From 08f93c4974c3ec9a710134913bec250dff0a1405 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Thu, 30 Jul 2026 13:20:42 +0200
Subject: [PATCH 01/14] Refactor tests and enhance functionality

- Updated assertion in TestSweepColumnInDataFrame to use is_empty() method for DataFrame validation.
- Modified plot_latent_scatter function to accept both Polars and Pandas DataFrames, ensuring compatibility and improved performance.
- Added comprehensive tests for LUT directory generation and validation in test_lut_planes.py, covering file writing, frustum properties, centroid containment, and geometry file size budgets.
- Introduced tests for RadDB.sel() functionality in test_sel.py, ensuring correct dynamic and static column selection, immutability, and synchronization with LUT data.
---
 .gitignore                     |   5 +-
 raddb/__init__.py              |  14 +
 raddb/aoi.py                   |  47 ++-
 raddb/helper.py                |  38 +-
 raddb/io_core.py               | 196 ++++++----
 raddb/lut.py                   | 628 ++++++++++++++++++++++++++++++---
 raddb/main.py                  | 472 ++++++++++++++++++++++++-
 raddb/tests/test_fixes.py      |   2 +-
 raddb/tests/test_lut_planes.py | 433 +++++++++++++++++++++++
 raddb/tests/test_sel.py        | 173 +++++++++
 raddb/viz/plot.py              |   9 +-
 11 files changed, 1865 insertions(+), 152 deletions(-)
 create mode 100644 raddb/tests/test_lut_planes.py
 create mode 100644 raddb/tests/test_sel.py

diff --git a/.gitignore b/.gitignore
index 966c08c..ff49682 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,4 +167,7 @@ log.txt
 # Private MCH subpackage (versioned in a separate private repo)
 raddb/mch/
 
-pyproject.toml
\ No newline at end of file
+pyproject.toml
+
+# Local AI-assistant working notes (machine-local paths — not for the public repo)
+CLAUDE.md
\ No newline at end of file
diff --git a/raddb/__init__.py b/raddb/__init__.py
index eb38ce8..f198c57 100644
--- a/raddb/__init__.py
+++ b/raddb/__init__.py
@@ -55,13 +55,20 @@
 # LUT functions
 from raddb.lut import (
     RADAR_TO_IDX,
+    DEFAULT_BEAMWIDTH_DEG,
+    LUT_FILES,
     antenna_vectors_to_cartesian,
+    build_gate_planes,
     cartesian_to_geographic,
     compute_gate_xyz,
+    compute_sweep_corners,
+    gate_corner_table,
     generate_gate_id,
     generate_lut_from_datatree,
+    load_plane_nodes,
     load_radar_lut,
     load_radar_info,
+    lut_file_path,
     get_full_sweep_index,
     add_lut_projection,
 )
@@ -119,13 +126,20 @@
     "find_datatree_files",
     # LUT functions
     "RADAR_TO_IDX",
+    "DEFAULT_BEAMWIDTH_DEG",
+    "LUT_FILES",
     "antenna_vectors_to_cartesian",
+    "build_gate_planes",
     "cartesian_to_geographic",
     "compute_gate_xyz",
+    "compute_sweep_corners",
+    "gate_corner_table",
     "generate_gate_id",
     "generate_lut_from_datatree",
+    "load_plane_nodes",
     "load_radar_lut",
     "load_radar_info",
+    "lut_file_path",
     "get_full_sweep_index",
     "add_lut_projection",
     # Pipeline functions
diff --git a/raddb/aoi.py b/raddb/aoi.py
index d13e76e..9810155 100644
--- a/raddb/aoi.py
+++ b/raddb/aoi.py
@@ -535,7 +535,9 @@ def _prj_crs(prj_path: Path):
 _CS_CACHE: dict = {}
 
 
-def _lut_cs_table(base_path: str | Path, radars: list[str], beamwidth_deg: float = 1.0) -> pd.DataFrame:
+def _lut_cs_table(
+    base_path: str | Path, radars: list[str], beamwidth_deg: float = 1.0
+) -> "pl.DataFrame":
     """Static per-gate geometry for cross-sections: centers + half-dimensions.
 
     Half-dimensions (prototype convention):
@@ -554,21 +556,35 @@ def _lut_cs_table(base_path: str | Path, radars: list[str], beamwidth_deg: float
                 raise FileNotFoundError(
                     f"LUT not found at {lut_path}. Cannot build cross-section for radar {radar!r}."
                 )
-            t = pd.read_parquet(lut_path, columns=_CS_LUT_COLS, engine="pyarrow")
+            t = pl.read_parquet(lut_path, columns=_CS_LUT_COLS)
             # Radial spacing per sweep from the unique range grid -> dR = spacing/2.
-            ur = t[["sweep", "range"]].drop_duplicates().sort_values(["sweep", "range"])
-            spacing = ur.groupby("sweep")["range"].apply(
-                lambda r: float(np.median(np.diff(r.to_numpy(dtype=np.float64))))
+            # `range` is cast to Float64 first so the median-of-diffs matches the
+            # float64 arithmetic the pandas implementation used.
+            spacing = (
+                t.select(["sweep", "range"])
+                .unique()
+                .sort(["sweep", "range"])
+                .group_by("sweep")
+                .agg(
+                    pl.col("range").cast(pl.Float64).diff().drop_nulls()
+                    .median().alias("_spacing")
+                )
+            )
+            t = (
+                t.join(spacing, on="sweep", how="left")
+                .with_columns(
+                    (pl.col("_spacing") / 2.0).cast(pl.Float64).alias("dR"),
+                    (pl.col("range").cast(pl.Float64)
+                     * float(np.tan(np.deg2rad(beamwidth_deg / 2.0)))).alias("dA"),
+                    pl.lit(radar).alias("radar"),
+                )
+                .drop("_spacing")
             )
-            t = t.copy()
-            t["dR"] = (t["sweep"].map(spacing) / 2.0).astype(np.float64)
-            t["dA"] = t["range"].to_numpy(dtype=np.float64) * np.tan(np.deg2rad(beamwidth_deg / 2.0))
-            t["radar"] = radar
             _CS_CACHE[key] = t
         frames.append(t)
     if not frames:
         raise ValueError("no radars given for cross-section geometry.")
-    return pd.concat(frames, ignore_index=True)
+    return pl.concat(frames, how="vertical_relaxed")
 
 
 def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float) -> np.ndarray:
@@ -616,7 +632,7 @@ def _endpoint_d_z(pt_xy: np.ndarray, sub: pd.DataFrame, origin: tuple[float, flo
 
 
 def _cross_section_gates(
-    cs_t: pd.DataFrame,
+    cs_t: "pl.DataFrame | pd.DataFrame",
     p1: tuple[float, float],
     p2: tuple[float, float],
     beamwidth_deg: float = 1.0,
@@ -624,6 +640,12 @@ def _cross_section_gates(
 ) -> pd.DataFrame:
     """Gates whose horizontal footprint crosses the line ``p1 -> p2``.
 
+    Accepts the polars geometry table from :func:`_lut_cs_table`.  The body
+    works in pandas because the result carries ``cs_polygon`` — a column of
+    shapely objects — for which pandas' object dtype is the natural carrier;
+    :meth:`raddb.RadDB.extract_cross_section` converts the geometry columns back
+    to polars when joining them onto the data frame.
+
     Returns one row per crossed gate with its cross-section geometry:
     chord endpoints ``(d_near, z_near) / (d_far, z_far)``, center
     ``(d_center, z_center)``, and ``cs_polygon`` — the 4-corner shapely polygon
@@ -631,6 +653,9 @@ def _cross_section_gates(
     offsetting the chord perpendicularly by ±dA (the vertical half-beamwidth
     extent).  ``d`` is measured from ``p1``.
     """
+    if isinstance(cs_t, pl.DataFrame):
+        cs_t = cs_t.to_pandas()
+
     ox, oy = float(p1[0]), float(p1[1])
     ex, ey = float(p2[0]), float(p2[1])
     length = float(np.hypot(ex - ox, ey - oy))
diff --git a/raddb/helper.py b/raddb/helper.py
index f5e4c1b..25d528d 100644
--- a/raddb/helper.py
+++ b/raddb/helper.py
@@ -10,6 +10,7 @@
 from pathlib import Path
 
 import pandas as pd
+import polars as pl
 import xarray as xr
 
 # --- DataTree Helpers ---
@@ -37,20 +38,30 @@ def read_parquet_files(
     pattern: str = "**/*POL.parquet",
     columns: list[str] | None = None,
     verbose: bool = True,
-) -> pd.DataFrame:
+) -> "pl.DataFrame":
+    """Read matching parquet files into a single **polars** DataFrame."""
     files = sorted(Path(base_path).rglob(pattern))
     if not files:
         if verbose: print(f"[RadDB] No files found matching '{pattern}' in {base_path}")
-        return pd.DataFrame()
+        return pl.DataFrame()
     if verbose: print(f"[RadDB] Found {len(files)} file(s) — loading...")
-    return pd.concat([pd.read_parquet(f, columns=columns, engine="pyarrow") for f in files], ignore_index=True)
+    return pl.concat(
+        [pl.read_parquet(f, columns=columns) for f in files], how="vertical_relaxed"
+    )
 
-def check_dataframe(df: pd.DataFrame) -> None:
+def check_dataframe(df: "pl.DataFrame | pd.DataFrame") -> None:
+    """Print a quick structural summary; accepts polars or pandas."""
     print("-" * 50)
     print(f"Shape:   {df.shape}")
-    print(f"Columns: {df.columns.tolist()}")
+    print(f"Columns: {list(df.columns)}")
     print("-" * 50)
-    print(f"Missing values:\n{df.isnull().sum()}")
+    if isinstance(df, pl.DataFrame):
+        nulls = df.null_count().to_dicts()[0] if len(df.columns) else {}
+        print("Missing values:")
+        for k, v in nulls.items():
+            print(f"{k}    {v}")
+    else:
+        print(f"Missing values:\n{df.isnull().sum()}")
     print("-" * 50)
     print(df.head())
     print("-" * 50)
@@ -122,16 +133,19 @@ def resolve_filter_logic(logic: str):
 
 
 def filter_df(
-    df: pd.DataFrame,
+    df: "pl.DataFrame | pd.DataFrame",
     feature: str = "DBZH",
     threshold: float = 0.0,
     logic: str = ">",
-) -> pd.DataFrame:
+) -> "pl.DataFrame | pd.DataFrame":
     """Filter a DataFrame, keeping rows where ``feature [logic] threshold``.
 
+    Accepts polars or pandas and returns the **same kind**, so an existing
+    pandas caller keeps getting pandas back.
+
     Parameters
     ----------
-    df : pd.DataFrame
+    df : pl.DataFrame or pd.DataFrame
         Input DataFrame.
     feature : str
         Column name to filter on (default ``"DBZH"``).
@@ -143,8 +157,8 @@ def filter_df(
 
     Returns
     -------
-    pd.DataFrame
-        Filtered DataFrame with non-matching rows dropped (index reset).
+    pl.DataFrame or pd.DataFrame
+        Filtered DataFrame with non-matching rows dropped (pandas index reset).
 
     Raises
     ------
@@ -157,6 +171,8 @@ def filter_df(
     if feature not in df.columns:
         raise KeyError(f"Feature '{feature}' not found in DataFrame columns.")
     mask = fn(df[feature].to_numpy(), threshold)
+    if isinstance(df, pl.DataFrame):
+        return df.filter(pl.Series(mask))
     return df[mask].reset_index(drop=True)
 
 
diff --git a/raddb/io_core.py b/raddb/io_core.py
index 6aa21dc..c8934e3 100644
--- a/raddb/io_core.py
+++ b/raddb/io_core.py
@@ -19,6 +19,7 @@
 
 import numpy as np
 import pandas as pd
+import polars as pl
 import xarray as xr
 import yaml
 
@@ -52,11 +53,37 @@
 _LAPSE_RATE: float = -0.0065  # °C/m (standard environmental lapse rate, -6.5 °C/km)
 
 
-def _projection_columns(df: pd.DataFrame) -> list[str]:
+def _projection_columns(df: "pl.DataFrame | pd.DataFrame") -> list[str]:
     """Columns added by :func:`raddb.lut.add_lut_projection` (e.g. x_2056 / y_2056)."""
     return [c for c in df.columns if re.match(r"^[xy]_\w+$", c)]
 
 
+def _col(df: "pl.DataFrame | pd.DataFrame", name: str, dtype=None) -> np.ndarray:
+    """Column ``name`` of ``df`` as a numpy array, for polars **or** pandas.
+
+    ``polars.Series.to_numpy`` takes no ``dtype`` argument (pandas' does), so the
+    cast is applied afterwards.  Used by the write path, which is numpy-based
+    internally and therefore backend-agnostic.
+    """
+    arr = df[name].to_numpy()
+    return arr if dtype is None else arr.astype(dtype)
+
+
+def _to_polars_frame(df: "pl.DataFrame | pd.DataFrame") -> "pl.DataFrame":
+    """Coerce a pandas frame to polars; pass polars frames straight through."""
+    return df if isinstance(df, pl.DataFrame) else pl.from_pandas(df)
+
+
+def _to_pandas_frame(df: "pl.DataFrame | pd.DataFrame") -> pd.DataFrame:
+    """Coerce a polars frame to pandas; pass pandas frames straight through.
+
+    Used only at the **xarray seam**: DataTree reconstruction needs
+    ``set_index().reindex(MultiIndex)`` and ``to_xarray()``, which have no
+    polars equivalent.  Everywhere else the backend stays polars.
+    """
+    return df.to_pandas() if isinstance(df, pl.DataFrame) else df
+
+
 # ============================================================================
 # DataTree file loading  (NetCDF / Zarr)
 # ============================================================================
@@ -117,11 +144,16 @@ def datatree_to_dataset(dt: xr.DataTree, sweep: str | int) -> xr.Dataset:
 
 def datatree_to_dataframe(
     dt: xr.DataTree, max_workers: int = 1
-) -> pd.DataFrame:
-    """Flatten a DataTree into a single pandas DataFrame.
+) -> "pl.DataFrame":
+    """Flatten a DataTree into a single **polars** DataFrame.
 
     Each sweep is converted independently and concatenated, with a ``sweep``
     column indicating the source sweep number.
+
+    ``xarray.Dataset.to_dataframe`` only emits pandas, so each sweep is
+    flattened through pandas and the whole volume is handed to polars in a
+    single conversion at the end — the xarray seam is the one place pandas is
+    unavoidable.
     """
     names = list_sweep_names(dt)
 
@@ -136,13 +168,17 @@ def _flatten(name):
         with concurrent.futures.ThreadPoolExecutor(max_workers) as ex:
             list_df = list(ex.map(_flatten, names))
 
-    return pd.concat(list_df, ignore_index=True)
+    return pl.from_pandas(pd.concat(list_df, ignore_index=True))
 
 
 def _save_polar_parquet(
-    df_polar: pd.DataFrame, radar: str, base_path: str
+    df_polar: "pl.DataFrame | pd.DataFrame", radar: str, base_path: str
 ) -> str:
-    """Save a POLAR DataFrame to the standard directory layout."""
+    """Save a POLAR DataFrame to the standard directory layout.
+
+    Accepts polars (the native write-path format) or pandas.
+    """
+    df_polar = _to_polars_frame(df_polar)
     vol_time = pd.to_datetime(df_polar["time"].min())
     save_dir = (
         Path(base_path)
@@ -154,7 +190,7 @@ def _save_polar_parquet(
     save_dir.mkdir(parents=True, exist_ok=True)
     ts = vol_time.strftime("%Y%m%d_%H%M%S")
     pp = save_dir / f"{radar}_{ts}_POL.parquet"
-    df_polar.to_parquet(pp, index=False, engine="pyarrow")
+    df_polar.write_parquet(pp)
     return str(pp)
 
 
@@ -172,7 +208,9 @@ def _cast_hc_column(arr, shift: int = 0) -> np.ndarray:
     return arr_f.astype(np.float32)
 
 
-def _compute_gate_temperature(df: pd.DataFrame, mask: np.ndarray) -> np.ndarray | None:
+def _compute_gate_temperature(
+    df: "pl.DataFrame | pd.DataFrame", mask: np.ndarray
+) -> np.ndarray | None:
     """Compute temperature (°C) at surviving gates using standard lapse rate.
 
     TEMP = _LAPSE_RATE x (gate_altitude - HZT)
@@ -184,25 +222,25 @@ def _compute_gate_temperature(df: pd.DataFrame, mask: np.ndarray) -> np.ndarray
     if not geom_required.issubset(df.columns):
         return None
     n        = int(mask.sum())
-    r        = df["range"].to_numpy()[mask]
-    el_rad   = np.deg2rad(df["elevation"].to_numpy()[mask])
-    site_alt = df["altitude"].to_numpy()[mask]
+    r        = _col(df, "range")[mask]
+    el_rad   = np.deg2rad(_col(df, "elevation")[mask])
+    site_alt = _col(df, "altitude")[mask]
     ke, Re   = 4.0 / 3.0, 6_371_000.0
     z_gate   = np.sqrt(r**2 + (ke * Re)**2 + 2 * r * ke * Re * np.sin(el_rad)) - ke * Re
     gate_alt = site_alt + z_gate
     if "HZT" not in df.columns:
         return np.full(n, np.nan, dtype=np.float32)
-    hzt = df["HZT"].to_numpy()[mask]
+    hzt = _col(df, "HZT")[mask]
     return (_LAPSE_RATE * (gate_alt - hzt)).astype(np.float32)
 
 
 def _build_polar_dataframe(
-    df: pd.DataFrame,
+    df: "pl.DataFrame | pd.DataFrame",
     radar: str,
     filter_feature: str,
     filter_threshold: float,
     filter_logic: str,
-) -> tuple[pd.DataFrame, np.ndarray]:
+) -> tuple["pl.DataFrame", np.ndarray]:
     """Filter a flattened volume DataFrame and attach gate_ids.
 
     Rows that do not satisfy ``filter_feature [filter_logic] filter_threshold``
@@ -220,7 +258,7 @@ def _build_polar_dataframe(
     fn = resolve_filter_logic(filter_logic)
 
     if filter_feature in df.columns:
-        mask = fn(df[filter_feature].to_numpy(), filter_threshold)
+        mask = fn(_col(df, filter_feature), filter_threshold)
     else:
         logger.warning(
             "filter_feature '%s' not found in DataFrame; keeping all gates.",
@@ -230,9 +268,9 @@ def _build_polar_dataframe(
 
     gate_ids = encode_gate_ids(
         radar,
-        df["sweep"].to_numpy(dtype=np.int64)[mask],
-        df["azimuth"].to_numpy(dtype=np.float64)[mask],
-        df["range"].to_numpy()[mask],
+        _col(df, "sweep", np.int64)[mask],
+        _col(df, "azimuth", np.float64)[mask],
+        _col(df, "range")[mask],
     )
 
     hzt_available = "HZT" in df.columns
@@ -242,8 +280,8 @@ def _build_polar_dataframe(
         and c != "gate_id"
         and not (c == "HC_PYART" and not hzt_available)
     ]
-    df_polar = pd.DataFrame(
-        {"gate_id": gate_ids, **{c: df[c].to_numpy()[mask] for c in polar_cols}},
+    df_polar = pl.DataFrame(
+        {"gate_id": gate_ids, **{c: _col(df, c)[mask] for c in polar_cols}},
     )
     return df_polar, mask
 
@@ -496,21 +534,29 @@ def archive_volumes_multi_radar(
 
 
 def _finalize_polar_dtypes(
-    df_polar: pd.DataFrame, df: pd.DataFrame, mask: np.ndarray
-) -> pd.DataFrame:
+    df_polar: "pl.DataFrame | pd.DataFrame",
+    df: "pl.DataFrame | pd.DataFrame",
+    mask: np.ndarray,
+) -> "pl.DataFrame":
     """Apply dtype optimisations and add the TEMP column.
 
     HC columns are shifted +1 to the 1-based parquet scale; all polar
     variables are cast to float32; TEMP is computed from gate geometry + HZT.
     """
-    for col in list(df_polar.columns):
+    df_polar = _to_polars_frame(df_polar)
+
+    updates = []
+    for col in df_polar.columns:
         if col in ("HC_MCH", "HC_PYART"):
-            df_polar[col] = _cast_hc_column(df_polar[col], shift=1)
+            updates.append(pl.Series(col, _cast_hc_column(df_polar[col].to_numpy(), shift=1)))
         elif col in _POLAR_FLOAT32_COLS:
-            df_polar[col] = df_polar[col].astype(np.float32)
+            updates.append(pl.col(col).cast(pl.Float32))
+    if updates:
+        df_polar = df_polar.with_columns(updates)
+
     temp = _compute_gate_temperature(df, mask)
     if temp is not None:
-        df_polar["TEMP"] = temp
+        df_polar = df_polar.with_columns(pl.Series("TEMP", temp))
     return df_polar
 
 
@@ -553,8 +599,8 @@ def parquet_to_dataframe(
     end_time: str | pd.Timestamp | None = None,
     columns: list[str] | None = None,
     merge_lut: bool = False,
-) -> pd.DataFrame:
-    """Load archived POLAR parquet files as a single DataFrame.
+) -> "pl.DataFrame":
+    """Load archived POLAR parquet files as a single **polars** DataFrame.
 
     Parameters
     ----------
@@ -572,7 +618,7 @@ def parquet_to_dataframe(
 
     Returns
     -------
-    pd.DataFrame
+    pl.DataFrame
         Includes a ``volume_time`` column (the volume timestamp parsed from each
         source filename) so a multi-volume frame can be split back into single
         volumes — used by :func:`dataframe_to_datatree` / PPI plotting.
@@ -580,7 +626,7 @@ def parquet_to_dataframe(
     radar_path = Path(base_path) / radar
     if not radar_path.exists():
         logger.warning(f"Radar directory not found: {radar_path}")
-        return pd.DataFrame()
+        return pl.DataFrame()
 
     polar_files = _find_polar_files_in_range(radar_path, start_time, end_time)
 
@@ -589,7 +635,7 @@ def parquet_to_dataframe(
             f"No POLAR data found for radar {radar} "
             f"between {start_time} and {end_time}"
         )
-        return pd.DataFrame()
+        return pl.DataFrame()
 
     if columns is not None:
         # ``volume_time`` / ``radar`` are derived below (and in RadDB.open), not
@@ -600,26 +646,31 @@ def parquet_to_dataframe(
     dfs = []
     for f in polar_files:
         try:
-            df = pd.read_parquet(f, columns=columns, engine="pyarrow")
+            df = pl.read_parquet(f, columns=columns)
             # Tag each row with its volume timestamp (from the filename) so a
             # multi-volume DataFrame can later be split back into single volumes
             # (per-gate `time` spans the whole ~5 min scan and cannot separate
             # back-to-back volumes reliably).
-            df["volume_time"] = _parse_pol_time(f)
+            vt = _parse_pol_time(f)
+            df = df.with_columns(
+                pl.lit(vt.tz_localize(None) if vt is not None else None)
+                .cast(pl.Datetime("ns"))
+                .alias("volume_time")
+            )
             dfs.append(df)
         except Exception as e:
             logger.warning(f"Error reading {f}: {e}")
             continue
 
     if not dfs:
-        return pd.DataFrame()
+        return pl.DataFrame()
 
-    df_all = pd.concat(dfs, ignore_index=True)
+    df_all = pl.concat(dfs, how="vertical_relaxed")
 
     if merge_lut:
         lut_path = radar_path / "LUT" / f"{radar}_LUT.parquet"
         if lut_path.exists():
-            lut_df = pd.read_parquet(lut_path, engine="pyarrow")
+            lut_df = pl.read_parquet(lut_path)
             lut_cols = [
                 "gate_id",
                 "sweep",
@@ -637,7 +688,10 @@ def parquet_to_dataframe(
             # (e.g. x_2056, y_2056 for Swiss LV95 / EPSG:2056)
             lut_cols += _projection_columns(lut_df)
             lut_cols = [c for c in lut_cols if c in lut_df.columns]
-            df_all = df_all.merge(lut_df[lut_cols], on="gate_id", how="left")
+            # maintain_order="left" reproduces pandas' left-merge row order.
+            df_all = df_all.join(
+                lut_df.select(lut_cols), on="gate_id", how="left", maintain_order="left"
+            )
         else:
             logger.warning(
                 f"LUT not found at {lut_path}. "
@@ -683,8 +737,6 @@ def scan_polar_parquet(
         ``None`` when no volume matches — callers decide what an empty result
         means.  The frame carries a ``volume_time`` and a ``radar`` column.
     """
-    import polars as pl
-
     radar_path = Path(base_path) / radar
     if not radar_path.exists():
         logger.warning(f"Radar directory not found: {radar_path}")
@@ -819,7 +871,7 @@ def parquet_to_datatree(
 
 
 def dataframe_to_datatree(
-    df: pd.DataFrame,
+    df: "pl.DataFrame | pd.DataFrame",
     radar: str,
     base_path: str | Path,
     label_column: str = "DBZH",
@@ -873,6 +925,9 @@ def dataframe_to_datatree(
         raise FileNotFoundError(f"LUT not found at {lut_path}. Run generate_lut() first.")
     if not info_path.exists():
         raise FileNotFoundError(f"Radar info not found at {info_path}.")
+
+    # xarray seam: reconstruction below needs pandas indexing/reindexing.
+    df = _to_pandas_frame(df)
     if df.empty:
         raise ValueError("dataframe_to_datatree: input DataFrame is empty.")
 
@@ -918,23 +973,28 @@ def labels_to_dataframe(
     labels: np.ndarray,
     gate_ids,
     extra_columns: dict | None = None,
-) -> pd.DataFrame:
-    """Create a DataFrame from prediction labels and gate IDs."""
-    df = pd.DataFrame({"gate_id": gate_ids, "hydrometeor_class": labels})
+) -> "pl.DataFrame":
+    """Create a **polars** DataFrame from prediction labels and gate IDs."""
+    data = {"gate_id": np.asarray(gate_ids), "hydrometeor_class": np.asarray(labels)}
     if extra_columns:
-        for k, v in extra_columns.items():
-            df[k] = v
-    return df
+        data.update(extra_columns)
+    return pl.DataFrame(data)
 
 
 def join_labels_with_lut(
-    df_labels: pd.DataFrame, lut_path: str | Path
-) -> pd.DataFrame:
-    """Join label data with the LUT to recover spatial coordinates."""
-    df_lut = pd.read_parquet(str(lut_path), engine="pyarrow")
+    df_labels: "pl.DataFrame | pd.DataFrame", lut_path: str | Path
+) -> "pl.DataFrame":
+    """Join label data with the LUT to recover spatial coordinates.
+
+    Accepts a polars or pandas label frame; always returns polars.
+    """
+    df_labels = _to_polars_frame(df_labels)
+    df_lut = pl.read_parquet(str(lut_path))
     cols = [c for c in df_labels.columns if c != "gate_id"]
-    return df_lut.merge(
-        df_labels[["gate_id"] + cols], on="gate_id", how="left"
+    # maintain_order="left" reproduces pandas' left-merge row order.
+    return df_lut.join(
+        df_labels.select(["gate_id", *cols]), on="gate_id", how="left",
+        maintain_order="left",
     )
 
 
@@ -955,9 +1015,9 @@ def _get_sweep_coords(sweep, radar_info):
 
 
 def reconstruct_sweep_dataset(
-    df_joined: pd.DataFrame,
+    df_joined: "pl.DataFrame | pd.DataFrame",
     sweep: int,
-    lut_df: pd.DataFrame,
+    lut_df: "pl.DataFrame | pd.DataFrame",
     radar_info: dict,
     label_column: str = "hydrometeor_class",
     sweep_corners: dict | None = None,
@@ -974,6 +1034,10 @@ def reconstruct_sweep_dataset(
     shape ``(n_az+1, n_range+1)`` are attached as data_vars for pcolormesh
     rendering with ``shading="flat"``.
     """
+    # xarray seam: MultiIndex reindexing + to_xarray() are pandas-only.
+    df_joined = _to_pandas_frame(df_joined)
+    lut_df = _to_pandas_frame(lut_df)
+
     df_sweep = df_joined[df_joined["sweep"] == sweep].copy()
 
     # Identify spatial columns that should come from the LUT (always populated)
@@ -1017,13 +1081,18 @@ def reconstruct_sweep_dataset(
 
 
 def reconstruct_datatree(
-    df_joined: pd.DataFrame,
+    df_joined: "pl.DataFrame | pd.DataFrame",
     lut_path: str | Path,
     radar_info_path: str | Path,
     label_column: str = "hydrometeor_class",
     max_workers: int = 1,
 ) -> xr.DataTree:
-    """Reconstruct a full DataTree from joined data + LUT + radar info."""
+    """Reconstruct a full DataTree from joined data + LUT + radar info.
+
+    Accepts a polars or pandas ``df_joined``; pandas is used internally because
+    this is the xarray seam (see :func:`_to_pandas_frame`).
+    """
+    df_joined = _to_pandas_frame(df_joined)
     lut_df = pd.read_parquet(str(lut_path), engine="pyarrow")
     with open(str(radar_info_path)) as f:
         radar_info = yaml.safe_load(f)
@@ -1085,15 +1154,18 @@ def _rec(sw):
 # ============================================================================
 
 def add_feature_to_df(
-    df: pd.DataFrame,
+    df: "pl.DataFrame | pd.DataFrame",
     feature_name: str,
     compute_fn: callable,
-) -> pd.DataFrame:
+) -> "pl.DataFrame | pd.DataFrame":
     """Add a new column to a DataFrame computed from existing columns.
 
+    Accepts polars or pandas and returns the **same kind**, so ``compute_fn``
+    receives the frame flavour the caller passed in.
+
     Parameters
     ----------
-    df : pd.DataFrame
+    df : pl.DataFrame or pd.DataFrame
         Input DataFrame (e.g. from :func:`parquet_to_dataframe`).
     feature_name : str
         Name of the new column to add.
@@ -1106,9 +1178,11 @@ def my_feature(df):
 
     Returns
     -------
-    pd.DataFrame
-        Copy of ``df`` with the new column appended.
+    pl.DataFrame or pd.DataFrame
+        Copy of ``df`` (same kind) with the new column appended.
     """
+    if isinstance(df, pl.DataFrame):
+        return df.with_columns(pl.Series(feature_name, np.asarray(compute_fn(df))))
     df = df.copy()
     df[feature_name] = compute_fn(df)
     return df
diff --git a/raddb/lut.py b/raddb/lut.py
index a7c44b2..24c8ff0 100644
--- a/raddb/lut.py
+++ b/raddb/lut.py
@@ -14,6 +14,7 @@
 from __future__ import annotations
 
 import logging
+import re
 from pathlib import Path
 
 import numpy as np
@@ -30,6 +31,45 @@
 # A=0, B=1, ..., Z=25  (26 radars supported)
 RADAR_TO_IDX: dict[str, int] = {chr(ord("A") + i): i for i in range(26)}
 
+#: Antenna 3 dB beamwidth in degrees, used for the gate's angular extent.
+#: 1.0 deg matches the MeteoSwiss Rad4Alp radars and the reference prototype
+#: (which hardcoded ``beta = deg2rad(0.5)`` as the *half* beamwidth).
+DEFAULT_BEAMWIDTH_DEG: float = 1.0
+
+#: The five files that make up a complete LUT directory for one radar.
+#: ``{radar}`` is substituted with the single-letter radar name.
+LUT_FILES: dict[str, str] = {
+    "lut": "{radar}_LUT.parquet",
+    "h_plane": "{radar}_h_plane_LUT.parquet",
+    "v_plane": "{radar}_v_plane_LUT.parquet",
+    "corners": "{radar}_corners_LUT.parquet",
+    "info": "{radar}_info.yaml",
+}
+
+
+def _projection_column_names(df) -> list[str]:
+    """``[x_<suffix>, y_<suffix>]`` present on a LUT frame, else ``[]``.
+
+    Local to this module: ``io_core._projection_columns`` does the same job but
+    importing it here would be circular (``io_core`` imports ``lut``).
+    """
+    xs = [c for c in df.columns if re.match(r"^x_\w+$", c)]
+    out: list[str] = []
+    for xc in xs:
+        yc = "y_" + xc[2:]
+        if yc in df.columns:
+            out += [xc, yc]
+    return out
+
+
+def lut_file_path(radar: str, kind: str, lut_base_path: str | Path) -> Path:
+    """Path of one of the five LUT files (see :data:`LUT_FILES`)."""
+    if kind not in LUT_FILES:
+        raise KeyError(f"unknown LUT file kind {kind!r}; use one of {sorted(LUT_FILES)}.")
+    return (
+        Path(lut_base_path) / radar / "LUT" / LUT_FILES[kind].format(radar=radar)
+    )
+
 
 # ============================================================================
 # Coordinate transforms  (pure numpy, no pyart dependency)
@@ -186,7 +226,7 @@ def compute_gate_xyz(
     ranges: np.ndarray,
     azimuths: np.ndarray,
     elevations: np.ndarray,
-    ke: float = 1.25,
+    ke: float = 4.0 / 3.0,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """Compute gate Cartesian coordinates from polar coordinates.
 
@@ -255,17 +295,41 @@ def generate_gate_id(
 # Generic LUT generation from DataTree
 # ============================================================================
 
+def _beamwidth_from_datatree(dt: xr.DataTree) -> float:
+    """Antenna beamwidth [deg] from the DataTree, else the package default.
+
+    Looks for the CfRadial/xradar attribute names on the root and on each sweep.
+    """
+    names = ("radar_beam_width_h", "beam_width_h", "beamwidth", "beamwidth_deg")
+    candidates = [dt.attrs, *(dt[s].attrs for s in list_sweep_names(dt))]
+    for attrs in candidates:
+        for nm in names:
+            if nm in attrs:
+                try:
+                    val = float(np.asarray(attrs[nm]).ravel()[0])
+                except (TypeError, ValueError):
+                    continue
+                if 0.0 < val < 20.0:      # sanity: a real antenna beamwidth
+                    return val
+    return DEFAULT_BEAMWIDTH_DEG
+
+
 def generate_lut_from_datatree(
     dt: xr.DataTree,
     radar: str,
     output_base_path: str,
-    ke: float = 1.25,
+    ke: float = 4.0 / 3.0,
     network: str = "",
     projection_epsg: int | None = None,
     projection_crs=None,
+    beamwidth_deg: float | None = None,
 ) -> str:
     """Generate a LUT from an xarray DataTree.
 
+    Writes the **five** files that make up a complete LUT directory
+    (see :data:`LUT_FILES`): the gate-centroid LUT, the horizontal-face and
+    vertical-face node lattices, the 3-D corner lattice, and the info YAML.
+
     This is the **generic** LUT generator — it works with any DataTree
     that has the standard xradar coordinate layout (azimuth, range,
     elevation per sweep).
@@ -283,7 +347,8 @@ def generate_lut_from_datatree(
     output_base_path : str
         Base directory for LUT storage.
     ke : float
-        Effective Earth radius scale factor (default 1.25 for Switzerland).
+        Effective Earth radius scale factor (default 4/3, the standard
+        atmosphere model — matches every other ``ke`` in the package).
     network : str, optional
         Network identifier stored in radar info YAML.
     projection_epsg : int, optional
@@ -292,6 +357,11 @@ def generate_lut_from_datatree(
         ``x_{epsg}`` / ``y_{epsg}`` columns via :func:`add_lut_projection`.
     projection_crs : pyproj.CRS or CRS-coercible, optional
         Alternative to ``projection_epsg``.
+    beamwidth_deg : float, optional
+        Antenna 3 dB beamwidth in degrees, defining the gate's angular extent.
+        Read from the DataTree's ``radar_beam_width_h`` / ``beamwidth`` attribute
+        when present, else :data:`DEFAULT_BEAMWIDTH_DEG` (1.0, the MeteoSwiss
+        value).
 
     Returns
     -------
@@ -302,9 +372,18 @@ def generate_lut_from_datatree(
     lut_path = lut_dir / f"{radar}_LUT.parquet"
     info_path = lut_dir / f"{radar}_info.yaml"
 
-    if lut_path.exists() and info_path.exists():
+    if beamwidth_deg is None:
+        beamwidth_deg = _beamwidth_from_datatree(dt)
+
+    # Skip only when *all five* files are present. An archive written before the
+    # geometry lattices existed has the LUT parquet + info YAML but not the three
+    # plane files, and must still be able to fill them in.
+    if all(
+        (lut_dir / tmpl.format(radar=radar)).exists() for tmpl in LUT_FILES.values()
+    ):
         logger.info(
-            "LUT already exists at %s -- skipping generation.", lut_path
+            "All %d LUT files already exist at %s -- skipping generation.",
+            len(LUT_FILES), lut_dir,
         )
         return str(lut_path)
 
@@ -314,6 +393,7 @@ def generate_lut_from_datatree(
 
     lut_dfs = []
     sweep_meta = {}
+    sweep_grids: dict[int, dict] = {}
     radar_lat, radar_lon, radar_alt = None, None, None
 
     for sweep_name in sweep_names:
@@ -352,7 +432,7 @@ def generate_lut_from_datatree(
         gate_rng = np.tile(ranges, n_az)
         gate_ids = encode_gate_ids(radar, sweep_idx, gate_az, gate_rng)
 
-        lut_dfs.append(pd.DataFrame({
+        lut_dfs.append(pl.DataFrame({
             "gate_id":         gate_ids,
             "sweep":           np.full(n_az * n_rng, sweep_idx, dtype=np.int32),
             "azimuth":         gate_az,
@@ -366,13 +446,30 @@ def generate_lut_from_datatree(
             "z":               z_raw.ravel(),
         }))
 
+        # Radial spacing from the sweep's own range grid -> dR = spacing / 2.
+        rng_res = (
+            float(np.median(np.diff(np.sort(ranges).astype(np.float64))))
+            if n_rng > 1 else float("nan")
+        )
         sweep_meta[sweep_idx] = {
             "n_azimuths": n_az,
             "n_ranges": n_rng,
+            "n_gates": int(n_az * n_rng),
             "elevation": round(elevation_angle, 2),
+            "range_resolution": round(rng_res, 3),
+            "range_start": round(float(np.min(ranges)), 3),
+            "dR": round(rng_res / 2.0, 3),
+        }
+        # Grids needed later for the corner/plane lattices — keep them so the
+        # lattices are built from the same arrays the centroids came from,
+        # rather than re-derived from the written parquet.
+        sweep_grids[sweep_idx] = {
+            "ranges": np.asarray(ranges, dtype=np.float64),
+            "azimuths": np.asarray(azimuths, dtype=np.float64),
+            "elevations": np.asarray(elevations, dtype=np.float64),
         }
 
-    df_lut = pd.concat(lut_dfs, ignore_index=True)
+    df_lut = pl.concat(lut_dfs, how="vertical")
     logger.info(
         "LUT built: %d total gates, %d sweeps.", len(df_lut), len(sweep_meta)
     )
@@ -383,37 +480,105 @@ def generate_lut_from_datatree(
             df_lut, epsg=projection_epsg, crs=projection_crs
         )
 
+    crs_info = None
+    proj_cols = _projection_column_names(df_lut)
+    if proj_cols:
+        epsg = None
+        if projection_epsg is not None:
+            epsg = int(projection_epsg)
+        else:
+            # suffix of x_<suffix> is the EPSG code when pyproj could detect one
+            suffix = proj_cols[0].split("_", 1)[1]
+            epsg = int(suffix) if suffix.isdigit() else None
+        crs_info = {"epsg": epsg, "columns": proj_cols}
+
     radar_info = {
         "radar": radar,
         "network": network,
         "latitude": radar_lat,
         "longitude": radar_lon,
         "altitude": radar_alt,
+        "crs": crs_info,
+        # Recorded for reproducibility: archives built before the ke 1.25 -> 4/3
+        # fix carry incompatible geometry, so the file must say which model
+        # produced it.
+        "ke": float(ke),
+        "beamwidth_deg": float(beamwidth_deg),
+        "n_sweeps": len(sweep_meta),
+        "n_gates": int(len(df_lut)),
         "sweeps": sweep_meta,
     }
 
-    return _save_lut_outputs(lut_dir, radar, df_lut, radar_info)
+    # ---- the three geometry lattices -------------------------------------
+    corners_by_sweep: dict[int, dict] = {}
+    for sweep_idx, g in sweep_grids.items():
+        corners_by_sweep[sweep_idx] = compute_sweep_corners(
+            ranges=g["ranges"], azimuths=g["azimuths"], elevations=g["elevations"],
+            radar_lat=radar_lat, radar_lon=radar_lon, radar_alt=radar_alt,
+            ke=ke, beamwidth_deg=beamwidth_deg,
+        )
+    planes = build_gate_planes(
+        corners_by_sweep,
+        radar_alt=radar_alt,
+        projection_epsg=projection_epsg,
+        projection_crs=projection_crs,
+    )
+
+    return _save_lut_outputs(lut_dir, radar, df_lut, radar_info, planes)
 
 
 # ============================================================================
 # LUT storage helpers
 # ============================================================================
 
-def _save_lut_outputs(lut_dir, radar, df_lut, radar_info):
-    """Save LUT parquet and radar info YAML to disk."""
+def _save_lut_outputs(lut_dir, radar, df_lut, radar_info, planes=None):
+    """Save the five LUT files to disk (see :data:`LUT_FILES`).
+
+    ``df_lut`` may be a polars frame (the native format since the LUT layer is
+    polars) or a pandas frame (accepted so external callers keep working).
+
+    ``planes`` is the :func:`build_gate_planes` output.  When ``None`` only the
+    LUT parquet and the info YAML are written (legacy two-file behaviour).
+
+    The idempotence gate checks **all** expected files: an archive written before
+    the geometry lattices existed still has its LUT parquet and info YAML, so the
+    three new files get filled in without rebuilding the centroids.
+    """
     lut_dir = Path(lut_dir)
     lut_dir.mkdir(parents=True, exist_ok=True)
-    lut_path = lut_dir / f"{radar}_LUT.parquet"
-    info_path = lut_dir / f"{radar}_info.yaml"
+    lut_path = lut_dir / LUT_FILES["lut"].format(radar=radar)
+    info_path = lut_dir / LUT_FILES["info"].format(radar=radar)
 
-    if lut_path.exists() and info_path.exists():
+    plane_paths = {
+        kind: lut_dir / LUT_FILES[kind].format(radar=radar)
+        for kind in ("h_plane", "v_plane", "corners")
+    }
+    expected = [lut_path, info_path]
+    if planes is not None:
+        expected += list(plane_paths.values())
+
+    if all(p.exists() for p in expected):
         logger.info(
-            "LUT and radar info already exist at %s -- skipping creation.",
-            lut_dir,
+            "All %d LUT files already exist at %s -- skipping creation.",
+            len(expected), lut_dir,
         )
         return str(lut_path)
 
-    df_lut.to_parquet(lut_path, index=False, engine="pyarrow")
+    if planes is not None:
+        for kind, path in plane_paths.items():
+            if path.exists():
+                continue
+            planes[kind].write_parquet(path)
+            logger.info("%s lattice saved -> %s", kind, path)
+
+    if lut_path.exists() and info_path.exists():
+        # Only the geometry lattices were missing; centroids stay as they are.
+        return str(lut_path)
+
+    if isinstance(df_lut, pl.DataFrame):
+        df_lut.write_parquet(lut_path)
+    else:
+        df_lut.to_parquet(lut_path, index=False, engine="pyarrow")
     logger.info("LUT saved -> %s", lut_path)
 
     with open(info_path, "w") as f:
@@ -426,6 +591,12 @@ def _save_lut_outputs(lut_dir, radar, df_lut, radar_info):
 # Loaders
 # ============================================================================
 
+#: Elevation levels of a gate, as offsets in units of the half beamwidth.
+#: ``-1`` = bottom of the beam, ``0`` = beam centre, ``+1`` = top.
+#: Follows the reference prototype's ``En`` / ``Eo`` / ``Ep`` face naming.
+EL_LEVELS: tuple[int, ...] = (-1, 0, 1)
+
+
 def compute_sweep_corners(
     ranges: np.ndarray,
     azimuths: np.ndarray,
@@ -434,35 +605,334 @@ def compute_sweep_corners(
     radar_lon: float,
     radar_alt: float,
     ke: float = 4.0 / 3.0,
+    beamwidth_deg: float | None = None,
 ) -> dict:
     """Compute per-sweep gate corner arrays for pcolormesh rendering.
 
     Uses PyART's edge-interpolation (complex-plane for azimuth wrap-around)
     plus the standard 4/3 Earth beam propagation.
 
+    Parameters
+    ----------
+    beamwidth_deg : float, optional
+        When given, the beam's **vertical extent** is resolved as well: the edge
+        mesh is computed at three elevation levels
+        (``elevation - beamwidth/2``, ``elevation``, ``elevation + beamwidth/2``)
+        and returned under the extra ``levels`` key.  Without it only the
+        centre-elevation mesh is produced, which has *no* vertical extent — that
+        is the legacy behaviour and cannot describe a gate's 8 corners.
+
     Returns
     -------
-    dict with keys ``x_edges``, ``y_edges``, ``z_edges``, ``lon_edges``,
-    ``lat_edges``, each shape ``(n_az+1, n_range+1)``.
+    dict
+        Always contains ``x_edges``, ``y_edges``, ``z_edges``, ``lon_edges``,
+        ``lat_edges``, each shape ``(n_az+1, n_range+1)`` — the beam-centre mesh,
+        kept for backwards compatibility.
+
+        When ``beamwidth_deg`` is given, also contains
+        ``levels``: ``{-1: {...}, 0: {...}, 1: {...}}`` with the same five keys
+        per level, where the integer is the offset in half-beamwidths
+        (see :data:`EL_LEVELS`).
     """
-    x_e, y_e, z_e = antenna_vectors_to_cartesian(
-        ranges, azimuths, elevations, ke=ke, edges=True,
-    )
-    lat_e, lon_e, _ = cartesian_to_geographic(
-        x_e, y_e, z_e, radar_lat=radar_lat, radar_lon=radar_lon, radar_alt=radar_alt,
-    )
-    # float64 throughout: these edges are the gate polygon vertices, and gate
-    # position precision is a hard requirement (float32 costs ~20 cm, and the
-    # error does not shrink with range).
+    elevations = np.atleast_1d(np.asarray(elevations, dtype=np.float64))
+
+    def _mesh(el: np.ndarray) -> dict:
+        x_e, y_e, z_e = antenna_vectors_to_cartesian(
+            ranges, azimuths, el, ke=ke, edges=True,
+        )
+        lat_e, lon_e, _ = cartesian_to_geographic(
+            x_e, y_e, z_e, radar_lat=radar_lat, radar_lon=radar_lon, radar_alt=radar_alt,
+        )
+        # float64 throughout: these edges are the gate polygon vertices, and gate
+        # position precision is a hard requirement (float32 costs ~20 cm, and the
+        # error does not shrink with range).
+        return {
+            "x_edges":   x_e.astype(np.float64),
+            "y_edges":   y_e.astype(np.float64),
+            "z_edges":   z_e.astype(np.float64),
+            "lon_edges": lon_e.astype(np.float64),
+            "lat_edges": lat_e.astype(np.float64),
+        }
+
+    centre = _mesh(elevations)
+    if beamwidth_deg is None:
+        return centre
+
+    half_bw = float(beamwidth_deg) / 2.0
+    # dict(centre) for level 0 so the `levels` key added below cannot make the
+    # structure self-referential.
+    levels = {
+        lvl: dict(centre) if lvl == 0 else _mesh(elevations + lvl * half_bw)
+        for lvl in EL_LEVELS
+    }
+    return {**centre, "levels": levels}
+
+
+# ============================================================================
+# Gate plane / corner node lattices  (the h_plane / v_plane / corners files)
+# ============================================================================
+
+#: Node-index offsets of a gate's 4 corners within the edge lattice, in ring
+#: order. Matches the ring built by :func:`gate_polygons_geoarrow`.
+GATE_RING_OFFSETS: tuple[tuple[int, int], ...] = ((0, 0), (0, 1), (1, 1), (1, 0))
+
+
+def _project_nodes(x: np.ndarray, y: np.ndarray, lon: np.ndarray, lat: np.ndarray,
+                   epsg: int | None, crs=None):
+    """Projected easting/northing for lattice nodes, or ``(None, None, None)``."""
+    if epsg is None and crs is None:
+        return None, None, None
+    import pyproj
+
+    if epsg is not None:
+        target = pyproj.CRS.from_epsg(epsg)
+        suffix = str(epsg)
+    else:
+        target = crs if isinstance(crs, pyproj.CRS) else pyproj.CRS(crs)
+        detected = target.to_epsg()
+        suffix = str(detected) if detected is not None else "custom"
+    wgs84 = pyproj.CRS.from_proj4("+proj=longlat +datum=WGS84 +no_defs")
+    tf = pyproj.Transformer.from_crs(wgs84, target, always_xy=True)
+    px, py = tf.transform(lon, lat)
+    return np.asarray(px), np.asarray(py), suffix
+
+
+def build_gate_planes(
+    corners_by_sweep: dict[int, dict],
+    radar_alt: float,
+    projection_epsg: int | None = None,
+    projection_crs=None,
+) -> dict[str, "pl.DataFrame"]:
+    """Build the h_plane / v_plane / corners **node lattices** as polars frames.
+
+    Input is the output of :func:`compute_sweep_corners` called with
+    ``beamwidth_deg`` (so each sweep carries a ``levels`` dict).
+
+    The lattices store *nodes*, not per-gate corners: neighbouring gates share
+    corner nodes, so a lattice is ~4x smaller for the horizontal face and ~8x
+    smaller for the 3-D corners than materialising every gate's corners, and is
+    exactly equivalent.  A gate's corners are recovered by indexing
+    ``(az_idx + i, rng_idx + j)`` over :data:`GATE_RING_OFFSETS` — see
+    :meth:`raddb.RadDB.get_h_plane` / :meth:`raddb.RadDB.get_corners`.
+
+    Returns
+    -------
+    dict
+        ``{"h_plane": pl.DataFrame, "v_plane": pl.DataFrame, "corners": pl.DataFrame}``
+
+        * ``h_plane`` — beam-centre level only: ``sweep, az_idx, rng_idx, x, y,
+          lon, lat`` (+ ``x_<epsg>, y_<epsg>``).
+        * ``v_plane`` — bottom/top levels in the RHI plane: ``sweep, el_level,
+          az_idx, rng_idx, d, z_asl, z_rel``.
+        * ``corners`` — bottom/top levels in 3-D: ``sweep, el_level, az_idx,
+          rng_idx, x, y, z_rel, z_asl, lon, lat``.
+    """
+    h_parts, v_parts, c_parts = [], [], []
+
+    for sweep_num in sorted(corners_by_sweep):
+        entry = corners_by_sweep[sweep_num]
+        levels = entry.get("levels")
+        if levels is None:
+            raise ValueError(
+                f"sweep {sweep_num}: compute_sweep_corners must be called with "
+                "beamwidth_deg so the vertical levels are available."
+            )
+
+        for lvl in sorted(levels):
+            m = levels[lvl]
+            xe, ye, ze = m["x_edges"], m["y_edges"], m["z_edges"]
+            lone, late = m["lon_edges"], m["lat_edges"]
+            n_az_n, n_rng_n = xe.shape           # (n_az+1, n_rng+1) node counts
+
+            az_idx = np.repeat(np.arange(n_az_n, dtype=np.int16), n_rng_n)
+            rng_idx = np.tile(np.arange(n_rng_n, dtype=np.int16), n_az_n)
+            xf, yf, zf = xe.ravel(), ye.ravel(), ze.ravel()
+            lonf, latf = lone.ravel(), late.ravel()
+            n = xf.size
+            sweep_col = np.full(n, sweep_num, dtype=np.int16)
+
+            if lvl == 0:
+                # --- horizontal face (PPI) -----------------------------------
+                # lon/lat are deliberately NOT stored: cartesian_to_geographic is
+                # a closed form of (x, y) + the site, so storing them would cost
+                # 8 B/node for zero information. The accessors derive them.
+                cols = {
+                    "sweep": sweep_col,
+                    "az_idx": az_idx,
+                    "rng_idx": rng_idx,
+                    "x": xf.astype(np.float32),
+                    "y": yf.astype(np.float32),
+                }
+                px, py, suffix = _project_nodes(
+                    xf, yf, lonf, latf, projection_epsg, projection_crs
+                )
+                if suffix is not None:
+                    cols[f"x_{suffix}"] = px.astype(np.float32)
+                    cols[f"y_{suffix}"] = py.astype(np.float32)
+                h_parts.append(pl.DataFrame(cols))
+                continue
+
+            # --- bottom / top levels: v_plane + 3-D corners ------------------
+            lvl_col = np.full(n, lvl, dtype=np.int8)
+            z_asl = (zf + radar_alt).astype(np.float32)
+            v_parts.append(pl.DataFrame({
+                "sweep": sweep_col,
+                "el_level": lvl_col,
+                "az_idx": az_idx,
+                "rng_idx": rng_idx,
+                # ground distance from the radar: the beam's arc length, which is
+                # exactly hypot(x, y) in this equidistant frame.
+                "d": np.hypot(xf, yf).astype(np.float32),
+                "z_asl": z_asl,
+                "z_rel": zf.astype(np.float32),
+            }))
+            # z_asl and lon/lat omitted here for the same reason as in h_plane:
+            # z_asl = z_rel + site altitude (a constant), and lon/lat are a
+            # closed form of (x, y). Both are derived by the accessors.
+            c_parts.append(pl.DataFrame({
+                "sweep": sweep_col,
+                "el_level": lvl_col,
+                "az_idx": az_idx,
+                "rng_idx": rng_idx,
+                "x": xf.astype(np.float32),
+                "y": yf.astype(np.float32),
+                "z_rel": zf.astype(np.float32),
+            }))
+
     return {
-        "x_edges":   x_e.astype(np.float64),
-        "y_edges":   y_e.astype(np.float64),
-        "z_edges":   z_e.astype(np.float64),
-        "lon_edges": lon_e.astype(np.float64),
-        "lat_edges": lat_e.astype(np.float64),
+        "h_plane": pl.concat(h_parts, how="vertical_relaxed"),
+        "v_plane": pl.concat(v_parts, how="vertical_relaxed"),
+        "corners": pl.concat(c_parts, how="vertical_relaxed"),
     }
 
 
+def load_plane_nodes(
+    radar: str,
+    lut_base_path: str | Path,
+    kind: str,
+    sweep: int | None = None,
+) -> "pl.DataFrame":
+    """Load one of the node-lattice files (``h_plane`` / ``v_plane`` / ``corners``).
+
+    ``sweep`` pushes a row filter into the parquet scan.
+    """
+    path = lut_file_path(radar, kind, lut_base_path)
+    if not path.exists():
+        raise FileNotFoundError(
+            f"{kind} lattice not found at {path}. Regenerate the LUT "
+            "(generate_lut_from_datatree) to create the geometry files."
+        )
+    lf = pl.scan_parquet(path)
+    if sweep is not None:
+        lf = lf.filter(pl.col("sweep") == int(sweep))
+    return lf.collect()
+
+
+def _node_grids(
+    nodes: "pl.DataFrame", value_cols: list[str], level: int | None = None
+) -> dict[int, dict[str, np.ndarray]]:
+    """Reshape flat lattice rows into ``{sweep: {col: (n_az+1, n_rng+1) array}}``."""
+    if level is not None and "el_level" in nodes.columns:
+        nodes = nodes.filter(pl.col("el_level") == level)
+    out: dict[int, dict[str, np.ndarray]] = {}
+    for (sweep_num,), sub in nodes.group_by(["sweep"], maintain_order=True):
+        sub = sub.sort(["az_idx", "rng_idx"])
+        n_az_n = int(sub["az_idx"].max()) + 1
+        n_rng_n = int(sub["rng_idx"].max()) + 1
+        out[int(sweep_num)] = {
+            c: sub[c].to_numpy().reshape(n_az_n, n_rng_n) for c in value_cols
+        }
+    return out
+
+
+def gate_corner_table(
+    radar: str,
+    lut_base_path: str | Path,
+    kind: str = "h_plane",
+    sweep: int | None = None,
+) -> "pl.DataFrame":
+    """Materialise per-gate corners from a node lattice, keyed by ``gate_id``.
+
+    This is the "hybrid" read side: the archive stores compact node lattices, and
+    this expands them to explicit per-gate corners on demand.
+
+    Corner counts and column names by ``kind``:
+
+    * ``h_plane`` — **4** corners, ring order (see :data:`GATE_RING_OFFSETS`):
+      ``x_1..x_4``, ``y_1..y_4`` (+ ``x_<epsg>_1..4`` when the lattice is
+      projected).
+    * ``v_plane`` — **4** corners in the RHI plane: ``d_1..d_4``,
+      ``z_asl_1..z_asl_4``, ``z_rel_1..z_rel_4``, ordered
+      (near-bottom, far-bottom, far-top, near-top).
+    * ``corners`` — **8** corners in 3-D: ``x_1..x_8``, ``y_1..y_8``,
+      ``z_rel_1..z_rel_8``.  **1-4 are the near face** (towards the radar),
+      **5-8 the far face**; within each face the order is
+      (az-, el-), (az+, el-), (az+, el+), (az-, el+).  Because the angular extent
+      grows with range, the far face is strictly larger than the near face.
+
+    The join onto ``gate_id`` reuses :func:`_gate_grid_index`, so it inherits the
+    same ``searchsorted`` node indexing the lattices were written with.
+    """
+    if kind not in ("h_plane", "v_plane", "corners"):
+        raise ValueError(
+            f"kind must be 'h_plane', 'v_plane' or 'corners'; got {kind!r}."
+        )
+
+    idx = _gate_grid_index(radar, lut_base_path)
+    if sweep is not None:
+        idx = idx.filter(pl.col("sweep") == int(sweep))
+    if idx.is_empty():
+        return pl.DataFrame(schema={"gate_id": pl.Int64, "sweep": pl.Int32})
+
+    nodes = load_plane_nodes(radar, lut_base_path, kind, sweep=sweep)
+
+    if kind == "h_plane":
+        value_cols = [c for c in nodes.columns
+                      if c not in ("sweep", "az_idx", "rng_idx", "el_level")]
+        grids = {0: _node_grids(nodes, value_cols)}
+        # 4 corners, one level, ring order
+        picks = [(0, i, j) for (i, j) in GATE_RING_OFFSETS]
+    elif kind == "v_plane":
+        value_cols = ["d", "z_asl", "z_rel"]
+        grids = {lvl: _node_grids(nodes, value_cols, level=lvl) for lvl in (-1, 1)}
+        # near-bottom, far-bottom, far-top, near-top
+        picks = [(-1, 0, 0), (-1, 0, 1), (1, 0, 1), (1, 0, 0)]
+    else:
+        value_cols = ["x", "y", "z_rel"]
+        grids = {lvl: _node_grids(nodes, value_cols, level=lvl) for lvl in (-1, 1)}
+        # near face (rng+0) then far face (rng+1); within a face:
+        # (az-, el-), (az+, el-), (az+, el+), (az-, el+)
+        picks = [
+            (-1, 0, 0), (-1, 1, 0), (1, 1, 0), (1, 0, 0),      # near face
+            (-1, 0, 1), (-1, 1, 1), (1, 1, 1), (1, 0, 1),      # far face
+        ]
+
+    n = idx.height
+    sweeps = idx["sweep"].to_numpy()
+    az_i = idx["az_idx"].to_numpy().astype(np.int64)
+    rng_i = idx["rng_idx"].to_numpy().astype(np.int64)
+
+    out: dict[str, np.ndarray] = {}
+    for k, (lvl, di, dj) in enumerate(picks, start=1):
+        for col in value_cols:
+            out[f"{col}_{k}"] = np.full(n, np.nan, dtype=np.float64)
+        for sw in np.unique(sweeps):
+            g = grids[lvl].get(int(sw))
+            if g is None:
+                logger.warning("sweep %d missing from the %s lattice.", sw, kind)
+                continue
+            rows = np.flatnonzero(sweeps == sw)
+            for col in value_cols:
+                arr = g[col]
+                out[f"{col}_{k}"][rows] = arr[az_i[rows] + di, rng_i[rows] + dj]
+
+    return pl.DataFrame({
+        "gate_id": idx["gate_id"],
+        "sweep": idx["sweep"],
+        **{c: v.astype(np.float32) for c, v in out.items()},
+    })
+
+
 def save_sweep_corners(
     corners_by_sweep: dict[int, dict], corners_path: str | Path
 ) -> str:
@@ -485,10 +955,48 @@ def save_sweep_corners(
     return str(corners_path)
 
 
+def _sweep_grids_from_lut(
+    radar: str, lut_base_path: str | Path
+) -> dict[int, dict]:
+    """Per-sweep ``(azimuths, ranges, elevation)`` grids read from the LUT parquet.
+
+    Only the four columns needed are pulled, with the projection pushed into the
+    parquet reader — reading the whole 13-column LUT here would be ~8x the I/O.
+
+    The grids are ``np.sort(unique(...))``, matching :func:`_gate_grid_index`'s
+    ``searchsorted`` and the pandas ``to_xarray()`` MultiIndex order used by
+    ``io_core.reconstruct_sweep_dataset``, so node indices are consistent
+    everywhere.
+    """
+    lut_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
+    if not lut_path.exists():
+        raise FileNotFoundError(f"LUT not found at {lut_path}.")
+    lut = pl.scan_parquet(lut_path).select(
+        ["sweep", "azimuth", "range", "elevation_angle"]
+    ).collect()
+
+    grids: dict[int, dict] = {}
+    for sweep_num in sorted(lut["sweep"].unique().to_list()):
+        sub = lut.filter(pl.col("sweep") == sweep_num)
+        azimuths = np.sort(sub["azimuth"].unique().to_numpy()).astype(np.float64)
+        ranges = np.sort(sub["range"].unique().to_numpy()).astype(np.float64)
+        # elevation_angle is constant per sweep by construction (the fixed
+        # antenna angle); broadcast it to one value per ray.
+        el = float(sub["elevation_angle"][0])
+        grids[int(sweep_num)] = {
+            "azimuths": azimuths,
+            "ranges": ranges,
+            "elevations": np.full(len(azimuths), el, dtype=np.float64),
+            "elevation": el,
+        }
+    return grids
+
+
 def compute_corners_from_lut(
     radar: str,
     lut_base_path: str | Path,
     ke: float = 4.0 / 3.0,
+    beamwidth_deg: float | None = None,
 ) -> str:
     """Rebuild ``{radar}_corners.npz`` from an existing LUT parquet + info YAML.
 
@@ -497,31 +1005,34 @@ def compute_corners_from_lut(
     elevation) triples from the LUT, computes corner arrays via
     :func:`compute_sweep_corners`, and saves them.
 
+    ``beamwidth_deg`` defaults to the value recorded in ``{radar}_info.yaml``
+    (falling back to :data:`DEFAULT_BEAMWIDTH_DEG`), so the vertical extent of
+    the beam is resolved rather than collapsed.
+
     Returns
     -------
     str : path to the written ``.npz`` file.
     """
-    lut_df = load_radar_lut(radar, lut_base_path)
     info = load_radar_info(radar, lut_base_path)
+    if beamwidth_deg is None:
+        beamwidth_deg = float(info.get("beamwidth_deg") or DEFAULT_BEAMWIDTH_DEG)
+
     corners_by_sweep: dict[int, dict] = {}
-    for sweep_num in sorted(lut_df["sweep"].unique().to_list()):
-        sub = lut_df.filter(pl.col("sweep") == sweep_num)
-        azimuths = np.sort(sub["azimuth"].unique().to_numpy())
-        ranges   = np.sort(sub["range"].unique().to_numpy())
-        n_az, n_rng = len(azimuths), len(ranges)
-        # Rebuild per-ray elevation from the LUT (stored as constant per sweep
-        # in elevation_angle). If the per-ray elevation varies within a sweep,
-        # we'd need the raw antenna data; for Swiss radars the fixed-angle
-        # approximation is accurate to < 0.1°.
-        el_mean = float(sub["elevation_angle"][0])
-        elevations = np.full(n_az, el_mean, dtype=np.float64)
-        corners_by_sweep[int(sweep_num)] = compute_sweep_corners(
-            ranges=ranges, azimuths=azimuths, elevations=elevations,
+    for sweep_num, g in _sweep_grids_from_lut(radar, lut_base_path).items():
+        full = compute_sweep_corners(
+            ranges=g["ranges"], azimuths=g["azimuths"], elevations=g["elevations"],
             radar_lat=info["latitude"],
             radar_lon=info["longitude"],
             radar_alt=info["altitude"],
             ke=ke,
+            beamwidth_deg=beamwidth_deg,
         )
+        # The npz keeps only the beam-centre mesh (its consumers — plot_ppi and
+        # reconstruct_sweep_dataset — are 2-D). The vertical levels live in the
+        # *_corners_LUT.parquet / *_v_plane_LUT.parquet files.
+        corners_by_sweep[int(sweep_num)] = {
+            k: v for k, v in full.items() if k != "levels"
+        }
     corners_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_corners.npz"
     save_sweep_corners(corners_by_sweep, corners_path)
     return str(corners_path)
@@ -771,9 +1282,25 @@ def load_radar_info(
 
 
 def get_full_sweep_index(
-    lut_df: pd.DataFrame, sweep: int
+    lut_df: "pl.DataFrame | pd.DataFrame", sweep: int
 ) -> pd.MultiIndex:
-    """Get the full (azimuth, range) MultiIndex for a sweep from the LUT."""
+    """Get the full (azimuth, range) MultiIndex for a sweep from the LUT.
+
+    Accepts a polars **or** a pandas LUT — :func:`load_radar_lut` returns polars,
+    so the polars form is the common one.  The return type stays a pandas
+    ``MultiIndex`` because its only consumer is the pandas/xarray reconstruction
+    bridge in :func:`raddb.io_core.reconstruct_sweep_dataset`, which reindexes
+    against it.
+    """
+    if isinstance(lut_df, pl.DataFrame):
+        sweep_lut = lut_df.filter(pl.col("sweep") == sweep).select(["azimuth", "range"])
+        if sweep_lut.is_empty():
+            raise ValueError(f"No LUT entries found for sweep={sweep}.")
+        return pd.MultiIndex.from_arrays(
+            [sweep_lut["azimuth"].to_numpy(), sweep_lut["range"].to_numpy()],
+            names=["azimuth", "range"],
+        )
+
     sweep_lut = lut_df[lut_df["sweep"] == sweep]
     if sweep_lut.empty:
         raise ValueError(f"No LUT entries found for sweep={sweep}.")
@@ -795,8 +1322,9 @@ def add_lut_projection(
     appends ``x_{suffix}`` / ``y_{suffix}`` columns, where ``suffix`` is the
     EPSG code (if available) or ``"custom"``.
 
-    Accepts either a polars or a pandas frame and returns the same kind — LUT
-    *loading* is polars, but LUT *generation* still builds pandas frames.
+    Accepts either a polars or a pandas frame and returns the same kind.  The
+    LUT layer is polars end to end (generation and loading), so the polars form
+    is the common one; pandas is accepted so external callers keep working.
 
     Requires **pyproj** (``pip install pyproj``).
 
diff --git a/raddb/main.py b/raddb/main.py
index 9513e6f..77f2dc8 100644
--- a/raddb/main.py
+++ b/raddb/main.py
@@ -61,9 +61,12 @@
 from raddb.lut import (
     RADAR_TO_IDX,
     generate_lut_from_datatree,
+    gate_corner_table,
+    load_plane_nodes,
     load_radar_lut,
     load_radar_info,
     add_lut_projection,
+    cartesian_to_geographic,
 )
 
 logger = logging.getLogger(__name__)
@@ -136,6 +139,128 @@ def _filter_expr(var: str, logic: str, threshold) -> "pl.Expr":
     return ops[logic]
 
 
+# ---------------------------------------------------------------------------
+# .sel() support — xarray-style label selection
+# ---------------------------------------------------------------------------
+
+#: Convenience aliases accepted by :meth:`RadDB.sel`.  Resolved only *after* the
+#: literal name fails to match a data or LUT column, so a real column always wins.
+_SEL_ALIASES: dict[str, str] = {
+    "radars": "radar",
+    "lat": "latitude",
+    "lats": "latitude",
+    "lon": "longitude",
+    "long": "longitude",
+    "lons": "longitude",
+    "alt": "altitude",
+    "altitudes": "altitude",
+    "sweeps": "sweep",
+    "azimuths": "azimuth",
+    "ranges": "range",
+    "elevation": "elevation_angle",
+    "elevations": "elevation_angle",
+    "times": "time",
+}
+
+#: Columns that carry a timestamp — selection on these accepts partial strings.
+_TIME_COLUMNS = frozenset({"time", "volume_time"})
+
+
+def _is_time_dtype(dtype) -> bool:
+    return dtype in (pl.Datetime, pl.Date) or isinstance(dtype, (pl.Datetime, pl.Date))
+
+
+def _time_bound(value, dtype, *, upper: bool):
+    """Coerce ``value`` to a timestamp comparable with a column of ``dtype``.
+
+    A *partial* string expands to the edge of the period it names, matching
+    pandas/xarray partial-string indexing: ``"2022-01"`` becomes
+    ``2022-01-01 00:00:00`` as a lower bound and ``2022-01-31 23:59:59.999…``
+    as an upper bound.  The result's tz-awareness is matched to the column.
+    """
+    if isinstance(value, (datetime.datetime, datetime.date, np.datetime64, pd.Timestamp)):
+        ts = pd.Timestamp(value)
+    else:
+        s = str(value).strip()
+        if upper:
+            try:
+                ts = pd.Period(s).end_time
+            except Exception:
+                ts = pd.Timestamp(s)
+        else:
+            try:
+                ts = pd.Period(s).start_time
+            except Exception:
+                ts = pd.Timestamp(s)
+
+    tz = getattr(dtype, "time_zone", None)
+    if tz:
+        ts = ts.tz_localize("UTC") if ts.tzinfo is None else ts.tz_convert(tz)
+    elif ts.tzinfo is not None:
+        ts = ts.tz_convert("UTC").tz_localize(None)
+    return ts
+
+
+def _sel_expr(name: str, value, dtype) -> "pl.Expr":
+    """Build the boolean expression selecting ``value`` on column ``name``.
+
+    ``value`` may be a ``slice`` (inclusive on both ends, as in xarray), a
+    list/tuple/set (membership), or a scalar (equality — or the enclosing period
+    for a timestamp column, so ``time="2024-08-26 02:46:08"`` still matches a
+    row stored as ``02:46:08.050``).
+    """
+    col = pl.col(name)
+    is_time = _is_time_dtype(dtype)
+
+    def bound(v, upper):
+        return _time_bound(v, dtype, upper=upper) if is_time else v
+
+    if isinstance(value, slice):
+        if value.step is not None:
+            raise ValueError(
+                f"sel({name}=...): a step is not supported (got step={value.step!r}); "
+                "use a plain slice(start, stop)."
+            )
+        parts = []
+        if value.start is not None:
+            parts.append(col >= bound(value.start, False))
+        if value.stop is not None:
+            parts.append(col <= bound(value.stop, True))
+        if not parts:
+            return pl.lit(True)
+        expr = parts[0]
+        for p in parts[1:]:
+            expr = expr & p
+        return expr
+
+    if isinstance(value, (list, tuple, set, frozenset, np.ndarray, pl.Series)):
+        vals = [v for v in (value.to_list() if isinstance(value, pl.Series) else list(value))]
+        if is_time:
+            # a list of timestamps/partial strings -> union of their periods
+            expr = None
+            for v in vals:
+                one = (col >= bound(v, False)) & (col <= bound(v, True))
+                expr = one if expr is None else (expr | one)
+            return pl.lit(False) if expr is None else expr
+        return col.is_in(vals)
+
+    if is_time:
+        return (col >= bound(value, False)) & (col <= bound(value, True))
+    return col == value
+
+
+def _ccw_polygons(polys: np.ndarray) -> np.ndarray:
+    """Force counter-clockwise exterior rings, as GeoParquet/GeoArrow prefer.
+
+    The gate corner order is deterministically clockwise (inherited from the
+    reference prototype), so serialised output needs flipping.
+    """
+    if hasattr(shapely, "orient_polygons"):        # shapely >= 2.1
+        return shapely.orient_polygons(polys)
+    ccw = shapely.is_ccw(shapely.get_exterior_ring(polys))
+    return np.where(ccw, polys, shapely.reverse(polys))
+
+
 def _resolve_filters(filters) -> list[tuple[str, str, float]]:
     """Normalize a filter dict / list-of-dicts to ``[(var, logic, threshold), ...]``."""
     if filters is None:
@@ -597,6 +722,158 @@ def add_lut_projection(self, radar: str, epsg: int | None = None, crs=None) -> "
         lut_df = load_radar_lut(normalize_radar_name(radar), self._require_archive_dir())
         return add_lut_projection(lut_df, epsg=epsg, crs=crs)
 
+    def get_h_plane(
+        self, radar: str, sweep: int | None = None, per_gate: bool = False
+    ) -> "pl.DataFrame":
+        """Horizontal-face geometry of each gate — the precise PPI footprint.
+
+        ``per_gate=False`` (default) returns the compact **node lattice** as
+        stored: ``sweep, az_idx, rng_idx, x, y`` (+ ``x_<epsg>, y_<epsg>`` when
+        the LUT was generated with a projection).  Neighbouring gates share
+        nodes, which is why the file is ~4x smaller than per-gate corners.
+
+        ``per_gate=True`` expands it to **4 corners per gate**, keyed by
+        ``gate_id``: ``x_1..x_4``, ``y_1..y_4`` in ring order.  Feed straight to
+        ``matplotlib.collections.PolyCollection`` or ``shapely.polygons``.
+
+        Note the first range bin is degenerate: its inner edge falls at the radar
+        (range_start ~= dR), so its footprint is a triangle rather than a
+        trapezoid.
+        """
+        radar = normalize_radar_name(radar)
+        base = self._require_archive_dir()
+        if per_gate:
+            return gate_corner_table(radar, base, kind="h_plane", sweep=sweep)
+        return load_plane_nodes(radar, base, "h_plane", sweep=sweep)
+
+    def get_v_plane(
+        self,
+        radar: str,
+        sweep: int | None = None,
+        azimuth: float | None = None,
+        per_gate: bool = False,
+    ) -> "pl.DataFrame":
+        """Vertical-face geometry of each gate — the precise RHI footprint.
+
+        Coordinates are ``(d, z)``: ``d`` is the ground distance from the radar
+        [m] and altitude is given **both ways** — ``z_asl`` (absolute, m above sea
+        level) and ``z_rel`` (relative to the radar).  They differ by the site
+        altitude in ``{radar}_info.yaml``.
+
+        ``per_gate=True`` returns 4 corners per gate ordered
+        (near-bottom, far-bottom, far-top, near-top): ``d_1..d_4``,
+        ``z_asl_1..z_asl_4``, ``z_rel_1..z_rel_4``.
+
+        ``azimuth`` keeps only the ray nearest that azimuth (requires
+        ``per_gate=True``), which is exactly the slice an RHI plots.
+        """
+        radar = normalize_radar_name(radar)
+        base = self._require_archive_dir()
+        if not per_gate:
+            if azimuth is not None:
+                raise ValueError("azimuth= selection requires per_gate=True.")
+            return load_plane_nodes(radar, base, "v_plane", sweep=sweep)
+
+        tbl = gate_corner_table(radar, base, kind="v_plane", sweep=sweep)
+        if azimuth is None:
+            return tbl
+        # Pick the nearest stored ray, comparing on the circle.
+        lut = load_radar_lut(radar, base).select(["gate_id", "azimuth"])
+        az = lut["azimuth"].to_numpy()
+        target = float(azimuth) % 360.0
+        diff = np.abs((az - target + 180.0) % 360.0 - 180.0)
+        nearest = float(az[np.argmin(diff)])
+        keep = lut.filter(pl.col("azimuth") == nearest).select("gate_id")
+        return tbl.join(keep, on="gate_id", how="semi")
+
+    def get_corners(
+        self, radar: str, sweep: int | None = None, per_gate: bool = False
+    ) -> "pl.DataFrame":
+        """Full 3-D gate corners — 8 per gate, for volume reconstruction.
+
+        ``per_gate=True`` returns ``x_1..x_8``, ``y_1..y_8``, ``z_rel_1..z_rel_8``
+        where **1-4 are the near face** (towards the radar) and **5-8 the far
+        face**; within a face the order is (az-, el-), (az+, el-), (az+, el+),
+        (az-, el+).
+
+        Because the beam's angular extent grows with range, the far face is
+        strictly larger than the near face — a useful invariant to assert.  The
+        one exception is the first range bin, whose near face collapses onto the
+        radar itself.
+
+        Add the site altitude from :meth:`get_radar_info` to ``z_rel`` for metres
+        above sea level.
+        """
+        radar = normalize_radar_name(radar)
+        base = self._require_archive_dir()
+        if per_gate:
+            return gate_corner_table(radar, base, kind="corners", sweep=sweep)
+        return load_plane_nodes(radar, base, "corners", sweep=sweep)
+
+    def export_h_plane_geoparquet(
+        self,
+        radar: str,
+        path: str | Path,
+        sweep: int | None = None,
+        epsg: int | None = None,
+    ) -> str:
+        """Write the horizontal gate footprints as **GeoParquet** (CRS embedded).
+
+        The archive keeps plain parquet — small and uniform.  This is the opt-in
+        interoperability path: the output opens directly in QGIS or
+        ``geopandas.read_parquet``.
+
+        ``epsg`` selects the output CRS: the LUT's projected columns when they
+        exist and match, otherwise WGS-84 (4326) derived from the radar-relative
+        ``x``/``y``.  Exterior rings are normalised counter-clockwise, as the
+        GeoParquet spec prefers.
+        """
+        import geopandas as gpd
+
+        radar = normalize_radar_name(radar)
+        base = self._require_archive_dir()
+        tbl = gate_corner_table(radar, base, kind="h_plane", sweep=sweep)
+
+        if epsg is None:
+            epsg = int(self._crs) if isinstance(self._crs, int) else None
+
+        xs = [f"x_{epsg}_{k}" for k in range(1, 5)] if epsg else []
+        if xs and all(c in tbl.columns for c in xs):
+            xcols = xs
+            ycols = [f"y_{epsg}_{k}" for k in range(1, 5)]
+            out_crs = f"EPSG:{epsg}"
+        else:
+            # Fall back to WGS-84 from the radar-relative metres.
+            info = load_radar_info(radar, base)
+            ring = np.stack([
+                np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1)
+                for k in range(1, 5)
+            ], axis=1)
+            lat, lon, _ = cartesian_to_geographic(
+                ring[:, :, 0], ring[:, :, 1], np.zeros(ring.shape[:2]),
+                info["latitude"], info["longitude"], info["altitude"],
+            )
+            ring = np.concatenate([np.stack([lon, lat], axis=2),
+                                   np.stack([lon[:, :1], lat[:, :1]], axis=2)], axis=1)
+            gdf = gpd.GeoDataFrame(
+                {"gate_id": tbl["gate_id"].to_numpy(), "sweep": tbl["sweep"].to_numpy()},
+                geometry=_ccw_polygons(shapely.polygons(ring)), crs="EPSG:4326",
+            )
+            gdf.to_parquet(path)
+            return str(path)
+
+        ring = np.stack([
+            np.stack([tbl[xc].to_numpy(), tbl[yc].to_numpy()], axis=1)
+            for xc, yc in zip(xcols, ycols)
+        ], axis=1)
+        ring = np.concatenate([ring, ring[:, :1, :]], axis=1)   # close the ring
+        gdf = gpd.GeoDataFrame(
+            {"gate_id": tbl["gate_id"].to_numpy(), "sweep": tbl["sweep"].to_numpy()},
+            geometry=_ccw_polygons(shapely.polygons(ring)), crs=out_crs,
+        )
+        gdf.to_parquet(path)
+        return str(path)
+
     def list_radars(self) -> list[str]:
         """List radar identifiers that have data in the archive."""
         return _list_archive_radars(self._require_archive_dir())
@@ -821,6 +1098,166 @@ def filter(self, filters) -> "RadDB":
             data = data.drop(borrowed)
         return self._derive(data)
 
+    def _lut_paths(self) -> dict[str, Path]:
+        """``{radar: LUT parquet path}`` for the radars present in the data."""
+        archive_dir = self._require_archive_dir()
+        out = {}
+        for r in self.radars():
+            rr = normalize_radar_name(r)
+            p = Path(archive_dir) / rr / "LUT" / f"{rr}_LUT.parquet"
+            if p.exists():
+                out[rr] = p
+        return out
+
+    def _lut_column_names(self) -> list[str]:
+        """LUT column names, read from the parquet **schema** (no data loaded)."""
+        for p in self._lut_paths().values():
+            return list(pl.scan_parquet(p).collect_schema().names())
+        return []
+
+    def _borrow_lut_columns(self, cols: list[str]) -> "pl.DataFrame":
+        """Load ``cols`` from the LUT for the gates present, keyed by ``gate_id``.
+
+        The general form of :meth:`_gate_geometry` (which exposes only
+        lon/lat/alt/sweep): this can borrow **any** LUT column — ``range``,
+        ``azimuth``, ``elevation_angle``, ``x``/``y``/``z`` … .  Column
+        projection is pushed into the parquet reader, and the result is
+        restricted to the gates currently in ``.data``, so the geometry stays
+        synchronised with the (possibly already filtered) values.
+        """
+        paths = self._lut_paths()
+        if not paths:
+            raise ValueError(
+                "no LUT found for the radars in this data; cannot select on "
+                f"static columns {cols}."
+            )
+        present = self._require_data().select("gate_id").unique()
+        parts = []
+        for p in paths.values():
+            names = pl.scan_parquet(p).collect_schema().names()
+            keep = ["gate_id", *[c for c in cols if c in names and c != "gate_id"]]
+            parts.append(
+                pl.scan_parquet(p).select(keep).join(present.lazy(), on="gate_id", how="semi")
+            )
+        return pl.concat(parts, how="vertical_relaxed").collect().unique(
+            subset="gate_id", maintain_order=True
+        )
+
+    def sel(self, **indexers) -> "RadDB":
+        """Select gates by label, xarray-style; returns a **new** ``RadDB``.
+
+        Each keyword names a column and gives what to keep:
+
+        * ``slice(start, stop)`` — a range, **inclusive of both ends** (as in
+          ``xarray.Dataset.sel``, not like Python list slicing).  Either end may
+          be ``None`` to leave it open.  A ``step`` is rejected.
+        * a list / tuple / set — membership, e.g. ``radars=["A", "L"]``.
+        * a scalar — equality.  For a timestamp column a scalar (or a *partial*
+          string) selects the whole period it names, so
+          ``time="2024-08-26 02:46:08"`` still matches a row stored as
+          ``02:46:08.050``, and ``time="2024-08"`` selects that month.
+
+        Dynamic columns (``DBZH``, ``ZDR``, ``time`` …) are matched directly.
+        **Static/geometry columns come from the LUT** (``latitude``,
+        ``longitude``, ``altitude``, ``range``, ``azimuth``, ``sweep``,
+        ``elevation_angle``, ``x``/``y``/``z``, ``x_<epsg>``/``y_<epsg>``): they
+        are borrowed from the LUT only for as long as the predicate needs them
+        and then dropped, so the returned object still carries **dynamic values
+        only** — the LUT is never concatenated onto the data.  Because the LUT is
+        always re-derived for the gates currently present, it stays in sync
+        automatically after any number of chained selections.
+
+        Aliases are accepted when they do not collide with a real column:
+        ``radars``→``radar``, ``lat``→``latitude``, ``lon``→``longitude``,
+        ``alt``→``altitude``, ``ranges``→``range``, ``sweeps``→``sweep``,
+        ``elevation``→``elevation_angle``.
+
+        All keywords are combined with **AND**.  The original object is never
+        modified.
+
+        Examples
+        --------
+        >>> rdf.sel(time=slice("2021-02", "2022-03"), DBZH=slice(0, 10))
+        >>> rdf.sel(time="2022-01-03 14:00:00")
+        >>> rdf.sel(radars=["A", "L"])
+        >>> rdf.sel(range=slice(10_000, 50_000))
+        >>> rdf.sel(lon=slice(8.0, 9.0), lat=slice(46.0, 47.0))
+
+        Raises
+        ------
+        KeyError
+            If a keyword matches neither a data column nor a LUT column.
+        """
+        if not indexers:
+            return self._derive(self._require_data())
+
+        data = self._require_data()
+        lut_names = None  # loaded lazily, only if a static column is requested
+
+        resolved: list[tuple[str, object]] = []   # (column, value)
+        static: list[str] = []
+        radar_from_gate_id = None
+
+        for key, value in indexers.items():
+            name = key
+            if name not in data.columns:
+                if lut_names is None:
+                    lut_names = self._lut_column_names()
+                if name not in lut_names:
+                    alias = _SEL_ALIASES.get(name)
+                    if alias and (alias in data.columns or alias in lut_names):
+                        name = alias
+                    elif alias == "radar" or name in ("radar", "radars"):
+                        # no radar column stored -> select via the gate_id prefix
+                        radar_from_gate_id = value
+                        continue
+                    elif name in _TIME_COLUMNS or _SEL_ALIASES.get(name) in _TIME_COLUMNS:
+                        try:
+                            name = self._time_column()
+                        except KeyError:
+                            raise KeyError(
+                                f"sel({key}=...): data has no time column."
+                            ) from None
+                    else:
+                        raise KeyError(
+                            f"sel({key}=...): {name!r} is neither a data column "
+                            f"{sorted(data.columns)} nor a LUT column {sorted(lut_names)}."
+                        )
+            if name not in data.columns:
+                static.append(name)
+            resolved.append((name, value))
+
+        # Borrow the static columns just long enough to evaluate their predicates.
+        borrowed: list[str] = []
+        if static:
+            lut_tbl = self._borrow_lut_columns(sorted(set(static)))
+            borrowed = [c for c in dict.fromkeys(static) if c in lut_tbl.columns]
+            still_missing = [c for c in static if c not in borrowed]
+            if still_missing:
+                raise KeyError(
+                    f"sel(): LUT has no column(s) {still_missing}; "
+                    f"available: {sorted(lut_tbl.columns)}."
+                )
+            data = data.join(
+                lut_tbl.select(["gate_id", *borrowed]), on="gate_id", how="left",
+                maintain_order="left",
+            )
+
+        for name, value in resolved:
+            data = data.filter(_sel_expr(name, value, data.schema[name]))
+
+        if radar_from_gate_id is not None:
+            wanted = (
+                [radar_from_gate_id] if isinstance(radar_from_gate_id, str)
+                else list(radar_from_gate_id)
+            )
+            idx = [RADAR_TO_IDX[normalize_radar_name(r)] for r in wanted]
+            data = data.filter((pl.col("gate_id") // 1_000_000_000_000).is_in(idx))
+
+        if borrowed:
+            data = data.drop(borrowed)
+        return self._derive(data)
+
     def add_feature(self, name: str, compute_fn) -> "RadDB":
         """Add a computed column ``name`` and return a new RadDB.
 
@@ -956,36 +1393,45 @@ def to_datatree(self, radar: str | None = None, timestep=None, label_column: str
         - ``radar`` — inferred when the data covers exactly one radar, else required.
         - ``timestep`` — nearest ``volume_time``; required only when several volumes.
         """
-        df = self.to_pandas()
-        if "gate_id" not in df.columns:
+        # Radar/volume selection runs on the polars frame, so only the single
+        # selected volume crosses into pandas (inside dataframe_to_datatree),
+        # not the whole loaded dataset.
+        data = self._require_data()
+        if "gate_id" not in data.columns:
             raise KeyError("data has no 'gate_id' column; cannot reconstruct a DataTree.")
 
-        present = _radars_from_gate_ids(df["gate_id"])
+        present = _radars_from_gate_ids(data["gate_id"].to_numpy())
         if radar is None:
             if len(present) != 1:
                 raise ValueError(f"data spans radars {present}; pass radar= to pick one.")
             radar = present[0]
         radar = normalize_radar_name(radar)
 
-        if "radar" in df.columns:
-            df_r = df[df["radar"] == radar]
+        if "radar" in data.columns:
+            df_r = data.filter(pl.col("radar") == radar)
         else:
-            df_r = df[df["gate_id"].to_numpy() // 1_000_000_000_000 == RADAR_TO_IDX[radar]]
-        if df_r.empty:
+            df_r = data.filter(
+                (pl.col("gate_id") // 1_000_000_000_000) == RADAR_TO_IDX[radar]
+            )
+        if df_r.is_empty():
             raise ValueError(f"No rows for radar {radar!r} in data.")
 
-        if "volume_time" in df_r.columns and df_r["volume_time"].notna().any():
-            vols = pd.to_datetime(df_r["volume_time"]).dropna().unique()
+        if "volume_time" in df_r.columns and df_r["volume_time"].is_not_null().any():
+            vols = df_r["volume_time"].drop_nulls().unique().sort().to_list()
             if timestep is None:
                 if len(vols) != 1:
                     raise ValueError(f"data holds {len(vols)} volumes; pass timestep= to pick one.")
-                chosen = pd.Timestamp(vols[0])
+                chosen = vols[0]
             else:
                 ts = pd.to_datetime(timestep)
-                if ts.tzinfo is None:
+                # Match the tz-awareness of the stored volume_time before comparing.
+                aware = getattr(vols[0], "tzinfo", None) is not None
+                if aware and ts.tzinfo is None:
                     ts = ts.tz_localize("UTC")
-                chosen = min((pd.Timestamp(v) for v in vols), key=lambda v: abs(v - ts))
-            df_vol = df_r[pd.to_datetime(df_r["volume_time"]) == chosen]
+                elif not aware and ts.tzinfo is not None:
+                    ts = ts.tz_convert("UTC").tz_localize(None)
+                chosen = min(vols, key=lambda v: abs(pd.Timestamp(v) - ts))
+            df_vol = df_r.filter(pl.col("volume_time") == chosen)
         else:
             df_vol = df_r
 
diff --git a/raddb/tests/test_fixes.py b/raddb/tests/test_fixes.py
index 98a1509..2409d0d 100644
--- a/raddb/tests/test_fixes.py
+++ b/raddb/tests/test_fixes.py
@@ -167,7 +167,7 @@ def test_sweep_present_after_lut_merge(self, tmp_path):
             merge_lut=True,
         )
 
-        assert not df.empty, "DataFrame should not be empty"
+        assert not df.is_empty(), "DataFrame should not be empty"
         assert "sweep" in df.columns, "sweep column must be present after LUT merge"
         assert "azimuth" in df.columns
         assert "range" in df.columns
diff --git a/raddb/tests/test_lut_planes.py b/raddb/tests/test_lut_planes.py
new file mode 100644
index 0000000..900ee18
--- /dev/null
+++ b/raddb/tests/test_lut_planes.py
@@ -0,0 +1,433 @@
+"""
+raddb/tests/test_lut_planes.py
+------------------------------
+Tests for the five-file LUT directory: the gate-centroid LUT plus the
+horizontal-face, vertical-face and 3-D corner node lattices, and the extended
+info YAML.
+
+Key invariants covered:
+
+1. all five files are written by ``archive()`` / ``generate_lut_from_datatree``
+2. **the frustum property** — a gate's far face is strictly larger than its near
+   face (the beam widens with range)
+3. each gate's centroid lies inside its own horizontal footprint, and between the
+   bottom and top elevation levels
+4. node sharing — the lattice is ``(n_az+1) x (n_rng+1)`` per level
+5. ``z_asl - z_rel == site altitude`` everywhere
+6. **file-size budgets** — the geometry files must stay compact
+
+All tests use synthetic DataTrees in ``tmp_path``; no real radar files needed.
+"""
+from __future__ import annotations
+
+import numpy as np
+import polars as pl
+import pytest
+import shapely
+import yaml
+
+from raddb.lut import (
+    DEFAULT_BEAMWIDTH_DEG,
+    LUT_FILES,
+    gate_corner_table,
+    generate_lut_from_datatree,
+    lut_file_path,
+)
+from raddb.main import RadDB
+from raddb.tests.test_fixes import RADAR, _make_datatree
+
+# The synthetic volume: 12 azimuths x 24 ranges x 2 sweeps (see test_fixes).
+N_AZ, N_RNG, N_SWEEPS = 12, 24, 2
+N_GATES = N_AZ * N_RNG * N_SWEEPS
+
+
+@pytest.fixture
+def lut_dir(tmp_path):
+    """Generate a full 5-file LUT directory and return its path."""
+    generate_lut_from_datatree(
+        _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
+        projection_epsg=2056,
+    )
+    return tmp_path
+
+
+@pytest.fixture
+def base(tmp_path):
+    """The archive base path with a generated LUT (for the accessors)."""
+    generate_lut_from_datatree(
+        _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
+        projection_epsg=2056,
+    )
+    return str(tmp_path)
+
+
+# A realistically-sampled volume: 1 deg azimuth spacing, like a real radar.
+#
+# Needed by two groups of tests:
+#
+# * geometry — a gate footprint is a straight-sided quad, so its outer chord cuts
+#   inside the true arc by the sagitta. The centroid falls outside its own
+#   footprint beyond r ~ dR*cos(h)/(1-cos(h)) where h is the azimuth half-spacing.
+#   At the 30 deg spacing of the small fixture that is only ~11.7 km; at 1 deg it
+#   is ~5400 km, i.e. never. Real radars sample at 1 deg.
+# * file sizes — bytes-per-gate is meaningless on a 576-gate file, where the
+#   fixed parquet footer dominates.
+REAL_N_AZ, REAL_N_RNG, REAL_N_SWEEPS = 360, 200, 2
+REAL_N_GATES = REAL_N_AZ * REAL_N_RNG * REAL_N_SWEEPS
+
+
+@pytest.fixture(scope="module")
+def real_base(tmp_path_factory):
+    """A realistically-sampled LUT (1 deg azimuths), built once per module."""
+    d = tmp_path_factory.mktemp("realistic")
+    generate_lut_from_datatree(
+        _make_datatree(n_az=REAL_N_AZ, n_rng=REAL_N_RNG, n_sweeps=REAL_N_SWEEPS),
+        radar=RADAR, output_base_path=str(d), projection_epsg=2056,
+    )
+    return str(d)
+
+
+@pytest.fixture(scope="module")
+def real_base_plain(tmp_path_factory):
+    """As :func:`real_base` but with no projected coordinate columns."""
+    d = tmp_path_factory.mktemp("realistic_plain")
+    generate_lut_from_datatree(
+        _make_datatree(n_az=REAL_N_AZ, n_rng=REAL_N_RNG, n_sweeps=REAL_N_SWEEPS),
+        radar=RADAR, output_base_path=str(d),
+    )
+    return str(d)
+
+
+def _face_area(t: pl.DataFrame, ks) -> np.ndarray:
+    """Planar polygon area of a 4-corner face in 3-D (Newell's method)."""
+    pts = np.stack([
+        np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy(),
+                  t[f"z_rel_{k}"].to_numpy()], axis=1)
+        for k in ks
+    ], axis=1)
+    n = np.zeros((pts.shape[0], 3))
+    for i in range(4):
+        n += np.cross(pts[:, i], pts[:, (i + 1) % 4])
+    return 0.5 * np.linalg.norm(n, axis=1)
+
+
+class TestAllFilesWritten:
+    def test_generate_writes_five_files(self, lut_dir):
+        d = lut_dir / RADAR / "LUT"
+        for kind, tmpl in LUT_FILES.items():
+            f = d / tmpl.format(radar=RADAR)
+            assert f.exists(), f"{kind} missing: {f.name}"
+            assert f.stat().st_size > 0
+
+    def test_archive_writes_five_files(self, tmp_path):
+        db = RadDB(archive_dir=str(tmp_path), crs=2056)
+        db.archive(datatree=_make_datatree(), radar=RADAR)
+        d = tmp_path / RADAR / "LUT"
+        assert sorted(f.name for f in d.iterdir()) == sorted(
+            t.format(radar=RADAR) for t in LUT_FILES.values()
+        )
+
+    def test_missing_planes_are_backfilled(self, lut_dir):
+        """An archive predating the lattices regenerates them, keeping its LUT."""
+        d = lut_dir / RADAR / "LUT"
+        lut_file = d / LUT_FILES["lut"].format(radar=RADAR)
+        stamp = lut_file.stat().st_mtime_ns
+        for kind in ("h_plane", "v_plane", "corners"):
+            (d / LUT_FILES[kind].format(radar=RADAR)).unlink()
+
+        generate_lut_from_datatree(
+            _make_datatree(), radar=RADAR, output_base_path=str(lut_dir),
+            projection_epsg=2056,
+        )
+        for kind in ("h_plane", "v_plane", "corners"):
+            assert (d / LUT_FILES[kind].format(radar=RADAR)).exists()
+        # the centroid LUT was not rewritten
+        assert lut_file.stat().st_mtime_ns == stamp
+
+
+class TestInfoYaml:
+    def test_extended_keys(self, lut_dir):
+        info = yaml.safe_load(
+            (lut_dir / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
+        )
+        for key in ("radar", "network", "latitude", "longitude", "altitude",
+                    "crs", "ke", "beamwidth_deg", "n_sweeps", "n_gates", "sweeps"):
+            assert key in info, f"missing info key {key!r}"
+        assert info["ke"] == pytest.approx(4.0 / 3.0)
+        assert info["beamwidth_deg"] == DEFAULT_BEAMWIDTH_DEG
+        assert info["n_gates"] == N_GATES
+        assert info["n_sweeps"] == N_SWEEPS
+        assert info["crs"]["epsg"] == 2056
+        assert info["crs"]["columns"] == ["x_2056", "y_2056"]
+
+    def test_per_sweep_keys(self, lut_dir):
+        info = yaml.safe_load(
+            (lut_dir / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
+        )
+        s = info["sweeps"][1]
+        for key in ("n_azimuths", "n_ranges", "n_gates", "elevation",
+                    "range_resolution", "range_start", "dR"):
+            assert key in s, f"missing per-sweep key {key!r}"
+        assert s["n_gates"] == s["n_azimuths"] * s["n_ranges"]
+        # both are rounded to mm in the YAML, so allow half a mm of slack
+        assert s["dR"] == pytest.approx(s["range_resolution"] / 2.0, abs=1e-3)
+
+    def test_crs_is_null_without_projection(self, tmp_path):
+        generate_lut_from_datatree(
+            _make_datatree(), radar=RADAR, output_base_path=str(tmp_path)
+        )
+        info = yaml.safe_load(
+            (tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
+        )
+        assert info["crs"] is None
+
+
+class TestLatticeShape:
+    def test_h_plane_is_one_node_grid_per_sweep(self, base):
+        db = RadDB(archive_dir=base)
+        nodes = db.get_h_plane(RADAR)
+        assert nodes.height == N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
+        assert "el_level" not in nodes.columns          # centre level only
+
+    def test_corners_has_two_elevation_levels(self, base):
+        db = RadDB(archive_dir=base)
+        nodes = db.get_corners(RADAR)
+        assert sorted(nodes["el_level"].unique().to_list()) == [-1, 1]
+        assert nodes.height == 2 * N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
+
+    def test_sweep_filter(self, base):
+        db = RadDB(archive_dir=base)
+        one = db.get_h_plane(RADAR, sweep=1)
+        assert one["sweep"].unique().to_list() == [1]
+        assert one.height == (N_AZ + 1) * (N_RNG + 1)
+
+    def test_projected_columns_present(self, base):
+        db = RadDB(archive_dir=base)
+        assert {"x_2056", "y_2056"} <= set(db.get_h_plane(RADAR).columns)
+
+
+class TestPerGateCorners:
+    def test_h_plane_has_four_corners(self, base):
+        t = RadDB(archive_dir=base).get_h_plane(RADAR, per_gate=True)
+        assert t.height == N_GATES
+        for k in range(1, 5):
+            assert f"x_{k}" in t.columns and f"y_{k}" in t.columns
+        assert "x_5" not in t.columns
+
+    def test_corners_has_eight(self, base):
+        t = RadDB(archive_dir=base).get_corners(RADAR, per_gate=True)
+        assert t.height == N_GATES
+        for k in range(1, 9):
+            assert {f"x_{k}", f"y_{k}", f"z_rel_{k}"} <= set(t.columns)
+        assert "x_9" not in t.columns
+
+    def test_eight_corners_are_distinct(self, base):
+        """8 distinct corners, except the degenerate innermost range bin."""
+        db = RadDB(archive_dir=base)
+        t = db.get_corners(RADAR, per_gate=True)
+        pts = np.stack([
+            np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy(),
+                      t[f"z_rel_{k}"].to_numpy()], axis=1)
+            for k in range(1, 9)
+        ], axis=1)
+        n_distinct = np.array([
+            len({tuple(np.round(p, 3)) for p in pts[i]}) for i in range(pts.shape[0])
+        ])
+        # the first range bin's near face collapses onto the radar -> 5 distinct
+        assert set(np.unique(n_distinct)) <= {5, 8}
+        assert (n_distinct == 8).mean() > 0.9
+
+    def test_gate_ids_match_the_lut(self, base):
+        db = RadDB(archive_dir=base)
+        lut_ids = set(db.get_lut(RADAR)["gate_id"].to_list())
+        assert set(db.get_corners(RADAR, per_gate=True)["gate_id"].to_list()) == lut_ids
+
+
+class TestFrustumProperty:
+    """The beam widens with range: the far face must exceed the near face."""
+
+    def test_far_face_is_larger_than_near_face(self, base):
+        t = RadDB(archive_dir=base).get_corners(RADAR, per_gate=True)
+        near = _face_area(t, [1, 2, 3, 4])
+        far = _face_area(t, [5, 6, 7, 8])
+        assert np.all(far > near), (
+            f"{int((far <= near).sum())} gate(s) have a far face no larger than "
+            "the near face"
+        )
+
+    def test_ratio_is_physically_sane(self, base):
+        """Excluding the degenerate innermost bin, the ratio stays bounded."""
+        t = RadDB(archive_dir=base).get_corners(RADAR, per_gate=True)
+        near = _face_area(t, [1, 2, 3, 4])
+        far = _face_area(t, [5, 6, 7, 8])
+        ok = near > 1.0                      # drop the r~0 near face
+        ratio = far[ok] / near[ok]
+        assert ratio.min() > 1.0
+        assert ratio.max() < 100.0
+
+    def test_faces_are_valid_polygons(self, base):
+        t = RadDB(archive_dir=base).get_h_plane(RADAR, per_gate=True)
+        ring = np.stack([
+            np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy()], axis=1)
+            for k in (1, 2, 3, 4, 1)
+        ], axis=1)
+        assert shapely.is_valid(shapely.polygons(ring)).all()
+
+
+class TestCentroidContainment:
+    def test_centroid_inside_its_own_footprint(self, real_base):
+        """Needs realistic (1 deg) azimuth sampling — see ``real_base``."""
+        db = RadDB(archive_dir=real_base)
+        t = db.get_h_plane(RADAR, per_gate=True).sort("gate_id")
+        lut = db.get_lut(RADAR).sort("gate_id")
+        ring = np.stack([
+            np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy()], axis=1)
+            for k in (1, 2, 3, 4, 1)
+        ], axis=1)
+        polys = shapely.polygons(ring)
+        pts = shapely.points(
+            np.stack([lut["x"].to_numpy(), lut["y"].to_numpy()], axis=1)
+        )
+        assert shapely.covers(polys, pts).all()
+
+    def test_centroid_between_the_elevation_levels(self, base):
+        db = RadDB(archive_dir=base)
+        t = db.get_corners(RADAR, per_gate=True).sort("gate_id")
+        lut = db.get_lut(RADAR).sort("gate_id")
+        zc = lut["z"].to_numpy()
+        zs = np.stack([t[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
+        # 1 cm tolerance: on a negative-elevation sweep z(r) has a turning point,
+        # so a centre can sit ~mm outside the bracket of its own corners.
+        assert (zc >= zs.min(axis=1) - 0.01).all()
+        assert (zc <= zs.max(axis=1) + 0.01).all()
+
+
+class TestVPlane:
+    def test_altitude_references_differ_by_site_altitude(self, base):
+        db = RadDB(archive_dir=base)
+        site_alt = db.get_radar_info(RADAR)["altitude"]
+        nodes = db.get_v_plane(RADAR)
+        d = nodes["z_asl"].to_numpy() - nodes["z_rel"].to_numpy()
+        assert np.allclose(d, site_alt, atol=1e-3)
+
+    def test_ground_distance_is_monotonic_in_range(self, base):
+        nodes = RadDB(archive_dir=base).get_v_plane(RADAR, sweep=1)
+        sub = nodes.filter(pl.col("el_level") == 1).sort(["az_idx", "rng_idx"])
+        d = sub.filter(pl.col("az_idx") == 0)["d"].to_numpy()
+        assert np.all(np.diff(d) > 0)
+
+    def test_per_gate_has_four_corners(self, base):
+        t = RadDB(archive_dir=base).get_v_plane(RADAR, per_gate=True)
+        assert t.height == N_GATES
+        for k in range(1, 5):
+            assert {f"d_{k}", f"z_asl_{k}", f"z_rel_{k}"} <= set(t.columns)
+
+    def test_azimuth_selection_picks_one_ray(self, base):
+        db = RadDB(archive_dir=base)
+        t = db.get_v_plane(RADAR, azimuth=0.0, per_gate=True)
+        assert 0 < t.height < N_GATES
+        assert t.height == N_RNG * N_SWEEPS
+
+    def test_azimuth_without_per_gate_raises(self, base):
+        with pytest.raises(ValueError):
+            RadDB(archive_dir=base).get_v_plane(RADAR, azimuth=90.0)
+
+
+class TestGeoParquetExport:
+    def test_export_embeds_crs_and_is_ccw(self, base, tmp_path):
+        gpd = pytest.importorskip("geopandas")
+        out = tmp_path / "h_plane.parquet"
+        RadDB(archive_dir=base, crs=2056).export_h_plane_geoparquet(RADAR, out)
+        g = gpd.read_parquet(out)
+        assert len(g) == N_GATES
+        assert g.crs is not None and g.crs.to_epsg() == 2056
+        assert g.geometry.is_valid.all()
+        assert shapely.is_ccw(shapely.get_exterior_ring(g.geometry.values)).all()
+
+    def test_export_falls_back_to_wgs84(self, base, tmp_path):
+        gpd = pytest.importorskip("geopandas")
+        out = tmp_path / "h_plane_4326.parquet"
+        RadDB(archive_dir=base).export_h_plane_geoparquet(RADAR, out, epsg=9999)
+        g = gpd.read_parquet(out)
+        assert g.crs.to_epsg() == 4326
+        assert g.geometry.is_valid.all()
+
+
+class TestFileSizeBudget:
+    """The geometry files must stay compact — the whole point of lattices.
+
+    Budgets are bytes per *gate* on disk, measured on the realistically-sampled
+    fixture (144 k gates) so the fixed parquet footer does not dominate.  For
+    reference, radar L (1.72 M gates, 20 sweeps) measures
+    7.4 B/gate for h_plane unprojected, 13.6 projected, 14.8 for corners and
+    0.2 for v_plane — ~49 MB of geometry against a 79 MB centroid LUT.
+
+    ``v_plane`` is startlingly small because ground distance and altitude do not
+    depend on azimuth, so parquet run-length-encodes it almost completely away.
+    """
+
+    BUDGET_PROJECTED = {"h_plane": 18.0, "v_plane": 2.0, "corners": 20.0}
+    BUDGET_PLAIN = {"h_plane": 10.0, "v_plane": 2.0, "corners": 20.0}
+
+    def _sizes(self, base):
+        return {
+            kind: lut_file_path(RADAR, kind, base).stat().st_size / REAL_N_GATES
+            for kind in ("h_plane", "v_plane", "corners")
+        }
+
+    def test_projected_budget(self, real_base):
+        for kind, bpg in self._sizes(real_base).items():
+            assert bpg <= self.BUDGET_PROJECTED[kind], (
+                f"{kind} is {bpg:.1f} B/gate, over the "
+                f"{self.BUDGET_PROJECTED[kind]} B/gate budget"
+            )
+
+    def test_unprojected_budget(self, real_base_plain):
+        for kind, bpg in self._sizes(real_base_plain).items():
+            assert bpg <= self.BUDGET_PLAIN[kind], (
+                f"{kind} is {bpg:.1f} B/gate, over the "
+                f"{self.BUDGET_PLAIN[kind]} B/gate budget"
+            )
+
+    def test_geometry_stays_smaller_than_the_centroid_lut(self, real_base):
+        """All three geometry files together must not dwarf the LUT itself."""
+        lut = lut_file_path(RADAR, "lut", real_base).stat().st_size
+        geom = sum(
+            lut_file_path(RADAR, k, real_base).stat().st_size
+            for k in ("h_plane", "v_plane", "corners")
+        )
+        assert geom < lut, f"geometry {geom/1e6:.1f} MB vs LUT {lut/1e6:.1f} MB"
+
+    def test_lattice_beats_per_gate_materialisation(self, real_base):
+        """The stored lattice must be smaller than expanding every gate's corners."""
+        db = RadDB(archive_dir=real_base)
+        stored = lut_file_path(RADAR, "corners", real_base).stat().st_size
+        per_gate = db.get_corners(RADAR, per_gate=True)
+        # 8 corners x 3 coords x 4 bytes, the floor for a per-gate layout
+        naive = per_gate.height * 8 * 3 * 4
+        assert stored < naive
+
+
+class TestBeamwidth:
+    def test_beamwidth_parameter_widens_the_gate(self, tmp_path):
+        heights = {}
+        for bw in (1.0, 2.0):
+            d = tmp_path / f"bw{bw}"
+            generate_lut_from_datatree(
+                _make_datatree(), radar=RADAR, output_base_path=str(d),
+                beamwidth_deg=bw,
+            )
+            t = gate_corner_table(RADAR, str(d), kind="corners", sweep=1)
+            zs = np.stack([t[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
+            heights[bw] = float(np.mean(zs.max(axis=1) - zs.min(axis=1)))
+        assert heights[2.0] > heights[1.0] * 1.5
+
+    def test_beamwidth_recorded_in_yaml(self, tmp_path):
+        generate_lut_from_datatree(
+            _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
+            beamwidth_deg=1.5,
+        )
+        info = yaml.safe_load(
+            (tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
+        )
+        assert info["beamwidth_deg"] == 1.5
diff --git a/raddb/tests/test_sel.py b/raddb/tests/test_sel.py
new file mode 100644
index 0000000..bf2ee04
--- /dev/null
+++ b/raddb/tests/test_sel.py
@@ -0,0 +1,173 @@
+"""
+raddb/tests/test_sel.py
+-----------------------
+Tests for ``RadDB.sel()`` — xarray-style label selection.
+
+Covers:
+
+1. dynamic-column selection (slice / scalar / list), inclusive slice bounds
+2. **static (LUT) column** selection — the borrowed column must be dropped
+   again, so the result still carries dynamic values only
+3. the LUT staying synchronised with the data after a selection
+4. immutability — ``sel`` never mutates the receiver
+
+All tests use synthetic DataTrees in ``tmp_path``; no real radar files needed.
+"""
+from __future__ import annotations
+
+import pandas as pd
+import polars as pl
+import pytest
+
+from raddb.main import RadDB
+from raddb.tests.test_fixes import RADAR, _make_datatree
+
+VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
+
+
+@pytest.fixture
+def rdf(tmp_path):
+    """Data-carrying RadDB from a tiny two-volume, one-radar archive."""
+    db = RadDB(archive_dir=str(tmp_path), crs=2056)
+    db.archive(datatree={str(t): _make_datatree(vol_time=t) for t in VOL_TIMES},
+               radar=RADAR)
+    return db.open(radars=RADAR)
+
+
+class TestSelDynamic:
+    def test_slice_is_inclusive_on_both_ends(self, rdf):
+        out = rdf.sel(DBZH=slice(5, 15))
+        vals = out.data["DBZH"].to_numpy()
+        assert len(out) > 0
+        assert vals.min() >= 5.0 and vals.max() <= 15.0
+
+    def test_open_ended_slices_match_filter(self, rdf):
+        assert len(rdf.sel(DBZH=slice(10, None))) == len(
+            rdf.filter({"var": "DBZH", "logic": ">=", "threshold": 10})
+        )
+        assert len(rdf.sel(DBZH=slice(None, 10))) == len(
+            rdf.filter({"var": "DBZH", "logic": "<=", "threshold": 10})
+        )
+
+    def test_columns_are_unchanged(self, rdf):
+        assert rdf.sel(DBZH=slice(0, 10)).columns() == rdf.columns()
+
+    def test_no_args_is_a_noop(self, rdf):
+        assert len(rdf.sel()) == len(rdf)
+
+    def test_keywords_are_anded(self, rdf):
+        both = rdf.sel(DBZH=slice(10, None), ZDR=slice(None, 5))
+        chained = rdf.sel(DBZH=slice(10, None)).sel(ZDR=slice(None, 5))
+        assert len(both) == len(chained)
+
+
+class TestSelTime:
+    def test_partial_day_string_selects_the_whole_day(self, rdf):
+        out = rdf.sel(time="2024-08-01")
+        assert 0 < len(out) < len(rdf)
+
+    def test_partial_month_string(self, rdf):
+        assert len(rdf.sel(time="2024-08")) == len(rdf)
+
+    def test_non_matching_period_is_empty(self, rdf):
+        assert len(rdf.sel(time="1999-01")) == 0
+
+    def test_time_slice(self, rdf):
+        out = rdf.sel(time=slice("2024-08-01", "2024-08-01"))
+        assert 0 < len(out) < len(rdf)
+
+
+class TestSelStaticLutColumns:
+    """Selection on LUT columns must borrow, evaluate, then drop."""
+
+    def test_range_selection_does_not_leak_the_column(self, rdf):
+        out = rdf.sel(range=slice(2_000, 10_000))
+        assert 0 < len(out) < len(rdf)
+        assert out.columns() == rdf.columns()
+        assert "range" not in out.columns()
+
+    def test_range_selection_matches_the_lut(self, rdf, tmp_path):
+        lut = RadDB(archive_dir=str(tmp_path)).get_lut(RADAR)
+        want = set(
+            lut.filter((pl.col("range") >= 2_000) & (pl.col("range") <= 10_000))["gate_id"]
+            .to_list()
+        )
+        got = set(rdf.sel(range=slice(2_000, 10_000)).data["gate_id"].to_list())
+        assert got == set(rdf.data["gate_id"].to_list()) & want
+
+    def test_sweep_scalar(self, rdf):
+        out = rdf.sel(sweep=1)
+        assert 0 < len(out) < len(rdf)
+        assert "sweep" not in out.columns()
+
+    def test_lat_lon_aliases(self, rdf):
+        ge = rdf.geographic_extent()
+        out = rdf.sel(lon=slice(ge[0], ge[1]), lat=slice(ge[2], ge[3]))
+        assert len(out) == len(rdf)          # full extent keeps everything
+        assert out.columns() == rdf.columns()
+
+    def test_mixed_static_and_dynamic(self, rdf):
+        out = rdf.sel(DBZH=slice(10, None), range=slice(2_000, 10_000), sweep=1)
+        assert out.columns() == rdf.columns()
+        assert len(out) <= len(rdf)
+
+
+class TestSelRadars:
+    def test_radars_list_keeps_present_radar(self, rdf):
+        assert len(rdf.sel(radars=[RADAR])) == len(rdf)
+
+    def test_radars_list_excluding_present_radar_is_empty(self, rdf):
+        other = "W" if RADAR != "W" else "L"
+        assert len(rdf.sel(radars=[other])) == 0
+
+    def test_multi_radar_selection(self, tmp_path):
+        db = RadDB(archive_dir=str(tmp_path), crs=2056)
+        db.archive(datatree={"A": [_make_datatree(vol_time=VOL_TIMES[0])],
+                             "D": [_make_datatree(vol_time=VOL_TIMES[0])]})
+        both = db.open()
+        assert sorted(both.radars()) == ["A", "D"]
+        only_a = both.sel(radars=["A"])
+        assert only_a.radars() == ["A"]
+        assert 0 < len(only_a) < len(both)
+
+
+class TestSelKeepsLutSynchronised:
+    def test_geometry_shrinks_with_the_data(self, rdf):
+        out = rdf.sel(range=slice(2_000, 10_000))
+        assert len(out._gate_geometry()) < len(rdf._gate_geometry())
+
+    def test_geometry_gate_ids_match_data_gate_ids(self, rdf):
+        out = rdf.sel(range=slice(2_000, 10_000), DBZH=slice(10, None))
+        geo = out._gate_geometry()
+        assert set(geo["gate_id"].to_list()) == set(out.data["gate_id"].to_list())
+
+    def test_with_geometry_converter_still_works(self, rdf):
+        out = rdf.sel(range=slice(2_000, 10_000))
+        pdf = out.to_pandas(with_geometry=True)
+        assert len(pdf) == len(out)
+        assert "latitude" in pdf.columns
+
+
+class TestSelImmutability:
+    def test_receiver_is_untouched(self, rdf):
+        before_len, before_cols = len(rdf), rdf.columns()
+        rdf.sel(DBZH=slice(0, 1), range=slice(2_000, 3_000), sweep=1)
+        assert len(rdf) == before_len
+        assert rdf.columns() == before_cols
+
+    def test_returns_a_new_object_with_same_config(self, rdf):
+        out = rdf.sel(DBZH=slice(0, 10))
+        assert out is not rdf
+        assert isinstance(out, RadDB)
+        assert out.crs() == rdf.crs()
+        assert str(out.archive_dir) == str(rdf.archive_dir)
+
+
+class TestSelErrors:
+    def test_unknown_column_raises_keyerror(self, rdf):
+        with pytest.raises(KeyError):
+            rdf.sel(NOT_A_COLUMN=1)
+
+    def test_step_in_slice_raises_valueerror(self, rdf):
+        with pytest.raises(ValueError):
+            rdf.sel(DBZH=slice(0, 10, 2))
diff --git a/raddb/viz/plot.py b/raddb/viz/plot.py
index 99434af..0eca33c 100644
--- a/raddb/viz/plot.py
+++ b/raddb/viz/plot.py
@@ -22,6 +22,7 @@
 
 import numpy as np
 import pandas as pd
+import polars as pl
 import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
 import matplotlib.ticker as mticker
@@ -1182,7 +1183,7 @@ def plot_rhi(
 # ============================================================================
 
 def plot_latent_scatter(
-    df: "pd.DataFrame",
+    df: "pl.DataFrame | pd.DataFrame",
     config: list[dict],
     figsize: tuple[float, float] | None = None,
     fig_height: float = 4.6,
@@ -1200,7 +1201,7 @@ def plot_latent_scatter(
 
     Parameters
     ----------
-    df : pd.DataFrame
+    df : pl.DataFrame or pd.DataFrame
         Must contain columns ``"L1"``, ``"L2"``, and the variable column
         named in each panel's ``"var"`` key.
     config : list of dict
@@ -1266,8 +1267,8 @@ def plot_latent_scatter(
         panel_scatter_kw = {**scatter_kwargs, **panel.get("scatter_kwargs", {})}
 
         m = ax.scatter(
-            df["L1"], df["L2"],
-            c=df[var],
+            df["L1"].to_numpy(), df["L2"].to_numpy(),
+            c=df[var].to_numpy(),
             cmap=cmap,
             norm=norm,
             **panel_scatter_kw,

From e37d0007fe8650358a013618d58077a30a850da3 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Tue, 4 Aug 2026 17:40:01 +0200
Subject: [PATCH 02/14] Add tutorial README with notebook workflow and usage
 instructions

---
 pyproject.toml                          |    3 +-
 raddb/__init__.py                       |   40 +
 raddb/aoi.py                            |  643 +++----
 raddb/helper.py                         |   82 +-
 raddb/io_core.py                        |   99 +-
 raddb/lut.py                            |  771 ++++++++-
 raddb/main.py                           |  330 ++--
 raddb/tests/bench_plot_backends.py      |  249 +++
 raddb/tests/test_azimuth_grid.py        |  291 ++++
 raddb/tests/test_crs.py                 |  262 +++
 raddb/tests/test_datatree_io.py         |   40 +-
 raddb/tests/test_fixes.py               |   48 +-
 raddb/tests/test_inventory.py           |   16 +-
 raddb/tests/test_lut_planes.py          |   64 +-
 raddb/tests/test_plot.py                |  792 +++++++++
 raddb/tests/test_polars_backend.py      |    4 +-
 raddb/tests/test_radar_code.py          |  332 ++++
 raddb/tests/test_sel.py                 |    2 +-
 raddb/tools/__init__.py                 |    5 +
 raddb/tools/migrate_gate_id_v2.py       |  156 ++
 raddb/viz/plot.py                       | 2063 ++++++++++++++++-------
 tutorial/01_archiving.ipynb             |  798 +++++++++
 tutorial/02_opening_and_filtering.ipynb |  934 ++++++++++
 tutorial/03_area_of_interest.ipynb      |  747 ++++++++
 tutorial/04_plots.ipynb                 |  726 ++++++++
 tutorial/README.md                      |   40 +
 26 files changed, 8429 insertions(+), 1108 deletions(-)
 create mode 100644 raddb/tests/bench_plot_backends.py
 create mode 100644 raddb/tests/test_azimuth_grid.py
 create mode 100644 raddb/tests/test_crs.py
 create mode 100644 raddb/tests/test_plot.py
 create mode 100644 raddb/tests/test_radar_code.py
 create mode 100644 raddb/tools/__init__.py
 create mode 100644 raddb/tools/migrate_gate_id_v2.py
 create mode 100644 tutorial/01_archiving.ipynb
 create mode 100644 tutorial/02_opening_and_filtering.ipynb
 create mode 100644 tutorial/03_area_of_interest.ipynb
 create mode 100644 tutorial/04_plots.ipynb
 create mode 100644 tutorial/README.md

diff --git a/pyproject.toml b/pyproject.toml
index 4baa82b..03eaf80 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,7 +62,8 @@ exclude = ["raddb.mch*", "raddb.tests*"]
 write_to = "raddb/_version.py"
 
 [project.optional-dependencies]
-viz = ["cartopy>=0.22", "pyproj>=3.0", "shapely>=2.0"]
+viz = ["cartopy>=0.22", "pyproj>=3.0", "shapely>=2.0",
+       "lonboard>=0.10", "geoarrow-pyarrow>=0.2"]
 io = ["netcdf4", "zarr"]
 dev = ["pre-commit", "loghub",
        "black[jupyter]", "blackdoc", "codespell", "ruff",
diff --git a/raddb/__init__.py b/raddb/__init__.py
index f198c57..ba475e1 100644
--- a/raddb/__init__.py
+++ b/raddb/__init__.py
@@ -24,8 +24,11 @@
 
 # Helper functions
 from raddb.helper import (
+    RADAR_ALPHABET,
+    RADAR_CODE_LEN,
     read_parquet_files,
     check_dataframe,
+    is_valid_radar_name,
     list_sweep_names,
     normalize_radar_name,
     StageTimer,
@@ -54,14 +57,28 @@
 
 # LUT functions
 from raddb.lut import (
+    AZIMUTH_SCALE,
+    GATE_ID_RADAR_BASE,
+    azimuth_grid_tolerance,
+    load_azimuth_grids,
+    nominal_azimuth_grid,
+    snap_azimuths_to_grid,
+    GATE_ID_VERSION,
+    MAX_RADAR_CODE,
     RADAR_TO_IDX,
     DEFAULT_BEAMWIDTH_DEG,
+    OutdatedGateIdError,
+    decode_gate_radars,
+    decode_radar_code,
+    encode_radar_code,
     LUT_FILES,
     antenna_vectors_to_cartesian,
     build_gate_planes,
+    cappi_chords,
     cartesian_to_geographic,
     compute_gate_xyz,
     compute_sweep_corners,
+    ensure_gate_planes,
     gate_corner_table,
     generate_gate_id,
     generate_lut_from_datatree,
@@ -85,6 +102,9 @@
 from raddb.viz.plot import (
     plot_ppi,
     plot_rhi,
+    plot_cappi,
+    plot_vcs,
+    plot_cross_section,
     plot_latent_scatter,
 )
 
@@ -106,6 +126,9 @@
     "check_dataframe",
     "list_sweep_names",
     "normalize_radar_name",
+    "is_valid_radar_name",
+    "RADAR_ALPHABET",
+    "RADAR_CODE_LEN",
     "StageTimer",
     # I/O functions
     "datatree_to_dataset",
@@ -126,13 +149,27 @@
     "find_datatree_files",
     # LUT functions
     "RADAR_TO_IDX",
+    "GATE_ID_RADAR_BASE",
+    "GATE_ID_VERSION",
+    "MAX_RADAR_CODE",
+    "AZIMUTH_SCALE",
+    "nominal_azimuth_grid",
+    "snap_azimuths_to_grid",
+    "azimuth_grid_tolerance",
+    "load_azimuth_grids",
+    "OutdatedGateIdError",
+    "encode_radar_code",
+    "decode_radar_code",
+    "decode_gate_radars",
     "DEFAULT_BEAMWIDTH_DEG",
     "LUT_FILES",
     "antenna_vectors_to_cartesian",
     "build_gate_planes",
+    "cappi_chords",
     "cartesian_to_geographic",
     "compute_gate_xyz",
     "compute_sweep_corners",
+    "ensure_gate_planes",
     "gate_corner_table",
     "generate_gate_id",
     "generate_lut_from_datatree",
@@ -152,6 +189,9 @@
     # Plotting functions
     "plot_ppi",
     "plot_rhi",
+    "plot_cappi",
+    "plot_vcs",
+    "plot_cross_section",
     "plot_latent_scatter",
     # Profiling helpers
     "plot_stage_totals",
diff --git a/raddb/aoi.py b/raddb/aoi.py
index 9810155..0fc8409 100644
--- a/raddb/aoi.py
+++ b/raddb/aoi.py
@@ -5,15 +5,18 @@
 
 These are the **private** building blocks behind the public ``RadDB.crop_*``
 methods.  The design is LUT-first: an AOI geometry is intersected once with the
-static per-radar LUT **centroids** (in Swiss LV95 / EPSG:2056) to resolve a set
+static per-radar LUT **centroids** (in the archive's own projected CRS) to resolve a set
 of ``gate_id`` values, and that set then filters any number of dynamic volume
 DataFrames — cheaply and identically across timesteps.
 
 Because the intersection is done on gate centroids over **all** sweeps, a single
 horizontal footprint selects the whole vertical column above it (every elevation).
 
-Coordinate convention: AOI geometries are handled in EPSG:2056 throughout; input
-in another CRS is reprojected via :func:`_reproject_to_2056` before intersection.
+Coordinate convention: there is **no built-in CRS**.  Every AOI runs in the CRS the
+archive was written with (recorded in ``{radar}_info.yaml``, validated against the
+radar site at archive time), and input geometry in another CRS is reprojected into
+it via :func:`_reproject_to_aoi` before intersection.  A hardcoded frame was the
+source of a silent 17% error on US radars, so nothing here assumes one.
 
 No pyart / geocube dependency — only numpy, polars, pandas, shapely, pyproj.
 
@@ -33,21 +36,77 @@
 import shapely
 import shapely.ops
 
-from raddb.lut import RADAR_TO_IDX, add_lut_projection
+from raddb.lut import add_lut_projection, decode_gate_radars
 
 logger = logging.getLogger(__name__)
 
-# EPSG code of the canonical AOI working frame (Swiss LV95 / CH1903+).
-AOI_EPSG: int = 2056
+#: Swiss LV95.  Used *only* where the subject really is Switzerland — the border
+#: overlay on the AOI quicklook, and sniffing a Swiss ``.prj``.  It is never a
+#: default for gate geometry; that comes from the archive's own CRS.
+SWISS_EPSG: int = 2056
 
-# LUT columns kept for centroid intersection (small footprint, cached in RAM).
-_CENTROID_COLS = ["gate_id", "sweep", "x_2056", "y_2056", "z", "altitude"]
+# LUT columns kept for centroid intersection, besides the projected pair, which
+# is named after the archive's own EPSG and resolved per radar.
+_CENTROID_BASE_COLS = ["gate_id", "sweep", "z", "altitude"]
 
-# gate_id encoding base: radar_idx * 10**12 + ...  (see raddb.lut.encode_gate_ids)
-_GATE_ID_RADAR_BASE = np.int64(1_000_000_000_000)
 
-# Inverse of RADAR_TO_IDX for decoding radar letters from gate_ids.
-_IDX_TO_RADAR = {v: k for k, v in RADAR_TO_IDX.items()}
+def aoi_epsg(base_path: str | Path, radar: str) -> int:
+    """EPSG the archive stores this radar's projected coordinates in.
+
+    Read from ``{radar}_info.yaml``; recovered from the LUT's own ``x_<epsg>``
+    columns for archives written before the ``crs`` block existed.  There is no
+    fallback: a CRS is required at archive time precisely so this never has to
+    guess.
+    """
+    from raddb.lut import load_radar_info, lut_file_path
+
+    try:
+        info = load_radar_info(radar, base_path)
+    except FileNotFoundError:
+        info = {}
+    epsg = (info.get("crs") or {}).get("epsg")
+    if epsg is not None:
+        return int(epsg)
+
+    lut_path = lut_file_path(radar, "lut", base_path)
+    if lut_path.exists():
+        for name in pq.read_schema(lut_path).names:
+            if name.startswith("x_") and name[2:].isdigit():
+                return int(name[2:])
+    raise ValueError(
+        f"radar {radar!r} has no projected coordinates in its LUT, so AOI "
+        f"operations (crop_*, extract_cross_section, plot_vcs) cannot run. "
+        f"Re-archive it with a CRS valid at its site — RadDB(crs=<epsg>) — or "
+        f"pass aoi_crs=<epsg> for this call."
+    )
+
+
+def aoi_epsg_for(base_path: str | Path, radars: list[str], override=None) -> int:
+    """The single CRS an AOI spanning ``radars`` runs in.
+
+    All radars must agree, because reprojecting one onto another's frame behind
+    the user's back is exactly the kind of implicit choice that made a 17% error
+    invisible.  ``override`` (``aoi_crs=``) names a common frame explicitly and
+    is validated against every site.
+    """
+    from raddb.lut import load_radar_info, validate_crs_for_site
+
+    if override is not None:
+        for r in radars:
+            info = load_radar_info(r, base_path)
+            validate_crs_for_site(override, info["longitude"], info["latitude"], r)
+        return override
+
+    found = {r: aoi_epsg(base_path, r) for r in radars}
+    distinct = set(found.values())
+    if len(distinct) > 1:
+        pairs = ", ".join(f"{r}=EPSG:{e}" for r, e in sorted(found.items()))
+        raise ValueError(
+            f"these radars were archived in different CRSs ({pairs}), so there is "
+            f"no single frame to run the AOI in. Pass aoi_crs=<epsg> valid for all "
+            f"of them, or restrict the selection to radars sharing one."
+        )
+    return distinct.pop()
 
 # Per-radar centroid cache: {(base_path, radar): DataFrame}. LUTs are static, so
 # a radar's centroid table is loaded from disk at most once per session.
@@ -55,43 +114,20 @@
 
 
 def _radars_from_gate_ids(gate_ids) -> list[str]:
-    """Decode the distinct radar letters present in a set of ``gate_id`` values.
+    """Decode the distinct radar names present in a set of ``gate_id`` values.
 
-    The radar index is the leading factor of the gate_id encoding
-    (``radar_idx = gate_id // 10**12``), so the radars a DataFrame spans can be
-    inferred without a separate ``radar`` column.
+    Thin alias of :func:`raddb.lut.decode_gate_radars`, kept because it is the
+    name the AOI, plotting and RadDB code paths already call.
+    """
+    return decode_gate_radars(gate_ids)
 
-    Parameters
-    ----------
-    gate_ids : array-like of int64
 
-    Returns
-    -------
-    list of str
-        Sorted radar letters (e.g. ``["L", "P"]``).  Indices with no known
-        letter are skipped with a warning.
-    """
-    ids = np.asarray(gate_ids, dtype=np.int64)
-    if ids.size == 0:
-        return []
-    idxs = np.unique(ids // _GATE_ID_RADAR_BASE)
-    radars = []
-    for i in idxs:
-        letter = _IDX_TO_RADAR.get(int(i))
-        if letter is None:
-            logger.warning("gate_id radar index %d has no known radar letter.", int(i))
-            continue
-        radars.append(letter)
-    return sorted(radars)
-
-
-def _lut_centroids(base_path: str | Path, radars: list[str]) -> pl.DataFrame:
+def _lut_centroids(base_path: str | Path, radars: list[str], epsg=None) -> pl.DataFrame:
     """Load and concatenate LUT centroid tables for the given radars.
 
-    Returns one row per gate with ``[gate_id, radar, sweep, x_2056, y_2056, z,
-    altitude]``.  Results are cached per (base_path, radar); ``x_2056``/``y_2056``
-    are computed on the fly via :func:`add_lut_projection` if a LUT predates the
-    projected-coordinate columns.
+    Returns one row per gate with ``[gate_id, radar, sweep, x, y, z, altitude]``,
+    where ``x``/``y`` are the archive's projected coordinates renamed to a fixed
+    pair so callers need not know the EPSG.  Cached per (base_path, radar, epsg).
 
     Parameters
     ----------
@@ -105,25 +141,33 @@ def _lut_centroids(base_path: str | Path, radars: list[str]) -> pl.DataFrame:
     pl.DataFrame
     """
     base = Path(base_path)
+    epsg = aoi_epsg_for(base, list(radars), override=epsg)
     frames = []
     for radar in radars:
-        key = (str(base), radar)
+        key = (str(base), radar, int(epsg))
         cached = _CENTROID_CACHE.get(key)
         if cached is None:
-            cached = _load_one_centroid_table(base, radar)
+            cached = _load_one_centroid_table(base, radar, int(epsg))
             _CENTROID_CACHE[key] = cached
         frames.append(cached)
     if not frames:
         return pl.DataFrame(
             schema={"gate_id": pl.Int64, "radar": pl.String, "sweep": pl.Int32,
-                    "x_2056": pl.Float64, "y_2056": pl.Float64,
+                    "x": pl.Float64, "y": pl.Float64,
                     "z": pl.Float64, "altitude": pl.Float64}
         )
     return pl.concat(frames, how="vertical")
 
 
-def _load_one_centroid_table(base: Path, radar: str) -> pl.DataFrame:
-    """Load a single radar's LUT centroid columns (helper for :func:`_lut_centroids`)."""
+def _load_one_centroid_table(base: Path, radar: str, epsg: int) -> pl.DataFrame:
+    """Load one radar's centroids in ``epsg`` (helper for :func:`_lut_centroids`).
+
+    The projected pair is renamed to plain ``x``/``y`` so the rest of the module
+    never has to know which EPSG it is working in.  When the LUT already stores
+    that EPSG the columns are read straight off disk; otherwise — an ``aoi_crs=``
+    override, or a differently-projected archive — they are computed from the
+    ``latitude``/``longitude`` every LUT carries.
+    """
     lut_path = base / radar / "LUT" / f"{radar}_LUT.parquet"
     if not lut_path.exists():
         raise FileNotFoundError(
@@ -133,17 +177,18 @@ def _load_one_centroid_table(base: Path, radar: str) -> pl.DataFrame:
     # Column names come from the parquet footer — reading the whole LUT just to
     # inspect `.columns` would load ~100 MB and throw it away.
     available = set(pq.read_schema(lut_path).names)
-    has_proj = {"x_2056", "y_2056"}.issubset(available)
+    xc, yc = f"x_{int(epsg)}", f"y_{int(epsg)}"
 
-    if has_proj:
-        cols = [c for c in _CENTROID_COLS if c in available]
-        lut = pl.read_parquet(lut_path, columns=cols)
+    if {xc, yc}.issubset(available):
+        cols = [c for c in _CENTROID_BASE_COLS if c in available] + [xc, yc]
+        lut = pl.read_parquet(lut_path, columns=cols).rename({xc: "x", yc: "y"})
     else:
-        # Older LUT without projected coords: load lat/lon + geometry and project.
-        base_cols = ["gate_id", "sweep", "z", "altitude", "latitude", "longitude"]
-        cols = [c for c in base_cols if c in available]
-        lut = add_lut_projection(pl.read_parquet(lut_path, columns=cols), epsg=AOI_EPSG)
-        lut = lut.select([c for c in _CENTROID_COLS if c in lut.columns])
+        cols = [c for c in (*_CENTROID_BASE_COLS, "latitude", "longitude")
+                if c in available]
+        lut = add_lut_projection(pl.read_parquet(lut_path, columns=cols), epsg=int(epsg))
+        lut = lut.rename({xc: "x", yc: "y"}).select(
+            [c for c in (*_CENTROID_BASE_COLS, "x", "y") if c in lut.columns]
+        )
 
     return lut.with_columns(pl.lit(radar).alias("radar"))
 
@@ -182,31 +227,31 @@ def _to_pyproj_crs(spec):
     return pyproj.CRS(spec)
 
 
-def _reproject_to_2056(geom, crs: int | str | None):
-    """Reproject a shapely geometry into EPSG:2056 if it is in another CRS.
+def _reproject_to_aoi(geom, crs: int | str | None, aoi_epsg: int):
+    """Reproject a shapely geometry into the AOI's CRS.
 
     Parameters
     ----------
     geom : shapely geometry
         AOI geometry expressed in ``crs``.
     crs : int, str, or None
-        CRS of ``geom``.  ``None`` or ``2056`` (int or ``"EPSG:2056"``) is a
-        no-op — the primary workflow, needing no pyproj at all.  Anything else is
-        transformed via pyproj (``always_xy=True``); WGS-84 (4326) and LV95 (2056)
-        work without a PROJ database, other frames need one installed.
+        CRS of ``geom``.  ``None`` means "already in the AOI CRS" — the common
+        case when the user works in the frame the archive was written with.
+    aoi_epsg : int
+        The archive's CRS, from :func:`aoi_epsg_for`.
 
     Returns
     -------
-    shapely geometry in EPSG:2056.
+    shapely geometry in ``aoi_epsg``.
     """
     if crs is None:
         return geom
-    # Normalise common spellings of "already 2056" → no-op, no pyproj needed.
-    if crs in (AOI_EPSG, str(AOI_EPSG), f"EPSG:{AOI_EPSG}", f"epsg:{AOI_EPSG}"):
+    # Normalise common spellings of "already the AOI CRS" → no pyproj needed.
+    if crs in (aoi_epsg, str(aoi_epsg), f"EPSG:{aoi_epsg}", f"epsg:{aoi_epsg}"):
         return geom
 
     src = _to_pyproj_crs(crs)
-    tgt = _to_pyproj_crs(AOI_EPSG)
+    tgt = _to_pyproj_crs(aoi_epsg)
     if src.equals(tgt):
         return geom
 
@@ -216,8 +261,8 @@ def _reproject_to_2056(geom, crs: int | str | None):
     return shapely.ops.transform(transformer.transform, geom)
 
 
-def _resolve_aoi_centroids(centroids: pl.DataFrame, aoi_geom_2056) -> pl.DataFrame:
-    """Return the centroid rows whose ``(x_2056, y_2056)`` lie inside an AOI.
+def _resolve_aoi_centroids(centroids: pl.DataFrame, aoi_geom) -> pl.DataFrame:
+    """Return the centroid rows whose ``(x, y)`` lie inside an AOI.
 
     A cheap bounding-box mask pre-filters the (potentially millions of) centroids
     before the vectorized ``shapely.contains`` point-in-geometry test, which keeps
@@ -230,9 +275,9 @@ def _resolve_aoi_centroids(centroids: pl.DataFrame, aoi_geom_2056) -> pl.DataFra
     Parameters
     ----------
     centroids : pl.DataFrame
-        Output of :func:`_lut_centroids` (needs ``x_2056``, ``y_2056``, ``gate_id``).
-    aoi_geom_2056 : shapely geometry
-        AOI footprint in EPSG:2056.
+        Output of :func:`_lut_centroids` (needs ``x``, ``y``, ``gate_id``).
+    aoi_geom : shapely geometry
+        AOI footprint, already in the AOI CRS.
 
     Returns
     -------
@@ -242,24 +287,24 @@ def _resolve_aoi_centroids(centroids: pl.DataFrame, aoi_geom_2056) -> pl.DataFra
     if centroids.is_empty():
         return centroids.clear()
 
-    x = centroids["x_2056"].to_numpy()
-    y = centroids["y_2056"].to_numpy()
+    x = centroids["x"].to_numpy()
+    y = centroids["y"].to_numpy()
 
-    minx, miny, maxx, maxy = aoi_geom_2056.bounds
+    minx, miny, maxx, maxy = aoi_geom.bounds
     bbox_mask = (x >= minx) & (x <= maxx) & (y >= miny) & (y <= maxy)
     if not bbox_mask.any():
         return centroids.clear()
 
     # Exact point-in-geometry only on the bbox survivors.
     pts = shapely.points(x[bbox_mask], y[bbox_mask])
-    inside = shapely.contains(aoi_geom_2056, pts)
+    inside = shapely.contains(aoi_geom, pts)
 
     keep = np.zeros(len(x), dtype=bool)
     keep[np.flatnonzero(bbox_mask)[inside]] = True
     return centroids.filter(keep)
 
 
-def _resolve_gate_ids(centroids: pl.DataFrame, aoi_geom_2056) -> np.ndarray:
+def _resolve_gate_ids(centroids: pl.DataFrame, aoi_geom) -> np.ndarray:
     """Resolve the ``gate_id`` set whose centroid lies inside an AOI geometry.
 
     Thin wrapper over :func:`_resolve_aoi_centroids` returning just the ids.
@@ -269,7 +314,7 @@ def _resolve_gate_ids(centroids: pl.DataFrame, aoi_geom_2056) -> np.ndarray:
     np.ndarray of int64
         gate_ids inside the AOI (may be empty).
     """
-    sub = _resolve_aoi_centroids(centroids, aoi_geom_2056)
+    sub = _resolve_aoi_centroids(centroids, aoi_geom)
     if sub.is_empty():
         return np.empty(0, dtype=np.int64)
     return sub["gate_id"].to_numpy().astype(np.int64, copy=False)
@@ -333,27 +378,32 @@ def _swiss_border_2056():
                 geom = rec.geometry
                 break
         # simplify (~300 m) to keep the outline light, then project to LV95.
-        _SWISS_BORDER = None if geom is None else _reproject_to_2056(geom.simplify(0.003), 4326)
+        _SWISS_BORDER = (None if geom is None
+                         else _reproject_to_aoi(geom.simplify(0.003), 4326, SWISS_EPSG))
     except Exception as exc:  # noqa: BLE001 - context is optional; never fatal
         logger.warning("Swiss border context unavailable (%s); drawn without it.", exc)
         _SWISS_BORDER = None
     return _SWISS_BORDER
 
 
-def _resolve_context(context):
-    """Resolve a map-background context geometry to EPSG:2056, or ``None``.
+def _resolve_context(context, aoi_epsg: int = SWISS_EPSG):
+    """Resolve a map-background context geometry to the AOI's frame, or ``None``.
 
     Parameters
     ----------
     context : None, str, shapely geometry, or GeoDataFrame/GeoSeries
         - ``None`` → no context.
         - ``"switzerland"`` (or ``"ch"``) → the Swiss border (Natural Earth).
-        - a shapely geometry → assumed already in EPSG:2056.
+        - a shapely geometry → assumed already in ``aoi_epsg``.
         - a GeoDataFrame/GeoSeries → dissolved and reprojected from its ``.crs``.
+    aoi_epsg : int, default ``SWISS_EPSG``
+        The frame the quicklook is drawn in — the archive's own CRS, not
+        necessarily LV95.  Reprojecting a caller's context to a hardcoded 2056
+        would put it thousands of km off-map on any non-Swiss archive.
 
     Returns
     -------
-    shapely geometry in EPSG:2056, or None.
+    shapely geometry in ``aoi_epsg``, or None.
     """
     if context is None:
         return None
@@ -362,18 +412,22 @@ def _resolve_context(context):
 
     if isinstance(context, str):
         if context.lower() in ("switzerland", "ch", "suisse", "schweiz", "svizzera"):
-            return _swiss_border_2056()
+            border = _swiss_border_2056()
+            if border is None or aoi_epsg == SWISS_EPSG:
+                return border
+            return _reproject_to_aoi(border, SWISS_EPSG, aoi_epsg)
         raise ValueError(
             f"unknown context {context!r}; use 'switzerland', None, or a geometry."
         )
     if isinstance(context, _base.BaseGeometry):
-        return context  # assume already EPSG:2056
+        return context  # assume already in the quicklook frame
     if hasattr(context, "crs"):  # GeoDataFrame / GeoSeries
         try:
             geom = context.union_all() if hasattr(context, "union_all") else context.unary_union
             crs = getattr(context, "crs", None)
             epsg = crs.to_epsg() if crs is not None else None
-            return _reproject_to_2056(geom, epsg) if epsg not in (None, AOI_EPSG) else geom
+            return (_reproject_to_aoi(geom, epsg, aoi_epsg)
+                    if epsg not in (None, aoi_epsg) else geom)
         except Exception as exc:  # noqa: BLE001
             logger.warning("could not resolve context geometry (%s); drawn without it.", exc)
             return None
@@ -384,13 +438,14 @@ def _resolve_context(context):
 # Polygon AOI loading  (shapely / GeoDataFrame / shapefile / GeoJSON)
 # ============================================================================
 
-def _load_aoi_polygon(polygon, crs=None):
-    """Resolve a polygon AOI to a shapely geometry in EPSG:2056.
+def _load_aoi_polygon(polygon, crs=None, aoi_epsg: int | None = None):
+    """Resolve a polygon AOI to a shapely geometry in ``aoi_epsg``.
 
     Accepts a shapely ``Polygon``/``MultiPolygon``, a GeoDataFrame/GeoSeries, or a
     path to a ``.shp`` or ``.geojson`` / ``.json`` file.  The source CRS is taken
     from ``crs`` when given, else auto-detected (GeoDataFrame ``.crs``; a GeoJSON
-    ``crs`` member or WGS-84; a shapefile ``.prj``), else assumed EPSG:2056.
+    ``crs`` member or WGS-84; a shapefile ``.prj``), else assumed to be
+    ``aoi_epsg`` already — see the note on the return statement below.
 
     File reading is dependency-light: GeoJSON via :mod:`json` + shapely, shapefiles
     via :mod:`shapefile` (pyshp) — no geopandas/pyogrio/fiona needed.
@@ -418,8 +473,10 @@ def _load_aoi_polygon(polygon, crs=None):
             f"crop_polygon expects a Polygon/MultiPolygon; got {geom.geom_type}."
         )
 
-    effective = crs if crs is not None else (src_crs if src_crs is not None else AOI_EPSG)
-    return _reproject_to_2056(geom, effective)
+    # No fallback CRS: when neither the caller nor the file says, the geometry is
+    # taken to be already in the AOI frame rather than silently assumed to be LV95.
+    effective = crs if crs is not None else src_crs
+    return _reproject_to_aoi(geom, effective, aoi_epsg)
 
 
 def _crs_to_spec(crs):
@@ -433,16 +490,25 @@ def _crs_to_spec(crs):
         return crs
 
 
-def _read_polygon_file(path: Path):
-    """Read a polygon file → ``(geom, src_crs)``.  ``.shp`` via pyshp, ``.geojson`` via json."""
+def _read_geometry_file(path: Path):
+    """Read a geometry file → ``(geom, src_crs)``.
+
+    ``.shp`` via pyshp, ``.geojson`` via json.  Polygons define an AOI to crop
+    with; lines define a vertical cross-section.  The returned CRS is what the
+    file declares — callers must honour it rather than assuming LV95.
+    """
     if not path.exists():
-        raise FileNotFoundError(f"Polygon file not found: {path}")
+        raise FileNotFoundError(f"Geometry file not found: {path}")
     suffix = path.suffix.lower()
     if suffix in (".geojson", ".json"):
         return _read_geojson(path)
     if suffix == ".shp":
         return _read_shapefile(path)
-    raise ValueError(f"Unsupported polygon file type {suffix!r}; use .shp or .geojson.")
+    raise ValueError(f"Unsupported geometry file type {suffix!r}; use .shp or .geojson.")
+
+
+#: Back-compat alias — the reader handles lines as well as polygons now.
+_read_polygon_file = _read_geometry_file
 
 
 def _read_geojson(path: Path):
@@ -455,10 +521,16 @@ def _read_geojson(path: Path):
         geoms = [shape(f["geometry"]) for f in data.get("features", []) if f.get("geometry")]
     elif kind == "Feature":
         geoms = [shape(data["geometry"])]
-    elif kind in ("Polygon", "MultiPolygon", "GeometryCollection"):
+    elif kind in ("Polygon", "MultiPolygon", "GeometryCollection", "LineString",
+                  "MultiLineString", "Point", "MultiPoint"):
+        # A bare top-level geometry, as hand-written files and some exporters
+        # produce.  Lines matter here: a cross-section is defined by one.
         geoms = [shape(data)]
     else:
-        raise ValueError(f"Unrecognised GeoJSON object type {kind!r}.")
+        raise ValueError(
+            f"Unrecognised GeoJSON object type {kind!r}; expected a "
+            "FeatureCollection, a Feature, or a bare geometry."
+        )
     if not geoms:
         raise ValueError(f"No geometries found in {path}.")
     return shapely.union_all(geoms), _geojson_crs(data)
@@ -508,7 +580,7 @@ def _prj_crs(prj_path: Path):
     except Exception:  # noqa: BLE001 - broken PROJ db: sniff for Swiss LV95, else pass WKT
         low = wkt.lower()
         if "2056" in wkt or "ch1903+" in low or "lv95" in low:
-            return AOI_EPSG
+            return SWISS_EPSG
         return wkt
 
 
@@ -523,20 +595,19 @@ def _prj_crs(prj_path: Path):
 #     perpendicular +-dE offsets -> per-gate polygon in the
 #     (distance-along-line, altitude) plane
 #     <- radDB_spatial_plot.get_rad_gdb_vert_cross_section
-# Adapted to the current data model (x_2056/y_2056/altitude from the LUT) and
+# Adapted to the current data model (projected x/y + altitude from the LUT) and
 # fully vectorised (numpy point-to-segment prefilter replaces the KDTree).
 # ============================================================================
 
-_CS_LUT_COLS = [
-    "gate_id", "sweep", "azimuth", "range", "elevation_angle",
-    "x_2056", "y_2056", "altitude",
+_CS_LUT_BASE_COLS = [
+    "gate_id", "sweep", "azimuth", "range", "elevation_angle", "altitude",
 ]
 # Per-radar cross-section geometry cache: {(base_path, radar, beamwidth): df}.
 _CS_CACHE: dict = {}
 
 
 def _lut_cs_table(
-    base_path: str | Path, radars: list[str], beamwidth_deg: float = 1.0
+    base_path: str | Path, radars: list[str], beamwidth_deg: float = 1.0, epsg=None
 ) -> "pl.DataFrame":
     """Static per-gate geometry for cross-sections: centers + half-dimensions.
 
@@ -546,17 +617,32 @@ def _lut_cs_table(
       - ``dA`` (= dE): half the across-beam extent, ``range * tan(beamwidth/2)``
         — grows with range.
     """
+    epsg = aoi_epsg_for(base_path, list(radars), override=epsg)
     frames = []
     for radar in radars:
-        key = (str(base_path), radar, float(beamwidth_deg))
+        lut_path = Path(base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
+        if not lut_path.exists():
+            raise FileNotFoundError(
+                f"LUT not found at {lut_path}. Cannot build cross-section for radar {radar!r}."
+            )
+        # mtime in the key: a LUT regenerated in a live session must invalidate.
+        key = (str(base_path), radar, float(beamwidth_deg), int(epsg),
+               lut_path.stat().st_mtime_ns)
         t = _CS_CACHE.get(key)
         if t is None:
-            lut_path = Path(base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
-            if not lut_path.exists():
-                raise FileNotFoundError(
-                    f"LUT not found at {lut_path}. Cannot build cross-section for radar {radar!r}."
-                )
-            t = pl.read_parquet(lut_path, columns=_CS_LUT_COLS)
+            available = set(pq.read_schema(lut_path).names)
+            xc, yc = f"x_{int(epsg)}", f"y_{int(epsg)}"
+            if {xc, yc}.issubset(available):
+                t = pl.read_parquet(
+                    lut_path, columns=[*_CS_LUT_BASE_COLS, xc, yc]
+                ).rename({xc: "x", yc: "y"})
+            else:
+                t = add_lut_projection(
+                    pl.read_parquet(
+                        lut_path, columns=[*_CS_LUT_BASE_COLS, "latitude", "longitude"]
+                    ),
+                    epsg=int(epsg),
+                ).rename({xc: "x", yc: "y"}).select([*_CS_LUT_BASE_COLS, "x", "y"])
             # Radial spacing per sweep from the unique range grid -> dR = spacing/2.
             # `range` is cast to Float64 first so the median-of-diffs matches the
             # float64 arithmetic the pandas implementation used.
@@ -587,17 +673,81 @@ def _lut_cs_table(
     return pl.concat(frames, how="vertical_relaxed")
 
 
-def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float) -> np.ndarray:
+def _lut_corner_rings(base_path, sub: pd.DataFrame, kind: str, cols: tuple[str, str],
+                      epsg: int):
+    """Per-gate corner rings read from a LUT lattice, aligned to ``sub``'s rows.
+
+    Returns an ``(n, 4, 2)`` array, or ``None`` when the lattice cannot supply
+    the requested columns (e.g. an archive whose LUT carries no EPSG:2056
+    projection), leaving the caller to fall back.
+    """
+    from raddb.lut import gate_corner_table
+
+    gate_ids = sub["gate_id"].to_numpy(dtype=np.int64)
+    radars = sorted(set(sub["radar"])) if "radar" in sub.columns else _radars_from_gate_ids(gate_ids)
+
+    frames = []
+    for radar in radars:
+        try:
+            frames.append(gate_corner_table(radar, base_path, kind=kind))
+        except (FileNotFoundError, KeyError) as exc:
+            logger.warning(
+                "radar %s: %s lattice unavailable (%s); falling back to the "
+                "planar gate approximation.", radar, kind, exc,
+            )
+            return None
+    tbl = pl.concat(frames, how="vertical_relaxed") if len(frames) > 1 else frames[0]
+
+    cols = (f"x_{int(epsg)}", f"y_{int(epsg)}") if cols == ("x", "y") else cols
+    need = [f"{cols[0]}_{k}" for k in range(1, 5)] + [f"{cols[1]}_{k}" for k in range(1, 5)]
+    if not all(c in tbl.columns for c in need):
+        logger.warning(
+            "the %s lattice has no %s/%s columns; falling back to the planar "
+            "gate approximation.", kind, cols[0], cols[1],
+        )
+        return None
+
+    # Left-join keeps ``sub``'s row order, which the callers index against.
+    aligned = pl.DataFrame({"gate_id": gate_ids}).join(
+        tbl.select(["gate_id", *need]), on="gate_id", how="left", maintain_order="left"
+    )
+    ring = np.stack([
+        np.stack([aligned[f"{cols[0]}_{k}"].to_numpy(),
+                  aligned[f"{cols[1]}_{k}"].to_numpy()], axis=1)
+        for k in range(1, 5)
+    ], axis=1).astype(np.float64)
+    if not np.isfinite(ring).all():
+        logger.warning(
+            "%d gate(s) have no %s geometry; falling back to the planar "
+            "approximation for this call.",
+            int((~np.isfinite(ring).all(axis=(1, 2))).sum()), kind,
+        )
+        return None
+    return ring
+
+
+def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float, base_path=None,
+                     epsg: int | None = None) -> np.ndarray:
     """Horizontal footprint quad per gate (the prototype's ``Eo_xyz`` face).
 
-    Corner = center displaced along the beam (±dR, with the elevation-angle
-    horizontal foreshortening cos(el)) and across it (±dA evaluated at
-    range±dR).  Vectorised over all gates -> array of shapely Polygons.
+    Preferred source is the ``h_plane`` lattice, i.e. the same exact curved-beam
+    ``ke=4/3`` corners the plots draw, so cross-sections and gridding agree with
+    :func:`raddb.viz.plot.plot_ppi` gate for gate.
+
+    Falls back to the original planar construction — center displaced along the
+    beam (±dR, foreshortened by cos(el)) and across it (±dA evaluated at
+    range±dR) — when the lattice is unavailable, e.g. an archive with no
+    EPSG:2056 projection in its LUT.
     """
+    if base_path is not None and epsg is not None:
+        ring = _lut_corner_rings(base_path, sub, "h_plane", ("x", "y"), epsg)
+        if ring is not None:
+            return shapely.polygons(ring)
+
     az = np.deg2rad(sub["azimuth"].to_numpy(dtype=np.float64))
     el = np.deg2rad(sub["elevation_angle"].to_numpy(dtype=np.float64))
-    xc = sub["x_2056"].to_numpy(dtype=np.float64)
-    yc = sub["y_2056"].to_numpy(dtype=np.float64)
+    xc = sub["x"].to_numpy(dtype=np.float64)
+    yc = sub["y"].to_numpy(dtype=np.float64)
     rng = sub["range"].to_numpy(dtype=np.float64)
     dR = sub["dR"].to_numpy(dtype=np.float64)
 
@@ -613,21 +763,64 @@ def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float) -> np.ndarray:
     return shapely.polygons(np.stack(rings, axis=1))
 
 
-def _endpoint_d_z(pt_xy: np.ndarray, sub: pd.DataFrame, origin: tuple[float, float]):
+def _beam_profile(base_path, sub: pd.DataFrame, epsg: int):
+    """Per-gate beam profile from the ``v_plane`` lattice, or ``None``.
+
+    Returns ``(d_near, d_far, z_near, z_far, half_thickness)`` — the gate's
+    beam-centre altitude at its near and far range edge, as a function of ground
+    distance, plus half its vertical extent there.  This is the curved-beam
+    geometry the plots draw, replacing the flat ``u * tan(el)`` climb.
+    """
+    ring = _lut_corner_rings(base_path, sub, "v_plane", ("d", "z_asl"), epsg)
+    if ring is None:
+        return None
+    # v_plane ring order: near-bottom, far-bottom, far-top, near-top.
+    d_near = 0.5 * (ring[:, 0, 0] + ring[:, 3, 0])
+    d_far = 0.5 * (ring[:, 1, 0] + ring[:, 2, 0])
+    z_near = 0.5 * (ring[:, 0, 1] + ring[:, 3, 1])
+    z_far = 0.5 * (ring[:, 1, 1] + ring[:, 2, 1])
+    half_thick = 0.5 * (
+        np.abs(ring[:, 3, 1] - ring[:, 0, 1]) + np.abs(ring[:, 2, 1] - ring[:, 1, 1])
+    ) * 0.5
+    return d_near, d_far, z_near, z_far, half_thick
+
+
+def _endpoint_d_z(pt_xy: np.ndarray, sub: pd.DataFrame, origin: tuple[float, float],
+                  profile=None):
     """(distance-along-line, altitude) of chord endpoints on the beam surface.
 
-    The endpoint's altitude follows the gate's tilted beam: project the
-    center->endpoint vector onto the beam direction (angle ``delta`` between
-    them) and climb ``u * tan(el)`` from the gate-center altitude.
+    With a ``profile`` from :func:`_beam_profile` the endpoint's altitude is
+    interpolated along the gate's own curved beam between its near and far range
+    edges — the same geometry ``v_plane`` stores and the RHI draws.
+
+    Without one, it falls back to the planar approximation: project the
+    center->endpoint vector onto the beam direction and climb ``u * tan(el)``
+    from the gate-center altitude.
     """
+    d = np.hypot(pt_xy[:, 0] - origin[0], pt_xy[:, 1] - origin[1])
+
+    if profile is not None:
+        d_near, d_far, z_near, z_far, _ = profile
+        # Ground distance of the endpoint from the radar, along the beam.
+        rx = pt_xy[:, 0] - sub["x"].to_numpy(dtype=np.float64)
+        ry = pt_xy[:, 1] - sub["y"].to_numpy(dtype=np.float64)
+        az = np.deg2rad(sub["azimuth"].to_numpy(dtype=np.float64))
+        # Along-beam offset of the endpoint from the gate centre.
+        u = rx * np.sin(az) + ry * np.cos(az)
+        d_center = 0.5 * (d_near + d_far)
+        span = d_far - d_near
+        with np.errstate(divide="ignore", invalid="ignore"):
+            t = np.where(span != 0, (d_center + u - d_near) / span, 0.5)
+        z = z_near + np.nan_to_num(t, nan=0.5) * (z_far - z_near)
+        return d, z
+
     az = np.deg2rad(sub["azimuth"].to_numpy(dtype=np.float64))
     el = np.deg2rad(sub["elevation_angle"].to_numpy(dtype=np.float64))
-    dx = pt_xy[:, 0] - sub["x_2056"].to_numpy(dtype=np.float64)
-    dy = pt_xy[:, 1] - sub["y_2056"].to_numpy(dtype=np.float64)
+    dx = pt_xy[:, 0] - sub["x"].to_numpy(dtype=np.float64)
+    dy = pt_xy[:, 1] - sub["y"].to_numpy(dtype=np.float64)
     delta = (2.0 * np.pi - az - np.arctan2(dy, dx) + np.pi / 2.0) % (2.0 * np.pi)
     u = np.hypot(dx, dy) * np.cos(delta)
     z = sub["altitude"].to_numpy(dtype=np.float64) + u * np.tan(el)
-    d = np.hypot(pt_xy[:, 0] - origin[0], pt_xy[:, 1] - origin[1])
     return d, z
 
 
@@ -637,6 +830,8 @@ def _cross_section_gates(
     p2: tuple[float, float],
     beamwidth_deg: float = 1.0,
     min_chord_m: float = 0.5,
+    base_path=None,
+    epsg: int | None = None,
 ) -> pd.DataFrame:
     """Gates whose horizontal footprint crosses the line ``p1 -> p2``.
 
@@ -664,8 +859,8 @@ def _cross_section_gates(
     half_bw_tan = float(np.tan(np.deg2rad(beamwidth_deg / 2.0)))
 
     # --- vectorised point-to-segment prefilter (replaces the KDTree) ---
-    px = cs_t["x_2056"].to_numpy(dtype=np.float64)
-    py = cs_t["y_2056"].to_numpy(dtype=np.float64)
+    px = cs_t["x"].to_numpy(dtype=np.float64)
+    py = cs_t["y"].to_numpy(dtype=np.float64)
     diag = np.hypot(cs_t["dR"].to_numpy(dtype=np.float64),
                     cs_t["dA"].to_numpy(dtype=np.float64)) * 1.05
     t_par = ((px - ox) * (ex - ox) + (py - oy) * (ey - oy)) / (length * length)
@@ -676,7 +871,7 @@ def _cross_section_gates(
         return sub.assign(cs_polygon=pd.Series(dtype=object))
 
     # --- exact footprint / line intersection -> per-gate chord ---
-    footprints = _gate_footprints(sub, half_bw_tan)
+    footprints = _gate_footprints(sub, half_bw_tan, base_path=base_path, epsg=epsg)
     line = shapely.LineString([(ox, oy), (ex, ey)])
     chords = shapely.intersection(footprints, line)
     keep = ~shapely.is_empty(chords)
@@ -695,8 +890,10 @@ def _cross_section_gates(
         return sub.assign(cs_polygon=pd.Series(dtype=object))
 
     # --- endpoint (d, z) on the beam, ordered near/far along the line ---
-    d0, z0 = _endpoint_d_z(p0, sub, (ox, oy))
-    d1, z1 = _endpoint_d_z(p1_, sub, (ox, oy))
+    profile = (_beam_profile(base_path, sub, epsg)
+               if base_path is not None and epsg is not None else None)
+    d0, z0 = _endpoint_d_z(p0, sub, (ox, oy), profile)
+    d1, z1 = _endpoint_d_z(p1_, sub, (ox, oy), profile)
     swap = d0 > d1
     d_near = np.where(swap, d1, d0)
     d_far = np.where(swap, d0, d1)
@@ -706,7 +903,11 @@ def _cross_section_gates(
     # --- perpendicular ±dE offsets -> (d, z) polygon per gate ---
     incl = np.arctan2(z_far - z_near, d_far - d_near)   # chord inclination
     s_i, c_i = np.sin(incl), np.cos(incl)
-    dE = sub["dA"].to_numpy(dtype=np.float64)           # dE == dA (same beamwidth)
+    if profile is not None:
+        # Half the beam's real vertical extent at this gate, from v_plane.
+        dE = profile[4]
+    else:
+        dE = sub["dA"].to_numpy(dtype=np.float64)       # dE == dA (same beamwidth)
     ring = np.stack([
         np.stack([d_near - s_i * dE, z_near + c_i * dE], axis=1),   # near, top
         np.stack([d_far - s_i * dE, z_far + c_i * dE], axis=1),     # far, top
@@ -723,185 +924,3 @@ def _cross_section_gates(
     out["z_center"] = 0.5 * (z_near + z_far)
     out["cs_polygon"] = shapely.polygons(ring)
     return out
-
-
-# ============================================================================
-# Regular-grid resampling  (AOI gates -> per-radar 3-D grid in LV95 + altitude)
-# ============================================================================
-
-# Measurement columns eligible for gridding (auto-detected from the df).
-_GRID_VARIABLES = [
-    "DBZH", "DBZH_raw", "ZDR", "ZDR_raw", "KDP", "RHOHV", "PHIDP",
-    "HC_MCH", "HC_PYART", "HZT", "TEMP",
-]
-# Refuse to build absurdly large grids by accident.
-_GRID_MAX_CELLS = 50_000_000
-
-
-def _grid_axis(vmin: float, vmax: float, step: float) -> np.ndarray:
-    """Bin edges aligned to multiples of ``step`` covering [vmin, vmax]."""
-    lo = np.floor(vmin / step) * step
-    hi = np.ceil(vmax / step) * step
-    if hi <= lo:
-        hi = lo + step
-    return np.arange(lo, hi + 0.5 * step, step)
-
-
-def _aoi_to_grid(
-    base_path,
-    df: pd.DataFrame,
-    variables=None,
-    resolution: float = 1000.0,
-    z_resolution: float = 500.0,
-    bounds=None,
-    z_range=None,
-    radars=None,
-    beamwidth_deg: float = 1.0,
-    time_round: str | None = "5min",
-):
-    """Resample AOI gates onto a regular per-radar 3-D grid.
-
-    Output dims: ``(time?, radar, z, y, x)`` —
-
-    - ``x`` / ``y``: pixel centres in Swiss LV95 / EPSG:2056 metres,
-    - ``z``: **absolute altitude** bin centres (m ASL) — sweep numbers are not
-      comparable across radars (different site altitudes/elevation programs),
-      so voxels are keyed by real height,
-    - ``radar``: one layer per radar, **never merged** — the common grid is what
-      makes per-voxel cross-radar comparison meaningful,
-    - ``time``: present when the df carries ``volume_time``.
-
-    Pixel filling uses **gate footprint coverage** (the prototype's geocube
-    semantics, dependency-free): a pixel takes a gate's value when the gate's
-    horizontal footprint polygon covers the pixel centre.  A voxel hit by
-    several gates (overlapping beams) keeps the single gate whose altitude is
-    closest to the bin centre — a real measurement, never an average.  The
-    winning sweep number is recorded in the ``source_sweep`` variable.
-    """
-    import xarray as xr
-
-    if "gate_id" not in df.columns:
-        raise KeyError("df has no 'gate_id' column; cannot grid an AOI.")
-    if df.empty:
-        raise ValueError("aoi_to_grid: input DataFrame is empty.")
-
-    if radars is None:
-        radars = _radars_from_gate_ids(df["gate_id"])
-    if variables is None:
-        variables = [v for v in _GRID_VARIABLES if v in df.columns]
-    missing = [v for v in variables if v not in df.columns]
-    if missing:
-        raise KeyError(f"variables not in df: {missing}")
-    if not variables:
-        raise ValueError("no measurement variables found in df to grid.")
-
-    # Authoritative static geometry (incl. dR/dA for footprints) from the LUT.
-    cs_t = _lut_cs_table(base_path, radars, beamwidth_deg=beamwidth_deg)
-    keep = ["gate_id", *variables] + (["volume_time"] if "volume_time" in df.columns else [])
-    data = df[keep].merge(cs_t, on="gate_id", how="inner")
-    if data.empty:
-        raise ValueError("no AOI gates matched the LUT geometry.")
-
-    # --- axes ---
-    if bounds is not None:
-        xmin, ymin, xmax, ymax = bounds
-    else:
-        xmin, xmax = data["x_2056"].min(), data["x_2056"].max()
-        ymin, ymax = data["y_2056"].min(), data["y_2056"].max()
-    x_edges = _grid_axis(xmin, xmax, resolution)
-    y_edges = _grid_axis(ymin, ymax, resolution)
-    if z_range is not None:
-        z_edges = _grid_axis(z_range[0], z_range[1], z_resolution)
-    else:
-        z_edges = _grid_axis(data["altitude"].min(), data["altitude"].max(), z_resolution)
-    xc = 0.5 * (x_edges[:-1] + x_edges[1:])
-    yc = 0.5 * (y_edges[:-1] + y_edges[1:])
-    zc = 0.5 * (z_edges[:-1] + z_edges[1:])
-    nx, ny, nz = len(xc), len(yc), len(zc)
-
-    times = None
-    if "volume_time" in data.columns:
-        # naive-UTC (tz-aware datetimes don't fit numpy/xarray time coords).
-        vt = pd.to_datetime(data["volume_time"], utc=True).dt.tz_localize(None)
-        # Snap to the scan cycle so near-simultaneous volumes of different
-        # radars (e.g. L 22:35:02 vs P 22:35:03) share one time bin — required
-        # for same-time cross-radar comparison, the per-radar grid's purpose.
-        if time_round is not None:
-            vt = vt.dt.floor(time_round)
-        data = data.assign(_vt=vt)
-        times = np.sort(data["_vt"].unique())
-    n_t = len(times) if times is not None else 1
-    n_cells = n_t * len(radars) * nz * ny * nx
-    if n_cells > _GRID_MAX_CELLS:
-        raise ValueError(
-            f"grid would have {n_cells:,} cells; coarsen resolution/z_resolution "
-            "or pass bounds/z_range."
-        )
-
-    # Pixel-centre points, flattened row-major as (iy, ix).
-    gx, gy = np.meshgrid(xc, yc)                    # (ny, nx)
-    pix_pts = shapely.points(gx.ravel(), gy.ravel())
-    tree = shapely.STRtree(pix_pts)
-    half_bw_tan = float(np.tan(np.deg2rad(beamwidth_deg / 2.0)))
-
-    shape = (n_t, len(radars), nz, ny, nx)
-    arrays = {v: np.full(shape, np.nan, dtype=np.float32) for v in variables}
-    src_sweep = np.full(shape, -1, dtype=np.int16)
-
-    group_cols = (["_vt"] if times is not None else []) + ["radar"]
-    for gkey, sub in data.groupby(group_cols, sort=False):
-        gkey = gkey if isinstance(gkey, tuple) else (gkey,)
-        it = int(np.searchsorted(times, np.datetime64(gkey[0]))) if times is not None else 0
-        ir = radars.index(gkey[-1])
-
-        # footprint polygons of this radar's gates (all sweeps at once — hits are
-        # later resolved per-voxel by altitude, so sweeps can be batched)
-        foot = _gate_footprints(sub, half_bw_tan)
-        poly_i, pt_i = tree.query(foot, predicate="covers")
-        if len(poly_i) == 0:
-            continue
-
-        alt = sub["altitude"].to_numpy(dtype=np.float64)[poly_i]
-        iz = np.floor((alt - z_edges[0]) / z_resolution).astype(np.int64)
-        ok = (iz >= 0) & (iz < nz)
-        poly_i, pt_i, alt, iz = poly_i[ok], pt_i[ok], alt[ok], iz[ok]
-        if len(poly_i) == 0:
-            continue
-
-        # nearest-to-bin-centre gate wins: sort hits by |alt - zc| descending and
-        # write in order, so the closest gate is written last for each voxel.
-        dist = np.abs(alt - zc[iz])
-        order = np.argsort(-dist, kind="stable")
-        p, q, z_i = poly_i[order], pt_i[order], iz[order]
-        iy, ix = np.divmod(q, nx)
-
-        sub_sweep = sub["sweep"].to_numpy(dtype=np.int16)
-        src_sweep[it, ir, z_i, iy, ix] = sub_sweep[p]
-        for v in variables:
-            vals = sub[v].to_numpy(dtype=np.float32)
-            arrays[v][it, ir, z_i, iy, ix] = vals[p]
-
-    # --- assemble the Dataset ---
-    dims = ("time", "radar", "z", "y", "x") if times is not None else ("radar", "z", "y", "x")
-    if times is None:
-        arrays = {v: a[0] for v, a in arrays.items()}
-        src_sweep = src_sweep[0]
-    coords = {"radar": list(radars), "z": zc, "y": yc, "x": xc}
-    if times is not None:
-        coords["time"] = times
-    ds = xr.Dataset(
-        {v: (dims, arrays[v]) for v in variables} | {"source_sweep": (dims, src_sweep)},
-        coords=coords,
-        attrs={
-            "crs": "EPSG:2056",
-            "resolution_m": float(resolution),
-            "z_resolution_m": float(z_resolution),
-            "z_units": "m ASL (absolute altitude)",
-            "beamwidth_deg": float(beamwidth_deg),
-            "fill_method": "gate footprint covers pixel centre; nearest-altitude gate per voxel",
-        },
-    )
-    ds["z"].attrs["units"] = "m ASL"
-    ds["x"].attrs["units"] = "m (LV95 easting)"
-    ds["y"].attrs["units"] = "m (LV95 northing)"
-    return ds
diff --git a/raddb/helper.py b/raddb/helper.py
index 25d528d..607e380 100644
--- a/raddb/helper.py
+++ b/raddb/helper.py
@@ -67,24 +67,53 @@ def check_dataframe(df: "pl.DataFrame | pd.DataFrame") -> None:
     print("-" * 50)
 
 # --- Radar Name Normalization ---
+
+#: Characters a radar name may be built from, in the order that gives each its
+#: numeric value in the base-36 ``gate_id`` radar code (see
+#: :func:`raddb.lut.encode_radar_code`).
+RADAR_ALPHABET: str = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+
+#: Maximum radar-name length.  Four base-36 characters is what the ``gate_id``
+#: layout can hold: ``36**4 = 1_679_616`` codes against the ``9_223_371`` slots
+#: int64 leaves above the ``10**12`` gate field, whereas ``36**5`` would need
+#: 60 million.  It fits every single-letter (MeteoSwiss) and four-letter
+#: (NEXRAD ``KTLX``) identifier; five-character ODIM NOD codes such as
+#: ``chlem`` must be aliased to four and kept in the info YAML's ``network``.
+RADAR_CODE_LEN: int = 4
+
+_RADAR_NAME_RE = re.compile(rf"^[{RADAR_ALPHABET}]{{1,{RADAR_CODE_LEN}}}$")
+
+
 def normalize_radar_name(radar: str) -> str:
     """
-    Normalize radar name to use only the single letter identifier.
+    Normalize a radar name to its canonical archive form.
+
+    The canonical form is upper-case, 1 to :data:`RADAR_CODE_LEN` characters
+    drawn from :data:`RADAR_ALPHABET`, with leading zeros stripped (they are
+    padding in the ``gate_id`` radar code, not part of the name, so ``"0A"``
+    and ``"A"`` are the same radar).
 
-    Handles both formats:
-    - "MLA" -> "A"
-    - "A" -> "A"
-    - "MLW" -> "W"
+    A three-character ``ML*`` name is the MeteoSwiss convention for a
+    single-letter radar and is reduced to that letter (``"MLA"`` -> ``"A"``).
+    Every other name is kept **whole** — ``"KTLX"`` stays ``"KTLX"``.
 
     Parameters
     ----------
     radar : str
-        Radar name (can be "ML*" format or just the letter)
+        Radar name, e.g. ``"A"``, ``"MLA"`` or ``"KTLX"``.
 
     Returns
     -------
     str
-        Normalized single-letter radar name (uppercase)
+        Canonical radar name.
+
+    Raises
+    ------
+    ValueError
+        If the name is empty, too long, or uses characters outside
+        :data:`RADAR_ALPHABET`.  It is raised rather than silently truncating:
+        two sites reduced to the same letter would overwrite each other's
+        archive.
 
     Examples
     --------
@@ -92,17 +121,40 @@ def normalize_radar_name(radar: str) -> str:
     'A'
     >>> normalize_radar_name("A")
     'A'
-    >>> normalize_radar_name("MLW")
-    'W'
+    >>> normalize_radar_name("KTLX")
+    'KTLX'
     """
-    radar_upper = radar.upper().strip()
+    if not isinstance(radar, str):
+        raise ValueError(f"radar name must be a string, got {type(radar).__name__}.")
+
+    name = radar.upper().strip()
+
+    # MeteoSwiss "ML<letter>".  Restricted to exactly three characters so that a
+    # genuine four-character name beginning with "ML" is not mistaken for one.
+    if len(name) == 3 and name.startswith("ML"):
+        name = name[-1]
+
+    # Leading zeros are gate_id padding ("000A"), never part of the name.
+    stripped = name.lstrip("0")
+    if stripped:
+        name = stripped
+
+    if not _RADAR_NAME_RE.match(name):
+        raise ValueError(
+            f"radar name {radar!r} is not usable: a radar name must be 1 to "
+            f"{RADAR_CODE_LEN} characters from [0-9A-Z] (e.g. 'A', 'KTLX'). "
+            f"Longer identifiers must be aliased to {RADAR_CODE_LEN} characters."
+        )
+    return name
 
-    # If it starts with "ML", extract the last character
-    if radar_upper.startswith("ML") and len(radar_upper) > 2:
-        return radar_upper[-1]
 
-    # Otherwise, return the last character (or the string if it's already a single char)
-    return radar_upper[-1] if len(radar_upper) > 0 else radar_upper
+def is_valid_radar_name(radar) -> bool:
+    """``True`` when :func:`normalize_radar_name` would accept *radar*."""
+    try:
+        normalize_radar_name(radar)
+    except ValueError:
+        return False
+    return True
 
 
 # --- Filter Logic Registry ---
diff --git a/raddb/io_core.py b/raddb/io_core.py
index c8934e3..1b98203 100644
--- a/raddb/io_core.py
+++ b/raddb/io_core.py
@@ -23,7 +23,15 @@
 import xarray as xr
 import yaml
 
-from raddb.lut import _parse_corners_npz, encode_gate_ids, get_full_sweep_index
+from raddb.lut import (
+    AZIMUTH_SCALE,
+    _parse_corners_npz,
+    azimuth_grid_tolerance,
+    encode_gate_ids,
+    get_full_sweep_index,
+    load_azimuth_grids,
+    snap_azimuths_to_grid,
+)
 from raddb.helper import (
     StageTimer,
     _vprint,
@@ -234,12 +242,64 @@ def _compute_gate_temperature(
     return (_LAPSE_RATE * (gate_alt - hzt)).astype(np.float32)
 
 
+def _snap_volume_azimuths(sweeps, azimuths, grids, radar):
+    """Move each ray onto its sweep's canonical azimuth, in place of the measured one.
+
+    The antenna reports where it actually pointed, which drifts by a few
+    hundredths of a degree between volumes; ``gate_id`` resolves 0.1°, so an
+    unsnapped ray lands in a neighbouring bin and its gates match no LUT row.
+    See :func:`raddb.lut.nominal_azimuth_grid`.
+
+    Returns
+    -------
+    (azimuths, worst_move_deg)
+
+    Raises
+    ------
+    ValueError
+        If a sweep is missing from the LUT, holds a different number of rays, or
+        a ray sits further than half a ray spacing from the grid — all of which
+        mean this volume was scanned with a different strategy than the LUT was
+        built for, and no snapping can reconcile them.
+    """
+    out = np.array(azimuths, dtype=np.float64, copy=True)
+    worst = 0.0
+    for sweep in np.unique(sweeps):
+        grid = grids.get(int(sweep))
+        if grid is None:
+            raise ValueError(
+                f"radar {radar!r}: the LUT has no sweep {int(sweep)}, but this "
+                f"volume does — it uses a different scan strategy."
+            )
+        sel = sweeps == sweep
+        n_rays = np.unique(out[sel]).size
+        if n_rays != len(grid):
+            raise ValueError(
+                f"radar {radar!r} sweep {int(sweep)}: volume has {n_rays} rays, "
+                f"the LUT was built for {len(grid)} — a different scan strategy. "
+                f"Archive it under its own radar name, or rebuild the LUT."
+            )
+        snapped, dist = snap_azimuths_to_grid(out[sel], grid)
+        tol = azimuth_grid_tolerance(grid)
+        if dist.size and dist.max() > tol:
+            raise ValueError(
+                f"radar {radar!r} sweep {int(sweep)}: a ray sits "
+                f"{dist.max() / AZIMUTH_SCALE:.3f}° from the nearest LUT azimuth, "
+                f"beyond the half-spacing tolerance of {tol / AZIMUTH_SCALE:.3f}° "
+                f"— this is not antenna drift."
+            )
+        out[sel] = snapped.astype(np.float64) / AZIMUTH_SCALE
+        worst = max(worst, float(dist.max()) if dist.size else 0.0)
+    return out, worst / AZIMUTH_SCALE
+
+
 def _build_polar_dataframe(
     df: "pl.DataFrame | pd.DataFrame",
     radar: str,
     filter_feature: str,
     filter_threshold: float,
     filter_logic: str,
+    azimuth_grids: dict | None = None,
 ) -> tuple["pl.DataFrame", np.ndarray]:
     """Filter a flattened volume DataFrame and attach gate_ids.
 
@@ -250,6 +310,11 @@ def _build_polar_dataframe(
     This is the single shared core of :func:`datatree_to_parquet` and
     :func:`archive_volume`.
 
+    ``azimuth_grids`` maps sweep -> canonical azimuths (tenths of a degree); when
+    given, every ray is snapped onto it before its ``gate_id`` is built, so the
+    volume joins its LUT exactly.  ``None`` — an archive predating the grid —
+    keeps the measured azimuths, i.e. the previous behaviour.
+
     Returns
     -------
     (df_polar, mask) : the polar DataFrame (gate_id + polar columns) and the
@@ -266,10 +331,30 @@ def _build_polar_dataframe(
         )
         mask = np.ones(len(df), dtype=bool)
 
+    sweeps_all = _col(df, "sweep", np.int64)
+    azimuths_all = _col(df, "azimuth", np.float64)
+    if azimuth_grids:
+        # Snapped before the filter, so the scan-strategy check counts the
+        # volume's rays rather than only those that survived the filter.
+        azimuths_all, worst = _snap_volume_azimuths(
+            sweeps_all, azimuths_all, azimuth_grids, radar
+        )
+        logger.debug("radar %s: rays snapped, max move %.3f deg.", radar, worst)
+    else:
+        # The old, silent failure mode: without a grid the measured azimuths go
+        # straight into gate_id, and every ray whose 0.1° bin drifted since the
+        # LUT was built produces gates that match no LUT row and vanish from
+        # every join.  Say so rather than losing 6-35% of the volume quietly.
+        logger.warning(
+            "radar %s: the LUT records no nominal azimuth grid, so measured "
+            "azimuths are used as-is and some gates may not join it. Regenerate "
+            "the LUT to fix this.", radar,
+        )
+
     gate_ids = encode_gate_ids(
         radar,
-        _col(df, "sweep", np.int64)[mask],
-        _col(df, "azimuth", np.float64)[mask],
+        sweeps_all[mask],
+        azimuths_all[mask],
         _col(df, "range")[mask],
     )
 
@@ -316,7 +401,7 @@ def archive_volume(
     dt : xr.DataTree
         Processed volume (any radar network).
     radar : str
-        Radar identifier (single letter, e.g. ``"A"``).
+        Radar identifier, e.g. ``"A"`` or ``"KTLX"``.
     base_output_path : str
         Base output directory for parquet files.
     filter_feature : str
@@ -351,7 +436,8 @@ def archive_volume(
         else _nullctx()
     ):
         df_polar, _mask = _build_polar_dataframe(
-            df, radar, filter_feature, filter_threshold, filter_logic
+            df, radar, filter_feature, filter_threshold, filter_logic,
+            azimuth_grids=load_azimuth_grids(radar, base_output_path),
         )
 
     with (
@@ -582,7 +668,8 @@ def datatree_to_parquet(
 
     df = datatree_to_dataframe(dt, max_workers)
     df_polar, mask = _build_polar_dataframe(
-        df, radar, filter_feature, filter_threshold, filter_logic
+        df, radar, filter_feature, filter_threshold, filter_logic,
+        azimuth_grids=load_azimuth_grids(radar, base_output_path),
     )
     df_polar = _finalize_polar_dtypes(df_polar, df, mask)
     return _save_polar_parquet(df_polar, radar, base_output_path)
diff --git a/raddb/lut.py b/raddb/lut.py
index 24c8ff0..afb11fa 100644
--- a/raddb/lut.py
+++ b/raddb/lut.py
@@ -23,13 +23,126 @@
 import xarray as xr
 import yaml
 
-from raddb.helper import list_sweep_names
+from raddb.helper import (
+    RADAR_ALPHABET,
+    RADAR_CODE_LEN,
+    list_sweep_names,
+    normalize_radar_name,
+)
 
 logger = logging.getLogger(__name__)
 
-# Maps single-letter radar identifiers to integer indices for numeric gate_id.
-# A=0, B=1, ..., Z=25  (26 radars supported)
-RADAR_TO_IDX: dict[str, int] = {chr(ord("A") + i): i for i in range(26)}
+# ----------------------------------------------------------------------------
+# Radar code — the leading field of a gate_id
+# ----------------------------------------------------------------------------
+
+#: Multiplier of the radar field in a ``gate_id`` — everything below it is the
+#: gate's own ``sweep``/``azimuth``/``range`` (see :func:`encode_gate_ids`).
+GATE_ID_RADAR_BASE: int = 1_000_000_000_000
+
+#: Number of distinct radar codes: ``36**4 - 1`` is the largest, ``0`` the
+#: smallest.  ``radar_code * 10**12`` must stay inside int64, which allows
+#: ``9_223_371``; base-36 over four characters needs only ``1_679_615``.
+MAX_RADAR_CODE: int = 36 ** RADAR_CODE_LEN - 1
+
+#: Version of the ``gate_id`` encoding written into each radar's info YAML.
+#:
+#: 1. radar index ``A=0 … Z=25`` — 26 radars, single letters only.
+#: 2. radar code = base-36 of the zero-padded 4-character name (``"A"`` ->
+#:    ``"000A"`` -> 10, ``"KTLX"`` -> 971493) — 1,679,616 radars.
+#:
+#: The two disagree for every name (``"L"`` is 11 under v1, 21 under v2), so a
+#: v1 archive must be migrated before it is read; see
+#: ``raddb/tools/migrate_gate_id_v2.py``.
+GATE_ID_VERSION: int = 2
+
+#: The v1 radar index, kept only so the migration script can compute the offset
+#: between an archived ``gate_id`` and its v2 replacement.
+LEGACY_RADAR_TO_IDX: dict[str, int] = {chr(ord("A") + i): i for i in range(26)}
+
+
+def encode_radar_code(radar: str) -> int:
+    """Base-36 radar code for the leading ``gate_id`` field.
+
+    The name is normalised, right-aligned and zero-padded to
+    :data:`~raddb.helper.RADAR_CODE_LEN` characters, then read as a base-36
+    integer over :data:`~raddb.helper.RADAR_ALPHABET`.
+
+    Parameters
+    ----------
+    radar : str
+        Radar name, e.g. ``"A"``, ``"MLA"`` or ``"KTLX"``.
+
+    Returns
+    -------
+    int
+        A value in ``[0, MAX_RADAR_CODE]``.
+
+    Examples
+    --------
+    >>> encode_radar_code("A")
+    10
+    >>> encode_radar_code("KTLX")
+    971493
+    """
+    name = normalize_radar_name(radar).rjust(RADAR_CODE_LEN, "0")
+    code = 0
+    for char in name:
+        code = code * 36 + RADAR_ALPHABET.index(char)
+    return code
+
+
+def decode_radar_code(code: int) -> str:
+    """Inverse of :func:`encode_radar_code`.
+
+    Parameters
+    ----------
+    code : int
+        A radar code in ``[0, MAX_RADAR_CODE]``.
+
+    ``decode_radar_code(encode_radar_code(name)) == name`` for every canonical
+    name (all 1,679,580 of them).  The reverse holds for every code the encoder
+    can emit; the 36 codes spelling ``"ML0".."MLZ"`` are the exception, because
+    :func:`~raddb.helper.normalize_radar_name` resolves that MeteoSwiss spelling
+    to its final letter before encoding, so no ``gate_id`` ever carries one.
+
+    Returns
+    -------
+    str
+        The canonical radar name, without its zero padding.
+
+    Raises
+    ------
+    ValueError
+        If *code* is outside the representable range.
+
+    Examples
+    --------
+    >>> decode_radar_code(971493)
+    'KTLX'
+    """
+    value = int(code)
+    if not 0 <= value <= MAX_RADAR_CODE:
+        raise ValueError(
+            f"radar code {value} is outside [0, {MAX_RADAR_CODE}] and names no radar."
+        )
+    chars = []
+    for _ in range(RADAR_CODE_LEN):
+        value, rem = divmod(value, 36)
+        chars.append(RADAR_ALPHABET[rem])
+    return "".join(reversed(chars)).lstrip("0") or "0"
+
+
+#: Radar code of every single-letter name.
+#:
+#: .. deprecated::
+#:    Superseded by :func:`encode_radar_code`, which is not limited to A-Z.
+#:    Kept because it is part of the public API surface; do **not** use it as a
+#:    membership test for "is this a usable radar name" — see
+#:    :func:`raddb.helper.is_valid_radar_name`.
+RADAR_TO_IDX: dict[str, int] = {
+    chr(ord("A") + i): encode_radar_code(chr(ord("A") + i)) for i in range(26)
+}
 
 #: Antenna 3 dB beamwidth in degrees, used for the gate's angular extent.
 #: 1.0 deg matches the MeteoSwiss Rad4Alp radars and the reference prototype
@@ -37,7 +150,7 @@
 DEFAULT_BEAMWIDTH_DEG: float = 1.0
 
 #: The five files that make up a complete LUT directory for one radar.
-#: ``{radar}`` is substituted with the single-letter radar name.
+#: ``{radar}`` is substituted with the canonical radar name.
 LUT_FILES: dict[str, str] = {
     "lut": "{radar}_LUT.parquet",
     "h_plane": "{radar}_h_plane_LUT.parquet",
@@ -236,6 +349,173 @@ def compute_gate_xyz(
     return antenna_vectors_to_cartesian(ranges, azimuths, elevations, ke=ke)
 
 
+# ============================================================================
+# The nominal azimuth grid
+# ============================================================================
+
+#: Azimuths are stored in ``gate_id`` as ``round(azimuth * 10)`` — tenths of a
+#: degree.  A full turn is therefore ``360 * 10`` steps.
+AZIMUTH_SCALE: int = 10
+AZIMUTH_STEPS: int = 360 * AZIMUTH_SCALE
+
+
+def _round_half_up(values) -> np.ndarray:
+    """``round`` that always breaks .5 upwards, unlike numpy's banker's rounding.
+
+    A nominal grid lands exactly on a half-step whenever the ray spacing is an
+    odd multiple of 0.05° — 720-ray NEXRAD sweeps put every ray centre on
+    ``x.x5``.  Banker's rounding would then alternate down/up and turn a uniform
+    0.5° grid into an irregular 0.4°/0.6° one.
+    """
+    return np.floor(np.asarray(values, dtype=np.float64) + 0.5)
+
+
+def nominal_azimuth_grid(azimuths) -> np.ndarray:
+    """The canonical ray azimuths of one sweep, as tenths of a degree.
+
+    A radar's scan strategy fixes how many rays a sweep has and how they are
+    spaced; what varies volume to volume is only where the antenna *reports*
+    itself, which drifts by a few hundredths of a degree.  Rounding those
+    measured angles to 0.1° therefore puts the same physical ray in different
+    ``gate_id`` bins on different volumes, and every gate whose bin moved has no
+    LUT row — 6% of gates per volume on Rad4Alp, 35% on WSR-88D.
+
+    So the grid is derived from the scan strategy rather than from one volume's
+    measurements: ``step = 360 / n_rays``, and the offset is the circular mean
+    of the measured residuals (circular because the offset is only defined
+    modulo one step).  That gives 1.0° for a 360-ray Rad4Alp sweep and 0.5° for
+    a 720-ray NEXRAD super-resolution sweep, from the same rule — which is why
+    no per-network resolution has to be configured.
+
+    Parameters
+    ----------
+    azimuths : array-like
+        One sweep's measured ray azimuths [deg], any order.
+
+    Returns
+    -------
+    np.ndarray of int64
+        ``n_rays`` sorted azimuths in tenths of a degree, in ``[0, 3600)``.
+
+    Raises
+    ------
+    ValueError
+        If the sweep has no rays, if the spacing is finer than the 0.1°
+        ``gate_id`` resolution, or if two grid points collide after rounding.
+    """
+    az = np.asarray(azimuths, dtype=np.float64).ravel() % 360.0
+    n = az.size
+    if n == 0:
+        raise ValueError("cannot derive an azimuth grid from a sweep with no rays.")
+
+    step = 360.0 / n
+    if step * AZIMUTH_SCALE < 1.0:
+        raise ValueError(
+            f"{n} rays give a {step:.4f}° ray spacing, finer than the "
+            f"{1 / AZIMUTH_SCALE}° azimuth resolution of gate_id; two rays would "
+            f"share one gate_id."
+        )
+
+    # Residual of each ray against a step grid anchored at 0.  It is defined
+    # only modulo one step, so it is averaged as an angle on that period —
+    # a plain mean would be wrong whenever the residuals straddle the wrap.
+    resid = np.sort(az) - np.arange(n) * step
+    phase = 2.0 * np.pi * resid / step
+    offset = step * np.arctan2(np.sin(phase).mean(), np.cos(phase).mean()) / (2.0 * np.pi)
+
+    grid = (np.arange(n) * step + offset) % 360.0
+    az_int = np.sort(_round_half_up(grid * AZIMUTH_SCALE).astype(np.int64) % AZIMUTH_STEPS)
+    if np.unique(az_int).size != n:
+        raise ValueError(
+            f"the {n}-ray azimuth grid collides after rounding to "
+            f"{1 / AZIMUTH_SCALE}°; this scan strategy cannot be stored in gate_id."
+        )
+
+    # The grid is only meaningful if it actually fits the rays it came from.
+    # It assumes a full rotation of evenly spaced rays, so a sector scan (or a
+    # sweep with a large gap) would otherwise be silently mangled: 90 rays over
+    # a 90° sector get a 4° grid and collapse onto 23 of its points.
+    snapped, dist = snap_azimuths_to_grid(az, az_int)
+    tol = azimuth_grid_tolerance(az_int)
+    if np.unique(snapped).size != n or (dist.size and dist.max() > tol):
+        raise ValueError(
+            f"these {n} rays do not form a full rotation of evenly spaced rays "
+            f"(they span {np.ptp(np.sort(az)):.1f}° with a derived spacing of "
+            f"{step:.4f}°), so they have no nominal azimuth grid. Sector scans and "
+            f"irregular sweeps are not supported."
+        )
+    return az_int
+
+
+def azimuth_grid_tolerance(grid) -> float:
+    """Largest snap distance accepted for *grid*, in tenths of a degree.
+
+    Half the ray spacing: beyond that a ray is closer to its neighbour than to
+    itself, so it is not antenna drift but a different scan strategy.
+    """
+    n = len(grid)
+    return (AZIMUTH_STEPS / n) / 2.0 if n else float("inf")
+
+
+def snap_azimuths_to_grid(azimuths, grid) -> tuple[np.ndarray, np.ndarray]:
+    """Match measured azimuths onto a sweep's canonical grid.
+
+    Nearest neighbour **on the circle** — a ray reported at 359.97° belongs to
+    the grid point at 0.0°, not to the one at 359.5°.
+
+    Parameters
+    ----------
+    azimuths : array-like
+        Measured ray azimuths [deg].
+    grid : array-like of int
+        The sweep's canonical azimuths in tenths of a degree
+        (:func:`nominal_azimuth_grid`).
+
+    Returns
+    -------
+    (snapped, distance) : np.ndarray
+        ``snapped`` is the matched azimuth in tenths of a degree (int64);
+        ``distance`` is how far each ray moved, in the same units (float).
+    """
+    # The comparison runs at full precision, *not* on the 0.1°-rounded azimuth:
+    # rounding first would make a ray at 0.02° equidistant from 0.5° and 359.5°
+    # and let the tie-break send it the wrong way across the seam.
+    az_t = np.asarray(azimuths, dtype=np.float64).ravel() % 360.0 * AZIMUTH_SCALE
+    g = np.unique(np.asarray(grid, dtype=np.int64))
+    if g.size == 0:
+        raise ValueError("cannot snap to an empty azimuth grid.")
+
+    # Wrap the grid once each way so the nearest neighbour of a ray near 0° or
+    # 360° is found across the seam.
+    ext = np.concatenate([g - AZIMUTH_STEPS, g, g + AZIMUTH_STEPS]).astype(np.float64)
+    idx = np.clip(np.searchsorted(ext, az_t), 1, ext.size - 1)
+    lo, hi = ext[idx - 1], ext[idx]
+    take_lo = (az_t - lo) <= (hi - az_t)
+    snapped = np.where(take_lo, lo, hi).astype(np.int64) % AZIMUTH_STEPS
+    distance = np.where(take_lo, az_t - lo, hi - az_t)
+    return snapped, distance
+
+
+def load_azimuth_grids(radar: str, base_path: str | Path) -> dict[int, np.ndarray] | None:
+    """``{sweep: canonical azimuths}`` from a radar's info YAML, or ``None``.
+
+    ``None`` means the archive predates the nominal grid, in which case the
+    caller must keep using the measured azimuths — snapping to a grid that was
+    never recorded would move gates off their own LUT rows.
+    """
+    try:
+        info = load_radar_info(radar, base_path)
+    except (FileNotFoundError, OutdatedGateIdError):
+        return None
+    sweeps = (info or {}).get("sweeps") or {}
+    grids = {
+        int(sweep): np.asarray(meta["azimuths"], dtype=np.int64)
+        for sweep, meta in sweeps.items()
+        if isinstance(meta, dict) and meta.get("azimuths")
+    }
+    return grids or None
+
+
 # ============================================================================
 # Gate ID generation
 # ============================================================================
@@ -248,16 +528,17 @@ def encode_gate_ids(
 ) -> np.ndarray:
     """Vectorised 64-bit gate identifier encoding.
 
-    Encoding: ``radar_idx * 10^12 + sweep * 10^10 + az_int * 10^6 + range_int``
+    Encoding: ``radar_code * 10^12 + sweep * 10^10 + az_int * 10^6 + range_int``
 
-    where ``az_int = round(azimuth * 10)`` (1 decimal place precision) and
+    where ``radar_code`` is :func:`encode_radar_code`,
+    ``az_int = round(azimuth * 10)`` (1 decimal place precision) and
     ``range_int = int(range_m)`` (integer metres).  This is the single
     canonical implementation, used by LUT generation and volume archiving.
 
     Parameters
     ----------
     radar : str
-        Radar identifier (single letter, e.g. ``"A"``).
+        Radar identifier, e.g. ``"A"`` or ``"KTLX"``.
     sweeps : int or array
         Sweep number(s) — a scalar (applied to all gates) or an array
         aligned with ``azimuths`` / ``ranges``.
@@ -268,12 +549,12 @@ def encode_gate_ids(
     -------
     np.ndarray of int64
     """
-    radar_idx = np.int64(RADAR_TO_IDX[radar.upper()])
+    radar_code = np.int64(encode_radar_code(radar))
     sweep_v = np.asarray(sweeps, dtype=np.int64)
     az_int  = np.round(np.asarray(azimuths, dtype=np.float64) * 10).astype(np.int64)
     rng_int = np.asarray(ranges).astype(np.int64)
     return (
-        radar_idx * np.int64(1_000_000_000_000)
+        radar_code * np.int64(GATE_ID_RADAR_BASE)
         + sweep_v  * np.int64(   10_000_000_000)
         + az_int   * np.int64(        1_000_000)
         + rng_int
@@ -314,6 +595,131 @@ def _beamwidth_from_datatree(dt: xr.DataTree) -> float:
     return DEFAULT_BEAMWIDTH_DEG
 
 
+#: Distance error above which a CRS is refused for a radar site, in percent.
+#: Every legitimate case measured sits under 0.06%; every wrong one over 20%.
+CRS_REFUSE_PCT: float = 1.0
+#: Above this, the CRS is accepted but warned about.
+CRS_WARN_PCT: float = 0.1
+
+
+def suggest_crs(longitude: float, latitude: float) -> int:
+    """EPSG code of the UTM zone covering a site — a safe default suggestion.
+
+    UTM is not always the best choice (a national grid may fit better, and a
+    long-range radar can reach past its zone), but it is defined worldwide and
+    accurate to well under a percent near its central meridian, so it is a sound
+    thing to put in an error message when the user has to pick one.
+    """
+    zone = int((float(longitude) + 180.0) // 6) + 1
+    return (32600 if float(latitude) >= 0 else 32700) + zone
+
+
+def crs_distance_error(crs, longitude: float, latitude: float,
+                       baseline_m: float = 100_000.0) -> float:
+    """Worst relative distance error of ``crs`` at a site, in percent.
+
+    Projects a ``baseline_m`` geodesic in eight directions from the site and
+    compares each projected length with the true one.  This is measured rather
+    than read from the CRS's declared ``area_of_use``, because that metadata is
+    both incomplete (a custom proj4 definition may declare none) and
+    insufficient (EPSG:3857 claims the whole world, then reports a 100 km
+    baseline as 145 km in Switzerland).
+    """
+    import pyproj
+    from raddb.aoi import _to_pyproj_crs
+
+    geod = pyproj.Geod(ellps="WGS84")
+    tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(crs),
+                                     always_xy=True)
+    x0, y0 = tf.transform(longitude, latitude)
+    worst = 0.0
+    for azimuth in range(0, 360, 45):
+        lon2, lat2, _ = geod.fwd(longitude, latitude, azimuth, baseline_m)
+        x1, y1 = tf.transform(lon2, lat2)
+        worst = max(worst, abs(np.hypot(x1 - x0, y1 - y0) - baseline_m) / baseline_m)
+    return worst * 100.0
+
+
+def _crs_label(crs) -> str:
+    """Human name for a CRS, for error messages.
+
+    ``aoi._to_pyproj_crs`` resolves the common EPSG codes through DB-free proj4
+    strings, which lose the name — so look the code up directly when we have one.
+    """
+    import pyproj
+
+    if isinstance(crs, (int, np.integer)):
+        try:
+            named = pyproj.CRS.from_epsg(int(crs))
+            return f"EPSG:{int(crs)} ({named.name})"
+        except Exception:                                          # noqa: BLE001
+            return f"EPSG:{int(crs)}"
+    name = getattr(crs, "name", None)
+    return str(name) if name and name != "unknown" else str(crs)
+
+
+def validate_crs_for_site(crs, longitude: float, latitude: float, radar: str = "") -> float:
+    """Check that ``crs`` can carry radar geometry at this site; return the error %.
+
+    Raises when the CRS is geographic (degrees cannot express a crop radius) or
+    when it distorts distance by more than :data:`CRS_REFUSE_PCT` — which is what
+    using EPSG:2056 outside Switzerland does, to the tune of 20%.
+
+    Parameters
+    ----------
+    crs : int or CRS-like
+        The CRS the LUT will store projected coordinates in.
+    longitude, latitude : float
+        Radar site, WGS-84 degrees.
+    radar : str, optional
+        Used only to make the message concrete.
+
+    Returns
+    -------
+    float
+        Worst distance error at this site, in percent.
+    """
+    import warnings as _warnings
+    import pyproj
+    from raddb.aoi import _to_pyproj_crs
+
+    who = f"radar {radar} " if radar else ""
+    resolved = _to_pyproj_crs(crs)
+    label = _crs_label(crs)
+    if not resolved.is_projected:
+        raise ValueError(
+            f"{label} is a geographic CRS — its units are "
+            f"degrees, so it cannot express gate geometry, a crop radius or a "
+            f"cross-section distance. Pass a projected CRS; for {who}at "
+            f"({longitude:.4f}, {latitude:.4f}) try "
+            f"EPSG:{suggest_crs(longitude, latitude)}."
+        )
+
+    err = crs_distance_error(resolved, longitude, latitude)
+    if err > CRS_REFUSE_PCT:
+        area = getattr(resolved.area_of_use, "name", None)
+        if area is None and isinstance(crs, (int, np.integer)):
+            import pyproj
+            try:
+                area = getattr(pyproj.CRS.from_epsg(int(crs)).area_of_use, "name", None)
+            except Exception:                                      # noqa: BLE001
+                area = None
+        where = f", valid for {area}" if area else ""
+        raise ValueError(
+            f"{label}{where} distorts distance by {err:.1f}% at "
+            f"{who}({longitude:.4f}, {latitude:.4f}) — gate geometry, crops and "
+            f"cross-sections would all be wrong by that much. Suggested for this "
+            f"site: EPSG:{suggest_crs(longitude, latitude)}."
+        )
+    if err > CRS_WARN_PCT:
+        _warnings.warn(
+            f"{label} distorts distance by {err:.2f}% at "
+            f"{who}({longitude:.4f}, {latitude:.4f}).",
+            stacklevel=3,
+        )
+    return err
+
+
 def generate_lut_from_datatree(
     dt: xr.DataTree,
     radar: str,
@@ -343,7 +749,7 @@ def generate_lut_from_datatree(
         DataTree with ``sweep_N`` groups, each containing ``azimuth``,
         ``range``, and ``elevation`` coordinates.
     radar : str
-        Radar identifier (single letter, e.g. ``"A"``).
+        Radar identifier, e.g. ``"A"`` or ``"KTLX"``.
     output_base_path : str
         Base directory for LUT storage.
     ke : float
@@ -400,10 +806,30 @@ def generate_lut_from_datatree(
         sweep_idx = int(sweep_name.split("_")[-1])
         ds = dt[sweep_name].to_dataset()
 
-        azimuths = ds["azimuth"].values
+        measured_az = np.asarray(ds["azimuth"].values, dtype=np.float64)
         ranges = ds["range"].values
         elevations = ds["elevation"].values
+
+        # The LUT is the radar's *nominal* scan geometry, not a snapshot of this
+        # one volume's antenna readings.  Every later volume snaps onto this same
+        # grid, so a gate keeps its gate_id for the life of the archive; keying
+        # off the measured angles instead loses 6% (Rad4Alp) to 35% (WSR-88D) of
+        # the gates of every volume after the first.
+        az_grid = nominal_azimuth_grid(measured_az)
+        snapped, snap_dist = snap_azimuths_to_grid(measured_az, az_grid)
+        if np.unique(snapped).size != measured_az.size:
+            raise ValueError(
+                f"radar {radar!r} sweep {sweep_idx}: the {measured_az.size} rays do "
+                f"not sit one-per-point on a regular {360 / measured_az.size:.4f}° "
+                f"grid (two rays snap together), so this sweep has no nominal "
+                f"azimuth grid."
+            )
+        azimuths = snapped.astype(np.float64) / AZIMUTH_SCALE
         n_az, n_rng = len(azimuths), len(ranges)
+        logger.debug(
+            "sweep %d: %d rays snapped to the nominal grid, max move %.3f°.",
+            sweep_idx, n_az, snap_dist.max() / AZIMUTH_SCALE,
+        )
 
         # Compute Cartesian coordinates
         x_raw, y_raw, z_raw = antenna_vectors_to_cartesian(
@@ -459,6 +885,11 @@ def generate_lut_from_datatree(
             "range_resolution": round(rng_res, 3),
             "range_start": round(float(np.min(ranges)), 3),
             "dR": round(rng_res / 2.0, 3),
+            # The canonical ray azimuths, in tenths of a degree — exactly the
+            # values gate_id carries.  Written so archiving a later volume need
+            # not re-read the (80 MB) LUT to recover them.
+            "azimuth_scale": AZIMUTH_SCALE,
+            "azimuths": [int(v) for v in az_grid],
         }
         # Grids needed later for the corner/plane lattices — keep them so the
         # lattices are built from the same arrays the centroids came from,
@@ -474,11 +905,23 @@ def generate_lut_from_datatree(
         "LUT built: %d total gates, %d sweeps.", len(df_lut), len(sweep_meta)
     )
 
-    # Add projected coordinates if requested
-    if projection_epsg is not None or projection_crs is not None:
-        df_lut = add_lut_projection(
-            df_lut, epsg=projection_epsg, crs=projection_crs
+    # A CRS is required, and must hold at this radar's site.  There is no
+    # default: a silently wrong projection corrupts every crop and cross-section
+    # downstream, and the archive is the only place to catch it.
+    if projection_epsg is None and projection_crs is None:
+        raise ValueError(
+            f"archiving radar {radar!r} requires a CRS: the LUT stores projected "
+            f"gate coordinates, and crops and cross-sections are computed in them. "
+            f"There is no default because a wrong one is silently wrong. Radar "
+            f"{radar!r} is at ({radar_lon:.4f}, {radar_lat:.4f}); suggested: "
+            f"RadDB(crs={suggest_crs(radar_lon, radar_lat)})  "
+            f"# UTM zone {int((radar_lon + 180) // 6) + 1}"
         )
+    validate_crs_for_site(
+        projection_epsg if projection_epsg is not None else projection_crs,
+        radar_lon, radar_lat, radar,
+    )
+    df_lut = add_lut_projection(df_lut, epsg=projection_epsg, crs=projection_crs)
 
     crs_info = None
     proj_cols = _projection_column_names(df_lut)
@@ -499,6 +942,10 @@ def generate_lut_from_datatree(
         "longitude": radar_lon,
         "altitude": radar_alt,
         "crs": crs_info,
+        # Which gate_id encoding the stored ids use.  v1 (A=0..Z=25) and v2
+        # (base-36 of the name) disagree for every radar, so the reader has to
+        # be told rather than guess.
+        "gate_id_version": GATE_ID_VERSION,
         # Recorded for reproducibility: archives built before the ke 1.25 -> 4/3
         # fix carry incompatible geometry, so the file must say which model
         # produced it.
@@ -815,12 +1262,17 @@ def load_plane_nodes(
     """Load one of the node-lattice files (``h_plane`` / ``v_plane`` / ``corners``).
 
     ``sweep`` pushes a row filter into the parquet scan.
+
+    A pre-geometry archive (centroid LUT only) is backfilled on first read via
+    :func:`ensure_gate_planes` rather than raising.
     """
     path = lut_file_path(radar, kind, lut_base_path)
+    if not path.exists():
+        ensure_gate_planes(radar, lut_base_path)
     if not path.exists():
         raise FileNotFoundError(
-            f"{kind} lattice not found at {path}. Regenerate the LUT "
-            "(generate_lut_from_datatree) to create the geometry files."
+            f"{kind} lattice not found at {path}, and it could not be rebuilt from "
+            f"{radar}_LUT.parquet. Regenerate the LUT (generate_lut_from_datatree)."
         )
     lf = pl.scan_parquet(path)
     if sweep is not None:
@@ -933,6 +1385,201 @@ def gate_corner_table(
     })
 
 
+def ensure_gate_planes(
+    radar: str,
+    lut_base_path: str | Path,
+    ke: float = 4.0 / 3.0,
+    beamwidth_deg: float | None = None,
+) -> bool:
+    """Backfill the three geometry lattices from the centroid LUT if they are missing.
+
+    Archives written before the geometry files existed hold only
+    ``{radar}_LUT.parquet`` + ``{radar}_info.yaml``.  Everything the lattices need
+    is derivable from those two, so rather than failing, rebuild them in place —
+    the centroid LUT is never rewritten (see :func:`_save_lut_outputs`).
+
+    The projection is taken from the ``crs`` block recorded in the info YAML, so a
+    backfilled ``h_plane`` carries the same ``x_<epsg>`` / ``y_<epsg>`` columns a
+    freshly generated one would.
+
+    Returns
+    -------
+    bool
+        ``True`` if files were written, ``False`` if all three already existed.
+    """
+    missing = [
+        kind for kind in ("h_plane", "v_plane", "corners")
+        if not lut_file_path(radar, kind, lut_base_path).exists()
+    ]
+    if not missing:
+        return False
+
+    info = load_radar_info(radar, lut_base_path)
+    if beamwidth_deg is None:
+        beamwidth_deg = float(info.get("beamwidth_deg") or DEFAULT_BEAMWIDTH_DEG)
+    logger.info(
+        "radar %s: geometry lattices %s missing -- rebuilding from the centroid LUT.",
+        radar, missing,
+    )
+
+    corners_by_sweep: dict[int, dict] = {}
+    for sweep_num, g in _sweep_grids_from_lut(radar, lut_base_path).items():
+        corners_by_sweep[int(sweep_num)] = compute_sweep_corners(
+            ranges=g["ranges"], azimuths=g["azimuths"], elevations=g["elevations"],
+            radar_lat=info["latitude"],
+            radar_lon=info["longitude"],
+            radar_alt=info["altitude"],
+            ke=ke,
+            beamwidth_deg=beamwidth_deg,
+        )
+
+    # Pre-geometry info YAMLs have no ``crs`` block, but the centroid LUT still
+    # carries its x_<epsg>/y_<epsg> columns — recover the EPSG from those so a
+    # backfilled h_plane is projected exactly like a freshly generated one.
+    epsg = (info.get("crs") or {}).get("epsg")
+    if epsg is None:
+        lut_cols = pl.scan_parquet(
+            lut_file_path(radar, "lut", lut_base_path)
+        ).collect_schema().names()
+        for name in _projection_column_names(pl.DataFrame(schema={c: pl.Float64 for c in lut_cols})):
+            suffix = name.split("_", 1)[1]
+            if name.startswith("x_") and suffix.isdigit():
+                epsg = int(suffix)
+                logger.info("radar %s: EPSG:%d recovered from the LUT columns.", radar, epsg)
+                break
+
+    planes = build_gate_planes(
+        corners_by_sweep,
+        radar_alt=float(info["altitude"]),
+        projection_epsg=epsg,
+    )
+    lut_dir = Path(lut_base_path) / radar / "LUT"
+    for kind in ("h_plane", "v_plane", "corners"):
+        path = lut_dir / LUT_FILES[kind].format(radar=radar)
+        if not path.exists():
+            planes[kind].write_parquet(path)
+            logger.info("%s lattice backfilled -> %s", kind, path)
+    return True
+
+
+def cappi_chords(
+    radar: str,
+    lut_base_path: str | Path,
+    altitude: float,
+    height: str = "asl",
+) -> "pl.DataFrame":
+    """Where a constant-altitude surface cuts each range bin — the CAPPI slice.
+
+    A CAPPI is a horizontal slice through the volume, so the question it asks of
+    the geometry is *vertical*: which gates does the plane ``z = altitude`` pass
+    through, and where along the beam does it enter and leave each one.  That is
+    answered by the ``v_plane`` lattice, whose ``(d, z)`` quads are the gates'
+    vertical faces.  ``h_plane`` has no altitude column at all, so it cannot
+    answer it; its role comes afterwards, turning each chord into an ``(x, y)``
+    polygon (see :func:`raddb.viz.plot.plot_cappi`).
+
+    The whole computation is **per sweep and range bin, not per gate**: ``d`` and
+    ``z`` do not depend on azimuth (elevation is constant across a sweep), which
+    is why ``v_plane`` compresses to a fraction of ``h_plane``.  So a few
+    thousand rows here serve every azimuth of the volume.
+
+    Parameters
+    ----------
+    radar : str
+    lut_base_path : str or Path
+        Archive root (the directory holding ``{radar}/LUT/``).
+    altitude : float
+        Slice altitude in metres.
+    height : {"asl", "rel"}
+        Whether ``altitude`` is above sea level (default) or above the radar.
+
+    Returns
+    -------
+    pl.DataFrame
+        One row per ``(sweep, rng_idx)`` the surface intersects:
+
+        * ``d_near`` / ``d_far`` — ground distance [m] where the slice enters and
+          leaves the gate.  Interior bins of a band clip to the bin edges; only
+          the first and last bin of a band are genuinely trimmed.
+        * ``z_center`` — the gate's mid-face altitude, in the same reference as
+          ``altitude``.  Used to resolve overlapping sweeps by taking the beam
+          whose centre is closest to the slice.
+        * ``dz_center`` — ``abs(z_center - altitude)``.
+
+        Empty when no beam reaches that altitude.
+
+    Notes
+    -----
+    Beam thickness grows with range (~1.7 km at 100 km for a 1° beam) and far
+    exceeds the height gained across one range bin, so a sweep typically
+    intersects a **wide contiguous band** of bins rather than a thin ring, and
+    neighbouring sweeps overlap heavily.
+    """
+    if height not in ("asl", "rel"):
+        raise ValueError(f"height must be 'asl' or 'rel'; got {height!r}.")
+    z_col = "z_asl" if height == "asl" else "z_rel"
+    z0 = float(altitude)
+
+    nodes = load_plane_nodes(radar, lut_base_path, "v_plane")
+    # Azimuth-independent: one ray of nodes describes every azimuth.
+    nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
+
+    bottom = _node_grids(nodes, ["d", z_col], level=-1)
+    top = _node_grids(nodes, ["d", z_col], level=1)
+
+    parts = []
+    for sweep_num in sorted(bottom):
+        if sweep_num not in top:
+            logger.warning("sweep %d missing the top level of the v_plane lattice.", sweep_num)
+            continue
+        # Node rows are (n_az+1, n_rng+1); one azimuth was kept, so row 0 is it.
+        d_b, z_b = bottom[sweep_num]["d"][0], bottom[sweep_num][z_col][0]
+        d_t, z_t = top[sweep_num]["d"][0], top[sweep_num][z_col][0]
+        if d_b.size < 2:
+            continue
+
+        # Vertical face of bin j, clockwise:
+        # near-bottom, far-bottom, far-top, near-top.
+        ring_d = np.stack([d_b[:-1], d_b[1:], d_t[1:], d_t[:-1]], axis=1)
+        ring_z = np.stack([z_b[:-1], z_b[1:], z_t[1:], z_t[:-1]], axis=1)
+
+        # Clip the quad against z == z0, edge by edge.  Convex quad -> at most
+        # one entry and one exit, so min/max of the crossings is the chord.
+        za, zb = ring_z, np.roll(ring_z, -1, axis=1)
+        da, db = ring_d, np.roll(ring_d, -1, axis=1)
+        sa, sb = za - z0, zb - z0
+        crosses = ((sa <= 0) & (sb >= 0)) | ((sa >= 0) & (sb <= 0))
+        with np.errstate(divide="ignore", invalid="ignore"):
+            t = np.where(zb != za, (z0 - za) / (zb - za), 0.0)
+        d_cross = np.where(crosses, da + np.clip(t, 0.0, 1.0) * (db - da), np.nan)
+
+        hit = np.isfinite(d_cross).any(axis=1)
+        if not hit.any():
+            continue
+        # Mask before reducing: non-intersecting rows are all-NaN by design.
+        d_hit = d_cross[hit]
+        d_near = np.nanmin(d_hit, axis=1)
+        d_far = np.nanmax(d_hit, axis=1)
+        z_center = ring_z.mean(axis=1)[hit]
+
+        parts.append(pl.DataFrame({
+            "sweep": np.full(hit.sum(), sweep_num, dtype=np.int32),
+            "rng_idx": np.flatnonzero(hit).astype(np.int32),
+            "d_near": d_near.astype(np.float32),
+            "d_far": d_far.astype(np.float32),
+            "z_center": z_center.astype(np.float32),
+            "dz_center": np.abs(z_center - z0).astype(np.float32),
+        }))
+
+    if not parts:
+        return pl.DataFrame(schema={
+            "sweep": pl.Int32, "rng_idx": pl.Int32,
+            "d_near": pl.Float32, "d_far": pl.Float32,
+            "z_center": pl.Float32, "dz_center": pl.Float32,
+        })
+    return pl.concat(parts, how="vertical")
+
+
 def save_sweep_corners(
     corners_by_sweep: dict[int, dict], corners_path: str | Path
 ) -> str:
@@ -1078,11 +1725,13 @@ def load_sweep_corners(
 # Per-radar cache of gate_id -> (sweep, azimuth index, range index) into the
 # corner arrays: {(base_path, radar): pl.DataFrame}.  ~27 MB per radar, built
 # once per session.
-_GRID_CACHE: dict[tuple[str, str], "pl.DataFrame"] = {}
+_GRID_CACHE: dict[tuple[str, str, int], "pl.DataFrame"] = {}
 
 
 def decode_gate_ids(gate_ids) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Inverse of :func:`encode_gate_ids` (radar index excluded).
+    """Inverse of :func:`encode_gate_ids` (radar code excluded).
+
+    Use :func:`decode_gate_radars` for the radar names.
 
     Returns
     -------
@@ -1097,6 +1746,36 @@ def decode_gate_ids(gate_ids) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     return sweeps, az_int.astype(np.float64) / 10.0, rng_m.astype(np.float64)
 
 
+def decode_gate_radars(gate_ids) -> list[str]:
+    """Sorted distinct radar names encoded in a set of ``gate_id`` values.
+
+    The radar code is the leading field of the encoding
+    (``code = gate_id // 10**12``), so the radars a frame spans can be read off
+    without a separate ``radar`` column.  Codes that name no radar are skipped
+    with a warning rather than raising — a frame is still usable when one of
+    its radars cannot be identified.
+
+    Parameters
+    ----------
+    gate_ids : array-like of int64
+
+    Returns
+    -------
+    list of str
+        e.g. ``["KTLX", "L"]``.
+    """
+    ids = np.asarray(gate_ids, dtype=np.int64)
+    if ids.size == 0:
+        return []
+    names = []
+    for code in np.unique(ids // np.int64(GATE_ID_RADAR_BASE)):
+        try:
+            names.append(decode_radar_code(int(code)))
+        except ValueError:
+            logger.warning("gate_id radar code %d names no radar; skipped.", int(code))
+    return sorted(names)
+
+
 def _gate_grid_index(radar: str, lut_base_path: str | Path) -> "pl.DataFrame":
     """Map every ``gate_id`` to its position in the per-sweep corner arrays.
 
@@ -1109,14 +1788,17 @@ def _gate_grid_index(radar: str, lut_base_path: str | Path) -> "pl.DataFrame":
     because ``gate_id`` stores azimuth rounded to 0.1° while the LUT keeps the
     raw antenna azimuth (~0.03° jitter); matching the floats would fail.
     """
-    key = (str(lut_base_path), radar)
+    lut_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
+    if not lut_path.exists():
+        raise FileNotFoundError(f"LUT not found at {lut_path}.")
+
+    # mtime is part of the key: regenerating a LUT in a live session must not
+    # keep serving the geometry of the previous one.
+    key = (str(lut_base_path), radar, lut_path.stat().st_mtime_ns)
     cached = _GRID_CACHE.get(key)
     if cached is not None:
         return cached
 
-    lut_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
-    if not lut_path.exists():
-        raise FileNotFoundError(f"LUT not found at {lut_path}.")
     lut = pl.read_parquet(lut_path, columns=["gate_id", "sweep", "azimuth", "range"])
 
     parts = []
@@ -1271,14 +1953,51 @@ def load_radar_lut(
 def load_radar_info(
     radar: str, lut_base_path: str | Path
 ) -> dict:
-    """Load the radar info YAML for a radar."""
+    """Load the radar info YAML for a radar.
+
+    Raises
+    ------
+    OutdatedGateIdError
+        If the archive was written with the v1 ``gate_id`` encoding.
+    """
     info_path = (
         Path(lut_base_path) / radar / "LUT" / f"{radar}_info.yaml"
     )
     if not info_path.exists():
         raise FileNotFoundError(f"Info not found at {info_path}.")
     with open(info_path) as f:
-        return yaml.safe_load(f)
+        info = yaml.safe_load(f)
+    check_gate_id_version(info, radar=radar, base_path=lut_base_path)
+    return info
+
+
+class OutdatedGateIdError(RuntimeError):
+    """An archive still holds v1 ``gate_id`` values and must be migrated."""
+
+
+def check_gate_id_version(info: dict, radar: str, base_path: str | Path) -> int:
+    """Validate the ``gate_id`` encoding version recorded in a radar's info.
+
+    Archives written before the base-36 radar code carry no ``gate_id_version``
+    key at all, so a missing key means v1.  Reading one as v2 would silently
+    rename its radars (a v1 ``"L"`` prefix of 11 decodes to ``"B"`` under v2),
+    which is worse than refusing, hence the hard error.
+
+    Returns
+    -------
+    int
+        The archive's ``gate_id`` version, always :data:`GATE_ID_VERSION`.
+    """
+    version = int((info or {}).get("gate_id_version", 1))
+    if version != GATE_ID_VERSION:
+        raise OutdatedGateIdError(
+            f"radar {radar!r} in {base_path} uses gate_id encoding v{version}, "
+            f"but this version of RadDB writes and reads v{GATE_ID_VERSION} "
+            f"(radar codes are base-36 of the name, so v1 ids decode to the "
+            f"wrong radar). Migrate the archive in place with:\n"
+            f"    python -m raddb.tools.migrate_gate_id_v2 {base_path}"
+        )
+    return version
 
 
 def get_full_sweep_index(
diff --git a/raddb/main.py b/raddb/main.py
index 77f2dc8..ec72f33 100644
--- a/raddb/main.py
+++ b/raddb/main.py
@@ -41,7 +41,12 @@
     scan_polar_parquet,
     dataframe_to_datatree,
 )
-from raddb.helper import ensure_utc, normalize_radar_name
+from raddb.helper import (
+    RADAR_CODE_LEN,
+    ensure_utc,
+    is_valid_radar_name,
+    normalize_radar_name,
+)
 from raddb.discovery import (
     find_datatree_files,
     _find_polar_files_in_range,
@@ -55,11 +60,13 @@
     _lut_centroids,
     _lut_cs_table,
     _radars_from_gate_ids,
-    _reproject_to_2056,
+    _reproject_to_aoi,
+    aoi_epsg_for,
     _resolve_aoi_centroids,
 )
 from raddb.lut import (
-    RADAR_TO_IDX,
+    GATE_ID_RADAR_BASE,
+    encode_radar_code,
     generate_lut_from_datatree,
     gate_corner_table,
     load_plane_nodes,
@@ -361,13 +368,18 @@ def _print_daily_breakdown(times: list, indent: str = "      ") -> None:
 
 
 def _list_archive_radars(archive_dir: Path) -> list[str]:
-    """Single-letter radar subdirectories that hold archived data."""
+    """Radar subdirectories that hold archived data.
+
+    A directory counts as a radar when its name is a usable radar name *and* it
+    holds a ``LUT/`` subdirectory — the name test alone would also match
+    scratch directories, and RadDB cannot read a radar without its LUT anyway.
+    """
     if not archive_dir.exists():
         return []
     return sorted(
         p.name
         for p in archive_dir.iterdir()
-        if p.is_dir() and len(p.name) == 1 and p.name.isalpha()
+        if p.is_dir() and is_valid_radar_name(p.name) and (p / "LUT").is_dir()
     )
 
 
@@ -537,6 +549,19 @@ def archive(
         if archive_dir is None:
             raise ValueError("archive_dir must be given (via RadDB(...) or this call).")
         crs = crs if crs is not None else self._crs
+        if crs is None:
+            # Checked up front: archive() reports per-volume failures rather than
+            # raising, so without this a missing CRS would read as "0 archived"
+            # instead of saying what is actually wrong.
+            raise ValueError(
+                "archiving requires a CRS — the LUT stores projected gate "
+                "coordinates, and crops and cross-sections are computed in them. "
+                "There is no default because a wrong one is silently wrong: "
+                "EPSG:2056 outside Switzerland mis-measures distance by ~20%. "
+                "Pass RadDB(crs=<epsg>) or archive(crs=<epsg>), choosing one valid "
+                "at your radar's site (raddb.lut.suggest_crs(lon, lat) gives the "
+                "UTM zone)."
+            )
 
         if (datatree is None) == (datatree_dir is None):
             raise ValueError(
@@ -586,6 +611,11 @@ def _ensure_lut(self, radar: str, sample_dt, archive_dir: Path, crs) -> None:
                 projection_epsg=crs if isinstance(crs, int) else None,
                 projection_crs=None if isinstance(crs, int) else crs,
             )
+        except ValueError:
+            # A rejected CRS is fatal, not a per-volume hiccup: continuing would
+            # write POL files against a LUT that does not exist or is wrong, and
+            # report "archived" for data no crop or section could ever use.
+            raise
         except Exception as e:  # noqa: BLE001
             print(f"  [{radar}] LUT generation failed: {e}")
 
@@ -605,8 +635,12 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
                 filter_feature=feat, filter_threshold=thr, filter_logic=logic,
                 verbose=False,
             )
-            n = sum(len(v) for v in results.values())
-            return list(results.keys()), n, 0
+            # Count outcomes, not attempts: archive_multiple_volumes reports a
+            # failed volume as a record with success=False, and calling every
+            # record an archive is how a broken volume gets announced as stored.
+            flat = [r for v in results.values() for r in v]
+            n_ok = sum(1 for r in flat if r.get("success"))
+            return list(results.keys()), n_ok, len(flat) - n_ok
 
         if radar is None or not isinstance(radar, str):
             raise ValueError(
@@ -629,7 +663,8 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
             filter_feature=feat, filter_threshold=thr, filter_logic=logic,
             verbose=False,
         )
-        return [r], len(results), 0
+        n_ok = sum(1 for res in results if res.get("success"))
+        return [r], n_ok, len(results) - n_ok
 
     def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic, thr, time_period):
         archive_dir = Path(archive_dir)
@@ -643,9 +678,14 @@ def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic,
             by_radar: dict[str, list] = {normalize_radar_name(radar): list(files)}
         else:
             # Infer the radar per file from its filename (``<RADAR>_...``).
+            # Group on the canonical name so that case and the MeteoSwiss
+            # ``ML*`` spelling collapse together; an unusable prefix is kept
+            # verbatim so the per-radar loop can report and skip it.
             by_radar = defaultdict(list)
             for f in files:
-                by_radar[_radar_from_filename(f)].append(f)
+                prefix = _radar_from_filename(f)
+                key = normalize_radar_name(prefix) if is_valid_radar_name(prefix) else prefix
+                by_radar[key].append(f)
             if radar is not None:  # a list of radars -> keep only those
                 wanted = {normalize_radar_name(x) for x in radar}
                 by_radar = {r: fs for r, fs in by_radar.items() if r in wanted}
@@ -662,11 +702,10 @@ def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic,
 
     def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic, thr):
         """Archive every saved DataTree file for one radar (LUT autogen, resume)."""
-        if radar not in RADAR_TO_IDX:
-            print(
-                f"  [skip] radar {radar!r} is not a single letter A-Z "
-                f"(gate_id encoding limit); skipping {len(files)} file(s)."
-            )
+        try:
+            radar = normalize_radar_name(radar)
+        except ValueError as exc:
+            print(f"  [skip] {exc} Skipping {len(files)} file(s).")
             return (0, 0)
         archive_dir.mkdir(parents=True, exist_ok=True)
         ckpt = archive_dir / f"_archive_checkpoint_datatrees_{radar}.txt"
@@ -940,9 +979,10 @@ def _inventory_datatrees(self, directory: Path, detailed: bool) -> None:
             print(f"  {r:<7}{len(rfiles):>8}  {_time_span(times):<45}{_format_size(size):>12}")
             if detailed:
                 _print_daily_breakdown(times)
-                if r not in RADAR_TO_IDX:
-                    print(f"      [!] {r!r} is not a single letter A-Z — archive() would skip it "
-                          f"unless you pass radar='<letter>'")
+                if not is_valid_radar_name(r):
+                    print(f"      [!] {r!r} is not a usable radar name (1-{RADAR_CODE_LEN} "
+                          f"characters from [0-9A-Z]) — archive() would skip it unless you "
+                          f"pass radar='<name>'")
         print("-" * 78)
         print(f"  archive with: db.archive(datatree_dir={str(directory)!r})")
         print("=" * 78)
@@ -1251,8 +1291,10 @@ def sel(self, **indexers) -> "RadDB":
                 [radar_from_gate_id] if isinstance(radar_from_gate_id, str)
                 else list(radar_from_gate_id)
             )
-            idx = [RADAR_TO_IDX[normalize_radar_name(r)] for r in wanted]
-            data = data.filter((pl.col("gate_id") // 1_000_000_000_000).is_in(idx))
+            codes = [encode_radar_code(r) for r in wanted]
+            data = data.filter(
+                (pl.col("gate_id") // GATE_ID_RADAR_BASE).is_in(codes)
+            )
 
         if borrowed:
             data = data.drop(borrowed)
@@ -1275,23 +1317,37 @@ def add_feature(self, name: str, compute_fn) -> "RadDB":
 
     # ---- converters ----
 
-    def to_pandas(self, with_geometry: bool = False) -> pd.DataFrame:
+    def to_pandas(self, with_geometry: bool = False,
+                  with_polar_coords: bool = False) -> pd.DataFrame:
         """Return the data as a pandas DataFrame.
 
-        With ``with_geometry=True`` the per-gate LUT geometry
-        (``latitude``/``longitude``/``altitude`` and, if ``crs`` is set,
-        ``x_{epsg}``/``y_{epsg}``) is merged in on ``gate_id``.
+        Parameters
+        ----------
+        with_geometry : bool
+            Merge the per-gate LUT geometry on ``gate_id`` —
+            ``latitude``/``longitude``/``altitude`` and, if ``crs`` is set,
+            ``x_{epsg}``/``y_{epsg}``.
+        with_polar_coords : bool
+            Also merge the polar coordinates the geometry was derived from:
+            ``range``, ``azimuth``, ``elevation_angle``.  Off by default because
+            they duplicate information already in the Cartesian columns; turn them
+            on to work in polar space.  Implies ``with_geometry``.
         """
         data = self._require_data()
-        if with_geometry:
-            data = data.join(self._gate_geometry(), on="gate_id", how="left", suffix="_lut")
+        if with_geometry or with_polar_coords:
+            data = data.join(self._gate_geometry(with_polar_coords=with_polar_coords),
+                             on="gate_id", how="left", suffix="_lut")
         return _decode_geometry(data.to_pandas())
 
-    def to_geopandas(self):
-        """Return a GeoDataFrame with a per-gate point geometry and CRS."""
+    def to_geopandas(self, with_polar_coords: bool = False):
+        """Return a GeoDataFrame with a per-gate point geometry and CRS.
+
+        ``with_polar_coords=True`` adds ``range`` / ``azimuth`` / ``elevation_angle``
+        (see :meth:`to_pandas`).
+        """
         import geopandas as gpd
 
-        df = self.to_pandas(with_geometry=True)
+        df = self.to_pandas(with_geometry=True, with_polar_coords=with_polar_coords)
         epsg = int(self._crs) if isinstance(self._crs, int) else None
         xcol, ycol = (f"x_{epsg}", f"y_{epsg}") if epsg else (None, None)
         if xcol and xcol in df.columns:
@@ -1411,7 +1467,7 @@ def to_datatree(self, radar: str | None = None, timestep=None, label_column: str
             df_r = data.filter(pl.col("radar") == radar)
         else:
             df_r = data.filter(
-                (pl.col("gate_id") // 1_000_000_000_000) == RADAR_TO_IDX[radar]
+                (pl.col("gate_id") // GATE_ID_RADAR_BASE) == encode_radar_code(radar)
             )
         if df_r.is_empty():
             raise ValueError(f"No rows for radar {radar!r} in data.")
@@ -1476,7 +1532,15 @@ def end_time(self) -> datetime.datetime:
         """Latest volume/ray time in the loaded data."""
         return self._require_data()[self._time_column()].max()
 
-    def _gate_geometry(self) -> "pl.DataFrame":
+    #: LUT columns the ``to_*`` converters attach as gate geometry.
+    _GEOMETRY_COLS = ("gate_id", "latitude", "longitude", "altitude", "sweep")
+
+    #: Polar coordinates, attached only on request — they are what the gate
+    #: geometry was *derived from*, so they are redundant with the Cartesian
+    #: columns unless you are working in polar (antenna) space.
+    _POLAR_COLS = ("range", "azimuth", "elevation_angle")
+
+    def _gate_geometry(self, with_polar_coords: bool = False) -> "pl.DataFrame":
         """LUT geometry (lon/lat/alt [+ projected x/y]) for the gates present.
 
         Returned as its **own** table — the LUT is never carried alongside the
@@ -1484,16 +1548,17 @@ def _gate_geometry(self) -> "pl.DataFrame":
         """
         archive_dir = self._require_archive_dir()
         epsg = int(self._crs) if isinstance(self._crs, int) else None
+        wanted = list(self._GEOMETRY_COLS) + (list(self._POLAR_COLS) if with_polar_coords else [])
         radars = self.radars()
         if not radars:  # empty selection (e.g. a crop that matched nothing)
-            cols = ["gate_id", "latitude", "longitude", "altitude", "sweep"]
+            cols = list(wanted)
             if epsg:
                 cols += [f"x_{epsg}", f"y_{epsg}"]
             return pl.DataFrame(schema={c: (pl.Int64 if c == "gate_id" else pl.Float64) for c in cols})
         parts = []
         for r in radars:
             lut = load_radar_lut(normalize_radar_name(r), archive_dir)
-            keep = [c for c in ("gate_id", "latitude", "longitude", "altitude", "sweep") if c in lut.columns]
+            keep = [c for c in wanted if c in lut.columns]
             if epsg and f"x_{epsg}" in lut.columns:
                 keep += [f"x_{epsg}", f"y_{epsg}"]
             parts.append(lut.select(keep))
@@ -1522,12 +1587,29 @@ def geographic_extent(self) -> list[float]:
                 float(geo["latitude"].min()), float(geo["latitude"].max())]
 
     def crs(self):
-        """Projected CRS (x, y) as a ``pyproj.CRS``."""
+        """Projected CRS (x, y) as a ``pyproj.CRS``.
+
+        Falls back to the CRS the archive was written with, so reading never
+        requires restating it — that is recorded precisely so it need not be
+        guessed or repeated.
+        """
         import pyproj
 
-        if self._crs is None:
-            raise ValueError("No projected crs set; pass RadDB(crs=<epsg>).")
-        return pyproj.CRS.from_user_input(self._crs)
+        spec = self._crs
+        if spec is None and self.archive_dir is not None:
+            from raddb.aoi import aoi_epsg
+            for radar in (self.radars() if self.data is not None else []):
+                try:
+                    spec = aoi_epsg(self.archive_dir, radar)
+                    break
+                except (ValueError, FileNotFoundError):
+                    continue
+        if spec is None:
+            raise ValueError(
+                "no projected CRS: this object has none and no archive records "
+                "one. Pass RadDB(crs=<epsg>)."
+            )
+        return pyproj.CRS.from_user_input(spec)
 
     def geographic_crs(self):
         """Geographic CRS (lat, lon) as a ``pyproj.CRS`` (EPSG:4326)."""
@@ -1539,7 +1621,8 @@ def geographic_crs(self):
     # EXTRACT AREA OF INTEREST
     # ================================================================
 
-    def crop_by_bbox(self, bounds=None, extent=None, crs: int | str = 2056, quicklook: bool = False) -> "RadDB":
+    def crop_by_bbox(self, bounds=None, extent=None, crs: int | str | None = None,
+                     quicklook: bool = False, aoi_crs=None) -> "RadDB":
         """Crop to a rectangle; returns a new RadDB.
 
         Give **exactly one** of ``bounds=(xmin, ymin, xmax, ymax)`` or
@@ -1555,17 +1638,21 @@ def crop_by_bbox(self, bounds=None, extent=None, crs: int | str = 2056, quickloo
             xmin, ymin, xmax, ymax = bounds
         if not (xmin < xmax and ymin < ymax):
             raise ValueError("expected xmin < xmax and ymin < ymax.")
-        geom = _reproject_to_2056(shapely.box(xmin, ymin, xmax, ymax), crs)
-        return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook))
+        epsg = self._aoi_epsg(aoi_crs)
+        geom = _reproject_to_aoi(shapely.box(xmin, ymin, xmax, ymax), crs, epsg)
+        return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook, epsg))
 
-    def crop_by_polygone(self, polygon, crs: int | str | None = None, quicklook: bool = False) -> "RadDB":
+    def crop_by_polygone(self, polygon, crs: int | str | None = None,
+                         quicklook: bool = False, aoi_crs=None) -> "RadDB":
         """Crop to an arbitrary polygon (shapely, GeoDataFrame/GeoSeries, or a
         ``.shp``/``.geojson`` path); returns a new RadDB.  ``crs=None`` auto-detects.
         """
-        geom = _load_aoi_polygon(polygon, crs)
-        return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook))
+        epsg = self._aoi_epsg(aoi_crs)
+        geom = _load_aoi_polygon(polygon, crs, epsg)
+        return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook, epsg))
 
-    def crop_around_point(self, point, distance: float, crs: int | str | None = None, quicklook: bool = False) -> "RadDB":
+    def crop_around_point(self, point, distance: float, crs: int | str | None = None,
+                          quicklook: bool = False, aoi_crs=None) -> "RadDB":
         """Crop to a circle of radius ``distance`` (metres) around ``point``;
         returns a new RadDB.  ``point`` is ``(x, y)`` or a shapely Point in ``crs``.
         """
@@ -1579,11 +1666,25 @@ def crop_around_point(self, point, distance: float, crs: int | str | None = None
             pt = shapely.Point(float(point[0]), float(point[1]))
         else:
             raise TypeError(f"point must be (x, y) or a shapely Point; got {type(point).__name__}.")
-        geom = _reproject_to_2056(pt, crs).buffer(distance)
-        return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook))
+        epsg = self._aoi_epsg(aoi_crs)
+        geom = _reproject_to_aoi(pt, crs, epsg).buffer(distance)
+        return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook, epsg))
+
+    def _aoi_epsg(self, override=None) -> int:
+        """The CRS this object's AOI operations run in.
+
+        The archive's own, from ``info.yaml`` — never a built-in default, so a
+        crop radius always means metres in a frame that is valid where the radar
+        actually is.  ``aoi_crs=`` names a different one explicitly and is
+        validated against every site before use.
+        """
+        data = self._require_data()
+        radars = _radars_from_gate_ids(data["gate_id"].to_numpy())
+        return aoi_epsg_for(self._require_archive_dir(), radars, override=override)
 
-    def _crop_to_aoi(self, data: "pl.DataFrame", geom, quicklook: bool = False) -> "pl.DataFrame":
-        """Intersect ``geom`` (EPSG:2056) with LUT centroids and keep the matching rows.
+    def _crop_to_aoi(self, data: "pl.DataFrame", geom, quicklook: bool = False,
+                     epsg: int | None = None) -> "pl.DataFrame":
+        """Intersect ``geom`` (in the AOI CRS) with LUT centroids and keep matching rows.
 
         This **selects** rows, it never widens them: the LUT geometry stays in its
         own table, reachable through the ``to_*`` converters.  The intersection
@@ -1593,7 +1694,7 @@ def _crop_to_aoi(self, data: "pl.DataFrame", geom, quicklook: bool = False) -> "
         if "gate_id" not in data.columns:
             raise KeyError("data has no 'gate_id' column; cannot crop by AOI.")
         radars = _radars_from_gate_ids(data["gate_id"].to_numpy())
-        centroids = _lut_centroids(self._require_archive_dir(), radars)
+        centroids = _lut_centroids(self._require_archive_dir(), radars, epsg=epsg)
         aoi_cen = _resolve_aoi_centroids(centroids, geom)
         data_aoi = _apply_gate_ids(data, aoi_cen["gate_id"].to_numpy())
 
@@ -1601,10 +1702,13 @@ def _crop_to_aoi(self, data: "pl.DataFrame", geom, quicklook: bool = False) -> "
             from raddb.viz.plot import plot_aoi_quicklook
             # ponytail: the quicklook is the one consumer that needs coordinates,
             # so join them for the plot only — never into the returned frame.
+            # _lut_centroids returns the archive's projected pair as plain x/y,
+            # whatever EPSG that is — the quicklook draws in the AOI frame.
             selected = data_aoi.join(
-                aoi_cen.select("gate_id", "x_2056", "y_2056"), on="gate_id", how="left",
+                aoi_cen.select("gate_id", "x", "y"), on="gate_id", how="left",
             )
-            plot_aoi_quicklook(geom, selected=selected, radars=radars, base_path=self.archive_dir)
+            plot_aoi_quicklook(geom, selected=selected, radars=radars,
+                               base_path=self.archive_dir, epsg=epsg)
         return data_aoi
 
     def interactive_crop(self, **kwargs):
@@ -1621,7 +1725,9 @@ def interactive_crop(self, **kwargs):
     # EXTRACT CROSS-SECTION
     # ================================================================
 
-    def extract_cross_section(self, p1, p2, crs: int | str | None = None, beamwidth_deg: float = 1.0, quicklook: bool = False) -> "RadDB":
+    def extract_cross_section(self, p1, p2, crs: int | str | None = None,
+                              beamwidth_deg: float = 1.0, quicklook: bool = False,
+                              aoi_crs=None) -> "RadDB":
         """Extract a vertical cross-section along the line ``p1 -> p2``; returns a new RadDB.
 
         The line need not pass through a radar.  Each selected gate gets a polygon
@@ -1634,6 +1740,7 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None, beamwidth_
         if "gate_id" not in data.columns:
             raise KeyError("data has no 'gate_id' column; cannot extract a cross-section.")
 
+        epsg = self._aoi_epsg(aoi_crs)
         pts = []
         for name, p in (("p1", p1), ("p2", p2)):
             if hasattr(p, "geom_type"):
@@ -1644,13 +1751,17 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None, beamwidth_
                 pt = shapely.Point(float(p[0]), float(p[1]))
             else:
                 raise TypeError(f"{name} must be (x, y) or a shapely Point.")
-            pt = _reproject_to_2056(pt, crs)
+            pt = _reproject_to_aoi(pt, crs, epsg)
             pts.append((pt.x, pt.y))
         (x1, y1), (x2, y2) = pts
 
         radars = _radars_from_gate_ids(data["gate_id"].to_numpy())
-        cs_t = _lut_cs_table(self._require_archive_dir(), radars, beamwidth_deg=beamwidth_deg)
-        cs_geom = _cross_section_gates(cs_t, (x1, y1), (x2, y2), beamwidth_deg=beamwidth_deg)
+        base = self._require_archive_dir()
+        cs_t = _lut_cs_table(base, radars, beamwidth_deg=beamwidth_deg, epsg=epsg)
+        cs_geom = _cross_section_gates(
+            cs_t, (x1, y1), (x2, y2), beamwidth_deg=beamwidth_deg, base_path=base,
+            epsg=epsg,
+        )
 
         gate_ids = cs_geom["gate_id"].to_numpy(dtype=np.int64) if len(cs_geom) else np.empty(0, dtype=np.int64)
         data_cs = _apply_gate_ids(data, gate_ids)
@@ -1661,7 +1772,7 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None, beamwidth_
         geom_cols = [
             c for c in (
                 "radar", "sweep", "azimuth", "range", "elevation_angle",
-                "x_2056", "y_2056", "altitude",
+                "x", "y", "altitude",
                 "d_near", "d_far", "z_near", "z_far", "d_center", "z_center",
                 "cs_polygon",
             )
@@ -1677,6 +1788,10 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None, beamwidth_
             plot_aoi_quicklook(
                 shapely.LineString([(x1, y1), (x2, y2)]),
                 selected=data_cs, radars=radars, base_path=self.archive_dir,
+                # The section was resolved in `epsg`; without it the quicklook
+                # falls back to LV95 and frames a non-Swiss archive over
+                # Switzerland.
+                epsg=epsg,
             )
         return self._derive(data_cs)
 
@@ -1693,53 +1808,66 @@ def _save_fig(ret, save, kwargs):
         fig.savefig(save, bbox_inches="tight", dpi=kwargs.get("dpi", 150))
 
     def plot_ppi(self, sweep: int | str = 1, variable: str = "DBZH", radar: str | None = None,
-                 timestep=None, save: str | None = None, **kwargs):
-        """Plot a PPI of one sweep from the loaded data. Returns the matplotlib artist."""
+                 timestep=None, **kwargs):
+        """Plot a PPI of one sweep — one plot, one figure.
+
+        Gate footprints come from the ``h_plane`` lattice, so a filtered, ``sel``-ed
+        or cropped RadDB draws exactly the gates it still holds.  Pass ``ax=`` to
+        compose several of these into a multi-panel figure.
+
+        See :func:`raddb.viz.plot.plot_ppi` for the full parameter list
+        (``coords``, ``context``, ``save``, ...).
+        """
         from raddb.viz.plot import plot_ppi as _plot_ppi
-        dt = self.to_datatree(radar=radar, timestep=timestep, label_column=variable)
-        ret = _plot_ppi(dt, sweep=sweep, variable=variable, **kwargs)
-        self._save_fig(ret, save, kwargs)
-        return ret
-
-    def plot_rhi(self, azimuth: float, variable: str = "DBZH", radar: str | None = None,
-                 timestep=None, save: str | None = None, **kwargs):
-        """Plot a pseudo-RHI through a radar site at ``azimuth``. Returns the matplotlib artist."""
+        return _plot_ppi(self, sweep=sweep, variable=variable, radar=radar,
+                         timestep=timestep, **kwargs)
+
+    def plot_rhi(self, azimuth: float = 0.0, variable: str = "DBZH", radar: str | None = None,
+                 timestep=None, **kwargs):
+        """Plot an RHI along one azimuth, stacking every sweep.
+
+        Gate faces come from the ``v_plane`` lattice in the
+        ``(ground distance, altitude)`` plane.  See
+        :func:`raddb.viz.plot.plot_rhi` for the full parameter list.
+        """
         from raddb.viz.plot import plot_rhi as _plot_rhi
-        dt = self.to_datatree(radar=radar, timestep=timestep, label_column=variable)
-        r = radar or (self.radars()[0] if self.radars() else "")
-        ret = _plot_rhi(dt, azimuth=azimuth, variable=variable, radar=r, **kwargs)
-        self._save_fig(ret, save, kwargs)
-        return ret
+        return _plot_rhi(self, azimuth=azimuth, variable=variable, radar=radar,
+                         timestep=timestep, **kwargs)
+
+    def plot_cappi(self, altitude: float, variable: str = "DBZH", radar: str | None = None,
+                   timestep=None, **kwargs):
+        """Plot a CAPPI — a horizontal slice at constant ``altitude`` [m].
+
+        Where :meth:`plot_ppi` fixes the sweep, this fixes the altitude and pulls
+        from whichever elevation angles sample it.  See
+        :func:`raddb.viz.plot.plot_cappi` for how the slice geometry is built and
+        for ``overlap`` / ``fill_lowest``.
+        """
+        from raddb.viz.plot import plot_cappi as _plot_cappi
+        return _plot_cappi(self, altitude=altitude, variable=variable, radar=radar,
+                           timestep=timestep, **kwargs)
+
+    def plot_vcs(self, line=None, variable: str = "DBZH", radar: str | None = None,
+                 timestep=None, **kwargs):
+        """Plot a vertical cross-section along an arbitrary line.
+
+        Either pass ``line=`` — ``(p1, p2)``, a shapely ``LineString``, or a
+        ``.shp``/``.geojson`` path — or call this on an
+        :meth:`extract_cross_section` result, which already carries the section.
+        Giving both is an error, as is giving neither.  A DataTree cannot be used:
+        archive it first.  See :func:`raddb.viz.plot.plot_vcs`.
+        """
+        from raddb.viz.plot import plot_vcs as _plot_vcs
+        return _plot_vcs(self, line=line, variable=variable, radar=radar,
+                         timestep=timestep, **kwargs)
 
     def plot_cross_section(self, variable: str = "DBZH", radar: str | None = None,
-                           timestep=None, save: str | None = None, **kwargs):
-        """Plot a vertical cross-section from an :meth:`extract_cross_section` result."""
-        from raddb.viz.plot import plot_cross_section as _plot_cs
-        data = self.to_pandas()
-        if "cs_polygon" not in data.columns:
-            raise ValueError(
-                "no 'cs_polygon' column; call extract_cross_section() first."
-            )
-        if "radar" in data.columns:
-            present = sorted(data["radar"].dropna().unique())
-            if radar is None:
-                if len(present) > 1:
-                    raise ValueError(f"data spans radars {present}; pass radar= to pick one.")
-            else:
-                data = data[data["radar"] == normalize_radar_name(radar)]
-                if data.empty:
-                    raise ValueError(f"no rows for radar {radar!r}.")
-        if "volume_time" in data.columns and data["volume_time"].notna().any():
-            vols = pd.to_datetime(data["volume_time"]).dropna().unique()
-            if timestep is None:
-                if len(vols) > 1:
-                    raise ValueError(f"data holds {len(vols)} volumes; pass timestep= to pick one.")
-            else:
-                ts = pd.to_datetime(timestep)
-                if ts.tzinfo is None:
-                    ts = ts.tz_localize("UTC")
-                chosen = min((pd.Timestamp(v) for v in vols), key=lambda v: abs(v - ts))
-                data = data[pd.to_datetime(data["volume_time"]) == chosen]
-        ret = _plot_cs(data, variable=variable, **kwargs)
-        self._save_fig(ret, save, kwargs)
-        return ret
+                           timestep=None, **kwargs):
+        """Deprecated alias of :meth:`plot_vcs`."""
+        import warnings
+        warnings.warn(
+            "RadDB.plot_cross_section is deprecated; use plot_vcs() instead.",
+            DeprecationWarning, stacklevel=2,
+        )
+        return self.plot_vcs(variable=variable, radar=radar, timestep=timestep, **kwargs)
+
diff --git a/raddb/tests/bench_plot_backends.py b/raddb/tests/bench_plot_backends.py
new file mode 100644
index 0000000..362c37c
--- /dev/null
+++ b/raddb/tests/bench_plot_backends.py
@@ -0,0 +1,249 @@
+"""
+raddb/tests/bench_plot_backends.py
+----------------------------------
+Measured comparison of the ways a RadDB frame can be turned into a PPI.
+
+Not a test — ``pytest`` does not collect it (the filename is not ``test_*``).
+Run it directly against a real archive::
+
+    python -m raddb.tests.bench_plot_backends --archive /path/to/archive --radar L
+
+Five paths are compared:
+
+1. ``polygons``  — polars -> numpy -> ``PolyCollection`` (what the four plots use)
+2. ``geopandas`` — ``to_geopandas()`` -> ``GeoDataFrame.plot``
+3. ``lonboard``  — ``to_geoarrow()`` -> deck.gl widget (interactive, not matplotlib)
+
+For each: geometric deviation from the exact ``h_plane`` corners, wall time,
+peak RSS, and the size of the saved PNG and PDF.  Every path is run on a full
+sweep **and** on a small crop — the crop is where ``datatree`` falls apart,
+because it reindexes onto the complete azimuth x range grid regardless of how
+few gates survive.
+"""
+from __future__ import annotations
+
+import argparse
+import gc
+import threading
+import time
+import warnings
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+import polars as pl
+import shapely
+
+import raddb
+from raddb.main import RadDB
+from raddb.lut import gate_corner_table
+
+VARIABLE = "DBZH"
+SWEEP = 1
+
+
+# --------------------------------------------------------------------------- util
+
+def _rss_mb() -> float:
+    """Current resident set size in MB."""
+    with open("/proc/self/statm") as f:
+        return int(f.read().split()[1]) * 4096 / 1e6
+
+
+class _RssSampler:
+    """Per-call peak RSS.
+
+    ``ru_maxrss`` is a monotonic high-water mark for the whole process, so it
+    reports 0 for every backend that runs after a heavier one.  Sampling VmRSS in
+    a side thread gives each backend its own peak, independent of run order.
+    """
+
+    def __init__(self, interval: float = 0.005):
+        self.interval = interval
+        self.peak = 0.0
+        self._stop = threading.Event()
+        self._thread = None
+
+    def __enter__(self):
+        self.base = _rss_mb()
+        self.peak = self.base
+
+        def poll():
+            while not self._stop.wait(self.interval):
+                self.peak = max(self.peak, _rss_mb())
+
+        self._thread = threading.Thread(target=poll, daemon=True)
+        self._thread.start()
+        return self
+
+    def __exit__(self, *exc):
+        self._stop.set()
+        self._thread.join()
+        self.peak = max(self.peak, _rss_mb())
+        return False
+
+    @property
+    def delta(self) -> float:
+        return self.peak - self.base
+
+
+def _sizes(fig, tmp: Path, tag: str) -> tuple[float, float]:
+    """Saved PNG and PDF size in kB."""
+    png, pdf = tmp / f"{tag}.png", tmp / f"{tag}.pdf"
+    fig.savefig(png, dpi=150, bbox_inches="tight")
+    fig.savefig(pdf, bbox_inches="tight")
+    return png.stat().st_size / 1e3, pdf.stat().st_size / 1e3
+
+
+def _reference_corners(radar: str, base: Path, gate_ids) -> np.ndarray:
+    """Exact h_plane corners for the given gates, as an (n, 4, 2) array."""
+    tbl = gate_corner_table(radar, base, kind="h_plane")
+    aligned = pl.DataFrame({"gate_id": np.asarray(gate_ids, dtype=np.int64)}).join(
+        tbl, on="gate_id", how="left", maintain_order="left"
+    )
+    return np.stack([
+        np.stack([aligned[f"x_{k}"].to_numpy(), aligned[f"y_{k}"].to_numpy()], axis=1)
+        for k in range(1, 5)
+    ], axis=1).astype(np.float64)
+
+
+# ----------------------------------------------------------------------- backends
+
+def bench_polygons(rdf, radar, base, tmp, tag):
+    t0 = time.perf_counter()
+    p = rdf.plot_ppi(sweep=SWEEP, variable=VARIABLE, coords="xy")
+    t_draw = time.perf_counter() - t0
+    fig = p.figure
+    drawn = np.array([path.vertices[:4] for path in p.get_paths()])
+    png, pdf = _sizes(fig, tmp, f"{tag}_polygons")
+    plt.close(fig)
+    return dict(n=len(drawn), t=t_draw, png=png, pdf=pdf, dev=0.0, drawn=drawn)
+
+
+def bench_geopandas(rdf, radar, base, tmp, tag):
+    t0 = time.perf_counter()
+    gdf = rdf.to_geopandas()
+    gdf = gdf[gdf[VARIABLE].notna()]
+    fig, ax = plt.subplots(figsize=(6, 6))
+    gdf.plot(column=VARIABLE, ax=ax, markersize=1)
+    t_draw = time.perf_counter() - t0
+    png, pdf = _sizes(fig, tmp, f"{tag}_geopandas")
+    plt.close(fig)
+    return dict(n=len(gdf), t=t_draw, png=png, pdf=pdf, dev=float("nan"))
+
+
+def bench_lonboard(rdf, radar, base, tmp, tag):
+    import lonboard
+    t0 = time.perf_counter()
+    table = rdf.to_geoarrow(geometry="polygon")
+    layer = lonboard.PolygonLayer(table=table)
+    m = lonboard.Map(layers=[layer])
+    t_draw = time.perf_counter() - t0
+    html = tmp / f"{tag}_lonboard.html"
+    try:
+        m.to_html(str(html))
+        size = html.stat().st_size / 1e3
+    except Exception as exc:                                   # noqa: BLE001
+        print(f"      (lonboard to_html failed: {exc})")
+        size = float("nan")
+    return dict(n=len(table), t=t_draw, png=size, pdf=float("nan"), dev=0.0)
+
+
+BACKENDS = [
+    ("polygons  (PolyCollection)", bench_polygons),
+    ("geopandas  (GeoDataFrame)", bench_geopandas),
+    ("lonboard   (deck.gl)", bench_lonboard),
+]
+
+
+def run(rdf, radar, base, tmp, tag):
+    print(f"\n{'=' * 92}\n{tag}: {len(rdf):,} gates\n{'=' * 92}")
+    print(f"{'backend':30s} {'drawn':>10s} {'time [s]':>9s} {'peakRSS':>9s} "
+          f"{'PNG [kB]':>10s} {'PDF [kB]':>10s}")
+    print("-" * 92)
+    results = {}
+    for name, fn in BACKENDS:
+        gc.collect()
+        try:
+            with warnings.catch_warnings(), _RssSampler() as rss:
+                warnings.simplefilter("ignore")
+                r = fn(rdf, radar, base, tmp, tag)
+        except Exception as exc:                               # noqa: BLE001
+            print(f"{name:30s} {'FAILED':>10s}  {type(exc).__name__}: {exc}")
+            plt.close("all")
+            continue
+        r["rss"] = rss.delta
+        results[name] = r
+        pdf = f"{r['pdf']:10.0f}" if np.isfinite(r["pdf"]) else f"{'-':>10s}"
+        print(f"{name:30s} {r['n']:10,d} {r['t']:9.2f} {r['rss']:8.0f}M "
+              f"{r['png']:10.0f} {pdf}")
+    return results
+
+
+def check_precision(rdf, radar, base):
+    """How far each path's geometry sits from the exact frustum corners."""
+    print(f"\n{'=' * 92}\ngeometric precision vs the exact h_plane corners\n{'=' * 92}")
+
+    p = rdf.plot_ppi(sweep=SWEEP, variable=VARIABLE, coords="xy")
+    drawn = np.array([path.vertices[:4] for path in p.get_paths()])
+    ids = (
+        rdf.data.select(["gate_id", VARIABLE])
+        .join(gate_corner_table(radar, base, "h_plane", sweep=SWEEP).select("gate_id"),
+              on="gate_id", how="semi")
+        .filter(pl.col(VARIABLE).is_not_nan())["gate_id"].to_numpy()
+    )
+    plt.close("all")
+    ref = _reference_corners(radar, base, ids)
+    dev = np.abs(drawn - ref).max() if len(drawn) == len(ref) else float("nan")
+    print(f"  polygons   : {dev:.3e} m   (exact — these ARE the stored corners)")
+
+    # The centroid mesh matplotlib would infer if it had no corner nodes.
+    lut = raddb.load_radar_lut(radar, base).filter(pl.col("sweep") == SWEEP)
+    n_az = lut["azimuth"].n_unique()
+    n_rng = lut["range"].n_unique()
+    cx = lut.sort(["azimuth", "range"])["x"].to_numpy().reshape(n_az, n_rng)
+    cy = lut.sort(["azimuth", "range"])["y"].to_numpy().reshape(n_az, n_rng)
+    mid_x = 0.25 * (cx[:-1, :-1] + cx[:-1, 1:] + cx[1:, :-1] + cx[1:, 1:])
+    mid_y = 0.25 * (cy[:-1, :-1] + cy[:-1, 1:] + cy[1:, :-1] + cy[1:, 1:])
+    from raddb.lut import load_plane_nodes, _node_grids
+    nodes = load_plane_nodes(radar, base, "h_plane", sweep=SWEEP)
+    g = _node_grids(nodes, ["x", "y"])[SWEEP]
+    off = np.hypot(mid_x - g["x"][1:-1, 1:-1], mid_y - g["y"][1:-1, 1:-1])
+    print(f"  centroid   : {off.mean():.1f} m mean, {off.max():.1f} m max "
+          f"(what shading='auto' invents when no corner nodes exist)")
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--archive", required=True)
+    ap.add_argument("--radar", default="L")
+    ap.add_argument("--crop-km", type=float, default=10.0)
+    ap.add_argument("--out", default=None, help="where to write the figures")
+    args = ap.parse_args()
+
+    base = Path(args.archive)
+    tmp = Path(args.out) if args.out else Path("bench_out")
+    tmp.mkdir(parents=True, exist_ok=True)
+
+    db = RadDB(archive_dir=str(base), crs=2056)
+    info = db.get_radar_info(args.radar)
+
+    # Every backend must draw the same thing, so restrict to one sweep up front:
+    # plot_ppi selects the sweep itself, but geopandas/lonboard would otherwise
+    # render the whole volume and the timings would not be comparable.
+    rdf = db.open(radars=args.radar).sel(sweep=SWEEP)
+
+    check_precision(rdf, args.radar, base)
+    run(rdf, args.radar, base, tmp, f"full sweep {SWEEP}")
+
+    from raddb.aoi import _reproject_to_aoi
+    site = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, 2056)
+    crop = rdf.crop_around_point((site.x, site.y), distance=args.crop_km * 1000)
+    run(crop, args.radar, base, tmp, f"sweep {SWEEP} cropped to {args.crop_km:g} km")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/raddb/tests/test_azimuth_grid.py b/raddb/tests/test_azimuth_grid.py
new file mode 100644
index 0000000..33ae579
--- /dev/null
+++ b/raddb/tests/test_azimuth_grid.py
@@ -0,0 +1,291 @@
+"""
+raddb/tests/test_azimuth_grid.py
+--------------------------------
+The nominal azimuth grid: the LUT stores a radar's *scan strategy*, and every
+volume's rays are snapped onto it before their ``gate_id`` is built.
+
+This exists because the LUT used to freeze the measured azimuths of whichever
+volume was archived first.  An antenna reports where it actually pointed, which
+drifts a few hundredths of a degree between rotations, and ``gate_id`` resolves
+0.1° — so a drifting ray changed bin and its gates matched no LUT row.  Measured
+on real data: **6%** of gates lost per volume on Rad4Alp, **35%** on WSR-88D,
+silently, on every volume after the first.
+
+Synthetic throughout; the drift is injected to match what the real files show.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import xarray as xr
+
+_PKG_ROOT = Path(__file__).resolve().parents[2]
+if str(_PKG_ROOT) not in sys.path:
+    sys.path.insert(0, str(_PKG_ROOT))
+
+from raddb.lut import (  # noqa: E402
+    AZIMUTH_SCALE,
+    AZIMUTH_STEPS,
+    azimuth_grid_tolerance,
+    nominal_azimuth_grid,
+    snap_azimuths_to_grid,
+    load_azimuth_grids,
+)
+from raddb.main import RadDB  # noqa: E402
+from raddb.tests.test_fixes import _make_datatree  # noqa: E402
+
+# Real drift, measured from the files themselves: Rad4Alp reports every ray
+# ~0.033 deg past its nominal angle with a ~0.007 deg spread; WSR-88D is
+# zero-mean with a spread up to ~0.045 deg.
+MCH_BIAS, MCH_SPREAD = 0.0327, 0.0069
+NEXRAD_SPREAD = 0.045
+
+
+def _jitter(az, rng, bias=0.0, spread=MCH_SPREAD):
+    return (np.asarray(az, float) + bias + rng.normal(0, spread, len(az))) % 360.0
+
+
+def _retime(dt, when, rng, bias=0.0, spread=MCH_SPREAD):
+    """Copy a DataTree at a new time, with the antenna pointing slightly differently."""
+    out = {}
+    for name, node in dt.children.items():
+        ds = node.to_dataset()
+        n = ds.sizes["azimuth"]
+        ds = ds.assign_coords(azimuth=_jitter(ds["azimuth"].values, rng, bias, spread))
+        ds["time"] = ("azimuth", np.array([when] * n, dtype="datetime64[ns]"))
+        ds.attrs.update(node.attrs)
+        out[name] = ds
+    return xr.DataTree.from_dict(out)
+
+
+# ===========================================================================
+# nominal_azimuth_grid
+# ===========================================================================
+
+class TestNominalGrid:
+
+    @pytest.mark.parametrize("n_rays,step_tenths", [(360, 10), (720, 5), (180, 20)])
+    def test_spacing_follows_the_ray_count(self, n_rays, step_tenths):
+        """One rule, no per-network constant: the grid comes from n_rays."""
+        grid = nominal_azimuth_grid(np.arange(n_rays) * (360 / n_rays) + 0.25)
+        assert grid.size == n_rays
+        assert np.all(np.diff(grid) == step_tenths)
+
+    def test_recovers_the_grid_from_jittered_rays(self):
+        rng = np.random.default_rng(0)
+        nominal = np.arange(360) + 0.5
+        grid = nominal_azimuth_grid(_jitter(nominal, rng, MCH_BIAS))
+        assert np.array_equal(grid, np.round(nominal * AZIMUTH_SCALE).astype(np.int64))
+
+    def test_is_stable_across_volumes(self):
+        """The whole point: different volumes must derive the *same* grid."""
+        rng = np.random.default_rng(1)
+        nominal = np.arange(360) + 0.5
+        grids = [nominal_azimuth_grid(_jitter(nominal, rng, MCH_BIAS)) for _ in range(25)]
+        assert all(np.array_equal(g, grids[0]) for g in grids)
+
+    def test_super_resolution_grid_stays_uniform(self):
+        """720 rays put every centre on x.x5; banker's rounding would alternate 0.4/0.6."""
+        grid = nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)
+        assert np.all(np.diff(grid) == 5)
+
+    def test_offset_is_circular(self):
+        """Rays straddling 0 deg must not drag the offset to the middle of the step."""
+        rng = np.random.default_rng(2)
+        nominal = np.arange(360) * 1.0          # rays centred on 0, 1, 2, ...
+        grid = nominal_azimuth_grid(_jitter(nominal, rng, 0.0, 0.02))
+        assert np.array_equal(grid, np.arange(360) * 10)
+
+    def test_rejects_spacing_finer_than_the_resolution(self):
+        with pytest.raises(ValueError, match="finer than"):
+            nominal_azimuth_grid(np.arange(7200) * 0.05)
+
+    def test_rejects_empty_sweep(self):
+        with pytest.raises(ValueError, match="no rays"):
+            nominal_azimuth_grid([])
+
+    def test_rejects_a_sector_scan(self):
+        """90 rays over 90 deg would silently get a 4 deg grid and collapse."""
+        with pytest.raises(ValueError, match="full rotation"):
+            nominal_azimuth_grid(np.arange(90, 180, 1.0))
+
+    def test_rejects_a_sweep_with_a_large_gap(self):
+        az = np.concatenate([np.arange(0, 120, 1.0), np.arange(240, 360, 1.0)])
+        with pytest.raises(ValueError, match="full rotation"):
+            nominal_azimuth_grid(az)
+
+
+# ===========================================================================
+# snap_azimuths_to_grid
+# ===========================================================================
+
+class TestSnapping:
+
+    @pytest.fixture
+    def grid(self):
+        return nominal_azimuth_grid(np.arange(360) + 0.5)   # 0.5, 1.5, ... 359.5
+
+    def test_snaps_to_the_nearest_point(self, grid):
+        snapped, dist = snap_azimuths_to_grid([0.49, 0.51, 1.44, 1.56], grid)
+        assert list(snapped) == [5, 5, 15, 15]
+        assert np.allclose(dist, [0.1, 0.1, 0.6, 0.6])
+
+    @pytest.mark.parametrize("az,expected", [
+        (359.97, 3595),   # 0.47 from 359.5, 0.53 from 0.5 -> stays below the seam
+        (0.02, 5),        # 0.48 from 0.5, 0.52 from 359.5 -> stays above it
+        (359.60, 3595),
+        (0.60, 5),
+    ])
+    def test_seam_is_measured_the_short_way(self, grid, az, expected):
+        """Distance across 0/360 must go the short way round, not through 180."""
+        snapped, _ = snap_azimuths_to_grid([az], grid)
+        assert snapped[0] == expected
+
+    def test_a_ray_below_360_can_snap_to_a_grid_point_at_zero(self):
+        """With rays centred on 0, 1, 2 ..., 359.7 deg belongs to 0.0, not 359.0."""
+        grid = nominal_azimuth_grid(np.arange(360) * 1.0)
+        assert grid[0] == 0
+        snapped, dist = snap_azimuths_to_grid([359.7, 359.4, 0.3], grid)
+        assert list(snapped) == [0, 3590, 0]
+        assert np.allclose(dist, [3.0, 4.0, 3.0])
+
+    def test_full_precision_decides_the_match(self, grid):
+        """Rounding to 0.1 deg first would make 0.02 deg an ambiguous tie."""
+        snapped, _ = snap_azimuths_to_grid([0.02], grid)
+        assert snapped[0] == 5          # not 3595
+
+    def test_is_a_bijection_under_real_drift(self, grid):
+        rng = np.random.default_rng(3)
+        snapped, dist = snap_azimuths_to_grid(
+            _jitter(np.arange(360) + 0.5, rng, MCH_BIAS), grid
+        )
+        assert np.unique(snapped).size == 360        # no two rays collapse
+        assert dist.max() <= azimuth_grid_tolerance(grid)
+
+    def test_survives_nexrad_scale_drift(self):
+        grid = nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)
+        rng = np.random.default_rng(4)
+        snapped, dist = snap_azimuths_to_grid(
+            _jitter(np.arange(720) * 0.5 + 0.25, rng, 0.0, NEXRAD_SPREAD), grid
+        )
+        assert np.unique(snapped).size == 720
+        assert dist.max() <= azimuth_grid_tolerance(grid)
+
+    def test_output_is_inside_one_turn(self, grid):
+        snapped, _ = snap_azimuths_to_grid([0.0, 180.0, 359.999, 360.0], grid)
+        assert np.all((snapped >= 0) & (snapped < AZIMUTH_STEPS))
+
+    def test_rejects_empty_grid(self):
+        with pytest.raises(ValueError, match="empty azimuth grid"):
+            snap_azimuths_to_grid([1.0], [])
+
+
+# ===========================================================================
+# End to end: the bug this was written for
+# ===========================================================================
+
+class TestVolumesJoinTheirLut:
+
+    @pytest.fixture
+    def archive(self, tmp_path):
+        """One LUT-defining volume plus four later ones with drifting azimuths."""
+        db = RadDB(archive_dir=str(tmp_path / "a"), crs=2056)
+        base = _make_datatree(n_az=360, n_rng=40, n_sweeps=3)
+        db.archive(datatree=base, radar="A")
+        rng = np.random.default_rng(7)
+        for k in range(1, 5):
+            when = pd.Timestamp("2024-08-01 12:00:00") + pd.Timedelta(minutes=5 * k)
+            db.archive(datatree=_retime(base, when, rng, MCH_BIAS), radar="A")
+        return tmp_path / "a"
+
+    def test_every_volume_joins_completely(self, archive):
+        lut = pl.read_parquet(archive / "A" / "LUT" / "A_LUT.parquet", columns=["gate_id"])
+        pols = sorted((archive / "A").rglob("*_POL.parquet"))
+        assert len(pols) == 5
+        for f in pols:
+            pol = pl.read_parquet(f, columns=["gate_id"])
+            matched = pol.join(lut, on="gate_id", how="semi").height
+            assert matched == pol.height, f"{f.name}: {matched}/{pol.height} joined"
+
+    def test_the_same_ray_keeps_its_gate_id(self, archive):
+        """Across volumes a gate must keep one identity, or nothing can be compared."""
+        pols = sorted((archive / "A").rglob("*_POL.parquet"))
+        sets = [set(pl.read_parquet(f, columns=["gate_id"])["gate_id"].to_list()) for f in pols]
+        assert all(s == sets[0] for s in sets)
+
+    def test_grid_is_recorded_in_the_info_yaml(self, archive):
+        grids = load_azimuth_grids("A", archive)
+        assert grids is not None and set(grids) == {1, 2, 3}
+        for g in grids.values():
+            assert g.size == 360 and np.all(np.diff(g) == 10)
+
+    def test_lut_azimuths_are_the_grid(self, archive):
+        """The LUT holds nominal angles now, not one volume's measurements."""
+        lut = pl.read_parquet(archive / "A" / "LUT" / "A_LUT.parquet", columns=["sweep", "azimuth"])
+        az = np.unique(lut.filter(pl.col("sweep") == 1)["azimuth"].to_numpy())
+        assert np.allclose(az * AZIMUTH_SCALE, np.round(az * AZIMUTH_SCALE))
+
+    def test_plots_and_crops_see_every_gate(self, archive):
+        """The loss was invisible because it only showed up in LUT joins."""
+        rdf = RadDB(archive_dir=str(archive)).open(radars="A")
+        assert rdf.to_geopandas().shape[0] == rdf.data.height
+
+
+class TestScanStrategyGuardrails:
+
+    @pytest.fixture
+    def db(self, tmp_path):
+        d = RadDB(archive_dir=str(tmp_path / "a"), crs=2056)
+        d.archive(datatree=_make_datatree(n_az=360, n_rng=20, n_sweeps=2), radar="A")
+        return d
+
+    def test_refuses_a_different_ray_count(self, db):
+        with pytest.raises(ValueError, match="different scan strategy"):
+            db.archive(
+                datatree=_make_datatree(n_az=720, n_rng=20, n_sweeps=2,
+                                        vol_time=pd.Timestamp("2024-08-01 13:00")),
+                radar="A",
+            )
+
+    def test_refuses_an_unknown_sweep(self, db):
+        with pytest.raises(ValueError, match="no sweep"):
+            db.archive(
+                datatree=_make_datatree(n_az=360, n_rng=20, n_sweeps=4,
+                                        vol_time=pd.Timestamp("2024-08-01 14:00")),
+                radar="A",
+            )
+
+    def test_accepts_ordinary_drift(self, db):
+        rng = np.random.default_rng(11)
+        base = _make_datatree(n_az=360, n_rng=20, n_sweeps=2)
+        res = db.archive(
+            datatree=_retime(base, pd.Timestamp("2024-08-01 15:00"), rng, MCH_BIAS),
+            radar="A",
+        )
+        assert (res["n_archived"], res["n_failed"]) == (1, 0)
+
+    def test_batch_reports_the_refusal_instead_of_aborting(self, db, tmp_path):
+        """One incompatible volume must not take the whole batch down."""
+        good = _retime(_make_datatree(n_az=360, n_rng=20, n_sweeps=2),
+                       pd.Timestamp("2024-08-01 16:00"), np.random.default_rng(12), MCH_BIAS)
+        bad = _make_datatree(n_az=720, n_rng=20, n_sweeps=2,
+                             vol_time=pd.Timestamp("2024-08-01 17:00"))
+        res = db.archive(datatree={"good": good, "bad": bad}, radar="A")
+        assert res["n_archived"] == 1 and res["n_failed"] == 1
+
+    def test_warns_when_the_lut_has_no_grid(self, tmp_path, caplog):
+        """A pre-grid archive keeps working, but says that gates may not join."""
+        from raddb.io_core import _build_polar_dataframe
+
+        df = pl.DataFrame({
+            "sweep": [1, 1], "azimuth": [0.53, 1.53], "range": [1000.0, 1000.0],
+            "DBZH": [10.0, 20.0], "time": [pd.Timestamp("2024-01-01")] * 2,
+        })
+        with caplog.at_level("WARNING"):
+            _build_polar_dataframe(df, "A", "DBZH", 0.0, ">", azimuth_grids=None)
+        assert "no nominal azimuth grid" in caplog.text
diff --git a/raddb/tests/test_crs.py b/raddb/tests/test_crs.py
new file mode 100644
index 0000000..ed01861
--- /dev/null
+++ b/raddb/tests/test_crs.py
@@ -0,0 +1,262 @@
+"""
+raddb/tests/test_crs.py
+-----------------------
+The CRS contract: a projection must be declared at archive time and must be
+valid where the radar actually is.
+
+This exists because a hardcoded EPSG:2056 silently mis-selected US gates by
+**17%** — a "50 km" crop reached only ~46 km — while looking entirely normal.
+Nothing here may be inferred, defaulted or guessed.
+
+Synthetic throughout; the fixture radar is relocated to test non-Swiss sites.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pyproj
+import pytest
+import shapely
+import xarray as xr
+
+from raddb.main import RadDB
+from raddb.lut import (
+    CRS_REFUSE_PCT, crs_distance_error, generate_lut_from_datatree,
+    suggest_crs, validate_crs_for_site,
+)
+from raddb.tests.test_fixes import RADAR, _make_datatree
+
+CH = (7.0, 46.0)          # the fixture's own site
+US = (-97.2775, 35.3331)  # KTLX, Oklahoma
+
+
+def relocate(dt, lon, lat):
+    """Move a synthetic volume to another place on Earth."""
+    out = {}
+    for name, node in dt.children.items():
+        ds = node.to_dataset().assign_coords(latitude=lat, longitude=lon)
+        ds.attrs.update(node.attrs)
+        out[name] = ds
+    return xr.DataTree.from_dict(out)
+
+
+class TestSuggestion:
+    def test_suggests_the_utm_zone(self):
+        assert suggest_crs(*CH) == 32632      # zone 32N
+        assert suggest_crs(*US) == 32614      # zone 14N
+
+    def test_southern_hemisphere_gets_a_south_zone(self):
+        assert suggest_crs(151.2, -33.9) == 32756   # Sydney, zone 56S
+
+
+class TestMeasuredValidation:
+    """Validity is measured, because declared metadata is not enough."""
+
+    @pytest.mark.parametrize("crs,site,ok", [
+        (2056, CH, True),      # LV95 at home
+        (32632, CH, True),     # UTM 32N at home
+        (32614, US, True),     # UTM 14N at KTLX
+        (2056, US, False),     # the bug: LV95 in Oklahoma
+        (3857, CH, False),     # Web Mercator: claims the world, distorts hugely
+        (3857, US, False),
+    ])
+    def test_accepts_and_refuses_by_measurement(self, crs, site, ok):
+        if ok:
+            assert validate_crs_for_site(crs, *site) < CRS_REFUSE_PCT
+        else:
+            with pytest.raises(ValueError, match="distorts distance"):
+                validate_crs_for_site(crs, *site)
+
+    def test_area_of_use_alone_would_not_catch_web_mercator(self):
+        """EPSG:3857 declares the whole world, so bounds checks pass it."""
+        au = pyproj.CRS.from_epsg(3857).area_of_use
+        assert au.west <= CH[0] <= au.east and au.south <= CH[1] <= au.north
+        assert crs_distance_error(3857, *CH) > 10.0
+
+    def test_geographic_crs_is_refused(self):
+        with pytest.raises(ValueError, match="geographic"):
+            validate_crs_for_site(4326, *CH)
+
+    def test_refusal_names_a_replacement(self):
+        with pytest.raises(ValueError, match="32614"):
+            validate_crs_for_site(2056, *US)
+
+
+class TestArchiveRequiresACrs:
+    def test_no_crs_raises(self, tmp_path):
+        with pytest.raises(ValueError, match="requires a CRS"):
+            RadDB(archive_dir=str(tmp_path)).archive(datatree={RADAR: [_make_datatree()]})
+
+    def test_bad_crs_aborts_and_writes_nothing(self, tmp_path):
+        """A rejected CRS must not leave POL files behind with no usable LUT."""
+        dt = relocate(_make_datatree(), *US)
+        with pytest.raises(ValueError, match="distorts distance"):
+            RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree={RADAR: [dt]})
+        assert not list(tmp_path.rglob("*POL.parquet"))
+
+    def test_correct_crs_archives(self, tmp_path):
+        dt = relocate(_make_datatree(), *US)
+        RadDB(archive_dir=str(tmp_path), crs=32614).archive(datatree={RADAR: [dt]})
+        assert list(tmp_path.rglob("*POL.parquet"))
+        info = RadDB(archive_dir=str(tmp_path)).get_radar_info(RADAR)
+        assert info["crs"]["epsg"] == 32614
+
+
+class TestAoiUsesTheArchiveCrs:
+    @pytest.fixture(scope="class")
+    def us_archive(self, tmp_path_factory):
+        base = tmp_path_factory.mktemp("us")
+        dt = relocate(_make_datatree(n_az=72, n_rng=60, n_sweeps=3), *US)
+        RadDB(archive_dir=str(base), crs=32614).archive(datatree={RADAR: [dt]})
+        return base
+
+    def test_aoi_epsg_comes_from_the_archive(self, us_archive):
+        from raddb.aoi import aoi_epsg
+        assert aoi_epsg(us_archive, RADAR) == 32614
+
+    def test_crop_radius_is_true_metres(self, us_archive):
+        """The bug in one assertion: a 10 km crop must select a 10 km radius."""
+        from raddb.aoi import _lut_centroids, _resolve_gate_ids, _reproject_to_aoi, aoi_epsg
+
+        db = RadDB(archive_dir=str(us_archive))
+        lut = db.get_lut(RADAR)
+        geod = pyproj.Geod(ellps="WGS84")
+        lon = lut["longitude"].to_numpy(); lat = lut["latitude"].to_numpy()
+        _, _, d = geod.inv(np.full(lon.size, US[0]), np.full(lat.size, US[1]), lon, lat)
+
+        epsg = aoi_epsg(us_archive, RADAR)
+        centroids = _lut_centroids(us_archive, [RADAR])
+        pt = _reproject_to_aoi(shapely.Point(*US), 4326, epsg)
+        for radius in (10_000, 15_000):
+            truth = int((d <= radius).sum())
+            got = len(_resolve_gate_ids(centroids, pt.buffer(radius)))
+            assert abs(got - truth) <= 0.01 * truth, (
+                f"{radius/1000:.0f} km crop selected {got} gates, truth {truth}"
+            )
+
+    def test_cross_section_distance_is_true_metres(self, us_archive):
+        """A section line outside Switzerland must measure real ground distance."""
+        rdf = RadDB(archive_dir=str(us_archive)).open(radars=RADAR)
+        p1 = (US[0] - 0.2, US[1])
+        p2 = (US[0] + 0.2, US[1])
+        truth = pyproj.Geod(ellps="WGS84").inv(p1[0], p1[1], p2[0], p2[1])[2]
+
+        cs = rdf.extract_cross_section(p1=p1, p2=p2, crs=4326)
+        assert cs.data.height > 0, "the section selected no gates"
+        span = float(cs.data["d_far"].max())
+        # UTM 14N at KTLX is accurate to ~0.03%; a hardcoded LV95 would be ~20% out.
+        assert abs(span - truth) <= 0.005 * truth, (
+            f"section spans {span:,.0f} m, true geodesic {truth:,.0f} m"
+        )
+
+    def test_quicklook_context_lands_in_the_archive_frame(self, us_archive):
+        """A caller's context geometry must not be reprojected to LV95."""
+        from raddb.aoi import _resolve_context, _reproject_to_aoi, aoi_epsg
+
+        epsg = aoi_epsg(us_archive, RADAR)
+        gpd = pytest.importorskip("geopandas")
+        box = shapely.box(US[0] - 1, US[1] - 1, US[0] + 1, US[1] + 1)   # WGS-84
+        gdf = gpd.GeoDataFrame(geometry=[box], crs="EPSG:4326")
+
+        got = _resolve_context(gdf, aoi_epsg=epsg)
+        want = _reproject_to_aoi(box, 4326, epsg)
+        assert got.distance(want) < 1.0
+
+        # ... and it must sit on top of the radar, not thousands of km away.
+        site = _reproject_to_aoi(shapely.Point(*US), 4326, epsg)
+        assert got.contains(site)
+
+    def test_quicklook_context_defaults_to_the_aoi_frame(self, us_archive):
+        """A bare shapely geometry is taken as already being in the AOI frame."""
+        from raddb.aoi import _resolve_context, _reproject_to_aoi, aoi_epsg
+
+        epsg = aoi_epsg(us_archive, RADAR)
+        site = _reproject_to_aoi(shapely.Point(*US), 4326, epsg)
+        geom = site.buffer(50_000)
+        assert _resolve_context(geom, aoi_epsg=epsg).equals(geom)
+
+    @pytest.mark.parametrize("kind", ["crop", "section"])
+    def test_quicklook_is_framed_on_the_archive(self, us_archive, kind):
+        """Both quicklook call sites must pass the AOI frame, not default to LV95."""
+        matplotlib = pytest.importorskip("matplotlib")
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+        from raddb.aoi import _reproject_to_aoi, aoi_epsg
+
+        rdf = RadDB(archive_dir=str(us_archive)).open(radars=RADAR)
+        if kind == "crop":
+            rdf.crop_around_point(US, distance=20_000, crs=4326, quicklook=True)
+        else:
+            rdf.extract_cross_section(
+                p1=(US[0] - 0.2, US[1]), p2=(US[0] + 0.2, US[1]),
+                crs=4326, quicklook=True,
+            )
+        ax = plt.gcf().axes[0]
+        site = _reproject_to_aoi(shapely.Point(*US), 4326, aoi_epsg(us_archive, RADAR))
+        (x0, x1), (y0, y1) = ax.get_xlim(), ax.get_ylim()
+        plt.close("all")
+        # Axes hold metres (labelled in km by _KmFormatter); a Swiss-framed view
+        # would put the site millions of metres off-axis.
+        assert x0 <= site.x <= x1, f"{kind}: site outside x-range {(x0, x1)}"
+        assert y0 <= site.y <= y1, f"{kind}: site outside y-range {(y0, y1)}"
+
+    def test_aoi_crs_override_is_validated(self, us_archive):
+        rdf = RadDB(archive_dir=str(us_archive)).open(radars=RADAR)
+        with pytest.raises(ValueError, match="distorts distance"):
+            rdf.crop_around_point(US, distance=10_000, crs=4326, aoi_crs=2056)
+
+    def test_mixed_crs_radars_refuse_a_shared_aoi(self, tmp_path):
+        """No silent reprojection: the user must name the common frame."""
+        from raddb.aoi import aoi_epsg_for
+
+        base = tmp_path
+        RadDB(archive_dir=str(base), crs=2056).archive(
+            datatree={"A": [_make_datatree(n_az=24, n_rng=20, n_sweeps=2)]})
+        RadDB(archive_dir=str(base), crs=32614).archive(
+            datatree={"D": [relocate(_make_datatree(n_az=24, n_rng=20, n_sweeps=2), *US)]})
+        assert aoi_epsg_for(base, ["A"]) == 2056
+        assert aoi_epsg_for(base, ["D"]) == 32614
+        with pytest.raises(ValueError, match="different CRSs"):
+            aoi_epsg_for(base, ["A", "D"])
+
+
+class TestQuicklookFollowsTheFrame:
+    """The AOI quicklook draws in the archive's CRS, not always in LV95."""
+
+    @pytest.fixture(scope="class")
+    def archives(self, tmp_path_factory):
+        ch = tmp_path_factory.mktemp("ql_ch")
+        us = tmp_path_factory.mktemp("ql_us")
+        RadDB(archive_dir=str(ch), crs=2056).archive(
+            datatree={RADAR: [_make_datatree(n_az=36, n_rng=30, n_sweeps=2)]})
+        RadDB(archive_dir=str(us), crs=32614).archive(
+            datatree={RADAR: [relocate(_make_datatree(n_az=36, n_rng=30, n_sweeps=2), *US)]})
+        return ch, us
+
+    @pytest.mark.parametrize("which,epsg,site", [(0, 2056, CH), (1, 32614, US)])
+    def test_crop_is_in_view(self, archives, which, epsg, site):
+        """A hardcoded Swiss y-band used to push a US AOI off-screen entirely."""
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+        from raddb.aoi import _reproject_to_aoi
+
+        base = archives[which]
+        rdf = RadDB(archive_dir=str(base)).open(radars=RADAR)
+        pt = _reproject_to_aoi(shapely.Point(*site), 4326, epsg)
+        rdf.crop_around_point((pt.x, pt.y), distance=8_000, quicklook=True)
+        ax = plt.gcf().axes[0]
+        assert ax.get_xlim()[0] <= pt.x <= ax.get_xlim()[1]
+        assert ax.get_ylim()[0] <= pt.y <= ax.get_ylim()[1]
+        plt.close("all")
+
+    def test_quicklook_runs_without_a_declared_crs(self, archives):
+        """Reading needs no CRS, so neither does the quicklook."""
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+        from raddb.aoi import _reproject_to_aoi
+
+        rdf = RadDB(archive_dir=str(archives[1])).open(radars=RADAR)
+        pt = _reproject_to_aoi(shapely.Point(*US), 4326, 32614)
+        rdf.crop_around_point((pt.x, pt.y), distance=8_000, quicklook=True)
+        plt.close("all")
diff --git a/raddb/tests/test_datatree_io.py b/raddb/tests/test_datatree_io.py
index dbf032f..99c66cb 100644
--- a/raddb/tests/test_datatree_io.py
+++ b/raddb/tests/test_datatree_io.py
@@ -191,7 +191,7 @@ def test_end_to_end(self, tmp_path):
         out = tmp_path / "archive"
         _write_nc_volumes(src)
 
-        db = RadDB(archive_dir=str(out))
+        db = RadDB(archive_dir=str(out), crs=2056)
         res = db.archive(datatree_dir=src, radar=RADAR)
         assert (res["n_archived"], res["n_failed"]) == (3, 0)
 
@@ -217,7 +217,7 @@ def test_resume_skips_archived(self, tmp_path):
         out = tmp_path / "archive"
         _write_nc_volumes(src)
 
-        db = RadDB(archive_dir=str(out))
+        db = RadDB(archive_dir=str(out), crs=2056)
         assert db.archive(datatree_dir=src, radar=RADAR)["n_archived"] == 3
         # second run: everything checkpointed
         assert db.archive(datatree_dir=src, radar=RADAR)["n_archived"] == 0
@@ -230,7 +230,7 @@ def test_time_period_subset(self, tmp_path):
         out = tmp_path / "archive"
         _write_nc_volumes(src, minutes=(0, 5, 10))
 
-        db = RadDB(archive_dir=str(out))
+        db = RadDB(archive_dir=str(out), crs=2056)
         res = db.archive(
             datatree_dir=src, radar=RADAR,
             time_period=("2024-01-01 12:04", "2024-01-01 12:11"),
@@ -242,17 +242,41 @@ def test_unrecognized_radar_skipped(self, tmp_path):
         from raddb.main import RadDB
 
         src = tmp_path / "input"
+        src.mkdir(parents=True)
         out = tmp_path / "archive"
-        _write_nc_volumes(src)  # files are named vol_* -> radar "vol" (not A-Z)
+        # "OVERLONG" is 8 characters -> not a usable radar name, so the file is
+        # skipped rather than silently archived under its last letter.
+        _make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")).to_netcdf(
+            src / "OVERLONG_20240101_120000.nc"
+        )
 
-        db = RadDB(archive_dir=str(out))
-        res = db.archive(datatree_dir=src)  # radar=None -> infer per file; "vol" skipped
+        db = RadDB(archive_dir=str(out), crs=2056)
+        res = db.archive(datatree_dir=src)  # radar=None -> infer per file
         assert res["n_archived"] == 0
+        assert not (out / "N").exists()  # not filed under the last letter either
+
+    def test_four_letter_radar_archives(self, tmp_path):
+        """A NEXRAD-style 4-character name survives whole (gate_id v2)."""
+        pytest.importorskip("netCDF4")
+        from raddb.main import RadDB
+
+        src = tmp_path / "input"
+        src.mkdir(parents=True)
+        out = tmp_path / "archive"
+        _make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")).to_netcdf(
+            src / "KTLX_20240101_120000.nc"
+        )
+
+        db = RadDB(archive_dir=str(out), crs=2056)
+        res = db.archive(datatree_dir=src)
+        assert (res["n_archived"], res["n_failed"]) == (1, 0)
+        assert (out / "KTLX" / "LUT" / "KTLX_LUT.parquet").exists()
+        assert db.list_radars() == ["KTLX"]
 
     def test_both_sources_raises(self, tmp_path):
         from raddb.main import RadDB
 
-        db = RadDB(archive_dir=str(tmp_path))
+        db = RadDB(archive_dir=str(tmp_path), crs=2056)
         with pytest.raises(ValueError, match="exactly one"):
             db.archive(datatree_dir=tmp_path, datatree=object())
 
@@ -261,5 +285,5 @@ def test_empty_source_returns_zero(self, tmp_path):
 
         src = tmp_path / "empty"
         src.mkdir()
-        db = RadDB(archive_dir=str(tmp_path / "out"))
+        db = RadDB(archive_dir=str(tmp_path / "out"), crs=2056)
         assert db.archive(datatree_dir=src, radar=RADAR)["n_archived"] == 0
diff --git a/raddb/tests/test_fixes.py b/raddb/tests/test_fixes.py
index 2409d0d..0332650 100644
--- a/raddb/tests/test_fixes.py
+++ b/raddb/tests/test_fixes.py
@@ -19,6 +19,8 @@
 import pytest
 import xarray as xr
 
+from raddb.lut import generate_lut_from_datatree
+
 _PKG_ROOT = Path(__file__).resolve().parents[2]
 if str(_PKG_ROOT) not in sys.path:
     sys.path.insert(0, str(_PKG_ROOT))
@@ -87,7 +89,7 @@ def test_single_volume_round_trip(self, tmp_path):
         base = str(tmp_path)
 
         # Generate LUT
-        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base)
+        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base, projection_epsg=2056)
 
         # Archive volume
         datatree_to_parquet(dt, radar=RADAR, base_output_path=base)
@@ -123,7 +125,7 @@ def test_multi_volume_keeps_latest(self, tmp_path):
         dt2 = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00"))
 
         # Generate LUT from first volume
-        generate_lut_from_datatree(dt1, radar=RADAR, output_base_path=base)
+        generate_lut_from_datatree(dt1, radar=RADAR, output_base_path=base, projection_epsg=2056)
 
         # Archive both volumes
         datatree_to_parquet(dt1, radar=RADAR, base_output_path=base)
@@ -158,7 +160,7 @@ def test_sweep_present_after_lut_merge(self, tmp_path):
         base = str(tmp_path)
         dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
 
-        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base)
+        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base, projection_epsg=2056)
         datatree_to_parquet(dt, radar=RADAR, base_output_path=base)
 
         df = parquet_to_dataframe(
@@ -191,10 +193,10 @@ def test_sweep_present_in_multi_radar(self, tmp_path):
 
         # Set up two radars
         for r in ["A", "D"]:
-            generate_lut_from_datatree(dt, radar=r, output_base_path=base)
+            generate_lut_from_datatree(dt, radar=r, output_base_path=base, projection_epsg=2056)
             datatree_to_parquet(dt, radar=r, base_output_path=base)
 
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         rdf = db.open(
             radars=["A", "D"],
             time_period=("2024-08-01 00:00", "2024-08-02 00:00"),
@@ -249,17 +251,31 @@ def test_projection_columns_in_saved_lut(self, tmp_path):
         assert len(proj_y_cols) == 1, f"Expected one y_ projection column, got {proj_y_cols}"
         assert lut[proj_x_cols[0]].is_not_null().any(), "Projected x values should not all be NaN"
 
-    def test_lut_without_projection_has_no_extra_cols(self, tmp_path):
-        from raddb.lut import generate_lut_from_datatree, load_radar_lut
-
-        base = str(tmp_path)
-        dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-
-        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base)
-
-        lut = load_radar_lut(RADAR, base)
-        proj_cols = [c for c in lut.columns if c.startswith("x_") or c.startswith("y_")]
-        assert len(proj_cols) == 0, f"No projection columns expected, got {proj_cols}"
+    def test_archiving_without_a_crs_is_refused(self, tmp_path):
+        """A CRS is mandatory: a wrong or absent one silently breaks every AOI."""
+        with pytest.raises(ValueError, match="requires a CRS"):
+            generate_lut_from_datatree(
+                _make_datatree(), radar=RADAR, output_base_path=str(tmp_path)
+            )
+
+    def test_refusal_names_a_usable_crs(self, tmp_path):
+        """The message must tell the user what to pass, not just complain."""
+        with pytest.raises(ValueError, match=r"RadDB\(crs=32632\)"):
+            generate_lut_from_datatree(
+                _make_datatree(), radar=RADAR, output_base_path=str(tmp_path)
+            )
+
+    def test_a_crs_invalid_at_the_site_is_refused(self, tmp_path):
+        """EPSG:2056 outside Switzerland distorts distance ~20%."""
+        from raddb.tests.test_fixes import _make_datatree as mk
+        dt = mk()
+        for name in list(dt.children):
+            ds = dt[name].to_dataset().assign_coords(latitude=35.33, longitude=-97.28)
+            dt[name] = xr.DataTree(ds)
+        with pytest.raises(ValueError, match="distorts distance"):
+            generate_lut_from_datatree(
+                dt, radar=RADAR, output_base_path=str(tmp_path), projection_epsg=2056
+            )
 
     def test_api_archive_with_projection(self, tmp_path):
         """archive() auto-generates a LUT with projected columns when crs is set."""
diff --git a/raddb/tests/test_inventory.py b/raddb/tests/test_inventory.py
index fa9fcf0..b57c9cc 100644
--- a/raddb/tests/test_inventory.py
+++ b/raddb/tests/test_inventory.py
@@ -55,7 +55,7 @@ def test_detailed_adds_lut_columns_and_days(self, archive, capsys):
         assert "volume(s)" in out
 
     def test_empty_archive_dir(self, tmp_path, capsys):
-        RadDB(archive_dir=str(tmp_path)).inventory()
+        RadDB(archive_dir=str(tmp_path), crs=2056).inventory()
         assert "nothing archived here yet" in capsys.readouterr().out
 
     def test_without_archive_dir_raises(self):
@@ -71,14 +71,24 @@ def test_lists_files_radar_and_time_range(self, datatree_dir, capsys):
         assert "files     : 2" in out
         assert "2024-08-01 12:00:00 .. 2024-08-02 06:30:00" in out
 
-    def test_warns_on_non_letter_radar(self, tmp_path, capsys):
+    def test_four_letter_radar_not_warned(self, tmp_path, capsys):
+        """A NEXRAD-style name is archivable under gate_id v2 — no warning."""
         d = tmp_path / "nexrad"
         d.mkdir()
         _make_datatree(vol_time=VOL_TIMES[0]).to_netcdf(d / "KTLX_20240801_120000.nc")
         RadDB().inventory(datatree_dir=str(d), detailed=True)
         out = capsys.readouterr().out
         assert "KTLX" in out
-        assert "not a single letter" in out
+        assert "not a usable radar name" not in out
+
+    def test_warns_on_unusable_radar_name(self, tmp_path, capsys):
+        d = tmp_path / "odd"
+        d.mkdir()
+        _make_datatree(vol_time=VOL_TIMES[0]).to_netcdf(d / "OVERLONG_20240801_120000.nc")
+        RadDB().inventory(datatree_dir=str(d), detailed=True)
+        out = capsys.readouterr().out
+        assert "OVERLONG" in out
+        assert "not a usable radar name" in out
 
     def test_missing_directory_raises(self, tmp_path):
         with pytest.raises(FileNotFoundError):
diff --git a/raddb/tests/test_lut_planes.py b/raddb/tests/test_lut_planes.py
index 900ee18..272326a 100644
--- a/raddb/tests/test_lut_planes.py
+++ b/raddb/tests/test_lut_planes.py
@@ -20,6 +20,8 @@
 """
 from __future__ import annotations
 
+from pathlib import Path
+
 import numpy as np
 import polars as pl
 import pytest
@@ -94,6 +96,7 @@ def real_base_plain(tmp_path_factory):
     generate_lut_from_datatree(
         _make_datatree(n_az=REAL_N_AZ, n_rng=REAL_N_RNG, n_sweeps=REAL_N_SWEEPS),
         radar=RADAR, output_base_path=str(d),
+        projection_epsg=2056,
     )
     return str(d)
 
@@ -172,50 +175,49 @@ def test_per_sweep_keys(self, lut_dir):
         # both are rounded to mm in the YAML, so allow half a mm of slack
         assert s["dR"] == pytest.approx(s["range_resolution"] / 2.0, abs=1e-3)
 
-    def test_crs_is_null_without_projection(self, tmp_path):
-        generate_lut_from_datatree(
-            _make_datatree(), radar=RADAR, output_base_path=str(tmp_path)
-        )
+    def test_crs_block_records_what_was_used(self, real_base):
+        """A CRS is mandatory, so the block is always populated."""
         info = yaml.safe_load(
-            (tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
-        )
-        assert info["crs"] is None
+            (Path(real_base) / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text())
+        assert info["crs"]["epsg"] == 2056
+        assert info["crs"]["columns"] == ["x_2056", "y_2056"]
+
 
 
 class TestLatticeShape:
     def test_h_plane_is_one_node_grid_per_sweep(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         nodes = db.get_h_plane(RADAR)
         assert nodes.height == N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
         assert "el_level" not in nodes.columns          # centre level only
 
     def test_corners_has_two_elevation_levels(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         nodes = db.get_corners(RADAR)
         assert sorted(nodes["el_level"].unique().to_list()) == [-1, 1]
         assert nodes.height == 2 * N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
 
     def test_sweep_filter(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         one = db.get_h_plane(RADAR, sweep=1)
         assert one["sweep"].unique().to_list() == [1]
         assert one.height == (N_AZ + 1) * (N_RNG + 1)
 
     def test_projected_columns_present(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         assert {"x_2056", "y_2056"} <= set(db.get_h_plane(RADAR).columns)
 
 
 class TestPerGateCorners:
     def test_h_plane_has_four_corners(self, base):
-        t = RadDB(archive_dir=base).get_h_plane(RADAR, per_gate=True)
+        t = RadDB(archive_dir=base, crs=2056).get_h_plane(RADAR, per_gate=True)
         assert t.height == N_GATES
         for k in range(1, 5):
             assert f"x_{k}" in t.columns and f"y_{k}" in t.columns
         assert "x_5" not in t.columns
 
     def test_corners_has_eight(self, base):
-        t = RadDB(archive_dir=base).get_corners(RADAR, per_gate=True)
+        t = RadDB(archive_dir=base, crs=2056).get_corners(RADAR, per_gate=True)
         assert t.height == N_GATES
         for k in range(1, 9):
             assert {f"x_{k}", f"y_{k}", f"z_rel_{k}"} <= set(t.columns)
@@ -223,7 +225,7 @@ def test_corners_has_eight(self, base):
 
     def test_eight_corners_are_distinct(self, base):
         """8 distinct corners, except the degenerate innermost range bin."""
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         t = db.get_corners(RADAR, per_gate=True)
         pts = np.stack([
             np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy(),
@@ -238,7 +240,7 @@ def test_eight_corners_are_distinct(self, base):
         assert (n_distinct == 8).mean() > 0.9
 
     def test_gate_ids_match_the_lut(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         lut_ids = set(db.get_lut(RADAR)["gate_id"].to_list())
         assert set(db.get_corners(RADAR, per_gate=True)["gate_id"].to_list()) == lut_ids
 
@@ -247,7 +249,7 @@ class TestFrustumProperty:
     """The beam widens with range: the far face must exceed the near face."""
 
     def test_far_face_is_larger_than_near_face(self, base):
-        t = RadDB(archive_dir=base).get_corners(RADAR, per_gate=True)
+        t = RadDB(archive_dir=base, crs=2056).get_corners(RADAR, per_gate=True)
         near = _face_area(t, [1, 2, 3, 4])
         far = _face_area(t, [5, 6, 7, 8])
         assert np.all(far > near), (
@@ -257,7 +259,7 @@ def test_far_face_is_larger_than_near_face(self, base):
 
     def test_ratio_is_physically_sane(self, base):
         """Excluding the degenerate innermost bin, the ratio stays bounded."""
-        t = RadDB(archive_dir=base).get_corners(RADAR, per_gate=True)
+        t = RadDB(archive_dir=base, crs=2056).get_corners(RADAR, per_gate=True)
         near = _face_area(t, [1, 2, 3, 4])
         far = _face_area(t, [5, 6, 7, 8])
         ok = near > 1.0                      # drop the r~0 near face
@@ -266,7 +268,7 @@ def test_ratio_is_physically_sane(self, base):
         assert ratio.max() < 100.0
 
     def test_faces_are_valid_polygons(self, base):
-        t = RadDB(archive_dir=base).get_h_plane(RADAR, per_gate=True)
+        t = RadDB(archive_dir=base, crs=2056).get_h_plane(RADAR, per_gate=True)
         ring = np.stack([
             np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy()], axis=1)
             for k in (1, 2, 3, 4, 1)
@@ -277,7 +279,7 @@ def test_faces_are_valid_polygons(self, base):
 class TestCentroidContainment:
     def test_centroid_inside_its_own_footprint(self, real_base):
         """Needs realistic (1 deg) azimuth sampling — see ``real_base``."""
-        db = RadDB(archive_dir=real_base)
+        db = RadDB(archive_dir=real_base, crs=2056)
         t = db.get_h_plane(RADAR, per_gate=True).sort("gate_id")
         lut = db.get_lut(RADAR).sort("gate_id")
         ring = np.stack([
@@ -291,7 +293,7 @@ def test_centroid_inside_its_own_footprint(self, real_base):
         assert shapely.covers(polys, pts).all()
 
     def test_centroid_between_the_elevation_levels(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         t = db.get_corners(RADAR, per_gate=True).sort("gate_id")
         lut = db.get_lut(RADAR).sort("gate_id")
         zc = lut["z"].to_numpy()
@@ -304,33 +306,33 @@ def test_centroid_between_the_elevation_levels(self, base):
 
 class TestVPlane:
     def test_altitude_references_differ_by_site_altitude(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         site_alt = db.get_radar_info(RADAR)["altitude"]
         nodes = db.get_v_plane(RADAR)
         d = nodes["z_asl"].to_numpy() - nodes["z_rel"].to_numpy()
         assert np.allclose(d, site_alt, atol=1e-3)
 
     def test_ground_distance_is_monotonic_in_range(self, base):
-        nodes = RadDB(archive_dir=base).get_v_plane(RADAR, sweep=1)
+        nodes = RadDB(archive_dir=base, crs=2056).get_v_plane(RADAR, sweep=1)
         sub = nodes.filter(pl.col("el_level") == 1).sort(["az_idx", "rng_idx"])
         d = sub.filter(pl.col("az_idx") == 0)["d"].to_numpy()
         assert np.all(np.diff(d) > 0)
 
     def test_per_gate_has_four_corners(self, base):
-        t = RadDB(archive_dir=base).get_v_plane(RADAR, per_gate=True)
+        t = RadDB(archive_dir=base, crs=2056).get_v_plane(RADAR, per_gate=True)
         assert t.height == N_GATES
         for k in range(1, 5):
             assert {f"d_{k}", f"z_asl_{k}", f"z_rel_{k}"} <= set(t.columns)
 
     def test_azimuth_selection_picks_one_ray(self, base):
-        db = RadDB(archive_dir=base)
+        db = RadDB(archive_dir=base, crs=2056)
         t = db.get_v_plane(RADAR, azimuth=0.0, per_gate=True)
         assert 0 < t.height < N_GATES
         assert t.height == N_RNG * N_SWEEPS
 
     def test_azimuth_without_per_gate_raises(self, base):
         with pytest.raises(ValueError):
-            RadDB(archive_dir=base).get_v_plane(RADAR, azimuth=90.0)
+            RadDB(archive_dir=base, crs=2056).get_v_plane(RADAR, azimuth=90.0)
 
 
 class TestGeoParquetExport:
@@ -347,7 +349,7 @@ def test_export_embeds_crs_and_is_ccw(self, base, tmp_path):
     def test_export_falls_back_to_wgs84(self, base, tmp_path):
         gpd = pytest.importorskip("geopandas")
         out = tmp_path / "h_plane_4326.parquet"
-        RadDB(archive_dir=base).export_h_plane_geoparquet(RADAR, out, epsg=9999)
+        RadDB(archive_dir=base, crs=2056).export_h_plane_geoparquet(RADAR, out, epsg=9999)
         g = gpd.read_parquet(out)
         assert g.crs.to_epsg() == 4326
         assert g.geometry.is_valid.all()
@@ -382,12 +384,6 @@ def test_projected_budget(self, real_base):
                 f"{self.BUDGET_PROJECTED[kind]} B/gate budget"
             )
 
-    def test_unprojected_budget(self, real_base_plain):
-        for kind, bpg in self._sizes(real_base_plain).items():
-            assert bpg <= self.BUDGET_PLAIN[kind], (
-                f"{kind} is {bpg:.1f} B/gate, over the "
-                f"{self.BUDGET_PLAIN[kind]} B/gate budget"
-            )
 
     def test_geometry_stays_smaller_than_the_centroid_lut(self, real_base):
         """All three geometry files together must not dwarf the LUT itself."""
@@ -400,7 +396,7 @@ def test_geometry_stays_smaller_than_the_centroid_lut(self, real_base):
 
     def test_lattice_beats_per_gate_materialisation(self, real_base):
         """The stored lattice must be smaller than expanding every gate's corners."""
-        db = RadDB(archive_dir=real_base)
+        db = RadDB(archive_dir=real_base, crs=2056)
         stored = lut_file_path(RADAR, "corners", real_base).stat().st_size
         per_gate = db.get_corners(RADAR, per_gate=True)
         # 8 corners x 3 coords x 4 bytes, the floor for a per-gate layout
@@ -416,6 +412,7 @@ def test_beamwidth_parameter_widens_the_gate(self, tmp_path):
             generate_lut_from_datatree(
                 _make_datatree(), radar=RADAR, output_base_path=str(d),
                 beamwidth_deg=bw,
+                projection_epsg=2056,
             )
             t = gate_corner_table(RADAR, str(d), kind="corners", sweep=1)
             zs = np.stack([t[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
@@ -426,6 +423,7 @@ def test_beamwidth_recorded_in_yaml(self, tmp_path):
         generate_lut_from_datatree(
             _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
             beamwidth_deg=1.5,
+            projection_epsg=2056,
         )
         info = yaml.safe_load(
             (tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
diff --git a/raddb/tests/test_plot.py b/raddb/tests/test_plot.py
new file mode 100644
index 0000000..c09af00
--- /dev/null
+++ b/raddb/tests/test_plot.py
@@ -0,0 +1,792 @@
+"""
+raddb/tests/test_plot.py
+------------------------
+Tests for the four gate-accurate plots — ``plot_ppi``, ``plot_rhi``,
+``plot_cappi`` and ``plot_vcs`` — plus the LUT geometry they read.
+
+Covers:
+
+1. each plot returns a matplotlib artist and honours ``ax=`` (subplot composition)
+2. filtered / ``sel``-ed / cropped frames plot exactly the gates they still hold
+3. one exact geometry path; beamwidth resolution and its warning
+4. DataTree input — geometry computed from its own coords, no archive
+5. GeoDataFrame input and its error contract
+6. CAPPI slice invariants — every drawn gate really spans the altitude, chords
+   stay inside their range bin, overlap resolution leaves no double coverage
+7. coordinate frames, volume selection, and the error paths
+8. ``aoi.py`` now derives gate footprints from the ``h_plane`` lattice
+
+All tests are synthetic (``tmp_path``); no real radar files needed.
+"""
+from __future__ import annotations
+
+import matplotlib
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import shapely
+
+from raddb.main import RadDB
+from raddb.lut import cappi_chords, ensure_gate_planes, load_plane_nodes, lut_file_path
+from raddb.tests.test_fixes import _make_datatree
+
+RADAR = "L"
+N_AZ, N_RNG, N_SWEEPS = 72, 60, 6
+VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
+
+
+@pytest.fixture(scope="module")
+def archive(tmp_path_factory):
+    """A one-radar, one-volume archive with the full 5-file LUT."""
+    base = tmp_path_factory.mktemp("plot_archive")
+    db = RadDB(archive_dir=str(base), crs=2056)
+    db.archive(datatree={RADAR: [_make_datatree(N_AZ, N_RNG, n_sweeps=N_SWEEPS)]})
+    return base
+
+
+@pytest.fixture(scope="module")
+def rdf(archive):
+    return RadDB(archive_dir=str(archive), crs=2056).open(radars=RADAR)
+
+
+@pytest.fixture(scope="module")
+def site(archive):
+    """Radar site in EPSG:2056."""
+    from raddb.aoi import _reproject_to_aoi
+    info = RadDB(archive_dir=str(archive), crs=2056).get_radar_info(RADAR)
+    p = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, 2056)
+    return (p.x, p.y)
+
+
+@pytest.fixture(autouse=True)
+def _close_figures():
+    yield
+    plt.close("all")
+
+
+def _n_polys(artist):
+    return len(artist.get_paths())
+
+
+# ===========================================================================
+# 1. Each plot draws one plot, returns an artist, and composes via ax=
+# ===========================================================================
+
+class TestBasicRendering:
+    def test_ppi_returns_polycollection(self, rdf):
+        from matplotlib.collections import PolyCollection
+        p = rdf.plot_ppi(sweep=1)
+        assert isinstance(p, PolyCollection)
+        assert _n_polys(p) > 0
+
+    def test_rhi_returns_polycollection(self, rdf):
+        assert _n_polys(rdf.plot_rhi(azimuth=0)) > 0
+
+    def test_cappi_returns_polycollection(self, rdf):
+        assert _n_polys(rdf.plot_cappi(altitude=1200)) > 0
+
+    def test_vcr_returns_polycollection(self, rdf, site):
+        line = ((site[0] - 12_000, site[1] - 12_000), (site[0] + 12_000, site[1] + 12_000))
+        assert _n_polys(rdf.plot_vcs(line=line)) > 0
+
+    @pytest.mark.parametrize("call", [
+        lambda r, ax: r.plot_ppi(sweep=1, ax=ax),
+        lambda r, ax: r.plot_rhi(azimuth=0, ax=ax),
+        lambda r, ax: r.plot_cappi(altitude=1200, ax=ax),
+    ])
+    def test_draws_into_the_supplied_axes(self, rdf, call):
+        fig, ax = plt.subplots()
+        p = call(rdf, ax)
+        assert p.axes is ax
+        assert len(ax.collections) == 1
+
+    def test_composes_a_multi_panel_figure(self, rdf):
+        """One plot per Axes — the user builds the panel, not the plot function."""
+        fig, axes = plt.subplots(2, 2)
+        for ax, var in zip(axes.ravel(), ["DBZH", "ZDR", "RHOHV", "PHIDP"]):
+            rdf.plot_ppi(sweep=1, variable=var, ax=ax)
+        assert all(len(ax.collections) == 1 for ax in axes.ravel())
+
+    def test_save_writes_a_file(self, rdf, tmp_path):
+        out = tmp_path / "ppi.png"
+        rdf.plot_ppi(sweep=1, save=str(out))
+        assert out.exists() and out.stat().st_size > 0
+
+    def test_title_and_colorbar_are_optional(self, rdf):
+        p = rdf.plot_ppi(sweep=1, add_colorbar=False, title="custom")
+        assert p.axes.get_title() == "custom"
+
+
+# ===========================================================================
+# 2. Partial frames — a crop plots exactly the gates it still holds
+# ===========================================================================
+
+class TestPartialFrames:
+    def test_filtered_frame_draws_fewer_gates(self, rdf):
+        sub = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
+        assert 0 < len(sub) < len(rdf)
+        assert _n_polys(sub.plot_ppi(sweep=1)) < _n_polys(rdf.plot_ppi(sweep=1))
+
+    def test_polygon_count_equals_surviving_gate_count(self, rdf):
+        sub = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
+        n_sweep1 = (
+            sub.data.select("gate_id")
+            .join(RadDB(archive_dir=str(sub.archive_dir), crs=2056).get_lut(RADAR)
+                  .filter(pl.col("sweep") == 1).select("gate_id"),
+                  on="gate_id", how="semi")
+            .height
+        )
+        assert _n_polys(sub.plot_ppi(sweep=1)) == n_sweep1
+
+    def test_cropped_frame_plots(self, rdf, site):
+        crop = rdf.crop_around_point(site, distance=8_000)
+        assert 0 < len(crop) < len(rdf)
+        assert _n_polys(crop.plot_ppi(sweep=1)) > 0
+        assert _n_polys(crop.plot_cappi(altitude=1100)) > 0
+
+    def test_sel_frame_plots(self, rdf):
+        sel = rdf.sel(range=slice(2_000, 12_000))
+        assert 0 < len(sel) < len(rdf)
+        assert _n_polys(sel.plot_ppi(sweep=1)) > 0
+
+    def test_empty_selection_raises(self, rdf):
+        empty = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 1e9})
+        with pytest.raises(ValueError):
+            empty.plot_ppi(sweep=1)
+# ===========================================================================
+# 3. Geometry source — one exact path, chosen by input type
+# ===========================================================================
+
+class TestGeometrySource:
+    """There is no approximate mode: every plot draws the exact frustum."""
+
+    @pytest.mark.parametrize("fn", ["plot_ppi", "plot_rhi", "plot_cappi"])
+    def test_no_plot_takes_a_beamwidth(self, fn):
+        """Beamwidth belongs to LUT generation, not to plotting."""
+        import inspect
+        import raddb.viz.plot as vp
+        assert "beamwidth_deg" not in inspect.signature(getattr(vp, fn)).parameters
+
+    def test_datatree_beamwidth_comes_from_the_file(self):
+        """No archive to bake it in, so it is inferred as LUT generation does."""
+        from raddb.viz.plot import _beamwidth
+        from raddb.lut import DEFAULT_BEAMWIDTH_DEG
+
+        dt = _make_datatree(N_AZ, N_RNG, n_sweeps=N_SWEEPS)
+        src = type("S", (), {"kind": "datatree", "dtree": dt})()
+        assert _beamwidth(src) == DEFAULT_BEAMWIDTH_DEG
+
+        dt.attrs["radar_beam_width_h"] = 0.5
+        assert _beamwidth(src) == 0.5
+
+
+# ===========================================================================
+# 3b. DataTree input — geometry from its own coordinates, no archive
+# ===========================================================================
+
+class TestDataTreeInput:
+    @pytest.fixture(scope="class")
+    def dtree(self):
+        return _make_datatree(N_AZ, N_RNG, n_sweeps=N_SWEEPS)
+
+    def test_ppi_exact_from_a_raw_datatree(self, dtree):
+        from raddb.viz.plot import plot_ppi
+        assert _n_polys(plot_ppi(dtree, sweep=1, variable="DBZH")) == N_AZ * N_RNG
+
+    def test_cappi_uses_the_files_beamwidth(self, dtree):
+        """A wider declared beam reaches the slice altitude over more bins."""
+        from raddb.viz.plot import plot_cappi
+        import copy
+        narrow = copy.deepcopy(dtree); narrow.attrs["radar_beam_width_h"] = 0.5
+        wide = copy.deepcopy(dtree); wide.attrs["radar_beam_width_h"] = 2.0
+        assert (_n_polys(plot_cappi(wide, altitude=1200, variable="DBZH"))
+                > _n_polys(plot_cappi(narrow, altitude=1200, variable="DBZH")))
+
+    def test_no_archive_is_needed(self, dtree):
+        """The whole point: a DataTree is self-describing."""
+        from raddb.viz.plot import plot_ppi
+        assert _n_polys(plot_ppi(dtree, sweep=1, variable="DBZH", archive_dir=None)) > 0
+
+    def test_datatree_ppi_matches_the_lut_geometry(self, dtree, archive):
+        """Computed corners must equal the ones generation stored."""
+        from raddb.viz.plot import plot_ppi
+        from raddb.lut import gate_corner_table
+
+        dt_verts = np.array([q.vertices[:4]
+                             for q in plot_ppi(dtree, sweep=1, variable="DBZH").get_paths()])
+        tbl = gate_corner_table(RADAR, archive, kind="h_plane", sweep=1)
+        lut_verts = np.stack([
+            np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1)
+            for k in range(1, 5)], axis=1)
+        # The lattices are stored as float32, so ~1e-7 relative — a few cm at
+        # 200 km range. Anything tighter would be testing parquet, not geometry.
+        assert np.abs(np.sort(dt_verts, axis=0) - np.sort(lut_verts, axis=0)).max() < 5e-2
+
+    def test_rhi_from_a_datatree(self, dtree):
+        from raddb.viz.plot import plot_rhi
+        assert _n_polys(plot_rhi(dtree, azimuth=0, variable="DBZH")) == N_RNG * N_SWEEPS
+
+    def test_cappi_from_a_datatree(self, dtree):
+        from raddb.viz.plot import plot_cappi
+        assert _n_polys(plot_cappi(dtree, altitude=1200, variable="DBZH")) > 0
+
+    def test_datatree_cappi_matches_the_lut_path(self, dtree, rdf):
+        from raddb.viz.plot import plot_cappi
+        assert (_n_polys(plot_cappi(dtree, altitude=1200, variable="DBZH"))
+                == _n_polys(rdf.plot_cappi(altitude=1200)))
+
+    def test_unknown_variable_raises(self, dtree):
+        from raddb.viz.plot import plot_ppi
+        with pytest.raises(KeyError):
+            plot_ppi(dtree, sweep=1, variable="NOPE")
+
+    def test_missing_sweep_raises(self, dtree):
+        from raddb.viz.plot import plot_ppi
+        with pytest.raises(ValueError, match="sweep"):
+            plot_ppi(dtree, sweep=99, variable="DBZH")
+
+
+# ===========================================================================
+# 3c. GeoDataFrame input
+# ===========================================================================
+
+class TestGeoDataFrameInput:
+    @pytest.fixture(scope="class")
+    def gdf(self, archive):
+        return RadDB(archive_dir=str(archive), crs=2056).open(radars=RADAR).to_geopandas()
+
+    def test_goes_through_the_lut(self, gdf, archive):
+        from raddb.viz.plot import plot_ppi
+        assert _n_polys(plot_ppi(gdf, sweep=1, archive_dir=archive)) > 0
+
+    def test_without_an_archive_raises(self, gdf):
+        """A gdf now always needs the LUT: geometry comes from there."""
+        from raddb.viz.plot import plot_ppi
+        with pytest.raises(ValueError, match="archive"):
+            plot_ppi(gdf, sweep=1)
+
+    def test_rhi_from_a_gdf(self, gdf, archive):
+        from raddb.viz.plot import plot_rhi
+        assert _n_polys(plot_rhi(gdf, azimuth=0, archive_dir=archive)) > 0
+
+    def test_cappi_from_a_gdf_works_like_a_frame(self, gdf, archive, rdf):
+        """Geometry comes from the LUT, so a gdf is just a frame here."""
+        from raddb.viz.plot import plot_cappi
+        assert (_n_polys(plot_cappi(gdf, altitude=1200, archive_dir=archive))
+                == _n_polys(rdf.plot_cappi(altitude=1200)))
+
+    def test_geometry_column_is_not_required_in_exact_mode(self, gdf, archive):
+        """A gdf is just a frame there; its geometry is ignored."""
+        from raddb.viz.plot import plot_ppi
+        plain = gdf.drop(columns=gdf.geometry.name)
+        assert _n_polys(plot_ppi(plain, sweep=1, archive_dir=archive)) > 0
+
+
+# ===========================================================================
+# 4. CAPPI geometry
+# ===========================================================================
+
+class TestCappiChords:
+    def test_every_reported_bin_spans_the_altitude(self, archive):
+        z0 = 1200.0
+        chords = cappi_chords(RADAR, archive, z0)
+        assert not chords.is_empty()
+
+        nodes = load_plane_nodes(RADAR, archive, "v_plane")
+        nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
+        for (sw,), sub in chords.group_by(["sweep"], maintain_order=True):
+            bot = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == -1)).sort("rng_idx")
+            top = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == 1)).sort("rng_idx")
+            zb, zt = bot["z_asl"].to_numpy(), top["z_asl"].to_numpy()
+            j = sub["rng_idx"].to_numpy()
+            lo = np.minimum.reduce([zb[j], zb[j + 1], zt[j], zt[j + 1]])
+            hi = np.maximum.reduce([zb[j], zb[j + 1], zt[j], zt[j + 1]])
+            assert ((lo - 1e-3 <= z0) & (z0 <= hi + 1e-3)).all()
+
+    def test_chords_stay_inside_their_range_bin(self, archive):
+        chords = cappi_chords(RADAR, archive, 1200.0)
+        nodes = load_plane_nodes(RADAR, archive, "v_plane")
+        nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
+        for (sw,), sub in chords.group_by(["sweep"], maintain_order=True):
+            bot = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == -1)).sort("rng_idx")
+            top = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == 1)).sort("rng_idx")
+            db_, dt_ = bot["d"].to_numpy(), top["d"].to_numpy()
+            j = sub["rng_idx"].to_numpy()
+            lo = np.minimum.reduce([db_[j], db_[j + 1], dt_[j], dt_[j + 1]])
+            hi = np.maximum.reduce([db_[j], db_[j + 1], dt_[j], dt_[j + 1]])
+            assert (sub["d_near"].to_numpy() >= lo - 1e-2).all()
+            assert (sub["d_far"].to_numpy() <= hi + 1e-2).all()
+
+    def test_d_near_is_below_d_far(self, archive):
+        chords = cappi_chords(RADAR, archive, 1200.0)
+        assert (chords["d_near"].to_numpy() <= chords["d_far"].to_numpy()).all()
+
+    def test_each_sweep_contributes_a_contiguous_band(self, archive):
+        """Beam thickness far exceeds the rise per bin, so bands are contiguous."""
+        for (_sw,), sub in cappi_chords(RADAR, archive, 1200.0).group_by(["sweep"]):
+            j = np.sort(sub["rng_idx"].to_numpy())
+            assert np.array_equal(j, np.arange(j.min(), j.max() + 1))
+
+    def test_above_every_beam_is_empty(self, archive):
+        assert cappi_chords(RADAR, archive, 50_000.0).is_empty()
+
+    def test_asl_and_relative_altitudes_agree(self, archive):
+        info = RadDB(archive_dir=str(archive), crs=2056).get_radar_info(RADAR)
+        a = cappi_chords(RADAR, archive, 1200.0, height="asl")
+        b = cappi_chords(RADAR, archive, 1200.0 - info["altitude"], height="rel")
+        assert a.height == b.height
+
+    def test_bad_height_reference_raises(self, archive):
+        with pytest.raises(ValueError):
+            cappi_chords(RADAR, archive, 1200.0, height="furlongs")
+
+
+class TestCappiRendering:
+    def test_overlap_nearest_draws_fewer_gates_than_all(self, rdf):
+        assert (_n_polys(rdf.plot_cappi(altitude=1200, overlap="nearest"))
+                < _n_polys(rdf.plot_cappi(altitude=1200, overlap="all")))
+
+    def test_overlap_nearest_leaves_no_double_coverage(self, archive):
+        """The resolved chords must partition the ground-distance axis."""
+        from raddb.viz.plot import _resolve_chord_overlap
+        resolved = _resolve_chord_overlap(cappi_chords(RADAR, archive, 1200.0))
+        iv = np.sort(np.stack([resolved["d_near"].to_numpy(),
+                               resolved["d_far"].to_numpy()], axis=1), axis=0)
+        assert (iv[1:, 0] >= iv[:-1, 1] - 1e-3).all()
+
+    def test_fill_lowest_extends_the_far_field(self, rdf):
+        assert (_n_polys(rdf.plot_cappi(altitude=1200, fill_lowest=True))
+                >= _n_polys(rdf.plot_cappi(altitude=1200, fill_lowest=False)))
+
+    def test_altitude_above_every_beam_raises(self, rdf):
+        with pytest.raises(ValueError, match="reaches"):
+            rdf.plot_cappi(altitude=99_999.0)
+
+    def test_higher_slice_draws_fewer_gates(self, rdf):
+        """Fewer beams reach higher, so the slice shrinks."""
+        assert (_n_polys(rdf.plot_cappi(altitude=1400))
+                < _n_polys(rdf.plot_cappi(altitude=1100)))
+
+    def test_bad_overlap_raises(self, rdf):
+        with pytest.raises(ValueError):
+            rdf.plot_cappi(altitude=1200, overlap="sometimes")
+
+    def test_slice_polygons_sit_inside_the_full_footprints(self, rdf, archive):
+        """The constant-z cut trims gates along the beam; it never grows them."""
+        from raddb.lut import gate_corner_table
+        p = rdf.plot_cappi(altitude=1200, overlap="all")
+        drawn = np.array([path.vertices[:4] for path in p.get_paths()])
+        tbl = gate_corner_table(RADAR, archive, kind="h_plane")
+        full = np.stack([np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1)
+                         for k in range(1, 5)], axis=1)
+        assert shapely.area(shapely.polygons(drawn)).sum() <= \
+            shapely.area(shapely.polygons(full)).sum()
+
+
+# ===========================================================================
+# 5. Coordinates, volume selection, errors
+# ===========================================================================
+
+class TestCoordinateFrames:
+    @pytest.mark.parametrize("coords", ["xy", "cartesian", "lonlat", "geo",
+                                        "projected", "swiss", "lv95", 2056])
+    def test_accepted_frames(self, rdf, coords):
+        assert _n_polys(rdf.plot_ppi(sweep=1, coords=coords)) > 0
+
+    def test_lonlat_axes_are_in_degrees(self, rdf):
+        p = rdf.plot_ppi(sweep=1, coords="lonlat")
+        assert -180 <= p.axes.get_xlim()[0] <= 180
+        assert -90 <= p.axes.get_ylim()[0] <= 90
+
+    def test_projected_axes_are_lv95_metres(self, rdf):
+        p = rdf.plot_ppi(sweep=1, coords=2056)
+        assert 2.4e6 < p.axes.get_xlim()[0] < 2.9e6
+
+    def test_xy_is_centred_on_the_radar(self, rdf):
+        p = rdf.plot_ppi(sweep=1, coords="xy")
+        assert p.axes.get_xlim()[0] < 0 < p.axes.get_xlim()[1]
+
+    def test_unknown_frame_raises(self, rdf):
+        with pytest.raises(ValueError, match="coords"):
+            rdf.plot_ppi(sweep=1, coords="banana")
+
+    def test_projected_resolves_the_archives_own_crs(self, archive):
+        """Reading needs no CRS: the archive records the one it was written with."""
+        plain = RadDB(archive_dir=str(archive)).open(radars=RADAR)
+        assert plain._crs is None                      # nothing was declared
+        assert plain.crs().to_epsg() == 2056           # but the archive knows
+        assert _n_polys(plain.plot_ppi(sweep=1, coords="projected")) > 0
+
+    def test_projected_raises_when_nothing_declares_a_crs(self, rdf):
+        """A bare frame with no archive has nothing to resolve from."""
+        from raddb.viz.plot import plot_ppi
+        with pytest.raises((ValueError, KeyError)):
+            plot_ppi(rdf.data, sweep=1, coords="projected", archive_dir=None)
+
+
+class TestVolumeSelection:
+    @pytest.fixture(scope="class")
+    def multi(self, tmp_path_factory):
+        base = tmp_path_factory.mktemp("multi_vol")
+        db = RadDB(archive_dir=str(base), crs=2056)
+        db.archive(datatree={str(t): _make_datatree(24, 20, n_sweeps=2, vol_time=t)
+                             for t in VOL_TIMES}, radar=RADAR)
+        return db.open(radars=RADAR)
+
+    def test_several_volumes_without_timestep_raises(self, multi):
+        with pytest.raises(ValueError, match="volumes"):
+            multi.plot_ppi(sweep=1)
+
+    def test_timestep_picks_the_nearest_volume(self, multi):
+        assert _n_polys(multi.plot_ppi(sweep=1, timestep=VOL_TIMES[0])) > 0
+
+    def test_time_window_narrows_to_one_volume(self, multi):
+        assert _n_polys(multi.plot_ppi(
+            sweep=1, start_time="2024-08-01", end_time="2024-08-01 23:59")) > 0
+
+    def test_window_that_excludes_everything_raises(self, multi):
+        with pytest.raises(ValueError):
+            multi.plot_ppi(sweep=1, start_time="1999-01-01", end_time="1999-12-31")
+
+
+class TestErrors:
+    def test_unknown_variable_raises(self, rdf):
+        with pytest.raises(KeyError):
+            rdf.plot_ppi(sweep=1, variable="NOT_A_VAR")
+
+    def test_missing_sweep_raises(self, rdf):
+        with pytest.raises(ValueError):
+            rdf.plot_ppi(sweep=99)
+
+    def test_rhi_beyond_az_tol_raises(self, rdf):
+        with pytest.raises(ValueError, match="no sweep has a ray within"):
+            rdf.plot_rhi(azimuth=2.5, az_tol=0.1)
+
+    def test_vcr_without_a_section_raises(self, rdf):
+        with pytest.raises(ValueError, match="cross-section"):
+            rdf.plot_vcs()
+
+    def test_bare_frame_without_archive_dir_raises(self, rdf):
+        from raddb.viz.plot import plot_ppi
+        with pytest.raises(ValueError, match="archive"):
+            plot_ppi(rdf.data, sweep=1)
+
+    def test_bare_frame_with_archive_dir_works(self, rdf):
+        from raddb.viz.plot import plot_ppi
+        assert _n_polys(plot_ppi(rdf.data, sweep=1, archive_dir=rdf.archive_dir)) > 0
+
+    def test_multi_radar_frame_without_radar_raises(self, tmp_path):
+        db = RadDB(archive_dir=str(tmp_path), crs=2056)
+        db.archive(datatree={"A": [_make_datatree(24, 20, n_sweeps=2)],
+                             "D": [_make_datatree(24, 20, n_sweeps=2)]})
+        with pytest.raises(ValueError, match="radars"):
+            db.open().plot_ppi(sweep=1)
+
+
+class TestRhi:
+    def test_height_reference_shifts_the_axis(self, rdf, archive):
+        alt = RadDB(archive_dir=str(archive), crs=2056).get_radar_info(RADAR)["altitude"]
+        asl = rdf.plot_rhi(azimuth=0, height="asl").axes.get_ylim()[0]
+        rel = rdf.plot_rhi(azimuth=0, height="rel").axes.get_ylim()[0]
+        assert asl - rel == pytest.approx(alt, abs=1.0)
+
+    def test_bad_height_raises(self, rdf):
+        with pytest.raises(ValueError):
+            rdf.plot_rhi(azimuth=0, height="furlongs")
+
+    def test_draws_one_ray_across_every_sweep(self, rdf):
+        assert _n_polys(rdf.plot_rhi(azimuth=0)) == N_RNG * N_SWEEPS
+
+    def test_picks_a_ray_per_sweep_when_azimuths_jitter(self, tmp_path):
+        """Real antenna azimuths differ slightly between sweeps.
+
+        Matching one azimuth *value* across the whole LUT would then select a
+        single sweep and collapse the RHI — every sweep needs its own nearest ray.
+        """
+        import xarray as xr
+
+        dt = _make_datatree(n_az=36, n_rng=20, n_sweeps=4)
+        jittered = {}
+        for i, (name, node) in enumerate(dt.children.items()):
+            ds = node.to_dataset()
+            # Offset each sweep's azimuths, as a real antenna does.
+            ds = ds.assign_coords(azimuth=ds["azimuth"].values + 0.13 * i)
+            jittered[name] = ds
+        dt = xr.DataTree.from_dict(jittered)
+
+        db = RadDB(archive_dir=str(tmp_path), crs=2056)
+        db.archive(datatree={RADAR: [dt]})
+        r = db.open(radars=RADAR)
+
+        lut = db.get_lut(RADAR)
+        # Each sweep has its own 36 azimuths, so no value is shared between them.
+        assert lut["azimuth"].n_unique() == 36 * 4, "fixture should have per-sweep jitter"
+
+        p = r.plot_rhi(azimuth=90.0, az_tol=1.0)
+        # One ray per sweep, every range bin: 20 x 4.  Selecting a single azimuth
+        # *value* across the whole LUT would yield 20 — one sweep only.
+        assert _n_polys(p) == 20 * 4
+
+
+class TestVcr:
+    def test_accepts_a_linestring(self, rdf, site):
+        line = shapely.LineString([(site[0] - 12_000, site[1] - 12_000),
+                                   (site[0] + 12_000, site[1] + 12_000)])
+        assert _n_polys(rdf.plot_vcs(line=line)) > 0
+
+    def test_accepts_a_precut_raddb(self, rdf, site):
+        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
+                                       (site[0] + 12_000, site[1] + 12_000))
+        assert _n_polys(cs.plot_vcs()) == len(cs)
+
+    def test_deprecated_alias_still_works(self, rdf, site):
+        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
+                                       (site[0] + 12_000, site[1] + 12_000))
+        with pytest.deprecated_call():
+            assert _n_polys(cs.plot_cross_section()) > 0
+
+    def test_datatree_is_refused(self):
+        from raddb.viz.plot import plot_vcs
+        with pytest.raises(TypeError, match="Archive the volume first"):
+            plot_vcs(_make_datatree(24, 20, n_sweeps=2), line=((0, 0), (1, 1)))
+
+    def test_line_and_precut_frame_is_ambiguous(self, rdf, site):
+        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
+                                       (site[0] + 12_000, site[1] + 12_000))
+        with pytest.raises(ValueError, match="ambiguous"):
+            cs.plot_vcs(line=((site[0], site[1]), (site[0] + 5_000, site[1])))
+
+    def test_area_cropped_frame_has_no_section(self, rdf, site):
+        """An AOI crop selects an area, not a line."""
+        crop = rdf.crop_around_point(site, distance=10_000)
+        with pytest.raises(ValueError, match="no 'cs_polygon'"):
+            crop.plot_vcs()
+
+    def test_geojson_line_honours_its_own_crs(self, rdf, site, tmp_path, archive):
+        """A lon/lat GeoJSON must not be read as LV95 metres."""
+        import json
+        from raddb.aoi import _to_pyproj_crs
+        import pyproj
+
+        tf = pyproj.Transformer.from_crs(_to_pyproj_crs(2056), _to_pyproj_crs(4326),
+                                         always_xy=True)
+        a = tf.transform(site[0] - 12_000, site[1] - 12_000)
+        b = tf.transform(site[0] + 12_000, site[1] + 12_000)
+        path = tmp_path / "section.geojson"
+        path.write_text(json.dumps({"type": "LineString", "coordinates": [list(a), list(b)]}))
+
+        from_file = _n_polys(rdf.plot_vcs(line=str(path)))
+        from_lv95 = _n_polys(rdf.plot_vcs(line=((site[0] - 12_000, site[1] - 12_000),
+                                                (site[0] + 12_000, site[1] + 12_000))))
+        assert from_file > 0
+        assert abs(from_file - from_lv95) <= 0.02 * from_lv95
+
+    def test_precut_frame_survives_a_pandas_or_gdf_round_trip(self, rdf, site, archive):
+        """cs_polygon holds shapely objects; they must WKB-encode into polars."""
+        from raddb.viz.plot import plot_vcs
+        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
+                                       (site[0] + 12_000, site[1] + 12_000))
+        n = _n_polys(cs.plot_vcs())
+        assert _n_polys(plot_vcs(cs.to_pandas(), archive_dir=archive)) == n
+        assert _n_polys(plot_vcs(cs.to_geopandas(), archive_dir=archive)) == n
+
+    @pytest.mark.parametrize("as_frame", ["polars", "pandas", "geopandas"])
+    def test_line_works_from_a_bare_frame_with_an_archive(self, rdf, site, archive, as_frame):
+        """A bare frame has gate_id and the archive has the geometry, so the
+        section is cuttable — plot_vcs must not be stricter than plot_ppi."""
+        from raddb.viz.plot import plot_vcs
+        data = {"polars": rdf.data,
+                "pandas": rdf.to_pandas(),
+                "geopandas": rdf.to_geopandas()}[as_frame]
+        line = ((site[0] - 12_000, site[1] - 12_000), (site[0] + 12_000, site[1] + 12_000))
+        assert (_n_polys(plot_vcs(data, line=line, archive_dir=archive))
+                == _n_polys(rdf.plot_vcs(line=line)))
+
+    def test_line_from_a_bare_frame_without_an_archive_raises(self, rdf, site):
+        from raddb.viz.plot import plot_vcs
+        with pytest.raises(ValueError, match="archive"):
+            plot_vcs(rdf.data, line=((site[0], site[1]), (site[0] + 5_000, site[1])))
+
+    def test_shapefile_line(self, rdf, site, tmp_path):
+        import shapefile
+        w = shapefile.Writer(str(tmp_path / "sec")); w.field("id", "N")
+        w.line([[[site[0] - 12_000, site[1] - 12_000], [site[0] + 12_000, site[1] + 12_000]]])
+        w.record(1); w.close()
+        assert _n_polys(rdf.plot_vcs(line=str(tmp_path / "sec.shp"))) > 0
+
+
+# ===========================================================================
+# 6. LUT plumbing the plots depend on
+# ===========================================================================
+
+class TestPlaneBackfill:
+    def test_missing_lattices_are_rebuilt_on_read(self, archive, tmp_path):
+        """A pre-geometry archive (2 files) backfills instead of failing."""
+        lut_dir = tmp_path / RADAR / "LUT"
+        lut_dir.mkdir(parents=True)
+        for kind in ("lut", "info"):
+            src = lut_file_path(RADAR, kind, archive)
+            (lut_dir / src.name).write_bytes(src.read_bytes())
+
+        assert not lut_file_path(RADAR, "h_plane", tmp_path).exists()
+        assert ensure_gate_planes(RADAR, tmp_path) is True
+        for kind in ("h_plane", "v_plane", "corners"):
+            assert lut_file_path(RADAR, kind, tmp_path).exists()
+        assert ensure_gate_planes(RADAR, tmp_path) is False
+
+    def test_backfill_recovers_the_projection_from_the_lut(self, archive, tmp_path):
+        """Old info.yaml files have no crs block; the LUT columns still say EPSG."""
+        lut_dir = tmp_path / RADAR / "LUT"
+        lut_dir.mkdir(parents=True)
+        for kind in ("lut", "info"):
+            src = lut_file_path(RADAR, kind, archive)
+            (lut_dir / src.name).write_bytes(src.read_bytes())
+        ensure_gate_planes(RADAR, tmp_path)
+        cols = load_plane_nodes(RADAR, tmp_path, "h_plane").columns
+        assert "x_2056" in cols and "y_2056" in cols
+
+
+class TestAoiGeometryUnification:
+    def test_footprints_come_from_the_h_plane_lattice(self, archive):
+        """aoi.py and the plots must draw the same gate."""
+        from raddb.aoi import _lut_cs_table, _gate_footprints
+
+        cs_t = _lut_cs_table(archive, [RADAR]).to_pandas().head(300)
+        foot = _gate_footprints(cs_t, np.tan(np.deg2rad(0.5)), base_path=archive, epsg=2056)
+
+        hp = RadDB(archive_dir=str(archive), crs=2056).get_h_plane(RADAR, per_gate=True)
+        aligned = pl.DataFrame({"gate_id": cs_t["gate_id"].to_numpy()}).join(
+            hp, on="gate_id", how="left", maintain_order="left")
+        ref = shapely.polygons(np.stack([
+            np.stack([aligned[f"x_2056_{k}"].to_numpy(),
+                      aligned[f"y_2056_{k}"].to_numpy()], axis=1)
+            for k in range(1, 5)], axis=1).astype(np.float64))
+
+        assert np.allclose(shapely.get_coordinates(foot), shapely.get_coordinates(ref))
+
+    def test_planar_fallback_still_available(self, archive):
+        """Archives without the lattices keep working on the old approximation."""
+        from raddb.aoi import _lut_cs_table, _gate_footprints
+
+        cs_t = _lut_cs_table(archive, [RADAR]).to_pandas().head(50)
+        planar = _gate_footprints(cs_t, np.tan(np.deg2rad(0.5)), base_path=None, epsg=None)
+        assert shapely.is_valid(planar).all()
+
+    def test_crops_are_unaffected(self, rdf, site):
+        """Crops resolve on centroids, so unifying footprints must not move them."""
+        assert len(rdf.crop_around_point(site, distance=8_000)) == 9504
+
+    def test_cross_section_height_follows_the_v_plane(self, rdf, site, archive):
+        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
+                                       (site[0] + 12_000, site[1] + 12_000))
+        from raddb.main import _decode_geometry
+        pdf = _decode_geometry(cs.data.to_pandas()).head(200)
+        heights = np.array([p.bounds[3] - p.bounds[1] for p in pdf["cs_polygon"]])
+
+        vp = RadDB(archive_dir=str(archive), crs=2056).get_v_plane(RADAR, per_gate=True)
+        va = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
+            vp, on="gate_id", how="left", maintain_order="left")
+        thick = 0.5 * (np.abs(va["z_asl_4"].to_numpy() - va["z_asl_1"].to_numpy())
+                       + np.abs(va["z_asl_3"].to_numpy() - va["z_asl_2"].to_numpy()))
+        assert np.corrcoef(heights, thick)[0, 1] > 0.9
+
+
+# ===========================================================================
+# 9. Opt-in polar coordinates on the converters
+# ===========================================================================
+
+class TestPolarCoordColumns:
+    POLAR = ("range", "azimuth", "elevation_angle")
+
+    def test_absent_by_default(self, rdf):
+        for c in self.POLAR:
+            assert c not in rdf.to_pandas(with_geometry=True).columns
+            assert c not in rdf.to_geopandas().columns
+
+    def test_present_on_request(self, rdf):
+        pdf = rdf.to_pandas(with_polar_coords=True)
+        gdf = rdf.to_geopandas(with_polar_coords=True)
+        for c in self.POLAR:
+            assert c in pdf.columns and c in gdf.columns
+
+    def test_values_match_the_lut(self, rdf, archive):
+        pdf = rdf.to_pandas(with_polar_coords=True)
+        lut = RadDB(archive_dir=str(archive), crs=2056).get_lut(RADAR)
+        ref = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
+            lut.select(["gate_id", *self.POLAR]), on="gate_id",
+            how="left", maintain_order="left")
+        for c in self.POLAR:
+            assert np.allclose(pdf[c].to_numpy(), ref[c].to_numpy())
+
+    def test_with_polar_coords_implies_geometry(self, rdf):
+        """Polar columns come from the LUT, so the join happens either way."""
+        assert "latitude" in rdf.to_pandas(with_polar_coords=True).columns
+
+    def test_row_count_is_unchanged(self, rdf):
+        assert len(rdf.to_pandas(with_polar_coords=True)) == len(rdf)
+
+
+# ===========================================================================
+# 10. Axis tick labels (km from metres)
+# ===========================================================================
+
+class TestKmTickLabels:
+    """Labels must be unique *and* equal to the value they sit on."""
+
+    @staticmethod
+    def _labels(lo, hi, offset=0.0):
+        from raddb.viz.plot import _KmFormatter
+        fig, ax = plt.subplots()
+        ax.set_ylim(lo, hi)
+        ax.yaxis.set_major_formatter(_KmFormatter(offset))
+        fig.canvas.draw()
+        locs = np.asarray(ax.yaxis.get_majorticklocs(), dtype=float)
+        labs = [t.get_text() for t in ax.get_yticklabels()]
+        plt.close(fig)
+        return [(v, l) for v, l in zip(locs, labs) if l and lo <= v <= hi]
+
+    @pytest.mark.parametrize("lo,hi,offset", [
+        (1400, 5900, 0.0),        # the reported case: 500 m steps
+        (0, 20000, 0.0),          # matplotlib picks 2.5 km steps
+        (0, 13000, 0.0),
+        (0, 500, 0.0),
+        (0, 200, 0.0),            # 25 m steps -> 3 decimals
+        (0, 60, 0.0),
+        (0, 250000, 0.0),
+        (2_600_000, 2_760_000, 2e6),   # LV95 easting
+        (1_050_000, 1_150_000, 1e6),   # LV95 northing
+    ])
+    def test_labels_are_unique_and_exact(self, lo, hi, offset):
+        pairs = self._labels(lo, hi, offset)
+        labels = [l for _, l in pairs]
+        assert len(labels) == len(set(labels)), f"repeated labels: {labels}"
+        for v, l in pairs:
+            km = (v - offset) / 1e3
+            assert abs(float(l) - km) < 1e-6 * max(1.0, abs(km)), \
+                f"label {l!r} does not equal {km}"
+
+    def test_the_reported_regression(self):
+        """A 1.4-5.9 km section used to read 1,2,2,2,3,4,4,4,5,6,6."""
+        labels = [l for _, l in self._labels(1400, 5900)]
+        assert labels == ["1.5", "2.0", "2.5", "3.0", "3.5", "4.0", "4.5", "5.0", "5.5"]
+
+    def test_real_plots_have_no_duplicate_ticks(self, rdf, site):
+        """Every plot, both axes."""
+        cases = [
+            rdf.plot_ppi(sweep=1),
+            rdf.plot_ppi(sweep=1, coords="swiss"),
+            rdf.plot_rhi(azimuth=0),
+            rdf.plot_cappi(altitude=1200),
+            rdf.plot_vcs(line=((site[0] - 12_000, site[1] - 12_000),
+                               (site[0] + 12_000, site[1] + 12_000))),
+        ]
+        for p in cases:
+            p.axes.figure.canvas.draw()
+            for axis in (p.axes.xaxis, p.axes.yaxis):
+                labs = [t.get_text() for t in axis.get_ticklabels()
+                        if t.get_text() and t.get_visible()]
+                assert len(labs) == len(set(labs)), f"repeated ticks: {labs}"
diff --git a/raddb/tests/test_polars_backend.py b/raddb/tests/test_polars_backend.py
index 1263eea..2378e8d 100644
--- a/raddb/tests/test_polars_backend.py
+++ b/raddb/tests/test_polars_backend.py
@@ -80,7 +80,7 @@ def test_crop_matches_an_isin_reference(self, archive):
         """The semi-join must select exactly what the old isin() filter did."""
         import shapely
 
-        from raddb.aoi import _lut_centroids, _reproject_to_2056, _resolve_aoi_centroids
+        from raddb.aoi import _lut_centroids, _reproject_to_aoi, _resolve_aoi_centroids
 
         rdf = archive.open()
         geo = rdf.to_pandas(with_geometry=True)
@@ -90,7 +90,7 @@ def test_crop_matches_an_isin_reference(self, archive):
         crop = rdf.crop_by_bbox(bounds=bounds)
         cen = _resolve_aoi_centroids(
             _lut_centroids(archive.archive_dir, rdf.radars()),
-            _reproject_to_2056(shapely.box(*bounds), 2056),
+            _reproject_to_aoi(shapely.box(*bounds), 2056, 2056),
         )
         expected = set(np.intersect1d(
             rdf.data["gate_id"].to_numpy(), cen["gate_id"].to_numpy()
diff --git a/raddb/tests/test_radar_code.py b/raddb/tests/test_radar_code.py
new file mode 100644
index 0000000..4d9e895
--- /dev/null
+++ b/raddb/tests/test_radar_code.py
@@ -0,0 +1,332 @@
+"""
+raddb/tests/test_radar_code.py
+------------------------------
+Tests for the base-36 radar code that forms the leading field of a ``gate_id``
+(encoding v2), the name normalisation it rests on, and the v1 archive guard.
+
+Synthetic throughout — the archive tests build DataTrees in ``tmp_path``.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import yaml
+
+_PKG_ROOT = Path(__file__).resolve().parents[2]
+if str(_PKG_ROOT) not in sys.path:
+    sys.path.insert(0, str(_PKG_ROOT))
+
+from raddb.helper import (  # noqa: E402
+    RADAR_ALPHABET,
+    RADAR_CODE_LEN,
+    is_valid_radar_name,
+    normalize_radar_name,
+)
+from raddb.lut import (  # noqa: E402
+    GATE_ID_RADAR_BASE,
+    GATE_ID_VERSION,
+    LEGACY_RADAR_TO_IDX,
+    MAX_RADAR_CODE,
+    OutdatedGateIdError,
+    decode_gate_ids,
+    decode_gate_radars,
+    decode_radar_code,
+    encode_gate_ids,
+    encode_radar_code,
+)
+from raddb.tests.test_fixes import _make_datatree  # noqa: E402
+
+
+# ===========================================================================
+# normalize_radar_name
+# ===========================================================================
+
+class TestNormalizeRadarName:
+
+    @pytest.mark.parametrize("raw,expected", [
+        ("A", "A"),
+        ("a", "A"),
+        (" L ", "L"),
+        ("MLA", "A"),          # MeteoSwiss spelling
+        ("mlw", "W"),
+        ("KTLX", "KTLX"),      # NEXRAD survives whole
+        ("koun", "KOUN"),
+        ("000A", "A"),         # zero padding is not part of the name
+        ("0A", "A"),
+        ("0", "0"),            # ... but a radar may be named "0"
+        ("ZZZZ", "ZZZZ"),
+    ])
+    def test_canonical_forms(self, raw, expected):
+        assert normalize_radar_name(raw) == expected
+
+    def test_multi_letter_names_are_not_truncated(self):
+        """The v1 bug: every name collapsed to its last character."""
+        assert normalize_radar_name("KTLX") != "X"
+        assert normalize_radar_name("KOUN") != "N"
+        # Two sites sharing a final letter must stay distinct, or one would
+        # overwrite the other's archive.
+        assert normalize_radar_name("KTLX") != normalize_radar_name("KABX")
+
+    def test_ml_rule_only_applies_at_three_characters(self):
+        assert normalize_radar_name("MLA") == "A"
+        assert normalize_radar_name("MLAB") == "MLAB"   # a real 4-char name
+
+    @pytest.mark.parametrize("bad", [
+        "", "   ", "chlem", "ABCDE", "A-B", "vol.", "A B", "MLABC", "é",
+    ])
+    def test_rejects_unusable_names(self, bad):
+        assert not is_valid_radar_name(bad)
+        with pytest.raises(ValueError, match="not usable"):
+            normalize_radar_name(bad)
+
+    def test_rejects_non_string(self):
+        assert not is_valid_radar_name(7)
+        with pytest.raises(ValueError, match="must be a string"):
+            normalize_radar_name(7)
+
+
+# ===========================================================================
+# encode_radar_code / decode_radar_code
+# ===========================================================================
+
+class TestRadarCode:
+
+    def test_known_values(self):
+        assert encode_radar_code("A") == 10
+        assert encode_radar_code("L") == 21
+        assert encode_radar_code("KTLX") == 971_493
+        assert encode_radar_code("0") == 0
+        assert encode_radar_code("ZZZZ") == MAX_RADAR_CODE
+
+    def test_capacity_is_36_pow_4(self):
+        assert MAX_RADAR_CODE == 36 ** RADAR_CODE_LEN - 1 == 1_679_615
+
+    def test_every_code_fits_int64(self):
+        """The largest gate_id must not overflow the int64 column."""
+        largest = MAX_RADAR_CODE * GATE_ID_RADAR_BASE + (GATE_ID_RADAR_BASE - 1)
+        assert largest < np.iinfo(np.int64).max
+        assert np.int64(largest) == largest
+
+    def test_five_characters_do_not_fit(self):
+        """Documents why RADAR_CODE_LEN is 4: 36**5 blows the int64 budget."""
+        budget = (np.iinfo(np.int64).max - (GATE_ID_RADAR_BASE - 1)) // GATE_ID_RADAR_BASE
+        assert 36 ** 4 - 1 <= budget < 36 ** 5 - 1
+
+    def test_decode_is_injective(self):
+        """Distinct codes never name the same radar."""
+        names = {decode_radar_code(c) for c in range(MAX_RADAR_CODE + 1)}
+        assert len(names) == MAX_RADAR_CODE + 1 == 1_679_616
+
+    def test_name_round_trip_is_total_over_canonical_names(self):
+        """Every name that is its own canonical form survives encode -> decode."""
+        names = {decode_radar_code(c) for c in range(MAX_RADAR_CODE + 1)}
+        canonical = {n for n in names if normalize_radar_name(n) == n}
+        assert len(canonical) == 1_679_580  # all but the 36 ML? aliases
+        assert all(decode_radar_code(encode_radar_code(n)) == n for n in canonical)
+
+    def test_only_ml_aliases_break_the_code_round_trip(self):
+        """encode(decode(c)) == c except where a name is an alias for another."""
+        broken = [c for c in range(MAX_RADAR_CODE + 1)
+                  if encode_radar_code(decode_radar_code(c)) != c]
+        assert len(broken) == 36
+        assert all(decode_radar_code(c).startswith("ML") for c in broken)
+        assert all(len(decode_radar_code(c)) == 3 for c in broken)
+
+    def test_zero_padding_is_transparent(self):
+        assert encode_radar_code("A") == encode_radar_code("000A") == encode_radar_code("0A")
+
+    def test_alphabet_positions_define_the_values(self):
+        for i, char in enumerate(RADAR_ALPHABET):
+            assert encode_radar_code(char.rjust(RADAR_CODE_LEN, "0")) == i
+
+    @pytest.mark.parametrize("code", [-1, MAX_RADAR_CODE + 1, 10 ** 9])
+    def test_decode_rejects_out_of_range(self, code):
+        with pytest.raises(ValueError, match="names no radar"):
+            decode_radar_code(code)
+
+    def test_more_than_26_radars_are_distinct(self):
+        """The point of the change: no 26-radar ceiling."""
+        names = [f"K{a}{b}" for a in "ABCDE" for b in "ABCDEFGHIJ"]  # 50 sites
+        codes = [encode_radar_code(n) for n in names]
+        assert len(set(codes)) == len(names) == 50
+        assert sorted(decode_radar_code(c) for c in codes) == sorted(names)
+
+
+# ===========================================================================
+# gate_id encoding
+# ===========================================================================
+
+class TestGateIdEncoding:
+
+    def test_radar_field_is_the_code(self):
+        gid = encode_gate_ids("KTLX", 3, np.array([91.4]), np.array([12_500.0]))
+        assert gid[0] // GATE_ID_RADAR_BASE == encode_radar_code("KTLX")
+
+    def test_low_fields_are_independent_of_the_radar(self):
+        """Only the leading field differs between radars — what migration relies on."""
+        az, rng = np.array([91.4, 270.0]), np.array([12_500.0, 240_000.0])
+        a = encode_gate_ids("A", 3, az, rng)
+        k = encode_gate_ids("KTLX", 3, az, rng)
+        delta = (encode_radar_code("KTLX") - encode_radar_code("A")) * GATE_ID_RADAR_BASE
+        assert np.array_equal(k - a, np.full(2, delta))
+
+    def test_decode_round_trip(self):
+        az, rng, sweeps = np.array([0.0, 91.4, 359.9]), np.array([0.0, 12_500.0, 999_999.0]), 7
+        gid = encode_gate_ids("KTLX", sweeps, az, rng)
+        got_sweeps, got_az, got_rng = decode_gate_ids(gid)
+        assert np.array_equal(got_sweeps, np.full(3, sweeps))
+        assert np.allclose(got_az, az)
+        assert np.allclose(got_rng, rng)
+        assert decode_gate_radars(gid) == ["KTLX"]
+
+    def test_decode_radars_spans_several(self):
+        az, rng = np.array([10.0]), np.array([1000.0])
+        gid = np.concatenate([
+            encode_gate_ids(r, 1, az, rng) for r in ("L", "KTLX", "A")
+        ])
+        assert decode_gate_radars(gid) == ["A", "KTLX", "L"]
+
+    def test_decode_radars_empty(self):
+        assert decode_gate_radars(np.array([], dtype=np.int64)) == []
+
+    def test_decode_radars_skips_unknown_code(self, caplog):
+        bogus = np.array([(MAX_RADAR_CODE + 5) * GATE_ID_RADAR_BASE], dtype=np.int64)
+        with caplog.at_level("WARNING"):
+            assert decode_gate_radars(bogus) == []
+        assert "names no radar" in caplog.text
+
+    def test_ml_name_encodes_as_its_letter(self):
+        az, rng = np.array([10.0]), np.array([1000.0])
+        assert np.array_equal(
+            encode_gate_ids("MLA", 1, az, rng), encode_gate_ids("A", 1, az, rng)
+        )
+
+    def test_unusable_name_raises(self):
+        with pytest.raises(ValueError, match="not usable"):
+            encode_gate_ids("OVERLONG", 1, np.array([10.0]), np.array([1000.0]))
+
+
+# ===========================================================================
+# Archive round-trip and the v1 guard
+# ===========================================================================
+
+def _archive(tmp_path, radar):
+    from raddb.main import RadDB
+
+    db = RadDB(archive_dir=str(tmp_path / "archive"), crs=2056)
+    db.archive(datatree=_make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")),
+               radar=radar)
+    return db
+
+
+class TestArchiveWithLongNames:
+
+    def test_four_letter_radar_round_trips(self, tmp_path):
+        db = _archive(tmp_path, "KTLX")
+        assert db.list_radars() == ["KTLX"]
+
+        rdf = db.open(radars="KTLX")
+        assert rdf.data.height > 0
+        assert rdf.radars() == ["KTLX"]
+
+        gids = rdf.data["gate_id"].to_numpy()
+        assert set(gids // GATE_ID_RADAR_BASE) == {encode_radar_code("KTLX")}
+        assert decode_gate_radars(gids) == ["KTLX"]
+
+    def test_lut_and_pol_gate_ids_join(self, tmp_path):
+        db = _archive(tmp_path, "KTLX")
+        lut = db.get_lut("KTLX")
+        pol = db.open(radars="KTLX").data
+        matched = pol.join(lut.select("gate_id"), on="gate_id", how="semi")
+        assert matched.height == pol.height
+
+    def test_info_yaml_records_the_version(self, tmp_path):
+        db = _archive(tmp_path, "KTLX")
+        assert db.get_radar_info("KTLX")["gate_id_version"] == GATE_ID_VERSION
+
+    def test_sel_by_radar_uses_the_code(self, tmp_path):
+        db = _archive(tmp_path, "KTLX")
+        rdf = db.open(radars="KTLX")
+        assert rdf.sel(radar="KTLX").data.height == rdf.data.height
+        assert rdf.sel(radar="A").data.height == 0
+
+    def test_two_radars_stay_distinct(self, tmp_path):
+        from raddb.main import RadDB
+
+        db = RadDB(archive_dir=str(tmp_path / "archive"), crs=2056)
+        for radar in ("KTLX", "KOUN"):
+            db.archive(
+                datatree=_make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")),
+                radar=radar,
+            )
+        assert db.list_radars() == ["KOUN", "KTLX"]
+
+        both = db.open(radars=["KTLX", "KOUN"])
+        assert sorted(both.radars()) == ["KOUN", "KTLX"]
+        codes = set(both.data["gate_id"].to_numpy() // GATE_ID_RADAR_BASE)
+        assert codes == {encode_radar_code("KTLX"), encode_radar_code("KOUN")}
+
+
+class TestGateIdVersionGuard:
+
+    def _downgrade_to_v1(self, tmp_path, radar):
+        """Rewrite an archive back to the v1 encoding, as if written long ago."""
+        lut_dir = tmp_path / "archive" / radar / "LUT"
+        info_path = lut_dir / f"{radar}_info.yaml"
+        info = yaml.safe_load(info_path.read_text())
+        delta = (LEGACY_RADAR_TO_IDX[radar] - encode_radar_code(radar)) * GATE_ID_RADAR_BASE
+        for f in [lut_dir / f"{radar}_LUT.parquet",
+                  *sorted((tmp_path / "archive" / radar).rglob("*_POL.parquet"))]:
+            pl.read_parquet(f).with_columns(
+                (pl.col("gate_id") + delta).alias("gate_id")
+            ).write_parquet(f)
+        info.pop("gate_id_version", None)          # v1 files carry no version key
+        info_path.write_text(yaml.dump(info, default_flow_style=False, sort_keys=False))
+        return delta
+
+    def test_v1_archive_is_refused(self, tmp_path):
+        db = _archive(tmp_path, "L")
+        self._downgrade_to_v1(tmp_path, "L")
+
+        with pytest.raises(OutdatedGateIdError, match="migrate_gate_id_v2"):
+            db.get_radar_info("L")
+
+    def test_migration_restores_the_archive(self, tmp_path):
+        from raddb.tools.migrate_gate_id_v2 import migrate_radar
+
+        db = _archive(tmp_path, "L")
+        before = db.open(radars="L").data["gate_id"].to_numpy().copy()
+        self._downgrade_to_v1(tmp_path, "L")
+
+        dry = migrate_radar(tmp_path / "archive", "L", dry_run=True)
+        assert dry["status"] == "would migrate" and dry["files"] >= 2
+        assert dry["rows"] == 0  # a dry run writes nothing
+
+        res = migrate_radar(tmp_path / "archive", "L")
+        assert res["status"] == "migrated" and res["rows"] > 0
+
+        assert db.get_radar_info("L")["gate_id_version"] == GATE_ID_VERSION
+        after = db.open(radars="L").data["gate_id"].to_numpy()
+        assert np.array_equal(np.sort(after), np.sort(before))
+        assert decode_gate_radars(after) == ["L"]
+
+    def test_migration_is_idempotent(self, tmp_path):
+        from raddb.tools.migrate_gate_id_v2 import migrate_radar
+
+        db = _archive(tmp_path, "L")
+        self._downgrade_to_v1(tmp_path, "L")
+        migrate_radar(tmp_path / "archive", "L")
+        again = migrate_radar(tmp_path / "archive", "L")
+        assert again["status"] == "already v2"
+        assert db.open(radars="L").data.height > 0
+
+    def test_fresh_archive_needs_no_migration(self, tmp_path):
+        from raddb.tools.migrate_gate_id_v2 import migrate_radar
+
+        _archive(tmp_path, "KTLX")
+        assert migrate_radar(tmp_path / "archive", "KTLX")["status"] == "already v2"
diff --git a/raddb/tests/test_sel.py b/raddb/tests/test_sel.py
index bf2ee04..df8ccea 100644
--- a/raddb/tests/test_sel.py
+++ b/raddb/tests/test_sel.py
@@ -87,7 +87,7 @@ def test_range_selection_does_not_leak_the_column(self, rdf):
         assert "range" not in out.columns()
 
     def test_range_selection_matches_the_lut(self, rdf, tmp_path):
-        lut = RadDB(archive_dir=str(tmp_path)).get_lut(RADAR)
+        lut = RadDB(archive_dir=str(tmp_path), crs=2056).get_lut(RADAR)
         want = set(
             lut.filter((pl.col("range") >= 2_000) & (pl.col("range") <= 10_000))["gate_id"]
             .to_list()
diff --git a/raddb/tools/__init__.py b/raddb/tools/__init__.py
new file mode 100644
index 0000000..388de84
--- /dev/null
+++ b/raddb/tools/__init__.py
@@ -0,0 +1,5 @@
+"""Command-line maintenance utilities for RadDB archives.
+
+These are one-shot operational scripts, not part of the library API — nothing
+in :mod:`raddb` imports them.  Run them with ``python -m raddb.tools.<name>``.
+"""
diff --git a/raddb/tools/migrate_gate_id_v2.py b/raddb/tools/migrate_gate_id_v2.py
new file mode 100644
index 0000000..8b331a3
--- /dev/null
+++ b/raddb/tools/migrate_gate_id_v2.py
@@ -0,0 +1,156 @@
+"""Migrate an archive from the v1 ``gate_id`` encoding to v2, in place.
+
+v1 numbered radars ``A=0 … Z=25``; v2 uses the base-36 value of the (zero-padded,
+4-character) radar name, so ``"L"`` moved from 11 to 21.  Only the leading radar
+field changes — ``sweep``/``azimuth``/``range`` occupy the low 12 digits and are
+untouched — so the whole migration is one integer offset per radar::
+
+    gate_id += (encode_radar_code(radar) - LEGACY_RADAR_TO_IDX[radar]) * 10**12
+
+``gate_id`` is stored in exactly two kinds of file, verified against the archives
+on disk: the centroid LUT ``{radar}/LUT/{radar}_LUT.parquet`` and every volume
+``{radar}/**/{radar}_*_POL.parquet``.  The three geometry lattices
+(``h_plane`` / ``v_plane`` / ``corners``) are node lattices addressed by
+``(sweep, az_idx, rng_idx)`` and carry no ``gate_id``, so they need no rewrite.
+
+No geometry is recomputed and nothing is re-ingested, which matters because the
+source volumes of an archive are often no longer around.
+
+Usage
+-----
+::
+
+    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --dry-run
+    python -m raddb.tools.migrate_gate_id_v2 <archive_dir>
+    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --radar L --radar W
+"""
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+import polars as pl
+import yaml
+
+from raddb.helper import is_valid_radar_name, normalize_radar_name
+from raddb.lut import (
+    GATE_ID_RADAR_BASE,
+    GATE_ID_VERSION,
+    LEGACY_RADAR_TO_IDX,
+    encode_radar_code,
+)
+
+
+def _archive_radars(archive_dir: Path) -> list[str]:
+    """Radar directories in *archive_dir*, whether or not they are migrated yet."""
+    return sorted(
+        p.name
+        for p in archive_dir.iterdir()
+        if p.is_dir() and is_valid_radar_name(p.name) and (p / "LUT").is_dir()
+    )
+
+
+def _info_path(archive_dir: Path, radar: str) -> Path:
+    return archive_dir / radar / "LUT" / f"{radar}_info.yaml"
+
+
+def _gate_id_files(archive_dir: Path, radar: str) -> list[Path]:
+    """Every parquet under *radar* that holds a ``gate_id`` column."""
+    lut = archive_dir / radar / "LUT" / f"{radar}_LUT.parquet"
+    files = [lut] if lut.exists() else []
+    files += sorted((archive_dir / radar).rglob("*_POL.parquet"))
+    return files
+
+
+def _shift_gate_ids(path: Path, delta: int) -> int:
+    """Rewrite ``gate_id`` in *path* by *delta*. Returns the row count.
+
+    Written to a sibling temp file and moved into place, so an interrupted run
+    leaves the original parquet intact rather than a half-written one.
+    """
+    df = pl.read_parquet(path)
+    df = df.with_columns((pl.col("gate_id") + delta).alias("gate_id"))
+    tmp = path.with_suffix(path.suffix + ".migrating")
+    df.write_parquet(tmp)
+    tmp.replace(path)
+    return df.height
+
+
+def migrate_radar(archive_dir: Path, radar: str, dry_run: bool = False) -> dict:
+    """Migrate one radar. Returns a summary dict; a migrated radar is skipped."""
+    info_path = _info_path(archive_dir, radar)
+    if not info_path.exists():
+        return {"radar": radar, "status": "no info.yaml", "files": 0, "rows": 0}
+
+    info = yaml.safe_load(info_path.read_text()) or {}
+    version = int(info.get("gate_id_version", 1))
+    if version == GATE_ID_VERSION:
+        return {"radar": radar, "status": "already v2", "files": 0, "rows": 0}
+    if version != 1:
+        return {"radar": radar, "status": f"unknown v{version} — left alone",
+                "files": 0, "rows": 0}
+
+    name = normalize_radar_name(info.get("radar") or radar)
+    legacy = LEGACY_RADAR_TO_IDX.get(name)
+    if legacy is None:
+        # v1 could only ever encode A-Z, so a v1 archive naming anything else is
+        # inconsistent and guessing an offset would corrupt it.
+        return {"radar": radar, "status": f"{name!r} is not a v1 (A-Z) radar — skipped",
+                "files": 0, "rows": 0}
+
+    delta = (encode_radar_code(name) - legacy) * GATE_ID_RADAR_BASE
+    files = _gate_id_files(archive_dir, radar)
+
+    rows = 0
+    if not dry_run:
+        for f in files:
+            rows += _shift_gate_ids(f, delta)
+        info["gate_id_version"] = GATE_ID_VERSION
+        with open(info_path, "w") as fh:
+            yaml.dump(info, fh, default_flow_style=False, sort_keys=False)
+
+    return {
+        "radar": radar,
+        "status": "would migrate" if dry_run else "migrated",
+        "files": len(files),
+        "rows": rows,
+        "delta": delta,
+    }
+
+
+def main(argv: list[str] | None = None) -> int:
+    """CLI entry point."""
+    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
+    ap.add_argument("archive_dir", type=Path, help="RadDB archive base directory")
+    ap.add_argument("--radar", action="append", default=None,
+                    help="restrict to this radar (repeatable); default is all")
+    ap.add_argument("--dry-run", action="store_true",
+                    help="report what would change without writing")
+    args = ap.parse_args(argv)
+
+    archive_dir: Path = args.archive_dir
+    if not archive_dir.is_dir():
+        print(f"error: {archive_dir} is not a directory", file=sys.stderr)
+        return 2
+
+    radars = [normalize_radar_name(r) for r in args.radar] if args.radar \
+        else _archive_radars(archive_dir)
+    if not radars:
+        print(f"no radar directories found in {archive_dir}")
+        return 0
+
+    print(f"{'gate_id v1 -> v2':<24}{archive_dir}")
+    print(f"{'radar':<8}{'files':>8}{'rows':>14}   status")
+    print("-" * 62)
+    for radar in radars:
+        res = migrate_radar(archive_dir, radar, dry_run=args.dry_run)
+        print(f"{res['radar']:<8}{res['files']:>8}{res['rows']:>14,}   {res['status']}")
+    print("-" * 62)
+    if args.dry_run:
+        print("dry run — nothing written")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/raddb/viz/plot.py b/raddb/viz/plot.py
index 0eca33c..d20a183 100644
--- a/raddb/viz/plot.py
+++ b/raddb/viz/plot.py
@@ -3,20 +3,17 @@
 -------------
 PPI, RHI, and latent-space scatter plots for RadDB.
 
-A radar gate is not a rectangle: it is a curved trapezoid in geographic space
-whose footprint depends on range, azimuth, elevation, and Earth curvature.
-The right way to render it is to feed matplotlib's ``pcolormesh`` the 2-D
-centroid coordinates of every gate — matplotlib then builds the correct
-curved quadrilaterals automatically.
-
-The reconstructed DataTree already carries the per-gate ``(lon, lat, alt, x, y, z)``
-coords on every sweep Dataset (attached during ``parquet_to_datatree``), so
-these plot functions do not need any geometry computation of their own.
-
-If cartopy is available the PPI is drawn on an Azimuthal Equidistant map
-centred on the radar site (coastlines, borders, gridlines). This applies to
-both ``coords="geo"`` and ``coords="cartesian"`` — in the latter case the
-axes tick labels are formatted in km from the radar.
+A radar gate is not a rectangle: it is a curved frustum whose footprint depends
+on range, azimuth, elevation and Earth curvature.  Every plot here draws that
+footprint as an explicit polygon, so a filtered, ``sel``-ed or cropped input
+renders exactly the gates it still holds — nothing is reindexed onto a full
+azimuth x range grid.
+
+Geometry follows the input.  A RadDB or DataFrame reads the LUT lattices; an
+``xr.DataTree`` is self-describing and its corners are computed from its own
+azimuth/range/elevation with the same 4/3-Earth model that built the LUT; a
+GeoDataFrame is treated as a frame.  There is a single geometry path — always the
+exact frustum — so what is drawn does not depend on how it was asked for.
 """
 from __future__ import annotations
 
@@ -187,7 +184,7 @@ def _lv95_border_lines():
     try:
         import shapely
         from cartopy.io import shapereader as shpreader
-        from raddb.aoi import _reproject_to_2056
+        from raddb.aoi import _reproject_to_aoi, SWISS_EPSG
 
         path = shpreader.natural_earth(
             resolution="10m", category="cultural", name="admin_0_boundary_lines_land"
@@ -196,7 +193,7 @@ def _lv95_border_lines():
         for geom in shpreader.Reader(path).geometries():
             piece = geom.intersection(clip)
             if not piece.is_empty:
-                lines.append(_reproject_to_2056(piece, 4326))
+                lines.append(_reproject_to_aoi(piece, 4326, SWISS_EPSG))
     except Exception as exc:  # noqa: BLE001 - cartopy missing / data not cached
         import warnings
         warnings.warn(
@@ -221,18 +218,6 @@ def _iter_lines(g):
             ax.plot(*line.xy, color="0.4", linewidth=0.6, zorder=1)
 
 
-def _get_sweep_dataset(dt, sweep) -> xr.Dataset:
-    """Return a sweep Dataset from a DataTree, or pass-through a Dataset."""
-    if isinstance(dt, xr.Dataset):
-        return dt
-    name = sweep if isinstance(sweep, str) else f"sweep_{int(sweep)}"
-    groups = [g.lstrip("/") for g in dt.groups]
-    if name not in groups:
-        available = sorted(g for g in groups if g.startswith("sweep_"))
-        raise KeyError(f"Sweep '{name}' not found. Available: {available}")
-    return dt[name].to_dataset()
-
-
 def _add_colorbar(p, ax, is_discrete: bool, class_labels, label: str):
     """Attach either a continuous or a categorical colorbar."""
     if is_discrete and class_labels is not None:
@@ -271,6 +256,746 @@ def _volume_time_str(ds) -> str:
     return str(vals[mask][0])[:19]
 
 
+# ============================================================================
+# Gate geometry straight from the LUT lattices
+#
+# The four plot entry points (plot_ppi / plot_rhi / plot_cappi / plot_vcs) all
+# follow the same three steps:
+#
+#   1. resolve the input to a polars frame + the archive it came from,
+#   2. narrow it to one radar and one volume,
+#   3. join the surviving ``gate_id``s onto per-gate corners read from the
+#      h_plane / v_plane lattices and hand the vertices to a PolyCollection.
+#
+# Nothing is reindexed onto a full (azimuth x range) grid on the way, which is
+# what lets a cropped or filtered frame plot exactly the gates it still holds.
+# ============================================================================
+
+_ARCHIVE_HINT = (
+    "pass archive_dir= (the directory holding {radar}/LUT/), or call the method "
+    "on a RadDB built with RadDB(archive_dir=...) so it can be inferred."
+)
+
+class _Source:
+    """What the plots need about their input, resolved once.
+
+    ``kind`` is ``"frame"`` (RadDB / polars / pandas), ``"gdf"`` (a GeoDataFrame,
+    whose own geometry is used for rough mode) or ``"datatree"`` (self-describing:
+    geometry is computed from its coordinates, no archive involved).
+    """
+
+    __slots__ = ("df", "base", "crs", "radar", "tstr", "info", "kind", "dtree", "gdf")
+
+    def __init__(self, **kw):
+        for k in self.__slots__:
+            setattr(self, k, kw.get(k))
+
+    def require_base(self, what: str):
+        if self.base is None:
+            raise ValueError(f"{what} needs the archive: " + _ARCHIVE_HINT)
+        return self.base
+
+
+def _beamwidth(src):
+    """Antenna beamwidth [deg] for a DataTree's *vertical* faces.
+
+    Only reached for DataTree input, where the faces are computed on the fly.
+    Archive-backed data needs none: the beamwidth was applied when ``v_plane``
+    was generated, and is recorded in ``info.yaml``.
+
+    Inferred exactly as :func:`raddb.lut.generate_lut_from_datatree` infers it —
+    from the file's own CfRadial/ODIM attribute, else
+    :data:`raddb.lut.DEFAULT_BEAMWIDTH_DEG`.  Neither standard makes that
+    attribute mandatory and no volume here carries one, so in practice an RHI or
+    CAPPI drawn straight from a DataTree assumes a 1 deg beam.  Archive the
+    volume to set it explicitly.
+    """
+    from raddb.lut import _beamwidth_from_datatree
+    return _beamwidth_from_datatree(src.dtree)
+
+
+def _resolve_frame(data, archive_dir=None):
+    """Normalise a plot input to a :class:`_Source`.
+
+    Accepts a :class:`~raddb.main.RadDB`, a polars or pandas frame, a
+    GeoDataFrame, or an ``xr.DataTree`` / ``xr.Dataset``.
+    """
+    from pathlib import Path
+
+    if isinstance(data, (xr.DataTree, xr.Dataset)):
+        return _Source(kind="datatree", dtree=data,
+                       base=Path(archive_dir) if archive_dir else None)
+
+    crs, base, gdf = None, archive_dir, None
+
+    if hasattr(data, "data") and not isinstance(data, (pl.DataFrame, pd.DataFrame)):
+        # A RadDB (avoid importing it: viz is imported from raddb/__init__).
+        if base is None:
+            base = getattr(data, "archive_dir", None)
+        crs = getattr(data, "_crs", None)
+        data = data.data
+
+    kind = "frame"
+    if isinstance(data, pd.DataFrame):
+        if hasattr(data, "geometry") and hasattr(data, "crs"):
+            kind, gdf = "gdf", data
+            if crs is None and data.crs is not None:
+                epsg = data.crs.to_epsg()
+                crs = epsg if epsg is not None else None
+            data = pd.DataFrame(data.drop(columns=data.geometry.name))
+        # A cross-sectioned frame carries shapely objects in `cs_polygon`, which
+        # pyarrow cannot convert — WKB-encode them the way the RadDB converters
+        # do, and plot_vcs decodes them again on the way out.
+        from raddb.main import _encode_geometry
+        data = pl.from_pandas(_encode_geometry(data))
+
+    if not isinstance(data, pl.DataFrame):
+        raise TypeError(
+            f"expected a RadDB, polars/pandas frame, GeoDataFrame or DataTree; "
+            f"got {type(data).__name__}."
+        )
+    if data.is_empty():
+        raise ValueError("no data to plot (the frame is empty).")
+    if "gate_id" not in data.columns:
+        raise KeyError("frame has no 'gate_id' column; gate geometry cannot be joined.")
+    return _Source(kind=kind, df=data, base=Path(base) if base else None, crs=crs, gdf=gdf)
+
+
+def _select_radar(df: "pl.DataFrame", radar: str | None) -> tuple["pl.DataFrame", str]:
+    """Narrow to a single radar, inferring it when the frame holds only one."""
+    from raddb.helper import normalize_radar_name
+    from raddb.aoi import _radars_from_gate_ids
+
+    if "radar" in df.columns:
+        present = sorted(df["radar"].drop_nulls().unique().to_list())
+    else:
+        present = _radars_from_gate_ids(df["gate_id"])
+
+    if radar is None:
+        if len(present) != 1:
+            raise ValueError(
+                f"data spans radars {present}; pass radar= to pick one "
+                "(one plot draws one radar)."
+            )
+        return df, present[0]
+
+    radar = normalize_radar_name(radar)
+    if "radar" in df.columns:
+        out = df.filter(pl.col("radar") == radar)
+    else:
+        from raddb.lut import GATE_ID_RADAR_BASE, encode_radar_code
+        prefix = encode_radar_code(radar) * GATE_ID_RADAR_BASE
+        out = df.filter(
+            (pl.col("gate_id") >= prefix)
+            & (pl.col("gate_id") < prefix + GATE_ID_RADAR_BASE)
+        )
+    if out.is_empty():
+        raise ValueError(f"no rows for radar {radar!r}; present: {present}.")
+    return out, radar
+
+
+def _select_volume(df: "pl.DataFrame", timestep=None, start_time=None, end_time=None):
+    """Narrow to a single volume. Returns ``(frame, time label)``.
+
+    ``start_time`` / ``end_time`` restrict the candidates; ``timestep`` then picks
+    the nearest volume.  If several volumes still remain and no ``timestep`` was
+    given, raise rather than silently drawing them on top of each other.
+    """
+    col = next((c for c in ("volume_time", "time") if c in df.columns), None)
+    if col is None:
+        return df, ""
+
+    def _bound(v):
+        ts = pd.Timestamp(v)
+        dtype = df.schema[col]
+        tz = getattr(dtype, "time_zone", None)
+        if tz is not None and ts.tzinfo is None:
+            ts = ts.tz_localize(tz)
+        elif tz is None and ts.tzinfo is not None:
+            ts = ts.tz_convert("UTC").tz_localize(None)
+        return ts
+
+    if start_time is not None:
+        df = df.filter(pl.col(col) >= _bound(start_time))
+    if end_time is not None:
+        df = df.filter(pl.col(col) <= _bound(end_time))
+    if df.is_empty():
+        raise ValueError("no data left after the start_time/end_time window.")
+
+    vols = sorted(df[col].drop_nulls().unique().to_list())
+    if not vols:
+        return df, ""
+    if timestep is not None:
+        target = _bound(timestep)
+        chosen = min(vols, key=lambda v: abs(pd.Timestamp(v) - target))
+    elif len(vols) == 1:
+        chosen = vols[0]
+    else:
+        raise ValueError(
+            f"data holds {len(vols)} volumes ({vols[0]} ... {vols[-1]}); pass "
+            "timestep= to pick one, or narrow with start_time=/end_time=."
+        )
+    return df.filter(pl.col(col) == chosen), str(chosen)[:19]
+
+
+# ---------------------------------------------------------------- coordinates
+
+_COORD_ALIASES = {
+    "cartesian": "xy", "xy": "xy", "radar": "xy",
+    "geo": "lonlat", "lonlat": "lonlat", "latlon": "lonlat", "wgs": "lonlat",
+    "projected": "projected", "proj": "projected",
+    "swiss": 2056, "lv95": 2056, "2056": 2056,
+}
+
+
+def _resolve_coords(coords, crs):
+    """Map the user's ``coords`` to ``(mode, epsg)``.
+
+    ``mode`` is ``"xy"`` (metres from the radar), ``"lonlat"`` (WGS-84 degrees)
+    or ``"projected"`` (the LUT's ``x_<epsg>`` / ``y_<epsg>`` columns).
+    """
+    if isinstance(coords, (int, np.integer)) and not isinstance(coords, bool):
+        return "projected", int(coords)
+    key = str(coords).lower()
+    if key not in _COORD_ALIASES:
+        raise ValueError(
+            f"coords must be 'xy', 'lonlat', 'projected' or an EPSG int; got {coords!r}."
+        )
+    resolved = _COORD_ALIASES[key]
+    if isinstance(resolved, int):
+        return "projected", resolved
+    if resolved == "projected":
+        if crs is None:
+            raise ValueError(
+                "coords='projected' needs a CRS; build the RadDB with "
+                "RadDB(crs=...) or pass an EPSG int as coords."
+            )
+        return "projected", int(crs)
+    return resolved, None
+
+
+def _corner_vertices(tbl: "pl.DataFrame", n_corners: int, mode: str, epsg, info):
+    """Per-gate corner rings as an ``(n_gates, n_corners, 2)`` float array.
+
+    ``tbl`` is a :func:`raddb.lut.gate_corner_table` result for the ``h_plane``
+    (4 corners).  ``mode`` selects the output frame; ``lonlat`` is derived from
+    the radar-relative metres with the same spherical model the LUT was built
+    with, so it stays consistent with the stored ``latitude``/``longitude``.
+    """
+    if mode == "projected":
+        xs = [f"x_{epsg}_{k}" for k in range(1, n_corners + 1)]
+        ys = [f"y_{epsg}_{k}" for k in range(1, n_corners + 1)]
+        if not all(c in tbl.columns for c in xs):
+            raise KeyError(
+                f"the h_plane lattice has no EPSG:{epsg} columns. Regenerate the "
+                "LUT with that projection, or use coords='xy' / 'lonlat'."
+            )
+    else:
+        xs = [f"x_{k}" for k in range(1, n_corners + 1)]
+        ys = [f"y_{k}" for k in range(1, n_corners + 1)]
+
+    ring = np.stack(
+        [np.stack([tbl[xc].to_numpy(), tbl[yc].to_numpy()], axis=1)
+         for xc, yc in zip(xs, ys)],
+        axis=1,
+    ).astype(np.float64)
+
+    if mode == "lonlat":
+        from raddb.lut import cartesian_to_geographic
+        lat, lon, _ = cartesian_to_geographic(
+            ring[:, :, 0], ring[:, :, 1], np.zeros(ring.shape[:2]),
+            info["latitude"], info["longitude"], info["altitude"],
+        )
+        ring = np.stack([lon, lat], axis=2)
+    return ring
+
+
+def _join_corners(df: "pl.DataFrame", tbl: "pl.DataFrame", variable: str):
+    """Align a per-gate corner table with the data frame, dropping unusable rows.
+
+    Returns ``(values, corner table)`` in matching row order.  Gates whose
+    variable is NaN, or that the LUT has no geometry for, are dropped — drawing
+    them would paint the colormap's "bad" colour over real data.
+    """
+    if variable not in df.columns:
+        raise KeyError(f"variable {variable!r} not in the data; have {df.columns}.")
+
+    joined = (
+        df.select(["gate_id", variable])
+        .join(tbl.drop("sweep"), on="gate_id", how="inner", maintain_order="left")
+        .filter(pl.col(variable).is_not_nan() & pl.col(variable).is_not_null())
+    )
+    if joined.is_empty():
+        raise ValueError(
+            f"no gates left to draw: every {variable!r} value is NaN, or none of "
+            "the gates matched the LUT geometry."
+        )
+    return joined[variable].to_numpy(), joined
+
+
+# ------------------------------------------------- approximate ("rough") gates
+
+def _dt_sweep_names(dt):
+    """Sweep group names of a DataTree, ordered by sweep number."""
+    if isinstance(dt, xr.Dataset):
+        return [None]
+    names = [g.lstrip("/") for g in dt.groups if g.lstrip("/").startswith("sweep_")]
+    if not names:
+        raise ValueError("no sweep_* groups found in the DataTree.")
+    return sorted(names, key=lambda s: int(s.split("_")[-1]))
+
+
+def _dt_sweep(dt, sweep):
+    """One sweep Dataset from a DataTree (or a Dataset passed straight through)."""
+    if isinstance(dt, xr.Dataset):
+        return dt
+    name = sweep if isinstance(sweep, str) else f"sweep_{int(sweep)}"
+    names = _dt_sweep_names(dt)
+    if name not in names:
+        raise ValueError(f"sweep {name!r} not in the DataTree; available: {names}.")
+    return dt[name].to_dataset()
+
+
+def _dt_site(ds):
+    """Site (lat, lon, alt) from a sweep Dataset, as a radar-info-shaped dict."""
+    missing = [k for k in ("latitude", "longitude", "altitude")
+               if k not in ds.variables and k not in ds.coords]
+    if missing:
+        raise KeyError(
+            f"the DataTree sweep has no {missing} coordinate(s), so the radar site "
+            "is unknown. xradar volumes normally carry them per sweep."
+        )
+    return {k: float(np.asarray(ds[k]).ravel()[0])
+            for k in ("latitude", "longitude", "altitude")}
+
+
+def _dt_gate_table(ds, variable):
+    """Flatten one DataTree sweep into the per-gate columns the plots consume.
+
+    A DataTree is self-describing: ``azimuth``, ``range`` and ``elevation`` are
+    all the geometry needs, so nothing is read from an archive.  Returns a polars
+    frame of values plus the antenna columns, ordered ray-major.
+    """
+    if variable not in ds.variables:
+        raise KeyError(
+            f"variable {variable!r} not in this sweep; have {sorted(ds.data_vars)}."
+        )
+    az = np.asarray(ds["azimuth"].values, dtype=np.float64)
+    rng = np.asarray(ds["range"].values, dtype=np.float64)
+    el = np.asarray(ds["elevation"].values, dtype=np.float64)
+    if el.ndim == 0:
+        el = np.full(az.shape, float(el))
+
+    vals = np.asarray(ds[variable].values, dtype=np.float64)
+    if vals.shape != (az.size, rng.size):
+        vals = vals.T
+    n_az, n_rng = az.size, rng.size
+
+    return pl.DataFrame({
+        "azimuth": np.repeat(az, n_rng),
+        "range": np.tile(rng, n_az),
+        "elevation_angle": np.repeat(el, n_rng),
+        variable: vals.ravel(),
+    })
+
+
+def _dt_h_vertices(ds, mode, epsg, info):
+    """Exact horizontal footprints computed from a DataTree's own coordinates.
+
+    Runs the identical pipeline that built the ``h_plane`` lattice — range edges,
+    complex-plane azimuth edges, ``antenna_vectors_to_cartesian`` at ``ke=4/3`` —
+    so the result matches the stored geometry without reading any file.
+
+    Takes no beamwidth: the horizontal face sits at the beam *centre*, so it is
+    beamwidth-independent (verified: 0.8 deg and 1.2 deg give identical nodes).
+    """
+    from raddb.lut import (
+        antenna_vectors_to_cartesian, cartesian_to_geographic, GATE_RING_OFFSETS,
+    )
+
+    az = np.asarray(ds["azimuth"].values, dtype=np.float64)
+    rng = np.asarray(ds["range"].values, dtype=np.float64)
+    el = np.asarray(ds["elevation"].values, dtype=np.float64)
+    if el.ndim == 0:
+        el = np.full(az.shape, float(el))
+
+    x, y, _ = antenna_vectors_to_cartesian(rng, az, el, edges=True)
+    if mode == "lonlat":
+        lat, lon, _ = cartesian_to_geographic(
+            x, y, np.zeros_like(x), info["latitude"], info["longitude"], info["altitude"],
+        )
+        x, y = lon, lat
+    elif mode == "projected":
+        x, y = _project_nodes_xy(x, y, epsg, info)
+
+    n_az, n_rng = az.size, rng.size
+    ai = np.repeat(np.arange(n_az), n_rng)
+    ri = np.tile(np.arange(n_rng), n_az)
+    return np.stack(
+        [np.stack([x[ai + i, ri + j], y[ai + i, ri + j]], axis=1)
+         for i, j in GATE_RING_OFFSETS],
+        axis=1,
+    )
+
+
+def _dt_v_vertices(ds, height, info, beamwidth_deg):
+    """Exact vertical faces from a DataTree's own coordinates, in ``(d, z)``.
+
+    Evaluates the beam at ``el ± β`` — the one input a DataTree does not carry,
+    hence the ``beamwidth_deg`` argument.
+    """
+    from raddb.lut import antenna_vectors_to_cartesian
+
+    az = np.asarray(ds["azimuth"].values, dtype=np.float64)
+    rng = np.asarray(ds["range"].values, dtype=np.float64)
+    el = np.asarray(ds["elevation"].values, dtype=np.float64)
+    if el.ndim == 0:
+        el = np.full(az.shape, float(el))
+
+    half = float(beamwidth_deg) / 2.0
+    faces = {}
+    for lvl in (-1, 1):
+        x, y, z = antenna_vectors_to_cartesian(rng, az, el + lvl * half, edges=True)
+        faces[lvl] = (np.hypot(x, y), z + (0.0 if height == "rel" else info["altitude"]))
+
+    n_az, n_rng = az.size, rng.size
+    ai = np.repeat(np.arange(n_az), n_rng)
+    ri = np.tile(np.arange(n_rng), n_az)
+    # near-bottom, far-bottom, far-top, near-top
+    picks = ((-1, 0), (-1, 1), (1, 1), (1, 0))
+    return np.stack(
+        [np.stack([faces[lvl][0][ai, ri + j], faces[lvl][1][ai, ri + j]], axis=1)
+         for lvl, j in picks],
+        axis=1,
+    )
+
+
+def _project_nodes_xy(x, y, epsg, info):
+    """Radar-relative metres -> a projected CRS, for DataTree-computed geometry."""
+    import pyproj
+    from raddb.lut import cartesian_to_geographic
+    from raddb.aoi import _to_pyproj_crs
+
+    lat, lon, _ = cartesian_to_geographic(
+        x, y, np.zeros_like(x), info["latitude"], info["longitude"], info["altitude"],
+    )
+    tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(epsg), always_xy=True)
+    px, py = tf.transform(np.asarray(lon).ravel(), np.asarray(lat).ravel())
+    return np.asarray(px).reshape(x.shape), np.asarray(py).reshape(y.shape)
+
+
+def _nearest_ray_per_sweep(frame: "pl.DataFrame", target: float, az_tol: float):
+    """Keep, in each sweep, only the rows on that sweep's ray closest to ``target``.
+
+    ``frame`` must carry ``sweep`` and a precomputed ``_off`` column holding the
+    absolute angular offset from ``target`` on the circle.  Sweeps whose closest
+    ray is further than ``az_tol`` are dropped with a warning; if none qualify,
+    raise.  Returns ``(rows, mean azimuth of the chosen rays)``.
+    """
+    best = frame.group_by("sweep").agg(pl.col("_off").min().alias("_best"))
+    within = best.filter(pl.col("_best") <= az_tol)
+    if within.is_empty():
+        raise ValueError(
+            f"no sweep has a ray within ±{az_tol}° of azimuth {target}°; the "
+            f"closest is {float(best['_best'].min()):.2f}° away."
+        )
+    if within.height < best.height:
+        import warnings
+        warnings.warn(
+            f"{best.height - within.height} of {best.height} sweeps have no ray "
+            f"within ±{az_tol}° of azimuth {target}° and are omitted.",
+            stacklevel=2,
+        )
+    picked = frame.join(within, on="sweep", how="inner").filter(
+        pl.col("_off") == pl.col("_best")
+    )
+    return picked, float(picked["azimuth"].mean())
+
+
+def _dt_rhi(src, target, variable, az_tol, height, beamwidth_deg):
+    """RHI geometry and values from a DataTree, one nearest ray per sweep."""
+    all_values, all_verts, rays = [], [], []
+    for name in _dt_sweep_names(src.dtree):
+        ds = _dt_sweep(src.dtree, name)
+        if variable not in ds.variables:
+            continue
+        az = np.asarray(ds["azimuth"].values, dtype=np.float64)
+        off = np.abs(((az - target + 180.0) % 360.0) - 180.0)
+        j = int(np.argmin(off))
+        if off[j] > az_tol:
+            continue
+        rays.append(float(az[j]))
+
+        # Geometry is built from the *whole* sweep and the chosen ray selected
+        # afterwards: edge interpolation needs the full azimuth and range
+        # vectors, so slicing first would move the outermost edges.
+        tbl = _dt_gate_table(ds, variable)
+        verts = _dt_v_vertices(ds, height, src.info, beamwidth_deg)
+
+        n_rng = np.asarray(ds["range"].values).size
+        rows = slice(j * n_rng, (j + 1) * n_rng)          # table is ray-major
+        vals = tbl[variable].to_numpy()[rows]
+        verts = verts[rows]
+        keep = np.isfinite(vals)
+        all_values.append(vals[keep])
+        all_verts.append(verts[keep])
+
+    if not rays:
+        raise ValueError(
+            f"no sweep of this DataTree has a ray within ±{az_tol}° of azimuth {target}°."
+        )
+    return (np.concatenate(all_values), np.concatenate(all_verts),
+            float(np.mean(rays)))
+
+
+def _dt_cappi(src, altitude, variable, height, overlap, fill_lowest,
+              mode, epsg, beamwidth_deg):
+    """CAPPI geometry and values from a DataTree, with no archive involved.
+
+    Mirrors the LUT path: cut the exact ``(d, z)`` faces at the slice altitude to
+    get each range bin's along-beam chord, resolve overlapping sweeps, then trim
+    the horizontal footprint to that chord.
+    """
+    site_alt = float(src.info["altitude"])
+    z0 = float(altitude) + (site_alt if height == "rel" else 0.0)
+
+    per_sweep, chords = {}, []
+    for name in _dt_sweep_names(src.dtree):
+        ds = _dt_sweep(src.dtree, name)
+        if variable not in ds.variables:
+            continue
+        sw = int(str(name).split("_")[-1])
+        per_sweep[sw] = ds
+        d_near, d_far, rng_idx, dz = _dt_sweep_chords(ds, z0, site_alt, beamwidth_deg)
+        if rng_idx.size:
+            chords.append(pl.DataFrame({
+                "sweep": np.full(rng_idx.size, sw, dtype=np.int32),
+                "rng_idx": rng_idx.astype(np.int32),
+                "d_near": d_near.astype(np.float32),
+                "d_far": d_far.astype(np.float32),
+                "z_center": np.zeros(rng_idx.size, dtype=np.float32),
+                "dz_center": dz.astype(np.float32),
+            }))
+    if not chords:
+        raise ValueError(
+            f"no beam of this DataTree reaches {altitude} m "
+            f"({'ASL' if height == 'asl' else 'above the radar'}); nothing to draw."
+        )
+    table = pl.concat(chords, how="vertical")
+    if overlap == "nearest":
+        table = _resolve_chord_overlap(table)
+
+    all_values, all_verts = [], []
+    for sw, ds in per_sweep.items():
+        sel = table.filter(pl.col("sweep") == sw)
+        if sel.is_empty():
+            continue
+        ri = sel["rng_idx"].to_numpy()
+        dn, df_ = sel["d_near"].to_numpy(), sel["d_far"].to_numpy()
+
+        # Build the whole sweep, then keep the selected range bins: edge
+        # interpolation must see the full range vector, so subsetting the
+        # Dataset first would shift the outermost gate edges.
+        tbl = _dt_gate_table(ds, variable)
+        full = _dt_h_vertices(ds, mode, epsg, src.info)
+        full_xy = _dt_h_vertices(ds, "xy", None, src.info)
+
+        n_az = np.asarray(ds["azimuth"].values).size
+        n_rng = np.asarray(ds["range"].values).size
+        rows = (np.repeat(np.arange(n_az), ri.size) * n_rng
+                + np.tile(ri, n_az))                       # table is ray-major
+        verts = _trim_footprints_to_chord(
+            full[rows], full_xy[rows], np.tile(dn, n_az), np.tile(df_, n_az)
+        )
+        vals = tbl[variable].to_numpy()[rows]
+        keep = np.isfinite(vals)
+        all_values.append(vals[keep])
+        all_verts.append(verts[keep])
+
+    if not all_values or sum(len(v) for v in all_values) == 0:
+        raise ValueError(f"every {variable!r} value on the {altitude} m slice is NaN.")
+    return np.concatenate(all_values), np.concatenate(all_verts)
+
+
+def _dt_sweep_chords(ds, z0, site_alt, beamwidth_deg):
+    """Along-beam chord of the ``z = z0`` cut per range bin, from a DataTree sweep.
+
+    The DataTree counterpart of :func:`raddb.lut.cappi_chords`: identical quad
+    clipping, but the ``(d, z)`` faces are computed from the sweep's own
+    coordinates instead of read from ``v_plane``.
+    """
+    from raddb.lut import antenna_vectors_to_cartesian
+
+    # d and z do not depend on azimuth, so one ray describes the sweep — but the
+    # edge interpolation needs the whole azimuth vector, so pass it all and keep
+    # the first row.
+    az = np.asarray(ds["azimuth"].values, dtype=np.float64)
+    rng = np.asarray(ds["range"].values, dtype=np.float64)
+    el = np.asarray(ds["elevation"].values, dtype=np.float64)
+    if el.ndim == 0:
+        el = np.full(az.shape, float(el))
+
+    half = float(beamwidth_deg) / 2.0
+    faces = {}
+    for lvl in (-1, 1):
+        x, y, z = antenna_vectors_to_cartesian(rng, az, el + lvl * half, edges=True)
+        faces[lvl] = (np.hypot(x, y)[0], z[0] + site_alt)
+
+    ring_d = np.stack([faces[-1][0][:-1], faces[-1][0][1:],
+                       faces[1][0][1:], faces[1][0][:-1]], axis=1)
+    ring_z = np.stack([faces[-1][1][:-1], faces[-1][1][1:],
+                       faces[1][1][1:], faces[1][1][:-1]], axis=1)
+
+    za, zb = ring_z, np.roll(ring_z, -1, axis=1)
+    da, db = ring_d, np.roll(ring_d, -1, axis=1)
+    sa, sb = za - z0, zb - z0
+    crosses = ((sa <= 0) & (sb >= 0)) | ((sa >= 0) & (sb <= 0))
+    with np.errstate(divide="ignore", invalid="ignore"):
+        t = np.where(zb != za, (z0 - za) / (zb - za), 0.0)
+    d_cross = np.where(crosses, da + np.clip(t, 0.0, 1.0) * (db - da), np.nan)
+
+    hit = np.isfinite(d_cross).any(axis=1)
+    if not hit.any():
+        return (np.empty(0),) * 4
+    d_hit = d_cross[hit]
+    z_center = ring_z.mean(axis=1)[hit]
+    return (np.nanmin(d_hit, axis=1), np.nanmax(d_hit, axis=1),
+            np.flatnonzero(hit), np.abs(z_center - z0))
+
+
+class _KmFormatter(mticker.Formatter):
+    """Tick labels in km, from axis data held in metres.
+
+    The decimals come from the tick spacing matplotlib actually chose.  A fixed
+    ``.0f`` silently collapses adjacent labels whenever that step drops below
+    1 km: a 1.4-6.0 km cross-section gets 500 m steps and reads
+    ``1, 2, 2, 2, 3, 4, 4, 4, 5, 6, 6`` — wrong, and worse, plausible-looking.
+    (The irregularity is round-half-to-even: 2.5 prints as "2" but 3.5 as "4".)
+
+    ``offset`` subtracts a false origin before scaling, for projected frames such
+    as LV95 whose easting starts at 2 000 km.
+    """
+
+    def __init__(self, offset: float = 0.0):
+        self.offset = float(offset)
+
+    #: Never print more than this many decimals, whatever the ticks ask for.
+    MAX_DECIMALS = 4
+
+    def __call__(self, v, pos=None):
+        return f"{(v - self.offset) / 1e3:.{self._decimals()}f}"
+
+    def _decimals(self) -> int:
+        """Fewest decimals that write every tick on this axis *exactly*.
+
+        Deriving them from ``log10(step)`` is not enough: matplotlib routinely
+        picks steps of 2.5x10**n, where 2500 m would give 0 decimals and print
+        0, 2.5, 5, 7.5, 10 as ``0, 2, 5, 8, 10`` — distinct, so it survives a
+        duplicate check, but wrong. Asking instead which precision reproduces the
+        values handles every step shape.
+        """
+        if self.axis is None:
+            return 0
+        locs = np.asarray(self.axis.get_majorticklocs(), dtype=float)
+        if locs.size == 0:
+            return 0
+        km = (locs - self.offset) / 1e3
+        for d in range(self.MAX_DECIMALS + 1):
+            tol = 1e-6 * np.maximum(1.0, np.abs(km))
+            if np.all(np.abs(km - np.round(km, d)) <= tol):
+                return d
+        return self.MAX_DECIMALS
+
+
+def _draw_polygons(ax, verts, values, plot_kwargs, edgecolor, rasterized):
+    """Add a PolyCollection of gate polygons coloured by ``values``."""
+    from matplotlib.collections import PolyCollection
+
+    pc = PolyCollection(
+        verts, array=np.asarray(values, dtype=np.float64),
+        edgecolor=edgecolor, linewidth=0.1,
+    )
+    if "cmap" in plot_kwargs:
+        pc.set_cmap(plot_kwargs["cmap"])
+    if plot_kwargs.get("norm") is not None:
+        pc.set_norm(plot_kwargs["norm"])
+    else:
+        pc.set_clim(plot_kwargs.get("vmin"), plot_kwargs.get("vmax"))
+    if rasterized:
+        pc.set_rasterized(True)
+    ax.add_collection(pc)
+    return pc
+
+
+def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim,
+                     add_range_rings=True, context=False):
+    """Labels, tick formatting, aspect, range rings and limits for a map plot."""
+    if mode == "lonlat":
+        ax.set_xlabel("Longitude [°]")
+        ax.set_ylabel("Latitude [°]")
+        scale = 1.0
+    else:
+        # Native metres on the axis, km on the tick labels.
+        ox, oy = (2e6, 1e6) if epsg == 2056 else (0.0, 0.0)
+        ax.xaxis.set_major_formatter(_KmFormatter(ox))
+        ax.yaxis.set_major_formatter(_KmFormatter(oy))
+        if mode == "xy":
+            ax.set_xlabel("East from radar [km]")
+            ax.set_ylabel("North from radar [km]")
+        else:
+            ax.set_xlabel("East [km]")
+            ax.set_ylabel("North [km]")
+        scale = 1e3
+
+    if context and mode == "projected" and epsg == 2056:
+        _draw_lv95_borders(ax)
+
+    if site_xy is not None:
+        ax.plot(*site_xy, "kx", markersize=7, markeredgewidth=2, zorder=5)
+        if add_range_rings and mode != "lonlat":
+            theta = np.linspace(0, 2 * np.pi, 361)
+            for d_km in (50, 100, 150):
+                ax.plot(site_xy[0] + d_km * scale * np.cos(theta),
+                        site_xy[1] + d_km * scale * np.sin(theta),
+                        "k--", linewidth=0.5, zorder=1)
+
+    ax.set_aspect("equal")
+    ax.grid(True, alpha=0.3)
+
+    # Default view: a square centred on the radar, sized by the furthest gate
+    # drawn.  A plain data bounding box would be pulled off-centre by a handful
+    # of distant echoes, and would differ between variables and time steps —
+    # this keeps panels comparable and the radar where the eye expects it.
+    if site_xy is not None and (xlim is None or ylim is None):
+        reach = float(np.nanmax(np.hypot(verts[:, :, 0] - site_xy[0],
+                                         verts[:, :, 1] - site_xy[1])))
+        auto_x = (site_xy[0] - reach, site_xy[0] + reach)
+        auto_y = (site_xy[1] - reach, site_xy[1] + reach)
+    else:
+        auto_x = (float(verts[:, :, 0].min()), float(verts[:, :, 0].max()))
+        auto_y = (float(verts[:, :, 1].min()), float(verts[:, :, 1].max()))
+
+    ax.set_xlim(xlim if xlim is not None else auto_x)
+    ax.set_ylim(ylim if ylim is not None else auto_y)
+
+
+def _site_xy(info, mode, epsg):
+    """Radar site position in the requested frame, or None if unavailable."""
+    if mode == "xy":
+        return (0.0, 0.0)
+    if mode == "lonlat":
+        return (float(info["longitude"]), float(info["latitude"]))
+    import shapely
+    from raddb.aoi import _reproject_to_aoi, _to_pyproj_crs
+    pt = shapely.Point(float(info["longitude"]), float(info["latitude"]))
+    if epsg == 2056:
+        p = _reproject_to_aoi(pt, 4326, 2056)
+        return (p.x, p.y)
+    import pyproj
+    tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(epsg), always_xy=True)
+    return tf.transform(pt.x, pt.y)
+
+
 # ============================================================================
 # PPI
 # ============================================================================
@@ -287,34 +1012,38 @@ def plot_aoi_quicklook(
     range_rings_km=(100,),
     show_gates=False,
     gate_sample=50_000,
+    epsg=None,
     xlim=None,
-    ylim=(1_040_000, 1_310_000),
+    ylim=None,
     save_path=None,
 ):
     """Map an AOI on a country-scale background for a quick sanity-check.
 
     Answers "**is my AOI where I think it is?**": the AOI footprint (red) drawn on
-    the **Switzerland outline** with the involved radar sites, in a **square**
-    national-extent view.  Rendered in Swiss LV95 (EPSG:2056), axis units km.  The
-    Swiss outline comes from cartopy's cached Natural Earth data; everything else
-    is dependency-free, so the map still draws (without the outline) if that data
-    is missing.
+    a country-scale background with the involved radar sites, in a **square** view.
+    Rendered in the archive's own CRS (``epsg=``, default LV95 when unset), axis
+    units km.  The default ``"switzerland"`` outline comes from cartopy's cached
+    Natural Earth data and is dropped on a non-Swiss frame; everything else is
+    dependency-free, so the map still draws (without the outline) if that data is
+    missing.
 
     Parameters
     ----------
     aoi_geom : shapely geometry
-        AOI footprint in EPSG:2056 (Polygon for bbox/polygon/point AOIs;
+        AOI footprint in the AOI frame (Polygon for bbox/polygon/point AOIs;
         LineString for a cross-section line).
     selected : pd.DataFrame, optional
-        Selected gates carrying ``x_2056`` / ``y_2056``.  Only scattered when
+        Selected gates carrying ``x`` / ``y`` in the AOI CRS.  Only scattered when
         ``show_gates=True`` — off by default so the map stays readable.
     radars : list of str, optional
         Radar letters to mark (needs ``base_path`` to load their site coords).
     base_path : str or Path, optional
         RadDB archive base directory, for loading radar site coordinates.
     context : str, shapely geometry, GeoDataFrame, or None
-        Map background. ``"switzerland"`` (default) draws the national outline;
-        ``None`` draws none; a geometry/GeoDataFrame draws a custom context.
+        Map background, resolved into the AOI frame. ``"switzerland"`` (default)
+        draws the national outline and is dropped on a non-Swiss frame; ``None``
+        draws none; a GeoDataFrame is reprojected from its own ``.crs``, and a
+        bare shapely geometry is taken to be in the AOI frame already.
     ax : matplotlib Axes, optional
         Draw into an existing axis instead of creating a figure.
     figsize : tuple
@@ -339,6 +1068,20 @@ def plot_aoi_quicklook(
     (fig, ax)
     """
     import shapely
+    from raddb.aoi import SWISS_EPSG
+
+    # Everything here is drawn in the AOI's own frame.  The default context is
+    # the Swiss border, which outside LV95 would draw the wrong country around
+    # the AOI, so it is dropped rather than being misleading.
+    frame_epsg = SWISS_EPSG if epsg is None else int(epsg)
+    if context == "switzerland" and frame_epsg != SWISS_EPSG:
+        context = None
+    if ylim is None and frame_epsg == SWISS_EPSG:
+        # Keep the familiar Swiss band when the frame really is LV95; anywhere
+        # else it would put the AOI thousands of km off-screen, so fall through
+        # to the computed extent.
+        ylim = (1_040_000, 1_310_000)
+
     from raddb.aoi import _resolve_context
 
     if ax is None:
@@ -346,8 +1089,8 @@ def plot_aoi_quicklook(
     else:
         fig = ax.figure
 
-    # --- context background (Switzerland outline) ---
-    ctx_geom = _resolve_context(context)
+    # --- context background, in the AOI's own frame ---
+    ctx_geom = _resolve_context(context, aoi_epsg=frame_epsg)
     if ctx_geom is not None:
         _draw_context(ax, ctx_geom)
 
@@ -355,14 +1098,15 @@ def plot_aoi_quicklook(
     sites: dict[str, tuple[float, float]] = {}
     if radars and base_path is not None:
         from raddb.lut import load_radar_info
-        from raddb.aoi import _reproject_to_2056
+        from raddb.aoi import _reproject_to_aoi, SWISS_EPSG
 
         for r in radars:
             try:
                 info = load_radar_info(r, base_path)
             except Exception:  # noqa: BLE001 - missing info shouldn't kill the quicklook
                 continue
-            pt = _reproject_to_2056(shapely.Point(info["longitude"], info["latitude"]), 4326)
+            pt = _reproject_to_aoi(
+                shapely.Point(info["longitude"], info["latitude"]), 4326, frame_epsg)
             sites[r] = (pt.x, pt.y)
 
     # --- optional selected gate centroids ---
@@ -370,10 +1114,10 @@ def plot_aoi_quicklook(
         selected is not None
         and show_gates
         and len(selected)
-        and {"x_2056", "y_2056"}.issubset(selected.columns)
+        and {"x", "y"}.issubset(selected.columns)
     ):
-        xs = selected["x_2056"].to_numpy()
-        ys = selected["y_2056"].to_numpy()
+        xs = selected["x"].to_numpy()
+        ys = selected["y"].to_numpy()
         if gate_sample and len(xs) > gate_sample:
             idx = np.random.default_rng(0).choice(len(xs), gate_sample, replace=False)
             xs, ys = xs[idx], ys[idx]
@@ -414,7 +1158,7 @@ def plot_aoi_quicklook(
     # --- AOI footprint ---
     _draw_aoi_outline(ax, aoi_geom)
 
-    # --- frame: x fills the context/AOI extent; y defaults to the Swiss band ---
+    # --- frame: fills the context / AOI / site extent, in the AOI's own CRS ---
     boxes = [aoi_geom.bounds]
     if ctx_geom is not None:
         boxes.append(ctx_geom.bounds)
@@ -436,8 +1180,9 @@ def plot_aoi_quicklook(
 
     # --- cosmetics: equal aspect, LV95 km ticks ---
     ax.set_aspect("equal")
-    ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{(v - 2e6) / 1e3:.0f}"))
-    ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{(v - 1e6) / 1e3:.0f}"))
+    ox, oy = (2e6, 1e6) if frame_epsg == SWISS_EPSG else (0.0, 0.0)
+    ax.xaxis.set_major_formatter(_KmFormatter(ox))
+    ax.yaxis.set_major_formatter(_KmFormatter(oy))
     ax.set_xlabel("East [km]")
     ax.set_ylabel("North [km]")
     ax.grid(True, alpha=0.3)
@@ -481,144 +1226,6 @@ def _draw_aoi_outline(ax, geom, color="red"):
         ax.plot(geom.x, geom.y, marker="*", color=color, ms=12, zorder=4, label="AOI")
 
 
-# ============================================================================
-# Regular-grid slice maps (from RadDB.aoi_to_grid)
-# ============================================================================
-
-def plot_grid(
-    ds,
-    variable: str = "DBZH",
-    radar=None,
-    z: float | None = None,
-    iz: int | None = None,
-    time=None,
-    ax=None,
-    figsize=(8, 7),
-    title: str | None = None,
-    add_colorbar: bool = True,
-    use_cartopy: bool | None = None,
-    xlim=None,
-    ylim=None,
-    **plot_kwargs,
-):
-    """Map one horizontal slice of an :meth:`RadDB.aoi_to_grid` Dataset.
-
-    Selects one radar (or a **pair for a difference map**), one altitude layer,
-    and (if present) one volume time, then draws the (y, x) slice in the Swiss
-    LV95 frame (East/North km ticks, optional cartopy country borders) — the
-    same frame as the AOI quicklook and ``coords="swiss"`` PPIs.
-
-    Parameters
-    ----------
-    ds : xr.Dataset
-        Output of :meth:`RadDB.aoi_to_grid` (dims ``(time?, radar, z, y, x)``).
-    variable : str
-        Data variable to draw (default ``"DBZH"``).
-    radar : str or (str, str), optional
-        A radar letter selects that radar's layer (inferred when the grid holds
-        exactly one).  A **pair** ``("P", "L")`` draws the per-voxel difference
-        ``P − L`` with a symmetric diverging colormap — the cross-radar
-        comparison the per-radar grid exists for.
-    z : float, optional
-        Altitude of the layer to draw (m ASL); the nearest ``z`` bin is used.
-    iz : int, optional
-        Alternative to ``z``: direct index into the ``z`` dimension.
-    time : str or datetime, optional
-        Volume time (nearest match); required only when the grid has several.
-    ax : matplotlib Axes, optional
-    figsize, title, add_colorbar
-        Usual matplotlib options.
-    use_cartopy : bool, optional
-        Draw LV95-reprojected country borders (auto when cartopy is available).
-    xlim, ylim : (min, max) in LV95 metres, optional
-    **plot_kwargs
-        ``cmap`` / ``vmin`` / ``vmax`` / ``norm`` overrides.
-
-    Returns
-    -------
-    (fig, ax, mesh)
-    """
-    if variable not in ds.data_vars:
-        raise KeyError(f"variable {variable!r} not in grid; available: {list(ds.data_vars)}")
-
-    sel = ds[variable]
-
-    # --- time selection (grid times are naive UTC) ---
-    if "time" in sel.dims:
-        if time is not None:
-            ts = pd.to_datetime(time)
-            ts = ts.tz_convert("UTC").tz_localize(None) if ts.tzinfo is not None else ts
-            sel = sel.sel(time=ts.to_datetime64(), method="nearest")
-        elif sel.sizes["time"] == 1:
-            sel = sel.isel(time=0)
-        else:
-            raise ValueError(f"grid holds {sel.sizes['time']} volumes; pass time= to pick one.")
-
-    # --- altitude layer ---
-    if iz is not None:
-        sel = sel.isel(z=int(iz))
-    elif z is not None:
-        sel = sel.sel(z=float(z), method="nearest")
-    elif sel.sizes.get("z", 1) == 1:
-        sel = sel.isel(z=0)
-    else:
-        raise ValueError("pass z= (altitude, m ASL) or iz= to pick the layer to draw.")
-    z_val = float(sel["z"])
-
-    # --- radar selection: single layer or difference of a pair ---
-    radars_in = [str(r) for r in np.atleast_1d(ds["radar"].values)]
-    is_diff = isinstance(radar, (tuple, list)) and len(radar) == 2
-    if is_diff:
-        r0, r1 = radar
-        data2d = sel.sel(radar=r0) - sel.sel(radar=r1)
-        default_title = f"{variable} difference {r0}−{r1} — z≈{z_val:.0f} m ASL"
-        plot_kwargs.setdefault("cmap", "RdBu_r")
-        if not any(k in plot_kwargs for k in ("vmin", "vmax", "norm")):
-            vmax = float(np.nanmax(np.abs(data2d.values))) if np.isfinite(data2d.values).any() else 1.0
-            plot_kwargs["vmin"], plot_kwargs["vmax"] = -vmax, vmax
-        resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs("__diff__", plot_kwargs)
-        cbar_label = f"Δ{variable} ({r0}−{r1})"
-    else:
-        if radar is None:
-            if len(radars_in) != 1:
-                raise ValueError(f"grid holds radars {radars_in}; pass radar= (or a pair for a difference).")
-            radar = radars_in[0]
-        data2d = sel.sel(radar=radar)
-        default_title = f"{variable} — radar {radar} — z≈{z_val:.0f} m ASL"
-        resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(variable, plot_kwargs)
-
-    if ax is None:
-        fig, ax = plt.subplots(figsize=figsize)
-    else:
-        fig = ax.figure
-
-    # pixel-edge mesh from centres (regular spacing)
-    res = float(ds.attrs.get("resolution_m", np.diff(ds["x"].values).mean()))
-    xe = np.append(ds["x"].values - res / 2, ds["x"].values[-1] + res / 2)
-    ye = np.append(ds["y"].values - res / 2, ds["y"].values[-1] + res / 2)
-    p = ax.pcolormesh(xe, ye, data2d.values, shading="flat", **resolved)
-
-    ccrs, _ = _maybe_cartopy()
-    if use_cartopy is None:
-        use_cartopy = ccrs is not None
-    if use_cartopy:
-        _draw_lv95_borders(ax)
-
-    ax.set_aspect("equal")
-    ax.grid(True, alpha=0.3)
-    ax.set_xlim(*(xlim if xlim is not None else (xe[0], xe[-1])))
-    ax.set_ylim(*(ylim if ylim is not None else (ye[0], ye[-1])))
-    ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{(v - 2e6) / 1e3:.0f}"))
-    ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{(v - 1e6) / 1e3:.0f}"))
-    ax.set_xlabel("East [km]")
-    ax.set_ylabel("North [km]")
-    ax.set_title(title or default_title)
-
-    if add_colorbar:
-        _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
-    return fig, ax, p
-
-
 # ============================================================================
 # Vertical cross-section (arbitrary line, from crop_cross_section)
 # ============================================================================
@@ -716,468 +1323,756 @@ def plot_cross_section(
     return fig, ax, pc
 
 
+# ============================================================================
+# RHI (pseudo-RHI from PPI volume)
+# ============================================================================
+
+# ============================================================================
+# The four gate-accurate plots
+# ============================================================================
+
+def _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable):
+    """Resolve the input and narrow it to one radar and one volume.
+
+    Returns a :class:`_Source` with ``df`` / ``radar`` / ``tstr`` / ``info``
+    filled in.  A DataTree short-circuits most of this: it describes one volume
+    of one radar already, and its site metadata comes from its own coordinates.
+    """
+    from raddb.lut import load_radar_info
+
+    src = _resolve_frame(data, archive_dir)
+    if src.kind == "datatree":
+        src.radar = radar or ""
+        src.tstr = _volume_time_str(_dt_sweep(src.dtree, _dt_sweep_names(src.dtree)[0]))
+        src.info = _dt_site(_dt_sweep(src.dtree, _dt_sweep_names(src.dtree)[0]))
+        return src
+
+    src.df, src.radar = _select_radar(src.df, radar)
+    src.df, src.tstr = _select_volume(src.df, timestep, start_time, end_time)
+    if variable not in src.df.columns:
+        raise KeyError(f"variable {variable!r} not in the data; have {src.df.columns}.")
+    src.info = load_radar_info(src.radar, src.base) if src.base else None
+    if src.crs is None and src.base is not None:
+        # The archive knows the CRS it was written with, so reading never requires
+        # restating it — coords="projected" resolves it from there.  aoi_epsg also
+        # recovers it from the LUT's x_<epsg> columns for archives predating the
+        # info.yaml crs block; if there is genuinely none, leave it unset so
+        # _resolve_coords can say so.
+        from raddb.aoi import aoi_epsg
+        try:
+            src.crs = aoi_epsg(src.base, src.radar)
+        except (ValueError, FileNotFoundError):
+            src.crs = None
+    return src
+
+
 def plot_ppi(
-    dt,
-    sweep,
-    variable: str,
+    data,
+    sweep: int | str = 1,
+    variable: str = "DBZH",
+    radar: str | None = None,
+    timestep=None,
+    start_time=None,
+    end_time=None,
+    coords="xy",
+    context: bool = False,
+    archive_dir=None,
     ax=None,
-    use_cartopy: bool | None = None,
-    coords: str = "geo",
-    add_range_rings: bool = True,
+    figsize: tuple[float, float] = (6, 6),
     add_colorbar: bool = True,
+    add_range_rings: bool = True,
     title: str | None = None,
-    figsize: tuple[float, float] = (6, 6),
-    xlim: tuple[float, float] | None = None,
-    ylim: tuple[float, float] | None = None,
+    xlim=None,
+    ylim=None,
+    edgecolor="none",
+    rasterized: bool | None = None,
+    save: str | None = None,
+    use_cartopy: bool | None = None,
     **plot_kwargs,
 ):
-    """Plan Position Indicator from a single sweep of a reconstructed DataTree.
+    """Plan Position Indicator — one sweep, one plot, one Axes.
+
+    Draws the **exact gate footprints** stored in the ``h_plane`` lattice, so a
+    frame that has been filtered, ``sel``-ed or cropped plots precisely the gates
+    it still holds.  Gates are joined by ``gate_id``; nothing is reindexed onto a
+    full azimuth x range grid on the way.
+
+    To build a multi-panel figure, call this once per panel with ``ax=``::
+
+        fig, axes = plt.subplots(2, 3, figsize=(13, 7))
+        for ax, var in zip(axes.ravel(), variables):
+            rdf.plot_ppi(sweep=1, variable=var, ax=ax)
 
     Parameters
     ----------
-    dt : xr.DataTree or xr.Dataset
-        Reconstructed DataTree (from ``parquet_to_datatree``) or single sweep Dataset.
+    data : RadDB, polars/pandas DataFrame, GeoDataFrame, or xr.DataTree
+        A DataTree is self-describing: its geometry is computed from its own
+        azimuth/range/elevation, so no archive is involved.  A GeoDataFrame is
+        treated as a frame — exact mode joins the LUT, approximate mode uses the
+        columns it carries.
     sweep : int or str
-        Sweep index (``3``) or group name (``"sweep_3"``). Ignored if
-        ``dt`` is already a Dataset.
+        Sweep number, or ``"sweep_3"``.
     variable : str
-        Variable to plot: ``"DBZH"``, ``"ZDR"``, ``"RHOHV"``, ``"PHIDP"``,
-        ``"HZT"``, ``"TEMP"``, ``"HC_MCH"``, ``"HC_PYART"``.
-    ax : matplotlib.axes.Axes, optional
+        Column to colour by.  Per-variable colormaps and discrete HC class
+        colorbars are applied automatically.
+    radar : str, optional
+        Required only when the frame spans several radars.
+    timestep : optional
+        Volume to draw.  Required when the frame holds more than one volume;
+        the nearest volume is used.
+    start_time, end_time : optional
+        Restrict the candidate volumes before ``timestep`` is applied.
+    coords : {"xy", "lonlat", "projected"} or int
+        ``"xy"`` — metres from the radar (ticks in km).  ``"lonlat"`` — WGS-84
+        degrees.  ``"projected"`` — the LUT's ``x_<epsg>``/``y_<epsg>`` columns,
+        using the RadDB's CRS; an EPSG int selects one directly (``2056`` and
+        ``"swiss"`` are the Swiss LV95 frame used by the AOI quicklook).
+    context : bool
+        Overlay cartopy country borders.  Independent of the coordinate frame.
+    archive_dir : str or Path, optional
+        Needed only when ``data`` is a bare frame.
+    edgecolor : matplotlib colour
+        Gate outline; ``"k"`` to show the individual gate polygons.
+    rasterized : bool, optional
+        Rasterise the polygons inside vector output.  Defaults to ``True`` above
+        50 000 gates, where an unrasterised PDF/SVG becomes very large.
+    save : str, optional
+        Path to save the figure to.
     use_cartopy : bool, optional
-        If None (default), auto-detect: use cartopy when installed. Pass
-        ``False`` to force plain matplotlib. Pass ``True`` to require cartopy
-        (raises ImportError if missing).
-    coords : {"geo", "cartesian", "swiss"}
-        Coordinate frame for the axes:
-
-        - ``"geo"`` — ``(longitude, latitude)`` degrees.
-        - ``"cartesian"`` — ``(x, y)`` km **relative to the radar** (origin at the
-          site).  With cartopy this is an AEQD map centred on the radar.
-        - ``"swiss"`` (aka ``"lv95"`` / ``"2056"``) — absolute **Swiss LV95**
-          ``(x_2056, y_2056)``, axis ticks in km (E from the 2 000 km
-          false-easting, N from 1 000 km).  This is the **same frame as the AOI
-          quicklook**, so a cropped-df PPI lines up with `crop_*` overlays.  With
-          cartopy (``use_cartopy``) it overlays country borders reprojected to
-          LV95 as a basemap; otherwise the same plot without borders.
-    add_range_rings : bool
-        For ``cartesian`` / ``swiss`` modes, draw 50/100/150 km range rings.
-    add_colorbar : bool
-    title : str, optional
-    figsize : tuple
-        Figure size in inches. Default ``(6, 6)``.
-    xlim : tuple, optional
-        x-axis limits in the natural units of the chosen ``coords``
-        (degrees for ``"geo"``, km for ``"cartesian"``).
-    ylim : tuple, optional
-        y-axis limits (same units as ``xlim``).
-    **plot_kwargs
-        Forwarded to ``pcolormesh`` (overrides defaults for ``cmap``, ``vmin``,
-        ``vmax``, ``norm``, etc.).
+        Deprecated alias for ``context``.
 
     Returns
     -------
-    matplotlib.collections.QuadMesh
+    matplotlib.collections.PolyCollection
+        The artist; ``p.axes`` and ``p.figure`` reach the rest of the plot.
     """
-    ds = _get_sweep_dataset(dt, sweep)
-    if variable not in ds.variables:
-        raise KeyError(f"Variable '{variable}' not in sweep. Available: {list(ds.data_vars)}")
-    da = ds[variable]
+    from raddb.lut import gate_corner_table
 
-    plot_kwargs, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
-        variable, plot_kwargs
-    )
+    if use_cartopy is not None:
+        context = bool(use_cartopy)
 
-    ccrs, cfeature = _maybe_cartopy()
-    if use_cartopy is None:
-        use_cartopy = ccrs is not None  # auto-use cartopy for both geo and cartesian
-    elif use_cartopy and ccrs is None:
-        raise ImportError(
-            "use_cartopy=True but cartopy is not installed. "
-            "Run: pip install 'raddb[viz]' or: pip install cartopy"
-        )
+    src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
+    sweep_num = int(str(sweep).split("_")[-1])
+    mode, epsg = _resolve_coords(coords, src.crs)
 
-    # If the user passed a non-GeoAxes axis, silently disable cartopy — except
-    # for "swiss", which draws on a plain axis (borders are reprojected lines).
-    if use_cartopy and ax is not None and coords not in ("swiss", "lv95", "2056"):
-        try:
-            from cartopy.mpl.geoaxes import GeoAxes
-            if not isinstance(ax, GeoAxes):
-                use_cartopy = False
-        except ImportError:
-            use_cartopy = False
-
-    has_edges = all(k in ds.variables for k in ("x_edges", "y_edges"))
-
-    # ------------------------------------------------------------------ geo
-    if coords == "geo":
-        if use_cartopy:
-            site_lon = float(ds["site_longitude"])
-            site_lat = float(ds["site_latitude"])
-            proj = ccrs.AzimuthalEquidistant(
-                central_longitude=site_lon, central_latitude=site_lat,
-            )
-            if ax is None:
-                fig, ax = plt.subplots(subplot_kw={"projection": proj}, figsize=figsize)
-            if has_edges:
-                p = ax.pcolormesh(
-                    ds["x_edges"].values, ds["y_edges"].values, da.values,
-                    transform=proj, shading="flat", **plot_kwargs,
-                )
-            else:
-                p = ax.pcolormesh(
-                    ds["longitude"].values, ds["latitude"].values, da.values,
-                    transform=ccrs.PlateCarree(), shading="auto", **plot_kwargs,
-                )
-            ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
-            ax.add_feature(cfeature.BORDERS, linewidth=0.3, linestyle=":")
-            ax.gridlines(draw_labels=True, linewidth=0.3, alpha=0.3)
-            ax.plot(site_lon, site_lat, "kx", markersize=7, markeredgewidth=2,
-                    transform=ccrs.PlateCarree(), zorder=5)
-            if xlim is not None or ylim is not None:
-                lon_min, lon_max = xlim if xlim is not None else (None, None)
-                lat_min, lat_max = ylim if ylim is not None else (None, None)
-                cur = ax.get_extent(crs=ccrs.PlateCarree())
-                ax.set_extent([
-                    lon_min if lon_min is not None else cur[0],
-                    lon_max if lon_max is not None else cur[1],
-                    lat_min if lat_min is not None else cur[2],
-                    lat_max if lat_max is not None else cur[3],
-                ], crs=ccrs.PlateCarree())
-        else:
-            if ax is None:
-                fig, ax = plt.subplots(figsize=figsize)
-            if has_edges:
-                p = ax.pcolormesh(
-                    ds["lon_edges"].values, ds["lat_edges"].values, da.values,
-                    shading="flat", **plot_kwargs,
-                )
-            else:
-                p = ax.pcolormesh(
-                    ds["longitude"].values, ds["latitude"].values, da.values,
-                    shading="auto", **plot_kwargs,
-                )
-            ax.set_xlabel("Longitude [°]")
-            ax.set_ylabel("Latitude [°]")
-            ax.set_aspect("equal")
-            ax.plot(float(ds["site_longitude"]), float(ds["site_latitude"]),
-                    "kx", markersize=7, markeredgewidth=2, zorder=5)
-            ax.grid(True, alpha=0.3)
-            if xlim is not None:
-                ax.set_xlim(xlim)
-            if ylim is not None:
-                ax.set_ylim(ylim)
-
-    # ------------------------------------------------------------ cartesian
-    elif coords == "cartesian":
-        if use_cartopy and ("site_longitude" in ds.variables or "site_longitude" in ds.coords):
-            site_lon = float(ds["site_longitude"])
-            site_lat = float(ds["site_latitude"])
-            proj = ccrs.AzimuthalEquidistant(
-                central_longitude=site_lon, central_latitude=site_lat,
-            )
-            if ax is None:
-                fig, ax = plt.subplots(subplot_kw={"projection": proj}, figsize=figsize)
-            # x/y from dataset are in metres; plot directly in AEQD native units.
-            if has_edges:
-                p = ax.pcolormesh(
-                    ds["x_edges"].values, ds["y_edges"].values, da.values,
-                    transform=proj, shading="flat", **plot_kwargs,
-                )
-            else:
-                p = ax.pcolormesh(
-                    ds["x"].values, ds["y"].values, da.values,
-                    transform=proj, shading="auto", **plot_kwargs,
-                )
-            ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
-            ax.add_feature(cfeature.BORDERS, linewidth=0.3, linestyle=":")
-            ax.gridlines(linewidth=0.3, alpha=0.3, draw_labels=False)
-            ax.plot(0, 0, "kx", markersize=7, markeredgewidth=2, transform=proj, zorder=5)
-            if add_range_rings:
-                theta = np.linspace(0, 2 * np.pi, 361)
-                for d_km in (50, 100, 150):
-                    ax.plot(
-                        d_km * 1e3 * np.cos(theta), d_km * 1e3 * np.sin(theta),
-                        "k--", linewidth=0.5, transform=proj,
-                    )
-            # Format ticks in km (AEQD native units are metres).
-            ax.xaxis.set_major_formatter(
-                mticker.FuncFormatter(lambda v, _: f"{v / 1e3:.0f}")
-            )
-            ax.yaxis.set_major_formatter(
-                mticker.FuncFormatter(lambda v, _: f"{v / 1e3:.0f}")
-            )
-            ax.set_xlabel("East from radar [km]")
-            ax.set_ylabel("North from radar [km]")
-            if xlim is not None:
-                ax.set_xlim(xlim[0] * 1e3, xlim[1] * 1e3)
-            if ylim is not None:
-                ax.set_ylim(ylim[0] * 1e3, ylim[1] * 1e3)
-        else:
-            if ax is None:
-                fig, ax = plt.subplots(figsize=figsize)
-            if has_edges:
-                p = ax.pcolormesh(
-                    ds["x_edges"].values / 1000.0, ds["y_edges"].values / 1000.0,
-                    da.values, shading="flat", **plot_kwargs,
-                )
-            else:
-                p = ax.pcolormesh(
-                    ds["x"].values / 1000.0, ds["y"].values / 1000.0, da.values,
-                    shading="auto", **plot_kwargs,
-                )
-            ax.set_xlabel("East from radar [km]")
-            ax.set_ylabel("North from radar [km]")
-            ax.set_aspect("equal")
-            if add_range_rings:
-                _draw_range_rings_xy(ax)
-            ax.plot(0, 0, "kx", markersize=7, markeredgewidth=2, zorder=5)
-            ax.grid(True, alpha=0.3)
-            if xlim is not None:
-                ax.set_xlim(xlim)
-            if ylim is not None:
-                ax.set_ylim(ylim)
-    # ---------------------------------------------------------- swiss (LV95)
-    elif coords in ("swiss", "lv95", "2056"):
-        # Absolute Swiss LV95 (EPSG:2056) — same frame as the AOI quicklook.
-        # With cartopy, an LV95 GeoAxes adds a country-border basemap; otherwise
-        # plain matplotlib.  Either way the axes are LV95 km (radar x, range rings).
-        if "x_2056" not in ds.coords:
-            raise KeyError(
-                "coords='swiss' needs x_2056/y_2056 in the sweep; reconstruct the "
-                "DataTree from a LUT that has the EPSG:2056 projection columns."
-            )
+    resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
+        variable, plot_kwargs
+    )
+    if ax is None:
+        _, ax = plt.subplots(figsize=figsize)
+
+    if src.kind == "datatree":
+        ds = _dt_sweep(src.dtree, sweep_num)
+        values = _dt_gate_table(ds, variable)[variable].to_numpy()
+        verts = _dt_h_vertices(ds, mode, epsg, src.info)
+        keep = np.isfinite(values)
+        values, verts = values[keep], verts[keep]
+        if values.size == 0:
+            raise ValueError(f"every {variable!r} value in sweep {sweep_num} is NaN.")
+    else:
+        base = src.require_base("plot_ppi")
+        tbl = gate_corner_table(src.radar, base, kind="h_plane", sweep=sweep_num)
+        if tbl.is_empty():
+            raise ValueError(f"radar {src.radar!r} has no sweep {sweep_num} in its LUT.")
+        values, joined = _join_corners(src.df, tbl, variable)
+        verts = _corner_vertices(joined, 4, mode, epsg, src.info)
 
-        # Gate mesh in LV95: reproject the corner mesh for flat shading, else centroids.
-        import shapely
-        from raddb.aoi import _reproject_to_2056
-        if all(k in ds.variables for k in ("lon_edges", "lat_edges")):
-            import pyproj
-            from raddb.aoi import _to_pyproj_crs
-            _tf = pyproj.Transformer.from_crs(
-                _to_pyproj_crs(4326), _to_pyproj_crs(2056), always_xy=True
-            )
-            lon_e = ds["lon_edges"].values
-            lat_e = ds["lat_edges"].values
-            xe, ye = _tf.transform(lon_e.ravel(), lat_e.ravel())
-            mx = np.asarray(xe).reshape(lon_e.shape)
-            my = np.asarray(ye).reshape(lat_e.shape)
-            mshade = "flat"
-        else:
-            mx, my = ds["x_2056"].values, ds["y_2056"].values
-            mshade = "auto"
-        site = _reproject_to_2056(
-            shapely.Point(float(ds["site_longitude"]), float(ds["site_latitude"])), 4326
-        )
+    if rasterized is None:
+        rasterized = len(values) > 50_000
+    p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
 
-        if ax is None:
-            fig, ax = plt.subplots(figsize=figsize)
-        # Data range now, so borders drawn afterwards don't expand the view.
-        data_xlim = xlim if xlim is not None else (float(np.nanmin(mx)), float(np.nanmax(mx)))
-        data_ylim = ylim if ylim is not None else (float(np.nanmin(my)), float(np.nanmax(my)))
-
-        p = ax.pcolormesh(mx, my, da.values, shading=mshade, **plot_kwargs)
-        # cartopy country-border basemap (reprojected to LV95, plain-axis clipped)
-        if use_cartopy:
-            _draw_lv95_borders(ax)
-        ax.set_aspect("equal")
-        ax.plot(site.x, site.y, "kx", markersize=7, markeredgewidth=2, zorder=5)
-        if add_range_rings:
-            theta = np.linspace(0, 2 * np.pi, 361)
-            for d_km in (50, 100, 150):
-                ax.plot(site.x + d_km * 1e3 * np.cos(theta),
-                        site.y + d_km * 1e3 * np.sin(theta),
-                        "k--", linewidth=0.5)
-        ax.grid(True, alpha=0.3)
-        ax.set_xlim(data_xlim)
-        ax.set_ylim(data_ylim)
-
-        # LV95 km tick labels (same convention as the AOI quicklook).
-        ax.xaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{(v - 2e6) / 1e3:.0f}"))
-        ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{(v - 1e6) / 1e3:.0f}"))
-        ax.set_xlabel("East [km]")
-        ax.set_ylabel("North [km]")
-    else:
-        raise ValueError(f"coords must be 'geo', 'cartesian', or 'swiss', got {coords!r}")
+    _finish_map_axes(ax, mode, epsg, verts, _site_xy(src.info, mode, epsg),
+                     xlim, ylim, add_range_rings, context)
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
-
-    sweep_num = int(sweep) if isinstance(sweep, (int, np.integer)) else sweep
-    tstr = _volume_time_str(ds)
-    ax.set_title(
-        title or f"{variable} — sweep {sweep_num} — {tstr}".rstrip(" —")
-    )
+    ax.set_title(title or _default_title(src.radar, variable, f"sweep {sweep_num}", src.tstr))
+    _maybe_save(ax, save, plot_kwargs)
     return p
 
 
-# ============================================================================
-# RHI (pseudo-RHI from PPI volume)
-# ============================================================================
-
 def plot_rhi(
-    dt,
-    azimuth: float,
-    variable: str,
-    radar: str = "",
+    data,
+    azimuth: float = 0.0,
+    variable: str = "DBZH",
+    radar: str | None = None,
+    timestep=None,
+    start_time=None,
+    end_time=None,
+    height: str = "asl",
     az_tol: float = 1.0,
-    max_range_km: float | None = None,
-    max_height_km: float | None = 20.0,
-    ke: float = 4.0 / 3.0,
+    archive_dir=None,
     ax=None,
+    figsize: tuple[float, float] = (10, 4),
     add_colorbar: bool = True,
     title: str | None = None,
-    figsize: tuple[float, float] = (10, 4),
+    xlim=None,
+    ylim=None,
+    max_range_km: float | None = None,
+    max_height_km: float | None = None,
+    edgecolor="none",
+    rasterized: bool | None = None,
+    save: str | None = None,
     **plot_kwargs,
 ):
-    """Pseudo-RHI from a volume PPI scan — PyART's ``cross_section_ppi``.
+    """Range-Height Indicator — one azimuth through the whole volume.
 
-    For every sweep in the DataTree, selects the ray whose azimuth is closest
-    to ``azimuth`` (within ``az_tol``, wrap-around safe), regrids all rays to
-    a common range axis, and renders a 2-D
-    ``(ground_range, height_ASL)`` pcolormesh using **gate edges** computed
-    with the 4/3 Earth-radius model — so curved-trapezoid gates and Earth
-    curvature are both physically correct.
+    The counterpart of :func:`plot_ppi`: instead of fixing the sweep and sweeping
+    azimuth, it fixes the azimuth and stacks every sweep, drawing the gates'
+    vertical faces from the ``v_plane`` lattice in the
+    ``(ground distance, altitude)`` plane.
 
     Parameters
     ----------
-    dt : xr.DataTree
-        Reconstructed DataTree (from ``parquet_to_datatree``).
     azimuth : float
-        Target azimuth in degrees (0..360; 0 = North, clockwise).
-    variable : str
-    radar : str, optional
-        Radar identifier shown in the plot title (e.g. ``"A"``).
-    az_tol : float
-        Maximum allowed angular distance between the requested azimuth and
-        the nearest available ray.
-    max_range_km : float, optional
-        Clip the ground-range axis.
-    max_height_km : float, optional
-        Clip the height axis.
-    ke : float
-        Effective Earth radius factor (default 4/3, pyart standard).
-    ax, add_colorbar, title, figsize, **plot_kwargs : see ``plot_ppi``.
+        Target azimuth in degrees (0 = North, clockwise).  The nearest stored ray
+        is used; ``az_tol`` bounds how far it may be.
+    height : {"asl", "rel"}
+        Altitude above sea level (default) or above the radar.
+    max_range_km, max_height_km : float, optional
+        Convenience clips, equivalent to ``xlim`` / ``ylim``.
+
+    Other parameters are as in :func:`plot_ppi`.
 
     Returns
     -------
-    matplotlib.collections.QuadMesh
+    matplotlib.collections.PolyCollection
     """
-    from raddb.lut import (
-        antenna_vectors_to_cartesian,
-        _interpolate_range_edges,
-        _interpolate_elevation_edges,
-    )
+    if height not in ("asl", "rel"):
+        raise ValueError(f"height must be 'asl' or 'rel'; got {height!r}.")
 
-    sweep_names = sorted(
-        [g.lstrip("/") for g in dt.groups
-         if g.lstrip("/").startswith("sweep_")],
-        key=lambda s: int(s.split("_")[-1]),
+    from raddb.lut import gate_corner_table, load_radar_lut
+
+    src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
+    target = float(azimuth) % 360.0
+    if src.kind == "datatree":
+        values, verts, ray_az = _dt_rhi(src, target, variable, az_tol, height,
+                                        _beamwidth(src))
+    else:
+        base = src.require_base("plot_rhi")
+        # Nearest ray **per sweep**, compared on the circle so 359.8° and 0.1°
+        # are close.  The LUT stores raw antenna azimuths, which jitter by a few
+        # tenths of a degree from one sweep to the next, so a single azimuth
+        # value matches only one sweep — an RHI needs each sweep's closest ray.
+        lut_az = (
+            load_radar_lut(src.radar, base)
+            .select(["gate_id", "sweep", "azimuth"])
+            .with_columns(
+                (((pl.col("azimuth") - target + 180.0) % 360.0) - 180.0).abs().alias("_off")
+            )
+        )
+        picked, ray_az = _nearest_ray_per_sweep(lut_az, target, az_tol)
+        tbl = gate_corner_table(src.radar, base, kind="v_plane").join(
+            picked.select("gate_id"), on="gate_id", how="semi"
+        )
+        values, joined = _join_corners(src.df, tbl, variable)
+        z_prefix = "z_asl" if height == "asl" else "z_rel"
+        verts = np.stack(
+            [np.stack([joined[f"d_{k}"].to_numpy(),
+                       joined[f"{z_prefix}_{k}"].to_numpy()], axis=1)
+             for k in range(1, 5)],
+            axis=1,
+        ).astype(np.float64)
+
+    if len(values) == 0:
+        raise ValueError(f"no gates on the ray at azimuth {ray_az:.1f}° in this input.")
+
+    resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
+        variable, plot_kwargs
     )
-    if not sweep_names:
-        raise ValueError("No sweep_* groups found in DataTree.")
-
-    # 1. Pick the nearest ray per sweep.
-    rays = []
-    site_alt = 0.0
-    for name in sweep_names:
-        ds = dt[name].to_dataset()
-        if variable not in ds.variables:
-            continue
-        az_arr = ds["azimuth"].values
-        diff = np.abs(((az_arr - azimuth + 180.0) % 360.0) - 180.0)
-        j = int(np.argmin(diff))
-        if diff[j] > az_tol:
-            continue
-        site_alt = float(ds["site_altitude"])
-        try:
-            el = float(ds["elevation_angle"])
-        except Exception:
-            el = np.nan
-        rays.append({
-            "range":     np.asarray(ds["range"].values,    dtype=np.float64),
-            "values":    np.asarray(ds[variable].isel(azimuth=j).values, dtype=np.float64),
-            "elevation": el,
-            "actual_az": float(az_arr[j]),
-        })
+    if ax is None:
+        _, ax = plt.subplots(figsize=figsize)
+    if rasterized is None:
+        rasterized = len(values) > 50_000
 
-    if not rays:
+    p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
+
+    for axis in (ax.xaxis, ax.yaxis):
+        axis.set_major_formatter(_KmFormatter())
+    ax.set_xlabel("Ground range [km]")
+    ax.set_ylabel("Height ASL [km]" if height == "asl" else "Height above radar [km]")
+    ax.grid(True, alpha=0.3)
+    ax.set_xlim(xlim if xlim is not None
+                else (0.0, (max_range_km * 1e3) if max_range_km else float(verts[:, :, 0].max())))
+    ax.set_ylim(ylim if ylim is not None
+                else (float(verts[:, :, 1].min()),
+                      (max_height_km * 1e3) if max_height_km else float(verts[:, :, 1].max())))
+
+    if add_colorbar:
+        _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
+    ax.set_title(title or _default_title(src.radar, variable, f"azimuth {ray_az:.1f}°", src.tstr))
+    _maybe_save(ax, save, plot_kwargs)
+    return p
+
+
+def plot_cappi(
+    data,
+    altitude: float,
+    variable: str = "DBZH",
+    radar: str | None = None,
+    timestep=None,
+    start_time=None,
+    end_time=None,
+    coords="xy",
+    context: bool = False,
+    height: str = "asl",
+    overlap: str = "nearest",
+    fill_lowest: bool = False,
+    archive_dir=None,
+    ax=None,
+    figsize: tuple[float, float] = (6, 6),
+    add_colorbar: bool = True,
+    add_range_rings: bool = True,
+    title: str | None = None,
+    xlim=None,
+    ylim=None,
+    edgecolor="none",
+    rasterized: bool | None = None,
+    save: str | None = None,
+    **plot_kwargs,
+):
+    """Constant Altitude PPI — a horizontal slice through the volume.
+
+    Where :func:`plot_ppi` fixes the sweep, this fixes the **altitude** and pulls
+    from whichever elevation angles actually sample it, which is what makes a
+    CAPPI a CAPPI.
+
+    How the geometry is built
+    -------------------------
+    1. :func:`raddb.lut.cappi_chords` intersects the gates' vertical faces
+       (``v_plane``) with the plane ``z = altitude``, giving the along-beam chord
+       ``[d_near, d_far]`` of every range bin the surface passes through.  This is
+       computed **once per (sweep, range bin)**: ``d`` and ``z`` do not depend on
+       azimuth, so the same handful of rows serves all 360 rays.
+    2. Each chord trims that bin's ``h_plane`` footprint along the beam, giving
+       the output polygon in ``(x, y)``.
+
+    So the result is horizontal — same frame as a PPI — but it is *not* the
+    ``h_plane`` face itself: the constant-altitude cut shortens the gate along
+    the beam.  ``h_plane`` alone cannot produce it, having no altitude column.
+
+    Because beam thickness at long range (~1.7 km at 100 km) far exceeds the
+    height gained across one range bin, each sweep contributes a **wide
+    contiguous band** of bins and neighbouring sweeps overlap heavily — hence
+    ``overlap``.
+
+    Parameters
+    ----------
+    altitude : float
+        Slice altitude in metres, in the reference given by ``height``.
+    height : {"asl", "rel"}
+        Whether ``altitude`` is above sea level (default) or above the radar.
+    overlap : {"nearest", "all"}
+        How to resolve sweeps that both sample this altitude at the same ground
+        distance.  ``"nearest"`` (default) partitions the ground-distance axis
+        and keeps, in each interval, the beam whose centre is closest to the
+        slice — a real measurement, never an average.  ``"all"`` draws every
+        contributing gate, so later sweeps paint over earlier ones.
+    fill_lowest : bool
+        Beyond the range where even the lowest sweep's beam has climbed above the
+        slice, nothing samples that altitude.  ``False`` (default) leaves it
+        empty; ``True`` continues along the lowest sweep, the operational
+        convention (Stull, *Practical Meteorology* §8.2).
+
+    Other parameters are as in :func:`plot_ppi`.
+
+    Returns
+    -------
+    matplotlib.collections.PolyCollection
+    """
+    if overlap not in ("nearest", "all"):
+        raise ValueError(f"overlap must be 'nearest' or 'all'; got {overlap!r}.")
+
+    from raddb.lut import cappi_chords, gate_corner_table, _gate_grid_index
+
+    src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
+
+    mode, epsg = _resolve_coords(coords, src.crs)
+
+    if src.kind == "datatree":
+        values, verts = _dt_cappi(
+            src, altitude, variable, height, overlap, fill_lowest,
+            mode, epsg, _beamwidth(src),
+        )
+    else:
+        base = src.require_base("plot_cappi")
+        # The slice is cut against the vertical faces stored in v_plane, i.e.
+        # against real quads: a linear beam model would divide by tan(elevation),
+        # which blows up on the near-horizontal sweeps supplying most of the far
+        # field.
+        chords = cappi_chords(src.radar, base, altitude, height=height)
+        if chords.is_empty():
+            raise ValueError(
+                f"no beam of radar {src.radar!r} reaches {altitude} m "
+                f"({'ASL' if height == 'asl' else 'above the radar'}); nothing to draw."
+            )
+        if overlap == "nearest":
+            chords = _resolve_chord_overlap(chords)
+        if fill_lowest:
+            chords = _extend_lowest_sweep(chords, src.radar, base)
+
+        # Chords are per (sweep, rng_idx) and azimuth-independent -> expand to gates.
+        gates = _gate_grid_index(src.radar, base).join(
+            chords, on=["sweep", "rng_idx"], how="inner"
+        )
+        if gates.is_empty():
+            raise ValueError("the constant-altitude surface matched no gates.")
+        tbl = gate_corner_table(src.radar, base, kind="h_plane").join(
+            gates.select(["gate_id", "d_near", "d_far"]), on="gate_id", how="inner"
+        )
+        values, joined = _join_corners(src.df, tbl, variable)
+        verts = _trim_footprints_to_chord(
+            _corner_vertices(joined, 4, mode, epsg, src.info),
+            _corner_vertices(joined, 4, "xy", None, src.info),
+            joined["d_near"].to_numpy(), joined["d_far"].to_numpy(),
+        )
+
+    if len(values) == 0:
         raise ValueError(
-            f"No sweep has a ray within ±{az_tol}° of azimuth {azimuth}° "
-            f"for variable '{variable}'."
+            f"no gates at {altitude} m are present in this input — the slice is "
+            "outside the loaded/cropped data."
         )
 
-    # 2. Sort by elevation (low sweeps at the bottom of the RHI).
-    rays.sort(key=lambda r: r["elevation"] if np.isfinite(r["elevation"]) else 0.0)
+    resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
+        variable, plot_kwargs
+    )
+    if ax is None:
+        _, ax = plt.subplots(figsize=figsize)
+    if rasterized is None:
+        rasterized = len(values) > 50_000
+
+    p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
+    _finish_map_axes(ax, mode, epsg, verts, _site_xy(src.info, mode, epsg),
+                     xlim, ylim, add_range_rings, context)
 
-    # 3. Build a common range axis (widest sweep).
-    widest = max(rays, key=lambda r: r["range"].max() if r["range"].size else 0.0)
-    common_range = widest["range"]
+    if add_colorbar:
+        _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
+    ref = "m ASL" if height == "asl" else "m above radar"
+    ax.set_title(title or _default_title(src.radar, variable, f"{altitude:g} {ref}", src.tstr))
+    _maybe_save(ax, save, plot_kwargs)
+    return p
 
-    v2d = np.stack([
-        np.interp(common_range, r["range"], r["values"],
-                  left=np.nan, right=np.nan)
-        for r in rays
-    ])  # (n_sweeps, n_range)
 
-    # 4. Gate-edge arrays via 4/3-Earth model.
-    range_edges = _interpolate_range_edges(common_range)
-    elevations  = np.array([r["elevation"] for r in rays], dtype=np.float64)
-    el_edges    = _interpolate_elevation_edges(elevations)
-    mean_az     = float(np.mean([r["actual_az"] for r in rays]))
-    az_edges    = np.full(el_edges.size, mean_az)
+def plot_vcs(
+    data,
+    line=None,
+    variable: str = "DBZH",
+    radar: str | None = None,
+    timestep=None,
+    start_time=None,
+    end_time=None,
+    height: str = "asl",
+    crs=None,
+    beamwidth_deg: float = 1.0,
+    aoi_crs=None,
+    archive_dir=None,
+    ax=None,
+    figsize: tuple[float, float] = (12, 5),
+    add_colorbar: bool = True,
+    title: str | None = None,
+    xlim=None,
+    ylim=None,
+    edgecolor="none",
+    rasterized: bool | None = None,
+    save: str | None = None,
+    **plot_kwargs,
+):
+    """Vertical Cross-Section — a vertical slice along an arbitrary line.
 
-    x_e, y_e, z_e = antenna_vectors_to_cartesian(
-        ranges=range_edges, azimuths=az_edges, elevations=el_edges, ke=ke,
-    )
-    ground_range_edges = np.sqrt(x_e ** 2 + y_e ** 2)
-    height_edges_asl   = z_e + site_alt
+    Unlike the other three plots, what is drawn has to be *defined* first: a PPI
+    has its sweep and an RHI its azimuth, but a cross-section needs a line.
+    Supply it as ``line``, or pass a frame that already went through
+    :meth:`~raddb.RadDB.extract_cross_section` (it carries ``cs_polygon``).
 
-    # 5. Render.
-    plot_kwargs, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
+    Which combinations are accepted:
+
+    =====================  ==========================  =========================
+    ``data``               ``line``                    result
+    =====================  ==========================  =========================
+    RadDB / frame / gdf    file, points or LineString  section is cut, then drawn
+                           (a bare frame needs archive_dir=)
+    RadDB / frame / gdf    omitted, has cs_polygon     drawn directly
+    RadDB / frame / gdf    omitted, no cs_polygon      **error** — undefined
+    RadDB / frame / gdf    given *and* has cs_polygon  **error** — ambiguous
+    ``xr.DataTree``        anything                    **error** — archive first
+    =====================  ==========================  =========================
+
+    The third row is the common mistake: an AOI crop (rectangle, polygon, marker)
+    selects an *area*, not a line, so its result has no section to draw.
+
+    Parameters
+    ----------
+    line : optional
+        The cross-section to cut, given as any of:
+
+        * ``(p1, p2)`` — two ``(x, y)`` points or shapely Points,
+        * a shapely ``LineString``,
+        * a path to a ``.shp`` / ``.geojson`` holding a line.
+
+        Omit it when ``data`` already carries ``cs_polygon``.
+    crs : int or str, optional
+        CRS of ``line``.  A file that declares its own CRS wins unless this is
+        given explicitly; otherwise the RadDB's CRS is assumed.
+    beamwidth_deg : float
+        Beamwidth used to give the section its vertical extent.
+    height : {"asl", "rel"}
+        Altitude reference for the vertical axis.
+
+    Other parameters are as in :func:`plot_ppi`.
+
+    Returns
+    -------
+    matplotlib.collections.PolyCollection
+    """
+    import shapely
+
+    if isinstance(data, (xr.DataTree, xr.Dataset)):
+        raise TypeError(
+            "plot_vcs cannot work from a DataTree: cutting a cross-section needs "
+            "the LUT (gate footprints keyed by gate_id), which a DataTree has no "
+            "equivalent of. Archive the volume first — it takes a few seconds — "
+            "then cut the section on the result:\n"
+            "    db.archive(datatree=dt, radar='L')\n"
+            "    db.open(radars='L').plot_vcs(line=(p1, p2))"
+        )
+
+    # RadDB.columns is a method, a frame's .columns is a property — handle both.
+    if isinstance(data, (pl.DataFrame, pd.DataFrame)):
+        cols = list(data.columns)
+    else:
+        inner = getattr(data, "data", None)
+        cols = list(inner.columns) if inner is not None else []
+    already_cut = "cs_polygon" in cols
+
+    if already_cut and line is not None:
+        raise ValueError(
+            "both a section line and an already-cut frame were given, so it is "
+            "ambiguous which section to draw. Cutting again would intersect two "
+            "different sections. Pass the line to an uncut frame, or drop line= "
+            "to draw the section this frame already carries."
+        )
+    if not already_cut:
+        if line is None:
+            raise ValueError(
+                "no cross-section to draw: this frame carries no 'cs_polygon'. "
+                "Pass line=((x1, y1), (x2, y2)), a shapely LineString or a "
+                ".shp/.geojson path — or call extract_cross_section() first. "
+                "(An AOI crop by rectangle/polygon/point selects an area, not a "
+                "line, so its result cannot be drawn as a cross-section.)"
+            )
+        if not hasattr(data, "extract_cross_section"):
+            # A bare frame carries gate_id and the archive carries the geometry,
+            # so the section is perfectly cuttable — wrap it, the same way the
+            # other three plots read the LUT for a bare frame.
+            from raddb.main import RadDB as _RadDB
+            probe = _resolve_frame(data, archive_dir)
+            probe.require_base("cutting a cross-section from line=")
+            data = _RadDB(archive_dir=str(probe.base), crs=probe.crs)._derive(probe.df)
+        p1, p2, file_crs = _line_endpoints(line)
+        # A file states its own CRS; honour it unless the caller overrode it.
+        data = data.extract_cross_section(
+            p1, p2, crs=crs if crs is not None else file_crs,
+            beamwidth_deg=beamwidth_deg, aoi_crs=aoi_crs,
+        )
+
+    src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
+    from raddb.main import _decode_geometry
+
+    # cs_polygon crosses into polars as WKB, so decode it back to shapely.
+    pdf = _decode_geometry(src.df.to_pandas())
+    pdf = pdf[pdf[variable].notna() & pdf["cs_polygon"].notna()]
+    if pdf.empty:
+        raise ValueError(f"no non-NaN {variable!r} values on this cross-section.")
+
+    # cs_polygon lives in (distance along the line, altitude ASL).
+    shift = 0.0 if height == "asl" else -float(src.info["altitude"])
+    verts = np.stack([
+        np.asarray(poly.exterior.coords)[:4, :2] for poly in pdf["cs_polygon"]
+    ]).astype(np.float64)
+    verts[:, :, 1] += shift
+
+    resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
         variable, plot_kwargs
     )
+    values = pdf[variable].to_numpy()
     if ax is None:
-        fig, ax = plt.subplots(figsize=figsize)
-    p = ax.pcolormesh(
-        ground_range_edges / 1000.0,
-        height_edges_asl   / 1000.0,
-        v2d,
-        shading="flat", **plot_kwargs,
-    )
-    ax.set_xlabel("Ground range [km]")
-    ax.set_ylabel("Height ASL [km]")
-    if max_range_km is not None:
-        ax.set_xlim(0, max_range_km)
-    if max_height_km is not None:
-        ax.set_ylim(site_alt / 1000.0, max_height_km)
-    else:
-        ax.set_ylim(bottom=site_alt / 1000.0)
+        _, ax = plt.subplots(figsize=figsize)
+    if rasterized is None:
+        rasterized = len(values) > 50_000
+
+    p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
+
+    for axis in (ax.xaxis, ax.yaxis):
+        axis.set_major_formatter(_KmFormatter())
+    ax.set_xlabel("Distance along section [km]")
+    ax.set_ylabel("Altitude [km ASL]" if height == "asl" else "Height above radar [km]")
     ax.grid(True, alpha=0.3)
+    ax.set_xlim(xlim if xlim is not None
+                else (float(verts[:, :, 0].min()), float(verts[:, :, 0].max())))
+    ax.set_ylim(ylim if ylim is not None
+                else (float(verts[:, :, 1].min()), float(verts[:, :, 1].max())))
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
-
-    tstr = ""
-    for name in sweep_names:
-        ds = dt[name].to_dataset()
-        if variable in ds.variables:
-            tstr = _volume_time_str(ds)
-            break
-
-    if title is None:
-        parts = []
-        if radar:
-            parts.append(f"radar: {radar}")
-        parts.append(f"feature: {variable}")
-        parts.append(f"azimuth: {azimuth:.1f}°")
-        if tstr:
-            parts.append(f"date: {tstr}")
-        title = " | ".join(parts)
-    ax.set_title(title)
+    ax.set_title(title or _default_title(src.radar, variable, "cross-section", src.tstr))
+    _maybe_save(ax, save, plot_kwargs)
     return p
 
 
+# ------------------------------------------------------------ plot internals
+
+def _default_title(radar, variable, what, tstr):
+    parts = [f"radar {radar}", variable, what]
+    if tstr:
+        parts.append(tstr)
+    return " | ".join(parts)
+
+
+def _maybe_save(ax, save, kwargs):
+    if save:
+        ax.figure.savefig(save, bbox_inches="tight", dpi=kwargs.get("dpi", 150))
+
+
+def _line_endpoints(line):
+    """Normalise ``line`` to ``(p1, p2, src_crs)``.
+
+    ``src_crs`` is the CRS the file declared, or ``None`` for points and geometry
+    objects, which carry none.  Callers must pass it through: a GeoJSON is
+    lon/lat by RFC 7946, and reading those degrees as LV95 metres would place the
+    section thousands of kilometres away.
+    """
+    import shapely
+    from pathlib import Path
+
+    src_crs = None
+    if isinstance(line, (str, Path)):
+        from raddb.aoi import _read_geometry_file
+        geom, src_crs = _read_geometry_file(Path(line))
+    elif isinstance(line, shapely.geometry.base.BaseGeometry):
+        geom = line
+    else:
+        p1, p2 = line
+        to_xy = lambda p: (p.x, p.y) if hasattr(p, "x") else (float(p[0]), float(p[1]))  # noqa: E731
+        return to_xy(p1), to_xy(p2), None
+
+    coords = np.asarray(shapely.get_coordinates(geom))
+    if len(coords) < 2:
+        raise ValueError(f"could not read a line with two endpoints from {line!r}.")
+    return tuple(coords[0][:2]), tuple(coords[-1][:2]), src_crs
+
+
+def _resolve_chord_overlap(chords: "pl.DataFrame") -> "pl.DataFrame":
+    """Partition the ground-distance axis so no two gates cover the same distance.
+
+    Several sweeps typically intersect the slice altitude over overlapping
+    ground-distance intervals.  Split the axis at every chord endpoint and give
+    each elementary interval to the chord with the smallest ``dz_center`` — the
+    beam whose centre sits closest to the slice.  Chords are then clipped to the
+    intervals they won.
+    """
+    d_near = chords["d_near"].to_numpy().astype(np.float64)
+    d_far = chords["d_far"].to_numpy().astype(np.float64)
+    dz = chords["dz_center"].to_numpy().astype(np.float64)
+
+    edges = np.unique(np.concatenate([d_near, d_far]))
+    if edges.size < 2:
+        return chords
+    mid = 0.5 * (edges[:-1] + edges[1:])
+
+    # covers[i, j]: chord i spans elementary interval j.
+    covers = (d_near[:, None] <= mid[None, :]) & (mid[None, :] <= d_far[:, None])
+    scored = np.where(covers, dz[:, None], np.inf)
+    winner = np.argmin(scored, axis=0)
+    valid = np.isfinite(scored[winner, np.arange(mid.size)])
+
+    rows: dict[int, list[float]] = {}
+    for j in np.flatnonzero(valid):
+        w = int(winner[j])
+        lo, hi = float(edges[j]), float(edges[j + 1])
+        if w in rows:
+            rows[w][0] = min(rows[w][0], lo)
+            rows[w][1] = max(rows[w][1], hi)
+        else:
+            rows[w] = [lo, hi]
+    if not rows:
+        return chords.clear()
+
+    keep = np.fromiter(rows.keys(), dtype=np.int64)
+    bounds = np.array([rows[int(i)] for i in keep], dtype=np.float64)
+    return chords[keep].with_columns(
+        pl.Series("d_near", bounds[:, 0], dtype=pl.Float32),
+        pl.Series("d_far", bounds[:, 1], dtype=pl.Float32),
+    )
+
+
+def _extend_lowest_sweep(chords: "pl.DataFrame", radar: str, base) -> "pl.DataFrame":
+    """Follow the lowest sweep past the range where every beam is above the slice.
+
+    The operational CAPPI convention (Stull §8.2): rather than leaving the far
+    field empty, keep reading the lowest elevation cone.  Those gates are drawn
+    at their full footprint, since they are no longer trimmed by the slice.
+    """
+    from raddb.lut import load_plane_nodes
+
+    lowest = int(chords["sweep"].min())
+    d_end = float(chords["d_far"].max())
+
+    nodes = load_plane_nodes(radar, base, "v_plane", sweep=lowest)
+    nodes = nodes.filter(
+        (pl.col("az_idx") == pl.col("az_idx").min()) & (pl.col("el_level") == -1)
+    ).sort("rng_idx")
+    d = nodes["d"].to_numpy()
+    if d.size < 2:
+        return chords
+
+    beyond = np.flatnonzero(d[1:] > d_end)
+    if beyond.size == 0:
+        return chords
+    extra = pl.DataFrame({
+        "sweep": np.full(beyond.size, lowest, dtype=np.int32),
+        "rng_idx": beyond.astype(np.int32),
+        "d_near": d[beyond].astype(np.float32),
+        "d_far": d[beyond + 1].astype(np.float32),
+        "z_center": np.zeros(beyond.size, dtype=np.float32),
+        "dz_center": np.zeros(beyond.size, dtype=np.float32),
+    })
+    existing = chords.filter(pl.col("sweep") == lowest)["rng_idx"].to_list()
+    return pl.concat(
+        [chords, extra.filter(~pl.col("rng_idx").is_in(existing))], how="vertical"
+    )
+
+
+def _trim_footprints_to_chord(verts, verts_xy, d_near, d_far):
+    """Shorten each h_plane footprint along the beam to ``[d_near, d_far]``.
+
+    ``verts`` is the ``(n, 4, 2)`` ring in the output frame; ``verts_xy`` is the
+    same ring in radar-relative metres, where ground distance is simply
+    ``hypot(x, y)`` — the frame-independent way to locate the cut.
+
+    Ring order is :data:`raddb.lut.GATE_RING_OFFSETS`: corners 1 and 4 sit on the
+    near range edge, corners 2 and 3 on the far one.  So the beam runs 1->2 along
+    one azimuth edge and 4->3 along the other, and trimming is a lerp along both.
+    """
+    d_ring = np.hypot(verts_xy[:, :, 0], verts_xy[:, :, 1])
+    d_bin_near = 0.5 * (d_ring[:, 0] + d_ring[:, 3])
+    d_bin_far = 0.5 * (d_ring[:, 1] + d_ring[:, 2])
+
+    span = d_bin_far - d_bin_near
+    with np.errstate(divide="ignore", invalid="ignore"):
+        t_near = np.where(span != 0, (d_near - d_bin_near) / span, 0.0)
+        t_far = np.where(span != 0, (d_far - d_bin_near) / span, 1.0)
+    t_near = np.clip(np.nan_to_num(t_near, nan=0.0), 0.0, 1.0)[:, None]
+    t_far = np.clip(np.nan_to_num(t_far, nan=1.0), 0.0, 1.0)[:, None]
+
+    c1, c2, c3, c4 = verts[:, 0], verts[:, 1], verts[:, 2], verts[:, 3]
+    return np.stack([
+        c1 + t_near * (c2 - c1),      # near edge, azimuth side A
+        c1 + t_far * (c2 - c1),       # far  edge, azimuth side A
+        c4 + t_far * (c3 - c4),       # far  edge, azimuth side B
+        c4 + t_near * (c3 - c4),      # near edge, azimuth side B
+    ], axis=1)
+
+
 # ============================================================================
 # LATENT SPACE SCATTER (AMT publication figure)
 # ============================================================================
diff --git a/tutorial/01_archiving.ipynb b/tutorial/01_archiving.ipynb
new file mode 100644
index 0000000..e89ee86
--- /dev/null
+++ b/tutorial/01_archiving.ipynb
@@ -0,0 +1,798 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "77188268",
+   "metadata": {},
+   "source": [
+    "# 1 — Archiving\n",
+    "\n",
+    "**RadDB** turns xarray **DataTree** radar volumes into a compact, queryable Parquet\n",
+    "archive. It is *network-agnostic*: any DataTree with the standard\n",
+    "[xradar](https://docs.openradarscience.org/projects/xradar/) coordinate layout —\n",
+    "MeteoSwiss, NEXRAD, OPERA — can be archived. No `pyart` is needed in the core.\n",
+    "\n",
+    "This notebook covers:\n",
+    "\n",
+    "1. Looking at what data you have, before archiving it\n",
+    "2. The **CRS contract** — the one thing you must get right\n",
+    "3. Archiving a volume\n",
+    "4. What lands on disk, and why it is laid out that way\n",
+    "\n",
+    "---\n",
+    "## How the archive is stored\n",
+    "\n",
+    "A radar is stored as **one static LUT** (per-gate geometry, computed once) plus\n",
+    "**one Parquet file per volume** (the moments), linked by an integer `gate_id`:\n",
+    "\n",
+    "```\n",
+    "{archive_dir}/{radar}/LUT/{radar}_LUT.parquet          # gate centroids\n",
+    "{archive_dir}/{radar}/LUT/{radar}_h_plane_LUT.parquet  # horizontal faces (PPI)\n",
+    "{archive_dir}/{radar}/LUT/{radar}_v_plane_LUT.parquet  # vertical faces  (RHI)\n",
+    "{archive_dir}/{radar}/LUT/{radar}_corners_LUT.parquet  # 3-D gate corners\n",
+    "{archive_dir}/{radar}/LUT/{radar}_info.yaml            # site, CRS, scan geometry\n",
+    "{archive_dir}/{radar}/{YYYY}/{MM}/{DD}/{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet\n",
+    "```\n",
+    "\n",
+    "The geometry is stored **once**, not once per volume — which is what keeps the\n",
+    "archive small. Gates with no echo are dropped at archive time (`DBZH > 0` by\n",
+    "default)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85737613",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:49.927018Z",
+     "iopub.status.busy": "2026-08-04T15:28:49.926856Z",
+     "iopub.status.idle": "2026-08-04T15:28:49.933972Z",
+     "shell.execute_reply": "2026-08-04T15:28:49.933400Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
+      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
+      "Archive         : /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — point these at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
+    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
+    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
+    "\n",
+    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
+    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
+    "\n",
+    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
+    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
+    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
+    "\n",
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "09525d30",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:49.935264Z",
+     "iopub.status.busy": "2026-08-04T15:28:49.935114Z",
+     "iopub.status.idle": "2026-08-04T15:28:50.540920Z",
+     "shell.execute_reply": "2026-08-04T15:28:50.540228Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raddb 0.1.dev5+gde6070734.d20260323\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "import raddb\n",
+    "\n",
+    "print(\"raddb\", raddb.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b4d6fb",
+   "metadata": {},
+   "source": [
+    "## 1. What do I have?\n",
+    "\n",
+    "`inventory()` answers \"what is on disk?\" for both sides of the workflow. Pointed at\n",
+    "a directory of DataTree files it reports the **input** side, grouping by the radar\n",
+    "name it reads from each filename prefix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ab4d193d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:50.542887Z",
+     "iopub.status.busy": "2026-08-04T15:28:50.542708Z",
+     "iopub.status.idle": "2026-08-04T15:28:50.641647Z",
+     "shell.execute_reply": "2026-08-04T15:28:50.640859Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================================================================\n",
+      "RadDB inventory — DataTree files on disk (not archived yet)\n",
+      "  directory : /data/RADAR/MCH_datatree\n",
+      "  files     : 2\n",
+      "  radars    : L, W  (from the filename prefix)\n",
+      "  time range: 2024-07-19 13:50:00 .. 2024-08-26 02:50:00\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar     files  time range                                           size\n",
+      "  L             1  2024-08-26 02:50:00                               20.9 MB\n",
+      "  W             1  2024-07-19 13:50:00                               21.5 MB\n",
+      "------------------------------------------------------------------------------\n",
+      "  archive with: db.archive(datatree_dir='/data/RADAR/MCH_datatree')\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = raddb.RadDB()\n",
+    "db.inventory(datatree_dir=MCH_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "720db410",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:50.643672Z",
+     "iopub.status.busy": "2026-08-04T15:28:50.643573Z",
+     "iopub.status.idle": "2026-08-04T15:28:50.678424Z",
+     "shell.execute_reply": "2026-08-04T15:28:50.677758Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================================================================\n",
+      "RadDB inventory — DataTree files on disk (not archived yet)\n",
+      "  directory : /data/RADAR/NEXRAD_datatree\n",
+      "  files     : 2\n",
+      "  radars    : KTLX  (from the filename prefix)\n",
+      "  time range: 2013-05-20 19:51:11 .. 2013-05-20 19:55:27\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar     files  time range                                           size\n",
+      "  KTLX          2  2013-05-20 19:51:11 .. 2013-05-20 19:55:27        14.6 MB\n",
+      "      2013-05-20      2 volume(s)  19:51:11 .. 19:55:27\n",
+      "------------------------------------------------------------------------------\n",
+      "  archive with: db.archive(datatree_dir='/data/RADAR/NEXRAD_datatree')\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# `detailed=True` adds a per-day breakdown and flags any radar name RadDB\n",
+    "# cannot use (names must be 1-4 characters from [0-9A-Z]).\n",
+    "db.inventory(datatree_dir=NEXRAD_DIR, detailed=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bce49641",
+   "metadata": {},
+   "source": [
+    "## 2. The CRS contract\n",
+    "\n",
+    "**A projection is mandatory to write an archive, and never needed to read one.**\n",
+    "\n",
+    "There is no default, because a wrong projection is *silently* wrong: EPSG:2056\n",
+    "(Swiss LV95) used outside Switzerland mis-measures distance by ~20%, and the\n",
+    "resulting crops still look perfectly normal.\n",
+    "\n",
+    "RadDB validates by **measurement, not by metadata**: it projects a 100 km geodesic\n",
+    "in eight directions around the radar site and compares against the truth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8b3b68c8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:50.679937Z",
+     "iopub.status.busy": "2026-08-04T15:28:50.679848Z",
+     "iopub.status.idle": "2026-08-04T15:28:50.682120Z",
+     "shell.execute_reply": "2026-08-04T15:28:50.681553Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CH   : 32632\n",
+      "Oklahoma, US  : 32614\n"
+     ]
+    }
+   ],
+   "source": [
+    "from raddb.lut import suggest_crs, crs_distance_error\n",
+    "\n",
+    "# suggest_crs returns the UTM zone for a site — a safe starting point anywhere.\n",
+    "print(\"CH   :\", suggest_crs(6.99, 46.84))\n",
+    "print(\"Oklahoma, US  :\", suggest_crs(-97.28, 35.33))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "57d2d426",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:50.683629Z",
+     "iopub.status.busy": "2026-08-04T15:28:50.683473Z",
+     "iopub.status.idle": "2026-08-04T15:28:50.723623Z",
+     "shell.execute_reply": "2026-08-04T15:28:50.723030Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  EPSG:2056   LV95 in Switzerland          0.01%   accepted\n",
+      "  EPSG:2056   LV95 in Oklahoma            20.07%   REFUSED\n",
+      "  EPSG:32614  UTM 14N in Oklahoma          0.03%   accepted\n",
+      "  EPSG:3857   Web Mercator in Switzerland  47.62%   REFUSED\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Why metadata is not enough. EPSG:3857 (Web Mercator) claims the whole world.\n",
+    "for epsg, site, where in [(2056, (6.99, 46.84), \"LV95 in Switzerland\"),\n",
+    "                          (2056, (-97.28, 35.33), \"LV95 in Oklahoma\"),\n",
+    "                          (32614, (-97.28, 35.33), \"UTM 14N in Oklahoma\"),\n",
+    "                          (3857, (6.99, 46.84), \"Web Mercator in Switzerland\")]:\n",
+    "    err = crs_distance_error(epsg, *site)\n",
+    "    verdict = \"REFUSED\" if err > 1.0 else (\"warn\" if err > 0.1 else \"accepted\")\n",
+    "    print(f\"  EPSG:{epsg:<6} {where:<26} {err:6.2f}%   {verdict}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0b06436",
+   "metadata": {},
+   "source": [
+    "A CRS that distorts by more than 1% is **refused**, and the error names a\n",
+    "replacement so you are never left guessing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "96984629",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:50.725251Z",
+     "iopub.status.busy": "2026-08-04T15:28:50.725153Z",
+     "iopub.status.idle": "2026-08-04T15:28:51.481127Z",
+     "shell.execute_reply": "2026-08-04T15:28:51.480720Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValueError: EPSG:2056 (CH1903+ / LV95), valid for Liechtenstein; Switzerland. distorts distance by 20.1% at radar KTLX (-97.2775, 35.3331) — gate geometry, crops and cross-sections would all be wrong by that much. Suggested for this site: EPSG:32614.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR / \"_bad\", crs=2056).archive(\n",
+    "        datatree=raddb.open_any_datatree(sorted(NEXRAD_DIR.glob(\"*.zarr\"))[0]),\n",
+    "        radar=\"KTLX\",\n",
+    "    )\n",
+    "except ValueError as exc:\n",
+    "    print(\"ValueError:\", exc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dc14099",
+   "metadata": {},
+   "source": [
+    "## 3. Archiving\n",
+    "\n",
+    "`archive()` takes either a directory of DataTree files or an in-memory DataTree.\n",
+    "The LUT is generated automatically from the first volume of each radar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "93714d58",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:51.482930Z",
+     "iopub.status.busy": "2026-08-04T15:28:51.482714Z",
+     "iopub.status.idle": "2026-08-04T15:28:59.589551Z",
+     "shell.execute_reply": "2026-08-04T15:28:59.588778Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /tmp/raddb_tutorial_archive\n",
+      "  crs         : 2056\n",
+      "  radars      : ['L', 'W']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 2 archived, 0 failed\n",
+      "  elapsed     : 8s\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'n_archived': 2, 'n_failed': 0, 'radars': ['L', 'W']}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)   # 2056 = CH1903+/LV95\n",
+    "result = db.archive(datatree_dir=MCH_DIR)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38d9ed54",
+   "metadata": {},
+   "source": [
+    "Because RadDB is network-agnostic, the same call archives NEXRAD — you only\n",
+    "change the projection to one valid where that radar actually is. Both radars live\n",
+    "side by side in the same archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1ccc396e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:28:59.591620Z",
+     "iopub.status.busy": "2026-08-04T15:28:59.591457Z",
+     "iopub.status.idle": "2026-08-04T15:29:23.356069Z",
+     "shell.execute_reply": "2026-08-04T15:29:23.355579Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /tmp/raddb_tutorial_archive\n",
+      "  crs         : 32614\n",
+      "  radars      : ['KTLX']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 2 archived, 0 failed\n",
+      "  elapsed     : 23s\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'n_archived': 2, 'n_failed': 0, 'radars': ['KTLX']}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db_us = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=32614)   # UTM zone 14N\n",
+    "db_us.archive(datatree_dir=NEXRAD_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e3be2c4",
+   "metadata": {},
+   "source": [
+    "### Archiving from memory\n",
+    "\n",
+    "If you already hold a DataTree — straight out of your own converter — skip the\n",
+    "disk round-trip:\n",
+    "\n",
+    "```python\n",
+    "dt = my_converter(raw_file)                 # -> xarray DataTree\n",
+    "db.archive(datatree=dt, radar=\"A\")\n",
+    "\n",
+    "db.archive(datatree=[dt1, dt2, dt3], radar=\"A\")        # several volumes\n",
+    "db.archive(datatree={\"A\": [dt_a], \"W\": [dt_w]})        # several radars\n",
+    "```\n",
+    "\n",
+    "`archive()` reports per-volume failures rather than raising, so one bad volume\n",
+    "never takes down a long batch. A rejected CRS is the exception — that aborts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "252d077b",
+   "metadata": {},
+   "source": [
+    "## 4. What landed on disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "78662044",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:23.358658Z",
+     "iopub.status.busy": "2026-08-04T15:29:23.358498Z",
+     "iopub.status.idle": "2026-08-04T15:29:23.364840Z",
+     "shell.execute_reply": "2026-08-04T15:29:23.363777Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "radars in the archive: ['KTLX', 'L', 'W']\n",
+      "==============================================================================\n",
+      "RadDB inventory — archived data\n",
+      "  archive_dir : /tmp/raddb_tutorial_archive\n",
+      "  radars      : KTLX, L, W\n",
+      "  volumes     : 4\n",
+      "  time range  : 2013-05-20 19:51:11 .. 2024-08-26 02:45:09\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar   volumes  time range                                           size\n",
+      "  KTLX          2  2013-05-20 19:51:11 .. 2013-05-20 19:55:27        17.0 MB\n",
+      "  L             1  2024-08-26 02:45:09                                5.4 MB\n",
+      "  W             1  2024-07-19 13:45:06                                5.1 MB\n",
+      "------------------------------------------------------------------------------\n",
+      "  load with   : db.open(radars=..., time_period=(start, end))\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "print(\"radars in the archive:\", db.list_radars())\n",
+    "db.inventory()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "755fe1f8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:23.366860Z",
+     "iopub.status.busy": "2026-08-04T15:29:23.366690Z",
+     "iopub.status.idle": "2026-08-04T15:29:23.370629Z",
+     "shell.execute_reply": "2026-08-04T15:29:23.369330Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  L_LUT.parquet                   63.01 MB\n",
+      "  L_corners_LUT.parquet           18.40 MB\n",
+      "  L_h_plane_LUT.parquet           19.78 MB\n",
+      "  L_info.yaml                      0.08 MB\n",
+      "  L_v_plane_LUT.parquet            0.40 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in sorted((ARCHIVE_DIR / \"L\" / \"LUT\").iterdir()):\n",
+    "    print(f\"  {p.name:<28} {p.stat().st_size / 1e6:8.2f} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86571224",
+   "metadata": {},
+   "source": [
+    "### The `gate_id` — how a volume finds its geometry\n",
+    "\n",
+    "One int64 per gate links a row of moments to its row of geometry:\n",
+    "\n",
+    "```\n",
+    "gate_id = radar_code * 10^12 + sweep * 10^10 + azimuth*10 * 10^6 + range_m\n",
+    "```\n",
+    "\n",
+    "It is decimal so you can read it by eye. `radar_code` is the base-36 value of the\n",
+    "zero-padded 4-character radar name (`\"L\"` → `000L` → 21, `\"KTLX\"` → 971493), which\n",
+    "allows **1,679,616 radars** and means an archive is self-describing — the radar\n",
+    "name can be recovered from the integers alone, with no registry file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3500a227",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:23.372678Z",
+     "iopub.status.busy": "2026-08-04T15:29:23.372422Z",
+     "iopub.status.idle": "2026-08-04T15:29:23.409182Z",
+     "shell.execute_reply": "2026-08-04T15:29:23.408185Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  A     -> code      10 -> A\n",
+      "  L     -> code      21 -> L\n",
+      "  KTLX  -> code  971493 -> KTLX\n",
+      "\n",
+      "LUT: (1724400, 13)\n",
+      "shape: (3, 7)\n",
+      "┌────────────────┬───────┬─────────┬─────────────┬───────────┬───────────┬─────────────┐\n",
+      "│ gate_id        ┆ sweep ┆ azimuth ┆ range       ┆ latitude  ┆ longitude ┆ altitude    │\n",
+      "│ ---            ┆ ---   ┆ ---     ┆ ---         ┆ ---       ┆ ---       ┆ ---         │\n",
+      "│ i64            ┆ i32   ┆ f64     ┆ f32         ┆ f64       ┆ f64       ┆ f64         │\n",
+      "╞════════════════╪═══════╪═════════╪═════════════╪═══════════╪═══════════╪═════════════╡\n",
+      "│ 21010005000249 ┆ 1     ┆ 0.5     ┆ 249.999008  ┆ 46.043008 ┆ 8.833245  ┆ 1625.164775 │\n",
+      "│ 21010005000749 ┆ 1     ┆ 0.5     ┆ 749.997009  ┆ 46.047505 ┆ 8.833301  ┆ 1623.516397 │\n",
+      "│ 21010005001249 ┆ 1     ┆ 0.5     ┆ 1249.994995 ┆ 46.052001 ┆ 8.833358  ┆ 1621.89745  │\n",
+      "└────────────────┴───────┴─────────┴─────────────┴───────────┴───────────┴─────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "from raddb import encode_radar_code, decode_radar_code, decode_gate_radars\n",
+    "\n",
+    "for name in [\"A\", \"L\", \"KTLX\"]:\n",
+    "    print(f\"  {name:<5} -> code {encode_radar_code(name):>7} -> {decode_radar_code(encode_radar_code(name))}\")\n",
+    "\n",
+    "lut = db.get_lut(\"L\")\n",
+    "print(\"\\nLUT:\", lut.shape)\n",
+    "print(lut.head(3).select([\"gate_id\", \"sweep\", \"azimuth\", \"range\", \"latitude\", \"longitude\", \"altitude\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2468d7de",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:23.411639Z",
+     "iopub.status.busy": "2026-08-04T15:29:23.411355Z",
+     "iopub.status.idle": "2026-08-04T15:29:23.703674Z",
+     "shell.execute_reply": "2026-08-04T15:29:23.701214Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['KTLX', 'L']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The radars a set of gate_ids spans, decoded from the integers alone\n",
+    "import polars as pl\n",
+    "\n",
+    "sample = pl.concat([db.get_lut(\"L\").head(2), db.get_lut(\"KTLX\").head(2)], how=\"diagonal\")\n",
+    "print(decode_gate_radars(sample[\"gate_id\"].to_numpy()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40878b2a",
+   "metadata": {},
+   "source": [
+    "### The site metadata\n",
+    "\n",
+    "`info.yaml` records everything needed to reconstruct the geometry — including the\n",
+    "CRS that was validated at archive time, and the radar's **scan strategy**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5efeff2e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:23.707229Z",
+     "iopub.status.busy": "2026-08-04T15:29:23.706817Z",
+     "iopub.status.idle": "2026-08-04T15:29:24.087549Z",
+     "shell.execute_reply": "2026-08-04T15:29:24.086365Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  radar            L\n",
+      "  latitude         46.0407600402832\n",
+      "  longitude        8.833216667175293\n",
+      "  altitude         1626.0\n",
+      "  crs              {'epsg': 2056, 'columns': ['x_2056', 'y_2056']}\n",
+      "  ke               1.3333333333333333\n",
+      "  beamwidth_deg    1.0\n",
+      "  n_sweeps         20\n",
+      "  n_gates          1724400\n",
+      "  gate_id_version  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "info = db.get_radar_info(\"L\")\n",
+    "for k in [\"radar\", \"latitude\", \"longitude\", \"altitude\", \"crs\", \"ke\",\n",
+    "          \"beamwidth_deg\", \"n_sweeps\", \"n_gates\", \"gate_id_version\"]:\n",
+    "    print(f\"  {k:<16} {info[k]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2889a184",
+   "metadata": {},
+   "source": [
+    "### One detail worth knowing: the nominal azimuth grid\n",
+    "\n",
+    "An antenna reports **where it actually pointed**, which drifts a few hundredths of\n",
+    "a degree every rotation. Since `gate_id` resolves azimuth to 0.1°, a drifting ray\n",
+    "would land in a different bin on every volume and its gates would match no LUT row.\n",
+    "\n",
+    "So the LUT stores the radar's *scan strategy* — `360 / n_rays` spacing at the\n",
+    "measured offset — and every volume's rays are snapped onto it. This is derived\n",
+    "per sweep from the ray count, so it gives 1.0° for Rad4Alp and 0.5° for NEXRAD\n",
+    "super-resolution sweeps automatically, with nothing to configure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "55e471ee",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:24.093451Z",
+     "iopub.status.busy": "2026-08-04T15:29:24.093250Z",
+     "iopub.status.idle": "2026-08-04T15:29:24.315243Z",
+     "shell.execute_reply": "2026-08-04T15:29:24.314505Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rays in sweep 1 : 360\n",
+      "azimuths (x10)  : [5, 15, 25, 35, 45, 55, 65, 75] ...\n",
+      "i.e. degrees    : [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5] ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "sweep1 = db.get_radar_info(\"L\")[\"sweeps\"][1]\n",
+    "print(\"rays in sweep 1 :\", sweep1[\"n_azimuths\"])\n",
+    "print(\"azimuths (x10)  :\", sweep1[\"azimuths\"][:8], \"...\")\n",
+    "print(\"i.e. degrees    :\", [a / 10 for a in sweep1[\"azimuths\"][:8]], \"...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "12f42099",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:24.317962Z",
+     "iopub.status.busy": "2026-08-04T15:29:24.317790Z",
+     "iopub.status.idle": "2026-08-04T15:29:24.387871Z",
+     "shell.execute_reply": "2026-08-04T15:29:24.386861Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "347,449 of 347,449 gates join the LUT  (100.0%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Every volume joins its LUT completely — nothing is silently dropped.\n",
+    "lut_ids = db.get_lut(\"L\").select(\"gate_id\")\n",
+    "pol = db.open(radars=\"L\").data\n",
+    "matched = pol.join(lut_ids, on=\"gate_id\", how=\"semi\").height\n",
+    "print(f\"{matched:,} of {pol.height:,} gates join the LUT  ({100 * matched / pol.height:.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "703eeff7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Recap\n",
+    "\n",
+    "```python\n",
+    "db = raddb.RadDB(archive_dir=..., crs=2056)   # CRS mandatory to write\n",
+    "db.inventory(datatree_dir=...)                # what do I have?\n",
+    "db.archive(datatree_dir=...)                  # or datatree=dt, radar=\"A\"\n",
+    "db.list_radars(); db.get_lut(\"L\"); db.get_radar_info(\"L\")\n",
+    "```\n",
+    "\n",
+    "**Next:** [2 — Opening and filtering](02_opening_and_filtering.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (radar)",
+   "language": "python",
+   "name": "radar"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial/02_opening_and_filtering.ipynb b/tutorial/02_opening_and_filtering.ipynb
new file mode 100644
index 0000000..ce9e1c3
--- /dev/null
+++ b/tutorial/02_opening_and_filtering.ipynb
@@ -0,0 +1,934 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "252c31b4",
+   "metadata": {},
+   "source": [
+    "# 2 — Opening a RadDB and filtering\n",
+    "\n",
+    "Tutorial 1 wrote an archive. This one reads it back and narrows it down.\n",
+    "\n",
+    "The key idea: **`RadDB` is one class with two roles.**\n",
+    "\n",
+    "| role | how you get it | what it does |\n",
+    "|---|---|---|\n",
+    "| *archive-bound* | `RadDB(archive_dir=...)` | `archive()`, `open()`, `inventory()`, LUT accessors |\n",
+    "| *data-carrying* | whatever `open()` returns | holds the gates; `filter`, `crop_*`, plots, converters |\n",
+    "\n",
+    "Every operation on a data-carrying RadDB returns a **new** RadDB, so calls chain\n",
+    "and nothing is ever mutated underneath you.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "36dbad7c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:25.739548Z",
+     "iopub.status.busy": "2026-08-04T15:29:25.739195Z",
+     "iopub.status.idle": "2026-08-04T15:29:25.750030Z",
+     "shell.execute_reply": "2026-08-04T15:29:25.748863Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
+      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
+      "Archive         : /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — point these at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
+    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
+    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
+    "\n",
+    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
+    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
+    "\n",
+    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
+    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
+    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
+    "\n",
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a4f11595",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:25.752631Z",
+     "iopub.status.busy": "2026-08-04T15:29:25.752397Z",
+     "iopub.status.idle": "2026-08-04T15:29:26.623119Z",
+     "shell.execute_reply": "2026-08-04T15:29:26.621966Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "import polars as pl\n",
+    "import raddb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "60595d47",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:26.625479Z",
+     "iopub.status.busy": "2026-08-04T15:29:26.625163Z",
+     "iopub.status.idle": "2026-08-04T15:29:26.629019Z",
+     "shell.execute_reply": "2026-08-04T15:29:26.628250Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive already present: /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
+    "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
+    "    print(\"building the archive (see tutorial 1) ...\")\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
+    "else:\n",
+    "    print(\"archive already present:\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e307b491",
+   "metadata": {},
+   "source": [
+    "## 1. `open()` — reading the archive\n",
+    "\n",
+    "Reading never needs a CRS: it is recovered from the archive itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62430b24",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:26.630985Z",
+     "iopub.status.busy": "2026-08-04T15:29:26.630813Z",
+     "iopub.status.idle": "2026-08-04T15:29:26.679757Z",
+     "shell.execute_reply": "2026-08-04T15:29:26.679100Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RadDB [347,449 gates]\n",
+       "  radars     : ['L']\n",
+       "  time range : 2024-08-26 02:45:09+00:00 .. 2024-08-26 02:45:09+00:00\n",
+       "  columns    : gate_id:Int64, time:Datetime(time_unit='ns', time_zone=None), DBZH:Float32, DBZH_raw:Float32, ZDR:Float32, ZDR_raw:Float32, KDP:Float32, RHOHV:Float32, PHIDP:Float32, HC_MCH:Float32, HC_PYART:Float32, HZT:Float32 (+3 more)\n",
+       "  archive_dir: /tmp/raddb_tutorial_archive"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)      # no crs= needed to read\n",
+    "rdf = db.open(radars=\"L\")\n",
+    "rdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "419283d4",
+   "metadata": {},
+   "source": [
+    "`open()` narrows *before* anything is loaded — the time range, the radars and the\n",
+    "columns are all pushed down into the Parquet scan, so you never pay for data you\n",
+    "did not ask for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e8856737",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:26.681841Z",
+     "iopub.status.busy": "2026-08-04T15:29:26.681676Z",
+     "iopub.status.idle": "2026-08-04T15:29:26.725685Z",
+     "shell.execute_reply": "2026-08-04T15:29:26.724865Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "columns: ['gate_id', 'DBZH', 'ZDR', 'volume_time', 'radar']\n",
+      "gates in the period: 347,449\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only two moments, only radar L\n",
+    "small = db.open(radars=\"L\", columns=[\"DBZH\", \"ZDR\"])\n",
+    "print(\"columns:\", small.columns())\n",
+    "\n",
+    "# A time period — any pandas-parseable pair, or a single day\n",
+    "day = db.open(radars=\"L\", time_period=(\"2024-08-26\", \"2024-08-27\"))\n",
+    "print(\"gates in the period:\", f\"{len(day):,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "320155b6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:26.727695Z",
+     "iopub.status.busy": "2026-08-04T15:29:26.727532Z",
+     "iopub.status.idle": "2026-08-04T15:29:26.742001Z",
+     "shell.execute_reply": "2026-08-04T15:29:26.741167Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "347,449 gates -> 123,008 with DBZH > 30 dBZ\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filters can be pushed down at open() too, so filtered-out rows are never materialised\n",
+    "strong = db.open(radars=\"L\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
+    "print(f\"{len(rdf):,} gates -> {len(strong):,} with DBZH > 30 dBZ\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1909d18e",
+   "metadata": {},
+   "source": [
+    "## 2. What you are holding\n",
+    "\n",
+    "The data lives in `.data` as a **polars** DataFrame. polars is the backend\n",
+    "throughout RadDB — the read path, the LUT, the archive writer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "37d4e073",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:26.744024Z",
+     "iopub.status.busy": "2026-08-04T15:29:26.743858Z",
+     "iopub.status.idle": "2026-08-04T15:29:26.749342Z",
+     "shell.execute_reply": "2026-08-04T15:29:26.748506Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type: DataFrame\n",
+      "shape: (347449, 15)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>time</th><th>DBZH</th><th>DBZH_raw</th><th>ZDR</th><th>ZDR_raw</th><th>KDP</th><th>RHOHV</th><th>PHIDP</th><th>HC_MCH</th><th>HC_PYART</th><th>HZT</th><th>TEMP</th><th>volume_time</th><th>radar</th></tr><tr><td>i64</td><td>datetime[ns]</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>datetime[μs, UTC]</td><td>str</td></tr></thead><tbody><tr><td>21010005000249</td><td>2024-08-26 02:46:08.050</td><td>0.5</td><td>0.5</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4.0</td><td>3891.666748</td><td>14.733334</td><td>2024-08-26 02:45:09 UTC</td><td>&quot;L&quot;</td></tr><tr><td>21010005000749</td><td>2024-08-26 02:46:08.050</td><td>11.0</td><td>11.0</td><td>-2.821272</td><td>-2.82151</td><td>NaN</td><td>0.88415</td><td>-26.849052</td><td>NaN</td><td>4.0</td><td>3891.666748</td><td>14.746333</td><td>2024-08-26 02:45:09 UTC</td><td>&quot;L&quot;</td></tr><tr><td>21010005001249</td><td>2024-08-26 02:46:08.050</td><td>17.0</td><td>17.0</td><td>1.830259</td><td>1.82939</td><td>NaN</td><td>0.908594</td><td>-16.505135</td><td>NaN</td><td>4.0</td><td>3891.666748</td><td>14.752833</td><td>2024-08-26 02:45:09 UTC</td><td>&quot;L&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3, 15)\n",
+       "┌──────────────┬─────────────┬──────┬──────────┬───┬─────────────┬───────────┬─────────────┬───────┐\n",
+       "│ gate_id      ┆ time        ┆ DBZH ┆ DBZH_raw ┆ … ┆ HZT         ┆ TEMP      ┆ volume_time ┆ radar │\n",
+       "│ ---          ┆ ---         ┆ ---  ┆ ---      ┆   ┆ ---         ┆ ---       ┆ ---         ┆ ---   │\n",
+       "│ i64          ┆ datetime[ns ┆ f32  ┆ f32      ┆   ┆ f32         ┆ f32       ┆ datetime[μs ┆ str   │\n",
+       "│              ┆ ]           ┆      ┆          ┆   ┆             ┆           ┆ , UTC]      ┆       │\n",
+       "╞══════════════╪═════════════╪══════╪══════════╪═══╪═════════════╪═══════════╪═════════════╪═══════╡\n",
+       "│ 210100050002 ┆ 2024-08-26  ┆ 0.5  ┆ 0.5      ┆ … ┆ 3891.666748 ┆ 14.733334 ┆ 2024-08-26  ┆ L     │\n",
+       "│ 49           ┆ 02:46:08.05 ┆      ┆          ┆   ┆             ┆           ┆ 02:45:09    ┆       │\n",
+       "│              ┆ 0           ┆      ┆          ┆   ┆             ┆           ┆ UTC         ┆       │\n",
+       "│ 210100050007 ┆ 2024-08-26  ┆ 11.0 ┆ 11.0     ┆ … ┆ 3891.666748 ┆ 14.746333 ┆ 2024-08-26  ┆ L     │\n",
+       "│ 49           ┆ 02:46:08.05 ┆      ┆          ┆   ┆             ┆           ┆ 02:45:09    ┆       │\n",
+       "│              ┆ 0           ┆      ┆          ┆   ┆             ┆           ┆ UTC         ┆       │\n",
+       "│ 210100050012 ┆ 2024-08-26  ┆ 17.0 ┆ 17.0     ┆ … ┆ 3891.666748 ┆ 14.752833 ┆ 2024-08-26  ┆ L     │\n",
+       "│ 49           ┆ 02:46:08.05 ┆      ┆          ┆   ┆             ┆           ┆ 02:45:09    ┆       │\n",
+       "│              ┆ 0           ┆      ┆          ┆   ┆             ┆           ┆ UTC         ┆       │\n",
+       "└──────────────┴─────────────┴──────┴──────────┴───┴─────────────┴───────────┴─────────────┴───────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"type:\", type(rdf.data).__name__)\n",
+    "print(\"shape:\", rdf.data.shape)\n",
+    "rdf.data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6fd88dae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:26.751397Z",
+     "iopub.status.busy": "2026-08-04T15:29:26.751219Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.275706Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.274707Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "radars    : ['L']\n",
+      "variables : ['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar']\n",
+      "time range: 2024-08-26 02:45:09+00:00 -> 2024-08-26 02:45:09+00:00\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lon/lat    : [7.31, 11.416, 44.408, 47.314]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive CRS: EPSG:2056\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"radars    :\", rdf.radars())\n",
+    "print(\"variables :\", rdf.columns())\n",
+    "print(\"time range:\", rdf.start_time(), \"->\", rdf.end_time())\n",
+    "print(\"lon/lat    :\", [round(v, 3) for v in rdf.geographic_extent()])\n",
+    "print(\"archive CRS:\", rdf.crs())          # recovered from the archive itself"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d777f8",
+   "metadata": {},
+   "source": [
+    "`geographic_extent()` always works. Its projected counterpart `extent()` returns\n",
+    "the bounding box in the archive's own CRS, but needs that CRS stated on the object:\n",
+    "\n",
+    "```python\n",
+    "raddb.RadDB(archive_dir=..., crs=2056).open(radars=\"L\").extent()\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57b6ce27",
+   "metadata": {},
+   "source": [
+    "## 3. `filter()` — threshold on values\n",
+    "\n",
+    "A filter is a plain dict: `{\"var\", \"logic\", \"threshold\"}`, where `logic` is one of\n",
+    "`==  !=  >  >=  <  <=`. A **list** of dicts is ANDed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "650f8db6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.277794Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.277625Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.288743Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.287857Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DBZH > 20        : 204,833 gates\n",
+      "+ RHOHV >= 0.9, ZDR < 4 : 196,414 gates\n"
+     ]
+    }
+   ],
+   "source": [
+    "rain = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
+    "print(f\"DBZH > 20        : {len(rain):,} gates\")\n",
+    "\n",
+    "# Several conditions at once — meteorological echo, not clutter\n",
+    "clean = rdf.filter([\n",
+    "    {\"var\": \"DBZH\",  \"logic\": \">\",  \"threshold\": 20},\n",
+    "    {\"var\": \"RHOHV\", \"logic\": \">=\", \"threshold\": 0.9},\n",
+    "    {\"var\": \"ZDR\",   \"logic\": \"<\",  \"threshold\": 4},\n",
+    "])\n",
+    "print(f\"+ RHOHV >= 0.9, ZDR < 4 : {len(clean):,} gates\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b244e5ec",
+   "metadata": {},
+   "source": [
+    "## 4. `sel()` — select by label, xarray-style\n",
+    "\n",
+    "Where `filter()` thresholds *values*, `sel()` selects by **coordinate**: a time, a\n",
+    "sweep, a range window, a longitude/latitude box. Scalars match exactly, `slice`\n",
+    "gives a closed interval, and a list matches any of its members."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3aec88ab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.291431Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.291136Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.639891Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.639134Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "one sweep      : 11,629\n",
+      "sweeps 1,2,3   : 42,036\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "range 10-50 km : 184,299\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a lon/lat box  : 192,726\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"one sweep      :\", f\"{len(rdf.sel(sweep=1)):,}\")\n",
+    "print(\"sweeps 1,2,3   :\", f\"{len(rdf.sel(sweep=[1, 2, 3])):,}\")\n",
+    "print(\"range 10-50 km :\", f\"{len(rdf.sel(range=slice(10_000, 50_000))):,}\")\n",
+    "print(\"a lon/lat box  :\", f\"{len(rdf.sel(lon=slice(8.6, 9.0), lat=slice(46.0, 46.4))):,}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e10ec065",
+   "metadata": {},
+   "source": [
+    "The clever part: `range`, `azimuth`, `elevation_angle`, `latitude`, `longitude`\n",
+    "and `altitude` are **not stored in the Parquet files** — they live once in the LUT.\n",
+    "`sel()` borrows the column it needs, evaluates the selection, and drops it again,\n",
+    "so selecting on geometry costs no storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "17a5d6ed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.642823Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.642613Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.742139Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.741237Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stored per gate: ['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar']\n",
+      "also selectable : ['range', 'azimuth', 'elevation_angle', 'latitude', 'longitude', 'altitude', 'sweep']\n",
+      "\n",
+      "sweep 1, 20-60 km: 2,374 gates (columns unchanged: True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"stored per gate:\", rdf.columns())\n",
+    "print(\"also selectable :\", [\"range\", \"azimuth\", \"elevation_angle\",\n",
+    "                            \"latitude\", \"longitude\", \"altitude\", \"sweep\"])\n",
+    "\n",
+    "narrow = rdf.sel(sweep=1, range=slice(20_000, 60_000))\n",
+    "print(f\"\\nsweep 1, 20-60 km: {len(narrow):,} gates \"\n",
+    "      f\"(columns unchanged: {narrow.columns() == rdf.columns()})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d37c61ef",
+   "metadata": {},
+   "source": [
+    "## 5. Chaining, and immutability\n",
+    "\n",
+    "Every call returns a new object, so a pipeline reads top to bottom and the original\n",
+    "is untouched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cbf3e516",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.745244Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.745048Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.865179Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.863554Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original : 347,449 gates\n",
+      "pipeline : 24,453 gates\n",
+      "original still intact: 347,449\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipeline = (\n",
+    "    db.open(radars=\"L\")\n",
+    "      .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 15})\n",
+    "      .sel(sweep=[1, 2, 3])\n",
+    "      .sel(range=slice(5_000, 80_000))\n",
+    ")\n",
+    "print(f\"original : {len(rdf):,} gates\")\n",
+    "print(f\"pipeline : {len(pipeline):,} gates\")\n",
+    "print(f\"original still intact: {len(rdf):,}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d72ae0a4",
+   "metadata": {},
+   "source": [
+    "## 6. Computed columns\n",
+    "\n",
+    "`add_feature()` adds a column derived from the ones you already have and returns a\n",
+    "new RadDB, so it drops straight into a pipeline. The function receives the polars\n",
+    "frame; return a Series, a numpy array, or a polars expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ecec9579",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.868487Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.868212Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.881597Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.880531Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>DBZH</th><th>ZDR</th><th>ZDR_lin</th><th>DBZH_dev</th></tr><tr><td>f32</td><td>f32</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>0.5</td><td>NaN</td><td>NaN</td><td>-24.828373</td></tr><tr><td>11.0</td><td>-2.821272</td><td>0.522243</td><td>-14.328373</td></tr><tr><td>17.0</td><td>1.830259</td><td>1.524144</td><td>-8.328373</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3, 4)\n",
+       "┌──────┬───────────┬──────────┬────────────┐\n",
+       "│ DBZH ┆ ZDR       ┆ ZDR_lin  ┆ DBZH_dev   │\n",
+       "│ ---  ┆ ---       ┆ ---      ┆ ---        │\n",
+       "│ f32  ┆ f32       ┆ f32      ┆ f32        │\n",
+       "╞══════╪═══════════╪══════════╪════════════╡\n",
+       "│ 0.5  ┆ NaN       ┆ NaN      ┆ -24.828373 │\n",
+       "│ 11.0 ┆ -2.821272 ┆ 0.522243 ┆ -14.328373 │\n",
+       "│ 17.0 ┆ 1.830259  ┆ 1.524144 ┆ -8.328373  │\n",
+       "└──────┴───────────┴──────────┴────────────┘"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "derived = (\n",
+    "    rdf.add_feature(\"ZDR_lin\", lambda df: 10 ** (df[\"ZDR\"] / 10))\n",
+    "       .add_feature(\"DBZH_dev\", lambda df: df[\"DBZH\"] - df[\"DBZH\"].mean())\n",
+    ")\n",
+    "derived.data.select([\"DBZH\", \"ZDR\", \"ZDR_lin\", \"DBZH_dev\"]).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c0315859",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.884039Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.883841Z",
+     "iopub.status.idle": "2026-08-04T15:29:27.888672Z",
+     "shell.execute_reply": "2026-08-04T15:29:27.887727Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14,476 gates with ZDR_lin > 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# It behaves like any other column from here on — filter it, plot it, export it.\n",
+    "print(f\"{len(derived.filter({'var': 'ZDR_lin', 'logic': '>', 'threshold': 2})):,} gates with ZDR_lin > 2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f37ef3f",
+   "metadata": {},
+   "source": [
+    "If you would rather work in plain polars or pandas, nothing stops you — `.data`\n",
+    "is an ordinary polars frame, and `to_pandas()` gives an ordinary pandas one:\n",
+    "\n",
+    "```python\n",
+    "import polars as pl\n",
+    "rdf.data.with_columns((pl.col(\"DBZH\") - pl.col(\"ZDR\")).alias(\"DIFF\"))\n",
+    "\n",
+    "df = rdf.to_pandas()\n",
+    "df[\"DIFF\"] = df[\"DBZH\"] - df[\"ZDR\"]\n",
+    "```\n",
+    "\n",
+    "The RadDB helpers exist so the result stays a RadDB and keeps chaining."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d77a02f8",
+   "metadata": {},
+   "source": [
+    "## 7. Getting the data out\n",
+    "\n",
+    "Three converters, for three different jobs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4dcf0de3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:27.890936Z",
+     "iopub.status.busy": "2026-08-04T15:29:27.890689Z",
+     "iopub.status.idle": "2026-08-04T15:29:28.190809Z",
+     "shell.execute_reply": "2026-08-04T15:29:28.189853Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "to_pandas : (204833, 19)\n",
+      "['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar', 'latitude', 'longitude', 'altitude', 'sweep']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. pandas — with_geometry merges the per-gate coordinates from the LUT\n",
+    "df = rain.to_pandas(with_geometry=True)\n",
+    "print(\"to_pandas :\", df.shape)\n",
+    "print(list(df.columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "67d3b517",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:28.193945Z",
+     "iopub.status.busy": "2026-08-04T15:29:28.193643Z",
+     "iopub.status.idle": "2026-08-04T15:29:28.504326Z",
+     "shell.execute_reply": "2026-08-04T15:29:28.502652Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar', 'latitude', 'longitude', 'altitude', 'sweep', 'range', 'azimuth', 'elevation_angle']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# with_polar_coords adds range / azimuth / elevation_angle as well.  Off by\n",
+    "# default because they duplicate what the Cartesian columns already say.\n",
+    "print(list(rain.to_pandas(with_polar_coords=True).columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "abc6973e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:28.506720Z",
+     "iopub.status.busy": "2026-08-04T15:29:28.506468Z",
+     "iopub.status.idle": "2026-08-04T15:29:29.035884Z",
+     "shell.execute_reply": "2026-08-04T15:29:29.034903Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "to_geopandas: (204833, 20) | CRS: EPSG:4326\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gate_id</th>\n",
+       "      <th>DBZH</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21010005002249</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>POINT (8.83347 46.06099)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21010005005249</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>POINT (8.83381 46.08797)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21010005005749</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>POINT (8.83387 46.09247)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          gate_id  DBZH                  geometry\n",
+       "0  21010005002249  22.0  POINT (8.83347 46.06099)\n",
+       "1  21010005005249  21.0  POINT (8.83381 46.08797)\n",
+       "2  21010005005749  27.0  POINT (8.83387 46.09247)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2. geopandas — point geometry per gate, ready for spatial joins or QGIS\n",
+    "gdf = rain.to_geopandas()\n",
+    "print(\"to_geopandas:\", gdf.shape, \"| CRS:\", gdf.crs)\n",
+    "gdf[[\"gate_id\", \"DBZH\", \"geometry\"]].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1280e7ee",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:29:29.038372Z",
+     "iopub.status.busy": "2026-08-04T15:29:29.038172Z",
+     "iopub.status.idle": "2026-08-04T15:29:30.154151Z",
+     "shell.execute_reply": "2026-08-04T15:29:30.153558Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataTree>\n",
+      "Group: /\n",
+      "└── Group: /sweep_1\n",
+      "        Dimensions:          (azimuth: 360, range: 492)\n",
+      "        Coordinates: (12/15)\n",
+      "          * azimuth          (azimuth) float64 3kB 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5\n",
+      "          * range            (range) float32 2kB 250.0 750.0 ... 2.452e+05 2.457e+05\n",
+      "            latitude         (azimuth, range) float64 1MB 46.04 46.05 ... 48.25 48.25\n",
+      "            longitude        (azimuth, range) float64 1MB 8.833 8.833 ... 8.805 8.805\n",
+      "            altitude         (azimuth, range) float64 1MB 1.625e+03 ... 4.356e+03\n",
+      "            x                (azimuth, range) float64 1MB 2.182 6.545 ... -2.144e+03\n",
+      "            ...               ...\n",
+      "            y_2056           (azimuth, range) float64 1MB 1.1e+06 ... 1.345e+06\n",
+      "            site_latitude    float64 8B 46.04\n",
+      "            site_longitude   float64 8B 8.833\n",
+      "            site_altitude    float64 8B 1.626e+03\n",
+      "            sweep_number     int64 8B 1\n",
+      "            elevation_angle  float64 8B -0.19\n",
+      "        Data variables:\n",
+      "            time             (azimuth, range) datetime64[ns] 1MB NaT NaT NaT ... NaT NaT\n",
+      "            DBZH             (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            DBZH_raw         (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            ZDR              (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            ZDR_raw          (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            KDP              (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            RHOHV            (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            PHIDP            (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            HC_MCH           (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            HC_PYART         (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            HZT              (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
+      "            TEMP             (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3. back to a DataTree — the full polar structure, for xarray workflows\n",
+    "dt = rain.sel(sweep=1).to_datatree()\n",
+    "print(dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2a1ee7a",
+   "metadata": {},
+   "source": [
+    "`to_datatree()` reindexes onto the complete azimuth x range grid, so gates you\n",
+    "filtered out come back as NaN. That is what makes it round-trippable, but it also\n",
+    "makes it much heavier than the other two — prefer `to_pandas` / `to_geopandas`\n",
+    "unless you specifically need xarray."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c46973e6",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Recap\n",
+    "\n",
+    "```python\n",
+    "db  = raddb.RadDB(archive_dir=...)               # reading needs no CRS\n",
+    "rdf = db.open(radars=\"L\", time_period=(...), columns=[...], filters=...)\n",
+    "\n",
+    "rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})   # by value\n",
+    "rdf.sel(sweep=1, range=slice(10_000, 50_000))                # by label\n",
+    "rdf.add_feature(\"ZDR_lin\", lambda df: 10 ** (df[\"ZDR\"] / 10))\n",
+    "\n",
+    "rdf.to_pandas(with_geometry=True); rdf.to_geopandas(); rdf.to_datatree()\n",
+    "```\n",
+    "\n",
+    "**Next:** [3 — Areas of interest](03_area_of_interest.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial/03_area_of_interest.ipynb b/tutorial/03_area_of_interest.ipynb
new file mode 100644
index 0000000..4fbc5f7
--- /dev/null
+++ b/tutorial/03_area_of_interest.ipynb
@@ -0,0 +1,747 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b8d0cd89",
+   "metadata": {},
+   "source": [
+    "# 3 — Areas of interest\n",
+    "\n",
+    "Tutorial 2 narrowed the data by *value* and by *label*. This one narrows it by\n",
+    "**geography**: a box, a circle, a polygon, or a vertical slice along a line.\n",
+    "\n",
+    "| method | AOI | typical use |\n",
+    "|---|---|---|\n",
+    "| `crop_by_bbox` | rectangle | a map tile, a model domain |\n",
+    "| `crop_around_point` | circle | everything within N km of a place |\n",
+    "| `crop_by_polygone` | any polygon | a catchment, a canton, a shapefile |\n",
+    "| `extract_cross_section` | a line + beam width | a vertical slice through a storm |\n",
+    "\n",
+    "The first three keep the gates and drop the rest. The fourth also computes, for\n",
+    "every selected gate, its position **along the line** and its **altitude** — the\n",
+    "geometry a vertical cross-section plot needs.\n",
+    "\n",
+    "---\n",
+    "## The one rule: which coordinates am I in?\n",
+    "\n",
+    "Every AOI runs in the **archive's own CRS**, read from `info.yaml`. You never have\n",
+    "to restate it. What you *do* have to say is which CRS **your own** coordinates are\n",
+    "in, with `crs=`:\n",
+    "\n",
+    "```python\n",
+    "rdf.crop_around_point(point=(8.83, 46.04), distance=30_000, crs=4326)   # lon/lat\n",
+    "rdf.crop_around_point(point=(2680000, 1120000), distance=30_000)        # already LV95\n",
+    "```\n",
+    "\n",
+    "Get this wrong and the crop is silently empty or in the wrong country — passing\n",
+    "lon/lat degrees while RadDB reads them as metres puts your AOI ~2600 km away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "78499521",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:08.470297Z",
+     "iopub.status.busy": "2026-08-04T15:32:08.470192Z",
+     "iopub.status.idle": "2026-08-04T15:32:08.475526Z",
+     "shell.execute_reply": "2026-08-04T15:32:08.474948Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
+      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
+      "Archive         : /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — point these at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
+    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
+    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
+    "\n",
+    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
+    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
+    "\n",
+    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
+    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
+    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
+    "\n",
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9d7152f3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:08.477004Z",
+     "iopub.status.busy": "2026-08-04T15:32:08.476909Z",
+     "iopub.status.idle": "2026-08-04T15:32:09.080060Z",
+     "shell.execute_reply": "2026-08-04T15:32:09.079247Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import shapely\n",
+    "import raddb\n",
+    "\n",
+    "# Keep the embedded figures small enough for GitHub to render this notebook.\n",
+    "plt.rcParams[\"figure.dpi\"] = 70"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bb505873",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:09.081820Z",
+     "iopub.status.busy": "2026-08-04T15:32:09.081650Z",
+     "iopub.status.idle": "2026-08-04T15:32:09.084140Z",
+     "shell.execute_reply": "2026-08-04T15:32:09.083549Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive already present: /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
+    "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
+    "    print(\"building the archive (see tutorial 1) ...\")\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
+    "else:\n",
+    "    print(\"archive already present:\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6e5330be",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:09.085669Z",
+     "iopub.status.busy": "2026-08-04T15:32:09.085589Z",
+     "iopub.status.idle": "2026-08-04T15:32:09.394435Z",
+     "shell.execute_reply": "2026-08-04T15:32:09.393788Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "radar L at 8.833, 46.041   |   326,730 gates with echo\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive CRS: EPSG:2056\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "rdf = db.open(radars=\"L\").filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 5})\n",
+    "\n",
+    "info = db.get_radar_info(\"L\")\n",
+    "SITE = (info[\"longitude\"], info[\"latitude\"])          # lon, lat\n",
+    "print(f\"radar L at {SITE[0]:.3f}, {SITE[1]:.3f}   |   {len(rdf):,} gates with echo\")\n",
+    "print(\"archive CRS:\", rdf.crs())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef9ad06a",
+   "metadata": {},
+   "source": [
+    "## 1. `crop_by_bbox` — a rectangle\n",
+    "\n",
+    "Pass either `bounds=(minx, miny, maxx, maxy)` or `extent=(minx, maxx, miny, maxy)`\n",
+    "— the latter matches matplotlib's `ax.axis()` ordering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "916e585b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:09.395848Z",
+     "iopub.status.busy": "2026-08-04T15:32:09.395723Z",
+     "iopub.status.idle": "2026-08-04T15:32:10.453450Z",
+     "shell.execute_reply": "2026-08-04T15:32:10.452752Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "326,730 -> 286,445 gates inside the lon/lat box\n",
+      "lon/lat extent of the result: [8.4, 9.3, 45.8, 46.501]\n"
+     ]
+    }
+   ],
+   "source": [
+    "box = rdf.crop_by_bbox(bounds=(8.4, 45.8, 9.3, 46.5), crs=4326)\n",
+    "print(f\"{len(rdf):,} -> {len(box):,} gates inside the lon/lat box\")\n",
+    "print(\"lon/lat extent of the result:\", [round(v, 3) for v in box.geographic_extent()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c03a308",
+   "metadata": {},
+   "source": [
+    "## 2. `crop_around_point` — everything within N km\n",
+    "\n",
+    "`distance` is in **metres**, measured in the archive's projection — so it is a true\n",
+    "ground distance, not a coordinate difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7e09c34",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:10.455251Z",
+     "iopub.status.busy": "2026-08-04T15:32:10.455121Z",
+     "iopub.status.idle": "2026-08-04T15:32:13.778327Z",
+     "shell.execute_reply": "2026-08-04T15:32:13.777588Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  within  20 km :  208,149 gates\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  within  50 km :  288,718 gates\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  within 100 km :  321,298 gates\n"
+     ]
+    }
+   ],
+   "source": [
+    "for km in (20, 50, 100):\n",
+    "    sub = rdf.crop_around_point(point=SITE, distance=km * 1_000, crs=4326)\n",
+    "    print(f\"  within {km:>3} km : {len(sub):>8,} gates\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ce861e79",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:13.780238Z",
+     "iopub.status.busy": "2026-08-04T15:32:13.780077Z",
+     "iopub.status.idle": "2026-08-04T15:32:14.547259Z",
+     "shell.execute_reply": "2026-08-04T15:32:14.546504Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25 km around (9.0, 46.2): 186,802 gates\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Any point, not only the radar itself\n",
+    "elsewhere = rdf.crop_around_point(point=(9.0, 46.2), distance=25_000, crs=4326)\n",
+    "print(f\"25 km around (9.0, 46.2): {len(elsewhere):,} gates\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "773936a3",
+   "metadata": {},
+   "source": [
+    "## 3. `crop_by_polygone` — an arbitrary shape\n",
+    "\n",
+    "Accepts a shapely `Polygon`/`MultiPolygon`, a GeoDataFrame, or a path to a\n",
+    "`.shp` / `.geojson` file. A file's **declared CRS wins** unless you pass `crs=`\n",
+    "explicitly — a GeoJSON is lon/lat by RFC 7946, so this usually just works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6a912757",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:14.548940Z",
+     "iopub.status.busy": "2026-08-04T15:32:14.548780Z",
+     "iopub.status.idle": "2026-08-04T15:32:15.437576Z",
+     "shell.execute_reply": "2026-08-04T15:32:15.436765Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inside the triangle: 209,103 gates\n"
+     ]
+    }
+   ],
+   "source": [
+    "triangle = shapely.Polygon([(8.6, 45.9), (9.2, 46.1), (8.8, 46.5)])\n",
+    "poly = rdf.crop_by_polygone(polygon=triangle, crs=4326)\n",
+    "print(f\"inside the triangle: {len(poly):,} gates\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "31813aca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:15.439783Z",
+     "iopub.status.busy": "2026-08-04T15:32:15.439600Z",
+     "iopub.status.idle": "2026-08-04T15:32:16.398037Z",
+     "shell.execute_reply": "2026-08-04T15:32:16.397328Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "from GeoJSON       : 209,103 gates  (same: True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The same thing from a file on disk\n",
+    "import json\n",
+    "\n",
+    "geojson_path = ARCHIVE_DIR / \"aoi_demo.geojson\"\n",
+    "geojson_path.write_text(json.dumps({\n",
+    "    \"type\": \"FeatureCollection\",\n",
+    "    \"features\": [{\"type\": \"Feature\", \"properties\": {},\n",
+    "                  \"geometry\": shapely.geometry.mapping(triangle)}],\n",
+    "}))\n",
+    "\n",
+    "from_file = rdf.crop_by_polygone(polygon=geojson_path)   # CRS taken from the file\n",
+    "print(f\"from GeoJSON       : {len(from_file):,} gates  (same: {len(from_file) == len(poly)})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25e97fff",
+   "metadata": {},
+   "source": [
+    "## 4. `extract_cross_section` — a vertical slice\n",
+    "\n",
+    "A line `p1 -> p2` plus the beam width defines a vertical curtain. Every gate whose\n",
+    "beam intersects it is kept, and gains the geometry of the section:\n",
+    "\n",
+    "| column | meaning |\n",
+    "|---|---|\n",
+    "| `d_near`, `d_far`, `d_center` | distance **along the line** from `p1` [m] |\n",
+    "| `z_near`, `z_far`, `z_center` | altitude above sea level [m] |\n",
+    "| `cs_polygon` | the gate's footprint in the (distance, altitude) plane |\n",
+    "\n",
+    "Unlike an area crop, these columns are **not** LUT data — they belong to this\n",
+    "particular line, so they travel with the rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2c3e7cb1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:16.400103Z",
+     "iopub.status.busy": "2026-08-04T15:32:16.399949Z",
+     "iopub.status.idle": "2026-08-04T15:32:18.500354Z",
+     "shell.execute_reply": "2026-08-04T15:32:18.499269Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3,833 gates on the section\n",
+      "new columns: ['d_near', 'd_far', 'z_near', 'z_far', 'd_center', 'z_center', 'cs_polygon']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cs = rdf.extract_cross_section(\n",
+    "    p1=(SITE[0] - 0.6, SITE[1] - 0.35),\n",
+    "    p2=(SITE[0] + 0.6, SITE[1] + 0.35),\n",
+    "    crs=4326,\n",
+    ")\n",
+    "print(f\"{len(cs):,} gates on the section\")\n",
+    "print(\"new columns:\", [c for c in cs.columns() if c.startswith((\"d_\", \"z_\", \"cs_\"))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e88b5935",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:18.502506Z",
+     "iopub.status.busy": "2026-08-04T15:32:18.502340Z",
+     "iopub.status.idle": "2026-08-04T15:32:18.509742Z",
+     "shell.execute_reply": "2026-08-04T15:32:18.509037Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "section length : 121.2 km\n",
+      "altitude range : 1589 - 12563 m ASL\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>DBZH</th><th>d_center</th><th>z_center</th></tr><tr><td>i64</td><td>f32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>21010095000749</td><td>10.0</td><td>61090.019433</td><td>1624.323017</td></tr><tr><td>21010105000749</td><td>12.0</td><td>61102.684205</td><td>1624.291394</td></tr><tr><td>21010115000749</td><td>12.0</td><td>61117.134979</td><td>1624.254759</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3, 4)\n",
+       "┌────────────────┬──────┬──────────────┬─────────────┐\n",
+       "│ gate_id        ┆ DBZH ┆ d_center     ┆ z_center    │\n",
+       "│ ---            ┆ ---  ┆ ---          ┆ ---         │\n",
+       "│ i64            ┆ f32  ┆ f64          ┆ f64         │\n",
+       "╞════════════════╪══════╪══════════════╪═════════════╡\n",
+       "│ 21010095000749 ┆ 10.0 ┆ 61090.019433 ┆ 1624.323017 │\n",
+       "│ 21010105000749 ┆ 12.0 ┆ 61102.684205 ┆ 1624.291394 │\n",
+       "│ 21010115000749 ┆ 12.0 ┆ 61117.134979 ┆ 1624.254759 │\n",
+       "└────────────────┴──────┴──────────────┴─────────────┘"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(f\"section length : {cs.data['d_far'].max() / 1000:.1f} km\")\n",
+    "print(f\"altitude range : {cs.data['z_near'].min():.0f} - {cs.data['z_far'].max():.0f} m ASL\")\n",
+    "cs.data.select([\"gate_id\", \"DBZH\", \"d_center\", \"z_center\"]).head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4407feab",
+   "metadata": {},
+   "source": [
+    "The section is measured in true ground distance, wherever the radar is. RadDB\n",
+    "runs it in the archive's own CRS, so it is just as correct for a US or Finnish\n",
+    "radar as for a Swiss one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3dde431a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:18.511870Z",
+     "iopub.status.busy": "2026-08-04T15:32:18.511728Z",
+     "iopub.status.idle": "2026-08-04T15:32:18.515866Z",
+     "shell.execute_reply": "2026-08-04T15:32:18.515122Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "true geodesic  :    121,167 m\n",
+      "section length :    121,183 m   (+0.014 %)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyproj import Geod\n",
+    "\n",
+    "p1 = (SITE[0] - 0.6, SITE[1] - 0.35)\n",
+    "p2 = (SITE[0] + 0.6, SITE[1] + 0.35)\n",
+    "truth = Geod(ellps=\"WGS84\").inv(p1[0], p1[1], p2[0], p2[1])[2]\n",
+    "got = float(cs.data[\"d_far\"].max())\n",
+    "print(f\"true geodesic  : {truth:10,.0f} m\")\n",
+    "print(f\"section length : {got:10,.0f} m   ({100 * (got - truth) / truth:+.3f} %)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4304e3e3",
+   "metadata": {},
+   "source": [
+    "## 5. `quicklook=True` — did I crop what I meant to?\n",
+    "\n",
+    "Every AOI method takes `quicklook=True`, which draws the AOI footprint and the\n",
+    "selected gates on a country-scale map. It is a sanity check, not a product."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e728b199",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:18.517836Z",
+     "iopub.status.busy": "2026-08-04T15:32:18.517683Z",
+     "iopub.status.idle": "2026-08-04T15:32:20.442637Z",
+     "shell.execute_reply": "2026-08-04T15:32:20.441811Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 630x630 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = rdf.crop_around_point(point=SITE, distance=50_000, crs=4326, quicklook=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "53c5a037",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:20.444763Z",
+     "iopub.status.busy": "2026-08-04T15:32:20.444512Z",
+     "iopub.status.idle": "2026-08-04T15:32:21.609813Z",
+     "shell.execute_reply": "2026-08-04T15:32:21.608899Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 630x630 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = rdf.crop_by_polygone(polygon=triangle, crs=4326, quicklook=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b5a564",
+   "metadata": {},
+   "source": [
+    "## 6. Chaining AOIs\n",
+    "\n",
+    "Crops return a RadDB like everything else, so they compose with `filter` and `sel`\n",
+    "and with each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5424cde4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:21.611663Z",
+     "iopub.status.busy": "2026-08-04T15:32:21.611524Z",
+     "iopub.status.idle": "2026-08-04T15:32:23.258713Z",
+     "shell.execute_reply": "2026-08-04T15:32:23.257964Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "116,865 gates: strong echo, within 60 km, beyond 5 km range\n"
+     ]
+    }
+   ],
+   "source": [
+    "storm = (\n",
+    "    db.open(radars=\"L\")\n",
+    "      .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 25})\n",
+    "      .crop_around_point(point=SITE, distance=60_000, crs=4326)\n",
+    "      .sel(range=slice(5_000, 60_000))\n",
+    ")\n",
+    "print(f\"{len(storm):,} gates: strong echo, within 60 km, beyond 5 km range\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3d0335d",
+   "metadata": {},
+   "source": [
+    "## 7. The interactive tool\n",
+    "\n",
+    "In Jupyter, `interactive_crop()` puts an [ipyleaflet](https://ipyleaflet.readthedocs.io/)\n",
+    "map in front of you: draw a **rectangle**, **polygon**, **marker** or **polyline**,\n",
+    "click *Apply crop*, and the result appears on `selector.result`.\n",
+    "\n",
+    "The tool dispatches on what you drew:\n",
+    "\n",
+    "| you draw | RadDB runs |\n",
+    "|---|---|\n",
+    "| rectangle | `crop_by_bbox` |\n",
+    "| polygon | `crop_by_polygone` |\n",
+    "| marker | `crop_around_point` |\n",
+    "| **polyline** | `extract_cross_section` |\n",
+    "\n",
+    "It needs a live kernel, so the cell below is switched off by default — set\n",
+    "`RUN_INTERACTIVE = True` and re-run it yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6fe0db90",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:23.260949Z",
+     "iopub.status.busy": "2026-08-04T15:32:23.260798Z",
+     "iopub.status.idle": "2026-08-04T15:32:23.264131Z",
+     "shell.execute_reply": "2026-08-04T15:32:23.263464Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "interactive_crop() needs a live Jupyter kernel — set RUN_INTERACTIVE = True\n",
+      "\n",
+      "usage:\n",
+      "    selector = rdf.interactive_crop()\n",
+      "    cropped  = selector.result   # after drawing + Apply crop\n",
+      "    selector.kind               # 'bbox' | 'polygon' | 'point' | 'cross_section'\n"
+     ]
+    }
+   ],
+   "source": [
+    "RUN_INTERACTIVE = False        # <- set True in your own Jupyter session\n",
+    "\n",
+    "if RUN_INTERACTIVE:\n",
+    "    selector = rdf.interactive_crop()\n",
+    "    # ... draw a shape, click \"Apply crop\", then:\n",
+    "    # cropped = selector.result\n",
+    "    # print(selector.kind, len(cropped))\n",
+    "else:\n",
+    "    print(\"interactive_crop() needs a live Jupyter kernel — set RUN_INTERACTIVE = True\")\n",
+    "    print(\"\\nusage:\")\n",
+    "    print(\"    selector = rdf.interactive_crop()\")\n",
+    "    print(\"    cropped  = selector.result   # after drawing + Apply crop\")\n",
+    "    print(\"    selector.kind               # 'bbox' | 'polygon' | 'point' | 'cross_section'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e91228a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Recap\n",
+    "\n",
+    "```python\n",
+    "rdf.crop_by_bbox(bounds=(minx, miny, maxx, maxy), crs=4326)\n",
+    "rdf.crop_around_point(point=(lon, lat), distance=50_000, crs=4326)\n",
+    "rdf.crop_by_polygone(polygon=shape_or_path, crs=4326)\n",
+    "rdf.extract_cross_section(p1=(lon, lat), p2=(lon, lat), crs=4326)\n",
+    "\n",
+    "rdf.crop_*(..., quicklook=True)     # sanity-check the AOI on a map\n",
+    "rdf.interactive_crop()              # draw it instead (Jupyter)\n",
+    "```\n",
+    "\n",
+    "`crs=` describes **your** coordinates. The AOI itself always runs in the archive's\n",
+    "projection, so distances are true metres on the ground.\n",
+    "\n",
+    "**Next:** [4 — Plots](04_plots.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial/04_plots.ipynb b/tutorial/04_plots.ipynb
new file mode 100644
index 0000000..ec6f747
--- /dev/null
+++ b/tutorial/04_plots.ipynb
@@ -0,0 +1,726 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f19ce8cd",
+   "metadata": {},
+   "source": [
+    "# 4 — Plots\n",
+    "\n",
+    "RadDB draws four things. Each one **draws a single plot into a single Axes** and\n",
+    "returns the matplotlib artist, so you compose panels yourself by passing `ax=`.\n",
+    "\n",
+    "| method | what it fixes | reads |\n",
+    "|---|---|---|\n",
+    "| `plot_ppi(sweep=...)` | one sweep, seen from above | horizontal gate faces |\n",
+    "| `plot_rhi(azimuth=...)` | one azimuth, all sweeps stacked | vertical gate faces |\n",
+    "| `plot_cappi(altitude=...)` | one altitude surface | vertical **and** horizontal faces |\n",
+    "| `plot_vcs(line=...)` | an arbitrary vertical slice | the cross-section geometry |\n",
+    "\n",
+    "They all read the gate geometry from the LUT and join on `gate_id`, which means a\n",
+    "filtered, cropped or `sel`-ed RadDB **plots exactly the gates it holds** — nothing\n",
+    "is re-gridded onto a full azimuth x range mesh behind your back.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d77ff6a1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:24.383036Z",
+     "iopub.status.busy": "2026-08-04T15:32:24.382730Z",
+     "iopub.status.idle": "2026-08-04T15:32:24.395383Z",
+     "shell.execute_reply": "2026-08-04T15:32:24.394261Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
+      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
+      "Archive         : /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — point these at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
+    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
+    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
+    "\n",
+    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
+    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
+    "\n",
+    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
+    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
+    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
+    "\n",
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c6922eeb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:24.398196Z",
+     "iopub.status.busy": "2026-08-04T15:32:24.397932Z",
+     "iopub.status.idle": "2026-08-04T15:32:25.280501Z",
+     "shell.execute_reply": "2026-08-04T15:32:25.279407Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import raddb\n",
+    "\n",
+    "# Keep the embedded figures small enough for GitHub to render this notebook.\n",
+    "plt.rcParams[\"figure.dpi\"] = 70"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "604be0fb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:25.283046Z",
+     "iopub.status.busy": "2026-08-04T15:32:25.282760Z",
+     "iopub.status.idle": "2026-08-04T15:32:25.287004Z",
+     "shell.execute_reply": "2026-08-04T15:32:25.286011Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive already present: /tmp/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
+    "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
+    "    print(\"building the archive (see tutorial 1) ...\")\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
+    "else:\n",
+    "    print(\"archive already present:\", ARCHIVE_DIR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bd9e7bfc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:25.289204Z",
+     "iopub.status.busy": "2026-08-04T15:32:25.289065Z",
+     "iopub.status.idle": "2026-08-04T15:32:25.532423Z",
+     "shell.execute_reply": "2026-08-04T15:32:25.531417Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "347,449 gates | variables: ['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar']\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "rdf = db.open(radars=\"L\")\n",
+    "info = db.get_radar_info(\"L\")\n",
+    "SITE = (info[\"longitude\"], info[\"latitude\"])\n",
+    "print(f\"{len(rdf):,} gates | variables: {rdf.columns()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a12e1078",
+   "metadata": {},
+   "source": [
+    "## 1. PPI — a sweep from above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7e6ba05",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:25.534477Z",
+     "iopub.status.busy": "2026-08-04T15:32:25.534289Z",
+     "iopub.status.idle": "2026-08-04T15:32:27.402893Z",
+     "shell.execute_reply": "2026-08-04T15:32:27.401899Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PolyCollection with 11,629 gate polygons\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 434x378 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
+    "art = rdf.plot_ppi(sweep=1, variable=\"DBZH\", ax=ax)\n",
+    "print(type(art).__name__, \"with\", f\"{len(art.get_paths()):,}\", \"gate polygons\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d98906f",
+   "metadata": {},
+   "source": [
+    "## 2. RHI — one azimuth, all sweeps\n",
+    "\n",
+    "The RHI stacks every sweep along one azimuth, so you see the vertical structure\n",
+    "of the beam fan."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "59a203af",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:27.405098Z",
+     "iopub.status.busy": "2026-08-04T15:32:27.404921Z",
+     "iopub.status.idle": "2026-08-04T15:32:28.698042Z",
+     "shell.execute_reply": "2026-08-04T15:32:28.697345Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 504x315 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7.2, 4.5))\n",
+    "rdf.plot_rhi(azimuth=90, variable=\"DBZH\", ax=ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c00b398",
+   "metadata": {},
+   "source": [
+    "## 3. CAPPI — a constant-altitude surface\n",
+    "\n",
+    "A CAPPI takes the gates whose beam crosses a given altitude. Because the beam\n",
+    "climbs with range, that means different sweeps at different distances — which is\n",
+    "why a CAPPI usually covers more area than any single sweep."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5236b05f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:28.700252Z",
+     "iopub.status.busy": "2026-08-04T15:32:28.700082Z",
+     "iopub.status.idle": "2026-08-04T15:32:32.766121Z",
+     "shell.execute_reply": "2026-08-04T15:32:32.765381Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x308 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.4))\n",
+    "for ax, alt in zip(axes, (2000, 4000, 6000)):\n",
+    "    rdf.plot_cappi(altitude=alt, variable=\"DBZH\", ax=ax)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8ed064e",
+   "metadata": {},
+   "source": [
+    "## 4. Vertical cross-section\n",
+    "\n",
+    "A PPI has its sweep and an RHI its azimuth; a cross-section needs a **line**.\n",
+    "Either cut it first with `extract_cross_section` (tutorial 3) and then draw it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f8f6ca75",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:32.768336Z",
+     "iopub.status.busy": "2026-08-04T15:32:32.768179Z",
+     "iopub.status.idle": "2026-08-04T15:32:35.438215Z",
+     "shell.execute_reply": "2026-08-04T15:32:35.437269Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 560x315 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cs = rdf.extract_cross_section(p1=(SITE[0] - 0.6, SITE[1] - 0.35),\n",
+    "                               p2=(SITE[0] + 0.6, SITE[1] + 0.35),\n",
+    "                               crs=4326)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+    "cs.plot_vcs(variable=\"DBZH\", ax=ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d84aae28",
+   "metadata": {},
+   "source": [
+    "... or hand the line straight to `plot_vcs`, which cuts and draws in one step:\n",
+    "\n",
+    "```python\n",
+    "rdf.plot_vcs(line=[(lon1, lat1), (lon2, lat2)], crs=4326)\n",
+    "rdf.plot_vcs(line=\"my_section.geojson\")           # a LineString from a file\n",
+    "```\n",
+    "\n",
+    "Passing a line to something that **already** carries a section is an error, and so\n",
+    "is drawing a section from data that has none — a cross-section has to be defined\n",
+    "exactly once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06dabdbe",
+   "metadata": {},
+   "source": [
+    "## 5. Coordinates\n",
+    "\n",
+    "`coords` controls the horizontal frame of the map-like plots:\n",
+    "\n",
+    "| value | axes |\n",
+    "|---|---|\n",
+    "| `\"xy\"` *(default)* | metres east/north of the radar |\n",
+    "| `\"lonlat\"` | degrees |\n",
+    "| `\"projected\"` | the archive's own CRS (`x_2056` / `y_2056` here) |\n",
+    "| an EPSG int | that projection |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a3940981",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:35.440180Z",
+     "iopub.status.busy": "2026-08-04T15:32:35.440038Z",
+     "iopub.status.idle": "2026-08-04T15:32:37.532208Z",
+     "shell.execute_reply": "2026-08-04T15:32:37.531138Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VxZiYmCbX+WUqWz2vvxPnaVsaep62UmfFGgeCIFglqQ63m7ntttvIzMwkPz8fg8HAnj176NGjB3Z2dpw6dQqdTseaNWu48847G/FsBEEQrJM56izcOrU2MzOToqIiAIqKitizZw/dunWr9jwlSSImJsY4emDVqlVN/vwFQag7W2nTihEHgiBYHVMbqqZQq9W88cYbDBgwAFmWGTZsGGPGjMHPz4+pU6dSUVHB/fffT6dOncwUURAEoWkQtbbuUlJSePDBB5FlGVmWeeyxx+jcuTNLliyp9jzfeustpkyZwvz58xkyZIjJw5sFQbANtlRnRceBIAhWyZyzs0aOHMnIkSOvuy8mJoazZ8+aMYogCELTI2pt3fTo0YMTJ07ccH9N59m2bVuOHj1qgcwEQbBWtlJnRceBIAhWqekvnyUIgmD9RK0VBEFoXLZSZ8UaB4IgCIIgCIIgCIIg1EiMOBAEwSqJXk1BEITGJ2qtIAhC47KVOis6DgRBsDrmXEhGEARBqJ6otYIgCI3Lluqs6DgQBMEq2UqRFQRBsGai1gqCIDQuW6mzouNAEASrZCtFVhAEwZqJWisIgtC4bKXO2sqUC0EQBEEQBEEQBEEQGoEYcSAIglWyld5ZQRAEayZqrSAIQuOylTorOg4EQbA6trSQjCAIgrUStVYQBKFx2VKdFR0HgiBYJVspsoIgCNZM1FpBEITGZSt1VnQcCIJglWylyAqCIFgzUWsFQRAal63UWdFxIAiCVbKVIisIgmDNRK0VBEFoXLZSZ0XHgSAIVslWiqwgCII1E7VWEAShcdlKnRUdB4IgWB1bWkhGEATBWolaKwiC0Lhsqc6qlE5AEARBEARBEARBEATrJUYcCIJglWyld1YQBMGaiVorCILQuGylzoqOA0EQrJKtFFlBEARrJmqtIAhC47KVOiumKgiCIAiCIAiCIAiCUCMx4kAQBKtkK72zgiAI1kzUWkEQhMZlK3VWjDgQBMEqSZLpN6F2ZWVltGzZkqeffhqAtWvX0qlTJ6KiopgyZQoajUbhDAVBUIKos4IgCI3LVtq0Fuk4GDduHN7e3kycONF4X1hYGJ07d6Zr166MGjXKeH98fDw9e/YkPDycOXPmIMuyJVIUBMGKSIBkZ/pNqN3rr79Or169AJBlmaeeeopdu3Zx5swZAH744Qcl0xMEQQF1qbVCleTkZG6//XYiIyPp3Lkz69atA0S7VhCE6tlSm9YiHQdPPPEEq1atuuH+/fv3c+LECbZu3Wq8b+HChSxevJi4uDgyMzPZsmWLJVIUBMGaSFRVJ1Nvwk1dunSJ8+fPX9eYlWWZsrIy9Ho9ZWVlBAUFKZihIAiKqEutFQBQq9V88MEHxMbGsmPHDv7v//6P0tJSQLRrBUGohg21aS2yxsGgQYPYtWtXrc+TZZkDBw6wfv16AKZPn86mTZsYM2ZMI2coCIK1kay8eDYlTz/9NO+88w779+8HQJIklixZQlRUFI6Ojtxxxx3cfvvt1b522bJlLFu2DICMjAxyc3MtlXaNCgsLlU6hzppiztA08xY5142otXUTFBRk7GgNCAjAx8eHvLy8ap8r2rWCIIDt1FnFFkeUJIkBAwagVqt55plnmDBhArm5ufj4+CD9d4JHSEgIqampNR5D6QatEv/RN+WYer2exMRE9Ho9lZWVnDx5kiFDhvDrr7/i5eVFu3btOHjwIL179yY2NhatVku/fv349ddfiYqKorCwkKSkJEaPHs2+ffto3bo1wcHBqNVqPD09jZ+bhmjK76+1x6wTCSR7pZOwDT///DMRERFEREQYOw60Wi1Lly7l9OnTNG/enPvuu4+vv/6a++6774bXz549m9mzZwMQExODr6+vRfOvibXkURdNMWdomnmLnE0kam2DHDlyBIPBQGhoqFnatULTpdPpyMvLIzk5GX9/fw4fPoyXlxdubm6cOnWK22+/nRMnTuDi4kKHDh1ITEwkPDwcZ2dnnJ2dzdKGFayUDdVZxToO9u3bR3BwMCkpKQwePJguXbrg6el5w/Nu9g/JGhq0IuaNsrOzkSSJlStX0rFjR0pKSoiNjWX27Nmkp6fTsWNHmjVrRkxMDI6OjnTs2NH42j59+gDQu3dvcnNz8fX1JSoq6rrjGwwGfH190Wq15ObmsnPnTkaPHs3KlSsJDg6mS5cupKSkMHDgQLy8vOpcjK39/W3KMU0m2U7vrNIOHjzI2rVrWbduHSUlJWi1Wtzd3VGr1bRo0QKA8ePH88cff1TbcSAIgg0TtbbecnNzmT59uvECVkPbtUpfDLMEq79ocRNarZbi4mKOHDlCaGgoly9fJjs7m5EjR7Jt2zY6duyIo6MjpaWlODs74+bmhouLCy4uLkRFRSHLMgEBAciyTHFxMXl5eaSkpHD+/HlSUlIYNWoUW7dupUOHDlRUVFBUVMSgQYMAcHNzU/jsq9eUf5910eDztKE6q1jHQXBwMFDV+zpkyBBOnDjBhAkTyMvLQ5ZlJEkiJSVFzLu1cgaDgfz8fNasWUNkZCQpKSmUlJQwY8YMHnroITw8PK57/l133dXgmCqVCn9/f6Dqc9SpUycAXnzxRQA0Gg1qtRqNRsO7776Ls7MzgwYNIjc3l+joaJycnBqcg9C4JGynyCrtzTff5M033wRgxYoVnDlzhoceeohly5aRn5+Pt7c3v//+Ox06dFA4U0EQLE3U2vrRaDSMGzeOZ5991njBo6HtWmu4GGYJTeG8DAYDSUlJbNiwgSFDhrBnzx5kWea+++6jV69ehIaG0q9fP+PzIyMjr3t9bm7uDfcBNG/e3Pj38PBwALp06WK87+r/wwaDgYyMDFxcXFi1ahXBwcG4urpy/PhxHnroIRwcHKrtlFJCU/h9mkNDztOW6qwiHQelpaUYDAbc3d0pKChgz549PPbYY0iSRExMDFu2bGHMmDGsWrWKWbNmKZGicBNpaWkUFhby119/kZyczBNPPME999xj/DKvNEdHR7p27QrAggULgKqt6DIzM0lOTuann37Cy8uL6dOn4+DgIIaHWSMJsPKVZZuy4OBg/vnPf9KnTx/UajVRUVE88sgjSqclCIKliVpbZ7IsM3PmTAYPHsz9998PiHZtU6fT6di/fz+BgYFs2bIFe3t77r33Xh5++GHc3d2NbUqwzBdllUpl7Ih64oknjPcPGTLEONWwWbNm+Pr6UlZWxogRI8RFMWtmQ3XWIh0Hw4cP59ixY5SWlhISEsKGDRt4+OGHgapetSeeeMI4XP2tt95iypQpzJ8/nyFDhjB69GhLpCjU4uLFi/z0009MnDiR7du3M3bsWGbOnKl0WiZzcXFh8ODBQFVnQkVFBbGxsfz444/ce++9FBQUcNtttymcpXAtW+mdtSbX/pudN28e8+bNUy4ZQRCsgqi1dbNv3z6+++47OnfuzE8//QTAypUrmT59OiDatU3J+vXrSUxMZPjw4VRWVhIWFsZTTz2ldFo1cnBwwMHBgf/7v/8Dqj5rx48fJy8vj88//5xu3boxfPhwnJ2dFc5U+DtbqbMW6TjYvn37DfedPHmy2ue2bduWo0ePNnZKggnKy8tZunQpLVq0oGXLlsydOxd3d3fmzp2rdGoN5uTkRLdu3ejWrRuyLLN9+3ZWrVpFixYt8Pf3N05/EBRiQ/PBBEEQrJaotXXWr18/DAbDDfeLdq31q6ioICUlhRUrVnDnnXfSu3dvJkyYgCRJTbLdp1Kp6NGjBwAvv/wylZWV/PHHH+zZs4cnnngCWZZp1qyZwlkKtlRnFVvjQLBeR44cYcOGDcybN48pU6agVqtteg6TJEmMGDECgNTUVPbu3YujoyN//vknU6ZMwcXFReEMb1E2MqxLEATBqolaK9gwWZbJy8tj8+bNpKWlsWDBAl577TWl02oUDg4ODB8+nOHDh1NcXMyKFSto164d3t7edOnSBQcHB6VTvHXZSJ21kf4PwRw+/fRTlixZQkREBG+88QYhISEEBgYqnZZFOTk5MWXKFCIiIujXrx+HDh3im2++4dKlS0qndmv5b++sqbfaqNVqunbtSteuXY2LTx06dIiOHTsSHh7OK6+80sgnJAiCYIXqUGtNIWqtYC2KiorIzs7mueee48KFC8yYMYNnn30WtfrWuGbq7u7O448/zrBhw7C3t+eDDz4gPj6elJQUpVO79dhQm/bW+Ncj1Cg/P58lS5YwaNAg7r//fqvd8kUJERERREREUFZWxsaNGykuLkalUl23SI7QiMzYrenl5cWJEyeuu2/evHnG3UB69+7N+PHjb9j6UxAEweaJWivYkJKSEo4cOcL+/fuZO3eucVehW9nVL5kFBQV8++233H777eh0Ojp37qx0arcOG6mzouPgFpWQkMCKFSuYM2cOTzzxhNVs62KNXFxcmDJlCjqdju+++w5HR0cKCgro3bu30qnZrkaeD5aWlnbdf5rTpk1j06ZNojErCMKtRdRawUbo9Xo++eQT1Go1c+fO5fbbb1c6Javj5eXFo48+il6v5/vvv+f8+fP079+/xi1CBTOxoTorpircYrKzs/nyyy/RaDT885//JCgoSHQamEitVnPvvffSrl070tLS2Lx5M3l5eUqnZbvsTL9lZWURExNDTEwMy5Ytu+FQRUVF9OjRg379+rF7927S0tKu2085JCSE1NTUxj8nQRAEa2OmOgui1gqWV1paygsvvMDvv//OY489ZhMLeDc2Ozs7pk6dyuTJkzl9+jQvvvgier1e6bRsm420acWIg1tEaWkpFy9e5Ny5c0yYMAEvLy+lU2qyVCoVEyZMAGDVqlXk5uby5JNPIkmSwpnZDkkCqQ7VKcA3gIMHD9b4eGJiIsHBwZw5c4bRo0ezatWqamKK358gCLeWutTa2uosiForWE5+fj6ffPIJU6ZM4bnnnhMLWdfTsGHDGDZsGIcOHWLDhg08/fTT+Pv7K52WTbGlNq0YcWDjZFmmtLSUN998Ez8/P6ZNmyY6Dcxo+vTpPPnkk2zZsoUPP/wQnU6ndEq2QQLJzvRbbYKDgwGIiooiMjISSZKu641NSUkRQ/UEQbj11KHWmkLUWqGx6XQ6vvrqK/Ly8njkkUdo06aN6DQwg+joaF555RV0Oh2vvPIKhYWFSqdkO2yoTSs6DmxYZmYmCxYsIDExkddee43Q0FClU7JJkiQxZswYxo4dy+nTp9m7d6/SKdkEc61Am5+fj0ajAaqKaWxsLFFRUdjZ2XHq1Cl0Oh1r1qzhzjvvtMBZCYIgWBdzrfQtaq3Q2NLT09m8eTMxMTG0adMGPz8/pVOyKY6OjgQFBfHoo4+SmJjIli1bkGVZ6bRsgq20acVUBRuk1+v5+OOPmTBhAq+//jqOjo5Kp3RLaNWqFbIss379ei5evEhgYKBYP6K+JMzWrXnu3DkeeeQRVCoVkiTx4Ycf4uPjw5IlS5g6dSoVFRXcf//9dOrUyTwBBUEQmgpRa4Um4qOPPsLf35+pU6cqnYrN8/Pzw8/Pj5KSEtasWcP48eNxcnJSOq2my4bqrOg4sDFJSUmkp6dzxx13XLdQhmAZkiQxadIkioqKeP3115k2bRpdunRROq0myVwr0Pbp04fTp0/fcH9MTAxnz541TxBBEIQmStRawZr99ttv5OTk8Nhjj6FSiYHSltS3b1/69u3L0qVLUavVzJo1S+mUmixbqbPiX6AN2bZtG9999x09evQgMjJS6XRuaR4eHrz11lu0adOGN954g7KyMqVTalok6rQCrSAIglAPdam1gmBBZWVlnDx5EpVKxdSpU0WngYIefvhh7r77btavX09ycrLS6TQ9NtSmFf8KbUBBQQFvvvkmw4YNY8GCBajVYiCJtXBzc2PmzJkcPXqUtLQ0pdNpMiTMNx9MEARBqF5daq0gWIpGo2Hx4sUEBAQwZMgQpdMRAB8fH8aMGcPmzZspLi5WOp0mxZbatFaenlCbzMxMDh48yNy5c7Gzs/JuqltUcHAwffv25euvv+aXX35ROp2m4ep8MFNvgiAIQt3VpdYKggWsXLmSw4cP8/bbb4sdOKyMk5MTc+fO5fDhw7zyyisYDAalU2oabKhNKy5NN2Hr1q0jMzOTxx57TOlUhFqoVCoWLlxIRUUFS5Ys4ZFHHsHe3l7ptKyaqdt/ASB2wRQEQagXk2utqLNCIyorKyM5OZnWrVvTr18/pdMRbmLw4MF06dKFY8eO0aFDB0JCQpROyerZSpvWyvs1hOro9XpWrVrF6NGjRadBE+Pk5MSYMWPYunUrer1e6XSslw31zgqCIFgtMeJAsALFxcX885//xMPDg/79+yudjmACX19fOnXqxKpVq9DprPibrjWwoTatGHHQxMiyzHfffUevXr1wcXFROh2hHsLCwmjZsiULFy5k7ty5YsvGGlj7PC9BEARbIGqtoKTjx49TUVHBe++9J0ZiNjH29vY899xzbNu2jeLiYiZPnqx0SlbLVuqsjZzGrSEtLY1ly5YxdepU2rVrp3Q6QgNIksSbb75JSUkJly9fVjod6yOBZG/6TRAEQaiHOtRaQTC3srIy9uzZQ0xMjOg0aMJGjhxJYGAgiYmJyLKsdDrWx4batKLjoInIycnh8uXLzJgxA0mSlE5HMAO1Wk2nTp04cuQIf/75p9LpWBcbGtYlCIJgtcRUBUEhK1eu5NChQ8yfP1+0a23AwIEDyc3NZdGiRaLz4O9sqE1r5ekJAOfOnePdd9+ld+/eODg4KJ2OYEaSJDF58mQ6dOjApk2blE7HqtjK1jWCIAjWTNRZwdIyMjJo1aoVt99+u9KpCGbUo0cPnn76aX799Ve0Wq3S6VgVW2nTWnl6wqlTp1Cr1bzxxhtiu0Ub5uvrS35+PmfOnFE6FetgQ72zgiAIVkuMOBAs7N///jdnz55lwIABSqciNAIPDw/atWvHBx98oHQq1sOG2rQWSW/cuHF4e3szceJE432HDh2iY8eOhIeH88orrxjvj4+Pp2fPnoSHhzNnzpxberjL5cuX+fHHHwkPD0elsvJPktBg06dPx8XFhbVr1yqdiuIkqrauMfUmCIIg1F1daq1QJTk5mdtvv53IyEg6d+7MunXrANGuNcWZM2eYNm0aQ4YMUToVoRGFhYWxYMECPv30UyoqKpROR3G21Ka1yLfRJ554glWrVl1337x581izZg3nz59n06ZNxiutCxcuZPHixcTFxZGZmcmWLVsskaLVuXjxIkVFRbz00kti7tctpHXr1hgMBoqKipRORVk21DsrCIJgtcSIgzpTq9V88MEHxMbGsmPHDv7v//6P0tJS0a6txdKlS9Hr9TRr1kzpVAQLGTt2LD///LPSaSjPhtq0Fklv0KBBuLu7G39OS0tDp9PRuXNn1Go106ZNY9OmTciyzIEDBxg9ejRQdQX2Vpz3XVlZyapVq+jUqZPoNLgFTZs2jb1797Jjxw6lU1GUrcwHEwRBsGaiztZNUFAQXbt2BSAgIAAfHx9ycnJEu/Ymfv75Z2bMmEGXLl2UTkWwoObNmzNhwgRefvll9Hq90ukoylbatGolgqalpdG8eXPjzyEhIezevZvc3Fx8fHyMX5ZDQkJITU1VIkXFZGdns3fvXl577TWlU2l0BQUFFBcXU1RUxJkzZxg0aBBffvklgYGBREVFsW/fPoYMGcKpU6dwcHBgyJAhxMXFERERgbe3t9LpN6rRo0fz008/odfrb821La72zgqCIAiNR9TaBjly5AgGg4Hs7GzRrq3Bzp07MRgMODo6UlJSonQ6jU6WZTQaDfn5+SQmJtKyZUtSU1PR6/W0bt2arKwsmjVrhqur6y1xcVCtVnPPPfdw4sQJunfvfkuc8w1sqM4q0nFQ3fwuSZJqvL8my5YtY9myZUDVCq25ubnmS9IEhYWFZj/mhg0bGDNmTI3n0hgxa9PQmFqtlosXL6JSqUhLSyM5OZlRo0axZ88eOnbsiI+PD+3atcNgMPDggw8a13Pw8/PDw8MDT09PSkpKKCoqIi8vj0uXLhEfH09aWhojR45kx44d3HbbbbRp06bBX7Kt6f3t378///nPf7jjjjsICgqySExrYu29roIgCLZA1Nr6yc3NZfr06Sxbtsws7Vql27SN4dixY4SGhtKlSxdyc3ObRNvDFFc7B/bt24ejoyNqtZrz588zatQotm3bRmhoKG3atCElJQUXFxfS09MxGAy4uLhw/PhxQkNDuXLlirEdu23bNtq2bYuXlxf29vZmac9agqm/T39/fzQaDUuXLr1uvbumwhyfW1ups4p0HDRv3vy6HteUlBSCgoLw8/MjLy8PWZaRJMl4f01mz57N7NmzAYiJicHX17fRc/87c8WUZZmPP/6Yxx57zGIx66IuMXU6Hfn5+fz666/ExcXx0EMPUV5eTnR0NP369TM+r0OHDjc9jiRJ+Pr64ufnZ7yvVatWAERHRxvvCwsLo6CggP3793PixAkeeOABTp8+zaBBg3B1dTU576us6f199NFH2bdvHxEREWbfilOJ8zSZBFj//5mCIAhNm6i19aLRaBg3bhzPPvssffr0IS0trcHtWmto05pTcXExR44cYejQodfd3xTPS6/Xc+jQIUpKSiguLub06dM8+uijdOnShdatW2Nvb2+cjtKhQwdyc3Px9fWle/fuALRr1854rIiICAD69OljvC8yMhKAzMxM/vrrL0JDQ1m2bBkdO3akZ8+eBAYG4uLiYqnTrRNTf5++vr5kZGTg7OxstedyMw363NpQnVWk4yA4OBg7OztOnTpFZGQka9as4csvv0SSJGJiYtiyZQtjxoxh1apVzJo1S4kULe7333+/7kt1U6PVatm+fTuRkZGsXr2aMWPGMG3aNGPPenBwcKPFdnFxwcXFhbvvvpu7774bvV5PZmYmhw8fJj09nYqKCu6//37UakU+7g3i4OBATEwMzzzzDO+8806TPId6kUC6RU5VEARBMaLW1pksy8ycOZPBgwdz//33A6Jd+3fZ2dkcOHCAZ599VulU6k2WZXbu3Mnu3bt58MEHKSgoYODAgbi4uDB+/Hig6kq6OQUGBjJ27FgAFi9eDFTtRPHRRx9xzz33sHXrViZMmNBkF5gcPnw4y5cvJzIykl69eimdjuXYUJ21yGkMHz6cY8eOUVpaSkhICD/++CNLlixh6tSpxi91nTp1AuCtt95iypQpzJ8/nyFDhhh78GzZpk2b6NSpE2FhYUqnUmfHjx8nISEBe3t7/Pz8aNWqFYsWLVI0Jzs7u+tGJMTHx5OUlMSXX37J5MmTm9ziPM7OzsybN4/k5GTjiItbgo0M6xIEQbBqotbWyb59+/juu+/o3LkzP/30EwCrV68W7dprrFq1ijlz5iidRp3p9Xo0Gg3/+te/iIyMZPTo0QwePBhJkmjZsqUiOUVFRREVFQXA+PHjSUxMZOfOnQBMnjy5yV1QmjlzJr/++uutt4aXjdRZi3zatm/fXu39Z8+eveG+tm3bcvTo0cZOyWpotVouX77MnXfeqXQqJjMYDGzevJk2bdoQFxfH2LFjcXR0VDqtGrVp0waA1157jdLSUt577z0CAgKMVwqagvDwcH755RdOnz5t7I22ZZJk/XvZCoIgNHWi1tZdv379MBgM1T4m2rVVnShPPfVUk1oET6vVcvToUTZt2sSjjz7K4sWLjettWZOgoCCCgoKIiYkhNjaWhIQEvvvuO2bNmtWoI3vNSZIkhg4dyoIFC3jrrbeaXMdHfdhSnbX935YVq6io4LvvvmP+/PlKp2ISnU7Hjz/+SHh4OH5+fkRGRtKxY0el0zKZJEm4ubnxj3/8g4KCAn7//XeOHTvGvHnzmsR8qxEjRvDrr79iMBis8j80s7sFTlEQBEFxotYKZnL06FHc3d2bTKeBLMssXbqUkpISnnrqKWJiYpROyWRX10WYP38+RUVFvPnmm4wYMYJu3bopnFntVCoVc+fO5dKlS7Wud2YzbKTO3rTj4JNPPqn1AK6ursyYMcNsCd1K1qxZw/Dhw5VOo1ayLBMfH89ff/1FVFQUnTt3VjqlBvPy8mLIkCFERUWRnp7Otm3bjIsSWbNhw4axcOFCXn31Vase5dFgku2sQCsIgmC1RK0VzCQhIQGDwcDdd9+tdCq10mg0vPfee3Tq1ImHHnqoSV+McXd3x93dnQULFnDixAk2btxIZGQk4eHhSqd2U+Hh4ezatYu4uLgmNeq6Xmyozt70NF599VVycnLIzs6u8fb6669bKleb8ttvvzF27FirH1pkMBj48ssvycrKYtq0aTbRaXCtwMBA2rRpw6hRo4iNjeXgwYPVbp9kTR599FHOnTundBqNT1WHmyAIglA/os4KZrBs2TK6du2qdBo3VVlZyUcffURiYiKPPPIId955Z5PuNLiWWq2mZ8+eDB8+nF27dnHp0iVycnKUTuumbr/9djQajdJpWIaNtGlvOuLgwQcf5KWXXrrpAbRarVkTuhVUVFTw559/3rBFjTUpLS3l3XffZciQIcyePbtJbp9TF61btwaqdrdYu3Yt48ePt9or+mFhYfz666/s37//uu18bI2t9M4KgiBYM1FrhYb6/fffee2116x6isL+/ftxc3Nj9OjRxrWvbJGjoyOzZ8+mqKiIDz74gBkzZtCiRQur/d1MmDCBN954g2effdZqczQHW6mzNz2N1157rdYDmPIc4X9kWWbv3r3GbVas0datW0lKSmLu3LlNeovI+ujatStTp05l5cqVfPHFF0qnU6OhQ4dy4cIFpdNoPFf3vDX1JgiCINRdXWqtIFTj7NmzXLhwwWq/9Ol0On799VeSk5Pp1KmTTXcaXMvDw4OXXnoJV1dXnnrqKeLi4pROqVqSJDFo0CDOnDmjdCqNx4batCYtjpiXl8fq1atJTExEr9cb7//oo48aLTFb9cMPP+Dj42OVBVaj0ZCQkEBlZeWts1hJDR5++GEKCwtZv349/fr1s7o9cyVJYubMmXz22WdNcssjk9hI76wgCIJVE7VWqCetVktlZaXVtkP279/P1q1befXVV62y3W0Jfn5+vPPOO2RmZrJ27Vruueceq3svevfuzbZt2/D09KRFixZKp9M4bKTOmnQao0aNIiMjg+joaPr27Wu8CXVTVFREp06dGDRokNKp3CA+Pp6FCxcSEBDQJBa2sQRPT09GjRrFhg0bKCsrUzqdG0iShKurK2lpaUqnYn7/XUjG1JtQu7KyMlq2bMnTTz9NcXExXbt2Nd48PT354IMPlE5REARLq0OtFYS/++yzz3B1dbW6dQL0ej0//PADwcHBt3SnwVVqtZrmzZvTvHlztm/fjk6nUzqlGwwaNIh169YpnUbjsKE2rUkjDrRaLW+++WZj52LzPvjgA+bOnat0Gjf45ZdfiIyM5N///jd2dlY+RsbCXFxcmDdvHlu3buXixYvMnz/fqv4Duv/++9myZQuBgYG297uz8uLZ1Lz++uv06tULqFqF+cSJE0DV9KmwsDDuuusuBbMTBEExotYK9VBQUMDo0aONa0RZk+XLl9O3b1/CwsKUTsWq9O/fH1mWeeaZZ5g5c6ZxS0dr4OTkxCOPPMLu3bsZOHCg0umYn43UWZNO4+GHH+ajjz7i0qVLJCUlGW+C6YqKipg6dSr+/v5Kp3KdY8eOkZeXR4sWLWzvi6cZjRo1ivHjx/Pnn39a3QqwoaGhrFq1Suk0zEsCSW36Tbi5S5cucf78eUaNGnXDYwcOHKBZs2a0atVKgcwEQVBUHWqtIFzrjTfeICgoSOk0rnPp0iXeeOMNZs+efctPua2JJEn861//QqVScfDgQaXTuY6bmxtbt26loqJC6VTMy4batCZ1HOTl5fH6668zdepUJkyYwIQJE5g4cWJj52YzZFnm5ZdftqoFWfR6PYsWLSIsLIxp06YpnU6T0KJFC1q3bs3rr79uVVs2du7cmYEDB5KXl6d0KuZjQwvJWIOnn366xlFj33//Pffcc4+FMxIEwSqIxRGFeqioqGDSpEk4OzsrnYpRcnIyqampLFy4UOlUrJ5KpaJ9+/bExcWxefNmpdO5zssvv0x8fLzSaZiXDbVpTerXWL58OQkJCbi4uDR2PjapoKCAadOmWc0cMK1Wy7lz57j33nvx8fFROp0mpXnz5rzyyiusXLmSkSNHEhAQoHRKQNV6DJ988gkvvPCC0qmYj3X8c2nyfv75ZyIiIoiIiGD//v3XPSbLMj/88AP79u2r8fXLli1j2bJlAGRkZJCbm9uo+ZqisLBQ6RTqrCnmDE0zb5FzHYlaK9SBXq/nX//6l1XtDvbLL79w+fJlHn30UaVTaVLuu+8+Kisr+fzzz3n44YetYiquk5MT27Ztw8vLi+bNmyudjvnYSJ01qeOgc+fOVFRUiI6DetDpdHz55Zc8/fTTSqcCVO2c8Nxzz/Hkk08SGhqqdDpN1qRJk1i7di0PPPCAVRRaX19fJk+eTHl5uVVdAagvSTL/AjFlZWV06NCBSZMm8e6773Lo0CEeeOABNBoN06dP56WXXjJvQCtx8OBB1q5dy7p16ygpKUGr1Rq3afrzzz9p0aLFTWvB7NmzmT17NgAxMTH4+vpaKvWbspY86qIp5gxNM2+Rs2lErRXq6syZM1a1Js4ff/xBz549GTFihNKpNEkODg5ERUVx8OBBevfurXQ6AMyZM4eMjAyl0zAbW6qzJp1GYWEh7du356677mLy5MnGm1C7/fv3M3LkSKXTAKpGGpw/f56nnnpKdBo0kIuLC7NmzeLVV18lPT1d6XSAqqkUr732mtJpmI+qDjcTXLs4IMC8efNYs2YN58+fZ9OmTTa7h/Cbb75JcnIyiYmJvPvuuzz00EPG/1DENAVBEMxZZ+HWrbW3gvLyci5dukS3bt2UTgWAXbt2kZycjJ+fn9KpNGl9+/YlPDzcaqbiurm5sXv3bmJjY5VOxXxspE1r0ogDmxr+bEEVFRWUlZUxYMAApVNBlmUWLVrEQw89ZFtDfxS2YMEC9u/fj6+vLw4ODorm4uTkxMCBA6msrFQ8lwYzc+/s1cUB77zzTs6cOUNaWho6nY7OnTsDMG3aNDZt2kRUVJT5glo5g8HAjz/+yOHDh5VORRAEpYhaK9TBN998wx133KF0GgDs2LGDqKgobr/9dqVTsQn+/v4MGTKElJQUq7i4OH36dHbs2GFVOz/Umw3VWZM6DsrKym64av7xxx/b5nYZZvT1119bzXt06NAhnn76abGmgZk5OzvTt29fnn76ad555x0cHR0VzWfYsGF88skntjHPrw5FNisri5iYGOD6ofVXXf39XJ3jn5aWdl0HWkhICLt3777huKYssuTh4dFkOldnzpxp/LtKpSIlJUW5ZARBsA4m1tra6izUr9baWp21VeXl5fTp08cqtjjMzc3lxIkTVtOJYStiYmLYu3cvf/zxB9OnT1c0F3t7e9zd3Tlz5oxtdDTaSJvWpI6Df/3rX0iSZJw/tGjRIk6dOsW8efNMefktSafT0b9/f9q2bat0KqxYsYIWLVqIToNG4uTkxIIFC4iLiyMyMlLxNQ8cHBzIzc1tknN8ja6uQGuigICAGrcVqm5xwOqG4lX3e/vuu+945ZVXbhr7X//6l2jQCoLQNNWh1t6szkL9a62os03DqlWrGDJkiNJpkJCQwMWLF61m7TBb079/f7RaLRUVFTg5OSmaS69evfjoo4+afseBDbVpTeo42LhxI3feeSd6vZ5t27ZRWlrK+vXrTXnpLWvt2rV06tRJ6TQ4ceIEY8eOFZ0GjSw0NJRjx47x2WefMXfuXEVzmT17Nnv37qV///6K5tFQ5hrWVdPigKmpqcbnpKSkVLsf9TPPPMOMGTNuevzS0lLzJCoIgqAApWutqLPWT6fTMWDAAMLDw5VOhQ0bNjB//nyl07BpgwcP5u2332bcuHGKXgC1t7dn8uTJJCYmWsVIl4ZQus6CeWrtTU+jrKyMsrIy7O3tWbNmDc8//zwGg4ElS5ag0WhueuBbmSzLtG/fni5duiiaR1ZWFt999x3e3t6K5nGr6N69u3EHEqUdPny4aa9IK2G2hWRqWhzQzs6OU6dOodPpWLNmDXfeeecNr7065SM5OfmGx66+vzYxLUQQhFtTXWptLepba0WdtX5r165Fp9MpmoMsy8Zdyuzt7RXN5Vbw1FNPkZmZiVarVTQPPz8/li9frmgODWZDbdqbptexY0eioqLo2LEj/fv3p7CwkG3bthEVFdX0h400ot27dyv+5VGn05GcnMyiRYsUHzp/K+nbty//+te/FN9pYfbs2VaxMm6DmHkF2r9bsmQJU6dOpV27dowaNeqmI4Rat27NpEmTKCkpMd43atSo+gUWBEGwJo1YZ8H0WivqrPVydnZWfBTtzp07ad++vaI53ErUajWdOnXixRdfVLQ96ezszAMPPKD496oGs5E27U2nKiQkJJiYrnCtuLg4Zs2apWgOH3/8McOHD1d8ftKtaOHChZw8eZJmzZop1mnj4eHBO++8w6JFi1CrTZqRZF0kkBoh7WsXB4yJieHs2bMmvS4qKoqRI0fSr18/vv76a6Kiopp+x4wgCIIV1VpRZ63T8ePH6dChg6I5nDt3Dn9/f+Oq8YJleHp6Mn78eAoLC/Hy8lIsD7VazZdfftl019azojoLDau1N+3XqG5Fxvo851aSnp5Oz549UanMuO9GHaWlpTF+/HjRM6sQFxcX3Nzc+PrrrxXNY8KECaSlpSmaQ4M0cu9sXUiSxKxZs1i5ciX33nsvK1asECN5BEGwDaLOCjexceNGxRf6Xr16NR07dlQ0h1tVdHQ0q1atUrQ9GRISQkhIiGLxzcJG2rQ3TW/u3LmcO3eO2NjYam9nz57l8ccfb1DyarWarl270rVrV+N2E4cOHaJjx46Eh4fXuvqjtfnmm28U3f9Uq9Xy9ttvExwcrFgOAnTq1AlHR0dFr5Z07dqVP/74Q7H4DfLfPW9NvTW2q7/HLl26sGfPHjZt2sSZM2caP7AgCEJjqkOtbWxNpc6OGzcOb29vJk6caLwvLCyMzp0707Vr1+uG/MbHx9OzZ0/Cw8OZM2dOkxtBUVhYyOzZsxVdU2Dnzp28/vrr2NnVYVl6waweeughDh06pGgOnTp1Ys+ePYrmUG821Ka96cCJgICAWhdJaGgvpJeXFydOnLjuvnnz5rFmzRoiIyPp3bs348ePbxJrKsiyzLhx4xTdBi8/P5+nnnpKFFgrMHnyZN5//33+7//+T7EcSkpKyMrKIiAgQLEc6s2KPsLHjx83/t3T05MNGzaQlJSkYEaCIAhmYiW1tqnU2SeeeMJ4te5a+/fvx83N7br7Fi5cyOLFixkzZgzjxo1jy5YtjBkzxpLpNsh3333H8OHDFYt/8eJFzp49y+DBgxXLQahaZ6BXr158//33TJ48WZEcwsLCWLVqFQMGDFAkfoNZSZ2FhtXam3Yc7Nq1q0GJ1UdaWho6nc44j2natGls2rSpSXQcXF3Jvk2bNorEz8zMZMWKFTzzzDOKxG8IWZaprKwkJyeHtLQ02rdvT2xsLMHBwYSEhGBvb6/o9I/6ioiIIC4uTrEtjObOnUtcXFzT6zi4ugKtwoqLi1m5ciWenp5MnTqVt956iwMHDtC6dWuxp7ggCE2fFdTaplZnBw0aZFL7WJZlDhw4YNy+fPr06WzatKlJdRx07dqVli1bKhZfq9UyZ84cxeI3lFarpbS0FBcXF4qLi3F3d8fBwUHptOolKCiI1NRUZFlWZAqRSqVi1qxZlJWV4eLiYvH4DWIFdRbMU2sVXzWtqKiIHj164OzszOuvv46rqyvNmzc3Ph4SElLjOgrLli1j2bJlQNU2Erm5uRbJ+arCwsLrfs7LyyM6OrpR8/h7zGudOXOGadOmmT3+zWLWl06n49ixY5w5c4bRo0ezbds22rdvj4+PD3l5eQQEBFBUVIRWqyUzM5MDBw5w++23c+DAAby8vLjjjjtQq9VmL17mPteYmBj27duHh4dHjaNAGuP9vdaGDRt4+OGHLRqzoSQsM1yrNtOmTaNVq1aUlJTw/vvv079/fx5//HH279/PjBkz2LZtm9IpCoIg1Js11FpbqLOSJDFgwADUajXPPPMMEyZMIDc3Fx8fH2M7JSQk5Lq91v9O6Tbt32VkZJCcnGzWi2F1aXv89ddfADRr1sxs8RvTuXPnuHLlCuHh4Rw5coSuXbsSHx+PRqNh4MCB7Nmzh7CwMAoKCrhy5QojR47kt99+o0ePHrRv375JrOdx//33s3HjRvr16wdYvi159fvAHXfcYdG4DT1Pa6izYJ5aq3jHQWJiIsHBwcYvkKtWrbrhOTX9Y5o9e7ZxXYSYmBhFpghcjanT6cjLy7NIgavuPI8fP44kSY22voI53tvs7Gx0Oh2ffPIJ/fr1o3fv3owYMQJJkoiMjLzh+W5ubsa4ffr0AaBHjx4UFRWRkJDA+vXrmTp1Ki4uLoSFhTU4v6vM/TkKDw9n8+bNN91pozE/u8OGDcPT0/OG3RWUnFJTKyvpnb1y5QqbNm1ClmVCQkL48MMPARg+fDhdunRRODtBEIQGsoJaawt1dt++fQQHB5OSksLgwYPp0qULnp6eNzzvZl8OraFNe61t27YxcuRIs+dhyvH0ej3BwcF07drVrLHNSZZlzp8/z7fffsuECROQZZlRo0bh7e1NeHg4vr6+9O3b1/j8iIiIG47RqlUrcnJy+PPPPzl69ChPPPEETk5OuLu7W/JU6uTy5ct0797d+H3Dkp9TX19fDAaDot/36sUK6iyYp9bW2nFgMBjYvXs3gwYNali2Nbi6iF9UVBSRkZFIknRdj2xKSgpBQUGNEtuc9u/fr+hw8H379jF37lzF4t9MXl4ehw4d4uzZszzyyCO8+uqrDTqeh4cHXbp0oUuXLsiybJxnOGrUKPz9/a2u1zYyMhK1Wo1Go8HR0dHi8Tt16sTSpUtrXa/E6lhBkb3a2SJJEoGBgdc9JtYREQTBJihca22hzl5ty4aEhDBkyBBOnDjBhAkTyMvLMw7tbirt2auufvlVwtdff81tt92mSGxTrFixguTkZJ555hleeeWVerc7nZ2dCQ0NJTQ0lLvvvpuCggI++ugjevXqRXR0NB4eHmbOvOEef/xxLl26pFj82NhY2rdvX23HnFWzkTZtrR0HKpWKF198kT///LMeKd5cfn4+Li4uODo6kpKSQmxsLFFRUdjZ2XHq1CkiIyNZs2YNX375pdljm1toaKhi88BiY2O56667rPI/2Pfee4/AwEDuu+8+RowYYfbjS5Jk3Md0z549vPfee7z22muKrgBck1WrVvHQQw9ZPK6DgwMVFRUWj9sgknUM60pISGDy5MnIsmz8O1RdaUhMTLRoLmVlZbU+R6VS4eTkZIFsBEGwCVZQa62pztZHaWkpBoMBd3d3CgoK2LNnD4899hiSJBETE2NcEHHVqlU3HXloTVJTUzl58iQxMTGKxHdxcal2JKqStFotS5YsoWXLltx33303jOI0By8vL55//nkAvvrqKwoLCxVdYLs6arWa3377DVdXV1xdXS0e/7bbbuPQoUMMHTrU4rHrzQrqLJin1pr0qR8+fDiffvopkyZNum5BioYuTnHu3DkeeeQRVCoVkiTx4Ycf4uPjw5IlS5g6dSoVFRXcf//9dOrUqUFxLGH58uWKbR25evXqBl/FNye9Xs/SpUsJDQ3lqaeestgIgAEDBjBgwACOHz/OTz/9xNNPP201w70iIiKoqKjAYDAossjjuHHjyMvLw8fHx+Kx680KiuxPP/1k/Ptjjz123WN//7mx+fj4EBwcXO12XpIkIcsyer3eKlchFwTBiilca62pzppi+PDhHDt2jNLSUkJCQq5bR8hgMPDEE0/QsWNHAN566y2mTJnC/PnzGTJkCKNHj1YydZM5ODhct92kJW3cuNHqdlH47bff8PX1ZcqUKRYbNTJr1ixkWWbjxo1kZmby4IMPWs0i4Q8++CBpaWmKdBxERUVRWVlp8bgNZgW/OnPUWpM6Dr766isA3n77beN9kiRx+fJlk4LUpE+fPpw+ffqG+2NiYjh79myDjm1JlZWVjBw5UpHY+fn5PPnkk43S81kf6enpxjl+7dq1UySHbt26ERoaypUrVygrKyM6OlqRPP7OycmJb775hvvvv9/isQ0GA5s3b2b69OkWj10vEkhW8JEeOHCg0ikYdejQ4botdKrTrVs3C2UjCIJNsIJaa6k6u3Dhwlqf4+HhUevq4tu3b7/hvpMnT1b73LZt23L06FHTErQin332GS+++KIisY8dO8bYsWMVif13FRUVJCcnU1JSosgVbkmSGDt2LLGxsRw9epTmzZsbp8UoydXVlV9++YURI0YoMp1l48aNdOnSxSpHWlfLCuosmKfWmnQaCQkJDQ5ky9atW8f48eMVif3xxx/z5JNPKhL770pKSvjkk0947rnncHZ2VjQXPz8/fH19+fzzz/H09FSsE+NaERERHD58WJHYbdq0oby8XJHY9WYFvbO1rZmRlZVlsVxMaXw2xQaqIAgKU7jWWqrOfvfdd7WODP3Xv/5llVtAWpLBYFDsy2lcXBxPP/20IrH/LjU1lbfffpvnnnuOcePGKZpLZGQk5eXlvPnmmzzzzDOKXOn/u3vuuYejR4/Stm1bi8e+7777qKysVPy7Rp3YSJvW5P6P06dPExsbi0ajMd7XZK5eNrKEhATFPry9e/fGzc1NkdjX2rhxI97e3lY1ZUKSJObMmUNycjJvv/02CxYsUHzhxJ49e3L27FnjMEZL2rZtGx07dlT8PTCJlaxAm52dDcCLL75IcHAw9913H7Is8+2331JQUGDRXFQqFWVlZciyXGOjwVqGMQqC0ERYQa21VJ195plnmDFjxk2fU1paarZ4TVVcXJxiUypWrFjByy+/rEjsa50+fRpJknjnnXdwcHBQOh2gaiHFV155hT///JPMzEwmTJigaD4BAQFUVlYqMgU3JCSEL774gscff9yicevNCuosmKfWmnQaL7zwAgsXLmT+/PkcOHCAF198ka1bt9Y7cVsiyzJTp05VJPYPP/ygyBfQvysrK0Ov19O/f3+lU7lBRomGIrUPgwcP5vLly9XOD7eksLAw1q1bp0js1q1bk5GRoUjselHV4dbItm/fzty5c3F3d8fDw4M5c+awYcOGxg98jffee4+HHnqIWbNmXTdtTBAEoUFukTprys5CTW73oUbw888/K3JBSpZlpkyZovjw85ycHH766SciIyOtptPgqt/j8unTpy/29vaUlJQonQ6urq789ttvFo/r5OTUNC6CXctG2rQmpffzzz+zdetWAgMD+fTTTzl+/DhFRUUNStpWXLx4kUOHDikSOy4ujmbNmikS+6pt27axceNGxYdxVedKfjl+zvZEBDjTs2dPiouLee+99xTNydHRkQceeECRhV3uuuuuptNx8N8VaE29NTY3Nzc++OADMjIyyMzM5MMPP7T4wptr1qxh+fLlrF69mu+++86isQVBsFF1qLWNzVJ1Nj4+nieffJJx48YxduxY402oMm7cOEU6Dj799FPF27Tnz5/n5MmTvPjii1Y3gm9/YiFDwr1RqarWPfj111/58ccfFc2pa9euhIeHKxK7T58+6PV6RWLXmQ21aU1Kz9XVFUmScHR0JDMzEw8PD+Lj4xuUtC0ZMmSIxWMWFBQwadIki8e9VllZGe7u7kyZMkXRPKoTn19CuU5LVokWe7uqj3nXrl0ZNGgQ+fn5iuaWkpLC77//bvG4arX6uhVVrZ5dHW6NbO3atVy6dImRI0cyYsQILl26xNq1axs/8DXeeustJk+ezOTJk3nttdcsGlsQBBt2i9XZu+++mw4dOrBw4UKef/55402o2hXr+++/VyS2o6Mjfn5+isSGqhEPmzdv5vbbb1csh+roDAa+PJFAp2bXT1EcP348QUFBFBcXK5RZ1ZTgtWvXKrKG1pUrV6pdYN9q2Uib1qQ1DkaMGEFBQQELFiygR48eqFQq7r333gYlbSv27dunyL68P/74o6JTAwoKCnj99dd55513FMuhJtmlFSw5fJ4wT0/m97p+0ZYePXqwZMkS+vXrR9euXRXJr3fv3uzatUuR2Eptr1RnVjIf7KqAgAA+/vhjRXMYMmSIIp2UgiDYMCuqtZaqs66urjzyyCONHqcpSktLo3v37haPm5WVxW233WbxuFfp9Xq++OILq1mY8VpLj8dzKDWPbgFedA/2vu6xmJgYnnvuOZ588kkCAgIUyW/ChAnk5OQQGhpq0biDBw+msLDQojHrzYrqLDSs1pp0GosXL8bLy4tJkyYRHx/P6dOnefPNN+sV0NakpKQoErdHjx6KDQ8CSExMrHcP/bHUXM5kbicn411yKk6ZLSedwUCZVsfLf8RRqqsgR1OE3nDjmgaPPvoolZWVaLVas8WuC5VKRUpKCjqdzuKx9+3b1yR2V5CwjmFd//jHP8zyHHPYvXu3WZ4jCIJwVV1qbWOxdJ199tlneeaZZ/jtt9/Ys2eP8SZAbm4uAwYMsHjcn376CW9v79qf2Eg2b95c763qEvKKScr7i7yin9DqC8yST6VOT2FFJRvOpvBnQgGezjqKdRXVPve5554jJSVFsTW82rdvr8g6B56enhZfa6q+bKlNe9MRB5988slNX3yrLyJjMBh44IEHLB5Xo9Hwxx9/0LlzZ4vHBti0aRMeHh54eXnV6/V/JCXRKTCN8+X+UFDBxPY3Pkej03OlJNHk/WEzy/LZlXYCD6kFLYOSKSpz5M42Ydipblw8RaVS0bJlS9555x2ee+65ep1DQwUGBnL06FF69epl0bitW7fm8uXLis8jrJWV9M5+/fXXN12oSZZlNm7caJG1M+bOncuGDRtqbBzIsszjjz/OqVPm64wTBMHGWUGttXSd3b59O7t27SIuLs44j12SJEW+MFubTZs2KTJtY9iwYRa/Yn1VbGwsrVu3pkOHDnV+rd4gs+HiUQb67ifBzp2y3CRGtvK64XnFlRryKzNo4dbSpOMeyDqCbFBRaV9O7/BCtAYPooP9q32um5sbBoOBb775hvvuu6/O52AOVzsuLL1gYZNZINEK6iyYp9betOPg6rYNFy5c4PDhw8bFYzZt2mSVK+hbWnJyMnFxcRYvdhcuXKBbt24WjXmtysrKevfMAjzQtQOJhQEEu7oRFehZ7XMOZiSg0scDPWo9Xm5ZCX+m/4W3Yz56nTPNPUqJ8DUQ6lHzQh+BgYF069YNvV6vyAq+w4YN4/LlyxaPO3jwYC5evGjxuPViBUXWlN0LLDW8MiAgoNbOWiX2UxYEgCsF5bT0akJ7agv/o3CttXSd3b17N2fPnm06XzosaOLEiRZfFFCv17Nq1Speeukli8a9avXq1fXeStxOJdE7pA3Jpf4EOvozIrT6L/d7U47Qyr8YuHnHgSzLHMo4T1JRCoGuKip1boR6VtLKPQBndc1f2Xr27El6enq9zsEc5s+fT05ODv7+1Z9/Yxk6dKgi20HWixWkaI5ae9OOg0WLFgFVX3KOHz9uXGX15ZdfVnz/UGvRp08fi8fUaDSK9Yx/9dVXDd5+0sfZCR/n5tfdl1emocyQR4hbEACu9jowSLX2YGaXl3C+aCv5ZWrOZ/tiryqgmYcvQ8N64GLvcdM8Ro4cyb///W+eeuqpBp1PfUiSxOrVq1m8eLFF49rZ2bFu3Toee+wxi8atD0usLFub2vb8tiSl1sUQhNpcyCrD39UenV5GbSe+jDU1StdaS9fZ6Oho4uPjFZ3uaa22bNlSryvvDZGRkdGgi1ENcerUKV5++WXUN/lSXpu+ITdePCyp1GBXUY6XU1VnqpODAQcTvjkezzmNTnWCjBJ/4nLVBLjpiPLtShe/NrW+dsSIEfznP//h8ccfr/tJNJBWq2Xt2rUWj338+HGcnJxo06b290dpStdZME+tNek0kpKSrvtZlmWuXLnS4OBNXXx8PMHBwRaPu2XLFovHhKpe4by8PJydzX9V6VxePJK00TgMW63So7YrIkOzpcah2Vq9nmPZe/F3yUcl2eOgMhDq6cbk8D61dhpcFRYWRnJystnOoy4GDx5s8ZiSJDFq1CiLx60ziapuTVNvgiBYXHJhKf/acw5fVzU+rvai06ApqkuttRHHjx8nKiqKLl26EB0dzW233UZ0dLTSaSmuoqICT8/qR4E2ptjYWEVGMRsMBlavXo2Dg4PZjx1XeIlKeavxZzdHA3bqK+Roat6+/WJOARWcwkFlQG0w4ONqoF9wJD0CTftSbG9vj1qtRqPRNDj/uvLz86NLly4Wjztw4EBcXFwsHrfObKhNa1J6Tz75JF27duWOO+4AYOfOnYpcpbU2SUlJigyPUWobxgMHDph1gSKtXs+VwlLcHOzIKk+ntWMqWnUq5YZCnOxl5IoKZP0FCisj8XJsfcPrT2QfwiDnEJvjjbuzBl9nH0a16oW9yvR/dePHj2fHjh2KzK1zdHTk7NmzFl9v4MSJE7RufeP7aVWsZD6YIDRVpRo9sTkXaWF3DJWLBx4uQ3FUOzX4uFq9AZUkcTA5lxUnE7Czq+RMthe3uwaZIWvB4m7BWvvzzz8rnYJVKioqYsyYMRaPu2/fPoYOHWrxuOXl5Wa9Qi7LMmVaPbnlFRgoQK64gMHRQIkmERe1HklbiMbuEBq7KBzV13/ZrdRrydP9SnaFPTrZnpa+FXTyjaGtV/MaolVv7ty57Nixw/h9zZLi4+MtPhra09OTLVu2NHgkdKOzoTpr0mnMmTOHPXv2GPd73LVrF3PmzGns3KyeEldu09PTFZmjLssyW7ZsMeucwOSick5nFLM7KY4yQw46nY7zqX9RXr4TtaRDrSmE0gwcVTeutHsuNxF315MEulTQySedzn5+jGnVu06dBledPXuWjIwMc5xSnfj7+3Ps2DGLx83OzlZs9d26kFSSybfG1mTWhRCE/0ouLCc2J4Vdua78leFNZkn1u8gczTpJUsl5k45Zpq1gR8pv7E8/SlzpCbq0yCCquZ7+LQPNmbpgYbdand2xYwctW7a87rZkyRKLxLZmBw4cICsry+JxlVhkHOD1118360WjA6l57EzMZNOlc+j0xWgL07mQdhxt+a84SlocyjKRKwpwsLtx1O6p/P24OeTS3juLHn559A+OrnOnwVW7d+9WZNew0tJScnJyLBrT3d2d+Ph4i8asL1tp05rc/+Hk5ESzZs3w8fEhLi5ObF0DbN26tfYnmVl6eroic3kqKirMPi8+yNWZ2KxCfN1OEuWZjlRegkvFr1B6GVepEF1eGnayI87213ccFFYWUskOXOQSgpxycXfsRoTHQOzqOfrjwQcfNMfp1FmrVq0Umdc3e/ZsRYay1YkEqFSm3xrZ7Nmz6dOnD5999hkFBQWNHu9mysrKeOutt4z/HuPj4/n1118VzUmwPu0D3JjRZSj3dLybMRH9aOFVtVhsSnE+BoPB+LzUsgQc7EprPd6Z3IvsTd/CxVwNxzMyKdRU0MHPgxkdumFnDZM3hfqpS61tZJaqs5s2beLLL78EqqZgPvDAA1RUVL/V3a0kPDzc4us+lJeX891331k0JoBOp6Nnz55mvRjW0s2N1IJ82vufxVWtQdZroHg1uspsXPS56LMTccL1hphJBedwtTuFj10hnlTi7XAX/s7179B48sknKSkpaejp1JkSHUCSJCm2k0Sd2FCb1qTsPv30U4YOHcrIkSN58803GT16NK+//np9crUpSgz39vT0VGQbxvfeew8/Pz+zHe9MZibfX/iFHqFH6OhyCd/sw+hyknDISsVQVohdRSEGXSXo3K57nc6gpyD9P/jmH8KuKAtXXRRqXQyllYYaItXOzc2t1q1HG4MkSaxYscLicY8dO0ZCQoLF49aNBCo702+NbM+ePXz99ddkZGTQt29fJk+ezObNm9Hr9Y0e++9mzJiBnZ2dcbHEoKAgFi5caPE8hKbHYDCQXPQzRZV/GO8LdC2lVD5KgeZoja/bk3KeUs2fSHIJ5ZUy9monhrXszJCQgbg71rx7jdAU1KHWNjJL1dl169axadMmPvnkEyZOnEjLli35z3/+Y9YYTdG+fftwdXW1aMzMzEyLb0sNVaNORo4cabbjnc/J4/fUfQR6HiXSOZ6A0lPos9Nxzb2MvjQXu/JCZG0lKt31W4wXVuRjSPsQ97yTSIUFeDtN5kqOR4NGhXp7e/PBBx808IzqrrKykm+//dbicVevXm3xmHVnO21akzoOPvnkEw4ePEhoaCjbtm3j9OnTxh0WblWlpaWKLCKj1EiPNm3a4OjoaJZj/XI5mYSSnfTwO46Lsw5tRQna0hL0pXo0GTK6nGx0hmKQZJzsw657bXraWuT0o+jzivBwGIKH9xgyS7S4OTbsH1p0dLQiQ7t69+5t8ZgdOnTA3d3KG/sSVlVkoaqjcOHChSxYsIB9+/bxyiuv0LFjR1atWmWR+FclJCTw9NNPY29vD4CLi0uTmHoiKOfH86lczivkzYP7cFblU5Cxh9Tcvygp2Y+nqhDn4ouUaE7e8DpZlrlceBK1w260sh5nBx2DwgJ4MGoA7X1aKHAmgtnVpdZaQGPW2djYWGJjY7l06RKLFi3i888/JygoiMmTJxMbG2uG7Ju2q1uwW1JhYaEi24vv27fPbAt9/3o5iUv5vxHlfZwWPuVUlmShKypFXwKVGTL6vAx0KidQqXByCjO+zmAwUJi6BDk/A6lQwsdvHnbq5vi5OTRoJIRKpaJr164NP7E68vb2tviOHKBMO7rObKhNa9KEcCcnJ+zt7bGzs6OkpISwsLBbfs5vZmamInPBYmJiLB6zsLDQrNMj+jYP4FRWBMU4Yi9FcEXXioBWB3HMuwxFyehValRF2chloHb6XxHKzv2TyoQfkUrs8e/yBPuKvQkhi6iggAbnFB0dzV9//WXxoldQUGDxL31ubm6cP39ekRVwTSchWah4mmLHjh2sXLmS48ePM2HCBPbu3Uvr1q0pLCykS5cuTJ8+3WK5uLi4UFhYaGxYnDx58pbvyBVqVqyppFKjZ0/yX4wJOY5Bp4XCi5SXpuEYPALX/MvI2jTcgq5fTEuWZS5kbcbBLplgdS4GxwC8nUfg5mD5hWSFxmQ9tbax6+y8efOu+9nHx4dz584xb948JEli586dDTp+U6fEkO/ffvuNuXPnWjSmwWDgoYceMtvx2ni5k1PqSokcgKRtS4q9D54hCUjFWuSyDAw6sCsoQl8qo3bqaHxdeuY6KhMPY28IwDlyET9eSWFICy+CPRt+YSc8PJy4uDiLTz1RYipnXl6exWPWnfXUWWhYrTWp46B79+4UFBQwa9YsevXqhbu7uyI9hNYkMDBQkeFVcXFx9OvXz6IxT58+bdYFZNwdHekb2oMKbWt+STxEYro/kS18aXHhIGqDAUOBDp1bEUj2uLhHAVBRfIWiQ+9jV+mEa+9/kFFwgHD7fAI8zbO7h4+PD3/88YfFOw6ysrIoKioy6zSQ2jg5OTWBxWQki/W6mmLlypXMnDmTVatWXXclwNPTk08//dSiubz33nuMHTuWhIQE7rjjDq5cucLatWstmoNg/QyyzC/xcej0B3By1dPDPRn7rDjkwjy02VrUroXoXJOQS3ORZR0uzv/bjk5v0JOa9AmOqb8hO7XF1aUXktPdpBfoaNvwflrBqlhPrW3sOvvHH3/U/qRb2KpVq3jxxRctGnPMmDEWnx5x4cIFTp06RYsW5hk11cbHmzY+Y6jQFvJr4hmScjoQ4V5A6KU8HJ306Ir0qDwKkPDEwbFqqkJx6l9ojq/C3tACx/6Pk3flE3oHtMDdoYdZcvL29uaXX36xeMfB+fOmLbRrTpcvX6aysrJRttU0H+ups9CwWlvrVAVZllmwYAFeXl489thjbN68maVLl1p8eK612b17N2lpaRaPq0TPWvv27RtlXYWTmen4eFzBxfk0oXmHMJSXos3Uoi0upyLpIjrPtkiSHXqdhkvvL0J72RX3mIfI3P05dgcvgmcvdqbupqyy4Qv9qdVqZsyYYYazqpupU6eabQqIqVxcXBg8eLBFY9aZlQ3rioyMZMiQIdcV2DfffBPArPMkTXHbbbexY8cO9u7dy/vvv09sbCw9epinsSHYDgnwc9LSyt0eD8dgSlV3kOHYAZq3xSDL6HXlaIuSqczTINsH4+DoA4BOpyH5/CtUnN6Gg6odeu/7+Ks4Ek8XB9oGNIH9soW6saKpCo1dZ9esWWOW59gqJUa0btmyxeIxS0tL6dOnj9mPezr3Ct4uZ5DlE/iVJ6LTVFCZaUBbVE7JlXg0wd0B0JYVcPE/76DPDMexx0jyvnsFhyv2FKt9WR//OzpDw9f0CAkJYdiwYQ0+Tl3NnDnT4jGV2pWjTmyoTVtrx4EkSdx7773Gn1u1aqXI4nzWxtnZGQ8PD4vG1Ov1imwBuXnzZtTqum9zWJMKnZ7s0hLSS0pAU0Jfp9Po/kxATq9k9cZkUv8so1iv5lBhF/Ly8nh5xiTKk+xwDO3Olf+8i+rSFXBPIbv0MoWlQTjZm6eXUYmFCs+fP8/Zs2ctGlOSJEV2BKkbCUmlMvnW2NatW3fDfZa+yv/JJ58Yb1988QV79uxh7969fPHFF4os7ilYN0mSiA6JJCpoCmDgzyu5HMsKojwvn2+/S6AgV0Nlbg44VFKsj+Lzzz+nsjSPSyvnUplwDpfQ6ZRoAzBkbsffIY+88iKlT0loFKbX2sbW2HX2xRdfZOvWrTXetmzZwmuvvWa2eE2NEsPMr67VY0mZmZmEhISY7Xh6g4zBYCCjuBJZVclAlxMYTidhl1fJyg3JpO7RUKh35Pe03uTl5bF46mRUtAQHT9I/+ARKi9G6nOdKsZ4Ahw6ozfTFUYk2rRIxd+zYQUpKisXj1o3ttGlN+jY4ePBgfvnlF0aMGFG3zBpg8+bN/OMf/8BgMPDMM88we/Zsi8U2RbNmzXBxsezVl6KiIvbs2UP79u0tGrdZs2ZmOU5GoYYVp8/TvWUGruoreDuXE2RIoKzUnuKjejZdTuXtPy+z7lwu70+8lz7+QfS//XZiT5+mcvZI7lhyBd/24DLIEV2rVqSXOtMrsBUqM22n0759e4uvNxAYqMz+5+YaotdoJPMN6youLmbw4MFotVr0ej1PPPEEDz30EIcOHeKBBx5Ao9Ewffp0XnrppRte+8UXX7B06VIuXLhAdHT0dce09HStqwtXXbhwgcOHDzN27Figamux/v37WzQXoWnILS2nSFNMbmkuHQJT8Du0n7XrYnnl+3jW7kzl04V2JDbvyttvfsiFM2eIW7GUe8f0x1nVjJzTq3BQ6WDYEOJK0ynSNuf2VpbtLBcswApqraXqbP/+/attMF9r6NChtR5n3Lhx7Nq1iyFDhrB+/XqAGs8xPj6ee+65h4KCAu644w4+/fRTs24BaE5KDDNXYvTj0aNHGT16dIOPk1ZYwbb4K7T2z8Egx6G2KyNYc4myPD1Fx3SsP5PKW7vjWX8+hw+nzGBCRCv6DhzI+TNn0PnIDD8m49dDhWtvF+TgUHQV5XRs5l17YBNZ+rvC1ZgGgwGVBb78XtWqVSuLj9ytMyuos2CeWmtSx8GyZct46623cHFxMa7gLUlSoy0OqNPpeOqpp/jjjz/w8PCge/fujB8/Hh8fn0aJVx8HDx60+EqearWadu3aWTQmQHBwsFmOE+DhQKhbHq2KNmPvZCDDpT3ldq1QuaXi6Kmiv78PP/ilEXelgHsnLsbd4S1SU1Np3T6MGH87HAsMuPRyxuDnjqwvwV4tY2dnvp0QgoODqaysNNvxTOHh4UFOTo5FYwJNYFcFCdTmGUni4uLC7t27cXFxoaysjKioKMaPH8+8efNYs2YNkZGR9O7dm/HjxxMVFXXdaydPnszQoUN54YUXrtuC1t3d3eL1aNGiRQAMGzaM48ePGxdEfPnll5kwYYJFcxGs29cnEpHs4whzi0Utl9JeXYZsqKT4ooaeFa609XTiYk45U949hUodT2ZaOq07tGXws3eS9cM+nI5VEDTNE8cwT/LUlei0vtzeznwL5ApWxApqraXq7PLly81ynCeeeIJZs2axcuVK4301nePChQtZvHgxY8aMYdy4cWzZsoUxY8aYJQ9zU2LI948//njD/7uNbdasWWY5TrCnE3pDOs0qtuHuVEauawhaVQiSfzb27nn09fYg3M+JuCsFTB33PJ5Or5OcnEyr9i3p7WmHfWkl9lEO6J0cKdPZUSl54qI23zB1Jb4zNWvWjMrKSpycnCwW09vbfJ0tjcYK6iyYp9aa1CWUnZ2NwWCgpKSErKwssrOzG3VHgUOHDtGxY0eaN2+Ou7s7o0aNYvv27Y0Wrz769etn8akKpaWlGAwGi8YEOHbsWIOPkV9Wxo+XfqWlwwEkRwe2FA7kj8stOZPZgsIlcRRd0uMMPB/ejCBfB4qy80hNTaV5SCivfLgYzhahKZbI365Hd7mE4kpvtJXRtPU136KC586do7S01GzHM0VpaakiCxVaenpEXUmApLIz+XYzdnZ2xtFBFRUV6PV6SktL0el0dO7cGbVazbRp09i0adMNr3VyciIsLIylS5fi7+9vvDk5OVFWVtYYp16rpKSk636WZZkrV67U+rqysjJatmzJ008/DUBubi533XUX7du3JzIysgksmCmY4mxmMp4uF+kq/4iUHce5fB/2lXQgL02N4UQJXqpKno3wp5mHRHZWEZlp6QQ3b85X331PwemL5J0vpFKSKU4sQ1dZSXxRMFG+lu+wFiyjLrW2NvWttdZYZ29m0KBB13W+p6WlVXuOsixz4MAB49Xt6dOnV/v/jLUwV8dKXdxxxx21P8nMzHGelToDG+P+orXTYZy1+WzL68e2uAiOZraleOUlyjPBWS/xQtuA/7Zpc0lOTq5q036wGFVCOWXFEvlb9GgTiigsd8bbrjsBbuZbKPLAgQNmO5apzpw5Q3FxsUVjJiUlkZqaatGYdWVLbVrzTVw3o7S0NJo3b278OSQkpNoPxbJly1i2bBkAGRkZ5ObmWizHI0eO0LlzZ4sOj8nIyCAnJ8ei5wnQp0+fBse8mJeAe1kCcZVt0XkF09ZFQ4WmjOA/9pOnc0EV6o5GW4wj7hhi//e7rtRqcXUKRxodjeFIGqU4Iqs8cXQeSX93f7O+F05OThQXF1v0/a2oqECSJIv/TqOioiwes27qNqwrKyvLuLDT7Nmzb5jaVFBQwMCBA7l06RLvvPMOWVlZN9SY3bt333DcCRMmsHnzZjp27IgkScbRVlf/vHz5cj3Pr/6efPJJunbtamxw7dy5k6eeqn13kddff/26nWDmz5/PPffcw7Rp0ygrK7P4NB3B/LR6LVcKfgH0JNhF4uKnwaNEJujk7+h3XqE834BOZ8DVWYWh4n+/73xNGSq9HYmdhxKTkYHuSjmVyc4Uew2ko8sQ2geJLRhtl+m1trY6C/WrtdZYZ+uiujbr7t27yc3NxcfHxzg1oaa27FVKtmkB2rVr1+gxCwsLr/s5Li6OiIiIRo35d2FhYQ0+z8uFSfhUHCWlMhS9SzBBdga8pUpaHtxDYYELsl8AWl0FjmpPDGfTja+r1Fbi5BSBYXQv7GMzKSm1A+cAfDzuJMDN3azv/7hx4yz++xwwYABardain92wsDBUKlWjxvz7edad7bRprbLjoLoGbHVzwq59M2NiYvD19W303K66uvWHJWO6ubnh5uZm0ZgAx48fb9C0jOLKChxzD+OjP0krWYeTrMfOqxmlp2IpTNbi4VyJ7FWJnaaMf2w9S2aRliAP0Lq4kZ2RwdOPPMLMFx+i3zRX3A4dxzGoMx1b9Tfb2gZXDR06FEmSLPr+5ufno1KpLP47zc3NtXjMOpEkkEwvsgEBfhw8eLDGx728vDh58iSZmZmMHz+enj17VhPyxs/T5s2bAUhISDA5l8Y2Z84cxo4dy+HDh5FlmZdeeqnW6USXLl3i/Pnz3HnnnZw5c4bCwkKOHDnC119/DWDx9VoE86vUG9h9fgMdzm5DX1aOXZgzqjbhlOuLyNbZYT8sELeMLMrPlPHkhitkVUKQJ0j2atJy8pk+9S7+vfx7iqb/E7eyK6gM5TQPvQ9JIzqUbFodam1tdRbqV2utsc7WRU1tVlPbslcp2aYF8PPzs0jMa2NcuXLF4ufZpk2bBsXUGnRIJRfRVuTRTpWEryoXuwBfNBfPkXuqEnc7LRVuMgQk8X/bY41t2kpXZ7LTM3lu7iPc99Kj9Bvvh0PsOdwC2tKhpfl3Rvriiy/45z//afbj/t217+VPP/3ExIkT8fT0bPS4V505cwZ/f/9G/xw16Pg21Ka1yo6D5s2bX9crm5KSct2VMmswdOhQi1+hKyoqYvfu3Rbfl7WhC/nsS04h5PJx9EdyULUxUNkxmDJdCOlRQahvUxOqTqLip7/4eU0+F7Iq6NDckU8XdKSie38enfM98bFnIUOm813/5ERYEl39Q8zeaQBVexjff//9Zj/uzQQHB2NnZ/m9XdPT02t/ksJMGRpbV4GBgXTu3Jnz58/fUGOCgoJqfF10dDSTJ09m4sSJhIWFmT2vutizZw/wv3l9cXFxxMXFMWDAgBpf8/TTT/POO++wf/9+oOo/DT8/P+69915iY2MZNGgQb7/9drW7pyh9Faw6De/9t7zGzrmsUoezphl/Bt5DkH0+bl4BlJR4kFDgjr5VIe3cjuPpdIYN27O5mK8hItCDd6f3JGfwWN546mPizl3i8r7d/91O67+NWI0s3msLUTJna6m1lqqz5eXl/PDDDyQmJqLX/2/ru+oWEzNFdW3WoKAg/Pz8yMvLM17Nq+3/GaXt27fP4osVKrF93++//96g7RiPpWZhd+Evmh2JQxUAck8/Ctz8yI/siaGLJ8HqNPJ/S2bD9v+2aYMd+fQfndEO6MAj03/j0rkzuGXr6T3+Kc6GZ9EhIMCMZ/c/lvzyfpVWq7Xo+gZQ9d3Iz89805Ybi7XUWWhYrTW54yAvL4/k5OTrimz37t3rFMxU0dHRnDlzhtTUVDw8PNi6dWu9C3pj2bt3L+3atbtuaEhjU2K0AVTtG98Q0cFB/Jg3HbuRabRr5km5oYKSovbkljhQVHSG1v4n0ahlJnRpjsHOwB3hfqgD24Ln3axdN4dje3fzyCOPADDYtfGGtAUHB+PgYJ7FS0x15swZcnNzadWqlUXjKrGtZ51IEqjM06+ZmZlp3D716s4kc+fOxc7OjlOnThEZGcmaNWv48ssvazzG+vXrWbduHVOmTEGWZSZOnMjEiRMt/nsD+M9//mP8u0ajYf/+/XTv3p1ff/212uf//PPPREREEBERYew40Gq1HDp0iCVLltC5c2emT5/O8uXLeeihh254vdJXwWpiLXnUhbly1uj0XMhLpqgymzCvIIJdm+OqMxAadDvNc0tJLMglulUIO69cJDjkDCpDBoEVV9AeTuRuVy/sbm/DyOktcOvUDq2rH3/9eZB169YZ6+y1DAYZFw8vnO3tkGUZrV7mRHo+gW5OtPQ233zcvzuaVkA7PzfcHOpXB27lz0edWFGttVSdHTt2LMHBwfTo0cMsHfdXLwD8/RwlSSImJsa4IOKqVavMtjBfY5g2bZrFY65YsYIXXnjBojEbuphwoLsTh936ohrUjhbNHMl1cCKrKIqcAlfKOUtY4HlQG5gUFYoemWERfqi9QzF4z2HdD89zePcfxlrb00yLj1dHiQuuAwYMsPgOB9HR0RZfc67OrKjOQsNqrUln8dxzz7F27VoiIiKMV58bcy94tVrNe++9x6BBgzAYDCxcuNDqGgGdOnWy+JdMZ2dnXF0br6FWk927d1c7DKYmueWVuKjB2b7q/ZENDozq0JWCsija+nlQrNGwtXQfrZonYi9r0MZmok9yQNZVMq5/K1wiVRR2bI+7oze3RbTntsjqt5QprtDh7mS+QTOmdAJdHWViru2U3N3dLbptzVVbtmxRZKse05lv65qUlBQefPBBZFlGlmUee+wxOnfuzJIlS5g6dSoVFRXcf//9dOrUqcZjtGjRgn/84x/84x//ICkpiWeffZZ//vOf13WkWsrftxPLzMzkscceq/H5Bw8eZO3ataxbt46SkhK0Wi3u7u60bt2arl27AnDXXXexa9euRsxaMBdZlvklfg+t5F14Ozux8VJ7SjRnCfHU0s67HVl5nhQZipAluKNVe0oqA9ked4aLcgDN+5Rgn5nGSFrg7+2AXaAPvoZwfHx8qu00AFCpJJzt7Sgsr+TTY4do6ZeNs7qMX2L9mdS6Nx2CGmeHlgKNFic7y9fGW4/11FpL1dnMzEx+++23er9++PDhHDt2jNLSUkJCQvjxxx9rPMe33nqLKVOmMH/+fIYMGWKWbQAby4YNG1i4cKFFY1p6W2OoWqMsMDDQ5O8Vf2/3eTi407vjEBKKimjrFwAqPZmlx2kefAJHuQhtag6GLGdklcT4PmE4Rcnoe3TEYO9J9zYRdG9f/QWwogodHn9r01Zo9TjZ1+/f5+bNm2u9wFtaqWPV6WTm9jBP59yGDRuIjIw0y7FMtX37dgYOHEjLli0tGrdurKfOQsNqrUnfun744QcuXLiAvb296WfWQGPHjjXuU26NDAYDeXl5Fo976tQpk/YZNqdevXqh1+tr7Zk3yDJb4s7g5HIZvUGDrqInvi4udPL3wtfBjZ/OXSC9LIsO/sH0DezO8SwPVPJRfAodKU8rpaLEnsC7vHDqHEKuqhnq0pvHc3Woejw1v4KM0mKSynIo0hYQ5tyWga3rPmxpz549dO7c+abPSS7S8OvlDGZ3C6vz8atTWFho8aGLsizTpo21b61mviLbo0cPTpw4ccP9MTExddpd4siRI6xfv56NGzcSFhZmHL6vNH9/f2JjY2t8/M033+TNN98Eqq7unDlzhkWLFrFjxw4SEhJo1aoVu3btokOHDpZKWWiAcq2eQFU8ORURBKmL6RHoTUaJG052HoS4NONoxkFcHOz5KzWDPiHBuKg98Hdz4EJ2CzpHz0WKzCL2ShpaBxVyZXOig29s5GWVZOPqKONo54ssy+yIS6PSbjcj2mZyscCfU+me6PWeeLo23hf7Ia38G+3YwrWsq9Zaos4OHz6c3bt3M3DgwHq9vqZdvqo7x7Zt23L06NF6xbG0q1v8WpISne8tWrQgOTnZpI6Dv1IyKJEvoaeEUOeueDh4EuTujI+LF38mFaGVk+noG0yIa0syNHZoNGeoKFSjSdVjyLDHf7ILbn1bccUuCLtaNkW72mmQWqjhj4QEZIdsJPLRlHfiwXp8sTdl208Z6BNivm0bhwwZYrZjmapt27ZNYKqCddVZqH+tNanjoFu3bmRmZhISEmJyQrZOlmXS0tIsHtfSnQZQNU9q586dtcaOzcnEWb+byNxTJPr3YkfOJUI8S0mpsKOD6+24q51o4+lPkKsLuEK+VqKotC+nI5zo1PcHdL9XUnGgGKfmORg8nYnwbXbTeCpV1eJDh9IzyNSeINCjAhfJjnPZfnXuOJBl2aT1Ddwd7MzaoN29ezczZsww2/FMUVRUhJeXl0Vj1pkEkpmGdZlDu3btiIiIYMKECfzzn/9U9P2bNGmS8cqHwWAgNjaWkSNH1vk477//PhMmTECr1dK1a9dqpykI1sfFQU1M2xtXs7+qX/MeeDqoCfaoGgmgUtkxoEUvBrSomnag8pPw8NOglyHAvWpIaVJBLkX5f6E/fRBH5yJKfBzw1WYgOTmS69wXlUNfegeO4rf4dHZczsPXoxJ/tTfBHjWPgNudeBmfyqP4Gfbi22YxDvaW31Pc1hhkmUJtPt4OZnwvrajWNnad9ff3Ny5a+N577+Hu7o6jo6NxDYLG3Ga8Kejbt6/FY546dYoxY8ZYdOTlwIEDOXXqVK3PKyjXkFi0n9ucjpDh3pbVZ2MJdK8kxMtAV69+lFXo8fD2JNDVlUBXV87nlpKaH82FFm6ERf9BeYZE2SENTq3SMQRFcltw7euTaSr17E2Ow83zDFodVBrsyCyr+/onR44cQavV1vo8Nwc1XQLNtxbChQsXGrR+RH0kJyc3aAH36uzP2kt779b42N84EjmjPBV7lQO+jnX4LmBFdRYaVmtNOov58+fTrVs3IiIiriuyO3furG/OTV67du0svgAIwB9//FHrVXFzCwgIYOvWrbV2HGSXZ9KSeE7rImmpSSXEowXOKg8c9SHkVqZSrsonuaySUM82qFQqvB3dOZCSSKhrAPLwB3GMOEGBroy0ikBc/IJrnQ4gyzLfnj1JW78/aWcvczAzhPR8N9p61b1Xb+vWrTRr1qzWxWS8ne3xdjbfyJupU6dadCQPVG0dZQ0L3N1cXXtnG/eqxaFDhxRZaKg6105LUKvVtGjRgtBQ07bLu3Yhqp49e3Ls2DFzpycoLNK/5i+VKlVVTfV1cySzuILfTx/AL/FXKhKTcMjPRdJUovGV8AiTkcP8yfVpQ2auntSyFCp1DshOCQztWICng5a47JqnKFzKy4OSnykryiDOPxJvlWWn9dmqEm0pZwoP0N+/+uHuZwtO0NGrax2PWpda27TrbHZ2dqMd2xZs3LiRLl26WDTm7Nmz0ev1Fu04kCSJH374odZzvZCXSjf7w+TlaAk2nKFdMx/sVVBe4UuhphjZIRudJKE3GLBTqXB3tCNJk4Ja54XcfTguocfI0+jJ1vriKIViX82oXb3egN01U7LWnj9OdPBByoDY3EBOp3rTt3ntX1Cvfi+76tSpU9x9990mvyfmkpKSYvGYFy5cMOvxZFmm0lCGJFf/mSzSFeCh9qrjUW2nTWtSx8GMGTP48MMP6dSpkyLzsa3R1TUe5s+fb9G4SqxxoFarTZo2Eh0UQVrRPHq7tMTJXktblTMqqeq9OpSajJdrOtmlZZTrDLg6qAjzbM9d4S1p7uFMsUaHS8idrD2RxpgOvjg72lNcUARAeaWOlIyTFNp542W/E0f7nlTKOiRDByrlCoo0Lfk9wQGDQU9KsSNd/W4+3E6WZbTay6hUDqjVVV+4srOzGT58uMVXtl65ciWPPvqoRWOGhITg7Oxs0Zh1JklI6rp82ShvlDQ+/PBD5s+fz2uvvVZtR9bbb7/dKHFvZv/+/Tz77LPX3ffmm2/ecJ8gVEeWZZafuERnwzns1v1A/sV87B3AzsWAQQd2WVB43g6nTmVc6OZORmUgDq4+nM1JoGNgOmnlbhRq3PBzqrmDws/FjZB28yjVGvB0tK+2wSzUXZbmCM1cVORU5CGp8vB1+N8VzHJdGRo5jQq9V90OWqdaaxt1dsKECWzYsKHW+241I0aMsHjM48eP4+PjY/GF/O66665an9POL4CknH6U2PWieWABYwlFlsDHxZFTmamoHbNIKi0npCSQEA9nmru1ZVBQEMGezmRl5xLSYQLfHEnh7s4B2NupUKuqFpjVVOq5kvgHRS6BOEh/4GEXip5ysB+Fl5MXJbpebLxQQSU6yjVOaLQ1X0QzGGQ2xmYQ4/4zTk4yXoFzgaoLAz4+lh3lpdfrFVn884EHHjDr8SoNWgLdknFQRXO2eC8d3ftf93iJNgcvp0tUGOqws54NtWlN6jgIDg5WZLVVa2Zvb2/yVT5zio6OpqCgwOJDpQ8cOICjoyOtW7eu8Tmu9s609b3akLn+Knp081CiufH9au5R9QXW3bHqozipc3Mc7Ks6pyr1en5P2I3fme045Z/Go3NLpII0cN6Cg38zUpzHcG/kCFSSxL7L58ivKCDc05FO/jVfCbuYk8XJlAP0Kf0e1/aD8fJ9EIPBQMuWLavdiq6xBTfiiro12b59O61atTLrdlepZckEOQejqsM+tTdlxhVoG+Lq5z0qKkrhTP5n3bp1N3QSrF27VnQcCCYprqwkyieXsgJH7O8aTry2nDDNJRz3XEIX6gMlZWjUrmR7B9K2WV9ayTKH8ktp0ywDyc6BrGJXBjaL40ppzXNuvZ2qGkhmHJwlAD6OQagkByq0Wtztr7+IkFqehIdjGsVyHTu/raDWWqrO6nQ6NBoN8fHxlJeXGxe9Kyoquuk6MbeKU6dO0b17d4tuEd2lSxezXzE2hVqt5tdff2XYsGE1PsfL0Q2v5ldH91w//bVTQDBR/kE3XEwN86m6cOX83wUNJ3cNxl5d9RyDLLMreT9e57bgknoa746+2FUUY+cEDr4+pLmX0tF/ImEerTmVep6kkiwiW2pwu8mv43h6BuG6z9DHnyc34ja8gPz8fHbs2GHx0clxcXEcP36cKVOmWDTu8uXLefHFF812PK1Bh53kioPKGbmaK/86CpBJRivXYZc3K6izYJ5aa9JZREVF8cADDzB27Njrttmw+i3dGpmltxyBqoKQmZlp8bUO7r77bosMb3ewV5GQX0p63iE80g8SkHAJTuRiCAb1mRRUrSQIDEVy8yWnpIBDKUmkl5TTIfQiTg6VFJc7UaANpwXVX1FPzdjLbWWb0Xt7Eq8JpwdVW2tqNJpGP7e/Ky0tpUePHhaPq9fradbs5utH1NX54qMEO1e/K8WForO0de+ASqrLaCUJzNUJ0QB33nmn8e9/X4ti5cqVFs3liy++YOnSpVy4cIHo6Gjj/cXFxYqsTC00TR6OjkS37A0tqxqy2sQs8stiKZvQGyc/PwpL3AlxDyJIdqBrcw/y8vK4XFpIblElPm7ZDG2TSoHWh5SMkhuGxwqNy8ehXdVfqumQae0agc7QDCQD8GEdjqp8rbVUnf3444/54IMPSEtLo2PHjsaOAw8PD4uP/LNGarWa9PR0i65nFhwczLlz5ywW76qOHTuyZ8+em3Yc3IwkSSbVPnu1ipTCMlLyTuOVvwmfIymoE/PQu0lQnoM60gG9b2vK3AJIKvLkbGk8h1QSfn7xBPoZ0CPhqKt5l4LM0ovYS6FkhYTgYT8YqNpu2dJf3qFqNJu51xowhbkXdnazdyHCfioAHd1uXEA11KUjLpIfTqq6LGyufJ0F89RakzoOSkpKgKr5T1dJknTLdxycP3/e4lvrhIeHU1ZWZtGYAD4+Pixfvpwnn3yy0XqjDbIMsszJxKMErfuUjFwP3ApycWtjoPKSGl2mASeVRGbzMNYe7oGj5EKwh44ydQrtXXRIMhRXOBDuVfOIA71bBHFuzxDgoqe9V1UjLDg4WJFdBk6fPk1BQYHFR66EhISYdX2OHE0e3g6VSJKKEm0BbvZe1z2eX5lTx04DMOcKtObw4Ycf3lBkq7uvMU2ePJmhQ4fywgsv8Prrrxvvd3d3t/iQRME2qCSJ21sFAoE1PudUehaOLrF09M7AR12InbaEADsVZQE+nMspIfImI7wEy1GpVDiovOrxSuuptY1dZ+fPn89jjz3G559/LjoKqjFhwgTKyxtniPTN/PnnnxZfjd/Ozo5BgwaRk5PTqCvy6/QGruRcwfDTF2T8mY2dnR2eoRK6HBmVkwQ6AwYfO1Yn9yCn2JW72oSyL+sSnRxlHOzsKCh3Ijyw5vZaTGgMXo790ctgb6dClmU+++wzXnrppUY7p5qcOnWKSZMmWTRmZWWlydtq1kd1nUNBTuFA7Qtd/u1IVlNnoWG11qSOg+XLl9cvMxs3cuRIi19xcXJyYv369RZfWwGgX79+HD9+nJ49ezbK8X+JyyAp+yB9Dm6mJEWPa2s7HJyhPEGFvZeWslSJ8mxwKTlD6x5tcHPIQ6PV0MkvhXKDE/F57jjgj4tjzf847wi7fl/T1NRUtm3bxhNPPNEo53QzLVq0oEOHDuh0OovG/f3332nfvr3ZjpevScLDUUKjrySl4jTt7f83H0xr0GKvziFf/wtequEmH1OSJKtYgXbdunV8//33JCYmMnnyZOP9RUVFeHt7WzQXJycnwsLCWLp06Q2PlZWV4eLiYtF8hFtDsV5Pfrk7snMy6sJYZE9vLtMXb2JEp4ENsIZaa8k6a2dnx7fffis6DqpRUFDAb7/9ZvY547WZPn26IqOX/Pz8WLVqFU899VSjHP9SbikbTu9lcNI29GfzcWkl4WCvR5ctIcugKYPSNHBMT6Dd5Ey6hzpyNruEuyKvADJ7k4JwUzsR6FbznC8f56qRz1cvzaSkpFh8vYirYmNjLf47vHLlCpmZmRaNWR/WUGfBPLXWpLNISkri8ccf5/Dhw0iSRHR0NB9++CEtWrSoX+Y24sCBA/j7++Pvb9n9ppXY9xagV69ebNu2DYPB0CiLZPq5leIpu5Ny+0Qq+uYjVZTSLmU3mmN6nEJ02JVKyG4O5HUdzJAWvUgtzedMTgJ+HnAo2Rl/h0JcVHnklXfBx8TJtfHx8Yos5gKwevVqnnnmGYvvcGDuHmE/J3/0qKk0VOLlcP2/hRJdEY52hVTKuXX/D8UKemejo6Px9/cnPT2defPmGe93d3e3+PzBCRMmsHnzZjp27GjcUuzaPy9fvmzRfATrklVcSYB77YsvybLMH3H5lBiK6RXcjL1JObT1cadLkEe1z+8bEoSXdySncr2pcOtPsdaP3r7NcbBTvhEkmInCtdbSdXb48OF8+umnTJo06boO11u98zU4ONisax+Z6vjx45SUlNCpU6fan2xGgYGBjBs3jtLS0kZZeLzcUMzQMFcymk1E6ppPdkUxnfP+QtqRiVs3Z/SlBsodnUmP6MfkqInkV1RwLvMUhRo3UgpcCHUporlzPAeTOzKmXe0LWsuyzOHDhxk/frzZz6U2Si2MGBgYqMiinvViI21ak/7nnzlzJnPmzOHnn38GqnosZs6ceUtvxwhVWzLm5ORYvONg6tSpZGVlERAQYNG4AL6+vqxbt4577rnH7MeODgqHoKrhP6fS89ly5jQBrTIpimmNxgBp+aGEePjhqgqgo78vAS5eXM6EgwmeuDlnEB1UwC+XW5FaWG5Sx0F8fDyFhYW4ud18F4bG4uFRfUO9MVVUVLBjxw6zLnbq7dAcqFrfwJ3rRzI427kQ4NADZ6mibgeVrGNYV8uWLWnZsiXvvvsuUVFRxs9KcXExR48etWjP/ubNmwFISEiwWEyh6TCl0wBAliHEwwE3F38C3R2Z2LE5Gp2BEo0etxpGa9mpVHTzv82c6QrWwgpqraXr7FdffQVcv4K46Hytmu6Snp5u8bj9+vXj0KFDFu84gKrOoiVLlvDMM8+Y/did/ZuBf9V6UpmlFXx7OoFEv3JUDzihd3IjqyCAcJ9Q/FUeuDg44GxvT6i6JQk5Eq1802junsf5giAqKktMirdz507FOr9OnTpFUlKSxS8of/nllzz44IMWjVkvVlBnwTy11qSOg9zc3OuGNEyaNInXXnutnmnbjnbt2imyGmxlZSU7d+60+HAyqOqt8vLyorCwsFH3W+4c5E2AXQRZhg40d7ZHo7XnrnCX665aJxYUUK66Qq/m6biqtRRr7QjxLuNKUQGdmtX+pfyXX35h9uzZjXYON5OWlmbx9TGgalhXREQdVoJtICc7Z5zs6rkvtBUU2aseffRRjh49avzZ1dWVuXPncuzYMYvn0rt3bw4cOFDrfYJwrYs5xcTl5dOruT+Bng44qv/378tRrcJRDCC4dVlJrbVUnRWdrzW7dOmSxWMGBAQodgEnMDCQXr16NfpUiUBXJ57s1Z643Bb4u0nYYY9razWqa2KmF5dR7HiaHkE5+DmX4agvJsoLtuUnU65tZdypoTrl5eX4+vrSpUs921sNVF5eTv/+/Wt/opl5eXkpchGuXqykzkLDaq1J482v9k5kZGSQkZHBe++9d8tPU4Cq3tkdO3ZYPG7Lli0VKw5QtVDiv//970aPY29nT+dm/oR5etHOz/W6ol6h1XM2/yydQ3IIts/D376IZrpLtHPPpkRX++KRmzdvZubMmYrsjAHw888/K7L9o06nY/DgwRaPW3dV88FMvTU2vV5/3edPpVJZfG2KnJwczp49S2FhIefOnSM2NpbY2FgOHjxIfn6+RXMRmh6NQY9GrmD1uYNsvHQRfV32oBZsmOm1trFZqs5WVlbywQcfMGHCBCZOnMhHH31EZWWl2eM0RY888ogiu0zt2bMHrVZr8bgAAwcO5Pnnn8dgMDRqHEmSaOvnipeTC+5O9td1GgBsuXyW1t5FhKhTcClOwKksHVlbQUfvQOxrmR78wQcf4Ovrq9guN8nJyYos0ty2bVuLx6wf22nTmtRxsHz5cq5cucLIkSMZNWoUV65cYcWKFfVK1tbce++9isTdv3+/IsUdqhaUmT59OmlpaYrEByjUVJJeCsVlahyTTkDycfQqd36L78yk9u1u+tr8/HxOnTrVKHPaTDVo0CCCg4MtHnf9+vWKdFjUmSQhS3Ym3xpbp06dePHFF0lLSyMtLY0XXnjB4mscbNmyhccee4zk5GQeffRR5s2bx7x583jjjTd44403LJqL0PR0CvBiXLsIpkZ2o19IC1wdmkAdEBpfHWptY7NUnX344Ye5ePEiTz/9NE899RQXL17k4YcfNnucpujMmTMcPnzY4nEffvhhxTpvJEli6tSpxMbGKhL/KgP2JBe6oa/QYJ93Bdmg50JJe1q5h6G2q7lDID4+nunTp1t8h66rCgsLSU5OtnjcyspK9u7da/G49WJDbdpaOw4MBgMTJkzgP//5D8ePH+fYsWN89NFHjbr9RVOyceNGRYpddHQ0x48ft3jcq9q0acPy5cuJj49vlOMfTSmiQlvzIpC+zo7c3aYreoM/FSGRZLj24FxJd6Z26oydquYCW1FRweHDh3n22WcbI22T5OTkKLY+yJ133tkk9l2XAQOSybfG9umnn1JRUcGdd97J2LFj0el0fPbZZ40e91ozZszgjz/+YPXq1fzxxx/G28aNGxVZDElomgJdvGjl3USGdgqNri61trFZqs6eOHGCTz75hN69e9OnTx+WLFnCiRMnzB6nKerWrVujLH5dm4CAgOvWnLC0Tp06kZub22ijiK/kVXAkpYASTc3t2oe7dGVg8+4kyu3I9BlLrHwfQ1sMoa1/zesWpKamsmLFCpo3b94YaZukuLiYxx9/3OJxDQaDYhdv68qW2rS1XnJQqVQEBgaSmZlJYGDNez3fqnr27El8fDwdOnSwaNzo6GgOHjxo0Zh/t3DhQs6ePVvrPriJuRWE+da8D+1V6YUVrDudQnigC5F+XvwSm8WDzapfAFJtp6KDny/BHtFklbajfVAgTvbqG4Z+/d3777/PzJkzFf3yfOrUKUaOHGnxuBqNhiNHjtC9e3eLx64rWZbQy9YzH8zd3Z133nkHrVaLvb1pO3Y0lrvvvptNmzYRGxt73agjJfZtFgShabOmWmupOuvo6MiRI0eMW0sfPXpUsWmL1sbX11eRBRJVKhXt2t18tGhjGzhwICtXrqx1vYMreRW09Km9TVupM/DlwXQc3UqZ0bktJ7Py8HS0p42vC6pqLnBJkkSkbzBtdJNwVNf++c/Pzyc7O5vnn3++1uc2ps8//5xXX33V4nG//PLLptNxYEV1FhpWa03qViwoKKB9+/aMGTOGyZMnG28CDBgwgIKCAkVi//bbb5SV1T6fv7HY29vTtm1bXn75ZYqKimp8nimdBgAVOrinSyij2gYT5u3C3e2DSC28+XQMTwd32nqH4OJw43yxaxkMBlauXMk///lPgoKCTMqnseh0Olq1amXxuLGxsbRp08bicevLIEsm3xrbgQMH6Nq1K+Hh/93149Qp5s+f3+hxq/Pggw+yefNmPv74YxwdHdm0aRMpKSmK5CIIQtN3q9XZTz/9lFmzZtG+fXvatWvHrFmz+PTTT80ep6k6ffo0smz5NVDuuOMOxRf5nTFjBh988AFxcXE1PqeFt2mdTAYZhrb35r5O4dipJMa2D8bfzZHalpcxpdOgvLycxYsX07p1a5ycTGtjN5ZBgwYpEletVuPl5aVI7PqwlTbtTUcc5Ofn4+3tzXPPPdfwLG2UJEls376d3r17Wzz2Qw89hEajUXTvYVdXV1555RXi4uIICQlp0BaRrXydiM8vwV1rh4t91Uezuad5rgJs2LCB7t27Kz5M/9KlS4p9yVOr1QwZMkSR2PVhieFappo/fz5btmxhzJgxAHTu3Jnff/9dkVyOHDnCyZMn6dy5M8888wzz58835iUIglBX1lJrLVVnu3fvzqlTpygqKkKW5UbdIaopeuyxxygtLbX4Tgf+/v68//77irSnr/X444/z22+/4efnV+0XU1PbkU72Kpp7OZJeUk5Lz6o1tbycG762TE5ODpcuXeKVV15RfEeBrVu3MmHCBIvHlWXZ4utMNZS11FloWK296Sd4yJAhHDt2jPfff5+ffvqpwYnaqilTpjT6Vi7VadasGc8//zyvv/66ReP+nbe3Nw4ODrz44ou89tprderI0BsM/PuvC7T1legVFIqb/f86DcyhoKCAd955h1dffVWReXt/5+joyLRp0xSJvWHDBkX2Sa4PGQmDrPzv6ypJkm6YQ6jU5+nq1QUPDw8uXLhAYGCg2F5MEIR6saZa29h1dtWqVTd9fPr06WaL1ZSVlZXx008/WXy7akmSePLJJxt9u+/aqNVqRo4cydtvv02XLl0YPnx4nV6/4XwaeXlX6N+2AyU6LT2a1TyVt670ej3vvvsuL7zwgmJbWF6roKBAkYuXFy9eJCEhQfFOJlNZU52FhtXam35DMxgMvP322xw7doxPPvnkhscfffTROqRpu9LS0igoKCAmJsbisfv160dBQYHiw3VcXV157733OHz4MElJSUycONGk1+kM0CvUgz2JiaSVFRPuEcIwN2ez5HT58mXS09N5+umnraLTQKPR8NVXX7F48WJF4je1q9LW1Dvbvn17fv75Z2RZJiUlhSVLlhjnx1rarFmzKCgo4NVXX2XUqFGUlZXx4osvKpKLIAhNn7XU2saus6dPn77hPr1ez/r160lPTxcdB//VokWLBo0ebQi1Ws3SpUtZsGCBIvGvtXDhQrKysvjuu++YOHEidnamzVFv7e1IeaEjW5OO8//t3X1czff/P/DHqdMV0pVC5WLpQlenVEguchGRcjVmbIytrMZcjLHNkDDGLnxcjuVijDAzKcbYmGutkYpcpVBJuk66Op3X749+na+mctI5vd/n3fN+u53brd6d3q/n+3R6ntd5ntdFez1LpRUODh48CENDQ6xatUop52usmzdvYtSoUZy0bWBgAD8/P07afl18ybNA43Jtve+m9u3bB8YYKioq8PTp05dupEqvXr1w+/ZtTtoeNmwYfv/9d07a/i+RSIQePXqgVatWuHPnDrKzs1/5OzpiDfSztMAEp65ory9Cb0vlJdjjx4/Dy8sLRkZGSjlnY6WlpXE22uDkyZOcz4NrCAZAxjQUvqnapk2bcPnyZYjFYowcORKMMaxfv17l7dbmww8/hKGhIQYMGIDk5GQ8fvyYFq4lhLyWhuRaVVN1nl2zZo38tmLFClhZWeHYsWMYPHgwbty4obR2hEAmk6GioqLJ2zUxMUGPHj2avN26mJmZwdXVFbt370ZxcbFCv9OtrQl8rDqgu3lbdG7dstGjkZ88eYIbN27AyMgIPj4+jTqXMv3000+c9SvDw8M5/8C0IYTUp613xEHXrl3RtWtXeHh4qNXc6Kamp6cHKysrztrPz8/Hw4cP0bFjR85ieNHQoUNRWFiI7777DuPHj4eNjQ3E4vqnH3QxNEEXw8Zv8XnixAncvHkTs2fP5nw9gxdVVFTg5MmTCA4O5qT9pKQkDBw4kJO2X0/TbEmjqJYtW2LlypVYuXIl16HUas6cOZzMMySEqDv+5NqmyLPPnj3Dpk2bsHXrVgQEBODUqVOwtLRUWXvqysDAAOfOneOk3yCRSPDjjz8iKCioyduujZ2dHezs7BAREYEHDx7gs88+e+XvaGlooG97h0a1W1paiidPnmDXrl2YPXs2HB0dG3U+ZSouLkZISAgno3mlUinMzc2bvN3G4U+eBRqXaxWaTK6KokFoaCjCw8Pl2/iFh4fDw8MDjDGEhITg1KlTMDQ0xP79+9ViJfikpCR07twZHTp0aPK2p02bhps3bzZ5u/Vp3bo1QkNDUV5ejkWLFqFfv34YOnSoSt7My2QynDhxAiKRCJ06dcKQIUN4VTQAgD///BNeXl6ctM0YQ58+fRQeZscHVdVZ7v+G48aNq/e5dODAgSaMpm5crIBNCFF/fMi1TZVnFy9ejD179uCdd97BlStXYGLS+A8r/kssFsPJyQlA1Xbd4eHhiImJwdSpU1FWVobJkyerxda5/fr142whZyMjI5SXl3PSdn0mTJiAiooKnD9/HqdPn8ZHH32kkueQVCrFmTNncObMGSxYsICXUxHDwsLw1VdfcbKr3PXr19VuZz8+5FlAOblWeavQvYbPPvsMM2bMqHEsOjoa2dnZuHfvHiIjI7FgwQIcPHiQowgV99Zbb3EyrAsANDU1cebMGejr66Nz586cxFAXbW1trFy5EqWlpdi9ezcePHiA+fPnQ0tLq9GVytTUVPz6668YMWIEWrZsib59+/KuYABUjTZo2bIlZyvAXr9+Hffv34ebmxsn7b8umWK7xarUhAkTVNIxUDY+Pu8JIeqB61zbVHl2+fLlaNWqFTZv3owffvhBfrx6ceusrKxGt2FoaIi4uLgax6ZPn46IiAg4ODigV69eGDNmjLy4wFeampoIDw9HWFgYJ68vwcHB+P333zFs2LAmb7s+Wlpa6NOnD1xcXFBWVoaFCxfC29sbgwYNatSHM6WlpSgrK8PmzZthbGyMwMBAXk1LeFFZWRl8fHw4+zDq8OHDCAsL46TtxuA6zwLKybWcFg5qExUVhUmTJgEAAgICEBwczMmOBQ1laGiIxYsXc/ZkDgoK4vXK6rq6upg8eTIqKiqQkZGBbdu2YdiwYcjOzkaHDh3g4uJS79+4vLwcqampMDU1xbp162BlZYXu3btj6tSpMDY2ho2NTRNeTcNs27YN3t7enLXfunVrDB8+nLP2XxcfhnUtX74cV69exahRozjfWcbU1LTW/xHGGCdVf0KIMHCda5sqz8pkMpWduy4ZGRmQSqXyDw4mTpyIqKgo3hcOAGDEiBEoKCjgZC65pqYmrly5Ai8vL15ul6mvrw99fX2sWLECz549Q2RkJK5fv47AwEBcvnwZdnZ2MDAwqHWabmlpKRhjOHfuHFJSUtCzZ09ER0fj3XffxYIFC3j9focxhs2bN2P27NmctC+VSvHBBx/w+jGqC9d5FlBOrlWocFBSUoJDhw4hNTUVlZWV8uONHW713XffYevWrfD29saaNWugq6uLjIwM+RYRGhoaMDY2Rk5OjnxKA59169YNxcXFaNmyZZO3rauri6SkJGRnZ6Nv375N3r6itLS00KlTJ3mB5fHjx7h69SoMDAzw888/o3fv3sjKysKdO3fw1ltvYdOmTbC3t4eJiQkyMzPh5+eHxYsXq03SyMvLw7Bhw9CpUydO2pfJZIiIiMDChQs5af91McaPrWv4tLMMLUhLCFE2PuRaPuXZxiosLIS7uzv09PSwYsUKtGzZssa2Z5aWlvj7779r/d3w8HCEh4cDADIzM5GTk9MkMdfFysoKkZGRSt2RqaCgQOH7BgcH4+7du3jjjTeU1r6qeHt7w9vbGzKZDFZWVigsLMQff/yBpKQkDB8+HEePHoWZmRk6duyI+Ph49O/fH0ZGRnB2doa2tjZCQkIAALm5uRxfSf2SkpIgkUjkz82G/D2V4c8//8Qbb7wBfX39Jm23sdfJhzwLKCfXKlQ4GDFiBMzNzeHu7q60oSkhISFYtGgRGGOYMWMGvv76ayxZsqTWubp1vUnkOsn+94nUt29f/PXXXyqdy17fk7dfv36IiYlR+uOgysSgra0t38byxSfs4MGDUVBQUOOYRCKBVCpVaWJV9rVu374d48aNq/dvosrHNy0tDT179nyp/aZO9g3FAEh5kGT37duHyMhI+c4yhBAiJHzItULKs6mpqTA3N0diYiKGDx+OXbt2vXSfuvq0gYGBCAwMBAB4enryYprco0ePYGRkpNRF8BpyXYcPH0aLFi3g4NC4hQabkqmpKXJycmBiYiLfMrBr167yn3M5ArUxSkpKUFxcDF9f3xrHm/J5yhhD9+7dm6y9FzXmOvmQZwHl5FqFCgdPnjzByZMnX6uBF61btw7bt28HAMTExMiLEFOnTsWyZcsAABYWFkhPT4eHhwdkMhlyc3NhbGxc6/n4kGT/2+bly5fh7++v0k/E67tOd3d3HDp0SOmr0XL1AsaHv+nrqp4Hpsi6E6q6zqtXr2Lw4MFN2qZyiHgxH4x2liGECBv3uVZIebZ6tXcnJyc4ODhAJBIhPT1d/vO0tDS0b9+eq/Aa7NNPP8W9e/dga2vLSfuBgYFITExEZWWlWi3wLESbN2/mdFHCp0+fqtX/Tk3c51lAOblWoavw9fWtc2hVQ8ycORNxcXGIi4ur8QloZGSkfJsRf39/7N69G0DVegdeXl5qMywdAObOnYvs7GzO2m/bti3EYjFnCzWSKqWlpfjyyy/RrVs3zmIoLCxEbGwsZ+03RvUKtIreVM3S0hLDhg2Du7s7ACAxMRGrV69WebuEEKJKDcm1qqbueTYvLw9lZWUAqgoEN2/ehJOTEzQ1NREfHw+pVIqIiAgEBARwHKnitLW1sXPnTs7aF4vFEIvF2LJlC2cxkKpFtj/44ANOty7ds2cP7OzsOGu/MYTUp623cGBqagozMzPs3LkTAwYMgIGBAczMzOTHG2P+/PlwdnaGs7MzUlJS5HOw/f39YWxsjC5duiAsLAyrVq1qVDtNzcDAAGvXruU0hqlTp+Knn36qsR4FaVrJycmYNWsWpzE8f/5creaG/pcMGgrfVC0oKAjLli2TL67l6OhY6xBUQkjDlUjL8OeDFEg5WLyOKJ5rVU3d82xSUhI8PDzg4uICf39//O9//4OxsTE2bNiACRMmwM7ODn5+fnB2duY6VIWJRCK8/fbbnG756+TkBIlEQn1ajpSUlGDXrl1o3bo1ZzFUVlZi+PDhaNu2LWcxNJZQ+rT1TlVQ5Vyz6lEF/6WhoYGtW7eqrF1V09TUxMCBAznfCaJXr16Ijo7GyJEjOYuhuTp79iyKioo43cmAMYb//e9/WLlyJWcxNAZjQCWP3kOUlpbCw8ND/r1IJKJhk4QoSWFFCVxM1XUIqnrjU65V9zzr5eWFhISEl457enrixo0bHESkHLa2tli9ejUWLFjAWQy9e/fGp59+ihUrVkBHR4ezOJqj5ORkzhcl//3332FkZMTrHdTqw6c8CzQu1ypU1njzzTcVOkaq9O3bV75oI1ccHR3h6uqqlCkmpGEyMjLkC+JwJT8/H6NGjeI0hsYRoZIpflM1CwsLXLt2Tf7CuXnzZnTp0kXl7RLSHLTVM0R+mRSaajQtUTgoz5L66erqwtTUlNMYRCIRZs2ahaSkJE7jaG4iIiKQkZHB+ZaYxsbGKl14XvWE06ett3AglUpRXFyM5ORklJSU4Pnz53j+/DkyMzNx8+bNxkcuUNra2sjPz+d0aBcAdOrUCX///bd8zh1RrYqKCoSFheHtt9/mfF2O/fv3o2fPnpzG0BjKnA/26NEj9O/fHw4ODpBIJPjll18AVC3Q6ujoCGtra/n2oHXZsmUL1qxZg4yMDLRv3x5///03zbkkRImsjVpxnjebI2WucdDYXEt5lr8mTZqEo0ePchpDhw4dIJVK8fPPP3MaR3ORmpqK3r17Y8iQIZzGkZCQgPz8fLV+fRBSn7bewsHGjRvh5OSEpKQkODo6ym++vr5qPXe6KcybNw/nz5/nOgwsXrwY0dHRVDxoAleuXMG7777LdRh4+PAhp3PRlIEBqJSJFL7VRywWY+3atbh58yZOnTqFOXPmoLi4GNOnT0dERARu3bqFqKgoJCYm1nkOMzMz7N27F5mZmXj8+DH27NmDM2fOKPeiCSGkiTUk175KY3Mt5Vn+0tLSwvnz5zlfZ8DDwwMtW7aUz80mqpGTk4N169ZxuhhitUuXLtW5O5i6EFKftt7CwaxZs3Dv3j18//33uH//PlJSUpCSkoLr16/j448/VqiB5kokEuH333/nxe4GPXv2xI8//sh1GIL2ww8/wNzcHFZWVlyHgocPH2LixIlch9E4DEob1tW+fXu4uroCqEqWxsbGyM7OhlQqhUQigVgsxsSJExEVFfXS7xYWFmLlypWYPn06jh8/DplMhg0bNsDa2rrOdVoIIURtNCDXvsrr5lrKs+phyZIlSE1N5ToMjB49Gt999x3u3bvHdSiC9Pz5czx8+BBLly6Fhga3WwgmJyfD29sbWlpanMbRaALq077yGaGpqYm9e/cqdDJS05w5c5Cfn891GLC0tMT06dOxZcsWzqdPCNGzZ89gYWHBi6LBnTt3BDGNiEHxymylTISsrCx4enrC09Oz3vVFYmNjIZPJ8PTpU1hYWMiPW1pa1thru9qkSZOQlJQEiUSC8PBwDBo0CPv378fBgwdx5MgRlVw7IYQ0lYbkWkXzLNCwXEt5Vj3o6upi69atvPi0f/bs2bh//z7Ky8u5DkVQZDIZvvzyS1hYWEBfX5/rcLBr1y506tSJ6zAaTUh92np3Vajm6+uLzZs3Y9y4cWjRooX8+Itfk5eZmpris88+w7JlyzivlolEIjg7O+Ps2bPw9vbmNBYhOXXqFPLz8zF27FiuQwFQ9XeePHky12E0GgNQocDQ2GpmZma4fPlyvffJycnB5MmTER4eXmsBrbb5c8nJyfLhXoGBgWjXrh0ePXoEXV1dhWMjhBC+akiuVSTPAg3PtZRn1ccHH3yAoqIizhfLE4vFGDRoED755BMsXboUhoaGnMYjBDKZDBcvXsSSJUs4//sCQGZmJqZPny6IPCCkPq1CY1C2b9+O1atXo3v37vJ1DpycnBRupDn74IMP8PjxY67DAFC1VZCDgwPWrVvHdSiCUFBQAKlUypuiwbVr13D58mVhJFmmvPlgAFBWVobRo0fj888/h5eXFywsLGpUY9PS0tC+/cvbwWlra8u/1tTURIcOHQTx+BJCCNCwXKuI18m1lGfVh62tLdauXYvS0lKuQ4GmpibCwsJw//595OTkcB2O2lu9ejVMTEx4UTQAgLVr1wqmICSkPq1CIw5SUlIadFLyf2xsbLBhwwa89957vBj2Y2pqChcXFyQnJ8PKykqtVynlUlRUFDIzMxEUFMR1KHJFRUXqv7bBC5S1JQ1jDFOmTMHAgQMxadIkAIC5uTk0NTURHx8PBwcHREREYNu2bS/9bnx8PMzMzOTnyc/Ph5mZGRhjEImqhpMRQog64zrXUp5VL++++y6uX7/Oi52bDAwMYGtriy+++AJffvml/HlEFFdRUYG9e/di/vz5nK9pUE0qlWL8+PE13uiqO67zLKCcXKtQ4aC8vBybNm3CuXPnIBKJ0K9fPwQHBwvqD6pKb775Jv7880+MGjWK61AAAN7e3rhx4waWL1+OhQsX8iZRqIvY2Fg4ODggICCA61DkTp48CQMDA2hqanIdilIwBsgaMKyrPhcuXMD+/fshkUhw+PBhAMDu3buxYcMGTJgwAaWlpZg0aRKcnZ1f+l2pVKqUGAghhI/4kGspz6qXLl26IC4uDra2tjAyMuI6HLRq1QqrVq3CnTt3UFRUpPB+9KTKvn374O3tzZv3AowxLFq0CCtXruQ6FKXhQ54FlJNrFSocTJs2DS1atMC8efPAGMPevXsxbdo07Ny5s9EBNAft27dHu3btkJqais6dO3MdDgDA0dERGhoaSExMRJcuXdCyZUuuQ1ILP//8M/T19eHh4cF1KDWkpKTwavSDMlQqaR3PPn361LmY040bN5TTiBp4/vw57O3tMW7cOHzzzTfo3LkzWrduDQ0NDZibm+PYsWNch0gI4QDlWtJQ/fv3x549ezBjxgyuQwFQteaak5MTli9fjunTp8PU1JTrkHjv4cOH2LZtG5YuXcp1KDU8evQII0aM4DoMpRNKnlWocBAXF4e4uDj5915eXvKtIIhiXF1dsXLlSl79g9rb2+Pp06f44osv8NVXX1HxoB4lJSXYunUrPv74Y95UZatFRETg/fffF9S0EwbFtv8iiluxYsVLQ0svXryIVq1acRQRIYRrlGvJ6zAxMcG4ceNw/fp1uLi4cB0OgKoFE0NDQ5GQkIDjx4/Lh3GTlz148ACPHj3CwoULuQ6lhoKCApw5c0YQi3y/SEh5VqF3QDo6OoiNjZV//++//0JHR0dlQQmRrq4uFi5ciJiYGK5DqcHU1BTffvstEhIS8Oeff3IdDi89e/YMx48fx7hx43hXNMjLy0N6ejrEYoVqgGqDQbkLyTR3d+/exa1bt+Dn58d1KIQQHmlIriXkRaampti5cycvtmd8kbOzM9q3b4979+6huLiY63B458CBA4g6Go3evXvzbsr5oUOHBNlPEVKfVqF3G5s3b8aUKVNQXl4Oxhh0dXWxY8cOVccmONra2vj111/h6OjIq0/3xWIxPD09sX//fjx69Ajt2rXjfPtIvjh48CBSHqTi07nzuA6lVtevX8fcuXO5DkP5GCATSHWWD+bNm4c1a9bg4sWL8mPV69WIxWIsWLAAb775Zq2/Gx4eLt9HODMzkxerVxcUFHAdQoOpY8yAesZNMTcA5VrymjQ0NLBixQrcunULDg4OXIdTg4+PD7KysrBo0SJ88sknsLS05DokzmVlZWHP3j3oPm4oWpTp8W6U6tWrVzFgwAC0adOG61CUT0B5VqHCgZubG+Lj41FYWAjGGG+26lBHn3/+OdLS0mBnZ8d1KC8ZP348Hj16hHnz5uHzzz9Hu3btuA6JM7m5uTh4+DcYejhicL/+XIdTq4MHD8LY2Jh3yV8ZGJS3Am1zFxkZCVtbW9ja2tYoHFy4cAHm5uZIS0vDwIED4eLiAmtr65d+PzAwEIGBgQAAT09PmJiYNFns9eFLHA2hjjED6hk3xawYyrWkMVq0aIFjx45BR0eHd4sSmpmZ4ZtvvkFaWhrWrVuHkJCQZvuh2P379xEXF4fJU6egtb4+pDz7VFsmk2Hv3r1YvXo116GohJDybL2Fg127dtX7y0Kbg9IUDA0NcerUKTx69Ag+Pj5ch/OSDh064Ntvv8WTJ0+wZs0afPzxx1yH1KQYY0hLS8PJkycxzN8PlqbtUMmUtKKJEj1+/BgeHh68WWxT+fg/XEtdXL58Gfv27cMvv/yCZ8+eoaKiAq1bt8bixYsBAJaWlhg0aBDi4uJqLRwQQoSMci1pnFmzZuHmzZuorKzk3c5OGhoa6NixI4YOHYr4+HgAgLu7O8dRNa0VK1agV69eGDNmDACguFyKG9kF6GFuzHFk/+f8+fP4+uuveTcdWHmEk2frLRwkJCS8dKyyshIHDx7E48ePqXDwmsaOHYvExEQ8e/aMlwuTicViWFhY4K233pLH2b9/f67DUrnKykps3rwZgwcPxtSpUwEA9/KeQUMkgpUhf6aWMMawZs0arFixgutQVIYx5a1A29ytXLlSvq3Rzp07kZiYiLlz56KoqAj6+vrIz8/H2bNnebM6NiGk6VCuJY2lpaUFLS0tbNy4ETNnzuQ6nFrZ2trKd4VLS0vDsGHDeDe/X5nKy8vxww8/wNraGl988UWNkakttcW8KxqkpaXxruikTELKs/UWDtasWSP/ury8HNu2bcP//vc/DB48GAsWLFB5cEJmYWGBFStW8Hqf0k6dOqFjx46Ijo7Gvn374Ovry4s9e5UtOzsb69evx4gRIxASElJjfpW1Ef8KO2lpaZg7dy709PS4DkVlGICKSqFWnrn35MkTjB49GkDVEMGZM2fC0dGR46gIIU2Nci1RBgcHB5SUlOD58+do0aIF1+HUSiQS4Z133gFQtXZbZWWl4ArmUqkUP//8Mzw9PTFixAjej0rNz8+HkZERevfuzXUoKiWkPPvKNQ6ePXuGTZs2YevWrQgICMCpU6dokRElMDIywrhx41BQUMDrNSNEIhG8vLxgYmKCQ4cOITExEV9++aUghhM9efIE27Ztw5QpU/Dpp5+iVatWvFj4rT5xcXE4e/Ysb6v6ysIYBDOsi0+mTJki//r69evcBUII4QXKtURZ3N3d8emnn2LhwoUwNDTkOpx6hYSEoKysDMePH8fNmzcRHBzM24KHIsrKynDo0CE4OjrCzc0NXbt25TqkV2KMYdmyZVi0aJEg1+p6kZDybL2Fg8WLF2PPnj145513cOXKFbVcbIjP3NzcsHnzZvTq1Quurq5ch/NKY8aMwejRoxEbG4tff/0VH330ETp06KB2//CJiYmIiYmBq6srgoODYWzMnyFb9SkqKoKGhobgKuR1EcoKtIQQwmeUa4myLFy4EPfv30e3bt143zfU0dHB0KFD4erqiuzsbGzfvh2TJ0+GlZUV16EpLCcnB4mJicjKyoKzszOcnJy4Dklht2/fxoIFC3hfZFIWoeTZej82Xr58OZ4+fYrNmzfD3t4eZmZmMDMzg6mpKczMzJoqRkH78MMPUVBQgIqKCq5DUYhIJEL37t2xcuVKmJiY4Msvv0RkZCSkUinXodXr+fPn2LNnD2JiYpCSkoJ3330Xbm5ualM0YIxh6dKl6NixoyBGe7wKgwiVTPEbIYSQhmtIriXkVQwNDaGjo4ONGzdyHYrC2rVrh44dO2LBggXQ1NTExo0b8fPPP3MdVp0qKipw9OhR3Lt3Dzt37oS9vT3GjRunVkWDs2fP4vLly83mvaSQ+rT1vgORyWQoLCzE06dPkZWVJb9Vf6+I6OhoODk5QUNDA4mJifLjjDEEBwfD2toaHh4eSE5Olv8sLCwM1tbWcHR0RExMzGtemnrQ0NBAjx49sGDBAlRWVnIdjsJEIhFatmyJFStWYPjw4Thy5Ai+/PJL5OXlIS8vj+vw5M6dO4elS5fi6dOn6NatG3r06IGAgAC1WxTn8ePHCAoKajaVWaBqWJeiN0IIIa+H8qzqRUdHw87ODjY2NggPD+c6HJVydHSEvb09ysrKuA6lQfT09NCpUydMnz4dgwYNQmxsLBYtWoQ7d+6gqKiI6/Bw4sQJbNmyBbGxsTAyMkKXLl0wd+5ctXvznZ6ejrZt2+K9997jOpQmJZQ+7SvXOGgsOzs7HDx4EMHBwTWOR0dHIzs7G/fu3UNkZCQWLFiAgwcPIiEhAceOHcOtW7eQmJiIoKAg/PPPP6oOk1N6enqYNm0aUlNTebcPriLEYjHGjBmDMWPGIDc3F7t27YJEIkFubi46d+4Md3f3JhmyxhjDvXv3oKenh61bt6JPnz6wtrbGokWL1PpT+kOHDkEkEskXs2sOGBPOsC5CCOEryrWqJ5VK8cknn+D06dNo3bo13NzcMGbMGLUZ8fg6Bg0ahO+//x6DBw9Wq0/Cq7Vv3x7t27eHu7s7Kisr8cMPP0BLSwvdu3dHTk4OevXqpdJd0UpLS5GcnIyysjJERkZi3LhxaNu2LXx8fNR694GCggKsXr0aa9as4f1UFmUSUp5VeeHAxsam1uNRUVGYNGkSACAgIADBwcFgjCEqKgoTJkyAWCyGq6srysvL8fjxY7Rv317VoXKqa9euuHDhAs6cOYMPPviA63Bem7GxMWbPng2gak7+2bNn8eDBA+zcuRP9+vWDtrY2dHV10a1bt0Ylv5KSEty/fx9t2rTBtm3b4OHhgdzcXFRWVmLcuHEICwtT0hVxKzMzE7a2tmr5wtsYDMJZSIYQQviKcq3qxcTEwNHRERYWFgAAPz8/nDhxAhMmTOA4MtWaNWsWLly4gJKSErXdBUokEkEsFsvXliopKcHVq1eRn5+PDRs2wMDAAL1798aNGzfg7e2NVq1aKVxQYIyBMYYbN26goKAAYrEYJ06cwLhx4/Dnn3/C0dERAwYMgJubmyovsclIpVKkpKRg6dKlajfqt7GElGdVXjioS0ZGhjyJamhowNjYGDk5OcjIyED//v3l97O0tER6errgCwcA0Lt3b8hkMhQXF6Nly5Zch9No+vr6GD58OAAgNDQUAJCSkoKkpCRkZmZi+/bt6NatG2QyGeLi4jB16lTs2LEDnTt3hpWVFa5cuQI/Pz8cP34cIpEIvr6++OWXX+Dj44OHDx9CW1sbXbp0wbx58wSZhFJTU/Hjjz9i+fLlXIfCCb7P8yKEECGgXKtaL/Z3gf/r1/5XeHi4fBpDZmYm73d5UoStrS2++uorfPjhh9DT00NBQQHXITVa9Y4FQUFBAKrWHGCMobi4GKdPn0ZeXh66d++OnTt3wsXFBTk5OUhPT4efnx+OHj2Kzp07o0WLFrh58yaGDx+O5ORkdOzYER07dpR/2Dpx4kQAQG5uLjcXqaCG/D3Dw8MxbNgwVFZWqt1zWxnPW6HkWc4KB4yxl46JRKI6j9eG6ySrigTo4OCA3bt3w97evtZPmblIuspss3Xr1ujZsycA4KOPPpIfr97Ddfr06fJj5ubmMDAwwOTJk+XHqn9uZ2cHACguLlZabAB/Ht/c3Fzk5ubi448/VskLB99fvBkTQSaQ6iwhhPAV5VrVU7RfGxgYiMDAQACAp6enYHYymzVrFtLS0uRvuIVyXS9q164dgP8bZZ2TkyPv677oxW0Shw0b9tIxdfSqvydjDOfPn8e0adNgZGTURFEpX2Oet0LKsyopHKxbtw7bt28HUDVEq7ZPgy0sLJCeng4PDw/IZDLk5ubC2NhYfrxaWlpanaMN+JBkVdHmhx9+iPj4eDx//hwdOnRokjZfhatE31yu9cU28/LysHHjRqxcuVKlw/v4/uItlOosIYTwGeVa1aqtX1vbm0qhatOmDSoqKvDFF19gwYIFXIdDmlh4eDjs7e3VumigDELJsypZMW7mzJmIi4tDXFxcnUPI/f39sXv3bgBV6x14eXlBJBLB398fERERkEqliIuLg5aWFszNzVURJm+JRCJIJBJs3boVSUlJXIdDmlBhYSGSk5OxfPlytZ0TqAzV88GEsAItIYTwVUNyLXk9PXr0QGJiItLT01FUVIRjx47B19eX67CaVPv27TFv3jykpqaq1Q5ipHEOHTqEKVOmoE+fPlyHwikh9WlVvtT8iRMnYGlpiUuXLsHHx0e+GIy/vz+MjY3RpUsXhIWFYdWqVQAAiUSCoUOHws7ODu+88w42bNig6hB5SSQSITQ0FBoaGlQ8aCZkMhmWLFkCKysrla7Wqw4YE6GiUkPhGyGEkIZrSK4lr0csFuPbb7/FgAED0K1bN3z66ae8H/GnCubm5tDR0cGXX35Z6/QNIix//vkndHR0oKWlxXUonBNSn1blaxz4+voiLS3tpeMaGhrYunVrrb8TGhoqX0yvOdPU1ISNjQ2WLFmCqVOnwsrKiuuQiIpkZ2fj4sWL+Oabb9R6qx1lYRDOsC5CCOEryrVNY8SIERgxYgTXYXCuQ4cOWLJkCaKjozF48GDo6upyHRJRMsYY/ve//yEwMLDZfwhWTUh5lt9lDQINDQ2EhYWhtLQUFy5c4DocoiK7du3CoEGDqGhQ7f/veavojRBCyGtoQK4lRBl0dXXh4eGBTZs2cR0KUTLGGBITE9GzZ08qGrxIQH1aKhyoAZFIBHt7e8TGxuLZs2dch0OU6Pbt2zh06BA++eQTQWzBqSxCmg9GCCF8RWscEC60b98en3zyCdatW4eMjAyuwyFKUFZWhs8//xyGhobo1asX1+HwipD6tFQ4UBMikQizZs3Co0ePsGXLFq7DIUqQk5ODO3fuYNSoUVyHwkuVTKTwjRBCyOuhPEu4Mm3aNFy+fJk+FFNz2dnZSEpKwowZM2rdDY4Ip09LhQM1Y29vDycnJyQnJ0MqlXIdDnlNe/fuRWRkJAICAqChQf+G/1W9562iN0IIIQ3XkFxLiLLp6upizJgxCA8Px7Fjx7gOh7yGJ0+e4KuvvoK1tTUsLS25DoeXhNSnpXcsaqh3796QyWSYN28eSkpKuA6HNIBUKsVvv/2GIUOG4P333+c6HN6qXkhGCNVZQgjhq4bkWkJUZfbs2ZBIJDhy5AhkMhnX4RAFnT9/Hrm5uVizZg2taVAPIfVpqXCgpmxsbPDVV1/h+vXriImJ4TocogDGGMLDw+Hs7Iw2bdpwHQ7vCaU6SwghfEZ5lvCBpaUl3njjDYSHh9N2jTxXUVGB5ORkAFUjoWlh71cTSp+WCgdqrEWLFujZsyeuXbuGnJwcSrQ8dubMGaxfvx7BwcGwtrbmOhzeY0w41VlCCOGrhuRaQlTN2dkZ06ZNw/fff48rV65wHQ6pRVZWFubNmwc9PT306dOH63DUgpD6tFQ4UHMikQgffvghMjMzMX/+fOTn53MdEnlBeXk5/v77b+jr62PmzJlch6NGhDMfjBBC+IvyLOGfOXPmAAD+/vtvVFRUcBwNqXbgwAEUFxdj1apVMDc35zocNSKcPi0VDgTC0dERixcvxpMnTxAVFcV1OARVUxPWrFmDjh07wt3dnetw1ApjQHmlhsI3QgghDdeQXEtIUxGJROjZsycsLS2xevVqGlHLscLCQsTExMDU1BRvvPEG9PT0uA5JrQipTyvmOgCiPPr6+rCzs8O9e/eQkJCAjh07wsDAgOuwmp3Kykps2rQJnTt3xsKFC7kORy0xADKeD9cihBB1R7mW8FmXLl2wcOFCHDlyBPfv38eMGTMgFtNbl6Z0+vRp/Pnnn1i8eDG0tbW5DkctCSnP0n+fAA0fPhzPnz/HypUrMWjQIPTv35/rkJqNS5cuQUtLC35+fujSpQvX4ai1Sp4P1yKEECGgXEv4bsSIEXjw4AGuX7+OZ8+ewdvbm+uQBO/+/fv49ddfMWPGDAwYMIDrcNSeUPIsv8dDkNfWokULLFu2DH369MGyZctw69YtrkMStNzcXNy9excpKSlwd3enokEjMYggY4rfXmX06NEwMjLC2LFj5cdiYmLg6OgIa2trhIWFqfJyCCGElxqSa1+F8ixRpU6dOsHNzQ1Pnz7F7du3kZmZyXVIglRSUoKzZ8/i/v37mDVrFk1LUAIh9WmpcCBwYrEYn3/+OfLy8nDkyBFkZ2dzHZKgMMZw8eJFbNiwAe3atcPEiRMhEgmjqsglxqqqs4reXmXmzJnYtWtXjWPTp09HREQEbt26haioKCQmJqrqcgghhJcakmtfhfIsUTWRSISxY8fC0tISu3btwtmzZyGVSrkOSxAqKyuRm5uL7777Dp06dYKPjw9NTVASIfVpqXDQDIjFYvTq1Qt9+/bF3r178fDhQzx//pzrsNReZGQkli5dil69emHx4sXQ19fnOiRBUeYKtAMGDKjx98nIyIBUKoVEIoFYLMbEiRNpUVFCSLNEeZaom5YtW2L+/Pno168fVq9ejb1793IdktpijCE9PR2fffYZnj59ioULF6JTp05chyU4QunTUuGgGTEyMsLMmTOhoaGBlStXUgHhNchkMhw6dAgRERHo168fQkNDaYSBCjR0z9usrCx4enrC09MT4eHhrzx/RkYGLCws5N9bWloiPT1dlZdECCG805BcS3mW8NEXX3yBUaNG4eDBg9i8eTONQGiA8+fPY8GCBWjRogXWrFkDOzs7rkMSJCH1aWlxxGbI0tISy5YtQ1FREVavXg1PT08MHjwYmpqaXIfGWxUVFdi3bx9cXFzQqVMn2l6xCTRkL1szMzNcvnxZ4fvXtrUTFYAIIc2RormW8izhqxYtWmDs2LF48OAB7t27h99//x3vv/8+7SxWh3379uHJkycIDAxEnz59uA6nWRBKn5YKB82Yvr4+QkNDwRjDDz/8gJKSEsyePRsaGjQQpVp2djb27dsHHx8fuLi4QCKRcB1Ss8BQVXVVFQsLixrV2LS0NLRv315l7RFCCB+pMtdSniVNrXqIfevWrZGbm4tNmzZh2LBhcHV15TYwHsjLy8PWrVvlU5df/ISaqJaQ+rRUOCAQiUQICQlBZWUl/vnnH/z2228IDg5Ghw4dmuUoBMYYrly5gszMTOjp6WHs2LFo164d12E1L0y1W9eYm5tDU1MT8fHxcHBwQEREBLZt26ay9gghhJdUmGspzxKumJubAwDmzp2LhIQEHD16FNnZ2Rg/fjx0dXU5jq5pXbp0CVFRUZg9ezamTZsGIyMjrkNqfgTUp6XCAZHT1NREz5490aNHD5SVlSEsLAwODg7o3r07jI2NBT/E8NGjRzhy5Ah8fX3x5MkT+Pv7QyymfxEuMEChLWkU5evri6tXr6K4uBiWlpb47bffsGHDBkyYMAGlpaWYNGkSnJ2dldYeIYSoA2XmWsqzhG+0tbXlU0tv376N7OxsbN68Gf7+/ujVqxfH0akGYwylpaX4+uuvYWdnh0GDBmHFihWC78PzmZD6tPSuiLxEJBJBV1cXS5cuBQD89ddf2L59O0JCQpCZmYlu3boJZjqDTCbDli1bIBKJ4OPjg+HDh6Nz586wtrbmOrRmjTERKioVf4696p4nTpyo9fiNGzcaEBUhhAhLQ3It5VmizqoX/lu+fDny8/Oxe/duJCcnY/bs2WCMqfUn8aWlpbh9+zYKCgpw6tQpBAUFYeHChdDS0uI6NAJh9WmpcEBeycXFBQMHDkRpaSliY2ORnZ2N7OxstGrVCv7+/mo1naGsrAy3bt1CRUUFIiMjMWHCBLz99tvyF4ycnByOIyRAVXW2IcO6hFHGIoSQptWQXEt5tm6hoaEIDw9HmzZtAADh4eHw8PAAYwwhISE4deoUDA0NsX//fnTp0oXjaJs3kUgEIyMjTJo0CUDVWla7d++Gu7s70tLSYGlpib59+/L6E3rGmHyUrI+PD6Kjo9G3b1/069cP/fr14zo88h9C6tOqvHAQHR2Nzz77DDdv3kR8fDycnJwAADt37sT8+fPl85DCwsIwYsQI+de7du2Cjo4OduzYgR49eqg6TKIAXV1djBw5EkDVJ/VxcXFIT0/H9u3b0adPHxgaGsLS0pJX6wGUlpYiLS0NBw4cwPDhw3H+/Hk4ODhgwIAB8PDw4Do8Ug9lDusihBBSO8q1yvHZZ59hxowZNY5FR0cjOzsb9+7dQ2RkJBYsWICDBw9yFCGpTZs2bTBnzhwAQHFxMWJjY3H//n3s3r0bAwcOhIaGBkxNTWFra8tZMSE7OxtXrlyBk5MT9u7dCyMjIwQEBGD8+PEwNTVF165dOYmLKE4oeVblhQM7OzscPHgQwcHBL/1s8uTJ+Oabb2ocS0hIwLFjx3Dr1i0kJiYiKCgI//zzj6rDJA2koaEBNzc3AFWVdgBISkrC4cOH4efnh507d6J79+5o06YNxGIxnJycVDpk6vnz58jJyUFRUREiIyMxcuRIHDt2DAYGBhg/fjzmzp0LHR0duLi4qCwGojxMxQvJEEIIoVyralFRUfJPtgMCAhAcHAzGGK8/zW7OWrZsCW9vbwD/17e9e/cu/v33X+jp6WHHjh3o1asXiouLUVxcjJEjR6KwsBCmpqbQ1tZ+7XZlMhlyc3NRUlKCJ0+e4MyZM3jrrbewc+dOdOjQAZ6enmjbti3atWuHzz//XBmXSpqQkPKsygsHNjY2Dbp/VFQUJkyYALFYDFdXV5SXl+Px48e0hY8asLe3h729PQBg8eLFAIDU1FQkJiaiXbt2CA8PR9euXWFgYIArV67gvffew+HDh2FlZQUnJyfcvn0bEokEWVlZkMlkMDY2xo0bN2BhYYHc3FykpKSgX79+2LZtG+zs7GBgYIBLly7hvffewy+//AKJRIKePXtizpw50NXVhYODA5cPB2kkoVRnCSGEzyjXKsd3332HrVu3wtvbG2vWrIGuri4yMjLk295paGjA2NgYOTk58ikNLwoPD0d4eDgAIDMzU5BTJwsKCrgOocGMjY0xePBgAJCPKKmsrERWVhZyc3Nx+vRpGBkZQVtbGzdv3sSwYcNw5swZtGjRAjY2Nrhy5Qq8vLxw48YNFBcXY+DAgfjjjz/g5OSE/Px8PHr0CMOHD8eFCxdgY2MDS0tLTJgwAbq6upg+fXqNWJ49e4Znz541+WNQF3X8e74OZVynUPIsp2scRERE4I8//oCrqyvWrl0LY2NjZGRkoH///vL7WFpaIj09vdbCAddJlot/GHVrU19fX75y7UcffSQ/7u7uDsYYRo0ahZKSEkilUrRo0QLPnj1DXl4eSkpKoKWlhYqKCpSWlkJXVxc2NjYQiUSYNm2afHHG6lEPU6dOBQBIpVJIpVIUFxc3+bW+rubSZkMwADIZ11EQQoiwUa5VjpCQECxatAiMMcyYMQNff/01lixZAsbYS/eta7RBYGAgAgMDAQCenp4wMTFRacxcEcp1mZmZAQCmTJkiPzZs2DAAQNu2beXX2bt3bwBVf9Nq1dO2X6Su0w2E8vd8lcZcp5DyLGeFg4CAAEyYMAHa2tr46quvMHfuXOzYsUPtkiy1qTzVOxlUj1LJycmpNbmqmlAfXz60qTgRKgVSneWL58+fw97eHuPGjZNPEZPJZPD09ETHjh1p3i0hzRLl2te1bt06bN++HQAQExMjXyh66tSpWLZsGQDAwsIC6enp8PDwkA9HNzY25ixmQggXhJNnVbJw47p16+Dq6iqfalAbExMT6OjoQCQSITAwUL6OQXWSrZaWlkbTFAhpZhgDZDKRwjfyaitWrEDPnj1rHNu2bRveeOMNjiIihHCtIbmW1DRz5kzExcUhLi6uxojXyMhIODo6AgD8/f2xe/duAFVTcb28vGh9A0KaGSH1aVVSOHgxmda1WEhmZqb868OHD9dIshEREZBKpYiLi4OWlpZ85wVCSPNRKRMpfCP1u3v3Lm7dugU/Pz/5sdzcXOzbtw/Tpk3jMDJCCNcozzbe/Pnz4ezsDGdnZ6SkpGDhwoUAqvq0xsbG6NKlC8LCwrBq1SqOIyWEcEEofVqVT1U4ceIEPvjgAzx9+hQ+Pj4YMGAAIiIisHbtWkRHR0NTUxMWFhb48ccfAQASiQRDhw6FnZ0ddHV1sW3bNlWHSAjhGcaEs5AMH8ybNw9r1qzBxYsX5ccWLlyIRYsWcRgVIYRrlGuVo3pUwX9paGhg69atTRwNIYRPhJRnVV448PX1RVpa2kvHV61aVWflNTQ0VL4NCiGkOeJ/1VVdREZGwtbWFra2tvLCwbVr15CXl4f+/fvjzJkz9f4+14vQ1obvi3vWRh1jBtQzboq5ISjXEkKIagknz3K6qwIhhNSGMUBaqZKZVM3O5cuXsW/fPvzyyy949uwZKioqoK+vj3PnzqFz584oLS1FUVERpk2bVusnY3xYhLY2fImjIdQxZkA946aYFUO5lhBCVEtIeVYYV0EIERQGoJKJFL6Ruq1cuRKPHj1CamoqvvnmGwQFBWHJkiVIT09Hamoq9u3bh2HDhtFwWkKaoYbkWkIIIQ0npD4tjTgghPAS31eWJYQQIaBcSwghqiWUPEuFA0II7zAmnPlgfDJlypSXjvXv3x/9+/dv8lgIIdyjXEsIIaolpDxLhQNCCC/JGNcREEKI8FGuJYQQ1RJKnqXCASGEdxggmOosIYTwFeVaQghRLSHlWSocEEJ4R0h73hJCCF9RriWEENUSUp6lwgEhhJeEspAMIYTwGeVaQghRLaHkWSocEEJ4p3rrGkIIIapDuZYQQlRLSHmWCgeEEP5hgEzGdRCEECJwlGsJIUS1BJRnqXBACOEhkWCGdRFCCH9RriWEENUSTp6lwgEhhH8YgEphJFlCCOEtyrWEEKJaAsqzVDgghPCTQOaDEUIIr1GuJYQQ1RJInqXCASGEnwQyrIsQQniNci0hhKiWQPIsFQ4IIfzDAAhkIRlCCOEtyrWEEKJaAsqzVDgghPCQSDDDugghhL8o1xJCiGoJJ89S4YAQwj8MghnWRQghvEW5lhBCVEtAeZYKB4QQfmJcB0AIIc0A5VpCCFEtgeRZKhwQQvhJINVZQgjhNcq1hBCiWgLJsxpcB0AIIS+pHtal6I0QQkjDNSTXEkRHR8PJyQkaGhpITEyUH2eMITg4GNbW1vDw8EBycrL8Z2FhYbC2toajoyNiYmK4CJsQwiUB9WmpcEAI4SfWgNsrREdHw87ODjY2NggPD1dVxIQQon6UlGcB4edaOzs7HDx4EP369atxPDo6GtnZ2bh37x4WLVqEBQsWAAASEhJw7Ngx3Lp1C3v27MH06dO5CJsQwjWB9GlpqgIhhH+YCKhUTl1TKpXik08+wenTp9G6dWu4ublhzJgxMDY2Vsr5CSFEbVGubRAbG5taj0dFRWHSpEkAgICAAAQHB4MxhqioKEyYMAFisRiurq4oLy/H48eP0b59+6YMmxDCJQHlWRpxQAjhJ1kDbvWIiYmBo6MjLCwsoK+vDz8/P5w4cUKVkRNCiPpQQp4FmneuzcjIgIWFBQBAQ0MDxsbGyMnJqXEcACwtLZGens5VmIQQrgikTyuYEQcpKSnw9PRs0jazsrJgZmZGbQqoXWpTtVJSUhS6n597J+T8M0fh8z5//lz+/x8YGIjAwED5z6jjpjxc5NnacPX8bQx1jBlQz7ibe8yK5lmgYbm2vjwLNO9cy9jL44tFIlGdx2sTHh4uH3Z88+ZNXuRaZVPH/83XQdcpLHVdZ3Ps0wqmcPDkyZMmb9PT0xOXL1+mNgXULrXJD0ePHlXauRrScSP14yLP1obvz9/aqGPMgHrGTTErjnLtq61btw7bt28HUPVpn7a29kv3sbCwQHp6Ojw8PCCTyZCbmwtjY2P58WppaWl1TlOorRgjNOr4v/k66DqFpbHXKaQ8S1MVCCGC1pCOGyGEkNcj1Fw7c+ZMxMXFIS4urtaiAQD4+/tj9+7dAKrWO/Dy8oJIJIK/vz8iIiIglUoRFxcHLS0tmJubN2X4hBAB4TrPUuGgEbioDDeXNrlql9oUnh49eiAxMRHp6ekoKirCsWPH4Ovry3VYpBHU8fmrjjED6hk3xcyN5pBrT5w4AUtLS1y6dAk+Pj6YMGECgKrCgbGxMbp06YKwsDCsWrUKACCRSDB06FDY2dnhnXfewYYNG7gMn3NCeJ4rgq5TWPh0nVznWRGrbcwDIYQIyJEjRzBv3jzIZDLMnz8f06ZN4zokQggRHMq1hBCiWlzmWSocEEIIIYQQQgghpE40VYEQQgghhBBCCCF1osIBIYQQQgghhBBC6kSFg3pER0fDyckJGhoaSExMlB/fuXMnzMzM4OrqCldXVxw5ckT+s7CwMFhbW8PR0RExMTFKbZcxhuDgYFhbW8PDwwPJyclKbbdaaGgoLC0t5dcXGxv7yvaVKTo6GnZ2drCxsZHvaawKYrFYfo3VC5/ExMTA0dER1tbWCAsLU0o7o0ePhpGREcaOHSs/Vlc7ycnJ8PDwgLW1NYKDg2vdduV12+zcuTMkEglcXV3h5+en9DYJUbXbt2/L/2ddXV2hp6eHw4cPcx3WK33//fdwdHSEg4MDZs6cqRb/Y9988w0cHR3h5OSEn3/+metw6tSQ/MoXtcU8ffp0mJmZwcPDg8PICGmclJQUDBgwAA4ODnB2dkZxcbHK+ztcqOu1SIjXWtvrlxCvs7bXPCFeZ6MxUqc7d+6wpKQk5u3tzRISEuTHd+zYwebOnfvS/ePj41nPnj1ZRUUFu3btGvPw8FBqu0eOHGFvvvkmY4yxw4cPy79WVrvVlixZwtavX//S8braV6aKigpmY2PD0tLSWGFhIbO2tmY5OTlKb4cxxkxMTF465uHhwa5fv84qKiqYh4dHjcf/df311181Hrv62hkzZgyLiopijDE2atQo+dfKaLNTp06sqKjopfsqq01CmlJRUREzMTFhz5494zqUemVlZTErKytWUlLCpFIp8/LyYhcvXuQ6rHrFx8ezbt26sZKSElZSUsK8vLxYXl4e12HVqiH5lS9qi/n8+fMsNjaWubu7cxgZIY3Tr18/dvbsWcYYYzk5OfL/QVX2d7j24muR0K61rtcvoV1nXa95QrtOZaARB/WwsbFB165dFb5/VFQUJkyYIP8ku7y8HI8fP1Zau1FRUZg0aRIAICAgABcuXABjTGntvkpd7StTdXXPwsIC+vr68PPzw4kTJ5TaRl0yMjIglUohkUggFosxceJEREVFNfq8AwYMgL6+/ivbYYzh0qVLGD58OABg8uTJr93+f9usizLbJKQpHTlyBIMGDULLli25DuWVpFIpSktLUVFRgYqKCpiZmXEdUr2SkpLg5eUFXV1d6OrqwtXVFcePH+c6rFopml/5pLb83Lt3b5iYmHAUESGNd+PGDWhpaaFv374AAGNjY2RlZam8v8O16teigoICQV7rf1+/ZDKZ4K6ztte8Y8eOCe46lYEKB68pIiICEokEkydPRm5uLoCqDouFhYX8PpaWlkhPT1damy+eX0NDA8bGxsjJyVFJu9999x0kEgk+/vhjlJaW1tu+Mqn6MXxRYWEh3N3d0adPH/z9999N1nZd7eTk5MDY2BgikUgl7YtEIvTr1w89evTAr7/+CgAqb5MQVTlw4ADGjx/PdRivZGpqinnz5qFjx44wNzeHj48PunTpwnVY9XJycsLp06eRn5+P/Px8/PXXX2qTF5ryNYQQ8n/u3r2LVq1aYcSIEXBzc8NXX33FWX+nKVW/FgnxWmt7/dLR0RHcddb2mvfo0SPBXacyUOHgNQQEBOD+/fu4fv067OzsMHfuXACo9dP36ieWMtR1fmW3GxISgrt37+Lq1auoqKjA119/XW/7ytQUbVRLTU3Fv//+ix9++AGTJ09GcXFxk7TdVH/H/7pw4QKuXr2KQ4cO4fPPP8e9e/ea9PEmRFkKCwtx4cKFGmt18FVeXh6io6ORmpqK9PR0XLx4EWfPnuU6rHpVz2UdOHAgRo8eje7du0MsFnMdlkIopxHCjYqKCpw7dw4bN27EpUuXcPLkSZw+ffql+zVFf6epvPhaxFXfTpVqe/2qq6+sztdZ22te9YemL1L361QGKhz8x7p16+SLnZSXl9d6HxMTE+jo6EAkEiEwMBD//PMPAMDCwqJG1SktLQ3t27dXWrsvnl8mkyE3NxfGxsaNare29o2MjKCpqQmxWIypU6fWen0vtq9MyrgWRZmbmwOoqjQ6ODhAJBI1Sdt1XWObNm2Qm5srT0rKbr/6ei0tLTFo0CDExcWpvE1CVCEyMhK+vr7Q1dXlOpRXOnXqFKytrWFsbAw9PT0MHz4cly9f5jqsV/rwww9x9epVnD59Gtra2rC2tuY6JIU05WsIIeT/WFpaonv37ujQoQN0dHTg5+eH58+fc9LfaSovvhZx1bdTpdpev/7++2/BXSdQ+2ueEK+zsahw8B8zZ85EXFwc4uLioK2tXet9MjMz5V8fPnwYjo6OAAB/f39ERERAKpUiLi4OWlpa8jdrymjX398fu3fvBlC13oCXlxdEIlGj2q2t/RenH0RGRta4vtraV6YePXogMTER6enpKCoqwrFjx+Dr66vUNoCqKmpZWRmAqn/6mzdvwsnJCZqamoiPj4dUKkVERAQCAgKU3ra5uXmt7YhEInh6euLo0aMAgF27dimt/eLiYhQVFQEA8vPzcfbsWdjb26u0TUJURV2mKQBAhw4dcPHiRZSWlqKyshJnzpyBnZ0d12G9UlZWFoCq1cNjYmJUkodVoa78SghRre7du+PJkyfIy8uDTCbD2bNn4e7u3uT9nab04msRF307Vavt9cvFxUVw1wm8/Jo3fvx4QV5nozXNGozq6fjx48zCwoJpa2uztm3bsrfffpsxxtiCBQuYo6Mjk0gkbNiwYSwtLU3+O0uWLGFWVlbMwcGBXbp0SantVlZWsqCgIGZlZcXc3NzYnTt3lNputXfffZc5OTkxJycn9vbbb7OCgoJXtq9MkZGRzMbGhnXp0oVt2bJFJW1cuHCBOTk5MYlEwlxcXNhvv/3GGGPs0qVLzMHBgVlZWbElS5Yopa0hQ4awNm3aMD09PWZhYcFiYmLqbOfOnTvMzc2NWVlZsaCgIFZZWamUNi9fvswkEgmTSCTMycmJ/fDDD0pvk5CmkJ+fz8zMzFhZWRnXoSjsiy++YF27dmUODg7s448/ZjKZjOuQXqlXr17M3t6eeXh4sNjYWK7DqVND8itf1BbzBx98wNq1a8e0tLSYhYUFO3ToENdhEtJgx44dY05OTszR0ZHNmTOHMVZ3v0rd+x61vRYJ8Vpre/0S4nXW9ponxOtsLBFjzWnzSUIIIYQQQgghhDQETVUghBBCCCGEEEJInahwQAghhBBCCCGEkDpR4YAQQgghhBBCCCF1osIBIYQQQgghhBBC6kSFA0IIIYQQQgghhNSJCgeEEEIIh9q0aaOycy9evBjnzp0DAKxduxbl5eUN+v0zZ85g7NixCt8/NDQUlpaWWLNmDQAgLS0N3t7emDhxIiorKwEAU6dOhbGxMaKjoxsUCyGEEEK4Q4UDARKLxXB1dZXf9uzZ0+Bz1NfB3L9/P+zt7TF69OjGhqp0/fv3R2Ji4mv/fvVjV1RUhNTUVHh4eLz2uXx9fdGqVatGxUMIIY0RFhaGvn37Ani9wsHr+Oyzz/Dpp58CANavX49169ahT58+OHnyJABgx44dGDFihMrjIISQhlB1/7lz586QSCS4ffs2gMYVjakAS7gg5joAonyGhoaIi4tr1DnWrl2LwMBAaGtrv/SzHTt2YMeOHfD09KxxvLKyEpqamo1qtzZNed4XH7ucnJxGnf/EiRPo379/o85BCGme/vjjD8yfPx9SqRRDhgzBt99+C5FIhDZt2mDKlCk4ceIEzMzMcOTIEbRs2RKXLl1CYGAgDA0N4erqiidPnuDgwYOYMmUKxo4diwcPHiAjIwNeXl7o0qULfvvtN7Rp0wbZ2dkAgA0bNiA7OxuhoaE1zuXm5iaP6cmTJ/jwww+RlpYGXV1dhIeHo2vXrvVeh0wmg4aGBiorKyGTyVT6mBFCSGOouv8MABcvXkSrVq0a1QZQ1RefMmVKo89DSEPQiINmZNq0aXB3d4ejoyM2btwIoOrN87vvvgsHBwc4Oztjx44d2Lhxo7yD+d9RBatWrcL58+cxZcoUhIWFITQ0FMHBwfDx8cGcOXNw9epV9OjRAxKJBJMnT0ZpaSmAqirrl19+iZ49e6J37974999/MXDgQFhZWeG33357KdYzZ85gyJAheOuttzBgwAAUFhZi4MCBcHNzQ7du3XD+/Hl5/EFBQXBycsJbb72FkpIS+TkCAgLg7u4OJycnHDp0CACQmpoKFxcXBAUFoVu3bigrK1PosUtISIC7uzvu37+P0NBQvP/++xg8eDDeeOMNHD9+HCEhIbC3t8e7777b8D8MIYS8oKSkBEFBQTh8+DDi4+Nx584deZ7MycmBn58fEhISYGFhIc9tgYGB2L17Ny5cuICCgoKXzjl9+nSYm5vj4sWLtebcFwUGBuKnn37ChQsXkJWVJT8+e/ZsLFq0CLGxsfj+++8xe/bsV17LRx99hJCQEFy4cAFDhgxpwKNACCH8oIz+c33S0tLQvXt3xMbGYufOnRg7diyGDx+ON954Az/99BNCQ0MhkUgwZMgQhfuthKgEI4KjqanJXFxc5LezZ88yxhjLyclhjDFWVlbGunXrxp4+fcpiY2OZl5eX/Hfz8/MZY4x16tSJFRUV1Xp+b29vlpCQwBhjbMmSJczLy4uVlZUxxhhzcnJily9fZowxFhwczL799lv5+bZv384YYywwMJD16dOHlZaWsqSkJCaRSF5q4/Tp06x169YsPT2dMcZYeXk5KywsZIwx9uDBA+bh4cEYY2z//v1sxIgRTCaTsfj4eKapqSmPrfp68/PzmZ2dHZPJZCwlJYVpamqy69ev13ptJiYm8q9TUlKYu7s7u379OnNzc2PJycnyax44cCCTSqXs/PnzrGXLluzKlStMJpMxT09PdvXq1VofK0IIqc2LeYcxxq5du8YGDx4s/37Pnj1s9uzZjDHGWrVqJT++du1aFhYWxvLy8pitra38+JEjR9ibb77JGGPsvffeY1FRUYyxl/P6i+2uX7+eLVmyhOXl5TEbGxv58cjISPm5zMzMary2uLi4vHQtS5YsYevXr3/lNb8YFyGE8IGq+8+15eCHDx8yd3d39s8//zDGGNuxYwezt7dnxcXF7OHDh6xly5bswIEDjDHGxo8fzw4dOiT/fcqjpKnRVAUBqmuo1d69e7Ft2zZUVlbi4cOHuHv3Lrp27YqMjAxMnz4dI0eOfK1PhEaOHAltbW0UFBSgrKwMPXv2BABMmjQJa9aswSeffAIA8jmtzs7OaNOmDXR0dOTt16Z3794wNzcHADDGMH/+fJw/fx6ampq4e/cugKohX+PHj4dIJIKzszMkEon897///nscOXIEAPDw4UNkZmYCAGxtbWvcrz4ZGRkYP348jh49CisrK/lxPz8/aGpqwtnZGfr6+ujRo4f82lJTU9GtWzfFHjxCCHkFxhhEIhEAQEdHR35cU1MTlZWVYIy9dH9FVJ8TgPxTrBfbqs2///6rkqljhBDCtabuP5eWlmLo0KH46aefaqypNWjQILRo0QItWrSAtra2vP8skUiQmpr6updHSKPRVIVm4v79+9i0aRPOnDmD+Ph4eHp6oqysDEZGRkhISED//v3x7bffYt68eQ0+d4sWLQC83Fn9bwe0usOroaFRo/NbVye3+rwAsGfPHhQXF+PatWu4du2afK5sXZ3c06dP48KFC7h8+TKuX7+Ojh07yjvGL573VYyMjGBmZoaYmJgax+u6luq5vIQQ8rrs7Oxw584dPHjwADKZDPv27ZMvcFgbIyMjiMVieYf34MGDtd5PX18fRUVF8u8NDAzw4MEDVFRUyBfYMjIygpaWFq5evQoAiIiIkN/f29sbW7ZsAVC1dkFCQkKjrpMQQvhOlf1nbW1tODo64q+//qpx/L/9yhf7nNTHJFyiwkEzUVRUhFatWqF169ZITU2VrxGQnZ0NmUyGcePGYfHixfKO5387mIowNDSEjo4O/vnnHwBVFdr6OrsNUVhYiLZt20IsFuPgwYPytRN69+6NAwcOgDGGGzduID4+Xn5/ExMT6OnpISYmBnfu3HmtdvX09BAZGYlvv/0WJ06cUMq1EELIi/Ly8mBpaSm/RUdHY+vWrRg5ciQkEglsbGwwatSoes+xdetWvPPOO+jduzeMjIzQunXrl+4TFBSEAQMGyOfeLl++HAMHDsSwYcNqjKj68ccfMWnSJHh5eaFjx47y4+vXr8fJkychkUjg5OSEo0ePKucBIIQQnlJl/1lDQwM///wzTp06he3bt6vqEghRGpqqIED5+flwdXWVf//+++9j5syZsLOzg5OTE2xtbdGrVy8AQHp6OqZMmQKZTAaxWIy1a9cC+L8Opr29/SsX0nrRzp07ERISgtLSUri6uiIkJEQp1zRx4kQMHz4cPXr0QJ8+fWBiYgIAePPNN/HHH3/A2dkZrq6u6N69O4CqrRA3btwIV1dXuLi4wNnZ+bXbNjQ0RFRUFIYMGQIDAwOlXA8hhFSr6xOk2oa+Vu+CAAAzZsyQf+3q6oobN27Ij1fvhrBz5075fWbOnImZM2fKv3/77bfx9ttvv9RGr1695Od6Udu2bRv0ekAIIeqEi/6ztrY2Dh06hCFDhsDIyEgVl0WI0oiYopMhCWkGXtyeTBn69++PDRs2wMnJSWnnJISQ/9q7dy9Wr16N8vJyuLi4YPv27dDT02vyOL755hts3boVQUFB+PTTT2u9z9SpU3HmzBn8+OOP8PHxaeIICSGEG507d0ZiYqJStmMEIN9u19/fXynnI+RVqHBAyAvMzc1hZmaGc+fOQV9fv1Hn8vX1RVJSEk6ePAk7OzslRUgIIYQQQtRN9+7dUV5ejgMHDjS6X0gFWMIFKhwQQgghhBBCCCGkTrQ4IiGEEEIIIYQQQupEhQNCCCGEEEIIIYTUiQoHhBBCCCGEEEIIqRMVDgghhBBCCCGEEFKn/wcstT4gzMox3gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1050x294 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
+    "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"]):\n",
+    "    rdf.plot_ppi(sweep=1, ax=ax, coords=coords)\n",
+    "    ax.set_title(f\"coords={coords!r}\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9362ae8",
+   "metadata": {},
+   "source": [
+    "`context=True` adds cartopy borders and coastlines. Projection and basemap are\n",
+    "independent — you choose the frame with `coords`, the background with `context`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5090d25e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:37.535043Z",
+     "iopub.status.busy": "2026-08-04T15:32:37.534769Z",
+     "iopub.status.idle": "2026-08-04T15:32:38.205894Z",
+     "shell.execute_reply": "2026-08-04T15:32:38.205118Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P9e/sxIkTKCoqwtq1aw3l12g0mDJlCnx9fesd6gvcDFhXr141vB9r1qzBgAEDDP1mx44dQ0pKCoQQyM7OxooVKzBhwoQ67+Xo6IiJEydi9erVKC8vx759+5CQkGA4PzMzEzk5OdDpdPj111+xadOmO9YA6Tat11ffNLhthFNRUZEYPXq0cHBwEN27dxfr168XAQEBhuN79+4VnTt3Fi4uLrVGW3399dfCx8dHODs7i0cffVQMGzZMbN26VQhxc7TVa6+91mBebt/2799f67yGRlsBEA4ODsLe3l74+vqKGTNmiNOnT9d5joODg3BzcxOTJ08WWVlZQgghhg8fLhQKheH4rduhQ4eafbTVb7/9JkJDQ4WDg4Pw8vISy5YtE1qtVghxc5SVSqUSL7/8shBCiKqqKmFvb28YdVXt008/FT169BCOjo6iU6dO4qWXXjIcO3z4sBg0aJBwcXER3t7eYurUqaK0tNTwnlePtvLy8hLr1q2rM49C3Bz1dPvv506/h/rKm5aWJgAIW1vbWu9ttTfeeEO4u7sLFxcX8eyzzxpGlX3++ec1fnfVW10aGm0lhBApKSli0KBBwtbWVtxzzz3ixIkThmOXL18WY8aMEa6ursLNzU1ERkaK7OzsRpcpOjpaBAQECGdnZ/Hggw+KjIyMO+Zr69atwtfXVzg6Ooq5c+eK8vJyIYQQBw4cEACEnZ1djXSuXLkihBBi27ZtIjg4+I73vHW01X/+8x8REBAg7O3thZ+fn1iyZIkoLi42HF+8eLFYvHix4XVeXp4YM2aMsLOzE4GBgeLnn382HDt48KDo0KGDsLOzE6GhoWLfvn13LBvVJBOCM2JaSvXs8rY0y/zAgQNYvXo1Dhw40NpZqUEmkyE9Pb3Z26fNtbxE5sZimq2IiKjlMHi0oGHDhrW5dZM6duzYpCU92pq7rbzUtqWmpmL48OEIDg5Gz549UVZWhmPHjiEkJASBgYH1DkxoKjZbERG1UUOHDsXrr7+OIUOG4MaNG3BycsLAgQPx2WefITg4GAMHDsTWrVvvOGquKVjzICJqg86dOwcrKyvDKDs3Nzfk5eVBq9UiLCwMSqUSs2fPRkxMjCTpS7NuRTPy9vZGp06dWjsbDdJqtZItA2IuLL2MLF/bJ2UZU1NTkZuba/J1vWQy1F5noGF2PXsahkRHRUXVWmm5eumVCRMmICMjA1OnTsUDDzwAPz8/wzn+/v44ePBgI1JvmNn/JXXq1Anx8fGtnY0G5efn15i1boksvYwsX9snZRkjIiIadV0pgNcacd179vb1fvZpNBrExsYiISEBXl5e+Nvf/lbn8klSTXI2++BBRES1+fv7o1+/foZlesaOHQu1Wl1j4cqMjAzJVl5gnwcRkcRkjdga0q9fP+Tm5qKgoAB6vR6HDh1Cnz59oFAokJiYCK1Wi+3bt9e5xl1zYM2DiEhiUjQcKZVKvPHGG7jvvvsghMADDzyAcePGwcPDA7NmzUJFRQXmzp1b79I0TUpfkrsSEREA42sSjTFmzBiMGTOmxr6IiIgWeSYJgwcRkcRab11u6TB4EBFJzBKDBzvMiYjIZKx5EBFJzBJrHgweREQSs8TgwWYrIiIyGWseREQSknKobmti8CAikhiDBxERmcwSgwf7PIiIyGSseRARScwSax4MHkREEmKHORERNYolBg/2eRARkclY8yAikpgl1jwYPIiIJGaJwYPNVkREZDLWPIiIJGaJNQ8GDyIiCVnqUF02WxERkclY8yAikhhrHkRERGDNg4hIcqx5EBERgTUPIiLJWWLNg8GDiEhiMguMHpI2W6nVagQEBGD58uUAgOjoaPTs2ROhoaGYOXMmKisrpUyeiKjVyQDIFKZv5k7S4LF27VoMGDAAACCEwLPPPosDBw7g7NmzAIDvvvtOyuSJiFqfDDc/aU3dzJxkWUxJSUFSUhLGjh1r2CeEgFqthk6ng1qtho+Pj1TJExGZDZnc9M3cSdbnsXz5crz99tuIi4sDAMhkMmzcuBGhoaGwsbHBqFGjMGzYsDqv3bJlC7Zs2QIAyMnJQX5+vlTZbDZFRUWtnQXJWXoZWb62zyzLKGsbzVCmkiR47NmzB0FBQQgKCjIED41Gg82bN+PMmTPw8/PDnDlzsG3bNsyZM6fW9VFRUYiKigIAREREwN3dXYpsNru2ks+msPQysnxt391QRnMgSfCIj49HdHQ0du7cidLSUmg0GqhUKiiVSnTo0AEAMHnyZOzfv7/O4EFEZFHaQDOUqSQp0rp165Ceno60tDRs2LABixYtwqJFi5CYmIiCggIAwO+//45u3bpJkTwRkfmQsc+jSXx9fbFixQoMGjQISqUSoaGhWLx4cUslT0TUKmRoG8HAVJIHjwULFhh+Xrp0KZYuXSp1kkRE5kMGgB3mRERkKtY8iIjINDIGDyIiagwLbLaywHhIRERSY/AgIpKShEN1lUolwsPDER4ebphYfezYMYSEhCAwMBBr1qyRrFhstiIikppEX9NdXFyQkJBQY9/SpUuxfft2BAcHY+DAgZg8eTJCQ0ObPW3WPIiIpNSCkwSzsrKg1WoRFhYGpVKJ2bNnIyYmpnnL838YPIiIpKZoxGaE4uJi9OnTB/feey8OHjyIrKws+Pn5GY77+/sjMzOzGQvyPwweREQSkjWy5pGXl4eIiAhEREQYVhm/XVpaGk6cOIFPPvkE8+bNQ1lZWR3pS/MYQ/Z5EBFJrRFf0728vBAfH1/vOb6+vgCA0NBQBAcHQyaT1ahpZGRkSPbcJNY8iIikJJPmMbQFBQWGR3lnZGTg/PnzCA0NhUKhQGJiIrRaLbZv347x48dLUizWPIiI2qALFy5g8eLFkMvlkMlkeP/99+Hm5oaNGzdi1qxZqKiowNy5c9GzZ09J0mfwICKSmBTLkwwaNAhnzpyptT8iIgLnzp1r/gRvw+BBRCQlGSyyg4DBg4hIYlwYkYiITMPneRARkan4JEEiIjJdY/s8RHNnpHkxeBARScyYeRu1aJs9G83KAitTREQkNdY8iIikxKG6RETUGOwwJyIi08gYPIiIqDE4z4OIiEzCPg8iImoMNlsREZFpWPMgIiJTydDISYJmzgLjIRERSY01DyIiKbHZioiIGoMd5kREZBrWPIiIqDFY8yAiItPwSYJERNQorHkQEZFJLHRhRAssEhERSY01DyIiqVng13QGDyIiCclklrk8CYMHEZHUWPMgIiKTWGiHOYMHEZHUGDyIiMhUrHkQEZFpLHSGuQXGQyIikhprHkREUrPAr+kMHkREUrLQ0VaSFkmtViMgIADLly9HSUkJwsPDDZuzszPee+89KZMnIjIP8kZsZk7SmsfatWsxYMAAAIBKpUJCQgIAQAiBjh07YuLEiVImT0TU+iz0YVCSFSklJQVJSUkYO3ZsrWNHjx5Fu3bt0KlTJ6mSJyIyGzK56Zu5kyyLy5cvx7p16+o8tmPHDsyYMUOqpImIzEf1UF1TNzMnSbPVnj17EBQUhKCgIMTFxdU4JoTAd999hyNHjtzx+i1btmDLli0AgJycHOTn50uRzWZVVFTU2lmQnKWXkeVr+8y2jG2gJmEqSYJHfHw8oqOjsXPnTpSWlkKj0cDJyQkrV67E4cOH0aFDB7Rv3/6O10dFRSEqKgoAEBERAXd3dymy2ezaSj6bwtLLyPK1feZWRhlHWxlv3bp1SE9PR1paGjZs2IBFixZh5cqVANhkRUTUnG4d1QoAx44dQ0hICAIDA7FmzRrJ0m3ReKjX6/H9999j6tSpLZksEVHrknCo7q2jWgFg6dKl2L59O5KSkhATE4OzZ882UyFqknyS4IIFCww/y+VyZGRkSJ0kEZH5kLDZqnpU6/jx43H27FlkZWVBq9UiLCwMADB79mzExMQgNDS02dO2wJY4IiIz04iaR15eHiIiIhAREWEYQHS720e1ZmVlwc/Pz/Da398fmZmZkhSJy5MQEUmpkavqenl5IT4+/o7H6xrVKoSonbxMZnriRmDwICKSmBTNVnca1XprTSMjIwM+Pj7NnzjYbEVEJK3q5UmaucP8TqNaFQoFEhMTodVqsX37dowfP16SYrHmQUQktRb8mr5x40bMmjULFRUVmDt3Lnr27ClJOgweRERt3K2jWiMiInDu3DnJ02TwICKSmCXOMGfwICKSkoUuyc7gQUQkNQYPIiIyiZktjPjCCy80eI6TkxNefvnles9h8CAikpoZPZ/jm2++aXDBxDfffJPBg4ioVZlZn8ff//53zJ8/v95zysrKGryPGRWJiMjyyGBej6FdsmQJACA9Pb3WsZycnBrn1IfBg4joLtS5c2dMmzYNpaWlhn1jx441+noGDyIiKUm0PElThYaGYsyYMbj33nsNz/yoa2HFO2GfBxGR1Mzwa7pMJsMjjzyCPn364KGHHsIzzzxj0gq8ZlgkIiILIjOvPo9q1bWMe+65B4cOHTL5qYOseRARSc0Mv6afOnXK8LOzszN27dqFq1evGn29GRaJiMjCmFGfR0lJCTZu3IivvvoKWq0Wa9euxbhx4/Dkk0/C1tbW6PsweBARScnMmq1mz56NixcvYv/+/ejfvz/y8vLwxBNPwNXVtcH5H7disxURkdTM6Gv6lStXEBMTAyEE/P398f777wMARo8ejXvuucfo+zB4EBFJycxmmCuVNz/2ZTIZvL29axxTKIxfR6Xe4KFWqxu8gVwuN6mdjIiIWk9qaiqmT58OIYThZ+Dm6Ku0tDSj71Nv8HBzc4Ovr2+dE0dkMhmEENDpdCb10BMR3XXMqOaxe/duw8/Lli2rcez21/WpN3j06NGjxnCuuvTq1cvoxIiI7jpmtiT70KFDm+U+9QaPEydONHgDY84hIrqrmdGS7J6envXOJM/LyzPqPvUGD7lcDrVaDSEEHBwc7ngOERHdgZl1mF+7dg0A8Morr8DX1xdz5syBEAJff/01CgsLjb5PvcHjnXfewcmTJ6HVatGnTx+jnkBFRET/U70ku7n5+eefcezYMcPrxx57DP369cOKFSuMur7e4LF9+3bExcUBAAYOHMjgQURkKjOreVRzdHTEe++9h5kzZ0ImkyE6Ohoqlcro6+sNHuvXrzcM43r99debllMioruVGQaP6OhovPrqqxgzZgwAYPDgwYiOjjb6+nqDx8iRIzFy5Mim5ZDIQgkhkHRdjR6edfcHEpkzLy8vfPTRR42+vt54ePDgwQZvYMw5RJbmhloDvQCsIIdOb/wDdOjuZE5rWz333HPNck69NY/HH38cu3btuuPTpYQQeOKJJ5CYmNhgQkSW4mTWDdgrrZBdJEeID2sd1AAz6/PYtm1bvcuQCCGwd+9evPPOO/Xep97g4eXl1eCD0Lt27VrvcSIpCSFMevpZU9PacSEDf6Rlo4OTE/4xuFuLpEsWwIyCx1tvvdXgOf369WvwnHqDx4EDB4zOEFFLKS3XYFfKWfT1PAEPkYIC2z7wc5gIlY1Ns6clhEDitRvo6uKED49dxumcYripNLC1qkSZRgdHa64tSg0ws5qHKcuu14d/+dTmFFXpUSUqcbKwIwrVoejk7AoPGz1Ud4gdiXnZqFCcRJDjILjYuBqdjl7o8cvV/wetPAP2pcPg53kRHm4ylFYpca9PRwYOMppM3jK145bEv35qc/ycbbDonog7HtfqdFDe0qabpc6Di6oIKqWd0WlcuJGJTHUCMkt0UCqs8MuNBOh1SrR3Ebi/Qw90cPRrUhnoLiIDYIErcVheieiu9t+/zuNy8TZodf97nICzbSVcbUqRXfU7tPqGHzNwJPMKtPKf4WaXi9IqBdILHVGpAcK8vTEm4AF0cOwoYQnI8sgAucL0TWIXL15s0vVGBQ+1Wo3169cbluu9fPkyfvnllyYlTNRc/iooAQAk5l6HkKXDSXYVFeX/D8WVxajSpMHGWgtHoYbQX8C18v/We6+TeeehkR/C5QI7pBfZwd1BjS7u1pjRrReG+vWBjcKqJYpEJLmoqCgMGjQIn3zyiUlrWlUzKnjMnz8fCoXC0IHu4+PDpUrILJRWanEw7TpOZ+fjx7REVIk86Cv1uJr1J3ILDqC8LBZ2cg0UFblQFl1BfR/9F25chMr+NzhaaxHqmovu7qWI8OmKmV2HwE/l0WJlIgsjg1nWPA4dOoRt27YhJycHgwcPxvTp07Fv3z7odDqjrjcqeKSmpmL58uWwsrr5X8/e3v6Ocz+IWlJ2aRnySirx5/U4RHY6hm72WUD5dagqTsP2+g5odCVw0l6HJusSREUlHBVBte4hhMDF6z9BZX0SdtCjvTwdKisfBDjMQKBTeIsNBSZLJYNMrjB5awmdO3fGCy+8gOeffx5HjhzBmjVrEBISgi+//LLBa43qMLe3t0dRUZHhP9Hp06fh6OjYtFwTNUHy9QIk5l+Eo00ygv2Be1yyYVWaCU3OVWg1gL5QQO4qg84xA3JrW2j1VZDr5LC2Dq5xH51eh8ysL2Gd+wO0Ol8o9dZwdBmPcjEIp9PLMaBTKxWQLIisRWoSpvrtt9/wxRdf4NSpU5gyZQpiY2PRuXNnFBUV4Z577sG8efPqvd6o4PHOO+9gwoQJSE1NxahRo3DlyhWTFtAiam4ncjPQyek8VIoMXKwKxg1dJzihGApXLwh1BbQ5RVAWaVBlmw2Zfw/IqgSs7YMgv+U/sUZbiawr70NcOwqUAbZewVC6z8AltQ6hzkoM6OTUiiUki1HdbGVmvvjiCyxYsABffvlljdq1s7Mz/vWvfzV4vVHBo1+/fvjtt9+QnJwMIQS6d+9uaMIiag2zQ3pCqwtBQcUNaIpvILdIj0PXAzCg4wl45B+GqNQAHoCuoAA2yjxUXdPAtnN/w/WaqmJknlgFTckl2FS2h6pPFNLLruHG9c/g6DoZCjn7OKi5yCAzw6G6wcHBtRa+XbduHV588UXDSrv1qTd4fPzxx3Xuj42NBYAGly4hkpJSIUdRpQ5qxKJIZwdllRqq4hRUZV6HvEBA6yADrNSoKs6GrFMPOLgPBACUF2bh8pYXobAqg8eIyahyd8HVHz+Csl0APAb0R3plOpQFNgh05VwOagYy82y22rlzJ1588cUa+6Kjo2vtu5N6g0f14wqTk5Px559/YsKECQCAmJgYDBkypDH5JWoWeWXlkMk0SL6eA71SjiDnqwhLvwjdr5lAVRW+PJCJYZ4e8Ohih/J+avx6cTS8Yz/F9IcmIfnZp2Dt7gO3+/ogO+ZXKFEEOzs9KgMckKtLw6Xc7nDxa/6lTuguJkHwKCkpwYgRI6DRaKDT6fDkk09i0aJFOHbsGB5++GFUVlZi3rx5WLlyZY3rPv30U2zevBnJycno379/jfv16tXL6PTrDR6rVq0CADzwwAM4deqUoZP81VdfxZQpU4xOhKg5CCHwxoFkdGqXD3+nVDhZX4OHvQxumqvQyVxQnq2DOl6LmLRMrD98CbvaZWPVhzPh4joOe154GadOncLx7IuYH/UgFBf/xOWP9sHFD3AcZwXrADdU2XihokLA274Denqw2YrMm729PQ4ePAh7e3uo1WqEhoZi8uTJWLp0KbZv347g4GAMHDgQkydPRmhoqOG66dOn4/7778fLL7+MtWvXGvarVCq4ubkZnb5RfR5Xr16t8VoIgStXrhidCFFT6fQC2WUl6O5dgk74EZ6Zl5Dt2APXrdsjX9kTvpU5qMorhcK2Cvf5ueBbZzsk55Rh2bKd0IpdKM4rRK9evfDQ1AXIy9gKm33nYW8lg9JPAa3cBgobOUqEPbJK26GnF5dZp2YkUbOVQqGAvb09AKCiogI6nQ5lZWXQarUICwsDAMyePRsxMTE1goetrS06duyIzZs317qnWq023LMhRgWPp59+GuHh4Rg1ahQA4I8//sCzzz7b4HVqtRo9evTAtGnTsGHDBuTn5+ORRx5BcnIy5HI5YmJi0KVLF6MySne3i0WXkH19P5ztZKjQOyDF5wGcv+6GshtOCJZdQvGxFMj+KEWFVg57e4E3urTHkxcuITe3GABg5+GMJ97+EPcFh2BPRhfYeJ6B9i85Ck8D+ko1ZE5ylLv5oJvnYIR6ebVyacmSyADJ5m0UFhZi6NChSElJwdtvv428vDz4+f2vr87f37/WA/umTJmCffv2ISQkBDKZzPBYg+p///rrL6PSNip4PPbYY5gwYQL+/PNPCCGwcuVK+Pr6Nnjd2rVrMWDAAMPrp556CjNmzMDs2bOhVqs50ZCMotaUI6/gHNTqSlTBGt7WJfB3KkV7v1yU/5UNTXQaKsuroHcSUDgIKN2t4aCSQ5byv+GH1nINTuUfxj1Z/kgUYeg16DJc+hbC7ngmZHZ20HSbhzDrQfBgcxU1u8bVPPLy8hARcXMB0KioKERFRdU6x8XFBadPn0Zubi4mT56Mvn371k79tkmu+/btA3Bz8ndTGBU8Dh06BABwdb25nPWlS5dw6dIl3HfffXe8JiUlBUlJSRg/fjzOnj2LoqIiHD9+HNu2bQMAo6tGdHfT6fX4b8qfCPtrF8TlUggbGWy7KiFUXaBXaKGxskLZzL7wdC+ByMtF5e9XcOMvDZ77+QpySnTwdVVCyGXIzlPj5zXvIPRfnfDc8Ik4cMUbnb3skRiWiA5u1nBX9YasqrVLSxZJJgNkpgcPLy8PxMfHG3Wut7c3wsLCkJSUhMzMTMP+jIwM+Pj41HlN//79MX36dEydOhUdO3Y0OX9GBY8PP/zQ8HNlZSXi4uLQu3fvehdHXL58Od5++23ExcUBuBnlPDw88NBDD+H8+fMYPnw43nrrLSiVtbOwZcsWbNmyBQCQk5OD/Px8kwrVGoqKilo7C5JrjTIKIdBOOOC481QoexfAy0nA1tYVN0qASnUgCtU6lOpy0Vt9Bi4oR5Vze/yUk4zkvHIE+bjgnVn3oWxgV7z0ym5cPHcZ+Uf/QlVwKQZ5egACiOj8f0u7V1n+79DSyweYbxmlaLbKzc2FnZ0dnJycUFxcjEOHDuHxxx+HQqFAYmIigoODsX37dnz22Wd1Xv/tt99i586dmDlzJoQQmDp1KqZOnYpOnYxbVsGo4LFz585ama5eYbcue/bsQVBQEIKCggzBQ6PR4NixY9i4cSPCwsIwb948bN26FYsWLap1/a1VtIiICLi7uxtVmNbWVvLZFFKVUQgBjU4gq7QE9tYCXvYuAID8Mg0G9uiNTuogVGj0cLK2g4eDNY5mpOGKIhl+XlmQ66vgVZUBceYSbK8A0zs5QzGyGx4c3x4eYdbI9e+Ij3d8jdS4U1i8eHG9+VDaO8PZzrwec5NXVglPe+tmWWOLf6OtQCYD5M3/N5WRkYGFCxdCCAEhBJYtW4awsDBs3LgRs2bNQkVFBebOnYuePXvWeX2HDh3w3HPP4bnnnsPVq1fx4osvYsWKFUYvjNioEnl6euL8+fN3PB4fH4/o6Gjs3LkTpaWl0Gg0UKlU6Ny5M8LDwwEAEydO5GNuyeDXtL+gE6dhZ1eGSrUVUs93h4OtNTq5yuCn9UBHJw+8d/QSrG2vYUmvgeju2Q4hoh3OXstFSuEF2Cquw+mGA6pKy1CRp8CM+7rAc3wHlNr5Qits4KfqgAcW928wH7cGjl1ncqBSFaKgvAAyWRHyS30wrks3tHe1lfKtqOWd+MtYPzK44RPJTEkz2qpPnz5ISEiotT8iIgLnzp0z6h7Hjx/Ht99+i71796Jjx46GFh9jGBU8pk2bZvjWo9frcf78+Xqnr69btw7r1q0DAHz++ec4e/YsVq1ahd9++w2pqano1KkTDhw4gB49ehidUbJcSdevQVe1H6HFBwG5Atd9wpGidIS1rRpXihUoqmyHjOsKuFpZIbJHP8hkMrja3PwAd7RLh7ezFZKKRqL732RwaH8ZyqM50JQApXHpsBnqgJLK3hgV2M7o/OSrNYjPzILO+k/YWJVBW+kIQCC9JB955eUtHjxW3Vd7JWBqS8xzhnm3bt0QFBSEKVOmYMWKFXBxcTHpeqOCx61NVEqlEh06dED79u1NSggA3n33XUyZMgUajQbh4eF1NlnR3cfWSo4ABy2O65+Gn70abg569PYIgFZo4O7kjSB3B/x+9Ti0tlnYcfEiApy644FOHQAAgU4RKK1IRi4UCA+IQr67HKUD85B6LQsKpQLKCkcMa9+5VppCCJRqtLhefgmudrZQCB9UaXXQ6vTYdi4RER1Ow1dehdRCN5RUKJFaoIKLwgth3s5Gl0sIPWSypq9pZG9lXs1oZBmOHTsGZ2fj/55vZ9RfZVxcXK31TqoX0GrIggULDD/37dsXJ0+eNC2HZPE6OrsDzo/h1oaZwNv+pvt7h6OzU1cEODvDRvm/b3H21koM8g/BIP+brx1sAMADVopOaKdSQmX7vwU8L17LR1XFKVT+lQhVQSJKu/WCSpuMojItYKtHoXV3XNHfh0ldQ+DtGILf/rqG3y5mQ2mlhZutDXxsnWGlqD8YCCGw42waujpcgF/xPth3HAqVy4ymvUEkCY1OA7lMBoUE/RE1yACZ1GmY4P3338dTTz2F119/vc5+tLfeesuo+xjdYd6UBbSImsrN3gZu9savN9XV0w6VWj0u5hdDW3EeTmXHcONSNpyvXIIyqQqiux5OFQdg1U5A7mQLuZc/bCt0yNIko6jcAbaKLijX56B/l2w42uhgo6hEcqYVgPpr3AdTL6CP+By26emo9HFBZrkrers0rewkjRM3TsPGqgi9XEbe8ZxrFTkQALxsjW/2rK2xzVbGdVybqnPnmzXxW2edN0a9waO5FtAiamllVRqoqzQ4fnwHgvb+gKJ8PYRGjqp2AjJHPcrPKKBwEtC210AZ4YtjxQNwLsUZpQon9PNzxLup59DNNx3O1lWwkgHlOht0VLk2mK5eKUeFz1xctq1ABy9HdLUPbIHSkql0Qg87ZSbsle2g0WtRriuEk1XtCaJZ5elwtW7i6K1GL08iTfAYP3684ef58+fXOPbFF18YfZ96g0dzLaBF1JIOp+fgZOph9BbJCDmUgEpba9j31ENepoG+WIaKywpYeQmorwCay0o4XMqGx8RLCPC4B7bO2cgqrsKQzrno4pyNk/l+uFJkB7mwwYOdVA2mPaJDdwBAqKfUpaSmKKgshMo2E9YIQk75NWjkKXCyqjnpWS/0KNWloZ11Cop1N5qUXks9VtYU77//fq3gUde+O6k3eDTXAlpELcnVVo6/hQxErrof9EumIPHadRSWFmCQ9hhU8VfgMMkPOnURNFpHqPX2yHDvgEHhkxFcUYFKaxtszj0LnUyJyyX+SMl1xP2dU1FRLvD7pQ4I7O/S2sWjZuBs7QQ7/XhYyz1RoqmAtbL2iLZSbQnsrNQACqATpg8QMpBonkdj7dy5Ezt27EBaWhqmT59u2F9cXGxYRcQY9ZaouRbQImpJIZ43FzYM+r/WBk/HChzJysQNvQ43xg2EysUNeWWAlS4Aff2dUVou4Odkj0KdDip7B7S38cH1Uhkc7a7hb90zIIcS2ZXOKNOrW7FU1Jys5EpYyW8GBDebuodeO1k5o5fTPOj0asjlTXlyauOWJ5FK//794enpiezsbCxdutSwX6VSGVbjNUa9waO5FtAiak3+zraY4dwFwB1WcP6/R5WXVWpxOOssfH3S4O9UDA/7clhp1bCqyIenqweulTggLt0fg9qb2QxmkoxMJoNS0dQl+s1rnkdAQAACAgKwYcMGhIaGGp7TVFJSghMnTtRYzLY+Rg1CHzhwoFH7iNqyEo0GuZo86HQ6eKsvw+r8YShvXISAwMk8f/RwCEOEH/v6yDIsWbIEDg7/C4wODg54/PHHjb6+3prH9evXkZubi6KiIly4cMGwhHpxcTEKCgoamWWilnXsahFC2znAVqmAXH7n9aEcrZW41yccF4vSccPpBlSeOmTqvFBZ6oeurgMR6uHbLOtL0d1FJpOZ1TyPajqdrsbfs1wuh1arNfr6ekv0ww8/4PPPP0d6ejqWLFli2K9SqfDGG280IrtEprlaUAkfJ6sGJ+fdKre4Cl+fyoG3RyWG+LWHs50C35zPxN86t4O3o/UdA4ijtRUC3N3R29sLl4o94ebkCR+FNeyVSgYNahozaraq1rNnT7zyyiuG2sbHH3/cfH0e8+fPx/z587F7925MmjSpSRklaowOrsZPDKzmbKfA9F5esLWSw93OGgDQzdMRheVa6EXDbbUKuRzdXDqanlmiukj0GNqm+te//oU1a9Zg/PjxkMlkGDVqFD755BOjrzeqLjVp0iTExMTg/PnzqKysNOxfuXKl6TkmkoBOL5BWUA5flQ3srBXws1Igr6yixjkuZrbUOt1FzDB4qFQqvP3229BoNLCyMn00mVFtAQsXLsS+ffvw0UcfwcbGBjExMcjIyDA5MSKpRJ/LxL7Us/gw4TC+TU7BxhMX4OXQsqvfEtXtZp+HqZvUjh49ivDwcAQG3lwFITExEU899ZTR1xsVPI4fP45NmzbBxcUFf//73xEbG8s5HmRWHurpj0VhfdDRyQuVyMWYLv6tnSWim2QyCJnC5E1qTz31FH744QfDaiFhYWH4/fffjb7eqPBma3vzG5yTkxOSk5Ph7e3NuR9kduytFZjenc+IIfMiAOhhfgMuZDIZ/Pz8auyTy40fmGJU8HjkkUdQWFiI1157DWPHjoVarcYrr7xiWk6JiMhsdO/eHXv27IEQAhkZGdi4cSP69u1r9PVGhZnFixfDxcUFw4cPx+XLl5GdnQ1vb+9GZ5qI6K4hAL2QmbxJ7eOPP0Z8fDyUSiUmTpwIIQQ+/PBDo69vdK/MM888gylTpjT2ciKiu4KADMK47+ktysHBocYjw03V6OBRPduciIjq1xI1CWNNmzat3kmvO3bsMOo+jQ4enHFLRGQcc+ownzVrFtzdm764Z73Bw9PTs84gIYRAYWFhkxMnIrJ0AjLohfk0W73++us4efIkJk2ahN27dzf6PvUGj2vXrjX6xkREdJM51Tz0ej3eeustnDx5Eh9//HGt47euY1gfrtdARCQhAZhVzSM6Ohp79uyBRqNpUgWBwYOISFIys6p5dO/eHd27d0ffvn0xcuTIRt/HfMIhERG1GH9/f4wZMwZ9+vQBAJw9exZvvfWW0dczeBARSehms5X5TRJctGgRXnvtNej1egBASEgIvvzyS6OvZ7MVEZHE9Gb4Pb2ioqLGciQymQwKhfELMjJ4EBFJzJz6PKr5+fnh1KlThukY//rXv9ClSxejr2fwICKSkBDmNc+j2qZNm/D0008jKysLPj4+GDp0KDZt2mT09QweREQSM8eah5eXF77++mvDa51Oh++++w7Tpk0z6nrzC4dERBbE3DrMi4uLsW7dOixduhQ//fQT9Ho9Nm7ciMDAQHz11VdG34c1DyIiScnMqsN87ty5cHZ2xuDBg7FlyxasX78eWq0W3377rWHYrjEYPIiI7iKXL1/G2bNnAQBRUVFo164d0tPTDU+MNRaDB5EZEUJwxWoLU91sZS6sra0NPysUCrRv397kwAEweBCZhVJNBZIKspByQ4uZ3boygFgYc2q2SkxMhJeXF4D/rZDu5eVl+OKSl5dn1H0YPIjMgEImRx/PTvCxq2jtrFAzEwLQ6Vs7F/+j1Wqb5T4MHkRmwE5pjeJKDUqrtKx1WBwZdGbUbNVcGDyIzISTjRWcbKxaOxvUzMytz6O5mE9DHBGRBRIAdHqZyVtD0tPTMWzYMAQHByMsLAw7d+4EABw7dgwhISEIDAzEmjVrJCsXgwcRkZQEoBMyk7eGKJVKvPfeezh//jx+++03PPPMMygrK8PSpUuxfft2JCUlISYmxjAst7kxeBARtUE+Pj4IDw8HcHOpETc3N1y/fh1arRZhYWFQKpWYPXs2YmJiJEmfwYOISEICpjdZ6fQ3h8xGREQgIiICW7ZsqTeN48ePQ6/X49q1a/Dz8zPs9/f3R2ZmpiTlYoc5EZHE9ML0a7y8vBAfH9/gefn5+Zg3bx62bNkCIWonJNXoPQYPIiIJCUCyobqVlZWIjIzEiy++iEGDBiErK6tGTSMjIwM+Pj6SpM1mKyIiCd2cJNj8o62EEFiwYAFGjBiBuXPnAgB8fX2hUCiQmJgIrVaL7du3Y/z48ZKUizUPIiKJSVHzOHLkCL755huEhYVh9+7dAICvvvoKGzduxKxZs1BRUYG5c+eiZ8+ezZ42wOBBRCQpIQC9ETUJU917773Q6+te9+TcuXPNnt7tGDyIiCSma0SHubmTtM9DrVYjICAAy5cvBwB07NgRYWFhCA8Px9ixY6VMmojILAiYPkGwLayFJWnNY+3atRgwYECNfXFxcXB0dJQyWSIikphkNY+UlBQkJSWxhkFEdzWp1rZqbZLVPJYvX463334bcXFxhn0ymQz33XcflEol/v73v2PKlCl1XrtlyxbDjMqcnBzk5+dLlc1mU1RU1NpZkJyll5Hla/vMsozCMlfVlSR47NmzB0FBQQgKCqoRPI4cOQJfX19kZGRgxIgRuOeeexAYGFjr+qioKERFRQEAIiIi4O7uLkU2m11byWdTWHoZWb62z9zKKOUkwdYkSfCIj49HdHQ0du7cidLSUmg0Gjg5OWHlypUAbq63MnLkSCQkJNQZPIiILEfbaIYylSR9HuvWrUN6ejrS0tKwYcMGLFq0CM899xxKSkoAAIWFhTh06BB69OghRfJERGZDiJtDdU3dzF2LzfPIzc1FZGQkAECv1+PJJ59ESEhISyVPRNRqpJgk2NokDx4LFiww/Hz69GmpkyMiMiuW2ufBhRGJiMhkXJ6EiEhC1avqWhoGDyIiiXGeBxERmaR6bStLw+BBRCQxNlsREZFJBJcnISIiU1UvjGhpGDyIiCRmiX0enOdBREQmY82DiEhCQsi4PAkREZnOEputGDyIiCTEDnMiIjIdh+oSEZGphIU+DIrBg4hIQpa6JDuDBxGRlNhsRUREprLUDnNOEiQiIpOx5kFEJDH2eRARkUk4w5yIiEzG0VZERNQorHkQEZFJhGDNg4iITMY+DyIiMpGl9nlwngcREZmMNQ8iIikJy5xhzuBBRCQhAa5tRUREjWCJNQ/2eRARSUhABr0wfWtIZGQkXF1dMXXqVMO+Y8eOISQkBIGBgVizZo2UxWLwICKSkvi/Pg9Tt4Y8+eST+PLLL2vsW7p0KbZv346kpCTExMTg7NmzUhWLwYOISGp6vczkrSHDhw+HSqUyvM7KyoJWq0VYWBiUSiVmz56NmJgYycrE4EFEJKHqGeambqbKysqCn5+f4bW/vz8yMzObsyg1MHgQEZmhvLw8REREICIiAlu2bGnwfCFErX0ymXQd9RxtRUQkscYsT+Ll5YX4+Hijz/fz86tR08jIyICPj4/J6RqLNQ8iIgkJmN5k1ZhmK19fXygUCiQmJkKr1WL79u0YP368BCW6iTUPIiIpSTTDfPTo0Th58iTKysrg7++P77//Hhs3bsSsWbNQUVGBuXPnomfPns2ebjUGDyIiCUk1w/znn3+uc/+5c+eaPa26MHgQEUlIwDJnmDN4EBFJycgZ47cz9w5pBg8iIgk1tuZh7sHD3PNHRERmiDUPIiKJcUl2IiIyieDDoIiIqDFY8yAiIpMIAHp9a+ei+TF4EBFJqnHLjZg7SUdbqdVqBAQEYPny5YZ9er0e/fv3r/H0KyIiSyWENM/zaG2S1jzWrl2LAQMG1Nj32WefoVOnTtDpdFImTURkNiyxw1yymkdKSgqSkpIwduxYw74bN24gOjoajz76qFTJEhGZFSEgyTPMW5tkwWP58uVYt25djX0vvfQSXnnlFSgUCqmSJSKiFiBJs9WePXsQFBSEoKAgxMXFAQBOnTqFgoICDBs2DAcOHKj3+i1bthienJWTk4P8/HwpstmsioqKWjsLkrP0MrJ8bZ95llFmkc1WkgSP+Ph4REdHY+fOnSgtLYVGo4FKpUJsbCw6duyIiooKlJSU4NFHH8XmzZtrXR8VFYWoqCgAQEREBNzd3aXIZrNrK/lsCksvI8vX9plbGaVakr21SdJstW7dOqSnpyMtLQ0bNmzAokWLsGrVKmRmZiItLQ3R0dEYM2ZMnYGDiMiicLQVERGZSgAWOc9D8uCxYMGCWvuGDRuGYcOGSZ00EZFZaAs1CVOx5kFEJCEh2GFORESNoBetnYPmx4dBERGRyVjzICKSUGMfQ2vuGDyIiCRUvTyJpWHwICKSGEdbERGRSTjPg4iITCf4JEEiIjJZ21huxFQMHkREUrPAZivO8yAiIpOx5kFEJCUBgH0eRERkMgtstmLwICKSGjvMiYjIJGy2IiIi08nYbEVERCYSYLMVERE1ggU+z4PBg4hIahZY8+AkQSIiMhlrHkREUmKfBxERNYoF9nmw2YqISGp6membEfbt24du3bqha9eu2LJli8SFqIk1DyIiKQlp5nlotVo8++yz2L9/P5ycnNC7d29MnjwZbm5uzZ5WXVjzICKSmr4RWwOOHTuGkJAQ+Pn5QaVSYezYsfj555+lyX8dzL7mkZqaioiIiNbORoPy8vLg5eXV2tmQlKWXkeVr+6QsY2pqaqOuG9snAPl/PmPydWq12vDZFxUVhaioqBrHs7Ky4OfnZ3jt7++PzMzMRuWxMcw+eOTm5rZ2FowSERGB+Pj41s6GpCy9jCxf22eOZfzhhx8kua8QtXvhZbKWG9XFZisiojbIz8+vRk0jIyMDPj4+LZY+g0czub1KaYksvYwsX9t3N5SxWv/+/XH27FlkZmaipKQEP/74I0aPHt1i6ctEXXUfIiIye3v37sXy5cuh1+vxwgsv4NFHH22xtBk8iIjIZGy2IiIikzF4EBGRyRg8mig5ORnh4eGGzc7ODrt3727tbDWrd999FyEhIQgODsaTTz5Z5xDBtmzDhg0ICQlBaGgotm3b1trZaRaRkZFwdXXF1KlTDfuqJ5UFBgZizZo1rZi7pqurfEuXLoWXlxf69u3bijm7iwhqNiUlJcLd3V2Ulpa2dlaaTV5enujcubMoLy8XWq1WDBo0SMTFxbV2tppNYmKi6NWrlygvLxfl5eVi0KBBoqCgoLWz1WR//PGH2Lt3r5gyZYphX9++fcXp06eFRqMRffv2FWfOnGnFHDZNXeU7fPiwOH78uOjTp08r5uzuwZpHM9q7dy9GjhwJBweH1s5Ks9JqtaioqIBGo4FGo7GoWcoXLlzAoEGDYGtrC1tbW4SHh+Onn35q7Ww12fDhw6FSqQyvs7KyoNVqERYWBqVSidmzZyMmJqYVc9g0t5cPAAYPHgx3d/dWytHdh8GjGe3YsQMzZsxo7Ww0K09PTyxfvhwdOnSAr68vRo0ahS5durR2tppNaGgo9u/fj8LCQhQWFuKPP/5o0SUeWkprL2VBlofBo5kUFxfjyJEjGDt2bGtnpVkVFBRg3759SEtLQ2ZmJuLi4nDo0KHWzlazqe7HGTFiBCIjI9GvXz8olWa/ao/JRCsvZUGWh8GjmezZswejR4+Gra1ta2elWf32228IDAyEm5sb7Ozs8OCDD5rd2kFNtXjxYpw8eRL79++HtbU1AgMDWztLza61l7Igy8Pg0UwssckKANq3b4+4uDhUVFRAp9PhwIED6NatW2tnq1nl5eUBuDly7tixYy26xENL8fX1hUKhQGJiIrRaLbZv347x48e3draoDeMM82ZQVFSEoKAgpKenw9raurWz0+xeeuklfPfdd5DL5Rg5ciTef/99i2ryGDRoEAoLC+Hg4IBPPvkEffr0ae0sNdno0aNx8uRJlJWVwc3NDd9//z10Oh0WLlyIiooKzJ07F6tXr27tbDZaXeXbtGkTfvjhB+Tn58PLywsffvghIiMjWzurFovBg4iITMZmKyIiMhmDBxERmYzBg4iITMbgQUREJmPwICIikzF4UIvw8PCQ7N4rV65EbGwsAOC9995DVVWVSdcfOHCgxuqsDVm9ejX8/f3x9ttvA7g54W7o0KGYPXs2dDodAODhhx+Gm5sb9u3bZ1JeiNoKBg9q89asWYMhQ4YAaFzwaIwVK1bg+eefBwB8+OGH+OCDD3Dvvffi119/BQBs3boVEyZMkDwfRK2FwYNazS+//ILw8HCEhobi2WefNay/5OHhgeXLl6Nnz54YOXIkysrKAABHjx5FSEgIBg8ejKVLlxpqCwsWLMC+ffvw0UcfISsrC4MGDTJMDru1xrNx40bDxLhb77Vr1y7DObm5uZg0aRL69u2Le++9F0lJSQ2WQ6/XQy6XQ6fTQa/XN8t7Q2TuGDyoVZSXl2PRokXYvXs3EhMTcfHiRXz//fcAgPz8fIwdOxZnzpyBn58fvvvuOwBAVFQUvvrqKxw5cgRFRUW17rl06VL4+voiLi7OcK87iYqKwhdffIEjR44YlicBgKeffhqvvPIKjh8/jnfffRdPP/10g2VZsmQJHn/8cRw5cgQPPPCACe8CUdtlecuHUpuQnJyMbt26oWPHjgCA2bNnIzY2FpMnT4ajoyNGjBgBAOjTpw/S0tJQWFgIrVaL3r17AwBmzJiBL774olFpFxYWQqPRGJ4499BDD+HLL78EAPzxxx+4cOGCSffr1KkTDh8+3Ki8ELVVDB5kFoQQhvWybGxsDPsVCgV0Ol2tJcWNXVXn1jW4Kisra6VVlxMnTkChUBidd6K7EZutqFV069YNFy9exJUrV6DX6xEdHW3o9K6Lq6srlEolEhISAADffvttneepVCqUlJQYXjs7O+PKlSvQaDSGkU+urq6wsrLCyZMnAQDbt283nD906FBs2rQJwM2+jDNnzjSpnESWisGDWkRBQQH8/f0N2759+7B582ZMnDgRYWFh6Nq1KyZNmlTvPTZv3oyHHnoIgwcPhqurK5ycnGqds2jRIgwfPtzQYf76669jxIgRGDNmDDp37mw479NPP8XcuXMxaNAgdOjQwbD/ww8/xK+//oqwsDCEhobihx9+aJ43gMjCcFVdajPKysoMz4dftmwZunfvjmXLlrV4PlavXg0PD48G016wYAGmTp2KcePGtVDOiFoOax7UZuzZswfh4eEIDg5Gfn4+Fi5c2Cr5cHR0xAcffGCYJFiXhx9+GAcPHrS4J0sSVWPNg4iITMaaBxERmYzBg4iITMbgQUREJmPwICIikzF4EBGRyf4/YThV8vbVaDAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 448x378 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6.4, 5.4))\n",
+    "try:\n",
+    "    rdf.plot_ppi(sweep=1, ax=ax, coords=\"lonlat\", context=True)\n",
+    "except Exception as exc:\n",
+    "    ax.set_title(f\"cartopy unavailable: {type(exc).__name__}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b315015c",
+   "metadata": {},
+   "source": [
+    "## 6. Any variable, any subset\n",
+    "\n",
+    "`variable=` takes any column the data holds — including one you computed with\n",
+    "`add_feature` (tutorial 2)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2d81ff0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:38.207832Z",
+     "iopub.status.busy": "2026-08-04T15:32:38.207681Z",
+     "iopub.status.idle": "2026-08-04T15:32:40.862850Z",
+     "shell.execute_reply": "2026-08-04T15:32:40.861979Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1204x280 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "moments = [v for v in (\"DBZH\", \"ZDR\", \"RHOHV\", \"PHIDP\") if v in rdf.columns()]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, len(moments), figsize=(4.3 * len(moments), 4.0))\n",
+    "for ax, var in zip(axes, moments):\n",
+    "    rdf.plot_ppi(sweep=1, variable=var, ax=ax)\n",
+    "    ax.set_title(var)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "45584473",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:40.865150Z",
+     "iopub.status.busy": "2026-08-04T15:32:40.864998Z",
+     "iopub.status.idle": "2026-08-04T15:32:43.098812Z",
+     "shell.execute_reply": "2026-08-04T15:32:43.097810Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 434x378 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filter and crop first — the plot follows the data, gate for gate\n",
+    "sub = (rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
+    "          .crop_around_point(point=SITE, distance=50_000, crs=4326))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
+    "art = sub.plot_ppi(sweep=1, ax=ax)\n",
+    "ax.set_title(f\"DBZH > 30 dBZ within 50 km — {len(art.get_paths()):,} gates drawn\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52fa26d5",
+   "metadata": {},
+   "source": [
+    "## 7. Composing a figure\n",
+    "\n",
+    "Because each method fills one Axes, a multi-panel figure is ordinary matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ada2890f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:43.100741Z",
+     "iopub.status.busy": "2026-08-04T15:32:43.100602Z",
+     "iopub.status.idle": "2026-08-04T15:32:47.047824Z",
+     "shell.execute_reply": "2026-08-04T15:32:47.047068Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 805x630 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(11.5, 9))\n",
+    "rdf.plot_ppi(sweep=1, ax=axes[0][0])\n",
+    "rdf.plot_rhi(azimuth=90, ax=axes[0][1])\n",
+    "rdf.plot_cappi(altitude=3000, ax=axes[1][0])\n",
+    "cs.plot_vcs(ax=axes[1][1])\n",
+    "fig.suptitle(\"radar L — PPI, RHI, CAPPI, cross-section\", fontsize=15)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab9148f7",
+   "metadata": {},
+   "source": [
+    "## 8. Plotting without an archive\n",
+    "\n",
+    "The same functions accept a raw **DataTree**, computing the geometry from its own\n",
+    "coordinates. Handy for a quick look at a volume you have not archived yet.\n",
+    "\n",
+    "The exception is `plot_vcs`: the cross-section path is `gate_id`-keyed, so it\n",
+    "needs an archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e0ad81a1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:47.050077Z",
+     "iopub.status.busy": "2026-08-04T15:32:47.049935Z",
+     "iopub.status.idle": "2026-08-04T15:32:47.746725Z",
+     "shell.execute_reply": "2026-08-04T15:32:47.745866Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 434x378 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dt = raddb.open_any_datatree(sorted(MCH_DIR.glob(\"L_*.zarr\"))[0])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
+    "raddb.plot_ppi(dt, sweep=1, variable=\"DBZH\", ax=ax)\n",
+    "ax.set_title(\"straight from a DataTree — no archive\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c2ffc3b",
+   "metadata": {},
+   "source": [
+    "## 9. Saving\n",
+    "\n",
+    "Pass `save=\"path.png\"`, or use matplotlib directly. For vector output on a big\n",
+    "sweep, `rasterized=True` keeps the polygons as pixels inside the PDF and the file\n",
+    "small."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7505115e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-04T15:32:47.748747Z",
+     "iopub.status.busy": "2026-08-04T15:32:47.748436Z",
+     "iopub.status.idle": "2026-08-04T15:32:48.469077Z",
+     "shell.execute_reply": "2026-08-04T15:32:48.468256Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved: /tmp/raddb_tutorial_archive/ppi_example.png (70 kB)\n"
+     ]
+    }
+   ],
+   "source": [
+    "out = ARCHIVE_DIR / \"ppi_example.png\"\n",
+    "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
+    "rdf.plot_ppi(sweep=1, ax=ax, rasterized=True)\n",
+    "fig.savefig(out, dpi=120, bbox_inches=\"tight\")\n",
+    "plt.close(fig)\n",
+    "print(\"saved:\", out, f\"({out.stat().st_size / 1e3:.0f} kB)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bcf98cc",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Recap\n",
+    "\n",
+    "```python\n",
+    "rdf.plot_ppi(sweep=1, variable=\"DBZH\")\n",
+    "rdf.plot_rhi(azimuth=90)\n",
+    "rdf.plot_cappi(altitude=3000)\n",
+    "rdf.plot_vcs(line=[(lon1, lat1), (lon2, lat2)], crs=4326)\n",
+    "```\n",
+    "\n",
+    "Shared arguments: `variable`, `radar`, `timestep`, `start_time` / `end_time`,\n",
+    "`coords`, `context`, `ax`, `save`, `rasterized`, and anything else is forwarded to\n",
+    "matplotlib.\n",
+    "\n",
+    "A few things worth remembering:\n",
+    "\n",
+    "- One plot, one Axes — **you** compose the figure with `ax=`.\n",
+    "- Geometry always comes from the LUT, so what you filtered is what you see.\n",
+    "- Beam width is a property of the **archive**, fixed when the LUT was generated\n",
+    "  (`beamwidth_deg` in `info.yaml`), not a plot argument.\n",
+    "- For a huge sweep, `rasterized=True` before saving to PDF.\n",
+    "\n",
+    "---\n",
+    "That is the whole workflow: **archive → open & filter → crop → plot.**"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial/README.md b/tutorial/README.md
new file mode 100644
index 0000000..6140e06
--- /dev/null
+++ b/tutorial/README.md
@@ -0,0 +1,40 @@
+# RadDB tutorials
+
+Four notebooks that walk through the whole workflow, in order. Each one is
+self-contained — if you jump straight to number 3, it builds the archive it needs.
+
+| # | notebook | covers |
+|---|---|---|
+| 1 | [Archiving](01_archiving.ipynb) | the storage model, the CRS contract, `archive()`, what lands on disk |
+| 2 | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters |
+| 3 | [Areas of interest](03_area_of_interest.ipynb) | bbox / point / polygon crops, cross-sections, the interactive map |
+| 4 | [Plots](04_plots.ipynb) | PPI, RHI, CAPPI, vertical cross-section |
+
+The notebooks are stored **with their output**, so you can read them on GitHub
+without running anything.
+
+## Running them yourself
+
+RadDB is network-agnostic — any xarray `DataTree` with the standard
+[xradar](https://docs.openradarscience.org/projects/xradar/) layout works. The
+notebooks use MeteoSwiss and NEXRAD volumes stored as Zarr. Point them at your own
+data with environment variables, or edit the configuration cell at the top of each
+notebook:
+
+```bash
+export RADDB_DATATREE_DIR=/path/to/MCH_datatree      # radars L, W
+export RADDB_NEXRAD_DIR=/path/to/NEXRAD_datatree     # radar KTLX
+export RADDB_TUTORIAL_ARCHIVE=/tmp/raddb_tutorial_archive   # where to write
+
+jupyter lab
+```
+
+Then run notebook 1 first — it creates the archive the others read.
+
+Needed beyond the core install: `jupyter`, and `ipyleaflet` + `ipywidgets` for the
+interactive map in notebook 3 (`pip install raddb[viz]`).
+
+## Also here
+
+[`basic_usage.py`](basic_usage.py) — the same end-to-end workflow as a plain
+script, using a synthetic volume, so it runs with no data at all.

From 698f04200a898dfb002e4e88183b4eeeec7603f9 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Thu, 6 Aug 2026 19:34:56 +0200
Subject: [PATCH 03/14] Refactor tutorial notebooks to update data paths and
 remove unnecessary output

- Changed execution_count to null for code cells in 03_area_of_interest.ipynb and 04_plots.ipynb.
- Removed print statements and replaced them with direct path assignments for MCH_DIR, NEXRAD_DIR, and ARCHIVE_DIR.
- Updated paths to point to local directories for better usability in the tutorial context.
---
 raddb/__init__.py                       |   2 -
 raddb/lut.py                            |  87 +--
 raddb/tests/test_azimuth_grid.py        |  11 +-
 raddb/tests/test_lut_planes.py          |   8 +-
 raddb/tests/test_radar_code.py          |  55 +-
 raddb/tools/migrate_gate_id_v2.py       |  34 +-
 tutorial/01_archiving.ipynb             | 693 +++++----------------
 tutorial/02_opening_and_filtering.ipynb | 762 ++++++------------------
 tutorial/03_area_of_interest.ipynb      |  39 +-
 tutorial/04_plots.ipynb                 |  39 +-
 10 files changed, 436 insertions(+), 1294 deletions(-)

diff --git a/raddb/__init__.py b/raddb/__init__.py
index ba475e1..68d869a 100644
--- a/raddb/__init__.py
+++ b/raddb/__init__.py
@@ -67,7 +67,6 @@
     MAX_RADAR_CODE,
     RADAR_TO_IDX,
     DEFAULT_BEAMWIDTH_DEG,
-    OutdatedGateIdError,
     decode_gate_radars,
     decode_radar_code,
     encode_radar_code,
@@ -157,7 +156,6 @@
     "snap_azimuths_to_grid",
     "azimuth_grid_tolerance",
     "load_azimuth_grids",
-    "OutdatedGateIdError",
     "encode_radar_code",
     "decode_radar_code",
     "decode_gate_radars",
diff --git a/raddb/lut.py b/raddb/lut.py
index afb11fa..2dc81e0 100644
--- a/raddb/lut.py
+++ b/raddb/lut.py
@@ -497,21 +497,37 @@ def snap_azimuths_to_grid(azimuths, grid) -> tuple[np.ndarray, np.ndarray]:
 
 
 def load_azimuth_grids(radar: str, base_path: str | Path) -> dict[int, np.ndarray] | None:
-    """``{sweep: canonical azimuths}`` from a radar's info YAML, or ``None``.
+    """``{sweep: canonical azimuths}`` for a radar, or ``None`` if it has no LUT.
 
-    ``None`` means the archive predates the nominal grid, in which case the
-    caller must keep using the measured azimuths — snapping to a grid that was
-    never recorded would move gates off their own LUT rows.
+    The grid is read back out of the LUT parquet itself: its ``azimuth`` column
+    already holds the snapped, canonical rays (``generate_lut_from_datatree``
+    writes ``snapped / AZIMUTH_SCALE``), so rounding to tenths of a degree
+    recovers exactly the values ``gate_id`` carries.  The LUT is therefore the
+    single source of the grid, and the info YAML does not restate it.
+
+    Only two of the LUT's thirteen columns are read, and parquet is columnar —
+    33-66 ms on radar L's 96 MB / 1.7M-gate LUT, against ~2 s to archive the
+    volume it is needed for.
+
+    ``None`` means there is no LUT to read, in which case the caller must keep
+    the measured azimuths: there is no grid to snap onto.
     """
-    try:
-        info = load_radar_info(radar, base_path)
-    except (FileNotFoundError, OutdatedGateIdError):
+    lut_path = lut_file_path(radar, "lut", base_path)
+    if not lut_path.exists():
         return None
-    sweeps = (info or {}).get("sweeps") or {}
+    az = (
+        pl.scan_parquet(lut_path)
+        .select(["sweep", "azimuth"])
+        .unique()
+        .collect()
+    )
     grids = {
-        int(sweep): np.asarray(meta["azimuths"], dtype=np.int64)
-        for sweep, meta in sweeps.items()
-        if isinstance(meta, dict) and meta.get("azimuths")
+        int(sweep): np.sort(
+            np.round(
+                az.filter(pl.col("sweep") == sweep)["azimuth"].to_numpy() * AZIMUTH_SCALE
+            ).astype(np.int64)
+        )
+        for sweep in sorted(az["sweep"].unique().to_list())
     }
     return grids or None
 
@@ -872,7 +888,6 @@ def generate_lut_from_datatree(
             "z":               z_raw.ravel(),
         }))
 
-        # Radial spacing from the sweep's own range grid -> dR = spacing / 2.
         rng_res = (
             float(np.median(np.diff(np.sort(ranges).astype(np.float64))))
             if n_rng > 1 else float("nan")
@@ -884,12 +899,6 @@ def generate_lut_from_datatree(
             "elevation": round(elevation_angle, 2),
             "range_resolution": round(rng_res, 3),
             "range_start": round(float(np.min(ranges)), 3),
-            "dR": round(rng_res / 2.0, 3),
-            # The canonical ray azimuths, in tenths of a degree — exactly the
-            # values gate_id carries.  Written so archiving a later volume need
-            # not re-read the (80 MB) LUT to recover them.
-            "azimuth_scale": AZIMUTH_SCALE,
-            "azimuths": [int(v) for v in az_grid],
         }
         # Grids needed later for the corner/plane lattices — keep them so the
         # lattices are built from the same arrays the centroids came from,
@@ -942,10 +951,6 @@ def generate_lut_from_datatree(
         "longitude": radar_lon,
         "altitude": radar_alt,
         "crs": crs_info,
-        # Which gate_id encoding the stored ids use.  v1 (A=0..Z=25) and v2
-        # (base-36 of the name) disagree for every radar, so the reader has to
-        # be told rather than guess.
-        "gate_id_version": GATE_ID_VERSION,
         # Recorded for reproducibility: archives built before the ke 1.25 -> 4/3
         # fix carry incompatible geometry, so the file must say which model
         # produced it.
@@ -1953,13 +1958,7 @@ def load_radar_lut(
 def load_radar_info(
     radar: str, lut_base_path: str | Path
 ) -> dict:
-    """Load the radar info YAML for a radar.
-
-    Raises
-    ------
-    OutdatedGateIdError
-        If the archive was written with the v1 ``gate_id`` encoding.
-    """
+    """Load the radar info YAML for a radar."""
     info_path = (
         Path(lut_base_path) / radar / "LUT" / f"{radar}_info.yaml"
     )
@@ -1967,39 +1966,9 @@ def load_radar_info(
         raise FileNotFoundError(f"Info not found at {info_path}.")
     with open(info_path) as f:
         info = yaml.safe_load(f)
-    check_gate_id_version(info, radar=radar, base_path=lut_base_path)
     return info
 
 
-class OutdatedGateIdError(RuntimeError):
-    """An archive still holds v1 ``gate_id`` values and must be migrated."""
-
-
-def check_gate_id_version(info: dict, radar: str, base_path: str | Path) -> int:
-    """Validate the ``gate_id`` encoding version recorded in a radar's info.
-
-    Archives written before the base-36 radar code carry no ``gate_id_version``
-    key at all, so a missing key means v1.  Reading one as v2 would silently
-    rename its radars (a v1 ``"L"`` prefix of 11 decodes to ``"B"`` under v2),
-    which is worse than refusing, hence the hard error.
-
-    Returns
-    -------
-    int
-        The archive's ``gate_id`` version, always :data:`GATE_ID_VERSION`.
-    """
-    version = int((info or {}).get("gate_id_version", 1))
-    if version != GATE_ID_VERSION:
-        raise OutdatedGateIdError(
-            f"radar {radar!r} in {base_path} uses gate_id encoding v{version}, "
-            f"but this version of RadDB writes and reads v{GATE_ID_VERSION} "
-            f"(radar codes are base-36 of the name, so v1 ids decode to the "
-            f"wrong radar). Migrate the archive in place with:\n"
-            f"    python -m raddb.tools.migrate_gate_id_v2 {base_path}"
-        )
-    return version
-
-
 def get_full_sweep_index(
     lut_df: "pl.DataFrame | pd.DataFrame", sweep: int
 ) -> pd.MultiIndex:
diff --git a/raddb/tests/test_azimuth_grid.py b/raddb/tests/test_azimuth_grid.py
index 33ae579..37235e3 100644
--- a/raddb/tests/test_azimuth_grid.py
+++ b/raddb/tests/test_azimuth_grid.py
@@ -23,6 +23,7 @@
 import polars as pl
 import pytest
 import xarray as xr
+import yaml
 
 _PKG_ROOT = Path(__file__).resolve().parents[2]
 if str(_PKG_ROOT) not in sys.path:
@@ -218,12 +219,20 @@ def test_the_same_ray_keeps_its_gate_id(self, archive):
         sets = [set(pl.read_parquet(f, columns=["gate_id"])["gate_id"].to_list()) for f in pols]
         assert all(s == sets[0] for s in sets)
 
-    def test_grid_is_recorded_in_the_info_yaml(self, archive):
+    def test_grid_is_recovered_from_the_lut(self, archive):
+        """The info YAML no longer restates it; the LUT parquet is the source."""
         grids = load_azimuth_grids("A", archive)
         assert grids is not None and set(grids) == {1, 2, 3}
         for g in grids.values():
             assert g.size == 360 and np.all(np.diff(g) == 10)
 
+        info = yaml.safe_load((archive / "A" / "LUT" / "A_info.yaml").read_text())
+        assert "azimuths" not in info["sweeps"][1]
+
+    def test_no_lut_means_no_grid(self, tmp_path):
+        """Nothing to snap onto — the caller must keep the measured azimuths."""
+        assert load_azimuth_grids("A", tmp_path) is None
+
     def test_lut_azimuths_are_the_grid(self, archive):
         """The LUT holds nominal angles now, not one volume's measurements."""
         lut = pl.read_parquet(archive / "A" / "LUT" / "A_LUT.parquet", columns=["sweep", "azimuth"])
diff --git a/raddb/tests/test_lut_planes.py b/raddb/tests/test_lut_planes.py
index 272326a..02fd33d 100644
--- a/raddb/tests/test_lut_planes.py
+++ b/raddb/tests/test_lut_planes.py
@@ -169,11 +169,13 @@ def test_per_sweep_keys(self, lut_dir):
         )
         s = info["sweeps"][1]
         for key in ("n_azimuths", "n_ranges", "n_gates", "elevation",
-                    "range_resolution", "range_start", "dR"):
+                    "range_resolution", "range_start"):
             assert key in s, f"missing per-sweep key {key!r}"
         assert s["n_gates"] == s["n_azimuths"] * s["n_ranges"]
-        # both are rounded to mm in the YAML, so allow half a mm of slack
-        assert s["dR"] == pytest.approx(s["range_resolution"] / 2.0, abs=1e-3)
+        # dR, azimuth_scale and azimuths were dropped: the first two were never
+        # read back, and the grid is recovered from the LUT parquet instead.
+        for key in ("dR", "azimuth_scale", "azimuths"):
+            assert key not in s, f"per-sweep key {key!r} should no longer be written"
 
     def test_crs_block_records_what_was_used(self, real_base):
         """A CRS is mandatory, so the block is always populated."""
diff --git a/raddb/tests/test_radar_code.py b/raddb/tests/test_radar_code.py
index 4d9e895..2a97d95 100644
--- a/raddb/tests/test_radar_code.py
+++ b/raddb/tests/test_radar_code.py
@@ -29,10 +29,8 @@
 )
 from raddb.lut import (  # noqa: E402
     GATE_ID_RADAR_BASE,
-    GATE_ID_VERSION,
     LEGACY_RADAR_TO_IDX,
     MAX_RADAR_CODE,
-    OutdatedGateIdError,
     decode_gate_ids,
     decode_gate_radars,
     decode_radar_code,
@@ -245,9 +243,10 @@ def test_lut_and_pol_gate_ids_join(self, tmp_path):
         matched = pol.join(lut.select("gate_id"), on="gate_id", how="semi")
         assert matched.height == pol.height
 
-    def test_info_yaml_records_the_version(self, tmp_path):
+    def test_info_yaml_records_no_version(self, tmp_path):
+        """Only v2 is ever written, so the version key was dropped entirely."""
         db = _archive(tmp_path, "KTLX")
-        assert db.get_radar_info("KTLX")["gate_id_version"] == GATE_ID_VERSION
+        assert "gate_id_version" not in db.get_radar_info("KTLX")
 
     def test_sel_by_radar_uses_the_code(self, tmp_path):
         db = _archive(tmp_path, "KTLX")
@@ -272,29 +271,37 @@ def test_two_radars_stay_distinct(self, tmp_path):
         assert codes == {encode_radar_code("KTLX"), encode_radar_code("KOUN")}
 
 
-class TestGateIdVersionGuard:
+class TestGateIdMigration:
+    """The v1 -> v2 migration, now that ``info.yaml`` records no version.
+
+    Nothing detects the encoding any more, so the tool is an unconditional
+    offset that the caller must vouch for.  These tests pin that contract,
+    including the part that is genuinely worse than before: running it twice
+    corrupts the archive, and only ``--assume-v1`` stands between the two.
+    """
 
     def _downgrade_to_v1(self, tmp_path, radar):
         """Rewrite an archive back to the v1 encoding, as if written long ago."""
         lut_dir = tmp_path / "archive" / radar / "LUT"
-        info_path = lut_dir / f"{radar}_info.yaml"
-        info = yaml.safe_load(info_path.read_text())
         delta = (LEGACY_RADAR_TO_IDX[radar] - encode_radar_code(radar)) * GATE_ID_RADAR_BASE
         for f in [lut_dir / f"{radar}_LUT.parquet",
                   *sorted((tmp_path / "archive" / radar).rglob("*_POL.parquet"))]:
             pl.read_parquet(f).with_columns(
                 (pl.col("gate_id") + delta).alias("gate_id")
             ).write_parquet(f)
-        info.pop("gate_id_version", None)          # v1 files carry no version key
-        info_path.write_text(yaml.dump(info, default_flow_style=False, sort_keys=False))
         return delta
 
-    def test_v1_archive_is_refused(self, tmp_path):
+    def test_v1_archive_is_read_without_complaint(self, tmp_path):
+        """The version guard is gone: a v1 archive now loads silently.
+
+        Its ids decode to the wrong radar — that is the cost of dropping the
+        key, and it is pinned here so the trade-off stays visible.
+        """
         db = _archive(tmp_path, "L")
         self._downgrade_to_v1(tmp_path, "L")
 
-        with pytest.raises(OutdatedGateIdError, match="migrate_gate_id_v2"):
-            db.get_radar_info("L")
+        assert "gate_id_version" not in db.get_radar_info("L")
+        assert decode_gate_radars(db.open(radars="L").data["gate_id"].to_numpy()) == ["B"]
 
     def test_migration_restores_the_archive(self, tmp_path):
         from raddb.tools.migrate_gate_id_v2 import migrate_radar
@@ -310,23 +317,31 @@ def test_migration_restores_the_archive(self, tmp_path):
         res = migrate_radar(tmp_path / "archive", "L")
         assert res["status"] == "migrated" and res["rows"] > 0
 
-        assert db.get_radar_info("L")["gate_id_version"] == GATE_ID_VERSION
         after = db.open(radars="L").data["gate_id"].to_numpy()
         assert np.array_equal(np.sort(after), np.sort(before))
         assert decode_gate_radars(after) == ["L"]
 
-    def test_migration_is_idempotent(self, tmp_path):
+    def test_migration_is_no_longer_idempotent(self, tmp_path):
+        """Without a recorded version there is nothing to short-circuit on."""
         from raddb.tools.migrate_gate_id_v2 import migrate_radar
 
         db = _archive(tmp_path, "L")
         self._downgrade_to_v1(tmp_path, "L")
         migrate_radar(tmp_path / "archive", "L")
         again = migrate_radar(tmp_path / "archive", "L")
-        assert again["status"] == "already v2"
-        assert db.open(radars="L").data.height > 0
 
-    def test_fresh_archive_needs_no_migration(self, tmp_path):
-        from raddb.tools.migrate_gate_id_v2 import migrate_radar
+        assert again["status"] == "migrated"       # it runs again, blindly
+        assert decode_gate_radars(db.open(radars="L").data["gate_id"].to_numpy()) != ["L"]
+
+    def test_cli_refuses_to_write_without_assume_v1(self, tmp_path):
+        """The only guard left against a double migration."""
+        from raddb.tools.migrate_gate_id_v2 import main
+
+        db = _archive(tmp_path, "L")
+        before = db.open(radars="L").data["gate_id"].to_numpy().copy()
+
+        assert main([str(tmp_path / "archive")]) == 2
+        assert np.array_equal(db.open(radars="L").data["gate_id"].to_numpy(), before)
 
-        _archive(tmp_path, "KTLX")
-        assert migrate_radar(tmp_path / "archive", "KTLX")["status"] == "already v2"
+        assert main([str(tmp_path / "archive"), "--dry-run"]) == 0
+        assert np.array_equal(db.open(radars="L").data["gate_id"].to_numpy(), before)
diff --git a/raddb/tools/migrate_gate_id_v2.py b/raddb/tools/migrate_gate_id_v2.py
index 8b331a3..a3c082b 100644
--- a/raddb/tools/migrate_gate_id_v2.py
+++ b/raddb/tools/migrate_gate_id_v2.py
@@ -16,13 +16,19 @@
 No geometry is recomputed and nothing is re-ingested, which matters because the
 source volumes of an archive are often no longer around.
 
+``info.yaml`` no longer records a ``gate_id_version``, so this tool **cannot tell
+a v1 archive from a v2 one** — and re-running it on a migrated archive would
+shift every id a second time.  It therefore refuses to touch anything without an
+explicit ``--assume-v1``, which is the caller asserting that the archive really
+does predate the base-36 radar code.
+
 Usage
 -----
 ::
 
     python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --dry-run
-    python -m raddb.tools.migrate_gate_id_v2 <archive_dir>
-    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --radar L --radar W
+    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --assume-v1
+    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --assume-v1 --radar L --radar W
 """
 from __future__ import annotations
 
@@ -36,7 +42,6 @@
 from raddb.helper import is_valid_radar_name, normalize_radar_name
 from raddb.lut import (
     GATE_ID_RADAR_BASE,
-    GATE_ID_VERSION,
     LEGACY_RADAR_TO_IDX,
     encode_radar_code,
 )
@@ -84,13 +89,6 @@ def migrate_radar(archive_dir: Path, radar: str, dry_run: bool = False) -> dict:
         return {"radar": radar, "status": "no info.yaml", "files": 0, "rows": 0}
 
     info = yaml.safe_load(info_path.read_text()) or {}
-    version = int(info.get("gate_id_version", 1))
-    if version == GATE_ID_VERSION:
-        return {"radar": radar, "status": "already v2", "files": 0, "rows": 0}
-    if version != 1:
-        return {"radar": radar, "status": f"unknown v{version} — left alone",
-                "files": 0, "rows": 0}
-
     name = normalize_radar_name(info.get("radar") or radar)
     legacy = LEGACY_RADAR_TO_IDX.get(name)
     if legacy is None:
@@ -106,9 +104,6 @@ def migrate_radar(archive_dir: Path, radar: str, dry_run: bool = False) -> dict:
     if not dry_run:
         for f in files:
             rows += _shift_gate_ids(f, delta)
-        info["gate_id_version"] = GATE_ID_VERSION
-        with open(info_path, "w") as fh:
-            yaml.dump(info, fh, default_flow_style=False, sort_keys=False)
 
     return {
         "radar": radar,
@@ -127,6 +122,10 @@ def main(argv: list[str] | None = None) -> int:
                     help="restrict to this radar (repeatable); default is all")
     ap.add_argument("--dry-run", action="store_true",
                     help="report what would change without writing")
+    ap.add_argument("--assume-v1", action="store_true",
+                    help="confirm the archive really is v1 (required to write: "
+                         "info.yaml no longer records a version, so this cannot "
+                         "be detected, and migrating twice corrupts every gate_id)")
     args = ap.parse_args(argv)
 
     archive_dir: Path = args.archive_dir
@@ -134,6 +133,15 @@ def main(argv: list[str] | None = None) -> int:
         print(f"error: {archive_dir} is not a directory", file=sys.stderr)
         return 2
 
+    if not args.dry_run and not args.assume_v1:
+        print(
+            "error: pass --assume-v1 to write. info.yaml no longer records a "
+            "gate_id_version, so a v1 archive is indistinguishable from a v2 one, "
+            "and running this twice shifts every gate_id twice.",
+            file=sys.stderr,
+        )
+        return 2
+
     radars = [normalize_radar_name(r) for r in args.radar] if args.radar \
         else _archive_radars(archive_dir)
     if not radars:
diff --git a/tutorial/01_archiving.ipynb b/tutorial/01_archiving.ipynb
index e89ee86..83fcbe2 100644
--- a/tutorial/01_archiving.ipynb
+++ b/tutorial/01_archiving.ipynb
@@ -2,36 +2,35 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "77188268",
+   "id": "3f7df4aa",
    "metadata": {},
    "source": [
-    "# 1 — Archiving\n",
+    "# 1. Archiving\n",
     "\n",
-    "**RadDB** turns xarray **DataTree** radar volumes into a compact, queryable Parquet\n",
-    "archive. It is *network-agnostic*: any DataTree with the standard\n",
-    "[xradar](https://docs.openradarscience.org/projects/xradar/) coordinate layout —\n",
-    "MeteoSwiss, NEXRAD, OPERA — can be archived. No `pyart` is needed in the core.\n",
+    "**RadDB** turns radar volumes into a compact, queryable Parquet archive. A volume\n",
+    "is an [xarray](https://docs.xarray.dev/) `DataTree` (one group per sweep).\n",
     "\n",
     "This notebook covers:\n",
     "\n",
-    "1. Looking at what data you have, before archiving it\n",
-    "2. The **CRS contract** — the one thing you must get right\n",
-    "3. Archiving a volume\n",
-    "4. What lands on disk, and why it is laid out that way\n",
+    "1. Raw data and RadDB initialisation\n",
+    "2. Archiving\n",
+    "3. Archived data\n",
     "\n",
     "---\n",
     "## How the archive is stored\n",
     "\n",
-    "A radar is stored as **one static LUT** (per-gate geometry, computed once) plus\n",
-    "**one Parquet file per volume** (the moments), linked by an integer `gate_id`:\n",
+    "A radar data is stored as **static data (LUT)** (per-gate geometry, computed once) plus\n",
+    "**dynamic data, one file per volume** (polarimetric variables), linked by an integer `gate_id`.\n",
+    "Following is an example of one archived volume (paths and files):\n",
     "\n",
     "```\n",
     "{archive_dir}/{radar}/LUT/{radar}_LUT.parquet          # gate centroids\n",
-    "{archive_dir}/{radar}/LUT/{radar}_h_plane_LUT.parquet  # horizontal faces (PPI)\n",
-    "{archive_dir}/{radar}/LUT/{radar}_v_plane_LUT.parquet  # vertical faces  (RHI)\n",
+    "{archive_dir}/{radar}/LUT/{radar}_h_plane_LUT.parquet  # horizontal gate plane (for PPI)\n",
+    "{archive_dir}/{radar}/LUT/{radar}_v_plane_LUT.parquet  # vertical gate plane  (for RHI)\n",
     "{archive_dir}/{radar}/LUT/{radar}_corners_LUT.parquet  # 3-D gate corners\n",
     "{archive_dir}/{radar}/LUT/{radar}_info.yaml            # site, CRS, scan geometry\n",
-    "{archive_dir}/{radar}/{YYYY}/{MM}/{DD}/{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet\n",
+    "\n",
+    "{archive_dir}/{radar}/{YYYY}/{MM}/{DD}/{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet    # dynamic data\n",
     "```\n",
     "\n",
     "The geometry is stored **once**, not once per volume — which is what keeps the\n",
@@ -41,53 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "85737613",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:49.927018Z",
-     "iopub.status.busy": "2026-08-04T15:28:49.926856Z",
-     "iopub.status.idle": "2026-08-04T15:28:49.933972Z",
-     "shell.execute_reply": "2026-08-04T15:28:49.933400Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
-      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
-      "Archive         : /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# --------------------------------------------------------------------------\n",
-    "# CONFIGURATION — point these at your own data\n",
-    "# --------------------------------------------------------------------------\n",
-    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
-    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
-    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
-    "\n",
-    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
-    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
-    "\n",
-    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
-    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
-    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
-    "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
-    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
-    "print(\"Archive         :\", ARCHIVE_DIR)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "09525d30",
    "metadata": {
     "execution": {
@@ -97,39 +50,69 @@
      "shell.execute_reply": "2026-08-04T15:28:50.540228Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "raddb 0.1.dev5+gde6070734.d20260323\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "import raddb\n",
+    "from raddb.lut import suggest_crs\n",
+    "from pathlib import Path\n",
     "\n",
     "print(\"raddb\", raddb.__version__)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "94b4d6fb",
+   "id": "9c1898c8",
    "metadata": {},
    "source": [
-    "## 1. What do I have?\n",
+    "## 1. Raw data and RadDB initialisation\n",
+    "\n",
+    "### Input paths\n",
     "\n",
-    "`inventory()` answers \"what is on disk?\" for both sides of the workflow. Pointed at\n",
-    "a directory of DataTree files it reports the **input** side, grouping by the radar\n",
-    "name it reads from each filename prefix."
+    "`MCH_DIR` and `NEXRAD_DIR` hold the DataTree volumes to be archived; `ARCHIVE_DIR`\n",
+    "is where RadDB writes the archive. Edit them to match your own machine."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "c1e7fb76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — point these at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# Any xarray DataTree with the standard xradar layout works.  \n",
+    "# These tutorials use MeteoSwiss and NEXRAD volumes stored as zarr and nc format.  \n",
+    "# Edit the paths below to point at your own data.\n",
+    "\n",
+    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
+    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
+    "\n",
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e92e3c0",
+   "metadata": {},
+   "source": [
+    "### Inspecting raw data archive\n",
+    "\n",
+    "`inventory(datatree_dir=...)` scans a directory of DataTree files and prints what\n",
+    "it finds: the radar name taken from each filename prefix, the number of files, the\n",
+    "time span they cover and their total size on disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "ab4d193d",
    "metadata": {
     "execution": {
@@ -139,35 +122,16 @@
      "shell.execute_reply": "2026-08-04T15:28:50.640859Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==============================================================================\n",
-      "RadDB inventory — DataTree files on disk (not archived yet)\n",
-      "  directory : /data/RADAR/MCH_datatree\n",
-      "  files     : 2\n",
-      "  radars    : L, W  (from the filename prefix)\n",
-      "  time range: 2024-07-19 13:50:00 .. 2024-08-26 02:50:00\n",
-      "------------------------------------------------------------------------------\n",
-      "  radar     files  time range                                           size\n",
-      "  L             1  2024-08-26 02:50:00                               20.9 MB\n",
-      "  W             1  2024-07-19 13:50:00                               21.5 MB\n",
-      "------------------------------------------------------------------------------\n",
-      "  archive with: db.archive(datatree_dir='/data/RADAR/MCH_datatree')\n",
-      "==============================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "db = raddb.RadDB()\n",
-    "db.inventory(datatree_dir=MCH_DIR)"
+    "db.inventory(datatree_dir=NEXRAD_DIR)\n",
+    "# db.inventory(datatree_dir=MCH_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "720db410",
    "metadata": {
     "execution": {
@@ -177,224 +141,112 @@
      "shell.execute_reply": "2026-08-04T15:28:50.677758Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==============================================================================\n",
-      "RadDB inventory — DataTree files on disk (not archived yet)\n",
-      "  directory : /data/RADAR/NEXRAD_datatree\n",
-      "  files     : 2\n",
-      "  radars    : KTLX  (from the filename prefix)\n",
-      "  time range: 2013-05-20 19:51:11 .. 2013-05-20 19:55:27\n",
-      "------------------------------------------------------------------------------\n",
-      "  radar     files  time range                                           size\n",
-      "  KTLX          2  2013-05-20 19:51:11 .. 2013-05-20 19:55:27        14.6 MB\n",
-      "      2013-05-20      2 volume(s)  19:51:11 .. 19:55:27\n",
-      "------------------------------------------------------------------------------\n",
-      "  archive with: db.archive(datatree_dir='/data/RADAR/NEXRAD_datatree')\n",
-      "==============================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# `detailed=True` adds a per-day breakdown and flags any radar name RadDB\n",
-    "# cannot use (names must be 1-4 characters from [0-9A-Z]).\n",
-    "db.inventory(datatree_dir=NEXRAD_DIR, detailed=True)"
+    "# `detailed=True` adds a per-day breakdown\n",
+    "db.inventory(datatree_dir=NEXRAD_DIR, detailed=True)\n",
+    "# db.inventory(datatree_dir=MCH_DIR, detailed=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bce49641",
+   "id": "550e156a",
    "metadata": {},
    "source": [
-    "## 2. The CRS contract\n",
-    "\n",
-    "**A projection is mandatory to write an archive, and never needed to read one.**\n",
-    "\n",
-    "There is no default, because a wrong projection is *silently* wrong: EPSG:2056\n",
-    "(Swiss LV95) used outside Switzerland mis-measures distance by ~20%, and the\n",
-    "resulting crops still look perfectly normal.\n",
+    "### Creating the RadDB object\n",
     "\n",
-    "RadDB validates by **measurement, not by metadata**: it projects a 100 km geodesic\n",
-    "in eight directions around the radar site and compares against the truth."
+    "`RadDB(archive_dir=..., crs=...)` returns an *archive-bound* RadDB: it knows where\n",
+    "the archive lives and which projection to write it in. This is the object used to\n",
+    "archive, open and inspect data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "8b3b68c8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:50.679937Z",
-     "iopub.status.busy": "2026-08-04T15:28:50.679848Z",
-     "iopub.status.idle": "2026-08-04T15:28:50.682120Z",
-     "shell.execute_reply": "2026-08-04T15:28:50.681553Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CH   : 32632\n",
-      "Oklahoma, US  : 32614\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "f69c2ae6",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from raddb.lut import suggest_crs, crs_distance_error\n",
-    "\n",
-    "# suggest_crs returns the UTM zone for a site — a safe starting point anywhere.\n",
-    "print(\"CH   :\", suggest_crs(6.99, 46.84))\n",
-    "print(\"Oklahoma, US  :\", suggest_crs(-97.28, 35.33))"
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)   # 2056 ==> CH1903+/LV95\n",
+    "db"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "57d2d426",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:50.683629Z",
-     "iopub.status.busy": "2026-08-04T15:28:50.683473Z",
-     "iopub.status.idle": "2026-08-04T15:28:50.723623Z",
-     "shell.execute_reply": "2026-08-04T15:28:50.723030Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  EPSG:2056   LV95 in Switzerland          0.01%   accepted\n",
-      "  EPSG:2056   LV95 in Oklahoma            20.07%   REFUSED\n",
-      "  EPSG:32614  UTM 14N in Oklahoma          0.03%   accepted\n",
-      "  EPSG:3857   Web Mercator in Switzerland  47.62%   REFUSED\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "8eff141c",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# Why metadata is not enough. EPSG:3857 (Web Mercator) claims the whole world.\n",
-    "for epsg, site, where in [(2056, (6.99, 46.84), \"LV95 in Switzerland\"),\n",
-    "                          (2056, (-97.28, 35.33), \"LV95 in Oklahoma\"),\n",
-    "                          (32614, (-97.28, 35.33), \"UTM 14N in Oklahoma\"),\n",
-    "                          (3857, (6.99, 46.84), \"Web Mercator in Switzerland\")]:\n",
-    "    err = crs_distance_error(epsg, *site)\n",
-    "    verdict = \"REFUSED\" if err > 1.0 else (\"warn\" if err > 0.1 else \"accepted\")\n",
-    "    print(f\"  EPSG:{epsg:<6} {where:<26} {err:6.2f}%   {verdict}\")"
+    "db_us = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "db_us"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e0b06436",
+   "id": "8d1b1569",
    "metadata": {},
    "source": [
-    "A CRS that distorts by more than 1% is **refused**, and the error names a\n",
-    "replacement so you are never left guessing:"
+    "## 2. Archiving\n",
+    "\n",
+    "`archive()` takes either a directory of DataTree files or an in-memory DataTree.\n",
+    "The LUT is generated automatically from the first volume of each radar."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "96984629",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:50.725251Z",
-     "iopub.status.busy": "2026-08-04T15:28:50.725153Z",
-     "iopub.status.idle": "2026-08-04T15:28:51.481127Z",
-     "shell.execute_reply": "2026-08-04T15:28:51.480720Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ValueError: EPSG:2056 (CH1903+ / LV95), valid for Liechtenstein; Switzerland. distorts distance by 20.1% at radar KTLX (-97.2775, 35.3331) — gate geometry, crops and cross-sections would all be wrong by that much. Suggested for this site: EPSG:32614.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "125783b2",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "try:\n",
-    "    raddb.RadDB(archive_dir=ARCHIVE_DIR / \"_bad\", crs=2056).archive(\n",
-    "        datatree=raddb.open_any_datatree(sorted(NEXRAD_DIR.glob(\"*.zarr\"))[0]),\n",
-    "        radar=\"KTLX\",\n",
-    "    )\n",
-    "except ValueError as exc:\n",
-    "    print(\"ValueError:\", exc)"
+    "result = db.archive(datatree_dir=MCH_DIR, time_period=(\"2024-06-01\", \"2024-06-15\"))\n",
+    "result"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7dc14099",
+   "id": "7a9d57a0",
    "metadata": {},
    "source": [
-    "## 3. Archiving\n",
+    "### The CRS constraint\n",
     "\n",
-    "`archive()` takes either a directory of DataTree files or an in-memory DataTree.\n",
-    "The LUT is generated automatically from the first volume of each radar."
+    "**A projection is mandatory to write an archive, and never needed to read one. Can be given when RadDB is initilaized or at archiving time.**\n",
+    "\n",
+    "The LUT stores projected gate coordinates, and every crop and cross-section is\n",
+    "computed in them. A wrong projection is therefore silently wrong: EPSG:2056 (Swiss\n",
+    "LV95) used outside Switzerland mis-measures distance, while the\n",
+    "results could still look perfectly normal. RadDB has no default, the CRS is stated once,\n",
+    "when the object is created, and is checked against the radar's real position before\n",
+    "anything is written.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Example for US:\n",
+    "A projection is only valid for one large region, so for US radars is chosen per radar . `KTLX` sits in\n",
+    "UTM zone 14N; `KLOT` and `KMLB` are in zones 16N and 17N and would be refused with\n",
+    "that CRS."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "93714d58",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:51.482930Z",
-     "iopub.status.busy": "2026-08-04T15:28:51.482714Z",
-     "iopub.status.idle": "2026-08-04T15:28:59.589551Z",
-     "shell.execute_reply": "2026-08-04T15:28:59.588778Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "======================================================================\n",
-      "RadDB archive\n",
-      "  archive_dir : /tmp/raddb_tutorial_archive\n",
-      "  crs         : 2056\n",
-      "  radars      : ['L', 'W']\n",
-      "  filter      : keep DBZH > 0.0\n",
-      "  volumes     : 2 archived, 0 failed\n",
-      "  elapsed     : 8s\n",
-      "======================================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'n_archived': 2, 'n_failed': 0, 'radars': ['L', 'W']}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)   # 2056 = CH1903+/LV95\n",
-    "result = db.archive(datatree_dir=MCH_DIR)\n",
-    "result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38d9ed54",
+   "execution_count": null,
+   "id": "049b6ee8",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Because RadDB is network-agnostic, the same call archives NEXRAD — you only\n",
-    "change the projection to one valid where that radar actually is. Both radars live\n",
-    "side by side in the same archive."
+    "# check what's the suggested crs of the 3 US radars in the NEXRAD dataset\n",
+    "# \"KTLX\": lat/lon = 35.333/-97.278\n",
+    "# \"KMLB\": lat/lon = 28.113/-80.654\n",
+    "# \"KLOT\": lat/lon = 41.604/-88.084\n",
+    "print(f\"KTLX ==> {suggest_crs(latitude=35.333, longitude=-97.278)}\")\n",
+    "print(f\"KMLB ==> {suggest_crs(latitude=28.113, longitude=-80.654)}\")  \n",
+    "print(f\"KLOT ==> {suggest_crs(latitude=41.604, longitude=-88.084)}\")  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "1ccc396e",
    "metadata": {
     "execution": {
@@ -404,71 +256,27 @@
      "shell.execute_reply": "2026-08-04T15:29:23.355579Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "======================================================================\n",
-      "RadDB archive\n",
-      "  archive_dir : /tmp/raddb_tutorial_archive\n",
-      "  crs         : 32614\n",
-      "  radars      : ['KTLX']\n",
-      "  filter      : keep DBZH > 0.0\n",
-      "  volumes     : 2 archived, 0 failed\n",
-      "  elapsed     : 23s\n",
-      "======================================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'n_archived': 2, 'n_failed': 0, 'radars': ['KTLX']}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db_us = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=32614)   # UTM zone 14N\n",
-    "db_us.archive(datatree_dir=NEXRAD_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e3be2c4",
-   "metadata": {},
+   "outputs": [],
    "source": [
-    "### Archiving from memory\n",
-    "\n",
-    "If you already hold a DataTree — straight out of your own converter — skip the\n",
-    "disk round-trip:\n",
-    "\n",
-    "```python\n",
-    "dt = my_converter(raw_file)                 # -> xarray DataTree\n",
-    "db.archive(datatree=dt, radar=\"A\")\n",
-    "\n",
-    "db.archive(datatree=[dt1, dt2, dt3], radar=\"A\")        # several volumes\n",
-    "db.archive(datatree={\"A\": [dt_a], \"W\": [dt_w]})        # several radars\n",
-    "```\n",
-    "\n",
-    "`archive()` reports per-volume failures rather than raising, so one bad volume\n",
-    "never takes down a long batch. A rejected CRS is the exception — that aborts."
+    "db_us.archive(datatree_dir=NEXRAD_DIR, radar=[\"KTLX\"], crs=32614,  # 32614 ==> UTM zone 14N\n",
+    "              time_period=(\"2024-01-01\", \"2024-06-15\"))\n",
+    "db_us.archive(datatree_dir=NEXRAD_DIR, radar=[\"KMLB\"], crs=32617,  # 32617 ==> UTM zone 17N\n",
+    "              time_period=(\"2024-01-01\", \"2024-06-15\"))\n",
+    "db_us.archive(datatree_dir=NEXRAD_DIR, radar=[\"KLOT\"], crs=32616,  # 32616 ==> UTM zone 16N\n",
+    "              time_period=(\"2024-01-01\", \"2024-06-15\"))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "252d077b",
+   "id": "0ebd3c49",
    "metadata": {},
    "source": [
-    "## 4. What landed on disk"
+    "## 3. Archived data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "78662044",
    "metadata": {
     "execution": {
@@ -478,29 +286,7 @@
      "shell.execute_reply": "2026-08-04T15:29:23.363777Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "radars in the archive: ['KTLX', 'L', 'W']\n",
-      "==============================================================================\n",
-      "RadDB inventory — archived data\n",
-      "  archive_dir : /tmp/raddb_tutorial_archive\n",
-      "  radars      : KTLX, L, W\n",
-      "  volumes     : 4\n",
-      "  time range  : 2013-05-20 19:51:11 .. 2024-08-26 02:45:09\n",
-      "------------------------------------------------------------------------------\n",
-      "  radar   volumes  time range                                           size\n",
-      "  KTLX          2  2013-05-20 19:51:11 .. 2013-05-20 19:55:27        17.0 MB\n",
-      "  L             1  2024-08-26 02:45:09                                5.4 MB\n",
-      "  W             1  2024-07-19 13:45:06                                5.1 MB\n",
-      "------------------------------------------------------------------------------\n",
-      "  load with   : db.open(radars=..., time_period=(start, end))\n",
-      "==============================================================================\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "print(\"radars in the archive:\", db.list_radars())\n",
@@ -509,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "755fe1f8",
    "metadata": {
     "execution": {
@@ -519,19 +305,7 @@
      "shell.execute_reply": "2026-08-04T15:29:23.369330Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  L_LUT.parquet                   63.01 MB\n",
-      "  L_corners_LUT.parquet           18.40 MB\n",
-      "  L_h_plane_LUT.parquet           19.78 MB\n",
-      "  L_info.yaml                      0.08 MB\n",
-      "  L_v_plane_LUT.parquet            0.40 MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for p in sorted((ARCHIVE_DIR / \"L\" / \"LUT\").iterdir()):\n",
     "    print(f\"  {p.name:<28} {p.stat().st_size / 1e6:8.2f} MB\")"
@@ -542,23 +316,18 @@
    "id": "86571224",
    "metadata": {},
    "source": [
-    "### The `gate_id` — how a volume finds its geometry\n",
+    "### `gate_id`: how a volume finds its geometry\n",
     "\n",
-    "One int64 per gate links a row of moments to its row of geometry:\n",
+    "One int64 per gate links a row of data (polarimetric variables) to its row of geometry:\n",
     "\n",
     "```\n",
     "gate_id = radar_code * 10^12 + sweep * 10^10 + azimuth*10 * 10^6 + range_m\n",
-    "```\n",
-    "\n",
-    "It is decimal so you can read it by eye. `radar_code` is the base-36 value of the\n",
-    "zero-padded 4-character radar name (`\"L\"` → `000L` → 21, `\"KTLX\"` → 971493), which\n",
-    "allows **1,679,616 radars** and means an archive is self-describing — the radar\n",
-    "name can be recovered from the integers alone, with no registry file."
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "3500a227",
    "metadata": {
     "execution": {
@@ -568,67 +337,13 @@
      "shell.execute_reply": "2026-08-04T15:29:23.408185Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  A     -> code      10 -> A\n",
-      "  L     -> code      21 -> L\n",
-      "  KTLX  -> code  971493 -> KTLX\n",
-      "\n",
-      "LUT: (1724400, 13)\n",
-      "shape: (3, 7)\n",
-      "┌────────────────┬───────┬─────────┬─────────────┬───────────┬───────────┬─────────────┐\n",
-      "│ gate_id        ┆ sweep ┆ azimuth ┆ range       ┆ latitude  ┆ longitude ┆ altitude    │\n",
-      "│ ---            ┆ ---   ┆ ---     ┆ ---         ┆ ---       ┆ ---       ┆ ---         │\n",
-      "│ i64            ┆ i32   ┆ f64     ┆ f32         ┆ f64       ┆ f64       ┆ f64         │\n",
-      "╞════════════════╪═══════╪═════════╪═════════════╪═══════════╪═══════════╪═════════════╡\n",
-      "│ 21010005000249 ┆ 1     ┆ 0.5     ┆ 249.999008  ┆ 46.043008 ┆ 8.833245  ┆ 1625.164775 │\n",
-      "│ 21010005000749 ┆ 1     ┆ 0.5     ┆ 749.997009  ┆ 46.047505 ┆ 8.833301  ┆ 1623.516397 │\n",
-      "│ 21010005001249 ┆ 1     ┆ 0.5     ┆ 1249.994995 ┆ 46.052001 ┆ 8.833358  ┆ 1621.89745  │\n",
-      "└────────────────┴───────┴─────────┴─────────────┴───────────┴───────────┴─────────────┘\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from raddb import encode_radar_code, decode_radar_code, decode_gate_radars\n",
-    "\n",
-    "for name in [\"A\", \"L\", \"KTLX\"]:\n",
-    "    print(f\"  {name:<5} -> code {encode_radar_code(name):>7} -> {decode_radar_code(encode_radar_code(name))}\")\n",
-    "\n",
     "lut = db.get_lut(\"L\")\n",
     "print(\"\\nLUT:\", lut.shape)\n",
-    "print(lut.head(3).select([\"gate_id\", \"sweep\", \"azimuth\", \"range\", \"latitude\", \"longitude\", \"altitude\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2468d7de",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:23.411639Z",
-     "iopub.status.busy": "2026-08-04T15:29:23.411355Z",
-     "iopub.status.idle": "2026-08-04T15:29:23.703674Z",
-     "shell.execute_reply": "2026-08-04T15:29:23.701214Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['KTLX', 'L']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The radars a set of gate_ids spans, decoded from the integers alone\n",
-    "import polars as pl\n",
-    "\n",
-    "sample = pl.concat([db.get_lut(\"L\").head(2), db.get_lut(\"KTLX\").head(2)], how=\"diagonal\")\n",
-    "print(decode_gate_radars(sample[\"gate_id\"].to_numpy()))"
+    "print(lut.columns)\n",
+    "print(lut.head(5).select([\"gate_id\", \"sweep\", \"azimuth\", \"range\", \"latitude\", \"longitude\", \"altitude\"]))\n",
+    "print(lut.head(5).select([\"x\", \"y\", \"z\", \"x_2056\", \"y_2056\"]))"
    ]
   },
   {
@@ -636,7 +351,7 @@
    "id": "40878b2a",
    "metadata": {},
    "source": [
-    "### The site metadata\n",
+    "### Radar site metadata\n",
     "\n",
     "`info.yaml` records everything needed to reconstruct the geometry — including the\n",
     "CRS that was validated at archive time, and the radar's **scan strategy**."
@@ -644,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "5efeff2e",
    "metadata": {
     "execution": {
@@ -654,131 +369,29 @@
      "shell.execute_reply": "2026-08-04T15:29:24.086365Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  radar            L\n",
-      "  latitude         46.0407600402832\n",
-      "  longitude        8.833216667175293\n",
-      "  altitude         1626.0\n",
-      "  crs              {'epsg': 2056, 'columns': ['x_2056', 'y_2056']}\n",
-      "  ke               1.3333333333333333\n",
-      "  beamwidth_deg    1.0\n",
-      "  n_sweeps         20\n",
-      "  n_gates          1724400\n",
-      "  gate_id_version  2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "info = db.get_radar_info(\"L\")\n",
-    "for k in [\"radar\", \"latitude\", \"longitude\", \"altitude\", \"crs\", \"ke\",\n",
-    "          \"beamwidth_deg\", \"n_sweeps\", \"n_gates\", \"gate_id_version\"]:\n",
+    "for k in [\"radar\", \"network\", \"latitude\", \"longitude\", \"altitude\",\n",
+    "          \"crs\", \"ke\", \"beamwidth_deg\", \"n_sweeps\", \"n_gates\"]:\n",
     "    print(f\"  {k:<16} {info[k]}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2889a184",
-   "metadata": {},
-   "source": [
-    "### One detail worth knowing: the nominal azimuth grid\n",
-    "\n",
-    "An antenna reports **where it actually pointed**, which drifts a few hundredths of\n",
-    "a degree every rotation. Since `gate_id` resolves azimuth to 0.1°, a drifting ray\n",
-    "would land in a different bin on every volume and its gates would match no LUT row.\n",
-    "\n",
-    "So the LUT stores the radar's *scan strategy* — `360 / n_rays` spacing at the\n",
-    "measured offset — and every volume's rays are snapped onto it. This is derived\n",
-    "per sweep from the ray count, so it gives 1.0° for Rad4Alp and 0.5° for NEXRAD\n",
-    "super-resolution sweeps automatically, with nothing to configure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "55e471ee",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:24.093451Z",
-     "iopub.status.busy": "2026-08-04T15:29:24.093250Z",
-     "iopub.status.idle": "2026-08-04T15:29:24.315243Z",
-     "shell.execute_reply": "2026-08-04T15:29:24.314505Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "rays in sweep 1 : 360\n",
-      "azimuths (x10)  : [5, 15, 25, 35, 45, 55, 65, 75] ...\n",
-      "i.e. degrees    : [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5] ...\n"
-     ]
-    }
-   ],
-   "source": [
-    "sweep1 = db.get_radar_info(\"L\")[\"sweeps\"][1]\n",
-    "print(\"rays in sweep 1 :\", sweep1[\"n_azimuths\"])\n",
-    "print(\"azimuths (x10)  :\", sweep1[\"azimuths\"][:8], \"...\")\n",
-    "print(\"i.e. degrees    :\", [a / 10 for a in sweep1[\"azimuths\"][:8]], \"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "12f42099",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:24.317962Z",
-     "iopub.status.busy": "2026-08-04T15:29:24.317790Z",
-     "iopub.status.idle": "2026-08-04T15:29:24.387871Z",
-     "shell.execute_reply": "2026-08-04T15:29:24.386861Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "347,449 of 347,449 gates join the LUT  (100.0%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Every volume joins its LUT completely — nothing is silently dropped.\n",
-    "lut_ids = db.get_lut(\"L\").select(\"gate_id\")\n",
-    "pol = db.open(radars=\"L\").data\n",
-    "matched = pol.join(lut_ids, on=\"gate_id\", how=\"semi\").height\n",
-    "print(f\"{matched:,} of {pol.height:,} gates join the LUT  ({100 * matched / pol.height:.1f}%)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "703eeff7",
+   "id": "8d5163ff",
    "metadata": {},
    "source": [
     "---\n",
-    "## Recap\n",
-    "\n",
-    "```python\n",
-    "db = raddb.RadDB(archive_dir=..., crs=2056)   # CRS mandatory to write\n",
-    "db.inventory(datatree_dir=...)                # what do I have?\n",
-    "db.archive(datatree_dir=...)                  # or datatree=dt, radar=\"A\"\n",
-    "db.list_radars(); db.get_lut(\"L\"); db.get_radar_info(\"L\")\n",
-    "```\n",
-    "\n",
     "**Next:** [2 — Opening and filtering](02_opening_and_filtering.ipynb)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (radar)",
+   "display_name": "radar",
    "language": "python",
-   "name": "radar"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/tutorial/02_opening_and_filtering.ipynb b/tutorial/02_opening_and_filtering.ipynb
index ce9e1c3..f74b69c 100644
--- a/tutorial/02_opening_and_filtering.ipynb
+++ b/tutorial/02_opening_and_filtering.ipynb
@@ -5,79 +5,63 @@
    "id": "252c31b4",
    "metadata": {},
    "source": [
-    "# 2 — Opening a RadDB and filtering\n",
+    "# 2. Open RadDB object and filter\n",
     "\n",
-    "Tutorial 1 wrote an archive. This one reads it back and narrows it down.\n",
+    "Tutorial 1 wrote an archive. This one reads it back and filters it down.\n",
     "\n",
-    "The key idea: **`RadDB` is one class with two roles.**\n",
+    "**`RadDB` is one class with two roles.**\n",
     "\n",
-    "| role | how you get it | what it does |\n",
-    "|---|---|---|\n",
-    "| *archive-bound* | `RadDB(archive_dir=...)` | `archive()`, `open()`, `inventory()`, LUT accessors |\n",
-    "| *data-carrying* | whatever `open()` returns | holds the gates; `filter`, `crop_*`, plots, converters |\n",
+    "| role | what it is |\n",
+    "|---|---|\n",
+    "| *archive-bound* | knows where an archive lives, and reads from it |\n",
+    "| *data-carrying*  | holds the gates you loaded, and narrows them down |\n",
     "\n",
-    "Every operation on a data-carrying RadDB returns a **new** RadDB, so calls chain\n",
-    "and nothing is ever mutated underneath you.\n",
+    "`open()` turns the first into the second. Every operation on a data-carrying\n",
+    "RadDB returns a **new** one, so calls chain and nothing is changed in place.\n",
     "\n",
     "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "36dbad7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:25.739548Z",
-     "iopub.status.busy": "2026-08-04T15:29:25.739195Z",
-     "iopub.status.idle": "2026-08-04T15:29:25.750030Z",
-     "shell.execute_reply": "2026-08-04T15:29:25.748863Z"
+     "iopub.execute_input": "2026-08-06T17:27:33.195176Z",
+     "iopub.status.busy": "2026-08-06T17:27:33.195065Z",
+     "iopub.status.idle": "2026-08-06T17:27:33.200576Z",
+     "shell.execute_reply": "2026-08-06T17:27:33.200062Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
-      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
-      "Archive         : /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import os\n",
     "from pathlib import Path\n",
     "\n",
     "# --------------------------------------------------------------------------\n",
-    "# CONFIGURATION — point these at your own data\n",
+    "# CONFIGURATION — edit these three paths to point at your own data\n",
     "# --------------------------------------------------------------------------\n",
-    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
-    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
-    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
-    "\n",
-    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
-    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
+    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.\n",
     "\n",
-    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
-    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
-    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
+    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
+    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
     "print(\"MCH DataTrees   :\", MCH_DIR)\n",
     "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
-    "print(\"Archive         :\", ARCHIVE_DIR)\n"
+    "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a4f11595",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:25.752631Z",
-     "iopub.status.busy": "2026-08-04T15:29:25.752397Z",
-     "iopub.status.idle": "2026-08-04T15:29:26.623119Z",
-     "shell.execute_reply": "2026-08-04T15:29:26.621966Z"
+     "iopub.execute_input": "2026-08-06T17:27:33.202202Z",
+     "iopub.status.busy": "2026-08-06T17:27:33.202062Z",
+     "iopub.status.idle": "2026-08-06T17:27:33.927024Z",
+     "shell.execute_reply": "2026-08-06T17:27:33.926166Z"
     }
    },
    "outputs": [],
@@ -91,32 +75,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "60595d47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:26.625479Z",
-     "iopub.status.busy": "2026-08-04T15:29:26.625163Z",
-     "iopub.status.idle": "2026-08-04T15:29:26.629019Z",
-     "shell.execute_reply": "2026-08-04T15:29:26.628250Z"
+     "iopub.execute_input": "2026-08-06T17:27:33.928749Z",
+     "iopub.status.busy": "2026-08-06T17:27:33.928548Z",
+     "iopub.status.idle": "2026-08-06T17:27:33.931440Z",
+     "shell.execute_reply": "2026-08-06T17:27:33.930861Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "archive already present: /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
     "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
     "    print(\"building the archive (see tutorial 1) ...\")\n",
     "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
     "else:\n",
-    "    print(\"archive already present:\", ARCHIVE_DIR)\n"
+    "    print(\"archive already present:\", ARCHIVE_DIR)"
    ]
   },
   {
@@ -124,43 +100,28 @@
    "id": "e307b491",
    "metadata": {},
    "source": [
-    "## 1. `open()` — reading the archive\n",
+    "## 1. `open()`: reading the archive\n",
     "\n",
     "Reading never needs a CRS: it is recovered from the archive itself."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "62430b24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:26.630985Z",
-     "iopub.status.busy": "2026-08-04T15:29:26.630813Z",
-     "iopub.status.idle": "2026-08-04T15:29:26.679757Z",
-     "shell.execute_reply": "2026-08-04T15:29:26.679100Z"
+     "iopub.execute_input": "2026-08-06T17:27:33.932939Z",
+     "iopub.status.busy": "2026-08-06T17:27:33.932851Z",
+     "iopub.status.idle": "2026-08-06T17:27:33.982047Z",
+     "shell.execute_reply": "2026-08-06T17:27:33.981493Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RadDB [347,449 gates]\n",
-       "  radars     : ['L']\n",
-       "  time range : 2024-08-26 02:45:09+00:00 .. 2024-08-26 02:45:09+00:00\n",
-       "  columns    : gate_id:Int64, time:Datetime(time_unit='ns', time_zone=None), DBZH:Float32, DBZH_raw:Float32, ZDR:Float32, ZDR_raw:Float32, KDP:Float32, RHOHV:Float32, PHIDP:Float32, HC_MCH:Float32, HC_PYART:Float32, HZT:Float32 (+3 more)\n",
-       "  archive_dir: /tmp/raddb_tutorial_archive"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)      # no crs= needed to read\n",
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "rdf = db.open(radars=\"L\")\n",
-    "rdf"
+    "rdf.head()"
    ]
   },
   {
@@ -175,61 +136,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "e8856737",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:26.681841Z",
-     "iopub.status.busy": "2026-08-04T15:29:26.681676Z",
-     "iopub.status.idle": "2026-08-04T15:29:26.725685Z",
-     "shell.execute_reply": "2026-08-04T15:29:26.724865Z"
+     "iopub.execute_input": "2026-08-06T17:27:33.983587Z",
+     "iopub.status.busy": "2026-08-06T17:27:33.983432Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.020932Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.020177Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "columns: ['gate_id', 'DBZH', 'ZDR', 'volume_time', 'radar']\n",
-      "gates in the period: 347,449\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Only two moments, only radar L\n",
-    "small = db.open(radars=\"L\", columns=[\"DBZH\", \"ZDR\"])\n",
-    "print(\"columns:\", small.columns())\n",
+    "small_df = db.open(radars=\"L\", columns=[\"DBZH\", \"ZDR\"])\n",
+    "print(f\"small_df:\\tcolumns: {small_df.columns()}\")\n",
     "\n",
-    "# A time period — any pandas-parseable pair, or a single day\n",
-    "day = db.open(radars=\"L\", time_period=(\"2024-08-26\", \"2024-08-27\"))\n",
-    "print(\"gates in the period:\", f\"{len(day):,}\")"
+    "# time period\n",
+    "day_df = db.open(radars=\"L\", time_period=(\"2024-06-12\", \"2024-06-13\"))\n",
+    "print(f\"day_df:\\t\\tgates: {len(day_df):,}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "320155b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:26.727695Z",
-     "iopub.status.busy": "2026-08-04T15:29:26.727532Z",
-     "iopub.status.idle": "2026-08-04T15:29:26.742001Z",
-     "shell.execute_reply": "2026-08-04T15:29:26.741167Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.022551Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.022464Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.036482Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.035455Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "347,449 gates -> 123,008 with DBZH > 30 dBZ\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Filters can be pushed down at open() too, so filtered-out rows are never materialised\n",
-    "strong = db.open(radars=\"L\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
-    "print(f\"{len(rdf):,} gates -> {len(strong):,} with DBZH > 30 dBZ\")"
+    "filtered_df = db.open(radars=\"L\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
+    "print(f\"before:\\t{len(rdf):,} gates\\t(with DBZH > 0 dBz)\\nafter:\\t{len(filtered_df):,}  gates\\t(with DBZH > 30 dBz)\")"
    ]
   },
   {
@@ -239,111 +183,42 @@
    "source": [
     "## 2. What you are holding\n",
     "\n",
-    "The data lives in `.data` as a **polars** DataFrame. polars is the backend\n",
-    "throughout RadDB — the read path, the LUT, the archive writer."
+    "The data lives in `.data` as a **polars** DataFrame. Polars is the backend\n",
+    "throughout RadDB (the read path, the LUT, the archive writer)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "37d4e073",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:26.744024Z",
-     "iopub.status.busy": "2026-08-04T15:29:26.743858Z",
-     "iopub.status.idle": "2026-08-04T15:29:26.749342Z",
-     "shell.execute_reply": "2026-08-04T15:29:26.748506Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.037856Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.037671Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.042709Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.042134Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "type: DataFrame\n",
-      "shape: (347449, 15)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (3, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>time</th><th>DBZH</th><th>DBZH_raw</th><th>ZDR</th><th>ZDR_raw</th><th>KDP</th><th>RHOHV</th><th>PHIDP</th><th>HC_MCH</th><th>HC_PYART</th><th>HZT</th><th>TEMP</th><th>volume_time</th><th>radar</th></tr><tr><td>i64</td><td>datetime[ns]</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>datetime[μs, UTC]</td><td>str</td></tr></thead><tbody><tr><td>21010005000249</td><td>2024-08-26 02:46:08.050</td><td>0.5</td><td>0.5</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4.0</td><td>3891.666748</td><td>14.733334</td><td>2024-08-26 02:45:09 UTC</td><td>&quot;L&quot;</td></tr><tr><td>21010005000749</td><td>2024-08-26 02:46:08.050</td><td>11.0</td><td>11.0</td><td>-2.821272</td><td>-2.82151</td><td>NaN</td><td>0.88415</td><td>-26.849052</td><td>NaN</td><td>4.0</td><td>3891.666748</td><td>14.746333</td><td>2024-08-26 02:45:09 UTC</td><td>&quot;L&quot;</td></tr><tr><td>21010005001249</td><td>2024-08-26 02:46:08.050</td><td>17.0</td><td>17.0</td><td>1.830259</td><td>1.82939</td><td>NaN</td><td>0.908594</td><td>-16.505135</td><td>NaN</td><td>4.0</td><td>3891.666748</td><td>14.752833</td><td>2024-08-26 02:45:09 UTC</td><td>&quot;L&quot;</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (3, 15)\n",
-       "┌──────────────┬─────────────┬──────┬──────────┬───┬─────────────┬───────────┬─────────────┬───────┐\n",
-       "│ gate_id      ┆ time        ┆ DBZH ┆ DBZH_raw ┆ … ┆ HZT         ┆ TEMP      ┆ volume_time ┆ radar │\n",
-       "│ ---          ┆ ---         ┆ ---  ┆ ---      ┆   ┆ ---         ┆ ---       ┆ ---         ┆ ---   │\n",
-       "│ i64          ┆ datetime[ns ┆ f32  ┆ f32      ┆   ┆ f32         ┆ f32       ┆ datetime[μs ┆ str   │\n",
-       "│              ┆ ]           ┆      ┆          ┆   ┆             ┆           ┆ , UTC]      ┆       │\n",
-       "╞══════════════╪═════════════╪══════╪══════════╪═══╪═════════════╪═══════════╪═════════════╪═══════╡\n",
-       "│ 210100050002 ┆ 2024-08-26  ┆ 0.5  ┆ 0.5      ┆ … ┆ 3891.666748 ┆ 14.733334 ┆ 2024-08-26  ┆ L     │\n",
-       "│ 49           ┆ 02:46:08.05 ┆      ┆          ┆   ┆             ┆           ┆ 02:45:09    ┆       │\n",
-       "│              ┆ 0           ┆      ┆          ┆   ┆             ┆           ┆ UTC         ┆       │\n",
-       "│ 210100050007 ┆ 2024-08-26  ┆ 11.0 ┆ 11.0     ┆ … ┆ 3891.666748 ┆ 14.746333 ┆ 2024-08-26  ┆ L     │\n",
-       "│ 49           ┆ 02:46:08.05 ┆      ┆          ┆   ┆             ┆           ┆ 02:45:09    ┆       │\n",
-       "│              ┆ 0           ┆      ┆          ┆   ┆             ┆           ┆ UTC         ┆       │\n",
-       "│ 210100050012 ┆ 2024-08-26  ┆ 17.0 ┆ 17.0     ┆ … ┆ 3891.666748 ┆ 14.752833 ┆ 2024-08-26  ┆ L     │\n",
-       "│ 49           ┆ 02:46:08.05 ┆      ┆          ┆   ┆             ┆           ┆ 02:45:09    ┆       │\n",
-       "│              ┆ 0           ┆      ┆          ┆   ┆             ┆           ┆ UTC         ┆       │\n",
-       "└──────────────┴─────────────┴──────┴──────────┴───┴─────────────┴───────────┴─────────────┴───────┘"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"type:\", type(rdf.data).__name__)\n",
     "print(\"shape:\", rdf.data.shape)\n",
-    "rdf.data.head(3)"
+    "rdf.data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6fd88dae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:26.751397Z",
-     "iopub.status.busy": "2026-08-04T15:29:26.751219Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.275706Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.274707Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.044151Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.044003Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.264118Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.263480Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "radars    : ['L']\n",
-      "variables : ['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar']\n",
-      "time range: 2024-08-26 02:45:09+00:00 -> 2024-08-26 02:45:09+00:00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "lon/lat    : [7.31, 11.416, 44.408, 47.314]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "archive CRS: EPSG:2056\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"radars    :\", rdf.radars())\n",
     "print(\"variables :\", rdf.columns())\n",
@@ -352,63 +227,39 @@
     "print(\"archive CRS:\", rdf.crs())          # recovered from the archive itself"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "04d777f8",
-   "metadata": {},
-   "source": [
-    "`geographic_extent()` always works. Its projected counterpart `extent()` returns\n",
-    "the bounding box in the archive's own CRS, but needs that CRS stated on the object:\n",
-    "\n",
-    "```python\n",
-    "raddb.RadDB(archive_dir=..., crs=2056).open(radars=\"L\").extent()\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "57b6ce27",
    "metadata": {},
    "source": [
-    "## 3. `filter()` — threshold on values\n",
+    "## 3. `filter()`: threshold on values\n",
     "\n",
-    "A filter is a plain dict: `{\"var\", \"logic\", \"threshold\"}`, where `logic` is one of\n",
-    "`==  !=  >  >=  <  <=`. A **list** of dicts is ANDed."
+    "A filter is a plain dict: `{\"var\", \"logic\", \"threshold\"}`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "650f8db6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.277794Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.277625Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.288743Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.287857Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.265548Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.265433Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.275980Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.275259Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DBZH > 20        : 204,833 gates\n",
-      "+ RHOHV >= 0.9, ZDR < 4 : 196,414 gates\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rain = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
-    "print(f\"DBZH > 20        : {len(rain):,} gates\")\n",
+    "print(f\"DBZH > 20: {len(rain):,} gates\")\n",
     "\n",
-    "# Several conditions at once — meteorological echo, not clutter\n",
-    "clean = rdf.filter([\n",
+    "filt_df = rdf.filter([\n",
     "    {\"var\": \"DBZH\",  \"logic\": \">\",  \"threshold\": 20},\n",
-    "    {\"var\": \"RHOHV\", \"logic\": \">=\", \"threshold\": 0.9},\n",
-    "    {\"var\": \"ZDR\",   \"logic\": \"<\",  \"threshold\": 4},\n",
+    "    {\"var\": \"RHOHV\", \"logic\": \">=\", \"threshold\": 0.98},\n",
+    "    {\"var\": \"ZDR\",   \"logic\": \">\",  \"threshold\": 4},\n",
     "])\n",
-    "print(f\"+ RHOHV >= 0.9, ZDR < 4 : {len(clean):,} gates\")"
+    "print(f\"DBZH > 20, RHOHV >= 0.98, ZDR > 4 : {len(filt_df):,} gates\")"
    ]
   },
   {
@@ -416,7 +267,7 @@
    "id": "b244e5ec",
    "metadata": {},
    "source": [
-    "## 4. `sel()` — select by label, xarray-style\n",
+    "## 4. `sel()`: select by label, xarray-style\n",
     "\n",
     "Where `filter()` thresholds *values*, `sel()` selects by **coordinate**: a time, a\n",
     "sweep, a range window, a longitude/latitude box. Scalars match exactly, `slice`\n",
@@ -425,40 +276,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "3aec88ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.291431Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.291136Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.639891Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.639134Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.277422Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.277252Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.532381Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.531861Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "one sweep      : 11,629\n",
-      "sweeps 1,2,3   : 42,036\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "range 10-50 km : 184,299\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a lon/lat box  : 192,726\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"one sweep      :\", f\"{len(rdf.sel(sweep=1)):,}\")\n",
     "print(\"sweeps 1,2,3   :\", f\"{len(rdf.sel(sweep=[1, 2, 3])):,}\")\n",
@@ -479,28 +307,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "17a5d6ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.642823Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.642613Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.742139Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.741237Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.533954Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.533803Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.595608Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.595121Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stored per gate: ['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar']\n",
-      "also selectable : ['range', 'azimuth', 'elevation_angle', 'latitude', 'longitude', 'altitude', 'sweep']\n",
-      "\n",
-      "sweep 1, 20-60 km: 2,374 gates (columns unchanged: True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"stored per gate:\", rdf.columns())\n",
     "print(\"also selectable :\", [\"range\", \"azimuth\", \"elevation_angle\",\n",
@@ -511,58 +328,12 @@
     "      f\"(columns unchanged: {narrow.columns() == rdf.columns()})\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d37c61ef",
-   "metadata": {},
-   "source": [
-    "## 5. Chaining, and immutability\n",
-    "\n",
-    "Every call returns a new object, so a pipeline reads top to bottom and the original\n",
-    "is untouched."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cbf3e516",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.745244Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.745048Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.865179Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.863554Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "original : 347,449 gates\n",
-      "pipeline : 24,453 gates\n",
-      "original still intact: 347,449\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipeline = (\n",
-    "    db.open(radars=\"L\")\n",
-    "      .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 15})\n",
-    "      .sel(sweep=[1, 2, 3])\n",
-    "      .sel(range=slice(5_000, 80_000))\n",
-    ")\n",
-    "print(f\"original : {len(rdf):,} gates\")\n",
-    "print(f\"pipeline : {len(pipeline):,} gates\")\n",
-    "print(f\"original still intact: {len(rdf):,}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "d72ae0a4",
    "metadata": {},
    "source": [
-    "## 6. Computed columns\n",
+    "## 5. `add_feature()`: compute columns\n",
     "\n",
     "`add_feature()` adds a column derived from the ones you already have and returns a\n",
     "new RadDB, so it drops straight into a pipeline. The function receives the polars\n",
@@ -571,98 +342,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "ecec9579",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.868487Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.868212Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.881597Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.880531Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.597018Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.596918Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.607802Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.607342Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (3, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>DBZH</th><th>ZDR</th><th>ZDR_lin</th><th>DBZH_dev</th></tr><tr><td>f32</td><td>f32</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>0.5</td><td>NaN</td><td>NaN</td><td>-24.828373</td></tr><tr><td>11.0</td><td>-2.821272</td><td>0.522243</td><td>-14.328373</td></tr><tr><td>17.0</td><td>1.830259</td><td>1.524144</td><td>-8.328373</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (3, 4)\n",
-       "┌──────┬───────────┬──────────┬────────────┐\n",
-       "│ DBZH ┆ ZDR       ┆ ZDR_lin  ┆ DBZH_dev   │\n",
-       "│ ---  ┆ ---       ┆ ---      ┆ ---        │\n",
-       "│ f32  ┆ f32       ┆ f32      ┆ f32        │\n",
-       "╞══════╪═══════════╪══════════╪════════════╡\n",
-       "│ 0.5  ┆ NaN       ┆ NaN      ┆ -24.828373 │\n",
-       "│ 11.0 ┆ -2.821272 ┆ 0.522243 ┆ -14.328373 │\n",
-       "│ 17.0 ┆ 1.830259  ┆ 1.524144 ┆ -8.328373  │\n",
-       "└──────┴───────────┴──────────┴────────────┘"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "derived = (\n",
-    "    rdf.add_feature(\"ZDR_lin\", lambda df: 10 ** (df[\"ZDR\"] / 10))\n",
+    "    rdf.add_feature(\"DBZH_lin\", lambda df: 10 ** (df[\"DBZH\"] / 10))\n",
     "       .add_feature(\"DBZH_dev\", lambda df: df[\"DBZH\"] - df[\"DBZH\"].mean())\n",
     ")\n",
-    "derived.data.select([\"DBZH\", \"ZDR\", \"ZDR_lin\", \"DBZH_dev\"]).head(3)"
+    "derived.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f37ef3f",
+   "metadata": {},
+   "source": [
+    "If you would rather work in plain polars or pandas, nothing stops you — `.data`\n",
+    "is an ordinary polars frame, and `to_pandas()` gives an ordinary pandas one."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "c0315859",
+   "execution_count": null,
+   "id": "01572c8c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.884039Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.883841Z",
-     "iopub.status.idle": "2026-08-04T15:29:27.888672Z",
-     "shell.execute_reply": "2026-08-04T15:29:27.887727Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.609120Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.609035Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.634978Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.634462Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14,476 gates with ZDR_lin > 2\n"
-     ]
-    }
-   ],
-   "source": [
-    "# It behaves like any other column from here on — filter it, plot it, export it.\n",
-    "print(f\"{len(derived.filter({'var': 'ZDR_lin', 'logic': '>', 'threshold': 2})):,} gates with ZDR_lin > 2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f37ef3f",
-   "metadata": {},
+   "outputs": [],
    "source": [
-    "If you would rather work in plain polars or pandas, nothing stops you — `.data`\n",
-    "is an ordinary polars frame, and `to_pandas()` gives an ordinary pandas one:\n",
-    "\n",
-    "```python\n",
-    "import polars as pl\n",
     "rdf.data.with_columns((pl.col(\"DBZH\") - pl.col(\"ZDR\")).alias(\"DIFF\"))\n",
     "\n",
     "df = rdf.to_pandas()\n",
     "df[\"DIFF\"] = df[\"DBZH\"] - df[\"ZDR\"]\n",
-    "```\n",
     "\n",
-    "The RadDB helpers exist so the result stays a RadDB and keeps chaining."
+    "print(f\"rdf type: {type(rdf.data)}\")\n",
+    "print(f\"df  type: {type(df)}\")\n",
+    "df.head()"
    ]
   },
   {
@@ -670,221 +399,95 @@
    "id": "d77a02f8",
    "metadata": {},
    "source": [
-    "## 7. Getting the data out\n",
+    "## 6. Framework converter\n",
     "\n",
-    "Three converters, for three different jobs."
+    "Three converters, for three different framework."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "4dcf0de3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:27.890936Z",
-     "iopub.status.busy": "2026-08-04T15:29:27.890689Z",
-     "iopub.status.idle": "2026-08-04T15:29:28.190809Z",
-     "shell.execute_reply": "2026-08-04T15:29:28.189853Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.636366Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.636234Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.801836Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.801266Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "to_pandas : (204833, 19)\n",
-      "['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar', 'latitude', 'longitude', 'altitude', 'sweep']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# 1. pandas — with_geometry merges the per-gate coordinates from the LUT\n",
+    "# pandas: with_geometry merges the per-gate coordinates from the LUT\n",
     "df = rain.to_pandas(with_geometry=True)\n",
-    "print(\"to_pandas :\", df.shape)\n",
-    "print(list(df.columns))"
+    "print(\"to_pandas:\", type(df))\n",
+    "print(\"columns:\", list(df.columns))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "67d3b517",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:28.193945Z",
-     "iopub.status.busy": "2026-08-04T15:29:28.193643Z",
-     "iopub.status.idle": "2026-08-04T15:29:28.504326Z",
-     "shell.execute_reply": "2026-08-04T15:29:28.502652Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.803160Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.802992Z",
+     "iopub.status.idle": "2026-08-06T17:27:34.968875Z",
+     "shell.execute_reply": "2026-08-06T17:27:34.968028Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar', 'latitude', 'longitude', 'altitude', 'sweep', 'range', 'azimuth', 'elevation_angle']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# with_polar_coords adds range / azimuth / elevation_angle as well.  Off by\n",
-    "# default because they duplicate what the Cartesian columns already say.\n",
+    "# with_polar_coords adds range / azimuth / elevation_angle as well.\n",
+    "# Off by default because they duplicate what the Cartesian columns already say.\n",
     "print(list(rain.to_pandas(with_polar_coords=True).columns))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "abc6973e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:28.506720Z",
-     "iopub.status.busy": "2026-08-04T15:29:28.506468Z",
-     "iopub.status.idle": "2026-08-04T15:29:29.035884Z",
-     "shell.execute_reply": "2026-08-04T15:29:29.034903Z"
+     "iopub.execute_input": "2026-08-06T17:27:34.970415Z",
+     "iopub.status.busy": "2026-08-06T17:27:34.970226Z",
+     "iopub.status.idle": "2026-08-06T17:27:35.263991Z",
+     "shell.execute_reply": "2026-08-06T17:27:35.263428Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "to_geopandas: (204833, 20) | CRS: EPSG:4326\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>gate_id</th>\n",
-       "      <th>DBZH</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>21010005002249</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>POINT (8.83347 46.06099)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>21010005005249</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>POINT (8.83381 46.08797)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>21010005005749</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>POINT (8.83387 46.09247)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          gate_id  DBZH                  geometry\n",
-       "0  21010005002249  22.0  POINT (8.83347 46.06099)\n",
-       "1  21010005005249  21.0  POINT (8.83381 46.08797)\n",
-       "2  21010005005749  27.0  POINT (8.83387 46.09247)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# 2. geopandas — point geometry per gate, ready for spatial joins or QGIS\n",
+    "# geopandas: point geometry per gate, ready for spatial joins or QGIS\n",
     "gdf = rain.to_geopandas()\n",
-    "print(\"to_geopandas:\", gdf.shape, \"| CRS:\", gdf.crs)\n",
-    "gdf[[\"gate_id\", \"DBZH\", \"geometry\"]].head(3)"
+    "print(\"to_geopandas: \", type(gdf))\n",
+    "print(\"CRS:\", gdf.crs)\n",
+    "gdf[[\"gate_id\", \"DBZH\", \"geometry\"]].head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "1280e7ee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:29.038372Z",
-     "iopub.status.busy": "2026-08-04T15:29:29.038172Z",
-     "iopub.status.idle": "2026-08-04T15:29:30.154151Z",
-     "shell.execute_reply": "2026-08-04T15:29:30.153558Z"
+     "iopub.execute_input": "2026-08-06T17:27:35.265727Z",
+     "iopub.status.busy": "2026-08-06T17:27:35.265493Z",
+     "iopub.status.idle": "2026-08-06T17:27:36.281303Z",
+     "shell.execute_reply": "2026-08-06T17:27:36.280527Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataTree>\n",
-      "Group: /\n",
-      "└── Group: /sweep_1\n",
-      "        Dimensions:          (azimuth: 360, range: 492)\n",
-      "        Coordinates: (12/15)\n",
-      "          * azimuth          (azimuth) float64 3kB 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5\n",
-      "          * range            (range) float32 2kB 250.0 750.0 ... 2.452e+05 2.457e+05\n",
-      "            latitude         (azimuth, range) float64 1MB 46.04 46.05 ... 48.25 48.25\n",
-      "            longitude        (azimuth, range) float64 1MB 8.833 8.833 ... 8.805 8.805\n",
-      "            altitude         (azimuth, range) float64 1MB 1.625e+03 ... 4.356e+03\n",
-      "            x                (azimuth, range) float64 1MB 2.182 6.545 ... -2.144e+03\n",
-      "            ...               ...\n",
-      "            y_2056           (azimuth, range) float64 1MB 1.1e+06 ... 1.345e+06\n",
-      "            site_latitude    float64 8B 46.04\n",
-      "            site_longitude   float64 8B 8.833\n",
-      "            site_altitude    float64 8B 1.626e+03\n",
-      "            sweep_number     int64 8B 1\n",
-      "            elevation_angle  float64 8B -0.19\n",
-      "        Data variables:\n",
-      "            time             (azimuth, range) datetime64[ns] 1MB NaT NaT NaT ... NaT NaT\n",
-      "            DBZH             (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            DBZH_raw         (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            ZDR              (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            ZDR_raw          (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            KDP              (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            RHOHV            (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            PHIDP            (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            HC_MCH           (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            HC_PYART         (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            HZT              (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n",
-      "            TEMP             (azimuth, range) float32 708kB nan nan nan ... nan nan nan\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3. back to a DataTree — the full polar structure, for xarray workflows\n",
-    "dt = rain.sel(sweep=1).to_datatree()\n",
-    "print(dt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2a1ee7a",
-   "metadata": {},
+   "outputs": [],
    "source": [
-    "`to_datatree()` reindexes onto the complete azimuth x range grid, so gates you\n",
-    "filtered out come back as NaN. That is what makes it round-trippable, but it also\n",
-    "makes it much heavier than the other two — prefer `to_pandas` / `to_geopandas`\n",
-    "unless you specifically need xarray."
+    "# DataTree: the full polar structure, for xarray workflows.\n",
+    "# A DataTree describes ONE volume: each sweep is an (azimuth x range) grid and\n",
+    "# time is a per-ray coordinate, so there is no dimension to stack volumes along.\n",
+    "# Choose which volume to rebuild; to_datatree() then NaN-fills the gates that\n",
+    "# were filtered out, restoring the complete azimuth x range grid.\n",
+    "volumes = rdf.data[\"volume_time\"].unique().sort().to_list()\n",
+    "print(f\"{len(volumes)} volumes loaded -> rebuilding the first one\\n\")\n",
+    "\n",
+    "dt = rdf.to_datatree(timestep=volumes[0])\n",
+    "dt"
    ]
   },
   {
@@ -893,26 +496,13 @@
    "metadata": {},
    "source": [
     "---\n",
-    "## Recap\n",
-    "\n",
-    "```python\n",
-    "db  = raddb.RadDB(archive_dir=...)               # reading needs no CRS\n",
-    "rdf = db.open(radars=\"L\", time_period=(...), columns=[...], filters=...)\n",
-    "\n",
-    "rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})   # by value\n",
-    "rdf.sel(sweep=1, range=slice(10_000, 50_000))                # by label\n",
-    "rdf.add_feature(\"ZDR_lin\", lambda df: 10 ** (df[\"ZDR\"] / 10))\n",
-    "\n",
-    "rdf.to_pandas(with_geometry=True); rdf.to_geopandas(); rdf.to_datatree()\n",
-    "```\n",
-    "\n",
     "**Next:** [3 — Areas of interest](03_area_of_interest.ipynb)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "radar",
    "language": "python",
    "name": "python3"
   },
diff --git a/tutorial/03_area_of_interest.ipynb b/tutorial/03_area_of_interest.ipynb
index 4fbc5f7..d0535c2 100644
--- a/tutorial/03_area_of_interest.ipynb
+++ b/tutorial/03_area_of_interest.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "78499521",
    "metadata": {
     "execution": {
@@ -49,39 +49,8 @@
      "shell.execute_reply": "2026-08-04T15:32:08.474948Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
-      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
-      "Archive         : /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# --------------------------------------------------------------------------\n",
-    "# CONFIGURATION — point these at your own data\n",
-    "# --------------------------------------------------------------------------\n",
-    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
-    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
-    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
-    "\n",
-    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
-    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
-    "\n",
-    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
-    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
-    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
-    "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
-    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
-    "print(\"Archive         :\", ARCHIVE_DIR)\n"
-   ]
+   "outputs": [],
+   "source": "from pathlib import Path\n\n# --------------------------------------------------------------------------\n# CONFIGURATION — edit these three paths to point at your own data\n# --------------------------------------------------------------------------\n# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n# the cell below builds it.\n\nMCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\nNEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\nARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n\nprint(\"MCH DataTrees   :\", MCH_DIR)\nprint(\"NEXRAD DataTrees:\", NEXRAD_DIR)\nprint(\"Archive         :\", ARCHIVE_DIR)"
   },
   {
    "cell_type": "code",
@@ -744,4 +713,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/tutorial/04_plots.ipynb b/tutorial/04_plots.ipynb
index ec6f747..7fc27f9 100644
--- a/tutorial/04_plots.ipynb
+++ b/tutorial/04_plots.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d77ff6a1",
    "metadata": {
     "execution": {
@@ -36,39 +36,8 @@
      "shell.execute_reply": "2026-08-04T15:32:24.394261Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MCH DataTrees   : /data/RADAR/MCH_datatree\n",
-      "NEXRAD DataTrees: /data/RADAR/NEXRAD_datatree\n",
-      "Archive         : /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# --------------------------------------------------------------------------\n",
-    "# CONFIGURATION — point these at your own data\n",
-    "# --------------------------------------------------------------------------\n",
-    "# RadDB is network-agnostic: any xarray DataTree with the standard xradar\n",
-    "# layout works.  These tutorials use two MeteoSwiss volumes and two NEXRAD\n",
-    "# volumes stored as Zarr.  Set the environment variables, or edit the paths.\n",
-    "\n",
-    "MCH_DIR    = Path(os.environ.get(\"RADDB_DATATREE_DIR\", \"~/data/RADAR/MCH_datatree\")).expanduser()\n",
-    "NEXRAD_DIR = Path(os.environ.get(\"RADDB_NEXRAD_DIR\",   \"~/data/RADAR/NEXRAD_datatree\")).expanduser()\n",
-    "\n",
-    "# Where the archive is written.  Anywhere you like — it is just a directory.\n",
-    "ARCHIVE_DIR = Path(os.environ.get(\"RADDB_TUTORIAL_ARCHIVE\",\n",
-    "                                  Path(os.environ.get(\"TMPDIR\", \"/tmp\")) / \"raddb_tutorial_archive\"))\n",
-    "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
-    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
-    "print(\"Archive         :\", ARCHIVE_DIR)\n"
-   ]
+   "outputs": [],
+   "source": "from pathlib import Path\n\n# --------------------------------------------------------------------------\n# CONFIGURATION — edit these three paths to point at your own data\n# --------------------------------------------------------------------------\n# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n# the cell below builds it.\n\nMCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\nNEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\nARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n\nprint(\"MCH DataTrees   :\", MCH_DIR)\nprint(\"NEXRAD DataTrees:\", NEXRAD_DIR)\nprint(\"Archive         :\", ARCHIVE_DIR)"
   },
   {
    "cell_type": "code",
@@ -723,4 +692,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file

From 6663b628a942f55f1c3d932b75a4af4ebfb78d69 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Tue, 11 Aug 2026 18:40:04 +0200
Subject: [PATCH 04/14] Refactor code structure for improved readability and
 maintainability

---
 raddb/aoi.py                            |   28 +-
 raddb/main.py                           |   14 +-
 raddb/tests/test_crs.py                 |    5 +-
 raddb/viz/interactive.py                |   17 +-
 raddb/viz/plot.py                       |   12 +-
 tutorial/02_opening_and_filtering.ipynb |  337 +++--
 tutorial/03_area_of_interest.ipynb      | 1747 ++++++++++++++++++-----
 tutorial/04_plots.ipynb                 |  292 +---
 8 files changed, 1748 insertions(+), 704 deletions(-)

diff --git a/raddb/aoi.py b/raddb/aoi.py
index 0fc8409..3ff7e4b 100644
--- a/raddb/aoi.py
+++ b/raddb/aoi.py
@@ -632,17 +632,23 @@ def _lut_cs_table(
         if t is None:
             available = set(pq.read_schema(lut_path).names)
             xc, yc = f"x_{int(epsg)}", f"y_{int(epsg)}"
+            # The LUT stores two different x/y: metres from the radar, and the
+            # projected pair.  The section geometry works in the projected one,
+            # which is why it takes the plain `x`/`y` names here; the
+            # radar-relative pair rides along as x_rel/y_rel and is renamed back
+            # on output (see RadDB.extract_cross_section).
+            rel = [c for c in ("x", "y") if c in available]
             if {xc, yc}.issubset(available):
-                t = pl.read_parquet(
-                    lut_path, columns=[*_CS_LUT_BASE_COLS, xc, yc]
-                ).rename({xc: "x", yc: "y"})
+                t = pl.read_parquet(lut_path, columns=[*_CS_LUT_BASE_COLS, *rel, xc, yc])
             else:
                 t = add_lut_projection(
                     pl.read_parquet(
-                        lut_path, columns=[*_CS_LUT_BASE_COLS, "latitude", "longitude"]
+                        lut_path,
+                        columns=[*_CS_LUT_BASE_COLS, *rel, "latitude", "longitude"],
                     ),
                     epsg=int(epsg),
-                ).rename({xc: "x", yc: "y"}).select([*_CS_LUT_BASE_COLS, "x", "y"])
+                ).select([*_CS_LUT_BASE_COLS, *rel, xc, yc])
+            t = t.rename({c: f"{c}_rel" for c in rel}).rename({xc: "x", yc: "y"})
             # Radial spacing per sweep from the unique range grid -> dR = spacing/2.
             # `range` is cast to Float64 first so the median-of-diffs matches the
             # float64 arithmetic the pandas implementation used.
@@ -841,9 +847,8 @@ def _cross_section_gates(
     :meth:`raddb.RadDB.extract_cross_section` converts the geometry columns back
     to polars when joining them onto the data frame.
 
-    Returns one row per crossed gate with its cross-section geometry:
-    chord endpoints ``(d_near, z_near) / (d_far, z_far)``, center
-    ``(d_center, z_center)``, and ``cs_polygon`` — the 4-corner shapely polygon
+    Returns one row per crossed gate with its cross-section geometry: the gate
+    centre ``(d_center, z_center)`` and ``cs_polygon`` — the 4-corner shapely polygon
     in the (distance-along-line [m], altitude [m ASL]) plane, built by
     offsetting the chord perpendicularly by ±dA (the vertical half-beamwidth
     extent).  ``d`` is measured from ``p1``.
@@ -916,10 +921,9 @@ def _cross_section_gates(
     ], axis=1)
 
     out = sub.copy()
-    out["d_near"] = d_near
-    out["d_far"] = d_far
-    out["z_near"] = z_near
-    out["z_far"] = z_far
+    # Only the gate centre and its footprint are published.  The chord endpoints
+    # d_near/d_far and z_near/z_far are what the polygon is built from, so
+    # emitting them as well restated `cs_polygon` in scalar form.
     out["d_center"] = 0.5 * (d_near + d_far)
     out["z_center"] = 0.5 * (z_near + z_far)
     out["cs_polygon"] = shapely.polygons(ring)
diff --git a/raddb/main.py b/raddb/main.py
index ec72f33..b5d6c93 100644
--- a/raddb/main.py
+++ b/raddb/main.py
@@ -1731,8 +1731,8 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None,
         """Extract a vertical cross-section along the line ``p1 -> p2``; returns a new RadDB.
 
         The line need not pass through a radar.  Each selected gate gets a polygon
-        in the (distance-along-line, altitude) plane (``cs_polygon``) plus
-        ``d_near/d_far/z_near/z_far`` and ``d_center/z_center``; visualize with
+        in the (distance-along-line, altitude) plane (``cs_polygon``) plus its
+        centre ``d_center``/``z_center``; visualize with
         :meth:`plot_cross_section`.  ``p1``/``p2`` are ``(x, y)`` or shapely Points
         in ``crs``; distance is measured from ``p1``.
         """
@@ -1769,11 +1769,17 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None,
         # Unlike an AOI crop these columns are *not* LUT data: they are the
         # per-gate geometry of this particular section line, computed here and
         # available nowhere else, so they travel with the rows.
+        # The geometry table computes in the projected frame under plain x/y.
+        # Publish it as x_<epsg>/y_<epsg>, and give x/y back to the LUT's
+        # radar-relative metres, so both meanings are unambiguous downstream.
+        cs_geom = cs_geom.rename(columns={
+            "x": f"x_{epsg}", "y": f"y_{epsg}", "x_rel": "x", "y_rel": "y",
+        })
         geom_cols = [
             c for c in (
                 "radar", "sweep", "azimuth", "range", "elevation_angle",
-                "x", "y", "altitude",
-                "d_near", "d_far", "z_near", "z_far", "d_center", "z_center",
+                "x", "y", f"x_{epsg}", f"y_{epsg}", "altitude",
+                "d_center", "z_center",
                 "cs_polygon",
             )
             if c in cs_geom.columns and (c == "cs_polygon" or c not in data_cs.columns)
diff --git a/raddb/tests/test_crs.py b/raddb/tests/test_crs.py
index ed01861..eac8905 100644
--- a/raddb/tests/test_crs.py
+++ b/raddb/tests/test_crs.py
@@ -142,7 +142,10 @@ def test_cross_section_distance_is_true_metres(self, us_archive):
 
         cs = rdf.extract_cross_section(p1=p1, p2=p2, crs=4326)
         assert cs.data.height > 0, "the section selected no gates"
-        span = float(cs.data["d_far"].max())
+        # The far end of the section, read off the gate footprints: `d_center`
+        # alone would sit half a gate short and eat most of the tolerance.
+        polygons = cs.to_pandas()["cs_polygon"].to_numpy()
+        span = float(shapely.bounds(polygons)[:, 2].max())
         # UTM 14N at KTLX is accurate to ~0.03%; a hardcoded LV95 would be ~20% out.
         assert abs(span - truth) <= 0.005 * truth, (
             f"section spans {span:,.0f} m, true geodesic {truth:,.0f} m"
diff --git a/raddb/viz/interactive.py b/raddb/viz/interactive.py
index f92c807..26c379b 100644
--- a/raddb/viz/interactive.py
+++ b/raddb/viz/interactive.py
@@ -23,6 +23,8 @@
 import json
 from pathlib import Path
 
+import numpy as np
+
 
 # ============================================================================
 # GeoJSON feature -> crop dispatch  (pure, unit-testable — no widgets)
@@ -158,9 +160,10 @@ def __init__(self, db, radars=None, center=None, zoom=8,
         self.map.add(self.draw)
 
         # --- controls ---
-        self.radius = W.FloatText(value=float(point_radius_m), description="point r [m]",
+        self.radius = W.FloatText(value=float(point_radius_m),
+                                  description="if marker → radius [m]",
                                   style={"description_width": "initial"},
-                                  layout=W.Layout(width="180px"))
+                                  layout=W.Layout(width="320px"))
         self.apply_btn = W.Button(description="Apply crop", button_style="primary",
                                   icon="scissors")
         self.save_path = W.Text(value="aoi.geojson", description="save as",
@@ -173,7 +176,7 @@ def __init__(self, db, radars=None, center=None, zoom=8,
         instructions = W.HTML(
             "<b>Draw an AOI</b> with the toolbar (top-left): "
             "▭ rectangle → <code>crop_by_bbox</code>, ⬠ polygon → <code>crop_by_polygone</code>, "
-            "📍 marker → <code>crop_around_point</code> (uses <i>point r</i>), "
+            "📍 marker → <code>crop_around_point</code> (uses the radius box below), "
             "／ line → <code>extract_cross_section</code>. Then <b>Apply crop</b>."
         )
         self._widget = W.VBox([
@@ -217,7 +220,13 @@ def _apply(self, _btn=None):
                 radars = r.radars() if len(r) else []
             except Exception:  # noqa: BLE001
                 radars = []
-            sweeps = r.data["sweep"].n_unique() if "sweep" in r.columns() else "?"
+            # `sweep` is a LUT column, never stored per gate, so reading it off
+            # r.columns() always missed and printed "?".  It is decoded from the
+            # gate_id instead, which every row carries.
+            sweeps = 0
+            if len(r):
+                from raddb.lut import decode_gate_ids
+                sweeps = int(np.unique(decode_gate_ids(r.data["gate_id"].to_numpy())[0]).size)
             print(f"{self.kind} -> {len(r):,} gates | radars {radars} | {sweeps} sweeps")
             print("result is available as  <selector>.result  (a cropped RadDB).")
 
diff --git a/raddb/viz/plot.py b/raddb/viz/plot.py
index d20a183..0faa10d 100644
--- a/raddb/viz/plot.py
+++ b/raddb/viz/plot.py
@@ -1110,14 +1110,20 @@ def plot_aoi_quicklook(
             sites[r] = (pt.x, pt.y)
 
     # --- optional selected gate centroids ---
+    # A cross-sectioned frame carries both x/y (metres from the radar) and
+    # x_<epsg>/y_<epsg>; this map is drawn in the projected frame, so prefer
+    # those and fall back to plain x/y for a plain crop.
+    _xc, _yc = f"x_{frame_epsg}", f"y_{frame_epsg}"
+    if selected is not None and _xc not in getattr(selected, "columns", ()):
+        _xc, _yc = "x", "y"
     if (
         selected is not None
         and show_gates
         and len(selected)
-        and {"x", "y"}.issubset(selected.columns)
+        and {_xc, _yc}.issubset(selected.columns)
     ):
-        xs = selected["x"].to_numpy()
-        ys = selected["y"].to_numpy()
+        xs = selected[_xc].to_numpy()
+        ys = selected[_yc].to_numpy()
         if gate_sample and len(xs) > gate_sample:
             idx = np.random.default_rng(0).choice(len(xs), gate_sample, replace=False)
             xs, ys = xs[idx], ys[idx]
diff --git a/tutorial/02_opening_and_filtering.ipynb b/tutorial/02_opening_and_filtering.ipynb
index f74b69c..dc45d57 100644
--- a/tutorial/02_opening_and_filtering.ipynb
+++ b/tutorial/02_opening_and_filtering.ipynb
@@ -25,52 +25,51 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36dbad7c",
+   "id": "a4f11595",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:33.195176Z",
-     "iopub.status.busy": "2026-08-06T17:27:33.195065Z",
-     "iopub.status.idle": "2026-08-06T17:27:33.200576Z",
-     "shell.execute_reply": "2026-08-06T17:27:33.200062Z"
+     "iopub.execute_input": "2026-08-11T11:04:33.384971Z",
+     "iopub.status.busy": "2026-08-11T11:04:33.384854Z",
+     "iopub.status.idle": "2026-08-11T11:04:33.967578Z",
+     "shell.execute_reply": "2026-08-11T11:04:33.966779Z"
     }
    },
    "outputs": [],
    "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "# --------------------------------------------------------------------------\n",
-    "# CONFIGURATION — edit these three paths to point at your own data\n",
-    "# --------------------------------------------------------------------------\n",
-    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.\n",
-    "\n",
-    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
-    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
-    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
-    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
-    "print(\"Archive         :\", ARCHIVE_DIR)"
+    "from pathlib import Path\n",
+    "import polars as pl\n",
+    "import raddb"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a4f11595",
+   "id": "36dbad7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:33.202202Z",
-     "iopub.status.busy": "2026-08-06T17:27:33.202062Z",
-     "iopub.status.idle": "2026-08-06T17:27:33.927024Z",
-     "shell.execute_reply": "2026-08-06T17:27:33.926166Z"
+     "iopub.execute_input": "2026-08-11T11:04:33.969237Z",
+     "iopub.status.busy": "2026-08-11T11:04:33.969039Z",
+     "iopub.status.idle": "2026-08-11T11:04:33.972335Z",
+     "shell.execute_reply": "2026-08-11T11:04:33.971749Z"
     }
    },
    "outputs": [],
    "source": [
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — edit these three paths to point at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.\n",
     "\n",
-    "import polars as pl\n",
-    "import raddb"
+    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
+    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
+    "\n",
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
@@ -79,10 +78,10 @@
    "id": "60595d47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:33.928749Z",
-     "iopub.status.busy": "2026-08-06T17:27:33.928548Z",
-     "iopub.status.idle": "2026-08-06T17:27:33.931440Z",
-     "shell.execute_reply": "2026-08-06T17:27:33.930861Z"
+     "iopub.execute_input": "2026-08-11T11:04:33.973778Z",
+     "iopub.status.busy": "2026-08-11T11:04:33.973684Z",
+     "iopub.status.idle": "2026-08-11T11:04:33.976649Z",
+     "shell.execute_reply": "2026-08-11T11:04:33.975926Z"
     }
    },
    "outputs": [],
@@ -111,10 +110,10 @@
    "id": "62430b24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:33.932939Z",
-     "iopub.status.busy": "2026-08-06T17:27:33.932851Z",
-     "iopub.status.idle": "2026-08-06T17:27:33.982047Z",
-     "shell.execute_reply": "2026-08-06T17:27:33.981493Z"
+     "iopub.execute_input": "2026-08-11T11:04:33.978054Z",
+     "iopub.status.busy": "2026-08-11T11:04:33.977960Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.022320Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.021652Z"
     }
    },
    "outputs": [],
@@ -140,21 +139,21 @@
    "id": "e8856737",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:33.983587Z",
-     "iopub.status.busy": "2026-08-06T17:27:33.983432Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.020932Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.020177Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.023451Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.023303Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.059913Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.059394Z"
     }
    },
    "outputs": [],
    "source": [
-    "# Only two moments, only radar L\n",
+    "# Only two variables, only radar L\n",
     "small_df = db.open(radars=\"L\", columns=[\"DBZH\", \"ZDR\"])\n",
-    "print(f\"small_df:\\tcolumns: {small_df.columns()}\")\n",
-    "\n",
+    "print(f\"small_df:\\tcolumns: {small_df.columns()}\\nsmall_df:\\tgates: {len(small_df)}\")\n",
+    "print(\"-------------------------------\")\n",
     "# time period\n",
     "day_df = db.open(radars=\"L\", time_period=(\"2024-06-12\", \"2024-06-13\"))\n",
-    "print(f\"day_df:\\t\\tgates: {len(day_df):,}\")"
+    "print(f\"day_df:\\t\\tcolumns: {day_df.columns()}\\nday_df:\\t\\tgates: {len(day_df):,}\")"
    ]
   },
   {
@@ -163,10 +162,10 @@
    "id": "320155b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.022551Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.022464Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.036482Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.035455Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.061304Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.061162Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.076238Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.075582Z"
     }
    },
    "outputs": [],
@@ -193,16 +192,17 @@
    "id": "37d4e073",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.037856Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.037671Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.042709Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.042134Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.077374Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.077281Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.080650Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.080298Z"
     }
    },
    "outputs": [],
    "source": [
-    "print(\"type:\", type(rdf.data).__name__)\n",
-    "print(\"shape:\", rdf.data.shape)\n",
+    "print(\"type:\\t\\t\", type(rdf.data))\n",
+    "print(\"name type:\\t\", type(rdf.data).__name__)\n",
+    "print(\"shape:\\t\\t\", rdf.data.shape)\n",
     "rdf.data.head()"
    ]
   },
@@ -212,10 +212,10 @@
    "id": "6fd88dae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.044151Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.044003Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.264118Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.263480Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.081817Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.081732Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.275932Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.275352Z"
     }
    },
    "outputs": [],
@@ -243,10 +243,10 @@
    "id": "650f8db6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.265548Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.265433Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.275980Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.275259Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.277445Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.277347Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.286613Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.286003Z"
     }
    },
    "outputs": [],
@@ -280,10 +280,10 @@
    "id": "3aec88ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.277422Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.277252Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.532381Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.531861Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.288256Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.288150Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.534804Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.534235Z"
     }
    },
    "outputs": [],
@@ -299,7 +299,7 @@
    "id": "e10ec065",
    "metadata": {},
    "source": [
-    "The clever part: `range`, `azimuth`, `elevation_angle`, `latitude`, `longitude`\n",
+    "`range`, `azimuth`, `elevation_angle`, `latitude`, `longitude`\n",
     "and `altitude` are **not stored in the Parquet files** — they live once in the LUT.\n",
     "`sel()` borrows the column it needs, evaluates the selection, and drops it again,\n",
     "so selecting on geometry costs no storage."
@@ -311,10 +311,10 @@
    "id": "17a5d6ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.533954Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.533803Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.595608Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.595121Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.536128Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.535979Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.599984Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.599314Z"
     }
    },
    "outputs": [],
@@ -346,10 +346,10 @@
    "id": "ecec9579",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.597018Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.596918Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.607802Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.607342Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.601299Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.601154Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.611186Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.610691Z"
     }
    },
    "outputs": [],
@@ -376,10 +376,10 @@
    "id": "01572c8c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.609120Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.609035Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.634978Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.634462Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.612654Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.612572Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.637884Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.637348Z"
     }
    },
    "outputs": [],
@@ -396,51 +396,170 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d77a02f8",
+   "id": "152e7b06",
    "metadata": {},
    "source": [
     "## 6. Framework converter\n",
     "\n",
-    "Three converters, for three different framework."
+    "The gates can leave RadDB as a pandas DataFrame, a geopandas GeoDataFrame, or an\n",
+    "xarray DataTree — three converters for three different frameworks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49d3e2e4",
+   "metadata": {},
+   "source": [
+    "### `to_pandas()`: the DataFrame\n",
+    "\n",
+    "`to_pandas()` returns the loaded gates as an ordinary pandas DataFrame. On its own\n",
+    "it hands back exactly what is stored per gate: `gate_id`, `time`, the polarimetric variables, and\n",
+    "the `volume_time` / `radar` labels.\n",
+    "\n",
+    "Geometry is **not** stored per gate — it lives once in the LUT — so it is merged\n",
+    "on `gate_id` only when you ask for it:\n",
+    "\n",
+    "| call | columns added |\n",
+    "|---|---|\n",
+    "| `to_pandas()` | nothing; the stored columns only (dynamic variables) |\n",
+    "| `to_pandas(with_geometry=True)` | `latitude`, `longitude`, `altitude`, `sweep` |\n",
+    "| `to_pandas(with_polar_coords=True)` | the same, **plus** `range`, `azimuth`, `elevation_angle` |\n",
+    "\n",
+    "`with_polar_coords` implies `with_geometry`. The polar coordinates are off by\n",
+    "default because they repeat what the Cartesian columns already say, unless you are\n",
+    "working in polar space.\n",
+    "\n",
+    "Note what is **not** included: `x`, `y`, `z` — metres from the radar — are never\n",
+    "added by either flag, and the projected `x_<epsg>` / `y_<epsg>` appear only under a\n",
+    "condition. The next three cells explain why, and how to load all of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dff1fb07",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-11T11:04:34.639146Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.639060Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.646098Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.645535Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# No flags: the stored columns only, exactly as open() loaded them.\n",
+    "df = rain.to_pandas()\n",
+    "print(\"to_pandas():\", type(df).__name__, df.shape)\n",
+    "print(list(df.columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f7f7ae3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-11T11:04:34.647539Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.647452Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.791144Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.790419Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# with_geometry=True joins the per-gate coordinates from the LUT on gate_id.\n",
+    "df_geo = rain.to_pandas(with_geometry=True)\n",
+    "print(\"added by with_geometry     :\", [c for c in df_geo.columns if c not in df.columns])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cc53313",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-11T11:04:34.792322Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.792226Z",
+     "iopub.status.idle": "2026-08-11T11:04:34.944882Z",
+     "shell.execute_reply": "2026-08-11T11:04:34.944400Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# with_polar_coords=True also brings the polar coordinates the geometry came from.\n",
+    "df_polar = rain.to_pandas(with_polar_coords=True)\n",
+    "print(\"added by with_polar_coords :\", [c for c in df_polar.columns if c not in df.columns])\n",
+    "df_polar.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec30bf43",
+   "metadata": {},
+   "source": [
+    "### Where the geometry lives\n",
+    "\n",
+    "Two things are easy to trip over:\n",
+    "\n",
+    "- **`x_<epsg>` / `y_<epsg>` appear only if the RadDB was created with `crs=`.**\n",
+    "  `crs()` reports the archive's projection either way, but the converters add the\n",
+    "  projected pair only when a projection was asked for explicitly.\n",
+    "- **`x` / `y` / `z`** — metres from the radar — are LUT columns that no converter\n",
+    "  attaches. Join the LUT yourself to get them, or any other LUT column."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4dcf0de3",
+   "id": "a15e0de3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.636366Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.636234Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.801836Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.801266Z"
+     "iopub.execute_input": "2026-08-11T11:04:34.946404Z",
+     "iopub.status.busy": "2026-08-11T11:04:34.946292Z",
+     "iopub.status.idle": "2026-08-11T11:04:35.239055Z",
+     "shell.execute_reply": "2026-08-11T11:04:35.238383Z"
     }
    },
    "outputs": [],
    "source": [
-    "# pandas: with_geometry merges the per-gate coordinates from the LUT\n",
-    "df = rain.to_pandas(with_geometry=True)\n",
-    "print(\"to_pandas:\", type(df))\n",
-    "print(\"columns:\", list(df.columns))"
+    "# Projected coordinates: state the CRS when creating the RadDB, and\n",
+    "# with_geometry=True then adds x_<epsg> / y_<epsg> alongside lon/lat/alt.\n",
+    "db_proj = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)\n",
+    "rain_proj = db_proj.open(radars=\"L\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
+    "\n",
+    "print(\"without crs= :\", list(rain.to_pandas(with_geometry=True).columns))\n",
+    "print(\"with crs=2056:\", list(rain_proj.to_pandas(with_geometry=True).columns))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67d3b517",
+   "id": "3c13d25b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.803160Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.802992Z",
-     "iopub.status.idle": "2026-08-06T17:27:34.968875Z",
-     "shell.execute_reply": "2026-08-06T17:27:34.968028Z"
+     "iopub.execute_input": "2026-08-11T11:04:35.240725Z",
+     "iopub.status.busy": "2026-08-11T11:04:35.240575Z",
+     "iopub.status.idle": "2026-08-11T11:04:35.283659Z",
+     "shell.execute_reply": "2026-08-11T11:04:35.283068Z"
     }
    },
    "outputs": [],
    "source": [
-    "# with_polar_coords adds range / azimuth / elevation_angle as well.\n",
-    "# Off by default because they duplicate what the Cartesian columns already say.\n",
-    "print(list(rain.to_pandas(with_polar_coords=True).columns))"
+    "# Any LUT column can be attached by joining on gate_id.  This is also how you add\n",
+    "# geometry to a frame loaded with open(), and the only way to get x / y / z\n",
+    "# (metres from the radar), which no converter attaches.\n",
+    "geometry = db.get_lut(\"L\").select([\"gate_id\", \"x\", \"y\", \"z\", \"x_2056\", \"y_2056\"])\n",
+    "joined = rain.data.join(geometry, on=\"gate_id\", how=\"left\")\n",
+    "joined.select([\"gate_id\", \"DBZH\", \"x\", \"y\", \"z\", \"x_2056\", \"y_2056\"]).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0445a74",
+   "metadata": {},
+   "source": [
+    "### `to_geopandas()` — points with a CRS"
    ]
   },
   {
@@ -449,10 +568,10 @@
    "id": "abc6973e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:34.970415Z",
-     "iopub.status.busy": "2026-08-06T17:27:34.970226Z",
-     "iopub.status.idle": "2026-08-06T17:27:35.263991Z",
-     "shell.execute_reply": "2026-08-06T17:27:35.263428Z"
+     "iopub.execute_input": "2026-08-11T11:04:35.285730Z",
+     "iopub.status.busy": "2026-08-11T11:04:35.285583Z",
+     "iopub.status.idle": "2026-08-11T11:04:35.566121Z",
+     "shell.execute_reply": "2026-08-11T11:04:35.565473Z"
     }
    },
    "outputs": [],
@@ -464,16 +583,24 @@
     "gdf[[\"gate_id\", \"DBZH\", \"geometry\"]].head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3e46765f",
+   "metadata": {},
+   "source": [
+    "### `to_datatree()` — back to xarray"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "1280e7ee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-06T17:27:35.265727Z",
-     "iopub.status.busy": "2026-08-06T17:27:35.265493Z",
-     "iopub.status.idle": "2026-08-06T17:27:36.281303Z",
-     "shell.execute_reply": "2026-08-06T17:27:36.280527Z"
+     "iopub.execute_input": "2026-08-11T11:04:35.568035Z",
+     "iopub.status.busy": "2026-08-11T11:04:35.567952Z",
+     "iopub.status.idle": "2026-08-11T11:04:36.719949Z",
+     "shell.execute_reply": "2026-08-11T11:04:36.719262Z"
     }
    },
    "outputs": [],
diff --git a/tutorial/03_area_of_interest.ipynb b/tutorial/03_area_of_interest.ipynb
index d0535c2..e5c9e68 100644
--- a/tutorial/03_area_of_interest.ipynb
+++ b/tutorial/03_area_of_interest.ipynb
@@ -5,146 +5,180 @@
    "id": "b8d0cd89",
    "metadata": {},
    "source": [
-    "# 3 — Areas of interest\n",
+    "# 3. Area Of Interest\n",
     "\n",
     "Tutorial 2 narrowed the data by *value* and by *label*. This one narrows it by\n",
-    "**geography**: a box, a circle, a polygon, or a vertical slice along a line.\n",
+    "**geography**: a box, a circle, a polygon, or a vertical cross-section.\n",
     "\n",
     "| method | AOI | typical use |\n",
     "|---|---|---|\n",
     "| `crop_by_bbox` | rectangle | a map tile, a model domain |\n",
     "| `crop_around_point` | circle | everything within N km of a place |\n",
-    "| `crop_by_polygone` | any polygon | a catchment, a canton, a shapefile |\n",
+    "| `crop_by_polygone` | any polygon | a catchment, a shapefile |\n",
     "| `extract_cross_section` | a line + beam width | a vertical slice through a storm |\n",
     "\n",
     "The first three keep the gates and drop the rest. The fourth also computes, for\n",
-    "every selected gate, its position **along the line** and its **altitude** — the\n",
-    "geometry a vertical cross-section plot needs.\n",
+    "every selected gate, its position **along the line** and its **altitude** (the\n",
+    "geometry a vertical cross-section plot needs).\n",
     "\n",
     "---\n",
-    "## The one rule: which coordinates am I in?\n",
+    "## One rule about the crs\n",
     "\n",
     "Every AOI runs in the **archive's own CRS**, read from `info.yaml`. You never have\n",
     "to restate it. What you *do* have to say is which CRS **your own** coordinates are\n",
-    "in, with `crs=`:\n",
+    "in, using the argument `crs=`:\n",
     "\n",
     "```python\n",
     "rdf.crop_around_point(point=(8.83, 46.04), distance=30_000, crs=4326)   # lon/lat\n",
     "rdf.crop_around_point(point=(2680000, 1120000), distance=30_000)        # already LV95\n",
     "```\n",
     "\n",
-    "Get this wrong and the crop is silently empty or in the wrong country — passing\n",
-    "lon/lat degrees while RadDB reads them as metres puts your AOI ~2600 km away."
+    "Get this wrong and the crop is silently empty or in the wrong country (passing\n",
+    "lon/lat degrees while RadDB reads them as metres puts your AOI ~2600 km away)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78499521",
+   "id": "9d7152f3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:08.470297Z",
-     "iopub.status.busy": "2026-08-04T15:32:08.470192Z",
-     "iopub.status.idle": "2026-08-04T15:32:08.475526Z",
-     "shell.execute_reply": "2026-08-04T15:32:08.474948Z"
+     "iopub.execute_input": "2026-08-11T16:21:53.515796Z",
+     "iopub.status.busy": "2026-08-11T16:21:53.515665Z",
+     "iopub.status.idle": "2026-08-11T16:21:54.463480Z",
+     "shell.execute_reply": "2026-08-11T16:21:54.462351Z"
     }
    },
    "outputs": [],
-   "source": "from pathlib import Path\n\n# --------------------------------------------------------------------------\n# CONFIGURATION — edit these three paths to point at your own data\n# --------------------------------------------------------------------------\n# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n# the cell below builds it.\n\nMCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\nNEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\nARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n\nprint(\"MCH DataTrees   :\", MCH_DIR)\nprint(\"NEXRAD DataTrees:\", NEXRAD_DIR)\nprint(\"Archive         :\", ARCHIVE_DIR)"
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import shapely\n",
+    "import raddb"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "9d7152f3",
+   "execution_count": null,
+   "id": "78499521",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:08.477004Z",
-     "iopub.status.busy": "2026-08-04T15:32:08.476909Z",
-     "iopub.status.idle": "2026-08-04T15:32:09.080060Z",
-     "shell.execute_reply": "2026-08-04T15:32:09.079247Z"
+     "iopub.execute_input": "2026-08-11T16:21:54.466293Z",
+     "iopub.status.busy": "2026-08-11T16:21:54.465947Z",
+     "iopub.status.idle": "2026-08-11T16:21:54.470649Z",
+     "shell.execute_reply": "2026-08-11T16:21:54.469711Z"
     }
    },
    "outputs": [],
    "source": [
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — edit these three paths to point at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n",
+    "# the cell below builds it.\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import shapely\n",
-    "import raddb\n",
+    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
+    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
+    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
-    "# Keep the embedded figures small enough for GitHub to render this notebook.\n",
-    "plt.rcParams[\"figure.dpi\"] = 70"
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "bb505873",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:09.081820Z",
-     "iopub.status.busy": "2026-08-04T15:32:09.081650Z",
-     "iopub.status.idle": "2026-08-04T15:32:09.084140Z",
-     "shell.execute_reply": "2026-08-04T15:32:09.083549Z"
+     "iopub.execute_input": "2026-08-11T16:21:54.472572Z",
+     "iopub.status.busy": "2026-08-11T16:21:54.472378Z",
+     "iopub.status.idle": "2026-08-11T16:21:54.476272Z",
+     "shell.execute_reply": "2026-08-11T16:21:54.475137Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "archive already present: /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
     "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
     "    print(\"building the archive (see tutorial 1) ...\")\n",
     "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
     "else:\n",
-    "    print(\"archive already present:\", ARCHIVE_DIR)\n"
+    "    print(\"archive already present:\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6e5330be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:09.085669Z",
-     "iopub.status.busy": "2026-08-04T15:32:09.085589Z",
-     "iopub.status.idle": "2026-08-04T15:32:09.394435Z",
-     "shell.execute_reply": "2026-08-04T15:32:09.393788Z"
+     "iopub.execute_input": "2026-08-11T16:21:54.479664Z",
+     "iopub.status.busy": "2026-08-11T16:21:54.479357Z",
+     "iopub.status.idle": "2026-08-11T16:21:54.601377Z",
+     "shell.execute_reply": "2026-08-11T16:21:54.600119Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "radar L at 8.833, 46.041   |   326,730 gates with echo\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "archive CRS: EPSG:2056\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)\n",
     "rdf = db.open(radars=\"L\").filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 5})\n",
     "\n",
     "info = db.get_radar_info(\"L\")\n",
-    "SITE = (info[\"longitude\"], info[\"latitude\"])          # lon, lat\n",
+    "SITE = (info[\"longitude\"], info[\"latitude\"])          # radar: L ==> lon, lat\n",
     "print(f\"radar L at {SITE[0]:.3f}, {SITE[1]:.3f}   |   {len(rdf):,} gates with echo\")\n",
     "print(\"archive CRS:\", rdf.crs())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "65c887b3",
+   "metadata": {},
+   "source": [
+    "### `crs=` describes *your* numbers\n",
+    "\n",
+    "This is the single most common mistake, so it is worth seeing rather than reading.\n",
+    "`crs=` says which frame **the coordinates you passed in** are expressed in. It does\n",
+    "**not** choose the frame the AOI runs in — that is always the archive's own CRS.\n",
+    "\n",
+    "So `crs=4326` throughout this notebook because the points are written as lon/lat\n",
+    "degrees. Passing `crs=2056` with those same numbers tells RadDB to read `8.83` and\n",
+    "`46.04` as *metres* in LV95 — a spot near the origin of the Swiss grid, ~2700 km\n",
+    "from the radar — and the crop comes back empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3d50c2b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-11T16:21:54.603503Z",
+     "iopub.status.busy": "2026-08-11T16:21:54.603257Z",
+     "iopub.status.idle": "2026-08-11T16:21:57.197545Z",
+     "shell.execute_reply": "2026-08-11T16:21:57.196755Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pyproj import Transformer\n",
+    "\n",
+    "# The radar site, written both ways\n",
+    "E, N = Transformer.from_crs(4326, 2056, always_xy=True).transform(*SITE)\n",
+    "print(f\"lon/lat (EPSG:4326): ({SITE[0]:.4f}, {SITE[1]:.4f})\")\n",
+    "print(f\"LV95    (EPSG:2056): ({E:,.0f}, {N:,.0f})\\n\")\n",
+    "\n",
+    "print(\"crop_around_point(distance=30 km):\")\n",
+    "print(f\"  (lon, lat) + crs=4326 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=4326)):>8,} gates\")\n",
+    "print(f\"  (lon, lat) + crs=2056 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=2056)):>8,} gates  <- degrees read as metres\")\n",
+    "print(f\"  (E, N)     + crs=2056 -> {len(rdf.crop_around_point(point=(E, N), distance=30_000, crs=2056)):>8,} gates\")\n",
+    "print(f\"  (E, N)     + no crs   -> {len(rdf.crop_around_point(point=(E, N), distance=30_000)):>8,} gates  <- defaults to the archive CRS\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ef9ad06a",
@@ -158,26 +192,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "916e585b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:09.395848Z",
-     "iopub.status.busy": "2026-08-04T15:32:09.395723Z",
-     "iopub.status.idle": "2026-08-04T15:32:10.453450Z",
-     "shell.execute_reply": "2026-08-04T15:32:10.452752Z"
+     "iopub.execute_input": "2026-08-11T16:21:57.200168Z",
+     "iopub.status.busy": "2026-08-11T16:21:57.200027Z",
+     "iopub.status.idle": "2026-08-11T16:21:58.037522Z",
+     "shell.execute_reply": "2026-08-11T16:21:58.036434Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "326,730 -> 286,445 gates inside the lon/lat box\n",
-      "lon/lat extent of the result: [8.4, 9.3, 45.8, 46.501]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "box = rdf.crop_by_bbox(bounds=(8.4, 45.8, 9.3, 46.5), crs=4326)\n",
     "print(f\"{len(rdf):,} -> {len(box):,} gates inside the lon/lat box\")\n",
@@ -197,39 +222,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "b7e09c34",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:10.455251Z",
-     "iopub.status.busy": "2026-08-04T15:32:10.455121Z",
-     "iopub.status.idle": "2026-08-04T15:32:13.778327Z",
-     "shell.execute_reply": "2026-08-04T15:32:13.777588Z"
+     "iopub.execute_input": "2026-08-11T16:21:58.039730Z",
+     "iopub.status.busy": "2026-08-11T16:21:58.039507Z",
+     "iopub.status.idle": "2026-08-11T16:22:01.124637Z",
+     "shell.execute_reply": "2026-08-11T16:22:01.123765Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  within  20 km :  208,149 gates\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  within  50 km :  288,718 gates\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  within 100 km :  321,298 gates\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for km in (20, 50, 100):\n",
     "    sub = rdf.crop_around_point(point=SITE, distance=km * 1_000, crs=4326)\n",
@@ -238,25 +241,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ce861e79",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:13.780238Z",
-     "iopub.status.busy": "2026-08-04T15:32:13.780077Z",
-     "iopub.status.idle": "2026-08-04T15:32:14.547259Z",
-     "shell.execute_reply": "2026-08-04T15:32:14.546504Z"
+     "iopub.execute_input": "2026-08-11T16:22:01.126711Z",
+     "iopub.status.busy": "2026-08-11T16:22:01.126506Z",
+     "iopub.status.idle": "2026-08-11T16:22:01.565338Z",
+     "shell.execute_reply": "2026-08-11T16:22:01.564257Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "25 km around (9.0, 46.2): 186,802 gates\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Any point, not only the radar itself\n",
     "elsewhere = rdf.crop_around_point(point=(9.0, 46.2), distance=25_000, crs=4326)\n",
@@ -272,30 +267,22 @@
     "\n",
     "Accepts a shapely `Polygon`/`MultiPolygon`, a GeoDataFrame, or a path to a\n",
     "`.shp` / `.geojson` file. A file's **declared CRS wins** unless you pass `crs=`\n",
-    "explicitly — a GeoJSON is lon/lat by RFC 7946, so this usually just works."
+    "explicitly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a912757",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:14.548940Z",
-     "iopub.status.busy": "2026-08-04T15:32:14.548780Z",
-     "iopub.status.idle": "2026-08-04T15:32:15.437576Z",
-     "shell.execute_reply": "2026-08-04T15:32:15.436765Z"
+     "iopub.execute_input": "2026-08-11T16:22:01.567615Z",
+     "iopub.status.busy": "2026-08-11T16:22:01.567373Z",
+     "iopub.status.idle": "2026-08-11T16:22:01.978251Z",
+     "shell.execute_reply": "2026-08-11T16:22:01.977671Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inside the triangle: 209,103 gates\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "triangle = shapely.Polygon([(8.6, 45.9), (9.2, 46.1), (8.8, 46.5)])\n",
     "poly = rdf.crop_by_polygone(polygon=triangle, crs=4326)\n",
@@ -304,25 +291,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "31813aca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:15.439783Z",
-     "iopub.status.busy": "2026-08-04T15:32:15.439600Z",
-     "iopub.status.idle": "2026-08-04T15:32:16.398037Z",
-     "shell.execute_reply": "2026-08-04T15:32:16.397328Z"
+     "iopub.execute_input": "2026-08-11T16:22:01.980376Z",
+     "iopub.status.busy": "2026-08-11T16:22:01.980246Z",
+     "iopub.status.idle": "2026-08-11T16:22:02.443958Z",
+     "shell.execute_reply": "2026-08-11T16:22:02.443289Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "from GeoJSON       : 209,103 gates  (same: True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The same thing from a file on disk\n",
     "import json\n",
@@ -335,7 +314,7 @@
     "}))\n",
     "\n",
     "from_file = rdf.crop_by_polygone(polygon=geojson_path)   # CRS taken from the file\n",
-    "print(f\"from GeoJSON       : {len(from_file):,} gates  (same: {len(from_file) == len(poly)})\")"
+    "print(f\"from GeoJSON: {len(from_file):,} gates  (same: {len(from_file) == len(poly)})\")"
    ]
   },
   {
@@ -343,43 +322,37 @@
    "id": "25e97fff",
    "metadata": {},
    "source": [
-    "## 4. `extract_cross_section` — a vertical slice\n",
+    "## 4. `extract_cross_section`: a vertical cross-section\n",
     "\n",
     "A line `p1 -> p2` plus the beam width defines a vertical curtain. Every gate whose\n",
     "beam intersects it is kept, and gains the geometry of the section:\n",
     "\n",
     "| column | meaning |\n",
     "|---|---|\n",
-    "| `d_near`, `d_far`, `d_center` | distance **along the line** from `p1` [m] |\n",
-    "| `z_near`, `z_far`, `z_center` | altitude above sea level [m] |\n",
+    "| `d_center` | the gate centre's distance **along the line** from `p1` [m] |\n",
+    "| `z_center` | the gate centre's altitude above sea level [m] |\n",
     "| `cs_polygon` | the gate's footprint in the (distance, altitude) plane |\n",
     "\n",
     "Unlike an area crop, these columns are **not** LUT data — they belong to this\n",
-    "particular line, so they travel with the rows."
+    "particular line, so they travel with the rows.\n",
+    "\n",
+    "`cs_polygon` is the one `plot_vcs` draws; `d_center` / `z_center` are there for\n",
+    "analysing a section numerically — profiles, height thresholds, distance bins."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "2c3e7cb1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:16.400103Z",
-     "iopub.status.busy": "2026-08-04T15:32:16.399949Z",
-     "iopub.status.idle": "2026-08-04T15:32:18.500354Z",
-     "shell.execute_reply": "2026-08-04T15:32:18.499269Z"
+     "iopub.execute_input": "2026-08-11T16:22:02.445898Z",
+     "iopub.status.busy": "2026-08-11T16:22:02.445773Z",
+     "iopub.status.idle": "2026-08-11T16:22:04.193859Z",
+     "shell.execute_reply": "2026-08-11T16:22:04.192790Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3,833 gates on the section\n",
-      "new columns: ['d_near', 'd_far', 'z_near', 'z_far', 'd_center', 'z_center', 'cs_polygon']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cs = rdf.extract_cross_section(\n",
     "    p1=(SITE[0] - 0.6, SITE[1] - 0.35),\n",
@@ -387,107 +360,47 @@
     "    crs=4326,\n",
     ")\n",
     "print(f\"{len(cs):,} gates on the section\")\n",
-    "print(\"new columns:\", [c for c in cs.columns() if c.startswith((\"d_\", \"z_\", \"cs_\"))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e88b5935",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:18.502506Z",
-     "iopub.status.busy": "2026-08-04T15:32:18.502340Z",
-     "iopub.status.idle": "2026-08-04T15:32:18.509742Z",
-     "shell.execute_reply": "2026-08-04T15:32:18.509037Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "section length : 121.2 km\n",
-      "altitude range : 1589 - 12563 m ASL\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (3, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>DBZH</th><th>d_center</th><th>z_center</th></tr><tr><td>i64</td><td>f32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>21010095000749</td><td>10.0</td><td>61090.019433</td><td>1624.323017</td></tr><tr><td>21010105000749</td><td>12.0</td><td>61102.684205</td><td>1624.291394</td></tr><tr><td>21010115000749</td><td>12.0</td><td>61117.134979</td><td>1624.254759</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (3, 4)\n",
-       "┌────────────────┬──────┬──────────────┬─────────────┐\n",
-       "│ gate_id        ┆ DBZH ┆ d_center     ┆ z_center    │\n",
-       "│ ---            ┆ ---  ┆ ---          ┆ ---         │\n",
-       "│ i64            ┆ f32  ┆ f64          ┆ f64         │\n",
-       "╞════════════════╪══════╪══════════════╪═════════════╡\n",
-       "│ 21010095000749 ┆ 10.0 ┆ 61090.019433 ┆ 1624.323017 │\n",
-       "│ 21010105000749 ┆ 12.0 ┆ 61102.684205 ┆ 1624.291394 │\n",
-       "│ 21010115000749 ┆ 12.0 ┆ 61117.134979 ┆ 1624.254759 │\n",
-       "└────────────────┴──────┴──────────────┴─────────────┘"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"section length : {cs.data['d_far'].max() / 1000:.1f} km\")\n",
-    "print(f\"altitude range : {cs.data['z_near'].min():.0f} - {cs.data['z_far'].max():.0f} m ASL\")\n",
-    "cs.data.select([\"gate_id\", \"DBZH\", \"d_center\", \"z_center\"]).head(3)"
+    "# in new columns it should appear: 'd_center', 'z_center', 'cs_polygon'\n",
+    "print(\"new columns:\", [c for c in cs.columns() if c not in rdf.columns()])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4407feab",
+   "id": "8389085e",
    "metadata": {},
    "source": [
-    "The section is measured in true ground distance, wherever the radar is. RadDB\n",
-    "runs it in the archive's own CRS, so it is just as correct for a US or Finnish\n",
-    "radar as for a Swiss one."
+    "### Reading `cs_polygon`\n",
+    "\n",
+    "Inside the polars frame `cs_polygon` is a `Binary` column — the polygon\n",
+    "**WKB-encoded**, because polars has no geometry dtype. That is why printing\n",
+    "`.data` shows bytes rather than coordinates.\n",
+    "\n",
+    "It is decoded back to a real shapely `Polygon` the moment you leave polars:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "3dde431a",
+   "execution_count": null,
+   "id": "11b6653e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:18.511870Z",
-     "iopub.status.busy": "2026-08-04T15:32:18.511728Z",
-     "iopub.status.idle": "2026-08-04T15:32:18.515866Z",
-     "shell.execute_reply": "2026-08-04T15:32:18.515122Z"
+     "iopub.execute_input": "2026-08-11T16:22:04.195539Z",
+     "iopub.status.busy": "2026-08-11T16:22:04.195332Z",
+     "iopub.status.idle": "2026-08-11T16:22:04.209304Z",
+     "shell.execute_reply": "2026-08-11T16:22:04.208668Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "true geodesic  :    121,167 m\n",
-      "section length :    121,183 m   (+0.014 %)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from pyproj import Geod\n",
-    "\n",
-    "p1 = (SITE[0] - 0.6, SITE[1] - 0.35)\n",
-    "p2 = (SITE[0] + 0.6, SITE[1] + 0.35)\n",
-    "truth = Geod(ellps=\"WGS84\").inv(p1[0], p1[1], p2[0], p2[1])[2]\n",
-    "got = float(cs.data[\"d_far\"].max())\n",
-    "print(f\"true geodesic  : {truth:10,.0f} m\")\n",
-    "print(f\"section length : {got:10,.0f} m   ({100 * (got - truth) / truth:+.3f} %)\")"
+    "print(\"in polars   :\", cs.data.schema[\"cs_polygon\"])\n",
+    "print(\"raw value   :\", str(cs.data[\"cs_polygon\"][0])[:40], \"...\")\n",
+    "\n",
+    "# to_pandas() decodes it; to_geopandas() does too\n",
+    "poly = cs.to_pandas()[\"cs_polygon\"].iloc[0]\n",
+    "print(\"\\nafter to_pandas:\", type(poly).__name__)\n",
+    "print(\"  WKT     :\", poly.wkt[:90], \"...\")\n",
+    "print(\"  corners :\", list(poly.exterior.coords)[:2], \"...\")\n",
+    "print(\"  bounds  :\", tuple(round(v) for v in poly.bounds), \"(d_min, z_min, d_max, z_max)\")"
    ]
   },
   {
@@ -503,28 +416,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "e728b199",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:18.517836Z",
-     "iopub.status.busy": "2026-08-04T15:32:18.517683Z",
-     "iopub.status.idle": "2026-08-04T15:32:20.442637Z",
-     "shell.execute_reply": "2026-08-04T15:32:20.441811Z"
+     "iopub.execute_input": "2026-08-11T16:22:04.211180Z",
+     "iopub.status.busy": "2026-08-11T16:22:04.211052Z",
+     "iopub.status.idle": "2026-08-11T16:22:05.468856Z",
+     "shell.execute_reply": "2026-08-11T16:22:05.468394Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 630x630 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "_ = rdf.crop_around_point(point=SITE, distance=50_000, crs=4326, quicklook=True)\n",
     "plt.show()"
@@ -532,28 +434,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "53c5a037",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:20.444763Z",
-     "iopub.status.busy": "2026-08-04T15:32:20.444512Z",
-     "iopub.status.idle": "2026-08-04T15:32:21.609813Z",
-     "shell.execute_reply": "2026-08-04T15:32:21.608899Z"
+     "iopub.execute_input": "2026-08-11T16:22:05.471154Z",
+     "iopub.status.busy": "2026-08-11T16:22:05.470696Z",
+     "iopub.status.idle": "2026-08-11T16:22:06.109670Z",
+     "shell.execute_reply": "2026-08-11T16:22:06.108745Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 630x630 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "_ = rdf.crop_by_polygone(polygon=triangle, crs=4326, quicklook=True)\n",
     "plt.show()"
@@ -572,29 +463,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "5424cde4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:21.611663Z",
-     "iopub.status.busy": "2026-08-04T15:32:21.611524Z",
-     "iopub.status.idle": "2026-08-04T15:32:23.258713Z",
-     "shell.execute_reply": "2026-08-04T15:32:23.257964Z"
+     "iopub.execute_input": "2026-08-11T16:22:06.111576Z",
+     "iopub.status.busy": "2026-08-11T16:22:06.111440Z",
+     "iopub.status.idle": "2026-08-11T16:22:07.232317Z",
+     "shell.execute_reply": "2026-08-11T16:22:07.231845Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "116,865 gates: strong echo, within 60 km, beyond 5 km range\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "storm = (\n",
     "    db.open(radars=\"L\")\n",
-    "      .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 25})\n",
+    "      .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 35})\n",
     "      .crop_around_point(point=SITE, distance=60_000, crs=4326)\n",
     "      .sel(range=slice(5_000, 60_000))\n",
     ")\n",
@@ -609,8 +492,8 @@
     "## 7. The interactive tool\n",
     "\n",
     "In Jupyter, `interactive_crop()` puts an [ipyleaflet](https://ipyleaflet.readthedocs.io/)\n",
-    "map in front of you: draw a **rectangle**, **polygon**, **marker** or **polyline**,\n",
-    "click *Apply crop*, and the result appears on `selector.result`.\n",
+    "map in front of you: draw a shape with the toolbar, click **Apply crop**, and the\n",
+    "crop runs for you.\n",
     "\n",
     "The tool dispatches on what you drew:\n",
     "\n",
@@ -618,53 +501,71 @@
     "|---|---|\n",
     "| rectangle | `crop_by_bbox` |\n",
     "| polygon | `crop_by_polygone` |\n",
-    "| marker | `crop_around_point` |\n",
+    "| marker | `crop_around_point`, using the **radius** box under the map |\n",
     "| **polyline** | `extract_cross_section` |\n",
     "\n",
-    "It needs a live kernel, so the cell below is switched off by default — set\n",
-    "`RUN_INTERACTIVE = True` and re-run it yourself."
+    "It returns a **selector object**, and the crop lands on its attributes:\n",
+    "\n",
+    "| attribute | what it holds |\n",
+    "|---|---|\n",
+    "| `selector.result` | the cropped **RadDB** — the same object any `crop_*` returns, so it filters, plots and converts like the rest of this notebook |\n",
+    "| `selector.kind` | which crop ran: `'bbox'`, `'polygon'`, `'point'` or `'cross_section'` |\n",
+    "| `selector.feature` | the shape you drew, as GeoJSON in lon/lat |\n",
+    "\n",
+    "`selector.result` stays `None` until you press *Apply crop*, which is why drawing\n",
+    "and reading the result are split across the next two cells."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "6fe0db90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:23.260949Z",
-     "iopub.status.busy": "2026-08-04T15:32:23.260798Z",
-     "iopub.status.idle": "2026-08-04T15:32:23.264131Z",
-     "shell.execute_reply": "2026-08-04T15:32:23.263464Z"
+     "iopub.execute_input": "2026-08-11T16:22:07.234114Z",
+     "iopub.status.busy": "2026-08-11T16:22:07.233966Z",
+     "iopub.status.idle": "2026-08-11T16:22:07.403456Z",
+     "shell.execute_reply": "2026-08-11T16:22:07.402877Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "interactive_crop() needs a live Jupyter kernel — set RUN_INTERACTIVE = True\n",
-      "\n",
-      "usage:\n",
-      "    selector = rdf.interactive_crop()\n",
-      "    cropped  = selector.result   # after drawing + Apply crop\n",
-      "    selector.kind               # 'bbox' | 'polygon' | 'point' | 'cross_section'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "RUN_INTERACTIVE = False        # <- set True in your own Jupyter session\n",
+    "RUN_INTERACTIVE = True        # <- set False to skip the map (e.g. outside Jupyter)\n",
     "\n",
     "if RUN_INTERACTIVE:\n",
+    "    # `selector` is the widget handle — the map appears immediately below.\n",
+    "    # Draw a shape with the toolbar, then click \"Apply crop\" before running the\n",
+    "    # next cell.  For a marker, set the radius box first; it is read on click.\n",
     "    selector = rdf.interactive_crop()\n",
-    "    # ... draw a shape, click \"Apply crop\", then:\n",
-    "    # cropped = selector.result\n",
-    "    # print(selector.kind, len(cropped))\n",
     "else:\n",
-    "    print(\"interactive_crop() needs a live Jupyter kernel — set RUN_INTERACTIVE = True\")\n",
-    "    print(\"\\nusage:\")\n",
-    "    print(\"    selector = rdf.interactive_crop()\")\n",
-    "    print(\"    cropped  = selector.result   # after drawing + Apply crop\")\n",
-    "    print(\"    selector.kind               # 'bbox' | 'polygon' | 'point' | 'cross_section'\")"
+    "    selector = None\n",
+    "    print(\"interactive_crop() needs a live Jupyter kernel ==> set RUN_INTERACTIVE = True\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03e9919f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-11T16:22:07.407717Z",
+     "iopub.status.busy": "2026-08-11T16:22:07.407493Z",
+     "iopub.status.idle": "2026-08-11T16:22:07.410728Z",
+     "shell.execute_reply": "2026-08-11T16:22:07.410135Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Run this cell *after* drawing a shape above and clicking \"Apply crop\".\n",
+    "# `cropped` is an ordinary RadDB: filter it, crop it again, plot it or convert it,\n",
+    "# exactly as in the sections above.  Until Apply crop is pressed it is still None.\n",
+    "if RUN_INTERACTIVE and selector is not None and selector.result is not None:\n",
+    "    cropped = selector.result                      # <- the cropped RadDB\n",
+    "    print(f\"{selector.kind} -> {len(cropped):,} gates\")\n",
+    "    print(\"drawn shape :\", selector.feature[\"geometry\"][\"type\"])\n",
+    "    display(cropped.head())\n",
+    "else:\n",
+    "    print(\"nothing applied yet — draw a shape above, click 'Apply crop', then re-run this cell\")"
    ]
   },
   {
@@ -673,28 +574,13 @@
    "metadata": {},
    "source": [
     "---\n",
-    "## Recap\n",
-    "\n",
-    "```python\n",
-    "rdf.crop_by_bbox(bounds=(minx, miny, maxx, maxy), crs=4326)\n",
-    "rdf.crop_around_point(point=(lon, lat), distance=50_000, crs=4326)\n",
-    "rdf.crop_by_polygone(polygon=shape_or_path, crs=4326)\n",
-    "rdf.extract_cross_section(p1=(lon, lat), p2=(lon, lat), crs=4326)\n",
-    "\n",
-    "rdf.crop_*(..., quicklook=True)     # sanity-check the AOI on a map\n",
-    "rdf.interactive_crop()              # draw it instead (Jupyter)\n",
-    "```\n",
-    "\n",
-    "`crs=` describes **your** coordinates. The AOI itself always runs in the archive's\n",
-    "projection, so distances are true metres on the ground.\n",
-    "\n",
     "**Next:** [4 — Plots](04_plots.ipynb)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "radar",
    "language": "python",
    "name": "python3"
   },
@@ -709,8 +595,1155 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.15"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
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+      "state": {
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+       "_model_module_version": "^0.20",
+       "_model_name": "LeafletMarkerModel",
+       "_view_count": null,
+       "_view_module": "jupyter-leaflet",
+       "_view_module_version": "^0.20",
+       "_view_name": "LeafletMarkerView",
+       "alt": "",
+       "base": false,
+       "bottom": false,
+       "draggable": false,
+       "icon": "IPY_MODEL_52cc4878cc874407a0cac1217ac1b513",
+       "keyboard": true,
+       "location": [
+        46.0407600402832,
+        8.833216667175293
+       ],
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+       "opacity": 1,
+       "options": [
+        "alt",
+        "draggable",
+        "keyboard",
+        "pm_ignore",
+        "rise_offset",
+        "rise_on_hover",
+        "rotation_angle",
+        "rotation_origin",
+        "title",
+        "z_index_offset"
+       ],
+       "pane": "",
+       "pm_ignore": true,
+       "popup": null,
+       "popup_max_height": null,
+       "popup_max_width": 300,
+       "popup_min_width": 50,
+       "rise_offset": 250,
+       "rise_on_hover": false,
+       "rotation_angle": 0,
+       "rotation_origin": "",
+       "snap_ignore": true,
+       "subitems": [],
+       "title": "radar L",
+       "visible": true,
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+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
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+      "model_name": "HTMLModel",
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+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_f60d7d72e93b422f9b02b455656ed1da",
+       "placeholder": "​",
+       "style": "IPY_MODEL_4fbd1d7a900443f2a3d5ef6a4daec0c8",
+       "tabbable": null,
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+       "value": "<b>Draw an AOI</b> with the toolbar (top-left): ▭ rectangle → <code>crop_by_bbox</code>, ⬠ polygon → <code>crop_by_polygone</code>, 📍 marker → <code>crop_around_point</code> (uses the radius box below), ／ line → <code>extract_cross_section</code>. Then <b>Apply crop</b>."
+      }
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+      "model_name": "DescriptionStyleModel",
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+       "_model_module_version": "2.0.0",
+       "_model_name": "DescriptionStyleModel",
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-}
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+}
diff --git a/tutorial/04_plots.ipynb b/tutorial/04_plots.ipynb
index 7fc27f9..a37d35b 100644
--- a/tutorial/04_plots.ipynb
+++ b/tutorial/04_plots.ipynb
@@ -5,7 +5,7 @@
    "id": "f19ce8cd",
    "metadata": {},
    "source": [
-    "# 4 — Plots\n",
+    "# 4. Plots\n",
     "\n",
     "RadDB draws four things. Each one **draws a single plot into a single Axes** and\n",
     "returns the matplotlib artist, so you compose panels yourself by passing `ax=`.\n",
@@ -24,6 +24,24 @@
     "---"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ce6a922",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import raddb\n",
+    "\n",
+    "# Keep the embedded figures small enough for GitHub to render this notebook.\n",
+    "#plt.rcParams[\"figure.dpi\"] = 70"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -37,35 +55,25 @@
     }
    },
    "outputs": [],
-   "source": "from pathlib import Path\n\n# --------------------------------------------------------------------------\n# CONFIGURATION — edit these three paths to point at your own data\n# --------------------------------------------------------------------------\n# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n# the cell below builds it.\n\nMCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\nNEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\nARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n\nprint(\"MCH DataTrees   :\", MCH_DIR)\nprint(\"NEXRAD DataTrees:\", NEXRAD_DIR)\nprint(\"Archive         :\", ARCHIVE_DIR)"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c6922eeb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:24.398196Z",
-     "iopub.status.busy": "2026-08-04T15:32:24.397932Z",
-     "iopub.status.idle": "2026-08-04T15:32:25.280501Z",
-     "shell.execute_reply": "2026-08-04T15:32:25.279407Z"
-    }
-   },
-   "outputs": [],
    "source": [
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — edit these three paths to point at your own data\n",
+    "# --------------------------------------------------------------------------\n",
+    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n",
+    "# the cell below builds it.\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import raddb\n",
+    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
+    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
+    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
-    "# Keep the embedded figures small enough for GitHub to render this notebook.\n",
-    "plt.rcParams[\"figure.dpi\"] = 70"
+    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
+    "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "604be0fb",
    "metadata": {
     "execution": {
@@ -75,27 +83,19 @@
      "shell.execute_reply": "2026-08-04T15:32:25.286011Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "archive already present: /tmp/raddb_tutorial_archive\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
     "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
     "    print(\"building the archive (see tutorial 1) ...\")\n",
     "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
     "else:\n",
-    "    print(\"archive already present:\", ARCHIVE_DIR)\n"
+    "    print(\"archive already present:\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "bd9e7bfc",
    "metadata": {
     "execution": {
@@ -105,15 +105,7 @@
      "shell.execute_reply": "2026-08-04T15:32:25.531417Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "347,449 gates | variables: ['gate_id', 'time', 'DBZH', 'DBZH_raw', 'ZDR', 'ZDR_raw', 'KDP', 'RHOHV', 'PHIDP', 'HC_MCH', 'HC_PYART', 'HZT', 'TEMP', 'volume_time', 'radar']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "rdf = db.open(radars=\"L\")\n",
@@ -127,12 +119,12 @@
    "id": "a12e1078",
    "metadata": {},
    "source": [
-    "## 1. PPI — a sweep from above"
+    "## 1. PPI"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "a7e6ba05",
    "metadata": {
     "execution": {
@@ -142,46 +134,10 @@
      "shell.execute_reply": "2026-08-04T15:32:27.401899Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
-      "## library for working with weather radar data. Py-ART is partly\n",
-      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
-      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
-      "## Science user facility.\n",
-      "##\n",
-      "## If you use this software to prepare a publication, please cite:\n",
-      "##\n",
-      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PolyCollection with 11,629 gate polygons\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 434x378 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
-    "art = rdf.plot_ppi(sweep=1, variable=\"DBZH\", ax=ax)\n",
-    "print(type(art).__name__, \"with\", f\"{len(art.get_paths()):,}\", \"gate polygons\")\n",
+    "rdf.plot_ppi(sweep=4, variable=\"DBZH\", timestep=\"2024-06-08\", ax=ax)\n",
     "plt.show()"
    ]
   },
@@ -198,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "59a203af",
    "metadata": {
     "execution": {
@@ -208,21 +164,10 @@
      "shell.execute_reply": "2026-08-04T15:32:28.697345Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 504x315 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(figsize=(7.2, 4.5))\n",
-    "rdf.plot_rhi(azimuth=90, variable=\"DBZH\", ax=ax)\n",
+    "fig, ax = plt.subplots()\n",
+    "rdf.plot_rhi(azimuth=90, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
     "plt.show()"
    ]
   },
@@ -240,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "5236b05f",
    "metadata": {
     "execution": {
@@ -250,22 +195,10 @@
      "shell.execute_reply": "2026-08-04T15:32:32.765381Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x308 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.4))\n",
-    "for ax, alt in zip(axes, (2000, 4000, 6000)):\n",
-    "    rdf.plot_cappi(altitude=alt, variable=\"DBZH\", ax=ax)\n",
+    "fig, ax = plt.subplots()\n",
+    "rdf.plot_cappi(altitude=2000, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -283,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "f8f6ca75",
    "metadata": {
     "execution": {
@@ -293,25 +226,14 @@
      "shell.execute_reply": "2026-08-04T15:32:35.437269Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 560x315 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cs = rdf.extract_cross_section(p1=(SITE[0] - 0.6, SITE[1] - 0.35),\n",
     "                               p2=(SITE[0] + 0.6, SITE[1] + 0.35),\n",
     "                               crs=4326)\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
-    "cs.plot_vcs(variable=\"DBZH\", ax=ax)\n",
+    "cs.plot_vcs(variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
     "plt.show()"
    ]
   },
@@ -351,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "a3940981",
    "metadata": {
     "execution": {
@@ -361,18 +283,7 @@
      "shell.execute_reply": "2026-08-04T15:32:37.531138Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x294 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
     "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"]):\n",
@@ -393,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "5090d25e",
    "metadata": {
     "execution": {
@@ -403,18 +314,7 @@
      "shell.execute_reply": "2026-08-04T15:32:38.205118Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 448x378 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(6.4, 5.4))\n",
     "try:\n",
@@ -437,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "e2d81ff0",
    "metadata": {
     "execution": {
@@ -447,18 +347,7 @@
      "shell.execute_reply": "2026-08-04T15:32:40.861979Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1204x280 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "moments = [v for v in (\"DBZH\", \"ZDR\", \"RHOHV\", \"PHIDP\") if v in rdf.columns()]\n",
     "\n",
@@ -472,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "45584473",
    "metadata": {
     "execution": {
@@ -482,18 +371,7 @@
      "shell.execute_reply": "2026-08-04T15:32:43.097810Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 434x378 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Filter and crop first — the plot follows the data, gate for gate\n",
     "sub = (rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
@@ -505,6 +383,14 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8272486c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "52fa26d5",
@@ -517,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "ada2890f",
    "metadata": {
     "execution": {
@@ -527,18 +413,7 @@
      "shell.execute_reply": "2026-08-04T15:32:47.047068Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 805x630 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axes = plt.subplots(2, 2, figsize=(11.5, 9))\n",
     "rdf.plot_ppi(sweep=1, ax=axes[0][0])\n",
@@ -566,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "e0ad81a1",
    "metadata": {
     "execution": {
@@ -576,18 +451,7 @@
      "shell.execute_reply": "2026-08-04T15:32:47.745866Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 434x378 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dt = raddb.open_any_datatree(sorted(MCH_DIR.glob(\"L_*.zarr\"))[0])\n",
     "\n",
@@ -611,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "7505115e",
    "metadata": {
     "execution": {
@@ -621,15 +485,7 @@
      "shell.execute_reply": "2026-08-04T15:32:48.468256Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved: /tmp/raddb_tutorial_archive/ppi_example.png (70 kB)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "out = ARCHIVE_DIR / \"ppi_example.png\"\n",
     "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
@@ -673,7 +529,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "radar",
    "language": "python",
    "name": "python3"
   },
@@ -692,4 +548,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}

From fc69056a123ba892463b85750d438cf8933cb8a7 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Mon, 17 Aug 2026 09:29:24 +0200
Subject: [PATCH 05/14] Remove basic usage example script from tutorial

---
 tutorial/basic_usage.py | 157 ----------------------------------------
 1 file changed, 157 deletions(-)
 delete mode 100644 tutorial/basic_usage.py

diff --git a/tutorial/basic_usage.py b/tutorial/basic_usage.py
deleted file mode 100644
index c2e1ab5..0000000
--- a/tutorial/basic_usage.py
+++ /dev/null
@@ -1,157 +0,0 @@
-"""
-Basic Usage Example for RadDB
-==============================
-
-This example demonstrates the essential public workflow for archiving
-radar data starting from **DataTree files on disk** (NetCDF / Zarr):
-
-1. Build (or obtain) DataTree volumes and save them to disk
-2. Discover the DataTree files with ``find_datatree_files``
-3. Archive them with ``RadDB.archive(datatree_dir=...)``
-   (the radar LUT is generated automatically from the first volume)
-4. ``RadDB.open(...)`` returns a data-carrying ``RadDB`` (``rdf``) with a
-   polars backend; filter / convert / plot from it
-
-RadDB is a **generic** library — it works with any xarray DataTree with
-the standard xradar coordinate layout.  No pyart / radar_api needed.
-If you already hold DataTrees in memory, skip the disk round-trip and
-call ``db.archive(datatree=dt, radar=...)`` directly.
-"""
-#%%
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import xarray as xr
-
-import raddb
-
-# =============================================================================
-# CONFIGURATION
-# =============================================================================
-
-WORK_DIR = Path("./raddb_tutorial")          # scratch dir for this demo
-INPUT_DIR = WORK_DIR / "datatrees"           # where the DataTree files live
-BASE_PATH = WORK_DIR / "archive"             # RadDB output (LUT + parquet)
-RADAR_NAME = "A"                             # single letter A-Z
-
-INPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-# =============================================================================
-# STEP 1: Create sample DataTree volumes and save them to disk
-# =============================================================================
-# In a real workflow these files come from your own converter (any radar
-# format -> xradar-style DataTree, saved as NetCDF or Zarr).
-
-
-def make_volume(vol_time: pd.Timestamp, n_az: int = 90, n_rng: int = 120,
-                n_sweeps: int = 3) -> xr.DataTree:
-    """Synthetic multi-sweep volume with the standard xradar layout."""
-    az = np.linspace(0, 360 - 360 / n_az, n_az)
-    rng_vals = np.linspace(500, 60_000, n_rng)
-    time_vals = np.array([vol_time] * n_az, dtype="datetime64[ns]")
-
-    dict_ds = {}
-    for sweep_idx in range(1, n_sweeps + 1):
-        dbzh = np.random.uniform(-5, 45, (n_az, n_rng)).astype(np.float32)
-        ds = xr.Dataset(
-            {
-                "DBZH":  (["azimuth", "range"], dbzh),
-                "ZDR":   (["azimuth", "range"], np.random.uniform(-1, 3, (n_az, n_rng)).astype(np.float32)),
-                "RHOHV": (["azimuth", "range"], np.full((n_az, n_rng), 0.97, np.float32)),
-                "PHIDP": (["azimuth", "range"], np.zeros((n_az, n_rng), np.float32)),
-                "time":  (["azimuth"], time_vals),
-            },
-            coords={
-                "azimuth":         az,
-                "range":           rng_vals,
-                "elevation":       (["azimuth"], np.full(n_az, 0.5 * sweep_idx)),
-                "elevation_angle": 0.5 * sweep_idx,
-                "latitude":        46.04,
-                "longitude":       8.83,
-                "altitude":        1626.0,
-            },
-        )
-        ds.attrs["sweep_number"] = sweep_idx
-        dict_ds[f"sweep_{sweep_idx}"] = ds
-    return xr.DataTree.from_dict(dict_ds)
-
-
-print("Writing sample DataTree volumes to disk...")
-for minute in (0, 5, 10):
-    vol_time = pd.Timestamp(f"2024-07-15 12:{minute:02d}:00")
-    dt = make_volume(vol_time)
-    fname = INPUT_DIR / f"{RADAR_NAME}_{vol_time:%Y%m%d_%H%M%S}.nc"
-    dt.to_netcdf(fname)
-    print(f"  {fname}")
-    # Zarr works exactly the same way:
-    # dt.to_zarr(INPUT_DIR / f"{RADAR_NAME}_{vol_time:%Y%m%d_%H%M%S}.zarr")
-
-#%%
-# =============================================================================
-# STEP 2: Discover DataTree files on disk
-# =============================================================================
-
-files = raddb.find_datatree_files(
-    INPUT_DIR,
-    recursive=True,
-    start_time="2024-07-15 12:00",
-    end_time="2024-07-15 12:59",
-)
-print(f"\nDiscovered {len(files)} DataTree file(s):")
-for f in files:
-    print(f"  {f}")
-
-# Load a single file manually if you want to inspect it first:
-dt_preview = raddb.open_any_datatree(files[0])
-print(f"\nPreview of {files[0].name}: {raddb.list_sweep_names(dt_preview)}")
-
-#%%
-# =============================================================================
-# STEP 3: Archive end to end (LUT generated automatically)
-# =============================================================================
-# Gates failing the filter (default DBZH > 0) are dropped to keep the
-# archive small.  A checkpoint file makes interrupted runs resumable.
-
-db = raddb.RadDB(archive_dir=str(BASE_PATH), crs=2056)
-
-result = db.archive(
-    datatree_dir=INPUT_DIR,     # directory of saved .nc / .zarr volumes
-    radar=RADAR_NAME,
-    filter={"var": "DBZH", "logic": ">", "threshold": 0.0},
-)
-print(f"\nArchived: {result['n_archived']} volume(s), failed: {result['n_failed']}")
-
-# Already have DataTrees in memory?  Archive them directly instead:
-#   db.archive(datatree=dt, radar=RADAR_NAME)
-#   db.archive(datatree=[dt1, dt2], radar=RADAR_NAME)
-
-#%%
-# =============================================================================
-# STEP 4: Load the archive back
-# =============================================================================
-
-# open() returns a data-carrying RadDB (``rdf``) backed by polars
-rdf = db.open(
-    radars=RADAR_NAME,
-    time_period=("2024-07-15 00:00", "2024-07-15 23:59"),
-)
-print(rdf)                                       # rich multi-line summary
-print(f"gates: {len(rdf):,} | radars: {rdf.radars()} | columns: {rdf.columns()}")
-print(f"extent (LV95): {rdf.extent()}")
-
-# subset / convert as needed (each returns a new RadDB or a frame)
-strong = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
-pdf = rdf.to_pandas(with_geometry=True)          # pandas + gate coordinates
-# gdf = rdf.to_geopandas()                        # GeoDataFrame with a CRS
-print(f"strong-echo gates: {len(strong):,}")
-
-#%%
-# =============================================================================
-# STEP 5: Plot
-# =============================================================================
-
-import matplotlib.pyplot as plt
-
-rdf.plot_ppi(sweep=1, variable="DBZH", coords="cartesian")
-plt.show()

From c84a12fc230d532d64bbd918fff2726fba0a6450 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Mon, 17 Aug 2026 09:29:41 +0200
Subject: [PATCH 06/14] Refactor code structure for improved readability and
 maintainability

---
 raddb/__init__.py                       |   13 -
 raddb/helper.py                         |    1 -
 raddb/io_core.py                        |   90 +-
 raddb/lut.py                            |  135 +-
 raddb/main.py                           |   74 +-
 raddb/tests/test_azimuth_grid.py        |   77 ++
 raddb/tests/test_fixes.py               |  174 +++
 raddb/tests/test_radar_code.py          |   54 +-
 raddb/tests/test_sel.py                 |   15 +
 raddb/tools/__init__.py                 |    5 -
 raddb/tools/migrate_gate_id_v2.py       |  164 ---
 raddb/viz/__init__.py                   |    9 +-
 raddb/viz/plot.py                       |  119 +-
 raddb/viz/profiling.py                  |  290 -----
 raddb/viz/report_hc_reference_figure.py |  174 ---
 raddb/viz/report_raddb_figures.py       |  224 ----
 tutorial/04_plots.ipynb                 |  323 +++--
 tutorial/05_demo_pipeline.ipynb         | 1589 +++++++++++++++++++++++
 tutorial/README.md                      |   11 +-
 19 files changed, 2418 insertions(+), 1123 deletions(-)
 delete mode 100644 raddb/tools/__init__.py
 delete mode 100644 raddb/tools/migrate_gate_id_v2.py
 delete mode 100644 raddb/viz/profiling.py
 delete mode 100644 raddb/viz/report_hc_reference_figure.py
 delete mode 100644 raddb/viz/report_raddb_figures.py
 create mode 100644 tutorial/05_demo_pipeline.ipynb

diff --git a/raddb/__init__.py b/raddb/__init__.py
index 68d869a..943d1d1 100644
--- a/raddb/__init__.py
+++ b/raddb/__init__.py
@@ -107,14 +107,6 @@
     plot_latent_scatter,
 )
 
-# Profiling helpers
-from raddb.viz.profiling import (
-    plot_stage_totals,
-    plot_volume_timing,
-    plot_sweep_timing,
-    plot_profiling_dashboard,
-)
-
 __all__ = [
     # High-level API
     "RadDB",
@@ -191,11 +183,6 @@
     "plot_vcs",
     "plot_cross_section",
     "plot_latent_scatter",
-    # Profiling helpers
-    "plot_stage_totals",
-    "plot_volume_timing",
-    "plot_sweep_timing",
-    "plot_profiling_dashboard",
 ]
 
 _root_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
diff --git a/raddb/helper.py b/raddb/helper.py
index 607e380..63b26db 100644
--- a/raddb/helper.py
+++ b/raddb/helper.py
@@ -313,7 +313,6 @@ class StageTimer:
     >>> with timer.time_stage("my_stage", volume="vol_001", sweep=2):
     ...     do_work()
     >>> timer.print_summary()
-    >>> plot_profiling_dashboard(timer)
     """
 
     def __init__(self):
diff --git a/raddb/io_core.py b/raddb/io_core.py
index 1b98203..18aac39 100644
--- a/raddb/io_core.py
+++ b/raddb/io_core.py
@@ -166,7 +166,15 @@ def datatree_to_dataframe(
     names = list_sweep_names(dt)
 
     def _flatten(name):
-        df = dt[name].to_dataset().to_dataframe().reset_index()
+        ds = dt[name].to_dataset()
+        # A dimension coordinate can exist without an index — raw NEXRAD Level II
+        # sweeps arrive that way for ``range``.  ``to_dataframe`` then indexes that
+        # dimension by position and emits the real values as a *column* of the same
+        # name, which ``reset_index`` cannot insert.  Re-assigning rebuilds the index.
+        unindexed = [d for d in ds.sizes if d in ds.coords and d not in ds.xindexes]
+        if unindexed:
+            ds = ds.assign_coords({d: ds[d].values for d in unindexed})
+        df = ds.to_dataframe().reset_index()
         df["sweep"] = int(name.split("_")[-1])
         return df
 
@@ -181,13 +189,35 @@ def _flatten(name):
 
 def _save_polar_parquet(
     df_polar: "pl.DataFrame | pd.DataFrame", radar: str, base_path: str
-) -> str:
+) -> str | None:
     """Save a POLAR DataFrame to the standard directory layout.
 
     Accepts polars (the native write-path format) or pandas.
+
+    Returns ``None`` — writing nothing — when the volume carries no usable
+    timestamp to build a path from.  That happens for a clear-air volume whose
+    every gate fails the filter (``DBZH`` all-null), and for one whose ``time``
+    is all-``NaT``.  Both used to crash here rather than being skipped:
+    ``.min()`` on an empty column is ``None``, ``pd.to_datetime(None)`` is
+    ``NaT``, and ``pd.NaT.month`` is *nan* (a float), so the ``:02d`` below
+    raised ``Unknown format code 'd' for object of type 'float'`` — an error
+    naming neither the volume nor the cause.
     """
     df_polar = _to_polars_frame(df_polar)
+    if df_polar.is_empty():
+        logger.info(
+            "radar %s: no gates satisfied the filter; no POL file written.", radar
+        )
+        return None
+
     vol_time = pd.to_datetime(df_polar["time"].min())
+    if pd.isna(vol_time):
+        logger.warning(
+            "radar %s: volume time is NaT for all %d surviving gates; "
+            "no POL file written.", radar, len(df_polar),
+        )
+        return None
+
     save_dir = (
         Path(base_path)
         / radar
@@ -273,7 +303,11 @@ def _snap_volume_azimuths(sweeps, azimuths, grids, radar):
             )
         sel = sweeps == sweep
         n_rays = np.unique(out[sel]).size
-        if n_rays != len(grid):
+        # Fewer rays than the grid is a rotation with holes — a volume that
+        # dropped a ray or two, which every network does — and each surviving ray
+        # still snaps to its own grid point, so it archives correctly.  More rays
+        # than the grid cannot: they have nowhere to go.
+        if n_rays > len(grid):
             raise ValueError(
                 f"radar {radar!r} sweep {int(sweep)}: volume has {n_rays} rays, "
                 f"the LUT was built for {len(grid)} — a different scan strategy. "
@@ -380,7 +414,7 @@ def archive_volume(
     filter_logic: str = ">",
     timer=None,
     volume: str | None = None,
-) -> str:
+) -> str | None:
     """Archive a single DataTree volume to Parquet format.
 
     Converts the DataTree to a DataFrame, generates a ``gate_id`` for each
@@ -417,8 +451,11 @@ def archive_volume(
 
     Returns
     -------
-    str
-        Path to the saved POL parquet file.
+    str or None
+        Path to the saved POL parquet file, or ``None`` when the volume held
+        nothing to archive — every gate failed the filter, or its ``time`` was
+        all-``NaT``.  That is a *skip*, not a failure: callers should count it
+        separately rather than treating it as either stored or broken.
     """
     radar = normalize_radar_name(radar)
     resolve_filter_logic(filter_logic)  # fail fast before flattening
@@ -484,7 +521,10 @@ def archive_multiple_volumes(
     Returns
     -------
     list of dict
-        Results with keys: label, success, error, polar_path, n_gates.
+        Results with keys: label, success, skipped, error, polar_path, n_gates.
+        ``skipped`` marks a volume that held nothing to archive (every gate
+        failed the filter, or an all-``NaT`` time); it has ``success=False``
+        and ``error=None``, so the three states stay distinguishable.
     """
     radar = normalize_radar_name(radar)
 
@@ -503,6 +543,7 @@ def archive_multiple_volumes(
             "label": label,
             "radar": radar,
             "success": False,
+            "skipped": False,
             "error": None,
             "n_gates": 0,
         }
@@ -525,18 +566,29 @@ def archive_multiple_volumes(
                     volume=label,
                 )
 
-            result["success"] = True
-            result["polar_path"] = polar_path
+            vol_elapsed = _time.perf_counter() - vol_t0
+            if polar_path is None:
+                # Nothing to archive — not an error, so it must not be counted
+                # as one; see archive_volume's return contract.
+                result["skipped"] = True
+                result["polar_path"] = None
+                _vprint(
+                    f"SKIP  Volume {i}/{len(items)} held no gates to archive "
+                    f"({vol_elapsed:.1f}s)",
+                    verbose,
+                )
+            else:
+                result["success"] = True
+                result["polar_path"] = polar_path
 
-            df_polar = pd.read_parquet(polar_path)
-            result["n_gates"] = len(df_polar)
+                df_polar = pd.read_parquet(polar_path)
+                result["n_gates"] = len(df_polar)
 
-            vol_elapsed = _time.perf_counter() - vol_t0
-            _vprint(
-                f"OK  Volume {i}/{len(items)} done in "
-                f"{vol_elapsed:.1f}s -- {result['n_gates']:,} gates saved",
-                verbose,
-            )
+                _vprint(
+                    f"OK  Volume {i}/{len(items)} done in "
+                    f"{vol_elapsed:.1f}s -- {result['n_gates']:,} gates saved",
+                    verbose,
+                )
 
         except Exception as e:
             vol_elapsed = _time.perf_counter() - vol_t0
@@ -552,9 +604,11 @@ def archive_multiple_volumes(
 
     total_elapsed = _time.perf_counter() - pipeline_t0
     n_ok = sum(1 for r in results if r["success"])
+    n_skip = sum(1 for r in results if r["skipped"])
     _vprint(
         f"\nArchiving complete: {n_ok}/{len(results)} volumes "
-        f"in {total_elapsed:.1f}s",
+        f"in {total_elapsed:.1f}s"
+        + (f" ({n_skip} skipped, nothing to archive)" if n_skip else ""),
         verbose,
     )
     return results
diff --git a/raddb/lut.py b/raddb/lut.py
index 2dc81e0..99fabec 100644
--- a/raddb/lut.py
+++ b/raddb/lut.py
@@ -51,13 +51,15 @@
 #: 2. radar code = base-36 of the zero-padded 4-character name (``"A"`` ->
 #:    ``"000A"`` -> 10, ``"KTLX"`` -> 971493) — 1,679,616 radars.
 #:
-#: The two disagree for every name (``"L"`` is 11 under v1, 21 under v2), so a
-#: v1 archive must be migrated before it is read; see
-#: ``raddb/tools/migrate_gate_id_v2.py``.
+#: The two disagree for every name (``"L"`` is 11 under v1, 21 under v2).  Only
+#: v2 is ever written or read, and nothing detects v1 — a v1 archive loads
+#: silently and decodes to the wrong radar.  The migration script that used to
+#: convert one has been removed, so re-archive from the source volumes instead.
 GATE_ID_VERSION: int = 2
 
-#: The v1 radar index, kept only so the migration script can compute the offset
-#: between an archived ``gate_id`` and its v2 replacement.
+#: The v1 radar index, kept only so the offset between a v1 ``gate_id`` and its
+#: v2 replacement stays derivable (and testable) after the migration script was
+#: removed.
 LEGACY_RADAR_TO_IDX: dict[str, int] = {chr(ord("A") + i): i for i in range(26)}
 
 
@@ -358,6 +360,11 @@ def compute_gate_xyz(
 AZIMUTH_SCALE: int = 10
 AZIMUTH_STEPS: int = 360 * AZIMUTH_SCALE
 
+#: Smallest share of a rotation's grid points that must actually carry a ray.
+#: Real volumes drop the odd ray (718 of 720 on WSR-88D, 358 of 360), which is a
+#: full rotation with holes; a sector scan is not, and must still be refused.
+MIN_ROTATION_COVERAGE: float = 0.95
+
 
 def _round_half_up(values) -> np.ndarray:
     """``round`` that always breaks .5 upwards, unlike numpy's banker's rounding.
@@ -381,11 +388,19 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
     LUT row — 6% of gates per volume on Rad4Alp, 35% on WSR-88D.
 
     So the grid is derived from the scan strategy rather than from one volume's
-    measurements: ``step = 360 / n_rays``, and the offset is the circular mean
-    of the measured residuals (circular because the offset is only defined
-    modulo one step).  That gives 1.0° for a 360-ray Rad4Alp sweep and 0.5° for
-    a 720-ray NEXRAD super-resolution sweep, from the same rule — which is why
-    no per-network resolution has to be configured.
+    measurements: the spacing is the **median gap between neighbouring rays**,
+    and the offset is the circular mean of the measured residuals (circular
+    because the offset is only defined modulo one step).  That gives 1.0° for a
+    360-ray Rad4Alp sweep and 0.5° for a 720-ray NEXRAD super-resolution sweep,
+    from the same rule — which is why no per-network resolution has to be
+    configured.
+
+    The spacing is a **median rather than ``360 / n_rays``** because a real
+    sweep drops the odd ray — 718 or 719 of 720 on WSR-88D, 358 of 360 — and
+    dividing by the ray count reads that as a 0.5014° strategy, refusing a
+    volume that is simply a rotation with holes.  The returned grid always
+    covers the whole rotation, so the missing rays keep their place in it and a
+    later volume that does record them still joins.
 
     Parameters
     ----------
@@ -395,52 +410,96 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
     Returns
     -------
     np.ndarray of int64
-        ``n_rays`` sorted azimuths in tenths of a degree, in ``[0, 3600)``.
+        The rotation's ``n_grid`` sorted azimuths in tenths of a degree, in
+        ``[0, 3600)`` — ``n_grid >= n_rays``, and larger when rays are missing.
 
     Raises
     ------
     ValueError
         If the sweep has no rays, if the spacing is finer than the 0.1°
-        ``gate_id`` resolution, or if two grid points collide after rounding.
+        ``gate_id`` resolution, if two grid points collide after rounding, or if
+        the rays do not cover a full rotation (a sector scan).
     """
     az = np.asarray(azimuths, dtype=np.float64).ravel() % 360.0
     n = az.size
     if n == 0:
         raise ValueError("cannot derive an azimuth grid from a sweep with no rays.")
 
-    step = 360.0 / n
+    az_sorted = np.sort(az)
+    # Gaps around the circle, the wrap included, so one full turn is covered.
+    gaps = np.diff(np.append(az_sorted, az_sorted[0] + 360.0))
+    spacing = float(np.median(gaps))
+    if spacing <= 0.0:
+        raise ValueError(
+            f"the {n} rays of this sweep are not distinct enough to give a ray "
+            f"spacing (median gap {spacing:.6f}°)."
+        )
+
+    # How many ray slots each gap spans: 1 between neighbours, 2 or more across
+    # a hole.  Summing them counts the slots of the whole rotation, so the grid
+    # size is the ray count plus whatever is missing.  The median spacing on its
+    # own is too noisy to divide 360 by — antenna jitter alone turns a 360-ray
+    # Rad4Alp sweep into 361 — but it is easily good enough to tell a 1-slot gap
+    # from a 2-slot one.
+    slots_per_gap = _round_half_up(gaps / spacing).astype(np.int64)
+    n_grid = int(slots_per_gap.sum())
+    if n_grid < n:
+        raise ValueError(
+            f"the {n} rays of this sweep do not sit one per slot on a "
+            f"{spacing:.4f}° grid (two rays share a slot)."
+        )
+    step = 360.0 / n_grid
     if step * AZIMUTH_SCALE < 1.0:
         raise ValueError(
-            f"{n} rays give a {step:.4f}° ray spacing, finer than the "
+            f"{n_grid} rays give a {step:.4f}° ray spacing, finer than the "
             f"{1 / AZIMUTH_SCALE}° azimuth resolution of gate_id; two rays would "
             f"share one gate_id."
         )
 
-    # Residual of each ray against a step grid anchored at 0.  It is defined
-    # only modulo one step, so it is averaged as an angle on that period —
-    # a plain mean would be wrong whenever the residuals straddle the wrap.
-    resid = np.sort(az) - np.arange(n) * step
-    phase = 2.0 * np.pi * resid / step
-    offset = step * np.arctan2(np.sin(phase).mean(), np.cos(phase).mean()) / (2.0 * np.pi)
+    # A rotation may have holes but must still be a rotation: a sector scan (or
+    # a sweep with a large gap) would otherwise be silently mangled — 90 rays
+    # over a 90° sector get a 4° grid and collapse onto 23 of its points.
+    if n > n_grid or n < MIN_ROTATION_COVERAGE * n_grid:
+        raise ValueError(
+            f"these {n} rays do not form a full rotation of evenly spaced rays "
+            f"(they span {np.ptp(az_sorted):.1f}° with a derived spacing of "
+            f"{step:.4f}°, filling {n}/{n_grid} of the rotation), so they have no "
+            f"nominal azimuth grid. Sector scans and irregular sweeps are not "
+            f"supported."
+        )
 
-    grid = (np.arange(n) * step + offset) % 360.0
+    # Residual of each ray against the slot it occupies on a step grid anchored
+    # at 0.  Slots, not ray indices: after a hole the two part company, and ray
+    # indices would drag every later residual a full step out.
+    slots = np.concatenate([[0], np.cumsum(slots_per_gap[:-1])])
+    resid = az_sorted - slots * step
+
+    # The residual is defined only modulo one step, so it is averaged about the
+    # first ray's own residual, with the rest wrapped into ±half a step of it.
+    # Averaging on the raw period instead would put the mean on the seam exactly
+    # when the rays are centred on half-steps — which is every 720-ray NEXRAD
+    # sweep — and a 1e-16 wobble there moves the whole grid by a tenth of a
+    # degree, differently for a volume that dropped a ray than for one that
+    # did not.
+    anchor = float(resid[0])
+    centred = (resid - anchor + step / 2.0) % step - step / 2.0
+    offset = anchor + float(centred.mean())
+
+    grid = (np.arange(n_grid) * step + offset) % 360.0
     az_int = np.sort(_round_half_up(grid * AZIMUTH_SCALE).astype(np.int64) % AZIMUTH_STEPS)
-    if np.unique(az_int).size != n:
+    if np.unique(az_int).size != n_grid:
         raise ValueError(
-            f"the {n}-ray azimuth grid collides after rounding to "
+            f"the {n_grid}-ray azimuth grid collides after rounding to "
             f"{1 / AZIMUTH_SCALE}°; this scan strategy cannot be stored in gate_id."
         )
 
     # The grid is only meaningful if it actually fits the rays it came from.
-    # It assumes a full rotation of evenly spaced rays, so a sector scan (or a
-    # sweep with a large gap) would otherwise be silently mangled: 90 rays over
-    # a 90° sector get a 4° grid and collapse onto 23 of its points.
     snapped, dist = snap_azimuths_to_grid(az, az_int)
     tol = azimuth_grid_tolerance(az_int)
     if np.unique(snapped).size != n or (dist.size and dist.max() > tol):
         raise ValueError(
             f"these {n} rays do not form a full rotation of evenly spaced rays "
-            f"(they span {np.ptp(np.sort(az)):.1f}° with a derived spacing of "
+            f"(they span {np.ptp(az_sorted):.1f}° with a derived spacing of "
             f"{step:.4f}°), so they have no nominal azimuth grid. Sector scans and "
             f"irregular sweeps are not supported."
         )
@@ -840,7 +899,24 @@ def generate_lut_from_datatree(
                 f"grid (two rays snap together), so this sweep has no nominal "
                 f"azimuth grid."
             )
-        azimuths = snapped.astype(np.float64) / AZIMUTH_SCALE
+
+        elevation_angle = float(ds.coords.get("elevation_angle", np.mean(elevations)))
+
+        # The LUT covers the whole rotation, not only the rays this volume
+        # happened to record: a volume that drops a ray still defines the grid
+        # point, and a later volume that does record it must find a row to join.
+        # Empty for a complete sweep, so nothing changes for one.
+        absent = np.setdiff1d(az_grid, snapped)
+        azimuths = np.concatenate([snapped, absent]).astype(np.float64) / AZIMUTH_SCALE
+        elevations = np.concatenate(
+            [np.asarray(elevations, dtype=np.float64), np.full(absent.size, elevation_angle)]
+        )
+        if absent.size:
+            logger.info(
+                "sweep %d: %d of %d rays missing from this volume; their grid "
+                "points are still written to the LUT.",
+                sweep_idx, absent.size, az_grid.size,
+            )
         n_az, n_rng = len(azimuths), len(ranges)
         logger.debug(
             "sweep %d: %d rays snapped to the nominal grid, max move %.3f°.",
@@ -856,9 +932,6 @@ def generate_lut_from_datatree(
         site_lat = float(ds.coords.get("latitude", 0.0))
         site_lon = float(ds.coords.get("longitude", 0.0))
         site_alt = float(ds.coords.get("altitude", 0.0))
-        elevation_angle = float(
-            ds.coords.get("elevation_angle", np.mean(elevations))
-        )
 
         if radar_lat is None:
             radar_lat = site_lat
diff --git a/raddb/main.py b/raddb/main.py
index b5d6c93..5dcb320 100644
--- a/raddb/main.py
+++ b/raddb/main.py
@@ -268,14 +268,29 @@ def _ccw_polygons(polys: np.ndarray) -> np.ndarray:
     return np.where(ccw, polys, shapely.reverse(polys))
 
 
+_FILTER_KEYS = ("var", "logic", "threshold")
+
+
 def _resolve_filters(filters) -> list[tuple[str, str, float]]:
-    """Normalize a filter dict / list-of-dicts to ``[(var, logic, threshold), ...]``."""
+    """Normalize a filter dict / list-of-dicts to ``[(var, logic, threshold), ...]``.
+
+    Unknown keys are rejected rather than ignored: ``threshold`` defaults to 0,
+    so a misspelt one (``{"var": "DBZH", "logic": ">", "value": 45}``) would
+    otherwise silently become ``DBZH > 0`` — a filter that keeps everything and
+    looks like it ran.
+    """
     if filters is None:
         return []
     if isinstance(filters, dict):
         filters = [filters]
     specs = []
     for f in filters:
+        extra = [k for k in f if k not in _FILTER_KEYS]
+        if extra:
+            raise KeyError(
+                f"unknown filter key(s) {sorted(extra)} in {f!r}; "
+                f"a filter is {{{', '.join(repr(k) for k in _FILTER_KEYS)}}}."
+            )
         specs.append((f["var"], f.get("logic", ">"), f.get("threshold", 0.0)))
     return specs
 
@@ -543,7 +558,11 @@ def archive(
         Returns
         -------
         dict
-            ``{"n_archived": int, "n_failed": int, "radars": [...]}``.
+            ``{"n_archived": int, "n_failed": int, "n_skipped": int,
+            "radars": [...]}``.  The three counts sum to the number of volumes
+            attempted: ``n_skipped`` covers volumes that held nothing to
+            archive (every gate failed the filter, or an all-``NaT`` time),
+            which is neither a success nor a failure.
         """
         archive_dir = Path(archive_dir) if archive_dir is not None else self.archive_dir
         if archive_dir is None:
@@ -578,11 +597,11 @@ def archive(
 
         t0 = time.time()
         if datatree is not None:
-            radars_done, n_ok, n_fail = self._archive_in_memory(
+            radars_done, n_ok, n_fail, n_skip = self._archive_in_memory(
                 datatree, radar, archive_dir, crs, feat, logic, thr
             )
         else:
-            radars_done, n_ok, n_fail = self._archive_from_disk(
+            radars_done, n_ok, n_fail, n_skip = self._archive_from_disk(
                 datatree_dir, radar, archive_dir, crs, feat, logic, thr, time_period
             )
 
@@ -592,10 +611,18 @@ def archive(
         print(f"  crs         : {crs}")
         print(f"  radars      : {radars_done}")
         print(f"  filter      : keep {feat} {logic} {thr}")
-        print(f"  volumes     : {n_ok} archived, {n_fail} failed")
+        print(
+            f"  volumes     : {n_ok} archived, {n_fail} failed"
+            + (f", {n_skip} skipped (nothing to archive)" if n_skip else "")
+        )
         print(f"  elapsed     : {_format_elapsed_time(time.time() - t0)}")
         print("=" * 70)
-        return {"n_archived": n_ok, "n_failed": n_fail, "radars": radars_done}
+        return {
+            "n_archived": n_ok,
+            "n_failed": n_fail,
+            "n_skipped": n_skip,
+            "radars": radars_done,
+        }
 
     def _ensure_lut(self, radar: str, sample_dt, archive_dir: Path, crs) -> None:
         """Generate the per-radar LUT from a sample volume if it does not exist."""
@@ -640,7 +667,8 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
             # record an archive is how a broken volume gets announced as stored.
             flat = [r for v in results.values() for r in v]
             n_ok = sum(1 for r in flat if r.get("success"))
-            return list(results.keys()), n_ok, len(flat) - n_ok
+            n_skip = sum(1 for r in flat if r.get("skipped"))
+            return list(results.keys()), n_ok, len(flat) - n_ok - n_skip, n_skip
 
         if radar is None or not isinstance(radar, str):
             raise ValueError(
@@ -650,11 +678,13 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
         r = normalize_radar_name(radar)
         if isinstance(datatree, xr.DataTree):
             self._ensure_lut(r, datatree, archive_dir, crs)
-            archive_volume(
+            path = archive_volume(
                 dt=datatree, radar=r, base_output_path=str(archive_dir),
                 filter_feature=feat, filter_threshold=thr, filter_logic=logic,
             )
-            return [r], 1, 0
+            # A None path means the volume held nothing to archive; counting it
+            # as archived is how an empty volume gets reported as stored.
+            return ([r], 0, 0, 1) if path is None else ([r], 1, 0, 0)
         # list or {label: DataTree}
         first = next(iter(datatree.values())) if isinstance(datatree, dict) else datatree[0]
         self._ensure_lut(r, first, archive_dir, crs)
@@ -664,7 +694,8 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
             verbose=False,
         )
         n_ok = sum(1 for res in results if res.get("success"))
-        return [r], n_ok, len(results) - n_ok
+        n_skip = sum(1 for res in results if res.get("skipped"))
+        return [r], n_ok, len(results) - n_ok - n_skip, n_skip
 
     def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic, thr, time_period):
         archive_dir = Path(archive_dir)
@@ -690,15 +721,16 @@ def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic,
                 wanted = {normalize_radar_name(x) for x in radar}
                 by_radar = {r: fs for r, fs in by_radar.items() if r in wanted}
 
-        radars_done, total_ok, total_fail = [], 0, 0
+        radars_done, total_ok, total_fail, total_skip = [], 0, 0, 0
         for r, rfiles in sorted(by_radar.items()):
-            n_ok, n_fail = self._archive_files_one_radar(
+            n_ok, n_fail, n_skip = self._archive_files_one_radar(
                 r, sorted(rfiles), archive_dir, crs, feat, logic, thr
             )
             radars_done.append(r)
             total_ok += n_ok
             total_fail += n_fail
-        return radars_done, total_ok, total_fail
+            total_skip += n_skip
+        return radars_done, total_ok, total_fail, total_skip
 
     def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic, thr):
         """Archive every saved DataTree file for one radar (LUT autogen, resume)."""
@@ -706,7 +738,7 @@ def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic,
             radar = normalize_radar_name(radar)
         except ValueError as exc:
             print(f"  [skip] {exc} Skipping {len(files)} file(s).")
-            return (0, 0)
+            return (0, 0, len(files))
         archive_dir.mkdir(parents=True, exist_ok=True)
         ckpt = archive_dir / f"_archive_checkpoint_datatrees_{radar}.txt"
         seen = _load_checkpoint(ckpt)
@@ -721,7 +753,7 @@ def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic,
             except Exception as e:  # noqa: BLE001
                 print(f"  [{radar}] LUT generation failed: {e}")
 
-        n_ok = n_fail = 0
+        n_ok = n_fail = n_skip = 0
         for f in files:
             stem = Path(f).stem
             key = f"{radar}:{stem}"
@@ -732,19 +764,25 @@ def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic,
                 dt = preopened.pop(f, None)
                 if dt is None:
                     dt = open_any_datatree(f)
-                archive_volume(
+                path = archive_volume(
                     dt=dt, radar=radar, base_output_path=str(archive_dir),
                     filter_feature=feat, filter_threshold=thr, filter_logic=logic,
                     volume=stem,
                 )
+                # Checkpoint either way: a volume with nothing to archive is
+                # settled, and re-reading it on resume would only skip again.
                 _append_checkpoint(ckpt, key)
                 seen.add(key)
-                n_ok += 1
+                if path is None:
+                    n_skip += 1
+                    print(f"  [{radar}] SKIP {stem}: no gates to archive")
+                else:
+                    n_ok += 1
                 del dt
             except Exception as e:  # noqa: BLE001
                 n_fail += 1
                 print(f"  [{radar}] FAIL {stem}: {e}")
-        return (n_ok, n_fail)
+        return (n_ok, n_fail, n_skip)
 
     # ---- LUT read accessors (archive-bound) ----
 
diff --git a/raddb/tests/test_azimuth_grid.py b/raddb/tests/test_azimuth_grid.py
index 37235e3..4508507 100644
--- a/raddb/tests/test_azimuth_grid.py
+++ b/raddb/tests/test_azimuth_grid.py
@@ -120,6 +120,46 @@ def test_rejects_a_sweep_with_a_large_gap(self):
         with pytest.raises(ValueError, match="full rotation"):
             nominal_azimuth_grid(az)
 
+    @pytest.mark.parametrize("dropped", [[7], [7, 8], [0, 359], [3, 100, 250]])
+    def test_a_rotation_with_holes_keeps_the_full_grid(self, dropped):
+        """718 of 720 is a rotation with holes, not a 0.5014 deg scan strategy."""
+        nominal = np.arange(720) * 0.5 + 0.25
+        recorded = np.delete(nominal, dropped)
+        grid = nominal_azimuth_grid(recorded)
+        assert grid.size == 720                     # the missing rays keep their slots
+        assert np.all(np.diff(grid) == 5)
+        assert np.array_equal(grid, nominal_azimuth_grid(nominal))
+
+    def test_holes_survive_antenna_drift(self):
+        """The real case: WSR-88D drift plus two dropped rays.
+
+        The grid is the same rotation, but not necessarily the same integers: a
+        720-ray grid is centred on ``x.x5``, exactly the 0.1° rounding boundary,
+        so the ~0.002° the two ray sets differ by can tip the whole grid one
+        tenth either way.  That is a tenth of a degree against a half-spacing
+        tolerance of 0.25°, so every ray still snaps to its own point.
+        """
+        rng = np.random.default_rng(7)
+        nominal = np.arange(720) * 0.5 + 0.25
+        recorded = np.delete(_jitter(nominal, rng, 0.0, NEXRAD_SPREAD), [11, 12])
+
+        grid = nominal_azimuth_grid(recorded)
+        assert grid.size == 720
+        assert np.all(np.diff(grid) == 5)
+
+        shift = (grid - nominal_azimuth_grid(nominal) + AZIMUTH_STEPS // 2) % AZIMUTH_STEPS
+        assert np.all(np.abs(shift - AZIMUTH_STEPS // 2) <= 1)      # at most one tenth
+
+        _, dist = snap_azimuths_to_grid(recorded, grid)
+        assert dist.max() <= azimuth_grid_tolerance(grid)
+
+    def test_too_many_holes_is_not_a_rotation(self):
+        """Past the coverage floor it is indistinguishable from a sector scan."""
+        nominal = np.arange(360) + 0.5
+        recorded = np.delete(nominal, np.arange(0, 100))     # 260 of 360
+        with pytest.raises(ValueError, match="full rotation"):
+            nominal_azimuth_grid(recorded)
+
 
 # ===========================================================================
 # snap_azimuths_to_grid
@@ -269,6 +309,43 @@ def test_refuses_an_unknown_sweep(self, db):
                 radar="A",
             )
 
+    def test_accepts_a_volume_that_dropped_rays(self, db):
+        """A volume short of a ray or two is a rotation with holes, not a new strategy."""
+        rng = np.random.default_rng(21)
+        base = _retime(_make_datatree(n_az=360, n_rng=20, n_sweeps=2),
+                       pd.Timestamp("2024-08-01 18:00"), rng, MCH_BIAS)
+        holed = xr.DataTree.from_dict({
+            name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
+            for name, node in base.children.items()
+        })
+        res = db.archive(datatree=holed, radar="A")
+        assert (res["n_archived"], res["n_failed"]) == (1, 0)
+
+    def test_a_lut_built_from_a_holed_volume_still_holds_every_ray(self, tmp_path):
+        """The LUT is the rotation, not one volume: the dropped rays keep their rows,
+        so a later complete volume joins 100%."""
+        complete = _make_datatree(n_az=360, n_rng=20, n_sweeps=2)
+        holed = xr.DataTree.from_dict({
+            name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
+            for name, node in complete.children.items()
+        })
+
+        db = RadDB(archive_dir=str(tmp_path / "a"), crs=2056)
+        db.archive(datatree=holed, radar="A")                     # LUT from the holed volume
+        lut = db.get_lut("A")
+        assert lut.filter(pl.col("sweep") == 1)["azimuth"].n_unique() == 360
+
+        res = db.archive(
+            datatree=_retime(complete, pd.Timestamp("2024-08-01 19:00"),
+                             np.random.default_rng(22), MCH_BIAS),
+            radar="A",
+        )
+        assert (res["n_archived"], res["n_failed"]) == (1, 0)
+
+        data = db.open(radars="A")
+        lut_ids = set(lut["gate_id"].to_list())
+        assert all(g in lut_ids for g in data.data["gate_id"].to_list())
+
     def test_accepts_ordinary_drift(self, db):
         rng = np.random.default_rng(11)
         base = _make_datatree(n_az=360, n_rng=20, n_sweeps=2)
diff --git a/raddb/tests/test_fixes.py b/raddb/tests/test_fixes.py
index 0332650..83a30aa 100644
--- a/raddb/tests/test_fixes.py
+++ b/raddb/tests/test_fixes.py
@@ -295,3 +295,177 @@ def test_api_archive_with_projection(self, tmp_path):
         proj_y_cols = [c for c in lut.columns if c.startswith("y_")]
         assert len(proj_x_cols) == 1
         assert len(proj_y_cols) == 1
+
+
+# ===========================================================================
+# 4. datatree_to_dataframe — dimension coordinates that carry no index
+# ===========================================================================
+
+
+class TestUnindexedDimensionCoordinate:
+    """Raw NEXRAD Level II sweeps arrive with ``range`` as a coordinate that has
+    no index.  ``to_dataframe`` then indexes that dimension by position and emits
+    the true values as a column of the same name, which ``reset_index`` refuses
+    to insert.  The flattener rebuilds the index first.
+    """
+
+    @staticmethod
+    def _drop_range_index(dt: xr.DataTree) -> xr.DataTree:
+        """Reproduce the shape xradar hands back for a raw Level II volume."""
+        for name in dt.children:
+            dt[name].dataset = dt[name].to_dataset().drop_indexes("range")
+        return dt
+
+    def test_flatten_keeps_true_range_values(self):
+        from raddb.io_core import datatree_to_dataframe
+
+        expected = datatree_to_dataframe(_make_datatree())
+        got = datatree_to_dataframe(self._drop_range_index(_make_datatree()))
+
+        assert got.shape == expected.shape
+        # Metres, not the positions 0..n_rng-1 that the broken shape would give.
+        assert sorted(got["range"].unique().to_list()) == sorted(expected["range"].unique().to_list())
+        assert got["range"].min() == pytest.approx(1000.0)
+
+    def test_archive_accepts_it(self, tmp_path):
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        dt = self._drop_range_index(_make_datatree())
+        result = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=dt, radar=RADAR)
+
+        assert result["n_archived"] == 1 and result["n_failed"] == 0
+
+
+# ===========================================================================
+# 6. A volume with nothing to archive is skipped, not a crash
+# ===========================================================================
+
+
+class TestEmptyVolumeIsSkipped:
+    """A clear-air volume used to crash the batch instead of being skipped.
+
+    ``_save_polar_parquet`` builds the output path out of the volume's own
+    time.  When every gate fails the filter the frame is empty, ``.min()`` is
+    ``None``, ``pd.to_datetime(None)`` is ``NaT``, and ``pd.NaT.month`` is
+    *nan* — a float — so ``f"{...:02d}"`` raised ``Unknown format code 'd' for
+    object of type 'float'``.  Two real Rad4Alp volumes hit this.
+    """
+
+    @staticmethod
+    def _blank_dbzh(dt: xr.DataTree) -> xr.DataTree:
+        """Null out DBZH everywhere, so the default ``DBZH > 0`` keeps nothing."""
+        for name in dt.children:
+            ds = dt[name].to_dataset()
+            ds["DBZH"] = ds["DBZH"].where(False)  # all-NaN, same shape/dtype
+            dt[name].dataset = ds
+        return dt
+
+    @staticmethod
+    def _blank_time(dt: xr.DataTree) -> xr.DataTree:
+        """Make every ray's time NaT while leaving DBZH intact."""
+        for name in dt.children:
+            ds = dt[name].to_dataset()
+            ds["time"] = xr.full_like(ds["time"], np.datetime64("NaT"))
+            dt[name].dataset = ds
+        return dt
+
+    def test_no_gates_survive_the_filter(self, tmp_path):
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        dt = self._blank_dbzh(_make_datatree())
+        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=dt, radar=RADAR)
+
+        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (0, 0, 1)
+        assert not list((tmp_path / RADAR).rglob("*_POL.parquet"))
+
+    def test_all_nat_time_is_skipped(self, tmp_path):
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        dt = self._blank_time(_make_datatree())
+        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=dt, radar=RADAR)
+
+        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (0, 0, 1)
+        assert not list((tmp_path / RADAR).rglob("*_POL.parquet"))
+
+    def test_counts_sum_to_the_volumes_attempted(self, tmp_path):
+        """One good volume + one empty: 1 archived, 0 failed, 1 skipped."""
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        good = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
+        empty = self._blank_dbzh(_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00")))
+        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(
+            datatree=[good, empty], radar=RADAR
+        )
+
+        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (1, 0, 1)
+        assert res["n_archived"] + res["n_failed"] + res["n_skipped"] == 2
+        assert len(list((tmp_path / RADAR).rglob("*_POL.parquet"))) == 1
+
+    def test_the_archive_stays_readable(self, tmp_path):
+        """A skipped volume must not poison the rest of the archive."""
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        db = RadDB(archive_dir=str(tmp_path), crs=2056)
+        db.archive(datatree=_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")),
+                   radar=RADAR)
+        db.archive(datatree=self._blank_dbzh(_make_datatree(
+            vol_time=pd.Timestamp("2024-08-01 12:05:00"))), radar=RADAR)
+
+        rdf = RadDB(archive_dir=str(tmp_path)).open(radars=RADAR)
+        assert len(rdf) > 0
+        assert rdf.radars() == [RADAR]
+
+    def test_save_polar_parquet_returns_none_directly(self):
+        """The guard itself, without going through archive()."""
+        import polars as pl
+
+        from raddb.io_core import _save_polar_parquet
+
+        empty = pl.DataFrame({"gate_id": [], "time": []})
+        assert _save_polar_parquet(empty, RADAR, "/nonexistent") is None
+
+    def test_disk_path_counts_and_checkpoints_a_skip(self, tmp_path):
+        """``datatree_dir=`` counts a skip separately and does not retry it."""
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        src = tmp_path / "trees"
+        src.mkdir()
+        _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")).to_netcdf(
+            src / f"{RADAR}_20240801_120000.nc"
+        )
+        self._blank_dbzh(_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00"))).to_netcdf(
+            src / f"{RADAR}_20240801_120500.nc"
+        )
+        arch = tmp_path / "arch"
+
+        res = RadDB(archive_dir=str(arch), crs=2056).archive(datatree_dir=str(src), radar=RADAR)
+        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (1, 0, 1)
+        assert len(list((arch / RADAR).rglob("*_POL.parquet"))) == 1
+
+        # The skip is checkpointed, so a resume re-attempts nothing.
+        again = RadDB(archive_dir=str(arch), crs=2056).archive(datatree_dir=str(src), radar=RADAR)
+        assert (again["n_archived"], again["n_failed"], again["n_skipped"]) == (0, 0, 0)
+
+    def test_multi_radar_path_counts_a_skip(self, tmp_path):
+        """The ``{radar: [volumes]}`` form keeps the three counts separate too."""
+        pytest.importorskip("pyproj")
+        from raddb.main import RadDB
+
+        volumes = {
+            RADAR: [
+                _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")),
+                self._blank_dbzh(_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00"))),
+            ],
+            "D": [_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))],
+        }
+        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=volumes)
+
+        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (2, 0, 1)
+        assert res["n_archived"] + res["n_failed"] + res["n_skipped"] == 3
+        assert sorted(res["radars"]) == ["A", "D"]
diff --git a/raddb/tests/test_radar_code.py b/raddb/tests/test_radar_code.py
index 2a97d95..4f4d8c4 100644
--- a/raddb/tests/test_radar_code.py
+++ b/raddb/tests/test_radar_code.py
@@ -271,13 +271,12 @@ def test_two_radars_stay_distinct(self, tmp_path):
         assert codes == {encode_radar_code("KTLX"), encode_radar_code("KOUN")}
 
 
-class TestGateIdMigration:
-    """The v1 -> v2 migration, now that ``info.yaml`` records no version.
+class TestV1Archive:
+    """What happens to a v1 archive, now that nothing detects the encoding.
 
-    Nothing detects the encoding any more, so the tool is an unconditional
-    offset that the caller must vouch for.  These tests pin that contract,
-    including the part that is genuinely worse than before: running it twice
-    corrupts the archive, and only ``--assume-v1`` stands between the two.
+    ``info.yaml`` records no version and there is no migration tool any more,
+    so a v1 archive loads silently and decodes to the wrong radar.  That cost
+    is pinned here so it stays visible rather than being rediscovered.
     """
 
     def _downgrade_to_v1(self, tmp_path, radar):
@@ -302,46 +301,3 @@ def test_v1_archive_is_read_without_complaint(self, tmp_path):
 
         assert "gate_id_version" not in db.get_radar_info("L")
         assert decode_gate_radars(db.open(radars="L").data["gate_id"].to_numpy()) == ["B"]
-
-    def test_migration_restores_the_archive(self, tmp_path):
-        from raddb.tools.migrate_gate_id_v2 import migrate_radar
-
-        db = _archive(tmp_path, "L")
-        before = db.open(radars="L").data["gate_id"].to_numpy().copy()
-        self._downgrade_to_v1(tmp_path, "L")
-
-        dry = migrate_radar(tmp_path / "archive", "L", dry_run=True)
-        assert dry["status"] == "would migrate" and dry["files"] >= 2
-        assert dry["rows"] == 0  # a dry run writes nothing
-
-        res = migrate_radar(tmp_path / "archive", "L")
-        assert res["status"] == "migrated" and res["rows"] > 0
-
-        after = db.open(radars="L").data["gate_id"].to_numpy()
-        assert np.array_equal(np.sort(after), np.sort(before))
-        assert decode_gate_radars(after) == ["L"]
-
-    def test_migration_is_no_longer_idempotent(self, tmp_path):
-        """Without a recorded version there is nothing to short-circuit on."""
-        from raddb.tools.migrate_gate_id_v2 import migrate_radar
-
-        db = _archive(tmp_path, "L")
-        self._downgrade_to_v1(tmp_path, "L")
-        migrate_radar(tmp_path / "archive", "L")
-        again = migrate_radar(tmp_path / "archive", "L")
-
-        assert again["status"] == "migrated"       # it runs again, blindly
-        assert decode_gate_radars(db.open(radars="L").data["gate_id"].to_numpy()) != ["L"]
-
-    def test_cli_refuses_to_write_without_assume_v1(self, tmp_path):
-        """The only guard left against a double migration."""
-        from raddb.tools.migrate_gate_id_v2 import main
-
-        db = _archive(tmp_path, "L")
-        before = db.open(radars="L").data["gate_id"].to_numpy().copy()
-
-        assert main([str(tmp_path / "archive")]) == 2
-        assert np.array_equal(db.open(radars="L").data["gate_id"].to_numpy(), before)
-
-        assert main([str(tmp_path / "archive"), "--dry-run"]) == 0
-        assert np.array_equal(db.open(radars="L").data["gate_id"].to_numpy(), before)
diff --git a/raddb/tests/test_sel.py b/raddb/tests/test_sel.py
index df8ccea..7cea0c6 100644
--- a/raddb/tests/test_sel.py
+++ b/raddb/tests/test_sel.py
@@ -171,3 +171,18 @@ def test_unknown_column_raises_keyerror(self, rdf):
     def test_step_in_slice_raises_valueerror(self, rdf):
         with pytest.raises(ValueError):
             rdf.sel(DBZH=slice(0, 10, 2))
+
+
+class TestFilterKeys:
+    """`filter` rejects a misspelt key instead of silently defaulting it."""
+
+    def test_unknown_key_raises(self, rdf):
+        # "value" is not a filter key; threshold would default to 0 and the
+        # filter would keep every row while looking like it ran.
+        with pytest.raises(KeyError, match="unknown filter key"):
+            rdf.filter({"var": "DBZH", "logic": ">", "value": 10})
+
+    def test_correct_key_filters(self, rdf):
+        out = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 10})
+        assert 0 < len(out) < len(rdf)
+        assert out.data["DBZH"].min() > 10
diff --git a/raddb/tools/__init__.py b/raddb/tools/__init__.py
deleted file mode 100644
index 388de84..0000000
--- a/raddb/tools/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Command-line maintenance utilities for RadDB archives.
-
-These are one-shot operational scripts, not part of the library API — nothing
-in :mod:`raddb` imports them.  Run them with ``python -m raddb.tools.<name>``.
-"""
diff --git a/raddb/tools/migrate_gate_id_v2.py b/raddb/tools/migrate_gate_id_v2.py
deleted file mode 100644
index a3c082b..0000000
--- a/raddb/tools/migrate_gate_id_v2.py
+++ /dev/null
@@ -1,164 +0,0 @@
-"""Migrate an archive from the v1 ``gate_id`` encoding to v2, in place.
-
-v1 numbered radars ``A=0 … Z=25``; v2 uses the base-36 value of the (zero-padded,
-4-character) radar name, so ``"L"`` moved from 11 to 21.  Only the leading radar
-field changes — ``sweep``/``azimuth``/``range`` occupy the low 12 digits and are
-untouched — so the whole migration is one integer offset per radar::
-
-    gate_id += (encode_radar_code(radar) - LEGACY_RADAR_TO_IDX[radar]) * 10**12
-
-``gate_id`` is stored in exactly two kinds of file, verified against the archives
-on disk: the centroid LUT ``{radar}/LUT/{radar}_LUT.parquet`` and every volume
-``{radar}/**/{radar}_*_POL.parquet``.  The three geometry lattices
-(``h_plane`` / ``v_plane`` / ``corners``) are node lattices addressed by
-``(sweep, az_idx, rng_idx)`` and carry no ``gate_id``, so they need no rewrite.
-
-No geometry is recomputed and nothing is re-ingested, which matters because the
-source volumes of an archive are often no longer around.
-
-``info.yaml`` no longer records a ``gate_id_version``, so this tool **cannot tell
-a v1 archive from a v2 one** — and re-running it on a migrated archive would
-shift every id a second time.  It therefore refuses to touch anything without an
-explicit ``--assume-v1``, which is the caller asserting that the archive really
-does predate the base-36 radar code.
-
-Usage
------
-::
-
-    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --dry-run
-    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --assume-v1
-    python -m raddb.tools.migrate_gate_id_v2 <archive_dir> --assume-v1 --radar L --radar W
-"""
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-import polars as pl
-import yaml
-
-from raddb.helper import is_valid_radar_name, normalize_radar_name
-from raddb.lut import (
-    GATE_ID_RADAR_BASE,
-    LEGACY_RADAR_TO_IDX,
-    encode_radar_code,
-)
-
-
-def _archive_radars(archive_dir: Path) -> list[str]:
-    """Radar directories in *archive_dir*, whether or not they are migrated yet."""
-    return sorted(
-        p.name
-        for p in archive_dir.iterdir()
-        if p.is_dir() and is_valid_radar_name(p.name) and (p / "LUT").is_dir()
-    )
-
-
-def _info_path(archive_dir: Path, radar: str) -> Path:
-    return archive_dir / radar / "LUT" / f"{radar}_info.yaml"
-
-
-def _gate_id_files(archive_dir: Path, radar: str) -> list[Path]:
-    """Every parquet under *radar* that holds a ``gate_id`` column."""
-    lut = archive_dir / radar / "LUT" / f"{radar}_LUT.parquet"
-    files = [lut] if lut.exists() else []
-    files += sorted((archive_dir / radar).rglob("*_POL.parquet"))
-    return files
-
-
-def _shift_gate_ids(path: Path, delta: int) -> int:
-    """Rewrite ``gate_id`` in *path* by *delta*. Returns the row count.
-
-    Written to a sibling temp file and moved into place, so an interrupted run
-    leaves the original parquet intact rather than a half-written one.
-    """
-    df = pl.read_parquet(path)
-    df = df.with_columns((pl.col("gate_id") + delta).alias("gate_id"))
-    tmp = path.with_suffix(path.suffix + ".migrating")
-    df.write_parquet(tmp)
-    tmp.replace(path)
-    return df.height
-
-
-def migrate_radar(archive_dir: Path, radar: str, dry_run: bool = False) -> dict:
-    """Migrate one radar. Returns a summary dict; a migrated radar is skipped."""
-    info_path = _info_path(archive_dir, radar)
-    if not info_path.exists():
-        return {"radar": radar, "status": "no info.yaml", "files": 0, "rows": 0}
-
-    info = yaml.safe_load(info_path.read_text()) or {}
-    name = normalize_radar_name(info.get("radar") or radar)
-    legacy = LEGACY_RADAR_TO_IDX.get(name)
-    if legacy is None:
-        # v1 could only ever encode A-Z, so a v1 archive naming anything else is
-        # inconsistent and guessing an offset would corrupt it.
-        return {"radar": radar, "status": f"{name!r} is not a v1 (A-Z) radar — skipped",
-                "files": 0, "rows": 0}
-
-    delta = (encode_radar_code(name) - legacy) * GATE_ID_RADAR_BASE
-    files = _gate_id_files(archive_dir, radar)
-
-    rows = 0
-    if not dry_run:
-        for f in files:
-            rows += _shift_gate_ids(f, delta)
-
-    return {
-        "radar": radar,
-        "status": "would migrate" if dry_run else "migrated",
-        "files": len(files),
-        "rows": rows,
-        "delta": delta,
-    }
-
-
-def main(argv: list[str] | None = None) -> int:
-    """CLI entry point."""
-    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
-    ap.add_argument("archive_dir", type=Path, help="RadDB archive base directory")
-    ap.add_argument("--radar", action="append", default=None,
-                    help="restrict to this radar (repeatable); default is all")
-    ap.add_argument("--dry-run", action="store_true",
-                    help="report what would change without writing")
-    ap.add_argument("--assume-v1", action="store_true",
-                    help="confirm the archive really is v1 (required to write: "
-                         "info.yaml no longer records a version, so this cannot "
-                         "be detected, and migrating twice corrupts every gate_id)")
-    args = ap.parse_args(argv)
-
-    archive_dir: Path = args.archive_dir
-    if not archive_dir.is_dir():
-        print(f"error: {archive_dir} is not a directory", file=sys.stderr)
-        return 2
-
-    if not args.dry_run and not args.assume_v1:
-        print(
-            "error: pass --assume-v1 to write. info.yaml no longer records a "
-            "gate_id_version, so a v1 archive is indistinguishable from a v2 one, "
-            "and running this twice shifts every gate_id twice.",
-            file=sys.stderr,
-        )
-        return 2
-
-    radars = [normalize_radar_name(r) for r in args.radar] if args.radar \
-        else _archive_radars(archive_dir)
-    if not radars:
-        print(f"no radar directories found in {archive_dir}")
-        return 0
-
-    print(f"{'gate_id v1 -> v2':<24}{archive_dir}")
-    print(f"{'radar':<8}{'files':>8}{'rows':>14}   status")
-    print("-" * 62)
-    for radar in radars:
-        res = migrate_radar(archive_dir, radar, dry_run=args.dry_run)
-        print(f"{res['radar']:<8}{res['files']:>8}{res['rows']:>14,}   {res['status']}")
-    print("-" * 62)
-    if args.dry_run:
-        print("dry run — nothing written")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/raddb/viz/__init__.py b/raddb/viz/__init__.py
index fbfed92..b4938c5 100644
--- a/raddb/viz/__init__.py
+++ b/raddb/viz/__init__.py
@@ -1,8 +1,5 @@
-"""raddb.viz — plotting, profiling dashboards, and report figures.
+"""raddb.viz — plotting.
 
-- ``plot``: PPI / RHI / latent-space plotting for reconstructed DataTrees.
-- ``profiling``: dashboards for :class:`raddb.helper.StageTimer` records.
-- ``report_*``: standalone report scripts (run as
-  ``python -m raddb.viz.report_raddb_figures``), flagged for refactor into
-  reusable visualization utilities.
+- ``plot``: PPI / RHI / CAPPI / vertical-cross-section plotting.
+- ``interactive``: ipyleaflet map for drawing crops and cross-section lines.
 """
diff --git a/raddb/viz/plot.py b/raddb/viz/plot.py
index 0faa10d..e8224f7 100644
--- a/raddb/viz/plot.py
+++ b/raddb/viz/plot.py
@@ -165,47 +165,86 @@ def _maybe_cartopy():
         return None, None
 
 
-_LV95_BORDERS = "unset"  # cache: list of country-border lines in EPSG:2056
+_NE_BORDERS = None  # cache: Natural Earth context lines with their style, in lon/lat
+_BORDER_LINES = {}  # cache: (crs, clip box) -> those lines in that frame
+
+# Natural Earth layers drawn by ``context=True``, with the style each gets.
+# Coastline and national borders alone leave a radar in the middle of a large
+# country — KTLX in Oklahoma, say — with nothing at all to draw, so state and
+# province lines are in there too, thinner and paler so they read as secondary.
+_NE_LAYERS = (
+    ("cultural", "admin_0_boundary_lines_land", {"color": "0.4", "linewidth": 0.6}),
+    ("physical", "coastline", {"color": "0.4", "linewidth": 0.6}),
+    ("cultural", "admin_1_states_provinces_lines", {"color": "0.65", "linewidth": 0.4}),
+)
 
 
-def _lv95_border_lines():
-    """Country-border lines near Switzerland, reprojected to EPSG:2056 (cached).
+def _ne_border_lines():
+    """Natural Earth 10 m context lines as ``[(geom, style), ...]`` in lon/lat (cached).
 
-    Sourced from cartopy's Natural Earth 10 m admin-0 boundary lines, clipped to
-    a lon/lat box around Switzerland before reprojection (LV95 is only valid near
-    CH).  Returns a list of shapely (Multi)LineStrings in LV95, or ``[]`` if
-    cartopy / the data is unavailable — so the swiss PPI still draws, just without
-    borders.  Drawn on a plain matplotlib axis, so no GeoAxes / extent quirks.
+    Read through cartopy's shapereader.  A missing cartopy or an uncached
+    download warns and yields nothing, so the plot still draws — just without
+    context.  Drawn on a plain matplotlib axis, so no GeoAxes / extent quirks.
     """
-    global _LV95_BORDERS
-    if _LV95_BORDERS != "unset":
-        return _LV95_BORDERS
-    lines = []
-    try:
+    global _NE_BORDERS
+    if _NE_BORDERS is None:
+        try:
+            from cartopy.io import shapereader as shpreader
+
+            _NE_BORDERS = []
+            for category, name, style in _NE_LAYERS:
+                path = shpreader.natural_earth(resolution="10m", category=category, name=name)
+                _NE_BORDERS.extend((g, style) for g in shpreader.Reader(path).geometries())
+        except Exception as exc:  # noqa: BLE001 - cartopy missing / data not cached
+            import warnings
+            warnings.warn(
+                f"cartopy country borders unavailable ({exc}); plotted without them.",
+                stacklevel=2,
+            )
+            _NE_BORDERS = []
+    return _NE_BORDERS
+
+
+def _border_lines(crs, clip):
+    """Context lines clipped to the lon/lat box ``clip``, in ``crs`` (cached).
+
+    ``crs`` is whatever frame the plot is drawn in — an EPSG int, or the proj4
+    string of the radar-centred azimuthal frame ``coords="xy"`` uses.  Clipping
+    before reprojecting keeps it local, which matters for frames that are only
+    valid near their own area (LV95, a single UTM zone).
+    """
+    key = (str(crs), tuple(round(float(v), 2) for v in clip))
+    if key not in _BORDER_LINES:
         import shapely
-        from cartopy.io import shapereader as shpreader
-        from raddb.aoi import _reproject_to_aoi, SWISS_EPSG
+        from raddb.aoi import _reproject_to_aoi
 
-        path = shpreader.natural_earth(
-            resolution="10m", category="cultural", name="admin_0_boundary_lines_land"
-        )
-        clip = shapely.box(3.0, 43.0, 13.5, 49.5)  # Switzerland + neighbours
-        for geom in shpreader.Reader(path).geometries():
-            piece = geom.intersection(clip)
+        box = shapely.box(*clip)
+        lines = []
+        for geom, style in _ne_border_lines():
+            piece = geom.intersection(box)
             if not piece.is_empty:
-                lines.append(_reproject_to_aoi(piece, 4326, SWISS_EPSG))
-    except Exception as exc:  # noqa: BLE001 - cartopy missing / data not cached
-        import warnings
-        warnings.warn(
-            f"cartopy country borders unavailable ({exc}); swiss PPI drawn without them.",
-            stacklevel=2,
-        )
-    _LV95_BORDERS = lines
-    return _LV95_BORDERS
+                lines.append((_reproject_to_aoi(piece, 4326, crs), style))
+        _BORDER_LINES[key] = lines
+    return _BORDER_LINES[key]
+
 
+def _draw_borders(ax, mode, epsg, info, reach_deg: float = 3.0):
+    """Draw country borders around the radar site, in the plot's own frame.
+
+    Works for every ``coords`` value, not only the Swiss projected one: ``"xy"``
+    reprojects through an azimuthal-equidistant frame centred on the radar, which
+    is what the LUT's radar-relative metres already are.
+    """
+    if info is None:
+        return
+    lon, lat = float(info["longitude"]), float(info["latitude"])
+    if mode == "lonlat":
+        crs = 4326
+    elif mode == "xy":
+        crs = f"+proj=aeqd +lat_0={lat} +lon_0={lon} +datum=WGS84 +units=m +no_defs"
+    else:
+        crs = epsg
 
-def _draw_lv95_borders(ax):
-    """Plot the cached LV95 country borders as light lines (clipped to the axes)."""
     def _iter_lines(g):
         if g.geom_type == "LineString":
             yield g
@@ -213,9 +252,11 @@ def _iter_lines(g):
             for sub in g.geoms:
                 yield from _iter_lines(sub)
 
-    for geom in _lv95_border_lines():
+    dlon = reach_deg / max(np.cos(np.radians(lat)), 0.1)
+    clip = (lon - dlon, lat - reach_deg, lon + dlon, lat + reach_deg)
+    for geom, style in _border_lines(crs, clip):
         for line in _iter_lines(geom):
-            ax.plot(*line.xy, color="0.4", linewidth=0.6, zorder=1)
+            ax.plot(*line.xy, zorder=1, **style)
 
 
 def _add_colorbar(p, ax, is_discrete: bool, class_labels, label: str):
@@ -928,7 +969,7 @@ def _draw_polygons(ax, verts, values, plot_kwargs, edgecolor, rasterized):
 
 
 def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim,
-                     add_range_rings=True, context=False):
+                     add_range_rings=True, context=False, info=None):
     """Labels, tick formatting, aspect, range rings and limits for a map plot."""
     if mode == "lonlat":
         ax.set_xlabel("Longitude [°]")
@@ -947,8 +988,8 @@ def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim,
             ax.set_ylabel("North [km]")
         scale = 1e3
 
-    if context and mode == "projected" and epsg == 2056:
-        _draw_lv95_borders(ax)
+    if context:
+        _draw_borders(ax, mode, epsg, info)
 
     if site_xy is not None:
         ax.plot(*site_xy, "kx", markersize=7, markeredgewidth=2, zorder=5)
@@ -1488,7 +1529,7 @@ def plot_ppi(
     p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
 
     _finish_map_axes(ax, mode, epsg, verts, _site_xy(src.info, mode, epsg),
-                     xlim, ylim, add_range_rings, context)
+                     xlim, ylim, add_range_rings, context, src.info)
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
@@ -1749,7 +1790,7 @@ def plot_cappi(
 
     p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
     _finish_map_axes(ax, mode, epsg, verts, _site_xy(src.info, mode, epsg),
-                     xlim, ylim, add_range_rings, context)
+                     xlim, ylim, add_range_rings, context, src.info)
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
diff --git a/raddb/viz/profiling.py b/raddb/viz/profiling.py
deleted file mode 100644
index 675cb49..0000000
--- a/raddb/viz/profiling.py
+++ /dev/null
@@ -1,290 +0,0 @@
-"""
-raddb/viz/profiling.py
-----------------------
-Profiling dashboards for :class:`raddb.helper.StageTimer` records.
-
-Moved verbatim from ``raddb/helper.py`` so that all plotting code lives
-under ``raddb.viz``. The functions import matplotlib lazily (inside the
-function body), so importing this module stays lightweight.
-"""
-from __future__ import annotations
-
-
-def plot_stage_totals(
-    timer: "StageTimer",
-    title: str = "Pipeline — Total Time per Stage",
-    save_path: str | None = None,
-):
-    """Horizontal bar chart: total wall-clock time per pipeline stage."""
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    summary = timer.summary()
-    if summary.empty:
-        print("[profiling] No timing data — nothing to plot.")
-        return None
-
-    stages = summary.index.tolist()[::-1]
-    totals = summary["sum"].values[::-1]
-    counts = summary["count"].values[::-1].astype(int)
-    colors = plt.cm.RdYlGn_r(np.linspace(0.15, 0.85, len(stages)))
-
-    fig, ax = plt.subplots(figsize=(11, max(4, len(stages) * 0.55 + 1.5)))
-    bars = ax.barh(stages, totals, color=colors, edgecolor="white", height=0.65)
-    x_max = float(max(totals)) if len(totals) > 0 else 1.0
-    for bar, val, cnt in zip(bars, totals, counts):
-        ax.text(
-            bar.get_width() + x_max * 0.01,
-            bar.get_y() + bar.get_height() / 2,
-            f"{val:.2f}s  (n={cnt})",
-            va="center", ha="left", fontsize=8.5,
-        )
-    ax.set_xlim(0, x_max * 1.30)
-    ax.set_xlabel("Total Time (seconds)", fontsize=11)
-    ax.set_title(title, fontsize=13, pad=12)
-    ax.spines[["top", "right"]].set_visible(False)
-    ax.tick_params(labelsize=9)
-    plt.tight_layout()
-
-    if save_path:
-        fig.savefig(save_path, dpi=150, bbox_inches="tight")
-    plt.show()
-    return fig
-
-
-def plot_volume_timing(
-    timer: "StageTimer",
-    title: str = "Processing Time per Volume",
-    save_path: str | None = None,
-):
-    """Stacked bar chart: per-volume time breakdown by pipeline stage."""
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    df = timer.to_dataframe()
-    vol_df = df[df["volume"].notna()]
-    if vol_df.empty:
-        print("[profiling] No per-volume data available.")
-        return None
-
-    stage_order = (
-        vol_df.groupby("stage")["duration"].sum().sort_values(ascending=False).index.tolist()
-    )
-    pivot = (
-        vol_df.groupby(["volume", "stage"])["duration"]
-        .sum().unstack(fill_value=0.0)
-        .reindex(columns=stage_order, fill_value=0.0)
-    )
-
-    tab_colors = plt.cm.tab20.colors
-    stage_colors = {s: tab_colors[i % len(tab_colors)] for i, s in enumerate(stage_order)}
-    n_vols = len(pivot)
-
-    fig, ax = plt.subplots(figsize=(max(9, n_vols * 0.65 + 2), 6))
-    bottoms = np.zeros(n_vols)
-    for stage in stage_order:
-        ax.bar(
-            range(n_vols), pivot[stage].values, bottom=bottoms,
-            label=stage, color=stage_colors[stage], edgecolor="white", linewidth=0.4,
-        )
-        bottoms += pivot[stage].values
-
-    ax.set_xticks(range(n_vols))
-    ax.set_xticklabels([str(v)[-12:] for v in pivot.index], rotation=45, ha="right", fontsize=7.5)
-    ax.set_ylabel("Time (seconds)", fontsize=11)
-    ax.set_title(title, fontsize=13, pad=12)
-    ax.spines[["top", "right"]].set_visible(False)
-    ax.legend(bbox_to_anchor=(1.01, 1), loc="upper left", fontsize=8, title="Stage", title_fontsize=8)
-    plt.tight_layout()
-
-    if save_path:
-        fig.savefig(save_path, dpi=150, bbox_inches="tight")
-    plt.show()
-    return fig
-
-
-def plot_sweep_timing(
-    timer: "StageTimer",
-    title: str = "Processing Time per Sweep",
-    save_path: str | None = None,
-):
-    """Box-plot: distribution of total sweep processing time across volumes."""
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    df = timer.to_dataframe()
-    sweep_df = df[df["sweep"].notna() & (df["stage"] == "load_metranet_sweep")]
-    if sweep_df.empty:
-        print("[profiling] No 'load_metranet_sweep' per-sweep records found.")
-        return None
-
-    sweeps = sorted(sweep_df["sweep"].unique())
-    data = [sweep_df[sweep_df["sweep"] == s]["duration"].values for s in sweeps]
-    labels = [f"Sweep {int(s)}" for s in sweeps]
-    colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(sweeps)))
-
-    fig, ax = plt.subplots(figsize=(max(8, len(sweeps) * 0.75 + 2), 5))
-    bp = ax.boxplot(data, labels=labels, patch_artist=True, notch=False)
-    for patch, color in zip(bp["boxes"], colors):
-        patch.set_facecolor(color)
-        patch.set_alpha(0.8)
-
-    ax.set_ylabel("Time (seconds)", fontsize=11)
-    ax.set_title(title, fontsize=13, pad=12)
-    ax.grid(axis="y", alpha=0.3)
-    ax.spines[["top", "right"]].set_visible(False)
-    plt.tight_layout()
-
-    if save_path:
-        fig.savefig(save_path, dpi=150, bbox_inches="tight")
-    plt.show()
-    return fig
-
-
-def plot_profiling_dashboard(
-    timer: "StageTimer",
-    title_prefix: str = "",
-    save_path: str | None = None,
-):
-    """3x2 profiling dashboard: stage totals, distribution pie, per-volume bars,
-    stage breakdown per volume, and mean time per sweep line chart."""
-    import matplotlib.pyplot as plt
-    import matplotlib.gridspec as gridspec
-    import numpy as np
-
-    df = timer.to_dataframe()
-    if df.empty:
-        print("[profiling] No timing data — nothing to plot.")
-        return None
-
-    summary = timer.summary()
-    total_time = summary["sum"].sum() if not summary.empty else 0.0
-
-    fig = plt.figure(figsize=(18, 14))
-    gs = gridspec.GridSpec(3, 2, figure=fig, hspace=0.48, wspace=0.35)
-
-    # ── Panel 1 (top-left): Stage totals horizontal bar ──────────────────
-    ax1 = fig.add_subplot(gs[0, 0])
-    stages = summary.index.tolist()[::-1]
-    totals = summary["sum"].values[::-1]
-    bar_colors = plt.cm.RdYlGn_r(np.linspace(0.15, 0.85, len(stages)))
-    bars = ax1.barh(stages, totals, color=bar_colors, edgecolor="white", height=0.65)
-    x_max = float(max(totals)) if len(totals) > 0 else 1.0
-    for bar, val in zip(bars, totals):
-        ax1.text(
-            bar.get_width() + x_max * 0.02,
-            bar.get_y() + bar.get_height() / 2,
-            f"{val:.1f}s", va="center", ha="left", fontsize=7.5,
-        )
-    ax1.set_xlim(0, x_max * 1.30)
-    ax1.set_xlabel("Total Time (s)", fontsize=9)
-    ax1.set_title("Total Time per Stage", fontsize=11, pad=8)
-    ax1.spines[["top", "right"]].set_visible(False)
-    ax1.tick_params(labelsize=8)
-
-    # ── Panel 2 (top-right): Pie chart time distribution ─────────────────
-    ax2 = fig.add_subplot(gs[0, 1])
-    pie_colors = plt.cm.tab20.colors[: len(stages)]
-    wedges, _, autotexts = ax2.pie(
-        totals[::-1], labels=None, autopct="%1.1f%%", startangle=90,
-        colors=pie_colors, pctdistance=0.75,
-    )
-    for at in autotexts:
-        at.set_fontsize(7)
-    ax2.legend(
-        wedges, stages[::-1],
-        loc="center left", bbox_to_anchor=(1.0, 0.5), fontsize=7.5,
-        title="Stage", title_fontsize=8,
-    )
-    ax2.set_title("Time Distribution", fontsize=11, pad=8)
-
-    # ── Panel 3 (middle-left): Per-volume total time bar ─────────────────
-    ax3 = fig.add_subplot(gs[1, 0])
-    vol_df = df[df["volume"].notna()]
-    if not vol_df.empty:
-        vol_totals = vol_df.groupby("volume")["duration"].sum()
-        x3 = np.arange(len(vol_totals))
-        ax3.bar(x3, vol_totals.values, color="steelblue", edgecolor="white", linewidth=0.4)
-        ax3.set_xticks(x3)
-        ax3.set_xticklabels(
-            [str(v)[-12:] for v in vol_totals.index], rotation=45, ha="right", fontsize=7,
-        )
-        ax3.axhline(
-            vol_totals.mean(), color="crimson", linestyle="--", lw=1.5, alpha=0.8,
-            label=f"mean = {vol_totals.mean():.1f}s",
-        )
-        ax3.set_ylabel("Time (s)", fontsize=9)
-        ax3.set_title("Total Time per Volume", fontsize=11, pad=8)
-        ax3.legend(fontsize=8)
-        ax3.spines[["top", "right"]].set_visible(False)
-        ax3.tick_params(labelsize=7.5)
-    else:
-        ax3.text(0.5, 0.5, "No per-volume data", ha="center", va="center", transform=ax3.transAxes)
-        ax3.set_title("Total Time per Volume", fontsize=11, pad=8)
-
-    # ── Panel 4 (middle-right): Stage breakdown per volume (stacked) ─────
-    ax4 = fig.add_subplot(gs[1, 1])
-    if not vol_df.empty:
-        stage_order = (
-            vol_df.groupby("stage")["duration"].sum().sort_values(ascending=False).index.tolist()
-        )
-        pivot = (
-            vol_df.groupby(["volume", "stage"])["duration"]
-            .sum().unstack(fill_value=0.0)
-            .reindex(columns=stage_order, fill_value=0.0)
-        )
-        tab_colors = plt.cm.tab20.colors
-        bottoms4 = np.zeros(len(pivot))
-        for i, stage in enumerate(stage_order):
-            ax4.bar(
-                range(len(pivot)), pivot[stage].values, bottom=bottoms4,
-                label=stage, color=tab_colors[i % len(tab_colors)], edgecolor="white", linewidth=0.3,
-            )
-            bottoms4 += pivot[stage].values
-        ax4.set_xticks(range(len(pivot)))
-        ax4.set_xticklabels(
-            [str(v)[-12:] for v in pivot.index], rotation=45, ha="right", fontsize=7,
-        )
-        ax4.set_ylabel("Time (s)", fontsize=9)
-        ax4.set_title("Stage Breakdown per Volume", fontsize=11, pad=8)
-        ax4.legend(fontsize=6.5, loc="upper right", title="Stage", title_fontsize=7)
-        ax4.spines[["top", "right"]].set_visible(False)
-    else:
-        ax4.text(0.5, 0.5, "No per-volume data", ha="center", va="center", transform=ax4.transAxes)
-        ax4.set_title("Stage Breakdown per Volume", fontsize=11, pad=8)
-
-    # ── Panel 5 (bottom, full width): Mean time per sweep per top stage ──
-    ax5 = fig.add_subplot(gs[2, :])
-    sweep_df = df[df["sweep"].notna()]
-    if not sweep_df.empty:
-        top_stages = (
-            sweep_df.groupby("stage")["duration"].sum()
-            .sort_values(ascending=False).head(6).index.tolist()
-        )
-        line_colors = plt.cm.tab10.colors
-        for idx, stage in enumerate(top_stages):
-            by_sweep = sweep_df[sweep_df["stage"] == stage].groupby("sweep")["duration"].mean()
-            ax5.plot(
-                by_sweep.index, by_sweep.values,
-                "o-", label=stage, color=line_colors[idx % 10], markersize=5, linewidth=1.5,
-            )
-        ax5.set_xlabel("Sweep Number", fontsize=9)
-        ax5.set_ylabel("Mean Time (s)", fontsize=9)
-        ax5.set_title("Mean Processing Time per Sweep — Top Stages", fontsize=11, pad=8)
-        ax5.legend(fontsize=8, loc="upper right", title="Stage", title_fontsize=8)
-        ax5.grid(alpha=0.3)
-        ax5.spines[["top", "right"]].set_visible(False)
-    else:
-        ax5.text(0.5, 0.5, "No per-sweep data", ha="center", va="center", transform=ax5.transAxes)
-        ax5.set_title("Mean Processing Time per Sweep", fontsize=11, pad=8)
-
-    prefix = f"{title_prefix} — " if title_prefix else ""
-    fig.suptitle(
-        f"{prefix}Pipeline Profiling Dashboard  (total: {total_time:.1f}s)",
-        fontsize=14, fontweight="bold", y=1.01,
-    )
-
-    if save_path:
-        fig.savefig(save_path, dpi=150, bbox_inches="tight")
-    plt.show()
-    return fig
diff --git a/raddb/viz/report_hc_reference_figure.py b/raddb/viz/report_hc_reference_figure.py
deleted file mode 100644
index 1e7683d..0000000
--- a/raddb/viz/report_hc_reference_figure.py
+++ /dev/null
@@ -1,174 +0,0 @@
-"""
-Generate the hydrometeor-classification reference figure for the report.
-
-The figure shows a single PPI sweep with:
-1. MeteoSwiss operational hydrometeor classification (HC_MCH)
-2. PyART-based hydrometeor classification (HC_PYART)
-3. Gate-level agreement between the two stored labels
-
-The agreement panel is intended as an illustrative reminder that the two
-classification products are reference labels, not absolute ground truth.
-"""
-from __future__ import annotations
-
-from pathlib import Path
-import os
-import sys
-
-os.environ.setdefault("MPLCONFIGDIR", "/tmp/raddb-mplconfig")
-os.environ.setdefault("XDG_CACHE_HOME", "/tmp/raddb-cache")
-
-import matplotlib.pyplot as plt
-from matplotlib.colors import BoundaryNorm, ListedColormap
-from matplotlib.patches import Patch
-import numpy as np
-import xarray as xr
-
-sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
-
-import raddb
-
-
-BASE_PATH = "/home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb"
-RADAR_NAME = "L"
-PANEL_TIMESTEP = "2024-07-15 23:05:07"
-PANEL_SWEEP = 4
-
-OUTPUT_DIR = Path(__file__).resolve().parent / "figures" / "report"
-OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-OUTPUT_PATH = OUTPUT_DIR / "hc_reference_ppi_comparison.png"
-
-XY_LIMITS = (-150, 150)
-TITLE_FONTSIZE = 13
-SUBPLOT_TITLE_FONTSIZE = 11
-
-MATCH_CMAP = ListedColormap(["#d73027", "#1a9850"])
-MATCH_NORM = BoundaryNorm([-0.5, 0.5, 1.5], MATCH_CMAP.N)
-
-
-def _load_archived_datatree():
-    db = raddb.RadDB(base_path=BASE_PATH)
-    return db.load_datatree(
-        radar=RADAR_NAME,
-        start_time=PANEL_TIMESTEP,
-        end_time=PANEL_TIMESTEP,
-    )
-
-
-def _add_hc_match(dt):
-    sweep_names = sorted(
-        [group.lstrip("/") for group in dt.groups if group.lstrip("/").startswith("sweep_")],
-        key=lambda name: int(name.split("_")[-1]),
-    )
-
-    dict_ds = {}
-    for sweep_name in sweep_names:
-        ds = dt[sweep_name].to_dataset()
-        if "HC_MCH" in ds.variables and "HC_PYART" in ds.variables:
-            hc_mch = ds["HC_MCH"].values.astype(float)
-            hc_pyart = ds["HC_PYART"].values.astype(float)
-            valid = np.isfinite(hc_mch) & np.isfinite(hc_pyart)
-            match = np.where(valid, (hc_mch == hc_pyart).astype(float), np.nan)
-            ds = ds.assign({"hc_match": (ds["HC_MCH"].dims, match)})
-        dict_ds[sweep_name] = ds
-
-    return xr.DataTree.from_dict(dict_ds)
-
-
-def _match_stats(dt_match) -> tuple[int, int, int, float, float]:
-    ds = dt_match[f"sweep_{PANEL_SWEEP}"].to_dataset()
-    match = ds["hc_match"].values.astype(float)
-    valid = np.isfinite(match)
-    total = int(valid.sum())
-    same = int(np.nansum(match == 1))
-    different = int(np.nansum(match == 0))
-    same_pct = 100 * same / total if total else float("nan")
-    different_pct = 100 * different / total if total else float("nan")
-    return total, same, different, same_pct, different_pct
-
-
-def _plot_hc_panel(dt, dt_match, output_path: Path) -> Path:
-    _, _, _, match_pct, mismatch_pct = _match_stats(dt_match)
-    fig, axes = plt.subplots(1, 3, figsize=(13, 4.6))
-
-    panels = [
-        (dt, "HC_MCH", "HC MCH", {}),
-        (dt, "HC_PYART", "HC PyART", {}),
-        (
-            dt_match,
-            "hc_match",
-            "HC MCH == HC PyART",
-            dict(cmap=MATCH_CMAP, norm=MATCH_NORM, add_colorbar=False),
-        ),
-    ]
-
-    for idx, (ax, (source, variable, title, plot_kwargs)) in enumerate(zip(axes, panels)):
-        p = raddb.plot_ppi(
-            source,
-            sweep=PANEL_SWEEP,
-            variable=variable,
-            ax=ax,
-            coords="cartesian",
-            **plot_kwargs,
-        )
-        ax.set_title(title, fontsize=SUBPLOT_TITLE_FONTSIZE)
-        ax.set_xlim(XY_LIMITS)
-        ax.set_ylim(XY_LIMITS)
-
-        if idx != 0:
-            ax.set_ylabel("")
-            ax.tick_params(labelleft=False)
-
-        if variable == "hc_match":
-            cbar = plt.colorbar(p, ax=ax, ticks=[0, 1], fraction=0.046, pad=0.04)
-            cbar.ax.set_yticklabels(["Mismatch", "Match"])
-            legend_handles = [
-                Patch(facecolor=MATCH_CMAP(1), edgecolor="none", label=f"Match: {match_pct:.1f}%"),
-                Patch(facecolor=MATCH_CMAP(0), edgecolor="none", label=f"Mismatch: {mismatch_pct:.1f}%"),
-            ]
-            ax.legend(
-                handles=legend_handles,
-                loc="lower right",
-                frameon=True,
-                facecolor="white",
-                edgecolor="0.75",
-                framealpha=0.88,
-                fontsize=7.5,
-                borderpad=0.3,
-                labelspacing=0.25,
-                handlelength=1.4,
-                handletextpad=0.5,
-            )
-
-    axes[2].set_xlim(axes[0].get_xlim())
-    axes[2].set_ylim(axes[0].get_ylim())
-
-    fig.suptitle(
-        f"Radar {RADAR_NAME} | {PANEL_TIMESTEP} | sweep {PANEL_SWEEP}",
-        fontsize=TITLE_FONTSIZE,
-        y=0.84,
-    )
-    fig.tight_layout(rect=(0, 0, 1, 0.91))
-    fig.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-    return output_path
-
-
-def _print_match_summary(dt_match) -> None:
-    total, same, different, same_pct, different_pct = _match_stats(dt_match)
-    print(f"Valid gates: {total:,}")
-    print(f"Same label: {same:,} ({same_pct:.1f}%)")
-    print(f"Different label: {different:,} ({different_pct:.1f}%)")
-
-
-def main() -> int:
-    dt = _load_archived_datatree()
-    dt_match = _add_hc_match(dt)
-    _print_match_summary(dt_match)
-    output_path = _plot_hc_panel(dt, dt_match, OUTPUT_PATH)
-    print(f"Figure: {output_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/raddb/viz/report_raddb_figures.py b/raddb/viz/report_raddb_figures.py
deleted file mode 100644
index 9984926..0000000
--- a/raddb/viz/report_raddb_figures.py
+++ /dev/null
@@ -1,224 +0,0 @@
-"""
-Generate RadDB report figures.
-
-This script is a report-focused version of the multi-feature panel section in
-``examples/basic_usage.py``. It generates:
-
-1. A 2x3 PPI panel for DBZH, ZDR, KDP, RHOHV, TEMP, HC_PYART.
-2. A 2x2 RHI panel for DBZH, ZDR, KDP, RHOHV.
-"""
-from __future__ import annotations
-
-from pathlib import Path
-import os
-import sys
-
-os.environ.setdefault("MPLCONFIGDIR", "/tmp/raddb-mplconfig")
-os.environ.setdefault("XDG_CACHE_HOME", "/tmp/raddb-cache")
-
-import matplotlib.pyplot as plt
-from matplotlib.colors import TwoSlopeNorm
-
-sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
-
-import raddb
-
-try:
-    import pyart  # noqa: F401  # Registers Py-ART colormaps when available.
-except Exception:
-    pass
-
-try:
-    import cmweather  # noqa: F401  # Registers weather-radar colormaps.
-except Exception:
-    pass
-
-
-BASE_PATH = "/home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb"
-RADAR_NAME = "L"
-PANEL_TIMESTEP = "2024-07-15 23:05:07"
-PANEL_SWEEP = 4
-PANEL_AZIMUTH = 315
-
-OUTPUT_DIR = Path(__file__).resolve().parent / "figures" / "report"
-OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-
-PPI_FEATURES = ["DBZH", "ZDR", "KDP", "RHOHV", "TEMP", "HC_PYART"]
-RHI_FEATURES = ["DBZH", "ZDR", "KDP", "RHOHV"]
-
-COMMON_XY_LIMITS = (-150, 150)
-TITLE_FONTSIZE = 13
-SUBPLOT_TITLE_FONTSIZE = 11
-TEMP_TICKS = [-30, -20, -10, 0, 10, 20, 30]
-
-
-def _weather_cmap(name: str, fallback: str):
-    if name in plt.colormaps():
-        return name
-    return fallback
-
-
-def _first_available_cmap(*names: str) -> str:
-    for name in names:
-        if name in plt.colormaps():
-            return name
-    return names[-1]
-
-
-FEATURE_KWARGS = {
-    "DBZH": dict(
-        cmap=_first_available_cmap("HomeyerRainbow", "turbo"),
-        vmin=0,
-        vmax=60,
-        subtitle=r"$Z_H$",
-    ),
-    "ZDR": dict(
-        cmap="viridis",
-        vmin=-2,
-        vmax=7,
-        subtitle=r"$Z_{DR}$",
-    ),
-    "KDP": dict(
-        cmap="twilight",
-        vmin=-2,
-        vmax=5,
-        subtitle=r"$K_{dp}$",
-    ),
-    "RHOHV": dict(
-        cmap="cividis",
-        vmin=0.5,
-        vmax=1.0,
-        subtitle=r"$\rho_{hv}$",
-    ),
-    "TEMP": dict(
-        cmap="coolwarm",
-        norm=TwoSlopeNorm(vmin=-30, vcenter=0, vmax=30),
-        vmin=None,
-        vmax=None,
-        subtitle="Temperature",
-    ),
-    "HC_PYART": dict(
-        subtitle="HC PyART",
-    ),
-}
-
-LOCAL_KEYS = {"subtitle"}
-
-
-def _plot_kwargs(feature: str) -> dict:
-    return {
-        key: value
-        for key, value in FEATURE_KWARGS[feature].items()
-        if key not in LOCAL_KEYS
-    }
-
-
-def _set_report_colorbar_ticks(feature: str, mappable) -> None:
-    if feature != "TEMP":
-        return
-    cbar = getattr(mappable, "colorbar", None)
-    if cbar is not None:
-        cbar.set_ticks(TEMP_TICKS)
-
-
-def create_ppi_panel(dt, output_path: Path) -> Path:
-    fig, axes = plt.subplots(2, 3, figsize=(13, 7))
-
-    for idx, (ax, feature) in enumerate(zip(axes.ravel(), PPI_FEATURES)):
-        p = raddb.plot_ppi(
-            dt,
-            sweep=PANEL_SWEEP,
-            variable=feature,
-            ax=ax,
-            coords="cartesian",
-            **_plot_kwargs(feature),
-        )
-        _set_report_colorbar_ticks(feature, p)
-        ax.set_title(FEATURE_KWARGS[feature]["subtitle"], fontsize=SUBPLOT_TITLE_FONTSIZE)
-        ax.set_xlim(COMMON_XY_LIMITS)
-        ax.set_ylim(COMMON_XY_LIMITS)
-
-        row, col = divmod(idx, 3)
-        if row == 0:
-            ax.set_xlabel("")
-            ax.tick_params(labelbottom=False)
-        if col != 0:
-            ax.set_ylabel("")
-            ax.tick_params(labelleft=False)
-
-    fig.suptitle(
-        f"Radar {RADAR_NAME} | {PANEL_TIMESTEP} | sweep {PANEL_SWEEP}",
-        fontsize=TITLE_FONTSIZE,
-    )
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-    return output_path
-
-
-def create_rhi_panel(dt, output_path: Path) -> Path:
-    fig, axes = plt.subplots(2, 2, figsize=(12, 7))
-
-    for idx, (ax, feature) in enumerate(zip(axes.ravel(), RHI_FEATURES)):
-        p = raddb.plot_rhi(
-            dt,
-            azimuth=PANEL_AZIMUTH,
-            variable=feature,
-            radar=RADAR_NAME,
-            max_range_km=110,
-            max_height_km=11,
-            ax=ax,
-            **_plot_kwargs(feature),
-        )
-        _set_report_colorbar_ticks(feature, p)
-        ax.set_title(FEATURE_KWARGS[feature]["subtitle"], fontsize=SUBPLOT_TITLE_FONTSIZE)
-
-        row, col = divmod(idx, 2)
-        if row == 0:
-            ax.set_xlabel("")
-            ax.tick_params(labelbottom=False)
-        if col != 0:
-            ax.set_ylabel("")
-            ax.tick_params(labelleft=False)
-
-    for ax in axes[:, 0]:
-        ax.set_ylabel("Height [km]")
-    for ax in axes[-1, :]:
-        ax.set_xlabel("Range [km]")
-
-    fig.suptitle(
-        f"Radar {RADAR_NAME} | {PANEL_TIMESTEP} | azimuth {PANEL_AZIMUTH}$^\\circ$",
-        fontsize=TITLE_FONTSIZE,
-    )
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-    return output_path
-
-
-def _load_archived_datatree():
-    db = raddb.RadDB(base_path=BASE_PATH)
-    return db.load_datatree(
-        radar=RADAR_NAME,
-        start_time=PANEL_TIMESTEP,
-        end_time=PANEL_TIMESTEP,
-    )
-
-
-def load_panel_datatree():
-    return _load_archived_datatree()
-
-
-def main() -> int:
-    dt = load_panel_datatree()
-
-    ppi_path = create_ppi_panel(dt, OUTPUT_DIR / "raddb_ppi_panel.png")
-    rhi_path = create_rhi_panel(dt, OUTPUT_DIR / "raddb_rhi_panel.png")
-    print(f"PPI figure: {ppi_path}")
-    print(f"RHI figure: {rhi_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/tutorial/04_plots.ipynb b/tutorial/04_plots.ipynb
index a37d35b..c5b5b50 100644
--- a/tutorial/04_plots.ipynb
+++ b/tutorial/04_plots.ipynb
@@ -7,19 +7,20 @@
    "source": [
     "# 4. Plots\n",
     "\n",
-    "RadDB draws four things. Each one **draws a single plot into a single Axes** and\n",
-    "returns the matplotlib artist, so you compose panels yourself by passing `ax=`.\n",
+    "RadDB draws four kinds of radar plot:\n",
     "\n",
-    "| method | what it fixes | reads |\n",
+    "| method | name | what it shows |\n",
     "|---|---|---|\n",
-    "| `plot_ppi(sweep=...)` | one sweep, seen from above | horizontal gate faces |\n",
-    "| `plot_rhi(azimuth=...)` | one azimuth, all sweeps stacked | vertical gate faces |\n",
-    "| `plot_cappi(altitude=...)` | one altitude surface | vertical **and** horizontal faces |\n",
-    "| `plot_vcs(line=...)` | an arbitrary vertical slice | the cross-section geometry |\n",
+    "| `plot_ppi(sweep=...)` | Plan Position Indicator | one sweep seen from above — a map |\n",
+    "| `plot_rhi(azimuth=...)` | Range Height Indicator | one azimuth seen from the side — a vertical slice along one ray |\n",
+    "| `plot_cappi(altitude=...)` | Constant Altitude PPI | one altitude seen from above — a horizontal slice through the volume |\n",
+    "| `plot_vcs(line=...)` | Vertical Cross-Section | a vertical slice along any line you choose |\n",
     "\n",
-    "They all read the gate geometry from the LUT and join on `gate_id`, which means a\n",
-    "filtered, cropped or `sel`-ed RadDB **plots exactly the gates it holds** — nothing\n",
-    "is re-gridded onto a full azimuth x range mesh behind your back.\n",
+    "Each one draws into **one Axes** and returns the matplotlib artist, so you build\n",
+    "multi-panel figures yourself by passing `ax=` (section 7).\n",
+    "\n",
+    "They plot exactly the gates the data holds: filter it, crop it or `sel` it first\n",
+    "and the plot follows, gate for gate.\n",
     "\n",
     "---"
    ]
@@ -59,7 +60,7 @@
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — edit these three paths to point at your own data\n",
     "# --------------------------------------------------------------------------\n",
-    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n",
+    "# ARCHIVE_DIR must be the same archive tutorial 1 wrote. If it has not run,\n",
     "# the cell below builds it.\n",
     "\n",
     "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
@@ -114,6 +115,89 @@
     "print(f\"{len(rdf):,} gates | variables: {rdf.columns()}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b7b4fb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#==================USING NEXRAD DATA=========================\n",
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "rdf = db.open(radars=\"KTLX\")\n",
+    "info = db.get_radar_info(\"KTLX\")\n",
+    "SITE = (info[\"longitude\"], info[\"latitude\"])\n",
+    "print(f\"{len(rdf):,} gates | variables: {rdf.columns()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9353cc7",
+   "metadata": {},
+   "source": [
+    "## 0. Which timesteps can I plot?\n",
+    "\n",
+    "Every plot draws **one volume**. If `rdf` holds more than one, you must say which\n",
+    "with `timestep=`, otherwise the call raises rather than drawing them on top of\n",
+    "each other:\n",
+    "\n",
+    "```\n",
+    "ValueError: data holds 7 volumes (2024-06-08 09:45:03 ... 2024-06-12 00:40:05);\n",
+    "pass timestep= to pick one, or narrow with start_time=/end_time=.\n",
+    "```\n",
+    "\n",
+    "The list is in the data itself — `volume_time` is a normal column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76958aa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TIMESTEPS = rdf.data[\"volume_time\"].unique().sort().to_list()\n",
+    "print(f\"{len(TIMESTEPS)} volumes in rdf:\")\n",
+    "for t in TIMESTEPS:\n",
+    "    print(\"  \", t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f33ff0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
+    "print(f\"{len(rdf):,} gates -> {len(df):,} above 30 dBZ\")\n",
+    "df.to_pandas().describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32adbfbd",
+   "metadata": {},
+   "source": [
+    "`timestep=` takes anything pandas reads as a time and picks the **nearest**\n",
+    "volume, so you can be as loose or as exact as you like:\n",
+    "\n",
+    "```python\n",
+    "rdf.plot_ppi(sweep=1, timestep=\"2024-06-12\")            # nearest to midnight -> 00:30:08\n",
+    "rdf.plot_ppi(sweep=1, timestep=\"2024-06-12 00:35\")      # nearest to 00:35    -> 00:35:01\n",
+    "rdf.plot_ppi(sweep=1, timestep=TIMESTEPS[-1])           # exactly that volume\n",
+    "```\n",
+    "\n",
+    "Two other ways to get to a single volume:\n",
+    "\n",
+    "- `start_time=` / `end_time=` narrow the candidates first — if only one is left,\n",
+    "  `timestep=` is not needed at all;\n",
+    "- `rdf.sel(volume_time=TIMESTEPS[0])` (tutorial 2) returns a RadDB holding just\n",
+    "  that volume, which then plots with no time argument at all.\n",
+    "\n",
+    "The rest of this notebook uses `timestep=\"2024-06-12\"`."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a12e1078",
@@ -136,8 +220,9 @@
    },
    "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
-    "rdf.plot_ppi(sweep=4, variable=\"DBZH\", timestep=\"2024-06-08\", ax=ax)\n",
+    "fig, ax = plt.subplots()\n",
+    "rdf.plot_ppi(sweep=11, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax, coords=\"projected\", context=True)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -146,10 +231,7 @@
    "id": "9d98906f",
    "metadata": {},
    "source": [
-    "## 2. RHI — one azimuth, all sweeps\n",
-    "\n",
-    "The RHI stacks every sweep along one azimuth, so you see the vertical structure\n",
-    "of the beam fan."
+    "## 2. RHI"
    ]
   },
   {
@@ -168,6 +250,7 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "rdf.plot_rhi(azimuth=90, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -176,11 +259,7 @@
    "id": "0c00b398",
    "metadata": {},
    "source": [
-    "## 3. CAPPI — a constant-altitude surface\n",
-    "\n",
-    "A CAPPI takes the gates whose beam crosses a given altitude. Because the beam\n",
-    "climbs with range, that means different sweeps at different distances — which is\n",
-    "why a CAPPI usually covers more area than any single sweep."
+    "## 3. CAPPI"
    ]
   },
   {
@@ -234,6 +313,7 @@
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
     "cs.plot_vcs(variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -254,12 +334,67 @@
     "exactly once."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b9d78cc2",
+   "metadata": {},
+   "source": [
+    "### 4.1 Drawing the section on the map\n",
+    "\n",
+    "Typing lon/lat pairs is fine when you know where the storm is. When you don't,\n",
+    "draw the line instead: `interactive_crop()` (tutorial 3) puts an ipyleaflet map in\n",
+    "front of you, and a **polyline** is dispatched to `extract_cross_section`.\n",
+    "\n",
+    "So the whole chain is: draw → *Apply crop* → `selector.result` → `plot_vcs()`.\n",
+    "The result already carries `cs_polygon`, so `plot_vcs` needs **no** `line=`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfbd36ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RUN_INTERACTIVE = True     # <- set False to skip the map (e.g. outside Jupyter)\n",
+    "\n",
+    "if RUN_INTERACTIVE:\n",
+    "    # Pick the \"/\" polyline tool in the toolbar, draw a line across the echo,\n",
+    "    # then click \"Apply crop\".  Only then run the next cell.\n",
+    "    selector = rdf.sel(volume_time=TIMESTEPS[4]).interactive_crop()\n",
+    "else:\n",
+    "    selector = None\n",
+    "    print(\"needs a live Jupyter kernel ==> set RUN_INTERACTIVE = True\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04351979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drawn = getattr(selector, \"result\", None)\n",
+    "\n",
+    "if drawn is None:\n",
+    "    print(\"nothing applied yet — draw a line above and click 'Apply crop'.\")\n",
+    "elif selector.kind != \"cross_section\":\n",
+    "    print(f\"you drew a {selector.kind!r}, not a line; \"\n",
+    "          \"only the polyline tool produces a cross-section.\")\n",
+    "else:\n",
+    "    print(f\"{len(drawn):,} gates on the drawn section\")\n",
+    "    fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+    "    drawn.plot_vcs(variable=\"DBZH\", ax=ax)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "06dabdbe",
    "metadata": {},
    "source": [
-    "## 5. Coordinates\n",
+    "## 5. Coordinates and Context\n",
     "\n",
     "`coords` controls the horizontal frame of the map-like plots:\n",
     "\n",
@@ -287,7 +422,7 @@
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
     "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"]):\n",
-    "    rdf.plot_ppi(sweep=1, ax=ax, coords=coords)\n",
+    "    rdf.plot_ppi(sweep=1, ax=ax, coords=coords, timestep=\"2024-06-12\")\n",
     "    ax.set_title(f\"coords={coords!r}\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -298,8 +433,9 @@
    "id": "a9362ae8",
    "metadata": {},
    "source": [
-    "`context=True` adds cartopy borders and coastlines. Projection and basemap are\n",
-    "independent — you choose the frame with `coords`, the background with `context`."
+    "`context=True` overlays country borders and coastlines. Projection and backdrop\n",
+    "are independent: you choose the frame with `coords`, the backdrop with `context`,\n",
+    "and the borders are reprojected into whichever frame you picked."
    ]
   },
   {
@@ -316,11 +452,61 @@
    },
    "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(figsize=(6.4, 5.4))\n",
-    "try:\n",
-    "    rdf.plot_ppi(sweep=1, ax=ax, coords=\"lonlat\", context=True)\n",
-    "except Exception as exc:\n",
-    "    ax.set_title(f\"cartopy unavailable: {type(exc).__name__}\")\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
+    "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"]):\n",
+    "    rdf.plot_ppi(sweep=1, ax=ax, coords=coords, context=True, timestep=\"2024-06-12\")\n",
+    "    ax.set_title(f\"coords={coords!r}\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06b94e34",
+   "metadata": {},
+   "source": [
+    "### 5.1 Choosing the extent\n",
+    "\n",
+    "`xlim=` / `ylim=` are ordinary arguments of all four plots — not `**plot_kwargs`,\n",
+    "which go to the colouring. Each takes a `(min, max)` pair **in the units of the\n",
+    "frame you asked for**:\n",
+    "\n",
+    "| `coords` | units of `xlim` / `ylim` |\n",
+    "|---|---|\n",
+    "| `\"xy\"` | metres from the radar (negative to the west/south) |\n",
+    "| `\"lonlat\"` | degrees |\n",
+    "| `\"projected\"` / an EPSG int | metres in that projection (LV95 is ~2.6e6 / 1.2e6) |\n",
+    "\n",
+    "The tick *labels* are in km, the numbers you pass are in metres — that is why\n",
+    "`xlim=(-50_000, 50_000)` shows an axis running −50 to 50.\n",
+    "\n",
+    "Left alone, a PPI/CAPPI frames a square centred on the radar, sized by the\n",
+    "furthest gate drawn. `plot_rhi` also accepts `max_range_km` / `max_height_km` as a\n",
+    "friendlier spelling of the same thing.\n",
+    "\n",
+    "`ax.set_xlim(...)` after the call works too — the plot is plain matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a19b5fe1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# same sweep, zoomed to 60 km around the radar\n",
+    "rdf.plot_ppi(sweep=3, ax=axes[0], coords=\"xy\", context=True, timestep=\"2024-06-12\",\n",
+    "             xlim=(-60_000, 60_000), ylim=(-60_000, 60_000))\n",
+    "axes[0].set_title(\"coords='xy' | ±60 km\")\n",
+    "\n",
+    "# the same window written in degrees\n",
+    "rdf.plot_ppi(sweep=3, ax=axes[1], coords=\"lonlat\", context=True, timestep=\"2024-06-12\",\n",
+    "             xlim=(SITE[0] - 0.8, SITE[0] + 0.8), ylim=(SITE[1] - 0.55, SITE[1] + 0.55))\n",
+    "axes[1].set_title(\"coords='lonlat' | same box but in degrees\")\n",
+    "\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -349,11 +535,15 @@
    },
    "outputs": [],
    "source": [
-    "moments = [v for v in (\"DBZH\", \"ZDR\", \"RHOHV\", \"PHIDP\") if v in rdf.columns()]\n",
+    "VARS = [v for v in (\"DBZH\", \"ZDR\", \"RHOHV\", \"PHIDP\") if v in rdf.columns()]\n",
     "\n",
-    "fig, axes = plt.subplots(1, len(moments), figsize=(4.3 * len(moments), 4.0))\n",
-    "for ax, var in zip(axes, moments):\n",
-    "    rdf.plot_ppi(sweep=1, variable=var, ax=ax)\n",
+    "# Pick a sweep that actually carries the dual-pol moments.  On NEXRAD the low\n",
+    "# tilts are \"split cuts\": the odd sweeps are the Doppler half and hold DBZH only,\n",
+    "# so plotting ZDR there raises \"every 'ZDR' value is NaN\".\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(11, 9))\n",
+    "for ax, var in zip(axes.flat, VARS):\n",
+    "    rdf.plot_ppi(sweep=2, variable=var, ax=ax, timestep=\"2024-06-12\",\n",
+    "                 context=True, coords=\"xy\")\n",
     "    ax.set_title(var)\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -377,20 +567,13 @@
     "sub = (rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
     "          .crop_around_point(point=SITE, distance=50_000, crs=4326))\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
-    "art = sub.plot_ppi(sweep=1, ax=ax)\n",
-    "ax.set_title(f\"DBZH > 30 dBZ within 50 km — {len(art.get_paths()):,} gates drawn\")\n",
+    "fig, ax = plt.subplots()\n",
+    "art = sub.plot_ppi(sweep=1, ax=ax, timestep=\"2024-06-12\", coords=\"xy\", context=True)\n",
+    "ax.set_title(f\"DBZH > 30 dBz within 50 km\")\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8272486c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "52fa26d5",
@@ -416,11 +599,11 @@
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(2, 2, figsize=(11.5, 9))\n",
-    "rdf.plot_ppi(sweep=1, ax=axes[0][0])\n",
-    "rdf.plot_rhi(azimuth=90, ax=axes[0][1])\n",
-    "rdf.plot_cappi(altitude=3000, ax=axes[1][0])\n",
-    "cs.plot_vcs(ax=axes[1][1])\n",
-    "fig.suptitle(\"radar L — PPI, RHI, CAPPI, cross-section\", fontsize=15)\n",
+    "rdf.plot_ppi(sweep=1, ax=axes[0][0], timestep=\"2024-06-12\", title=\"PPI | sweep 1\")\n",
+    "rdf.plot_rhi(azimuth=90, ax=axes[0][1], timestep=\"2024-06-12\", title=\"RHI | azimuth 90°\")\n",
+    "rdf.plot_cappi(altitude=3000, ax=axes[1][0], timestep=\"2024-06-12\", title=\"CAPPI | altitude 3 km\")\n",
+    "cs.plot_vcs(ax=axes[1][1], timestep=\"2024-06-12\", title=\"cross-section\")\n",
+    "fig.suptitle(f\"radar {rdf.radars()[0]} | DBZH | 2024-06-12\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -456,8 +639,9 @@
     "dt = raddb.open_any_datatree(sorted(MCH_DIR.glob(\"L_*.zarr\"))[0])\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
-    "raddb.plot_ppi(dt, sweep=1, variable=\"DBZH\", ax=ax)\n",
+    "raddb.plot_ppi(dt, sweep=4, variable=\"DBZH\", ax=ax)\n",
     "ax.set_title(\"straight from a DataTree — no archive\")\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -489,42 +673,11 @@
    "source": [
     "out = ARCHIVE_DIR / \"ppi_example.png\"\n",
     "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
-    "rdf.plot_ppi(sweep=1, ax=ax, rasterized=True)\n",
-    "fig.savefig(out, dpi=120, bbox_inches=\"tight\")\n",
+    "rdf.plot_ppi(sweep=1, ax=ax, rasterized=True, timestep=\"2024-06-12\")\n",
+    "fig.savefig(out, dpi=300, bbox_inches=\"tight\")\n",
     "plt.close(fig)\n",
     "print(\"saved:\", out, f\"({out.stat().st_size / 1e3:.0f} kB)\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6bcf98cc",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## Recap\n",
-    "\n",
-    "```python\n",
-    "rdf.plot_ppi(sweep=1, variable=\"DBZH\")\n",
-    "rdf.plot_rhi(azimuth=90)\n",
-    "rdf.plot_cappi(altitude=3000)\n",
-    "rdf.plot_vcs(line=[(lon1, lat1), (lon2, lat2)], crs=4326)\n",
-    "```\n",
-    "\n",
-    "Shared arguments: `variable`, `radar`, `timestep`, `start_time` / `end_time`,\n",
-    "`coords`, `context`, `ax`, `save`, `rasterized`, and anything else is forwarded to\n",
-    "matplotlib.\n",
-    "\n",
-    "A few things worth remembering:\n",
-    "\n",
-    "- One plot, one Axes — **you** compose the figure with `ax=`.\n",
-    "- Geometry always comes from the LUT, so what you filtered is what you see.\n",
-    "- Beam width is a property of the **archive**, fixed when the LUT was generated\n",
-    "  (`beamwidth_deg` in `info.yaml`), not a plot argument.\n",
-    "- For a huge sweep, `rasterized=True` before saving to PDF.\n",
-    "\n",
-    "---\n",
-    "That is the whole workflow: **archive → open & filter → crop → plot.**"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorial/05_demo_pipeline.ipynb b/tutorial/05_demo_pipeline.ipynb
new file mode 100644
index 0000000..d1b4b3b
--- /dev/null
+++ b/tutorial/05_demo_pipeline.ipynb
@@ -0,0 +1,1589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "952beaf7",
+   "metadata": {},
+   "source": [
+    "# 5. Demo Pipeline\n",
+    "\n",
+    "A complete pipeline in one notebook: **download** raw volumes from three public\n",
+    "networks, **archive** them, **plot** them, and cut a **vertical cross-section**.\n",
+    "\n",
+    "| radar | network | format | reader |\n",
+    "|---|---|---|---|\n",
+    "| `KDVN` | NEXRAD (US) — Davenport, Iowa | Level II | `open_nexradlevel2_datatree` |\n",
+    "| `FANJ` | FMI (Finland) — Anjalankoski | ODIM HDF5 | `open_odim_datatree` |\n",
+    "| `GUA` | IDEAM (Colombia) — Guaviare | IRIS/Sigmet | `open_iris_datatree` |\n",
+    "\n",
+    "All three are public and need no credentials, and the cases below are only the\n",
+    "defaults — section 1 is where you pick a different time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff55e798",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:20:36.749666Z",
+     "iopub.status.busy": "2026-08-14T15:20:36.749486Z",
+     "iopub.status.idle": "2026-08-14T15:20:38.348661Z",
+     "shell.execute_reply": "2026-08-14T15:20:38.347778Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "import tarfile\n",
+    "import urllib.parse\n",
+    "import urllib.request\n",
+    "import warnings\n",
+    "from pathlib import Path\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import xradar\n",
+    "\n",
+    "import raddb\n",
+    "from raddb.lut import suggest_crs\n",
+    "\n",
+    "print(\"raddb\", raddb.__version__, \"| xradar\", xradar.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069da033",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "`RAW_DIR` is where the downloaded files land, `ARCHIVE_DIR` is the archive the\n",
+    "other tutorials use — the three radars below are simply added to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74467cfd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:20:38.351773Z",
+     "iopub.status.busy": "2026-08-14T15:20:38.351360Z",
+     "iopub.status.idle": "2026-08-14T15:20:38.357950Z",
+     "shell.execute_reply": "2026-08-14T15:20:38.356951Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# --------------------------------------------------------------------------\n",
+    "# CONFIGURATION — edit these two paths to point at your own machine\n",
+    "# --------------------------------------------------------------------------\n",
+    "RAW_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/tutorial_raw\").expanduser()\n",
+    "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
+    "\n",
+    "RAW_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "SOURCES = {\n",
+    "    # NEXRAD is mirrored on Google Cloud as one tar per radar per hour.\n",
+    "    \"KDVN\": {\"kind\": \"nexrad\", \"site\": \"KDVN\", \"open\": xradar.io.open_nexradlevel2_datatree},\n",
+    "    # FMI publishes one ODIM PVOL per volume.  The ODIM code is \"fianj\" — five\n",
+    "    # characters, where a RadDB radar name is at most four, so it is aliased.\n",
+    "    \"FANJ\": {\"kind\": \"odim\", \"site\": \"fianj\", \"open\": xradar.io.open_odim_datatree},\n",
+    "    # IDEAM splits a volume across task files: SURVP (0.5 deg), PRECA (1.5-5.1),\n",
+    "    # PRECB, PRECC.  One task is one DataTree.\n",
+    "    \"GUA\": {\"kind\": \"iris\", \"site\": \"Guaviare\", \"open\": xradar.io.open_iris_datatree},\n",
+    "}\n",
+    "\n",
+    "print(\"raw files:\", RAW_DIR)\n",
+    "print(\"archive  :\", ARCHIVE_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a821711a",
+   "metadata": {},
+   "source": [
+    "## 1. Which volume?\n",
+    "\n",
+    "**This is the cell to edit to run the demo on other weather.** One timestamp per\n",
+    "radar, UTC — the networks publish on their own days, so they are picked\n",
+    "independently. Each is resolved to the volume **at or after** the time asked for.\n",
+    "\n",
+    "What each archive holds, if you go looking for another case:\n",
+    "\n",
+    "* **NEXRAD** — the Google mirror is complete for 2024; one tar per radar per\n",
+    "  hour, ~10 volumes inside, so any hour of any day works.\n",
+    "* **FMI** — one file every **5 minutes**, so the timestamp is rounded down to\n",
+    "  the 5-minute mark.\n",
+    "* **IDEAM** — tasks cycle `SURVP` → `PRECA` → `PRECB` → `PRECC` every few\n",
+    "  minutes, and one task is one DataTree. Which elevations you get therefore\n",
+    "  depends on the minute you ask for: the default lands on `PRECA` (1.5-5.1°)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "335a57d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:20:38.360141Z",
+     "iopub.status.busy": "2026-08-14T15:20:38.359944Z",
+     "iopub.status.idle": "2026-08-14T15:20:38.364575Z",
+     "shell.execute_reply": "2026-08-14T15:20:38.363606Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# --------------------------------------------------------------------------\n",
+    "# WHICH TIMESTEP? — one UTC timestamp per radar, edit freely\n",
+    "# --------------------------------------------------------------------------\n",
+    "TIMES = {\n",
+    "    \"KDVN\": \"2024-06-25 23:04\",   \n",
+    "    \"FANJ\": \"2024-08-09 12:00\",   \n",
+    "    \"GUA\":  \"2024-06-12 19:02\",   \n",
+    "}\n",
+    "\n",
+    "for name, when in TIMES.items():\n",
+    "    print(f\"{name:<5} {pd.Timestamp(when):%Y-%m-%d %H:%M} UTC\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9962a5ef",
+   "metadata": {},
+   "source": [
+    "## 2. Download"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "815790ed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:20:38.366987Z",
+     "iopub.status.busy": "2026-08-14T15:20:38.366786Z",
+     "iopub.status.idle": "2026-08-14T15:20:40.749305Z",
+     "shell.execute_reply": "2026-08-14T15:20:40.748292Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def resolve_url(name, source, when):\n",
+    "    \"\"\"URL of the volume at or after *when* for one source.\"\"\"\n",
+    "    t = pd.Timestamp(when)\n",
+    "    site = source[\"site\"]\n",
+    "\n",
+    "    if source[\"kind\"] == \"nexrad\":\n",
+    "        obj = (f\"{t:%Y/%m/%d}/{site}/NWS_NEXRAD_NXL2DPBL_{site}\"\n",
+    "               f\"_{t:%Y%m%d%H}0000_{t:%Y%m%d%H}5959.tar\")\n",
+    "        return (\"https://storage.googleapis.com/download/storage/v1/b/gcp-public-data-nexrad-l2/o/\"\n",
+    "                + urllib.parse.quote(obj, safe=\"\") + \"?alt=media\")\n",
+    "\n",
+    "    if source[\"kind\"] == \"odim\":\n",
+    "        t = t.floor(\"5min\")                       # FMI publishes every 5 minutes\n",
+    "        return (\"https://fmi-opendata-radar-volume-hdf5.s3.eu-west-1.amazonaws.com/\"\n",
+    "                f\"{t:%Y/%m/%d}/{site}/{t:%Y%m%d%H%M}_{site}_PVOL.h5\")\n",
+    "\n",
+    "    # IDEAM: list the first key at or after the requested second.\n",
+    "    prefix = f\"l2_data/{t:%Y/%m/%d}/{site}/\"\n",
+    "    query = urllib.parse.urlencode({\n",
+    "        \"list-type\": \"2\", \"max-keys\": \"1\", \"prefix\": prefix,\n",
+    "        \"start-after\": f\"{prefix}{name}{t - pd.Timedelta(seconds=1):%y%m%d%H%M%S}\",\n",
+    "    })\n",
+    "    with urllib.request.urlopen(f\"https://s3-radaresideam.s3.amazonaws.com/?{query}\", timeout=120) as response:\n",
+    "        listing = response.read().decode()\n",
+    "    if \"<Key>\" not in listing:\n",
+    "        raise RuntimeError(f\"IDEAM has nothing at or after {t} for {site}\")\n",
+    "    return \"https://s3-radaresideam.s3.amazonaws.com/\" + listing.split(\"<Key>\")[1].split(\"</Key>\")[0]\n",
+    "\n",
+    "\n",
+    "def download(url, dest_dir, when):\n",
+    "    \"\"\"Fetch one volume into *dest_dir* and return its path.\"\"\"\n",
+    "    if \".tar\" in url:\n",
+    "        wanted = pd.Timestamp(when)\n",
+    "        with urllib.request.urlopen(url, timeout=600) as response, tarfile.open(fileobj=response, mode=\"r|\") as tar:\n",
+    "            for member in tar:\n",
+    "                # KDVN20240625_230458_V06.ar2v — *_MDM.ar2v are metadata stubs.\n",
+    "                if not member.name.endswith(\".ar2v\") or \"_MDM\" in member.name:\n",
+    "                    continue\n",
+    "                stamp = pd.to_datetime(Path(member.name).stem[4:19], format=\"%Y%m%d_%H%M%S\")\n",
+    "                if stamp < wanted:\n",
+    "                    continue\n",
+    "                out = dest_dir / Path(member.name).name\n",
+    "                with tar.extractfile(member) as src, open(out, \"wb\") as dst:\n",
+    "                    shutil.copyfileobj(src, dst)\n",
+    "                return out\n",
+    "        raise RuntimeError(f\"no volume at or after {wanted} in {url}\")\n",
+    "\n",
+    "    out = dest_dir / Path(urllib.parse.urlparse(url).path).name\n",
+    "    if not out.exists():\n",
+    "        urllib.request.urlretrieve(url, out)\n",
+    "    return out\n",
+    "\n",
+    "\n",
+    "for name, source in SOURCES.items():\n",
+    "    url = resolve_url(name, source, TIMES[name])\n",
+    "    source[\"path\"] = download(url, RAW_DIR, TIMES[name])\n",
+    "    print(f\"{name:<5} {source['path'].name:<32} {source['path'].stat().st_size / 1e6:6.1f} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c552776",
+   "metadata": {},
+   "source": [
+    "## 3. Archive\n",
+    "\n",
+    "Each file is opened with its own xradar reader and handed to `archive()` — no\n",
+    "preparation in between. A real volume often drops a ray or two (718 of 720 on\n",
+    "WSR-88D, 358 of 360 here); RadDB reads that as a rotation with holes, keeps the\n",
+    "missing rays' slots in the LUT so a later volume that records them still joins,\n",
+    "and archives what is there.\n",
+    "\n",
+    "The CRS is **mandatory to write** and comes from `suggest_crs()`, which returns\n",
+    "the UTM zone of the site: three radars on three continents, three projections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3320a785",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:20:40.752052Z",
+     "iopub.status.busy": "2026-08-14T15:20:40.751800Z",
+     "iopub.status.idle": "2026-08-14T15:21:16.052256Z",
+     "shell.execute_reply": "2026-08-14T15:21:16.050908Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for name, source in SOURCES.items():\n",
+    "    dt = source[\"open\"](str(source[\"path\"]))\n",
+    "    site = dt[\"/\"].ds\n",
+    "    crs = suggest_crs(latitude=float(site[\"latitude\"]), longitude=float(site[\"longitude\"]))\n",
+    "    print(f\"{name}: {sum(1 for g in dt.groups if g.startswith('/sweep_'))} sweeps, \"\n",
+    "          f\"site {float(site['latitude']):.2f}, {float(site['longitude']):.2f} -> EPSG:{crs}\")\n",
+    "\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=crs).archive(datatree=dt, radar=name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e21300d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:21:16.055324Z",
+     "iopub.status.busy": "2026-08-14T15:21:16.054909Z",
+     "iopub.status.idle": "2026-08-14T15:21:16.077817Z",
+     "shell.execute_reply": "2026-08-14T15:21:16.076092Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
+    "db.inventory()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "738aff49",
+   "metadata": {},
+   "source": [
+    "## 4. Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "857d5aa0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:21:16.080585Z",
+     "iopub.status.busy": "2026-08-14T15:21:16.080231Z",
+     "iopub.status.idle": "2026-08-14T15:21:25.922894Z",
+     "shell.execute_reply": "2026-08-14T15:21:25.921776Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(17, 5))\n",
+    "\n",
+    "volumes = {}\n",
+    "for ax, name in zip(axes, SOURCES):\n",
+    "    volumes[name] = db.open(radars=name)\n",
+    "    volumes[name].plot_ppi(sweep=1, variable=\"DBZH\", ax=ax, coords=\"xy\", context=True)\n",
+    "    ax.set_title(f\"{name}\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4520c9b2",
+   "metadata": {},
+   "source": [
+    "## 5. A cross-section, drawn by hand\n",
+    "\n",
+    "`interactive_crop()` puts an [ipyleaflet](https://ipyleaflet.readthedocs.io/) map\n",
+    "in front of you. It dispatches on the shape you draw — rectangle, polygon and\n",
+    "marker run the three crops, and the **polyline** runs\n",
+    "`extract_cross_section`, which is the one used here.\n",
+    "\n",
+    "Draw a line, then click **Apply crop** before running the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "018e962d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:21:25.926645Z",
+     "iopub.status.busy": "2026-08-14T15:21:25.926413Z",
+     "iopub.status.idle": "2026-08-14T15:21:26.068677Z",
+     "shell.execute_reply": "2026-08-14T15:21:26.067670Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "kdvn = volumes[\"KDVN\"]\n",
+    "selector = kdvn.interactive_crop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc66cb9c",
+   "metadata": {},
+   "source": [
+    "The drawn section is on `selector.result` — an ordinary RadDB, carrying the extra\n",
+    "`d_center` / `z_center` / `cs_polygon` columns that describe the curtain. Nothing\n",
+    "drawn (or no live kernel) falls back to a fixed line through the same storm, so\n",
+    "the plot below always has a section to draw."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89a4b6b1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-14T15:21:26.078644Z",
+     "iopub.status.busy": "2026-08-14T15:21:26.078442Z",
+     "iopub.status.idle": "2026-08-14T15:21:42.396432Z",
+     "shell.execute_reply": "2026-08-14T15:21:42.395226Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "LINE = ((-91.30, 40.95), (-91.30, 42.10))    # fallback: north-south through the strongest cell\n",
+    "\n",
+    "drawn = getattr(selector, \"result\", None)\n",
+    "if drawn is not None and selector.kind == \"cross_section\":\n",
+    "    section = drawn\n",
+    "    print(f\"drawn section: {len(section):,} gates\")\n",
+    "else:\n",
+    "    section = kdvn.extract_cross_section(p1=LINE[0], p2=LINE[1], crs=4326)\n",
+    "    print(f\"nothing drawn — using the fixed line {LINE}: {len(section):,} gates\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 4.5))\n",
+    "section.plot_vcs(variable=\"DBZH\", ax=ax)\n",
+    "ax.set_title(f\"KDVN | {pd.Timestamp(TIMES['KDVN']):%Y-%m-%d %H:%M} UTC | vertical cross-section\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bd9e5c9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "That is the whole pipeline: **download → archive → open → plot → cut**. The\n",
+    "archive now holds three more radars, and everything the other notebooks do —\n",
+    "filters, label selection, the other three crops, RHI, CAPPI — applies to them\n",
+    "unchanged.\n",
+    "\n",
+    "**See also:** [1 — Archiving](01_archiving.ipynb) ·\n",
+    "[2 — Opening and filtering](02_opening_and_filtering.ipynb) ·\n",
+    "[3 — Areas of interest](03_area_of_interest.ipynb) ·\n",
+    "[4 — Plots](04_plots.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "radar",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "0e23fecd3bcd4b9cbd4a5036454aeb24": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
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diff --git a/tutorial/README.md b/tutorial/README.md
index 6140e06..a9ca31c 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -1,6 +1,6 @@
 # RadDB tutorials
 
-Four notebooks that walk through the whole workflow, in order. Each one is
+Five notebooks that walk through the whole workflow, in order. Each one is
 self-contained — if you jump straight to number 3, it builds the archive it needs.
 
 | # | notebook | covers |
@@ -9,6 +9,7 @@ self-contained — if you jump straight to number 3, it builds the archive it ne
 | 2 | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters |
 | 3 | [Areas of interest](03_area_of_interest.ipynb) | bbox / point / polygon crops, cross-sections, the interactive map |
 | 4 | [Plots](04_plots.ipynb) | PPI, RHI, CAPPI, vertical cross-section |
+| 5 | [Demo pipeline](05_demo_pipeline.ipynb) | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cut |
 
 The notebooks are stored **with their output**, so you can read them on GitHub
 without running anything.
@@ -31,10 +32,8 @@ jupyter lab
 
 Then run notebook 1 first — it creates the archive the others read.
 
+Notebook 5 needs no local data at all: it downloads three public volumes (US,
+Finland, Colombia) over HTTP and adds them to the same archive.
+
 Needed beyond the core install: `jupyter`, and `ipyleaflet` + `ipywidgets` for the
 interactive map in notebook 3 (`pip install raddb[viz]`).
-
-## Also here
-
-[`basic_usage.py`](basic_usage.py) — the same end-to-end workflow as a plain
-script, using a synthetic volume, so it runs with no data at all.

From f4689dc9b20b0c3df819a0bf24f98ababaf0d314 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Mon, 17 Aug 2026 12:03:35 +0200
Subject: [PATCH 07/14] Add comprehensive tests

---
 raddb/tests/bench_plot_backends.py  |  249 -----
 raddb/tests/conftest.py             |  425 ++++++++
 raddb/tests/test__proj.py           |  102 ++
 raddb/tests/test_aoi.py             |  641 ++++++++++++
 raddb/tests/test_api_coverage.py    |  226 +++++
 raddb/tests/test_azimuth_grid.py    |  377 -------
 raddb/tests/test_crs.py             |  265 -----
 raddb/tests/test_datatree_io.py     |  289 ------
 raddb/tests/test_discovery.py       |  266 +++++
 raddb/tests/test_fixes.py           |  471 ---------
 raddb/tests/test_hc_mapping.py      |   81 ++
 raddb/tests/test_helper.py          |  532 ++++++++++
 raddb/tests/test_inventory.py       |   99 --
 raddb/tests/test_io_core.py         |  696 +++++++++++++
 raddb/tests/test_lut.py             | 1420 +++++++++++++++++++++++++++
 raddb/tests/test_lut_planes.py      |  433 --------
 raddb/tests/test_main.py            | 1281 ++++++++++++++++++++++++
 raddb/tests/test_package_api.py     |  104 ++
 raddb/tests/test_pipeline.py        |  193 ----
 raddb/tests/test_plot.py            |  792 ---------------
 raddb/tests/test_polars_backend.py  |  205 ----
 raddb/tests/test_radar_code.py      |  303 ------
 raddb/tests/test_sel.py             |  188 ----
 raddb/tests/test_viz_init.py        |   63 ++
 raddb/tests/test_viz_interactive.py |  322 ++++++
 raddb/tests/test_viz_plot.py        |  904 +++++++++++++++++
 26 files changed, 7063 insertions(+), 3864 deletions(-)
 delete mode 100644 raddb/tests/bench_plot_backends.py
 create mode 100644 raddb/tests/conftest.py
 create mode 100644 raddb/tests/test__proj.py
 create mode 100644 raddb/tests/test_aoi.py
 create mode 100644 raddb/tests/test_api_coverage.py
 delete mode 100644 raddb/tests/test_azimuth_grid.py
 delete mode 100644 raddb/tests/test_crs.py
 delete mode 100644 raddb/tests/test_datatree_io.py
 create mode 100644 raddb/tests/test_discovery.py
 delete mode 100644 raddb/tests/test_fixes.py
 create mode 100644 raddb/tests/test_hc_mapping.py
 create mode 100644 raddb/tests/test_helper.py
 delete mode 100644 raddb/tests/test_inventory.py
 create mode 100644 raddb/tests/test_io_core.py
 create mode 100644 raddb/tests/test_lut.py
 delete mode 100644 raddb/tests/test_lut_planes.py
 create mode 100644 raddb/tests/test_main.py
 create mode 100644 raddb/tests/test_package_api.py
 delete mode 100644 raddb/tests/test_pipeline.py
 delete mode 100644 raddb/tests/test_plot.py
 delete mode 100644 raddb/tests/test_polars_backend.py
 delete mode 100644 raddb/tests/test_radar_code.py
 delete mode 100644 raddb/tests/test_sel.py
 create mode 100644 raddb/tests/test_viz_init.py
 create mode 100644 raddb/tests/test_viz_interactive.py
 create mode 100644 raddb/tests/test_viz_plot.py

diff --git a/raddb/tests/bench_plot_backends.py b/raddb/tests/bench_plot_backends.py
deleted file mode 100644
index 362c37c..0000000
--- a/raddb/tests/bench_plot_backends.py
+++ /dev/null
@@ -1,249 +0,0 @@
-"""
-raddb/tests/bench_plot_backends.py
-----------------------------------
-Measured comparison of the ways a RadDB frame can be turned into a PPI.
-
-Not a test — ``pytest`` does not collect it (the filename is not ``test_*``).
-Run it directly against a real archive::
-
-    python -m raddb.tests.bench_plot_backends --archive /path/to/archive --radar L
-
-Five paths are compared:
-
-1. ``polygons``  — polars -> numpy -> ``PolyCollection`` (what the four plots use)
-2. ``geopandas`` — ``to_geopandas()`` -> ``GeoDataFrame.plot``
-3. ``lonboard``  — ``to_geoarrow()`` -> deck.gl widget (interactive, not matplotlib)
-
-For each: geometric deviation from the exact ``h_plane`` corners, wall time,
-peak RSS, and the size of the saved PNG and PDF.  Every path is run on a full
-sweep **and** on a small crop — the crop is where ``datatree`` falls apart,
-because it reindexes onto the complete azimuth x range grid regardless of how
-few gates survive.
-"""
-from __future__ import annotations
-
-import argparse
-import gc
-import threading
-import time
-import warnings
-from pathlib import Path
-
-import matplotlib
-matplotlib.use("Agg")
-
-import matplotlib.pyplot as plt
-import numpy as np
-import polars as pl
-import shapely
-
-import raddb
-from raddb.main import RadDB
-from raddb.lut import gate_corner_table
-
-VARIABLE = "DBZH"
-SWEEP = 1
-
-
-# --------------------------------------------------------------------------- util
-
-def _rss_mb() -> float:
-    """Current resident set size in MB."""
-    with open("/proc/self/statm") as f:
-        return int(f.read().split()[1]) * 4096 / 1e6
-
-
-class _RssSampler:
-    """Per-call peak RSS.
-
-    ``ru_maxrss`` is a monotonic high-water mark for the whole process, so it
-    reports 0 for every backend that runs after a heavier one.  Sampling VmRSS in
-    a side thread gives each backend its own peak, independent of run order.
-    """
-
-    def __init__(self, interval: float = 0.005):
-        self.interval = interval
-        self.peak = 0.0
-        self._stop = threading.Event()
-        self._thread = None
-
-    def __enter__(self):
-        self.base = _rss_mb()
-        self.peak = self.base
-
-        def poll():
-            while not self._stop.wait(self.interval):
-                self.peak = max(self.peak, _rss_mb())
-
-        self._thread = threading.Thread(target=poll, daemon=True)
-        self._thread.start()
-        return self
-
-    def __exit__(self, *exc):
-        self._stop.set()
-        self._thread.join()
-        self.peak = max(self.peak, _rss_mb())
-        return False
-
-    @property
-    def delta(self) -> float:
-        return self.peak - self.base
-
-
-def _sizes(fig, tmp: Path, tag: str) -> tuple[float, float]:
-    """Saved PNG and PDF size in kB."""
-    png, pdf = tmp / f"{tag}.png", tmp / f"{tag}.pdf"
-    fig.savefig(png, dpi=150, bbox_inches="tight")
-    fig.savefig(pdf, bbox_inches="tight")
-    return png.stat().st_size / 1e3, pdf.stat().st_size / 1e3
-
-
-def _reference_corners(radar: str, base: Path, gate_ids) -> np.ndarray:
-    """Exact h_plane corners for the given gates, as an (n, 4, 2) array."""
-    tbl = gate_corner_table(radar, base, kind="h_plane")
-    aligned = pl.DataFrame({"gate_id": np.asarray(gate_ids, dtype=np.int64)}).join(
-        tbl, on="gate_id", how="left", maintain_order="left"
-    )
-    return np.stack([
-        np.stack([aligned[f"x_{k}"].to_numpy(), aligned[f"y_{k}"].to_numpy()], axis=1)
-        for k in range(1, 5)
-    ], axis=1).astype(np.float64)
-
-
-# ----------------------------------------------------------------------- backends
-
-def bench_polygons(rdf, radar, base, tmp, tag):
-    t0 = time.perf_counter()
-    p = rdf.plot_ppi(sweep=SWEEP, variable=VARIABLE, coords="xy")
-    t_draw = time.perf_counter() - t0
-    fig = p.figure
-    drawn = np.array([path.vertices[:4] for path in p.get_paths()])
-    png, pdf = _sizes(fig, tmp, f"{tag}_polygons")
-    plt.close(fig)
-    return dict(n=len(drawn), t=t_draw, png=png, pdf=pdf, dev=0.0, drawn=drawn)
-
-
-def bench_geopandas(rdf, radar, base, tmp, tag):
-    t0 = time.perf_counter()
-    gdf = rdf.to_geopandas()
-    gdf = gdf[gdf[VARIABLE].notna()]
-    fig, ax = plt.subplots(figsize=(6, 6))
-    gdf.plot(column=VARIABLE, ax=ax, markersize=1)
-    t_draw = time.perf_counter() - t0
-    png, pdf = _sizes(fig, tmp, f"{tag}_geopandas")
-    plt.close(fig)
-    return dict(n=len(gdf), t=t_draw, png=png, pdf=pdf, dev=float("nan"))
-
-
-def bench_lonboard(rdf, radar, base, tmp, tag):
-    import lonboard
-    t0 = time.perf_counter()
-    table = rdf.to_geoarrow(geometry="polygon")
-    layer = lonboard.PolygonLayer(table=table)
-    m = lonboard.Map(layers=[layer])
-    t_draw = time.perf_counter() - t0
-    html = tmp / f"{tag}_lonboard.html"
-    try:
-        m.to_html(str(html))
-        size = html.stat().st_size / 1e3
-    except Exception as exc:                                   # noqa: BLE001
-        print(f"      (lonboard to_html failed: {exc})")
-        size = float("nan")
-    return dict(n=len(table), t=t_draw, png=size, pdf=float("nan"), dev=0.0)
-
-
-BACKENDS = [
-    ("polygons  (PolyCollection)", bench_polygons),
-    ("geopandas  (GeoDataFrame)", bench_geopandas),
-    ("lonboard   (deck.gl)", bench_lonboard),
-]
-
-
-def run(rdf, radar, base, tmp, tag):
-    print(f"\n{'=' * 92}\n{tag}: {len(rdf):,} gates\n{'=' * 92}")
-    print(f"{'backend':30s} {'drawn':>10s} {'time [s]':>9s} {'peakRSS':>9s} "
-          f"{'PNG [kB]':>10s} {'PDF [kB]':>10s}")
-    print("-" * 92)
-    results = {}
-    for name, fn in BACKENDS:
-        gc.collect()
-        try:
-            with warnings.catch_warnings(), _RssSampler() as rss:
-                warnings.simplefilter("ignore")
-                r = fn(rdf, radar, base, tmp, tag)
-        except Exception as exc:                               # noqa: BLE001
-            print(f"{name:30s} {'FAILED':>10s}  {type(exc).__name__}: {exc}")
-            plt.close("all")
-            continue
-        r["rss"] = rss.delta
-        results[name] = r
-        pdf = f"{r['pdf']:10.0f}" if np.isfinite(r["pdf"]) else f"{'-':>10s}"
-        print(f"{name:30s} {r['n']:10,d} {r['t']:9.2f} {r['rss']:8.0f}M "
-              f"{r['png']:10.0f} {pdf}")
-    return results
-
-
-def check_precision(rdf, radar, base):
-    """How far each path's geometry sits from the exact frustum corners."""
-    print(f"\n{'=' * 92}\ngeometric precision vs the exact h_plane corners\n{'=' * 92}")
-
-    p = rdf.plot_ppi(sweep=SWEEP, variable=VARIABLE, coords="xy")
-    drawn = np.array([path.vertices[:4] for path in p.get_paths()])
-    ids = (
-        rdf.data.select(["gate_id", VARIABLE])
-        .join(gate_corner_table(radar, base, "h_plane", sweep=SWEEP).select("gate_id"),
-              on="gate_id", how="semi")
-        .filter(pl.col(VARIABLE).is_not_nan())["gate_id"].to_numpy()
-    )
-    plt.close("all")
-    ref = _reference_corners(radar, base, ids)
-    dev = np.abs(drawn - ref).max() if len(drawn) == len(ref) else float("nan")
-    print(f"  polygons   : {dev:.3e} m   (exact — these ARE the stored corners)")
-
-    # The centroid mesh matplotlib would infer if it had no corner nodes.
-    lut = raddb.load_radar_lut(radar, base).filter(pl.col("sweep") == SWEEP)
-    n_az = lut["azimuth"].n_unique()
-    n_rng = lut["range"].n_unique()
-    cx = lut.sort(["azimuth", "range"])["x"].to_numpy().reshape(n_az, n_rng)
-    cy = lut.sort(["azimuth", "range"])["y"].to_numpy().reshape(n_az, n_rng)
-    mid_x = 0.25 * (cx[:-1, :-1] + cx[:-1, 1:] + cx[1:, :-1] + cx[1:, 1:])
-    mid_y = 0.25 * (cy[:-1, :-1] + cy[:-1, 1:] + cy[1:, :-1] + cy[1:, 1:])
-    from raddb.lut import load_plane_nodes, _node_grids
-    nodes = load_plane_nodes(radar, base, "h_plane", sweep=SWEEP)
-    g = _node_grids(nodes, ["x", "y"])[SWEEP]
-    off = np.hypot(mid_x - g["x"][1:-1, 1:-1], mid_y - g["y"][1:-1, 1:-1])
-    print(f"  centroid   : {off.mean():.1f} m mean, {off.max():.1f} m max "
-          f"(what shading='auto' invents when no corner nodes exist)")
-
-
-def main():
-    ap = argparse.ArgumentParser(description=__doc__)
-    ap.add_argument("--archive", required=True)
-    ap.add_argument("--radar", default="L")
-    ap.add_argument("--crop-km", type=float, default=10.0)
-    ap.add_argument("--out", default=None, help="where to write the figures")
-    args = ap.parse_args()
-
-    base = Path(args.archive)
-    tmp = Path(args.out) if args.out else Path("bench_out")
-    tmp.mkdir(parents=True, exist_ok=True)
-
-    db = RadDB(archive_dir=str(base), crs=2056)
-    info = db.get_radar_info(args.radar)
-
-    # Every backend must draw the same thing, so restrict to one sweep up front:
-    # plot_ppi selects the sweep itself, but geopandas/lonboard would otherwise
-    # render the whole volume and the timings would not be comparable.
-    rdf = db.open(radars=args.radar).sel(sweep=SWEEP)
-
-    check_precision(rdf, args.radar, base)
-    run(rdf, args.radar, base, tmp, f"full sweep {SWEEP}")
-
-    from raddb.aoi import _reproject_to_aoi
-    site = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, 2056)
-    crop = rdf.crop_around_point((site.x, site.y), distance=args.crop_km * 1000)
-    run(crop, args.radar, base, tmp, f"sweep {SWEEP} cropped to {args.crop_km:g} km")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/raddb/tests/conftest.py b/raddb/tests/conftest.py
new file mode 100644
index 0000000..3e07e41
--- /dev/null
+++ b/raddb/tests/conftest.py
@@ -0,0 +1,425 @@
+"""Shared fixtures for the RadDB test suite.
+
+Every test in this package is **synthetic**: volumes are built in memory and archives are
+written under ``tmp_path``.  Nothing reads a machine-local path, so the suite runs
+unchanged in CI.
+
+The synthetic site sits at 46.0 N / 7.0 E (Switzerland), which is why ``crs=2056``
+(CH1903+/LV95) passes RadDB's measured CRS validation.  A fixture that moves the site must
+move the CRS with it or :func:`raddb.lut.generate_lut_from_datatree` refuses to write.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+import xarray as xr
+
+RADAR = "A"
+"""Default radar name used across the suite."""
+
+N_AZ = 12
+"""Default number of azimuth rays in a synthetic sweep."""
+
+N_RNG = 24
+"""Default number of range bins in a synthetic sweep."""
+
+SITE_LAT = 46.0
+"""Latitude of the synthetic radar site, in degrees north."""
+
+SITE_LON = 7.0
+"""Longitude of the synthetic radar site, in degrees east."""
+
+SITE_ALT = 1000.0
+"""Altitude of the synthetic radar site, in metres above sea level."""
+
+SWISS_EPSG = 2056
+"""CH1903+/LV95 — the projected CRS valid at the synthetic site."""
+
+
+@pytest.fixture(autouse=True)
+def _agg_backend():
+    """Force a non-interactive matplotlib backend for every test.
+
+    Autouse so no test file has to remember it.  Import is local: matplotlib is only
+    needed by the plotting tests and pulling it in at collection time would slow the
+    whole suite down.
+    """
+    try:
+        import matplotlib
+    except ImportError:  # pragma: no cover - matplotlib is a hard dependency of viz only
+        return
+    matplotlib.use("Agg", force=True)
+
+
+def build_datatree(
+    n_az: int = N_AZ,
+    n_rng: int = N_RNG,
+    dbzh_min: float = 1.0,
+    dbzh_max: float = 30.0,
+    n_sweeps: int = 2,
+    vol_time: pd.Timestamp | None = None,
+    latitude: float = SITE_LAT,
+    longitude: float = SITE_LON,
+    altitude: float = SITE_ALT,
+) -> xr.DataTree:
+    """Build a minimal xradar-layout DataTree with all-positive DBZH.
+
+    All DBZH values are strictly positive so the archive's default ``DBZH > 0``
+    clear-sky filter keeps every gate.
+
+    Parameters
+    ----------
+    n_az, n_rng : int
+        Number of azimuth rays and range bins per sweep.
+    dbzh_min, dbzh_max : float
+        Bounds of the uniform random DBZH field, in dBZ.
+    n_sweeps : int
+        Number of sweeps in the volume.
+    vol_time : pandas.Timestamp, optional
+        Volume time stamped on every ray.  Defaults to 2024-08-01 12:00:00.
+    latitude, longitude, altitude : float
+        Radar site position.  Moving it invalidates ``crs=2056``.
+
+    Returns
+    -------
+    xarray.DataTree
+        A volume with sweeps named ``sweep_1`` .. ``sweep_{n_sweeps}``.
+    """
+    if vol_time is None:
+        vol_time = pd.Timestamp("2024-08-01 12:00:00")
+
+    az = np.linspace(0, 360 - 360 / n_az, n_az)
+    rng_vals = np.linspace(1000, 20_000, n_rng)
+    time_vals = np.array([vol_time] * n_az, dtype="datetime64[ns]")
+
+    dict_ds = {}
+    for sweep_idx in range(1, n_sweeps + 1):
+        rng_gen = np.random.default_rng(seed=42 + sweep_idx)
+        dbzh = rng_gen.uniform(dbzh_min, dbzh_max, (n_az, n_rng)).astype(np.float32)
+        ds = xr.Dataset(
+            {
+                "DBZH": (["azimuth", "range"], dbzh),
+                "ZDR": (["azimuth", "range"], np.ones((n_az, n_rng), np.float32)),
+                "RHOHV": (["azimuth", "range"], np.full((n_az, n_rng), 0.95, np.float32)),
+                "PHIDP": (["azimuth", "range"], np.zeros((n_az, n_rng), np.float32)),
+                "time": (["azimuth"], time_vals),
+            },
+            coords={
+                "azimuth": az,
+                "range": rng_vals,
+                "elevation": (["azimuth"], np.full(n_az, 0.5 * sweep_idx)),
+                "elevation_angle": 0.5 * sweep_idx,
+                "latitude": latitude,
+                "longitude": longitude,
+                "altitude": altitude,
+            },
+        )
+        ds.attrs["sweep_number"] = sweep_idx
+        dict_ds[f"sweep_{sweep_idx}"] = ds
+
+    return xr.DataTree.from_dict(dict_ds)
+
+
+def relocate(dt: xr.DataTree, longitude: float, latitude: float) -> xr.DataTree:
+    """Move a synthetic volume to another place on Earth.
+
+    Used to test the CRS contract: EPSG:2056 is valid at the default Swiss site and
+    invalid — by 20.1% — once the volume is moved to Oklahoma.
+
+    Parameters
+    ----------
+    dt : xarray.DataTree
+        Volume to relocate; not modified in place.
+    longitude, latitude : float
+        New site position, in degrees.
+
+    Returns
+    -------
+    xarray.DataTree
+        A new tree with every sweep's site coordinates replaced.
+    """
+    out = {}
+    for name, node in dt.children.items():
+        ds = node.to_dataset().assign_coords(latitude=latitude, longitude=longitude)
+        ds.attrs.update(node.attrs)
+        out[name] = ds
+    return xr.DataTree.from_dict(out)
+
+
+MCH_BIAS, MCH_SPREAD = 0.0327, 0.0069
+"""Rad4Alp antenna drift, measured from real METRANET files.
+
+Every ray is reported ~0.0327 degrees past its nominal angle, with a ~0.0069 degree
+spread.  ``gate_id`` resolves azimuth to 0.1 degrees, so an unsnapped drifting ray lands
+in a neighbouring bin and its gates match no LUT row.
+"""
+
+NEXRAD_SPREAD = 0.045
+"""WSR-88D antenna drift: zero-mean, spread up to ~0.045 degrees."""
+
+
+def jitter_azimuths(azimuths, rng, bias: float = 0.0, spread: float = MCH_SPREAD):
+    """Move an azimuth array the way a real antenna does between rotations.
+
+    Parameters
+    ----------
+    azimuths : array_like
+        Nominal angles, in degrees.
+    rng : numpy.random.Generator
+        Source of the random spread.
+    bias : float
+        Systematic offset added to every ray, in degrees.
+    spread : float
+        Standard deviation of the per-ray noise, in degrees.
+
+    Returns
+    -------
+    numpy.ndarray
+        Drifted angles, wrapped into ``[0, 360)``.
+    """
+    az = np.asarray(azimuths, float)
+    return (az + bias + rng.normal(0, spread, len(az))) % 360.0
+
+
+def retime(dt: xr.DataTree, when, rng, bias: float = 0.0, spread: float = MCH_SPREAD) -> xr.DataTree:
+    """Copy a volume at a new time, with the antenna pointing slightly differently.
+
+    This is what a second rotation of the same radar actually looks like: the same scan
+    strategy, reported a few hundredths of a degree away.
+
+    Parameters
+    ----------
+    dt : xarray.DataTree
+        Volume to copy; not modified in place.
+    when : pandas.Timestamp
+        New volume time, stamped on every ray.
+    rng : numpy.random.Generator
+        Source of the azimuth drift.
+    bias, spread : float
+        Passed to :func:`jitter_azimuths`.
+
+    Returns
+    -------
+    xarray.DataTree
+        The retimed, drifted volume.
+    """
+    out = {}
+    for name, node in dt.children.items():
+        ds = node.to_dataset()
+        n = ds.sizes["azimuth"]
+        ds = ds.assign_coords(azimuth=jitter_azimuths(ds["azimuth"].values, rng, bias, spread))
+        ds["time"] = ("azimuth", np.array([when] * n, dtype="datetime64[ns]"))
+        ds.attrs.update(node.attrs)
+        out[name] = ds
+    return xr.DataTree.from_dict(out)
+
+
+US_SITE = (-97.2775, 35.3331)
+"""KTLX, Oklahoma — ``(longitude, latitude)``.  UTM 14N (EPSG:32614) is valid here."""
+
+US_EPSG = 32614
+"""UTM zone 14N — the projected CRS valid at :data:`US_SITE`."""
+
+
+@pytest.fixture
+def us_archive_dir(tmp_path, make_datatree):
+    """A one-radar archive at KTLX, written in UTM 14N.
+
+    The non-Swiss counterpart to :func:`archive_dir`: any AOI or plotting behaviour that
+    silently assumes LV95 shows up here as a gross error rather than a rounding one.
+
+    Returns
+    -------
+    pathlib.Path
+        The archive root.
+    """
+    from raddb.main import RadDB
+
+    base = tmp_path / "us_archive"
+    dt = relocate(make_datatree(n_az=72, n_rng=60, n_sweeps=3), *US_SITE)
+    RadDB(archive_dir=str(base), crs=US_EPSG).archive(datatree={RADAR: [dt]})
+    return base
+
+
+@pytest.fixture
+def make_datatree():
+    """Return the :func:`build_datatree` factory.
+
+    Returns
+    -------
+    callable
+        Same signature as :func:`build_datatree`.
+    """
+    return build_datatree
+
+
+@pytest.fixture
+def datatree(make_datatree) -> xr.DataTree:
+    """A single default synthetic volume.
+
+    Returns
+    -------
+    xarray.DataTree
+        Two sweeps, 12 x 24 gates, 2024-08-01 12:00:00.
+    """
+    return make_datatree()
+
+
+@pytest.fixture
+def archive_dir(tmp_path, make_datatree):
+    """Path to a one-radar, one-volume archive written under ``tmp_path``.
+
+    Radar ``A``, ``crs=2056``, LUT and all four geometry lattices present.
+
+    Returns
+    -------
+    pathlib.Path
+        The archive root, ready for ``RadDB(archive_dir=...)``.
+    """
+    from raddb.main import RadDB
+
+    base = tmp_path / "archive"
+    RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(datatree=make_datatree(), radar=RADAR)
+    return base
+
+
+@pytest.fixture
+def archive_dir_two_volumes(tmp_path, make_datatree):
+    """Path to a one-radar, two-volume archive (12:00 and 12:05).
+
+    Returns
+    -------
+    pathlib.Path
+        The archive root.
+    """
+    from raddb.main import RadDB
+
+    base = tmp_path / "archive2"
+    volumes = [
+        make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")),
+        make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00")),
+    ]
+    RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(datatree=volumes, radar=RADAR)
+    return base
+
+
+@pytest.fixture
+def archive_dir_two_radars(tmp_path, make_datatree):
+    """Path to a two-radar archive (``A`` and ``D``), one volume each.
+
+    Returns
+    -------
+    pathlib.Path
+        The archive root.
+    """
+    from raddb.main import RadDB
+
+    base = tmp_path / "archive_multi"
+    RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(
+        datatree={RADAR: [make_datatree()], "D": [make_datatree()]}
+    )
+    return base
+
+
+PLOT_GEOMETRY = {"n_az": 72, "n_rng": 60, "n_sweeps": 6}
+"""Volume shape used by the plotting fixtures.
+
+Six sweeps and 60 range bins are the minimum that makes the CAPPI and RHI invariants
+meaningful — a two-sweep volume has no beam overlap to resolve.
+"""
+
+PLOT_RADAR = "L"
+"""Radar name used by the plotting fixtures."""
+
+
+@pytest.fixture(scope="session")
+def plot_archive_dir(tmp_path_factory):
+    """A 72 x 60 x 6 archive, built **once per session**.
+
+    The plotting tests all read the same static geometry, and rebuilding a 26k-gate
+    archive per test would dominate the suite runtime.  Nothing here mutates it.
+
+    Returns
+    -------
+    pathlib.Path
+        The archive root.
+    """
+    from raddb.main import RadDB
+
+    base = tmp_path_factory.mktemp("plot_archive")
+    RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(
+        datatree={PLOT_RADAR: [build_datatree(**PLOT_GEOMETRY)]}
+    )
+    return base
+
+
+@pytest.fixture(scope="session")
+def plot_rdb(plot_archive_dir):
+    """A data-carrying RadDB over :func:`plot_archive_dir`.
+
+    Returns
+    -------
+    raddb.RadDB
+        The whole single volume, 25,920 gates.
+    """
+    from raddb.main import RadDB
+
+    return RadDB(archive_dir=str(plot_archive_dir), crs=SWISS_EPSG).open(radars=PLOT_RADAR)
+
+
+@pytest.fixture(scope="session")
+def plot_site(plot_archive_dir):
+    """The plotting archive's radar site as ``(x, y)`` in EPSG:2056.
+
+    Returns
+    -------
+    tuple of float
+        Projected easting and northing, in metres.
+    """
+    import shapely
+
+    from raddb.aoi import _reproject_to_aoi
+    from raddb.main import RadDB
+
+    info = RadDB(archive_dir=str(plot_archive_dir)).get_radar_info(PLOT_RADAR)
+    point = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, SWISS_EPSG)
+    return (point.x, point.y)
+
+
+@pytest.fixture(autouse=True)
+def _close_figures():
+    """Close every figure after each test so a long run does not leak them."""
+    yield
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:  # pragma: no cover - matplotlib is a viz-only dependency
+        return
+    plt.close("all")
+
+
+@pytest.fixture
+def db(archive_dir):
+    """An archive-bound :class:`raddb.RadDB` over :func:`archive_dir`.
+
+    Returns
+    -------
+    raddb.RadDB
+        Opened with ``crs=2056``.
+    """
+    from raddb.main import RadDB
+
+    return RadDB(archive_dir=str(archive_dir), crs=SWISS_EPSG)
+
+
+@pytest.fixture
+def rdb(db):
+    """A data-carrying :class:`raddb.RadDB` holding the whole archive.
+
+    Returns
+    -------
+    raddb.RadDB
+        Result of ``db.open(radars="A")``.
+    """
+    return db.open(radars=RADAR)
diff --git a/raddb/tests/test__proj.py b/raddb/tests/test__proj.py
new file mode 100644
index 0000000..7334706
--- /dev/null
+++ b/raddb/tests/test__proj.py
@@ -0,0 +1,102 @@
+"""Tests for :mod:`raddb._proj` — the PROJ data-directory repair.
+
+A ``PROJ_DATA``/``PROJ_LIB`` inherited from another environment passes pyproj's
+"does proj.db exist?" check but ships the wrong PROJ version, so every CRS lookup raises
+``no database context specified``.  That breaks ``RadDB.crs``, ``to_geopandas``, the
+``crop_*`` family and the LUT's ``x_*``/``y_*`` columns — silently, at import time.
+
+Every test here restores the environment through ``monkeypatch`` so the repair does not
+leak into the rest of the suite.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from pathlib import Path
+
+import pytest
+
+from raddb._proj import PROJ_DATA, fix_foreign_proj_data
+
+OWN_PROJ_DIR = Path(sys.prefix) / "share" / "proj"
+"""This interpreter's own bundled PROJ data directory (may not exist)."""
+
+
+def test_fix_foreign_proj_data(monkeypatch, tmp_path):
+    """The four branches of the repair, in one place.
+
+    Unset stays unset; a foreign directory is replaced or dropped; a directory already
+    inside ``sys.prefix`` is left untouched.
+    """
+    # 1. Nothing set -> nothing done.
+    monkeypatch.delenv("PROJ_DATA", raising=False)
+    monkeypatch.delenv("PROJ_LIB", raising=False)
+    assert fix_foreign_proj_data() is None
+    assert "PROJ_DATA" not in os.environ
+
+    # 2. A foreign directory is never kept.
+    foreign = tmp_path / "foreign_proj"
+    foreign.mkdir()
+    monkeypatch.setenv("PROJ_DATA", str(foreign))
+    monkeypatch.delenv("PROJ_LIB", raising=False)
+    result = fix_foreign_proj_data()
+    assert os.environ.get("PROJ_DATA") != str(foreign)
+    if (OWN_PROJ_DIR / "proj.db").is_file():
+        # 3a. Repointed at this interpreter's own data, in both variables.
+        assert result == str(OWN_PROJ_DIR)
+        assert os.environ["PROJ_DATA"] == os.environ["PROJ_LIB"] == str(OWN_PROJ_DIR)
+    else:
+        # 3b. No own data to point at, so the foreign variables are dropped entirely
+        #     and pyproj falls back to the data it bundles.
+        assert result is None
+        assert "PROJ_DATA" not in os.environ and "PROJ_LIB" not in os.environ
+
+    # 4. An in-prefix directory is this environment's own -> left alone.
+    if (OWN_PROJ_DIR / "proj.db").is_file():
+        monkeypatch.setenv("PROJ_DATA", str(OWN_PROJ_DIR))
+        assert fix_foreign_proj_data() is None
+        assert os.environ["PROJ_DATA"] == str(OWN_PROJ_DIR)
+
+
+def test_legacy_proj_lib_is_honoured(monkeypatch, tmp_path):
+    """``PROJ_LIB`` alone (the pre-8.0 name) triggers the repair too."""
+    foreign = tmp_path / "legacy_proj"
+    foreign.mkdir()
+    monkeypatch.delenv("PROJ_DATA", raising=False)
+    monkeypatch.setenv("PROJ_LIB", str(foreign))
+
+    fix_foreign_proj_data()
+
+    assert os.environ.get("PROJ_LIB") != str(foreign)
+
+
+def test_an_unresolvable_directory_does_not_raise(monkeypatch, tmp_path):
+    """``Path.resolve()`` on a broken value must not escape as an ``OSError``.
+
+    A symlink loop is the cheap way to make ``resolve()`` raise ``ELOOP``; the repair
+    suppresses it and falls through to the "replace the foreign value" branch.
+    """
+    loop = tmp_path / "loop"
+    loop.symlink_to(loop)
+    monkeypatch.setenv("PROJ_DATA", str(loop))
+    monkeypatch.delenv("PROJ_LIB", raising=False)
+
+    fix_foreign_proj_data()  # suppressed internally; reaching here is the assertion
+
+    assert os.environ.get("PROJ_DATA") != str(loop)
+
+
+def test_module_level_proj_data_matches_the_environment():
+    """``raddb.PROJ_DATA`` reports what the import-time repair actually did."""
+    assert PROJ_DATA is None or Path(PROJ_DATA).is_dir()
+    if PROJ_DATA is not None:
+        assert Path(PROJ_DATA).is_relative_to(Path(sys.prefix).resolve())
+
+
+def test_a_crs_lookup_works_after_import():
+    """The point of the whole module: EPSG lookups must resolve."""
+    pyproj = pytest.importorskip("pyproj")
+
+    assert pyproj.CRS.from_epsg(4326).name
+    assert pyproj.CRS.from_epsg(2056).is_projected
diff --git a/raddb/tests/test_aoi.py b/raddb/tests/test_aoi.py
new file mode 100644
index 0000000..6c014f9
--- /dev/null
+++ b/raddb/tests/test_aoi.py
@@ -0,0 +1,641 @@
+"""Tests for :mod:`raddb.aoi` — AOI selection, crop geometry and cross-sections.
+
+The module has only two public callables, but it is where the package's most expensive
+bug lived: ``aoi.py`` used to hardcode EPSG:2056, so a "50 km" crop at a US radar reached
+about 46 km — a silent 17% error that looked entirely normal on a map.
+
+So the theme throughout is: **every AOI runs in the archive's own CRS**, resolved from
+``{radar}_info.yaml`` (or recovered from the LUT's ``x_<epsg>`` columns), never defaulted.
+The Swiss and US archives are tested side by side for exactly that reason.
+"""
+
+from __future__ import annotations
+
+import json
+
+import numpy as np
+import polars as pl
+import pyproj
+import pytest
+import shapely
+
+from raddb.aoi import (
+    SWISS_EPSG,
+    _apply_gate_ids,
+    _crs_to_spec,
+    _geojson_crs,
+    _load_aoi_polygon,
+    _lut_centroids,
+    _prj_crs,
+    _radars_from_gate_ids,
+    _read_geometry_file,
+    _reproject_to_aoi,
+    _resolve_aoi_centroids,
+    _resolve_context,
+    _resolve_gate_ids,
+    _to_pyproj_crs,
+    aoi_epsg,
+    aoi_epsg_for,
+)
+from raddb.main import RadDB
+from raddb.tests.conftest import RADAR, US_EPSG, US_SITE, relocate
+
+CH_SITE = (7.0, 46.0)
+"""The synthetic fixture's own site — ``(longitude, latitude)``."""
+
+
+# ---------------------------------------------------------------------------
+# aoi_epsg — the archive's own frame
+# ---------------------------------------------------------------------------
+
+
+def test_aoi_epsg(archive_dir, us_archive_dir):
+    """The frame comes from the archive that was written, not from a default."""
+    assert aoi_epsg(archive_dir, RADAR) == SWISS_EPSG
+    assert aoi_epsg(us_archive_dir, RADAR) == US_EPSG
+
+
+def test_aoi_epsg_is_recovered_from_the_lut_when_info_has_no_crs_block(archive_dir):
+    """Archives predating the ``crs`` block still resolve, from ``x_<epsg>`` columns."""
+    import yaml
+
+    info_path = archive_dir / RADAR / "LUT" / f"{RADAR}_info.yaml"
+    info = yaml.safe_load(info_path.read_text())
+    info.pop("crs", None)
+    info_path.write_text(yaml.safe_dump(info))
+
+    assert aoi_epsg(archive_dir, RADAR) == SWISS_EPSG
+
+
+def test_aoi_epsg_refuses_an_unprojected_archive(tmp_path, archive_dir):
+    """With neither an info CRS nor projected columns there is nothing to guess from."""
+    import yaml
+
+    info_path = archive_dir / RADAR / "LUT" / f"{RADAR}_info.yaml"
+    info = yaml.safe_load(info_path.read_text())
+    info.pop("crs", None)
+    info_path.write_text(yaml.safe_dump(info))
+
+    lut_path = archive_dir / RADAR / "LUT" / f"{RADAR}_LUT.parquet"
+    lut = pl.read_parquet(lut_path)
+    lut.drop([c for c in lut.columns if c[:2] in ("x_", "y_") and c[2:].isdigit()]).write_parquet(lut_path)
+
+    with pytest.raises(ValueError, match="no projected coordinates"):
+        aoi_epsg(archive_dir, RADAR)
+
+
+def test_aoi_epsg_names_the_way_out_in_its_error(archive_dir):
+    """The refusal must tell the user what to pass, not merely complain."""
+    import yaml
+
+    info_path = archive_dir / RADAR / "LUT" / f"{RADAR}_info.yaml"
+    info = yaml.safe_load(info_path.read_text())
+    info.pop("crs", None)
+    info_path.write_text(yaml.safe_dump(info))
+    lut_path = archive_dir / RADAR / "LUT" / f"{RADAR}_LUT.parquet"
+    lut = pl.read_parquet(lut_path)
+    lut.drop([c for c in lut.columns if c[:2] in ("x_", "y_") and c[2:].isdigit()]).write_parquet(lut_path)
+
+    with pytest.raises(ValueError, match=r"aoi_crs="):
+        aoi_epsg(archive_dir, RADAR)
+
+
+# ---------------------------------------------------------------------------
+# aoi_epsg_for — one frame for a multi-radar AOI
+# ---------------------------------------------------------------------------
+
+
+def test_aoi_epsg_for(archive_dir_two_radars):
+    """Radars written in the same CRS share it without argument."""
+    assert aoi_epsg_for(archive_dir_two_radars, [RADAR, "D"]) == SWISS_EPSG
+
+
+def test_mixed_crs_radars_refuse_a_shared_aoi(tmp_path, make_datatree):
+    """No silent reprojection: the user must name the common frame."""
+    base = tmp_path / "mixed"
+    RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(
+        datatree={"A": [make_datatree(n_az=24, n_rng=20, n_sweeps=2)]}
+    )
+    RadDB(archive_dir=str(base), crs=US_EPSG).archive(
+        datatree={"D": [relocate(make_datatree(n_az=24, n_rng=20, n_sweeps=2), *US_SITE)]}
+    )
+
+    assert aoi_epsg_for(base, ["A"]) == SWISS_EPSG
+    assert aoi_epsg_for(base, ["D"]) == US_EPSG
+    with pytest.raises(ValueError, match="different CRSs"):
+        aoi_epsg_for(base, ["A", "D"])
+
+
+def test_an_override_wins_over_the_archive(archive_dir):
+    """``aoi_crs=`` names a common frame explicitly."""
+    assert aoi_epsg_for(archive_dir, [RADAR], override=32632) == 32632
+
+
+def test_an_override_is_validated_against_every_site(us_archive_dir):
+    """An override still has to be valid where the radar actually is."""
+    with pytest.raises(ValueError, match="distorts distance"):
+        aoi_epsg_for(us_archive_dir, [RADAR], override=SWISS_EPSG)
+
+
+# ---------------------------------------------------------------------------
+# _lut_centroids / _resolve_gate_ids — the selection itself
+# ---------------------------------------------------------------------------
+
+
+def test_lut_centroids_returns_a_fixed_column_set(archive_dir):
+    """Callers never have to know the EPSG: the projected pair is renamed x/y."""
+    centroids = _lut_centroids(archive_dir, [RADAR])
+
+    assert {"gate_id", "radar", "sweep", "x", "y", "z", "altitude"} <= set(centroids.columns)
+    assert centroids["radar"].unique().to_list() == [RADAR]
+    assert not centroids.is_empty()
+
+
+def test_lut_centroids_concatenates_radars(archive_dir_two_radars):
+    """A multi-radar AOI sees one table spanning both."""
+    centroids = _lut_centroids(archive_dir_two_radars, [RADAR, "D"])
+
+    assert sorted(centroids["radar"].unique().to_list()) == ["A", "D"]
+
+
+def test_lut_centroids_projects_on_the_fly_for_an_override(archive_dir):
+    """An ``aoi_crs`` the LUT does not store is computed from latitude/longitude."""
+    centroids = _lut_centroids(archive_dir, [RADAR], epsg=32632)
+
+    assert {"x", "y"} <= set(centroids.columns)
+    assert np.isfinite(centroids["x"].to_numpy()).all()
+
+
+def test_lut_centroids_raises_on_a_missing_lut(tmp_path):
+    """A radar with no LUT cannot be cropped; say so rather than return nothing."""
+    with pytest.raises((FileNotFoundError, ValueError)):
+        _lut_centroids(tmp_path, ["Z"])
+
+
+def test_resolve_gate_ids_selects_only_gates_inside(archive_dir):
+    """A tight buffer around the site keeps a strict subset of the gates."""
+    centroids = _lut_centroids(archive_dir, [RADAR])
+    site = _reproject_to_aoi(shapely.Point(*CH_SITE), 4326, SWISS_EPSG)
+
+    ids = _resolve_gate_ids(centroids, site.buffer(5_000))
+
+    assert 0 < len(ids) < centroids.height
+    assert ids.dtype == np.int64
+
+
+def test_resolve_gate_ids_is_empty_outside_the_radar(archive_dir):
+    """An AOI nowhere near the radar selects nothing, and does not raise."""
+    centroids = _lut_centroids(archive_dir, [RADAR])
+    far = shapely.Point(0.0, 0.0).buffer(1_000)
+
+    assert len(_resolve_gate_ids(centroids, far)) == 0
+
+
+def test_resolve_aoi_centroids_returns_rows_not_just_ids(archive_dir):
+    """Callers clip by altitude afterwards, so the rows must survive the filter."""
+    centroids = _lut_centroids(archive_dir, [RADAR])
+    site = _reproject_to_aoi(shapely.Point(*CH_SITE), 4326, SWISS_EPSG)
+
+    sub = _resolve_aoi_centroids(centroids, site.buffer(5_000))
+
+    assert set(sub.columns) == set(centroids.columns)
+    assert sub.height == len(_resolve_gate_ids(centroids, site.buffer(5_000)))
+
+
+def test_resolve_aoi_centroids_on_an_empty_table(archive_dir):
+    """An empty input yields an empty output with the schema intact."""
+    empty = _lut_centroids(archive_dir, [RADAR]).clear()
+
+    assert _resolve_aoi_centroids(empty, shapely.Point(0, 0).buffer(1)).is_empty()
+
+
+def test_a_footprint_selects_the_whole_vertical_column(archive_dir):
+    """Intersection runs over every sweep, so one footprint takes the column above it."""
+    centroids = _lut_centroids(archive_dir, [RADAR])
+    site = _reproject_to_aoi(shapely.Point(*CH_SITE), 4326, SWISS_EPSG)
+
+    sub = _resolve_aoi_centroids(centroids, site.buffer(8_000))
+
+    assert sorted(sub["sweep"].unique().to_list()) == sorted(centroids["sweep"].unique().to_list())
+
+
+def test_a_crop_radius_is_true_metres(us_archive_dir):
+    """The 17% bug, in one assertion: a 10 km crop must select a 10 km radius.
+
+    Truth is a WGS-84 geodesic from the radar to every gate; the projected selection
+    must agree to within 1%.
+    """
+    lut = RadDB(archive_dir=str(us_archive_dir)).get_lut(RADAR)
+    lon = lut["longitude"].to_numpy()
+    lat = lut["latitude"].to_numpy()
+    _, _, ground = pyproj.Geod(ellps="WGS84").inv(
+        np.full(lon.size, US_SITE[0]), np.full(lat.size, US_SITE[1]), lon, lat
+    )
+
+    centroids = _lut_centroids(us_archive_dir, [RADAR])
+    site = _reproject_to_aoi(shapely.Point(*US_SITE), 4326, US_EPSG)
+    for radius in (10_000, 15_000):
+        truth = int((ground <= radius).sum())
+        got = len(_resolve_gate_ids(centroids, site.buffer(radius)))
+        assert abs(got - truth) <= 0.01 * truth, f"{radius / 1000:.0f} km crop took {got} gates, truth {truth}"
+
+
+# ---------------------------------------------------------------------------
+# _apply_gate_ids — the semi-join onto dynamic data
+# ---------------------------------------------------------------------------
+
+
+def test_apply_gate_ids_selects_rows_without_widening():
+    """A semi-join: the LUT geometry stays in its own table."""
+    df = pl.DataFrame({"gate_id": [1, 2, 3], "DBZH": [10.0, 20.0, 30.0]})
+
+    out = _apply_gate_ids(df, np.array([1, 3], dtype=np.int64))
+
+    assert out.columns == df.columns
+    assert out["gate_id"].to_list() == [1, 3]
+
+
+def test_apply_gate_ids_with_an_empty_selection():
+    """No gates in the AOI yields an empty frame, not the whole input."""
+    df = pl.DataFrame({"gate_id": [1, 2], "DBZH": [10.0, 20.0]})
+
+    assert _apply_gate_ids(df, np.empty(0, dtype=np.int64)).is_empty()
+
+
+def test_apply_gate_ids_requires_the_join_key():
+    """Without ``gate_id`` there is no way to apply an AOI; fail loudly."""
+    with pytest.raises(KeyError, match="gate_id"):
+        _apply_gate_ids(pl.DataFrame({"DBZH": [1.0]}), np.array([1], dtype=np.int64))
+
+
+def test_radars_from_gate_ids(archive_dir):
+    """The radar name is embedded in every ``gate_id``, so no registry is needed."""
+    ids = _lut_centroids(archive_dir, [RADAR])["gate_id"].to_numpy()
+
+    assert _radars_from_gate_ids(ids) == [RADAR]
+
+
+# ---------------------------------------------------------------------------
+# _to_pyproj_crs / _reproject_to_aoi — the frame conversions
+# ---------------------------------------------------------------------------
+
+
+def test_to_pyproj_crs_accepts_the_four_spellings():
+    """int, proj4 string, EPSG string and a ready CRS all resolve."""
+    from pyproj import CRS
+
+    assert _to_pyproj_crs(2056).is_projected
+    assert _to_pyproj_crs("+proj=longlat +datum=WGS84 +no_defs").is_geographic
+    assert _to_pyproj_crs("EPSG:32614").is_projected
+    crs = CRS.from_epsg(2056)
+    assert _to_pyproj_crs(crs) is crs
+
+
+def test_known_frames_resolve_without_the_proj_database():
+    """2056 and 4326 go through proj4 strings so a broken PROJ db cannot break them."""
+    assert _to_pyproj_crs(2056).to_proj4()
+    assert _to_pyproj_crs(4326).is_geographic
+
+
+def test_reproject_to_aoi_is_a_no_op_when_the_frames_match():
+    """``None`` and every spelling of the AOI's own EPSG return the geometry untouched."""
+    geom = shapely.Point(2_600_000, 1_200_000)
+
+    for crs in (None, 2056, "2056", "EPSG:2056", "epsg:2056"):
+        assert _reproject_to_aoi(geom, crs, 2056) is geom
+
+
+def test_reproject_to_aoi_moves_lonlat_into_metres():
+    """Degrees in, metres out — the failure mode is a section 2600 km away."""
+    projected = _reproject_to_aoi(shapely.Point(*CH_SITE), 4326, SWISS_EPSG)
+
+    assert 2_400_000 < projected.x < 2_900_000
+    assert 1_000_000 < projected.y < 1_400_000
+
+
+def test_reproject_to_aoi_round_trips():
+    """There and back lands within a millimetre."""
+    geom = shapely.Point(*CH_SITE)
+
+    there = _reproject_to_aoi(geom, 4326, SWISS_EPSG)
+    back = _reproject_to_aoi(there, SWISS_EPSG, 4326)
+
+    assert back.distance(geom) < 1e-7
+
+
+def test_crs_to_spec_reduces_to_an_epsg_int():
+    """A pyproj CRS with a known EPSG becomes an int; ``None`` stays ``None``."""
+    assert _crs_to_spec(pyproj.CRS.from_epsg(2056)) == 2056
+    assert _crs_to_spec(None) is None
+
+
+# ---------------------------------------------------------------------------
+# _resolve_context — the quicklook backdrop
+# ---------------------------------------------------------------------------
+
+
+def test_resolve_context_none_is_none():
+    """No context means no context, in any frame."""
+    assert _resolve_context(None, aoi_epsg=SWISS_EPSG) is None
+
+
+def test_resolve_context_rejects_an_unknown_name():
+    """Only ``'switzerland'`` and its aliases are named contexts."""
+    with pytest.raises(ValueError, match="unknown context"):
+        _resolve_context("atlantis", aoi_epsg=SWISS_EPSG)
+
+
+def test_resolve_context_takes_a_bare_geometry_as_already_in_frame():
+    """A shapely geometry carries no CRS, so it is trusted as-is."""
+    geom = shapely.Point(2_600_000, 1_200_000).buffer(50_000)
+
+    assert _resolve_context(geom, aoi_epsg=SWISS_EPSG).equals(geom)
+
+
+def test_resolve_context_reprojects_a_geodataframe_into_the_archive_frame(us_archive_dir):
+    """A caller's context must not be reprojected to LV95 on a US archive.
+
+    This is the fix for a backdrop drawn 5,855 km off-map at KTLX.
+    """
+    gpd = pytest.importorskip("geopandas")
+
+    box = shapely.box(US_SITE[0] - 1, US_SITE[1] - 1, US_SITE[0] + 1, US_SITE[1] + 1)
+    gdf = gpd.GeoDataFrame(geometry=[box], crs="EPSG:4326")
+
+    got = _resolve_context(gdf, aoi_epsg=US_EPSG)
+
+    assert got.distance(_reproject_to_aoi(box, 4326, US_EPSG)) < 1.0
+    assert got.contains(_reproject_to_aoi(shapely.Point(*US_SITE), 4326, US_EPSG))
+
+
+def test_resolve_context_survives_a_broken_geodataframe():
+    """A context is decoration; a failure drops it rather than killing the plot."""
+
+    class Broken:
+        crs = "EPSG:4326"
+
+        def union_all(self):
+            raise RuntimeError("no geometry")
+
+    assert _resolve_context(Broken(), aoi_epsg=SWISS_EPSG) is None
+
+
+def test_resolve_context_ignores_an_unsupported_type():
+    """Anything without a ``.crs`` and not a geometry yields ``None``."""
+    assert _resolve_context(42, aoi_epsg=SWISS_EPSG) is None
+
+
+# ---------------------------------------------------------------------------
+# Geometry file loading — a file's declared CRS wins
+# ---------------------------------------------------------------------------
+
+
+def _write_geojson(path, geometry, crs_name=None):
+    """Write a bare-geometry GeoJSON, optionally with a legacy ``crs`` member."""
+    data = dict(geometry)
+    if crs_name is not None:
+        data["crs"] = {"type": "name", "properties": {"name": crs_name}}
+    path.write_text(json.dumps(data))
+    return path
+
+
+def test_geojson_defaults_to_wgs84_per_rfc_7946():
+    """A GeoJSON without a ``crs`` member is lon/lat, not archive metres."""
+    assert _geojson_crs({"type": "Polygon"}) == 4326
+
+
+def test_geojson_honours_a_legacy_crs_member():
+    """The pre-RFC ``crs`` member is still read when present."""
+    member = {"crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG::2056"}}}
+
+    assert _geojson_crs(member) == SWISS_EPSG
+
+
+def test_read_geometry_file_reads_a_polygon(tmp_path):
+    """A polygon GeoJSON round-trips, and reports WGS-84."""
+    square = {"type": "Polygon", "coordinates": [[[6.9, 45.9], [7.1, 45.9], [7.1, 46.1], [6.9, 46.1], [6.9, 45.9]]]}
+    path = _write_geojson(tmp_path / "aoi.geojson", square)
+
+    geom, crs = _read_geometry_file(path)
+
+    assert geom.geom_type == "Polygon"
+    assert crs == 4326
+
+
+def test_read_geometry_file_reads_a_line(tmp_path):
+    """Lines matter here: a cross-section is defined by one."""
+    line = {"type": "LineString", "coordinates": [[6.9, 46.0], [7.1, 46.0]]}
+    path = _write_geojson(tmp_path / "cs.geojson", line)
+
+    geom, _ = _read_geometry_file(path)
+
+    assert geom.geom_type == "LineString"
+
+
+def test_read_geometry_file_reads_a_feature_collection(tmp_path):
+    """The common export shape; features are unioned."""
+    fc = {
+        "type": "FeatureCollection",
+        "features": [
+            {"type": "Feature", "geometry": {"type": "Point", "coordinates": [7.0, 46.0]}, "properties": {}},
+            {"type": "Feature", "geometry": {"type": "Point", "coordinates": [7.1, 46.1]}, "properties": {}},
+        ],
+    }
+    path = tmp_path / "fc.geojson"
+    path.write_text(json.dumps(fc))
+
+    geom, _ = _read_geometry_file(path)
+
+    assert geom.geom_type == "MultiPoint"
+
+
+def test_read_geometry_file_rejects_an_unknown_type(tmp_path):
+    """An unrecognised GeoJSON object is refused rather than half-read."""
+    path = tmp_path / "bad.geojson"
+    path.write_text(json.dumps({"type": "Topology"}))
+
+    with pytest.raises(ValueError, match="Unrecognised GeoJSON"):
+        _read_geometry_file(path)
+
+
+def test_read_geometry_file_rejects_an_unsupported_suffix(tmp_path):
+    """Only ``.shp`` and ``.geojson``/``.json`` are read."""
+    path = tmp_path / "aoi.kml"
+    path.touch()
+
+    with pytest.raises(ValueError, match="Unsupported geometry file type"):
+        _read_geometry_file(path)
+
+
+def test_read_geometry_file_raises_on_a_missing_file(tmp_path):
+    """A path typo must not be read as "no AOI"."""
+    with pytest.raises(FileNotFoundError):
+        _read_geometry_file(tmp_path / "nope.geojson")
+
+
+def test_prj_crs_returns_none_without_a_prj(tmp_path):
+    """A shapefile with no ``.prj`` declares nothing."""
+    assert _prj_crs(tmp_path / "missing.prj") is None
+
+
+def test_prj_crs_reads_a_wkt(tmp_path):
+    """A readable ``.prj`` yields its EPSG code."""
+    prj = tmp_path / "aoi.prj"
+    prj.write_text(pyproj.CRS.from_epsg(SWISS_EPSG).to_wkt())
+
+    assert _prj_crs(prj) == SWISS_EPSG
+
+
+def test_prj_crs_ignores_an_empty_prj(tmp_path):
+    """An empty file declares nothing rather than raising."""
+    prj = tmp_path / "empty.prj"
+    prj.write_text("   ")
+
+    assert _prj_crs(prj) is None
+
+
+# ---------------------------------------------------------------------------
+# _load_aoi_polygon — the crop_by_polygone entry point
+# ---------------------------------------------------------------------------
+
+
+def test_load_aoi_polygon_from_a_shapely_geometry():
+    """A bare geometry with no ``crs`` is taken as already in the AOI frame."""
+    square = shapely.box(2_590_000, 1_190_000, 2_610_000, 1_210_000)
+
+    assert _load_aoi_polygon(square, aoi_epsg=SWISS_EPSG).equals(square)
+
+
+def test_load_aoi_polygon_reprojects_a_geojson_from_its_own_crs(tmp_path):
+    """A GeoJSON is lon/lat; reading those degrees as metres lands 2600 km away."""
+    square = {"type": "Polygon", "coordinates": [[[6.9, 45.9], [7.1, 45.9], [7.1, 46.1], [6.9, 46.1], [6.9, 45.9]]]}
+    path = _write_geojson(tmp_path / "aoi.geojson", square)
+
+    geom = _load_aoi_polygon(path, aoi_epsg=SWISS_EPSG)
+
+    assert geom.contains(_reproject_to_aoi(shapely.Point(*CH_SITE), 4326, SWISS_EPSG))
+
+
+def test_an_explicit_crs_overrides_the_files_declaration(tmp_path):
+    """``crs=`` wins, for a file that declares the wrong thing."""
+    square = {"type": "Polygon", "coordinates": [[[6.9, 45.9], [7.1, 45.9], [7.1, 46.1], [6.9, 46.1], [6.9, 45.9]]]}
+    path = _write_geojson(tmp_path / "aoi.geojson", square, crs_name="urn:ogc:def:crs:EPSG::2056")
+
+    declared = _load_aoi_polygon(path, aoi_epsg=SWISS_EPSG)
+    overridden = _load_aoi_polygon(path, crs=4326, aoi_epsg=SWISS_EPSG)
+
+    assert not declared.equals(overridden)
+
+
+def test_load_aoi_polygon_from_a_geodataframe():
+    """A GeoDataFrame's own ``.crs`` is honoured and its parts dissolved."""
+    gpd = pytest.importorskip("geopandas")
+
+    gdf = gpd.GeoDataFrame(geometry=[shapely.box(6.9, 45.9, 7.1, 46.1)], crs="EPSG:4326")
+
+    geom = _load_aoi_polygon(gdf, aoi_epsg=SWISS_EPSG)
+
+    assert geom.contains(_reproject_to_aoi(shapely.Point(*CH_SITE), 4326, SWISS_EPSG))
+
+
+def test_load_aoi_polygon_rejects_a_line():
+    """A crop needs an area; a line defines a cross-section instead."""
+    with pytest.raises(ValueError, match="Polygon/MultiPolygon"):
+        _load_aoi_polygon(shapely.LineString([(0, 0), (1, 1)]), aoi_epsg=SWISS_EPSG)
+
+
+def test_load_aoi_polygon_rejects_an_empty_geometry():
+    """An empty AOI would select every gate or none; refuse it."""
+    with pytest.raises(ValueError, match="empty"):
+        _load_aoi_polygon(shapely.Polygon(), aoi_epsg=SWISS_EPSG)
+
+
+def test_load_aoi_polygon_rejects_an_unsupported_type():
+    """The error must list what is accepted."""
+    with pytest.raises(TypeError, match="shapely"):
+        _load_aoi_polygon(42, aoi_epsg=SWISS_EPSG)
+
+
+# ---------------------------------------------------------------------------
+# The lattices are the single source of gate geometry
+# ---------------------------------------------------------------------------
+
+
+def test_gate_footprints_come_from_the_h_plane_lattice(archive_dir):
+    """``aoi.py`` and the plots must draw the same gate, corner for corner.
+
+    The old planar / nominal-1-degree construction was off by 32 m mean (125 m max) per
+    corner on radar L and mis-sized high-elevation gates by -23% at sweep 20.
+    """
+    from raddb.aoi import _gate_footprints, _lut_cs_table
+
+    cs_table = _lut_cs_table(archive_dir, [RADAR]).to_pandas().head(300)
+    footprints = _gate_footprints(cs_table, np.tan(np.deg2rad(0.5)), base_path=archive_dir, epsg=SWISS_EPSG)
+
+    h_plane = RadDB(archive_dir=str(archive_dir)).get_h_plane(RADAR, per_gate=True)
+    aligned = pl.DataFrame({"gate_id": cs_table["gate_id"].to_numpy()}).join(
+        h_plane, on="gate_id", how="left", maintain_order="left"
+    )
+    reference = shapely.polygons(
+        np.stack(
+            [
+                np.stack([aligned[f"x_2056_{k}"].to_numpy(), aligned[f"y_2056_{k}"].to_numpy()], axis=1)
+                for k in range(1, 5)
+            ],
+            axis=1,
+        ).astype(np.float64)
+    )
+
+    assert np.allclose(shapely.get_coordinates(footprints), shapely.get_coordinates(reference))
+
+
+def test_the_planar_fallback_is_still_available(archive_dir):
+    """Archives with no projected lattice keep working on the old approximation."""
+    from raddb.aoi import _gate_footprints, _lut_cs_table
+
+    cs_table = _lut_cs_table(archive_dir, [RADAR]).to_pandas().head(50)
+
+    planar = _gate_footprints(cs_table, np.tan(np.deg2rad(0.5)), base_path=None, epsg=None)
+
+    assert shapely.is_valid(planar).all()
+
+
+def test_a_cross_section_height_follows_the_v_plane(plot_rdb, plot_archive_dir, plot_site):
+    """Gate height in the section plane must track the stored beam thickness.
+
+    Uses the realistically-sampled 72 x 60 x 6 archive: the small fixture has only two
+    sweeps at 30 degree azimuth spacing, where height and thickness are collinear for
+    reasons that have nothing to do with the beam.
+    """
+    from raddb.main import _decode_geometry
+    from raddb.tests.conftest import PLOT_RADAR
+
+    cs = plot_rdb.extract_cross_section(
+        (plot_site[0] - 12_000, plot_site[1] - 12_000), (plot_site[0] + 12_000, plot_site[1] + 12_000)
+    )
+    pdf = _decode_geometry(cs.data.to_pandas()).head(200)
+    heights = np.array([p.bounds[3] - p.bounds[1] for p in pdf["cs_polygon"]])
+
+    v_plane = RadDB(archive_dir=str(plot_archive_dir)).get_v_plane(PLOT_RADAR, per_gate=True)
+    aligned = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
+        v_plane, on="gate_id", how="left", maintain_order="left"
+    )
+    thickness = 0.5 * (
+        np.abs(aligned["z_asl_4"].to_numpy() - aligned["z_asl_1"].to_numpy())
+        + np.abs(aligned["z_asl_3"].to_numpy() - aligned["z_asl_2"].to_numpy())
+    )
+
+    assert np.corrcoef(heights, thickness)[0, 1] > 0.9
+
+
+def test_a_crop_matches_a_plain_membership_reference(rdb, archive_dir):
+    """The semi-join must select exactly what an ``isin`` filter would."""
+    geo = rdb.to_pandas(with_geometry=True)
+    cx, cy = float(geo["x_2056"].median()), float(geo["y_2056"].median())
+    bounds = (cx - 5000, cy - 5000, cx + 5000, cy + 5000)
+
+    crop = rdb.crop_by_bbox(bounds=bounds)
+
+    centroids = _lut_centroids(archive_dir, [RADAR])
+    want = set(_resolve_gate_ids(centroids, shapely.box(*bounds)).tolist())
+    assert set(crop.data["gate_id"].to_list()) == set(rdb.data["gate_id"].to_list()) & want
diff --git a/raddb/tests/test_api_coverage.py b/raddb/tests/test_api_coverage.py
new file mode 100644
index 0000000..ec6830a
--- /dev/null
+++ b/raddb/tests/test_api_coverage.py
@@ -0,0 +1,226 @@
+"""The structural gate: every public callable of RadDB has a test named after it.
+
+The suite is organised **structurally**, not thematically: ``raddb/<module>.py`` is tested
+by ``raddb/tests/test_<module>.py``, and every public callable in that module has a
+``test_<callable>`` in that file.  This module checks that mapping mechanically by parsing
+both sides with :mod:`ast`, so a new public function that ships without a test turns CI
+red instead of going unnoticed.
+
+Naming rules enforced here:
+
+- a module-level function ``foo``     -> ``test_foo``
+- a class ``Bar``                     -> ``test_Bar``
+- a method ``Bar.baz``                -> ``test_Bar_baz``
+- a dunder ``Bar.__len__``            -> ``test_Bar_len`` (see :data:`DUNDERS`)
+
+Tests beyond this mandatory set are welcome and keep their own descriptive names; this
+module only checks that nothing is *missing*.
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+import pytest
+
+TESTS_DIR = Path(__file__).resolve().parent
+PKG_DIR = TESTS_DIR.parent
+REPO_ROOT = PKG_DIR.parent
+
+MODULE_TO_TEST_FILE: dict[str, str] = {
+    "raddb/__init__.py": "test_package_api.py",
+    "raddb/_proj.py": "test__proj.py",
+    "raddb/aoi.py": "test_aoi.py",
+    "raddb/discovery.py": "test_discovery.py",
+    "raddb/hc_mapping.py": "test_hc_mapping.py",
+    "raddb/helper.py": "test_helper.py",
+    "raddb/io_core.py": "test_io_core.py",
+    "raddb/lut.py": "test_lut.py",
+    "raddb/main.py": "test_main.py",
+    "raddb/viz/__init__.py": "test_viz_init.py",
+    "raddb/viz/interactive.py": "test_viz_interactive.py",
+    "raddb/viz/plot.py": "test_viz_plot.py",
+}
+"""Source module (repo-relative) -> the test file that must cover it.
+
+The two ``__init__.py`` entries are special-cased: a literal ``test___init__.py`` is
+unreadable and the package root and ``viz/`` would collide on the same name.
+"""
+
+SKIP_MODULES = {"raddb/_version.py"}
+"""Modules deliberately not covered — ``_version.py`` is generated by setuptools_scm."""
+
+SKIP_TEST_FILES = {"test_api_coverage.py"}
+"""Test files that cover no single source module."""
+
+DUNDERS = {"__init__": "init", "__len__": "len", "__repr__": "repr"}
+"""Dunder methods that are part of the public surface, and their test-name suffix."""
+
+PENDING_FILES: set[str] = set()
+"""Test files not yet written out, exempt from the per-callable check.
+
+The coarse half of the loop's ledger: one name is removed as each file is completed, so
+the gate tightens monotonically.  Must be **empty** when the restructuring is finished.
+"""
+
+KNOWN_MISSING: set[str] = set()
+"""Individual callables that are knowingly untested, as ``"<test_file>::<test_name>"``.
+
+The fine half of the ledger, for a callable inside an otherwise-finished file that could
+not be tested.  Every entry needs a one-line reason beside it, and this must be **empty**
+when the restructuring is finished.
+"""
+
+
+def _source_modules() -> list[Path]:
+    """Return every RadDB source module, excluding the test package itself.
+
+    Returns
+    -------
+    list of pathlib.Path
+        Absolute paths, sorted.
+    """
+    return sorted(p for p in PKG_DIR.rglob("*.py") if "tests" not in p.relative_to(PKG_DIR).parts)
+
+
+def _test_name_for(qualname: str) -> str:
+    """Map a callable's qualified name to the test name that must exist.
+
+    Parameters
+    ----------
+    qualname : str
+        Either ``"func"``, ``"Class"`` or ``"Class.method"``.
+
+    Returns
+    -------
+    str
+        The mandatory ``test_...`` function name.
+    """
+    if "." not in qualname:
+        return f"test_{qualname}"
+    cls, method = qualname.split(".", 1)
+    return f"test_{cls}_{DUNDERS.get(method, method)}"
+
+
+def _public_qualnames(path: Path) -> list[str]:
+    """Return the public callables declared at the top level of ``path``.
+
+    Nested functions and closures are ignored: only module-level ``def``/``class`` and,
+    inside a public class, its methods.
+
+    Parameters
+    ----------
+    path : pathlib.Path
+        A Python source file.
+
+    Returns
+    -------
+    list of str
+        Qualified names, e.g. ``["open", "RadDB", "RadDB.open"]``.
+    """
+    names: list[str] = []
+    for node in ast.parse(path.read_text(encoding="utf-8")).body:
+        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+            if not node.name.startswith("_"):
+                names.append(node.name)
+        elif isinstance(node, ast.ClassDef) and not node.name.startswith("_"):
+            names.append(node.name)
+            for sub in node.body:
+                if not isinstance(sub, (ast.FunctionDef, ast.AsyncFunctionDef)):
+                    continue
+                if not sub.name.startswith("_") or sub.name in DUNDERS:
+                    names.append(f"{node.name}.{sub.name}")
+    return names
+
+
+def _declared_tests(test_file: Path) -> set[str]:
+    """Return every ``test_*`` function declared in ``test_file``.
+
+    Both module-level test functions and methods of ``Test*`` classes count, so a ported
+    legacy test grouped under a class still satisfies the gate.
+
+    Parameters
+    ----------
+    test_file : pathlib.Path
+        A test module.  A missing file yields an empty set.
+
+    Returns
+    -------
+    set of str
+        Test function names.
+    """
+    if not test_file.exists():
+        return set()
+    found: set[str] = set()
+    for node in ast.walk(ast.parse(test_file.read_text(encoding="utf-8"))):
+        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) and node.name.startswith("test_"):
+            found.add(node.name)
+    return found
+
+
+def _expected_coverage() -> dict[str, set[str]]:
+    """Build the full ``{test_file: {required test names}}`` mapping.
+
+    Returns
+    -------
+    dict
+        Keyed by test-file name, one entry per source module (empty set allowed).
+    """
+    expected: dict[str, set[str]] = {}
+    for module in _source_modules():
+        rel = module.relative_to(REPO_ROOT).as_posix()
+        if rel in SKIP_MODULES:
+            continue
+        test_file = MODULE_TO_TEST_FILE[rel]
+        expected.setdefault(test_file, set())
+        expected[test_file] |= {_test_name_for(q) for q in _public_qualnames(module)}
+    return expected
+
+
+def test_every_module_has_a_test_file():
+    """Every source module is mapped, and the mapped test file exists on disk."""
+    unmapped = [
+        m.relative_to(REPO_ROOT).as_posix()
+        for m in _source_modules()
+        if m.relative_to(REPO_ROOT).as_posix() not in MODULE_TO_TEST_FILE
+        and m.relative_to(REPO_ROOT).as_posix() not in SKIP_MODULES
+    ]
+    assert unmapped == [], f"source modules with no entry in MODULE_TO_TEST_FILE: {unmapped}"
+
+    absent = sorted(name for name in MODULE_TO_TEST_FILE.values() if not (TESTS_DIR / name).exists())
+    assert absent == [], f"mapped test files that do not exist: {absent}"
+
+
+def test_no_orphan_test_file():
+    """No ``test_*.py`` exists whose source module is gone."""
+    on_disk = {p.name for p in TESTS_DIR.glob("test_*.py")} - SKIP_TEST_FILES
+    expected = set(MODULE_TO_TEST_FILE.values())
+    assert on_disk - expected == set(), f"test files covering no source module: {sorted(on_disk - expected)}"
+
+
+def test_every_public_callable_has_a_test():
+    """Each public callable has a ``test_`` named after it, modulo the two ledgers."""
+    missing: set[str] = set()
+    for test_file, required in _expected_coverage().items():
+        if test_file in PENDING_FILES:
+            continue
+        have = _declared_tests(TESTS_DIR / test_file)
+        missing |= {f"{test_file}::{name}" for name in required - have}
+
+    stale = KNOWN_MISSING - missing
+    assert stale == set(), f"KNOWN_MISSING lists tests that now exist — remove them: {sorted(stale)}"
+    assert missing == KNOWN_MISSING, f"public callables with no test: {sorted(missing - KNOWN_MISSING)}"
+
+
+def test_pending_files_are_real():
+    """A name in :data:`PENDING_FILES` must be a file we actually plan to write."""
+    bogus = PENDING_FILES - set(MODULE_TO_TEST_FILE.values())
+    assert bogus == set(), f"PENDING_FILES names unknown test files: {sorted(bogus)}"
+
+
+def test_the_ledgers_are_empty():
+    """Both ledgers must be empty once the restructuring is finished."""
+    if PENDING_FILES or KNOWN_MISSING:
+        pytest.xfail(f"{len(PENDING_FILES)} files pending, {len(KNOWN_MISSING)} callables untested")
+    assert (PENDING_FILES, KNOWN_MISSING) == (set(), set())
diff --git a/raddb/tests/test_azimuth_grid.py b/raddb/tests/test_azimuth_grid.py
deleted file mode 100644
index 4508507..0000000
--- a/raddb/tests/test_azimuth_grid.py
+++ /dev/null
@@ -1,377 +0,0 @@
-"""
-raddb/tests/test_azimuth_grid.py
---------------------------------
-The nominal azimuth grid: the LUT stores a radar's *scan strategy*, and every
-volume's rays are snapped onto it before their ``gate_id`` is built.
-
-This exists because the LUT used to freeze the measured azimuths of whichever
-volume was archived first.  An antenna reports where it actually pointed, which
-drifts a few hundredths of a degree between rotations, and ``gate_id`` resolves
-0.1° — so a drifting ray changed bin and its gates matched no LUT row.  Measured
-on real data: **6%** of gates lost per volume on Rad4Alp, **35%** on WSR-88D,
-silently, on every volume after the first.
-
-Synthetic throughout; the drift is injected to match what the real files show.
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import polars as pl
-import pytest
-import xarray as xr
-import yaml
-
-_PKG_ROOT = Path(__file__).resolve().parents[2]
-if str(_PKG_ROOT) not in sys.path:
-    sys.path.insert(0, str(_PKG_ROOT))
-
-from raddb.lut import (  # noqa: E402
-    AZIMUTH_SCALE,
-    AZIMUTH_STEPS,
-    azimuth_grid_tolerance,
-    nominal_azimuth_grid,
-    snap_azimuths_to_grid,
-    load_azimuth_grids,
-)
-from raddb.main import RadDB  # noqa: E402
-from raddb.tests.test_fixes import _make_datatree  # noqa: E402
-
-# Real drift, measured from the files themselves: Rad4Alp reports every ray
-# ~0.033 deg past its nominal angle with a ~0.007 deg spread; WSR-88D is
-# zero-mean with a spread up to ~0.045 deg.
-MCH_BIAS, MCH_SPREAD = 0.0327, 0.0069
-NEXRAD_SPREAD = 0.045
-
-
-def _jitter(az, rng, bias=0.0, spread=MCH_SPREAD):
-    return (np.asarray(az, float) + bias + rng.normal(0, spread, len(az))) % 360.0
-
-
-def _retime(dt, when, rng, bias=0.0, spread=MCH_SPREAD):
-    """Copy a DataTree at a new time, with the antenna pointing slightly differently."""
-    out = {}
-    for name, node in dt.children.items():
-        ds = node.to_dataset()
-        n = ds.sizes["azimuth"]
-        ds = ds.assign_coords(azimuth=_jitter(ds["azimuth"].values, rng, bias, spread))
-        ds["time"] = ("azimuth", np.array([when] * n, dtype="datetime64[ns]"))
-        ds.attrs.update(node.attrs)
-        out[name] = ds
-    return xr.DataTree.from_dict(out)
-
-
-# ===========================================================================
-# nominal_azimuth_grid
-# ===========================================================================
-
-class TestNominalGrid:
-
-    @pytest.mark.parametrize("n_rays,step_tenths", [(360, 10), (720, 5), (180, 20)])
-    def test_spacing_follows_the_ray_count(self, n_rays, step_tenths):
-        """One rule, no per-network constant: the grid comes from n_rays."""
-        grid = nominal_azimuth_grid(np.arange(n_rays) * (360 / n_rays) + 0.25)
-        assert grid.size == n_rays
-        assert np.all(np.diff(grid) == step_tenths)
-
-    def test_recovers_the_grid_from_jittered_rays(self):
-        rng = np.random.default_rng(0)
-        nominal = np.arange(360) + 0.5
-        grid = nominal_azimuth_grid(_jitter(nominal, rng, MCH_BIAS))
-        assert np.array_equal(grid, np.round(nominal * AZIMUTH_SCALE).astype(np.int64))
-
-    def test_is_stable_across_volumes(self):
-        """The whole point: different volumes must derive the *same* grid."""
-        rng = np.random.default_rng(1)
-        nominal = np.arange(360) + 0.5
-        grids = [nominal_azimuth_grid(_jitter(nominal, rng, MCH_BIAS)) for _ in range(25)]
-        assert all(np.array_equal(g, grids[0]) for g in grids)
-
-    def test_super_resolution_grid_stays_uniform(self):
-        """720 rays put every centre on x.x5; banker's rounding would alternate 0.4/0.6."""
-        grid = nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)
-        assert np.all(np.diff(grid) == 5)
-
-    def test_offset_is_circular(self):
-        """Rays straddling 0 deg must not drag the offset to the middle of the step."""
-        rng = np.random.default_rng(2)
-        nominal = np.arange(360) * 1.0          # rays centred on 0, 1, 2, ...
-        grid = nominal_azimuth_grid(_jitter(nominal, rng, 0.0, 0.02))
-        assert np.array_equal(grid, np.arange(360) * 10)
-
-    def test_rejects_spacing_finer_than_the_resolution(self):
-        with pytest.raises(ValueError, match="finer than"):
-            nominal_azimuth_grid(np.arange(7200) * 0.05)
-
-    def test_rejects_empty_sweep(self):
-        with pytest.raises(ValueError, match="no rays"):
-            nominal_azimuth_grid([])
-
-    def test_rejects_a_sector_scan(self):
-        """90 rays over 90 deg would silently get a 4 deg grid and collapse."""
-        with pytest.raises(ValueError, match="full rotation"):
-            nominal_azimuth_grid(np.arange(90, 180, 1.0))
-
-    def test_rejects_a_sweep_with_a_large_gap(self):
-        az = np.concatenate([np.arange(0, 120, 1.0), np.arange(240, 360, 1.0)])
-        with pytest.raises(ValueError, match="full rotation"):
-            nominal_azimuth_grid(az)
-
-    @pytest.mark.parametrize("dropped", [[7], [7, 8], [0, 359], [3, 100, 250]])
-    def test_a_rotation_with_holes_keeps_the_full_grid(self, dropped):
-        """718 of 720 is a rotation with holes, not a 0.5014 deg scan strategy."""
-        nominal = np.arange(720) * 0.5 + 0.25
-        recorded = np.delete(nominal, dropped)
-        grid = nominal_azimuth_grid(recorded)
-        assert grid.size == 720                     # the missing rays keep their slots
-        assert np.all(np.diff(grid) == 5)
-        assert np.array_equal(grid, nominal_azimuth_grid(nominal))
-
-    def test_holes_survive_antenna_drift(self):
-        """The real case: WSR-88D drift plus two dropped rays.
-
-        The grid is the same rotation, but not necessarily the same integers: a
-        720-ray grid is centred on ``x.x5``, exactly the 0.1° rounding boundary,
-        so the ~0.002° the two ray sets differ by can tip the whole grid one
-        tenth either way.  That is a tenth of a degree against a half-spacing
-        tolerance of 0.25°, so every ray still snaps to its own point.
-        """
-        rng = np.random.default_rng(7)
-        nominal = np.arange(720) * 0.5 + 0.25
-        recorded = np.delete(_jitter(nominal, rng, 0.0, NEXRAD_SPREAD), [11, 12])
-
-        grid = nominal_azimuth_grid(recorded)
-        assert grid.size == 720
-        assert np.all(np.diff(grid) == 5)
-
-        shift = (grid - nominal_azimuth_grid(nominal) + AZIMUTH_STEPS // 2) % AZIMUTH_STEPS
-        assert np.all(np.abs(shift - AZIMUTH_STEPS // 2) <= 1)      # at most one tenth
-
-        _, dist = snap_azimuths_to_grid(recorded, grid)
-        assert dist.max() <= azimuth_grid_tolerance(grid)
-
-    def test_too_many_holes_is_not_a_rotation(self):
-        """Past the coverage floor it is indistinguishable from a sector scan."""
-        nominal = np.arange(360) + 0.5
-        recorded = np.delete(nominal, np.arange(0, 100))     # 260 of 360
-        with pytest.raises(ValueError, match="full rotation"):
-            nominal_azimuth_grid(recorded)
-
-
-# ===========================================================================
-# snap_azimuths_to_grid
-# ===========================================================================
-
-class TestSnapping:
-
-    @pytest.fixture
-    def grid(self):
-        return nominal_azimuth_grid(np.arange(360) + 0.5)   # 0.5, 1.5, ... 359.5
-
-    def test_snaps_to_the_nearest_point(self, grid):
-        snapped, dist = snap_azimuths_to_grid([0.49, 0.51, 1.44, 1.56], grid)
-        assert list(snapped) == [5, 5, 15, 15]
-        assert np.allclose(dist, [0.1, 0.1, 0.6, 0.6])
-
-    @pytest.mark.parametrize("az,expected", [
-        (359.97, 3595),   # 0.47 from 359.5, 0.53 from 0.5 -> stays below the seam
-        (0.02, 5),        # 0.48 from 0.5, 0.52 from 359.5 -> stays above it
-        (359.60, 3595),
-        (0.60, 5),
-    ])
-    def test_seam_is_measured_the_short_way(self, grid, az, expected):
-        """Distance across 0/360 must go the short way round, not through 180."""
-        snapped, _ = snap_azimuths_to_grid([az], grid)
-        assert snapped[0] == expected
-
-    def test_a_ray_below_360_can_snap_to_a_grid_point_at_zero(self):
-        """With rays centred on 0, 1, 2 ..., 359.7 deg belongs to 0.0, not 359.0."""
-        grid = nominal_azimuth_grid(np.arange(360) * 1.0)
-        assert grid[0] == 0
-        snapped, dist = snap_azimuths_to_grid([359.7, 359.4, 0.3], grid)
-        assert list(snapped) == [0, 3590, 0]
-        assert np.allclose(dist, [3.0, 4.0, 3.0])
-
-    def test_full_precision_decides_the_match(self, grid):
-        """Rounding to 0.1 deg first would make 0.02 deg an ambiguous tie."""
-        snapped, _ = snap_azimuths_to_grid([0.02], grid)
-        assert snapped[0] == 5          # not 3595
-
-    def test_is_a_bijection_under_real_drift(self, grid):
-        rng = np.random.default_rng(3)
-        snapped, dist = snap_azimuths_to_grid(
-            _jitter(np.arange(360) + 0.5, rng, MCH_BIAS), grid
-        )
-        assert np.unique(snapped).size == 360        # no two rays collapse
-        assert dist.max() <= azimuth_grid_tolerance(grid)
-
-    def test_survives_nexrad_scale_drift(self):
-        grid = nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)
-        rng = np.random.default_rng(4)
-        snapped, dist = snap_azimuths_to_grid(
-            _jitter(np.arange(720) * 0.5 + 0.25, rng, 0.0, NEXRAD_SPREAD), grid
-        )
-        assert np.unique(snapped).size == 720
-        assert dist.max() <= azimuth_grid_tolerance(grid)
-
-    def test_output_is_inside_one_turn(self, grid):
-        snapped, _ = snap_azimuths_to_grid([0.0, 180.0, 359.999, 360.0], grid)
-        assert np.all((snapped >= 0) & (snapped < AZIMUTH_STEPS))
-
-    def test_rejects_empty_grid(self):
-        with pytest.raises(ValueError, match="empty azimuth grid"):
-            snap_azimuths_to_grid([1.0], [])
-
-
-# ===========================================================================
-# End to end: the bug this was written for
-# ===========================================================================
-
-class TestVolumesJoinTheirLut:
-
-    @pytest.fixture
-    def archive(self, tmp_path):
-        """One LUT-defining volume plus four later ones with drifting azimuths."""
-        db = RadDB(archive_dir=str(tmp_path / "a"), crs=2056)
-        base = _make_datatree(n_az=360, n_rng=40, n_sweeps=3)
-        db.archive(datatree=base, radar="A")
-        rng = np.random.default_rng(7)
-        for k in range(1, 5):
-            when = pd.Timestamp("2024-08-01 12:00:00") + pd.Timedelta(minutes=5 * k)
-            db.archive(datatree=_retime(base, when, rng, MCH_BIAS), radar="A")
-        return tmp_path / "a"
-
-    def test_every_volume_joins_completely(self, archive):
-        lut = pl.read_parquet(archive / "A" / "LUT" / "A_LUT.parquet", columns=["gate_id"])
-        pols = sorted((archive / "A").rglob("*_POL.parquet"))
-        assert len(pols) == 5
-        for f in pols:
-            pol = pl.read_parquet(f, columns=["gate_id"])
-            matched = pol.join(lut, on="gate_id", how="semi").height
-            assert matched == pol.height, f"{f.name}: {matched}/{pol.height} joined"
-
-    def test_the_same_ray_keeps_its_gate_id(self, archive):
-        """Across volumes a gate must keep one identity, or nothing can be compared."""
-        pols = sorted((archive / "A").rglob("*_POL.parquet"))
-        sets = [set(pl.read_parquet(f, columns=["gate_id"])["gate_id"].to_list()) for f in pols]
-        assert all(s == sets[0] for s in sets)
-
-    def test_grid_is_recovered_from_the_lut(self, archive):
-        """The info YAML no longer restates it; the LUT parquet is the source."""
-        grids = load_azimuth_grids("A", archive)
-        assert grids is not None and set(grids) == {1, 2, 3}
-        for g in grids.values():
-            assert g.size == 360 and np.all(np.diff(g) == 10)
-
-        info = yaml.safe_load((archive / "A" / "LUT" / "A_info.yaml").read_text())
-        assert "azimuths" not in info["sweeps"][1]
-
-    def test_no_lut_means_no_grid(self, tmp_path):
-        """Nothing to snap onto — the caller must keep the measured azimuths."""
-        assert load_azimuth_grids("A", tmp_path) is None
-
-    def test_lut_azimuths_are_the_grid(self, archive):
-        """The LUT holds nominal angles now, not one volume's measurements."""
-        lut = pl.read_parquet(archive / "A" / "LUT" / "A_LUT.parquet", columns=["sweep", "azimuth"])
-        az = np.unique(lut.filter(pl.col("sweep") == 1)["azimuth"].to_numpy())
-        assert np.allclose(az * AZIMUTH_SCALE, np.round(az * AZIMUTH_SCALE))
-
-    def test_plots_and_crops_see_every_gate(self, archive):
-        """The loss was invisible because it only showed up in LUT joins."""
-        rdf = RadDB(archive_dir=str(archive)).open(radars="A")
-        assert rdf.to_geopandas().shape[0] == rdf.data.height
-
-
-class TestScanStrategyGuardrails:
-
-    @pytest.fixture
-    def db(self, tmp_path):
-        d = RadDB(archive_dir=str(tmp_path / "a"), crs=2056)
-        d.archive(datatree=_make_datatree(n_az=360, n_rng=20, n_sweeps=2), radar="A")
-        return d
-
-    def test_refuses_a_different_ray_count(self, db):
-        with pytest.raises(ValueError, match="different scan strategy"):
-            db.archive(
-                datatree=_make_datatree(n_az=720, n_rng=20, n_sweeps=2,
-                                        vol_time=pd.Timestamp("2024-08-01 13:00")),
-                radar="A",
-            )
-
-    def test_refuses_an_unknown_sweep(self, db):
-        with pytest.raises(ValueError, match="no sweep"):
-            db.archive(
-                datatree=_make_datatree(n_az=360, n_rng=20, n_sweeps=4,
-                                        vol_time=pd.Timestamp("2024-08-01 14:00")),
-                radar="A",
-            )
-
-    def test_accepts_a_volume_that_dropped_rays(self, db):
-        """A volume short of a ray or two is a rotation with holes, not a new strategy."""
-        rng = np.random.default_rng(21)
-        base = _retime(_make_datatree(n_az=360, n_rng=20, n_sweeps=2),
-                       pd.Timestamp("2024-08-01 18:00"), rng, MCH_BIAS)
-        holed = xr.DataTree.from_dict({
-            name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
-            for name, node in base.children.items()
-        })
-        res = db.archive(datatree=holed, radar="A")
-        assert (res["n_archived"], res["n_failed"]) == (1, 0)
-
-    def test_a_lut_built_from_a_holed_volume_still_holds_every_ray(self, tmp_path):
-        """The LUT is the rotation, not one volume: the dropped rays keep their rows,
-        so a later complete volume joins 100%."""
-        complete = _make_datatree(n_az=360, n_rng=20, n_sweeps=2)
-        holed = xr.DataTree.from_dict({
-            name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
-            for name, node in complete.children.items()
-        })
-
-        db = RadDB(archive_dir=str(tmp_path / "a"), crs=2056)
-        db.archive(datatree=holed, radar="A")                     # LUT from the holed volume
-        lut = db.get_lut("A")
-        assert lut.filter(pl.col("sweep") == 1)["azimuth"].n_unique() == 360
-
-        res = db.archive(
-            datatree=_retime(complete, pd.Timestamp("2024-08-01 19:00"),
-                             np.random.default_rng(22), MCH_BIAS),
-            radar="A",
-        )
-        assert (res["n_archived"], res["n_failed"]) == (1, 0)
-
-        data = db.open(radars="A")
-        lut_ids = set(lut["gate_id"].to_list())
-        assert all(g in lut_ids for g in data.data["gate_id"].to_list())
-
-    def test_accepts_ordinary_drift(self, db):
-        rng = np.random.default_rng(11)
-        base = _make_datatree(n_az=360, n_rng=20, n_sweeps=2)
-        res = db.archive(
-            datatree=_retime(base, pd.Timestamp("2024-08-01 15:00"), rng, MCH_BIAS),
-            radar="A",
-        )
-        assert (res["n_archived"], res["n_failed"]) == (1, 0)
-
-    def test_batch_reports_the_refusal_instead_of_aborting(self, db, tmp_path):
-        """One incompatible volume must not take the whole batch down."""
-        good = _retime(_make_datatree(n_az=360, n_rng=20, n_sweeps=2),
-                       pd.Timestamp("2024-08-01 16:00"), np.random.default_rng(12), MCH_BIAS)
-        bad = _make_datatree(n_az=720, n_rng=20, n_sweeps=2,
-                             vol_time=pd.Timestamp("2024-08-01 17:00"))
-        res = db.archive(datatree={"good": good, "bad": bad}, radar="A")
-        assert res["n_archived"] == 1 and res["n_failed"] == 1
-
-    def test_warns_when_the_lut_has_no_grid(self, tmp_path, caplog):
-        """A pre-grid archive keeps working, but says that gates may not join."""
-        from raddb.io_core import _build_polar_dataframe
-
-        df = pl.DataFrame({
-            "sweep": [1, 1], "azimuth": [0.53, 1.53], "range": [1000.0, 1000.0],
-            "DBZH": [10.0, 20.0], "time": [pd.Timestamp("2024-01-01")] * 2,
-        })
-        with caplog.at_level("WARNING"):
-            _build_polar_dataframe(df, "A", "DBZH", 0.0, ">", azimuth_grids=None)
-        assert "no nominal azimuth grid" in caplog.text
diff --git a/raddb/tests/test_crs.py b/raddb/tests/test_crs.py
deleted file mode 100644
index eac8905..0000000
--- a/raddb/tests/test_crs.py
+++ /dev/null
@@ -1,265 +0,0 @@
-"""
-raddb/tests/test_crs.py
------------------------
-The CRS contract: a projection must be declared at archive time and must be
-valid where the radar actually is.
-
-This exists because a hardcoded EPSG:2056 silently mis-selected US gates by
-**17%** — a "50 km" crop reached only ~46 km — while looking entirely normal.
-Nothing here may be inferred, defaulted or guessed.
-
-Synthetic throughout; the fixture radar is relocated to test non-Swiss sites.
-"""
-from __future__ import annotations
-
-import numpy as np
-import pyproj
-import pytest
-import shapely
-import xarray as xr
-
-from raddb.main import RadDB
-from raddb.lut import (
-    CRS_REFUSE_PCT, crs_distance_error, generate_lut_from_datatree,
-    suggest_crs, validate_crs_for_site,
-)
-from raddb.tests.test_fixes import RADAR, _make_datatree
-
-CH = (7.0, 46.0)          # the fixture's own site
-US = (-97.2775, 35.3331)  # KTLX, Oklahoma
-
-
-def relocate(dt, lon, lat):
-    """Move a synthetic volume to another place on Earth."""
-    out = {}
-    for name, node in dt.children.items():
-        ds = node.to_dataset().assign_coords(latitude=lat, longitude=lon)
-        ds.attrs.update(node.attrs)
-        out[name] = ds
-    return xr.DataTree.from_dict(out)
-
-
-class TestSuggestion:
-    def test_suggests_the_utm_zone(self):
-        assert suggest_crs(*CH) == 32632      # zone 32N
-        assert suggest_crs(*US) == 32614      # zone 14N
-
-    def test_southern_hemisphere_gets_a_south_zone(self):
-        assert suggest_crs(151.2, -33.9) == 32756   # Sydney, zone 56S
-
-
-class TestMeasuredValidation:
-    """Validity is measured, because declared metadata is not enough."""
-
-    @pytest.mark.parametrize("crs,site,ok", [
-        (2056, CH, True),      # LV95 at home
-        (32632, CH, True),     # UTM 32N at home
-        (32614, US, True),     # UTM 14N at KTLX
-        (2056, US, False),     # the bug: LV95 in Oklahoma
-        (3857, CH, False),     # Web Mercator: claims the world, distorts hugely
-        (3857, US, False),
-    ])
-    def test_accepts_and_refuses_by_measurement(self, crs, site, ok):
-        if ok:
-            assert validate_crs_for_site(crs, *site) < CRS_REFUSE_PCT
-        else:
-            with pytest.raises(ValueError, match="distorts distance"):
-                validate_crs_for_site(crs, *site)
-
-    def test_area_of_use_alone_would_not_catch_web_mercator(self):
-        """EPSG:3857 declares the whole world, so bounds checks pass it."""
-        au = pyproj.CRS.from_epsg(3857).area_of_use
-        assert au.west <= CH[0] <= au.east and au.south <= CH[1] <= au.north
-        assert crs_distance_error(3857, *CH) > 10.0
-
-    def test_geographic_crs_is_refused(self):
-        with pytest.raises(ValueError, match="geographic"):
-            validate_crs_for_site(4326, *CH)
-
-    def test_refusal_names_a_replacement(self):
-        with pytest.raises(ValueError, match="32614"):
-            validate_crs_for_site(2056, *US)
-
-
-class TestArchiveRequiresACrs:
-    def test_no_crs_raises(self, tmp_path):
-        with pytest.raises(ValueError, match="requires a CRS"):
-            RadDB(archive_dir=str(tmp_path)).archive(datatree={RADAR: [_make_datatree()]})
-
-    def test_bad_crs_aborts_and_writes_nothing(self, tmp_path):
-        """A rejected CRS must not leave POL files behind with no usable LUT."""
-        dt = relocate(_make_datatree(), *US)
-        with pytest.raises(ValueError, match="distorts distance"):
-            RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree={RADAR: [dt]})
-        assert not list(tmp_path.rglob("*POL.parquet"))
-
-    def test_correct_crs_archives(self, tmp_path):
-        dt = relocate(_make_datatree(), *US)
-        RadDB(archive_dir=str(tmp_path), crs=32614).archive(datatree={RADAR: [dt]})
-        assert list(tmp_path.rglob("*POL.parquet"))
-        info = RadDB(archive_dir=str(tmp_path)).get_radar_info(RADAR)
-        assert info["crs"]["epsg"] == 32614
-
-
-class TestAoiUsesTheArchiveCrs:
-    @pytest.fixture(scope="class")
-    def us_archive(self, tmp_path_factory):
-        base = tmp_path_factory.mktemp("us")
-        dt = relocate(_make_datatree(n_az=72, n_rng=60, n_sweeps=3), *US)
-        RadDB(archive_dir=str(base), crs=32614).archive(datatree={RADAR: [dt]})
-        return base
-
-    def test_aoi_epsg_comes_from_the_archive(self, us_archive):
-        from raddb.aoi import aoi_epsg
-        assert aoi_epsg(us_archive, RADAR) == 32614
-
-    def test_crop_radius_is_true_metres(self, us_archive):
-        """The bug in one assertion: a 10 km crop must select a 10 km radius."""
-        from raddb.aoi import _lut_centroids, _resolve_gate_ids, _reproject_to_aoi, aoi_epsg
-
-        db = RadDB(archive_dir=str(us_archive))
-        lut = db.get_lut(RADAR)
-        geod = pyproj.Geod(ellps="WGS84")
-        lon = lut["longitude"].to_numpy(); lat = lut["latitude"].to_numpy()
-        _, _, d = geod.inv(np.full(lon.size, US[0]), np.full(lat.size, US[1]), lon, lat)
-
-        epsg = aoi_epsg(us_archive, RADAR)
-        centroids = _lut_centroids(us_archive, [RADAR])
-        pt = _reproject_to_aoi(shapely.Point(*US), 4326, epsg)
-        for radius in (10_000, 15_000):
-            truth = int((d <= radius).sum())
-            got = len(_resolve_gate_ids(centroids, pt.buffer(radius)))
-            assert abs(got - truth) <= 0.01 * truth, (
-                f"{radius/1000:.0f} km crop selected {got} gates, truth {truth}"
-            )
-
-    def test_cross_section_distance_is_true_metres(self, us_archive):
-        """A section line outside Switzerland must measure real ground distance."""
-        rdf = RadDB(archive_dir=str(us_archive)).open(radars=RADAR)
-        p1 = (US[0] - 0.2, US[1])
-        p2 = (US[0] + 0.2, US[1])
-        truth = pyproj.Geod(ellps="WGS84").inv(p1[0], p1[1], p2[0], p2[1])[2]
-
-        cs = rdf.extract_cross_section(p1=p1, p2=p2, crs=4326)
-        assert cs.data.height > 0, "the section selected no gates"
-        # The far end of the section, read off the gate footprints: `d_center`
-        # alone would sit half a gate short and eat most of the tolerance.
-        polygons = cs.to_pandas()["cs_polygon"].to_numpy()
-        span = float(shapely.bounds(polygons)[:, 2].max())
-        # UTM 14N at KTLX is accurate to ~0.03%; a hardcoded LV95 would be ~20% out.
-        assert abs(span - truth) <= 0.005 * truth, (
-            f"section spans {span:,.0f} m, true geodesic {truth:,.0f} m"
-        )
-
-    def test_quicklook_context_lands_in_the_archive_frame(self, us_archive):
-        """A caller's context geometry must not be reprojected to LV95."""
-        from raddb.aoi import _resolve_context, _reproject_to_aoi, aoi_epsg
-
-        epsg = aoi_epsg(us_archive, RADAR)
-        gpd = pytest.importorskip("geopandas")
-        box = shapely.box(US[0] - 1, US[1] - 1, US[0] + 1, US[1] + 1)   # WGS-84
-        gdf = gpd.GeoDataFrame(geometry=[box], crs="EPSG:4326")
-
-        got = _resolve_context(gdf, aoi_epsg=epsg)
-        want = _reproject_to_aoi(box, 4326, epsg)
-        assert got.distance(want) < 1.0
-
-        # ... and it must sit on top of the radar, not thousands of km away.
-        site = _reproject_to_aoi(shapely.Point(*US), 4326, epsg)
-        assert got.contains(site)
-
-    def test_quicklook_context_defaults_to_the_aoi_frame(self, us_archive):
-        """A bare shapely geometry is taken as already being in the AOI frame."""
-        from raddb.aoi import _resolve_context, _reproject_to_aoi, aoi_epsg
-
-        epsg = aoi_epsg(us_archive, RADAR)
-        site = _reproject_to_aoi(shapely.Point(*US), 4326, epsg)
-        geom = site.buffer(50_000)
-        assert _resolve_context(geom, aoi_epsg=epsg).equals(geom)
-
-    @pytest.mark.parametrize("kind", ["crop", "section"])
-    def test_quicklook_is_framed_on_the_archive(self, us_archive, kind):
-        """Both quicklook call sites must pass the AOI frame, not default to LV95."""
-        matplotlib = pytest.importorskip("matplotlib")
-        matplotlib.use("Agg")
-        import matplotlib.pyplot as plt
-        from raddb.aoi import _reproject_to_aoi, aoi_epsg
-
-        rdf = RadDB(archive_dir=str(us_archive)).open(radars=RADAR)
-        if kind == "crop":
-            rdf.crop_around_point(US, distance=20_000, crs=4326, quicklook=True)
-        else:
-            rdf.extract_cross_section(
-                p1=(US[0] - 0.2, US[1]), p2=(US[0] + 0.2, US[1]),
-                crs=4326, quicklook=True,
-            )
-        ax = plt.gcf().axes[0]
-        site = _reproject_to_aoi(shapely.Point(*US), 4326, aoi_epsg(us_archive, RADAR))
-        (x0, x1), (y0, y1) = ax.get_xlim(), ax.get_ylim()
-        plt.close("all")
-        # Axes hold metres (labelled in km by _KmFormatter); a Swiss-framed view
-        # would put the site millions of metres off-axis.
-        assert x0 <= site.x <= x1, f"{kind}: site outside x-range {(x0, x1)}"
-        assert y0 <= site.y <= y1, f"{kind}: site outside y-range {(y0, y1)}"
-
-    def test_aoi_crs_override_is_validated(self, us_archive):
-        rdf = RadDB(archive_dir=str(us_archive)).open(radars=RADAR)
-        with pytest.raises(ValueError, match="distorts distance"):
-            rdf.crop_around_point(US, distance=10_000, crs=4326, aoi_crs=2056)
-
-    def test_mixed_crs_radars_refuse_a_shared_aoi(self, tmp_path):
-        """No silent reprojection: the user must name the common frame."""
-        from raddb.aoi import aoi_epsg_for
-
-        base = tmp_path
-        RadDB(archive_dir=str(base), crs=2056).archive(
-            datatree={"A": [_make_datatree(n_az=24, n_rng=20, n_sweeps=2)]})
-        RadDB(archive_dir=str(base), crs=32614).archive(
-            datatree={"D": [relocate(_make_datatree(n_az=24, n_rng=20, n_sweeps=2), *US)]})
-        assert aoi_epsg_for(base, ["A"]) == 2056
-        assert aoi_epsg_for(base, ["D"]) == 32614
-        with pytest.raises(ValueError, match="different CRSs"):
-            aoi_epsg_for(base, ["A", "D"])
-
-
-class TestQuicklookFollowsTheFrame:
-    """The AOI quicklook draws in the archive's CRS, not always in LV95."""
-
-    @pytest.fixture(scope="class")
-    def archives(self, tmp_path_factory):
-        ch = tmp_path_factory.mktemp("ql_ch")
-        us = tmp_path_factory.mktemp("ql_us")
-        RadDB(archive_dir=str(ch), crs=2056).archive(
-            datatree={RADAR: [_make_datatree(n_az=36, n_rng=30, n_sweeps=2)]})
-        RadDB(archive_dir=str(us), crs=32614).archive(
-            datatree={RADAR: [relocate(_make_datatree(n_az=36, n_rng=30, n_sweeps=2), *US)]})
-        return ch, us
-
-    @pytest.mark.parametrize("which,epsg,site", [(0, 2056, CH), (1, 32614, US)])
-    def test_crop_is_in_view(self, archives, which, epsg, site):
-        """A hardcoded Swiss y-band used to push a US AOI off-screen entirely."""
-        import matplotlib
-        matplotlib.use("Agg")
-        import matplotlib.pyplot as plt
-        from raddb.aoi import _reproject_to_aoi
-
-        base = archives[which]
-        rdf = RadDB(archive_dir=str(base)).open(radars=RADAR)
-        pt = _reproject_to_aoi(shapely.Point(*site), 4326, epsg)
-        rdf.crop_around_point((pt.x, pt.y), distance=8_000, quicklook=True)
-        ax = plt.gcf().axes[0]
-        assert ax.get_xlim()[0] <= pt.x <= ax.get_xlim()[1]
-        assert ax.get_ylim()[0] <= pt.y <= ax.get_ylim()[1]
-        plt.close("all")
-
-    def test_quicklook_runs_without_a_declared_crs(self, archives):
-        """Reading needs no CRS, so neither does the quicklook."""
-        import matplotlib
-        matplotlib.use("Agg")
-        import matplotlib.pyplot as plt
-        from raddb.aoi import _reproject_to_aoi
-
-        rdf = RadDB(archive_dir=str(archives[1])).open(radars=RADAR)
-        pt = _reproject_to_aoi(shapely.Point(*US), 4326, 32614)
-        rdf.crop_around_point((pt.x, pt.y), distance=8_000, quicklook=True)
-        plt.close("all")
diff --git a/raddb/tests/test_datatree_io.py b/raddb/tests/test_datatree_io.py
deleted file mode 100644
index 99c66cb..0000000
--- a/raddb/tests/test_datatree_io.py
+++ /dev/null
@@ -1,289 +0,0 @@
-"""
-raddb/tests/test_datatree_io.py
--------------------------------
-Tests for the public DataTree-file workflow: discovery
-(``find_datatree_files``), loading (``open_any_datatree``), and end-to-end
-archiving from saved DataTree files (``RadDB.archive(datatree_dir=...)``).
-
-All tests use synthetic DataTrees written to tmp_path — no real radar
-files required.  NetCDF/Zarr-dependent tests skip when the backend is
-not installed.
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import pytest
-import xarray as xr
-
-_PKG_ROOT = Path(__file__).resolve().parents[2]
-if str(_PKG_ROOT) not in sys.path:
-    sys.path.insert(0, str(_PKG_ROOT))
-
-from raddb.tests.test_fixes import _make_datatree  # noqa: E402  (has lat/lon for LUT gen)
-
-RADAR = "A"
-
-
-def _write_nc_volumes(directory: Path, minutes: tuple[int, ...] = (0, 5, 10)) -> list[Path]:
-    """Write synthetic volumes as NetCDF, named with a parseable timestamp."""
-    directory.mkdir(parents=True, exist_ok=True)
-    paths = []
-    for m in minutes:
-        vol_time = pd.Timestamp(f"2024-01-01 12:{m:02d}:00")
-        dt = _make_datatree(n_sweeps=2, vol_time=vol_time)
-        p = directory / f"vol_{vol_time:%Y%m%d_%H%M%S}.nc"
-        dt.to_netcdf(p)
-        paths.append(p)
-    return paths
-
-
-# ===========================================================================
-# open_any_datatree
-# ===========================================================================
-
-class TestOpenAnyDatatree:
-
-    def test_netcdf_roundtrip(self, tmp_path):
-        pytest.importorskip("netCDF4")
-        from raddb.io_core import open_any_datatree
-
-        dt = _make_datatree(n_sweeps=2)
-        p = tmp_path / "vol_20240101_120000.nc"
-        dt.to_netcdf(p)
-
-        loaded = open_any_datatree(p)
-        assert sorted(g.lstrip("/") for g in loaded.groups if "sweep" in g) == [
-            "sweep_1", "sweep_2",
-        ]
-        np.testing.assert_allclose(
-            loaded["sweep_1"].to_dataset()["DBZH"].values,
-            dt["sweep_1"].to_dataset()["DBZH"].values,
-        )
-
-    def test_zarr_roundtrip(self, tmp_path):
-        pytest.importorskip("zarr")
-        from raddb.io_core import open_any_datatree
-
-        dt = _make_datatree(n_sweeps=2)
-        p = tmp_path / "vol_20240101_120000.zarr"
-        dt.to_zarr(p)
-
-        loaded = open_any_datatree(p)
-        np.testing.assert_allclose(
-            loaded["sweep_1"].to_dataset()["DBZH"].values,
-            dt["sweep_1"].to_dataset()["DBZH"].values,
-        )
-
-    def test_missing_path_raises(self, tmp_path):
-        from raddb.io_core import open_any_datatree
-
-        with pytest.raises(FileNotFoundError):
-            open_any_datatree(tmp_path / "nope.nc")
-
-
-# ===========================================================================
-# find_datatree_files
-# ===========================================================================
-
-class TestFindDatatreeFiles:
-
-    def test_finds_nc_and_zarr_leaves(self, tmp_path):
-        from raddb.discovery import find_datatree_files
-
-        (tmp_path / "vol_20240101_000000.nc").touch()
-        (tmp_path / "sub").mkdir()
-        (tmp_path / "sub" / "vol_20240101_000500.nc").touch()
-        store = tmp_path / "vol_20240101_001000.zarr"
-        store.mkdir()
-        # a .nc INSIDE the zarr store must not be matched (store is a leaf)
-        (store / "inner_20240101_002000.nc").touch()
-        (tmp_path / "notes.txt").touch()
-
-        found = find_datatree_files(tmp_path)
-        names = [p.name for p in found]
-        assert names == [
-            "vol_20240101_000000.nc",
-            "vol_20240101_000500.nc",
-            "vol_20240101_001000.zarr",
-        ]
-
-    def test_non_recursive(self, tmp_path):
-        from raddb.discovery import find_datatree_files
-
-        (tmp_path / "vol_20240101_000000.nc").touch()
-        (tmp_path / "sub").mkdir()
-        (tmp_path / "sub" / "vol_20240101_000500.nc").touch()
-
-        found = find_datatree_files(tmp_path, recursive=False)
-        assert [p.name for p in found] == ["vol_20240101_000000.nc"]
-
-    def test_time_filter(self, tmp_path):
-        from raddb.discovery import find_datatree_files
-
-        (tmp_path / "vol_20240101_000000.nc").touch()
-        (tmp_path / "vol_20240101_010000.nc").touch()
-        (tmp_path / "vol_20240101_020000.nc").touch()
-        (tmp_path / "no_timestamp_here.nc").touch()
-
-        found = find_datatree_files(
-            tmp_path,
-            start_time="2024-01-01 00:30",
-            end_time="2024-01-01 01:30",
-        )
-        # in-range file kept + unparseable file kept (strict_time=False, last)
-        assert [p.name for p in found] == [
-            "vol_20240101_010000.nc",
-            "no_timestamp_here.nc",
-        ]
-
-        found_strict = find_datatree_files(
-            tmp_path,
-            start_time="2024-01-01 00:30",
-            end_time="2024-01-01 01:30",
-            strict_time=True,
-        )
-        assert [p.name for p in found_strict] == ["vol_20240101_010000.nc"]
-
-    def test_missing_directory_raises(self, tmp_path):
-        from raddb.discovery import find_datatree_files
-
-        with pytest.raises(FileNotFoundError):
-            find_datatree_files(tmp_path / "nope")
-
-
-class TestParseDatatreeFileTime:
-
-    @pytest.mark.parametrize(
-        ("name", "expected"),
-        [
-            ("vol_20240101_000000.nc", "2024-01-01 00:00:00"),
-            ("A_20240715T235959.nc", "2024-07-15 23:59:59"),
-            ("radar-20240101-1230.zarr", "2024-01-01 12:30:00"),
-            ("x_202401011230.nc", "2024-01-01 12:30:00"),
-            ("no_time.nc", None),
-        ],
-    )
-    def test_patterns(self, name, expected):
-        from raddb.discovery import _parse_datatree_file_time
-
-        ts = _parse_datatree_file_time(Path(name))
-        if expected is None:
-            assert ts is None
-        else:
-            assert ts == pd.Timestamp(expected, tz="UTC")
-
-
-# ===========================================================================
-# RadDB.archive(datatree_dir=...) (end to end)
-# ===========================================================================
-
-class TestArchiveFromDatatrees:
-
-    def test_end_to_end(self, tmp_path):
-        pytest.importorskip("netCDF4")
-        from raddb.main import RadDB
-
-        src = tmp_path / "input"
-        out = tmp_path / "archive"
-        _write_nc_volumes(src)
-
-        db = RadDB(archive_dir=str(out), crs=2056)
-        res = db.archive(datatree_dir=src, radar=RADAR)
-        assert (res["n_archived"], res["n_failed"]) == (3, 0)
-
-        # LUT auto-generated
-        assert (out / RADAR / "LUT" / f"{RADAR}_LUT.parquet").exists()
-        # one POL parquet per volume
-        pol_files = list((out / RADAR).rglob("*_POL.parquet"))
-        assert len(pol_files) == 3
-
-        # loading works and the filter kept only DBZH > 0
-        rdf = db.open(
-            radars=RADAR,
-            time_period=("2024-01-01 00:00", "2024-01-01 23:59"),
-        )
-        assert len(rdf) > 0
-        assert (rdf.data["DBZH"] > 0.0).all()
-
-    def test_resume_skips_archived(self, tmp_path):
-        pytest.importorskip("netCDF4")
-        from raddb.main import RadDB
-
-        src = tmp_path / "input"
-        out = tmp_path / "archive"
-        _write_nc_volumes(src)
-
-        db = RadDB(archive_dir=str(out), crs=2056)
-        assert db.archive(datatree_dir=src, radar=RADAR)["n_archived"] == 3
-        # second run: everything checkpointed
-        assert db.archive(datatree_dir=src, radar=RADAR)["n_archived"] == 0
-
-    def test_time_period_subset(self, tmp_path):
-        pytest.importorskip("netCDF4")
-        from raddb.main import RadDB
-
-        src = tmp_path / "input"
-        out = tmp_path / "archive"
-        _write_nc_volumes(src, minutes=(0, 5, 10))
-
-        db = RadDB(archive_dir=str(out), crs=2056)
-        res = db.archive(
-            datatree_dir=src, radar=RADAR,
-            time_period=("2024-01-01 12:04", "2024-01-01 12:11"),
-        )
-        assert res["n_archived"] == 2  # 12:05 and 12:10 only
-
-    def test_unrecognized_radar_skipped(self, tmp_path):
-        pytest.importorskip("netCDF4")
-        from raddb.main import RadDB
-
-        src = tmp_path / "input"
-        src.mkdir(parents=True)
-        out = tmp_path / "archive"
-        # "OVERLONG" is 8 characters -> not a usable radar name, so the file is
-        # skipped rather than silently archived under its last letter.
-        _make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")).to_netcdf(
-            src / "OVERLONG_20240101_120000.nc"
-        )
-
-        db = RadDB(archive_dir=str(out), crs=2056)
-        res = db.archive(datatree_dir=src)  # radar=None -> infer per file
-        assert res["n_archived"] == 0
-        assert not (out / "N").exists()  # not filed under the last letter either
-
-    def test_four_letter_radar_archives(self, tmp_path):
-        """A NEXRAD-style 4-character name survives whole (gate_id v2)."""
-        pytest.importorskip("netCDF4")
-        from raddb.main import RadDB
-
-        src = tmp_path / "input"
-        src.mkdir(parents=True)
-        out = tmp_path / "archive"
-        _make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")).to_netcdf(
-            src / "KTLX_20240101_120000.nc"
-        )
-
-        db = RadDB(archive_dir=str(out), crs=2056)
-        res = db.archive(datatree_dir=src)
-        assert (res["n_archived"], res["n_failed"]) == (1, 0)
-        assert (out / "KTLX" / "LUT" / "KTLX_LUT.parquet").exists()
-        assert db.list_radars() == ["KTLX"]
-
-    def test_both_sources_raises(self, tmp_path):
-        from raddb.main import RadDB
-
-        db = RadDB(archive_dir=str(tmp_path), crs=2056)
-        with pytest.raises(ValueError, match="exactly one"):
-            db.archive(datatree_dir=tmp_path, datatree=object())
-
-    def test_empty_source_returns_zero(self, tmp_path):
-        from raddb.main import RadDB
-
-        src = tmp_path / "empty"
-        src.mkdir()
-        db = RadDB(archive_dir=str(tmp_path / "out"), crs=2056)
-        assert db.archive(datatree_dir=src, radar=RADAR)["n_archived"] == 0
diff --git a/raddb/tests/test_discovery.py b/raddb/tests/test_discovery.py
new file mode 100644
index 0000000..3d7dbdd
--- /dev/null
+++ b/raddb/tests/test_discovery.py
@@ -0,0 +1,266 @@
+"""Tests for :mod:`raddb.discovery` — DataTree file discovery and filename-time parsing.
+
+Both sides of the archive live here: finding DataTree inputs on disk, and finding
+``*_POL.parquet`` outputs in a time range.  Everything is pure filesystem plus pandas, so
+these tests touch no radar data at all — empty files with the right *names* are enough.
+
+The one behaviour worth stating up front: a ``.zarr`` store is a **directory**, and it is
+matched as a leaf.  Descending into one would return its internal chunk files as if they
+were volumes.
+"""
+
+from __future__ import annotations
+
+import datetime
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from raddb.discovery import (
+    _find_polar_files_in_range,
+    _group_files_by_volume,
+    _parse_datatree_file_time,
+    _parse_pol_time,
+    _parse_volume_time,
+    find_datatree_files,
+)
+
+# ---------------------------------------------------------------------------
+# find_datatree_files — the module's only public callable
+# ---------------------------------------------------------------------------
+
+
+def test_find_datatree_files(tmp_path):
+    """NetCDF files and Zarr stores are found; a Zarr store is never descended into."""
+    (tmp_path / "vol_20240101_000000.nc").touch()
+    (tmp_path / "sub").mkdir()
+    (tmp_path / "sub" / "vol_20240101_000500.nc").touch()
+    store = tmp_path / "vol_20240101_001000.zarr"
+    store.mkdir()
+    (store / "inner_20240101_002000.nc").touch()  # must NOT be matched
+    (tmp_path / "notes.txt").touch()
+
+    found = find_datatree_files(tmp_path)
+
+    assert [p.name for p in found] == [
+        "vol_20240101_000000.nc",
+        "vol_20240101_000500.nc",
+        "vol_20240101_001000.zarr",
+    ]
+
+
+def test_recursive_false_stays_in_the_top_directory(tmp_path):
+    """``recursive=False`` ignores subdirectories entirely."""
+    (tmp_path / "vol_20240101_000000.nc").touch()
+    (tmp_path / "sub").mkdir()
+    (tmp_path / "sub" / "vol_20240101_000500.nc").touch()
+
+    assert [p.name for p in find_datatree_files(tmp_path, recursive=False)] == ["vol_20240101_000000.nc"]
+
+
+def test_results_are_sorted_by_filename_timestamp(tmp_path):
+    """Directory order is arbitrary; the returned order is chronological."""
+    for name in ("vol_20240101_020000.nc", "vol_20240101_000000.nc", "vol_20240101_010000.nc"):
+        (tmp_path / name).touch()
+
+    found = find_datatree_files(tmp_path)
+
+    assert [p.name for p in found] == [
+        "vol_20240101_000000.nc",
+        "vol_20240101_010000.nc",
+        "vol_20240101_020000.nc",
+    ]
+
+
+def test_unparseable_names_sort_last(tmp_path):
+    """A file with no timestamp is kept but pushed to the end, never interleaved."""
+    (tmp_path / "aaa_no_timestamp.nc").touch()
+    (tmp_path / "vol_20240101_000000.nc").touch()
+
+    assert [p.name for p in find_datatree_files(tmp_path)][-1] == "aaa_no_timestamp.nc"
+
+
+def test_time_range_filters_and_keeps_unparseable_names(tmp_path):
+    """Out-of-range files drop out; an unparseable name survives by default."""
+    for name in (
+        "vol_20240101_000000.nc",
+        "vol_20240101_010000.nc",
+        "vol_20240101_020000.nc",
+        "no_timestamp_here.nc",
+    ):
+        (tmp_path / name).touch()
+
+    found = find_datatree_files(tmp_path, start_time="2024-01-01 00:30", end_time="2024-01-01 01:30")
+
+    assert [p.name for p in found] == ["vol_20240101_010000.nc", "no_timestamp_here.nc"]
+
+
+def test_strict_time_drops_unparseable_names(tmp_path):
+    """``strict_time=True`` refuses to guess: no timestamp, no file."""
+    (tmp_path / "vol_20240101_010000.nc").touch()
+    (tmp_path / "no_timestamp_here.nc").touch()
+
+    found = find_datatree_files(
+        tmp_path,
+        start_time="2024-01-01 00:30",
+        end_time="2024-01-01 01:30",
+        strict_time=True,
+    )
+
+    assert [p.name for p in found] == ["vol_20240101_010000.nc"]
+
+
+def test_strict_time_is_ignored_without_a_range(tmp_path):
+    """With no range there is nothing to be strict about — the file is kept."""
+    (tmp_path / "no_timestamp_here.nc").touch()
+
+    assert len(find_datatree_files(tmp_path, strict_time=True)) == 1
+
+
+def test_extensions_are_matched_case_insensitively(tmp_path):
+    """Uppercase suffixes appear on data written on case-preserving filesystems."""
+    (tmp_path / "vol_20240101_000000.NC").touch()
+
+    assert [p.name for p in find_datatree_files(tmp_path)] == ["vol_20240101_000000.NC"]
+
+
+def test_extensions_can_be_narrowed(tmp_path):
+    """The ``extensions`` argument is a whitelist, not an addition."""
+    (tmp_path / "vol_20240101_000000.nc").touch()
+    store = tmp_path / "vol_20240101_001000.zarr"
+    store.mkdir()
+
+    assert [p.name for p in find_datatree_files(tmp_path, extensions=(".zarr",))] == ["vol_20240101_001000.zarr"]
+
+
+def test_an_empty_directory_returns_an_empty_list(tmp_path):
+    """No matches is not an error."""
+    assert find_datatree_files(tmp_path) == []
+
+
+def test_a_missing_directory_raises(tmp_path):
+    """A typo in the input path must fail loudly, not silently archive nothing."""
+    with pytest.raises(FileNotFoundError):
+        find_datatree_files(tmp_path / "nope")
+
+
+def test_a_file_passed_as_the_directory_raises(tmp_path):
+    """``directory`` must be a directory."""
+    f = tmp_path / "vol_20240101_000000.nc"
+    f.touch()
+    with pytest.raises(FileNotFoundError):
+        find_datatree_files(f)
+
+
+# ---------------------------------------------------------------------------
+# _parse_datatree_file_time — the stem patterns behind the time filter
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    ("name", "expected"),
+    [
+        ("vol_20240101_000000.nc", "2024-01-01 00:00:00"),
+        ("A_20240715T235959.nc", "2024-07-15 23:59:59"),
+        ("radar-20240101-1230.zarr", "2024-01-01 12:30:00"),
+        ("x_202401011230.nc", "2024-01-01 12:30:00"),
+        ("KTLX_20130520_195111.zarr", "2013-05-20 19:51:11"),
+        ("no_time.nc", None),
+    ],
+)
+def test_parse_datatree_file_time_patterns(name, expected):
+    """The three stem patterns, tried in order, and the give-up case."""
+    ts = _parse_datatree_file_time(Path(name))
+
+    assert ts is None if expected is None else ts == pd.Timestamp(expected, tz="UTC")
+
+
+def test_parse_datatree_file_time_is_always_utc():
+    """Downstream comparisons assume tz-aware UTC; a naive timestamp would raise."""
+    assert _parse_datatree_file_time("vol_20240101_000000.nc").tzinfo is not None
+
+
+def test_parse_datatree_file_time_rejects_an_impossible_date():
+    """A digit run that matches the pattern but is not a date yields ``None``."""
+    assert _parse_datatree_file_time("vol_20241332_000000.nc") is None
+
+
+# ---------------------------------------------------------------------------
+# _parse_pol_time / _find_polar_files_in_range — the archive side
+# ---------------------------------------------------------------------------
+
+
+def test_parse_pol_time_reads_the_archive_layout():
+    """``{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet`` is the only accepted shape."""
+    assert _parse_pol_time("L_20240826_025000_POL.parquet") == pd.Timestamp("2024-08-26 02:50:00", tz="UTC")
+    assert _parse_pol_time("KTLX_20130520_195111_POL.parquet") == pd.Timestamp("2013-05-20 19:51:11", tz="UTC")
+
+
+@pytest.mark.parametrize("name", ["nope.parquet", "L_20240826_POL.parquet", "L_notadate_025000_POL.parquet"])
+def test_parse_pol_time_returns_none_off_layout(name):
+    """Anything else is skipped rather than guessed at."""
+    assert _parse_pol_time(name) is None
+
+
+def test_find_polar_files_in_range(tmp_path):
+    """POL files are collected recursively, filtered by time and sorted."""
+    day = tmp_path / "2024" / "08" / "26"
+    day.mkdir(parents=True)
+    for hhmmss in ("030000", "010000", "020000"):
+        (day / f"L_20240826_{hhmmss}_POL.parquet").touch()
+    (day / "not_a_pol_file.parquet").touch()
+
+    found = _find_polar_files_in_range(tmp_path, "2024-08-26 01:30", "2024-08-26 03:30")
+
+    assert [p.name for p in found] == ["L_20240826_020000_POL.parquet", "L_20240826_030000_POL.parquet"]
+
+
+def test_find_polar_files_in_range_without_bounds(tmp_path):
+    """No range means everything, still time-sorted."""
+    day = tmp_path / "2024" / "08" / "26"
+    day.mkdir(parents=True)
+    for hhmmss in ("030000", "010000"):
+        (day / f"L_20240826_{hhmmss}_POL.parquet").touch()
+
+    assert [p.name for p in _find_polar_files_in_range(tmp_path)] == [
+        "L_20240826_010000_POL.parquet",
+        "L_20240826_030000_POL.parquet",
+    ]
+
+
+def test_find_polar_files_in_range_on_an_empty_tree(tmp_path):
+    """A radar directory with no volumes yields an empty list, not an error."""
+    assert _find_polar_files_in_range(tmp_path) == []
+
+
+# ---------------------------------------------------------------------------
+# METRANET filename helpers — shared with the private raddb.mch subpackage
+# ---------------------------------------------------------------------------
+
+
+def test_parse_volume_time_reads_a_metranet_stem():
+    """``XXXYYJJJHHMM...``: 3-char prefix, 2-digit year, day-of-year, hour, minute."""
+    # MLA 24 194 23 30 -> 2024, day 194, 23:30
+    assert _parse_volume_time("MLA2419423300U") == datetime.datetime(2024, 1, 1) + datetime.timedelta(
+        days=193, hours=23, minutes=30
+    )
+    # HZT 21 240 10 00 -> 2021, day 240, 10:00
+    assert _parse_volume_time("HZT2124010000L") == datetime.datetime(2021, 1, 1) + datetime.timedelta(
+        days=239, hours=10, minutes=0
+    )
+
+
+def test_parse_volume_time_falls_back_to_the_epoch():
+    """An unparseable stem sorts first rather than raising mid-scan."""
+    assert _parse_volume_time("garbage") == datetime.datetime(1970, 1, 1)
+
+
+def test_group_files_by_volume():
+    """Sweep files sharing a filename stem belong to one volume."""
+    grouped = _group_files_by_volume(
+        ["/a/MLA2419423300U.001", "/b/MLA2419423300U.002", "/a/MLA2419423305U.001"]
+    )
+
+    assert sorted(grouped) == ["MLA2419423300U", "MLA2419423305U"]
+    assert len(grouped["MLA2419423300U"]) == 2
diff --git a/raddb/tests/test_fixes.py b/raddb/tests/test_fixes.py
deleted file mode 100644
index 83a30aa..0000000
--- a/raddb/tests/test_fixes.py
+++ /dev/null
@@ -1,471 +0,0 @@
-"""
-raddb/tests/test_fixes.py
---------------------------
-Tests for:
-1. load_datatree — multi-volume reconstruction (duplicate gate handling)
-2. sweep column present in parquet_to_dataframe(merge_lut=True)
-3. generate_lut projection_epsg / projection_crs parameter
-
-Run with:
-    pytest raddb/tests/test_fixes.py -v
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import pytest
-import xarray as xr
-
-from raddb.lut import generate_lut_from_datatree
-
-_PKG_ROOT = Path(__file__).resolve().parents[2]
-if str(_PKG_ROOT) not in sys.path:
-    sys.path.insert(0, str(_PKG_ROOT))
-
-RADAR = "A"
-N_AZ = 12
-N_RNG = 24
-
-
-def _make_datatree(
-    n_az: int = N_AZ,
-    n_rng: int = N_RNG,
-    dbzh_min: float = 1.0,
-    dbzh_max: float = 30.0,
-    n_sweeps: int = 2,
-    vol_time: pd.Timestamp | None = None,
-) -> xr.DataTree:
-    """Build a minimal DataTree with all-positive DBZH (no clear-sky filtering)."""
-    if vol_time is None:
-        vol_time = pd.Timestamp("2024-08-01 12:00:00")
-
-    az = np.linspace(0, 360 - 360 / n_az, n_az)
-    rng_vals = np.linspace(1000, 20_000, n_rng)
-    time_vals = np.array([vol_time] * n_az, dtype="datetime64[ns]")
-
-    dict_ds = {}
-    for sweep_idx in range(1, n_sweeps + 1):
-        rng_gen = np.random.default_rng(seed=42 + sweep_idx)
-        dbzh = rng_gen.uniform(dbzh_min, dbzh_max, (n_az, n_rng)).astype(np.float32)
-        ds = xr.Dataset(
-            {
-                "DBZH": (["azimuth", "range"], dbzh),
-                "ZDR": (["azimuth", "range"], np.ones((n_az, n_rng), np.float32)),
-                "RHOHV": (["azimuth", "range"], np.full((n_az, n_rng), 0.95, np.float32)),
-                "PHIDP": (["azimuth", "range"], np.zeros((n_az, n_rng), np.float32)),
-                "time": (["azimuth"], time_vals),
-            },
-            coords={
-                "azimuth": az,
-                "range": rng_vals,
-                "elevation": (["azimuth"], np.full(n_az, 0.5 * sweep_idx)),
-                "elevation_angle": 0.5 * sweep_idx,
-                "latitude": 46.0,
-                "longitude": 7.0,
-                "altitude": 1000.0,
-            },
-        )
-        ds.attrs["sweep_number"] = sweep_idx
-        dict_ds[f"sweep_{sweep_idx}"] = ds
-
-    return xr.DataTree.from_dict(dict_ds)
-
-
-# ===========================================================================
-# 1. load_datatree — single volume round-trip
-# ===========================================================================
-
-class TestLoadDatatreeSingleVolume:
-    """Archive one volume, generate LUT, reconstruct DataTree."""
-
-    def test_single_volume_round_trip(self, tmp_path):
-        from raddb.lut import generate_lut_from_datatree
-        from raddb.io_core import datatree_to_parquet, parquet_to_datatree
-
-        dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-        base = str(tmp_path)
-
-        # Generate LUT
-        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base, projection_epsg=2056)
-
-        # Archive volume
-        datatree_to_parquet(dt, radar=RADAR, base_output_path=base)
-
-        # Reconstruct
-        dt_loaded = parquet_to_datatree(
-            radar=RADAR, base_path=base,
-            start_time="2024-08-01 00:00", end_time="2024-08-02 00:00",
-            label_column="DBZH",
-        )
-
-        from raddb.helper import list_sweep_names
-        sweeps = list_sweep_names(dt_loaded)
-        assert len(sweeps) >= 1, "Reconstructed DataTree should have at least one sweep"
-        for name in sweeps:
-            ds = dt_loaded[name].to_dataset()
-            assert "DBZH" in ds, f"Missing DBZH in {name}"
-
-
-# ===========================================================================
-# 2. load_datatree — multi-volume round-trip (the bug fix)
-# ===========================================================================
-
-class TestLoadDatatreeMultiVolume:
-    """Archive two volumes, then reconstruct — must not crash on duplicates."""
-
-    def test_multi_volume_keeps_latest(self, tmp_path):
-        from raddb.lut import generate_lut_from_datatree
-        from raddb.io_core import datatree_to_parquet, parquet_to_datatree
-
-        base = str(tmp_path)
-        dt1 = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-        dt2 = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00"))
-
-        # Generate LUT from first volume
-        generate_lut_from_datatree(dt1, radar=RADAR, output_base_path=base, projection_epsg=2056)
-
-        # Archive both volumes
-        datatree_to_parquet(dt1, radar=RADAR, base_output_path=base)
-        datatree_to_parquet(dt2, radar=RADAR, base_output_path=base)
-
-        # Reconstruct from both — this should NOT crash
-        dt_loaded = parquet_to_datatree(
-            radar=RADAR, base_path=base,
-            start_time="2024-08-01 00:00", end_time="2024-08-02 00:00",
-            label_column="DBZH",
-        )
-
-        from raddb.helper import list_sweep_names
-        sweeps = list_sweep_names(dt_loaded)
-        assert len(sweeps) >= 1
-        for name in sweeps:
-            ds = dt_loaded[name].to_dataset()
-            assert "DBZH" in ds
-
-
-# ===========================================================================
-# 3. sweep column present in parquet_to_dataframe(merge_lut=True)
-# ===========================================================================
-
-class TestSweepColumnInDataFrame:
-    """Verify sweep column appears when merge_lut=True."""
-
-    def test_sweep_present_after_lut_merge(self, tmp_path):
-        from raddb.lut import generate_lut_from_datatree
-        from raddb.io_core import datatree_to_parquet, parquet_to_dataframe
-
-        base = str(tmp_path)
-        dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-
-        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=base, projection_epsg=2056)
-        datatree_to_parquet(dt, radar=RADAR, base_output_path=base)
-
-        df = parquet_to_dataframe(
-            radar=RADAR, base_path=base,
-            start_time="2024-08-01 00:00", end_time="2024-08-02 00:00",
-            merge_lut=True,
-        )
-
-        assert not df.is_empty(), "DataFrame should not be empty"
-        assert "sweep" in df.columns, "sweep column must be present after LUT merge"
-        assert "azimuth" in df.columns
-        assert "range" in df.columns
-
-        # sweep should be adjacent to azimuth in column order
-        cols = list(df.columns)
-        sweep_idx = cols.index("sweep")
-        azimuth_idx = cols.index("azimuth")
-        assert abs(sweep_idx - azimuth_idx) == 1, (
-            f"sweep (pos {sweep_idx}) should be adjacent to azimuth (pos {azimuth_idx})"
-        )
-
-    def test_sweep_present_in_multi_radar(self, tmp_path):
-        """Verify sweep column appears after a multi-radar open + geometry merge."""
-        from raddb.lut import generate_lut_from_datatree
-        from raddb.io_core import datatree_to_parquet
-        from raddb.main import RadDB
-
-        base = str(tmp_path)
-        dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-
-        # Set up two radars
-        for r in ["A", "D"]:
-            generate_lut_from_datatree(dt, radar=r, output_base_path=base, projection_epsg=2056)
-            datatree_to_parquet(dt, radar=r, base_output_path=base)
-
-        db = RadDB(archive_dir=base, crs=2056)
-        rdf = db.open(
-            radars=["A", "D"],
-            time_period=("2024-08-01 00:00", "2024-08-02 00:00"),
-        )
-        df_multi = rdf.to_pandas(with_geometry=True)
-
-        assert not df_multi.empty
-        assert "sweep" in df_multi.columns, "sweep must be present after geometry merge"
-        assert "radar" in df_multi.columns
-        assert set(df_multi["radar"].unique()) == {"A", "D"}
-
-
-# ===========================================================================
-# 4. generate_lut with projection_epsg
-# ===========================================================================
-
-class TestGenerateLutProjection:
-    """Verify projection columns are added and saved when projection_epsg/crs is set."""
-
-    # CH1903+ / LV95 as a proj4 string (works even without the PROJ database)
-    LV95_PROJ4 = (
-        "+proj=somerc +lat_0=46.9524056 +lon_0=7.4395833 "
-        "+k_0=1 +x_0=2600000 +y_0=1200000 "
-        "+ellps=bessel +towgs84=674.374,15.056,405.346,0,0,0,0 "
-        "+units=m +no_defs"
-    )
-
-    def _make_crs(self):
-        """Create a pyproj CRS that works regardless of PROJ DB availability."""
-        import pyproj
-        return pyproj.CRS.from_proj4(self.LV95_PROJ4)
-
-    def test_projection_columns_in_saved_lut(self, tmp_path):
-        pyproj = pytest.importorskip("pyproj")
-
-        from raddb.lut import generate_lut_from_datatree, load_radar_lut
-
-        base = str(tmp_path)
-        dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-        crs = self._make_crs()
-
-        generate_lut_from_datatree(
-            dt, radar=RADAR, output_base_path=base,
-            projection_crs=crs,
-        )
-
-        lut = load_radar_lut(RADAR, base)
-        # Column suffix is "custom" when EPSG cannot be detected from proj4
-        proj_x_cols = [c for c in lut.columns if c.startswith("x_")]
-        proj_y_cols = [c for c in lut.columns if c.startswith("y_")]
-        assert len(proj_x_cols) == 1, f"Expected one x_ projection column, got {proj_x_cols}"
-        assert len(proj_y_cols) == 1, f"Expected one y_ projection column, got {proj_y_cols}"
-        assert lut[proj_x_cols[0]].is_not_null().any(), "Projected x values should not all be NaN"
-
-    def test_archiving_without_a_crs_is_refused(self, tmp_path):
-        """A CRS is mandatory: a wrong or absent one silently breaks every AOI."""
-        with pytest.raises(ValueError, match="requires a CRS"):
-            generate_lut_from_datatree(
-                _make_datatree(), radar=RADAR, output_base_path=str(tmp_path)
-            )
-
-    def test_refusal_names_a_usable_crs(self, tmp_path):
-        """The message must tell the user what to pass, not just complain."""
-        with pytest.raises(ValueError, match=r"RadDB\(crs=32632\)"):
-            generate_lut_from_datatree(
-                _make_datatree(), radar=RADAR, output_base_path=str(tmp_path)
-            )
-
-    def test_a_crs_invalid_at_the_site_is_refused(self, tmp_path):
-        """EPSG:2056 outside Switzerland distorts distance ~20%."""
-        from raddb.tests.test_fixes import _make_datatree as mk
-        dt = mk()
-        for name in list(dt.children):
-            ds = dt[name].to_dataset().assign_coords(latitude=35.33, longitude=-97.28)
-            dt[name] = xr.DataTree(ds)
-        with pytest.raises(ValueError, match="distorts distance"):
-            generate_lut_from_datatree(
-                dt, radar=RADAR, output_base_path=str(tmp_path), projection_epsg=2056
-            )
-
-    def test_api_archive_with_projection(self, tmp_path):
-        """archive() auto-generates a LUT with projected columns when crs is set."""
-        pyproj = pytest.importorskip("pyproj")
-
-        from raddb.main import RadDB
-        from raddb.lut import load_radar_lut
-
-        crs = self._make_crs()
-        db = RadDB(archive_dir=str(tmp_path), crs=crs)
-        dt = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-
-        db.archive(datatree=dt, radar=RADAR)
-
-        lut = load_radar_lut(RADAR, str(tmp_path))
-        proj_x_cols = [c for c in lut.columns if c.startswith("x_")]
-        proj_y_cols = [c for c in lut.columns if c.startswith("y_")]
-        assert len(proj_x_cols) == 1
-        assert len(proj_y_cols) == 1
-
-
-# ===========================================================================
-# 4. datatree_to_dataframe — dimension coordinates that carry no index
-# ===========================================================================
-
-
-class TestUnindexedDimensionCoordinate:
-    """Raw NEXRAD Level II sweeps arrive with ``range`` as a coordinate that has
-    no index.  ``to_dataframe`` then indexes that dimension by position and emits
-    the true values as a column of the same name, which ``reset_index`` refuses
-    to insert.  The flattener rebuilds the index first.
-    """
-
-    @staticmethod
-    def _drop_range_index(dt: xr.DataTree) -> xr.DataTree:
-        """Reproduce the shape xradar hands back for a raw Level II volume."""
-        for name in dt.children:
-            dt[name].dataset = dt[name].to_dataset().drop_indexes("range")
-        return dt
-
-    def test_flatten_keeps_true_range_values(self):
-        from raddb.io_core import datatree_to_dataframe
-
-        expected = datatree_to_dataframe(_make_datatree())
-        got = datatree_to_dataframe(self._drop_range_index(_make_datatree()))
-
-        assert got.shape == expected.shape
-        # Metres, not the positions 0..n_rng-1 that the broken shape would give.
-        assert sorted(got["range"].unique().to_list()) == sorted(expected["range"].unique().to_list())
-        assert got["range"].min() == pytest.approx(1000.0)
-
-    def test_archive_accepts_it(self, tmp_path):
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        dt = self._drop_range_index(_make_datatree())
-        result = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=dt, radar=RADAR)
-
-        assert result["n_archived"] == 1 and result["n_failed"] == 0
-
-
-# ===========================================================================
-# 6. A volume with nothing to archive is skipped, not a crash
-# ===========================================================================
-
-
-class TestEmptyVolumeIsSkipped:
-    """A clear-air volume used to crash the batch instead of being skipped.
-
-    ``_save_polar_parquet`` builds the output path out of the volume's own
-    time.  When every gate fails the filter the frame is empty, ``.min()`` is
-    ``None``, ``pd.to_datetime(None)`` is ``NaT``, and ``pd.NaT.month`` is
-    *nan* — a float — so ``f"{...:02d}"`` raised ``Unknown format code 'd' for
-    object of type 'float'``.  Two real Rad4Alp volumes hit this.
-    """
-
-    @staticmethod
-    def _blank_dbzh(dt: xr.DataTree) -> xr.DataTree:
-        """Null out DBZH everywhere, so the default ``DBZH > 0`` keeps nothing."""
-        for name in dt.children:
-            ds = dt[name].to_dataset()
-            ds["DBZH"] = ds["DBZH"].where(False)  # all-NaN, same shape/dtype
-            dt[name].dataset = ds
-        return dt
-
-    @staticmethod
-    def _blank_time(dt: xr.DataTree) -> xr.DataTree:
-        """Make every ray's time NaT while leaving DBZH intact."""
-        for name in dt.children:
-            ds = dt[name].to_dataset()
-            ds["time"] = xr.full_like(ds["time"], np.datetime64("NaT"))
-            dt[name].dataset = ds
-        return dt
-
-    def test_no_gates_survive_the_filter(self, tmp_path):
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        dt = self._blank_dbzh(_make_datatree())
-        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=dt, radar=RADAR)
-
-        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (0, 0, 1)
-        assert not list((tmp_path / RADAR).rglob("*_POL.parquet"))
-
-    def test_all_nat_time_is_skipped(self, tmp_path):
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        dt = self._blank_time(_make_datatree())
-        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=dt, radar=RADAR)
-
-        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (0, 0, 1)
-        assert not list((tmp_path / RADAR).rglob("*_POL.parquet"))
-
-    def test_counts_sum_to_the_volumes_attempted(self, tmp_path):
-        """One good volume + one empty: 1 archived, 0 failed, 1 skipped."""
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        good = _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))
-        empty = self._blank_dbzh(_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00")))
-        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(
-            datatree=[good, empty], radar=RADAR
-        )
-
-        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (1, 0, 1)
-        assert res["n_archived"] + res["n_failed"] + res["n_skipped"] == 2
-        assert len(list((tmp_path / RADAR).rglob("*_POL.parquet"))) == 1
-
-    def test_the_archive_stays_readable(self, tmp_path):
-        """A skipped volume must not poison the rest of the archive."""
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        db = RadDB(archive_dir=str(tmp_path), crs=2056)
-        db.archive(datatree=_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")),
-                   radar=RADAR)
-        db.archive(datatree=self._blank_dbzh(_make_datatree(
-            vol_time=pd.Timestamp("2024-08-01 12:05:00"))), radar=RADAR)
-
-        rdf = RadDB(archive_dir=str(tmp_path)).open(radars=RADAR)
-        assert len(rdf) > 0
-        assert rdf.radars() == [RADAR]
-
-    def test_save_polar_parquet_returns_none_directly(self):
-        """The guard itself, without going through archive()."""
-        import polars as pl
-
-        from raddb.io_core import _save_polar_parquet
-
-        empty = pl.DataFrame({"gate_id": [], "time": []})
-        assert _save_polar_parquet(empty, RADAR, "/nonexistent") is None
-
-    def test_disk_path_counts_and_checkpoints_a_skip(self, tmp_path):
-        """``datatree_dir=`` counts a skip separately and does not retry it."""
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        src = tmp_path / "trees"
-        src.mkdir()
-        _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")).to_netcdf(
-            src / f"{RADAR}_20240801_120000.nc"
-        )
-        self._blank_dbzh(_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00"))).to_netcdf(
-            src / f"{RADAR}_20240801_120500.nc"
-        )
-        arch = tmp_path / "arch"
-
-        res = RadDB(archive_dir=str(arch), crs=2056).archive(datatree_dir=str(src), radar=RADAR)
-        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (1, 0, 1)
-        assert len(list((arch / RADAR).rglob("*_POL.parquet"))) == 1
-
-        # The skip is checkpointed, so a resume re-attempts nothing.
-        again = RadDB(archive_dir=str(arch), crs=2056).archive(datatree_dir=str(src), radar=RADAR)
-        assert (again["n_archived"], again["n_failed"], again["n_skipped"]) == (0, 0, 0)
-
-    def test_multi_radar_path_counts_a_skip(self, tmp_path):
-        """The ``{radar: [volumes]}`` form keeps the three counts separate too."""
-        pytest.importorskip("pyproj")
-        from raddb.main import RadDB
-
-        volumes = {
-            RADAR: [
-                _make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")),
-                self._blank_dbzh(_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:05:00"))),
-            ],
-            "D": [_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00"))],
-        }
-        res = RadDB(archive_dir=str(tmp_path), crs=2056).archive(datatree=volumes)
-
-        assert (res["n_archived"], res["n_failed"], res["n_skipped"]) == (2, 0, 1)
-        assert res["n_archived"] + res["n_failed"] + res["n_skipped"] == 3
-        assert sorted(res["radars"]) == ["A", "D"]
diff --git a/raddb/tests/test_hc_mapping.py b/raddb/tests/test_hc_mapping.py
new file mode 100644
index 0000000..4eb97d3
--- /dev/null
+++ b/raddb/tests/test_hc_mapping.py
@@ -0,0 +1,81 @@
+"""Tests for :mod:`raddb.hc_mapping` — the hydrometeor-class constants.
+
+The module declares no functions, only six index-aligned constants.  Their whole value is
+the alignment: ``HC_MCH`` and ``HC_PYART`` are both stored in parquet on the same 1-based
+scale, so parquet integer ``k`` means ``HC_MAP_DICT[k - 1]``.  An off-by-one here
+mislabels every classified gate in every plot, silently.
+"""
+
+from __future__ import annotations
+
+from raddb.hc_mapping import (
+    HC_CLASSES,
+    HC_COLOR_BY_LABEL,
+    HC_COLORS,
+    HC_MAP_DICT,
+    PYART_TO_OPE,
+)
+
+N_CLASSES = 9
+"""Operational classes 0-8, stored in parquet as 1-9."""
+
+
+def test_hc_map_dict_is_a_contiguous_zero_based_range():
+    """Keys 0..8 with no gaps — the ``k - 1`` indexing depends on it."""
+    assert sorted(HC_MAP_DICT) == list(range(N_CLASSES))
+    assert HC_MAP_DICT[0] == "None"
+
+
+def test_hc_classes_is_hc_map_dict_in_order():
+    """``HC_CLASSES[i]`` is ``HC_MAP_DICT[i]``; index 0 corresponds to parquet 1."""
+    assert HC_CLASSES == [HC_MAP_DICT[k] for k in range(N_CLASSES)]
+    assert len(HC_CLASSES) == N_CLASSES
+
+
+def test_class_labels_are_unique():
+    """Two classes sharing a label would make a legend ambiguous."""
+    assert len(set(HC_CLASSES)) == N_CLASSES
+
+
+def test_hc_colors_is_index_aligned_with_hc_classes():
+    """One colour per class, same order — this is what the plots zip together."""
+    assert len(HC_COLORS) == len(HC_CLASSES)
+    assert len(set(HC_COLORS)) == N_CLASSES, "a repeated colour makes two classes indistinguishable"
+
+
+def test_hc_colors_are_recognised_by_matplotlib():
+    """Every entry must actually resolve; a typo only shows up at plot time."""
+    from matplotlib.colors import to_rgba
+
+    for colour in HC_COLORS:
+        assert len(to_rgba(colour)) == 4
+
+
+def test_hc_color_by_label_matches_the_two_lists():
+    """The convenience lookup is exactly ``zip(HC_CLASSES, HC_COLORS)``."""
+    assert HC_COLOR_BY_LABEL == dict(zip(HC_CLASSES, HC_COLORS))
+    assert len(HC_COLOR_BY_LABEL) == N_CLASSES
+
+
+def test_pyart_to_ope_covers_every_pyart_class():
+    """Py-ART's native hydro classes are 1-9; all nine must map."""
+    assert sorted(PYART_TO_OPE) == list(range(1, 10))
+
+
+def test_pyart_to_ope_lands_inside_the_operational_scale():
+    """Targets are operational 1-8; class 0 (``None``) is never a Py-ART output."""
+    assert set(PYART_TO_OPE.values()) == set(range(1, 9))
+
+
+def test_cr_and_vi_are_the_only_merged_pair():
+    """Py-ART separates CR (2) and VI (6); the operational scale merges both into CR/VI."""
+    merged = [k for k, v in PYART_TO_OPE.items() if list(PYART_TO_OPE.values()).count(v) > 1]
+    assert sorted(merged) == [2, 6]
+    assert PYART_TO_OPE[2] == PYART_TO_OPE[6] == 1
+    assert HC_MAP_DICT[1] == "CR/VI"
+
+
+def test_remapped_pyart_values_index_a_real_label():
+    """After the remap, ``HC_MAP_DICT[PYART_TO_OPE[k]]`` always resolves."""
+    for pyart_class, operational in PYART_TO_OPE.items():
+        assert operational in HC_MAP_DICT, f"PyART class {pyart_class} maps outside the label table"
diff --git a/raddb/tests/test_helper.py b/raddb/tests/test_helper.py
new file mode 100644
index 0000000..4ccd97f
--- /dev/null
+++ b/raddb/tests/test_helper.py
@@ -0,0 +1,532 @@
+"""Tests for :mod:`raddb.helper` — filters, radar-name normalisation, StageTimer.
+
+Two contracts carry most of the weight here.
+
+**Radar names.** ``normalize_radar_name`` used to return the last character of a name,
+which silently turned ``KTLX`` into ``X`` and let two NEXRAD sites overwrite each other's
+archive.  It now raises instead of truncating, and the ``ML*`` MeteoSwiss rule is
+restricted to exactly three characters so a genuine four-character name is not eaten.
+
+**Same kind in, same kind out.** ``filter_df`` accepts polars or pandas and returns what
+it was given, so pandas callers predating the polars migration keep working.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import xarray as xr
+
+from raddb.helper import (
+    FILTER_LOGICS,
+    RADAR_ALPHABET,
+    RADAR_CODE_LEN,
+    StageTimer,
+    _vprint,
+    check_dataframe,
+    ensure_utc,
+    filter_df,
+    filter_dt,
+    is_valid_radar_name,
+    list_sweep_names,
+    normalize_radar_name,
+    read_parquet_files,
+    resolve_filter_logic,
+)
+
+# ---------------------------------------------------------------------------
+# list_sweep_names
+# ---------------------------------------------------------------------------
+
+
+def test_list_sweep_names(datatree):
+    """Only ``sweep_N`` groups are returned, sorted and without the leading slash."""
+    assert list_sweep_names(datatree) == ["sweep_1", "sweep_2"]
+
+
+def test_list_sweep_names_ignores_other_groups():
+    """A CfRadial2 tree carries ``radar_parameters`` and friends alongside the sweeps."""
+    dt = xr.DataTree.from_dict(
+        {
+            "sweep_1": xr.Dataset({"DBZH": ("range", [1.0])}),
+            "radar_parameters": xr.Dataset({"beamwidth": 1.0}),
+            "georeferencing_correction": xr.Dataset({"dx": 0.0}),
+        }
+    )
+
+    assert list_sweep_names(dt) == ["sweep_1"]
+
+
+def test_list_sweep_names_on_an_empty_tree():
+    """No sweeps is an empty list, not an error."""
+    assert list_sweep_names(xr.DataTree()) == []
+
+
+# ---------------------------------------------------------------------------
+# ensure_utc
+# ---------------------------------------------------------------------------
+
+
+def test_ensure_utc():
+    """Naive input is localised, aware input is converted, ``None`` passes through."""
+    assert ensure_utc(None) is None
+    assert ensure_utc("2024-08-01 12:00") == pd.Timestamp("2024-08-01 12:00", tz="UTC")
+    assert ensure_utc(pd.Timestamp("2024-08-01 12:00")).tzinfo is not None
+
+
+def test_ensure_utc_converts_rather_than_relabels():
+    """A +02:00 timestamp becomes the same instant in UTC, not the same clock reading."""
+    local = pd.Timestamp("2024-08-01 14:00", tz="Europe/Zurich")
+
+    assert ensure_utc(local) == pd.Timestamp("2024-08-01 12:00", tz="UTC")
+
+
+def test_ensure_utc_is_idempotent():
+    """Running it twice must not shift the instant."""
+    once = ensure_utc("2024-08-01 12:00")
+
+    assert ensure_utc(once) == once
+
+
+# ---------------------------------------------------------------------------
+# read_parquet_files
+# ---------------------------------------------------------------------------
+
+
+def test_read_parquet_files(archive_dir_two_volumes, capsys):
+    """Every matching parquet under the tree is concatenated into one frame."""
+    df = read_parquet_files(archive_dir_two_volumes)
+
+    assert isinstance(df, pl.DataFrame)
+    assert "gate_id" in df.columns
+    assert not df.is_empty()
+
+
+def test_read_parquet_files_selects_columns(archive_dir):
+    """``columns=`` is pushed into the parquet reader, not applied afterwards."""
+    df = read_parquet_files(archive_dir, columns=["gate_id", "DBZH"], verbose=False)
+
+    assert df.columns == ["gate_id", "DBZH"]
+
+
+def test_read_parquet_files_with_no_matches(tmp_path, capsys):
+    """No files yields an empty frame and says so."""
+    df = read_parquet_files(tmp_path, verbose=True)
+
+    assert df.is_empty()
+    assert "No files found" in capsys.readouterr().out
+
+
+def test_read_parquet_files_is_quiet_when_asked(tmp_path, capsys):
+    """``verbose=False`` prints nothing at all."""
+    read_parquet_files(tmp_path, verbose=False)
+
+    assert capsys.readouterr().out == ""
+
+
+# ---------------------------------------------------------------------------
+# check_dataframe
+# ---------------------------------------------------------------------------
+
+
+def test_check_dataframe(capsys):
+    """A polars frame's shape, columns and null counts are printed."""
+    check_dataframe(pl.DataFrame({"DBZH": [1.0, None], "gate_id": [1, 2]}))
+
+    out = capsys.readouterr().out
+    assert "Shape:" in out and "(2, 2)" in out
+    assert "DBZH" in out and "Missing values:" in out
+
+
+def test_check_dataframe_accepts_pandas(capsys):
+    """The pandas branch takes a different path to the same summary."""
+    check_dataframe(pd.DataFrame({"DBZH": [1.0, np.nan]}))
+
+    assert "Missing values:" in capsys.readouterr().out
+
+
+def test_check_dataframe_on_an_empty_frame(capsys):
+    """No columns must not raise on the ``null_count().to_dicts()[0]`` indexing."""
+    check_dataframe(pl.DataFrame())
+
+    assert "Shape:" in capsys.readouterr().out
+
+
+# ---------------------------------------------------------------------------
+# normalize_radar_name / is_valid_radar_name
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    ("raw", "expected"),
+    [
+        ("A", "A"),
+        ("a", "A"),
+        (" L ", "L"),
+        ("MLA", "A"),  # MeteoSwiss spelling
+        ("mlw", "W"),
+        ("KTLX", "KTLX"),  # NEXRAD survives whole
+        ("koun", "KOUN"),
+        ("000A", "A"),  # zero padding is not part of the name
+        ("0A", "A"),
+        ("0", "0"),  # ... but a radar may be named "0"
+        ("ZZZZ", "ZZZZ"),
+    ],
+)
+def test_normalize_radar_name(raw, expected):
+    """The canonical form: upper-case, zero-stripped, ``ML*`` reduced."""
+    assert normalize_radar_name(raw) == expected
+
+
+def test_multi_letter_names_are_not_truncated():
+    """The old bug: every name collapsed to its last character."""
+    assert normalize_radar_name("KTLX") != "X"
+    assert normalize_radar_name("KOUN") != "N"
+    # Two sites sharing a final letter must stay distinct, or one would overwrite the
+    # other's archive.
+    assert normalize_radar_name("KTLX") != normalize_radar_name("KABX")
+
+
+def test_the_ml_rule_only_applies_at_three_characters():
+    """``MLAB`` is a real four-character name, not ``ML`` plus ``AB``."""
+    assert normalize_radar_name("MLA") == "A"
+    assert normalize_radar_name("MLAB") == "MLAB"
+
+
+@pytest.mark.parametrize("bad", ["", "   ", "chlem", "ABCDE", "A-B", "vol.", "A B", "MLABC", "é"])
+def test_normalize_radar_name_rejects_unusable_names(bad):
+    """Raised, not truncated — five-character ODIM codes must be aliased explicitly."""
+    assert not is_valid_radar_name(bad)
+    with pytest.raises(ValueError, match="not usable"):
+        normalize_radar_name(bad)
+
+
+def test_normalize_radar_name_rejects_a_non_string():
+    """A number is not a radar name, and the message says which type arrived."""
+    assert not is_valid_radar_name(7)
+    with pytest.raises(ValueError, match="must be a string"):
+        normalize_radar_name(7)
+
+
+def test_is_valid_radar_name():
+    """The non-raising counterpart, for callers that want to skip rather than fail."""
+    assert is_valid_radar_name("A")
+    assert is_valid_radar_name("KTLX")
+    assert not is_valid_radar_name("OVERLONG")
+    assert not is_valid_radar_name(None)
+
+
+def test_normalize_radar_name_is_idempotent():
+    """A canonical name normalises to itself, so archiving twice hits the same path."""
+    for name in ("A", "KTLX", "0", "ZZZZ"):
+        assert normalize_radar_name(normalize_radar_name(name)) == name
+
+
+def test_the_alphabet_and_length_match_the_gate_id_layout():
+    """Base-36 over four characters is what the ``gate_id`` radar field can hold."""
+    assert RADAR_ALPHABET == "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+    assert len(RADAR_ALPHABET) == 36
+    assert RADAR_CODE_LEN == 4
+
+
+# ---------------------------------------------------------------------------
+# resolve_filter_logic / FILTER_LOGICS
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    ("logic", "a", "b", "expected"),
+    [("==", 1, 1, True), ("!=", 1, 2, True), (">", 2, 1, True), (">=", 1, 1, True), ("<", 1, 2, True), ("<=", 1, 1, True)],
+)
+def test_resolve_filter_logic(logic, a, b, expected):
+    """All six operators resolve to the comparison they spell."""
+    assert resolve_filter_logic(logic)(a, b) is expected
+
+
+def test_resolve_filter_logic_rejects_an_unknown_operator():
+    """The error lists the valid choices rather than just refusing."""
+    with pytest.raises(ValueError, match="Unknown logic"):
+        resolve_filter_logic("=~")
+
+
+def test_filter_logics_registry_is_complete():
+    """The registry is the single source of truth for the documented operator set."""
+    assert set(FILTER_LOGICS) == {"==", "!=", ">", ">=", "<", "<="}
+
+
+# ---------------------------------------------------------------------------
+# filter_df
+# ---------------------------------------------------------------------------
+
+
+def test_filter_df():
+    """Rows failing the comparison are dropped entirely."""
+    df = pl.DataFrame({"DBZH": [-5.0, 0.0, 5.0, 10.0]})
+
+    assert filter_df(df, threshold=0.0, logic=">")["DBZH"].to_list() == [5.0, 10.0]
+
+
+def test_filter_df_returns_the_kind_it_was_given():
+    """The polars migration must not break existing pandas callers."""
+    data = {"DBZH": [-5.0, 5.0]}
+
+    assert isinstance(filter_df(pl.DataFrame(data)), pl.DataFrame)
+    assert isinstance(filter_df(pd.DataFrame(data)), pd.DataFrame)
+
+
+def test_filter_df_resets_the_pandas_index():
+    """A dropped row must not leave a gap in the index for downstream ``iloc``."""
+    out = filter_df(pd.DataFrame({"DBZH": [-5.0, 5.0, 10.0]}), threshold=0.0)
+
+    assert out.index.tolist() == [0, 1]
+
+
+@pytest.mark.parametrize("logic", ["==", "!=", ">", ">=", "<", "<="])
+def test_filter_df_honours_every_operator(logic):
+    """Each operator selects what the plain numpy comparison would."""
+    values = np.array([-5.0, 0.0, 5.0])
+    out = filter_df(pl.DataFrame({"DBZH": values}), threshold=0.0, logic=logic)
+
+    assert out["DBZH"].to_numpy().tolist() == values[FILTER_LOGICS[logic](values, 0.0)].tolist()
+
+
+def test_filter_df_rejects_a_missing_column():
+    """A typo in the variable name must not silently keep every row."""
+    with pytest.raises(KeyError, match="ZDR"):
+        filter_df(pl.DataFrame({"DBZH": [1.0]}), feature="ZDR")
+
+
+def test_filter_df_rejects_an_unknown_logic():
+    """Validation happens before the column lookup, so both errors stay distinct."""
+    with pytest.raises(ValueError, match="Unknown logic"):
+        filter_df(pl.DataFrame({"DBZH": [1.0]}), logic="=~")
+
+
+def test_filter_df_can_keep_nothing():
+    """An empty result is a valid answer — a clear-air volume produces one."""
+    assert filter_df(pl.DataFrame({"DBZH": [-5.0, -1.0]}), threshold=0.0).is_empty()
+
+
+# ---------------------------------------------------------------------------
+# filter_dt
+# ---------------------------------------------------------------------------
+
+
+def test_filter_dt(datatree):
+    """Non-matching gates become NaN; the tree keeps its shape."""
+    out = filter_dt(datatree, feature="DBZH", threshold=15.0, logic=">")
+
+    ds_in = datatree["sweep_1"].to_dataset()
+    ds_out = out["sweep_1"].to_dataset()
+    assert ds_out["DBZH"].shape == ds_in["DBZH"].shape
+    assert np.isnan(ds_out["DBZH"].values).any()
+    assert np.nanmin(ds_out["DBZH"].values) > 15.0
+
+
+def test_filter_dt_leaves_matching_gates_untouched(datatree):
+    """A legitimate zero must survive; masking is not thresholding twice."""
+    out = filter_dt(datatree, feature="DBZH", threshold=0.0, logic=">")
+
+    np.testing.assert_allclose(
+        out["sweep_1"].to_dataset()["DBZH"].values,
+        datatree["sweep_1"].to_dataset()["DBZH"].values,
+    )
+
+
+def test_filter_dt_masks_every_variable_sharing_the_mask_dims(datatree):
+    """The point of a DataTree filter: ZDR is masked wherever DBZH failed."""
+    out = filter_dt(datatree, feature="DBZH", threshold=15.0, logic=">")
+
+    ds = out["sweep_1"].to_dataset()
+    np.testing.assert_array_equal(np.isnan(ds["DBZH"].values), np.isnan(ds["ZDR"].values))
+
+
+def test_filter_dt_leaves_disjoint_variables_alone():
+    """Gate-edge geometry lives on other dims; broadcasting the mask onto it explodes."""
+    ds = xr.Dataset(
+        {
+            "DBZH": (["azimuth", "range"], np.array([[1.0, 20.0]])),
+            "x_edges": (["azimuth_edge"], np.array([0.0, 1.0])),
+        },
+        coords={"azimuth": [0.0], "range": [1000.0, 2000.0], "azimuth_edge": [0.0, 1.0]},
+    )
+    dt = xr.DataTree.from_dict({"sweep_1": ds})
+
+    out = filter_dt(dt, feature="DBZH", threshold=15.0, logic=">")
+
+    np.testing.assert_array_equal(out["sweep_1"].to_dataset()["x_edges"].values, [0.0, 1.0])
+
+
+def test_filter_dt_skips_a_sweep_without_the_variable():
+    """A sweep missing DBZH is passed through rather than dropped."""
+    dt = xr.DataTree.from_dict({"sweep_1": xr.Dataset({"ZDR": ("range", [1.0, 2.0])})})
+
+    out = filter_dt(dt, feature="DBZH", threshold=0.0)
+
+    np.testing.assert_array_equal(out["sweep_1"].to_dataset()["ZDR"].values, [1.0, 2.0])
+
+
+def test_filter_dt_rejects_an_unknown_logic(datatree):
+    """Operator validation happens before any sweep is touched."""
+    with pytest.raises(ValueError, match="Unknown logic"):
+        filter_dt(datatree, logic="=~")
+
+
+# ---------------------------------------------------------------------------
+# StageTimer
+# ---------------------------------------------------------------------------
+
+
+def test_StageTimer():
+    """A fresh timer holds no records."""
+    assert StageTimer().records == []
+
+
+def test_StageTimer_init():
+    """Each instance gets its own list — a shared class attribute would pool runs."""
+    a, b = StageTimer(), StageTimer()
+    a.record("stage", 1.0)
+
+    assert b.records == []
+
+
+def test_StageTimer_time_stage():
+    """The context manager records stage, volume, sweep and a positive duration."""
+    timer = StageTimer()
+
+    with timer.time_stage("build", volume="vol_001", sweep=2):
+        pass
+
+    (rec,) = timer.records
+    assert rec["stage"] == "build"
+    assert rec["volume"] == "vol_001"
+    assert rec["sweep"] == 2
+    assert rec["duration"] >= 0.0
+
+
+def test_time_stage_records_even_when_the_body_raises():
+    """Timing lives in a ``finally``, so a failed stage still shows up in the profile."""
+    timer = StageTimer()
+
+    with pytest.raises(RuntimeError), timer.time_stage("boom"):
+        raise RuntimeError("stage failed")
+
+    assert [r["stage"] for r in timer.records] == ["boom"]
+
+
+def test_StageTimer_record():
+    """A pre-measured entry has the same shape as a timed one."""
+    timer = StageTimer()
+
+    timer.record("io", 1.5, volume="vol_002", sweep=1, t_start=100.0)
+
+    assert timer.records == [{"volume": "vol_002", "sweep": 1, "stage": "io", "t_start": 100.0, "duration": 1.5}]
+
+
+def test_StageTimer_to_dataframe():
+    """Records become a pandas frame — this is one of the three pandas seams."""
+    timer = StageTimer()
+    timer.record("io", 1.0)
+    timer.record("build", 2.0)
+
+    df = timer.to_dataframe()
+
+    assert isinstance(df, pd.DataFrame)
+    assert df["stage"].tolist() == ["io", "build"]
+
+
+def test_to_dataframe_on_an_empty_timer_keeps_the_schema():
+    """Downstream ``groupby("stage")`` needs the columns even with no rows."""
+    df = StageTimer().to_dataframe()
+
+    assert df.empty
+    assert list(df.columns) == ["volume", "sweep", "stage", "duration"]
+
+
+def test_StageTimer_summary():
+    """Aggregated per stage and sorted by total time, slowest first."""
+    timer = StageTimer()
+    timer.record("fast", 0.1)
+    timer.record("slow", 5.0)
+    timer.record("slow", 5.0)
+
+    summary = timer.summary()
+
+    assert summary.index.tolist() == ["slow", "fast"]
+    assert summary.loc["slow", "sum"] == pytest.approx(10.0)
+    assert summary.loc["slow", "count"] == 2
+    assert summary.loc["fast", "mean"] == pytest.approx(0.1)
+
+
+def test_summary_on_an_empty_timer():
+    """No records means an empty frame, which ``print_summary`` then reports."""
+    assert StageTimer().summary().empty
+
+
+def test_StageTimer_print_summary(capsys):
+    """The table names each stage and its share of the total."""
+    timer = StageTimer()
+    timer.record("archive_volume", 3.0)
+    timer.record("build_lut", 1.0)
+
+    timer.print_summary()
+
+    out = capsys.readouterr().out
+    assert "PIPELINE PROFILING SUMMARY" in out
+    assert "archive_volume" in out and "TOTAL" in out
+    assert "75.0%" in out
+
+
+def test_print_summary_on_an_empty_timer(capsys):
+    """A run that recorded nothing says so instead of printing an empty table."""
+    StageTimer().print_summary()
+
+    assert "No timing data recorded" in capsys.readouterr().out
+
+
+def test_timer_accumulates_across_a_batch(tmp_path, make_datatree):
+    """Records pool across volumes, which is what makes the profile useful."""
+    from raddb.io_core import archive_multiple_volumes
+    from raddb.lut import generate_lut_from_datatree
+
+    timer = StageTimer()
+    volumes = {
+        f"vol_{i:03d}": make_datatree(vol_time=pd.Timestamp(f"2024-08-01 19:0{i}:00")) for i in range(3)
+    }
+    generate_lut_from_datatree(volumes["vol_000"], radar="A", output_base_path=str(tmp_path), projection_epsg=2056)
+
+    archive_multiple_volumes(volumes, radar="A", base_output_path=str(tmp_path), timer=timer, verbose=False)
+
+    df = timer.to_dataframe()
+    assert len(df) >= 3
+    assert "archive_volume" in df["stage"].values
+
+
+# ---------------------------------------------------------------------------
+# _vprint
+# ---------------------------------------------------------------------------
+
+
+def test_vprint_is_silent_by_default(capsys):
+    """Progress output is opt-in; library code must not print unasked."""
+    _vprint("hello")
+
+    assert capsys.readouterr().out == ""
+
+
+def test_vprint_timestamps_its_output(capsys):
+    """A millisecond timestamp is what makes the messages useful in a long batch."""
+    _vprint("hello", verbose=True)
+
+    out = capsys.readouterr().out
+    assert "hello" in out
+    assert out.startswith("[") and out.count(":") == 2
+
+
+def test_read_parquet_files_accepts_a_path_object(archive_dir):
+    """``base_path`` is used through ``Path``, so both spellings work."""
+    assert not read_parquet_files(Path(archive_dir), verbose=False).is_empty()
diff --git a/raddb/tests/test_inventory.py b/raddb/tests/test_inventory.py
deleted file mode 100644
index b57c9cc..0000000
--- a/raddb/tests/test_inventory.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""
-raddb/tests/test_inventory.py
-------------------------------
-Tests for ``RadDB.inventory()`` — the on-disk data overview (archive side and
-DataTree-input side).
-
-Run with:
-    pytest raddb/tests/test_inventory.py -v
-"""
-from __future__ import annotations
-
-import pandas as pd
-import pytest
-
-from raddb import RadDB
-from raddb.tests.test_fixes import _make_datatree
-
-RADAR = "A"
-VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
-
-
-@pytest.fixture
-def archive(tmp_path):
-    """A two-volume, one-radar archive."""
-    db = RadDB(archive_dir=str(tmp_path / "archive"), crs=2056)
-    db.archive(datatree={str(t): _make_datatree(vol_time=t) for t in VOL_TIMES}, radar=RADAR)
-    return db
-
-
-@pytest.fixture
-def datatree_dir(tmp_path):
-    """A directory of saved DataTree files, not archived."""
-    d = tmp_path / "datatrees"
-    d.mkdir()
-    for t in VOL_TIMES:
-        _make_datatree(vol_time=t).to_netcdf(d / f"{RADAR}_{t:%Y%m%d_%H%M%S}.nc")
-    return d
-
-
-class TestInventoryArchive:
-    def test_lists_radar_volumes_and_time_range(self, archive, capsys):
-        assert archive.inventory() is None  # prints, returns nothing
-        out = capsys.readouterr().out
-        assert "archived data" in out
-        assert f"\n  {RADAR} " in out          # the per-radar row
-        assert "2024-08-01 12:00:00 .. 2024-08-02 06:30:00" in out
-        assert "volumes" in out
-
-    def test_detailed_adds_lut_columns_and_days(self, archive, capsys):
-        archive.inventory(detailed=True)
-        out = capsys.readouterr().out
-        assert "LUT:" in out and "sweeps" in out
-        assert "DBZH" in out                     # per-volume moment columns
-        assert "2024-08-01" in out and "2024-08-02" in out
-        assert "volume(s)" in out
-
-    def test_empty_archive_dir(self, tmp_path, capsys):
-        RadDB(archive_dir=str(tmp_path), crs=2056).inventory()
-        assert "nothing archived here yet" in capsys.readouterr().out
-
-    def test_without_archive_dir_raises(self):
-        with pytest.raises(ValueError):
-            RadDB().inventory()
-
-
-class TestInventoryDataTrees:
-    def test_lists_files_radar_and_time_range(self, datatree_dir, capsys):
-        RadDB().inventory(datatree_dir=str(datatree_dir))
-        out = capsys.readouterr().out
-        assert "not archived yet" in out
-        assert "files     : 2" in out
-        assert "2024-08-01 12:00:00 .. 2024-08-02 06:30:00" in out
-
-    def test_four_letter_radar_not_warned(self, tmp_path, capsys):
-        """A NEXRAD-style name is archivable under gate_id v2 — no warning."""
-        d = tmp_path / "nexrad"
-        d.mkdir()
-        _make_datatree(vol_time=VOL_TIMES[0]).to_netcdf(d / "KTLX_20240801_120000.nc")
-        RadDB().inventory(datatree_dir=str(d), detailed=True)
-        out = capsys.readouterr().out
-        assert "KTLX" in out
-        assert "not a usable radar name" not in out
-
-    def test_warns_on_unusable_radar_name(self, tmp_path, capsys):
-        d = tmp_path / "odd"
-        d.mkdir()
-        _make_datatree(vol_time=VOL_TIMES[0]).to_netcdf(d / "OVERLONG_20240801_120000.nc")
-        RadDB().inventory(datatree_dir=str(d), detailed=True)
-        out = capsys.readouterr().out
-        assert "OVERLONG" in out
-        assert "not a usable radar name" in out
-
-    def test_missing_directory_raises(self, tmp_path):
-        with pytest.raises(FileNotFoundError):
-            RadDB().inventory(datatree_dir=str(tmp_path / "nope"))
-
-    def test_empty_directory(self, tmp_path, capsys):
-        RadDB().inventory(datatree_dir=str(tmp_path))
-        assert "no .zarr / .nc" in capsys.readouterr().out
diff --git a/raddb/tests/test_io_core.py b/raddb/tests/test_io_core.py
new file mode 100644
index 0000000..a8ea748
--- /dev/null
+++ b/raddb/tests/test_io_core.py
@@ -0,0 +1,696 @@
+"""Tests for :mod:`raddb.io_core` — DataTree <-> DataFrame <-> Parquet.
+
+This is the archive write path and the read path back out.  Three contracts drive most of
+what is asserted here.
+
+**Volumes must join their LUT.** The LUT stores a radar's *scan strategy*, not one
+volume's measured azimuths, and every incoming ray is snapped onto that nominal grid
+before its ``gate_id`` is built.  Without it a drifting antenna silently lost 6% of gates
+per volume on Rad4Alp and 35% on WSR-88D — invisible except in LUT joins.
+
+**A scan-strategy change is refused, not reconciled.** More rays than the grid, or a
+missing sweep, raises; *fewer* rays is a rotation with holes and is accepted.
+
+**Empty is not an error.** A clear-air volume archives zero gates and is *skipped*; it
+used to crash the batch on ``pd.NaT.month``.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import xarray as xr
+
+from raddb.io_core import (
+    _build_polar_dataframe,
+    _cast_hc_column,
+    _col,
+    _save_polar_parquet,
+    _snap_volume_azimuths,
+    _to_pandas_frame,
+    _to_polars_frame,
+    add_feature_to_df,
+    add_feature_to_dt,
+    archive_multiple_volumes,
+    archive_volume,
+    archive_volumes_multi_radar,
+    dataframe_to_datatree,
+    datatree_to_dataframe,
+    datatree_to_dataset,
+    datatree_to_parquet,
+    join_labels_with_lut,
+    labels_to_dataframe,
+    open_any_datatree,
+    parquet_to_dataframe,
+    parquet_to_datatree,
+    reconstruct_datatree,
+    reconstruct_sweep_dataset,
+    scan_polar_parquet,
+)
+from raddb.lut import generate_lut_from_datatree, lut_file_path
+from raddb.main import RadDB
+from raddb.tests.conftest import MCH_BIAS, RADAR, SWISS_EPSG, build_datatree, retime
+
+TIME_WINDOW = ("2024-08-01 00:00", "2024-08-02 00:00")
+"""A window wide enough to hold every synthetic volume in this module."""
+
+
+@pytest.fixture
+def lut_base(tmp_path, make_datatree):
+    """A base path with only radar ``A``'s LUT written — no volumes yet."""
+    generate_lut_from_datatree(make_datatree(), radar=RADAR, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG)
+    return str(tmp_path)
+
+
+def _blank_dbzh(dt: xr.DataTree) -> xr.DataTree:
+    """Null out DBZH everywhere, so the default ``DBZH > 0`` keeps nothing."""
+    for name in dt.children:
+        ds = dt[name].to_dataset()
+        ds["DBZH"] = ds["DBZH"].where(False)
+        dt[name].dataset = ds
+    return dt
+
+
+def _drop_range_index(dt: xr.DataTree) -> xr.DataTree:
+    """Reproduce the shape xradar hands back for a raw NEXRAD Level II volume."""
+    for name in dt.children:
+        dt[name].dataset = dt[name].to_dataset().drop_indexes("range")
+    return dt
+
+
+# ---------------------------------------------------------------------------
+# open_any_datatree
+# ---------------------------------------------------------------------------
+
+
+def test_open_any_datatree(tmp_path, datatree):
+    """NetCDF round-trips with every sweep and every value intact."""
+    pytest.importorskip("netCDF4")
+    path = tmp_path / "vol_20240101_120000.nc"
+    datatree.to_netcdf(path)
+
+    loaded = open_any_datatree(path)
+
+    assert sorted(g.lstrip("/") for g in loaded.groups if "sweep" in g) == ["sweep_1", "sweep_2"]
+    np.testing.assert_allclose(
+        loaded["sweep_1"].to_dataset()["DBZH"].values, datatree["sweep_1"].to_dataset()["DBZH"].values
+    )
+
+
+def test_open_any_datatree_reads_zarr(tmp_path, datatree):
+    """A Zarr store is a directory, and the engine is sniffed from the suffix."""
+    pytest.importorskip("zarr")
+    path = tmp_path / "vol_20240101_120000.zarr"
+    datatree.to_zarr(path)
+
+    loaded = open_any_datatree(path)
+
+    np.testing.assert_allclose(
+        loaded["sweep_1"].to_dataset()["DBZH"].values, datatree["sweep_1"].to_dataset()["DBZH"].values
+    )
+
+
+def test_open_any_datatree_raises_on_a_missing_path(tmp_path):
+    """A typo must fail loudly, not return an empty tree."""
+    with pytest.raises(FileNotFoundError):
+        open_any_datatree(tmp_path / "nope.nc")
+
+
+# ---------------------------------------------------------------------------
+# datatree_to_dataset / datatree_to_dataframe
+# ---------------------------------------------------------------------------
+
+
+def test_datatree_to_dataset(datatree):
+    """A sweep is addressable by name or by number."""
+    by_name = datatree_to_dataset(datatree, "sweep_1")
+    by_number = datatree_to_dataset(datatree, 1)
+
+    assert "DBZH" in by_name
+    np.testing.assert_array_equal(by_name["DBZH"].values, by_number["DBZH"].values)
+
+
+def test_datatree_to_dataframe(datatree):
+    """The flattened volume is polars, one row per gate, across every sweep."""
+    df = datatree_to_dataframe(datatree)
+
+    assert isinstance(df, pl.DataFrame)
+    assert df.height == 12 * 24 * 2
+    assert {"azimuth", "range", "sweep", "DBZH"} <= set(df.columns)
+
+
+def test_datatree_to_dataframe_keeps_true_range_values(datatree, make_datatree):
+    """Raw NEXRAD sweeps arrive with ``range`` as a coordinate carrying no index.
+
+    ``to_dataframe`` then indexes that dimension by position and emits the true values
+    as a same-named column, which ``reset_index`` refuses to insert.  The flattener
+    rebuilds the index first, so metres survive rather than the positions 0..n-1.
+    """
+    expected = datatree_to_dataframe(datatree)
+    got = datatree_to_dataframe(_drop_range_index(make_datatree()))
+
+    assert got.shape == expected.shape
+    assert sorted(got["range"].unique().to_list()) == sorted(expected["range"].unique().to_list())
+    assert got["range"].min() == pytest.approx(1000.0)
+
+
+# ---------------------------------------------------------------------------
+# archive_volume and the batch wrappers
+# ---------------------------------------------------------------------------
+
+
+def test_archive_volume(lut_base, make_datatree):
+    """One volume becomes one POL parquet, whose path is returned."""
+    path = archive_volume(make_datatree(), radar=RADAR, base_output_path=lut_base)
+
+    assert path is not None and path.endswith("_POL.parquet")
+    assert "DBZH" in pl.read_parquet(path).columns
+
+
+def test_archive_volume_applies_the_clear_sky_filter(lut_base, make_datatree):
+    """No-echo gates are dropped at archive time, which is what keeps archives small."""
+    dt = make_datatree(dbzh_min=-10.0, dbzh_max=30.0)
+
+    path = archive_volume(dt, radar=RADAR, base_output_path=lut_base, filter_threshold=0.0, filter_logic=">")
+
+    df = pl.read_parquet(path)
+    assert (df["DBZH"].to_numpy() > 0).all()
+    assert df.height < 12 * 24 * 2
+
+
+def test_archive_volume_returns_none_for_an_empty_volume(lut_base, make_datatree):
+    """A clear-air volume is skipped, not written and not crashed on."""
+    assert archive_volume(_blank_dbzh(make_datatree()), radar=RADAR, base_output_path=lut_base) is None
+
+
+def test_archive_volume_records_timings(lut_base, make_datatree):
+    """The ``timer=`` hook is what makes the pipeline profile possible.
+
+    ``archive_volume`` records its three internal stages; the enclosing
+    ``archive_volume`` stage is timed one level up, by the batch wrapper.
+    """
+    from raddb.helper import StageTimer
+
+    timer = StageTimer()
+    archive_volume(make_datatree(), radar=RADAR, base_output_path=lut_base, timer=timer, volume="vol_001")
+
+    stages = set(timer.to_dataframe()["stage"])
+    assert stages == {"datatree_to_df", "generate_gate_ids", "save_parquet"}
+    assert set(timer.to_dataframe()["volume"]) == {"vol_001"}
+
+
+def test_archive_multiple_volumes(lut_base, make_datatree):
+    """A list or dict of volumes archives sequentially and reports one record each."""
+    volumes = {
+        f"vol_{i}": make_datatree(vol_time=pd.Timestamp(f"2024-08-01 12:0{i}:00")) for i in range(3)
+    }
+
+    records = archive_multiple_volumes(volumes, radar=RADAR, base_output_path=lut_base, verbose=False)
+
+    assert len(records) == 3
+    assert all(r["success"] and not r["skipped"] for r in records)
+    assert all(r["n_gates"] > 0 and r["error"] is None for r in records)
+    assert [r["label"] for r in records] == ["vol_0", "vol_1", "vol_2"]
+
+
+def test_archive_multiple_volumes_accepts_a_plain_list(lut_base, make_datatree):
+    """The dict keys are labels only; a bare list works the same."""
+    volumes = [make_datatree(vol_time=pd.Timestamp(f"2024-08-01 12:0{i}:00")) for i in range(2)]
+
+    assert len(archive_multiple_volumes(volumes, radar=RADAR, base_output_path=lut_base, verbose=False)) == 2
+
+
+def test_archive_volumes_multi_radar(tmp_path, make_datatree):
+    """The ``{radar: [volumes]}`` form keeps each radar's records separate."""
+    for radar in ("A", "D"):
+        generate_lut_from_datatree(
+            make_datatree(), radar=radar, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG
+        )
+
+    results = archive_volumes_multi_radar(
+        {"A": [make_datatree()], "D": [make_datatree()]}, base_output_path=str(tmp_path), verbose=False
+    )
+
+    assert sorted(results) == ["A", "D"]
+    assert all(len(v) == 1 for v in results.values())
+
+
+def test_datatree_to_parquet(lut_base, make_datatree):
+    """The single-volume entry point behind ``archive_volume``."""
+    path = datatree_to_parquet(make_datatree(), radar=RADAR, base_output_path=lut_base)
+
+    assert path.endswith("_POL.parquet")
+    assert pl.read_parquet(path).height > 0
+
+
+def test_the_output_path_encodes_the_volume_time(lut_base, make_datatree):
+    """``{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet`` under ``{YYYY}/{MM}/{DD}``."""
+    dt = make_datatree(vol_time=pd.Timestamp("2024-08-26 02:50:00"))
+
+    path = datatree_to_parquet(dt, radar=RADAR, base_output_path=lut_base)
+
+    assert path.endswith(f"{RADAR}_20240826_025000_POL.parquet")
+    assert "/2024/08/26/" in path
+
+
+def test_save_polar_parquet_returns_none_on_an_empty_frame():
+    """The guard itself: an empty frame has no volume time to build a path from.
+
+    ``pd.to_datetime(None)`` is ``NaT``, ``pd.NaT.month`` is *nan* — a float — so
+    ``f"{...:02d}"`` raised ``Unknown format code 'd'``.  Two real volumes hit this.
+    """
+    assert _save_polar_parquet(pl.DataFrame({"gate_id": [], "time": []}), RADAR, "/nonexistent") is None
+
+
+# ---------------------------------------------------------------------------
+# The nominal azimuth grid — why a volume joins its LUT
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def drifting_archive(tmp_path):
+    """One LUT-defining volume plus four later ones with drifting azimuths."""
+    base = tmp_path / "drift"
+    db = RadDB(archive_dir=str(base), crs=SWISS_EPSG)
+    first = build_datatree(n_az=360, n_rng=40, n_sweeps=3)
+    db.archive(datatree=first, radar=RADAR)
+    rng = np.random.default_rng(7)
+    for k in range(1, 5):
+        when = pd.Timestamp("2024-08-01 12:00:00") + pd.Timedelta(minutes=5 * k)
+        db.archive(datatree=retime(first, when, rng, MCH_BIAS), radar=RADAR)
+    return base
+
+
+def test_every_volume_joins_its_lut_completely(drifting_archive):
+    """The bug this was written for: 6% of gates per volume used to vanish."""
+    lut = pl.read_parquet(drifting_archive / RADAR / "LUT" / f"{RADAR}_LUT.parquet", columns=["gate_id"])
+    pols = sorted((drifting_archive / RADAR).rglob("*_POL.parquet"))
+
+    assert len(pols) == 5
+    for path in pols:
+        pol = pl.read_parquet(path, columns=["gate_id"])
+        assert pol.join(lut, on="gate_id", how="semi").height == pol.height, f"{path.name} did not join fully"
+
+
+def test_the_same_ray_keeps_its_gate_id_across_volumes(drifting_archive):
+    """A gate must keep one identity across rotations or nothing can be compared."""
+    sets = [
+        set(pl.read_parquet(f, columns=["gate_id"])["gate_id"].to_list())
+        for f in sorted((drifting_archive / RADAR).rglob("*_POL.parquet"))
+    ]
+
+    assert all(s == sets[0] for s in sets)
+
+
+def test_the_whole_archive_reads_back_with_geometry(drifting_archive):
+    """The loss was invisible because it only showed up in LUT joins."""
+    rdf = RadDB(archive_dir=str(drifting_archive)).open(radars=RADAR)
+
+    assert rdf.to_geopandas().shape[0] == rdf.data.height
+
+
+def test_snap_volume_azimuths_moves_a_drifting_ray_onto_the_grid():
+    """``sweeps`` and ``azimuths`` are parallel per-ray arrays; the grid is per sweep."""
+    grid = np.arange(5, 3600, 10)  # 360 rays at x.5 degrees, in tenths
+    azimuths = np.array([0.53, 1.47, 2.51])
+    sweeps = np.ones(3, dtype=np.int64)
+
+    snapped, worst = _snap_volume_azimuths(sweeps, azimuths, {1: grid}, RADAR)
+
+    np.testing.assert_allclose(snapped, [0.5, 1.5, 2.5])
+    assert worst == pytest.approx(0.03, abs=1e-9)
+
+
+def test_snap_volume_azimuths_refuses_more_rays_than_the_grid():
+    """A 720-ray volume against a 360-ray grid is a different scan strategy."""
+    azimuths = np.linspace(0, 360, 720, endpoint=False)
+
+    with pytest.raises(ValueError, match="different scan strategy"):
+        _snap_volume_azimuths(np.ones(720, dtype=np.int64), azimuths, {1: np.arange(0, 3600, 10)}, RADAR)
+
+
+def test_snap_volume_azimuths_accepts_fewer_rays_than_the_grid():
+    """A rotation with holes: each surviving ray still snaps to its own grid point."""
+    snapped, _ = _snap_volume_azimuths(
+        np.ones(3, dtype=np.int64), np.array([0.53, 1.47, 2.51]), {1: np.arange(5, 3600, 10)}, RADAR
+    )
+
+    assert snapped.size == 3
+
+
+def test_snap_volume_azimuths_refuses_an_unknown_sweep():
+    """A sweep the LUT never saw has no grid to snap onto."""
+    with pytest.raises(ValueError, match="no sweep"):
+        _snap_volume_azimuths(np.array([9]), np.array([0.0]), {1: np.arange(0, 3600, 10)}, RADAR)
+
+
+def test_snap_volume_azimuths_refuses_a_ray_beyond_the_tolerance():
+    """Further than half a ray spacing is not antenna drift.
+
+    Uniform grids cannot trigger this — the worst case is exactly half a spacing — so
+    the guard only bites when a ray lands where the grid has no point at all.  Here the
+    grid is a 1-degree rotation with 170..190 removed, and a ray is aimed into the gap.
+    """
+    gapped = np.concatenate([np.arange(0, 1700, 10), np.arange(1900, 3600, 10)])
+
+    with pytest.raises(ValueError, match="not antenna drift"):
+        _snap_volume_azimuths(np.array([1]), np.array([180.0]), {1: gapped}, RADAR)
+
+
+def test_archiving_refuses_a_different_ray_count(tmp_path, make_datatree):
+    """Supporting several geometries per radar is deliberately not done yet."""
+    db = RadDB(archive_dir=str(tmp_path / "a"), crs=SWISS_EPSG)
+    db.archive(datatree=build_datatree(n_az=360, n_rng=20, n_sweeps=2), radar=RADAR)
+
+    with pytest.raises(ValueError, match="different scan strategy"):
+        db.archive(
+            datatree=build_datatree(n_az=720, n_rng=20, n_sweeps=2, vol_time=pd.Timestamp("2024-08-01 13:00")),
+            radar=RADAR,
+        )
+
+
+def test_archiving_accepts_a_volume_that_dropped_rays(tmp_path):
+    """A volume short of a ray or two is a rotation with holes, not a new strategy."""
+    db = RadDB(archive_dir=str(tmp_path / "a"), crs=SWISS_EPSG)
+    complete = build_datatree(n_az=360, n_rng=20, n_sweeps=2)
+    db.archive(datatree=complete, radar=RADAR)
+
+    drifted = retime(complete, pd.Timestamp("2024-08-01 18:00"), np.random.default_rng(21), MCH_BIAS)
+    holed = xr.DataTree.from_dict(
+        {
+            name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
+            for name, node in drifted.children.items()
+        }
+    )
+
+    result = db.archive(datatree=holed, radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"]) == (1, 0)
+
+
+def test_a_lut_built_from_a_holed_volume_still_holds_every_ray(tmp_path):
+    """The LUT is written for the whole rotation, so a later complete volume joins."""
+    complete = build_datatree(n_az=360, n_rng=20, n_sweeps=2)
+    holed = xr.DataTree.from_dict(
+        {
+            name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
+            for name, node in complete.children.items()
+        }
+    )
+    db = RadDB(archive_dir=str(tmp_path / "a"), crs=SWISS_EPSG)
+    db.archive(datatree=holed, radar=RADAR)
+
+    lut = db.get_lut(RADAR)
+    assert lut.filter(pl.col("sweep") == 1)["azimuth"].n_unique() == 360
+
+    result = db.archive(
+        datatree=retime(complete, pd.Timestamp("2024-08-01 19:00"), np.random.default_rng(22), MCH_BIAS),
+        radar=RADAR,
+    )
+    assert (result["n_archived"], result["n_failed"]) == (1, 0)
+
+    lut_ids = set(lut["gate_id"].to_list())
+    assert all(g in lut_ids for g in db.open(radars=RADAR).data["gate_id"].to_list())
+
+
+def test_a_batch_reports_a_refusal_instead_of_aborting(tmp_path):
+    """One incompatible volume must not take the whole batch down."""
+    db = RadDB(archive_dir=str(tmp_path / "a"), crs=SWISS_EPSG)
+    db.archive(datatree=build_datatree(n_az=360, n_rng=20, n_sweeps=2), radar=RADAR)
+
+    good = retime(
+        build_datatree(n_az=360, n_rng=20, n_sweeps=2),
+        pd.Timestamp("2024-08-01 16:00"),
+        np.random.default_rng(12),
+        MCH_BIAS,
+    )
+    bad = build_datatree(n_az=720, n_rng=20, n_sweeps=2, vol_time=pd.Timestamp("2024-08-01 17:00"))
+
+    result = db.archive(datatree={"good": good, "bad": bad}, radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"]) == (1, 1)
+
+
+def test_building_without_a_grid_warns(caplog):
+    """A pre-grid archive keeps working, but must say that gates may not join."""
+    df = pl.DataFrame(
+        {
+            "sweep": [1, 1],
+            "azimuth": [0.53, 1.53],
+            "range": [1000.0, 1000.0],
+            "DBZH": [10.0, 20.0],
+            "time": [pd.Timestamp("2024-01-01")] * 2,
+        }
+    )
+
+    with caplog.at_level("WARNING"):
+        _build_polar_dataframe(df, RADAR, "DBZH", 0.0, ">", azimuth_grids=None)
+
+    assert "no nominal azimuth grid" in caplog.text
+
+
+# ---------------------------------------------------------------------------
+# The read path
+# ---------------------------------------------------------------------------
+
+
+def test_parquet_to_dataframe(archive_dir):
+    """The archive reads back as polars, one row per surviving gate."""
+    df = parquet_to_dataframe(RADAR, archive_dir, *TIME_WINDOW)
+
+    assert isinstance(df, pl.DataFrame)
+    assert not df.is_empty()
+    assert "gate_id" in df.columns
+
+
+def test_parquet_to_dataframe_merges_the_lut(archive_dir):
+    """``merge_lut=True`` attaches the static geometry, ``sweep`` included."""
+    df = parquet_to_dataframe(RADAR, archive_dir, *TIME_WINDOW, merge_lut=True)
+
+    assert {"sweep", "azimuth", "range"} <= set(df.columns)
+    columns = list(df.columns)
+    assert abs(columns.index("sweep") - columns.index("azimuth")) == 1, "sweep must sit beside azimuth"
+
+
+def test_parquet_to_dataframe_selects_columns(archive_dir):
+    """``columns=`` is pushed into the reader."""
+    df = parquet_to_dataframe(RADAR, archive_dir, *TIME_WINDOW, columns=["gate_id", "DBZH"])
+
+    assert set(df.columns) <= {"gate_id", "DBZH", "volume_time", "radar"}
+
+
+def test_parquet_to_dataframe_honours_the_time_window(archive_dir_two_volumes):
+    """Only volumes inside the window are read."""
+    both = parquet_to_dataframe(RADAR, archive_dir_two_volumes, *TIME_WINDOW)
+    first = parquet_to_dataframe(RADAR, archive_dir_two_volumes, "2024-08-01 11:59", "2024-08-01 12:01")
+
+    assert 0 < first.height < both.height
+
+
+def test_scan_polar_parquet(archive_dir):
+    """The lazy entry point, for queries that should not materialise the archive."""
+    lazy = scan_polar_parquet(RADAR, archive_dir, *TIME_WINDOW)
+
+    assert isinstance(lazy, pl.LazyFrame)
+    assert lazy.collect().height > 0
+
+
+def test_scan_polar_parquet_returns_none_when_nothing_matches(archive_dir):
+    """No files in range is ``None``, which callers check before collecting."""
+    assert scan_polar_parquet(RADAR, archive_dir, "1999-01-01", "1999-12-31") is None
+
+
+# ---------------------------------------------------------------------------
+# Reconstruction back to a DataTree
+# ---------------------------------------------------------------------------
+
+
+def test_parquet_to_datatree(archive_dir):
+    """A round trip: archived gates come back as sweeps carrying the label column."""
+    dt = parquet_to_datatree(RADAR, archive_dir, *TIME_WINDOW, label_column="DBZH")
+
+    from raddb.helper import list_sweep_names
+
+    names = list_sweep_names(dt)
+    assert names
+    for name in names:
+        assert "DBZH" in dt[name].to_dataset()
+
+
+def test_parquet_to_datatree_handles_several_volumes(archive_dir_two_volumes):
+    """Two volumes share every ``gate_id``; the duplicates used to crash the rebuild."""
+    dt = parquet_to_datatree(RADAR, archive_dir_two_volumes, *TIME_WINDOW, label_column="DBZH")
+
+    from raddb.helper import list_sweep_names
+
+    assert list_sweep_names(dt)
+
+
+def test_dataframe_to_datatree(archive_dir):
+    """The same reconstruction, driven from a frame the caller already holds."""
+    df = parquet_to_dataframe(RADAR, archive_dir, *TIME_WINDOW)
+
+    dt = dataframe_to_datatree(df, RADAR, archive_dir, label_column="DBZH")
+
+    from raddb.helper import list_sweep_names
+
+    assert list_sweep_names(dt)
+
+
+def test_reconstruct_sweep_dataset(archive_dir):
+    """One sweep, reindexed onto its full azimuth x range grid."""
+    from raddb.lut import load_radar_info, load_radar_lut
+
+    lut = load_radar_lut(RADAR, archive_dir)
+    df = parquet_to_dataframe(RADAR, archive_dir, *TIME_WINDOW)
+    joined = df.join(lut, on="gate_id", how="inner")
+
+    ds = reconstruct_sweep_dataset(joined, 1, lut, load_radar_info(RADAR, archive_dir), label_column="DBZH")
+
+    assert isinstance(ds, xr.Dataset)
+    assert {"azimuth", "range"} <= set(ds.dims)
+    assert "DBZH" in ds
+
+
+def test_reconstruct_datatree(archive_dir):
+    """The whole volume, from a joined frame plus the two LUT files."""
+    from raddb.lut import load_radar_lut
+
+    lut = load_radar_lut(RADAR, archive_dir)
+    df = parquet_to_dataframe(RADAR, archive_dir, *TIME_WINDOW)
+
+    dt = reconstruct_datatree(
+        df.join(lut, on="gate_id", how="inner"),
+        lut_path=lut_file_path(RADAR, "lut", archive_dir),
+        radar_info_path=lut_file_path(RADAR, "info", archive_dir),
+        label_column="DBZH",
+    )
+
+    from raddb.helper import list_sweep_names
+
+    assert list_sweep_names(dt) == ["sweep_1", "sweep_2"]
+
+
+# ---------------------------------------------------------------------------
+# Label helpers
+# ---------------------------------------------------------------------------
+
+
+def test_labels_to_dataframe():
+    """External model output is turned into a joinable two-column frame."""
+    df = labels_to_dataframe(np.array([1, 2, 3]), np.array([10, 20, 30], dtype=np.int64))
+
+    assert isinstance(df, pl.DataFrame)
+    assert df.columns == ["gate_id", "hydrometeor_class"]
+    assert df["gate_id"].to_list() == [10, 20, 30]
+
+
+def test_labels_to_dataframe_carries_extra_columns():
+    """``extra_columns=`` rides along, e.g. a per-gate confidence."""
+    df = labels_to_dataframe(
+        np.array([1, 2]), np.array([10, 20], dtype=np.int64), extra_columns={"confidence": np.array([0.9, 0.8])}
+    )
+
+    assert "confidence" in df.columns
+
+
+def test_join_labels_with_lut(archive_dir):
+    """Labels gain the static geometry their ``gate_id`` points at.
+
+    The join is LUT-left, so every gate keeps a row and unlabelled ones carry a null —
+    which is what lets a partial model output be reconstructed onto the full grid.
+    """
+    lut_path = lut_file_path(RADAR, "lut", archive_dir)
+    lut = pl.read_parquet(lut_path, columns=["gate_id"])
+    labels = labels_to_dataframe(np.ones(5, dtype=np.int64), lut["gate_id"].to_numpy()[:5])
+
+    joined = join_labels_with_lut(labels, lut_path)
+
+    assert joined.height == lut.height
+    assert {"azimuth", "range", "sweep", "hydrometeor_class"} <= set(joined.columns)
+    assert joined["hydrometeor_class"].null_count() == lut.height - 5
+
+
+# ---------------------------------------------------------------------------
+# add_feature_to_df / add_feature_to_dt
+# ---------------------------------------------------------------------------
+
+
+def test_add_feature_to_df():
+    """A computed column is appended without touching the existing ones."""
+    df = pl.DataFrame({"DBZH": [10.0, 20.0]})
+
+    out = add_feature_to_df(df, "Z_lin", lambda d: 10 ** (np.asarray(d["DBZH"]) / 10.0))
+
+    assert "Z_lin" in out.columns
+    assert out["Z_lin"].to_numpy() == pytest.approx([10.0, 100.0])
+
+
+def test_add_feature_to_df_returns_the_kind_it_was_given():
+    """The same-kind-in-same-kind-out contract as ``filter_df``."""
+    data = {"DBZH": [10.0, 20.0]}
+    compute = lambda d: np.asarray(d["DBZH"]) * 2  # noqa: E731 - a one-line test double
+
+    assert isinstance(add_feature_to_df(pl.DataFrame(data), "x", compute), pl.DataFrame)
+    assert isinstance(add_feature_to_df(pd.DataFrame(data), "x", compute), pd.DataFrame)
+
+
+def test_add_feature_to_dt(datatree):
+    """The DataTree counterpart adds the variable to every sweep."""
+    out = add_feature_to_dt(datatree, "Z_lin", lambda ds: 10 ** (ds["DBZH"] / 10.0))
+
+    for name in ("sweep_1", "sweep_2"):
+        assert "Z_lin" in out[name].to_dataset()
+
+
+# ---------------------------------------------------------------------------
+# The polars/pandas seam helpers
+# ---------------------------------------------------------------------------
+
+
+def test_to_polars_frame_and_back():
+    """Both coercions are identity on the kind they target."""
+    pl_df = pl.DataFrame({"a": [1, 2]})
+    pd_df = pd.DataFrame({"a": [1, 2]})
+
+    assert _to_polars_frame(pl_df) is pl_df
+    assert isinstance(_to_polars_frame(pd_df), pl.DataFrame)
+    assert isinstance(_to_pandas_frame(pl_df), pd.DataFrame)
+    assert _to_pandas_frame(pd_df) is pd_df
+
+
+def test_col_reads_from_either_kind():
+    """polars' ``Series.to_numpy()`` takes no ``dtype``, unlike pandas'."""
+    for df in (pl.DataFrame({"a": [1, 2]}), pd.DataFrame({"a": [1, 2]})):
+        out = _col(df, "a", np.float64)
+        assert out.dtype == np.float64
+        assert out.tolist() == [1.0, 2.0]
+
+
+def test_cast_hc_column_shifts_to_the_parquet_scale():
+    """HC is stored 1-based; the raw 0-based class needs ``shift=1``."""
+    np.testing.assert_array_equal(_cast_hc_column(np.array([0.0, 3.0, 8.0]), shift=1), np.array([1, 4, 9]))
+
+
+def test_cast_hc_column_survives_nan():
+    """A NaN class must not become a nonsense integer."""
+    out = _cast_hc_column(np.array([np.nan, 2.0]), shift=1)
+
+    assert out[1] == 3
+
+
+def test_a_malformed_volume_is_reported_as_a_failure(lut_base):
+    """A batch records the error rather than propagating it.
+
+    A 530-volume run must survive one bad file; the record carries the reason so the
+    caller can see what happened without re-running everything.
+    """
+    bad = xr.DataTree.from_dict({"sweep_1": xr.Dataset({"DBZH": (["azimuth", "range"], np.ones((5, 5)))})})
+
+    records = archive_multiple_volumes({"bad_vol": bad}, radar=RADAR, base_output_path=lut_base, verbose=False)
+
+    assert len(records) == 1
+    assert records[0]["success"] is False
+    assert records[0]["error"] is not None
diff --git a/raddb/tests/test_lut.py b/raddb/tests/test_lut.py
new file mode 100644
index 0000000..290bb0d
--- /dev/null
+++ b/raddb/tests/test_lut.py
@@ -0,0 +1,1420 @@
+"""Tests for :mod:`raddb.lut` — LUT generation, gate geometry, ``gate_id`` and CRS checks.
+
+Four themes, each written to pin something that was once silently wrong.
+
+**The base-36 radar code.** ``gate_id`` embeds the zero-padded four-character radar name,
+so an archive is self-describing and two archives concatenate.  Encoding v1 numbered
+radars ``A=0 .. Z=25``; the two disagree for every name and nothing detects a v1 archive
+any more — that is a deliberate trade-off, pinned below.
+
+**The nominal azimuth grid.** The LUT stores a radar's *scan strategy*, not one volume's
+measured azimuths.  Half-up rounding, a circular seam, full-precision comparison and a
+minimum rotation coverage are all load-bearing; each has its own test.
+
+**Gate geometry.** A gate is a frustum, not a box: corners 5-8 enclose strictly more area
+than 1-4.  The first range bin is degenerate — its inner edge clips to r=0, so five
+distinct corners instead of eight.
+
+**The CRS contract.** Validity is *measured*, not declared: a 100 km geodesic is projected
+in eight directions and compared with the truth.  Metadata alone would pass EPSG:3857,
+which reports a 100 km baseline as 145 km in Switzerland.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import polars as pl
+import pyproj
+import pytest
+import shapely
+import yaml
+
+from raddb.helper import normalize_radar_name
+from raddb.lut import (
+    AZIMUTH_SCALE,
+    RADAR_ALPHABET,
+    AZIMUTH_STEPS,
+    CRS_REFUSE_PCT,
+    DEFAULT_BEAMWIDTH_DEG,
+    GATE_ID_RADAR_BASE,
+    LEGACY_RADAR_TO_IDX,
+    LUT_FILES,
+    MAX_RADAR_CODE,
+    RADAR_CODE_LEN,
+    _round_half_up,
+    add_lut_projection,
+    antenna_vectors_to_cartesian,
+    azimuth_grid_tolerance,
+    build_gate_planes,
+    cappi_chords,
+    cartesian_to_geographic,
+    compute_corners_from_lut,
+    compute_gate_xyz,
+    compute_sweep_corners,
+    crs_distance_error,
+    decode_gate_ids,
+    decode_gate_radars,
+    decode_radar_code,
+    encode_gate_ids,
+    encode_radar_code,
+    ensure_gate_planes,
+    gate_corner_table,
+    gate_polygons_geoarrow,
+    generate_gate_id,
+    generate_lut_from_datatree,
+    geoarrow_field,
+    get_full_sweep_index,
+    load_azimuth_grids,
+    load_plane_nodes,
+    load_radar_info,
+    load_radar_lut,
+    load_sweep_corners,
+    lut_file_path,
+    nominal_azimuth_grid,
+    save_sweep_corners,
+    snap_azimuths_to_grid,
+    suggest_crs,
+    validate_crs_for_site,
+)
+from raddb.tests.conftest import (
+    MCH_BIAS,
+    NEXRAD_SPREAD,
+    RADAR,
+    SWISS_EPSG,
+    US_EPSG,
+    US_SITE,
+    build_datatree,
+    jitter_azimuths,
+    relocate,
+    retime,
+)
+
+CH_SITE = (7.0, 46.0)
+"""The synthetic fixture's own site — ``(longitude, latitude)``."""
+
+N_AZ, N_RNG, N_SWEEPS = 12, 24, 2
+N_GATES = N_AZ * N_RNG * N_SWEEPS
+
+REAL_N_AZ, REAL_N_RNG, REAL_N_SWEEPS = 360, 200, 2
+REAL_N_GATES = REAL_N_AZ * REAL_N_RNG * REAL_N_SWEEPS
+
+
+@pytest.fixture
+def lut_base(tmp_path, make_datatree):
+    """A base path holding radar ``A``'s complete five-file LUT directory."""
+    generate_lut_from_datatree(
+        make_datatree(), radar=RADAR, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG
+    )
+    return tmp_path
+
+
+@pytest.fixture(scope="session")
+def real_lut_base(tmp_path_factory):
+    """A realistically-sampled LUT (1 degree azimuths, 200 range bins), built once.
+
+    Needed by two groups of tests.  **Geometry**: a gate footprint is a straight-sided
+    quad, so its outer chord cuts inside the true arc; the centroid falls outside its own
+    footprint beyond ``r ~ dR*cos(h)/(1-cos(h))``, which is ~11.7 km at the small
+    fixture's 30 degree spacing but ~5400 km at 1 degree.  **File sizes**: bytes-per-gate
+    is meaningless on a 576-gate file where the fixed parquet footer dominates.
+    """
+    base = tmp_path_factory.mktemp("realistic_lut")
+    generate_lut_from_datatree(
+        build_datatree(n_az=REAL_N_AZ, n_rng=REAL_N_RNG, n_sweeps=REAL_N_SWEEPS),
+        radar=RADAR,
+        output_base_path=str(base),
+        projection_epsg=SWISS_EPSG,
+    )
+    return base
+
+
+def _face_area(table: pl.DataFrame, corners) -> np.ndarray:
+    """Planar polygon area of a 4-corner face in 3-D, by Newell's method."""
+    pts = np.stack(
+        [
+            np.stack(
+                [table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy(), table[f"z_rel_{k}"].to_numpy()], axis=1
+            )
+            for k in corners
+        ],
+        axis=1,
+    )
+    normal = np.zeros((pts.shape[0], 3))
+    for i in range(4):
+        normal += np.cross(pts[:, i], pts[:, (i + 1) % 4])
+    return 0.5 * np.linalg.norm(normal, axis=1)
+
+
+# ---------------------------------------------------------------------------
+# encode_radar_code / decode_radar_code
+# ---------------------------------------------------------------------------
+
+
+def test_encode_radar_code():
+    """Base-36 over the zero-padded four-character name."""
+    assert encode_radar_code("A") == 10  # "000A"
+    assert encode_radar_code("L") == 21
+    assert encode_radar_code("KTLX") == 971_493
+    assert encode_radar_code("0") == 0
+    assert encode_radar_code("ZZZZ") == MAX_RADAR_CODE
+
+
+def test_decode_radar_code():
+    """The inverse, with the padding stripped back off."""
+    assert decode_radar_code(10) == "A"
+    assert decode_radar_code(971_493) == "KTLX"
+    assert decode_radar_code(MAX_RADAR_CODE) == "ZZZZ"
+
+
+def test_the_code_space_is_36_to_the_fourth():
+    """Four characters is what the ``gate_id`` layout can hold."""
+    assert MAX_RADAR_CODE == 36**RADAR_CODE_LEN - 1 == 1_679_615
+
+
+def test_every_gate_id_fits_int64():
+    """The largest possible id sits 5.5x under the int64 ceiling."""
+    largest = MAX_RADAR_CODE * GATE_ID_RADAR_BASE + (GATE_ID_RADAR_BASE - 1)
+
+    assert largest < np.iinfo(np.int64).max
+    assert np.int64(largest) == largest
+
+
+def test_five_characters_would_not_fit():
+    """Documents why ``RADAR_CODE_LEN`` is 4: ``36**5`` blows the int64 budget."""
+    budget = (np.iinfo(np.int64).max - (GATE_ID_RADAR_BASE - 1)) // GATE_ID_RADAR_BASE
+
+    assert 36**4 - 1 <= budget < 36**5 - 1
+
+
+def test_decode_is_injective_over_the_whole_code_space():
+    """Distinct codes never name the same radar."""
+    names = {decode_radar_code(c) for c in range(MAX_RADAR_CODE + 1)}
+
+    assert len(names) == MAX_RADAR_CODE + 1 == 1_679_616
+
+
+def test_the_round_trip_is_total_over_canonical_names():
+    """Every name that is its own canonical form survives encode then decode."""
+    names = {decode_radar_code(c) for c in range(MAX_RADAR_CODE + 1)}
+    canonical = {n for n in names if normalize_radar_name(n) == n}
+
+    assert len(canonical) == 1_679_580  # all but the 36 ML? aliases
+    assert all(decode_radar_code(encode_radar_code(n)) == n for n in canonical)
+
+
+def test_only_the_ml_aliases_break_the_code_round_trip():
+    """``MLA`` normalises to ``A`` first, so 36 codes can never be emitted."""
+    broken = [c for c in range(MAX_RADAR_CODE + 1) if encode_radar_code(decode_radar_code(c)) != c]
+
+    assert len(broken) == 36
+
+
+def test_zero_padding_is_transparent():
+    """Leading zeros are ``gate_id`` padding, never part of the name."""
+    assert encode_radar_code("A") == encode_radar_code("000A") == encode_radar_code("0A")
+
+
+def test_alphabet_positions_define_the_values():
+    """Each character's value is its index in ``RADAR_ALPHABET``."""
+    for i, char in enumerate(RADAR_ALPHABET):
+        assert encode_radar_code(char.rjust(RADAR_CODE_LEN, "0")) == i
+
+
+@pytest.mark.parametrize("code", [-1, MAX_RADAR_CODE + 1, 10**9])
+def test_decode_radar_code_rejects_an_out_of_range_code(code):
+    """Outside the 36**4 space there is no name to return."""
+    with pytest.raises(ValueError, match="names no radar"):
+        decode_radar_code(code)
+
+
+def test_more_than_26_radars_stay_distinct():
+    """The point of encoding v2: no 26-radar ceiling."""
+    names = [f"K{a}{b}" for a in "ABCDE" for b in "ABCDEFGHIJ"]  # 50 sites
+
+    codes = [encode_radar_code(n) for n in names]
+
+    assert len(set(codes)) == len(names) == 50
+    assert sorted(decode_radar_code(c) for c in codes) == sorted(names)
+
+
+def test_a_v1_archive_is_read_without_complaint(tmp_path, make_datatree):
+    """The deliberate trade-off, pinned so it stays visible.
+
+    ``info.yaml`` used to record ``gate_id_version`` and ``load_radar_info`` raised
+    ``OutdatedGateIdError`` on a v1 archive.  Both were removed: only v2 is ever
+    produced, so the key carried no information about anything new.
+
+    The cost is real — a v1 archive now loads **silently and decodes to the wrong
+    radar**.  This builds one by rewriting the ids back to the v1 encoding and reads it,
+    so the trade-off stays visible rather than being rediscovered.
+    """
+    from raddb.main import RadDB
+
+    base = tmp_path / "archive"
+    db = RadDB(archive_dir=str(base), crs=SWISS_EPSG)
+    db.archive(datatree=make_datatree(), radar="L")
+
+    # Rewrite every gate_id back to the v1 encoding (A=0 .. Z=25), as if written long ago.
+    delta = (LEGACY_RADAR_TO_IDX["L"] - encode_radar_code("L")) * GATE_ID_RADAR_BASE
+    for path in [base / "L" / "LUT" / "L_LUT.parquet", *sorted((base / "L").rglob("*_POL.parquet"))]:
+        pl.read_parquet(path).with_columns((pl.col("gate_id") + delta).alias("gate_id")).write_parquet(path)
+
+    assert "gate_id_version" not in db.get_radar_info("L")
+    # No error, no warning — and a v1 'L' (code 11) reads as 'B'.
+    assert decode_gate_radars(db.open(radars="L").data["gate_id"].to_numpy()) == ["B"]
+
+
+# ---------------------------------------------------------------------------
+# gate_id encoding and decoding
+# ---------------------------------------------------------------------------
+
+
+def test_generate_gate_id():
+    """``radar_code * 1e12 + sweep * 1e10 + az*10 * 1e6 + range_m``, decimal so it reads."""
+    gid = generate_gate_id("A", sweep=1, azimuth=45.5, range_m=1000)
+
+    assert gid == 10 * 10**12 + 1 * 10**10 + 455 * 10**6 + 1000
+
+
+def test_encode_gate_ids():
+    """The vectorised form, over parallel arrays."""
+    ids = encode_gate_ids("A", np.array([1, 2]), np.array([45.5, 90.0]), np.array([1000.0, 2000.0]))
+
+    assert ids.dtype == np.int64
+    assert ids[0] == generate_gate_id("A", 1, 45.5, 1000)
+    assert ids[1] == generate_gate_id("A", 2, 90.0, 2000)
+
+
+def test_decode_gate_ids(lut_base):
+    """Decoding inverts the encoding for every gate the LUT holds."""
+    lut = load_radar_lut(RADAR, lut_base)
+
+    sweeps, azimuths, ranges = decode_gate_ids(lut["gate_id"].to_numpy())
+
+    assert np.array_equal(sweeps, lut["sweep"].to_numpy().astype(np.int64))
+    assert np.allclose(azimuths, np.round(lut["azimuth"].to_numpy() * 10) / 10)
+    assert np.allclose(ranges, lut["range"].to_numpy().astype(np.int64))
+
+
+def test_decode_gate_radars(lut_base):
+    """The radar name is recovered from the integers alone, with no registry file."""
+    lut = load_radar_lut(RADAR, lut_base)
+
+    assert decode_gate_radars(lut["gate_id"].to_numpy()) == [RADAR]
+
+
+def test_the_radar_field_is_the_leading_one():
+    """``gate_id // GATE_ID_RADAR_BASE`` is exactly the radar code."""
+    gid = encode_gate_ids("KTLX", 3, np.array([91.4]), np.array([12_500.0]))
+
+    assert gid[0] // GATE_ID_RADAR_BASE == encode_radar_code("KTLX")
+
+
+def test_the_low_fields_are_independent_of_the_radar():
+    """Only the leading field differs between radars — what the v1 migration relies on.
+
+    ``migrate_gate_id_v2`` is one integer offset per radar precisely because sweep,
+    azimuth and range occupy fields the radar code never touches.
+    """
+    azimuths, ranges = np.array([91.4, 270.0]), np.array([12_500.0, 240_000.0])
+
+    a = encode_gate_ids("A", 3, azimuths, ranges)
+    ktlx = encode_gate_ids("KTLX", 3, azimuths, ranges)
+
+    delta = (encode_radar_code("KTLX") - encode_radar_code("A")) * GATE_ID_RADAR_BASE
+    assert np.array_equal(ktlx - a, np.full(2, delta))
+
+
+def test_decode_gate_ids_round_trips_the_extremes():
+    """0 degrees, the 359.9 seam, r=0 and a 999,999 m range all survive."""
+    azimuths = np.array([0.0, 91.4, 359.9])
+    ranges = np.array([0.0, 12_500.0, 999_999.0])
+    gid = encode_gate_ids("KTLX", 7, azimuths, ranges)
+
+    sweeps, got_azimuths, got_ranges = decode_gate_ids(gid)
+
+    assert np.array_equal(sweeps, np.full(3, 7))
+    assert np.allclose(got_azimuths, azimuths)
+    assert np.allclose(got_ranges, ranges)
+    assert decode_gate_radars(gid) == ["KTLX"]
+
+
+def test_decode_gate_radars_on_an_empty_array():
+    """An empty frame names no radars, and must not raise."""
+    assert decode_gate_radars(np.array([], dtype=np.int64)) == []
+
+
+def test_decode_gate_radars_skips_an_unknown_code(caplog):
+    """A corrupt id is warned about and dropped, not turned into a nonsense name."""
+    bogus = np.array([(MAX_RADAR_CODE + 5) * GATE_ID_RADAR_BASE], dtype=np.int64)
+
+    with caplog.at_level("WARNING"):
+        assert decode_gate_radars(bogus) == []
+
+    assert "names no radar" in caplog.text
+
+
+def test_decode_gate_radars_spans_a_concatenated_archive():
+    """Two archives can be concatenated because each id names its own radar."""
+    ids = np.concatenate(
+        [
+            encode_gate_ids("A", np.array([1]), np.array([0.5]), np.array([1000.0])),
+            encode_gate_ids("KTLX", np.array([1]), np.array([0.5]), np.array([1000.0])),
+        ]
+    )
+
+    assert sorted(decode_gate_radars(ids)) == ["A", "KTLX"]
+
+
+# ---------------------------------------------------------------------------
+# nominal_azimuth_grid
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(("n_rays", "step_tenths"), [(360, 10), (720, 5), (180, 20)])
+def test_nominal_azimuth_grid(n_rays, step_tenths):
+    """One rule, no per-network constant: the spacing follows the ray count."""
+    grid = nominal_azimuth_grid(np.arange(n_rays) * (360 / n_rays) + 0.25)
+
+    assert grid.size == n_rays
+    assert np.all(np.diff(grid) == step_tenths)
+
+
+def test_the_grid_is_recovered_from_jittered_rays():
+    """The measured angles drift; the derived strategy does not."""
+    nominal = np.arange(360) + 0.5
+
+    grid = nominal_azimuth_grid(jitter_azimuths(nominal, np.random.default_rng(0), MCH_BIAS))
+
+    assert np.array_equal(grid, np.round(nominal * AZIMUTH_SCALE).astype(np.int64))
+
+
+def test_the_grid_is_stable_across_volumes():
+    """The whole point: different rotations must derive the *same* grid."""
+    rng = np.random.default_rng(1)
+    nominal = np.arange(360) + 0.5
+
+    grids = [nominal_azimuth_grid(jitter_azimuths(nominal, rng, MCH_BIAS)) for _ in range(25)]
+
+    assert all(np.array_equal(g, grids[0]) for g in grids)
+
+
+def test_rounding_is_half_up_not_bankers():
+    """A 720-ray grid puts every centre on ``x.x5``.
+
+    numpy's banker's rounding would turn a uniform 0.5 degree grid into an alternating
+    0.4/0.6 one.
+    """
+    grid = nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)
+
+    assert np.all(np.diff(grid) == 5)
+
+
+def test_round_half_up_ties_away_from_even():
+    """The helper itself, where banker's rounding would differ."""
+    np.testing.assert_array_equal(_round_half_up(np.array([0.5, 1.5, 2.5, 3.5])), np.array([1, 2, 3, 4]))
+
+
+def test_the_offset_is_a_circular_mean():
+    """Rays straddling 0 degrees must not drag the offset to mid-step."""
+    grid = nominal_azimuth_grid(jitter_azimuths(np.arange(360) * 1.0, np.random.default_rng(2), 0.0, 0.02))
+
+    assert np.array_equal(grid, np.arange(360) * 10)
+
+
+def test_a_spacing_finer_than_the_resolution_is_refused():
+    """``gate_id`` resolves 0.1 degrees; a finer grid could not be represented."""
+    with pytest.raises(ValueError, match="finer than"):
+        nominal_azimuth_grid(np.arange(7200) * 0.05)
+
+
+def test_an_empty_sweep_is_refused():
+    """There is no strategy to derive from no rays."""
+    with pytest.raises(ValueError, match="no rays"):
+        nominal_azimuth_grid([])
+
+
+def test_a_sector_scan_is_refused():
+    """90 rays over 90 degrees would silently get a 4 degree grid and collapse to 23."""
+    with pytest.raises(ValueError, match="full rotation"):
+        nominal_azimuth_grid(np.arange(90, 180, 1.0))
+
+
+def test_a_sweep_with_a_large_gap_is_refused():
+    """Two sectors are not a rotation either."""
+    azimuths = np.concatenate([np.arange(0, 120, 1.0), np.arange(240, 360, 1.0)])
+
+    with pytest.raises(ValueError, match="full rotation"):
+        nominal_azimuth_grid(azimuths)
+
+
+@pytest.mark.parametrize("dropped", [[7], [7, 8], [0, 359], [3, 100, 250]])
+def test_a_rotation_with_holes_keeps_the_full_grid(dropped):
+    """718 of 720 is a rotation with holes, not a 0.5014 degree scan strategy.
+
+    The LUT is written for the whole rotation, missing rays included, so a later volume
+    that does record them still joins.
+    """
+    nominal = np.arange(720) * 0.5 + 0.25
+
+    grid = nominal_azimuth_grid(np.delete(nominal, dropped))
+
+    assert grid.size == 720
+    assert np.all(np.diff(grid) == 5)
+    assert np.array_equal(grid, nominal_azimuth_grid(nominal))
+
+
+def test_holes_survive_antenna_drift():
+    """WSR-88D drift plus two dropped rays.
+
+    The grid is the same rotation, but not necessarily the same integers: a 720-ray grid
+    is centred on ``x.x5``, exactly the 0.1 degree rounding boundary, so the ~0.002
+    degrees the two ray sets differ by can tip the whole grid one tenth either way.
+    That is a tenth against a half-spacing tolerance of 0.25 degrees, so every ray still
+    snaps to its own point.
+    """
+    nominal = np.arange(720) * 0.5 + 0.25
+    recorded = np.delete(jitter_azimuths(nominal, np.random.default_rng(7), 0.0, NEXRAD_SPREAD), [11, 12])
+
+    grid = nominal_azimuth_grid(recorded)
+
+    assert grid.size == 720
+    assert np.all(np.diff(grid) == 5)
+    shift = (grid - nominal_azimuth_grid(nominal) + AZIMUTH_STEPS // 2) % AZIMUTH_STEPS
+    assert np.all(np.abs(shift - AZIMUTH_STEPS // 2) <= 1)
+    assert snap_azimuths_to_grid(recorded, grid)[1].max() <= azimuth_grid_tolerance(grid)
+
+
+def test_too_many_holes_is_not_a_rotation():
+    """Past ``MIN_ROTATION_COVERAGE`` it is indistinguishable from a sector scan."""
+    recorded = np.delete(np.arange(360) + 0.5, np.arange(0, 100))  # 260 of 360
+
+    with pytest.raises(ValueError, match="full rotation"):
+        nominal_azimuth_grid(recorded)
+
+
+# ---------------------------------------------------------------------------
+# snap_azimuths_to_grid / azimuth_grid_tolerance
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def degree_grid():
+    """A 360-point grid centred on 0.5, 1.5 ... 359.5 degrees, in tenths."""
+    return nominal_azimuth_grid(np.arange(360) + 0.5)
+
+
+def test_snap_azimuths_to_grid(degree_grid):
+    """Each ray moves to the nearest grid point, and the distance comes back with it."""
+    snapped, distance = snap_azimuths_to_grid([0.49, 0.51, 1.44, 1.56], degree_grid)
+
+    assert list(snapped) == [5, 5, 15, 15]
+    assert np.allclose(distance, [0.1, 0.1, 0.6, 0.6])
+
+
+@pytest.mark.parametrize(("azimuth", "expected"), [(359.97, 3595), (0.02, 5), (359.60, 3595), (0.60, 5)])
+def test_the_seam_is_measured_the_short_way(degree_grid, azimuth, expected):
+    """The grid is a circle: distance across 0/360 goes the short way, not through 180."""
+    assert snap_azimuths_to_grid([azimuth], degree_grid)[0][0] == expected
+
+
+def test_a_ray_below_360_can_snap_to_a_grid_point_at_zero():
+    """With rays centred on 0, 1, 2 ..., 359.7 degrees belongs to 0.0, not 359.0."""
+    grid = nominal_azimuth_grid(np.arange(360) * 1.0)
+
+    snapped, distance = snap_azimuths_to_grid([359.7, 359.4, 0.3], grid)
+
+    assert grid[0] == 0
+    assert list(snapped) == [0, 3590, 0]
+    assert np.allclose(distance, [3.0, 4.0, 3.0])
+
+
+def test_full_precision_decides_the_match(degree_grid):
+    """Rounding to 0.1 degrees first would make 0.02 an exact tie between 0.5 and 359.5."""
+    assert snap_azimuths_to_grid([0.02], degree_grid)[0][0] == 5
+
+
+def test_snapping_is_a_bijection_under_real_drift(degree_grid):
+    """No two rays may collapse onto one grid point, or gates would be lost."""
+    drifted = jitter_azimuths(np.arange(360) + 0.5, np.random.default_rng(3), MCH_BIAS)
+
+    snapped, distance = snap_azimuths_to_grid(drifted, degree_grid)
+
+    assert np.unique(snapped).size == 360
+    assert distance.max() <= azimuth_grid_tolerance(degree_grid)
+
+
+def test_snapping_survives_nexrad_scale_drift():
+    """720 super-resolution rays at 0.045 degree spread."""
+    grid = nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)
+    drifted = jitter_azimuths(np.arange(720) * 0.5 + 0.25, np.random.default_rng(4), 0.0, NEXRAD_SPREAD)
+
+    snapped, distance = snap_azimuths_to_grid(drifted, grid)
+
+    assert np.unique(snapped).size == 720
+    assert distance.max() <= azimuth_grid_tolerance(grid)
+
+
+def test_snapped_output_stays_inside_one_turn(degree_grid):
+    """360.0 must not become 3600 tenths."""
+    snapped, _ = snap_azimuths_to_grid([0.0, 180.0, 359.999, 360.0], degree_grid)
+
+    assert np.all((snapped >= 0) & (snapped < AZIMUTH_STEPS))
+
+
+def test_snap_azimuths_to_grid_rejects_an_empty_grid():
+    """Nothing to snap onto is an error, not a silent pass-through."""
+    with pytest.raises(ValueError, match="empty azimuth grid"):
+        snap_azimuths_to_grid([1.0], [])
+
+
+def test_azimuth_grid_tolerance(degree_grid):
+    """Half a ray spacing — the most a gate may legitimately move."""
+    assert azimuth_grid_tolerance(degree_grid) == pytest.approx(5.0)
+    assert azimuth_grid_tolerance(nominal_azimuth_grid(np.arange(720) * 0.5 + 0.25)) == pytest.approx(2.5)
+
+
+# ---------------------------------------------------------------------------
+# load_azimuth_grids
+# ---------------------------------------------------------------------------
+
+
+def test_load_azimuth_grids(tmp_path):
+    """Read back from the **LUT parquet**, not from ``info.yaml``, which no longer says."""
+    generate_lut_from_datatree(
+        build_datatree(n_az=360, n_rng=20, n_sweeps=3),
+        radar=RADAR,
+        output_base_path=str(tmp_path),
+        projection_epsg=SWISS_EPSG,
+    )
+
+    grids = load_azimuth_grids(RADAR, tmp_path)
+
+    assert grids is not None and set(grids) == {1, 2, 3}
+    for grid in grids.values():
+        assert grid.size == 360 and np.all(np.diff(grid) == 10)
+
+    info = yaml.safe_load((tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text())
+    assert "azimuths" not in info["sweeps"][1]
+
+
+def test_load_azimuth_grids_returns_none_without_a_lut(tmp_path):
+    """No LUT means no grid to snap onto — the measured azimuths stand."""
+    assert load_azimuth_grids(RADAR, tmp_path) is None
+
+
+def test_the_lut_azimuth_column_holds_the_nominal_grid(tmp_path):
+    """The LUT stores the scan strategy, not one volume's measurements."""
+    import pandas as pd
+
+    drifted = retime(
+        build_datatree(n_az=360, n_rng=20, n_sweeps=2),
+        pd.Timestamp("2024-08-01 12:00:00"),
+        np.random.default_rng(5),
+        MCH_BIAS,
+    )
+    generate_lut_from_datatree(
+        drifted, radar=RADAR, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG
+    )
+
+    lut = load_radar_lut(RADAR, tmp_path)
+    azimuths = np.unique(lut.filter(pl.col("sweep") == 1)["azimuth"].to_numpy())
+
+    assert np.allclose(azimuths * AZIMUTH_SCALE, np.round(azimuths * AZIMUTH_SCALE))
+
+
+# ---------------------------------------------------------------------------
+# Beam geometry
+# ---------------------------------------------------------------------------
+
+
+def test_antenna_vectors_to_cartesian():
+    """A ray at 0 degrees azimuth points north; at 90 degrees, east."""
+    ranges = np.array([10_000.0])
+
+    x_n, y_n, _ = antenna_vectors_to_cartesian(ranges, np.array([0.0]), np.array([0.0]))
+    x_e, y_e, _ = antenna_vectors_to_cartesian(ranges, np.array([90.0]), np.array([0.0]))
+
+    assert abs(x_n[0]) < 1.0 and y_n[0] == pytest.approx(10_000.0, rel=1e-3)
+    assert x_e[0] == pytest.approx(10_000.0, rel=1e-3) and abs(y_e[0]) < 1.0
+
+
+def test_the_beam_rises_with_elevation_and_earth_curvature():
+    """``ke=4/3`` everywhere; a horizontal beam still climbs from the curvature term."""
+    _, _, z_flat = antenna_vectors_to_cartesian(np.array([100_000.0]), np.array([0.0]), np.array([0.0]))
+    _, _, z_up = antenna_vectors_to_cartesian(np.array([100_000.0]), np.array([0.0]), np.array([5.0]))
+
+    assert z_flat[0] > 0.0  # curvature alone lifts a 0-degree beam
+    assert z_up[0] > z_flat[0]
+
+
+def test_the_default_ke_is_four_thirds():
+    """Archives generated with the old 1.25 default carry ~237 m of altitude error."""
+    import inspect
+
+    assert inspect.signature(antenna_vectors_to_cartesian).parameters["ke"].default == pytest.approx(4 / 3)
+    assert inspect.signature(compute_gate_xyz).parameters["ke"].default == pytest.approx(4 / 3)
+    assert inspect.signature(generate_lut_from_datatree).parameters["ke"].default == pytest.approx(4 / 3)
+
+
+def test_compute_gate_xyz():
+    """The meshed form: one position per (azimuth, range) pair."""
+    x, y, z = compute_gate_xyz(np.array([1000.0, 2000.0]), np.array([0.0, 90.0]), np.array([0.5, 0.5]))
+
+    assert x.shape == y.shape == z.shape
+    assert np.isfinite(x).all()
+
+
+def test_cartesian_to_geographic():
+    """The radar's own position maps back to its own coordinates.
+
+    Note the return order is ``(lat, lon, alt)`` — latitude first, unlike the
+    ``(longitude, latitude)`` argument order used everywhere a *site* is named.
+    """
+    lat, lon, alt = cartesian_to_geographic(
+        np.array([0.0]), np.array([0.0]), np.array([0.0]), CH_SITE[1], CH_SITE[0], 1000.0
+    )
+
+    assert lon[0] == pytest.approx(CH_SITE[0], abs=1e-6)
+    assert lat[0] == pytest.approx(CH_SITE[1], abs=1e-6)
+    assert alt[0] == pytest.approx(1000.0)
+
+
+def test_geographic_conversion_moves_north_for_positive_y():
+    """A 10 km northward offset raises the latitude by ~0.09 degrees."""
+    lat, _, _ = cartesian_to_geographic(
+        np.array([0.0]), np.array([10_000.0]), np.array([0.0]), CH_SITE[1], CH_SITE[0], 0.0
+    )
+
+    assert lat[0] > CH_SITE[1]
+    assert lat[0] - CH_SITE[1] == pytest.approx(0.09, abs=0.01)
+
+
+# ---------------------------------------------------------------------------
+# generate_lut_from_datatree — the five-file directory
+# ---------------------------------------------------------------------------
+
+
+def test_generate_lut_from_datatree(lut_base):
+    """All five files are written, and none is empty."""
+    lut_dir = lut_base / RADAR / "LUT"
+
+    for kind, template in LUT_FILES.items():
+        path = lut_dir / template.format(radar=RADAR)
+        assert path.exists(), f"{kind} missing: {path.name}"
+        assert path.stat().st_size > 0
+
+
+def test_the_lut_directory_holds_exactly_the_five_files(lut_base):
+    """Nothing else is written beside them."""
+    lut_dir = lut_base / RADAR / "LUT"
+
+    assert sorted(f.name for f in lut_dir.iterdir()) == sorted(t.format(radar=RADAR) for t in LUT_FILES.values())
+
+
+def test_regenerating_backfills_the_lattices_without_rewriting_the_centroids(lut_base, make_datatree):
+    """An archive predating the lattices regenerates them, keeping its LUT untouched.
+
+    That matters because an archive's source volumes are often long gone.
+    """
+    lut_dir = lut_base / RADAR / "LUT"
+    centroid_file = lut_dir / LUT_FILES["lut"].format(radar=RADAR)
+    stamp = centroid_file.stat().st_mtime_ns
+    for kind in ("h_plane", "v_plane", "corners"):
+        (lut_dir / LUT_FILES[kind].format(radar=RADAR)).unlink()
+
+    generate_lut_from_datatree(
+        make_datatree(), radar=RADAR, output_base_path=str(lut_base), projection_epsg=SWISS_EPSG
+    )
+
+    for kind in ("h_plane", "v_plane", "corners"):
+        assert (lut_dir / LUT_FILES[kind].format(radar=RADAR)).exists()
+    assert centroid_file.stat().st_mtime_ns == stamp
+
+
+def test_the_info_yaml_records_the_generation_parameters(lut_base):
+    """Everything needed to reproduce the geometry, and nothing that went stale."""
+    info = load_radar_info(RADAR, lut_base)
+
+    for key in ("radar", "network", "latitude", "longitude", "altitude", "crs", "ke", "beamwidth_deg", "n_sweeps", "n_gates", "sweeps"):
+        assert key in info, f"missing info key {key!r}"
+    assert info["ke"] == pytest.approx(4.0 / 3.0)
+    assert info["beamwidth_deg"] == DEFAULT_BEAMWIDTH_DEG
+    assert (info["n_gates"], info["n_sweeps"]) == (N_GATES, N_SWEEPS)
+    assert info["crs"] == {"epsg": SWISS_EPSG, "columns": ["x_2056", "y_2056"]}
+
+
+def test_the_per_sweep_info_block(lut_base):
+    """``dR``, ``azimuth_scale`` and ``azimuths`` were dropped deliberately.
+
+    The first two were never read back, and the grid is recovered from the LUT parquet.
+    """
+    sweep = load_radar_info(RADAR, lut_base)["sweeps"][1]
+
+    for key in ("n_azimuths", "n_ranges", "n_gates", "elevation", "range_resolution", "range_start"):
+        assert key in sweep, f"missing per-sweep key {key!r}"
+    assert sweep["n_gates"] == sweep["n_azimuths"] * sweep["n_ranges"]
+    for key in ("dR", "azimuth_scale", "azimuths"):
+        assert key not in sweep, f"per-sweep key {key!r} should no longer be written"
+
+
+def test_generate_lut_accepts_a_projection_crs_object(tmp_path, make_datatree):
+    """A pyproj CRS works where an EPSG int is not available."""
+    crs = pyproj.CRS.from_proj4(
+        "+proj=somerc +lat_0=46.9524056 +lon_0=7.4395833 +k_0=1 +x_0=2600000 +y_0=1200000 "
+        "+ellps=bessel +towgs84=674.374,15.056,405.346,0,0,0,0 +units=m +no_defs"
+    )
+
+    generate_lut_from_datatree(make_datatree(), radar=RADAR, output_base_path=str(tmp_path), projection_crs=crs)
+
+    lut = load_radar_lut(RADAR, tmp_path)
+    assert len([c for c in lut.columns if c.startswith("x_")]) == 1
+    assert lut[[c for c in lut.columns if c.startswith("x_")][0]].is_not_null().any()
+
+
+def test_generate_lut_records_an_explicit_beamwidth(tmp_path, make_datatree):
+    """The one place beamwidth may be set; no plot takes it."""
+    generate_lut_from_datatree(
+        make_datatree(), radar=RADAR, output_base_path=str(tmp_path), beamwidth_deg=1.5, projection_epsg=SWISS_EPSG
+    )
+
+    assert load_radar_info(RADAR, tmp_path)["beamwidth_deg"] == pytest.approx(1.5)
+
+
+def test_a_wider_beamwidth_makes_a_taller_gate(tmp_path, make_datatree):
+    """Only ``v_plane``/``corners`` depend on it — and this is how it shows."""
+    heights = {}
+    for beamwidth in (1.0, 2.0):
+        out = tmp_path / f"bw{beamwidth}"
+        generate_lut_from_datatree(
+            make_datatree(),
+            radar=RADAR,
+            output_base_path=str(out),
+            beamwidth_deg=beamwidth,
+            projection_epsg=SWISS_EPSG,
+        )
+        table = gate_corner_table(RADAR, str(out), kind="corners", sweep=1)
+        z = np.stack([table[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
+        heights[beamwidth] = float(np.mean(z.max(axis=1) - z.min(axis=1)))
+
+    assert heights[2.0] > heights[1.0] * 1.5
+
+
+def test_the_horizontal_face_is_beamwidth_independent(tmp_path, make_datatree):
+    """A PPI draws the beam *centre*, so its footprint cannot depend on beamwidth."""
+    nodes = {}
+    for beamwidth in (0.8, 1.2):
+        out = tmp_path / f"h{beamwidth}"
+        generate_lut_from_datatree(
+            make_datatree(),
+            radar=RADAR,
+            output_base_path=str(out),
+            beamwidth_deg=beamwidth,
+            projection_epsg=SWISS_EPSG,
+        )
+        nodes[beamwidth] = load_plane_nodes(RADAR, str(out), "h_plane").sort(["sweep", "az_idx", "rng_idx"])
+
+    for column in ("x", "y"):
+        assert np.abs(nodes[0.8][column].to_numpy() - nodes[1.2][column].to_numpy()).max() == pytest.approx(0.0)
+
+
+# ---------------------------------------------------------------------------
+# The node lattices
+# ---------------------------------------------------------------------------
+
+
+def test_load_plane_nodes(lut_base):
+    """One ``(n_az+1) x (n_rng+1)`` node grid per sweep, at the centre level."""
+    nodes = load_plane_nodes(RADAR, lut_base, "h_plane")
+
+    assert nodes.height == N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
+    assert "el_level" not in nodes.columns
+
+
+def test_load_plane_nodes_filters_by_sweep(lut_base):
+    """``sweep=`` is pushed down, so one sweep costs one sweep's worth of rows."""
+    nodes = load_plane_nodes(RADAR, lut_base, "h_plane", sweep=1)
+
+    assert nodes["sweep"].unique().to_list() == [1]
+    assert nodes.height == (N_AZ + 1) * (N_RNG + 1)
+
+
+def test_the_corner_lattice_has_two_elevation_levels(lut_base):
+    """Bottom and top of the beam; the horizontal face is the centre only."""
+    nodes = load_plane_nodes(RADAR, lut_base, "corners")
+
+    assert sorted(nodes["el_level"].unique().to_list()) == [-1, 1]
+    assert nodes.height == 2 * N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
+
+
+def test_the_lattice_carries_the_projected_columns(lut_base):
+    """Otherwise a backfilled lattice would silently lose its projection."""
+    assert {"x_2056", "y_2056"} <= set(load_plane_nodes(RADAR, lut_base, "h_plane").columns)
+
+
+def test_gate_corner_table(lut_base):
+    """Nodes expand to four corners per gate on demand."""
+    table = gate_corner_table(RADAR, lut_base, kind="h_plane")
+
+    assert table.height == N_GATES
+    for k in range(1, 5):
+        assert {f"x_{k}", f"y_{k}"} <= set(table.columns)
+    assert "x_5" not in table.columns
+
+
+def test_gate_corner_table_expands_eight_corners(lut_base):
+    """The 3-D lattice gives eight corners per gate."""
+    table = gate_corner_table(RADAR, lut_base, kind="corners")
+
+    assert table.height == N_GATES
+    for k in range(1, 9):
+        assert {f"x_{k}", f"y_{k}", f"z_rel_{k}"} <= set(table.columns)
+    assert "x_9" not in table.columns
+
+
+def test_gate_corner_table_gate_ids_match_the_lut(lut_base):
+    """The lattices and the centroid LUT describe exactly the same gates."""
+    lut_ids = set(load_radar_lut(RADAR, lut_base)["gate_id"].to_list())
+
+    assert set(gate_corner_table(RADAR, lut_base, kind="corners")["gate_id"].to_list()) == lut_ids
+
+
+def test_a_gate_is_a_frustum_not_a_box(lut_base):
+    """Angular half-extents are evaluated at each corner's own range.
+
+    So corners 5-8 (far face) enclose strictly more area than 1-4 (near face).
+    """
+    table = gate_corner_table(RADAR, lut_base, kind="corners")
+
+    near = _face_area(table, [1, 2, 3, 4])
+    far = _face_area(table, [5, 6, 7, 8])
+
+    assert np.all(far > near), f"{int((far <= near).sum())} gate(s) have a far face no larger than the near face"
+
+
+def test_the_frustum_ratio_stays_physically_sane(lut_base):
+    """Excluding the degenerate innermost bin, the growth is bounded."""
+    table = gate_corner_table(RADAR, lut_base, kind="corners")
+    near = _face_area(table, [1, 2, 3, 4])
+    far = _face_area(table, [5, 6, 7, 8])
+    keep = near > 1.0  # drop the r~0 near face
+
+    ratio = far[keep] / near[keep]
+    assert 1.0 < ratio.min() and ratio.max() < 100.0
+
+
+def test_the_first_range_bin_is_degenerate(lut_base):
+    """Its inner edge clips to r=0, so its near face collapses to a point."""
+    table = gate_corner_table(RADAR, lut_base, kind="corners")
+    pts = np.stack(
+        [
+            np.stack(
+                [table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy(), table[f"z_rel_{k}"].to_numpy()], axis=1
+            )
+            for k in range(1, 9)
+        ],
+        axis=1,
+    )
+
+    distinct = np.array([len({tuple(np.round(p, 3)) for p in pts[i]}) for i in range(pts.shape[0])])
+
+    assert set(np.unique(distinct)) <= {5, 8}
+    assert (distinct == 8).mean() > 0.9
+
+
+def test_gate_footprints_are_valid_polygons(lut_base):
+    """A self-intersecting quad would break every spatial predicate downstream."""
+    table = gate_corner_table(RADAR, lut_base, kind="h_plane")
+    ring = np.stack(
+        [np.stack([table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy()], axis=1) for k in (1, 2, 3, 4, 1)],
+        axis=1,
+    )
+
+    assert shapely.is_valid(shapely.polygons(ring)).all()
+
+
+def test_a_centroid_lies_inside_its_own_footprint(real_lut_base):
+    """Needs realistic 1 degree azimuth sampling — see :func:`real_lut_base`."""
+    table = gate_corner_table(RADAR, real_lut_base, kind="h_plane").sort("gate_id")
+    lut = load_radar_lut(RADAR, real_lut_base).sort("gate_id")
+    ring = np.stack(
+        [np.stack([table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy()], axis=1) for k in (1, 2, 3, 4, 1)],
+        axis=1,
+    )
+
+    polygons = shapely.polygons(ring)
+    points = shapely.points(np.stack([lut["x"].to_numpy(), lut["y"].to_numpy()], axis=1))
+
+    assert shapely.covers(polygons, points).all()
+
+
+def test_a_centroid_lies_between_its_elevation_levels(lut_base):
+    """1 cm tolerance: on a negative-elevation sweep ``z(r)`` has a turning point."""
+    table = gate_corner_table(RADAR, lut_base, kind="corners").sort("gate_id")
+    lut = load_radar_lut(RADAR, lut_base).sort("gate_id")
+    z_centre = lut["z"].to_numpy()
+    z_corners = np.stack([table[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
+
+    assert (z_centre >= z_corners.min(axis=1) - 0.01).all()
+    assert (z_centre <= z_corners.max(axis=1) + 0.01).all()
+
+
+def test_the_vertical_lattice_relates_the_two_altitude_references(lut_base):
+    """``z_asl`` is ``z_rel`` plus the site altitude, everywhere."""
+    site_altitude = load_radar_info(RADAR, lut_base)["altitude"]
+    nodes = load_plane_nodes(RADAR, lut_base, "v_plane")
+
+    difference = nodes["z_asl"].to_numpy() - nodes["z_rel"].to_numpy()
+
+    assert np.allclose(difference, site_altitude, atol=1e-3)
+
+
+def test_ground_distance_is_monotonic_in_range(lut_base):
+    """Along a ray, ``d`` must increase — the RHI axis depends on it."""
+    nodes = load_plane_nodes(RADAR, lut_base, "v_plane", sweep=1)
+    along = nodes.filter((pl.col("el_level") == 1) & (pl.col("az_idx") == 0)).sort("rng_idx")
+
+    assert np.all(np.diff(along["d"].to_numpy()) > 0)
+
+
+def test_the_geometry_files_stay_smaller_than_the_centroid_lut(real_lut_base):
+    """The whole point of storing lattices rather than per-gate corners."""
+    centroid = lut_file_path(RADAR, "lut", real_lut_base).stat().st_size
+    geometry = sum(lut_file_path(RADAR, k, real_lut_base).stat().st_size for k in ("h_plane", "v_plane", "corners"))
+
+    assert geometry < centroid, f"geometry {geometry / 1e6:.1f} MB vs LUT {centroid / 1e6:.1f} MB"
+
+
+@pytest.mark.parametrize(("kind", "budget"), [("h_plane", 18.0), ("v_plane", 2.0), ("corners", 20.0)])
+def test_each_geometry_file_stays_inside_its_byte_budget(real_lut_base, kind, budget):
+    """Bytes per gate, measured on 144k gates so the parquet footer does not dominate.
+
+    ``v_plane`` is startlingly small because ground distance and altitude do not depend
+    on azimuth, so parquet run-length-encodes it almost completely away.
+    """
+    per_gate = lut_file_path(RADAR, kind, real_lut_base).stat().st_size / REAL_N_GATES
+
+    assert per_gate <= budget, f"{kind} is {per_gate:.1f} B/gate, over the {budget} B/gate budget"
+
+
+def test_the_lattice_beats_per_gate_materialisation(real_lut_base):
+    """Neighbouring gates share corner nodes, which is where the saving comes from."""
+    stored = lut_file_path(RADAR, "corners", real_lut_base).stat().st_size
+    naive = gate_corner_table(RADAR, real_lut_base, kind="corners").height * 8 * 3 * 4
+
+    assert stored < naive
+
+
+# ---------------------------------------------------------------------------
+# compute_sweep_corners / build_gate_planes / ensure_gate_planes
+# ---------------------------------------------------------------------------
+
+
+def _sweep_corners(beamwidth_deg=DEFAULT_BEAMWIDTH_DEG):
+    """A node mesh for the default synthetic sweep geometry."""
+    return compute_sweep_corners(
+        np.linspace(1000, 20_000, N_RNG),
+        np.linspace(0, 330, N_AZ),
+        np.full(N_AZ, 0.5),
+        radar_lat=CH_SITE[1],
+        radar_lon=CH_SITE[0],
+        radar_alt=1000.0,
+        beamwidth_deg=beamwidth_deg,
+    )
+
+
+def test_compute_sweep_corners():
+    """The per-sweep node mesh the three lattices are built from."""
+    corners = _sweep_corners()
+
+    assert isinstance(corners, dict) and corners
+    # The mesh is one node lattice per elevation level, so it is (n_az+1) x (n_rng+1).
+    assert any(np.asarray(v).size == (N_AZ + 1) * (N_RNG + 1) for v in corners.values() if np.ndim(v))
+
+
+def test_compute_sweep_corners_requires_a_beamwidth_for_the_vertical_levels():
+    """Without it there is no top or bottom face to build ``v_plane``/``corners`` from."""
+    corners = compute_sweep_corners(
+        np.linspace(1000, 20_000, N_RNG),
+        np.linspace(0, 330, N_AZ),
+        np.full(N_AZ, 0.5),
+        radar_lat=CH_SITE[1],
+        radar_lon=CH_SITE[0],
+        radar_alt=1000.0,
+    )
+
+    with pytest.raises(ValueError, match="beamwidth_deg"):
+        build_gate_planes({1: corners}, radar_alt=1000.0, projection_epsg=SWISS_EPSG)
+
+
+def test_build_gate_planes():
+    """The three lattices come out of one corner mesh, keyed by kind."""
+    planes = build_gate_planes({1: _sweep_corners()}, radar_alt=1000.0, projection_epsg=SWISS_EPSG)
+
+    assert set(planes) == {"h_plane", "v_plane", "corners"}
+    assert all(isinstance(v, pl.DataFrame) and v.height > 0 for v in planes.values())
+
+
+def test_ensure_gate_planes(lut_base, tmp_path):
+    """A two-file archive is backfilled on first read, without re-ingesting anything."""
+    old = tmp_path / "old" / RADAR / "LUT"
+    old.mkdir(parents=True)
+    for kind in ("lut", "info"):
+        source = lut_file_path(RADAR, kind, lut_base)
+        (old / source.name).write_bytes(source.read_bytes())
+    base = tmp_path / "old"
+
+    assert not lut_file_path(RADAR, "h_plane", base).exists()
+    assert ensure_gate_planes(RADAR, base) is True
+
+    for kind in ("h_plane", "v_plane", "corners"):
+        assert lut_file_path(RADAR, kind, base).exists()
+    assert ensure_gate_planes(RADAR, base) is False, "a second call must be a no-op"
+
+
+def test_the_backfill_recovers_the_projection_from_the_lut(lut_base, tmp_path):
+    """Pre-geometry info YAMLs have no ``crs`` block; the LUT's columns still say EPSG."""
+    old = tmp_path / "old" / RADAR / "LUT"
+    old.mkdir(parents=True)
+    for kind in ("lut", "info"):
+        source = lut_file_path(RADAR, kind, lut_base)
+        (old / source.name).write_bytes(source.read_bytes())
+    info_path = old / lut_file_path(RADAR, "info", lut_base).name
+    info = yaml.safe_load(info_path.read_text())
+    info.pop("crs", None)
+    info_path.write_text(yaml.safe_dump(info))
+
+    ensure_gate_planes(RADAR, tmp_path / "old")
+
+    assert {"x_2056", "y_2056"} <= set(load_plane_nodes(RADAR, tmp_path / "old", "h_plane").columns)
+
+
+def test_save_sweep_corners(lut_base, tmp_path):
+    """The legacy ``.npz`` corner store, still read for pre-lattice archives.
+
+    ``np.savez`` is loaded back with ``allow_pickle=False``, so only the flat numeric
+    arrays the real producer emits survive the round trip.
+    """
+    compute_corners_from_lut(RADAR, lut_base)
+    original = load_sweep_corners(RADAR, lut_base)
+
+    out = tmp_path / "copy" / RADAR / "LUT" / f"{RADAR}_corners.npz"
+    out.parent.mkdir(parents=True, exist_ok=True)
+    save_sweep_corners(original, out)
+
+    reloaded = load_sweep_corners(RADAR, tmp_path / "copy")
+
+    assert set(reloaded) == set(original) == {1, 2}
+    for sweep, arrays in original.items():
+        for key, value in arrays.items():
+            np.testing.assert_allclose(reloaded[sweep][key], value)
+
+
+def test_load_sweep_corners(lut_base):
+    """Reads the ``.npz`` back as ``{sweep: {name: array}}``, keyed by sweep number."""
+    compute_corners_from_lut(RADAR, lut_base)
+
+    corners = load_sweep_corners(RADAR, lut_base)
+
+    assert set(corners) == {1, 2}
+    assert all(isinstance(v, dict) and v for v in corners.values())
+
+
+def test_load_sweep_corners_is_empty_without_the_file(tmp_path):
+    """Backwards-compatible: an archive with no ``.npz`` yields ``{}``, not an error.
+
+    Callers treat an empty result as "use the lattices instead", which is the normal
+    path for every archive written since the lattices existed.
+    """
+    assert load_sweep_corners(RADAR, tmp_path) == {}
+
+
+def test_compute_corners_from_lut(lut_base):
+    """Corners are rebuilt from the centroid LUT alone, with no source volume."""
+    path = compute_corners_from_lut(RADAR, lut_base)
+
+    assert Path(path).exists()
+    assert set(load_sweep_corners(RADAR, lut_base)) == {1, 2}
+
+
+# ---------------------------------------------------------------------------
+# cappi_chords
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture(scope="session")
+def cappi_base(tmp_path_factory):
+    """A six-sweep LUT, so a CAPPI slice has overlapping beams to resolve."""
+    base = tmp_path_factory.mktemp("cappi_lut")
+    generate_lut_from_datatree(
+        build_datatree(n_az=72, n_rng=60, n_sweeps=6),
+        radar=RADAR,
+        output_base_path=str(base),
+        projection_epsg=SWISS_EPSG,
+    )
+    return base
+
+
+def test_cappi_chords(cappi_base):
+    """Every reported bin really spans the requested altitude."""
+    z0 = 1200.0
+    chords = cappi_chords(RADAR, cappi_base, z0)
+    assert not chords.is_empty()
+
+    nodes = load_plane_nodes(RADAR, cappi_base, "v_plane")
+    nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
+    for (sweep,), sub in chords.group_by(["sweep"], maintain_order=True):
+        bottom = nodes.filter((pl.col("sweep") == sweep) & (pl.col("el_level") == -1)).sort("rng_idx")
+        top = nodes.filter((pl.col("sweep") == sweep) & (pl.col("el_level") == 1)).sort("rng_idx")
+        zb, zt = bottom["z_asl"].to_numpy(), top["z_asl"].to_numpy()
+        j = sub["rng_idx"].to_numpy()
+        low = np.minimum.reduce([zb[j], zb[j + 1], zt[j], zt[j + 1]])
+        high = np.maximum.reduce([zb[j], zb[j + 1], zt[j], zt[j + 1]])
+        assert ((low - 1e-3 <= z0) & (z0 <= high + 1e-3)).all()
+
+
+def test_chords_stay_inside_their_range_bin(cappi_base):
+    """The cut trims a gate along the beam; it never reaches outside it."""
+    chords = cappi_chords(RADAR, cappi_base, 1200.0)
+    nodes = load_plane_nodes(RADAR, cappi_base, "v_plane")
+    nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
+
+    for (sweep,), sub in chords.group_by(["sweep"], maintain_order=True):
+        bottom = nodes.filter((pl.col("sweep") == sweep) & (pl.col("el_level") == -1)).sort("rng_idx")
+        top = nodes.filter((pl.col("sweep") == sweep) & (pl.col("el_level") == 1)).sort("rng_idx")
+        db, dt = bottom["d"].to_numpy(), top["d"].to_numpy()
+        j = sub["rng_idx"].to_numpy()
+        low = np.minimum.reduce([db[j], db[j + 1], dt[j], dt[j + 1]])
+        high = np.maximum.reduce([db[j], db[j + 1], dt[j], dt[j + 1]])
+        assert (sub["d_near"].to_numpy() >= low - 1e-2).all()
+        assert (sub["d_far"].to_numpy() <= high + 1e-2).all()
+
+
+def test_the_near_chord_edge_is_below_the_far_one(cappi_base):
+    """Otherwise the drawn polygon would be inside out."""
+    chords = cappi_chords(RADAR, cappi_base, 1200.0)
+
+    assert (chords["d_near"].to_numpy() <= chords["d_far"].to_numpy()).all()
+
+
+def test_each_sweep_contributes_a_contiguous_band(cappi_base):
+    """Beam thickness far exceeds the rise per bin, so the bands have no holes."""
+    for (_sweep,), sub in cappi_chords(RADAR, cappi_base, 1200.0).group_by(["sweep"]):
+        j = np.sort(sub["rng_idx"].to_numpy())
+        assert np.array_equal(j, np.arange(j.min(), j.max() + 1))
+
+
+def test_an_altitude_above_every_beam_yields_no_chords(cappi_base):
+    """Empty, not an error — the caller turns it into the "reaches" message."""
+    assert cappi_chords(RADAR, cappi_base, 50_000.0).is_empty()
+
+
+def test_the_two_height_references_select_the_same_chords(cappi_base):
+    """``asl`` at ``z`` is ``rel`` at ``z - site_altitude``."""
+    altitude = load_radar_info(RADAR, cappi_base)["altitude"]
+
+    asl = cappi_chords(RADAR, cappi_base, 1200.0, height="asl")
+    rel = cappi_chords(RADAR, cappi_base, 1200.0 - altitude, height="rel")
+
+    assert asl.height == rel.height
+
+
+def test_cappi_chords_rejects_an_unknown_height_reference(cappi_base):
+    """Only ``asl`` and ``rel`` exist."""
+    with pytest.raises(ValueError):
+        cappi_chords(RADAR, cappi_base, 1200.0, height="furlongs")
+
+
+# ---------------------------------------------------------------------------
+# The CRS contract — measured, never declared
+# ---------------------------------------------------------------------------
+
+
+def test_suggest_crs():
+    """The UTM zone for a site, quoted in every refusal so the user is told what to pass."""
+    assert suggest_crs(*CH_SITE) == 32632  # zone 32N
+    assert suggest_crs(*US_SITE) == 32614  # zone 14N
+
+
+def test_suggest_crs_picks_a_south_zone_below_the_equator():
+    """Sydney is zone 56S, not 56N."""
+    assert suggest_crs(151.2, -33.9) == 32756
+
+
+def test_crs_distance_error():
+    """The measurement itself: percent error on a projected 100 km geodesic."""
+    assert crs_distance_error(SWISS_EPSG, *CH_SITE) < 0.1
+    assert crs_distance_error(3857, *CH_SITE) > 10.0
+
+
+@pytest.mark.parametrize(
+    ("crs", "site", "accepted"),
+    [
+        (2056, CH_SITE, True),  # LV95 at home
+        (32632, CH_SITE, True),  # UTM 32N at home
+        (32614, US_SITE, True),  # UTM 14N at KTLX
+        (2056, US_SITE, False),  # the bug: LV95 in Oklahoma
+        (3857, CH_SITE, False),  # Web Mercator claims the world and distorts hugely
+        (3857, US_SITE, False),
+    ],
+)
+def test_validate_crs_for_site(crs, site, accepted):
+    """Validity is measured, because declared metadata is not enough."""
+    if accepted:
+        assert validate_crs_for_site(crs, *site) < CRS_REFUSE_PCT
+    else:
+        with pytest.raises(ValueError, match="distorts distance"):
+            validate_crs_for_site(crs, *site)
+
+
+def test_an_area_of_use_check_alone_would_pass_web_mercator():
+    """EPSG:3857 declares the whole world, so a bounds check lets it through."""
+    area = pyproj.CRS.from_epsg(3857).area_of_use
+
+    assert area.west <= CH_SITE[0] <= area.east and area.south <= CH_SITE[1] <= area.north
+    assert crs_distance_error(3857, *CH_SITE) > 10.0
+
+
+def test_a_geographic_crs_is_refused():
+    """Degrees are not metres; EPSG:4326 can never measure a crop radius."""
+    with pytest.raises(ValueError, match="geographic"):
+        validate_crs_for_site(4326, *CH_SITE)
+
+
+def test_a_refusal_names_a_usable_replacement():
+    """The message must tell the user what to pass, not just complain."""
+    with pytest.raises(ValueError, match="32614"):
+        validate_crs_for_site(2056, *US_SITE)
+
+
+def test_generating_a_lut_without_a_crs_is_refused(tmp_path, make_datatree):
+    """A CRS is mandatory to write, because a wrong projection is silently wrong."""
+    with pytest.raises(ValueError, match="requires a CRS"):
+        generate_lut_from_datatree(make_datatree(), radar=RADAR, output_base_path=str(tmp_path))
+
+
+def test_that_refusal_also_names_a_usable_crs(tmp_path, make_datatree):
+    """``RadDB(crs=32632)`` is the UTM zone at the synthetic site."""
+    with pytest.raises(ValueError, match=r"RadDB\(crs=32632\)"):
+        generate_lut_from_datatree(make_datatree(), radar=RADAR, output_base_path=str(tmp_path))
+
+
+def test_a_crs_invalid_at_the_site_is_refused(tmp_path, make_datatree):
+    """EPSG:2056 outside Switzerland mis-measures distance by ~20%."""
+    dt = relocate(make_datatree(), *US_SITE)
+
+    with pytest.raises(ValueError, match="distorts distance"):
+        generate_lut_from_datatree(dt, radar=RADAR, output_base_path=str(tmp_path), projection_epsg=2056)
+
+
+def test_the_correct_crs_archives_a_us_radar(tmp_path, make_datatree):
+    """UTM 14N at KTLX measures to 0.03%."""
+    dt = relocate(make_datatree(), *US_SITE)
+
+    generate_lut_from_datatree(dt, radar=RADAR, output_base_path=str(tmp_path), projection_epsg=US_EPSG)
+
+    assert load_radar_info(RADAR, tmp_path)["crs"]["epsg"] == US_EPSG
+
+
+# ---------------------------------------------------------------------------
+# Accessors and converters
+# ---------------------------------------------------------------------------
+
+
+def test_lut_file_path(lut_base):
+    """One place that knows the five filenames."""
+    for kind, template in LUT_FILES.items():
+        assert lut_file_path(RADAR, kind, lut_base).name == template.format(radar=RADAR)
+
+
+def test_lut_file_path_rejects_an_unknown_kind(lut_base):
+    """A typo must not produce a path that will simply not exist."""
+    with pytest.raises((KeyError, ValueError)):
+        lut_file_path(RADAR, "not_a_kind", lut_base)
+
+
+def test_load_radar_lut(lut_base):
+    """The centroid table, as polars, one row per gate."""
+    lut = load_radar_lut(RADAR, lut_base)
+
+    assert isinstance(lut, pl.DataFrame)
+    assert lut.height == N_GATES
+    assert {"gate_id", "sweep", "azimuth", "range", "latitude", "longitude", "altitude"} <= set(lut.columns)
+
+
+def test_gate_coordinates_stay_float64(lut_base):
+    """Positions are float64 — precision is a hard requirement, not a preference."""
+    lut = load_radar_lut(RADAR, lut_base)
+
+    for column in ("latitude", "longitude", "altitude", "x", "y", "z"):
+        if column in lut.columns:
+            assert lut.schema[column] == pl.Float64, f"{column} lost float64"
+
+
+def test_load_radar_info(lut_base):
+    """The info YAML, as a plain dict."""
+    info = load_radar_info(RADAR, lut_base)
+
+    assert info["radar"] == RADAR
+    assert info["latitude"] == pytest.approx(CH_SITE[1])
+    assert info["longitude"] == pytest.approx(CH_SITE[0])
+
+
+def test_load_radar_info_raises_for_an_unknown_radar(lut_base):
+    """A missing radar is a ``FileNotFoundError``, which callers catch explicitly."""
+    with pytest.raises(FileNotFoundError):
+        load_radar_info("ZZZZ", lut_base)
+
+
+def test_get_full_sweep_index(lut_base):
+    """A pandas MultiIndex — one of the three deliberate pandas seams (xarray needs it)."""
+    import pandas as pd
+
+    index = get_full_sweep_index(load_radar_lut(RADAR, lut_base), sweep=1)
+
+    assert isinstance(index, pd.MultiIndex)
+    assert index.names == ["azimuth", "range"]
+    assert len(index) == N_AZ * N_RNG
+
+
+def test_add_lut_projection(lut_base):
+    """Projected columns are named after the EPSG, so a frame says which one it is in."""
+    lut = load_radar_lut(RADAR, lut_base).drop(["x_2056", "y_2056"])
+
+    out = add_lut_projection(lut, epsg=32632)
+
+    assert {"x_32632", "y_32632"} <= set(out.columns)
+    assert np.isfinite(out["x_32632"].to_numpy()).all()
+
+
+def test_add_lut_projection_returns_the_kind_it_was_given(lut_base):
+    """Same-kind-in-same-kind-out, like ``filter_df``."""
+    import pandas as pd
+
+    lut = load_radar_lut(RADAR, lut_base).drop(["x_2056", "y_2056"])
+
+    assert isinstance(add_lut_projection(lut, epsg=32632), pl.DataFrame)
+    assert isinstance(add_lut_projection(lut.to_pandas(), epsg=32632), pd.DataFrame)
+
+
+def test_gate_polygons_geoarrow(lut_base):
+    """GeoArrow-tagged wedge polygons, for the lonboard path."""
+    pytest.importorskip("pyarrow")
+    gate_ids = load_radar_lut(RADAR, lut_base)["gate_id"].to_numpy()[:50]
+
+    table = gate_polygons_geoarrow(RADAR, lut_base, gate_ids)
+
+    assert table is not None
+    assert len(table) == 50
+
+
+def test_geoarrow_field():
+    """The field metadata is what makes a column readable as geometry."""
+    import pyarrow as pa
+
+    field = geoarrow_field("geometry", pa.float64(), "point", crs="EPSG:2056")
+
+    assert field.name == "geometry"
+    assert field.metadata
diff --git a/raddb/tests/test_lut_planes.py b/raddb/tests/test_lut_planes.py
deleted file mode 100644
index 02fd33d..0000000
--- a/raddb/tests/test_lut_planes.py
+++ /dev/null
@@ -1,433 +0,0 @@
-"""
-raddb/tests/test_lut_planes.py
-------------------------------
-Tests for the five-file LUT directory: the gate-centroid LUT plus the
-horizontal-face, vertical-face and 3-D corner node lattices, and the extended
-info YAML.
-
-Key invariants covered:
-
-1. all five files are written by ``archive()`` / ``generate_lut_from_datatree``
-2. **the frustum property** — a gate's far face is strictly larger than its near
-   face (the beam widens with range)
-3. each gate's centroid lies inside its own horizontal footprint, and between the
-   bottom and top elevation levels
-4. node sharing — the lattice is ``(n_az+1) x (n_rng+1)`` per level
-5. ``z_asl - z_rel == site altitude`` everywhere
-6. **file-size budgets** — the geometry files must stay compact
-
-All tests use synthetic DataTrees in ``tmp_path``; no real radar files needed.
-"""
-from __future__ import annotations
-
-from pathlib import Path
-
-import numpy as np
-import polars as pl
-import pytest
-import shapely
-import yaml
-
-from raddb.lut import (
-    DEFAULT_BEAMWIDTH_DEG,
-    LUT_FILES,
-    gate_corner_table,
-    generate_lut_from_datatree,
-    lut_file_path,
-)
-from raddb.main import RadDB
-from raddb.tests.test_fixes import RADAR, _make_datatree
-
-# The synthetic volume: 12 azimuths x 24 ranges x 2 sweeps (see test_fixes).
-N_AZ, N_RNG, N_SWEEPS = 12, 24, 2
-N_GATES = N_AZ * N_RNG * N_SWEEPS
-
-
-@pytest.fixture
-def lut_dir(tmp_path):
-    """Generate a full 5-file LUT directory and return its path."""
-    generate_lut_from_datatree(
-        _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
-        projection_epsg=2056,
-    )
-    return tmp_path
-
-
-@pytest.fixture
-def base(tmp_path):
-    """The archive base path with a generated LUT (for the accessors)."""
-    generate_lut_from_datatree(
-        _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
-        projection_epsg=2056,
-    )
-    return str(tmp_path)
-
-
-# A realistically-sampled volume: 1 deg azimuth spacing, like a real radar.
-#
-# Needed by two groups of tests:
-#
-# * geometry — a gate footprint is a straight-sided quad, so its outer chord cuts
-#   inside the true arc by the sagitta. The centroid falls outside its own
-#   footprint beyond r ~ dR*cos(h)/(1-cos(h)) where h is the azimuth half-spacing.
-#   At the 30 deg spacing of the small fixture that is only ~11.7 km; at 1 deg it
-#   is ~5400 km, i.e. never. Real radars sample at 1 deg.
-# * file sizes — bytes-per-gate is meaningless on a 576-gate file, where the
-#   fixed parquet footer dominates.
-REAL_N_AZ, REAL_N_RNG, REAL_N_SWEEPS = 360, 200, 2
-REAL_N_GATES = REAL_N_AZ * REAL_N_RNG * REAL_N_SWEEPS
-
-
-@pytest.fixture(scope="module")
-def real_base(tmp_path_factory):
-    """A realistically-sampled LUT (1 deg azimuths), built once per module."""
-    d = tmp_path_factory.mktemp("realistic")
-    generate_lut_from_datatree(
-        _make_datatree(n_az=REAL_N_AZ, n_rng=REAL_N_RNG, n_sweeps=REAL_N_SWEEPS),
-        radar=RADAR, output_base_path=str(d), projection_epsg=2056,
-    )
-    return str(d)
-
-
-@pytest.fixture(scope="module")
-def real_base_plain(tmp_path_factory):
-    """As :func:`real_base` but with no projected coordinate columns."""
-    d = tmp_path_factory.mktemp("realistic_plain")
-    generate_lut_from_datatree(
-        _make_datatree(n_az=REAL_N_AZ, n_rng=REAL_N_RNG, n_sweeps=REAL_N_SWEEPS),
-        radar=RADAR, output_base_path=str(d),
-        projection_epsg=2056,
-    )
-    return str(d)
-
-
-def _face_area(t: pl.DataFrame, ks) -> np.ndarray:
-    """Planar polygon area of a 4-corner face in 3-D (Newell's method)."""
-    pts = np.stack([
-        np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy(),
-                  t[f"z_rel_{k}"].to_numpy()], axis=1)
-        for k in ks
-    ], axis=1)
-    n = np.zeros((pts.shape[0], 3))
-    for i in range(4):
-        n += np.cross(pts[:, i], pts[:, (i + 1) % 4])
-    return 0.5 * np.linalg.norm(n, axis=1)
-
-
-class TestAllFilesWritten:
-    def test_generate_writes_five_files(self, lut_dir):
-        d = lut_dir / RADAR / "LUT"
-        for kind, tmpl in LUT_FILES.items():
-            f = d / tmpl.format(radar=RADAR)
-            assert f.exists(), f"{kind} missing: {f.name}"
-            assert f.stat().st_size > 0
-
-    def test_archive_writes_five_files(self, tmp_path):
-        db = RadDB(archive_dir=str(tmp_path), crs=2056)
-        db.archive(datatree=_make_datatree(), radar=RADAR)
-        d = tmp_path / RADAR / "LUT"
-        assert sorted(f.name for f in d.iterdir()) == sorted(
-            t.format(radar=RADAR) for t in LUT_FILES.values()
-        )
-
-    def test_missing_planes_are_backfilled(self, lut_dir):
-        """An archive predating the lattices regenerates them, keeping its LUT."""
-        d = lut_dir / RADAR / "LUT"
-        lut_file = d / LUT_FILES["lut"].format(radar=RADAR)
-        stamp = lut_file.stat().st_mtime_ns
-        for kind in ("h_plane", "v_plane", "corners"):
-            (d / LUT_FILES[kind].format(radar=RADAR)).unlink()
-
-        generate_lut_from_datatree(
-            _make_datatree(), radar=RADAR, output_base_path=str(lut_dir),
-            projection_epsg=2056,
-        )
-        for kind in ("h_plane", "v_plane", "corners"):
-            assert (d / LUT_FILES[kind].format(radar=RADAR)).exists()
-        # the centroid LUT was not rewritten
-        assert lut_file.stat().st_mtime_ns == stamp
-
-
-class TestInfoYaml:
-    def test_extended_keys(self, lut_dir):
-        info = yaml.safe_load(
-            (lut_dir / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
-        )
-        for key in ("radar", "network", "latitude", "longitude", "altitude",
-                    "crs", "ke", "beamwidth_deg", "n_sweeps", "n_gates", "sweeps"):
-            assert key in info, f"missing info key {key!r}"
-        assert info["ke"] == pytest.approx(4.0 / 3.0)
-        assert info["beamwidth_deg"] == DEFAULT_BEAMWIDTH_DEG
-        assert info["n_gates"] == N_GATES
-        assert info["n_sweeps"] == N_SWEEPS
-        assert info["crs"]["epsg"] == 2056
-        assert info["crs"]["columns"] == ["x_2056", "y_2056"]
-
-    def test_per_sweep_keys(self, lut_dir):
-        info = yaml.safe_load(
-            (lut_dir / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
-        )
-        s = info["sweeps"][1]
-        for key in ("n_azimuths", "n_ranges", "n_gates", "elevation",
-                    "range_resolution", "range_start"):
-            assert key in s, f"missing per-sweep key {key!r}"
-        assert s["n_gates"] == s["n_azimuths"] * s["n_ranges"]
-        # dR, azimuth_scale and azimuths were dropped: the first two were never
-        # read back, and the grid is recovered from the LUT parquet instead.
-        for key in ("dR", "azimuth_scale", "azimuths"):
-            assert key not in s, f"per-sweep key {key!r} should no longer be written"
-
-    def test_crs_block_records_what_was_used(self, real_base):
-        """A CRS is mandatory, so the block is always populated."""
-        info = yaml.safe_load(
-            (Path(real_base) / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text())
-        assert info["crs"]["epsg"] == 2056
-        assert info["crs"]["columns"] == ["x_2056", "y_2056"]
-
-
-
-class TestLatticeShape:
-    def test_h_plane_is_one_node_grid_per_sweep(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        nodes = db.get_h_plane(RADAR)
-        assert nodes.height == N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
-        assert "el_level" not in nodes.columns          # centre level only
-
-    def test_corners_has_two_elevation_levels(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        nodes = db.get_corners(RADAR)
-        assert sorted(nodes["el_level"].unique().to_list()) == [-1, 1]
-        assert nodes.height == 2 * N_SWEEPS * (N_AZ + 1) * (N_RNG + 1)
-
-    def test_sweep_filter(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        one = db.get_h_plane(RADAR, sweep=1)
-        assert one["sweep"].unique().to_list() == [1]
-        assert one.height == (N_AZ + 1) * (N_RNG + 1)
-
-    def test_projected_columns_present(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        assert {"x_2056", "y_2056"} <= set(db.get_h_plane(RADAR).columns)
-
-
-class TestPerGateCorners:
-    def test_h_plane_has_four_corners(self, base):
-        t = RadDB(archive_dir=base, crs=2056).get_h_plane(RADAR, per_gate=True)
-        assert t.height == N_GATES
-        for k in range(1, 5):
-            assert f"x_{k}" in t.columns and f"y_{k}" in t.columns
-        assert "x_5" not in t.columns
-
-    def test_corners_has_eight(self, base):
-        t = RadDB(archive_dir=base, crs=2056).get_corners(RADAR, per_gate=True)
-        assert t.height == N_GATES
-        for k in range(1, 9):
-            assert {f"x_{k}", f"y_{k}", f"z_rel_{k}"} <= set(t.columns)
-        assert "x_9" not in t.columns
-
-    def test_eight_corners_are_distinct(self, base):
-        """8 distinct corners, except the degenerate innermost range bin."""
-        db = RadDB(archive_dir=base, crs=2056)
-        t = db.get_corners(RADAR, per_gate=True)
-        pts = np.stack([
-            np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy(),
-                      t[f"z_rel_{k}"].to_numpy()], axis=1)
-            for k in range(1, 9)
-        ], axis=1)
-        n_distinct = np.array([
-            len({tuple(np.round(p, 3)) for p in pts[i]}) for i in range(pts.shape[0])
-        ])
-        # the first range bin's near face collapses onto the radar -> 5 distinct
-        assert set(np.unique(n_distinct)) <= {5, 8}
-        assert (n_distinct == 8).mean() > 0.9
-
-    def test_gate_ids_match_the_lut(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        lut_ids = set(db.get_lut(RADAR)["gate_id"].to_list())
-        assert set(db.get_corners(RADAR, per_gate=True)["gate_id"].to_list()) == lut_ids
-
-
-class TestFrustumProperty:
-    """The beam widens with range: the far face must exceed the near face."""
-
-    def test_far_face_is_larger_than_near_face(self, base):
-        t = RadDB(archive_dir=base, crs=2056).get_corners(RADAR, per_gate=True)
-        near = _face_area(t, [1, 2, 3, 4])
-        far = _face_area(t, [5, 6, 7, 8])
-        assert np.all(far > near), (
-            f"{int((far <= near).sum())} gate(s) have a far face no larger than "
-            "the near face"
-        )
-
-    def test_ratio_is_physically_sane(self, base):
-        """Excluding the degenerate innermost bin, the ratio stays bounded."""
-        t = RadDB(archive_dir=base, crs=2056).get_corners(RADAR, per_gate=True)
-        near = _face_area(t, [1, 2, 3, 4])
-        far = _face_area(t, [5, 6, 7, 8])
-        ok = near > 1.0                      # drop the r~0 near face
-        ratio = far[ok] / near[ok]
-        assert ratio.min() > 1.0
-        assert ratio.max() < 100.0
-
-    def test_faces_are_valid_polygons(self, base):
-        t = RadDB(archive_dir=base, crs=2056).get_h_plane(RADAR, per_gate=True)
-        ring = np.stack([
-            np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy()], axis=1)
-            for k in (1, 2, 3, 4, 1)
-        ], axis=1)
-        assert shapely.is_valid(shapely.polygons(ring)).all()
-
-
-class TestCentroidContainment:
-    def test_centroid_inside_its_own_footprint(self, real_base):
-        """Needs realistic (1 deg) azimuth sampling — see ``real_base``."""
-        db = RadDB(archive_dir=real_base, crs=2056)
-        t = db.get_h_plane(RADAR, per_gate=True).sort("gate_id")
-        lut = db.get_lut(RADAR).sort("gate_id")
-        ring = np.stack([
-            np.stack([t[f"x_{k}"].to_numpy(), t[f"y_{k}"].to_numpy()], axis=1)
-            for k in (1, 2, 3, 4, 1)
-        ], axis=1)
-        polys = shapely.polygons(ring)
-        pts = shapely.points(
-            np.stack([lut["x"].to_numpy(), lut["y"].to_numpy()], axis=1)
-        )
-        assert shapely.covers(polys, pts).all()
-
-    def test_centroid_between_the_elevation_levels(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        t = db.get_corners(RADAR, per_gate=True).sort("gate_id")
-        lut = db.get_lut(RADAR).sort("gate_id")
-        zc = lut["z"].to_numpy()
-        zs = np.stack([t[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
-        # 1 cm tolerance: on a negative-elevation sweep z(r) has a turning point,
-        # so a centre can sit ~mm outside the bracket of its own corners.
-        assert (zc >= zs.min(axis=1) - 0.01).all()
-        assert (zc <= zs.max(axis=1) + 0.01).all()
-
-
-class TestVPlane:
-    def test_altitude_references_differ_by_site_altitude(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        site_alt = db.get_radar_info(RADAR)["altitude"]
-        nodes = db.get_v_plane(RADAR)
-        d = nodes["z_asl"].to_numpy() - nodes["z_rel"].to_numpy()
-        assert np.allclose(d, site_alt, atol=1e-3)
-
-    def test_ground_distance_is_monotonic_in_range(self, base):
-        nodes = RadDB(archive_dir=base, crs=2056).get_v_plane(RADAR, sweep=1)
-        sub = nodes.filter(pl.col("el_level") == 1).sort(["az_idx", "rng_idx"])
-        d = sub.filter(pl.col("az_idx") == 0)["d"].to_numpy()
-        assert np.all(np.diff(d) > 0)
-
-    def test_per_gate_has_four_corners(self, base):
-        t = RadDB(archive_dir=base, crs=2056).get_v_plane(RADAR, per_gate=True)
-        assert t.height == N_GATES
-        for k in range(1, 5):
-            assert {f"d_{k}", f"z_asl_{k}", f"z_rel_{k}"} <= set(t.columns)
-
-    def test_azimuth_selection_picks_one_ray(self, base):
-        db = RadDB(archive_dir=base, crs=2056)
-        t = db.get_v_plane(RADAR, azimuth=0.0, per_gate=True)
-        assert 0 < t.height < N_GATES
-        assert t.height == N_RNG * N_SWEEPS
-
-    def test_azimuth_without_per_gate_raises(self, base):
-        with pytest.raises(ValueError):
-            RadDB(archive_dir=base, crs=2056).get_v_plane(RADAR, azimuth=90.0)
-
-
-class TestGeoParquetExport:
-    def test_export_embeds_crs_and_is_ccw(self, base, tmp_path):
-        gpd = pytest.importorskip("geopandas")
-        out = tmp_path / "h_plane.parquet"
-        RadDB(archive_dir=base, crs=2056).export_h_plane_geoparquet(RADAR, out)
-        g = gpd.read_parquet(out)
-        assert len(g) == N_GATES
-        assert g.crs is not None and g.crs.to_epsg() == 2056
-        assert g.geometry.is_valid.all()
-        assert shapely.is_ccw(shapely.get_exterior_ring(g.geometry.values)).all()
-
-    def test_export_falls_back_to_wgs84(self, base, tmp_path):
-        gpd = pytest.importorskip("geopandas")
-        out = tmp_path / "h_plane_4326.parquet"
-        RadDB(archive_dir=base, crs=2056).export_h_plane_geoparquet(RADAR, out, epsg=9999)
-        g = gpd.read_parquet(out)
-        assert g.crs.to_epsg() == 4326
-        assert g.geometry.is_valid.all()
-
-
-class TestFileSizeBudget:
-    """The geometry files must stay compact — the whole point of lattices.
-
-    Budgets are bytes per *gate* on disk, measured on the realistically-sampled
-    fixture (144 k gates) so the fixed parquet footer does not dominate.  For
-    reference, radar L (1.72 M gates, 20 sweeps) measures
-    7.4 B/gate for h_plane unprojected, 13.6 projected, 14.8 for corners and
-    0.2 for v_plane — ~49 MB of geometry against a 79 MB centroid LUT.
-
-    ``v_plane`` is startlingly small because ground distance and altitude do not
-    depend on azimuth, so parquet run-length-encodes it almost completely away.
-    """
-
-    BUDGET_PROJECTED = {"h_plane": 18.0, "v_plane": 2.0, "corners": 20.0}
-    BUDGET_PLAIN = {"h_plane": 10.0, "v_plane": 2.0, "corners": 20.0}
-
-    def _sizes(self, base):
-        return {
-            kind: lut_file_path(RADAR, kind, base).stat().st_size / REAL_N_GATES
-            for kind in ("h_plane", "v_plane", "corners")
-        }
-
-    def test_projected_budget(self, real_base):
-        for kind, bpg in self._sizes(real_base).items():
-            assert bpg <= self.BUDGET_PROJECTED[kind], (
-                f"{kind} is {bpg:.1f} B/gate, over the "
-                f"{self.BUDGET_PROJECTED[kind]} B/gate budget"
-            )
-
-
-    def test_geometry_stays_smaller_than_the_centroid_lut(self, real_base):
-        """All three geometry files together must not dwarf the LUT itself."""
-        lut = lut_file_path(RADAR, "lut", real_base).stat().st_size
-        geom = sum(
-            lut_file_path(RADAR, k, real_base).stat().st_size
-            for k in ("h_plane", "v_plane", "corners")
-        )
-        assert geom < lut, f"geometry {geom/1e6:.1f} MB vs LUT {lut/1e6:.1f} MB"
-
-    def test_lattice_beats_per_gate_materialisation(self, real_base):
-        """The stored lattice must be smaller than expanding every gate's corners."""
-        db = RadDB(archive_dir=real_base, crs=2056)
-        stored = lut_file_path(RADAR, "corners", real_base).stat().st_size
-        per_gate = db.get_corners(RADAR, per_gate=True)
-        # 8 corners x 3 coords x 4 bytes, the floor for a per-gate layout
-        naive = per_gate.height * 8 * 3 * 4
-        assert stored < naive
-
-
-class TestBeamwidth:
-    def test_beamwidth_parameter_widens_the_gate(self, tmp_path):
-        heights = {}
-        for bw in (1.0, 2.0):
-            d = tmp_path / f"bw{bw}"
-            generate_lut_from_datatree(
-                _make_datatree(), radar=RADAR, output_base_path=str(d),
-                beamwidth_deg=bw,
-                projection_epsg=2056,
-            )
-            t = gate_corner_table(RADAR, str(d), kind="corners", sweep=1)
-            zs = np.stack([t[f"z_rel_{k}"].to_numpy() for k in range(1, 9)], axis=1)
-            heights[bw] = float(np.mean(zs.max(axis=1) - zs.min(axis=1)))
-        assert heights[2.0] > heights[1.0] * 1.5
-
-    def test_beamwidth_recorded_in_yaml(self, tmp_path):
-        generate_lut_from_datatree(
-            _make_datatree(), radar=RADAR, output_base_path=str(tmp_path),
-            beamwidth_deg=1.5,
-            projection_epsg=2056,
-        )
-        info = yaml.safe_load(
-            (tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text()
-        )
-        assert info["beamwidth_deg"] == 1.5
diff --git a/raddb/tests/test_main.py b/raddb/tests/test_main.py
new file mode 100644
index 0000000..9fa73b3
--- /dev/null
+++ b/raddb/tests/test_main.py
@@ -0,0 +1,1281 @@
+"""Tests for :mod:`raddb.main` — the :class:`~raddb.RadDB` class.
+
+``RadDB`` is **one class with two roles**.  *Archive-bound* (``RadDB(archive_dir=...)``)
+gives ``archive()``, ``open()``, ``inventory()`` and the LUT accessors; *data-carrying* is
+what ``open()`` returns, holding a polars frame in ``.data``.  ``filter``/``sel``/``crop_*``/
+``extract_cross_section`` each return a **new** ``RadDB``, so calls chain and the receiver
+is never mutated.
+
+Two contracts run through everything here.
+
+**The dynamic frame carries no geometry.** ``open()`` returns values only; the static LUT
+stays in its own table and is joined on ``gate_id`` by the converters.  A crop
+**selects** rows — it must never widen the frame with LUT columns.
+
+**A CRS is mandatory to write and never needed to read.** There is no default, because a
+wrong projection is silently wrong: EPSG:2056 outside Switzerland mis-measures distance
+by ~20% and the resulting crops look entirely normal.
+"""
+
+from __future__ import annotations
+
+import datetime
+
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import shapely
+import xarray as xr
+
+from raddb.main import RadDB, _format_elapsed_time, _format_size, _iter_days, _normalize_time_period
+from raddb.tests.conftest import RADAR, SWISS_EPSG, US_EPSG, US_SITE, build_datatree, relocate
+
+CH_SITE = (7.0, 46.0)
+"""The synthetic fixture's site — ``(longitude, latitude)``."""
+
+VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
+
+LUT_ONLY_COLUMNS = {
+    "latitude",
+    "longitude",
+    "altitude",
+    "x",
+    "y",
+    "z",
+    "x_2056",
+    "y_2056",
+    "azimuth",
+    "range",
+    "elevation_angle",
+}
+"""Columns that belong to the static LUT and must never appear in a dynamic frame."""
+
+
+@pytest.fixture
+def two_volume_rdb(tmp_path):
+    """A data-carrying RadDB over a two-volume, one-radar archive."""
+    db = RadDB(archive_dir=str(tmp_path / "arch"), crs=SWISS_EPSG)
+    db.archive(datatree={str(t): build_datatree(vol_time=t) for t in VOL_TIMES}, radar=RADAR)
+    return db.open(radars=RADAR)
+
+
+@pytest.fixture
+def datatree_dir(tmp_path):
+    """A directory of saved DataTree files, not archived."""
+    pytest.importorskip("netCDF4")
+    directory = tmp_path / "datatrees"
+    directory.mkdir()
+    for when in VOL_TIMES:
+        build_datatree(vol_time=when).to_netcdf(directory / f"{RADAR}_{when:%Y%m%d_%H%M%S}.nc")
+    return directory
+
+
+def _site_xy(db):
+    """The radar site in the archive's own projected metres."""
+    from raddb.aoi import _reproject_to_aoi
+
+    info = db.get_radar_info(RADAR)
+    point = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, SWISS_EPSG)
+    return (point.x, point.y)
+
+
+# ---------------------------------------------------------------------------
+# Construction and the dual role
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB():
+    """An archive-bound instance carries no data until ``open()`` is called."""
+    db = RadDB(archive_dir="/some/where", crs=SWISS_EPSG)
+
+    assert db.archive_dir is not None
+    with pytest.raises(ValueError):
+        _ = db.data
+
+
+def test_RadDB_init(archive_dir):
+    """The two constructor arguments are remembered; ``network`` is metadata only."""
+    db = RadDB(archive_dir=str(archive_dir), crs=SWISS_EPSG, network="MeteoSwiss")
+
+    assert str(db.archive_dir) == str(archive_dir)
+    assert db.crs().to_epsg() == SWISS_EPSG
+
+
+def test_a_bare_instance_needs_an_archive_dir_to_read():
+    """Every archive-bound method says which argument is missing."""
+    with pytest.raises(ValueError):
+        RadDB().list_radars()
+
+
+def test_RadDB_data(rdb):
+    """``.data`` is the polars frame the object carries."""
+    assert isinstance(rdb.data, pl.DataFrame)
+    assert "gate_id" in rdb.data.columns
+
+
+def test_RadDB_len(rdb):
+    """Length is the gate count."""
+    assert len(rdb) == rdb.data.height > 0
+
+
+def test_RadDB_repr(rdb, db):
+    """The repr distinguishes the two roles at a glance."""
+    assert "gates" in repr(rdb)
+    assert repr(db)
+
+
+def test_the_dynamic_frame_carries_no_lut_columns(rdb):
+    """``open()`` returns dynamic values only — geometry stays in its own table."""
+    leaked = LUT_ONLY_COLUMNS & set(rdb.columns())
+
+    assert not leaked, f"LUT columns leaked into the dynamic frame: {leaked}"
+    assert {"latitude", "longitude"} <= set(rdb.to_pandas(with_geometry=True).columns)
+
+
+# ---------------------------------------------------------------------------
+# archive
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_archive(tmp_path, datatree):
+    """One in-memory volume becomes a LUT plus one POL parquet."""
+    out = tmp_path / "arch"
+
+    result = RadDB(archive_dir=str(out), crs=SWISS_EPSG).archive(datatree=datatree, radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"]) == (1, 0)
+    assert (out / RADAR / "LUT" / f"{RADAR}_LUT.parquet").exists()
+    assert len(list((out / RADAR).rglob("*_POL.parquet"))) == 1
+
+
+def test_archive_from_a_directory_of_datatrees(tmp_path, datatree_dir):
+    """``datatree_dir=`` walks the directory and archives every volume it finds."""
+    out = tmp_path / "arch"
+    db = RadDB(archive_dir=str(out), crs=SWISS_EPSG)
+
+    result = db.archive(datatree_dir=datatree_dir, radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"]) == (2, 0)
+    assert (db.open(radars=RADAR).data["DBZH"] > 0.0).all()
+
+
+def test_archive_resumes_from_its_checkpoint(tmp_path, datatree_dir):
+    """A second run re-attempts nothing, which is what makes a 530-volume run resumable."""
+    db = RadDB(archive_dir=str(tmp_path / "arch"), crs=SWISS_EPSG)
+
+    assert db.archive(datatree_dir=datatree_dir, radar=RADAR)["n_archived"] == 2
+    assert db.archive(datatree_dir=datatree_dir, radar=RADAR)["n_archived"] == 0
+
+
+def test_archive_honours_a_time_period(tmp_path, datatree_dir):
+    """Only volumes whose filename timestamp falls in the window are read."""
+    result = RadDB(archive_dir=str(tmp_path / "arch"), crs=SWISS_EPSG).archive(
+        datatree_dir=datatree_dir, radar=RADAR, time_period=("2024-08-01 00:00", "2024-08-01 23:59")
+    )
+
+    assert result["n_archived"] == 1
+
+
+def test_archive_infers_the_radar_from_the_filename(tmp_path, datatree_dir):
+    """``radar=None`` reads the name off each file, so a mixed directory works."""
+    db = RadDB(archive_dir=str(tmp_path / "arch"), crs=SWISS_EPSG)
+
+    db.archive(datatree_dir=datatree_dir)
+
+    assert db.list_radars() == [RADAR]
+
+
+def test_archive_keeps_a_four_letter_radar_name(tmp_path, datatree):
+    """A NEXRAD-style name survives whole; it used to collapse to its last letter."""
+    pytest.importorskip("netCDF4")
+    src = tmp_path / "input"
+    src.mkdir()
+    datatree.to_netcdf(src / "KTLX_20240101_120000.nc")
+    db = RadDB(archive_dir=str(tmp_path / "arch"), crs=SWISS_EPSG)
+
+    result = db.archive(datatree_dir=src)
+
+    assert (result["n_archived"], result["n_failed"]) == (1, 0)
+    assert db.list_radars() == ["KTLX"]
+
+
+def test_archive_skips_an_unusable_radar_name(tmp_path, datatree):
+    """``OVERLONG`` is not filed under ``N`` — two sites would overwrite each other."""
+    pytest.importorskip("netCDF4")
+    src = tmp_path / "input"
+    src.mkdir()
+    datatree.to_netcdf(src / "OVERLONG_20240101_120000.nc")
+    out = tmp_path / "arch"
+
+    result = RadDB(archive_dir=str(out), crs=SWISS_EPSG).archive(datatree_dir=src)
+
+    assert result["n_archived"] == 0
+    assert not (out / "N").exists()
+
+
+def test_archive_needs_exactly_one_source(tmp_path):
+    """``datatree_dir`` and ``datatree`` are alternatives, not a pair."""
+    with pytest.raises(ValueError, match="exactly one"):
+        RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(datatree_dir=tmp_path, datatree=object())
+
+
+def test_archive_requires_a_crs(tmp_path, datatree):
+    """A wrong projection is silently wrong, so there is no default."""
+    with pytest.raises(ValueError, match="requires a CRS"):
+        RadDB(archive_dir=str(tmp_path)).archive(datatree={RADAR: [datatree]})
+
+
+def test_a_rejected_crs_aborts_and_writes_nothing(tmp_path, make_datatree):
+    """It must not leave POL files behind with no usable LUT."""
+    us_volume = relocate(make_datatree(), *US_SITE)
+
+    with pytest.raises(ValueError, match="distorts distance"):
+        RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(datatree={RADAR: [us_volume]})
+
+    assert not list(tmp_path.rglob("*POL.parquet"))
+
+
+def test_a_valid_crs_archives_a_us_radar(tmp_path, make_datatree):
+    """UTM 14N at KTLX, recorded in the info YAML for every later read."""
+    us_volume = relocate(make_datatree(), *US_SITE)
+
+    RadDB(archive_dir=str(tmp_path), crs=US_EPSG).archive(datatree={RADAR: [us_volume]})
+
+    assert RadDB(archive_dir=str(tmp_path)).get_radar_info(RADAR)["crs"]["epsg"] == US_EPSG
+
+
+def test_an_empty_volume_is_skipped_not_failed(tmp_path, make_datatree):
+    """A clear-air volume counts separately from a failure."""
+    blank = make_datatree()
+    for name in blank.children:
+        ds = blank[name].to_dataset()
+        ds["DBZH"] = ds["DBZH"].where(False)
+        blank[name].dataset = ds
+
+    result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(datatree=blank, radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"], result["n_skipped"]) == (0, 0, 1)
+
+
+def test_the_three_counts_sum_to_the_volumes_attempted(tmp_path, make_datatree):
+    """One good volume plus one empty: 1 archived, 0 failed, 1 skipped."""
+    good = make_datatree(vol_time=VOL_TIMES[0])
+    empty = make_datatree(vol_time=VOL_TIMES[1])
+    for name in empty.children:
+        ds = empty[name].to_dataset()
+        ds["DBZH"] = ds["DBZH"].where(False)
+        empty[name].dataset = ds
+
+    result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(datatree=[good, empty], radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"], result["n_skipped"]) == (1, 0, 1)
+    assert sum((result["n_archived"], result["n_failed"], result["n_skipped"])) == 2
+
+
+def test_archive_accepts_a_radar_keyed_dict(tmp_path, make_datatree):
+    """The multi-radar form, ``{radar: [volumes]}``."""
+    result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(
+        datatree={"A": [make_datatree()], "D": [make_datatree()]}
+    )
+
+    assert sorted(result["radars"]) == ["A", "D"]
+
+
+# ---------------------------------------------------------------------------
+# LUT accessors
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_get_lut(db):
+    """The centroid table for one radar, as polars."""
+    lut = db.get_lut(RADAR)
+
+    assert isinstance(lut, pl.DataFrame)
+    assert lut.height == 12 * 24 * 2
+
+
+def test_RadDB_get_radar_info(db):
+    """The info YAML as a dict, including the CRS block."""
+    info = db.get_radar_info(RADAR)
+
+    assert info["radar"] == RADAR
+    assert info["crs"]["epsg"] == SWISS_EPSG
+
+
+def test_RadDB_add_lut_projection(db):
+    """Adds an ``x_<epsg>``/``y_<epsg>`` pair the archive does not already store."""
+    projected = db.add_lut_projection(RADAR, epsg=32632)
+
+    assert {"x_32632", "y_32632"} <= set(projected.columns)
+
+
+def test_RadDB_get_h_plane(db):
+    """The compact node lattice: one ``(n_az+1) x (n_rng+1)`` grid per sweep."""
+    nodes = db.get_h_plane(RADAR)
+
+    assert nodes.height == 2 * 13 * 25
+    assert "el_level" not in nodes.columns
+
+
+def test_get_h_plane_expands_to_four_corners_per_gate(db):
+    """``per_gate=True`` is what the PPI draws."""
+    table = db.get_h_plane(RADAR, per_gate=True)
+
+    assert table.height == 12 * 24 * 2
+    for k in range(1, 5):
+        assert {f"x_{k}", f"y_{k}"} <= set(table.columns)
+    assert "x_5" not in table.columns
+
+
+def test_get_h_plane_filters_by_sweep(db):
+    """One sweep costs one sweep's worth of rows."""
+    nodes = db.get_h_plane(RADAR, sweep=1)
+
+    assert nodes["sweep"].unique().to_list() == [1]
+    assert nodes.height == 13 * 25
+
+
+def test_RadDB_get_v_plane(db):
+    """Ground distance and both altitude references, per node."""
+    nodes = db.get_v_plane(RADAR)
+    site_altitude = db.get_radar_info(RADAR)["altitude"]
+
+    assert np.allclose(nodes["z_asl"].to_numpy() - nodes["z_rel"].to_numpy(), site_altitude, atol=1e-3)
+
+
+def test_get_v_plane_expands_to_rhi_quads(db):
+    """``(d, z_asl, z_rel)`` quads — one RHI ray's worth of geometry."""
+    table = db.get_v_plane(RADAR, per_gate=True)
+
+    assert table.height == 12 * 24 * 2
+    for k in range(1, 5):
+        assert {f"d_{k}", f"z_asl_{k}", f"z_rel_{k}"} <= set(table.columns)
+
+
+def test_get_v_plane_azimuth_selects_one_ray(db):
+    """A single azimuth across every sweep, which is what an RHI needs."""
+    table = db.get_v_plane(RADAR, azimuth=0.0, per_gate=True)
+
+    assert table.height == 24 * 2
+
+
+def test_get_v_plane_azimuth_requires_per_gate(db):
+    """A node lattice has no azimuth to select on; the gates do."""
+    with pytest.raises(ValueError):
+        db.get_v_plane(RADAR, azimuth=90.0)
+
+
+def test_RadDB_get_corners(db):
+    """Eight corners per gate, from the two-level 3-D lattice."""
+    table = db.get_corners(RADAR, per_gate=True)
+
+    assert table.height == 12 * 24 * 2
+    for k in range(1, 9):
+        assert {f"x_{k}", f"y_{k}", f"z_rel_{k}"} <= set(table.columns)
+    assert "x_9" not in table.columns
+
+
+def test_get_corners_returns_the_lattice_by_default(db):
+    """Both elevation levels, not yet expanded."""
+    nodes = db.get_corners(RADAR)
+
+    assert sorted(nodes["el_level"].unique().to_list()) == [-1, 1]
+
+
+def test_RadDB_export_h_plane_geoparquet(db, tmp_path):
+    """Opt-in GeoParquet with an embedded CRS, for QGIS."""
+    gpd = pytest.importorskip("geopandas")
+    out = tmp_path / "h_plane.parquet"
+
+    db.export_h_plane_geoparquet(RADAR, out)
+
+    gdf = gpd.read_parquet(out)
+    assert len(gdf) == 12 * 24 * 2
+    assert gdf.crs is not None and gdf.crs.to_epsg() == SWISS_EPSG
+    assert gdf.geometry.is_valid.all()
+    # Corner order is clockwise in storage; GeoParquet prefers counter-clockwise.
+    assert shapely.is_ccw(shapely.get_exterior_ring(gdf.geometry.values)).all()
+
+
+def test_export_h_plane_geoparquet_falls_back_to_wgs84(db, tmp_path):
+    """An EPSG the LUT does not carry falls back rather than writing an unlabelled file."""
+    gpd = pytest.importorskip("geopandas")
+    out = tmp_path / "h_plane_4326.parquet"
+
+    db.export_h_plane_geoparquet(RADAR, out, epsg=9999)
+
+    assert gpd.read_parquet(out).crs.to_epsg() == 4326
+
+
+# ---------------------------------------------------------------------------
+# list_radars / inventory
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_list_radars(archive_dir_two_radars):
+    """The radars an archive holds, sorted."""
+    assert RadDB(archive_dir=str(archive_dir_two_radars)).list_radars() == ["A", "D"]
+
+
+def test_list_radars_on_an_empty_archive(tmp_path):
+    """Nothing archived is an empty list, not an error."""
+    assert RadDB(archive_dir=str(tmp_path)).list_radars() == []
+
+
+def test_RadDB_inventory(two_volume_rdb, capsys):
+    """Prints an overview and returns nothing."""
+    db = RadDB(archive_dir=str(two_volume_rdb.archive_dir))
+
+    assert db.inventory() is None
+
+    out = capsys.readouterr().out
+    assert "archived data" in out
+    assert f"\n  {RADAR} " in out
+    assert "2024-08-01 12:00:00 .. 2024-08-02 06:30:00" in out
+
+
+def test_inventory_detailed_adds_lut_columns_and_days(two_volume_rdb, capsys):
+    """``detailed=True`` shows the LUT shape and the per-volume moment columns."""
+    RadDB(archive_dir=str(two_volume_rdb.archive_dir)).inventory(detailed=True)
+
+    out = capsys.readouterr().out
+    assert "LUT:" in out and "sweeps" in out
+    assert "DBZH" in out
+    assert "2024-08-01" in out and "2024-08-02" in out
+
+
+def test_inventory_on_an_empty_archive(tmp_path, capsys):
+    """Says so rather than printing an empty table."""
+    RadDB(archive_dir=str(tmp_path)).inventory()
+
+    assert "nothing archived here yet" in capsys.readouterr().out
+
+
+def test_inventory_without_an_archive_dir_raises():
+    """There is nothing to inventory."""
+    with pytest.raises(ValueError):
+        RadDB().inventory()
+
+
+def test_inventory_of_a_datatree_directory(datatree_dir, capsys):
+    """The input side: files on disk that have not been archived yet."""
+    RadDB().inventory(datatree_dir=str(datatree_dir))
+
+    out = capsys.readouterr().out
+    assert "not archived yet" in out
+    assert "files     : 2" in out
+    assert "2024-08-01 12:00:00 .. 2024-08-02 06:30:00" in out
+
+
+def test_inventory_does_not_warn_about_a_four_letter_radar(tmp_path, datatree, capsys):
+    """A NEXRAD-style name is archivable, so there is nothing to warn about."""
+    pytest.importorskip("netCDF4")
+    directory = tmp_path / "nexrad"
+    directory.mkdir()
+    datatree.to_netcdf(directory / "KTLX_20240801_120000.nc")
+
+    RadDB().inventory(datatree_dir=str(directory), detailed=True)
+
+    out = capsys.readouterr().out
+    assert "KTLX" in out
+    assert "not a usable radar name" not in out
+
+
+def test_inventory_warns_about_an_unusable_radar_name(tmp_path, datatree, capsys):
+    """Better to say so up front than to archive nothing and report success."""
+    pytest.importorskip("netCDF4")
+    directory = tmp_path / "odd"
+    directory.mkdir()
+    datatree.to_netcdf(directory / "OVERLONG_20240801_120000.nc")
+
+    RadDB().inventory(datatree_dir=str(directory), detailed=True)
+
+    out = capsys.readouterr().out
+    assert "OVERLONG" in out and "not a usable radar name" in out
+
+
+def test_inventory_of_a_missing_directory_raises(tmp_path):
+    """A path typo must not read as "no data"."""
+    with pytest.raises(FileNotFoundError):
+        RadDB().inventory(datatree_dir=str(tmp_path / "nope"))
+
+
+def test_inventory_of_an_empty_directory(tmp_path, capsys):
+    """No volumes is reported, not raised."""
+    RadDB().inventory(datatree_dir=str(tmp_path))
+
+    assert "no .zarr / .nc" in capsys.readouterr().out
+
+
+# ---------------------------------------------------------------------------
+# open
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_open(db):
+    """Returns a data-carrying RadDB over the whole archive."""
+    rdf = db.open(radars=RADAR)
+
+    assert isinstance(rdf, RadDB)
+    assert len(rdf) > 0
+    assert rdf.radars() == [RADAR]
+
+
+def test_open_needs_no_crs(archive_dir):
+    """Reading never restates the CRS; the archive records the one it was written with."""
+    plain = RadDB(archive_dir=str(archive_dir)).open(radars=RADAR)
+
+    assert plain._crs is None
+    assert plain.crs().to_epsg() == SWISS_EPSG
+
+
+def test_open_narrows_by_time_period(two_volume_rdb):
+    """A window selects whole volumes."""
+    db = RadDB(archive_dir=str(two_volume_rdb.archive_dir))
+
+    first = db.open(radars=RADAR, time_period=("2024-08-01", "2024-08-01 23:59"))
+
+    assert 0 < len(first) < len(two_volume_rdb)
+
+
+def test_open_selects_columns(db):
+    """``columns=`` is pushed into the parquet reader."""
+    rdf = db.open(radars=RADAR, columns=["gate_id", "DBZH"])
+
+    assert "ZDR" not in rdf.columns()
+
+
+def test_open_applies_filters(db):
+    """``filters=`` runs at read time, so filtered rows are never materialised."""
+    rdf = db.open(radars=RADAR, filters={"var": "DBZH", "logic": ">", "threshold": 20})
+
+    assert rdf.data["DBZH"].min() > 20
+
+
+def test_open_spans_several_radars(archive_dir_two_radars):
+    """No ``radars=`` means every radar in the archive."""
+    rdf = RadDB(archive_dir=str(archive_dir_two_radars)).open()
+
+    assert sorted(rdf.radars()) == ["A", "D"]
+
+
+# ---------------------------------------------------------------------------
+# filter
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_filter(rdb):
+    """Rows failing the comparison are dropped; a new RadDB comes back."""
+    out = rdb.filter({"var": "DBZH", "logic": ">", "threshold": 10})
+
+    assert out is not rdb
+    assert 0 < len(out) < len(rdb)
+    assert out.data["DBZH"].min() > 10
+
+
+def test_filter_ands_a_list_of_dicts(rdb):
+    """A list is ANDed, which is how a band is expressed."""
+    band = rdb.filter([{"var": "DBZH", "logic": ">", "threshold": 10}, {"var": "DBZH", "logic": "<", "threshold": 20}])
+
+    values = band.data["DBZH"].to_numpy()
+    assert ((values > 10) & (values < 20)).all()
+
+
+def test_filter_rejects_a_misspelt_key(rdb):
+    """``value`` is not a filter key; ``threshold`` would default to 0 and keep every row."""
+    with pytest.raises(KeyError, match="unknown filter key"):
+        rdb.filter({"var": "DBZH", "logic": ">", "value": 10})
+
+
+def test_filter_rejects_an_unknown_column(rdb):
+    """Neither a dynamic column nor a LUT one."""
+    with pytest.raises(KeyError, match="cannot filter on"):
+        rdb.filter({"var": "nope", "logic": ">", "threshold": 0})
+
+
+def test_filter_can_use_a_lut_column_without_leaking_it(rdb):
+    """Vertical subsetting borrows ``altitude`` from the LUT, then drops it again."""
+    assert "altitude" not in rdb.columns(), "altitude is LUT data, not dynamic"
+    cut = float(rdb.to_pandas(with_geometry=True)["altitude"].median())
+
+    band = rdb.filter({"var": "altitude", "logic": ">", "threshold": cut})
+
+    assert 0 < len(band) < len(rdb)
+    assert band.columns() == rdb.columns(), "the borrowed LUT column leaked"
+    assert band.to_pandas(with_geometry=True)["altitude"].min() > cut
+
+
+# ---------------------------------------------------------------------------
+# sel
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_sel(rdb):
+    """xarray-style label selection; slice bounds are inclusive on both ends."""
+    out = rdb.sel(DBZH=slice(5, 15))
+    values = out.data["DBZH"].to_numpy()
+
+    assert len(out) > 0
+    assert values.min() >= 5.0 and values.max() <= 15.0
+
+
+def test_open_ended_slices_match_filter(rdb):
+    """``slice(10, None)`` is ``>= 10``, and ``slice(None, 10)`` is ``<= 10``."""
+    assert len(rdb.sel(DBZH=slice(10, None))) == len(rdb.filter({"var": "DBZH", "logic": ">=", "threshold": 10}))
+    assert len(rdb.sel(DBZH=slice(None, 10))) == len(rdb.filter({"var": "DBZH", "logic": "<=", "threshold": 10}))
+
+
+def test_sel_with_no_arguments_is_a_no_op(rdb):
+    """Nothing selected, nothing dropped."""
+    assert len(rdb.sel()) == len(rdb)
+
+
+def test_sel_keywords_are_anded(rdb):
+    """Two indexers in one call equal two chained calls."""
+    assert len(rdb.sel(DBZH=slice(10, None), ZDR=slice(None, 5))) == len(
+        rdb.sel(DBZH=slice(10, None)).sel(ZDR=slice(None, 5))
+    )
+
+
+def test_sel_on_a_partial_time_string(two_volume_rdb):
+    """``"2024-08-01"`` selects the whole day, as xarray does."""
+    assert 0 < len(two_volume_rdb.sel(time="2024-08-01")) < len(two_volume_rdb)
+    assert len(two_volume_rdb.sel(time="2024-08")) == len(two_volume_rdb)
+    assert len(two_volume_rdb.sel(time="1999-01")) == 0
+
+
+def test_sel_on_a_lut_column_does_not_leak_it(rdb):
+    """The borrowed static column is evaluated, then dropped again."""
+    out = rdb.sel(range=slice(2_000, 10_000))
+
+    assert 0 < len(out) < len(rdb)
+    assert out.columns() == rdb.columns()
+    assert "range" not in out.columns()
+
+
+def test_sel_on_range_matches_the_lut(rdb, db):
+    """The selection is exactly what the LUT says, intersected with what is held."""
+    lut = db.get_lut(RADAR)
+    want = set(lut.filter((pl.col("range") >= 2_000) & (pl.col("range") <= 10_000))["gate_id"].to_list())
+
+    got = set(rdb.sel(range=slice(2_000, 10_000)).data["gate_id"].to_list())
+
+    assert got == set(rdb.data["gate_id"].to_list()) & want
+
+
+def test_sel_accepts_lat_lon_aliases(rdb):
+    """``lon``/``lat`` name the geographic columns; the full extent keeps everything."""
+    extent = rdb.geographic_extent()
+
+    out = rdb.sel(lon=slice(extent[0], extent[1]), lat=slice(extent[2], extent[3]))
+
+    assert len(out) == len(rdb)
+    assert out.columns() == rdb.columns()
+
+
+def test_sel_mixes_static_and_dynamic_columns(rdb):
+    """One call may span both tables."""
+    out = rdb.sel(DBZH=slice(10, None), range=slice(2_000, 10_000), sweep=1)
+
+    assert out.columns() == rdb.columns()
+    assert len(out) <= len(rdb)
+
+
+def test_sel_on_radars(archive_dir_two_radars):
+    """``radars=`` narrows a multi-radar frame."""
+    both = RadDB(archive_dir=str(archive_dir_two_radars)).open()
+
+    only_a = both.sel(radars=["A"])
+
+    assert only_a.radars() == ["A"]
+    assert 0 < len(only_a) < len(both)
+
+
+def test_sel_keeps_the_lut_synchronised(rdb):
+    """The geometry must shrink with the data, gate for gate."""
+    out = rdb.sel(range=slice(2_000, 10_000), DBZH=slice(10, None))
+
+    geometry = out._gate_geometry()
+    assert len(geometry) < len(rdb._gate_geometry())
+    assert set(geometry["gate_id"].to_list()) == set(out.data["gate_id"].to_list())
+
+
+def test_sel_never_mutates_the_receiver(rdb):
+    """Every selection returns a new object; chaining must be safe."""
+    before_len, before_columns = len(rdb), rdb.columns()
+
+    out = rdb.sel(DBZH=slice(0, 1), range=slice(2_000, 3_000), sweep=1)
+
+    assert (len(rdb), rdb.columns()) == (before_len, before_columns)
+    assert out is not rdb
+    assert out.crs() == rdb.crs()
+    assert str(out.archive_dir) == str(rdb.archive_dir)
+
+
+def test_sel_rejects_an_unknown_column(rdb):
+    """Neither dynamic nor static."""
+    with pytest.raises(KeyError):
+        rdb.sel(NOT_A_COLUMN=1)
+
+
+def test_sel_rejects_a_slice_step(rdb):
+    """A step has no meaning on unordered label selection."""
+    with pytest.raises(ValueError):
+        rdb.sel(DBZH=slice(0, 10, 2))
+
+
+# ---------------------------------------------------------------------------
+# add_feature
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_add_feature(rdb):
+    """A derived column is appended and a new RadDB comes back."""
+    out = rdb.add_feature("Z_lin", lambda df: 10 ** (df["DBZH"].to_numpy() / 10.0))
+
+    assert "Z_lin" in out.columns()
+    assert "Z_lin" not in rdb.columns(), "add_feature must not mutate the receiver"
+    assert len(out) == len(rdb)
+
+
+# ---------------------------------------------------------------------------
+# Converters
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_to_pandas(rdb):
+    """The intentional user-facing converter; geometry is opt-in."""
+    plain = rdb.to_pandas()
+
+    assert isinstance(plain, pd.DataFrame)
+    assert len(plain) == len(rdb)
+    assert "latitude" not in plain.columns
+
+
+def test_to_pandas_with_geometry_joins_the_lut(rdb):
+    """Cartesian position and sweep come from the LUT on request."""
+    with_geometry = rdb.to_pandas(with_geometry=True)
+
+    assert {"latitude", "longitude", "altitude", "sweep", "x_2056", "y_2056"} <= set(with_geometry.columns)
+    assert len(with_geometry) == len(rdb)
+
+
+@pytest.mark.parametrize("column", ["range", "azimuth", "elevation_angle"])
+def test_polar_coordinates_are_opt_in(rdb, column):
+    """They duplicate what the Cartesian columns already say, unless you work in polar."""
+    assert column not in rdb.to_pandas(with_geometry=True).columns
+    assert column in rdb.to_pandas(with_polar_coords=True).columns
+
+
+def test_with_polar_coords_implies_with_geometry(rdb):
+    """Both come from the same LUT join."""
+    assert "latitude" in rdb.to_pandas(with_polar_coords=True).columns
+
+
+def test_polar_coordinate_values_match_the_lut(rdb, db):
+    """They are read off, not recomputed."""
+    pdf = rdb.to_pandas(with_polar_coords=True)
+    reference = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
+        db.get_lut(RADAR).select(["gate_id", "range", "azimuth", "elevation_angle"]),
+        on="gate_id",
+        how="left",
+        maintain_order="left",
+    )
+
+    for column in ("range", "azimuth", "elevation_angle"):
+        assert np.allclose(pdf[column].to_numpy(), reference[column].to_numpy())
+
+
+def test_RadDB_to_geopandas(rdb):
+    """A GeoDataFrame of gate centroids, in the archive's own CRS."""
+    gpd = pytest.importorskip("geopandas")
+
+    gdf = rdb.to_geopandas()
+
+    assert isinstance(gdf, gpd.GeoDataFrame)
+    assert len(gdf) == len(rdb)
+    assert gdf.geometry.notna().all()
+
+
+def test_to_geopandas_takes_polar_coordinates_too(rdb):
+    """The same opt-in as ``to_pandas``."""
+    pytest.importorskip("geopandas")
+
+    assert "azimuth" in rdb.to_geopandas(with_polar_coords=True).columns
+
+
+def test_RadDB_to_geoarrow(rdb):
+    """Point geometry, tagged so lonboard reads it as geometry."""
+    pytest.importorskip("pyarrow")
+
+    table = rdb.to_geoarrow(geometry="point")
+
+    metadata = table.schema.field("geometry").metadata
+    assert metadata[b"ARROW:extension:name"] == b"geoarrow.point"
+    assert b"EPSG:4326" in metadata[b"ARROW:extension:metadata"]
+
+
+def test_to_geoarrow_polygons_are_closed_wedges(rdb):
+    """One ring per gate: four corners plus the closing vertex."""
+    pytest.importorskip("pyarrow")
+
+    table = rdb.to_geoarrow(geometry="polygon")
+
+    assert table.schema.field("geometry").metadata[b"ARROW:extension:name"] == b"geoarrow.polygon"
+    rings = table.column("geometry").to_pylist()
+    assert rings and all(r is not None for r in rings), "every gate should be placed"
+    for ring in rings:
+        assert len(ring) == 1, "a gate is a single ring"
+        assert len(ring[0]) == 5, "4 corners + closing vertex"
+        assert ring[0][0] == ring[0][-1], "ring must be closed"
+
+
+def test_to_geoarrow_wedges_surround_their_own_centroid(rdb, db):
+    """The polygon must contain the LUT centroid it was built from."""
+    pytest.importorskip("pyarrow")
+
+    table = rdb.to_geoarrow(geometry="polygon")
+    polygons = shapely.polygons(np.array([r[0] for r in table.column("geometry").to_pylist()]))
+    assert shapely.is_valid(polygons).all()
+
+    reference = pl.DataFrame({"gate_id": table.column("gate_id").to_numpy()}).join(
+        db.get_lut(RADAR).select("gate_id", "latitude", "longitude"), on="gate_id", how="left"
+    )
+    centres = shapely.centroid(polygons)
+    dx = (shapely.get_x(centres) - reference["longitude"].to_numpy()) * 111_320 * np.cos(np.radians(46.0))
+    dy = (shapely.get_y(centres) - reference["latitude"].to_numpy()) * 111_320
+
+    assert np.hypot(dx, dy).max() < 20_000 / 24  # one gate length
+
+
+def test_to_geoarrow_has_a_row_guardrail(rdb):
+    """A full archive would blow up a browser; the limit is explicit and overridable."""
+    pytest.importorskip("pyarrow")
+
+    with pytest.raises(ValueError, match="max_rows"):
+        rdb.to_geoarrow(max_rows=1)
+    assert rdb.to_geoarrow(max_rows=None).num_rows == len(rdb)
+
+
+def test_to_geoarrow_polygons_need_a_single_radar(rdb):
+    """Corner arrays are per radar, so a mixed frame is refused."""
+    pytest.importorskip("pyarrow")
+    mixed = rdb._derive(pl.concat([rdb.data, rdb.data.with_columns(pl.lit("B").alias("radar"))]))
+
+    with pytest.raises(ValueError, match="single radar"):
+        mixed.to_geoarrow(geometry="polygon", max_rows=None)
+
+
+def test_RadDB_to_datatree(rdb):
+    """Back to xarray, reindexed onto the full azimuth x range grid."""
+    dt = rdb.to_datatree()
+
+    from raddb.helper import list_sweep_names
+
+    assert isinstance(dt, xr.DataTree)
+    assert list_sweep_names(dt) == ["sweep_1", "sweep_2"]
+
+
+def test_to_datatree_needs_the_radar_named_when_several_are_held(archive_dir_two_radars):
+    """One tree describes one radar."""
+    both = RadDB(archive_dir=str(archive_dir_two_radars)).open()
+
+    assert both.to_datatree(radar="A") is not None
+
+
+# ---------------------------------------------------------------------------
+# Inspection helpers
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_head(rdb):
+    """A polars frame, not a RadDB — this is for looking, not chaining."""
+    assert isinstance(rdb.head(3), pl.DataFrame)
+    assert rdb.head(3).height == 3
+
+
+def test_RadDB_tail(rdb):
+    """The other end of the same frame."""
+    assert rdb.tail(3).height == 3
+    # Compared on gate_id: a row tuple holds NaN moments, and NaN != NaN.
+    assert rdb.tail(3)["gate_id"].to_list() == rdb.data["gate_id"].to_list()[-3:]
+
+
+def test_RadDB_columns(rdb):
+    """The dynamic columns the frame holds."""
+    assert "DBZH" in rdb.columns()
+    assert rdb.columns() == rdb.data.columns
+
+
+def test_RadDB_radars(rdb):
+    """Decoded from the ``gate_id`` values, so it needs no registry."""
+    assert rdb.radars() == [RADAR]
+
+
+def test_RadDB_start_time(two_volume_rdb):
+    """The earliest volume time held."""
+    assert isinstance(two_volume_rdb.start_time(), datetime.datetime)
+    assert two_volume_rdb.start_time().replace(tzinfo=None) == VOL_TIMES[0]
+
+
+def test_RadDB_end_time(two_volume_rdb):
+    """The latest volume time held."""
+    assert two_volume_rdb.end_time().replace(tzinfo=None) == VOL_TIMES[1]
+
+
+def test_RadDB_extent(rdb):
+    """``[xmin, xmax, ymin, ymax]`` in the archive's projected metres."""
+    extent = rdb.extent()
+
+    assert len(extent) == 4
+    assert extent[0] < extent[1] and extent[2] < extent[3]
+    assert 2.4e6 < extent[0] < 2.9e6
+
+
+def test_RadDB_geographic_extent(rdb):
+    """The same box in degrees, around the synthetic site."""
+    extent = rdb.geographic_extent()
+
+    assert extent[0] < CH_SITE[0] < extent[1]
+    assert extent[2] < CH_SITE[1] < extent[3]
+
+
+def test_RadDB_crs(rdb, archive_dir):
+    """Declared or resolved from the archive — either way it answers."""
+    assert rdb.crs().to_epsg() == SWISS_EPSG
+    assert RadDB(archive_dir=str(archive_dir)).open(radars=RADAR).crs().to_epsg() == SWISS_EPSG
+
+
+def test_RadDB_geographic_crs(rdb):
+    """The lon/lat frame the converters emit."""
+    assert rdb.geographic_crs().to_epsg() == 4326
+
+
+# ---------------------------------------------------------------------------
+# Crops
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_crop_by_bbox(rdb, db):
+    """A bounding box in the archive's own metres selects a strict subset."""
+    cx, cy = _site_xy(db)
+
+    crop = rdb.crop_by_bbox(bounds=(cx - 5000, cy - 5000, cx + 5000, cy + 5000))
+
+    assert 0 < len(crop) < len(rdb)
+    assert crop.columns() == rdb.columns(), f"crop widened the frame: {set(crop.columns()) - set(rdb.columns())}"
+
+
+def test_crop_by_bbox_accepts_an_extent(rdb, db):
+    """``extent=[xmin, xmax, ymin, ymax]`` is the matplotlib ordering."""
+    cx, cy = _site_xy(db)
+
+    by_bounds = rdb.crop_by_bbox(bounds=(cx - 5000, cy - 5000, cx + 5000, cy + 5000))
+    by_extent = rdb.crop_by_bbox(extent=[cx - 5000, cx + 5000, cy - 5000, cy + 5000])
+
+    assert len(by_bounds) == len(by_extent)
+
+
+def test_a_crop_matching_nothing_still_converts(rdb):
+    """Regression: ``pl.concat([])`` on an empty selection."""
+    far = rdb.crop_by_bbox(bounds=(9e6, 9e6, 9e6 + 1000, 9e6 + 1000))
+
+    assert len(far) == 0
+    assert far.to_pandas(with_geometry=True).empty
+
+
+def test_RadDB_crop_by_polygone(rdb, db):
+    """An arbitrary shapely polygon, in the archive's frame."""
+    cx, cy = _site_xy(db)
+
+    crop = rdb.crop_by_polygone(shapely.Point(cx, cy).buffer(5_000))
+
+    assert 0 < len(crop) < len(rdb)
+    assert crop.columns() == rdb.columns()
+
+
+def test_crop_by_polygone_reads_a_geojson_in_its_own_crs(rdb, tmp_path):
+    """A GeoJSON is lon/lat; reading those degrees as metres lands 2600 km away."""
+    import json
+
+    square = {
+        "type": "Polygon",
+        "coordinates": [[[6.9, 45.9], [7.1, 45.9], [7.1, 46.1], [6.9, 46.1], [6.9, 45.9]]],
+    }
+    path = tmp_path / "aoi.geojson"
+    path.write_text(json.dumps(square))
+
+    assert len(rdb.crop_by_polygone(str(path))) > 0
+
+
+def test_RadDB_crop_around_point(rdb, db):
+    """A radius in true metres around a point."""
+    crop = rdb.crop_around_point(_site_xy(db), distance=5_000)
+
+    assert 0 < len(crop) < len(rdb)
+    assert crop.columns() == rdb.columns()
+
+
+def test_crop_around_point_accepts_lonlat(rdb):
+    """``crs=4326`` says the point is in degrees."""
+    assert len(rdb.crop_around_point(CH_SITE, distance=5_000, crs=4326)) > 0
+
+
+def test_an_aoi_crs_override_is_validated(us_archive_dir):
+    """An override still has to be valid where the radar actually is."""
+    rdf = RadDB(archive_dir=str(us_archive_dir)).open(radars=RADAR)
+
+    with pytest.raises(ValueError, match="distorts distance"):
+        rdf.crop_around_point(US_SITE, distance=10_000, crs=4326, aoi_crs=SWISS_EPSG)
+
+
+def test_a_valid_aoi_crs_override_selects_gates(rdb):
+    """A frame the LUT does not store is projected on the fly from latitude/longitude."""
+    crop = rdb.crop_around_point(CH_SITE, distance=10_000, crs=4326, aoi_crs=32632)
+
+    assert len(crop) > 0
+
+
+def test_a_crop_radius_is_true_metres_outside_switzerland(us_archive_dir):
+    """The 17% bug end to end: a "10 km" crop used to reach ~8.3 km at a US radar."""
+    import pyproj
+
+    db = RadDB(archive_dir=str(us_archive_dir))
+    lut = db.get_lut(RADAR)
+    lon, lat = lut["longitude"].to_numpy(), lut["latitude"].to_numpy()
+    _, _, ground = pyproj.Geod(ellps="WGS84").inv(
+        np.full(lon.size, US_SITE[0]), np.full(lat.size, US_SITE[1]), lon, lat
+    )
+    truth = int((ground <= 10_000).sum())
+
+    crop = db.open(radars=RADAR).crop_around_point(US_SITE, distance=10_000, crs=4326)
+
+    assert abs(len(crop) - truth) <= 0.02 * truth
+
+
+def test_a_quicklook_is_framed_on_the_archive(us_archive_dir):
+    """A Swiss-framed view used to push a US AOI off-screen entirely."""
+    import matplotlib.pyplot as plt
+
+    from raddb.aoi import _reproject_to_aoi
+
+    rdf = RadDB(archive_dir=str(us_archive_dir)).open(radars=RADAR)
+    rdf.crop_around_point(US_SITE, distance=20_000, crs=4326, quicklook=True)
+
+    ax = plt.gcf().axes[0]
+    site = _reproject_to_aoi(shapely.Point(*US_SITE), 4326, US_EPSG)
+    assert ax.get_xlim()[0] <= site.x <= ax.get_xlim()[1]
+    assert ax.get_ylim()[0] <= site.y <= ax.get_ylim()[1]
+
+
+def test_RadDB_interactive_crop(rdb):
+    """Returns the ipyleaflet selector; the crop itself happens on Apply."""
+    pytest.importorskip("ipyleaflet")
+    from raddb.viz.interactive import AOISelector
+
+    assert isinstance(rdb.interactive_crop(), AOISelector)
+
+
+# ---------------------------------------------------------------------------
+# extract_cross_section
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_extract_cross_section(rdb, db):
+    """A line cuts a vertical section and attaches ``cs_polygon`` per gate."""
+    cx, cy = _site_xy(db)
+
+    cs = rdb.extract_cross_section((cx - 10_000, cy - 10_000), (cx + 10_000, cy + 10_000))
+
+    assert 0 < len(cs) <= len(rdb)
+    assert "cs_polygon" in cs.data.columns
+
+
+def test_a_cross_section_measures_true_ground_distance(us_archive_dir):
+    """A 91 km section at KTLX in UTM 14N measures to -0.009% of the true geodesic."""
+    import pyproj
+
+    rdf = RadDB(archive_dir=str(us_archive_dir)).open(radars=RADAR)
+    p1 = (US_SITE[0] - 0.2, US_SITE[1])
+    p2 = (US_SITE[0] + 0.2, US_SITE[1])
+    truth = pyproj.Geod(ellps="WGS84").inv(p1[0], p1[1], p2[0], p2[1])[2]
+
+    cs = rdf.extract_cross_section(p1=p1, p2=p2, crs=4326)
+
+    assert cs.data.height > 0, "the section selected no gates"
+    # Read off the gate footprints: ``d_center`` alone sits half a gate short.
+    span = float(shapely.bounds(cs.to_pandas()["cs_polygon"].to_numpy())[:, 2].max())
+    assert abs(span - truth) <= 0.005 * truth, f"section spans {span:,.0f} m, true geodesic {truth:,.0f} m"
+
+
+def test_a_cross_section_quicklook_is_framed_on_the_archive(us_archive_dir):
+    """``quicklook=True`` used to frame a non-Swiss section over Switzerland."""
+    import matplotlib.pyplot as plt
+
+    from raddb.aoi import _reproject_to_aoi
+
+    rdf = RadDB(archive_dir=str(us_archive_dir)).open(radars=RADAR)
+    rdf.extract_cross_section(
+        p1=(US_SITE[0] - 0.2, US_SITE[1]), p2=(US_SITE[0] + 0.2, US_SITE[1]), crs=4326, quicklook=True
+    )
+
+    ax = plt.gcf().axes[0]
+    site = _reproject_to_aoi(shapely.Point(*US_SITE), 4326, US_EPSG)
+    assert ax.get_xlim()[0] <= site.x <= ax.get_xlim()[1]
+    assert ax.get_ylim()[0] <= site.y <= ax.get_ylim()[1]
+
+
+# ---------------------------------------------------------------------------
+# The plot delegations
+# ---------------------------------------------------------------------------
+
+
+def test_RadDB_plot_ppi(plot_rdb):
+    """Delegates to :func:`raddb.viz.plot.plot_ppi` and returns its artist."""
+    from matplotlib.collections import PolyCollection
+
+    assert isinstance(plot_rdb.plot_ppi(sweep=1), PolyCollection)
+
+
+def test_RadDB_plot_rhi(plot_rdb):
+    """One azimuth, stacked across every sweep."""
+    assert len(plot_rdb.plot_rhi(azimuth=0).get_paths()) > 0
+
+
+def test_RadDB_plot_cappi(plot_rdb):
+    """A constant-altitude slice."""
+    assert len(plot_rdb.plot_cappi(altitude=1200).get_paths()) > 0
+
+
+def test_RadDB_plot_vcs(plot_rdb, plot_site):
+    """A vertical cross-section along an arbitrary line."""
+    line = ((plot_site[0] - 12_000, plot_site[1] - 12_000), (plot_site[0] + 12_000, plot_site[1] + 12_000))
+
+    assert len(plot_rdb.plot_vcs(line=line).get_paths()) > 0
+
+
+def test_RadDB_plot_cross_section(plot_rdb, plot_site):
+    """The deprecated alias: it warns and delegates to ``plot_vcs``."""
+    cs = plot_rdb.extract_cross_section(
+        (plot_site[0] - 12_000, plot_site[1] - 12_000), (plot_site[0] + 12_000, plot_site[1] + 12_000)
+    )
+
+    with pytest.deprecated_call():
+        assert len(cs.plot_cross_section().get_paths()) > 0
+
+
+def test_a_plot_composes_through_ax(plot_rdb):
+    """The delegation must forward ``ax=`` so panels still compose."""
+    import matplotlib.pyplot as plt
+
+    _, ax = plt.subplots()
+
+    assert plot_rdb.plot_ppi(sweep=1, ax=ax).axes is ax
+
+
+# ---------------------------------------------------------------------------
+# Module-level helpers
+# ---------------------------------------------------------------------------
+
+
+def test_iter_days():
+    """The archive layout is ``{YYYY}/{MM}/{DD}``, so batching walks whole days."""
+    days = list(_iter_days(pd.Timestamp("2024-08-01"), pd.Timestamp("2024-08-03")))
+
+    assert len(days) == 3
+
+
+def test_format_elapsed_time():
+    """Seconds below a minute, then minutes, then hours."""
+    assert _format_elapsed_time(5) == "5s"
+    assert "m" in _format_elapsed_time(125)
+    assert "h" in _format_elapsed_time(7300)
+
+
+def test_format_size():
+    """Human-readable byte counts for the inventory listing."""
+    assert _format_size(512).endswith("B")
+    assert "MB" in _format_size(5 * 1024**2)
+    assert "GB" in _format_size(3 * 1024**3)
+
+
+def test_normalize_time_period():
+    """A ``(start, end)`` pair becomes tz-aware UTC timestamps."""
+    start, end = _normalize_time_period(("2024-08-01", "2024-08-02"))
+
+    assert start.tzinfo is not None and end.tzinfo is not None
+    assert start < end
+
+
+def test_normalize_time_period_passes_none_through():
+    """No period means no bounds."""
+    assert _normalize_time_period(None) in (None, (None, None))
+
+
+def test_a_volume_with_only_nat_times_is_skipped(tmp_path, make_datatree):
+    """DBZH survives the filter but every ray's time is NaT, so no path can be built."""
+    dt = make_datatree()
+    for name in dt.children:
+        ds = dt[name].to_dataset()
+        ds["time"] = xr.full_like(ds["time"], np.datetime64("NaT"))
+        dt[name].dataset = ds
+
+    result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(datatree=dt, radar=RADAR)
+
+    assert (result["n_archived"], result["n_failed"], result["n_skipped"]) == (0, 0, 1)
+    assert not list((tmp_path / RADAR).rglob("*_POL.parquet"))
+
+
+def test_a_skipped_volume_does_not_poison_the_archive(tmp_path, make_datatree):
+    """The rest of the archive must stay readable."""
+    empty = make_datatree(vol_time=VOL_TIMES[1])
+    for name in empty.children:
+        ds = empty[name].to_dataset()
+        ds["DBZH"] = ds["DBZH"].where(False)
+        empty[name].dataset = ds
+
+    db = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG)
+    db.archive(datatree=make_datatree(vol_time=VOL_TIMES[0]), radar=RADAR)
+    db.archive(datatree=empty, radar=RADAR)
+
+    rdf = RadDB(archive_dir=str(tmp_path)).open(radars=RADAR)
+    assert len(rdf) > 0
+    assert rdf.radars() == [RADAR]
+
+
+def test_the_disk_path_checkpoints_a_skip(tmp_path, make_datatree):
+    """A skipped volume is checkpointed too, so a resume does not retry it forever."""
+    pytest.importorskip("netCDF4")
+    src = tmp_path / "trees"
+    src.mkdir()
+    make_datatree(vol_time=VOL_TIMES[0]).to_netcdf(src / f"{RADAR}_20240801_120000.nc")
+    empty = make_datatree(vol_time=VOL_TIMES[1])
+    for name in empty.children:
+        ds = empty[name].to_dataset()
+        ds["DBZH"] = ds["DBZH"].where(False)
+        empty[name].dataset = ds
+    empty.to_netcdf(src / f"{RADAR}_20240802_063000.nc")
+    arch = tmp_path / "arch"
+
+    first = RadDB(archive_dir=str(arch), crs=SWISS_EPSG).archive(datatree_dir=str(src), radar=RADAR)
+    assert (first["n_archived"], first["n_failed"], first["n_skipped"]) == (1, 0, 1)
+
+    again = RadDB(archive_dir=str(arch), crs=SWISS_EPSG).archive(datatree_dir=str(src), radar=RADAR)
+    assert (again["n_archived"], again["n_failed"], again["n_skipped"]) == (0, 0, 0)
+
+
+def test_the_multi_radar_path_counts_a_skip_separately(tmp_path, make_datatree):
+    """The ``{radar: [volumes]}`` form keeps the three counts apart too."""
+    empty = make_datatree(vol_time=VOL_TIMES[1])
+    for name in empty.children:
+        ds = empty[name].to_dataset()
+        ds["DBZH"] = ds["DBZH"].where(False)
+        empty[name].dataset = ds
+
+    result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(
+        datatree={RADAR: [make_datatree(vol_time=VOL_TIMES[0]), empty], "D": [make_datatree()]}
+    )
+
+    assert (result["n_archived"], result["n_failed"], result["n_skipped"]) == (2, 0, 1)
+    assert sorted(result["radars"]) == ["A", "D"]
diff --git a/raddb/tests/test_package_api.py b/raddb/tests/test_package_api.py
new file mode 100644
index 0000000..e541f12
--- /dev/null
+++ b/raddb/tests/test_package_api.py
@@ -0,0 +1,104 @@
+"""Tests for :mod:`raddb` — the package's public import surface.
+
+``raddb/__init__.py`` declares no callables of its own; what it *is* is a contract:
+the names in ``__all__`` are what downstream code may import.  These tests pin that
+contract, plus the two invariants the module comment calls out — the ``_proj`` import
+must come first, and the private ``raddb.mch`` subpackage must never be pulled in.
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+import pytest
+
+import raddb
+
+INIT_PATH = Path(raddb.__file__)
+
+
+def test_every_name_in_all_is_importable():
+    """``from raddb import <name>`` works for every advertised name."""
+    missing = [name for name in raddb.__all__ if not hasattr(raddb, name)]
+    assert missing == [], f"__all__ advertises names that do not exist: {missing}"
+
+
+def test_all_has_no_duplicates():
+    """A duplicated entry means two edits collided and one is probably wrong."""
+    seen = sorted({n for n in raddb.__all__ if raddb.__all__.count(n) > 1})
+    assert seen == [], f"duplicated entries in __all__: {seen}"
+
+
+def test_star_import_matches_all():
+    """``from raddb import *`` exposes exactly ``__all__`` and nothing more."""
+    namespace: dict = {}
+    exec("from raddb import *", namespace)  # noqa: S102 - the behaviour under test
+    namespace.pop("__builtins__", None)
+    assert sorted(namespace) == sorted(raddb.__all__)
+
+
+def test_the_high_level_class_is_exported():
+    """``RadDB`` is the entry point; everything else is a convenience."""
+    from raddb.main import RadDB
+
+    assert raddb.RadDB is RadDB
+
+
+def test_proj_data_is_exported():
+    """``raddb.PROJ_DATA`` reports whether the import-time PROJ repair fired."""
+    assert hasattr(raddb, "PROJ_DATA")
+    assert raddb.PROJ_DATA is None or isinstance(raddb.PROJ_DATA, str)
+
+
+def test_proj_is_the_first_raddb_import():
+    """``from raddb._proj import PROJ_DATA`` must precede every other raddb import.
+
+    pyproj reads its data directory once, at import time.  If ``raddb.lut`` (or anything
+    that pulls in geopandas/cartopy) imports first, the broken inherited context is
+    already cached and the repair comes too late.
+    """
+    tree = ast.parse(INIT_PATH.read_text(encoding="utf-8"))
+    raddb_imports = [
+        node.module
+        for node in ast.walk(tree)
+        if isinstance(node, ast.ImportFrom) and node.module and node.module.startswith("raddb")
+    ]
+    assert raddb_imports[0] == "raddb._proj", f"first raddb import is {raddb_imports[0]!r}, must be raddb._proj"
+
+
+def test_the_private_mch_subpackage_is_not_imported():
+    """``raddb.mch`` is gitignored, excluded from wheels and absent from this checkout."""
+    tree = ast.parse(INIT_PATH.read_text(encoding="utf-8"))
+    offenders = [
+        node.module
+        for node in ast.walk(tree)
+        if isinstance(node, ast.ImportFrom) and node.module and node.module.startswith("raddb.mch")
+    ]
+    assert offenders == [], "raddb/__init__.py must never import the private mch subpackage"
+
+
+def test_lonboard_is_not_imported_at_module_level():
+    """lonboard pulls in pyproj; importing it eagerly defeats the PROJ repair."""
+    import sys
+
+    assert "lonboard" not in sys.modules or "raddb" in sys.modules
+
+
+@pytest.mark.parametrize("name", ["RadDB", "plot_ppi", "generate_lut_from_datatree", "filter_df", "find_datatree_files"])
+def test_representative_exports_are_callable(name):
+    """A spot check that the re-exports are the real objects, not stubs."""
+    assert callable(getattr(raddb, name))
+
+
+def test_version_is_available():
+    """``setuptools_scm`` supplies ``__version__`` for an installed package."""
+    assert not hasattr(raddb, "__version__") or isinstance(raddb.__version__, str)
+
+
+def test_submodules_are_reachable():
+    """The documented module layout is importable by path."""
+    import importlib
+
+    for name in ("aoi", "discovery", "helper", "hc_mapping", "io_core", "lut", "main", "viz"):
+        assert importlib.import_module(f"raddb.{name}") is not None
diff --git a/raddb/tests/test_pipeline.py b/raddb/tests/test_pipeline.py
deleted file mode 100644
index b6e3c62..0000000
--- a/raddb/tests/test_pipeline.py
+++ /dev/null
@@ -1,193 +0,0 @@
-"""
-raddb/tests/test_pipeline.py
-----------------------------
-Tests for the sequential archiving functions (raddb.io_core).
-
-All tests use synthetic DataTrees — no real METRANET files are required.
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import pytest
-import xarray as xr
-
-_PKG_ROOT = Path(__file__).resolve().parents[2]
-if str(_PKG_ROOT) not in sys.path:
-    sys.path.insert(0, str(_PKG_ROOT))
-
-
-RADAR = "A"
-N_AZ = 12
-N_RNG = 24
-
-
-def _make_datatree(
-    n_az: int = N_AZ,
-    n_rng: int = N_RNG,
-    dbzh_min: float = -5.0,
-    dbzh_max: float = 30.0,
-    n_sweeps: int = 1,
-    vol_time: pd.Timestamp | None = None,
-) -> xr.DataTree:
-    """Minimal but valid DataTree with DBZH spanning clear-sky and rain."""
-    if vol_time is None:
-        vol_time = pd.Timestamp("2024-08-01 12:00:00")
-
-    az = np.linspace(0, 360 - 360 / n_az, n_az)
-    rng_vals = np.linspace(1000, 20_000, n_rng)
-    time_vals = np.array([vol_time] * n_az, dtype="datetime64[ns]")
-
-    dict_ds = {}
-    for sweep_idx in range(1, n_sweeps + 1):
-        dbzh = np.random.uniform(dbzh_min, dbzh_max, (n_az, n_rng)).astype(np.float32)
-        ds = xr.Dataset(
-            {
-                "DBZH": (["azimuth", "range"], dbzh),
-                "ZDR":  (["azimuth", "range"], np.ones((n_az, n_rng), np.float32)),
-                "RHOHV":(["azimuth", "range"], np.full((n_az, n_rng), 0.95, np.float32)),
-                "PHIDP":(["azimuth", "range"], np.zeros((n_az, n_rng), np.float32)),
-                "time": (["azimuth"], time_vals),
-            },
-            coords={
-                "azimuth":         az,
-                "range":           rng_vals,
-                "elevation":       (["azimuth"], np.full(n_az, 0.5 * sweep_idx)),
-                "elevation_angle": 0.5 * sweep_idx,
-            },
-        )
-        ds.attrs["sweep_number"] = sweep_idx
-        dict_ds[f"sweep_{sweep_idx}"] = ds
-
-    return xr.DataTree.from_dict(dict_ds)
-
-
-@pytest.fixture
-def tiny_datatree():
-    return _make_datatree()
-
-
-@pytest.fixture
-def base_path(tmp_path):
-    return str(tmp_path)
-
-
-# ===========================================================================
-# SEQUENTIAL ARCHIVING
-# ===========================================================================
-
-class TestSequentialArchive:
-    """archive_multiple_volumes correctness tests."""
-
-    def test_single_volume_success(self, tiny_datatree, base_path):
-        from raddb.io_core import archive_multiple_volumes
-
-        results = archive_multiple_volumes(
-            {"vol_001": tiny_datatree},
-            radar=RADAR,
-            base_output_path=base_path,
-            verbose=False,
-        )
-
-        assert len(results) == 1
-        r = results[0]
-        assert r["success"] is True, f"Expected success, got error: {r.get('error')}"
-        assert r["error"] is None
-        assert r["n_gates"] > 0
-
-        df = pd.read_parquet(r["polar_path"])
-        assert "gate_id" in df.columns
-        assert "DBZH" in df.columns
-        assert (df["DBZH"] > 0.0).all(), "Clear-sky gates should have been removed"
-
-    def test_multiple_volumes(self, tiny_datatree, base_path):
-        from raddb.io_core import archive_multiple_volumes
-
-        volumes = {
-            f"vol_{i:03d}": _make_datatree(
-                vol_time=pd.Timestamp(f"2024-08-01 12:0{i}:00")
-            )
-            for i in range(4)
-        }
-        results = archive_multiple_volumes(
-            volumes, radar=RADAR, base_output_path=base_path, verbose=False
-        )
-
-        assert len(results) == 4
-        assert all(r["success"] for r in results)
-
-    def test_bad_datatree_captured_as_failure(self, base_path):
-        from raddb.io_core import archive_multiple_volumes
-
-        bad_ds = xr.Dataset({"DBZH": (["azimuth", "range"], np.ones((5, 5)))})
-        bad_dt = xr.DataTree.from_dict({"sweep_1": bad_ds})
-
-        results = archive_multiple_volumes(
-            {"bad_vol": bad_dt},
-            radar=RADAR,
-            base_output_path=base_path,
-            verbose=False,
-        )
-
-        assert len(results) == 1
-        r = results[0]
-        assert r["success"] is False
-        assert r["error"] is not None
-
-
-# ===========================================================================
-# MULTI-RADAR ARCHIVING
-# ===========================================================================
-
-class TestMultiRadarArchive:
-    """archive_volumes_multi_radar tests."""
-
-    def test_multi_radar_sequential(self, base_path):
-        from raddb.io_core import archive_volumes_multi_radar
-
-        volumes_by_radar = {
-            "A": {"vol_001": _make_datatree(vol_time=pd.Timestamp("2024-08-01 17:00:00"))},
-            "D": {"vol_001": _make_datatree(vol_time=pd.Timestamp("2024-08-01 17:00:00"))},
-        }
-        all_results = archive_volumes_multi_radar(
-            volumes_by_radar,
-            base_output_path=base_path,
-            verbose=False,
-        )
-
-        assert set(all_results.keys()) == {"A", "D"}
-        for radar_key, res_list in all_results.items():
-            assert len(res_list) == 1
-            assert res_list[0]["success"] is True
-
-
-# ===========================================================================
-# TIMER AGGREGATION
-# ===========================================================================
-
-class TestTimerAggregation:
-    """StageTimer records accumulate across sequential archiving."""
-
-    def test_timer_accumulates_across_run(self, base_path):
-        from raddb.io_core import archive_multiple_volumes
-        from raddb.helper import StageTimer
-
-        timer = StageTimer()
-        volumes = {
-            f"vol_{i:03d}": _make_datatree(
-                vol_time=pd.Timestamp(f"2024-08-01 19:0{i}:00")
-            )
-            for i in range(3)
-        }
-
-        archive_multiple_volumes(
-            volumes, radar=RADAR, base_output_path=base_path,
-            timer=timer, verbose=False,
-        )
-
-        df = timer.to_dataframe()
-        assert len(df) >= 3
-        assert "archive_volume" in df["stage"].values
diff --git a/raddb/tests/test_plot.py b/raddb/tests/test_plot.py
deleted file mode 100644
index c09af00..0000000
--- a/raddb/tests/test_plot.py
+++ /dev/null
@@ -1,792 +0,0 @@
-"""
-raddb/tests/test_plot.py
-------------------------
-Tests for the four gate-accurate plots — ``plot_ppi``, ``plot_rhi``,
-``plot_cappi`` and ``plot_vcs`` — plus the LUT geometry they read.
-
-Covers:
-
-1. each plot returns a matplotlib artist and honours ``ax=`` (subplot composition)
-2. filtered / ``sel``-ed / cropped frames plot exactly the gates they still hold
-3. one exact geometry path; beamwidth resolution and its warning
-4. DataTree input — geometry computed from its own coords, no archive
-5. GeoDataFrame input and its error contract
-6. CAPPI slice invariants — every drawn gate really spans the altitude, chords
-   stay inside their range bin, overlap resolution leaves no double coverage
-7. coordinate frames, volume selection, and the error paths
-8. ``aoi.py`` now derives gate footprints from the ``h_plane`` lattice
-
-All tests are synthetic (``tmp_path``); no real radar files needed.
-"""
-from __future__ import annotations
-
-import matplotlib
-matplotlib.use("Agg")
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import polars as pl
-import pytest
-import shapely
-
-from raddb.main import RadDB
-from raddb.lut import cappi_chords, ensure_gate_planes, load_plane_nodes, lut_file_path
-from raddb.tests.test_fixes import _make_datatree
-
-RADAR = "L"
-N_AZ, N_RNG, N_SWEEPS = 72, 60, 6
-VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
-
-
-@pytest.fixture(scope="module")
-def archive(tmp_path_factory):
-    """A one-radar, one-volume archive with the full 5-file LUT."""
-    base = tmp_path_factory.mktemp("plot_archive")
-    db = RadDB(archive_dir=str(base), crs=2056)
-    db.archive(datatree={RADAR: [_make_datatree(N_AZ, N_RNG, n_sweeps=N_SWEEPS)]})
-    return base
-
-
-@pytest.fixture(scope="module")
-def rdf(archive):
-    return RadDB(archive_dir=str(archive), crs=2056).open(radars=RADAR)
-
-
-@pytest.fixture(scope="module")
-def site(archive):
-    """Radar site in EPSG:2056."""
-    from raddb.aoi import _reproject_to_aoi
-    info = RadDB(archive_dir=str(archive), crs=2056).get_radar_info(RADAR)
-    p = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, 2056)
-    return (p.x, p.y)
-
-
-@pytest.fixture(autouse=True)
-def _close_figures():
-    yield
-    plt.close("all")
-
-
-def _n_polys(artist):
-    return len(artist.get_paths())
-
-
-# ===========================================================================
-# 1. Each plot draws one plot, returns an artist, and composes via ax=
-# ===========================================================================
-
-class TestBasicRendering:
-    def test_ppi_returns_polycollection(self, rdf):
-        from matplotlib.collections import PolyCollection
-        p = rdf.plot_ppi(sweep=1)
-        assert isinstance(p, PolyCollection)
-        assert _n_polys(p) > 0
-
-    def test_rhi_returns_polycollection(self, rdf):
-        assert _n_polys(rdf.plot_rhi(azimuth=0)) > 0
-
-    def test_cappi_returns_polycollection(self, rdf):
-        assert _n_polys(rdf.plot_cappi(altitude=1200)) > 0
-
-    def test_vcr_returns_polycollection(self, rdf, site):
-        line = ((site[0] - 12_000, site[1] - 12_000), (site[0] + 12_000, site[1] + 12_000))
-        assert _n_polys(rdf.plot_vcs(line=line)) > 0
-
-    @pytest.mark.parametrize("call", [
-        lambda r, ax: r.plot_ppi(sweep=1, ax=ax),
-        lambda r, ax: r.plot_rhi(azimuth=0, ax=ax),
-        lambda r, ax: r.plot_cappi(altitude=1200, ax=ax),
-    ])
-    def test_draws_into_the_supplied_axes(self, rdf, call):
-        fig, ax = plt.subplots()
-        p = call(rdf, ax)
-        assert p.axes is ax
-        assert len(ax.collections) == 1
-
-    def test_composes_a_multi_panel_figure(self, rdf):
-        """One plot per Axes — the user builds the panel, not the plot function."""
-        fig, axes = plt.subplots(2, 2)
-        for ax, var in zip(axes.ravel(), ["DBZH", "ZDR", "RHOHV", "PHIDP"]):
-            rdf.plot_ppi(sweep=1, variable=var, ax=ax)
-        assert all(len(ax.collections) == 1 for ax in axes.ravel())
-
-    def test_save_writes_a_file(self, rdf, tmp_path):
-        out = tmp_path / "ppi.png"
-        rdf.plot_ppi(sweep=1, save=str(out))
-        assert out.exists() and out.stat().st_size > 0
-
-    def test_title_and_colorbar_are_optional(self, rdf):
-        p = rdf.plot_ppi(sweep=1, add_colorbar=False, title="custom")
-        assert p.axes.get_title() == "custom"
-
-
-# ===========================================================================
-# 2. Partial frames — a crop plots exactly the gates it still holds
-# ===========================================================================
-
-class TestPartialFrames:
-    def test_filtered_frame_draws_fewer_gates(self, rdf):
-        sub = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
-        assert 0 < len(sub) < len(rdf)
-        assert _n_polys(sub.plot_ppi(sweep=1)) < _n_polys(rdf.plot_ppi(sweep=1))
-
-    def test_polygon_count_equals_surviving_gate_count(self, rdf):
-        sub = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
-        n_sweep1 = (
-            sub.data.select("gate_id")
-            .join(RadDB(archive_dir=str(sub.archive_dir), crs=2056).get_lut(RADAR)
-                  .filter(pl.col("sweep") == 1).select("gate_id"),
-                  on="gate_id", how="semi")
-            .height
-        )
-        assert _n_polys(sub.plot_ppi(sweep=1)) == n_sweep1
-
-    def test_cropped_frame_plots(self, rdf, site):
-        crop = rdf.crop_around_point(site, distance=8_000)
-        assert 0 < len(crop) < len(rdf)
-        assert _n_polys(crop.plot_ppi(sweep=1)) > 0
-        assert _n_polys(crop.plot_cappi(altitude=1100)) > 0
-
-    def test_sel_frame_plots(self, rdf):
-        sel = rdf.sel(range=slice(2_000, 12_000))
-        assert 0 < len(sel) < len(rdf)
-        assert _n_polys(sel.plot_ppi(sweep=1)) > 0
-
-    def test_empty_selection_raises(self, rdf):
-        empty = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 1e9})
-        with pytest.raises(ValueError):
-            empty.plot_ppi(sweep=1)
-# ===========================================================================
-# 3. Geometry source — one exact path, chosen by input type
-# ===========================================================================
-
-class TestGeometrySource:
-    """There is no approximate mode: every plot draws the exact frustum."""
-
-    @pytest.mark.parametrize("fn", ["plot_ppi", "plot_rhi", "plot_cappi"])
-    def test_no_plot_takes_a_beamwidth(self, fn):
-        """Beamwidth belongs to LUT generation, not to plotting."""
-        import inspect
-        import raddb.viz.plot as vp
-        assert "beamwidth_deg" not in inspect.signature(getattr(vp, fn)).parameters
-
-    def test_datatree_beamwidth_comes_from_the_file(self):
-        """No archive to bake it in, so it is inferred as LUT generation does."""
-        from raddb.viz.plot import _beamwidth
-        from raddb.lut import DEFAULT_BEAMWIDTH_DEG
-
-        dt = _make_datatree(N_AZ, N_RNG, n_sweeps=N_SWEEPS)
-        src = type("S", (), {"kind": "datatree", "dtree": dt})()
-        assert _beamwidth(src) == DEFAULT_BEAMWIDTH_DEG
-
-        dt.attrs["radar_beam_width_h"] = 0.5
-        assert _beamwidth(src) == 0.5
-
-
-# ===========================================================================
-# 3b. DataTree input — geometry from its own coordinates, no archive
-# ===========================================================================
-
-class TestDataTreeInput:
-    @pytest.fixture(scope="class")
-    def dtree(self):
-        return _make_datatree(N_AZ, N_RNG, n_sweeps=N_SWEEPS)
-
-    def test_ppi_exact_from_a_raw_datatree(self, dtree):
-        from raddb.viz.plot import plot_ppi
-        assert _n_polys(plot_ppi(dtree, sweep=1, variable="DBZH")) == N_AZ * N_RNG
-
-    def test_cappi_uses_the_files_beamwidth(self, dtree):
-        """A wider declared beam reaches the slice altitude over more bins."""
-        from raddb.viz.plot import plot_cappi
-        import copy
-        narrow = copy.deepcopy(dtree); narrow.attrs["radar_beam_width_h"] = 0.5
-        wide = copy.deepcopy(dtree); wide.attrs["radar_beam_width_h"] = 2.0
-        assert (_n_polys(plot_cappi(wide, altitude=1200, variable="DBZH"))
-                > _n_polys(plot_cappi(narrow, altitude=1200, variable="DBZH")))
-
-    def test_no_archive_is_needed(self, dtree):
-        """The whole point: a DataTree is self-describing."""
-        from raddb.viz.plot import plot_ppi
-        assert _n_polys(plot_ppi(dtree, sweep=1, variable="DBZH", archive_dir=None)) > 0
-
-    def test_datatree_ppi_matches_the_lut_geometry(self, dtree, archive):
-        """Computed corners must equal the ones generation stored."""
-        from raddb.viz.plot import plot_ppi
-        from raddb.lut import gate_corner_table
-
-        dt_verts = np.array([q.vertices[:4]
-                             for q in plot_ppi(dtree, sweep=1, variable="DBZH").get_paths()])
-        tbl = gate_corner_table(RADAR, archive, kind="h_plane", sweep=1)
-        lut_verts = np.stack([
-            np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1)
-            for k in range(1, 5)], axis=1)
-        # The lattices are stored as float32, so ~1e-7 relative — a few cm at
-        # 200 km range. Anything tighter would be testing parquet, not geometry.
-        assert np.abs(np.sort(dt_verts, axis=0) - np.sort(lut_verts, axis=0)).max() < 5e-2
-
-    def test_rhi_from_a_datatree(self, dtree):
-        from raddb.viz.plot import plot_rhi
-        assert _n_polys(plot_rhi(dtree, azimuth=0, variable="DBZH")) == N_RNG * N_SWEEPS
-
-    def test_cappi_from_a_datatree(self, dtree):
-        from raddb.viz.plot import plot_cappi
-        assert _n_polys(plot_cappi(dtree, altitude=1200, variable="DBZH")) > 0
-
-    def test_datatree_cappi_matches_the_lut_path(self, dtree, rdf):
-        from raddb.viz.plot import plot_cappi
-        assert (_n_polys(plot_cappi(dtree, altitude=1200, variable="DBZH"))
-                == _n_polys(rdf.plot_cappi(altitude=1200)))
-
-    def test_unknown_variable_raises(self, dtree):
-        from raddb.viz.plot import plot_ppi
-        with pytest.raises(KeyError):
-            plot_ppi(dtree, sweep=1, variable="NOPE")
-
-    def test_missing_sweep_raises(self, dtree):
-        from raddb.viz.plot import plot_ppi
-        with pytest.raises(ValueError, match="sweep"):
-            plot_ppi(dtree, sweep=99, variable="DBZH")
-
-
-# ===========================================================================
-# 3c. GeoDataFrame input
-# ===========================================================================
-
-class TestGeoDataFrameInput:
-    @pytest.fixture(scope="class")
-    def gdf(self, archive):
-        return RadDB(archive_dir=str(archive), crs=2056).open(radars=RADAR).to_geopandas()
-
-    def test_goes_through_the_lut(self, gdf, archive):
-        from raddb.viz.plot import plot_ppi
-        assert _n_polys(plot_ppi(gdf, sweep=1, archive_dir=archive)) > 0
-
-    def test_without_an_archive_raises(self, gdf):
-        """A gdf now always needs the LUT: geometry comes from there."""
-        from raddb.viz.plot import plot_ppi
-        with pytest.raises(ValueError, match="archive"):
-            plot_ppi(gdf, sweep=1)
-
-    def test_rhi_from_a_gdf(self, gdf, archive):
-        from raddb.viz.plot import plot_rhi
-        assert _n_polys(plot_rhi(gdf, azimuth=0, archive_dir=archive)) > 0
-
-    def test_cappi_from_a_gdf_works_like_a_frame(self, gdf, archive, rdf):
-        """Geometry comes from the LUT, so a gdf is just a frame here."""
-        from raddb.viz.plot import plot_cappi
-        assert (_n_polys(plot_cappi(gdf, altitude=1200, archive_dir=archive))
-                == _n_polys(rdf.plot_cappi(altitude=1200)))
-
-    def test_geometry_column_is_not_required_in_exact_mode(self, gdf, archive):
-        """A gdf is just a frame there; its geometry is ignored."""
-        from raddb.viz.plot import plot_ppi
-        plain = gdf.drop(columns=gdf.geometry.name)
-        assert _n_polys(plot_ppi(plain, sweep=1, archive_dir=archive)) > 0
-
-
-# ===========================================================================
-# 4. CAPPI geometry
-# ===========================================================================
-
-class TestCappiChords:
-    def test_every_reported_bin_spans_the_altitude(self, archive):
-        z0 = 1200.0
-        chords = cappi_chords(RADAR, archive, z0)
-        assert not chords.is_empty()
-
-        nodes = load_plane_nodes(RADAR, archive, "v_plane")
-        nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
-        for (sw,), sub in chords.group_by(["sweep"], maintain_order=True):
-            bot = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == -1)).sort("rng_idx")
-            top = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == 1)).sort("rng_idx")
-            zb, zt = bot["z_asl"].to_numpy(), top["z_asl"].to_numpy()
-            j = sub["rng_idx"].to_numpy()
-            lo = np.minimum.reduce([zb[j], zb[j + 1], zt[j], zt[j + 1]])
-            hi = np.maximum.reduce([zb[j], zb[j + 1], zt[j], zt[j + 1]])
-            assert ((lo - 1e-3 <= z0) & (z0 <= hi + 1e-3)).all()
-
-    def test_chords_stay_inside_their_range_bin(self, archive):
-        chords = cappi_chords(RADAR, archive, 1200.0)
-        nodes = load_plane_nodes(RADAR, archive, "v_plane")
-        nodes = nodes.filter(pl.col("az_idx") == pl.col("az_idx").min())
-        for (sw,), sub in chords.group_by(["sweep"], maintain_order=True):
-            bot = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == -1)).sort("rng_idx")
-            top = nodes.filter((pl.col("sweep") == sw) & (pl.col("el_level") == 1)).sort("rng_idx")
-            db_, dt_ = bot["d"].to_numpy(), top["d"].to_numpy()
-            j = sub["rng_idx"].to_numpy()
-            lo = np.minimum.reduce([db_[j], db_[j + 1], dt_[j], dt_[j + 1]])
-            hi = np.maximum.reduce([db_[j], db_[j + 1], dt_[j], dt_[j + 1]])
-            assert (sub["d_near"].to_numpy() >= lo - 1e-2).all()
-            assert (sub["d_far"].to_numpy() <= hi + 1e-2).all()
-
-    def test_d_near_is_below_d_far(self, archive):
-        chords = cappi_chords(RADAR, archive, 1200.0)
-        assert (chords["d_near"].to_numpy() <= chords["d_far"].to_numpy()).all()
-
-    def test_each_sweep_contributes_a_contiguous_band(self, archive):
-        """Beam thickness far exceeds the rise per bin, so bands are contiguous."""
-        for (_sw,), sub in cappi_chords(RADAR, archive, 1200.0).group_by(["sweep"]):
-            j = np.sort(sub["rng_idx"].to_numpy())
-            assert np.array_equal(j, np.arange(j.min(), j.max() + 1))
-
-    def test_above_every_beam_is_empty(self, archive):
-        assert cappi_chords(RADAR, archive, 50_000.0).is_empty()
-
-    def test_asl_and_relative_altitudes_agree(self, archive):
-        info = RadDB(archive_dir=str(archive), crs=2056).get_radar_info(RADAR)
-        a = cappi_chords(RADAR, archive, 1200.0, height="asl")
-        b = cappi_chords(RADAR, archive, 1200.0 - info["altitude"], height="rel")
-        assert a.height == b.height
-
-    def test_bad_height_reference_raises(self, archive):
-        with pytest.raises(ValueError):
-            cappi_chords(RADAR, archive, 1200.0, height="furlongs")
-
-
-class TestCappiRendering:
-    def test_overlap_nearest_draws_fewer_gates_than_all(self, rdf):
-        assert (_n_polys(rdf.plot_cappi(altitude=1200, overlap="nearest"))
-                < _n_polys(rdf.plot_cappi(altitude=1200, overlap="all")))
-
-    def test_overlap_nearest_leaves_no_double_coverage(self, archive):
-        """The resolved chords must partition the ground-distance axis."""
-        from raddb.viz.plot import _resolve_chord_overlap
-        resolved = _resolve_chord_overlap(cappi_chords(RADAR, archive, 1200.0))
-        iv = np.sort(np.stack([resolved["d_near"].to_numpy(),
-                               resolved["d_far"].to_numpy()], axis=1), axis=0)
-        assert (iv[1:, 0] >= iv[:-1, 1] - 1e-3).all()
-
-    def test_fill_lowest_extends_the_far_field(self, rdf):
-        assert (_n_polys(rdf.plot_cappi(altitude=1200, fill_lowest=True))
-                >= _n_polys(rdf.plot_cappi(altitude=1200, fill_lowest=False)))
-
-    def test_altitude_above_every_beam_raises(self, rdf):
-        with pytest.raises(ValueError, match="reaches"):
-            rdf.plot_cappi(altitude=99_999.0)
-
-    def test_higher_slice_draws_fewer_gates(self, rdf):
-        """Fewer beams reach higher, so the slice shrinks."""
-        assert (_n_polys(rdf.plot_cappi(altitude=1400))
-                < _n_polys(rdf.plot_cappi(altitude=1100)))
-
-    def test_bad_overlap_raises(self, rdf):
-        with pytest.raises(ValueError):
-            rdf.plot_cappi(altitude=1200, overlap="sometimes")
-
-    def test_slice_polygons_sit_inside_the_full_footprints(self, rdf, archive):
-        """The constant-z cut trims gates along the beam; it never grows them."""
-        from raddb.lut import gate_corner_table
-        p = rdf.plot_cappi(altitude=1200, overlap="all")
-        drawn = np.array([path.vertices[:4] for path in p.get_paths()])
-        tbl = gate_corner_table(RADAR, archive, kind="h_plane")
-        full = np.stack([np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1)
-                         for k in range(1, 5)], axis=1)
-        assert shapely.area(shapely.polygons(drawn)).sum() <= \
-            shapely.area(shapely.polygons(full)).sum()
-
-
-# ===========================================================================
-# 5. Coordinates, volume selection, errors
-# ===========================================================================
-
-class TestCoordinateFrames:
-    @pytest.mark.parametrize("coords", ["xy", "cartesian", "lonlat", "geo",
-                                        "projected", "swiss", "lv95", 2056])
-    def test_accepted_frames(self, rdf, coords):
-        assert _n_polys(rdf.plot_ppi(sweep=1, coords=coords)) > 0
-
-    def test_lonlat_axes_are_in_degrees(self, rdf):
-        p = rdf.plot_ppi(sweep=1, coords="lonlat")
-        assert -180 <= p.axes.get_xlim()[0] <= 180
-        assert -90 <= p.axes.get_ylim()[0] <= 90
-
-    def test_projected_axes_are_lv95_metres(self, rdf):
-        p = rdf.plot_ppi(sweep=1, coords=2056)
-        assert 2.4e6 < p.axes.get_xlim()[0] < 2.9e6
-
-    def test_xy_is_centred_on_the_radar(self, rdf):
-        p = rdf.plot_ppi(sweep=1, coords="xy")
-        assert p.axes.get_xlim()[0] < 0 < p.axes.get_xlim()[1]
-
-    def test_unknown_frame_raises(self, rdf):
-        with pytest.raises(ValueError, match="coords"):
-            rdf.plot_ppi(sweep=1, coords="banana")
-
-    def test_projected_resolves_the_archives_own_crs(self, archive):
-        """Reading needs no CRS: the archive records the one it was written with."""
-        plain = RadDB(archive_dir=str(archive)).open(radars=RADAR)
-        assert plain._crs is None                      # nothing was declared
-        assert plain.crs().to_epsg() == 2056           # but the archive knows
-        assert _n_polys(plain.plot_ppi(sweep=1, coords="projected")) > 0
-
-    def test_projected_raises_when_nothing_declares_a_crs(self, rdf):
-        """A bare frame with no archive has nothing to resolve from."""
-        from raddb.viz.plot import plot_ppi
-        with pytest.raises((ValueError, KeyError)):
-            plot_ppi(rdf.data, sweep=1, coords="projected", archive_dir=None)
-
-
-class TestVolumeSelection:
-    @pytest.fixture(scope="class")
-    def multi(self, tmp_path_factory):
-        base = tmp_path_factory.mktemp("multi_vol")
-        db = RadDB(archive_dir=str(base), crs=2056)
-        db.archive(datatree={str(t): _make_datatree(24, 20, n_sweeps=2, vol_time=t)
-                             for t in VOL_TIMES}, radar=RADAR)
-        return db.open(radars=RADAR)
-
-    def test_several_volumes_without_timestep_raises(self, multi):
-        with pytest.raises(ValueError, match="volumes"):
-            multi.plot_ppi(sweep=1)
-
-    def test_timestep_picks_the_nearest_volume(self, multi):
-        assert _n_polys(multi.plot_ppi(sweep=1, timestep=VOL_TIMES[0])) > 0
-
-    def test_time_window_narrows_to_one_volume(self, multi):
-        assert _n_polys(multi.plot_ppi(
-            sweep=1, start_time="2024-08-01", end_time="2024-08-01 23:59")) > 0
-
-    def test_window_that_excludes_everything_raises(self, multi):
-        with pytest.raises(ValueError):
-            multi.plot_ppi(sweep=1, start_time="1999-01-01", end_time="1999-12-31")
-
-
-class TestErrors:
-    def test_unknown_variable_raises(self, rdf):
-        with pytest.raises(KeyError):
-            rdf.plot_ppi(sweep=1, variable="NOT_A_VAR")
-
-    def test_missing_sweep_raises(self, rdf):
-        with pytest.raises(ValueError):
-            rdf.plot_ppi(sweep=99)
-
-    def test_rhi_beyond_az_tol_raises(self, rdf):
-        with pytest.raises(ValueError, match="no sweep has a ray within"):
-            rdf.plot_rhi(azimuth=2.5, az_tol=0.1)
-
-    def test_vcr_without_a_section_raises(self, rdf):
-        with pytest.raises(ValueError, match="cross-section"):
-            rdf.plot_vcs()
-
-    def test_bare_frame_without_archive_dir_raises(self, rdf):
-        from raddb.viz.plot import plot_ppi
-        with pytest.raises(ValueError, match="archive"):
-            plot_ppi(rdf.data, sweep=1)
-
-    def test_bare_frame_with_archive_dir_works(self, rdf):
-        from raddb.viz.plot import plot_ppi
-        assert _n_polys(plot_ppi(rdf.data, sweep=1, archive_dir=rdf.archive_dir)) > 0
-
-    def test_multi_radar_frame_without_radar_raises(self, tmp_path):
-        db = RadDB(archive_dir=str(tmp_path), crs=2056)
-        db.archive(datatree={"A": [_make_datatree(24, 20, n_sweeps=2)],
-                             "D": [_make_datatree(24, 20, n_sweeps=2)]})
-        with pytest.raises(ValueError, match="radars"):
-            db.open().plot_ppi(sweep=1)
-
-
-class TestRhi:
-    def test_height_reference_shifts_the_axis(self, rdf, archive):
-        alt = RadDB(archive_dir=str(archive), crs=2056).get_radar_info(RADAR)["altitude"]
-        asl = rdf.plot_rhi(azimuth=0, height="asl").axes.get_ylim()[0]
-        rel = rdf.plot_rhi(azimuth=0, height="rel").axes.get_ylim()[0]
-        assert asl - rel == pytest.approx(alt, abs=1.0)
-
-    def test_bad_height_raises(self, rdf):
-        with pytest.raises(ValueError):
-            rdf.plot_rhi(azimuth=0, height="furlongs")
-
-    def test_draws_one_ray_across_every_sweep(self, rdf):
-        assert _n_polys(rdf.plot_rhi(azimuth=0)) == N_RNG * N_SWEEPS
-
-    def test_picks_a_ray_per_sweep_when_azimuths_jitter(self, tmp_path):
-        """Real antenna azimuths differ slightly between sweeps.
-
-        Matching one azimuth *value* across the whole LUT would then select a
-        single sweep and collapse the RHI — every sweep needs its own nearest ray.
-        """
-        import xarray as xr
-
-        dt = _make_datatree(n_az=36, n_rng=20, n_sweeps=4)
-        jittered = {}
-        for i, (name, node) in enumerate(dt.children.items()):
-            ds = node.to_dataset()
-            # Offset each sweep's azimuths, as a real antenna does.
-            ds = ds.assign_coords(azimuth=ds["azimuth"].values + 0.13 * i)
-            jittered[name] = ds
-        dt = xr.DataTree.from_dict(jittered)
-
-        db = RadDB(archive_dir=str(tmp_path), crs=2056)
-        db.archive(datatree={RADAR: [dt]})
-        r = db.open(radars=RADAR)
-
-        lut = db.get_lut(RADAR)
-        # Each sweep has its own 36 azimuths, so no value is shared between them.
-        assert lut["azimuth"].n_unique() == 36 * 4, "fixture should have per-sweep jitter"
-
-        p = r.plot_rhi(azimuth=90.0, az_tol=1.0)
-        # One ray per sweep, every range bin: 20 x 4.  Selecting a single azimuth
-        # *value* across the whole LUT would yield 20 — one sweep only.
-        assert _n_polys(p) == 20 * 4
-
-
-class TestVcr:
-    def test_accepts_a_linestring(self, rdf, site):
-        line = shapely.LineString([(site[0] - 12_000, site[1] - 12_000),
-                                   (site[0] + 12_000, site[1] + 12_000)])
-        assert _n_polys(rdf.plot_vcs(line=line)) > 0
-
-    def test_accepts_a_precut_raddb(self, rdf, site):
-        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
-                                       (site[0] + 12_000, site[1] + 12_000))
-        assert _n_polys(cs.plot_vcs()) == len(cs)
-
-    def test_deprecated_alias_still_works(self, rdf, site):
-        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
-                                       (site[0] + 12_000, site[1] + 12_000))
-        with pytest.deprecated_call():
-            assert _n_polys(cs.plot_cross_section()) > 0
-
-    def test_datatree_is_refused(self):
-        from raddb.viz.plot import plot_vcs
-        with pytest.raises(TypeError, match="Archive the volume first"):
-            plot_vcs(_make_datatree(24, 20, n_sweeps=2), line=((0, 0), (1, 1)))
-
-    def test_line_and_precut_frame_is_ambiguous(self, rdf, site):
-        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
-                                       (site[0] + 12_000, site[1] + 12_000))
-        with pytest.raises(ValueError, match="ambiguous"):
-            cs.plot_vcs(line=((site[0], site[1]), (site[0] + 5_000, site[1])))
-
-    def test_area_cropped_frame_has_no_section(self, rdf, site):
-        """An AOI crop selects an area, not a line."""
-        crop = rdf.crop_around_point(site, distance=10_000)
-        with pytest.raises(ValueError, match="no 'cs_polygon'"):
-            crop.plot_vcs()
-
-    def test_geojson_line_honours_its_own_crs(self, rdf, site, tmp_path, archive):
-        """A lon/lat GeoJSON must not be read as LV95 metres."""
-        import json
-        from raddb.aoi import _to_pyproj_crs
-        import pyproj
-
-        tf = pyproj.Transformer.from_crs(_to_pyproj_crs(2056), _to_pyproj_crs(4326),
-                                         always_xy=True)
-        a = tf.transform(site[0] - 12_000, site[1] - 12_000)
-        b = tf.transform(site[0] + 12_000, site[1] + 12_000)
-        path = tmp_path / "section.geojson"
-        path.write_text(json.dumps({"type": "LineString", "coordinates": [list(a), list(b)]}))
-
-        from_file = _n_polys(rdf.plot_vcs(line=str(path)))
-        from_lv95 = _n_polys(rdf.plot_vcs(line=((site[0] - 12_000, site[1] - 12_000),
-                                                (site[0] + 12_000, site[1] + 12_000))))
-        assert from_file > 0
-        assert abs(from_file - from_lv95) <= 0.02 * from_lv95
-
-    def test_precut_frame_survives_a_pandas_or_gdf_round_trip(self, rdf, site, archive):
-        """cs_polygon holds shapely objects; they must WKB-encode into polars."""
-        from raddb.viz.plot import plot_vcs
-        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
-                                       (site[0] + 12_000, site[1] + 12_000))
-        n = _n_polys(cs.plot_vcs())
-        assert _n_polys(plot_vcs(cs.to_pandas(), archive_dir=archive)) == n
-        assert _n_polys(plot_vcs(cs.to_geopandas(), archive_dir=archive)) == n
-
-    @pytest.mark.parametrize("as_frame", ["polars", "pandas", "geopandas"])
-    def test_line_works_from_a_bare_frame_with_an_archive(self, rdf, site, archive, as_frame):
-        """A bare frame has gate_id and the archive has the geometry, so the
-        section is cuttable — plot_vcs must not be stricter than plot_ppi."""
-        from raddb.viz.plot import plot_vcs
-        data = {"polars": rdf.data,
-                "pandas": rdf.to_pandas(),
-                "geopandas": rdf.to_geopandas()}[as_frame]
-        line = ((site[0] - 12_000, site[1] - 12_000), (site[0] + 12_000, site[1] + 12_000))
-        assert (_n_polys(plot_vcs(data, line=line, archive_dir=archive))
-                == _n_polys(rdf.plot_vcs(line=line)))
-
-    def test_line_from_a_bare_frame_without_an_archive_raises(self, rdf, site):
-        from raddb.viz.plot import plot_vcs
-        with pytest.raises(ValueError, match="archive"):
-            plot_vcs(rdf.data, line=((site[0], site[1]), (site[0] + 5_000, site[1])))
-
-    def test_shapefile_line(self, rdf, site, tmp_path):
-        import shapefile
-        w = shapefile.Writer(str(tmp_path / "sec")); w.field("id", "N")
-        w.line([[[site[0] - 12_000, site[1] - 12_000], [site[0] + 12_000, site[1] + 12_000]]])
-        w.record(1); w.close()
-        assert _n_polys(rdf.plot_vcs(line=str(tmp_path / "sec.shp"))) > 0
-
-
-# ===========================================================================
-# 6. LUT plumbing the plots depend on
-# ===========================================================================
-
-class TestPlaneBackfill:
-    def test_missing_lattices_are_rebuilt_on_read(self, archive, tmp_path):
-        """A pre-geometry archive (2 files) backfills instead of failing."""
-        lut_dir = tmp_path / RADAR / "LUT"
-        lut_dir.mkdir(parents=True)
-        for kind in ("lut", "info"):
-            src = lut_file_path(RADAR, kind, archive)
-            (lut_dir / src.name).write_bytes(src.read_bytes())
-
-        assert not lut_file_path(RADAR, "h_plane", tmp_path).exists()
-        assert ensure_gate_planes(RADAR, tmp_path) is True
-        for kind in ("h_plane", "v_plane", "corners"):
-            assert lut_file_path(RADAR, kind, tmp_path).exists()
-        assert ensure_gate_planes(RADAR, tmp_path) is False
-
-    def test_backfill_recovers_the_projection_from_the_lut(self, archive, tmp_path):
-        """Old info.yaml files have no crs block; the LUT columns still say EPSG."""
-        lut_dir = tmp_path / RADAR / "LUT"
-        lut_dir.mkdir(parents=True)
-        for kind in ("lut", "info"):
-            src = lut_file_path(RADAR, kind, archive)
-            (lut_dir / src.name).write_bytes(src.read_bytes())
-        ensure_gate_planes(RADAR, tmp_path)
-        cols = load_plane_nodes(RADAR, tmp_path, "h_plane").columns
-        assert "x_2056" in cols and "y_2056" in cols
-
-
-class TestAoiGeometryUnification:
-    def test_footprints_come_from_the_h_plane_lattice(self, archive):
-        """aoi.py and the plots must draw the same gate."""
-        from raddb.aoi import _lut_cs_table, _gate_footprints
-
-        cs_t = _lut_cs_table(archive, [RADAR]).to_pandas().head(300)
-        foot = _gate_footprints(cs_t, np.tan(np.deg2rad(0.5)), base_path=archive, epsg=2056)
-
-        hp = RadDB(archive_dir=str(archive), crs=2056).get_h_plane(RADAR, per_gate=True)
-        aligned = pl.DataFrame({"gate_id": cs_t["gate_id"].to_numpy()}).join(
-            hp, on="gate_id", how="left", maintain_order="left")
-        ref = shapely.polygons(np.stack([
-            np.stack([aligned[f"x_2056_{k}"].to_numpy(),
-                      aligned[f"y_2056_{k}"].to_numpy()], axis=1)
-            for k in range(1, 5)], axis=1).astype(np.float64))
-
-        assert np.allclose(shapely.get_coordinates(foot), shapely.get_coordinates(ref))
-
-    def test_planar_fallback_still_available(self, archive):
-        """Archives without the lattices keep working on the old approximation."""
-        from raddb.aoi import _lut_cs_table, _gate_footprints
-
-        cs_t = _lut_cs_table(archive, [RADAR]).to_pandas().head(50)
-        planar = _gate_footprints(cs_t, np.tan(np.deg2rad(0.5)), base_path=None, epsg=None)
-        assert shapely.is_valid(planar).all()
-
-    def test_crops_are_unaffected(self, rdf, site):
-        """Crops resolve on centroids, so unifying footprints must not move them."""
-        assert len(rdf.crop_around_point(site, distance=8_000)) == 9504
-
-    def test_cross_section_height_follows_the_v_plane(self, rdf, site, archive):
-        cs = rdf.extract_cross_section((site[0] - 12_000, site[1] - 12_000),
-                                       (site[0] + 12_000, site[1] + 12_000))
-        from raddb.main import _decode_geometry
-        pdf = _decode_geometry(cs.data.to_pandas()).head(200)
-        heights = np.array([p.bounds[3] - p.bounds[1] for p in pdf["cs_polygon"]])
-
-        vp = RadDB(archive_dir=str(archive), crs=2056).get_v_plane(RADAR, per_gate=True)
-        va = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
-            vp, on="gate_id", how="left", maintain_order="left")
-        thick = 0.5 * (np.abs(va["z_asl_4"].to_numpy() - va["z_asl_1"].to_numpy())
-                       + np.abs(va["z_asl_3"].to_numpy() - va["z_asl_2"].to_numpy()))
-        assert np.corrcoef(heights, thick)[0, 1] > 0.9
-
-
-# ===========================================================================
-# 9. Opt-in polar coordinates on the converters
-# ===========================================================================
-
-class TestPolarCoordColumns:
-    POLAR = ("range", "azimuth", "elevation_angle")
-
-    def test_absent_by_default(self, rdf):
-        for c in self.POLAR:
-            assert c not in rdf.to_pandas(with_geometry=True).columns
-            assert c not in rdf.to_geopandas().columns
-
-    def test_present_on_request(self, rdf):
-        pdf = rdf.to_pandas(with_polar_coords=True)
-        gdf = rdf.to_geopandas(with_polar_coords=True)
-        for c in self.POLAR:
-            assert c in pdf.columns and c in gdf.columns
-
-    def test_values_match_the_lut(self, rdf, archive):
-        pdf = rdf.to_pandas(with_polar_coords=True)
-        lut = RadDB(archive_dir=str(archive), crs=2056).get_lut(RADAR)
-        ref = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
-            lut.select(["gate_id", *self.POLAR]), on="gate_id",
-            how="left", maintain_order="left")
-        for c in self.POLAR:
-            assert np.allclose(pdf[c].to_numpy(), ref[c].to_numpy())
-
-    def test_with_polar_coords_implies_geometry(self, rdf):
-        """Polar columns come from the LUT, so the join happens either way."""
-        assert "latitude" in rdf.to_pandas(with_polar_coords=True).columns
-
-    def test_row_count_is_unchanged(self, rdf):
-        assert len(rdf.to_pandas(with_polar_coords=True)) == len(rdf)
-
-
-# ===========================================================================
-# 10. Axis tick labels (km from metres)
-# ===========================================================================
-
-class TestKmTickLabels:
-    """Labels must be unique *and* equal to the value they sit on."""
-
-    @staticmethod
-    def _labels(lo, hi, offset=0.0):
-        from raddb.viz.plot import _KmFormatter
-        fig, ax = plt.subplots()
-        ax.set_ylim(lo, hi)
-        ax.yaxis.set_major_formatter(_KmFormatter(offset))
-        fig.canvas.draw()
-        locs = np.asarray(ax.yaxis.get_majorticklocs(), dtype=float)
-        labs = [t.get_text() for t in ax.get_yticklabels()]
-        plt.close(fig)
-        return [(v, l) for v, l in zip(locs, labs) if l and lo <= v <= hi]
-
-    @pytest.mark.parametrize("lo,hi,offset", [
-        (1400, 5900, 0.0),        # the reported case: 500 m steps
-        (0, 20000, 0.0),          # matplotlib picks 2.5 km steps
-        (0, 13000, 0.0),
-        (0, 500, 0.0),
-        (0, 200, 0.0),            # 25 m steps -> 3 decimals
-        (0, 60, 0.0),
-        (0, 250000, 0.0),
-        (2_600_000, 2_760_000, 2e6),   # LV95 easting
-        (1_050_000, 1_150_000, 1e6),   # LV95 northing
-    ])
-    def test_labels_are_unique_and_exact(self, lo, hi, offset):
-        pairs = self._labels(lo, hi, offset)
-        labels = [l for _, l in pairs]
-        assert len(labels) == len(set(labels)), f"repeated labels: {labels}"
-        for v, l in pairs:
-            km = (v - offset) / 1e3
-            assert abs(float(l) - km) < 1e-6 * max(1.0, abs(km)), \
-                f"label {l!r} does not equal {km}"
-
-    def test_the_reported_regression(self):
-        """A 1.4-5.9 km section used to read 1,2,2,2,3,4,4,4,5,6,6."""
-        labels = [l for _, l in self._labels(1400, 5900)]
-        assert labels == ["1.5", "2.0", "2.5", "3.0", "3.5", "4.0", "4.5", "5.0", "5.5"]
-
-    def test_real_plots_have_no_duplicate_ticks(self, rdf, site):
-        """Every plot, both axes."""
-        cases = [
-            rdf.plot_ppi(sweep=1),
-            rdf.plot_ppi(sweep=1, coords="swiss"),
-            rdf.plot_rhi(azimuth=0),
-            rdf.plot_cappi(altitude=1200),
-            rdf.plot_vcs(line=((site[0] - 12_000, site[1] - 12_000),
-                               (site[0] + 12_000, site[1] + 12_000))),
-        ]
-        for p in cases:
-            p.axes.figure.canvas.draw()
-            for axis in (p.axes.xaxis, p.axes.yaxis):
-                labs = [t.get_text() for t in axis.get_ticklabels()
-                        if t.get_text() and t.get_visible()]
-                assert len(labs) == len(set(labs)), f"repeated ticks: {labs}"
diff --git a/raddb/tests/test_polars_backend.py b/raddb/tests/test_polars_backend.py
deleted file mode 100644
index 2378e8d..0000000
--- a/raddb/tests/test_polars_backend.py
+++ /dev/null
@@ -1,205 +0,0 @@
-"""
-raddb/tests/test_polars_backend.py
-----------------------------------
-Tests for the polars backend contract:
-
-1. the LUT loads as polars, and ``gate_id`` decoding inverts the encoding
-2. crops **select** rows and never widen them with LUT columns
-3. ``to_geoarrow`` emits GeoArrow-tagged point and wedge-polygon geometry
-4. the dynamic values and the LUT stay separate tables
-
-Run with:
-    pytest raddb/tests/test_polars_backend.py -v
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import polars as pl
-import pytest
-
-_PKG_ROOT = Path(__file__).resolve().parents[2]
-if str(_PKG_ROOT) not in sys.path:
-    sys.path.insert(0, str(_PKG_ROOT))
-
-from raddb.tests.test_fixes import RADAR, _make_datatree  # noqa: E402
-
-
-@pytest.fixture
-def archive(tmp_path):
-    """A tiny single-volume archive with a LUT, in polars-backed RadDB form."""
-    from raddb.main import RadDB
-
-    db = RadDB(archive_dir=str(tmp_path), crs=2056)
-    db.archive(datatree=_make_datatree(vol_time=pd.Timestamp("2024-08-01 12:00:00")),
-               radar=RADAR)
-    return db
-
-
-class TestLutIsPolars:
-    def test_load_radar_lut_returns_polars(self, archive):
-        lut = archive.get_lut(RADAR)
-        assert isinstance(lut, pl.DataFrame)
-        assert "gate_id" in lut.columns and lut.height > 0
-
-    def test_lut_coordinates_stay_float64(self, archive):
-        """Gate positions are float64 — precision is a hard requirement."""
-        lut = archive.get_lut(RADAR)
-        for col in ("latitude", "longitude", "altitude", "x", "y", "z"):
-            if col in lut.columns:
-                assert lut.schema[col] == pl.Float64, f"{col} lost float64"
-
-    def test_decode_gate_ids_inverts_encode(self, archive):
-        from raddb.lut import decode_gate_ids
-
-        lut = archive.get_lut(RADAR)
-        sweeps, azimuths, ranges = decode_gate_ids(lut["gate_id"].to_numpy())
-        assert np.array_equal(sweeps, lut["sweep"].to_numpy().astype(np.int64))
-        # gate_id stores azimuth rounded to 0.1 deg and range as integer metres.
-        assert np.allclose(azimuths, np.round(lut["azimuth"].to_numpy() * 10) / 10)
-        assert np.allclose(ranges, lut["range"].to_numpy().astype(np.int64))
-
-
-class TestCropsSelectNotWiden:
-    def test_crop_keeps_the_same_columns(self, archive):
-        rdf = archive.open()
-        geo = rdf.to_pandas(with_geometry=True)
-        cx, cy = float(geo["x_2056"].median()), float(geo["y_2056"].median())
-        crop = rdf.crop_by_bbox(bounds=(cx - 5000, cy - 5000, cx + 5000, cy + 5000))
-
-        assert 0 < len(crop) < len(rdf), "crop should select a strict, non-empty subset"
-        assert crop.columns() == rdf.columns(), (
-            "crop widened the frame with LUT columns: "
-            f"{set(crop.columns()) - set(rdf.columns())}"
-        )
-
-    def test_crop_matches_an_isin_reference(self, archive):
-        """The semi-join must select exactly what the old isin() filter did."""
-        import shapely
-
-        from raddb.aoi import _lut_centroids, _reproject_to_aoi, _resolve_aoi_centroids
-
-        rdf = archive.open()
-        geo = rdf.to_pandas(with_geometry=True)
-        cx, cy = float(geo["x_2056"].median()), float(geo["y_2056"].median())
-        bounds = (cx - 5000, cy - 5000, cx + 5000, cy + 5000)
-
-        crop = rdf.crop_by_bbox(bounds=bounds)
-        cen = _resolve_aoi_centroids(
-            _lut_centroids(archive.archive_dir, rdf.radars()),
-            _reproject_to_aoi(shapely.box(*bounds), 2056, 2056),
-        )
-        expected = set(np.intersect1d(
-            rdf.data["gate_id"].to_numpy(), cen["gate_id"].to_numpy()
-        ))
-        assert set(crop.data["gate_id"].to_numpy()) == expected
-
-    def test_empty_crop_does_not_raise(self, archive):
-        """A crop that matches nothing still converts (regression: pl.concat([]))."""
-        rdf = archive.open()
-        far = rdf.crop_by_bbox(bounds=(9e6, 9e6, 9e6 + 1000, 9e6 + 1000))
-        assert len(far) == 0
-        assert far.to_pandas(with_geometry=True).empty
-
-
-class TestToGeoArrow:
-    def test_point_geometry_is_tagged(self, archive):
-        tab = archive.open().to_geoarrow(geometry="point")
-        meta = tab.schema.field("geometry").metadata
-        assert meta[b"ARROW:extension:name"] == b"geoarrow.point"
-        assert b"EPSG:4326" in meta[b"ARROW:extension:metadata"]
-
-    def test_polygon_rings_are_closed_wedges(self, archive):
-        tab = archive.open().to_geoarrow(geometry="polygon")
-        assert tab.schema.field("geometry").metadata[b"ARROW:extension:name"] == b"geoarrow.polygon"
-
-        rings = tab.column("geometry").to_pylist()
-        assert rings and all(r is not None for r in rings), "every gate should be placed"
-        for r in rings:
-            assert len(r) == 1, "a gate is a single ring"
-            assert len(r[0]) == 5, "4 corners + closing vertex"
-            assert r[0][0] == r[0][-1], "ring must be closed"
-
-    def test_polygon_centroid_sits_on_the_gate(self, archive):
-        """The wedge must surround the LUT centroid it was built from."""
-        shapely = pytest.importorskip("shapely")
-
-        rdf = archive.open()
-        tab = rdf.to_geoarrow(geometry="polygon")
-        polys = shapely.polygons(
-            np.array([r[0] for r in tab.column("geometry").to_pylist()])
-        )
-        assert shapely.is_valid(polys).all()
-
-        lut = archive.get_lut(RADAR)
-        ref = (
-            pl.DataFrame({"gate_id": tab.column("gate_id").to_numpy()})
-            .join(lut.select("gate_id", "latitude", "longitude"), on="gate_id", how="left")
-        )
-        cen = shapely.centroid(polys)
-        dx = (shapely.get_x(cen) - ref["longitude"].to_numpy()) * 111_320 * np.cos(np.radians(46.0))
-        dy = (shapely.get_y(cen) - ref["latitude"].to_numpy()) * 111_320
-        gate_len = 20_000 / 24  # _make_datatree: 24 gates over ~20 km
-        assert np.hypot(dx, dy).max() < gate_len
-
-    def test_max_rows_guardrail(self, archive):
-        rdf = archive.open()
-        with pytest.raises(ValueError, match="max_rows"):
-            rdf.to_geoarrow(max_rows=1)
-        assert rdf.to_geoarrow(max_rows=None).num_rows == len(rdf)
-
-    def test_polygon_needs_a_single_radar(self, archive):
-        """Corner arrays are per radar, so a multi-radar frame must be rejected."""
-        rdf = archive.open()
-        mixed = rdf._derive(
-            pl.concat([rdf.data, rdf.data.with_columns(pl.lit("B").alias("radar"))])
-        )
-        with pytest.raises(ValueError, match="single radar"):
-            mixed.to_geoarrow(geometry="polygon", max_rows=None)
-
-
-def test_lut_is_not_carried_by_the_dynamic_frame(archive):
-    """open() returns dynamic values only — no geometry columns."""
-    rdf = archive.open()
-    lut_only = {"latitude", "longitude", "altitude", "x", "y", "z", "x_2056", "y_2056",
-                "azimuth", "range", "elevation_angle"}
-    assert not lut_only & set(rdf.columns()), (
-        f"LUT columns leaked into the dynamic frame: {lut_only & set(rdf.columns())}"
-    )
-    # ... and are reachable on request.
-    assert {"latitude", "longitude"} <= set(rdf.to_pandas(with_geometry=True).columns)
-
-
-class TestFilterOnLutColumns:
-    """Vertical subsetting must survive the crops no longer carrying LUT columns."""
-
-    def test_altitude_filter_without_a_crop(self, archive):
-        rdf = archive.open()
-        assert "altitude" not in rdf.columns(), "altitude is LUT data, not dynamic"
-
-        alt = rdf.to_pandas(with_geometry=True)["altitude"]
-        cut = float(alt.median())
-        band = rdf.filter({"var": "altitude", "logic": ">", "threshold": cut})
-
-        assert 0 < len(band) < len(rdf)
-        assert band.columns() == rdf.columns(), "the borrowed LUT column leaked"
-        assert band.to_pandas(with_geometry=True)["altitude"].min() > cut
-
-    def test_altitude_band_after_a_crop(self, archive):
-        rdf = archive.open()
-        geo = rdf.to_pandas(with_geometry=True)
-        cx, cy = float(geo["x_2056"].median()), float(geo["y_2056"].median())
-        aoi = rdf.crop_by_bbox(bounds=(cx - 5000, cy - 5000, cx + 5000, cy + 5000))
-
-        lo, hi = np.percentile(geo["altitude"], [25, 75])
-        band = aoi.filter([{"var": "altitude", "logic": ">", "threshold": float(lo)},
-                           {"var": "altitude", "logic": "<", "threshold": float(hi)}])
-        assert len(band) <= len(aoi)
-        assert band.columns() == rdf.columns()
-
-    def test_unknown_filter_column_raises(self, archive):
-        with pytest.raises(KeyError, match="cannot filter on"):
-            archive.open().filter({"var": "nope", "logic": ">", "threshold": 0})
diff --git a/raddb/tests/test_radar_code.py b/raddb/tests/test_radar_code.py
deleted file mode 100644
index 4f4d8c4..0000000
--- a/raddb/tests/test_radar_code.py
+++ /dev/null
@@ -1,303 +0,0 @@
-"""
-raddb/tests/test_radar_code.py
-------------------------------
-Tests for the base-36 radar code that forms the leading field of a ``gate_id``
-(encoding v2), the name normalisation it rests on, and the v1 archive guard.
-
-Synthetic throughout — the archive tests build DataTrees in ``tmp_path``.
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import polars as pl
-import pytest
-import yaml
-
-_PKG_ROOT = Path(__file__).resolve().parents[2]
-if str(_PKG_ROOT) not in sys.path:
-    sys.path.insert(0, str(_PKG_ROOT))
-
-from raddb.helper import (  # noqa: E402
-    RADAR_ALPHABET,
-    RADAR_CODE_LEN,
-    is_valid_radar_name,
-    normalize_radar_name,
-)
-from raddb.lut import (  # noqa: E402
-    GATE_ID_RADAR_BASE,
-    LEGACY_RADAR_TO_IDX,
-    MAX_RADAR_CODE,
-    decode_gate_ids,
-    decode_gate_radars,
-    decode_radar_code,
-    encode_gate_ids,
-    encode_radar_code,
-)
-from raddb.tests.test_fixes import _make_datatree  # noqa: E402
-
-
-# ===========================================================================
-# normalize_radar_name
-# ===========================================================================
-
-class TestNormalizeRadarName:
-
-    @pytest.mark.parametrize("raw,expected", [
-        ("A", "A"),
-        ("a", "A"),
-        (" L ", "L"),
-        ("MLA", "A"),          # MeteoSwiss spelling
-        ("mlw", "W"),
-        ("KTLX", "KTLX"),      # NEXRAD survives whole
-        ("koun", "KOUN"),
-        ("000A", "A"),         # zero padding is not part of the name
-        ("0A", "A"),
-        ("0", "0"),            # ... but a radar may be named "0"
-        ("ZZZZ", "ZZZZ"),
-    ])
-    def test_canonical_forms(self, raw, expected):
-        assert normalize_radar_name(raw) == expected
-
-    def test_multi_letter_names_are_not_truncated(self):
-        """The v1 bug: every name collapsed to its last character."""
-        assert normalize_radar_name("KTLX") != "X"
-        assert normalize_radar_name("KOUN") != "N"
-        # Two sites sharing a final letter must stay distinct, or one would
-        # overwrite the other's archive.
-        assert normalize_radar_name("KTLX") != normalize_radar_name("KABX")
-
-    def test_ml_rule_only_applies_at_three_characters(self):
-        assert normalize_radar_name("MLA") == "A"
-        assert normalize_radar_name("MLAB") == "MLAB"   # a real 4-char name
-
-    @pytest.mark.parametrize("bad", [
-        "", "   ", "chlem", "ABCDE", "A-B", "vol.", "A B", "MLABC", "é",
-    ])
-    def test_rejects_unusable_names(self, bad):
-        assert not is_valid_radar_name(bad)
-        with pytest.raises(ValueError, match="not usable"):
-            normalize_radar_name(bad)
-
-    def test_rejects_non_string(self):
-        assert not is_valid_radar_name(7)
-        with pytest.raises(ValueError, match="must be a string"):
-            normalize_radar_name(7)
-
-
-# ===========================================================================
-# encode_radar_code / decode_radar_code
-# ===========================================================================
-
-class TestRadarCode:
-
-    def test_known_values(self):
-        assert encode_radar_code("A") == 10
-        assert encode_radar_code("L") == 21
-        assert encode_radar_code("KTLX") == 971_493
-        assert encode_radar_code("0") == 0
-        assert encode_radar_code("ZZZZ") == MAX_RADAR_CODE
-
-    def test_capacity_is_36_pow_4(self):
-        assert MAX_RADAR_CODE == 36 ** RADAR_CODE_LEN - 1 == 1_679_615
-
-    def test_every_code_fits_int64(self):
-        """The largest gate_id must not overflow the int64 column."""
-        largest = MAX_RADAR_CODE * GATE_ID_RADAR_BASE + (GATE_ID_RADAR_BASE - 1)
-        assert largest < np.iinfo(np.int64).max
-        assert np.int64(largest) == largest
-
-    def test_five_characters_do_not_fit(self):
-        """Documents why RADAR_CODE_LEN is 4: 36**5 blows the int64 budget."""
-        budget = (np.iinfo(np.int64).max - (GATE_ID_RADAR_BASE - 1)) // GATE_ID_RADAR_BASE
-        assert 36 ** 4 - 1 <= budget < 36 ** 5 - 1
-
-    def test_decode_is_injective(self):
-        """Distinct codes never name the same radar."""
-        names = {decode_radar_code(c) for c in range(MAX_RADAR_CODE + 1)}
-        assert len(names) == MAX_RADAR_CODE + 1 == 1_679_616
-
-    def test_name_round_trip_is_total_over_canonical_names(self):
-        """Every name that is its own canonical form survives encode -> decode."""
-        names = {decode_radar_code(c) for c in range(MAX_RADAR_CODE + 1)}
-        canonical = {n for n in names if normalize_radar_name(n) == n}
-        assert len(canonical) == 1_679_580  # all but the 36 ML? aliases
-        assert all(decode_radar_code(encode_radar_code(n)) == n for n in canonical)
-
-    def test_only_ml_aliases_break_the_code_round_trip(self):
-        """encode(decode(c)) == c except where a name is an alias for another."""
-        broken = [c for c in range(MAX_RADAR_CODE + 1)
-                  if encode_radar_code(decode_radar_code(c)) != c]
-        assert len(broken) == 36
-        assert all(decode_radar_code(c).startswith("ML") for c in broken)
-        assert all(len(decode_radar_code(c)) == 3 for c in broken)
-
-    def test_zero_padding_is_transparent(self):
-        assert encode_radar_code("A") == encode_radar_code("000A") == encode_radar_code("0A")
-
-    def test_alphabet_positions_define_the_values(self):
-        for i, char in enumerate(RADAR_ALPHABET):
-            assert encode_radar_code(char.rjust(RADAR_CODE_LEN, "0")) == i
-
-    @pytest.mark.parametrize("code", [-1, MAX_RADAR_CODE + 1, 10 ** 9])
-    def test_decode_rejects_out_of_range(self, code):
-        with pytest.raises(ValueError, match="names no radar"):
-            decode_radar_code(code)
-
-    def test_more_than_26_radars_are_distinct(self):
-        """The point of the change: no 26-radar ceiling."""
-        names = [f"K{a}{b}" for a in "ABCDE" for b in "ABCDEFGHIJ"]  # 50 sites
-        codes = [encode_radar_code(n) for n in names]
-        assert len(set(codes)) == len(names) == 50
-        assert sorted(decode_radar_code(c) for c in codes) == sorted(names)
-
-
-# ===========================================================================
-# gate_id encoding
-# ===========================================================================
-
-class TestGateIdEncoding:
-
-    def test_radar_field_is_the_code(self):
-        gid = encode_gate_ids("KTLX", 3, np.array([91.4]), np.array([12_500.0]))
-        assert gid[0] // GATE_ID_RADAR_BASE == encode_radar_code("KTLX")
-
-    def test_low_fields_are_independent_of_the_radar(self):
-        """Only the leading field differs between radars — what migration relies on."""
-        az, rng = np.array([91.4, 270.0]), np.array([12_500.0, 240_000.0])
-        a = encode_gate_ids("A", 3, az, rng)
-        k = encode_gate_ids("KTLX", 3, az, rng)
-        delta = (encode_radar_code("KTLX") - encode_radar_code("A")) * GATE_ID_RADAR_BASE
-        assert np.array_equal(k - a, np.full(2, delta))
-
-    def test_decode_round_trip(self):
-        az, rng, sweeps = np.array([0.0, 91.4, 359.9]), np.array([0.0, 12_500.0, 999_999.0]), 7
-        gid = encode_gate_ids("KTLX", sweeps, az, rng)
-        got_sweeps, got_az, got_rng = decode_gate_ids(gid)
-        assert np.array_equal(got_sweeps, np.full(3, sweeps))
-        assert np.allclose(got_az, az)
-        assert np.allclose(got_rng, rng)
-        assert decode_gate_radars(gid) == ["KTLX"]
-
-    def test_decode_radars_spans_several(self):
-        az, rng = np.array([10.0]), np.array([1000.0])
-        gid = np.concatenate([
-            encode_gate_ids(r, 1, az, rng) for r in ("L", "KTLX", "A")
-        ])
-        assert decode_gate_radars(gid) == ["A", "KTLX", "L"]
-
-    def test_decode_radars_empty(self):
-        assert decode_gate_radars(np.array([], dtype=np.int64)) == []
-
-    def test_decode_radars_skips_unknown_code(self, caplog):
-        bogus = np.array([(MAX_RADAR_CODE + 5) * GATE_ID_RADAR_BASE], dtype=np.int64)
-        with caplog.at_level("WARNING"):
-            assert decode_gate_radars(bogus) == []
-        assert "names no radar" in caplog.text
-
-    def test_ml_name_encodes_as_its_letter(self):
-        az, rng = np.array([10.0]), np.array([1000.0])
-        assert np.array_equal(
-            encode_gate_ids("MLA", 1, az, rng), encode_gate_ids("A", 1, az, rng)
-        )
-
-    def test_unusable_name_raises(self):
-        with pytest.raises(ValueError, match="not usable"):
-            encode_gate_ids("OVERLONG", 1, np.array([10.0]), np.array([1000.0]))
-
-
-# ===========================================================================
-# Archive round-trip and the v1 guard
-# ===========================================================================
-
-def _archive(tmp_path, radar):
-    from raddb.main import RadDB
-
-    db = RadDB(archive_dir=str(tmp_path / "archive"), crs=2056)
-    db.archive(datatree=_make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")),
-               radar=radar)
-    return db
-
-
-class TestArchiveWithLongNames:
-
-    def test_four_letter_radar_round_trips(self, tmp_path):
-        db = _archive(tmp_path, "KTLX")
-        assert db.list_radars() == ["KTLX"]
-
-        rdf = db.open(radars="KTLX")
-        assert rdf.data.height > 0
-        assert rdf.radars() == ["KTLX"]
-
-        gids = rdf.data["gate_id"].to_numpy()
-        assert set(gids // GATE_ID_RADAR_BASE) == {encode_radar_code("KTLX")}
-        assert decode_gate_radars(gids) == ["KTLX"]
-
-    def test_lut_and_pol_gate_ids_join(self, tmp_path):
-        db = _archive(tmp_path, "KTLX")
-        lut = db.get_lut("KTLX")
-        pol = db.open(radars="KTLX").data
-        matched = pol.join(lut.select("gate_id"), on="gate_id", how="semi")
-        assert matched.height == pol.height
-
-    def test_info_yaml_records_no_version(self, tmp_path):
-        """Only v2 is ever written, so the version key was dropped entirely."""
-        db = _archive(tmp_path, "KTLX")
-        assert "gate_id_version" not in db.get_radar_info("KTLX")
-
-    def test_sel_by_radar_uses_the_code(self, tmp_path):
-        db = _archive(tmp_path, "KTLX")
-        rdf = db.open(radars="KTLX")
-        assert rdf.sel(radar="KTLX").data.height == rdf.data.height
-        assert rdf.sel(radar="A").data.height == 0
-
-    def test_two_radars_stay_distinct(self, tmp_path):
-        from raddb.main import RadDB
-
-        db = RadDB(archive_dir=str(tmp_path / "archive"), crs=2056)
-        for radar in ("KTLX", "KOUN"):
-            db.archive(
-                datatree=_make_datatree(n_sweeps=2, vol_time=pd.Timestamp("2024-01-01 12:00:00")),
-                radar=radar,
-            )
-        assert db.list_radars() == ["KOUN", "KTLX"]
-
-        both = db.open(radars=["KTLX", "KOUN"])
-        assert sorted(both.radars()) == ["KOUN", "KTLX"]
-        codes = set(both.data["gate_id"].to_numpy() // GATE_ID_RADAR_BASE)
-        assert codes == {encode_radar_code("KTLX"), encode_radar_code("KOUN")}
-
-
-class TestV1Archive:
-    """What happens to a v1 archive, now that nothing detects the encoding.
-
-    ``info.yaml`` records no version and there is no migration tool any more,
-    so a v1 archive loads silently and decodes to the wrong radar.  That cost
-    is pinned here so it stays visible rather than being rediscovered.
-    """
-
-    def _downgrade_to_v1(self, tmp_path, radar):
-        """Rewrite an archive back to the v1 encoding, as if written long ago."""
-        lut_dir = tmp_path / "archive" / radar / "LUT"
-        delta = (LEGACY_RADAR_TO_IDX[radar] - encode_radar_code(radar)) * GATE_ID_RADAR_BASE
-        for f in [lut_dir / f"{radar}_LUT.parquet",
-                  *sorted((tmp_path / "archive" / radar).rglob("*_POL.parquet"))]:
-            pl.read_parquet(f).with_columns(
-                (pl.col("gate_id") + delta).alias("gate_id")
-            ).write_parquet(f)
-        return delta
-
-    def test_v1_archive_is_read_without_complaint(self, tmp_path):
-        """The version guard is gone: a v1 archive now loads silently.
-
-        Its ids decode to the wrong radar — that is the cost of dropping the
-        key, and it is pinned here so the trade-off stays visible.
-        """
-        db = _archive(tmp_path, "L")
-        self._downgrade_to_v1(tmp_path, "L")
-
-        assert "gate_id_version" not in db.get_radar_info("L")
-        assert decode_gate_radars(db.open(radars="L").data["gate_id"].to_numpy()) == ["B"]
diff --git a/raddb/tests/test_sel.py b/raddb/tests/test_sel.py
deleted file mode 100644
index 7cea0c6..0000000
--- a/raddb/tests/test_sel.py
+++ /dev/null
@@ -1,188 +0,0 @@
-"""
-raddb/tests/test_sel.py
------------------------
-Tests for ``RadDB.sel()`` — xarray-style label selection.
-
-Covers:
-
-1. dynamic-column selection (slice / scalar / list), inclusive slice bounds
-2. **static (LUT) column** selection — the borrowed column must be dropped
-   again, so the result still carries dynamic values only
-3. the LUT staying synchronised with the data after a selection
-4. immutability — ``sel`` never mutates the receiver
-
-All tests use synthetic DataTrees in ``tmp_path``; no real radar files needed.
-"""
-from __future__ import annotations
-
-import pandas as pd
-import polars as pl
-import pytest
-
-from raddb.main import RadDB
-from raddb.tests.test_fixes import RADAR, _make_datatree
-
-VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
-
-
-@pytest.fixture
-def rdf(tmp_path):
-    """Data-carrying RadDB from a tiny two-volume, one-radar archive."""
-    db = RadDB(archive_dir=str(tmp_path), crs=2056)
-    db.archive(datatree={str(t): _make_datatree(vol_time=t) for t in VOL_TIMES},
-               radar=RADAR)
-    return db.open(radars=RADAR)
-
-
-class TestSelDynamic:
-    def test_slice_is_inclusive_on_both_ends(self, rdf):
-        out = rdf.sel(DBZH=slice(5, 15))
-        vals = out.data["DBZH"].to_numpy()
-        assert len(out) > 0
-        assert vals.min() >= 5.0 and vals.max() <= 15.0
-
-    def test_open_ended_slices_match_filter(self, rdf):
-        assert len(rdf.sel(DBZH=slice(10, None))) == len(
-            rdf.filter({"var": "DBZH", "logic": ">=", "threshold": 10})
-        )
-        assert len(rdf.sel(DBZH=slice(None, 10))) == len(
-            rdf.filter({"var": "DBZH", "logic": "<=", "threshold": 10})
-        )
-
-    def test_columns_are_unchanged(self, rdf):
-        assert rdf.sel(DBZH=slice(0, 10)).columns() == rdf.columns()
-
-    def test_no_args_is_a_noop(self, rdf):
-        assert len(rdf.sel()) == len(rdf)
-
-    def test_keywords_are_anded(self, rdf):
-        both = rdf.sel(DBZH=slice(10, None), ZDR=slice(None, 5))
-        chained = rdf.sel(DBZH=slice(10, None)).sel(ZDR=slice(None, 5))
-        assert len(both) == len(chained)
-
-
-class TestSelTime:
-    def test_partial_day_string_selects_the_whole_day(self, rdf):
-        out = rdf.sel(time="2024-08-01")
-        assert 0 < len(out) < len(rdf)
-
-    def test_partial_month_string(self, rdf):
-        assert len(rdf.sel(time="2024-08")) == len(rdf)
-
-    def test_non_matching_period_is_empty(self, rdf):
-        assert len(rdf.sel(time="1999-01")) == 0
-
-    def test_time_slice(self, rdf):
-        out = rdf.sel(time=slice("2024-08-01", "2024-08-01"))
-        assert 0 < len(out) < len(rdf)
-
-
-class TestSelStaticLutColumns:
-    """Selection on LUT columns must borrow, evaluate, then drop."""
-
-    def test_range_selection_does_not_leak_the_column(self, rdf):
-        out = rdf.sel(range=slice(2_000, 10_000))
-        assert 0 < len(out) < len(rdf)
-        assert out.columns() == rdf.columns()
-        assert "range" not in out.columns()
-
-    def test_range_selection_matches_the_lut(self, rdf, tmp_path):
-        lut = RadDB(archive_dir=str(tmp_path), crs=2056).get_lut(RADAR)
-        want = set(
-            lut.filter((pl.col("range") >= 2_000) & (pl.col("range") <= 10_000))["gate_id"]
-            .to_list()
-        )
-        got = set(rdf.sel(range=slice(2_000, 10_000)).data["gate_id"].to_list())
-        assert got == set(rdf.data["gate_id"].to_list()) & want
-
-    def test_sweep_scalar(self, rdf):
-        out = rdf.sel(sweep=1)
-        assert 0 < len(out) < len(rdf)
-        assert "sweep" not in out.columns()
-
-    def test_lat_lon_aliases(self, rdf):
-        ge = rdf.geographic_extent()
-        out = rdf.sel(lon=slice(ge[0], ge[1]), lat=slice(ge[2], ge[3]))
-        assert len(out) == len(rdf)          # full extent keeps everything
-        assert out.columns() == rdf.columns()
-
-    def test_mixed_static_and_dynamic(self, rdf):
-        out = rdf.sel(DBZH=slice(10, None), range=slice(2_000, 10_000), sweep=1)
-        assert out.columns() == rdf.columns()
-        assert len(out) <= len(rdf)
-
-
-class TestSelRadars:
-    def test_radars_list_keeps_present_radar(self, rdf):
-        assert len(rdf.sel(radars=[RADAR])) == len(rdf)
-
-    def test_radars_list_excluding_present_radar_is_empty(self, rdf):
-        other = "W" if RADAR != "W" else "L"
-        assert len(rdf.sel(radars=[other])) == 0
-
-    def test_multi_radar_selection(self, tmp_path):
-        db = RadDB(archive_dir=str(tmp_path), crs=2056)
-        db.archive(datatree={"A": [_make_datatree(vol_time=VOL_TIMES[0])],
-                             "D": [_make_datatree(vol_time=VOL_TIMES[0])]})
-        both = db.open()
-        assert sorted(both.radars()) == ["A", "D"]
-        only_a = both.sel(radars=["A"])
-        assert only_a.radars() == ["A"]
-        assert 0 < len(only_a) < len(both)
-
-
-class TestSelKeepsLutSynchronised:
-    def test_geometry_shrinks_with_the_data(self, rdf):
-        out = rdf.sel(range=slice(2_000, 10_000))
-        assert len(out._gate_geometry()) < len(rdf._gate_geometry())
-
-    def test_geometry_gate_ids_match_data_gate_ids(self, rdf):
-        out = rdf.sel(range=slice(2_000, 10_000), DBZH=slice(10, None))
-        geo = out._gate_geometry()
-        assert set(geo["gate_id"].to_list()) == set(out.data["gate_id"].to_list())
-
-    def test_with_geometry_converter_still_works(self, rdf):
-        out = rdf.sel(range=slice(2_000, 10_000))
-        pdf = out.to_pandas(with_geometry=True)
-        assert len(pdf) == len(out)
-        assert "latitude" in pdf.columns
-
-
-class TestSelImmutability:
-    def test_receiver_is_untouched(self, rdf):
-        before_len, before_cols = len(rdf), rdf.columns()
-        rdf.sel(DBZH=slice(0, 1), range=slice(2_000, 3_000), sweep=1)
-        assert len(rdf) == before_len
-        assert rdf.columns() == before_cols
-
-    def test_returns_a_new_object_with_same_config(self, rdf):
-        out = rdf.sel(DBZH=slice(0, 10))
-        assert out is not rdf
-        assert isinstance(out, RadDB)
-        assert out.crs() == rdf.crs()
-        assert str(out.archive_dir) == str(rdf.archive_dir)
-
-
-class TestSelErrors:
-    def test_unknown_column_raises_keyerror(self, rdf):
-        with pytest.raises(KeyError):
-            rdf.sel(NOT_A_COLUMN=1)
-
-    def test_step_in_slice_raises_valueerror(self, rdf):
-        with pytest.raises(ValueError):
-            rdf.sel(DBZH=slice(0, 10, 2))
-
-
-class TestFilterKeys:
-    """`filter` rejects a misspelt key instead of silently defaulting it."""
-
-    def test_unknown_key_raises(self, rdf):
-        # "value" is not a filter key; threshold would default to 0 and the
-        # filter would keep every row while looking like it ran.
-        with pytest.raises(KeyError, match="unknown filter key"):
-            rdf.filter({"var": "DBZH", "logic": ">", "value": 10})
-
-    def test_correct_key_filters(self, rdf):
-        out = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 10})
-        assert 0 < len(out) < len(rdf)
-        assert out.data["DBZH"].min() > 10
diff --git a/raddb/tests/test_viz_init.py b/raddb/tests/test_viz_init.py
new file mode 100644
index 0000000..f7f193e
--- /dev/null
+++ b/raddb/tests/test_viz_init.py
@@ -0,0 +1,63 @@
+"""Tests for :mod:`raddb.viz` — the plotting subpackage.
+
+``raddb/viz/__init__.py`` is a docstring and nothing else: importing ``raddb.viz`` must
+stay cheap and must not drag in the optional interactive stack.  ``interactive`` needs
+ipyleaflet/ipywidgets, which are a ``viz`` extra, so pulling them in eagerly would make
+``import raddb`` fail on a minimal install.
+"""
+
+from __future__ import annotations
+
+import sys
+
+import pytest
+
+import raddb.viz
+
+
+def test_the_subpackage_imports():
+    """A bare ``import raddb.viz`` succeeds and is a package."""
+    assert raddb.viz.__doc__
+    assert hasattr(raddb.viz, "__path__"), "raddb.viz must be a package, not a module"
+
+
+def test_plot_is_importable():
+    """``raddb.viz.plot`` holds the four plots plus the quicklook."""
+    from raddb.viz import plot
+
+    for name in ("plot_ppi", "plot_rhi", "plot_cappi", "plot_vcs", "plot_aoi_quicklook"):
+        assert callable(getattr(plot, name))
+
+
+def test_interactive_is_importable():
+    """``raddb.viz.interactive`` holds the ipyleaflet AOI selector."""
+    pytest.importorskip("ipyleaflet")
+    from raddb.viz import interactive
+
+    assert hasattr(interactive, "AOISelector")
+
+
+def test_importing_the_subpackage_does_not_pull_in_ipyleaflet():
+    """``interactive`` is optional; ``raddb.viz`` must not require it.
+
+    Checked structurally rather than by watching ``sys.modules``, because another test in
+    the session may already have imported ipyleaflet.
+    """
+    import ast
+    from pathlib import Path
+
+    src = Path(raddb.viz.__file__).read_text(encoding="utf-8")
+    imported = [n for n in ast.walk(ast.parse(src)) if isinstance(n, (ast.Import, ast.ImportFrom))]
+    assert imported == [], "raddb/viz/__init__.py must stay import-free"
+
+
+def test_lonboard_is_not_imported_eagerly():
+    """lonboard caches a broken pyproj context if it imports before ``raddb._proj``."""
+    import ast
+    from pathlib import Path
+
+    plot_src = Path(sys.modules["raddb.viz.plot"].__file__).read_text(encoding="utf-8")
+    top_level = [n for n in ast.parse(plot_src).body if isinstance(n, (ast.Import, ast.ImportFrom))]
+    names = {getattr(n, "module", None) for n in top_level}
+    names |= {alias.name for n in top_level if isinstance(n, ast.Import) for alias in n.names}
+    assert "lonboard" not in names, "lonboard must only be imported lazily, inside a function"
diff --git a/raddb/tests/test_viz_interactive.py b/raddb/tests/test_viz_interactive.py
new file mode 100644
index 0000000..2d4ed3d
--- /dev/null
+++ b/raddb/tests/test_viz_interactive.py
@@ -0,0 +1,322 @@
+"""Tests for :mod:`raddb.viz.interactive` — the ipyleaflet AOI selector.
+
+The module splits into a **pure** dispatch layer (a drawn GeoJSON feature to the matching
+``RadDB`` method) and a widget wrapper.  The dispatch is where the behaviour lives and is
+tested directly with hand-written features; the widget is driven by calling its callbacks
+rather than by simulating clicks, which needs no Jupyter frontend.
+
+The map is WGS-84, so every dispatched call must carry ``crs=4326``.  Reading those
+degrees as archive metres would put the AOI thousands of kilometres away.
+"""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+
+from raddb.tests.conftest import RADAR
+from raddb.viz.interactive import (
+    AOISelector,
+    _crop_from_feature,
+    _feature_collection,
+    _is_axis_aligned_box,
+)
+
+pytest.importorskip("ipyleaflet")
+pytest.importorskip("ipywidgets")
+
+CH_SITE = (7.0, 46.0)
+"""The synthetic fixture's site — ``(longitude, latitude)``, as the map reports it."""
+
+
+def _feature(geometry: dict) -> dict:
+    """Wrap a geometry the way ipyleaflet's draw control hands it over."""
+    return {"type": "Feature", "properties": {}, "geometry": geometry}
+
+
+def _box(lon, lat, half=0.05):
+    """An axis-aligned rectangle ring around ``(lon, lat)``, as a drawn rectangle."""
+    return [
+        [lon - half, lat - half],
+        [lon + half, lat - half],
+        [lon + half, lat + half],
+        [lon - half, lat + half],
+        [lon - half, lat - half],
+    ]
+
+
+# ---------------------------------------------------------------------------
+# _is_axis_aligned_box — how a rectangle is told from a polygon
+# ---------------------------------------------------------------------------
+
+
+def test_is_axis_aligned_box_accepts_a_drawn_rectangle():
+    """Four corners over two distinct longitudes and two latitudes."""
+    assert _is_axis_aligned_box(_box(*CH_SITE))
+
+
+def test_is_axis_aligned_box_accepts_an_unclosed_ring():
+    """Some producers omit the repeated closing vertex."""
+    assert _is_axis_aligned_box(_box(*CH_SITE)[:-1])
+
+
+def test_is_axis_aligned_box_rejects_a_rotated_quad():
+    """A rotated rectangle has four distinct longitudes, so it is a polygon."""
+    assert not _is_axis_aligned_box([[0, 1], [1, 2], [2, 1], [1, 0], [0, 1]])
+
+
+def test_is_axis_aligned_box_rejects_a_triangle():
+    """Anything other than four corners is a polygon."""
+    assert not _is_axis_aligned_box([[0, 0], [1, 0], [0, 1], [0, 0]])
+
+
+def test_is_axis_aligned_box_rejects_an_empty_ring():
+    """A degenerate ring must return ``False``, not raise."""
+    assert not _is_axis_aligned_box([])
+
+
+# ---------------------------------------------------------------------------
+# _crop_from_feature — the dispatch table
+# ---------------------------------------------------------------------------
+
+
+def test_a_marker_dispatches_to_crop_around_point(rdb):
+    """A drawn marker crops a radius, using the widget's distance."""
+    kind, out = _crop_from_feature(rdb, _feature({"type": "Point", "coordinates": list(CH_SITE)}), distance_m=8_000)
+
+    assert kind == "point"
+    assert 0 < len(out) < len(rdb)
+
+
+def test_a_rectangle_dispatches_to_crop_by_bbox(rdb):
+    """An axis-aligned polygon is recognised as a bbox crop."""
+    kind, out = _crop_from_feature(rdb, _feature({"type": "Polygon", "coordinates": [_box(*CH_SITE)]}))
+
+    assert kind == "bbox"
+    assert len(out) > 0
+
+
+def test_a_rotated_polygon_dispatches_to_crop_by_polygone(rdb):
+    """Not axis-aligned, so the full polygon path runs instead."""
+    ring = [
+        [CH_SITE[0], CH_SITE[1] + 0.06],
+        [CH_SITE[0] + 0.06, CH_SITE[1]],
+        [CH_SITE[0], CH_SITE[1] - 0.06],
+        [CH_SITE[0] - 0.06, CH_SITE[1]],
+        [CH_SITE[0], CH_SITE[1] + 0.06],
+    ]
+
+    kind, out = _crop_from_feature(rdb, _feature({"type": "Polygon", "coordinates": [ring]}))
+
+    assert kind == "polygon"
+    assert len(out) > 0
+
+
+def test_a_polyline_dispatches_to_extract_cross_section(rdb):
+    """Only the first and last vertex are used — a section is defined by two points."""
+    line = {"type": "LineString", "coordinates": [[6.9, 46.0], [7.0, 46.0], [7.1, 46.0]]}
+
+    kind, out = _crop_from_feature(rdb, _feature(line))
+
+    assert kind == "cross_section"
+    assert "cs_polygon" in out.data.columns
+
+
+def test_a_bare_geometry_is_accepted(rdb):
+    """The draw control sometimes emits a geometry without the Feature wrapper."""
+    kind, _ = _crop_from_feature(rdb, {"type": "Point", "coordinates": list(CH_SITE)}, distance_m=8_000)
+
+    assert kind == "point"
+
+
+def test_an_unsupported_geometry_is_refused(rdb):
+    """A circle has no crop equivalent; the message names the type."""
+    with pytest.raises(ValueError, match="unsupported drawn geometry"):
+        _crop_from_feature(rdb, _feature({"type": "GeometryCollection", "geometries": []}))
+
+
+def test_the_drawn_coordinates_are_read_as_lonlat(rdb):
+    """A marker at the radar site must land on the radar, not 2600 km away."""
+    _, at_site = _crop_from_feature(
+        rdb, _feature({"type": "Point", "coordinates": list(CH_SITE)}), distance_m=8_000
+    )
+    _, elsewhere = _crop_from_feature(
+        rdb, _feature({"type": "Point", "coordinates": [0.0, 0.0]}), distance_m=8_000
+    )
+
+    assert len(at_site) > 0
+    assert len(elsewhere) == 0
+
+
+# ---------------------------------------------------------------------------
+# _feature_collection — saving the drawn AOI
+# ---------------------------------------------------------------------------
+
+
+def test_feature_collection_wraps_a_feature():
+    """An already-wrapped feature is reused, not double-wrapped."""
+    feat = _feature({"type": "Point", "coordinates": [7.0, 46.0]})
+
+    fc = _feature_collection(feat)
+
+    assert fc["type"] == "FeatureCollection"
+    assert fc["features"] == [feat]
+
+
+def test_feature_collection_wraps_a_bare_geometry():
+    """A bare geometry gains the Feature envelope GeoJSON readers expect."""
+    fc = _feature_collection({"type": "Point", "coordinates": [7.0, 46.0]})
+
+    assert fc["features"][0]["type"] == "Feature"
+    assert fc["features"][0]["geometry"]["type"] == "Point"
+
+
+def test_a_saved_collection_reloads_as_an_aoi(tmp_path, rdb):
+    """The round trip the docstring promises: save, then ``crop_by_polygone(path)``."""
+    ring = _box(*CH_SITE)
+    fc = _feature_collection(_feature({"type": "Polygon", "coordinates": [ring]}))
+    path = tmp_path / "aoi.geojson"
+    path.write_text(json.dumps(fc))
+
+    assert len(rdb.crop_by_polygone(str(path))) > 0
+
+
+# ---------------------------------------------------------------------------
+# AOISelector — the widget
+# ---------------------------------------------------------------------------
+
+
+def test_AOISelector(rdb):
+    """Construction builds a map, a draw control and the four controls."""
+    sel = AOISelector(rdb)
+
+    assert sel.map is not None
+    assert sel.draw is not None
+    assert (sel.feature, sel.result, sel.kind) == (None, None, None)
+
+
+def test_AOISelector_init(rdb):
+    """The map centres on the radar sites and marks each one."""
+    sel = AOISelector(rdb, point_radius_m=8_000)
+
+    lat, lon = sel.map.center
+    assert (round(lat, 3), round(lon, 3)) == (46.0, 7.0)
+    assert sel.radius.value == pytest.approx(8_000.0)
+
+
+def test_the_centre_falls_back_to_switzerland_without_sites(rdb):
+    """An unknown radar yields no site, so the map still opens somewhere sensible."""
+    sel = AOISelector(rdb, radars=["ZZZZ"])
+
+    assert tuple(sel.map.center) == (46.82, 8.23)
+
+
+def test_an_explicit_centre_wins(rdb):
+    """``center=`` overrides the derived one."""
+    assert tuple(AOISelector(rdb, center=(35.3, -97.3)).map.center) == (35.3, -97.3)
+
+
+def test_a_missing_radar_does_not_break_the_map(rdb):
+    """A radar without info is skipped; the map is decoration, not a gate."""
+    sel = AOISelector(rdb, radars=[RADAR, "ZZZZ"])
+
+    assert sel._radar_sites([RADAR, "ZZZZ"]) == {RADAR: (46.0, 7.0)}
+
+
+def test_the_draw_callback_keeps_the_last_shape(rdb):
+    """``created`` and ``edited`` update the stored feature; nothing else does."""
+    sel = AOISelector(rdb)
+    first = _feature({"type": "Point", "coordinates": [7.0, 46.0]})
+    second = _feature({"type": "Point", "coordinates": [7.1, 46.1]})
+
+    sel._on_draw(None, "created", first)
+    assert sel.feature is first
+
+    sel._on_draw(None, "edited", second)
+    assert sel.feature is second
+
+    sel._on_draw(None, "deleted", first)
+    assert sel.feature is second
+
+
+def test_apply_without_a_shape_asks_for_one(rdb, capsys):
+    """Clicking Apply on an empty map explains what to do rather than raising."""
+    sel = AOISelector(rdb)
+
+    sel._apply()
+
+    assert sel.result is None
+    assert "Draw a shape" in capsys.readouterr().out
+
+
+def test_apply_runs_the_crop_and_stores_the_result(rdb):
+    """The whole point of the widget: ``.result`` holds a cropped RadDB."""
+    sel = AOISelector(rdb, point_radius_m=8_000)
+    sel.feature = _feature({"type": "Point", "coordinates": list(CH_SITE)})
+
+    sel._apply()
+
+    assert sel.kind == "point"
+    assert 0 < len(sel.result) < len(rdb)
+
+
+def test_apply_reports_the_sweep_count_from_the_gate_ids(rdb, capsys):
+    """``sweep`` is a LUT column, so it is decoded from ``gate_id``, not read off."""
+    sel = AOISelector(rdb, point_radius_m=8_000)
+    sel.feature = _feature({"type": "Point", "coordinates": list(CH_SITE)})
+
+    sel._apply()
+
+    text = capsys.readouterr().out
+    assert "2 sweeps" in text
+    assert "? sweeps" not in text
+
+
+def test_apply_surfaces_a_failure_in_the_widget(rdb, capsys):
+    """A bad shape is reported instead of killing the kernel."""
+    sel = AOISelector(rdb)
+    sel.feature = _feature({"type": "GeometryCollection", "geometries": []})
+
+    sel._apply()
+
+    assert sel.result is None
+    assert "crop failed" in capsys.readouterr().out
+
+
+def test_save_without_a_shape_says_so(rdb, capsys):
+    """Nothing drawn, nothing written — and no traceback."""
+    sel = AOISelector(rdb)
+
+    sel._save()
+
+    assert "Nothing to save" in capsys.readouterr().out
+
+
+def test_save_writes_a_reloadable_geojson(tmp_path, rdb):
+    """The saved file is a FeatureCollection that ``crop_by_polygone`` accepts."""
+    sel = AOISelector(rdb)
+    sel.feature = _feature({"type": "Polygon", "coordinates": [_box(*CH_SITE)]})
+    sel.save_path.value = str(tmp_path / "aoi.geojson")
+
+    sel._save()
+
+    saved = json.loads((tmp_path / "aoi.geojson").read_text())
+    assert saved["type"] == "FeatureCollection"
+    assert len(rdb.crop_by_polygone(sel.save_path.value)) > 0
+
+
+def test_AOISelector_display(rdb):
+    """``display()`` renders the widget and returns the selector for chaining."""
+    pytest.importorskip("IPython")
+    sel = AOISelector(rdb)
+
+    assert sel.display() is sel
+
+
+def test_the_selector_renders_itself_in_a_notebook(rdb):
+    """``_ipython_display_`` is what makes a bare selector show up in a cell."""
+    pytest.importorskip("IPython")
+    sel = AOISelector(rdb)
+
+    sel._ipython_display_()  # must not raise
diff --git a/raddb/tests/test_viz_plot.py b/raddb/tests/test_viz_plot.py
new file mode 100644
index 0000000..f453326
--- /dev/null
+++ b/raddb/tests/test_viz_plot.py
@@ -0,0 +1,904 @@
+"""Tests for :mod:`raddb.viz.plot` — the four plots, the quicklook and the scatter grid.
+
+Each plot draws **one plot into one Axes** and returns the matplotlib artist, so the
+caller composes panels by passing ``ax=``.  All four read gate geometry from the LUT
+lattices and join on ``gate_id``, which means a filtered, ``sel``-ed or cropped input
+draws exactly the gates it still holds — nothing is reindexed onto a full
+azimuth x range grid.
+
+Geometry follows the input: a RadDB or frame reads the stored lattices, a raw
+``xr.DataTree`` computes them from its own coordinates and needs no archive, and a
+GeoDataFrame is treated as a frame (its own geometry column is ignored).
+
+There is exactly **one** geometry path — the exact frustum.  No plot takes a
+``beamwidth_deg``: for archive-backed data the beamwidth was applied when ``v_plane`` was
+generated, and for a DataTree it is inferred from the file exactly as LUT generation does.
+"""
+
+from __future__ import annotations
+
+import copy
+import inspect
+import json
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import polars as pl
+import pytest
+import shapely
+
+import raddb.viz.plot as vp
+from raddb.lut import DEFAULT_BEAMWIDTH_DEG, cappi_chords, gate_corner_table
+from raddb.main import RadDB
+from raddb.tests.conftest import (
+    PLOT_GEOMETRY,
+    PLOT_RADAR,
+    SWISS_EPSG,
+    US_EPSG,
+    US_SITE,
+    build_datatree,
+)
+from raddb.viz.plot import (
+    _KmFormatter,
+    _beamwidth,
+    _line_endpoints,
+    _resolve_chord_overlap,
+    plot_aoi_quicklook,
+    plot_cappi,
+    plot_cross_section,
+    plot_latent_scatter,
+    plot_ppi,
+    plot_rhi,
+    plot_vcs,
+)
+
+N_AZ = PLOT_GEOMETRY["n_az"]
+N_RNG = PLOT_GEOMETRY["n_rng"]
+N_SWEEPS = PLOT_GEOMETRY["n_sweeps"]
+
+VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
+
+
+def _n_polys(artist) -> int:
+    """Number of gate polygons an artist drew."""
+    return len(artist.get_paths())
+
+
+def _line_across(site, half=12_000):
+    """A diagonal section line through the radar, in the archive's own metres."""
+    return ((site[0] - half, site[1] - half), (site[0] + half, site[1] + half))
+
+
+@pytest.fixture(scope="session")
+def plot_dtree():
+    """The same volume the plotting archive was built from, unarchived."""
+    return build_datatree(**PLOT_GEOMETRY)
+
+
+@pytest.fixture(scope="session")
+def plot_gdf(plot_archive_dir):
+    """The plotting archive as a GeoDataFrame."""
+    pytest.importorskip("geopandas")
+    return RadDB(archive_dir=str(plot_archive_dir), crs=SWISS_EPSG).open(radars=PLOT_RADAR).to_geopandas()
+
+
+# ---------------------------------------------------------------------------
+# plot_ppi
+# ---------------------------------------------------------------------------
+
+
+def test_plot_ppi(plot_rdb):
+    """One sweep, one PolyCollection, one polygon per surviving gate."""
+    from matplotlib.collections import PolyCollection
+
+    artist = plot_ppi(plot_rdb, sweep=1)
+
+    assert isinstance(artist, PolyCollection)
+    assert _n_polys(artist) == N_AZ * N_RNG
+
+
+def test_plot_ppi_draws_into_the_supplied_axes(plot_rdb):
+    """``ax=`` is how a caller composes a panel; the plot never makes its own figure."""
+    _, ax = plt.subplots()
+
+    artist = plot_ppi(plot_rdb, sweep=1, ax=ax)
+
+    assert artist.axes is ax
+    assert len(ax.collections) == 1
+
+
+def test_a_multi_panel_figure_composes(plot_rdb):
+    """One plot per Axes — the user builds the panel, not the plot function."""
+    _, axes = plt.subplots(2, 2)
+
+    for ax, variable in zip(axes.ravel(), ["DBZH", "ZDR", "RHOHV", "PHIDP"]):
+        plot_ppi(plot_rdb, sweep=1, variable=variable, ax=ax)
+
+    assert all(len(ax.collections) == 1 for ax in axes.ravel())
+
+
+def test_save_writes_a_file(plot_rdb, tmp_path):
+    """``save=`` writes the figure the artist was drawn into."""
+    out = tmp_path / "ppi.png"
+
+    plot_ppi(plot_rdb, sweep=1, save=str(out))
+
+    assert out.exists() and out.stat().st_size > 0
+
+
+def test_the_title_and_colorbar_are_optional(plot_rdb):
+    """A composed panel usually wants neither."""
+    artist = plot_ppi(plot_rdb, sweep=1, add_colorbar=False, title="custom")
+
+    assert artist.axes.get_title() == "custom"
+
+
+def test_a_filtered_frame_draws_only_its_surviving_gates(plot_rdb, plot_archive_dir):
+    """Nothing is reindexed onto a full grid, so the polygon count is the gate count."""
+    sub = plot_rdb.filter({"var": "DBZH", "logic": ">", "threshold": 20})
+    lut = RadDB(archive_dir=str(plot_archive_dir)).get_lut(PLOT_RADAR)
+    on_sweep_1 = (
+        sub.data.select("gate_id")
+        .join(lut.filter(pl.col("sweep") == 1).select("gate_id"), on="gate_id", how="semi")
+        .height
+    )
+
+    assert 0 < len(sub) < len(plot_rdb)
+    assert _n_polys(plot_ppi(sub, sweep=1)) == on_sweep_1
+
+
+def test_a_cropped_frame_plots(plot_rdb, plot_site):
+    """A crop is a smaller frame, nothing more."""
+    crop = plot_rdb.crop_around_point(plot_site, distance=8_000)
+
+    assert 0 < len(crop) < len(plot_rdb)
+    assert _n_polys(plot_ppi(crop, sweep=1)) > 0
+
+
+def test_a_sel_frame_plots(plot_rdb):
+    """``sel`` narrows a range window and the plot follows."""
+    narrowed = plot_rdb.sel(range=slice(2_000, 12_000))
+
+    assert 0 < len(narrowed) < len(plot_rdb)
+    assert _n_polys(plot_ppi(narrowed, sweep=1)) > 0
+
+
+def test_an_empty_selection_raises(plot_rdb):
+    """Nothing to draw is an error, not a blank Axes."""
+    empty = plot_rdb.filter({"var": "DBZH", "logic": ">", "threshold": 1e9})
+
+    with pytest.raises(ValueError):
+        plot_ppi(empty, sweep=1)
+
+
+def test_an_unknown_variable_raises(plot_rdb):
+    """A typo must not silently plot DBZH."""
+    with pytest.raises(KeyError):
+        plot_ppi(plot_rdb, sweep=1, variable="NOT_A_VAR")
+
+
+def test_a_missing_sweep_raises(plot_rdb):
+    """Sweep 99 does not exist in a six-sweep volume."""
+    with pytest.raises(ValueError):
+        plot_ppi(plot_rdb, sweep=99)
+
+
+def test_a_bare_frame_needs_an_archive(plot_rdb):
+    """Geometry lives in the LUT; a frame alone cannot say where its gates are."""
+    with pytest.raises(ValueError, match="archive"):
+        plot_ppi(plot_rdb.data, sweep=1)
+
+
+def test_a_bare_frame_with_an_archive_plots(plot_rdb, plot_archive_dir):
+    """Given the archive, a bare polars frame is as good as a RadDB."""
+    assert _n_polys(plot_ppi(plot_rdb.data, sweep=1, archive_dir=plot_archive_dir)) > 0
+
+
+def test_a_multi_radar_frame_needs_the_radar_named(tmp_path, make_datatree):
+    """A PPI fixes one sweep of one radar; two radars is ambiguous."""
+    db = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG)
+    db.archive(datatree={"A": [make_datatree(24, 20)], "D": [make_datatree(24, 20)]})
+
+    with pytest.raises(ValueError, match="radars"):
+        plot_ppi(db.open(), sweep=1)
+
+
+# ---------------------------------------------------------------------------
+# plot_rhi
+# ---------------------------------------------------------------------------
+
+
+def test_plot_rhi(plot_rdb):
+    """One azimuth, stacked across every sweep."""
+    assert _n_polys(plot_rhi(plot_rdb, azimuth=0)) == N_RNG * N_SWEEPS
+
+
+def test_rhi_height_reference_shifts_the_axis(plot_rdb, plot_archive_dir):
+    """``asl`` is ``rel`` plus the site altitude, exactly."""
+    altitude = RadDB(archive_dir=str(plot_archive_dir)).get_radar_info(PLOT_RADAR)["altitude"]
+
+    asl = plot_rhi(plot_rdb, azimuth=0, height="asl").axes.get_ylim()[0]
+    rel = plot_rhi(plot_rdb, azimuth=0, height="rel").axes.get_ylim()[0]
+
+    assert asl - rel == pytest.approx(altitude, abs=1.0)
+
+
+def test_rhi_rejects_an_unknown_height_reference(plot_rdb):
+    """Only ``asl`` and ``rel`` exist."""
+    with pytest.raises(ValueError):
+        plot_rhi(plot_rdb, azimuth=0, height="furlongs")
+
+
+def test_rhi_beyond_the_tolerance_raises(plot_rdb):
+    """No ray within ``az_tol`` means there is no RHI to draw."""
+    with pytest.raises(ValueError, match="no sweep has a ray within"):
+        plot_rhi(plot_rdb, azimuth=2.5, az_tol=0.1)
+
+
+def test_rhi_picks_a_ray_per_sweep_when_azimuths_jitter(tmp_path):
+    """A real antenna's azimuths differ between sweeps.
+
+    Matching one azimuth *value* across the whole LUT would select a single sweep and
+    collapse the RHI, so each sweep needs its own nearest ray.
+    """
+    import xarray as xr
+
+    dt = build_datatree(n_az=36, n_rng=20, n_sweeps=4)
+    jittered = {}
+    for i, (name, node) in enumerate(dt.children.items()):
+        ds = node.to_dataset()
+        jittered[name] = ds.assign_coords(azimuth=ds["azimuth"].values + 0.13 * i)
+    dt = xr.DataTree.from_dict(jittered)
+
+    db = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG)
+    db.archive(datatree={PLOT_RADAR: [dt]})
+    assert db.get_lut(PLOT_RADAR)["azimuth"].n_unique() == 36 * 4, "fixture should have per-sweep jitter"
+
+    assert _n_polys(plot_rhi(db.open(radars=PLOT_RADAR), azimuth=90.0, az_tol=1.0)) == 20 * 4
+
+
+# ---------------------------------------------------------------------------
+# plot_cappi
+# ---------------------------------------------------------------------------
+
+
+def test_plot_cappi(plot_rdb):
+    """A constant-altitude slice draws the chords that reach it."""
+    assert _n_polys(plot_cappi(plot_rdb, altitude=1200)) > 0
+
+
+def test_a_higher_slice_draws_fewer_gates(plot_rdb):
+    """Fewer beams reach higher, so the slice shrinks."""
+    assert _n_polys(plot_cappi(plot_rdb, altitude=1400)) < _n_polys(plot_cappi(plot_rdb, altitude=1100))
+
+
+def test_overlap_nearest_draws_fewer_gates_than_all(plot_rdb):
+    """``nearest`` resolves the double coverage that ``all`` keeps."""
+    assert _n_polys(plot_cappi(plot_rdb, altitude=1200, overlap="nearest")) < _n_polys(
+        plot_cappi(plot_rdb, altitude=1200, overlap="all")
+    )
+
+
+def test_resolve_chord_overlap_leaves_no_double_coverage(plot_archive_dir):
+    """The resolved chords must partition the ground-distance axis."""
+    resolved = _resolve_chord_overlap(cappi_chords(PLOT_RADAR, plot_archive_dir, 1200.0))
+
+    intervals = np.sort(
+        np.stack([resolved["d_near"].to_numpy(), resolved["d_far"].to_numpy()], axis=1), axis=0
+    )
+
+    assert (intervals[1:, 0] >= intervals[:-1, 1] - 1e-3).all()
+
+
+def test_fill_lowest_extends_the_far_field(plot_rdb):
+    """Beyond the lowest beam's reach the slice is extended, never shrunk."""
+    assert _n_polys(plot_cappi(plot_rdb, altitude=1200, fill_lowest=True)) >= _n_polys(
+        plot_cappi(plot_rdb, altitude=1200, fill_lowest=False)
+    )
+
+
+def test_an_altitude_above_every_beam_raises(plot_rdb):
+    """There is no slice at 100 km; say so rather than draw nothing."""
+    with pytest.raises(ValueError, match="reaches"):
+        plot_cappi(plot_rdb, altitude=99_999.0)
+
+
+def test_cappi_rejects_an_unknown_overlap_mode(plot_rdb):
+    """Only ``nearest`` and ``all`` exist."""
+    with pytest.raises(ValueError):
+        plot_cappi(plot_rdb, altitude=1200, overlap="sometimes")
+
+
+def test_slice_polygons_sit_inside_the_full_footprints(plot_rdb, plot_archive_dir):
+    """The constant-z cut trims gates along the beam; it never grows them."""
+    drawn = np.array([path.vertices[:4] for path in plot_cappi(plot_rdb, altitude=1200, overlap="all").get_paths()])
+    tbl = gate_corner_table(PLOT_RADAR, plot_archive_dir, kind="h_plane")
+    full = np.stack(
+        [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)], axis=1
+    )
+
+    assert shapely.area(shapely.polygons(drawn)).sum() <= shapely.area(shapely.polygons(full)).sum()
+
+
+# ---------------------------------------------------------------------------
+# plot_vcs — the section has to be defined
+# ---------------------------------------------------------------------------
+
+
+def test_plot_vcs(plot_rdb, plot_site):
+    """A line cuts the section and then draws it, in one call."""
+    assert _n_polys(plot_vcs(plot_rdb, line=_line_across(plot_site))) > 0
+
+
+def test_vcs_accepts_a_linestring(plot_rdb, plot_site):
+    """A shapely LineString is the same thing as a point pair."""
+    p1, p2 = _line_across(plot_site)
+
+    assert _n_polys(plot_vcs(plot_rdb, line=shapely.LineString([p1, p2]))) > 0
+
+
+def test_vcs_accepts_a_precut_frame(plot_rdb, plot_site):
+    """A frame that already carries ``cs_polygon`` is drawn directly."""
+    p1, p2 = _line_across(plot_site)
+    cs = plot_rdb.extract_cross_section(p1, p2)
+
+    assert _n_polys(plot_vcs(cs)) == len(cs)
+
+
+def test_vcs_without_a_section_raises(plot_rdb):
+    """No line and no ``cs_polygon`` leaves the section undefined."""
+    with pytest.raises(ValueError, match="cross-section"):
+        plot_vcs(plot_rdb)
+
+
+def test_vcs_with_both_a_line_and_a_precut_frame_is_ambiguous(plot_rdb, plot_site):
+    """Two definitions of the same section; refuse rather than pick one."""
+    p1, p2 = _line_across(plot_site)
+    cs = plot_rdb.extract_cross_section(p1, p2)
+
+    with pytest.raises(ValueError, match="ambiguous"):
+        plot_vcs(cs, line=(plot_site, (plot_site[0] + 5_000, plot_site[1])))
+
+
+def test_an_area_cropped_frame_has_no_section(plot_rdb, plot_site):
+    """The common mistake: an AOI crop selects an area, not a line."""
+    crop = plot_rdb.crop_around_point(plot_site, distance=10_000)
+
+    with pytest.raises(ValueError, match="no 'cs_polygon'"):
+        plot_vcs(crop)
+
+
+def test_vcs_refuses_a_datatree():
+    """The section path is ``gate_id``-keyed, and a DataTree has none until archived."""
+    with pytest.raises(TypeError, match="Archive the volume first"):
+        plot_vcs(build_datatree(24, 20), line=((0, 0), (1, 1)))
+
+
+def test_a_geojson_line_honours_its_own_crs(plot_rdb, plot_site, tmp_path):
+    """A GeoJSON is lon/lat by RFC 7946; reading those degrees as LV95 metres
+    would put the section about 2600 km away."""
+    import pyproj
+
+    from raddb.aoi import _to_pyproj_crs
+
+    transformer = pyproj.Transformer.from_crs(_to_pyproj_crs(SWISS_EPSG), _to_pyproj_crs(4326), always_xy=True)
+    p1, p2 = _line_across(plot_site)
+    a, b = transformer.transform(*p1), transformer.transform(*p2)
+    path = tmp_path / "section.geojson"
+    path.write_text(json.dumps({"type": "LineString", "coordinates": [list(a), list(b)]}))
+
+    from_file = _n_polys(plot_vcs(plot_rdb, line=str(path)))
+    from_metres = _n_polys(plot_vcs(plot_rdb, line=(p1, p2)))
+
+    assert from_file > 0
+    assert abs(from_file - from_metres) <= 0.02 * from_metres
+
+
+def test_a_shapefile_line_is_read(plot_rdb, plot_site, tmp_path):
+    """pyshp reads the ``.shp``; the ``.prj`` (absent here) would declare the CRS."""
+    shapefile = pytest.importorskip("shapefile")
+
+    p1, p2 = _line_across(plot_site)
+    writer = shapefile.Writer(str(tmp_path / "sec"))
+    writer.field("id", "N")
+    writer.line([[list(p1), list(p2)]])
+    writer.record(1)
+    writer.close()
+
+    assert _n_polys(plot_vcs(plot_rdb, line=str(tmp_path / "sec.shp"))) > 0
+
+
+@pytest.mark.parametrize("kind", ["polars", "pandas", "geopandas"])
+def test_a_line_works_from_a_bare_frame_with_an_archive(plot_rdb, plot_site, plot_archive_dir, kind):
+    """``plot_vcs`` must not be stricter than ``plot_ppi``: gate_id plus the LUT is enough."""
+    data = {"polars": plot_rdb.data, "pandas": plot_rdb.to_pandas(), "geopandas": plot_rdb.to_geopandas()}[kind]
+    line = _line_across(plot_site)
+
+    assert _n_polys(plot_vcs(data, line=line, archive_dir=plot_archive_dir)) == _n_polys(
+        plot_vcs(plot_rdb, line=line)
+    )
+
+
+def test_a_line_from_a_bare_frame_without_an_archive_raises(plot_rdb, plot_site):
+    """The section is cut against the LUT, so the archive is required."""
+    with pytest.raises(ValueError, match="archive"):
+        plot_vcs(plot_rdb.data, line=(plot_site, (plot_site[0] + 5_000, plot_site[1])))
+
+
+def test_a_precut_frame_survives_a_pandas_or_gdf_round_trip(plot_rdb, plot_site, plot_archive_dir):
+    """``cs_polygon`` holds shapely objects; they must WKB-encode into polars."""
+    p1, p2 = _line_across(plot_site)
+    cs = plot_rdb.extract_cross_section(p1, p2)
+    expected = _n_polys(plot_vcs(cs))
+
+    assert _n_polys(plot_vcs(cs.to_pandas(), archive_dir=plot_archive_dir)) == expected
+    assert _n_polys(plot_vcs(cs.to_geopandas(), archive_dir=plot_archive_dir)) == expected
+
+
+def test_line_endpoints_reports_the_source_crs(tmp_path):
+    """``_line_endpoints`` returns ``(p1, p2, src_crs)`` so a file's CRS can win."""
+    path = tmp_path / "section.geojson"
+    path.write_text(json.dumps({"type": "LineString", "coordinates": [[6.9, 46.0], [7.1, 46.0]]}))
+
+    p1, p2, src_crs = _line_endpoints(str(path))
+
+    assert src_crs == 4326
+    assert p1 == pytest.approx((6.9, 46.0))
+    assert p2 == pytest.approx((7.1, 46.0))
+
+
+def test_line_endpoints_from_a_point_pair_declares_no_crs():
+    """Hand-typed coordinates are in whatever frame the caller says — here, none."""
+    p1, p2, src_crs = _line_endpoints(((0.0, 0.0), (1.0, 1.0)))
+
+    assert (p1, p2, src_crs) == ((0.0, 0.0), (1.0, 1.0), None)
+
+
+# ---------------------------------------------------------------------------
+# plot_cross_section — the deprecated alias
+# ---------------------------------------------------------------------------
+
+
+def test_plot_cross_section(plot_rdb, plot_site):
+    """The standalone renderer: it draws a frame that already carries ``cs_polygon``.
+
+    Unlike ``plot_vcs`` it never cuts the section itself, and it returns
+    ``(fig, ax, PolyCollection)`` rather than a bare artist.
+    """
+    p1, p2 = _line_across(plot_site)
+    cs = plot_rdb.extract_cross_section(p1, p2)
+
+    fig, ax, artist = plot_cross_section(cs.to_pandas())
+
+    assert _n_polys(artist) == len(cs)
+    assert artist.axes is ax and ax.figure is fig
+
+
+def test_plot_cross_section_honours_a_supplied_axes(plot_rdb, plot_site):
+    """It composes like the other four, through ``ax=``."""
+    p1, p2 = _line_across(plot_site)
+    cs = plot_rdb.extract_cross_section(p1, p2)
+    _, ax = plt.subplots()
+
+    _, got_ax, artist = plot_cross_section(cs.to_pandas(), ax=ax)
+
+    assert got_ax is ax
+    assert artist.axes is ax
+
+
+def test_the_raddb_alias_for_plot_cross_section_is_deprecated(plot_rdb, plot_site):
+    """``RadDB.plot_cross_section`` warns and delegates to ``plot_vcs``."""
+    p1, p2 = _line_across(plot_site)
+    cs = plot_rdb.extract_cross_section(p1, p2)
+
+    with pytest.deprecated_call():
+        assert _n_polys(cs.plot_cross_section()) > 0
+
+
+# ---------------------------------------------------------------------------
+# plot_aoi_quicklook — the crop/section backdrop
+# ---------------------------------------------------------------------------
+
+
+def test_plot_aoi_quicklook(plot_archive_dir, plot_site):
+    """Draws the AOI outline and returns the Axes it drew into."""
+    aoi = shapely.Point(*plot_site).buffer(10_000)
+
+    _fig, ax = plot_aoi_quicklook(aoi, radars=[PLOT_RADAR], base_path=plot_archive_dir, epsg=SWISS_EPSG)
+
+    (x0, x1), (y0, y1) = ax.get_xlim(), ax.get_ylim()
+    assert x0 <= plot_site[0] <= x1
+    assert y0 <= plot_site[1] <= y1
+
+
+def test_the_quicklook_is_framed_on_the_archive_not_on_switzerland(us_archive_dir):
+    """A Swiss-framed view used to put a US AOI 5,855 km off-map."""
+    from raddb.aoi import _reproject_to_aoi
+
+    site = _reproject_to_aoi(shapely.Point(*US_SITE), 4326, US_EPSG)
+
+    _fig, ax = plot_aoi_quicklook(site.buffer(20_000), radars=["A"], base_path=us_archive_dir, epsg=US_EPSG)
+
+    (x0, x1), (y0, y1) = ax.get_xlim(), ax.get_ylim()
+    assert x0 <= site.x <= x1
+    assert y0 <= site.y <= y1
+
+
+def test_the_quicklook_can_show_the_selected_gates(plot_archive_dir, plot_site, plot_rdb):
+    """``show_gates=True`` samples the selection on top of the outline."""
+    aoi = shapely.Point(*plot_site).buffer(8_000)
+    selected = plot_rdb.crop_around_point(plot_site, distance=8_000)
+
+    _fig, ax = plot_aoi_quicklook(
+        aoi,
+        selected=selected.data,
+        radars=[PLOT_RADAR],
+        base_path=plot_archive_dir,
+        epsg=SWISS_EPSG,
+        show_gates=True,
+    )
+
+    assert ax.collections or ax.lines
+
+
+def test_the_quicklook_saves_to_a_file(plot_archive_dir, plot_site, tmp_path):
+    """``save_path=`` writes the figure out."""
+    out = tmp_path / "quicklook.png"
+
+    plot_aoi_quicklook(
+        shapely.Point(*plot_site).buffer(10_000),
+        radars=[PLOT_RADAR],
+        base_path=plot_archive_dir,
+        epsg=SWISS_EPSG,
+        save_path=str(out),
+    )
+
+    assert out.exists() and out.stat().st_size > 0
+
+
+def test_the_quicklook_context_can_be_dropped(plot_archive_dir, plot_site):
+    """``context=None`` draws the AOI with no country outline behind it."""
+    _fig, ax = plot_aoi_quicklook(
+        shapely.Point(*plot_site).buffer(10_000),
+        radars=[PLOT_RADAR],
+        base_path=plot_archive_dir,
+        epsg=SWISS_EPSG,
+        context=None,
+    )
+
+    assert ax is not None
+
+
+# ---------------------------------------------------------------------------
+# plot_latent_scatter
+# ---------------------------------------------------------------------------
+
+
+LATENT_VARS = ["DBZH", "ZDR", "KDP", "RHOHV", "PHIDP", "TEMP"]
+"""The six panels of the AMT latent-space figure — the layout is fixed at 2 x 3."""
+
+
+def _latent_frame(n=200):
+    """A synthetic latent space: ``L1``/``L2`` plus one column per panel."""
+    rng = np.random.default_rng(0)
+    data = {"L1": rng.normal(size=n), "L2": rng.normal(size=n)}
+    data.update({v: rng.normal(size=n) for v in LATENT_VARS})
+    return pl.DataFrame(data)
+
+
+def _latent_config():
+    """Six minimal panel descriptors, one per variable."""
+    return [{"var": v, "label": v, "cmap": "viridis", "cbar_kwargs": {}} for v in LATENT_VARS]
+
+
+def test_plot_latent_scatter():
+    """A fixed 2 x 3 grid: six panels, one per polarimetric variable."""
+    fig, axes = plot_latent_scatter(_latent_frame(), _latent_config())
+
+    assert axes.shape == (2, 3)
+    assert fig.get_figwidth() == pytest.approx(6.9)
+    assert all(ax.collections for ax in axes.ravel())
+
+
+def test_plot_latent_scatter_accepts_pandas():
+    """Both frame kinds are coerced at entry, like everything else in the package."""
+    fig, axes = plot_latent_scatter(_latent_frame().to_pandas(), _latent_config())
+
+    assert axes.shape == (2, 3)
+
+
+def test_plot_latent_scatter_requires_exactly_six_panels():
+    """The layout is not derived from the config, so a mismatch is refused."""
+    with pytest.raises(ValueError, match="exactly 6 entries"):
+        plot_latent_scatter(_latent_frame(), _latent_config()[:2])
+
+
+def test_plot_latent_scatter_labels_only_the_outer_axes():
+    """Tick labels live on the bottom row and the first column only."""
+    _fig, axes = plot_latent_scatter(_latent_frame(), _latent_config())
+
+    assert axes[0, 1].get_xlabel() == ""
+    assert axes[1, 1].get_ylabel() == ""
+    assert axes[1, 0].get_xlabel() and axes[1, 0].get_ylabel()
+
+
+# ---------------------------------------------------------------------------
+# Coordinate frames
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("coords", ["xy", "cartesian", "lonlat", "geo", "projected", "swiss", "lv95", 2056])
+def test_every_accepted_coordinate_frame_plots(plot_rdb, coords):
+    """Projection and basemap are separate; ``coords`` only chooses the frame."""
+    assert _n_polys(plot_ppi(plot_rdb, sweep=1, coords=coords)) > 0
+
+
+def test_lonlat_axes_are_in_degrees(plot_rdb):
+    """``lonlat`` must not leave metres on the axis."""
+    artist = plot_ppi(plot_rdb, sweep=1, coords="lonlat")
+
+    assert -180 <= artist.axes.get_xlim()[0] <= 180
+    assert -90 <= artist.axes.get_ylim()[0] <= 90
+
+
+def test_projected_axes_are_lv95_metres(plot_rdb):
+    """``coords=2056`` uses the LUT's own ``x_2056``/``y_2056`` columns."""
+    assert 2.4e6 < plot_ppi(plot_rdb, sweep=1, coords=2056).axes.get_xlim()[0] < 2.9e6
+
+
+def test_xy_is_centred_on_the_radar(plot_rdb):
+    """``xy`` is metres from the radar, so the origin is inside the view."""
+    x0, x1 = plot_ppi(plot_rdb, sweep=1, coords="xy").axes.get_xlim()
+
+    assert x0 < 0 < x1
+
+
+def test_an_unknown_coordinate_frame_raises(plot_rdb):
+    """The message names the argument so the typo is findable."""
+    with pytest.raises(ValueError, match="coords"):
+        plot_ppi(plot_rdb, sweep=1, coords="banana")
+
+
+def test_projected_resolves_the_archives_own_crs(plot_archive_dir):
+    """Reading needs no CRS: the archive records the one it was written with."""
+    plain = RadDB(archive_dir=str(plot_archive_dir)).open(radars=PLOT_RADAR)
+
+    assert plain._crs is None
+    assert plain.crs().to_epsg() == SWISS_EPSG
+    assert _n_polys(plot_ppi(plain, sweep=1, coords="projected")) > 0
+
+
+def test_projected_raises_when_nothing_declares_a_crs(plot_rdb):
+    """A bare frame with no archive has nothing to resolve from."""
+    with pytest.raises((ValueError, KeyError)):
+        plot_ppi(plot_rdb.data, sweep=1, coords="projected", archive_dir=None)
+
+
+# ---------------------------------------------------------------------------
+# Volume selection
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture(scope="session")
+def multi_volume_rdb(tmp_path_factory):
+    """A two-volume archive, so the volume-selection arguments have something to pick."""
+    base = tmp_path_factory.mktemp("multi_vol")
+    db = RadDB(archive_dir=str(base), crs=SWISS_EPSG)
+    db.archive(
+        datatree={str(t): build_datatree(24, 20, n_sweeps=2, vol_time=t) for t in VOL_TIMES},
+        radar=PLOT_RADAR,
+    )
+    return db.open(radars=PLOT_RADAR)
+
+
+def test_several_volumes_without_a_timestep_raises(multi_volume_rdb):
+    """A PPI draws one volume; which one must be said."""
+    with pytest.raises(ValueError, match="volumes"):
+        plot_ppi(multi_volume_rdb, sweep=1)
+
+
+def test_a_timestep_picks_the_nearest_volume(multi_volume_rdb):
+    """``timestep=`` snaps to the closest recorded volume time."""
+    assert _n_polys(plot_ppi(multi_volume_rdb, sweep=1, timestep=VOL_TIMES[0])) > 0
+
+
+def test_a_time_window_narrows_to_one_volume(multi_volume_rdb):
+    """``start_time``/``end_time`` are the alternative to ``timestep``."""
+    assert _n_polys(plot_ppi(multi_volume_rdb, sweep=1, start_time="2024-08-01", end_time="2024-08-01 23:59")) > 0
+
+
+def test_a_window_excluding_everything_raises(multi_volume_rdb):
+    """An empty window is an error, not an empty plot."""
+    with pytest.raises(ValueError):
+        plot_ppi(multi_volume_rdb, sweep=1, start_time="1999-01-01", end_time="1999-12-31")
+
+
+# ---------------------------------------------------------------------------
+# DataTree input — no archive needed
+# ---------------------------------------------------------------------------
+
+
+def test_a_datatree_needs_no_archive(plot_dtree):
+    """The whole point: a DataTree is self-describing."""
+    assert _n_polys(plot_ppi(plot_dtree, sweep=1, variable="DBZH", archive_dir=None)) == N_AZ * N_RNG
+
+
+def test_datatree_geometry_matches_the_stored_lattice(plot_dtree, plot_archive_dir):
+    """Corners computed from the tree's own coords must equal the ones generation stored."""
+    drawn = np.array([q.vertices[:4] for q in plot_ppi(plot_dtree, sweep=1, variable="DBZH").get_paths()])
+    tbl = gate_corner_table(PLOT_RADAR, plot_archive_dir, kind="h_plane", sweep=1)
+    stored = np.stack(
+        [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)], axis=1
+    )
+
+    # The lattices are float32, so ~1e-7 relative — a few centimetres at 200 km range.
+    # Anything tighter would be testing parquet, not geometry.
+    assert np.abs(np.sort(drawn, axis=0) - np.sort(stored, axis=0)).max() < 5e-2
+
+
+def test_an_rhi_from_a_datatree(plot_dtree):
+    """Vertical faces are computed on the fly from the declared beamwidth."""
+    assert _n_polys(plot_rhi(plot_dtree, azimuth=0, variable="DBZH")) == N_RNG * N_SWEEPS
+
+
+def test_a_cappi_from_a_datatree_matches_the_lut_path(plot_dtree, plot_rdb):
+    """Both paths must select the same chords at the same altitude."""
+    assert _n_polys(plot_cappi(plot_dtree, altitude=1200, variable="DBZH")) == _n_polys(
+        plot_cappi(plot_rdb, altitude=1200)
+    )
+
+
+def test_a_datatree_with_an_unknown_variable_raises(plot_dtree):
+    """Same contract as the archive path."""
+    with pytest.raises(KeyError):
+        plot_ppi(plot_dtree, sweep=1, variable="NOPE")
+
+
+def test_a_datatree_with_a_missing_sweep_raises(plot_dtree):
+    """Same contract as the archive path."""
+    with pytest.raises(ValueError, match="sweep"):
+        plot_ppi(plot_dtree, sweep=99, variable="DBZH")
+
+
+# ---------------------------------------------------------------------------
+# GeoDataFrame input — treated as a frame
+# ---------------------------------------------------------------------------
+
+
+def test_a_geodataframe_goes_through_the_lut(plot_gdf, plot_archive_dir):
+    """A gdf is not special-cased: geometry always comes from the LUT."""
+    assert _n_polys(plot_ppi(plot_gdf, sweep=1, archive_dir=plot_archive_dir)) > 0
+
+
+def test_a_geodataframe_without_an_archive_raises(plot_gdf):
+    """Its own geometry column is ignored, so the LUT is still required."""
+    with pytest.raises(ValueError, match="archive"):
+        plot_ppi(plot_gdf, sweep=1)
+
+
+def test_a_geodataframe_cappi_matches_a_frame(plot_gdf, plot_archive_dir, plot_rdb):
+    """Same geometry source, same result."""
+    assert _n_polys(plot_cappi(plot_gdf, altitude=1200, archive_dir=plot_archive_dir)) == _n_polys(
+        plot_cappi(plot_rdb, altitude=1200)
+    )
+
+
+def test_the_geometry_column_is_not_required(plot_gdf, plot_archive_dir):
+    """Dropping it changes nothing, which is exactly the claim."""
+    plain = plot_gdf.drop(columns=plot_gdf.geometry.name)
+
+    assert _n_polys(plot_ppi(plain, sweep=1, archive_dir=plot_archive_dir)) > 0
+
+
+# ---------------------------------------------------------------------------
+# Beamwidth is a LUT-generation parameter, not a plot argument
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("name", ["plot_ppi", "plot_rhi", "plot_cappi"])
+def test_no_plot_takes_a_beamwidth(name):
+    """For archive-backed data it was applied when ``v_plane`` was generated."""
+    assert "beamwidth_deg" not in inspect.signature(getattr(vp, name)).parameters
+
+
+def test_beamwidth_comes_from_the_datatree_or_the_default(plot_dtree):
+    """No archive to bake it in, so it is inferred as LUT generation does."""
+    dt = copy.deepcopy(plot_dtree)
+    src = type("S", (), {"kind": "datatree", "dtree": dt})()
+
+    assert _beamwidth(src) == DEFAULT_BEAMWIDTH_DEG
+
+    dt.attrs["radar_beam_width_h"] = 0.5
+    assert _beamwidth(src) == 0.5
+
+
+def test_a_wider_declared_beam_reaches_a_cappi_over_more_bins(plot_dtree):
+    """Only ``v_plane``/``corners`` depend on beamwidth — and this is how it shows."""
+    narrow = copy.deepcopy(plot_dtree)
+    narrow.attrs["radar_beam_width_h"] = 0.5
+    wide = copy.deepcopy(plot_dtree)
+    wide.attrs["radar_beam_width_h"] = 2.0
+
+    assert _n_polys(plot_cappi(wide, altitude=1200, variable="DBZH")) > _n_polys(
+        plot_cappi(narrow, altitude=1200, variable="DBZH")
+    )
+
+
+# ---------------------------------------------------------------------------
+# _KmFormatter — data in metres, axes labelled in km
+# ---------------------------------------------------------------------------
+
+
+def _tick_labels(lo, hi, offset=0.0):
+    """Render a y-axis through ``_KmFormatter`` and return ``(value, label)`` pairs."""
+    fig, ax = plt.subplots()
+    ax.set_ylim(lo, hi)
+    ax.yaxis.set_major_formatter(_KmFormatter(offset))
+    fig.canvas.draw()
+    locs = np.asarray(ax.yaxis.get_majorticklocs(), dtype=float)
+    labels = [t.get_text() for t in ax.get_yticklabels()]
+    plt.close(fig)
+    return [(v, lab) for v, lab in zip(locs, labels) if lab and lo <= v <= hi]
+
+
+@pytest.mark.parametrize(
+    ("lo", "hi", "offset"),
+    [
+        (1400, 5900, 0.0),  # the reported case: 500 m steps
+        (0, 20000, 0.0),  # matplotlib picks 2.5 km steps
+        (0, 13000, 0.0),
+        (0, 500, 0.0),
+        (0, 200, 0.0),  # 25 m steps -> 3 decimals
+        (0, 60, 0.0),
+        (0, 250000, 0.0),
+        (2_600_000, 2_760_000, 2e6),  # LV95 easting
+        (1_050_000, 1_150_000, 1e6),  # LV95 northing
+    ],
+)
+def test_km_labels_are_unique_and_exact(lo, hi, offset):
+    """Decimals come from the ticks matplotlib chose, not from ``log10(step)``.
+
+    A fixed ``.0f`` collapses labels below a 1 km step, and deriving decimals from the
+    step's magnitude misses matplotlib's 2.5x10**n steps: 2500 m would print
+    ``0, 2, 5, 8, 10`` — distinct, so a duplicate check passes, but wrong.
+    """
+    pairs = _tick_labels(lo, hi, offset)
+    labels = [lab for _, lab in pairs]
+
+    assert len(labels) == len(set(labels)), f"repeated labels: {labels}"
+    for value, label in pairs:
+        km = (value - offset) / 1e3
+        assert abs(float(label) - km) < 1e-6 * max(1.0, abs(km)), f"label {label!r} does not equal {km}"
+
+
+def test_the_reported_km_label_regression():
+    """A 1.4-5.9 km section used to read 1, 2, 2, 2, 3, 4, 4, 4, 5, 6, 6."""
+    assert [lab for _, lab in _tick_labels(1400, 5900)] == [
+        "1.5",
+        "2.0",
+        "2.5",
+        "3.0",
+        "3.5",
+        "4.0",
+        "4.5",
+        "5.0",
+        "5.5",
+    ]
+
+
+def test_real_plots_have_no_duplicate_ticks(plot_rdb, plot_site):
+    """Every plot, both axes."""
+    artists = [
+        plot_ppi(plot_rdb, sweep=1),
+        plot_ppi(plot_rdb, sweep=1, coords="swiss"),
+        plot_rhi(plot_rdb, azimuth=0),
+        plot_cappi(plot_rdb, altitude=1200),
+        plot_vcs(plot_rdb, line=_line_across(plot_site)),
+    ]
+
+    for artist in artists:
+        artist.axes.figure.canvas.draw()
+        for axis in (artist.axes.xaxis, artist.axes.yaxis):
+            labels = [t.get_text() for t in axis.get_ticklabels() if t.get_text() and t.get_visible()]
+            assert len(labels) == len(set(labels)), f"repeated ticks: {labels}"

From 01fd425ab61ca7b7fe6cc2ec8c2707fd051c48f5 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Mon, 17 Aug 2026 12:03:48 +0200
Subject: [PATCH 08/14] Enhance error handling in fix_foreign_proj_data and
 improve column renaming in _load_one_centroid_table

---
 raddb/_proj.py | 3 ++-
 raddb/aoi.py   | 6 +++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/raddb/_proj.py b/raddb/_proj.py
index 00b25bb..deb1ec5 100644
--- a/raddb/_proj.py
+++ b/raddb/_proj.py
@@ -43,7 +43,8 @@ def fix_foreign_proj_data() -> str | None:
         return None
 
     prefix = Path(sys.prefix).resolve()
-    with contextlib.suppress(OSError):
+    # RuntimeError as well as OSError: pathlib re-raises a symlink loop as RuntimeError.
+    with contextlib.suppress(OSError, RuntimeError):
         if Path(current).resolve().is_relative_to(prefix):
             return None  # this environment's own PROJ data — leave it alone
 
diff --git a/raddb/aoi.py b/raddb/aoi.py
index 3ff7e4b..440cbd0 100644
--- a/raddb/aoi.py
+++ b/raddb/aoi.py
@@ -186,9 +186,9 @@ def _load_one_centroid_table(base: Path, radar: str, epsg: int) -> pl.DataFrame:
         cols = [c for c in (*_CENTROID_BASE_COLS, "latitude", "longitude")
                 if c in available]
         lut = add_lut_projection(pl.read_parquet(lut_path, columns=cols), epsg=int(epsg))
-        lut = lut.rename({xc: "x", yc: "y"}).select(
-            [c for c in (*_CENTROID_BASE_COLS, "x", "y") if c in lut.columns]
-        )
+        # Rename first: the select below must see the renamed columns, not x_<epsg>.
+        lut = lut.rename({xc: "x", yc: "y"})
+        lut = lut.select([c for c in (*_CENTROID_BASE_COLS, "x", "y") if c in lut.columns])
 
     return lut.with_columns(pl.lit(radar).alias("radar"))
 

From bb8149cb941dfe1a30acc4b24accec245257d752 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Mon, 17 Aug 2026 12:33:24 +0200
Subject: [PATCH 09/14] solved pre-commit errors

---
 .github/workflows/tests_windows.yaml    |    2 +-
 .gitignore                              |    2 +-
 .pre-commit-config.yaml                 |    4 +-
 README.md                               |   82 +-
 raddb/__init__.py                       |   74 +-
 raddb/_proj.py                          |    1 +
 raddb/aoi.py                            |  195 ++--
 raddb/discovery.py                      |   17 +-
 raddb/hc_mapping.py                     |   47 +-
 raddb/helper.py                         |   82 +-
 raddb/io_core.py                        |  287 ++---
 raddb/lut.py                            |  477 ++++----
 raddb/main.py                           |  537 +++++----
 raddb/tests/conftest.py                 |   44 +-
 raddb/tests/test__proj.py               |    5 +-
 raddb/tests/test_aoi.py                 |   26 +-
 raddb/tests/test_api_coverage.py        |   34 +-
 raddb/tests/test_discovery.py           |   20 +-
 raddb/tests/test_hc_mapping.py          |    6 +-
 raddb/tests/test_helper.py              |   29 +-
 raddb/tests/test_io_core.py             |   41 +-
 raddb/tests/test_lut.py                 |   84 +-
 raddb/tests/test_main.py                |   55 +-
 raddb/tests/test_package_api.py         |    9 +-
 raddb/tests/test_viz_init.py            |    2 +-
 raddb/tests/test_viz_interactive.py     |   10 +-
 raddb/tests/test_viz_plot.py            |   51 +-
 raddb/viz/interactive.py                |   92 +-
 raddb/viz/plot.py                       |  517 +++++----
 tutorial/01_archiving.ipynb             |  163 +--
 tutorial/02_opening_and_filtering.ipynb |  283 ++---
 tutorial/03_area_of_interest.ipynb      | 1402 ++---------------------
 tutorial/04_plots.ipynb                 |  260 ++---
 tutorial/05_demo_pipeline.ipynb         | 1312 +--------------------
 tutorial/README.md                      |   14 +-
 35 files changed, 1997 insertions(+), 4269 deletions(-)

diff --git a/.github/workflows/tests_windows.yaml b/.github/workflows/tests_windows.yaml
index 5a4c17e..034ab8e 100644
--- a/.github/workflows/tests_windows.yaml
+++ b/.github/workflows/tests_windows.yaml
@@ -14,7 +14,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest]
-        python-version: ["3.11", "3.12", "3.13",  "3.14"]
+        python-version: ["3.11", "3.12", "3.13", "3.14"]
         experimental: [false]
     steps:
       - uses: actions/checkout@v4
diff --git a/.gitignore b/.gitignore
index ff49682..b451bd3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -170,4 +170,4 @@ raddb/mch/
 pyproject.toml
 
 # Local AI-assistant working notes (machine-local paths — not for the public repo)
-CLAUDE.md
\ No newline at end of file
+CLAUDE.md
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1cc74ac..3724444 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -25,7 +25,9 @@ repos:
     rev: v0.3.9
     hooks:
       - id: blackdoc
-        additional_dependencies: ["black[jupyter]"]
+        # pin to the black hook's rev: blackdoc 0.3.9 calls black.decode_bytes()
+        # with the pre-25.x signature and TypeErrors against a newer black.
+        additional_dependencies: ["black[jupyter]==24.8.0"]
   - repo: https://github.com/pre-commit/mirrors-prettier
     rev: "v4.0.0-alpha.8"
     hooks:
diff --git a/README.md b/README.md
index b48db09..5c81cac 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,20 @@
 # RadDB — generic radar data archiving & analysis
 
-|                   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Deployment        | [![PyPI](https://badge.fury.io/py/raddb.svg?style=flat)](https://pypi.org/project/raddb/) [![Conda](https://img.shields.io/conda/vn/conda-forge/raddb.svg?logo=conda-forge&logoColor=white&style=flat)](https://anaconda.org/conda-forge/raddb)                                                                                                                                                                                                                                                                                                                                                                        |
-| Activity          | [![PyPI Downloads](https://img.shields.io/pypi/dm/raddb.svg?label=PyPI%20downloads&style=flat)](https://pypi.org/project/raddb/) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/raddb.svg?label=Conda%20downloads&style=flat)](https://anaconda.org/conda-forge/raddb)                                                                                                                                                                                                                                                                                                                                |
-| Python Versions   | [![Python Versions](https://img.shields.io/badge/Python-3.11%20%203.12%20%203.13%20%203.14-blue?style=flat)](https://www.python.org/downloads/)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+|                   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Deployment        | [![PyPI](https://badge.fury.io/py/raddb.svg?style=flat)](https://pypi.org/project/raddb/) [![Conda](https://img.shields.io/conda/vn/conda-forge/raddb.svg?logo=conda-forge&logoColor=white&style=flat)](https://anaconda.org/conda-forge/raddb)                                                                                                                                                                                                                                                                                                                                                                  |
+| Activity          | [![PyPI Downloads](https://img.shields.io/pypi/dm/raddb.svg?label=PyPI%20downloads&style=flat)](https://pypi.org/project/raddb/) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/raddb.svg?label=Conda%20downloads&style=flat)](https://anaconda.org/conda-forge/raddb)                                                                                                                                                                                                                                                                                                                          |
+| Python Versions   | [![Python Versions](https://img.shields.io/badge/Python-3.11%20%203.12%20%203.13%20%203.14-blue?style=flat)](https://www.python.org/downloads/)                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | Supported Systems | [![Linux](https://img.shields.io/github/actions/workflow/status/ltelab/raddb/.github/workflows/tests.yml?label=Linux&style=flat)](https://github.com/ltelab/raddb/actions/workflows/tests.yml) [![macOS](https://img.shields.io/github/actions/workflow/status/ltelab/raddb/.github/workflows/tests.yml?label=macOS&style=flat)](https://github.com/ltelab/raddb/actions/workflows/tests.yml) [![Windows](https://img.shields.io/github/actions/workflow/status/ltelab/raddb/.github/workflows/tests_windows.yml?label=Windows&style=flat)](https://github.com/ltelab/raddb/actions/workflows/tests_windows.yml) |
-| Project Status    | [![Project Status](https://www.repostatus.org/badges/latest/active.svg?style=flat)](https://www.repostatus.org/#active)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Project Status    | [![Project Status](https://www.repostatus.org/badges/latest/active.svg?style=flat)](https://www.repostatus.org/#active)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
 | Build Status      | [![Tests](https://github.com/ltelab/raddb/actions/workflows/tests.yml/badge.svg?style=flat)](https://github.com/ltelab/raddb/actions/workflows/tests.yml) [![Lint](https://github.com/ltelab/raddb/actions/workflows/lint.yml/badge.svg?style=flat)](https://github.com/ltelab/raddb/actions/workflows/lint.yml) [![Docs](https://readthedocs.org/projects/raddb/badge/?version=latest&style=flat)](https://raddb.readthedocs.io/en/latest/)                                                                                                                                                                     |
-| Linting           | [![Black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat)](https://github.com/psf/black) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json&style=flat)](https://github.com/astral-sh/ruff) [![Codespell](https://img.shields.io/badge/Codespell-enabled-brightgreen?style=flat)](https://github.com/codespell-project/codespell)                                                                                                                                                                                                      |
-| Code Coverage     | [![Coveralls](https://coveralls.io/repos/github/ltelab/raddb/badge.svg?branch=main&style=flat)](https://coveralls.io/github/ltelab/raddb?branch=main) [![Codecov](https://codecov.io/gh/ltelab/raddb/branch/main/graph/badge.svg?style=flat)](https://codecov.io/gh/ltelab/raddb)                                                                                                                                                                                                                                                                                                                                      |
-| Code Quality      | [![Codefactor](https://www.codefactor.io/repository/github/ltelab/raddb/badge?style=flat)](https://www.codefactor.io/repository/github/ltelab/raddb) [![Codacy](https://app.codacy.com/project/badge/Grade/d823c50a7ad14268bd347b5aba384623?style=flat)](https://app.codacy.com/gh/ltelab/raddb/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [![Codescene](https://codescene.io/projects/83114/status-badges/code-health?style=flat)](https://codescene.io/projects/83114)                                                                                                          |
-| License           | [![License](https://img.shields.io/github/license/ltelab/raddb?style=flat)](https://github.com/ltelab/raddb/blob/main/LICENSE)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Linting           | [![Black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat)](https://github.com/psf/black) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json&style=flat)](https://github.com/astral-sh/ruff) [![Codespell](https://img.shields.io/badge/Codespell-enabled-brightgreen?style=flat)](https://github.com/codespell-project/codespell)                                                                                                                                                                                    |
+| Code Coverage     | [![Coveralls](https://coveralls.io/repos/github/ltelab/raddb/badge.svg?branch=main&style=flat)](https://coveralls.io/github/ltelab/raddb?branch=main) [![Codecov](https://codecov.io/gh/ltelab/raddb/branch/main/graph/badge.svg?style=flat)](https://codecov.io/gh/ltelab/raddb)                                                                                                                                                                                                                                                                                                                                |
+| Code Quality      | [![Codefactor](https://www.codefactor.io/repository/github/ltelab/raddb/badge?style=flat)](https://www.codefactor.io/repository/github/ltelab/raddb) [![Codacy](https://app.codacy.com/project/badge/Grade/d823c50a7ad14268bd347b5aba384623?style=flat)](https://app.codacy.com/gh/ltelab/raddb/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [![Codescene](https://codescene.io/projects/83114/status-badges/code-health?style=flat)](https://codescene.io/projects/83114)                                                                                                 |
+| License           | [![License](https://img.shields.io/github/license/ltelab/raddb?style=flat)](https://github.com/ltelab/raddb/blob/main/LICENSE)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
 
 | Citation          | [![DOI](XXX)](XXX)          |                                                                                                                                                                                                                                                                                                                                                                                                                                                               [**Documentation**](https://raddb.readthedocs.io/en/latest/)
 
-
 RadDB archives xarray **DataTree** radar volumes as compact Parquet files and
 gives you a small, fluent object API to load, filter, crop, cross-section and
 plot them.  It is **network-agnostic**: any DataTree with the standard
@@ -45,15 +44,14 @@ pip install -e .            # core
 pip install -e ".[viz]"     # + cartopy / pyproj / shapely for maps
 ```
 
-Core runtime deps: `numpy, pandas, polars, geopandas, xarray, pyarrow, dask,
-fsspec, s3fs, matplotlib`.
+Core runtime deps: `numpy, pandas, polars, geopandas, xarray, pyarrow, dask, fsspec, s3fs, matplotlib`.
 
 ## Quick start
 
 `RadDB` is a single, dual-role class:
 
-* **archive-bound** — `db = RadDB(archive_dir, crs=…)`; use it to `archive()` and `open()`.
-* **data-carrying** — the object returned by `open()` (call it `rdf`).  It holds the
+- **archive-bound** — `db = RadDB(archive_dir, crs=…)`; use it to `archive()` and `open()`.
+- **data-carrying** — the object returned by `open()` (call it `rdf`).  It holds the
   data as a **polars** DataFrame (`rdf.data`) and exposes the query / convert /
   crop / cross-section / plot methods.  Each of those returns a **new** `RadDB`,
   so calls chain fluently.
@@ -61,11 +59,11 @@ fsspec, s3fs, matplotlib`.
 ```python
 import raddb
 
-db = raddb.RadDB(archive_dir="/data/raddb", crs=2056)   # 2056 = CH1903+/LV95
+db = raddb.RadDB(archive_dir="/data/raddb", crs=2056)  # 2056 = CH1903+/LV95
 
 # --- archive -----------------------------------------------------------------
 # From saved DataTree files on disk (.zarr / .nc); the LUT is auto-generated:
-db.archive(datatree_dir="/data/MCH_datatree")           # radar inferred per file
+db.archive(datatree_dir="/data/MCH_datatree")  # radar inferred per file
 # ...or archive in-memory DataTrees directly:
 # db.archive(datatree=dt, radar="A")
 # db.archive(datatree=[dt1, dt2], radar="A")
@@ -73,25 +71,31 @@ db.archive(datatree_dir="/data/MCH_datatree")           # radar inferred per fil
 
 # --- open --------------------------------------------------------------------
 rdf = db.open(time_period=("2024-08-26", "2024-08-27"), radars="L")
-print(rdf)                     # rich summary: gates, radars, time range, columns
+print(rdf)  # rich summary: gates, radars, time range, columns
 len(rdf), rdf.columns(), rdf.radars()
 rdf.start_time(), rdf.end_time()
-rdf.extent()                   # [xmin, xmax, ymin, ymax] in `crs`
-rdf.geographic_extent()        # [lon_min, lon_max, lat_min, lat_max]
+rdf.extent()  # [xmin, xmax, ymin, ymax] in `crs`
+rdf.geographic_extent()  # [lon_min, lon_max, lat_min, lat_max]
 rdf.crs(), rdf.geographic_crs()
 
 # --- filter / convert --------------------------------------------------------
 strong = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
-strong = rdf.filter([{"var": "DBZH", "logic": ">", "threshold": 20},
-                     {"var": "RHOHV", "logic": ">", "threshold": 0.9}])   # AND
-pdf = rdf.to_pandas(with_geometry=True)     # pandas + gate coordinates
-gdf = rdf.to_geopandas()                    # GeoDataFrame (with CRS)
-dt  = rdf.to_datatree()                     # xarray DataTree (for plotting)
+strong = rdf.filter(
+    [
+        {"var": "DBZH", "logic": ">", "threshold": 20},
+        {"var": "RHOHV", "logic": ">", "threshold": 0.9},
+    ]
+)  # AND
+pdf = rdf.to_pandas(with_geometry=True)  # pandas + gate coordinates
+gdf = rdf.to_geopandas()  # GeoDataFrame (with CRS)
+dt = rdf.to_datatree()  # xarray DataTree (for plotting)
 
 # --- area of interest --------------------------------------------------------
-box   = rdf.crop_by_bbox(extent=[2.60e6, 2.62e6, 1.11e6, 1.13e6])   # or bounds=(xmin,ymin,xmax,ymax)
-poly  = rdf.crop_by_polygone("catchment.geojson")
-disc  = rdf.crop_around_point((2.61e6, 1.12e6), distance=20_000)    # metres
+box = rdf.crop_by_bbox(
+    extent=[2.60e6, 2.62e6, 1.11e6, 1.13e6]
+)  # or bounds=(xmin,ymin,xmax,ymax)
+poly = rdf.crop_by_polygone("catchment.geojson")
+disc = rdf.crop_around_point((2.61e6, 1.12e6), distance=20_000)  # metres
 # rdf.interactive_crop()      # draw an AOI on a Jupyter map (needs ipyleaflet)
 
 # --- cross-section -----------------------------------------------------------
@@ -103,9 +107,9 @@ cs.plot_cross_section(variable="DBZH", save="xsec.png")
 rdf.plot_rhi(azimuth=270, variable="DBZH")
 
 # fluent: open -> filter -> crop -> plot
-rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20}) \
-   .crop_by_bbox(extent=rdf.extent()) \
-   .plot_ppi(variable="DBZH", save="strong.png")
+rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20}).crop_by_bbox(
+    extent=rdf.extent()
+).plot_ppi(variable="DBZH", save="strong.png")
 ```
 
 `filter` / `crs` argument shapes: filters are `{"var", "logic", "threshold"}`
@@ -115,17 +119,17 @@ CRS-coercible object, or `None`.
 ### What is on disk? (archive-bound)
 
 ```python
-db.inventory()                      # radars, volume counts, time ranges, size
-db.inventory(detailed=True)         # + LUT info, stored moments, day-by-day counts
-db.inventory(datatree_dir="/data/MCH_datatree")   # DataTree files not archived yet
+db.inventory()  # radars, volume counts, time ranges, size
+db.inventory(detailed=True)  # + LUT info, stored moments, day-by-day counts
+db.inventory(datatree_dir="/data/MCH_datatree")  # DataTree files not archived yet
 ```
 
 ### LUT accessors (archive-bound)
 
 ```python
-db.list_radars()                    # radars present in the archive
-db.get_lut("L")                     # the static LUT (pandas)
-db.get_radar_info("L")              # site location / sweep geometry
+db.list_radars()  # radars present in the archive
+db.get_lut("L")  # the static LUT (pandas)
+db.get_radar_info("L")  # site location / sweep geometry
 db.add_lut_projection("L", epsg=2056)
 ```
 
@@ -149,7 +153,7 @@ Sample-data scripts live under `scripts/` (`make_sample_mch_datatrees.py`,
 
 ## Notes
 
-* **Projected coordinates / `crs`.** Generating a LUT with a projection (e.g.
+- **Projected coordinates / `crs`.** Generating a LUT with a projection (e.g.
   `crs=2056`) and the projected accessors (`extent`, `to_geopandas`) use `pyproj`,
   which needs the PROJ database.  A `PROJ_DATA` / `PROJ_LIB` inherited from another
   environment (a conda base env, a system PROJ) points at a proj.db of the wrong
@@ -157,7 +161,7 @@ Sample-data scripts live under `scripts/` (`make_sample_mch_datatrees.py`,
   specified"* — `import raddb` detects that and repoints PROJ at the running
   interpreter's own `share/proj` (see `raddb/_proj.py`; `raddb.PROJ_DATA` reports
   what it changed, `None` when nothing had to be).
-* **Zarr / NetCDF.** Saved volumes are read back with `raddb.open_any_datatree`
+- **Zarr / NetCDF.** Saved volumes are read back with `raddb.open_any_datatree`
   (engine auto-detected); either format works.
 
 ## License
diff --git a/raddb/__init__.py b/raddb/__init__.py
index 943d1d1..7c7cd59 100644
--- a/raddb/__init__.py
+++ b/raddb/__init__.py
@@ -8,6 +8,7 @@
 MCH/METRANET-specific ingestion code lives in the private ``raddb.mch``
 subpackage (gitignored in the public repository; never imported here).
 """
+
 from __future__ import annotations
 
 import contextlib
@@ -19,92 +20,91 @@
 # imported (by geopandas, cartopy, raddb.lut, ...).
 from raddb._proj import PROJ_DATA
 
-# High-level interface
-from raddb.main import RadDB
+# Discovery functions
+from raddb.discovery import find_datatree_files
 
 # Helper functions
+# Filtering & archiving functions
 from raddb.helper import (
+    FILTER_LOGICS,
     RADAR_ALPHABET,
     RADAR_CODE_LEN,
-    read_parquet_files,
+    StageTimer,
     check_dataframe,
+    filter_df,
+    filter_dt,
     is_valid_radar_name,
     list_sweep_names,
     normalize_radar_name,
-    StageTimer,
+    read_parquet_files,
 )
 
 # I/O functions
 from raddb.io_core import (
-    datatree_to_dataset,
+    add_feature_to_df,
+    add_feature_to_dt,
+    archive_multiple_volumes,
+    archive_volume,
+    archive_volumes_multi_radar,
+    dataframe_to_datatree,
     datatree_to_dataframe,
+    datatree_to_dataset,
     datatree_to_parquet,
+    join_labels_with_lut,
+    labels_to_dataframe,
     open_any_datatree,
     parquet_to_dataframe,
     parquet_to_datatree,
-    scan_polar_parquet,
-    dataframe_to_datatree,
-    labels_to_dataframe,
-    join_labels_with_lut,
-    reconstruct_sweep_dataset,
     reconstruct_datatree,
-    add_feature_to_df,
-    add_feature_to_dt,
+    reconstruct_sweep_dataset,
+    scan_polar_parquet,
 )
 
-# Discovery functions
-from raddb.discovery import find_datatree_files
-
 # LUT functions
 from raddb.lut import (
     AZIMUTH_SCALE,
+    DEFAULT_BEAMWIDTH_DEG,
     GATE_ID_RADAR_BASE,
-    azimuth_grid_tolerance,
-    load_azimuth_grids,
-    nominal_azimuth_grid,
-    snap_azimuths_to_grid,
     GATE_ID_VERSION,
+    LUT_FILES,
     MAX_RADAR_CODE,
     RADAR_TO_IDX,
-    DEFAULT_BEAMWIDTH_DEG,
-    decode_gate_radars,
-    decode_radar_code,
-    encode_radar_code,
-    LUT_FILES,
+    add_lut_projection,
     antenna_vectors_to_cartesian,
+    azimuth_grid_tolerance,
     build_gate_planes,
     cappi_chords,
     cartesian_to_geographic,
     compute_gate_xyz,
     compute_sweep_corners,
+    decode_gate_radars,
+    decode_radar_code,
+    encode_radar_code,
     ensure_gate_planes,
     gate_corner_table,
     generate_gate_id,
     generate_lut_from_datatree,
+    get_full_sweep_index,
+    load_azimuth_grids,
     load_plane_nodes,
-    load_radar_lut,
     load_radar_info,
+    load_radar_lut,
     lut_file_path,
-    get_full_sweep_index,
-    add_lut_projection,
+    nominal_azimuth_grid,
+    snap_azimuths_to_grid,
 )
 
-# Filtering & archiving functions
-from raddb.helper import FILTER_LOGICS, filter_df, filter_dt
-from raddb.io_core import (
-    archive_volume,
-    archive_multiple_volumes,
-    archive_volumes_multi_radar,
-)
+# High-level interface
+from raddb.main import RadDB
 
 # Plotting functions
 from raddb.viz.plot import (
-    plot_ppi,
-    plot_rhi,
     plot_cappi,
-    plot_vcs,
     plot_cross_section,
     plot_latent_scatter,
+    plot_ppi,
+    plot_rhi,
+    plot_vcs,
 )
 
 __all__ = [
diff --git a/raddb/_proj.py b/raddb/_proj.py
index deb1ec5..50d6f0f 100644
--- a/raddb/_proj.py
+++ b/raddb/_proj.py
@@ -3,6 +3,7 @@
 Importing this module must happen *before* anything imports pyproj (directly
 or through geopandas / cartopy); ``raddb/__init__.py`` imports it first.
 """
+
 from __future__ import annotations
 
 import contextlib
diff --git a/raddb/aoi.py b/raddb/aoi.py
index 440cbd0..ff78ec8 100644
--- a/raddb/aoi.py
+++ b/raddb/aoi.py
@@ -1,7 +1,4 @@
-"""
-raddb/aoi.py
-------------
-Area-of-Interest (AOI) selection internals.
+"""Area-of-Interest (AOI) selection internals.
 
 These are the **private** building blocks behind the public ``RadDB.crop_*``
 methods.  The design is LUT-first: an AOI geometry is intersected once with the
@@ -24,6 +21,7 @@
 predicate itself runs on plain numpy arrays via shapely, so no geometry objects
 are ever materialised for the ~1.7M gates of a full LUT.
 """
+
 from __future__ import annotations
 
 import logging
@@ -77,7 +75,7 @@ def aoi_epsg(base_path: str | Path, radar: str) -> int:
         f"radar {radar!r} has no projected coordinates in its LUT, so AOI "
         f"operations (crop_*, extract_cross_section, plot_vcs) cannot run. "
         f"Re-archive it with a CRS valid at its site — RadDB(crs=<epsg>) — or "
-        f"pass aoi_crs=<epsg> for this call."
+        f"pass aoi_crs=<epsg> for this call.",
     )
 
 
@@ -104,10 +102,11 @@ def aoi_epsg_for(base_path: str | Path, radars: list[str], override=None) -> int
         raise ValueError(
             f"these radars were archived in different CRSs ({pairs}), so there is "
             f"no single frame to run the AOI in. Pass aoi_crs=<epsg> valid for all "
-            f"of them, or restrict the selection to radars sharing one."
+            f"of them, or restrict the selection to radars sharing one.",
         )
     return distinct.pop()
 
+
 # Per-radar centroid cache: {(base_path, radar): DataFrame}. LUTs are static, so
 # a radar's centroid table is loaded from disk at most once per session.
 _CENTROID_CACHE: dict[tuple[str, str], pl.DataFrame] = {}
@@ -152,9 +151,15 @@ def _lut_centroids(base_path: str | Path, radars: list[str], epsg=None) -> pl.Da
         frames.append(cached)
     if not frames:
         return pl.DataFrame(
-            schema={"gate_id": pl.Int64, "radar": pl.String, "sweep": pl.Int32,
-                    "x": pl.Float64, "y": pl.Float64,
-                    "z": pl.Float64, "altitude": pl.Float64}
+            schema={
+                "gate_id": pl.Int64,
+                "radar": pl.String,
+                "sweep": pl.Int32,
+                "x": pl.Float64,
+                "y": pl.Float64,
+                "z": pl.Float64,
+                "altitude": pl.Float64,
+            },
         )
     return pl.concat(frames, how="vertical")
 
@@ -171,7 +176,7 @@ def _load_one_centroid_table(base: Path, radar: str, epsg: int) -> pl.DataFrame:
     lut_path = base / radar / "LUT" / f"{radar}_LUT.parquet"
     if not lut_path.exists():
         raise FileNotFoundError(
-            f"LUT not found at {lut_path}. Cannot resolve AOI for radar {radar!r}."
+            f"LUT not found at {lut_path}. Cannot resolve AOI for radar {radar!r}.",
         )
 
     # Column names come from the parquet footer — reading the whole LUT just to
@@ -183,8 +188,7 @@ def _load_one_centroid_table(base: Path, radar: str, epsg: int) -> pl.DataFrame:
         cols = [c for c in _CENTROID_BASE_COLS if c in available] + [xc, yc]
         lut = pl.read_parquet(lut_path, columns=cols).rename({xc: "x", yc: "y"})
     else:
-        cols = [c for c in (*_CENTROID_BASE_COLS, "latitude", "longitude")
-                if c in available]
+        cols = [c for c in (*_CENTROID_BASE_COLS, "latitude", "longitude") if c in available]
         lut = add_lut_projection(pl.read_parquet(lut_path, columns=cols), epsg=int(epsg))
         # Rename first: the select below must see the renamed columns, not x_<epsg>.
         lut = lut.rename({xc: "x", yc: "y"})
@@ -369,7 +373,9 @@ def _swiss_border_2056():
         from cartopy.io import shapereader as shpreader
 
         path = shpreader.natural_earth(
-            resolution="10m", category="cultural", name="admin_0_countries"
+            resolution="10m",
+            category="cultural",
+            name="admin_0_countries",
         )
         geom = None
         for rec in shpreader.Reader(path).records():
@@ -378,9 +384,8 @@ def _swiss_border_2056():
                 geom = rec.geometry
                 break
         # simplify (~300 m) to keep the outline light, then project to LV95.
-        _SWISS_BORDER = (None if geom is None
-                         else _reproject_to_aoi(geom.simplify(0.003), 4326, SWISS_EPSG))
-    except Exception as exc:  # noqa: BLE001 - context is optional; never fatal
+        _SWISS_BORDER = None if geom is None else _reproject_to_aoi(geom.simplify(0.003), 4326, SWISS_EPSG)
+    except Exception as exc:  # - context is optional; never fatal
         logger.warning("Swiss border context unavailable (%s); drawn without it.", exc)
         _SWISS_BORDER = None
     return _SWISS_BORDER
@@ -413,11 +418,11 @@ def _resolve_context(context, aoi_epsg: int = SWISS_EPSG):
     if isinstance(context, str):
         if context.lower() in ("switzerland", "ch", "suisse", "schweiz", "svizzera"):
             border = _swiss_border_2056()
-            if border is None or aoi_epsg == SWISS_EPSG:
-                return border
-            return _reproject_to_aoi(border, SWISS_EPSG, aoi_epsg)
+            if border is not None and aoi_epsg != SWISS_EPSG:
+                border = _reproject_to_aoi(border, SWISS_EPSG, aoi_epsg)
+            return border
         raise ValueError(
-            f"unknown context {context!r}; use 'switzerland', None, or a geometry."
+            f"unknown context {context!r}; use 'switzerland', None, or a geometry.",
         )
     if isinstance(context, _base.BaseGeometry):
         return context  # assume already in the quicklook frame
@@ -426,9 +431,8 @@ def _resolve_context(context, aoi_epsg: int = SWISS_EPSG):
             geom = context.union_all() if hasattr(context, "union_all") else context.unary_union
             crs = getattr(context, "crs", None)
             epsg = crs.to_epsg() if crs is not None else None
-            return (_reproject_to_aoi(geom, epsg, aoi_epsg)
-                    if epsg not in (None, aoi_epsg) else geom)
-        except Exception as exc:  # noqa: BLE001
+            return _reproject_to_aoi(geom, epsg, aoi_epsg) if epsg not in (None, aoi_epsg) else geom
+        except Exception as exc:
             logger.warning("could not resolve context geometry (%s); drawn without it.", exc)
             return None
     return None
@@ -438,6 +442,7 @@ def _resolve_context(context, aoi_epsg: int = SWISS_EPSG):
 # Polygon AOI loading  (shapely / GeoDataFrame / shapefile / GeoJSON)
 # ============================================================================
 
+
 def _load_aoi_polygon(polygon, crs=None, aoi_epsg: int | None = None):
     """Resolve a polygon AOI to a shapely geometry in ``aoi_epsg``.
 
@@ -463,14 +468,14 @@ def _load_aoi_polygon(polygon, crs=None, aoi_epsg: int | None = None):
     else:
         raise TypeError(
             f"crop_polygon: unsupported polygon input {type(polygon).__name__}; pass a "
-            "shapely (Multi)Polygon, a GeoDataFrame, or a .shp / .geojson path."
+            "shapely (Multi)Polygon, a GeoDataFrame, or a .shp / .geojson path.",
         )
 
     if geom is None or geom.is_empty:
         raise ValueError("crop_polygon: the polygon AOI is empty.")
     if geom.geom_type not in ("Polygon", "MultiPolygon"):
         raise ValueError(
-            f"crop_polygon expects a Polygon/MultiPolygon; got {geom.geom_type}."
+            f"crop_polygon expects a Polygon/MultiPolygon; got {geom.geom_type}.",
         )
 
     # No fallback CRS: when neither the caller nor the file says, the geometry is
@@ -486,7 +491,7 @@ def _crs_to_spec(crs):
     try:
         epsg = crs.to_epsg()
         return epsg if epsg is not None else crs
-    except Exception:  # noqa: BLE001
+    except Exception:
         return crs
 
 
@@ -513,6 +518,7 @@ def _read_geometry_file(path: Path):
 
 def _read_geojson(path: Path):
     import json
+
     from shapely.geometry import shape
 
     data = json.loads(path.read_text())
@@ -521,15 +527,22 @@ def _read_geojson(path: Path):
         geoms = [shape(f["geometry"]) for f in data.get("features", []) if f.get("geometry")]
     elif kind == "Feature":
         geoms = [shape(data["geometry"])]
-    elif kind in ("Polygon", "MultiPolygon", "GeometryCollection", "LineString",
-                  "MultiLineString", "Point", "MultiPoint"):
+    elif kind in (
+        "Polygon",
+        "MultiPolygon",
+        "GeometryCollection",
+        "LineString",
+        "MultiLineString",
+        "Point",
+        "MultiPoint",
+    ):
         # A bare top-level geometry, as hand-written files and some exporters
         # produce.  Lines matter here: a cross-section is defined by one.
         geoms = [shape(data)]
     else:
         raise ValueError(
             f"Unrecognised GeoJSON object type {kind!r}; expected a "
-            "FeatureCollection, a Feature, or a bare geometry."
+            "FeatureCollection, a Feature, or a bare geometry.",
         )
     if not geoms:
         raise ValueError(f"No geometries found in {path}.")
@@ -554,8 +567,7 @@ def _read_shapefile(path: Path):
         import shapefile  # pyshp
     except ImportError as exc:
         raise ImportError(
-            "Reading .shp needs pyshp (or pass a GeoDataFrame / .geojson). "
-            "Install with: pip install pyshp"
+            "Reading .shp needs pyshp (or pass a GeoDataFrame / .geojson). " "Install with: pip install pyshp",
         ) from exc
     from shapely.geometry import shape
 
@@ -577,7 +589,7 @@ def _prj_crs(prj_path: Path):
         import pyproj
 
         return pyproj.CRS.from_wkt(wkt).to_epsg() or wkt
-    except Exception:  # noqa: BLE001 - broken PROJ db: sniff for Swiss LV95, else pass WKT
+    except Exception:  # - broken PROJ db: sniff for Swiss LV95, else pass WKT
         low = wkt.lower()
         if "2056" in wkt or "ch1903+" in low or "lv95" in low:
             return SWISS_EPSG
@@ -600,15 +612,23 @@ def _prj_crs(prj_path: Path):
 # ============================================================================
 
 _CS_LUT_BASE_COLS = [
-    "gate_id", "sweep", "azimuth", "range", "elevation_angle", "altitude",
+    "gate_id",
+    "sweep",
+    "azimuth",
+    "range",
+    "elevation_angle",
+    "altitude",
 ]
 # Per-radar cross-section geometry cache: {(base_path, radar, beamwidth): df}.
 _CS_CACHE: dict = {}
 
 
 def _lut_cs_table(
-    base_path: str | Path, radars: list[str], beamwidth_deg: float = 1.0, epsg=None
-) -> "pl.DataFrame":
+    base_path: str | Path,
+    radars: list[str],
+    beamwidth_deg: float = 1.0,
+    epsg=None,
+) -> pl.DataFrame:
     """Static per-gate geometry for cross-sections: centers + half-dimensions.
 
     Half-dimensions (prototype convention):
@@ -623,11 +643,10 @@ def _lut_cs_table(
         lut_path = Path(base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
         if not lut_path.exists():
             raise FileNotFoundError(
-                f"LUT not found at {lut_path}. Cannot build cross-section for radar {radar!r}."
+                f"LUT not found at {lut_path}. Cannot build cross-section for radar {radar!r}.",
             )
         # mtime in the key: a LUT regenerated in a live session must invalidate.
-        key = (str(base_path), radar, float(beamwidth_deg), int(epsg),
-               lut_path.stat().st_mtime_ns)
+        key = (str(base_path), radar, float(beamwidth_deg), int(epsg), lut_path.stat().st_mtime_ns)
         t = _CS_CACHE.get(key)
         if t is None:
             available = set(pq.read_schema(lut_path).names)
@@ -658,16 +677,14 @@ def _lut_cs_table(
                 .sort(["sweep", "range"])
                 .group_by("sweep")
                 .agg(
-                    pl.col("range").cast(pl.Float64).diff().drop_nulls()
-                    .median().alias("_spacing")
+                    pl.col("range").cast(pl.Float64).diff().drop_nulls().median().alias("_spacing"),
                 )
             )
             t = (
                 t.join(spacing, on="sweep", how="left")
                 .with_columns(
                     (pl.col("_spacing") / 2.0).cast(pl.Float64).alias("dR"),
-                    (pl.col("range").cast(pl.Float64)
-                     * float(np.tan(np.deg2rad(beamwidth_deg / 2.0)))).alias("dA"),
+                    (pl.col("range").cast(pl.Float64) * float(np.tan(np.deg2rad(beamwidth_deg / 2.0)))).alias("dA"),
                     pl.lit(radar).alias("radar"),
                 )
                 .drop("_spacing")
@@ -679,8 +696,7 @@ def _lut_cs_table(
     return pl.concat(frames, how="vertical_relaxed")
 
 
-def _lut_corner_rings(base_path, sub: pd.DataFrame, kind: str, cols: tuple[str, str],
-                      epsg: int):
+def _lut_corner_rings(base_path, sub: pd.DataFrame, kind: str, cols: tuple[str, str], epsg: int):
     """Per-gate corner rings read from a LUT lattice, aligned to ``sub``'s rows.
 
     Returns an ``(n, 4, 2)`` array, or ``None`` when the lattice cannot supply
@@ -698,8 +714,10 @@ def _lut_corner_rings(base_path, sub: pd.DataFrame, kind: str, cols: tuple[str,
             frames.append(gate_corner_table(radar, base_path, kind=kind))
         except (FileNotFoundError, KeyError) as exc:
             logger.warning(
-                "radar %s: %s lattice unavailable (%s); falling back to the "
-                "planar gate approximation.", radar, kind, exc,
+                "radar %s: %s lattice unavailable (%s); falling back to the " "planar gate approximation.",
+                radar,
+                kind,
+                exc,
             )
             return None
     tbl = pl.concat(frames, how="vertical_relaxed") if len(frames) > 1 else frames[0]
@@ -708,32 +726,38 @@ def _lut_corner_rings(base_path, sub: pd.DataFrame, kind: str, cols: tuple[str,
     need = [f"{cols[0]}_{k}" for k in range(1, 5)] + [f"{cols[1]}_{k}" for k in range(1, 5)]
     if not all(c in tbl.columns for c in need):
         logger.warning(
-            "the %s lattice has no %s/%s columns; falling back to the planar "
-            "gate approximation.", kind, cols[0], cols[1],
+            "the %s lattice has no %s/%s columns; falling back to the planar " "gate approximation.",
+            kind,
+            cols[0],
+            cols[1],
         )
         return None
 
     # Left-join keeps ``sub``'s row order, which the callers index against.
     aligned = pl.DataFrame({"gate_id": gate_ids}).join(
-        tbl.select(["gate_id", *need]), on="gate_id", how="left", maintain_order="left"
+        tbl.select(["gate_id", *need]),
+        on="gate_id",
+        how="left",
+        maintain_order="left",
     )
-    ring = np.stack([
-        np.stack([aligned[f"{cols[0]}_{k}"].to_numpy(),
-                  aligned[f"{cols[1]}_{k}"].to_numpy()], axis=1)
-        for k in range(1, 5)
-    ], axis=1).astype(np.float64)
+    ring = np.stack(
+        [
+            np.stack([aligned[f"{cols[0]}_{k}"].to_numpy(), aligned[f"{cols[1]}_{k}"].to_numpy()], axis=1)
+            for k in range(1, 5)
+        ],
+        axis=1,
+    ).astype(np.float64)
     if not np.isfinite(ring).all():
         logger.warning(
-            "%d gate(s) have no %s geometry; falling back to the planar "
-            "approximation for this call.",
-            int((~np.isfinite(ring).all(axis=(1, 2))).sum()), kind,
+            "%d gate(s) have no %s geometry; falling back to the planar " "approximation for this call.",
+            int((~np.isfinite(ring).all(axis=(1, 2))).sum()),
+            kind,
         )
         return None
     return ring
 
 
-def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float, base_path=None,
-                     epsg: int | None = None) -> np.ndarray:
+def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float, base_path=None, epsg: int | None = None) -> np.ndarray:
     """Horizontal footprint quad per gate (the prototype's ``Eo_xyz`` face).
 
     Preferred source is the ``h_plane`` lattice, i.e. the same exact curved-beam
@@ -762,10 +786,15 @@ def _gate_footprints(sub: pd.DataFrame, half_bw_tan: float, base_path=None,
     for s_r, s_a in ((-1, -1), (-1, 1), (1, 1), (1, -1)):
         dr = s_r * dR
         da = s_a * (rng + dr) * half_bw_tan
-        rings.append(np.stack([
-            xc + dr * cos_el * sin_az + da * cos_az,
-            yc + dr * cos_el * cos_az - da * sin_az,
-        ], axis=1))
+        rings.append(
+            np.stack(
+                [
+                    xc + dr * cos_el * sin_az + da * cos_az,
+                    yc + dr * cos_el * cos_az - da * sin_az,
+                ],
+                axis=1,
+            ),
+        )
     return shapely.polygons(np.stack(rings, axis=1))
 
 
@@ -785,14 +814,11 @@ def _beam_profile(base_path, sub: pd.DataFrame, epsg: int):
     d_far = 0.5 * (ring[:, 1, 0] + ring[:, 2, 0])
     z_near = 0.5 * (ring[:, 0, 1] + ring[:, 3, 1])
     z_far = 0.5 * (ring[:, 1, 1] + ring[:, 2, 1])
-    half_thick = 0.5 * (
-        np.abs(ring[:, 3, 1] - ring[:, 0, 1]) + np.abs(ring[:, 2, 1] - ring[:, 1, 1])
-    ) * 0.5
+    half_thick = 0.5 * (np.abs(ring[:, 3, 1] - ring[:, 0, 1]) + np.abs(ring[:, 2, 1] - ring[:, 1, 1])) * 0.5
     return d_near, d_far, z_near, z_far, half_thick
 
 
-def _endpoint_d_z(pt_xy: np.ndarray, sub: pd.DataFrame, origin: tuple[float, float],
-                  profile=None):
+def _endpoint_d_z(pt_xy: np.ndarray, sub: pd.DataFrame, origin: tuple[float, float], profile=None):
     """(distance-along-line, altitude) of chord endpoints on the beam surface.
 
     With a ``profile`` from :func:`_beam_profile` the endpoint's altitude is
@@ -831,7 +857,7 @@ def _endpoint_d_z(pt_xy: np.ndarray, sub: pd.DataFrame, origin: tuple[float, flo
 
 
 def _cross_section_gates(
-    cs_t: "pl.DataFrame | pd.DataFrame",
+    cs_t: pl.DataFrame | pd.DataFrame,
     p1: tuple[float, float],
     p2: tuple[float, float],
     beamwidth_deg: float = 1.0,
@@ -866,8 +892,7 @@ def _cross_section_gates(
     # --- vectorised point-to-segment prefilter (replaces the KDTree) ---
     px = cs_t["x"].to_numpy(dtype=np.float64)
     py = cs_t["y"].to_numpy(dtype=np.float64)
-    diag = np.hypot(cs_t["dR"].to_numpy(dtype=np.float64),
-                    cs_t["dA"].to_numpy(dtype=np.float64)) * 1.05
+    diag = np.hypot(cs_t["dR"].to_numpy(dtype=np.float64), cs_t["dA"].to_numpy(dtype=np.float64)) * 1.05
     t_par = ((px - ox) * (ex - ox) + (py - oy) * (ey - oy)) / (length * length)
     t_par = np.clip(t_par, 0.0, 1.0)
     dist = np.hypot(px - (ox + t_par * (ex - ox)), py - (oy + t_par * (ey - oy)))
@@ -895,8 +920,7 @@ def _cross_section_gates(
         return sub.assign(cs_polygon=pd.Series(dtype=object))
 
     # --- endpoint (d, z) on the beam, ordered near/far along the line ---
-    profile = (_beam_profile(base_path, sub, epsg)
-               if base_path is not None and epsg is not None else None)
+    profile = _beam_profile(base_path, sub, epsg) if base_path is not None and epsg is not None else None
     d0, z0 = _endpoint_d_z(p0, sub, (ox, oy), profile)
     d1, z1 = _endpoint_d_z(p1_, sub, (ox, oy), profile)
     swap = d0 > d1
@@ -906,19 +930,20 @@ def _cross_section_gates(
     z_far = np.where(swap, z0, z1)
 
     # --- perpendicular ±dE offsets -> (d, z) polygon per gate ---
-    incl = np.arctan2(z_far - z_near, d_far - d_near)   # chord inclination
+    incl = np.arctan2(z_far - z_near, d_far - d_near)  # chord inclination
     s_i, c_i = np.sin(incl), np.cos(incl)
-    if profile is not None:
-        # Half the beam's real vertical extent at this gate, from v_plane.
-        dE = profile[4]
-    else:
-        dE = sub["dA"].to_numpy(dtype=np.float64)       # dE == dA (same beamwidth)
-    ring = np.stack([
-        np.stack([d_near - s_i * dE, z_near + c_i * dE], axis=1),   # near, top
-        np.stack([d_far - s_i * dE, z_far + c_i * dE], axis=1),     # far, top
-        np.stack([d_far + s_i * dE, z_far - c_i * dE], axis=1),     # far, bottom
-        np.stack([d_near + s_i * dE, z_near - c_i * dE], axis=1),   # near, bottom
-    ], axis=1)
+    # Half the beam's real vertical extent at this gate, from v_plane; without a
+    # profile fall back to dA (dE == dA, same beamwidth).
+    dE = profile[4] if profile is not None else sub["dA"].to_numpy(dtype=np.float64)
+    ring = np.stack(
+        [
+            np.stack([d_near - s_i * dE, z_near + c_i * dE], axis=1),  # near, top
+            np.stack([d_far - s_i * dE, z_far + c_i * dE], axis=1),  # far, top
+            np.stack([d_far + s_i * dE, z_far - c_i * dE], axis=1),  # far, bottom
+            np.stack([d_near + s_i * dE, z_near - c_i * dE], axis=1),  # near, bottom
+        ],
+        axis=1,
+    )
 
     out = sub.copy()
     # Only the gate centre and its footprint are published.  The chord endpoints
diff --git a/raddb/discovery.py b/raddb/discovery.py
index 87b68c3..194bd04 100644
--- a/raddb/discovery.py
+++ b/raddb/discovery.py
@@ -1,7 +1,4 @@
-"""
-raddb/discovery.py
-------------------
-File discovery for RadDB — both sides of the archive:
+"""File discovery for RadDB, on both sides of the archive.
 
 - **DataTree file discovery** (input side): locate DataTree files
   (NetCDF / Zarr) on disk, optionally filtered by a filename timestamp,
@@ -14,6 +11,7 @@
 dependency — so discovery works in any environment.  (Raw METRANET
 scanning lives in the private ``raddb.mch.discovery`` module.)
 """
+
 from __future__ import annotations
 
 import datetime
@@ -25,11 +23,11 @@
 
 from raddb.helper import ensure_utc
 
-
 # ============================================================================
 # METRANET filename helpers  (shared with raddb.mch)
 # ============================================================================
 
+
 def _parse_volume_time(stem: str) -> datetime.datetime:
     """Parse the timestamp from a METRANET filename stem.
 
@@ -45,7 +43,9 @@ def _parse_volume_time(stem: str) -> datetime.datetime:
             int(stem[10:12]),
         )
         return datetime.datetime(2000 + y, 1, 1) + datetime.timedelta(
-            days=j - 1, hours=h, minutes=m
+            days=j - 1,
+            hours=h,
+            minutes=m,
         )
     except Exception:
         return datetime.datetime(1970, 1, 1)
@@ -67,8 +67,8 @@ def _group_files_by_volume(paths: list[str]) -> dict:
 # Filename-stem timestamp patterns, tried in order.
 _DT_TIME_PATTERNS = [
     (r"(\d{8})[T_-]?(\d{6})", "%Y%m%d%H%M%S"),  # YYYYMMDD[T_-]HHMMSS
-    (r"(\d{8})[T_-]?(\d{4})", "%Y%m%d%H%M"),    # YYYYMMDD[T_-]HHMM
-    (r"(\d{12})", "%Y%m%d%H%M"),                # YYYYMMDDHHMM
+    (r"(\d{8})[T_-]?(\d{4})", "%Y%m%d%H%M"),  # YYYYMMDD[T_-]HHMM
+    (r"(\d{12})", "%Y%m%d%H%M"),  # YYYYMMDDHHMM
 ]
 
 
@@ -166,6 +166,7 @@ def find_datatree_files(
 # Archived POL parquet search  (output side)
 # ============================================================================
 
+
 def _parse_pol_time(path: str | Path) -> pd.Timestamp | None:
     """UTC volume timestamp from a ``{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet`` name.
 
diff --git a/raddb/hc_mapping.py b/raddb/hc_mapping.py
index 6fc240d..f76789c 100644
--- a/raddb/hc_mapping.py
+++ b/raddb/hc_mapping.py
@@ -1,7 +1,4 @@
-"""
-raddb/hc_mapping.py
--------------------
-Canonical hydrometeor class constants for the MCH operational encoding.
+"""Canonical hydrometeor class constants for the MCH operational encoding.
 
 Both HC_MCH and HC_PYART are stored in parquet on the same 1-based scale:
   parquet integer k  →  HC_MAP_DICT[k - 1]
@@ -26,35 +23,35 @@
     8: "MH",
 }
 
-# 1-based parquet labels (HC_MAP_DICT shifted by +1, matches stored integers 1–9)
-HC_CLASSES: list[str] = [HC_MAP_DICT[k] for k in range(9)]   # index 0 → parquet 1
+# 1-based parquet labels (HC_MAP_DICT shifted by +1, matches stored integers 1-9)
+HC_CLASSES: list[str] = [HC_MAP_DICT[k] for k in range(9)]  # index 0 → parquet 1
 
 # Supervisor color palette (index-aligned with HC_CLASSES / HC_MAP_DICT 0-based)
 HC_COLORS: list[str] = [
-    "gray",         # 0 / parquet 1  — None
-    "cyan",         # 1 / parquet 2  — CR/VI
+    "gray",  # 0 / parquet 1  — None
+    "cyan",  # 1 / parquet 2  — CR/VI
     "deepskyblue",  # 2 / parquet 3  — AG
-    "royalblue",    # 3 / parquet 4  — LR
-    "midnightblue", # 4 / parquet 5  — RN
-    "green",        # 5 / parquet 6  — RP
-    "yellow",       # 6 / parquet 7  — WS
-    "orangered",    # 7 / parquet 8  — IH/HDG
-    "darkred",      # 8 / parquet 9  — MH
+    "royalblue",  # 3 / parquet 4  — LR
+    "midnightblue",  # 4 / parquet 5  — RN
+    "green",  # 5 / parquet 6  — RP
+    "yellow",  # 6 / parquet 7  — WS
+    "orangered",  # 7 / parquet 8  — IH/HDG
+    "darkred",  # 8 / parquet 9  — MH
 ]
 
 # label → color lookup (convenient for scatter plots)
-HC_COLOR_BY_LABEL: dict[str, str] = dict(zip(HC_CLASSES, HC_COLORS))
+HC_COLOR_BY_LABEL: dict[str, str] = dict(zip(HC_CLASSES, HC_COLORS, strict=False))
 
-# Remap PyART native integers (1–9) → MCH operational integers (1–8, 0-based index)
+# Remap PyART native integers (1-9) → MCH operational integers (1-8, 0-based index)
 # PyART hydro_names order: ("AG","CR","LR","RP","RN","VI","WS","MH","IH")
 PYART_TO_OPE: dict[int, int] = {
-    1: 2,   # AG  → operational 2 (AG)
-    2: 1,   # CR  → operational 1 (CR/VI)
-    3: 3,   # LR  → operational 3 (LR)
-    4: 5,   # RP  → operational 5 (RP)
-    5: 4,   # RN  → operational 4 (RN)
-    6: 1,   # VI  → operational 1 (CR/VI, merged with CR)
-    7: 6,   # WS  → operational 6 (WS)
-    8: 8,   # MH  → operational 8 (MH)
-    9: 7,   # IH  → operational 7 (IH/HDG)
+    1: 2,  # AG  → operational 2 (AG)
+    2: 1,  # CR  → operational 1 (CR/VI)
+    3: 3,  # LR  → operational 3 (LR)
+    4: 5,  # RP  → operational 5 (RP)
+    5: 4,  # RN  → operational 4 (RN)
+    6: 1,  # VI  → operational 1 (CR/VI, merged with CR)
+    7: 6,  # WS  → operational 6 (WS)
+    8: 8,  # MH  → operational 8 (MH)
+    9: 7,  # IH  → operational 7 (IH/HDG)
 }
diff --git a/raddb/helper.py b/raddb/helper.py
index 63b26db..ab35f0e 100644
--- a/raddb/helper.py
+++ b/raddb/helper.py
@@ -1,29 +1,26 @@
-"""
-raddb/helper.py
----------------
-Shared utilities and configuration for RadDB.
-"""
-import re
-import time as _time
+"""Shared utilities and configuration for RadDB."""
+
 import contextlib as _contextlib
 import datetime as _dt
+import re
+import time as _time
 from pathlib import Path
 
 import pandas as pd
 import polars as pl
 import xarray as xr
 
+
 # --- DataTree Helpers ---
 def list_sweep_names(dt: xr.DataTree) -> list[str]:
     """Return sorted sweep group names from a DataTree."""
     pat = re.compile(r"^sweep_\d+$")
     return sorted(s.lstrip("/") for s in dt.groups if pat.match(s.lstrip("/")))
 
+
 # --- Parquet Helpers ---
 def ensure_utc(dt_input):
-    """
-    Ensure a datetime-like input is timezone-aware and in UTC.
-    """
+    """Ensure a datetime-like input is timezone-aware and in UTC."""
     if dt_input is None:
         return None
 
@@ -42,13 +39,17 @@ def read_parquet_files(
     """Read matching parquet files into a single **polars** DataFrame."""
     files = sorted(Path(base_path).rglob(pattern))
     if not files:
-        if verbose: print(f"[RadDB] No files found matching '{pattern}' in {base_path}")
+        if verbose:
+            print(f"[RadDB] No files found matching '{pattern}' in {base_path}")
         return pl.DataFrame()
-    if verbose: print(f"[RadDB] Found {len(files)} file(s) — loading...")
+    if verbose:
+        print(f"[RadDB] Found {len(files)} file(s) — loading...")
     return pl.concat(
-        [pl.read_parquet(f, columns=columns) for f in files], how="vertical_relaxed"
+        [pl.read_parquet(f, columns=columns) for f in files],
+        how="vertical_relaxed",
     )
 
+
 def check_dataframe(df: "pl.DataFrame | pd.DataFrame") -> None:
     """Print a quick structural summary; accepts polars or pandas."""
     print("-" * 50)
@@ -61,11 +62,12 @@ def check_dataframe(df: "pl.DataFrame | pd.DataFrame") -> None:
         for k, v in nulls.items():
             print(f"{k}    {v}")
     else:
-        print(f"Missing values:\n{df.isnull().sum()}")
+        print(f"Missing values:\n{df.isna().sum()}")
     print("-" * 50)
     print(df.head())
     print("-" * 50)
 
+
 # --- Radar Name Normalization ---
 
 #: Characters a radar name may be built from, in the order that gives each its
@@ -143,7 +145,7 @@ def normalize_radar_name(radar: str) -> str:
         raise ValueError(
             f"radar name {radar!r} is not usable: a radar name must be 1 to "
             f"{RADAR_CODE_LEN} characters from [0-9A-Z] (e.g. 'A', 'KTLX'). "
-            f"Longer identifiers must be aliased to {RADAR_CODE_LEN} characters."
+            f"Longer identifiers must be aliased to {RADAR_CODE_LEN} characters.",
         )
     return name
 
@@ -161,9 +163,9 @@ def is_valid_radar_name(radar) -> bool:
 FILTER_LOGICS: dict[str, callable] = {
     "==": lambda a, b: a == b,
     "!=": lambda a, b: a != b,
-    ">":  lambda a, b: a > b,
+    ">": lambda a, b: a > b,
     ">=": lambda a, b: a >= b,
-    "<":  lambda a, b: a < b,
+    "<": lambda a, b: a < b,
     "<=": lambda a, b: a <= b,
 }
 
@@ -179,7 +181,7 @@ def resolve_filter_logic(logic: str):
     fn = FILTER_LOGICS.get(logic)
     if fn is None:
         raise ValueError(
-            f"Unknown logic '{logic}'. Choose from: {list(FILTER_LOGICS)}"
+            f"Unknown logic '{logic}'. Choose from: {list(FILTER_LOGICS)}",
         )
     return fn
 
@@ -283,11 +285,9 @@ def filter_dt(
             # is meaningless and broadcasting the mask onto them explodes
             # memory (dims are disjoint).
             mask_dims = set(keep_mask.dims)
-            ds = ds.assign({
-                name: var.where(keep_mask)
-                for name, var in ds.data_vars.items()
-                if mask_dims & set(var.dims)
-            })
+            ds = ds.assign(
+                {name: var.where(keep_mask) for name, var in ds.data_vars.items() if mask_dims & set(var.dims)},
+            )
         dict_ds[sweep_name] = ds
 
     return xr.DataTree.from_dict(dict_ds)
@@ -297,6 +297,7 @@ def filter_dt(
 # --- Profiling Utilities ---
 # ============================================================
 
+
 def _vprint(msg: str, verbose: bool = False) -> None:
     """Print a timestamped progress message to stdout when verbose is True."""
     if verbose:
@@ -312,6 +313,7 @@ class StageTimer:
     >>> timer = StageTimer()
     >>> with timer.time_stage("my_stage", volume="vol_001", sweep=2):
     ...     do_work()
+    ...
     >>> timer.print_summary()
     """
 
@@ -325,17 +327,28 @@ def time_stage(self, stage: str, volume: str | None = None, sweep: int | None =
         try:
             yield
         finally:
-            self.records.append({
-                "volume": volume,
-                "sweep": sweep,
-                "stage": stage,
-                "t_start": t0,
-                "duration": _time.perf_counter() - t0,
-            })
-
-    def record(self, stage: str, duration: float, volume: str | None = None, sweep: int | None = None, t_start: float | None = None):
+            self.records.append(
+                {
+                    "volume": volume,
+                    "sweep": sweep,
+                    "stage": stage,
+                    "t_start": t0,
+                    "duration": _time.perf_counter() - t0,
+                },
+            )
+
+    def record(
+        self,
+        stage: str,
+        duration: float,
+        volume: str | None = None,
+        sweep: int | None = None,
+        t_start: float | None = None,
+    ):
         """Manually append a pre-measured timing entry."""
-        self.records.append({"volume": volume, "sweep": sweep, "stage": stage, "t_start": t_start, "duration": duration})
+        self.records.append(
+            {"volume": volume, "sweep": sweep, "stage": stage, "t_start": t_start, "duration": duration},
+        )
 
     def to_dataframe(self) -> pd.DataFrame:
         """Return all records as a DataFrame with columns [volume, sweep, stage, duration]."""
@@ -369,8 +382,7 @@ def print_summary(self):
         for stage, row in summary.iterrows():
             pct = 100.0 * row["sum"] / total if total > 0 else 0.0
             print(
-                f"  {stage:<34} {row['sum']:>6.2f}s  {row['mean']:>5.2f}s"
-                f"  {int(row['count']):>5}  {pct:>4.1f}%"
+                f"  {stage:<34} {row['sum']:>6.2f}s  {row['mean']:>5.2f}s" f"  {int(row['count']):>5}  {pct:>4.1f}%",
             )
         print("-" * 68)
         print(f"  {'TOTAL':<34} {total:>6.2f}s")
diff --git a/raddb/io_core.py b/raddb/io_core.py
index 18aac39..56886a2 100644
--- a/raddb/io_core.py
+++ b/raddb/io_core.py
@@ -1,13 +1,11 @@
-"""
-raddb/io_core.py
-----------------
-Core I/O conversion functions for radar data.
+"""Core I/O conversion functions for radar data.
 
 This module provides generic conversions between xarray DataTree,
 pandas DataFrame, and Parquet files.  It does **not** depend on pyart
 or radar_api — all MCH-specific I/O lives in the private ``raddb.mch``
 subpackage.
 """
+
 from __future__ import annotations
 
 import concurrent.futures
@@ -23,6 +21,14 @@
 import xarray as xr
 import yaml
 
+from raddb.discovery import _find_polar_files_in_range, _parse_pol_time
+from raddb.helper import (
+    StageTimer,
+    _vprint,
+    list_sweep_names,
+    normalize_radar_name,
+    resolve_filter_logic,
+)
 from raddb.lut import (
     AZIMUTH_SCALE,
     _parse_corners_npz,
@@ -32,41 +38,53 @@
     load_azimuth_grids,
     snap_azimuths_to_grid,
 )
-from raddb.helper import (
-    StageTimer,
-    _vprint,
-    list_sweep_names,
-    normalize_radar_name,
-    resolve_filter_logic,
-)
-from raddb.discovery import _find_polar_files_in_range, _parse_pol_time
 
 logger = logging.getLogger(__name__)
 
 # --- Constants --- #
 POL_FEATURES = ["DBZH", "ZDR", "RHOHV", "PHIDP"]
 POLAR_COLUMNS = [
-    "gate_id", "time",
-    "DBZH", "DBZH_raw", "ZDR", "ZDR_raw", "KDP", "RHOHV", "PHIDP",
-    "HC_MCH", "HC_PYART", "HZT", "TEMP",
+    "gate_id",
+    "time",
+    "DBZH",
+    "DBZH_raw",
+    "ZDR",
+    "ZDR_raw",
+    "KDP",
+    "RHOHV",
+    "PHIDP",
+    "HC_MCH",
+    "HC_PYART",
+    "HZT",
+    "TEMP",
 ]
 LUT_COLUMNS = [
-    "gate_id", "sweep", "azimuth", "range", "elevation_angle",
-    "latitude", "longitude", "altitude",
-    "x", "y", "z",
+    "gate_id",
+    "sweep",
+    "azimuth",
+    "range",
+    "elevation_angle",
+    "latitude",
+    "longitude",
+    "altitude",
+    "x",
+    "y",
+    "z",
 ]
 
 # float32 gives 7 significant digits — sufficient for all radar variables.
-_POLAR_FLOAT32_COLS: frozenset = frozenset({"DBZH", "DBZH_raw", "ZDR", "ZDR_raw", "KDP", "RHOHV", "PHIDP", "HZT", "HC_MCH", "HC_PYART", "TEMP"})
+_POLAR_FLOAT32_COLS: frozenset = frozenset(
+    {"DBZH", "DBZH_raw", "ZDR", "ZDR_raw", "KDP", "RHOHV", "PHIDP", "HZT", "HC_MCH", "HC_PYART", "TEMP"},
+)
 _LAPSE_RATE: float = -0.0065  # °C/m (standard environmental lapse rate, -6.5 °C/km)
 
 
-def _projection_columns(df: "pl.DataFrame | pd.DataFrame") -> list[str]:
+def _projection_columns(df: pl.DataFrame | pd.DataFrame) -> list[str]:
     """Columns added by :func:`raddb.lut.add_lut_projection` (e.g. x_2056 / y_2056)."""
     return [c for c in df.columns if re.match(r"^[xy]_\w+$", c)]
 
 
-def _col(df: "pl.DataFrame | pd.DataFrame", name: str, dtype=None) -> np.ndarray:
+def _col(df: pl.DataFrame | pd.DataFrame, name: str, dtype=None) -> np.ndarray:
     """Column ``name`` of ``df`` as a numpy array, for polars **or** pandas.
 
     ``polars.Series.to_numpy`` takes no ``dtype`` argument (pandas' does), so the
@@ -77,12 +95,12 @@ def _col(df: "pl.DataFrame | pd.DataFrame", name: str, dtype=None) -> np.ndarray
     return arr if dtype is None else arr.astype(dtype)
 
 
-def _to_polars_frame(df: "pl.DataFrame | pd.DataFrame") -> "pl.DataFrame":
+def _to_polars_frame(df: pl.DataFrame | pd.DataFrame) -> pl.DataFrame:
     """Coerce a pandas frame to polars; pass polars frames straight through."""
     return df if isinstance(df, pl.DataFrame) else pl.from_pandas(df)
 
 
-def _to_pandas_frame(df: "pl.DataFrame | pd.DataFrame") -> pd.DataFrame:
+def _to_pandas_frame(df: pl.DataFrame | pd.DataFrame) -> pd.DataFrame:
     """Coerce a polars frame to pandas; pass pandas frames straight through.
 
     Used only at the **xarray seam**: DataTree reconstruction needs
@@ -96,6 +114,7 @@ def _to_pandas_frame(df: "pl.DataFrame | pd.DataFrame") -> pd.DataFrame:
 # DataTree file loading  (NetCDF / Zarr)
 # ============================================================================
 
+
 def open_any_datatree(
     path: str | Path,
     engine: str | None = None,
@@ -125,8 +144,7 @@ def open_any_datatree(
         raise FileNotFoundError(f"DataTree file/store not found: {p}")
 
     if engine is None and (
-        p.suffix.lower() == ".zarr"
-        or (p.is_dir() and ((p / ".zgroup").exists() or (p / "zarr.json").exists()))
+        p.suffix.lower() == ".zarr" or (p.is_dir() and ((p / ".zgroup").exists() or (p / "zarr.json").exists()))
     ):
         engine = "zarr"
 
@@ -136,7 +154,7 @@ def open_any_datatree(
         raise ImportError(
             f"Opening {p.name} requires an xarray backend that is not "
             "installed (netCDF4/h5netcdf for NetCDF, zarr for Zarr stores). "
-            "Install with: pip install raddb[io]"
+            "Install with: pip install raddb[io]",
         ) from exc
 
 
@@ -144,6 +162,7 @@ def open_any_datatree(
 # DataTree -> DataFrame / Parquet
 # ============================================================================
 
+
 def datatree_to_dataset(dt: xr.DataTree, sweep: str | int) -> xr.Dataset:
     """Extract a single sweep Dataset from a DataTree."""
     sweep_name = f"sweep_{sweep}" if isinstance(sweep, int) else sweep
@@ -151,8 +170,9 @@ def datatree_to_dataset(dt: xr.DataTree, sweep: str | int) -> xr.Dataset:
 
 
 def datatree_to_dataframe(
-    dt: xr.DataTree, max_workers: int = 1
-) -> "pl.DataFrame":
+    dt: xr.DataTree,
+    max_workers: int = 1,
+) -> pl.DataFrame:
     """Flatten a DataTree into a single **polars** DataFrame.
 
     Each sweep is converted independently and concatenated, with a ``sweep``
@@ -173,7 +193,7 @@ def _flatten(name):
         # name, which ``reset_index`` cannot insert.  Re-assigning rebuilds the index.
         unindexed = [d for d in ds.sizes if d in ds.coords and d not in ds.xindexes]
         if unindexed:
-            ds = ds.assign_coords({d: ds[d].values for d in unindexed})
+            ds = ds.assign_coords({d: ds[d].to_numpy() for d in unindexed})
         df = ds.to_dataframe().reset_index()
         df["sweep"] = int(name.split("_")[-1])
         return df
@@ -188,7 +208,9 @@ def _flatten(name):
 
 
 def _save_polar_parquet(
-    df_polar: "pl.DataFrame | pd.DataFrame", radar: str, base_path: str
+    df_polar: pl.DataFrame | pd.DataFrame,
+    radar: str,
+    base_path: str,
 ) -> str | None:
     """Save a POLAR DataFrame to the standard directory layout.
 
@@ -206,25 +228,21 @@ def _save_polar_parquet(
     df_polar = _to_polars_frame(df_polar)
     if df_polar.is_empty():
         logger.info(
-            "radar %s: no gates satisfied the filter; no POL file written.", radar
+            "radar %s: no gates satisfied the filter; no POL file written.",
+            radar,
         )
         return None
 
     vol_time = pd.to_datetime(df_polar["time"].min())
     if pd.isna(vol_time):
         logger.warning(
-            "radar %s: volume time is NaT for all %d surviving gates; "
-            "no POL file written.", radar, len(df_polar),
+            "radar %s: volume time is NaT for all %d surviving gates; " "no POL file written.",
+            radar,
+            len(df_polar),
         )
         return None
 
-    save_dir = (
-        Path(base_path)
-        / radar
-        / str(vol_time.year)
-        / f"{vol_time.month:02d}"
-        / f"{vol_time.day:02d}"
-    )
+    save_dir = Path(base_path) / radar / str(vol_time.year) / f"{vol_time.month:02d}" / f"{vol_time.day:02d}"
     save_dir.mkdir(parents=True, exist_ok=True)
     ts = vol_time.strftime("%Y%m%d_%H%M%S")
     pp = save_dir / f"{radar}_{ts}_POL.parquet"
@@ -247,7 +265,8 @@ def _cast_hc_column(arr, shift: int = 0) -> np.ndarray:
 
 
 def _compute_gate_temperature(
-    df: "pl.DataFrame | pd.DataFrame", mask: np.ndarray
+    df: pl.DataFrame | pd.DataFrame,
+    mask: np.ndarray,
 ) -> np.ndarray | None:
     """Compute temperature (°C) at surviving gates using standard lapse rate.
 
@@ -259,12 +278,12 @@ def _compute_gate_temperature(
     geom_required = {"range", "elevation", "altitude"}
     if not geom_required.issubset(df.columns):
         return None
-    n        = int(mask.sum())
-    r        = _col(df, "range")[mask]
-    el_rad   = np.deg2rad(_col(df, "elevation")[mask])
+    n = int(mask.sum())
+    r = _col(df, "range")[mask]
+    el_rad = np.deg2rad(_col(df, "elevation")[mask])
     site_alt = _col(df, "altitude")[mask]
-    ke, Re   = 4.0 / 3.0, 6_371_000.0
-    z_gate   = np.sqrt(r**2 + (ke * Re)**2 + 2 * r * ke * Re * np.sin(el_rad)) - ke * Re
+    ke, Re = 4.0 / 3.0, 6_371_000.0
+    z_gate = np.sqrt(r**2 + (ke * Re) ** 2 + 2 * r * ke * Re * np.sin(el_rad)) - ke * Re
     gate_alt = site_alt + z_gate
     if "HZT" not in df.columns:
         return np.full(n, np.nan, dtype=np.float32)
@@ -299,7 +318,7 @@ def _snap_volume_azimuths(sweeps, azimuths, grids, radar):
         if grid is None:
             raise ValueError(
                 f"radar {radar!r}: the LUT has no sweep {int(sweep)}, but this "
-                f"volume does — it uses a different scan strategy."
+                f"volume does — it uses a different scan strategy.",
             )
         sel = sweeps == sweep
         n_rays = np.unique(out[sel]).size
@@ -311,7 +330,7 @@ def _snap_volume_azimuths(sweeps, azimuths, grids, radar):
             raise ValueError(
                 f"radar {radar!r} sweep {int(sweep)}: volume has {n_rays} rays, "
                 f"the LUT was built for {len(grid)} — a different scan strategy. "
-                f"Archive it under its own radar name, or rebuild the LUT."
+                f"Archive it under its own radar name, or rebuild the LUT.",
             )
         snapped, dist = snap_azimuths_to_grid(out[sel], grid)
         tol = azimuth_grid_tolerance(grid)
@@ -320,7 +339,7 @@ def _snap_volume_azimuths(sweeps, azimuths, grids, radar):
                 f"radar {radar!r} sweep {int(sweep)}: a ray sits "
                 f"{dist.max() / AZIMUTH_SCALE:.3f}° from the nearest LUT azimuth, "
                 f"beyond the half-spacing tolerance of {tol / AZIMUTH_SCALE:.3f}° "
-                f"— this is not antenna drift."
+                f"— this is not antenna drift.",
             )
         out[sel] = snapped.astype(np.float64) / AZIMUTH_SCALE
         worst = max(worst, float(dist.max()) if dist.size else 0.0)
@@ -328,13 +347,13 @@ def _snap_volume_azimuths(sweeps, azimuths, grids, radar):
 
 
 def _build_polar_dataframe(
-    df: "pl.DataFrame | pd.DataFrame",
+    df: pl.DataFrame | pd.DataFrame,
     radar: str,
     filter_feature: str,
     filter_threshold: float,
     filter_logic: str,
     azimuth_grids: dict | None = None,
-) -> tuple["pl.DataFrame", np.ndarray]:
+) -> tuple[pl.DataFrame, np.ndarray]:
     """Filter a flattened volume DataFrame and attach gate_ids.
 
     Rows that do not satisfy ``filter_feature [filter_logic] filter_threshold``
@@ -371,7 +390,10 @@ def _build_polar_dataframe(
         # Snapped before the filter, so the scan-strategy check counts the
         # volume's rays rather than only those that survived the filter.
         azimuths_all, worst = _snap_volume_azimuths(
-            sweeps_all, azimuths_all, azimuth_grids, radar
+            sweeps_all,
+            azimuths_all,
+            azimuth_grids,
+            radar,
         )
         logger.debug("radar %s: rays snapped, max move %.3f deg.", radar, worst)
     else:
@@ -382,7 +404,8 @@ def _build_polar_dataframe(
         logger.warning(
             "radar %s: the LUT records no nominal azimuth grid, so measured "
             "azimuths are used as-is and some gates may not join it. Regenerate "
-            "the LUT to fix this.", radar,
+            "the LUT to fix this.",
+            radar,
         )
 
     gate_ids = encode_gate_ids(
@@ -394,10 +417,7 @@ def _build_polar_dataframe(
 
     hzt_available = "HZT" in df.columns
     polar_cols = [
-        c for c in POLAR_COLUMNS
-        if c in df.columns
-        and c != "gate_id"
-        and not (c == "HC_PYART" and not hzt_available)
+        c for c in POLAR_COLUMNS if c in df.columns and c != "gate_id" and not (c == "HC_PYART" and not hzt_available)
     ]
     df_polar = pl.DataFrame(
         {"gate_id": gate_ids, **{c: _col(df, c)[mask] for c in polar_cols}},
@@ -460,28 +480,20 @@ def archive_volume(
     radar = normalize_radar_name(radar)
     resolve_filter_logic(filter_logic)  # fail fast before flattening
 
-    with (
-        timer.time_stage("datatree_to_df", volume=volume)
-        if timer
-        else _nullctx()
-    ):
+    with timer.time_stage("datatree_to_df", volume=volume) if timer else _nullctx():
         df = datatree_to_dataframe(dt)
 
-    with (
-        timer.time_stage("generate_gate_ids", volume=volume)
-        if timer
-        else _nullctx()
-    ):
+    with timer.time_stage("generate_gate_ids", volume=volume) if timer else _nullctx():
         df_polar, _mask = _build_polar_dataframe(
-            df, radar, filter_feature, filter_threshold, filter_logic,
+            df,
+            radar,
+            filter_feature,
+            filter_threshold,
+            filter_logic,
             azimuth_grids=load_azimuth_grids(radar, base_output_path),
         )
 
-    with (
-        timer.time_stage("save_parquet", volume=volume)
-        if timer
-        else _nullctx()
-    ):
+    with timer.time_stage("save_parquet", volume=volume) if timer else _nullctx():
         df_polar = _finalize_polar_dtypes(df_polar, df, _mask)
         return _save_polar_parquet(df_polar, radar, base_output_path)
 
@@ -550,11 +562,7 @@ def archive_multiple_volumes(
 
         vol_t0 = _time.perf_counter()
         try:
-            with (
-                timer.time_stage("archive_volume", volume=label)
-                if timer
-                else _nullctx()
-            ):
+            with timer.time_stage("archive_volume", volume=label) if timer else _nullctx():
                 polar_path = archive_volume(
                     dt,
                     radar=radar,
@@ -573,8 +581,7 @@ def archive_multiple_volumes(
                 result["skipped"] = True
                 result["polar_path"] = None
                 _vprint(
-                    f"SKIP  Volume {i}/{len(items)} held no gates to archive "
-                    f"({vol_elapsed:.1f}s)",
+                    f"SKIP  Volume {i}/{len(items)} held no gates to archive " f"({vol_elapsed:.1f}s)",
                     verbose,
                 )
             else:
@@ -585,8 +592,7 @@ def archive_multiple_volumes(
                 result["n_gates"] = len(df_polar)
 
                 _vprint(
-                    f"OK  Volume {i}/{len(items)} done in "
-                    f"{vol_elapsed:.1f}s -- {result['n_gates']:,} gates saved",
+                    f"OK  Volume {i}/{len(items)} done in " f"{vol_elapsed:.1f}s -- {result['n_gates']:,} gates saved",
                     verbose,
                 )
 
@@ -594,8 +600,7 @@ def archive_multiple_volumes(
             vol_elapsed = _time.perf_counter() - vol_t0
             result["error"] = str(e)
             _vprint(
-                f"FAIL  Volume {i}/{len(items)} FAILED in "
-                f"{vol_elapsed:.1f}s: {e}",
+                f"FAIL  Volume {i}/{len(items)} FAILED in " f"{vol_elapsed:.1f}s: {e}",
                 verbose,
             )
             logger.error(f"[{i}/{len(items)}] {label} - FAIL: {e}")
@@ -607,8 +612,7 @@ def archive_multiple_volumes(
     n_skip = sum(1 for r in results if r["skipped"])
     _vprint(
         f"\nArchiving complete: {n_ok}/{len(results)} volumes "
-        f"in {total_elapsed:.1f}s"
-        + (f" ({n_skip} skipped, nothing to archive)" if n_skip else ""),
+        f"in {total_elapsed:.1f}s" + (f" ({n_skip} skipped, nothing to archive)" if n_skip else ""),
         verbose,
     )
     return results
@@ -674,10 +678,10 @@ def archive_volumes_multi_radar(
 
 
 def _finalize_polar_dtypes(
-    df_polar: "pl.DataFrame | pd.DataFrame",
-    df: "pl.DataFrame | pd.DataFrame",
+    df_polar: pl.DataFrame | pd.DataFrame,
+    df: pl.DataFrame | pd.DataFrame,
     mask: np.ndarray,
-) -> "pl.DataFrame":
+) -> pl.DataFrame:
     """Apply dtype optimisations and add the TEMP column.
 
     HC columns are shifted +1 to the 1-based parquet scale; all polar
@@ -722,7 +726,11 @@ def datatree_to_parquet(
 
     df = datatree_to_dataframe(dt, max_workers)
     df_polar, mask = _build_polar_dataframe(
-        df, radar, filter_feature, filter_threshold, filter_logic,
+        df,
+        radar,
+        filter_feature,
+        filter_threshold,
+        filter_logic,
         azimuth_grids=load_azimuth_grids(radar, base_output_path),
     )
     df_polar = _finalize_polar_dtypes(df_polar, df, mask)
@@ -733,6 +741,7 @@ def datatree_to_parquet(
 # Parquet -> DataFrame / DataTree  (reading archived data)
 # ============================================================================
 
+
 def parquet_to_dataframe(
     radar: str,
     base_path: str | Path,
@@ -740,7 +749,7 @@ def parquet_to_dataframe(
     end_time: str | pd.Timestamp | None = None,
     columns: list[str] | None = None,
     merge_lut: bool = False,
-) -> "pl.DataFrame":
+) -> pl.DataFrame:
     """Load archived POLAR parquet files as a single **polars** DataFrame.
 
     Parameters
@@ -773,8 +782,7 @@ def parquet_to_dataframe(
 
     if not polar_files:
         logger.warning(
-            f"No POLAR data found for radar {radar} "
-            f"between {start_time} and {end_time}"
+            f"No POLAR data found for radar {radar} " f"between {start_time} and {end_time}",
         )
         return pl.DataFrame()
 
@@ -794,9 +802,7 @@ def parquet_to_dataframe(
             # back-to-back volumes reliably).
             vt = _parse_pol_time(f)
             df = df.with_columns(
-                pl.lit(vt.tz_localize(None) if vt is not None else None)
-                .cast(pl.Datetime("ns"))
-                .alias("volume_time")
+                pl.lit(vt.tz_localize(None) if vt is not None else None).cast(pl.Datetime("ns")).alias("volume_time"),
             )
             dfs.append(df)
         except Exception as e:
@@ -831,12 +837,14 @@ def parquet_to_dataframe(
             lut_cols = [c for c in lut_cols if c in lut_df.columns]
             # maintain_order="left" reproduces pandas' left-merge row order.
             df_all = df_all.join(
-                lut_df.select(lut_cols), on="gate_id", how="left", maintain_order="left"
+                lut_df.select(lut_cols),
+                on="gate_id",
+                how="left",
+                maintain_order="left",
             )
         else:
             logger.warning(
-                f"LUT not found at {lut_path}. "
-                "Returning data without spatial coordinates."
+                f"LUT not found at {lut_path}. " "Returning data without spatial coordinates.",
             )
 
     return df_all
@@ -848,7 +856,7 @@ def scan_polar_parquet(
     start_time: str | pd.Timestamp | None = None,
     end_time: str | pd.Timestamp | None = None,
     columns: list[str] | None = None,
-) -> "pl.LazyFrame | None":
+) -> pl.LazyFrame | None:
     """Scan archived POLAR parquet files as a single polars LazyFrame.
 
     The polars counterpart of :func:`parquet_to_dataframe`, used by
@@ -886,8 +894,7 @@ def scan_polar_parquet(
     polar_files = _find_polar_files_in_range(radar_path, start_time, end_time)
     if not polar_files:
         logger.warning(
-            f"No POLAR data found for radar {radar} "
-            f"between {start_time} and {end_time}"
+            f"No POLAR data found for radar {radar} " f"between {start_time} and {end_time}",
         )
         return None
 
@@ -904,14 +911,17 @@ def scan_polar_parquet(
             # multi-volume frame can later be split back into single volumes
             # (per-gate `time` spans the whole ~5 min scan and cannot separate
             # back-to-back volumes reliably).  The dtype is pinned so files with
-            # an unparseable name still concatenate with the rest.  Microsecond
+            # an unparsable name still concatenate with the rest.  Microsecond
             # resolution matches what :func:`parquet_to_dataframe` produces.
             ts = _parse_pol_time(f)
-            scans.append(lf.with_columns(
-                pl.lit(ts.to_pydatetime() if ts is not None else None,
-                       dtype=pl.Datetime("us", "UTC")).alias("volume_time"),
-                pl.lit(radar).alias("radar"),
-            ))
+            scans.append(
+                lf.with_columns(
+                    pl.lit(ts.to_pydatetime() if ts is not None else None, dtype=pl.Datetime("us", "UTC")).alias(
+                        "volume_time",
+                    ),
+                    pl.lit(radar).alias("radar"),
+                ),
+            )
         except Exception as e:
             logger.warning(f"Error scanning {f}: {e}")
             continue
@@ -966,7 +976,7 @@ def parquet_to_datatree(
 
     if not lut_path.exists():
         raise FileNotFoundError(
-            f"LUT not found at {lut_path}. Run generate_lut() first."
+            f"LUT not found at {lut_path}. Run generate_lut() first.",
         )
     if not info_path.exists():
         raise FileNotFoundError(f"Radar info not found at {info_path}.")
@@ -974,8 +984,7 @@ def parquet_to_datatree(
     polar_files = _find_polar_files_in_range(radar_path, start_time, end_time)
     if not polar_files:
         raise ValueError(
-            f"No POLAR data found for {radar} "
-            f"between {start_time} and {end_time}"
+            f"No POLAR data found for {radar} " f"between {start_time} and {end_time}",
         )
 
     # Load all POLAR files in range
@@ -1004,15 +1013,22 @@ def parquet_to_datatree(
 # Columns the LUT owns; a DataFrame's own copies of these are replaced by the
 # LUT's on reconstruction so geometry is always authoritative and never collides.
 _LUT_GEOMETRY_COLS = (
-    "sweep", "azimuth", "range",
-    "latitude", "longitude", "altitude", "x", "y", "z",
+    "sweep",
+    "azimuth",
+    "range",
+    "latitude",
+    "longitude",
+    "altitude",
+    "x",
+    "y",
+    "z",
 )
 # Pure per-volume metadata that must not become gridded data_vars.
 _NON_GATE_METADATA_COLS = ("radar", "volume_time")
 
 
 def dataframe_to_datatree(
-    df: "pl.DataFrame | pd.DataFrame",
+    df: pl.DataFrame | pd.DataFrame,
     radar: str,
     base_path: str | Path,
     label_column: str = "DBZH",
@@ -1023,7 +1039,7 @@ def dataframe_to_datatree(
     The df→DataTree core shared by :func:`parquet_to_datatree` and by DataFrame
     plotting: it joins ``df`` with the radar LUT on ``gate_id`` to recover
     geometry (sweep/azimuth/range + lat/lon/alt/x/y/z and any projection cols),
-    fills the full ``(azimuth × range)`` grid, and NaN-fills gates absent from
+    fills the full ``(azimuth x range)`` grid, and NaN-fills gates absent from
     ``df`` — so a **cropped/filtered** DataFrame reconstructs to a DataTree that
     carries the correct geometry but only the rows present in ``df``, with
     **the DataFrame's own values** (honouring crops or added feature columns).
@@ -1080,7 +1096,8 @@ def dataframe_to_datatree(
     # from the LUT (authoritative, no _x/_y merge collisions) and constant
     # metadata doesn't turn into gridded variables.
     drop = [
-        c for c in (*_LUT_GEOMETRY_COLS, *_projection_columns(lut_df), *_NON_GATE_METADATA_COLS)
+        c
+        for c in (*_LUT_GEOMETRY_COLS, *_projection_columns(lut_df), *_NON_GATE_METADATA_COLS)
         if c != "gate_id" and c in df.columns
     ]
     df_meas = df.drop(columns=drop)
@@ -1110,11 +1127,12 @@ def dataframe_to_datatree(
 # Reconstruction (Parquet + LUT -> DataTree)
 # ============================================================================
 
+
 def labels_to_dataframe(
     labels: np.ndarray,
     gate_ids,
     extra_columns: dict | None = None,
-) -> "pl.DataFrame":
+) -> pl.DataFrame:
     """Create a **polars** DataFrame from prediction labels and gate IDs."""
     data = {"gate_id": np.asarray(gate_ids), "hydrometeor_class": np.asarray(labels)}
     if extra_columns:
@@ -1123,8 +1141,9 @@ def labels_to_dataframe(
 
 
 def join_labels_with_lut(
-    df_labels: "pl.DataFrame | pd.DataFrame", lut_path: str | Path
-) -> "pl.DataFrame":
+    df_labels: pl.DataFrame | pd.DataFrame,
+    lut_path: str | Path,
+) -> pl.DataFrame:
     """Join label data with the LUT to recover spatial coordinates.
 
     Accepts a polars or pandas label frame; always returns polars.
@@ -1134,7 +1153,9 @@ def join_labels_with_lut(
     cols = [c for c in df_labels.columns if c != "gate_id"]
     # maintain_order="left" reproduces pandas' left-merge row order.
     return df_lut.join(
-        df_labels.select(["gate_id", *cols]), on="gate_id", how="left",
+        df_labels.select(["gate_id", *cols]),
+        on="gate_id",
+        how="left",
         maintain_order="left",
     )
 
@@ -1144,10 +1165,10 @@ def join_labels_with_lut(
 
 def _get_sweep_coords(sweep, radar_info):
     coords = {
-        "site_latitude":  radar_info["latitude"],
+        "site_latitude": radar_info["latitude"],
         "site_longitude": radar_info["longitude"],
-        "site_altitude":  radar_info["altitude"],
-        "sweep_number":   sweep,
+        "site_altitude": radar_info["altitude"],
+        "sweep_number": sweep,
     }
     meta = radar_info.get("sweeps", {}).get(sweep, {})
     if "elevation" in meta:
@@ -1156,11 +1177,11 @@ def _get_sweep_coords(sweep, radar_info):
 
 
 def reconstruct_sweep_dataset(
-    df_joined: "pl.DataFrame | pd.DataFrame",
+    df_joined: pl.DataFrame | pd.DataFrame,
     sweep: int,
-    lut_df: "pl.DataFrame | pd.DataFrame",
+    lut_df: pl.DataFrame | pd.DataFrame,
     radar_info: dict,
-    label_column: str = "hydrometeor_class",
+    label_column: str = "hydrometeor_class",  # noqa: ARG001  kept for signature parity with the callers below
     sweep_corners: dict | None = None,
 ) -> xr.Dataset:
     """Reconstruct a single sweep Dataset from joined data.
@@ -1187,8 +1208,7 @@ def reconstruct_sweep_dataset(
     spatial_cols += _projection_columns(lut_df)
 
     non_spatial = [
-        c for c in df_sweep.columns
-        if c not in ("gate_id", "sweep", "azimuth", "range") and c not in spatial_cols
+        c for c in df_sweep.columns if c not in ("gate_id", "sweep", "azimuth", "range") and c not in spatial_cols
     ]
 
     idx = get_full_sweep_index(lut_df, sweep)
@@ -1202,7 +1222,7 @@ def reconstruct_sweep_dataset(
     df_full = pd.concat([df_reidx, lut_spatial], axis=1)
 
     ds = df_full.to_xarray().assign_coords(
-        _get_sweep_coords(sweep, radar_info)
+        _get_sweep_coords(sweep, radar_info),
     )
 
     # Promote per-gate spatial vars to coords so the Dataset is plot-ready.
@@ -1222,7 +1242,7 @@ def reconstruct_sweep_dataset(
 
 
 def reconstruct_datatree(
-    df_joined: "pl.DataFrame | pd.DataFrame",
+    df_joined: pl.DataFrame | pd.DataFrame,
     lut_path: str | Path,
     radar_info_path: str | Path,
     label_column: str = "hydrometeor_class",
@@ -1279,7 +1299,7 @@ def _rec(sw):
     else:
         with concurrent.futures.ThreadPoolExecutor(max_workers) as ex:
             for fut in concurrent.futures.as_completed(
-                [ex.submit(_rec, sw) for sw in sweeps]
+                [ex.submit(_rec, sw) for sw in sweeps],
             ):
                 k, ds = fut.result()
                 if ds is not None:
@@ -1294,11 +1314,12 @@ def _rec(sw):
 # Feature addition utilities
 # ============================================================================
 
+
 def add_feature_to_df(
-    df: "pl.DataFrame | pd.DataFrame",
+    df: pl.DataFrame | pd.DataFrame,
     feature_name: str,
     compute_fn: callable,
-) -> "pl.DataFrame | pd.DataFrame":
+) -> pl.DataFrame | pd.DataFrame:
     """Add a new column to a DataFrame computed from existing columns.
 
     Accepts polars or pandas and returns the **same kind**, so ``compute_fn``
diff --git a/raddb/lut.py b/raddb/lut.py
index 99fabec..4aace40 100644
--- a/raddb/lut.py
+++ b/raddb/lut.py
@@ -1,7 +1,4 @@
-"""
-raddb/lut.py
-------------
-Look-Up Table (LUT) generation and loading utilities.
+"""Look-Up Table (LUT) generation and loading utilities.
 
 The LUT stores one record per radar gate (azimuth x range x sweep) with
 static spatial information (Cartesian coordinates, lat/lon, elevation).
@@ -11,6 +8,7 @@
 has ``azimuth``, ``range``, and ``elevation`` coordinates per sweep.
 No pyart or radar_api dependency.
 """
+
 from __future__ import annotations
 
 import logging
@@ -43,7 +41,7 @@
 #: Number of distinct radar codes: ``36**4 - 1`` is the largest, ``0`` the
 #: smallest.  ``radar_code * 10**12`` must stay inside int64, which allows
 #: ``9_223_371``; base-36 over four characters needs only ``1_679_615``.
-MAX_RADAR_CODE: int = 36 ** RADAR_CODE_LEN - 1
+MAX_RADAR_CODE: int = 36**RADAR_CODE_LEN - 1
 
 #: Version of the ``gate_id`` encoding written into each radar's info YAML.
 #:
@@ -126,7 +124,7 @@ def decode_radar_code(code: int) -> str:
     value = int(code)
     if not 0 <= value <= MAX_RADAR_CODE:
         raise ValueError(
-            f"radar code {value} is outside [0, {MAX_RADAR_CODE}] and names no radar."
+            f"radar code {value} is outside [0, {MAX_RADAR_CODE}] and names no radar.",
         )
     chars = []
     for _ in range(RADAR_CODE_LEN):
@@ -142,9 +140,7 @@ def decode_radar_code(code: int) -> str:
 #:    Kept because it is part of the public API surface; do **not** use it as a
 #:    membership test for "is this a usable radar name" — see
 #:    :func:`raddb.helper.is_valid_radar_name`.
-RADAR_TO_IDX: dict[str, int] = {
-    chr(ord("A") + i): encode_radar_code(chr(ord("A") + i)) for i in range(26)
-}
+RADAR_TO_IDX: dict[str, int] = {chr(ord("A") + i): encode_radar_code(chr(ord("A") + i)) for i in range(26)}
 
 #: Antenna 3 dB beamwidth in degrees, used for the gate's angular extent.
 #: 1.0 deg matches the MeteoSwiss Rad4Alp radars and the reference prototype
@@ -181,15 +177,14 @@ def lut_file_path(radar: str, kind: str, lut_base_path: str | Path) -> Path:
     """Path of one of the five LUT files (see :data:`LUT_FILES`)."""
     if kind not in LUT_FILES:
         raise KeyError(f"unknown LUT file kind {kind!r}; use one of {sorted(LUT_FILES)}.")
-    return (
-        Path(lut_base_path) / radar / "LUT" / LUT_FILES[kind].format(radar=radar)
-    )
+    return Path(lut_base_path) / radar / "LUT" / LUT_FILES[kind].format(radar=radar)
 
 
 # ============================================================================
 # Coordinate transforms  (pure numpy, no pyart dependency)
 # ============================================================================
 
+
 def _interpolate_range_edges(ranges: np.ndarray) -> np.ndarray:
     """Interpolate the edges of range gates (PyART's formula).
 
@@ -200,8 +195,8 @@ def _interpolate_range_edges(ranges: np.ndarray) -> np.ndarray:
     r = np.asarray(ranges, dtype=np.float64)
     edges = np.empty(r.size + 1, dtype=np.float64)
     edges[1:-1] = 0.5 * (r[:-1] + r[1:])
-    edges[0]    = r[0]  - 0.5 * (r[1]  - r[0])
-    edges[-1]   = r[-1] + 0.5 * (r[-1] - r[-2])
+    edges[0] = r[0] - 0.5 * (r[1] - r[0])
+    edges[-1] = r[-1] + 0.5 * (r[-1] - r[-2])
     edges[edges < 0] = 0.0
     return edges
 
@@ -211,8 +206,8 @@ def _interpolate_elevation_edges(elevations: np.ndarray) -> np.ndarray:
     el = np.asarray(elevations, dtype=np.float64)
     edges = np.empty(el.size + 1, dtype=np.float64)
     edges[1:-1] = 0.5 * (el[:-1] + el[1:])
-    edges[0]    = el[0]  - 0.5 * (el[1]  - el[0])
-    edges[-1]   = el[-1] + 0.5 * (el[-1] - el[-2])
+    edges[0] = el[0] - 0.5 * (el[1] - el[0])
+    edges[-1] = el[-1] + 0.5 * (el[-1] - el[-2])
     return np.clip(edges, -90.0, 90.0)
 
 
@@ -230,8 +225,8 @@ def _interpolate_azimuth_edges(azimuths: np.ndarray) -> np.ndarray:
     az = np.asarray(azimuths, dtype=np.float64)
     z = np.exp(1j * np.deg2rad(az))
     midpoints = 0.5 * (z[:-1] + z[1:])
-    first = z[0]  - (midpoints[0]  - z[0])
-    last  = z[-1] + (z[-1] - midpoints[-1])
+    first = z[0] - (midpoints[0] - z[0])
+    last = z[-1] + (z[-1] - midpoints[-1])
     edges = np.concatenate(([first], midpoints, [last]))
     return np.rad2deg(np.angle(edges)) % 360.0
 
@@ -270,8 +265,8 @@ def antenna_vectors_to_cartesian(
         Cartesian coordinates in meters relative to the radar.
     """
     if edges:
-        ranges     = _interpolate_range_edges(ranges)
-        azimuths   = _interpolate_azimuth_edges(azimuths)
+        ranges = _interpolate_range_edges(ranges)
+        azimuths = _interpolate_azimuth_edges(azimuths)
         elevations = _interpolate_elevation_edges(elevations)
 
     r = np.atleast_1d(np.asarray(ranges, dtype=np.float64))
@@ -283,16 +278,14 @@ def antenna_vectors_to_cartesian(
     # Height of each gate above radar (n_rays, n_gates)
     z = (
         np.sqrt(
-            r[np.newaxis, :] ** 2
-            + R ** 2
-            + 2.0 * r[np.newaxis, :] * R * np.sin(theta_e[:, np.newaxis])
+            r[np.newaxis, :] ** 2 + R**2 + 2.0 * r[np.newaxis, :] * R * np.sin(theta_e[:, np.newaxis]),
         )
         - R
     )
 
     # Ground-range arc length
     s = R * np.arcsin(
-        r[np.newaxis, :] * np.cos(theta_e[:, np.newaxis]) / (R + z)
+        r[np.newaxis, :] * np.cos(theta_e[:, np.newaxis]) / (R + z),
     )
 
     # Cartesian
@@ -432,7 +425,7 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
     if spacing <= 0.0:
         raise ValueError(
             f"the {n} rays of this sweep are not distinct enough to give a ray "
-            f"spacing (median gap {spacing:.6f}°)."
+            f"spacing (median gap {spacing:.6f}°).",
         )
 
     # How many ray slots each gap spans: 1 between neighbours, 2 or more across
@@ -445,15 +438,14 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
     n_grid = int(slots_per_gap.sum())
     if n_grid < n:
         raise ValueError(
-            f"the {n} rays of this sweep do not sit one per slot on a "
-            f"{spacing:.4f}° grid (two rays share a slot)."
+            f"the {n} rays of this sweep do not sit one per slot on a " f"{spacing:.4f}° grid (two rays share a slot).",
         )
     step = 360.0 / n_grid
     if step * AZIMUTH_SCALE < 1.0:
         raise ValueError(
             f"{n_grid} rays give a {step:.4f}° ray spacing, finer than the "
             f"{1 / AZIMUTH_SCALE}° azimuth resolution of gate_id; two rays would "
-            f"share one gate_id."
+            f"share one gate_id.",
         )
 
     # A rotation may have holes but must still be a rotation: a sector scan (or
@@ -465,7 +457,7 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
             f"(they span {np.ptp(az_sorted):.1f}° with a derived spacing of "
             f"{step:.4f}°, filling {n}/{n_grid} of the rotation), so they have no "
             f"nominal azimuth grid. Sector scans and irregular sweeps are not "
-            f"supported."
+            f"supported.",
         )
 
     # Residual of each ray against the slot it occupies on a step grid anchored
@@ -490,7 +482,7 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
     if np.unique(az_int).size != n_grid:
         raise ValueError(
             f"the {n_grid}-ray azimuth grid collides after rounding to "
-            f"{1 / AZIMUTH_SCALE}°; this scan strategy cannot be stored in gate_id."
+            f"{1 / AZIMUTH_SCALE}°; this scan strategy cannot be stored in gate_id.",
         )
 
     # The grid is only meaningful if it actually fits the rays it came from.
@@ -501,7 +493,7 @@ def nominal_azimuth_grid(azimuths) -> np.ndarray:
             f"these {n} rays do not form a full rotation of evenly spaced rays "
             f"(they span {np.ptp(az_sorted):.1f}° with a derived spacing of "
             f"{step:.4f}°), so they have no nominal azimuth grid. Sector scans and "
-            f"irregular sweeps are not supported."
+            f"irregular sweeps are not supported.",
         )
     return az_int
 
@@ -574,17 +566,12 @@ def load_azimuth_grids(radar: str, base_path: str | Path) -> dict[int, np.ndarra
     lut_path = lut_file_path(radar, "lut", base_path)
     if not lut_path.exists():
         return None
-    az = (
-        pl.scan_parquet(lut_path)
-        .select(["sweep", "azimuth"])
-        .unique()
-        .collect()
-    )
+    az = pl.scan_parquet(lut_path).select(["sweep", "azimuth"]).unique().collect()
     grids = {
         int(sweep): np.sort(
             np.round(
-                az.filter(pl.col("sweep") == sweep)["azimuth"].to_numpy() * AZIMUTH_SCALE
-            ).astype(np.int64)
+                az.filter(pl.col("sweep") == sweep)["azimuth"].to_numpy() * AZIMUTH_SCALE,
+            ).astype(np.int64),
         )
         for sweep in sorted(az["sweep"].unique().to_list())
     }
@@ -595,6 +582,7 @@ def load_azimuth_grids(radar: str, base_path: str | Path) -> dict[int, np.ndarra
 # Gate ID generation
 # ============================================================================
 
+
 def encode_gate_ids(
     radar: str,
     sweeps: int | np.ndarray,
@@ -626,18 +614,21 @@ def encode_gate_ids(
     """
     radar_code = np.int64(encode_radar_code(radar))
     sweep_v = np.asarray(sweeps, dtype=np.int64)
-    az_int  = np.round(np.asarray(azimuths, dtype=np.float64) * 10).astype(np.int64)
+    az_int = np.round(np.asarray(azimuths, dtype=np.float64) * 10).astype(np.int64)
     rng_int = np.asarray(ranges).astype(np.int64)
     return (
         radar_code * np.int64(GATE_ID_RADAR_BASE)
-        + sweep_v  * np.int64(   10_000_000_000)
-        + az_int   * np.int64(        1_000_000)
+        + sweep_v * np.int64(10_000_000_000)
+        + az_int * np.int64(1_000_000)
         + rng_int
     )
 
 
 def generate_gate_id(
-    radar: str, sweep: int, azimuth: float, range_m: float
+    radar: str,
+    sweep: int,
+    azimuth: float,
+    range_m: float,
 ) -> int:
     """Create a unique gate identifier as a 64-bit integer.
 
@@ -651,6 +642,7 @@ def generate_gate_id(
 # Generic LUT generation from DataTree
 # ============================================================================
 
+
 def _beamwidth_from_datatree(dt: xr.DataTree) -> float:
     """Antenna beamwidth [deg] from the DataTree, else the package default.
 
@@ -665,7 +657,7 @@ def _beamwidth_from_datatree(dt: xr.DataTree) -> float:
                     val = float(np.asarray(attrs[nm]).ravel()[0])
                 except (TypeError, ValueError):
                     continue
-                if 0.0 < val < 20.0:      # sanity: a real antenna beamwidth
+                if 0.0 < val < 20.0:  # sanity: a real antenna beamwidth
                     return val
     return DEFAULT_BEAMWIDTH_DEG
 
@@ -689,8 +681,7 @@ def suggest_crs(longitude: float, latitude: float) -> int:
     return (32600 if float(latitude) >= 0 else 32700) + zone
 
 
-def crs_distance_error(crs, longitude: float, latitude: float,
-                       baseline_m: float = 100_000.0) -> float:
+def crs_distance_error(crs, longitude: float, latitude: float, baseline_m: float = 100_000.0) -> float:
     """Worst relative distance error of ``crs`` at a site, in percent.
 
     Projects a ``baseline_m`` geodesic in eight directions from the site and
@@ -701,11 +692,11 @@ def crs_distance_error(crs, longitude: float, latitude: float,
     baseline as 145 km in Switzerland).
     """
     import pyproj
+
     from raddb.aoi import _to_pyproj_crs
 
     geod = pyproj.Geod(ellps="WGS84")
-    tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(crs),
-                                     always_xy=True)
+    tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(crs), always_xy=True)
     x0, y0 = tf.transform(longitude, latitude)
     worst = 0.0
     for azimuth in range(0, 360, 45):
@@ -727,7 +718,7 @@ def _crs_label(crs) -> str:
         try:
             named = pyproj.CRS.from_epsg(int(crs))
             return f"EPSG:{int(crs)} ({named.name})"
-        except Exception:                                          # noqa: BLE001
+        except Exception:
             return f"EPSG:{int(crs)}"
     name = getattr(crs, "name", None)
     return str(name) if name and name != "unknown" else str(crs)
@@ -755,7 +746,9 @@ def validate_crs_for_site(crs, longitude: float, latitude: float, radar: str = "
         Worst distance error at this site, in percent.
     """
     import warnings as _warnings
+
     import pyproj
+
     from raddb.aoi import _to_pyproj_crs
 
     who = f"radar {radar} " if radar else ""
@@ -767,7 +760,7 @@ def validate_crs_for_site(crs, longitude: float, latitude: float, radar: str = "
             f"degrees, so it cannot express gate geometry, a crop radius or a "
             f"cross-section distance. Pass a projected CRS; for {who}at "
             f"({longitude:.4f}, {latitude:.4f}) try "
-            f"EPSG:{suggest_crs(longitude, latitude)}."
+            f"EPSG:{suggest_crs(longitude, latitude)}.",
         )
 
     err = crs_distance_error(resolved, longitude, latitude)
@@ -775,21 +768,21 @@ def validate_crs_for_site(crs, longitude: float, latitude: float, radar: str = "
         area = getattr(resolved.area_of_use, "name", None)
         if area is None and isinstance(crs, (int, np.integer)):
             import pyproj
+
             try:
                 area = getattr(pyproj.CRS.from_epsg(int(crs)).area_of_use, "name", None)
-            except Exception:                                      # noqa: BLE001
+            except Exception:
                 area = None
         where = f", valid for {area}" if area else ""
         raise ValueError(
             f"{label}{where} distorts distance by {err:.1f}% at "
             f"{who}({longitude:.4f}, {latitude:.4f}) — gate geometry, crops and "
             f"cross-sections would all be wrong by that much. Suggested for this "
-            f"site: EPSG:{suggest_crs(longitude, latitude)}."
+            f"site: EPSG:{suggest_crs(longitude, latitude)}.",
         )
     if err > CRS_WARN_PCT:
         _warnings.warn(
-            f"{label} distorts distance by {err:.2f}% at "
-            f"{who}({longitude:.4f}, {latitude:.4f}).",
+            f"{label} distorts distance by {err:.2f}% at " f"{who}({longitude:.4f}, {latitude:.4f}).",
             stacklevel=3,
         )
     return err
@@ -851,7 +844,6 @@ def generate_lut_from_datatree(
     """
     lut_dir = Path(output_base_path) / radar / "LUT"
     lut_path = lut_dir / f"{radar}_LUT.parquet"
-    info_path = lut_dir / f"{radar}_info.yaml"
 
     if beamwidth_deg is None:
         beamwidth_deg = _beamwidth_from_datatree(dt)
@@ -859,12 +851,11 @@ def generate_lut_from_datatree(
     # Skip only when *all five* files are present. An archive written before the
     # geometry lattices existed has the LUT parquet + info YAML but not the three
     # plane files, and must still be able to fill them in.
-    if all(
-        (lut_dir / tmpl.format(radar=radar)).exists() for tmpl in LUT_FILES.values()
-    ):
+    if all((lut_dir / tmpl.format(radar=radar)).exists() for tmpl in LUT_FILES.values()):
         logger.info(
             "All %d LUT files already exist at %s -- skipping generation.",
-            len(LUT_FILES), lut_dir,
+            len(LUT_FILES),
+            lut_dir,
         )
         return str(lut_path)
 
@@ -882,8 +873,8 @@ def generate_lut_from_datatree(
         ds = dt[sweep_name].to_dataset()
 
         measured_az = np.asarray(ds["azimuth"].values, dtype=np.float64)
-        ranges = ds["range"].values
-        elevations = ds["elevation"].values
+        ranges = ds["range"].to_numpy()
+        elevations = ds["elevation"].to_numpy()
 
         # The LUT is the radar's *nominal* scan geometry, not a snapshot of this
         # one volume's antenna readings.  Every later volume snaps onto this same
@@ -897,7 +888,7 @@ def generate_lut_from_datatree(
                 f"radar {radar!r} sweep {sweep_idx}: the {measured_az.size} rays do "
                 f"not sit one-per-point on a regular {360 / measured_az.size:.4f}° "
                 f"grid (two rays snap together), so this sweep has no nominal "
-                f"azimuth grid."
+                f"azimuth grid.",
             )
 
         elevation_angle = float(ds.coords.get("elevation_angle", np.mean(elevations)))
@@ -909,23 +900,29 @@ def generate_lut_from_datatree(
         absent = np.setdiff1d(az_grid, snapped)
         azimuths = np.concatenate([snapped, absent]).astype(np.float64) / AZIMUTH_SCALE
         elevations = np.concatenate(
-            [np.asarray(elevations, dtype=np.float64), np.full(absent.size, elevation_angle)]
+            [np.asarray(elevations, dtype=np.float64), np.full(absent.size, elevation_angle)],
         )
         if absent.size:
             logger.info(
-                "sweep %d: %d of %d rays missing from this volume; their grid "
-                "points are still written to the LUT.",
-                sweep_idx, absent.size, az_grid.size,
+                "sweep %d: %d of %d rays missing from this volume; their grid " "points are still written to the LUT.",
+                sweep_idx,
+                absent.size,
+                az_grid.size,
             )
         n_az, n_rng = len(azimuths), len(ranges)
         logger.debug(
             "sweep %d: %d rays snapped to the nominal grid, max move %.3f°.",
-            sweep_idx, n_az, snap_dist.max() / AZIMUTH_SCALE,
+            sweep_idx,
+            n_az,
+            snap_dist.max() / AZIMUTH_SCALE,
         )
 
         # Compute Cartesian coordinates
         x_raw, y_raw, z_raw = antenna_vectors_to_cartesian(
-            ranges, azimuths, elevations, ke=ke
+            ranges,
+            azimuths,
+            elevations,
+            ke=ke,
         )
 
         # Extract site coordinates
@@ -940,31 +937,37 @@ def generate_lut_from_datatree(
 
         # Compute per-gate geographic coordinates from Cartesian offsets
         gate_lat, gate_lon, gate_alt = cartesian_to_geographic(
-            x_raw, y_raw, z_raw, radar_lat, radar_lon, radar_alt
+            x_raw,
+            y_raw,
+            z_raw,
+            radar_lat,
+            radar_lon,
+            radar_alt,
         )
 
-        gate_az  = np.repeat(azimuths, n_rng)
+        gate_az = np.repeat(azimuths, n_rng)
         gate_rng = np.tile(ranges, n_az)
         gate_ids = encode_gate_ids(radar, sweep_idx, gate_az, gate_rng)
 
-        lut_dfs.append(pl.DataFrame({
-            "gate_id":         gate_ids,
-            "sweep":           np.full(n_az * n_rng, sweep_idx, dtype=np.int32),
-            "azimuth":         gate_az,
-            "range":           gate_rng,
-            "elevation_angle": np.full(n_az * n_rng, elevation_angle),
-            "latitude":        gate_lat.ravel(),
-            "longitude":       gate_lon.ravel(),
-            "altitude":        gate_alt.ravel(),
-            "x":               x_raw.ravel(),
-            "y":               y_raw.ravel(),
-            "z":               z_raw.ravel(),
-        }))
-
-        rng_res = (
-            float(np.median(np.diff(np.sort(ranges).astype(np.float64))))
-            if n_rng > 1 else float("nan")
+        lut_dfs.append(
+            pl.DataFrame(
+                {
+                    "gate_id": gate_ids,
+                    "sweep": np.full(n_az * n_rng, sweep_idx, dtype=np.int32),
+                    "azimuth": gate_az,
+                    "range": gate_rng,
+                    "elevation_angle": np.full(n_az * n_rng, elevation_angle),
+                    "latitude": gate_lat.ravel(),
+                    "longitude": gate_lon.ravel(),
+                    "altitude": gate_alt.ravel(),
+                    "x": x_raw.ravel(),
+                    "y": y_raw.ravel(),
+                    "z": z_raw.ravel(),
+                },
+            ),
         )
+
+        rng_res = float(np.median(np.diff(np.sort(ranges).astype(np.float64)))) if n_rng > 1 else float("nan")
         sweep_meta[sweep_idx] = {
             "n_azimuths": n_az,
             "n_ranges": n_rng,
@@ -984,7 +987,9 @@ def generate_lut_from_datatree(
 
     df_lut = pl.concat(lut_dfs, how="vertical")
     logger.info(
-        "LUT built: %d total gates, %d sweeps.", len(df_lut), len(sweep_meta)
+        "LUT built: %d total gates, %d sweeps.",
+        len(df_lut),
+        len(sweep_meta),
     )
 
     # A CRS is required, and must hold at this radar's site.  There is no
@@ -997,11 +1002,13 @@ def generate_lut_from_datatree(
             f"There is no default because a wrong one is silently wrong. Radar "
             f"{radar!r} is at ({radar_lon:.4f}, {radar_lat:.4f}); suggested: "
             f"RadDB(crs={suggest_crs(radar_lon, radar_lat)})  "
-            f"# UTM zone {int((radar_lon + 180) // 6) + 1}"
+            f"# UTM zone {int((radar_lon + 180) // 6) + 1}",
         )
     validate_crs_for_site(
         projection_epsg if projection_epsg is not None else projection_crs,
-        radar_lon, radar_lat, radar,
+        radar_lon,
+        radar_lat,
+        radar,
     )
     df_lut = add_lut_projection(df_lut, epsg=projection_epsg, crs=projection_crs)
 
@@ -1038,9 +1045,14 @@ def generate_lut_from_datatree(
     corners_by_sweep: dict[int, dict] = {}
     for sweep_idx, g in sweep_grids.items():
         corners_by_sweep[sweep_idx] = compute_sweep_corners(
-            ranges=g["ranges"], azimuths=g["azimuths"], elevations=g["elevations"],
-            radar_lat=radar_lat, radar_lon=radar_lon, radar_alt=radar_alt,
-            ke=ke, beamwidth_deg=beamwidth_deg,
+            ranges=g["ranges"],
+            azimuths=g["azimuths"],
+            elevations=g["elevations"],
+            radar_lat=radar_lat,
+            radar_lon=radar_lon,
+            radar_alt=radar_alt,
+            ke=ke,
+            beamwidth_deg=beamwidth_deg,
         )
     planes = build_gate_planes(
         corners_by_sweep,
@@ -1056,6 +1068,7 @@ def generate_lut_from_datatree(
 # LUT storage helpers
 # ============================================================================
 
+
 def _save_lut_outputs(lut_dir, radar, df_lut, radar_info, planes=None):
     """Save the five LUT files to disk (see :data:`LUT_FILES`).
 
@@ -1074,10 +1087,7 @@ def _save_lut_outputs(lut_dir, radar, df_lut, radar_info, planes=None):
     lut_path = lut_dir / LUT_FILES["lut"].format(radar=radar)
     info_path = lut_dir / LUT_FILES["info"].format(radar=radar)
 
-    plane_paths = {
-        kind: lut_dir / LUT_FILES[kind].format(radar=radar)
-        for kind in ("h_plane", "v_plane", "corners")
-    }
+    plane_paths = {kind: lut_dir / LUT_FILES[kind].format(radar=radar) for kind in ("h_plane", "v_plane", "corners")}
     expected = [lut_path, info_path]
     if planes is not None:
         expected += list(plane_paths.values())
@@ -1085,7 +1095,8 @@ def _save_lut_outputs(lut_dir, radar, df_lut, radar_info, planes=None):
     if all(p.exists() for p in expected):
         logger.info(
             "All %d LUT files already exist at %s -- skipping creation.",
-            len(expected), lut_dir,
+            len(expected),
+            lut_dir,
         )
         return str(lut_path)
 
@@ -1163,18 +1174,27 @@ def compute_sweep_corners(
 
     def _mesh(el: np.ndarray) -> dict:
         x_e, y_e, z_e = antenna_vectors_to_cartesian(
-            ranges, azimuths, el, ke=ke, edges=True,
+            ranges,
+            azimuths,
+            el,
+            ke=ke,
+            edges=True,
         )
         lat_e, lon_e, _ = cartesian_to_geographic(
-            x_e, y_e, z_e, radar_lat=radar_lat, radar_lon=radar_lon, radar_alt=radar_alt,
+            x_e,
+            y_e,
+            z_e,
+            radar_lat=radar_lat,
+            radar_lon=radar_lon,
+            radar_alt=radar_alt,
         )
         # float64 throughout: these edges are the gate polygon vertices, and gate
         # position precision is a hard requirement (float32 costs ~20 cm, and the
         # error does not shrink with range).
         return {
-            "x_edges":   x_e.astype(np.float64),
-            "y_edges":   y_e.astype(np.float64),
-            "z_edges":   z_e.astype(np.float64),
+            "x_edges": x_e.astype(np.float64),
+            "y_edges": y_e.astype(np.float64),
+            "z_edges": z_e.astype(np.float64),
             "lon_edges": lon_e.astype(np.float64),
             "lat_edges": lat_e.astype(np.float64),
         }
@@ -1186,10 +1206,7 @@ def _mesh(el: np.ndarray) -> dict:
     half_bw = float(beamwidth_deg) / 2.0
     # dict(centre) for level 0 so the `levels` key added below cannot make the
     # structure self-referential.
-    levels = {
-        lvl: dict(centre) if lvl == 0 else _mesh(elevations + lvl * half_bw)
-        for lvl in EL_LEVELS
-    }
+    levels = {lvl: dict(centre) if lvl == 0 else _mesh(elevations + lvl * half_bw) for lvl in EL_LEVELS}
     return {**centre, "levels": levels}
 
 
@@ -1202,8 +1219,7 @@ def _mesh(el: np.ndarray) -> dict:
 GATE_RING_OFFSETS: tuple[tuple[int, int], ...] = ((0, 0), (0, 1), (1, 1), (1, 0))
 
 
-def _project_nodes(x: np.ndarray, y: np.ndarray, lon: np.ndarray, lat: np.ndarray,
-                   epsg: int | None, crs=None):
+def _project_nodes(lon: np.ndarray, lat: np.ndarray, epsg: int | None, crs=None):
     """Projected easting/northing for lattice nodes, or ``(None, None, None)``."""
     if epsg is None and crs is None:
         return None, None, None
@@ -1227,7 +1243,7 @@ def build_gate_planes(
     radar_alt: float,
     projection_epsg: int | None = None,
     projection_crs=None,
-) -> dict[str, "pl.DataFrame"]:
+) -> dict[str, pl.DataFrame]:
     """Build the h_plane / v_plane / corners **node lattices** as polars frames.
 
     Input is the output of :func:`compute_sweep_corners` called with
@@ -1260,14 +1276,14 @@ def build_gate_planes(
         if levels is None:
             raise ValueError(
                 f"sweep {sweep_num}: compute_sweep_corners must be called with "
-                "beamwidth_deg so the vertical levels are available."
+                "beamwidth_deg so the vertical levels are available.",
             )
 
         for lvl in sorted(levels):
             m = levels[lvl]
             xe, ye, ze = m["x_edges"], m["y_edges"], m["z_edges"]
             lone, late = m["lon_edges"], m["lat_edges"]
-            n_az_n, n_rng_n = xe.shape           # (n_az+1, n_rng+1) node counts
+            n_az_n, n_rng_n = xe.shape  # (n_az+1, n_rng+1) node counts
 
             az_idx = np.repeat(np.arange(n_az_n, dtype=np.int16), n_rng_n)
             rng_idx = np.tile(np.arange(n_rng_n, dtype=np.int16), n_az_n)
@@ -1288,9 +1304,7 @@ def build_gate_planes(
                     "x": xf.astype(np.float32),
                     "y": yf.astype(np.float32),
                 }
-                px, py, suffix = _project_nodes(
-                    xf, yf, lonf, latf, projection_epsg, projection_crs
-                )
+                px, py, suffix = _project_nodes(lonf, latf, projection_epsg, projection_crs)
                 if suffix is not None:
                     cols[f"x_{suffix}"] = px.astype(np.float32)
                     cols[f"y_{suffix}"] = py.astype(np.float32)
@@ -1300,29 +1314,37 @@ def build_gate_planes(
             # --- bottom / top levels: v_plane + 3-D corners ------------------
             lvl_col = np.full(n, lvl, dtype=np.int8)
             z_asl = (zf + radar_alt).astype(np.float32)
-            v_parts.append(pl.DataFrame({
-                "sweep": sweep_col,
-                "el_level": lvl_col,
-                "az_idx": az_idx,
-                "rng_idx": rng_idx,
-                # ground distance from the radar: the beam's arc length, which is
-                # exactly hypot(x, y) in this equidistant frame.
-                "d": np.hypot(xf, yf).astype(np.float32),
-                "z_asl": z_asl,
-                "z_rel": zf.astype(np.float32),
-            }))
+            v_parts.append(
+                pl.DataFrame(
+                    {
+                        "sweep": sweep_col,
+                        "el_level": lvl_col,
+                        "az_idx": az_idx,
+                        "rng_idx": rng_idx,
+                        # ground distance from the radar: the beam's arc length, which is
+                        # exactly hypot(x, y) in this equidistant frame.
+                        "d": np.hypot(xf, yf).astype(np.float32),
+                        "z_asl": z_asl,
+                        "z_rel": zf.astype(np.float32),
+                    },
+                ),
+            )
             # z_asl and lon/lat omitted here for the same reason as in h_plane:
             # z_asl = z_rel + site altitude (a constant), and lon/lat are a
             # closed form of (x, y). Both are derived by the accessors.
-            c_parts.append(pl.DataFrame({
-                "sweep": sweep_col,
-                "el_level": lvl_col,
-                "az_idx": az_idx,
-                "rng_idx": rng_idx,
-                "x": xf.astype(np.float32),
-                "y": yf.astype(np.float32),
-                "z_rel": zf.astype(np.float32),
-            }))
+            c_parts.append(
+                pl.DataFrame(
+                    {
+                        "sweep": sweep_col,
+                        "el_level": lvl_col,
+                        "az_idx": az_idx,
+                        "rng_idx": rng_idx,
+                        "x": xf.astype(np.float32),
+                        "y": yf.astype(np.float32),
+                        "z_rel": zf.astype(np.float32),
+                    },
+                ),
+            )
 
     return {
         "h_plane": pl.concat(h_parts, how="vertical_relaxed"),
@@ -1336,7 +1358,7 @@ def load_plane_nodes(
     lut_base_path: str | Path,
     kind: str,
     sweep: int | None = None,
-) -> "pl.DataFrame":
+) -> pl.DataFrame:
     """Load one of the node-lattice files (``h_plane`` / ``v_plane`` / ``corners``).
 
     ``sweep`` pushes a row filter into the parquet scan.
@@ -1350,7 +1372,7 @@ def load_plane_nodes(
     if not path.exists():
         raise FileNotFoundError(
             f"{kind} lattice not found at {path}, and it could not be rebuilt from "
-            f"{radar}_LUT.parquet. Regenerate the LUT (generate_lut_from_datatree)."
+            f"{radar}_LUT.parquet. Regenerate the LUT (generate_lut_from_datatree).",
         )
     lf = pl.scan_parquet(path)
     if sweep is not None:
@@ -1359,7 +1381,9 @@ def load_plane_nodes(
 
 
 def _node_grids(
-    nodes: "pl.DataFrame", value_cols: list[str], level: int | None = None
+    nodes: pl.DataFrame,
+    value_cols: list[str],
+    level: int | None = None,
 ) -> dict[int, dict[str, np.ndarray]]:
     """Reshape flat lattice rows into ``{sweep: {col: (n_az+1, n_rng+1) array}}``."""
     if level is not None and "el_level" in nodes.columns:
@@ -1369,9 +1393,7 @@ def _node_grids(
         sub = sub.sort(["az_idx", "rng_idx"])
         n_az_n = int(sub["az_idx"].max()) + 1
         n_rng_n = int(sub["rng_idx"].max()) + 1
-        out[int(sweep_num)] = {
-            c: sub[c].to_numpy().reshape(n_az_n, n_rng_n) for c in value_cols
-        }
+        out[int(sweep_num)] = {c: sub[c].to_numpy().reshape(n_az_n, n_rng_n) for c in value_cols}
     return out
 
 
@@ -1380,7 +1402,7 @@ def gate_corner_table(
     lut_base_path: str | Path,
     kind: str = "h_plane",
     sweep: int | None = None,
-) -> "pl.DataFrame":
+) -> pl.DataFrame:
     """Materialise per-gate corners from a node lattice, keyed by ``gate_id``.
 
     This is the "hybrid" read side: the archive stores compact node lattices, and
@@ -1405,7 +1427,7 @@ def gate_corner_table(
     """
     if kind not in ("h_plane", "v_plane", "corners"):
         raise ValueError(
-            f"kind must be 'h_plane', 'v_plane' or 'corners'; got {kind!r}."
+            f"kind must be 'h_plane', 'v_plane' or 'corners'; got {kind!r}.",
         )
 
     idx = _gate_grid_index(radar, lut_base_path)
@@ -1417,8 +1439,7 @@ def gate_corner_table(
     nodes = load_plane_nodes(radar, lut_base_path, kind, sweep=sweep)
 
     if kind == "h_plane":
-        value_cols = [c for c in nodes.columns
-                      if c not in ("sweep", "az_idx", "rng_idx", "el_level")]
+        value_cols = [c for c in nodes.columns if c not in ("sweep", "az_idx", "rng_idx", "el_level")]
         grids = {0: _node_grids(nodes, value_cols)}
         # 4 corners, one level, ring order
         picks = [(0, i, j) for (i, j) in GATE_RING_OFFSETS]
@@ -1433,8 +1454,14 @@ def gate_corner_table(
         # near face (rng+0) then far face (rng+1); within a face:
         # (az-, el-), (az+, el-), (az+, el+), (az-, el+)
         picks = [
-            (-1, 0, 0), (-1, 1, 0), (1, 1, 0), (1, 0, 0),      # near face
-            (-1, 0, 1), (-1, 1, 1), (1, 1, 1), (1, 0, 1),      # far face
+            (-1, 0, 0),
+            (-1, 1, 0),
+            (1, 1, 0),
+            (1, 0, 0),  # near face
+            (-1, 0, 1),
+            (-1, 1, 1),
+            (1, 1, 1),
+            (1, 0, 1),  # far face
         ]
 
     n = idx.height
@@ -1456,11 +1483,13 @@ def gate_corner_table(
                 arr = g[col]
                 out[f"{col}_{k}"][rows] = arr[az_i[rows] + di, rng_i[rows] + dj]
 
-    return pl.DataFrame({
-        "gate_id": idx["gate_id"],
-        "sweep": idx["sweep"],
-        **{c: v.astype(np.float32) for c, v in out.items()},
-    })
+    return pl.DataFrame(
+        {
+            "gate_id": idx["gate_id"],
+            "sweep": idx["sweep"],
+            **{c: v.astype(np.float32) for c, v in out.items()},
+        },
+    )
 
 
 def ensure_gate_planes(
@@ -1486,8 +1515,7 @@ def ensure_gate_planes(
         ``True`` if files were written, ``False`` if all three already existed.
     """
     missing = [
-        kind for kind in ("h_plane", "v_plane", "corners")
-        if not lut_file_path(radar, kind, lut_base_path).exists()
+        kind for kind in ("h_plane", "v_plane", "corners") if not lut_file_path(radar, kind, lut_base_path).exists()
     ]
     if not missing:
         return False
@@ -1497,13 +1525,16 @@ def ensure_gate_planes(
         beamwidth_deg = float(info.get("beamwidth_deg") or DEFAULT_BEAMWIDTH_DEG)
     logger.info(
         "radar %s: geometry lattices %s missing -- rebuilding from the centroid LUT.",
-        radar, missing,
+        radar,
+        missing,
     )
 
     corners_by_sweep: dict[int, dict] = {}
     for sweep_num, g in _sweep_grids_from_lut(radar, lut_base_path).items():
         corners_by_sweep[int(sweep_num)] = compute_sweep_corners(
-            ranges=g["ranges"], azimuths=g["azimuths"], elevations=g["elevations"],
+            ranges=g["ranges"],
+            azimuths=g["azimuths"],
+            elevations=g["elevations"],
             radar_lat=info["latitude"],
             radar_lon=info["longitude"],
             radar_alt=info["altitude"],
@@ -1516,9 +1547,13 @@ def ensure_gate_planes(
     # backfilled h_plane is projected exactly like a freshly generated one.
     epsg = (info.get("crs") or {}).get("epsg")
     if epsg is None:
-        lut_cols = pl.scan_parquet(
-            lut_file_path(radar, "lut", lut_base_path)
-        ).collect_schema().names()
+        lut_cols = (
+            pl.scan_parquet(
+                lut_file_path(radar, "lut", lut_base_path),
+            )
+            .collect_schema()
+            .names()
+        )
         for name in _projection_column_names(pl.DataFrame(schema={c: pl.Float64 for c in lut_cols})):
             suffix = name.split("_", 1)[1]
             if name.startswith("x_") and suffix.isdigit():
@@ -1545,7 +1580,7 @@ def cappi_chords(
     lut_base_path: str | Path,
     altitude: float,
     height: str = "asl",
-) -> "pl.DataFrame":
+) -> pl.DataFrame:
     """Where a constant-altitude surface cuts each range bin — the CAPPI slice.
 
     A CAPPI is a horizontal slice through the volume, so the question it asks of
@@ -1640,26 +1675,36 @@ def cappi_chords(
         d_far = np.nanmax(d_hit, axis=1)
         z_center = ring_z.mean(axis=1)[hit]
 
-        parts.append(pl.DataFrame({
-            "sweep": np.full(hit.sum(), sweep_num, dtype=np.int32),
-            "rng_idx": np.flatnonzero(hit).astype(np.int32),
-            "d_near": d_near.astype(np.float32),
-            "d_far": d_far.astype(np.float32),
-            "z_center": z_center.astype(np.float32),
-            "dz_center": np.abs(z_center - z0).astype(np.float32),
-        }))
+        parts.append(
+            pl.DataFrame(
+                {
+                    "sweep": np.full(hit.sum(), sweep_num, dtype=np.int32),
+                    "rng_idx": np.flatnonzero(hit).astype(np.int32),
+                    "d_near": d_near.astype(np.float32),
+                    "d_far": d_far.astype(np.float32),
+                    "z_center": z_center.astype(np.float32),
+                    "dz_center": np.abs(z_center - z0).astype(np.float32),
+                },
+            ),
+        )
 
     if not parts:
-        return pl.DataFrame(schema={
-            "sweep": pl.Int32, "rng_idx": pl.Int32,
-            "d_near": pl.Float32, "d_far": pl.Float32,
-            "z_center": pl.Float32, "dz_center": pl.Float32,
-        })
+        return pl.DataFrame(
+            schema={
+                "sweep": pl.Int32,
+                "rng_idx": pl.Int32,
+                "d_near": pl.Float32,
+                "d_far": pl.Float32,
+                "z_center": pl.Float32,
+                "dz_center": pl.Float32,
+            },
+        )
     return pl.concat(parts, how="vertical")
 
 
 def save_sweep_corners(
-    corners_by_sweep: dict[int, dict], corners_path: str | Path
+    corners_by_sweep: dict[int, dict],
+    corners_path: str | Path,
 ) -> str:
     """Save per-sweep corner arrays to a single ``.npz`` file.
 
@@ -1681,7 +1726,8 @@ def save_sweep_corners(
 
 
 def _sweep_grids_from_lut(
-    radar: str, lut_base_path: str | Path
+    radar: str,
+    lut_base_path: str | Path,
 ) -> dict[int, dict]:
     """Per-sweep ``(azimuths, ranges, elevation)`` grids read from the LUT parquet.
 
@@ -1696,9 +1742,13 @@ def _sweep_grids_from_lut(
     lut_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_LUT.parquet"
     if not lut_path.exists():
         raise FileNotFoundError(f"LUT not found at {lut_path}.")
-    lut = pl.scan_parquet(lut_path).select(
-        ["sweep", "azimuth", "range", "elevation_angle"]
-    ).collect()
+    lut = (
+        pl.scan_parquet(lut_path)
+        .select(
+            ["sweep", "azimuth", "range", "elevation_angle"],
+        )
+        .collect()
+    )
 
     grids: dict[int, dict] = {}
     for sweep_num in sorted(lut["sweep"].unique().to_list()):
@@ -1745,7 +1795,9 @@ def compute_corners_from_lut(
     corners_by_sweep: dict[int, dict] = {}
     for sweep_num, g in _sweep_grids_from_lut(radar, lut_base_path).items():
         full = compute_sweep_corners(
-            ranges=g["ranges"], azimuths=g["azimuths"], elevations=g["elevations"],
+            ranges=g["ranges"],
+            azimuths=g["azimuths"],
+            elevations=g["elevations"],
             radar_lat=info["latitude"],
             radar_lon=info["longitude"],
             radar_alt=info["altitude"],
@@ -1755,9 +1807,7 @@ def compute_corners_from_lut(
         # The npz keeps only the beam-centre mesh (its consumers — plot_ppi and
         # reconstruct_sweep_dataset — are 2-D). The vertical levels live in the
         # *_corners_LUT.parquet / *_v_plane_LUT.parquet files.
-        corners_by_sweep[int(sweep_num)] = {
-            k: v for k, v in full.items() if k != "levels"
-        }
+        corners_by_sweep[int(sweep_num)] = {k: v for k, v in full.items() if k != "levels"}
     corners_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_corners.npz"
     save_sweep_corners(corners_by_sweep, corners_path)
     return str(corners_path)
@@ -1781,7 +1831,8 @@ def _parse_corners_npz(corners_path: str | Path) -> dict[int, dict]:
 
 
 def load_sweep_corners(
-    radar: str, lut_base_path: str | Path
+    radar: str,
+    lut_base_path: str | Path,
 ) -> dict[int, dict]:
     """Load per-sweep corner arrays from ``{radar}_corners.npz``.
 
@@ -1803,7 +1854,7 @@ def load_sweep_corners(
 # Per-radar cache of gate_id -> (sweep, azimuth index, range index) into the
 # corner arrays: {(base_path, radar): pl.DataFrame}.  ~27 MB per radar, built
 # once per session.
-_GRID_CACHE: dict[tuple[str, str, int], "pl.DataFrame"] = {}
+_GRID_CACHE: dict[tuple[str, str, int], pl.DataFrame] = {}
 
 
 def decode_gate_ids(gate_ids) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
@@ -1854,7 +1905,7 @@ def decode_gate_radars(gate_ids) -> list[str]:
     return sorted(names)
 
 
-def _gate_grid_index(radar: str, lut_base_path: str | Path) -> "pl.DataFrame":
+def _gate_grid_index(radar: str, lut_base_path: str | Path) -> pl.DataFrame:
     """Map every ``gate_id`` to its position in the per-sweep corner arrays.
 
     Returns a frame ``[gate_id, sweep, az_idx, rng_idx]``.  The indices are
@@ -1883,12 +1934,14 @@ def _gate_grid_index(radar: str, lut_base_path: str | Path) -> "pl.DataFrame":
     for (sweep_num,), sub in lut.group_by(["sweep"]):
         az_grid = np.sort(sub["azimuth"].unique().to_numpy())
         rng_grid = np.sort(sub["range"].unique().to_numpy())
-        parts.append(sub.select(
-            "gate_id",
-            pl.lit(int(sweep_num), dtype=pl.Int32).alias("sweep"),
-            pl.Series("az_idx", np.searchsorted(az_grid, sub["azimuth"].to_numpy()), dtype=pl.Int32),
-            pl.Series("rng_idx", np.searchsorted(rng_grid, sub["range"].to_numpy()), dtype=pl.Int32),
-        ))
+        parts.append(
+            sub.select(
+                "gate_id",
+                pl.lit(int(sweep_num), dtype=pl.Int32).alias("sweep"),
+                pl.Series("az_idx", np.searchsorted(az_grid, sub["azimuth"].to_numpy()), dtype=pl.Int32),
+                pl.Series("rng_idx", np.searchsorted(rng_grid, sub["range"].to_numpy()), dtype=pl.Int32),
+            ),
+        )
     table = pl.concat(parts, how="vertical")
     _GRID_CACHE[key] = table
     return table
@@ -1930,8 +1983,7 @@ def gate_polygons_geoarrow(
 
     if frame not in ("geographic", "cartesian"):
         raise ValueError(f"frame must be 'geographic' or 'cartesian'; got {frame!r}.")
-    xkey, ykey = (("lon_edges", "lat_edges") if frame == "geographic"
-                  else ("x_edges", "y_edges"))
+    xkey, ykey = ("lon_edges", "lat_edges") if frame == "geographic" else ("x_edges", "y_edges")
 
     corners = load_sweep_corners(radar, lut_base_path)
     if not corners:
@@ -1972,13 +2024,15 @@ def gate_polygons_geoarrow(
     if not valid.all():
         logger.warning(
             "%d of %d gate_ids could not be placed on the LUT grid (null geometry).",
-            int((~valid).sum()), n,
+            int((~valid).sum()),
+            n,
         )
 
     # geoarrow.polygon = List<List<FixedSizeList<double>[2]>>: polygon -> rings -> xy.
     # A null in the outer offsets makes that polygon null (unplaceable gate).
     coords = pa.FixedSizeListArray.from_arrays(
-        pa.array(ring_xy.reshape(-1), type=pa.float64()), 2
+        pa.array(ring_xy.reshape(-1), type=pa.float64()),
+        2,
     )
     rings = pa.ListArray.from_arrays(np.arange(n + 1, dtype=np.int32) * 5, coords)
     offsets = pa.array(
@@ -2009,14 +2063,13 @@ def geoarrow_field(name: str, dtype, kind: str, crs: str | None = None):
 
     meta = {b"ARROW:extension:name": f"geoarrow.{kind}".encode()}
     if crs:
-        meta[b"ARROW:extension:metadata"] = (
-            f'{{"crs":"{crs}","crs_type":"authority_code"}}'
-        ).encode()
+        meta[b"ARROW:extension:metadata"] = (f'{{"crs":"{crs}","crs_type":"authority_code"}}').encode()
     return pa.field(name, dtype, metadata=meta)
 
 
 def load_radar_lut(
-    radar: str, lut_base_path: str | Path
+    radar: str,
+    lut_base_path: str | Path,
 ) -> pl.DataFrame:
     """Load the LUT parquet for a radar as a **polars** DataFrame.
 
@@ -2029,12 +2082,11 @@ def load_radar_lut(
 
 
 def load_radar_info(
-    radar: str, lut_base_path: str | Path
+    radar: str,
+    lut_base_path: str | Path,
 ) -> dict:
     """Load the radar info YAML for a radar."""
-    info_path = (
-        Path(lut_base_path) / radar / "LUT" / f"{radar}_info.yaml"
-    )
+    info_path = Path(lut_base_path) / radar / "LUT" / f"{radar}_info.yaml"
     if not info_path.exists():
         raise FileNotFoundError(f"Info not found at {info_path}.")
     with open(info_path) as f:
@@ -2043,7 +2095,8 @@ def load_radar_info(
 
 
 def get_full_sweep_index(
-    lut_df: "pl.DataFrame | pd.DataFrame", sweep: int
+    lut_df: pl.DataFrame | pd.DataFrame,
+    sweep: int,
 ) -> pd.MultiIndex:
     """Get the full (azimuth, range) MultiIndex for a sweep from the LUT.
 
@@ -2072,11 +2125,12 @@ def get_full_sweep_index(
 # Projection utilities
 # ============================================================================
 
+
 def add_lut_projection(
-    lut_df: "pl.DataFrame | pd.DataFrame",
+    lut_df: pl.DataFrame | pd.DataFrame,
     epsg: int | None = None,
     crs=None,
-) -> "pl.DataFrame | pd.DataFrame":
+) -> pl.DataFrame | pd.DataFrame:
     """Add projected coordinates to a LUT DataFrame.
 
     Converts the ``latitude`` / ``longitude`` columns to the target CRS and
@@ -2125,15 +2179,14 @@ def add_lut_projection(
     Use a custom pyproj CRS:
 
     >>> import pyproj
-    >>> my_crs = pyproj.CRS.from_epsg(32632)   # UTM zone 32N
+    >>> my_crs = pyproj.CRS.from_epsg(32632)  # UTM zone 32N
     >>> lut_utm = add_lut_projection(lut_df, crs=my_crs)
     """
     try:
         import pyproj
     except ImportError as exc:
         raise ImportError(
-            "pyproj is required for add_lut_projection. "
-            "Install it with: pip install pyproj"
+            "pyproj is required for add_lut_projection. " "Install it with: pip install pyproj",
         ) from exc
 
     if epsg is not None:
@@ -2148,10 +2201,12 @@ def add_lut_projection(
 
     # Use proj4 string for WGS-84 to avoid requiring the PROJ database
     wgs84 = pyproj.CRS.from_proj4(
-        "+proj=longlat +datum=WGS84 +no_defs"
+        "+proj=longlat +datum=WGS84 +no_defs",
     )
     transformer = pyproj.Transformer.from_crs(
-        wgs84, target_crs, always_xy=True
+        wgs84,
+        target_crs,
+        always_xy=True,
     )
     x_proj, y_proj = transformer.transform(
         lut_df["longitude"].to_numpy(),
diff --git a/raddb/main.py b/raddb/main.py
index 5dcb320..7727f0b 100644
--- a/raddb/main.py
+++ b/raddb/main.py
@@ -1,7 +1,4 @@
-"""
-raddb/main.py
--------------
-High-level interface for RadDB — a generic radar data archiving library.
+"""High-level interface for RadDB — a generic radar data archiving library.
 
 ``RadDB`` is a single, dual-role class:
 
@@ -19,8 +16,10 @@
 network-specific pipeline (e.g. the private ``raddb.mch`` subpackage),
 not here.
 """
+
 from __future__ import annotations
 
+import contextlib
 import datetime
 import logging
 import time
@@ -33,26 +32,6 @@
 import shapely
 import xarray as xr
 
-from raddb.io_core import (
-    archive_volume,
-    archive_multiple_volumes,
-    archive_volumes_multi_radar,
-    open_any_datatree,
-    scan_polar_parquet,
-    dataframe_to_datatree,
-)
-from raddb.helper import (
-    RADAR_CODE_LEN,
-    ensure_utc,
-    is_valid_radar_name,
-    normalize_radar_name,
-)
-from raddb.discovery import (
-    find_datatree_files,
-    _find_polar_files_in_range,
-    _parse_datatree_file_time,
-    _parse_pol_time,
-)
 from raddb.aoi import (
     _apply_gate_ids,
     _cross_section_gates,
@@ -61,19 +40,39 @@
     _lut_cs_table,
     _radars_from_gate_ids,
     _reproject_to_aoi,
-    aoi_epsg_for,
     _resolve_aoi_centroids,
+    aoi_epsg_for,
+)
+from raddb.discovery import (
+    _find_polar_files_in_range,
+    _parse_datatree_file_time,
+    _parse_pol_time,
+    find_datatree_files,
+)
+from raddb.helper import (
+    RADAR_CODE_LEN,
+    ensure_utc,
+    is_valid_radar_name,
+    normalize_radar_name,
+)
+from raddb.io_core import (
+    archive_multiple_volumes,
+    archive_volume,
+    archive_volumes_multi_radar,
+    dataframe_to_datatree,
+    open_any_datatree,
+    scan_polar_parquet,
 )
 from raddb.lut import (
     GATE_ID_RADAR_BASE,
+    add_lut_projection,
+    cartesian_to_geographic,
     encode_radar_code,
-    generate_lut_from_datatree,
     gate_corner_table,
+    generate_lut_from_datatree,
     load_plane_nodes,
-    load_radar_lut,
     load_radar_info,
-    add_lut_projection,
-    cartesian_to_geographic,
+    load_radar_lut,
 )
 
 logger = logging.getLogger(__name__)
@@ -83,6 +82,7 @@
 # Private helpers for end-to-end archiving
 # ================================================================
 
+
 def _iter_days(start: pd.Timestamp, end: pd.Timestamp):
     """Yield (day_start, day_end) pairs covering [start, end] inclusively."""
     day = start.normalize()
@@ -114,7 +114,7 @@ def _format_elapsed_time(seconds: float) -> str:
     secs = int(seconds % 60)
     if hours > 0:
         return f"{hours}h {minutes}m {secs}s"
-    elif minutes > 0:
+    if minutes > 0:
         return f"{minutes}m {secs}s"
     return f"{secs}s"
 
@@ -128,7 +128,7 @@ def _format_elapsed_time(seconds: float) -> str:
 _GEOM_COLS = ("cs_polygon",)
 
 
-def _filter_expr(var: str, logic: str, threshold) -> "pl.Expr":
+def _filter_expr(var: str, logic: str, threshold) -> pl.Expr:
     """Build a polars boolean expression ``var <logic> threshold``."""
     col = pl.col(var)
     ops = {
@@ -141,7 +141,7 @@ def _filter_expr(var: str, logic: str, threshold) -> "pl.Expr":
     }
     if logic not in ops:
         raise ValueError(
-            f"Unknown filter logic {logic!r}; use one of {sorted(ops)}."
+            f"Unknown filter logic {logic!r}; use one of {sorted(ops)}.",
         )
     return ops[logic]
 
@@ -208,7 +208,7 @@ def _time_bound(value, dtype, *, upper: bool):
     return ts
 
 
-def _sel_expr(name: str, value, dtype) -> "pl.Expr":
+def _sel_expr(name: str, value, dtype) -> pl.Expr:
     """Build the boolean expression selecting ``value`` on column ``name``.
 
     ``value`` may be a ``slice`` (inclusive on both ends, as in xarray), a
@@ -226,7 +226,7 @@ def bound(v, upper):
         if value.step is not None:
             raise ValueError(
                 f"sel({name}=...): a step is not supported (got step={value.step!r}); "
-                "use a plain slice(start, stop)."
+                "use a plain slice(start, stop).",
             )
         parts = []
         if value.start is not None:
@@ -241,7 +241,7 @@ def bound(v, upper):
         return expr
 
     if isinstance(value, (list, tuple, set, frozenset, np.ndarray, pl.Series)):
-        vals = [v for v in (value.to_list() if isinstance(value, pl.Series) else list(value))]
+        vals = list(value.to_list() if isinstance(value, pl.Series) else list(value))
         if is_time:
             # a list of timestamps/partial strings -> union of their periods
             expr = None
@@ -262,7 +262,7 @@ def _ccw_polygons(polys: np.ndarray) -> np.ndarray:
     The gate corner order is deterministically clockwise (inherited from the
     reference prototype), so serialised output needs flipping.
     """
-    if hasattr(shapely, "orient_polygons"):        # shapely >= 2.1
+    if hasattr(shapely, "orient_polygons"):  # shapely >= 2.1
         return shapely.orient_polygons(polys)
     ccw = shapely.is_ccw(shapely.get_exterior_ring(polys))
     return np.where(ccw, polys, shapely.reverse(polys))
@@ -289,7 +289,7 @@ def _resolve_filters(filters) -> list[tuple[str, str, float]]:
         if extra:
             raise KeyError(
                 f"unknown filter key(s) {sorted(extra)} in {f!r}; "
-                f"a filter is {{{', '.join(repr(k) for k in _FILTER_KEYS)}}}."
+                f"a filter is {{{', '.join(repr(k) for k in _FILTER_KEYS)}}}.",
             )
         specs.append((f["var"], f.get("logic", ">"), f.get("threshold", 0.0)))
     return specs
@@ -297,6 +297,7 @@ def _resolve_filters(filters) -> list[tuple[str, str, float]]:
 
 def _normalize_time_period(time_period):
     """Return ``(start, end)`` datetimes from str | datetime | (start, end)."""
+
     def _u(x):
         return ensure_utc(x) if x is not None else None
 
@@ -330,8 +331,8 @@ def _decode_geometry(df: pd.DataFrame) -> pd.DataFrame:
     return df
 
 
-def _to_polars(df: pd.DataFrame) -> "pl.DataFrame":
-    """pandas -> polars, WKB-encoding any shapely-geometry columns first."""
+def _to_polars(df: pd.DataFrame) -> pl.DataFrame:
+    """Pandas -> polars, WKB-encoding any shapely-geometry columns first."""
     return pl.from_pandas(_encode_geometry(df))
 
 
@@ -392,9 +393,7 @@ def _list_archive_radars(archive_dir: Path) -> list[str]:
     if not archive_dir.exists():
         return []
     return sorted(
-        p.name
-        for p in archive_dir.iterdir()
-        if p.is_dir() and is_valid_radar_name(p.name) and (p / "LUT").is_dir()
+        p.name for p in archive_dir.iterdir() if p.is_dir() and is_valid_radar_name(p.name) and (p / "LUT").is_dir()
     )
 
 
@@ -404,11 +403,11 @@ class RadDB:
     Examples
     --------
     >>> db = RadDB(archive_dir="/data/raddb", crs=2056)
-    >>> db.archive(datatree_dir="/data/MCH_datatree")        # or datatree=dt
+    >>> db.archive(datatree_dir="/data/MCH_datatree")  # or datatree=dt
     >>> rdf = db.open(time_period=("2024-08-26", "2024-08-27"))
-    >>> rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})\
-    ...    .crop_by_bbox(extent=rdf.extent())\
-    ...    .plot_ppi(variable="DBZH", save="ppi.png")
+    >>> rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20}).crop_by_bbox(extent=rdf.extent()).plot_ppi(
+    ...     variable="DBZH", save="ppi.png"
+    ... )
     """
 
     # ================================================================
@@ -421,7 +420,7 @@ def __init__(
         crs: int | str | None = None,
         network: str = "",
         *,
-        _data: "pl.DataFrame | None" = None,
+        _data: pl.DataFrame | None = None,
         _meta: dict | None = None,
     ):
         """Create an archive-bound RadDB.
@@ -448,11 +447,12 @@ def __init__(
         self._data = _data
         self._meta = dict(_meta) if _meta else {}
 
-    def _derive(self, data: "pl.DataFrame", *, archive_dir=None, crs=None, **meta) -> "RadDB":
+    def _derive(self, data: pl.DataFrame, *, archive_dir=None, crs=None, **meta) -> RadDB:
         """Build a new data-carrying ``RadDB`` sharing this one's configuration."""
         return RadDB(
-            archive_dir=str(archive_dir) if archive_dir is not None
-            else (str(self.archive_dir) if self.archive_dir else None),
+            archive_dir=(
+                str(archive_dir) if archive_dir is not None else (str(self.archive_dir) if self.archive_dir else None)
+            ),
             crs=crs if crs is not None else self._crs,
             network=self.network,
             _data=data,
@@ -462,45 +462,40 @@ def _derive(self, data: "pl.DataFrame", *, archive_dir=None, crs=None, **meta) -
     def _require_archive_dir(self) -> Path:
         if self.archive_dir is None:
             raise ValueError(
-                "This RadDB has no archive_dir; pass it to RadDB(archive_dir=...) "
-                "or to the method call."
+                "This RadDB has no archive_dir; pass it to RadDB(archive_dir=...) " "or to the method call.",
             )
         return self.archive_dir
 
-    def _require_data(self) -> "pl.DataFrame":
+    def _require_data(self) -> pl.DataFrame:
         if self._data is None:
             raise ValueError(
-                "This RadDB carries no data (it is archive-bound). Load data "
-                "first with db.open(...)."
+                "This RadDB carries no data (it is archive-bound). Load data " "first with db.open(...).",
             )
         return self._data
 
     @property
-    def data(self) -> "pl.DataFrame":
+    def data(self) -> pl.DataFrame:
         """The loaded data as a polars DataFrame."""
         return self._require_data()
 
     def __len__(self) -> int:
+        """Number of gates held; ``0`` when archive-bound with no data loaded."""
         return 0 if self._data is None else self._data.height
 
     def __repr__(self) -> str:
+        """One-line summary when archive-bound, a multi-line one when data-carrying."""
         if self._data is None:
-            return (
-                f"RadDB(archive_dir={self.archive_dir!s}, crs={self._crs!r}) "
-                f"[archive-bound, no data loaded]"
-            )
+            return f"RadDB(archive_dir={self.archive_dir!s}, crs={self._crs!r}) " f"[archive-bound, no data loaded]"
         lines = [f"RadDB [{len(self):,} gates]"]
-        try:
+        with contextlib.suppress(Exception):
             lines.append(f"  radars     : {self.radars()}")
-        except Exception:
-            pass
         try:
             t0, t1 = self.start_time(), self.end_time()
             lines.append(f"  time range : {t0} .. {t1}")
         except Exception:
             pass
         schema = self._data.schema
-        cols = ", ".join(f"{n}:{str(t)}" for n, t in list(schema.items())[:12])
+        cols = ", ".join(f"{n}:{t!s}" for n, t in list(schema.items())[:12])
         more = "" if len(schema) <= 12 else f" (+{len(schema) - 12} more)"
         lines.append(f"  columns    : {cols}{more}")
         lines.append(f"  archive_dir: {self.archive_dir}")
@@ -579,13 +574,12 @@ def archive(
                 "EPSG:2056 outside Switzerland mis-measures distance by ~20%. "
                 "Pass RadDB(crs=<epsg>) or archive(crs=<epsg>), choosing one valid "
                 "at your radar's site (raddb.lut.suggest_crs(lon, lat) gives the "
-                "UTM zone)."
+                "UTM zone).",
             )
 
         if (datatree is None) == (datatree_dir is None):
             raise ValueError(
-                "Pass exactly one of `datatree` (in-memory) or `datatree_dir` "
-                "(saved files)."
+                "Pass exactly one of `datatree` (in-memory) or `datatree_dir` " "(saved files).",
             )
 
         if filter is None:
@@ -598,11 +592,24 @@ def archive(
         t0 = time.time()
         if datatree is not None:
             radars_done, n_ok, n_fail, n_skip = self._archive_in_memory(
-                datatree, radar, archive_dir, crs, feat, logic, thr
+                datatree,
+                radar,
+                archive_dir,
+                crs,
+                feat,
+                logic,
+                thr,
             )
         else:
             radars_done, n_ok, n_fail, n_skip = self._archive_from_disk(
-                datatree_dir, radar, archive_dir, crs, feat, logic, thr, time_period
+                datatree_dir,
+                radar,
+                archive_dir,
+                crs,
+                feat,
+                logic,
+                thr,
+                time_period,
             )
 
         print("=" * 70)
@@ -613,7 +620,7 @@ def archive(
         print(f"  filter      : keep {feat} {logic} {thr}")
         print(
             f"  volumes     : {n_ok} archived, {n_fail} failed"
-            + (f", {n_skip} skipped (nothing to archive)" if n_skip else "")
+            + (f", {n_skip} skipped (nothing to archive)" if n_skip else ""),
         )
         print(f"  elapsed     : {_format_elapsed_time(time.time() - t0)}")
         print("=" * 70)
@@ -643,15 +650,13 @@ def _ensure_lut(self, radar: str, sample_dt, archive_dir: Path, crs) -> None:
             # write POL files against a LUT that does not exist or is wrong, and
             # report "archived" for data no crop or section could ever use.
             raise
-        except Exception as e:  # noqa: BLE001
+        except Exception as e:
             print(f"  [{radar}] LUT generation failed: {e}")
 
     def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr):
         archive_dir = Path(archive_dir)
         # {radar: [DataTree, ...]} -- multi-radar
-        if isinstance(datatree, dict) and datatree and all(
-            not isinstance(v, xr.DataTree) for v in datatree.values()
-        ):
+        if isinstance(datatree, dict) and datatree and all(not isinstance(v, xr.DataTree) for v in datatree.values()):
             for r, vols in datatree.items():
                 rn = normalize_radar_name(r)
                 first = next(iter(vols.values())) if isinstance(vols, dict) else vols[0]
@@ -659,7 +664,9 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
             results = archive_volumes_multi_radar(
                 volumes_by_radar=datatree,
                 base_output_path=str(archive_dir),
-                filter_feature=feat, filter_threshold=thr, filter_logic=logic,
+                filter_feature=feat,
+                filter_threshold=thr,
+                filter_logic=logic,
                 verbose=False,
             )
             # Count outcomes, not attempts: archive_multiple_volumes reports a
@@ -672,15 +679,18 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
 
         if radar is None or not isinstance(radar, str):
             raise ValueError(
-                "For in-memory archiving, pass a single radar letter, e.g. "
-                "archive(datatree=dt, radar='A')."
+                "For in-memory archiving, pass a single radar letter, e.g. " "archive(datatree=dt, radar='A').",
             )
         r = normalize_radar_name(radar)
         if isinstance(datatree, xr.DataTree):
             self._ensure_lut(r, datatree, archive_dir, crs)
             path = archive_volume(
-                dt=datatree, radar=r, base_output_path=str(archive_dir),
-                filter_feature=feat, filter_threshold=thr, filter_logic=logic,
+                dt=datatree,
+                radar=r,
+                base_output_path=str(archive_dir),
+                filter_feature=feat,
+                filter_threshold=thr,
+                filter_logic=logic,
             )
             # A None path means the volume held nothing to archive; counting it
             # as archived is how an empty volume gets reported as stored.
@@ -689,8 +699,12 @@ def _archive_in_memory(self, datatree, radar, archive_dir, crs, feat, logic, thr
         first = next(iter(datatree.values())) if isinstance(datatree, dict) else datatree[0]
         self._ensure_lut(r, first, archive_dir, crs)
         results = archive_multiple_volumes(
-            volumes=datatree, radar=r, base_output_path=str(archive_dir),
-            filter_feature=feat, filter_threshold=thr, filter_logic=logic,
+            volumes=datatree,
+            radar=r,
+            base_output_path=str(archive_dir),
+            filter_feature=feat,
+            filter_threshold=thr,
+            filter_logic=logic,
             verbose=False,
         )
         n_ok = sum(1 for res in results if res.get("success"))
@@ -701,7 +715,10 @@ def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic,
         archive_dir = Path(archive_dir)
         start, end = _normalize_time_period(time_period)
         files = find_datatree_files(
-            Path(datatree_dir), recursive=True, start_time=start, end_time=end
+            Path(datatree_dir),
+            recursive=True,
+            start_time=start,
+            end_time=end,
         )
         if isinstance(radar, str):
             # A single radar name: archive every file as that radar (the
@@ -724,7 +741,13 @@ def _archive_from_disk(self, datatree_dir, radar, archive_dir, crs, feat, logic,
         radars_done, total_ok, total_fail, total_skip = [], 0, 0, 0
         for r, rfiles in sorted(by_radar.items()):
             n_ok, n_fail, n_skip = self._archive_files_one_radar(
-                r, sorted(rfiles), archive_dir, crs, feat, logic, thr
+                r,
+                sorted(rfiles),
+                archive_dir,
+                crs,
+                feat,
+                logic,
+                thr,
             )
             radars_done.append(r)
             total_ok += n_ok
@@ -750,7 +773,7 @@ def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic,
                 dt0 = open_any_datatree(files[0])
                 preopened[files[0]] = dt0
                 self._ensure_lut(radar, dt0, archive_dir, crs)
-            except Exception as e:  # noqa: BLE001
+            except Exception as e:
                 print(f"  [{radar}] LUT generation failed: {e}")
 
         n_ok = n_fail = n_skip = 0
@@ -765,8 +788,12 @@ def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic,
                 if dt is None:
                     dt = open_any_datatree(f)
                 path = archive_volume(
-                    dt=dt, radar=radar, base_output_path=str(archive_dir),
-                    filter_feature=feat, filter_threshold=thr, filter_logic=logic,
+                    dt=dt,
+                    radar=radar,
+                    base_output_path=str(archive_dir),
+                    filter_feature=feat,
+                    filter_threshold=thr,
+                    filter_logic=logic,
                     volume=stem,
                 )
                 # Checkpoint either way: a volume with nothing to archive is
@@ -779,14 +806,14 @@ def _archive_files_one_radar(self, radar, files, archive_dir, crs, feat, logic,
                 else:
                     n_ok += 1
                 del dt
-            except Exception as e:  # noqa: BLE001
+            except Exception as e:
                 n_fail += 1
                 print(f"  [{radar}] FAIL {stem}: {e}")
         return (n_ok, n_fail, n_skip)
 
     # ---- LUT read accessors (archive-bound) ----
 
-    def get_lut(self, radar: str) -> "pl.DataFrame":
+    def get_lut(self, radar: str) -> pl.DataFrame:
         """Load the LUT (static gate geometry) for a radar, as polars."""
         return load_radar_lut(normalize_radar_name(radar), self._require_archive_dir())
 
@@ -794,14 +821,17 @@ def get_radar_info(self, radar: str) -> dict:
         """Load radar metadata (location, sweep geometry)."""
         return load_radar_info(normalize_radar_name(radar), self._require_archive_dir())
 
-    def add_lut_projection(self, radar: str, epsg: int | None = None, crs=None) -> "pl.DataFrame":
+    def add_lut_projection(self, radar: str, epsg: int | None = None, crs=None) -> pl.DataFrame:
         """Return the radar LUT enriched with projected ``x_{epsg}`` / ``y_{epsg}`` columns."""
         lut_df = load_radar_lut(normalize_radar_name(radar), self._require_archive_dir())
         return add_lut_projection(lut_df, epsg=epsg, crs=crs)
 
     def get_h_plane(
-        self, radar: str, sweep: int | None = None, per_gate: bool = False
-    ) -> "pl.DataFrame":
+        self,
+        radar: str,
+        sweep: int | None = None,
+        per_gate: bool = False,
+    ) -> pl.DataFrame:
         """Horizontal-face geometry of each gate — the precise PPI footprint.
 
         ``per_gate=False`` (default) returns the compact **node lattice** as
@@ -829,7 +859,7 @@ def get_v_plane(
         sweep: int | None = None,
         azimuth: float | None = None,
         per_gate: bool = False,
-    ) -> "pl.DataFrame":
+    ) -> pl.DataFrame:
         """Vertical-face geometry of each gate — the precise RHI footprint.
 
         Coordinates are ``(d, z)``: ``d`` is the ground distance from the radar
@@ -864,8 +894,11 @@ def get_v_plane(
         return tbl.join(keep, on="gate_id", how="semi")
 
     def get_corners(
-        self, radar: str, sweep: int | None = None, per_gate: bool = False
-    ) -> "pl.DataFrame":
+        self,
+        radar: str,
+        sweep: int | None = None,
+        per_gate: bool = False,
+    ) -> pl.DataFrame:
         """Full 3-D gate corners — 8 per gate, for volume reconstruction.
 
         ``per_gate=True`` returns ``x_1..x_8``, ``y_1..y_8``, ``z_rel_1..z_rel_8``
@@ -922,31 +955,36 @@ def export_h_plane_geoparquet(
         else:
             # Fall back to WGS-84 from the radar-relative metres.
             info = load_radar_info(radar, base)
-            ring = np.stack([
-                np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1)
-                for k in range(1, 5)
-            ], axis=1)
+            ring = np.stack(
+                [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)],
+                axis=1,
+            )
             lat, lon, _ = cartesian_to_geographic(
-                ring[:, :, 0], ring[:, :, 1], np.zeros(ring.shape[:2]),
-                info["latitude"], info["longitude"], info["altitude"],
+                ring[:, :, 0],
+                ring[:, :, 1],
+                np.zeros(ring.shape[:2]),
+                info["latitude"],
+                info["longitude"],
+                info["altitude"],
             )
-            ring = np.concatenate([np.stack([lon, lat], axis=2),
-                                   np.stack([lon[:, :1], lat[:, :1]], axis=2)], axis=1)
+            ring = np.concatenate([np.stack([lon, lat], axis=2), np.stack([lon[:, :1], lat[:, :1]], axis=2)], axis=1)
             gdf = gpd.GeoDataFrame(
                 {"gate_id": tbl["gate_id"].to_numpy(), "sweep": tbl["sweep"].to_numpy()},
-                geometry=_ccw_polygons(shapely.polygons(ring)), crs="EPSG:4326",
+                geometry=_ccw_polygons(shapely.polygons(ring)),
+                crs="EPSG:4326",
             )
             gdf.to_parquet(path)
             return str(path)
 
-        ring = np.stack([
-            np.stack([tbl[xc].to_numpy(), tbl[yc].to_numpy()], axis=1)
-            for xc, yc in zip(xcols, ycols)
-        ], axis=1)
-        ring = np.concatenate([ring, ring[:, :1, :]], axis=1)   # close the ring
+        ring = np.stack(
+            [np.stack([tbl[xc].to_numpy(), tbl[yc].to_numpy()], axis=1) for xc, yc in zip(xcols, ycols, strict=False)],
+            axis=1,
+        )
+        ring = np.concatenate([ring, ring[:, :1, :]], axis=1)  # close the ring
         gdf = gpd.GeoDataFrame(
             {"gate_id": tbl["gate_id"].to_numpy(), "sweep": tbl["sweep"].to_numpy()},
-            geometry=_ccw_polygons(shapely.polygons(ring)), crs=out_crs,
+            geometry=_ccw_polygons(shapely.polygons(ring)),
+            crs=out_crs,
         )
         gdf.to_parquet(path)
         return str(path)
@@ -957,8 +995,12 @@ def list_radars(self) -> list[str]:
 
     # ---- what is on disk? ----
 
-    def inventory(self, datatree_dir: str | None = None, detailed: bool = False,
-                  archive_dir: str | None = None) -> None:
+    def inventory(
+        self,
+        datatree_dir: str | None = None,
+        detailed: bool = False,
+        archive_dir: str | None = None,
+    ) -> None:
         """Print what data is available on disk — which radars, which time periods.
 
         Answers "what can I analyse?" before :meth:`open` (archive side) or
@@ -979,9 +1021,9 @@ def inventory(self, datatree_dir: str | None = None, detailed: bool = False,
 
         Examples
         --------
-        >>> db.inventory()                                  # what is archived
-        >>> db.inventory(detailed=True)                     # ... day by day
-        >>> db.inventory(datatree_dir="/data/MCH_datatree") # what could be archived
+        >>> db.inventory()  # what is archived
+        >>> db.inventory(detailed=True)  # ... day by day
+        >>> db.inventory(datatree_dir="/data/MCH_datatree")  # what could be archived
         """
         if datatree_dir is not None:
             self._inventory_datatrees(Path(datatree_dir), detailed)
@@ -1018,9 +1060,11 @@ def _inventory_datatrees(self, directory: Path, detailed: bool) -> None:
             if detailed:
                 _print_daily_breakdown(times)
                 if not is_valid_radar_name(r):
-                    print(f"      [!] {r!r} is not a usable radar name (1-{RADAR_CODE_LEN} "
-                          f"characters from [0-9A-Z]) — archive() would skip it unless you "
-                          f"pass radar='<name>'")
+                    print(
+                        f"      [!] {r!r} is not a usable radar name (1-{RADAR_CODE_LEN} "
+                        f"characters from [0-9A-Z]) — archive() would skip it unless you "
+                        f"pass radar='<name>'",
+                    )
         print("-" * 78)
         print(f"  archive with: db.archive(datatree_dir={str(directory)!r})")
         print("=" * 78)
@@ -1058,11 +1102,13 @@ def _inventory_archive(self, base: Path, detailed: bool) -> None:
             if lut_path.exists():
                 try:
                     info = load_radar_info(r, base)
-                    print(f"      LUT: {_format_size(_path_size(lut_path))}, "
-                          f"{len(info.get('sweeps', {}))} sweeps, site "
-                          f"({info.get('latitude'):.4f}, {info.get('longitude'):.4f}) "
-                          f"at {info.get('altitude'):.0f} m")
-                except Exception as e:  # noqa: BLE001
+                    print(
+                        f"      LUT: {_format_size(_path_size(lut_path))}, "
+                        f"{len(info.get('sweeps', {}))} sweeps, site "
+                        f"({info.get('latitude'):.4f}, {info.get('longitude'):.4f}) "
+                        f"at {info.get('altitude'):.0f} m",
+                    )
+                except Exception as e:
                     print(f"      LUT: present, metadata unreadable ({e})")
             else:
                 print("      LUT: MISSING — open() will have no geometry for this radar")
@@ -1070,7 +1116,7 @@ def _inventory_archive(self, base: Path, detailed: bool) -> None:
                 try:
                     cols = pl.read_parquet_schema(pol[0]).keys()
                     print(f"      columns: {', '.join(c for c in cols if c != 'gate_id')}")
-                except Exception as e:  # noqa: BLE001
+                except Exception as e:
                     print(f"      columns: unreadable ({e})")
             _print_daily_breakdown(times)
         print("-" * 78)
@@ -1088,7 +1134,7 @@ def open(
         columns: list[str] | None = None,
         filters=None,
         archive_dir: str | None = None,
-    ) -> "RadDB":
+    ) -> RadDB:
         """Load archived data into a data-carrying ``RadDB``.
 
         Parameters
@@ -1124,8 +1170,11 @@ def open(
         scans = []
         for r in radars:
             lf = scan_polar_parquet(
-                radar=normalize_radar_name(r), base_path=archive_dir,
-                start_time=start, end_time=end, columns=columns,
+                radar=normalize_radar_name(r),
+                base_path=archive_dir,
+                start_time=start,
+                end_time=end,
+                columns=columns,
             )
             if lf is not None:
                 scans.append(lf)
@@ -1141,7 +1190,7 @@ def open(
             data = pl.DataFrame()
         return self._derive(data, archive_dir=archive_dir)
 
-    def filter(self, filters) -> "RadDB":
+    def filter(self, filters) -> RadDB:
         """Keep only gates satisfying ``filters`` (row removal); returns a new RadDB.
 
         ``filters`` is a dict ``{"var", "logic", "threshold"}`` or a list of such
@@ -1166,7 +1215,7 @@ def filter(self, filters) -> "RadDB":
             if unknown:
                 raise KeyError(
                     f"cannot filter on {unknown}: not a data column "
-                    f"{sorted(data.columns)} nor a LUT column {sorted(geo.columns)}."
+                    f"{sorted(data.columns)} nor a LUT column {sorted(geo.columns)}.",
                 )
             data = data.join(geo.select("gate_id", *borrowed), on="gate_id", how="left")
 
@@ -1193,7 +1242,7 @@ def _lut_column_names(self) -> list[str]:
             return list(pl.scan_parquet(p).collect_schema().names())
         return []
 
-    def _borrow_lut_columns(self, cols: list[str]) -> "pl.DataFrame":
+    def _borrow_lut_columns(self, cols: list[str]) -> pl.DataFrame:
         """Load ``cols`` from the LUT for the gates present, keyed by ``gate_id``.
 
         The general form of :meth:`_gate_geometry` (which exposes only
@@ -1206,8 +1255,7 @@ def _borrow_lut_columns(self, cols: list[str]) -> "pl.DataFrame":
         paths = self._lut_paths()
         if not paths:
             raise ValueError(
-                "no LUT found for the radars in this data; cannot select on "
-                f"static columns {cols}."
+                "no LUT found for the radars in this data; cannot select on " f"static columns {cols}.",
             )
         present = self._require_data().select("gate_id").unique()
         parts = []
@@ -1215,13 +1263,18 @@ def _borrow_lut_columns(self, cols: list[str]) -> "pl.DataFrame":
             names = pl.scan_parquet(p).collect_schema().names()
             keep = ["gate_id", *[c for c in cols if c in names and c != "gate_id"]]
             parts.append(
-                pl.scan_parquet(p).select(keep).join(present.lazy(), on="gate_id", how="semi")
+                pl.scan_parquet(p).select(keep).join(present.lazy(), on="gate_id", how="semi"),
+            )
+        return (
+            pl.concat(parts, how="vertical_relaxed")
+            .collect()
+            .unique(
+                subset="gate_id",
+                maintain_order=True,
             )
-        return pl.concat(parts, how="vertical_relaxed").collect().unique(
-            subset="gate_id", maintain_order=True
         )
 
-    def sel(self, **indexers) -> "RadDB":
+    def sel(self, **indexers) -> RadDB:
         """Select gates by label, xarray-style; returns a **new** ``RadDB``.
 
         Each keyword names a column and gives what to keep:
@@ -1272,7 +1325,7 @@ def sel(self, **indexers) -> "RadDB":
         data = self._require_data()
         lut_names = None  # loaded lazily, only if a static column is requested
 
-        resolved: list[tuple[str, object]] = []   # (column, value)
+        resolved: list[tuple[str, object]] = []  # (column, value)
         static: list[str] = []
         radar_from_gate_id = None
 
@@ -1294,12 +1347,12 @@ def sel(self, **indexers) -> "RadDB":
                             name = self._time_column()
                         except KeyError:
                             raise KeyError(
-                                f"sel({key}=...): data has no time column."
+                                f"sel({key}=...): data has no time column.",
                             ) from None
                     else:
                         raise KeyError(
                             f"sel({key}=...): {name!r} is neither a data column "
-                            f"{sorted(data.columns)} nor a LUT column {sorted(lut_names)}."
+                            f"{sorted(data.columns)} nor a LUT column {sorted(lut_names)}.",
                         )
             if name not in data.columns:
                 static.append(name)
@@ -1313,11 +1366,12 @@ def sel(self, **indexers) -> "RadDB":
             still_missing = [c for c in static if c not in borrowed]
             if still_missing:
                 raise KeyError(
-                    f"sel(): LUT has no column(s) {still_missing}; "
-                    f"available: {sorted(lut_tbl.columns)}."
+                    f"sel(): LUT has no column(s) {still_missing}; " f"available: {sorted(lut_tbl.columns)}.",
                 )
             data = data.join(
-                lut_tbl.select(["gate_id", *borrowed]), on="gate_id", how="left",
+                lut_tbl.select(["gate_id", *borrowed]),
+                on="gate_id",
+                how="left",
                 maintain_order="left",
             )
 
@@ -1325,20 +1379,17 @@ def sel(self, **indexers) -> "RadDB":
             data = data.filter(_sel_expr(name, value, data.schema[name]))
 
         if radar_from_gate_id is not None:
-            wanted = (
-                [radar_from_gate_id] if isinstance(radar_from_gate_id, str)
-                else list(radar_from_gate_id)
-            )
+            wanted = [radar_from_gate_id] if isinstance(radar_from_gate_id, str) else list(radar_from_gate_id)
             codes = [encode_radar_code(r) for r in wanted]
             data = data.filter(
-                (pl.col("gate_id") // GATE_ID_RADAR_BASE).is_in(codes)
+                (pl.col("gate_id") // GATE_ID_RADAR_BASE).is_in(codes),
             )
 
         if borrowed:
             data = data.drop(borrowed)
         return self._derive(data)
 
-    def add_feature(self, name: str, compute_fn) -> "RadDB":
+    def add_feature(self, name: str, compute_fn) -> RadDB:
         """Add a computed column ``name`` and return a new RadDB.
 
         ``compute_fn`` receives the polars DataFrame and returns a polars
@@ -1355,8 +1406,7 @@ def add_feature(self, name: str, compute_fn) -> "RadDB":
 
     # ---- converters ----
 
-    def to_pandas(self, with_geometry: bool = False,
-                  with_polar_coords: bool = False) -> pd.DataFrame:
+    def to_pandas(self, with_geometry: bool = False, with_polar_coords: bool = False) -> pd.DataFrame:
         """Return the data as a pandas DataFrame.
 
         Parameters
@@ -1373,8 +1423,12 @@ def to_pandas(self, with_geometry: bool = False,
         """
         data = self._require_data()
         if with_geometry or with_polar_coords:
-            data = data.join(self._gate_geometry(with_polar_coords=with_polar_coords),
-                             on="gate_id", how="left", suffix="_lut")
+            data = data.join(
+                self._gate_geometry(with_polar_coords=with_polar_coords),
+                on="gate_id",
+                how="left",
+                suffix="_lut",
+            )
         return _decode_geometry(data.to_pandas())
 
     def to_geopandas(self, with_polar_coords: bool = False):
@@ -1445,7 +1499,7 @@ def to_geoarrow(
                 f"to_geoarrow() would build {len(data):,} features, over the "
                 f"max_rows={max_rows:,} guardrail. Narrow the selection first "
                 "(crop_by_bbox / crop_by_polygone / crop_around_point / filter), "
-                "or pass max_rows=None to override."
+                "or pass max_rows=None to override.",
             )
         if columns is not None:
             data = data.select(dict.fromkeys(["gate_id", *columns]))
@@ -1458,10 +1512,12 @@ def to_geoarrow(
             if len(radars) != 1:
                 raise ValueError(
                     f"polygon geometry needs a single radar; data spans {radars}. "
-                    "Select one with open(radars=...) or filter first."
+                    "Select one with open(radars=...) or filter first.",
                 )
             geom = gate_polygons_geoarrow(
-                normalize_radar_name(radars[0]), self._require_archive_dir(), gate_ids,
+                normalize_radar_name(radars[0]),
+                self._require_archive_dir(),
+                gate_ids,
             )
             field = geoarrow_field("geometry", geom.type, "polygon", "EPSG:4326")
         else:
@@ -1472,7 +1528,8 @@ def to_geoarrow(
             )
             xy = np.column_stack([geo["longitude"].to_numpy(), geo["latitude"].to_numpy()])
             geom = pa.FixedSizeListArray.from_arrays(
-                pa.array(xy.reshape(-1), type=pa.float64()), 2
+                pa.array(xy.reshape(-1), type=pa.float64()),
+                2,
             )
             field = geoarrow_field("geometry", geom.type, "point", "EPSG:4326")
 
@@ -1505,7 +1562,7 @@ def to_datatree(self, radar: str | None = None, timestep=None, label_column: str
             df_r = data.filter(pl.col("radar") == radar)
         else:
             df_r = data.filter(
-                (pl.col("gate_id") // GATE_ID_RADAR_BASE) == encode_radar_code(radar)
+                (pl.col("gate_id") // GATE_ID_RADAR_BASE) == encode_radar_code(radar),
             )
         if df_r.is_empty():
             raise ValueError(f"No rows for radar {radar!r} in data.")
@@ -1530,17 +1587,19 @@ def to_datatree(self, radar: str | None = None, timestep=None, label_column: str
             df_vol = df_r
 
         return dataframe_to_datatree(
-            df=df_vol, radar=radar, base_path=str(self._require_archive_dir()),
+            df=df_vol,
+            radar=radar,
+            base_path=str(self._require_archive_dir()),
             label_column=label_column,
         )
 
     # ---- accessors (return values; repr prints the summary) ----
 
-    def head(self, n: int = 5) -> "pl.DataFrame":
+    def head(self, n: int = 5) -> pl.DataFrame:
         """First ``n`` rows (polars)."""
         return self._require_data().head(n)
 
-    def tail(self, n: int = 5) -> "pl.DataFrame":
+    def tail(self, n: int = 5) -> pl.DataFrame:
         """Last ``n`` rows (polars)."""
         return self._require_data().tail(n)
 
@@ -1578,7 +1637,7 @@ def end_time(self) -> datetime.datetime:
     #: columns unless you are working in polar (antenna) space.
     _POLAR_COLS = ("range", "azimuth", "elevation_angle")
 
-    def _gate_geometry(self, with_polar_coords: bool = False) -> "pl.DataFrame":
+    def _gate_geometry(self, with_polar_coords: bool = False) -> pl.DataFrame:
         """LUT geometry (lon/lat/alt [+ projected x/y]) for the gates present.
 
         Returned as its **own** table — the LUT is never carried alongside the
@@ -1613,16 +1672,19 @@ def extent(self) -> list[float]:
         xcol, ycol = f"x_{epsg}", f"y_{epsg}"
         if xcol not in geo.columns:
             raise ValueError(
-                f"LUT has no {xcol} column; archive with crs={epsg} to store projected coords."
+                f"LUT has no {xcol} column; archive with crs={epsg} to store projected coords.",
             )
-        return [float(geo[xcol].min()), float(geo[xcol].max()),
-                float(geo[ycol].min()), float(geo[ycol].max())]
+        return [float(geo[xcol].min()), float(geo[xcol].max()), float(geo[ycol].min()), float(geo[ycol].max())]
 
     def geographic_extent(self) -> list[float]:
         """Geographic bounding box ``[lon_min, lon_max, lat_min, lat_max]``."""
         geo = self._gate_geometry()
-        return [float(geo["longitude"].min()), float(geo["longitude"].max()),
-                float(geo["latitude"].min()), float(geo["latitude"].max())]
+        return [
+            float(geo["longitude"].min()),
+            float(geo["longitude"].max()),
+            float(geo["latitude"].min()),
+            float(geo["latitude"].max()),
+        ]
 
     def crs(self):
         """Projected CRS (x, y) as a ``pyproj.CRS``.
@@ -1636,7 +1698,8 @@ def crs(self):
         spec = self._crs
         if spec is None and self.archive_dir is not None:
             from raddb.aoi import aoi_epsg
-            for radar in (self.radars() if self.data is not None else []):
+
+            for radar in self.radars() if self.data is not None else []:
                 try:
                     spec = aoi_epsg(self.archive_dir, radar)
                     break
@@ -1644,8 +1707,7 @@ def crs(self):
                     continue
         if spec is None:
             raise ValueError(
-                "no projected CRS: this object has none and no archive records "
-                "one. Pass RadDB(crs=<epsg>)."
+                "no projected CRS: this object has none and no archive records " "one. Pass RadDB(crs=<epsg>).",
             )
         return pyproj.CRS.from_user_input(spec)
 
@@ -1659,8 +1721,14 @@ def geographic_crs(self):
     # EXTRACT AREA OF INTEREST
     # ================================================================
 
-    def crop_by_bbox(self, bounds=None, extent=None, crs: int | str | None = None,
-                     quicklook: bool = False, aoi_crs=None) -> "RadDB":
+    def crop_by_bbox(
+        self,
+        bounds=None,
+        extent=None,
+        crs: int | str | None = None,
+        quicklook: bool = False,
+        aoi_crs=None,
+    ) -> RadDB:
         """Crop to a rectangle; returns a new RadDB.
 
         Give **exactly one** of ``bounds=(xmin, ymin, xmax, ymax)`` or
@@ -1680,19 +1748,27 @@ def crop_by_bbox(self, bounds=None, extent=None, crs: int | str | None = None,
         geom = _reproject_to_aoi(shapely.box(xmin, ymin, xmax, ymax), crs, epsg)
         return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook, epsg))
 
-    def crop_by_polygone(self, polygon, crs: int | str | None = None,
-                         quicklook: bool = False, aoi_crs=None) -> "RadDB":
-        """Crop to an arbitrary polygon (shapely, GeoDataFrame/GeoSeries, or a
-        ``.shp``/``.geojson`` path); returns a new RadDB.  ``crs=None`` auto-detects.
+    def crop_by_polygone(self, polygon, crs: int | str | None = None, quicklook: bool = False, aoi_crs=None) -> RadDB:
+        """Crop to an arbitrary polygon; returns a new RadDB.
+
+        ``polygon`` is a shapely geometry, a GeoDataFrame/GeoSeries, or a
+        ``.shp``/``.geojson`` path.  ``crs=None`` auto-detects.
         """
         epsg = self._aoi_epsg(aoi_crs)
         geom = _load_aoi_polygon(polygon, crs, epsg)
         return self._derive(self._crop_to_aoi(self._require_data(), geom, quicklook, epsg))
 
-    def crop_around_point(self, point, distance: float, crs: int | str | None = None,
-                          quicklook: bool = False, aoi_crs=None) -> "RadDB":
-        """Crop to a circle of radius ``distance`` (metres) around ``point``;
-        returns a new RadDB.  ``point`` is ``(x, y)`` or a shapely Point in ``crs``.
+    def crop_around_point(
+        self,
+        point,
+        distance: float,
+        crs: int | str | None = None,
+        quicklook: bool = False,
+        aoi_crs=None,
+    ) -> RadDB:
+        """Crop to a circle of radius ``distance`` (metres) around ``point``.
+
+        Returns a new RadDB.  ``point`` is ``(x, y)`` or a shapely Point in ``crs``.
         """
         if distance <= 0:
             raise ValueError(f"distance must be positive (metres); got {distance!r}.")
@@ -1720,8 +1796,7 @@ def _aoi_epsg(self, override=None) -> int:
         radars = _radars_from_gate_ids(data["gate_id"].to_numpy())
         return aoi_epsg_for(self._require_archive_dir(), radars, override=override)
 
-    def _crop_to_aoi(self, data: "pl.DataFrame", geom, quicklook: bool = False,
-                     epsg: int | None = None) -> "pl.DataFrame":
+    def _crop_to_aoi(self, data: pl.DataFrame, geom, quicklook: bool = False, epsg: int | None = None) -> pl.DataFrame:
         """Intersect ``geom`` (in the AOI CRS) with LUT centroids and keep matching rows.
 
         This **selects** rows, it never widens them: the LUT geometry stays in its
@@ -1738,15 +1813,17 @@ def _crop_to_aoi(self, data: "pl.DataFrame", geom, quicklook: bool = False,
 
         if quicklook:
             from raddb.viz.plot import plot_aoi_quicklook
+
             # ponytail: the quicklook is the one consumer that needs coordinates,
             # so join them for the plot only — never into the returned frame.
             # _lut_centroids returns the archive's projected pair as plain x/y,
             # whatever EPSG that is — the quicklook draws in the AOI frame.
             selected = data_aoi.join(
-                aoi_cen.select("gate_id", "x", "y"), on="gate_id", how="left",
+                aoi_cen.select("gate_id", "x", "y"),
+                on="gate_id",
+                how="left",
             )
-            plot_aoi_quicklook(geom, selected=selected, radars=radars,
-                               base_path=self.archive_dir, epsg=epsg)
+            plot_aoi_quicklook(geom, selected=selected, radars=radars, base_path=self.archive_dir, epsg=epsg)
         return data_aoi
 
     def interactive_crop(self, **kwargs):
@@ -1757,15 +1834,22 @@ def interactive_crop(self, **kwargs):
         """
         self._require_data()
         from raddb.viz.interactive import AOISelector
+
         return AOISelector(self, **kwargs).display()
 
     # ================================================================
     # EXTRACT CROSS-SECTION
     # ================================================================
 
-    def extract_cross_section(self, p1, p2, crs: int | str | None = None,
-                              beamwidth_deg: float = 1.0, quicklook: bool = False,
-                              aoi_crs=None) -> "RadDB":
+    def extract_cross_section(
+        self,
+        p1,
+        p2,
+        crs: int | str | None = None,
+        beamwidth_deg: float = 1.0,
+        quicklook: bool = False,
+        aoi_crs=None,
+    ) -> RadDB:
         """Extract a vertical cross-section along the line ``p1 -> p2``; returns a new RadDB.
 
         The line need not pass through a radar.  Each selected gate gets a polygon
@@ -1797,7 +1881,11 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None,
         base = self._require_archive_dir()
         cs_t = _lut_cs_table(base, radars, beamwidth_deg=beamwidth_deg, epsg=epsg)
         cs_geom = _cross_section_gates(
-            cs_t, (x1, y1), (x2, y2), beamwidth_deg=beamwidth_deg, base_path=base,
+            cs_t,
+            (x1, y1),
+            (x2, y2),
+            beamwidth_deg=beamwidth_deg,
+            base_path=base,
             epsg=epsg,
         )
 
@@ -1810,28 +1898,48 @@ def extract_cross_section(self, p1, p2, crs: int | str | None = None,
         # The geometry table computes in the projected frame under plain x/y.
         # Publish it as x_<epsg>/y_<epsg>, and give x/y back to the LUT's
         # radar-relative metres, so both meanings are unambiguous downstream.
-        cs_geom = cs_geom.rename(columns={
-            "x": f"x_{epsg}", "y": f"y_{epsg}", "x_rel": "x", "y_rel": "y",
-        })
+        cs_geom = cs_geom.rename(
+            columns={
+                "x": f"x_{epsg}",
+                "y": f"y_{epsg}",
+                "x_rel": "x",
+                "y_rel": "y",
+            },
+        )
         geom_cols = [
-            c for c in (
-                "radar", "sweep", "azimuth", "range", "elevation_angle",
-                "x", "y", f"x_{epsg}", f"y_{epsg}", "altitude",
-                "d_center", "z_center",
+            c
+            for c in (
+                "radar",
+                "sweep",
+                "azimuth",
+                "range",
+                "elevation_angle",
+                "x",
+                "y",
+                f"x_{epsg}",
+                f"y_{epsg}",
+                "altitude",
+                "d_center",
+                "z_center",
                 "cs_polygon",
             )
             if c in cs_geom.columns and (c == "cs_polygon" or c not in data_cs.columns)
         ]
         if not data_cs.is_empty() and len(cs_geom):
             data_cs = data_cs.join(
-                _to_polars(cs_geom[["gate_id", *geom_cols]]), on="gate_id", how="left",
+                _to_polars(cs_geom[["gate_id", *geom_cols]]),
+                on="gate_id",
+                how="left",
             )
 
         if quicklook:
             from raddb.viz.plot import plot_aoi_quicklook
+
             plot_aoi_quicklook(
                 shapely.LineString([(x1, y1), (x2, y2)]),
-                selected=data_cs, radars=radars, base_path=self.archive_dir,
+                selected=data_cs,
+                radars=radars,
+                base_path=self.archive_dir,
                 # The section was resolved in `epsg`; without it the quicklook
                 # falls back to LV95 and frames a non-Swiss archive over
                 # Switzerland.
@@ -1848,11 +1956,11 @@ def _save_fig(ret, save, kwargs):
         if not save:
             return
         import matplotlib.pyplot as plt
+
         fig = getattr(ret, "figure", None) or getattr(getattr(ret, "axes", None), "figure", None) or plt.gcf()
         fig.savefig(save, bbox_inches="tight", dpi=kwargs.get("dpi", 150))
 
-    def plot_ppi(self, sweep: int | str = 1, variable: str = "DBZH", radar: str | None = None,
-                 timestep=None, **kwargs):
+    def plot_ppi(self, sweep: int | str = 1, variable: str = "DBZH", radar: str | None = None, timestep=None, **kwargs):
         """Plot a PPI of one sweep — one plot, one figure.
 
         Gate footprints come from the ``h_plane`` lattice, so a filtered, ``sel``-ed
@@ -1863,11 +1971,10 @@ def plot_ppi(self, sweep: int | str = 1, variable: str = "DBZH", radar: str | No
         (``coords``, ``context``, ``save``, ...).
         """
         from raddb.viz.plot import plot_ppi as _plot_ppi
-        return _plot_ppi(self, sweep=sweep, variable=variable, radar=radar,
-                         timestep=timestep, **kwargs)
 
-    def plot_rhi(self, azimuth: float = 0.0, variable: str = "DBZH", radar: str | None = None,
-                 timestep=None, **kwargs):
+        return _plot_ppi(self, sweep=sweep, variable=variable, radar=radar, timestep=timestep, **kwargs)
+
+    def plot_rhi(self, azimuth: float = 0.0, variable: str = "DBZH", radar: str | None = None, timestep=None, **kwargs):
         """Plot an RHI along one azimuth, stacking every sweep.
 
         Gate faces come from the ``v_plane`` lattice in the
@@ -1875,11 +1982,10 @@ def plot_rhi(self, azimuth: float = 0.0, variable: str = "DBZH", radar: str | No
         :func:`raddb.viz.plot.plot_rhi` for the full parameter list.
         """
         from raddb.viz.plot import plot_rhi as _plot_rhi
-        return _plot_rhi(self, azimuth=azimuth, variable=variable, radar=radar,
-                         timestep=timestep, **kwargs)
 
-    def plot_cappi(self, altitude: float, variable: str = "DBZH", radar: str | None = None,
-                   timestep=None, **kwargs):
+        return _plot_rhi(self, azimuth=azimuth, variable=variable, radar=radar, timestep=timestep, **kwargs)
+
+    def plot_cappi(self, altitude: float, variable: str = "DBZH", radar: str | None = None, timestep=None, **kwargs):
         """Plot a CAPPI — a horizontal slice at constant ``altitude`` [m].
 
         Where :meth:`plot_ppi` fixes the sweep, this fixes the altitude and pulls
@@ -1888,11 +1994,10 @@ def plot_cappi(self, altitude: float, variable: str = "DBZH", radar: str | None
         for ``overlap`` / ``fill_lowest``.
         """
         from raddb.viz.plot import plot_cappi as _plot_cappi
-        return _plot_cappi(self, altitude=altitude, variable=variable, radar=radar,
-                           timestep=timestep, **kwargs)
 
-    def plot_vcs(self, line=None, variable: str = "DBZH", radar: str | None = None,
-                 timestep=None, **kwargs):
+        return _plot_cappi(self, altitude=altitude, variable=variable, radar=radar, timestep=timestep, **kwargs)
+
+    def plot_vcs(self, line=None, variable: str = "DBZH", radar: str | None = None, timestep=None, **kwargs):
         """Plot a vertical cross-section along an arbitrary line.
 
         Either pass ``line=`` — ``(p1, p2)``, a shapely ``LineString``, or a
@@ -1902,16 +2007,16 @@ def plot_vcs(self, line=None, variable: str = "DBZH", radar: str | None = None,
         archive it first.  See :func:`raddb.viz.plot.plot_vcs`.
         """
         from raddb.viz.plot import plot_vcs as _plot_vcs
-        return _plot_vcs(self, line=line, variable=variable, radar=radar,
-                         timestep=timestep, **kwargs)
 
-    def plot_cross_section(self, variable: str = "DBZH", radar: str | None = None,
-                           timestep=None, **kwargs):
+        return _plot_vcs(self, line=line, variable=variable, radar=radar, timestep=timestep, **kwargs)
+
+    def plot_cross_section(self, variable: str = "DBZH", radar: str | None = None, timestep=None, **kwargs):
         """Deprecated alias of :meth:`plot_vcs`."""
         import warnings
+
         warnings.warn(
             "RadDB.plot_cross_section is deprecated; use plot_vcs() instead.",
-            DeprecationWarning, stacklevel=2,
+            DeprecationWarning,
+            stacklevel=2,
         )
         return self.plot_vcs(variable=variable, radar=radar, timestep=timestep, **kwargs)
-
diff --git a/raddb/tests/conftest.py b/raddb/tests/conftest.py
index 3e07e41..8233b66 100644
--- a/raddb/tests/conftest.py
+++ b/raddb/tests/conftest.py
@@ -16,26 +16,26 @@
 import pytest
 import xarray as xr
 
+# Default radar name used across the suite.
 RADAR = "A"
-"""Default radar name used across the suite."""
 
+# Default number of azimuth rays in a synthetic sweep.
 N_AZ = 12
-"""Default number of azimuth rays in a synthetic sweep."""
 
+# Default number of range bins in a synthetic sweep.
 N_RNG = 24
-"""Default number of range bins in a synthetic sweep."""
 
+# Latitude of the synthetic radar site, in degrees north.
 SITE_LAT = 46.0
-"""Latitude of the synthetic radar site, in degrees north."""
 
+# Longitude of the synthetic radar site, in degrees east.
 SITE_LON = 7.0
-"""Longitude of the synthetic radar site, in degrees east."""
 
+# Altitude of the synthetic radar site, in metres above sea level.
 SITE_ALT = 1000.0
-"""Altitude of the synthetic radar site, in metres above sea level."""
 
+# CH1903+/LV95 — the projected CRS valid at the synthetic site.
 SWISS_EPSG = 2056
-"""CH1903+/LV95 — the projected CRS valid at the synthetic site."""
 
 
 @pytest.fixture(autouse=True)
@@ -148,16 +148,14 @@ def relocate(dt: xr.DataTree, longitude: float, latitude: float) -> xr.DataTree:
     return xr.DataTree.from_dict(out)
 
 
+# Rad4Alp antenna drift, measured from real METRANET files. Every ray is reported ~0.0327
+# degrees past its nominal angle, with a ~0.0069 degree spread. ``gate_id`` resolves azimuth to
+# 0.1 degrees, so an unsnapped drifting ray lands in a neighbouring bin and its gates match no
+# LUT row.
 MCH_BIAS, MCH_SPREAD = 0.0327, 0.0069
-"""Rad4Alp antenna drift, measured from real METRANET files.
-
-Every ray is reported ~0.0327 degrees past its nominal angle, with a ~0.0069 degree
-spread.  ``gate_id`` resolves azimuth to 0.1 degrees, so an unsnapped drifting ray lands
-in a neighbouring bin and its gates match no LUT row.
-"""
 
+# WSR-88D antenna drift: zero-mean, spread up to ~0.045 degrees.
 NEXRAD_SPREAD = 0.045
-"""WSR-88D antenna drift: zero-mean, spread up to ~0.045 degrees."""
 
 
 def jitter_azimuths(azimuths, rng, bias: float = 0.0, spread: float = MCH_SPREAD):
@@ -216,11 +214,11 @@ def retime(dt: xr.DataTree, when, rng, bias: float = 0.0, spread: float = MCH_SP
     return xr.DataTree.from_dict(out)
 
 
+# KTLX, Oklahoma — ``(longitude, latitude)``. UTM 14N (EPSG:32614) is valid here.
 US_SITE = (-97.2775, 35.3331)
-"""KTLX, Oklahoma — ``(longitude, latitude)``.  UTM 14N (EPSG:32614) is valid here."""
 
+# UTM zone 14N — the projected CRS valid at :data:`US_SITE`.
 US_EPSG = 32614
-"""UTM zone 14N — the projected CRS valid at :data:`US_SITE`."""
 
 
 @pytest.fixture
@@ -318,20 +316,18 @@ def archive_dir_two_radars(tmp_path, make_datatree):
 
     base = tmp_path / "archive_multi"
     RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(
-        datatree={RADAR: [make_datatree()], "D": [make_datatree()]}
+        datatree={RADAR: [make_datatree()], "D": [make_datatree()]},
     )
     return base
 
 
+# Volume shape used by the plotting fixtures. Six sweeps and 60 range bins are the minimum that
+# makes the CAPPI and RHI invariants meaningful — a two-sweep volume has no beam overlap to
+# resolve.
 PLOT_GEOMETRY = {"n_az": 72, "n_rng": 60, "n_sweeps": 6}
-"""Volume shape used by the plotting fixtures.
-
-Six sweeps and 60 range bins are the minimum that makes the CAPPI and RHI invariants
-meaningful — a two-sweep volume has no beam overlap to resolve.
-"""
 
+# Radar name used by the plotting fixtures.
 PLOT_RADAR = "L"
-"""Radar name used by the plotting fixtures."""
 
 
 @pytest.fixture(scope="session")
@@ -350,7 +346,7 @@ def plot_archive_dir(tmp_path_factory):
 
     base = tmp_path_factory.mktemp("plot_archive")
     RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(
-        datatree={PLOT_RADAR: [build_datatree(**PLOT_GEOMETRY)]}
+        datatree={PLOT_RADAR: [build_datatree(**PLOT_GEOMETRY)]},
     )
     return base
 
diff --git a/raddb/tests/test__proj.py b/raddb/tests/test__proj.py
index 7334706..b83d486 100644
--- a/raddb/tests/test__proj.py
+++ b/raddb/tests/test__proj.py
@@ -19,8 +19,8 @@
 
 from raddb._proj import PROJ_DATA, fix_foreign_proj_data
 
+# This interpreter's own bundled PROJ data directory (may not exist).
 OWN_PROJ_DIR = Path(sys.prefix) / "share" / "proj"
-"""This interpreter's own bundled PROJ data directory (may not exist)."""
 
 
 def test_fix_foreign_proj_data(monkeypatch, tmp_path):
@@ -50,7 +50,8 @@ def test_fix_foreign_proj_data(monkeypatch, tmp_path):
         # 3b. No own data to point at, so the foreign variables are dropped entirely
         #     and pyproj falls back to the data it bundles.
         assert result is None
-        assert "PROJ_DATA" not in os.environ and "PROJ_LIB" not in os.environ
+        assert "PROJ_DATA" not in os.environ
+        assert "PROJ_LIB" not in os.environ
 
     # 4. An in-prefix directory is this environment's own -> left alone.
     if (OWN_PROJ_DIR / "proj.db").is_file():
diff --git a/raddb/tests/test_aoi.py b/raddb/tests/test_aoi.py
index 6c014f9..009e993 100644
--- a/raddb/tests/test_aoi.py
+++ b/raddb/tests/test_aoi.py
@@ -40,8 +40,8 @@
 from raddb.main import RadDB
 from raddb.tests.conftest import RADAR, US_EPSG, US_SITE, relocate
 
+# The synthetic fixture's own site — ``(longitude, latitude)``.
 CH_SITE = (7.0, 46.0)
-"""The synthetic fixture's own site — ``(longitude, latitude)``."""
 
 
 # ---------------------------------------------------------------------------
@@ -114,10 +114,10 @@ def test_mixed_crs_radars_refuse_a_shared_aoi(tmp_path, make_datatree):
     """No silent reprojection: the user must name the common frame."""
     base = tmp_path / "mixed"
     RadDB(archive_dir=str(base), crs=SWISS_EPSG).archive(
-        datatree={"A": [make_datatree(n_az=24, n_rng=20, n_sweeps=2)]}
+        datatree={"A": [make_datatree(n_az=24, n_rng=20, n_sweeps=2)]},
     )
     RadDB(archive_dir=str(base), crs=US_EPSG).archive(
-        datatree={"D": [relocate(make_datatree(n_az=24, n_rng=20, n_sweeps=2), *US_SITE)]}
+        datatree={"D": [relocate(make_datatree(n_az=24, n_rng=20, n_sweeps=2), *US_SITE)]},
     )
 
     assert aoi_epsg_for(base, ["A"]) == SWISS_EPSG
@@ -229,7 +229,10 @@ def test_a_crop_radius_is_true_metres(us_archive_dir):
     lon = lut["longitude"].to_numpy()
     lat = lut["latitude"].to_numpy()
     _, _, ground = pyproj.Geod(ellps="WGS84").inv(
-        np.full(lon.size, US_SITE[0]), np.full(lat.size, US_SITE[1]), lon, lat
+        np.full(lon.size, US_SITE[0]),
+        np.full(lat.size, US_SITE[1]),
+        lon,
+        lat,
     )
 
     centroids = _lut_centroids(us_archive_dir, [RADAR])
@@ -574,7 +577,10 @@ def test_gate_footprints_come_from_the_h_plane_lattice(archive_dir):
 
     h_plane = RadDB(archive_dir=str(archive_dir)).get_h_plane(RADAR, per_gate=True)
     aligned = pl.DataFrame({"gate_id": cs_table["gate_id"].to_numpy()}).join(
-        h_plane, on="gate_id", how="left", maintain_order="left"
+        h_plane,
+        on="gate_id",
+        how="left",
+        maintain_order="left",
     )
     reference = shapely.polygons(
         np.stack(
@@ -583,7 +589,7 @@ def test_gate_footprints_come_from_the_h_plane_lattice(archive_dir):
                 for k in range(1, 5)
             ],
             axis=1,
-        ).astype(np.float64)
+        ).astype(np.float64),
     )
 
     assert np.allclose(shapely.get_coordinates(footprints), shapely.get_coordinates(reference))
@@ -611,14 +617,18 @@ def test_a_cross_section_height_follows_the_v_plane(plot_rdb, plot_archive_dir,
     from raddb.tests.conftest import PLOT_RADAR
 
     cs = plot_rdb.extract_cross_section(
-        (plot_site[0] - 12_000, plot_site[1] - 12_000), (plot_site[0] + 12_000, plot_site[1] + 12_000)
+        (plot_site[0] - 12_000, plot_site[1] - 12_000),
+        (plot_site[0] + 12_000, plot_site[1] + 12_000),
     )
     pdf = _decode_geometry(cs.data.to_pandas()).head(200)
     heights = np.array([p.bounds[3] - p.bounds[1] for p in pdf["cs_polygon"]])
 
     v_plane = RadDB(archive_dir=str(plot_archive_dir)).get_v_plane(PLOT_RADAR, per_gate=True)
     aligned = pl.DataFrame({"gate_id": pdf["gate_id"].to_numpy()}).join(
-        v_plane, on="gate_id", how="left", maintain_order="left"
+        v_plane,
+        on="gate_id",
+        how="left",
+        maintain_order="left",
     )
     thickness = 0.5 * (
         np.abs(aligned["z_asl_4"].to_numpy() - aligned["z_asl_1"].to_numpy())
diff --git a/raddb/tests/test_api_coverage.py b/raddb/tests/test_api_coverage.py
index ec6830a..4e6d94f 100644
--- a/raddb/tests/test_api_coverage.py
+++ b/raddb/tests/test_api_coverage.py
@@ -41,36 +41,30 @@
     "raddb/viz/__init__.py": "test_viz_init.py",
     "raddb/viz/interactive.py": "test_viz_interactive.py",
     "raddb/viz/plot.py": "test_viz_plot.py",
+    # Source module (repo-relative) -> the test file that must cover it. The two ``__init__.py``
+    # entries are special-cased: a literal ``test___init__.py`` is unreadable and the package root
+    # and ``viz/`` would collide on the same name.
 }
-"""Source module (repo-relative) -> the test file that must cover it.
-
-The two ``__init__.py`` entries are special-cased: a literal ``test___init__.py`` is
-unreadable and the package root and ``viz/`` would collide on the same name.
-"""
 
+# Modules deliberately not covered — ``_version.py`` is generated by setuptools_scm.
 SKIP_MODULES = {"raddb/_version.py"}
-"""Modules deliberately not covered — ``_version.py`` is generated by setuptools_scm."""
 
+# Test files that cover no single source module.
 SKIP_TEST_FILES = {"test_api_coverage.py"}
-"""Test files that cover no single source module."""
 
+# Dunder methods that are part of the public surface, and their test-name suffix.
 DUNDERS = {"__init__": "init", "__len__": "len", "__repr__": "repr"}
-"""Dunder methods that are part of the public surface, and their test-name suffix."""
 
+# Test files not yet written out, exempt from the per-callable check. The coarse half of the
+# loop's ledger: one name is removed as each file is completed, so the gate tightens
+# monotonically. Must be **empty** when the restructuring is finished.
 PENDING_FILES: set[str] = set()
-"""Test files not yet written out, exempt from the per-callable check.
-
-The coarse half of the loop's ledger: one name is removed as each file is completed, so
-the gate tightens monotonically.  Must be **empty** when the restructuring is finished.
-"""
 
+# Individual callables that are knowingly untested, as ``"<test_file>::<test_name>"``. The fine
+# half of the ledger, for a callable inside an otherwise-finished file that could not be tested.
+# Every entry needs a one-line reason beside it, and this must be **empty** when the
+# restructuring is finished.
 KNOWN_MISSING: set[str] = set()
-"""Individual callables that are knowingly untested, as ``"<test_file>::<test_name>"``.
-
-The fine half of the ledger, for a callable inside an otherwise-finished file that could
-not be tested.  Every entry needs a one-line reason beside it, and this must be **empty**
-when the restructuring is finished.
-"""
 
 
 def _source_modules() -> list[Path]:
@@ -223,4 +217,4 @@ def test_the_ledgers_are_empty():
     """Both ledgers must be empty once the restructuring is finished."""
     if PENDING_FILES or KNOWN_MISSING:
         pytest.xfail(f"{len(PENDING_FILES)} files pending, {len(KNOWN_MISSING)} callables untested")
-    assert (PENDING_FILES, KNOWN_MISSING) == (set(), set())
+    assert (set(), set()) == (PENDING_FILES, KNOWN_MISSING)
diff --git a/raddb/tests/test_discovery.py b/raddb/tests/test_discovery.py
index 3d7dbdd..0d45185 100644
--- a/raddb/tests/test_discovery.py
+++ b/raddb/tests/test_discovery.py
@@ -73,7 +73,7 @@ def test_results_are_sorted_by_filename_timestamp(tmp_path):
     ]
 
 
-def test_unparseable_names_sort_last(tmp_path):
+def test_unparsable_names_sort_last(tmp_path):
     """A file with no timestamp is kept but pushed to the end, never interleaved."""
     (tmp_path / "aaa_no_timestamp.nc").touch()
     (tmp_path / "vol_20240101_000000.nc").touch()
@@ -81,8 +81,8 @@ def test_unparseable_names_sort_last(tmp_path):
     assert [p.name for p in find_datatree_files(tmp_path)][-1] == "aaa_no_timestamp.nc"
 
 
-def test_time_range_filters_and_keeps_unparseable_names(tmp_path):
-    """Out-of-range files drop out; an unparseable name survives by default."""
+def test_time_range_filters_and_keeps_unparsable_names(tmp_path):
+    """Out-of-range files drop out; an unparsable name survives by default."""
     for name in (
         "vol_20240101_000000.nc",
         "vol_20240101_010000.nc",
@@ -96,7 +96,7 @@ def test_time_range_filters_and_keeps_unparseable_names(tmp_path):
     assert [p.name for p in found] == ["vol_20240101_010000.nc", "no_timestamp_here.nc"]
 
 
-def test_strict_time_drops_unparseable_names(tmp_path):
+def test_strict_time_drops_unparsable_names(tmp_path):
     """``strict_time=True`` refuses to guess: no timestamp, no file."""
     (tmp_path / "vol_20240101_010000.nc").touch()
     (tmp_path / "no_timestamp_here.nc").touch()
@@ -243,23 +243,27 @@ def test_parse_volume_time_reads_a_metranet_stem():
     """``XXXYYJJJHHMM...``: 3-char prefix, 2-digit year, day-of-year, hour, minute."""
     # MLA 24 194 23 30 -> 2024, day 194, 23:30
     assert _parse_volume_time("MLA2419423300U") == datetime.datetime(2024, 1, 1) + datetime.timedelta(
-        days=193, hours=23, minutes=30
+        days=193,
+        hours=23,
+        minutes=30,
     )
     # HZT 21 240 10 00 -> 2021, day 240, 10:00
     assert _parse_volume_time("HZT2124010000L") == datetime.datetime(2021, 1, 1) + datetime.timedelta(
-        days=239, hours=10, minutes=0
+        days=239,
+        hours=10,
+        minutes=0,
     )
 
 
 def test_parse_volume_time_falls_back_to_the_epoch():
-    """An unparseable stem sorts first rather than raising mid-scan."""
+    """An unparsable stem sorts first rather than raising mid-scan."""
     assert _parse_volume_time("garbage") == datetime.datetime(1970, 1, 1)
 
 
 def test_group_files_by_volume():
     """Sweep files sharing a filename stem belong to one volume."""
     grouped = _group_files_by_volume(
-        ["/a/MLA2419423300U.001", "/b/MLA2419423300U.002", "/a/MLA2419423305U.001"]
+        ["/a/MLA2419423300U.001", "/b/MLA2419423300U.002", "/a/MLA2419423305U.001"],
     )
 
     assert sorted(grouped) == ["MLA2419423300U", "MLA2419423305U"]
diff --git a/raddb/tests/test_hc_mapping.py b/raddb/tests/test_hc_mapping.py
index 4eb97d3..ff8c1b7 100644
--- a/raddb/tests/test_hc_mapping.py
+++ b/raddb/tests/test_hc_mapping.py
@@ -16,8 +16,8 @@
     PYART_TO_OPE,
 )
 
+# Operational classes 0-8, stored in parquet as 1-9.
 N_CLASSES = 9
-"""Operational classes 0-8, stored in parquet as 1-9."""
 
 
 def test_hc_map_dict_is_a_contiguous_zero_based_range():
@@ -28,7 +28,7 @@ def test_hc_map_dict_is_a_contiguous_zero_based_range():
 
 def test_hc_classes_is_hc_map_dict_in_order():
     """``HC_CLASSES[i]`` is ``HC_MAP_DICT[i]``; index 0 corresponds to parquet 1."""
-    assert HC_CLASSES == [HC_MAP_DICT[k] for k in range(N_CLASSES)]
+    assert [HC_MAP_DICT[k] for k in range(N_CLASSES)] == HC_CLASSES
     assert len(HC_CLASSES) == N_CLASSES
 
 
@@ -53,7 +53,7 @@ def test_hc_colors_are_recognised_by_matplotlib():
 
 def test_hc_color_by_label_matches_the_two_lists():
     """The convenience lookup is exactly ``zip(HC_CLASSES, HC_COLORS)``."""
-    assert HC_COLOR_BY_LABEL == dict(zip(HC_CLASSES, HC_COLORS))
+    assert dict(zip(HC_CLASSES, HC_COLORS, strict=False)) == HC_COLOR_BY_LABEL
     assert len(HC_COLOR_BY_LABEL) == N_CLASSES
 
 
diff --git a/raddb/tests/test_helper.py b/raddb/tests/test_helper.py
index 4ccd97f..54a1f89 100644
--- a/raddb/tests/test_helper.py
+++ b/raddb/tests/test_helper.py
@@ -55,7 +55,7 @@ def test_list_sweep_names_ignores_other_groups():
             "sweep_1": xr.Dataset({"DBZH": ("range", [1.0])}),
             "radar_parameters": xr.Dataset({"beamwidth": 1.0}),
             "georeferencing_correction": xr.Dataset({"dx": 0.0}),
-        }
+        },
     )
 
     assert list_sweep_names(dt) == ["sweep_1"]
@@ -138,8 +138,10 @@ def test_check_dataframe(capsys):
     check_dataframe(pl.DataFrame({"DBZH": [1.0, None], "gate_id": [1, 2]}))
 
     out = capsys.readouterr().out
-    assert "Shape:" in out and "(2, 2)" in out
-    assert "DBZH" in out and "Missing values:" in out
+    assert "Shape:" in out
+    assert "(2, 2)" in out
+    assert "DBZH" in out
+    assert "Missing values:" in out
 
 
 def test_check_dataframe_accepts_pandas(capsys):
@@ -240,7 +242,14 @@ def test_the_alphabet_and_length_match_the_gate_id_layout():
 
 @pytest.mark.parametrize(
     ("logic", "a", "b", "expected"),
-    [("==", 1, 1, True), ("!=", 1, 2, True), (">", 2, 1, True), (">=", 1, 1, True), ("<", 1, 2, True), ("<=", 1, 1, True)],
+    [
+        ("==", 1, 1, True),
+        ("!=", 1, 2, True),
+        (">", 2, 1, True),
+        (">=", 1, 1, True),
+        ("<", 1, 2, True),
+        ("<=", 1, 1, True),
+    ],
 )
 def test_resolve_filter_logic(logic, a, b, expected):
     """All six operators resolve to the comparison they spell."""
@@ -477,7 +486,8 @@ def test_StageTimer_print_summary(capsys):
 
     out = capsys.readouterr().out
     assert "PIPELINE PROFILING SUMMARY" in out
-    assert "archive_volume" in out and "TOTAL" in out
+    assert "archive_volume" in out
+    assert "TOTAL" in out
     assert "75.0%" in out
 
 
@@ -494,16 +504,14 @@ def test_timer_accumulates_across_a_batch(tmp_path, make_datatree):
     from raddb.lut import generate_lut_from_datatree
 
     timer = StageTimer()
-    volumes = {
-        f"vol_{i:03d}": make_datatree(vol_time=pd.Timestamp(f"2024-08-01 19:0{i}:00")) for i in range(3)
-    }
+    volumes = {f"vol_{i:03d}": make_datatree(vol_time=pd.Timestamp(f"2024-08-01 19:0{i}:00")) for i in range(3)}
     generate_lut_from_datatree(volumes["vol_000"], radar="A", output_base_path=str(tmp_path), projection_epsg=2056)
 
     archive_multiple_volumes(volumes, radar="A", base_output_path=str(tmp_path), timer=timer, verbose=False)
 
     df = timer.to_dataframe()
     assert len(df) >= 3
-    assert "archive_volume" in df["stage"].values
+    assert "archive_volume" in df["stage"].to_numpy()
 
 
 # ---------------------------------------------------------------------------
@@ -524,7 +532,8 @@ def test_vprint_timestamps_its_output(capsys):
 
     out = capsys.readouterr().out
     assert "hello" in out
-    assert out.startswith("[") and out.count(":") == 2
+    assert out.startswith("[")
+    assert out.count(":") == 2
 
 
 def test_read_parquet_files_accepts_a_path_object(archive_dir):
diff --git a/raddb/tests/test_io_core.py b/raddb/tests/test_io_core.py
index a8ea748..714a3d0 100644
--- a/raddb/tests/test_io_core.py
+++ b/raddb/tests/test_io_core.py
@@ -53,8 +53,8 @@
 from raddb.main import RadDB
 from raddb.tests.conftest import MCH_BIAS, RADAR, SWISS_EPSG, build_datatree, retime
 
+# A window wide enough to hold every synthetic volume in this module.
 TIME_WINDOW = ("2024-08-01 00:00", "2024-08-02 00:00")
-"""A window wide enough to hold every synthetic volume in this module."""
 
 
 @pytest.fixture
@@ -95,7 +95,8 @@ def test_open_any_datatree(tmp_path, datatree):
 
     assert sorted(g.lstrip("/") for g in loaded.groups if "sweep" in g) == ["sweep_1", "sweep_2"]
     np.testing.assert_allclose(
-        loaded["sweep_1"].to_dataset()["DBZH"].values, datatree["sweep_1"].to_dataset()["DBZH"].values
+        loaded["sweep_1"].to_dataset()["DBZH"].values,
+        datatree["sweep_1"].to_dataset()["DBZH"].values,
     )
 
 
@@ -108,7 +109,8 @@ def test_open_any_datatree_reads_zarr(tmp_path, datatree):
     loaded = open_any_datatree(path)
 
     np.testing.assert_allclose(
-        loaded["sweep_1"].to_dataset()["DBZH"].values, datatree["sweep_1"].to_dataset()["DBZH"].values
+        loaded["sweep_1"].to_dataset()["DBZH"].values,
+        datatree["sweep_1"].to_dataset()["DBZH"].values,
     )
 
 
@@ -165,7 +167,8 @@ def test_archive_volume(lut_base, make_datatree):
     """One volume becomes one POL parquet, whose path is returned."""
     path = archive_volume(make_datatree(), radar=RADAR, base_output_path=lut_base)
 
-    assert path is not None and path.endswith("_POL.parquet")
+    assert path is not None
+    assert path.endswith("_POL.parquet")
     assert "DBZH" in pl.read_parquet(path).columns
 
 
@@ -203,9 +206,7 @@ def test_archive_volume_records_timings(lut_base, make_datatree):
 
 def test_archive_multiple_volumes(lut_base, make_datatree):
     """A list or dict of volumes archives sequentially and reports one record each."""
-    volumes = {
-        f"vol_{i}": make_datatree(vol_time=pd.Timestamp(f"2024-08-01 12:0{i}:00")) for i in range(3)
-    }
+    volumes = {f"vol_{i}": make_datatree(vol_time=pd.Timestamp(f"2024-08-01 12:0{i}:00")) for i in range(3)}
 
     records = archive_multiple_volumes(volumes, radar=RADAR, base_output_path=lut_base, verbose=False)
 
@@ -226,11 +227,16 @@ def test_archive_volumes_multi_radar(tmp_path, make_datatree):
     """The ``{radar: [volumes]}`` form keeps each radar's records separate."""
     for radar in ("A", "D"):
         generate_lut_from_datatree(
-            make_datatree(), radar=radar, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG
+            make_datatree(),
+            radar=radar,
+            output_base_path=str(tmp_path),
+            projection_epsg=SWISS_EPSG,
         )
 
     results = archive_volumes_multi_radar(
-        {"A": [make_datatree()], "D": [make_datatree()]}, base_output_path=str(tmp_path), verbose=False
+        {"A": [make_datatree()], "D": [make_datatree()]},
+        base_output_path=str(tmp_path),
+        verbose=False,
     )
 
     assert sorted(results) == ["A", "D"]
@@ -334,7 +340,10 @@ def test_snap_volume_azimuths_refuses_more_rays_than_the_grid():
 def test_snap_volume_azimuths_accepts_fewer_rays_than_the_grid():
     """A rotation with holes: each surviving ray still snaps to its own grid point."""
     snapped, _ = _snap_volume_azimuths(
-        np.ones(3, dtype=np.int64), np.array([0.53, 1.47, 2.51]), {1: np.arange(5, 3600, 10)}, RADAR
+        np.ones(3, dtype=np.int64),
+        np.array([0.53, 1.47, 2.51]),
+        {1: np.arange(5, 3600, 10)},
+        RADAR,
     )
 
     assert snapped.size == 3
@@ -382,7 +391,7 @@ def test_archiving_accepts_a_volume_that_dropped_rays(tmp_path):
         {
             name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
             for name, node in drifted.children.items()
-        }
+        },
     )
 
     result = db.archive(datatree=holed, radar=RADAR)
@@ -397,7 +406,7 @@ def test_a_lut_built_from_a_holed_volume_still_holds_every_ray(tmp_path):
         {
             name: node.to_dataset().isel(azimuth=np.delete(np.arange(360), [5, 6, 200]))
             for name, node in complete.children.items()
-        }
+        },
     )
     db = RadDB(archive_dir=str(tmp_path / "a"), crs=SWISS_EPSG)
     db.archive(datatree=holed, radar=RADAR)
@@ -442,7 +451,7 @@ def test_building_without_a_grid_warns(caplog):
             "range": [1000.0, 1000.0],
             "DBZH": [10.0, 20.0],
             "time": [pd.Timestamp("2024-01-01")] * 2,
-        }
+        },
     )
 
     with caplog.at_level("WARNING"):
@@ -590,7 +599,9 @@ def test_labels_to_dataframe():
 def test_labels_to_dataframe_carries_extra_columns():
     """``extra_columns=`` rides along, e.g. a per-gate confidence."""
     df = labels_to_dataframe(
-        np.array([1, 2]), np.array([10, 20], dtype=np.int64), extra_columns={"confidence": np.array([0.9, 0.8])}
+        np.array([1, 2]),
+        np.array([10, 20], dtype=np.int64),
+        extra_columns={"confidence": np.array([0.9, 0.8])},
     )
 
     assert "confidence" in df.columns
@@ -662,7 +673,7 @@ def test_to_polars_frame_and_back():
 
 
 def test_col_reads_from_either_kind():
-    """polars' ``Series.to_numpy()`` takes no ``dtype``, unlike pandas'."""
+    """Polars' ``Series.to_numpy()`` takes no ``dtype``, unlike pandas'."""
     for df in (pl.DataFrame({"a": [1, 2]}), pd.DataFrame({"a": [1, 2]})):
         out = _col(df, "a", np.float64)
         assert out.dtype == np.float64
diff --git a/raddb/tests/test_lut.py b/raddb/tests/test_lut.py
index 290bb0d..9d76c8c 100644
--- a/raddb/tests/test_lut.py
+++ b/raddb/tests/test_lut.py
@@ -34,7 +34,6 @@
 from raddb.helper import normalize_radar_name
 from raddb.lut import (
     AZIMUTH_SCALE,
-    RADAR_ALPHABET,
     AZIMUTH_STEPS,
     CRS_REFUSE_PCT,
     DEFAULT_BEAMWIDTH_DEG,
@@ -42,6 +41,7 @@
     LEGACY_RADAR_TO_IDX,
     LUT_FILES,
     MAX_RADAR_CODE,
+    RADAR_ALPHABET,
     RADAR_CODE_LEN,
     _round_half_up,
     add_lut_projection,
@@ -91,8 +91,8 @@
     retime,
 )
 
+# The synthetic fixture's own site — ``(longitude, latitude)``.
 CH_SITE = (7.0, 46.0)
-"""The synthetic fixture's own site — ``(longitude, latitude)``."""
 
 N_AZ, N_RNG, N_SWEEPS = 12, 24, 2
 N_GATES = N_AZ * N_RNG * N_SWEEPS
@@ -105,7 +105,10 @@
 def lut_base(tmp_path, make_datatree):
     """A base path holding radar ``A``'s complete five-file LUT directory."""
     generate_lut_from_datatree(
-        make_datatree(), radar=RADAR, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG
+        make_datatree(),
+        radar=RADAR,
+        output_base_path=str(tmp_path),
+        projection_epsg=SWISS_EPSG,
     )
     return tmp_path
 
@@ -135,7 +138,8 @@ def _face_area(table: pl.DataFrame, corners) -> np.ndarray:
     pts = np.stack(
         [
             np.stack(
-                [table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy(), table[f"z_rel_{k}"].to_numpy()], axis=1
+                [table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy(), table[f"z_rel_{k}"].to_numpy()],
+                axis=1,
             )
             for k in corners
         ],
@@ -362,7 +366,7 @@ def test_decode_gate_radars_spans_a_concatenated_archive():
         [
             encode_gate_ids("A", np.array([1]), np.array([0.5]), np.array([1000.0])),
             encode_gate_ids("KTLX", np.array([1]), np.array([0.5]), np.array([1000.0])),
-        ]
+        ],
     )
 
     assert sorted(decode_gate_radars(ids)) == ["A", "KTLX"]
@@ -592,9 +596,11 @@ def test_load_azimuth_grids(tmp_path):
 
     grids = load_azimuth_grids(RADAR, tmp_path)
 
-    assert grids is not None and set(grids) == {1, 2, 3}
+    assert grids is not None
+    assert set(grids) == {1, 2, 3}
     for grid in grids.values():
-        assert grid.size == 360 and np.all(np.diff(grid) == 10)
+        assert grid.size == 360
+        assert np.all(np.diff(grid) == 10)
 
     info = yaml.safe_load((tmp_path / RADAR / "LUT" / f"{RADAR}_info.yaml").read_text())
     assert "azimuths" not in info["sweeps"][1]
@@ -616,7 +622,10 @@ def test_the_lut_azimuth_column_holds_the_nominal_grid(tmp_path):
         MCH_BIAS,
     )
     generate_lut_from_datatree(
-        drifted, radar=RADAR, output_base_path=str(tmp_path), projection_epsg=SWISS_EPSG
+        drifted,
+        radar=RADAR,
+        output_base_path=str(tmp_path),
+        projection_epsg=SWISS_EPSG,
     )
 
     lut = load_radar_lut(RADAR, tmp_path)
@@ -637,8 +646,10 @@ def test_antenna_vectors_to_cartesian():
     x_n, y_n, _ = antenna_vectors_to_cartesian(ranges, np.array([0.0]), np.array([0.0]))
     x_e, y_e, _ = antenna_vectors_to_cartesian(ranges, np.array([90.0]), np.array([0.0]))
 
-    assert abs(x_n[0]) < 1.0 and y_n[0] == pytest.approx(10_000.0, rel=1e-3)
-    assert x_e[0] == pytest.approx(10_000.0, rel=1e-3) and abs(y_e[0]) < 1.0
+    assert abs(x_n[0]) < 1.0
+    assert y_n[0] == pytest.approx(10_000.0, rel=1e-3)
+    assert x_e[0] == pytest.approx(10_000.0, rel=1e-3)
+    assert abs(y_e[0]) < 1.0
 
 
 def test_the_beam_rises_with_elevation_and_earth_curvature():
@@ -674,7 +685,12 @@ def test_cartesian_to_geographic():
     ``(longitude, latitude)`` argument order used everywhere a *site* is named.
     """
     lat, lon, alt = cartesian_to_geographic(
-        np.array([0.0]), np.array([0.0]), np.array([0.0]), CH_SITE[1], CH_SITE[0], 1000.0
+        np.array([0.0]),
+        np.array([0.0]),
+        np.array([0.0]),
+        CH_SITE[1],
+        CH_SITE[0],
+        1000.0,
     )
 
     assert lon[0] == pytest.approx(CH_SITE[0], abs=1e-6)
@@ -685,7 +701,12 @@ def test_cartesian_to_geographic():
 def test_geographic_conversion_moves_north_for_positive_y():
     """A 10 km northward offset raises the latitude by ~0.09 degrees."""
     lat, _, _ = cartesian_to_geographic(
-        np.array([0.0]), np.array([10_000.0]), np.array([0.0]), CH_SITE[1], CH_SITE[0], 0.0
+        np.array([0.0]),
+        np.array([10_000.0]),
+        np.array([0.0]),
+        CH_SITE[1],
+        CH_SITE[0],
+        0.0,
     )
 
     assert lat[0] > CH_SITE[1]
@@ -726,7 +747,10 @@ def test_regenerating_backfills_the_lattices_without_rewriting_the_centroids(lut
         (lut_dir / LUT_FILES[kind].format(radar=RADAR)).unlink()
 
     generate_lut_from_datatree(
-        make_datatree(), radar=RADAR, output_base_path=str(lut_base), projection_epsg=SWISS_EPSG
+        make_datatree(),
+        radar=RADAR,
+        output_base_path=str(lut_base),
+        projection_epsg=SWISS_EPSG,
     )
 
     for kind in ("h_plane", "v_plane", "corners"):
@@ -738,7 +762,19 @@ def test_the_info_yaml_records_the_generation_parameters(lut_base):
     """Everything needed to reproduce the geometry, and nothing that went stale."""
     info = load_radar_info(RADAR, lut_base)
 
-    for key in ("radar", "network", "latitude", "longitude", "altitude", "crs", "ke", "beamwidth_deg", "n_sweeps", "n_gates", "sweeps"):
+    for key in (
+        "radar",
+        "network",
+        "latitude",
+        "longitude",
+        "altitude",
+        "crs",
+        "ke",
+        "beamwidth_deg",
+        "n_sweeps",
+        "n_gates",
+        "sweeps",
+    ):
         assert key in info, f"missing info key {key!r}"
     assert info["ke"] == pytest.approx(4.0 / 3.0)
     assert info["beamwidth_deg"] == DEFAULT_BEAMWIDTH_DEG
@@ -764,7 +800,7 @@ def test_generate_lut_accepts_a_projection_crs_object(tmp_path, make_datatree):
     """A pyproj CRS works where an EPSG int is not available."""
     crs = pyproj.CRS.from_proj4(
         "+proj=somerc +lat_0=46.9524056 +lon_0=7.4395833 +k_0=1 +x_0=2600000 +y_0=1200000 "
-        "+ellps=bessel +towgs84=674.374,15.056,405.346,0,0,0,0 +units=m +no_defs"
+        "+ellps=bessel +towgs84=674.374,15.056,405.346,0,0,0,0 +units=m +no_defs",
     )
 
     generate_lut_from_datatree(make_datatree(), radar=RADAR, output_base_path=str(tmp_path), projection_crs=crs)
@@ -777,7 +813,11 @@ def test_generate_lut_accepts_a_projection_crs_object(tmp_path, make_datatree):
 def test_generate_lut_records_an_explicit_beamwidth(tmp_path, make_datatree):
     """The one place beamwidth may be set; no plot takes it."""
     generate_lut_from_datatree(
-        make_datatree(), radar=RADAR, output_base_path=str(tmp_path), beamwidth_deg=1.5, projection_epsg=SWISS_EPSG
+        make_datatree(),
+        radar=RADAR,
+        output_base_path=str(tmp_path),
+        beamwidth_deg=1.5,
+        projection_epsg=SWISS_EPSG,
     )
 
     assert load_radar_info(RADAR, tmp_path)["beamwidth_deg"] == pytest.approx(1.5)
@@ -902,7 +942,8 @@ def test_the_frustum_ratio_stays_physically_sane(lut_base):
     keep = near > 1.0  # drop the r~0 near face
 
     ratio = far[keep] / near[keep]
-    assert 1.0 < ratio.min() and ratio.max() < 100.0
+    assert ratio.min() > 1.0
+    assert ratio.max() < 100.0
 
 
 def test_the_first_range_bin_is_degenerate(lut_base):
@@ -911,7 +952,8 @@ def test_the_first_range_bin_is_degenerate(lut_base):
     pts = np.stack(
         [
             np.stack(
-                [table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy(), table[f"z_rel_{k}"].to_numpy()], axis=1
+                [table[f"x_{k}"].to_numpy(), table[f"y_{k}"].to_numpy(), table[f"z_rel_{k}"].to_numpy()],
+                axis=1,
             )
             for k in range(1, 9)
         ],
@@ -1029,7 +1071,8 @@ def test_compute_sweep_corners():
     """The per-sweep node mesh the three lattices are built from."""
     corners = _sweep_corners()
 
-    assert isinstance(corners, dict) and corners
+    assert isinstance(corners, dict)
+    assert corners
     # The mesh is one node lattice per elevation level, so it is (n_az+1) x (n_rng+1).
     assert any(np.asarray(v).size == (N_AZ + 1) * (N_RNG + 1) for v in corners.values() if np.ndim(v))
 
@@ -1273,7 +1316,8 @@ def test_an_area_of_use_check_alone_would_pass_web_mercator():
     """EPSG:3857 declares the whole world, so a bounds check lets it through."""
     area = pyproj.CRS.from_epsg(3857).area_of_use
 
-    assert area.west <= CH_SITE[0] <= area.east and area.south <= CH_SITE[1] <= area.north
+    assert area.west <= CH_SITE[0] <= area.east
+    assert area.south <= CH_SITE[1] <= area.north
     assert crs_distance_error(3857, *CH_SITE) > 10.0
 
 
diff --git a/raddb/tests/test_main.py b/raddb/tests/test_main.py
index 9fa73b3..c770308 100644
--- a/raddb/tests/test_main.py
+++ b/raddb/tests/test_main.py
@@ -31,8 +31,8 @@
 from raddb.main import RadDB, _format_elapsed_time, _format_size, _iter_days, _normalize_time_period
 from raddb.tests.conftest import RADAR, SWISS_EPSG, US_EPSG, US_SITE, build_datatree, relocate
 
+# The synthetic fixture's site — ``(longitude, latitude)``.
 CH_SITE = (7.0, 46.0)
-"""The synthetic fixture's site — ``(longitude, latitude)``."""
 
 VOL_TIMES = [pd.Timestamp("2024-08-01 12:00:00"), pd.Timestamp("2024-08-02 06:30:00")]
 
@@ -48,8 +48,8 @@
     "azimuth",
     "range",
     "elevation_angle",
+    # Columns that belong to the static LUT and must never appear in a dynamic frame.
 }
-"""Columns that belong to the static LUT and must never appear in a dynamic frame."""
 
 
 @pytest.fixture
@@ -171,7 +171,9 @@ def test_archive_resumes_from_its_checkpoint(tmp_path, datatree_dir):
 def test_archive_honours_a_time_period(tmp_path, datatree_dir):
     """Only volumes whose filename timestamp falls in the window are read."""
     result = RadDB(archive_dir=str(tmp_path / "arch"), crs=SWISS_EPSG).archive(
-        datatree_dir=datatree_dir, radar=RADAR, time_period=("2024-08-01 00:00", "2024-08-01 23:59")
+        datatree_dir=datatree_dir,
+        radar=RADAR,
+        time_period=("2024-08-01 00:00", "2024-08-01 23:59"),
     )
 
     assert result["n_archived"] == 1
@@ -276,7 +278,7 @@ def test_the_three_counts_sum_to_the_volumes_attempted(tmp_path, make_datatree):
 def test_archive_accepts_a_radar_keyed_dict(tmp_path, make_datatree):
     """The multi-radar form, ``{radar: [volumes]}``."""
     result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(
-        datatree={"A": [make_datatree()], "D": [make_datatree()]}
+        datatree={"A": [make_datatree()], "D": [make_datatree()]},
     )
 
     assert sorted(result["radars"]) == ["A", "D"]
@@ -392,7 +394,8 @@ def test_RadDB_export_h_plane_geoparquet(db, tmp_path):
 
     gdf = gpd.read_parquet(out)
     assert len(gdf) == 12 * 24 * 2
-    assert gdf.crs is not None and gdf.crs.to_epsg() == SWISS_EPSG
+    assert gdf.crs is not None
+    assert gdf.crs.to_epsg() == SWISS_EPSG
     assert gdf.geometry.is_valid.all()
     # Corner order is clockwise in storage; GeoParquet prefers counter-clockwise.
     assert shapely.is_ccw(shapely.get_exterior_ring(gdf.geometry.values)).all()
@@ -440,9 +443,11 @@ def test_inventory_detailed_adds_lut_columns_and_days(two_volume_rdb, capsys):
     RadDB(archive_dir=str(two_volume_rdb.archive_dir)).inventory(detailed=True)
 
     out = capsys.readouterr().out
-    assert "LUT:" in out and "sweeps" in out
+    assert "LUT:" in out
+    assert "sweeps" in out
     assert "DBZH" in out
-    assert "2024-08-01" in out and "2024-08-02" in out
+    assert "2024-08-01" in out
+    assert "2024-08-02" in out
 
 
 def test_inventory_on_an_empty_archive(tmp_path, capsys):
@@ -492,7 +497,8 @@ def test_inventory_warns_about_an_unusable_radar_name(tmp_path, datatree, capsys
     RadDB().inventory(datatree_dir=str(directory), detailed=True)
 
     out = capsys.readouterr().out
-    assert "OVERLONG" in out and "not a usable radar name" in out
+    assert "OVERLONG" in out
+    assert "not a usable radar name" in out
 
 
 def test_inventory_of_a_missing_directory_raises(tmp_path):
@@ -617,7 +623,8 @@ def test_RadDB_sel(rdb):
     values = out.data["DBZH"].to_numpy()
 
     assert len(out) > 0
-    assert values.min() >= 5.0 and values.max() <= 15.0
+    assert values.min() >= 5.0
+    assert values.max() <= 15.0
 
 
 def test_open_ended_slices_match_filter(rdb):
@@ -634,7 +641,7 @@ def test_sel_with_no_arguments_is_a_no_op(rdb):
 def test_sel_keywords_are_anded(rdb):
     """Two indexers in one call equal two chained calls."""
     assert len(rdb.sel(DBZH=slice(10, None), ZDR=slice(None, 5))) == len(
-        rdb.sel(DBZH=slice(10, None)).sel(ZDR=slice(None, 5))
+        rdb.sel(DBZH=slice(10, None)).sel(ZDR=slice(None, 5)),
     )
 
 
@@ -824,7 +831,8 @@ def test_to_geoarrow_polygons_are_closed_wedges(rdb):
 
     assert table.schema.field("geometry").metadata[b"ARROW:extension:name"] == b"geoarrow.polygon"
     rings = table.column("geometry").to_pylist()
-    assert rings and all(r is not None for r in rings), "every gate should be placed"
+    assert rings
+    assert all(r is not None for r in rings), "every gate should be placed"
     for ring in rings:
         assert len(ring) == 1, "a gate is a single ring"
         assert len(ring[0]) == 5, "4 corners + closing vertex"
@@ -840,7 +848,9 @@ def test_to_geoarrow_wedges_surround_their_own_centroid(rdb, db):
     assert shapely.is_valid(polygons).all()
 
     reference = pl.DataFrame({"gate_id": table.column("gate_id").to_numpy()}).join(
-        db.get_lut(RADAR).select("gate_id", "latitude", "longitude"), on="gate_id", how="left"
+        db.get_lut(RADAR).select("gate_id", "latitude", "longitude"),
+        on="gate_id",
+        how="left",
     )
     centres = shapely.centroid(polygons)
     dx = (shapely.get_x(centres) - reference["longitude"].to_numpy()) * 111_320 * np.cos(np.radians(46.0))
@@ -929,7 +939,8 @@ def test_RadDB_extent(rdb):
     extent = rdb.extent()
 
     assert len(extent) == 4
-    assert extent[0] < extent[1] and extent[2] < extent[3]
+    assert extent[0] < extent[1]
+    assert extent[2] < extent[3]
     assert 2.4e6 < extent[0] < 2.9e6
 
 
@@ -1045,7 +1056,10 @@ def test_a_crop_radius_is_true_metres_outside_switzerland(us_archive_dir):
     lut = db.get_lut(RADAR)
     lon, lat = lut["longitude"].to_numpy(), lut["latitude"].to_numpy()
     _, _, ground = pyproj.Geod(ellps="WGS84").inv(
-        np.full(lon.size, US_SITE[0]), np.full(lat.size, US_SITE[1]), lon, lat
+        np.full(lon.size, US_SITE[0]),
+        np.full(lat.size, US_SITE[1]),
+        lon,
+        lat,
     )
     truth = int((ground <= 10_000).sum())
 
@@ -1117,7 +1131,10 @@ def test_a_cross_section_quicklook_is_framed_on_the_archive(us_archive_dir):
 
     rdf = RadDB(archive_dir=str(us_archive_dir)).open(radars=RADAR)
     rdf.extract_cross_section(
-        p1=(US_SITE[0] - 0.2, US_SITE[1]), p2=(US_SITE[0] + 0.2, US_SITE[1]), crs=4326, quicklook=True
+        p1=(US_SITE[0] - 0.2, US_SITE[1]),
+        p2=(US_SITE[0] + 0.2, US_SITE[1]),
+        crs=4326,
+        quicklook=True,
     )
 
     ax = plt.gcf().axes[0]
@@ -1158,7 +1175,8 @@ def test_RadDB_plot_vcs(plot_rdb, plot_site):
 def test_RadDB_plot_cross_section(plot_rdb, plot_site):
     """The deprecated alias: it warns and delegates to ``plot_vcs``."""
     cs = plot_rdb.extract_cross_section(
-        (plot_site[0] - 12_000, plot_site[1] - 12_000), (plot_site[0] + 12_000, plot_site[1] + 12_000)
+        (plot_site[0] - 12_000, plot_site[1] - 12_000),
+        (plot_site[0] + 12_000, plot_site[1] + 12_000),
     )
 
     with pytest.deprecated_call():
@@ -1204,7 +1222,8 @@ def test_normalize_time_period():
     """A ``(start, end)`` pair becomes tz-aware UTC timestamps."""
     start, end = _normalize_time_period(("2024-08-01", "2024-08-02"))
 
-    assert start.tzinfo is not None and end.tzinfo is not None
+    assert start.tzinfo is not None
+    assert end.tzinfo is not None
     assert start < end
 
 
@@ -1274,7 +1293,7 @@ def test_the_multi_radar_path_counts_a_skip_separately(tmp_path, make_datatree):
         empty[name].dataset = ds
 
     result = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG).archive(
-        datatree={RADAR: [make_datatree(vol_time=VOL_TIMES[0]), empty], "D": [make_datatree()]}
+        datatree={RADAR: [make_datatree(vol_time=VOL_TIMES[0]), empty], "D": [make_datatree()]},
     )
 
     assert (result["n_archived"], result["n_failed"], result["n_skipped"]) == (2, 0, 1)
diff --git a/raddb/tests/test_package_api.py b/raddb/tests/test_package_api.py
index e541f12..a7082b9 100644
--- a/raddb/tests/test_package_api.py
+++ b/raddb/tests/test_package_api.py
@@ -33,7 +33,7 @@ def test_all_has_no_duplicates():
 def test_star_import_matches_all():
     """``from raddb import *`` exposes exactly ``__all__`` and nothing more."""
     namespace: dict = {}
-    exec("from raddb import *", namespace)  # noqa: S102 - the behaviour under test
+    exec("from raddb import *", namespace)  # - the behaviour under test
     namespace.pop("__builtins__", None)
     assert sorted(namespace) == sorted(raddb.__all__)
 
@@ -79,13 +79,16 @@ def test_the_private_mch_subpackage_is_not_imported():
 
 
 def test_lonboard_is_not_imported_at_module_level():
-    """lonboard pulls in pyproj; importing it eagerly defeats the PROJ repair."""
+    """Lonboard pulls in pyproj; importing it eagerly defeats the PROJ repair."""
     import sys
 
     assert "lonboard" not in sys.modules or "raddb" in sys.modules
 
 
-@pytest.mark.parametrize("name", ["RadDB", "plot_ppi", "generate_lut_from_datatree", "filter_df", "find_datatree_files"])
+@pytest.mark.parametrize(
+    "name",
+    ["RadDB", "plot_ppi", "generate_lut_from_datatree", "filter_df", "find_datatree_files"],
+)
 def test_representative_exports_are_callable(name):
     """A spot check that the re-exports are the real objects, not stubs."""
     assert callable(getattr(raddb, name))
diff --git a/raddb/tests/test_viz_init.py b/raddb/tests/test_viz_init.py
index f7f193e..ef90588 100644
--- a/raddb/tests/test_viz_init.py
+++ b/raddb/tests/test_viz_init.py
@@ -52,7 +52,7 @@ def test_importing_the_subpackage_does_not_pull_in_ipyleaflet():
 
 
 def test_lonboard_is_not_imported_eagerly():
-    """lonboard caches a broken pyproj context if it imports before ``raddb._proj``."""
+    """Lonboard caches a broken pyproj context if it imports before ``raddb._proj``."""
     import ast
     from pathlib import Path
 
diff --git a/raddb/tests/test_viz_interactive.py b/raddb/tests/test_viz_interactive.py
index 2d4ed3d..cd9863b 100644
--- a/raddb/tests/test_viz_interactive.py
+++ b/raddb/tests/test_viz_interactive.py
@@ -26,8 +26,8 @@
 pytest.importorskip("ipyleaflet")
 pytest.importorskip("ipywidgets")
 
+# The synthetic fixture's site — ``(longitude, latitude)``, as the map reports it.
 CH_SITE = (7.0, 46.0)
-"""The synthetic fixture's site — ``(longitude, latitude)``, as the map reports it."""
 
 
 def _feature(geometry: dict) -> dict:
@@ -139,10 +139,14 @@ def test_an_unsupported_geometry_is_refused(rdb):
 def test_the_drawn_coordinates_are_read_as_lonlat(rdb):
     """A marker at the radar site must land on the radar, not 2600 km away."""
     _, at_site = _crop_from_feature(
-        rdb, _feature({"type": "Point", "coordinates": list(CH_SITE)}), distance_m=8_000
+        rdb,
+        _feature({"type": "Point", "coordinates": list(CH_SITE)}),
+        distance_m=8_000,
     )
     _, elsewhere = _crop_from_feature(
-        rdb, _feature({"type": "Point", "coordinates": [0.0, 0.0]}), distance_m=8_000
+        rdb,
+        _feature({"type": "Point", "coordinates": [0.0, 0.0]}),
+        distance_m=8_000,
     )
 
     assert len(at_site) > 0
diff --git a/raddb/tests/test_viz_plot.py b/raddb/tests/test_viz_plot.py
index f453326..7523b4c 100644
--- a/raddb/tests/test_viz_plot.py
+++ b/raddb/tests/test_viz_plot.py
@@ -40,8 +40,8 @@
     build_datatree,
 )
 from raddb.viz.plot import (
-    _KmFormatter,
     _beamwidth,
+    _KmFormatter,
     _line_endpoints,
     _resolve_chord_overlap,
     plot_aoi_quicklook,
@@ -112,7 +112,7 @@ def test_a_multi_panel_figure_composes(plot_rdb):
     """One plot per Axes — the user builds the panel, not the plot function."""
     _, axes = plt.subplots(2, 2)
 
-    for ax, variable in zip(axes.ravel(), ["DBZH", "ZDR", "RHOHV", "PHIDP"]):
+    for ax, variable in zip(axes.ravel(), ["DBZH", "ZDR", "RHOHV", "PHIDP"], strict=False):
         plot_ppi(plot_rdb, sweep=1, variable=variable, ax=ax)
 
     assert all(len(ax.collections) == 1 for ax in axes.ravel())
@@ -124,7 +124,8 @@ def test_save_writes_a_file(plot_rdb, tmp_path):
 
     plot_ppi(plot_rdb, sweep=1, save=str(out))
 
-    assert out.exists() and out.stat().st_size > 0
+    assert out.exists()
+    assert out.stat().st_size > 0
 
 
 def test_the_title_and_colorbar_are_optional(plot_rdb):
@@ -248,7 +249,7 @@ def test_rhi_picks_a_ray_per_sweep_when_azimuths_jitter(tmp_path):
     jittered = {}
     for i, (name, node) in enumerate(dt.children.items()):
         ds = node.to_dataset()
-        jittered[name] = ds.assign_coords(azimuth=ds["azimuth"].values + 0.13 * i)
+        jittered[name] = ds.assign_coords(azimuth=ds["azimuth"].to_numpy() + 0.13 * i)
     dt = xr.DataTree.from_dict(jittered)
 
     db = RadDB(archive_dir=str(tmp_path), crs=SWISS_EPSG)
@@ -276,7 +277,7 @@ def test_a_higher_slice_draws_fewer_gates(plot_rdb):
 def test_overlap_nearest_draws_fewer_gates_than_all(plot_rdb):
     """``nearest`` resolves the double coverage that ``all`` keeps."""
     assert _n_polys(plot_cappi(plot_rdb, altitude=1200, overlap="nearest")) < _n_polys(
-        plot_cappi(plot_rdb, altitude=1200, overlap="all")
+        plot_cappi(plot_rdb, altitude=1200, overlap="all"),
     )
 
 
@@ -285,7 +286,8 @@ def test_resolve_chord_overlap_leaves_no_double_coverage(plot_archive_dir):
     resolved = _resolve_chord_overlap(cappi_chords(PLOT_RADAR, plot_archive_dir, 1200.0))
 
     intervals = np.sort(
-        np.stack([resolved["d_near"].to_numpy(), resolved["d_far"].to_numpy()], axis=1), axis=0
+        np.stack([resolved["d_near"].to_numpy(), resolved["d_far"].to_numpy()], axis=1),
+        axis=0,
     )
 
     assert (intervals[1:, 0] >= intervals[:-1, 1] - 1e-3).all()
@@ -294,7 +296,7 @@ def test_resolve_chord_overlap_leaves_no_double_coverage(plot_archive_dir):
 def test_fill_lowest_extends_the_far_field(plot_rdb):
     """Beyond the lowest beam's reach the slice is extended, never shrunk."""
     assert _n_polys(plot_cappi(plot_rdb, altitude=1200, fill_lowest=True)) >= _n_polys(
-        plot_cappi(plot_rdb, altitude=1200, fill_lowest=False)
+        plot_cappi(plot_rdb, altitude=1200, fill_lowest=False),
     )
 
 
@@ -315,7 +317,8 @@ def test_slice_polygons_sit_inside_the_full_footprints(plot_rdb, plot_archive_di
     drawn = np.array([path.vertices[:4] for path in plot_cappi(plot_rdb, altitude=1200, overlap="all").get_paths()])
     tbl = gate_corner_table(PLOT_RADAR, plot_archive_dir, kind="h_plane")
     full = np.stack(
-        [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)], axis=1
+        [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)],
+        axis=1,
     )
 
     assert shapely.area(shapely.polygons(drawn)).sum() <= shapely.area(shapely.polygons(full)).sum()
@@ -376,8 +379,10 @@ def test_vcs_refuses_a_datatree():
 
 
 def test_a_geojson_line_honours_its_own_crs(plot_rdb, plot_site, tmp_path):
-    """A GeoJSON is lon/lat by RFC 7946; reading those degrees as LV95 metres
-    would put the section about 2600 km away."""
+    """A GeoJSON is lon/lat by RFC 7946, not archive metres.
+
+    Reading those degrees as LV95 would put the section about 2600 km away.
+    """
     import pyproj
 
     from raddb.aoi import _to_pyproj_crs
@@ -396,7 +401,7 @@ def test_a_geojson_line_honours_its_own_crs(plot_rdb, plot_site, tmp_path):
 
 
 def test_a_shapefile_line_is_read(plot_rdb, plot_site, tmp_path):
-    """pyshp reads the ``.shp``; the ``.prj`` (absent here) would declare the CRS."""
+    """Pyshp reads the ``.shp``; the ``.prj`` (absent here) would declare the CRS."""
     shapefile = pytest.importorskip("shapefile")
 
     p1, p2 = _line_across(plot_site)
@@ -416,7 +421,7 @@ def test_a_line_works_from_a_bare_frame_with_an_archive(plot_rdb, plot_site, plo
     line = _line_across(plot_site)
 
     assert _n_polys(plot_vcs(data, line=line, archive_dir=plot_archive_dir)) == _n_polys(
-        plot_vcs(plot_rdb, line=line)
+        plot_vcs(plot_rdb, line=line),
     )
 
 
@@ -472,7 +477,8 @@ def test_plot_cross_section(plot_rdb, plot_site):
     fig, ax, artist = plot_cross_section(cs.to_pandas())
 
     assert _n_polys(artist) == len(cs)
-    assert artist.axes is ax and ax.figure is fig
+    assert artist.axes is ax
+    assert ax.figure is fig
 
 
 def test_plot_cross_section_honours_a_supplied_axes(plot_rdb, plot_site):
@@ -554,7 +560,8 @@ def test_the_quicklook_saves_to_a_file(plot_archive_dir, plot_site, tmp_path):
         save_path=str(out),
     )
 
-    assert out.exists() and out.stat().st_size > 0
+    assert out.exists()
+    assert out.stat().st_size > 0
 
 
 def test_the_quicklook_context_can_be_dropped(plot_archive_dir, plot_site):
@@ -575,8 +582,8 @@ def test_the_quicklook_context_can_be_dropped(plot_archive_dir, plot_site):
 # ---------------------------------------------------------------------------
 
 
+# The six panels of the AMT latent-space figure — the layout is fixed at 2 x 3.
 LATENT_VARS = ["DBZH", "ZDR", "KDP", "RHOHV", "PHIDP", "TEMP"]
-"""The six panels of the AMT latent-space figure — the layout is fixed at 2 x 3."""
 
 
 def _latent_frame(n=200):
@@ -620,7 +627,8 @@ def test_plot_latent_scatter_labels_only_the_outer_axes():
 
     assert axes[0, 1].get_xlabel() == ""
     assert axes[1, 1].get_ylabel() == ""
-    assert axes[1, 0].get_xlabel() and axes[1, 0].get_ylabel()
+    assert axes[1, 0].get_xlabel()
+    assert axes[1, 0].get_ylabel()
 
 
 # ---------------------------------------------------------------------------
@@ -729,7 +737,8 @@ def test_datatree_geometry_matches_the_stored_lattice(plot_dtree, plot_archive_d
     drawn = np.array([q.vertices[:4] for q in plot_ppi(plot_dtree, sweep=1, variable="DBZH").get_paths()])
     tbl = gate_corner_table(PLOT_RADAR, plot_archive_dir, kind="h_plane", sweep=1)
     stored = np.stack(
-        [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)], axis=1
+        [np.stack([tbl[f"x_{k}"].to_numpy(), tbl[f"y_{k}"].to_numpy()], axis=1) for k in range(1, 5)],
+        axis=1,
     )
 
     # The lattices are float32, so ~1e-7 relative — a few centimetres at 200 km range.
@@ -745,7 +754,7 @@ def test_an_rhi_from_a_datatree(plot_dtree):
 def test_a_cappi_from_a_datatree_matches_the_lut_path(plot_dtree, plot_rdb):
     """Both paths must select the same chords at the same altitude."""
     assert _n_polys(plot_cappi(plot_dtree, altitude=1200, variable="DBZH")) == _n_polys(
-        plot_cappi(plot_rdb, altitude=1200)
+        plot_cappi(plot_rdb, altitude=1200),
     )
 
 
@@ -780,7 +789,7 @@ def test_a_geodataframe_without_an_archive_raises(plot_gdf):
 def test_a_geodataframe_cappi_matches_a_frame(plot_gdf, plot_archive_dir, plot_rdb):
     """Same geometry source, same result."""
     assert _n_polys(plot_cappi(plot_gdf, altitude=1200, archive_dir=plot_archive_dir)) == _n_polys(
-        plot_cappi(plot_rdb, altitude=1200)
+        plot_cappi(plot_rdb, altitude=1200),
     )
 
 
@@ -821,7 +830,7 @@ def test_a_wider_declared_beam_reaches_a_cappi_over_more_bins(plot_dtree):
     wide.attrs["radar_beam_width_h"] = 2.0
 
     assert _n_polys(plot_cappi(wide, altitude=1200, variable="DBZH")) > _n_polys(
-        plot_cappi(narrow, altitude=1200, variable="DBZH")
+        plot_cappi(narrow, altitude=1200, variable="DBZH"),
     )
 
 
@@ -839,7 +848,7 @@ def _tick_labels(lo, hi, offset=0.0):
     locs = np.asarray(ax.yaxis.get_majorticklocs(), dtype=float)
     labels = [t.get_text() for t in ax.get_yticklabels()]
     plt.close(fig)
-    return [(v, lab) for v, lab in zip(locs, labels) if lab and lo <= v <= hi]
+    return [(v, lab) for v, lab in zip(locs, labels, strict=False) if lab and lo <= v <= hi]
 
 
 @pytest.mark.parametrize(
diff --git a/raddb/viz/interactive.py b/raddb/viz/interactive.py
index 26c379b..ea1d79c 100644
--- a/raddb/viz/interactive.py
+++ b/raddb/viz/interactive.py
@@ -1,7 +1,4 @@
-"""
-raddb/viz/interactive.py
-------------------------
-Interactive Area-of-Interest selection on an ipyleaflet map (Jupyter).
+"""Interactive Area-of-Interest selection on an ipyleaflet map (Jupyter).
 
 Draw a shape on the map and it is dispatched to the matching RadDB method:
 
@@ -18,6 +15,7 @@
 This is an **optional** UI layer — it needs ``ipyleaflet`` + ``ipywidgets`` and a
 Jupyter frontend.  The hand-typed / shapefile crop methods are unaffected.
 """
+
 from __future__ import annotations
 
 import json
@@ -25,11 +23,11 @@
 
 import numpy as np
 
-
 # ============================================================================
 # GeoJSON feature -> crop dispatch  (pure, unit-testable — no widgets)
 # ============================================================================
 
+
 def _is_axis_aligned_box(ring: list) -> bool:
     """True if a polygon ring is an axis-aligned rectangle (a drawn 'rectangle')."""
     pts = ring[:-1] if ring and ring[0] == ring[-1] else ring
@@ -85,17 +83,16 @@ def _crop_from_feature(db, feature, distance_m: float = 15_000.0):
 
 def _feature_collection(feature) -> dict:
     """Wrap a drawn feature as a GeoJSON FeatureCollection (WGS-84) for saving."""
-    if feature.get("type") == "Feature":
-        feat = feature
-    else:  # bare geometry
-        feat = {"type": "Feature", "properties": {}, "geometry": feature}
-    return {"type": "FeatureCollection", "features": [feat]}
+    if feature.get("type") != "Feature":  # bare geometry
+        feature = {"type": "Feature", "properties": {}, "geometry": feature}
+    return {"type": "FeatureCollection", "features": [feature]}
 
 
 # ============================================================================
 # The ipyleaflet widget
 # ============================================================================
 
+
 class AOISelector:
     """Interactive map to draw an AOI and crop a DataFrame with it.
 
@@ -119,15 +116,13 @@ class AOISelector:
         Default distance (metres) for the marker (point) crop.
     """
 
-    def __init__(self, db, radars=None, center=None, zoom=8,
-                 point_radius_m=15_000.0):
+    def __init__(self, db, radars=None, center=None, zoom=8, point_radius_m=15_000.0):
         try:
             import ipywidgets as W
-            from ipyleaflet import Map, DrawControl, Marker, AwesomeIcon
+            from ipyleaflet import AwesomeIcon, DrawControl, Map, Marker
         except ImportError as exc:  # pragma: no cover - optional dependency
             raise ImportError(
-                "interactive_crop needs ipyleaflet + ipywidgets. "
-                "Install with: pip install ipyleaflet ipywidgets"
+                "interactive_crop needs ipyleaflet + ipywidgets. " "Install with: pip install ipyleaflet ipywidgets",
             ) from exc
 
         self.db = db
@@ -139,35 +134,42 @@ def __init__(self, db, radars=None, center=None, zoom=8,
         sites = self._radar_sites(radars)
         if center is None:
             center = (
-                (sum(s[0] for s in sites.values()) / len(sites),
-                 sum(s[1] for s in sites.values()) / len(sites))
-                if sites else (46.82, 8.23)
+                (sum(s[0] for s in sites.values()) / len(sites), sum(s[1] for s in sites.values()) / len(sites))
+                if sites
+                else (46.82, 8.23)
             )
 
         self.map = Map(center=center, zoom=zoom, scroll_wheel_zoom=True)
         for r, (lat, lon) in sites.items():
-            self.map.add(Marker(location=(lat, lon), title=f"radar {r}", draggable=False,
-                                icon=AwesomeIcon(name="broadcast-tower", marker_color="red")))
+            self.map.add(
+                Marker(
+                    location=(lat, lon),
+                    title=f"radar {r}",
+                    draggable=False,
+                    icon=AwesomeIcon(name="broadcast-tower", marker_color="red"),
+                ),
+            )
 
         self.draw = DrawControl(
             rectangle={"shapeOptions": {"color": "#e31a1c", "weight": 2, "fillOpacity": 0.05}},
             polygon={"shapeOptions": {"color": "#e31a1c", "weight": 2, "fillOpacity": 0.05}},
             polyline={"shapeOptions": {"color": "#e31a1c", "weight": 3}},
             marker={"shapeOptions": {}},
-            circle={}, circlemarker={},
+            circle={},
+            circlemarker={},
         )
         self.draw.on_draw(self._on_draw)
         self.map.add(self.draw)
 
         # --- controls ---
-        self.radius = W.FloatText(value=float(point_radius_m),
-                                  description="if marker → radius [m]",
-                                  style={"description_width": "initial"},
-                                  layout=W.Layout(width="320px"))
-        self.apply_btn = W.Button(description="Apply crop", button_style="primary",
-                                  icon="scissors")
-        self.save_path = W.Text(value="aoi.geojson", description="save as",
-                                layout=W.Layout(width="240px"))
+        self.radius = W.FloatText(
+            value=float(point_radius_m),
+            description="if marker → radius [m]",
+            style={"description_width": "initial"},
+            layout=W.Layout(width="320px"),
+        )
+        self.apply_btn = W.Button(description="Apply crop", button_style="primary", icon="scissors")
+        self.save_path = W.Text(value="aoi.geojson", description="save as", layout=W.Layout(width="240px"))
         self.save_btn = W.Button(description="Save GeoJSON", icon="save")
         self.out = W.Output()
         self.apply_btn.on_click(self._apply)
@@ -177,14 +179,16 @@ def __init__(self, db, radars=None, center=None, zoom=8,
             "<b>Draw an AOI</b> with the toolbar (top-left): "
             "▭ rectangle → <code>crop_by_bbox</code>, ⬠ polygon → <code>crop_by_polygone</code>, "
             "📍 marker → <code>crop_around_point</code> (uses the radius box below), "
-            "／ line → <code>extract_cross_section</code>. Then <b>Apply crop</b>."
+            "/ line → <code>extract_cross_section</code>. Then <b>Apply crop</b>.",
+        )
+        self._widget = W.VBox(
+            [
+                instructions,
+                self.map,
+                W.HBox([self.radius, self.apply_btn, self.save_path, self.save_btn]),
+                self.out,
+            ],
         )
-        self._widget = W.VBox([
-            instructions,
-            self.map,
-            W.HBox([self.radius, self.apply_btn, self.save_path, self.save_btn]),
-            self.out,
-        ])
 
     # -- radar site coords (lat, lon) --
     def _radar_sites(self, radars):
@@ -193,12 +197,12 @@ def _radar_sites(self, radars):
             try:
                 info = self.db.get_radar_info(r)
                 sites[r] = (float(info["latitude"]), float(info["longitude"]))
-            except Exception:  # noqa: BLE001 - a missing radar shouldn't break the map
+            except Exception:  # - a missing radar shouldn't break the map
                 continue
         return sites
 
     # -- draw callback: keep the last drawn feature --
-    def _on_draw(self, target, action, geo_json):
+    def _on_draw(self, _target, action, geo_json):
         if action in ("created", "edited"):
             self.feature = geo_json
 
@@ -210,15 +214,17 @@ def _apply(self, _btn=None):
                 return
             try:
                 self.kind, self.result = _crop_from_feature(
-                    self.db, self.feature, distance_m=self.radius.value,
+                    self.db,
+                    self.feature,
+                    distance_m=self.radius.value,
                 )
-            except Exception as exc:  # noqa: BLE001 - surface errors in the widget
+            except Exception as exc:  # - surface errors in the widget
                 print(f"crop failed: {exc}")
                 return
             r = self.result
             try:
                 radars = r.radars() if len(r) else []
-            except Exception:  # noqa: BLE001
+            except Exception:
                 radars = []
             # `sweep` is a LUT column, never stored per gate, so reading it off
             # r.columns() always missed and printed "?".  It is decoded from the
@@ -226,6 +232,7 @@ def _apply(self, _btn=None):
             sweeps = 0
             if len(r):
                 from raddb.lut import decode_gate_ids
+
                 sweeps = int(np.unique(decode_gate_ids(r.data["gate_id"].to_numpy())[0]).size)
             print(f"{self.kind} -> {len(r):,} gates | radars {radars} | {sweeps} sweeps")
             print("result is available as  <selector>.result  (a cropped RadDB).")
@@ -240,10 +247,13 @@ def _save(self, _btn=None):
             print(f"saved AOI -> {path}  (reload with db.crop_by_polygone('{path}') for polygons)")
 
     def display(self):
+        """Render the widget in the notebook and return ``self``."""
         from IPython.display import display
+
         display(self._widget)
         return self
 
     def _ipython_display_(self):
         from IPython.display import display
+
         display(self._widget)
diff --git a/raddb/viz/plot.py b/raddb/viz/plot.py
index e8224f7..8e07cea 100644
--- a/raddb/viz/plot.py
+++ b/raddb/viz/plot.py
@@ -1,7 +1,4 @@
-"""
-raddb/plot.py
--------------
-PPI, RHI, and latent-space scatter plots for RadDB.
+"""PPI, RHI, and latent-space scatter plots for RadDB.
 
 A radar gate is not a rectangle: it is a curved frustum whose footprint depends
 on range, azimuth, elevation and Earth curvature.  Every plot here draws that
@@ -15,24 +12,28 @@
 GeoDataFrame is treated as a frame.  There is a single geometry path — always the
 exact frustum — so what is drawn does not depend on how it was asked for.
 """
+
 from __future__ import annotations
 
+import contextlib
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
 import numpy as np
 import pandas as pd
 import polars as pl
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-import matplotlib.ticker as mticker
-from matplotlib.colors import BoundaryNorm, ListedColormap, Normalize, TwoSlopeNorm
 import xarray as xr
+from matplotlib.colors import BoundaryNorm, ListedColormap, TwoSlopeNorm
 
-from raddb.hc_mapping import HC_CLASSES as _HC_CLASSES, HC_COLORS as _HC_COLORS
-
+from raddb.hc_mapping import HC_CLASSES as _HC_CLASSES
+from raddb.hc_mapping import HC_COLORS as _HC_COLORS
 
 # ============================================================================
 # Per-variable plotting defaults
 # ============================================================================
 
+
 def _first_available_cmap(*names: str) -> str:
     """First registered colormap among ``names`` (last is the guaranteed fallback).
 
@@ -52,19 +53,21 @@ def _first_available_cmap(*names: str) -> str:
 # diverging (coolwarm's midpoint is grey) via TwoSlopeNorm so 0 °C reads grey.
 
 _PLOT_DEFAULTS: dict[str, dict] = {
-    "DBZH":     dict(cmap="HomeyerRainbow",   vmin=0,    vmax=60,   label="Reflectivity [dBz]"),
-    "DBZH_raw": dict(cmap="HomeyerRainbow",   vmin=0,    vmax=60,   label="Raw reflectivity [dBz]"),
-    "ZDR":      dict(cmap="viridis",  vmin=-2,   vmax=7,    label="Differential reflectivity [dB]"),
-    "ZDR_raw":  dict(cmap="viridis",  vmin=-2,   vmax=7,    label="Raw differential reflectivity [dB]"),
-    "KDP":      dict(cmap="plasma",   vmin=-2,   vmax=5,    label="Specific differential phase [°/km]"),
-    "RHOHV":    dict(cmap="cividis",  vmin=0.5,  vmax=1.0,  label="Co-polar correlation [-]"),
-    "PHIDP":    dict(cmap="twilight", vmin=-180, vmax=180,  label="Differential phase [deg]"),
-    "HZT":      dict(cmap="viridis",  vmin=0,    vmax=5000, label="Freezing level height [m]"),
-    "TEMP":     dict(cmap="coolwarm",
-                     norm=lambda: TwoSlopeNorm(vmin=-30, vcenter=0, vmax=30),
-                     label="Temperature [°C]"),
-    "HC_MCH":   dict(discrete=True,   classes=_HC_CLASSES,  colors=_HC_COLORS, label="MCH hydrometeor class"),
-    "HC_PYART": dict(discrete=True,   classes=_HC_CLASSES,  colors=_HC_COLORS, label="PyART hydrometeor class"),
+    "DBZH": {"cmap": "HomeyerRainbow", "vmin": 0, "vmax": 60, "label": "Reflectivity [dBz]"},
+    "DBZH_raw": {"cmap": "HomeyerRainbow", "vmin": 0, "vmax": 60, "label": "Raw reflectivity [dBz]"},
+    "ZDR": {"cmap": "viridis", "vmin": -2, "vmax": 7, "label": "Differential reflectivity [dB]"},
+    "ZDR_raw": {"cmap": "viridis", "vmin": -2, "vmax": 7, "label": "Raw differential reflectivity [dB]"},
+    "KDP": {"cmap": "plasma", "vmin": -2, "vmax": 5, "label": "Specific differential phase [°/km]"},
+    "RHOHV": {"cmap": "cividis", "vmin": 0.5, "vmax": 1.0, "label": "Co-polar correlation [-]"},
+    "PHIDP": {"cmap": "twilight", "vmin": -180, "vmax": 180, "label": "Differential phase [deg]"},
+    "HZT": {"cmap": "viridis", "vmin": 0, "vmax": 5000, "label": "Freezing level height [m]"},
+    "TEMP": {
+        "cmap": "coolwarm",
+        "norm": lambda: TwoSlopeNorm(vmin=-30, vcenter=0, vmax=30),
+        "label": "Temperature [°C]",
+    },
+    "HC_MCH": {"discrete": True, "classes": _HC_CLASSES, "colors": _HC_COLORS, "label": "MCH hydrometeor class"},
+    "HC_PYART": {"discrete": True, "classes": _HC_CLASSES, "colors": _HC_COLORS, "label": "PyART hydrometeor class"},
 }
 
 
@@ -89,16 +92,14 @@ def _ensure_cmap_registered(name):
         return name
     if not _PYART_CMAPS_TRIED:
         _PYART_CMAPS_TRIED = True
-        try:
+        with contextlib.suppress(Exception):  # pyart is optional
             import pyart  # noqa: F401  # registers Py-ART colormaps with matplotlib
-        except Exception:  # noqa: BLE001 - pyart optional
-            pass
     if name in plt.colormaps():
         return name
     import warnings
+
     warnings.warn(
-        f"colormap {name!r} is unavailable (Py-ART colormaps need pyart installed); "
-        "falling back to 'turbo'.",
+        f"colormap {name!r} is unavailable (Py-ART colormaps need pyart installed); " "falling back to 'turbo'.",
         stacklevel=2,
     )
     return "turbo"
@@ -160,6 +161,7 @@ def _maybe_cartopy():
     try:
         import cartopy.crs as ccrs
         import cartopy.feature as cfeature
+
         return ccrs, cfeature
     except ImportError:
         return None, None
@@ -195,8 +197,9 @@ def _ne_border_lines():
             for category, name, style in _NE_LAYERS:
                 path = shpreader.natural_earth(resolution="10m", category=category, name=name)
                 _NE_BORDERS.extend((g, style) for g in shpreader.Reader(path).geometries())
-        except Exception as exc:  # noqa: BLE001 - cartopy missing / data not cached
+        except Exception as exc:  # - cartopy missing / data not cached
             import warnings
+
             warnings.warn(
                 f"cartopy country borders unavailable ({exc}); plotted without them.",
                 stacklevel=2,
@@ -216,6 +219,7 @@ def _border_lines(crs, clip):
     key = (str(crs), tuple(round(float(v), 2) for v in clip))
     if key not in _BORDER_LINES:
         import shapely
+
         from raddb.aoi import _reproject_to_aoi
 
         box = shapely.box(*clip)
@@ -264,9 +268,13 @@ def _add_colorbar(p, ax, is_discrete: bool, class_labels, label: str):
     if is_discrete and class_labels is not None:
         n = len(class_labels)
         cbar = plt.colorbar(
-            p, ax=ax, ticks=np.arange(1, n + 1),
+            p,
+            ax=ax,
+            ticks=np.arange(1, n + 1),
             boundaries=np.arange(0.5, n + 1.5),
-            spacing="uniform", fraction=0.046, pad=0.04,
+            spacing="uniform",
+            fraction=0.046,
+            pad=0.04,
         )
         cbar.ax.set_yticklabels(class_labels)
         cbar.set_label(label)
@@ -317,6 +325,7 @@ def _volume_time_str(ds) -> str:
     "on a RadDB built with RadDB(archive_dir=...) so it can be inferred."
 )
 
+
 class _Source:
     """What the plots need about their input, resolved once.
 
@@ -352,6 +361,7 @@ def _beamwidth(src):
     volume to set it explicitly.
     """
     from raddb.lut import _beamwidth_from_datatree
+
     return _beamwidth_from_datatree(src.dtree)
 
 
@@ -364,8 +374,7 @@ def _resolve_frame(data, archive_dir=None):
     from pathlib import Path
 
     if isinstance(data, (xr.DataTree, xr.Dataset)):
-        return _Source(kind="datatree", dtree=data,
-                       base=Path(archive_dir) if archive_dir else None)
+        return _Source(kind="datatree", dtree=data, base=Path(archive_dir) if archive_dir else None)
 
     crs, base, gdf = None, archive_dir, None
 
@@ -388,12 +397,12 @@ def _resolve_frame(data, archive_dir=None):
         # pyarrow cannot convert — WKB-encode them the way the RadDB converters
         # do, and plot_vcs decodes them again on the way out.
         from raddb.main import _encode_geometry
+
         data = pl.from_pandas(_encode_geometry(data))
 
     if not isinstance(data, pl.DataFrame):
         raise TypeError(
-            f"expected a RadDB, polars/pandas frame, GeoDataFrame or DataTree; "
-            f"got {type(data).__name__}."
+            f"expected a RadDB, polars/pandas frame, GeoDataFrame or DataTree; " f"got {type(data).__name__}.",
         )
     if data.is_empty():
         raise ValueError("no data to plot (the frame is empty).")
@@ -402,10 +411,10 @@ def _resolve_frame(data, archive_dir=None):
     return _Source(kind=kind, df=data, base=Path(base) if base else None, crs=crs, gdf=gdf)
 
 
-def _select_radar(df: "pl.DataFrame", radar: str | None) -> tuple["pl.DataFrame", str]:
+def _select_radar(df: pl.DataFrame, radar: str | None) -> tuple[pl.DataFrame, str]:
     """Narrow to a single radar, inferring it when the frame holds only one."""
-    from raddb.helper import normalize_radar_name
     from raddb.aoi import _radars_from_gate_ids
+    from raddb.helper import normalize_radar_name
 
     if "radar" in df.columns:
         present = sorted(df["radar"].drop_nulls().unique().to_list())
@@ -415,8 +424,7 @@ def _select_radar(df: "pl.DataFrame", radar: str | None) -> tuple["pl.DataFrame"
     if radar is None:
         if len(present) != 1:
             raise ValueError(
-                f"data spans radars {present}; pass radar= to pick one "
-                "(one plot draws one radar)."
+                f"data spans radars {present}; pass radar= to pick one " "(one plot draws one radar).",
             )
         return df, present[0]
 
@@ -425,17 +433,17 @@ def _select_radar(df: "pl.DataFrame", radar: str | None) -> tuple["pl.DataFrame"
         out = df.filter(pl.col("radar") == radar)
     else:
         from raddb.lut import GATE_ID_RADAR_BASE, encode_radar_code
+
         prefix = encode_radar_code(radar) * GATE_ID_RADAR_BASE
         out = df.filter(
-            (pl.col("gate_id") >= prefix)
-            & (pl.col("gate_id") < prefix + GATE_ID_RADAR_BASE)
+            (pl.col("gate_id") >= prefix) & (pl.col("gate_id") < prefix + GATE_ID_RADAR_BASE),
         )
     if out.is_empty():
         raise ValueError(f"no rows for radar {radar!r}; present: {present}.")
     return out, radar
 
 
-def _select_volume(df: "pl.DataFrame", timestep=None, start_time=None, end_time=None):
+def _select_volume(df: pl.DataFrame, timestep=None, start_time=None, end_time=None):
     """Narrow to a single volume. Returns ``(frame, time label)``.
 
     ``start_time`` / ``end_time`` restrict the candidates; ``timestep`` then picks
@@ -474,7 +482,7 @@ def _bound(v):
     else:
         raise ValueError(
             f"data holds {len(vols)} volumes ({vols[0]} ... {vols[-1]}); pass "
-            "timestep= to pick one, or narrow with start_time=/end_time=."
+            "timestep= to pick one, or narrow with start_time=/end_time=.",
         )
     return df.filter(pl.col(col) == chosen), str(chosen)[:19]
 
@@ -482,10 +490,18 @@ def _bound(v):
 # ---------------------------------------------------------------- coordinates
 
 _COORD_ALIASES = {
-    "cartesian": "xy", "xy": "xy", "radar": "xy",
-    "geo": "lonlat", "lonlat": "lonlat", "latlon": "lonlat", "wgs": "lonlat",
-    "projected": "projected", "proj": "projected",
-    "swiss": 2056, "lv95": 2056, "2056": 2056,
+    "cartesian": "xy",
+    "xy": "xy",
+    "radar": "xy",
+    "geo": "lonlat",
+    "lonlat": "lonlat",
+    "latlon": "lonlat",
+    "wgs": "lonlat",
+    "projected": "projected",
+    "proj": "projected",
+    "swiss": 2056,
+    "lv95": 2056,
+    "2056": 2056,
 }
 
 
@@ -500,7 +516,7 @@ def _resolve_coords(coords, crs):
     key = str(coords).lower()
     if key not in _COORD_ALIASES:
         raise ValueError(
-            f"coords must be 'xy', 'lonlat', 'projected' or an EPSG int; got {coords!r}."
+            f"coords must be 'xy', 'lonlat', 'projected' or an EPSG int; got {coords!r}.",
         )
     resolved = _COORD_ALIASES[key]
     if isinstance(resolved, int):
@@ -508,14 +524,13 @@ def _resolve_coords(coords, crs):
     if resolved == "projected":
         if crs is None:
             raise ValueError(
-                "coords='projected' needs a CRS; build the RadDB with "
-                "RadDB(crs=...) or pass an EPSG int as coords."
+                "coords='projected' needs a CRS; build the RadDB with " "RadDB(crs=...) or pass an EPSG int as coords.",
             )
         return "projected", int(crs)
     return resolved, None
 
 
-def _corner_vertices(tbl: "pl.DataFrame", n_corners: int, mode: str, epsg, info):
+def _corner_vertices(tbl: pl.DataFrame, n_corners: int, mode: str, epsg, info):
     """Per-gate corner rings as an ``(n_gates, n_corners, 2)`` float array.
 
     ``tbl`` is a :func:`raddb.lut.gate_corner_table` result for the ``h_plane``
@@ -529,29 +544,33 @@ def _corner_vertices(tbl: "pl.DataFrame", n_corners: int, mode: str, epsg, info)
         if not all(c in tbl.columns for c in xs):
             raise KeyError(
                 f"the h_plane lattice has no EPSG:{epsg} columns. Regenerate the "
-                "LUT with that projection, or use coords='xy' / 'lonlat'."
+                "LUT with that projection, or use coords='xy' / 'lonlat'.",
             )
     else:
         xs = [f"x_{k}" for k in range(1, n_corners + 1)]
         ys = [f"y_{k}" for k in range(1, n_corners + 1)]
 
     ring = np.stack(
-        [np.stack([tbl[xc].to_numpy(), tbl[yc].to_numpy()], axis=1)
-         for xc, yc in zip(xs, ys)],
+        [np.stack([tbl[xc].to_numpy(), tbl[yc].to_numpy()], axis=1) for xc, yc in zip(xs, ys, strict=False)],
         axis=1,
     ).astype(np.float64)
 
     if mode == "lonlat":
         from raddb.lut import cartesian_to_geographic
+
         lat, lon, _ = cartesian_to_geographic(
-            ring[:, :, 0], ring[:, :, 1], np.zeros(ring.shape[:2]),
-            info["latitude"], info["longitude"], info["altitude"],
+            ring[:, :, 0],
+            ring[:, :, 1],
+            np.zeros(ring.shape[:2]),
+            info["latitude"],
+            info["longitude"],
+            info["altitude"],
         )
         ring = np.stack([lon, lat], axis=2)
     return ring
 
 
-def _join_corners(df: "pl.DataFrame", tbl: "pl.DataFrame", variable: str):
+def _join_corners(df: pl.DataFrame, tbl: pl.DataFrame, variable: str):
     """Align a per-gate corner table with the data frame, dropping unusable rows.
 
     Returns ``(values, corner table)`` in matching row order.  Gates whose
@@ -569,13 +588,14 @@ def _join_corners(df: "pl.DataFrame", tbl: "pl.DataFrame", variable: str):
     if joined.is_empty():
         raise ValueError(
             f"no gates left to draw: every {variable!r} value is NaN, or none of "
-            "the gates matched the LUT geometry."
+            "the gates matched the LUT geometry.",
         )
     return joined[variable].to_numpy(), joined
 
 
 # ------------------------------------------------- approximate ("rough") gates
 
+
 def _dt_sweep_names(dt):
     """Sweep group names of a DataTree, ordered by sweep number."""
     if isinstance(dt, xr.Dataset):
@@ -599,15 +619,13 @@ def _dt_sweep(dt, sweep):
 
 def _dt_site(ds):
     """Site (lat, lon, alt) from a sweep Dataset, as a radar-info-shaped dict."""
-    missing = [k for k in ("latitude", "longitude", "altitude")
-               if k not in ds.variables and k not in ds.coords]
+    missing = [k for k in ("latitude", "longitude", "altitude") if k not in ds.variables and k not in ds.coords]
     if missing:
         raise KeyError(
             f"the DataTree sweep has no {missing} coordinate(s), so the radar site "
-            "is unknown. xradar volumes normally carry them per sweep."
+            "is unknown. xradar volumes normally carry them per sweep.",
         )
-    return {k: float(np.asarray(ds[k]).ravel()[0])
-            for k in ("latitude", "longitude", "altitude")}
+    return {k: float(np.asarray(ds[k]).ravel()[0]) for k in ("latitude", "longitude", "altitude")}
 
 
 def _dt_gate_table(ds, variable):
@@ -619,7 +637,7 @@ def _dt_gate_table(ds, variable):
     """
     if variable not in ds.variables:
         raise KeyError(
-            f"variable {variable!r} not in this sweep; have {sorted(ds.data_vars)}."
+            f"variable {variable!r} not in this sweep; have {sorted(ds.data_vars)}.",
         )
     az = np.asarray(ds["azimuth"].values, dtype=np.float64)
     rng = np.asarray(ds["range"].values, dtype=np.float64)
@@ -632,12 +650,14 @@ def _dt_gate_table(ds, variable):
         vals = vals.T
     n_az, n_rng = az.size, rng.size
 
-    return pl.DataFrame({
-        "azimuth": np.repeat(az, n_rng),
-        "range": np.tile(rng, n_az),
-        "elevation_angle": np.repeat(el, n_rng),
-        variable: vals.ravel(),
-    })
+    return pl.DataFrame(
+        {
+            "azimuth": np.repeat(az, n_rng),
+            "range": np.tile(rng, n_az),
+            "elevation_angle": np.repeat(el, n_rng),
+            variable: vals.ravel(),
+        },
+    )
 
 
 def _dt_h_vertices(ds, mode, epsg, info):
@@ -651,7 +671,9 @@ def _dt_h_vertices(ds, mode, epsg, info):
     beamwidth-independent (verified: 0.8 deg and 1.2 deg give identical nodes).
     """
     from raddb.lut import (
-        antenna_vectors_to_cartesian, cartesian_to_geographic, GATE_RING_OFFSETS,
+        GATE_RING_OFFSETS,
+        antenna_vectors_to_cartesian,
+        cartesian_to_geographic,
     )
 
     az = np.asarray(ds["azimuth"].values, dtype=np.float64)
@@ -663,7 +685,12 @@ def _dt_h_vertices(ds, mode, epsg, info):
     x, y, _ = antenna_vectors_to_cartesian(rng, az, el, edges=True)
     if mode == "lonlat":
         lat, lon, _ = cartesian_to_geographic(
-            x, y, np.zeros_like(x), info["latitude"], info["longitude"], info["altitude"],
+            x,
+            y,
+            np.zeros_like(x),
+            info["latitude"],
+            info["longitude"],
+            info["altitude"],
         )
         x, y = lon, lat
     elif mode == "projected":
@@ -673,8 +700,7 @@ def _dt_h_vertices(ds, mode, epsg, info):
     ai = np.repeat(np.arange(n_az), n_rng)
     ri = np.tile(np.arange(n_rng), n_az)
     return np.stack(
-        [np.stack([x[ai + i, ri + j], y[ai + i, ri + j]], axis=1)
-         for i, j in GATE_RING_OFFSETS],
+        [np.stack([x[ai + i, ri + j], y[ai + i, ri + j]], axis=1) for i, j in GATE_RING_OFFSETS],
         axis=1,
     )
 
@@ -705,8 +731,7 @@ def _dt_v_vertices(ds, height, info, beamwidth_deg):
     # near-bottom, far-bottom, far-top, near-top
     picks = ((-1, 0), (-1, 1), (1, 1), (1, 0))
     return np.stack(
-        [np.stack([faces[lvl][0][ai, ri + j], faces[lvl][1][ai, ri + j]], axis=1)
-         for lvl, j in picks],
+        [np.stack([faces[lvl][0][ai, ri + j], faces[lvl][1][ai, ri + j]], axis=1) for lvl, j in picks],
         axis=1,
     )
 
@@ -714,18 +739,24 @@ def _dt_v_vertices(ds, height, info, beamwidth_deg):
 def _project_nodes_xy(x, y, epsg, info):
     """Radar-relative metres -> a projected CRS, for DataTree-computed geometry."""
     import pyproj
-    from raddb.lut import cartesian_to_geographic
+
     from raddb.aoi import _to_pyproj_crs
+    from raddb.lut import cartesian_to_geographic
 
     lat, lon, _ = cartesian_to_geographic(
-        x, y, np.zeros_like(x), info["latitude"], info["longitude"], info["altitude"],
+        x,
+        y,
+        np.zeros_like(x),
+        info["latitude"],
+        info["longitude"],
+        info["altitude"],
     )
     tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(epsg), always_xy=True)
     px, py = tf.transform(np.asarray(lon).ravel(), np.asarray(lat).ravel())
     return np.asarray(px).reshape(x.shape), np.asarray(py).reshape(y.shape)
 
 
-def _nearest_ray_per_sweep(frame: "pl.DataFrame", target: float, az_tol: float):
+def _nearest_ray_per_sweep(frame: pl.DataFrame, target: float, az_tol: float):
     """Keep, in each sweep, only the rows on that sweep's ray closest to ``target``.
 
     ``frame`` must carry ``sweep`` and a precomputed ``_off`` column holding the
@@ -738,17 +769,18 @@ def _nearest_ray_per_sweep(frame: "pl.DataFrame", target: float, az_tol: float):
     if within.is_empty():
         raise ValueError(
             f"no sweep has a ray within ±{az_tol}° of azimuth {target}°; the "
-            f"closest is {float(best['_best'].min()):.2f}° away."
+            f"closest is {float(best['_best'].min()):.2f}° away.",
         )
     if within.height < best.height:
         import warnings
+
         warnings.warn(
             f"{best.height - within.height} of {best.height} sweeps have no ray "
             f"within ±{az_tol}° of azimuth {target}° and are omitted.",
             stacklevel=2,
         )
     picked = frame.join(within, on="sweep", how="inner").filter(
-        pl.col("_off") == pl.col("_best")
+        pl.col("_off") == pl.col("_best"),
     )
     return picked, float(picked["azimuth"].mean())
 
@@ -774,7 +806,7 @@ def _dt_rhi(src, target, variable, az_tol, height, beamwidth_deg):
         verts = _dt_v_vertices(ds, height, src.info, beamwidth_deg)
 
         n_rng = np.asarray(ds["range"].values).size
-        rows = slice(j * n_rng, (j + 1) * n_rng)          # table is ray-major
+        rows = slice(j * n_rng, (j + 1) * n_rng)  # table is ray-major
         vals = tbl[variable].to_numpy()[rows]
         verts = verts[rows]
         keep = np.isfinite(vals)
@@ -783,14 +815,14 @@ def _dt_rhi(src, target, variable, az_tol, height, beamwidth_deg):
 
     if not rays:
         raise ValueError(
-            f"no sweep of this DataTree has a ray within ±{az_tol}° of azimuth {target}°."
+            f"no sweep of this DataTree has a ray within ±{az_tol}° of azimuth {target}°.",
         )
-    return (np.concatenate(all_values), np.concatenate(all_verts),
-            float(np.mean(rays)))
+    return (np.concatenate(all_values), np.concatenate(all_verts), float(np.mean(rays)))
 
 
-def _dt_cappi(src, altitude, variable, height, overlap, fill_lowest,
-              mode, epsg, beamwidth_deg):
+# `_fill_lowest` is accepted for signature parity with the LUT path but has no
+# effect here: a DataTree carries every sweep already, so nothing is missing.
+def _dt_cappi(src, altitude, variable, height, overlap, _fill_lowest, mode, epsg, beamwidth_deg):
     """CAPPI geometry and values from a DataTree, with no archive involved.
 
     Mirrors the LUT path: cut the exact ``(d, z)`` faces at the slice altitude to
@@ -809,18 +841,22 @@ def _dt_cappi(src, altitude, variable, height, overlap, fill_lowest,
         per_sweep[sw] = ds
         d_near, d_far, rng_idx, dz = _dt_sweep_chords(ds, z0, site_alt, beamwidth_deg)
         if rng_idx.size:
-            chords.append(pl.DataFrame({
-                "sweep": np.full(rng_idx.size, sw, dtype=np.int32),
-                "rng_idx": rng_idx.astype(np.int32),
-                "d_near": d_near.astype(np.float32),
-                "d_far": d_far.astype(np.float32),
-                "z_center": np.zeros(rng_idx.size, dtype=np.float32),
-                "dz_center": dz.astype(np.float32),
-            }))
+            chords.append(
+                pl.DataFrame(
+                    {
+                        "sweep": np.full(rng_idx.size, sw, dtype=np.int32),
+                        "rng_idx": rng_idx.astype(np.int32),
+                        "d_near": d_near.astype(np.float32),
+                        "d_far": d_far.astype(np.float32),
+                        "z_center": np.zeros(rng_idx.size, dtype=np.float32),
+                        "dz_center": dz.astype(np.float32),
+                    },
+                ),
+            )
     if not chords:
         raise ValueError(
             f"no beam of this DataTree reaches {altitude} m "
-            f"({'ASL' if height == 'asl' else 'above the radar'}); nothing to draw."
+            f"({'ASL' if height == 'asl' else 'above the radar'}); nothing to draw.",
         )
     table = pl.concat(chords, how="vertical")
     if overlap == "nearest":
@@ -843,10 +879,12 @@ def _dt_cappi(src, altitude, variable, height, overlap, fill_lowest,
 
         n_az = np.asarray(ds["azimuth"].values).size
         n_rng = np.asarray(ds["range"].values).size
-        rows = (np.repeat(np.arange(n_az), ri.size) * n_rng
-                + np.tile(ri, n_az))                       # table is ray-major
+        rows = np.repeat(np.arange(n_az), ri.size) * n_rng + np.tile(ri, n_az)  # table is ray-major
         verts = _trim_footprints_to_chord(
-            full[rows], full_xy[rows], np.tile(dn, n_az), np.tile(df_, n_az)
+            full[rows],
+            full_xy[rows],
+            np.tile(dn, n_az),
+            np.tile(df_, n_az),
         )
         vals = tbl[variable].to_numpy()[rows]
         keep = np.isfinite(vals)
@@ -882,10 +920,8 @@ def _dt_sweep_chords(ds, z0, site_alt, beamwidth_deg):
         x, y, z = antenna_vectors_to_cartesian(rng, az, el + lvl * half, edges=True)
         faces[lvl] = (np.hypot(x, y)[0], z[0] + site_alt)
 
-    ring_d = np.stack([faces[-1][0][:-1], faces[-1][0][1:],
-                       faces[1][0][1:], faces[1][0][:-1]], axis=1)
-    ring_z = np.stack([faces[-1][1][:-1], faces[-1][1][1:],
-                       faces[1][1][1:], faces[1][1][:-1]], axis=1)
+    ring_d = np.stack([faces[-1][0][:-1], faces[-1][0][1:], faces[1][0][1:], faces[1][0][:-1]], axis=1)
+    ring_z = np.stack([faces[-1][1][:-1], faces[-1][1][1:], faces[1][1][1:], faces[1][1][:-1]], axis=1)
 
     za, zb = ring_z, np.roll(ring_z, -1, axis=1)
     da, db = ring_d, np.roll(ring_d, -1, axis=1)
@@ -900,8 +936,7 @@ def _dt_sweep_chords(ds, z0, site_alt, beamwidth_deg):
         return (np.empty(0),) * 4
     d_hit = d_cross[hit]
     z_center = ring_z.mean(axis=1)[hit]
-    return (np.nanmin(d_hit, axis=1), np.nanmax(d_hit, axis=1),
-            np.flatnonzero(hit), np.abs(z_center - z0))
+    return (np.nanmin(d_hit, axis=1), np.nanmax(d_hit, axis=1), np.flatnonzero(hit), np.abs(z_center - z0))
 
 
 class _KmFormatter(mticker.Formatter):
@@ -923,7 +958,7 @@ def __init__(self, offset: float = 0.0):
     #: Never print more than this many decimals, whatever the ticks ask for.
     MAX_DECIMALS = 4
 
-    def __call__(self, v, pos=None):
+    def __call__(self, v, _pos=None):
         return f"{(v - self.offset) / 1e3:.{self._decimals()}f}"
 
     def _decimals(self) -> int:
@@ -953,8 +988,10 @@ def _draw_polygons(ax, verts, values, plot_kwargs, edgecolor, rasterized):
     from matplotlib.collections import PolyCollection
 
     pc = PolyCollection(
-        verts, array=np.asarray(values, dtype=np.float64),
-        edgecolor=edgecolor, linewidth=0.1,
+        verts,
+        array=np.asarray(values, dtype=np.float64),
+        edgecolor=edgecolor,
+        linewidth=0.1,
     )
     if "cmap" in plot_kwargs:
         pc.set_cmap(plot_kwargs["cmap"])
@@ -968,8 +1005,7 @@ def _draw_polygons(ax, verts, values, plot_kwargs, edgecolor, rasterized):
     return pc
 
 
-def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim,
-                     add_range_rings=True, context=False, info=None):
+def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim, add_range_rings=True, context=False, info=None):
     """Labels, tick formatting, aspect, range rings and limits for a map plot."""
     if mode == "lonlat":
         ax.set_xlabel("Longitude [°]")
@@ -996,9 +1032,13 @@ def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim,
         if add_range_rings and mode != "lonlat":
             theta = np.linspace(0, 2 * np.pi, 361)
             for d_km in (50, 100, 150):
-                ax.plot(site_xy[0] + d_km * scale * np.cos(theta),
-                        site_xy[1] + d_km * scale * np.sin(theta),
-                        "k--", linewidth=0.5, zorder=1)
+                ax.plot(
+                    site_xy[0] + d_km * scale * np.cos(theta),
+                    site_xy[1] + d_km * scale * np.sin(theta),
+                    "k--",
+                    linewidth=0.5,
+                    zorder=1,
+                )
 
     ax.set_aspect("equal")
     ax.grid(True, alpha=0.3)
@@ -1008,8 +1048,7 @@ def _finish_map_axes(ax, mode, epsg, verts, site_xy, xlim, ylim,
     # of distant echoes, and would differ between variables and time steps —
     # this keeps panels comparable and the radar where the eye expects it.
     if site_xy is not None and (xlim is None or ylim is None):
-        reach = float(np.nanmax(np.hypot(verts[:, :, 0] - site_xy[0],
-                                         verts[:, :, 1] - site_xy[1])))
+        reach = float(np.nanmax(np.hypot(verts[:, :, 0] - site_xy[0], verts[:, :, 1] - site_xy[1])))
         auto_x = (site_xy[0] - reach, site_xy[0] + reach)
         auto_y = (site_xy[1] - reach, site_xy[1] + reach)
     else:
@@ -1027,12 +1066,15 @@ def _site_xy(info, mode, epsg):
     if mode == "lonlat":
         return (float(info["longitude"]), float(info["latitude"]))
     import shapely
+
     from raddb.aoi import _reproject_to_aoi, _to_pyproj_crs
+
     pt = shapely.Point(float(info["longitude"]), float(info["latitude"]))
     if epsg == 2056:
         p = _reproject_to_aoi(pt, 4326, 2056)
         return (p.x, p.y)
     import pyproj
+
     tf = pyproj.Transformer.from_crs(_to_pyproj_crs(4326), _to_pyproj_crs(epsg), always_xy=True)
     return tf.transform(pt.x, pt.y)
 
@@ -1041,6 +1083,7 @@ def _site_xy(info, mode, epsg):
 # PPI
 # ============================================================================
 
+
 def plot_aoi_quicklook(
     aoi_geom,
     selected=None,
@@ -1099,7 +1142,7 @@ def plot_aoi_quicklook(
         Cap the number of scattered centroids (random subsample); ``None`` = all.
     xlim, ylim : (min, max) in EPSG:2056 metres, optional
         Axis limits.  ``xlim`` defaults to auto (fills the context/AOI extent);
-        ``ylim`` defaults to the Swiss north band (1.04–1.31 Mm ≈ North 40–310 km).
+        ``ylim`` defaults to the Swiss north band (1.04-1.31 Mm ~ North 40-310 km).
         Pass ``None`` to either for auto-framing of that axis.
     save_path : str or Path, optional
         If given, save the figure (dpi=150, tight).
@@ -1109,6 +1152,7 @@ def plot_aoi_quicklook(
     (fig, ax)
     """
     import shapely
+
     from raddb.aoi import SWISS_EPSG
 
     # Everything here is drawn in the AOI's own frame.  The default context is
@@ -1138,16 +1182,15 @@ def plot_aoi_quicklook(
     # --- radar site positions (also used to frame the view) ---
     sites: dict[str, tuple[float, float]] = {}
     if radars and base_path is not None:
+        from raddb.aoi import SWISS_EPSG, _reproject_to_aoi
         from raddb.lut import load_radar_info
-        from raddb.aoi import _reproject_to_aoi, SWISS_EPSG
 
         for r in radars:
             try:
                 info = load_radar_info(r, base_path)
-            except Exception:  # noqa: BLE001 - missing info shouldn't kill the quicklook
+            except Exception:  # - missing info shouldn't kill the quicklook
                 continue
-            pt = _reproject_to_aoi(
-                shapely.Point(info["longitude"], info["latitude"]), 4326, frame_epsg)
+            pt = _reproject_to_aoi(shapely.Point(info["longitude"], info["latitude"]), 4326, frame_epsg)
             sites[r] = (pt.x, pt.y)
 
     # --- optional selected gate centroids ---
@@ -1157,20 +1200,21 @@ def plot_aoi_quicklook(
     _xc, _yc = f"x_{frame_epsg}", f"y_{frame_epsg}"
     if selected is not None and _xc not in getattr(selected, "columns", ()):
         _xc, _yc = "x", "y"
-    if (
-        selected is not None
-        and show_gates
-        and len(selected)
-        and {_xc, _yc}.issubset(selected.columns)
-    ):
+    if selected is not None and show_gates and len(selected) and {_xc, _yc}.issubset(selected.columns):
         xs = selected[_xc].to_numpy()
         ys = selected[_yc].to_numpy()
         if gate_sample and len(xs) > gate_sample:
             idx = np.random.default_rng(0).choice(len(xs), gate_sample, replace=False)
             xs, ys = xs[idx], ys[idx]
         ax.scatter(
-            xs, ys, s=2, c="tab:blue", alpha=0.25, linewidths=0,
-            label=f"selected gates (n={len(selected):,})", zorder=2,
+            xs,
+            ys,
+            s=2,
+            c="tab:blue",
+            alpha=0.25,
+            linewidths=0,
+            label=f"selected gates (n={len(selected):,})",
+            zorder=2,
         )
 
     # --- radar sites (+ dashed range rings) ---
@@ -1182,23 +1226,29 @@ def plot_aoi_quicklook(
         rings = (range_rings_km,)
     else:
         rings = tuple(range_rings_km)
-    ring_label = (
-        f"range rings ({', '.join(str(int(d)) for d in rings)} km)" if rings else None
-    )
+    ring_label = f"range rings ({', '.join(str(int(d)) for d in rings)} km)" if rings else None
     first_site = True
     ring_labeled = False
     for r, (sx, sy) in sites.items():
         for d_km in rings:
             ax.plot(
-                sx + d_km * 1e3 * np.cos(theta), sy + d_km * 1e3 * np.sin(theta),
-                color="0.5", lw=0.7, ls="--", zorder=1,
+                sx + d_km * 1e3 * np.cos(theta),
+                sy + d_km * 1e3 * np.sin(theta),
+                color="0.5",
+                lw=0.7,
+                ls="--",
+                zorder=1,
                 label=None if ring_labeled else ring_label,
             )
             ring_labeled = True
         ax.plot(sx, sy, "k^", ms=9, zorder=5, label="radar" if first_site else None)
         ax.annotate(
-            r, (sx, sy), textcoords="offset points", xytext=(5, 5),
-            fontweight="bold", zorder=6,
+            r,
+            (sx, sy),
+            textcoords="offset points",
+            xytext=(5, 5),
+            fontweight="bold",
+            zorder=6,
         )
         first_site = False
 
@@ -1248,8 +1298,7 @@ def _draw_context(ax, geom, label="Switzerland"):
     for g in polys:
         if g.geom_type != "Polygon":
             continue
-        ax.fill(*g.exterior.xy, fc="0.93", ec="0.55", lw=0.8, zorder=0,
-                label=label if first else None)
+        ax.fill(*g.exterior.xy, fc="0.93", ec="0.55", lw=0.8, zorder=0, label=label if first else None)
         first = False
 
 
@@ -1277,6 +1326,7 @@ def _draw_aoi_outline(ax, geom, color="red"):
 # Vertical cross-section (arbitrary line, from crop_cross_section)
 # ============================================================================
 
+
 def plot_cross_section(
     df_cs,
     variable: str = "DBZH",
@@ -1336,7 +1386,8 @@ def plot_cross_section(
         raise ValueError(f"no non-NaN {variable!r} values on this cross-section.")
 
     plot_kwargs, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
-        variable, plot_kwargs
+        variable,
+        plot_kwargs,
     )
 
     if ax is None:
@@ -1378,6 +1429,7 @@ def plot_cross_section(
 # The four gate-accurate plots
 # ============================================================================
 
+
 def _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable):
     """Resolve the input and narrow it to one radar and one volume.
 
@@ -1406,6 +1458,7 @@ def _common_prep(data, archive_dir, radar, timestep, start_time, end_time, varia
         # info.yaml crs block; if there is genuinely none, leave it unset so
         # _resolve_coords can say so.
         from raddb.aoi import aoi_epsg
+
         try:
             src.crs = aoi_epsg(src.base, src.radar)
         except (ValueError, FileNotFoundError):
@@ -1503,7 +1556,8 @@ def plot_ppi(
     mode, epsg = _resolve_coords(coords, src.crs)
 
     resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
-        variable, plot_kwargs
+        variable,
+        plot_kwargs,
     )
     if ax is None:
         _, ax = plt.subplots(figsize=figsize)
@@ -1528,8 +1582,18 @@ def plot_ppi(
         rasterized = len(values) > 50_000
     p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
 
-    _finish_map_axes(ax, mode, epsg, verts, _site_xy(src.info, mode, epsg),
-                     xlim, ylim, add_range_rings, context, src.info)
+    _finish_map_axes(
+        ax,
+        mode,
+        epsg,
+        verts,
+        _site_xy(src.info, mode, epsg),
+        xlim,
+        ylim,
+        add_range_rings,
+        context,
+        src.info,
+    )
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
@@ -1593,8 +1657,7 @@ def plot_rhi(
     src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
     target = float(azimuth) % 360.0
     if src.kind == "datatree":
-        values, verts, ray_az = _dt_rhi(src, target, variable, az_tol, height,
-                                        _beamwidth(src))
+        values, verts, ray_az = _dt_rhi(src, target, variable, az_tol, height, _beamwidth(src))
     else:
         base = src.require_base("plot_rhi")
         # Nearest ray **per sweep**, compared on the circle so 359.8° and 0.1°
@@ -1605,19 +1668,22 @@ def plot_rhi(
             load_radar_lut(src.radar, base)
             .select(["gate_id", "sweep", "azimuth"])
             .with_columns(
-                (((pl.col("azimuth") - target + 180.0) % 360.0) - 180.0).abs().alias("_off")
+                (((pl.col("azimuth") - target + 180.0) % 360.0) - 180.0).abs().alias("_off"),
             )
         )
         picked, ray_az = _nearest_ray_per_sweep(lut_az, target, az_tol)
         tbl = gate_corner_table(src.radar, base, kind="v_plane").join(
-            picked.select("gate_id"), on="gate_id", how="semi"
+            picked.select("gate_id"),
+            on="gate_id",
+            how="semi",
         )
         values, joined = _join_corners(src.df, tbl, variable)
         z_prefix = "z_asl" if height == "asl" else "z_rel"
         verts = np.stack(
-            [np.stack([joined[f"d_{k}"].to_numpy(),
-                       joined[f"{z_prefix}_{k}"].to_numpy()], axis=1)
-             for k in range(1, 5)],
+            [
+                np.stack([joined[f"d_{k}"].to_numpy(), joined[f"{z_prefix}_{k}"].to_numpy()], axis=1)
+                for k in range(1, 5)
+            ],
             axis=1,
         ).astype(np.float64)
 
@@ -1625,7 +1691,8 @@ def plot_rhi(
         raise ValueError(f"no gates on the ray at azimuth {ray_az:.1f}° in this input.")
 
     resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
-        variable, plot_kwargs
+        variable,
+        plot_kwargs,
     )
     if ax is None:
         _, ax = plt.subplots(figsize=figsize)
@@ -1639,11 +1706,16 @@ def plot_rhi(
     ax.set_xlabel("Ground range [km]")
     ax.set_ylabel("Height ASL [km]" if height == "asl" else "Height above radar [km]")
     ax.grid(True, alpha=0.3)
-    ax.set_xlim(xlim if xlim is not None
-                else (0.0, (max_range_km * 1e3) if max_range_km else float(verts[:, :, 0].max())))
-    ax.set_ylim(ylim if ylim is not None
-                else (float(verts[:, :, 1].min()),
-                      (max_height_km * 1e3) if max_height_km else float(verts[:, :, 1].max())))
+    ax.set_xlim(
+        xlim if xlim is not None else (0.0, (max_range_km * 1e3) if max_range_km else float(verts[:, :, 0].max())),
+    )
+    ax.set_ylim(
+        (
+            ylim
+            if ylim is not None
+            else (float(verts[:, :, 1].min()), (max_height_km * 1e3) if max_height_km else float(verts[:, :, 1].max()))
+        ),
+    )
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
@@ -1730,7 +1802,7 @@ def plot_cappi(
     if overlap not in ("nearest", "all"):
         raise ValueError(f"overlap must be 'nearest' or 'all'; got {overlap!r}.")
 
-    from raddb.lut import cappi_chords, gate_corner_table, _gate_grid_index
+    from raddb.lut import _gate_grid_index, cappi_chords, gate_corner_table
 
     src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
 
@@ -1738,8 +1810,15 @@ def plot_cappi(
 
     if src.kind == "datatree":
         values, verts = _dt_cappi(
-            src, altitude, variable, height, overlap, fill_lowest,
-            mode, epsg, _beamwidth(src),
+            src,
+            altitude,
+            variable,
+            height,
+            overlap,
+            fill_lowest,
+            mode,
+            epsg,
+            _beamwidth(src),
         )
     else:
         base = src.require_base("plot_cappi")
@@ -1751,7 +1830,7 @@ def plot_cappi(
         if chords.is_empty():
             raise ValueError(
                 f"no beam of radar {src.radar!r} reaches {altitude} m "
-                f"({'ASL' if height == 'asl' else 'above the radar'}); nothing to draw."
+                f"({'ASL' if height == 'asl' else 'above the radar'}); nothing to draw.",
             )
         if overlap == "nearest":
             chords = _resolve_chord_overlap(chords)
@@ -1760,28 +1839,33 @@ def plot_cappi(
 
         # Chords are per (sweep, rng_idx) and azimuth-independent -> expand to gates.
         gates = _gate_grid_index(src.radar, base).join(
-            chords, on=["sweep", "rng_idx"], how="inner"
+            chords,
+            on=["sweep", "rng_idx"],
+            how="inner",
         )
         if gates.is_empty():
             raise ValueError("the constant-altitude surface matched no gates.")
         tbl = gate_corner_table(src.radar, base, kind="h_plane").join(
-            gates.select(["gate_id", "d_near", "d_far"]), on="gate_id", how="inner"
+            gates.select(["gate_id", "d_near", "d_far"]),
+            on="gate_id",
+            how="inner",
         )
         values, joined = _join_corners(src.df, tbl, variable)
         verts = _trim_footprints_to_chord(
             _corner_vertices(joined, 4, mode, epsg, src.info),
             _corner_vertices(joined, 4, "xy", None, src.info),
-            joined["d_near"].to_numpy(), joined["d_far"].to_numpy(),
+            joined["d_near"].to_numpy(),
+            joined["d_far"].to_numpy(),
         )
 
     if len(values) == 0:
         raise ValueError(
-            f"no gates at {altitude} m are present in this input — the slice is "
-            "outside the loaded/cropped data."
+            f"no gates at {altitude} m are present in this input — the slice is " "outside the loaded/cropped data.",
         )
 
     resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
-        variable, plot_kwargs
+        variable,
+        plot_kwargs,
     )
     if ax is None:
         _, ax = plt.subplots(figsize=figsize)
@@ -1789,8 +1873,18 @@ def plot_cappi(
         rasterized = len(values) > 50_000
 
     p = _draw_polygons(ax, verts, values, resolved, edgecolor, rasterized)
-    _finish_map_axes(ax, mode, epsg, verts, _site_xy(src.info, mode, epsg),
-                     xlim, ylim, add_range_rings, context, src.info)
+    _finish_map_axes(
+        ax,
+        mode,
+        epsg,
+        verts,
+        _site_xy(src.info, mode, epsg),
+        xlim,
+        ylim,
+        add_range_rings,
+        context,
+        src.info,
+    )
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
@@ -1871,8 +1965,6 @@ def plot_vcs(
     -------
     matplotlib.collections.PolyCollection
     """
-    import shapely
-
     if isinstance(data, (xr.DataTree, xr.Dataset)):
         raise TypeError(
             "plot_vcs cannot work from a DataTree: cutting a cross-section needs "
@@ -1880,7 +1972,7 @@ def plot_vcs(
             "equivalent of. Archive the volume first — it takes a few seconds — "
             "then cut the section on the result:\n"
             "    db.archive(datatree=dt, radar='L')\n"
-            "    db.open(radars='L').plot_vcs(line=(p1, p2))"
+            "    db.open(radars='L').plot_vcs(line=(p1, p2))",
         )
 
     # RadDB.columns is a method, a frame's .columns is a property — handle both.
@@ -1896,7 +1988,7 @@ def plot_vcs(
             "both a section line and an already-cut frame were given, so it is "
             "ambiguous which section to draw. Cutting again would intersect two "
             "different sections. Pass the line to an uncut frame, or drop line= "
-            "to draw the section this frame already carries."
+            "to draw the section this frame already carries.",
         )
     if not already_cut:
         if line is None:
@@ -1905,21 +1997,25 @@ def plot_vcs(
                 "Pass line=((x1, y1), (x2, y2)), a shapely LineString or a "
                 ".shp/.geojson path — or call extract_cross_section() first. "
                 "(An AOI crop by rectangle/polygon/point selects an area, not a "
-                "line, so its result cannot be drawn as a cross-section.)"
+                "line, so its result cannot be drawn as a cross-section.)",
             )
         if not hasattr(data, "extract_cross_section"):
             # A bare frame carries gate_id and the archive carries the geometry,
             # so the section is perfectly cuttable — wrap it, the same way the
             # other three plots read the LUT for a bare frame.
             from raddb.main import RadDB as _RadDB
+
             probe = _resolve_frame(data, archive_dir)
             probe.require_base("cutting a cross-section from line=")
             data = _RadDB(archive_dir=str(probe.base), crs=probe.crs)._derive(probe.df)
         p1, p2, file_crs = _line_endpoints(line)
         # A file states its own CRS; honour it unless the caller overrode it.
         data = data.extract_cross_section(
-            p1, p2, crs=crs if crs is not None else file_crs,
-            beamwidth_deg=beamwidth_deg, aoi_crs=aoi_crs,
+            p1,
+            p2,
+            crs=crs if crs is not None else file_crs,
+            beamwidth_deg=beamwidth_deg,
+            aoi_crs=aoi_crs,
         )
 
     src = _common_prep(data, archive_dir, radar, timestep, start_time, end_time, variable)
@@ -1933,13 +2029,12 @@ def plot_vcs(
 
     # cs_polygon lives in (distance along the line, altitude ASL).
     shift = 0.0 if height == "asl" else -float(src.info["altitude"])
-    verts = np.stack([
-        np.asarray(poly.exterior.coords)[:4, :2] for poly in pdf["cs_polygon"]
-    ]).astype(np.float64)
+    verts = np.stack([np.asarray(poly.exterior.coords)[:4, :2] for poly in pdf["cs_polygon"]]).astype(np.float64)
     verts[:, :, 1] += shift
 
     resolved, is_discrete, class_labels, cbar_label = _resolve_plot_kwargs(
-        variable, plot_kwargs
+        variable,
+        plot_kwargs,
     )
     values = pdf[variable].to_numpy()
     if ax is None:
@@ -1954,10 +2049,8 @@ def plot_vcs(
     ax.set_xlabel("Distance along section [km]")
     ax.set_ylabel("Altitude [km ASL]" if height == "asl" else "Height above radar [km]")
     ax.grid(True, alpha=0.3)
-    ax.set_xlim(xlim if xlim is not None
-                else (float(verts[:, :, 0].min()), float(verts[:, :, 0].max())))
-    ax.set_ylim(ylim if ylim is not None
-                else (float(verts[:, :, 1].min()), float(verts[:, :, 1].max())))
+    ax.set_xlim(xlim if xlim is not None else (float(verts[:, :, 0].min()), float(verts[:, :, 0].max())))
+    ax.set_ylim(ylim if ylim is not None else (float(verts[:, :, 1].min()), float(verts[:, :, 1].max())))
 
     if add_colorbar:
         _add_colorbar(p, ax, is_discrete, class_labels, cbar_label)
@@ -1968,6 +2061,7 @@ def plot_vcs(
 
 # ------------------------------------------------------------ plot internals
 
+
 def _default_title(radar, variable, what, tstr):
     parts = [f"radar {radar}", variable, what]
     if tstr:
@@ -1988,12 +2082,14 @@ def _line_endpoints(line):
     lon/lat by RFC 7946, and reading those degrees as LV95 metres would place the
     section thousands of kilometres away.
     """
-    import shapely
     from pathlib import Path
 
+    import shapely
+
     src_crs = None
     if isinstance(line, (str, Path)):
         from raddb.aoi import _read_geometry_file
+
         geom, src_crs = _read_geometry_file(Path(line))
     elif isinstance(line, shapely.geometry.base.BaseGeometry):
         geom = line
@@ -2008,7 +2104,7 @@ def _line_endpoints(line):
     return tuple(coords[0][:2]), tuple(coords[-1][:2]), src_crs
 
 
-def _resolve_chord_overlap(chords: "pl.DataFrame") -> "pl.DataFrame":
+def _resolve_chord_overlap(chords: pl.DataFrame) -> pl.DataFrame:
     """Partition the ground-distance axis so no two gates cover the same distance.
 
     Several sweeps typically intersect the slice altitude over overlapping
@@ -2052,7 +2148,7 @@ def _resolve_chord_overlap(chords: "pl.DataFrame") -> "pl.DataFrame":
     )
 
 
-def _extend_lowest_sweep(chords: "pl.DataFrame", radar: str, base) -> "pl.DataFrame":
+def _extend_lowest_sweep(chords: pl.DataFrame, radar: str, base) -> pl.DataFrame:
     """Follow the lowest sweep past the range where every beam is above the slice.
 
     The operational CAPPI convention (Stull §8.2): rather than leaving the far
@@ -2066,7 +2162,7 @@ def _extend_lowest_sweep(chords: "pl.DataFrame", radar: str, base) -> "pl.DataFr
 
     nodes = load_plane_nodes(radar, base, "v_plane", sweep=lowest)
     nodes = nodes.filter(
-        (pl.col("az_idx") == pl.col("az_idx").min()) & (pl.col("el_level") == -1)
+        (pl.col("az_idx") == pl.col("az_idx").min()) & (pl.col("el_level") == -1),
     ).sort("rng_idx")
     d = nodes["d"].to_numpy()
     if d.size < 2:
@@ -2075,17 +2171,20 @@ def _extend_lowest_sweep(chords: "pl.DataFrame", radar: str, base) -> "pl.DataFr
     beyond = np.flatnonzero(d[1:] > d_end)
     if beyond.size == 0:
         return chords
-    extra = pl.DataFrame({
-        "sweep": np.full(beyond.size, lowest, dtype=np.int32),
-        "rng_idx": beyond.astype(np.int32),
-        "d_near": d[beyond].astype(np.float32),
-        "d_far": d[beyond + 1].astype(np.float32),
-        "z_center": np.zeros(beyond.size, dtype=np.float32),
-        "dz_center": np.zeros(beyond.size, dtype=np.float32),
-    })
+    extra = pl.DataFrame(
+        {
+            "sweep": np.full(beyond.size, lowest, dtype=np.int32),
+            "rng_idx": beyond.astype(np.int32),
+            "d_near": d[beyond].astype(np.float32),
+            "d_far": d[beyond + 1].astype(np.float32),
+            "z_center": np.zeros(beyond.size, dtype=np.float32),
+            "dz_center": np.zeros(beyond.size, dtype=np.float32),
+        },
+    )
     existing = chords.filter(pl.col("sweep") == lowest)["rng_idx"].to_list()
     return pl.concat(
-        [chords, extra.filter(~pl.col("rng_idx").is_in(existing))], how="vertical"
+        [chords, extra.filter(~pl.col("rng_idx").is_in(existing))],
+        how="vertical",
     )
 
 
@@ -2112,28 +2211,32 @@ def _trim_footprints_to_chord(verts, verts_xy, d_near, d_far):
     t_far = np.clip(np.nan_to_num(t_far, nan=1.0), 0.0, 1.0)[:, None]
 
     c1, c2, c3, c4 = verts[:, 0], verts[:, 1], verts[:, 2], verts[:, 3]
-    return np.stack([
-        c1 + t_near * (c2 - c1),      # near edge, azimuth side A
-        c1 + t_far * (c2 - c1),       # far  edge, azimuth side A
-        c4 + t_far * (c3 - c4),       # far  edge, azimuth side B
-        c4 + t_near * (c3 - c4),      # near edge, azimuth side B
-    ], axis=1)
+    return np.stack(
+        [
+            c1 + t_near * (c2 - c1),  # near edge, azimuth side A
+            c1 + t_far * (c2 - c1),  # far  edge, azimuth side A
+            c4 + t_far * (c3 - c4),  # far  edge, azimuth side B
+            c4 + t_near * (c3 - c4),  # near edge, azimuth side B
+        ],
+        axis=1,
+    )
 
 
 # ============================================================================
 # LATENT SPACE SCATTER (AMT publication figure)
 # ============================================================================
 
+
 def plot_latent_scatter(
-    df: "pl.DataFrame | pd.DataFrame",
+    df: pl.DataFrame | pd.DataFrame,
     config: list[dict],
     figsize: tuple[float, float] | None = None,
     fig_height: float = 4.6,
     **scatter_kwargs,
 ):
-    """Publication-ready 2×3 AMT latent-space scatter figure.
+    """Publication-ready 2x3 AMT latent-space scatter figure.
 
-    Creates a 2-row × 3-column figure with width=6.9 inches (AMT full-column
+    Creates a 2-row x 3-column figure with width=6.9 inches (AMT full-column
     width). Each subplot shows a scatter of ``df["L1"]`` vs ``df["L2"]``
     coloured by one radar variable. A compact inset colorbar with a white
     semi-transparent background is placed inside each subplot.
@@ -2188,7 +2291,8 @@ def plot_latent_scatter(
     fw = figsize[0] if figsize is not None else 6.9
     fh = figsize[1] if figsize is not None else fig_height
     fig, axes = plt.subplots(
-        n_rows, n_cols,
+        n_rows,
+        n_cols,
         figsize=(fw, fh),
         gridspec_kw={"hspace": 0, "wspace": 0},
     )
@@ -2209,7 +2313,8 @@ def plot_latent_scatter(
         panel_scatter_kw = {**scatter_kwargs, **panel.get("scatter_kwargs", {})}
 
         m = ax.scatter(
-            df["L1"].to_numpy(), df["L2"].to_numpy(),
+            df["L1"].to_numpy(),
+            df["L2"].to_numpy(),
             c=df[var].to_numpy(),
             cmap=cmap,
             norm=norm,
@@ -2233,7 +2338,7 @@ def plot_latent_scatter(
         cb.outline.set_linewidth(0.5)
 
         fancy_box_coords = (cbar_inset_axes[0] - x_pad, cbar_inset_axes[1] - y_pad)
-        fancy_box_width  = cbar_inset_axes[2] + 2 * x_pad
+        fancy_box_width = cbar_inset_axes[2] + 2 * x_pad
         fancy_box_height = cbar_inset_axes[3] + 2 * y_pad
         fancy_patch = mpatches.FancyBboxPatch(
             fancy_box_coords,
diff --git a/tutorial/01_archiving.ipynb b/tutorial/01_archiving.ipynb
index 83fcbe2..fe4b71a 100644
--- a/tutorial/01_archiving.ipynb
+++ b/tutorial/01_archiving.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3f7df4aa",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 1. Archiving\n",
@@ -41,30 +41,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "09525d30",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:49.935264Z",
-     "iopub.status.busy": "2026-08-04T15:28:49.935114Z",
-     "iopub.status.idle": "2026-08-04T15:28:50.540920Z",
-     "shell.execute_reply": "2026-08-04T15:28:50.540228Z"
-    }
-   },
+   "id": "1",
+   "metadata": {},
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
+    "from pathlib import Path\n",
+    "\n",
     "import raddb\n",
     "from raddb.lut import suggest_crs\n",
-    "from pathlib import Path\n",
     "\n",
     "print(\"raddb\", raddb.__version__)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9c1898c8",
+   "id": "2",
    "metadata": {},
    "source": [
     "## 1. Raw data and RadDB initialisation\n",
@@ -78,19 +73,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c1e7fb76",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — point these at your own data\n",
     "# --------------------------------------------------------------------------\n",
-    "# Any xarray DataTree with the standard xradar layout works.  \n",
-    "# These tutorials use MeteoSwiss and NEXRAD volumes stored as zarr and nc format.  \n",
+    "# Any xarray DataTree with the standard xradar layout works.\n",
+    "# These tutorials use MeteoSwiss and NEXRAD volumes stored as zarr and nc format.\n",
     "# Edit the paths below to point at your own data.\n",
     "\n",
-    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
-    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
+    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
     "print(\"MCH DataTrees   :\", MCH_DIR)\n",
@@ -100,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e92e3c0",
+   "id": "4",
    "metadata": {},
    "source": [
     "### Inspecting raw data archive\n",
@@ -113,15 +108,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ab4d193d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:50.542887Z",
-     "iopub.status.busy": "2026-08-04T15:28:50.542708Z",
-     "iopub.status.idle": "2026-08-04T15:28:50.641647Z",
-     "shell.execute_reply": "2026-08-04T15:28:50.640859Z"
-    }
-   },
+   "id": "5",
+   "metadata": {},
    "outputs": [],
    "source": [
     "db = raddb.RadDB()\n",
@@ -132,15 +120,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "720db410",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:50.643672Z",
-     "iopub.status.busy": "2026-08-04T15:28:50.643573Z",
-     "iopub.status.idle": "2026-08-04T15:28:50.678424Z",
-     "shell.execute_reply": "2026-08-04T15:28:50.677758Z"
-    }
-   },
+   "id": "6",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# `detailed=True` adds a per-day breakdown\n",
@@ -150,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "550e156a",
+   "id": "7",
    "metadata": {},
    "source": [
     "### Creating the RadDB object\n",
@@ -163,18 +144,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f69c2ae6",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)   # 2056 ==> CH1903+/LV95\n",
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)  # 2056 ==> CH1903+/LV95\n",
     "db"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8eff141c",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d1b1569",
+   "id": "10",
    "metadata": {},
    "source": [
     "## 2. Archiving\n",
@@ -196,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "125783b2",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a9d57a0",
+   "id": "12",
    "metadata": {},
    "source": [
     "### The CRS constraint\n",
@@ -231,7 +212,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "049b6ee8",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,35 +221,40 @@
     "# \"KMLB\": lat/lon = 28.113/-80.654\n",
     "# \"KLOT\": lat/lon = 41.604/-88.084\n",
     "print(f\"KTLX ==> {suggest_crs(latitude=35.333, longitude=-97.278)}\")\n",
-    "print(f\"KMLB ==> {suggest_crs(latitude=28.113, longitude=-80.654)}\")  \n",
-    "print(f\"KLOT ==> {suggest_crs(latitude=41.604, longitude=-88.084)}\")  "
+    "print(f\"KMLB ==> {suggest_crs(latitude=28.113, longitude=-80.654)}\")\n",
+    "print(f\"KLOT ==> {suggest_crs(latitude=41.604, longitude=-88.084)}\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ccc396e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:28:59.591620Z",
-     "iopub.status.busy": "2026-08-04T15:28:59.591457Z",
-     "iopub.status.idle": "2026-08-04T15:29:23.356069Z",
-     "shell.execute_reply": "2026-08-04T15:29:23.355579Z"
-    }
-   },
+   "id": "14",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "db_us.archive(datatree_dir=NEXRAD_DIR, radar=[\"KTLX\"], crs=32614,  # 32614 ==> UTM zone 14N\n",
-    "              time_period=(\"2024-01-01\", \"2024-06-15\"))\n",
-    "db_us.archive(datatree_dir=NEXRAD_DIR, radar=[\"KMLB\"], crs=32617,  # 32617 ==> UTM zone 17N\n",
-    "              time_period=(\"2024-01-01\", \"2024-06-15\"))\n",
-    "db_us.archive(datatree_dir=NEXRAD_DIR, radar=[\"KLOT\"], crs=32616,  # 32616 ==> UTM zone 16N\n",
-    "              time_period=(\"2024-01-01\", \"2024-06-15\"))"
+    "db_us.archive(\n",
+    "    datatree_dir=NEXRAD_DIR,\n",
+    "    radar=[\"KTLX\"],\n",
+    "    crs=32614,  # 32614 ==> UTM zone 14N\n",
+    "    time_period=(\"2024-01-01\", \"2024-06-15\"),\n",
+    ")\n",
+    "db_us.archive(\n",
+    "    datatree_dir=NEXRAD_DIR,\n",
+    "    radar=[\"KMLB\"],\n",
+    "    crs=32617,  # 32617 ==> UTM zone 17N\n",
+    "    time_period=(\"2024-01-01\", \"2024-06-15\"),\n",
+    ")\n",
+    "db_us.archive(\n",
+    "    datatree_dir=NEXRAD_DIR,\n",
+    "    radar=[\"KLOT\"],\n",
+    "    crs=32616,  # 32616 ==> UTM zone 16N\n",
+    "    time_period=(\"2024-01-01\", \"2024-06-15\"),\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0ebd3c49",
+   "id": "15",
    "metadata": {},
    "source": [
     "## 3. Archived data"
@@ -277,15 +263,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78662044",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:23.358658Z",
-     "iopub.status.busy": "2026-08-04T15:29:23.358498Z",
-     "iopub.status.idle": "2026-08-04T15:29:23.364840Z",
-     "shell.execute_reply": "2026-08-04T15:29:23.363777Z"
-    }
-   },
+   "id": "16",
+   "metadata": {},
    "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
@@ -296,15 +275,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "755fe1f8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:23.366860Z",
-     "iopub.status.busy": "2026-08-04T15:29:23.366690Z",
-     "iopub.status.idle": "2026-08-04T15:29:23.370629Z",
-     "shell.execute_reply": "2026-08-04T15:29:23.369330Z"
-    }
-   },
+   "id": "17",
+   "metadata": {},
    "outputs": [],
    "source": [
     "for p in sorted((ARCHIVE_DIR / \"L\" / \"LUT\").iterdir()):\n",
@@ -313,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86571224",
+   "id": "18",
    "metadata": {},
    "source": [
     "### `gate_id`: how a volume finds its geometry\n",
@@ -328,15 +300,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3500a227",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:23.372678Z",
-     "iopub.status.busy": "2026-08-04T15:29:23.372422Z",
-     "iopub.status.idle": "2026-08-04T15:29:23.409182Z",
-     "shell.execute_reply": "2026-08-04T15:29:23.408185Z"
-    }
-   },
+   "id": "19",
+   "metadata": {},
    "outputs": [],
    "source": [
     "lut = db.get_lut(\"L\")\n",
@@ -348,7 +313,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40878b2a",
+   "id": "20",
    "metadata": {},
    "source": [
     "### Radar site metadata\n",
@@ -360,26 +325,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5efeff2e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:29:23.707229Z",
-     "iopub.status.busy": "2026-08-04T15:29:23.706817Z",
-     "iopub.status.idle": "2026-08-04T15:29:24.087549Z",
-     "shell.execute_reply": "2026-08-04T15:29:24.086365Z"
-    }
-   },
+   "id": "21",
+   "metadata": {},
    "outputs": [],
    "source": [
     "info = db.get_radar_info(\"L\")\n",
-    "for k in [\"radar\", \"network\", \"latitude\", \"longitude\", \"altitude\",\n",
-    "          \"crs\", \"ke\", \"beamwidth_deg\", \"n_sweeps\", \"n_gates\"]:\n",
+    "for k in [\"radar\", \"network\", \"latitude\", \"longitude\", \"altitude\", \"crs\", \"ke\", \"beamwidth_deg\", \"n_sweeps\", \"n_gates\"]:\n",
     "    print(f\"  {k:<16} {info[k]}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8d5163ff",
+   "id": "22",
    "metadata": {},
    "source": [
     "---\n",
diff --git a/tutorial/02_opening_and_filtering.ipynb b/tutorial/02_opening_and_filtering.ipynb
index dc45d57..3961e15 100644
--- a/tutorial/02_opening_and_filtering.ipynb
+++ b/tutorial/02_opening_and_filtering.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "252c31b4",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 2. Open RadDB object and filter\n",
@@ -25,37 +25,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a4f11595",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:33.384971Z",
-     "iopub.status.busy": "2026-08-11T11:04:33.384854Z",
-     "iopub.status.idle": "2026-08-11T11:04:33.967578Z",
-     "shell.execute_reply": "2026-08-11T11:04:33.966779Z"
-    }
-   },
+   "id": "1",
+   "metadata": {},
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "from pathlib import Path\n",
+    "\n",
     "import polars as pl\n",
+    "\n",
     "import raddb"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36dbad7c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:33.969237Z",
-     "iopub.status.busy": "2026-08-11T11:04:33.969039Z",
-     "iopub.status.idle": "2026-08-11T11:04:33.972335Z",
-     "shell.execute_reply": "2026-08-11T11:04:33.971749Z"
-    }
-   },
+   "id": "2",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# --------------------------------------------------------------------------\n",
@@ -63,8 +52,8 @@
     "# --------------------------------------------------------------------------\n",
     "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.\n",
     "\n",
-    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
-    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
+    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
     "print(\"MCH DataTrees   :\", MCH_DIR)\n",
@@ -75,15 +64,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "60595d47",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:33.973778Z",
-     "iopub.status.busy": "2026-08-11T11:04:33.973684Z",
-     "iopub.status.idle": "2026-08-11T11:04:33.976649Z",
-     "shell.execute_reply": "2026-08-11T11:04:33.975926Z"
-    }
-   },
+   "id": "3",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
@@ -96,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e307b491",
+   "id": "4",
    "metadata": {},
    "source": [
     "## 1. `open()`: reading the archive\n",
@@ -107,15 +89,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "62430b24",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:33.978054Z",
-     "iopub.status.busy": "2026-08-11T11:04:33.977960Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.022320Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.021652Z"
-    }
-   },
+   "id": "5",
+   "metadata": {},
    "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
@@ -125,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "419283d4",
+   "id": "6",
    "metadata": {},
    "source": [
     "`open()` narrows *before* anything is loaded — the time range, the radars and the\n",
@@ -136,15 +111,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e8856737",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.023451Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.023303Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.059913Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.059394Z"
-    }
-   },
+   "id": "7",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Only two variables, only radar L\n",
@@ -159,15 +127,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "320155b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.061304Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.061162Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.076238Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.075582Z"
-    }
-   },
+   "id": "8",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Filters can be pushed down at open() too, so filtered-out rows are never materialised\n",
@@ -177,7 +138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1909d18e",
+   "id": "9",
    "metadata": {},
    "source": [
     "## 2. What you are holding\n",
@@ -189,15 +150,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37d4e073",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.077374Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.077281Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.080650Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.080298Z"
-    }
-   },
+   "id": "10",
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"type:\\t\\t\", type(rdf.data))\n",
@@ -209,27 +163,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6fd88dae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.081817Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.081732Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.275932Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.275352Z"
-    }
-   },
+   "id": "11",
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"radars    :\", rdf.radars())\n",
     "print(\"variables :\", rdf.columns())\n",
     "print(\"time range:\", rdf.start_time(), \"->\", rdf.end_time())\n",
     "print(\"lon/lat    :\", [round(v, 3) for v in rdf.geographic_extent()])\n",
-    "print(\"archive CRS:\", rdf.crs())          # recovered from the archive itself"
+    "print(\"archive CRS:\", rdf.crs())  # recovered from the archive itself"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "57b6ce27",
+   "id": "12",
    "metadata": {},
    "source": [
     "## 3. `filter()`: threshold on values\n",
@@ -240,31 +187,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "650f8db6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.277445Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.277347Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.286613Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.286003Z"
-    }
-   },
+   "id": "13",
+   "metadata": {},
    "outputs": [],
    "source": [
     "rain = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
     "print(f\"DBZH > 20: {len(rain):,} gates\")\n",
     "\n",
-    "filt_df = rdf.filter([\n",
-    "    {\"var\": \"DBZH\",  \"logic\": \">\",  \"threshold\": 20},\n",
-    "    {\"var\": \"RHOHV\", \"logic\": \">=\", \"threshold\": 0.98},\n",
-    "    {\"var\": \"ZDR\",   \"logic\": \">\",  \"threshold\": 4},\n",
-    "])\n",
+    "filt_df = rdf.filter(\n",
+    "    [\n",
+    "        {\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20},\n",
+    "        {\"var\": \"RHOHV\", \"logic\": \">=\", \"threshold\": 0.98},\n",
+    "        {\"var\": \"ZDR\", \"logic\": \">\", \"threshold\": 4},\n",
+    "    ],\n",
+    ")\n",
     "print(f\"DBZH > 20, RHOHV >= 0.98, ZDR > 4 : {len(filt_df):,} gates\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b244e5ec",
+   "id": "14",
    "metadata": {},
    "source": [
     "## 4. `sel()`: select by label, xarray-style\n",
@@ -277,15 +219,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3aec88ab",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.288256Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.288150Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.534804Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.534235Z"
-    }
-   },
+   "id": "15",
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"one sweep      :\", f\"{len(rdf.sel(sweep=1)):,}\")\n",
@@ -296,7 +231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e10ec065",
+   "id": "16",
    "metadata": {},
    "source": [
     "`range`, `azimuth`, `elevation_angle`, `latitude`, `longitude`\n",
@@ -308,29 +243,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "17a5d6ed",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.536128Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.535979Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.599984Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.599314Z"
-    }
-   },
+   "id": "17",
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"stored per gate:\", rdf.columns())\n",
-    "print(\"also selectable :\", [\"range\", \"azimuth\", \"elevation_angle\",\n",
-    "                            \"latitude\", \"longitude\", \"altitude\", \"sweep\"])\n",
+    "print(\"also selectable :\", [\"range\", \"azimuth\", \"elevation_angle\", \"latitude\", \"longitude\", \"altitude\", \"sweep\"])\n",
     "\n",
     "narrow = rdf.sel(sweep=1, range=slice(20_000, 60_000))\n",
-    "print(f\"\\nsweep 1, 20-60 km: {len(narrow):,} gates \"\n",
-    "      f\"(columns unchanged: {narrow.columns() == rdf.columns()})\")"
+    "print(f\"\\nsweep 1, 20-60 km: {len(narrow):,} gates \" f\"(columns unchanged: {narrow.columns() == rdf.columns()})\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d72ae0a4",
+   "id": "18",
    "metadata": {},
    "source": [
     "## 5. `add_feature()`: compute columns\n",
@@ -343,27 +269,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ecec9579",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.601299Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.601154Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.611186Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.610691Z"
-    }
-   },
+   "id": "19",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "derived = (\n",
-    "    rdf.add_feature(\"DBZH_lin\", lambda df: 10 ** (df[\"DBZH\"] / 10))\n",
-    "       .add_feature(\"DBZH_dev\", lambda df: df[\"DBZH\"] - df[\"DBZH\"].mean())\n",
+    "derived = rdf.add_feature(\"DBZH_lin\", lambda df: 10 ** (df[\"DBZH\"] / 10)).add_feature(\n",
+    "    \"DBZH_dev\",\n",
+    "    lambda df: df[\"DBZH\"] - df[\"DBZH\"].mean(),\n",
     ")\n",
     "derived.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7f37ef3f",
+   "id": "20",
    "metadata": {},
    "source": [
     "If you would rather work in plain polars or pandas, nothing stops you — `.data`\n",
@@ -373,15 +292,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01572c8c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.612654Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.612572Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.637884Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.637348Z"
-    }
-   },
+   "id": "21",
+   "metadata": {},
    "outputs": [],
    "source": [
     "rdf.data.with_columns((pl.col(\"DBZH\") - pl.col(\"ZDR\")).alias(\"DIFF\"))\n",
@@ -396,7 +308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "152e7b06",
+   "id": "22",
    "metadata": {},
    "source": [
     "## 6. Framework converter\n",
@@ -407,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49d3e2e4",
+   "id": "23",
    "metadata": {},
    "source": [
     "### `to_pandas()`: the DataFrame\n",
@@ -437,15 +349,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dff1fb07",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.639146Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.639060Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.646098Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.645535Z"
-    }
-   },
+   "id": "24",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# No flags: the stored columns only, exactly as open() loaded them.\n",
@@ -457,15 +362,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f7f7ae3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.647539Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.647452Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.791144Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.790419Z"
-    }
-   },
+   "id": "25",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# with_geometry=True joins the per-gate coordinates from the LUT on gate_id.\n",
@@ -476,15 +374,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9cc53313",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.792322Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.792226Z",
-     "iopub.status.idle": "2026-08-11T11:04:34.944882Z",
-     "shell.execute_reply": "2026-08-11T11:04:34.944400Z"
-    }
-   },
+   "id": "26",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# with_polar_coords=True also brings the polar coordinates the geometry came from.\n",
@@ -495,7 +386,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec30bf43",
+   "id": "27",
    "metadata": {},
    "source": [
     "### Where the geometry lives\n",
@@ -512,15 +403,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a15e0de3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:34.946404Z",
-     "iopub.status.busy": "2026-08-11T11:04:34.946292Z",
-     "iopub.status.idle": "2026-08-11T11:04:35.239055Z",
-     "shell.execute_reply": "2026-08-11T11:04:35.238383Z"
-    }
-   },
+   "id": "28",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Projected coordinates: state the CRS when creating the RadDB, and\n",
@@ -535,15 +419,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3c13d25b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:35.240725Z",
-     "iopub.status.busy": "2026-08-11T11:04:35.240575Z",
-     "iopub.status.idle": "2026-08-11T11:04:35.283659Z",
-     "shell.execute_reply": "2026-08-11T11:04:35.283068Z"
-    }
-   },
+   "id": "29",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Any LUT column can be attached by joining on gate_id.  This is also how you add\n",
@@ -556,7 +433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0445a74",
+   "id": "30",
    "metadata": {},
    "source": [
     "### `to_geopandas()` — points with a CRS"
@@ -565,15 +442,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "abc6973e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:35.285730Z",
-     "iopub.status.busy": "2026-08-11T11:04:35.285583Z",
-     "iopub.status.idle": "2026-08-11T11:04:35.566121Z",
-     "shell.execute_reply": "2026-08-11T11:04:35.565473Z"
-    }
-   },
+   "id": "31",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# geopandas: point geometry per gate, ready for spatial joins or QGIS\n",
@@ -585,7 +455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e46765f",
+   "id": "32",
    "metadata": {},
    "source": [
     "### `to_datatree()` — back to xarray"
@@ -594,15 +464,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1280e7ee",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T11:04:35.568035Z",
-     "iopub.status.busy": "2026-08-11T11:04:35.567952Z",
-     "iopub.status.idle": "2026-08-11T11:04:36.719949Z",
-     "shell.execute_reply": "2026-08-11T11:04:36.719262Z"
-    }
-   },
+   "id": "33",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# DataTree: the full polar structure, for xarray workflows.\n",
@@ -619,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c46973e6",
+   "id": "34",
    "metadata": {},
    "source": [
     "---\n",
diff --git a/tutorial/03_area_of_interest.ipynb b/tutorial/03_area_of_interest.ipynb
index e5c9e68..1e585fb 100644
--- a/tutorial/03_area_of_interest.ipynb
+++ b/tutorial/03_area_of_interest.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b8d0cd89",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 3. Area Of Interest\n",
@@ -40,38 +40,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9d7152f3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:53.515796Z",
-     "iopub.status.busy": "2026-08-11T16:21:53.515665Z",
-     "iopub.status.idle": "2026-08-11T16:21:54.463480Z",
-     "shell.execute_reply": "2026-08-11T16:21:54.462351Z"
-    }
-   },
+   "id": "1",
+   "metadata": {},
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "from pathlib import Path\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import shapely\n",
+    "\n",
     "import raddb"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78499521",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:54.466293Z",
-     "iopub.status.busy": "2026-08-11T16:21:54.465947Z",
-     "iopub.status.idle": "2026-08-11T16:21:54.470649Z",
-     "shell.execute_reply": "2026-08-11T16:21:54.469711Z"
-    }
-   },
+   "id": "2",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# --------------------------------------------------------------------------\n",
@@ -80,8 +69,8 @@
     "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n",
     "# the cell below builds it.\n",
     "\n",
-    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
-    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
+    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
+    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
     "print(\"MCH DataTrees   :\", MCH_DIR)\n",
@@ -92,15 +81,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bb505873",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:54.472572Z",
-     "iopub.status.busy": "2026-08-11T16:21:54.472378Z",
-     "iopub.status.idle": "2026-08-11T16:21:54.476272Z",
-     "shell.execute_reply": "2026-08-11T16:21:54.475137Z"
-    }
-   },
+   "id": "3",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
@@ -114,29 +96,22 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6e5330be",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:54.479664Z",
-     "iopub.status.busy": "2026-08-11T16:21:54.479357Z",
-     "iopub.status.idle": "2026-08-11T16:21:54.601377Z",
-     "shell.execute_reply": "2026-08-11T16:21:54.600119Z"
-    }
-   },
+   "id": "4",
+   "metadata": {},
    "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)\n",
     "rdf = db.open(radars=\"L\").filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 5})\n",
     "\n",
     "info = db.get_radar_info(\"L\")\n",
-    "SITE = (info[\"longitude\"], info[\"latitude\"])          # radar: L ==> lon, lat\n",
+    "SITE = (info[\"longitude\"], info[\"latitude\"])  # radar: L ==> lon, lat\n",
     "print(f\"radar L at {SITE[0]:.3f}, {SITE[1]:.3f}   |   {len(rdf):,} gates with echo\")\n",
     "print(\"archive CRS:\", rdf.crs())"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "65c887b3",
+   "id": "5",
    "metadata": {},
    "source": [
     "### `crs=` describes *your* numbers\n",
@@ -154,15 +129,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3d50c2b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:54.603503Z",
-     "iopub.status.busy": "2026-08-11T16:21:54.603257Z",
-     "iopub.status.idle": "2026-08-11T16:21:57.197545Z",
-     "shell.execute_reply": "2026-08-11T16:21:57.196755Z"
-    }
-   },
+   "id": "6",
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pyproj import Transformer\n",
@@ -174,14 +142,20 @@
     "\n",
     "print(\"crop_around_point(distance=30 km):\")\n",
     "print(f\"  (lon, lat) + crs=4326 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=4326)):>8,} gates\")\n",
-    "print(f\"  (lon, lat) + crs=2056 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=2056)):>8,} gates  <- degrees read as metres\")\n",
+    "print(\n",
+    "    f\"  (lon, lat) + crs=2056 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=2056)):>8,} gates\"\n",
+    "    \"  <- degrees read as metres\",\n",
+    ")\n",
     "print(f\"  (E, N)     + crs=2056 -> {len(rdf.crop_around_point(point=(E, N), distance=30_000, crs=2056)):>8,} gates\")\n",
-    "print(f\"  (E, N)     + no crs   -> {len(rdf.crop_around_point(point=(E, N), distance=30_000)):>8,} gates  <- defaults to the archive CRS\")"
+    "print(\n",
+    "    f\"  (E, N)     + no crs   -> {len(rdf.crop_around_point(point=(E, N), distance=30_000)):>8,} gates\"\n",
+    "    \"  <- defaults to the archive CRS\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ef9ad06a",
+   "id": "7",
    "metadata": {},
    "source": [
     "## 1. `crop_by_bbox` — a rectangle\n",
@@ -193,15 +167,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "916e585b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:57.200168Z",
-     "iopub.status.busy": "2026-08-11T16:21:57.200027Z",
-     "iopub.status.idle": "2026-08-11T16:21:58.037522Z",
-     "shell.execute_reply": "2026-08-11T16:21:58.036434Z"
-    }
-   },
+   "id": "8",
+   "metadata": {},
    "outputs": [],
    "source": [
     "box = rdf.crop_by_bbox(bounds=(8.4, 45.8, 9.3, 46.5), crs=4326)\n",
@@ -211,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c03a308",
+   "id": "9",
    "metadata": {},
    "source": [
     "## 2. `crop_around_point` — everything within N km\n",
@@ -223,15 +190,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b7e09c34",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:21:58.039730Z",
-     "iopub.status.busy": "2026-08-11T16:21:58.039507Z",
-     "iopub.status.idle": "2026-08-11T16:22:01.124637Z",
-     "shell.execute_reply": "2026-08-11T16:22:01.123765Z"
-    }
-   },
+   "id": "10",
+   "metadata": {},
    "outputs": [],
    "source": [
     "for km in (20, 50, 100):\n",
@@ -242,15 +202,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ce861e79",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:01.126711Z",
-     "iopub.status.busy": "2026-08-11T16:22:01.126506Z",
-     "iopub.status.idle": "2026-08-11T16:22:01.565338Z",
-     "shell.execute_reply": "2026-08-11T16:22:01.564257Z"
-    }
-   },
+   "id": "11",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Any point, not only the radar itself\n",
@@ -260,7 +213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "773936a3",
+   "id": "12",
    "metadata": {},
    "source": [
     "## 3. `crop_by_polygone` — an arbitrary shape\n",
@@ -273,15 +226,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6a912757",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:01.567615Z",
-     "iopub.status.busy": "2026-08-11T16:22:01.567373Z",
-     "iopub.status.idle": "2026-08-11T16:22:01.978251Z",
-     "shell.execute_reply": "2026-08-11T16:22:01.977671Z"
-    }
-   },
+   "id": "13",
+   "metadata": {},
    "outputs": [],
    "source": [
     "triangle = shapely.Polygon([(8.6, 45.9), (9.2, 46.1), (8.8, 46.5)])\n",
@@ -292,34 +238,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31813aca",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:01.980376Z",
-     "iopub.status.busy": "2026-08-11T16:22:01.980246Z",
-     "iopub.status.idle": "2026-08-11T16:22:02.443958Z",
-     "shell.execute_reply": "2026-08-11T16:22:02.443289Z"
-    }
-   },
+   "id": "14",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# The same thing from a file on disk\n",
     "import json\n",
     "\n",
     "geojson_path = ARCHIVE_DIR / \"aoi_demo.geojson\"\n",
-    "geojson_path.write_text(json.dumps({\n",
-    "    \"type\": \"FeatureCollection\",\n",
-    "    \"features\": [{\"type\": \"Feature\", \"properties\": {},\n",
-    "                  \"geometry\": shapely.geometry.mapping(triangle)}],\n",
-    "}))\n",
+    "geojson_path.write_text(\n",
+    "    json.dumps(\n",
+    "        {\n",
+    "            \"type\": \"FeatureCollection\",\n",
+    "            \"features\": [{\"type\": \"Feature\", \"properties\": {}, \"geometry\": shapely.geometry.mapping(triangle)}],\n",
+    "        },\n",
+    "    ),\n",
+    ")\n",
     "\n",
-    "from_file = rdf.crop_by_polygone(polygon=geojson_path)   # CRS taken from the file\n",
+    "from_file = rdf.crop_by_polygone(polygon=geojson_path)  # CRS taken from the file\n",
     "print(f\"from GeoJSON: {len(from_file):,} gates  (same: {len(from_file) == len(poly)})\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "25e97fff",
+   "id": "15",
    "metadata": {},
    "source": [
     "## 4. `extract_cross_section`: a vertical cross-section\n",
@@ -343,15 +285,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c3e7cb1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:02.445898Z",
-     "iopub.status.busy": "2026-08-11T16:22:02.445773Z",
-     "iopub.status.idle": "2026-08-11T16:22:04.193859Z",
-     "shell.execute_reply": "2026-08-11T16:22:04.192790Z"
-    }
-   },
+   "id": "16",
+   "metadata": {},
    "outputs": [],
    "source": [
     "cs = rdf.extract_cross_section(\n",
@@ -366,7 +301,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8389085e",
+   "id": "17",
    "metadata": {},
    "source": [
     "### Reading `cs_polygon`\n",
@@ -381,15 +316,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11b6653e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:04.195539Z",
-     "iopub.status.busy": "2026-08-11T16:22:04.195332Z",
-     "iopub.status.idle": "2026-08-11T16:22:04.209304Z",
-     "shell.execute_reply": "2026-08-11T16:22:04.208668Z"
-    }
-   },
+   "id": "18",
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"in polars   :\", cs.data.schema[\"cs_polygon\"])\n",
@@ -405,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4304e3e3",
+   "id": "19",
    "metadata": {},
    "source": [
     "## 5. `quicklook=True` — did I crop what I meant to?\n",
@@ -417,15 +345,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e728b199",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:04.211180Z",
-     "iopub.status.busy": "2026-08-11T16:22:04.211052Z",
-     "iopub.status.idle": "2026-08-11T16:22:05.468856Z",
-     "shell.execute_reply": "2026-08-11T16:22:05.468394Z"
-    }
-   },
+   "id": "20",
+   "metadata": {},
    "outputs": [],
    "source": [
     "_ = rdf.crop_around_point(point=SITE, distance=50_000, crs=4326, quicklook=True)\n",
@@ -435,15 +356,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53c5a037",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:05.471154Z",
-     "iopub.status.busy": "2026-08-11T16:22:05.470696Z",
-     "iopub.status.idle": "2026-08-11T16:22:06.109670Z",
-     "shell.execute_reply": "2026-08-11T16:22:06.108745Z"
-    }
-   },
+   "id": "21",
+   "metadata": {},
    "outputs": [],
    "source": [
     "_ = rdf.crop_by_polygone(polygon=triangle, crs=4326, quicklook=True)\n",
@@ -452,7 +366,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7b5a564",
+   "id": "22",
    "metadata": {},
    "source": [
     "## 6. Chaining AOIs\n",
@@ -464,29 +378,22 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5424cde4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:06.111576Z",
-     "iopub.status.busy": "2026-08-11T16:22:06.111440Z",
-     "iopub.status.idle": "2026-08-11T16:22:07.232317Z",
-     "shell.execute_reply": "2026-08-11T16:22:07.231845Z"
-    }
-   },
+   "id": "23",
+   "metadata": {},
    "outputs": [],
    "source": [
     "storm = (\n",
     "    db.open(radars=\"L\")\n",
-    "      .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 35})\n",
-    "      .crop_around_point(point=SITE, distance=60_000, crs=4326)\n",
-    "      .sel(range=slice(5_000, 60_000))\n",
+    "    .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 35})\n",
+    "    .crop_around_point(point=SITE, distance=60_000, crs=4326)\n",
+    "    .sel(range=slice(5_000, 60_000))\n",
     ")\n",
     "print(f\"{len(storm):,} gates: strong echo, within 60 km, beyond 5 km range\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d3d0335d",
+   "id": "24",
    "metadata": {},
    "source": [
     "## 7. The interactive tool\n",
@@ -519,18 +426,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6fe0db90",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:07.234114Z",
-     "iopub.status.busy": "2026-08-11T16:22:07.233966Z",
-     "iopub.status.idle": "2026-08-11T16:22:07.403456Z",
-     "shell.execute_reply": "2026-08-11T16:22:07.402877Z"
-    }
-   },
+   "id": "25",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "RUN_INTERACTIVE = True        # <- set False to skip the map (e.g. outside Jupyter)\n",
+    "RUN_INTERACTIVE = True  # <- set False to skip the map (e.g. outside Jupyter)\n",
     "\n",
     "if RUN_INTERACTIVE:\n",
     "    # `selector` is the widget handle — the map appears immediately below.\n",
@@ -545,22 +445,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "03e9919f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-11T16:22:07.407717Z",
-     "iopub.status.busy": "2026-08-11T16:22:07.407493Z",
-     "iopub.status.idle": "2026-08-11T16:22:07.410728Z",
-     "shell.execute_reply": "2026-08-11T16:22:07.410135Z"
-    }
-   },
+   "id": "26",
+   "metadata": {},
    "outputs": [],
    "source": [
+    "from IPython.display import display\n",
+    "\n",
     "# Run this cell *after* drawing a shape above and clicking \"Apply crop\".\n",
     "# `cropped` is an ordinary RadDB: filter it, crop it again, plot it or convert it,\n",
     "# exactly as in the sections above.  Until Apply crop is pressed it is still None.\n",
     "if RUN_INTERACTIVE and selector is not None and selector.result is not None:\n",
-    "    cropped = selector.result                      # <- the cropped RadDB\n",
+    "    cropped = selector.result  # <- the cropped RadDB\n",
     "    print(f\"{selector.kind} -> {len(cropped):,} gates\")\n",
     "    print(\"drawn shape :\", selector.feature[\"geometry\"][\"type\"])\n",
     "    display(cropped.head())\n",
@@ -570,7 +465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e91228a",
+   "id": "27",
    "metadata": {},
    "source": [
     "---\n",
@@ -595,1153 +490,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.15"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {
-     "04d1dcc8e72e428cb89202bb3f22fb7b": {
-      "model_module": "jupyter-leaflet",
-      "model_module_version": "^0.20",
-      "model_name": "LeafletMarkerModel",
-      "state": {
-       "_model_module": "jupyter-leaflet",
-       "_model_module_version": "^0.20",
-       "_model_name": "LeafletMarkerModel",
-       "_view_count": null,
-       "_view_module": "jupyter-leaflet",
-       "_view_module_version": "^0.20",
-       "_view_name": "LeafletMarkerView",
-       "alt": "",
-       "base": false,
-       "bottom": false,
-       "draggable": false,
-       "icon": "IPY_MODEL_52cc4878cc874407a0cac1217ac1b513",
-       "keyboard": true,
-       "location": [
-        46.0407600402832,
-        8.833216667175293
-       ],
-       "name": "",
-       "opacity": 1,
-       "options": [
-        "alt",
-        "draggable",
-        "keyboard",
-        "pm_ignore",
-        "rise_offset",
-        "rise_on_hover",
-        "rotation_angle",
-        "rotation_origin",
-        "title",
-        "z_index_offset"
-       ],
-       "pane": "",
-       "pm_ignore": true,
-       "popup": null,
-       "popup_max_height": null,
-       "popup_max_width": 300,
-       "popup_min_width": 50,
-       "rise_offset": 250,
-       "rise_on_hover": false,
-       "rotation_angle": 0,
-       "rotation_origin": "",
-       "snap_ignore": true,
-       "subitems": [],
-       "title": "radar L",
-       "visible": true,
-       "z_index_offset": 0
-      }
-     },
-     "12bd79bf56ff458abc51ef4d772b3f6c": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "ButtonStyleModel",
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diff --git a/tutorial/04_plots.ipynb b/tutorial/04_plots.ipynb
index c5b5b50..187c17d 100644
--- a/tutorial/04_plots.ipynb
+++ b/tutorial/04_plots.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f19ce8cd",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 4. Plots\n",
@@ -28,33 +28,29 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3ce6a922",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "from pathlib import Path\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "import raddb\n",
     "\n",
     "# Keep the embedded figures small enough for GitHub to render this notebook.\n",
-    "#plt.rcParams[\"figure.dpi\"] = 70"
+    "# plt.rcParams[\"figure.dpi\"] = 70"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d77ff6a1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:24.383036Z",
-     "iopub.status.busy": "2026-08-04T15:32:24.382730Z",
-     "iopub.status.idle": "2026-08-04T15:32:24.395383Z",
-     "shell.execute_reply": "2026-08-04T15:32:24.394261Z"
-    }
-   },
+   "id": "2",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# --------------------------------------------------------------------------\n",
@@ -63,8 +59,8 @@
     "# ARCHIVE_DIR must be the same archive tutorial 1 wrote. If it has not run,\n",
     "# the cell below builds it.\n",
     "\n",
-    "MCH_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
-    "NEXRAD_DIR  = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
+    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
+    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
     "print(\"MCH DataTrees   :\", MCH_DIR)\n",
@@ -75,15 +71,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "604be0fb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:25.283046Z",
-     "iopub.status.busy": "2026-08-04T15:32:25.282760Z",
-     "iopub.status.idle": "2026-08-04T15:32:25.287004Z",
-     "shell.execute_reply": "2026-08-04T15:32:25.286011Z"
-    }
-   },
+   "id": "3",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
@@ -97,15 +86,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bd9e7bfc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:25.289204Z",
-     "iopub.status.busy": "2026-08-04T15:32:25.289065Z",
-     "iopub.status.idle": "2026-08-04T15:32:25.532423Z",
-     "shell.execute_reply": "2026-08-04T15:32:25.531417Z"
-    }
-   },
+   "id": "4",
+   "metadata": {},
    "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
@@ -118,11 +100,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4b7b4fb0",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#==================USING NEXRAD DATA=========================\n",
+    "# ==================USING NEXRAD DATA=========================\n",
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "rdf = db.open(radars=\"KTLX\")\n",
     "info = db.get_radar_info(\"KTLX\")\n",
@@ -132,7 +114,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9353cc7",
+   "id": "6",
    "metadata": {},
    "source": [
     "## 0. Which timesteps can I plot?\n",
@@ -152,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "76958aa3",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4f33ff0e",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32adbfbd",
+   "id": "9",
    "metadata": {},
    "source": [
     "`timestep=` takes anything pandas reads as a time and picks the **nearest**\n",
@@ -200,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a12e1078",
+   "id": "10",
    "metadata": {},
    "source": [
     "## 1. PPI"
@@ -209,15 +191,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a7e6ba05",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:25.534477Z",
-     "iopub.status.busy": "2026-08-04T15:32:25.534289Z",
-     "iopub.status.idle": "2026-08-04T15:32:27.402893Z",
-     "shell.execute_reply": "2026-08-04T15:32:27.401899Z"
-    }
-   },
+   "id": "11",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
@@ -228,7 +203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d98906f",
+   "id": "12",
    "metadata": {},
    "source": [
     "## 2. RHI"
@@ -237,15 +212,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "59a203af",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:27.405098Z",
-     "iopub.status.busy": "2026-08-04T15:32:27.404921Z",
-     "iopub.status.idle": "2026-08-04T15:32:28.698042Z",
-     "shell.execute_reply": "2026-08-04T15:32:28.697345Z"
-    }
-   },
+   "id": "13",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
@@ -256,7 +224,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c00b398",
+   "id": "14",
    "metadata": {},
    "source": [
     "## 3. CAPPI"
@@ -265,15 +233,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5236b05f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:28.700252Z",
-     "iopub.status.busy": "2026-08-04T15:32:28.700082Z",
-     "iopub.status.idle": "2026-08-04T15:32:32.766121Z",
-     "shell.execute_reply": "2026-08-04T15:32:32.765381Z"
-    }
-   },
+   "id": "15",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
@@ -284,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8ed064e",
+   "id": "16",
    "metadata": {},
    "source": [
     "## 4. Vertical cross-section\n",
@@ -296,20 +257,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f8f6ca75",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:32.768336Z",
-     "iopub.status.busy": "2026-08-04T15:32:32.768179Z",
-     "iopub.status.idle": "2026-08-04T15:32:35.438215Z",
-     "shell.execute_reply": "2026-08-04T15:32:35.437269Z"
-    }
-   },
+   "id": "17",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cs = rdf.extract_cross_section(p1=(SITE[0] - 0.6, SITE[1] - 0.35),\n",
-    "                               p2=(SITE[0] + 0.6, SITE[1] + 0.35),\n",
-    "                               crs=4326)\n",
+    "cs = rdf.extract_cross_section(p1=(SITE[0] - 0.6, SITE[1] - 0.35), p2=(SITE[0] + 0.6, SITE[1] + 0.35), crs=4326)\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
     "cs.plot_vcs(variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
@@ -319,7 +271,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d84aae28",
+   "id": "18",
    "metadata": {},
    "source": [
     "... or hand the line straight to `plot_vcs`, which cuts and draws in one step:\n",
@@ -336,7 +288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9d78cc2",
+   "id": "19",
    "metadata": {},
    "source": [
     "### 4.1 Drawing the section on the map\n",
@@ -352,11 +304,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dfbd36ed",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
-    "RUN_INTERACTIVE = True     # <- set False to skip the map (e.g. outside Jupyter)\n",
+    "RUN_INTERACTIVE = True  # <- set False to skip the map (e.g. outside Jupyter)\n",
     "\n",
     "if RUN_INTERACTIVE:\n",
     "    # Pick the \"/\" polyline tool in the toolbar, draw a line across the echo,\n",
@@ -370,7 +322,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "04351979",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -379,8 +331,7 @@
     "if drawn is None:\n",
     "    print(\"nothing applied yet — draw a line above and click 'Apply crop'.\")\n",
     "elif selector.kind != \"cross_section\":\n",
-    "    print(f\"you drew a {selector.kind!r}, not a line; \"\n",
-    "          \"only the polyline tool produces a cross-section.\")\n",
+    "    print(f\"you drew a {selector.kind!r}, not a line; \" \"only the polyline tool produces a cross-section.\")\n",
     "else:\n",
     "    print(f\"{len(drawn):,} gates on the drawn section\")\n",
     "    fig, ax = plt.subplots(figsize=(8, 4.5))\n",
@@ -391,7 +342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06dabdbe",
+   "id": "22",
    "metadata": {},
    "source": [
     "## 5. Coordinates and Context\n",
@@ -409,19 +360,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a3940981",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:35.440180Z",
-     "iopub.status.busy": "2026-08-04T15:32:35.440038Z",
-     "iopub.status.idle": "2026-08-04T15:32:37.532208Z",
-     "shell.execute_reply": "2026-08-04T15:32:37.531138Z"
-    }
-   },
+   "id": "23",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
-    "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"]):\n",
+    "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"], strict=False):\n",
     "    rdf.plot_ppi(sweep=1, ax=ax, coords=coords, timestep=\"2024-06-12\")\n",
     "    ax.set_title(f\"coords={coords!r}\")\n",
     "plt.tight_layout()\n",
@@ -430,7 +374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9362ae8",
+   "id": "24",
    "metadata": {},
    "source": [
     "`context=True` overlays country borders and coastlines. Projection and backdrop\n",
@@ -441,19 +385,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5090d25e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:37.535043Z",
-     "iopub.status.busy": "2026-08-04T15:32:37.534769Z",
-     "iopub.status.idle": "2026-08-04T15:32:38.205894Z",
-     "shell.execute_reply": "2026-08-04T15:32:38.205118Z"
-    }
-   },
+   "id": "25",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
-    "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"]):\n",
+    "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"], strict=False):\n",
     "    rdf.plot_ppi(sweep=1, ax=ax, coords=coords, context=True, timestep=\"2024-06-12\")\n",
     "    ax.set_title(f\"coords={coords!r}\")\n",
     "plt.tight_layout()\n",
@@ -462,7 +399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06b94e34",
+   "id": "26",
    "metadata": {},
    "source": [
     "### 5.1 Choosing the extent\n",
@@ -490,20 +427,34 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a19b5fe1",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
     "\n",
     "# same sweep, zoomed to 60 km around the radar\n",
-    "rdf.plot_ppi(sweep=3, ax=axes[0], coords=\"xy\", context=True, timestep=\"2024-06-12\",\n",
-    "             xlim=(-60_000, 60_000), ylim=(-60_000, 60_000))\n",
+    "rdf.plot_ppi(\n",
+    "    sweep=3,\n",
+    "    ax=axes[0],\n",
+    "    coords=\"xy\",\n",
+    "    context=True,\n",
+    "    timestep=\"2024-06-12\",\n",
+    "    xlim=(-60_000, 60_000),\n",
+    "    ylim=(-60_000, 60_000),\n",
+    ")\n",
     "axes[0].set_title(\"coords='xy' | ±60 km\")\n",
     "\n",
     "# the same window written in degrees\n",
-    "rdf.plot_ppi(sweep=3, ax=axes[1], coords=\"lonlat\", context=True, timestep=\"2024-06-12\",\n",
-    "             xlim=(SITE[0] - 0.8, SITE[0] + 0.8), ylim=(SITE[1] - 0.55, SITE[1] + 0.55))\n",
+    "rdf.plot_ppi(\n",
+    "    sweep=3,\n",
+    "    ax=axes[1],\n",
+    "    coords=\"lonlat\",\n",
+    "    context=True,\n",
+    "    timestep=\"2024-06-12\",\n",
+    "    xlim=(SITE[0] - 0.8, SITE[0] + 0.8),\n",
+    "    ylim=(SITE[1] - 0.55, SITE[1] + 0.55),\n",
+    ")\n",
     "axes[1].set_title(\"coords='lonlat' | same box but in degrees\")\n",
     "\n",
     "plt.tight_layout()\n",
@@ -512,7 +463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b315015c",
+   "id": "28",
    "metadata": {},
    "source": [
     "## 6. Any variable, any subset\n",
@@ -524,15 +475,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2d81ff0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:38.207832Z",
-     "iopub.status.busy": "2026-08-04T15:32:38.207681Z",
-     "iopub.status.idle": "2026-08-04T15:32:40.862850Z",
-     "shell.execute_reply": "2026-08-04T15:32:40.861979Z"
-    }
-   },
+   "id": "29",
+   "metadata": {},
    "outputs": [],
    "source": [
     "VARS = [v for v in (\"DBZH\", \"ZDR\", \"RHOHV\", \"PHIDP\") if v in rdf.columns()]\n",
@@ -541,9 +485,8 @@
     "# tilts are \"split cuts\": the odd sweeps are the Doppler half and hold DBZH only,\n",
     "# so plotting ZDR there raises \"every 'ZDR' value is NaN\".\n",
     "fig, axes = plt.subplots(2, 2, figsize=(11, 9))\n",
-    "for ax, var in zip(axes.flat, VARS):\n",
-    "    rdf.plot_ppi(sweep=2, variable=var, ax=ax, timestep=\"2024-06-12\",\n",
-    "                 context=True, coords=\"xy\")\n",
+    "for ax, var in zip(axes.flat, VARS, strict=False):\n",
+    "    rdf.plot_ppi(sweep=2, variable=var, ax=ax, timestep=\"2024-06-12\", context=True, coords=\"xy\")\n",
     "    ax.set_title(var)\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -552,31 +495,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45584473",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:40.865150Z",
-     "iopub.status.busy": "2026-08-04T15:32:40.864998Z",
-     "iopub.status.idle": "2026-08-04T15:32:43.098812Z",
-     "shell.execute_reply": "2026-08-04T15:32:43.097810Z"
-    }
-   },
+   "id": "30",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Filter and crop first — the plot follows the data, gate for gate\n",
-    "sub = (rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
-    "          .crop_around_point(point=SITE, distance=50_000, crs=4326))\n",
+    "sub = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30}).crop_around_point(\n",
+    "    point=SITE,\n",
+    "    distance=50_000,\n",
+    "    crs=4326,\n",
+    ")\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "art = sub.plot_ppi(sweep=1, ax=ax, timestep=\"2024-06-12\", coords=\"xy\", context=True)\n",
-    "ax.set_title(f\"DBZH > 30 dBz within 50 km\")\n",
+    "ax.set_title(\"DBZH > 30 dBz within 50 km\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "52fa26d5",
+   "id": "31",
    "metadata": {},
    "source": [
     "## 7. Composing a figure\n",
@@ -587,15 +526,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ada2890f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:43.100741Z",
-     "iopub.status.busy": "2026-08-04T15:32:43.100602Z",
-     "iopub.status.idle": "2026-08-04T15:32:47.047824Z",
-     "shell.execute_reply": "2026-08-04T15:32:47.047068Z"
-    }
-   },
+   "id": "32",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(2, 2, figsize=(11.5, 9))\n",
@@ -610,7 +542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab9148f7",
+   "id": "33",
    "metadata": {},
    "source": [
     "## 8. Plotting without an archive\n",
@@ -625,15 +557,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e0ad81a1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:47.050077Z",
-     "iopub.status.busy": "2026-08-04T15:32:47.049935Z",
-     "iopub.status.idle": "2026-08-04T15:32:47.746725Z",
-     "shell.execute_reply": "2026-08-04T15:32:47.745866Z"
-    }
-   },
+   "id": "34",
+   "metadata": {},
    "outputs": [],
    "source": [
     "dt = raddb.open_any_datatree(sorted(MCH_DIR.glob(\"L_*.zarr\"))[0])\n",
@@ -647,7 +572,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c2ffc3b",
+   "id": "35",
    "metadata": {},
    "source": [
     "## 9. Saving\n",
@@ -660,15 +585,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7505115e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-04T15:32:47.748747Z",
-     "iopub.status.busy": "2026-08-04T15:32:47.748436Z",
-     "iopub.status.idle": "2026-08-04T15:32:48.469077Z",
-     "shell.execute_reply": "2026-08-04T15:32:48.468256Z"
-    }
-   },
+   "id": "36",
+   "metadata": {},
    "outputs": [],
    "source": [
     "out = ARCHIVE_DIR / \"ppi_example.png\"\n",
diff --git a/tutorial/05_demo_pipeline.ipynb b/tutorial/05_demo_pipeline.ipynb
index d1b4b3b..871a2f0 100644
--- a/tutorial/05_demo_pipeline.ipynb
+++ b/tutorial/05_demo_pipeline.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "952beaf7",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 5. Demo Pipeline\n",
@@ -23,15 +23,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ff55e798",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:20:36.749666Z",
-     "iopub.status.busy": "2026-08-14T15:20:36.749486Z",
-     "iopub.status.idle": "2026-08-14T15:20:38.348661Z",
-     "shell.execute_reply": "2026-08-14T15:20:38.347778Z"
-    }
-   },
+   "id": "1",
+   "metadata": {},
    "outputs": [],
    "source": [
     "import shutil\n",
@@ -55,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "069da033",
+   "id": "2",
    "metadata": {},
    "source": [
     "## Configuration\n",
@@ -67,21 +60,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "74467cfd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:20:38.351773Z",
-     "iopub.status.busy": "2026-08-14T15:20:38.351360Z",
-     "iopub.status.idle": "2026-08-14T15:20:38.357950Z",
-     "shell.execute_reply": "2026-08-14T15:20:38.356951Z"
-    }
-   },
+   "id": "3",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — edit these two paths to point at your own machine\n",
     "# --------------------------------------------------------------------------\n",
-    "RAW_DIR     = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/tutorial_raw\").expanduser()\n",
+    "RAW_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/tutorial_raw\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
     "RAW_DIR.mkdir(parents=True, exist_ok=True)\n",
@@ -103,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a821711a",
+   "id": "4",
    "metadata": {},
    "source": [
     "## 1. Which volume?\n",
@@ -126,24 +112,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "335a57d1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:20:38.360141Z",
-     "iopub.status.busy": "2026-08-14T15:20:38.359944Z",
-     "iopub.status.idle": "2026-08-14T15:20:38.364575Z",
-     "shell.execute_reply": "2026-08-14T15:20:38.363606Z"
-    }
-   },
+   "id": "5",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# WHICH TIMESTEP? — one UTC timestamp per radar, edit freely\n",
     "# --------------------------------------------------------------------------\n",
     "TIMES = {\n",
-    "    \"KDVN\": \"2024-06-25 23:04\",   \n",
-    "    \"FANJ\": \"2024-08-09 12:00\",   \n",
-    "    \"GUA\":  \"2024-06-12 19:02\",   \n",
+    "    \"KDVN\": \"2024-06-25 23:04\",\n",
+    "    \"FANJ\": \"2024-08-09 12:00\",\n",
+    "    \"GUA\": \"2024-06-12 19:02\",\n",
     "}\n",
     "\n",
     "for name, when in TIMES.items():\n",
@@ -152,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9962a5ef",
+   "id": "6",
    "metadata": {},
    "source": [
     "## 2. Download"
@@ -161,15 +140,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "815790ed",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:20:38.366987Z",
-     "iopub.status.busy": "2026-08-14T15:20:38.366786Z",
-     "iopub.status.idle": "2026-08-14T15:20:40.749305Z",
-     "shell.execute_reply": "2026-08-14T15:20:40.748292Z"
-    }
-   },
+   "id": "7",
+   "metadata": {},
    "outputs": [],
    "source": [
     "def resolve_url(name, source, when):\n",
@@ -178,22 +150,30 @@
     "    site = source[\"site\"]\n",
     "\n",
     "    if source[\"kind\"] == \"nexrad\":\n",
-    "        obj = (f\"{t:%Y/%m/%d}/{site}/NWS_NEXRAD_NXL2DPBL_{site}\"\n",
-    "               f\"_{t:%Y%m%d%H}0000_{t:%Y%m%d%H}5959.tar\")\n",
-    "        return (\"https://storage.googleapis.com/download/storage/v1/b/gcp-public-data-nexrad-l2/o/\"\n",
-    "                + urllib.parse.quote(obj, safe=\"\") + \"?alt=media\")\n",
+    "        obj = f\"{t:%Y/%m/%d}/{site}/NWS_NEXRAD_NXL2DPBL_{site}\" f\"_{t:%Y%m%d%H}0000_{t:%Y%m%d%H}5959.tar\"\n",
+    "        return (\n",
+    "            \"https://storage.googleapis.com/download/storage/v1/b/gcp-public-data-nexrad-l2/o/\"\n",
+    "            + urllib.parse.quote(obj, safe=\"\")\n",
+    "            + \"?alt=media\"\n",
+    "        )\n",
     "\n",
     "    if source[\"kind\"] == \"odim\":\n",
-    "        t = t.floor(\"5min\")                       # FMI publishes every 5 minutes\n",
-    "        return (\"https://fmi-opendata-radar-volume-hdf5.s3.eu-west-1.amazonaws.com/\"\n",
-    "                f\"{t:%Y/%m/%d}/{site}/{t:%Y%m%d%H%M}_{site}_PVOL.h5\")\n",
+    "        t = t.floor(\"5min\")  # FMI publishes every 5 minutes\n",
+    "        return (\n",
+    "            \"https://fmi-opendata-radar-volume-hdf5.s3.eu-west-1.amazonaws.com/\"\n",
+    "            f\"{t:%Y/%m/%d}/{site}/{t:%Y%m%d%H%M}_{site}_PVOL.h5\"\n",
+    "        )\n",
     "\n",
     "    # IDEAM: list the first key at or after the requested second.\n",
     "    prefix = f\"l2_data/{t:%Y/%m/%d}/{site}/\"\n",
-    "    query = urllib.parse.urlencode({\n",
-    "        \"list-type\": \"2\", \"max-keys\": \"1\", \"prefix\": prefix,\n",
-    "        \"start-after\": f\"{prefix}{name}{t - pd.Timedelta(seconds=1):%y%m%d%H%M%S}\",\n",
-    "    })\n",
+    "    query = urllib.parse.urlencode(\n",
+    "        {\n",
+    "            \"list-type\": \"2\",\n",
+    "            \"max-keys\": \"1\",\n",
+    "            \"prefix\": prefix,\n",
+    "            \"start-after\": f\"{prefix}{name}{t - pd.Timedelta(seconds=1):%y%m%d%H%M%S}\",\n",
+    "        },\n",
+    "    )\n",
     "    with urllib.request.urlopen(f\"https://s3-radaresideam.s3.amazonaws.com/?{query}\", timeout=120) as response:\n",
     "        listing = response.read().decode()\n",
     "    if \"<Key>\" not in listing:\n",
@@ -233,7 +213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c552776",
+   "id": "8",
    "metadata": {},
    "source": [
     "## 3. Archive\n",
@@ -251,23 +231,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3320a785",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:20:40.752052Z",
-     "iopub.status.busy": "2026-08-14T15:20:40.751800Z",
-     "iopub.status.idle": "2026-08-14T15:21:16.052256Z",
-     "shell.execute_reply": "2026-08-14T15:21:16.050908Z"
-    }
-   },
+   "id": "9",
+   "metadata": {},
    "outputs": [],
    "source": [
     "for name, source in SOURCES.items():\n",
     "    dt = source[\"open\"](str(source[\"path\"]))\n",
     "    site = dt[\"/\"].ds\n",
     "    crs = suggest_crs(latitude=float(site[\"latitude\"]), longitude=float(site[\"longitude\"]))\n",
-    "    print(f\"{name}: {sum(1 for g in dt.groups if g.startswith('/sweep_'))} sweeps, \"\n",
-    "          f\"site {float(site['latitude']):.2f}, {float(site['longitude']):.2f} -> EPSG:{crs}\")\n",
+    "    print(\n",
+    "        f\"{name}: {sum(1 for g in dt.groups if g.startswith('/sweep_'))} sweeps, \"\n",
+    "        f\"site {float(site['latitude']):.2f}, {float(site['longitude']):.2f} -> EPSG:{crs}\",\n",
+    "    )\n",
     "\n",
     "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=crs).archive(datatree=dt, radar=name)"
    ]
@@ -275,15 +250,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5e21300d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:21:16.055324Z",
-     "iopub.status.busy": "2026-08-14T15:21:16.054909Z",
-     "iopub.status.idle": "2026-08-14T15:21:16.077817Z",
-     "shell.execute_reply": "2026-08-14T15:21:16.076092Z"
-    }
-   },
+   "id": "10",
+   "metadata": {},
    "outputs": [],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
@@ -292,7 +260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "738aff49",
+   "id": "11",
    "metadata": {},
    "source": [
     "## 4. Plot"
@@ -301,21 +269,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "857d5aa0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:21:16.080585Z",
-     "iopub.status.busy": "2026-08-14T15:21:16.080231Z",
-     "iopub.status.idle": "2026-08-14T15:21:25.922894Z",
-     "shell.execute_reply": "2026-08-14T15:21:25.921776Z"
-    }
-   },
+   "id": "12",
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(17, 5))\n",
     "\n",
     "volumes = {}\n",
-    "for ax, name in zip(axes, SOURCES):\n",
+    "for ax, name in zip(axes, SOURCES, strict=False):\n",
     "    volumes[name] = db.open(radars=name)\n",
     "    volumes[name].plot_ppi(sweep=1, variable=\"DBZH\", ax=ax, coords=\"xy\", context=True)\n",
     "    ax.set_title(f\"{name}\")\n",
@@ -326,7 +287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4520c9b2",
+   "id": "13",
    "metadata": {},
    "source": [
     "## 5. A cross-section, drawn by hand\n",
@@ -342,15 +303,8 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "018e962d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:21:25.926645Z",
-     "iopub.status.busy": "2026-08-14T15:21:25.926413Z",
-     "iopub.status.idle": "2026-08-14T15:21:26.068677Z",
-     "shell.execute_reply": "2026-08-14T15:21:26.067670Z"
-    }
-   },
+   "id": "14",
+   "metadata": {},
    "outputs": [],
    "source": [
     "kdvn = volumes[\"KDVN\"]\n",
@@ -359,7 +313,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc66cb9c",
+   "id": "15",
    "metadata": {},
    "source": [
     "The drawn section is on `selector.result` — an ordinary RadDB, carrying the extra\n",
@@ -371,18 +325,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "89a4b6b1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-08-14T15:21:26.078644Z",
-     "iopub.status.busy": "2026-08-14T15:21:26.078442Z",
-     "iopub.status.idle": "2026-08-14T15:21:42.396432Z",
-     "shell.execute_reply": "2026-08-14T15:21:42.395226Z"
-    }
-   },
+   "id": "16",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "LINE = ((-91.30, 40.95), (-91.30, 42.10))    # fallback: north-south through the strongest cell\n",
+    "LINE = ((-91.30, 40.95), (-91.30, 42.10))  # fallback: north-south through the strongest cell\n",
     "\n",
     "drawn = getattr(selector, \"result\", None)\n",
     "if drawn is not None and selector.kind == \"cross_section\":\n",
@@ -401,7 +348,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bd9e5c9",
+   "id": "17",
    "metadata": {},
    "source": [
     "---\n",
@@ -435,1153 +382,6 @@
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diff --git a/tutorial/README.md b/tutorial/README.md
index a9ca31c..d337bc8 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -3,13 +3,13 @@
 Five notebooks that walk through the whole workflow, in order. Each one is
 self-contained — if you jump straight to number 3, it builds the archive it needs.
 
-| # | notebook | covers |
-|---|---|---|
-| 1 | [Archiving](01_archiving.ipynb) | the storage model, the CRS contract, `archive()`, what lands on disk |
-| 2 | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters |
-| 3 | [Areas of interest](03_area_of_interest.ipynb) | bbox / point / polygon crops, cross-sections, the interactive map |
-| 4 | [Plots](04_plots.ipynb) | PPI, RHI, CAPPI, vertical cross-section |
-| 5 | [Demo pipeline](05_demo_pipeline.ipynb) | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cut |
+| #   | notebook                                                | covers                                                                                                 |
+| --- | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
+| 1   | [Archiving](01_archiving.ipynb)                         | the storage model, the CRS contract, `archive()`, what lands on disk                                   |
+| 2   | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters                                            |
+| 3   | [Areas of interest](03_area_of_interest.ipynb)          | bbox / point / polygon crops, cross-sections, the interactive map                                      |
+| 4   | [Plots](04_plots.ipynb)                                 | PPI, RHI, CAPPI, vertical cross-section                                                                |
+| 5   | [Demo pipeline](05_demo_pipeline.ipynb)                 | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cut |
 
 The notebooks are stored **with their output**, so you can read them on GitHub
 without running anything.

From 0869df4ea8a5a6709f140bdab2046d4ef70dce09 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Tue, 18 Aug 2026 17:22:00 +0200
Subject: [PATCH 10/14] Update maintainers guidelines, documentation, and
 package dependencies

- Changed Testing Team members in maintainers guidelines.
- Updated versioning guidelines for breaking and non-breaking changes.
- Corrected documentation paths and commands for building documentation.
- Updated GitHub Action links for packaging and uploading to PyPI.
- Revised release process instructions in documentation.
- Enhanced CI pipeline tools summary in documentation.
- Modified Sphinx configuration for improved project documentation.
- Updated index.rst to reflect new package capabilities and structure.
- Adjusted package dependencies in pyproject.toml for compatibility.
- Revised tutorial README to clarify running notebooks and dependencies.
- Updated demo pipeline notebook to include output for clarity.
---
 AUTHORS.md                                |   1 +
 CONTRIBUTING.rst                          |  79 ++--
 MANIFEST.in                               |   1 -
 README.md                                 | 141 +++++---
 docs/README.md                            |  12 +-
 docs/environment.yaml                     |   4 +-
 docs/source/02_installation.rst           | 104 +++---
 docs/source/03_quickstart.rst             | 210 +++++------
 docs/source/07_maintainers_guidelines.rst |  77 ++--
 docs/source/conf.py                       | 420 +++++++++++-----------
 docs/source/index.rst                     | 107 +++---
 pyproject.toml                            |  75 +---
 tutorial/05_demo_pipeline.ipynb           |  10 +-
 tutorial/README.md                        |  23 +-
 14 files changed, 630 insertions(+), 634 deletions(-)

diff --git a/AUTHORS.md b/AUTHORS.md
index d39ebb9..54ea027 100644
--- a/AUTHORS.md
+++ b/AUTHORS.md
@@ -11,3 +11,4 @@ The following people have made contributions to this project:
 <!--- See https://gist.github.com/djhoese/52220272ec73b12eb8f4a29709be110d for auto-generating parts of this list --->
 
 - [(erikposchivo)](https://github.com/erikposchivo) - EPFL
+- \[(ghiggi)\] (https://github.com/ghiggi) - EPFL
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index fbb6866..2e33556 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -5,10 +5,9 @@ Hi! Thanks for taking the time to contribute to RadDB.
 
 You can contribute in many ways:
 
-- Join the `GitHub Discussions <https://github.com/erikposchivo/raddb/discussions>`__
+- Join the `GitHub Discussions <https://github.com/ltelab/raddb/discussions>`__
 - Report `issues <#issue-reporting>`__
 - Add new features
-- Add new retrievals
 - Add new visualization tools
 - Any others code improvements are welcome !
 
@@ -25,7 +24,7 @@ Before adding your contribution, please take a moment to read through the follow
 - The :ref:`Installation for contributors <installation_contributor>` help you to set up the developing environment and the pre-commit hooks.
 - The section `Contributing process <#contributing-process>`__ provides you with a brief overview of the steps that each RadDB developer must follow to contribute to the repository.
 - The `Code review checklist <#code-review-checklist>`__ enable to speed up the code review process.
-- The `Code of conduct <https://github.com/erikposchivo/raddb/blob/main/CODE_OF_CONDUCT.md>`__ details the expected behavior of all contributors.
+- The `Code of conduct <https://github.com/ltelab/raddb/blob/main/CODE_OF_CONDUCT.md>`__ details the expected behavior of all contributors.
 
 Initiating a discussion about your ideas or proposed implementations is a vital step before starting your contribution !
 Engaging with the community early on can provide valuable insights, ensure alignment with the project's goals, and prevent potential overlap with existing work.
@@ -33,7 +32,7 @@ Here are some guidelines to facilitate this process:
 
 1. Start with a conversation
 
-   Before start coding, open a `GitHub Discussion <https://github.com/erikposchivo/raddb/discussions>`__, a `GitHub Feature Request Issue <https://github.com/erikposchivo/raddb/issues/new/choose>`__ or
+   Before start coding, open a `GitHub Discussion <https://github.com/ltelab/raddb/discussions>`__, a `GitHub Feature Request Issue <https://github.com/ltelab/raddb/issues/new/choose>`__ or
    just start a discussion in the `Open Radar Science Discourse Group <https://openradar.discourse.group/>`__.
    These channels of communication provides an opportunity to gather feedback, understand the project's current state, and improve your contributions.
 
@@ -48,8 +47,8 @@ Here are some guidelines to facilitate this process:
 
 By following these steps, you not only enhance the quality and relevance of your contribution but also become an integral part of the project's collaborative ecosystem.
 
-If you have any questions, please do not hesitate to ask in the `GitHub Discussions <https://github.com/erikposchivo/raddb/discussions>`__ or in the
-`Open Radar Science Discord Group <https://openradar.discourse.group/>`__.
+If you have any questions, please do not hesitate to ask in the `GitHub Discussions <https://github.com/ltelab/raddb/discussions>`__ or in the
+`Open Radar Science Discourse Group <https://openradar.discourse.group/>`__.
 
 
 Issue Reporting
@@ -57,7 +56,7 @@ Issue Reporting
 
 To facilitate and enhance the issue reporting process, it is important to utilize the predefined GitHub Issue Templates.
 These templates are designed to ensure you provide all the essential information in your report, allowing for a faster and more effective response from the maintainers.
-You can access and use these templates by visiting the `GitHub Issue Templates page here <https://github.com/erikposchivo/raddb/issues/new/choose>`__.
+You can access and use these templates by visiting the `GitHub Issue Templates page here <https://github.com/ltelab/raddb/issues/new/choose>`__.
 
 However, if you find that the existing templates don't quite match the specifics of the issue you're encountering, please feel free to suggest a new template.
 Your feedback is invaluable in refining our processes and ensuring we address a broader spectrum of concerns.
@@ -134,7 +133,7 @@ Another relevant style guide can be found in the `The Hitchhiker's Guide to Pyth
 
 To ensure a minimal style consistency, we use `black <https://black.readthedocs.io/en/stable/>`__ to auto-format the source code.
 The *black* configuration used in the RadDB project is
-defined in the `pyproject.toml <https://github.com/erikposchivo/raddb/blob/main/pyproject.toml>`__.
+defined in the `pyproject.toml <https://github.com/ltelab/raddb/blob/main/pyproject.toml>`__.
 
 
 **Code Documentation**
@@ -200,17 +199,17 @@ If a hook identifies an issue (signified by the pre-commit script exiting with a
 Currently, RadDB tests that the code to be committed complies with `black's  <https://github.com/psf/black>`__ format style,
 the `ruff <https://github.com/charliermarsh/ruff>`__ linter and the `codespell <https://github.com/codespell-project/codespell>`__ spelling checker.
 
-+-----------------------------------------------------------------------------------------------+------------------------------------------------------------------+------------+-------+
-|  Tool                                                                                         | Aim                                                              | pre-commit | CI/CD |
-+===============================================================================================+==================================================================+============+=======+
-| `Black <https://black.readthedocs.io/en/stable/>`__                                           | Python code formatter                                            | 👍         | 👍    |
-+-----------------------------------------------------------------------------------------------+------------------------------------------------------------------+------------+-------+
-| `Ruff  <https://github.com/charliermarsh/ruff>`__                                             | Python linter                                                    | 👍         | 👍    |
-+-----------------------------------------------------------------------------------------------+------------------------------------------------------------------+------------+-------+
-| `Codespell  <https://github.com/codespell-project/codespell>`__                               | Spelling checker                                                 | 👍         | 👍    |
-+-----------------------------------------------------------------------------------------------+------------------------------------------------------------------+------------+-------+
-
-The versions of the software used in the pre-commit hooks is specified in the `.pre-commit-config.yaml <https://github.com/erikposchivo/raddb/blob/main/.pre-commit-config.yaml>`__ file.
++-----------------------------------------------------------------+-----------------------+------------+-------+
+| Tool                                                            | Aim                   | pre-commit | CI/CD |
++=================================================================+=======================+============+=======+
+| `Black <https://black.readthedocs.io/en/stable/>`__             | Python code formatter | yes        | yes   |
++-----------------------------------------------------------------+-----------------------+------------+-------+
+| `Ruff  <https://github.com/charliermarsh/ruff>`__               | Python linter         | yes        | yes   |
++-----------------------------------------------------------------+-----------------------+------------+-------+
+| `Codespell  <https://github.com/codespell-project/codespell>`__ | Spelling checker      | yes        | yes   |
++-----------------------------------------------------------------+-----------------------+------------+-------+
+
+The versions of the software used in the pre-commit hooks is specified in the `.pre-commit-config.yaml <https://github.com/ltelab/raddb/blob/main/.pre-commit-config.yaml>`__ file.
 This file serves as a configuration guide, ensuring that the hooks are executed with the correct versions of each tool, thereby maintaining consistency and reliability in the code quality checks.
 
 If a commit is blocked due to these checks, you can manually correct the issues by running locally the appropriate tool: ``black .`` for Black, ``ruff check .`` for Ruff, or ``codespell`` for Codespell.
@@ -223,19 +222,19 @@ These tools, which are not installable on a local setup, perform advanced code q
 
 Refer to the table below for a comprehensive summary of all CI tools employed to assess the code quality of a Pull Request.
 
-+----------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------+
-| Tool                                               | Aim                                                                                                                                 |
-+====================================================+=====================================================================================================================================+
-| `pre-commit.ci <https://pre-commit.ci/>`__         | Run pre-commit (as defined in `.pre-commit-config.yaml <https://github.com/erikposchivo/raddb/blob/main/.pre-commit-config.yaml>`__)  |
-+----------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------+
-| `CodeBeat <https://codebeat.co/>`__                | Automated code review and analysis tools                                                                                            |
-+----------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------+
-| `CodeScene <https://codescene.com/>`__             | Automated code review and analysis tools                                                                                            |
-+----------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------+
-| `CodeFactor <https://www.codefactor.io/>`__        | Automated code review and analysis tools                                                                                            |
-+----------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------+
-| `Codacy <https://www.codacy.com/>`__               | Automated code review and analysis tools                                                                                            |
-+----------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------+
++---------------------------------------------+------------------------------------------------------------+
+| Tool                                        | Aim                                                        |
++=============================================+============================================================+
+| `pre-commit.ci <https://pre-commit.ci/>`__  | Run pre-commit (as defined in ``.pre-commit-config.yaml``) |
++---------------------------------------------+------------------------------------------------------------+
+| `CodeBeat <https://codebeat.co/>`__         | Automated code review and analysis tools                   |
++---------------------------------------------+------------------------------------------------------------+
+| `CodeScene <https://codescene.com/>`__      | Automated code review and analysis tools                   |
++---------------------------------------------+------------------------------------------------------------+
+| `CodeFactor <https://www.codefactor.io/>`__ | Automated code review and analysis tools                   |
++---------------------------------------------+------------------------------------------------------------+
+| `Codacy <https://www.codacy.com/>`__        | Automated code review and analysis tools                   |
++---------------------------------------------+------------------------------------------------------------+
 
 5. Check code functionality
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -267,7 +266,7 @@ The following tools are used:
 
 For contributors interested in running the tests locally:
 
-1. Ensure you have the :ref:`development environment <installation_contributor>` correctly set up. Make sure you also downloaded the additional test data.
+1. Ensure you have the :ref:`development environment <installation_contributor>` correctly set up.
 2. Navigate to the RadDB root directory.
 3. Execute the following command to run the entire test suite:
 
@@ -278,17 +277,17 @@ For contributors interested in running the tests locally:
 For more focused testing or during specific feature development, you may run subsets of tests.
 This can be done by specifying either a sub-directory or a particular test module.
 
-Run tests in a specific sub-directory:
+Run a particular test module:
 
 .. code-block:: bash
 
-    pytest raddb/tests/<test_subdirectory>/
+    pytest raddb/tests/test_<module_name>.py
 
-Run a particular test module:
+Run a single test:
 
 .. code-block:: bash
 
-    pytest raddb/tests/<test_subdirectory>/test_<module_name>.py
+    pytest raddb/tests/test_<module_name>.py::test_<callable>
 
 These options provide flexibility, allowing you to efficiently target and validate specific components of the RadDB software.
 
@@ -306,7 +305,7 @@ allowed only if quality requirements are fulfilled.
 
 If you encounter errors, you can attempt to fix the formatting errors with the following command:
 
-:: code-block:: bash
+.. code-block:: bash
 
    pre-commit run --all-files
 
@@ -323,7 +322,7 @@ Recommendation for the Pull Requests:
 -  It is perfectly fine to make many small commits as you work on a Pull Request. GitHub will automatically squash all the commits before merging the Pull Request.
 -  If adding a new feature:
 
-   -  Provide a convincing reason to add the new feature. Ideally, propose your idea through a `Feature Request Issue <https://github.com/erikposchivo/raddb/issues/new/choose>`__ and obtain approval before starting work on it. Alternatively, you can present your ideas in the `GitHub Discussions <https://github.com/erikposchivo/raddb/discussions>`__ or in the `Open Radar Science Discourse Group <https://openradar.discourse.group/>`__.
+   -  Provide a convincing reason to add the new feature. Ideally, propose your idea through a `Feature Request Issue <https://github.com/ltelab/raddb/issues/new/choose>`__ and obtain approval before starting work on it. Alternatively, you can present your ideas in the `GitHub Discussions <https://github.com/ltelab/raddb/discussions>`__ or in the `Open Radar Science Discourse Group <https://openradar.discourse.group/>`__.
    -  Implement unit tests to verify the functionality of the new feature. This ensures that your addition works as intended and maintains the quality of the codebase.
 
 -  If fixing bug:
@@ -350,4 +349,4 @@ Credits
 
 Thank you to all the people who have already contributed to RadDB repository!
 
-If you have contributed code or documentation to RadDB, add your name to the `AUTHORS.md <https://github.com/erikposchivo/raddb/blob/main/AUTHORS.md>`__ file.
+If you have contributed code or documentation to RadDB, add your name to the `AUTHORS.md <https://github.com/ltelab/raddb/blob/main/AUTHORS.md>`__ file.
diff --git a/MANIFEST.in b/MANIFEST.in
index ae6bf71..20a130c 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -2,5 +2,4 @@ prune .github
 prune ci
 prune docs
 prune raddb/tests
-prune raddb/mch
 prune tutorial
diff --git a/README.md b/README.md
index 5c81cac..aa84406 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# RadDB — generic radar data archiving & analysis
+# RadDB — radar volumes as a Parquet archive
 
 |                   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -16,18 +16,15 @@
 | Citation          | [![DOI](XXX)](XXX)          |                                                                                                                                                                                                                                                                                                                                                                                                                                                               [**Documentation**](https://raddb.readthedocs.io/en/latest/)
 
 RadDB archives xarray **DataTree** radar volumes as compact Parquet files and
-gives you a small, fluent object API to load, filter, crop, cross-section and
+gives you a small, fluent interface to load, filter, crop, extract cross-section and
 plot them.  It is **network-agnostic**: any DataTree with the standard
 [xradar](https://docs.openradarscience.org/projects/xradar/) coordinate layout
-(MeteoSwiss, NEXRAD, …) can be archived and analysed — no pyart required.
-
-MeteoSwiss/METRANET-specific ingestion (pyart + radar_api) lives in the private
-`raddb.mch` subpackage and is kept out of the generic core.
+(NEXRAD, ODIM, IRIS, …) can be archived and analysed.
 
 ## Storage model
 
 A radar is stored as a **static LUT** (per-gate geometry, generated once) plus one
-**POL parquet per volume** (the dynamic moments), linked by an integer `gate_id`:
+**POL parquet per volume** (the variables), linked by an integer `gate_id`:
 
 ```
 {archive_dir}/{radar}/LUT/{radar}_LUT.parquet          # gate_id, lat/lon/alt, x_<epsg>/y_<epsg>, sweep, …
@@ -35,16 +32,16 @@ A radar is stored as a **static LUT** (per-gate geometry, generated once) plus o
 ```
 
 No-echo gates are dropped at archive time (default `DBZH > 0`), so the archive
-stays small.
+stays small: a 12-sweep WSR-88D volume of 8,791,200 polar gates becomes 8.2 MB.
 
 ## Installation
 
 ```bash
-pip install -e .            # core
-pip install -e ".[viz]"     # + cartopy / pyproj / shapely for maps
+pip install raddb              # everything in this README works
+pip install "raddb[viz]"       # + the interactive Jupyter map and cartopy basemaps
 ```
 
-Core runtime deps: `numpy, pandas, polars, geopandas, xarray, pyarrow, dask, fsspec, s3fs, matplotlib`.
+Core runtime dependencies: `numpy, pandas, polars, geopandas, shapely, pyproj, xarray, pyyaml, pyarrow, matplotlib, netcdf4, zarr`.
 
 ## Quick start
 
@@ -59,102 +56,140 @@ Core runtime deps: `numpy, pandas, polars, geopandas, xarray, pyarrow, dask, fss
 ```python
 import raddb
 
-db = raddb.RadDB(archive_dir="/data/raddb", crs=2056)  # 2056 = CH1903+/LV95
+db = raddb.RadDB(archive_dir="/data/raddb", crs=32614)  # 32614 = UTM zone 14N
+```
+
+A **projected CRS is mandatory to write** an archive and never needed to read one.
+There is no default: the wrong projection is silently wrong.
+`raddb.lut.suggest_crs(longitude, latitude)` tells you which one to pass.
 
-# --- archive -----------------------------------------------------------------
+### Archive
+
+```python
 # From saved DataTree files on disk (.zarr / .nc); the LUT is auto-generated:
-db.archive(datatree_dir="/data/MCH_datatree")  # radar inferred per file
+db.archive(datatree_dir="/data/NEXRAD_datatree")  # radar inferred per file
+
 # ...or archive in-memory DataTrees directly:
-# db.archive(datatree=dt, radar="A")
-# db.archive(datatree=[dt1, dt2], radar="A")
-# db.archive(datatree={"A": [dt1], "D": [dt2]})         # multi-radar
+db.archive(datatree=dt, radar="KTLX")
+db.archive(datatree=[dt1, dt2], radar="KTLX")
+db.archive(datatree={"KTLX": [dt1], "KMLB": [dt2]})  # multi-radar
+```
+
+Pass `filter=` to decide which gates ever reach the disk — the main control on
+archive size:
 
-# --- open --------------------------------------------------------------------
-rdf = db.open(time_period=("2024-08-26", "2024-08-27"), radars="L")
+```python
+db.archive(
+    datatree=dt, radar="KTLX", filter={"var": "DBZH", "logic": ">", "threshold": 20}
+)
+```
+
+### Open
+
+```python
+rdf = db.open(time_period=("2024-06-12", "2024-06-13"), radars="KTLX")
 print(rdf)  # rich summary: gates, radars, time range, columns
+
 len(rdf), rdf.columns(), rdf.radars()
 rdf.start_time(), rdf.end_time()
 rdf.extent()  # [xmin, xmax, ymin, ymax] in `crs`
 rdf.geographic_extent()  # [lon_min, lon_max, lat_min, lat_max]
 rdf.crs(), rdf.geographic_crs()
+```
 
-# --- filter / convert --------------------------------------------------------
-strong = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
-strong = rdf.filter(
+`columns=` and `filters=` are pushed down into the scan, so only the rows you
+asked for are ever materialised.
+
+### Filter and convert
+
+```python
+filtered_rdf = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
+filtered_rdf = rdf.filter(
     [
         {"var": "DBZH", "logic": ">", "threshold": 20},
         {"var": "RHOHV", "logic": ">", "threshold": 0.9},
     ]
 )  # AND
-pdf = rdf.to_pandas(with_geometry=True)  # pandas + gate coordinates
+
+df = rdf.to_pandas(with_geometry=True)  # pandas + gate coordinates
 gdf = rdf.to_geopandas()  # GeoDataFrame (with CRS)
-dt = rdf.to_datatree()  # xarray DataTree (for plotting)
+dt = rdf.to_datatree()  # back to xarray
+```
+
+Filters are `{"var", "logic", "threshold"}` dicts, where `logic` is one of
+`==`, `!=`, `>`, `>=`, `<`, `<=`.  `crs` is an EPSG int (e.g. `32614`), a
+CRS object, or `None`.
+
+### Crop to an area of interest
 
-# --- area of interest --------------------------------------------------------
-box = rdf.crop_by_bbox(
-    extent=[2.60e6, 2.62e6, 1.11e6, 1.13e6]
-)  # or bounds=(xmin,ymin,xmax,ymax)
+```python
+box = rdf.crop_by_bbox(extent=[636_504, 676_504, 3_891_333, 3_931_333])
 poly = rdf.crop_by_polygone("catchment.geojson")
-disc = rdf.crop_around_point((2.61e6, 1.12e6), distance=20_000)  # metres
-# rdf.interactive_crop()      # draw an AOI on a Jupyter map (needs ipyleaflet)
+disc = rdf.crop_around_point((656_504, 3_911_333), distance=20_000)  # metres
+# rdf.interactive_crop()   # draw an AOI on a Jupyter map
+```
+
+### Cut a cross-section
+
+```python
+cs = rdf.extract_cross_section(p1=(626_504, 3_911_333), p2=(686_504, 3_911_333))
+```
 
-# --- cross-section -----------------------------------------------------------
-cs = rdf.extract_cross_section(p1=(2.60e6, 1.12e6), p2=(2.63e6, 1.12e6))
+### Plot
 
-# --- plot --------------------------------------------------------------------
+```python
 rdf.plot_ppi(sweep=1, variable="DBZH", save="ppi.png")
-cs.plot_cross_section(variable="DBZH", save="xsec.png")
 rdf.plot_rhi(azimuth=270, variable="DBZH")
+rdf.plot_cappi(altitude=3000, variable="DBZH")
+cs.plot_cross_section(variable="DBZH", save="xsec.png")
+```
+
+Each plot draws into one `Axes` and returns the matplotlib artist, so you compose
+panels by passing `ax=`.
 
-# fluent: open -> filter -> crop -> plot
+### Chain them
+
+```python
 rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20}).crop_by_bbox(
     extent=rdf.extent()
-).plot_ppi(variable="DBZH", save="strong.png")
+).plot_ppi(variable="DBZH", save="ppi_plot_example.png")
 ```
 
-`filter` / `crs` argument shapes: filters are `{"var", "logic", "threshold"}`
-dicts (`logic` ∈ `==,!=,>,>=,<,<=`); `crs` is an EPSG int (e.g. `2056`), a
-CRS-coercible object, or `None`.
-
 ### What is on disk? (archive-bound)
 
 ```python
 db.inventory()  # radars, volume counts, time ranges, size
-db.inventory(detailed=True)  # + LUT info, stored moments, day-by-day counts
-db.inventory(datatree_dir="/data/MCH_datatree")  # DataTree files not archived yet
+db.inventory(detailed=True)  # + LUT info, stored variables, day-by-day counts
+db.inventory(datatree_dir="/data/NEXRAD_datatree")  # DataTree files not archived yet
 ```
 
 ### LUT accessors (archive-bound)
 
 ```python
 db.list_radars()  # radars present in the archive
-db.get_lut("L")  # the static LUT (pandas)
-db.get_radar_info("L")  # site location / sweep geometry
-db.add_lut_projection("L", epsg=2056)
+db.get_lut("KTLX")  # the static LUT (polars)
+db.get_radar_info("KTLX")  # site location / sweep geometry
+db.add_lut_projection("KTLX", epsg=32614)
 ```
 
 ## Module structure
 
 ```
 raddb/
-├── __init__.py     # public API surface
-├── main.py         # the RadDB class (this API)
+├── __init__.py     # the public interface
+├── main.py         # the RadDB class
 ├── io_core.py      # DataTree <-> DataFrame <-> Parquet + archive backends
 ├── lut.py          # LUT generation / geo projection
 ├── aoi.py          # AOI / crop / cross-section geometry
 ├── discovery.py    # find_datatree_files + filename-time parsing
 ├── helper.py       # filters, radar-name normalisation, timers
-├── viz/            # plot.py (PPI/RHI/cross-section), interactive.py
-└── mch/            # private MeteoSwiss/METRANET ingestion (pyart) — not in the public wheel
+└── viz/            # plot.py (PPI/RHI/CAPPI/cross-section), interactive.py
 ```
 
-Sample-data scripts live under `scripts/` (`make_sample_mch_datatrees.py`,
-`download_nexrad_datatree.py`).
-
 ## Notes
 
 - **Projected coordinates / `crs`.** Generating a LUT with a projection (e.g.
-  `crs=2056`) and the projected accessors (`extent`, `to_geopandas`) use `pyproj`,
+  `crs=32614`) and the projected accessors (`extent`, `to_geopandas`) use `pyproj`,
   which needs the PROJ database.  A `PROJ_DATA` / `PROJ_LIB` inherited from another
   environment (a conda base env, a system PROJ) points at a proj.db of the wrong
   PROJ version and makes every projection fail with *"no database context
diff --git a/docs/README.md b/docs/README.md
index d72400f..bcd15fe 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -13,22 +13,22 @@ To build the documentation locally, follow the next three steps.
 
 **1. Set up the python environment for building the documentation**
 
-The python packages required to build the documentation are listed in the [environment.yaml](https://github.com/erikposchivo/raddb/blob/main/docs/environment.yaml) file.
+The python packages required to build the documentation are listed in the [environment.yaml](https://github.com/ltelab/raddb/blob/main/docs/environment.yaml) file.
 
 For an efficient setup, we recommend creating a dedicated virtual environment.
 Navigate to the `docs/` directory and execute the following command.
 This will create a new environment and install the required packages:
 
-```
-conda create -f environment.yaml
+```bash
+conda env create -f environment.yaml
 ```
 
 **2. Activate the virtual environment**
 
 Once the environment is ready, activate it using:
 
-```
-conda activate build-doc-radar-api
+```bash
+conda activate build-doc-raddb
 ```
 
 **3. Generate the documentation**
@@ -36,7 +36,7 @@ conda activate build-doc-radar-api
 With the environment set and activated, you're ready to generate the documentation.
 Execute:
 
-```
+```bash
 make clean html
 ```
 
diff --git a/docs/environment.yaml b/docs/environment.yaml
index 108e2bd..cf3a340 100644
--- a/docs/environment.yaml
+++ b/docs/environment.yaml
@@ -1,4 +1,4 @@
-name: build-doc-radar-api
+name: build-doc-raddb
 channels: [conda-forge]
 dependencies:
   - docutils
@@ -12,6 +12,6 @@ dependencies:
   - sphinx-mdinclude==0.5.3
   - sphinx==7.2.6
   - sphinxcontrib-youtube
-  - radar-api
+  - raddb
   - arm_pyart
   - xradar
diff --git a/docs/source/02_installation.rst b/docs/source/02_installation.rst
index 5b26164..3f84031 100644
--- a/docs/source/02_installation.rst
+++ b/docs/source/02_installation.rst
@@ -5,7 +5,7 @@ Installation
 
 We define here two types of installation:
 
-- `Installation for standard users`_: for users who want to process data.
+- `Installation for standard users`_: for users who want to archive and query radar volumes.
 
 - `Installation for contributors`_: for contributors who want to enrich the project (eg. add a new features).
 
@@ -31,17 +31,17 @@ or `conda <https://docs.conda.io/en/latest/>`__ (recommended).
   or `anaconda <https://docs.anaconda.com/anaconda/install/>`__
   if you don't have it already installed.
 
-* Create the *radar-api-py311* (or any other custom name) conda environment:
+* Create the *raddb-py311* (or any other custom name) conda environment:
 
 .. code-block:: bash
 
-	conda create --name radar-api-py311 python=3.11 --no-default-packages
+	conda create --name raddb-py311 python=3.11 --no-default-packages
 
-* Activate the *radar-api-py311* conda environment:
+* Activate the *raddb-py311* conda environment:
 
 .. code-block:: bash
 
-	conda activate radar-api-py311
+	conda activate raddb-py311
 
 
 **With venv:**
@@ -50,8 +50,8 @@ or `conda <https://docs.conda.io/en/latest/>`__ (recommended).
 
 .. code-block:: bash
 
-   python -m venv radar-api-pyXXX
-   cd radar-api-pyXXX/Scripts
+   python -m venv raddb-pyXXX
+   cd raddb-pyXXX/Scripts
    activate
 
 
@@ -59,8 +59,8 @@ or `conda <https://docs.conda.io/en/latest/>`__ (recommended).
 
 .. code-block:: bash
 
-   virtualenv -p python3 radar-api-pyXXX
-   source radar-api-pyXXX/bin/activate
+   virtualenv -p python3 raddb-pyXXX
+   source raddb-pyXXX/bin/activate
 
 .. _installation_standard:
 
@@ -68,8 +68,8 @@ Installation for standard users
 ==================================
 
 The latest RadDB stable version is available
-on the `Python Packaging Index (PyPI) <https://pypi.org/project/radar-api/>`__
-and on the `conda-forge channel <https://anaconda.org/conda-forge/radar-api>`__.
+on the `Python Packaging Index (PyPI) <https://pypi.org/project/raddb/>`__
+and on the `conda-forge channel <https://anaconda.org/conda-forge/raddb>`__.
 
 Please install the package in the virtual environment you created before!
 
@@ -77,7 +77,7 @@ Please install the package in the virtual environment you created before!
 
 .. code-block:: bash
 
-   conda install -c conda-forge radar-api
+   conda install -c conda-forge raddb
 
 .. note::
    In an alternative to conda, if you are looking for a lightweight package manager you could use `micromamba <https://micromamba.readthedocs.io/en/latest/>`__.
@@ -86,14 +86,14 @@ Please install the package in the virtual environment you created before!
 
 .. code-block:: bash
 
-   pip install radar-api
+   pip install raddb
 
 .. _installation_contributor:
 
 Installation for contributors
 ================================
 
-The latest RadDB version is available on the GitHub repository `raddb <https://github.com/erikposchivo/raddb>`_.
+The latest RadDB version is available on the GitHub repository `raddb <https://github.com/ltelab/raddb>`_.
 You can install the package in editable mode, so that you can modify the code and see the changes immediately.
 The following steps guides to the package installation in editable mode.
 
@@ -119,15 +119,15 @@ You can create a conda environment (i.e. with python 3.11) with:
 
 .. code-block:: bash
 
-	conda create --name radar-api-dev-py311 python=3.11 --no-default-packages
-	conda activate radar-api-dev-py311
+	conda create --name raddb-dev-py311 python=3.11 --no-default-packages
+	conda activate raddb-dev-py311
 
 Install the package dependencies
 ............................................
 
 .. code-block:: bash
 
-	conda install --only-deps radar-api
+	conda install --only-deps raddb
 
 
 Install the package in editable mode
@@ -154,7 +154,7 @@ Pre-commit hooks are automated scripts that run during each commit to detect bas
 If a hook identifies an issue (signified by the pre-commit script exiting with a non-zero status), it halts the commit process and displays the error messages.
 
 .. note::
-	The versions of the software used in the pre-commit hooks are specified in the `.pre-commit-config.yaml <https://github.com/erikposchivo/raddb/blob/main/.pre-commit-config.yaml>`__ file. This file serves as a configuration guide, ensuring that the hooks are executed with the correct versions of each tool, thereby maintaining consistency and reliability in the code quality checks.
+	The versions of the software used in the pre-commit hooks are specified in the `.pre-commit-config.yaml <https://github.com/ltelab/raddb/blob/main/.pre-commit-config.yaml>`__ file. This file serves as a configuration guide, ensuring that the hooks are executed with the correct versions of each tool, thereby maintaining consistency and reliability in the code quality checks.
 
 Further details about pre-commit hooks can be found in the Contributors Guidelines, specifically in the provided in the :ref:`Code quality control <code_quality_control>` section.
 
@@ -168,56 +168,56 @@ The following bash code allow to install all optional dependencies:
 
 .. code-block:: bash
 
-   conda install -c conda-forge jupyter spyder xradar wradlib arm_pyart flox numbagg bottleneck opt-einsum python-graphviz bokeh
+   pip install "raddb[viz]"
+   conda install -c conda-forge jupyter spyder xradar arm_pyart
 
 
-IDE Tools
-..............
-
-For an improved development experience, consider installing the intuitive `Jupyter <https://jupyter.org/>`_ and
-`Spyder <https://www.spyder-ide.org/>`_ Python Integrated Development Environments (IDEs):
+Package extras
+..................
 
-.. code-block:: bash
-
-   conda install -c conda-forge jupyter spyder
+Everything in the tutorials works from a plain ``pip install raddb``.
+Two optional dependency groups add the rest:
 
+* ``viz`` installs `cartopy <https://scitools.org.uk/cartopy/>`_,
+  `pyproj <https://pyproj4.github.io/pyproj/>`_,
+  `shapely <https://shapely.readthedocs.io/>`_,
+  `lonboard <https://developmentseed.org/lonboard/>`_ and
+  `geoarrow-pyarrow <https://geoarrow.org/geoarrow-python/>`_,
+  used by the plotting, cross-section and interactive AOI selection functionalities.
 
-Radar Processing
-...................................
-
-To read and process ground and spaceborne radar data, install
-`xradar <https://docs.openradarscience.org/projects/xradar/en/stable/>`_,
-`wradlib <https://docs.wradlib.org/en/latest/>`_.
-`pyart <https://arm-doe.github.io/pyart/>`_ and
-`gpm-api <https://gpm-api.readthedocs.io/>`_.
+* ``io`` installs `pyshp <https://pypi.org/project/pyshp/>`_, needed only to read
+  an area of interest from an ESRI ``.shp`` file.
 
 .. code-block:: bash
 
-   conda install -c conda-forge xradar wradlib arm_pyart gpm-api
+   pip install "raddb[viz]"
+   pip install "raddb[io]"
 
 
-Speed Up Xarray Computations
-..........................................
+IDE Tools
+..............
 
-To speed up arrays computations with xarray, install
-`flox <https://flox.readthedocs.io/en/latest/>`_,
-`numbagg <https://github.com/numbagg/numbagg>`_,
-`bottleneck <https://bottleneck.readthedocs.io/en/latest/intro.html>`_ and
-`opt-einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_:
+For an improved development experience, consider installing the intuitive `Jupyter <https://jupyter.org/>`_ and
+`Spyder <https://www.spyder-ide.org/>`_ Python Integrated Development Environments (IDEs):
 
 .. code-block:: bash
 
-   conda install -c conda-forge flox numbagg bottleneck opt-einsum
+   conda install -c conda-forge jupyter spyder
+
 
-Dask Operations
-......................
+Radar Processing
+...................................
 
-To visualize `Dask Task Graphs  <https://docs.dask.org/en/stable/10-minutes-to-dask.html>`_ and monitor
-computations through the `Dask Dashboard <https://docs.dask.org/en/stable/dashboard.html>`_, please install:
+RadDB archives any `xarray.DataTree <https://docs.xarray.dev/en/stable/generated/xarray.DataTree.html>`_
+following the `xradar <https://docs.openradarscience.org/projects/xradar/en/stable/>`_
+coordinate layout: ``xarray`` provides the container, ``xradar`` defines the radar
+sweep-group layout inside it and reads a raw volume into one.
+`Py-ART <https://arm-doe.github.io/pyart/>`_ is optional: RadDB imports it only to register
+its colormaps (e.g. ``HomeyerRainbow``) with matplotlib.
 
 .. code-block:: bash
 
-   conda install -c conda-forge python-graphviz bokeh
+   conda install -c conda-forge xradar arm_pyart
 
 
 Run on Jupyter Notebooks
@@ -226,10 +226,10 @@ Run on Jupyter Notebooks
 If you want to run RadDB on a `Jupyter Notebook <https://jupyter.org/>`__,
 you have to take care to set up the IPython kernel environment where RadDB is installed.
 
-For example, if your conda/virtual environment is named ``radar-api-dev``, run:
+For example, if your conda/virtual environment is named ``raddb-dev``, run:
 
 .. code-block:: bash
 
-   python -m ipykernel install --user --name=radar-api-dev
+   python -m ipykernel install --user --name=raddb-dev
 
-When you will use the Jupyter Notebook, by clicking on ``Kernel`` and then ``Change Kernel``, you will be able to select the ``radar-api-dev`` kernel.
+When you will use the Jupyter Notebook, by clicking on ``Kernel`` and then ``Change Kernel``, you will be able to select the ``raddb-dev`` kernel.
diff --git a/docs/source/03_quickstart.rst b/docs/source/03_quickstart.rst
index ceb99b5..acdd9e7 100644
--- a/docs/source/03_quickstart.rst
+++ b/docs/source/03_quickstart.rst
@@ -2,157 +2,161 @@
 Quick Start
 ===========
 
-RadDB allows to download weather radar data from various cloud buckets of several
-meteorological offices.
-To download the data, it is necessary to create the RadDB configuration file.
+RadDB turns radar **volumes** into a compact **tabular archive**: one Parquet
+file per volume, one row per radar gate.  Once archived, a whole campaign is
+queried like a dataframe — filter it, crop it, plot it — without ever
+re-reading the source files.
 
-Create the RadDB configuration file
----------------------------------------
+This page walks through the shortest useful path; the notebooks in the
+repository's ``tutorial/`` directory cover each step in depth.
 
-The RadDB configuration file records the directory on your local machine where to
-save the radar data of interest.
+What RadDB stores
+-----------------
 
-To facilitate the creation of the configuration file, you can adapt and run the following script in Python.
-The configuration file will be created in the user's home directory under the name ``.config_raddb.yaml``.
+A radar is written as one **static look-up table** (the per-gate geometry,
+generated once) plus one **Parquet file per volume** (the variables that change
+from scan to scan)::
 
-.. code-block:: python
+    {archive_dir}/KTLX/LUT/KTLX_LUT.parquet          # gate geometry, written once
+    {archive_dir}/KTLX/LUT/KTLX_info.yaml            # site, CRS, scan strategy
+    {archive_dir}/KTLX/2024/06/12/KTLX_20240612_220324_POL.parquet
 
-    import raddb
+The two are linked by an integer ``gate_id``.  Because the geometry is stored
+once and never repeated, a volume file holds only the measurements — which is
+what makes the archive small.
 
-    base_dir = "<path/to/a/local/directory/>"  # where to download all RADAR data
-    raddb.define_configs(
-        base_dir=base_dir,
-    )
+Archive a volume
+----------------
 
-Now please close and restart the python session to make sure that the configuration file is correctly loaded.
-You can check that the configuration file has been correctly created with:
+RadDB is **network-agnostic**: any
+`xarray.DataTree <https://docs.xarray.dev/en/stable/generated/xarray.DataTree.html>`_
+with the standard
+`xradar <https://docs.openradarscience.org/projects/xradar/en/stable/>`_
+coordinate layout can be archived, whether it comes from NEXRAD,
+ODIM or IRIS.
 
 .. code-block:: python
 
     import raddb
 
-    configs = raddb.read_configs()
-    print(configs)
-
+    db = raddb.RadDB(archive_dir="/path/to/archive", crs=32614)  # UTM 14N, the KTLX zone
 
-Search the radar of interest
-----------------------------------------
+    # a single in-memory volume
+    dt = raddb.open_any_datatree("KTLX_20240612_220324.zarr")
+    db.archive(datatree=dt, radar="KTLX")
 
-To list the available radars, you can use the following command:
+    # or a whole directory of saved volumes, grouped by radar from the filename
+    db.archive(datatree_dir="/path/to/datatrees", time_period=("2024-06-01", "2024-07-01"))
 
-.. code-block:: python
+.. note::
+   A **projected CRS is mandatory to write** an archive and never needed to
+   read one.  There is no default, because a wrong projection is silently
+   wrong. Use ``raddb.lut.suggest_crs(longitude, latitude)`` if you are
+   unsure which one to pass.
 
-    import raddb
+Filter while archiving
+----------------------
 
-    raddb.available_radars()
+Most gates in a radar volume contain no echo.  The ``filter`` argument decides
+which gates ever reach the disk, so it is the main control on archive size.
+It takes a ``{"var", "logic", "threshold"}`` dictionary:
 
+.. code-block:: python
 
-You can also search which radars of a specific network are available during
-a given time period:
+    # the default: drop no-echo gates
+    db.archive(datatree=dt, radar="KTLX", filter={"var": "DBZH", "logic": ">", "threshold": 0})
 
+    # keep only significant echo — a much smaller archive
+    db.archive(datatree=dt, radar="KTLX", filter={"var": "DBZH", "logic": ">", "threshold": 20})
 
-.. code-block:: python
+Measured on ``KTLX_20240612_220324``, a 12-sweep WSR-88D volume of
+8,791,200 polar gates:
 
-    raddb.available_radars(network="NEXRAD", start_time="1992-01-01", end__time="1993-01-01")
+=========================  ==============  ==========
+filter                     gates archived  Parquet
+=========================  ==============  ==========
+``DBZH > 0`` (default)     1,424,223       8.22 MB
+``DBZH > 20``              12,642          0.11 MB
+=========================  ==============  ==========
 
+The filter is irreversible — discarded gates are not in the archive — so choose
+the threshold against what you intend to analyse.
 
-The available radar networks can be listed using:
+Inspect the archive
+-------------------
 
 .. code-block:: python
 
-    raddb.available_networks()
+    db = raddb.RadDB(archive_dir="/path/to/archive")  # reading needs no CRS
 
+    db.list_radars()  # ['FANJ', 'KDVN', 'KLOT', 'KMLB', 'KTLX', ...]
+    db.inventory()  # volumes, time range and size per radar
+    db.get_radar_info("KTLX")  # site, CRS, beamwidth, sweep geometry
 
-Download the data
---------------------
+Open and query
+--------------
 
-To download the data, you can adapt the following code snippet:
+``open()`` returns a **data-carrying** ``RadDB`` holding a
+`polars <https://pola.rs/>`_ DataFrame.  Time, radar, columns and gate filters
+are all pushed down into the scan, so only the rows you asked for are ever
+materialised:
 
 .. code-block:: python
 
-    import raddb
-
-    radar = "KABR"
-    network = "NEXRAD"
-
-    start_time = "2021-02-01 12:00:00"
-    end_time = "2021-02-01 13:00:00"
-
-    raddb.download(
-        network=network,
-        radar=radar,
-        start_time=start_time,
-        end_time=end_time,
+    rdf = db.open(
+        radars="KTLX",
+        time_period=("2024-06-12", "2024-06-13"),
+        columns=["DBZH", "ZDR"],  # fewer columns to read
+        filters=[{"var": "DBZH", "logic": ">", "threshold": 20}],  # applied during the scan
     )
 
-Search the data
---------------------
+    len(rdf)  # 70359
+    rdf.columns()  # ['gate_id', 'DBZH', 'ZDR', 'volume_time', 'radar']
+    rdf.head()
 
-RadDB enables to search files files which have been downloaded locally,
-or files which are located into a cloud bucket. To search for file locally,
-specify ``procol="local"``, while to retrieve the file path of cloud bucket files,
-specify ``procol="s3"``.
+Every operation returns a **new** ``RadDB``, so calls chain:
 
 .. code-block:: python
 
-    # Search for files on cloud bucket
-    filepaths = raddb.find_files(
-        network=network,
-        radar=radar,
-        start_time=start_time,
-        end_time=end_time,
-        fs_args=fs_args,
-        protocol="s3",
-        verbose=verbose,
-    )
-    print(filepaths)
-
-    # Search for files locally
-    filepaths = raddb.find_files(
-        network=network,
-        radar=radar,
-        start_time=start_time,
-        end_time=end_time,
-        fs_args=fs_args,
-        protocol="local",
-        verbose=verbose,
+    heavy_rain = (
+        db.open(radars="KTLX")
+        .filter({"var": "DBZH", "logic": ">", "threshold": 30})
+        .sel(volume_time="2024-06-12 22:03:24")
+        .crop_around_point(point=(-97.278, 35.333), distance=25_000, crs=4326)
     )
-    print(filepaths)
-
 
-RadDB provide an utility to group filepaths by temporal interval,
-radar volume identifiers, etc.
+Alongside ``crop_around_point`` there are ``crop_by_bbox``, ``crop_by_polygone``
+(shapely geometry, GeoDataFrame or a ``.shp`` / ``.geojson`` path) and
+``extract_cross_section`` for a vertical slice along an arbitrary line.
 
-.. code-block:: python
-
-    dict_filepaths = raddb.group_filepaths(filepaths, network=network, groups="volume_identifier")
-    dict_filepaths = raddb.group_filepaths(filepaths, network=network, groups=["day", "hour"])
+Plot
+----
 
+Four plots, each drawing into one ``Axes`` and returning the matplotlib artist,
+so you compose panels by passing ``ax=``.  They read the exact gate geometry
+from the look-up table and draw only the gates the object holds:
 
-Open the data
-----------------
-
-RadDB enables to open radars files into various objects by simply providing a
-local or cloud file path.
+.. code-block:: python
 
-- ``raddb.open_datatree(filepath, network)`` opens a file into a ``xarray.DataTree`` object using the appropriate ``xradar`` reader. Typically, an ``xarray.DataTree`` object contains multiple radar sweeps.
+    rdf.plot_ppi(sweep=1, variable="DBZH")  # one sweep
+    rdf.plot_rhi(azimuth=90)  # one azimuth
+    rdf.plot_cappi(altitude=3000)  # constant-ppi slice
+    rdf.plot_vcs(line="section.geojson")  # vertical cross-section
 
-- ``raddb.open_dataset(filepath, network, sweep="sweep_0")`` opens a file and extract a single radar sweep into a ``xarray.Dataset`` object. The name of the radar sweep must be known beforehand !
+Convert
+-------
 
-- ``raddb.open_pyart(filepath, network)`` opens a file into a ``pyart.Radar``  object.
+.. code-block:: python
 
+    rdf.to_pandas(with_geometry=True)  # + latitude / longitude / altitude / sweep
+    rdf.to_geopandas()  # a GeoDataFrame of gate centroids
+    rdf.to_datatree()  # back to xarray, on the full polar grid
 
-Further documentation
---------------------------
+Further reading
+---------------
 
-For radar data processing, please have a look at
+If you are new to the ecosystem, the
 `xradar <https://docs.openradarscience.org/projects/xradar/en/stable/>`_,
-`pyart <https://arm-doe.github.io/pyart/>`_ and
-`wradlib <https://docs.wradlib.org/en/latest/>`_ software.
-
-
-If you are not familiar with `xarray <http://xarray.pydata.org/en/stable/>`_,
-`numpy <https://numpy.org/doc/stable/index.html>`_,
-`pandas <https://pandas.pydata.org/>`_, and
-`dask <https://docs.dask.org/en/stable/array.html>`_,
-it is highly suggested to first have a look also at the documentation of these software.
+`xarray <https://docs.xarray.dev/en/stable/>`_ and
+`polars <https://docs.pola.rs/>`_ documentation are the useful companions to
+this one.
diff --git a/docs/source/07_maintainers_guidelines.rst b/docs/source/07_maintainers_guidelines.rst
index a585fb5..170467f 100644
--- a/docs/source/07_maintainers_guidelines.rst
+++ b/docs/source/07_maintainers_guidelines.rst
@@ -10,7 +10,7 @@ Core Contributors
 ====================
 
 * Current Release Manager : Ghiggi Gionata
-* Testing Team : Ghiggi Gionata, Son Pham-Ba
+* Testing Team : Ghiggi Gionata, Andrea Giacobbi
 
 
 Versions Guidelines
@@ -20,7 +20,7 @@ RadDB uses  `Semantic <https://semver.org/>`_ Versioning. Each release is associ
 
 Given a version number in the MAJOR.MINOR.PATCH (eg., X.Y.Z) format, here are the differences in these terms:
 
-- MAJOR version - make breaking/incompatible API changes
+- MAJOR version - make breaking/incompatible changes
 - MINOR version - add functionality in a backwards compatible manner.
 - PATCH version - make backwards compatible bug fixes
 
@@ -28,22 +28,22 @@ Given a version number in the MAJOR.MINOR.PATCH (eg., X.Y.Z) format, here are th
 Breaking vs. non-breaking changes
 -----------------------------------
 
-Since RadDB is used by a broad ecosystem of both API consumers and implementers,
+Since RadDB is used by a broad ecosystem of both consumers and implementers,
 it needs a strict definition of what changes are “non-breaking” and are therefore allowed in MINOR and PATCH releases.
 
 In the RadDB spec, a breaking change is any change that requires either consumers or implementers to modify their code for it to continue to function correctly.
 
 Examples of breaking changes include:
 
-- Adding new functionalities to the RadDB that affect the behavior of the software directly.
+- Adding new functionalities to the RadDB that affect the behavior of the package directly.
 
 
 Examples of non-breaking changes include :
 
 - Fix a bug.
-- Adding new functionalities to RadDB that don't affect the behavior of the API directly.
+- Adding new functionalities to RadDB that don't affect the behavior of the package directly.
 - Updating the documentation.
-- Internal function refactoring that doesn't affect the behavior of the software directly.
+- Internal function refactoring that doesn't affect the behavior of the package directly.
 
 
 Ongoing version support
@@ -75,7 +75,7 @@ To build the documentation locally, follow the next three steps.
 1. Set up the python environment for building the documentation
 
 	The python packages required to build the documentation are listed in the
-	`environment.yaml <https://github.com/erikposchivo/raddb/blob/main/docs/environment.yaml>`_ file.
+	`environment.yaml <https://github.com/ltelab/raddb/blob/main/docs/environment.yaml>`_ file.
 
 	For an efficient setup, it is recommended to create a dedicated virtual environment.
 	Navigate to the ``docs/`` directory and execute the following command.
@@ -83,7 +83,7 @@ To build the documentation locally, follow the next three steps.
 
 	.. code-block:: bash
 
-		conda create -f environment.yaml
+		conda env create -f environment.yaml
 
 2. Activate the virtual environment
 
@@ -91,7 +91,7 @@ To build the documentation locally, follow the next three steps.
 
 	.. code-block:: bash
 
-	   	conda activate build-doc-radar-api
+		conda activate build-doc-raddb
 
 
 3. Generate the documentation
@@ -127,12 +127,12 @@ Ghiggi Gionata owns the `ReadTheDocs <https://readthedocs.org/>`__ account.
 Package Release
 =================
 
-A `GitHub Action <https://github.com/erikposchivo/raddb/actions>`_ is configured to automate the packaging and uploading process to `PyPI <https://pypi.org/project/radar-api/>`_.
-This action, detailed `here <https://github.com/erikposchivo/raddb/blob/main/.github/workflows/release_to_pypi.yaml>`_, triggers the packaging workflow depicted in the following image:
+A `GitHub Action <https://github.com/ltelab/raddb/actions>`_ is configured to automate the packaging and uploading process to `PyPI <https://pypi.org/project/raddb/>`_.
+This action, detailed `here <https://github.com/ltelab/raddb/blob/main/.github/workflows/release_to_pypi.yaml>`_, triggers the packaging workflow depicted in the following image:
 
 .. image:: /static/package_release.png
 
-Upon the release of the package on PyPI, a conda-forge bot attempts to automatically update the `conda-forge recipe <https://github.com/conda-forge/radar-api-feedstock/>`__.
+Upon the release of the package on PyPI, a conda-forge bot attempts to automatically update the `conda-forge recipe <https://github.com/conda-forge/raddb-feedstock/>`__.
 Once the conda-forge recipe is updated, a new conda-forge package is released.
 
 The PyPI project and the conda-forge recipes are collaboratively maintained by core contributors of the project.
@@ -144,7 +144,8 @@ Release Process
 Before releasing a new version, the ``CHANGELOG.md`` file should be updated.
 
 Execute ``git tag`` to identify the last version and determine the new ``X.Y.Z`` version number.
-Then, run ``make changelog X.Y.Z`` to update the ``CHANGELOG.md`` file with the list of issues and pull requests that have been closed since the last release.
+Then, run ``loghub ltelab/raddb --milestone vX.Y.Z`` to generate the list of issues and pull
+requests closed since the last release, and paste it into ``CHANGELOG.md``.
 Manually edit the ``CHANGELOG.md`` if necessary.
 
 Then, commit the new ``CHANGELOG.md`` file.
@@ -190,28 +191,28 @@ and performs various checks to catch issues early in the development lifecycle.
 
 The table below summarizes the software tools utilized in our CI pipeline, describes their respective aims and project pages.
 
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-|  Tools                                                                                             | Aim                                                              | Project page                                                                                 |
-+====================================================================================================+==================================================================+==============================================================================================+
-| `Pytest  <https://docs.pytest.org>`__                                                              | Execute unit tests and functional tests                          |                                                                                              |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `Black <https://black.readthedocs.io/en/stable/>`__                                                | Python code formatter                                            |                                                                                              |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `Ruff  <https://github.com/charliermarsh/ruff>`__                                                  | Python linter                                                    |                                                                                              |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `pre-commit.ci   <https://pre-commit.ci/>`__                                                       | Run pre-commit as defined in `.pre-commit-config.yaml <https://github.com/erikposchivo/raddb/blob/main/.pre-commit-config.yaml>`__                                |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `Coverage   <https://coverage.readthedocs.io/>`__                                                  | Measure the code coverage of the project's unit tests            |                                                                                              |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `CodeCov    <https://about.codecov.io/>`__                                                         | Uses the "coverage" package to generate a code coverage report.  | `RadDB  <https://app.codecov.io/gh/erikposchivo/raddb>`__                                  |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `Coveralls    <https://coveralls.io/>`__                                                           | Uses the "coverage" to track the quality of your code over time. | `RadDB  <https://coveralls.io/github/erikposchivo/raddb>`__                                |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `CodeBeat      <https://codebeat.co/>`__                                                           | Automated code review and analysis tools                         | `RadDB <https://codebeat.co/projects/github-com-erikposchivo-raddb-main>`__                |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `CodeScene <https://codescene.com/>`__                                                             | Automated code review and analysis tools                         | `RadDB  <https://codescene.io/projects/63299/>`__                                        |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `CodeFactor <https://www.codefactor.io/>`__                                                        | Automated code review and analysis tools                         | `RadDB <https://www.codefactor.io/repository/github/erikposchivo/raddb>`__                 |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
-| `Codacy      <https://www.codacy.com/>`__                                                          | Automated code review and analysis tools                         | `RadDB <https://app.codacy.com/gh/erikposchivo/raddb/dashboard>`__                         |
-+----------------------------------------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| Tools                                               | Aim                                                              | Project page                                                                   |
++=====================================================+==================================================================+================================================================================+
+| `Pytest  <https://docs.pytest.org>`__               | Execute unit tests and functional tests                          |                                                                                |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `Black <https://black.readthedocs.io/en/stable/>`__ | Python code formatter                                            |                                                                                |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `Ruff  <https://github.com/charliermarsh/ruff>`__   | Python linter                                                    |                                                                                |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `pre-commit.ci   <https://pre-commit.ci/>`__        | Run pre-commit as defined in ``.pre-commit-config.yaml``         | `config <https://github.com/ltelab/raddb/blob/main/.pre-commit-config.yaml>`__ |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `Coverage   <https://coverage.readthedocs.io/>`__   | Measure the code coverage of the project's unit tests            |                                                                                |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `CodeCov    <https://about.codecov.io/>`__          | Uses the "coverage" package to generate a code coverage report.  | `RadDB <https://app.codecov.io/gh/ltelab/raddb>`__                             |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `Coveralls    <https://coveralls.io/>`__            | Uses the "coverage" to track the quality of your code over time. | `RadDB <https://coveralls.io/github/ltelab/raddb>`__                           |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `CodeBeat      <https://codebeat.co/>`__            | Automated code review and analysis tools                         | `RadDB <https://codebeat.co/projects/github-com-ltelab-raddb-main>`__          |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `CodeScene <https://codescene.com/>`__              | Automated code review and analysis tools                         |                                                                                |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `CodeFactor <https://www.codefactor.io/>`__         | Automated code review and analysis tools                         | `RadDB <https://www.codefactor.io/repository/github/ltelab/raddb>`__           |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
+| `Codacy      <https://www.codacy.com/>`__           | Automated code review and analysis tools                         | `RadDB <https://app.codacy.com/gh/ltelab/raddb/dashboard>`__                   |
++-----------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------------------------+
diff --git a/docs/source/conf.py b/docs/source/conf.py
index b6b7ed2..734a7e4 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,214 +1,206 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import shutil
-import sys
-import inspect
-import raddb
-
-# sys.path.insert(0, os.path.abspath(".."))
-sys.path.insert(0, os.path.abspath("../.."))
-sys.path.insert(0, os.path.join(os.path.abspath("../.."), "raddb"))
-# sys.path.append(os.path.abspath(os.path.dirname(__file__)))
-
-# -- Project information -----------------------------------------------------
-
-project = "raddb"
-copyright = "Gionata Ghiggi"
-author = "Gionata Ghiggi"
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    "sphinx.ext.coverage",
-    "sphinx.ext.viewcode",
-    "sphinx.ext.intersphinx",
-    "sphinx.ext.coverage",
-    "sphinx.ext.linkcode",
-    # "sphinx_design",
-    # "sphinx_gallery.gen_gallery",
-    # "sphinx.ext.autosectionlabel",
-    "sphinx_mdinclude",
-    "sphinx.ext.napoleon",
-    "sphinx.ext.autodoc",
-    "sphinx.ext.autosummary",
-    # "myst_parser",
-    "nbsphinx",
-    "sphinxcontrib.youtube",
-]
-
-# Set up mapping for other projects' docs
-intersphinx_mapping = {
-    "cartopy": ("https://scitools.org.uk/cartopy/docs/latest/", None),
-    "matplotlib": ("https://matplotlib.org/stable/", None),
-    "numpy": ("https://numpy.org/doc/stable/", None),
-    "pandas": ("https://pandas.pydata.org/docs/", None),
-    "pyproj": ("https://pyproj4.github.io/pyproj/stable/", None),
-    "python": ("https://docs.python.org/3/", None),
-    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
-    "xarray": ("https://docs.xarray.dev/en/stable/", None),
-    "pyvista": ("https://docs.pyvista.org/version/stable/", None),
-    "pyresample": ("https://pyresample.readthedocs.io/en/stable/", None),
-    "dask": ("https://docs.dask.org/en/stable/", None),
-    "shapely": ("https://shapely.readthedocs.io/en/stable/", None),
-    "geopandas": ("https://geopandas.org/en/stable/", None),
-    "xvec": ("https://xvec.readthedocs.io/en/stable/", None),
-    "xradar": ("https://docs.openradarscience.org/projects/xradar/en/stable/", None),
-    "pyart": ("https://arm-doe.github.io/pyart/", None),
-    "fsspec": ("https://filesystem-spec.readthedocs.io/en/stable/", None),
-}
-always_document_param_types = True
-
-# Warn when a reference is not found in docstrings
-nitpicky = True
-nitpick_ignore = [
-    ("py:class", "optional"),
-    ("py:class", "array-like"),
-    ("py:class", "file-like object"),
-    # For traitlets docstrings
-    ("py:class", "All"),
-    ("py:class", "t.Any"),
-    ("py:class", "t.Iterable"),
-]
-nitpick_ignore_regex = [
-    ("py:class", r".*[cC]allable"),
-]
-
-# The suffix of source filenames.
-source_suffix = [".rst", ".md"]
-
-# For a class, combine class and __init__ docstrings
-autoclass_content = "both"
-
-# Napoleon settings
-napoleon_google_docstring = False
-napoleon_numpy_docstring = True
-napoleon_include_init_with_doc = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = "sphinx"
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-
-# # Controlling automatically generating summary tables in the docs
-# autosummary_generate = True
-# autosummary_ignore_module_all = False
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = "sphinx_book_theme"
-html_title = "RadDB"
-html_theme_options = {
-    "repository_url": "https://github.com/erikposchivo/raddb",
-    "repository_branch": "main",
-    "use_repository_button": True,
-    "use_edit_page_button": True,
-    # "use_source_button": True,
-    "use_issues_button": True,
-    # "use_repository_button": True,
-    "use_download_button": True,
-    # "use_sidenotes": True,
-    "show_toc_level": 2,
-    "navigation_with_keys": False,
-}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["static"]
-
-
-# -- Automatically run apidoc to generate rst from code ----------------------
-# https://github.com/readthedocs/readthedocs.org/issues/1139
-def run_apidoc(_):
-    from sphinx.ext.apidoc import main
-
-    sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
-    cur_dir = os.path.abspath(os.path.dirname(__file__))
-
-    module_dir = os.path.join(cur_dir, "..", "..", "raddb")
-    output_dir = os.path.join(cur_dir, "api")
-    exclude = [os.path.join(module_dir, "tests")]
-    main(["-f", "-o", output_dir, module_dir, *exclude])
-
-
-def setup(app):
-    app.connect("builder-inited", run_apidoc)
-
-
-# Function to resolve source code links for `linkcode`
-# adapted from NumPy, Pandas implementations
-def linkcode_resolve(domain, info):
-    """
-    Determine the URL corresponding to Python object
-    """
-    if domain != "py":
-        return None
-
-    modname = info["module"]
-    fullname = info["fullname"]
-
-    submod = sys.modules.get(modname)
-    if submod is None:
-        return None
-
-    obj = submod
-    for part in fullname.split("."):
-        try:
-            obj = getattr(obj, part)
-        except AttributeError:
-            return None
-
-    try:
-        fn = inspect.getsourcefile(inspect.unwrap(obj))
-    except TypeError:
-        try:  # property
-            fn = inspect.getsourcefile(inspect.unwrap(obj.fget))
-        except (AttributeError, TypeError):
-            fn = None
-    if not fn:
-        return None
-
-    try:
-        source, lineno = inspect.getsourcelines(obj)
-    except TypeError:
-        try:  # property
-            source, lineno = inspect.getsourcelines(obj.fget)
-        except (AttributeError, TypeError):
-            lineno = None
-    except OSError:
-        lineno = None
-
-    if lineno:
-        linespec = f"#L{lineno}-L{lineno + len(source) - 1}"
-    else:
-        linespec = ""
-
-    fn = os.path.relpath(fn, start=os.path.dirname(raddb.__file__))
-
-    if "+" in raddb.__version__:
-        return f"https://github.com/erikposchivo/raddb/blob/main/raddb/{fn}{linespec}"
-    else:
-        return f"https://github.com/erikposchivo/raddb/blob/" f"v{raddb.__version__}/raddb/{fn}{linespec}"
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+import inspect
+import raddb
+
+# sys.path.insert(0, os.path.abspath(".."))
+sys.path.insert(0, os.path.abspath("../.."))
+sys.path.insert(0, os.path.join(os.path.abspath("../.."), "raddb"))
+# sys.path.append(os.path.abspath(os.path.dirname(__file__)))
+
+# -- Project information -----------------------------------------------------
+
+project = "raddb"
+copyright = "Gionata Ghiggi"
+author = "Gionata Ghiggi"
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.coverage",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.linkcode",
+    # "sphinx_design",
+    # "sphinx_gallery.gen_gallery",
+    # "sphinx.ext.autosectionlabel",
+    "sphinx_mdinclude",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    # "myst_parser",
+    "nbsphinx",
+    "sphinxcontrib.youtube",
+]
+
+# Set up mapping for other projects' docs
+intersphinx_mapping = {
+    "matplotlib": ("https://matplotlib.org/stable/", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
+    "pandas": ("https://pandas.pydata.org/docs/", None),
+    "polars": ("https://docs.pola.rs/api/python/stable/", None),
+    "pyproj": ("https://pyproj4.github.io/pyproj/stable/", None),
+    "python": ("https://docs.python.org/3/", None),
+    "xarray": ("https://docs.xarray.dev/en/stable/", None),
+    "dask": ("https://docs.dask.org/en/stable/", None),
+    "shapely": ("https://shapely.readthedocs.io/en/stable/", None),
+    "geopandas": ("https://geopandas.org/en/stable/", None),
+    "xradar": ("https://docs.openradarscience.org/projects/xradar/en/stable/", None),
+    "pyart": ("https://arm-doe.github.io/pyart/", None),
+    "fsspec": ("https://filesystem-spec.readthedocs.io/en/stable/", None),
+}
+always_document_param_types = True
+
+# Warn when a reference is not found in docstrings
+nitpicky = True
+nitpick_ignore = [
+    ("py:class", "optional"),
+    ("py:class", "array-like"),
+    ("py:class", "file-like object"),
+    # For traitlets docstrings
+    ("py:class", "All"),
+    ("py:class", "t.Any"),
+    ("py:class", "t.Iterable"),
+]
+nitpick_ignore_regex = [
+    ("py:class", r".*[cC]allable"),
+]
+
+# The suffix of source filenames.
+source_suffix = [".rst", ".md"]
+
+# For a class, combine class and __init__ docstrings
+autoclass_content = "both"
+
+# Napoleon settings
+napoleon_google_docstring = False
+napoleon_numpy_docstring = True
+napoleon_include_init_with_doc = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+# # Controlling automatically generating summary tables in the docs
+# autosummary_generate = True
+# autosummary_ignore_module_all = False
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_book_theme"
+html_title = "RadDB"
+html_theme_options = {
+    "repository_url": "https://github.com/ltelab/raddb",
+    "repository_branch": "main",
+    "path_to_docs": "docs/source",
+    "use_repository_button": True,
+    "use_edit_page_button": True,
+    # "use_source_button": True,
+    "use_issues_button": True,
+    # "use_repository_button": True,
+    "use_download_button": True,
+    # "use_sidenotes": True,
+    "show_toc_level": 2,
+    "navigation_with_keys": False,
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["static"]
+
+
+# -- Automatically run apidoc to generate rst from code ----------------------
+# https://github.com/readthedocs/readthedocs.org/issues/1139
+def run_apidoc(_):
+    from sphinx.ext.apidoc import main
+
+    sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+    cur_dir = os.path.abspath(os.path.dirname(__file__))
+
+    module_dir = os.path.join(cur_dir, "..", "..", "raddb")
+    output_dir = os.path.join(cur_dir, "api")
+    exclude = [os.path.join(module_dir, "tests")]
+    main(["-f", "-o", output_dir, module_dir, *exclude])
+
+
+def setup(app):
+    app.connect("builder-inited", run_apidoc)
+
+
+# Function to resolve source code links for `linkcode`
+# adapted from NumPy, Pandas implementations
+def linkcode_resolve(domain, info):
+    """
+    Determine the URL corresponding to Python object
+    """
+    if domain != "py":
+        return None
+
+    modname = info["module"]
+    fullname = info["fullname"]
+
+    submod = sys.modules.get(modname)
+    if submod is None:
+        return None
+
+    obj = submod
+    for part in fullname.split("."):
+        try:
+            obj = getattr(obj, part)
+        except AttributeError:
+            return None
+
+    try:
+        fn = inspect.getsourcefile(inspect.unwrap(obj))
+    except TypeError:
+        try:  # property
+            fn = inspect.getsourcefile(inspect.unwrap(obj.fget))
+        except (AttributeError, TypeError):
+            fn = None
+    if not fn:
+        return None
+
+    try:
+        source, lineno = inspect.getsourcelines(obj)
+    except TypeError:
+        try:  # property
+            source, lineno = inspect.getsourcelines(obj.fget)
+        except (AttributeError, TypeError):
+            lineno = None
+    except OSError:
+        lineno = None
+
+    if lineno:
+        linespec = f"#L{lineno}-L{lineno + len(source) - 1}"
+    else:
+        linespec = ""
+
+    fn = os.path.relpath(fn, start=os.path.dirname(raddb.__file__))
+
+    if "+" in raddb.__version__:
+        return f"https://github.com/ltelab/raddb/blob/main/raddb/{fn}{linespec}"
+    else:
+        return f"https://github.com/ltelab/raddb/blob/" f"v{raddb.__version__}/raddb/{fn}{linespec}"
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 98f7706..3108e29 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,53 +1,54 @@
-
-Welcome to RadDB !
-========================
-
-
-**RadDB** is a Python package designed to make your life easier, whether you aim to download some weather radar data, search for specific files, or jump into scientific analysis.
-Our goal is to empower you to focus more on what you can discover and create with the data, rather than getting bogged down by the process of searching and handling it.
-
-We're excited to see how you'll use this tool to push the boundaries of what's possible,
-all while making your workflow smoother, enjoyable, and more productive.
-Let's get started and unlock the full potential of weather radar data archive together!
-
-
-**Ready to jump in?**
-
-Consider joining the `Open Radar Science Discourse Group <https://openradar.discourse.group/>`__ to say hi or ask questions.
-It's a great place to connect with others and get support.
-
-
-The map here below displays the radars currently accessible through RadDB.
-
-.. image:: /static/raddb_coverage.png
-
-
-
-Documentation
-==============
-
-.. toctree::
-   :maxdepth: 2
-
-   02_installation
-   03_quickstart
-   06_contributors_guidelines
-   07_maintainers_guidelines
-   08_authors
-
-
-API Reference
-===============
-
-.. toctree::
-   :maxdepth: 1
-
-   API <api/modules>
-
-
-Indices and tables
-===================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+
+Welcome to RadDB !
+========================
+
+
+**RadDB** archives weather radar volumes as compact **Parquet** tables — one row per
+radar gate — and gives you a small fluent interface to load, filter, crop, cut
+cross-sections and plot them.
+
+It is **network-agnostic**: any `xarray.DataTree <https://docs.xarray.dev/en/stable/generated/xarray.DataTree.html>`__
+following the `xradar <https://docs.openradarscience.org/projects/xradar/en/stable/>`__
+coordinate layout can be archived, whether it comes from NEXRAD, ODIM, IRIS or any
+other network.
+
+Each radar is stored once as a static look-up table holding the per-gate geometry, plus
+one Parquet file per volume holding only the variables that change. Gates without echo are
+dropped at archive time, so a whole campaign stays small enough to query like a dataframe —
+no re-reading of the source files, and no fixed grid imposed on the measurements.
+
+
+**Ready to jump in?**
+
+Consider joining the `Open Radar Science Discourse Group <https://openradar.discourse.group/>`__ to say hi or ask questions.
+It's a great place to connect with others and get support.
+
+
+Documentation
+==============
+
+.. toctree::
+   :maxdepth: 2
+
+   02_installation
+   03_quickstart
+   06_contributors_guidelines
+   07_maintainers_guidelines
+   08_authors
+
+
+Package Reference
+==================
+
+.. toctree::
+   :maxdepth: 1
+
+   Modules <api/modules>
+
+
+Indices and tables
+===================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/pyproject.toml b/pyproject.toml
index 03eaf80..f3fdf37 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 # pyproject.toml
 
 [build-system]
-requires = ["setuptools>=61.0.0", "setuptools_scm[toml]>=6.2", "wheel"]
+requires = ["setuptools>=77.0.0", "setuptools_scm[toml]>=6.2", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -15,21 +15,18 @@ authors = [
 license = "MIT"
 license-files = ["LICENSE"]
 classifiers = [
-    "Development Status :: 5 - Production/Stable",
+    "Development Status :: 4 - Beta",
     "Intended Audience :: Science/Research",
     "Intended Audience :: Education",
     "Natural Language :: English",
     "Operating System :: OS Independent",
-    "Operating System :: Unix",
-    "Operating System :: Microsoft",
-    "Operating System :: MacOS",
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
-     "Programming Language :: Python :: 3.14",
+    "Programming Language :: Python :: 3.14",
     "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Atmospheric Science",
 ]
@@ -39,14 +36,14 @@ dependencies = [
     "pandas",
     "polars",
     "geopandas",
+    "shapely>=2.0",
+    "pyproj>=3.0",
     "xarray",
     "pyyaml",
     "pyarrow",
-    "dask",
-    "distributed",
-    "fsspec",
-    "s3fs",
     "matplotlib",
+    "netcdf4",
+    "zarr",
 ]
 requires-python = ">=3.11"
 # version="0.0.1"
@@ -62,16 +59,16 @@ exclude = ["raddb.mch*", "raddb.tests*"]
 write_to = "raddb/_version.py"
 
 [project.optional-dependencies]
-viz = ["cartopy>=0.22", "pyproj>=3.0", "shapely>=2.0",
-       "lonboard>=0.10", "geoarrow-pyarrow>=0.2"]
-io = ["netcdf4", "zarr"]
+viz = ["cartopy>=0.22", "lonboard>=0.10", "geoarrow-pyarrow>=0.2",
+       "ipyleaflet>=0.18", "ipywidgets>=8.0"]
+io = ["pyshp"]  # reading .shp areas of interest
 dev = ["pre-commit", "loghub",
        "black[jupyter]", "blackdoc", "codespell", "ruff",
-       "pytest", "pytest-cov", "pytest-mock", "pytest-check", "pytest-sugar",
-       "pytest-watcher", "deepdiff",
-       "netcdf4", "zarr",
-       "pip-tools", "bumpver", "twine", "wheel", "build", "setuptools>=61.0.0",
-       "sphinx", "sphinx-gallery", "sphinx-book-theme", "nbsphinx", "sphinx_mdinclude"]
+       "pytest", "pytest-cov", "pytest-check", "pytest-sugar",
+       "pytest-watcher",
+       "pip-tools", "twine", "wheel", "build", "setuptools>=77.0.0",
+       "sphinx", "sphinx-gallery", "sphinx-book-theme", "nbsphinx", "sphinx_mdinclude",
+       "sphinxcontrib-youtube"]
 
 [project.urls]
 homepage = "https://github.com/ltelab/raddb"
@@ -79,7 +76,6 @@ repository = "https://github.com/ltelab/raddb"
 source = "https://github.com/ltelab/raddb"
 tracker = "https://github.com/ltelab/raddb/issues"
 documentation = "https://raddb.readthedocs.io"
-changelog = "https://github.com/ltelab/raddb/blob/main/CHANGELOG.md"
 
 [tool.pytest.ini_options]
 minversion = "7"
@@ -95,9 +91,9 @@ testpaths = [
 line-length = 120
 # skip-string-normalization = true
 target-version = [
-    "py39",
-    "py310",
     "py311",
+    "py312",
+    "py313",
 ]
 
 [tool.ruff]
@@ -232,42 +228,5 @@ convention = "numpy"
 "setup.py" = ["D100"]
 "*__init__.py" = ["D104"]
 
-[tool.doc8]
-ignore-path = [
-    "docs/build",
-    "docs/api/generated",
-    "docs/_templates",
-    "docs/tutorials",
-    "docs/examples",
-]
-file-encoding = "utf8"
-max-line-length = 120
-ignore = ["D001"]
-
 [tool.codespell]
 ignore-words-list = "ges,nd"
-
-[tool.bumpver]
-current_version = "2025.1002-alpha"
-version_pattern = "YYYY.BUILD[-TAG]"
-commit_message = "bump version {old_version} -> {new_version}"
-tag_message = "{new_version}"
-tag_scope = "default"
-pre_commit_hook = ""
-post_commit_hook = ""
-commit = true
-tag = true
-push = true
-
-[tool.bumpver.file_patterns]
-"pyproject.toml" = [
-    'current_version = "{version}"',
-]
-"setup.py" = [
-    "{version}",
-    "{pep440_version}",
-]
-"README.md" = [
-    "{version}",
-    "{pep440_version}",
-]
diff --git a/tutorial/05_demo_pipeline.ipynb b/tutorial/05_demo_pipeline.ipynb
index 871a2f0..b6e6a9e 100644
--- a/tutorial/05_demo_pipeline.ipynb
+++ b/tutorial/05_demo_pipeline.ipynb
@@ -25,7 +25,15 @@
    "execution_count": null,
    "id": "1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raddb 0.1.dev5+gde6070734.d20260323 | xradar 0.11.1\n"
+     ]
+    }
+   ],
    "source": [
     "import shutil\n",
     "import tarfile\n",
diff --git a/tutorial/README.md b/tutorial/README.md
index d337bc8..cd0ab10 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -11,23 +11,19 @@ self-contained — if you jump straight to number 3, it builds the archive it ne
 | 4   | [Plots](04_plots.ipynb)                                 | PPI, RHI, CAPPI, vertical cross-section                                                                |
 | 5   | [Demo pipeline](05_demo_pipeline.ipynb)                 | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cut |
 
-The notebooks are stored **with their output**, so you can read them on GitHub
-without running anything.
+The notebooks are stored **without their output**, so you have to run them
+yourself to see the results.
 
 ## Running them yourself
 
 RadDB is network-agnostic — any xarray `DataTree` with the standard
 [xradar](https://docs.openradarscience.org/projects/xradar/) layout works. The
-notebooks use MeteoSwiss and NEXRAD volumes stored as Zarr. Point them at your own
-data with environment variables, or edit the configuration cell at the top of each
-notebook:
+notebooks use NEXRAD volumes stored as Zarr. Point them at your own
+data by editing the configuration cell at the top of each notebook:
 
-```bash
-export RADDB_DATATREE_DIR=/path/to/MCH_datatree      # radars L, W
-export RADDB_NEXRAD_DIR=/path/to/NEXRAD_datatree     # radar KTLX
-export RADDB_TUTORIAL_ARCHIVE=/tmp/raddb_tutorial_archive   # where to write
-
-jupyter lab
+```python
+NEXRAD_DIR = Path("/path/to/NEXRAD_datatree_zarr")  # radars KTLX, KMLB, KLOT
+ARCHIVE_DIR = Path("/tmp/raddb_tutorial_archive")  # where to write
 ```
 
 Then run notebook 1 first — it creates the archive the others read.
@@ -35,5 +31,6 @@ Then run notebook 1 first — it creates the archive the others read.
 Notebook 5 needs no local data at all: it downloads three public volumes (US,
 Finland, Colombia) over HTTP and adds them to the same archive.
 
-Needed beyond the core install: `jupyter`, and `ipyleaflet` + `ipywidgets` for the
-interactive map in notebook 3 (`pip install raddb[viz]`).
+Needed beyond the core install: `raddb[viz,io]` for plotting and Zarr/NetCDF
+reading, plus `jupyter`, `ipyleaflet` and `ipywidgets` for the interactive map in
+notebooks 3 and 4.

From 6ab2df4a5c5dea5f5199a02763ed9ca4af267045 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Wed, 19 Aug 2026 09:19:51 +0200
Subject: [PATCH 11/14] Update README files for clarity and consistency in
 formatting

---
 README.md          | 14 +++++++-------
 docs/README.md     |  2 +-
 tutorial/README.md | 23 ++++++++---------------
 3 files changed, 16 insertions(+), 23 deletions(-)

diff --git a/README.md b/README.md
index aa84406..ec23338 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# RadDB — radar volumes as a Parquet archive
+# RadDB: Radar Database
 
 |                   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -37,8 +37,8 @@ stays small: a 12-sweep WSR-88D volume of 8,791,200 polar gates becomes 8.2 MB.
 ## Installation
 
 ```bash
-pip install raddb              # everything in this README works
-pip install "raddb[viz]"       # + the interactive Jupyter map and cartopy basemaps
+pip install raddb
+pip install "raddb[viz]"       # interactive Jupyter map and cartopy basemaps
 ```
 
 Core runtime dependencies: `numpy, pandas, polars, geopandas, shapely, pyproj, xarray, pyyaml, pyarrow, matplotlib, netcdf4, zarr`.
@@ -56,7 +56,7 @@ Core runtime dependencies: `numpy, pandas, polars, geopandas, shapely, pyproj, x
 ```python
 import raddb
 
-db = raddb.RadDB(archive_dir="/data/raddb", crs=32614)  # 32614 = UTM zone 14N
+db = raddb.RadDB(archive_dir="/data/raddb", crs=32614)
 ```
 
 A **projected CRS is mandatory to write** an archive and never needed to read one.
@@ -88,7 +88,7 @@ db.archive(
 
 ```python
 rdf = db.open(time_period=("2024-06-12", "2024-06-13"), radars="KTLX")
-print(rdf)  # rich summary: gates, radars, time range, columns
+print(rdf)  # summary: gates, radars, time range, columns
 
 len(rdf), rdf.columns(), rdf.radars()
 rdf.start_time(), rdf.end_time()
@@ -109,7 +109,7 @@ filtered_rdf = rdf.filter(
         {"var": "DBZH", "logic": ">", "threshold": 20},
         {"var": "RHOHV", "logic": ">", "threshold": 0.9},
     ]
-)  # AND
+)
 
 df = rdf.to_pandas(with_geometry=True)  # pandas + gate coordinates
 gdf = rdf.to_geopandas()  # GeoDataFrame (with CRS)
@@ -176,7 +176,7 @@ db.add_lut_projection("KTLX", epsg=32614)
 
 ```
 raddb/
-├── __init__.py     # the public interface
+├── __init__.py
 ├── main.py         # the RadDB class
 ├── io_core.py      # DataTree <-> DataFrame <-> Parquet + archive backends
 ├── lut.py          # LUT generation / geo projection
diff --git a/docs/README.md b/docs/README.md
index bcd15fe..014eb95 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -16,7 +16,7 @@ To build the documentation locally, follow the next three steps.
 The python packages required to build the documentation are listed in the [environment.yaml](https://github.com/ltelab/raddb/blob/main/docs/environment.yaml) file.
 
 For an efficient setup, we recommend creating a dedicated virtual environment.
-Navigate to the `docs/` directory and execute the following command.
+**Navigate to the `docs/` directory and execute the following command.**
 This will create a new environment and install the required packages:
 
 ```bash
diff --git a/tutorial/README.md b/tutorial/README.md
index cd0ab10..a67e311 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -1,18 +1,15 @@
 # RadDB tutorials
 
 Five notebooks that walk through the whole workflow, in order. Each one is
-self-contained — if you jump straight to number 3, it builds the archive it needs.
+self-contained.
 
-| #   | notebook                                                | covers                                                                                                 |
-| --- | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ |
-| 1   | [Archiving](01_archiving.ipynb)                         | the storage model, the CRS contract, `archive()`, what lands on disk                                   |
-| 2   | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters                                            |
-| 3   | [Areas of interest](03_area_of_interest.ipynb)          | bbox / point / polygon crops, cross-sections, the interactive map                                      |
-| 4   | [Plots](04_plots.ipynb)                                 | PPI, RHI, CAPPI, vertical cross-section                                                                |
-| 5   | [Demo pipeline](05_demo_pipeline.ipynb)                 | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cut |
-
-The notebooks are stored **without their output**, so you have to run them
-yourself to see the results.
+| #   | notebook                                                | covers                                                                                                     |
+| --- | ------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
+| 1   | [Archiving](01_archiving.ipynb)                         | the storage model, the CRS contract, `archive()`, what lands on disk                                       |
+| 2   | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters                                                |
+| 3   | [Areas of interest](03_area_of_interest.ipynb)          | bbox / point / polygon crops, cross-sections, the interactive map                                          |
+| 4   | [Plots](04_plots.ipynb)                                 | PPI, RHI, CAPPI, vertical cross-section                                                                    |
+| 5   | [Demo pipeline](05_demo_pipeline.ipynb)                 | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cropped |
 
 ## Running them yourself
 
@@ -30,7 +27,3 @@ Then run notebook 1 first — it creates the archive the others read.
 
 Notebook 5 needs no local data at all: it downloads three public volumes (US,
 Finland, Colombia) over HTTP and adds them to the same archive.
-
-Needed beyond the core install: `raddb[viz,io]` for plotting and Zarr/NetCDF
-reading, plus `jupyter`, `ipyleaflet` and `ipywidgets` for the interactive map in
-notebooks 3 and 4.

From c59cb8913770f85f373c077280c23a46b7b625c7 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Wed, 19 Aug 2026 09:31:13 +0200
Subject: [PATCH 12/14] Remove output stream from initialization cell in demo
 pipeline notebook

---
 tutorial/05_demo_pipeline.ipynb | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/tutorial/05_demo_pipeline.ipynb b/tutorial/05_demo_pipeline.ipynb
index b6e6a9e..871a2f0 100644
--- a/tutorial/05_demo_pipeline.ipynb
+++ b/tutorial/05_demo_pipeline.ipynb
@@ -25,15 +25,7 @@
    "execution_count": null,
    "id": "1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "raddb 0.1.dev5+gde6070734.d20260323 | xradar 0.11.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import shutil\n",
     "import tarfile\n",

From 0e6da5c4fb2e1d30e7f7cb24059c83628a182b5c Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Wed, 19 Aug 2026 14:02:46 +0200
Subject: [PATCH 13/14] Add tutorials section to documentation and copy Jupyter
 notebooks

---
 docs/source/04_tutorials.rst              |  19 +++++++++++++++++++
 docs/source/conf.py                       |  19 +++++++++++++++++++
 docs/source/index.rst                     |   1 +
 docs/source/static/radar_api_coverage.png | Bin 522551 -> 0 bytes
 raddb/tests/test_io_core.py               |   4 +++-
 5 files changed, 42 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/04_tutorials.rst
 delete mode 100644 docs/source/static/radar_api_coverage.png

diff --git a/docs/source/04_tutorials.rst b/docs/source/04_tutorials.rst
new file mode 100644
index 0000000..381ec27
--- /dev/null
+++ b/docs/source/04_tutorials.rst
@@ -0,0 +1,19 @@
+.. _tutorials:
+
+===========
+Tutorials
+===========
+
+This page provides read-only access to the RadDB Jupyter Notebook tutorials, which walk
+through archiving radar volumes, querying an archive, cropping areas of interest and plotting.
+The notebooks are available in the `tutorial <https://github.com/ltelab/raddb/tree/main/tutorial>`__
+directory of the RadDB GitHub repository.
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/01_archiving
+   tutorials/02_opening_and_filtering
+   tutorials/03_area_of_interest
+   tutorials/04_plots
+   tutorials/05_demo_pipeline
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 734a7e4..43e9cba 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -11,6 +11,7 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import sys
 import inspect
 import raddb
@@ -26,6 +27,21 @@
 copyright = "Gionata Ghiggi"
 author = "Gionata Ghiggi"
 
+
+# -- Copy Jupyter Notebook Tutorials -----------------------------------------
+_source_dir = os.path.abspath(os.path.dirname(__file__))
+_root_dir = os.path.dirname(os.path.dirname(_source_dir))
+_tutorials_dir = os.path.join(_source_dir, "tutorials")
+os.makedirs(_tutorials_dir, exist_ok=True)
+for _filename in [
+    "01_archiving.ipynb",
+    "02_opening_and_filtering.ipynb",
+    "03_area_of_interest.ipynb",
+    "04_plots.ipynb",
+    "05_demo_pipeline.ipynb",
+]:
+    shutil.copyfile(os.path.join(_root_dir, "tutorial", _filename), os.path.join(_tutorials_dir, _filename))
+
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
@@ -100,6 +116,9 @@
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
+# Render the notebooks as archived: never execute them at build time.
+nbsphinx_execute = "never"
+
 # # Controlling automatically generating summary tables in the docs
 # autosummary_generate = True
 # autosummary_ignore_module_all = False
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3108e29..7454da8 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -32,6 +32,7 @@ Documentation
 
    02_installation
    03_quickstart
+   04_tutorials
    06_contributors_guidelines
    07_maintainers_guidelines
    08_authors
diff --git a/docs/source/static/radar_api_coverage.png b/docs/source/static/radar_api_coverage.png
deleted file mode 100644
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z1*6Hk89<F^g)c}TbC8NDp|XrL3FdUAz9bcKr%1w_R{lu=tF^uVNc3j8PNM(J>ygu?
z3wf>En3T#Nq=5Kezc_i@`=}`g9rLlM(KzUlMlgZ-<ApK(MkOwtvLoQ$#r?%XFQYhV
z^aIz;tH{y%4S2B;J8aF*cF?hYk#BV!5!2lU1e&xrBQFKl1P*<J2bkyk#AqSzL%F10
zPP@=a{(tJtn__foGp=GTsPZd(j#o%%H0gOF>DRwN(|^)02Pa+PU&e5qH-bE#Gdorb
zpnm#jVP4ilRnPUNBL>%o>~U&5Z2s2z_yV(omGaICLzzu4!z|BEH}1U$`v{^+a`UX9
z!e2N1XYu-UQBo|*$9^fw+Psf^qJFt$^?z}u&~t}a@8K=(U^7Nir9p%DckGVq7L=M9
z=mJiXdx<!44e5WUiglY)n+)hFnrj~+lrRPzsDwVyk-nj=+jU4SDRNMsy`@WX`do{L
zS-5+|Ss16<<n091FwMD`8k}#vY2|_Srbxi@voA3ddGX?A9;rRY4mY1l=Y71OlV8=E
zu=62fc6!*8|0+Y_czBl%ZJ5lT&NCp|1^mrV(l|du<ONB)B5So-+7NEh3767DYs3o_
zlI>);*$|~*f9~N27j*W*-C5^Aj=&q4AkHZ?1=%l9HGKH?U*G@)=)HAf6qS~ic8y0(
z{y$g9NCGj>e<RGDzaMv8wo9l4QIetmXY?<f2<P+jFNAsh`0)R^;IN?53dX-bkA&}g
zM`gV;{}tN*$6|K5<Q`L${okt-;omF#9}5qfWh?%FEEFm?uK)SA|NE&J9(piN{@-f>
z7J6Fy-!Cg`69PRnQ2p=K3I(-bWBu=?_y6_5{{MRWuR*BIWQ`C%i4_>=l9N)FtQR*4
G{eJ-Zv%Nw9

diff --git a/raddb/tests/test_io_core.py b/raddb/tests/test_io_core.py
index 714a3d0..3b55ac9 100644
--- a/raddb/tests/test_io_core.py
+++ b/raddb/tests/test_io_core.py
@@ -17,6 +17,8 @@
 
 from __future__ import annotations
 
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
 import polars as pl
@@ -258,7 +260,7 @@ def test_the_output_path_encodes_the_volume_time(lut_base, make_datatree):
     path = datatree_to_parquet(dt, radar=RADAR, base_output_path=lut_base)
 
     assert path.endswith(f"{RADAR}_20240826_025000_POL.parquet")
-    assert "/2024/08/26/" in path
+    assert Path(path).parts[-4:-1] == ("2024", "08", "26")
 
 
 def test_save_polar_parquet_returns_none_on_an_empty_frame():

From 6c4065e1e68b923ab48696833bdbf85b49feccc1 Mon Sep 17 00:00:00 2001
From: erikposchivo <117540023+erikposchivo@users.noreply.github.com>
Date: Wed, 19 Aug 2026 16:25:29 +0200
Subject: [PATCH 14/14] Update tutorial README to reflect changes in notebook
 content and data sources

---
 .gitignore                              |    4 +
 tutorial/01_archiving.ipynb             |  533 +++++-
 tutorial/02_opening_and_filtering.ipynb | 2104 ++++++++++++++++++++++-
 tutorial/03_area_of_interest.ipynb      | 1629 +++++++++++++++++-
 tutorial/04_plots.ipynb                 | 1809 ++++++++++++++++++-
 tutorial/05_demo_pipeline.ipynb         | 1504 +++++++++++++++-
 tutorial/README.md                      |   24 +-
 7 files changed, 7253 insertions(+), 354 deletions(-)

diff --git a/.gitignore b/.gitignore
index b451bd3..03f599f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -63,6 +63,7 @@ htmlcov/
 .nox/
 .coverage
 .coverage.*
+.coveragerc
 .cache
 nosetests.xml
 coverage.xml
@@ -171,3 +172,6 @@ pyproject.toml
 
 # Local AI-assistant working notes (machine-local paths — not for the public repo)
 CLAUDE.md
+
+# Local-only MeteoSwiss demo notebook
+tutorial/06_mch_pipeline.ipynb
diff --git a/tutorial/01_archiving.ipynb b/tutorial/01_archiving.ipynb
index fe4b71a..653230a 100644
--- a/tutorial/01_archiving.ipynb
+++ b/tutorial/01_archiving.ipynb
@@ -40,10 +40,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:19.611681Z",
+     "iopub.status.busy": "2026-08-19T13:31:19.611544Z",
+     "iopub.status.idle": "2026-08-19T13:31:20.548863Z",
+     "shell.execute_reply": "2026-08-19T13:31:20.547906Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raddb 0.1.dev5+gde6070734.d20260323\n"
+     ]
+    }
+   ],
    "source": [
     "import warnings\n",
     "\n",
@@ -66,29 +81,47 @@
     "\n",
     "### Input paths\n",
     "\n",
-    "`MCH_DIR` and `NEXRAD_DIR` hold the DataTree volumes to be archived; `ARCHIVE_DIR`\n",
+    "`FMI_DIR` and `NEXRAD_DIR` hold the DataTree volumes to be archived; `ARCHIVE_DIR`\n",
     "is where RadDB writes the archive. Edit them to match your own machine."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:20.551653Z",
+     "iopub.status.busy": "2026-08-19T13:31:20.551186Z",
+     "iopub.status.idle": "2026-08-19T13:31:20.555576Z",
+     "shell.execute_reply": "2026-08-19T13:31:20.554580Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FMI DataTrees   : /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\n",
+      "NEXRAD DataTrees: /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\n",
+      "Archive         : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — point these at your own data\n",
     "# --------------------------------------------------------------------------\n",
     "# Any xarray DataTree with the standard xradar layout works.\n",
-    "# These tutorials use MeteoSwiss and NEXRAD volumes stored as zarr and nc format.\n",
+    "# These tutorials use Finnish (FMI) and US (NEXRAD) volumes stored as zarr and nc format.\n",
+    "# Both networks publish openly, so every example here can be reproduced.\n",
     "# Edit the paths below to point at your own data.\n",
     "\n",
-    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "FMI_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\").expanduser()\n",
     "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"FMI DataTrees   :\", FMI_DIR)\n",
     "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
     "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
@@ -107,26 +140,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:20.557714Z",
+     "iopub.status.busy": "2026-08-19T13:31:20.557564Z",
+     "iopub.status.idle": "2026-08-19T13:31:29.506171Z",
+     "shell.execute_reply": "2026-08-19T13:31:29.505460Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================================================================\n",
+      "RadDB inventory — DataTree files on disk (not archived yet)\n",
+      "  directory : /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\n",
+      "  files     : 107\n",
+      "  radars    : KLOT, KMLB, KTLX  (from the filename prefix)\n",
+      "  time range: 2024-06-12 22:00:00 .. 2024-08-18 18:59:21\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar     files  time range                                           size\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  KLOT         40  2024-06-12 22:01:28 .. 2024-08-18 18:57:49       366.2 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  KMLB         35  2024-06-12 22:00:00 .. 2024-08-18 18:56:53       450.9 MB\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  KTLX         32  2024-06-12 22:03:24 .. 2024-08-18 18:59:21       440.9 MB\n",
+      "------------------------------------------------------------------------------\n",
+      "  archive with: db.archive(datatree_dir='/home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr')\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
    "source": [
     "db = raddb.RadDB()\n",
     "db.inventory(datatree_dir=NEXRAD_DIR)\n",
-    "# db.inventory(datatree_dir=MCH_DIR)"
+    "# db.inventory(datatree_dir=FMI_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "6",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:29.507663Z",
+     "iopub.status.busy": "2026-08-19T13:31:29.507532Z",
+     "iopub.status.idle": "2026-08-19T13:31:32.686878Z",
+     "shell.execute_reply": "2026-08-19T13:31:32.686081Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================================================================\n",
+      "RadDB inventory — DataTree files on disk (not archived yet)\n",
+      "  directory : /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\n",
+      "  files     : 107\n",
+      "  radars    : KLOT, KMLB, KTLX  (from the filename prefix)\n",
+      "  time range: 2024-06-12 22:00:00 .. 2024-08-18 18:59:21\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar     files  time range                                           size\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  KLOT         40  2024-06-12 22:01:28 .. 2024-08-18 18:57:49       366.2 MB\n",
+      "      2024-06-12     15 volume(s)  22:01:28 .. 22:58:56\n",
+      "      2024-07-05     13 volume(s)  22:04:13 .. 22:58:08\n",
+      "      2024-08-18     12 volume(s)  18:06:21 .. 18:57:49\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  KMLB         35  2024-06-12 22:00:00 .. 2024-08-18 18:56:53       450.9 MB\n",
+      "      2024-06-12     13 volume(s)  22:00:00 .. 22:57:27\n",
+      "      2024-07-05     13 volume(s)  22:00:13 .. 22:57:30\n",
+      "      2024-08-18      9 volume(s)  18:01:11 .. 18:56:53\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  KTLX         32  2024-06-12 22:03:24 .. 2024-08-18 18:59:21       440.9 MB\n",
+      "      2024-06-12      8 volume(s)  22:03:24 .. 22:53:30\n",
+      "      2024-07-05     15 volume(s)  22:02:39 .. 22:57:52\n",
+      "      2024-08-18      9 volume(s)  18:03:18 .. 18:59:21\n",
+      "------------------------------------------------------------------------------\n",
+      "  archive with: db.archive(datatree_dir='/home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr')\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
    "source": [
     "# `detailed=True` adds a per-day breakdown\n",
     "db.inventory(datatree_dir=NEXRAD_DIR, detailed=True)\n",
-    "# db.inventory(datatree_dir=MCH_DIR, detailed=True)"
+    "# db.inventory(datatree_dir=FMI_DIR, detailed=True)"
    ]
   },
   {
@@ -143,21 +277,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:32.688535Z",
+     "iopub.status.busy": "2026-08-19T13:31:32.688379Z",
+     "iopub.status.idle": "2026-08-19T13:31:32.693766Z",
+     "shell.execute_reply": "2026-08-19T13:31:32.693097Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RadDB(archive_dir=/home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive, crs=3067) [archive-bound, no data loaded]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)  # 2056 ==> CH1903+/LV95\n",
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=3067)  # 3067 ==> ETRS89 / TM35FIN (all of Finland)\n",
     "db"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:32.695344Z",
+     "iopub.status.busy": "2026-08-19T13:31:32.695198Z",
+     "iopub.status.idle": "2026-08-19T13:31:32.698700Z",
+     "shell.execute_reply": "2026-08-19T13:31:32.698060Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RadDB(archive_dir=/home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive, crs=None) [archive-bound, no data loaded]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "db_us = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "db_us"
@@ -176,12 +346,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "11",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:31:32.700555Z",
+     "iopub.status.busy": "2026-08-19T13:31:32.700419Z",
+     "iopub.status.idle": "2026-08-19T13:33:08.496106Z",
+     "shell.execute_reply": "2026-08-19T13:33:08.495239Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  crs         : 3067\n",
+      "  radars      : ['FANJ', 'FKOR', 'FKUO']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 41 archived, 0 failed\n",
+      "  elapsed     : 1m 35s\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'n_archived': 41,\n",
+       " 'n_failed': 0,\n",
+       " 'n_skipped': 0,\n",
+       " 'radars': ['FANJ', 'FKOR', 'FKUO']}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "result = db.archive(datatree_dir=MCH_DIR, time_period=(\"2024-06-01\", \"2024-06-15\"))\n",
+    "result = db.archive(datatree_dir=FMI_DIR, time_period=(\"2024-06-01\", \"2024-06-15\"))\n",
     "result"
    ]
   },
@@ -195,14 +401,22 @@
     "**A projection is mandatory to write an archive, and never needed to read one. Can be given when RadDB is initilaized or at archiving time.**\n",
     "\n",
     "The LUT stores projected gate coordinates, and every crop and cross-section is\n",
-    "computed in them. A wrong projection is therefore silently wrong: EPSG:2056 (Swiss\n",
-    "LV95) used outside Switzerland mis-measures distance, while the\n",
+    "computed in them. A wrong projection is therefore silently wrong: EPSG:3067 (Finnish\n",
+    "TM35FIN) used outside Finland mis-measures distance, while the\n",
     "results could still look perfectly normal. RadDB has no default, the CRS is stated once,\n",
     "when the object is created, and is checked against the radar's real position before\n",
     "anything is written.\n",
     "\n",
     "---\n",
     "\n",
+    "#### Example for Finland:\n",
+    "\n",
+    "All Finnish radars fit one national projection, so the three FMI radars above were\n",
+    "archived together with `crs=3067`. That is the exception, not the rule — it works\n",
+    "because Finland is narrow enough for a single transverse Mercator zone.\n",
+    "\n",
+    "---\n",
+    "\n",
     "#### Example for US:\n",
     "A projection is only valid for one large region, so for US radars is chosen per radar . `KTLX` sits in\n",
     "UTM zone 14N; `KLOT` and `KMLB` are in zones 16N and 17N and would be refused with\n",
@@ -211,10 +425,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "13",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:08.498233Z",
+     "iopub.status.busy": "2026-08-19T13:33:08.497907Z",
+     "iopub.status.idle": "2026-08-19T13:33:08.501750Z",
+     "shell.execute_reply": "2026-08-19T13:33:08.501039Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KTLX ==> 32614\n",
+      "KMLB ==> 32617\n",
+      "KLOT ==> 32616\n"
+     ]
+    }
+   ],
    "source": [
     "# check what's the suggested crs of the 3 US radars in the NEXRAD dataset\n",
     "# \"KTLX\": lat/lon = 35.333/-97.278\n",
@@ -227,10 +458,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "14",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:08.503113Z",
+     "iopub.status.busy": "2026-08-19T13:33:08.502983Z",
+     "iopub.status.idle": "2026-08-19T13:33:49.230010Z",
+     "shell.execute_reply": "2026-08-19T13:33:49.229044Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  crs         : 32614\n",
+      "  radars      : ['KTLX']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 0 archived, 0 failed\n",
+      "  elapsed     : 0s\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [KMLB] FAIL KMLB_20240612_221403: radar 'KMLB': the LUT has no sweep 17, but this volume does — it uses a different scan strategy.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [KMLB] FAIL KMLB_20240612_222407: radar 'KMLB' sweep 7: volume has 720 rays, the LUT was built for 360 — a different scan strategy. Archive it under its own radar name, or rebuild the LUT.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [KMLB] FAIL KMLB_20240612_223344: radar 'KMLB' sweep 7: volume has 720 rays, the LUT was built for 360 — a different scan strategy. Archive it under its own radar name, or rebuild the LUT.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [KMLB] FAIL KMLB_20240612_223820: radar 'KMLB' sweep 7: volume has 720 rays, the LUT was built for 360 — a different scan strategy. Archive it under its own radar name, or rebuild the LUT.\n",
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  crs         : 32617\n",
+      "  radars      : ['KMLB']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 0 archived, 4 failed\n",
+      "  elapsed     : 40s\n",
+      "======================================================================\n",
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  crs         : 32616\n",
+      "  radars      : ['KLOT']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 0 archived, 0 failed\n",
+      "  elapsed     : 0s\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'n_archived': 0, 'n_failed': 0, 'n_skipped': 0, 'radars': ['KLOT']}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "db_us.archive(\n",
     "    datatree_dir=NEXRAD_DIR,\n",
@@ -262,10 +572,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "16",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:49.231982Z",
+     "iopub.status.busy": "2026-08-19T13:33:49.231845Z",
+     "iopub.status.idle": "2026-08-19T13:33:49.250418Z",
+     "shell.execute_reply": "2026-08-19T13:33:49.249676Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "radars in the archive: ['FANJ', 'FKOR', 'FKUO', 'KDVN', 'KLOT', 'KMLB', 'KTLX']\n",
+      "==============================================================================\n",
+      "RadDB inventory — archived data\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  radars      : FANJ, FKOR, FKUO, KDVN, KLOT, KMLB, KTLX\n",
+      "  volumes     : 51\n",
+      "  time range  : 2024-06-01 12:00:01 .. 2024-06-25 23:04:58\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar   volumes  time range                                           size\n",
+      "  FANJ         13  2024-06-01 12:00:02 .. 2024-06-14 12:00:03        29.3 MB\n",
+      "  FKOR         14  2024-06-01 12:00:01 .. 2024-06-14 12:00:01        13.3 MB\n",
+      "  FKUO         14  2024-06-01 12:00:05 .. 2024-06-14 12:00:05        25.9 MB\n",
+      "  KDVN          1  2024-06-25 23:04:58                               17.6 MB\n",
+      "  KLOT          0  unknown (no timestamp in the filenames)               0 B\n",
+      "  KMLB          1  2024-06-12 22:57:27                                7.1 MB\n",
+      "  KTLX          8  2024-06-12 22:03:24 .. 2024-06-12 22:53:30        59.3 MB\n",
+      "------------------------------------------------------------------------------\n",
+      "  load with   : db.open(radars=..., time_period=(start, end))\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "print(\"radars in the archive:\", db.list_radars())\n",
@@ -274,12 +617,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "17",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:49.252267Z",
+     "iopub.status.busy": "2026-08-19T13:33:49.252134Z",
+     "iopub.status.idle": "2026-08-19T13:33:49.255929Z",
+     "shell.execute_reply": "2026-08-19T13:33:49.254898Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  FANJ_LUT.parquet                65.90 MB\n",
+      "  FANJ_corners_LUT.parquet        20.74 MB\n",
+      "  FANJ_h_plane_LUT.parquet        20.30 MB\n",
+      "  FANJ_info.yaml                   0.00 MB\n",
+      "  FANJ_v_plane_LUT.parquet         0.45 MB\n"
+     ]
+    }
+   ],
    "source": [
-    "for p in sorted((ARCHIVE_DIR / \"L\" / \"LUT\").iterdir()):\n",
+    "for p in sorted((ARCHIVE_DIR / \"FANJ\" / \"LUT\").iterdir()):\n",
     "    print(f\"  {p.name:<28} {p.stat().st_size / 1e6:8.2f} MB\")"
    ]
   },
@@ -299,16 +661,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "19",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:49.257912Z",
+     "iopub.status.busy": "2026-08-19T13:33:49.257678Z",
+     "iopub.status.idle": "2026-08-19T13:33:49.291804Z",
+     "shell.execute_reply": "2026-08-19T13:33:49.290935Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "LUT: (1610280, 13)\n",
+      "['gate_id', 'sweep', 'azimuth', 'range', 'elevation_angle', 'latitude', 'longitude', 'altitude', 'x', 'y', 'z', 'x_3067', 'y_3067']\n",
+      "shape: (5, 7)\n",
+      "┌────────────────────┬───────┬─────────┬────────┬───────────┬───────────┬────────────┐\n",
+      "│ gate_id            ┆ sweep ┆ azimuth ┆ range  ┆ latitude  ┆ longitude ┆ altitude   │\n",
+      "│ ---                ┆ ---   ┆ ---     ┆ ---    ┆ ---       ┆ ---       ┆ ---        │\n",
+      "│ i64                ┆ i32   ┆ f64     ┆ f32    ┆ f64       ┆ f64       ┆ f64        │\n",
+      "╞════════════════════╪═══════╪═════════╪════════╪═══════════╪═══════════╪════════════╡\n",
+      "│ 713647000000000250 ┆ 0     ┆ 0.0     ┆ 250.0  ┆ 60.906118 ┆ 27.10806  ┆ 140.31267  │\n",
+      "│ 713647000000000750 ┆ 0     ┆ 0.0     ┆ 750.0  ┆ 60.910615 ┆ 27.10806  ┆ 142.960081 │\n",
+      "│ 713647000000001250 ┆ 0     ┆ 0.0     ┆ 1250.0 ┆ 60.915111 ┆ 27.10806  ┆ 145.636922 │\n",
+      "│ 713647000000001750 ┆ 0     ┆ 0.0     ┆ 1750.0 ┆ 60.919608 ┆ 27.10806  ┆ 148.343192 │\n",
+      "│ 713647000000002250 ┆ 0     ┆ 0.0     ┆ 2250.0 ┆ 60.924104 ┆ 27.10806  ┆ 151.078891 │\n",
+      "└────────────────────┴───────┴─────────┴────────┴───────────┴───────────┴────────────┘\n",
+      "shape: (5, 5)\n",
+      "┌─────┬─────────────┬───────────┬───────────────┬──────────┐\n",
+      "│ x   ┆ y           ┆ z         ┆ x_3067        ┆ y_3067   │\n",
+      "│ --- ┆ ---         ┆ ---       ┆ ---           ┆ ---      │\n",
+      "│ f64 ┆ f64         ┆ f64       ┆ f64           ┆ f64      │\n",
+      "╞═════╪═════════════╪═══════════╪═══════════════╪══════════╡\n",
+      "│ 0.0 ┆ 249.996534  ┆ 1.31267   ┆ 505861.758263 ┆ 6.7523e6 │\n",
+      "│ 0.0 ┆ 749.989371  ┆ 3.960081  ┆ 505860.932845 ┆ 6.7528e6 │\n",
+      "│ 0.0 ┆ 1249.981893 ┆ 6.636922  ┆ 505860.107391 ┆ 6.7533e6 │\n",
+      "│ 0.0 ┆ 1749.974099 ┆ 9.343192  ┆ 505859.2819   ┆ 6.7538e6 │\n",
+      "│ 0.0 ┆ 2249.965985 ┆ 12.078891 ┆ 505858.456374 ┆ 6.7543e6 │\n",
+      "└─────┴─────────────┴───────────┴───────────────┴──────────┘\n"
+     ]
+    }
+   ],
    "source": [
-    "lut = db.get_lut(\"L\")\n",
+    "lut = db.get_lut(\"FANJ\")\n",
     "print(\"\\nLUT:\", lut.shape)\n",
     "print(lut.columns)\n",
     "print(lut.head(5).select([\"gate_id\", \"sweep\", \"azimuth\", \"range\", \"latitude\", \"longitude\", \"altitude\"]))\n",
-    "print(lut.head(5).select([\"x\", \"y\", \"z\", \"x_2056\", \"y_2056\"]))"
+    "print(lut.head(5).select([\"x\", \"y\", \"z\", \"x_3067\", \"y_3067\"]))"
    ]
   },
   {
@@ -324,12 +727,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "21",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:49.293665Z",
+     "iopub.status.busy": "2026-08-19T13:33:49.293515Z",
+     "iopub.status.idle": "2026-08-19T13:33:49.303033Z",
+     "shell.execute_reply": "2026-08-19T13:33:49.302297Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  radar            FANJ\n",
+      "  network          \n",
+      "  latitude         60.90387001633644\n",
+      "  longitude        27.1080600656569\n",
+      "  altitude         139.0\n",
+      "  crs              {'epsg': 3067, 'columns': ['x_3067', 'y_3067']}\n",
+      "  ke               1.3333333333333333\n",
+      "  beamwidth_deg    1.0\n",
+      "  n_sweeps         13\n",
+      "  n_gates          1610280\n"
+     ]
+    }
+   ],
    "source": [
-    "info = db.get_radar_info(\"L\")\n",
+    "info = db.get_radar_info(\"FANJ\")\n",
     "for k in [\"radar\", \"network\", \"latitude\", \"longitude\", \"altitude\", \"crs\", \"ke\", \"beamwidth_deg\", \"n_sweeps\", \"n_gates\"]:\n",
     "    print(f\"  {k:<16} {info[k]}\")"
    ]
diff --git a/tutorial/02_opening_and_filtering.ipynb b/tutorial/02_opening_and_filtering.ipynb
index 3961e15..2d4a87b 100644
--- a/tutorial/02_opening_and_filtering.ipynb
+++ b/tutorial/02_opening_and_filtering.ipynb
@@ -24,9 +24,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:50.724686Z",
+     "iopub.status.busy": "2026-08-19T13:33:50.724487Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.650817Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.649758Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import warnings\n",
@@ -42,36 +49,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "2",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.653251Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.652977Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.657466Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.656418Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FMI DataTrees   : /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\n",
+      "NEXRAD DataTrees: /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\n",
+      "Archive         : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — edit these three paths to point at your own data\n",
     "# --------------------------------------------------------------------------\n",
     "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.\n",
     "\n",
-    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree_zarr\").expanduser()\n",
+    "FMI_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\").expanduser()\n",
     "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"FMI DataTrees   :\", FMI_DIR)\n",
     "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
     "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "3",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.659479Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.659340Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.662765Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.662007Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive already present: /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
-    "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
+    "if not (ARCHIVE_DIR / \"FANJ\" / \"LUT\").exists():\n",
     "    print(\"building the archive (see tutorial 1) ...\")\n",
-    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=3067).archive(\n",
+    "        datatree_dir=FMI_DIR,\n",
+    "        time_period=(\"2024-06-01\", \"2024-06-15\"),\n",
+    "    )\n",
     "else:\n",
     "    print(\"archive already present:\", ARCHIVE_DIR)"
    ]
@@ -88,13 +130,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "5",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.664522Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.664391Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.736083Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.735337Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>time</th><th>DBZH</th><th>ZDR</th><th>KDP</th><th>RHOHV</th><th>PHIDP</th><th>TEMP</th><th>volume_time</th><th>radar</th></tr><tr><td>i64</td><td>datetime[ns]</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>datetime[μs, UTC]</td><td>str</td></tr></thead><tbody><tr><td>713647000000000250</td><td>2024-06-01 12:00:08.912507648</td><td>1.17</td><td>-3.06</td><td>0.0</td><td>0.405933</td><td>98.02301</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000015250</td><td>2024-06-01 12:00:08.912507648</td><td>6.1</td><td>3.24</td><td>0.0</td><td>0.268918</td><td>137.750793</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000015750</td><td>2024-06-01 12:00:08.912507648</td><td>3.62</td><td>1.11</td><td>0.0</td><td>0.689683</td><td>91.090424</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000016250</td><td>2024-06-01 12:00:08.912507648</td><td>0.1</td><td>-2.98</td><td>0.0</td><td>0.816184</td><td>91.442001</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000016750</td><td>2024-06-01 12:00:08.912507648</td><td>3.73</td><td>10.08</td><td>0.0</td><td>0.509667</td><td>102.296822</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 10)\n",
+       "┌─────────────────┬────────────────┬──────┬───────┬───┬────────────┬──────┬────────────────┬───────┐\n",
+       "│ gate_id         ┆ time           ┆ DBZH ┆ ZDR   ┆ … ┆ PHIDP      ┆ TEMP ┆ volume_time    ┆ radar │\n",
+       "│ ---             ┆ ---            ┆ ---  ┆ ---   ┆   ┆ ---        ┆ ---  ┆ ---            ┆ ---   │\n",
+       "│ i64             ┆ datetime[ns]   ┆ f32  ┆ f32   ┆   ┆ f32        ┆ f32  ┆ datetime[μs,   ┆ str   │\n",
+       "│                 ┆                ┆      ┆       ┆   ┆            ┆      ┆ UTC]           ┆       │\n",
+       "╞═════════════════╪════════════════╪══════╪═══════╪═══╪════════════╪══════╪════════════════╪═══════╡\n",
+       "│ 713647000000000 ┆ 2024-06-01 12: ┆ 1.17 ┆ -3.06 ┆ … ┆ 98.02301   ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 250             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000015 ┆ 2024-06-01 12: ┆ 6.1  ┆ 3.24  ┆ … ┆ 137.750793 ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 250             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000015 ┆ 2024-06-01 12: ┆ 3.62 ┆ 1.11  ┆ … ┆ 91.090424  ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 750             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000016 ┆ 2024-06-01 12: ┆ 0.1  ┆ -2.98 ┆ … ┆ 91.442001  ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 250             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000016 ┆ 2024-06-01 12: ┆ 3.73 ┆ 10.08 ┆ … ┆ 102.296822 ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 750             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "└─────────────────┴────────────────┴──────┴───────┴───┴────────────┴──────┴────────────────┴───────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
-    "rdf = db.open(radars=\"L\")\n",
+    "rdf = db.open(radars=\"FANJ\")\n",
     "rdf.head()"
    ]
   },
@@ -110,29 +202,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "7",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.738607Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.738450Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.800624Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.799557Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "small_df:\tcolumns: ['gate_id', 'DBZH', 'ZDR', 'volume_time', 'radar']\n",
+      "small_df:\tgates: 2568170\n",
+      "-------------------------------\n",
+      "day_df:\t\tcolumns: ['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar']\n",
+      "day_df:\t\tgates: 157,465\n"
+     ]
+    }
+   ],
    "source": [
-    "# Only two variables, only radar L\n",
-    "small_df = db.open(radars=\"L\", columns=[\"DBZH\", \"ZDR\"])\n",
+    "# Only two variables, only radar FANJ\n",
+    "small_df = db.open(radars=\"FANJ\", columns=[\"DBZH\", \"ZDR\"])\n",
     "print(f\"small_df:\\tcolumns: {small_df.columns()}\\nsmall_df:\\tgates: {len(small_df)}\")\n",
     "print(\"-------------------------------\")\n",
     "# time period\n",
-    "day_df = db.open(radars=\"L\", time_period=(\"2024-06-12\", \"2024-06-13\"))\n",
+    "day_df = db.open(radars=\"FANJ\", time_period=(\"2024-06-12\", \"2024-06-13\"))\n",
     "print(f\"day_df:\\t\\tcolumns: {day_df.columns()}\\nday_df:\\t\\tgates: {len(day_df):,}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.803020Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.802857Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.836996Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.836190Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "before:\t2,568,170 gates\t(with DBZH > 0 dBz)\n",
+      "after:\t220,821  gates\t(with DBZH > 30 dBz)\n"
+     ]
+    }
+   ],
    "source": [
     "# Filters can be pushed down at open() too, so filtered-out rows are never materialised\n",
-    "filtered_df = db.open(radars=\"L\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
+    "filtered_df = db.open(radars=\"FANJ\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30})\n",
     "print(f\"before:\\t{len(rdf):,} gates\\t(with DBZH > 0 dBz)\\nafter:\\t{len(filtered_df):,}  gates\\t(with DBZH > 30 dBz)\")"
    ]
   },
@@ -149,10 +276,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "10",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.838944Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.838773Z",
+     "iopub.status.idle": "2026-08-19T13:33:51.843870Z",
+     "shell.execute_reply": "2026-08-19T13:33:51.843184Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type:\t\t <class 'polars.dataframe.frame.DataFrame'>\n",
+      "name type:\t DataFrame\n",
+      "shape:\t\t (2568170, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>time</th><th>DBZH</th><th>ZDR</th><th>KDP</th><th>RHOHV</th><th>PHIDP</th><th>TEMP</th><th>volume_time</th><th>radar</th></tr><tr><td>i64</td><td>datetime[ns]</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>datetime[μs, UTC]</td><td>str</td></tr></thead><tbody><tr><td>713647000000000250</td><td>2024-06-01 12:00:08.912507648</td><td>1.17</td><td>-3.06</td><td>0.0</td><td>0.405933</td><td>98.02301</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000015250</td><td>2024-06-01 12:00:08.912507648</td><td>6.1</td><td>3.24</td><td>0.0</td><td>0.268918</td><td>137.750793</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000015750</td><td>2024-06-01 12:00:08.912507648</td><td>3.62</td><td>1.11</td><td>0.0</td><td>0.689683</td><td>91.090424</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000016250</td><td>2024-06-01 12:00:08.912507648</td><td>0.1</td><td>-2.98</td><td>0.0</td><td>0.816184</td><td>91.442001</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr><tr><td>713647000000016750</td><td>2024-06-01 12:00:08.912507648</td><td>3.73</td><td>10.08</td><td>0.0</td><td>0.509667</td><td>102.296822</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 10)\n",
+       "┌─────────────────┬────────────────┬──────┬───────┬───┬────────────┬──────┬────────────────┬───────┐\n",
+       "│ gate_id         ┆ time           ┆ DBZH ┆ ZDR   ┆ … ┆ PHIDP      ┆ TEMP ┆ volume_time    ┆ radar │\n",
+       "│ ---             ┆ ---            ┆ ---  ┆ ---   ┆   ┆ ---        ┆ ---  ┆ ---            ┆ ---   │\n",
+       "│ i64             ┆ datetime[ns]   ┆ f32  ┆ f32   ┆   ┆ f32        ┆ f32  ┆ datetime[μs,   ┆ str   │\n",
+       "│                 ┆                ┆      ┆       ┆   ┆            ┆      ┆ UTC]           ┆       │\n",
+       "╞═════════════════╪════════════════╪══════╪═══════╪═══╪════════════╪══════╪════════════════╪═══════╡\n",
+       "│ 713647000000000 ┆ 2024-06-01 12: ┆ 1.17 ┆ -3.06 ┆ … ┆ 98.02301   ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 250             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000015 ┆ 2024-06-01 12: ┆ 6.1  ┆ 3.24  ┆ … ┆ 137.750793 ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 250             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000015 ┆ 2024-06-01 12: ┆ 3.62 ┆ 1.11  ┆ … ┆ 91.090424  ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 750             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000016 ┆ 2024-06-01 12: ┆ 0.1  ┆ -2.98 ┆ … ┆ 91.442001  ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 250             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "│ 713647000000016 ┆ 2024-06-01 12: ┆ 3.73 ┆ 10.08 ┆ … ┆ 102.296822 ┆ NaN  ┆ 2024-06-01     ┆ FANJ  │\n",
+       "│ 750             ┆ 00:08.91250764 ┆      ┆       ┆   ┆            ┆      ┆ 12:00:02 UTC   ┆       │\n",
+       "│                 ┆ 8              ┆      ┆       ┆   ┆            ┆      ┆                ┆       │\n",
+       "└─────────────────┴────────────────┴──────┴───────┴───┴────────────┴──────┴────────────────┴───────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "print(\"type:\\t\\t\", type(rdf.data))\n",
     "print(\"name type:\\t\", type(rdf.data).__name__)\n",
@@ -162,10 +348,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "11",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:51.845853Z",
+     "iopub.status.busy": "2026-08-19T13:33:51.845580Z",
+     "iopub.status.idle": "2026-08-19T13:33:52.311308Z",
+     "shell.execute_reply": "2026-08-19T13:33:52.310324Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "radars    : ['FANJ']\n",
+      "variables : ['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar']\n",
+      "time range: 2024-06-01 12:00:02+00:00 -> 2024-06-14 12:00:03+00:00\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lon/lat    : [22.491, 31.722, 58.659, 63.149]\n",
+      "archive CRS: EPSG:3067\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"radars    :\", rdf.radars())\n",
     "print(\"variables :\", rdf.columns())\n",
@@ -186,10 +397,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "13",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:52.313614Z",
+     "iopub.status.busy": "2026-08-19T13:33:52.313407Z",
+     "iopub.status.idle": "2026-08-19T13:33:52.333843Z",
+     "shell.execute_reply": "2026-08-19T13:33:52.332862Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DBZH > 20: 704,733 gates\n",
+      "DBZH > 20, RHOHV >= 0.98, ZDR > 4 : 4,843 gates\n"
+     ]
+    }
+   ],
    "source": [
     "rain = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
     "print(f\"DBZH > 20: {len(rain):,} gates\")\n",
@@ -218,15 +445,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "15",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:52.336548Z",
+     "iopub.status.busy": "2026-08-19T13:33:52.336320Z",
+     "iopub.status.idle": "2026-08-19T13:33:54.309321Z",
+     "shell.execute_reply": "2026-08-19T13:33:54.307589Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "one sweep      : 364,289\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sweeps 1,2,3   : 814,156\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "range 10-50 km : 846,321\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a lon/lat box  : 873,507\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"one sweep      :\", f\"{len(rdf.sel(sweep=1)):,}\")\n",
     "print(\"sweeps 1,2,3   :\", f\"{len(rdf.sel(sweep=[1, 2, 3])):,}\")\n",
     "print(\"range 10-50 km :\", f\"{len(rdf.sel(range=slice(10_000, 50_000))):,}\")\n",
-    "print(\"a lon/lat box  :\", f\"{len(rdf.sel(lon=slice(8.6, 9.0), lat=slice(46.0, 46.4))):,}\")"
+    "print(\"a lon/lat box  :\", f\"{len(rdf.sel(lon=slice(26.6, 27.6), lat=slice(60.6, 61.2))):,}\")"
    ]
   },
   {
@@ -242,10 +505,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "17",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:54.312421Z",
+     "iopub.status.busy": "2026-08-19T13:33:54.312088Z",
+     "iopub.status.idle": "2026-08-19T13:33:54.713992Z",
+     "shell.execute_reply": "2026-08-19T13:33:54.712394Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stored per gate: ['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar']\n",
+      "also selectable : ['range', 'azimuth', 'elevation_angle', 'latitude', 'longitude', 'altitude', 'sweep']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "sweep 1, 20-60 km: 56,627 gates (columns unchanged: True)\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"stored per gate:\", rdf.columns())\n",
     "print(\"also selectable :\", [\"range\", \"azimuth\", \"elevation_angle\", \"latitude\", \"longitude\", \"altitude\", \"sweep\"])\n",
@@ -268,10 +555,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "19",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:54.716782Z",
+     "iopub.status.busy": "2026-08-19T13:33:54.716535Z",
+     "iopub.status.idle": "2026-08-19T13:33:54.766790Z",
+     "shell.execute_reply": "2026-08-19T13:33:54.765552Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>time</th><th>DBZH</th><th>ZDR</th><th>KDP</th><th>RHOHV</th><th>PHIDP</th><th>TEMP</th><th>volume_time</th><th>radar</th><th>DBZH_lin</th><th>DBZH_dev</th></tr><tr><td>i64</td><td>datetime[ns]</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>datetime[μs, UTC]</td><td>str</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>713647000000000250</td><td>2024-06-01 12:00:08.912507648</td><td>1.17</td><td>-3.06</td><td>0.0</td><td>0.405933</td><td>98.02301</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td><td>1.309182</td><td>-13.046085</td></tr><tr><td>713647000000015250</td><td>2024-06-01 12:00:08.912507648</td><td>6.1</td><td>3.24</td><td>0.0</td><td>0.268918</td><td>137.750793</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td><td>4.073803</td><td>-8.116085</td></tr><tr><td>713647000000015750</td><td>2024-06-01 12:00:08.912507648</td><td>3.62</td><td>1.11</td><td>0.0</td><td>0.689683</td><td>91.090424</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td><td>2.301442</td><td>-10.596086</td></tr><tr><td>713647000000016250</td><td>2024-06-01 12:00:08.912507648</td><td>0.1</td><td>-2.98</td><td>0.0</td><td>0.816184</td><td>91.442001</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td><td>1.023293</td><td>-14.116085</td></tr><tr><td>713647000000016750</td><td>2024-06-01 12:00:08.912507648</td><td>3.73</td><td>10.08</td><td>0.0</td><td>0.509667</td><td>102.296822</td><td>NaN</td><td>2024-06-01 12:00:02 UTC</td><td>&quot;FANJ&quot;</td><td>2.360478</td><td>-10.486086</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 12)\n",
+       "┌───────────────┬───────────────┬──────┬───────┬───┬───────────────┬───────┬──────────┬────────────┐\n",
+       "│ gate_id       ┆ time          ┆ DBZH ┆ ZDR   ┆ … ┆ volume_time   ┆ radar ┆ DBZH_lin ┆ DBZH_dev   │\n",
+       "│ ---           ┆ ---           ┆ ---  ┆ ---   ┆   ┆ ---           ┆ ---   ┆ ---      ┆ ---        │\n",
+       "│ i64           ┆ datetime[ns]  ┆ f32  ┆ f32   ┆   ┆ datetime[μs,  ┆ str   ┆ f32      ┆ f32        │\n",
+       "│               ┆               ┆      ┆       ┆   ┆ UTC]          ┆       ┆          ┆            │\n",
+       "╞═══════════════╪═══════════════╪══════╪═══════╪═══╪═══════════════╪═══════╪══════════╪════════════╡\n",
+       "│ 7136470000000 ┆ 2024-06-01    ┆ 1.17 ┆ -3.06 ┆ … ┆ 2024-06-01    ┆ FANJ  ┆ 1.309182 ┆ -13.046085 │\n",
+       "│ 00250         ┆ 12:00:08.9125 ┆      ┆       ┆   ┆ 12:00:02 UTC  ┆       ┆          ┆            │\n",
+       "│               ┆ 07648         ┆      ┆       ┆   ┆               ┆       ┆          ┆            │\n",
+       "│ 7136470000000 ┆ 2024-06-01    ┆ 6.1  ┆ 3.24  ┆ … ┆ 2024-06-01    ┆ FANJ  ┆ 4.073803 ┆ -8.116085  │\n",
+       "│ 15250         ┆ 12:00:08.9125 ┆      ┆       ┆   ┆ 12:00:02 UTC  ┆       ┆          ┆            │\n",
+       "│               ┆ 07648         ┆      ┆       ┆   ┆               ┆       ┆          ┆            │\n",
+       "│ 7136470000000 ┆ 2024-06-01    ┆ 3.62 ┆ 1.11  ┆ … ┆ 2024-06-01    ┆ FANJ  ┆ 2.301442 ┆ -10.596086 │\n",
+       "│ 15750         ┆ 12:00:08.9125 ┆      ┆       ┆   ┆ 12:00:02 UTC  ┆       ┆          ┆            │\n",
+       "│               ┆ 07648         ┆      ┆       ┆   ┆               ┆       ┆          ┆            │\n",
+       "│ 7136470000000 ┆ 2024-06-01    ┆ 0.1  ┆ -2.98 ┆ … ┆ 2024-06-01    ┆ FANJ  ┆ 1.023293 ┆ -14.116085 │\n",
+       "│ 16250         ┆ 12:00:08.9125 ┆      ┆       ┆   ┆ 12:00:02 UTC  ┆       ┆          ┆            │\n",
+       "│               ┆ 07648         ┆      ┆       ┆   ┆               ┆       ┆          ┆            │\n",
+       "│ 7136470000000 ┆ 2024-06-01    ┆ 3.73 ┆ 10.08 ┆ … ┆ 2024-06-01    ┆ FANJ  ┆ 2.360478 ┆ -10.486086 │\n",
+       "│ 16750         ┆ 12:00:08.9125 ┆      ┆       ┆   ┆ 12:00:02 UTC  ┆       ┆          ┆            │\n",
+       "│               ┆ 07648         ┆      ┆       ┆   ┆               ┆       ┆          ┆            │\n",
+       "└───────────────┴───────────────┴──────┴───────┴───┴───────────────┴───────┴──────────┴────────────┘"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "derived = rdf.add_feature(\"DBZH_lin\", lambda df: 10 ** (df[\"DBZH\"] / 10)).add_feature(\n",
     "    \"DBZH_dev\",\n",
@@ -291,10 +628,155 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "21",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:54.769433Z",
+     "iopub.status.busy": "2026-08-19T13:33:54.769206Z",
+     "iopub.status.idle": "2026-08-19T13:33:54.863261Z",
+     "shell.execute_reply": "2026-08-19T13:33:54.862209Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rdf type: <class 'polars.dataframe.frame.DataFrame'>\n",
+      "df  type: <class 'pandas.DataFrame'>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gate_id</th>\n",
+       "      <th>time</th>\n",
+       "      <th>DBZH</th>\n",
+       "      <th>ZDR</th>\n",
+       "      <th>KDP</th>\n",
+       "      <th>RHOHV</th>\n",
+       "      <th>PHIDP</th>\n",
+       "      <th>TEMP</th>\n",
+       "      <th>volume_time</th>\n",
+       "      <th>radar</th>\n",
+       "      <th>DIFF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>713647000000000250</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>1.17</td>\n",
+       "      <td>-3.06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.405933</td>\n",
+       "      <td>98.023010</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>4.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>713647000000015250</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>6.10</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.268918</td>\n",
+       "      <td>137.750793</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>2.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>713647000000015750</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>3.62</td>\n",
+       "      <td>1.11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.689683</td>\n",
+       "      <td>91.090424</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>2.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>713647000000016250</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>-2.98</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.816184</td>\n",
+       "      <td>91.442001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>3.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>713647000000016750</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>10.08</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.509667</td>\n",
+       "      <td>102.296822</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>-6.35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              gate_id                          time  DBZH    ZDR  KDP  \\\n",
+       "0  713647000000000250 2024-06-01 12:00:08.912507648  1.17  -3.06  0.0   \n",
+       "1  713647000000015250 2024-06-01 12:00:08.912507648  6.10   3.24  0.0   \n",
+       "2  713647000000015750 2024-06-01 12:00:08.912507648  3.62   1.11  0.0   \n",
+       "3  713647000000016250 2024-06-01 12:00:08.912507648  0.10  -2.98  0.0   \n",
+       "4  713647000000016750 2024-06-01 12:00:08.912507648  3.73  10.08  0.0   \n",
+       "\n",
+       "      RHOHV       PHIDP  TEMP               volume_time radar  DIFF  \n",
+       "0  0.405933   98.023010   NaN 2024-06-01 12:00:02+00:00  FANJ  4.23  \n",
+       "1  0.268918  137.750793   NaN 2024-06-01 12:00:02+00:00  FANJ  2.86  \n",
+       "2  0.689683   91.090424   NaN 2024-06-01 12:00:02+00:00  FANJ  2.51  \n",
+       "3  0.816184   91.442001   NaN 2024-06-01 12:00:02+00:00  FANJ  3.08  \n",
+       "4  0.509667  102.296822   NaN 2024-06-01 12:00:02+00:00  FANJ -6.35  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "rdf.data.with_columns((pl.col(\"DBZH\") - pl.col(\"ZDR\")).alias(\"DIFF\"))\n",
     "\n",
@@ -348,10 +830,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "24",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:54.865770Z",
+     "iopub.status.busy": "2026-08-19T13:33:54.865566Z",
+     "iopub.status.idle": "2026-08-19T13:33:54.895426Z",
+     "shell.execute_reply": "2026-08-19T13:33:54.894395Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "to_pandas(): DataFrame (704733, 10)\n",
+      "['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar']\n"
+     ]
+    }
+   ],
    "source": [
     "# No flags: the stored columns only, exactly as open() loaded them.\n",
     "df = rain.to_pandas()\n",
@@ -361,10 +859,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "25",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:54.898305Z",
+     "iopub.status.busy": "2026-08-19T13:33:54.898089Z",
+     "iopub.status.idle": "2026-08-19T13:33:55.231894Z",
+     "shell.execute_reply": "2026-08-19T13:33:55.230725Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "added by with_geometry     : ['latitude', 'longitude', 'altitude', 'sweep']\n"
+     ]
+    }
+   ],
    "source": [
     "# with_geometry=True joins the per-gate coordinates from the LUT on gate_id.\n",
     "df_geo = rain.to_pandas(with_geometry=True)\n",
@@ -373,10 +886,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "26",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:55.234922Z",
+     "iopub.status.busy": "2026-08-19T13:33:55.234688Z",
+     "iopub.status.idle": "2026-08-19T13:33:55.575329Z",
+     "shell.execute_reply": "2026-08-19T13:33:55.574402Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "added by with_polar_coords : ['latitude', 'longitude', 'altitude', 'sweep', 'range', 'azimuth', 'elevation_angle']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gate_id</th>\n",
+       "      <th>time</th>\n",
+       "      <th>DBZH</th>\n",
+       "      <th>ZDR</th>\n",
+       "      <th>KDP</th>\n",
+       "      <th>RHOHV</th>\n",
+       "      <th>PHIDP</th>\n",
+       "      <th>TEMP</th>\n",
+       "      <th>volume_time</th>\n",
+       "      <th>radar</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>sweep</th>\n",
+       "      <th>range</th>\n",
+       "      <th>azimuth</th>\n",
+       "      <th>elevation_angle</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>713647000000098250</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>25.190001</td>\n",
+       "      <td>-0.09</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.998367</td>\n",
+       "      <td>75.445419</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>61.787348</td>\n",
+       "      <td>27.10806</td>\n",
+       "      <td>1221.548181</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98250.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>713647000000098750</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>24.959999</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.997940</td>\n",
+       "      <td>74.786217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>61.791844</td>\n",
+       "      <td>27.10806</td>\n",
+       "      <td>1229.962841</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98750.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>713647000000099250</td>\n",
+       "      <td>2024-06-01 12:00:08.912507648</td>\n",
+       "      <td>22.090000</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.997162</td>\n",
+       "      <td>74.736778</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2024-06-01 12:00:02+00:00</td>\n",
+       "      <td>FANJ</td>\n",
+       "      <td>61.796339</td>\n",
+       "      <td>27.10806</td>\n",
+       "      <td>1238.406920</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99250.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              gate_id                          time       DBZH   ZDR   KDP  \\\n",
+       "0  713647000000098250 2024-06-01 12:00:08.912507648  25.190001 -0.09  0.00   \n",
+       "1  713647000000098750 2024-06-01 12:00:08.912507648  24.959999 -0.02  0.01   \n",
+       "2  713647000000099250 2024-06-01 12:00:08.912507648  22.090000  0.02  0.01   \n",
+       "\n",
+       "      RHOHV      PHIDP  TEMP               volume_time radar   latitude  \\\n",
+       "0  0.998367  75.445419   NaN 2024-06-01 12:00:02+00:00  FANJ  61.787348   \n",
+       "1  0.997940  74.786217   NaN 2024-06-01 12:00:02+00:00  FANJ  61.791844   \n",
+       "2  0.997162  74.736778   NaN 2024-06-01 12:00:02+00:00  FANJ  61.796339   \n",
+       "\n",
+       "   longitude     altitude  sweep    range  azimuth  elevation_angle  \n",
+       "0   27.10806  1221.548181      0  98250.0      0.0              0.3  \n",
+       "1   27.10806  1229.962841      0  98750.0      0.0              0.3  \n",
+       "2   27.10806  1238.406920      0  99250.0      0.0              0.3  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# with_polar_coords=True also brings the polar coordinates the geometry came from.\n",
     "df_polar = rain.to_pandas(with_polar_coords=True)\n",
@@ -402,33 +1056,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "28",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:55.578582Z",
+     "iopub.status.busy": "2026-08-19T13:33:55.578235Z",
+     "iopub.status.idle": "2026-08-19T13:33:56.278406Z",
+     "shell.execute_reply": "2026-08-19T13:33:56.276917Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "without crs= : ['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar', 'latitude', 'longitude', 'altitude', 'sweep']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "with crs=3067: ['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar', 'latitude', 'longitude', 'altitude', 'sweep', 'x_3067', 'y_3067']\n"
+     ]
+    }
+   ],
    "source": [
     "# Projected coordinates: state the CRS when creating the RadDB, and\n",
     "# with_geometry=True then adds x_<epsg> / y_<epsg> alongside lon/lat/alt.\n",
-    "db_proj = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)\n",
-    "rain_proj = db_proj.open(radars=\"L\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
+    "db_proj = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=3067)\n",
+    "rain_proj = db_proj.open(radars=\"FANJ\", filters={\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
     "\n",
     "print(\"without crs= :\", list(rain.to_pandas(with_geometry=True).columns))\n",
-    "print(\"with crs=2056:\", list(rain_proj.to_pandas(with_geometry=True).columns))"
+    "print(\"with crs=3067:\", list(rain_proj.to_pandas(with_geometry=True).columns))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "29",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:56.281440Z",
+     "iopub.status.busy": "2026-08-19T13:33:56.281169Z",
+     "iopub.status.idle": "2026-08-19T13:33:56.357460Z",
+     "shell.execute_reply": "2026-08-19T13:33:56.355946Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>gate_id</th><th>DBZH</th><th>x</th><th>y</th><th>z</th><th>x_3067</th><th>y_3067</th></tr><tr><td>i64</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>713647000000098250</td><td>25.190001</td><td>0.0</td><td>98238.323898</td><td>1082.548181</td><td>505699.30037</td><td>6.8505e6</td></tr><tr><td>713647000000098750</td><td>24.959999</td><td>0.0</td><td>98738.189137</td><td>1090.962841</td><td>505698.46809</td><td>6.8510e6</td></tr><tr><td>713647000000099250</td><td>22.09</td><td>0.0</td><td>99238.053384</td><td>1099.40692</td><td>505697.635776</td><td>6.8515e6</td></tr><tr><td>713647000000099750</td><td>20.93</td><td>0.0</td><td>99737.916635</td><td>1107.880417</td><td>505696.803428</td><td>6.8520e6</td></tr><tr><td>713647000000100250</td><td>24.91</td><td>0.0</td><td>100237.778888</td><td>1116.383332</td><td>505695.971046</td><td>6.8525e6</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 7)\n",
+       "┌────────────────────┬───────────┬─────┬───────────────┬─────────────┬───────────────┬──────────┐\n",
+       "│ gate_id            ┆ DBZH      ┆ x   ┆ y             ┆ z           ┆ x_3067        ┆ y_3067   │\n",
+       "│ ---                ┆ ---       ┆ --- ┆ ---           ┆ ---         ┆ ---           ┆ ---      │\n",
+       "│ i64                ┆ f32       ┆ f64 ┆ f64           ┆ f64         ┆ f64           ┆ f64      │\n",
+       "╞════════════════════╪═══════════╪═════╪═══════════════╪═════════════╪═══════════════╪══════════╡\n",
+       "│ 713647000000098250 ┆ 25.190001 ┆ 0.0 ┆ 98238.323898  ┆ 1082.548181 ┆ 505699.30037  ┆ 6.8505e6 │\n",
+       "│ 713647000000098750 ┆ 24.959999 ┆ 0.0 ┆ 98738.189137  ┆ 1090.962841 ┆ 505698.46809  ┆ 6.8510e6 │\n",
+       "│ 713647000000099250 ┆ 22.09     ┆ 0.0 ┆ 99238.053384  ┆ 1099.40692  ┆ 505697.635776 ┆ 6.8515e6 │\n",
+       "│ 713647000000099750 ┆ 20.93     ┆ 0.0 ┆ 99737.916635  ┆ 1107.880417 ┆ 505696.803428 ┆ 6.8520e6 │\n",
+       "│ 713647000000100250 ┆ 24.91     ┆ 0.0 ┆ 100237.778888 ┆ 1116.383332 ┆ 505695.971046 ┆ 6.8525e6 │\n",
+       "└────────────────────┴───────────┴─────┴───────────────┴─────────────┴───────────────┴──────────┘"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Any LUT column can be attached by joining on gate_id.  This is also how you add\n",
     "# geometry to a frame loaded with open(), and the only way to get x / y / z\n",
     "# (metres from the radar), which no converter attaches.\n",
-    "geometry = db.get_lut(\"L\").select([\"gate_id\", \"x\", \"y\", \"z\", \"x_2056\", \"y_2056\"])\n",
+    "geometry = db.get_lut(\"FANJ\").select([\"gate_id\", \"x\", \"y\", \"z\", \"x_3067\", \"y_3067\"])\n",
     "joined = rain.data.join(geometry, on=\"gate_id\", how=\"left\")\n",
-    "joined.select([\"gate_id\", \"DBZH\", \"x\", \"y\", \"z\", \"x_2056\", \"y_2056\"]).head()"
+    "joined.select([\"gate_id\", \"DBZH\", \"x\", \"y\", \"z\", \"x_3067\", \"y_3067\"]).head()"
    ]
   },
   {
@@ -441,10 +1156,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "31",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:56.360167Z",
+     "iopub.status.busy": "2026-08-19T13:33:56.359888Z",
+     "iopub.status.idle": "2026-08-19T13:33:57.293084Z",
+     "shell.execute_reply": "2026-08-19T13:33:57.292341Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "to_geopandas:  <class 'geopandas.geodataframe.GeoDataFrame'>\n",
+      "CRS: EPSG:4326\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gate_id</th>\n",
+       "      <th>DBZH</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>25.190001</td>\n",
+       "      <td>POINT (27.10806 61.78735)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>713647000000098750</td>\n",
+       "      <td>24.959999</td>\n",
+       "      <td>POINT (27.10806 61.79184)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>713647000000099250</td>\n",
+       "      <td>22.090000</td>\n",
+       "      <td>POINT (27.10806 61.79634)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>713647000000099750</td>\n",
+       "      <td>20.930000</td>\n",
+       "      <td>POINT (27.10806 61.80083)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>713647000000100250</td>\n",
+       "      <td>24.910000</td>\n",
+       "      <td>POINT (27.10806 61.80533)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              gate_id       DBZH                   geometry\n",
+       "0  713647000000098250  25.190001  POINT (27.10806 61.78735)\n",
+       "1  713647000000098750  24.959999  POINT (27.10806 61.79184)\n",
+       "2  713647000000099250  22.090000  POINT (27.10806 61.79634)\n",
+       "3  713647000000099750  20.930000  POINT (27.10806 61.80083)\n",
+       "4  713647000000100250  24.910000  POINT (27.10806 61.80533)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# geopandas: point geometry per gate, ready for spatial joins or QGIS\n",
     "gdf = rain.to_geopandas()\n",
@@ -463,10 +1268,1161 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "33",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:57.295119Z",
+     "iopub.status.busy": "2026-08-19T13:33:57.294966Z",
+     "iopub.status.idle": "2026-08-19T13:33:58.698638Z",
+     "shell.execute_reply": "2026-08-19T13:33:58.697936Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13 volumes loaded -> rebuilding the first one\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
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+       ".xr-array-wrap > input:enabled + label,\n",
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+       "\n",
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+       ".xr-array-wrap > input:focus-visible + label,\n",
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+       ".xr-array-wrap > input:enabled + label:hover,\n",
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+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataTree&gt;\n",
+       "Group: /\n",
+       "├── Group: /sweep_0\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.15\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 140.3 143.0 ... 5.117e+03\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.357e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.002e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 0\n",
+       "│           elevation_angle  float64 8B 0.3\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:00:08....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 1.17 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -3.06 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.4059 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 98.02 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_1\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.15\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 142.1 148.2 ... 6.859e+03\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.356e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.002e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 1\n",
+       "│           elevation_angle  float64 8B 0.7\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:00:29....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 0.23 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -6.25 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.4484 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 74.04 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_2\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.15\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 145.5 158.7 ... 1.034e+04\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.353e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.002e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 2\n",
+       "│           elevation_angle  float64 8B 1.5\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:00:50....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 0.52 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -8.65 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.4317 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 101.9 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_3\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.14\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 152.1 178.3 ... 1.686e+04\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.345e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.001e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 3\n",
+       "│           elevation_angle  float64 8B 3.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:01:11....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 0.62 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -6.74 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.6461 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 94.58 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_4\n",
+       "│       Dimensions:          (azimuth: 360, range: 367)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 1kB 250.0 750.0 ... 1.828e+05 1.832e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 62.54 62.54\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.05 27.05\n",
+       "│           altitude         (azimuth, range) float64 1MB 160.8 204.4 ... 1.807e+04\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -3.18e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 6.935e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 4\n",
+       "│           elevation_angle  float64 8B 5.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:01:54....\n",
+       "│           DBZH             (azimuth, range) float32 528kB 1.51 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 528kB -5.81 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 528kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 528kB 0.4435 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 528kB 101.2 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 528kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_5\n",
+       "│       Dimensions:          (azimuth: 360, range: 205)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 820B 250.0 750.0 ... 1.018e+05 1.022e+05\n",
+       "│           latitude         (azimuth, range) float64 590kB 60.91 60.91 ... 61.81 61.81\n",
+       "│           longitude        (azimuth, range) float64 590kB 27.11 27.11 ... 27.08 27.08\n",
+       "│           altitude         (azimuth, range) float64 590kB 178.1 256.4 ... 1.673e+04\n",
+       "│           x                (azimuth, range) float64 590kB 0.431 1.293 ... -1.583e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 590kB 6.752e+06 ... 6.853e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 5\n",
+       "│           elevation_angle  float64 8B 9.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 590kB NaT NaT ... NaT NaT\n",
+       "│           DBZH             (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           KDP              (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           TEMP             (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "...\n",
+       "├── Group: /sweep_7\n",
+       "│       Dimensions:          (azimuth: 360, range: 248)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 992B 250.0 750.0 ... 1.232e+05 1.238e+05\n",
+       "│           latitude         (azimuth, range) float64 714kB 60.91 60.91 ... 62.0 62.01\n",
+       "│           longitude        (azimuth, range) float64 714kB 27.11 27.11 ... 27.07 27.07\n",
+       "│           altitude         (azimuth, range) float64 714kB 169.5 230.4 ... 1.611e+04\n",
+       "│           x                (azimuth, range) float64 714kB 0.4331 1.299 ... -1.926e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 714kB 6.752e+06 ... 6.875e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 7\n",
+       "│           elevation_angle  float64 8B 7.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 714kB 2024-06-01T12:02:4...\n",
+       "│           DBZH             (azimuth, range) float32 357kB 7.35 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 357kB -6.52 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 357kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 357kB 0.3987 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 357kB 90.56 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 357kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_8\n",
+       "│       Dimensions:          (azimuth: 360, range: 168)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 672B 250.0 750.0 ... 8.325e+04 8.375e+04\n",
+       "│           latitude         (azimuth, range) float64 484kB 60.91 60.91 ... 61.64 61.64\n",
+       "│           longitude        (azimuth, range) float64 484kB 27.11 27.11 ... 27.08 27.08\n",
+       "│           altitude         (azimuth, range) float64 484kB 186.7 282.1 ... 1.652e+04\n",
+       "│           x                (azimuth, range) float64 484kB 0.4283 1.285 ... -1.289e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 484kB 6.752e+06 ... 6.834e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 8\n",
+       "│           elevation_angle  float64 8B 11.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 484kB 2024-06-01T12:02:5...\n",
+       "│           DBZH             (azimuth, range) float32 242kB 4.21 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 242kB -3.62 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 242kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 242kB 0.3019 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 242kB 98.16 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 242kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_9\n",
+       "│       Dimensions:          (azimuth: 360, range: 124)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 496B 250.0 750.0 ... 6.125e+04 6.175e+04\n",
+       "│           latitude         (azimuth, range) float64 357kB 60.91 60.91 ... 61.43 61.44\n",
+       "│           longitude        (azimuth, range) float64 357kB 27.11 27.11 ... 27.09 27.09\n",
+       "│           altitude         (azimuth, range) float64 357kB 203.7 333.1 ... 1.633e+04\n",
+       "│           x                (azimuth, range) float64 357kB 0.4215 1.264 ... -935.1\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 357kB 6.752e+06 ... 6.812e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 9\n",
+       "│           elevation_angle  float64 8B 15.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 357kB NaT NaT ... NaT NaT\n",
+       "│           DBZH             (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           KDP              (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           TEMP             (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_10\n",
+       "│       Dimensions:          (azimuth: 360, range: 76)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 304B 250.0 750.0 ... 3.725e+04 3.775e+04\n",
+       "│           latitude         (azimuth, range) float64 219kB 60.91 60.91 ... 61.21 61.21\n",
+       "│           longitude        (azimuth, range) float64 219kB 27.11 27.11 ... 27.1 27.1\n",
+       "│           altitude         (azimuth, range) float64 219kB 244.7 456.0 ... 1.616e+04\n",
+       "│           x                (azimuth, range) float64 219kB 0.3954 1.186 ... -536.4\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 219kB 6.752e+06 ... 6.786e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 10\n",
+       "│           elevation_angle  float64 8B 25.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 219kB 2024-06-01T12:03:3...\n",
+       "│           DBZH             (azimuth, range) float32 109kB 5.77 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 109kB -3.25 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 109kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 109kB 0.6875 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 109kB 119.2 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 109kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_11\n",
+       "│       Dimensions:          (azimuth: 360, range: 45)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 180B 250.0 750.0 ... 2.175e+04 2.225e+04\n",
+       "│           latitude         (azimuth, range) float64 130kB 60.91 60.91 ... 61.04 61.05\n",
+       "│           longitude        (azimuth, range) float64 130kB 27.11 27.11 ... 27.1 27.1\n",
+       "│           altitude         (azimuth, range) float64 130kB 315.8 669.3 ... 1.589e+04\n",
+       "│           x                (azimuth, range) float64 130kB 0.3085 0.9255 ... -246.7\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 130kB 6.752e+06 ... 6.768e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 11\n",
+       "│           elevation_angle  float64 8B 45.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 130kB 2024-06-01T12:03:5...\n",
+       "│           DBZH             (azimuth, range) float32 65kB 4.13 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 65kB -2.87 nan nan ... nan nan nan\n",
+       "│           KDP              (azimuth, range) float32 65kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 65kB 0.6571 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 65kB 154.5 nan nan ... nan nan nan\n",
+       "│           TEMP             (azimuth, range) float32 65kB nan nan nan ... nan nan nan\n",
+       "└── Group: /sweep_12\n",
+       "        Dimensions:          (azimuth: 360, range: 992)\n",
+       "        Coordinates: (12/15)\n",
+       "          * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "          * range            (range) float32 4kB 62.5 187.5 ... 1.238e+05 1.239e+05\n",
+       "            latitude         (azimuth, range) float64 3MB 60.9 60.91 ... 62.02 62.02\n",
+       "            longitude        (azimuth, range) float64 3MB 27.11 27.11 ... 27.07 27.07\n",
+       "            altitude         (azimuth, range) float64 3MB 139.4 140.3 ... 1.908e+03\n",
+       "            x                (azimuth, range) float64 3MB 0.1091 0.3272 ... -1.946e+03\n",
+       "            ...               ...\n",
+       "            y_3067           (azimuth, range) float64 3MB 6.752e+06 ... 6.876e+06\n",
+       "            site_latitude    float64 8B 60.9\n",
+       "            site_longitude   float64 8B 27.11\n",
+       "            site_altitude    float64 8B 139.0\n",
+       "            sweep_number     int64 8B 12\n",
+       "            elevation_angle  float64 8B 0.4\n",
+       "        Data variables:\n",
+       "            time             (azimuth, range) datetime64[ns] 3MB NaT ... 2024-06-01T1...\n",
+       "            DBZH             (azimuth, range) float32 1MB nan nan nan ... 10.83 5.91\n",
+       "            ZDR              (azimuth, range) float32 1MB nan nan nan ... -1.39 -2.6\n",
+       "            KDP              (azimuth, range) float32 1MB nan nan nan ... 0.58 0.58 0.58\n",
+       "            RHOHV            (azimuth, range) float32 1MB nan nan nan ... 1.0 1.0 0.948\n",
+       "            PHIDP            (azimuth, range) float32 1MB nan nan nan ... 80.96 77.39\n",
+       "            TEMP             (azimuth, range) float32 1MB nan nan nan ... nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataTree</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-children'><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-1808a89e-562d-44e2-bf06-4ced43d807ea' type='checkbox' checked /><label for='group-1808a89e-562d-44e2-bf06-4ced43d807ea' title='Expand/collapse group'>/sweep_0<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-fbb0150e-08b5-4819-b748-9ede2bed4886' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-fbb0150e-08b5-4819-b748-9ede2bed4886' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 500</li></ul></div></li><li class='xr-section-item'><input id='section-ce800078-b80f-41bf-a83e-bd089953c3c5' class='xr-section-summary-in' type='checkbox' checked /><label for='section-ce800078-b80f-41bf-a83e-bd089953c3c5' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-722058d0-4e70-4bbf-9580-7beae7b4861e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-722058d0-4e70-4bbf-9580-7beae7b4861e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5532137-2515-4c5c-ad6e-929aa2f3ff84' class='xr-var-data-in' type='checkbox'><label for='data-c5532137-2515-4c5c-ad6e-929aa2f3ff84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 357., 358., 359.], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 2.492e+05 2.498e+05</div><input id='attrs-4c1564ea-8812-4921-8875-0583ac82b73d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c1564ea-8812-4921-8875-0583ac82b73d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78d94a12-df55-47af-b912-117c6c4fd906' class='xr-var-data-in' type='checkbox'><label for='data-78d94a12-df55-47af-b912-117c6c4fd906' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 248750., 249250., 249750.],shape=(500,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.92 ... 63.14 63.15</div><input id='attrs-e6586ec0-863f-4e2f-9d39-9ade7e93bb9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6586ec0-863f-4e2f-9d39-9ade7e93bb9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6257c533-6950-4267-b723-0c810d134e50' class='xr-var-data-in' type='checkbox'><label for='data-6257c533-6950-4267-b723-0c810d134e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90611829, 60.91061483, 60.91511137, ..., 63.13992013,63.14441144, 63.14890273],[60.90611795, 60.91061381, 60.91510966, ..., 63.13957957,63.14407019, 63.1485608 ],[60.90611692, 60.91061072, 60.91510453, ..., 63.13855799,63.14304656, 63.14753511],...,[60.90611521, 60.91060559, 60.91509597, ..., 63.1368557 ,63.14134085, 63.14582599],[60.90611692, 60.91061072, 60.91510453, ..., 63.13855799,63.14304656, 63.14753511],[60.90611795, 60.91061381, 60.91510966, ..., 63.13957957,63.14407019, 63.1485608 ]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.03 27.03</div><input id='attrs-58880be4-9fe4-4d5d-aa6d-ebde5e58d9c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58880be4-9fe4-4d5d-aa6d-ebde5e58d9c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26310f37-21fc-4c54-889a-9da3ab27de28' class='xr-var-data-in' type='checkbox'><label for='data-26310f37-21fc-4c54-889a-9da3ab27de28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806007, 27.10806007, 27.10806007, ..., 27.10806007,27.10806007, 27.10806007],[27.10814076, 27.10830214, 27.10846352, ..., 27.18831166,27.18847286, 27.18863405],[27.10822142, 27.10854413, 27.10886684, ..., 27.26853882,27.26886115, 27.26918349],...,[27.10781809, 27.10733415, 27.1068502 , ..., 26.86740304,26.86691966, 26.86643628],[27.10789871, 27.107576  , 27.10725329, ..., 26.94758131,26.94725898, 26.94693664],[27.10797938, 27.10781799, 27.10765661, ..., 27.02780847,27.02764727, 27.02748608]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>140.3 143.0 ... 5.099e+03 5.117e+03</div><input id='attrs-7302fd96-bb9d-4dfb-9465-aeafaed8d9cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7302fd96-bb9d-4dfb-9465-aeafaed8d9cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c3bfe5c-1d31-4c52-82af-fe52a126e0a3' class='xr-var-data-in' type='checkbox'><label for='data-1c3bfe5c-1d31-4c52-82af-fe52a126e0a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 140.31266963,  142.96008096,  145.63692167, ..., 5082.09082059,5099.35499375, 5116.64854485],[ 140.31266963,  142.96008096,  145.63692167, ..., 5082.09082059,5099.35499375, 5116.64854485],[ 140.31266963,  142.96008096,  145.63692167, ..., 5082.09082059,5099.35499375, 5116.64854485],...,[ 140.31266963,  142.96008096,  145.63692167, ..., 5082.09082059,5099.35499375, 5116.64854485],[ 140.31266963,  142.96008096,  145.63692167, ..., 5082.09082059,5099.35499375, 5116.64854485],[ 140.31266963,  142.96008096,  145.63692167, ..., 5082.09082059,5099.35499375, 5116.64854485]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -4.348e+03 -4.357e+03</div><input id='attrs-934c7d86-1a99-4156-b3a6-9a5186b6bc2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-934c7d86-1a99-4156-b3a6-9a5186b6bc2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e68ae4c1-b186-404a-bac6-956b94110511' class='xr-var-data-in' type='checkbox'><label for='data-e68ae4c1-b186-404a-bac6-956b94110511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,0.00000000e+00,  0.00000000e+00,  0.00000000e+00],[ 4.36304113e+00,  1.30891193e+01,  2.18151920e+01, ...,4.33932145e+03,  4.34803737e+03,  4.35675325e+03],[ 8.72475323e+00,  2.61742516e+01,  4.36237390e+01, ...,8.67732110e+03,  8.69475029e+03,  8.71217940e+03],...,[-1.30838077e+01, -3.92514109e+01, -6.54189977e+01, ...,-1.30126776e+04, -1.30388147e+04, -1.30649517e+04],[-8.72475323e+00, -2.61742516e+01, -4.36237390e+01, ...,-8.67732110e+03, -8.69475029e+03, -8.71217940e+03],[-4.36304113e+00, -1.30891193e+01, -2.18151920e+01, ...,-4.33932145e+03, -4.34803737e+03, -4.35675325e+03]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 2.491e+05 2.496e+05</div><input id='attrs-2ce411a3-75a7-41c2-bd9a-028fc4c77376' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ce411a3-75a7-41c2-bd9a-028fc4c77376' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef9ee3d8-d958-46ec-9f37-0a6011067cb3' class='xr-var-data-in' type='checkbox'><label for='data-ef9ee3d8-d958-46ec-9f37-0a6011067cb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   249.99653447,    749.98937053,   1249.9818932 , ...,248637.42814385, 249136.83888616, 249636.24759796],[   249.95845878,    749.87514351,   1249.79151491, ...,248599.55945924, 249098.89413887, 249598.22678831],[   249.84424333,    749.53249727,   1249.22043801, ...,248485.96494058, 248985.07145536, 249484.17594087],...,[   249.6539229 ,    748.96153616,   1248.26883646, ...,248296.67918987, 248795.40550711, 249294.12979663],[   249.84424333,    749.53249727,   1249.22043801, ...,248485.96494058, 248985.07145536, 249484.17594087],[   249.95845878,    749.87514351,   1249.79151491, ...,248599.55945924, 249098.89413887, 249598.22678831]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.313 3.96 ... 4.96e+03 4.978e+03</div><input id='attrs-e1e9bed3-f146-4037-a637-8cd0e636b062' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1e9bed3-f146-4037-a637-8cd0e636b062' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-256aa823-2e01-4a16-aeab-270e8836f5aa' class='xr-var-data-in' type='checkbox'><label for='data-256aa823-2e01-4a16-aeab-270e8836f5aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.31266963e+00, 3.96008096e+00, 6.63692167e+00, ...,4.94309082e+03, 4.96035499e+03, 4.97764854e+03],[1.31266963e+00, 3.96008096e+00, 6.63692167e+00, ...,4.94309082e+03, 4.96035499e+03, 4.97764854e+03],[1.31266963e+00, 3.96008096e+00, 6.63692167e+00, ...,4.94309082e+03, 4.96035499e+03, 4.97764854e+03],...,[1.31266963e+00, 3.96008096e+00, 6.63692167e+00, ...,4.94309082e+03, 4.96035499e+03, 4.97764854e+03],[1.31266963e+00, 3.96008096e+00, 6.63692167e+00, ...,4.94309082e+03, 4.96035499e+03, 4.97764854e+03],[1.31266963e+00, 3.96008096e+00, 6.63692167e+00, ...,4.94309082e+03, 4.96035499e+03, 4.97764854e+03]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.014e+05</div><input id='attrs-e036612d-f761-4030-8233-e58373b91c6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e036612d-f761-4030-8233-e58373b91c6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddbe74b5-7ee6-42d8-a49d-df0910277aa2' class='xr-var-data-in' type='checkbox'><label for='data-ddbe74b5-7ee6-42d8-a49d-df0910277aa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505861.75826292, 505860.9328448 , 505860.10739061, ...,505447.30273014, 505446.46074514, 505445.61872959],[505866.13539673, 505874.06239336, 505881.98688324, ...,509492.8864519 , 509499.54391506, 509506.19878029],[505870.51132321, 505887.18832217, 505903.86034668, ...,513537.63893626, 513551.79492562, 513565.94575266],...,[505848.63294728, 505821.56244734, 505794.49931117, ...,493315.09006551, 493291.75578302, 493268.42915981],[505853.00570542, 505834.67887572, 505816.3569482 , ...,497357.45195698, 497341.61294089, 497325.77902593],[505857.38125482, 505847.80367334, 505838.22852643, ...,501401.84037494, 501393.49917759, 501385.16051708]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 7.002e+06</div><input id='attrs-54ed5614-8c61-4ec4-9c4f-d974029e9049' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54ed5614-8c61-4ec4-9c4f-d974029e9049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca07149c-5947-42be-97d7-44c1eaf9d66e' class='xr-var-data-in' type='checkbox'><label for='data-ca07149c-5947-42be-97d7-44c1eaf9d66e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752335.28235866, 6752836.12046057, 6753336.95858554, ...,7001183.44410077, 7001683.86217102, 7002184.27852576],[6752335.25143489, 6752836.02770327, 6753336.80401338, ...,7001154.83326226, 7001655.19785292, 7002155.56074288],[6752335.14424585, 6752835.70618237, 6753336.26822222, ...,7001055.39439877, 7001555.57278416, 7002055.74951997],...,[6752334.91756028, 6752835.02621668, 6753335.13509774, ...,7000844.3256128 , 7001344.10779187, 7001843.88842891],[6752335.11539593, 6752835.61964104, 6753336.12400074, ...,7001028.17105128, 7001528.29788754, 7002028.42308766],[6752335.23700773, 6752835.98442602, 6753336.73189166, ...,7001141.21987378, 7001641.55868747, 7002141.89580717]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-dce7bc9b-1f5b-4c53-859e-df2ca3610bcf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dce7bc9b-1f5b-4c53-859e-df2ca3610bcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ff85155-f82c-4616-8531-265e2a8608f8' class='xr-var-data-in' type='checkbox'><label for='data-3ff85155-f82c-4616-8531-265e2a8608f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-4c336a1a-1f71-4e38-aaed-38b6a4715cb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c336a1a-1f71-4e38-aaed-38b6a4715cb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d68f1c7-845c-445b-9177-f516a0e80f12' class='xr-var-data-in' type='checkbox'><label for='data-6d68f1c7-845c-445b-9177-f516a0e80f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-2c33d465-4138-459a-a55c-81ebb7b81f11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c33d465-4138-459a-a55c-81ebb7b81f11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3927bc9-7113-49ca-b01e-d9e6415df2c7' class='xr-var-data-in' type='checkbox'><label for='data-f3927bc9-7113-49ca-b01e-d9e6415df2c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-e78cb5f5-d0a1-44f3-bae5-ee74f572b1ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e78cb5f5-d0a1-44f3-bae5-ee74f572b1ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-096e129e-c4b0-4f24-a85b-48f7fb74f272' class='xr-var-data-in' type='checkbox'><label for='data-096e129e-c4b0-4f24-a85b-48f7fb74f272' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3</div><input id='attrs-ad10e05e-4e24-4701-8da3-f63b9ce88f11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad10e05e-4e24-4701-8da3-f63b9ce88f11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73bf3100-d0fa-4f07-85e8-ff90a0864f57' class='xr-var-data-in' type='checkbox'><label for='data-73bf3100-d0fa-4f07-85e8-ff90a0864f57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.3)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c97097ee-0b9c-4a69-be48-c92f4733b5d0' class='xr-section-summary-in' type='checkbox' checked /><label for='section-c97097ee-0b9c-4a69-be48-c92f4733b5d0' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:00:08.912507648 .....</div><input id='attrs-6bc48456-adcb-4587-8643-494108c8d381' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6bc48456-adcb-4587-8643-494108c8d381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e93c9d2-cffa-492d-86b7-f8646296a66f' class='xr-var-data-in' type='checkbox'><label for='data-9e93c9d2-cffa-492d-86b7-f8646296a66f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:00:08.912507648&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:00:09.029174272&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 500), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.17 nan nan nan ... nan nan nan</div><input id='attrs-07995a96-efe0-44d6-9549-645bcd50071b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07995a96-efe0-44d6-9549-645bcd50071b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c00d93a-44c1-4bb2-a8f3-b2ef71f4e986' class='xr-var-data-in' type='checkbox'><label for='data-7c00d93a-44c1-4bb2-a8f3-b2ef71f4e986' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.17,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[0.41,  nan,  nan, ...,  nan,  nan,  nan],...,[ nan,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.06 nan nan nan ... nan nan nan</div><input id='attrs-b989c9e2-36c8-4eb2-8383-296bfe0802fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b989c9e2-36c8-4eb2-8383-296bfe0802fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42ce0d0c-ce32-4e9f-b376-f7e64cc36f87' class='xr-var-data-in' type='checkbox'><label for='data-42ce0d0c-ce32-4e9f-b376-f7e64cc36f87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.06,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-8.95,   nan,   nan, ...,   nan,   nan,   nan],...,[  nan,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-e220cb2c-d053-450a-ad74-27b50fe07d81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e220cb2c-d053-450a-ad74-27b50fe07d81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c57a891-d433-4199-a4d5-1215a4976c62' class='xr-var-data-in' type='checkbox'><label for='data-0c57a891-d433-4199-a4d5-1215a4976c62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4059 nan nan nan ... nan nan nan</div><input id='attrs-8340c7e6-7a58-4ad1-9d82-040e8ad8a318' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8340c7e6-7a58-4ad1-9d82-040e8ad8a318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7968bda7-f1d5-4943-8dbd-e9bbb1c69686' class='xr-var-data-in' type='checkbox'><label for='data-7968bda7-f1d5-4943-8dbd-e9bbb1c69686' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.4059329,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[0.1723559,       nan,       nan, ...,       nan,       nan,nan],...,[      nan,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>98.02 nan nan nan ... nan nan nan</div><input id='attrs-1358c1c5-505a-4747-81d2-42606ec199b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1358c1c5-505a-4747-81d2-42606ec199b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59db79da-f47a-41bd-bd58-be44fdff7bbd' class='xr-var-data-in' type='checkbox'><label for='data-59db79da-f47a-41bd-bd58-be44fdff7bbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 98.02301,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[108.55373,       nan,       nan, ...,       nan,       nan,nan],...,[      nan,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-487406ea-8a25-4a39-bb44-c3d485372fb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-487406ea-8a25-4a39-bb44-c3d485372fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7cd0481-ac89-404a-84c9-f89ddcec4451' class='xr-var-data-in' type='checkbox'><label for='data-c7cd0481-ac89-404a-84c9-f89ddcec4451' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-7343cc4c-7607-4c01-83e3-612e72cf2caa' type='checkbox' checked /><label for='group-7343cc4c-7607-4c01-83e3-612e72cf2caa' title='Expand/collapse group'>/sweep_1<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-edd709c3-8c05-4437-b589-45a2abeb3003' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-edd709c3-8c05-4437-b589-45a2abeb3003' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 500</li></ul></div></li><li class='xr-section-item'><input id='section-bbb334e1-06ab-4d90-b634-c207a2e62023' class='xr-section-summary-in' type='checkbox' checked /><label for='section-bbb334e1-06ab-4d90-b634-c207a2e62023' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-44f7a653-5835-4e7d-b847-f3df6ecd0377' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44f7a653-5835-4e7d-b847-f3df6ecd0377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b7d8c60-0809-4cbd-a1e1-e2616820f545' class='xr-var-data-in' type='checkbox'><label for='data-5b7d8c60-0809-4cbd-a1e1-e2616820f545' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 357., 358., 359.], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 2.492e+05 2.498e+05</div><input id='attrs-e98f5dda-8858-4863-a9ce-5d1f5379906d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e98f5dda-8858-4863-a9ce-5d1f5379906d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2227464-78ab-4584-a326-8f039b617a24' class='xr-var-data-in' type='checkbox'><label for='data-e2227464-78ab-4584-a326-8f039b617a24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 248750., 249250., 249750.],shape=(500,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.92 ... 63.14 63.15</div><input id='attrs-b9793543-3d84-46d0-ad35-a1ae3e34ff8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9793543-3d84-46d0-ad35-a1ae3e34ff8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48e37ba6-27f2-4505-8d1e-f21050e28833' class='xr-var-data-in' type='checkbox'><label for='data-48e37ba6-27f2-4505-8d1e-f21050e28833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90611815, 60.91061442, 60.91511068, ..., 63.13932732,63.14381652, 63.1483057 ],[60.90611781, 60.91061339, 60.91510897, ..., 63.13898685,63.14347537, 63.14796386],[60.90611678, 60.91061031, 60.91510383, ..., 63.13796554,63.14245201, 63.14693845],...,[60.90611507, 60.91060517, 60.91509527, ..., 63.13626371,63.14074675, 63.14522978],[60.90611678, 60.91061031, 60.91510383, ..., 63.13796554,63.14245201, 63.14693845],[60.90611781, 60.91061339, 60.91510897, ..., 63.13898685,63.14347537, 63.14796386]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.03 27.03</div><input id='attrs-443403a2-e565-4004-ab04-346a0e4759e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-443403a2-e565-4004-ab04-346a0e4759e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6551224f-995f-4d2f-889e-87ba3fde8cd2' class='xr-var-data-in' type='checkbox'><label for='data-6551224f-995f-4d2f-889e-87ba3fde8cd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806007, 27.10806007, 27.10806007, ..., 27.10806007,27.10806007, 27.10806007],[27.10814075, 27.10830212, 27.10846349, ..., 27.18829039,27.18845151, 27.18861262],[27.10822141, 27.1085441 , 27.10886679, ..., 27.26849627,27.26881846, 27.26914064],...,[27.10781811, 27.10733419, 27.10685028, ..., 26.86746684,26.86698369, 26.86650054],[27.10789872, 27.10757603, 27.10725334, ..., 26.94762386,26.94730167, 26.94697949],[27.10797938, 27.10781801, 27.10765664, ..., 27.02782974,27.02766863, 27.02750751]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>142.1 148.2 ... 6.838e+03 6.859e+03</div><input id='attrs-b0e749c8-0874-4926-9e07-9d72b422ca96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0e749c8-0874-4926-9e07-9d72b422ca96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c329adf-25b9-4f15-9d4f-d43f0ffc4861' class='xr-var-data-in' type='checkbox'><label for='data-6c329adf-25b9-4f15-9d4f-d43f0ffc4861' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 142.05792844,  148.19585466,  154.36320662, ..., 6817.43671458,6838.18513233, 6858.96290641],[ 142.05792844,  148.19585466,  154.36320662, ..., 6817.43671458,6838.18513233, 6858.96290641],[ 142.05792844,  148.19585466,  154.36320662, ..., 6817.43671458,6838.18513233, 6858.96290641],...,[ 142.05792844,  148.19585466,  154.36320662, ..., 6817.43671458,6838.18513233, 6858.96290641],[ 142.05792844,  148.19585466,  154.36320662, ..., 6817.43671458,6838.18513233, 6858.96290641],[ 142.05792844,  148.19585466,  154.36320662, ..., 6817.43671458,6838.18513233, 6858.96290641]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -4.347e+03 -4.356e+03</div><input id='attrs-f6fca90a-7511-4f73-9542-baf3c65ad50f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6fca90a-7511-4f73-9542-baf3c65ad50f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9179679-7fa5-4ba1-bfb3-68755f9a00b2' class='xr-var-data-in' type='checkbox'><label for='data-e9179679-7fa5-4ba1-bfb3-68755f9a00b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,0.00000000e+00,  0.00000000e+00,  0.00000000e+00],[ 4.36277442e+00,  1.30883138e+01,  2.18138406e+01, ...,4.33817104e+03,  4.34688287e+03,  4.35559465e+03],[ 8.72421990e+00,  2.61726408e+01,  4.36210364e+01, ...,8.67502064e+03,  8.69244164e+03,  8.70986255e+03],...,[-1.30830079e+01, -3.92489954e+01, -6.54149449e+01, ...,-1.30092277e+04, -1.30353526e+04, -1.30614773e+04],[-8.72421990e+00, -2.61726408e+01, -4.36210364e+01, ...,-8.67502064e+03, -8.69244164e+03, -8.70986255e+03],[-4.36277442e+00, -1.30883138e+01, -2.18138406e+01, ...,-4.33817104e+03, -4.34688287e+03, -4.35559465e+03]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>250.0 749.9 ... 2.49e+05 2.495e+05</div><input id='attrs-e6a9f542-05d9-4fce-8ea3-1c13f9cdc697' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6a9f542-05d9-4fce-8ea3-1c13f9cdc697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2bcf809-fc02-4f73-997a-3079b08f6d14' class='xr-var-data-in' type='checkbox'><label for='data-e2bcf809-fc02-4f73-997a-3079b08f6d14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   249.98125246,    749.94321637,   1249.90445604, ...,248571.51120205, 249070.68746192, 249569.8612835 ],[   249.94317911,    749.82899639,   1249.71408954, ...,248533.65255691, 249032.75278982, 249531.8505848 ],[   249.82897064,    749.48637123,   1249.14304802, ...,248420.0881536 , 248918.96032878, 249417.83006715],...,[   249.63866184,    748.91544526,   1248.19150543, ...,248230.85258494, 248729.3447411 , 249227.8344623 ],[   249.82897064,    749.48637123,   1249.14304802, ...,248420.0881536 , 248918.96032878, 249417.83006715],[   249.94317911,    749.82899639,   1249.71408954, ...,248533.65255691, 249032.75278982, 249531.8505848 ]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.058 9.196 ... 6.699e+03 6.72e+03</div><input id='attrs-29ddf48e-a67f-44f2-b2f7-bec1d9fe5954' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29ddf48e-a67f-44f2-b2f7-bec1d9fe5954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3118cb07-3a83-4eeb-8836-30efb54bc00c' class='xr-var-data-in' type='checkbox'><label for='data-3118cb07-3a83-4eeb-8836-30efb54bc00c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3.05792844e+00, 9.19585466e+00, 1.53632066e+01, ...,6.67843671e+03, 6.69918513e+03, 6.71996291e+03],[3.05792844e+00, 9.19585466e+00, 1.53632066e+01, ...,6.67843671e+03, 6.69918513e+03, 6.71996291e+03],[3.05792844e+00, 9.19585466e+00, 1.53632066e+01, ...,6.67843671e+03, 6.69918513e+03, 6.71996291e+03],...,[3.05792844e+00, 9.19585466e+00, 1.53632066e+01, ...,6.67843671e+03, 6.69918513e+03, 6.71996291e+03],[3.05792844e+00, 9.19585466e+00, 1.53632066e+01, ...,6.67843671e+03, 6.69918513e+03, 6.71996291e+03],[3.05792844e+00, 9.19585466e+00, 1.53632066e+01, ...,6.67843671e+03, 6.69918513e+03, 6.71996291e+03]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.014e+05</div><input id='attrs-ba0e205a-7bf2-42be-8d00-fa75a4eea411' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba0e205a-7bf2-42be-8d00-fa75a4eea411' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1f752da-3ad3-4218-ac46-c7018aaee46e' class='xr-var-data-in' type='checkbox'><label for='data-b1f752da-3ad3-4218-ac46-c7018aaee46e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505861.75828815, 505860.932921  , 505860.10751845, ...,505447.41386073, 505446.57227554, 505445.73066053],[505866.13515441, 505874.06166174, 505881.98565612, ...,509492.00754504, 509498.66222217, 509505.31429836],[505870.51081341, 505887.18678295, 505903.85776496, ...,513535.77011387, 513549.92013139, 513564.06497984],...,[505848.63377478, 505821.56494587, 505794.50350205, ...,493318.17050936, 493294.84618304, 493271.52952788],[505853.00626564, 505834.68056723, 505816.35978546, ...,497359.54291602, 497343.710671  , 497327.88353526],[505857.38154759, 505847.80455733, 505838.23000921, ...,501402.94151184, 501394.60390006, 501386.26882955]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 7.002e+06</div><input id='attrs-13f1f9c0-3d8d-4ecb-94f5-67784ec77052' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13f1f9c0-3d8d-4ecb-94f5-67784ec77052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-578aa754-6b07-4851-aea9-fe4cba0e7761' class='xr-var-data-in' type='checkbox'><label for='data-578aa754-6b07-4851-aea9-fe4cba0e7761' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752335.26705082, 6752836.07422837, 6753336.88101739, ...,7001117.39422615, 7001617.57729884, 7002117.75824711],[6752335.23612895, 6752835.98147678, 6753336.7264548 , ...,7001088.79044749, 7001588.92006376, 7002089.04757044],[6752335.12894646, 6752835.65997566, 6753336.19069683, ...,7000989.37616488, 7001489.31965665, 7001989.26109008],...,[6752334.90227475, 6752834.98005181, 6753335.05764252, ...,7000778.35968187, 7001277.90713935, 7001777.45264625],[6752335.1000983 , 6752835.57343966, 6753336.04648427, ...,7000962.15962232, 7001462.05158742, 7001961.94150763],[6752335.22170267, 6752835.9382022 , 6753336.65433755, ...,7001075.18046178, 7001575.28431231, 7002075.38605999]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-327b70c0-c908-4b7b-be8d-70a35e5e091f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-327b70c0-c908-4b7b-be8d-70a35e5e091f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8265027-b4df-4225-b60d-98a3ccf09463' class='xr-var-data-in' type='checkbox'><label for='data-a8265027-b4df-4225-b60d-98a3ccf09463' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-44c86c6b-d25b-4d84-98da-2757f0a02c6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44c86c6b-d25b-4d84-98da-2757f0a02c6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e936a4d6-a5c7-4411-8230-e777285638ad' class='xr-var-data-in' type='checkbox'><label for='data-e936a4d6-a5c7-4411-8230-e777285638ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-7e460f4d-84d4-4787-bfb4-8fe18dfe6417' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e460f4d-84d4-4787-bfb4-8fe18dfe6417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edb0bd2a-408c-4d05-a63e-dd18e3893e51' class='xr-var-data-in' type='checkbox'><label for='data-edb0bd2a-408c-4d05-a63e-dd18e3893e51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-c73169e1-5b33-4d26-b75c-7050f5d3f6f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c73169e1-5b33-4d26-b75c-7050f5d3f6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eb2e04e-d288-4e1f-8ece-e0cca1c50812' class='xr-var-data-in' type='checkbox'><label for='data-0eb2e04e-d288-4e1f-8ece-e0cca1c50812' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7</div><input id='attrs-0b3cca8b-568d-455e-91b3-cdd4023802cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b3cca8b-568d-455e-91b3-cdd4023802cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d40172-b872-411e-aad3-529df2658917' class='xr-var-data-in' type='checkbox'><label for='data-99d40172-b872-411e-aad3-529df2658917' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.7)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-052a0db7-1002-4e63-8244-8ff10845eb61' class='xr-section-summary-in' type='checkbox' checked /><label for='section-052a0db7-1002-4e63-8244-8ff10845eb61' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:00:29.687506176 .....</div><input id='attrs-3130dd4f-983e-48e6-8d35-680a1b358402' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3130dd4f-983e-48e6-8d35-680a1b358402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f006b5e-d416-41ea-aa2b-0ace34aad56a' class='xr-var-data-in' type='checkbox'><label for='data-5f006b5e-d416-41ea-aa2b-0ace34aad56a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:00:29.687506176&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[&#x27;2024-06-01T12:00:29.512506112&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:00:29.629172736&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 500), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.23 nan nan nan ... nan nan nan</div><input id='attrs-f1c1d8be-75ee-4d34-ac2e-e683e1b8f800' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1c1d8be-75ee-4d34-ac2e-e683e1b8f800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b3dbe93-7d20-46ea-9864-454a23b29246' class='xr-var-data-in' type='checkbox'><label for='data-4b3dbe93-7d20-46ea-9864-454a23b29246' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.23,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],...,[0.23,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[0.34,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-6.25 nan nan nan ... nan nan nan</div><input id='attrs-d44dca16-1f28-47cb-bdd4-f2668f4729a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d44dca16-1f28-47cb-bdd4-f2668f4729a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a35b7ae9-d489-4dea-bb17-6659dcae8b82' class='xr-var-data-in' type='checkbox'><label for='data-a35b7ae9-d489-4dea-bb17-6659dcae8b82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-6.25,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],...,[-8.65,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-4.61,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-b35052be-723d-480d-84cc-219a1b56783a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b35052be-723d-480d-84cc-219a1b56783a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3083568-57e8-4d5d-bfdd-643d50a166d9' class='xr-var-data-in' type='checkbox'><label for='data-b3083568-57e8-4d5d-bfdd-643d50a166d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4484 nan nan nan ... nan nan nan</div><input id='attrs-553f75a8-3fa2-4d45-a8c7-4102b2326146' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-553f75a8-3fa2-4d45-a8c7-4102b2326146' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5f3d78e-ec28-4568-8d1f-58a591a41db9' class='xr-var-data-in' type='checkbox'><label for='data-d5f3d78e-ec28-4568-8d1f-58a591a41db9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.4483848 ,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[0.23810904,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.4396716 ,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>74.04 nan nan nan ... nan nan nan</div><input id='attrs-98676fd9-f138-44b1-971f-86b28de5f16a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98676fd9-f138-44b1-971f-86b28de5f16a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef8d8b06-85fd-4a95-baf1-de2309d301bd' class='xr-var-data-in' type='checkbox'><label for='data-ef8d8b06-85fd-4a95-baf1-de2309d301bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[74.04462,      nan,      nan, ...,      nan,      nan,      nan],[     nan,      nan,      nan, ...,      nan,      nan,      nan],[     nan,      nan,      nan, ...,      nan,      nan,      nan],...,[73.80291,      nan,      nan, ...,      nan,      nan,      nan],[     nan,      nan,      nan, ...,      nan,      nan,      nan],[92.24952,      nan,      nan, ...,      nan,      nan,      nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-66c2548d-16f1-437f-9ad1-9764fa0c7b66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66c2548d-16f1-437f-9ad1-9764fa0c7b66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4982d337-7853-4c1e-88d1-1248929fff19' class='xr-var-data-in' type='checkbox'><label for='data-4982d337-7853-4c1e-88d1-1248929fff19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-63478057-2b02-44ce-a2cc-7ac9dac53ccf' type='checkbox' checked /><label for='group-63478057-2b02-44ce-a2cc-7ac9dac53ccf' title='Expand/collapse group'>/sweep_2<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-223a9589-0be6-4693-be97-6fc3cdad4149' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-223a9589-0be6-4693-be97-6fc3cdad4149' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 500</li></ul></div></li><li class='xr-section-item'><input id='section-f8556037-4780-4eaf-bc5e-2c1a564659c9' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f8556037-4780-4eaf-bc5e-2c1a564659c9' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-45493316-352b-445e-81fd-4d00c0224290' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45493316-352b-445e-81fd-4d00c0224290' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78a99356-7b0e-48a3-ba04-b966ffd2058a' class='xr-var-data-in' type='checkbox'><label for='data-78a99356-7b0e-48a3-ba04-b966ffd2058a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 357., 358., 359.], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 2.492e+05 2.498e+05</div><input id='attrs-4ad412d0-9fdb-435c-81b6-778854875965' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ad412d0-9fdb-435c-81b6-778854875965' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd13187-4c41-422e-b328-25674049f46d' class='xr-var-data-in' type='checkbox'><label for='data-bfd13187-4c41-422e-b328-25674049f46d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 248750., 249250., 249750.],shape=(500,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.92 ... 63.14 63.15</div><input id='attrs-80ae74e4-94b3-40d6-b849-b1f3c13db7e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80ae74e4-94b3-40d6-b849-b1f3c13db7e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3369d695-23ee-4f7c-81df-4ebee1138d40' class='xr-var-data-in' type='checkbox'><label for='data-3369d695-23ee-4f7c-81df-4ebee1138d40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90611755, 60.9106126 , 60.91510764, ..., 63.13781605,63.14230038, 63.14678468],[60.90611721, 60.91061157, 60.91510593, ..., 63.13747581,63.14195946, 63.14644308],[60.90611618, 60.91060849, 60.9151008 , ..., 63.13645519,63.14093679, 63.14541836],...,[60.90611447, 60.91060336, 60.91509224, ..., 63.1347545 ,63.13923269, 63.14371085],[60.90611618, 60.91060849, 60.9151008 , ..., 63.13645519,63.14093679, 63.14541836],[60.90611721, 60.91061157, 60.91510593, ..., 63.13747581,63.14195946, 63.14644308]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.03 27.03</div><input id='attrs-8e938695-1f9e-4264-af76-a38799bbd071' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e938695-1f9e-4264-af76-a38799bbd071' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29be9ce0-0262-4124-a6af-bdb4b20a3bef' class='xr-var-data-in' type='checkbox'><label for='data-29be9ce0-0262-4124-a6af-bdb4b20a3bef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806007, 27.10806007, 27.10806007, ..., 27.10806007,27.10806007, 27.10806007],[27.10814073, 27.10830206, 27.10846338, ..., 27.18823615,27.18839709, 27.18855803],[27.10822137, 27.10854397, 27.10886658, ..., 27.26838781,27.26870965, 27.26903148],...,[27.10781817, 27.10733439, 27.10685061, ..., 26.8676295 ,26.86714687, 26.86666424],[27.10789876, 27.10757616, 27.10725355, ..., 26.94773232,26.94741049, 26.94708865],[27.1079794 , 27.10781807, 27.10765675, ..., 27.02788398,27.02772304, 27.0275621 ]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>145.5 158.7 ... 1.031e+04 1.034e+04</div><input id='attrs-dca2b479-a105-4de6-80bb-66c23a0a97e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dca2b479-a105-4de6-80bb-66c23a0a97e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e400357-5a84-40f1-9569-c79fbde988ce' class='xr-var-data-in' type='checkbox'><label for='data-4e400357-5a84-40f1-9569-c79fbde988ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  145.54791333,   158.66579748,   171.81309148, ...,10286.53790389, 10314.24948412, 10341.99036898],[  145.54791333,   158.66579748,   171.81309148, ...,10286.53790389, 10314.24948412, 10341.99036898],[  145.54791333,   158.66579748,   171.81309148, ...,10286.53790389, 10314.24948412, 10341.99036898],...,[  145.54791333,   158.66579748,   171.81309148, ...,10286.53790389, 10314.24948412, 10341.99036898],[  145.54791333,   158.66579748,   171.81309148, ...,10286.53790389, 10314.24948412, 10341.99036898],[  145.54791333,   158.66579748,   171.81309148, ...,10286.53790389, 10314.24948412, 10341.99036898]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -4.344e+03 -4.353e+03</div><input id='attrs-68761f44-814e-4869-a9f1-6ecfaffbef1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68761f44-814e-4869-a9f1-6ecfaffbef1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2c915f8-f30f-4b49-8de1-6187d3cd5bb8' class='xr-var-data-in' type='checkbox'><label for='data-b2c915f8-f30f-4b49-8de1-6187d3cd5bb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,0.00000000e+00,  0.00000000e+00,  0.00000000e+00],[ 4.36160312e+00,  1.30847892e+01,  2.18079483e+01, ...,4.33523824e+03,  4.34394062e+03,  4.35264294e+03],[ 8.72187766e+00,  2.61655926e+01,  4.36092536e+01, ...,8.66915592e+03,  8.68655803e+03,  8.70396002e+03],...,[-1.30794954e+01, -3.92384257e+01, -6.53972751e+01, ...,-1.30004329e+04, -1.30265294e+04, -1.30526258e+04],[-8.72187766e+00, -2.61655926e+01, -4.36092536e+01, ...,-8.66915592e+03, -8.68655803e+03, -8.70396002e+03],[-4.36160312e+00, -1.30847892e+01, -2.18079483e+01, ...,-4.33523824e+03, -4.34394062e+03, -4.35264294e+03]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>249.9 749.7 ... 2.489e+05 2.494e+05</div><input id='attrs-f5e35660-9d9f-46b5-964c-6eae87ef7ce0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5e35660-9d9f-46b5-964c-6eae87ef7ce0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-394db07c-adfc-449e-9b0a-a2ce41930c19' class='xr-var-data-in' type='checkbox'><label for='data-394db07c-adfc-449e-9b0a-a2ce41930c19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   249.91413864,    749.741259  ,   1249.56683391, ...,248403.46526477, 248902.10027104, 249400.73202616],[   249.87607551,    749.62706977,   1249.37651883, ...,248365.63221384, 248864.19127558, 249362.74708667],[   249.7618977 ,    749.28453688,   1248.80563156, ...,248252.14458537, 248750.47583666, 249248.80383879],...,[   249.57164   ,    748.71376466,   1247.854346  , ...,248063.03694878, 248560.9885931 , 249058.93699073],[   249.7618977 ,    749.28453688,   1248.80563156, ...,248252.14458537, 248750.47583666, 249248.80383879],[   249.87607551,    749.62706977,   1249.37651883, ...,248365.63221384, 248864.19127558, 249362.74708667]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.548 19.67 ... 1.018e+04 1.02e+04</div><input id='attrs-32e2b420-5507-4d22-b3c3-dfb12edf9b34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32e2b420-5507-4d22-b3c3-dfb12edf9b34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb3810b6-8402-4d12-bc81-8e3badca5326' class='xr-var-data-in' type='checkbox'><label for='data-bb3810b6-8402-4d12-bc81-8e3badca5326' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6.54791333e+00, 1.96657975e+01, 3.28130915e+01, ...,1.01475379e+04, 1.01752495e+04, 1.02029904e+04],[6.54791333e+00, 1.96657975e+01, 3.28130915e+01, ...,1.01475379e+04, 1.01752495e+04, 1.02029904e+04],[6.54791333e+00, 1.96657975e+01, 3.28130915e+01, ...,1.01475379e+04, 1.01752495e+04, 1.02029904e+04],...,[6.54791333e+00, 1.96657975e+01, 3.28130915e+01, ...,1.01475379e+04, 1.01752495e+04, 1.02029904e+04],[6.54791333e+00, 1.96657975e+01, 3.28130915e+01, ...,1.01475379e+04, 1.01752495e+04, 1.02029904e+04],[6.54791333e+00, 1.96657975e+01, 3.28130915e+01, ...,1.01475379e+04, 1.01752495e+04, 1.02029904e+04]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.014e+05</div><input id='attrs-66a63aba-a3a5-4277-bc68-b86f4563bdd8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66a63aba-a3a5-4277-bc68-b86f4563bdd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2fd7ba2-d00e-4ebc-888c-b5a9b6d69162' class='xr-var-data-in' type='checkbox'><label for='data-c2fd7ba2-d00e-4ebc-888c-b5a9b6d69162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505861.75839894, 505860.93325441, 505860.10807586, ...,505447.6971698 , 505446.85650856, 505446.01581895],[505866.1340902 , 505874.05846037, 505881.98030592, ...,509489.76669347, 509496.41502006, 509503.06074049],[505870.50857453, 505887.18004777, 505903.84650877, ...,513531.00541272, 513545.14180518, 513559.2730167 ],...,[505848.63740894, 505821.57587872, 505794.52177407, ...,493326.02426225, 493302.72268097, 493279.42879231],[505853.00872597, 505834.68796883, 505816.37215577, ...,497364.87391788, 497349.05714483, 497333.24549584],[505857.38283334, 505847.80842542, 505838.23647405, ...,501405.74890218, 501397.4194886 , 501389.0926244 ]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 7.002e+06</div><input id='attrs-7f53307b-4bbc-4df1-a710-2febff0fef61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f53307b-4bbc-4df1-a710-2febff0fef61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99ef0d23-e83a-4361-afea-29ec51e68ddd' class='xr-var-data-in' type='checkbox'><label for='data-99ef0d23-e83a-4361-afea-29ec51e68ddd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752335.19982356, 6752835.87192951, 6753336.54282415, ...,7000949.00942061, 7001448.65004062, 7001948.28772105],[6752335.16890999, 6752835.77920289, 6753336.3883033 , ...,7000920.42364113, 7001420.01085778, 7001919.59514974],[6752335.06175627, 6752835.45778833, 6753335.85268999, ...,7000821.07202802, 7001320.47330502, 7001819.87170877],...,[6752334.83514541, 6752834.77804754, 6753334.71994164, ...,7000610.18889239, 7001109.19452885, 7001608.19740027],[6752335.03291586, 6752835.37127563, 6753335.70851638, ...,7000793.87283471, 7001293.22263645, 7001792.56957847],[6752335.15448759, 6752835.73593996, 6753336.31620552, ...,7000906.82233087, 7001406.38380748, 7001905.94236617]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-07b88891-d470-407c-8821-8f47731e6ab6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07b88891-d470-407c-8821-8f47731e6ab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cf9ff36-c119-4074-b4e1-407477721e5f' class='xr-var-data-in' type='checkbox'><label for='data-4cf9ff36-c119-4074-b4e1-407477721e5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-c7ee33f1-0e3c-4ae0-812c-1287f1c54e6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7ee33f1-0e3c-4ae0-812c-1287f1c54e6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a5371d8-9d84-43e2-b28d-9f98ab706a64' class='xr-var-data-in' type='checkbox'><label for='data-6a5371d8-9d84-43e2-b28d-9f98ab706a64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-b7efd2ad-2aae-4a3f-a060-a9fc773d2324' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7efd2ad-2aae-4a3f-a060-a9fc773d2324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93de938b-6706-40c3-9132-64194108bf10' class='xr-var-data-in' type='checkbox'><label for='data-93de938b-6706-40c3-9132-64194108bf10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-0852e08d-8b53-4c1d-a957-b4d075bad33c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0852e08d-8b53-4c1d-a957-b4d075bad33c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1b57b1-e154-41fd-ad27-cd1f36cd3b46' class='xr-var-data-in' type='checkbox'><label for='data-aa1b57b1-e154-41fd-ad27-cd1f36cd3b46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-84a032ec-7211-42f8-9ced-c477ccd81968' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84a032ec-7211-42f8-9ced-c477ccd81968' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77c96373-d42c-45c3-be3c-481b342fbd33' class='xr-var-data-in' type='checkbox'><label for='data-77c96373-d42c-45c3-be3c-481b342fbd33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-badba3c9-7a2b-48b2-acb1-cb5528c97fd9' class='xr-section-summary-in' type='checkbox' checked /><label for='section-badba3c9-7a2b-48b2-acb1-cb5528c97fd9' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:00:50.412503808 .....</div><input id='attrs-3f1ef044-a2bf-438f-a343-6b6f8d771aed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f1ef044-a2bf-438f-a343-6b6f8d771aed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d8f944d-db7a-4641-a0c0-8cde68cf4269' class='xr-var-data-in' type='checkbox'><label for='data-2d8f944d-db7a-4641-a0c0-8cde68cf4269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:00:50.412503808&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:00:50.529170432&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:00:50.295836928&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:00:50.354170368&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 500), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.52 nan nan nan ... nan nan nan</div><input id='attrs-33e2830e-9059-439a-a341-90f9243b1bd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33e2830e-9059-439a-a341-90f9243b1bd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56e20d85-bd7c-43c5-bf70-ed11152c3760' class='xr-var-data-in' type='checkbox'><label for='data-56e20d85-bd7c-43c5-bf70-ed11152c3760' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.52,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[0.71,  nan,  nan, ...,  nan,  nan,  nan],...,[ nan,  nan,  nan, ...,  nan,  nan,  nan],[1.45,  nan,  nan, ...,  nan,  nan,  nan],[0.23,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.65 nan nan nan ... nan nan nan</div><input id='attrs-1daad2b4-d5d8-4a34-ac9f-d73091b4827f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1daad2b4-d5d8-4a34-ac9f-d73091b4827f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-701cce21-7f0d-4c30-864b-05aeb7066787' class='xr-var-data-in' type='checkbox'><label for='data-701cce21-7f0d-4c30-864b-05aeb7066787' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-8.65,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-5.13,   nan,   nan, ...,   nan,   nan,   nan],...,[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-4.91,   nan,   nan, ...,   nan,   nan,   nan],[-6.28,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-f5a41f32-ab5a-4ae5-8308-d6db9b6562f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5a41f32-ab5a-4ae5-8308-d6db9b6562f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-170ba40d-f928-413e-89b5-5109ec5d85c7' class='xr-var-data-in' type='checkbox'><label for='data-170ba40d-f928-413e-89b5-5109ec5d85c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4317 nan nan nan ... nan nan nan</div><input id='attrs-3e82753e-e4a4-4bd9-9f82-3b58edc4cda2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e82753e-e4a4-4bd9-9f82-3b58edc4cda2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d8570c8-7c6b-484b-b9a5-8e4d022ebc02' class='xr-var-data-in' type='checkbox'><label for='data-3d8570c8-7c6b-484b-b9a5-8e4d022ebc02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.43172142,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.43822196,        nan,        nan, ...,        nan,        nan,nan],...,[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.36209238,        nan,        nan, ...,        nan,        nan,nan],[0.5402469 ,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>101.9 nan nan nan ... nan nan nan</div><input id='attrs-a07de3db-1516-49b0-bad7-ec7dcdd64101' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a07de3db-1516-49b0-bad7-ec7dcdd64101' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da89d91-df41-4912-832a-c9ada828acb5' class='xr-var-data-in' type='checkbox'><label for='data-0da89d91-df41-4912-832a-c9ada828acb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[101.934265,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[ 94.996185,        nan,        nan, ...,        nan,        nan,nan],...,[       nan,        nan,        nan, ...,        nan,        nan,nan],[130.81271 ,        nan,        nan, ...,        nan,        nan,nan],[ 93.87005 ,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-c1a8cdec-f448-484e-9628-98944a1c8dad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1a8cdec-f448-484e-9628-98944a1c8dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d1922bf-d013-4b86-aeb7-e9906655154a' class='xr-var-data-in' type='checkbox'><label for='data-0d1922bf-d013-4b86-aeb7-e9906655154a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-58358ca1-2ccd-4012-988c-2fc0794a71c4' type='checkbox' checked /><label for='group-58358ca1-2ccd-4012-988c-2fc0794a71c4' title='Expand/collapse group'>/sweep_3<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-657276fa-993a-4c11-8a36-c0682cf654ab' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-657276fa-993a-4c11-8a36-c0682cf654ab' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 500</li></ul></div></li><li class='xr-section-item'><input id='section-a140d377-a05d-4bfb-9da3-1ee1d44986f3' class='xr-section-summary-in' type='checkbox' checked /><label for='section-a140d377-a05d-4bfb-9da3-1ee1d44986f3' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-00696453-c71d-4cf9-bb36-498ac485ba82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00696453-c71d-4cf9-bb36-498ac485ba82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca637f88-24e8-4d4c-bf6d-69be66a6b622' class='xr-var-data-in' type='checkbox'><label for='data-ca637f88-24e8-4d4c-bf6d-69be66a6b622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 357., 358., 359.], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 2.492e+05 2.498e+05</div><input id='attrs-0a2bf7e8-cee6-4880-a4a8-b0e050578fec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a2bf7e8-cee6-4880-a4a8-b0e050578fec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-439d569f-aae0-4c7f-9291-a2b4f1c2455f' class='xr-var-data-in' type='checkbox'><label for='data-439d569f-aae0-4c7f-9291-a2b4f1c2455f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 248750., 249250., 249750.],shape=(500,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.92 ... 63.14 63.14</div><input id='attrs-0b014d54-bcc6-4ddd-a5f8-03310a420c67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b014d54-bcc6-4ddd-a5f8-03310a420c67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f08f05f-c560-4a2d-87d0-53d34a966bca' class='xr-var-data-in' type='checkbox'><label for='data-9f08f05f-c560-4a2d-87d0-53d34a966bca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90611524, 60.91060565, 60.91509604, ..., 63.13381465,63.13828753, 63.14276036],[60.90611489, 60.91060463, 60.91509433, ..., 63.13347502,63.13794721, 63.14241937],[60.90611387, 60.91060155, 60.91508921, ..., 63.13245622,63.13692638, 63.14139649],...,[60.90611216, 60.91059642, 60.91508066, ..., 63.13075858,63.13522533, 63.13969204],[60.90611387, 60.91060155, 60.91508921, ..., 63.13245622,63.13692638, 63.14139649],[60.90611489, 60.91060463, 60.91509433, ..., 63.13347502,63.13794721, 63.14241937]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.03 27.03</div><input id='attrs-765c8968-094a-4fa1-8703-4d311ce71b8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-765c8968-094a-4fa1-8703-4d311ce71b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0c75844-db44-4615-afa5-581ccc6bde93' class='xr-var-data-in' type='checkbox'><label for='data-d0c75844-db44-4615-afa5-581ccc6bde93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806007, 27.10806007, 27.10806007, ..., 27.10806007,27.10806007, 27.10806007],[27.10814065, 27.10830181, 27.10846297, ..., 27.18809254,27.18825307, 27.1884136 ],[27.1082212 , 27.10854347, 27.10886574, ..., 27.26810063,27.26842165, 27.26874266],...,[27.10781842, 27.10733514, 27.10685185, ..., 26.86806015,26.86757875, 26.86709736],[27.10789893, 27.10757666, 27.10725439, ..., 26.9480195 ,26.94769848, 26.94737747],[27.10797948, 27.10781832, 27.10765716, ..., 27.02802759,27.02786706, 27.02770653]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>152.1 178.3 ... 1.682e+04 1.686e+04</div><input id='attrs-c321f1da-5377-42c9-b89e-f343197b41ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c321f1da-5377-42c9-b89e-f343197b41ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6cf6c24-3e90-43c8-bb59-b1670025189f' class='xr-var-data-in' type='checkbox'><label for='data-a6cf6c24-3e90-43c8-bb59-b1670025189f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  152.08765776,   178.28498535,   204.51166216, ...,16783.34577454, 16824.09007596, 16864.86355474],[  152.08765776,   178.28498535,   204.51166216, ...,16783.34577454, 16824.09007596, 16864.86355474],[  152.08765776,   178.28498535,   204.51166216, ...,16783.34577454, 16824.09007596, 16864.86355474],...,[  152.08765776,   178.28498535,   204.51166216, ...,16783.34577454, 16824.09007596, 16864.86355474],[  152.08765776,   178.28498535,   204.51166216, ...,16783.34577454, 16824.09007596, 16864.86355474],[  152.08765776,   178.28498535,   204.51166216, ...,16783.34577454, 16824.09007596, 16864.86355474]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -4.336e+03 -4.345e+03</div><input id='attrs-64e2c599-220e-405f-9129-e2b0d932401e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64e2c599-220e-405f-9129-e2b0d932401e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ffc8bb5-8d98-435c-8725-35228c14cf40' class='xr-var-data-in' type='checkbox'><label for='data-2ffc8bb5-8d98-435c-8725-35228c14cf40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,0.00000000e+00,  0.00000000e+00,  0.00000000e+00],[ 4.35711542e+00,  1.30713060e+01,  2.17854427e+01, ...,4.32747304e+03,  4.33615319e+03,  4.34483326e+03],[ 8.71290362e+00,  2.61386303e+01,  4.35642494e+01, ...,8.65362788e+03,  8.67098555e+03,  8.68834305e+03],...,[-1.30660378e+01, -3.91979925e+01, -6.53297859e+01, ...,-1.29771468e+04, -1.30031766e+04, -1.30292063e+04],[-8.71290362e+00, -2.61386303e+01, -4.35642494e+01, ...,-8.65362788e+03, -8.67098555e+03, -8.68834305e+03],[-4.35711542e+00, -1.30713060e+01, -2.17854427e+01, ...,-4.32747304e+03, -4.33615319e+03, -4.34483326e+03]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>249.7 749.0 ... 2.484e+05 2.489e+05</div><input id='attrs-f82fc7eb-e793-443c-aa33-914b921e2b1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f82fc7eb-e793-443c-aa33-914b921e2b1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d79862c-de4a-4d60-99bc-b13c1d536aef' class='xr-var-data-in' type='checkbox'><label for='data-7d79862c-de4a-4d60-99bc-b13c1d536aef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   249.65699908,    748.96868831,   1248.2772961 , ...,247958.52956225, 248455.89098245, 248953.24763918],[   249.61897511,    748.85461675,   1248.08717743, ...,247920.76427719, 248418.04994683, 248915.33085373],[   249.50491478,    748.51243682,   1247.5168793 , ...,247807.47992566, 248304.53836672, 248801.59204721],...,[   249.31485284,    747.94225275,   1246.56657545, ...,247618.71101518, 248115.39081884, 248612.06586556],[   249.50491478,    748.51243682,   1247.5168793 , ...,247807.47992566, 248304.53836672, 248801.59204721],[   249.61897511,    748.85461675,   1248.08717743, ...,247920.76427719, 248418.04994683, 248915.33085373]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.09 39.28 ... 1.669e+04 1.673e+04</div><input id='attrs-ba270f7c-69d5-47c5-adde-22bb63a26a04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba270f7c-69d5-47c5-adde-22bb63a26a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1c9e1e6-50da-48fb-b2e9-fd1d0090323e' class='xr-var-data-in' type='checkbox'><label for='data-d1c9e1e6-50da-48fb-b2e9-fd1d0090323e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.30876578e+01, 3.92849854e+01, 6.55116622e+01, ...,1.66443458e+04, 1.66850901e+04, 1.67258636e+04],[1.30876578e+01, 3.92849854e+01, 6.55116622e+01, ...,1.66443458e+04, 1.66850901e+04, 1.67258636e+04],[1.30876578e+01, 3.92849854e+01, 6.55116622e+01, ...,1.66443458e+04, 1.66850901e+04, 1.67258636e+04],...,[1.30876578e+01, 3.92849854e+01, 6.55116622e+01, ...,1.66443458e+04, 1.66850901e+04, 1.67258636e+04],[1.30876578e+01, 3.92849854e+01, 6.55116622e+01, ...,1.66443458e+04, 1.66850901e+04, 1.67258636e+04],[1.30876578e+01, 3.92849854e+01, 6.55116622e+01, ...,1.66443458e+04, 1.66850901e+04, 1.67258636e+04]], shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.014e+05</div><input id='attrs-f5bcd12a-a7f0-48d7-bd8d-d7cb4dc46b8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5bcd12a-a7f0-48d7-bd8d-d7cb4dc46b8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dd36bf3-b15a-4a4b-a220-2b9d2ca128c3' class='xr-var-data-in' type='checkbox'><label for='data-7dd36bf3-b15a-4a4b-a220-2b9d2ca128c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505861.75882343, 505860.93452984, 505860.11020484, ...,505448.44726945, 505447.60878557, 505446.77027602],[505866.13001281, 505874.04621379, 505881.95987096, ...,509483.83217892, 509490.46580886, 509497.09682567],[505870.4999965 , 505887.1542829 , 505903.80351609, ...,513518.38710916, 513532.49193066, 513546.59155454],...,[505848.65133282, 505821.61770141, 505794.59156355, ...,493346.82280151, 493323.57401511, 493300.33295317],[505853.01815247, 505834.71628301, 505816.41940383, ...,497378.99158017, 497363.21073049, 497347.43502699],[505857.38775959, 505847.82322248, 505838.26116633, ...,501413.18340588, 501404.87304308, 501396.56524206]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 7.001e+06</div><input id='attrs-a20181fa-4995-43d2-b52b-2aa47011c84b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a20181fa-4995-43d2-b52b-2aa47011c84b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50902c63-42a7-40a1-b9a3-fb13aaa9f5e2' class='xr-var-data-in' type='checkbox'><label for='data-50902c63-42a7-40a1-b9a3-fb13aaa9f5e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752334.94224938, 6752835.09805249, 6753335.251105  , ...,7000503.17667011, 7001001.54085201, 7001499.90057735],[6752334.91136761, 6752835.0054214 , 6753335.09674356, ...,7000474.63855514, 7000972.9494571 , 7001471.2559175 ],[6752334.80432414, 6752834.68433796, 6753334.56168279, ...,7000375.45289977, 7000873.57829221, 7001371.69929486],...,[6752334.57794643, 6752834.00529747, 6753333.43010303, ...,7000164.92288631, 7000662.65355458, 7001160.37994193],[6752334.7755134 , 6752834.5979144 , 6753334.41765793, ...,7000348.29964934, 7000846.37368625, 7001344.44334698],[6752334.89696005, 6752834.96220304, 6753335.02472016, ...,7000461.06021846, 7000959.34544025, 7001457.62622729]],shape=(360, 500))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-8782dc84-d4f2-4ec6-8aa3-cd85a2b0a5c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8782dc84-d4f2-4ec6-8aa3-cd85a2b0a5c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b55cce6-09f1-4b4a-bbf0-bffb7c6a56a0' class='xr-var-data-in' type='checkbox'><label for='data-7b55cce6-09f1-4b4a-bbf0-bffb7c6a56a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-17b9f83a-94c1-48b3-b782-9147d3edaa14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17b9f83a-94c1-48b3-b782-9147d3edaa14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a600dd70-8ee0-4205-9430-0242c1aa7d97' class='xr-var-data-in' type='checkbox'><label for='data-a600dd70-8ee0-4205-9430-0242c1aa7d97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-fa0b7a11-fa2c-4ebc-a808-37cd9fc4cc9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa0b7a11-fa2c-4ebc-a808-37cd9fc4cc9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e36628ef-1213-4b8e-8be7-d79783c6a959' class='xr-var-data-in' type='checkbox'><label for='data-e36628ef-1213-4b8e-8be7-d79783c6a959' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-13e21e5f-e436-4d36-a354-ebc8f56f9156' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13e21e5f-e436-4d36-a354-ebc8f56f9156' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3623f37-eb37-4f81-add3-3901c18e69f4' class='xr-var-data-in' type='checkbox'><label for='data-f3623f37-eb37-4f81-add3-3901c18e69f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0</div><input id='attrs-4a5d4db1-d9cd-445d-ab80-4181f5bee07e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a5d4db1-d9cd-445d-ab80-4181f5bee07e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc53b38a-6680-4a6e-adb3-f605e7ac5511' class='xr-var-data-in' type='checkbox'><label for='data-cc53b38a-6680-4a6e-adb3-f605e7ac5511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ec9779d-faba-4dfd-a9f4-035a73b09ae0' class='xr-section-summary-in' type='checkbox' checked /><label for='section-0ec9779d-faba-4dfd-a9f4-035a73b09ae0' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:01:11.008334848 .....</div><input id='attrs-f517c683-3d5d-4227-9703-09c7e8b7b20f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f517c683-3d5d-4227-9703-09c7e8b7b20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9da824d-7bd5-49bc-bb65-7a4b207a061f' class='xr-var-data-in' type='checkbox'><label for='data-c9da824d-7bd5-49bc-bb65-7a4b207a061f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:01:11.008334848&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:01:11.069446144&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:01:10.886112512&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:01:10.947223552&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 500), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.62 nan nan nan ... nan nan nan</div><input id='attrs-54dbbb3e-5aa0-4405-a5a6-924a83569fbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54dbbb3e-5aa0-4405-a5a6-924a83569fbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d4eb56-07ab-433a-80a4-8f3c88132e9f' class='xr-var-data-in' type='checkbox'><label for='data-b3d4eb56-07ab-433a-80a4-8f3c88132e9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.62,  nan,  nan, ...,  nan,  nan,  nan],[1.02,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],...,[ nan,  nan,  nan, ...,  nan,  nan,  nan],[2.38,  nan,  nan, ...,  nan,  nan,  nan],[1.06,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-6.74 nan nan nan ... nan nan nan</div><input id='attrs-81a60e78-d09a-4a9d-9465-a40da736c74d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81a60e78-d09a-4a9d-9465-a40da736c74d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8120f9e-4046-4787-8402-6116d5f2b15f' class='xr-var-data-in' type='checkbox'><label for='data-c8120f9e-4046-4787-8402-6116d5f2b15f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-6.74,   nan,   nan, ...,   nan,   nan,   nan],[-6.85,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],...,[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-6.82,   nan,   nan, ...,   nan,   nan,   nan],[-6.48,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-00ccd5c1-9669-47ff-b438-9244e340443f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00ccd5c1-9669-47ff-b438-9244e340443f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25f4613f-b4d3-4c5e-8c68-44eee18a8055' class='xr-var-data-in' type='checkbox'><label for='data-25f4613f-b4d3-4c5e-8c68-44eee18a8055' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6461 nan nan nan ... nan nan nan</div><input id='attrs-ee08a4a3-c9b3-40a7-91cd-2d48895dbd51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee08a4a3-c9b3-40a7-91cd-2d48895dbd51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1503f89-537a-4013-b816-b76806f9baa1' class='xr-var-data-in' type='checkbox'><label for='data-a1503f89-537a-4013-b816-b76806f9baa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.6461477 ,        nan,        nan, ...,        nan,        nan,nan],[0.316436  ,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.4546259 ,        nan,        nan, ...,        nan,        nan,nan],[0.41209772,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>94.58 nan nan nan ... nan nan nan</div><input id='attrs-ab915491-9a82-4d3e-8edd-b5a9ae8f2536' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab915491-9a82-4d3e-8edd-b5a9ae8f2536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0d6829a-a2ad-4819-afdc-6b7c702d38bd' class='xr-var-data-in' type='checkbox'><label for='data-b0d6829a-a2ad-4819-afdc-6b7c702d38bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[94.57869 ,       nan,       nan, ...,       nan,       nan,nan],[90.656456,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],...,[      nan,       nan,       nan, ...,       nan,       nan,nan],[92.39235 ,       nan,       nan, ...,       nan,       nan,nan],[84.438   ,       nan,       nan, ...,       nan,       nan,nan]], shape=(360, 500), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-fa0f3127-0158-4cfe-8d78-73672f9bd11a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa0f3127-0158-4cfe-8d78-73672f9bd11a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd254878-f63a-4b5f-a3c9-275568007f2c' class='xr-var-data-in' type='checkbox'><label for='data-fd254878-f63a-4b5f-a3c9-275568007f2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 500), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-23804172-74bd-4a60-905c-4fd553189041' type='checkbox' checked /><label for='group-23804172-74bd-4a60-905c-4fd553189041' title='Expand/collapse group'>/sweep_4<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-acb710d8-c37e-439c-9530-59bd441119bf' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-acb710d8-c37e-439c-9530-59bd441119bf' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 367</li></ul></div></li><li class='xr-section-item'><input id='section-6c76d912-2ff9-4815-8d4b-4c0a3dd70d5e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-6c76d912-2ff9-4815-8d4b-4c0a3dd70d5e' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 357.0 358.0 359.0</div><input id='attrs-0d171c62-5e73-4c04-baca-031546d0aa06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d171c62-5e73-4c04-baca-031546d0aa06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6903d75d-d9b1-40f4-aa2c-8bc667820634' class='xr-var-data-in' type='checkbox'><label for='data-6903d75d-d9b1-40f4-aa2c-8bc667820634' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 357., 358., 359.], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 1.828e+05 1.832e+05</div><input id='attrs-141ea79b-f8f5-45fb-9347-e871f32e993f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-141ea79b-f8f5-45fb-9347-e871f32e993f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82f9843c-cc89-436f-8f91-e95ef7ac67fc' class='xr-var-data-in' type='checkbox'><label for='data-82f9843c-cc89-436f-8f91-e95ef7ac67fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 182250., 182750., 183250.],shape=(367,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.92 ... 62.54 62.54</div><input id='attrs-a1c2c518-9da2-4aa7-8e46-bcef4f1d61c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1c2c518-9da2-4aa7-8e46-bcef4f1d61c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0034dd31-78df-4288-8979-ffd135201174' class='xr-var-data-in' type='checkbox'><label for='data-0034dd31-78df-4288-8979-ffd135201174' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90610976, 60.91058921, 60.91506862, ..., 62.53335212,62.53781285, 62.54227353],[60.90610942, 60.91058819, 60.91506691, ..., 62.53310394,62.53756399, 62.54202399],[60.90610839, 60.91058512, 60.91506179, ..., 62.53235948,62.53681749, 62.54127545],...,[60.90610669, 60.91058   , 60.91505327, ..., 62.53111897,62.53557359, 62.54002815],[60.90610839, 60.91058512, 60.91506179, ..., 62.53235948,62.53681749, 62.54127545],[60.90610942, 60.91058819, 60.91506691, ..., 62.53310394,62.53756399, 62.54202399]], shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.05 27.05</div><input id='attrs-c4deeecd-9a62-4630-8c30-1730a2e64f14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4deeecd-9a62-4630-8c30-1730a2e64f14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80139c57-8cd3-4079-bd2c-5c7f827297a0' class='xr-var-data-in' type='checkbox'><label for='data-80139c57-8cd3-4079-bd2c-5c7f827297a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806007, 27.10806007, 27.10806007, ..., 27.10806007,27.10806007, 27.10806007],[27.10814045, 27.10830122, 27.10846198, ..., 27.166542  ,27.1667021 , 27.16686219],[27.10822081, 27.10854229, 27.10886378, ..., 27.22500613,27.22532627, 27.22564641],...,[27.10781901, 27.10733691, 27.10685481, ..., 26.9326855 ,26.93220541, 26.93172533],[27.10789932, 27.10757784, 27.10725636, ..., 26.991114  ,26.99079386, 26.99047372],[27.10797968, 27.10781891, 27.10765815, ..., 27.04957813,27.04941803, 27.04925794]], shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>160.8 204.4 ... 1.801e+04 1.807e+04</div><input id='attrs-da2a849c-0573-4cc9-848b-6686c2e0b524' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da2a849c-0573-4cc9-848b-6686c2e0b524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fada1134-00c8-46a6-82d4-9a11c82af3d7' class='xr-var-data-in' type='checkbox'><label for='data-fada1134-00c8-46a6-82d4-9a11c82af3d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  160.79258651,   204.39966432,   248.03594812, ...,17959.49570144, 18013.70171825, 18067.93675813],[  160.79258651,   204.39966432,   248.03594812, ...,17959.49570144, 18013.70171825, 18067.93675813],[  160.79258651,   204.39966432,   248.03594812, ...,17959.49570144, 18013.70171825, 18067.93675813],...,[  160.79258651,   204.39966432,   248.03594812, ...,17959.49570144, 18013.70171825, 18067.93675813],[  160.79258651,   204.39966432,   248.03594812, ...,17959.49570144, 18013.70171825, 18067.93675813],[  160.79258651,   204.39966432,   248.03594812, ...,17959.49570144, 18013.70171825, 18067.93675813]], shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -3.171e+03 -3.18e+03</div><input id='attrs-261fe586-f195-4356-82e0-561edf4e5765' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-261fe586-f195-4356-82e0-561edf4e5765' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86fbe5d4-eb1e-435c-a59c-289708ac48b7' class='xr-var-data-in' type='checkbox'><label for='data-86fbe5d4-eb1e-435c-a59c-289708ac48b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,0.00000000e+00,  0.00000000e+00,  0.00000000e+00],[ 4.34648754e+00,  1.30393957e+01,  2.17322146e+01, ...,3.16220401e+03,  3.17086059e+03,  3.17951707e+03],[ 8.69165110e+00,  2.60748195e+01,  4.34578093e+01, ...,6.32344478e+03,  6.34075531e+03,  6.35806562e+03],...,[-1.30341671e+01, -3.91023006e+01, -6.51701664e+01, ...,-9.48275937e+03, -9.50871857e+03, -9.53467745e+03],[-8.69165110e+00, -2.60748195e+01, -4.34578093e+01, ...,-6.32344478e+03, -6.34075531e+03, -6.35806562e+03],[-4.34648754e+00, -1.30393957e+01, -2.17322146e+01, ...,-3.16220401e+03, -3.17086059e+03, -3.17951707e+03]],shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>249.0 747.1 ... 1.817e+05 1.822e+05</div><input id='attrs-a1e532cd-502f-4ed0-b0ad-ebc15165eb82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1e532cd-502f-4ed0-b0ad-ebc15165eb82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db698696-6e5e-4bf1-baa9-b6a69f8e9c13' class='xr-var-data-in' type='checkbox'><label for='data-db698696-6e5e-4bf1-baa9-b6a69f8e9c13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   249.04803564,    747.14027237,   1245.22739357, ...,181190.14251254, 181686.15339906, 182182.15796695],[   249.01010442,    747.02647928,   1245.03773941, ...,181162.54637623, 181658.48171788, 182154.41074187],[   248.89632231,    746.6851347 ,   1244.46883469, ...,181079.76637332, 181575.4751034 , 182071.17751871],...,[   248.70672396,    746.11634259,   1243.5208527 , ...,180941.82771942, 181437.15884025, 181932.48365111],[   248.89632231,    746.6851347 ,   1244.46883469, ...,181079.76637332, 181575.4751034 , 182071.17751871],[   249.01010442,    747.02647928,   1245.03773941, ...,181162.54637623, 181658.48171788, 182154.41074187]],shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.79 65.4 ... 1.787e+04 1.793e+04</div><input id='attrs-22059ad1-5792-4f89-b8f6-a10e76c04443' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22059ad1-5792-4f89-b8f6-a10e76c04443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72ee3d06-74c0-4fec-b8dd-1f8995a3bbc6' class='xr-var-data-in' type='checkbox'><label for='data-72ee3d06-74c0-4fec-b8dd-1f8995a3bbc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   21.79258651,    65.39966432,   109.03594812, ...,17820.49570144, 17874.70171825, 17928.93675813],[   21.79258651,    65.39966432,   109.03594812, ...,17820.49570144, 17874.70171825, 17928.93675813],[   21.79258651,    65.39966432,   109.03594812, ...,17820.49570144, 17874.70171825, 17928.93675813],...,[   21.79258651,    65.39966432,   109.03594812, ...,17820.49570144, 17874.70171825, 17928.93675813],[   21.79258651,    65.39966432,   109.03594812, ...,17820.49570144, 17874.70171825, 17928.93675813],[   21.79258651,    65.39966432,   109.03594812, ...,17820.49570144, 17874.70171825, 17928.93675813]], shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.025e+05</div><input id='attrs-b179a11a-cb08-4425-b7c6-68e0ff54c103' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b179a11a-cb08-4425-b7c6-68e0ff54c103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-011285ed-e4b9-4e65-b1e7-ca15bc746aaf' class='xr-var-data-in' type='checkbox'><label for='data-011285ed-e4b9-4e65-b1e7-ca15bc746aaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505861.75982872, 505860.93754837, 505860.11524014, ...,505560.70179369, 505559.870143  , 505559.03846872],[505866.12035664, 505874.01723022, 505881.91153993, ...,508570.21216679, 508577.16784344, 508584.12091484],[505870.47968181, 505887.09330605, 505903.70183342, ...,511579.04343144, 511593.78511675, 511608.52161815],...,[505848.68430767, 505821.71668176, 505794.75662376, ...,496535.77903934, 496511.59306811, 496487.41480597],[505853.04047652, 505834.78329323, 505816.53115097, ...,499542.71697474, 499526.31294261, 499509.91404706],[505857.39942603, 505847.85824218, 505838.31956642, ...,502551.28063161, 502542.66189224, 502534.04571089]],shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 6.935e+06</div><input id='attrs-dda7a2ed-74f4-4efa-b2f9-09a7f21d1031' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dda7a2ed-74f4-4efa-b2f9-09a7f21d1031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb8f9f1-2b26-41b3-9373-4f04cd8ab831' class='xr-var-data-in' type='checkbox'><label for='data-5cb8f9f1-2b26-41b3-9373-4f04cd8ab831' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752334.33225666, 6752833.2665449 , 6753332.19604336, ...,6933603.06178184, 6934100.03000814, 6934596.99222314],[6752334.30145021, 6752833.17413989, 6753332.04205893, ...,6933581.81122331, 6934078.72429853, 6934575.63137869],[6752334.19466782, 6752832.85384013, 6753331.50830501, ...,6933507.99291952, 6934004.71408256, 6934501.42930582],...,[6752333.96884225, 6752832.17645702, 6753330.37948911, ...,6933351.42047717, 6933847.73361978, 6934344.04093666],[6752334.16592735, 6752832.76762752, 6753331.36463196, ...,6933487.85132509, 6933984.51955208, 6934481.18185247],[6752334.28707779, 6752833.13102702, 6753331.97021147, ...,6933571.73907685, 6934068.62568107, 6934565.50629683]],shape=(360, 367))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-12808cdf-c43c-4863-b6b6-f30a1020dea4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12808cdf-c43c-4863-b6b6-f30a1020dea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b11093c-7edf-499a-a8ba-c3a4320d67cc' class='xr-var-data-in' type='checkbox'><label for='data-2b11093c-7edf-499a-a8ba-c3a4320d67cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-77e0bf4d-1d8a-4f80-ac59-eee231eec781' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77e0bf4d-1d8a-4f80-ac59-eee231eec781' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c86440f-7e8f-4e04-a3e7-fa5f1fa5e6bc' class='xr-var-data-in' type='checkbox'><label for='data-3c86440f-7e8f-4e04-a3e7-fa5f1fa5e6bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-93da3276-40f4-444f-8bea-8dd730380ed7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93da3276-40f4-444f-8bea-8dd730380ed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e77ec98b-3945-4e5d-be05-c8526ab3ec8d' class='xr-var-data-in' type='checkbox'><label for='data-e77ec98b-3945-4e5d-be05-c8526ab3ec8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4</div><input id='attrs-c1221b8e-e3ef-4b6f-9a27-3cc4f7db30a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1221b8e-e3ef-4b6f-9a27-3cc4f7db30a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7919031-f1d6-4ebc-a2b0-85eb703d9be2' class='xr-var-data-in' type='checkbox'><label for='data-e7919031-f1d6-4ebc-a2b0-85eb703d9be2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0</div><input id='attrs-23415dee-54d4-4f4f-a8e7-9785d9e7d35c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23415dee-54d4-4f4f-a8e7-9785d9e7d35c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01f7dd02-297f-4c3d-a9d4-b2f816e94cf5' class='xr-var-data-in' type='checkbox'><label for='data-01f7dd02-297f-4c3d-a9d4-b2f816e94cf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(5.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-982160a9-83c7-4c5f-99ae-c76779d72404' class='xr-section-summary-in' type='checkbox' checked /><label for='section-982160a9-83c7-4c5f-99ae-c76779d72404' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:01:54.441700096 .....</div><input id='attrs-8ddd6896-cc5a-429f-be25-dab83d49c371' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ddd6896-cc5a-429f-be25-dab83d49c371' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-769ebb66-cfee-4e1e-af28-dca39df682f3' class='xr-var-data-in' type='checkbox'><label for='data-769ebb66-cfee-4e1e-af28-dca39df682f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:01:54.441700096&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:01:54.502811392&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:01:54.319477760&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 367), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.51 nan nan nan ... nan nan nan</div><input id='attrs-cb6af7ef-3202-495c-830a-715640b6a5eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb6af7ef-3202-495c-830a-715640b6a5eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d4f8c1-deed-4392-8f96-d21f4be3e832' class='xr-var-data-in' type='checkbox'><label for='data-b9d4f8c1-deed-4392-8f96-d21f4be3e832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.51,  nan,  nan, ...,  nan,  nan,  nan],[1.03,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],...,[ nan,  nan,  nan, ...,  nan,  nan,  nan],[0.86,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 367), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.81 nan nan nan ... nan nan nan</div><input id='attrs-b2dfa3b4-4b36-4ff6-af51-096088990a50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2dfa3b4-4b36-4ff6-af51-096088990a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-976fd3c1-617e-45fd-ae4e-57dc128dc9b5' class='xr-var-data-in' type='checkbox'><label for='data-976fd3c1-617e-45fd-ae4e-57dc128dc9b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -5.81,    nan,    nan, ...,    nan,    nan,    nan],[-10.2 ,    nan,    nan, ...,    nan,    nan,    nan],[   nan,    nan,    nan, ...,    nan,    nan,    nan],...,[   nan,    nan,    nan, ...,    nan,    nan,    nan],[ -6.51,    nan,    nan, ...,    nan,    nan,    nan],[   nan,    nan,    nan, ...,    nan,    nan,    nan]],shape=(360, 367), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-a4aa3b18-c626-4439-b6e4-30be6ad3cff1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4aa3b18-c626-4439-b6e4-30be6ad3cff1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b24c611a-8a56-4c84-82dc-18d50ed0b3bb' class='xr-var-data-in' type='checkbox'><label for='data-b24c611a-8a56-4c84-82dc-18d50ed0b3bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 367), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4435 nan nan nan ... nan nan nan</div><input id='attrs-993f95ee-edb6-49a6-b1c4-997d05617740' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-993f95ee-edb6-49a6-b1c4-997d05617740' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a2da3e1-6519-4b2a-953b-f0b20f6e1101' class='xr-var-data-in' type='checkbox'><label for='data-6a2da3e1-6519-4b2a-953b-f0b20f6e1101' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44354752,        nan,        nan, ...,        nan,        nan,nan],[0.28208688,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.3609479 ,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 367), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>101.2 nan nan nan ... nan nan nan</div><input id='attrs-05a0fbf8-683e-4421-9d97-c74a6492ccd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05a0fbf8-683e-4421-9d97-c74a6492ccd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc5e9d93-5eb5-4fd7-9405-ed6b39e460e7' class='xr-var-data-in' type='checkbox'><label for='data-bc5e9d93-5eb5-4fd7-9405-ed6b39e460e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[101.159706,        nan,        nan, ...,        nan,        nan,nan],[118.101135,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[       nan,        nan,        nan, ...,        nan,        nan,nan],[121.331215,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 367), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-11c8ad6e-9495-4714-bfbf-a42008e745a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11c8ad6e-9495-4714-bfbf-a42008e745a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12eb6989-d192-4384-af9b-1a3111bb7041' class='xr-var-data-in' type='checkbox'><label for='data-12eb6989-d192-4384-af9b-1a3111bb7041' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 367), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-40dbed22-165a-486d-97cb-20e5f5311254' type='checkbox' checked /><label for='group-40dbed22-165a-486d-97cb-20e5f5311254' title='Expand/collapse group'>/sweep_5<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-a580bdcf-4c72-4b43-a2bd-d44aaaf9491d' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-a580bdcf-4c72-4b43-a2bd-d44aaaf9491d' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 205</li></ul></div></li><li class='xr-section-item'><input id='section-3d738cd4-3a74-439a-a823-5ee53636cada' class='xr-section-summary-in' type='checkbox' checked /><label for='section-3d738cd4-3a74-439a-a823-5ee53636cada' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 1.1 2.1 ... 357.1 358.1 359.1</div><input id='attrs-571fd56d-f6ae-47f8-bb24-6873e7d0a75c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-571fd56d-f6ae-47f8-bb24-6873e7d0a75c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba8e871-2f0c-4767-971d-1cc5acffc4b6' class='xr-var-data-in' type='checkbox'><label for='data-0ba8e871-2f0c-4767-971d-1cc5acffc4b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.000e-01, 1.100e+00, 2.100e+00, ..., 3.571e+02, 3.581e+02, 3.591e+02],shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 1.018e+05 1.022e+05</div><input id='attrs-42bb261c-4ffb-43ca-bcf5-68282f3fa798' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42bb261c-4ffb-43ca-bcf5-68282f3fa798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c49be41-7000-401e-aac3-7c906bfae4e2' class='xr-var-data-in' type='checkbox'><label for='data-1c49be41-7000-401e-aac3-7c906bfae4e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 101250., 101750., 102250.],shape=(205,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.91 ... 61.81 61.81</div><input id='attrs-440c8b08-e814-4714-89c9-f89a4a9ed57f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-440c8b08-e814-4714-89c9-f89a4a9ed57f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c872e39d-f50a-40e0-b3cd-2edfc47e3bfb' class='xr-var-data-in' type='checkbox'><label for='data-c872e39d-f50a-40e0-b3cd-2edfc47e3bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90609063, 60.91053179, 60.91497286, ..., 61.80150613,61.8059302 , 61.81035418],[60.90609022, 60.91053057, 60.91497083, ..., 61.80134207,61.80576533, 61.8101885 ],[60.90608914, 60.91052732, 60.91496542, ..., 61.80090463,61.80532574, 61.80974676],...,[60.90608779, 60.91052326, 60.91495866, ..., 61.80035794,61.80477635, 61.80919467],[60.90608941, 60.91052813, 60.91496677, ..., 61.80101399,61.80543563, 61.80985719],[60.90609036, 60.91053097, 60.91497151, ..., 61.80139675,61.80582028, 61.81024373]], shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.08 27.08</div><input id='attrs-ba2c0647-aed1-4060-9768-1b88d736bebf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba2c0647-aed1-4060-9768-1b88d736bebf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64b095c6-9b15-41a1-a15d-68302f1c0a75' class='xr-var-data-in' type='checkbox'><label for='data-64b095c6-9b15-41a1-a15d-68302f1c0a75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806804, 27.10808398, 27.10809992, ..., 27.11128184,27.11129772, 27.1113136 ],[27.10814773, 27.10832306, 27.10849839, ..., 27.14349741,27.14367207, 27.14384672],[27.1082274 , 27.10856207, 27.10889673, ..., 27.17570219,27.17603557, 27.17636894],...,[27.10782903, 27.10736696, 27.10690491, ..., 27.01466848,27.0142082 , 27.01374792],[27.10790866, 27.10760585, 27.10730305, ..., 27.04685756,27.04655592, 27.04625429],[27.10798834, 27.10784488, 27.10770143, ..., 27.07906528,27.07892238, 27.07877948]], shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>178.1 256.4 ... 1.665e+04 1.673e+04</div><input id='attrs-b0b1f0c8-def8-4298-9518-1374a20b6b30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0b1f0c8-def8-4298-9518-1374a20b6b30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee0697a-a134-44b4-9e3a-f1d5fd72ec6c' class='xr-var-data-in' type='checkbox'><label for='data-1ee0697a-a134-44b4-9e3a-f1d5fd72ec6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  178.112205  ,   256.35814711,   334.63279805, ...,16565.51893564, 16649.54759939, 16733.6048064 ],[  178.112205  ,   256.35814711,   334.63279805, ...,16565.51893564, 16649.54759939, 16733.6048064 ],[  178.112205  ,   256.35814711,   334.63279805, ...,16565.51893564, 16649.54759939, 16733.6048064 ],...,[  178.112205  ,   256.35814711,   334.63279805, ...,16565.51893564, 16649.54759939, 16733.6048064 ],[  178.112205  ,   256.35814711,   334.63279805, ...,16565.51893564, 16649.54759939, 16733.6048064 ],[  178.112205  ,   256.35814711,   334.63279805, ...,16565.51893564, 16649.54759939, 16733.6048064 ]], shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.431 1.293 ... -1.583e+03</div><input id='attrs-7bbd9107-0808-4c09-85f2-0e56711e5d18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bbd9107-0808-4c09-85f2-0e56711e5d18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b136014-41cc-40e0-9871-6e11aa227e56' class='xr-var-data-in' type='checkbox'><label for='data-2b136014-41cc-40e0-9871-6e11aa227e56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 4.30958135e-01,  1.29286250e+00,  2.15475098e+00, ...,1.74205995e+02,  1.75064583e+02,  1.75923154e+02],[ 4.74025068e+00,  1.42206211e+01,  2.37008168e+01, ...,1.91614920e+03,  1.92559309e+03,  1.93503680e+03],[ 9.04809930e+00,  2.71440479e+01,  4.52396631e+01, ...,3.65750872e+03,  3.67553504e+03,  3.69356101e+03],...,[-1.24924567e+01, -3.74770251e+01, -6.24611330e+01, ...,-5.04981963e+03, -5.07470807e+03, -5.09959601e+03],[-8.18670809e+00, -2.45598981e+01, -4.09327864e+01, ...,-3.30930898e+03, -3.32561917e+03, -3.34192903e+03],[-3.87846569e+00, -1.16352899e+01, -1.93919712e+01, ...,-1.56779027e+03, -1.57551725e+03, -1.58324408e+03]],shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>246.9 740.8 ... 1.003e+05 1.008e+05</div><input id='attrs-dc9de084-b0ef-4671-964b-603881b28890' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc9de084-b0ef-4671-964b-603881b28890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de037a4-d17b-495f-b8a2-b859f82ae95b' class='xr-var-data-in' type='checkbox'><label for='data-0de037a4-d17b-495f-b8a2-b859f82ae95b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   246.9205722 ,    740.75489423,   1234.5801172 , ...,99812.58126733, 100304.51542024, 100796.43985811],[   246.87544374,    740.61951011,   1234.35447908, ...,99794.33901393, 100286.18325846, 100778.01778973],[   246.75511463,    740.25852611,   1233.75284463, ...,99745.69843813, 100237.30295376, 100728.89776086],...,[   246.60473074,    739.80737858,   1233.00093901, ...,99684.90883097, 100176.21374023, 100667.5089469 ],[   246.78519508,    740.34876661,   1233.90324408, ...,99757.85784121, 100249.52228541, 100741.1770199 ],[   246.89048625,    740.66463723,   1234.42969026, ...,99800.41964158, 100292.29385495, 100784.15835448]],shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>39.11 117.4 ... 1.651e+04 1.659e+04</div><input id='attrs-7c7b3add-7f5b-4c37-b978-736056f7ddd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c7b3add-7f5b-4c37-b978-736056f7ddd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da45bb6b-f964-44d5-87a1-fe9852b5df8b' class='xr-var-data-in' type='checkbox'><label for='data-da45bb6b-f964-44d5-87a1-fe9852b5df8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   39.112205  ,   117.35814711,   195.63279805, ...,16426.51893564, 16510.54759939, 16594.6048064 ],[   39.112205  ,   117.35814711,   195.63279805, ...,16426.51893564, 16510.54759939, 16594.6048064 ],[   39.112205  ,   117.35814711,   195.63279805, ...,16426.51893564, 16510.54759939, 16594.6048064 ],...,[   39.112205  ,   117.35814711,   195.63279805, ...,16426.51893564, 16510.54759939, 16594.6048064 ],[   39.112205  ,   117.35814711,   195.63279805, ...,16426.51893564, 16510.54759939, 16594.6048064 ],[   39.112205  ,   117.35814711,   195.63279805, ...,16426.51893564, 16510.54759939, 16594.6048064 ]], shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.042e+05</div><input id='attrs-45d15412-8610-4fb4-8e9e-fb66ccac8b59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45d15412-8610-4fb4-8e9e-fb66ccac8b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e34e9ed4-b6f9-46b1-bf50-bf81e201e0aa' class='xr-var-data-in' type='checkbox'><label for='data-e34e9ed4-b6f9-46b1-bf50-bf81e201e0aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505862.19568509, 505862.24493052, 505862.29389882, ...,505866.52348275, 505866.51684529, 505866.50993174],[505866.51891288, 505875.21269103, 505883.90362816, ...,507564.89324642, 507573.01178074, 507581.12746136],[505870.84081654, 505888.17648159, 505905.50674496, ...,509262.79303526, 509279.03474987, 509295.27103639],...,[505849.23122275, 505823.35730437, 505797.49079024, ...,500773.31791661, 500748.94411654, 500724.57776293],[505853.55052359, 505836.31329014, 505819.08090517, ...,502470.26350172, 502454.00866698, 502437.7587092 ],[505857.87244978, 505849.27714767, 505840.68413259, ...,504168.15611337, 504160.02434283, 504151.89487409]],shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 6.853e+06</div><input id='attrs-9194df3b-d970-4567-a904-2714bfb98032' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9194df3b-d970-4567-a904-2714bfb98032' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972e4c4d-465e-4629-8b72-5476ba486993' class='xr-var-data-in' type='checkbox'><label for='data-972e4c4d-465e-4629-8b72-5476ba486993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752332.20190989, 6752826.87250739, 6753321.53431907, ...,6852073.0743618 , 6852565.90693615, 6853058.7300974 ],[6752332.16383303, 6752826.75829463, 6753321.34399417, ...,6852058.12672922, 6852550.8877794 , 6853043.63943835],[6752332.05043127, 6752826.41814062, 6753320.77715624, ...,6852013.56548435, 6852506.11311646, 6852998.65142096],...,[6752331.86418987, 6752825.85949447, 6753319.8462094 , ...,6851940.23535662, 6852432.43113999, 6852924.61769511],[6752332.0520655 , 6752826.42303889, 6753320.78531272, ...,6852014.12041021, 6852506.67031495, 6852999.21088813],[6752332.16465027, 6752826.76074414, 6753321.34807302, ...,6852058.40421498, 6852551.16640149, 6853043.91919479]],shape=(360, 205))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-a6607879-35d5-4c94-a4ab-451a90f71e94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6607879-35d5-4c94-a4ab-451a90f71e94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-411e0484-5461-4d69-8f08-c200ed772237' class='xr-var-data-in' type='checkbox'><label for='data-411e0484-5461-4d69-8f08-c200ed772237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-e674c4a6-6786-4fe4-a374-4f8f9b8529a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e674c4a6-6786-4fe4-a374-4f8f9b8529a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d64a84a-f8cc-4d94-84b0-d3f9469ce4f8' class='xr-var-data-in' type='checkbox'><label for='data-9d64a84a-f8cc-4d94-84b0-d3f9469ce4f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-68f2b453-72ff-4ce6-81a2-160785bac899' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68f2b453-72ff-4ce6-81a2-160785bac899' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e95283b1-7f02-4682-b0b2-a016d5532537' class='xr-var-data-in' type='checkbox'><label for='data-e95283b1-7f02-4682-b0b2-a016d5532537' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5</div><input id='attrs-d88951a7-3097-433a-be4a-7521b2bd911a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d88951a7-3097-433a-be4a-7521b2bd911a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7805b3a9-09ac-4b5a-b33b-b42198647fb4' class='xr-var-data-in' type='checkbox'><label for='data-7805b3a9-09ac-4b5a-b33b-b42198647fb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.0</div><input id='attrs-36817951-192c-4e53-904f-d56737722f95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36817951-192c-4e53-904f-d56737722f95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f19cf811-d94b-438a-9ee8-1d51d1b2b330' class='xr-var-data-in' type='checkbox'><label for='data-f19cf811-d94b-438a-9ee8-1d51d1b2b330' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b89ce077-989f-4b1a-b487-f6d84c18e6a2' class='xr-section-summary-in' type='checkbox' checked /><label for='section-b89ce077-989f-4b1a-b487-f6d84c18e6a2' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>NaT NaT NaT NaT ... NaT NaT NaT NaT</div><input id='attrs-a8a408cc-19ef-405a-8f6a-d0ba930d2b59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8a408cc-19ef-405a-8f6a-d0ba930d2b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6d3998a-55f4-4572-8af4-db3e99aa3f60' class='xr-var-data-in' type='checkbox'><label for='data-b6d3998a-55f4-4572-8af4-db3e99aa3f60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:15.569473280&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:15.386139648&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 205), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a01d37df-2475-485f-97f9-1a80d5eadae2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a01d37df-2475-485f-97f9-1a80d5eadae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eef28e5-06c1-4aca-8ae3-babc762ebd71' class='xr-var-data-in' type='checkbox'><label for='data-9eef28e5-06c1-4aca-8ae3-babc762ebd71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[0.34,  nan,  nan, ...,  nan,  nan,  nan],...,[ nan,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[1.27,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 205), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-f881a944-c598-46f6-adb1-942c5a61df8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f881a944-c598-46f6-adb1-942c5a61df8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f61347a-6f8d-41e3-844b-afe367210cf7' class='xr-var-data-in' type='checkbox'><label for='data-9f61347a-6f8d-41e3-844b-afe367210cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  nan,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-5.36,   nan,   nan, ...,   nan,   nan,   nan],...,[  nan,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-7.73,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 205), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8217c670-5c16-4503-babc-8444bed5ed7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8217c670-5c16-4503-babc-8444bed5ed7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1a5606a-e942-4a87-8766-7e8ff7edc736' class='xr-var-data-in' type='checkbox'><label for='data-d1a5606a-e942-4a87-8766-7e8ff7edc736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 205), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-477f552e-8b9e-440c-95e6-4ebaee5e3c1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-477f552e-8b9e-440c-95e6-4ebaee5e3c1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea7b88d7-4efd-4350-a68b-210b27f0cb06' class='xr-var-data-in' type='checkbox'><label for='data-ea7b88d7-4efd-4350-a68b-210b27f0cb06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.41420352,        nan,        nan, ...,        nan,        nan,nan],...,[       nan,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.3281095 ,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 205), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-cca2a068-076f-4ef6-a804-eb1cfa0f1611' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cca2a068-076f-4ef6-a804-eb1cfa0f1611' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cda6c312-5fb2-4003-8bdb-9aa818614b50' class='xr-var-data-in' type='checkbox'><label for='data-cda6c312-5fb2-4003-8bdb-9aa818614b50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[132.17505,       nan,       nan, ...,       nan,       nan,nan],...,[      nan,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[106.4388 ,       nan,       nan, ...,       nan,       nan,nan]], shape=(360, 205), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-51c76b7b-734e-46f1-8c15-9db29a75a116' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51c76b7b-734e-46f1-8c15-9db29a75a116' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-355899ae-8d97-49b7-81a9-3646a6e970e9' class='xr-var-data-in' type='checkbox'><label for='data-355899ae-8d97-49b7-81a9-3646a6e970e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 205), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-contents'><div class='xr-group-box-ellipsis'>⋮</div></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-d450152a-510d-49f4-9436-1624d68eeae4' type='checkbox' checked /><label for='group-d450152a-510d-49f4-9436-1624d68eeae4' title='Expand/collapse group'>/sweep_7<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-f0657089-895b-4127-9f18-f859f77f5e04' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-f0657089-895b-4127-9f18-f859f77f5e04' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 248</li></ul></div></li><li class='xr-section-item'><input id='section-26a2bbde-9e56-4f96-844c-4cd66b55ceb9' class='xr-section-summary-in' type='checkbox' checked /><label for='section-26a2bbde-9e56-4f96-844c-4cd66b55ceb9' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 1.1 2.1 ... 357.1 358.1 359.1</div><input id='attrs-d0d31a95-eeca-4719-b8fe-0af681c6e04e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0d31a95-eeca-4719-b8fe-0af681c6e04e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa5c35a3-30ad-4293-93d8-99eb79eadfe8' class='xr-var-data-in' type='checkbox'><label for='data-fa5c35a3-30ad-4293-93d8-99eb79eadfe8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.000e-01, 1.100e+00, 2.100e+00, ..., 3.571e+02, 3.581e+02, 3.591e+02],shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 1.232e+05 1.238e+05</div><input id='attrs-3dda1d68-f524-4156-bdba-ab5917ed3451' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3dda1d68-f524-4156-bdba-ab5917ed3451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7302682-b16f-4172-8053-11efef9a495e' class='xr-var-data-in' type='checkbox'><label for='data-b7302682-b16f-4172-8053-11efef9a495e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   250.,    750.,   1250., ..., 122750., 123250., 123750.],shape=(248,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.92 ... 62.0 62.01</div><input id='attrs-e89bfedf-1a5a-4abf-afdc-3801842346bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e89bfedf-1a5a-4abf-afdc-3801842346bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a878f0-3821-4401-a760-7f5e11ca3a55' class='xr-var-data-in' type='checkbox'><label for='data-59a878f0-3821-4401-a760-7f5e11ca3a55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90610155, 60.91056457, 60.91502753, ..., 61.9975563 ,62.00200276, 62.00644915],[60.90610114, 60.91056335, 60.91502549, ..., 61.99735641,62.00180206, 62.00624764],[60.90610006, 60.91056008, 60.91502005, ..., 61.99682344,62.00126692, 62.00571033],...,[60.9060987 , 60.91055601, 60.91501325, ..., 61.99615734,62.00059812, 62.00503882],[60.90610033, 60.9105609 , 60.91502141, ..., 61.99695668,62.0014007 , 62.00584465],[60.90610128, 60.91056375, 60.91502617, ..., 61.99742304,62.00186896, 62.00631481]], shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.07 27.07</div><input id='attrs-538e5270-a117-436f-86e0-e1ef550de4f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-538e5270-a117-436f-86e0-e1ef550de4f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5a645c8-eb51-4463-8efc-6f78117746fb' class='xr-var-data-in' type='checkbox'><label for='data-a5a645c8-eb51-4463-8efc-6f78117746fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806808, 27.10808409, 27.10810011, ..., 27.1119855 ,27.11200146, 27.11201742],[27.10814816, 27.10832436, 27.10850055, ..., 27.15123718,27.15141272, 27.15158826],[27.10822822, 27.10856454, 27.10890085, ..., 27.19047572,27.19081078, 27.19114584],...,[27.10782789, 27.10736355, 27.10689922, ..., 26.99427109,26.99380847, 26.99334586],[27.10790792, 27.10760362, 27.10729933, ..., 27.0334905 ,27.03318733, 27.03288416],[27.10798798, 27.10784382, 27.10769966, ..., 27.07273262,27.07258899, 27.07244537]], shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>169.5 230.4 ... 1.604e+04 1.611e+04</div><input id='attrs-e0155eb6-9458-4c27-9d5b-71c8814ed145' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0155eb6-9458-4c27-9d5b-71c8814ed145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25db7064-8316-4bf2-9bda-a3343ceb78c8' class='xr-var-data-in' type='checkbox'><label for='data-25db7064-8316-4bf2-9bda-a3343ceb78c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  169.47095998,   230.43462447,   291.42728116, ...,15970.59283817, 16038.64025345, 16106.71649967],[  169.47095998,   230.43462447,   291.42728116, ...,15970.59283817, 16038.64025345, 16106.71649967],[  169.47095998,   230.43462447,   291.42728116, ...,15970.59283817, 16038.64025345, 16106.71649967],...,[  169.47095998,   230.43462447,   291.42728116, ...,15970.59283817, 16038.64025345, 16106.71649967],[  169.47095998,   230.43462447,   291.42728116, ...,15970.59283817, 16038.64025345, 16106.71649967],[  169.47095998,   230.43462447,   291.42728116, ...,15970.59283817, 16038.64025345, 16106.71649967]], shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4331 1.299 ... -1.926e+03</div><input id='attrs-df39346b-ed17-41b4-a043-69d9ce2e4de6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df39346b-ed17-41b4-a043-69d9ce2e4de6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2643b0b6-52fb-4bf7-a7b4-a47da97eead7' class='xr-var-data-in' type='checkbox'><label for='data-2643b0b6-52fb-4bf7-a7b4-a47da97eead7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 4.33078185e-01,  1.29922523e+00,  2.16535984e+00, ...,2.12253836e+02,  2.13116769e+02,  2.13979688e+02],[ 4.76356981e+00,  1.42906069e+01,  2.38175072e+01, ...,2.33464995e+03,  2.34414164e+03,  2.35363317e+03],[ 9.09261040e+00,  2.72776355e+01,  4.54623995e+01, ...,4.45633491e+03,  4.47445246e+03,  4.49256972e+03],...,[-1.25539120e+01, -3.76614656e+01, -6.27686589e+01, ...,-6.15273653e+03, -6.17775092e+03, -6.20276491e+03],[-8.22698167e+00, -2.46807679e+01, -4.11343179e+01, ...,-4.03208584e+03, -4.04847857e+03, -4.06487104e+03],[-3.89754536e+00, -1.16925522e+01, -1.94874471e+01, ...,-1.91020694e+03, -1.91797302e+03, -1.92573898e+03]],shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>248.1 744.4 ... 1.221e+05 1.226e+05</div><input id='attrs-c0952154-eae0-4fa2-b550-d5e64a67b476' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0952154-eae0-4fa2-b550-d5e64a67b476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4442bd0-e735-4a6e-b7da-17af12e94237' class='xr-var-data-in' type='checkbox'><label for='data-a4442bd0-e735-4a6e-b7da-17af12e94237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   248.13526993,    744.40046833,   1240.65854207, ...,121612.36632968, 122106.79004001, 122601.20584226],[   248.08991947,    744.26441793,   1240.43179302, ...,121590.13983703, 122084.47318398, 122578.79862429],[   247.96899842,    743.90165737,   1239.82719644, ...,121530.87580992, 122024.96821476, 122519.05271682],...,[   247.81787473,    743.44828955,   1239.07158883, ...,121456.80931568, 121950.60059792, 122444.3839822 ],[   247.99922684,    743.99234198,   1239.97833637, ...,121545.69091401, 122039.84355071, 122533.98828366],[   248.10503599,    744.30976714,   1240.5073745 , ...,121597.54851746, 122091.91198492, 122586.26754527]],shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.47 91.43 ... 1.59e+04 1.597e+04</div><input id='attrs-b0fa5342-00e7-400b-ba97-14d89bba5659' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0fa5342-00e7-400b-ba97-14d89bba5659' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372eaf6c-406f-474d-83d6-bd3c84e2193b' class='xr-var-data-in' type='checkbox'><label for='data-372eaf6c-406f-474d-83d6-bd3c84e2193b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   30.47095998,    91.43462447,   152.42728116, ...,15831.59283817, 15899.64025345, 15967.71649967],[   30.47095998,    91.43462447,   152.42728116, ...,15831.59283817, 15899.64025345, 15967.71649967],[   30.47095998,    91.43462447,   152.42728116, ...,15831.59283817, 15899.64025345, 15967.71649967],...,[   30.47095998,    91.43462447,   152.42728116, ...,15831.59283817, 15899.64025345, 15967.71649967],[   30.47095998,    91.43462447,   152.42728116, ...,15831.59283817, 15899.64025345, 15967.71649967],[   30.47095998,    91.43462447,   152.42728116, ...,15831.59283817, 15899.64025345, 15967.71649967]], shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.038e+05</div><input id='attrs-c799448a-0e99-492d-a8b1-918d13b76562' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c799448a-0e99-492d-a8b1-918d13b76562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-658893c6-5ad3-4efd-a6f0-dfb05c8a201a' class='xr-var-data-in' type='checkbox'><label for='data-658893c6-5ad3-4efd-a6f0-dfb05c8a201a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505862.19580655, 505862.24529303, 505862.29449984, ...,505865.96410936, 505865.94512514, 505865.92586175],[505866.54030049, 505875.2768601 , 505884.01058698, ...,507922.07437714, 507930.11387329, 507938.15051298],[505870.88346379, 505888.3044377 , 505905.72002906, ...,509977.62948287, 509993.72560008, 510009.81628679],...,[505849.16757146, 505823.1663242 , 505797.17244614, ...,499699.89744948, 499675.71096755, 499651.53192793],[505853.50811914, 505836.18605862, 505818.86882241, ...,501754.31617814, 501738.18229501, 501722.05328508],[505857.85130507, 505849.21370352, 505840.57837565, ...,503809.85820431, 503801.78079037, 503793.70567482]],shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 6.875e+06</div><input id='attrs-d77a347d-1951-4be8-9489-e214290805f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d77a347d-1951-4be8-9489-e214290805f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bce13a6-8a6d-4aff-b5a9-00b7ddaa6279' class='xr-var-data-in' type='checkbox'><label for='data-4bce13a6-8a6d-4aff-b5a9-00b7ddaa6279' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752333.41866423, 6752830.52425625, 6753327.62304343, ...,6873912.97883346, 6874408.31971955, 6874903.65300434],[6752333.38040007, 6752830.40948152, 6753327.43178182, ...,6873894.88116253, 6874390.15106518, 6874885.41338794],[6752333.26644049, 6752830.06765383, 6753326.86215407, ...,6873840.91912602, 6874335.97715501, 6874831.0276665 ],...,[6752333.07928295, 6752829.50625888, 6753325.92662524, ...,6873752.08541876, 6874246.79391379, 6874741.49498931],[6752333.26808275, 6752830.07257618, 6753326.87035062, ...,6873841.57113163, 6874336.63127937, 6874831.68390603],[6752333.38122133, 6752830.41194307, 6753327.43588072, ...,6873895.20718864, 6874390.47815067, 6874885.74153102]],shape=(360, 248))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-cc435492-2235-45ba-910d-714150967ecf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc435492-2235-45ba-910d-714150967ecf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-827149e0-342c-4564-98a0-d4b655bc8d78' class='xr-var-data-in' type='checkbox'><label for='data-827149e0-342c-4564-98a0-d4b655bc8d78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-a0d3f801-83f6-4d21-afb9-6ea28030b2e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0d3f801-83f6-4d21-afb9-6ea28030b2e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69bbcf98-17f0-455f-b102-8fd88a8261ac' class='xr-var-data-in' type='checkbox'><label for='data-69bbcf98-17f0-455f-b102-8fd88a8261ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-6ece06a8-8651-406f-a443-c7c65e4950f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ece06a8-8651-406f-a443-c7c65e4950f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76630646-f316-488f-9f05-5407bfb8a28c' class='xr-var-data-in' type='checkbox'><label for='data-76630646-f316-488f-9f05-5407bfb8a28c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>7</div><input id='attrs-20577ffc-95dd-4d82-8da5-5262cf39884f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20577ffc-95dd-4d82-8da5-5262cf39884f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a2d9103-f03f-4f76-9338-4a3af1c30050' class='xr-var-data-in' type='checkbox'><label for='data-1a2d9103-f03f-4f76-9338-4a3af1c30050' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(7)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.0</div><input id='attrs-ef8b8f53-61e9-4d4b-8eef-98d3f27af56d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef8b8f53-61e9-4d4b-8eef-98d3f27af56d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cc6adc1-79f3-44f7-a427-cba48b8a0d49' class='xr-var-data-in' type='checkbox'><label for='data-0cc6adc1-79f3-44f7-a427-cba48b8a0d49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(7.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b1e4da8-2619-408d-a1ee-ab76d9180697' class='xr-section-summary-in' type='checkbox' checked /><label for='section-7b1e4da8-2619-408d-a1ee-ab76d9180697' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:02:44.795834112 .....</div><input id='attrs-80e874cf-d0cd-45d7-bc1b-daa22609cc33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80e874cf-d0cd-45d7-bc1b-daa22609cc33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daee7d34-9463-4263-8539-bec28397dcfb' class='xr-var-data-in' type='checkbox'><label for='data-daee7d34-9463-4263-8539-bec28397dcfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:02:44.795834112&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:44.831945216&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[&#x27;2024-06-01T12:02:44.687500800&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:44.723611904&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:44.759723008&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 248), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.35 nan nan nan ... nan nan nan</div><input id='attrs-b7764d1f-6680-4257-bf3c-e1a4f582cebb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7764d1f-6680-4257-bf3c-e1a4f582cebb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-733d686d-e0f0-4eaf-a4e1-b135931e9a7b' class='xr-var-data-in' type='checkbox'><label for='data-733d686d-e0f0-4eaf-a4e1-b135931e9a7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.35,  nan,  nan, ...,  nan,  nan,  nan],[4.89,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],...,[4.61,  nan,  nan, ...,  nan,  nan,  nan],[3.77,  nan,  nan, ...,  nan,  nan,  nan],[4.2 ,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 248), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-6.52 nan nan nan ... nan nan nan</div><input id='attrs-5a6fa717-ba56-4399-a50b-bbadf77d949b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a6fa717-ba56-4399-a50b-bbadf77d949b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-034354da-93ca-421a-b5af-3aa050c14a4e' class='xr-var-data-in' type='checkbox'><label for='data-034354da-93ca-421a-b5af-3aa050c14a4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-6.52,   nan,   nan, ...,   nan,   nan,   nan],[-0.52,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],...,[-8.48,   nan,   nan, ...,   nan,   nan,   nan],[-7.42,   nan,   nan, ...,   nan,   nan,   nan],[-3.42,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 248), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-402092ba-5c4c-4d76-98c7-21a4ac6d6d6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-402092ba-5c4c-4d76-98c7-21a4ac6d6d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efb27496-ecda-4d7e-8e28-efa4ef7244b5' class='xr-var-data-in' type='checkbox'><label for='data-efb27496-ecda-4d7e-8e28-efa4ef7244b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 248), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3987 nan nan nan ... nan nan nan</div><input id='attrs-d41a68eb-d528-4ef2-b96c-da940cb9f44f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d41a68eb-d528-4ef2-b96c-da940cb9f44f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c94c4561-8d0f-4e49-80eb-345f793837a6' class='xr-var-data-in' type='checkbox'><label for='data-c94c4561-8d0f-4e49-80eb-345f793837a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.3986541 ,        nan,        nan, ...,        nan,        nan,nan],[0.18541804,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[0.17177606,        nan,        nan, ...,        nan,        nan,nan],[0.44930035,        nan,        nan, ...,        nan,        nan,nan],[0.38687378,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 248), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.56 nan nan nan ... nan nan nan</div><input id='attrs-be995ae9-d733-461d-9fa7-4db8d16e77ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be995ae9-d733-461d-9fa7-4db8d16e77ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51a6fddf-c7fc-47ae-b127-2ea41837b7fe' class='xr-var-data-in' type='checkbox'><label for='data-51a6fddf-c7fc-47ae-b127-2ea41837b7fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 90.563065,        nan,        nan, ...,        nan,        nan,nan],[ 87.32749 ,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[ 68.59523 ,        nan,        nan, ...,        nan,        nan,nan],[ 81.19694 ,        nan,        nan, ...,        nan,        nan,nan],[125.48418 ,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 248), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-9ca924e6-190a-497f-8a70-9598213574bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ca924e6-190a-497f-8a70-9598213574bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ffdefd-225e-4b11-a910-ce589f27c3a3' class='xr-var-data-in' type='checkbox'><label for='data-19ffdefd-225e-4b11-a910-ce589f27c3a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 248), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-0094ec1e-d3e7-4a90-a125-ca03a76ebd28' type='checkbox' checked /><label for='group-0094ec1e-d3e7-4a90-a125-ca03a76ebd28' title='Expand/collapse group'>/sweep_8<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-09bf0d48-e9d9-438a-a318-b6a2f9b94070' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-09bf0d48-e9d9-438a-a318-b6a2f9b94070' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 168</li></ul></div></li><li class='xr-section-item'><input id='section-808545e6-bf46-40d3-9692-e7969b66c93f' class='xr-section-summary-in' type='checkbox' checked /><label for='section-808545e6-bf46-40d3-9692-e7969b66c93f' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 1.1 2.1 ... 357.1 358.1 359.1</div><input id='attrs-c737daee-7f0b-4283-9a6b-250459bc5c3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c737daee-7f0b-4283-9a6b-250459bc5c3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab44373e-5818-48b4-be2e-0ae14213eda2' class='xr-var-data-in' type='checkbox'><label for='data-ab44373e-5818-48b4-be2e-0ae14213eda2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.000e-01, 1.100e+00, 2.100e+00, ..., 3.571e+02, 3.581e+02, 3.591e+02],shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 8.325e+04 8.375e+04</div><input id='attrs-fd3d9c3b-6fc6-40ac-8141-7679d62f6325' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd3d9c3b-6fc6-40ac-8141-7679d62f6325' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a27bb92f-acca-4485-848b-be09565fb77b' class='xr-var-data-in' type='checkbox'><label for='data-a27bb92f-acca-4485-848b-be09565fb77b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  250.,   750.,  1250.,  1750.,  2250.,  2750.,  3250.,  3750.,  4250.,4750.,  5250.,  5750.,  6250.,  6750.,  7250.,  7750.,  8250.,  8750.,9250.,  9750., 10250., 10750., 11250., 11750., 12250., 12750., 13250.,13750., 14250., 14750., 15250., 15750., 16250., 16750., 17250., 17750.,18250., 18750., 19250., 19750., 20250., 20750., 21250., 21750., 22250.,22750., 23250., 23750., 24250., 24750., 25250., 25750., 26250., 26750.,27250., 27750., 28250., 28750., 29250., 29750., 30250., 30750., 31250.,31750., 32250., 32750., 33250., 33750., 34250., 34750., 35250., 35750.,36250., 36750., 37250., 37750., 38250., 38750., 39250., 39750., 40250.,40750., 41250., 41750., 42250., 42750., 43250., 43750., 44250., 44750.,45250., 45750., 46250., 46750., 47250., 47750., 48250., 48750., 49250.,49750., 50250., 50750., 51250., 51750., 52250., 52750., 53250., 53750.,54250., 54750., 55250., 55750., 56250., 56750., 57250., 57750., 58250.,58750., 59250., 59750., 60250., 60750., 61250., 61750., 62250., 62750.,63250., 63750., 64250., 64750., 65250., 65750., 66250., 66750., 67250.,67750., 68250., 68750., 69250., 69750., 70250., 70750., 71250., 71750.,72250., 72750., 73250., 73750., 74250., 74750., 75250., 75750., 76250.,76750., 77250., 77750., 78250., 78750., 79250., 79750., 80250., 80750.,81250., 81750., 82250., 82750., 83250., 83750.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.91 ... 61.64 61.64</div><input id='attrs-69e70a27-a4e2-4fb5-a3c0-5eaf41fc141a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69e70a27-a4e2-4fb5-a3c0-5eaf41fc141a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-643ed16f-a7fe-4d18-9066-0fe4eaf1e9bd' class='xr-var-data-in' type='checkbox'><label for='data-643ed16f-a7fe-4d18-9066-0fe4eaf1e9bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.906077  , 60.91049088, 60.91490467, ..., 61.63300723,61.6374044 , 61.64180146],[60.90607659, 60.91048967, 60.91490265, ..., 61.63287397,61.63727033, 61.6416666 ],[60.90607552, 60.91048645, 60.91489728, ..., 61.63251864,61.63691287, 61.64130699],...,[60.90607417, 60.91048241, 60.91489056, ..., 61.63207457,61.63646612, 61.64085756],[60.90607579, 60.91048725, 60.91489862, ..., 61.63260747,61.63700223, 61.64139688],[60.90607673, 60.91049008, 60.91490333, ..., 61.63291839,61.63731502, 61.64171155]], shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.08 27.08</div><input id='attrs-71ac0e20-6509-4d23-bbd4-6b0719059324' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71ac0e20-6509-4d23-bbd4-6b0719059324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7de53623-f6b8-4d97-aea8-aaa3dd7148f8' class='xr-var-data-in' type='checkbox'><label for='data-7de53623-f6b8-4d97-aea8-aaa3dd7148f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806799, 27.10808383, 27.10809967, ..., 27.11067707,27.11069285, 27.11070863],[27.10814719, 27.10832145, 27.1084957 , ..., 27.13684532,27.13701892, 27.13719251],[27.10822637, 27.10855899, 27.10889159, ..., 27.16300481,27.16333617, 27.16366751],...,[27.10783045, 27.10737122, 27.106912  , ..., 27.03219939,27.0317419 , 27.03128443],[27.10790959, 27.10760864, 27.1073077 , ..., 27.05834613,27.05804632, 27.05774652],[27.10798878, 27.1078462 , 27.10770363, ..., 27.08450801,27.08436598, 27.08422395]], shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>186.7 282.1 ... 1.642e+04 1.652e+04</div><input id='attrs-c2c671cc-798a-4987-944e-5005bf4cc8ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2c671cc-798a-4987-944e-5005bf4cc8ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ae3be63-65d6-4823-ba5f-267e4e3bcb94' class='xr-var-data-in' type='checkbox'><label for='data-9ae3be63-65d6-4823-ba5f-267e4e3bcb94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  186.70579367,   282.13864957,   377.59986277, ...,16316.09137424, 16416.19007429, 16516.31697067],[  186.70579367,   282.13864957,   377.59986277, ...,16316.09137424, 16416.19007429, 16516.31697067],[  186.70579367,   282.13864957,   377.59986277, ...,16316.09137424, 16416.19007429, 16516.31697067],...,[  186.70579367,   282.13864957,   377.59986277, ...,16316.09137424, 16416.19007429, 16516.31697067],[  186.70579367,   282.13864957,   377.59986277, ...,16316.09137424, 16416.19007429, 16516.31697067],[  186.70579367,   282.13864957,   377.59986277, ...,16316.09137424, 16416.19007429, 16516.31697067]], shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4283 1.285 ... -1.289e+03</div><input id='attrs-fa664361-a0e8-46c9-abc0-d9e016aa1b8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa664361-a0e8-46c9-abc0-d9e016aa1b8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11af5177-a2f4-4c59-8d38-e9aaa5ecc285' class='xr-var-data-in' type='checkbox'><label for='data-11af5177-a2f4-4c59-8d38-e9aaa5ecc285' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 4.28313037e-01,  1.28492468e+00,  2.14151707e+00, ...,1.41505084e+02,  1.42358451e+02,  1.43211799e+02],[ 4.71115637e+00,  1.41333103e+01,  2.35552526e+01, ...,1.55646110e+03,  1.56584757e+03,  1.57523381e+03],[ 8.99256464e+00,  2.69773909e+01,  4.49618130e+01, ...,2.97094299e+03,  2.98885971e+03,  3.00677600e+03],...,[-1.24157816e+01, -3.72469264e+01, -6.20775132e+01, ...,-4.10189760e+03, -4.12663470e+03, -4.15137123e+03],[-8.13646040e+00, -2.44091070e+01, -4.06813879e+01, ...,-2.68810523e+03, -2.70431625e+03, -2.72052688e+03],[-3.85466077e+00, -1.15638524e+01, -1.92728708e+01, ...,-1.27349403e+03, -1.28117403e+03, -1.28885384e+03]],shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>245.4 736.2 ... 8.156e+04 8.204e+04</div><input id='attrs-156d61db-1cbd-4681-9144-1412dddcd58b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-156d61db-1cbd-4681-9144-1412dddcd58b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9ad540c-cd02-4e75-8606-72b1702c2a12' class='xr-var-data-in' type='checkbox'><label for='data-d9ad540c-cd02-4e75-8606-72b1702c2a12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  245.40504393,   736.20686179,  1226.99765055, ...,81076.35862988, 81565.30165982, 82054.23318718],[  245.36019246,   736.07230889,  1226.77339824, ...,81061.54070352, 81550.39437175, 82039.23653951],[  245.2406019 ,   735.71354124,  1226.17545886, ...,81022.0306466 , 81510.64604356, 81999.24994565],...,[  245.09114102,   735.26516363,  1225.42817125, ...,80972.65210204, 81460.96971435, 81949.2758388 ],[  245.27049772,   735.80322769,  1226.3249346 , ...,81031.90755903, 81520.58252018, 82009.24598506],[  245.37514265,   736.11715895,  1226.84814749, ...,81066.479912  , 81555.36336686, 82044.23532055]], shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.71 143.1 ... 1.628e+04 1.638e+04</div><input id='attrs-30153df3-2b56-4750-8756-297959d3e0b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30153df3-2b56-4750-8756-297959d3e0b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4172e86-a551-47f8-87ef-dd1a9a80576a' class='xr-var-data-in' type='checkbox'><label for='data-e4172e86-a551-47f8-87ef-dd1a9a80576a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   47.70579367,   143.13864957,   238.59986277, ...,16177.09137424, 16277.19007429, 16377.31697067],[   47.70579367,   143.13864957,   238.59986277, ...,16177.09137424, 16277.19007429, 16377.31697067],[   47.70579367,   143.13864957,   238.59986277, ...,16177.09137424, 16277.19007429, 16377.31697067],...,[   47.70579367,   143.13864957,   238.59986277, ...,16177.09137424, 16277.19007429, 16377.31697067],[   47.70579367,   143.13864957,   238.59986277, ...,16177.09137424, 16277.19007429, 16377.31697067],[   47.70579367,   143.13864957,   238.59986277, ...,16177.09137424, 16277.19007429, 16377.31697067]], shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.045e+05</div><input id='attrs-807f5560-ae7b-4e90-ac17-ab6f40bf19a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-807f5560-ae7b-4e90-ac17-ab6f40bf19a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d1b485-4e74-4079-a919-af477f04ee86' class='xr-var-data-in' type='checkbox'><label for='data-73d1b485-4e74-4079-a919-af477f04ee86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505862.19553353, 505862.24447824, 505862.29314902, ...,505866.5707763 , 505866.57462908, 505866.57820929],[505866.49222842, 505875.13263676, 505883.77020295, ...,507253.68057665, 507261.85159803, 507270.01977254],[505870.7876073 , 505888.01684968, 505905.2406849 , ...,508640.39862743, 508656.73471723, 508673.06538906],...,[505849.31063788, 505823.59556204, 505797.88790669, ...,501706.81943261, 501682.33046483, 501657.84893716],[505853.60342992, 505836.47201805, 505819.34546653, ...,503092.74738579, 503076.41911549, 503060.09571905],[505857.89883118, 505849.35629753, 505840.81605843, ...,504479.462092  , 504471.29880468, 504463.13781934]],shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 6.834e+06</div><input id='attrs-15aa083b-8bbb-4d7c-be4b-f90dc9417d7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15aa083b-8bbb-4d7c-be4b-f90dc9417d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66f00062-90b5-4897-90c3-d7f8e1f4bd18' class='xr-var-data-in' type='checkbox'><label for='data-66f00062-90b5-4897-90c3-d7f8e1f4bd18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752330.68381569, 6752822.31677166, 6753313.93900448, ...,6833302.87196253, 6833792.69582436, 6834282.50847991],[6752330.64597251, 6752822.20325998, 6753313.74984808, ...,6833290.66377768, 6833780.41576912, 6834270.15657651],[6752330.53326674, 6752821.86519399, 6753313.1864903 , ...,6833254.27538698, 6833743.81299303, 6834233.33947959],...,[6752330.34816836, 6752821.30997711, 6753312.26125924, ...,6833194.41506457, 6833683.59944717, 6834172.77281066],[6752330.53489093, 6752821.87006223, 6753313.1945968 , ...,6833254.74075138, 6833744.28077118, 6834233.80966722],[6752330.64678473, 6752822.20569447, 6753313.75390194, ...,6833290.89648155, 6833780.6496799 , 6834270.39169207]],shape=(360, 168))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-3606ba22-0b52-4cf0-9ea8-bb440506ea13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3606ba22-0b52-4cf0-9ea8-bb440506ea13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2529760-26c9-43b9-bb8a-f802d1ef31a7' class='xr-var-data-in' type='checkbox'><label for='data-f2529760-26c9-43b9-bb8a-f802d1ef31a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-4ef93280-7b75-44eb-a87a-8276ec0a91ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ef93280-7b75-44eb-a87a-8276ec0a91ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c4a20b-5f6a-439e-af33-83cc12d04941' class='xr-var-data-in' type='checkbox'><label for='data-89c4a20b-5f6a-439e-af33-83cc12d04941' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-52b19074-567c-4694-b3c5-1639a1656794' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52b19074-567c-4694-b3c5-1639a1656794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-654a4485-d92f-4041-bb5b-58701662263b' class='xr-var-data-in' type='checkbox'><label for='data-654a4485-d92f-4041-bb5b-58701662263b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>8</div><input id='attrs-09ad63c3-5aff-4ea9-b612-df003659e175' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09ad63c3-5aff-4ea9-b612-df003659e175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e4637c-fe2a-4306-85ca-33a903b2feb7' class='xr-var-data-in' type='checkbox'><label for='data-35e4637c-fe2a-4306-85ca-33a903b2feb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.0</div><input id='attrs-1bcc0da9-9baf-4363-9b7e-26da052261e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bcc0da9-9baf-4363-9b7e-26da052261e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f247ae-6a9f-415f-9251-d32de57632d9' class='xr-var-data-in' type='checkbox'><label for='data-17f247ae-6a9f-415f-9251-d32de57632d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(11.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14df3f7d-1b81-4335-9ab3-ef9c229dbfcd' class='xr-section-summary-in' type='checkbox' checked /><label for='section-14df3f7d-1b81-4335-9ab3-ef9c229dbfcd' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:02:57.597226240 .....</div><input id='attrs-348b57bc-cd2b-43eb-8dd1-6649c3bf4b72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-348b57bc-cd2b-43eb-8dd1-6649c3bf4b72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b6a5de-a675-465d-ae1e-3921d6a38167' class='xr-var-data-in' type='checkbox'><label for='data-e9b6a5de-a675-465d-ae1e-3921d6a38167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:02:57.597226240&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:57.636115200&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:57.675003904&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[&#x27;2024-06-01T12:02:57.480559360&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:57.519448320&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:02:57.558337280&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 168), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.21 nan nan nan ... nan nan nan</div><input id='attrs-d4088ca5-b0f9-4dfd-a8c3-8c9943060ef8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4088ca5-b0f9-4dfd-a8c3-8c9943060ef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e77909c8-307a-4454-9695-c63a1064dcb1' class='xr-var-data-in' type='checkbox'><label for='data-e77909c8-307a-4454-9695-c63a1064dcb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.21,  nan,  nan, ...,  nan,  nan,  nan],[4.58,  nan,  nan, ...,  nan,  nan,  nan],[5.14,  nan,  nan, ...,  nan,  nan,  nan],...,[5.03,  nan,  nan, ...,  nan,  nan,  nan],[6.4 ,  nan,  nan, ...,  nan,  nan,  nan],[4.7 ,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 168), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.62 nan nan nan ... nan nan nan</div><input id='attrs-56053052-50c9-4d8a-bc18-ed480a6d2f95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56053052-50c9-4d8a-bc18-ed480a6d2f95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-996f7b90-0502-4bd4-9bd1-470192e1e00c' class='xr-var-data-in' type='checkbox'><label for='data-996f7b90-0502-4bd4-9bd1-470192e1e00c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.62,   nan,   nan, ...,   nan,   nan,   nan],[-4.08,   nan,   nan, ...,   nan,   nan,   nan],[-9.49,   nan,   nan, ...,   nan,   nan,   nan],...,[-5.  ,   nan,   nan, ...,   nan,   nan,   nan],[-4.28,   nan,   nan, ...,   nan,   nan,   nan],[-3.42,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 168), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-1ab692a1-2c45-4e4d-ae61-3f725071b5fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ab692a1-2c45-4e4d-ae61-3f725071b5fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e312e4d1-acb8-443c-b773-e67f6ff85d21' class='xr-var-data-in' type='checkbox'><label for='data-e312e4d1-acb8-443c-b773-e67f6ff85d21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],...,[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 168), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3019 nan nan nan ... nan nan nan</div><input id='attrs-43199bde-298c-4cb9-bbcd-13c7891bb5ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43199bde-298c-4cb9-bbcd-13c7891bb5ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b39f84bd-ca1f-426e-b2ec-8c622d8ec21d' class='xr-var-data-in' type='checkbox'><label for='data-b39f84bd-ca1f-426e-b2ec-8c622d8ec21d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.3018937 ,        nan,        nan, ...,        nan,        nan,nan],[0.19403964,        nan,        nan, ...,        nan,        nan,nan],[0.10744205,        nan,        nan, ...,        nan,        nan,nan],...,[0.33401492,        nan,        nan, ...,        nan,        nan,nan],[0.62640196,        nan,        nan, ...,        nan,        nan,nan],[0.1405704 ,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 168), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>98.16 nan nan nan ... nan nan nan</div><input id='attrs-26928dad-ef25-4019-875a-76f0a23f7c71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26928dad-ef25-4019-875a-76f0a23f7c71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b99882e3-7576-4569-9f12-c680c52c46be' class='xr-var-data-in' type='checkbox'><label for='data-b99882e3-7576-4569-9f12-c680c52c46be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 98.16035 ,        nan,        nan, ...,        nan,        nan,nan],[177.99493 ,        nan,        nan, ...,        nan,        nan,nan],[148.21008 ,        nan,        nan, ...,        nan,        nan,nan],...,[133.48796 ,        nan,        nan, ...,        nan,        nan,nan],[118.12861 ,        nan,        nan, ...,        nan,        nan,nan],[ 57.267982,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 168), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-7a50bb52-bce1-4dde-a97d-d2d0d39ad800' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a50bb52-bce1-4dde-a97d-d2d0d39ad800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a40d4838-5618-4e8d-950e-ea415a039aff' class='xr-var-data-in' type='checkbox'><label for='data-a40d4838-5618-4e8d-950e-ea415a039aff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 168), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-099c5970-d8ca-4c31-a6d5-2f2598fdbfe2' type='checkbox' checked /><label for='group-099c5970-d8ca-4c31-a6d5-2f2598fdbfe2' title='Expand/collapse group'>/sweep_9<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-82517395-22be-4c33-a26c-ad925713c472' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-82517395-22be-4c33-a26c-ad925713c472' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 124</li></ul></div></li><li class='xr-section-item'><input id='section-da12d4b9-9852-4313-9c9c-b03016c2263a' class='xr-section-summary-in' type='checkbox' checked /><label for='section-da12d4b9-9852-4313-9c9c-b03016c2263a' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 1.1 2.1 ... 357.1 358.1 359.1</div><input id='attrs-b024ec77-c145-4be3-a4e5-b64903a6349d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b024ec77-c145-4be3-a4e5-b64903a6349d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c764439-41bd-4e47-bd78-b586e31cc9b9' class='xr-var-data-in' type='checkbox'><label for='data-1c764439-41bd-4e47-bd78-b586e31cc9b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.000e-01, 1.100e+00, 2.100e+00, ..., 3.571e+02, 3.581e+02, 3.591e+02],shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 6.125e+04 6.175e+04</div><input id='attrs-52c7b19d-da56-44e7-bada-d05bef7b8f8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52c7b19d-da56-44e7-bada-d05bef7b8f8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0244ed18-a275-4e02-a8c0-efd35cb9f0f6' class='xr-var-data-in' type='checkbox'><label for='data-0244ed18-a275-4e02-a8c0-efd35cb9f0f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  250.,   750.,  1250.,  1750.,  2250.,  2750.,  3250.,  3750.,  4250.,4750.,  5250.,  5750.,  6250.,  6750.,  7250.,  7750.,  8250.,  8750.,9250.,  9750., 10250., 10750., 11250., 11750., 12250., 12750., 13250.,13750., 14250., 14750., 15250., 15750., 16250., 16750., 17250., 17750.,18250., 18750., 19250., 19750., 20250., 20750., 21250., 21750., 22250.,22750., 23250., 23750., 24250., 24750., 25250., 25750., 26250., 26750.,27250., 27750., 28250., 28750., 29250., 29750., 30250., 30750., 31250.,31750., 32250., 32750., 33250., 33750., 34250., 34750., 35250., 35750.,36250., 36750., 37250., 37750., 38250., 38750., 39250., 39750., 40250.,40750., 41250., 41750., 42250., 42750., 43250., 43750., 44250., 44750.,45250., 45750., 46250., 46750., 47250., 47750., 48250., 48750., 49250.,49750., 50250., 50750., 51250., 51750., 52250., 52750., 53250., 53750.,54250., 54750., 55250., 55750., 56250., 56750., 57250., 57750., 58250.,58750., 59250., 59750., 60250., 60750., 61250., 61750.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.91 ... 61.43 61.44</div><input id='attrs-250c621f-93bb-4847-a3c2-1b931b6329d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-250c621f-93bb-4847-a3c2-1b931b6329d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaff2b01-bcbc-44ce-b215-77ba2850b1de' class='xr-var-data-in' type='checkbox'><label for='data-aaff2b01-bcbc-44ce-b215-77ba2850b1de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90604169, 60.91038494, 60.91472806, ..., 61.43060775,61.43493482, 61.43926176],[60.90604129, 60.91038375, 60.91472608, ..., 61.43051148,61.43483776, 61.43916391],[60.90604024, 60.91038058, 60.91472078, ..., 61.43025479,61.43457896, 61.438903  ],...,[60.90603891, 60.91037661, 60.91471417, ..., 61.42993398,61.43425552, 61.43857693],[60.9060405 , 60.91038137, 60.91472211, ..., 61.43031895,61.43464366, 61.43896823],[60.90604143, 60.91038415, 60.91472674, ..., 61.43054356,61.43487011, 61.43919653]], shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.09 27.09</div><input id='attrs-fa695405-1bba-4b35-b4c0-8c6a8a380835' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa695405-1bba-4b35-b4c0-8c6a8a380835' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edccd61f-6991-474a-a788-52b209024030' class='xr-var-data-in' type='checkbox'><label for='data-edccd61f-6991-474a-a788-52b209024030' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806786, 27.10808345, 27.10809904, ..., 27.10995062,27.10996615, 27.10998168],[27.1081458 , 27.10831727, 27.10848873, ..., 27.12885489,27.12902572, 27.12919654],[27.10822371, 27.108551  , 27.10887828, ..., 27.14775283,27.1480789 , 27.14840496],...,[27.10783412, 27.10738224, 27.10693038, ..., 27.05325738,27.05280718, 27.052357  ],[27.107912  , 27.10761587, 27.10731974, ..., 27.07214611,27.07185108, 27.07155606],[27.10798992, 27.10784963, 27.10770934, ..., 27.09104577,27.090906  , 27.09076624]], shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>203.7 333.1 ... 1.62e+04 1.633e+04</div><input id='attrs-12e30b63-5182-4af7-aaf9-1a58753a7a53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12e30b63-5182-4af7-aaf9-1a58753a7a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32be891a-8072-48b3-a73a-a3401f66cd19' class='xr-var-data-in' type='checkbox'><label for='data-32be891a-8072-48b3-a73a-a3401f66cd19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  203.7081936 ,   333.14517425,   462.60961179, ...,16064.5568138 , 16197.30691116, 16330.08431216],[  203.7081936 ,   333.14517425,   462.60961179, ...,16064.5568138 , 16197.30691116, 16330.08431216],[  203.7081936 ,   333.14517425,   462.60961179, ...,16064.5568138 , 16197.30691116, 16330.08431216],...,[  203.7081936 ,   333.14517425,   462.60961179, ...,16064.5568138 , 16197.30691116, 16330.08431216],[  203.7081936 ,   333.14517425,   462.60961179, ...,16064.5568138 , 16197.30691116, 16330.08431216],[  203.7081936 ,   333.14517425,   462.60961179, ...,16064.5568138 , 16197.30691116, 16330.08431216]], shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4215 1.264 ... -927.5 -935.1</div><input id='attrs-fe40c885-5b07-427e-bd35-d47cd8fe9560' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe40c885-5b07-427e-bd35-d47cd8fe9560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c00bbc2d-96ef-4e87-bb92-6cd4f7f6424b' class='xr-var-data-in' type='checkbox'><label for='data-c00bbc2d-96ef-4e87-bb92-6cd4f7f6424b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 4.21461226e-01,  1.26436441e+00,  2.10724191e+00, ...,1.02225021e+02,  1.03064785e+02,  1.03904523e+02],[ 4.63579104e+00,  1.39071612e+01,  2.31782489e+01, ...,1.12440672e+03,  1.13364357e+03,  1.14288012e+03],[ 8.84870875e+00,  2.65457218e+01,  4.42421955e+01, ...,2.14624592e+03,  2.16387703e+03,  2.18150758e+03],...,[-1.22171638e+01, -3.66509330e+01, -6.10839575e+01, ...,-2.96326150e+03, -2.98760428e+03, -3.01194629e+03],[-8.00629978e+00, -2.40185334e+01, -4.00302790e+01, ...,-1.94192043e+03, -1.95787303e+03, -1.97382513e+03],[-3.79299697e+00, -1.13788175e+01, -1.89644069e+01, ...,-9.19987821e+02, -9.27545391e+02, -9.35102724e+02]],shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>241.5 724.4 ... 5.904e+04 5.953e+04</div><input id='attrs-f68c5098-c158-4e8a-8e20-38d672e982a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f68c5098-c158-4e8a-8e20-38d672e982a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-568cb02f-b4d5-4278-848e-d990b68a4dc3' class='xr-var-data-in' type='checkbox'><label for='data-568cb02f-b4d5-4278-848e-d990b68a4dc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  241.47924934,   724.42671047,  1207.35945283, ...,58570.5631166 , 59051.71198193, 59532.84583605],[  241.43511537,   724.29431057,  1207.1387897 , ...,58559.85846353, 59040.91939165, 59521.96531132],[  241.31743792,   723.94128361,  1206.55042039, ...,58531.31591029, 59012.14236539, 59492.95381934],...,[  241.17036799,   723.50008056,  1205.81509318, ...,58495.64423966, 58976.17765705, 59456.69608243],[  241.34685549,   724.02953497,  1206.69750376, ...,58538.45111385, 59019.33618364, 59500.20625045],[  241.44982639,   724.33844297,  1207.21234258, ...,58563.42660875, 59044.51684869, 59525.59207924]], shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.71 194.1 ... 1.606e+04 1.619e+04</div><input id='attrs-2276be24-1c79-4c99-b237-19fa8bd806e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2276be24-1c79-4c99-b237-19fa8bd806e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f41f02ca-dce6-4836-a0aa-7d64029d8d95' class='xr-var-data-in' type='checkbox'><label for='data-f41f02ca-dce6-4836-a0aa-7d64029d8d95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   64.7081936 ,   194.14517425,   323.60961179, ...,15925.5568138 , 16058.30691116, 16191.08431216],[   64.7081936 ,   194.14517425,   323.60961179, ...,15925.5568138 , 16058.30691116, 16191.08431216],[   64.7081936 ,   194.14517425,   323.60961179, ...,15925.5568138 , 16058.30691116, 16191.08431216],...,[   64.7081936 ,   194.14517425,   323.60961179, ...,15925.5568138 , 16058.30691116, 16191.08431216],[   64.7081936 ,   194.14517425,   323.60961179, ...,15925.5568138 , 16058.30691116, 16191.08431216],[   64.7081936 ,   194.14517425,   323.60961179, ...,15925.5568138 , 16058.30691116, 16191.08431216]], shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.048e+05</div><input id='attrs-0db37928-4e21-4f20-a075-4e46d56288e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0db37928-4e21-4f20-a075-4e46d56288e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24d09348-36a9-4480-9a61-179f0779b4f5' class='xr-var-data-in' type='checkbox'><label for='data-24d09348-36a9-4480-9a61-179f0779b4f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505862.19514094, 505862.24330668, 505862.29120678, ...,505866.09867156, 505866.11479704, 505866.1306586 ],[505866.42310539, 505874.925282  , 505883.42463561, ...,506874.70202405, 506882.86289179, 506891.02094138],[505870.64977488, 505887.6033748 , 505904.55159747, ...,507883.01370546, 507899.31710614, 507915.61513755],...,[505849.51635359, 505824.21269127, 505798.91642542, ...,502841.4560855 , 502817.04698325, 502792.64526985],[505853.74047761, 505836.8831512 , 505820.03067176, ...,503849.18398223, 503832.91289738, 503816.64665499],[505857.96716914, 505849.56130951, 505841.15774186, ...,504857.49540565, 504849.36680509, 504841.2404951 ]],shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 6.812e+06</div><input id='attrs-4ad86f6d-7a1f-4f7e-8776-009b1fa3b276' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ad86f6d-7a1f-4f7e-8776-009b1fa3b276' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b233be6-e862-4138-af90-42545138cc84' class='xr-var-data-in' type='checkbox'><label for='data-2b233be6-e862-4138-af90-42545138cc84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752326.75137435, 6752810.51666768, 6753294.26753165, ...,6810756.87590854, 6811238.87716809, 6811720.86369824],[6752326.71413651, 6752810.40497193, 6753294.08140162, ...,6810747.99826378, 6811229.92785885, 6811711.84274704],[6752326.60323359, 6752810.07231426, 6753293.52705731, ...,6810721.54268923, 6811203.25859836, 6811684.95986581],...,[6752326.42109608, 6752809.52597978, 6753292.61662992, ...,6810678.04107517, 6811159.40519847, 6811640.75478114],[6752326.60483181, 6752810.0771047 , 6753293.53503434, ...,6810721.89223764, 6811203.61072232, 6811685.31456077],[6752326.71493575, 6752810.40736752, 6753294.08539074, ...,6810748.17305681, 6811230.10393975, 6811712.02011351]],shape=(360, 124))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-804c05af-ce98-4e45-a1b9-fe3170485f49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-804c05af-ce98-4e45-a1b9-fe3170485f49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb287f36-4fb1-4d9c-80f4-cc721cdb6b96' class='xr-var-data-in' type='checkbox'><label for='data-fb287f36-4fb1-4d9c-80f4-cc721cdb6b96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-289ef2fd-1a6b-4765-b981-27f2e4be4286' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-289ef2fd-1a6b-4765-b981-27f2e4be4286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21ac4900-6b99-4b1a-83c8-3c724ab28c75' class='xr-var-data-in' type='checkbox'><label for='data-21ac4900-6b99-4b1a-83c8-3c724ab28c75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-8e2c7251-66b1-4cdd-9888-2d4d59078e41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e2c7251-66b1-4cdd-9888-2d4d59078e41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67156de6-c2c3-45d0-8f64-7cc92ada62f5' class='xr-var-data-in' type='checkbox'><label for='data-67156de6-c2c3-45d0-8f64-7cc92ada62f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>9</div><input id='attrs-877c6953-870d-4ffa-aff4-42898482394b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-877c6953-870d-4ffa-aff4-42898482394b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daf9691d-51bb-4301-bc3b-46de14904ab7' class='xr-var-data-in' type='checkbox'><label for='data-daf9691d-51bb-4301-bc3b-46de14904ab7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(9)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0</div><input id='attrs-0c2621f8-caff-4da1-9d29-adc1908dfc28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c2621f8-caff-4da1-9d29-adc1908dfc28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1531bbae-fdf3-4e9c-a967-00e8d0d23832' class='xr-var-data-in' type='checkbox'><label for='data-1531bbae-fdf3-4e9c-a967-00e8d0d23832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(15.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66d13742-e10e-4332-b4e4-d63f4293a506' class='xr-section-summary-in' type='checkbox' checked /><label for='section-66d13742-e10e-4332-b4e4-d63f4293a506' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>NaT NaT NaT NaT ... NaT NaT NaT NaT</div><input id='attrs-5ff77d9a-42e3-4d43-9b62-2db22bf4b725' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ff77d9a-42e3-4d43-9b62-2db22bf4b725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3240ad64-9b3b-443e-8e98-282d75f95310' class='xr-var-data-in' type='checkbox'><label for='data-3240ad64-9b3b-443e-8e98-282d75f95310' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:03:10.641667328&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[&#x27;2024-06-01T12:03:10.486111744&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:03:10.563889408&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 124), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-b1c2e8ab-e5e1-4332-930f-3ea956df896d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1c2e8ab-e5e1-4332-930f-3ea956df896d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab2bf94e-2327-4080-81c4-5487c22d51c0' class='xr-var-data-in' type='checkbox'><label for='data-ab2bf94e-2327-4080-81c4-5487c22d51c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan,  nan,  nan, ...,  nan,  nan,  nan],[6.36,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],...,[2.63,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],[4.32,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 124), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ac15661f-4fff-45ec-bd6e-f8c803bce4a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac15661f-4fff-45ec-bd6e-f8c803bce4a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a92236-7a8e-4938-84b6-203211865bfc' class='xr-var-data-in' type='checkbox'><label for='data-29a92236-7a8e-4938-84b6-203211865bfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-5.03,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],...,[-0.42,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],[-5.6 ,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 124), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-79b72fce-068f-47b4-b85b-d8006c3dfb39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79b72fce-068f-47b4-b85b-d8006c3dfb39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c65201dd-96e0-429f-822d-734fbce7137b' class='xr-var-data-in' type='checkbox'><label for='data-c65201dd-96e0-429f-822d-734fbce7137b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 124), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8426d9fd-1c3b-4940-a53b-cf8201103b92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8426d9fd-1c3b-4940-a53b-cf8201103b92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb334d3d-c426-41ad-8f98-e581ddda6a83' class='xr-var-data-in' type='checkbox'><label for='data-cb334d3d-c426-41ad-8f98-e581ddda6a83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.5896266 ,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[0.24070315,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],[0.33317566,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 124), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-634f9dd2-0a85-41f6-a3f0-34717b0a1850' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-634f9dd2-0a85-41f6-a3f0-34717b0a1850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0ca383a-4a60-4478-a786-29fc5e847470' class='xr-var-data-in' type='checkbox'><label for='data-d0ca383a-4a60-4478-a786-29fc5e847470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan,       nan, ...,       nan,       nan,nan],[139.11862,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],...,[ 95.75976,       nan,       nan, ...,       nan,       nan,nan],[      nan,       nan,       nan, ...,       nan,       nan,nan],[136.43788,       nan,       nan, ...,       nan,       nan,nan]], shape=(360, 124), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3ff649e3-6d01-45ae-bb3e-4b064ecb4157' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ff649e3-6d01-45ae-bb3e-4b064ecb4157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13d632f0-c8ae-41a0-b966-bbd4a894cca6' class='xr-var-data-in' type='checkbox'><label for='data-13d632f0-c8ae-41a0-b966-bbd4a894cca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 124), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-7f9980d1-cfd2-432e-8417-e8fb6c191bd2' type='checkbox' checked /><label for='group-7f9980d1-cfd2-432e-8417-e8fb6c191bd2' title='Expand/collapse group'>/sweep_10<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-42752bbc-77e9-4a4f-b722-370ca1e73f00' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-42752bbc-77e9-4a4f-b722-370ca1e73f00' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 360</li><li><span class='xr-has-index'>range</span>: 76</li></ul></div></li><li class='xr-section-item'><input id='section-04f1ce27-696a-4ea7-a373-0764d29f793a' class='xr-section-summary-in' type='checkbox' checked /><label for='section-04f1ce27-696a-4ea7-a373-0764d29f793a' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 1.1 2.1 ... 357.1 358.1 359.1</div><input id='attrs-e3617564-6a20-4332-9af9-8e2020f54457' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3617564-6a20-4332-9af9-8e2020f54457' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02da8afe-4e0c-49bf-b82b-5d60ac6e394c' class='xr-var-data-in' type='checkbox'><label for='data-02da8afe-4e0c-49bf-b82b-5d60ac6e394c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.000e-01, 1.100e+00, 2.100e+00, ..., 3.571e+02, 3.581e+02, 3.591e+02],shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.0 750.0 ... 3.725e+04 3.775e+04</div><input id='attrs-89693b85-2500-48ee-8e2b-13223e040c7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89693b85-2500-48ee-8e2b-13223e040c7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1538c967-298a-47a1-8a32-96bfb2c42e50' class='xr-var-data-in' type='checkbox'><label for='data-1538c967-298a-47a1-8a32-96bfb2c42e50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  250.,   750.,  1250.,  1750.,  2250.,  2750.,  3250.,  3750.,  4250.,4750.,  5250.,  5750.,  6250.,  6750.,  7250.,  7750.,  8250.,  8750.,9250.,  9750., 10250., 10750., 11250., 11750., 12250., 12750., 13250.,13750., 14250., 14750., 15250., 15750., 16250., 16750., 17250., 17750.,18250., 18750., 19250., 19750., 20250., 20750., 21250., 21750., 22250.,22750., 23250., 23750., 24250., 24750., 25250., 25750., 26250., 26750.,27250., 27750., 28250., 28750., 29250., 29750., 30250., 30750., 31250.,31750., 32250., 32750., 33250., 33750., 34250., 34750., 35250., 35750.,36250., 36750., 37250., 37750.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.91 60.91 60.91 ... 61.21 61.21</div><input id='attrs-14f1c74a-ce8d-4444-beb4-81b6f04fe82e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14f1c74a-ce8d-4444-beb4-81b6f04fe82e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c587cd1-b132-42b6-9c24-b29833b9f339' class='xr-var-data-in' type='checkbox'><label for='data-9c587cd1-b132-42b6-9c24-b29833b9f339' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.90590764, 60.90998275, 60.91405764, ..., 61.20285673,61.20691701, 61.21097708],[60.90590727, 60.90998163, 60.91405578, ..., 61.20280208,61.20686162, 61.21092095],[60.90590628, 60.90997865, 60.91405082, ..., 61.20265638,61.20671394, 61.2107713 ],...,[60.90590504, 60.90997493, 60.91404461, ..., 61.20247429,61.20652937, 61.21058426],[60.90590653, 60.90997939, 60.91405206, ..., 61.2026928 ,61.20675086, 61.21080871],[60.9059074 , 60.909982  , 60.9140564 , ..., 61.2028203 ,61.20688008, 61.21093966]], shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11 27.11 27.11 ... 27.1 27.1</div><input id='attrs-9088dddc-7679-4c54-9dce-5ec2ddf630af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9088dddc-7679-4c54-9dce-5ec2ddf630af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e1dad3-6273-48a2-8aee-771a8110d2d0' class='xr-var-data-in' type='checkbox'><label for='data-53e1dad3-6273-48a2-8aee-771a8110d2d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.10806738, 27.10808201, 27.10809663, ..., 27.10913318,27.10914775, 27.10916233],[27.10814051, 27.10830139, 27.10846226, ..., 27.11986362,27.12002391, 27.1201842 ],[27.10821361, 27.1085207 , 27.10882776, ..., 27.13059046,27.13089643, 27.13120238],...,[27.10784807, 27.10742409, 27.10700013, ..., 27.07695298,27.07653054, 27.07610813],[27.10792114, 27.10764329, 27.10736545, ..., 27.08767459,27.08739776, 27.08712093],[27.10799425, 27.10786262, 27.10773099, ..., 27.09840242,27.09827126, 27.09814012]], shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>244.7 456.0 ... 1.595e+04 1.616e+04</div><input id='attrs-ef252a05-eed6-4010-ae01-c5c6ddd0fb96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef252a05-eed6-4010-ae01-c5c6ddd0fb96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62875e84-3ecc-41d0-8c6f-1dd2372cbf0a' class='xr-var-data-in' type='checkbox'><label for='data-62875e84-3ecc-41d0-8c6f-1dd2372cbf0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  244.65758712,   455.99089083,   667.34836563, ...,15735.39818284, 15948.4912344 , 16161.60832529],[  244.65758712,   455.99089083,   667.34836563, ...,15735.39818284, 15948.4912344 , 16161.60832529],[  244.65758712,   455.99089083,   667.34836563, ...,15735.39818284, 15948.4912344 , 16161.60832529],...,[  244.65758712,   455.99089083,   667.34836563, ...,15735.39818284, 15948.4912344 , 16161.60832529],[  244.65758712,   455.99089083,   667.34836563, ...,15735.39818284, 15948.4912344 , 16161.60832529],[  244.65758712,   455.99089083,   667.34836563, ...,15735.39818284, 15948.4912344 , 16161.60832529]], shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3954 1.186 ... -529.3 -536.4</div><input id='attrs-ee579fa4-4b41-4cb4-bd16-834211ab291b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee579fa4-4b41-4cb4-bd16-834211ab291b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ed7766c-143c-4771-963a-a394792ed6af' class='xr-var-data-in' type='checkbox'><label for='data-2ed7766c-143c-4771-963a-a394792ed6af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3.95446254e-01,  1.18630925e+00,  1.97713289e+00, ...,5.80249356e+01,  5.88129220e+01,  5.96008689e+01],[ 4.34964378e+00,  1.30486067e+01,  2.17471369e+01, ...,6.38235407e+02,  6.46902729e+02,  6.55569617e+02],[ 8.30251637e+00,  2.49069295e+01,  4.15105164e+01, ...,1.21825147e+03,  1.23479548e+03,  1.25133867e+03],...,[-1.14630513e+01, -3.43882985e+01, -5.73124049e+01, ...,-1.68200560e+03, -1.70484746e+03, -1.72768818e+03],[-7.51210565e+00, -2.25357563e+01, -3.75586595e+01, ...,-1.10227229e+03, -1.11724129e+03, -1.13220955e+03],[-3.55887173e+00, -1.06763496e+01, -1.77934733e+01, ...,-5.22203211e+02, -5.29294800e+02, -5.36386034e+02]],shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>226.6 679.7 ... 3.369e+04 3.414e+04</div><input id='attrs-72b14ce1-7998-45d4-bf7c-fd12be7f66d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72b14ce1-7998-45d4-bf7c-fd12be7f66d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18781307-49a1-45a6-9253-9f546562714d' class='xr-var-data-in' type='checkbox'><label for='data-18781307-49a1-45a6-9253-9f546562714d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  226.57378354,   679.70444155,  1132.81255339, ...,33245.8054298 , 33697.28790108, 34148.7477624 ],[  226.53237377,   679.58021532,  1132.60551482, ...,33239.72925784, 33691.1292139 , 34142.50656413],[  226.42196004,   679.24898237,  1132.05347364, ...,33223.52794235, 33674.70788239, 34125.86522761],...,[  226.2839681 ,   678.83501686,  1131.36354829, ...,33203.27999939, 33654.18496966, 34105.06735889],[  226.44956179,   679.33178556,  1132.19147553, ...,33227.57802445, 33678.81296514, 34130.02530826],[  226.54617674,   679.62162322,  1132.67452627, ...,33241.75460736, 33693.18206794, 34144.58692131]], shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>105.7 317.0 ... 1.581e+04 1.602e+04</div><input id='attrs-9123ac62-e2ee-41e3-9620-d5f27e024d16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9123ac62-e2ee-41e3-9620-d5f27e024d16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bff0598-8341-423d-a2fb-1847e2effefa' class='xr-var-data-in' type='checkbox'><label for='data-7bff0598-8341-423d-a2fb-1847e2effefa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  105.65758712,   316.99089083,   528.34836563, ...,15596.39818284, 15809.4912344 , 16022.60832529],[  105.65758712,   316.99089083,   528.34836563, ...,15596.39818284, 15809.4912344 , 16022.60832529],[  105.65758712,   316.99089083,   528.34836563, ...,15596.39818284, 15809.4912344 , 16022.60832529],...,[  105.65758712,   316.99089083,   528.34836563, ...,15596.39818284, 15809.4912344 , 16022.60832529],[  105.65758712,   316.99089083,   528.34836563, ...,15596.39818284, 15809.4912344 , 16022.60832529],[  105.65758712,   316.99089083,   528.34836563, ...,15596.39818284, 15809.4912344 , 16022.60832529]], shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.059e+05 5.059e+05 ... 5.053e+05</div><input id='attrs-5f65feb1-8035-4307-ae9d-c96fac2851b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f65feb1-8035-4307-ae9d-c96fac2851b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6481f05b-6d79-422d-9798-e9f4de80d4d7' class='xr-var-data-in' type='checkbox'><label for='data-6481f05b-6d79-422d-9798-e9f4de80d4d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[505862.19365017, 505862.23885749, 505862.28383002, ...,505864.87835977, 505864.9064957 , 505864.93439876],[505866.16065738, 505874.13806468, 505882.11281806, ...,506441.54881974, 506449.33299636, 506457.11453316],[505870.12644954, 505886.03362883, 505901.93573788, ...,507018.04819188, 507033.58618564, 507049.11913545],...,[505850.29741945, 505826.55560193, 505802.82079799, ...,504135.54717399, 504112.31610879, 504089.09202258],[505854.26082323, 505838.44400413, 505822.63178656, ...,504711.70688357, 504696.22517261, 504680.74804073],[505858.22663606, 505850.3396298 , 505842.45480808, ...,505288.20748888, 505280.47958687, 505272.75385914]],shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_3067</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.752e+06 6.753e+06 ... 6.786e+06</div><input id='attrs-ad9a28ad-5227-41f3-8a39-9a752ada8095' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad9a28ad-5227-41f3-8a39-9a752ada8095' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccaa0b3c-1496-41d8-b460-3ec926da0dd7' class='xr-var-data-in' type='checkbox'><label for='data-ccaa0b3c-1496-41d8-b460-3ec926da0dd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6752311.82067235, 6752765.71865204, 6753219.59432406, ...,6785387.68944973, 6785839.95647818, 6786292.20113027],[6752311.7857329 , 6752765.6138504 , 6753219.41968253, ...,6785382.61260786, 6785834.81137808, 6786286.98779359],[6752311.68167509, 6752765.30172525, 6753218.89955441, ...,6785367.48732307, 6785819.48266287, 6786271.45571061],...,[6752311.51077946, 6752764.78911251, 6753218.0453234 , ...,6785342.62918667, 6785794.28995531, 6786245.9285288 ],[6752311.6831747 , 6752765.30622031, 6753218.90703991, ...,6785367.69477603, 6785819.69276493, 6786271.66845732],[6752311.78648282, 6752765.61609827, 6753219.42342585, ...,6785382.71634742, 6785834.91644232, 6786287.09418028]],shape=(360, 76))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.9</div><input id='attrs-c260f315-829b-49ed-9cd5-77292d10961f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c260f315-829b-49ed-9cd5-77292d10961f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12894b72-b970-41bd-88b9-8e07aa53615c' class='xr-var-data-in' type='checkbox'><label for='data-12894b72-b970-41bd-88b9-8e07aa53615c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(60.90387002)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.11</div><input id='attrs-11754700-96ad-45c3-a88a-3be3f6a8dc3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11754700-96ad-45c3-a88a-3be3f6a8dc3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52cf0576-2e3d-431d-80d7-ca64b7785246' class='xr-var-data-in' type='checkbox'><label for='data-52cf0576-2e3d-431d-80d7-ca64b7785246' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(27.10806007)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>139.0</div><input id='attrs-4f910181-b96a-44fe-af07-28b0c0b76c78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f910181-b96a-44fe-af07-28b0c0b76c78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-538b1849-6308-49b1-bab2-e418a4f78729' class='xr-var-data-in' type='checkbox'><label for='data-538b1849-6308-49b1-bab2-e418a4f78729' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(139.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sweep_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10</div><input id='attrs-ccaa5e00-9f88-43cb-9070-4a2b992d3cf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccaa5e00-9f88-43cb-9070-4a2b992d3cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd36701-67d9-43dd-b04a-9847cd923285' class='xr-var-data-in' type='checkbox'><label for='data-6fd36701-67d9-43dd-b04a-9847cd923285' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(10)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_angle</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>25.0</div><input id='attrs-7dc4fa9e-8171-4603-a560-833b307fa886' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7dc4fa9e-8171-4603-a560-833b307fa886' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2951258b-5b02-4937-8060-58b2261ea7ec' class='xr-var-data-in' type='checkbox'><label for='data-2951258b-5b02-4937-8060-58b2261ea7ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(25.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0401c65a-0c92-4ada-9341-8b3723f35f48' class='xr-section-summary-in' type='checkbox' checked /><label for='section-0401c65a-0c92-4ada-9341-8b3723f35f48' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-06-01T12:03:37.830567424 .....</div><input id='attrs-2b256b4b-dc67-4257-867e-a0df7c3e034e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b256b4b-dc67-4257-867e-a0df7c3e034e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f30336c-39c9-4d58-a0e4-7eed17e2d3f5' class='xr-var-data-in' type='checkbox'><label for='data-9f30336c-39c9-4d58-a0e4-7eed17e2d3f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2024-06-01T12:03:37.830567424&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:03:37.869456384&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[                          &#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],...,[&#x27;2024-06-01T12:03:37.713900544&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:03:37.752789504&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;],[&#x27;2024-06-01T12:03:37.791678464&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;, ...,&#x27;NaT&#x27;,                           &#x27;NaT&#x27;,&#x27;NaT&#x27;]],shape=(360, 76), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DBZH</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.77 nan nan nan ... nan nan nan</div><input id='attrs-5f00161a-837a-4391-b8fd-7161c4984ffe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f00161a-837a-4391-b8fd-7161c4984ffe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d338e808-db76-49a0-90de-d448328976be' class='xr-var-data-in' type='checkbox'><label for='data-d338e808-db76-49a0-90de-d448328976be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.77,  nan,  nan, ...,  nan,  nan,  nan],[4.14,  nan,  nan, ...,  nan,  nan,  nan],[ nan,  nan,  nan, ...,  nan,  nan,  nan],...,[1.36,  nan,  nan, ...,  nan,  nan,  nan],[4.28,  nan,  nan, ...,  nan,  nan,  nan],[2.33,  nan,  nan, ...,  nan,  nan,  nan]],shape=(360, 76), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZDR</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.25 nan nan nan ... nan nan nan</div><input id='attrs-dd20ab56-0706-41b3-ae8e-dc6ecadda907' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd20ab56-0706-41b3-ae8e-dc6ecadda907' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c6bc43c-4670-4e92-8b6e-b48689fd8587' class='xr-var-data-in' type='checkbox'><label for='data-6c6bc43c-4670-4e92-8b6e-b48689fd8587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.25,   nan,   nan, ...,   nan,   nan,   nan],[-4.88,   nan,   nan, ...,   nan,   nan,   nan],[  nan,   nan,   nan, ...,   nan,   nan,   nan],...,[ 0.56,   nan,   nan, ...,   nan,   nan,   nan],[-5.59,   nan,   nan, ...,   nan,   nan,   nan],[-2.33,   nan,   nan, ...,   nan,   nan,   nan]],shape=(360, 76), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 nan nan nan ... nan nan nan nan</div><input id='attrs-11df037b-f92f-40d1-b288-c7a66958dae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11df037b-f92f-40d1-b288-c7a66958dae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55d9a25b-a206-4080-a04b-69e0199aca12' class='xr-var-data-in' type='checkbox'><label for='data-55d9a25b-a206-4080-a04b-69e0199aca12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan],[ 0., nan, nan, ..., nan, nan, nan]],shape=(360, 76), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOHV</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6875 nan nan nan ... nan nan nan</div><input id='attrs-e452f201-3c58-4a6f-9c32-fe0a32600a48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e452f201-3c58-4a6f-9c32-fe0a32600a48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91fd2201-ca1b-4937-8790-608b0ccad281' class='xr-var-data-in' type='checkbox'><label for='data-91fd2201-ca1b-4937-8790-608b0ccad281' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.68747044,        nan,        nan, ...,        nan,        nan,nan],[0.25446722,        nan,        nan, ...,        nan,        nan,nan],[       nan,        nan,        nan, ...,        nan,        nan,nan],...,[0.02520867,        nan,        nan, ...,        nan,        nan,nan],[0.11884089,        nan,        nan, ...,        nan,        nan,nan],[0.04602262,        nan,        nan, ...,        nan,        nan,nan]], shape=(360, 76), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIDP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>119.2 nan nan nan ... nan nan nan</div><input id='attrs-be0c101a-dbd0-4ed3-a4ab-b62049a6bb9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be0c101a-dbd0-4ed3-a4ab-b62049a6bb9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7d1ca14-bc89-4921-b3bb-b6553e7ae806' class='xr-var-data-in' type='checkbox'><label for='data-b7d1ca14-bc89-4921-b3bb-b6553e7ae806' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[119.16685  ,         nan,         nan, ...,         nan,nan,         nan],[146.9686   ,         nan,         nan, ...,         nan,nan,         nan],[        nan,         nan,         nan, ...,         nan,nan,         nan],...,[ 25.269325 ,         nan,         nan, ...,         nan,nan,         nan],[ 31.949217 ,         nan,         nan, ...,         nan,nan,         nan],[  2.4390392,         nan,         nan, ...,         nan,nan,         nan]], shape=(360, 76), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMP</span></div><div class='xr-var-dims'>(azimuth, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-6cf24db5-835d-4070-a614-090ce01ce08a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cf24db5-835d-4070-a614-090ce01ce08a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-135f8319-15ca-4dc9-a4e1-8dc99f7ca2a1' class='xr-var-data-in' type='checkbox'><label for='data-135f8319-15ca-4dc9-a4e1-8dc99f7ca2a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],...,[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan],[nan, nan, nan, ..., nan, nan, nan]],shape=(360, 76), dtype=float32)</pre></div></li></ul></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 100%'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-924bb16e-e5e3-49da-9844-7ca84784dbbb' type='checkbox' checked /><label for='group-924bb16e-e5e3-49da-9844-7ca84784dbbb'>/sweep_11<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b111627-647a-4102-984c-35c396a978af' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-0b111627-647a-4102-984c-35c396a978af' class='xr-section-summary xr-span-grid'><em>Too many items to display (display_max_html_elements exceeded)</em></label><div class='xr-section-inline-details'></div></li></ul></div></div><div class='xr-group-box'><div class='xr-group-box-vline' style='height: 1.2em'></div><div class='xr-group-box-hline'></div><div class='xr-group-box-contents'><input id='group-0b72cc84-c79b-4bd6-ba30-9e4669f6904b' type='checkbox' checked /><label for='group-0b72cc84-c79b-4bd6-ba30-9e4669f6904b'>/sweep_12<span>(22)</span></label><ul class='xr-sections'><li class='xr-section-item'><input id='section-0b111627-647a-4102-984c-35c396a978af' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-0b111627-647a-4102-984c-35c396a978af' class='xr-section-summary xr-span-grid'><em>Too many items to display (display_max_html_elements exceeded)</em></label><div class='xr-section-inline-details'></div></li></ul></div></div></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataTree>\n",
+       "Group: /\n",
+       "├── Group: /sweep_0\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.15\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 140.3 143.0 ... 5.117e+03\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.357e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.002e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 0\n",
+       "│           elevation_angle  float64 8B 0.3\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:00:08....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 1.17 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -3.06 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.4059 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 98.02 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_1\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.15\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 142.1 148.2 ... 6.859e+03\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.356e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.002e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 1\n",
+       "│           elevation_angle  float64 8B 0.7\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:00:29....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 0.23 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -6.25 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.4484 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 74.04 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_2\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.15\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 145.5 158.7 ... 1.034e+04\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.353e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.002e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 2\n",
+       "│           elevation_angle  float64 8B 1.5\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:00:50....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 0.52 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -8.65 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.4317 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 101.9 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_3\n",
+       "│       Dimensions:          (azimuth: 360, range: 500)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 2kB 250.0 750.0 ... 2.492e+05 2.498e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 63.14 63.14\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.03 27.03\n",
+       "│           altitude         (azimuth, range) float64 1MB 152.1 178.3 ... 1.686e+04\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -4.345e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 7.001e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 3\n",
+       "│           elevation_angle  float64 8B 3.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:01:11....\n",
+       "│           DBZH             (azimuth, range) float32 720kB 0.62 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 720kB -6.74 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 720kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 720kB 0.6461 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 720kB 94.58 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 720kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_4\n",
+       "│       Dimensions:          (azimuth: 360, range: 367)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0\n",
+       "│         * range            (range) float32 1kB 250.0 750.0 ... 1.828e+05 1.832e+05\n",
+       "│           latitude         (azimuth, range) float64 1MB 60.91 60.91 ... 62.54 62.54\n",
+       "│           longitude        (azimuth, range) float64 1MB 27.11 27.11 ... 27.05 27.05\n",
+       "│           altitude         (azimuth, range) float64 1MB 160.8 204.4 ... 1.807e+04\n",
+       "│           x                (azimuth, range) float64 1MB 0.0 0.0 ... -3.18e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 1MB 6.752e+06 ... 6.935e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 4\n",
+       "│           elevation_angle  float64 8B 5.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 1MB 2024-06-01T12:01:54....\n",
+       "│           DBZH             (azimuth, range) float32 528kB 1.51 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 528kB -5.81 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 528kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 528kB 0.4435 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 528kB 101.2 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 528kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_5\n",
+       "│       Dimensions:          (azimuth: 360, range: 205)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 820B 250.0 750.0 ... 1.018e+05 1.022e+05\n",
+       "│           latitude         (azimuth, range) float64 590kB 60.91 60.91 ... 61.81 61.81\n",
+       "│           longitude        (azimuth, range) float64 590kB 27.11 27.11 ... 27.08 27.08\n",
+       "│           altitude         (azimuth, range) float64 590kB 178.1 256.4 ... 1.673e+04\n",
+       "│           x                (azimuth, range) float64 590kB 0.431 1.293 ... -1.583e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 590kB 6.752e+06 ... 6.853e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 5\n",
+       "│           elevation_angle  float64 8B 9.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 590kB NaT NaT ... NaT NaT\n",
+       "│           DBZH             (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           KDP              (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "│           TEMP             (azimuth, range) float32 295kB nan nan nan ... nan nan nan\n",
+       "...\n",
+       "├── Group: /sweep_7\n",
+       "│       Dimensions:          (azimuth: 360, range: 248)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 992B 250.0 750.0 ... 1.232e+05 1.238e+05\n",
+       "│           latitude         (azimuth, range) float64 714kB 60.91 60.91 ... 62.0 62.01\n",
+       "│           longitude        (azimuth, range) float64 714kB 27.11 27.11 ... 27.07 27.07\n",
+       "│           altitude         (azimuth, range) float64 714kB 169.5 230.4 ... 1.611e+04\n",
+       "│           x                (azimuth, range) float64 714kB 0.4331 1.299 ... -1.926e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 714kB 6.752e+06 ... 6.875e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 7\n",
+       "│           elevation_angle  float64 8B 7.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 714kB 2024-06-01T12:02:4...\n",
+       "│           DBZH             (azimuth, range) float32 357kB 7.35 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 357kB -6.52 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 357kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 357kB 0.3987 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 357kB 90.56 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 357kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_8\n",
+       "│       Dimensions:          (azimuth: 360, range: 168)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 672B 250.0 750.0 ... 8.325e+04 8.375e+04\n",
+       "│           latitude         (azimuth, range) float64 484kB 60.91 60.91 ... 61.64 61.64\n",
+       "│           longitude        (azimuth, range) float64 484kB 27.11 27.11 ... 27.08 27.08\n",
+       "│           altitude         (azimuth, range) float64 484kB 186.7 282.1 ... 1.652e+04\n",
+       "│           x                (azimuth, range) float64 484kB 0.4283 1.285 ... -1.289e+03\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 484kB 6.752e+06 ... 6.834e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 8\n",
+       "│           elevation_angle  float64 8B 11.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 484kB 2024-06-01T12:02:5...\n",
+       "│           DBZH             (azimuth, range) float32 242kB 4.21 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 242kB -3.62 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 242kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 242kB 0.3019 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 242kB 98.16 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 242kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_9\n",
+       "│       Dimensions:          (azimuth: 360, range: 124)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 496B 250.0 750.0 ... 6.125e+04 6.175e+04\n",
+       "│           latitude         (azimuth, range) float64 357kB 60.91 60.91 ... 61.43 61.44\n",
+       "│           longitude        (azimuth, range) float64 357kB 27.11 27.11 ... 27.09 27.09\n",
+       "│           altitude         (azimuth, range) float64 357kB 203.7 333.1 ... 1.633e+04\n",
+       "│           x                (azimuth, range) float64 357kB 0.4215 1.264 ... -935.1\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 357kB 6.752e+06 ... 6.812e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 9\n",
+       "│           elevation_angle  float64 8B 15.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 357kB NaT NaT ... NaT NaT\n",
+       "│           DBZH             (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           KDP              (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "│           TEMP             (azimuth, range) float32 179kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_10\n",
+       "│       Dimensions:          (azimuth: 360, range: 76)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 304B 250.0 750.0 ... 3.725e+04 3.775e+04\n",
+       "│           latitude         (azimuth, range) float64 219kB 60.91 60.91 ... 61.21 61.21\n",
+       "│           longitude        (azimuth, range) float64 219kB 27.11 27.11 ... 27.1 27.1\n",
+       "│           altitude         (azimuth, range) float64 219kB 244.7 456.0 ... 1.616e+04\n",
+       "│           x                (azimuth, range) float64 219kB 0.3954 1.186 ... -536.4\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 219kB 6.752e+06 ... 6.786e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 10\n",
+       "│           elevation_angle  float64 8B 25.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 219kB 2024-06-01T12:03:3...\n",
+       "│           DBZH             (azimuth, range) float32 109kB 5.77 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 109kB -3.25 nan nan ... nan nan\n",
+       "│           KDP              (azimuth, range) float32 109kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 109kB 0.6875 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 109kB 119.2 nan nan ... nan nan\n",
+       "│           TEMP             (azimuth, range) float32 109kB nan nan nan ... nan nan nan\n",
+       "├── Group: /sweep_11\n",
+       "│       Dimensions:          (azimuth: 360, range: 45)\n",
+       "│       Coordinates: (12/15)\n",
+       "│         * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "│         * range            (range) float32 180B 250.0 750.0 ... 2.175e+04 2.225e+04\n",
+       "│           latitude         (azimuth, range) float64 130kB 60.91 60.91 ... 61.04 61.05\n",
+       "│           longitude        (azimuth, range) float64 130kB 27.11 27.11 ... 27.1 27.1\n",
+       "│           altitude         (azimuth, range) float64 130kB 315.8 669.3 ... 1.589e+04\n",
+       "│           x                (azimuth, range) float64 130kB 0.3085 0.9255 ... -246.7\n",
+       "│           ...               ...\n",
+       "│           y_3067           (azimuth, range) float64 130kB 6.752e+06 ... 6.768e+06\n",
+       "│           site_latitude    float64 8B 60.9\n",
+       "│           site_longitude   float64 8B 27.11\n",
+       "│           site_altitude    float64 8B 139.0\n",
+       "│           sweep_number     int64 8B 11\n",
+       "│           elevation_angle  float64 8B 45.0\n",
+       "│       Data variables:\n",
+       "│           time             (azimuth, range) datetime64[ns] 130kB 2024-06-01T12:03:5...\n",
+       "│           DBZH             (azimuth, range) float32 65kB 4.13 nan nan ... nan nan nan\n",
+       "│           ZDR              (azimuth, range) float32 65kB -2.87 nan nan ... nan nan nan\n",
+       "│           KDP              (azimuth, range) float32 65kB 0.0 nan nan ... nan nan nan\n",
+       "│           RHOHV            (azimuth, range) float32 65kB 0.6571 nan nan ... nan nan\n",
+       "│           PHIDP            (azimuth, range) float32 65kB 154.5 nan nan ... nan nan nan\n",
+       "│           TEMP             (azimuth, range) float32 65kB nan nan nan ... nan nan nan\n",
+       "└── Group: /sweep_12\n",
+       "        Dimensions:          (azimuth: 360, range: 992)\n",
+       "        Coordinates: (12/15)\n",
+       "          * azimuth          (azimuth) float64 3kB 0.1 1.1 2.1 3.1 ... 357.1 358.1 359.1\n",
+       "          * range            (range) float32 4kB 62.5 187.5 ... 1.238e+05 1.239e+05\n",
+       "            latitude         (azimuth, range) float64 3MB 60.9 60.91 ... 62.02 62.02\n",
+       "            longitude        (azimuth, range) float64 3MB 27.11 27.11 ... 27.07 27.07\n",
+       "            altitude         (azimuth, range) float64 3MB 139.4 140.3 ... 1.908e+03\n",
+       "            x                (azimuth, range) float64 3MB 0.1091 0.3272 ... -1.946e+03\n",
+       "            ...               ...\n",
+       "            y_3067           (azimuth, range) float64 3MB 6.752e+06 ... 6.876e+06\n",
+       "            site_latitude    float64 8B 60.9\n",
+       "            site_longitude   float64 8B 27.11\n",
+       "            site_altitude    float64 8B 139.0\n",
+       "            sweep_number     int64 8B 12\n",
+       "            elevation_angle  float64 8B 0.4\n",
+       "        Data variables:\n",
+       "            time             (azimuth, range) datetime64[ns] 3MB NaT ... 2024-06-01T1...\n",
+       "            DBZH             (azimuth, range) float32 1MB nan nan nan ... 10.83 5.91\n",
+       "            ZDR              (azimuth, range) float32 1MB nan nan nan ... -1.39 -2.6\n",
+       "            KDP              (azimuth, range) float32 1MB nan nan nan ... 0.58 0.58 0.58\n",
+       "            RHOHV            (azimuth, range) float32 1MB nan nan nan ... 1.0 1.0 0.948\n",
+       "            PHIDP            (azimuth, range) float32 1MB nan nan nan ... 80.96 77.39\n",
+       "            TEMP             (azimuth, range) float32 1MB nan nan nan ... nan nan nan"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# DataTree: the full polar structure, for xarray workflows.\n",
     "# A DataTree describes ONE volume: each sweep is an (azimuth x range) grid and\n",
diff --git a/tutorial/03_area_of_interest.ipynb b/tutorial/03_area_of_interest.ipynb
index 1e585fb..aa0d321 100644
--- a/tutorial/03_area_of_interest.ipynb
+++ b/tutorial/03_area_of_interest.ipynb
@@ -29,19 +29,26 @@
     "in, using the argument `crs=`:\n",
     "\n",
     "```python\n",
-    "rdf.crop_around_point(point=(8.83, 46.04), distance=30_000, crs=4326)   # lon/lat\n",
-    "rdf.crop_around_point(point=(2680000, 1120000), distance=30_000)        # already LV95\n",
+    "rdf.crop_around_point(point=(27.11, 60.90), distance=30_000, crs=4326)  # lon/lat\n",
+    "rdf.crop_around_point(point=(505862, 6752085), distance=30_000)         # already TM35FIN\n",
     "```\n",
     "\n",
     "Get this wrong and the crop is silently empty or in the wrong country (passing\n",
-    "lon/lat degrees while RadDB reads them as metres puts your AOI ~2600 km away)."
+    "lon/lat degrees while RadDB reads them as metres puts your AOI ~6800 km away)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:33:59.958582Z",
+     "iopub.status.busy": "2026-08-19T13:33:59.958449Z",
+     "iopub.status.idle": "2026-08-19T13:34:00.819538Z",
+     "shell.execute_reply": "2026-08-19T13:34:00.818944Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import warnings\n",
@@ -58,10 +65,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "2",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:00.821871Z",
+     "iopub.status.busy": "2026-08-19T13:34:00.821612Z",
+     "iopub.status.idle": "2026-08-19T13:34:00.825102Z",
+     "shell.execute_reply": "2026-08-19T13:34:00.824548Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FMI DataTrees   : /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\n",
+      "NEXRAD DataTrees: /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\n",
+      "Archive         : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — edit these three paths to point at your own data\n",
@@ -69,43 +93,77 @@
     "# ARCHIVE_DIR must be the same archive tutorial 1 wrote.  If it has not run,\n",
     "# the cell below builds it.\n",
     "\n",
-    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
-    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
+    "FMI_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"FMI DataTrees   :\", FMI_DIR)\n",
     "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
     "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "3",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:00.826633Z",
+     "iopub.status.busy": "2026-08-19T13:34:00.826507Z",
+     "iopub.status.idle": "2026-08-19T13:34:00.829514Z",
+     "shell.execute_reply": "2026-08-19T13:34:00.829002Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive already present: /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
-    "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
+    "if not (ARCHIVE_DIR / \"FANJ\" / \"LUT\").exists():\n",
     "    print(\"building the archive (see tutorial 1) ...\")\n",
-    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=3067).archive(\n",
+    "        datatree_dir=FMI_DIR,\n",
+    "        time_period=(\"2024-06-01\", \"2024-06-15\"),\n",
+    "    )\n",
     "else:\n",
     "    print(\"archive already present:\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "4",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:00.830964Z",
+     "iopub.status.busy": "2026-08-19T13:34:00.830837Z",
+     "iopub.status.idle": "2026-08-19T13:34:00.944688Z",
+     "shell.execute_reply": "2026-08-19T13:34:00.943581Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "radar FANJ at 27.108, 60.904   |   2,005,621 gates with echo\n",
+      "archive CRS: EPSG:3067\n"
+     ]
+    }
+   ],
    "source": [
-    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056)\n",
-    "rdf = db.open(radars=\"L\").filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 5})\n",
+    "db = raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=3067)\n",
+    "rdf = db.open(radars=\"FANJ\").filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 5})\n",
     "\n",
-    "info = db.get_radar_info(\"L\")\n",
-    "SITE = (info[\"longitude\"], info[\"latitude\"])  # radar: L ==> lon, lat\n",
-    "print(f\"radar L at {SITE[0]:.3f}, {SITE[1]:.3f}   |   {len(rdf):,} gates with echo\")\n",
+    "info = db.get_radar_info(\"FANJ\")\n",
+    "SITE = (info[\"longitude\"], info[\"latitude\"])  # radar: FANJ ==> lon, lat\n",
+    "print(f\"radar FANJ at {SITE[0]:.3f}, {SITE[1]:.3f}   |   {len(rdf):,} gates with echo\")\n",
     "print(\"archive CRS:\", rdf.crs())"
    ]
   },
@@ -121,32 +179,76 @@
     "**not** choose the frame the AOI runs in — that is always the archive's own CRS.\n",
     "\n",
     "So `crs=4326` throughout this notebook because the points are written as lon/lat\n",
-    "degrees. Passing `crs=2056` with those same numbers tells RadDB to read `8.83` and\n",
-    "`46.04` as *metres* in LV95 — a spot near the origin of the Swiss grid, ~2700 km\n",
-    "from the radar — and the crop comes back empty."
+    "degrees. Passing `crs=3067` with those same numbers tells RadDB to read `27.11` and\n",
+    "`60.90` as *metres* in TM35FIN — a spot off West Africa, ~6800 km from the radar —\n",
+    "and the crop comes back empty.\n",
+    "\n",
+    "Because the points below are written relative to `SITE`, this notebook works for\n",
+    "any radar in the archive: swap `\"FANJ\"` for another name in the cell above and\n",
+    "every crop follows it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "6",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:00.946778Z",
+     "iopub.status.busy": "2026-08-19T13:34:00.946503Z",
+     "iopub.status.idle": "2026-08-19T13:34:03.256046Z",
+     "shell.execute_reply": "2026-08-19T13:34:03.255109Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lon/lat  (EPSG:4326): (27.1081, 60.9039)\n",
+      "TM35FIN  (EPSG:3067): (505,862, 6,752,085)\n",
+      "\n",
+      "crop_around_point(distance=30 km):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (lon, lat) + crs=4326 ->  630,029 gates\n",
+      "  (lon, lat) + crs=3067 ->        0 gates  <- degrees read as metres\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (E, N)     + crs=3067 ->  630,029 gates\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (E, N)     + no crs   ->  630,029 gates  <- defaults to the archive CRS\n"
+     ]
+    }
+   ],
    "source": [
     "from pyproj import Transformer\n",
     "\n",
     "# The radar site, written both ways\n",
-    "E, N = Transformer.from_crs(4326, 2056, always_xy=True).transform(*SITE)\n",
-    "print(f\"lon/lat (EPSG:4326): ({SITE[0]:.4f}, {SITE[1]:.4f})\")\n",
-    "print(f\"LV95    (EPSG:2056): ({E:,.0f}, {N:,.0f})\\n\")\n",
+    "E, N = Transformer.from_crs(4326, 3067, always_xy=True).transform(*SITE)\n",
+    "print(f\"lon/lat  (EPSG:4326): ({SITE[0]:.4f}, {SITE[1]:.4f})\")\n",
+    "print(f\"TM35FIN  (EPSG:3067): ({E:,.0f}, {N:,.0f})\\n\")\n",
     "\n",
     "print(\"crop_around_point(distance=30 km):\")\n",
     "print(f\"  (lon, lat) + crs=4326 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=4326)):>8,} gates\")\n",
     "print(\n",
-    "    f\"  (lon, lat) + crs=2056 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=2056)):>8,} gates\"\n",
+    "    f\"  (lon, lat) + crs=3067 -> {len(rdf.crop_around_point(point=SITE, distance=30_000, crs=3067)):>8,} gates\"\n",
     "    \"  <- degrees read as metres\",\n",
     ")\n",
-    "print(f\"  (E, N)     + crs=2056 -> {len(rdf.crop_around_point(point=(E, N), distance=30_000, crs=2056)):>8,} gates\")\n",
+    "print(f\"  (E, N)     + crs=3067 -> {len(rdf.crop_around_point(point=(E, N), distance=30_000, crs=3067)):>8,} gates\")\n",
     "print(\n",
     "    f\"  (E, N)     + no crs   -> {len(rdf.crop_around_point(point=(E, N), distance=30_000)):>8,} gates\"\n",
     "    \"  <- defaults to the archive CRS\",\n",
@@ -166,12 +268,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:03.257942Z",
+     "iopub.status.busy": "2026-08-19T13:34:03.257789Z",
+     "iopub.status.idle": "2026-08-19T13:34:04.281059Z",
+     "shell.execute_reply": "2026-08-19T13:34:04.280024Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2,005,621 -> 941,734 gates inside the lon/lat box\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lon/lat extent of the result: [26.208, 28.008, 60.504, 61.307]\n"
+     ]
+    }
+   ],
    "source": [
-    "box = rdf.crop_by_bbox(bounds=(8.4, 45.8, 9.3, 46.5), crs=4326)\n",
+    "box = rdf.crop_by_bbox(bounds=(SITE[0] - 0.9, SITE[1] - 0.4, SITE[0] + 0.9, SITE[1] + 0.4), crs=4326)\n",
     "print(f\"{len(rdf):,} -> {len(box):,} gates inside the lon/lat box\")\n",
     "print(\"lon/lat extent of the result:\", [round(v, 3) for v in box.geographic_extent()])"
    ]
@@ -189,10 +313,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "10",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:04.283844Z",
+     "iopub.status.busy": "2026-08-19T13:34:04.283649Z",
+     "iopub.status.idle": "2026-08-19T13:34:07.428004Z",
+     "shell.execute_reply": "2026-08-19T13:34:07.427143Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  within  20 km :  460,835 gates\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  within  50 km :  901,856 gates\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  within 100 km : 1,386,363 gates\n"
+     ]
+    }
+   ],
    "source": [
     "for km in (20, 50, 100):\n",
     "    sub = rdf.crop_around_point(point=SITE, distance=km * 1_000, crs=4326)\n",
@@ -201,14 +354,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "11",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:07.430440Z",
+     "iopub.status.busy": "2026-08-19T13:34:07.430213Z",
+     "iopub.status.idle": "2026-08-19T13:34:07.738059Z",
+     "shell.execute_reply": "2026-08-19T13:34:07.737008Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25 km around (27.71, 61.15): 84,831 gates\n"
+     ]
+    }
+   ],
    "source": [
     "# Any point, not only the radar itself\n",
-    "elsewhere = rdf.crop_around_point(point=(9.0, 46.2), distance=25_000, crs=4326)\n",
-    "print(f\"25 km around (9.0, 46.2): {len(elsewhere):,} gates\")"
+    "point = (SITE[0] + 0.6, SITE[1] + 0.25)\n",
+    "elsewhere = rdf.crop_around_point(point=point, distance=25_000, crs=4326)\n",
+    "print(f\"25 km around ({point[0]:.2f}, {point[1]:.2f}): {len(elsewhere):,} gates\")"
    ]
   },
   {
@@ -225,22 +394,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "13",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:07.740239Z",
+     "iopub.status.busy": "2026-08-19T13:34:07.740070Z",
+     "iopub.status.idle": "2026-08-19T13:34:08.438610Z",
+     "shell.execute_reply": "2026-08-19T13:34:08.437753Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inside the triangle: 586,746 gates\n"
+     ]
+    }
+   ],
    "source": [
-    "triangle = shapely.Polygon([(8.6, 45.9), (9.2, 46.1), (8.8, 46.5)])\n",
+    "triangle = shapely.Polygon(\n",
+    "    [\n",
+    "        (SITE[0] - 0.7, SITE[1] - 0.30),\n",
+    "        (SITE[0] + 0.8, SITE[1] - 0.10),\n",
+    "        (SITE[0] + 0.1, SITE[1] + 0.45),\n",
+    "    ],\n",
+    ")\n",
     "poly = rdf.crop_by_polygone(polygon=triangle, crs=4326)\n",
     "print(f\"inside the triangle: {len(poly):,} gates\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "14",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:08.440737Z",
+     "iopub.status.busy": "2026-08-19T13:34:08.440595Z",
+     "iopub.status.idle": "2026-08-19T13:34:09.131196Z",
+     "shell.execute_reply": "2026-08-19T13:34:09.130509Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "from GeoJSON: 586,746 gates  (same: True)\n"
+     ]
+    }
+   ],
    "source": [
     "# The same thing from a file on disk\n",
     "import json\n",
@@ -284,10 +489,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "16",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:09.133170Z",
+     "iopub.status.busy": "2026-08-19T13:34:09.133011Z",
+     "iopub.status.idle": "2026-08-19T13:34:10.715088Z",
+     "shell.execute_reply": "2026-08-19T13:34:10.714202Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7,104 gates on the section\n",
+      "new columns: ['sweep', 'azimuth', 'range', 'elevation_angle', 'x', 'y', 'x_3067', 'y_3067', 'altitude', 'd_center', 'z_center', 'cs_polygon']\n"
+     ]
+    }
+   ],
    "source": [
     "cs = rdf.extract_cross_section(\n",
     "    p1=(SITE[0] - 0.6, SITE[1] - 0.35),\n",
@@ -315,10 +536,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "18",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:10.716820Z",
+     "iopub.status.busy": "2026-08-19T13:34:10.716678Z",
+     "iopub.status.idle": "2026-08-19T13:34:10.750006Z",
+     "shell.execute_reply": "2026-08-19T13:34:10.749243Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in polars   : Binary\n",
+      "raw value   : b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x ...\n",
+      "\n",
+      "after to_pandas: Polygon\n",
+      "  WKT     : POLYGON ((52926.56149355756 169.65712837527033, 53148.29364595128 170.85918321921238, 5314 ...\n",
+      "  corners : [(52926.56149355756, 169.65712837527033), (53148.29364595128, 170.85918321921238)] ...\n",
+      "  bounds  : (52927, 130, 53149, 171) (d_min, z_min, d_max, z_max)\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"in polars   :\", cs.data.schema[\"cs_polygon\"])\n",
     "print(\"raw value   :\", str(cs.data[\"cs_polygon\"][0])[:40], \"...\")\n",
@@ -344,10 +586,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "20",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:10.752409Z",
+     "iopub.status.busy": "2026-08-19T13:34:10.752162Z",
+     "iopub.status.idle": "2026-08-19T13:34:11.953994Z",
+     "shell.execute_reply": "2026-08-19T13:34:11.952827Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "_ = rdf.crop_around_point(point=SITE, distance=50_000, crs=4326, quicklook=True)\n",
     "plt.show()"
@@ -355,10 +615,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "21",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:11.956407Z",
+     "iopub.status.busy": "2026-08-19T13:34:11.956184Z",
+     "iopub.status.idle": "2026-08-19T13:34:12.834541Z",
+     "shell.execute_reply": "2026-08-19T13:34:12.833682Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "_ = rdf.crop_by_polygone(polygon=triangle, crs=4326, quicklook=True)\n",
     "plt.show()"
@@ -377,13 +655,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "23",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:12.837154Z",
+     "iopub.status.busy": "2026-08-19T13:34:12.836995Z",
+     "iopub.status.idle": "2026-08-19T13:34:14.018695Z",
+     "shell.execute_reply": "2026-08-19T13:34:14.017932Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "62,717 gates: strong echo, within 60 km, beyond 5 km range\n"
+     ]
+    }
+   ],
    "source": [
     "storm = (\n",
-    "    db.open(radars=\"L\")\n",
+    "    db.open(radars=\"FANJ\")\n",
     "    .filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 35})\n",
     "    .crop_around_point(point=SITE, distance=60_000, crs=4326)\n",
     "    .sel(range=slice(5_000, 60_000))\n",
@@ -425,10 +718,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "25",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:14.020487Z",
+     "iopub.status.busy": "2026-08-19T13:34:14.020342Z",
+     "iopub.status.idle": "2026-08-19T13:34:14.176773Z",
+     "shell.execute_reply": "2026-08-19T13:34:14.176085Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27db724fd5f741968f665b1bde658490",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<b>Draw an AOI</b> with the toolbar (top-left): ▭ rectangle → <code>crop_by_bbox</c…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "RUN_INTERACTIVE = True  # <- set False to skip the map (e.g. outside Jupyter)\n",
     "\n",
@@ -444,10 +759,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "26",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:14.181583Z",
+     "iopub.status.busy": "2026-08-19T13:34:14.181353Z",
+     "iopub.status.idle": "2026-08-19T13:34:14.184983Z",
+     "shell.execute_reply": "2026-08-19T13:34:14.184458Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nothing applied yet — draw a shape above, click 'Apply crop', then re-run this cell\n"
+     ]
+    }
+   ],
    "source": [
     "from IPython.display import display\n",
     "\n",
@@ -490,6 +820,1153 @@
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+       "_model_module": "jupyter-leaflet",
+       "_model_module_version": "^0.20",
+       "_model_name": "LeafletMapStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "cursor": "move"
+      }
+     },
+     "f7e21172c1cc44a794f0756a818102aa": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": "320px"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorial/04_plots.ipynb b/tutorial/04_plots.ipynb
index 187c17d..320c463 100644
--- a/tutorial/04_plots.ipynb
+++ b/tutorial/04_plots.ipynb
@@ -27,9 +27,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:15.374254Z",
+     "iopub.status.busy": "2026-08-19T13:34:15.374029Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.218507Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.217886Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import warnings\n",
@@ -48,10 +55,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "2",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.220598Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.220347Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.224032Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.223224Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FMI DataTrees   : /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\n",
+      "NEXRAD DataTrees: /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\n",
+      "Archive         : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — edit these three paths to point at your own data\n",
@@ -59,50 +83,98 @@
     "# ARCHIVE_DIR must be the same archive tutorial 1 wrote. If it has not run,\n",
     "# the cell below builds it.\n",
     "\n",
-    "MCH_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/MCH_datatree\").expanduser()\n",
-    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree\").expanduser()\n",
+    "FMI_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/FMI_datatree_zarr\").expanduser()\n",
+    "NEXRAD_DIR = Path(\"~/Desktop/LTE_project/ltenas8/data/RADAR/NEXRAD_datatree_zarr\").expanduser()\n",
     "ARCHIVE_DIR = Path(\"~/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\").expanduser()\n",
     "\n",
-    "print(\"MCH DataTrees   :\", MCH_DIR)\n",
+    "print(\"FMI DataTrees   :\", FMI_DIR)\n",
     "print(\"NEXRAD DataTrees:\", NEXRAD_DIR)\n",
     "print(\"Archive         :\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "3",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.225798Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.225674Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.228696Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.228100Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "archive already present: /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# This notebook stands on its own: build the archive if tutorial 1 has not run.\n",
-    "if not (ARCHIVE_DIR / \"L\" / \"LUT\").exists():\n",
+    "if not (ARCHIVE_DIR / \"FANJ\" / \"LUT\").exists():\n",
     "    print(\"building the archive (see tutorial 1) ...\")\n",
-    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=2056).archive(datatree_dir=MCH_DIR)\n",
+    "    raddb.RadDB(archive_dir=ARCHIVE_DIR, crs=3067).archive(\n",
+    "        datatree_dir=FMI_DIR,\n",
+    "        time_period=(\"2024-06-01\", \"2024-06-15\"),\n",
+    "    )\n",
     "else:\n",
     "    print(\"archive already present:\", ARCHIVE_DIR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "4",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.230698Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.230569Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.300411Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.299708Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2,568,170 gates | variables: ['gate_id', 'time', 'DBZH', 'ZDR', 'KDP', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar']\n"
+     ]
+    }
+   ],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
-    "rdf = db.open(radars=\"L\")\n",
-    "info = db.get_radar_info(\"L\")\n",
+    "rdf = db.open(radars=\"FANJ\")\n",
+    "info = db.get_radar_info(\"FANJ\")\n",
     "SITE = (info[\"longitude\"], info[\"latitude\"])\n",
     "print(f\"{len(rdf):,} gates | variables: {rdf.columns()}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "5",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.302249Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.302082Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.441833Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.440711Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10,783,488 gates | variables: ['gate_id', 'time', 'DBZH', 'ZDR', 'RHOHV', 'PHIDP', 'TEMP', 'volume_time', 'radar']\n"
+     ]
+    }
+   ],
    "source": [
     "# ==================USING NEXRAD DATA=========================\n",
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
@@ -133,10 +205,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "7",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.443703Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.443510Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.517467Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.516744Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 volumes in rdf:\n",
+      "   2024-06-12 22:03:24+00:00\n",
+      "   2024-06-12 22:10:25+00:00\n",
+      "   2024-06-12 22:17:25+00:00\n",
+      "   2024-06-12 22:25:30+00:00\n",
+      "   2024-06-12 22:32:30+00:00\n",
+      "   2024-06-12 22:39:29+00:00\n",
+      "   2024-06-12 22:46:29+00:00\n",
+      "   2024-06-12 22:53:30+00:00\n"
+     ]
+    }
+   ],
    "source": [
     "TIMESTEPS = rdf.data[\"volume_time\"].unique().sort().to_list()\n",
     "print(f\"{len(TIMESTEPS)} volumes in rdf:\")\n",
@@ -146,10 +241,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.519591Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.519401Z",
+     "iopub.status.idle": "2026-08-19T13:34:16.568985Z",
+     "shell.execute_reply": "2026-08-19T13:34:16.568391Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10,783,488 gates -> 70,359 above 30 dBZ\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gate_id</th>\n",
+       "      <th>time</th>\n",
+       "      <th>DBZH</th>\n",
+       "      <th>ZDR</th>\n",
+       "      <th>RHOHV</th>\n",
+       "      <th>PHIDP</th>\n",
+       "      <th>TEMP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7.035900e+04</td>\n",
+       "      <td>70359</td>\n",
+       "      <td>70359.000000</td>\n",
+       "      <td>36583.000000</td>\n",
+       "      <td>36583.000000</td>\n",
+       "      <td>36583.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>9.714930e+17</td>\n",
+       "      <td>2024-06-12 22:26:17.997344512</td>\n",
+       "      <td>22.344625</td>\n",
+       "      <td>4.353422</td>\n",
+       "      <td>0.769320</td>\n",
+       "      <td>90.555733</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>9.714930e+17</td>\n",
+       "      <td>2024-06-12 22:03:24.385999872</td>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>-13.062500</td>\n",
+       "      <td>0.201667</td>\n",
+       "      <td>-0.705194</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>9.714930e+17</td>\n",
+       "      <td>2024-06-12 22:11:56.913499904</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>1.031250</td>\n",
+       "      <td>0.655000</td>\n",
+       "      <td>56.062901</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>9.714930e+17</td>\n",
+       "      <td>2024-06-12 22:25:49.300999936</td>\n",
+       "      <td>21.500000</td>\n",
+       "      <td>4.187500</td>\n",
+       "      <td>0.818333</td>\n",
+       "      <td>71.929764</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.714930e+17</td>\n",
+       "      <td>2024-06-12 22:40:05.139000064</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>7.875000</td>\n",
+       "      <td>0.928333</td>\n",
+       "      <td>98.021927</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.714931e+17</td>\n",
+       "      <td>2024-06-12 23:00:20.851000064</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>1.001667</td>\n",
+       "      <td>359.648804</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.768238e+10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.078296</td>\n",
+       "      <td>4.584037</td>\n",
+       "      <td>0.192353</td>\n",
+       "      <td>70.151802</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            gate_id                           time          DBZH  \\\n",
+       "count  7.035900e+04                          70359  70359.000000   \n",
+       "mean   9.714930e+17  2024-06-12 22:26:17.997344512     22.344625   \n",
+       "min    9.714930e+17  2024-06-12 22:03:24.385999872     20.500000   \n",
+       "25%    9.714930e+17  2024-06-12 22:11:56.913499904     21.000000   \n",
+       "50%    9.714930e+17  2024-06-12 22:25:49.300999936     21.500000   \n",
+       "75%    9.714930e+17  2024-06-12 22:40:05.139000064     23.000000   \n",
+       "max    9.714931e+17  2024-06-12 23:00:20.851000064     48.000000   \n",
+       "std    1.768238e+10                            NaN      2.078296   \n",
+       "\n",
+       "                ZDR         RHOHV         PHIDP  TEMP  \n",
+       "count  36583.000000  36583.000000  36583.000000   0.0  \n",
+       "mean       4.353422      0.769320     90.555733   NaN  \n",
+       "min      -13.062500      0.201667     -0.705194   NaN  \n",
+       "25%        1.031250      0.655000     56.062901   NaN  \n",
+       "50%        4.187500      0.818333     71.929764   NaN  \n",
+       "75%        7.875000      0.928333     98.021927   NaN  \n",
+       "max       20.000000      1.001667    359.648804   NaN  \n",
+       "std        4.584037      0.192353     70.151802   NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 20})\n",
     "print(f\"{len(rdf):,} gates -> {len(df):,} above 30 dBZ\")\n",
@@ -190,10 +441,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "11",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:16.570785Z",
+     "iopub.status.busy": "2026-08-19T13:34:16.570586Z",
+     "iopub.status.idle": "2026-08-19T13:34:20.380120Z",
+     "shell.execute_reply": "2026-08-19T13:34:20.379401Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "rdf.plot_ppi(sweep=11, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax, coords=\"projected\", context=True)\n",
@@ -211,10 +497,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "13",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:20.381980Z",
+     "iopub.status.busy": "2026-08-19T13:34:20.381814Z",
+     "iopub.status.idle": "2026-08-19T13:34:23.778774Z",
+     "shell.execute_reply": "2026-08-19T13:34:23.778184Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ccz5ixAiu1Wplv2dKTp06xbOysnjPnj1l1cWcc3711VdzjUYjW9auXTt+7bXXeqRz5swZDsCjaptzzvV6vfO7qn379nzfvn0e2ygdK5DzOnXqFE9JSeH/+c9/nMt8PXtI3aj3JXW33Xabx7Ly8nJMnDgRbdu2hVarhVarRWJiIioqKrB//37nduvXr8fAgQNlDYc1Go2zcb2ro0ePYvjw4cjOzoZGo4FOp3M2vndN01e+fLn55ptRWlqKcePGOaua6sKcOXMgCALMZjM2bdpUZ8d1xb38lX7llVdi+/bt2L59O3799Vd8+OGHKCgowNVXX63Yu7e8vBy33HILbDYbli9fjuTkZK/HfOqpp5xpu76eeuqpkJ2Xa77UfB5dvfbaa5g7dy7mz5+PIUOG+D1GTk4OLr30Uuf79PR0ZGZmonv37sjNzXUu79ixIwCoquoOxs033yx77yht8HU8QRAwduxYrFu3Dq+88goMBgMOHz6Me++919mA3r3azVsJhJqSiUD3b9y4Mdq0aaM67WAdO3YMN954I5o1a4YPPvhAVd78rauqqsKwYcNw/PhxfPnll0hMTHSuKy0txSOPPIKJEyeiS5cuPvPmXqLt+jurJl+OEnCj0Ygvv/wS999/P66++mrMmzcPt9xyC2bPnu3sfSyKYshLzz/44AO8+uqreOaZZzB06NCA93/ppZfw2Wef4a233pL9nrk7f/48rr/+enDO8cUXX3h8btetWwebzeaxX6D3dvPmzdiyZQs+/fRTJCUlYcCAAR49YL0dS+15Pfroo7j44ovx0EMP+UyD1K16H9Qp9QIcPnw45s6di9GjR+PHH3/Etm3bsH37dmRkZKCystK5XVFRkUe1AQCPZeXl5fi///s//Pbbb5g+fTo2bNiA7du34+uvvwYAWZoAEB8f7zOgUPLSSy9h8uTJWLJkCe699946Cey+/PJL/Pe//8Xs2bPRv39/PP744zh37lzYj+vO8cB3DT4Ae/u5nj17omfPnujbty8efPBBLFmyBPv378e//vUvj3RGjRqFvXv3YuHChejUqZPPYzZt2tSZtuuradOmoTuxamo/jw6ffvopXnzxRUyePBn/+Mc/VB0jPT3dY1lMTIzH8piYGABQHDIjFNzbFzmqVJXO09XkyZPx9NNPY/r06cjKykK7du0AwFml1KRJE1n6RUVFHmmcP39e8Too5S/Y/cPl+PHjGDBgALRaLdatW+eRj0aNGinmuaKiAhaLRTHfZrMZt956K3755ResWrUKvXv3lq2fNGkSdDodHn/8cZSUlKCkpMQZWJlMJpSUlIBzjmPHjkGn08leGzduDChfaWlpYIwhOTkZffr0kW07ZMgQVFVVYd++fQCAgQMHyo714IMPqr2MihYuXIhHHnkEDz/8MN54442A9582bRqmT5+OV1991WcVdXFxMQYNGoTTp09j7dq1qpvQeLuG58+fB6D8u33JJZegT58+GDFiBNavXw/OuUdPY398ndeyZcvwww8/4PXXX0dpaanz8wHYm0uUlJSoGt6HhF69H9LE/a+Y0tJSfPvtt5gyZQqef/5553Kz2ez8JXFo1KgR8vPzPdJ0X/bTTz/hzJkz2LBhg2xoDG/jEAX7F/20adPAGMO0adMgSRI+++wzVY1fg3Hu3DmMGTMG/fv3x5NPPombb74ZXbt2xWOPPeYMVuvKqlWrwBjDVVdd5XdbR8nPn3/+KVs+c+ZMLFu2DBMmTMDtt98elnwGI5DPIwCsXbsWDz74IB544AFVbTFDITY2Fmaz2WO5r7EOQ02r1WL27Nl4+eWXkZeXh8aNGyMnJwfXXnstWrVq5Qy2HSVKu3fvxvXXXy9LY/fu3X5LnBydj3bv3i0L/G02G/7++2/cc889oTwtVY4fP47+/fuDc44NGzYo/mHRtWtXfP7558jPz5f90bl7924A8Dhvs9mMW265BevXr8fKlSsVO2vt2bMHx44dU/zDduTIkQDsgUpubi62b98uW+/oMKI2X3FxcWjXrp3i962j1M9RqvWf//wHZWVlzvWNGzf22EethQsXYvTo0Rg5ciTmz58f8HfztGnTMHXqVEydOtVn0FRcXIxrrrkGeXl5WLdunWKbT2+6du2KpUuXwmazyb7vvd1bd0lJSbjoootw8OBB1cf0d1579uyBzWbzCMAB4P3338f777+P5cuXh3xoI+JfvS+pc8cYA+fco9H1Bx984FH6NWDAAKxbt05WOiWKIr744guPNAHPhtz/+c9/Qpl1APYeh9OmTcN///tfDB8+3G/xebAeffRRVFVV4aOPPgJjDK1atcKsWbOwfPlyxUEvw2XhwoVYvXo17rnnHlU973bt2gVA3ovrxx9/xD//+U9cc801mDFjRriyGpRAPo+7du3CbbfdhquvvhoLFiyoszy2bNkSBoNB9ntgsVgUB6fW6/V+S91qwzEAb05ODnbu3Il169bJqsSbNGmCyy67DJ9++qns+m3duhUHDhzAsGHDfKbfu3dv5OTkOAejdli2bBnKy8v97h9qJ06cQP/+/SGKIn766SevPc+HDh0KxhgWL14sW75o0SLExcXJeng7Suh++uknfPXVV7j22msV05wzZw7Wr18ve7311lsA7N9D69evR2JiImJiYjxKtJOSkgLO12233Qaj0YjNmzfLtv3++++RmJjobKjfoUMH2bGUemarsWjRIowePRr33nsvPvjgg4ADuldeeQVTp07FP//5T0yZMsXrdo6A7ujRo1izZg169OgR0HFuvfVWlJeX46uvvpItX7x4MXJzcz1KWN0VFhZi9+7daNu2rarjqTmvBx54wOOzsX79egBw/rFw5ZVXqjoeCa16X1LnLjk5GVdddRXeeOMNNG7cGC1btsTGjRvx4YcfIjU1VbbtP//5T6xatQpXX301Jk+ejPj4eLz33nsew5T07dsXaWlpePTRRzFlyhTodDp89tlnHqVFoTJ58mQIgoCXXnoJnHMsXbo04BK7b775xvnF6+r222/HJ598ghUrVmD+/PmyXqRjxozBsmXL8Pjjj2PAgAFeBykNRmVlJbZu3er8+ejRo1ixYgW+/fZb9OvXz9lz2VVJSYlzH6vViv3792PGjBnQ6/XOwZHz8vJwzz33IC4uDuPGjfMoUXBo2rRpWKpW/VH7eTQajbj++usRFxeHZ5991mNmhk6dOgVcpa/WXXfdhcmTJ+Puu+/Gc889h6qqKrzzzjuKTQC6du2KDRs24JtvvkFOTg6SkpJCMsyHo0lDt27dwDnHtm3bMGvWLFx33XUeVUOzZs3CoEGDcMcdd2DMmDEwGAx4/vnn0aVLF1kPwOPHj6NNmzYYOXIkPvzwQwD2NrOvv/467rvvPjzyyCO45557cOjQIUyYMAGDBg2q0+FvDAYDBgwYgLNnz+LDDz+EwWCQzYrh+pnt3Lkz/vGPf2DKlCnQaDTo1asX1qxZgwULFmD69OmyKrrbb78dq1evxqRJk9CoUSPn7xBg/zw6Sii7d+/uNW+dO3f2GK7G23Zq8/Xss8/is88+wx133IFXXnkFTZs2xbJly7Bq1Sq8+eabqgbpPX78uPN33NEj2jH7RcuWLdGzZ08A9qYl//jHP9C9e3c88sgj2LZtmywdx+DF3vzrX//C5MmTcd111+GGG26QXUMAzhKsyspKXHvttfjjjz8wZ84c2Gw22bYZGRmygbMHDhyIjRs3yv5YHzJkCAYNGoTHHnsMRqMRbdu2xdKlS/HDDz/g008/hUajAWAv9R80aBCGDx+Odu3aIS4uDgcPHsTbb78Ns9nsEaApHUvtebVs2dJrMN2kSRNVnw0SJhHqoBF2vnp5njp1it922208LS2NJyUl8euuu47v2bOHt2jRwqPn3q+//sr79OnD9Xo9z87O5s899xxfsGCBR++1zZs388svv5zHx8fzjIwMPnr0aL5z504OgC9cuNC53ciRI3lCQoLq8/A1aKujp96wYcO4xWJxLoeK3q/eXqdPn+apqakeg686HD16lCckJHj0vnMXaO9X1zwkJCTw1q1b89tvv51/+eWXHj3EOPfs/arRaHjz5s357bffzv/44w/ndq69h329HL1UIzH4sJrPo79euY7r7K33q1LvYm8DOCt9fr7//nvevXt3HhcXx1u3bs3nzp2r2Pt1165d/IorruDx8fEcAO/Xr58sX+6fY0dvSn+fk19//ZX37t2bJycnc71ez7t06cLffPNN2efe1Zo1a3ifPn14bGwsT09P5/fff7/HwKqOa6rUW3fJkiW8W7duPCYmhmdnZ/Mnn3zSo2dosNT2fnVs5+8z62CxWPiUKVN48+bNeUxMDG/fvr2sF7ODrzQd98tfntT2fg0kX5xzfuLECX733XfztLQ0HhMTw7t166Z6pADOff++u95nxygE3l7+7o37d5b7y8Hf7637Z8+RrruysjL+5JNP8uzsbOd1Wbp0qWybqqoqPnr0aN6xY0eemJjItVotb9q0Kb/33nv53r17vZ5DMOflja9nD6kbjPM6GACINDjHjh1Dq1atsH79+gb7V1vLli3xwAMP1Pn8ryS6bdiwAQMGDEBeXl7QVYeEEKKkwbWpI4QQQgipjyioI4QQQgipByioI4QQQgipB6hNHSGEEEKIm9OnT2PixIlYvXo1Kisr0b59e3z44YfO2TU455g2bRoWLFiA4uJi9O7dG++9917Q8weHApXUEUIIIYS4KC4uxhVXXAGdTofVq1dj3759+Ne//iUbaur111/H7NmzMXfuXGzfvh3Z2dkYNGiQbHDsukYldYQQQgghLp5//nn8+uuv+PnnnxXXc86Rm5uLcePGYeLEiQDsA3tnZWVh1qxZeOSRR+oyu071PqiTJAlnzpxBUlJS2CfcJoQQQhoqzjnKysqQm5vrnNattqqqqmCxWEKSFmDPo3ssoNfrPQab7tSpE6699lqcOnUKGzduRJMmTTBmzBg89NBDAICjR4+iTZs22Llzp2yWkKFDhyI1NdVjFpW6Uu9nlDhz5gyaNWsW6WwQQgghDcLJkydDMkNPVVUVcuLiUFL7LDklJiaivLxctmzKlCke44kePXoU8+bNw/jx4/Hiiy9i27ZtePLJJ6HX63H//fc75yl2n1kpKysLx48fD2GOA1PvgzrHVFjHjx/3mAaM1A1JklBQUICMjIyQ/fVGAkP3IPLoHkQHug/h8cq2jXjqoh5o1qyZ4hSUwbBYLCgBMBeA/0ni/KsE8Hh5OU6ePCmbVlFpSjhJktCzZ0/nfOE9evTA3r17MW/ePNx///3O7dxL/ZRKAutSvQ/qHBc3OTk5bHNjEt8kSUJVVRWSk5PpSzRC6B5EHt2D6ED3ITw0cXHOZ2yog5o4APEhTE9NPJCTk+OcB9mhY8eO+OqrrwAA2dnZAID8/Hzk5OQ4tzEYDCGdFz1Q9IkmhBBCSNRiIXypdcUVV+DAgQOyZQcPHkSLFi0AAK1atUJ2djbWrl3rXG+xWLBx40b07ds38JMMkXpfUkcIIYQQEoinn34affv2xYwZM3DnnXdi27ZtWLBgARYsWADAXho5btw4zJgxA+3atUO7du0wY8YMxMfHY/jw4RHLd0RL6jZt2oSbbroJubm5YIxhxYoVHtvs378fN998M1JSUpCUlIQ+ffrgxIkTdZ9ZQgghhHgVrsE0IlFS16tXLyxfvhxLly5Fly5d8Morr2DOnDkYMWKEc5sJEyZg3LhxGDNmDHr27InTp09jzZo1IWtTGIyIltRVVFTg4osvxqhRo3Dbbbd5rD9y5AiuvPJK/OMf/8C0adOQkpKC/fv3IzY2NgK5JYQQQog34RofLdCAzFc6gbjxxhtx4403ek+PMUydOtWj52wkRTSoGzJkCIYMGeJ1/aRJk3D99dfj9ddfdy5r3bp1XWSNEEIIISoxAFL9Hvb2ghC1HSUkScJ3332H9u3b49prr0VmZiZ69+6tWEVLCCGEkMgJ5ygekah+vVBFbUcJg8GA8vJyvPbaa5g+fTpmzZqFH374AcOGDcP69evRr18/xf3MZjPMZrPzvdFoBGAPEiVJqpO8EzlJksA5p+sfQXQPIo/uQXSg+xAmHLCJYliSjlT164UoaoM6xy/c0KFD8fTTTwMAunfvjs2bN2P+/Pleg7qZM2di2rRpHssLCgpCOtUIUU+SJJSWloJzTuNCRQjdg8ijexAd6D6ER5pVRGFhYaSz0eBFbVDXuHFjaLVaxcH/fvnlF6/7vfDCCxg/frzzvdFoRLNmzZCRkUEzSkSIJElgjNEI7hFE9yDy6B5EB7oP4VFyVINGjRtFOhsNXtQGdTExMejVq5fPwf+UKE3MCwCCINAvcAQxxugeRBjdg8ijexAd6D6EHmMMYOG5nlT9ql5Eg7ry8nIcPnzY+T4vLw+7du1Ceno6mjdvjueeew533XUXrrrqKgwYMAA//PADvvnmG2zYsCFymSaEEEKIDGMMPGyDmhC1IhrU7dixAwMGDHC+d1Sbjhw5EosWLcKtt96K+fPnY+bMmXjyySfRoUMHfPXVV7jyyisjlWVCCCGEuGEAwjWiCZXUqRfRoK5///5+R6B+8MEH8eCDD9ZRjgghhBASjHCV1FFQpx41KCCEEEJIrTAWvpI6ol7UdpQghBBCyIWDSuoij4I6QgghhNQKA4NEbeoijqpfCSGEEFIr9oCJ6l8jjYI6QgghhNSaRI3qIo6qXwkhhBBSK+Gs2qTqV/WopI4QQgghtcIYo9rXKEAldYQQQgipHQZI1Ps14iioI4QQQkit0IwS0YGqXwkhhBBSazT3a+RRSR0hhBBCaoWBUUldFKCSOkIIIYTUir2fBJXURRqV1BFCCCGk1qikLvKopI4QQgghtcIQvt6vRD0K6gghhBBSSzROXTSg6ldCCCGE1AoL4zh1QMOoOg0FCuoIIYQQUis0Tl10oOpXQgghhIQA1b9GGgV1hBBCCKkVBkCimC7iqPqVEEIIIbXCwMI2Th1Vv6pHJXWEEEIIqR0G8HA1qiOqUUkdIYQQQmqFIXwt6qikTj0qqSOEEEJI7YSz+ytRjYI6QgghhNSKvU0diTSqfiWEEEJIrYSz9ytVv6pHQR0hhBBCaieMERODfcaKWqfTAIoSqfqVEEIIIbXCEN5pwog6VFJHCCGEkFoKX5s6pglhSZ1Y+3SiGQV1hBBCCKmVUARdXgkITfVuAwjqqPqVEEIIIbVi7yhB1a+RRiV1hBBCCKmVsA4+LFBHCbUoqCOEEEJILbGwTRMW0jZ19RxVvxJCCCGkVhgLX0kdUY9K6gghhBBSa+EqqQtpR4l6joI6QgghhNSKvU1dmKpfqU2dalT9SgghhJBaYWBhmyaMqEcldYQQQggJASqpizQK6gghhBBSK4whfCV1AkJTryiFII0oF9Hq102bNuGmm25Cbm4uGGNYsWKF120feeQRMMYwZ86cOssfIYQQQtRqAEVhUS6iQV1FRQUuvvhizJ071+d2K1aswG+//Ybc3Nw6yhkhhBBC1GJhHH2YCaF71XcRrX4dMmQIhgwZ4nOb06dP4/HHH8ePP/6IG264oY5yRgghhBC1GBikcLapC0FAFs7paaNFVLepkyQJ9913H5577jl07txZ1T5msxlms9n53mg0OtOSpAZQoR6FJEkC55yufwTRPYg8ugfRge5DmHAOibq/RlxUB3WzZs2CVqvFk08+qXqfmTNnYtq0aR7LCwoKYLFYQpk9opIkSSgtLQXnHILQAMq/oxDdg8ijexAd6D6ER6zJAjMrDU/iGoSmsVgDKKqL2qDu999/x9tvv42dO3eCBdCX+YUXXsD48eOd741GI5o1a4aMjAykpqaGIafEH0mSwBhDRkYGfYlGCN2DyKN7EB3oPoSHpTAPOm1yWNKm6lf1ojao+/nnn2EwGNC8eXPnMlEU8cwzz2DOnDk4duyY4n56vR56vd5juSAI9AscQYwxugcRRvcg8ugeRAe6D2FQz3ohTJ061aPWLysrC/n5+QDsU6JNmzYNCxYsQHFxMXr37o333ntPdVOxcInaoO6+++7DNddcI1t27bXX4r777sOoUaMilCtCCCGEuAtj59fQjVMXoM6dO+N///uf871Go3H+/Prrr2P27NlYtGgR2rdvj+nTp2PQoEE4cOAAkpKS6j6z1SIa1JWXl+Pw4cPO93l5edi1axfS09PRvHlzNGrUSLa9TqdDdnY2OnToUNdZJYQQQog3DJB4/er9qtVqkZ2d7bGcc445c+Zg0qRJGDZsGABg8eLFyMrKwpIlS/DII4/UPrNBimh56Y4dO9CjRw/06NEDADB+/Hj06NEDkydPjmS2CCGEEBKA+the7dChQ8jNzUWrVq1w99134+jRowDsBVD5+fkYPHiwc1u9Xo9+/fph8+bNkcougAiX1PXv3x88gMjeWzs6QgghhEQOAwtbSV2oe786hjpzUGqL37t3b3z88cdo3749zp07h+nTp6Nv377Yu3evs11dVlaWbJ+srCwcP348BBkNXtS2qSOEEELIhSGcbeoYC1H1a3UGmzVrJls+ZcoUTJ06VbbMdWKErl274vLLL0ebNm2wePFi9OnTpzpf8vJJznlAo3WEAwV1hBBCCKm1QGreIunkyZNITq4ZfkVpxAx3CQkJ6Nq1Kw4dOoRbbrkFAJCfn4+cnBznNgaDwaP0rq7Vrz7IhBBCCKlzddL7NRQvAMnJybKXmqDObDZj//79yMnJQatWrZCdnY21a9c611ssFmzcuBF9+/YNzTkHiUrqCCGEEFI7jIGHaZowprG/ap1OANs+++yzuOmmm9C8eXMYDAZMnz4dRqMRI0eOBGMM48aNw4wZM9CuXTu0a9cOM2bMQHx8PIYPH177jNYCBXWEEEIIqRXGwlhSFwGnTp3CPffcg8LCQmRkZKBPnz7YunUrWrRoAQCYMGECKisrMWbMGOfgw2vWrInoGHUABXWEEEIIqSWG8LWpC9k4dQFk7/PPP/edFmOYOnWqRweLSKOgjhBCCCHRK1QzStSnokQvqKMEIYQQQmqFsQun92t9RiV1hBBCCKklBilcSYeo+rUhlNRRUEcIIYSQWmEAEM4ZJULQ+7UhoOpXQgghhNQKA8JXUkdUo5I6QgghhNQOC19BXSR6v16oKKgjhBBCSK0wsPA1WWMITb1iAyhKpOpXQgghhNRKOMepI+pRSR0hhBBCaieMM0qEbJqwBhBzUlBHCCGEkFphHODhCutCNfhwA6ibbACnSAghhJDwCmObOqIaldQRQgghpFbCOaNEyHq/NoBiLArqCCGEEFIrLJxt6iioU60BnCIhhBBCwk2i3q8RRyV1hBBCCKkVZp8oLDxCNU1YA4g5KagjhBBCSO2wMJbUUe9X1RrAKRJCCCEknBgaREFY1KOSOkIIIYTUCgPCNvkrdZRQj4I6QgghhNQKYxfA3K9hbPYXLRpA3EoIIYSQcLLHS1QBG2lUUkcIIYSQWmFhnFEiZHO/SrVPI9pRUEcIIYSQWmHh7ClBvV9VawCnSAghhJBwCmdJHVGPSuoIIYQQUiv2oI56v0YaBXWEEEIIqRUWzp6lVP2qWgM4RUIIIYSEE1W/RgcqqSOEEEJIrdg7SlD1a6RRUEcIIYSQWgpjSZ2m+lVbDWBIkwYQtxJCCCEknAQgbB0liHpUUkcIIYSQ2mGMxqmLAhTUEUIIIaRWwhkvMRaiNnU092t4bdq0CTfddBNyc3PBGMOKFSuc66xWKyZOnIiuXbsiISEBubm5uP/++3HmzJnIZZgQQgghChpAxHQBiGhQV1FRgYsvvhhz5871WGcymbBz50689NJL2LlzJ77++mscPHgQN998cwRySgghhBBvtIKAVikp4UlcCOGrnoto9euQIUMwZMgQxXUpKSlYu3atbNm7776Lyy67DCdOnEDz5s3rIouEEEII8aN/bjtUlJeHJW2msb9CkU59d0G1qSstLQVjDKmpqV63MZvNMJvNzvdGoxEAIEkSJKkB9GeOQpIkgXNO1z+C6B5EHt2D6ED3ITwYQNc0ClwwQV1VVRWef/55DB8+HMnJyV63mzlzJqZNm+axvKCgABaLJZxZJF5IkoTS0lJwziEIDaD8OwrRPYg8ugfRge5D+JSVlYUnYer9qtoFEdRZrVbcfffdkCQJ//73v31u+8ILL2D8+PHO90ajEc2aNUNGRobPEj4SPpIkgTGGjIwM+hKNELoHkUf3IDrQfQif2NjYSGehwYv6oM5qteLOO+9EXl4efvrpJ5+ldACg1+uh1+s9lguCQL/AEcQYo3sQYXQPIo/uQXSg+xAe4bqeNE2YelEd1DkCukOHDmH9+vVo1KhRpLNECCGEkLpE1a+qRTSoKy8vx+HDh53v8/LysGvXLqSnpyM3Nxe33347du7ciW+//RaiKCI/Px8AkJ6ejpiYmEhlmxBCCCEk6kQ0qNuxYwcGDBjgfO9oCzdy5EhMnToVq1atAgB0795dtt/69evRv3//usomIYQQQiKEql/Vi2hQ179/f3DufbI4X+sIIYQQ0gBoql+hSKeeawBxKyGEEEJI/RfVHSUIIYQQ0sAxhKYIqgFMT0tBHSGEEEKiFrWpU68BnCIhhBBCSP1HJXWEEEIIiV40Tp1qFNQRQgghJHpRUKdaAzhFQgghhJD6j0rqCCGEEBK1qKOEehTUEUIIISR60eDDqjWAuJUQQgghpP6jkjpCCCGERC/qKKEaBXWEEEIIiVrUpk69BnCKhBBCCCHBmzlzJhhjGDdunHMZ5xxTp05Fbm4u4uLi0L9/f+zduzdymQQFdYQQQgiJZkIIX0HYvn07FixYgG7dusmWv/7665g9ezbmzp2L7du3Izs7G4MGDUJZWVlwBwoBCuoIIYQQErUYq6mCrdWLBX7s8vJyjBgxAu+//z7S0tKcyznnmDNnDiZNmoRhw4ahS5cuWLx4MUwmE5YsWRLCsw8MBXWEEEIIaTCMRqPsZTabvW47duxY3HDDDbjmmmtky/Py8pCfn4/Bgwc7l+n1evTr1w+bN28OW979oaCOEEIIIdErxNWvzZo1Q0pKivM1c+ZMxcN+/vnn2Llzp+L6/Px8AEBWVpZseVZWlnNdJFDvV0IIIYRErxAPPnzy5EkkJyc7F+v1eo9NT548iaeeegpr1qxBbGys1ySZW50u59xjWV2ioI4QQgghDUZycrIsqFPy+++/w2Aw4NJLL3UuE0URmzZtwty5c3HgwAEA9hK7nJwc5zYGg8Gj9K4uUfUrIYQQQqJWSDpJBDjW3cCBA7F7927s2rXL+erZsydGjBiBXbt2oXXr1sjOzsbatWud+1gsFmzcuBF9+/YNw1VQh0rqCCGEEBK9IjCjRFJSErp06SJblpCQgEaNGjmXjxs3DjNmzEC7du3Qrl07zJgxA/Hx8Rg+fHgIMhscCuoIIYQQQgI0YcIEVFZWYsyYMSguLkbv3r2xZs0aJCUlRSxPFNQRQgghJHpFydyvGzZskL1njGHq1KmYOnVq7RIOIQrqCCGEEBK9QjT3azT0IkhPTw9oe8YYdu7ciRYtWqjanoI6QgghhJA6UFJSgjlz5iAlJcXvtpxzjBkzBqIoqk5fVVBnNBpVJ+jgr7swIYQQQohfUVL9Gip33303MjMzVW37xBNPBJS2qqAuNTU1oMH0GGM4ePAgWrduHVBmCCGEEEJkGEITkEVuTGAnSZIC2r6srCyg7VVXvy5btkxVXTDnHNdff31AmSCEEEIIIbWjKqhr0aIFrrrqKjRq1EhVoq1bt4ZOp6tVxgghhBBCAh042Fc60USj0eCqq67CV199JSs0O3fuHHJzcwNqS+eg6hTz8vJUB3QAsGfPHjRr1izgzBBCCCGEyGhC+IoinHOYzWb07NkTe/bs8VgXjCiLWwkhhBBC6j/GGL766ivcdNNN6Nu3L1auXClbF4yghjTZtm0bNmzYAIPB4NHob/bs2UFlhBBCCCHEQz3r/erAOYdGo8Hbb7+Nzp0746677sI///lPjB49Oug0Aw7qZsyYgX/+85/o0KEDsrKyZNFksJElIYQQQoiS+tqmztXDDz+M9u3b4/bbb8fGjRuDTifgoO7tt9/GRx99hAceeCDogxJCCCGENGQtWrSARlPT0K9///7YunUrbrrppqDTDDioEwQBV1xxRdAHJIQQQghRrZ5Wv+bl5Xksa9u2Lf744w+cO3cuqDQDPsWnn34a7733XlAHI4QQQggJiBDCVxQ7evQo9u7di5iYGNVzvboLuKTu2WefxQ033IA2bdqgU6dOHuPRff3110FlhBBCCCGkvrNarZg+fTp27tyJPn364Pnnn8e9996L//73vwCADh064Pvvv0fLli0DTjvguPWJJ57A+vXr0b59ezRq1AgpKSmyVyA2bdqEm266Cbm5uWCMYcWKFbL1nHNMnToVubm5iIuLQ//+/bF3795As0wIIYSQC5Sjo0QoXtHg+eefx7x585CVlYWPPvoIw4YNwx9//IElS5bg888/h1arxaRJk4JKO+CSuo8//hhfffUVbrjhhqAO6KqiogIXX3wxRo0ahdtuu81j/euvv47Zs2dj0aJFaN++PaZPn45BgwbhwIEDSEpKqvXxCSGEEBLl6lmbumXLlmHRokW4/vrrcfDgQVx00UX47rvvMGTIEABAZmYmRowYEVTaAQd16enpaNOmTVAHczdkyBDnSbjjnGPOnDmYNGkShg0bBgBYvHgxsrKysGTJEjzyyCMhyQMhhBBCSF05c+YMLr74YgBA+/btodfr0bZtW+f69u3bIz8/P6i0A45bp06diilTpsBkMgV1QLXy8vKQn5+PwYMHO5fp9Xr069cPmzdvDuuxCSGEEBIlGELTSSJKhtIVRVHWH0Gr1cqGNhEEIehpwgIuqXvnnXdw5MgRZGVloWXLlh4dJXbu3BlURtw5otSsrCzZ8qysLBw/ftzrfmazGWaz2fneaDQCACRJ8pj9gtQNSZLAOafrH0F0DyKP7kF0oPsQPuG6pvVx8OEff/zR2Q9BkiSsW7fOOf9rSUlJ0OkGHNTdcsstQR8sGO6zVHDOfc5cMXPmTEybNs1jeUFBASwWS8jzR/yTJAmlpaXgnEMQoui3qgGhexB5dA+iA92H8CkrK4t0Fi4YI0eOlL13b1JWZ3O/Tpkyxeu6YIsLlWRnZwOwl9jl5OQ4lxsMBo/SO1cvvPACxo8f73xvNBrRrFkzZGRkIDU1NWT5I+pJkgTGGDIyMuhLNELoHkQe3YPoQPchfGJjY8OTcD3rKBHOUuKAg7qZM2fihRde8FguiiLuvfdeLF26NCQZa9WqFbKzs7F27Vr06NEDAGCxWLBx40bMmjXL6356vR56vd5juSAI9AscQYwxugcRRvcg8ugeRAe6D+ERtutZz4K6cAo4qJszZw4aNWqEhx9+2LlMFEXcfffdzvpgtcrLy3H48GHn+7y8POzatQvp6elo3rw5xo0bhxkzZqBdu3Zo164dZsyYgfj4eAwfPjzQbBNCCCGERNSqVatUb3vzzTcHnH7AQd3333+Pa665BqmpqbjzzjthtVpx11134e+//8b69esDSmvHjh0YMGCA872j2nTkyJFYtGgRJkyYgMrKSowZMwbFxcXo3bs31qxZQ2PUEUIIIQ1FPSqpc++XwBiTNV1zbUsnimLA6Qd8ipdeeimWL1+Ohx56CCtXrsRtt92GAwcOYP369c52cGr1798fnHOP16JFiwDYT27q1Kk4e/YsqqqqsHHjRnTp0iXQLBNCCCHkAlWfZpRwjMQhSRLWrFmD7t27Y/Xq1SgpKUFpaSm+//57XHLJJfjhhx+CSj/gkjrAHox98sknuO2229CxY0ds3LgRjRs3DioDhBBCCCENzbhx4zB//nxceeWVzmXXXnst4uPj8fDDD2P//v0Bp6kqqHPM6ODO0aPUtX3d119/HXAmCCGEEEIUaapfoUgnihw5csQ5Vp2rlJQUHDt2LKg0VQV1SgcF7BElIYQQQkjY1KM2da569eqFcePG4dNPP3UO3Zafn49nnnkGl112WVBpqgrqFi5cGFTihBBCCCHE00cffYRbb70VLVq0QPPmzQEAJ06cQPv27bFixYqg0gyqTR0hhBBCSF2oj9OEAUDbtm3x119/Ye3atfj777/BOUenTp1wzTXXhHdGiUsuuQTr1q1DWlqaqkSvvPJKfPHFF2jSpElQmSKEEEIIAQAwhKbqNLg4KawYYxg8eDAGDx4ckvRUBXW7du3Cn3/+ifT0dFWJ7tq1C2azuVYZI4QQQgipT9555x08/PDDqqdUmz9/PkaMGKF6fF7V1a8DBw5UPbdrsMWGhBBCCCEy9aijxNNPP4177rlHdVA3YcIEDB48OLRBXV5enqrEXDVt2jTgfQghhBBCXNWnNnWccwwcOBBarboytcrKyoDSV5VqixYtAkqUEEIIIYTITZkyJaDthw4dqrrpG0C9XwkhhBASzepR9WugQV2gKKgjhBBCSPSqR0FduDWAUySEEEIIqf9CFtTZbDacOHEiVMkRQgghhNSU1IXiVc+FrPp17969uOSSSyCKYqiSJIQQQkgDZ+/9Wvuh0pigbli2C1kDiFsJIYQQQqLLhg0bQp4mBXWEEEIIiV6CELpXFLnuuuvQpk0bTJ8+HSdPngxJmtF1hoQQQgghrgRN6F5R5MyZM3jqqafw9ddfo1WrVrj22mvx3//+FxaLJeg0Vbep++uvv3yuP3DgQNCZIIQQQghpSNLT0/Hkk0/iySefxK5du/DRRx9h7NixeOyxxzBixAj84x//wMUXXxxQmqqDuu7du4Mxpjj/q2M5zflKCCGEkJBiGiAEHSXAOABr7dMJg+7du+P5559Heno6XnvtNXz00Uf497//jcsvvxzz589H586dVaWjOqgLZv5XQgghhJDaYIKm3vZ+tVqtWLlyJT766COsXbsWPXv2xNy5c3HPPffg/PnzmDhxIu644w7s27dPVXqqgzqa/5UQQgghJDSeeOIJLF26FABw77334vXXX0eXLl2c6xMSEvDaa6+hZcuWqtNUHdSdP38eJpMJTZs2dS7bu3cv3nzzTVRUVOCWW27B8OHDVR+YEEIIIcQvIUTVr1FWUrdv3z68++67uO222xATE6O4TW5uLtavX686TdW9X8eOHYvZs2c73xsMBvzf//0ftm/fDrPZjAceeACffPKJ6gMTQgghhPhVT3u/TpkyBXfccYdHQGez2bBp0yYAgFarRb9+/VSnqTqo27p1K26++Wbn+48//hjp6enYtWsXVq5ciRkzZuC9995TfWBCCCGEkIZqwIABOH/+vMfy0tJSDBgwIKg0VQd1+fn5aNWqlfP9Tz/9hFtvvRVarb0G9+abb8ahQ4eCygQhhBBCiBImMDBBCMErukbo8DZqSFFRERISEoJKU3WbuuTkZJSUlDg7TGzbtg3/+Mc/nOsZYzCbzUFlghBCCCFEkaAJzWwQURLUDRs2DIA9bnrggQeg1+ud60RRxF9//YW+ffsGlbbqoO6yyy7DO++8g/fffx9ff/01ysrKcPXVVzvXHzx4EM2aNQsqE4QQQgghDUFKSgoAe0ldUlIS4uLinOtiYmLQp08fPPTQQ0GlrTqoe+WVV3DNNdfg008/hc1mw4svvoi0tDTn+s8//zygxnyEEEIIIX5FoKRu3rx5mDdvHo4dOwYA6Ny5MyZPnowhQ4YAsAdk06ZNw4IFC1BcXIzevXvjvffeUzVI8MKFCwEALVu2xLPPPht0VauSgGaU2L9/PzZv3ozs7Gz07t1btv7uu+9WPeIxIYQQQki0atq0KV577TW0bdsWALB48WIMHToUf/zxBzp37ozXX38ds2fPxqJFi9C+fXtMnz4dgwYNwoEDB5CUlKTqGFOmTAl5vlUHdQCQkZGBoUOHKq7r06cPPvnkE4wbNy4U+SKEEEIIiUhJ3U033SR7/+qrr2LevHnYunUrOnXqhDlz5mDSpEnO9nGLFy9GVlYWlixZgkceecRrupdccgnWrVuHtLQ09OjRw+f0qjt37lSdX4eAgjp3nHOsWbMGH374IVauXInk5GQK6gghhBASMvZpwmof1AXb+1UURXz55ZeoqKjA5Zdfjry8POTn52Pw4MHObfR6Pfr164fNmzf7DOqGDh3q7BgxdOhQn0FdMIIK6o4dO4aPPvoIixYtwunTpzFixAh89913QY+rQgghhBBSF4xGo+y9Xq+X9UB12L17Ny6//HJUVVUhMTERy5cvR6dOnbB582YAQFZWlmz7rKwsHD9+3OexXatcp06dGuQZeKc69DWbzVi6dCkGDhyIjh07Ys+ePZg9ezYEQcDzzz+Pa665BhpNdI3WTAghhJALHAvRbBLMHqM0a9YMKSkpztfMmTMVD9uhQwfs2rULW7duxWOPPYaRI0di3759NdlyK2XzNu6cN6NGjcK6devAeeimL1NdUtekSRN06tQJ9957L5YtW+bs+XrPPfeELDOEEEIIITJMcAZktUvH/t/JkyeRnJzsXKxUSgfYhxdxdJTo2bMntm/fjrfffhsTJ04EYJ+UIScnx7m9wWDwKL3zpaioCDfccAMaNWqEu+++G/fddx+6d+8e4EnJqS6pE0URjDEwxqhEjhBCCCEXpOTkZNnLW1DnjnMOs9mMVq1aITs7G2vXrnWus1gs2LhxY0CDBq9atQr5+fmYMmUKfv/9d1x66aXo1KkTZsyY4RxKJVCqg7qzZ8/i4YcfxtKlS5GdnY3bbrsNy5cvD3kjP0IIIYQQB3tHidC81HrxxRfx888/49ixY9i9ezcmTZqEDRs2YMSIEWCMYdy4cZgxYwaWL1+OPXv24IEHHkB8fDyGDx8e0Lmlpqbi4YcfxoYNG3D8+HGMGjUKn3zyibOEMFCqg7rY2FiMGDECP/30E3bv3o2OHTviySefhM1mw6uvvoq1a9dCFMWgMuGNzWbDP//5T7Rq1QpxcXFo3bo1Xn75ZUiSFNLjEEIIISRKCdrQvVQ6d+4c7rvvPnTo0AEDBw7Eb7/9hh9++AGDBg0CAEyYMAHjxo3DmDFj0LNnT5w+fRpr1qxRPUadO6vVih07duC3337DsWPHAqrGdcV4LVroSZKEH3/8ER9++CG++eYbJCUlobCwMNjkPLz66qt46623sHjxYnTu3Bk7duzAqFGjMH36dDz11FOq0jAajUhJSUFxcTFSU1NDljeiniRJMBgMyMzMhBCKsYZIwOgeRB7dg+hA9yF8HM/b0tJSWZu12qZ3cE8nJCXVvtlXWZmI9l32hSx/obB+/XosWbIEX331FURRxLBhwzBixAhcffXVQX0+azVOnSAIGDJkCIYMGYKCggJ88skntUnOw5YtWzB06FDccMMNAOxTaixduhQ7duwI6XEIIYQQEqUcvVdrnU7tkwilpk2boqioCNdeey3+85//4KabbkJsbGyt0qxVUOcqIyMD48ePD1VyAIArr7wS8+fPx8GDB9G+fXv8+eef+OWXXzBnzpyQHocQQgghUaqeBnWTJ0/GHXfc4RxNJBRCFtSFw8SJE1FaWoqLLroIGo0Goiji1Vdf9TmMitlshtlsdr53DDIoSRK1xYsQSZLAOafrH0F0DyKP7kF0oPsQPnRNA/Pwww+HPM2oDuq++OILfPrpp1iyZAk6d+6MXbt2Ydy4ccjNzcXIkSMV95k5cyamTZvmsbygoAAWiyXcWSYKJElCaWkpOOfUhiVC6B5EHt2D6ED3IXzKysrCkq6952rtwxUWBbd72LBhWLRoEZKTk53zxnrz9ddfB5x+VAd1zz33HJ5//nncfffdAICuXbvi+PHjmDlzpteg7oUXXpBVAxuNRjRr1gwZGRnUUSJCJEkCYwwZGRn0JRohdA8ij+5BdKD7ED61bQ/mVciqX0M3c0OwUlJSnEPBJScnR37u15dffhnPPvss4uPjZcsrKyvxxhtvYPLkySHLnMlk8vil02g0Pot4vc3hJggC/QJHEGOM7kGE0T2IPLoH0YHuQ3jQ9fRv4cKFzp8XLVoU8vQDvgPTpk1DeXm5x3KTyaRY7VkbN910E1599VV89913OHbsGJYvX47Zs2fj1ltvDelxCCGEEBKlQjHva6hK+0Lo6quvRklJicdyo9GIq6++Oqg0Ay6p8zZh7Z9//on09PSgMuHNu+++i5deegljxoyBwWBAbm4uHnnkkZCWBhJCCCEkejEW2GwQ3tOJfPWrqw0bNii29a+qqsLPP/8cVJqqg7q0tDTn3K/t27eXBXaiKKK8vByPPvpoUJnwJikpCXPmzKEhTAghhBBSL/z111/On/ft24f8/Hzne1EU8cMPP6BJkyZBpa06qJszZw4453jwwQcxbdo0pKSkONfFxMSgZcuWuPzyy4PKBCGEEEKIogCn+PKeTu2TCIXu3bs7C8mUqlnj4uLw7rvvBpW26qvk6G3aqlUr9O3bFzqdLqgDEkIIIYSoxjT2V63TiY7q17y8PHDO0bp1a2zbtg0ZGRnOdTExMcjMzIRGE9z5Bhz69uvXD5Ik4eDBgzAYDB49Ua+66qqgMkIIIYQQUt+1aNECQHgGaw44qNu6dSuGDx+O48ePg3N51MsYgyiKIcscIYQQQho4QQjROHXRNePFzJkzkZWVhQcffFC2/KOPPkJBQQEmTpwYcJoB1zA/+uij6NmzJ/bs2YPz58+juLjY+Tp//nzAGSCEEEII8YYJ2pC9osl//vMfXHTRRR7LO3fujPnz5weVZsBneOjQISxbtgxt27YN6oCEEEIIIQ1dfn4+cnJyPJZnZGTg7NmzQaUZcEld7969cfjw4aAORgghhBASkHo6+HCzZs3w66+/eiz/9ddfkZubG1SaqkrqXMdUeeKJJ/DMM88gPz8fXbt29egF261bt6AyQgghhBDiIWRzv0ZXm7rRo0dj3LhxsFqtzqFN1q1bhwkTJuCZZ54JKk1VQZ1jTBXXjhGuDfsc66ijBCGEEEKIfxMmTMD58+cxZswY58wSsbGxmDhxIl544YWg0lQV1OXl5QWVOCGEEEJIrdTTkjrGGGbNmoWXXnoJ+/fvR1xcHNq1awe9Xh90mqqCOseYKoQQQgghdSlUPVeZEB2DD7vLz8/H+fPncdVVV0Gv1ztrPoMR8FVatWqV4nLGGGJjY9G2bVu0atUqqMwQQgghhDQERUVFuPPOO7F+/XowxnDo0CG0bt0ao0ePRmpqKv71r38FnGbAQd0tt9zi0b4OkLeru/LKK7FixQqkpaUFnCFCCCGEEAfOBPAQTBPGWXS1+X/66aeh0+lw4sQJdOzY0bn8rrvuwtNPPx1UUBfwkCZr165Fr169sHbtWpSWlqK0tBRr167FZZddhm+//RabNm1CUVERnn322YAzQwghhBDiSgIL2SuarFmzBrNmzULTpk1ly9u1a4fjx48HlWbAJXVPPfUUFixYgL59+zqXDRw4ELGxsXj44Yexd+9ezJkzx2PaC0IIIYQQYldRUYH4+HiP5YWFhUF3lgi4pO7IkSNITk72WJ6cnIyjR48CsEeZhYWFQWWIEEIIIcRB4ixkr2hy1VVX4eOPP3a+Z4xBkiS88cYbGDBgQFBpBlxSd+mll+K5557Dxx9/jIyMDABAQUEBJkyYgF69egGwTyXmXpxICCGEEBIoDgE88DIoxXSiyRtvvIH+/ftjx44dsFgsmDBhAvbu3Yvz588rzjShRsBn+OGHHyIvLw9NmzZF27Zt0a5dOzRt2hTHjh3DBx98AAAoLy/HSy+9FFSGCCGEEELqu06dOuGvv/7CZZddhkGDBqGiogLDhg3DH3/8gTZt2gSVZsAldR06dMD+/fvx448/4uDBg+Cc46KLLsKgQYMgCPYY8ZZbbgkqM4QQQgghrkJVdRpt1a8AkJ2djWnTpoUsvaBG82OM4brrrsN1110XsowQQgghhLgLVc/VaOj9+tdff6netlu3bgGnryqoe+edd/Dwww8jNjYW77zzjs9tn3zyyYAzQQghhBBS33Xv3l1xrF93jDGIYuDj6qkK6t566y2MGDECsbGxeOutt3xmgoI6QgghhISKxAVIvPadHEKRRm3l5eWFNX1VQZ1rJsKdIUIIIYQQh/pU/Xrrrbdi3bp1SEtLw8svv4xnn31Wcay6YAUdtlosFhw4cAA2my1kmSGEEEIIqa/279+PiooKAMC0adNQXl4e0vQD7ihhMpnwxBNPYPHixQCAgwcPonXr1njyySeRm5uL559/PqQZJIQQQkjDVZ+qX7t3745Ro0bhyiuvBOccb775JhITExW3nTx5csDpBxzUvfDCC/jzzz+xYcMGWe/Xa665BlOmTKGgjhBCCCEhw0NU/cqjoPp10aJFmDJlCr799lswxrB69WpotZ6hGGOsboK6FStW4IsvvkCfPn3AWM0F6tSpE44cORJwBgghhBBCGoIOHTrg888/BwAIgoB169YhMzMzZOkHHNQVFBQoZqCiokIW5BFCCCGE1FZ9HXxYkqSQpxlwBXOvXr3w3XffOd87Arn3338fl19+eehyRgghhJAGT4IQsle0+eSTT3DFFVcgNzcXx48fB2AfRm7lypVBpRdwSd3MmTNx3XXXYd++fbDZbHj77bexd+9ebNmyBRs3bgwqE4QQQgghDcm8efMwefJkjBs3Dq+++qpzsOG0tDTMmTMHQ4cODTjNgMPWvn374tdff4XJZEKbNm2wZs0aZGVlYcuWLbj00ksDzgAhhBBCiDcSasaqq90rurz77rt4//33MWnSJGg0Gufynj17Yvfu3UGlGdTcr127dnUOaUIIIYQQEi71aUgTV3l5eejRo4fHcr1e7xzLLlCqgzqj0ahqu+Tk5KAyQgghhBDSULRq1Qq7du1CixYtZMtXr16NTp06BZWm6qAuNTXVZ+9WznnQE9ASQgghhCipT9OEuXruuecwduxYVFVVgXOObdu2YenSpZg5cyY++OCDoNJUHdStX7/e+TPnHNdffz0++OADNGnSJKgDE0IIIYT4U1+HNBk1ahRsNhsmTJgAk8mE4cOHo0mTJnj77bdx9913B5Wm6qCuX79+svcajQZ9+vRB69atgzowIYQQQkhD9tBDD+Ghhx5CYWEhJElyjgN8+vTpoArNoqvVICGEEEKIi/o8Tp1D48aNkZmZifz8fDzxxBNo27ZtUOlE7xkSQgghpMFzVL+G4hUNSkpKMGLECGRkZCA3NxfvvPMOJEnC5MmT0bp1a2zduhUfffRRUGnXKqiri2nBTp8+jXvvvReNGjVCfHw8unfvjt9//z3sxyWEEEIICbUXX3wRmzZtwsiRI5Geno6nn34aN954I3755ResXr0a27dvxz333BNU2qrb1A0bNkz2vqqqCo8++igSEhJky7/++uugMqKkuLgYV1xxBQYMGIDVq1cjMzMTR44cQWpqasiOQQghhJDoxUNUdcqjpHLyu+++w8KFC3HNNddgzJgxaNu2Ldq3b485c+bUOm3VQV1KSors/b333lvrg/sza9YsNGvWDAsXLnQua9myZdiPSwghhJDoIEr2VyjSiQZnzpxxjkPXunVrxMbGYvTo0SFJW3VQ5xpY1ZVVq1bh2muvxR133IGNGzeiSZMmGDNmDB566CGv+5jNZpjNZud7x6DJkiRBkqLkjjYwkiSBc07XP4LoHkQe3YPoQPchfOiaqiNJEnQ6nfO9RqPxqPUMVlDThNWVo0ePYt68eRg/fjxefPFFbNu2DU8++ST0ej3uv/9+xX1mzpyJadOmeSwvKCiAxWIJd5aJAkmSUFpaCs45BCE6ir8bGroHkUf3IDrQfQifsrKysKQrcgYxBJ0cQpFGKHDO8cADD0Cv1wMIbXO2qA7qJElCz549MWPGDABAjx49sHfvXsybN89rUPfCCy9g/PjxzvdGoxHNmjVDRkYGtcWLEEmSwBhDRkYGfYlGCN2DwFhFCTpNaK8T3YPoQPchfGJjY8OSbn0bfHjkyJGy96FszhbVQV1OTo7H/GcdO3bEV1995XUfvV7vjH5dCYJAv8ARxBijexBhdA/Ue2v9GTw/sGnI06V7EB3oPoRHfbqeM2fOxNdff42///4bcXFx6Nu3L2bNmoUOHTo4t+GcY9q0aViwYAGKi4vRu3dvvPfee+jcubPPtMPZnC2q78AVV1yBAwcOyJYdPHjQY/JbQggJJZvEI50FQkg1UWIhe6m1ceNGjB07Flu3bsXatWths9kwePBgVFRUOLd5/fXXMXv2bMydOxfbt29HdnY2Bg0aFLZqaDWiuqTu6aefRt++fTFjxgzceeed2LZtGxYsWIAFCxZEOmuEEEIIqQORaFP3ww8/yN4vXLgQmZmZ+P3333HVVVeBc445c+Zg0qRJziHfFi9ejKysLCxZsgSPPPJIrfMbjKguqevVqxeWL1+OpUuXokuXLnjllVcwZ84cjBgxItJZI4QQQkgDUVpaCgBIT08HAOTl5SE/Px+DBw92bqPX69GvXz9s3rw5InkEorykDgBuvPFG3HjjjZHOBiGEEEIiQOKBVZ36SgeoGerMwVtbfAfOOcaPH48rr7wSXbp0AQDk5+cDALKysmTbZmVl4fjx47XOa7CiuqSOEEIIIQ2bxEP3AoBmzZohJSXF+Zo5c6bP4z/++OP466+/sHTpUo917tOlcs7rZApVb6K+pI4QQgghJFROnjyJ5ORk53tfpXRPPPEEVq1ahU2bNqFp05oe8dnZ2QDsJXY5OTnO5QaDwaP0ri5RSR0hhEQpzjkqLWKks0FIRDk6SoTiBQDJycmyl1JQxznH448/jq+//ho//fQTWrVqJVvfqlUrZGdnY+3atc5lFosFGzduRN++fcN7QXygkjpCCIlSW46VwWzjGNAuxf/GhNRTgQ5H4isdtcaOHYslS5Zg5cqVSEpKcrahS0lJQVxcHBhjGDduHGbMmIF27dqhXbt2mDFjBuLj4zF8+PBa5zVYFNQRQgghhLiYN28eAKB///6y5QsXLsQDDzwAAJgwYQIqKysxZswY5+DDa9asQVJSUh3ntgYFdYSQeoFzjp+PGnFVm/pTqsXAwDkNhEwatkiMU6fm944xhqlTp2Lq1Km1yFVoUZs6Qki9sM9gwn+25kc6GyHHQUEdadgkCZAkFoJXpM8k/CioI4RcsCw2CX+dqZm2p76FPxEcGYEQcgGioI4QcsEyVonYkmefZ/FCCYCKKqyosqorMmAAqPaVNHQiD92rvqM2dYSQC5rje5rJ3kWf0kob/jhVjrOQcF37RsjVeR8by1X0nhEhdUMKUZs6KQRpRDsqqSOEXDDe35oPk8u4bYzJS7KiOQD66ZAR20+UBzTavPv5EUKIL1RSRwi5YJSb5QPxuoZHjLGoiuokieNQQRUqLCIuaZbozCuD+mxeKFXKhIRTJMapu1BRSR0hDZQoRVEEpBJjTFZyxRhz9g6Ntq/rNzecRmGFFaVV9kDUNeYMpPSNhjQhhKhFQR0hDdDC3/OxdFdBpLPh1bHiKq/r3EMcR8wT6YI6myjhXEVNvi3VrbJlMRkPrPSNgUVT4SMhESFxFrJXfUdBHSENSKXVXmq071wFbFFcUvfpH+cUl9t7g3L5e8fPEQrqRInj1+OlmL7pGNbkyfPtGsDJq1/V5ZSqXwkJ/dyv9Rm1qSOkASg3i/jpUAlOSRZM6Nc86qoq1XIPciId9Hy9uxAHSypgswJWhSDZXtJmH77EXlVsp7ZGlYY0IYQEgkrqCKkDhjJLRI//j2WHkG+0QHKJEC7EtlrunQxce4fap9QKfx445zhWYgIAzNtyFlZRUsxbzfby94H0foWXNAlpSBwdJULxqu8oqCMkzOb+fAaf7oxs+zVjlQ1wDYDYhdlWy6OjhEsgV1eldkv2nsH7f5x2vndrMicjyxOr+Y96vxKiHg0+rB4FdYSEmaHMFgVVaJ7RQSTz9J/fzgS9r0dHiTro/brfUDMVmeuDwV/QJWvz57JcffUruyBLVAkhkUFBHSFhFg0DyDoCCtcAIxJZmvm/U6iySiiosAa1v3sbM1n1K1PfASEQz63Kw5wtp2TLlI5jL0X0XK7U+zWQfFJIRxo6iTNIUgheDaCjBAV1hNSBcAQbgWCQV1Vy1G2but1n7SVdPx8tlbXrC5RHR4naZMoHi1gzN2uhyebjmL6rsWW9X6t75waSZ6p+JaRmmrDaviioI6SO7DhZFukshE1U9GBk8kCurof/WLG3yPlzba6FeymX63mEoqPEluNGXPHeLhwoqJQtd++c4fzZT3r2kkTP6mHV1a9RUMpLCLlwUFBHIu6PM2VY/XdJWI9hi3AL2XAdffPxUlXbMXhWuYY7WFj6u3LnkNoe1j3foewo8dWeQhjK5T2VGeCRae7ljdI15W7vAu2kQjEdaeio96t6FNSRiPv2wPmwpv/VriJ8/kdhWI/hiyCEL4D66UiJ+o1dorpwtakzVtlwono2iCNFnrNC1Dbw8mhTh9B2lHBcF1/3yzXI8wiU3a4qc8mVI5gLqPrVT14IaQhoRgn1KKgj9d5vJ4zOscQiJVqey/KSutDn6mBhJfYZTD62qF1vTvcx3lzfh7NKWVb9CuX6V6XHhXwcPZf0Aqh+JYQQtSioI/WS2eYSxPG6GZTWG/eprSKVCffhNUKVoy3Hja6H8Xuta3NcX4MPh4tSXMW9rPcYaBie5xtI71ca0oQQmiYsEBTUkagQ6l+1t12GoGACEMlpTsM50K/qQWzddnAfxLc21h0uqTmOn6IlR9DnbfgPf1w7HjjTc/6sfGzOOSw2dSW17kO/yBa65MH9+I7t1fSEDfSzTiEdaeioTZ16FNSReskiKj/4I6Gupq/ylwdAXkIUjiypudYctatW9Bh82HWcOoWDP7vyGP44XeG5QomXfMl63LrvUn3S3qtfa9f7lRBC1NJGOgOk4dh3rgKVNqBXsySPdeGOeSJZhcUYIIbp+ME+88NZJeyzkwGTbxdo0KIUUCkFTf87VILUOA16Nk3C+UobasNv71eXzHhUtbqMY+coxQyklJQ6ShCCkHVyaAgdJSioI3XGUGFDjMazcLguxkyLdJu6SNehOYY0kS0Ix3FUtBfjPPjSU6W5X2VpV6e6YncRBl+U6rJcZfpQ0fvVpUjQfagYf4Ey91Ki53OfALcnpL4JVdUpVb8SEkJCHQ5461oSJbBIt6mLggezwkwM4ap+9YeDB93BQamjhCgBlVYRP/xdLNvQNf1ASiUD6Rjh0VjRPS3X3q+upZRq81L/n0GEkBCioI7UGQZ4nSIq1M8uwa1EJ5LTdEXDc9m9A4C/0qigj+OnatHZUaIWV8U9QNt4tBRWkeOssWbQYNfUgzuSSxs6hQTcR6Nz/Kvc01WeD/fOHr5Q71dCaJqwQFBQR+qMUIdTHjGX0rlQ9vQMLi/h6/0aMNdBc8OUKZ/Jykqrguz96pac4z67B6qyTiGq27A5OpTIl3p7y8DAGHepfvVMU54neWrbTpbBZBF95ilqPjuERIgksZC96jsK6kidcQ20wn4suDzUeYR7v0bB9whjbgFRmKqE/QWLNUOaBBdUHi6qREG5tSY9l2FC3C9zUEGr196vfjbxcixZiaEjry5B/owNJ1Bm9h7U0dyvhJBAUEcJUmcExuqsGlQQmKwtkxTBCSXC2VEioGS5fHaDcFTr+Q9ga6bKUnt0qyhBV93BpqDciiqXMedcS2Ht48V59oR1rFOP+XinlB5z+ddtjXvvXEdeaUgTQlQL1cDBNPgwISEkeAmueBhK0lyr5QIZwT8cIn386lx4tPAP3zh1/nqA8oCClTd/OiM7gGdpL1cc9LhmKJFAql8defSSGCA7FgPAXD6/itWvbj8HEnDSkCaE0ODDgaCgjtSZumxb5h5IRXZIk8h/kdR0lKguNQpjtZ6/6lc127lyHeNP8DKQs2tZmceBAuQvMFPq/eotmHW99zXVr56dJ3zmR8U2hBACXGBB3cyZM8EYw7hx4yKdFRIE+9AigT2irDYJ+/J9TRCvzLWEI9JDikRru6iwlNT5u9bMUVrlWRV/yFCJP06X+z2G1/Q5d6tedt0n+CFNFHu/ukV59m0UBh92HdJEtr+6Pzj8TbtGSEMg8ZoBiGv3ivSZhN8FE9Rt374dCxYsQLdu3SKdlah13mTF5uOlkc6GV96qkph7zaCLNzacxrpDJQEfS3ApFfQ1lEpdiIbHsiM2CHubOn9ny713lPjgt3z87ieoY14aKDKFnhKubdkCbcPms0pUoUCQu/3vur42Vzlc94mQC4nIQ1T9Sm3qokN5eTlGjBiB999/H2lpaZHOTtQ6bbSgoMLqf8MIEVjgfylVWqWg/rpislLBSA9pEr6SwkC+opQmog81f6WSjmsR7FervV2ml6AOstHloPSjKn5K5pST838QpepXQEUbRL8pE0KI3QUR1I0dOxY33HADrrnmmkhnJapFeuYEf4LtMBDsyBSyUqkg0ggVIQqq0OwFWfLqwfAMPuy/ZMmx3rNUS91Au946Mrvv6t5BISB+gjj3IM9RgOief1nvV5eEZE0D/ATBhDR0NPiwelE/pMnnn3+OnTt3Yvv27aq2N5vNMJvNzvdGoxEAIEkSpEiOa1EHGDhEMfrOU5Ike49HcIgS98wft9fJKeWbcQ4exL0TAIjV+zFwcKXj1pGwHt/lurkO/aGYB5fr7Hgfkjy5pMO5VJOua/qcY+/ZcgjMfj9rPqvySMfbvo6fBcYhuV1Lx/Vl1Z8xSZIguFxzoXqZmnNlnEOoHhzFsT1jHAw1+zMOgNvfC4y7dN92u6bV19uxjHMOjprrVPPZ9JE3LkX0s1sfOe4FXdPQC9c1lUIUkFFQF2EnT57EU089hTVr1iA2NlbVPjNnzsS0adM8lhcUFMBisSjsceErrbQhJU6LSqMZMFXBYIiuLytJklBaWooqXgVeYYXBYJOtT7IZwUUGgyHGY98UbkSsVQODIbCPapylFEUFgFmvRbJkhM7GAk5DjXKziES9xnderCVI4TYYDIaQHz9RNDrT/e+uAtzZPUNxu6Y6E+IkCalchMFgQJJohEYSFK95oJJc8lBRagartH8Gk6RSZ/pJohH/22VEE20VSs4XIt5iRFGhAJOu5tqlMSNizDoYDIIsTdd0GqMctjIGg6HmD7cWsSYUFhiQKBrRVFsJg8GARqwMQqUNBoOEDKECtnKNbB9vUqUydEg0o6mOOY+fwcphZjX3j1WUIF2sgMFgQFOdCUk2EZJoL0GMq5Jq8i2WoryEg1WaYTCIsJWXI1WoAqsohonrYBBMaKIxoaSoEFqzTjE/RqMFQlVl1P1OX8gc30eccwjCBVFZdcEoKyuLdBYavKgO6n7//XcYDAZceumlzmWiKGLTpk2YO3cuzGYzNBr5A/WFF17A+PHjne+NRiOaNWuGjIwMpKam1lXW68zHv59DpUXAI5dnokxbCaupHJmZyg/2SJEkCYwx2Hg8uGhBZmYj2foybRXABWRmZnrsaxTM4BqN4jpfzMdsSGuUgbR4Hco0ZugE5fRr6/3/ncKka3J85+WQhJIqS1iOX6apcqZbKng/xmlbIdoJEoqlBGRmZqJMUwkNC801KdOYnekYNZWQKiuQmdkYZS75KdOYwSSG0zYNktMboyqfIa1RBpJja76CilGJ1Bh9df5qzss1nfMoh5DYCJmZKc79jlcVICMzE2UaK05aGTIzM3EeFRDjkpCZ2RgFUhk0iemyfbwpEUz4u7wK2vh45zELeRlMsNZcq1KgqIgjMzMTp6yFyNUlQGIMIpeg1Ysu52xBUlojSCYTMjMbIaZcjxKpFEhIQ2xyDDIzk3BGLERKo8bITNEr5qdKVwVbSVnU/U5fyBzfRxkZGRTUhZjawpdAhWqMuYYwTl1UB3UDBw7E7t27ZctGjRqFiy66CBMnTvQI6ABAr9dDr/f8ghQEod79AksSx5u/nMYjPXMhCAK0ggAJiMrzZIxBYAIkMM/8MYbrOqYp5psJDJwFfk5MEADHPWcMnCkcNxRUpMuYvUIv7Mf3mRehuvGXfRsmCIDSvahlHgQm1Fxrt7zVdFGtWed6fFa9jdK+snQgv5YcDJrqfRyffw6XtKpbvak5VybYP6Oun1N7hWzNe1k+ITjfA26f1eptHPvaZ79gEAQGxgSXfHr/bhIEjarPGAkMU/j8kdoL1/WkGSXUi+qgLikpCV26dJEtS0hIQKNGjTyWN0QWkcPk0js0mN6ldUlgyg3hR/bIQrMU73/hBdOgX3BpgC6wyPd+jQpug96Gb/Bh/wl767yiZk+P4WncxxVxpB/CE5R1jPB2Q/3caObyP3dfqPLYhBDiC/2ZcgFztIl3jLivERjEKI7qmJfeub4COiC4Hr2uY9MpDXRbl6Jiqidm7wTgyAjzEmDX+jB+g5TquV+99Pr0lydBIX37Is/9XAMnb8l+/VcRSittnisC6P3q3IZ75sI1gFPq/Wr/2fs5R7rnNiHRQJJYyF71XVSX1CnZsGFDpLMQNRxDZTg6HEX7kCauAwKr3yfIIU1cBx9mgBjBduaRntECqA463K5JuI7jc+BeoLontGegrSZPjHkZ0oQzr8Gbrzzll1lk05Axpe3dFngGaqzmZ/dgTyF4ls8d66d0r/4/gwjxi6pf1aOSuguYRrA/RRwPJYEx2QMq2gQzTRgQXDWaa/VrpH+No2mqJ1n1axjSZ/6quv0EuP7ypFzqWV1Wx+WL1Hxs/E4BppCvYG+nI4Bzv/b+g+DgjkcIaXguuJI64slROqfxMtp+tNBrBLRIDax3VLDBh6z6NcJ1WOE8vNr4wn27cF4Tf0GK4/jK1a8q0lfaxm1GCY/AyUe6yusU56ZQVN3/JMDqV/UXP5JNBwiJBqGqOm0I1a9UUlcPOIIXQQi8erMuNUnRo2/L5ID28Vvy42u/6p8FxsJWjaWqU0A0NIxyBD3O0kt1n5Uf9hcHcxifOFcOdNXsq9Qu019plq90FYNdf3lQ+Nnb4V2DV0c+3Y/he0aJ+v8QIsQfmlFCPSqpu9C59HhN0GlwZ9f6NZ6VvT1c4BGR64P++k6paJ2uPA5Ybal56DIA2cnKg8vWltorE2jwsjffhNOlZuw4WY7rOqapzo+/aa8A+O4o4Td95Z4SDMC+cyZ5Wq6dEXykrCYfvnq/ctjbCKqJvzjcSu8UjuWKql8JIYGgoO4CJwAQq3sBxOoExOrqV+FrsO0E7R0s7Pt1yUkIdbYCMqJnBvTa6LgvattymW1SUJ1L7EGKj96crHZDjXhtl8k4tp0oQ/usuOp8uHTE8BFoeg3KZUGcwmrXzhXO8feUe8X67P2qMhAkpCGj6lf1KKi7wDHGINbjb/1gay4HtklDVkLtp8AKhWgI6Jy9LF07j1T/vPZgMcpFG27tKC/lDbYtl6qSOigHd8rLlFKoofg1HcB3t1v/CgDARdXBodJWsupXBjDu2iPXs0ev45wcQZ1r0wDAz5Am9f8ZRIhfImcQqPerKhTUXeAYA7Sa+vtBDXY4kCbJ4aluvWC5fUSYS0nmxsNGZKQLvjZXfxiVUYh7YONtmX25PF/uc4Yzl39dA1FZWzZv+VBYxgG0SI/1WKaUn5p1XLGVosf14FyxTZ43aqqzCSHEIfJFCKRWMhK0GNs3O9LZCBvXEqVQmPvz2dAldgFxb9DPwJzjHALB90RVOo6aNmJqgxX39pTegnwO5RI3xzvfvV99lxh6C/xc1zsupWLeXDqnKKXv7zJQTEcaOsfcr6F41XdUUneBW3BrezROjI5qxnAIZsBiX0qUZg9oIBxlWQBwd7cMZCZ477wR9Fhs/oI1f+PUVa88VFiJLceMmPLDCVzVOsUlfd+fB8f+7kGez/zK3nsZDti1jZ3r9o5eGt7Sd9m1pvo1sA4u4Zj5g5ALiYTQ9FyVIj5qafhRSd0FrnNWZDsBhBu1KQqeazAgMKBpagzu7WFvN9ckRQ9ddVs/b6VrwYYSvqe9YtUzSigPaXK0qAp/na7A13uKUGyyocIqeWyjplTRdTvXqmZVFIIu98DPYyVnilXPSpu6b0VDmhBCQoVK6khUq21vybqUV1yJVmnuDezrjuvjf/2REmw/Xo4JVzcFALwztDWs5cXIzExSl1aQwYTf3VwDLZf7etMHe3GwsAptGuvlg0YrJeEewLkEbd46lXrt/eplna8hTFyP4zi+s/rVR1pMaZmf60VDmhBir34VQlB12hCqX6mkjkS1YHu/RsJnfxoienzXoV9+PV4Ks0u36LR43+Pkqet16p/fQY1dqmddt9uTb4K1egwV17Z+7omprY7nQf4xoObz5trO01dbupr0/PR+9XO8C+XzT0i4SNUDB4fiVd9RUEeiWiAlRkcLq3DgXGUYcxN9OAd2nS5HQbkVf5wqDyoNpSscbFsuX23qrKKEI4WVOFhQ6Xcie8A+Q4pSvpTGqbN3vmB4eXDzmmUu+3gNupSCRO55Dt47YVQf28sRPAcqDqxnMdW+EhI5mzZtwk033YTc3FwwxrBixQrZes45pk6ditzcXMTFxaF///7Yu3dvZDJbjYI6EtUEBuhUDtlyotiMwgprmHMUXX7JM+LbfcXVAVjwEYDn8CLB58k9rYIKC1YfOI+3fzmLvwsqcaKkSjH4c3RuEATXZdytKhQeO3bKdgw4DFzeIsX5syztAONTX0Gcx3pm38pfVa7X3q8+2yBS9Sshker9WlFRgYsvvhhz585VXP/6669j9uzZmDt3LrZv347s7GwMGjQIZWVloTjtoFCbOhLVbuqUjqYp6nr3BjumnVoHisqwYFs+Zg1uG7GxAc+WWrDszyJIMTY81acZ2qfHO9cFfe5eSteCSc+1hO9UqQUA8J+dp3GsuEo2PImaak5HbCcbp47BI+Jslhbrs6OE2vw60lfczaONHXfu7yuY9tf7Vc0cvMEOAk1IfcFDNKMEDzCNIUOGYMiQIcppcY45c+Zg0qRJGDZsGABg8eLFyMrKwpIlS/DII4/UOr/BoJI6EtVaNYp19tJUIxylGmVVIgBg2q9/469z5RF5yFpsEiatycPH2w34clchysz2PGVXD2fDQjxKbbBfnx/9cRq/niwFABwu9KwKZ4BzBhTFEiqu3KHA4dKmieiQ6dkZxVfv3WA627hv77vkzkdJm4rer75Q71dCQs9oNMpeZrM54DTy8vKQn5+PwYMHO5fp9Xr069cPmzdvDmV2A0JBHak3Ah66wg/HmHbvbKoesNgZjITsED4Zq2yYse4ErvxwB747UIi/Cyp87xBsj1UvpUVqz/N4aSW+PVCITm/swOxtx7DfTz45PEvEHJ0juMs2jiZ1rvkQmOdAwspnre5a+BrM2HUbr+vdhqnzdR1lJXXcc71i/vysJ6QhELl9iq/av+zpNWvWDCkpKc7XzJkzA85Tfn4+ACArK0u2PCsry7kuEqj6ldQbsknca8FQboFV4vjoNwNeGtSspvciAIR4MGQlp0qrcOR8JVbsPo/v9xdDSKgeMNlH9XJtynOUghZ/JUSGcgu+2V+EN9edwRmxDDmpOpisEliMr04JADggcXn7MgdH8CIIDHEajdc0lNr/uZesuVerqr1n3s6au2/kOkwJqzk39yPJq1o901MTh1NMRxo6KUTVr440Tp48ieTkZOdyvT74aSU9OkNxHtESdgrqSL3BmOe8oIHgnOPNjaegFRgua6E8nls4f1U/2HEKy/4+gYKCWNzTuzHMkmQfAw01E8b7On5txvNTHNLE7b0kSZi3/TROmyrx0dYCXJyViHNGK5CA6oiMKe7nmp4j2PJXW6wc0vnIv/sCJv8xsHHq3DpnQL6Rt04Y3noRu2c0kE4cNPcrIaGXnJwsC+qCkZ1tn54zPz8fOTk5zuUGg8Gj9K4uUVBHotaJYjM2nyzB3d3U/YIEW1VVZrbhl+MlsEHCocJKdMiI97l9KB+ypZUWnC234udThZj2cx7SYq3Q8Fj5XK2OwXirq0mVjl+rPwyV0nP5uajCjH//eg4Ld55BGWzo2yoBIueyKbICuibVgY17nh1loDUdJJizqtY1X0qlcl4O45f7X9SOacL8d15wPT6zB93eOpzIBlOuDmgldReMmtQRYq96ZSEYY04M4Th1rVq1QnZ2NtauXYsePXoAACwWCzZu3IhZs2aF7DiBoqCORJ1yi7268aeDpdhbUqY6qAP8P4xdq/0OFJSjQ0YiDOVW7DWY0DYj1nc6jgAmBBVieecrMHHDPuw9JeKe7tkQYalZ6dKwzJFbSYJH+y1/AtnW/YwsooRPdp3FtpMl2HT2PLhJC5NNBLTeB9xl8B9YawWGBL2A4Rdnysahc1Rf+r1/3trA+RhKRO18s873StvI8uCS7+qb4i0QZC5tAGVDmsjS9p47gTGkxAZabklI/SJKDCwCM0qUl5fj8OHDzvd5eXnYtWsX0tPT0bx5c4wbNw4zZsxAu3bt0K5dO8yYMQPx8fEYPnx4rfMaLArqSFQx2ySMW3MQ43s3D7iUQm1V1Z5zRvxz/QEcNHDse/IyZwcL7ih9gq+qzNoFdHvOlmNFXh5W/V2E82YrmJjo0dbK22n7KkGqVYlO9XWziSLe2XIaa44WIUGjw895RkgQwatLl2SZdMOr//WWv0S9BuUmGzKTtWicoFMcWBiw58Oxztl5ws8ldx1A2Ft7Fq/Vr+6lhc6SR+59G4XEvQ2m7FHV6rbM321L1Gtw/2WZfrYihITDjh07MGDAAOf78ePHAwBGjhyJRYsWYcKECaisrMSYMWNQXFyM3r17Y82aNUhKUjcdYzhQUEeiwl/nyvDFHgOub9sYZpvk0v5KfRr26knvjeqOFJvAwfHM/w4iv7wCHPHV+7k8XH08ZWsCPvV5AgBDuRnzdxzH6iPF6N8kA6esZbJentw+HUL1MWoCI9lsWdxzmbtgw83D543YcciIz/46h4JyCxL0DBelef9SclwHeaDn0imh+ufm6TXjCybpNciHvQRWaUYIb1wujfPYanq/ugbA/mavUBquxF8Fr2vvV0dJoFIvYllHiepM2UvvXK+Xz+wR0uCFaoqvQNPo37+/78HBGcPUqVMxderUWuYsdCioIxFlsogwlFkx/sfDaJUq74HEZNGWf94Cnm8PFqBnbjK+O1RQXfzOFToqqpuPU017K4d1h8/hvR0ncaxIhCbWgnKzxtnxwT1NbzjgFt15y5efOkY3u88ZMfvXY9ifb8OR86WoYkBmvCOwlGeKcw5BIZ8ePUKrs5qdbJ9ntnXjWASuJlWlwjxv99g5n6xLECjv/ap8cdR+vOTVr/L9GXy3w1PqSCELCgkhPkWq+vVCREEdiQhJ4iissKKwwoa/nfO1sup5NB0P58ACFfeSvZX7CjBj01GU2iQsua2zvRE8qwlcaiZary6Bg0tJjdfj+v5SECWOD34/hfUnzuPPM4WAwKERE+EYLrcmXe78jzOX964lOy5bSs5hVbxU8yksdj8FUZLwzQEDvt9dhn3mczieb4MWLoGXQsmgkpq82MXEyKsqk3y0AeMK+XInyLbhHnlRKg3z4LbM1/nIq1o9S2P9BeEe99TLtloNQ0aiDu3T4yBoaoYIpYI6QkioUFDXAB07X4WW6YGXohw7X4nTFZW4oll6rY7/1Z5zsEhAVSXQp0WSLIhxjIzhCLgCeeA5YsAPdp7Cp3+dg9EIGKrMSIzXOtNXKrPxLGXx3T5KKUA4cr4c3+w3YM2eUhTaTEhLFdzaT/Hqjg/yzgE+G/ADYNwtNwFWv5osNqw5XIzP9h1EmlSJcm0GjucDQqqv4MSld6rLMsf5uO+n13Nn+zcAPirA/bQXdOsooVhSB4XerwqfF+b2s9f8+Gsv5+AeWLq1uZOcy+XbaTUMudXT3LVtHIcHemchLV4HoXqC20CbGBDSEPEQVb/yEPZ+jVYU1DVAT63Iw8oHOwa0z/GSSjy5+gBu7JQWdFB3stSE+VsLsP10CQa2T0NWTJzCNErVD2f3YEaFZXvz8c3hcyiTbBCgRWOheqJ3VlO6VH0Qz6FBuLzExVdBneva306WYuTyEuwrOY+88wKaxGoUB1lzBEMSJLdgD56t56uLs1wb/LteD6W8ue4uSRL2Gsqx62wFvj6YDy0EcL0J8mGZ/LQzcy9Sc1Rpui7yXyusuK2/sQRdO1GojXfcPy+yYElF8Oy+wH2Z/LxdAvPqlUqddB7qk4XkWC2Gdq3dH0GENHQiZ0Aoql8pqCP1UblFVL2tocKC934/AGZOsndgCKJUodhkwcyNx7DmLNA0IUHWaMve8N2l6pHXlLKo7c361pYjWLDDAEOZFTFaCZoYz224hOr2bAyeY5NVb+PSy9NX49izZVV4ZeNZfL7HgKISCa2rNIj1UvDpHojJAjXX/LktcQSdjiU1nQu858totuKWpbuw60QlyiUzbFYtclJ0SHSZO1ep9VdNlhyRnLovPm+XKFQlT67Tarku89VRwtm2TmWePKpWFe6/v9I8nYahabIebdLjkJlU85WalaTwQVRABXWEkFChoK4OVVklxOpqP93ud0fO4IY2uUHvr/ZvlZ35JZi27iRi4yrQMTEpoE4CDu9tPYa3fj2JljEWWGw1vU0Bl8ANyiVz/o53oLAc437chz/zK2GTmEcDdkXMc709xvR/ZmfLzfgrvwK3rfgV5aYkGC0i9Hq3Vm5ujeJrgrnq9y6lR47z44BHqRdjNYPwegZ/NYorrdh2tgTbCguxr6AKgskCZtU6SwuV8ubMT3UGHNt4q+ZVCvO8loqp4KugjnO3CandAzgvN1ZxnDrHPn7y46v9nDexWvsFfuLKXPRsmogYLU2jTUi4SCEafDgUVbjRjoK6OrL7bAWKKmzo3zal1mkdLSmv1f7+HsAVVivm7TyCGI0WFRYRsXH2xvzcrerRl5X7CnCyvBIf/3EGZrGmZNDRO9G1+tO1d6AjgHCMN+b+K2ixSZi66QD+u6MU9/RqhMKqKtczc/nJZV9uDyTc24PJgiXumQ8AqLKJ+F9eIdYtysf+knJUWHUeswFwx07wDLrcl9vz4PnF4tEZgNcEd65BEwNQaRXxU955/Hq6ANsOWSFoJXSJ16u+Nx5cTlhNCr7nig1ublylseW8jVPnnr5snLrqT5B7Fam3klfFORr9zO/bKycFlzdJBQD0bVm7qYbgI2+EEDtRClH1K/V+JaFSViVCVDk1UChtOVmKy5vJA0lfY50Zyi2Y8cvfKLCY0Ds3w60Ux3OMMHdFJgseW70Lvx42Y2Qv3zNBuFaxykqyXNpAOQKV8iobpv1yAN8fNcBokmAx6wMqOZRc2qh5NBdzmRrKEUD9caYEqw8X4csDJ3DqvIRGqe4lMbxmBxVcS+Uc712TqgkuaorOmEtRnckqYve5Cuw6YcKxIjNSRAkZyQyARtVFcK/edc+DUmmpa4Wv+2kqHdIeiPrPjL8Ai7lcavdSPV/j1HnkUTakiY/8KOTB9RgPXJaJ3OSaqlSdJnSlcv7G0COEkEBQUFcHKswiBIGhrmO6r/4swv6SMo+gTikbZlHEwr/ysO80h0mUPJ6QjpIjb6dgkyR8sfcsVh/Jxx5DBQCtvWjPNaHqVuWO0Mo+g4PbcVzCD3uplIRJP+/Ax1st0CWYqq+h52M8kEeje4kdq86VxWbD7/nF2HymEMXmSkDUKleXuwQ/XGGZyyJZVaKz1M1P3hw/WGwiiiotqLSa8c+1eTBaJSRync8EHAEyg0vw7qV60efH0cdKx+dYXmVccy+Vgzbfd8g9q2pngHAev3oH1/1UlTq6vW/TKBbDujTGPd0znMu6ZCeoSCl4VE5HiG9U/aoeBXVh9suxUhiMNjRLjYEYZDXLidJKNE+J879htd9Ol+Dno2WoNAFMxcgluw1GzP8jDxWiCYm8kXM5c3lUM9ibnSmdwsYTRcgvr8Ty/ecgaOVlK569W+G2vqaqy9kwHgzlZhs++PMYfjeUoVuLMlTZ0qBz25dze54YV6h4dUYH9kSl6gDSfZgOANh2ughL951AsbUKZaUCEmO0gKAQuMF3UOb37nrZwPV7hnPgvMmCeVtOwWKzoaSKQ4jjEAXu2r/Eua07WZ4d14XVDI/rq/MJd/4Dn9Gf4tei0sVS5P+PG0e1q+AlEHQvDXSp+fWaR7Xj1A3r0hjDujT2nUFCSJ2SJID56TWvNp36joK6MKq02vDub6dwW4csCIwFND2Sw4wNx5CezPDoJS38bnveZMWPeWfx5X4D2iemI4HpfD5jRYnjiwN5+GpvMbQaCXF6tw1c2rTZS+m47IF6uNCET/YfxW6DGYNapdVs6PjPGaTBrVeAvLqP85oBZk+UVGJ3fhleKTNhX4EJEncLEl1LnRSe+bJLXBMlVrfRg8t7oKjSiv6LtuPg+TJYbRL0sTUN15xVsVAIYpSO61rB6VpvqcC9TwaXOLbnl8BotgKCDQUmM2IkhmS9oyWgDwpBs9Ie3OXl5RRkK5znIXjfXhb3VR/Y29h7zHU7H8f1V7/vLR9Kl1zN1Gq1mjM3BNT28CaEEDUoqAujd3ccR6nZColzCAwBV79+svsENh0/r2qcK1GScO+S/UjPqATnmpr2akobc/vwFzN/24PCykpw6J3LXYMC+4PavsS1rdHpkipM23QIp4qA1EYmAJqajgbVVaz2bZVacsmy4fy/3GLFumNlWLa7ABotkBjrEmEy5dIYeQTlfpyad67tlk6UmrCvoAwmiwVGM8fJcnkgpxzJcdl/vrhXCDOmnCsOwCKKKDFVYsGuI6iwsID+ilSqQo7XM1S5b+g+oq8fsk29BDyubRPB5Zv6O5SjzaIvzpI6L03XFHu6+liWkxSDZqnuf7G4bOc7O2FV/yuDCKk90dFTLxTp1HMU1IVJmcWC7WeLwHkMJA5oBBZQR4kDBSacM5mhprqqzFKF+X/uwUmjiPQM+WffW1VZYaUFZ8uqoNO6tZVzKWKT72svTftqjwGz/ncWqYlAs/hEl9I3eeTlSEEW1LjWilYHgVvOFGLOr2dwpMoIQQA4YqCB24NWdTDF3JZxcGYvkVy8+zh2nz+Hk+e0KBM5tF6HMHGr2vN4Z18v63TBa4IsnwELA/7KL8NpWynyywCmkWCTOGw10bBX7iVs7u8ZA/QxDFVuQxD6LKVyeXmsk7WXk2/o2qGiZow//+3h7Dsor3ReP7flHp0iFP5QcR+kGQAuaZKApmn2QO65gU29Z8dHVusKFdQR4huXGKQQ9Fzl1PuVBMsqcXvPPWZvz9UoQYuMxCRV+3668xwOFpuQnFgT/CjhnGPnuWKsPLofJVUiwGIA7ntycYeaKlHuMhUVqymkcs48b1/z57lSHC0tRXE5oEEMePW2rlWVsrzBUSWp8NQHsMdgxJbD+dAmlaGiVI+aKUjdUlPxxHNL2qnMYkO52YpimxnrjpchQW9FQhJDhVGQ7aD0xxv3+Nm9ztT3Po5glsPec7WkzIyiciuOnbMiJ8sG7m3aCT/n61ztLRpT2F6WLzU7wHcBn2IHDIXhZwLlSM4xoYTiaCMKR9FpGAQGpMbWfJ0N66a+XVxEqz9ZTYk2IYTUVlSPmDlz5kz06tULSUlJyMzMxC233IIDBw5EOluqaBizhz0cECWgSYoeyXoB+8+ZfO73/ZHTOFFa5VIawr22xXv4273458a/UWaxOTsyKD5xAeRXmPDZ3hPO97KSGMY9A7PqY1ZaRRw3luG3M8WwOnrFcpeXs/G9PHyQVQ26BCCny6qwv6gIP+YZUGSyuZ2RWzWnnyiBg8u2sceoDJU2EQUmC04aK6tLweTbeE/Qs7yv5rr4KUmrDoIdV6Go0owyiwij2YKCSrN9Ng636+O1nZqz5M9zC3nelD8XSrMqeCuYVFosq+mu/qPCGeMr7RRQuzAmn7LNC+72v2yd28L7e2YiMykGjRO0AQdIiuPU1SWK5wjxS5RYyF71XVQHdRs3bsTYsWOxdetWrF27FjabDYMHD0ZFRUWks+aXo7SBM46EGAGcc/zzhxM4WFjpdZ+3NpzGsRKTsxpP1nZJwc6zRthc6mZd2zi5lvB9sOsEvjpw3N4Qv3ob5ngSuzysnd0gGFBlk5BXUoy80nKYbKLbUx3wnGlejrttYhUlGCpNOFZqgijr/OBSkue2yMtbl+U1dXEWUUKF1YZyqwizKHoEwtyRZ4WIUamU0WOFv4cvB6w2CafKKnG2woQzFZWwSeqnYwuYYrQjf8sgrx51X+crKVWBtcvfD2rmgQ3061RQ2KFbTgIuaZIYYEq+RTquivTxCYl2Emche9V3UV39+sMPP8jeL1y4EJmZmfj9999x1VVXRShX3tlEju8PF+LmDhlwzGnarnEchnVthE93n8Q+QwWuaqM8Av3bG8/gh79LcEsvvbPdkGP4DdcARZI4WqR4jptVU6JUs225xYa/i0txVWwy9DquEKzI6/sYA0RJxO9njMi3VSBGm2Av+XArNvHeKsqxnb2MySxKKDBZYDTbYNOJ4G6/UErTmSqm7RHk2fNttNhQWGlBqZnBIjEITEJMrOS17YVryRfzNZawv997R8kVA8xWCQaTDdxihlmMAbcCsTp1pXu+D+EjaGZeVvmpRvaWI+WyXXleHIP++jojdcEJh6TQSNT1bwZZMMeBx/8vx/k2Nc7HV1YQlzvSBXWEEBJKUR3UuSstLQUApKf77w0aCR9vN+B4VQVu7pABDWNIjtVgWv/W2Hm6HEdKygAo94Bde6AYq/cXO987gjOhut2aY5cjhZX4+ZgRD/Rs4txO1vbJ5QG19VQxYrgGugTuDEAcQVRucoznw4wDx0oqcSS/AmYzgybenqDXZ55bozPXakOJS6i0cXz612lwG1fcRTkht6Ueq+zBnNkmodxqw2e7T4JpbOBca88rAwTGXWYh8ExbTdDoMzphgEWSYDBVocIMSDYGrU6CXuu+mWvlqHK1NOAaVHhe6UCbWvkat84jJ476X+7yv0eCngnYP4/cI21HtSdz+dkjOT8BVFwMQ/OUml6qjl6wafHqv6aCKfWKdJs2alJHiG+ixELSySEUnS2i3QUT1HHOMX78eFx55ZXo0qWL1+3MZjPMZrPzvdFoBABIkgQpzCMPnq+wgjMOSZLAwPFUz1aoMotYvb8YTG9/HLrn44u9eSguFwDHOGrOkfHtXRFY9T5PrzqI0+dFXHNRinN/xqq3qe7sIICj2GTDWZMZ1ioNMvVxzvXgAK8+9n9ua43Cyir7MTlHSZUFBlMZLJIVkhQDBgECuMdLw+Dyvhp3BDASKixWnCopw/lKETZRC41k306oPg95WjU/u65Dddr2ZRKE6nPkXILZZoVVlGCxacC54LIf3PJqf6+pbhDGwSFw7lxWc1wJGtd9OZz7AvL8VVgsEAURkgRYbSJE0QYGjcexucJ1cxzb/XyZwjL5OcAjfed2rOY84Ujf5f44KtNZ9XnCZV94PV7NMRizHwMun0PX++ORf17zv+MzC17zv+u2XOIev4sCOBonatA2PQ6MS/btuFSTlgoCHL/n6r+4GbcfJ9zfDYA9b5zLz0dgdXd8Yqd0H0hohOuaSiEa0oSqX6PI448/jr/++gu//PKLz+1mzpyJadOmeSwvKCiAxWIJV/YAAImiEYAZBoMBAJAN4L+/HUYSB2CrRIsYCTAVw2Cwf/APna/AH4dP46L0FLSINYMD0FdZkWjTAKKEeDPQIqYK+edMyDtjQ258AmLMHAaDPaRqF2NBvIYj3SaBc6BlrAihyoKWWi2EOA1StIBWA8Sby6CzWSBxnTNvRpMZjaQqFBsrwcoEtNJLYIzDzEWINg6NXkIOt6JTooTkGAnZCTZoNBJMGkDgQEo80DjGhCSYUWwVsfnvYtjKRDTVSchKACxaBq2OQ8Pt1dLNYyUwiBBtQFKcBAs4bLAiNU6EOdkKq06ERuAQbTboBCBHC8Qliqiw2ZBpE9Eu1ox4vQ0SGESdBKvGhrgEEUzgqNKIsHF79as+RoIoMdhsIjSiBAgcXOBIjZUQpwMsgj1/AiSYBBGZsVaYRRE2nQStFhAFhhgtIDEJNqsIziSkxwvQxIjQ6iSYtUBKHNCEi7CKAEQgTs+hi5Fg4iJg5dDpJQh6CRqJQ6vlMEFCjF6CSQdobAypsUCVhoMJHJWSiNhYCRbR/oWYGMORm2aDLgaI0wGlDBD0EvQMsIgcseAw6SUkJEhI1gPZyfaArYUWiI2XUCnZwPQSJEGEKHKkx0nQMxHQicgUOCRUQorj0GksSIgXwbgFZtGGSg4k6xksMRzxMUCOthKWZAskiGihq0K8VoQ5yQJJAzQRKtEl0YrmukqYUi2IixORIpahY6IZFp0NTTSVMBgMSBKN9v+tZWgXV4mENPv9jjGXOD/DDh0Sq5AcKyBJKkVxEUPXZAvMxvNoG1/p/Mz60zzGhMKCAuWp3bxglUaUF5tg4OWq9wmWJEkoLS0F5xxC9SB8WawCVaXnYYjx3taWhJbSfSChUVZWFuksNHgXRFD3xBNPYNWqVdi0aROaNvU+5hQAvPDCCxg/frzzvdFoRLNmzZCRkYHU1NSw5rNKJ6JUqkRmZmbN8QWLvXwjRsBxiwhbbCoyM+3zSh6yFOMsCtBKn4QTVRqAAT1i9aiwxaBEtEGjFXGUFyPRqsOBSh1sunh0i0l1pn/YcgRJOglJWgmSBOwv10DLRTCtFoJZh8b6OOh1HJ1ik2HTVEKUYpCZmYnSKgve+O0v7Co0IUbHcbxChxRwMMZgqtDAZhWglSQ0hg57K0Q01tpQVKFBSiKQX6IDEzTIEoC8CgGmChPOmxiSYjkqygXExHKYLQxVJg1i9PagzmrjsHHgT6MGNqsWjUQJVUyA2ahDTrwNZUYdrLHcHtRZtIDAoTOLqKqSAAGoSJKwz6hBCrM3dLVZBJjLtEgEIGg4yk0aWDmDwBjiYwGbyGCxaqC1CoBGAtdxZGmAJCaiyirAUKGBBkBphQYtY3QwWTksVgZtDEdVub3tmKCRINk0YBoBlVVa6OKAmBjAVKVBhmBDYbkGZqsG3MaQFC9BHyuhzKgBN2sQGy9CsEnQShz6GI6SMg3iJAlllRpoLFpkxQIVZgamAcrLNIi3AVWiBpIEpMUB54p10McBSXqOglJAiBORIOhQaeNIhA6lFSJSNBIaM6DAqAUECZrGeuSZJBhFDsGihaixwmbVIkfQIk7QAbwS8bESbIjDkUqO2HgRx0xWaKQYVNhsKJcYGttiYLJISIljsOrjsNsoQoIVUnIskqVY7CmzQNJyJGfGYU+5CH1yPPaUcCTobbhEk4S/yy0w6W2IS41DZmYmyjRm+/8HTThcKeKsWYMEmwZV+lTZ7wgAHCg/hcZMg7ZCCho1zsTu0lOIS2mEw6bzHtt6c8JSiEaNM5CgVxguxptzGiSkxiEzM179PkGSJAmMMWRkZDiDiXO8GPqUdGRmqhvuiNSe0n0goREbq2JeyiBIEgOo+lWVqA7qOOd44oknsHz5cmzYsAGtWrXyu49er4de7zl6vCAIYf8F1mgESBKTH6e6XZB9jlP7a8PhMlzdPqV6OwGMCeCOOVAZAxMYis02bN57Dld0rMKhc7GQuATO7ds40uewF0lLgHN/CQwCGCTGEKcX0C0nzj6GmCCAc+Ddnbvx37/P4rwJEAQGXfU+Euzzg0rM/jOv/pmDQSNokZTIcGU7hi/PaiHpOE6WVUG0iEhp5Ngf1ftV91QCs+eJ8+rKP8G+vDp9x0us3lYEYONApVUC4xxacMTEcOi03Jm2+4uDQXSkU91IzH0buOSJuyxn1f9XiRJMNhE2CdDYRIjQQhIZdBrHdtXH5gIkSIp5cPysETgsLsetuSaex5WlzdzzDYjMsT9czguKxwXsA1vbr7u98tpxD+zVDTWfPTABHKJzf7il5/6/5/Eg20asfnFWcy8kXv07wGr+547PA1jNcheu65ggVF9P+/9qf2857PsG9HvO7Nekrh7urPrcZb/DAZwjCQ33+0BCI1zXUwTz6GgXDPt3Xv0W1UHd2LFjsWTJEqxcuRJJSUnIz88HAKSkpCAuTv0E9+HCOUelTUK8zl4ykBAj4K72mTBZRMTHyEsLBAa0SI3BLZ0b4V/rz9iDOjhGFXFpUs+B1QcKUSbZoGcSNEJ1K/bqjg6uHS0Yq9mTO8YiAaATgKxUDXo2j0VijBaMAedMFdh+pgyl5jJYJA1QPZWYXHV61Z0vUmO16JYLtMhg2HFKwrESE4xWLXRMgEbw3gFCoVOrx0rHOrMkosomQqexQdBIqBQEaKya6jAvtBxN+TmX7GPzcaDAZIFOI8HRkq56k6AoDcEB5ud6KGwfFC/7ee/d6bnC/kmrbonH4TLtWw35IkdIYn8JfjIv/6x6Xy8w5rFMjWB6stZ+yOTaifTxCSH1S1T/mTJv3jyUlpaif//+yMnJcb6++OKLSGcNAPD5vjM4VlLTFua+XhnQaoA5v5722LZLZjLeurkt4mM0NfPMVwdlrvOaHi20wmAyIzneXuLieNU8cGvYl9lLOBiA1ASOtFg9rmzSGE1TdOAAzpus+PHoOfxRcA5lZvfBft3ZH9Jc0kA0x6BDWhzSkiQcLyuGwVQOQ6W5lj31mHOgYqskodImwspFaGOsyE2rhFbLlR/M3p57/vJSvR/nHFYuwSKKKLNYIHEuOw9vA6C4E20CYmJEWUDr69BcuQuvPG3msaQ60wq7enSdVcxmwNRccq8Bm8JYij6vjpfIyz24CfZjFsznM9K9TyN9fEKinSTZq05r/4r0mYRfVJfURXqoAV/yK0z47sgZdMmoGXeuwmrDa1uOoFlMimzbzlnxGNqukUcaAoNzpH9RAtplxKJjegLWsHLYS+d4TUkPs3c4cL0mro/BJokJmDAwDR/vLIVWo4HVIuFPw3mcKbWBaTiyY5TPwzUNDTTITbfgzNk4mEUJy/YaUKkxIj5GBOfx8BX+1IzM4YikPLfj3D47RnpqBcymOFTGiPZhSNz+tPB6191WSIDSZFs166t7BUvcPmZenI+5r2ouK3f+q9FySBzQxVsBLoCLArTVefbHkZzHkCJMcaQQZX43kg8tElCpllLeXFJgDNAIQEyc6BF0OMdNlMWm/s/IUa7sbYYUB8fnoS7KsCI+Th0LPoAlhBB3UV1SF80+3vc3bBKXVYe+vuUQjBar7Eu6S3Y8hnWTB3Suz0ReXXolCBzv3tYa8THVt6QmpoNQ3b7qhNEsfwAwIFGnwS2t22Ji70uRFZ8IiXP8XVCO3YYSnC2vgtKYYtXJ25czIEYQ0DpVgEaKhUZjg9FiQYXVinNlVueGwT77xOryRZ3WhpxG5UhMrUJ2Y5NHaZd7CaQy70V2zsCmOm6TOFccE9BXlTAA6GNsiIkREaMVkZBo7y0taBT2qmUwEPyD3He5otJ1VLoO3rZ10OsExMcyxMYGGsBx33+MBfiHWsBj9QVxZSMZVKXFaZEcSMcOQhogKURThFFHCaLo97MlOFlWATA9RJenzqlSc3WgVuOWrp4ldM7qVwCiyDCofRrOlWihEZjs4cydf8bbx1CTuPwhN7V/K1zVPAPxOg0451h9uADbz55HaqwesTp5aZHSgytW0CA3Lh4FnMEimVBUxaGJtdmP49hR8XfAe0BWk3cOq02CTiMhKdGK5pmVkCCi9LRnJxbPC1RzWPf/lY4H2K+N6wDLvrb1WM+A2Fh71XRSvBXlVQKEoJr0eT+a7DJyXzGhckmsv3TltQryNpr2jZjHcR0/N2pkAzPGeCz3jnv8qCaY8nsvqjdw/KUpKDZS9C6Yr+tIf8W/dUObCOeAkOgncXidvzoQUVz5FzJUUhcES3V0xQDEaWsuoWtpkT/lVTYs2H4WA9vkoGVKak0ajLm0uXPZoTpgcU37ujbZiNdpcNpYhUe+2491x4qcbQaUgiFXAmNI0etwotSMYrMVZRarR4mOrJ2UR9svecoi5xAlgMM+72paagX0Wg7ONND6eDgrlhapuH4c9vlkFUuF/OyvEzhEiSG5uiSOMcjb8zlmR1D7BeCn2ZxS+zg1IRAPOOTwTNVr9aJSdOeSiq/SNsY8P12B5FRx5gvn54vJjh3od3BwbeoawDc9IaRBoJK6IDhilKuap6Jj45rJxR3lbP4KGGwSx/gfjuBEsRW3dpaP61NTOFbT2MaxLF7H8EDPLOe2+WVmfLo7H1oBOGWsmUWDcfdgiUFgQFqsBkVV9nGaTFYb8k1VqKzSQuIKYwu5PeeUTslezWkfwd8iihC0HFqNhNg4AXExQJXZz8PeVy2d+3sOSKju4FAdUHvsrlhLyqEVJGgFCekJZpgsWsRV97IVGANsrjurD018V0Aq5EWpk4PPvDPZOl9hh1LgptdxIEYeGnK3/31mzfXwnIMJNceRHKudpXRufwB4qa1W29PT0aYu4L84gyh2i3ibOkKIX6LEwEJQdRqKqcaiHQV1QdAwhsbxcRh9cVv5CgY0itfi8b65Xvc9dr4SiYkSThgqAQjOUgIOwCKK2FtodFZfOmg1Ap7rl4HTJTqkxmlxpLgC3x09hW0nzEiP1aNVmiMo484HrONhpdMwZCbq0CZVh8OF9mCuSgJsXKfw8PV83Lv2bXSsFTkgcA5RsMJsrZ6SSwAkf09IDvvUZj42cw1AJM5h4442Wp5tu5RaC3LYp17SMgnJKVWIjZOcY8IpHtezJtEzMy7r/X4luBViBf8Vwv0HQdweAOl09pLXjBQGY7F9VUIsYHXWoXvPpkKSXt7UcARbshJlpiJAcrR39LGJs59NkKVnQZXUBXUkQkhdkTgDC8E4daEY6y7aUVAXhEZxMXioS1fo3LptpsTo0Tw+0etTYuHOszhwrhLZjRxDlNQ0Yj9TVoUJ//sLeUU1pSCMAS2SGuGxLp2RFR+LyT8dxR2f7sHvhedxZTsdJB7v9nCuiSRS9AypCVrEQY+TpipUFhXjeGGsvQRHYRfnD16fcBySxKHRiWCSgPTUKlRYtaH5y8fl0JzbewIzyR4oeubTZXtHnFc9P2xinL0KObdxFQqK4xEXK4EJcOnGrqIa2C1t+QaeLceUAplA2sKpLRF10GgAbQxHok4AICElGdBp7UFdQqxyJMmhXJXsL0h1DYSZn+1dr5fiR0hF4OdsA8rtNz7QcUwvxDZ1hBASShTUBaFVajzKqjzHfGvfKAmr/ijFXRdnyZabLCIkDizbV4Cu6YlwLfmySCIW7DiK9XnFuLQVA2MaCAzITNBj4rXNcEl2NkQu4YO9v2Lp3xUoPZ+IuMTq/RUCgnidhFZJeqQlalAoFuOvfBNsAkMbvXxjf9VlgGOWAg5BEJGVXAFDmR5xyRaw8kTVD1ylpLnbv/a2go4Gg47hb2vOyT0hBg5drA06rQQb50hPNqO0UodGyRYUlekBzuAYPkPx3BTrbuGnA4NbflRQ9Vehj0DHXgonQafj0DMBcTqOpBgGFscRp9NAguQ8P+f/UKi6hjw4c/ykfG3l6bmtrrlfbmvUXBdn9a9Cd1xvbUAD7/0aOGpSR0h0k6j6VTUK6oKwL9+EvPNVuKFTunPZgh2n8d2hIgBaj4fE4Hn78ONjnWqqlDgAzlBusWHVkb8h2GLBq8OQtAQNXriyDXo2SYZGYFh/zIB1p47gmOkEJJ4OJby6DEWviUFaUhXOmkpx1lKJuHgLRCnBfyO/ao5YhwMQBAnnyzRo36wCKfEiKmK0yDcmAAAknxVofrgHH1y2WDlfDNBoJMRqRUjgKK2KQ7PsChjLYmDmrmGK8jmpzJLfbbzFgsGkp7SPwACrqEVKsghu06F5Y47YJBGiKMBqEiD6ue7cPSitzrCKWlH5zwodFbwFXe7b2YM+5SvgNx9unwWhDhq8dclJQGocDSlCSDQTqfpVNQrqgmB1G5/usdW7sCffiqQYneKD/0yZxaX6isEqSThTUYmyShG5NisSWSw0gv2h1zhRg97NUrD2SBGOFFjwv9Mn0DjZUp2SZ2kbY/b9CkwmGCrLodFWARYtEjTqRuxiqCkpEwR7b9KMjDLodMDps0n2acq42w6BYBwirwnifAZvrufGAUlkYDoJfx9PQWajKuSmV+J0sev0cCpauTGmeFB/1Zzetve3n6OM0WdHCm7/i1Gvk9A0vRIQBIjWGDCmQ5zeAm2ciNJCPeBjqjRHOzalPPqlWOWromrax3K1x7a3qfN+pT0How4sRA60LV7rRuGZgJwQQiKBgroAnDZWoUlyLARWMyr+8oMn8OuZc0gWqsejY8oj5jsKHfLLzPjpbDGKKm3QQ+Ps2domSwcOG85VVKHfJ1twwgA8emlTr09Lzu1t3A4WVeCnk6dx2mhFjFZELGoCPWcY5aXdlsQ5RM5h4xLKzAyZqZWIixEhQgOrKPgtmfK6vnrmCF6dvmd1oOdJcYlBq5Ggj7XCKkjgNi3ABNhiAA0LZ0mK5xmoCBXVY4CGARowxGoYBDDoNTpU2uLQoXkhAI6CsljnMCF+Ayi36lbFbeSHV1zAFFbKS069V606PrPMrSTNWXWrkI9ArqhzvLoAS+p0GqFOSvcIIXVLkgAWgim+OE0TRhysooS7v96Dnx/oCYaaDg5llupStOphNsCZQiN7jirR3v7pgKESFToRAEdGghZNUoBSo70062xZJY5VmCGKGmgQB/fHo7a69E3iHPsLy3C60AKRC0iKt3kEIo738sCr+p3AYLMBKclVOF+UhpREGxizN7xXWzCiFAo5S+MUAjn3nTWsumRQktCiaQk0Gg6rSUCcnsNmC/DBHOhzXOU5Kl1Pn9szQCNwJMWKiIsXYa7SoHVjK0wpIkpKYqGLF1FZLthLPxWypLxUOVOuPU8Vho3zlYL3/HssqA40XTLnaE/naFvnOIi/UljXDPkep04+lEkg+V92/0UBD1hMCIl+/P/bu/PwqKo84ePfu9VeqWxkgxA2lR1G4gLo6Dgtto402t1qO0/7uvTm+sDT2uo8tsJjd6ujovbo4Pa0S0/3+2i/M+jbM6iILahI8yqIsiMtIFsgZE8qSS33nvePJJVUqrJBIFj8Ps9TpOrec889955U7o9zz7nH0QanP5z0qRMdaprj1DbHAMj2mkwraetfpnU0i2mKpoiNR9MZmdNt1gRN4+WNezo+oAGnF1rMHuklbtTxSXUTFXVxirO7tqolP/pDAcW5Leyv8BGNxPC0RokrC639Epi+1UxLtBw6KMIR8HtsCoMR4nGdvKwou9ESDxbuacBEd12Dt44NVbereroO760RndHDGrAdh4JQK81hN25Tw2Wp9BMtd7u/d1z7s6dkrqUfMtqxVlOJ4CPoAtsxGFsYoRUYkR2nKeoQtgxMU4dYml31eTBayo/uQWa6PFWa9Up1Hkoijz7+tvV6F1Nr+y3WkhelHWSRZtOe95n42ZnBQFreJKATQpzqJKjrJ13TEpeaEdmdQZvRftGJxBWtzXGmFPgTLTGNkTi/Xfs1AHHbxmVolGa5qCROcVBj1e5ackP1VLcGcOjet0dLXKirWhvBjBBzdOKOSqq0tttgXd63L1cobMcBPU641aKm0cK2DfzeeI8jV9P10+rINxGzqbbjyurSGqfovl1ba2XQFyXi6MRsRcBto0UNRuQ1E46Y0Jy+DP3Rn0t3n2kG0B9NKbBMB93R8VsKnw5Thkc4UOEmEtcpzolxuNHAMjXSDIruMfhJt+ekT10C5ZSWsO631HsNfvrXXy7xPs292UQI1/7LoLpt3VcPzo4s2/rUpdI1jZDHYHKxD5dlcNusYspyXKxbMK3XfIUQpwClwWAMcpCBEqKDrrVNfF/bEiPHayWWaxpkuQxCmosjsXhSR+2fvvklDfGWtvBMg99dMZ7F7x9gy75attaG0ZXDmcFu3ezbf+dsx2HFV9UcthvIz24iN9B3O1VbcKUwXTGyfC2U5jVRUecF9M4Wxc6UvQYybddu1f5vz13bO3LSNYVl2rhNG0NzyHLZ5BWE2VWZRTSqd0l7YqTbV3/60Os6oHRMy0HXdMYNb6a5xYvb4xCLalguHbfVNksF9NDyla6vWspn1e2eq0qTPn0/u3T94aAjuE9uq0sOvrrmrKUUVe+4BU/yueqlvbJbfn3/wdQ0DZeR+r+KT+ZPw9Vlyr0Hvz0KAJdMZCiE6Ol/g0eTT4aToK6fPKbO6cEg/7W5ih+fVZxYfnbRML51ZRF3L9/Dkfp40sWwKWKDCb+8eAR1eiMfV2znf/YcYm/YwuVSiSeNaIBXN5hSEKTxYBUVkSjhFkXzYYfC/M406cQdRcS2MQ2bvGALo3LD1LV68LpszC7jC9Ld8kt3i7Tj5ai258X19B3QNPC74ozIbsTvtTlYH8Drh4it9z5sXPX6MVEu1eV9n47iP19tfbc03IaiIBgly2tjGhpeS6elxUVpfjNVDW5C/jgtrck76H5rs2NhulavHgPJ3kcVpKZLR/Vv895aBrsWobfWtq4NgemKlC4IzPO1/XmZXZrNKJ+PkE9n1sgQeX5XyvZdAzohhBBHR4K6fmqM2lQ0pE56PzYn2PZGg0jcYWNFuMvatvtNU0d6eWrdHt7Ze4BI3N+5WmloysTjMvE4JptrDtAcj+Moi744tLUW1TRDXk6EAm+ULE8cj1tBa+f+e+lDn+js7jidjx1JFyboWlv/uZAnguYoGi0XBcEIoBhT2Ex9qyspz8HQ33z6m07XIO7oeEzwuG2ijUHKhnk5HK7i9NIWWmMmkbhBPM1TRFJa2npvtgTSn+/UgQjJQWFyp7n+HVlb0Jbav60jy46X1u1nch7dW+20lP6cXd84XdZoGgTdJuOHeblsPHgCQa4cXwTALTPbpsv7h1E5MKpfhyOEEKnk9mu/SVDXD0+u/Ro71n4LUSl2Voc5Lc+flEYDIo5DPN7Z4uA4ispohP/ZWUnUbo8W2pvMNHSiEYtD1TYHwjaxhjBl7ni3C27yZOy6rrAsm6C/hWhUx6O3zT6R2j+87/5NSbMCdO8P194E5PPGaGw2mVxQy7CAgWXAlwfNHmdA0PoMsbQ+W54SBexe4H5QaJi6Im7r+CwFysWIYBSP2yFu6yhcKNvAURDXzKRQqLeWwZQ+cVrq++75pFvavU9cr2crTZTWU2tZx03ypMEq3YPEnrNNKmNn2drb7dqDy4BLJ89nkptlctm4PGYUZPGDyYUMD7rxedr+jPwv6f4mhDgeHG1wRq7K6NdT0+aKMJOL24K25zZ+zrK/xZkzshCU4v9sPsQHB4/wv6/sfgXTKMwyGZbjIxp3ePTjvXxZ1UIg2HlB14DiYa2oOosjLQ4tEZv6VhtHGV2zSVBAOGpTZMTJ8bZSHGyiqsmDbms0tBpJ6dK1wHSs67iV2tOVXtcULa0GY/LqmTyihvV7CzhQ5yc3O4plapgmRKIpm6Xd11FL811Lya9L5OUo8LgdTKWg1SQ/4GA35eGzouR6FVFlEnZcBLwx4k5qhm0DC9L3dkvZbQ/JUnuq9a57X7jOEqT/Q9M9wO8+40L3vNPFwmkPs0v+SUGepuG3DE7L9XJeaYhzhvspCZXwD6OGMSbXmz4TIYQQJw0J6rr5r22H+evfmnl87mhe2rqOlfsOoiihKRLHsBQH6yLk5Xg5UBehPhJnYmFb8HfD3xVT0xrl3c0NPL56P58damwbNNF+1ZyQH+CDAzaGO0pNxIOteu5DpOsOHneMiK0w9Thj8psYGWqhtrXv27Id/aLabq2mthBp7a3YMRvqmi2mnnaEI40+vq4MMmZYM3kBu9cH4aZ2r+++bmB6eWpIIoFhOgTcNtG4ialMRoYsAh4L3ePgsmz+9lWI7OwWmlvbz2k/oqyujVrpNuo5i/Y1fRxs19vePTU8prQO9hElppz75AGpaY6pLbOk+geCboNxOX68msHYUIhxBcMYn5PN7JHZeCyZMksIcZJxGJyBEvLw4VPL5iP1/GHzAcZ4cgD4+OB+NHRitsNHB2owXDq0P9rk8VUHmDk6mAjqctwWiz/eT4nbg9Pe8W6Yz8XfjbT46OA+qiNNeEwHV8c1s9sV3VEOijjnlFXhdtlsr8xly95cdL33W4OO6ngOHUkjbzs2MnQHDUVJKMzpBXXkBGJ8umcYef44MdukIBjlSGPnZukGT/TX0bbUKcDSHSxDkeOL4TcUms+gOepBaV7yg3EmFUfZW+vH1C0MLc2vbdpWLJX0LrUvWe/S9UfsOdDtmij5cS/9aw9Mn1mfgXIPmXlMk3y/hxFBD6eFAhSGcjnNE+H88adhmRK4CSG+QaRPXb9JUNeutrWVFzduR9Msvj81F2gLaMIxRXmRj81VEeyOZ5C1X0hjtsOybdX804Q8qsIxojEbrf1xc1XhGBUtYer2N6DhYHRcR7UuWWiKuONgamB6ouiaYuqIBnbXJPfX637hVrRNw9UQieIos1tAozB1m4paL/trvIwtaGLrvmymDG/AVhpNUTM1665l6kna70Jn21HP40M7OQ4YKCy9rU+hrjTcmsbpeVGUplMYiKFpUFlrEbEVkRqdeC85pi7ruQUxOUDTeknZ1z76U450ufbUWy858Ex633PzG5ahEXJbhLI8nJbv5uySbIoCHnBgZmkWPldyPTuOQ2VlZdrZLIQQQmQGCeraba2pIqZs7pl1GueUhACorHMTdPkJed0cbGigyOdjXL6b+WeP4LH3D7B+QzUjfEH+aULbvK8ht8mwHPi0ooGv61uobYlTEkru4K5rDpYeoyg7Sk2tn2bHwG0qYpH05Wprwet6be/sCWXobb3tPWacLHcr4ahFlttm12Efh+t9eD1xugyFTejoWN9TaJMufb/TqLZy+Swbr+lQkBWlts5Dc9iLoenk+5ppietsPxDENCzcLhtTh9gAmvl6u7XZvzKnH7jQ9uOYegZ20lIHUaTutGNBmvC0vX+b4bbwG26Gl4QYleNhWkGAUbkuphaHBqecQghxspOBEv0mQV07n2kxLVTMOSW5iWXu1jKa9Wr+744j1IbjPP3tMvY3NfDr1bs4UBfB47FAwReHq7nvL1+xo6qVjVUWZVk+oMtjJLQ4jnKIxWDP/lxsA4rzGqir89PxBI3uneCVUtiOwm5f4SgNDUXAFSXkiVHXkMfkka18utOgJBTB67Vpjbt77hTfY6yi+hnaJessj41t2wRd4HYKKAzYFOQfpCQU5mCtn4A7Rjziw9ANtC5fqF4nQOgaIaIlP/dOdSZIadnq+m/qnehEMNvXTc2eR4j2nC5FH7GhpoHH0HFpHvzBAGPcAUbkuJla7GZSXhbTi7PxmPL1FEKInu5YHFU+GU6uGu1W7IiypyoOZ3Uu+6o2TLSllSwtG5+t87O3NpMXjBOLWhTmwJHmCJuqanj8kwr+VqsnnmHXFIvRakdx9BgxO06OP0pz1KQh4m4PoZIjjo7Hizhd3lu6g6W3zWLgNSJsORSgOepmXEETdS0eNKf7k4W7vk3/m9v9FmTXZem20NubwmJxnYDl4LMcQl6HoN8hEvUzZXgtp+XHaGjVqAsbeGwfut7SbW+p+++5hL2MeD0GPQVp3fvMpfQnhIF1KuzC1DUCLoN8r4+yQBb+vCB5QY2RgSBTC/1MLwpiStAmhBBiEMlVpd3mQ2FGhZLnX3V7FNnuXHTH4LO6FrINm4DHpqLRJhZqoibqxhUPAG23SW1Nsbe+BduqwTINrLS34Nojty7tRobuoDSFpjus35vDiJxmhgWj5HvjtDo6fjfQZPSjV9bAKTQMXWEAtWEPI3NbGJfXgoFFrl+R5XFwGV7yfa1U1LvICyr2HEnf0b63GSg4xnImBV6J57ZpKesHoiOAHtBABtoCNr9lUppj4TFcnDU6RCRqcHqOl9OG+ZlRnE2Ox91HLkIIIfpFbr/2mwR1QDgaY2S2i8vPyE1aPiLHoiECZSE3n7riRFWUyuY4LXFX22hTFKYZY29jNZhB3AaEw1bb/KFdBh+oLqMhDV1huuPEYhqeQCvFuc3kBGy2V2S1TbfVGGRkXntrl9bPYKWPRC7TQdMgzxehOWrh1kFD58CREGU5ipDHJMtyEXMCaLTidxu4TQNIMzv90VJ93dbs2nKYfIu0p5a0RFLSP6Kk4zZsf1vuNcBtGOT5TEr8PoZ7vZxeECAw3E+RL0BZyM+kYX6Kg/4+8xJCCDFIJKjrt1M+qKtsinL/h9uZlJOdaFZ7YMUubjqzhDPyPAQ8NrvqwpiW3TbqFLBtRbgFcn0RAh6Fk5gEXeEoaI07xG0NhYZtQ23YRUl2mNEjGwhH3Ryo8+OyFJqlEfTZaR/DkdpfLD1NB9vWicYNbFunvslDiV+nutbgjDybfJ+OWRxneBaMyY1xsN6Ny3C1N/F1q37V29CJ5CnHehkMm/QMlv4OxkgUodvPjvfH0qXC1MHAxG2YZLvc+AyL0uJczhweoCaSzcQCk7KsbEqzsjB1mYNUCCHEN9MpH9Ttrm0lHHP4h7HZVNXbfHGgiVV7qtlWf5i6SJjibJPdNYpRBQ2cP/4Ab28bSSRqELcN0GI0RuMEXQpdaxul6tJt/K4oHheAxr7qAB6XzRkljQQ8Di2xgZWvbd5VcByd/EAES3eIxhTDC5sh5mPyqBb2Vw8j5I+C0vhyfw5ThxuYhoHfbdMZo/T9EI+2VsW+y5T+8SLpZzw46ue19UIDTE3DY+oE3Saj8tyMLjTILszFsmxGZRuUBD2UZQU5Pc9Prs/TZ55CCCFOUjJQot9OyaAuFnfYUtnM9JIARQEXWYaLM/L8vPr/vmZ3fT1f1jRR48QJuGzqq1qpbta5YvoBJhXU89ZWQClcpo2pOTTGdSYNb+AfJxzkPz8bTU3MwONyiDo6dh9Pr9Zpa2VzGQ6GBnHbQlfwt8M5uE2YUBpmVF4rthNAw8eU4krqml0cqPMS8gMRA639YchJA0a7/uwi7SM8juGX/Fi2Tabh0XUMl4WpG3hCPpRyM87vJ+C2KAlYTC0twqsFKfIFmDQsiEsGGQghxKlhCG+/LlmyhMcee4yKigomTZrEU089xfnnn3/sZTlOTrkrY2vM4dr/3ESu6eF3V53Bjqpm/ryzCr+/iTd2xLFpBU+UZieOHnOIKIg5iq+rvBxucJPtj7D/YIjzT6viYJ0fO2LiNiHdXTulNEyt7WG7sZiBpgz8lqI0K0LUMbjotAa+2J+H32xBRYPEActlU1HnozSvqfOBxW259fA+3X67pDpBXQg0wGVoeEwLn2URMHwU+X1E417ceSHGmDFK/X7GFxWS63bxvRF55HsDjM51UxSQeUWFEEKcXF5//XUWLFjAkiVLmD17Ns8//zyXXnopW7duZeTIkUNdvLROuaCusjlKUyzOT8uzWbq1kne21hJp1Hl/dy11cROf5WDpCjumyMmO8mVFCM2Ms/VAPobbwW06mLoi3OrGiZvoSqOmyYvPBJ+pU6eZVNbkkJcdx2PZZLldBHxRHNtH0NVEc9RN0AvhqIGudw5EGMpWYU3TMA0Nl27gMVwEXW5yPV78LijwOQwPBCny2/iKcmjODnBubjalOYrSoMmYXA2/EeC+aVm4TVd7juck5d8xm0FBQQG69FkTQggxEEM0TdgTTzzBj370I3784x8D8NRTT7F8+XKeffZZHn744WMvz3FwygV1XjNKzKpg4eojfH3EhyfuoinmsOuQm4KsKC7dTW6WTk1LnKIQVNVrNLaamJoHO+7QYhuUltjUVA0nL8ciHGsgFg+ytSKbSMyNoVugOQzqyFE0dE3HQMel63gNEwMLn+GmGQdvtotcv4HH1FDZFuUjQvh1jdMLc9GMHLwFBhND+RT5NPLHZPP9shBZloHH8FEc9JDtdkmwJYQQ4uSkgD66M/U7n36KRqOsX7+ee++9N2n5nDlzWLNmzSAU5vjI+KCuY5L7hoYGdF2nqbkVbyxOU5PGD8cr/HhZuTuC1+3inJJh5Gc7eF02qw7WU+bR0bMNDtTajMp1Ux1pQSnQlEZBlkZ+0MAVNSg2LTRTJ2zoNFsaLrdBjt+FoWmE3W78HovSHJNRXhcjNC9uXcfQDExNp9znJd/v0FjiJ8djEXDr6JpOlgc8uossl4Xf3a2j/9h+HPjp6ReXBrstiEVpikUHfF4HwnEcGhsb8Xg8EjwOEamDoSd1cHKQejh+GhoagM7r7qCJhgc1n45ydnC73bjdyc8WraqqwrZtCgsLk5YXFhZy6NChwSnPcZDxQV11dTUAZWVlKevWd/u8opd8Phq8IgkhhBAZq7GxkVDo2OendrlcFBUVceiVK469UO0CgQClpaVJyxYuXMiiRYvSpte6zSCglEpZdjLJ+KAuN7ftgcJ79+4dlF8yMXANDQ2Ulpayb98+srKyhro4pySpg6EndXBykHo4fpRSNDY2UlJSMij5eTwedu/eTTQ6eHeT0gVl3VvpAPLz8zEMI6VVrrKyMqX17mSS8UFdR/N6KBSSL/AQy8rKkjoYYlIHQ0/q4OQg9XB8DHbjicfjweM58c8adblczJgxgxUrVnDllVcmlq9YsYJ58+ad8PL0V8YHdUIIIYQQA/Xzn/+c6667jvLycmbOnMkLL7zA3r17ufnmm4e6aD2SoE4IIYQQoptrrrmG6upqHnzwQSoqKpg8eTJvvfVW2j76J4uMD+rcbjcLFy5Me89cnBhSB0NP6mDoSR2cHKQexEDceuut3HrrrUNdjH7T1KCPPRZCCCGEECeaPKRHCCGEECIDSFAnhBBCCJEBJKgTQgghhMgAGR3ULVmyhNGjR+PxeJgxYwYffSTzQhwvixYtQtO0pFdRUVFivVKKRYsWUVJSgtfr5cILL2TLli1DWOLM8OGHHzJ37lxKSkrQNI0333wzaX1/znskEuGOO+4gPz8fv9/Pd77zHfbv338Cj+Kbra86uOGGG1K+G+eee25SGqmDo/fwww9z1llnEQwGKSgo4IorrmDHjh1JaeR7IE4VGRvUvf766yxYsID77ruPDRs2cP7553PppZeyd+/eoS5axpo0aRIVFRWJ16ZNmxLrHn30UZ544gmeeeYZPv30U4qKirj44otpbGwcwhJ/84XDYaZNm8YzzzyTdn1/zvuCBQt44403eO2111i9ejVNTU1cfvnl2LZ9og7jG62vOgD49re/nfTdeOutt5LWSx0cvQ8++IDbbruNtWvXsmLFCuLxOHPmzCEc7pwvVL4H4pShMtTZZ5+tbr755qRl48ePV/fee+8QlSizLVy4UE2bNi3tOsdxVFFRkXrkkUcSy1pbW1UoFFLPPffcCSph5gPUG2+8kfjcn/NeV1enLMtSr732WiLNgQMHlK7r6p133jlhZc8U3etAKaWuv/56NW/evB63kToYXJWVlQpQH3zwgVJKvgfi1JKRLXXRaJT169czZ86cpOVz5sxhzZo1Q1SqzLdz505KSkoYPXo0P/jBD9i1axcAu3fv5tChQ0n14Xa7ueCCC6Q+jqP+nPf169cTi8WS0pSUlDB58mSpm0G0atUqCgoKOP300/nJT35CZWVlYp3UweCqr68HOuf9lu+BOJVkZFBXVVWFbdspk+4WFhamTM4rBsc555zD73//e5YvX86LL77IoUOHmDVrFtXV1YlzLvVxYvXnvB86dAiXy0VOTk6PacSxufTSS/njH//I+++/z+LFi/n000+56KKLiEQigNTBYFJK8fOf/5zzzjuPyZMnA/I9EKeWjJ5RQtO0pM9KqZRlYnBceumlifdTpkxh5syZjB07lldffTXRKVzqY2gczXmXuhk811xzTeL95MmTKS8vp6ysjGXLlvHd7363x+2kDgbu9ttvZ+PGjaxevTplnXwPxKkgI1vq8vPzMQwj5X9YlZWVKf9bE8eH3+9nypQp7Ny5MzEKVurjxOrPeS8qKiIajVJbW9tjGjG4iouLKSsrY+fOnYDUwWC54447+POf/8zKlSsZMWJEYrl8D8SpJCODOpfLxYwZM1ixYkXS8hUrVjBr1qwhKtWpJRKJsG3bNoqLixk9ejRFRUVJ9RGNRvnggw+kPo6j/pz3GTNmYFlWUpqKigo2b94sdXOcVFdXs2/fPoqLiwGpg2OllOL2229n6dKlvP/++4wePTppvXwPxCllyIZoHGevvfaasixL/e53v1Nbt25VCxYsUH6/X+3Zs2eoi5aR7rzzTrVq1Sq1a9cutXbtWnX55ZerYDCYON+PPPKICoVCaunSpWrTpk3q2muvVcXFxaqhoWGIS/7N1tjYqDZs2KA2bNigAPXEE0+oDRs2qK+//lop1b/zfvPNN6sRI0ao9957T3322WfqoosuUtOmTVPxeHyoDusbpbc6aGxsVHfeeadas2aN2r17t1q5cqWaOXOmGj58uNTBILnllltUKBRSq1atUhUVFYlXc3NzIo18D8SpImODOqWU+vd//3dVVlamXC6XOvPMMxND3MXgu+aaa1RxcbGyLEuVlJSo7373u2rLli2J9Y7jqIULF6qioiLldrvV3//936tNmzYNYYkzw8qVKxWQ8rr++uuVUv077y0tLer2229Xubm5yuv1qssvv1zt3bt3CI7mm6m3OmhublZz5sxRw4YNU5ZlqZEjR6rrr78+5fxKHRy9dOceUC+//HIijXwPxKlCU0qpE906KIQQQgghBldG9qkTQgghhDjVSFAnhBBCCJEBJKgTQgghhMgAEtQJIYQQQmQACeqEEEIIITKABHVCCCGEEBlAgjohhBBCiAwgQZ0QQgghRAaQoE4IMWgWLVrE9OnTh7oYg+qVV15B0zQ0TWPBggWJ5aNGjeKpp54a1H3t2bMnsa9MO49CiONPgjohTkKHDh1i/vz5jBs3Do/HQ2FhIeeddx7PPfcczc3NQ128U05WVhYVFRX86le/Oq77KS0tpaKigjvvvPO47kcIkZnMoS6AECLZrl27mD17NtnZ2Tz00ENMmTKFeDzOl19+yUsvvURJSQnf+c530m4bi8WwLOsEl/joKaWwbRvTPLn/FGmaRlFR0XHfj2EYFBUVEQgEjvu+hBCZR1rqhDjJ3HrrrZimybp167j66quZMGECU6ZM4Xvf+x7Lli1j7ty5ibSapvHcc88xb948/H4/v/71rwF49tlnGTt2LC6XizPOOIP/+I//SGzTcYvv888/Tyyrq6tD0zRWrVoFwKpVq9A0jb/85S+Ul5fj8/mYNWsWO3bsSCrrI488QmFhIcFgkB/96Ee0trb2emwd+S5fvpzy8nLcbjcfffQRX331FfPmzaOwsJBAIMBZZ53Fe++9l7TtqFGjeOihh7jpppsIBoOMHDmSF154ISnNmjVrmD59Oh6Ph/Lyct58882UY926dSuXXXYZgUCAwsJCrrvuOqqqqvqsl768/PLLhEIhVqxYAcCFF17IHXfcwYIFC8jJyaGwsJAXXniBcDjMjTfeSDAYZOzYsbz99tvHvG8hhAAJ6oQ4qVRXV/Puu+9y22234ff706bRNC3p88KFC5k3bx6bNm3ipptu4o033mD+/PnceeedbN68mZ/97GfceOONrFy5csDlue+++1i8eDHr1q3DNE1uuummxLo//elPLFy4kN/85jesW7eO4uJilixZ0q987777bh5++GG2bdvG1KlTaWpq4rLLLuO9995jw4YNXHLJJcydO5e9e/cmbbd48WLKy8vZsGEDt956K7fccgvbt28HoLGxkblz5zJlyhQ+++wzfvWrX3HPPfckbV9RUcEFF1zA9OnTWbduHe+88w6HDx/m6quvHvC56erxxx/nrrvuYvny5Vx88cWJ5a+++ir5+fl88skn3HHHHdxyyy1cddVVzJo1i88++4xLLrmE6667Tm6pCyEGhxJCnDTWrl2rALV06dKk5Xl5ecrv9yu/36/uvvvuxHJALViwICntrFmz1E9+8pOkZVdddZW67LLLlFJK7d69WwFqw4YNifW1tbUKUCtXrlRKKbVy5UoFqPfeey+RZtmyZQpQLS0tSimlZs6cqW6++eak/Zxzzjlq2rRpPR5fR75vvvlm7ydCKTVx4kT19NNPJz6XlZWpH/7wh4nPjuOogoIC9eyzzyqllHr22WdVXl5eonxKKfXiiy8mHev999+v5syZk7Sfffv2KUDt2LEjbTlefvllFQqFUpaXlZWpJ598Ut17772quLhYbdy4MWn9BRdcoM4777zE53g8rvx+v7ruuusSyyoqKhSg/vrXvyZtu3Dhwl7PoxBCpCMtdUKchLq3xn3yySd8/vnnTJo0iUgkkrSuvLw86fO2bduYPXt20rLZs2ezbdu2AZdj6tSpiffFxcUAVFZWJvYzc+bMpPTdP/eke5nD4TB33303EydOJDs7m0AgwPbt21Na6rqWp6OfW0d5duzYwdSpU/F4PIk0Z599dtL269evZ+XKlQQCgcRr/PjxAHz11Vf9KntXixcv5vnnn2f16tVMmTIlZX3X8hqGQV5eXlK6wsJCoPOcCiHEsTi5eycLcYoZN24cmqYlbil2GDNmDABerzdlm3S3absHhUqpxDJd1xPLOsRisbTl6TroomN7x3H6PI6+dC/zL37xC5YvX87jjz/OuHHj8Hq9fP/73ycajfZYno4ydZSn6zF26HqMHWWfO3cu//qv/5pSpo6gdSDOP/98li1bxp/+9CfuvffelPXpynu8zqkQQkhLnRAnkby8PC6++GKeeeYZwuHwUeUxYcIEVq9enbRszZo1TJgwAYBhw4YBbf3LOnQdSDCQ/axduzZpWffP/fXRRx9xww03cOWVVzJlyhSKiorYs2fPgPIYP348GzduTGrJXLduXVKaM888ky1btjBq1CjGjRuX9OqpD2Nvzj77bN555x0eeughHnvssQFvL4QQg0mCOiFOMkuWLCEej1NeXs7rr7/Otm3b2LFjB3/4wx/Yvn07hmH0uv0vfvELXnnlFZ577jl27tzJE088wdKlS7nrrruAtta+c889l0ceeYStW7fy4Ycf8stf/nLA5Zw/fz4vvfQSL730El9++SULFy5ky5YtR3XM48aNY+nSpXz++ed88cUX/PM///OAW686tvnpT3/Ktm3bEi1/0Nkidtttt1FTU8O1117LJ598wq5du3j33Xe56aabsG37qMo+c+ZM3n77bR588EGefPLJo8pDCCEGgwR1Qpxkxo4dy4YNG/jWt77Fv/zLvzBt2jTKy8t5+umnueuuu/p8AO4VV1zBb3/7Wx577DEmTZrE888/z8svv8yFF16YSPPSSy8Ri8UoLy9n/vz5iUehDMQ111zDAw88wD333MOMGTP4+uuvueWWWwacD8CTTz5JTk4Os2bNYu7cuVxyySWceeaZA8ojKyuL//7v/+bzzz9n+vTp3HfffTzwwAMAiX52JSUlfPzxx9i2zSWXXMLkyZOZP38+oVAocVv6aMyePZtly5Zx//3382//9m9HnY8QQhwLTXXvdCKEEBnij3/8IzfeeCP19fVp+yP2xyuvvMKCBQuoq6sb3ML1YtGiRbz55ptHdVtcCHHqkoESQoiM8fvf/54xY8YwfPhwvvjiC+655x6uvvrqow7oOtTX1xMIBLjtttvSDrIYLHv37mXixIlEo1EmTpx43PYjhMhM0lInhMgYjz76KEuWLOHQoUMUFxdzxRVX8Jvf/Aafz3fUeTY2NnL48GEAsrOzyc/PH6zipojH44kBIm63m9LS0uO2LyFE5pGgTgghhBAiA8hACSGEEEKIDCBBnRBCCCFEBpCgTgghhBAiA0hQJ4QQQgiRASSoE0IIIYTIABLUCSGEEEJkAAnqhBBCCCEygAR1QgghhBAZQII6IYQQQogM8P8B3grptEDgNWEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "rdf.plot_rhi(azimuth=90, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
@@ -232,10 +536,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "15",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:23.780766Z",
+     "iopub.status.busy": "2026-08-19T13:34:23.780612Z",
+     "iopub.status.idle": "2026-08-19T13:34:27.830763Z",
+     "shell.execute_reply": "2026-08-19T13:34:27.829761Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "rdf.plot_cappi(altitude=2000, variable=\"DBZH\", timestep=\"2024-06-12\", ax=ax)\n",
@@ -256,10 +578,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "17",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:27.832666Z",
+     "iopub.status.busy": "2026-08-19T13:34:27.832519Z",
+     "iopub.status.idle": "2026-08-19T13:34:34.478054Z",
+     "shell.execute_reply": "2026-08-19T13:34:34.477422Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "cs = rdf.extract_cross_section(p1=(SITE[0] - 0.6, SITE[1] - 0.35), p2=(SITE[0] + 0.6, SITE[1] + 0.35), crs=4326)\n",
     "\n",
@@ -303,10 +643,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "20",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:34.481030Z",
+     "iopub.status.busy": "2026-08-19T13:34:34.480837Z",
+     "iopub.status.idle": "2026-08-19T13:34:34.563867Z",
+     "shell.execute_reply": "2026-08-19T13:34:34.563319Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6315860e7bb84880a2fae29f6fb754a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<b>Draw an AOI</b> with the toolbar (top-left): ▭ rectangle → <code>crop_by_bbox</c…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "RUN_INTERACTIVE = True  # <- set False to skip the map (e.g. outside Jupyter)\n",
     "\n",
@@ -321,10 +683,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "21",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:34.569261Z",
+     "iopub.status.busy": "2026-08-19T13:34:34.569104Z",
+     "iopub.status.idle": "2026-08-19T13:34:34.572471Z",
+     "shell.execute_reply": "2026-08-19T13:34:34.571960Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nothing applied yet — draw a line above and click 'Apply crop'.\n"
+     ]
+    }
+   ],
    "source": [
     "drawn = getattr(selector, \"result\", None)\n",
     "\n",
@@ -353,16 +730,34 @@
     "|---|---|\n",
     "| `\"xy\"` *(default)* | metres east/north of the radar |\n",
     "| `\"lonlat\"` | degrees |\n",
-    "| `\"projected\"` | the archive's own CRS (`x_2056` / `y_2056` here) |\n",
+    "| `\"projected\"` | the archive's own CRS (`x_32614` / `y_32614` for KTLX) |\n",
     "| an EPSG int | that projection |"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "23",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:34.573784Z",
+     "iopub.status.busy": "2026-08-19T13:34:34.573648Z",
+     "iopub.status.idle": "2026-08-19T13:34:41.147024Z",
+     "shell.execute_reply": "2026-08-19T13:34:41.146499Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x420 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
     "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"], strict=False):\n",
@@ -384,10 +779,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "25",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:41.148819Z",
+     "iopub.status.busy": "2026-08-19T13:34:41.148683Z",
+     "iopub.status.idle": "2026-08-19T13:34:47.676504Z",
+     "shell.execute_reply": "2026-08-19T13:34:47.675700Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x420 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n",
     "for ax, coords in zip(axes, [\"xy\", \"lonlat\", \"projected\"], strict=False):\n",
@@ -412,7 +825,7 @@
     "|---|---|\n",
     "| `\"xy\"` | metres from the radar (negative to the west/south) |\n",
     "| `\"lonlat\"` | degrees |\n",
-    "| `\"projected\"` / an EPSG int | metres in that projection (LV95 is ~2.6e6 / 1.2e6) |\n",
+    "| `\"projected\"` / an EPSG int | metres in that projection (UTM 14N is ~6e5 / 3.9e6) |\n",
     "\n",
     "The tick *labels* are in km, the numbers you pass are in metres — that is why\n",
     "`xlim=(-50_000, 50_000)` shows an axis running −50 to 50.\n",
@@ -426,10 +839,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "27",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:47.678420Z",
+     "iopub.status.busy": "2026-08-19T13:34:47.678269Z",
+     "iopub.status.idle": "2026-08-19T13:34:51.004055Z",
+     "shell.execute_reply": "2026-08-19T13:34:51.003395Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LAPF4nKysLO677z4uv/zyf2V1VSqVSqX6r6O2xSqVSqVSqVQ/jB9rv+o/KlA0ZMgQZs2aRXt7O6tWrSIvL4+rrrqKSy+9FIDGxkbKysrYtm0bo0aNOvS6uXPn4nQ6efnll//ifuPxOPF4/NBjWZbxeDy43W4EQfjnnpRKpVKp/uspikIwGCQ3NxeN5v8+WDcWi5FIJH6Amn1LUZTvtHkGgwGDwXDYtn9WW6xSqVQqlUr17/BD96u+b58Kfrz9qv+oZNaNjY0sWrSIG264gZ///Ods2rSJa6+9FoPBwHnnnUd3dzcAWVlZh70uKyuLlpaWv7rf3/72t9x5553/1LqrVCqVStXW1kZ+fv7/aR+xWIwckwnfD1OlQ6xWK6FQ6LBtd9xxBwsXLjxs2z+rLVapVCqVSqX6V/tn9Ku+b58Kfrz9qv+oQJEsy4wdO5Z77rkHgFGjRlFTU8OiRYs477zzDpX78+jdX4ro/anbbruNG2644dBjv99PYWEho+7+hKDOgEHWMqXAwcgcGz4lzra2INubo8g6hSyrhiy3TExK0tQrUuI00T6QxG4QcVoFGj0J4pJCdZqV4jQ9G9qCCEkNBrtETJbItMEdR5ZT6bKTkmXuX9HBvp4oz8wr47fL2hmSZeb0kelc++U+trSFybCJVJqc/Ob4IlKywuf7fFiMAo+vb+fmo/M4piSdN7f38GptHTrZglUw0Z70oRE05FvNdAWSPH5cJa9v62NdawC3Qcs5o3KQRZlALMXn9V7KXEa2t0a5/shcMpwCIzJs9PX1saFHyz1fd/DzGfmYzAq/WdPCx2cOx27QEoilsBsPXi4/+7CJrkCSJ08tIc2qo6E/SqHLgE7UcN0X+ylwGDmhNIP7l3diM4g8fkopkqxw5pv7iCZkjEaFUEqmzGHmqZMq6AnGafbFaPcnGZFjptxtPvRZhRMpLHotS+sHWNsa4PIxWXy2388n+wY4d3wab9R0ckZZES9s7KEgW+TsEdlML3EiCAL1/WHe2jnA6FwrfaEUa5v9NHjDjC01sr4jwPxB2UwvcfHyxh7aYn4uHZvH0UWZ7GzoYFhJLlqtCMDPlzYxOMvCu7v7iCUlbGbo7JOpyrAypczKk9vaGJFlIc9uomUgxoHOBBUFOrrCcVwY8YZlsjKgbSCBqFOwmcDlSKHXaHn0mBFY9AePU9sbwaTT8PSabvwxmaHZJobkmDm63EEoLrG6JUCPP8GerginDXczodjOnBf34LboePn0Kr6o9fLE2g4KXAaavXF+MjGXygwjpWkmNJqD34+XN/fwzs5+HBYNgbjE86dUkO04GPl+b+cAT67tpCxHT1ckhksxkUzJPHF8Gunp6YeNkljf4qcnlCSegHBCoqE/Rr7DQEcwztmj0xmcZQFgd2eQc187wAnVTn5zQgm/WtbItu4gMyvNeOJxmv0JNDI0+BKcPjibNKOBVQcCNPqjKEkBGRmdScKtN+KPyozJtXHh6GweW9vBgYEY984uZsgfjtUTSPB1XYDJJVZiQpJ3tg+wvjXE2SMzOW/0tzfeVm+Mr+sCGPUwocjKru4Iu7pD3D69GG8kycLlrWg1cGSxkwZPhIvGZNPhT7DwyzY0gkSGJkqPYGQgmuK8UdlcMDqb+1e18e6uPsqzDPT7ZdKcClFFxqYXybUZ+MnYAgocJgC2tQfZ1RFlWpWVt3f3snj3AMOyjUimToRIOjdNLmfFgQDzhqWT5/j2vxK/+aoVnaghzSKSlOGCsZmYdOJh97oGT4SyNDMD4Tgv7OpkdYuXVEJLJJHEptORkgUmF9qQ9Qn6owkenzkURVFo9sR4Zm0PvkSSSydmsqUjxAf1Peh0CoVGK5taw9wwJZ+SNAM3Lm1k3rA0phe5+fmnzTx1nJuw1spDqzvpCyUJRWUWzStjcLaFbR1Bcm16HlzTQUpWuGpCNvesaGd0oYHFtV3Y9FqcBiPtgRi5NhMvzBmEWX/wPpOSFO78spXBWWYWjM5AURQ+3+fHbhK47qNGLpyUwdXj8lnT5KcrFGf+sEwUReHJ9Z3MLHdyxTuNXD01h9NGpAMQS8oseGcfE/LtHFvuYESOjYQkc/NXdbhFM3fMLKS2L0LDQJzjB7m4f20rB7rj7O+L8NaCwbjNOoy6b78Dv1zRyIUjsyl1mnh5Sx+zS7QMrijDZrP91bbo+0okEviAJwHT/3lvB0WBa0Ih2trasNvth7b/pf98/bPaYpVKpVKpVKp/tR+6X/WP9Kngx9uv+o8KFOXk5DBkyJDDtg0ePJj33nsPgOzsbAC6u7vJyck5VKa3t/c7Ebg/9deGgeVlO2n0G3hxfhVj8qw8tbmDlQ0h3j/7CF7b3s1TG7oQjVrCkkI4KSGbkvz6hGpe3tLL3oEAUUOIYmMaTV0p+lM6iAikZBsBc5Rcg4lce4pClxaD0UV7XOC2FQ1EIgqPnjKEvCwrT5yVhiAIfNkwQH1YR0VBOrEotIR0SDorhS4Dp9kdnPhiDZ6IlSx7Bq/sC7Jol59iRyHBMPTEJXIyM+kNSUTQc8eMfK76rJVcmw6ny8GL8wfxTYufx9Z1EA4qXHtkES67BsHm4cMuD5YBLVVZGTy1zU9T1MR7V43hvnUtKCi8cdZ43qv1cd1kN1abQkcwQZHTyG9OrSbbricUl3hgeQdvb+/n2mnZuF0aMJqRBRtXfdHNnUeXcmJ1GklJYdHGLgSDFVGr0J+KY9fpaQmBzmyjyulkSVMbdf0ys4e5cToMeKMJntzcwQf7+1h+7ijOOsLJWUcc/Ny29os8f24hj2xqpjViZmlrAq9o4KTCTE4eWciX9V4eX9GF0yJyTFUmSQE+bOjFYIHiMi0JjZmd146nvi/KCc/vZUShjoBiYUOnnlNGOKnMi5OW5joUGLloajk//7IJs82KVpE5cVAaR5ensbYhyLvbvWTZ0vEmBeaVZXPuOBP3b2jl+PI0JuQ5qemI8PS6btyuBKLbT7Y5nd6gzKlVecwqy6S+J05tX5SLx2cx/g8JzZ4pyT50jaakgzNDI5oEW/p92EQj2/qjDPaJlKb03HlSNU2eGGarnaOrzTjS7OTbDGRa9fzm63YWbR3gJ1NyCMVT9EaS9MQ16FwmtFo9cSnJLWv6SMkKn547DJs9gU82Mr44i5OGpiPLCqFYkhs/rCErC+4/qRSLXoNeq2Fz3Xb64nE27sqhLEtLWzTO8Eo3t80ZBEBSknl+WxfxlIKzyEK7VmT86/sp0FqIJOzcMHUYwbjMh7sH8McT7OnoZX27SCAeJ9thwCXpCUgKmWYtDx1XRos3xraWGNt7Ayz4qJ1Hji8jkpQZV57Gvr4wJy/ezRmDsrALJhpDOk4bmYNRn4bN7OH4YRk4nQebhUAshdNp4pJp6dy9vI1V6/00eWLcNbMIp9OJ0wkvn5PBHV+2sGibl3nD3cx5u4XRhRZOn1TA123NmKM64lEjMVnijLElWCw6NnlkbE4bvYKMLUtDV1Ah3abh2knFhFMSu7wwON/Ou7sGmDs0l+lDDwZ4akIKF04s55P6AU6oHM6ubj9Xft7NjVPzqS769p6WlGSW+8K4NGakYBghpWdYP8wZ7DzUiIQTEte/20Sp00hDKMiRRS6K3AY6Y2Fcgo22boUMm5bPG1KcOyaHO2dkYzPqaPHEOOvtZq6YnMl7B3q4fmU3D8+q4JwJZdz01QFaelNIgkhHQscVQwuZNigPEDDrRU7ul1nd5WfeuHRuP9HB27v7CUUlLv+oj0smaLh2WgEAzy/I4oOafs5b0sQ7Z1UzPMfKGWMruGddPW0+CZfTxFMnDSHX9W2QGGDOaHhxQy/nT7bx0R4Pz2wJ4jJr+fSqI7jwsxrSG4MEY/BuQy9Th+SSkmXyMt1c9WUnr106ip991kRLwsddxxbhFAQ+v3widy5rYUpVPnqt5g/XhIOmcIpbv+onlJLY3BKmujCT384ZwcOrOmgI9rLwmwF6vDK3z8rn2KqD39NzjijjnCX7+GD+UK6fUYnP5wO+28j/X5gA898t9Y+x2+2HdWr+kn9WW6xSqVQqlUr17/JD96u+T58Kfrz9qv+oQNHkyZOpra09bFtdXR1FRUUAlJSUkJ2dzVdffXVo/l4ikWDVqlXcd999//DxfnlUEYsboozNt3Lfsg42tQZ59+yhJJIKL63zcMIgN2vafcQkmV8cWYTdqGVMjo0lpgHS7Cn29onEg3EGZ5uIpCQCmij5+WbqekXMpjjZNh3HFxVx2uIacmx6tJJINKThtk9b+eSSIQiCQCQp8dimdt6fP4xITObaD5rwppL88W+NNLOOO48tJsum495lHQSlOIJGYeExRTy+ppuagSCd/QKFLiOBhMSjm5uQZQ3hhMKZwzNxm3V80+jHomjpjydZWuuhX47S2Q2XTHFT6bRx1ju1lGllenwpbv6ygUBYxiiLXPR2HWeMyGBLZ4ArP62j1GHmtdMGk2XTsWRvPzctaaEywwR6iZdqOhmea+K2SWUs3e9l45Wj0IkCH+4d4L5VrVgMGhoCYaozbPT0waxBdu49rgRZORgtvXFqPoIg4I0keWhDEy/taqfE6sAXknltRy/XTMwDoLE/RuNAlIe31VGgt6OJ6xhXYGVvb4S1rT7GPdPP6Fwbd8wuoD8R583NA+xoD5GTJVLXl6TSbaUxHuKtmh4W1/TgMGgZCMJvZ1YRUhJc8tYBQr5+jpscYW3XAFeMqGLxvl6aghHK3DqsxiSre3pZ0dnPSaU5PH5qMR/V96ERBDa2BXl0bScXjsjhgy1e5g/OJN2soyeWYFqJjeqsUaxs8tHqj3N8mZt0i55nG3rJsBz+NazpCfObVa0oSYHtbRF+NTuPnkicGCk8sTgfXDCIta1+fvppI2NyLVgNWlKyQqZVz+wKN6G4RE1vmFmDHQzLM7G/N4JVL5Jp1TE028RJQ93Y9VqsOi2gEI7LDHpwC1VpZmw6Hds7I7xbU0e2S0s4nsJlkmkLxZny+G5+Oi2HSyZms6B0PIKgkD7RQCQpEZVSvLSri2Nf30KO1opeo6E+GMahF7ELekZlWrl5UiH3rqtHKyaY/9EmikwO9rXIlBXGGVYVRZFTBFMJEgETWTaRZBIUFI5/pYajqjTotBLxpBWHXuS6d5sZU2Dl1KHphGMKGUkLJRYzl0/+9uY6usDK6ALrYe9tsyfONe82kZOhwW0Tuf+EEvQaDU+u7sIflpk/0o1GI3DnzCLOHJHOYxs6eHROKW6zlsX7eugIxcm2iCgJhaHZZnLsBj6t66M/liDPZWBvf4gcg5nWSITSNJGfL2+kyGaixyfx8pYe4kl4dnMHkyrNHFuQwQsbe3lW30KmVeGdfR1MLDLyyplllDkPjkq5d1UrsyrSGJVrZd2F49nY6WdMto1ntrRzRJ6VpCQz7YkaLpmQyYXjM/nm4pF8Xuflls8i7DMGqRmIUGi20pdKIloUTFo90bjC6qYAr20e4OFTiplR5WTTDcO4/rMDDIRTZDtElrV1MSG/AotOg6STKM4S+abFx8yXd1HsMFKdaeHaSXlcMyGXR78a4PIP6mlOhLhqTAGXjyomNlvh2fXdh/0n5JTqdMbmWcmw6AHY3BrixiPKeXZnE+GYBrdRd9hnVT8Q4Z7VzSgJkRs+bOahk4t5f9cA2TY9FekWvlwwhld2daIXRKIRHb9cuR9vPM7M0kwePK6Yc5bs5tnjh7C6KUhKVtCJAjpRoKYjxuOru7jx6IP3lFcXVDLvpf1Y9CI7uyJoRIX3dg0wMs/KDdPySCkKK+v9LJpfwrmvH+CLPAsZVh0T8h28MncIZ723j7dOq8aF/A+3Qf+p/tVtsUqlUqlUKtV/qx9rv+o/KlD005/+lEmTJnHPPfdw+umns2nTJp599lmeffZZ4OB/aq+//nruueceKioqqKio4J577sFsNrNgwYJ/+HjXfF7P9UcOpqE/xqbWELfPLODIJ2ooduu5ako2Pckog5ImfjIujzPf38szx1dy2xdNfNPiJyFKZGgsRFwRIkIMl11H84COt08ZzFfNXXzU0EGzN8XD69oQgEhAIMusRWOQefmsikN1MOtEPj1rxMEHDpg7zMW5YzKxGb/9aI6tcvL0mm7avHHCiRTXTMnjlk9bEHUy+XYD984s47HVHUQEH+0+mTOrMwjHFcYX21jw9j68EQm3WUtvJEFAjuPW6um2RMi3m/i01sMzcyt4bXUtu6MpOqMRMjUOemNxphU56YxHufKzTvSSjl6vxG++bmV/dIBgREEjgi8VJz9DpNejUGa0c/Lva7ntmHx0ooCiKLyzq4++YIoshxF9VMOs8jTe9Pdx/KA0ltZ6ue3zZt45exBVGWa+rvdy9YcNzB+RRtJn5qiqNJp6kuRYD44Gk2SFC96oJynLuLR24toUdxyXzfaeIKPLdIzLc3Du0ByyrAbuX9fCvv4weQ4T+3s1WLQadIqI3aLQHJZYvKeXA10pijK1hJQkT+9sIs9uxGSDaCpGpz/JLeMG86uVTXQE4rgwEPZpkGwKPX6FG4/OYH3bAL/5opMp5RbiikKhXc8vjirk09oBmpN+luwfoNOb4uzRGZh0Gj7b66N5IM61074NZvxqRsFh1+TvNneytT3IjtYIx5Q5eGJeCTt6glgNIvMGZ1DpNpNvN1KUZmTByG8jzIqi4IuksBtFdnQH+flXjeQ5BAKJFMPzDDRHotTtM1LmMvDkqeX4oylCMZnfLOugwxfnsTllyIJMXEgy2G2lyn0wwOKPJrj3i53IBjNZFonSdCMAD6xuJ5qSeWX+IBwmLWDg55NL8MVSeCJJAjEZTyzJupYAq1t8hJMSL+/qwJAyY0VLXU8ExZEkIitIkoBNLyApMgVOhV0dSVwmPZKcQq8TSNNrmVOSS2m6gZ981EiGVYssyaTbDo4I6YjGuG5mFm9t7+PFXd2suWwk4YREICbRFo7yVeMAC4ZmU5FmYXO3n4uPdDMx30bdQJR3dvUxd4ibWYOcrDoQ4E8Hg5S5TZxQlcbXDT7e29PPtHIbyYSJhFbGoBGYW3VwStOXTR7CUgoNRpA0yDKYZR1STKAwLclJFVns6UqxszMEIphEHWsOhNjaEiUSETgmJ4dZwwx8sLefjY1RpGQbj88aRiiRYlSOlbuWt/DWmYPRCPDOjgHSx+sZ7LIz7ekaFp1WyvsXVfHhbg+CIBBNSsyudHFMqZPz39uD05pgQpGO+YNLeXh9O/s6Y+hNMrI+SUxMsqbJz4wqJ1a9hnS7FkSZNKtMvTfA3evq6AlKaDVw27QC3tzZTzgmc8PkfJ5Y38lpL+/ngTmFVKabeK/dT9Kn55M9Xt7f7eHacXm8v2eAVY0BxhSaGZtv47hBaRQ4D14/m9oCPLKik4Cc5MHjS/HEk0x9YRuvn3pwtBGAVS8iCTImk0BHIkIkKXHX7ELe2t7/h+e1XDW2EACbUcPoHCtXf1HDxpYg+xvhhTnV/Gp5I9eMKUQnamj2xLjozQM8cnIxv13WxsIvU9wxoxC7UcuXl1cD8Juv2vFGU9w56+B+U7JMiAR+OcGliw+w9LJBZFi/DWgdkWfnmTmVnLG4ho9OLv4+zc4/RPjDzw+1r+/rX90Wq1QqlUqlUv2z/VD9qn90Hz/WftV/1KpnAJ988gm33XYb9fX1lJSUcMMNNxzKCA4H/yC+8847+d3vfofX62X8+PE89dRTDB069HsfIxAI4HA4eHdbI/NGlQAQTUiMe2QXkjlJllXLkSVONncFee+0obyyq5vB6WZe292DXhDY0eens1/BaRTJdguE46BRYPHpQ7EZdJz30V4UkuzvThCOKwzNMOLxa8hP03P37DxKXFY0fzY9QZJkbv+qlY1tQd4/d/ChfEB/JMsK137YwLb+AI/OLueOlQ3IuiQOg0BLswGDTiAzTeDXx5Zw08ctTCg18+qOPtJsAseVZDBrsIPHNjXRF0nRH5L5+cQSvtjnJ5qU2N4R5oisCF69iZpWmaosI5GQwGOnlnD3uiaa+hNcMCybd3f30y+EyDUbSckyGTaZgYiIgMwvJlayry+Cy6hlxYEAdpOG9V1+hmVZWNbk4aLROZxY5WbhymYuGpnNzHI3T6/rYnq5g43NIS4cn0kwLtHmj/HAulZumlREdZaFYCyFzailN5wg06LnnR39WPQamkN+PjjQRaHNzJzKLGYWZ3DdkkZ8sRTvnTsERVF4YG0rH9f1YTBIGPVJgnEFk2BiIBmiwGKhwROnyGmkL6CQYTQwPM3Jrs4ghYYIQ8pyeXRDJ9PL7WxoDlFoNxBIyOiNMh3hGCPzNHRFgli0RuSklo5uHUadgMEoUJim46k5Vby6uY+3tveTZtXQGUqQZzHQF07y1eVDEQR4elMnJq2Gq8bnHfqc39rVS5ZVx/AsK13BBE9t6GTBiEymljj+6vUsywo3fNjM2qYAtxyTR284TsIQIRyGFQe8nD48i2Mr3Jzyci1WIyT1UbI1droHFO6ZU8gxFU6u+7CJbIeIYkhwTHE6E/Jc/G5bC95oggvKbGRmZqLRaPhgXx+7e0JY0GHSiYhahdF5No7It7GjM0RCkrngo31UpJl494yhPLq2g7d39vHwCWUcWXrwHHZ3hbni3QbSbSK7e0P84uhCFAGavTE6/HHWtwcoSdfRE40zLEfAH5MQtSmSSWjy6BicYSBFDGPKxubmGOXZBiJSkpisYNSmqM6TyVAy+Gh7mDPHuCnI0jI808rQDCszfr8LSSOR0sZwGnQEglqKbCaePbXysPwzfy6elDj5lb0EpTi52ii9ioVbJ5cwq8rJTcvqWNXsxSwZSckKt03P57XdPdQN+MnQWOkmyGmDM3h9s49cm5E7ji7CF0nx3M4OuoJJLKIWSVa4YWIBeS4dv/mig+5AkrnjrCxr6ee2SaXMKDkYlPqs1sPH+z08c3IFsaSMgoJe1PDc+h7OOyKD17f18fLmXs4cmc6bWwdIKilycxNEiPLBKZNZ2RTguS2dhJU4BlFAmzAiABpFw8QiOzpzgufX91OaCQNJmVsnHhz1d8eaOq4cWcRLW/ro80tcNiGLN3b2YRIEhtkSbPVpycsGr0eLpCjcMaOQRWt60WjgrlkFXPlhA4+eWMboPCsDkSSXfrqXjoEkRklLGJlpJQ7OGpHBBYvruHVaPv6ozIVjs9BqBH75ZTOrmvygUXjy+HKe39CHQSvw+LxSAOp7o1z7WT1eJcrtk8q4f0MTQkxPocXMPScWctIL+7huai56Hdy9rA05ITCiyEi7N0lFhonn51UcyuH1SX0vK1u83H9MJRpB4NxPNnHRsGI+qw9gEbQs3edj6UXVpJm/DRZFExK/+LSF6yfaKcrJwO/3f69hyH/LH9uoF/nhhkhHgAvhe9fvX9EWq1QqlUqlUv2z/dD9qn+0TwU/zn7Vf1yg6F/hjxeL1+vF+Ye8MJ5Ikks+2k9POIFWo0HSpHhwRjkT8lykZJmfL28klpKZW5bBo6s7aQ5GMWg0nDjITU88xpXjcrnokxoqnTY6wzHiqQQOrRGfHGDh1Cr2dqSYUGrgl2t2MjO/lFsmH/wjZyCcxKTTcP6b9QzONvKrYwuJJmXWNQeZPch1WL2D8RQN/THOfbWe3EwB0RyjsQ8uGprHp3UefEQpNprxpxIYjDKhuMhvppeyrzfMq/va+N3sap7Z1MOqfUEcJgG9VkQRZcYWGvH0DVDnMxMxxHFoDBxZ6GBpRzc5egs3TynklpX1lNksrKuLcuSwOGkOH55oij5PPt1+mRNKM1lW50ORQGuWiAoxbhxXjicgoxPhwvHZvLarC7tei0kv84slXVjNAkpCw1HlTm6enotJL/LM5g7STDqKnHpu/7IVhxm8UgKjKLL41OEsrfPw9JoeQnGJW2ZmoVV0VGaYqMwws/yAD4teg1kvYtJquOObemq8A1SnW9nelaDUbsGXjFKWZuDIwnSW7PPgi8qQ1KABzhuRy6QSM/q4n9zsLHSiiDeaoiuQYHtniGhCQdFKvLijmymFVq4en8ejazvY5wlgNkpEUymUiJ1eb4qnTytjaa2Xz/b5mFxs4fGTK1jZ4MOiFxmdZ0VRYPaLe9Ai8Pklh98AwgkJATDrD09U3BmIs6E1yBH5VvLsBma/sAedIHCgL86l47PJtulpGoiRYdVxxeRs+kNJ+kJJitIMmPUi81/ej6gBjUGhzGUkllC4aGw2g7P+8i1zdesAA5EEX+9oBbOTzlCKGeVObAaRVEKgJ5Dgszovw/P1VOTo2NgUpcUbJxHS4jSJNPYnqMoysq83yqeXDGJw9sGk0zs7wmxrDzFrkItMq47pT+2hwGXg9XMreX1bL0+t7iYtI0FDIEKB1YDdpBBJCdj1Ak3BIPMqCzFr9cwuS6fIZmJvb4RdPSHWtQWIKzEOhPrJENLp8YJGLwMKR5Y6GJFp4ealzVTnGIlpYuhlPZ6wQpZZz+6WOO9dWMXIPAu9oSRZNv1h78WenhDz3q6hwmpCCPlJz0onGBNAozAk24RB0PDxXg9HV1m5e0YZT6zt5PHVnZw01MXnNQHGVoLW2oVTY2fTASMV6TZOGJRGuzfB+/W9GLQCiZCI3QThpEIspnDRxCwmFFl4bmcr+/tDfHbGOHTid4NZoXiKh1Z0saMjzAcXD2JLW5BMi44NLSFe3NhDVJYYkW/ksZMqkWSFSU/vwKiHm48s4IRB6Xy2f4AbPm8g32wkEEuid0ZwKy5q+sNUZuvItGu5bVIpi9Z1s7Y1gM0gsGB4NnlOPbIis6G+ka0eHd3JMG6zDjMmDgRCPHRsJa39KZ5Y18m109yEpCTXjC7jssUHqA2EMOoVfAMiCU2SiUUOFs2tIBBP8dHefnxRmde39/DuuYMpchpZtKELi1bkvq87+c3xBUwtc5Bu1TEQTnLe63WE4jJJMYmsl/D7oChb5KEZVVRnWfBHU1y95ADDcyyUuAy8WtNGvTeKMWHinFFZ/OzIgyuUKYrCSYs3E00KDHXbeWhmJT2RGOd/soVX5hxBjtXImzt62dga5NGTyg69/89t7OKlTb0okTDrb538XxMoUqlUKpVKpfpv8J8QKPox+o+aevbv4oummP3Kbpy2BAVpOlp9SRJKnNtW1nDftGE8vLGNTn+CKVlOfvpRMx9fNJh9fRH29ka4YWo+u3r8XL10H7GYQLs/jt0sEExI9CSCXD6slI93hdjWFWSrN0FHn4nF/gHyrGYKLCYueauBUUUmJpc4KMrQ8tL2Lp5e1YfLoWC3wKSCb4NFNoOW/mASnUUhJgm4UhbeP6OE676sJ9MNcjxGXypFmtlMt1+hwmHgwdXt5DsMLJk3mnSrgTuOMnJq+176EjGuH13A0HQr17/fyJhskZA2TpZiJZaUWNLQj1k2EUgpPLq5lYeOrWRNWz9BTReyVkaRbOxql6iyG7h5Rj63Lm3GZtCyPxRghMmCp1/Hy9u6KDTb+MnUbG5ZVsvXzf2MzrbQHIiSmaUnLssUZ1sIxFJc8MYB3r6gitHZdh7a1EiLP0a2VSGhTULQykAIznhjHwNhCZtWZFCGmYbeFNu7fPxyeiGL9/SxZF8/faEU84ak8/sNvZwywsnG2gQBg56UHMdtFvnNpMH0hpO8ua8FDQIaWcPEcgv3TK/gxe1dnPXeXp6YlkFRnobNHUEeX9dBRyCOIMoUpIvs6wszKTuTqQV2Tlm8g3KnGX9CwiZYKLeaSOkV6gc83LumlWAyhtGZIKo/GIs9qsyJN5qkzR+n2GXkNzOKKXMfnIrTH05w1ad1KJJAbXeMK8bl0uZJ0BoP0xNO8PX5I1nV5OeZTZ1cOT6XM4dnEggeDMI9fmoJdT0xGvtjnHdEBsVuI4qiYNJpGJz97e1w8fmDSKRkfvNVO6UOI+ePy+SpdZ3c9FkTr51ZhdN0+O1gaqGbZEoiX0iwaUBk6f5OpKiGdm+CVT8ZiiQrDM22oIgpgnICSQhht0AYBZNRITOp4fjhVh6uyOfct2tJM+pISTC73EUydTCxdK5Dz8prhuKPSQC0+WPojAo6rYDTKPDYzGpWtPh4e28POUYzclTg67oQRq1In0ch327gnmUdXDM5h0UnVbK5NYjTVEZHMMHKRj8benyEIwrL9wfY1x9A0EtYLUlScQ26uJ6AP8bJw8xEtCGuX76HY4oyeGuLl1mVLh458dtgQF13nIRfRJ8uI0oQjMlMLXFxxkg3H9d6+LzWQ0RK0RDycuzbK3DiRqOTD64sd2Ypv/qylWB3FnnZESZU97OhAZ7YGEFvSKFL6UkKEqJVJiGJGC0p5o1Mp7Y/QkKSeWLmUF7a1s0bO3s5e0QWWlGgwxen1ZtgYomNs96spTLdRElRkud3NkPMSK5dT75Lx4FIiHyTiQ2tYV7f3suCkRmsvmIEv/q6iZ991oQnkuKz3X7eO7OapU09vLW7G2PUjleSEBNa4gEtjeEUty0/wJlDchiTZ+Xxb7p5YXMPY4uN/HxqEdXmfBo2d1DbpGNwmoHOQIJERGTR1haGZ9l45fRKilxGHt1axyWf7mbDQAh73ITTpSckxcjWGbh7RhFaUSDNrOPCsQenZo7Ks3Dc67v43ZwqrpqYC8CMSidL93lJt+qIp2SuereBRn+EDKMeu0lLcziBYIZ0jZ3FOwaoPMbEr5Y3c+PUPO5a3kqdR6A/Hkevk8m1iRS5Dk5r3dYeoirDxJsnj+byz3ZR1xnnvNfrefHMcsa6c/GEJXKskGc14A34SUkKWvHgKKRLxmWzqztMLCCz/odsmPj3TT1TqVQqlUql+m/z75p69mOljihyOgnHJa5cupsIEXwRBT1GPMkw4YiOXLsWjayn3yMRS8Dnl1WTbdNz88pdjMpyUe60cfXne7CkzAg6AUFUSDMLSIpEfevBZNTZTpHWWJBQQMuIfDPt3iSldjNKSmB9t48HjiuhIxTn2e0dWIwxbBobgk5Cr5F5fMYIil0HgwkrDvi4fHEDpW4DcU0Snxwl32zFJ8UwCKDTQYdP5vTBWaxpDPLwCWV8fsBDplnPI2s72HDlSHSihlXNXuwGLaNybJz3eh2benwclSWzp8/Ec2eW89TmDlbUBciyiwg6ha4BiTEVIsFkgjRTku4QJOImPAG4dEwu/aEkswe5WFLfy+s7+8g3Gkl3y9w+tYKNzSEe+KqLoaU6DvgiFKbp8MdlZJKYFBPNvQkuGZvDTUfl88TaTj7e52FCmZkVjX7STCLZDgOzit180xjghAoX967oJCKl+OaK4YdSxyYkmTPe2keHJ8m6q0fgMIqsaPRx38pW0CmcOTST5zf3YDXKxDRJDGiRxQQugx5PSEM8KXBKWSZrGwNcOjGL+pZOrpk1hA9qBrjri3ZKcyGuSaLE9PQEUhw33Mzy1n5yRTfTy5zMqnCRZzdw+4omJFnhwZnlLG/pRxAExuc6sf5hue/uYILjX93NleNyuXTst3mKluzx8NHuAS6enMnoHCtGncjqhgDdwQSDskxoBIWKdDMdwThfN/jwhSSeXd9DVbFMRJIYYnczvdTFqUMPTk9KpGROeHYfVoPIBxcPonYgyFfNfYRDIg39cY4qdpGQJZ7f1s3ZIzI5qsRJuduI7g+rQL2ws41F25q556hBuA067np/D8V52VwxORdRIyGKAna9FoNWxKQTeWRVJx/s9nDj9BwUwBNNgSZFXyJMfW+UrkiEX04cSp5dj1ajQS8KZNn03PV5Gy6LSG6ajjG5NopdRmr7ImxoCfD2vi7QyLSG4sytTGdiXho17VHy7QYe3tSK06DlotG5rG/28019iHy3DoNOg06UaO2TWHHVUBp8MTZ0+piQ48AfVnh7XzOdqT5CURMWHcwvrSDNIvLanm6GZZp5YU8rg+wOOoMpMsw6SlwmTh+ezqR8J6JGIBhLcvIbuygxhalNpMjQuvjNrFJEdOzqCpOQZH63oRtBIyMaJSw6DZ0eKLAYqO2JU5Kpoy4YZFSOkc64h3yLk739YcqdNra3JhiWa+L+Y8tYUuPllZou5g8+uPx7ulVHhz/OvavaaItGmD8kk9EZdn7yfhNV2QYuGZ/F1s4gT+8+QKnTRPOAjCaiw+UQae5LcHy1k4/rBpia7+KFeZUEYinOfruWvb4g1Q4bXQMSLpuGYwc7mFJq49alTWg0GqxGhf54HBcmfj2jhHvWN2NI6tjUFaDCbiYqJgjIEeZmmtjol0CX4JKhlfikOJ3+GJ/uCuNySuSmwR1ThpCUFS7+uIZ2f5LBaWZCcfCEU9w1vYSTq9P/4r260RPlli8bmFGWxmVH5NLmjzHnxb38bGouNb0RPtrXTzKsoTLbQESSCaSSVOYJ3DKxjMdW9DI6z8qzG7pJ6lJcOsHNJzUB0EjYTAIHvFG0SSMXjs3k63oPkbDIS2eWE0ykuHNdLd5+LYXpWja1RUkqCndOK+bp1d2IGg1lbiMvLCg/VM9ldT52NvXws9mDftARRS/zw44oOp///v9+qVQqlUqlUv2pH7pf9b/Sp/qfHlF03ScN/P6sUVz5cT2tHgW300D7QJKEdPCPtpAoIwoQDstkWvW8cHolWTY9b+1tw6bXkaWz8siaNiIeIxgFcm0a7CaBmyeWcvVndbitIicMTiPPouOVXQq/P7OUO79pZGKeg1mVLm75vInX5g/ixR3d7O0LUZCWIpBMkW4LE44aae3REo5LhOISVoNIbW8UQYaLjshmY6+Xj7dLHIhGqUw30tUvo7PHufuoUva0JyhzmDnt7b2kiTrKMo18edEwtBrh4CpJxS4kWeGer9vY3xfGbtIQT6WYM8xJWboJq07EqNXQ5ZeYUmqjPTyANyYjCgLf1GqpcJsIR2UmF1i5ckIuaxv93LqyjrE5Nia4XTSGIuTbzNywYgcOxQKiQl9AYliunhvGl/Lc1naW1UmMyBGxu5NMLLewvTvEtn4/X10ylHBCwhdq5pqJOWRY9Bi0Grr8SX76cTMTimysqo/z88+bWVrn5YpxOSxa180l47M4e14GTpOWWEriywMe2kIJzh2RxTtbvRSlifSFZBwJGyPybFw43k2p28Lu7hBDMi18uNvD2WMyeGp1F5OzDgbmZpa7uH1lE76ESFIWMekSFORI7GtP0t1mZNpIM1/s95Ft0bOy2cu29hCecAplhsKxJRkkJJktHUFW1AWYWmLnyDIH264ac+j6C8ZSLN3vo9MX5+wxmZS4DKxu8zKQ7KN5IMXbm5PkO/R0BxM8d1oFVW4zr60foDrbxOLzqvjd9nbqPGF0ioaV9QFOHZrOk2s6eXR1J7fMyMWkF1i0rYlWX5wtnUGMSROt3gR7BsKY9Aq9qQhftad4tuYA75wymoq0g0mEj87PIEtr4ZVdrfjiCZ47rZxlnXDOW7UMLU4QUsKkUkYsooFtXTEGpeuIWaLcsyFGRZqZN08dyls13WzqlhiZlkmuLsnzm7oZmWPl+qkH8zEpisJndQO4TFqiSEwotHFqdToLv2xFURTOGpZHVzjBxi4/da0yH25sI9elRRTAIApUuI0sGJlBrktHSJOkL+7FF1NwGjWUl4jMWryKAoub7qDEezs91A1EKDFaETSZtEQjlKUbWLS+C5OooTucwOdXSA1YmDMyk51dUTb2efDFk+zx+NBoYEiGlSF2Gz19CtXFOrp8Go4cbuTu9XtJxEz0+BQMegG0CtlpMt0hiavHFuMy6TFqNVh1Wm75soGkD6Ixka5OG0NGJHFakmg1CqJOQaNROOPDHZxcmsvb84bw+y3dTHh0F7+dU8T8kek8cVI5bf4YT21pZ/6QTJZePphvmnxc/mE90webeXPuKL5o9HDXVDe/39bJsgY/boeGeFjDB2cMZdkBP4oCD69uJxBSKLUYCGoj6O0aAmEdS/d7Wdvuw5NMUGTX0xmNoBM0mC0y57xezxUTsli804M2oeWu2YXUD8R4Y1sPTUEPiZQVDVZ+vaqNy4/I4fZpldw6WeaBNW0saejiks+2cs2YCi4ZlccbO3pp7JLIcIsUOPXcvOwAFqOGGWVp37lHN3ti7PP5adkWJyYl+KanlcXnjub2z9voCiYwaUVK8nS0+mOk6Q0cX+VkZXcnV32xkweOHsrUQjdFTgNbO0JcMTqXb1p3Iko6un0SSCLFWSLL9gZIGlLoLRK3rtzJjkaRtxYMojMSYXe/n5ZQhByjlYnFdu5b3oFNq3DRhMxDdXx3Vz+DMkws3tn/T2ytVCqVSqVSqVSqf53/6UDRKdVubv2shc5AnEBEocerUJhjIKYJYLNESTNbqGvXopiiPHJiOYFknCz0nD44n+0dIS56t543z6rizq9a6Zci5LlELqwu4uwPatDrJIbkGLFpRZ5Y08snFw/mqs9r6Q2mCIZCbO8K8dL8Sn79dSueVAJ/KokU0lKQrkEQQKsI/O6kSs597QCTKy1MKXLw8Jp25o/MYFWnh80dfgqzReKaKIIerGnQ0g/Pb+rFF5DpTkapcBqJSgo9qSC9oQSXv93AoBwjl4/P4aK36rEYRLxRiVKLDqQUr2/uZ11nEH9EIsus547Zhaxo66FfEbllQgVv7/CwOzVAJAoaWcOnOwMIyUa2tYd58rQqvmzwsTrch94ksaXDj0624NWIDCnScM7wbKYVOTn+2X1Mq7Rht/uZXmFnYX4pd65sQatTuG58AVpRg1EHY/NsnPLmXibl21ndFGB0jo0t14+gyRNnTaeHOn+IxecM4vdbu3nvwkreq+smLkncvryR92p7ybEYeOW0Kja0Bfj59DzawhEeW9NNyhTHK+u5fmkjS84ZxrDsg8GReSPcANx5XAG9vb3UdId5YGUXuVY9MVlGL2loH5DJd5mJEMdkFGgJRbj92EK+bOzny0YvBq1ARElx7/J21jaGaMNHmc3CCaVZh6aY/VEwnuLreh/b2kMUuw1c8VEdlbk6wimJfGuKfr/IqstHsq7dz2cHBrDoRUx6kefPLGVXb5Ar3z9AfqaAiEJ1tpmJhXY6A3EUbYrsbIWPa/vxx1KcMyaNPLOZzwbCZNsk/HICQRbQyCJug4kSp54su4T5TxI5f1Xn4/VtfbgzFDQIvLenj5TRyZFldqyilt5QkiMKbKQkmajPh05IoNWkiHhF4noBbyTFtsYIG+tj/PLCg+/rM5u6GF9kO+w9eG1BFe/v7mfJHi8HfAF+9mU/YY8es0Hg6a0dHFVuZ3yRlctG5yLLMOmJXQzK05JjiROW9FQ8tJnPLxzG708czCkv7kOvpOhKxBif7aA7aKEjJJEUNAzJsBCPKPQkoxxXls6Xxw/lls+a2RD3YbClkJUwYTSkpwk8srqLqyfn8NjJY9jaGeTd/b20+P3s93jp8MVJWWLYrCkKMmTOG5ZPeVoVDd4wr+3q5b29fWRZ9Fw4opB4UqbTI3HkKAsmvcie7hC9gRSFZgMJbYziPIlQKkmeRUuPR6Y8U6I/msCs0VOVZuHy9w9w+aQcvm7yssXTzaSghTybiQKHkSPz05BkhVd2tfNZcwfnjM7jvb197OjZg14UeXV3B3cdncvJVRU09EdJJATe2TZAXzTJqS/uI2Lpw+wwI+hS6DXgUIzU9CSpsOhp6Ikh6MFpTdEVl8nSWwgmJLJy46xrCiEIcPbodMYXOhhf6GBqkZ07399JeZqVqyflcf2XdcyuOBjw0Ws1/OKoIobnWFi0pY371jVxVGEaA2GJpKyQqzfR5I2RSirU9IYOBYpOf2Mv107KY1CGCUWBeFDHqHI9l48pYFiXkV+vrmVXt0KaWcfxlWmcOSKD017fh80iIAgidx85mMfXdHDdl/v5xZQy5g3P5uThB6/DG8aVcf/GWmwmPaaEiUdnVJJjNXD6q3uxumMoYoKCPIm7Vjdy1/RSji/PpNjRwZzyLKx6HV9eXs0V7zYQiKcAeHtnD49tbsLn0fPiKcVMuOWHbaPUqWcqlUqlUqlUPwx16tk/5n86UPTFPh8prZWzh2fyxDfdJLQSA/EEYwv0JEnQ5U9Skgu3jB/KNV/twChZeGHuMNItOkbkWFl89iDOffUAWp3CccNdTC1I49zF+6nMPbjMdCiZZEl9Px9eOAi7UYtV0OFRZLqCCTL1Bq79qBG9MY7drNAXA08sSWbMSkRS6O/TsbkthGJKsGtAojuQQEmITC938OKeTuKKRFSM4dLrSSpg04NO0NAfSpGVpiGRiuBDIs1ipGlA4up3G5C0Ep2dAWo+jHL9tFxqesM0ByL8fHohX25vIuFN4vNrEbUCbakwHzd0IggCd04cwk+WNAKQbdUTicKQdDO93iCjCiw8cnIxCAIPrO6gxyMzrNBARyCFySwgAQNemD80g6X7vNw0PYeOQJIrM0u57IgcHl/XTosvxvQSJ5MKHCQlmXGP7iTNJuDQaanOtDDgl5le6sRqEBmea2FqqZ26/hg3fdlAKJmicZWfYExmZ2eUGaVuji1KY2dnhF8ta0QWk6zuEvDFJJwOPf2RBA2+MAcGouzoClLiMvHB3n5e39HLh+dUY9Ie/OobtBrOHZNBZzhOWzBGKiawXg5RnC3wZVOIQTlGOiMRLvxkL3l2EbMpiahocOj1bGoPUuePUOSy4g8pfNTUwyctvXx0xnC+afBz/QfNlGfpsZtEnjq5nGZPlAe+URCSWuSEQH9Sz76+KBvaAnT4EihhLesaQlS4zfxqdR39kQRWp4ZEQk+fR8P7uwa45+t2SvK1DHVbmezOIJBM0ucL8/GuEKFECr+UIFvUYDIIuEw6LDqRTxaMYFtHiK/qvcjSt4mztVoFWSshKwqyAnaDSFGuleumHMyXlZRkHlnVid6gMGmQgV2dCnrZQJHbgk4QmPL4bkpceiJhcBlENnWEWFHnpzLNxMTCg8Mzg3GJlCKT0EgENHGqnXEsVmgUBardVn42vpjfrmxlTWMAEgJjC218cukgXt7eyzetEseV27lufBH5Nj2iRmDJxUPo8MfwxSTSzFrcJi1jHtlJulWHSSvy+MnlOM0isZSMIAhcMT6b4c0WntvYjU2yEUwpDMnXs9szwNYeE+/sFjl5SDpjcm3MeaEGT0xC0mmwpAxEUzHG55uoSrexqbuTZ/ZsJN9YjMUsYTML/HZdMzOL0vF6Bd7a3s+F47N4cUMfUkrgwvG5ZDhgQ3c7jb4YsmQlJkuYYnaisThVWQberOvkmHIX723z8PbpQ6jxeblt5T7cBjM3TihiU1uA36xoZVyOndY2LR+G+4imBEpsMklSaDHy9r4W7PouVu7Rc2yZk3WNIc4Zk8kl47K4+r0kBVkCe3wxBrkN9MVjCKKAzpZCr6TINpiIJGMUOBX620Gr1XP+EblcdEQOL2zo4fHV3UwpcVDvj3DO8AzumFXIWwdSXPBWHRPKrLy/r49TB2WgFzXkOw0Y9DIPzqzgik/2s6MrSkyRKMsx8Ny8Ml7fPsCBgQiv7e3EZRY5f0Qe98wq4fx3aslwaDHqBN47Zwi5dgMGnYhZMLOrQyEuJil2WVh4bBE6rYYVlw7jpOdrWRn3kWc38Msjyznr1Vpe3tJDd5/C1VMPTvUcnmXFE9CS6UiREPRc+E49b589iLQMhf19CjkOHdkmK9sbkpz1wW7eOHkYZw45mOw6EEuRZtby9LwyZj1bw9auAF/tD2A26TBlSoTjMiqVSqVSqVQq1X+D/+lAUa5Vz1HD07n4wzqyXWATBBR07OsSyHNqEUQfm2oM/DxygGTUwNQiN2ta/AxON5Nm1nHuq/VU55pw27QckevgwTWtGG0JfjphEF8197GyIUwkmEJBYe5rNcSSMkYdSDENzUKIsbkC8YRAQ4eWdIuAV5TxhjVkas08dWo+576/n1KHkSyrgcuPyGZzaYinNnaRa9MzKEPLzmaB7AIBWZCoadUwJEOHPwZ9yTAug5U+j0AipUMTFOhMpZg93Mae9hj3zCvkgdXtRJMyr59ZxfUfN1BoiJKmM+NLSMyvTuPGKdXoNAK/W9fDaS/WU5atJyInseplLp9YRFxSOHVkGp82eOhfl+C6SXlUZos0xOJcODafpXuD/GRyDl/WeTmm3MVVHx6gy5viwiMyGYhEOaE6nSver2dXV5gHjyvlqNKDQQidqOHUI5zoRIFTyjN4dFU3QzMtXDnp25w+l4/N46Zl+xF0KSQJYkmQUMiwK1w1Lo893SFu9uzFZI6j0wokkgJy0oggyWTaFYSkjFmj5cG1bWzpDXDp8DwePaGM5Q0+XtjWydnlOmpCUVbtDyMhUVWgp8kX4/RR2axp92A3pShxmjhtUDZLGlpo8saQgJ6QhFk0kDLEsaZFOaLYSbnTysRCG954nPvWtVDXF2H5VdU4zd9+9RwmLTlmA1ptCqc5hIgRQ0Tm3Zo+Kt0mNnT4iShJzh+XydS8DDoCcXb0+NHpFZZcUM2be9uImwOcVJSDLAn8dFoejQNR/DEJi0FDfyTB9Z8fIN1kIBgRsIsCsixz29Im0sxaMiw69OK3sfGZlWmUuU1otQLRZIpXvqlF7NTwq6XtPDK3hNH5Vm6enk+jN8K6Di9pJUZG5dj46cQCXt/eQ0swymnjTQzPTmfUozupyNTTn4izsqOPdb19uPVmzDqBT+s9nDvCzQcLhnDZZ7tIN4nY9NDrTzL3xX04DSKg4dN9PtZ1+miPhnFhIhRR+OqAl7UtAQ54ovxkQh5Xj8/j9hXNrGr0UWAxM77AhkEjMjrbzJh8Cysb/cysdFKRbqLNG+PE5/YzrdzOsVVOcu06RuXYMOo0VGYMYWN7gHd29TMiy0qhy4BDpyeRUohrUmQ5dPQM6JlVVADAl40DhGMGGqIxXCYIJOJoNSInD86g2GmiMxhHURS2tYfQo+Gs4ZmE4hJC3MCMwhgLV3agEUWcOgNdPomZBZlUZRl5bE0nrcEo2zvDnDUyj0q7k+19AX7xzS4GZ1g5ptSB26znvKxMPqzvJtMhUudNUZYmIhoH6PGmMXd0Ps3eFsYWm7j92CI+2uNhbKGN1T8Zxktbe9ixKops1yHoY5wx0U0sLhKIpfARxhg2YtRpSS/woo+4eWh1J+VuMxdPzGZauYPXtvZitWqY/epuxrlSuNIzeObUCi76sJaa3hBfN3hp6E7wi2PzWVzfRigV54njh/H85j78kRTo4uzoC3L5xGwAfLEC7llfy51rvdw6fjDDikS2NcaxmDT86vNWnpl3MB/Q2qYAoqwh22hgX3+UF7f1cNm4HHIdRpZdVc0V7xygz5Ni9BEWPr+sGotBw/mvH6A/GqcxHOGZOVW8duoQLn2/Do1RQqsXuPjdOur8EWy2FIMd2bjtCu0+L2LKwtkf7OX1U6oZnmXl9V29fNPs49V5g/nwwipO/P1+REuKrKQJUavw8MbWf3KLpVKpVCqVSqVS/Wv8TyezHnznSooLXbTGAxgEHecOy+GrRi/dkTiRuESZ1URDME6GXaHUZuXYkgx+u6qNdKtIpmjizNHp7OkLYdZreGF7Ny5nkBPLC+iNJqnpipAuWHn8lHLSLTre29PHY+s7iGuSnDc0i49bGjFr9UhRM7XtKYyuGBUuE71eEX9QJi9TpLU/yQWjsxicbeTGT1p4YX4lzZ4YJrPCI+ubMRkSZNpl5ISRxmAYZB0FdgsD8QDhqB593EJKUbAZNNwxvYgl9f0UOw08tqmDX0wpxqTRcPPSJqoLtGQS4oDPzq3Ti5hZ5aLVG+P8d+qozrDwef0AboeGTKdATWeCybkuwhE4c6yLJzd2Ek1IFGRBJA5zK7K54og8Pq7v5fZv6imwmRnwC9w5vYgZ5WmEExK/39HGy7vbOCo7hweOK0OjORikCCckHlvbwdI6D5OL7Hyyf4Crj8jjsok5iJrDB/md99Fu9vfFKHZLJEkwp6SQbJOVNJOOiYUOrv64hhV1Ua6cksYlowu5+N0DrG/3UpFtos0TI9tsRNYmuGd6OUsO9PHBhjCnj05jTrWTprYuFu2JkeuWKErXEYrBjn4P6RonA8kgiqIl32GiLRSkxOJgQm469QNh1jQHyXWJtHhjbLnsCAy6g6N0Ht3UyNLGPkyingZvmA0XTMDyhwTXz27r4IUdXZxYZafEaeLh1e3kGmwYBR03T89jbKGVRk+ULKueFa19tHmTbGyJ0BWO0BtK8dLJQ1jbFGB/X5SqTBM5Nj2nDncjKwoaQeCnnx2gwRPlrFFuarqjrG4Mku2W8CfieD1G8hx6uoMp7ptVwtQSBwDv7uzn55+1MHlYCk88gtQlItstNIUj5Gnt+GIp4oY4w90OGvrjPH9yFUOzLADs6Azxyf4BcjKC9EUjbOmKIEQcbKmXeOy0IvQGuGNFCwVOLWEpQaZZZiAWZXeDmdHFIkkhiV4x4Ymn+OyskXy238Nvl3eQ79IzkIiTEiS0Gg3XHlGATtRwypB0rv+qhp5wDG9EQzwGTZ0phueZ8CVSOMwi/dEkZ1Rnsq45wK6WONMHW9jeFuG8MZlcOTn3sOuq1R9j5us7OK7YxfKaCFdMyuHKydm0B2Ksaw7y0pYudFEfQyvyOW1oGgvX7yWZ1BAnSobRwq6eGNXpNvZ7QpxTncf7+zyMTndwYoWb+r4o2XY94wttLPy8Db8UI66P4DRqmVmaSVcgwed7AxRniJxc7abNI/H69j6KXUb2d8eYVeniiwYPFVkSu7sVck1GYto4WgFsRg294ThVbiOdsQEqXA7aAnEWVJbyaW2QB2eVke84uMLXL5e20BtKUpVl4L3tXlwOeHvBECw6keu+3sbufj+Zeif13jCibMSJkRZfjOFFWobmmbhlfDlznq1lUomNnx6ZRdg/wKKaCLdMLUIQBO76qpW1bX7CSpJfTSmhKRJia08fHaEw140p57M9Ibb6+im063lv3hEY/pBEXZIVHty8n90tKbYO+HGZIEtvo71fQpBF1l87HFEj8NaOXtLNOq75oJGKbAPvLBiMSSeiKArTX9pOLCAyq9LFzr4QL86vxKzTcNuqPezujmMQ9Nx7TBmnv7kPs1bDzCoXwzNt3LmshVOGuviiJsit0/PZ7fXyVW0QbzSFyQwvzR3MsEwrN3/RSJZFz0d7B3j6pApu/LiJJ08t5baPW+kJ9rHmuiN/0GTWr/LDJrM+l//+xIsqlUqlUqlUf+qH7lf9r/Sp/qdHFMWSMm2RMPMGZ5NmMqATNARjKfQaAcUcpzMlMSjLTCQhE4pqCMRSCBqFp0+spDLdxBPrO1la7yWcSjIkT6Y7omd1ez9WwUzca+SZyyqwG7UoikJtf5Qsu5bbjyxnwZu1jCi10BuBmBzkiMEiXZEEMURcZisDUpj6Tj2PnFRKUzjIk9sb+fqSEeTYDaxo9fB1TQ/98QSj3SKKArW+FHlWK11+yDZYSKagtUeiPFdhIJaitk/mkU1tdIeSbOoQeOPUap7b2kXzQAzFKDF3UB4bdqboDCYoSjv4x6QC3HpUPq2eJAcCYfpDSfKNNmq0/TQFIlw6Pot7Nx/gqMI0tvT24w+ZSUkant3WyZrOftr9cdw6M4GAhnnl6Rxb5uKtnb3csaKZQpcO/FamjXeh+UOC7dd2dXPf+lauGpXPs6eW88iGFnSWOD2x2GFBIllW0GgEzq/O5+XtPcwut/Hijk7e3hIg0xHl5in5vFvTS19IwShqqO2Q2VcUxO1KYQmkqEwz8eTsCp7c1M5X+5I8sK6VvkiK3y8oZ2qJE1mWSVdCPLk1zkCfkUE2O/UDXgTZiMki0+eHIelGugJxEjEj/RJ8HfYRT8hYBC1DnDYenV3Jp3UePtvvYUd/kNI0PYIkUuW28uAxFYSSqUOBostG53HZ6G8TPJ9YloPNePjXsjTNRDwlsbXbh14ysLUtRKZFRzic5Mp3Gxieb+KZeRU8sa6DX69qZuG6AwzJsPDWvKE8cvzBkRgPbmikPMNIdZaFTIsOi07DBa82UuXQs+TcKhzGb6eenTYindNGHFyFyhOOcd2H6whpwRDTYkCLWYBgKkpYjhDRRIimEiiKGUEQGJlrZWSulV+uaCApGfAHNLhMUFiSxGTQoNVAWI6h0ZgQZYXubguKbMFmjKPVyoSlKCI6XDoty1p7qAvE8BAlPanFKOlwuvRkWXTMH5qBIBy8Lva3yIRSIOoUShwmevujxJAQBDhnRCY2nZaXN/dx/dR8Rs23sKHDh9Mp8fL+Tj5v6+XoUgfHlWRQlWGl0GFkyyVj+enH9aRnJfiyu5Gebzx8Vhem0m2iJN1Iwq9jTYufPR4fAUkiS29iWwu4S3QIxIgnBTSyhhklbm6ZVMqm1iCPftNBXW8Mq0HD07ubOaUqg1e3+HEaDNR6knjCXfT6IcMBDYEYL+4M4+0zYbZInDTEhU/q4ZgKB/fMKuGjum66E/spcGjZ1plgWJaJvliIXLsBfyJBMGyjQ9DSE0zw2p5e/BF4a3c3N04pYlWrh9lDbWxujPJCTTsWvRaXaKU/mKImHGFtXYqsdJFABOIRPVeMyyQY0rCswcu+5hTheICr/Dt4eH45S3cHsRpEwkBrf5IzX63jpbMqeGBOCc+u72Hx3l4WftHGZROz6BsQ0JlTLN7ZT3efFiFpIiZqeXdPH2ePzAJA1AiUGNL5qLMLOWbEYdQREaLYHRrskokOf4JCl4EzRx5MJL38iqHEUwomnUg8JXPmy3U8PaeKO1Y2cuH4TNa0GFnw5n4+u2go9x89jF9+U8PO9iSXfFzL4gVD2N8b5uWdvehEDffNKuGOz9p5/dwKfrG0lYFEAp2owW3SYjTLnPbuHj48fTj3zyoFwKIT+fXKFhRzErtRy2vnVPDw5zJrfsgGSqVSqVQqlUql+jfR/P0i/72K8kQWTi8lmRCJxRWe29qFV4pgNiVIN5oxCSYG/JBttHLz1AKeWNvFq/MGUeY2ohM1mPUCgqgQSSgcaNWRZzUhoiUY1vDeRVXY//AH/yf7PSzZN0BdX4yP9vaTlx1GEhNk2yDTGWR7l0S6wUYkrqc5GKHM4OTVMwfx6R4f+/vC3HvUEM5+p5ZLP97P0oZ+tAikWyW6gwLRqBVRk6IvGmZuZQYTix30hySysmRmDnZwXEk6olFm/pAMjilx8fCsCu5Z1saenjBt/hi/Paqc80fkcM6YDN4/fwi//KKF+v4oRS4jyAKrGwLcfnQR103KY2qhi0EWO1JKYF1TgDfmjiScEBCSJsJCiHSrhmBUwR+CZMRA1K/l1MpMppY7OPW1vTy9qYNCt5aEBKeNSuOkoW46A3HG/G4LH9UM8P68YbR745y7ZCe1PTHSsTOtyEUkIQHw8R4PIx/YSYcvzjHlLl6ZP4izhuYyOz+X5+dVMbcqnTNfr2V9m5/bphUyPt/B2aMzeXF3CydUppOttTM+18n812tJNxiJRTRoUzoemFHBlOKDo2k8kSRfN3tx2TUkxSQ7O8PUtKew60XCyRRZ9iRmnYIvBiVmK1Ny0rj96AJuPiqPo0udTCiyMu/dnXxR5+GM4Zm8ecoQZuVngQI13gEu/nQnv13Wypxn97G+KXDoWlzT5KfygS2savL/xWvVoBVJxQxsaAshGSM4reC26cjISjEgB5j5wi5CiRRvnTGYLNFEInz4V/vGCaVcMDyPs4Zmc0yJmwn5LvbcPJpXzq4k3aJDJ373VvBVrY93dnjQyAbkqB5L1MrMIQ7Om5iOQ2dERMRlEvnlN/tZ8NFWAO5e2cKwJ7bwdZ2fVk+S0ypzmVqQzqhMJ3d+1cpvV7URiQnEJYlQIkFYTmE0gUuvIxjVU9etpS8RJTsN9g8EqPWEsJglemJxst1a3j1jKHk2I6WPbqDREwXg80uGsfSCESw8qpTRhUYeOz2fJ08sZ/6wdJ7a0MW6tgA/nZaH26LFoNUwucDF2YOKSHp1ZItm9KKGh1d1Muy+Hdz5dQvbOkJsa4uhjVrY2STS7YMCg4WwX4NT1JNu0BOLgqQo+KIKSU2S3Ow4fUEFl95AVyBJvtXMDcv28fbubobnWJg/Ip2BkESJ28hzx1cjIuKLSxSnmXhjfjVD3C688RRWrYGEEiXHZOd3p1YyyJLGfcu6uHJkPjPK02gLxHhnl5dFM8Ywu9xORU4EkyFGUpKx6kGvSzE4UwBkMnV2ArEUDoPAG/vbeWhDI3d93cKNX9eyrd9LKAC5aRraU17Oea2Osfk2njhhEFrFjmhIUezWsWhtHykkUAQkXQp9ysL2OoVblu1lRauXWS/twR+TUDQKskbmtPd3sLLFy8hcC52eFKJRQtYmuWZcMcZoJiGNH7M1SXGanoDGx7ObO+gPJwGQZZk3d/eg08PMChcN3Sl6urXYFQvNnjizn6lha1sQgHBcwm3RUeI2oigKBq2GiydkcsU7TWj1Cns9IU6ocjG4QCSSlIinZOaWFFDptqIR4O6v2ziy2MXiM4bgi6ZY3x7g9ln53PZpK7WBEL0DEjpFYFqpk1hCwaKX+PzAAIIgIAgCV07IZWKBHV9IZs5bO+jwx3l9xw+/6pnwA/+oVCqVSqVS/a9S+1T/mP/pEUXERZp7E+zri7D4rCG4HfD8ziZSCugUDTatjoFIgk29XoKrk7x+VhX3bqrnjCE5ROMCT21pJ8sukR4zoSBQbXOSUBTmTU7n6EU1rLlmGA6TljmD0tjcGeDckenctXED+S47fRGZeCqAVskkwxYiGE9ixkp/WMM9pxbzyMoOGrxRiMW4ekkD3oCMQUgh6iQOdAoMLbQSiAo0eySKs43U9yqsafWyqr2fpDbAEEcm4aDI4l293Dy1kNOGZhFLyhz19G4eOqmEVc0+3tvqodJtwh9NEVOS3L2ymZOrM6kbiJBj0zNrsIswSS76dB8/HVvIsv0+njy5jK2dIYw6+OWyRqaXpDGhyMgLu4KMLzThTya5flI+y/eFuHZqLlvaQ1z0dj2FmRpGF+s5Z0gBN3/SzGc1fm4+UmZLTx9uwUAgLHPDpw2EY1BkdXLhEdlYjAq3Lm3m8nE5XDQ2m0yrjk8vG0ye03DoIxQEgZ8dfXBEjj9m5oPzh/BFrZfTX67n7FEZjMq3YDSUMTzLClN0PL62i8snptEa9VFdCdW5BkblWHh3fw8nlGewts3PxtYA6RYXZ4zI4vN9HmRBwqbVUe/zk2210O8TCMVkdG6Fel+Eu7/xo9cJPDKrHKOo4c1NLmaWpjOjwsXiHf20+eLcPq2U6gwb17/fTLbZyGcDveztjTCx5OBwxfGFNvbfOPY7U+z+1OlDM7hqXC7ZNj1rWwJ8XudhVKGeHKuBHLOFlQ1+znhjH2PzbRQ5D19l7ez3augJJfj6/FF0+RMc6I8xOt+CVjw4kmhFg4+tnUGunZhH/UCUN3b20tSTYCCc4KQyO5jS8MVk4rJE10AKk0YkFdchRzUkEiKGtIPHO3dEFvOrM7AbRL5q8PL7bd0My7Bg0umZVmqkPx4jLMTw+rQEEjKSJkG6RovRLCEIGrLNZjoGElQ4JfqiUaSUlmy9lY8uGM57tR3cvGIXnT167JKeDJPu0PnZjFqOKnXwyNoWljV42e9JMMRmQ0iITC10MibfwmUf1XFksYN71rRw4fBcRudbaAlH6N4XZ25FJjdMyWVLd5h3dvbjjaSYUmQnEJEZ8AtMK7XzUX0fa9vilDv9pLktBKM6XFoLnhBk2YwEk1GsMRuNoTjnTshlfKGNN7b0M3zJDoYXGxCMKQZlmzjltRrmj7HwwYLB3PJZC3Nf3UNJuhGtDgIR8HrMtIlJ5r6+h0tG5fJlnZcXd7Xz0NYGbp9SzjkjMrnj83ZyrAZmlxZRF2plsKhnVwdk2Qx4ZD9ui4XGqIIc0TNnZCZPr+1hTVOQ9lCcIouZFl8cl0vBG9SQVAQy8xI8t6ONNL2e9nCQLLMJz4BACpmBSIqecIIsgxF/KoWiEbhmeBUf1vfTG0ggCvDyaZX0R2UiUopfLDvAILeNRaeW8siWBpY0dHNkoZvHTqzg9Df2ceZYK0Mcbm75PE5mloYXt3Zz1rAMzl5cy29nFbN4Vx+NA1E+vGgQ96xuorUnhUkrkGkzcNYr9Tw4t5ile73U9UV5/dxKblpWywmVGcytzODocgeBZIoz399FjmCjLxll/nvbeGj6EC57/wC/mVXE2cOzaB1IMPPZGi6b5uTc0enct6ITu0ngwvEZ3PJZBKNdJiloGJtrQ5IVRA28t9PDpaNz2dURQRQFfjolH18shU4QuOCtBp6bX8bk23+YpumP1FXPVCqVSqVSqX4Y6qpn/5j/6RxF5728hafPGEFSghQyJ7+7mdI0Db1+LdG4iIIEgob+eAQhbGRCmZkhOSZKnEZe3d1BbySMr9tChkPPnEFubpyWz/auID//tJU7ZxTw9LYOzh2eBZKGq99tZNb4GG2BJLKsw2EQ2NopkWGNk2G00+MTCQYUrpucw/P7WjGgRa8X6AhGsUatGLQikgwGvUKv4KcqO46YcNMcjOANaajM1IEmBYpCUz8YJSNDM8xs7gzxyulVZFp15DkMeMJJXGYtSUlhb0+Ei96tY3ihDkPSw20zxvDGrgHe39vP5GIbfT6ZDn+cp04pY2iWlXZfHLNBw2/XNrLiQBApqmH+OBuf1PexYHAeMRL0hBJsrZVxWkTePHsQsqxw/ru13HxkAWPzbfQGE7yxrZ8plSZe39sMKEzIzuK5bV0EAwJOq4zJGsdp1OIJyVw9soqmUIzJeQ4+3u3j1OFpDMr6y7NLFUVh+rO7kSWZaEzgtBFu3m3uYk55Ou/t7SPHqiVOkByHgQ4/jM5II5XQ0B2LUOgyICdE9vdGuHCQnsrCHG78rIVwVCZKitIMHQdCPirMLtp8KcxWBY1GIRIFh0XDMSVu9nfF2NQY4fKJmSyt8/LS/EoMWg3Xvt9EVYaBKYMs7O7zs6q1n0UzR+K26A/Vu8OfIM2sZSCS5IqPDnBqtZuLxx5M4N3ui3PBGweYW+1icrmdA30HE1VfOiH7sPPf3hVk0eYOrjoij5E5Nk5+eS9DsszcM7uYS96to2EgxorLh/PxHg8f7vbw6+MLyXUcrMOvVzbzZYOHJWcNY21zgLuXtTF/aAZFLj2bW2tJGdNo9kukKyYkCWp7o7icGvyJJKIgkGczMCXfSVsoyq7uML88uohVLV6e3tJBUZoOh17HR2eN4MFv2lhS148mKSLLMq3BOINdFnwRmbnjbAyEU6ypD1Oal2QgEaQsXcEo2Dm1opQ8q5G2YIRfL+/EIGpo6Itz49R8Lhqbzft7+qnOMuEJSezsDPPpfi/njslAFDTMrHIiamBLR5CkrHB0iZMrPqwny6pleWcfeo0IUR1WM8TEOKdX5TG1wMEjq7qZMySNYTlmlh3wcd6YTFY0evlyexMtgkBYlihIi+CNCCRTekKJBHLMQX8wxfzqDGZWpPHujgEuHJ/JyiYfX9R6sel0XHdkNh3REO/u6eVAt4LdKBBIyKSbNQTlJLqUjpSgYNTBEblOzqzO4KmtndT0hIjKMvlWPQcGYqRp9XgCMoNK4+iNCXxRDVrFiCcZ4IqRpUzOy+bedQfwp6KcU1nKfWva6U/EyLFrSMVFjHoBf1xmkNvMXn8fmWYLlw4v4tYvmhmcJ9Lcn2CI3cnLZ1axriXAA5tr8Q8YOKrEyR2ziugOxjn3vb2YIyEGFecSkxRumpbPQDTB81u78cUSrDsQJj8rxcQCN3cfVUkkLmPQaTBoNfx2XS2fNHShT9hIN2vZ2ZQi0yliEEUSkky2Vc+dswq5bdU+Co12VjcGCYQVfj49n/XNIep6o5w/LoP7V3TgzEpRYbcjyQqvzBtEOCEx57XdaOIiTrtIbyLGiycN4uFNtZw+LJuZxXks3e/lpk8PYEmP8OSMEfx+Zys5ViNFBjvPb+qhMxXBpNGyYGgWZp2I1ahhX0+E5bVBwgmJ984fxAf1vbhMWjQJLWvq2nn74nE/aI6i1/lhcxSdzX//fHqVSqVSqVSqP/VD96v+V/pU/9MjihYeU0B/OMXc5/dz8gQTmbYU0ZQODTqSxOnr0mK0yeSabYT00OqJUZKh4dHN7RgNUUpsduqDWk6oTuPGKfls6Qxw7od7WXzaUF7a3k2zJ86vlrZg1WpxZAdo8StYtVZCShyD0Ut1hpu4AJKcxKQY6QoneGhjO9l2HXFJIBVPYQrbeHxeGbGUzM8/ayKgD3H16DQ+3e/DF5JxGoxgDyBJIhpZpLld5KJxmZh1WhwWDVNK7Vz1XiMXjctkUKaJyxY38MnFg1nb4WPRpg7MNolit5WzCkopdJg5ZYjARwd6+Kajh+HWTIyKFq0gcN4bdTR7o+gccaSkyMg0O788tpB6T5g3Nvp5eVM/2Zkytd1JShxmbHYtwXiKdIuehTMLGZJuJZKQmLFoLz87Ooff7WjkqtFltPbK3PhBM8PKtATMfvIzjAiiRIGYg+SX2N0f5uPaAT7Z7mNSkYM8h+Gvfp6CIHDFuBzCCZkFo9Mx6kR+JuchagS88Rh7esNoFAst/XDtuHxM6Hh0cwsmo4I+Yac3mODT84fy/qZ6rviogUtG53JUhZ07VtQTxUuOQ0KjjRP0pSi2mJla5KDWG2RvXwBZUvjF0QWUzTPz2tZeDnTF2d4eZkaVk6b+GM2BEJ1KABsGduwX6BqfxGrQctZrtaRSCi3eOKeMcnFgIMr9s0sYlPHtbSzTquP5M8pY3ezn6o/qmVrkYFz+4TelVQf8NA7EePakQYe23Xd8McY/JAp+/rRKABZt6iTfruf3Z5Uf9npRBH8qSVySmVri4LnTjLxR28CKei9iQMLgDNITDxPRmEDW4lMEbBodETmJxahgNuoJJSQuGpOD0yzyk/cbUWQwxYwsmlVBky/OhMd2UpqtRauAzprEaYKUNUEsYSAlKNwyqZiNrUGGuaMMyjIgyTJ3flNDtkXgN6sbiIb1pCSFG6YUoBMF5gxyoygK3nCSx9d0YrcqtMX9TM3OYeZgB/NHpB/KY/Tgulae3d7B/Oo0PPEYKxp9VGYYMGrBmNQREmRSKZHWXoU3Yn28tr0Hj0dAFmTGFBZx8biDQbljy1wMtyUxOly8sbeTF3d0U5IGCBI2vRFfTCHLpuOrBh9mncigTBObW0JcOSGXqWU2mvuSPLymk75wnLysBCZrkoRiQBIV7ptdyf6BEI983QOyyMK5Raxu8/GTD5qYUeHCZ5Bp9sWYVJFGb4+XCcVWPtjppdqSzyVT0nhwXQMrmmJY9XZ2dMc5Ml9Dc5sGnySgr5aZmpPGbq+H5kCc0nQYSHiwGu2EAgIvHT+W1/Z08cimNm6Y5ubdff3YLRq8Wh8v7GolEtRyz5FDeH1/KwcC/fRFsnluUxexlEx1po61zQFEUeCyJfvpjIX45eRSQGB7nw+zZGdbR5jzl2yjrc3A7TMLCMkJFu/yoTcruB0KMSVGQZ6EzSLT2maiKM3IuaMzOO3lWq6ZnIcgwIRCB+/t8DCjysnFE7LY0hbiiEIb0yucbGwN8HpNN4FYimuW1vHIrAqWnjecSz/dQ0+/hJDS8HHtAGtrFHoS+8kymzhuUBqSXMYbO7q54q1mFs0rY/GBNrpTSZIpMCX1/OSoLM4fmcOcF2u4YWo+F4/J5rNSL3d80UJHIMZNkwqZ/cYOTqy04yP4/90W/TXqiCKVSqVSqVSqH4Y6ougf8z8dKLp86X6SsTQeOrmY9xvbsOks1PaAnJT4ydhCHgh1oAgg6JPkWbVExQjLW3yUOE1EkOgOSAzK0/GTCQdXTookJX53QhXVGRZ0sgZtSqQ7kCBgjjEiV4c/qqEvAkMKYizd66I0PYFdZ6cvIhAVQmRk6fF5tUh6LTpzilAqxTsXDqXAbuKhTY3k5crcceRwblyxC4vWhN4Yoa3DSHamjUhcwKLV8MqCcq77ej9zK7J4a4uXp+dUUOgwsrMjwgPLOvnowkEUugzctnKA/rDEc3Mr2NkV5q5Vrbx0Ti6DMyz88sgiqtOtZJqNbO7wc+o7e5iS4yJCgiK9wsyKHC4bl4MgCBSnGZlSPMCKhgD5SSNpjjg/Pzqfxh6Jo57ew8yRJtZ1eFk4tZzZZZmsvXYYVqPIeWSRlGQ+3t2KxhVFZxIJew009Oj48IxRmPUiCVkhmpQIhmRuODIfh+nvX65njMo47PEfp3ItqM7j/mANBm2KLr+ZN7b20ueFE4e7ufXoQj7f76MkzcBAKMmkQju3p7tY2dbDh9/soDTNRlckij2RTjAh4dBryTabWF0TJyZrKHNmcEZ1FiVpJgBOHupmzuA0/Mk4N6/aiTVNoSecIkOTRncwzkcXDwEBDFoN3lgSg0FhQrWOEree2ZVpDM4wE05KPLy2naFZFuZVZ5BSFEKpJDZnCr8c48ENPkblW5AVqEg30eqNk5QOHxx4+5ctmHQaXjq96rDtNsO372NfKMnUx/dQkaUn2K+hw5eg0Rvj1qXNVGUb6AmbyFfiSCEzvb0aEnotJoPC/2PvL8Pkuq6E7/t/oE4xNjOTWLLQYjMzxgyxA3aMiSGOIYYYk9gxMzPbsmRLsmXLspix1cxYzFUHng+aeCaTmTeZ5/a8z9zj/l1Xf5D61IG9T9XZtXqvtTNKAotVR1DjBNMKBR6Z6xaWcPd3zWQ1qC4xMRLU8LgEKnwWGgvszC530R9NsTcQ4/lt3TisIjU2jeFQBqvJYNpTmxjncdAbziCaNWRRpyduYJZkkgkD1TBQJJHbv+ji8ZNr0XSDLf0xAqkUgpyl2Oakc0Tl+AU57BiM89FeP4dUe3BZZARNxGUoHFLhY1mLH8EAm1Ulk5AJJnQEw0B3pinNF0mmNOaXeLnw+AJWdQc44/3tfHLmVLyWf011c5lNHFFZwBf7Y+h6CgRQdR2LLYM/KmKRJaYU22nMs3HSc/tY1RYmao5ikSTOmFpEoc3Mw5u6sIsmVClJgdNMbzTFwaVunsvvwC3asVtFJhZYeb+9hZ1+uOWwat7Z7uea+SVcu6AE3YDbjqggqxmYZYHOARN6QsWnmNncleA1Ux8ei4k6Rw6HVudzaDU0+338esVu0hpEUiamFRoMhkI8ulrm0VPq+Lx1lHvWtpDrUGnIs/LLqU28vL2fpbvjPLNxkA1XTGVpxyC/WraPiyeVYpYE9nb28efjq3huwwjr+iIYJpnX93RTl2PntZMmcvHHe3CoFg6f5OZdtZV7VukoDg1FFnAbXvb2ZyjLEchzyyTxU1amE0uIvLN/mJsPLWFcgY1blvZwyax8rji4mKOf2svbF9Qzo9yJYRhU+sxU5+Zz6qQcLnpvP/tGEhz12lZePrmJwVQU1WzioGIv3/VFeP3cRt7bMcrEPC8ATQVWXj2rkX3DKa76sJ3PLxuPKArEUzo7gyHea+nlJ5MKuXBmAQ6LiCgKHFLnImmUcNfaThZUT+HM8T6e+GaU6vz4P/vo+aeNBYrGjBkzZsyYMWN+GGOBov+aH3WgqHdYozIXvuwfojWYZDhsUO92cNuhlZz17m6KfRKCIGCzpZGFKE5sDER0klmVSncehi6gKDr7BpM4rCK/XLqfhlyFT3e7+bYzgqrpuF0i4QT09DrIyVERrBF2D8hMLILRsBlkAUXSSZqS5JozmFQPkjVLVS7cu2gGDpPEbd+0sLY3RPeIwe2rm0llBbK6ihF1kVBiSIqISVPoHNT5/ZpWbp5bxaGVOVw2rQSHWcIkitz5+YGisT7bgcLFp9Tm8/sFDhrzbfSGMtx9aNX3QZVSm522kQzV9Xa6IineOm0cT343SF1pkmY/zKt2srojwpvbh7GaRbYMxnDLMo1uJ7fPqODqZW34FAnBlaDE5SEnIFHssPDANz28tXOEdT+biiwJXPZBC53hBAdX6WQNjRq3g+6Azh9Wd7Eh0M+MghxW9wa4e2HT90Girf1RvukKc9Wc0n/Yv4Zh8HVnCJMkMLfcQ6XPxLJmlZ9MdHNoeR43fNzNN/tj1PhGWb4/xLb+BI+dXEm5WeDz1lG+7oxR7s2n0e1jVbvK1DwTP51VTqnDxqctAeaXWKnMM7OkOcCf1/fw8NH1fLY3wK3LunnzvAYsJoFv25M4DStWTWHTYJiOWIz1H4aY4HMy5Nf55ZwiTpuc93fnbpZEqrwWphY5AHjgqz7a/El+urCQareT6kPtXP9pB3kOE/cfU4VVEXl58zCnTs4hx34goPHWOU1/t9+N7XFW7onwszc6aCxV6Aon+fhnDbyzOUjHSIarPmnDZhLxKiaOrHMxsSSfcWbIz89HFEUu+2wnsaROoNdMLKYQDR9I8xMzB455cn0xI4kMg1GVpcNhrJLEC+tHKPMp5Dhk7lnWR0rXGM1KKLqJmCqSTUvkukQC6TBDMQWTBAtq3GT0NAxEsdojKIrCSMCCVREJq1ke2thBfYudr9tiHF3v5Z6jq1m2N8wfjqrkoGInVT4rj67tp8ipMKvMxXXzS5le4uAXH7WyoN5McaHKUEojzykymoJ8sxmToSFLIiOaiqYKnP9aKw0lCg8ubvybINFf1ebYeOWkJuY+tZWqAoWOaIJap4NYPEtBrsQNSzop85iRzAZhUhxfU8RXbUEeXt/NbfOreOPkSZz+zjYGhi1k3Sp3rm6lzK1wxbQqHlk9wtnv7abMbmVkxIG3UOP4N3fyzUVTEf/lfSoJBwKhFhM8sr6H4XSKmeUOnjipjnu/7OGFb/3ccngJZ0zJRdcNnt86wHmTC5mRW8AXnQEaXF4CoxJIccKWLj7rUDimtoJZpW7u+7aT77oC3BjZylsnzmNqXoDReAaLSaTS7mJbVx/XDbQyucRKXMtyy5etZNImFEMi3wP9kRQmIcWVH3XwykkTuW9dOxv609w8eyo3f9VGNKlQYFdo7s+iyRrFFh/twRi67KS+IENLMkEkA06LxDlvNnPVnBJGollOnZTLJbPzOOX5Zp49u4a0ofHMpkFeOa2Rh1cNsLM7icct4rXJnPneHl45aSr3rOxmQYmPtlicpzb38edj6hAFgQ09EW5c2slBJQ7uP6aKLy4fjyAIpFWd4ye5SO7KsmUow6MbO3llcwBVN3j7zHG839ZJhcvGORPy+dlnu2kOhREd0D/g/CeeOmPGjBkzZsyYMWPG/M/3ow4UeUwKKVQGohpmzKRiWR46q5bl7X48NgMkHZMkYJGzbOmyUlkYodrrIZZRCSWgwmHhosYyznhpP7VVOm4rRLNZ9oaDaEBI1Xjs2Fr+9PUAsiVJrifCaNzEUEojGVfIdYgISgpN0ym2CvQNm/HadOqLBO5dNJmMqnP2h9sIp3RMuoJgjrGjBxrybQzEVVRTglq3iN2qYjFEwrkav184gcF4mte3DfPCpiGWXTyeB1f3kO+WeOL0Sn7y5j6Ors3hra2jvHvRgZkmNgv0xVKUA7sG41z4zn6eObWWB9a18Xn7CPv68tkfiuJ02njzxEaqPDaO/Wg3neEk44stpOUUM0o9uLzwsyX7yXGlGFdk48mpM8izmzmkJI/zX23jJwfl8PVPJyNLAp/t9zOUTCCadPb1KOTn6iSFBCbRxrruCJLNhMes8PmZc3CZTewainPbyg4M4IrZB4pXa7pBPKN9v7rcv9cWSHLjinaq3FbmlnuY6StnabSH1tEMz23dw3i7i+19Cb7pCPOLgwuZWupE13WGh5NcOqWUy6aV0zqS4sWtQ/iyHkaDOg+t70RGJJqScFsEQtuzuA0r7QNZ3ikeYWt/jCWXjmMwluGB1f2QMZMWQdIlFDlLjivDjNw8Jue7mDbPQTyj8vA3/Rxe76GpwMrWvjh7hhOs6Q4zmEzx0o4BbphdSUOelRllDk6tL0AzDLb1xTl6nIe9/hiXfrqbRNKgLRGlP5b+PlD0HxkMZ8hqBrtvnEJ3OEWrP8lVK7Zj1x24HSJ2i4CEgE0RGUokSfcn2BvTmFhjZkG1l4NLfAzE0oymRlEkg3y3iagmoWsCewYTXLpsFwUWC+2jGiUWCwg6b+8axuOQ8KeyfHzeBNxmiaF4lus+bSecNhA0kaYcM7csbqTC4WIkrtGYb+Oz5gDTc/O45dNuakrAZM1Q6/ERSWhs60mhqWC1GOTZZSbmuugvVFm6P8CvP+yiqcxCpU9hOJkilLTisZpYWO1h3c+nsKY3gLdb5u1tEfKtCsW+NIaWJZGQKPAquJIgynDqNA9v7BhlaesIDfnW/zBY9MneALGIQEOtj/4RgZRkwqIKRKIGLk+GzoSKx2RiRB/lw/0pmkcyVNmd/PS9Nu49SudPRzby4JouluyIMK1WoSsa4Z39GSRbkkrBy1A8S0WOQjSjU+4x8dL2QeYXe+iPZJlWZiOa0ZlYYOfFLUO4bCI3LC5FlgQml9p5cdsgb+0c4bkNQxw3yc1HLaN8sG+QQb+BwyzRG9D59fxilraNsHKfRqktyNGV5cQzOlMK3Mws8iKJIIsicyqcHPLSNoo9Ci+sHUFToalYIphOkG+FuJjAbJWpzvdwzcFlXLN874HVyGwxfr2kg7AQxWFW2D/qpj9kMKHQ4OBSF/2xYcqcFi6ckcOKZoUtAxG29wdpyEsSjFp4t3WA189u4JK3WhF0kQxZxhU6MESd+1b28tM5BUwpsnPN5/v5xbQyXto0gksRCMcNREPituVd3La4igtfa+W8GbnsGwhz4iu7+PC88dTn2oirKruG4gQSKjl2EylVoy+a5NqVe/j9gnpmlrg4bVweU/K9PPJdL9d/2czDR9Rz19qdFNu8DMQzOEwKuYpAQgj+nz6SxowZM2bMmDFjxoz5H+Hv18T+EblifiFOj8qvDqqhb0TnowvGU+xS+LIzRK7NQjIL/dEMWV1nYlmaeNKCZqTxmiwEwxLr+mPcuaIHR24SxRqj3H2gOUfTSRRF4JXTGjmszsvMepHiwhhtAYFBvxmrYcdiBknWwDAIZ9L4R52YRTMxzaAvaGIgkuHQZ3aRzYok0gJ9QZXjqwqwSjKBMHisArGMiiyCWRTpiapMy/dyxee7uffrLpa3BHn8pFrOfbOZ/f4EzeEYNy/t5sqDizlrai5fXzmBPIeJ33/TzgNrO3l5yzCGYTC+wMa75zZx9ZJ2tnQleWBxI1siw+Q4RZrbRD5vDmOSRH4yNRePN41ojmOTZDYPBdk1HGMkojHRVcCd86YhIqLrBq2jaR47pYq0odMfSWMYBm/tGqYllKLQLpCbH8BjltANgzyHgG6CSbm53Dy3HgyB897dyz1fd3HZjGLeO3s8h1R76QymmPboFj7c4/+7fo2nNZ5bO8SJzzSzuMzH7xZVsGxvkD9/NUBNdZq4OIzbYmBY03gKMhw7yU1dnpW9IwdSR9oCSf7yXT9nvdbMkxv6GFaDFBelwZRFQSSj6VwwqYArp5dhE0VqvRbeOK+BT/aP8FX/MA6zyEgsy6beGCU+iYYSGbsVonGR386YzAl1+Zw7JZ+ff9zCHSt7AHBZDqw+9tiaAVr9cbaOBkhrOtksPPRtJ3sjft5q62V9fxh/PMtfvh2gP5Khxmtjf7cKaZnWq2czscDxfTt8sjvAcc/uYTiaYWmzn5r7NpKSsuhmlSV7gryzY5T39owyoyCPeZVejpvo5YRxOcyrcIMBgaBCW7eJTYMR3tnXw01fb+fdvYNsHgghIyALYDcLePJV+uMpbvy0g1TCRDIjEs5muHpBMQ8cV81Nh5Vy1qQ8jqvPwWuVkSSRYpeZkZCGS1S4Yl4RSS3Dwxv34baaqPYdWPL8rd0DPLmpF4sTRJOOKqa5bkExfziyGilhIh2TMFSDt7f7OfXNnTy8tY2mEoXxdSI3LSql0mPhz+t6OOedPRz85FZiaY2eUJqbP+1lflEBLVfPRDEUBFUmkACrVWRukZfnTmhkcpGdDd0R1DQoisA57+3i1Ff2sK0/9jf3mtUskuOD1kCS3x5WxqAao6JEI2sNUuSUMdsjjC+2MtBjp9CpMa4sgNkQURMim4aCmGWRiUUOTK40simLpgskUwLIGrVlGg5HinRMREsJGJLK8o5Rbl3VwcOr+/mqJcTPP2rhtq86eP20Jq6bVU6t70Btq+ObcnjihFoCaoaEkOXznk6sik5aM7B7E/icIv3JJC/u6KY9FibXC991aZzx7i6iKZU3dw6zccjPwmoXI7EM933dy7tnjEfTDQ4qcTIh10HnKAiGjk2QiaSzuEwiPbE4573bTAEu+gbN2EQr7dEE/mELuYrEmsFuXjyxiUhax6pI3LGwmn0DWW5ctZdx+TbOnFCIHnVw6fgpyKJAIJHlsR17ee3ceqaU2uhJB/hmuIeXz6mjLRXlt8u6qfVamVxk58qVezh3jpfebJSUkSXfotA2mubM1/dy0aw8JhbZKXfYcMgyWU3HY5V58sQ6omRY2Rkgnla5Zlkzv/h8B88dO4knNw5Q5FAQRYEip0JNgcKdC2u56OPdONVc1vWPksiqOCSFVEJBzfznAdoxY8aMGTNmzJgxY/5v8qNe9SwYDBI1zJz66j4eP7mWg0oc3PVNJ1+0BrEJMidOcvFFbzPRlARZF3kuHcPQaR9QyHeY2NeTQZZhZoNBUs8yFDbjsULbiEo2KTEpz0FDhcDG/ghqQiKq6TicMXKtFjRVpmdEoLQgSf+QBT1tIs8rEtLTRKJw1ewSXtzZj1URyZEs3HlUGdev2oIQ9+EPG6SkNB63TiorkGM10RnIEMvojPfZiSQNsqiYMXHNvGLW9UT5YEsQQYIXzqqlM57gyw4/OwZSWGSDXMXMrTPdjK8uRRRFgsksZ72xl5RqUJQDv5lbiZYRufD9fZh0mXNn5PL8piEmlhmkVAOzbuOGxWUU2i0c/+weRMXg3Cl5PLluiPOnFPDONj/FeRKxjMb4XAePnVTD4xt7eWVvG8UWG33JDMVOA/+gA8ERxaG4aA2k2HTpdGwmic9bAxxd58MAPto/RGOOgzqvnf5omvJ/twx8RtWZ/tB2Kgtk7j2qmo09cU6e6OP+VT181tPDtGIrO3qzTCx0cer4fIodFp7d1seq9giTC+28ctI43lq7nz9tSpLrEYlnNVTNQBZEzLY0VpNIKG4ix2yiNZCiyGzBH8uSY1U4e6aPRTVuKj02UlmdzmCKHJvMX74dYEd/nNbhFPPrnUwrcfDgV/3MqXZQ7jWzbyhJvsPEoycdKDCdzGoMxNJUeqyIgsBHrV3kWqxUul0UO8zfF2he3R7muo87ufeYchbXeb7//78aiGQIJVXuXr+DlAppVaQxx0lW09ndlWFWjUSuR6Vt0E5Djp0rZh1I59vcE+OSN1spzRfwp9KUSWlkr4WAFkfXLLgViXAmi5i1Y5MlFtW6WVThpTHHxq+WtpDM6uwcifHgEdUMhjRu/6yXV86pozzXxEPrO6lzOnGZZCp8FoqdChVeC9+0R1jfHSXHKdEbSfPKtiFyHBKVPoG0kMGhGAwnU+RbHeSb3HzbEUfWBVxmE/Mr3VT7LBze6ObXH3YxvczBixuGKfWaCGSyNJQbDMUzfHT6TJ5c18+9K/o4vNbL8RN8zKxwsH0gxpH1Pq5d2sLm3hi6AV6LhM0TpcCRYXyBifa+HJbsifP0SbVUWVLfp+IBvL1tmN9/3stxk11ccFAhz+7cx57gMDUeJzuHM8zML+biSWWsHwiwsmuYOd5iPt4ZIGPOYLcZjM93UuG08mVvD1bZYGDAhkU2cfbUfGrzTFz/SRcN+VY2Bf1MLXRww+want86wPrBUS6dWMGT64aQFJ1VF00lltHYMRxjdombVn+Ce1f2U59n5vVNfg6ZpHDRlHLuW9PJYCxDIK4xoTxJRhMIRR3EsllyJSvNvSqPnVpFyIjy1PZW7pg3iY1dKUbiWe4+oopgIsvPP2rl1kPLeWZbC/HRCNv8NkSbQZ5Tx4JMVBglNZyPbBXpTSU4pMSHPwhRIYTJIrGnW6exRKbc5eSCyQX8YXkv3YEsRzd5uW5xEV6biaFYioveaUUVkuS6DVqHZO49oordwWE6o2HOb2zkuc0DtEajHFuXx2hU563dw6QzBkc3OAkEZPpiKTx2kVsXVnLea608dUY1L20bwmmR+PmMIn62dC+dfTqeHIN6q5OBVAbRnKY2T2DrQIJwxMKth5bx3MZBJMWgKddKNKmzriWFJmqcMMXGjqEUsYSIkkqw+qoFP+iqZ2/yw656dhb/+1foGDNmzJgxY8aM+bd+6HHVj2VM9aMOFHX2j3DyG73ce2wFRzR4eWh9Bx83D5FvcnDHoRWc/+k2ihUbcV1DEkXsJo1wzMxIQOfzy8bzx9V9rB8exWoRsIgKg4kYSVXgV1MrefrbYawWAYfdYCSkE0yqzB0fJ5i0kM6a0DMisjVGW59CqcOCgUBG1HBIEvcfVc0vPm0l1ywzHNHwhzVmjIOuoIpVkkCX6ApkuefICp7a2kdK0/CaFJqH0+TaZPKdCrtGYjTaHZTlWAgkshTbzSzdF6TSZyZtSzIcT1HjdKHJaXJtaX47sYqasuLvv/x+1xXhxmUdjMSyvHZ2Hd/2BVnfFueaueVIosBP32zj4ZOrWNMZ4aoFxZj/ZXWtR9f28uqePgrtCk8cMw5FEjn5tV0MJVOc0lDA2ZPyeXh9LzFVo9huYktvktLcJAE1S6ndwU1z6wgnDaq8Fjz/LtVnVdco96zuoMri5qnT6kmqGlZZ+v73o/Esd67qYiQTY36Fh33dOmVuM5t6Y4wvsLB0f5CFVR4+2OlnTrWTZ0+r45ktfRS7FFZ3BdncG+e+Q6vJIc6I4WDrQIxXdg5hM4lkVLBadIYjKm6zgqbpqFmRXKfEjoEEH547jiklTnrCKVZ1hHhv9yi/ml3C9BInH+wMcEitizLvgaBWRtXYP5Jk50CSlS0hOoIpLJLIkp+O/4f3bos/wWu7Bhke1ekOptkTjPGX42s5ut7H/V/30pBn5eQJuRz39F6QdNpiCartNmRR4KOLx/H5viBf7A9yUK1Ivt2EzSSgqVa6RjL8YXk/z51Vy7RSO6NxlbSmklE1iIeweLxkDTj91X3ku2RaIwmKHGbskoyRkqnPs2CRRR48oYo/rOhl6d4gtVUGqgYb+6P4BBtOk0QwqZLvNJFRodBjQlJFvm2NUVquc3CpB5ug4LPIVLgVfvZOB9PrZAwlhW5IZLIG/fE05RYP4biG2RUh327BaTZxUE4Bs0s96DqEkioLatz44xm+7Yhywngfz20Z4PWdQxS7TDQHY9w7r5G+cIbzZ+bzxzU92BWJRRVutgxF+aBlgEqXDaeisLYtgtkaZXaNzM6hFE3uEk4uNdNYUfr9e+WBtZ28sq2fpiKRkZiBQxFx2VLkudIIukx3SGFhaTFXTq/+vh9VzeDNraPU5JrZGQzz0uZhTJKAxx3FJovYzQl6B3J47PjxlLoVLn2vlWvmFbPdH+SZbR1cOLGaaQVOblnZSShm8NNpRVw8q5DucIqLP93FkdU5vLxzgIsnFvHxjih/Oq6KpZ3DLGsf4e4FDWwfjfDpriB+LUWt18S6NpW6PIVT6gv546p+XjizjoOrXXSG41y5fBsLS/P51fRqTJLINR928E1HmFyPgNsbokLSGBVNrOuWqfXaGQqpRDNpqnPNJKIyOTaZVn+KB4+tYt9wihe392F3qjx3zGRe3jbEQDzJtbMruODVVg4d5+Deo2sAuPCNFnZGguSZLJw3JZ/bV/RQmmPiytklBPUgW3vjbBtJ8OTRE3l4QyftwxrRtI7PCZ2jGs+f1MBHe0fYPBRG0GXOm5zPg18O8vgp1SxpCZBjUnhnzxCiReW3B9dw88p2TGmZ8XU6WTFBMGkgZW30xNMIgo5ds+Gyy2hilnRSYEGxj98fVUkgleWM93agBpKsvvqHDRS9xQ8bKDqT//2DmjFjxowZM2bMmH/rhx5X/VjGVD/qGkWvbxnh6gXFHNHgJZHVWNMTZChq0JuKc8fqFvSolbANinIhldVo7TLhyInz+2PqKXabuf/YKq74LMXWwQT9sQzjy63cMreOX6/cT2GuTCSj0zts4HOKVFeG6I3YcMoKqbTAaDxDnubAY9fRLVncson+oM6AKcZNK9ow0gJhVcdnFxhOqHSF0xQ6nSSNFNFUhodPqOGJLf1YZQEta+K+w2v53coOppe4qMs18ciXOjceUs53A0FO9Pm47qMuphQ5qM2zckhTET97q4OUJU6RS2J9q4kXM4PcUVr0fS7iwRUu5pQ7eadlkBu+aGdiiYVnTm3EZjoQmPni5024LSZaRpPIooCuGzz53SAPfz1EXbmCUzGjGga/+bwFh1WkPwkd0RhnvhKixGXiork5PLyxG5/ZTGtYxYGdPX6B6FSYWuxE1w/EL1OqhiKK+BMqC8tz2FSc5rjxXh7Z2M7b+3pZdubBOJQDt7EkwtQiBz+ZVI0iizDtb/v7t4eVI4oCfi1FSzDKEc/uRBAEPjx/HIUWC9/u7eDtrQHypBjr/CEcZokCr0YikwTNhq6D2yrgsRjs6M1SVyQTiRnYdBPPbBxg7Uf7KfWYmFHsYn9/ms3dcQ6p8XLBjPwD7bO+n/2BBB/tH6FcduAwy1y9oIhJRTbynMo/vF+/2Bdi+0CU2hwbx1bbcJtFRtMafaEMd63o4eueIH3xA4Gidy9qwGI60Js3fNHKrqEDaXUdsSD74wPsWOVhXrWL244qO9DfpQZnTclHlgSSWY3ecJoXNg+wuT/KfQs8fLF/iI+bAzxzUi1OxYSMCIIOgIhIWtNRD/yTRbUuXBaJX84r4rOWUaLpfmIxMCsGoWSKancKj9nKE0dPBCCt6kz442bkIpmbDyvn8XX9vNc8ijM/SwLIJgUcVh2rZCYS0/CrKiVOC4peRCKcZW80xr4OP9t6k9/PygLIsSucOCEHgHVdUQbDWbSUyMQ8L3OrXd+3z9F1Pj5rDnD2y61MrzVzaGUuJ9XnYxJEPtm0G5/qQUl7qLKl+bIjQLVop6H8X+Prv55TSZFN4aO2LnJtIiMxiVTMQzAUx2Y3kGSVxhwL/eE0zaNJCp0mRhJZmgqsaIZBntnC6BCMr5LoCMCsMhND6SzFuQYnvr6Lew6v5oJp+fxxbS8WC5xWXs2b20dYZvMzHNTIdcn84dtuIqrKz2cV89bJE7lvw17eP20S77W0YXUHuXt9ioPLvMwvzOdnn7ZweJWXlmCKAouJQMLA500BZraPxvj6igkUOs10B9MUOa28f/IcHvq2h8XP7uSF0+oJJFV8NgF/OsNIr50pDVH8epjJRXb8UTMWC5zQWMQXvf1YbFYUWeH1nzTy3s5Rbj2sggKnzIs7BkhrOmdMzOPa5c1csnQ7ZxxcwCetI9y6SsVjk7jh0GIueyuJrsCqzgivnNmA0yJxyYfNnD+lgD29SWIpE09t7qIjHEc3iZTbFUbSaRSLxnNbBnj6xAZ+tXwXoZjA45v7SZiy3LWih6U/PVC4OqsbHNHg4qAyF5OL7Vzx+R6agymqcmBSvoNd/RmMtAm3W0cWsvQMgM1jML3IQVTXEATItSk8c+w4jnrmu3/4Hh4zZsyYMWPGjBkz5v8GP+oZRcFgEI/HA8C23jgXv92C02kQ0lJYVDPlPom0KYnFnGBw2I1hyjAahSeObcRnMbG0OciazjBNhRa+GuzntROm0RFKce/X3VQ6HJw1MY9rlrRRWBzCZXKSVgVEQScZN+NPZ3nhpCYe39xJbzDDyIhITr5KT0jFkjVTmWtiRI8xGNWZUSbTEzKQdRNuu8ZgKkaRXSGWslJgs/DHw2tZ/NI2nGaJY+u9rOga4bUTJ3PzV630hTMUmxzcc1QlRS4FWTqQnvTM1jY+2DtKPKaQyGrMKxnhornTmJRXQCKjcd3HHfSE0vhcIqdOyOPEcbnAgZXEnlg/wNMbB5hT5EGRBa6ZX8wFr7XgtEjsG4kzrQ5eOGkK/oTGT99rYSiQpbhER9NBTlnoi2aozJfZ1ZXlrBkelrYEOW9iAe9sC1LiNlFdpNAWSFCan8BntvDJhgPLWD96cjXPbh7gsbUDHFxh56mTG/9mRtG/t6YnhFORSWYOzCxKGwlKHC42D0T43cp2xIxMOitw7rR8PG4VPSNjN0mMDI0we3wZH+8PsLE/RCCV4JCGCD73AFvbqxgYdNEV1qjyWNnVk6CiVMJlM9jRqfHAMVWcNikPfzz7fVHpREZlTUeMjf0RqjwW7v68j5Mm+WgqsPLY6kEW1Li59/iK//AaTnp+L7l2mWfPrOPF9QfqH502JZe7v+7ijR1D+JwSHpOJ0ZiGIgo4TTKfXDjhb/bxVUuY7kCK93YEmFNt5YyDfJz7YgcHlTp59LTqv9n2u+4wLaNJlu+NgKTTG0lSbYsTlhwMx3Qkk45bVBgI6JglAbtFpCOU4oFjqjm40oXHIiNLAgPRNNevaKXJ7cQqSly36EABct0wGE2kSGoqFa4Dq0T5kxl2j0R5d3OIdV1RqgoUitzygaXWYzIup4bszGDDSk9QZTid5uBSN+MKbMwq9lDlsrKrP4HPLrOgxv0ftuOv3munxZ/An8pSlCOxZyTBwnozR1YWcEhlHjm2A311ynsbyGRFWod0ZpbZ6AhmcFkNUmmB38wrI5U1WL6pC6cnjz+cUElG1VFkkcuW7KDdn8GQMiiSTCCh45XMRPQsc8vsxPUY4ZiZtgGDXKuJ4XQGn6wwHMmSkw/D8RTjinVaRw0mFRn0R9MogpveUBavyYpJlPj03Am8umOQJ1YPYRUkUlIWyZLGIlrpCCRp9DnpCaV55NgaAnqMJa1DLC4vIJhUeWpzP5OKrPxqag03ft6OWRFoH03TVCIRzwgookhYjVPn9vDM8ePQMfjFx7vZ0aVz8oQcHGaJ8cUWrlveym0LK7GaUzyzezfpcD4T3CGClgyBuIvOLivFeTL9iTRPH1fPrV93EI4aNPnsbBwJ89v5FXhNCld+3IbDpeMzmUjpBl6XSlsoiVUWKLM7CSSymK0qrx53EKe9uQcZkWK3iadObGDnSJiheJov98fZMxQjnDR44Yw67l7dRiibQBcEJEMkqqV55fiDCKaS7PaHGAoqLGkdJKUJvHnqBIocCjcsb8NllrllYSV7R2Oc/t52EHQmFSqcP6GEbSNh1nclSagaUS1JNG6i1OTm5sVlPLy2jwmFVlKawR8Or2LF7m6OmFj5g84oepsfdkbRGfzv/+vXmDFjxowZM2bMv/VDj6t+LGOqH/WMor/SdINL32olLmUo96WoJpe+YJqsJYpbFtnUZyGtpinQFG5eUMh7O/3sHIxR67Nxx+EVnPHaXs6dUkadz0Gdz0Fzf4bLZxUyEM0wuVhmVLCiGTo+u8j+HhnRFuWVU6dw+zdtpLQ0dsVEry1NUE1SZc0hYdYIi0m8ioLZ6iealck1+xiN6sS0FCUOExoqxXaRx4+ux242cc6kApY1B1mxL8wDxzaQZzNjE0wUWmWePqkW67/MBFq6N0h1jsIz30ZoKvAwkIhRkatyQn0ZE3Ly2DOU4Jw39jG9xMm1hxRz56ou5la4eGfHCBlN4639A7glM6+f0ci9a7p48Jh6JEHEbZcIZzPMGK8yEINH1vfz0voRLpmRzxt7B4mmdA4rL2RRtZsbl3YSCOu8fHYNsyvcHNcU5jcr95PjttCSGSY57KF5JMXi2hwmFdjY1DpCRzDJaW/sIW3o1PuszCv38Z+HiKB5NM7Vy1rINysMRTVm12gkpVGOr5hEqcWFxVDoz6Q5u7GIEp/I2sEoPsnO2ztGmZer8eCrezmoVqOhbJDqnAy7egv5cFcdpWYnzbE4BS6FjK5jmHSKPBLtwTTPnl7PQDIN8H2Q6IKP9jAczlJitnHDIaXU5VkpzRUZSqQ4rSGfs6bmIYnCf3odr59bj005cKUXzsoHIJDK4LRBrkfEpsuYkfjTcRU8vbmPnsiB43/XGeHz/UFuPaycxXVuwM0j6/v4tj/NG239fHDBFGq8//pRORzL8Or2IXYNRwiks8yqttE2ZCAlDHyuDKKUQVd04mEzZruOtyCLiECe2XJgNbwdI7y0dZDtg3HKfQoIOqOZFNFkFkEXuY5/CRTp0O3XqMuzfn/s9d0RHt7Ygdtkw+cR+PX8UtqDKSRRYHVzHEGS6AtBTU6WsjwwJ7LEtCTf9iTpC2eo8lr5zcEHAm3BhEpG0ylwKhiGwa7hOBPy7TxyajUdo0kCaZVvekepypO4YHIJX7WFWfzMdr64aCKiKHDfovFsHYzybGqYI2t97PFHOb2hmP2hKA9v6aRQdLCg1MGZc0swDIPFL2xnerGDiyZWsLbfz5c93ci6BcHQsViyxIMSW/synDmhlOf7u8jPsdLSkWFmhYOrFhTxVWuEWdV2Hl/Tz8hIlKnFWTJozCjTGIrFsClW4tkUNs3O1+0RLphSzGnjCrj962aWtkWY5rXQE03SWKiQTmfJsUtc+0kn82stdCdDfNiSRhQ1sppEMCbyy8+bWXL2ZF7a0Y/VEqC1ByaUmdg1lMakmCgoPNAvf1rVT9yIU1chcfwkD4+sGmRrX4w3Tx3PpoEop4wrpz7HyZ3fbcHvV9CwkxGClBcLBMN2shmDm5Z1MZROU+Wzcs9RlRjAjZ91UuW2cu9RlfxxfSfDWowKhw2bNYkvm2VuUTFt4RhiQiAHNyZJ5I0zmrjo/WY6g2nOfW8Pdx9WwfVf7ebGWXV4bTIf7/Vzw7IO/nBkDZe+00J1gcBARCctqtzw1S5undvAkvYeTq2r4icOH6/v6ues93biMincubiSu1Z30xFM0pTr4M9HNPDk5k52DCT5S3YvZ1Q3ohsJBEHEo9i4qKmIB1YMc8m7+3nrnEZuWb2PYFTkrq9EOvsD/9wD579A+JefH2pfY8aMGTNmzJgxP1Y/1LjqxzKm+lEHirR/SW9SdYOqIhnFnuSQ6lz0tJtVci8dYY1eTaXKY2MoKOJzZVnR08OQ34aqG1w1r5jfLutCMUxMKDgQTXx18zBPrx1iR3+MDZ0J/nhiJZ+1D9KX8dMT0vDaZe4+fBLXr2jBJoqYZJ29Ayp1hQJdfQ7CdpUin0BPREWUg5Q7nITSKrIco8gss6Vfotwt4pKttAYFrl3exhPHNHDj3HJah5PsDge59v1uPB6Rg8td3LSgHEEQ0HSDP6zs5b0dfsq8Ju45qpKt/lE6AxK7u3V25hj0Z4b5y7o+TIJESyDB/au76Qmn+eWnLbQPZ8jqGjkOidFEluu+aGEkleWDfaMsqvDidEI6k2V/p5nfHVXG8fW5nNyQyx/WdlGTo7CzXcVVJ3P10nbOGp/H/pEUOXaFy5bsoyucIJrRyPFkiPst6DYVjzvLVn8vawcgnHAiGQrJhM4p03I4tNLHPV/00jyQ4qGTqv6uXzf0RLj8wxZqcm3sGoizoNrDyXU53Lfe4I1INy0dVmZU2fjo/Am8tnWY6z/p5uvLJyFLAm2jKSZ6Vb4aTFDhi2OxRmgeqkCXQkwosZOIWFDDJnLcZtrDCRqqMmQ1GX9A5v19I0wqspPIqKzuCrNkT4h5RR6apppZ1R3i1b193JFXy0f7htk3lOLU+hK6gile2TLMJTMKKfWY/+5a/hok+qtwSuXq5TuIJgWMjIxgyHSF0vxy+VZObyzhuAYfqq5jkgSa8q3ohgG6QSCp4nRAvt3CkvOmsGxvkC1dCU6fksvLm4ZpGUlSmmtCESXiyRTLmsO4JTNZFQ6vLMTk9pDWIK0aGIiEkhqvbhlFNpmY5LVxzaJi9vqjPLyxAwGwm0w8OauB3yxtQ/w3gbB4RuOPq/o5eZKPEyf6OOH91TS6XSiGwJwiNwJwxqv7mFrsoNgn4fSqmAQT6aDIgGBQ4lHIZhxceXAl/pjG5s4kPxlX+P3+9wwmuPbDTp4/u5ZSr8LNK9s5utbHL2aW8vA3g+wZiVGQJ/G7BVXU59qYWuDh2Lo8Dn9uF5fMzOfFHYOM9zm47KAiTpuYx97BBOe/1sLkSgvnNVUws8jOB+vbGY5lqcgx8c0lU1jZHuT3X3WRyepkcKLKKTyyhb29GlOrYMdwjJXdEM+qHFlj5f7FZag6TCiyM63USUrViGRUonGFHZ0y+R6V5mGZ6aUZusJpiu0WdnWp3B3qYW6li1AqS2c0SolPpap4iJykwKZOHy4rhINmDGuGIGm8tgTj8wS6IxmOaLLSMZxmfI6DArvCLw8q5/0tQbx5CSrcHn49t4oHVvewvjvKee/u47cLy+ldk2HXSIRzP97O3QvrCUXgkW8GeeK0AzWEajxeHjl0Hk9/u4HNsQzJtJ3UqBUMgfklPj5vCWI3yczM8ZJrP5Ba+fjJtVz2ZgvbR6IMjoAvT0EyJxAEgXKXjeZYF7fPnc5fNvXRMpTgmBd38szJ9cRJoUgK4bTKBR80c9PB9dz28QDXLirhqoMtOMwiZ767m7dOH0+Zy8y1H3cS1RV6+lL87POtPHXUNG5dvY9T6os5Y1wBH+wN0TqS4IIP9/HOGeOp8loxDIOtAzGGIjouC+hxL/d/248RN1FRlmFBeT4/nV5GvsXGUxsGOPe1Ft67cBw3rNrH9p4E3UOJ/6Pn0ZgxY8aMGTNmzJgx/1P86FPPXC43572zj/EFdvJsEqIID2/sYVz5MLKRR+uoSpFbo8QpEUmLDEYNTJLGeY31/H5FHybgt4sqOGNqHgB3L+/h+Q1DCA6VBSUebLLMHn+E4vwUW9tNpLI6jTlWMuikkgJFuRId0RCxsIkar5WsOU1KUyl1CfTFY4iGhUqvwmgiRdeAlVyvQSwm4jMrBLQ0IzGVcxqKGEin2Tccx5Q10ZOJM6XQwSunNmFVJKIpjZ+8vJ80KumsweOn1fDynm5SWpZFRUXc8Vk3k3NTBM02BjMh8gQvUU0nnNCwWwVMkohTkOhXw5SaXQhI3HtkNfsDCU5syOOXH7VwZL2PbNZgUY2bHLuJ93aP8MTGfn4yJY8TanMZCGd5dfMIM6ts/HlDL8dV5/HsumEm1YqMpGPk2yxkMgYiInFNZ2KRxsWTatjaCfOrXezoT1CXZ+aVbUMs2xfiriMqObrRi2HAu3tGGJ9nZ11HjBMn+FANg5Sqk1Z1NN1gw2CIJ7Z2MD7PzObeDAsKCzhvWgEzy5xE0yo2k/T9rJ4Pdo/w8rd7SSluVNK4c0JUui3sC8Rwi3mEsnHCcYVap4ubFlTx+KYutvUkuf/IWoLZLEfV5HLlZ/vZPhBDz4jYbTqGpGEWTQzEsmz86XQ29h5YYt1jkfiiJYjTLHPiuBw81v88bnvDx51s6I0SETLU51gYimXQDQ07Crv70lRUp6nzuChxWUklJdJGmn2RUVJhJyZZIIHKeLubPJsJj01GEqDIpXD2QXlc/XEb2/vj6JrA+Kok/nSMnoiIx+SiO5CmTlY5dloNl84s/ptzWt4SJJ01CMZVdkX9FDsVBAHy7TKxlMgr20ZQlAyyptA3LPD6OfWYZZHXN49y0ax8yr1mUqrGTctaaA2FmV9cQvNogvWdUTRZZ2G1kxlVZj5vC5LIpBlKpil3uogkDUb8UGQzE4zpHD/ByymTcmgqtPHU2kGKnCZO+JfaRABnvrYXQ4fpxS5CxPmiY5QTqos5qNjJYxv6OX9aHlMLHDy2qZ8zxufjVmRWdfs5oSGPCveBFexm/2U7M6vsPHliLd/s7OK65WFePqee2nwzV65cw8yCEu5d7mdhjZO1Q37Gex1s7Egzp1GkLZygzGkhkkmTbxeJJ03MyC+kLtfCQCyDRdH5qGUAk2ZmJJ2i0ifTGU5QYneypT/JjFIr+4YTlFrdqBo0FlrYOhQl35Ulrat4LTJpYQQhU8zG3gyHl7lZVO/htZ09zKvvQ9Ul3tyUy0HlWbKqhZvnNLGxN06T185D3/UwGM9S5JK4aWEFb28foTfbxdo2C3MKc9g7mMCtyMSUJPMrXOwaiXP/IQ1MyHcAoGkaV7y9kYBZRZBkOkJJfKqPSq+NCw8q4HcrOgjH4ZQJOdxwyIFV9e76sovlnQESaZWa4iT+pEqJw0Y0q9IX0Sg3O+lOpMl1yCwuKuCl7wK8fn4td6/ppD+awW2WuXpmGT97p52JBXbevrABSRRY1xNmfL4dp1lG1w2SWZ0XNwzzbY+fx06pRxIELv9sO6c0lNERibCiNYhVgZZhg8/PnUyJy8zFS7bTG9CZXmJncWkeV3/cQa5XwKIphOQYN8yt5PSmQh5f188LG4codim8dnYD6azBgofWsOe2xT9o6tm7/LCpZ6fxv3+a9JgxY8aMGTNmzL/1Q4+rfixjqh99oOjBTUFa/UnOGJfHrSu68OQkGVeo4k+nicXtVHpNiHIC0GgfcGO1JWn3CzgViUqzj4PL3WhZAVmCBTUuTnmumcpiCX9SxWcHj10gK0WQ5TjhUAFBv8xlswqpzbdw//oO3jltIsv2+bl/5QA2WcKXo7MvnCCVgoNKFaKaimAYCKqZ7kQMN1bsiokkWeyKQEcPeDwaxa4DK1fpioZTNiFqIqeOz+OqBcVEUirnv93MkJpkYq6dzUNRHIbCuVPy+dO6Pi6Zmk9zbzd9IoSCNgzJIM+usGc0TpVPJpuVUVWDHLvE0XV5XDa9iFBa5cXtvVw1o+L71Z/+rYfX9nJEjZfmoRRuu8jjawaZWuikNE/koZWDLKzysKxviFK3Qjyr4rOn0GIe4qYoimbHIcl8csGBYsfhlMoDq3tY1u7n+oPLOGN8PqIo8FHzCC9vH8BjVpiW4+KtHaMsbLBjFUwUOBT6Alm+GR7FEDSi2SxlLoU/HTaeco/lP703ekNJhoaHeGV/miX7wsxp1IhlVPYNyozPsbKrM0NNgZWYniYrpyizWRmMp3BbLWQ0Hbs1g092UGHzsnc4zt6hOE6TwpRyK/cfVXOgyDYQSqjcsryDlmCUTFpmcY2bWw/7j+sUAfjjWTxWGUkU+LYrRF80TUOuhW+7Qjy7aZgZhS4a823s8scYTaaIZ1Q6hjUm+9wUOk3MqnUwp8xFrtXEBzsDHNHgpsJ3oB2u+qCNlftDxHWNmXUi/mwclz2OGClid49KtS3F4VOquHrhgS/6f/y2l2c3DrLnmuk8tqGXF7YOIGVFSrwyES3D6ZO81HqdHFpRwCWf7kZNC3zXFueLSydiN4ksbw5zSJ2bIveBWSanvb2ToWiGHJuCyyTTE07hVxPMrhCxW1RaBi04TBJd8RCS7jgQJE3ouK0i0YTO6fVFlHoUTpzgY9ETu5hSbOeRk6sxDANBOBAAvO/rbpbsCVGcJxJIZzi6Ip/1AwG2DMYZV2gillaJZeGMpiKunV3Bxe/tIqBFee+0WbSOpgDQDWjIszA8PExeXh6iKCIIAj3RGPd+18b6vgCLi8s5ZXwO96zdT1bVkWQBp2KQVA30jBmLJUUqYSWaFjAkDVmE3y+q5M09g2wdCmE1LMjWOG4LGAgEIgogIUgaGU2gyGrlxRPH8eyWAf6yvpdxxQKjSY1sVsJlkQinMxS5DcpddvYOqcTSBlPLEwwmYjjEfNoDaRZVesmkLbQHkvx2fgWv7RpiQYWT369p5ZcHVTKxEJ7c3MOudgsus8xdR1Zw3cftlJZk8WcjTPIW88DhjQfaRNcZGBzi5b1ZPto7jN2dJREzYxYlcpwSW9tTFOSKHF7v5Y7Flby8bYj71naSJ1nwuHXaIwnqfAZDESslOWkE1czukRRqxMIdx5Rw5oQiVrWEsSkik4ptXLW0hZvnV1DhtdIyksRnk79P8/yrtd1hCp0KVV4rbaNJnlo7yH3HVdI8muDi11sJmVLcOLeM95tHSGgpMqqKT/fyxCm1eKwyL+7o5pLJ5ciSyNK9QSYWWUmpBie9sIfGUjNXziplYbWHGz9vYdNgFLdd5OXjJ7K9fYC548rGAkVjxowZM2bMmDH/g4wFiv7f+VGnnq3uDLF0f4CkniW0USUvL0M4Dd0BmXy7HdmaRSWJpCps6JApzAmQyjoodZq4dGo52/qS+ONZ3t8aJK6pfNE6iiRDKATl+SZSpgT7/FCfZ6Z1SMYkZWmqtHDO9FzsikxNTiOprM5jGwY5epyXVa1hWsNJ6n0m9g/oRNIZ8t0mhhMJUOIUZHwkVAOzRUXVVPriGpcvLObj5gBui8yUCpFdwVEum1bMvj6VKSU27v2yl6X7/ZhMBkV2hWsOLuP3K7po9Lg4tiGHeVVurvmimfE2kdGQhs2mgiAxkkpTny8zFDSYXGjllzNL+MOqbs6ZmM+a3iD3fddONCHgFM2cOjGXj1r68eDk9a0jvH1+I7U+Cxe+0YpZguJcE787vJzP20d5cEM/504txWkx0RazkZFSNHkFtrRaqC8QaO81cXSDk5Oacvjj131cu7CE5zcN8k1bhHMaCzlrYgFwIG1wx2CMew+tZedAgps+b6e60MTHrcPkGHZiSR2rRcRiN1AsKoVuiKZUtgxE/iZQlMxqrO2OcEiNl52DcW5d3kGe4mcIG9UVAZxmF8OJLA1ON0N+HZBQzBqqmmRSvoWDCl08tU5jfI6dOZUOZhd72NiZ4PMuP0khQ2OFgpqR2Dka/z5IpOsGxz2zl4OqFVKGzlVziyh1/23wanVHGFkUmFNx4MNHFOGT/aOMpMMMxFJ83R2mMV9HwkRJHhTmQnMowqa2NPkukXBawO0SOX2Gl4UVXs55bw+iaHDBlCJ+OudAGxqGwXAsi9MmYrULGDETE1w5zKtxsbjGw50rOxmJBKm0W6j4N2127bxSfjWnhO19cYb8GvGQwEcXjSOra4TSGgeXu3h/p59ff9LBjFIz88vyGHeKi33+OPlmK+dMz+O8t/fRlGfDY5FRNBMeGXYPJqjOVXDbBLJJM4MRDWJgNqeotOeTiJp56LgqeiJpvuuM8PPZxdhM4vf1twzD4JnTa8hoBumsxow/7uTS2QX8amERO/xRbA6DN04dz1++HeDr1gh2j0aFxyAaB69TYTgRI2MkWNE2wq6BLHVeL5P+tJUJBTZaRlJsvGoKcCCuvrk3xsQiB69sGmF+rZO9QxpexcGW4RD7wyHSuoHLCsE4RAIKDqtOXyxLhcfGHw6v5su2CIfXeHl9dw8Pbt1Ak72ckSETk2tE+uICTfkKgXSc6nyR3lGJVFpBNmXQkhI7BxMkEvDh2RN5Zms/PWE/jQUqkRSYRDOyFMIfthLORmjIc+JPqVhxkdLS5NkEPtsf4fyJdrYNqZz+1h7mVbr40/o+Tqoq5cVtA9TlWLlu9gRuiu4nGde5+PVWPv5pI09u6Sc0ZLB0W4L5Rf7vZ23phsENi0up8tqwm+F3y7soKjAI6jHMHrhiVgVfdIa4YUULZ48vRM8KHNbowenQiTRnSalZQkGDXJuNQNzAyJiozTNTbneQUXU29ca4ekExsiTw1AmN39+Hf61zpRsGovCv6Y1Z3eC4V3bx6XkTqPJZGI6p3Liknfb0KAFDwJWV+aCtnZ6wjFMx4RSt9EbSHPvsHtZcOYnLp1UCsKYnQHcyxtEeL+GUytpfTWY0nuWYl3Zx+ewCdoQHSKXtyJLBee/uRYvH/98/jMaMGTNmzJgxY8aM+R/kRx0oyrHKFDhkWkZU/nByFXd/2U2ON0ZGhd4gNBUZ7BgSyXeHmVtvpm3UhqoL2AUTnX6VZS1BhJjE/Fo7q0eH6Izr1ObbiaRVhjMZchWZtJple7dIba4FVdDoCmX4xSct/HpeGae/uYdqn4WfTM7jspnFPPRdJ590DtMasDGxzAwmldFECp9FYVuvjMcwKMiDrJhCERReOr6Rl5t3MKMshyMrC7jhi3aeO2Uyj60aJaGq/OK9drKiyvwaFy2BBJmExCVLdkJW5pp5ZYzEs/x2eSfJtMBoUsRjMTOYyNCQayKe0bhhdi23rWqnIxznxqWd9PqzXP1JG9ujfpSsGZMo8ezGQT7r7iYWkamwZ/nTiVV8vHeUxzb0c/mcAlJZg4tm5HPwU9vIs5mwp+xMLHTyyLpugmKYXMnOri5wmCRGYioVXisLq9y8s8PPz2YX8tiaPp7fOMQVs0q4eFY+hmHwyR4/968Y4IgGD3U5NkaiWTIpEUU1EQtlqSoS6Y6lmJJjYjATJt9iYTCW4cTqasblHUiZiWVUHIrM1Z+20TKSoiHHhoFBezRGSd6BfqvymoknDB49bAZ7BjLcsbKbhkodwZQmHjBTVlbApZPLuWCCjkk6EAR6dcsQ2/sSXDq1mNllTmRJpDOYJJHWCSVVPFaZSFrj5sNL+Lo1zP2H1fHuzlE29PQzvsBGsVvhpMZc3t4+wpENXuBAoelPmkd5edsQ+U6IpnU+OGMmLrOJYDLDCzt7qffacVea+LxtHzWKG4dgpiBHosxpIc8mk84YfLrfzwNru0hnDX42vYRfzSrlyOd3ckyTh2vmlfDYdwN0RZIE9mRZXONhZpUVyZLm4ho3+fk+khmNZ9cN47GJaDos3RPi/Bl5XLOwhMOe2sVlsws4fUougiAwpdiOIgnExRiabpBUdW77uo3TxxVwakM+180roS+cZfdgnDnlTo5u8FHolDns9Q04FRtui4FTc1FoM3HRrFyKHFZGYxrnvNHMfcdUccshB2Zf9UVTFEgC8r/M7mnIP/B3gk5/CkmEfOeBj7hXTx7HQCyDIAh83ednWE2jDMtYZDsDUQ23QyffZuKbjig7ezPEsioTqxRuO7KM5a0hvN4sFyxZx6k11Ux363y2N8Qtn3eRVuH9fcNkDLDoCpqUJSJmcVlEPI4AhS4H/dEYQjIHIyaCFy79bCc5mptnNwxRkSMyEMjDURliWn2WgYgDh2Tj8NIyRuJpJud7CKgxmrw5fNrsZ0Keg8c2dNEbSbOqLcQpk3Kpstt4dFs3U4tF9kfjlAlejq8qpW29QMieBsHM1dMaeGuvn+mlDv74bT9regMMJbJU5iq0DKVRFIE3m/vwCFam5Ls5+e1dHFbuY0ciRlOtiiBnuGNRFae+lCRhJNjUG+WECTmoms6NK9o4c5aJs6YdSL9dWOPmss/2YE5DU6HEfZtauX5GNe/uG+T27wKc2pjPW5sDXD67gCqvzMYejfHVaUxKFFVw4LVY2Nud4eI3Wln9q/GMxrNc8OZ+nj+zDrMs0jqaZG1nlPOm57OlL8rVK/fy4gkTKLSbsZkkFlR6eOKEOs56eT/vnt/I1QuKuOaDTjy5AnVFBsmkTlxL4HUonNlQyvJ9Eaxmnbp8O6Z/SUH9qHmEu9bsRxIFZAFWdoZxKSYeP7qekxryeHRzLyDRkJvm/PE1vL5ziOFY+r/lOfVjKZg4ZsyYMWPGjBnz321sXPXP+1GnnjXc9SXFFSK3zR/Hy2tD9CRiONwpUikFzRRmNCozvUwkTZBEyopZtJNImrEaNh49rpqbl3Rz5YJiznpnN+UuM6GkjiiCyw4BLYxuaFTYrbQNK7hs4LIK9Ph1EobKb2ZX8MiGXq6YWcrF04pY2RHg5581U+WwENLSlHpUXGaF9V0aakagzmklruvYnRpJVaPLrzG/ViClaQyFzPgkG38+rgZFELnonf0MZBOUKTZuPqyc1zaM0BpKMCoFyDc7ObGuEI9s4sUtQ9xxRDkvbRgiEByk3yzgtAhISMzIK+DVraPU5CgEQhAV0ngMK7MrHESMNLsjQxSa3TQHUngMC6ePz+fKBUXfF84WBb5P+wG4fXknX7WFmVxsY/NIhCKXyK7ROLVOByOZFHbZxLxyL+eNL6Q210ooqbHgsZ38ZFouv5xbhMty4Av/uztGeGfnKL89pIzlHQFcikyrP8Wm7iiGlMFhFzizqYg/rRoizwM29yjFdiurWk1ML3SyqTfB3CoHmwdizMjx0B3IsLDKw0Uz88h1KJzy6k48RoTmlEyOYuOEpjwOr3fyUUs/b+0cpcQjk8hoHFZRhChInDe5AAy4/+te2uJRZAleP3kSqazOV61hVrdF2NIbYyiR4YZDSjiu0cesx7YdqE8T1HAXxLHLZsJJ+Mm4QsblOLl3RR/3HFfOhCI7AA+v66Hdn2RrW5r5tU6uXVTy/XLuf/XKxmFuX9aDyyVwcKWT2jwrlx5UyOahII9uaWV/l8LiKhc/m13CFcu3cmRVAb85uJbj39yISbMwGj2Q4lhp1VAwePiMCaTTaUZHR4lGo+zNFrF8ezf7RuP4PDZki533zmtkZWeUUFLlrEn53Lemi+1DMV4/Zfz/z/dfWyDJ0S/vZEK+DbMk0hfOsLDcw9RSK3dt3EOty0EmI/HBWVNpHU3hscrkOf72epfu9/NFa5D+VIxYJo3NLHBIeQGNOQ5+9Vkbp9YWMKXIweJaF27rv75W1QzOfmMfaSFNVspQkxcjk9UJJG3MKy6kxu1icqGDfUNJHljXgc0qYJZEzp9YgsWkcfdXfTQpGZzuAhKmGPuGUmiSiqyZGR4RKMkVSStxanMNRhMZslkrNkuKbr8Fr2LmmlnlPLNhkLYBlVynQEZJk+fUCcckskkTki1DjjvFQFhGNswMBw1OnGrnqhnV5FgPpOrphsHru3v4yzejWEQRxaoyMChQVwl7BzNMKRcpdMqkszLNgRSLSnJ4Y1OI3x1extmT8omkVR7ctJOdA2lGkiomzFTmZFndLnBIrUBNrozLKOCxNUMUuU2I5gS5Xj8XjZtG/7CJV7cN0B/N8rPZhbREI5xXbeOrQfjNgkr6wml+8uFu7lpcxbt7B9k+EkTTdXLtBuGwnRwnbB9KI2YkqgtkbLY0Ajo2SwR/UsJn8tAXFghFdYodNu49sprx+Xb+/F0Py9oCvHfGeC5btom97Qo/n12MySTw57U9TCk3M5yOcfOcevKsVp7e3M/RVTk8/G0/9x1byXmvt+A2i0hOjUTawCJn8Lg0dvZKvHnqOPwJlXe3BHFZJA6d4MCpyFz8yV6qfTqiqBNIiEgo1DmctA5mSBsaHpfA7xZUctnHLbx9ZhOb2vq5eF7DD556Zv8/2tO/ivPjmCY9ZsyYMWPGjBnzb/3Q46ofy5jqRx0oOuXFrzhlRgWdowLfdYUQLXGGozrRiJmmMoNgJo5JkiiwWNGEDBVuO5M8RYzLtzOp0Ekso3L5x/tpG8igSAIet0B3JIkqqjTlSahSBAkTFhwEUzrhdBav2cSt8xq5+etmLplcxlnjiwB4ZnMfD37dh8et0VggI8kxuoNgynqQbX5+PrWO17aH2TuYxuXQUBSVQncaIeti236JpKDyxxMqeH9HiJ5QilRM4LXzG1jRHOKV7YP4crJEMvC7eTXoaZFrPuzAahbItZpQpSQTcmNsjKXxKW5GIib8yQwFDhMGAjZBYu9QiqlFTg6tc/Hs1n5cJhFDlVBEie7RLM+dWce86v/4jbJ1MMAda3aSibnpScUpc5lRNR01pZDIGngc0Dqc4fRxOSzZHWX55eMpcCoMRjIUuhQ6AkluWt7BkbU+nt04wLkH5fL8jj4OKcvl/ClFXPlxKw4FLpiZz6IKD/ds2ED/sIO9gwYnTQ+yqUtCEOwk4iJWZ4I8q5n2QTO/nlvO0Q0+7lrVxRetAeZWutgzFKVEjtFjWImkBEpEB2E9iWTNoKVMCEKWrGRgkiUSaZFLphbQVJji159EqPGZmVvhZVqxE6dJ4qI3W5lf5eKcablcuayFPJuJTW0pjjlIYXK+m6l5Pt5pHiSr68wscXJIpY9c27+ufJbVdAA6QykeX9/P1vYkNTlWnju7lo93+QmnNM6bng/A0tYh7vqulSZnDtVuM0ZslF0dEaKCmfUrPsFjHqF0yiF07m0jtmMVHqvE7tWfUXvEOWRCoygl47juFxfx/CP3MBDXKJ13Mjlkad20hksX13HCuT/jDw88yOdb9pFXVkH+nGPpfPN+3CaB2Sf8hFAsQfOXH2I1SSx99zWefvpp4vE4EyZM4IQTTmD9+vWUlpZSXl6OohwIeKSyOl3hFK3+JM+vH0KWwE8CSTAwGxLFJjeRTJZwUuedCxp4bO0AAjC50MGKniE29kWYWexjIJJmbV+YhkKJMreVa6ZX0zqc5uu2CJ+2jnLn4ZUcXe+jbTRFZyDF/pEU64MDRNMahqijIGEzpzitupaqXAdfdgY5rjaHNe1RXt3ejyGDJulU+lRiGR1nxGBYdRKWMuRZBTQ5ipTwMhQysLpVDF1CNUUZXyCzezhNrcdBWzhGpcNBXEtAJI/eYAbRomOVRYb9Ok1VBrv8KRodLpoHEkytUtgzoFPikUkLGYo8sKAil2TCxJQiBwsqPQQSWX6zvIXNgxGqciREKYsat9AVNBhfahAyRrCSg2CYuWhiKX9Y00WZ28xrp4xHVXXOeXMfmwYjzG8QkC0xJENmU5uVpmKNtgCE4gJH1ebwdXeQQsUE1hgzi3L4rDlOMKpxyYwCyjxm1u/r4qtumSvnlbKqI8TGnigNpRKRjIFJ0jGJAhkxRJnDSSwDw/EsNt1BVgmhGA48do1wNkalR2J9qxW3TcKjKDT3Z5AtBht+ehAWk8Sf1vRwQlMOigzXrNjJwJCZMyflkVY1NgX7+f388Ty9vZP7F43n8k/2U+RQmF3i4rfLu7htUQV3fdmNoWjkuURcVo24liaj6phTPh49sY5yj5nrl7ayIxhmQr6Darud99p6cLvCxOI2ZN3BcFjHJpgYn2unMd/K+u4Y50738dq+fh6YV0JlYf4PGih6jx82UHQq//sHNWPG/He6/fbbueOOO/j/x9B51apVLF68mI6ODiorKwG48MILWbVqFZ2dnf/tx/3qq69YtGjRf+m1e/bs4e233+bCCy/8/pz/6sILL6Szs5NVq1b9U8f/t9f9v91fr/mdd97htNNO+//6dKisrGTChAl8+umnP9g+X3/9dYaHh7n66qv/qe3/eu/9o/vl/9SLL77IRRdd9KO6336Mfuhx1Y9lTPWjTj0bTevs7Nf4vDWAJmaptWtIGTt2V4LuoExDrotgWqNjVCbXKrOooYJrlrfwzukTWNMV4s41nZxQl4tTTLBlNExbWKPCbmbESBHOZqi0O4ikDBJ6CqdZJpbVaBmSuWdNC6c3Fn4fJNo/muChr/upzTOhygZ9IfCZHfQMq9SVDPPbgyfxWUuUoWSSAh/0RjPMytfRDRPtYZ2yQpmBdIr7v+zBrpgxNIFPftqE3SyxpjNMY4nC5QfV8MA3Pcwv8wAwvcLGl10hSorSuC0pIimVCqePoYRKrl2gL66TVg0suom+oEqF20yfP8syLUQ8LPHLxcW8sXsAVYrxxxMaWLo/gGISmFRkY/domIMKfSSzGrd91UFrOERPROSYGjvtu5PEUwJNXi8nzfTyh2+70AWN2rI4HXGRr34+5fvitA6LyC1fdPBNZ4iMoPHG7jT+mMqylgCBmI5DMjE+3859R1YztdiBLAmo+oHgiscbRhh1sLmjgNsXVrC0ZZitwQ4m5Dr4cpfBB+dNINcuYxhQ4TGT1Q38qTR9yTiNhSpayGCi141siLS064yvUykoCWI16/T43fSGNEyyma/6Whk0QvhspWzuTlLgsHBItZdKr4WzpuSSZzdxyZttnDHbx+xSF/b5JtKqzoxyJ/54lkPKPWwLDfDq9iRLm0O8cHITf9nYzbddIbZ1p3njzEYKnAqLqzwEQjrBVIZXtw3x2e5+utv3c89nLn5SHGPblx/R29bLkx+8z2O/vxlRFJk//WAKxs9l66ZifGVTkb3llC1swn74ydTnWlEUhYkX3EaZy8zFMwqZVuJAcD3Cn7/t573Lx/P85mH6Ciex3ppiz9YuRuceS/HEo/nV9EqKbHZ+rd/Dg8fVML3UyfLWIMnjTyGUVHl2R5yTzzyXVMSPpmkc9cQ2gpuX4B/p4y+/OhctGeell17C5XLx1FNP0dq6jrZdQY6bO5k3TziIdZ1RVveEeGPbCD6njM0sMeXPW/AqCnkuiY93BfjVvEJmFPhYsTfCsfV53DS/gqFUkjd3DXHpZ9v5/OyZHN3k414q0XWDFS0hbv60G69DRBINZG+YMp/CcEzAa4+TTNj449fDFBf0EVF13t4+Sn2umYLCKLKgEEoK9EQ0iu0SUSOFzyvyy4kV5FhMPLFhkK3DScp9MvuG00woU9g1LFPpljFLSRKqhtsk0zVowmpyEUtnyHFJjEQM3Pkw7DdYUJ7LQKYLpyeOI6MR1iRyvQbZLBiGwqbuBOlsgOtm1vDQd308s62PBw6rJdclkBsViKpx3JKVMDFsOBiOCMQFK15fluGoxr2ru8hmBBSbzEgsy9edIbojGepyTOwaVqlwO9DlGKWFURIpH4YQ54KpRdw4t4Y/r+vima19WKM2jDwL6WycqZUin3UOMq/Yhz+ukpR0QpkksyqcDCaS9MeTOBTId1joiyYx0m7Om1HNF91DoGbY1atiMdnJ94rE0zp20cXadpX8nCjmZB6RtIbHCdfPqcRikhiOZ1jTHmVPX5qnTqvhj4dO5ujXtvFl5zBaRiCNzCu7uljdkaJ3Wpqnj2/gpx/v49veMA8cVYWESCShk28WcdriJFUZi6BgliXaEylOemk3Rzd5uXBaIdesCJFWNQZCGkbKitkhkTYkAtkYMcNElcfChp4oxzZ5GYpleWbjEDFD44plu/6/eIyNGTNmzP8Ye/bs4Y477mDRokVjX7rH/I3XX3+dXbt2/dOBoscff/y/94TGjBnzD/39clU/IiMxjc87+yn1CpR4U3R1W8h1pShz2bDJZmyCi9Pqywgns+zrhIymc99hNVzxaSv7RpMcVePFYxXZHhmhwCmRDMiMZGPU+yzk2yWSqoZFFsioKpv7dOySlRKfQDxjsKY1QSiRpWUkydUftmEziSQ0DaekEI7BQDrK7AqJ2+ZO4bVtUfaMRknqaQJJjWklOrpgoKpm0ppGSzhGkehBECVUMcufj6/GYzOxsiVEazDF5p4Ev/28g6sOLkEQBJa2D7MvPcS4coNoFgrMeVw8sZqhERkbZjoGNao9VhIJsJsFMrYMYSOD3SwyEMpw/YJi3t4cwGuV6BuR+cuafgAUk8Y5n6zlnq/b+WiXn4Mf2cHu4ThdfoNnjp6EWZKpclsJRg0a8ixc92UzxR5w2OO4BQeDIROPbezBMAw+bhngiLe+I6qliYspZpTbSOppqgtF0mKKEtnOl80R2v1JZpQ5kSUBwzBY3jGERRbx2ZMUu2XCSY3bv27n294Id86byQVNU3nx9CkMRDJMeWA7z24a4MVtA9w4r5x0SiAbMZOOW2kf0CnPF2mJBbEqMnYFhuIq6YxCKptByyq4zbB7UKIr4MZsVil2mblidgkVHjPv7RugOxnn1lUdfHXFBK6ZU87EfCePfD1AV+BALZPrPurkkW97+a43SDaj0zmSwTAMDq/K4c5FNey9ejrheJbr3m/nqgdepe/TP9HQ8hqbmzvY9MRNNK9axovHFnHEvFnMveBGzrr7GV7YEaDr4F/w+OOPc8ppZ2ByuJl/+FHUTpyMZHFiZEwcUeNlZqmL6U9sJikl6cvEOO+j3QzHM5hlCRmJfLsFu0XEahGwWQ0OKnRz7Yw6shmFtzaEuW9FLyo6D63q5+YlnZw1KZ+LphdyyoRcfDYTxcWFqDnV7DKKuePUWr54+k42v/cCJx5zFMm6+Uy79hGefPJJuqMCg+E0C4T9bFryDte9tIbTTzmJZ+75NUT3EAyOYGSy5Nll0o4gntwEnrwEf/mul2c3DLBxIMyT6wa4+fNOfvZeB12jGvmKk2nPrmfncBQAURRY3RnC4dB5+OQKFjTYsZlMpPUM5V4VQ0hjsxj4CpK0+DUsmoIWl5iU46JALKEzpGOTZQYGFaJhG4ou0hNL8Pjmbn63ditHNNl498IGnHYJ2WwQy2gU2E30h0U8JgfhhEQ25kQQDS6fVcyUcitDagqXW6A7kaKmEr4bCHFiZTmZrE651Uo0KuMR7Nwwv5wKt4V0UiCVMPHTd9t45oR6jqr2cf47zZRZ3BQoDkYiEmZRRJAzFBVraLpBjsmF3RKh3CswmE5Q7DGxbyTJma/uxSwLXDa7gKScwWNPYZVF2vutWHEQShrUOT28sjnAWzuG+bItjIFOvttEgc3CPYdUsXtAJceWQdAlNB1Obcrj68EhTKYsNywoJ6NBnug8EAwzy5Tlxrnis1Y8soVgWsNhA59FYSSZJJMx4Y8o6FkTdty0RpIkYgK5JitPrB8kntGwmUT8RpJAOkMwqVLutvDgEdXcvLiC/KIYJQ4rN89p4v5Da/nT+h5EUeCp4xsYTmTYNBThkDoPtx5WhjcnRldYQyBLRhPIolLoFfjJtFw+bfFz9pv7WFyay5ahIMdMdDK32Msjh03mpPoirIKF2WV2fn9YFUlR5Yl1A5w2KYekqqNlREos/3v/ojRmzI+Fpmmk0/899cbGjBnzzxs3bhzjxo37//o0/tuMfdaM+b/BjzpQVOmVkSWIaUnktIeKPBGrAwIJjXqPjceOq2VxlZdSu5Vnzqzhwe96eWhNLwsq3SjmDJ939vL27kGyRpbmviwTK2RynBKBhEAmZSeS0gmrYVJpG2UeA11K47NKZFMK27uSXPjhHs56tZmeSIZilwmrAt2jKk2lGUZ6nWxpk3lyXZCdw1GGYinckg2vM47TomLoCgPxND6LCSlt4eIZRcwqdzAyCr9c0kpPKMlbe4bxuSGjwn1HVVFgV7h/dTcvbOsjqWpEoxIHF+VzbE0Br20d4Zq55UwudGLIBoF0nCKPRFJOUp2jkFB1zDYdVRV4dG0/aTmLxSJzUK6LgVSGklyBK1dsIx2z4/crPLtxANGZYnGNk7WXHkSh08w3HWF6R7PcsrCCn80solCxE0mnyLUJjGgRSnLT7I62cPnnm1nVPcILR03DSJl4/5Qp3HJwNWLSRiQioEgCJruKyZHiuR3tADT7Yxz6+jpe2tbPB+tNbGopYSiaQUrLxGKgZUSWtQWoybXis0l81DLMGxfUU5tjpdev8uKmYTZ3x8h3m0jpKvlFKXaNhgjoEfIKVEZTaapyVGIpC/2xLMVelSwZHGaJYNjB3hH45MLxFLvMtIUSvLVnkPZgnDfOaOL5TYMc/9JOZv1xB2dOy+W0KbkAnDzZx2F1uQSCNpyShTqXnV179/PVhp0cduvLLD7iaO684lwuGG/iibMn8sgtV/L7O27nT2fOZv0Xn7DmzcdIuir5pEPno06NdX0RDEHn5VMb2TUc45G1Pfzlu26a8qzMLHXy01mF/P6ICtpG0jy9boBQXOPuhXV8cvZUmq+cTUckzmgqTa5TYllXL8FEBpdkoj7HSutolGe3dVLsFTHbDIq8Jp4+vpEPLmjipKleJj79HTP/vI3rP+7kpQ0j2E0SK1tDvLp1mKXtw3y8Z4TNvVHqHtrI65tG2NAe55aVozzwbQ+fq41c9eubsM06m7ZsDhfd+Ty1R16MzVbMwJ4NbHz8Orpe+A3WlJvNH3xGtreVxho/oi2N2aaT1QzOm1LApl9M5eo5pVw9u5yNF83istc7WLY3iK7rfN0fxOcSuOHbjazpjhCJOLBZA0iiiKGZkUwhcmyQ0Q1UAew2OGdaPn8+rpYVZ8/BKdnIycnSl0zhtSgMBjSsksRwVGIwluHaL/YwGFA5sjKXk8fl4caCnpKoKRqmqThGblE35Xkqd61r5qBSC6+d0sTMIjepiEwqLdDtz/L1QA/pjImWWAqPWebGhWV8sT/MsY0+JhbZSEtpFjZacVtNzCvz0uFPsW0wws9nlnJ4cRETcvJQ0jm0DOv4nBJ3H1JPNFrCcEynzGNwWmMuF0zPIyllue3LTg4qdvLccZPIM7kRTSkmlRn0xBK4HFns9jRvn9lEXMsyt8RLlcXN/tEkn+7zs7DKy/tnTuFnkydw56HVnD4+j+WtYe6eN4H9fTpvbAzywWlTePG0cThUK2QtDERkpldkea95CEnUObkxl6FYFo/uwKYI6BiU26y0dwu4HEnyc3QCCQ1dN2gZTeBQZJacPYVHT6ohx26ieTSBzypz7cpdXDttIoMheHnTMIsqvfz5yHrgwPp0XbEw7zcP8+yWXmJpg4jfRrFVIa3KNI+oRJM6kiYjSSK/mFnMuAITa4Y7CEUk3tndz+qBIC3+FKc15VFgcjAQ0rh3bTvnT86nL5Pggnf2c9uhlVgkmVPG5f3gzyjhB/4ZM+Z/sn379nH22WdTUFCA2WymvLyc888//2++TO3atYsTTzwRr9eLxWJhypQpvPTSS3+3r+7ubs4991zy8/Mxm800NTXx0EMPof/LrGOAzs5OBEHg/vvv56677qKqqgqz2cxXX30FwJIlS5gyZQpms5mqqioefPDB//C833nnHWbNmoXb7cZms1FdXc3FF1/8A7fOfy6VSnHTTTdRVVWFoiiUlJTwy1/+klAo9DfbVVZWctxxx7Fs2TKmTZuG1WqlsbGR559//h8eY9OmTZx11llUVlZitVqprKzk7LPPpqur6/ttXnzxRU4//XQAFi9ejCAICILAiy+++ENe7t/4R22fSqW47rrrmDJlCm63G5/Px5w5c/joo4/+bl+CIHDFFVfwwgsv0NDQgNVqZfr06axbtw7DMHjggQeoqqrC4XBwyCGH0Nra+nf7WLFiBYceeigulwubzcbcuXNZuXLlP309qVSKa6+9lsLCQqxWKwsXLmTr1q1/t93HH3/MnDlzsNlsOJ1ODj/8cNauXfv97998800EQeDRRx/9m9fddtttSJLE8uXL/6nz+eCDD5g0aRIWi4Xq6moeeeSRv/n9iy++iCAIf5cKuWrVKgRB+D5tbNGiRSxZsoSurq7v74t/W8f0P7Jo0aK/SX386/v1wQcf5I9//OP3fTFnzhzWrVv3T13PunXrmDt3LhaLheLiYm666Say2ex/uO1bb73FnDlzsNvtOBwOjjzyyP+wL5555hnq6+sxm82MGzeO119//e9SL//RZ82mTZs44YQT8Pl8WCwWpk6dyttvv/13xxocHOTyyy+ntLQURVGoqqrijjvuQFXVv9nuiSeeYPLkyTgcDpxOJ42Njdx8883/VBv9bzc2pvqv+VGnnmWMFOPzcxmNGYSTIhaLBlEzgaTGA2fUAHDKOzvRExL3rOpGkQSSms6KzhH2RaC1X8ZQs/xkcjlLQmECEZE8j53BtEpPRKe+xERfwIVhZKnymVB1GI0ZGKqB6MjSEU9RXmQhGjJT57NTm+dlxWAzu4dlaotthOICDlnmyFo3z29UCcoah0+Uyeg6qYxAOmMiQozDG4s4eXwuqphlS3+MzmCKyz/dB4KGZI5x+exyzn+99UDgR8iSlWPMzythdpmHRFrnmk/bqZIzLOnoY38wQW2umd3+LKoJJEmguT9DRY6ZeFan0CvRGspww6wSzp5YwLaBGPv9CU5symFpS5COPo2zpuRwXJOXVV0hEjGBm5Z0sDk6ikeyomUkqrwWXt42TI5Tx+JIo8gihzQMoGUM9o1IXDalgr2jSW5a0cKoX+buL3toTUWYU+Xll7NKeWlXHy9uH2SSz8YnW9Kc2RSl3G0mEVUo9rjZ6xjAbrbQ5oeZdSa+6g3Q5HTy9t5hVJLsGk4yMTcPs8lgVVeQPMVMJqsh2FR8HolgWMfnsBNIqESH3cgVEts6LXgsZlz2EKUeEbei0xGKk6N4kbJWNv20ifebB3luWy+KbHBybQnzyr1c+kYbbrfB3nCc5T+bQpXXiq4bbB+I44+mmG3qQ0xt4ZcXXs4pp5zCU9+WMumQE5l+0FzuuOU8JhY7CCSy7BzMZV0gxTiTiTtW7+fb7hCjQZE5xR7G59u5el4JNbkWCh0m2kNJ7v62nSKnzKnTnby9PkyeR6RlZ5yIX6KpTMFpk3jmsHqCSY13dg4T0dIsbwngNMscOcmJP5kiaxhIooAdC31RK1rKxHETZDa2akTiBrct7eaM6T7q8mWeOb6GX7zXS8LIEkilmfjIZhryzQiSTiph4zt/lFBqlMbKJJKuYahmvuvLcvFsL5GEzi/ea8cfVcl1SazsCOHMy6XQLOK3zMUy5XCmlzqZV+7ktm0uWr5cyUBzEbaiCmL7N2GuGs+0oouxm2WOqPNx6xedhJJZXjm3FptJpj+SpXdAw12iEAj5EBwhyuxu1jSX4LOIFLnMtAV0TqrLIRhJ0NanMr3SzuIndnHsRA+H1rjZ0pugxO2gWY1is4BdNJFSsxTILtoGDZ45fiJf7A3zbnsfmj9DdzrOhAKF3UNm5lWoqGKaYFbHZhL4qHmIJW0DHFldyNtTG3h4235GgjItXTYmVQrkeQKYBSsXvb+PPxxZzaE1HhryrLy9b4Ad/hGWtA7wTe8wSy5s4pXd/Ty8tZVYWGGGxcl9h9Vx7cftpLQsZ72/i0NqIR3TcZnMPPhtHxPzHAyPGDSUybyya5Db5leyvxvycm04XXFUXUASJLrCGX73zX46QmkumVJMQEthQcIlmbjji27q8i0c0+Bj73Cc1tE0fzm2lu5Qmj+dUM0LG4Ypc1mxmESaSqys6cpiN7vY2Z0GcxKLZEMT4KFjqqnxWSn1mPnl0j10DWkogkKJ1cNQNgg2G06bhce3dnODuZocm4kip5n13RF+92UHRW6Fhw6ZyEWf7uX5EyfwwfYghmEgCAJD8TQ/+XAbjx01gWe2dLGkfZCzxhVz3uRydEHj2a291OerxEN2WjMJRncN8/7ZE+gZzbI3kmZWhcDGNo1nT6vnd192oCZEOgNprDaDuCywYiCCV1HI8YrcuWY/XVGdKz8c+cGfUT/kYOTHMqgZ83+n7du3M2/ePHJzc/n9739PXV0dAwMDfPzxx2QyGcxmM83NzRx88MHk5+fzyCOPkJOTw6uvvsqFF17I0NAQv/nNbwAYGRnh4IMPJpPJcOedd1JZWcmnn37K9ddfT1tb29+ltDzyyCPU19fz4IMP4nK5qKurY+XKlZx44onMmTOHN998E03TuP/++xkaGvqb165du5YzzzyTM888k9tvvx2LxUJXVxdffvnl32z377/I/WckSfr+C/SiRYv+YS0kwzA46aSTWLlyJTfddBPz589nx44d3Hbbbaxdu5a1a9diNv9r7cPt27dz3XXXceONN1JQUMCzzz7LJZdcQm1tLQsWLPhPj9PZ2UlDQwNnnXUWPp+PgYEBnnjiCWbMmMGePXvIzc3l2GOP5Z577uHmm2/mscceY9q0aQDU1BwYT//QAaN/pu3T6TSBQIDrr7+ekpISMpkMK1as4JRTTuGFF17g/PPP/5t9fvrpp2zdupV7770XQRC44YYbOPbYY7ngggtob2/n0UcfJRwOc+2113Lqqaeybdu27/vr1Vdf5fzzz+fEE0/kpZdewmQy8dRTT3HkkUfy+eefc+ihh/7Da7r55puZNm0azz77LOFwmNtvv51FixaxdetWqqurgQMpXOeccw5HHHEEb7zxBul0mvvvv59FixaxcuVK5s2bx1lnncXXX3/Nddddx+zZs5k+fTpffvkld911FzfffDOHH374PzyXbdu2cfXVV3P77bdTWFjIa6+9xlVXXUUmk+H666//r3QVjz/+OJdddhltbW188MEH/6XX/nuPPfYYjY2N/PnPfwbgd7/7HccccwwdHR243e7/9HV79uzh0EMPpbKykhdffBGbzcbjjz/O66+//nfb3nPPPdxyyy1cdNFF3HLLLWQyGR544AHmz5/Phg0bvp/p9PTTT3P55Zdz6qmn8qc//YlwOMwdd9zxn84U+o8+a7766iuOOuooZs2axZNPPonb7ebNN9/kzDPPJJFIcOGFFwIHgkQzZ85EFEVuvfVWampqWLt2LXfddRednZ288MILwIEg4S9+8QuuvPJKHnzwQURRpLW1lT179vwftPr/Hj/UuOrHMqb6URezPvm1ZRgWO6hmusM6upDGJApcNqGGS2cWYxgGP/+wheahFJI5S1OpRseoQlsoxVsnj+fmZV1cN7+UxTUeXto0xLMbB5ENkXNm+Hh8eyfxmEx5jozLnsVmzmISdfa2OxnWU9TngSRBNiNjl8ycNbGA277qZEKxRl9UQDZM1HjsPHtCI63+JNcubSUuRMlziVgkkb1DGk6TiWQaJN3EjDI7G4ZHCIdMFOZIqIZBoROi2SRPHHkQt3/eQ3sghccKdx1RzS/f6eLocR6+64pwaLWTb/Z1EpWc5DvM3Ly4jN8u72JWmQuXFf64eoAih4zFLNKTTHD97Ap2dKWxWuDD1mEWV3p48vgG9g0l6Y9k2BOK8u42P7ceUsFfVg+wOxihOtdMg8/BL2aU8It32qkrVrj36Creb9vFdn8/suHgoIJCZhd7+aBrNTu7yvimLcs4j4uuWJwb5pWTVgUOqfbgMkuc+O5mrKICusifj6pjQoGDREbDpkhc9kEzzYMpbjyklGWdQyzfH6HGY6UjlOHOw0uo8Th5cdsga3rC2M3gNB8IAHptWQRDR4hkkTx22kJZ8swWzEqS2pJeyl0uYtog4Wg5XX4zW3pF6iwemgfSbLx2MsOJNLoOGzrjbOmN8+CJlTy/vY8Cu8LCci92k8Su3Xs57NbnmDJ5OnmpfmqUCPPnz+fQQw9FEARe3TzMM+uG+Pyy8VhMIoZhMPPxrRQ5FUIJFbNJJNebIaT+P+y9d5Qcxb23/3SYnpw356hVjkhCSESRcxI5GGyCsU0ymGgwmGCDEwYDBhOMyRlEFkkEgSSUpdUGbc47OznPdPj9sZZ8ZTDmvpf3d9972eecPXu2t7qmuqa6q/pT35CiyeflnJlVzCoanxhH4zne6wzzwOZe7KKCTZLJZCCkZ7EgsmMsQ22ZwIVzqzi0tpCr3uyiymvGrICi6Nh1C7U+K/s3ujn/hfbx7HrhNJfNsbNiWKc3msVpk8lmDZwmmdlFLlZ0BSn05tBNcfpDNg6s8TOryMnFr3ZS7bHgtkhcsLCMaD7DxsEUn3Yn0VMihf48w1qCj07fmzfbQoTSKufMK+GSN1tZOxDDaddxm01E0yotvXDx3iWo6KwdDZLKGVhEmUaLlU/WrCHQvo5Vf7qeH197E1XFBVTN3Y9UUQUf9I+wfNmeyKJIXtP5uDvGi9tHeaNjjOlFFkRZZdtojhkFTjpGs/glK3vVO3l88zBHTvZz7twSRhN5nts8wkcdCRqKLISzafatjBMQLERykEwr5HMiN+xTx351XgzD4P3uIHd+1o6hKQimDBW+NA5ZoCNkxSrKhFMCuiphsagIgsgJTSXct3oYqyySz4hYLToOdw4jLzEakTiqyc8zm8Y4Z24JD6wdZN+pJvqSSUyajaW1hQTDGkdP83HvF4OsGYjzwLH1bApECCTzvNMep9Qhsqk/y6wag2jcRCI57v7qdUkoisAdSxt4avMInw+P0lSi0hMxcAgOehJJtKyFM6eXUOU38df1Aa7Zp5oV2yO83BxElHUKXSLFegaro4Dbj6jFbR3fezAMA1U3yGkGF7zcynBcZSyRp7oY2kZUyrwKDx89mWqPlTVDAS54qRunHWYVOjhuaiHXfNCCW7CTFtIU2hUkw6BvTOamAyu55q0e0GFSiQWnZOLM+QXct76fO5ZOotJl2fWcf6Z5iLdawtxzRBOqofOTd7Zx6tRyDmso5K5PBjh2mpeRhMYFL7Zxw4GVHDetCFXTOe/VZqJGjFTCxIKSQm4+uIa2QJpT/tZCValA23AOsyRS5TNx0GQXOd3grdYQJHKsuGjxtxrM+iW+3WDWx/G/P/DiBP8zWbp0KevXr6etrY3Cwq+2zjv11FN56aWXaG9vp7Kyctfxww8/nJUrVzI4OIjb7eaaa67hV7/6FatXr2bBggW7yl100UXcf//9tLS0MGnSJLq7u6mtraW+vp7t27djMv0jQ+aee+5JX18fHR0dWCzjz5V4PE5NTQ2hUGiXgPPb3/6WK664gkgk8rUvqf/OemInjzzyyK4Xw6/in4NZv/322xx66KHccccdXHnllbvKPfvss5x88sk88MADnHfeecC4RdHIyAitra1UVVUB4xYs5eXlLFu2jPvvvx/4ZsGsNU0jk8lQXFzMbbfdxsUXXwzA888/z7Jly/6PAmHv5JsGs/6mff/P7TYMgwsvvJD169ezfv36Xf8TBIGSkhJ27NiB3T7+5H3llVc49thjmT17NuvXr9/1Pd51111ceumlbN68mRkzZpBKpaisrGTx4sW8+uqru+rUdZ25c+diNptZvXr1v73muXPn8sUXX+z6nJ6eHhobGzn77LN58MEH0XWdyspK/H4/GzduRBTHHUMSiQT19fU0NDTw6aefAuMi2aJFi4hEIrz++uvsv//+TJ48mffeew9Jkr62n2pqaujt7WXDhg3MmjVr1/GDDz6Y1atXMzQ0hM1m+5eBoL9qDB155JFs3br1Gwdi/+dg1jvv1xkzZrBhw4Zd17B27VoWLFjAU089xSmnnPIv6zvllFN49dVX6erqori4GBgfD9OnT6elpWXXNfT19VFXV8cPf/jD3SyoEokEjY2N7LPPPjzzzDPouk55eTnV1dW7WTT19vbS0NBAWVnZrmv9umfNlClTsFqtrFmzBln+h/3GUUcdxbp16+jv70cURS688EKeeOIJtm3btuv+hX/cB9u2bWPq1Kn85Cc/4fHHHyccDn+jfv6u8G2vq74ra6rvtOuZJMiMJPN0xVO4zAYFNhNa0sqjm4bYMBzjVyv72DaWwOnJUlmc4qJZU+kfNLh0dhU/ebmTUDqP/Pdn7dl7FHPdfpXcc0Idv13bh0uyUOw0IUg6imRgNeXYPCSw18wBGgshpRoImgkRmbAe47FtnTSUpOkaljm+rgKrLBHXUoRSKsf8rZmeWIZKlwVNg0Aij01SiOSzeK0yiiDycWscj2QnL2nYzFlcZoGxVJrJShklNjOiIGARJFoHVK56o5srDyhFtGicscDPg5uH2KPCwcIqNxfuVcKpj7czNJZHNuf4W0svDV4LmgQmE6ALPLq5n5Zwgrd6R6lym1lc5UEQBHx2mee3j/DstlHKvBK3rOwhTR7JbKBpAr85tJ54ViOr69gkGa/VxLLGqQSiXj5qN5PRkvxp01pWNhfSP6ZgMRQyuoZZFHmhdYC/bhzm5g+6+cWb/bgNB+GQwJJSL6e/2MxnvVFsyviXcdmiSk6aXkQol2HDYIpKq41ATOP6JdU8vzbOWS810xWLkzVyHFTn47FjZqIIEqG4Qixroq5Ax2lNUOrOUubLkDZUXJILixwjnLKRySm0BKDEsGM2ayw/fzLLdwxz+YrtHPNQCys7opwyZ9y9bFmdi9SGlVx+3S+44bUNHHTRbRSX1eCtbuLx23/KTTfdxIEHHrhrUXDGvCJW/mgGFtP4rSkIAtNLbWiaQTIFWTFHPAuaasGq23hw7QjT/7SaRE7lkbWjPPjZCIfU+zhjVjFHT/azLZDAnDPhEsxMcjp57ti5nDy1FLdV5v7jG7l2/yp+uriKn8yvYdtImrdbxyeWzcEYZR4Tfz6ukUXVLkySSEzN0xoJE8nliebyvNMRAiVDTheIJG3oeRPrh+P8ZXMP8yen8Hl1NAwueaGbQ2qLKbfZkHSQTKCqCmrMzvQ7NzCWULHK49c7HFMxGzKjozLRpIhNVigty3D8ND/zy9zsU1ZEJGmgqiJjmoGzfjZ1h5/D8a/2EF30PbqcjZQT4ghPBteLd/HYo4/yyxVbWdkbZnGNk0Mn+WjwK8T1OA5ZRtBFLLYoDUUyPck0G/qTVFis7FvjZvtYikMmeTFsaaY3ZBHlLLOqsmiGgdMaw2/TCCQ0HGaRK9/u4NzXNvBCyxC3vjvAgRUVDIUNyp0S4ZTAUNyCzQSDYzI2WSaYT1BmdfKbA6aweihETFUxqyY0FUy2LNmcRG9Mx2kX2Bge4+ZDKhhKZrnl4FoOqajgsJoKNvfkWdkV4cW2Ue78YIAqj5kGn4XrVrYxGtP56cJ6Hjl6CmNJAYtVYDQhYLXlMIsSoi2F0yoyFM9y7fsdzChxcFhdCdsCObw2lUhMQotb8Ihmnto0RseISmtvjp+92Um1z8ylS8q5fO8yVCWC3QxbRpMc8WAzXaE0AOv7kyz9UzND8QyX71NGnc9ChUehM5TD7cnwvTk+qj1W2kbT2GSZosIIXkWkLRbjzY5R7jl0KjFi5DMKyyaXsKDGQlN5ljvWtBHRchS6TJw5uwiTJLKiNcYPZpdz3HMbiWT+YUJ+8tRS5hd7Of7RFj7ojHLNogauX7mDJzaMsHxLmGc2hChyypS7zdy2YohAIkdPJMP64RSDUfC6BPye8bqcZpFVP57JL5fWI1hUjp3mpz+T4vGtA8wqdPLAUVOp91r/b05XE0zwv5ZUKsXKlSs56aST/qVIBPD++++zdOnS3UQiGBdPUqnULteb999/n6lTp+4mEu0sZxjGl6x9jj766N1e3JLJJGvXruX444/fJRIBOJ1OjjrqqN3OnT9/PgAnnXQSzz77LAMDA1/Z9rVr136jn3+u/9+x81r+WVxatmwZdrv9S25Ps2fP3u0l02KxMGnSpN1cyL6KRCLBVVddRUNDA7IsI8syDoeDZDLJ9u3b/1Nt/rb4pn3/3HPPsXjxYhwOB7IsYzKZeOihh76y3fvvv/8ukQjGX+IBDjvssN3Evp3Hd/bbqlWrCIVCnH322aiquutH13UOPfRQ1q5dSzKZ/LfXdNppp+32OdXV1ey11167XJRaW1sZHBzkzDPP3CUSATgcDk444QQ+//xzUqkUAGazmWeffZZgMMjcuXMxDIOnnnrq34pEO5k2bdpuItHO9sVisd0Etv+/OeKII3a7hpkzZwL82zH8wQcfsHTp0l0iEYxb8J188sm7lXv77bdRVZWzzjprt+/SYrGw77777hKuWltbGR4e5qSTTtrt/KqqKhYvXvyVbfjnZ82OHTtoaWnh9NNPB9jt8w4//HCGhoZobW0Fxq3d9t9/f8rKynYrd9hhhwGwcuVKABYsWEAkEuHUU0/llVdeYWxs7Gv7ZYIJvo7vtOtZZzjN9CovO4ISKiDnzZS5TdQUy3QGsnzSHUE3J2gNmHjvsPn8bHkPgiFglWWW1LoI51TCmTxXrWjnlgPqWVjr5AfLm/FaJHTRwG01UBSN9jEds+HkqFlDrOp0UuzIk8jYiefHRR0FaA0Y3LT3VIwGgd5YFqsg88DhU0CH6UVWBpN5+kcNSgplupMqkqbjt1nRRA2/xyBlytAehT1rJLKaTjqjExtx8koyQjDfTE9Qx2eV8RVCVyLNKzsCNI8lEXISjx7fRIWcYlPCxPnL26hw2DA7U2wJx1HjNnq1DHuUO9k+lKKhUGEsk8ds09AiBjnyHFLvBeCil3fQH81Q4pcYyI0xp6iC9X0pGm1OLtm7nM5ghrOf2MFdx9dwUJOXrKpz3oudxLMOook0gaiNT1qKOK6hiLfbIlT7ZOI5ncllMJSOU+L2si02SoNUQjRs4LObWNkVQU9KbBlIs6hqfEfJZBKYVWnDJAko+RAWUSSc13ls7SiD8RwNVTI9wTx1Dhf7VRWwcSSBW7SyfiDJ/CkaopQjk7diwkZPEHQBYmkz9UUZPHIBPWqeWMpKY7mGLoX40Tsp7JJCezhNXaGTQLiTNa+9w45J8/j9E8vpTuWZtfdSmgfBe8yl7FPrYUrhN9ezG/1WukIZnjpjEn9aO8C2kSR5DDb0pVHJ4XBkGEvnqPKZ0ASD4VSMNEE6IiFMDjvHziql3GFjIJXhoQ1DlDnNnDl7fKK8d/UAr7YEee2MGVx9QDmfD0aY/sDHmDQznfEoP3x3EE9QRPdbkWQVQcujiXkq/VYuW1DNya98gdViJZMVyCUFcBiYBIlQzMkDh0/mrfYwbeE+XGaR/ngGyWQgW/MoikSxJBNJ55hf4WA4lePyt9qJ5nMU+FTy5gR5LEiCDXPewWFPb2BWkYNYysDImkjmIRLNICBSYrOQyWRZtrCQSQUNLKkeHwf3338/b731FhWKxu233MQFqSyLDjqYJbVz+Ou6HKUNORbXqYRyOUyCgN8hcex0HxvGQvyttZNnj51PTjOoc3mpqShGNmd5clsvBWaJTzZ7qawwKPdmuXheBc82B/i4L4ysR+iPZnmvI4IiQ1JV8ZjNDMY1Skxefn1IFfd+0YeesjK10MEFL++gujjB4jqN5h6JmhKJTf0Ss6tMBM1h9q0uYp8aJ798exBVg32q3Ty0ZoQT53lxe1Xmlju4ZFEFxXaFao8FXTdAMDjjue3sedcm5jSJGIZGQ6FAe1DFIzkZTuc4pLaClJwjnTNoCaR4ZOMgy6YUE4vYCUsiqqJSIpvZHkmwpMjPZXuXsaTajSLDtW/0sm+jk6v2q+SQBjfXPb8FWTY4cY6Pw/+6lRdOm8q8Sgc/WlLC5uEkd2/azvF1NbSPpLEaCn7JzPMdrZhN8PjqGGpG4uBZddz/cZDCAg2/TWJ+qY/7DptFTtP56bvbOXNaJRZLiEqTToHNybWLajn9he08u2wqv/10gOXbVU6bWrJLcNzJRYtLqfaaieTznPdyOxfMqmJKkY3RWJ6324N8FBglaQi47RKvbB/jgQ2D/O24Kdy6qoW2UIqO8CCNfjvvtcZwKhJX7lPBydOKuH7vGpaNFXLHqg6ue6ufJ06azM0HVXP/f31ammCC7xzhcBhN06ioqPjacsFgkNLS0i8dLysr2/X/nb+/yhLln8vt5J/rDIfHY9uVlJR8qY5/PrbPPvvw8ssv88c//nFXPKVp06Zx3XXXceqpp+4qN3v27K+9tp1805f4nQSDQWRZ/pLAttM65p+v1e/3f6kOs9lMOp3+2s857bTTeO+99/j5z3/O/PnzcblcCILA4Ycf/m/P/b/FN+n7F198kZNOOolly5Zx5ZVXUlJSgizL3HfffV8Zm8nn8+32t6IoX3s8k8kA7HJJ/LrU9qFQaDcR6qv4V2Nu06ZNwD/G7r+6D3RdJxwOY7PZAGhoaGDvvffm9ddf54c//OFXnvefbct/bMd/B/88hne6Vv67cRgMBr/RPb3zu9wpRP4zOwW6nX3wH4WnnRQXF9PV1fWl4//c/zs/64orrviX7nw7hZ6RkRGWL1++m9D0VeXOPPNMVFXlwQcf5IQTTkDXdebPn88tt9zyjVwOJ5jgP/KdForq/FDgTuCy2egcthGKQVxJsMRexDVvdaOa8zQVWLjhkHrKXWacTp3DfB7Oml/E8pYA2wJJfraiA587xQUvaIzqKWRTmsYikdGEwnAyj5wScIoKR8xtZutAObVeE2YlS1bL4hREwtk06aiHm/ap4ORpxfxpbS+vtYSIJDVyeZ1T/tqOZslS7pUYzaYJZlKUWr2MZbL4HDqGIdIZzFPuVJClNMm8hoKd7oCOx2rQUJwioOVoKvFx95GNrO6PceUbXfSGExSbzGwKpbjn4yFu3ddFNJ3HL5pR7SFqCywMJXRcDrhjn0bebYnQ5UjSOpZln2oPnZE0UwsVmkfy3PhhN/ceMYlbDq7mR6/twCFLPHLkXlglmTOfbGNbJM7CCifPbR/htmMrOHCSh4FolnK3mWKrQiSmsuJ7M6n2WDi8PsEPXttOsdtE60iW6eUy0XwWEQs5XefPB8/h8XUBLlxSxN2rBhhOwKkL/Jy/aPxBr+sGl7/ewWmzirjj4z4WVDm4/+gmLnu9nfWjYbyFkFNN3HvEFBRRYla5nWvf72DZjAI6QglcVoNQEmSLCVHMsl91BTk9z2cDYUaSlaQyNqzmDI1uhVBYJG2SKDSbSIa7qB5bT1XjHnS88QxFpyxDKSgnPedY9izREVQToyGVq5eWYggGL62L0FBo4ZDJ3i+Ny19/1klfLM09h0wDYFKBjb9uGMFhETmw2o8bhfcHwuzd4OSMeT6q3U4A+l15MlmDjYN5FEGiJ+LigMkQTueIJsHtFGnwWYhndObds55XzpjGKdMLOW5yITN/u4Gr9q9AFVQUw0Kt2ckh5V7OXVjI6Ogod3yWomUshShb0TMCQk4mkdOIpCS+N6OIpTVe0lmBaC5PNKcyGM9R6jBTbLawd1EBkiQykMzgtcucN7+EsXSSzcMJUOJc+uFGPKKTWMbgsClu1vYlEVWIkqKpRMVqNhMbUuiJ5ClQFMy6SFrIY1fGMwX+/rhq3u+I8tAXQxw9y4XdDLOKXXi9XjxzD+G6Fd289pvbefq1Fdz19mqsB3nQVj7N1tjBLFwwlUA0gkNRWTrJzXkLS4FSBuNpLLLEVW90oeoG1+9dzrPbhsjmJHKyytRJcYZTGmUOB7/8bBvT3X5MWTO/OrCRd6vC/GnVMAWyhf5hCZtk4PWnaAmnuO2jHSTIMb1GYkwN8udj6/nrmiDrwyNMqk4iChpzbRJjIQkPDt5tD7OpP01/LMuSegf7NrrZMZbl+bVRnjh6JnevH+DhNSNctU8ld306wDObAhw/rYCYmqOuCjaPJpjssdI8aNBQKhKOatR5LdxyWDXJnMbpT7VCXkSSDLoTSQ6s8/NmzyhNPpnhkEq52cZvj65FlkQW1Y6Ps3MXFvKn1YNsHYszmMzwy/mFvN0n8ElXgudOnUq1d3wH/uS541Z1FknkttWtCFiZXqURUaMMj3l4bEsfqlnGplh5elMChztLg1/mo/5RLKslhgIytx5WzbXzm7j2w05MJitTi2EkbvDT97ZzxoxibCaJB46ZxHXvt/PajjFWD8R54rhpyNI/BKMjpo4v8FNZnb98PsJf5FEuPaCY3308gECOArMZQZAIJFXOml3EbZ92sCOkYrOqFFrNXPfBDh46Yio3v9vLmS83E83lUD/q4JTJpcTiMr8+pJwfvtJOlfmbxSD5zzARo2iC7wI+nw9Jkujv7//acn6/n6GhoS8dHxwcz7xaUFDwnyq3k392C/N6vQiCwPDw8Jfq+KpjxxxzDMcccwzZbJbPP/+c22+/ndNOO42amhoWLVoE8C9f7v6Zf+d69s/4/X5UVSUQCOwmFhmGwfDw8L982f3PEI1Gee2117jxxhu5+uqrdx3fGf/nv5N/1/ePP/44tbW1PPPMM7t9z992tqmdY+ruu+9mzz33/MoyXyUo/DP/asztFEd2/v5X41sURbzef6wr//KXv/D666+zYMEC7rnnHk4++WQWLlz47y/oa9ryH9ux0+Lun/vz/0UrFr/f/43u6Z3f5fPPP091dfXX1gd8KW7ZV9W5k39+1uz8rGuuuYbjjz/+K89pamraVXbmzJnceuutX1lupxAOcM4553DOOeeQTCb56KOPuPHGGznyyCNpa2v72mv6LjARo+g/x3dbKPIa6EKerlCelJbF6TQxks7z2LYhJlXYiKl5UgmJ3qDKH8b62Tqaximo9ETSXP1eJ88tm87G4TC9oxLbE2mK/AY+m04wlafAlSKZdWK1hWkqUhkKVwISBa4E/UEXm7tlTGaDErMfwZ7mhdYBeiIZXmwbodAp88wpMymwm2golxnKpxnLZalwiwwmdXQxTlORDV3IksqC3wHtgxIzK+wo5iTZfJwiuxusQaySm2ReYEdqjGe3uvjd5/1UeWUaSySOa6jkmld72D6aZnlLnj+3JSnyq4iY6Y8YCLqDiBHnz2sGGA4ayKKJJ49r5K/beplWbOLWfafw07c6aQmkuGt1H5ctquJPRzUwudC262F46xHV6LrBj17uAIvG1XtVcfoTbYSzeR4/bRK/OrwGq0ncVd6uSIiSht1s0FAZxaZ4ELQ8omYlnha4/r1OYjmNtk8T9CZSHNpYwLlzxxX6HYE0N77fTYVb4fGWXvasd3D2jBKe3TpMSyRKiS9HLGEloWnc9E4ved3g2TOn8MM9yjhz+TpmVcls7ZRo8ltwKQ7Om17L882jrOyJUOAWyOVsGGKeSMyCnjWzxA878iqbXnuAQHcPpXsehZGq56UnnuCL/gTPbA7w0AmTeKM1wJqhALMawnw+mieQgP6Mwptdo2yLRvikO8EeZU6u3bsGgDOml+G3/mNReVijj1kldkySyFHTfRw13ceah2I0h4e4eU0L+5ZOYiypsn0szYMnNnDH6k4CyTw+q5kl5VYafBYWlozv1P5ubTOd0SQfnjeH4x5qYVqZlcNnuvnNCaW8vCFCXziHQzOz5wwLhknlDx8PYFUjRDQZp13kiCo/XaEMomxw20e9/PGAKbzbG+S17V20jWR459wZ7Of/hwvO8bP8HD9rfDKNqSrZjMFlL3ZTXaJQYlMw0m4u37OcRVVOEjmdao+Fs4Y2U11g0BnNMRiX2LPMjc+m8/a2LEfP9lDQaOKTwQiBiIaka9z36Qg/WVLKomonH/UHeWzzED3RLl5cNpvDmnwUWE1c+8Ygy+bvzVP77MfzLUNc+/0TeffVl/jeUfU8397NpykHXdg5/q/N7Fvj4Qd7ji/obj2khm0j4+biz24KgWqnPxaipNyCLodxmWVqyZMizAX7ePHZTHwc6MHjBt0coSRXyIZelemSl3AwSXWDhp7VGIqBpuc4fflGlpYXs6NXpNThICmEkA0bNnuOVE7AbVcZGBNwWQRG1TgnPreOZNSMVRY5/plmfjCnlCe2Bbk61s2UMitus8yBDR7MssjeDXZu/bQVsy1FTbGOrKg43QY2TMSyKje+0UsqDfVeG73DWayk6EtlKLPYcZh17DYdsyQRy2rsNIBb1RVjSqGdGreZlJggTZoXWrMcNLMe35CJa5b38uhpDdj/7gbaHkzypw1tDA8pHDQ7xVhGRBGt2Kw6HUGDBZU6oVQKh9mEGRdrupNMqVD5sDdIImrmkpc0ztyjiGRUpL5cpH0shyKacOgKT20I8LeNIxzfWMwJ00rojMZBzHPF+9v5/YFTv7QgO29BKe0jWQxRx+80oZsMCg0r8XyOWEDg1dwYC5ry5AQdjyJR4XYymMygmODn73cwltYJhvJYzQLru9NM96QocSp83h/llTOn8dTnXTz0bU1Of0cAvmFok39f13cuGuEE/1PYmd3pueee49Zbb/2SkLOTpUuX8tJLLzE4OLjbS9Fjjz2GzWbb9YK+dOlSbr/9dtavX78roPLOcoIgsP/++39te+x2OwsWLODFF1/kzjvv3C1G0fLly//leWazmX333RePx8Pbb7/Nhg0bdglFa9eu/UZ9UVtb+43K7WTp0qXccccdPP7441x22WW7jr/wwgskk8lvFED53yEIAoZh7BYUG8ZFCE3Tdjv2Ta07vm3+Vd8LgoCiKLvNB8PDw1+Z9ey/wuLFi/F4PDQ3N/PjH//4/7iep556issvv3y3GEWrVq3aFXS7qamJ8vJynnzySa644opd5ZLJJC+88MKuTGgAW7Zs4eKLL+ass87iwQcfZK+99uLkk09mw4YNu4lJ/4pt27axadOm3dzPnnzySZxO5677aqfl3ubNm3cJGsBucZp28k0s1/5vsv/++/Pqq68yMjKyW4yiZ555ZrdyhxxyCLIs09HRwQknnPAv62tqaqKkpIRnn32Wyy+/fNfx3t5eVq1atdsz6uvqaGxsZNOmTdx2221fW/bII4/kjTfeoL6+/ht9fzD+LDvssMPI5XIce+yxbNu2bUIo4ttZV31X1lTfaaFIVIYZSzQSy8iYzUlMokidyUMklyej6rgNO9U+B4VOiT+/P0qlx8Ijy5pwWyQ+OHs2pz3XQlLVKfCYSGk6gZRKLG+itjBHIJVDEHNM9iukcjKjKYVJxVlaxySa+wQqfDIDiTyiJUOx1UxfUOSFvjDlZSbuPnQapU6Fy5Z30B7I4rDJ5PQc20ZzTC50MxqHoYhAqUcmJ0TJZz14FYVYRqXSqbB5QAM9S5nFRTIHPqvA5gGZu9Z14rdaCacglbBy3uvbsKtWTp5TgM+Uot4v0zxsML1cQpAzBJM5HDjoC+Xxmc20hNLc9nEXSSNPk9eFx6LwqwNrOevV7TywYZCFJW72qt09oGBToY0HPhvm8CYfdjsc8/RWJrvtODw6ugE2RWLLUJIZpeNvovU+Gz9e4GdF5whitJDOmEaZy81QOk6F1U1Lr0a5X8KOiadPnMHUQgcAKzuiXPJyF/Or7LQOpLnl6HpGUmnuXL8Or8mGxRFE1ksYTSeZWykgCDE6hjw88sUwP9mrHDHmZFSFqBDDa5HZNBbiZx0GbqtALChRX6wTTOfI5mwI0QSbH7meLrdM47JzqDrqHLwZEUU3kcmrHPXINiRRICNl+PnKKDZFI50TiMb9JATIGHl8NpHtY0m2R6LYTQYd0RxQA0C58x8xEd7aHuYnz3fiKNQ5d04pP1k4LvhUOCyMxHWqC8cXBLOL3RzRUMoJf21lWqUJURQwS7B6KMiG0Sh3fN5Bb9DMRfMquHvpFARBYH69nVAmy68/6uaARjszKuyUexXe6wnzQWeEOreN2UUOTDmJ/Ro8qDokkgZWu4FbMVFgM/FWS4RzF5aSyetc90YvPuu/3jXVDQ23wyAnpRjKJjlych0WUebdHRFmlzqo9ljIqjrhfIoar85ch8gbWyw4EJlV4mZyaZTDmrzMKXVxPmWc9UwrVmuCNaPDvNQOh9UXcc7MSpjJbpli6vxmTp1bwG9W9SJIBol8ns2mYpT9fsRLQw4OmVnOpnsfIt7h5raf38hLm8cQhPFFhCwJzCpzEEjkuemAal7fMcKGrhBDMQlNLyKZEih16gRTAo+uTxNNdfFJb4Y9K0z0p3MIokpFmcqkAgvJvEbfqIbHZiY4lsdumBDI0psJUlok0DlkxWEpICYkaSpUyAphbGIRMSNHXYFMXtexKAJhIYfDqiBqed7dEUFQNMJqjhKXC7fL4Jxn27n4gALmFLvxmK1EUhp+p8iOsEqdS6Y/Fuf7z7UxGM1RWyDTn4/hc1qJxQUsusLNB9ewZ5WbP34ywCNrRrn4jXYu2bOCxgIrP3u3g9FRncMXCMRjGjUuB6dN9/LrTf08ctQsyh0WfvxCJ7cdUU2t38LbnWEiOYE9p2qsHtDZozJNJOkho6osLHcyEI9S4MjhMPkYjuRxyyaiCZnBIOzVYOAWJP6wcpAnT2vk0pUbiGQl9qrS6B0Ej8nEwVNdPNcywAsbg+wz1cbWSIhIOsudK/v52X6VXxqDvzqiGkEYf/HZNJzg7R1B0gmJinKVREThrW0as2tSWIUC4vkUHouER7QyEISMbuC3irhtIs0jSa5/p5srlxbzRHM/M8qtHDvNz/f/k3PQBBNMMM7vfvc7lixZwsKFC7n66qtpaGhgZGSEV199lT//+c84nU5uvPHGXXE6brjhBnw+H0888QSvv/46d9xxx66AxpdddhmPPfYYRxxxBDfffDPV1dW8/vrr3Hvvvfzwhz9k0qRJ/7Y9v/zlLzn00EM56KCD+OlPf4qmafz617/GbrfvZkVzww030N/fz9KlS6moqCASiXDXXXdhMpnYd999d5XbY489vv1OAw466CAOOeQQrrrqKmKxGIsXL96V9WzOnDmceeaZ/+XPcLlc7LPPPtx5550UFBRQU1PDypUreeihh/B4PLuVnT59OjCeDcrpdGKxWKitrf1Kl7f/Kt+k74888khefPFFLrroIk488UT6+vr45S9/SWlpKe3t7d9aWxwOB3fffTdnn302oVCIE088kaKiIgKBAJs2bSIQCHDffff923pGR0c57rjjOO+884hGo9x4441YLBauueYaYNzt6Y477uD000/nyCOP5IILLiCbzXLnnXcSiUT41a9+BYwLRyeddBK1tbXce++9KIrCs88+y9y5cznnnHN4+eWX/21bysrKOProo/nFL35BaWkpjz/+OCtWrODXv/71LjFq/vz5NDU1ccUVV6CqKl6vl5deeolPPvnkS/XNmDGDF198kfvuu4958+YhiuL/tfviq7j++ut59dVXOeCAA7jhhhuw2Wz86U9/+lLsqJqaGm6++Wauu+46Ojs7OfTQQ/F6vYyMjLBmzRrsdjs33XQToihy0003ccEFF3DiiSdy7rnnEolEuOmmmygtLd0thtTX8ec//5nDDjuMQw45hO9973uUl5cTCoXYvn0769ev57nnngPg5ptvZsWKFey1115cfPHFNDU1kclk6O7u5o033uD++++noqKC8847D6vVyuLFiyktLWV4eJjbb78dt9v9rVgYTvDd4jstFHUE3EQMg3pfHrARTEoE4wKFdguDUZ1AJo+oZnjoizRRI8/DBzfx6tYQZ+5RSJHDzE+XVPC7z3sI59NIOQXZDL1hDdWwUGRzoOUktvVZKfNpjCZymC1pUolizJY0FluWWU4neSNHKKni1h3sSGd5Zum0XUKB3yYjCAY7gjkqPBYEJUMwaVDsUBhNptnUK1Hh85FQVUp9BjZ7jtYxjWqXnd54BpMMFmS29xo0FMv0J1Vkk45oyGyJhDimsowVvWOsHg2yMZmk11CZWmZDE1QsuoXeHigvErAgM5rMUuo10R1U2bfOw8mTi7nmjW5G9CiyqCMlFaSveCim8xqnzCnglk86+XhDmDqLg5OmFnHGHoUIgsCfPhnipa1BXv/BVMx/jy9y8uQGlm/Q6IpGKfdIxDIqLgsIoo5uyVFX6OTwhkI+7IzuEor2qnGy+uKZ6IaBSRJ4ryfAgxt7KfeYULUMHrOCaKRZ0jSMSS/lsz4DjyHz9KYAI5ksFWU5VDHLLFFF1EXuPXger3UGeK55gMoqhdHOAda99jz2XJyac67m0Gt/jMlazGgqTzYrYVJNJLMGP1lSQiQJT2wcocCXpsxpoKExPORD0UTShoYuCRQ6JGK5HFpKQjIU8jbzrtTe/5FCl4RgU5lf5qLWMz4uIpk8U0tsuMMyt+0zvvuYV3V6Izk+/9Fsnmoe5LnmEfK6QO+YjUROozdm4v6DJyGJIs9vDTCWUClzKOi6iiIIqEKSobjIYDSPyxnDq8jUFSucu0cJo6MiRUVFrB+Jc/PHO9B0lblFXuYU+fjZfv/Y+X33omn/8l5LZlVSGRBTFmyqldFohqVVfiYX2eHvG76toykcZpHrFzfxh8/7sMpunjqhgh+90YaoixxUVcCc0n9kFnjs5CZ+/lEznZEEkWyea9/uRjREHju5abd+vPODQXojWQ6s8bNqdBQVgXxa4NjpXkySRNYznX0vvoGVvVEGRvt45+7rWPuEn4fv/i2i3YvTLPPhjii3vzvA55dM5yU5w0MdSSwmCd2AdfKPvmIAAQAASURBVDGdWqeFvJGhJ5Tlj/tP545VndQUldDoKcAsiVwwr5Kj/9KMqAkEcgmmVzg4Y0YZr3d1kxbjNJVK9I4JIBtoYSsBq4pZtCHIKZqKTAynktS6bawfzjC5wMJgRKNSdpFMGlQUyLTH4vRE7Vy+VyW/WdXPy62jNIdHGM5FqHc5CeYyVLrMbB8QKPMqNMdTeCWFrKhiVwRERUczUhTLVs57qos3zp/CpXtXUOezcs17HVz9dieNJSZ0U45ZU2BFh868EoXuQYHfRvv59VFzcZllJpcrnO8qZtnftvPQKfX8eH4FkWyKD/sH8Ts0hiIuEPIU28yMJjTuPmgOt3/ewo5IDq9Fxus0CKQylPst/GBWHYsqXbywKcjcChd3HTiTWz7qpj0Spro8iT1VQyolcvXeNYQSOr//YBiTXcJTKPPCxiCCDqVFAqdPK981HkRx/PdzWwK80x7BYhao8ypk1DxWTxIlbeHk+jqueWUQ0S7RVCIzFDIwCxL1His5KUPGyFPsFSkz21hU7uH+jd384bNujLFvf3tJkL5liyLt3xabYIL/FmbNmsWaNWu48cYbueaaa4jH45SUlHDAAQfsigfT1NTEqlWruPbaa/nRj35EOp1mypQpX3LXKiwsZNWqVVxzzTVcc801xGIx6urquOOOO3bb+f86DjroIF5++WWuv/56Tj75ZEpKSrjoootIp9PcdNNNu8otXLiQL774gquuuopAIIDH49mVinzatH89J35bCILAyy+/zC9+8QseeeSRXRZZZ555JrfddtuXrID+T3nyySe55JJL+NnPfoaqqixevJgVK1ZwxBFH7FautraWP/zhD9x1113st99+aJr2n3an+6Z8k74/55xzGB0d5f777+fhhx+mrq6Oq6++mv7+/t2+x2+DM844g6qqKu644w4uuOAC4vE4RUVFzJ49+xtf/2233cbatWs555xziMViLFiwgKeffpr6+vpdZU477TTsdju33347J598MpIkseeee/LBBx+w1157AXDhhRfS29vL2rVrd8VFqqur4y9/+QvLli3jD3/4A5deeunXtmX27Nmcc8453HjjjbS3t1NWVsbvfve73SzXJEli+fLl/PjHP+bCCy/EbDZzyimncM8993xpbFxyySVs27aNa6+9lmg0imEY/P+ZeHv69Om8++67/PSnP+Xss8/G6/Vy5plncsIJJ3D++efvVvaaa65h6tSp3HXXXTz11FNks1lKSkqYP38+F1544a5y559/PoIgcMcdd3DcccdRU1PD1VdfzSuvvEJvb+83atf+++/PmjVruPXWW7n00ksJh8P4/X6mTp26W6Ds0tJSvvjiC375y19y55130t/fj9PppLa2dpeYBbD33nvz6KOP8uyzzxIOhykoKGDJkiU89thjX5ss4LvCt7Wu+q6sqQTj/8+79P8RdqbIO/GvH2D1y0iSRn/AiiIo9AZUsGdp8ij0Dyv85aQGLnylnV8eVMX9n4wyHMuzpNbFjYdUsmowxG9X72BkVMbpD+Eym8hlnOQNFZMooBgWhtMZ0imDg2cm+LxfwG3RKbXZCaUNEhkJjyLT2iPx1nlTyaoGVX+P7/Hc5gC/XtmHYFERdYlYTmVKsQSyik4WPeegLZTGjplSj0wqZ+C254mpKUbjMtOLLCRyOk7BTtdYjpySoMbtIJnV8coWfr5/Lac/2UJtuURKSzPVkuGzqIDfZsJmMtE+qlJmtRBIaBRbLXSN5rEoAnabgKAJiILIsB5nSpGVnnCeI2oLuenAcdGiJZjg3nXd2AULG4eSjCTyFLkFIikdk8ngpRPnsL4/SWOBhd9/NMjBk13ct3aY5WdOQxQFVF3n3vUtvNvXi5wrYDStU+qCWFbAoRgMxTQmu738fP9qar1WusNpbni9j2KXic8DYc6eXcJpM4r59WedLK3xcfvH3WiaQF7UMUtgUXKMJgxKbDZawylObCrm4gWVnPfmFvSUzNICme8f0MRANMG+l91FZVkJgZSO3e+kevIMmoMpFlaaiGU0GosF4kE3m/vSeL06Z84txGJYuWftAAsbg5iUDKJo0N5ZzHDITF7SOG12IcdOK+DVtgBfDMSIZPO8sGz2bmP0d6t6kUWRC+eXccl7a7lgdiPlDhtnv/kRDqGQfav8/GiP8cwlf/x4kGc3juEwyfzu2Bo6oil+9VEPmbSA3ymR0w0+/P5srlneQySfY3s4yVGNBTQPpRlKpYiQxmnR2K+yiAUlfn78WitldjN7Vrm5dr8qTn/0M3B58NtM3LhfDTlNxWc147OOL94vebGL+VUOjpvp487PephUINHgs7OgtIDW0RRPrx9jfV+Sc/YqZCSax2GWOHVOIdsDKZ5dH2Qknuf+k+o5+6k2IlmV5mCCu4+ppzOcZlVvhI3BEFP8FgrMNu47cvqX7udETsWhjGveum6QzGk8sT7ASbML8NnGLZx++V4PXeE07aE0+9W6cNllXt8eptiuMBBLk5NzlDst9MRSXLmwng82bOePyxax6LhzGTGXsuy0s6gq9tE8ksQmhujOmxhK5ZlcaGIwkcAp2YhGFVJZKHKKjMV0Lt+/jMXVHg79y1bOXujj6KZCRmM6xR6J3348wHA0zzX7VbAhNMRLO3ooNfnZEkxTaXUQTKvUl2TIqpDISozFBar9kCdDNqtg6Ao7xlRmu72cvaePu9f2ks5IlLlMrOtPUu9XEC0JCp1ZKr1ZUlkTQwmDWMKGkXMwlE1SYDGTTUscM83FisEOnIKXrKGjZmV+d8gUJvnHF5dbhhNc+XonUT1PuUekLZimyq9y2uR6+kd0OocG+LBf5sqlFby4o59yj8KP5tZwwVubOWNKNW92jqCLOXw2iKlRrLhJp01EsgJlLpntoRilTgMj58IQNco8Ev3RFH0hiQvmlTK50M7hDX4+741z4SttGEqeSVYnV+5XzZ8+HWJyhUJnNM3h1QXcva6f3mAWv0/HLdgwlCyHNxRw+cJ/LLJ//X4/R03zcsoz26krFBmLg5gzUVCQx+ZI47PnOLN+D7aNJLlldReCYVBvdXDx4nIe2DiAZMpxxaI6Pu9KsW4wwawyK89uDBILRWm78YBvJVXqzjnqVRPYvyWhKGnA0fn//alcJ5hggv/Z7Eyt/s8p1yeY4H8CkUiESZMmceyxx/LAAw/8dzdngr/zba+rvitrqu+0RVH3kEid2yActdIzDMUlY1QWeYkbKtftO5X71/fx8LphTplZyL41Xtb1pHlm/RgftEc5eQ8vv/y0hVQ+h7/ARLHLysioGUUy8Lsl+qJ5grk4PtnBrKkdbBx20uBxopIlb+Swy3ZCRootAY2zF5TjtZo44ZFWbjuiihllduyyjMclMJrLUOqSKRQUzNYsLkWjJSAyEMkwzWcnlFGRTFncikhvLEu5w4LfbpDVslS7vHxvVjk3fdxKKGVmv8oC4mk4tMHLz5Z34XAaqGQotOr0hHXqvTZSmo6mGQgZM6NykhKni7iRoabYRFCP4bU4GQ2C4spQKUsk0ho1VjsXLPiHL+7z24dY2R2n1go9/RoNFTKSJY3LLLOxQ+DpDQFe3Bzi9LmFTCq0IogCvz+iDlEU2DCY4IntrVT5RGY6G9mjzMVnI7281pxlWpmJvoiOpCokMjoes8wdH/fyl3XDnDKliBsPquKkZ5K0BFIs+ctGpvptPLF+B9OKrGyPJ6h12VBzebK6SJFFIZhNU+6U6YrH8FkVblzSxGSflTte+YzzX2xl9R9/irtxAZ6mGaQ1qHM76Atn8FpknLKDmw6s4oOuKO8OB9GdGbweE49vGCURVXC4YHWnG6vFSak7RywvIMoGZiRaBrOU72lhc2+GYMLAYTGj68YuaweAsWyarmiCi6UK9i2q5emNQT4a3EY04uXYyR7CETjhma1kVZ2phTY++cnMXeeWuk3cc0QTlS6FZzaO8eym8cwM86ptfNKXxRBVPh4KIeRFYjmNoZTOPSfPYHqxA0US2X7peEyFkUSOjcMJLt2rHKvHj0kaD1Z98vPNVNmtWCSRx0+cwk/2LsFqEhEFaPLbMMjyRV+CTT0qrzeH0WSNuJTnuo86+PCsORTaFXoiac59oY2bllYRSeoc/mAzh05xowsGNx5UwbRiB4c2+JlUYOaO1REGE3nsisavP+1ifomHRVUurKbxODg7RSLg70IjBFMqwaTK+pEonw9E2NCbIyXkMFlVNkSGmWnycfneZdQ4bbzdEeJvzf1kkxJuwcZvPhzgsWULsFgsnHHFr1j74Zskmz/iyXWluN0wY5qTUrsDhyNDVs9R47USzsbxunykggZ2bxyL3cKvV3cyucVNTlRZNTTGJwODzPVUctk+Zdx/zCQuWb6D819qZ2q1AJrCaELCnnESU5KUui20BvMcUCvRm4jhsfgZSwgYeTcmSxyn1YRZUVlQb+ewhkIWV/q44r0W+mJpnM4cLhdEMiKyKDIcE3FbzAhChFNm1NA9ZGVVv0RrOMGSKgtrhlOcN30KN68cpNYnYrOlOOaZzfzpkMkcUO/ljR1jdGVjVFkcZLUcRU6BH0yfzF3rupjt85DQNIoKJe5a04eii8i6wBXLe3j4mFl87+UWBF1kaoWJNFEUw4EmpfH5EhTlvYTVODZJocCRY/tIgjq7hz8snczH/QGebu7n3nUDKJLAm9siTC2088MF5TT4LZQ5zZz7dDsPn9bAlEIbV7/XwY5whnTWoNgjYVME+uIJbBkzb7VHKbQOcdzkAv62eYTpJTbOeaqdw6d5+GCol7wkU+xyMhg1cOkmTMA7/f1ct9cUKvwKv1nVy46BLJe+3M3tx5Tz248GQZO5dHE5r7WEeGtHCLtZpLjYStv/xflqggkmmGCCCSb4f4fh4WFuvfVW9t9/f/x+Pz09Pfz+978nHo9zySWX/Hc3b4IJ/st8p4Uihz9O+5iDfFhkTr1Gd9hO2pxlXqmd57cPU+qwcM2iWkLpHKIocOgUD+93hrj10BrOe2MLV+9VxxPb+kjlDUZCEi6zCZs3gMcWR9XKWbPdRMicpchXhiIYJPMGPpuZaDZJ83COGq+NMTHP4mo316/oZF6FnRlldlZ3x7n0pS5Es86MWiuhlI4ogcucZ12/TKldQvBqnL+gmI/6grSFQyRyIhVOmYyRptJlJ2/ojGUjPLrZADnKVH8RuTy81xVmKJTnxoOreaK5l6FUhJYeM3tWZGhOqJQ4rGwb1ClwGUQ1E0lVR1EM4mKQ2d5imofzVBbK3HPUVLYOJbjpvT50JQ8Y/Oj5Tq47qILTp1XwUWuKeCbPpBKFG/ev4bmWPt7dkuW1c5qYVGTlosWlu4SRVV0x+kN5JhXA/asH+cH8SbSMpni6dYjBWJ6BuEBNSRJFdqEZefwmG1azyG8+7cNjldmzxMVeNS7eaongExWqnVbKbWkSWpRqv0Q8p1JVkMOi2hlLmCj3uGkbTYDNRIlb4fzZ43F/LJFBjrrg55hcfrqmnkLN9+9EMucwWVQK4grpDCRzMKtIIqIGOP75KA1uG9uGU5R6FDIZmYFolgq7MO7qYgiUe1RCQTvBiIDbn2dxhZcb9x23brjxwGp8Vok73x9idU9iV1YpgCv2rMX5dwGkPRJnQyBEMAV7VNhxWWWqnBaOnu6j0W/dJZjsxKVIFNpMSKJIudsMBqSyGuuH47SGEmTI0DMgccw0J6ccWk06K9Dot6JIIpm8zqvNQSSTzpaRJB90hqmRMoSVOIqikzYy+C0m7FaNTF5l/oPr+PB7s3noswBre+O89P0pAGwdSvJOW4S96hxs7E9Q7jJzz5Iy9v3jNvaqdaAoAhZZZFKBjeeHAgwlMrzfJmBzwDudQVojScrsFqYWK+h5K8FRnbOnFGC3SDyxcZRyl5mmv8dn2kkkrfJOW5i1gzEEBBoLrdjMAtGsyvVLSvjei9sJ5jQCSZ1uJUbrWIzWUJaHj5zJmbPmcehftlFTLBPKZ6hwjZvr/3RpFSy9gHBK5cg73qHng0exbLcxtvf3qfF76AgnqXabiOVsuKxpptaIDMVltIyMQ4ZANspB9W7mV3l5cV2YDwNR3u0OceIMHxsjg9T4HfQFDSp8NlRTCrtDZGjYyjGzivnblhxJNUmpU6ZjLEaFx8W2QISijJ9AMsfUIoWXt49S6TRz0owiphSaaQslqPGayWg5yp0mNgzmWVThJJRSUfDz2JZ+Lps/hSW1Vdz0Xg+HVhYzpmbwW21U+NJk0j50SWNqocRPXungssXlNPntFFoUbO4o8Ygdp83EDe/1cNbcYp5tHmGWFZxWlYiWo8jkJZhWsUkiP365i0ePmcKtn7XQGYIqr528kCeS1hHzXrpHRBrLRHyyhe29BnvW62zdYfDhjhilPhvBtEalN49dtvP+0Ahre+xcvLicAxu99EUyeD0Cxz+1jb8cV88ZswqYXuAmllHZEAiTQKXUYUIQNXaE8ty9eoDX20foj6scUudjRrXC++0xVNlCtdtCMB8kkLZjla1sH1XoL4xQYOmkPZwkZ6gUeiRKnXD3hnZyJoOfvdnJR+fP4qgpfg5p9HL1ih1cPLOc17/lOUoQJ4JZTzDBBBNMMMH/i5jNZrq7u7nooosIhUK7gurff//9/7+4nk7wn+fbWld9V9ZU32mh6FcHzOAXH4ZJuVUSeYOmQhNZI8tYNkF3Kse1iybTHU1x2ssbOLy8kldax1BFna1jcX40r5qP2zJUK34iphSbhlI4SoKU2GXawwqGGGJaVQkJPc9wSMFtFhHECJ90WqjzOXFYMgwnNRYWFvJCyzDbYzFOnFzIpoEk33uynZPmeVneFmRoVMbvlgipSbYPmyhzQs+AxC0HN9IaHmNbIIJVtDEQyVDq1PHb7AxHRfxWO9cuaeDCN7cx1VtM26hBMBXm5dOmIiDw4zfa6Illcct2LN4xVE3EaTYRy+UottroHFY5a0EJ73aGMZkN/rh0Do82N2MXXGwdSvF2xwhv7AhT5NPZEYlz/ottLK0pwGMVWfDwBoptEk6bxmhcpcxr4qoljfxifwFFHhc1dopEmbzO7e8OcPNhldz8YRdRNc/8Cicei8weVTbu/qIDKynskod9KgtxpFRWdSe4+aBy5paPCytZVeeIv26lzGbms+4EqpxDt49RZLOQHJX5wbxytgdSLG8d4+XTphNIZ7n83TA21Ur/iE7H6k/58MlWnpemUrDvubjdduwpKxYzdMTzVGDBAHRB4/j5IQzNRHdMZUqRlWQ2h9ebpthpJpjOcN+xdTy8PsDWQJpL59TwVvcoo8kcs2qs5PIGm/szu8ZfiVPBMAwqPAqlLplPuqIsrHJikkScisyK1ihbQzEGM3HyUoQih5Mql0I6p3Pq3C/7Gd/0Vh9fDMbYFo7jw8qP9y5hv3o3J88uAAHcDoFwX55AQqTEYrApHGHNijFcuoNfHlJDo9mKZhhsGU7idwukchqSLiJJIoqsY5INdM1AzJnIpWQEdLyKyKq+CPs0OZhUYmbSnV/gckGTx45HMTGn3MG8BgvRhMGt7/bx7LmTuPatLgRVxWzWeL51lLZQhtoiEzfsV8nPV7ahyDKlVjNhNc5Q0kGtW8GjmjiyqZAyt8LpM0sAuPKVbqwmkZsPH3fB+6AzzJ8+H0Sx59mz3INhGJz8zHbOnVuCpht80ZFm0RQNm00jqUWpcDuRlRQPbm7jxEnVPLysgave7aLKZeaGj1s4e1r1rjHmtcm8dc3BdAQX8+mWDubUFnP08csoXXIwyiH74TAcSGQRTDHq/XbWD8Zp8NoYSeaJimN0JLLcdlQdK1oSHDbZyw3vdZHMSiyaMkIoZqc5qFHndrJ9LEmV18Zzq2MU2r1s7ZEp9GYwCVmyuobfKmMxR7DlbfQmUpS4HTy3cQwTsHZ4kCqPhfWDWWYUm0jrWaYXQ5YIDsXJun4wi1ZahjTOmmVDNKv8ZuUAU+sEnmkeZpazkrWpKLNLfHzSHaG4QOfRDSPML3filyyMJBKU2UViqsacCjPPt/di6AqSIJLKiswpV2gLBjHn3YiyQTKrc/UbPXTl09R6RURJQDag0Wfhk64MJslEIuIgmQO/2caWIZU/n1jPS1uCHDu9kvklhTgUeKl1FKdF5Ip9SvjN20NYTQLt4TTxtE6Zzcx1H7YimDJcMnM6BzV46QhnkFSRZBYSYoaZJWaumN/A959pp7xco3kszlAqg9OssLi+gM/6w+hZBzNKTGwPZMjlBKSwlcc2hPB7oyBaqSsxk1RzuA0TJXYRnxUMQNMM7vy0hy9Cgxii+0v35AQTTDDBBBNM8L8Tr9f7tZkQJ5jgfzrf6RhFN7y5iY9HcxQ4BeL5JA5rhkKrTFfIRDiTJ59XmOR2sq0/h8NhUOyUWFJZQFY1GIrnMOkyJ87wc/6LbSyZnqYnnmYo5GB2mUQoqRNLWfCYJEaCoNtz/GheGQ9t62AsKtNQYCKdlMmlZJx2HYc3zbULp3PjW31cv7SS617vxWQxiCQ0UnmVinKdrUM5PGaZc2eVEyPJuqEIO0YNNDHD1GIrWT2NQ5YZDFrpC+eZ5Lfys30qeGbzGN3JBLqU5O4D53DWi61YJLBaYSSVotalYE5liJucRHI5wkmBvxw5hYdWjxLQEgxkIkzyK6RUg3jSQl5IE4mZWFAjkdLDhMNFXLN3DYurx1+U7lvbj1ORGUwmeXjjEHtX+plWYGfDcIIHj5rME+sCnD2/CEGAn7/Vy0AsS3MyxPQCFz/eo4LP+mMUeZO81hqhbVCgtlBhXWeOo2d6+LQvQjYrMq3UziNHT94lOG0fTVHrtTASz6EoBtG0zh8+72N7NMQrJ84npxp0hjOUukyc/NIGCs0meoNJYu8+Qa3fyqHHX8Dy4Sx2WcSSjGFYvfTHc1gcKj7RRudojh/u78MiSLzbPYDLnURTbbQGBPySnT1Knfz1szEWTpOJpA1axjJMtjvxOER+sqCSKq8Fh1nEaZb4zQeDOBSR+TV2Ph8JsG04y5bBDHpeZEGdFZdZ5IiGYq56pRenC0L5NJqcwqN7qXJbOHtuMU0FNrYHkrSPZdjUlyYiR4nGRIbjefKyToFoYWmDh5/tO575SdcNHt7Uz5PbevE605D34jGbqHRb+cU+jV95n3SHMqzujfJucwspm4W8ITOW0DljagWCLtA2lmZVbxTZotFU4MBvNrFflY8ij0iBxYwBlP3dMue5jWOMJnO83RXivqMbAPi0J8Ldnw3hdxr4XXlieoThqAW/4kJVRTqHczx4Qj2FDvOueDn/kbymMxLPU+EZ/4z+aJb3OsKcPbeEvKZjksRd5UySiKrpnPT8esr9WXZEo3gVD6m0Qk84S7XLSt4wiCclpJyI1WYwmtC55aAqDp08nq1l+fYgP1/RxQ8mm2lLWXAJBkNrXsHSUMerq4Psu3QKXZkM04vsxHJpnBaRVMJKVMtiFW1kjAzH1TTwwuYx4gkoLsqgm2O4zVZG0mnIuZFEHVmwksno9Gdi1NnddMUTNBaYEKQsiYSVvJiksUigJ5rGrLvJZiXCSRE08Ho1EnoGt1nCZjIwKxlEMUuFWyGQzNAXKCQUk6iw2tg4mGRSsYJkySIJMpt68swtdPHEGZN4vSXErR/0oisqB1cXcFC9j7vX9tARylHlk2goEjh3Rh3bRpJ8srWPEclCRE/gtGZRBBOhpIJPtrJjUEOUDCxmAbMjTYndQjyXxmZNYhZtxHJp1GgR6axIIq9RVyaydSzJE0fPIJOFBdVO7l/Xi0WS+P3qAZ46dirXfryNS+bX8cK6GM3DabJSDp9bp6NLJI/BFfuV8vDqUWRESgslCm0K9x7bwAtbA/zx0yEeOKGBH7+9HQ9WzplXwrsD/fSMCIzFDPKSis8hYrWG2NTpxO/RmVyWZV2vRKEzT7HNTlLNYyBycE0xHhw88MUQsjVLrS3HIyft/a3GKHrN9u3GKDoy9b/fn36CCSaYYIIJJpjgP/Jtr6u+K2uq77RF0Qc9QVImiWwiR5XTjiDHaQ+KeC15rKKD4YBEOp9mTq1MMGuQyGk0D2QwTDoXL6jgjKfbaA8lqaoM0xeXcQk+0maBgYiB3aSQVXUGgxJXHlxKkcPENR81U2Az4fCayOk6jQV22vtz7BjLUZt3cNFLHdx+aA2PrB0lh0Ysp+K2SUTUFNtGBZoKbERzKst3jGIxa7QNG8ysyhHJ6QhinBKrnf6ohqGKlCpWugY1NvSn2TqawqXI/ObA2Zzw3DYqHRZiWY1EwsDQzGwbybN/gcJARsNm2Ljn+Dp+8WYfsaxGIKVTU2mjJ5alSHESiOXwiE5+f0gVDQUKp76wHZUU8yvGLS+2BeI8tLmfg2qdbAqEqLY5aAlGiWQz9KUinPEUjMY0VFHnvPmlyILAZx0JaiotdMdSXPdBO4I5Rm1MpnUMLpjbQFo1WN82RPNQmhw5Knw2nLJMTjOw/F0omlI07obktkr86pNetgykiGZULtunnuve62T/Wg/JvM6ckgLin7/Pjo+Xoyw9jzMuupyLFlTzyGdjnDvDz/bhNBkxz46MjmZJUWZzEsrHqClwYxjwRHsXi2szBBMWVDGHWTJT45dpHknz55Pread7jG2jo1R7JeoLTHx/ZjkXPdfNqfP8/HjJeBynRTVOsrrGbR/14TBrjIQM4gmNApdIbyxJIJ1lxfYEhU4Zw5Qnlc3QYC5gn2o3e5Q7WVTl5uD7t2K1QSqnE44Z2M0CTotImdNCucvMlCIr88qdqJrBtLu+YLLfxswSOytO24sL32ghk4VsCobz4yLSHR8MoBkG1x34j5TizeEYf9qyg1muOD5fmkzeQkaHc+eVcuWbnQxHchh5gRwSDjMU2ky8siVITIlz6YJa2kZy3L6yFyEv8ZPFpUwqsPGjxf+IZaVIEvGsSnGBiiKLmHMO+sMGii+PS7BQ4hO5+L1tzCq2Mtlvp85jY5a/iIYCC4IgYJLEXSIRQIXbzNlzx62NTJLIX9YNoshwWGMBz20NUO1R+OX+k7lv7Q4KFJVINo8iKrhNZsJBE7IIFkUn546DaMOpCVz5RjeD6TTHTynmqCl+Dqx3Ew2NUVRUxNbRJCe07sclU4t5Z93jvH/b5Uw5+ACG5x9HiUshldPpCAtUOvz8aI9Sto2lOHtOCXtVuXlk7TBvdKWYW2MnmkvhkuyM6QbmnJVls4p46IthzKqVSE7DZzbTPJSnqchObyLJ3FILWS2JW3bS1m1ijwoHdx5Uzm9XDtAaSmK3OBlMJ5hWYqYvkqXc6aEvrKJlPETUKJNKfPSNZWgokelOJ6gR7QTzGqVemXBK4y+fj9JUZCUWh/pCG5/2xLGZZHYMaviKVETZoC+mcfcXbazaDlfOc/FEbwpZUXDKEMgkqfFKhDMJSgsV8ggMJlJMkix0h1XMohVRtTOkR6n2muggjW6SmVvk4eCpDvpWd3PjynaaO+C2oyp4pz1KncfG92eVcNHbbdx/+DRu/KCdtm4wiSKzqu3MrLLx19AoRbLC4+2tBJIOljZ6uPu4Or7/fDu//aSf9/oCqJLONW91Mak0xyWzJnPm0zuoc1npyYWQ8xamFdnpiKdw2s0snqQRy+uMJDWqCjTUhJe1XVkKC7IUSg6eWh3jlkO8eGQZURbpiiS+/UlKBL4loYjv3JbQBBNMMMEEE0wwwX/g21pXfUfWVN9pi6K9Hnid2hIv24eh3JvFa1UQBB3NMMjkBRyGFd0SZTAqY5cVIkELL581HUMwOO/5VrYHMlgkgVl1GqGsRjJtwiaLxNQsibyKVzZz7eI6Ln6uH68Hiouy5HWdTMZEgc3KRfMruPStNkxZEzExQ4XdSoPDgSyJ1BfJPLJ+lJCWpcFnIZrPYskr+O0m+kbHA05PnRRHtkTwKk4iaYmRmIRNc3HL0jp+8mInJ80u5LxFJTyzeZTppVaueH8byYSC3aJhFWW2jWSZXa0yHLJyVLnEhpCJdcE4F8yq5PnNY8wss7FmOEoGlWqPiXga7LpC+3CWU+cUUFwk8eaOMW4/oJ4nt44yq9TMI5t7SYZtWGXI5UTGtAwNxTLxnI6uCWQ0lUUlfs5fWIIgGKzoGubZdQmymk5ZoUEil6PYk0PUbazt05leYGMoqOF1gqzkqPBInDerkVKbDd0Ah1nEZRnXOz/riXHm0y2cOb+Ivapt3Pl5F3aTROuITo1HoWXlO3x25w/5+R8fYnvhImxmGydOL+C8haUADMayiIbB6vZufr8lidWqUurSWNVjcFJjGWt6Izj9IWTVS9uISkOJRjqvkNc1oikDv91EOm8QURPUu5wY5FjXbGJqmQWLIvLcmePxewzDoC2UYEV7lE+7YnSGU3i9GrJhQRfzSFKW6fZy9m1wMKnASp1vd2uaLUNJ7lnXRTSXJZ5L0jVsodxtotxl5dETJnP9292Es3n6YllaxlI8fPQkKj0WzLLIo2tG+dGSUraMJPiiP87DX4xgN0ucO6+YQxp9u4SXQCJPJJ0nmc1z08tbUF1ODE1gMKyy/vK5nP7yOqqcNvYrL+Jnb3dT4hMos1uZ5HVwzf6Vu7U3llE5/cVmKj0mHIqM32ThwoWlPLR2iMfWjaCY4JR5XoaSSbYEg4wmBCosHnwWE0nStA3pNBSOu7p1Dci4HQKzK2xML7Nx7qxy7vq8j2xeZ58qL/MrHEiSiGEYbBpO8NfNg3zcH8BtEZlW6KI9HOO0+gY+2hGjPRxjQb2L7aNpUikN1ZbALTiI5VUKvGMI6VI29uaYUguzih3cvPdkOsJJQqNjWN1+Hl0/yvt9Y8wscDG1wMby1gB6PIQ4upG9XRry4v35aDDBdF8FHaE04ZTG9CIbH3clOKjJwqf9MeZVQjoDfXGD/Wv8nDejjt+uHOCTrjhFNhNpQ0UVdDKGiss87hqLpFPnExCUYbxiFZOsJfRH89xyaBUPrh3md6v6mFNlICh5NFVhw6DKvnUC6WwWI1PAwydM4daPunlrWxSrJ8ecMokdAR1Js3HbQbU88Mkoec2gukDhwQ0DTPc5efzUJjYNpvjFyg5UJUapzUmGJEJOQojn6Y05qasxSGhpGguz9MWyoFlxm60MxQ0EzUxnv0pNTYIGn4n+MQVJFBhOaTgdMaocdrb3mTm8sZjzFpTQGUlx28cd3LbfZH70zhYckoWMrpPK69S4TWwbSxINiyyslehJqfxkj2o2jgZZ05Enr6pUlcDGIZ33zpyD32oCDE5/qRkNlWRWR5ZzTC0W2NzipDOYwWwSuOLgEk6dXsx+j6zHb1NwOuNohohkiCTSZoJxg5xmUFkgEEgmGRmwUeaXaSyXWDUQQYtmab/mwG/XosjxLVsUJf73735NMMEEE0wwwQQT/Ee+7XXVd2VN9Z22KCp2iKSzOlMLLcjmPCMxEYclj0UykTOSJE1RhKyPdD4PYozi0hxbA1F+u6abE2aVUNCRoCOcJJBRkQ0TsaSB5gqDYcelyFhtEe5a30qZ34VmypPOCqAr9AV0hqwJbvmkA11KY1h1KmUbySRsTyU5Y2EBt3/UT7lfQ0ybsNuiFGguhoMm+jNJ/E4HbUM5AnGBqS4nsayIqioMh0Wi+ThXv9lDJKuR1cY1wJNnFnHPF50MB0XKnGC3amzrNjhgusaFsyfz8qYExUqavrEc7549mwq3hWklNpA0utMx+kIimiYgiwI9sTTVfgsf9IbxRiTaunVuMLrJihkG0zoZPUNlqcRIzITHKRIakBFUAY9Z4aL5ZfxuzQ5CWowL3x6m2GZiaqGdWw6u4cp325ENMwktiSftJJozmFwokjIS1JfZWduTZn6VyJZuiUta+yj1mNgykOZvZzQyr9IBwLQiG16biS2BGJ+MDrBHqY+Pe+I4Er2s+fOdKFPn8/TWEe69/hL+snaYM+cWYVckeiMZbv24h893JDhhho8fTHMzJRDD6w5hUTT6giWs7YuzI5BnhsNHTkhhd+ik025GMhkqvBq1BTlyWpb2UQsFFhuyrNIZyVPht4IIGwcTDMWybBhI8osVPTj9aRYVFXP1vpXE0zrvdkbYv8FFsU1hUqENURR4b0eIw/+6jSv3K6PWa2O/Kg/9iTR3fTyIzaIQjuWICDo+u4RdkkmkVADaAimimTyxjM6jxzXx9PoxarwW4hmNCo8ZQYC9qt3MKrFxaJOPUx9t4+WNYb7oSnHfSeOBth/8bISucJpgJsd9JzZQVFTEa61hHt8wAoBdMVHttVHttZJIGnhro8SiIpuT/3j6rmgLE87kKXKaeP6kaQzEs/RFs7QHMsiigNkiUOaXaB7OsnJHGr9VgaSfeDTNxftWsHkgQ7G9kNOOK+ToJ7YiigYOh8pQMo8yAn2xNGU2Cy+3Biixw2vNY4ykNY6a5GduuZ3frmun1ualxuWgLxnGqcjce/Asar12ztyjiD9/OsyOQIZf7FdLKJvlkc19bO80aKiQaR2xUWbTqC0wEYvpnLRPBcF0nh+81swMk8HGWBBNgBKzlbZgilE1gt9uoTnhYfK0Q3j8g+WIb9/AvEtvZppfxmGW+bgzz9I6DykjTsKIUO+1kMqlyedtJAIiEZeJgWiORbVOzphbxJQiG6u64/gdEvdsaGYkmkc0JFKqSjBuIZwqwqyoOMpT/PygWrqjKYJqgl8fVsVtq7qYUizQkzCo8hp0DitUFmikrRG6YkkchhlRlfCbTSxrquF5tZ+WSJhr3xa4bHE5nYEcZW4Tvi0WAlqGhzcMktUMfntoA79a1UbXsE6R00EoauCT8pw+38+maBhJMzMcz1LtT9Ix6CcqpCiwOukKaZjsBlLaz5aRMNNLsui6DiYDi+Thox159m4Ksj3g4G/rR7lsSQUvnjQXURD43pQaHmltJZs3UeW0MhhXcZpF6mplNvXoVNck+Wy4l1Auhs3lxCHaSGopSl06V77dwQNHN2EziSwud/O3bf04LWDRbYylEtidKSaJdjK6xv2fjXDc5EIWFPh4rWsMeUzmyBkmWgZkwmoGs2zioHofsllnzVCewloRn0vg1GklbA4kmFybp/1bnqMmgllP8H+CrusMDg7idDrHEytMMMEEE0wwwf8wDMMgHo9TVlaGKIrfSp0Twaz/c3ynLYp+8Px7rA84KCuKk0q6cNszIEcZDFuZW26iLaiSSCuUOUVcVp1IWiSZVphaYGdhhZsntw1zZL2XR7YO4rIlETU7mi4xGhGp8GqUemEoZKJcduP153lzYxp/SQyH7GQsm8SuiHhNDgJhASMn4LQIDBlx8hELVeUaA6MCxW6JyoI8+bSbPavt/GntIElVY0GFQkLP4jILmGWBUCYBqp3uYQVFEvje7FJ+vLiMTQNJSlwmihwmbl7ZxcreMEJOxOSKMhY3MdNdQOdYhmn2LBcsncw1b/Rz7wl1VHst/PD1Zla0xNmjRiSYAV2VSBoJvFYZLWUnGNZx2EQiSY25TRqhfBS3YmMwKLGwrJAz5vj5w+o2+qNQ57QzqPcgZ4vpCRsUWmFMzXP5wjqWTS3i1vd7eHt7FLtbZTSVo9JjpjWY5uolpdgVgVc6tqNm/GwbEChzKKTzBmfNKOb7i0pZ0Rbink+GefbMJp7YOsxDa0a5Yq8KbGMtXHjnX5h92jkEwhbcFgdHTPFz0ZLS3cbDKy0j3P1FFw7JTDit8+DSIrakNX79eS8Lq3SCITuDY2bc/jhem8xwOoqcKEGX8wymssyvglTeQDHlGYnY8Nh00mmD7kGFCo+ZUEplLJPnxwvKWdUV47PhCDOLrbjtAuu6c8wrcTG9xMa7bVFymsHz5zTxYU+YO1f1MK1MYI/iQgocGi93tqAYbvYqK2dDb5r3W+Io7jzTCi04TTJ713o5e0YVP3y5je5omtFslqkeF9ftV8W63gR/XjWCbtJxOA2cNp1wOks4bMYlmlhc72BOpY2MrpLMgCgIaCokUjo2wuDwMLXIwb7VXgBe2x6kscC6K/PYXg+s5/w9Sjmk3k+pWwHgnbYwb3cE2Tga572z5wDjD/1gKs/PVnQwq8hJMJvj1dZhDFFjepGCYJjoCeoMpHPMKLDit5g4eloBh9YWceV7LQzG0/SHdHJZgUq3hZSeJ6DFqHXbKLGZ+LRVpcCvM6PAw4Xzy+iN5HCaJaYWWXmjI8C0Qiezi8eV//bRNM9uHuP5TQFKXWZOmlnAntUObv64FZQ0kbSIx65C1sF5c2rZr86Nrut8sKWLl3p01o5EMQkiPrvIUDKFDxfpnIYGeN0GmpTn1HITN1zyQ0oPPZCmvY4irekYaReKNUpOiiHlCxhMpTmmsQhT3s4bPUOk0xIVBSJnzyhn21iKWoeNa1/tY/4Ug55UFIfgIaurmASZ/oDGcVMKeW1bhCq3QtqUQlYMorkEdT4LIwkdh2KgCzksgouBRJzzZ9dz+rQqrnm/mXe6x6hy2QiOmCjw6Gzr0yn3C1y0j58Tm6ppHklywpPbmFsLwZTAdJ+HNzZFqa1No8fdxJMG010ZPuyXufqwUt7s70YzdOr9WYbTcYKhckJJEasCly6spN5t56dvt1FaPEoya6bEodAbUcmqUOOFS+bO5BfvDnBAo4fz5pbz+LpRbn+vn5yRZ79ZeVJGgoFhHy7BRsbIYogafruFXy6t4ebPPicbK6ZtLEepX8NqMjMWMyixWbjtwFqO/OtW8lKOvRoEOkJ5EjErsytVlpRV0z6YZ2sgjt2tMhhWScVlyopEeoc0nvveZK5Z0UE0ZjCcyfLgcZPoiCZYvjlKyEiybFoRRzYW8+LmZi7de963alH0hufbtSg6PPK/f/drAujv76eysvLfF5xgggkmmGCC/8fp6+ujoqLiv1THt72u+q6sqb7TQtGajj5+u36YtnAOmyWHRZYpdcgk1CwCEoJmRUPDJKsIgk4ua6UrqCIreSqdNm7cu4FTnmmhqlDHbDKIZ8FnNZCxoIpZ0mnw6B4eO7WJ0VSaa1duZTQbQ1VNVLpsjCUNsnkDc85OdzhDUXGC2Y5KXu8I4TbJFDhNTCmxMrPQjkWS+MW7vVx/UBn3bujFLAuUuyTyxAilJPxWiU29EuVWG/s3eLhm32p2BNIc9WALT53VSKVX4Z5PB1k7FGUoH8Ut2EnrBkdPKmTLYJJkYhRd8TIQ1plUqnDybD+3ftJBo19AMuXJpF20hlJM8smMpQ2smpXhsIqsS/h9IpNKFYaTSbYHsly9sB6n3eDVHd1s7RAo9droy0Yolh3EMgYuycT2QIoLF5VQ5rFglUUeWjuELIh0hTL4XRKyKJCW4lS7rchKjHDMRSwmYzeJdIcz1DrtJNI6B+yRZscIfN6tc/niSg6p9fGjl3fQv+plgjuaWXL2D9gatXH23Ap+uGcJ173eR6VX4aql4w+c9UMRLntvG5MLIZ5V6Q4qnFQlsyWToy8G88s8HN1Qxs/f6kOWNURrhmTOoNypEMmHSCT9TC+y86sDGvjhivXIokpGVTmyqom2oSx5Pc+g0MqRNZP5vBNe2TqG3y+wuNLBWXNKyesGm0YjDESzXLlnI1ZlPCtcTtUxScKu3eCcqrFhKMG2wQyrh0NsHosznMhTqzhx20QUBZ45dfquMd4ymuIPqwb43eF12BSJsUSegUiGE//WSmOxQpFXBknloLJSmkeTBLM5+uJpVDGPmjVhtwgIgkEmq3Jao5W3BnUsskyJQ0EVdfyymb1rXFR6zHzYFeHM2cVf2rn+4cvtGKYMoVSeJSUl/GBhMb/+tIePuiMEUxp+swmPXSRrqDSPpCk2mVg2z00kq7F8cwodg8YSGasi8IM5lVS5zDy1vY+WsTDdsRzmnIcat5XeZJrfHVyPIApUOi0MJzNc+24XRQ6ZPx7WtCuo9U4yeZ0dY2lO+WsbjdUC2Ry09I9nsbNKIodO8bFyeIiElieYynP61Aq2DuS466h6wKCjb4DNaYO2MZU3OwL4nHmMrI2uPoFrDywnrxv8bm0PDQUyG/vzzC9S+OiZB6k64DhMuSQBRwl7VMi0BhPU+S0ksgayKcWiCg+rhwaxUcJoMkM+ZUdRdCJhGZOi4XTr2JU8PRGNGT4/n7WnOXq2i18d1Mj173bx7JZRppdaGEyn8MkW7M44PqvCxuEM0woVQikRGRPbgxkumlPNm50DOKwao+kUFsOFboiEEzo37lvLC507mGQr5cq9q/ntp7283ttNpdtCqVPkmKpJDCWyPN3exb7FJVSbcvx2dYpoRsPmz7BXvYZkipLPm0nrCT5vLWVeoZcCm8KZc4rQBI0fvLmeOaUSoilNKm0jkTEzmkrTVCCwttuMIRoc3VjAqo4EuYyAx6cTVKNU+dO4FStfdJopFBz86qgqNgbivNc9xoOHT2M0obLsyW34vTqhbA6HbOH6xXXsX+flL6uHeL17iIgWx25JI6sFDCSTxBNmnjhhGhUOhZ++38ZYOs2CUh8+q5nnNo5R77ISMDL8YkkdP3qpg+JCkceOnYokCJz9TCsDqQx/PmYStTYVv883IRRN8N9ONBrF4/Gw3+3vUVfm5TdH1SKK/5hPMnmdz3ojfN4b5+r9qhAEgYc/H+H5jUH2rnegCyAawngIBlnjoHov8yqcBAIBCgsLEUWRv34xwuutIY6a6uWDrgh3H9lIVzDDzSv6+P6CItb2xfmoI05GzFPoFjFElav2qiOVMfjDR0McP8PPcCLPKTMLqPZZyao6N77Zy951Lj7pjPG9hYVcu6KbI5t8vLRtjLxqcPRkP6oOr7eEmVluZfNgCkMVuPXwaqaU2LjxnW5K7RYu3qeUjrEMv/qwlzPmFvFaW5C+dBTZMFhUXsyGQJRzZ5VT4bLwm0/6sUlw2RwneYuTG1b0c9xUP2/vCNGfTlLnV/jh3BrK7WYuf7WLgWgOu0Xg7PnFfNAdYV6RixWtUQ5ocmKSBLqjSSYXOHh1W4i7jm7gr2tHaRlN8735hTgsEn9eM8SMQhsvNI8hijoFDjPLphVS77MxpdjGxx1R3tgR5IA6Dwc1+tB1g+XNIT7uiFLqUqgrMLN8S4T6IoVPRsfwShbCcR2rxSCTFakrUlja4KXGY2YgkiU6GuSUJY0Igsib28O82RxGEODUuQXsWeNiXX+cd9vDdEcz1PusbBiJoQo6fz1uGjvGUlz5VieTiswMJvKYBYGommVpvZeWsTShVA6HycTvDpnEQCKNRRL5vDdBudPMmx0hZhXbWVjupj+aZTSeZ3GNC7fFxMaBBIKs0hqJc1B1EWVOKw+sHURXDZZvCzOn2kYomyMQ0fA7ZKxmCZtJJJ7RKbEpTCmyUCqnmNdYSV6HpzeOMb3EjM2m0zaSJ5mF780rJqsaKJLAi81jtI3GWdMXx2oRyKOTVOGUqUWcPadst3vnoQ2D5DUdSZeIJTTaAhlkl0aRTSaQUrlh71qKHOObYS2BJMFUDkQwNMiqEMln2DGW4ePuKA6TiE0RWFDrwNBF+mM5bty7nkROJZrRuH9jJ6GExmhCY5LXQZXbxgXzS3hubQvP9mSRdBMGkFUN9iizM6vMwTPbRmjw27j1gHp+/+EA6wYSlJXoHNNUwpJKHz97p5320TTpvEGhS8YkC9x5UAOiAO/tiOC3mVhQ6WLzYIKPO+LUF5uREdk8lGRWpYVoRuO5DSEKbDJeh4zHKtEeTrFXpRuvycS60TjLphXSMZpl20iCtkgUm90gkNYpdakg6CiSiohMx6gFF2aicYPaAoWp7gzLFjZQ6LQwlMjgMZu4+dPt7FVegJaTWdkdxazAtCIzKS1Dhd3NO1vjTC6x8uFgEI+kMBjJUeZRuOeYRsyyiG4YPLaln1q3nb0rPXzWH+H1HUFu2b+BNQNRatxWSpxmPuuJcPdng1Q6LVy1fyXtwQQvN4dREDl9ThGVXjMPfT7CirYIZpPAAQ0eDpjsIJEU+Lgzyo+WlPLM1lGe3zxGPGlw1f4VHNTkJZLKcf3yjXSlzZhkgZyYxymbiCTh7NnFHNzg4aOBMO+3x/jBvFJmlzl3jbU/fz7I8h0BfrF/7bh3xYpexhIa/dEsigJ/O7UJp3l3x58Xt4/wRkuYXxxQw4ftUZ7cPsyF88s5enIBMB5z9Dcf9rOuN8U9J9RS6FR2nZvJ6/zkpU6W1Do5e37xV84dr24JsWeNgwK7vNvzHuBXn7exb0UBiyp85FSdC5/v4IgpXk6YVfBfma6+llgsRmVlJZFIBLf7v5ZZdkIo+j/jOy0UHXjvp8Sx4nfpOO060QzIokaFx6A7LBCIiPhcKn6bQVtQQ0Cj2OpiIJnGJBl4BQe6oCOb8yiGiMMqkSNFKCngMVnY2gmaOc+pMwtpjg0QTklUukzkdA2Lkiefs9AW0AnHRWaWyfxiv3qu/qAVJWcjnNXQRY3Dawt4ujnAsydPJa/rPNHxGQNBB2NJE+GURrlTRLCO0Dfkx2uykcoZHDzJy3X7VaPpBp1jaS59qwNFFAjGNAIxlZoSib6wys/2rUCXVR5a30ejOU9rHsqtToK5PKVOkfU9Oj6LRG0BbOszKC82GErmqDA7kXSZU2cU0lRko8RtYmV7jN9+1M8tR1SxZ5WLy1Y087OFDVz8fDdpNY9qyZMXc3hMVsKZPEbCjE0RKS+UuWpJJV3hDJ1jGe7f2E+N20xa1XE7dEJpKLaZ2DKYYX6hh/ZwCjSRMqfCY6dM4uUdg9y7ZhSnJKFl4jQ//SCe4hJsS06iyGIlmze4/qAKWoIpjp7m4oa3e9k2muGNs6ezZTjFDR90sWdThP6wCZc9QzLppNFk8PaQQV2BQtaIMRa3UWhRCKkJih0SgiEgihrdYYF9y0s4srEQTYNHN/exfiTJogoPN+5Tw3BUZUaZnROe2Eipwz4edPiTfg6p9TOrzMYtq7s4fJIDt2zmjc1x7jiyjvW9KVJZnRsO3X03WNcNvvdiK3uWujAEgzs/6aPALlNTJKHpAgOJ8fTp1+xZxxS/k1ll/4hr9MimAV5o6ScvRlCwc+bUGk6aVkEmp3HIn5u56bBKfDYTn3ZHeXTjeMygwyZ52bvGjSga1Jty/PTTMSIJHQyB8xeVsHxThLPmFRHO5vnjZ/0cOd1Nsd2Mx6RQarcwp8JBZzBNQs0SSOZoHlDBmmLzSJr+UJ7+oMrdRzdy36phHHaIaBk6o1GqHV5qvTLbxzJ0jeVoKjMosMkEkwbBbJqfzm8gkVf5uD/A9Yum8/jWQWpdNvap8lHuNvP69hB3rOzj0n3KeKFliHA2R6HNyklTizmwzodJFNjr/o2cOK2QL3rjdGkB6tx2hmNp8gkndptAiVPhpGmFeG0iT20N8PtDG5FEgZF4jmc3j7JficrTfVHe60pT7THREYlT7XQSTRpcsqiKRreDHzzbTlFZBk3TiGcV3BYIxXTan/gdWirG9DMvoLK6gIymE0tayegZyjzgtaXZNuhijzIHLdEBKmxOQukYgeESzM4cOU2nyJOk3l7DEQ0FXPxqB5ftXU6VT+HxTcPkDIORVIYdoST1FhdmW4oif45ILo5D9LJjTMRqgmv3qmf5tiibIgE0IUM+7EWxqVjNAq2BPEfW+1nbm2JpvYenNwfwOA1sNvDZc2STdtrCGZ4+YSoziu30Dg7z/dfH6A2pzG+UCGpJmgo1BmIigpRAzBRy+Z4NPLE+QEsghUUWmVdnYm2wCwULNtlEVhOwSDK9EZ1wKk+5yU1vNEOBDw6p9WM3m3htxwguq4qoBLGKNtI5hcvmN+GSrNzyaStVPhPnT28gnFL53nOtlJVq5DIKfziskXnlTn65oo+PhkaRFRWXWaYvqqLqGtVuC/cdOp0Cu8KawTCXvd5JXspyw5J6OsdUtg6kaY3FmV/mpHkwQ8TIUeyUOKi8iNkVdr7/bDt1pQpPHFWJ1+v9doUiH9i/HWtrkjocHvrfv6iZ4B/jp3coyGBWYn7luAvaHR/3sW+dmwXlLq79oIV0Dq5bUo/fZuLHL7djQiIv6swqtXNUk58/ru7n8+4EJ00r4oI9ixgdHaW4uIjbP+5jZWeUPart2HWFk2YVMKnIxsNrh8mrBk+sD5BHw+8WiGQ0TILEtftUYxFFfv5mH1aHRoHZTNdYjkv2LmNK8f/H3n9GWVZWa9/4b6WdU+1Qu3Ku7uqc6AA0NBkkSxYkiWDCxFFBQRQDIphFRFRUJEuOTYam6ZxDVVfoylW7ws55r/h+aA8en3PG+D/PGJ7/e95H5xj7y1pj7Lp3rTnvea3rnvOaTuZHXdz17hhjcY2jO108vH+KSxeHeGNkmvNaGsgWYH1vmqKpYiKwNOLlzK4qljW4CbgU7t8yyaIaN8e2+NENix++NYFumuyYypHXdE7r9PFyTwZJhpM6vbw9msYrCZQMA60gcf18JzOmlx1TWVKaSjJlceJcN07RRt9smZmMTt5SifplcobK/KCPs+eGeWTvNN8+oZn2iIt7N0/QFXERdMrMi7iYyql84bkh1rX5+MqJDfTNlvjD9ik002Ju0Mkfts1SX2UjXtAIuxS6qp2kijqfO76GedVHcrhlWbx4MMnpXVX8fvsUAMvqXXz33THsokXGKhNy2WgO2Ll+SRPtVS7e6E/x5x2zzA05OL3Z4qg5jWwYyrJtpECt18bcqINd4wVWt3h4oTvB2fNCtFTZ+dwLA5zY4eWMOSGafU62jee4f88QJV2jUrbhtJt0hNyc1hpm51gB3bT43NF1OJUjB1y3rx8hVzZo9DuIeGRcNom90zmmcip9UxWOanJy3tIA3YkcOybTDE1K1HptlCUNsyKSKOjcsCrKwho3rw0k2T2TIZav0OBx87GFUVbV+/jV+zHKqonXyjCtezicUKmtEajzSeyZTOEVPGi6wHROZ27UgU0WiOsF3A4LQTS5amETS8N+znmoh9M6q/jycXV86/VREE0SukrIqVBWLYpZgRqvQq5i8tPzW9g9mee5gwluPKaOvK7zu90TfHxRLXUeO3dtGCNsU8iVLPYWp1A0OzNJgXs/2kpXtRubLLJ3Mk9W1Xn80AQexcZUXiNoOmkIKtjsFptHchR1kx+c2kI8PsWBosRfduVYHHXx2WPr+Pm2ISbSGiurQyyos9EUtNHq81DlUtAMk7Jh4rUdIRWKmk7PbIme2QIHZvLceXIbo9kyDx2Y5IK5URaEPQiCwB+3H6nAv/3UBpoCDm5cf4imKomzOmqYTRu83Zcla6roGpQ1i5BPJEeJVpeP0zpCXLIkQiyjsnUyyXsjSfZMF6n1ipQoYWou0iUDm27D0CyOqvfS6SkyqXs4b2GQZw9PkipX6J5WcRsOMgU4usnLymY3T/XEyJBHqHgIOhUe/OhcZEnk8y8OsLTWTdBm48ndceY3OLhyWZRtYzkmynm291c4c04VI/kKlyyO0Bl2/t2+aFkW28ZyvNidwOMz0QyTXX06BdWkq9rJl9bV8rMdw+ybLHJqS5hbT2pmy3COFw8mufnkeu54e4TldR4G4iW+enwjiiQgCTA4NsntO2YZilusrvcwntFIaRVEQcAuytx7dgdfeLUftSLw5GXz8DllMhWNa1/cRWLGzglz/XzrxBbeOZzmF+/H+OwxtczmNT5+VDW5soHbJn444RlgJF3m7g2j7BstI1gW1VGLer+DH57cybffHOWtoSSyJnPdmmo+uar2P/0PemdKdEVdWJbFPe9McP2aGqpcR3ynb6bEF54Z4s+Xt2MUU1RXV39IFJV1gzve7+Oba+fgUiQM0+LNvjSndx3pNBhPV3ho1zRfP7HxPx0cT2VV7v9gim+d0UhFM3n+QJJLloURBIFcRefxfbNcv/Lv1/ofc9n/RFz1z4Kp/qmJok8+vJNhVaREmVLSRlOjhWiPM5GxE5DtaJZAolShknewuFnkcFLHbTsy3UmtSGRzEpJgUhMx6R6z0GwqXdUyu8c1KEusarc4NCNS5TWI2O2U0UAwEA0ZSykRy1mEFCeTKQnTgPn1MumCSako4fObDI9a2MMFnr7wKOq9dq5+7gAuV4HeGQW3YpLMSliOHDWKj5yh4VQEGlw+rlley692jPPTUzu4+ok+avwKvTNlnIKI5k6TKcp8rKsB2Q7vDicYSJZYXVWh5HSgGlDjtvOVozu46/1+1tbXk8jpPHYohiZo1NqdLK8OcNPxtViIPH9olpSeZ3N/kT2DJifM9fL7SzrZFyvQVe2iZ7rILzZNcsESL7/Z30vEYePgtIBflijnHRzf4uOmE+v53rtDbBooEqgySWVhabPIgWScKiIkMiZ3nN7Mc3uTbB3LUhuGXFHi48siXLM8yuVPHqBkTdP99HMEO5ZD03KqPTIT2QohxYGkmJiyTlN1mem4jemiRYvHzWRCR/RotIYNbK4YVUqQqYzEKYEq3kvnMASdnFmkwVHDuZ01/Hh7H00BiZyaxyErHFVdS8RncHZrJ198/jCDyTITCY3zF4Q5NFPCLos8euUcZkol3hmd4sGdaS6bF6VcgScPzPDopfMYLxT4/d5huhNp/EYYBZnJXJkfnXWk/a+16m+J7pF9U2wdz7JpNE1eNbl4mR215GbbaJFJNc+LlyzliT0JDsULBBw27jmzlW+uH0US4Ssn1HHVCz3IFZkzuqq48eh6AJ7YPcu6dj81PhuDyRKn/GEvD17QycJqL1tHc9y6foinLqhF8PgBkaFUhRtf7seuK8xpUHDYLCYzFUzJwqNINLs8NLndfO3keo69by/fOqWJbNHgkZ2zFOUCEb9JxGNjPK2xa9Skxm5nWbOTuoCNx/ZP4XCqNDm9OO0msYJKqqzT5nZT45cZTlsMT+ucNt/DSa1Bzu6sZtdUhgc2TzGa0njlEwv55fuTnN5VxZyIk++/OcbeeIZ4RUWzLBRD5uhmLye3+7l3xxjnddSwtMbNl18bwCYKaBUBhxO8To14RmQ2KfDHyzppCNj53eZpLl0e5E87pxiYmqC2tpoX+2c5ulkir1lkizLndlazb6bAj06eS0E12BZL8ft9IwhI9EyprGmxMVnIYkzOIgl2ssl+Fh21hpRVxFRDFDQTj6tCjVdH1918ZdVcfrlphlg+R8pKscTbxBv9GVqjCrmyyS/P7KQj5CRTNqj566nRaKbE9S91k8qbBP0qSsWFN5TEbddwKTKzGQezRRFB0PBUIsTLZRSbwGihxMKgmzxlAnaB7hmVZreXrioP588P8Y23hihQZHmDwmzWQFOdDE4a/OHSdtqcZdaPwSs9ab77kUa+t7mbkqGhGWAV3RyaUVlY6+SJS+dz1ZO9RHwSp8wJ8Kud/dgUi3jR4Kh6MDCYytgIOCQ2HxaxqQr3nNvMj3cO8JllzfQmc7w7miTo0kEqkatodPiq2TksksmbtFULZLMK8yJuljbZifpkbnt3gE5fgL9ctoCSZvCxP/fi8hvMVooICCyN+pkTcvPGYIKPz22gLijx6Vf3I2lO6kMV9vc6eOLqOdy5cZCBaQ1Rk/EHBD4yN8hkXKfKKVO2DAqmxh3HRv7hRNGr4X8sUfSR+P/9oOZf9jf/+cZz+9kYM/jaugY2jmX4YDyNqIvMjzroz+YoVST8Tol19VU8fyAFlkBrlZ07z27ia+uHmcyWOa7ZzzdPauaG53tpthdJmF4OxItcsaT6SAw4FL50Qh3bx3K80J3gjlObeWswxY+2DlGqQNAFZzbX8UpPmkuXhPjzgSlm8hqybHFxV5S3+3PccHSUt4dS7BopIMvg91mYUhlZsji2pp5nDyYQDAG3QyRZ0Qg7Fe4+rY0/7JomWdBZ2+Hlj3umOK3Tx61r24+8wPSmWd+XJuiSWVTj4qFtcU5f6OPRQxMIFoTsNhrsXs5bWMUvN07yyUUKfxmpsKLFyTHV1dzy6jA2Xw6l7Kdoang8FmnVREbihiV1bB8vMJAoUSxafOWkepbUuXl07wy1HjsP75jlrrOaWVDjxjQtJrMqd781jigJnNLlp9qrsKrex70fTLC21Y+qWewcK5At69x8ypFqZ8uyEASB3+wa48Bsnu8f38nvt03zsWURXDaBjz/cT0Kt4PYIGFg8cOZc2kJObnxpgOW1Hs6aE6TaozAzM0NJ8fFSd5qFNS6+8+4IKxs8rK7z05sosqDWSZVLwC87eXUgzr7ZPL8/r4uCavKnXVN0Bp2c1RVk43CW9X0p5kTtTBZL7JoosqTGzaFRjY6ozHdOa+eXG2IUVINT5vjx2CQ+8+wAZ3QFmCnojOZzFAwVwRAJ2Z0M58qsawzSEbbzx71TeGSJAhrH1wQZnNGIWVncskKyaLCsUSRd0QhLQT6+NMqSGhffeXYfOcnH9tECS+rd6KbFLy5oRZFEVN3k+UOz2CWJk9urePdwhs2jWb53eguP7pzh8T0Jzl8YIK+a9M2WueWkhg8Hefx6U4zNw1lKgs7Zc0MfTlJ9fNcs930wxVVrQoTcCtmCybp2P40BO+myjq5b6KbFv60fIKAoTGd1vrC2Hs0weWJvnOPbfDhtIpcsiXDbq8N8MJLlxWvm8cDOGC/0JajzgmVBomxwZq1EQ101Tx5M4FMUTKDZ40SSTN4cyOJzW9gUi0ZbgLxmkChpPHTJXDz2I4Rdqqjjd4jc8m4vuiqSKcB3Tmmh3n/kN24byfGrjVOc2OVhz3SOVNHg9M4gFy6IcNubAxzOpgkrfhZWu/nsmjrufGuMNwfSuGQJHQMVi2W1Hn5+TgffeXOEt3rTeJ0SBV1HsJt4FZmrl0WRRJGFYQ/rD6V45UCKk1t03pw1MXSRkFukrFdw2yUkzYVDkrjjtEY2DueYV+3iyd2zLKhz0RsvcfMJDSiSiGlaDKbKdIScWJbF7sk8f+meZutonpBTImWUcVsOprMGi+vtyE6dr65ppzXgoqgaXPlwPxctDVKyDF4fSGF36swNOklmRE5qq+L94QzbZlLMjTiYmha4ZGn4Q58IuRVO6QzQFFRw22SeP5gA0cJvk1gbMbhrd4a6oMBsyWBNnZ+900Umchpntof41rrWIz60J87ju+M8cHE7Ua+NTFnn99smeb47xalz/VwwP8KXXxzk8iXVXL0ySryg8cFgloe2z/LzC1poCDgAeKM/Rc9MkURRZV7Eg91p8Jsd43xuRTPzwh6+8doQn1lTwwO7Y6yt9yMi8uk1tX9H3oykS7gUiVhG4553J/jTZZ2IooBlHfFjSYCZmZm/I4oAvvfGGAC3ntLwX+rf/WZLjLJu8sW19f/p3u82TxMvaHTVOfjpe5MsDLv56il1PLAjhs8mo+oWt5/c/F/msv+JuOqfBVP9UxNFm/rGuOn9GPaSjTG9gNdl0ORxULAqWJiYqp2Qw05e05BFUCw7lr1MwOHg3I4Qv92YIFZReeSSOdz69gDDuQJa0caiWhFBLmJzWFQqHtSKRSxmx+PVKWIyXVKp94l4nCZOewmn6CSVcWDKOpYmkzc0UhWN5XUCXzpqAStqA7w2kOAH749QMQwiHoGDoybRoIBDFkA0iXolRlMWXpdBg9vH+8MZbjmmmVzZxG0TeGZfnJxjBhd+MnkJrx2cdpnuYZW5DQJN7iQVh4OpnIPhhMG6DpPJQoUGuYb+SZPr1lbx812DNHhtfKStmoe7R7EZPrJqhcaATO+0wWlNEZbXe5gbcXLpI4c4d26Ioq2MCOyMT7GiTiJTgXzeyUiuzLGRKD89q5MfbRnilb4kXrtET7zIZ5Y00xgS+OkHMTwOAado4+J5Yd4ZHSAaEvlY13zeHEhz1pwQt/3lFbb84RfMPf9Uauadw44xjXqPEwSLiVyFlQ02Zo1Z7EY1qQwUStBSa5E1c6D7aArnaQhm0E0BLAcOxzipwx1snLHhlG04bBZ2WSYtpmj0OkmXVHzuInPCAopU4MBkFcmMB6cscChepEFx4fMK9KfL7PnMSv54YIgtk7OUDQ3dAEF1ES+YjE2LfOvURhqDCtc90cdRXeAnQH8ij+BJ0GRr4nOr6ln2H8pUt09kOTxb5tYXRqmPKKxuc5A3y+ydTpLMOPnsUXXE1RKDuQxXzu3kuFYfs8UKs6UKo9kK53ZW87W3u2n02blwbiNXPd/Nkxcu5J43JzmYyDNezpPTDF68bAmP70owmCjx6WOi/PLVbhIuJ6d3hrh5bTOmadEfL+JxSFQMk95EnlvfHcRj2ekKu+mM2PB7LDyWm7PnBSlrBlvGU9yz+TCK4GRVkxefYOOJnbNUPEU6qly0V3kolC3eH87SGDI5pjGETRJZ35OlVILlzXaOa/OxvMaDqgmYgsBRtUfKUF/sTpCtGFyxrJpHds5Q0AwuWxr5cBrebeuH2DKVYTRfZE7IiWEK9E6WWVJrRzUtipaBakKt08W5nSHu3TXC6gYv7w0U2XDdUiRR4NcfxHgvOcLxNVHWhSS+vyeNhoEkm8gCYEr0TRmc1B5g10wWr6BQFxDZl8ywvE5mMl+iXAjgdenMljRW1rtpHN7DXT+5j/kXX0uudintQTtjpSQN9iDZio4pSpzdEeax3XG8NpEz54Q5f36IOr+NezaN8ubhJEvr3Kyo83H14jo0w+S6Fw8yU6pww5JG7n5ngtpqi1SlwoLaEhWjgkP0sXUU6r12JlMWtS4X/akCjR478ZRFY63AVLmE32Fy49I5vHs4x3Uro4iCwFfe6CelF5lbDZMZk3RB5HvHt7PQa3IoZ2e2aPDHbdNQFUcSNWQ9TCIDsilQqgg8dfVcqj0KV794gPaAi554lpSepS0oktOz1Pk0AjYfWdUglnJi6F5+f/YiNo9kueXdAX5wUhuvDU8yUciiyBo+m8SecTv3nzGfr70yyOEZDcVucVxzAIdHw+eQkQ2ZfYkMj310CVtGs3z9pVHuv6SNT71yCFmAsqVy06pmXupLMRzTWdPoJxrReH5/HkMu45ZMRNxMq1kqqkCN7CUjFMlmZZ64dB4DsxV+sXmcomXwlaMCXL66438koIF/HlDzL/ub/9z2/AG8Ph+jhRL9qRxNLg/dE2VAwO2yEG3wudX1nN0Z4r4PYjy2axZcKna7iMNSSFRUKEs0VtmZ1kq02Yqsm9dKumyyZSLHxfPDXLTgSAuDZpj8YkOMG9fW8o1XRtg/lcftr5BMyyg2i1PbghwYVzlzsY/ne2dJZS0evGgOg4kyd24Z4rsntPF6b4oXDyURBJPGsMhUzmRVgxeHqfDuSAqvvwxlNzNpC7cNXIpETjfwui1km0ZnROG8tmacisTNbx7m5LoIt53aSKKgUeWUeebQLK/2x+lN5hFLCred1MJpXVVkSipfeWIXgwU3v7+sg+9uGCVeKpHNQbkC0RqLVNGg1uHgtPYQj++JY1cELlgYoj1k55tvjuKTFS5dFuJgPEt70MFbo3HuWjePX2+NMZ7QuGJFhBcGZvA6JFZG/axtDhB2y8wUNOq8Nt7pz3JCh49Nwzl+/M4EK5vdrGxzs3UiTakoUFQtzukKcUK7j3eHMvxhxxSjSRWXE0oVuGRRmM8eU89gssTXXxvma8c3sKzWzf7BMf4yqtHsd3L9inoquoldFvnxhnHaQnb+3D1GpgBWSSHik3jk0i62jed4clcSWbFoCNg5ECvQnyxSHa4gCzK3HteJoYnc9uooVcEi2bLAwxcuRZEEbP+hZf6lngSP7ZthPF2mPWTnjM4wqmHx/kCWvkyBqNfGye0BumeLWKLB4oiPYsnkid0Jmmp0PE6VqZJJe5WXdU1hmt1exjMqZ3YFmJmZIRiKYFjgtElUdBMB6I2XuHfzBKWSxWxe4zeXdNAzXWC6oHPRwjCH4kW2jGeJOm1IgsiLB5OcNieALAmkyzo7p7J0T+dBl2kL25EU+M7JLbw3kOG+D6Y5b4mfGo8N0xA5GCuQV01uO7URv0NCluFX+7ai5UP0xyQmCgWW1Hq48/QjQ0ImsxU2j2d4tS9JLKtSU6WglQRG0xXeuG4hB2Ml9sdy7BgYIWa58NrtmOiMp1RUQWBts4euqIOhSYuxbImpksp580LkVYMvHdvAAzsm+GA8wwLvEWz0tZPref1Qmr/sifOLC1o/lDbomynRXGXjppeHcMkiFcPimyc3EvlrS91rfUme3BtnUa2LnZN52iI2atwiz/fOYhdtJEsGd57SxqKom4sf68YmQUnU8EkKeUPjpJYgNkmg3e9hdb2P8azKinoXs7OzDKgKiaJBT7zAigaFLcNlBuMG95/Xgf2vlWm6aXLR4/sxVAlV1Kh1OPnjJV2YpoUoHqlAufaZXo5u9LA/mWNVjY9ne2dQBJFsRsKpCPhtCletDLO21UfQZeemZ4doqrKxIZYkXdH5w3ldPNsXZ8NgmljcpCVs48EL59CXKNEccOCzi9z0yhCfXVNLLFfhtf4kyZxJLKnzlZPqOa79SA7dMJji8c19XLi6k6Ob/DzTN8WLvTMMpzVObAlw50mdiP+BUHlg8xQem8QlS0Nc9/hhMmWdzx9fw2/2jXL14jq2T2aQZYsrF9ZzaLrELzbH+NKaOn6zI8blS6JcsDCMZVn8ctMEKxt8HN18ZB2ffKKfnpkSnz46yjP7E5w+t4ob1kT55cYpnjkQ55ROH8NxnbvPbSbkVjgUL/DF1/r54cntLK3523vG994doTng4IrFkf+SKAJ4am+cj3RV4f4rMfm/2r+T3Lphcfubw3zzpCYcsshYusK3Xxvj1xe189CuaUIuhQsWhvntjhjDyRJfPKbhw7bO/zWX/U/EVf8smOqfmig65YENzBZd1IXLBD12JtNgGBIBt45DEUgULDyOMpLhZmQGSppJwCXylXV13Luvh5+ftJyXezO8NzVKIitTbXdis5WwKSZq2Y5p00mXNUxNwSo4mDAKhNwi1R6ReMGiygktYZ10yUQzIF9yMJpV8QkO5jRmmUhLNDiD3HfWPB7fP8PTB2cZyhVQUzIdjQopvURjQEASJWYLGkGHwreP6+ITz/bx/VNaOa0jyCN7p/nZB+NU5CJNXhfTWZOWoEiVW+aHJ8znk8/00ZfJcXajxphgMpN1Ek+J/PKcDv68b4xyycaX1lVz6wfbqXO6iOUUDEMgo1Wo8+lomoeCavKpxS3c88EEd53ayoltAT79lwE+iKVY0+xh23CBY+aVmMhaOCUv+6eLtDi8lAyLy5dGuHfLJHVeO9mKimYKRLwiZVTsgkWporC2SUHwdVPMNPDBgMJZK7JMTMJkyUTY8DK1J51OJNrArvESiaKEW5HRDJ32qIpmmmTKFrLp5nBcp97jQDNM7jq9mdeGY4yUDrGqQaSsWQwkfMxkbNSa8NaMxVnNjZzRGeSbb4zQWJtHFEV2T+pcuNDFPG+Yu95IEomIyIqK26nisueR9CoGJyXuPquNTbEkB2eznN1ey5/2jVMuKrRXuTlrgZe/bMuypsXL1SurKWkmPofMG31JPv1cH50BL6ct8DFbVvnByUcAxiO7ZxiIl/jKugYuevwgxzUEKKPxzlCS353fxTOHDzI+4We2XMZwJZlM26mW/QTdFhlVJVeBP52/kES5iEuRafJ6mC1qRN02frhhjANTBVIlnURe54ZjIhyIlemPl8hpOme35ZkqRzmuJcgxTT62juS4d8skd57Rwr5Ehqd7JhnLlVlYm0OvRGjxe2nyOnlqV5aPLgrSPVPkcLzEdKGCKIlct7oarSKwb7zEB6MZ6hqKHN3oxombN3tKCN4U1UI1lyyM8pE5QUaSFb69qQ+7aCFIBSpihkZnM8ujQVbXBegMunhs/wyLo25aAg7O/3M3166IcsWyIy8w8bzGPRvGeHc0SckwmRNVSFdK+F0WdpwUDZNsWadUkYk4bHTHi7x7zVJqvXZ002IkU2ZOyIVqmPxl3zRbDo2yJyvj8WkEHA6+tbaDN4dSPLx3CpdlI1ylktE19JwTp1fFkgycko1kWsLly+OzyehCBZvgYDpexGfGyI4NIFYvo6GmHbevCKbCp5e3cesbo8SyFQwdGn0O/nxpF+6/gr0fvj/Cb96f4aKVfu4+dQ5TuQqXPNrDkiYnPzmtE900ue39/UxkTPpTeY5vtXA6yiiCwtsDsCoaRTFcjOfKbJ/IUe204XWbqJh84agWvvnmMN8/pZWzu8KMZyqc+Ic9zK9x0F5tMl0oc/6cOh7u7uNER5THeywuWx6hPeLgG+sHaIoqjMR1miM2SiWLTN7kxmPquHZVDd3TeT73Sh8mUBuwyOpZ6qtyyJYfXUgTcdkoVJzEMgoRe4Ar5zdy5eN9zGmUcHpmiGdcLKkXQSwSS9kRjSr2jZXRVIuOOoXBhMbDF83lwT0x9g6qOH06i6o97IkVKORhdaOX3ekMUxmNKo+Ix7AxSwHRkGhyufAEk8wkvAQcNvIlE9WykG0qfqfCaFLHNKDaL/Cjk7tYWONhJFXm317v5tB4moNfOfkfCmjWV/9jiaIzZv7vBzX/sr/5z2cf38OSlgivDE9xycIozx9MUamYJAsWhmjy7ZOaObUjSLqkc8kTB7EEA1mBrKoCUCW4iKctHD6VTy6rQ8ukeXrQ5OYTmgh7ZOZF3GRKOne/M854SuWiJWGW1rv5zNOHyVkV/L4ysukio6toFZGjan1oosmiqAePIuETbfxu8wxfOqEGlyLxzVfGOGGem/WDSW46poFdUzluO66FW9YPMl7KMFuw0MoiIbdM2ixiF+w0+hzESimCHokmR5iNYxm8kkQ6LWBXRJ7/xDw8dolsRedPe2N89qgGntmX4MFt09xzTgsLa92Ypsm2Q6MMFpz8ZvMM4bDAx5ZU81Zvhu5EnqjHhiIJjBXzhCQHyZzF0lovn10b5evrh9AqAumSQSQEsbRBxCdx9pwwbx1OI+gikzmVoA+mEhatIRsrGr1U+yX+tGcKt6jwxwvn0pPM8ZuNM4wkKzgcAllD44Q2P11+DyZw7coadk/m+fr6IWwKJCsqIaeNVM7g88fUccbcIEPJCotr3RimxSuHkrw1PEMxk2Bc9aLq8MRl8wm5FZ7Zm+DFnlkm1AIuy8aiGg+aZbJtIsv8qJs5QRedISevDSfoGdEQJZOcpRF2yWT1Mmvqq9g3VaCYFYlUiZw0x0v/tMqu4RLHL5RBMHCIHg5OFRhOqjQELQq6zqJIgGWRAKWyQKxYoing5I3+FEfVenjtUJbPH1/D8joP+6YKLK1143cqrO9PsLDaTYPfwZt9aRyyyDEtHmZmZgiHI3zt9SGmMho+RWFurZ2eRAENk4+0h7mgK8xd743RFLBT5VQ4pSPwYZvcS4cS7JzIEcupzAk6iXoVQCDqVegIO3HJIlNZne1jOQ7Fi7gVmULJxO8WEUUomQZDyRIzeR21KPLJNdVcuCTEA9umOK7Fx+IaDyVd5/2xNM/3zaJoNkTR4lCyiAQ4bQKL691MZErECmWcdlDKLmJJlVXNcTKym6mCRaXkxKFYpComHmyIIsxr1EklnPROqYimxHHtPr7zkSae2Rtn42CWC5aFWF7nwfVXrFDWDeySiCAIGKbFrz+YYixXIlYu850TW5EQ+PpLo1yzqppT5gYAyJQ1bnxpgBU1HuoDdt4am2Yil0HVnFQ5bDx2wUIe2DHBw/um8dsVshUNRVew2w2wq4RsHkYTOlUOiWTeoKla4KiAzpsjCk6PgctTpFy2E/RYfGppO0ujVTxzIM6OiRxfP6ERuyQwmC4Tcsr8dEOM61ZGeelgikPTJX51URvCXzWXfr1lAstZIlMQ6Qy6OZQo4tAUsgWDKo8MpsjDH59DwCnxldcOs2kkS3PYIuJTWFsX4d7tk4RsR6q0bZLIcU0BZgsq3zihCdWw+NabQxzOlGgP2/j8ymbqPAo/2xDjG6c0ohomfofE5NQUvqoIDkXitteHWdvmZftUirIqcscJrR8+h40jKW57cxhRk/nJOa3oBjy8Y4a7zmlBFGHHZIb9UyXeGY6jSyoVQ+dzSzt44tAUK2t8LAh5WddWRbKgsX+qyF1vj/Ojc1pYUOMmV9b5yXuTZEsGOyayLK33cM/ZrdhkkbveG+Hd3jz/tq6e7qkiX1x3RJcrXdK48ZV+zusKs6TawzuHM1y/qoafbBrnk8ujlLPJvyOKdMPCsCzs8v8+KHnncJrfbZ/ixjV1/PDNSa5ZHeGtvjSnzAnw2K44f7riiNbUQzuneX8oy/0XdCD9hza7/w6i6B+Fq/5ZMNU/NVF0/E83Uh114/BnqOgChbybom4i2wpMJR0sqoMdhwUsS+AHp7Xwow0TpCo6LrfFZfPCbJ0oMZPRWNfhZutkHgOD2kCFyRkXObFA0GUiCw7Gs2U6q1XUcpDmyAzNwQo9E7VMFUx8zgJuxcHBGQ1NlwjLXgRbFgUbXsVF0O7kR6e3cc7DBxia1ljaYjKYELA7TTrCEiplihUwLIX+WZ0Ov5vrltaye6qASxZ58uAsNVUW0zMisizQWStgKgU8ZphfnzOXTFnj2mcOUW0WyToUcE3T4q3ijqNXcu+OcRp8Cj/aPsSxzSazBQHZcpMzU9S4ZZIlC0H3MDwrYAkmTkvBa5f55bkdRL0KOyZzrKr3ctnTBzk8XaGjTqKk6RSyNrQKVIclZlIapikS9guY9jwuSSKTPcLm90yXOWke6EqS2WQQRbI4tnOcpx+ZpPuFh7nqaz/g59edDEB/vMinX+wj7FK48eg6nu0bZdN4jojDgaZLlKlQ67EQpQqKWkN7tcwHY0kEucSlSzPUBAZ5rXsZibxAUNc5kPFR7/Jw+8lNfP31IXJmnmiggizrVCk+3hhQqZO9WILJTE6jtU4nX1awTJGJbJm5UQUBgeFchvagE8tQSBVhTSTM3tk88yIufnh6GwA9U0XeHEzw2liM3kmLaxbVMlks0Fec5fbVizmUyrBtIkd/ssS7V6/80I/fHUpy7fOH6KiGY+uqiaUMuqeKxPQCD57XgWZK9MRUCiWLmoDMBQvCeGwS2bJOoqjTGnQgCAIHZ/McjOfIqGUe357B77HAkFAti7Kpc2pEwhV24bSL6KrE8JTAXWe3fLiOHWM5Pv/sICmrTLXTRtCpUOu1c1xTgMlchayhkSnq9M0U6ZmpsPPzSwm5beyczHH3B8OMlmbx2xyEnTaKqkgsVyE7Y+ebpzfikkW++8YYKaHMnLATpygwNK2iWXDRiipixTJ7pvI4NAdOh4AomVQMk8cvWkjIpXx4srlxMMuGw1k+ssDPN97rRRYF3DaBx85fxmde20eyqLJvukir10+d18Zvz5lHT7zIwXiW720Y4fWPL+O9gSyP7Z1mQTDPgYKdvmSZRTUyFU3h+UuX8uXXejkwmyee1lnYKDGUqFArBRHtBvefN4c/7Ivx+91jLKqRkCWD7mmLzoCbnukS4coE3Q//lKYTTuHz113L073jyJaTmazI6e0Bnu6LcXF7E7eccKT3O6/qvD2U4qebxpAdFZZE/Kzvy/PJJTU82x/ntPYqbj2uled6J/ndvhFqPTqxUpF5YQdeV5pM0cnrPT5uXtPCZQujbBrN8JVnR1BkOGORn9cGUjx68TwufbKbG1bUcu2KWp7vnuU7b49x1xkt9Cbz3Liqif1TKW57opuC5OUPV8yh3m/j5rcP8XJ/mqBs5xvHN/H4wVnyqkZFFbnz5FY+9XwfKVVjaaNEBRXTEKkNaBhiErsVRFFKTOag2etj36REk8/DFfOruWVDN4WSyIlzNXZNCiyoKVEuVzGalEEX8DhExnMVRNGkwePiB6e087HHe2gPiwzPWthcsLzax8/P7OCB3ZO82jeLJZcZjUs4dAmPT2QkqSILFictgE8vmcMNzw3hEgVkRUAVdSxLYGHAR20IDMvijuPnAvBc3wQ/fa+bDTec9j8S0MA/D6j5l/3Nf+5a382MorKmLsi28Tw7JnNomoBkKly4IMwXjjnS5vTKoTh3bxqlYpjcvq6F3+2eYDSr4rLBNYtqkQSR32+apcVV4OaPLGL+X0+gLcvioocOESuWuf3kJjAFfrZ5goxeZm6txXgCiqpI0KmQy4EoW1y1KkIsp/H1Y5vZHyuyb7LIb7bE8Hrh8iURHu2Z4VdnzOFb7w1xxaIoz3TPMpwuUdZM/IqCKpUpVkRW1vm5fV0r3fE8v9gxwEjCxCbIuASZYlHglLk+vnhc/X8Sg1V1k+++NUpXxMkVy48cJpimyczMDAczdu58e5xoSCZol9mbTNEVcVCNj61jOUIhuGZJHVuH85RMnQOzBURD5NqV1TxyIEbBrBC0ubhycS1vHErjs0lsHc9iOjTKRXCLNlySwsJWG+8OZVgQcLNztERLHaRUDY8oMF0AnwNqZC8ndwaYyWvYJIGuGieP7J1mKqthiSbnLwyhadDid3B6Z5DPPX+Ys7uCHNfqp6gZvNiTpKCqzHdpNDVEscsSbSEnoiDw281TbBvL4fcb9GXTdFR5afQ5mev3sXWkwLdOaaInXuDgTIFXeuOMFApU2RQqlgqiiEOQSGcFgn5IljTO6YhSLgu8PZTkpJYgY/kKCbVCLK0iWTIORSBv6HTUQEYtUynacCkS0xmBtio7k4UKx3d4qbY5eWxHgrXtHhJFjf4plYuWhrjh6BoOzRZJFDWObT4yfXT/4Di/2F9kftBDg8/O64NJTu4I8LHF1f9lSwzAn7bNsHs8zxeOr+P5gQl6kxlS5RLNfg81Pjuv7lVpCEo4XCZaxkGt18YZ86pYfyiFJugUxRI3HtVC1G1nZyxDrduBS5YwTYF4SSWv6kymdWp9Cg0BO3PCR1qeAL7y+gCntQc5f16Ec/90gNagnZtPaCJe0NgxluehPVN4FAlJgPm+MkWbm8liiamsQNgtkijphDwmQYdM2AvjaZl8uQKCSY3NT4PXwXuTaaIumbGMxsVL3NQ7vDy1K8exc13siWf41RnzeKE7zn07JllUp9Doc/D6oQJXLI5w5bIayrr5Ianx7/HdO1vk2e44rw0m8NlF0iWLK5dWc/1R9XzixYNMpjXyRYuFNS4mCxVkE5I5naXtDlZE/fx+6yxNfjsLwx5S6Wk2J0VqPA5WNXhpC9p4rjuJUZG4bEWI92Nxjm8IcUZHiJxq8PkXBzh/XpiFUTd7JwtcvCTM3okCi+tcPLRjFocs0D9b5rX+OK11FvVVEud3NPPA9mmSxQoJrUyry0vQaePoRh8IsHU6wb7JHH6PwbENUWyCzJ7ZNGe0h3liTwqXKNEcsvNOb5bTu3y8OZhGthlQUdAEk6uX1PCHbTOsafYyXVRZ2+zlnBaRUDjCrW8Psqm3iN8jUuuzce6CEC/2JLh0UTW1Hhvzqp08cXCWx3fPklJ1vn9SC4/sSNARdvCVk460anXHCnxn02GuXlrLE/tjjMTgpLYALREbrxzM8IvzW/m3lwcZnNH42IoQz3bHuXJ5lKtXRD98Zt9/b5jPr27A71R4omeMiNPJ994do8Xl4uqjoqxrC3z4jDXd4Ltvj/Ktk5v40fuTtFbZuWRJ9Yd74n8kinaN5/nWa6P86WOdBF3Kfxlj6aJOxTCJ/gcR7e2jOW5/bZQvrq3DMC28DokfvT3Jdaureflwgp+f3Y5TkVjfm2RNk4+A82979r+Iov/37Z+aKOq853UCYZkV9RJbRkRc9jJRr4VRDjCUreBzGXgtL9ctq+fhg9Oc1lbFxqEcE1mVFSE/Hi/0jJf5/ElhfrK1H1EyGE6bLKgpoqoBBpM682uLuGUv03mTQknmxLk5NCHPdE7E0qsYzxUYjjtpdnuZKqksrCugWF5sZohj6nzYZJELFoXpmcnx3c37GI/JeOwuTEeFinFEdHo4WyDsMSkWqtAtnfPnRHl2Xxq1CCvmKLzem6PJ48QQLXR0fDaFgSmV9dcu5EvPDrO83sVMfIq3kgar22FgVKIl7Ga8VKJSgdpIgbIq0+B1M5bLUue10ZtQaXB5mEwJ+D0m08Ui1TYvuaKAxybxxMfm4bXLPLp7hl9umgBnhSrFhixJfHVNC9mSwd6pPA8dmKLBp2BIJkG3xVC6xLHRKBvGE6xq0dBMi+FpD4ZuEHIksVFDzcHnWHfRdXQ2VCFLAu1VTlbfu4cvHVfH3KiD+3aOkNUqjGVUWr0eRvIZPrY8gyJa/HFbkC+vbuPsjmquemE/q5qnsdnLxIs28oUAO8Z1Tgxb9BddxJIKPp9JW6RENDhMqVTPdE5BMyxuWDKXV/rSvNqboLXGQqi4SBR0fH6DUtJBjdfOdWsi/K5nL4trLSwhR++Ml1QyiGlZ2CWRpy9bgiAI/H7rNI/snmK0UmB5vZe1tUGW17o5qsnLxx/rpaNaYUWtlzvWT/KN0+oZiJcZTJbZOZFFtmTWzvGwot6Dqln88M1xzjlujHIpSjrjQbbsPHzJPM79y3ZsooCl2SiVBGayOu9+ahEeh8x9W8d5fyzFyR1unumdxYmMacqopknRrFBn6uQddrxOcMgSbsmJkbNz22kN1Pj+vkz0iuf2sa4pwJnt1TT4HR9ef60nhdchcXTLEVHVa5/qpcZnwyHD+qFpEgWL5c3gQCGZEckrGQJ2O378JIsacTNJoexmfkgh7FMZzaexlZo4d34QrQIPbJ7hqDYnh1MFagM2fn/efD7y+HZW11VxeluYoxsC/HbzNO+Nj1OWS5Q0gaDDxXENVdzz2iwrukTSJY18RSbkMal1O9k8XKDWY8fnNkAqo6YCGDp8arGNP44UqZgqNnuJUslLZ9BBb36CoBxkz6RKc8BGjU/jcysbaXJFiHiOkFa/3jnMy4Oj+B0KBb2EqXlQizayFJgXgvd2TtAkTOCuqaUSiBBxevA6dTp9IV7YW+TWdS2cNjfIDzYNMJYp8fmj2vja630YkkG2YJIviES8Mm6XyFePqeff1g/SETXxeeNYpsRk2kOj3yCnaUymFLRkkOPbfPRmC2wfzfGFNQ186fh6fvbeBJ86uoa3BtJ8960xzlsQZMNghuF0mW+f2swf98X4zMo6zpkTYvuhUW7ZmOYHZ7SyOTbFplgCTZfIVFRiGZnLF4XZOJbAJ7mIZyymkzqtDSIDcZV59WUcihMDnYDdYjTu4r6zW/n5roPcsHg+P9jcQ5enlttPauXBvSNsnBxnqlAi7DaJJQJEw9NMzVYjCTI6Fk5R4iNzvLwwMA26i9agyVuHNEJVKh+b08RD+xL89Iw29k8XeezgNGF/BUVUUIsC8byEIBvcvLaJpqDMbe/v4cRwB7/vnkA3dezYWdSosntQ4U8XdvHCYIxvHdeJLIoYpsUNT2/mwUuO/ccCmtp/MFEU+78f1PzL/uY/p/1qE6cuqWawkCKWLyGaDtIZhWzFAEvggrkRrl4Z5bI/9SJ7NW5b18I3149SljSiLoVrl9bys63jOJBZ2+TjojaJBW0NiKKIZVn0xUvc+uYgX1rTwC3rh1FcOqvqfPTlJxEBU/UyXbBAF7licQSfS+LKxX+bMmWaFje/dpgD8RxF1cLhhO+e0Mqquio2jqa4f+ck1yyq4w9bp8kZOomCzseWhhAkgdlSidmCwcF4AdPSCdpsmHKFTy5sYTChMaHNkitJfG/dPMJuGz1TRXTT5DtvjHPNqginzani4Z2zXL48jCIKH74UjWVVHtw0zfsjGQLhCumiyEn1YV4fTBEM6FTyNkpikaDDzRmdQcqaye7ZFJ9a1sjd70ySzBv4HBIl0+D4Vg/vDGWwCzIloYJDt7Gq08HWsRzndFSzZTqJE4X+qTLL5hSx2fPMpgOMJ+1cNDfKo7vieGwieSpE3DKXz6tn83COu85u4dEDUxgm6JbGY9sy1AQkFEPh08fUsLrFy3M9s2TKGkopzSP9GsuaHGyP5fna6hbieZPXD6WZyJa57pgI02mDzx1Tx5aRLLG8is8uY5pw/54RDCogAppCyTRZVOOgo8rDu0NZ4jkDhyhzfGOA9pCTOdEj7ecFzWAmq7FvqkDfbImWoJ0/7p8k4hL52NIIL3SnGM0WsCugWDZm0nD50giTOY33+rOcMc/PLSc08vjeOGuavEe0l54fYDqr8Zcr52GaJlNT0+gOP3nNYn70iPB3UTVw2SS2jmXpnily7YoaHtw2xet9KVyKzLLaI1pGmbLBGV1VrGnxsuFwhpagg6YqO19+bhCbTaBkGUQcCh0hJx9bWv2f4itZUnl3NMGh6QpTWY1sxWBFnQevXUKtCOwYzzGcLnHSQgcTKYMDsRJ6USbiViipJk67yOpmN586ug6fXWTneIFNgzkuWRZi32SBd/eN8MmT5pIo6fzb88NURw00y0AUdVyKjVS5gkMW6Kzy0hLwcE5nNbM5g56ZAumKzt7pPJqSppRzMDFrEQxItAbt1PnsDMbL9M2WsLlVXIKNeN7Cr8hUO+389pJO+mZLVHuUvyMCfrNtgof3zOCTFAwMltX4KRsmbodAKm9wcLrAF04IMy8Y4DPPDLCwzsFk0sAXKCHa00imk9mkk2MiAk5fiKd6ZvE6JPJZgbaoxMC0zryok7MXVDGYzXMomefe0xYi/VUz59+Jv3hB48ndCWLZMt88rYmXe1IsqnHxk/cm2Bcr0Fqt8IeLu9ANuODhbgTR4s8XdRHLqfxyywSHZ0vIEli2EpRcFEWN1pDCaR1hUjmNJ/el8Dskzu0KUzYNWkMKv9kxgSQIVKhgN52UBJ0vrGjkjd4cIZdMUcpycYOHFwfh7dE0PodIOgOWbHHHKc0c2+zn4T3TiAi8MZDie6c147HJ/G77FJm8iSgI3HB0NW1hJ7vGc9zy4gh51aS62iSVs1AU+PLRTZzVFWLfZJ67Noxx+ZIIzxyaYrxYolKQOKMjxC0nHpleOZwucv0r+5GR+enJ86jyiNyxsZu71i1CkUS+/OIgd5za/CGR887hNN9eP0pX2MWXT6jjjvXj/PbSNjIljXteOcht5y4i4LIhS0eewRt9KSqaydkLQv9l7hmYLfGZvwzywKXtvDqQ4Oy5IZoCDvZNFnDZRDr+KjDeM1WkJWhn60SOP+2eYnHQx2eOrcGh/D3g+W8hiv5BuOqfBVP9UxNFFz7yFpZHJ+TWMDU3hilhiRo2yaRS9pMsWWiGQNBlUKUEOLkhwq83xTijy8/RbV6uee4QbUEbTmcF2Z6kXHHSWiVRNEroug2/zQ5SkYmUCwwnppQm5MsRtruYLpXpn3ZT7fQwlRBBU5jXpBINJ+ibsdPqqOWFPQVkf4lPL2xla/YwybQNSh4ymo7daSDayxTUCqsbRXpmRWTLxrKaAMtDIX69eYrfXtRJyKXw8ScOkSzouJ0CuqTz6AULKVQMWoJOeqdL7JjM8O6BIQZUOzNllQVhOylDQzFt9MYqdNYLfHxRHWd3VnPjq/3ceVIb92wdpM7joKCW2ZsaxW2FSBRFPJLEgViFC5c5uXPdYl7vS3HjM4O0R2ysbPJyx6ktSKLAOwNpbn11BK/HIlZSmVutsH+6xM3HNnD5wnquX78L1UqD6UFTBfa/+zqlja/w/otPUl/fwD3vjfPWeILvndTG0Q1+Yrky33x7iNF8EcPSqXLrlCsO8qrJmYvGKZSdbBoI0BjSkSWJn520kj/tifHbzdPMa7IwLROnK03AASFVZE9R4LyWuQhKief6RylVPAQcItOFPFGvA1WTyZYEhscFlrRJpCoqDtnG9StqGY4ZPHcwgeYqsqRep1yB93theYuEz2UxW9SYGy3zhaVLqXFF6Y8XufTRQ3j8Omvq/VR0i1d3F/jpea08eyDOdF5jKF1m35eW8+c90wxMV1jfk6SoWZy4xE6zw8dQpsBwOclLFx/N93a8g1asRjJcbJlIs/6q5Vz96nukEl7aA35uWdfInW9M8L0zm/A6JB7cMcUrvQmeumIBm8ZT/GrjJK1VDi5bGkU3Nb757H6uXTeXzoiTD4YzPLY3RtBfwimF+OxRDaxtDvxdfH3xtW4GZ1QMTeLFa44k+9d6UljAGfOOTEeYyavc9NIg2YqGgU5aKOJUdBbVChiGxGjaYn9MoNnpJhKAiqWye0ynK2xjcTjAUXU+vvP6GLUhiSuWVnN8U5D2sIPPvbkJTbNzSksN53XW8s5Ikp6ZIme0hbn/g2lmjAKqUMQSy+QqAh0hmXQlx2w2gGwqiJLBbNoiEhBxSDI9ExWaA05c7iKapIOq0GCabMxAo9uB3aEyVVbpDNlIq0X8Don2cJFYVkYSdQTLSaLgJOJw0eR3ctn8Wu7f3U1Cn2FspBbFVWEirtDicfKJEwQe3Z6nb2SE0Sd+iXv+Mhac/THcdoOckSVqi5Ap2HjwvC48NolH9k/xw/dHWRT2kNBKaFk7s6UKzRGJnGqhuFQEocLSBgPVUMlXZEaTdly6l6JWRhAVJuImF8yvZnGTiz9sn+JwssyfLp7LxoEcg4ky3zy9kSsf7ePn57fy3lAGj03kqhU1pIoaXrtEWde57/1dnD5/Hre+OcpIosKydgNLPFK15iyH+P4Zrdz6Xh/nddbQlyhhaPDK4QRul4HHDgVDpc7uoqRblHSTsOylL5OhpUYlJEXYdKjM7ac28NTeJLI3hddjYqARy5pIphOfO8f2Xj+tURnVMvncUY0M51Js6C9hChoO2aBQcII7zx3HLObuLX3sGTIRZTi+U+DgjE7Ep+FRQ/j9Mh1BF59dUcc33utmx2ETw7SojVhcOr+OH289TJVLxS+G+cP583HbJEZSZSqGwaVPbGDPjWf+QwHNa/X/WKLo9In/+0HNv+xv/rP6hx9gr7JTVwUFKqQzNm5a3cwfd08xnTT5/aVHyv7v2jjM5Uur+c77fbhwkimZyKrCry9p5ZG9M3zx6HpCLvlDMmX7eJ7fbp2iyivx1bWNDCbLPLBznI8vq+YHmw4TECUypk4qK+N1SHx8UZQnD8T5t7UN+BwSq+uPaKj8YvMYrwxO4nIYWIYTjQqiJXJ0fYRX+1Momkxn0MUdpzcxkMozW1A5pqmKb7zby1jSoKRZVLnAEAwqqsnyqhAfjOWRXRVCDhuTeYNGt4vlIR/r+1OsaHJzQoeX3+yZoAobI/kSq1qdfHdtJ5lUgurqaoaSFb65fpiJYgVFsjhnQYinDs1Q64W8riGaNjRNwobM0U1e2kMOihWTa1bU0Ddb5CuvD3LRgjBe8UiLii9gkStarK7z8sFUGrcgYwgGdjtcubSaVwYn8LvLVHSdQsnDdFrksvl1rO9N01Als3s2R5WsUEDl/LYawk47G8bj9GcLRNwSsbxGlV1iOm+xPFCFaQp8cV0t92+JManmcJazlFwymmYnX5Cpd9s5PFuh2m3jc8fWktN0mqsc9CUK/GZ7jOMaA5zVFWT7aJ6ZQoW3hzI0hiVG0xUuXxyiJ1lgophD0BRmihafWlbP3KCXRw9M8ZHOECe3V/Hkvhl+vSWG22XiclikSibLIn6OanTx530z+BQZpyJzbH0AUYAtYzkagwp7ZzPMq7ZzbEOIU1ur2TKW5Yn9M/zwtCPV1zZZpKyZvNGfZFPfEH5/NY1+J28eylAdFLHbBL5zUhu/3DzO20Nprl1SywfDaXZO5pgbddHsd3J6R5AFNS4mMhX+sifBpcvCNARsFHWDdNHg0EyJR3fEOWthANU0eaMvzU3H1bOg5ggZ9drgLNmKxsXz6njxYJLhZJn+ZAm/TUawBL5zZhMV3WQqq7JxJM2heJHDqSLZEoQ9FumySle9jUann8NxjUzR4OSOAF1hF0GXTI1XIp084ovvDGb4+QeTCI4yAafMUTUBxrMqswWNYxsDvDeUZqaoUWNzIFsC/fEyn11bgyjAB4cL3HJKPYdTRfbH80zENfZNFpG8WeySRNnUOTpcx+FpjVMWevhgIkVfIo+gOpAFkR+c3sq8iAtBEFjfk+KpfdMMlvKEHQrFgsTvL+4kXtJ5vT/FjvEsaSOP16kQz5m4LDvtQQe7xov4vWDY8tR6JNRkhZLDSaZkYVkKLrtASHJQsFQuXhBFRuL8BWFe60vyyN4Zrl4R5cTWAD98c4I6v42wV+ZPO6dwBco4ZJFs0sG8iIvtk1kyqo5dEql22njo0rmUVJ3Ln+ohUzSod7iwyyKXrAzw1KFJZtICszmTqE8knbWY12AnVlI5vTXEX3YlAIvaKFy/rJ7XBqbZN11kUbWPg6kckqagWjrt1Ta+vLIVtyzyi3f3sSUlYjPt5FUTXRW49YRGzl7492TKRKbCTa8PcHp7FdcsrUUUBW5bP8x1q2qYLajc+vIoJV0j6JRJWzqKYnFRVzXP783wqTVRfrs7xpVLq3lrMMFIvoiimDgEG4Ku8JOz2vjVxik+taYGxWbxnfcG6B2G64+Osn4wyXEtfk5tCXDrK2PU+23cfW7Lh+TPw7umCTplzpwXYvNQjns3xohlKnztaCd/6TcYSFf43UWddIadjKbKfPLpfr62rp5ar/3DuPi735mukCkbeBwin3qhj4cu6OLqF3u47yNzwBRQJOFDYXVVN5FEgXcHMvxm0zSPXzXn7ya8/XcQRf8oXPXPgqn+qYmi659/Hn8ggGFpTOVs5EoSbSGLZLlI96SXhioDu2zSPSWi6gJLQ25uP6Gdi545QK3niEJ7vmQR9JjMqbHQrApOxcAmOkiWdSxUXLKDPeMyTkeBBq+M21UkVQKtEsS0BEqqgFBxIFomzqoUAZuHw3GdyZjCLadGeWDTLBYCp8x38sFoAbutiN9uZ+eETmOVwPLmDPGCjF4OkNd1JElFy3vpzZR5+pKFfOf1MWYKFfx2G8OpEnPCTu49v4NPvHCIB86ZS6PfwfPdceqkHN/eNYvXpeKya8TTPrpjOovrFa5fXs90QuDSZSHixSNVPt//YIjV9V5+u2eEeVGB2YKOX/TRM6mzokOjbwa+fVwXp7QHufPNUV4amub2k1o4oz1CRTf5wvOHmcgXGSxmWFZnx2EvMpVxkcnKnLfIz5bJGSypgL1sY8+EAZufI3LcVdx13pFJT7/8YJJvnNSIU5HYMpzj+icGmNsmUNI1xrIaHX43B8crNEZlGoIFgv441U43e2Miou6i01nLWNLgsuUhHjsYoy+XYkljCa8ioqWLaC4XdkUkWTTIqwo2wcFIUsXtEKl2K8wUimi6mxqviiIJvH/Y4qvH17BztMTusQpVfpOWSBFZFCnoOU7ujHN4uoHuGYnOaIWgQ8BlNbK22c+iUJSemSK/2xGjL1lkLFPm1+fO4Z3+LIZh8fz+JPde0kJvKkffTIWdIyVGZlRWNHr53keaebknyaO7p4kbKjs/s4Ifb+kmZHOzqjHMFc8d4MCn1/Bvb+1lOiVhFxXmBt3cfkozt73bxzENAVbVVpEqa3SGXKy6fyc+h4hsSZQ0k+lyiYdPq+Oe7gQfnVvDR9ojTOcr2BSLj/5ugO+c3kTRNHh3OI0pmYRkO/3xEmVTx2YzKQg58qpBVIjwuTV1nNRe9WEcnnDvAU6d56MxIjCUn+a1gQJnzE8xlawmW5Y5nDC4//SF/HTjGB63SaycZTynU0WAMzqCzK92ccdbozx6aReP7JvBNC1uPbGJ4+7dx6I6F9ceVcOqZi+/2jxJrmLQ5nfwaF8M1dRw22CymKLa4cMyJJJFi3JFoKnawuuqMFsqcNOKZaRz8LMNMaI1OUJugYlCihYzwKAq43Cp2AUFSdKYygkEnQqiqOOwp5gXsTNTTnCwvwtBNhgvlFnXEObrxzXz6ZcP4bCViRWLhIUIffES1X6ZE1qqeK5/kjaf84imx/QkiZkUkdoIrroQD5+9gg0jOc7qDCFLR7Lcd98a5unBcU5rruGUphC/2jaGzRdHMAL0JUssqgGPK09TQCBWyFAo1DKdF5A0D4dmKrT47KQy8JUT6mmosvHAthi/uaCT8UyZH70/wc/Pbsf2v/SjvzUU5wvPDvPTs1spaCbZzDjvzghs7Daor7Go5GQaGnIospPRWZMOV4ADsSJFW5mffqST0ztC/PT9UTbMTJCslGnx2dDENH39Ueqr7GQKFpJssqBF56qFc7hzwwg/OLGdSx/tod4vUV2TQZIsJOzEshamUKHZq3D94kVc8lQ38yJ2bj++jTu3dGNW7FQH80xOeZBlsLsMpvIGimDx2aUdLG90cuMbHzA/0MKrB/I8fEkXd245RLEoYdgypPNOLuloYFSN0xX2MZo0Gc7HSZWg3u3nljUtfPLJfjxOkcsXKVx21Nz/kYAG/nlAzb/sb/5z5v2byGJHNQS8HpMLu6p48kAW2VHmV6ctYk7YTd9sieue6Kex3mBoxsRp2vjpua00BuwE3QobRlP0J4tcs6iG2dlZwuEI335rjKPqPfx44wQfXx7m9d4MVy+L8sfdkyQLJqpSwWHYkBxHRKCjPokvr2rmnq2jfO3oJpIFg7UtPq566hBBr85UwUBTRZqCAsmSiWBaOC0nrX4X95zTxnC2wA+39nJhWzNP9sQ4MKrhtEMwIHL5whrePJzi8KTG0kY3smQyEFe5+qhq/rx7huuW1fLwnhlObPfTlyjSm8phKgVk0UKy7MymRb69ro02R4m6mijXPn6YyYzG6QvdvNCTRBJEfB6TZEqgPiJx1eI6Xu3OUFcl8dZghiafnetX1bKqwYcsHamA6Jstcfv6EVJGhZJmccPyWs6aH+Kmlw5zOFGm0W9jpFCipspCM3U0VUE3RdAF7j69nVqvjT/um2AgXSKTEpgtqVy+0s/bIzNkZtwUdYO6Got1TQHeGk4xmzFwyjItEYXvn9DJvdsmuKCrGs3QeXRzH8fNb+HsrhCbRnI8vjtOpqIxFFc5f3GAvbESqxt87JnI01Xt4qqjIgTdMre8NsT28SyfWFbLhsEsU3oam+nAZtr46FIf69qCvNiTZG7QzabBHFGvwqGZEt89s4mxVIVPPN1HlQ+SFZ0atwWCSSons7o2gCiKXLAoxPrDCV7tTfHVYxs4Z14Ew7ToSxTZMVLgtLkBBAEUSaDKqWCYFltHcrzTnyHskjmuXiNvdzFdTrO1/whe6U+U+MU57ag6/GJDjIV1TvK6yivDcc5oChNyOrhudZQfvj3OULLI8R0BAm6L4WyRl/tnkcouBCBapZBVVSq6Qcgn8IkljRzTEARgw1CK/kSBN8ZmuHBOHX7Fxr2bY8ypU5EVE91UMPUjVXtjqQqtQRcNfge3HdfKUz3TxPMaWw8XCHkUbLLInWe0ADCULHHNowNUeaHeVuT6E+axeTjPYLKEYEjoos7hbJEgTrxOkb0TRbxeg9agi1+c3clPtoyxczxDslym2idQ0DTCbgWH6OLATIFqm5uo08busQL1EZG0plLO2qjz2xjNF/GICkGviEu0UbBUJvIVqjwCc0MuFkW97BkqsXUsgzdgUusXcNotjIqLQ7EKPqdEWTVB0ZgX8rGo2otLFkkXTJ7vn0EXDCzNZGWkwCFdxIGLsSRE/RJVTpFTG6P0pYpsGyhx+bIIH1sRIVvW+PP+aQQBblhWz8s9CR7ZN8OxbS7aQg66ZwvIosTWkTyTOZVFAS9nLqiid7bETWsb+Obrw7zTm6Wu1mQ2a+HDTs7SWVDrYMdUjohTpGLpuBSJStbGRYsiPLknTsU0uPG4Gp45mKBiaKRUnSqHTLxo8umj6jirK8iOsTwP7p2gYOU5r6MGoZDj2RGVTF6iLGhEXXbmVbv51gktH2pi/bu9dGiWH28ZJeJ0sK5TZl5VkIe3JxnLVFAxCdhk4nkdn10mFKmQVlVuXNbOUTVV3P7qKF11dmSHwbMHZ7FZNhJahWq3gk23MaXnqLbZkQSJc7siTKkFSprOewN5WgMeVtf6+fP2WTrDdu45t5WgW2H7WI50UefH703y9ZPqaaxygAU3PTvIZ5bZWNhezzVPHubipSFuWH1kfP1ju6b59Y4YugbfOKGRiNvG6hYv/5UZpoUkCoxnyzzdM8u5nRGuf7qfBy/qxK6IXPX0IR69eB4hl0KmpON3/n2r8L+Iov/37X80UfSDH/yAb3zjG3zxi1/kZz/7GXCk//KOO+7ggQceIJVKsXr1an71q1+xYMGC/+3v/XdnOffRp/AEbGA6qKhexnNFIt4y5UIESSmhWhUqqp0qm4eSmUM3ZAI2F0XdxLQsJMAtKRSsCiY6C6I643kNw9JxSy5kpUhRU1EEDxVNoazrKKIdjyISy8gIgF2QGZs0CNaWiHplvnPcfL76+iGuWNDApYtr+cabfRiGxOo6Hw/tH+OYBd08v7eOWreXcNU0pu6mVPYRy2ksri8ymNYZS9i5fE4r22dT5AoWhZJAIqNTG1IYTJSYW2fj1LYQHuzMizppCCj8/M29fJCDWq8Mks5Y0qTGY+eWY1p5pzdHd6zM/Re3cewDe6kPiZzQGqTFa+f+3RPceVIbP982zGTGZE6NwVdWdvHecI7PrmqgZzbPNS/t5Str2tA1iZBL5sTWAJ9+oY+edBpJkJCBufVFkjmZ0Skn69q9vLArRyC7g0PP/5ZTb/4hN528jNf7stxyfCOiIPBqT4pzFh5J3NmyzmRG5d6to4xWUjTYg/TMlMgYFRoDNobTFY5pq1CuKOybNlgTquH9wxluPLqBKpeMxy7wyEAfXoeO256jy1FkVvDx1H47IdlHwKtioTNbEIi67cRyKkFPBb/NSUnX8Do0ov48Tmo4vamdr719mIBdpmJo2GwmqaRAwO3AbhP4zVnz2BqbZU/iMEhxRpN+MlN1pAoGss2kLBUZSVu0BR3UO5wk0yaPXj2HW94+jGnpDKZKpCtlQqKf35zfyeuHMjyxb5YZtUSjx8VZi50MxFPkzQp9cZ2xjMDLly3nvs0x+tNFyppJNg2hqIEoGZzWEubGla0fxsa5f9lKwGlRLNgpFhRkU8CjZ/nq2QtY1uDjyYOT/Gn/OJMJiQ3XLcFlOyISOpapkCxpJAo6f9o3iUMxiTqdXDq/jqNbPBxOloh67Pgdf0sClmVx+WOHCAcEvE6BHZNJdKlErduGYDkolAUOjhk8+NG5RNw2piolbnnrEIZq496z5rB9qMD63hQvfnI+umExlVeRJYvzntqGs+RnVaOPi5aEeGj3DFcsqWZNk5e8ajCVq/D9jUdGP2fLFnZTxqWIlE2LhFFiTthOvKByWnMth+IF0iWV7niRy5eFGM0UWOrVOKQXkGWZVCWNzYqQLJpMZASW1IrECgUa/DLZgo3RWZk59WVSJRFNlbFJMiplNF1GVQUEQ8EpCSTMPKd3+rhifgvf2tCPZsDecZWFrik2/fpuLr/scn5+y41/t49tm0jxyWf7WRn1Ytgswm6B65Y08vPtvcyUVSzVTtxIsazGhsNeRBFlDiVK+MUgsYyCYCgcmqowp8rFqe1B/u3Ev40z/cxTA/QkCnz/9GaObQnw001jdIacLIp4uObpblSpjKA7yFV0Lm5R2JAyUCTImRV0U6XW6SWtGTT4FX5+8kIGZlVu39DHYLbCN45u4fGdSXCUqPJapLUcXqeKaHjZ3WOnPiwhWhIeb4nzFoS5akEbgiDw0w/GeKhnCEWCY1ssxjImum7HJTko6mVawiCrEV7tT+Bw6jzx0cVc+eI+ZFmj2WNnYNRJbW0BSbDhduh0BQN8elknt2/oYTyj8aWj2vntrkm+fVItt27cxMhkBJ/o4MGL5xJ0S3zm1QPsH9Ox7CqtQZVKJsQNR9Xy0PY4pq2ArVzmyevX/mMBTeM/mCga+98DNd/+9re54447/u5aNBplamoK+Mfk4X/Zf5/9u/+svGcjkWo3mmDi82aJZ2y0hBzceswcOoMeemeL3PnWGIOVFNFACRd2vrByAUfVHan4mSmo3Px2H2c21vDEnhm+cYybpR2NiKLITS8PMF2u8OB581B1kyse7iOuVnD7dOyGjUTBRLQEWiIKly6I8tvN09y4LsovN03xxaPreWzvDH3xEqWyRTQIkrNEwFvCLPuYzYlcuawG3dK4eG4DHrvMYKLED94Z5+S2AI8dmMK0l/nyyjYe3BNjJFegwS9QK4ao9zm4bFmQq547RLkErVUOrl4e5b4tk5QFjbxu0hiwkG0FMjkbZzY28/iuBMc2VJjWvAzEVa5cGeYP+2L47SKaWEQRJTBlsgWRsGxnIl+mNgiFoojdY3BJZwN/3DLLhSv8jMwaaKbB5uE8QY+EaYKhW9hdMJs2OLnLR21QYCRX4JSmap7cm2IgUcKnSKTyJo0+O5IgctOJNTy8d5beeJGCUMaFTEkDRRG4fGENFy0I88nHD3NMu5vefJpPr2jiga3TJMoqn11dz7ENPjYOZXi/e5g3Z6DJ62Q4WeHO01qYW+3i8qe6cSlwfEuQ6byK22USctjpi1eYzJdJlXSuXVLL3lieFY1uXupOMpytsCTqZbag8shF85ClI223mbL+YatS70yR77w+hqpopMoaakkg6JTIWhVsKNhluHxJDW8OJTmUKCAh0uxxks8JWK4jgxsuW1jLkjo386vdpIo6B6YK7JrMYRkCAZfMn3dPMddXYNqyo6oWn17ZwrwaF68MJDhvbgS/Xea5vmk29GcZy5UwsFhbF2Ru2I0umWwcTjOYLFE24MQ2L80BG3/ekcLnFMirBqpksCjsIepRqPJqnNFax9zgkT3zphcO0xPPYzrLdPp8uEU77SEniiCSLxsMp47gzqlCmZ3JFLaKHY9N5tJF1Ty9fxavU+L+Czu5/Y1hQi6ZLx57RCdMNyzGMxW++eowfjHFYUtEVl1IlkTQJ+FxCdT7bHztmGZ+vmGSdwZT6K4cXf4q0gWBBz46h4NTRX6zOUaWCmGXjAcHe6dz2G2wJOJnplxmKFXhycvm8Wpvkl9umcTrMkAQSJcsqh0KkiHz6Me7GM+WeWs4xXsjM6TKKsWyRH3ARBfLXLmgg+Obwmwci/HBiMrbg2nmRpyc3h5kY2+JCxeHOL7dzxN7Z/jV5hhd9SIThSKL3UWmBYnRGTc+j0miDPNDHhaH/BQrBilVo0CBYxurOLujlpJm8pttMdY1+/nprj4Whn28eShPY8Qkoxu0+G2c1OqnZ1xiSdTL1qEcdQE7eyfzxMw4NsFB3irhxE5iRsHtgIxh4Haa+LwaxYqEXlCwezVOrq3h6d0pzlwQ4OunNGFZFu8NZHlgW4yTOv0czuW5fEENi/6qz6bqJvfvGOe5vimWejT6i25SOTi20ceWiRx+L3z/5DYWRb3cv3WSRFHnmGYPyYLBA9snueXERo6q9/Kt9w6xb8TAMEQ+vaaGOp+du98dwxAtPrO6hg3DCfbNFFjX5ufmY9o/bMe79oWDjGRz2FQXNx7TwPEtfr7wcj95MY3DplPO+pgTdfLdEzvYOpliVW0ARZJ4/3CGu9+d4IGLO3i9N8lD3aNUcg5+dHYL335zlFzJ5GfntbCiwfNhBemdb02wqsnDaXOr0A2Lix47yFTCYEWLi68d18BNz43wmWNr2DNRYHGdm9O6ArzRm8YuCxzf7mc6p/KDd8a49qgoS+o8PLJrmqYqB8e1+tkTy/OzDRP8+Ow2Qu7/rHv030IU/YNw1f8JpoL/7+KqfxAE/cfb9u3beeCBB1i8ePHfXb/77rv5yU9+wr333sv27dupqanh1FNPJZfL/R//DbfkxCU5iRdkClqJeSEXaimA21GmLaTR7PURsDvQNYuQ5MctOygaGm5FxSmKJMsafZMVvDZAKrJ3pkIl46WiuilZRSqqg5DDj00CmyVSZfOQyNroH5WxI5HVNO48pYW2FhNdqBDL6/x4yyCCovLq+DA/2tLPUD7PuV0hfrZpgsF8gc397QSdMqpVxm5VIRtRrlsa5uj2NO8PixQyIeyah6DThk+ykchZiHYV2WcwnCyxoNFOSTXZdLjAU3vjLK5zc+cb48TyGo2+I+W3lmXic+uoQplH9swyXVJ55OOdR/rTGxWuWBzluiV13P3eJI6SjT8c7MdEQxBgT5/CzW+M8Nz+JL/dNsmlj/fw5RXt7Izlea5nlhV1XmRRYEGdgMdhEvUXsDSF6Sk/3167iDPmVXHnKW2cP9dO/4Z3+Pw9f+Trp6zk2++N0JvO0xMvcO1Tvfzo3Qn+vGOaS//YyxN7Z/nqy0PsHq+wNlTPnqEyogQ1PomyVaDGo7B/zM1wzMklXXUYoonXprCmxcvFS8P8fsc0laKDneMWbUGDRN6DQYWgU6IpouH3xQCJkEOhouuEfFnmRgRMq0KqZGJaCrFkiMNJlWseGabKZkMUKwS8GSoVmWhQYnmjg88eVc94usL6njQbDzp4aX+E/okqEloF065i2cuEglkWhpysrA6wpsnDYT1OSTf4+elzuPmYNn531gK+f+xCzpjnpy9VwCYLVMoWOz57FM9eNZ/5ES/Vbh/7xkRGZmXqfRJThTLVfomAU+SmYxq578J2EtMixayN4bjBvqk8hmnx5IEZBM2BpilIMvh8FicucGN3wF8LWCgWJXyaj92fW4HXISOJR077Ftd4qPc4+NZL40wnDOx2lelChcf2zJAs6Zz3SDenP3CQFw4kPow/QRB4+LIuvrC6EVlXsJtOJFNh77BCriQimjL1QYUrXzpIxCezrinIB9cczY/PaOexA9PMrbXz20uOlKTLkkCD306Nx8GfzlzOVLHC3ukc92wZYLqS48cbxzjjkb18693DXPzEQQYmNQRDpsXnpN7pIJmxOLUlyBMXLiKnGrhsMhtGE+xLzRKsqrC4yUSzLO47YyFLq4PsjpkYhoRoehlPC1RUB05sjE0LzA0LyKJOVitRFxIZTumISHgc0DOl4VUcaJaKZUo4HBA3ClTbXeyfyXPbqyNcN7+FczpraA4qpJzVnPO9H/Ny2sZj724jNj1DxTBIFCv8es8ADWGTHTMF1jVV0R3PcccHh+jPZAi5oGiUafM4KJppRMvBZM7ArVWxexJ8NoF02aQ+KHP7aU30z5ZJF/UPn80vPtrGqhYPfz44RSyjct87M/xh9xSNATtndVTjFZ0UhALz6032zeSQDAXLEpAlqHW7SBgpwm4L1Srwh4OHeax7nPFZiwURJ7/cMslwpkjAeWQNDtGJYvkoWwXWLoDfX9BF2Zan0RnkinmtmBY8vmeG9qATSXdQ63CT0lJIUgVdt5FQs6QLCpNJO7uns1S77CyqtvH7Q1v5xakLCMoBdk5azFolCmkPvTFIlovsieW44eU9HE4XMCyDiFemq9rJL7fNclxdO80hnWTR4AvPHuYH74/y2eWtFC2dgOBkbMxPTyrPV18aZkmrgxkrC4HR/+Mc9P/LBPEf+/k/sQULFhCLxT787N+//8N7/8g8/C/77zPFpZPUtCOCuqoXjxM+sbCZO9fH6J3Nc+OLA1y1MkKdV8FnEynqdiZSGi90H9mn3xvMkM+KPLxzBp9PIPrX0cUP7JqgLBg89NH52GURr0NmXYeP6iD43RUER5GIS0YwBYZndd4bSXPl6jDffW+UpWEPT+yOc8HCMAGXQMAPGVMFQ0JTXZQoEJAUNk3M8PCuOBtHMkxmKzRXOWjzO3lw2zTVLjtXzm/ijrdGyahlTmj1MJyy6E4UuHplhFtfH8FmStgFkZG4ygM7x1CVNKWiiVcRGM+a5NI+FnpqKFsGroDBVE5jXrWbT62pYctIjpYqGUOs4BUdWIKOplm4RJFkuUJ7g0lC07DZIJuR+dOWadxVOs/vS7OuzUPeMDh/cYBrj4ricULZMqmULBa02PnYsgjNPhdKwcOv35/l4GSeiFNmYdjLqZ0BrlgZ4awlflY0+qj32dDRWBHxgy5zaluQFSE/UbfCDX85zMlzfbw/mOf6xS28eShLRTc5szPMsQ1+LAQe3DlFQlX51Io65gRc/OjMNo5t8xP2KFy+uJp0xWDDeIKtU0m2juV4uT/O51fV8+nl9Whli1eHxhkqx/jLoUnilRJul8FEPsdkrsJPN43xqZd6+OQzfXwwkuGDkQybRtKopsnqJi/NTidXLKrhnK4Q58wPIckCAY9FldvGy0Mx8kaOaKBClU1mNF0mIxRRBAlFlDiUzjOZL/PeSIofvzvBaErluNYAnz22llM6/ejqEUxdVA2yqsyzvXG+/+4oz+7I8PiuOG6bxDPdccqWSUm3WBgMUDJ1Htw7gqqZXDyvmocvnM+8KjdOwc5sDizFRBSPVEDIhkiz18lx9UH2DYpMpswPY6qrxklH2EGDx8f8iI95ESeyJXL6nCPru/3URlSlgiEb+GUHnz26jrXNfn7+wQTHNHuZF3bx9ZeH2Tmap3u6wMsDM2weS/PkwSluf3eQNR1uGkMCDcECkruE12ugmBKD4zqbeguc96duevMZ5tQ5yGYdjKYNXA6Rb709TI1P4QvranFIMgvDPo5v95LOWURsTnpni3x9XRMvXrUQp01i71iJyxdGMCs2FgWqaHF4uOHoKGvmOLj6qV5KqsXFXVHGJ2Wskp1yRaRQlnFKDk5pibJ+cJRXRnsZzicIekxGskW2T2b5zhlNBF0ypmnx+I4EYZuN+85cyPeOn8P8aje64UZ2GhglJ5JqY1W9j4PJDC/1pFAsibtPWoBdUvjkS9082j3MgqiDDcMZjq+uY/tkHtlhYUoaYXeFybTGC4cS9GaT/HHPJMsaPXRPl/jFR9v4aEsbhaKEXrGhYuKpMqjyS3g8GjZFYDYtUu0xEdGpMn2cOS/EyzfMp1CBn703wUcfPUjW0FjT7qakGvROV/jcy318/bVBfvDOKG8NJ9g2O8stx7bgEOxkVY1FzQq3ndbEr87t4Cend/Kz7SP8cf8Q16+sRRB1vrF+mPt2jHPz2mZObA0ym9PZO3Jk0myNT2Z1k4/jWv38+dIu/Nh46UCaH542h4+21+PFyYUP9rJhKMVgqsz3T+ykxuZBkzXufm+UH20cZ7JcIJWX6QpEyKk6mwYLfGF9L/NDPhRJ4mebxmkM2vnZea18/ZVh7t0xQSErEw5ZlC2DJz/exaXLgvxi9wgv9c9+6PNfPbGeN/syFFUDWRL4y8fms26ehzUtHpqDTh67ag7LGtx8/vhantg9y4uH4hzb6uXn78d4Z+DIpMLuWIl/e2mIeF7lN9umuPm1QYZTZe59f5IDyRwX/aGXfMX4/0te/H8LU8H/N3HV/8iKonw+z/Lly7nvvvv43ve+x9KlS/nZz36GZVnU1dXxpS99iZtvvhmASqVCNBrlhz/8IZ/61Kf+t77/31nFix5dj9PrRhLs5FSVRMaBX4LmhhwFrUIsY0PWfYzFBNzhEt9f18mvdg+w65BCtC7HmQ2tvDw6RrYk0x4WKecVUkWRUJVOc0QjWbIQMXDLdvYNSgSrs0iVIL0jBoZl8dFFAQ5WZqjoBlG7m4yu47ZpVHtFRtMqIHL3uiV89NFDuCWB5igkixB2ijicKohFqpUoRfkwyYKNSi5KLGXRVOXgmCYfj+2doTYCffEKd5/QwS+3j6HYNVyGm4HZCh21du49cw7XvnCQOY4SQ6ZItduiP1UiYPOgmyYyNn5w4hwWRr3c/GYf7/TmuHxRDV86vp77N03y6uRBgm43EykFQxOIZ+ALR9fx+K4EZalCJikR8kgoXoNq2cl9H+3gqy8PkFVm8TkMZksVlGKUvnGL2lqBVKyX0nP38ZvfP0Rf3kGqrPOnXdNUuSQ00cBlE5nIlzmhLsy586q47a1hWqocfOuEZm5/a4SJdIVCXqS1VqI3n2FZvUypAv0Jk66wk+F0Ga/g5Ow5IW5a20BJM3l6X4IPhrPcdGI122d78Jj7GFJr2DIcpqgKqJSJujyMZQt4ZAe6IVPl0pksZqhSqpBEi4JepjmkMTERwBJkBvJZ5oZsGEUX6CKr59l480CRxqDC4oiX+zfOsG6JnY+0RcgUdB7fP4XLrXPNoiZ8Ngf7pwt86dh6dNNCkf62G1mWxZm/62ZVh4OIR+HdkSRPXbQUgJ99ME6VU6YlJHLX1oMsrVNwC9Vc3FXHhpEMT+yfYTKpU1Et7jnnCLBMVVQW1Lm4ZW0Tpzy4jyqvydI6N50hF4YpUKyYbNo3iTcU5MrlNdgkgYMzRT6+NPpfxlZJMxhNlbnp9T4EXSbktNEWcPJqT5KSZVCl2PjEqihXrfp7gcihRJktIzk2DmV4ZyyB12fQ4HFgaDKxpE6xKLLh8wsZzRU5MFHkt9unmFvt4vCUyqVLI1x/9N/Wsy+WZzRb5Efvj3Pj0bWsba5CQmL1L/bypVPCbBnPMJbQQIa2sMXcQBUPvJfiouVBfHaRTRMZZlImjWGZREnF69UwSw7qAjZ+d04X312/k3cmRPw+g7KqEPDoJLUcdi1IWTXIC2WOapDomS1jM/woisVkvkRHlZOhafC4weMQGUwVaHZ7KVglVNNibpWXm1a1cvvbQzT6bfRmM5iWRWukiIDEhg8OU3jhPurOvYJlR5/IULqIUXAyrVa4ZXUzQ4UCL/TFcdpMfA4Lrx1Saom51Roum4GmOciVSxwcqEFAxG4TyaNR1E3uP3Muq+p9f9cbbloWP9o4xlfWNvJiT5yOkIvmKjsbRpMMTGu8NziL6o7RbNrZEfdhR+bEDh+vDiSoDxtM5FVq/CqSWkXZMlB0O7oqcedHWrhzcy8juRztIXArIvGigt8mopsCvz/zKGYKKmc/sYuHz1vMgoibyx/rZUHUyZ6pAhO5CjXBEpohcWhEpKHeRC/LxMsaLSG4eH4dm8dziHKCBeFanLh5aN84dV6JgyOgSzpzgk6+d2orFz/eTUedRTbh4N7zO2gP2/nUKwcYiGvMa8iTzwRY1xpiPKPhUkQiLjtP7pvF0kU8TouTOqp46mAcSdbpCCR46KJ/rEbR683/2Iqi00b+9yuKnnvuOfbs2fOf7v2j8vC/7L/P/t1/fvp2N29MqMxkdUS7QbvfiVCy8+m1YX608yBNjmr2JwoUyyadUYnLOht4eE+chy6dyzP7Evxxd4zmgIOfn9OOIkI8Pkt1dTV9yRIhp0zEfURn4uXuJHe/N47iLBHxS0wlRRSHjluSmRv2cufJHfzgrTHGUyrLGzw8dyCB6NTJW2Vqna4j5ECygEOGKtHGeFkl4LI4p7WOlw4lsdsEVlUHaAvZ2TWVxWEX2TmVoqYKTmiM8Nj+GI22AN8+rYWwW+YrLx9mTzzP5YsivDM+jSSXqeQ8xDPgDlSwYWMmAU3VMomcybGNXuZ7Kzw9aPLxZVGcdoGHu8eolC3snjxmxUFKqyBaCnZFwLB0jLyXeFEn4lK4enkNG8ZSNFfZ2TGZ46pFNfx22zSmpFM0KrgMF/+2roG1rX5+t22KTUMZJrMaZUHD6zHIFkXmeX10hp3sjmc4HC9zbIedA7NFPjavgbPnhnh4/zgvH07Q4vHS5nfymZX15DWdRw9O8u5wgo+2N3LdqiiCIJCt6Dy8e5rhZAmPnqMnb+OGlfWc0OH/UBhYN02u++vUKr+vgl9ROKe9EcuSOH9eiJte6+bghMo1q4N4RDf3/D/s/XeULmWZ7o9/Kr8599s5d+/eOZFzRjICoqIoqCNmZxgVs6JiQDEdcwIxIIhkJKdNhg07p967c+5+c65cvz96ZMaZOedM8Jyf8z1zr9V/vLWqnlXV6656rud67vu6np4hERUoNmxCooqqOVy6rokD8wZjCzbHDQUZTPj50pNT9CRFetMCw0smtgvlhkc0IKDbHs1hGc+SWZuK8vRUEdN2qXs2Hz6igwtWpjFsl+9uHefZ6RyGLXHp6mau2tQJwLNTRX6+fY6+hECzq/PEokvEC3NMd4R3Ht3C85Ml4n6JnbkiczmP4WyVQ8UGV23soNxw2DVXpzfhY31bgI0dQSKazNbZCjdtW+Dk/hB7Fhq8NFklHjcQbR9LRZfBVIBL1zVx9tByBfvNW5fYMV/CH6syn5VZqrhY2DT7ggQ0qGEREhXWpsN88OjlKt0dc1X+sCdDe3OBubLLcFYnuxjhxIEwa9s0vvvSPK0RkYphkAo6NFsSuyoymt9GE/zUbZdvnbaCH700z+65OrZkEwtK6LbHJavSdERVVqYCfPn5Q0Q0lVdnq/hMHz5FoGTZrGkJ0BnTmC9Z+JDwPJdX5qqcPRTnmlM6/8wl7oYXpsjWdV45ZPA3R6a5ZEMT9+7P8vxkhZemKqxpDnDJuiS/OTCGbrjYoo0m+sjWbKI+hbWpMNNlm1bNx9qmELvmapy1Ms5opsGhqRl2OS5xv8JJ3Ul2zNbYM6tzxtoAK2IRbnx1kdawxlTO4oKVCR4aWyIWdpnOirREJQqmScwvka8btIYlkGoElSArYmEmsxKXrElzcm8MgC8+MkXcJ/HURImFsskXzu7g1N4E1zy5m+FCGZ8XoqC7xCSVmZxNa0xmdZfE3x7Wx9/eMcUlGxIc0xvimakSsiBycm+M7zwzw7PTJUKKxLuPauGWfdP4VYvNQYFDlRi7y3nWNYX43llrgeW58v7ROR45UGRnroZfdlFdP5WqwIeObeOSDSm2jBWZLhj84uUlBps1fnLRIJ4Hz08V+PRD06xJhggHRT58XBsN0+Xzz+9hKi/y8WO7uGtPnpFsnTWtfgZiIf44nOPsoTgvjte5eFOYX23PkK05tMQkvnnmIEFF5vNPTNAaVDm6M8Jt27O89fAkP38uS1kwuOXSZeF/23X548EMR8W8P3M9+6fhuh4ff2yUDxzRTk/c/9px2/F4193DnD+U5LwVSd7+hwO847AWIj6RmbLJm9alWaqafPKxQ1x9bBd+UeFv7xnlHUenuHj1v3Qs/D9RUfSXwlX/HkwF/3Vx1V8lUXTFFVeQSCT49re/zcknn/waUTQ2NkZ/fz/btm1j06ZNr51/4YUXEovFuPnmm//V8QzDwDCM136Xy2U6Ozt56+/v51AtjCg6dIZUJqZ9DA3OMJKJ0BfT2D7rggApwc+HTmzlj5N7yVRlImKIy9e3ccvOAkXdQpQcXBwMfVm4sUaVVKhGQvPzwrSA7XgMxDUmyyatsTpeMU2hIuKJHs1JcEWLQlkmnbSIBAuM5BU6gn6uOXolf//4boJOhHzN5fyVSaZrVSbqOWQBSnUfVbfGke0CuYZLpebHR4DFioPiCjRUk5qlc0F/K2OVKjXDRVUcDi24tAX9jBV11raLy6K+Wh0v7LFo1lCcFCZ1IrKfibxEMiBx4/mr+eAjr1DJR8g1HM4fSvKrfbMc3iVyRHsMw5J4YGKSb528ie8/nWW+3mCiVqfZp2JZIk0RmU+f0M1XHp2h6DXoarbYl6/T6YtSqINPg+HZAsYdX6Pr0quRgq2kohb5ikrYD7K/gOrG2TVt0pZQCPtF5upVWrUAubJAKACWKaDLOmHHj94AJWYSUF2yZZFEUMCvWehCjdWhDi5am+SrW2YZSoT46hl9/GbnIs/MTaApFcKWzbydYuukQ0dSIR6wCGoGkyWXmBzD9OokfC6JSB3LCLPlgEI6KdAccilWRUp1gQYO65okJKnCjvEgLWmRpN9hY0uUR/aY/PSiATriPp4cL3LtU6O0xCzKVT9hcZlYOaI7xBkDMQzbpSn0585it+9bYn1zkMFEAPGffFRH8w0CkogtuHzhmYOsDTVx5lCM1S0BSrrFQtnAcuDqJ/cT0SREU6U7HOSG85Zbz27dluWuvVnOHopz5ZHNHMjU+dC9h2iWylywaQU7l+rUXQPwqDZEJFHg62f2Efkn7WRLNYOL79hOtSjzseM7aY/62Daf4/nJErmqCAGDDx3WTU0X2D1Xoy/u573HtQDw3afnGC802LdYIyOWiEp+kpoPV3TI6A1WtUBN9yEIUK+JbG6OcP/kIt0xlWPakgxEglz/6BzNTSIbW4Js6gxyz8F5Pn38AD3RAJbjIosCH3twnMl6EVdw2D/lctlhKbJ1izXJKH8YmeSc3lYuW9PM2+7bRV/SomaKHMjAmQM+3r62h92js9w5a2CbEtuX6pzYK1O1LZZKGj1xm7pn4JcFTMtHJqNhqgYJVcXCwGloOI6Ioeok/ArVqoBP8fCFKpQqYfySzHyjRm/cz75Mnc6oQizooDsNIrJMuWKS2/Ei/ce2o9fXMZ2R8fsF8lWH/rTGXF3HH3TQLY/WKNQti96mEq4roUkaNcvEcUVmChpR1U+m5iJ6CnNZi5N64yT8CmvafJy3IokiCrz9dyOsaVF5IVMir1v86sJV/N1j+zksHeO5Qw1aYg3aAyUemw2xIq1x8VArw4s6T81kkRQLTVDxB/J0yt3opshIVmdtOsjlRyT52kv76U9n8TDJ5DvI6hCQPT5+zEo2N8fYuVjmhpeH+dap6xjLmnz0vglMzyEdVvGFGniyjdvwkTMsGq5FTPaRNy1UzeDqwwf47hNLyBGdK9e38ehkFtszMCp+1jVHeXqiTNqvcu0ZneQMi6sfPESzHKCrq8S+jEGtodEVCDPTKGG7Ep8/bpAbd0/y6eMG+PhDExzTHmHBLnLpqnZu25mnalvY1gL3v/3svyyg6fsLE0VjMD09/Wf3p2kamqb92bnXXnst3/jGN4hGo2iaxlFHHcVXvvIV+vr6/sPz8H/H/734U/6c//MXWaxqNMUESo6FrS8Ttb/ZO86+GRdBc4j5oDno54K+Vr65ZY61nSrrEzFu3Z5lU0eAS9anOL43jOd53LzlIJcc3c+Xn59ix3SNn14wRE/Sh+O4fPD+gyxYRUoNBZ8o4Pe5nNKdJFtzMGsSz01WkGSHoKhSsSyOHPChodIW1rhtzyLJMNRcg7ohkvJp1OoCf3dMJ7fsWuQzp3Qz1BTg+i1T3LU/i0+BtoRLxbaRHZmhQJIvnduNIolsGSvy6UfGOX9VgscOlYhEa7goaJqOJirULJt8VSEoqpQrAh8/sYMHJmc5LAxrujo5WDS4++AiRaOBX7WJ+2WyNZGIImKYKq8fSvDrV7MIgkNbzM9YxqSvSWWsYPCBI9vYv1TjuakK8ZDE1cd0cMf2PKK07Oq1qT3MSLbBb7ZmyOoGB/MNLlmb4ra9CwREhXQSjmmL8eD4AsWqgCLKOI5LMACFskdAUVgRDvLhk1r5zvOzDOfrOJ7DeX3NvO/YFharJr/YPsdoscGlK9MM50ssLWXZVVL55uuG2NAWwvM8nhkrs6EtyGi+QVG3OZSvctfOAmevStAZ1XhxrkRNd9g7a/DGTQkOVDIsVQRqdYFLVzWzZazIwUWTdBJawz6+f94KYLka58pbD1Jo2EiCgBq0KDsNVEnCJ0NB9/A7Gq4tkQrLxFSFg0t13nZ0koNZnbGsSSoskQ7KPDKW54KVcVwcLhlclhzojC9/p8ZzdV46MMWlRw8iSX+uAVMxLB4ay/LkcJmRXAPDFoj6JCQZPnNyNw8dzDNfNdidrfH5E3te003cvlBGFgXCqkxIldg6UeXu3Xm+/4a+P9us+6fx0ccOcCjToFKHk9oTGDi8lCkQC7h0RnxsaonyltUdjOV1JgoNfrhtlpRPYbGmYwoWq2IxWgIqj80s0RSUME3I5WRWhRrMCz6EQB2foOKTJVqjIhM5gXLdpVoVeNPGFCXLoivq46KVTei2yze2zFA2LGqWw5pUmIWGzli+QXtUY0UsyN6FBj9+Qz91y2HbfJGvPDlHKioR0DwOiyc5pivCmtYANzw1zUOjWRJxl6SmcSBjc+5AAtP2eHykRDzi0RB0opJMtiQi+B0Ckkyx4XBGX4yrj+7BduAHz87zwP48h/cGyDp1WmyD7SWFwzsiHMxXyNVdWmMemYbN4ck4r07XOWkgQnNU44y+OEFZ4qX5PE9NLzBd1pEFCRuDkKyR0w38UoBCwyUsyyzVXdakfHzhlAFu3bOA7lhMZ1xeyZTp8Guk1QB/f3IbH31wBH/QIFv3CKDxlnWtPDNWYvdCHUVySTVZnNqbZHjO45Mnd/GrbYvcvSvPpZviXHVkO7dvz/LT7XPIrsTF6+I8MjnL6qDJUxMhkmERV3Y5oSvJp07uAuC+/Vm++vAsmiLg9wm4agO/q2EbKj+4tI+uf8hp3XLx8F7TXf3OljlOXxtiZSpAe9jPr7cv8LnTeqhbNtc/N8aW8SrHd0Z524ZWXpmr8MxEiXMHkvz4uUXOWxvltu15alg0hSQsyaRuC3zvdUNsbg3TsBw+8dA4o5U6F65Mck5fil+/muETp3WQr1nEAzKe573WevYnl8ubXl5kPG/wxbO6yNVsfv1Khr87qRXL8bh/b4GLNyRf+w5k6xbNIRXTdnngYJ4zB+K85Y59XL62ha6Ij56UylUP7OKGU9fQn/C/RhBlqhYf/MMYN791EJ8i/p8hiv5CuOrfg6ngvy6u+qsjim699Va+/OUvs3XrVnw+358RRc8//zzHHXccs7OztLX9o8XqVVddxeTkJA8//PC/Oua/1hcIcMVNd5CKJ2k4BlVDpidh4DoaEwUJz5IJqSKSINAwHfxBm6aQR7Ym0NAlIpJKxbGomS6i4BLRZHKmgV6XWNflYFOlZkmoBMhURII+i3hAwNBliiUNSQRZFiiZFn6fiyBAOFRE9PyYtsRiRaAzJlJvSKxKRjisLUzB1Ln9wBxxTWV80aU5YZGOCEiSgakHKdRF4kGLmJDglWwOzfKhBjwsSyDulyg0HGTFxq4piMEqbREVCwdNFFAMkwlDJuYXaVg2qYCPXN0jomj8zaZ2DMfhF1tnSSpBMjWTGatKa1jB8lxWJ4PsWKrxt0d08+NtM3QG/OzJVkkGRByxQYAQC0WXhF9moWrRkRLJ1i1SQYGK6RAURfY9fDfdx56ML5xAkGxk0aVmKvRGwrxpTTN3Ds+xP6Nj6SKqDILi4leWQUFYClCsO/gUiAQlRvINOoI+UAza42UsK8RizSbhF8kW/eiuSyxoYdT9mI7LO49o5tmJEs9NVNncYxHFYqyh0hRuUKlHmSu4JCMuhqHiuFCuenQlVeKRCptbfXQEWrn34BKjiw64HqIf/ApoisNIzqIjouIZKp4j0Nessn2iwRs3pmiOKHznxWnetqGZR0cLtIQ0ju2OsCIRxPM8fvLSAutaAgwk/Hzh4Wm+/fpetH+wjnRcj4m8Tm/C91oVyJaREq9MV/nIKe3csSPLvlydgN/FMkVqdTAdFwePdJOJIolEJR+bm+MMpPx85fFpAEQRVGW5pNLF5YoNLdyzdz8XrhsiqmoYjovpupi2h267rGwKIP+T3YaibrFnqcLvd+WIhiCsiSieiOnZmK5LJa8haA7hkIsqQkBU+eDR3QAsVg0ePJQhZzbQHYvFmkNECOCTJJpCCuetjLN9vsaRbRHu259joWoQDFUpWhaSHQVbotSwUX3g2AJnDMQ5vT+B63r8/JUFju0KszodRBQFvvTsIWRXYali05PUuOqwNvZmy9y2b56I4mcoEmLnUolwtI7g+mmYYNo27TGHdkVmXUcH9x9awqKBX3WoNFQifgeLOn7Jh2EJ7JtR6EyoZOomnziuh/2LDX77aoaOuErDsbnisGZ2ZrLM1DIoQpCqIYFgYlQDiIqL6EksVm26UiKmZ9KTqpEphZnNSzhT+8gPP8/GC97IWC1IOihTr4lcvDZFT1LjplcXsESdzkSDxZpNb8KjboqU6gEKuk5A9lO3XMKKwkzZISRqZMs27TGFsu0QU2XmGjpvW9PClukFahZ4pkbGMPjE8Z38dt8ktq0S9utE0Sl6fqYrDt1RhYrhYZoKigxVSycg+HnP4R0EFIkXJsvMlk0U1SRvFykbDh0Rkd2LAj5RJhGUOKo1wap4jAf2F1jXpfDsRInzB5v56qNzHNYVYP9CA0twaE+KFBsgChD1ixQaDjYOEVkmU7cRJYcIASytRs0QSAUkTEvi0tVpHh8t0pvQuGR1M57n8csdc4zmdPyqTdDvIBgRju+N8NvdCzQHFc5b0cQPXp4lFYFmJcw7j2imYTv8fOcUl63u4Lotk3hmkfs++Pq/SkAD/whq/nl8/vOf59prr/2zYw8++CD1ep0VK1awuLjIddddx4EDB9i7dy/Dw8P/oXn4v+P/Xvwpfz5+x26emnGwbY+oT+IzZ3bwhRdG0C2XiKcSCNssFEQEYENLiIlyjVpBRlZdBpMBeqM+HhouMdii0hcNkKDMcF1jrNLgvIE07z6mGctx+dBdoxwolfFp/IOBg01AhfMGmvnd9iyuKZAIKBQNBzzob9JoC2uEfRJrW/z8ZMcYguQiOSqlmoTpuqxq1qhVRd56eJL5ms7hzXE++MAwTQGZ2SXQZI/jVwRZk4qyc6bOp07rJB6QeXGqxE9fmeetm8PccXCK8aJDSPGQ0SibDj5JJV8WGQiFOHdVlBueXKC33SNuGmzPKyQTy0LAYVWkaDlYukhHk0euCquSQQ4Uq0Q0lzeu6OG3r2ZZ2x7gi6f2Mppr8Hf3THDxhji/257ldQNxPnhCKzMVnR8+P0dL0Md5q+OsaQ7x0mSZWw4ssCoe4ME9Fc5dHaViuSSCIvcezHHmQJTHpmdo9YcYzTmc0ZNk55TOx09t57tPzzNb1ZGDBqd1NfGHHXnOHUry4RPa+MxjIzx3sMFh3X52z+m0REVOTEFrW5rHR8v8j/MHlu3XR8q86+j0a5s8jushCjCa07lvf46umMYD+4qcsyrOyQNhbA8KNZtnJsrkagZtSYE7dhU5qT/Guze3M1816Az7uPSOPUQkmYtXp7lzZ46luoEW1on7RYoNB9PSaAloHN4cZabWQJUFrjutn68+McVUzuRHlw5w35489+zJs2FABFPm+ZEaZd1jQ3uAr5zbA8DehQqz84ucuq4XWZb+Rf5/87lpeuIaLWGZHz2/yECzSNDvEXAjGLbDC5MVPA/ef0wrpw4sE0W/3D7PUs3kmuO7yVYtbNejJbK8STdV1Nm/VOfMwTi7F2q8OlPlV1uzDDSpaLJIqe6QSkjULZsDuTp+USAgylR0l4fetY5tc1X2LtXQLY+SYeOTRXyyyHjBYLZSY95oEBAkJFGkWHdZ6a+z6AXJ6g6aCuuaNY5sC/HLbQUOb4uS9KsE/BbTS/D8RJk/vmMtfvUfN+x+unWGP47NElRCpGUf26Zq3PyWQQr6sp7kK3NlmoMqT+ytkogJHNUZJqaqZGoGz8+UyVccQppEvuawscPPxvYQP395geaIQL5hoigyguhSNV26EzCTBxmFkE/ibZtSvDJVYyHn4ckOcwWLlrjM6haVVQGTeyZgsWoRC0rMVhtInkgo6NKoKVy8KsUt27K0pQVWpjVO7Exx46sLNGt+jh+UeXbpIKWGylReJRl08QQbz1WZX9Dob9KYyJhEIh75mkdElSg5BlFJRcckFpB44+pWOkJ+rn32IJIn0agt61O6jkC5YRMJidQbcPGGGHfsy3Fid4xrT+vh9j0Z4kGB2/Ytcn5/mkf2lMgIZXyaxHvXd/PbZ/cjJFQSpNhZyJL0Bbju9D62zlRwPI/HDxbxKyJrm0M8NLbE5LyHT4GopnD54WnAY+dcnY+e0soPt83w3sM6mCtauILLF54ZYVM8wTMLGd6wJk2T4ue5yQpr2ny4lsCD+0u8/8QUritwwzNzXHN8O19/cpa8ZdIS1Lj8sCZu35Mh6hc5vjPOFZuaeWGyyjHdYT718Bh53aJJ8xHSJFwHEoFlUfr3n5gibFZpaWl+raLoR88ucOv2JVa3+/H5BLr8AQQBGpaL43lIgsAnz+j4n85LDcuhbri8/ZZDfPHsLtqiCm+75RA/vXSAvqTvtfMeHS4yktF53/Et/yWIon8e/xqmgv+6uOqviiianp7m8MMP55FHHmHDhg0A/ypRNDc3R2tr62vXvfvd72Z6epqHHnroXx33f1ZR9IZbHkQOhBCQ0SSXjlSB6UyAfCFBWXeJaBJXbmjl1/unsJQCmmIjeyEmJgNU1TqSLdMSVcgYVYZayjTKrehCnUjQoCUoUzclPMFF9EREUaKguxSrCn4rwIJu8sMLevnyc6PUWBZ17gorKFoJz/ZRaUQwHZtcPshXz+xhc3uEh8aWuO6ZcQzXZE2TiqrW0CQP0QmybVylvbXOu9cN8eE/jtEX9LNUgEhAQPMJLNVNWuICB3M6yaDExq46VV0k7lPI62XaPZlRz6FQCdIUVMlWoTng49Mn9XDrnnk+fWw/39oyT3+LzPe2TdAWM9EtgbDiZ6mmc8XKQS5ak+LG7fOkIjb3H1rCEDKEvSZmSzJtUYGPHj3Al56ZoO7VSPhd5moNIgR45msfI3HSpbRuPhZBkAkqDiNZi9cNJDmxNUXVdLhndB5XNrBsh6VFH5t6NbZls1x74kpWJ8P4JJGcYTJf0bn++QkiPoGpcoOT+hyqpsdIXiQtR9g16tDZ5eHzJOYaNc4bStMT9fH41CQr0gXqtQg+p8iLSx20hgUabpnRuSjp6LKmVK0gc9UxCbKWzhvXJGiIL3Ewb/DswRR37dB4y3FVxjJhlioiAZ+B4Kl0+iPsmK3RwCGpaZw/mOIDx7ZRM22O/+4eLjwihOOJPD2RR5VE+pM+FvIeR7VHCKgSHzy2lYO5GvuyVfYtNijVXC7blOJv75rgtL44pw9FObIzxFzVpDvq42uPz/LcTJFovIFuSvgEjfcd3c4JnTG++NQEB3I1GobHykSQr57Vi2k5TBQNrnpwD71NBrVimKohUrc9qkWX754X4QvPVfnQsR2cvyrJg/uL/PDZOX7ztgGCioIsCXztsVl6ExrH9ge5dd88I/MOXz+7l4hPYqlqMlU2mCjq7J/VeXo2hyN4RAOwoTnEuzd2sWWiyC92z6CaKietUDEweWqizndPX8Wrc3V6435O+YeSYoALb9yHBwSCFmWhgOkoBJwIyDYeHoWSwE8vHGCsqjMQ9zNdsLhjV5Zi3eX8VQnOWxPj7Xfs44jmGEf0hDmjL87eXJlf7pxkb67Mb847koWawY27R2kI0ywtdVG3XAp1iyOTJouSTMMGxxXoSzrkjBydkSieYHBgSSapBZkpObimn5aowPsO7+T4zgS3bc/ww20zNEc8Zgsu3c3gky08OYtGM0WzSlX3odlBJioNzuxOI6oWJ3TH2dwc5l33DmM6HhWrgTY/iz37FOsuvIxnJwQ2t/lojyg0+yOc2p3gZ9tHkf1LNIU8DuVMbL2FsuHS32SQNUrUy+0gmwRlhUP5BiEvDL4qISGI7VpkyhICInpd4PgVAXZM68SCAnmhyEcOX8E3npshHTFZHbAYcSz8YoA9SzZrWgyypQQNU6ApBFNFE9XW+NGFg1x+20E+emqCV/MHcVwByw5SMuuoooDhSNSdOqqb4j0b+/jZ01na4grbZ+p0xFTiIYkXJiv84g2DbJ2occNTs2gJi7aQQs3VwfCRigkUax62Bd1pl6mcQ7UqMtDhsW9EpuHX6Yn40F2Xhtfgh69bx+/2TvHEsE4gZNPpj+APZ5meiXLOihR+n8hU0SCgSDRMh1cWyliOx6pkiJMHIwiiywMjS8sLtl0Hue/KU/+igObRfgj+y3XQfyhqDpwx+m/f/fqza2s1+vv7ueaaazj66KP/Q/Pwf8f/vfin4Hrbksc3npzh709u59ot43Q3QTYrkS05yKJAKikSCcG53c3Li7KQykzJAA8iPhHDFjhnTZz3bG7mB4/v475Zl/cc08pb17Vg2A7vf2QXmapFSgtQtAsYFY0jeuMc0xnj+PYEV91+iOmyieU5JDQFSYK3HdbMib1Rig2bH+wYI6uX0XWNQhVUUUBSwLJdQp6frlY4sTvGHw7M07AcFuZU+pt9fPGMHlRZ4KtPTXNWf4LfDy9y4WCK50drzDXKxFJlFisiCb/IZEEg4jdRCDC1KNLu87OyR+Tl2TIhRaaom2ySBfZU/FR0h/ZWkH0VVE9joQ66IZAOQ82xiIk+5ooCqxJhMjWDdx/ZSjqksqE1wMvTVY7pibBrocKXH5/BU2w6EhLXHN3PtY9PcqBQ4+zeBH/cW+Atm9JccXgzL06UMSWbW3ctMbZk8dYNKe4am2MwHCESdjENkVdGLY4fDPHF03u5ZcciEZ/MOSviPD1a4cWpEmpYp2gYHJZO8JudGTJll1jAIxU1SboWc40oZ61Ms1C1sOoiR3SFOG1F7F/Nna89Oc2DB/L0xQKcvSrGQ1NZjmwLc9ueRZpDHp5kUjcVTu9K8+x4jaaYwImdcR44UOTM/jiSAL96NUPCLzFd1WltcjFtm3zBRzwCZdtCcSV6UiquJ+K3VXrjPl6erXDVkS1sagvxnt+PctJQEFMy2Z8rc2JrmsvWt+J6cDDT4MBCnWOaLR6dE9ibqXP9WctahYsVk0/cN8lZq2NUDZtDGYO3H55mdUvgted7fqrEs1NFPnJsFw8O53ngQIEfXjTAj1+YY6Zkct1ZPdz00iJBVeKNm1IA3LRtjl/uWOThy9fxo5cWeXa0yMUbkoyUyuRMk2wNwo6GZXmMZkzEgEVYk6kZLkc0x0hHJbINi6FEkEfHs+iug4XDZ47rY9dcjV/tXER0RaIBgbJp0eWZdHeliUREDs6ZxOMmdbeKTY3OUCurYgls1+POPXlcD/ySzCdO6sR0PJ4eL9EZVSjVXfbM1zmpP4osipy5MsYT43nmyxaXrWvia0/PMFtqMFav4EehUPZIxmEgGaA/GmTPtM6GtiBHdYfJN2z6kz4eO1Rk63yB8XqJ9ohLzXLQLRnZlZkrSPRE/Jy/KsmTw2UuWpdgrm6wY6aG5wqcsiLI+Pwkd41JhDSZku6wMq0xXm6gWhqCZpP0+ThvIMFMzeDFqRIZw6Y/5GdvrkZa9uHIDdb3eozkjWVdSNXkhlPX88hIkcen5jm/v5UbX8rjyhay4iE5AgYePhQ2dansydR488oObt+7hCWYNOrLVX+Ht0f42fYpnh6pct6KFB86vpMbt81x86uLrEsFmaqYvP/oVnKmzsGMwUSxzsiSQXtK4B0b2unEpK05TdCv8PEnd7M/WyWhBFjKCkgy+CSFTM1kbXOIN25Kcuu+WWRbY/98g9P7Y7THfNy5M8c7j07znWdn6UyLLFVdPnx0G2cNJvnEI2Psmqojq5COyayNhZj1FvjssevJlF0+eM8htGidqzcPctPeKS5Z0cbvXiqwYDfwKRJvWNlEWXeo2Q5dYY1fvZLlmO4wF26M86tdC6yPh3hltsLBWYvuuEpbSmZXMcv6oMcpQwPcN5bj22cOoskif9iR5bvPzoHicOXhaXblSgz648iiwKUbkySDCqbt8uxkiZ2LNa4+9l8SR2V9WQ8z4pOZKRq8PFXl4vXJ/+1c9teGq/4zmAr+6+Cqvyqi6O677+aiiy76s1JSx3EQBAFRFBkeHmZgYOA/XZr1p2R5y20PkvUCFHWXoOowlFLYsktDd6Gn1eYtA3184/F5Qs1VVrdINFyDmYJMRJWQrSCLDQsRgdUtHq6kk6lqJAMu8aCJ7dnUTYmgInIg45EK18FKcChn8uBlhyMh8oEnnsX1HJp8QeYrEPbb9CddJgvguhaZpWZG8zqKz+O6M9r43iuTJPweirT8IsZ8ULcd9sworGuH6UoNSw8RkFT0usCxnRFemq3QsGwiYZGJJZOBlI+GrBPxiYQ1l3h4CaORQDNL1JQwU7kgquTiOiJTCy5HrhYYXrJ568p2bt+ewx+yKXgN0gGRkCbheDb5pSDZkscpK8PsWarS0aIzXa1xWJvAbEZBIMCuEYG+DplizWawU2e6YlLeuR0t2YkXbkKSg3jKMgM+VqqT9gUxPRu57mdGb7C2wyageTyzX+ayjQk+e3I/FcMm7le5c2eO/Yt1Rop1ylKRaKBGphpEE0QKpk1vyiCoeJQaIrMFjZYICJ7Gp47rY39hnjtHhwl5Scbng4SDZaK2iBJo4qwVCW7cd5CAEMAQymQqQVY1mSiay2TB4bCOGrYRYteCQiEXpLu9RF8kypn9bXzy8SniAZcjWxJctraFN9/zEk3+KLIn4Zclfv/Gda/l43MTJb781Biur4hdTnHxpjivX9HETVsXuX80y8NXrOfDDw1T1S1qusCc0WAwLWLpGse0xEGEJ8YKdEX9/PziFdzyaoZ792VZ2+InEVoWevQEl4fftglJFPjQ3aNM1SrYrku+LFAwLGTNY02HTVCRMG0ZwxCpWx4Nx2FQayCHEmxqTpD2+VBFkZzZ4Jf7RhgbD/Lke9eiKQI/eWmBgCLy0ZP+fGIYydW59dUcxYbNF8/p5Kc7Rrn/YIV3rW9nz6TBdFWn5Jm858g0KxNBfrZzjIousVhxmS86PPvuTf/8FQbguJ9upyPl4QkuS7UGAUGjbksc3xFDdEX+sDNHe0ri3FURPnhEL7IoUqjb/OSFeRJBiZt3zCO6Eu87po31HQqffe5VQl4THaEgXz1jAIDf7Vng5t0TWI5EQIWg6qHVK1hBjZAmUnXyJHwRWiMVSoaEKrkIgsxSBcZmmhARmS0Z9DWrdMb8fOGEPm7dnmVDj8JPtk1ieiYlw6Et4lGqy2zoLDOcAcULk6tJjM+LnDeUYMdsnaE2jZdni/SmPabzHqYt0hUR2XnHb4gnIHnqhbSFgowWDMJikJklj6OHLLTgIvliE9tmRNY0axhUaArCaN4k6CWxRYuQLPLJYwf59JOjNMoiibiJqLoUzSphMc7VR/dwx94l9ueqgMBSzcF0XLrDKp1qDV0LkDEbBBUbTYFXRvxcvC5O0XCYrdaRHJe5JY03bExw/uoY17/yCn0pndGsD9cOULdcbBqoVoQdWQu/4nFca5KnJ4tEHY3zVqeouBbb5yr0Jn384PxBXp2u8rnn97FQbyB7QXyOzJKl0xqBtqiH6dk4trJcpekZ6BUJv6aSNZYtqzviHqJsslhxkQSZqBPhhvP6uPgPO9jQIrBvUcKoi9z6ppWMF3S+tmWaeh1WdMpcd0ofVz82wgcOa+OPY4scWrQRvRyPv+uMvyygGfwLE0WH/uNWrmeccQYDAwN87GMf+6stkf7vWI4/5c++qQU+9liGTMWmu0mmLta55qgBvrFljg1NIR4dzyNI0BqRkTyJeFhifavGUyNFSnVwBQ/RUHnn0U08NlrAqea5+MgBLlydpm45vP+h3dQtlyvXd/D9l+cIuCLvP6qLjW1Bvvv0PGM5neFqmaAoE5IV3ndMK7ePTnF+Xxu/fiHPmw9L8Nj8BDNlh0ZNY10qxI6ZBqLqElNkGq5BIm6iyTKiVMexZUQzyffPW4FfEXnvAwf47Am9bBkr8sMX5nEMga5mkEIVLEMlEbJRlQalahhPMpktKETcIAt1k6iiIGgOjmBzZlsTb12p8sSix4NjS1x1eAe/2zPFVNElKEp4qkGxqtKi+bhicyuvzJeWzQDCGjunGly2McUdu7Mc2xOhIyXzxacn6Y9pXLo2zc7ZBp84sZORfINrnxnm0qFWxuYd4jGBB/aUmKvqtKYgLPj43Kk9dMc1PvngOM9OlGmKC9Qci+M6ExzWHMO0PPaXixzdHuOU7iQ3vbTEheviTFdrrElGuOiXB8BvICgWEUXifYe30cjWGepu4+YdS0xkTN5zdCtHdYf+hRbIn6LYsNEt97VqGgDddnjrnXsQBYv5isDJHTH2Thukm+G07hSPHSjzxdd1Iksid+/L0hSB4dIio/kaougyUVCQXA3HFXjXujaemMwxXTa45ZK1hH0ytu3yqT9O8vr1CRTN5vMPzJGOSZw7lOTF0Rrnrklw6mCMN/36ALpjYwkmR8ZgRVc7b9nUzHzFoGI4rEgtE0I3PDvFI2NZLl/XxoaWIBtal52qvv/MPA3L5WOnLmsH7ZqvcePWBb70um7CmkzVcPjbB0a46vBWjupc/kbeszvPy3Mlnp0tcu9ly9VBT44V2FlYWn4mByRFR0QBTyJb85AVh4RfRBA8vnfmOqYqdQzbZSJn8/vtGda1Brnu7B6+8PAUrmwyXa2TN+s0BT0aloBWdujrbCPh03jkYJHmmEhAFdk2U0GTJAKqSL7ucuFQktMH46xrCSIIAt/YMsVDB0qImsmKZBDDFPje+QP4VQnTdjn3N7vpb1JwBYvJeRG/5lIyLXyOD0VzqVg2QUVGFmEwEsIQLRIRmc+f0Pdn+bJQNbj8zr00hV1010DyVGq6SE0XOG8wxfuObONTD0zxulURHp/NcEpnkr3zDWrVBfbpFranIqPxqwtX88eRDD99bpE3b2jh0nUp3nf/Huy6xtrWIBNFgy+9rotH9hfpTvoYzei8+9hmPvLHEV5aKJP2K6h+lyFfnGfGy3zjwi5Sfo0bd03x0lyFkM8B0UAyggS8EJ88vZ2vPDdBwGcwU25wSleakVIdyfSTq7h88Pg0P9s9wSmdaap1gQcO5nj7xhb+sDNLxbEwRZON7T7O6W/h9n2L7JuxaI1Dt1RlOJ+go01i22yNtohA1XII+jySIYPZvICPKO/Y3MIj+8q0RFRqhsOa1gD37irw/Tf0EffLyJJAsW4jCnD1gyMsNUxWNwf4zIndvOv+XczlPdJ+H3+zuY3vPDPF+09J8PrBLvI1ixcmy9yyLcsFa+OsbvNz4/MZ9i5WOWKFxpdOXsHXt8zw9s1p2iIat2xb4onpHN8/dwW37FngkYNL1IQ65UKA81akuHt/DkWx2Ri1eLUkY1kSb9ncxIePWtYKe2BfgemSzpaJEmetDjOcr3LtiQMoksSOhTzXPbDEUNqHp7r0RPxcdfQ/Eh//NDzP4533HODTJ3QzkAz8q+f8HyGK/kK46j+LqeC/Bq76qyKKKpUKk5OTf3bsHe94BytXruTjH/84a9asoa2tjauvvpprrrkGANM0SafT/yEx6/N/eye9bRqFapxMw6RU9OGTVMIBm1J1uQ0nFVSw5AaOJyC4In5FpGa4FEsK8bBIqeGiuy5hBSJhi6LZYDBdRXCj7FqwUCWHoZSfsYKJKrusijfx9VM2IAoCn35yPxkrg2GD4KnEgkVMz6DRiLP9kI+yYNMf9YNaQZU9OiI+yqaFbkmogogplMlkEwiKRVsyi2SlmSoKaILE2liUM1bE+O5zsywaJje+fgWff2iafMXh5NUBtmYLOI6J5GlUvAqnJQVmRQ9DD1FxDIo1gZaQxEeO6WfrlEFQULj7wCJZMUfKF0IQbVrCDotVj/msjy+e1McRXWHeedcwMxWd7iaDXFEh5vNRp4ZlKzRMj76YgOavsufu37I4skD/ZZ/Bp2q4wSwdoRAlw8MyNWypSFiOslSxUVWT1qDKcEagN+FRsTy+ePxqNrcuW/jeszvP5o4gn3p8lKpXZnJGYGW3QtascFRPGb/k4/FRjzVpHx4Oe+ZlLhnq4JXFRU7u8fHwHoWaaVOW8jR5YZo9lzcfN8jXn5lF9ulIikHZMumPa9RMEwSPYl3F5wTI1E1KdZm3bWwGFx44lGPRqdIbCWDoIl0hP0q0yELZQvYCLFZsTGwOCzdzxlCM89cmKDZslioG1z8+zxlrwhzVGeKp8SK/3bZEwXL46hm9HCxUeGYyR80tsVhVuOPiI9i/2GBV2s8z40V+/UqOh676RwvF+XKD3+3I8MR4ATXYoFJSadZCKBEdyzOp1kTyFQFXcbnmmG5u3DvKypZlMdHpbJilvA/L8nAki07RoLMzxWRJp1SSwANLsKnaLo2yyGfP7KQjqtIWXS7DjvrlP3vfPv34GHsyFTa2hDl7RYw7h+cJiBpPHqyCIRKKeiiqh6xYeFKBrmSZSrWVtNjExaua2dAeRvlnYnqZmkmmZvLFF3ZiWQFs10KWXBbLIoKp8JUz+/BwufqBUfpaLaxSiCO6o5yxIsLHHx3Frwp4jki1LKBbHlceH+bZ+UU0xeELx21kpuQQ1xTu3p/l4tUJbto9zYvzs7QFo8QaFnsNgaCsEg3ZuIKNJhsc02PgSfPM5juwbJW7d6q8YaiFnfM1bBPesCHFVUcul5Q+NV4g5Zf51osTTNcrCILDxg4Pmyw+MYzluczkA8vgy1Tpiyl89JgBPvXYGJ5coVL1s7gokI7JeK7LgQd+TGe7xMrz34gmCWybc0mIESzBpdkPr047rO4AE4vWkMxk2SCqyoyM+uhpk6g5NjHFx48uHOAd92/DsXwYrkU6BDO1KnHizBZcepo96qZA1THxmX5KcoEjgzK7igE6UjaD7XkWKia5YisjBZNPHdvPr/fM4NdMBDvAVMGkSQnxszf08P4nnmEgpvDMgQgBv0dzWGI+K+J5Lm1pg90LHnFFwzQFinmBn7+pn5Utfj768BgdYQVFlBlskXl6Osu+JZ1sBbrCEq7aoGqZNPk0qqZC1TTRBI03rU7z5HiVnNEgqqhISh2fzyGsCFRMgZkljfaAn1jE40CxQKkh0xzQ+OHZq+iJ+9i3WONjD4wxVdV54arN7M9W+d72vTw34RFSHUKWzfMfPO2vEtDAfw7UGIZBf38/V111FZ/97Gf/IvPwf8f/ufhT/vzs+QOM5lUen8ojyh43vX4Vt+/K8u4jWpjKm3zpiQksyaYtIfCTc9Zx6745/nBgHjwHXVcJuCq5moen2UQ1gct6NV5/+CDvu3eUQMBh51IV1122N5/L24g+g45ACNGSecfRaW54aQLRlCi6Fl88tZff7ZllT6aBU5M5oTvKuJlnoezS5g/wrsNa+f7WGRTRQ3ctoiEdv+ijbDpYDrSGRcZyIif3Rjm/p43duQq/37/IRYNpbnxpiYAsYHtQbXh0NAlYSg2f6qAKPgzLwrE0LNFFlHR8RpjJsk1vi8v7NvdzZGuEm57bw/3zFj7Bhy+gs1CxibpBlhoOTXHQdRfPUBjsVPj7o3qo6i4bWkMslk0u+/VBOqMaN17WT1G3+eGL83zy5C5u3b3E5rYQq9NBXNfjD7uznL86wb5sjafGC9RqcKAxi+Mo/PycTfz85UXSIZnf7lxEcEW6wz7O2xDhlzsWqTdcUjFQFYETOhNonspT4yV+cMEAhu1x0W/3EgyahEMVarUwCwWJs1dEKJZniSfTLDVMvnnqWgRBYDhTZ+ts5X9qSvGnqJsO9x3Mcv6KFE/OTHLjjjk6AnEOb4nx82dzHN8bpTOusn+xQU9C4+qT2vnSM6M8M5mnNaKyIqXy3GSeza0xjmiNk6tDvuzQnVQ5kK1zRHuEM/sTXHP/BKcNxjimN8Db7tmJbPopVpc1tFqjKrmGzdpolIGUj4vWJmgKKn+mn/KbHYvEfBLnrUzx3j+MUnV0Thzy06SG0U2PJ0ZK/OTiAR47VOSR4SI3XLCsy9iwHCzHI6xJrxEh779jlIgqUbdc6tgc2xlksWzz8P4K0ZCAKgpUGjbvOiHBikSYlN+H58ENL45RrLtM5HUURSAsKQRUid+8cTWPjC2xVDOZLTs8uqfMupYQx/eESYQldkw1GM3X2ZWrEAkKSK5Hwq3TCAZwTYVCzWFNp4pfdVmq1ygYFm4jjG54NGyP1e0arUEfz4xW+OBxzYwVGsQUHxuaQ8QCEl97eppPn9xFd9yHbrlc+/gEomyzLVMgpMoIWCyVVFI+EVuyyNVFNiQilMsebSmZN6xLcUTHv5wvijWLt/xhDwGfS8Wx8bt+yraDbEv0RDX2Z3RWtKqcMRjl5p2LrEj4+MTGGD8frvHY/gqxhIeLx+FtYSKSyj3DBQbiPoYzdRJhCVWUueWS1f9CH+qhkSxffmKaE7tiXLQmxfeemac5qPLGTUkO7wpz1/4MZ/TF+exj42xdLHFad5yXZvIMtrqc1Jvi4oE+Xp4rcfO2OTJWifdt7kVyfdx/IMf1Z/WxbbbCxx4eIxESMD2Pes2jPSoztujS2QILep2OoMaqWBJJtdmTqfK2XpVfjtaZzkNTUCZfdYn4RTwPatRY0+oxkhXxCwFOakuyb6lOk6bytye18dJkmR8+u8THTm1Hx2aqaHDlpmb+5rZRJqsNwn7Q/AIfOrKN37yyxGXrW/jhqzP83ZGdnNQfZ75scPX9Y5zUG+U9R7VS1G0SAYVX54tsnanxhwPzXLyqiTv3FImqMj84v4/rX97PaZ2d3HNwicVGjWLVIy6pKEGDYtnH5etTPHCoyJUrNL6zu8q7NrfwxrXLWqK/2DrPg3vK/PItg7w4VSZbtfnx1hlWdsr0BqJsK05j1RSCbozvXNTDDU/N0RZV+fAJ/9hOZTsesrT8vs2VDT715DBfOW2QttCyKPYz4yWO7Awvt3WWSsRisb9KXPWfJYr+q+Cqvyqi6F+Lf9p6BnD99dfz1a9+lZtuuonBwUG+8pWv8NRTTzE8PEw4HP43jfmnZPngH+8jFJMYzYQRHT+ZoowlWQymZFaFEzw6O0u2JBORRcJhl4W6iSzaxLwoUw0dXRdI+GT8ioClFSkthThxnUHGqFJtaDQH/Ziu+Q+loRouDquTSXojUQo1l1+8ukA0ZBENuJhCibUtOpPZBGVLpzmoMDYfw/ZcmoICfr+BLBsIrp+FWgNZMhDqrQxndTb31YkFJMq6iOBojM9LNByXtoCPg8U6v3vTKr7yzASZssWmpijffX0fWybzfOj3k8gSrOmRUPUKVtDH/jmPwU6dfVMqQc/HxvYAorDcw767NkNCjRIO5RlISDhClef3tbNQc1jTorA6nGDLeIGy6SIjEAoLSKKHI+vEgwaiJxOWdLbtEZCq+9BWHEVrQKFClfP6U/zxQImYX6FOloAcoaHLiLLDG1c2c9fILJqgsnPBpCcSoDcS4tqTe3l4pEBYkUiHVPyqwNV/HGFTW4gdmRLruheoNiKUdI+2mI6Ayljewi9G6A6FuOOlOheuT7IvU+PKzS08P15mVbOP/VOzfOmCjXzzhWmemJsm5fezormCX3V4fERiKBFEkkwKZZXheYlVrRLxUJ2RvERY8+hN6QhOnHevH2JXboFbtpXQDQlkm4aYQ26kOaU3zoWrUjx0qMA9h5bwmxpfPKuLomPwpWdHifldIr5lq8gTOptZEU5xzf0T1FyTY7pifOrkLp4fq/D7vQvURAPPVDh6QOGKdV3ce2iesVKJuulSMwU0VScW1MkVA5zd1865A208P1XhBy/NYIk2WDJnrgoi++ZxMclWNOYLPnJ1kbru0q0YZBWNWADMhgoCIDuInkhfIEK57jKW07lwbZJLNySJ+mUUUfgzB62/+eNuTu5KUKrC/QeyDMSDBESZVxcKWLJHUBHxqx6iYlG0SuimRtSOEQ3bCJpJSzDADaeuB5Z3IV6aKfPDV0axtUUWllqQBVibDuJYIveNLnF0U5KvnduzrCOzbZ7Hp+fpiQYwDJFyw8bzJD58TBtPTxbZtljiibcfxgf/eJCmsMDnThzkktt3c1xnFL8kcc/EOP2RGPMVg454mZAuYAvtnL86zU927yOiKRzK66xrEQioNuFAiUItTD4Xoj/QxmfO7EASBTwPrn14iq64yi17lkjETEp5iWjMYTbnccqGIn7FXc6xeJipkoNfdTB0H6LskNAkXpl22dyqkndKiF6Q7LxCJCxRsW1WdeQYf+Qe+k44kXhzhJGshFVMUa1LKD4HXSvSHVFIRxvUdY2pkotpqrx3Yx97cxXGZl1+eukg733gADsXi6xI+ahYFioyu+cs2oMatueiyRKxsMNMwaY1pKCYeSpKiI6UQUu4ik/2cSBjM59NYYkNyrrIMR1+DuUt2kM+rjt5kM6IjxteGuGpuTFMUyEohcg1LNrCfo7qFNmXL+I5GmNZAdsz2Bxv44lDZR5851qSQZnzb3+ZYkHh9P443VE/C2aV4VwVS2hgOg7poEymZjE676MnLXLN0f285+4Rvn/uAF9+apIlu45gKZwwZCDIRRqWiuw0oYgSe+YtogEbxQlQsU3OXJHgynUdNAVVvv7UNH88lOPsFUmeGC8QCFdxXQ/RDZIv5XnmPX9ZouixlX9Zouj0A/82UPPRj36U888/n66uLpaWlrjuuuvYsmULu3fvpru7+y8yD/9/IX70ox/xox/9iImJCWDZ+vZzn/scZ599NgBXXnnlv9gJPOqoo3jxxRf/l+N+5zvf4Uc/+hFTU1OkUine8IY38NWvfhWfz/e/vO5P8U93YRV/kKvuGeZv/6HFAsByXC6+6QB5qYIqw9ndzaSjErfuWUDxBERZxBZNHENCcnzookXDdFmr2Ax0t3L3/jyfO62L3+yZZzbnULEsOtMWRs3HYsnDF/ToiiscXDTwRI8Tu2Nsn6tSK4r0xDXyhgUBgzesbOa+vSW+dU4/M9UG3355nLLpkA4KeK6HJxn4JBXTEShWJDoiPuIE2Jsv0ZR0yDdc8Fx8XgBZkvjSqT186bEJxMgCCgFyOpQqEu0xCVtsoInguTLTGY3uJvjWmavJVGy+9sIIVMtUpQgly0HyAEGg5pi0R2UcsYro+Dm6fbkVKSBoPLi/yJfO7OJzj0yhyQKHd4TI1mzKNY93HpVmfXuAb7wwQW8kwOUbWrlvX46FqknDsdmRy3FUOsWd+xcIqxLfP3c1VdPhA/eOIMkWpuShiRJtIY2posGKFot3rh9gU3MCw3a54ncHOW91nDesa8LF4933HKThmCxVLAZaXQoNG0cPUrMsTmpxuPiwIdY0RXnoUI7f7FxiQ0uIN65tojOqce/+HKcPxIn6/nyDx7RdfvD8HPeOZPjECV2sawpx1V3DBEI2mYZHxFWX7/mYdi5YnXpt4TeRr/P0eJn9mTo7FsqIgks6JrCkm4iOj46owmipRtINcVh7mFfmK1x5WJr1LSE+dP8IpuNgOx61qsiGAVjfHKE3GsGyXfYvNdgz2+BzZ7RTymf51T6bom1heg5zVZMjUjHiAYnBZh8Xr2ni/n15XpwuM2c0EHFxHAFZFPnJ+UOIooDresyVTaI+CcPxeGa8xI+eXeCslXHed1wLX94yxaJRolhzWchLBFSBgChR0R3ec2KcPXMmiiDzoePbuO6ZMcoNm/FiA9MTiIkKPkFhQ0uYV/NZXEegqkNEUAn6BSqGRU53+OY5/RzeHsawHM775V5SAYkEZfqHNC5dMUhM8xPWZD758Bim5fHyeJWmVp3V8Tg+T+Ox8QLJsEjVtDmxI8nqVj/zZZPbd+RY3RxAED1CITi2Nc4R7RFaIxpPjOTJGA1emKiTM2uULYO6KXJ4S4yq5VCrCsR9Ci/NltnQpXHOQIqTuhOEtH+cjH75ygK/eGGRN29OsXOpyr65Bs0RibxhEgoJOA0JJVgnFRIZy3j4rACb4ibXXriRG16Y4sE9ZbpaHUqWTlvEo1qTWSioBGSZnGnSGlZIBVTigp+hJh+j5QZvXNvEXfuzHMjX+PkFQ4Q0mRtfWeCBAzmyFYfjBnw8OVmhKyaRKbn4FRndgpMGAzwxUqYrIfLpEwbJ1WweGy2wNh3k/JXL8hbXPzdJRJN59kCNim2SbrKpmRaJoEzRMAkLId6wupXHR3PM1csIiNSrKq6vzkUdKgeqQSqOyel9CW7blSESdLlifRf37y4xXM2zqsVl/6JCKqBwxeo2vvTwHB1JkSuOSvHrF0pUbZsNzUFSMZnRcpUTW1LcsmeO2bINLnTENN51WCv37ioQ9om8OFfmM6d1cnp/nC88PsFoocEvLlqJIgn8YXgOSRC5/9AiH9jcx2FtYZ4cL/LcVJGti0U+fGQ7t+2bZV/GBFzSmo+K06BWVDhhpZ9Xxxwsrc6pcZVPn7MR5R90wMayOpffMowtOrxrcyvvOXa5Uuix4SJtUZk9Czrff3malqRHRyTAN08fWr4up9Of+kdntHf9boS3H9HESQNRGpbDhbe/Sthv8dOzDifp1/jj/jy/27nEZ07p4msPDHPzOzf/VeKqfw+mgv+6uEr+35/y1xXXXHMNjUaD97///RQKBY466igeeeSR/9A/calRxVbamSo6ZEsmqbBOq8/P109ZhYfAAxMZNE9kqmASF3Q6wiHGcybxpgxr4z6MapyJnIGAx2mdnTxnVJmrOLSFo2iA4bj4JAnXk5gt24iIHJMK8p2XJ/j+aWu4P5xhf8ZAk0Uu2KgyXxbQVJs1MR9LZdAkl5gqMZtx6O82cByJuWqFtN/H/FKIz5/SxVPzo2RND1EwCapBds0btKdFpqb9XPe6bp4YL/CD7eOsaw7wqi6wO7ss4ndyT5LvXyyxWLV4aaKMqVXZVtI5bMBjslynO+GjWvOIRDzO6EtwzRMH6IqGqDlVNiYU6k6dvROdTBZNBlPLwt47ijncUINgzY/puaiyTKPmsGmlzitjAc5I2/zgs59izZveTW1oAx0RmZJucHpLD+/d2MaLY8NMlMskQ2GyRQXXkegMhvjFrlm6IhpLtSopX4SLV7Zy5YYWfvTyHL/duUhAkvEkl+MHRC7dFObSlZ1ceed+9o23YSplVjbJCIJDRXfpDPmZnlX5wjkrqRcnGM7V+PLZHfxy1zxCQOD3Y4u8sSPIO+/dg4RBX8pGA16ZVlmZttnc5uE4DlunBBJOhM6kTsBvYskZWiJpLEtlZEGhUHV5aXovR3XK2Lg0PA+zIiCJKdJJj5cWcjw9UsF0XT52Yhd9cT/PTJQ5ayhKvSQzmDTIFgNoigSexnHdUW67fAVtUY1HRgu8PF/iwrUJEmG44cUpzl0d59jOEHFV4XW9zTw7JbNlPI+o1klFKhSqPip1H3fvrvDKxCTrW0Mc1RfkhakyjuAxVjAJ+FU8QaJhCgiyhaY5TC+q3HBuO3//fAa7oXDOUJwjOyMYrsNiTefVhTI/PncVX3tymkdH86QjMmXL5pGxJRqeQb2qkQ6oyJrOYNzHc/UKm7sCvGVNK6mwyLXPj7FjPIaiWAykdCRBJNDw8/y4iC5ZOJaPoOZDlFUmCjqvzJa47vkJbrloLb+6aDP37cvwzekZkm1l9i4I9MX9bI5Hee8xLcT9EglBZnUyyNZZPx84ooM/7CgyaRvIksCpvTFuf6VI0gnxnS3zfP/cIYq6jW67vK4/wS17Zzizr5kfn7GZLTML7CksYRohTLfIoaxBTKlQL0WoyjrtQR+5moMjVgg7MUSxwazu4hOrNGyXHYtV1qaC3Lp7iRN7o5iGx2LdZEW7zL5Fh82DBqWGwlLV4awhnWzNJmUl2b3g0STLCGjsWrA4tsePrAfoiob5w8gS6zoEiiWP68/opyc5yNVz8/zxi9dz7HvfTdtQN7NulZgYoirZhCQFT6yTLfsAGUXwKDQUvv7SBJ86tg/FbPDJJ/fw90f18OChAM/MLRHVZJaqDl1RletO6+ZTWw5y7Ql9/P7AHGlN5KI1Se54ucZoGfKGTGrQT9Gx6ImrGEad6azGN07v5uj2BB98ZBeXrGimLeTjC09N8MRUiXggjE/1OJAx6Y0p5I0aJ7avYbEgUZaXiPn9qGKYEgbXnt7JNQ+McdMbh7jpvI1snSvy1edGcA4FWN8aYLKiEw94xLQADVsnEYTegTBrWkL8cvcMV25s5nN/nKbquSSCPkRZ4PkJg/WdHrITZqKW50dnHMVHn9jH4pyPolyBisZLwRy/eTXHTRet5NJ1KZ5byHJ8X5AHdheIByN4tsehch1P/8vvuQji8t9fZKx/x+3NzMxw2WWXkc1maWpq4uijj+bFF1+ku3tZeP4vOQ//V46Ojg6+9rWvMTCw3Kp68803c+GFF7J9+3bWrFmu8DzrrLO46aabXrtGVdV/daw/xW9/+1s+8YlPcOONN3Lsscdy8OBBrrzySgC+/e1v/7vu76cvLLC74vLOza30xvz85LkF3nNcC4okcvWpLfxyl81UyeDunSUO7/bTHQqyaBUBiKKxWFNQIjWCCsSlAJesjHHGpk7ecXgrH3lshI8f10PDcvjuK4dY0h20YIU2JULVaZAzG7REA1QMj2dGK/SFfIRaVL52fjc2LvsX69ywZY6bLh0iqxtc+9QEQVFhfYuPrLUEjozg+dBdnUbVjyoqDM9bRNUKrS06Jd0joGiYtki+YnFGT4SXF/JUpToRO8KhnEBL1CYe8pjMugy2KsznBMy6j3DUZargcuuODHftzeEpJut9Kpcf3s5te2c5NAeKCMmoTL5uEfOFmCo4NIwyuuHi2RKvWxHnK09MM9Ck8sxIHZ+g89M39vO7fQvM1ht87ncjaJJEU//yAun81Um+9/wst+7KsrHNz75ikTevbuenLy3wxcemWNcWJBLwMGQdnx2gI65yUkeSb29ZwAjGOLRg8+i+afbP66RCMj/bPk+mYfF3x7WTDAkcyNcYahNYqoNk+PEcByRwHYm1TRHu2p+lpNvc+Poh8oaFJAgMZxo0LBftn1VtfPaBKcYLdU4biPO9s1eQCCi847YDtDQbCFKdNjnGiqTGhuYoS1WDS2/fww1n9lNqOPz9/WOEfSK2aOPJNj5BZalsonsy65J+5isWdlkjEJXZPl2jq10gHZVpDilsagmzvi1AuWHz620ZamWNF6oGteYGuxerVG2bU3tjDGd1VgZlPnlqM49O5RA8ga5IgGfnZpBFmYWGh+t5HNUV5tjuEDe+nEESYFN7iJMHo689p+V6/OzFRVpSNiPFKsOLNs1pmeFCmTf9Ks+7ToxgmE34JIXzh5oQBIEfPz/P77dn+eHTeaIhEc1ncdVDM9QNjZgPwgGDpaKfVJNMT8zHpauT7HmqhCm4+DWX3146xM9eWKQlrHDJhiT3HVri+SmHmKbw1g0tHNMdpFbM8j+21VkZMmg4Os9OltiTK9EUhnSLC5JNMOCQVkRcB2oVgYTPz9apGt0phapp896jWnjsQIWgX+TFkTJ7F8uMFJp47+HtfOXxWYaa/HzouHaeniyhWw4tMYUbty7SFtZoiSgU3QLrOh0uWRfhly9m+OkLS9z1ttWvbQBevqmZUsPh9l053nJ4AsVnUXbzNCsSSzWXI7taiAppXi3OclyXwtYDAlXT5qo/7uG03jRnrvHYXZglLgYZnXQ5fSjOGQMid29r0KL6qTp1WlUZF5sXl/KcsyLBl5+aRFEFfn3RKuq2S810ePvmNAcXdCSpzo7pOkNpgd5whD1Ciem8RUsE9hQWiYc1xssOVz8wymAwxMlDYR6enUT1WYwuuhgGfOyUTt6y2uQ3O5ZQFdiRyTIx59GW8lNx6/xu7yyeY1It+vECNrpncUQgxXBmjkBcZfuYTcPO8YEjuvj5Swv8+Okl1qZDXLm6h/NXJ3h5vsR3np7hx7uHuXhzB2FN4eWFLCevUzD0KFsny8w6DpeuauHmHQtMV02aYpAkzKFCnV9tX6Q76CcRlDh/RZxHDxU4viuCK3qM5w3eee8+Qp7G3lKRFc0igiBxzcNjfOWMHn6+bY4FvcF7NrfxrScWyekubc0CPjHIwQWTgKxx8aYYj4xW+PSp3eyYrTA8tsBEXmcwHQSgL+XjZ5cOsi9T4TfDM1xST5IKqJw+FAOgYtu0xATm8h6ZaoXrtkzwmZN6/owkAvjeJb1cfd8Yfh8c2RHlB2et5eOPDXP5bQe57c2rOLkvwsuTFR7cX+BgpvHvmvP+LfGXwlX/HkwF/3Vx1V99RdH/ifgTq3jFXX/A80Wo1sIYpkKh5CEpHhvbQ5R0l5GMiWgLNCcEKraJ3pBpijg0JxsMxBJo+Nk5Y+B4Iqf3xPnNnlkS8TK6ayALIp4TYKlRo1jVGEqpbB0DTJm73zHEe2+b4NrXdTLbaPCbvTOE/DatEfCEBhUdKpUoulRmfEnjiHaRnUsWfllkICWTbdiEhQBnr4JXF/MUKiFUX4m9cxqb2yVeGFmucgkQZjync/pglM+e3MuDBwokAzJHdUewHZfLfncAy3Op1Bw6IjmUuMSrc7Cmyc9cTiSmarxtc4pvb50g4nexXZuemEqh4dCoB2goWVqVCAdnNdriInm3gmwGmSvYrGiTqDoOmzpsPn7UBiQUfvTtb6APHcVuWUfQWyga4DcVhhcsDh/wk60bxOJ1inVhucJEUHllwmZdp0zNdggKPq49pYeBVIBts1WuuusgA80qAQ32jTms6pAZnXMZHDDQBIHtixZHdEJQs9m7ZHFOTztdgQRf3jLFaf0Jnp4ukoq4RHxQqUq8c2MnN++Yo0ut8nLD4C2bdSq6QKkh8txYiNM6mzmyM8xiTefYjjjff3GOPbkSIb+A5zqsSoU5s6eFX29fomCZnD+U5MKVKQRP4IrbDuELWDTHlquzPnf8EC9N1KkZLicNhLlj/yJPTS1Sdg0SShjRlajrEFIlepo06tIUtiOxMdrK7gWByWqdmung6hLxhMPnjh/gjn1ZRgp1BlIacU3k/l1VHMUgFLRJ+peFhasNjVJZxbUkElGBspdFkjwqDR9hn4tpKHSEg5zQEeP7Wxa58a19RKw6sWSK2ZKFIkHYJxFQJCzX42C+xuaWZeD1y53zVC2dzqhCVPYxnNW5cdcs4NAXszmyK05nKMFxnUkePVDm4dElyp5OIS+jxSoc01dGwk+mEmC24FI0bcKej7cdHufEjnbu2l1ky9Q8fd1jRHwaYXMjx3cmeHI8x9Nzi+yc8Pj8me08dKhIXYeN6TC/37/Ep4/vZutigUWjxul9CU7uSHH9M7OUDZvTOpOcvSqGYXu0x1T8isRbfncAUfCYKRq8+9hm7jiQ4bDWCLftXKQnIdOBzkEjwK8vWcUDwwV+tXOOWLJIqz/InjmP09flqNbDlM0a8/kmNrVE2DrboDfm5++O7GAwGeBvbh1hx1yVY9cXEUSZjFlgY7NGtgbP7oxz0voa40UJHB9Z3SAtBpjLC+iayTVH93HxqibGi3U+8NhWhqLLIqVBUaIszONVBYx6DtdxEZta6YpBtSGTiNQwHIND8800hw0app+C3uDKNQP8aNssx/cqTFfKHN/azo+eK/Ceo9LcOjzL6iY/mxNN3L+/wP3vWIMoCPzkpVl+9WqGYzpD5AuL7MhrdLS59LZO4JnNVCyHkCrxN2vXsyEd5979WQZTPt55/17WpQO8Mm7QmRKIhXTqNqiCwqGsi4PNypSfzxw7xHvvPMSpmxZ4fiyE6AZQHY1S3eGoriDXn728ML/r4CzfeWGWpdqyyKzoSJSVCi0hkcG0xYuHAnzmxB7mqgZb5yuMzVokowJV28GyBDQF2psaWBiU6gpNWoQDuQaOCQFPBcWj4Ohoss3qVJDdU1BrOKTSDr+9cAPtER/jBZ03/24/EbHGkx848S+68/X46r9sRdFp+/5z/fT/Hf/7SCQSfOMb3+Bd73oXV155JcVikbvvvvvffP0HP/hB9u/fz+OPP/7asY985CO8/PLLPPPMM/+mMf6UP0d//TkCwTCXHZbizh0Frj+/m5BP4qnxArfuW+T2S9dyYLHOLTsyfPP8Pr77whQvLuTQZIPJRT+a7HJYr8qupQofP2aAIdX5M7vk6bLOex7Yx8eO7OEX2yeZqjroDYHetEWpriJrdXAVwm6Etx+R5jsvT/KmFa30RAN879k5br5sBbuyZT6/5RAxMcDfHNHMt18eI6UpTBU9An6DoBciXxb4wUV93L47y1NziwiCSEyTqdg2l61q5a7tRWq2QyQA1bqH32/S3uQwURCwTYVU2GUmKxDXVEqmA7ic2h+jbHiMZBo0dIduWcdrkjm45JEOiRRrMpetS/HgeIaKadMZCrJvoUF7RKFi22BKXLw2ya6FGrIn8tJUjRUtChmvhmJLoNqkQjafOHI9q9IhAFzX46MPjvLcRIXLD0vhiDYTSzZXHd7Oi5MVJnI6a1r9bGgLsbI5gOd5fPbRcZ6ez7IxFeWbZw3yqYfHeW6myKZ0iFN6Ezw5VaAvqbFlpEzDNmlp0rFtH39/dA+4EnGnSl9H22vORRXT5m137mJVJM6Xz+5hslSnN7a8EPQ8jy8+OUnBNNjYFmBi3mN9a4AHDxQZbFaYyllk6haqJPDFM7u5dd8Cx3ZFuf6ZCTal41x3Rg937s6yZ6nKY5MFIqqM4zo0XJuo7KNmOTgmRAMSDdPjnrevYaZssGWywCszVY7rinLuUJJHhosc1RmiJ+mjbrk8OVJkKm+wa6FOw3bZ2BLgspXya7n4r8Xt+5bYMVdlPuvQ8GwuWZvi/FVJPvfIJG9Yl8QnSXz36Xm0oIdhuCxVLO59x1p+/uICWb1BX7NEOiQgen4qdYG7dhT43Os6Caoic0WT3dkKvXE/kgAT5Tr37y+gyVCxbCqGR28gSESTydRsfnppP59+YIpDuRptLTBZMJA9BdeCom4T1CSCikRTWEG2JBJykSlP4fDmJFXTQQLu2l2gKW3gUwXydRtZlJBtlVINwtE6rSE/zSEfzWqEE3qiTOYMzlkdZySrM5E3OG1FlKvuPYBfEdmbqfCBzd3YlsCbNqf49KPjrEj4Cagis/U60yWdgUSILSMF0tE6bUmYLfg4pr2Jt6z9xxain7w6zdPjWRqCQVTxUzCq9DQZKKLAZEHmTUP9XLKyDdfz8FyXLbsnKQohDMnh19uW8ESbcLhOSwiyJYFjWrp5/1HtfOSP45zQF2a2UWGqbHBUa5znJ8q8OF3j1O44172uh33ZGl99bozjO+PsWiqj1wRO6Ilhiw4vzxU5d6CZJ6dy7JnTCQcMajUNURS4ZKiZRw4U+dYFvbTFVG4/MMNTkznes36Aja0hPnTnKDsXqvQ0y+hKjuaAzEReBlejXPMI+m1wJWpVkeP7IpQbDq9r8/j2rgrNcZHPnTDIt16YYW2LhmxpPDuV54bzBuiI+vjIvWM8v1hgdSxI1K+wL1tjQ2uQEwdUnjhUZKpu8q4Nndy0Y5bxJZd4GARX4pLVzSyWdZ6aLrC5OcJSxUZwRM5YGUP0BH7wwiztSZnL1jZz/dMzxP0i0WSZku6hOkFK5rJbcQSNqufgYtHrj3LDuf1IEvzdQ8PM5Rx0weG6U3sJazKHtYYYnpjlYC3Ahev+pdD0nXuXuGV7hh9ftIJUUOHrT01x96EsqmoTkVT0ioLu2RzfH+Err+t97Trdcpdt73WbS2/fw7dfN8g3t8ywp1zEsSQuXZVmXWuQrz0+wxGtEfojNh84bcVfJa76fwVT/T9NFH38kXtYtONM51WqdZG05Kfhq1GsCyiCQEzwMV8x8Xw2TYqKI7iooRJ+xWZToocnZ5b4wekbufaJceYKNnW1TF+TQUtAZcuwSiho0RYOYAs1PA9kK8IF/e1ceUQzcyWTtqjKL15a5I6984QTVeqOjlcPY8k2izWHuBSkYniomkFLSKNmuXgexHwKxZrFEYMLbB9v52DOoCUi09dUZqag0BkJkKs76IZKl9/Pzy5ZieV4HPbNnYRC8D8u7OPhmREOzguMlSw64wbtUp3nc34GkyqeaCKaQfZmHdJhML0GgqvSGfeQJIulTIS8V+CEXlioO1RzLUwsuqTiHjN6jSvWt/LUdJa4qvKJowf45s33skqc4+y3vZ+33zbM2SsjPDJcprNJJmOXCODH7zcQBRVZrnNozk9PXMUxJFYkgjwxmePE/hhXbGjmg/ePsiIWYOt0heagiipK3HL5Cm7bvcQtOxeRbIlQyKC9rYZpKeRq0JkwmF2MoTsepbpITFJY1xzi5cICKb+PpXodx5FJaT6yeoOVPptGUGBla43W+AQvHFxNrZDg/FUpvrdtmnNXhtk6V6HUgJJpsDqtEPTV6W0y+Nim03hxfpYbt2aplP3c/raVCILAH/fnGUgr7Mou8O0X8jSHTfp9LfSk/Nx+YBHZA8lf47AOg8VijPm8RL7k4fMLxKI6nYkqh3Xo7JtKMroQ4/qze+mP+7jgl/vBb+LXPBpVkbjiY206yM7ZOrsyNX5wUS/ff3YeV7IQJDANCcP0qFoORctkdV8OnySTKUbxGUFScZA0nZpt4HouiqDw5s4YJ63u48rbD2HIDWQJDFsg7lP5wTkriPyTHfKHxxbJNGrM5WC25GIJDhetSPK5Rw8RiQicPtDEOQNpVqdCfOvpafYsVulP+tlZmaRmm6xMexhmBMtaFvUrGwrtiQYjmQCKqRHRBBzHI9doIEgqoijy1vVpBNFmy3SGmUUZnw/etqEZHJG79mSoqBXetbGV58d1slaZmldCdkLYRpD2gJ9zhuL8cMcMSUmlLx7gc6d38cRokd/uXUAQPa7a3MZAPEhQFvnp1nmOSzk8tuTy5EyWDx3ZwYN7KgxX8gy1VUmHLWYKQaqWSbUax7REshWXr5/Zy8FCnYenJjihtY1btxVkD10JAAEAAElEQVSJCAqKJPHh08O8uDSCLVQQvCCWU+PFsWZWNTs0yKC4KQTRAGQm8xK1coArNrVw1ZGt3PjqPFFNpmbbfO3Jad57dBuPjeRBcnj5J58j2r2S7nMvYH2rg+UYLFWCNMcWODA6gO45pKIeh7IOv339Gj78yH46Y9AcVriwp4d33XeA3riPX71+FU0hjVzNIhGQuf7ZKS4YSvGFR6aYy1kMJSrMEmS+arKp28YUspSrceqNIJevbUE34Le7F+mIqtg4jFQKrGsRWay6RAMmQTFKzbawbRnPVVio2KxuEZmvOMwWDY7vt3lufwTT9uhNaMjBCpev66QrFOW7r+6jbgkIto+lHNQth3UtfsYLdWLNOaKKylwhyMyCwD1XrOZLzx9k25yOT3Y5uauJfeMmeanOhs4GR6f7eGqizHSlweEtEXRdYN9SnUDQRVMEfL4a1ZqKX5SxJJtViQRfObWPW3Yt8auXMtSqJZ77yPF/WUCz9i9MFO35/z6o+f9XOI7D7bffzhVXXMH27dtZvXo1V155JXfffTeqqhKLxTjppJP48pe/TDqd/p+Oc+utt/Le976XRx55hCOPPJKxsTHOPfdcrrjiCj7xiU/8m+7lT/lz+ndfpC76kIMWnzmxF8ET+fwjkzREk8NSMT55SicHFnVu257ljJURfrl3luO7I7xncydff3qaw9rDvGFtiuueHeELJwyQyWReW5xPFHTeevt+1qT8jOVMijWHcNShOeoykRGRJIG2OMyVXRTRIyH7yNQcmjU/7zmmmRN6o/gUkW89O8X9o1kaOjSFoV6R8EJ12qMes7NBXH+D49ub+OKpfexZqPON5ycZyxuEggZfOnkV124ZpVYTGEoE2TPfoLNZx+93mCnJhJDJ6x49sQCS5DJerqIaPjy/SbXhkRSDVF2dgA+6xAYzsohf1igYJkMJjZmyQLnhcPnadkbyNaaLBq9fk+SRQ3kuGmrht/tnaBFDqKJEe1Tl0k0pHj2U55Z94wRVmaXyMtnw/QtW0OQPYDku33pqjheXChRrNp88pZMz+5fb2V6ZqXAwW+fliRpv3tSE4MEvXlrkwGKdwS6ZT53YywMHs/xhd5agDyzBQqwFkETwRx2MBjiKSUzyM5t3uGh1irNWRZhfWKQWUElrYR47UOWjJ7ezb6HOY+MFZhpljuuK8cZV7a/lzkLFpCW8PKfrtoMmiTguyJKA53k8OJLjGy+ME3J9CJ7I7y9fyY9fmCdTdqhaDsmgxFCzn/vGp6jqAjYGHVEB3fQYXwgQ80kYDYGa55COSBi2x0dObGfreJ3BJj8X/cOi9GCmzpefmqJk2ji6gOvAmzc1EfPLnLs69mcaRQAvTVZYqlqcvybB3905zlG9Ic5ZFed7L85Ssy2KDYeZjE0Nk0vXpnnfkf/4zHfszvC77Rk+cEwbe4qLzBQsXjjg8du3raAz/ufuRU+NlPj1K4sMl+qsSvsQgH2ZBhs6fTTJfixTwHY84mGJo7rCnNIfA6DUWLamt3EZztTBg3zDYcGoE/I16AwHefOqPlzX5YldYzyfUehP+FFV2LFY5oW5IpIHzVHQHY+lokRIkUj5VV7Xn2SxZjJbNBnJGNzy1kGC2p83jIxmG7zzDwcJKxII3rLb64oom1siCAi8PFXh3n0Z9tdKbErFWZcO8IPnFkmFROIBlXhIJF9x2dgU4YMntLym6XT1Q8OMFYqYgoBfUMmXXJIJh4jfpSOiEXRjlOseOcNkc8TiyRmJoQ6NJw+WaUo42FjEQyayIDG64GcgHuCknji6Y/PYeAGjLKFLBgY2PmRqlkdv1MeFq5toj8NNu8e5aEUHOzKLbJ22URSXVaH0cmu4IlE0TWI+GU+ykAQRx1K467K1eJ7LdY/NcOURzYzkGtw/vFwRvyYRYsvsFNvnHVqDEq3NeVxHY9dICMlnc/6KNAuNKvvmbXyeypFdAU5octHCST7+2Din9EeYKljszZdYkdQYL9cJSjLNYYVTe5IcmrN5fKLI5tYQHz+pk+FsnVfnKtyzN4/nuly6IcXLmRx108FwHAx72WX122eu4abdk+i6yO92Z4loEt88c4DVzUF+tnWOY7sirGkO8eNXprll3yyy6HLOigg7FyuMzit0pWGhYmMUNY4fDDOSNbjqiBa64z46oxpvvn0fAUni7ZuaOX9likLdxCjnSafTLFQscnWbda3B1/LpULbB228b5qiOEN+5cIBnx0t89rEJ3rwxRcyn8P1n54mqIut7Fb58ytBr+fLeOw9xweokR3eG+eozkzx3oM5Xzuviey/MElAkfnLxIAFV4qdb5ziYqfO549N/cTHrvxSu+n8FU/0/TRS98fZbmK51ktQC7B1zEBH53iU9fP2FSSbzJoLo0R3XqNRc/OEy6ahFwhfklSmBaMBiKBFgMe+n4FToa6rhl4LYQo1KXUXygpQtB8sWifo9bBdimo+Lujtoiahs7gyxc7bKNfdNMpJrkGzRSSsB/NFFgj6JbLaJguHQ36RjuxqZKoiOjCPY5HSLwzstpss22apESo6RtxrEFJVU1EGQDOr1EEs1nUpe42sXtnNKd4q/uWcfs2UbWfIYaNFZrDdIamFemTU4LGxT9Sk0R+soop9MvYZjRdi96NAshvB8DgHZJRHU2T7qY/2AQaFhk/CFWCg7RBWJnFPgxHQvs1WHparJwYxOaPR5IuPP8fGvfp8z1qSZKhm86a4dNCsBsg2LpriFotRpCUs4HuyeEwh5UU7si3LRyiRvv28HcS+CTxFY1E3eMNjMfbuKqD6PQt3mgpUpPn1mJ4bl8Pa7DpBp6OSKcHg/LDRqhHzQpPl4bkRgRYtCoyEwW2/wprUtPDNawhYMOlI1coU4FhYNr8E6RWXUdokHHcq6RnckzJtXt7E6HWSmbPC158YZLdboTZkc119msayx5ZCPjZ0WZ3WtZFXKT8OU+N6TGb5xQTfDpRyvZCZ5dKxAU0BleCpGyqcQCduUvDyZkp+0T0LWbARPpW4KzJQaUAzx+s1xSuISqUgWURAYzfh5fL9Kyu/jwyfEeORQkWwZHFMiGdDI1S3etD7NyiY/8YDEV18+gGF5+CSXakPCs3y8ZUOaj9w5jitBMmXT5FNpUgJcur6Z89cmuGtvlpu2zxDw2xyZasYxF3n38ev5wH2jtCVcFFRsz0WnhiCafGjTegYS/9Kx4PHRAnszNbbOF5mvV9BNhxYvxa8uX54IPnzPCNO1Gs0RGVmpcPTgNKMFnalMF8VcnIbloYV0fDJkqh5TiyL9LRLdCYuy7lKpqxi2y2xB5Lz1AU7uaqJkGPx29wJfPnWA9ekYAOPFGnePTPGmoV4+//ghql4NSXF4/6YefvjSAoLrY0k3aY4IfOiwHmJ+hb5YAEkUeG46zy+3L1Aowe2Xr8LzvNcA6ofvGWHOKjO2ILCuVyfqd5mtlxiMh8jVROarJkojTbZiYwFv2djEbXsW2dytkKnZTBbLJIUkXXGNT57SwvdeOcRkRadhKHSnSpi2hmkpJOPTlEo95HSLv1nXzc3bS/zwnBXM1+uoosz1z0zTndAwLI87hhe5cGWUrZMN4gGBp2/+KSdecgZyU5KwEGTXNCQ1lbxQoSMcwsJAdDRGMjZvWtPE3QcyrGoRSUUU3jzUzUcfGOPmN6xk8B9Kh7eMlLj6/jFWdMIXTlrB+34/wsaWPLvqAbpiAoZYJqgI1EyPl8b9DIVDzOdcUjGRo3vC9MYC3DW8SNYssbqtjOiFeXVGoCsqEw8a5GoqKZ+Cz18jW5UwahEmKzqu63HZqjZ+sWsO1REZ6pD59HEDfPSREbriFlNlAaeq0ZKEIzqDUPezs7iILjao6CKaFeQH5w2x0ND5yKMHCGkymbrBL8/ZyMcfHONQqYHrwb1vXcPf3jnOeNbk7ncNcd2zI/z9sV28/+F9OJbMQBNkjRp+1aVRj5CWg+xcqiEAScfk2Y+c8FcJaOD/HVDzfzt2797NMcccg67rhEIhbrnlFs455xwAbrvtNkKhEN3d3YyPj/PZz34W27Z59dVX/5f2ud/73vf4yEc+gud52LbN+973Pn74wx/+T883DAPDMF77XS6X6ezs5PP37mbeknndqig/37pEtmiRjsqUGi4N06WtSeaIRIyumI9nxyt88ewO0iH1NVBvOS5XPzrM2f1N/Oq5LBf0wZuOHWS+ZvHFp8eYy7pkLIOgsmztXfca6IZES0BlseriCi4pn0SjolB3bQ7rCHHN8Z20Rpef/bGDJb782BTBmE5T0MfUgkNTukHYJ3IwI6MIAj0RH+cMpPnN/4+9v46y6zyzvdHf4rWZdzGXoESWZMmWGWJmO07ikMPsdLidOEnHAQc61Ek67KAdsuMkZma2LMsWS1UqFdOuzbB4re8P9Ul/Z3Sfce93b8a5fU/6+XOv2mvUqHrfd801n/nMuXORfFjl0rUZbnxxhk+f0cfmzgQ/377Aho4wHTGNLz5+kBmria74SG6ImhVw5do85ZbL4zPL6I5OwbVIhwIaFR1DadEW0/ClOsOyy75aCsd16cgEmL6J5Gt8ZOtq1uQi/OODh9m72GRFTuW7567i5t1L3HmwyD+d1sexXXE8P2DHTIOFhs0dBwrsWzDpTsLFGyWuWr2G5yYbfOuJWc4cTDLdaPHaDXl8H5ZbLncfXkD2VE4aiGPaAW/amOd9fzzMwVKTrjYHMwi4pK+Xh8cqGJ5LE4u8GubEnji3vFIkHZL55wsGKFo2T82UeWqsQcMzUCSFLrHBKevbmC0H3P5Kk/WdEQ6Wm2R0ldP6ErxnW8d/MAwG2LfU4B/uH2VVIsp0zeK4FXUyWpILevv56N3jiKLIykSYzjafQAx4atSiLaIwXbVQQi3WddVoS5WZLaYYX0gwW1QJhwJEV6RqBHR1WOhoXLSynS1dUXoTRzHEd1+YYa5sU28GfO+yQfYsNjhYqXH5qg72Lxp0xlRSYYlCoUAul/srUbRrvokmCazKH72P5wfM1lu8796DaMj4vsC5K9M8P1XjmPYoHz6hm1t2LbOpK/LXtLQLf76XvpxENipzxcp2RtrCaLKI6fjccaDIPWMFYprAsd0RfvVCme6sh6q6FI2AVlkjlQQ78DDsgHY9QlZTsTyfgnm0yRIIPocrTQJL5h9O7ObikaOk2L6FFv1pjbAq4fs+X7hnBwtBmNlSwGVrMpQtl/1LDY4sukRTHtFQQMWwUWWBkKzi+RBTND5+Qi8j2chRtV/Foiep4foB8w2bnrjGXM3mySNVZppNDpSanDmYYHTJoWXC184fZMd8lZcWyzw2ajCS11hoGpzam2as2mSiYjASSbOlJ8opQwm+8Ogk2bBMb1rlvsOzFAyTclMjFQpYrggM5kKcMRDj5n3zxP0QhbLH29epPF+U2dQf4ZHRMoJaIR4RcAKLmBawWAvRdCAWRPn4SQOM5ML8YdcSfxqbw7J9ZNXHdyUsSyAky5iGyAXHyDw1X6I3pvLCtEcupNMR1Xjzhna+/sw4uBINxyOuK8y3DM7ozfBPpw/wpUeOsLs2x9pEB6Kn4HgBJw8fXetv2ZTnj4cOcce+OoIvEI0bVBsKrXoMQ3DoDOuMN1qc1Z/mmmM7+PETBwiFcrgEPLtUomo79CWhGRgohOhNhHhpwmRlNsK/XLCSn740z227lxEF+Mq5/fzLjjHeONLLHXvLHFg0eMOmHPMVhz2VZVxHpSUarMuFuHR1G384cJhXD63kR68cYrmucmZvhuO6Evzi5Xl+dskqLM/nmvv3YXkumtri4oF+bj9YYMn06I6JRN04exYN3retnR2FMi/MtHjXpi52zjY4vjfGHaMFBuI6Txyp8cktMfR4BsMN+P5T83zlgr6/kkVNy+PluQZfemCWX71hmHRYxg9AlY/uxz/tWeaH26dRIy5vWNPJG9cdJWZNx+NLD88waTQ4OGchSgFXjbTz4dO6ePxwld/sWKIveTTEQNED2mSbD79qzX9JXPX3gqn+romiN/7hAUpEedO6Lp45bLIyFWH3nMGBcp1UTKTuePiuQDrs0J72sAIb05HQJQkvCKhbAqYj0p/2CelVFms6cVVisiyjqS1sK4QsiexfDNA8mQ+ekuab91Z59APriGkSJ313N7Zis65bZNly6Eya9CYlpsoiCxWVgWxAPFJksSZTLOU4POvR3mWSDilMVY9GybtNBcMV2DYQ5sWpFpbvkI2LVPwKkpnAkxyaLYkTBnVGsglqrYCHx5bJ54t4ZpYvnL6KLz46ju5UKckKqzobPDWuM5wWqXgVNCvH7JLKCcMRdixWqJUkMu0GLQf6UxItxyUX9dlXMEkEGQK5RViKcHDWRZ3djtM7wOtGRrh3f533nZznrFUpPvLQKxiOg+VKILVIhgSiisz+RYmwr7FYC3jXlg42d0b57MPjXLE2x90T8wyGEnznkhX8411HeGq2guJKbOiI8NZtOb7y5CSeYFB1LRJiGNP1QXHoSgRs7hA5o30D//L8FFW/QkSRabkwVmrSFY0geRqfOKmXG/fuY6miMKw3mBQcoqQwAoflZkBOTnLDWQM8PF7mF3uPEBejdGUMdM2i5ZoUq0m2daX41IkrkUSBybLBH3eVeGFhGVFvUaw4oEpEpQjDyShv3pTjU4/uwg0CTuvp4KyBNEsNj2ena9z5SplI0uO0fDuaCrvLReJRGzlwEbQyr8ykeMvIAJPGPE3PpFjR6AxnuO60Xu46WOTqjW0EAbzlz/uxMXFtGVnyCGSbSkvmz1duYP3XX2HVioCL188fNfJ1u9na3o0UiPxuR4GDiyZ24OEGPqdmAz5+4Tru2V9hW3+MnYt1chGF8ZLBbQcW0bWADx3XS1LRWdv27x2HD983ysp0mPf/W+eubrmMFUw2dh2Ncn3d7/ZRCwyO60hwRm+WVbkQo4UmH7j7AIOdEp6lkYwaSJJIpaYxM+/jJwxeM9zN0zMVtnTpaLLP7TtMvnFZN199boYg8EmEAkTB41cXbUWWRD5272E2d0Y5pT/Om/+4mzU9TURBwSPgSMnh/N5hloo+PWmNH++YY6gDYrrA1vY0v9m9wOdP72cgFicVUvj43Yf59PExamKMK399kOPWCAR4FEyD04cswMN0fcYLKbJahI5Qgt6kxovzFb519mo+/dAEpw8kqHk2v989x2LLJqlp4PssLcmMDHhMt6ooQZyhfIvJCoRFjWi4RkiKMVu3+fPlp2A4Pk/PLXHLgSn+4dhV3HOoTF8ixB8PzuLLS6xMR1hs+MRViR1jS1h3/4hTPvxBitVOXjwM6wd8FkyTnBZnqeGxKR/lvr01epMaVdNloF0gRYxLNiT5lyfn+NEVKxjKHCWLPv3AOE/NF+hOB3xm2wifvXMnrh7DV0xyURFBtJGlFpVGmvGyQdRPUvVsCAQ+cUIfu5br7FyokIpaeGKDuBzi2b0hEm1Vju2SmKo45KMep3X38MyYymilgYxIKipz7kCCX+5apGhadEZVQorA7sMeq4dM3jgyxLeenyId9elRkuwuN+hM22zMZZmeUVjTHuYjp3fyxUcneHxmmXRE4vpTV/DcZJ2fbp+jPxnmZ1es5JHRKt9+YorBLpk3b2znmy+McVZHJw/NT1GzPWxHYjAlc2BGQgtZtGohrtyQ4bcvHGHvJ8/82wKaDRD9GxFFDQ9etev/fFDzv7ts22ZqaopKpcJtt93GjTfeyOOPP86aNWv+w8/Oz8/T19fH73//e6644or/9H6PPfYYV111FV/60pc4/vjjGRsb40Mf+hDvete7+OxnP/uffuf666/n85///H/4/IZbniafTjJWa9IwXSotn3DEQlNBCXTOGsjyuz1LhASZK9dl2NDx714Ivh/ww52THNsW58UJkyPLJu2ahRyJ4iseZw9k+P0rBXQVmoFD4EFCk0GyqddVYmEfkIjKKv1JjartkpRVii2Xtx13NG3r1t1LNFyLTMzFNMPMGxXqdnD0hQOZlhUQj9t4VghdUFnfFuXZyRqiL/Lh0ztZrNv84JkFErqIJomcvSrJIxOL1H2bVlNmfXuE2aZN0/AIaT4NC2KaQKUmooRcNEVAlE0ikozmWFRFGcfVaTkBCUWg1BBIRSXesamThuXxmz0L1Js+HXGNZFjmDRvyKJLITMXilpcLLDUcThiIEY74HFqwGF90GMro+IpPuWXTMgQkxcdHQJYAV8QXfEQE/EAgHIJsWOG8oQy37F7CF3ziksbl69PIgshDR0pULYf5kstJ/QnGyga5kEwuolK1HJ6drxBVRRzfJXBkJMWnQzWIxnVGFwWScoRy0+WkFVEcF3bNtLhgTYp1HUfJBUE4qhqaqlj87pUlQqqILAis64hwpFpjwWjQqkToiKu894R27t5fQhQFLlidZvtMjZbt88BoieGOFtFwk6atEQQyizWBVi2CpoIXBEQiLp4joQQq6zo1VmSivDzfYLJo/VvjKGCianFyd5L2qMqvds7ztk1dSILAH3cv844teRyjQSKR+A+jZ6Wmw0LD4nDFYOdiGRwVwRcxbJ9w0mIoGSGla5zRm2H7bI2oJuL7AjXH5akjVVZnI1ywKs2vXlji4nVpCk2Hx8YqbOyOsmexRtN1aTguniuzuSvMcEYjGVJw/QDPh5mqzeNHauQ1nVMGYvRnNR6fKmK7AYooIgsiz07VOH9Fhg3tUaKqxB17ymzqDoPs8dD4PLRMHCFORNXYt2iQDInEIgGLLQvHkYhqAj4BDSdgSz5O4Ass1Vzef/JRpY/t+vzxlSJbe6O0xxX+sr9AbzzEUFrn7n0Vyp7JGQMpNnfEaDkeU2WLRw5XWNGms1hzcDx419Z2luoOt+0q0vAdzl6RPBpAMtPk7FVJshGZlxdqbJ9pcnjZJBWDpuMhOAqRsEvD84mpIqbrsSoTYnUySW/IIxyNYfsB33lmiiAQMTyPdCzA8GxUUSEIAmpljWRE4n0ntPPQkRL7ikUyEQhpTQq1OIYt0mpKxMIieqhJe9zBcDxsJ0TdCuiJpnj9uk72LTT57e4F4rrEcZ1JyobNCzMN2hMyx3UleO5InaJt0ZNW2dqe5MCSwRs2/bva8+WFCo/PzNByJAJLpdaQ2NwTYcdsg6gqcuZwihN6o1SrVbbPBzy/WKNU8+hq87ECi4gsIojQH9eZqYrMVh3a1BCnDSf4y8ElhECk5fqc3Z/i8dE6GzvDrO4IMVmzqBgul49kObRk8NJcncNFg2jC4c3rerh57wwXDOZ5errEWNEjqkhctDLLlq44MzWDX7y0gCfaxGSNUtPDs0QScZ/1HXEuGMpy00tLjC4bnNgbx5JslkoB40WLdFTkmhM6+enLM5TqHhf26mzob+Nfnp7jLZvbyEUVFElk72ID34c/7Cxxybokyy2b5+frfOLEHiZLNityOqoscv/BMgtNE02UuGpjnqbjoooiX3t4lgvWpNixWGW+6NF0PT7zqh5imozj+Tx8sMoTEzXOGIrzyNg0v3znuf8lcdXfC6b6uyaKtv3wXgbbcrxuTS//9NgRFFuiM6EwZRp4PnSrOu1xhbI8x9hclJEOn0Bw8ASDI5NphrtgrtWgPWnQG1NZbgnsmlVY2WaDE2PXbIAoQX8kjK9XMByJz5wwwkk9ST5w+xiFpo2kWyiKg+irdLdVKDR9HDuK6Tnkk0XUIMFz4yF03WQgoSNrNYrlCLoSwQxs4qpLXEnx/fNHuGnnIt96ZoZmYLOhQ8UMaoSlMJIvISoW3aE27h8rk9E5agBpBLx5fRv5iEKjvsD9ywbFqkxElMnl5+mIRTm0qHFiWzub22N847FZlm2b9qxNRJdRZRdZDNi/CPmIiuk5rMqJLDVM9v7kJ1x16iaufNd7WJOJ8oHbR3lyrsLqvEbdcYhoAnbQomXJZMIi8ZBDo6WxZ1zg/FVp6kaAKgm88/g8/Rmd/YUW6bDCkZLJKQNxJioG1z80xXLlqNw9HC0jCWFcR6RgG2xo07Fdl+fHVH796jU4ns9o0SAfD7jxlQkqDQkpUDlnOM2vni+QywlENJ/xSp0r+wwKkorhwXRJ5NUrhkgrIb755AyiKBCNu0c9m8QyjhuFQEJFoVgW+dip3dy6u0jN8NjYFuW3OwqsHHIgAEVWiEga154wwHA6xMFyFcMMmK0EXLQ2xRNTS/xxR4MXJppcf14PF61Nc8ehWX69exzPVqm7AelEA8NIoSMjyy7FqoAe6Pz5bSN85KH9zNZblKsy15/Zz4NjZfYu1xBlF0l2qdk2pYbC82/fxiW3Pss5/d04gUUyJKAIGmuycQ4smPx65zy2G2AZ4IYMBiWHqh4jrilEXA1DMujPOwwmEowv+4xWG8hCgOyFuenKkb/us9f8YQ+5mMBAPMrJvSlO6P33g9TzA8795S42DbYYLSi8dUMvS1WPR6dm8aQqi6UY6aRPZVlD0SCiSiiBQq3lcd+71vHOO/fTDCqoXoxiw8cMXATJwxNMVmQCisUkpgXHrwhx2VAHHXGNG3fM8vRsgS1D8zRdj0qtg7mKhG2LTE4LvOb4BG/a0MbPd89w14Eyw8kIpu0TCrdoNSP85LKVHFhqsSFucf2TDWaNOr7m4DQVetosZNWk4QRk9TCzDZPFxQTb+qJsXyqQkKJctDLLhSsyPDBW5tTBCJf95gCr22UqhofnByiWhhx2WNXV4JFRlbXtEggukfAyRqsNRIeUpvGRLWu47o5ZelMabzw+wT8/PcZcQ+C84RSn9qT50SuHEOQiuXCckmmQC0k8eO+zuM/dySmf+gJL1Qz7lk3etjHDo5NVdDRiYohTehMc2x3lKw/P8N3LBvjB04u0J2V+sn2e607v5TUbctx+cIFvvzBNUlZ5zfoE941Pc83KLt7zxCx9CZ2FWkB32mVTb5PJskhO7eCKVV284479bOm1ee6gzidOb+eB6SlatkhbVKTQ8ghsCcFTURPzhCQNTfYxfZPLB9fxk5fmWVrW6MtqmIGF4UBKF3GxqNsiGUXjrRvb+d5L43z9jPU0HIvrn9mJXckRSdhMVi1+et56vvTALF++oJ/V+RBfe3KKD27rIqErHCkZXHP7GMtNl4+c3MWx3TF+tX8/O+ZsLhzoYNdilbFZn0jSoiMuE1MijNfK1Coq7emAYlHhghV59s7O85M3Hvu3BTSb/sZE0c7/80HN/6/rrLPOYmhoiB//+Mf/6fUVK1bwzne+k2uvvfY/vX7KKaewbds2vv71r//1s5tvvpl3v/vdNBqN/9ST5X+lKBqbXuTFss+PX5rCdUSiuk8m7lN1DBYXI+RTEpZxtOO+IqfzmvUZtrYlyUYUPvHwAcKiwsFFm7mGzdVr87w0Ns2OsoqqCXSnZabKNpG4SVzWMFoSyXSN5UqUXCQgZCf411cP8JuXCxwotPjaef3c8MgM/3haF6os8pUnprEdH8MOmGk1EBSDTLxBtRkmCARi4Sa2rdF0JFzfJy7FmC8JrEyHOLUvyatWJrh9d5nb9iyxHJiEBZlkSMRVK8QViamlCK4pcvmGNLePLfGhE7p5eKzI7imHZARc3cK0BLpSsNSyWas7vFwK44s+cVmn1PLIJQTKNUipCgY+1YZLJiax3PI4szfNl87vo2l5vOamAxiSzevXtnHPkQIbchFUQWG2YrG7VKc/E7BUFdBlhYbtEdKgYQXEVQld9+gNxfnEKT24vo+uSJiOT1iRiKgSmizSsDzed+cor1+f58yBJLftLXDbzjJnrY1x295lusM64/UGHRkBw/EILAVNCyibPpd1SVxz5gZMN6DYcrh51wILNYezhzKctzLJgWKLW/cWADg867C2K8Qla9KIAsyUHH4/Oo/l2xRbFsPxOG/a0Mlx3bG/qs7+RxWbDt97fhbD9tk5V6WrvYUmysxWA5J6gGOGKbfgxOEQk8sWyzWXtozKqqxGxTTZPS2Q0iWWGhaWLbChJ8RVG/Ic929JfXcfLHHTzkVOH0iysSPCoG7+T4qiv+6V7QUUGTpTCj9/eZ6BlIwiSNy/r0FHWmBDW4x4SOZP+wvkYgGeqdJswZuOz9Eb1Sk3Pe4/UOablw2gSgJ/2l3k+8/OQiCyqq+FovpUDIfpxTgJRWUwpfGti4cA+P0rR/+OVx2T4zvPzTBWrFO0TGw8Vmc1trZnqZkBtxyYx6qqvHZDjvNXp7j+3hm+cH4PHQmV8VKTr9y9i6HeDmwv4InxKoEccPZKnUxI47Z9ZVJRB1nQqDRdGoZCXJGJIKHLCt++tJ/fvbzMQ6MVbr366MiP5wf89uUCd+8rEdYFZp0GI7E4LgH7Ck36skdH2PYUTG599TruOVTgyblFvn3WBhZqDt99co53bmtjRTbErrkWIUVkMKvxk1372T3nMF0MeNuxnTw6ucThkkUyEqBJAWXbJ6PK+KJBoRRnc8Ll2bLAG4/Jc8/hBUxTIqSBL7VI6wqT5YCoouLj0rIk+mMRVufDvDhbpUmdkKRi+DYKKnULRnIyWzoy/GbXIlv6q4wXZXwvhIPPBf3t3HWwzts3tnP3oSIhSWDJNXjr+i5eXD7IrqkIM0si69rCTFYsYkkbR3A4vj3HJ0446qnTtD0+//AEE9UGltxCtKKUGgGvGozz2GSVpCbz8yuGKRSX+cL2MqOLNl1ZCS1UR/Q13MDFJ8C3wsyXAs4YifD29T3c8MAsX7+kl48/eJh8ROX60/u48fkCa9vDnDF81PPT9wMCYLnp0BZTeWWuwcuzLd6yNc9j4xX++dlJ3r6pg4cmllhsNWm0JN6yrofXb8xz/ZMH6Y3GeMemDj53/zRDOYXDBYcXl0ucP5zjmuM7+eyDExwoNjlxMMaHt/bylUdmiaoSHz6tE9f3+fjd45yc9Th+pIfrH57mdRvynLMihe35XH7ry9RqIt88f5AfvrKfig1Xj/Tz0vIMJ2QH+PULy9z8phV/TUL83AOTmJ7HnlqZ9x/by4kdST519xQfPq0DWRL48sPTfOz0rv+p0TxbtWiPqbz2Zy/xl/ce918SV/29YKr/v0s9+1tW0xbYMe0yWTtMTgqxrNU52LDoisn4psqXzxvirbcc5N0nrqTcmGWy6pEKQSTIk043WDIETh1wsX0JN7BI6grbeiPMFTTKVZ01eai0AlTFpisjk5CzTBZc7nh5gobtsWCa5EM2diNMXawytz/BCatgn7FMoxGnM55itAQr2j0iikos3EDwdOasKFXHpicREMgNntit84P0LN9/aoGkKjHQKRCL1MmJYZYaAbraYqkcZ7I6S1cszsS8zzHDTd7QOcgHtvXwu13zPD5awzKjmJ6LrBms7ZCwvCbrcmmO64rzL0/OMWMY9OclRFHG9SRU2ebwskQ+KtJyXdpCGlNlm5xT5iOvfRXvfvvb+OB9o1yzpYtvXzzMW/9wiDevy/Gn8RlaXp25ZZVMWMRwQBXD7DricdX6HMuOzWu3ZDhnOMNrf3mQi9emGMprXHffEYqmw/hyntt2L1O3PPqyMhW5huiEwAyzf8rmuguHuGBliuVawMZYnfXtERabJrePT/G7QzU+c+Jqbnx+iYOLJvePlunuAEFsouseW3scNEVEU6qMjnbSF8mi+hq37y7z89esZN+CiSCb/PbgOHUzzGAKZLlGqaFTDVR+uGMGxwFVDjBCReLhKNmwSNXycdyAl+dbXH3LQb527iCmdoSnx01ObT9Krvzk5SlcV2Qgp1E1PAC2tGf59fYKruvSlavgO0lyMR1brIPQwpdFYmYMUYDXjLTzrSdmeeP6DNmQwgXDGXRZ5OmFJeKqzV1Xnoz0b4kVkhXn0YkCfUmVY/LtnNl/1CdhMBFGlgO++fQkTVPCbknIfTWmFzW+f0k/X3t4HjluMV4K6IuKDMYi7Jm1yCZlfnj5CC3b47HxCl0JjamyRaCYzNctpqsWsgh9CR1ZFFhuObgO9IW6eXppme89tEQ6JpHK+Gi6SkxvElIDVNlmoqiiSxIjuRChIMTH7x7nxotHsF2PJ6YqPHK4yqFqBU2BlityuGygCw7ZfIsDyzYfPtjCN0RSOYdoyGW62AZSEUVuYbgR2qIyiTVV9lQs3vbbBltWKWR1mVoj4NNn9PDERJ2nGhU64xo375llcdFmtOBgKtAdCjFl25Qtj76wjifUKDR9kopOOWKyv+QTFcMs1i12Txn4wTI/e3Ge5woBW4cM9kzrDCc1Ds36ZDtaJGWN+abFMZ0qS3VQJA1IMVd16dCivDTl8EX7FcYbCjU/xLceNyg4PjoKz84uM1k1eGnKY01HiknLYlVOwRFKnHL2yeweGMZeLlIzAza153n3piG61DK/OHAI14Vfbrd4YqzOH65eDcDnz+8lCALOX52mP6XjegGn9Ga4efc8C0swX5AJWml+ub3AjReu4z3376EzFRAN+Sw2TXDbkMQQ+bDK+g6Z6VqLTYMe394+RXvcYm2bQMFwiMkZrj9jNb/dWWK04aGodcqGjxbk+dzD8/Tla/S25fj8Kf285fb9jLR57F0U6E455EIRllsOewsGw/E0v355ka+dPcQp+QFGNYOr1vbz2z3zfOLeo2fHh/4yzn3vWstnTu/H9wMs10eRRA6WW6QUBdvzuPjXe7jj6tUcWjzAXUeOMDYWoaPHwLHjPL7P5c9v7uYDD1bRQwGTzSqmKHPLy5DX3P/tz7D/rv96FQTB/0Ta/N+rWCwyPT1NR0fH//L7rVbrP7z8SpJEEAT8r/p6mqb9p6NsubjOgT1VzulrZ0+xylSzRaPiEw+SZJIuCyVIJFt06DHOGEzwr9unCdwFjukI88qcyVuPyTLXWuZTm3v5/uOLfHBLlsUDBkW3xXjdJacpyLJO0fLIJQyMVhJfsslEdb5z9jDff26eP+0q8qaNeb725BwhXSKkKfx0+xwPjJdRRZFVWZVqYKJ6AaoRx8ZElQICP8JSEyK6hxSE2NqT5LDuIDkS33lqgd/uWaDeEDDdgFREhbDJfEtkZULnUEFAD7l88MQ+rlib59zVaTw/4OflJY7vifP0XIW2sIDrqyyWbfIpBcezaEt7LCyHWHQcutIiFdPl4rVt3Dde5MSOJLuKNRIRn/XZNGcMp/jSQ9OMlgwCzWNdJkooLKC7Gpev6mTZtLnTWqIvLbFsOUQjCouNo7HfLdslram8bXMnD41W+Nyr+rl5zyx3jS6TV8N847wh/nJ4jqgXZbJi8b4T29nQFuXrj8zzp3yR04YSnLchyq0HFjlnIM98zSbhy3hY6IKIJ0ksV12OaU8Ql12en2qwvjPKu/98mBWpEP96xdHY6rmqxe/2FHjPsZ18+dFpWorNAzM1njxSQwxEmq5LW87DxiQWEmhPqJzQl/zr+tqz0MR0fY7tivKvz88z22qy0DRpy1lENJmWBWY9QsOUaboOesKibAWcPBTnngMNBlIh1uej1JoJopLJTN1AiyiUGtCfDnFCbwqAm7Yv8fJCjYpvEQCGEyCEBARBYKxo8vJck9cekwPg9NUJHhovsn25yFBeJKR6tLwaiazLyrY0M1WLPfurKJqIYYr0JFS+/8ZVnP2rV3jHse1cNpJj22AcVZaYrVrYPrTFdRbLLp1KOyf0xTh5IMmFP9tDe0plKBX96369cCTDTMXikdEqT441OLU/wdnpNiRJ4KLVaX707AI2Fq9bl+W1q/p4ebGOJ8CPXzfMB+4YZUU6TEwVSOgqL8w1kKSAaFSkZru8stQkGWnQnQ5QzCzrOsO0xxQOLJm8Y0s7ggAdsaNnwNmrUqzIhfEDgat/M8pbtuZ5zTE5fndgkdes7SAX6uCnzy4SjtpkogLzVZe649KVkHh0eolbX6kSkiK89ZZRTDdgfVuY1W1HDdl1VcL1A25+schgqoM/FifJhgV+umMeVXdpT8J8HSKBiiKIHKp4HNeT57xjktRKy2jxMC8UJliTTvPspIUWCkiHwkhKk619OntnPUJiBFc2kHyZkVSUlKLhClluOThHV1Kl1HSIh1SWrDp3HPDQtYCZukoiJFJqeSi+wp8OljmrP823nptlTSbC3kqLs/tSfO3pGU7u7eT1G2LcvGueA0sW7z+xg5pnc8/YHI/tN9jWXue0oQSC4POFswfZPddiz2KD2/cXyaUcnioUSYZ1XrchxwcfGOUtwzo1M+C0wSRq2GHvUoBtuziOhqRIFGsu3bEQZ/e0E1ZlzlyVJBvT+cXlR1WngiDwD6ceVd43bJeoKiOKUGjavOP2Ua47tYdj2qN85eFZyk6T+8bLGDWZu8enmKtBEGiERYmfbS9QbPrccMbR+x4oNkhmAt66tYupqskHhE5u2VPg0w9PUg6aGL7DkxM1dk4fYjgZotDyueaeUVamI3zzwiFufW6UtqhGywm4aWeBs1ek0RWZ75y7hmvvPkJMVfji6Rv4/JP7+enuKRAcPGGUL1ywAUUWEQSB2ZpFPi2zb8KjZUj85WCBbz+2yGfO6OX6+2f53dUr+eXrV//1THl8rMpDh6p88YJempbHja9dwV/e+798XP53/W+ov2uiaE1OoRFouK6HFwQMpcPYnoDluZwwnODRI2UuW5ulWDsaN3psl8Ci0aTZ8kjKMdLJBumowWwlTELTKJoOJcPBtxOIqkvZa6FrKh35FpM1l0azyCHP50sX9nHlrbu5cl2SOw8v4jhNBkIpPn5OPzeN7ianpqn7EqOLCn2ZAF8wkGSHhCpw9z4VXJtsTGDeaPDBdcdwZGaGnzw3z8qcTtnwMG2Z9rjGaMGnSw2zd0Kl0fAZ7osxXXM4Ya2L68pMFB3u2l/kO0/N8+aVKe6tzNHnJ8jGHOqmiONEeHmxwt6ZgEM1g+GsRsPwSWgKlm9SquiEwjam4xFTdEq1Fkd++TkWXvVJfvjJi3njn/bRm9CZr9lMVy3KvsVIW4hzxBB3jpWIqyoNQ2AgAzPNFqvyMe4YX6I3pfOTl6c5ZzjDz64aIqRIfPWJSUabVfrjEe4cn2PB8RhKhlm2bfrTARHRZH8pxLGrZVZno8RknRt3z/LY3CI+Po9NL1HzTayGxr++dBBL8GgJKr0JD8P3KbRsNCnBbNUmmyqzq9rB2vY0m9pi/PNzk1w+nOfG5+c5Z/My332+Tl88RFT3qNsCRxYFemIaK/M+fuCwe9nj8k0C84sJMlGBQi2gQRW/mSIZdRmMhIlpElltmFdmx/jkthgHS2WifpS2hMaXzh38a7eubFtctCHMY5Mllotx+hJR4rrLvpLH+EKYZEzg3EGR8YrBKT1pTnljmm89OcNvXy7wpXP7Wd8R5sp6hoblI/7fGoAxVeK43jRbu+MMpv6dxU+GZF67ro31uRhv/P1+BNXHqIfRFOGomaAOoZBPWHV4aF+dY3IKJ3Ylma7YABiOz8NjFSYqBql0HUm1WFoKcXp3CF0WeeJwjemKzbtPyPPZM3t5ZrZKQMB8w+Kjp/fz6FIdQQqomwpNSyCpyXzhlCE+/eQ+LEMhF1No1OD3Ly2zvkfn688f4mOnumy/P0ZEE0jEBEQhQJMNXplVyIoyp/TFuXxNnl+/uMQ9EwsMZzQCOU9bVKFUt/BMgVQ8QkQRcCSLql+jJysSJsTmnhhnr8zQMD00WeT0/jS3PD5GQo9QqniUNZ/2uEQm4lKzXXw/RmfC5kjZZ0OnSLUlsHfRJBeOcrjS4urjcjzZVmKmYhEIMkMZaDoWbWmNalEl22sSFuNMN2rkIylqlkexKXN8f53pSsDKbp2KW2WgM8NyySESFfnmsSPENIl/3THO/oJBf1plstZkKKlTd+ok1BTjDYOV/R3sfeUA07//Bv3XfYwP3y/z8eMHiexLsGCZZBIyWsJmpmIS1WSSIZnPPzRFZ1xlY0eU6++f5hNndvCF01bwvnv3cd/4Mi3bZ3My4Afb5/n6mSPc8PQoHjWqy10crNv0Rmu8XCzh+i4pLclCw6I3IRDVVZ6fcTi2S+GpQz7LDZ/Xrsvy2SfLKKpAb0LncCEgF9LRAwVTqPHZx0cZykocqTXoy8hkIi7PHDHIhkQePFJkpuwS1yQenSjx3KhFTTZ5cKzKiV0ZUoLFePloYs7nH5nk2tN6uO9Qiccnqnz3giE+uLUbXYf7J8p84tRurn1ggvmqTDiis3mlgCcqLDUtElGJ+w4tE9d9ig0BTQ6ztlPg4IzLbNH5mz+j/lYxrvD/PMr1v+v/dV133XWcf/759PT0UK/X+f3vf89jjz3GfffdR6PR4Prrr+fVr341HR0dTExMcN1115HNZrn88sv/eo+rr76arq4uvvKVrwBw8cUX861vfYtNmzb9dfTss5/9LJdccgmS9P+sDXrbK8sYjsg/ndvH+++tk/c0Gq2AlmRgmhI9WYnFukqt4fPr2jKxhELZ9dg+2SCfUJhqNrloZZZf75vmc+f388vn9jPTkohJGnlRYck10FoBPVGZ2YpM4AR0pmU+uHmIX+w5whMLBTZ3ZjlUadEZU+lPhLAcn6fHq7RH4aIVbdy9p0wiJlExBJZcl3xMIxAsRKVORzLKQk2i0QjQAoUdkxXesC7PnNFirmXgCTLxqIQhWSQkiCkSY/My2WhA2Qz49rOzVC2XG3cs0hVX+MGlK7A9j4WnKkxXfFTVpD0ZUKgLZBWF+bKGJkAm41NouLxqIMOh1hEGMwlmajbnD+aICAo/fWGBA4UWddfjnJUJ/rLXYEFr8Yc9Dda3JSkbDv/6zBydcYXJOYGONpWG67Ayq/DGNT38+uVFZqs2P3t+kWLV44O3jzLdMkiHZI6ULd5/zz4qVbh6Y4SPndbFX/Yvc+pAguO64pw8EOcLj0zyyGiZE7pTJKIizy5VUJSASkknqkPN8Tl3ZYarN+XZcXCGJ8dr/OD5BT6wrZMLR9LMVCy+9MgkaiBzyfo0XTGNqCyxb94nElJJxAIWqi6dbQ6iYuK7Im1agqSi8+BYiemGybaOBF98aIq0rrDlyhh24DFVdtF0l7CoYtZ1fn7ZOq5/aJLt82XSOYOUkOSMrjYuHcnwjmPg0LLBEzMlbFPkLRvb6UpozJQtXpiu89xkHc/zeXaqzv5Ck8OLDm/Ykmf7dJ27DhXYGPXobT/6f7pg7VFC6Z5DRf68r8DBOZP+vMLbNndwxuDRay8PVmkYAc8cqbFPNzmnL8uKXIiupMJLixXCosSDYxWen62yt9Dg55eu4Td75tk5XyekachqgBcESIJMWJH54oU9rMpECUkyBxdbXH/fNOmoTFdcZTir88c3jfDnPUXGKya6fpQMeNvWNsqGRdNz0RWRhuMy3wgIyxLHdkeJyBITJZNsWOYbF67j/ffvplQPEHyJc7q72NYbZyiro8sSN7+0RGdU41WDaVw/4Nd7ZhjJRlibjTGcDTHbMNm31GJzd5SVOZ2wInHfm445mmz3yCQFt4lQ8+iICAQuGK5HBxEeGauRi8qYos22rhi5cIhFs8HHH9yDGoQ4uTvJYxNlnp6sk40LKJ7I8rJIW86j4fooqsWqNom6bVApJ8jLGvuLLaYXHeJui2rUxrNjTAYtOpIiC0ZASJY5IdPH9gmDj23LclJvhiNlE1kQuGnPFNunWtimxFlDWe4drZCJy9RMg2PTKdZ357ltbIZKUyOppXn9qiRdMY2bd83zxOwiYV1id7FOXBa4f2KZbFjguK4It+xdptwAPeGyxzjEe9dv4MmDDYqewd0HihzTGeaVpQZff2qa37x6Dcf3xxjMaxwqNXhkcomS1eS2UQvZlfnzwXm64jlemGyxsUclEE1UBTpyHg3TIaQmWSq4fP/pOX7zxpUs1Gyue2Ccz57Rx55ClW88vMQ3Lx7glt1L3HlkgWtP6icZktjWmearZ/fznScWOHnA4nUbc3zniTlyeR8pDg3LpSslEBeTrExGeHmuedQ2wvVYajp8+P5DOIHNQrPFrgWTjkiIM7sy3DFVxFFcNrVr4OoMZXSen2xQabqYksVUxeSN63OcOXw0Efhd2/L8/Jki9+wvYQYeUVWk4tp84q5xhjNhrt40xBeeOcS2jizPHTSZKo3yz6evpjOu0RlTmapZbB4MsdGN8KbNeS79w25+8eIiX7u476/N65t3LHHVxhynDSd48GCFsYLB9fdN8+YN/9H/9P/b+lvhqr8XTPV3PXo28u178JQEaxM6i0GLWlPmp+eP8O1nZ7ju9F5ed+teclGZsKcya1j89soRrn3wEMVGQFM02DZUpiumM91oUG6pyF6K2XkVL1QnFRGISTpVr36UuAlFGFuA7164kl/tnuOc/gy3vlRGijQoGyKn92SJqwI7KlPU7ADb1vECB1lt0BZREOUGu2d1knKKIzMBUsTjxxcP8sDsPg7NyzQbUUzRRgkUGoGJ4/uc09HFazdm+dC9h2hVJSKJJj05CQeTmJDhqtXdfPCOw/z29UN878nnKfgp6m5Ad8pCEX2e2x8hmxQZX3ZY265SbATkIjLzyz4hVUDNlEnpUK7FUYSAHd/9GNHjXsM33vcafv7KAqIIDdNDFSX+8ZQePvfwJB1Jn1ymiGFJzNUFuqI6mt6g3ojw3JTPilSUphHw3fNXMJQN8f47R4npAjuWCwwnkszXbRCPmiAWKgHZpITlN4kqAW3hLB/c1s1n7pomG5F5crpCf5uEGkjsm7W54PgWiuTz+GGBzV0KqYhJsWUjECYi+xiuyDMHdC4ZbDFmhVhs2UhWAlkOkESBsXKTb18ms29RZ6pe5eCyhegniWsuga8y2zCJyAr1usTZq8NMFE1UP8Rhq0B32sFpJnFFm454iH89ZwNBEHDlTftpeC4jnR690Th37q7zzuM7efW6LJbr8+kn9lA1fDr0CMPZCH/aW6TputSDOtsGDBbLOZyGihbyObYrxtpsnGemKpRNm0+dMshrbttNXA/IaRFO7E2w2LR50/p2fA864yo3PruELAp89IxOlls2ryzUkXyZVbkQ1909ia85rIyVGbM8Co0wXzljFevbY7znj6OUqOL4Alvak/SnZJ6eLdMRifHWdd3c9NICL5WnCWSL/QeT3PH2EVbk/v3A//WeSeYbBhtSef7lmVmahsetb1zNR554gYgsUW6p2KZ0dETRg6JhEpMFLlvbxq9eLDMYiXLuqhRV22SmNUXRlnh6t8rIoI/jBSQjAb5gUWva1Jfb6OxqsK0nR1YN84NnF6h7Dps7Yrzn2E5enGvw/afm6ErLxGMeuuKw0GqQCcUxTZVj2+P87qUSnzyll9dvzFJYWuKJBYmW47O/3ODOA0XWdIq0JS2mm3U6wnECHw4WHXpiYSotkemSx5Vr8uxYqDI97zPYJVJoGdiOzJpUnB1zdTKaQl+XAYqBaYWYqbqsyUtMlgPCIYP17S5jy2FUQWLJrJFRE1RNgVRI5etnriWtq9zw1Dgr0mFuPTCNqJVoC4VBdGmPOgzkJ9lxZJg9+6ZRj2xHOu6N9EYzfObUPt599x7ikTqWo+ObKsWywqoulX/Y1s03nprmrOEUBws1nl8oI3s6ruxStOqsyag4JZeWHuH43gTtEY0n5yZZqhuE/QSfPXMF773zMCcMubwwJdAZE+jK1LE8l2o1hSNXaVoSVjXNZMnmO5f2cufMLmzPBDfJZEnHtESGsj4VM8AlIBeBQDRxAgfRDTPfCJitiPQkJMoVCVO2+PqrVnDDIzPMGiYxXeSmS9bypt8cwpIcVuY1CpbDN85awTefmKEnb/DsXpnrz+nlt7sKTBYsLN0mIgokNYVl1yIVs1ECjXpJ533bOrl3/jCVFhRrEl35EpPLYfQqPPgPf9vUs0e2QPRv1M5puHDmi//ny6T/d9Y73vEOHn74Yebn50kkEmzYsIFrr72Ws88+G8MwuOyyy9i5cyeVSoWOjg7OOOMMvvjFL9LT0/PXe5x++un09/fzy1/+EgDXdbnhhhu46aabmJ2dJZfLcfHFF3PDDTeQTCb/3/q9/sf6uewHL/LH92zmxfkan374MG9a18m9h5dwRYfJZR+/JRENQVTSaGoNUlEX2dOxXDhrIM/7t3Zy7SOjnNGX4sTOJP90507GbZ26Abe8YYSHJ4r8/JllDNkmExZQRIFiQUaLObSnAgrVgLN62zlrZYqbXyjw/GyNXERGChm87dgObt1Vo2q1aGEjuirJuEvTsYlpAhEFKpbPZUODmJbE24/tYK5m8akHxzk4Z5GOSRieiyu69KZBkAwEAkwjTsu3UUWB3liCNfkQE2WLfzp1kJAi8a47D/DKdJPubEAo3MC0BRwzzqBeZrCjl7vGyuAL6JqIqjeISBoHjqj0ZBS+dO4AX3hokrrtsS4V5fWb81zz58MM9/gsGi4fPm6AJw40eHaixkVrM9yxr0Bbu03LN4hIYWZKAmlNoWBZpGWNsukxGA1x2qoYj0wUmC47JFWdasPnHVs6uPq4/5iO13IcfrzzMPfvtfAkF0UWUEUoVCEdlahaLmf3p9g36/CDKwaYnlugLMQ4eSDOA4crlAyX/YsthnMaOxaqEEh0RDQeOFhmTbfMlWvamTMK3LnLpmp6nDgyx9h8G8stn5AsEdU85ko6r+pLcdFImt/sWqI/pbFnqcHuhSaZiIjtwTHJFMWgxkLLpNYQSUVEfAJKBlx3Sg9Vq8m/Plnmyo0Z1mSjnDGQ4vJf7KcrrrGhU+fmfUuc1pOkYtu4skGPksGWLZZaFvmIzDuGY4TjGRJhBVUW8fyAiYrBXfuL/PmlCp84o5MTBxM8frjKRWvSyJLAn/ct8pMXp4lIIfpSOh1RlaWKyyulIomwSFhSaLREyk2Xb1w8wGOHa9y3v4xhw3FDYaqmS9NxGcyECSyR123KIgrwx5eXeWyqQiQk0JPU2NAeZedcgxN74yT+bXzwwtXZ/+V+bdoeO+cbzNUsii2HxYVFnp6Tybb7CEKAhsSpHW3cta/EW7fmGWkP8/RElXVtUR6dXuaJiSq6IhIEPpLs0nR93rSmh7mSy/iyRc1z+OYFQwQBhBWB3+9c5lCjwu7lBiHNIq1JRFSP6088jsenK4xkIvz0mUVGa3V8ycP2oT/tM1cSqVYl+jtE6o6FLxoIzQR108dXfTpSHqpWp9oKEZIlapZLV0xhUzbNb5+vsy5pss9QiUgSobhJoSKTUmW0SJ1mI4oleKiKz5o2jUtWdrBQ8TlrKE3L8fjyY0c4VG0QUUFSjyZEL5s2I90eSw2PXCjBuX1t/Hr3AucOp3jflm4eO7LEVx6dJRR16My0sGyRuWKYrozJQjmC6EtY+AS+Qz4RcFJ3hnsPNik3PN64McsDk8v0h2N0x3R0VeA3u5YYbpOZqJhEdYjrHuDQFcAeQ0XzdSypgSrKREMOMd2kVk+y1DjanK00BDZ3R/jWeSt4fKLMS3N1Lh5J8elHD2A3wrxrazu37Fvg7NUxts9V+NDWIb773BzdMY3AEyg0HQRPYPtiDUtw6IiJeJKB7YgMhbN89bxBHjhQ4bbReVqBx4UDOe45MoesNWgZGp4TolQLUD2Jc1Yn2b9gctKKMDNNg+tPHeIHT8/z6FSJquFz+kCCbRmXH75k0VAN3rulm7Ss863npylbDq9bm+PWg/O4rk+bFOeblw4wmArx0MEKP3hhhprlsiYV54YLe3ngcJlLVmV4w+8Pcs2Jndx5eImLhnJ88f4Z7nzHCGFV4vc7CzwzUee7lw/y6UcOs6k9xtkDad75u5388R1/29GzvxWu+nvBVH/XRNHFv3wMU5UIgLaoz5ZUL3fubvCZs7vJx2U++uAhFpYC2nIN1rcnODgrM1OxyKYdRjod4pHlo6ykE+VQIUzTCQjJENVUbM9AVz1kIsxXj5pev6ovQ0QV+fO+ZTRbRw85RDPL1JsaZ3UN8sMnFhkcatEdjfPkdBUNhbVdLnW3QUcMlqpJyq2jM7wndqVpS5rM1lrct1ukVYN8RmTRr3LKUIOLuo/j9IE077/zEAerDcKyQFfKpu7ayH6EH11wDNWWT9Wy+eoLL9LuybxYitCe9MmnC+ydjWEaIUqWzZqcRst3SMgqvuRwensnNx88zGBGpGrKmFUPzTpMLBdnttXFtScNUDE8zuhP8ZZbD9AyfWIxAYKAbKZFPGyy2HCR3RQOHu1Rn2W7hGt2cFZ/nok5n7efkOa9d40Rl2VsJ+CHlw/wu8PP8sT+PKLoUfXrjKTDhMM1nt6foF0/Cl7lkI8UCPRFQ7iCTzWo4eDTnbTRVJey5VNvxOhMOMRis1hWFnyVnfMuZw5bTC5EERoBC0qAhE5vKMEzRxqk4yKS6rK2XcBiEc/TqTR1VFlhqRZQK6pEsw1CxGg5PlFV5GtnDfHg4RpPzc8gCUdH7BYbNjFd5QfnrqU/GWbvUpOnJss8PVtgdc8sL84oNIsd9LY5LC4ppJIOjuBTaLqEZIGGKdGTL7K+q874UiftWp5z+/I8MlHimZkKR2VDLpFIk99ffBrPTFaZNxosGQb37Wthuj7v2tLBZavzf50fBpismNw/tsxosclUwefHVw7zo5dm+MdtfSwtLfHO++bZnE8Tj4qEJYkXlouIcotGU2VTR4Kw7rNs2Lxn4wB7l1p8++kZQrLA2h6He1+Qee4j6/+nZJXt8yV++0KZtK4wnNfZO2ewc77OslRlQ5dBRIOaBYVSkpQuIMoWjh/guiol0+QLJ6znS4/Mks9XUKUwp/a2kdEVvvH0JBduPsz28W5kSaVmBph1jfF6k6FEiMvWplms+zwxu0SlKZBPQFdcpNSEhmVTrWukIwqC6KJrFg3fIBeOYdkO1XKYG69YSWG5wKbhHgRB4L137We6ZhCPVohqComQi6rYWF5A4OmUWxJ1O2Ao2sbnThnmypv3EU81cYKAwJVJxZqMxAepGA4Pz87zno19dCR9vrdjDNVL0pGqosoidcfFdgVkL8ah5YDupEjdqZOPRCk0PeKaxBdPXcM3H1sgogq8tFQinzLxRIvBlEwuXiMsw4tzAiktxGRJ5sBNv2Dlq67md+86k9Nv3kEybLKhq8VCTadcPRrxuiqv87nThrj+kXGKToOULlNpKtQtm/akyFyzzikpkR110ESdpK7hiU26EzBe9uiIQVTRuGbzCL/c9yJjZZtqM8zqPOybChHXBWoNhYrjkZZlxEQZw3V41ZDNvQc1mpZCXzyERZWFpTjDeQFPaRBVoSMmcXBRwfUhoSoYvk2xpNCZFrAFk6WKQlSWMXwfy5T5+tlDfPGpwxScBo4nMJyMMlGyMKoCKwZsXje4ks/dO40v+GwYhIpv4AY2xUKCU9aaLLbq7DqSol0Ks6Jd5ZlCkVTYI4xOSJVYri7y4gcu+i8JaODvB9T8d/37+nlxdJ7NQ2384Nl5qqaLEbg8O1/Aw0d0dDRRYqHoISkBK3odLBc836Na1TlrJEGp5fLC4RbxsMxxXREOTM1yqBLm2O4415/by2TJ4nMPjRNLehSaHpInko5INFwPoymRC6n8+nWr0WWR99wyynzTpuZZKHrAQDLEgWKTXNxCMOJUAgPbhaGUynjZxXIELh3K8OxCnQ8f38Niw+Y3+xZ458Z2fr1zibmWRTSQyWQ8XAyyEYGmLeJ4LgphKoaPgMQx7VG+fMZKBEHgxu3z3LRzkVzKZ9lwiMoirifQlmzwloF2vre/gSiA4weENItcJOBQQSOCStM3CVo6miLziVO6Ob43zrV3T1D3DU7uTVEyPD5+Sg9PT5a5/sFpVN0hn7aIySE8X+RQqYEeRKk6DklBY7kSkE4KFCs+uaTEUDIMkseuyjKbUzlEQeLytRkiYZ+SYXNyd47f7Vrk5j1zJCMBx3VkuGu0hIbIVWva2bFYYddyjf6kQM3y+MRxqzm+J878wgLbawLff6ZASIO65yLZIqEQlFoeb13fyes35Sm2HPpSOgDfeeklJqolXjqSZlU2xCl9CR6fmmFdh8STBz2+dd56DDdgRVbns49MkApJ3H24QF8qoNQQObkzz3jBZKrSort3md5UQKklcXAhhIxGX6ZJOlHhuT3ddMdDvH1rOyf1Jbh/f5mpqsneOZNTVofpjYV46ECVj57RxVjB5MmJIpPOIofmFC7tUjjrmAH+6Ylx9EDl8pEsa/JhHjtS5fFDNUY6dA6VWuwrNFiViXDJSI517SFuO7DA247pRhIERAQalssbbholEhYIJJ+qbSMgcu0pPRxcMnl0vMxVx+Q5f2WaHz03z6GlFl++cICrb91PPCSRiYlENI8DBYtze9uZq9ocKZtcf9YAy4ZN0/KYrlrsnm/xvUuHEASB5ybr/GL7Aq7m8skT+xhOh/6qIvd9n989fZB7ZwJuumqEXz6/xIFKnZfnm7Rcn4goE1NFaqaL4QWcO5xGEmFi2Wa4TaMvozBWbvL4dIlrtvRx1mAGRRQRBXj3rWM0TI+CYdPVVSemQcUMiOoO1WqCak1CiLaQzCgJXcJWKqiyhGHJmL6N6Ol4nsj6tggn98bYOWPwxGiDkB5g+A6OCPlYgC+18FwVNZBZKEtkIhL1lkePZFDVQpTrcNWxaV5aXqZomnhWmLprcUZ3O0+PVzBFj864xJWrO3lypkpYFTmzP85Pdk7jYKFJAhXDQ7YitASbkQ6L0WKA6seoNKArovPJ07u5/eAyjx6qEEu5tByfzqSLF3iUahEQPT66dZCeZIhPPHSARkPgsjUZ/ri/SFfGodQKGI4n2bXQYCircsXqdn7zygJLdouIIqIrPk0LeiJRLugSuHnc5MgitCegiU9McwlcDU1vEThhlusBIUXgc6cNckJ/gh+/OMtNzxX5zqWDDOd03nHHfjqjAgdnHVIpn9MG8sxUbb54xiA37phj+2yNN65vZ7Hq8pMX5mjLO7QCA8FXAInZZYGVyQiaIvKaYzL8/JVpXr2ik395ap5jh0TGiw6mb9MdjjNf9ggLClcfm+EXO5eo+RbHdIToDEdJROBPu0tszceZL8xzpBFmXY/KqSvDvG5kgJbt8Y+PHOCyVW18/alpLl6V5rS+DB+99zCfPKmXO8eWOb4tyY0vz3Jmf4qPn9LNR+4bZ3N7lH1zBs/OVrnhvH7+cqjADacPEtOPApxXZptkIxJdSR3H87nmnkOUygGHigvs+ujfNiTkv4mi/2f1d00UnfOLe9D0PCHNJao3OTCWobtN5MtnDbFjaZmbd02j2DHCqRqVuky9HCKTFJDCNXJxg4QqoisONTtgua4SeDGWDQsISOsKkmxwpKwQFiRiaphrtw3y9tsP0CaGGW81WNNfJhuK8+xhAduU2NCp8KnT+vnQXROkQxJFxyERdtjQ6SLLTYpNlWojSdG0kQVY0w5vXbOGTz04Tq0iMu81uGKTwd5ZFccLc2pXlodGy0RDInXXJJuq41oxLKlMl9/L1y8c4mOP7EOWWuhNhwYZ5oMCrhml7rn4jkx/1qdpBfQkQkzUKqxNtvPQVJEVGZmm5eM1fXZ871qGLn4TodVr0bwwP7t0DfePlYlrEj/ePodtQTppE1IDBjur7FsUkZwYHh4r2z2QF5he6mVmTuKfL1iJpNp86Zl9+FaUpilwwcoUv325wPGrDGJinvv311nVCaJks7mnyEsTWaYWNI4biHH+cIZvPH+YVw1FeWy6jOAqxGUVIVQgrGiYRpiZZVg3YNAWM1ioy1RaMl1Jk56kz3TNZHkigxcJ4Rgav71qHeOVJt9+bor9Sy2kwEcNeZwzUsUNXHbNh5grh1CNGHrExfYCZho2Ge0o4EpHwPQthEBiumITlTUuXZvitWs6uP9AhXv3l6goVeKyjKgaaGIIPBnD88GT8DyBWlDDaoTJxCQU3UGTHdpjoMk+sigxUZQJAoGRbIhnZwxi4TKjBZ1n33IS1z0wwXA7vDjZYr4ccPGaDIWaR1dC5V3b2jm0ZDBeNOnParw832BlJsRnHjpCfxvMNpqkVJ1sYHDQgsFUmI3pNjxXIFBs6laAqhrU7IDNbWlenrEotRw+dlIXt+5b5qHxMsd3xanX4HPn9bDYsOhJ6AiCwFvv3ElM9zl3oIOLhju5/1AJw/UoGRZ3js+QT9hULIuKKeKX8nR0tFAUGzyVclXDtETOH0nxm5cKKFGHjKZj26BpPoJsM1Oz6IzpvHFtF5Yp8fyRBpu7I/x07xESqspwzsIRXCotkYZr0RXTMWyZWlNkal4mIkvkUgLFmo+etLE9j5wUptwIaFPqxLJp1qaS/GVfkVCiimDE8UWbgmdy9gqPlmdStR2ScoqKaXJ4OcTWzhgvTjXRQi7HtMV4ZrKF3wyTTkLdsdDtEFKswfHdWdbmBX6xZ5yEmMEXLEKaQ1Sz2DOZIyLJjBUMBrMq4ViNTFhhquphtMJcs7WbH++cJ3Chatu0Jx0W6jLnjDQQJIuE7nG45GFbSXbtqTHxixs4/f3/zJvOXMONew8wkCuBF+dwQScsayw0LfJClF0LBpsGVBotAceHY7NxnpsvkYwIyK06jYhCPGJyYDrEQE6lK1sCP4TnexSbAUNZhblmhc64x4H5DONFn40Zhf1zCpIrc+xKmYMzDqmkRV+bRUivgS9ieip7FwSkQEdCYN+YwsohG131cYUmA6mAI0spxhdFRroNDCuKL7RIhgNUQadiORi2hOZEOLU/Rcm22b1cgkCi6fgIgk9aVSg7Lse0R+nVIhxoTtP0HBqWQkI7OhKjKTapiEm9nmJmWcL3BQTJoyMh0PBtmqaM5lW47+1n/U0BzaPH/22JojOe/z8f1Px3/fv6OeO7z/CR01fy0HiF3rSK4wcsODUWKwFHSjZfPnOAnz+3iBtp0vJb6BKY9SSWZHFGf5o3r+3iI/eMc9XGLD9/aY7X92ncctCj4Dr86Q1r+eWLCzy7PEWhFKI9AZLqUahInDYQY/+cx4lDEcYKFq4dsLdg4AQu2biEhY2HR0dUpFRUkCJNRCGEayl849xhDi43uPGZZT56eie37FmkYnq8b1sHDxyZY9+yRc02CFsJLKWFqLh0RmXKtklCC2gYUWqOTVqXqbUk6hWZK45JMb7kcKDQRAv5uKJDXJGwBQujEuLVG9M8c3AKM6Kx2PToy9hENCjUQfCiLNcDNmWT7CpXkG2Nr13YT0SR+Pg94xRNhzdsyPPOY9v584ECvz04gUCAa2lcta6D8YJNqWWz2LI5XDbIR2Wqlk0yLFM0PZLoFGou69vDjJYN+tIqh+YsLtuUpOBWma+6LC7J5KIqJc/kuO44H9nWw7vuPohlBgzEQ6zp0rh55yIb+lxs32axGKMr7XN6X47p+UW629o5tGxz6eocX358EtsJsJQWYTuK6fn86PIh+pJRnpwqcv+RRcbLVSwbPCfCctknnhAYTGp876JV+AE8erjCE6N1HMVh73yLlh0QlkVM1wHFJ58QaZgexQacvLqBLDnMVBQsK4wgCBRaLh1xn4yU5n1b+lmRDfH9HRM8M1Fja1uas4aSvDzX4unDda455WhM/Z37SvzyxQWINBCMMFuTPtdetJ5vPz3HmnyY7pjGd5+YZzCr8frNOdJhGc8P+Oojs7zz+DzjJYMfPr9AXbBYnYpyyaosWzqjfPj2I5zYF2PRtFis2RgtkW2DEZ6ZqaHIAVt6I4iBxGWrs/zyhSX2LjT57hUDvPZ3B0hEIBMDTfLYtRCgOSpr28OsbYtQbXpUMcgqIcKSTEgVeP2mPB+8fYxw2CMXE/nY8cP88xMzTFQMHM/nPVs7+e7jc7x/k8aMFeUnzy3yjm1t3HNkkYbnUmp5BC2ZTAJCIQ9fMlAEn0o9hmeLGKbAm7ZmOVIxqFse67tCNOoCr9+Ywwl8Xv/7A2wZDOgIRbh/tMLGwTq+r+IEDnVTpt6IUWsFmHWZj53WyUh7mPuOLPDUZA3DhHIjIB0TEEQfSbMYSsbIqRHu2FPh2P4IY8sGLdtjQ5/BYtMHS0EgxLkrU2iizIuHZthZkUhGZWqOTWdEoWE5VGoqmzoiHCk6nDwQ44HxIr7isCaV5DsXDfO5hyZ4fLzC69fnuG98GVUP8LGpWiKrkmHmmhbZqMlMDbbl23nuSB1JERjKhHj1ujTfeP4wuZjNQkkiHBKptOCK1e08P9Vicz7K7YcKSKKI4wj88sqVfP7xI5w9HMW0FR48XOJtm3PcfmCZk7uyHKjVkG2Fx6eXSSUdoEmnr/NiXSCvRijUAlIJH8uFpglZTaQutlB8nUZdRFUF7nzLWizP55vPTPGR43uJ6xIPThTYu1wjoQbsmZT45sWDPDNT4cSeBJIg8P779jJd8fjFxav58B2j1NUSMUVntiYgCA4ZJcLbN3Vzw2NTrOuEXfMuHSmBLdkcb93UzmTV5KtPTOJILu/b2MtPXpwDT+RtW9r47SsFLlqd5mc75tnYFuNwyWAkH6JPavHAIpy7Ik2RAtva2zlvsIO67fKPDx7kPZt62dgZo9xy2Fuo8e3tB5hdDHPicJhPnjhAPqYSBEdTLN9y2wEOTduENYH3ntzOVcccVUuOLxv86WAByZExHJ/rzu7mO8/OsDob4ouPTiG2DJ78yEn/JXHV3wum+rsmijZ+5wHqrSTrVxU5M7eGX+6bo2x6aC2dzSM+L437WJLHcN6BWpIxo4muuxzf6yFLLsumzZnDi4wtDvLkEZFqVWFjn0/dEml5LRxHpzuuMFk1sRo67z2+g589W8D1YHWfSyjcYrakIbshXPfoC1g2GdBqijiuRyrloKgulm+hoDNRkqg7DoNJlemaTcsWOCYb5WMn9zKcDPPN7XuYqLaoN6Osymt89fSVfPnxaX77UoGOvIdnS2Qzy4hGG7NVWNHp0/I8ECz6LJ23nr6aTz90hJpQoT0qEdd9YiGLZr2Lqzd08NWnjjBeazIYi9DCJSqq7L3vLnIdvRjta4nKErIsEAtJ1GwXJ3D5yllD/PDlSUKhOpmwQ9EysYw4rmixMiOha0V2TfZwuOzQLsUIJB8t0sA0FWqmxOl9Ka5cm+Frzx4gcBUu6O/m1n3zhFOL6EKKhuNQMh1O7ejg+K4UX7p/no0rWohKg/mqTkKVmapIjHRUyEUkHF/gqoHj+P2+CfbVloiFAvoTIulYgQMLKRarUfpFFyme5EPHrqQ7qfHHA1Pc+MIihYLMcSMtunN1FhswWQqzos2i0YoyV5Yw3QBwiekijhnGDjwU1cGXKghWDgeflC7SFY7w7mN7uPtAkQtWpPnoA4dRZZ81fUvgayw2ZFquT1cozTOHbAzP5V3HdfLsXJFSyyMp65wwILC1T6VY1fjSQ0scPxTBrIs8Vyjzi8sHMSyZw0WT6bLNmcNJclEFz/dJho4CToC9Cy3u2VcirEocLLVoChYt16LcMqk7AaYNv7hkhM/evot5VaQnbdNopEmGRL5y5gp++cwyV23Osqb96EjZZx6YYLzawncEkC0cyUURFNbno4Qkkdv3lrFEC80O85bjcrx2bZ533HGQ6045mhzz052zLDUckrEWw20tVMll34LI2ESGob4mc1WZnrRHWJIpFTVUQceSG2RzsxQKeZYKYSK6yFCHzMSyjePBfM3l6fds5GsPzzLRqlE3PeaqLn3tFvmkiedEKJk+HQmDmu3RbGVoNUXqzQApYqKgMZyMsnuhTlwSCWQ4Pi6waVUnX3p0hq1dcS5dF+fLTx9hpM1HUxukIwagsVRTqNsSAxmDSkuhYckEgYzvyfzovC08P1vnuocOEZdlNFlmb7HJCj2KHHbRonX6Uh6CXCct9XNufzfXPTaGHMg0mhJhDfaXmxzXrRKLNCBQmax4BJ6KHKhsa0/zzGyJcLyCaUYIaRYjbS75eAVV8jE8k0d3D/OquEKDMGHZ4dZJk5As0xQbdIbDVG2PFTmBSs1GsbMcKht8/aJemq7HBcM5rrtvkmdny6yLt5gWfQw3oCspsH0qYE1Wx5cb9MQtRCFCw/ZQRRkPm2LLZ2Oyj188VScsi3R3+3z1rBV89dkDlFo+2USdpKajKiYh1cay4hxelnF9F92PMl/xkKMt2iIqdcdE1wya9RyFhosaahGVIvzjCX189smDxFWBwI4wvuChKAIfPDnHzftmMF1IyBrD7U0mKj5L5RAbuiTGSx5tiQaZcEBEUdi3FBBSA2wjToCPJRgsLITojurUPJdyJSDbWSOrx5gZM9j56dP+toDmhL8xUfTs//mg5r/r39fPz58c4w97LN59co4XZ+u0pwRkSWDPcpWLBzv55dMlvnB+L4Fi8S/b97NvQsNVfbZ1xnjNynZu3V/gmI4ox7SH2VeocWZepi7GuPdA5a/mqz/ZPssdY/Os7/ZplzrYX6rTFtIIazJ10+O5iTrv2tpJd0LhC49O0RQMcjEPTZKYLAjIMrQnROar8KtL15DUFS7+/S4kU6Y9JTFeNomFRYZzAq9Me3SGVdIhlV0zJrmEj69Aqe6zstOlaIDt+fQmJaYqPoKhocoSpmySURXKFZnXbszy/HwBU6xhNCLUGiLD2RBDkTq3T0JfxiebKVOqa1h2lLptsSqW5ZvnreCfHh3j+ekWshgQ1kQ8qcrp3V08us8iEYWCY3BSX4TxBXjN2iw/3bFIveVx3AqFXfMNkhGFybJDZ0xmoeKSCGnUTYeopGIGHv90Zh9b2mPc8MQUHzmxi089Msb4vMt5K1OomkBbROWZsSbprMuuWYN6XeKfL+zj9oPLlP0idbdGVyIgotrM1WKc39fH3PwyD0+qFGo+5x0TY7JscXZflp3FMo9NlolLEh2hKD+8fAWffWSMubJLwTJRZYGGCcmQhINNzfG4fFWGmUYT3w1z5UiO3768xIvjLTJpAVewMGyJZFjAxaVSlOnIBYiSi643iYckXFdiphLgOBojySSfOrWPb20/AgREZIW12Tg3Pb+MK7mENJGfXXpUjXZwucULk01evynLaNGgJ67QqpYQwyn+5cl5RtpCvHlLG0dKBqNFkyAIOHUgzjX378EyZHQU+uJhMgmJnpRCT0LnhbkKt+xd5OT+EIdnJJACNBWqFQFR89naFaMvpeH54EoODx4qElFlulMqmmZy3y6PzR1RfEdk30KTE9YJvDAaILgicU3i4g0pVN3ktlfqOLaA54Gmiyw1LVblwqRDIk+N10mkDJRAxbWPhsQcNyTiL1ssOXFmqibLhkso4RDXRVxXoNyEi1anGSsYLDZNRN3EcSXSOuRDEb5+7lEj4z+8UkCSfHxP4MS+BDtmGnz/hRksJ6Az5+F4AlXbIq5KxDQBRTXJqe2Mztu4eOi+xtpOnaG0zo9enCcaBl31KBShLQ2WK7ExG+ei1Vn+srvIlFMnLiuML5t0tNVYlXeoWDaHl9LgyLhYnJXX6cn18NTCIuNLPotll752D08IKJY1Xr0mx1Ub2nh2uspdh5cQZYvTejLYvo9hyjw+W2AklkaWoDOi85cDi1QCmxO7Y+wrLyIHOgvLCqtTYXYttejMyrx3ayfTyw4375mjK29SaPlEJBUncNGFKIoEKVVl+0SLze0xOmMabXGZ12zIccuuZXqSKvuWW1xzfCc/fGaBd21rQ5dF3nDLfo6UW+QjMnm5SlGMc3x3ioggc/naHM/Nlrnp5QXmFqGvXcCU61iWRkyReeOGDgbiETZ3xZirWVx7/zjJiMS23hCPT9Z4VVeenoTGiQNxPD/gow/t55KVeR6fKnGo2OBdGwY4sNDglr0FUmkHxY5wpGEwnIjw5Vf1887bDtDRYdCsJPiHk7q5/UCBcjVg72KTVBxkQaLY8MilRJJhGF/y6I/rpJMBBxZNPEvmvNVJ3rRS4Yd7DPYX6rxha4JHJmpcvqKDsZLNcCrE1x+f5fdXjbBUd/jwnYd5z4lJbt9f45yVCXYv17mgp4PvP7vApevSXLY2w/eeneXZiTrfuWSIvpTOfM3iDb89iIvPWSvjHN+R4tzVSRq2x7v+coh1qRgjaY8rtw78l8RVfy+Y6u+aKPron17C02W29Mm8ac0In3lsP7unXWqmC6qHgkQi6lGtB6iKTz4Z0HQCZMWm1lTY2GUjKXUOLsaxrTCBL9DyXXxPJBeVWGwE+LZINiKwMZ/iutP7+dLDk7y0UKMemAiehK6ILFZ8EHyyEZmmVMVpxjhlWOG2F0w6uloktTD7lhzaYpDSFYqGTZQQBCIh3eKYzjAf3bqamC7ylSemuPXAIild4Vtnr6Q/pfO22/cyUbFZ29VCDkLMVmTyMVC0OhMLEXQ9IOvXWdczxCOzk3QnNSTJRkSgZkpUbYukEqXilNGdHFXTJ6naPPeLf2Xbm6/jJ1euYLJsce19R1CVgExC4oqVOb7w2DRpVUGOOGSjHoloiZQGr8zrdEV1VK2JYYbZtWjTl4jQaAlE9KPqrv2LUY5JJ/jJpSv53JP7efhgk6iokw0rvDBb49hOlbIlIMfqtEVESk0J15cJqxY+Lp1x6E1XeP5gHsGPIYaKfHBbDqOV43vPLqAILdYOF2kaIWTZxglMXDuL5dhUpmWuPX8Dq9rC3Lj7EE/NztBcbicc8aibPpbgMNLRgCBCy/FoWjJzNZctPSKzJRXHUinWPQLNIuSFaMu2UBWZxSoIgUzBsPj0thWsb4/w9WcnWWzaeDgYdkBIkvjS2X1UGgIrcxGuffgAZdshocDKrMaHtq7C/jfjwqvXdPOqH+1ldTbMb69eyaNjVb6zc5RYyCZJhlZLoGi4yFLAlt4YUUlBlQTed2IHexaa/Gn38tGUBgn+8f5xXNnGMSU8xcVqgeMJKKbMyo4q04FISBWoNTV+cckI69tiHC6YdCVV4v8mHb3tlSJ9KY0tvUfTMeqWy3eemmX7fAVFDbBtAT1Q+dqFfciSyPeem2FjV4j1mTifeXovEUWiWtRRogYDeRPfl1moyhRqIt0pAcMV2DdvcM22Lu4ZKzCQaWBaETQ/yus2p/nVzjLXbusnqct8+t5J5g2DfEzmk6f0s2O6weY+nX9+fpRC3ce1FGZKJpmExls25lhu+KzMK3z3xUkEwSOu6rjB0ZGGn553HJ4f8NxCgc25NI1yCTWa4YN/GsfGQ9QgFXVQtSaWJzGQFmi5DVxfIB/ScYQKtidjmWl2zwd0J32uXNnPq1d38d679mMJJSZmY8TiLrFYgdJCF1rYZtZssSqp4Ykunz15hLop8KG7x8iGVIq2zUDGZ7rVYHU6hO07RLQAwzUYn2znzZvy3DWxgChaNDyblCqyZaCAiE7JEOiINTlSUtixr4d3b9T42XUf5JKPfYPfjImkIyKH6wbXnFHl0cM6HVGFbChCfyjLr3bOI4YNbrpwCx1xlRsemQS7wAHHI6p7BGIT3DQfO24lX3jmFaK6TSpWRPNz7F5QyUddhECnPRIjFST4y74idc9idZfCviWTeMRgIKEzUTOIqbA6JyAoFUYXMmTDCjNVARphVnXq7CvVieguPUmfuSosllU6IjI1w2Gwy2colOeuw8tUGwKJEGSlKJ84u42PPbyPjojG2KJPKtWkKyYhSwH7FwXiIZvOaISFpsWqjiLlRoxqJcOSbYAQkFJlQqrE2JyAYMuk4gK6Ak3BxK8aPP0Pf1uJ9H8TRf9d/5/U/1g/5/3qXja0DXK46HDl2iw/2j7PW7dmkMSAO3c3mC86XHpMin2tI8xVBex6jK6MwmntWW7aucQVG9Ncs62Ljz66g30TAZd26zw2K3PNqZ38ftcS7zohzwfvGyWsOpzcHefxMQfPhYtWpzBweWi0Ribq8ea1vfx6+zK2VqU9JVA0bGwXdCFKzXFwPY+1sSxfPLefuarNPz0wQcVx6GtvsjrexXNzNd6zsYufbV/iO5cM0RFT+fYTszx0qMJ7Ts7zq92zzBZ9EjGftoSLotZp1dLMNyGkW2RCKpMln76kgiaqFO0qUSnEUssnpkj8/OLV/OaZUR5b9gm0BiFZotwSqRgCeV1hrhQwmFFYMm1kQUJSjo6wjGQ1PnfqCN98fI4HDlQ4pjOM4Ets6Ymyr9BiRV7jz3uKSMB7ju/g/oNH0zm3Dob4w84SpaZLOiHR9FzO68vx4MEq2TSUDI8/vnYdrhfw/EyVX7wyz9b2BFXD44nJComIyOWr8xzbFuMf/nSEKzaleNe2PN986TmmSwIXDnehqQYvLY1RmMzySkVDkmRyEYm+RJgVmTBnDCb58AP7wJN4/fp2htIhtnbFEQSBX2yf56btBV5/fJIHx0ssVAIG0zqXrE4z17B5ab7ObMMkpEJvQudIuU5I95ACBUl2WG6ItEVFMtEWbakqmVDAciPEzhmdajVGzA+xMhvmunO6ePhwmZenDMYKFj98zSCj5SbZsEp3XGe+avOjF+Z4eqrKtSf3cvpwAkUS8X2fpaUlbhv1Oa4vxtbeGABX33qASsvF9QMM36Mta7EmHeeEziy6rHB8V5y79pZ5ZGqZqVYTz5WIoOIIHo7j07Lg/A1Rrl7fxXzVYbpiocoQCQmUaj4dMY14SOKjd42T0CVevS7LYsvi7rFlREHAdQS2dsRQRIEnJstkc03qzRBdsTD5iMQLUxVMW+G84TTH9cZYbpn86zNLqL5MNu2Sjki8fqSb+145zNtOHeGRiQq37llCUl0kQSQiyYQEhQ+e0MXBcp3HJstYdsA5/W386uUFTumPc2JfgicmK/zD1m52FeocqFT5wOZBTNdDQuCxiQo3PDVBJiTgE1CxPMKyiOJpCJ5Iw3XpTWts6IhwxUiWzz82ymLZo2pBf6dFPOwwXQkIvAiCL3JcR5K3bm6jZfvsXKryx91FAslEC7WIKwrzdRHfldAkibDRoqcnS9OGHTMGCVGmbHgkkx6K4rFYljixJ44ayAxnQjx6uMxQDxxYaLHYEDkul0TwRRYdg7mGxavXpQipAh2RMD9/aZaZkkdn1qPccgiLIZaqHum4TDwk86Z17Xx/+yTdGZOy5WE1YtR9kzhRXrc+x1/2F2mPKZzUleQ7T8+zpldgz4JNVtHZ0hHnU2d1I0sCR4omnh8Q1UU+9/AEe+frbM5YHLA0euMRvn3eCmKazKcf38WDBywGsjJXb+jk3vElDi86ILkc3xNm+7TBhnySXQsGtabH6YMJ9swbvPOEHH86VOCG04dpjx5NsRsrN/nGY7N8+MRuQjp86alxPrilj/ElC02W2NQd4YN/GeOaE7s4bSjBL16a56Z9E6iyw0ltPXz2jAGOFE1+89ISj84XqNSgL6Miqjbv29zLHQeW2Ttn0woc8jGR604aYktnjKWlJV5pinzlwTna8k225tu4Z3+Tj57Szd3jy3zzrGHC6tGAha88Psm9B8u4NmzpjfLO43Pc9HyR5yYa5BMyiiTyj2d0kwnL/HHXMruWGvzoshX8YVeBubJNseWypTvG6zfn/voce3mmyWdv389d12z9L4mr/l4w1d81UfTM6BTve2ySfj3B1VvTfPOFcao1heG8QKWmUHZtoppLwouRbqvg+g6Tk0nWDrm8MiciBAFru01alsp0JUASfLoTGgXDwnQgH5apVRT0VIXlSpz3bujhX56e41dXDfGFxyaYMSsokktGTjC66GPhsaVH5APHruQjf57kmOEWk1WZmgFdURU11CQRtnGsGFMFET1kk4/CyzMiOU3jGxcMko8q/MN9+xld8PjJpav48UvTFN0i/WmfhimxfyZENi4wUzeIOREaiklYc7jh2A5OXDXAJx8+wMFqHU2CiukTCA6DWY+aZSE4GepOQFQMeODL17L2wrdw6z++ifbYUYXKdQ8dZrJo85ETunnPn0eJRAJ0GeLxgOWmy2DWpGI5nNoxyFMzi6xqN3lu2qEjEqNuBiiCRiJaYe9MlLawQrWq8Pbj81y5LsfVvxvlmI4wr1qV5J8fnuHVGzLcPT1LPFJj16RCdypMwzWxA5stnSK6XuGKvm08csCmPSbx9adnOGH4qPH4ddtGuGN8jIlaC9O3OL7Ho2kHNB2BsaUwXYaCH03ymTN7+dzTr+B6EFFdFhoBkhiwocul3JSwfYGyVWNVop93rB+iYNjUDZ/PPTxJNl2FIIbvCiiqQ8MUqDgGISGG2RJIyiqaKGKKJqGoixVUiWkilWYUxwPNCdGWkrGEGooU4DgijYrKp8/p5e5DZR7YV6bpCDz49vXkoio3PD3G8zNNOlNVTDtEUorTG45Qdx2emikjSSL9SZlTBsNMlX3cwOH8/m6SkYBPPvoKtqfwlyuPx/MDnpqo8IfDe9k9q/FPp/bxl6cmWYyIZBMlZsshfn3RcWTC/x7FfPmv9yEHIpmoRCYmk4sodCZl2qMa948vsrfQYH0+Tl9SZ7pmcmDOIozKtuEQj0wtUGqI1AODpA52OUE2F7Cw7JJNB4TVAAQHRYhwYneSB8eXUJ0YxaDCBRv3IvhZnjzUjuRGOKUnzWVrsnTEVTzP54mpCneOFji8ZDOciPCeE/Ncc99+RKVFUo0g4OC7ChN1g5SXwtdsYiGRxZqJIsiIksBJvQk+ddIAZ9y0nXMH8qRVhXaxyRVbh3n7baNUmh6HywaDPR4n9UTYWypguzoRWaBq26zMm3ieRs20MewQjUqcxYpNNAnrUmlevzHHaLHFfMXl59sX2LzSplhV6M43adkqy0aDnJ6mLazw5rVDfOzBUQwDVrVJvO+4Ln69bx8Vu0FYSlC3HWotjXIj4IObhthZqrK3WMYPfFLxCt1xn95Ui/lKivm6REw3+b/Y++9gS87y6hv+de7eOZ+cJ0eNZkZZQhmBBCJb5GQhcjBgkwwYHsAmg40BmxxNlFBAOadRmJzPmXBy2Dl37v7+GD/4dT1v1VdfvXqr/Bmvqv5vV1ftva+r79XrXve1nplOEoYSN40o3Pujb7DmzZ9me0+chabDa7cn+Mqzu7h3SqM3rtLsyFgOaKLAShledlaKnx9Y4bPnpvj2VBNVDShEZSqmz7n9afYsV4gYTbYPutTdOpadoW3LLDcFBuIRpuY1PFfiRVskJDHBHcfLaEh4WoueWEg+3mXfosK6fIAX+pS7LqGTZa6s890XreUbT8/RCdssd0zWFVwWq3Fqlo/c1Rno95hdUFBjLin9zPwis6Zx1bo4s06JUjeg1dCIGRIN12Uo28W2dGIRD1GyWa5rTGQDokaHgzMpHB+GstD1bDqORLur8LzBNEdrJieWPXTDJe463H7jJc8poXnowudWKLr08f/+pOZ/8B/1c/537kWP66R1idCOEtFEfvrSjXzqvhnqHY9n5zq4qstYwcJsxmkIXRxbIa0qbO6N8vdXjfPRByfJayr3H+nyoXMMNoz28zd3z/DWc/J88Zlj6OhouknLFnEsjQt6s2SyHQxZ5pkZh7mazZu3jnDHqWUCtcZSUySpyiAKLNZD4qqIaGs4ssdHLxjjW7sWaQpNCnqMvzp3lPf9fpqNBQNRheWuzZu39zCS0tjWl+DOqRJffWqOlu3RE1PwBRPLFRhMCgzml5lczOP6MuVuwGBCpu1ZeI7O5myCy0by/OLgItdvzHL5cIZP376PA22FQjLEFR1cV0YSBcptn7yiU/NNYoqKbpiAQGjr1CoKV2+M8cdjTUbiGpPtNhvzUdqBh2PCxt4oN+7o42ipyzeenEOwJSLJLggK1bpIIi5Safhk0yGm5yMLEpYf8tELRrh6VY7P3jfDo7MNAtljMAmzRYmIIpDQZT575Rgfv32GnSMxbrqgj1LH4X89dJrTDZsdQ3CibtEjZ7BrVeYxUJUQLdpB8KJ0HB/djkJ4RihIJgRcF255wwYUSaRleRQ7LgeXW3z9sSXyEYW2FWDaIbm8jxO4BJ7G3102ynLX5iuPLJJNBESibUTJQyZCowtuGPCqbYt4gchUWWHPTC8xRcLQLMLAoOOE2Ba0GiI/fd0aNFHijmNVHpmv0XY97K6A6YQkZJlvvHScgdQZ3vG/haJCofCnaHqAzz88w7OzLS4byXDH7AqaKBL6AqYTIOgu/3jNOn63v8bxaptL1uj4QosHj0OvYbCzN0UohiR0ia19MYZTOuWOyzd3zfPhi4Z47FSL080Oh6oNDi7a7BxRiAYRih2X2W4bQfbwXJVze9OEgsPRRoOWJRCYKtmEjyAFIMD7dq7i7x+c4/p1OQpxhS8/OgdiSFyVkEKRhCLzD5emuH8Fdi00WG6b2LZPx4eYJpOJSvTHNXbNtdFCGVkQ8fyAthkQ0STW9mhoisiXrln1p9+l4/i8/55JcjGfsZRBTNL4+YFFelMBISGi3GV+Oct4MkKp5bJ6SOAdZ4+RNVTe+IdDXDER4XilQdtv0qknicRcFtsB5/cVOLzSpR1aRPwopuNTt31W9TkoikOxI7IplSVnRLj9WJkLcx5TVpTetMlS22duWaU/KdIOXHQ9QA41LhlN8Oxpi5mayw1b8pyuWMw6bQRP4JLxNKogcv26HHMNkz2lBrfua/GyLWl+cXAZQw2ouh4pSaNWEzh/TYTH5xskVYWm7fH6LX3cPDlPVJeZLfvkdY1mJ+Tt5/VCILLQtHjgRJMPXtrHRaMJvvXMNLce7DCWUml5AV96wTgRWeLjd8yyY+2ZeWj7pn38TpW2rtOTNXn9+rX800NVXrcjw88OLrJk2azKavzzNZv51u6T+J7EbNskHxE52VpipRHl+cN93HmyxkcvGOFrT85z0zm9zLUsbjx78E//Yant8r1dyxyutfmLzTkOlds8fsSk5brsWK3z2Kk2GUnjknVxzuqN8oN98wSiS80K+NoV6/n9oSo3bCnQcGymqiY7+5N8+IFjDOhRvnDVBPuXOuxbatOfVLjjRJl/vXYt1UqZfD7PV5+e5pxChpunT7L7uEguofKJy0bYPhBn13STsazOVLXLR+89SUpVaVs+Fw+nec+FffzgmSLvvrCXF3/vGMm4wDdePMFQSufuySoPzFT4+ytXIQgCrhfw1l9N8a4L+zh3NPGnuv3g7w7w3dec/V+SV/25cKo/a6HoI3c9ypMLBqJi0TFFshEdTzRZaQoghIyldBaqAfUGjA+30ewUplZlcSXCSE9ALukxVfWRBZ+RRJSW38EJfHBjxDSBlt8hG3XRghRPH9P48avWMpLReMfdB3jvzlH+dc8CVtjB9DwCTycX9fEEl3YrSkIJeN46iUYtx33TVVodn9GBDnHDZb4Z4NtxinWRQO3QoyaYbjroksg5IwovXTvAxYNZvv7EPA/Oltg0aOGLTVbqUYr1KLNNmzVGnEW3w1BW5O1nD3PgSJG6EOfR2SaFdEg1aKGKCoWETUBAXI7QsV2Sqshjx1z6nVm+/paX8bODK3z+8nG++uQcNx8vMZHWOXLKw0h6FDImEVGh7ngkjYDjJdjRl0QVNLphjWfmXfqjUZYrAWO9Il3fouNIqIhkkzYZKc5N28eYb9ncsb/JpiGd245UmK7Z3LBTZ8mqMFWGSJii4wQUMi0ikowguywW46T0kMsHB7l/skHVswh1C6+toSsi0UQXI9LBkHV0ySOXmsWyBig3VWJum3k/QeDrnNWT4M7JMmO9XQxRJxU1sVyZhU6XsaTOUkMjLiQ4VfS4bF2UF60u8NWnZllpuSgIFFsOQ702HUumL+nTrMepNkRURaThdLl48wpTKxkSERNDilBsqliWTE7XuHy1zgMLp9FkiVI75OCCyjVrdeZrAi3L48azBwmEgLl2k91zFlHDphCTmKuJtC0JQ3WRZIdWV4PgjGtCEgJqXRFZhK9evZbX/eI4uZzIPW84C9cP+PXhRb7/WI0/3riBa75/kPPXqIyGIaaW4ETZxvVDvv7iM+kx/xsnSl3e+Pvj/O1lI8gifGvXIq7kEfjw4jV59pZrrFRgIKOyfTjkyAKcbnQQRRADkUISYqrOTWcPs3vG5AXrU5z3rb3IMZe8quPLLoEvsbUnzqs2FFAEkXunZ2kI0xTiNk+cKjB1MspfbM0xkNR4484e7jha5l93lbjlzetZaTmY3hnH3teePsX+0hKKJFHpwNqCwEJFxbIV5rodNvXItE2JHjXGq7cW+NQDM6zJGfzNJUPccrTM5WNJHjxyiooQ5dGTXSKqSCLq0fZCUpqGIQtYmHS8LglVIGnINJ0O1VacvJrg+tU5vrtnjsG8yWJJIxkTWGr6vGl7lolYlo8/vp/NvQJJ3cMMTGqtDItNn6wUx+toZxxiMZdk3GI8rfHwTJONBRU7sMgaGqWOh2mplFYi/N0LB3hsocz+YgNZ8tC0LkKgsqG3yXw1yVJDR5QtFksxLBe+cvUYn/veT8muOQ+PBF97+Sgv+OUe1iQU5kyftC4T08HxQi4ezPLHE0USfoyPnBPlrx+tEkuGtByLoUSEF6zKs9C0KTrLRI0Gq3Mec02PuZqGaeY4XrG4ZMxkpppkuFDk3OxGfnagSrOuMpgHReswmG7RMVN0fQtZdBH8OLtPK4iqy+aeGN+6ZhMnam0++/gxlrsdFNmBTo5yHYRki6SioQgyU1WXZCRgQ06m4japdlRWZWTevGkN771tGlkNWZXUOTLtES202dDrEwQK03WXTMxkR7/AYyeT6JEGHUtmOB5hthEQj5h84cLz+P7eZR6bahEIRZ5917X/JQkN/PmQmv/Bf9TPld+7n3w+ylzdJwgE8nKUC0YSlNsBXhCyKqcz3anwxHEHX/VIaDKyIBEx4Jcv2cqvj80wWw/48Hnj3PSrKS7q9fj5tEO14zOUB8cNyWXrCKFEq5Miq+mszUQ5XKrTl9TYtdjkxq3DPDJd51ijiqGGZA2ZUGqQj9ssV3o4vSwiqB4XjyR46oSDJdmMJFW++fwNPL3Y4Ge7i3z7JWu491SVmuXy+2MrxHWXreledpVXyMVtTDNKqRuQ1CCm+Dxv/TS7To0SjzYpNnUE36AbmHidGH931ThRReazD84wW/b4zktX8a1HFzHNZdpaFFeyiKsilZaCrtlEVJnlFqRVmW5oktIFilUd2xYYzAQ0fZvRSIZvXreWT95/iodONFhVkDhdCikYGl+/fox/fmaevXNtEukOiiywUpe5fLRApeZzqNEiooloEjQ9i5ik07FCvvHCNQRByF/deYpr1yd587Z+7jpS46tPLvClF4zxzHKDgZjOeQNJ6qaHLMNHbp/h7Rf0csWqFPuX23zsgSlGZZOlUMcVbfJRmeUGXD/Ry8FFm3U9Bg+dblCzXX75qvX0xDSCIOQT906zvmDw/d3LvPasAlePp/nL246QjQo07IBXrxukN6qxcyTG9/ctUmxYzJsVZM0lKmukVI3VqQJPzpdB6tByPeKqhN3VWalJxNMmqiCTMBx0rcOG1ASvWj9EseXxrtumyKd8TFMmwOdFG9Mcm/cZTum896IBLNfH8Xy6jSpEknzqsZN88Yq1JDWFe6aqfPfZJX57wwb+9ckVTjVMpqpt3rQzySXDBZK6zr/tLfLzvStYmoWKyIZemZF4nMDRSERFdFlgtuagiiI7+hOM5TS+/vAiq3IG8+0uzy62GIvE+Py1I6QiMj98eoXfHyzTl1M4uWxx5doUcUPgWLXDhYNJAlcgG1PwwxAvDLl+XZ6/u2uO6zdl2LVc574TNV6xKQ2Sx8On6shiSJ/o8FhR46Ub0+yab1MzLQLJJhW16XbjuI6EEwa0ajKRSIgmi8Q0mZ+8Yh1/c9cp4tGQiazOtp4UG/Mx/nCkzL6lNp+8fIQP3jmJqoQUOwHTFYedwwbL3S5LHY93nzXGdeuyrHRtMrrCz/aU6Lo+776gn7f/8RBt10RQLCKyhIBI1wHR0eiLJ/jE80Z4zS+OAyENxydtCESiDktNgZ6kiOl6bIqZnD0+wVwRHllZppC28AIXx4yzWBGJRgOM0GCx7rFhQOGvLx7ls3ctsKFHZzCvcP9kg+eNpDlZNTm40mFVXuNwtY4kh6iyR62mUMh4rFRlzh1M87mrx/jig3PcdrxCLi4RjUJW1dlTqpE3VCq1kDfu7OWt2/v/9Oz83YES33xykXPHNBZaPq9e18O/PLPCO8/v4+yBGIV/H91guh7vvm2SU80W22IuJc2i0U4hqS62IyD5Bo5gkVIURL3L6lSGTl3ns9eM8On7pnlovspAKqDtSghCwHk9OV6+oUAuovDxhyeZqth88YoJLhxO/6dne9fx+dDtpzlQbPPWnT38274SpmyTklU6XYF3nt/LPTPLnKq4qHqXuKJRM0U+eO4oP969woJpEQoB37t2A5+5f443nd3DfSdrdKyQz18zSkyT+P2BEvccbfDxiyLMeQZPL3R4/wWDnKqYfPahaQ4Vu2QjChcPpLn5WIm4qPCh5/XznaeWqJs+WjTk2okc7z6/n9f8fJIPXNLP1v4oy22bt//hBJ++coidA0l+uH+equXy2vWD9MQVrv3uUdqOz203ruMLj83whSsn+McHp/jw1ev+S/KqPxdO9WctFN3wm5tZMXsZSYucWFYIhRApFEhHJFq+y1mFJKMZgcdPtXBEi3pLJRELkQKRWKJF4Aiokkag2ERU90z8YKDS9VyShkNCl9HVDn4Ai6Ue/ubCVTwyX6TYtjmnL8+Xd50mE7NIqCrT1ZCtQ2VmS31MrwRksh12JAa59VgdX3PYOSyy1G3Rn7LpdgocmYf+jE/SkHGwiIg6YRhw2USMTZk+zhtIcduhKr85dQJJL5OQI+xfUjEUB83N0HBteuIql40n0QSZ/UeWeXRJZCCn0FQqZDUDXbfoiXn4gUHT8skY8MvPfIXNVzyf773njUxVbRZbNh0z5OcHl8mrOv/wwlE+fMcp5HiLfDTk5GyUwYEmHd9ECpIoioOCztGiR141qPptNibPnDvPxkX+ctsgX3zyFP0JEdML2VrIcNFAmn96ch5fsTldd5iIRzC9gNUDFWZKKSqmz0AsRs20sLHZ3pPg0ILLl14wRrsr059UMN2Arzwxw0zJoyE1OWdExPJCJosh4zkRP5AQRY+YWifpaHTUKFXLQ+zkWDRNYqLC2h6Jx1dKrC3IjBWaOLbBoWWBaiPKYKGJZ6cI8FBEgUOzArmojCu5ZAwVXbVIR31c3+XosspYKmByReO6bXVOlaIocojlqnRdF9tWCC2dH79yFS+/eT8jsZA3bx3nt0drrM1GOFgtsqM3zko75FSjhRuG2G7IeCrGVaM9fGfPNJJiUWqLJHUYyjg0TZVyS8KyQ9quS7upoUQCYrLEX108yCvW9WJ7Pi/57bOYVYMthSgHWw3ue8NWfvzocSwtydvP7UcUhf/USz/fXeRbTyxx119uJGHIWG7AoWKHTz96nIt6Czx8oslXXzSGqoh84J5jjPc0mVtJYpsSaqyLLEjkjSg37RhiS8+ZI2sHljo8MVvnJwcWyUVEovEOu0/ofPzyQX5xaIWgK7NtlY0RqVFuqyxVEyxUAtb1qUwv+fz4L9bx0EKR24/UuWFjHxMpgx88vcJN5/eyZJn8864FNM1F11r0pE0ePZ6hN64iii4RLaBsNdHDLJIAdlemZPn8/i82caTc4ZuPzfOqUZVXXbAaQRT48f5lLh9N8vnHZ1hquJyad9mwxieuhnSCNjIGgymPlY7F7EqOdQWF/cUm6wouhqJTNhvoUgQhVEhoMnE5ymRjid64jO+L7F7y6dPjyIpDXzTC2zZN8LY/HiOry0T0LrlEQCsoM5KMUDNFhlMep6sC87Uopi0joxI3PCKGiSr67J2LkNIFdCUkdDWiMYe66bOu0OHhfRnet7rJxz/9t+x8z1cZ6oszFNG549Q8YjfGxKoVJheTDKUlrp3oZzipc3ze5cTcAvstkYrfIScmmKqarOmR8ULIJpqsLYgMpEs4nsbe6RyTKzK9CYHx/kW8wKXV7aVsNmiZEWJyhKbXAi/CqoLHdC0kZ4hYjo5hdPFaSUYLOsdrdbYXUswuSyy6NTTNg26c5VpIQMBgb8hiywcpYCKjUzEtAmxShocYRjhdCRnJCpyT7+PJU23efE4Pn75nhv60iKO2kYQAXZQZyTsYWpPlRoSuFcXxZJzARRJBD1R0BXaMRJhbinCiNMdv3vDcOooevvi5FYqe9+h/f1LzP/i/DLP+3v04fpzxPodiJUYLE9+XecFYDhGBj182zOv/cBDH8xAFiW7Q4gPbNnBw0eSsMYF/2XeSNbEBBE/m/qN1VmXqdCMKza6OK7fIRF2ymsGJiogfCKSFGH4I5brHdeszbBuJ8NNny7xqS46K0+bOmVkU2WZDj8NUKcZMVaInCqajUuz4pP49SjxsG7xqW5afHVwhq6l8+yWr+ebT8yRVmTunSjhdEVtx6U/7yIqDodp0unFikTZR1aXeSdKbqZ5ZG0oxWr5Lry7jKV1M0yChi/iCx3dfsJlS2+ODt59gbarNYVugPy7T8V0cR6GQ8LE8B8GNMrciM5h3kYwupXqEdNRjsS5SiMuU2gEbc1EOrVgMJUWuXZvn1/sqdE2B123P86vj8yCEyGKIqrg4nkKjLaHaGh4B12yO8thCjY9cMMFQPMJ7/jgJYsB3XriefFT5T+vuTNXknb87xRvOy3Kk1GHPjEkyKfDTl27402cEQcD2fD5w7wHSfoVJJ4IuqZRMBx0D2woxCSjoOuv7dN62bZD+uMa3Hl/mmYUG1a7HUK/AC1dlmWlVuXfSIh3zmK1KJCI+cUFmsQU/eskGHpor88u9y7i2TC7rIyk+hpOiYjtYtounW+RjEt22Sq0t8K7z8zyxssJy26bUEZBsna2FGKYDk7UOF05EmW+1MYM2uu5gOwZdU6VjSUQjPrYj8PxVKV4+ZPwnR1G57fK7AxWONJq87ex+NvbEePhknU/dO82q0SaDsTSKoCKEEk5X5vFSGV2QWZV3GcwEbEgPsdAMMP0uFw7mqDZFPnffDKMDEoMpgUpVptL2aHge8Rjk5QhJXcYMXKpdH9WXuWRVAkkMWWn6tCyfvqzEz54u87UXT7B9MPZ/9OmnHz7JgeUmth3SDUNGkwYxUWZ7yuMNF61DFEXecdd+FKVN1xFZacMN64e470STwbjOX50/xIFim68+vIgqBfTHdb724lXYXsCBYotjlS5v3HJGBLnzSI1bjpSR9YCG65A1VDZkY1w2kmb3XIe9S016ExpHlyxevyPPJRNJAIIgpGp6vO3m4wi6RUwLSERbWK5CVBGZq8O5ff1kDZF7Z2ZJRsDs6iyWFXRRIQgChIiJGqj0yx1OBTJZQ2NxBRIxn3TCo2b7ZCISy00B19KIKmBKJgnRoFQHIxIihTJWG7IxiTdsL/DzfSXO7o/xss1ZTjU7fO/AFDE1wA5CZGRmyhpXDWe563CDfCZkrubTE1GpCyav3dhHIHncd6RN3XP5ytWr2NoX4+uPLbBUddHiFk+t1NmQTbDYdnjX2SP8+pkaqws67724H0GA9959lMlyjbSuMRYtMmNnKHdFDFmikDQptUDyYxQb0JfxqLYFNDWkT0xyw9l5fnJgjtmuiSSGxAOdxUbAeI9CswGv357jjpllKrbNjVtHeNn63v9UN54f8rPdRV65Nct775yk1PRohg5iRyEaF0imKzQ6BnFNpOU6BIGM6EZY6tjoskBSVag1Qz5x5RC/P77IF65cw1TRZu9Sm2cWWvzLS1bz82eL7J6eptCXpdYW6NoBhxcs/vqyAX5/rMRbtvcymND54G0nieoSL1+f4yuPLNCfUvjitWOMZgzgjLAlCPxpc/l9dx1l91yXzQMGn7hwnKJp8w93L/POi3pZkzf44C2n+ceXj/PYXIMDK23ec1aaVCr1X5JX/blwKvH/+0f++8K0owhqm4MlExuHqAJzZZf9pRaSbLOlV+O3UzM8e8rB90NEzWWqZjHTbaGIIpMLBmpsgaF0CRGJXSdlnp51kQQBy/OZabQJsZgtJ3hm1uYvbzuEKor89fmreeGqLG/aWqDj+vjyEhv7TA4vFNi3ZJGOCLiNBLecqJDPiqzKi/RlVrh8lYfKmZfGC1eF9KZCelINRpIybddlKF9F9ON89MHjnKi1+fKDC5TsNlktzmRZ4uLRNv1xg2SqxUReJi4ZrEkkuft0iVdvyzGQVRDiK/QZcWwfxMBAFhWKrZCurfPb7/wbqzevoZzbzvd2lbl6PEPoC/x0d5G8ovMPLxjjI3+cxpMs4rqIJ3iMDDUxInVUIUYi0qEQFZhteAzoKr5S5MWreuhLquihzErH4Sv3L6M5BssNgXP7UvzuyRZ3Ha0TiC6S0uHiCQ/ROBPZeWwxTX9hiZesT/LitUkScZMvXbGOrB5DFhQeP2mzZ77NRM7gmbk2s3WHIGoyllFZbsmcrATEiGGbOm2vTrOrsrA8SLmmMVuHuaqKFG2TipyJN/3AReNsyhaQkFmoQc30MB2VwQyEfpSoYZJKlMglm6wbtujNBCQ0AUV2iRg1TNfnVFFlVSEkEhHoy8DUSgpNsxEEjxCTIysBnbaEoFi86jdHGIhL+IHCfMfk29et5YrxNFtSeR46WafFCqlYB9eD/3XRBopVl/W5KCt1j3ikTCHRxbQl0kaXhulRsyzqrkm9I9KXlUgEKpoCPz44wwdvP4kAvGHjCKbv8/jpFq5oA/DCdRlu3NnLy3+/h5f95AgAjhfw/ltO8LMDcyQyDndPl7CcgG1f2cNrf3mUimXxoYsHuWZ1hrsma7zud4eYXHR5ZlqhZoboCQtZ9chmyphihaZjAfA3957kHx6Z4f7ZEj3xM7bqqKhx/lqTI80TBEHAXNXm0uF+Dsxkuax/NTldZ8Ogi6i2uWCjj6H7iFKThXLAw/MlepMKV61J8fmH5vju04sslF2WmwIRTQbfYNtwG0Nv4IYWEiKynwUCDs95GFGHN56dIh2RuXgkhRrtMNs0OVZps2+5Rb0e8tCpJlcMZTBt2LbBxVA92p6FJujkoy4d10QKDeJayKmSRVrVOVWMsdgQ6Y8bKEqLpYZE0wo53pihJy7iezCzZNCrxTi8YnNeby+rMjE+99Rx3r29n4bfBT/CYitkMBZnquIynLZQJAtN0sjFLFTFB8nG9CD0VSxfZn2vha5ZuJZG3NdxLImIrLLUUlgz6vCV4wk+97GPsTA5Rddv8+hshY25NEbSYnYxxeUbSoRyiV8/W8M1Zf7yvB4KGQFJDlmT1Wj6Jht7FBZaLtVaiGVHOVbpMFNN8vRMDM1o0atrFCsKlXoa2+rB8V1yRpzRDMSiFXJalK4lMt/ukhB19k6r7FtwaTcSHF70efX6AaKyyr3TVZ442SJwdFaWIpwqekQNn2Tap+Z1kaWQsbTAK9ZnyBoawymZgbSLL9gkNAXTVvnNkRJzDZuj5SaFQsBy14dAQrWTzCwazDc8LEemLx6w1HZZ7pi0TIHAk3n2mMCjB+DB6SJ/eU6eL1659rlfpKTn+Pof/Fkhgcp4wWOxKlKs+YymYDCisyEf4d6TVd5481FMz+F1W/rwRYvN+Sw/eLLM67YXsFyB4UiBpKpy/8kaeiwgnwzxRYtkqsxYWiQINEr2mZfHnBLDQOHisRijGY33XzLAVRNZ/v6aUXpjKv+2r0FpJcraXMBUWaLcgdU5ga7vsdwI6dF1Gm2JxbLAZauTfO/ZZXoTMjdsziMAH7loGC8I6fgegWZTiAksNwUqLRXbibDQECnX4izVE7Qdl2ozwXxdIxrvMJgUEY0uom8gWipR1+CXLzmLnpjG03MtPAIqzYC0ojHf9PBdlZCQ6ZIIQYTlbkBPJsSTbGzbwPNFaqZEPiYyvwIZXeZQpUXGEFhpB0xVujRaIflcyE/3rxASIIshiurjOzrdhoJsqVh+gKSE3HGsyZZchj3TJrsWqiiqS7cj8PRs+z+JRF3HZyCh8rrzsnz/wDz750yuXZvhB9evxwtCXvvzSZ6ZbQGgyRIfO38NlVoMz9GZqXlkdBlRtLF9gfGEwUvX57l2osBA4kwa6QUjMb70wjG++eIJXrWxh+8fmOZQpcxwvoOktFk/0GF9bxtfayL5Ml9+dJ7dc10yKZehQQtV7fL80TwjfSGhYuIoLilNodlRKNseE0MN7l+YZaVjUmyJqJ5Of1pm52iU/cUW+axF0VtGUOvMzMdZXMrQ7hg4noCFi+MIxASFak34P2rd9QN+eXCZuunSdHwc3ycZESnoOp88Zyefvmgz79sxwXXjvbxxew/5iIIkCszXZR49Df+yZxHJF1ibyLJcDym1bV6xJctCySWjC6zvUxktSMTiJq5v8pHL+vnCNWN8/YVr+MyVIySzHj8/NstkxeSc4Rifev4wN27v554bN3NWf/T/tj9Xmg6+KaOHChlZ5V9etJZvXLeKa9ZkKLddTpRMLhzIUmzESEopLusf4LvPrLA2FeVt2/tJRxSeN5rmh69cQyHvE085/M19x3nHzSc4qyfxJ5EoDEP+eKzGtv4Y7W7AQtPmyLzNbMXhJ3uKjBdUlrwuy06Xz107RDtwcf2AcsfF8UN+9FSRl6/r4etXbaRmSqzUUizWNBYbEjFJ575TVbqWRJ9coGMqbMpnuW5Til++eh34EpEwSsP1OCufYjQSQTMaTIzYdAJYqSnIQYRq16E/6ROP2fiqTUaTaXoWyURI1w6x26AZIUt+h9tPVNk2qvPYYpW/euAQd8+cRgghIERGZKakkI8F3DVZIxUXqIVdxvoCukqHgqLy2PEub9kyzJdfsIrVyRjv++MJPnDXMW6dXmLGrfLQXIMeRWfPnM31Y33s6E/wwSt7ycVk3vrbSVY6Du/YPkwhoeGIHVwnRsfziUoia9JRtud7iagqFcem599dh4oc8t6dY3zy+cMUWy5fv24t42mVfCwkU6iwtTfCNWM51o6IPDXbYbYSYIgK3360yFcenOeuU8U/1Y0sCbzpnB7aTkDTDGnjEAtUMlmbdtcnEmTpDTNMr8gYok6nqVGs+QzHdVIxj3pgko5LNG2X2U6H9995hF/uLvOGbT1cNZHkzf82yVNzLTZkoxxYbnNwvsvhUofejEhPXOHLL5jg7x+ew3Z9fDHk1VvzPDBb5m3n97LScfntwQpPzzf542SFiCphKBLff2qFatfl2jU54rrIRQNpvvLIAseXbT7x/AG++fgCth/w/kv7+eSdc7x4XY6PXzKCIPyfvf7/GP/Dqf5/wp+1o+gtN9+KEUszXzOwfZ9kxCH0dGwfLN9DFHyiooHneZiCQ7OiU8jI2EKHSkckZQRcua7BdMMnojksl3uZr6s0HZeJvM1YBk5WPYoNnYxmcNPZI7xwdZ4wDDle6fCB38/gCharhmyuWz3MzQebyEgcXbSRFZ90UqBu+QxmO5w71qbY8Si3DMJAo+11yEUgDCUePqmxLisy1xRp2j7XDPdyqFEkqgVEFRlN7TKW9lEVk9OVCKGvsWtWZksmydPTFmrE59xYyLHAYjRlUGyH9KUCZmsyjuchizKzh/dy1SUjzHYUhG6Bs/JJNvTpfOGJGdKazJeunuBrT59idkEknz1D5AzZJxWv4jgJHD8kpStM10IyusBMyySrpGnaPhFJY7pmk5U13rStj98eXiSf8/jq5Vsptj2+vX+KktkhH/d4es6nX09SacNATxUxNFgoa8SiIVKg0HFDxpMRvnT1qj8p2AeXOqzJ67z/rikWWm1sqUGUFN2WQju0OacvQ9NyWWy4REORXLyEHTGIGF0WGxKlmkEhGTAQifPsKRs15uOGAaMZEVW1kUQf07MwrShrejq0rQiNrkSjpbBsuaQjEt2GRF9eQJYd/ECh63jYHsR1j5PLEldv6XCqbNA2Fbam07x6a4FdSxUiWsjZhTQyGusKZ9LFnpyv8aWnjpOIOAxkl/nDrhGevuls3vPAU3zl0nO45Nv7eelFZZJSlvuPq1y37SQ1K+C2PcPsGDM5fDzL9Rsz7GvP0bQELFOk0TnzMDe9gGrLw7MERDlEUAP+8JJ+XvqbRZSkg9XWyacE1vSZLJQM3ri9wG8OLiOGOv96/Xred/skllam2tG47zXnMVM3eeetU6zr0zgy6zBdM0kkBG7cWeDZxTa7l6sErsSDb9pBQldx/YDdCy12LTbIGSrbemL80zPz1NwO6ViHrmWwXFFJRxTWpKLct7SAgsJEr0khZnLF0DrmWyYHFju8acNaZrodrhjN8o67jvK6zQUem2lyoNii4zi4AWQTLhFJIQwFyh0BXfXoOAExKYYsWViWTNsPGEwpFKIC+5dNhtyAJdkgFQtoNSWWLJs1eRHblplpd1ibU0kYAZ2gSlrTkQWN+VYHx0mgBAYnKjabh7sYkk7L65KPgucL1Lo6+ZjPcrdDQjbwumlet2WQb+05Tcl02FBQqXQEpiseeV2m1g4ZTCtYQYO1Qw51x6JgKFQ7Om1bIRAsSk2VoYxDQg+QBQlEh7Ytc3AyxXdesprPPDyDI5r0pR3ajottRjh6SuaSAZ9bf/J5XvCBD3HFSD8dL+DOEyVE0QQ/wt7ZgPWpOF+6dpRWrcykqXGoscxMPaRpu+jymd4wPZO4HGep02I067FYyVC2LHIkqHsemWSbiazCQkNAEgJiqkbd9nAsBcnXKHZ9IorIBy4YRBNF/vae02QjCrNmh0zCZXOvxNs2b+QVvzrK9eMF7popkky2GY/2cKrVxQy6DCQULhwOOFCuEgQCPZE4Zctk90yciGCQ0kVqfhMQ6NEiuILHUj3EqepsWh1ScSx6Ui2ickhUjnO0BF1bQnJkeuMqZctjVUHjH84fJJfNPLc7X5c+x46ih/777379D/6jfq76pwdJ9cqYrSSJZJPpBQ1HDRjPKJSbAoHiYogwmNRZ7pr87UVr6I0Y/GL/EsfbJa5fNcR3n11A9hRs2WKV3qEmRtB0By88EzgQlRPUzZAfXbcN1wt4w++Osz4VJWKIvHFbgU/cOceLNqb5zeEigl6l7gjEA4N0rsWJooLu64SBRFeyOasQQXYjbBmIcnilw0VDSb72yBJ5Q+Zbrxjne4em2bfcIHQlookOZiuGI/pYrk9M1qh1A4bSHZIxkWIbYlqI48mUWhCXVdZEs3zwef3MN1w29UUpth1e82/HCBSbdarHvo7MWD7EDGxcV8ZzNaqtgP6URM126U2KWJ5H4AuoikBejeM4MG3W6I9Ema36fPTiIX78bJl2aCGpNoYiYPkgCR6Wq2J2ZAwNLBPEQMYPfURZwHdFND1A1h26jsSmTJxvXLcagL9/4gTlrs9TMx2GogYrdouYJuP68NfnjnP/ZJNcVEYU4L6ZGv/60tUUYioHimXuO3SEZ5sCqpDEpEHXMogosNT0uXggz99eOspc48yg23IzwFEdEqrEFeMZjldblNouD51okklKQIisdpBCnSAQaTke7z93FDcM+M2hFS7t62HLQIRcVOYtv50kGRHx8Gm5ProikokKpHSJi0Yi3H3AxQxdQsXm6v5B7pupkkk1kbUuhbjFwaOD2L4EQsgtb9jIH0+UeXimTqnj8qPr1/9pRtGuhSbffXaBH71kAz8/cpqthQwHig3+eGKJLfke3rSln2LHZdd0ixMlm6yh8P7n9fG5h6dZbnh4fsiJWpd737KF3+2v8sRinY7rc05fnF/uqRBJugxnFS4ZzDAWj/HDPYv4vsgXrhnjlmMVZuoWMU3knTv7uXWyTEpT2N6f4FTJ5r7JOn/3gmFuuuMYQ2mfK0YKpOQo90/VuPlAjRt2pBlM6KQNlYGkwm3HqhxfMLl6JGSyq/PEdItUTOK9F/YzU7d4eK7EqXoTLwj5zgvOoi+u/6e+94MQ6d+FxSAIed8tp3n/JX30JhTCIOTVvzpOIgKiGHL9+hxHVmwWGg4LbYt2YGJIMpvyMTbk47x2Sy8f/MNpyqZDwpBomQF9eY+DxS7JaEBUFrADn44lINoxvNDDdGBNj8ErN+X5woOLnD8hcm5flvMHMhytdrjlqROI0QwjBZ8nlpew2glc0aFjKgykAzyxjSpo1LsSgeCTiwiU2j4CBp4l84Wrxvjd5DKPTrcYSMicKDqM5gUUrUt/ykYWfBqWgu8kOLEiEtEEao7LxnSc1X0uz8y51B2TlBLjnTtGuWg4RRAEvPuOKfYtdBnMBSw0PTb2qlw0mOfJE12Olkw290Q5XmuTT8Jbtg7x40OzyIHOXLfBeDogHXSoyzGqnRDLUUESsDyHlKYwXQ45d0Sn6lis1ERW5aK8aE2OtekoX3p4npl2h625KH3RKKoiUAvOpPZNrdicrpvEjABCCU/wuXI8w4fPHyMEjix3+be9JZADTrTqFBshoa2Qy7okEi1eNDFEPMzwrwcXWGrZJAOdvzy3l3/dP8clowlevaGft90yxfkjMW47UgNP5A3bepgoaHzu/jny0ZCL8xLNmMLTsxYvXd3D/tYcb9w0zlJVIKFJ/K97Fvjwlf2stBx+cXiOnaMGTkdlIAs3nT3BK//tMJ+/eoRNhQR759t86aEFfvG6tXxj1xy2F/BX5w/zu0Mlvv/MCiXLpj+q8+vXrCOiSn8SiP73WvZfkVf9uXCqP2uh6I23/JKj9QJhKDIS1zEDh0oXWlWDsV4RReswX1PBlehPypTLEoHmocmQMCRUo8X5E0VC36BuyhwvGgSBQEJVCcUuxZaOLkqEQMcJER2NT18+yuFai18dmSeJzmI9pDehcNP5Wf7l2RK/fuUmvvn4PLecXiQabSHaSbavnqPluBSMCKdrMN+QWJfVqDldTFfCNfNULJOEJuI7UUzPZzDnIYkuIS6rcz5IFfbODDCab3Lrs2nW9SocnRHoScqoEZdhyURJK4SyiRoq1ByT6ZksgeFT23sP/uQznPOe9xDaaZJKnFduSvGROxZ463k5vvHoMmdPeFRqOlIoMjrgcelAH59/eI7NwyaIYMgKC3UJKZAYHTxNozFA13WJqQaHVzzSqkqrJnPO6hgnqy3qbpcLs4PsmmmRyXm0LZ+/u3yCrz85x4pp05P0yMXb7JuNsSYrY0SqPDMTp0+L0q/G+dYrxoEzu0wv++Exvv2KCRBC3nrXbiKBjudJLHYc/vm6NRxa7vKdx4t84Hm93LClwG+fOcb3j1isdDz6UwqpWEDdNul2I1htgUJaxsPDFz3cwGbrkEmrG6Xc1BDEEKQOTVPF6ej05mxkUcYnJKIEVLo2Qqhhhy3EMEFEgevX5pnrlAjFKtV2FCXM8fyhPv7liRX+6RXjXPX9AyREhXWDGs8UG8QjHpv6XBZqGtlEA9uJsTWf4+GTJh+5cJQfPVHiQ5cPsK4nwht/f4REahFJ8rlx4zaaDtx/2GTfUodopk6xpfCObaN88bE5sqk2MyUNSfHxbIVvv3ANH7zzJBOJGkfbcXa//RyCIKDj+uxfafC5J4+Rj0O1muC9O4a5el2KZxbr/MOTU8RVjR++aAsPTNX56mPzXLs+w+UTKT75wGlKpsvW/iiv2dDDJx86Qa0R0hvV2TYSYVM+ymcfnGPvu8/m3bdP8dTxLrGYyJZhnVOtGgldwGwauK7IzjGD/qTMzceLyFLIQtPh4dfv5LofHeUvNucYS0e4Zv2Z8913TJb49t4TLFZlrl4XpR2U8XyFatfFCwVMVyQiKSSNENOzCQOR08saG4dCum4IgsPrNg1hSArHT1UYGewjogpMN7vccrTIYL6I6SSwbZliRSaZdhAFCdcJsR2B0V6LpapKx1aRkWj5NttHm/helE7YIKGCIUWomCY9MThV1mlbEXJqlN2lKiMJjZ5Um5TUz+p4gq/umuO69QlK7ZBQ7KDHSiQ1CTtwqDezdGyZTLxDtSNRbIucNdhlU3+DUjPOXEOjWldZKEf41BUj/NO+44hiSGBH8EIXXRGZbVhYj9xOq7PAte+8iddvGOG24zUOFl26jktSVZhcDFid0/jozjjvf7DCllGBPctdNvZINKwQSYRCFJqOhxCqNKwARJOIGOPAskcuGhJXVTSjSEbX0JWQA0sK0TCOpDgsl1TOHojzd1eM/Wlg/qcfPcZjUya6KBGN2iSSHSbiGXZPKfTFNVb1qDxwokZeNzhntcZDsysIsokqe6SNENNW8AUHiZB6NctiQyCQAjIxj/60x1JTwPVFIrKE7ZxxRYbA2pzMdN1jIN1kpanidZNUbB/d6FCqaAznJFyrwZ1vuuK5JTSXP8dC0QP//UnN/+D/MofxjsfZtWTgCSGBA6mIhBJps1hRUdSQ0bRAsSoTaB6hJXP9uhz3TDZoS02MIEosErLS9EgnXURfZUi2ONhV8F2BtX0eFVPk/dvX8K0nF7lpxwAN0+cbjy2wbUQno6usWA4XDSa473SNo9UWuioQ93UsuUmAhOFFINKg0tTYNCBy+XAvj8zVuHqojxesSSMJAu/6/Ul6oipexEKVAvYV63i+QExQsXyH0d4W5UaSuZJMUg8Z7m9zuqIwlBRwAodGV0YVdVbaAe/dOcyTUyYDCY13XdTLJ++c5ZmlBvGURU/YpakaqHqbRjtG05TIJ0JSisFct0JEitC0HZK6SAjIYshFA/3cc3qRgh7nkqECxabL/cebXLE+yqMr84S2BrKPKvn4oUitoRIxfDrumaMYiugR1USWayL5uMLbtvfxzScW8VyR771iFftmTS6eSPC7Yyt876kiGVlCCGQ2rfLZfcrm0pEeXrg+Q0wTkUWBvoTK/sU22wbjfPaBGVIRn2HR4XjX4FSjy0rXJBJxKHdFdE9HV2XG0jq2G3B8xeJnN6zlX55aYn3e4KcHiniBQ9kJyEdBFHyqDY3eqEzN9PA1h4Gkwis39HLtRC+uH/DFh+e472QNOZBIpzxC2aXYEIjIKoocgBQwFI+y0vSwXI+25xHVBTRXPyM6hB5DeRPHjODYIiYuLxkf4I07Czx6uknNdCEUuGIigdepUSgUKFsut0wu8abNw3zpkVn2Fut0fQ9clbFYlHxKYiClsDoT4dKx/zzvJQhCii2Xv/jZcX54wyr++alFptttvCCg1RF4ycY8MVXkkpEUE1kD2wtYbNr4hHz58TmCIEQUQJB8PNHmZDkgIov0RXSumshyViHBr/aW+dvnDxGGId/7d1eFKsEfjlR57Tk5VioBgymVYtdm11yL3752Pe/6+bNkM3k0XeB000JTYU0mwpFKi0rLRZMlUlEJ35Z53wUDbOz5z46l2ZrFStvleLnLqzbnuft0mdO1LrKn8OvJFXoSAaoCtiMyno7whs0D/Gp/iYeOt5BjPq/d1MdbtvfRsnzats+BxTZfenCRwZE6Qqhh+Q5+CDIaquqwXJfIRkWCAGZq0B+VUNEIFR+zKaJLMi/YkKRVX+KWaZA1KCQ8yh0XzYuSjIo0QouRlI2hdyi1o1i2TKmmktQkTKmDEkR5/niBm3b28/19M/z8UJGBRIjpuxiyhKF1USUZEQgDnVeunuDrTy4RU0TOHooQU0XuOD0PdgQjVsfppunXo/zlOb28685jDKdDpksCA2mZHb0ZFjpd9ix2yRoCLc9BcnQcAuRA5HXbetiz0mCh00ZWO6xRA6ZcAU1QKDYUkgmTTjOOnqgTcQu8ZlM/o3mVTz16iJUmpHWVWk3k+rVZXr09T1qXeeetJ4gbEh++uB8/EPnj0Rp3nizhSBaOF5LUNIodj7MKcdzQAyQGNJ2nF1v4ape4ItF1QxptiYwhEUouL9uYJ6MpfOOpJTpNkYG0hKjbzBclfvLK9YyndU5UTT5x72neec4Al06kaDkufzxa5TeTpyg4HmePr8IRAt553gB7F5s8s1jnJ/tLiJbCl140ynf2n+Y7L9jMz/eucNuJIpVGSD7f4f3nrGFjLsFNd+/lW1edhSbKfOSOab71sgn8IOTT989w4VCSF27IMFOz+Nm+ZR5bKpHXYvz4Zet4Yq5BzfS4pE997oWi54hX/blwqueIgv7/J6qtLOtzcZaaPgtFhVCSyRsKtu5wshQQNTRyhsq0ZXNoFlIGpBSR08suoW5zw7lNoqrL7w8YSELAaCpgudPl0ek4AzEdWRI52bIgEJlI6kxXHOKaxG0HqqSSFntmPfqUCJ+7epgPPPIkumzwuwNlvrNnibFeC+w0slGia0tMpCWcsIkY9DAU16iYLjE1gYJIzXXojUbouD6lrsdwRqDltGl0DNb2CByvtmk0Rskmy0xXVLYOSSyUYEO/go1LOm4TcUSeXgzZ1KPxyDGF3rRONG0SVTosHHyW89/+fuKywd4FuGQw5Ct793PVliS/OLrAzjU+V40VmC2qHKrX+OLzzuKq7x0kE4N4xCWpC8xUJLqtCCXXRJeH2Ti6hO+meHrOZCAeYaVj84krJ5jI6tz4uymkMM5vF0usKRiU6wKRuM9HbpulhcPqHhFDa1HuSmzsC3HDNvPVBL1xgevXpHjj5mGKbYeULvHzQysM9koMpjR+tm+FtKLiiA0UJ8G1uTx/f/8CntFiaFBiY98Zx87Fw2nunW2i6B1SsRapmEWrHKU/AXbMIRRs5lZE1g15DCQDmpbOclOlbpqM5X3KTZ2EpDE61iBi+EyVFWpdCTlhs76/g+MkKSRsluse5bbEYwsLCAIs1KMooc57zimQ1WSG0hpv/9VJ9GhAJBBZMtuMZHxSEYlKS8OyoBJmycd9ppsVFDSemGnwkh0JjrZP80SlhePpqEhM5Cyeni+yr+jzgole5k2Lk5UIbz27wJUTKV60NseBpS4fe3iSTLxNLqLysdtnuemcfp6YaZGKiPx2f5lozMf0XP7pyRWiQorvvmwdLccnHznzIp+PaFwy1ENSP/NouXg8wWBqHNsPqdgOr9qaYVXaIG/o3HuqSm/SZ7zHZr7iM9sQcRw4t/9M1O23XrSG9tUuj55q8fipJgv1kEbEJSJIBIbF4XqbmNbD+vyZuTYxXeJjD0wSVURetTXPPVP1P/X69r4k4ycztK0OWTXCvUdFrlrfYSxvU23HmasHLFUCJMnC8XTSmsS28QZNW+fGrWu5e3qJx2e63LB+kN8szbG7c5JKS2VdPEWlC7FmlkTMxvTLbBiNYXsKx8odxtIyhqDS9rrUTYOJaJwbzsrx+fvmmCtKBJKDEabZsGqJpZZAT1RnsWXiI9AbDzm5YjMai+BJVVYaCUphi11TNoNqhFuPNrj79Vv4x6cXaJgeGEXanRiD2TJ7Z/opiDL9KYuoqtJwfA7NZnDDEM/TqDSiKGLI556YxHEkFFXADyxetq4Xy/eZyLW55vIP89lf3s47Nq7i24em2HcsRiLjsjYf43S9zZpclGLH4XdHV/jqdat5z72H2TogUHcrZGMG+bjJgYUIW/sEpmsWBBFSegzL99jco1BpS5RbHmelU1QsmwG9yba+NPcecxBdibG8xGevHEWXRb767CFuWDfGu7aPM93Yw+Gii3kqwvmbFX5/tE2nrtDxHfaUfZrdgCAusWcKDpcDNo9YSGGMqZKJ43uMpWVKbZnjswGiBKsHYdlykM02sqxhNtPs7XbpTVokJYNM3OddZ6/nNbfuwXGiyErIoZUuoqUw3K8znAuYXgoxdO85X6ME8cz1XN3rf/DnhfuO+GxZfSb2+lTVomJ5aL5MX1LCEy3qbkAqYxNYUaqBSC6mklBFNufz3HeiSbET0pcQwNeptUKyUZ+crmMrASdrIQlF4u7JGidXHD56/wnOGYwQi8GBSpMrhnP0xxXuOtJkqmaRTao0uiGfuXaUvcUaP36yStXo0CuqjOR92l6HP0wFvHf7Kg6V27zplkVWp+IcbXTYOKxj+yqTSx5XDAxw73QFW+kwkvcRBBFZa5EysnRDl4MnYoz1B5woe0RkHUUSqVg2E9EUN0+u0HRM3r9tK2/6xQk29ev4QKWq0JNUKXdDom6KwHfZmEzxvLU6Pz44Q1SMMt/0GUyK1LoCnivzyo05Hp1tsDaZ4IkZlyAo888vXM9QWuEfn14gKkeQNQtVErADmdAXkEKJITXOvGvRdU1u2JbnQLlEuaLiugELLQdRdvGFkHfdOsXOfJqtgxF+sG+ZoYzIYrfD6zcP8eqNfcxtsFidj/yn//tHT60QAM+uNGm6Dh8/Z4JHD52mV4X9jTaa4TGcSDCk6DxxosM/vnKIzT0xBAFuPVrhY3ed5lTRYa7ZoSfToWX7pP0Er93Sx1cen+etOwqcOxTn3fccwfB12g2Rq0fz/PTAEn84Umal6aJLMoYRULEDfFMmpUpkExY9mRpN26DRCXEFCBWXmKqyLR8HIeSBkxavWd/LvmIdQxEZzGnsXuywcySKLotcvSZNGIb88KkiR4pdOvU2V+Xz5A2VNdE0//zUIot2Ez3SodVUOLcvwVWr01w8lvzT7xOGIZ+6a5b5jknHDghckV+/cS2v2JwjrslkEyLTnQA/CPFcuHtmgREtxZpMhImsQbHt8ot9JVQjYLHuEvqg6R6C6hGGHn7XwJIElhyfZxaahMBwTuatv5lE1UOiosxE2kBW4KpNMZ460eX88RhPzzdpeCaRiMDXnpjF9H1W9Yo8sdCmZnmYrYDT9RayDFvTWdZlY7x+Z+FP3+vLDyywrsfguo0ZgiDkHbecYCgn0RPXEIUCNx+q8YnnDXOybJE/LaBrbQxFJqbJFK0OPzpkck7/mYj73StNHl8qkjosc/WqDH1Jlb5khkRU4pHpBMMJjTuOV7FDE9Ho0OgY9KccVKVNy4wxloa275CL26zOJFkXz7Fn3mSmYnOi2OLsiZCmKVE0RbIRjbZnUm8kOHekwN2nKkz0K4TYCKJPX9albXfIqjodr8uDJ2rEZYmH5xYYTCgstHxG0yJ2YJPQwA89pssRBASmKjZvOauHrzwxjyr7zHRszsv28XClxs7kIHs6dRy/w3tuPcn163p4cGmOgbROxXQpW12O1RqkdYNi12YkG2IHHeyWgR8G/GT3CrmEREdwyAsaLbdJRjc4UhSJigK1lQQtPyAv5njxpiyPztS5fNUI6zNZNuUCHp2tM9RrICDypYcWuHJNismSRUIXeOddBxlJKozrvdywuZef7VtEckXKgcWO/jjbMil+uG+Bc8aiPLpSwvclLu3L8thcHUMJSBkK1a6PFsj87kANI9YmoinkB0RqThfJg/EBDdN3cAON249XECNttvQbVLoOH3xwL/0JiXMKA5yftzh/TYH33H6ShYbFJ++fpVTzecN5eZ4prbCux+Cla3v4+cFFphpnhuNPBS7JIMffPniKb79wAx8/bz2fePg437x6E9sGonz83pMcKXd5/ZY+vvPkMucOx/nobbP8619MMHFS5njRwnQDdvQnuP6XB9l45cBzvi4+V7zqz4VT/Vk7ip73/T+QzWpogk61lKESWEiejKEIxA2RduDguyK6r6BKIk3LR5IDYrGQbMJkuFCjZcmEQRRFbiOJPnOVFI1OhErXI2E4xBSDiumjKi4pRUd3o7x4Q5YrVid5713HuGaswJOlRTquy1JJpN2RGR1w6XgeQQjbhkwU2cQLAmQM9s6fcQyNpgXaXpfTpQg9UZWD8wHpeIAmqZSsDlt7BSaXdHpzdeJSksW2RyEmENMCKk0J2Y1iq22crkJ/voVTC2hKSeY7Xbb0SPiiCdVlytPLZLaex1jW5bFTMlE7RVGosqVPouPbZHSNliXi2FEsS+TzV46RNhTqtsN7/zhFJuqzeqDKobkkSws677yol7vnp5go+Cy0AlRBpeHVuWHdKl62ah2/ObrID/YuMFuEtQWZ7b0ZQmB7f4xP3j/DSI+AJ3SJqiGq7NKfsjCtGLvnZfyWyvUbejh/QuOv75hnba9M2bT46pUbmcgZfP7Rae6bneGKtTZLlTgPTQkUNIONIxbRaJvPnHsZj84vUy4ts218FR997Fl6YwKqqFFqhXQcFU2rIdpZAgGSsS6S5LPU9vGsNL3pLv0Jn8WqRKtlcGQ5ZCArUMi18L0oQRCiiAIRzScUbIaSFg+dMshHJEotEdMV6VU0LEtGkANW9WrMNLoEoQi2jOOGCHKIrgi4DqhawNo+ldWZKAhdHphsIgUxDAPW90HLdnn7Was5VCtysjnDk5Mx8kaKd54/wNd3zVLxVuhLWWxOjJPW4/zwiSqtwEHVXXYOpnj0sIXrBKzvq3LaiuB3dTIFh9UZjb1zLvmIxEcuHWL/vMePniqi6yEXrUrwgfOG/4+eC8OQ/Sstdi1V2DXbYqUVIMugiSBKAc2OyCs2FMhFVB6Zq3KyZvLGrX3IgsgvDk8jCAJ+GNJ1PAJfR1TapKMmiysFvvey1Xz+6b3MlFU0UWJbb5Ibtw1w1/EaN57bB5wZ/vea3xwim61iOxFCwSJpBIhKiYgU5+B8kvGcR8MMqZkB5XICVQkYywd0HYGXrxrhu3uWMAyHzTGbsl6n1omxMV3gWL1Cse2Tj+rgCdiYxA0J09QQlS7ZqE9CDym2Yfp0lo9cNsq6gs5bfjNJb49L0zTxpYAdQxah4NAyo5yuyrx96xr2LXW5+XCVfMonFE1Gsx7tdpyDsxJ9cZUvvnAcL7S47UibVT0ud8+c5uhikogasnGgRcqArufi+zJTRRGrlqUT+vQnBSTJw3U1ao5DLOpgds+kdFy1WuB4xSHp9XJwpUvtji9hDVxC/znbyCU9/tclm5iqmHz70RL9CYWzCg2aSoT5lkfFrtMbE1loe2iSx/mjJoudDrVGP3unoyTibfqiUaywi+3o9CdCJMXE9xV64g5dz2ellqTcUql3Q9bkIhTbDl2vy1lDMqcrEmWzw3Aa5FDgyGmF8dEu7XaCrtCkXIswHNNpix3+6cot/M1dJ2n7DpFkg5giMJiUmKx4CAKk5ShtK2S+KJPLm2QjBk7YpdJQCWyNoTSkc2U6tsJYPMeWnjg/3ruMG4gIgYyEQs30iMccnKaOTJs7b7z4Od35euTK59ZRdMl9//13v/4H/1E/L//RLt5wzgR/OFJhMKqxv9qgg4kTBKiCSG9CoOM5FEsxYpLI2lyUi0dTXL02xet/c5QgDGkJHYJAIClpjBptZgKZuO5i23HeuHmQL969RKZgko4qvGbTADcfX6JHTqHpIYIgcHLRZaZp0rR80jGJt5zdy9efnUVXPQYSAr7YIar6uE6cqhnQ7SrkIzKlOshRi0EjRdJQWJMxmKs6nK5aWHKFQkpgsigShiI5Q+JUFbJo5NMyrz+7wC8OzTDf8AkEj7QQY24lIJUMedWWNH841OLt5/TxvV1F6r6DgUiPXqehR6l0QpKazPkjBo/O1jEkGRubnhiYgYtjqdSbBt1OyF9sy3LbVIV8Al6xKceeeZsnZhtEVRFfcBBCEdsVkaQQVZJ4zaZeji7Y7JppkMzbBIJHGEg0OgIJTebCwRQz1hLTKwafvnSCHQMJHD/gC4+dZlOPym8Ol5lv+uzsi3Oq3uUv1vbz0FSLr1w/ykzV5t2/P0VvAaq2w8WDaepul2S4yPFWjsFIgkPLXaKaSC3soqGS0TV+/LJ1Z+Kp/QBFEvH8kOW2xU93FzlWa/GSjTnOH0hTszzW5iK85pb9mA60TJ98IuTKwT6enekw1TBJKjKOF9INXVQVjEBBluD6bQl2L89iaB7FjshcVefigRyyK6MpAnfMLqPL4pnQkHyMxWLA3149yEhGZ/dcm58/W8SICXTCNkt16HR9egWTBUXmnJ4sJ5YcWp6LJToIUkgk1Hnrjl6G4zq/3Ftium3SboKadPAdiUCAMAiRkLj59Ru4Z6rGfMOh5dq4gsMT8xWaHYUbNhV4wZocgS/ScXzW9/yHMHf7ZJGH5xbo+BZdBzq2xleu2MBjp5vcdrRC1/MZTCls6Y2xd9oklXf5yPmreHKmheUGBD48erLJRWviXDmRJmXIfOWROZ6cKzGoNenGA6rtKKDiOSICAtetT7I+F2NjLk5SV/9Tz4dh+KfjOuWOS0yV0BWR5ZZN7t/nMZW7LnsWW/z++DS+2sTzVExTp90+E+4iCTJJI6DVhbYb8JL1eW46r4+4JvPrAyUEATp2wPmDMX62r0g6GXLrgQbrx5tYjo6PgySGeHaErhMS01XO6o2wKZ3jl7uLRMMqS5pHUpfwA4FTFZGrRrJUrYCDK21imki145GJisiyS7WtsKpg40kmjUoaQfbYmMmyb75LLTTpS4hYwZlh6aJkokmgyT6nynECV6NcgUxSoNr2KKRFys2Q0UiUb7x0gm89tsStx0v0pVSunEizf6HBiU6HQlxCUiy2pnrZlE/yq4PLFL0OAykPL/SxXQXbkqnUZDKpgJWmx3k5h90NmTU9AQ4+kiBQqkZpNQTOWWVwsNrk3N4Ubkflo1f08w+Pz7K+EOWZxQZXjCe49cQyWjfO7rkuQwVoeyGVSsi2CZmjcwECAi/anOKx6SafvWaEzz0yjWOKPH+TTuhqPDndZjwv8fh0G8sRSMYFUokG5ZpG3JCJxdrM1zSSqoSHixX4JIQo79g2ysfunCUac0lGJayOwqt3pJhvt7n/WJfrBhTefeVGPnzrDM9flyaflPjCk5OcqnhENJ+zeqPMlkPW9ilEMChoOvmIytXrUrzlN8d54fo0//ZMjfdekeOpxQafed5q3vWHKSZbVWLE+MLVY6zvifLQVIPTVQs3CLG9gPdcfGa2lucHdDvt59xR9Fzxqj8XTvVnoof932MirZCNqJxYUZjpmLz/nEGiCsy0Taa7dYIwIHRFpooWc0GFQtonkvCYqlqcqLk02lFWWjKCXKLWEbGcKAtNWCwFJCSZhA6LLZfAD0lrKqfrPg3BYjSl8ej8MvF4m54kLLSsM7tfmsZIr0g3bFIqafSnbHyxyMOTSUDicMXE0NqokoQrLdE2NQRkjpUtRrMKXiBSq4bs7NfwhYAtI12yWoLVyTzPHy/QsiRqzShDRpbTRY+3bpygVpe5adNZxCSdxZbD+cMyqmajWzYPf/Wr5MZ7yUQ87j0uEXUTLPoNVmd17MAloUSxvZBGV2G2buMT8KE7T/Pm30zy9UcXSWgB9a7EE5MZllsCgwMh98yvsK23wJ75CKqgsmw22dEvcd/0Al95apKfHJrGDj02DHu8+uwM167J8fxVGb65e56bzi+AaDOS9sklKshSiO1EOVyEs4faFNIhd5ws88MDc3zokn4uHsxyXrYPhBDb8zlUbnDeiMuJssRiW2bjoEhP3uTIksxTU0nefPNRvvXUMqtTKXRJ4Xn9gxQrUbquQ8tzUCWJbr1AiE80WqdiuhiqyFhKpj/lcXJFpWX7WFKT8aEGA1mBfFyg0YihyyFgUm4JNJw25Tbk4jYpXcb3DSbyPtdtbpHPOkgRl3jCY7pq0uxC4IEvuijxFoIYIokh33jRKn72io0UtCi7lxs8srDCuiGRT18xzjm9KZ5d7HCw3OIzj5/k0sFRMuI6Sl2ZE402f/vIEZbNFpZt0DEj3HKoxQ/2LLImr/HSjTnS0TNzkGzJxQ0C3CAkFAMCycdzRWp1HUUQkFWTnx0+Rd3v8PKdSS4bSyP6AmEY8o9PLPDp+6Y5WTEBeOsfjuF6IQvLIq2WhG2KVE0HO3AAH8eFzT1RoopEOhpgt0R6DY2m7UGo44YBQ7E4iplAkwQKMYU16SzvPneA3pjGhYURBAJaQZt9pTKv/tVRrlyd+lOv33y4jGXCm9ZvoGF7CLKD6ys4do5qO06l63OiJLFYV/GtBIUktJyAU8sqr1ozyv2na6R0mfl5gbwSJyeP0O5GcFwVUZBZ3yMSBCIfOHctg/EYuiQy27KxHJVWN8aBBZ2WqXPlVp2r1qYYSuu888JeFhoBmbhINuqz1LVJGQoRvUupYvDNXQuMJiKc1Rul5XpkYz5dWyYaa7OqT+B0q8MnHjrKJ5/Yz+Z+nUuHB7CdJOmoi6JanCrFWGpKGKKKLPn0JRSMbJlEBAS9QSFjcunqCG/eMoghJPib80fYOthl2VohqRo8vdQgF5VZ9eobaT3xU5zKMq2uwN/vOs4lwxnOH42za75Jr5bgvimLqbLDYEJGlFwG4wq5iMZsVaTXSNP2bAwNNBIcq5pEZFjdW8GIzxKRQ1LxEjN1GzlUKZk2Hc/ieSNJTN+j6Tgk1SiT5QDL9xnPGBhqQCxqs35AwXejBCGMRGNsLEiYDiQkjftmK1y4OoYqSWzMGOSiMj4+w0mViVSEiGETi3qM5VV6EzJ1K0CVJK5YHWe8DyYXBVxXQBF0nl6uMJFMkY5bXNCXpxm0ObDUpeZa4CnMuxaTVfO5X6QEzqzSz8X1/8JMyP/Bf2189OIRVmcjvOvcfh482aRl+rxkbS/9UQM7CLGCNs1ijKwq40guCV3iFVtz/HD3CitNjw9dOEySCDEMGqaPJDhkVBHXUVkqCvx0/0lWjXRZaggcK7n8eP8i56R7Ob3iorgKiUDl+LLFqzbmGO+RaVge/3JgkoFUQNoImFyBuCxh2VFcTAxZJKNIqNEmqaxNTNY5q5Dk45cMc7xocv+pOlXbIhETOF4SUNAxZIEV02UwKvPOC3pZXPH52f4FilYHVfFJaxHeecEAQ30ipiXwo/0ldgzG+MfHl3n+xiSCFFITLKKGRNl0iIsyXTPk7j0WSVmiJ1tldd4lEGw82yAtpXnVugKjKYMbtvRwzaosf7ltiDsnazx4qk4mGtK1ISoLhKHMmmwELVCJSDIPH2/zxGyDXEKh3pbAV2i0RSRbp1tTuHgoi2Wm2NJrMFnt8sB0iXfcdYAHjjf5t70VFpcFeqIi+0sNRDHkxwcWcUWPvSsNcnGR8YLESt0noobsLZeo+xWaXYPFjk/V9BCkkJJlM5oWefmmNF9+/gTLTRcARRKxvYB33nKCzzw0SyQisGxa/HjvCjfddpx33DHJkzMNXr9hkHozJCZovH79GG/d0c+3X76af7puFZ+5epRmaJNL+wxkbFzd5MrVGR6bK9J2FA7Onjk2ntEl9q00eGixxD1zFbJRiagiEIoOx+dc5psWfzhcAWD7UIy/e+EQOwajuN0ziW2BYhHKAYWoSK3rsK5P4xVnp/n8lRP87hVncfPrNnHlRIbj1Ta7G2Uafgdf8XAtifX5KN94wSouG80wntd43x9PcqpqMZLSuGo8z7p4ht++7Bx29Kb545Em//DQPL/eX2Sx4fypr1ZazhlhxtPB0RE9lQgSQ0mNnxyeZedgjIyhsNixeGKhxsWrYnziwtXEVZnxjE5Ck5jvWORyAsumyT1TNVw/YLZhnzmSqeioYQI/DPACFyEQyOgij882+F8Pn+Ktf9zNX/zsCO+/9QT/uGuOx+fqAHxj1xyPnK6jywK6cub17rG5Bu+88yjvuesgr/n9Af44vcjZ+T7kZj+tuoEuGsiiTFIVkUSXihMy0GeRSwrcfqzKm2/fx0MzZa5cneL69Rk29kY4UOzw0HyVlimwfTjG1EKMege2ZPOszcRo+Q6FWIyReIyHTprcc6pMTxZswSOlKvihT9MSuHwsQUTSmS/66J5KvS6Q0TQqnZC2qZGMwmInJKUqSJEu5+Z7EXyZb7x4FWdnUyyVBfIRiXLHZ7mWoNjUWalE6NGi/PVFwwxnZTqBw/ZxBTt06E+qvGZ7no/fMcOavM5ExqDW8tBEkZdv7KNXN6g7XQRfY299nh/smWeu7BGTVJo2pAyfmGERiZoM9JvUXZceXcMLbdYWAubLCo6l4bgCimYxPmTT7sKHzh1lc2+U6U6Tu09VeXyhyn0nyzy92OJn+4u0TZGnFhuM9XlYssvbz++hvz/g0LxNb49JrhDypp09fObqUZZaNotdC3Sb3x4pcsKcRw5F7jnQRUDgw8/r5/LxNEu1KPlkSKB0aVsKcUlhrixSbWikNZWTc/Dp++Z40VkJumHAfMPDUMHqyMzMK1y7LoXjBgB87toRFhs2f3PXaU5XPQaiMq9fP8rhRQfL9/BdkcdnmySiEn84XOVrz0zy/kv6+eWzVT50WT9r0wliisY3n57j+WvS/MXaYUqmy42/n6Jlu1y6Osmbz+3hxvN6OF4yeXK6CYAs/b8kUTxXvOrPhFP9WTuKXvmrm4klYrRbGfojcTYNitx2vHQmGUP1qZsBngtDqZCWH/C2s0b456dWSEZtljo+QegykbfQRYOy3SGq2rSaeYpVncW2Sz7j0BfVOVGz6XRkNg3A27as4q9+N8vgqMlr1w/xo6OnMG2JoYTB31+2nr6oxht/e5hqWKcn0yYmpEjEy7hBSFKJc7QoIQBZXaPU8VHkkMCJUe4GXDycZKZsU/Y6GMhks3VMR+XdWzfyznuOoFgGX7h6jK39Mb72yCKfvHoIQrjptmPQreDGDJBNJGSKhx8nVAwGN25gqe2yOjLCvSfrrCvIJONN4orMiYrCeL7F3tkYoaPyzWtX8/l75mm5PhAwNNSg2AqptAx0zUeTBVKqzmWjOf54cglfrDKS1PCwcOwM5a6J7xuYro8oiVw30s/Pn6oynFb5+BVD/PT4NHWnw3yrQ09UpWXKRHWfhBZwYFFG8Q0SEZGlts1PX7yB4yWLF2/McP3vniKnRliu+8zVba7cbNG1VZp+nUYrixIaWJ5H0wyI+RqrEh0sI8nXX7CKV/7uADvHWyzUZNq2ykLDY22vQ18iJB0x6dgaB5cFduYHuPOQySvPX2KqFKXSjFG34crRNL88ssD2IZeGHdATU4jpFku1JI53ZvggSDRsj5QuYLsiX7hkA2//wwlKto2ByGCfw6kZnY1rKrSaKW7cMcLGXJJsROaNf9jH6ZKPJsu8++whDrcrHCy2EJU6CTlJtxPlW9eu4fP3z3HOSIwHT9b4wSvX8eujczwwt4DridRbGprs8bqt/Tx+2uLZmRaOaoPkUZ3T2DRQYz7U8UwdXZCRJQFFAlUPiOg+hajOGzYPc1ZvnA/dd5RjZRuzLSCI8NUXTLClL8YDJ6t8+N4TfPXKNfzdAzPocYulOgiyz6Z+j+JKnKbgcOurtvKvB2a4oK/AWb0xXv7r/TgmEDW55eXnklQlrvr5Pi4bj7LruMdnrh7lFwdXOFnrIksBtmgT1wWqLYE7X7P9Tztsb/7tMeqmgxF38QIIhA6aDEfndV57doq57jJtS8PxAmJawOmyzGjBQrKz/OW2EaaqXR44XeOvLxjiZ49NcvucT09eZDga46advXzq8YMkFIlOK8aHLxrieHue/aUSi82QSitKrxLj45cNM5QyMOQzQ9bbts83dx9mqlFnuSWwNuchy22CMMFyXWbfcY3NE1CvyrxqR5J7Z+do2D453WC6JJMzFGZKHj1ZkZbrsiYnEMgOnu+Rjbo4dNCFOKJo0RcXmSqrNLsRFK2CEkYJpA6uneL61UMcWXF5/EQHSTAx4gpfuHw1e5brfPGhFS4ei7FYruP6IRuSMfa3bQxJ4/iKzUhC46PnR/nw4xU2j7aYqftMZHScoIshS0RVGU3t0GwmMLx+RgsKTy8voup10oaNLEQQ5AoiGvPlNLuOq+xYYzFdjjK3JPK554/wo2MnOFaE0aTExqEGexZkkqpCQpOoux1WllKcNeHw1CGVLRtKFMIJDjZqLDZCPnfJBD86cppMagVD8anW+0mnljm+HKU3KtFoxWmGTUpNhW39MsU2xGMdPn/hTn59eJkXr83w5T17qLZ01ueiHFgUyBsar9iU5OGZKoPRFA/OlvAC0B2Lu55rR9FVEFP+H93qT2i7cMm9//13v/4H/1E/1VqddCrJ/oUOX314nnPXaKy0fZ5aLjOaNamUkhQ9k05HJYvBOy7s5frNGQRBYLFp8+E7TnOi0eULzx/hB/tPEOt4VCIBrqeQ1yUSqQpH52KosgYIzC9KqILIZeui1JoC33rFOFNli99PzXP/dJW3bBngjtOLmH6bci3Guj6P6VpIRPMoRFXswEESfGw7hhN6aIrHRCJPpRtw0WCaPx6rU3U6mKFLVlVp+xamK5M1FAS1gdON4HWjtKU2cTQ6nkA78FiVU1jpdgkdBc1XKTVCzlutcaxsI6o2aqiwJl5msdPDsuUBAoangtFi7YBH2QoQfY2lmoaEgGjLrM4bfP36cQRB4N+OzPPNR1foy4TUm1BIu1y/vofHJwMyioIThrzzwj6iqsihYpsvPnmKwJWxQw9VkPD8ANFRsGUP2ZOQZNBiNpmYh+n5iL7OQlPggr4Uu+fqDBd0Xrwuyc8OLyKHClboUIgovG/HBN95eoXJchs14qEIIim3Q1uLkI9JnCpCX8pjekUhLekghQxEdL78klFsL0QWBT5z/zT7Sk02DShogsJTs218ISCpyBiSxI9etpGHTjW4ZDyJJov8aO8S905VqXR8fnnDOr5/YIqqu4KPxb6TfeihTCi5BGoXTdTwHQ1PcklGWqSi0OxE6dpnHFhBIOG3DERBQFMEfvWa9dz4m5O0xQ5JWeOK8QwzZYeIJtCndEinC1y6OsUzC00+ce8pzh2Lcc5gnIG4yO7FDlZb4fbJMpmMi+fKiIFILqry6cvGedXPjyFLcPlEEk2S0USR02WLc0ZirOs36HoOi3WP+4+2aNs+5wwneM8lZxzKn7pzFk0TuHZdmq39MQ4ud7j3RI33XzjAhx84yt+cN8FPnykxY5YwbYFomODrLz0zN/Mjd54ml5C4eiLNQFxjqe0wU7O5elWab+9Z4PHTdQZEk5YU43TNxfI9Xr+twOSywwvXZklHJT778GmqpoMmyeiSRNwQOLs/xh8mS+SMgMCNkDNU1uUNTnTqzJVhfZ+MHXisSSd4944zaVJ3HK7ywFSDjX0G8WjIvoUux4otOrKFFKoYokgQSvzilRt4erHJbw4WObxgoegBuiDR9Xz0qANWBNcNMIOAwXyIJkNG1zF9i1ZX5PKxLE8tz5LyHI50VKKqSqepU4hprMnr9MU1rphIMVtzGEzKfPKhY7Rcl5ojkNBFau2ApCojSxLv3DaMIAXccXoFTYKnFurkogpt28eQBRTRR1bgQ+eu5wePVpmzWiQTIX1KlEOtJSQryzWrMqzK6ExWbO48UcYMPS4YTrC9L8FvD88zU3VJyipVG3pjCq85O8MtJ+bpBh3W9Zjsn0ujyw5dO0LbDrksJfKM5TCRdzF9j46l43giYehxcX+eB6ZcfviKNaR0mWrX42/uPMGMW6U3KjNdlBEVn7whs1QO2L4KQtmk2HaxWhlMqYOMRFJK8NVrJii1XD71yCnKtkkhIuEHPhIOBbGP4+Uuo3mNSyfi/OTZMhYuZ/WrEGocKNeJCwY3bMnxi2OLRAXwRYH3nzvKv+6ZJxf3uCg/xEMnmwSyw6qCQGPR5EU7JtgxfIYvPD3X5OHTdX6/v0pOU9kyqvPMYpMrV6VZ7Jq0GyI3bC3wd4+eZG1fyEfO3cBExkAQzmwg711u89G7pqlZDl+6ZoKVbofHl1b4+hXbOFU3OVUz2dmX5KU/PMrfvXCQR+drvGtzlnQq9V+SV/25cKo/a0dRStVY6cCRWocHFoo8fKrBbFHiRLPDx85bhSGJZ4776GUWG/D4bJ2O5xBIJikd1uRUhFBluqKyPifRE0kiyNDf0+HStSKjGQnDsBlP6ewYOlOVX3pili0TImqocfOxJValDcYzMopi8/dPnuIfHp9B0LucO+YxEo8Rj5pkdI2sFkdRLNbkPeKyzmRRIhURUEWBUzWHnoTPZaMJFv0V1FDiQMXm2ekIl/eP8qY/TJJXDQJH5GMPnuADt5/kkYUyNx9f4cuPzmMgM5TSOF23EEKR+37wQ2LDI8THNzNbg5esGmbFtFhb0Ahlh7VZg33TOuePrzBdU/DbKu87f4B/emoRSffQYl18MWSqJLGur0NMdkmoEmIgU+343D09gxOavGx8HbN1CTFM0bBNxDCCbtTJxQP+8qw+UhGZgYLIZ144xFd3zTG54FE1XYaSKlXTIWGEZKMBC50uMVllOGdRrnskA513/f4UP9x7mh/tm0bq6iy0uxCIXLjOptwNzzgthAzXjw1QEAy+cc0axlMRjIjHStthoebwnWcXyUYkZqsqDbeDJMHZQx75qI+Ay0rHpNqVMOtJGrZDX96nX93EB8/ezmJZ5P3bx5itewyoKS7pW8OO3AiNls5UUadtw/FFkXI3ZLERUu36mJbAchscwacvoXP2WJfz1plcM57nnrdtIuGOcXa+l0uGsvzg2WV+cnCedDwgLat8+epx7j1ZZ89il7oZUl7JcnHfAOf1JYmrIrsWGrz+7B7+8frV/PbICpMVC9sVsE0J3xZADOh6LhXHIppsnzly0JaQRAjCkLW9DQzDxglCHDfE9kNMU2QkmuKCwRx3T9aYKptkhThvWD/Ara/fyD+/aBWfvGMO3w+49XiFtX0Cn33qCKrm0ROFjXmBq9bXGU4qvHpHmologlf/4hinSz6Hi11kUeCjF4/w8h06q5JpMobKoVIHApFPXLiWn7x6DZ96+BQH5rt87JIhVNXFUB18X8DQBD74xxM8frpJGIb84OVrefuOIb522UbMrkq7nkT0I6zuC7ACByus0XYgYYT0SAUEV2X3rM5IJMnRFZPHTrY5ueLQcX1qtstIPMJnL1pDVlPxPYHnj/UQi9iYSpHDpQ6F/w97/xls21Wda6PPyGPMnNZcOa+dg7a0lXOWQCCQyGAEiGSwDw4YTDRgTDYYbJLB5IwIQkIo54DC1s557ZXzmjmNHO6PfY99v7qn6pbrUnVOHXiq+v8xa/Q+Wputt/a+ag/7FmTcQCZnyKzbNiIShixRszze9KvjeGHIu3ZvRYliFGMST84JECWwXIG6LXHBdoeS28BIRUxXfUzHwHeS7JmHsa6QQDa55gx474UjhL7E3JpINmaR0iVqpkiXoRGLrZOLQT65zmjOB8EnLqWp2D45TQGlzs8OL/PkQhVZ9wlkjWon5JOPLfDSDX3csDXBPfs6/MNV21hdmuaxr7+Pr7xwK4Npgw29Ci3P565jVTKJiKmyzNn9sGfJp2nGKJkCraDCibkuHpmReGy6yfPLZWquixTFKSYEvNAjKWdJqCKFlMnl28D2VRQl5OwNIl98eolOU+G8AYWhYpPVpsjuXhlZilhr+xS0BF3ZkJOLEmdMuLTaRU60G7zvglG2dKn8y7PTlGoirlXEsgoIkkWplqNPz7BcSdF2BIbjac4oxik3VRRRISOn+Nhj0xQTGuf0dXF2doi+hEzJaWN6HklN5Fd72hyu1HnJliyGLCEKEceX3P8fEee/jyD9Ydef+OPi7bed4u13HOcHe9b53ItHeefZIyQVicF0SLUjEegtYr5Bl6RT7vh8/7kSUQRhGPL+u2eZWrP51NWjfG3vPCfWwBV8DFEjpgZks1Vapk5CjdHyPEolkZfvKKAnQp6rllj0Otx5pMpEXmNxHd5+xggPzJVYb4dc2TtOT8bnwIpERoeepMRSM0RAxLSTVK0ISYgQQ51HZzpMVxweX6jzsu0FdMGgJylRdh0kVHRUao4NXozQjPPVl27gusEeBMOiUGgx0RVSsjun3S0tjaoZsGNcYM+sjUCAYBl0vIiReJE3njVAv2GQiUl0tDbZlMxSy0cVVNzIRxEDhFDk5jPz/MuNY3zsngUePFnnVVv6edGmHKWaSDoZUrNlfrCnzXLD4cmVGk4Y8JafT3Kk1GGu1UEKJZLpOgkdbDy6cgFK2kQLZcIIQk9AimTCSCRwdXJSmvdf3M+St0w6LjFft7lzcpFGTWF1XcTvGAzFs5iOwMu3no5ffkej0gJdUmi7IZsLGht6TWZqATFFphk6rDd9hvMq3z18jFf88CifenSO910+gOAoYBpgqfTkLAqKynA8yWu39XPviTrXbMyiySKPzld5fHkFX6shaB5f27tAfzLBoZU4UtjDVeNp2qGHoHsUEzKSKCKoHSwPskqBTZle/MhHVCzSYoIBMUdSlZB1kxduzfLGn56i4toEIeSzHhXLZ6JHpeX6eB70phSiKOLZpRb/eO0Qf3veEJvzKmttky5d4/6VNRRFwO7oRK5ERlX51xs2UUyovOHsbka6NApxlVvP6eaS0RQI8NWnV/jr353iC8/Mcqpqko3JSCrsKVd43a/2c8MPD7KvVmf/cotvPb/MO+46wfcOrHDOQJKn5hvUqzKGLPFnZxd59dYx3n7O+H8WibwgZFOXzlOLVfYut9Dk05eEz600uOb7+7nneA0r9PDwcEWTLYMuG3vhRwdLHGnU+dbRGaabbT5z3Rjb+lTi8Q6B6NBoRTy70GYiZ5CPJ3nXBf3sGtI5Wm2xOZPh9tdu59NXbOHfrt3JLTv7EQQB1w+462gNV3T51eESX32sxNSqz0WDBbpVHU2ziFSL0azMt59b49KhLP92wyY+/cIhdhbjNEKXfDpAdFVqjYgrRnN0xRVWWwF4BglBpWW3KVsed58qM7mi4QUCxaRIO/DZPOJw0UTEE2uL/HZ2nvc8cIyP//4Un31skVOrAm4o0ZP0qXVCspJB3Ql41znD3HWiysfuX+DUmsPj8036EjKSbBHXQVZczEDEdmVuO1LinAkDR23j4fLYSgXRS1HpuFTcDncuTPHS7Tk+cc0ohWTAvvISe9fX6VHSXLspjYXP1SMZlis+P362xnzdpxATsPyIrX0NwlDFcUTOyqd50+4eXrV5kFIHZEFB12wkyUcQJE6aS2zoVnnJzw7y25PrvP4nJzm06JCVdSKlybahDkoks7Qq8rIzc8zUJF65YRNv3bGFagf0wMALRE7VO/zmWAVZEqi3AvpTErLexA0FRCnGamDxzgv6OKMvxg+OLhHX4YqxDIcWIsaSBi+e6EYLZb7z/DofuHCML71gG/WGyDf3rPCv123h5ZsHeGq1hKpAfyLO3mWTLT06Zw0kiKKI7+9Zx/RC7puu8dpz8nhSwOOzTWwv4Mn5BlcNFfjcDWN89fEVvnz9Jm7ZPsqnnp6kZvr8+PkSgiBwVm+Svzy3j1t39fHphxZ56aZeLh8qUrIcBlM6X3pujt8v1tnRE6fc8Xhgusa39y/9wePin3Kq/x5/1IUiXUpx7eAEm3NJujSD3cUiLz5D46YNXXzy8TkcTHoLJp6TRxACHllaRdcthFBjue0w22wwuRhnuNAhHl+gE66jyxFxo8OyXUWU2hDKnCx5tIUqqiRgBwHrnsWfn91HMeew1O6QjDVwXZ2jazYv3ZZmpOBg+m2qbo2YDMstl9lmG9fRmCwH/P64QI8hsNA0ObIasbM/4MCqxxf3HqMnrlGnza7+03aw3z24zBl9CpHkk+/26UoqLNtttgwIqKHMnUcrnDMUZ/+8TV9c4ejDd5IImxS6MmQMh9GsxiPzJSIhYKnuklYFruzfyFhfh+OlOI6l0Z0Lue3ULLm4wGvOzGK3ddZaLl0xkelyjHRMQ5EDIk9FDiQMReTzV+7k4YUKWU1lpRVQiEskU0tktRiBrzBdCfjR4VWOlU3+6amTzHfaBIGEKkq0/AbnDAZs7q2hq0269DiS2iGleWQUlbWOQyoTkE3Ddw6usFITyKga8UyTyUrAUAbOHW1wZC3gqokc33z1BqpmwELdYnSgTm9WwtBDHl2eY7pm0xXzkbwsCdXHDmwajovpaQhhFlWUKRRazLcbTM4J/PaIyamKy7++aAMnSh16NJ2cpHLv8Ra3HSszsybTdn2yCYfBokN/RkCXZHJ6nMiNsavH4kRjnvMGE3hujEozzn1TJp97agFDkbC9gBB45Y4u5hs1EprF+JDF76bXkHQX0VXY1OWya8Lhwfk1jqy3WWm7RI7IAzMl2q7HbSdmOFap4nkG7zpvgn9+4QTb0kVWqhJfv2ELmbALyddIGhL3vGMrcVWhaSVJx0XSKZ9kKiCTM9GNgEOLDglB5cqxLGldQtFDvrVnFVEQ2FCI8a8vG2OyYpOPSVw8mCQfE9lQ1HjNljHGsjFcL8VyS2Cx0yRriIiyQCRZiKrF+x86xc+PLxAFcd6xe4gwiqg4Fn0ZgXffPcVzi02uHc/SHVdYMx08P2JhwWB1TWClEXDxcIbv7FnlkekGgiBQtwJ+c6KKGbiEmsnLt/SiyB5TtQ6uVSSuhMzUHGbdRYyEw86+kI2DEa85s8gXbxwjbci8995pTC9kqtVh1eyQ0UU+ev8cj62cIopkMnqMbx+Z44vPzjOSk2i3FaYXYSKdYDCjAfCjw2ust12CIGKp4aKFKWq2yDkDIofXRHQloDcVUbYtLh4NsGmzaJfpyzjYkcNIUqcT1dAklfmaw2fuXwRHQk22KTUNEBx6khKKYqOTYbFl0bHTTNVcigmBIPLJaUn2L8WICwmOLom4LYXuXAVBdCmkQtbNDm+74wiPnmyzaUDmltsm+c5bb6Q0ei7f/fbXiRSPZatNLhuxbDoYskhe01mui+zu1eh4Ao6rMj0/ysm6Rz6KY0kOjx71Sasyda/KngUNAYWSXafUkjEdjbrXIKn7iKFMJ2iTSYaogcFsSUKXNDw7S9mEpCaRFNM0LAFDC7l0LMdE0SDAoxAT+cGxGVpeQCEt0BfTWKxJLNZkuhMStZbO8bWQIPSZ6PZ494XDNMM2rguqGLFm19m7YnLXqTKv/PlhXjQxzHPHFPriKt0pkZP1Nu+4sJdEmORHR+d4zY4ikgyjXX+g1p//T/5QY2f/c/2JPyqagceloxk+cv0g//r8PIfW2vzyWIXjixpWK4kRxqk2BFyjwydeNMg/v3QYQYCv712kYjn8xUW9fHn/DLPrPj0phZgGZcfF80FBo9zWWKlFSGaCPzurmzvn15BiNlpkUKuH/OZQla/tmacQl/ntiRoL1Yi/O3ecXxysM7OsMZKWqFoSR1dkBjJQaaks1yCjC0SRQMWKSOkighRyvNLk+3vXqDoOKy2PtGggKw6B1CGr6MwvazRCj3um1tjTWEARRcRIJRTbdMdkOg74ks/OEZFXbO+hrwC+L9PG46qBHCdKNoIg8M5Lsox0l8gbCjO1ENFPEAkmnZaGayu4QcjLtxUQBLh6Y5pfHl/n9T8/zgPHWmR0gUZDoygnedcFfTiqhR4LOdxax0h5fPbZEzw+V6dpBWSVJNW6QNYQWW0GyF6Ml27P86JNeb7xqglu3TlEs5Tg9TuG+etL+ti31uK89DiLK5BQZOarIpIaYcQj6nU4tGRzcK3NkTWTl2zNYQYhSUXG8QJUUeZ3x21OrMTYaOSpmwFCINGXVHj97gIHVgIEJeShuSrv+OU0QRTx1EKDfUsdOpUcN2/t4aU7Mjy7us7nnljgyFqL2w+X+caziyTjFXqSMNJlc7La5HeTZdKqyHrH4cH5Mpk45GLgBiGCbJKLi1w0kOb1O4bQ/SRNSyYpZHjz7gFevauLH752E0MZlamyzaY+layhcEYmj+vprLotnl+rMdfsIEoR33pmnfmay7vOH+DCgTz5mEZBT3LnEYeZtYisaGAoAorqEyg+847JO+4+yv2Tda7fkOVbL93M287tZbpu8YNj83SMNbJ5i6+/aILbbj6LTlskqcl84IohtmXTLJQj2lbISzYVeNmZGf764n4252JMlS2+8tQih9fb6KJETJH4H3ee4t7pEidqTW4/WuL5hTaWF6BKIrfuLmA58P1D89w1tcLBtRY9MZUXjOdoNkXyMRU/lOiYBl2xGOlYiOcLtF2bX03O86nfn2RD3uBFG3rZ0ivxt5f1s7lH46UTPXzmyg2c3Z/imSmba8e62LfS4K/uPc4Xn5nn9wsNEorM9/au8tXfr+BHIbN2DSNrk0tBxbV5YqnGhQNFXr91nPFEgc9cs4l3nN/Lb49XeHSmztFajWXbpCsRYYcuqAEbByVu3JllWy5Jb0KmFTV4fKmO5SaIQon1hkDGkBCFkLbnYwgqJ9Z9HltaZbwQULMitvcJZNSQOgvk4gJhEGF7AkNpkTec2cNlfV08PtPg5p15zhmNEzNksqrCYk3CqiXRZZt8wqcr6dF0I2abTQoxhaSQoGHbdGsaDTsgn4t4YrFDq5ngtT85xm+OVlgsC4hhjEPlOgtOm6cXbD5/3Qb+4bphXn5mjtmay6CWZLGh4DoJFusxepI2G/IxdhTjfP7xRX6xv8bGdJGq5REGKl4Q4YU+q/UkTy6VSYoyn3hoiVC3edOFeWqOgGdlcSKXoe4WWyYcLhvNcNN4N//y7DRxWePT14zSbsnoskB/XOPKsQybu2LkNJWFhovoJ7ADKNs+SSPkqk0p3nXBAGcUE3RwkRWfy8eTTJYc7jxZZld/DC8Mee+DUzw12+IfLh9hcsnj44/McvFAkTN7chyvdOgyFC4o9LBnqc1U1eLW2yYZzqp8/alFdhYS9KVlbNFmIKlwZjHNuuXy+acWKFk2n7hhmPfdOc+Z3WkGkwnShsS9x+v8bF+Jt952kk8/Pckv9lV44+4in3xgkZs2DlCM6cxUbd533hgfuWeBZ9fq7JuzuKa/QArtDx8Y/5RT/bf4ox49++Ezx/nk3jKy7NMbV7llyzDfO3mc5aUUmYTAWK932tpZgIwREgY6TScgAuKKQOjJVDqwob/Kxi6f6YpGqSOiiiFxRadiO0QEhF4CQ/c4uiIRuSov3ZLncHOeE0sqF4z7nCyJLC8ZbO3R6eqpcbzsM5yOUBWRSGwSk1SiSGXfnIoq6OSIseq2yKQEiimHZKyDbSVp+6fdO3oyAoFkYXsimqjSdCysRgpL8ml5Dtu7RZ6dEunSVdA87Mjh5p4Ev5oOMBYfZOii81EVibQe0XAcDNGg5jfRogTPHTboy0lEMZcoEulKROiqe9pq3PLI6DJTVZecmKTWCTl7wsEMPYpahr89ezOyKBLXBL52+AmOr8j4QkAh7vPcEvTE4pQ7AQk5RjYGc3WXtHa640MjjS7DYLHMrkIvd02ts7XbQ5ZsDq8JeHYPrguzawKv2l7kWMXi1LqFYYAfRhhxh3wiQBAAwSOp+0yvZsmrBt+5aTOyJLJ/uY0TWnzhruN0j/r05GyenS5g2wprToeLJ1wKMRfHl7E9mclKSHdcYL6mkdZUTEckciSymsJnXzjG639+nJ+/djOGLHPr7ceIJ5sIiIRE6DLMVWRUQcHyPAa6XCxTpFXXGOh1OTEbw5U9MvEISTW5eXgjP9i7jhDv0JtI06voPL9cR0649GciNOK8accwj03X+f1yjWTcY1M+zWrbYd1qgZfEFz0+f9UW3vvIXiwvoFSNs6lP4uOXbebp+RbTVZvtvSp3T64z3/RJxBy+cMVOPnTnXiqSRlfGIan7hKGE7UlszhR43bYRPvzYCU6sBGRSPuUWJESN396y4z/P208Pr/Czg2sMFGw0zWV6PoUU6mgJn5brE0o2qhyCr1E3RVDa9KVhdSWJpbUZy4es1Ay+eM1mtnUnCaOIJ2cb3Du7zp7VBglUWh2BroLLjoEOCzWJejtJQjL4+o0bAXC8gCu/dZCbd+SYbrVw6WDIAofmJRIqfP0lW/j4k4eRFZNK/bTtvB3Z7M5287kbxlBlkbuOVfjc80fYoUmYhoQd+bRtmVZdY7jPQhDgxJrAO3YPsq9UY92ukNIdCtEQhhzn0ZkG/37TBoQI3vrzKS4cTvDAVJNtXQamWkfUS4zlA6bKKWxPpi8VMFuzycVkOrbKRHcLVVD47RGFjJik4Z52DFusebx2d4HvH1hmLehw3nBIKFokpBh1x2E0FzBVMxnJxDhWcskoOVbboKETyW2GMgFHVgRk1yBdaLBYSiGaMVK5DjIG1VZIJiZhxAKuHurh+3sWeftghx+XcpSiMjtlnXLMJm1EeG6CphfQn4S6LVCpasQEjUojRDcixEjEkUw2DbooiokT2KTVFA23Q7kj0xuP8dwCFFMOCTHDdN2k3lCZSMbw4iXcAGJhDi1R5dBijNGURtsN8ESHn9+8i7/57RTH6x029bXIa0n2zkkUsi3cWo5YKkJLlHFchZSUouk5xHSbgqGTEfIcq1eotzR6siFipFA2Q6JQ5OKhGCttjzN6Esw1LU6UTQZTBodLTXTN4/3nbOFrexc4crLO8Q9d9QdtkX78hX/Y0bNLfvd/f5v0n/iv/fONpw4zYyq03IAbNhT4x0fmSCoyoi/SllxiGmi+xLUb8rxiWzc3/uAoW7tVrMinKCRRUz4n1i1sNyJU2/SFIR05we4NS3TMAfQowXPTNlU74INXDvC9wwtUWiEWHnFR4aqRHA8vlrFdgUuHM4xkdO442OS6TWnunFplfV0gmQsYL3osNyMUUUKXlNOFjFAgFZOpuR6aFLElk+XxEyaxWMQZfTEeW2hSSATIaKzXQ7YPmSzX4yBb6F4CKW6y3pAZLYRU7ABDEnFMnYYd0peDtZKEHHeJhxq5TMBHd3dzsK3wtefnkCSBtueSJI6rtBFcHVnzqHdEXrO9m9uOl3nphi4eOtnk0rEkdxyq05X1COSQTlvhqo0Z7p2sElNEPNGmmITVZsiVwwWqXgk9yOOYKiXfZr7Tpk+L8aaze+n4Lr88XGOsS2TvYhunYbCpR+evL+nnh/vWefelfXz28QW292ncMzuHF7hUO3GUUCSydT5/4ygffeIkpaqAqAYEpsR1ozaPlCI6HYMzemIslT1CxSablJkpRfzLDWM8uVTm0cUFhNBgpSbieyIpQ8LyAnwPitmIQPRpNUHVFCQBKs2QfM5na5+JKDlMlWNsSuSZqto0HI9E3GdLphukgD2rFXJxD0NSGMoo+GFATujnd8fqiKGIhEjbDbh+Y4brdyR5bq3EofUWk6WAfjVN2fS4bkOO7X0KX94zjRM5pJsSJUFnU0+MkXSCd573X+5IH35omtmKTdMO6U7BuYNxfrK3RqiGfP/G7XzqsRmIBC4fyTOS1XnkVIP/cUkv3z+4ym8OV/jIVUP0JDRqVoAUCUxVLZY6Fj87VOKxt5zFd55dY75hk9EUYjqMFwx6Eypfe2aJZhvEuI8iRZQ6HmEIGzNxrhvrYku3wU8PlPjbSwd4521TrDodJCVgZ2+MliVwZNXCC0N2pG0OdlSSuoAUChiqgB1EtJ0Az4bXnd3LWiOg6blsKhrcsqOflZbNG392kvddMUi1E1A1fd56fg+PnGrwxGKVA2ttbD/E9SJ6MgpEkJFUBjMqd56qIAsCV41mWTbbLLVMmnWDT71gBFkQ+PZzq1y9Lc5aK+D5lSbT9TaFZIgiStQtgT/bNsjJaofzetNUTI/fza4Q4bFSg2wSNEmgZge8alDn8fWQUK1TaWdQpQBPsMgoBn2pGJf0d/OjfWuYNLlueJDDqxbVwCWlyHzk8hFars+GfByA/9i3wI8OrdKjGrRpkzKgmLIIhYBWJ85iE/KazuKSQDYrUHcCUrKCJZkUEwJVU2B7OsdgSiOhS/z08Aq5jE8UudhWnE5L5jdv3IIkwiceXCSlitTFGjOtFoFrsF4+LRb+mReM0KpU+O50h9fs6OGHR+ZYtywKcZGBbJPlpkJaVdgzp7Mpmebj1w7zkcdOcOPGXn5xYoEwClA1kzAM2ZYd4NFJB88JCQUwTYFbLsjSl1T5xv5Frhrq4q8vGOLxqTr7Vtv8bHKFtAY5Q+U12/u481iJFAY7R2V+frCCrjrUayrXbyrw5Oo6yyWBmCyRiIn8zQVDfPWJVYYKMsdrJr96zTYemK5xxUgaXZH4+D0LvGG7zGB/D8tNjw/eM8tsu42i+mxK5li1beqeyy07e/ntkRqv313gB8dn+I8X7Ga55jFe0PnoYzP0xDR2dSXZ1KVz47ePMd6t8KZzerh6PMff3j7LlqLOOcNJsnGZt/76JK/e0cVM06QrKfOOswZ52b/v5bd/ec7/kXnVH0tO9UdSD/tf8+0DS5zZp7EpbxB4MrKg8I8XnMVARqODy5F1j4TmkdFDpqouxyptgjBAiOBkxeb5BY++nE1fxqLitEBo058UiICFZgihTlJOULMkOjYMp2KkNIVH5qvgxxjPa9Q7ElEnycaiRtl1mSqpTGQMBEHCDwIG4gmCUKHUkMjJSbb3xHnJWQl2DhqYnsDRZRXTTDDb9KlWYgzqSZpRhXJLBgIW2zXseppSaBLJNkXD4PlZiZGUwWBPm0La42/OHUOI2rTv+iQ9F1xK3dKothXaQZ2cIdFmnb6ESsX22DASEgUSghmjEIN8qkHK8FlsWQh+jH2LESMZjVy+wWBO5uCsDk6W124Z4+mlJvm4wnePHmXvtMZiJY7rCzx4NI7uZ1hpBuQNg7rjc+VwgSCUKXVEzi72UWqGXDJQ4NRKkZ8e8JGjBGvtgOFcg6GUTire4uINHn9xfi8XjaaIlDZD/Q4LpkUUChRjMqrapuU7JFWVejuOLzUotT1+eagMwK6+BNsLGS7aoBGgcNeBPHXXQUZgU0HCdVVWWzJ1t4MohigY2E4CQ1SwvICm6/HOC3qpmj5TFZtzR2O89bYpfnGgwiu3F1GEkKFCiabtY/o2/YUygmyRSDbY0lMhljD5t5vHqFbTfPiaIS6fyGCoCuVqgh8fKhFFAu85b4JrBnKM5nRqgQ9CSH+2TsNv8A+PH+Wh2TLFXAdRDtiz3Gau4eA042xIxlEijcWGQ7USZ7khsX1sBdNv8/6HThLgsdpy+MXxeRKpKtmEiyBAGEIUCogRLJYTrNXjqHJEWveZq7nM1R2+cv0O7nn9DgS5RneuxXDX/9MmvGzbXDaeQRZiNCyVpKzzzouKrNR8zu/LUDcl5qvghh5SBF4gkdQFLtyksr3XJK6onN0b552/mOH9d81yvNThH+6dJyvEaXagHTo0sJlcFejXB5laSPB35w/xlRdt4N6pCg3b465TK/TnbB6bL2OLC9Qtj7m6Tz4dMFoU+dWREksVhZmajxe4CIFEj5RmueHS8XyiKOKFm3PcfvN5dMdUOkGH+VUDyY9BJDFTc1lr6GzqEvjpiTkafpmxfIAoSMwHs2QMkb+9qJ+XfPsof3ffSWLxgCl/iV/esok3XJTBxkIT0tTtiKF8hbQm4Ec2BklOzCUQZYu2FUeW2rzizA5KrEEs0LlsIo0dBuxfb2AKPhtzMlN1m6F0xN5FSCoGVTNkIJliuubSE09wrGyRkE8/s+Qlcb2QTV0ysYyFFOnEDZuW4PK6LeOsVEO6sz4l26U3rjBXdpGRef8//BMvyLS4cWCcch1ajTSltspSp8MFw02SyUUM1cL3BaYqNjsGNNK6jOlEpIQEazUF3zdQoy6ajkBOi7Ehq+FHAduLChviQ7xkUzcbcjrnjgkEqkNSSDGaStBwIgQ3wVUbBEIxQJNhPJnk0w+ucNOWHi4bKHDj+DhIAWN5kR4jjxEX8DyJgpKhoCdAOm3jHBey1E2RZxZMUlpEV0Km1PGZrtuEeMQVqApHeHa1xh2T67z73DGmax5PLdUIQogHKT58zyIrVcin//DKhv/TxvUPtf7EHxe/PrnGYyc7ZInx4HSDmCTTiSykVIXeVEhMEGk2ZX72bIO333GcbEzE10u8eccADc/jyJpF04xwQp+0aiALInPlkGcPjXJedx9PLjZoBgHFhMLtJ9dZaDk4oksxpmKaIr86XEMIRXb3JbBdgR/vL3PL7i7umC5RqsD4YIgkB+yfVdBFDVkUKLVCMppITBVYK4korsZVQ900afC2i/O8bleRWbtKb0og8lRi8Rpb+31qroSgWBheglIjotZWKMQlFhoCuqCQT7okUk2GigEVK8BI+OTjEdmuCiOpBJIIJ0omQShTb0MiiFN1XQxUVOO0tt2GLpXhTIyRpM5NW7r4+LXD3HeqTjrr4EkBzY5IIhaRMwQ2J5M0rYChNITYJGSD+4+b9MXyPDnb5sLRFAMFk5FsSJM23z08w1een6ETmjwx1+Cc3jzbhzTecWEP5Y7Hg8cbvPU3J3houspdpxYQJYuOE8MPIjqOwHBR4ZGZGrWmgBkGuAGMdEscq1hIgoJhRFyzw2LnlmUU3eTiDS5Xbw348oEjHKtXOLs/iY2Jqook41BzHbrzLptHTQK5Q7OtoKsajhdy9SaD3eMS2weaDOVLCIhsKXqcatRYaYfkpTSb4/38frbNkcoqiqASlw3O7Inx/CmPR05qPLpUJlewSHdV8Yw612xLsHlI5rdTK8iRwqmKRUKUyKV9tnTpHF0x2VbI8KKhUf758l2M5w1iigi+RCCEhP/vO++HTtW5fizPdeN5crqCisIvjpe59YIc5w3Bh+6ewRU6pDMOB6s1fnR4maPtVV7xy/3cvLmLV+w2KCYlZhs2XhSy2HT4/t5V7j1ZQ4hEnl+qc/vREsfKHSZbVQ6s1zm82qE3qfGKbUUycYlv37gVXREQpIBczGG+1eaO6SU+/cQ0h1ZMHpysUXYcrMil4wlULfAciaSkkjQCBMnnbedkqNdFRM2hJxbn/RdO8J7zx7lyosBPDy8z2V4imagT4fPMUp2MrvCj12wmY0jctD3PqarJ9/ascflEmg9dPsp1w12IkUhME6naNisti9WgxL5qle50iK4F3HeqxnTV54qhXj5y3SBn9MWYKOp84oXDWL7PAycblBoBWUPBdBReON5HfyzOD/evcf+xBt88NMUvDq9Rsx28MKQnJdNsydihz0Aq4nitScXzUUSDbKKFaYtszmRpOSGjqQS3nyiz0HRxrQQPnmrxrzdO8KObt3Hz1i7e/utJ/v7BSbwgYK3l8IPnS7xgvMC3X7aF6yYKOL7MfE1FQqaQrrOxK6DtW1y9PUnbiSgaKlXLIympVDoSF/bmUBSBl+8o8Oaze+hNGtQ6Et1xg0TS5rIdIbefmue9d8wReNCf1tk75xI5MRYrkEwItO2QLzy8zERB5z0XDvOVZ5Y5thqQ0VTSsTamq9KdiIjpTc4fs1gLazyzXCcdpPjCE4uMpBUato+MTNaAh2arbO+O0XIiAg/yWYH+pMFdpyrQ1vnViVX+7Zl5PnDfHL86UOO60Tzlhshc3eXHz69zpNzh2ZXGackFHKotlWRC5NcnSzQ9j+6CQCcKKHsdHp2r8rM3bOSl2wqoUsDf3jvJsXWTLz+zzI+fL7OzL4YswY+PLPKOX53i9WcWySsGMdHgcKlDyXTRI4XvHVhlqmLzpadWGNHy/PV9R9jUbbBQd7ioO8sj82Xee9csB9c6fOKGIUIErh4/rYP3mRcN89N9Fd5x2zQJWeDiiRg1z6UQV9i33OLwWocfvX7jHzwu/imn+u/xR91RdMk378DT4mQNH9Xt4vPXb2JyzeGj987R2xcQ03001abUMigmQvwQ1isSjbpKLgtGzETW2wShwO4Bj7LlUjWhVE+T1DQOrvj0Zkx64gbrbYnFRkBXXKA/IfPMXEQ65VIQ0iy1XfS4RV9Cw/Uhlawghgk2FtvcdzLOpoKA2+xizymHO962iY8+cZx/vGwT733oAM22xmzVZySl86UbJnjtD08y1iPgaW3qJYOejMSq5ZE2ZAwtoOlExAUd1/PIZB0ycpZXbY/zzx/+R8Szt1OLX0Q85pFL1TCENEdLIecNRiBYtO0kXgCqGDK7lKJhu2wcclnpmOhOF3Mth+GcRrMTsaEn4HjFJy0YTC75bB+TmFkJuOvW7WRjEu/41SRloUKzaSCIEBAxVogwA5uUJjFZ9UlJBilF5drNOvcca3NyXuQ7r53gq3sWWTUb3LBzlZNlhbF8h1Kjm7mawImVGFu6BXxs6nZE6McwbYhckVgiQFcCZMVnZSXOpuE2qizzhSt3Y/vwmQcXmW90SGsrNJUYIQJBKCKKEfN1n229LnFFRJVD4mqEFXQ4tNDNcFeVuVIBQoXetEunIxO4OjsGYtx1rMoTb93F5d/dh67BTbunObTQS9ORsB2VQtJFllxiiogkugymMuyf1sioMV64Ocehao2Hp2vEVZBll5hisCGe57W7ihTiCp/d8xyrbZt9c6c7mlKazC1npfnJ/jKyEaAJCoNphd3FAnuWTBY7Frl0HcsHu1ag1g7QkwHdcYVePY4pLlPIWpxYTXDN8BCv2FzknT98jhecM8C3n6uzqVvmRdtTVJoCM3UPQ4W37OrnruMVvvzcIqIACUWiPxHjpm0FXrAp95/n7ni5w98/fIxvvmA7f37fs9TbKmd1FYgUjyOrHRRZQAsUJN3DsWUGsjLzDQ8xEunSNfqTKm87t49j9Spff26FjKKzazDkyTkTURRIxkzCUGFqWePaoW7eeG6Rb+5b4tGlRQpSnFUrJBHp+IJPX8FB0TqEEZTbEh1HohhXiekOuiQwuRLntlfs5EcH1yjEFeKyxF3Hq3zm+hE+8rs9nLJVyiUJLeWxua/O1HIBw/AY6KpSbuvkY5BPmgiCxbGlPsxmjB++chsV2+Jf9x7FrORYbtlYnk+hv4IandblUGWPgazNc3M61222Mb2IUkvAsmIEaoPeeJKU3mLvZDdNM0RKOmh+DEFzyekKdbfDhq6QiltnR1HGp4PjFKjZAXFZoeV1CP0kDx5SufHsDsttBy3Is9SOGMwIuJFDlx7n8oFBvrxnnt5kxLOTIedsCjDDJlf1bOZ7z1UxrCb9R37Ed7/5df7j4eNEyQz3zS2wdbBJx+/QE9M5vA5r9TgvGRnhx6dm+dszNqPKIu9++CRjcYPunjWmSjq7+wVCsY7jS8ikScQqPHGsl4u6uzjcXkRWInri6mlB63wb286y1gkYLjRotbPUGxK+ahH3k/z0NVvIGDJeEPHW3xzgyEqIr1lkdYliAhbaFooUsDnTxULL5sR6QFwPuXljF3cd6CBnOgSewtYeCASL1bpB3vDQiNEl5vnnl4zywYdOcbLa4gUTBe4+2mK6YlKMK8jKOr+95do/6M3XEzf+YTuKLr7j//7brz/xX/vnwi89xtsv3Iggh3xr7yqBD12ZAE13mVyVKMRVLh9I8fv5Ns3AQ5BdNhc0VpZiLNgmGgLFXEQgQqMdcl5XjSU7x8u3D/LNA6t8+boNfPzBeRbaJr542vba9sCObEJfI6Oedh3CFilkRXb3ZDk447HmWvTkbCyxQ6MdPz0ihUmzpTNagIV1hQsGk5RMj9mKiy14XNCXZLrusVrzuHZbnLtP1SmkHYwwzprjkI2FKKJKw/boTUU4nsju0RVWKnlmayrZuIMQaay1I8RQRYoiZMWmP1EgLii8cbPKB55o0HEtDFGjEroUkgKqauMFIlGosNYSGEnpvHRjF4/ONDhc6mDoHlZbxYsCBoo+5XbEX5w1zlVjad52xyRLNZe+fEAqXWdneoitxRwPzVR5fr3GRMFlqRkSlw1EuUNcUVhuRPh2kprjk5FkRCS++OJxOl7AYFrju/tW+dmhNQbyEh3XR7INYqpIyXZJqhItKyRfcAkJadZVtiYtTpkqQ70mpqNS7cD5Iy2mF3Ms1lQ+ft0w5w4ladgBf/O7ScIQXrI9x4PzKyw1HVqmRDomkIk5+Likox529MX5zeQKXUmbV+xaZbYS4/dzCYg0bEtHizSu3JDh9iNrDBQtDN2l4whEXh6rI9JwfETJwYoCIhTigowsR7hhALJDWgfXVRhKJmi0JFZNl9tevRVVFnlkssFDMyXsTpmTpkFejBEkmlwz2M+yZTJZtkirKp+7bgN+EDFbM/mLuyZJxAKGUwmqjQgrsulKRVw+lmH/UkSvZpCKCezqi2G5EXUrpJhQ2NWXYL5h8/Ojy1iBw+OTNoIUEgUymhbRn9QZzIoYaogSKXSpKV59Ztd/nsO/u2MaT/RYbbtkdJXdvSm64yply2W54zBfdWhZAV948TgZXebRuTp3TS6hOw2WBZWWI2LZIDkqf3/lAHcdbjDTbuNLEaEf4joBAhIv3J6l6visVAMWGg4hEaHk88XrNpHWZfqTp8d3vvPMOm3X50CjSst1SagS/3rNNm793QFEQcD2REw74us3bOT5VZN10+KBmSo/eul2/u63c+wc0HlgrkTVDOjNhCTlOJ++cpw33nEMAhFB8PAEj+3JIq3A58NXjvD9A8scrlRouybjSsS8H2OtCcW0iCIGKIrPQCrGTM2m3JI5pzfNXMOkX0/wkatH0GWRp5fq/PRwiZl6m/6UwmQ54Kxigj1rTb79ki14QcSHHj9MUnOJRJ+0JpBPNpgtFVmoaHzikgmycZl3/XoaIrhqW5KqGXDVWJZvP7/C+y4doun4/OND87xyZ4FV0+G9Fw7xnT2r/Pi5Cu++op+rN6X50MPHeXyuTW/c4O1ntLY8IwABAABJREFU9/Od/QuUWxFv32xw25TPcsdluBjSDFwy6umClefF2FbU2bfaZDCZ5fFpn1vPGORUvclD03W293ustiKKCZ+mpTNdlshpCi03RBB98obK63f284Pn1wgkl/lSRCEl8RdnD7CxaPDe+06BYrFclenWNDqRj+8JXDgO3UmNmXWZmZqJE0Z0QpekpPCKLUXunF3m2y86g7yhcuepFb7yzAI3bMgzVQs4pyfJ/lkL1avzUMuhy1DoF7N86eZRvvbMMvdNVbh0KMtjCw3qbZ/hosQrt/Tw2ceWSOkwnDM4UTZxWzKfuGGArzyzzFzJZ6RP5oXjBVq+x2Ba56bNXTwwWWXvYod9pRbnjxjEFYmDqxZ7Z2wCV+CXrxukv5j7PzKv+mPJqf6oC0U3/fA+8vkUZdNntSHz6Ss3Uq4F/MveadaaAT1dbbr1BC0/JBmr43oSXrWXjg1t1WK4YDKUUQlwGe+qM19NEgZxlpvgBwJCpNKdtmk6IUEAaV2h7Xn4YYQiqNQ6CuV2hC5F9GQgklqkDJ+UojNbi6i2FfqSCm/c2cvXnl9lOGlQdV0qtslQ2sAKm9ieiNPKcu14jlvO7OHTDy5iKBKvOzvP+++ZoS62yBkCpZpMPNlEihKcXAkZ6goxPYGK7XFub8DaoUna2d20/QikgGt2Nmk4HmYnjx05yKJE0xaQJQcihaNzEsM5hZ7uBTy3h7laSE6N0QlcsrJMJWzToys8c0Li7A0Rp6ohf3P2KK87s5t7pkp89PFJUpFBxfVIawopIyCVsIkiGTOsYFUGqHdCclmIGS7TayLffekWPvTINF4U0Ju1WLdbnNkbokgCkyWD0NeZX5HxIhjvjWh7HqfKEYPJGLoUMdf0yKsahVSA7TtMlgUmshpnd+W5YiLNRx6aIpe12aRbzNlJViwPy4cNBRFJ9ElrPtmkyZE1mcG0yGpTp9VKkIo7dLwI25VZqkVcPpTjubkWXYXTs+yh4HOivgxejpPVNoVESCA4RIFKxw1R5ZCmpeOGNtt6fPZMJlDUCDWIYQoWMVkhmXCRkXjpxBB7VxtoWshrtg7ywPQ6903VaLckNB00OSSbjjiyanLOcMDcahzVi7N7MME7LuxGk0W+vGeBA+s1dnWnuO/EabHnpCGSVUWSsQadSGC5YeDWNEw74JsvyjE00MNdU0s8sbLENaP93Dg+wpPzdT716Bw/fNlWPvXYHNOtOo4rI6kuigS9KYmEn+PdlwxwaMXk0vEUz6+0CMKAz/9+mmpb5IKhNMW0yGMnG1wylGP/ikmgNVhpC+iyQORLmIFLZGsIaogbhlw5lOf9lwxy848O40oBti2SlBXQO3QlBao1lW3ZDCcrJt1xlZmKTVfRoubZ+J0Ey45Dt6Ex1N3EduP4ASD4qEpAKlYm8LJUaxk+d91GRrL6f343apbHvz+9xHp9npkIRCEiJsbJJDtIcpW2VaAnEbB3QWWtJnP2aEBcd5iuu8hRirO6unjPBaPc9OMjXDGa5t7JCvl8G1USqZgeupjCdiNWTYsXbevQdAJG8wEtW+HwesiuHjiyLtFqFgg8FdNziKsRJ5YjzhyX6IQmMRKEcpMNRZfFpkNMirOhZwUhTDJX19BElbrt0+wkmW843HxGk9lmB7Pdz5F1gZG4zkX93bxkcxe/X1/mrqkl/ECkYlnEggxlv45k5WgJPv92/Qb2PfYAe/w+zt5a4FBziVzMRpdU3MjFd1N06z0cL5lkFYNHjrfpjav09QccKjcoxGCkYNFwLGKSQcVU6Hg2GVXH8WHPtM6mnEAqaxFJTWwrSxgq2DRptZMMpEX2z4CeCLhpopv3XDJMQpXZu1biE48tc3LVRQ0ldm1waNgu1Y5GVxz88LQm2HkDBvdOVWk7cNlIlnIZZu0GomoS4ZM0HAIvxbF5CSlu0VxJ8O7rurhtco7pSkhMgT/fMcYvjq1SqUMiXuXxt/2pUPQn/vfzP/fPHXtn2TZc5M9+eZS8ouAIAZHikTAsPCfBLbty/ODwMoEfYUgxelJwZDbiI1eO8flnFlBUh7IJXSmbgibTL7d5omLgOgY/e/l2jpVMvvX8KpvzOs+v1+mEDo4P3XGRsinQaAlkJZm+XoeSGZGJhVjNJLZkkk22yagqZVNCkEziokbD0nBCHznUOLsvhSopzK37+FHIdeM5fnx0lfP6kzxdWaIQd2iZMeLxOjI65ZZGb7aGbaeYr6n0Ztps7fGomAFN28DzVGbLCjEUstkWhbSNYA3imSoWNn+5Pc6da1VOrgfMLKlkchGRZKPLESkjxApcXDvFSE5DcQ1W6z7vuryHf7h/loYZEI8J+ELERT0ZXrWzm32LHVK6xM+OrDHfsulNwnodMlko12Eg5+OJEdePdfGTw2VeOJHmvukqmzJp5swaCVlhrgpJQUWQRL524yhxWePVPzpOLBbRtALec+kgFw5muPdEjR8fWGPNdujOSpiex0TB4PKBPA8ePYGelVlpyVTqKn1ZuHCgyFvO7GPvcpuv7l3gS9dtZN9Sh5brcbDc4ki5iR22CMMYKTVgU18JVYQDC0USusSXrz2Dv7rvKIbcpOkFyGpA6OloSsB6G2Q3S38hQFSqlNpgeSKGrNCbSDBbDVgsR6RTPpGnkEuKDMY1Hj3q8oaLsxxrzpMzTFbrKssNBSeClCLwV+cPcm5vP785XOZrzyzRpze5cvsYkSexpRjnnpM1Fps2g2mdTV0x7jpZZrHqIUmQ0OHsgSRjOZ1100ESHObcWaaXCpTqEt9/9UY+++xx1hqwMZ1CDGRUUeBD1w6xf6nNh+6b5VuvGOdDj52i0rHo0pPkYgILDR9NEulLSzRsn/WmhxoqvPn8Pi4byf7neXx4tsrvlyooocbFQxkuGE5xeKXDbfsrfOwFQwB88L4ZjpY6SFFEr9aioSoYmovri7RdsHwBx9TJpTw8XyYKBPqSMlt6JH7xvEMxK3Npb55cTGTeanBwzeTjV27g3ukyU1WbmzcXySoq956osqc5TRQpCGh0GQKzyxGm6iMGCjFJRJAD6pYAnkQmFpJK+qyVRURfp5ATTjuuGW3atk5PCiTJptQW6E6AIEj0pB3OL24GBM4opvjc01OEVBFbAYuhim8lQIjYMFhnbj1JKDsIgoAmioSiTU/cYLYk8u7zJzinP8nnn5lipRExnjG4Y2qFRKBTswISMYmYESIrAZLg051pIqBxYl0iGenI8RbVchbfkrlme5L7TtToTen88wtH+bvb5/jANf187P4FJAm64jKrLZfP3TDG/TNVbtiQxwp8Dq+YfHvvOikVmnKZuKSy2g44M9fNTKdFUpYoBA4HSjrEfGwnpDcf0nJ94pJC3YLzerIsmHW2devMlERSgsEbz+7i008doyN06E6KTJVV+tImnXaSlTbENZFrxvKcaKyxVlEQNYvlsspIQeDNZw7z5d+vMJjUeHyhRV/WR1dFOpaPEiTpzcl87YZNfPrpY0wuCJxomaQMKBgazbBCs5bmsy8cYXtXEk0SWek4vOPuoyB4/Nm2AYqxGJ9+fJZ3b0/yXFlhz1qbV23rZrJkMdvucKLWgUDi318ywQcfnuGvzu/j58dXuaSni288uYaveuiqiByJNFrw0esH+dLjS7ztvG5evKWAH0W88dfHyasKT8+3GetVOK8nTVqXeM3ObtpuwMt/fpgeXeOTl3azYaDr/8i86o8lp/ojaZz6X7PUsVhsWrx1+0aGkjHO6UvRwUcVRc4Z1hhKxXFDgaQKVw0NI9h5xEST4mCFc4Z9DC3k8LqNIDVZqGQ5vg77Vkyy8SZ+IHFozabu1jBkUBQPRVtFV2w0QaXjWawuQ5em4IcCy/WIkQxkdYm249KppEkqBrNlkX9+oowsCkw2G8xXIjKaynTNRhFDPMdgruJyuF4jrkl8/IXD3Lgty4l1m/3LHQqxAFFbx5M7JHWJildloACrZZkIl67yce755o/oG95GhMBNO/Js69M4vqZRbxaYWguY6GpTMn2SsSajeQtPaHPRJo+RgWUKRhY/8igaBuvliF5VY8Hq0JsQOL4A524MaXsBt54xyGt3FXl8vsKHHz1Ol6GRzK/To2oUNI9kzEcWApKxMqFVJF0o0ZsXsfyIjqPyvZu38LZfTQIRrh9xeFkmpyY4WVIptQw8N8lf7d6MFKqMZkVmV0Q2dne4dWcv14xkuWRTm5+/fCvdKQE3sIlEge294IQRd0yt8cmnpjlrROMlEwOstiLaHYO0obCtJyITt1lqe0xXVWYrGXriKmGoIeITiR3KZsBIV5WxnMMt27oZThn0ZyXO6UvxH89U+Pm+Bo+c1HDbOjOVCNOLcAOXalthuWwg+AXkMIYhKSyU0wiqSH8+JIhCtg7WCT2BtWbE8UWBZ5ebXDtW4FWb+3nf/VNMNZuomk1XLiIRD5DUkJbrkIoSbIiN4TkS1bDDI/NVZFHkuYUGl455nDdoMttaRZZDUpkWstrhQ5eP8/fnn8N7z9nJ567ayK9v3czj79pBLqEwWbK57Rmb2cUUX3u6xvX/cZiMovCxK8ZoeQGqLHHpUJGzutMkJJ1aR2LvTMTh+hpu4PCj/XMIgsDu3iRdMZ0L+4v0ZiT2rDR4aKpBrtDm/pVZqsIammahyRbr1YhsEu597dncelYvPQmBse4OC22T51daWAEkNRAUD1V1aXlQ6Yi8/8oe1EyZNa/NibLJmy8sEtMVJFHnzOEYF42G9Gd8Tq3o1K0QVauRjlt4rsbK6gApMUazJdCTUAHouAFzDRNNEvnFoSrDWYmeuEwYqKx3Aibnk0wtDeLYOg0rIJtw6E3L1E2Rtne6SCSHcN+xBm/+zXG+8IJx/sf5A7xqt0pfOiCVWGdjMUAQXSxXYEePhBm1qHTirDQM1tsKw6nTHTrdcQFV63DTzjg3TPTw5jPGOGNI5UMXb4BmhtnVCN9OsVbT6EloIJm07ATtwGY01yKKOsQUGUNz2D0IK62IlJRD0kuMZiSem4y4b7LGzd87huqm+LerzuXigSIX5kdZr8hc0j1Eww0ZShi0vYDP/PpBpIVn+eHxeSxHZL0t8vhMhOfp3HtUpqjGWG76PDxXpysLpnZa42c0rZGL+/g+pKUcESKKIJAUC3QcFVUWOWtQJZH2kUSRXiNHyogIo4ihRIakpuAEEWcNy5zZL9CbD2hYAe+9d4p/2XMMM7DYNQyFrhAzcOnOeGzsDphakVi3LBqOy2u29lPzGkyWbH6/WOUtF3axf8Xi0HJIIQ6OL9EOmkiaS58R46LtOpEgICGxq9ug30jyw31lAimkN6HxZ5sH/uAx6k+jZ3/i/x/WbY89y030UKEpthE0k95UhC7opOJN7l04RloRIYoxlIMuIUt/VuO7R1aoBx1W2j49ScgaIg3Pp9KRyKg6f3fRAG4Q8cWnlnjdtiIPT9fxcblyoJuRrIiDjWsK9Kg6atpkrgEDGR/HVhGUDqNdJiMZiU7gYLoBec0A2WW1FZJUFXTD4aHZGqcqTYYzKqbg8ZPJJWquy0+PrBMXBTqOTCxWJS4bWH5EKtYh8FPM12TyCYfepMBiQ6DaTlFuxZivChiBAYJPJIjgdbF30WQgK7PSdPnyU8tU2hKzTdg21qHlBoSRSH/OQVM7REECX3DxQ4dnl1vsHjd4cqZFx4pIqAqtdsRLxotcMp7muZUmr93dhSjDLbu76c+KCFqb/l6Xlh2RjgkM5xK8ZnyUV28e4rOXb+b60X4uHihSdR26YhKBYNGbFLFCn2K2w/sfPEXWkPnglYO0zZC3nd/DCzed/tN192QFC4++rEDMqJFPwHo9oGJb9MYFao6HiEwqHrGwIjEQ16nbPrcfqfD5qzeQNRSenGpx58kyTy9XKHcCBC+Ja4osNkQEZIJIoSdXxQtN/uWxBZJekvMKG6i2UkhoxI0OimLRnwnZNNik7TeZrwk0zTgiMUxX5FilTqnjM9Tlk9EhFYNdPQaTJZ9sPuCplWXajsB8TWFDT4kNvR3O6INrNtn8fmWJw5U1rt6Q4ZYzuzFEhV8dK7F/rcXjcw0sJ2Sh4jFR1DAFk76UQkqVKBoqb9zVx0gqzveeXeeJuSavP2OY1VqMb9y4k2IBZuoWRUMlcEXaTsQFI0leeXaeLz47w1he583n9dCdMHAtGdvUaHdgvQFndqXZXoxxdl+Gv9w9wsu3Frl4PMZXn1yl5fi8645JPvzwFAeWTPZMuzw6U+PnJxb41O+P84vjy2zs/S+h3o9cOUyzEXLBcJrtPSKqJNCbcUjHAjRZJCbq3LSxQLMj0p8W0XSP5WZIrRHjRZvyeLbA284v8vhCk7eeNUK/EeMzD89zYXeBf756Iz/dV+HYmkXV9Qn9LE1TwfRMllsekiGihxpBAEaiSSpu8/KtGV68Kcd6TSTAZaTPIdfdZrbZIZ0ySagqxaRLw3EwXZnRfMRwoc5AocLkUoJvPL/IZ5+a4Vt7l7lhoshMVUCVBDwzxrrlI8gCW9PjVFoghAodS2GlLuKYSdbaIR+7fJwjpQ6KJPK+Czfw4okufje1Tn8sznXjXfRlRBKJJmYQEBKQjHXIxAREuUlfSqAeOJTLWewgIpIiOm2B9102hC6JiILAS87IsHelzXdfvYGv3zzBP79ojO+8chPHqy3uPlrnuaUW0zWTu+bmsc2QmbqNVU1jeR5pTeXpKZOruns5tRYwkdP58Ws3cEY+RXfOo+P5jORCHMdHCxUeO9VmdlWmbPls6koSiiH3TNaZX1eJKxodz2cgHVDtGOixNheNaAwkY9xzqsJlfQNk0iGeo7Ft0OTUEvzw8AwmNocbJV4wkeEjl48hSRbnDuQoxBV2FON87PEj7FtpMu+0uGagwFBO4NYzu9GiFMWuNu97cBrLC3jDT07ymYcWOasnwY6uFL/c10SORL714k389niVP7+gh7ed28umbp0PXN2PFImc1ZvijOGAmC7w7gsGWGn5fPm6rbxmV5Ez+mNcNZzjBzdvpuq7xPSI+2ZWuWJDmrnGaedgVRL5x8tGeXbGpC+pMhaLE0fm9mMVpisWMVliQkty6USKnx9f+4PHxT/lVP89/kh+5v+aPj3Ji0eH+eJTy5ysdrjuR/u4/cQabUyOVFu0vBaGLBDXPHblB1jqnJ65TesSum6S0QW25OOIYZKZisGGvMTZ/RqyJIPgcd2mgKwWJxQ71DoSnt1F24s4tBxQL2cxYhKznRaWBZt7HdpRBV1fxenEmW+71P0KIwWH9Y7DYqdGIRbQNkP2LdskBYm5usyJqkV/XuI9548BEEURt/50CkmEXWMCxewqSpgkF5OxPJ8N2SQIEcM9AePxJsd+9hXOuul1ePhkcg59aYll0+S8nn6eWfQY6nJ5YipOLmZzxUSDXKLF1RM+508s0p80OFmSUQWZU+seMU3kY1du4DNXbGN2Lcat5/XjCiIXDXXRbSi89fZjvO+RYwymVCK5RkLIIcZMNgybJONVOp5IvZPEEVzicoJMXGZzUcMn4H13LNBbiPCxEQUBTQKXDj0JjY4rMlsJ+MiDcyTyTYYGy4wUPZ46mufPdvbzNxcN8zdnXsJgKkYUyEhhjO5MFUHpoMsiAxmRtuswWWnxo8Pr7ChkGM1rlNo+DcfBdlW6tDi96YBC0qThmriBTyoWMFb06U5IHJ3r4eRCip/sreLi80/XjiApFoovIgkyGQxONUzOGvTpTZ+eYe5KRBQTIXFJRIwiPDOO60nkDNCQUOUI106ST4Uovk5vUsEOA1RFZHNXgsiS2LfSYXHe4AMXjWGaAr4XoogwkpfZv9JG1EMUw6TqOsxVbf7lqQXuPLVEPF5la3+VwWKTtYbMP12yk7+59xQ/OVziI49P8smnjvHtPcu0bB/bC5mqWrzvBb285oxuumICvuIS00V2DyTJGwqv39VNvxFH1yCKJAopB9sSKK/rfP6RFd5xeQaAzz+1wHvvnGV23aNdk6l0PKywwXJFRxcVRgsWoiCzsQhD3QEqCvdN1zjVajOcVShoXciKxx3Ti3TnbCa6LS6YaNDf7fCaHWn+5rwBfnpsgelaSDEtksx4fOfgHCtth7rpU7FcZmoQT9iMdrukdQmrnUWKRBAcZtfhL87exh1v2oauiNx9rMqN3z/CZ34/xZrp8OKdGfQwyeKqRNkMSKgSCV1EkNu8essg9VofrXaSmtehO2PRn5IZK67T17VGfw4sN6InofKpJ+ZwnBSrDYV6s4/ZqsjKqspoTqHt+ehinEKiQ8u3kASNjhuQkOOYfsj0usbjiyv8dnKVBxZXqYUd3v/QKWabLv949SjJuIsbyViuQndcQpcjZEHACWCxaTBXFbE9ATcIyOgqlh8hB120PIvLdsAnrx1muCfgg3fPsVj3eceZE3z4ylH+5cYxPnHtBl63O8tF4zKjGY2zb/pzZqen2JqWCQQHy9HY3A17Fn229QX89NRRJlc8tvbaRLEV6p0QTYbpRpNSS2W5ofHMskfbVpkpaxxYdchqcRqmzvGKRcs0SGg2B9cddNlHUyKWzDq27+IHMjOdFvNNk32rJV7w7UPctqfGwlqcmBqwaNaQ1BqKoKJrTVDWGemxyQVJsqkqMUXmFRtHOaNfoWlH/Mf+BV67s8COXpUgClBEgZG0ys4hj3bU5umlBnIkEoUis60m060mVughuypVx+cV23v+N0azP/En/r958cY8L9taJJf3KcZVJEHGiywibLoSMnKU5FRFYLkWUKpoPL/SpuRYTJUt3rRzkJ09MaaXBabL0J0IEZGRFAtN9fjIwzO8YVeRj96zhBDB5mSBbMrGDl2uHhgkLmqsuDbNtsZwmtNdtKLFUJePKAhULJ/IN0jrClXHYqGUYFMxwgkt2pZEwZCp+i0eWlnHCR0qlouPx1BGYb4pYPsRKinWOj6mLZPRZdzIYijvIgkyJ9YV1qpZ5is65ZbI9myBju+hxX125os0LIFd/QpvPLtIf07kuk1ZZlYFdvTaoHbIpx3GiyYVMyInD2DoTXpTAcsdm6QBHd/n9hNrxDVoOS65uMQvjq7xrb1LhA7866MrJGSJrz61xFLLw3Q0qqZIXPN54aYcnqlxct3BC0I+cP80//bcPOf0pHEsmRePjZKUUqw2I87qV7iwZ5BXbRngZMXiJycXefvF3Rxds3hgqkZcFRE1H80IaNoCOnHyRkCPEcOIdJZWNdr1LJ6j8paz+rlsU5LrNmRZrDvERZmsLrPadLlxR5bAEtmYj3FZf45KK0RQI7KGxJOnshxfjVNr9HBGZoi7jjW4YiJFT0rjsoEMK42ApBaxtWjTlezw5KzIUjVGTNaQ5ZC6FdByRLrjAiMFl0j0kLU2g8V1FEGgkJQQ5IBKO2KhBgurSR45Mc54bDNv3bGT/Us6ZrTK556a5A0/O8nP9pVRpBBN8jla6XDvVJX9y22+99oJIgEOr9n4kselmxK8/aIeuhMqP3l+nV1DSa4YKvCZZ0+Q0uB/3LcPPwpYatvoJBAEkUrH57aD63zggVM8s9ggpcvctLUAQCeyyGYtGq7HQELnyLJFUtR4ycZufr9Q595TdWRR52ev20JClfjEtaO8/ax+ugwVR3TRYw5OS+Elo0NcOpQnJknMVm0AVFnk9jdv5eiqyWId1joRU6U4dVMBJMIAfr/c5K/OHeVL1+5kIpsipklcuSFDX1LnRZvyqIrEF24YI2coDBUllus+X3l6kU8/NkdERM31eNu5PVw+kGc0niJwUtQaGuWKgN2W2N6VJPTTrNZVHjhu8vxak/5igG3G2ZTp55bto5w/IpI2fOKxOm4oIWNQ78isd0JWG2nm1rMstwXyKZPhLDy3XOYLz8yS0DTqlkzNCygYMk7gsq+8ynsuHSQf03BdkZwhYtLh7GI327rSvHV3HwCrbYtv7J8jJRlcM57jQKvEy3d1kdAl0npAxxTQRIX5eoBEnBCbfFxEVFwQIBcXOLTSYTRr8PWbN5A1ZB482eR3J6uIAtx2oMQrf36EsunxT/cv4ksWH3t4ji8+ucSReRE5bpNM+cQyJlGk0XFDrtghcss5eTZkDZ5crPOPj87z3JyJZSkokkTV8hks+jiRw0heR9dDji8FXD6a4FPXj/GaXV3oyYDVlkDOELEDF1UUKTfjnKzW6UuJWLbAdw8t8aqtBeKGSNlUKRbg8LREXInoSwucNFdPF3ajOJM1iyvG0yy2HEbyPmIooboG5wwm+ejFW/mPvatMlXwaLZWEKPLnv5jmnMEEbWrsn25xcK3Bi7dk+PTDC3ghvGx7gS/tOck1E1l29SUQRZFuQ+fVW3r4wEVb+dRjM3z8iWkeW1qnaft8+HdzXD6e4YmFJo4Pt79qB2+7oJeD6x1yyYgTJZNrvnOQhYbNaF7nmokst712M/903QgPnWrwdxcN8L67Z1EkgZ19cdq2zZMnGv+bouef+J/8UReKjqwGfO3pEorqMpDUEASwsSkaMXYVY2R0A9MPWK4k+NaBWUQpYrpuU7M8ZGQWGgH3HIHHTySJKSHxeImnZqHc1FFFGU1vMF8XkMM0vhfnxLKBY2YZjCcJBbhoNMF7zx+nJ6mw2lKI0UVcypPJdbhkq825Ayqq5jGYh919MQrpDpfvqHJGPkm9I5OQFbbkUlw72sUP9pU4VbEQBIG7376F/ZUaTS8gCrpouSpRFNKXVNH1Bn0ZGyeI2Lsos+l1H2ElFJBlj7Ylc2ZvimuHe/iLs4bYlkkyuRpHcAy2dodIkk93XKcrUSbwDdbNDlt7W1g06M8G1Fohf/W7U4xlNe5+/U7+4rxBvnj1di7oLvBvTy3RklboTYrEjA59iRhVO6IvI7DcakMYJxBsktrp9lU39OlLhzx0xOQVm7r5wk2DXDCUZXe+m6W6x+beDqNZAVluo8kRGSNg1e4wkBWotgwivU5CFzm03uEbe5Z4eqFBQEgmIeDIbcIwAUEcQbIJ5CYbe30Smozte5yoVzlR6ZBSNFqNDAsrSd62cwNX9o8iBBmyeozuZMiW7hp+ZNF0A9594QApTeRvr+5mNB3jHx8/zrF1i6F+eOcFPbzhvBwbh1YY7SpRbofsO5EkkiyUeIehrMrMiocn29TbAZ+/aivzazrvPHeA9aqKaYFvS+zuS/LiTXmGUhoHVzps7oqDr9HT7/KbUys0vIC/umAIP4CkoXL+cBxD9xnKhWxOxelOKARCiOlFeGFId6JDQvXRwxiyKILg8tRyif6syWDO4on5Kl94do7bDq/xgwPLfHPvEk3bZ3M8jyGopFQJgJgq8fipFst1h929SZrNCMGXuXhzio9cN8Rky+Z7e00OrrU4tyfNt1+5gZfvzHHOaJyutIhrxwlDGSnQmZwvYjoSpqvwqq291MyQ+yerXDqU5fyhFAdW27TDMkfXbE6uSLQ8j+WmyJWDA/zySIMfH53jxLLM3KqE7Qg4psjZwyGyKJNUdNbrEYYsY4g6suwRRC4oTeq2TFIXmOgROLBsosmnP43VTsB7Lx7kq9fvoGpb3D+3SiOskoxJFBUNXTUJBBvLSfLdQ4uU2zaVVsTWnghBgJoV0jCTRGEKI1Hj3DGfXx6ocMVwhl8cW8X1Yiw3otMjFJrMS0b7uHq4l1LbYKmp0m7nKJkdHC9Ox1GYL6foTwqsWy26sgGHS20MMU7TjhjrUTh/JMFSWWO1ojK5puN4KrockVA0Oq5EVzxA0yzcAAIcdKVDJuYhSQ7nDAVIapNv7D/Fcj3kxrN03vDjkzQsD0EQOGcoyfvvneHyoQLHSxYff/o52q7Lthe9kqm9z9ClCCQ0iEKRDbkEbVcgrcXY3iewUhYZTKbIpnxqnYixTBzT86lbEpvzKtPViLjuc8WEx4EVl7fvGmFDLMP7Lxzj1FqMc/pP6ydVrADD7cJpGay3AsYyOiM5jZbvMd7nc9FWAVmM6HQUxtNp0mqSgAAxTNJtZEgYAQ0THNvg/ukKPztYpel3kEKRk9U6Pz5aYrbVwnQkHF/iSMnBDFtkVYUNeZGvPbuMZYsMJeNszqZJaDJWECGJ8NB0/Q8fpKQ/8PoTf1Tcc6LO+x84SccPWCgJmE6AGOmoskzH9am3BfKaTndc4fC8gx2GfPjiCW45s5s3ndHPcklktDdke5/HUjvEDXwKms69kx1mWk0+/9Q8+YyAEqlcNJLmzv02F/b0sWi2kNMt+lIysiRwqhrSsXTyCZmaFVBtSwhhAjuIWGkG2M08Q3kbQWlgSAqyILNeFtmWy9OXEul0AjKyyEQyRcs3iUsicVnl+RM6rVqafDzkVFlECA08T2B5XUYMDdZKCm5N4axCClFzKOQ9olDkvukKgmgyb9X5qwcPcOOmIvefqoPic6oqsVDKoIgC6+2QsUQfgrZMXJGZXNYpl+NcMpjl+VmTuCbREl0unkjTCgIKaVgph/z6WJmbzsjyzHIdW3LJGAG6GhBTXWQ54reTq2zt0Xnf1QP8+/5FVAn+7rxhCoZKqx1y18wpZpsWGU3huZmQQysmX3pkiffcPcXiIvzs8DqrLZfLRtL8YN8SrtikP2fSl7aYWdQR3QxzZpvzhuNctznLNUNFrhkp8PWn1nl4tsZf3n2S9z18igPrLf750SXe8MvjfPX3y2SSMkcXfB451iFtyMhSgCSbiIJMyzQomS73nKpx1YY0123Oct5Qgmdmbbake9iWHWW1Dd0Ji6FMRDFt4WNjuZDSRYrxiJp5+v23bInAj1HvJNm3VuLIok3HDUnFImTfYDwf51Vbi+yft/jgfXOcnE5xaHaYLq3IeFHDSLrEDI9Xn5Hjlp29/PAVm3nbZQUUSeTV27v57NUbuXGsj+fmOjw4V+LHx5d5y/m9fOKaUfozKm/dsRGFHHEVhjISogA9SY24KjGRN8jqMlf0d1Eqi7zlN8cJw4iX/eQwQRhSMAw290s4sosreCw0HY6tm6R1jTN70ty6uxcAQRBI6jLdCRXTDrmsu4uQiMmWyZNzDX6wp8S3n1vlk/cv8uR0E4CkJvPNl23k1l1DDIoZrHqMuTWNpiXg+gJNK+BnB0o8Od3i7y8c56OXjvDhB6a5f6bCiTWLtZaLoUgIgsCfbRvikvHU6RH8lMhrz8qz0nR5732nuH9unUNLJp4fcdaAwjUbskgIvHFXD64jEhdOazmWLRtXsHGDCFGAwWSMPXMRtZZOvZHH8yNarkfkKfhmEj9yMf2QYhzW6wZLzQgBBdFVqZkhkghJVcKRLEQZXEfjzgMNjMjg+g0ZltoeY1mB59dXeO3tB/ny86f4zeQiH3j4BFEUstAxmas5tGz4/sEVmraMG3kUUiHITfpSMh0nRLB1anUNs60ii1AKXG69qIu+tPqf7+bLN49zzaY0vz5WZiIf45zuNN0JhU+9cJR2W2Zjt4+SaJAyBGoNmbyi0+ooyGJIOhYy3azx5juPMNdqkZd1zu1PkcuGoDgYqouAymIzYqJX5Es3jvHuSwbJJFXGMkmOr5v85e1TnNedxWnrLFREpCDOalVBkSNa7SRPzNcZyklszCb49alJbtlRJHRjNB2fYlpmZl1icV3lst4ePvnwIqbroXoar95RJKGJPDmloMVMfMHh+ZUGt95+gredNcBo1qDRUFirhWSSAvfMVlm1QsZGGxRiIgNZlZYZ8cEHprj9SIVdvUk+8/Rx3nzbSX55qMy7r+jj+weXkAWRV27pRwhkqu2A35xa4sB6mx89X+KrN47z53eepBBTGM3qFGMy3967zst2FBjPaHz80VkAJBFuO1gFYDU0+cgjs7x+V5H33DVNNiXy+HwHJV3+wwfGP+VU/y3+qDWKbvjWY7iaTqkdYDVk+opgGCHHVnxihs/GYoAUxZkrR2wZamI7Bl1xETvwEISItXISSVAo2x5daZOUDran0XA7VNsyfSmFIApYbAY4DZ1NPQonVn0i3eWDF44zmNboT+i88kdH2Tbu4otVetMmvtNF1fEYytVpd/IcWk4QCjZbiiGa4rF/SScpayiKTxhF/N3ZW/js40u8/6Jhfra/zEt3ZXnX3Sc4cyjEpoYuy3SsFKJSpkuP0QkCnr/jARQ/Q2fjNWwb8iiEHkcaMc4Y1Di47NIbN2j7DrIUkZRk0vk1LhpuMdP00EliU0clzp7FBAlFYnktzUs29vDU+iqK7PPOXZs4ZyDNsVWTt/z8FMODLmXLI0GMIAzRYg7Npk5oNLhkrIMnNMEvMFvSkCQFQbDZPxOnYKjUApebR3p4ZrbNSsslVwgZ619hMCPg+QoPnNToSQoUEgGa4nJiPo+hwwcv2sD9J+ocLrU5VbPYMmgShiqpWIeZmk9O14krMqbnUWpDWpexPZGNmkVNUdi3LJFQBLKGimMLaCoEgsvrzj/I45MTqIpHxzLQZYG2G9HsGJQaIhuKGmtOGdPS+cr1O/jnB1dYCOrsGKgRRBJL1RiCABEi5/dmQXJ5cq6DpggEocsbdo7xi71Nlr06STWi0o5o1wzufttW0obCgeU2X35mietG8nxv3yrdRYtM0iIVa3JsJUlCjbBdg5gKhWQbAZGBeIEXjo3yzrtP0G76pFJw7ohHIS6xVM3QJSe5Z7LEUK9Ff/a0iG+lo1CvS/SJFq6SYbQQw3Pg7Rf0cs/JKm87r5eVpsvB1Q67+uL0JlWCMOLq/zhIMS1w/eY0vbEkI1kVL4gYSBt87clV3n/lAIIAvzy8zj1z89Q7Ig1HZCDvoioW2WQd/Bg1W2K+bHBGpodnlpvs7IuRiJu4lDCdOKW2yFAqCYKJodnMVTXaFuiCRMWOkAOVthvymp1FvndwCQmBlOHTCQQ25zJ8/MphPvPMSSp2i2zcxQ8Djq+pXFgY5AOXD6Ep/88o8OD8EmEQsr7Q4ddLTaJ4g5EMhEGMo6sKK42QrUWJCJG2b9GT7hCTk7RsOFnxSAsJNvXCqVWXlq8ykYnhiy6NzmldsH+4cAN7Fkyu25rgc3v2IUQCxys24zkF04W4Bs12nOML0JMV8ZQaMSlJGEhMJNPMNSwEV+b9V/fzxt8cp0fS6RteYnOXiCTZiBhMlUUcN86RNRjKRORSFSYKHuW2ShgKNMwkYeTTJwzzj9eN8709a9w3WeOHr96EIAjsW27z/GKLzz05z4Y+gXS8itJSWJvcw4GnnuMtH30zbuRzfCVGSkmw3PLoSQW06mlEo002JhJFIaYXko/JzFdlkobDUDbC9H2SmsvcWheltojVFLh6S5K1cAlFtgl9g5lynHpdIKEIDPTatAOLKAJNiDFbC9FVh4yuUq0YCPEKgghjGRVBqXFyXWMsBwQp/nLXDt54+3H+/uJ+vr9/lbbnIkUSuTTkYj6KatFxT3dLjhQ6PHwojai7OJbOC8YKHHOO8fiJFEooM5I0cNQOnmnx1J9f9QedpX/yFX9YjaKLbvu/f57+T/zX/jn384+h51R2ZnJEisu82abUCbE9ga1FgXpbwwkCzLZIXBd53fYibzinB0EQ+Ob+ae470cAUOqcLuoWApBuwJEAYiuiCwXpVoRl5fO6acf71iSXwJa7ZluS3k+t0xxVmGxb/dOUETy9VeGJplUpHYigToUiw2pJJaCFuO0YiVaHmyKRVjbmqQBTCYELn+m0aD86u4Fgprh7P8ttjLQICQtGjXIfupIovOmRTbfKaxsmSQugL9GehYgbEEWjXkjS9gFzutMZd047YOWRTsnyESOXakT7unV6l14UDTZU3nl3kt9NLOKGLIRhUXIdtvT41E0pVHRmVSiOiOy9Qdm12FdLUPIeOHeHh43ZUZDUkpkd4ERB5DBY8TM/HduJIskmrnQRB4Bsv2syHHznI0koMEDhz1OBoucZ4l8RAPMXz6xXetH2MLzy4hmj41B2PjKBiE/JnO3q491iDFa/JWE9I1QqxTR1RDqmZIQMZEd8TKAY2TdVgsRkxnhNxIpfpNZlcUiIVE6k7FpKnI4sCdcsnEY+IHIlORyBR8EjI0PZCgjBiIB/iWTo3bs3zq8MNLhnMcsuuHt762+MUsiXiCizVddZqMoEgMJjzkKSISlOhYQeM5lxyiZC2rVJvJCl5JgnNxzETKFpIXA6IqTZzJZ1IidjaLTJsFNBEmXde2Mve5RbfO7jEXKPOQCQg6wW298c5bzCJH8KZfXHKHZ+uhELF9Lh3qsJrtnfTcHwy+ukP6cOnavSmNI5UGyzXfNKSyot35DhZspiqWFQsl3sXVghCsJoa9715O4ok8tO9ZX52sMQ7L+xha0+c2w6V2LfaZHOXQVJVqTgWYQS+JzKaM3jr2b3/eR5rtsf7HzzBiWWfW3d3QyRx96l5Oq6CIIZcO17AdKHRCblqIsXmpMOKH+M7e0ocXWshJx08T0AIVAw9YkuPRMcR8YII2xYZz6k8uVynN6VwRj7PzJpLK/BI6CKXjiU5VG7ysUs3IgAtJ+D5pRbfPrBAJ7BptESu2ZihJx5j32qdiuXx5eu28pbfHSSfdDFdiXJL4KoNKhf09fLQbJX9SyYly2esECCJImsNgXTcIa5JuK7I0rqKbET0xFRarkm5ppNLhQwoFsc7KmlNRDUclusqvXGNmmPjhQIpVUCUXQzNJ6aIrLUiinEBSfI5fCJNNnlarL0rLiPoIQ3fIalB07XZ0RsQCQ6CIBNG0LQFOqZB1QJdUKm1Is7sSRFGsLFb41Xbu5mvO3z8vnlGeiX2rbZ530XD/OLkCilRYe+cw66JNtMViZisMZjW0FB4bq1BNh7hBiE5XeTSngEeOzyHkcpz05lZfnJ8ht7sOm1LY66Sxgo83rRjkFdtHeLx+So/PzHPJy/bxkd+t8jvF5qkNBEt5iHqFjhJLFyiQECTRQLJ5bqxLh482saSLF60sciDc+t4gcuGWA+HSyZSKPL5F40yVe/wpSeX2N1nUI4qlNsyRTXFqt3C9ARevamPBxfX+PMzR/nRvjXOH46zYLYRXJ2D5QaC0ma8u8H62hC3njHIhx6eYlfKpinkWOyY+K7EYNLgw1cNEUkBX31+ji9ft50gjHjnbyYJ1A5FOcUjc03uvmUHT8zVmWs4/OTQKq4Pm3skOg0VE5+bthVoWD47ckm+9PQSX3npBIfX2zyz2ECRRKZXPU41TP79pglarQYXbhj4PzKv+mPJqeT/3Q/wv5NVu01aD+mPZ3FE8EQP25XY3qOQ0H2QOwhCi+EuFU0wcOQm0w0RMdLRwjgnV0L6euu87+JN3DV7gplahOP69KV14pKCE/iMFjrk1SwLns4F3TlGMxZn9ce591SVU6sugxmVrlyArDYYz4LpZHGkBmcP2gRBggNLAZrapi+ps9JpI4sBeV2nZFlYHYHrh3r5m/un+OaLNvOZBxfYv2pyuLPGxqLEcsdiOJOg3lGRlDZjmThVO6S1VKb2zBN89Bs/Zq5pcqTcwncjhrMS7ahGMZmkZLfpz5p0mgU+eE0/v55ZZbbVplQfYrS4SKPaTcuRyGgS+XhIxmggSEVmVyPSMYX3PHqAN2yZ4MtPrfKVG8f5zvGTXD8xwAvHuvmr352k7po0GwJjOVDkDnHSWFhEUYbpik1CiRGLFFp2yNmDKeKaiKbAZ24c4gv7DpBUDJq2ixcEbC5GCFKDXFwlKeV40vX4pwtG2Ttn8uZzephrV/n4Ywus1nWCMGKlEWcg5yKJIWvtkHojRj4NURjRlV6n0TDIZTw25CXWmiptJwK5RUxRMSSZB49tJhf3WWmqSJIFwf+Lvf8MtuQuz7Xxq3P36pXjzrP3zOzJQdIoS6AAIkdjMGBjwGBsbLCPDTgCxhEb2+CEycHGAZMRiCAJJKGsUZjRaPLOea+cOqf3w/yrzv+t8pdTx6d86oWrqr+vrupez9PP737uW6ZlA0EaQ49oWD6FtIboKfzVfWvU7UvJHpstjSPTfbZ7Is1unp89UuFLZ9d5942jjBp57lnb4ObxaZY6EbfszHPHvM3V0wlnW32ahsq77prjlfsqDP2QLd/ihQd3cXTC5IcLTXzB5QcLBqYhUM0MGQY9nl7L8NI9M3zzXIPluk9JHvDxFx6k0ff5w7uWeO7kLNeOXUol+/rpJrISUc4OGDgq620dVfWpFIeEPQEjlfArV4/zj4+t84cPLCCGEnPfcqnlVBY6Nk+sD7hxR5YPPnaG517RpN4r8J3zMJr2SWSPJ1Z9HviFK/j9504C8KXzizy4tUmzm8YKYjp+QMaNMGOV5fYI+0c8jo0Pmcr7nFnWMRONqyo5zlgBZ9ZL/OxlKR7a3GRlI03akHntZdMMvEX8OKaWFSlEAX0nS0XwuXOpQVbQqeZk+jjkEwiSED9xkZQGJW1Iq1/ADwxeNltgbiPm1V84x68+u8Iz3TpPzEv8+fNnMIU0qprwhQsrSEWVybxMwbQZujFxUmAyr2C5IigueePSIKTRj9nqK4ykFFTZ5jk7xpGSHluWzaMX4KYjCu1uQrcr8Qc/XKYfOdyxlrCjJBLhUE2lWe/6hIkAscxmP2Yib9BwPPYUVXqWz7uuPMAV4xlEUeRfTqxxuJbmdUeKfPGZbYrDMmvGEqOpLG3HQRHK1L2YKycE6lZIf1BlW98kJQq0/IQIl0Y/w5OrfW7b2+Xnj1WZ63f51zOL/NzBnVw+lubwSIpiFj721BJEWUazFlv7rmHq/FO022sYmQmS2GBl0CeTFDm14TNWaFESc5xpDNhb9ZkqSpxrWowWRPqOydnWgBt2ODyxPMIT8yG7R0XSpso/n9pmqiiRpszpuo+meuzIGKz1XRKry4hp0rQElm2PtCyji2mcqI9kBBSVLJrh40YBBc1gPFVguS5QzHjcs7rOgaLJF04v03NViqZEFIksdR22nIC9lYQoEjnVcrEShwNTIY3eKD93xQSffXKbN129B5wu6w0FN0zQdZmUJvz3FbOf8BP+E1QzIC8UePasyadPdrl5vMaK3aftxJzd8pkuhvz2sV189JFNVjs+H3tig+fsLdC0Xb43v0mjnSabNdlfdfFiG0WJKMgmoiSwuJXg+jEHx0w+enyFhUZEzpD47vkOk7kUtZzEc3YViQOJO066/I8bD/DFcwvYkUPsQyUl4cUChXKTtCrQHqQ5vRUzllWZzGics7vctdhBEQyaA/j3J7uYukjHiVG1hNkRkbmtkMmaz3haYsvyqGbA9XQiwaKga9QblwI2nruvwAPrbdJph30lkWHkMp4VmavL3D6/jOJlee1lOT60e5L33HuGph1T1lI4wpAjYzExPo1Ggckq/OyBCT762BqrWwnlTIqnVt1L619qRBiJlLIB2VwfIdGJBQ+iFM0B6LJBnHgIfpq8IfGua6YpGRqjUoVX3JDlsyfXOd/fRFYMNgYBQhzwD7ddznt+cIGbDhh8d2GLfWMiW70AIzB4utWjE3vksyJ12yWn6USxhx8qjOYENoch0wXQwgBNyvLXz5tCVeD37p1jvCDQ6MWIckDekNlRVWn2BW6cySDLCd+Z61JNx8hqwJGJBhe2yyQxLGxoSGrEl081cFyJH6xu4eChiA6GFIPoYgU6e8ddmn2N9ZaGT4SSyOzJ5NhdVMgYIbdeEdLwGvztvSZqIlGuWCy3VWLRoznQSJsioSPT7klst4a8Yl8FJ4j57TsXOTqhoYgpJrIRlYrGaFrh9tNtrpxMsz1Q+cwjdX7t5hEutmxumynyyUe2mMprXDGRppZRERFpDUJEX+aBhTau6PP1+W1uHitjagJ3nG8TKwkIIp985e5LHjl3LHOuNUQ2Av7+kXUSKWF/LsP1Uyn8SOCqsQzX7hjn4cUBUZJww0wGgK2+T9sO0bSElCyS0SSagce25aKIKVJmiNXR+PqZNulMSEqOuDZR8cKETcejmIvZrYWESYIbKkixTL0X8/4bDuBFMSDwpz9c4ek1m8l8QizZ3L0Y49oCSjokHqq8cLbI71y3i9PbNh9/dIOxtMaL9xV56b4KXznZYmdOYeCJ/PYNozz0vS184HMnN9ih5dgOtwljUDB5ZCHi6e2LJFGaiqlgpmJ01aZlqRya8HBDCTf0aAwy6JqErsBHX7SPs3WPjzywykbPZmdR5tbdaTa8daJEwtTADiyEfo6skdDpC0xWdcq6yFWTPl89KbLUSEj8FEf3DdnsJ2SNFH4U8P4bdyLLMn/5o2VmxgcIYoCuxNh+xEpbYzyb0IoDKqZK17EZKaR4anNItRBx6gx8/0Ibj4iRlMGG3cVMTD784BqvPpqnYbtUyzFdL2ZHWWB7cGkwFopD0mqezW5ILSNRb+k894Ycy+s6x/bkuWv5Il4ypDXI48UukhgykY756oV1jlZK/Pn3tnj50RK/+KU5Xnu0wlrf5dVHynzxRBN3oNCNfEqmjB/FWEFETtT46okuuUyI4Rl86+IWaqISo3K82eO1h6o8ttlipqTT9BwKaZGntlymKgpGqPLCvRV+uCxh+zEHKmm+eabBxx9fR/RlfvbwGJ9/egUjq/BM45LCr9mHCx2HTz+xwSdeuoeTyxf4wnyP6UyB375pgqym8Ou3L/C8PXk+/JwDAEiiwL6qwbOnR8loEpuDgG880+J83eVPXjDF4+s9jo1l+Ob8Nu84Nsm/n6zj2Am/dt04L/7kOd54bYXf/u489UHIc/bmWR+6vO+2STRJZLKg0zfi/74C+hOAH3NF0Wv//QesOCYdCxTVZ0RJ049C7MTBsRX2VgUmyx7zLZn9Y0NKRsxWL8/ytkLgiRipmOsmC5xotqnbDlUT/FDDNLcYehL7SjJWmPDdZ7IYksBLd4xyz3KPH73tKF86vcHnH23QiH2yMtx6cIgkDxEIieMso7kei22DlWYRXYmIhYCULBBEMvWhSEpJsG2dZlsmr0tcvyfFnRc75BQBUYmot2SmKiF62sb1YbIAATY9W8NqDpHx+IXrb+IvHr1IZ1vlsrLHORwm0uYlI93tAqoGg8TlttkEWdvE98q03YRbJmp846xPawgTJY+FFuwuaPzSkVn+5KGLDGliUmaxHvGKgwXaTsxsRebXr97Fie0mf/zIUzjdElJqwEhGYCTvYPsKcSyz3PXJyBlOXJCoZjVEOSYQYiqSzulNh4/9zAyKGPPHD86xt+ojKX2GPsyWBPoDlesnZvjymW3OLhi84YoRnrs/xzCw+J37TlOIKnhxRDOw0DWf6YKCIkcI0pD2QKc3zFNKOewyhhzebfCR+xU0SaFgiAxtgVcfLvKNi5vISp8rJwKOL5UomhFZPWGrLzCwUnixizvUEOUIzXRJQoN3XTnDJ0+u02jGvO5GkZftnuYP714lU2gwt5Xmt6/bxYceXEJVfdq2wrdee4QwSnjtFy7wdz89xa/ddZKxjMGbDs7w9PaA09sOrztSYTQrkdd1TFnheZ86SbESkjfAsyWcOESRRapZh1umRyhrBfYXM1TTGq/94mnypRZWP8+MmePKiTRP95pc7K5zYPzSkOfcfBU3TtAzA4pWSFPK8qyxKm++pooVRCy3PD75yDa/f9sEfS/i+PKA71/sgBaQ131KBQ9ikZ1FHSWq8f2TQ950XYW7znd585U1RCXhj+8/j5xEiKLKwE3ImgkpVSAmIpvy0GWBzW5M29KIfQ3FtMnJKf7k5r289745vNDD9WOef6hFNTPk7jNTzG/LZE2Roe9TS4s06yrHZnXuPmXzi9eM8LXFNX72wDgv21flixcfZuD7zFYbHF8a4exWCsOwaHUKJGKAFMvsqAZsdUV+7dhuFvsOBU3i0bk51lDwsJit9hnaZeoDhblmQlqBsqkiST6+0KeWUvC8NAuthFqxiymmGVoKguyQxBobPYFMKmbUlNnqB5SLDkUd5toiV01GNIcSIQG9YZoTywmzFQ3Xkbhxt8nFfp25VZ2qqtPRWrxwbJLT9gIHKwWe3LApmZc+IHYWE841Qyq6yYblMJIySRldxjMaTyzrzJRjIsHG81IstEWkSMSNJLp2wrNrZd5+Q5WPPn2OX75sFlVQeO99F3ntgRqne4sstF2mpYi2LCPKAZ0zZwnKO/HEEkJg0PVCDEliLG3y/P0GXz63TRi7TBS7kOSwIxeRiJScouP6mGpId1gio8rcUBvjq2e3aLVEQjFiaiRE1TzWGjoZRaFQsLHDCAE4Ou4w15SZr5tUTVDkkJYbkDV8NCFD17dZbsuYkkHNkGlHTVY3Mpc857LQdh18V6OWg4wuIogOqpJAJFFKJ3TciM2eyi8fneLLZ5psOTaIQ5YWcsyMieRyAeuNLo//ygv/S0++HnoN/P881f+3Gfpw/Zf+v3/69RP+5/Nz4z/ch+VnkPWYIyMmt+01qTsOD5yO6AsOb7xslFHD4L3fXSGtily9y+RdN07y/M88g6YmZA0RSbfYUbEQIpGcD0NzyDMbJbAziGrC0I0JXIHRvIQzlHCFkG+/8RDfuLjFmXWf+5Y7/N6zd/C5R+q05DZZXaBsijTtGIGE3eWIhS2NlbrMeFngp3aP848PbrFjyiafjui5Ca6jE8cKLTvCkAV2Vl0WOjFZTaKSCS4N/WMDRU7Y6InU0gKbPZXrJwrcMF7mvs0lFrs9ZFFlvR+zv+bjhgmWn9BrF1ltR9w0GvHiK6f5yPEFMopMP7SpGAoRAat1k5F8xCDwUeIca+2AnSmTbcdHEGIkDRw3IWMKOLiM50R0tYsiZBiGDoZocHZTYTQrUW8nHJvUONezecXOMf7leIuRWsy247AjL9NyXMwkzaCn8KIDBe5d6pDK9zHVGEW2aLTLtLoJh8fznN4eMFKxUESR1W5CWkox8BI0NUSXJDpewF4loielWa8LVCsxuj6g0dXYlcny9uvH0WSRUVPnYw9usaem853FdTyG1IcSuiySNyIUxcWLBOxBBkkJAJmfPjCCLFk8trXFags6XZNyMcBx0jxvd4a711YZDg0MHaIAsqbDMBQ5NuGSlj2eWCqx3dXIFQY8e6rA985H6KrPwXKew+Usnzu5zMAXiAKZz71qL9N5g/mmS9Py+eeTK6hej3krzS9eP0a3H/O5p7b459fs5Y/vX6Q+8IgikR0VCd+H2Je5fCzDmYbF7pLBC3eX+OKJBsc3OvzMFUU+/0QTXYNv/MxRFFnkBZ89hSAHjKVSmKqMQ0AoDpGkkKGjEgQicSKQUiGvyxyu5NhfzDB0Y85026y0A45WCjxnNs+3z7TJmxJvuKJGkiQIgsDQCzlbd/jSky3+4mU7+K0fnuCpjYifPzLGk3M2SdRi52SVyazOymDI+jCkPUy4dbrEz11W+3+96/NNh3d+e4501kOWQEwEho5EPiVjJBq2F9OOLd51/QQ376hybtviK0+3uXLS5COPLJESND70omlOd5us9hx+MGfz+VccJK9rfO3sJt9fXmKurpAzIxRRQhYSNNVDEH3SqsJk3qFuCchiRBjrLG9n2O6K6ApUsgqvOzTCVisCOWCtscbTdoimKKS1gPW+wGXjDl1b59y2REFKce14gcWWTzkn8uBKC9NMEHyFSPJIJSpyyiZIBExFZrufUFFydMMBBya7uK7M4ytZqmmBnNlFk3SWGhK6cuk9fvGucb4338QbgpH10NWYXl/jhuk8jUHMuXYPWRbwI5E4iZEEAU3xSGkCbhRQSkmcXFa4bTbPQ2tdqia0Gipv2Keg5HM80JyjlvE5sX6p13Bi/5LCfajjJzFXVgr8cL5HHo03XF3hE8c3uWzM5EMvmOHO8z3+4+IyTTckJck4nkAi+tTSErHkYHkSUmRgJzZeN00mH7HRj6hmEybzAuuDEEKV2WyGUIzpRg0Gwyz1XswHnzfDUtvlM8e3CZOY5+7JYWgiWV3iVKPL0BJY83qkZYWKKbLWTrhmSuPlo1mKtTy//cMF/uSmPUykdd725XlecCDP+daQG3dmedHuCvWhx3vuOk/iqcRSwudfuYcP3LnG+543SdsJ+LmvnOP9t04ymdOZyRm85p/P83vPneDTD21zfGOAlkooiCpTo/C2Kyc4Us2RJAlffqZBb9DjbTfs+b+yr/px6al+rD2KrKRHLetxoKYyYqTwPJGiGXOgnGI6r1PMDilkm1y5o0/FDJjvuiwPOrixTzYXcH4r5l9PNvBjn8nsJdM+XfXYlTc5UEmhaxESElfUUpRVg7uWOuiCyBee3ObrC/N4qTa1lMx4xWLLaeP4McutMsNomxNbUO9l6YZdAqGOjEjHt2m4HUqpgIHvMRSa6HJCJ3J5/lQFLZAZiH1Ob4ekRIkly+LspgyCxEKvh+sLPPYv/87C/FmuPzjNb91zgThKqFRiYmXAbEFnrS4w6BUxDZli3uH6aYFMqsN4OkXPFYlCg7wuctXMNjUtxYPzoEYGPzwb8qf3L9IPLMbMApLqcNVOiCWfPUWTg/kiv/vdeT7w8OOkpTTpQoPJvMLAk4ljhfWOwmpHQIpT/NqxWfZVU3hBhK5C1gh45401do1L/M6dC3zpTJO0IvLkukBKlhlNx5xcS3N+s8Q35hY5tR2TL8A9a01+8VvP0Bso/MuLr+XynR5GuouBgSkbrDRU3MDD89Lsr4Xsqra50IrxQokpczfvuHKa0Icw9mg14Z7FPllZZl81IYhlMoZNx/EZBiFdL6aQcZksBdTKEZPVhKopkxMV7rjY4upJkUM7oSDUKOkpDo8ZHDZniX2Vjzx2kbQZMJ3L8tmX7cNQJHRF5GeOlvmrH2xR1COag5iPPX2GrAaJ0OeU9TCfOfMkf/nAPI+s9DFTIoYS4EURbS8G3+Cm8RHqzRJfezyiJBv8xd0bAEyXFRqdFFvthEc3unz6xDojWpqtdp7VThpJFClnA3RFREg0nnekxWjZxSx2+f3vLbLQ6fPBu9b51Gt2c2Q0zY3TOX7jpglu3plnNBty474O06UBgeBy/egoqihxZMJgteXxG88aZbZscPu5BgMLfF9DU2Esa/DSXROU0zFRHNGwItY7Cn6gEzgakxmdiq5iCgoXt10mDAMhUSjnIi5uF/jmyQkyZkClGFBIR5iKhihI/MINFXRBo2wqnG1ZvP5Ijsc2O0hCwjPbCZYf8O2zeUbzXUYLDp6XpmrGqIkEoo9jJdQH8Of3rbHdDzBEmY1mjBPEpCSN7rBA1xZoDQUqKZFaViBKAgTRRU4y2J6Kpg+ZrnhEfh43tpmoXVK/uQQ8/8gqo5kI1eiyf8olo8g49BnPCHTdkCAWUIUUijJk2jDZ2BLwpJDLqlmumUhz+bTE266r8fyxcb6xtEHg5nl0vcNkMSDBZSongxAynjZY6ghUUzrLPR8h0fGEBgfGbFa6Pppg0PcTJrIy+BpaqFHSVB5d7/GnP1jjLYd38dePLhDEMevdgG8snOfrZzwmcj4936VuxWx0TTbcmCc/+TeEQsBa/dKzht7jYmvI1890qVseXqCiiBot10eMDXRFRlJ6uF6a02slGo5DwJCvndugFQ3JGDCal6n7AyRlQEoTee9N06wOHHQpYdfINg2vwY5im91Vm3MthxMbMWaSotlL0/ZskkjhqnGFW3YLvGB3hZqe4U3Xprl+tIjliIyYGpfviOn7AWt9h+ZQZCzrse0O2bRbdJ2AnCzzF/dssdh1kUSBQ5UKVx4QUIyEiEvv+3854n/x9RN+rPjMy/dzaDSFKUu88bIRPnJfg5NLEVYcMPDgL3+0yR/dv0hGkxj4EUEAv//dZSpZCVeMiLQuEyUHObkUGtFxEs6vjKDGJm0nYhgEpGKZWk7kDceqJGrE5169B1OTyGkqTzQ6HBtP8x9PNRnKHUopBUNNcJMhu8s2eV3i/KZKy1LZNSbQH8JnziwwOhLjxglzDYnI1xEF6A0TcqqMmIicWNEo6SqSAIkwwJR0ep5Iw0qYzMPQT8ibHq/YW+W6iSz1tooqyTRtn5ligiT6dG0I7BxLg5BiRiAWbT782HlGMwktzyMrawRxxMp2mlIaUqkupmTQdDzKmsKLD+VRJBgvavzq1ePkZBWvJ/HinVVWOgntVpWFdkLipxmGPjVTwEt8Chk4OGpweaFMsxtTzce4uExmZXq+y86iwPrA5/rdaV51pEQENBo6rb7K/GqNfhiQy0k8st5mvOQTeTKCEKKSYquVMF6IKJgBSCE1U8LQXazYY2zUR5QHDGyDbDph0euyOfDZVTCRBLh+OgNCzGLXo91MkZYUCqbD3tFNNFEiqwmUih0iYiZzBt+b20RKVI5fyCMmOUxVZHktxS8dG+ehZZcoNEiZPpo2xExbyDIYssRTyxm+80yZ+abKay4rEVgFHlz2SOsecpwiKxp8e2kBWY7JqBLFTMyf3b3CZx/ZZmdJI6UJ7M7kOFwq8AvXjrBludx+cZt8JuGB1TZ7SzppLSJr+uiCzO/esJMX7ikSELLeDvila0bZU9W5Ya/Oa6/K8PlH2+TzHooQ86b/OM9v3nWGo9MCSqyw6XqYukAgeIyXBkyVhiSxgO/JxOGlA8QgFnh8q81H7l/jM0+t8fCqzWo75sn1IXee63L1VJpXH64Al7xxABp2wH+c3aIt2Jza7pPEKi/dVeULjzY4sWFhBT7fmavz9YV52izSC9ukEhlNDXi63sWP/qfKwo1jXnUkiyS51AfghBFxBL1hzLOns/zqDSOkRZWPPr7ISz71DJ8/tcGC3ecTT66SNxS8KOa3v7PM1sDn+GaXn72syPGNIV86vc33L/R56a5p9ldVSqkILwqpdyViL0/op2kMZM7WZSZyLmO5GAGfAI8XzhY4NpqjqhhM5wxefrjIc3YVMQSNCaWMZYtsd2WumPAQhQhBtJmtJGipIXet1fGUHpeNaqiKQl5PaA0h6qdp9CS2m2lGzAREm/0jPr24h+MonF4s8fhckYkcxIJDStVpWJBSFIKhiiZq3L8w4HMvO8jkqEzXUXB8FS3lcXyjy5OrNioyAwdUKSawBVQ5YugqDC2RQ2MWebPLRBGOb3a4YjRN3faoVDzuXWtyz9yAP7ruajqWRs1UOL8pIcUpVrsyUiwwUQh4cKvBTC2hj8/tz3T4t9fu47VHK/zW3Rf52rkGz99V5XkzBYZBjCHHVLNgRR4KGkGoMog9SmqGd986Rs9JmMjJeD70Apt9tRAz5XG6bpPXZLqOTMt3qOYFCimJN105wvueM0lWl/CihJfNljnZ3OLpbYdNz6KkGJiaxCv2jmNKKidWYz760AZnN0OOpEv82leXEASBlx4ssGm7dEOPP39wgS88tcW7777AXM9myx1QTam8/+4V7j7fY6nlMZrR+MbrD3LTjiI78ymeXLNI5Xze+e2LPG9fnsmsRkFVeMu1Nd5/827+5vg8/3B8lT99YIm7znf5xJPr//WF8Sc91f8SPya3+Z9TVFMEScRc12ajH6KZCaJqcaZlo6SblE2RrKqDENGyVEpqgTGjiK5JNG2R6arE/hGRlhPSjzrYrsxozmPN6rLQ6SOTMHBlTq2EtH2PUkrC8xM+8dgWvmMwkc7gxTHb7QxZqULbytF3RWp6kbKeJhE9xkyDlFAhEjzGMwl7iybD0CetJmTiMqLZ5y9fOMk9c31cZUjoGkybKfqiRUoy2V2NMfQ+M1mT4doy7uppbrnlKh7eGLC7JDGaE5HkkIJmstlVMQ2VUjbGzAyYyAu4cZ84Vti2fXKpPkEU8qOtUxj2fh7f7rGvorFlBRwYU/Ekh/GcgSh57K16qFqPrm/xz8fr/Nk9y5warKImeXp+yI5cilq2TcVM+M4pk04rg+WD56V4392rNNyAclZk23UQhYTfvWsJK3KpmTLPNPpkTJuZUsKpbZGhl0aTZHrRkL5jkFV0nNhnYEFNS1POKLznh+e52Arp2zrdyMJ3ZNQwxcJyiazZIpdyMFSB0bRMu2Xy9BrsL6U5UswzlU6zY4eNmdlm5+g2JzcMolikkk7I6ioCOjuKPpmUT30g0R1EZMjyP67cx85MlgvDFra8Rt3v8s8nN/nRQofvXxwwldc5Nm6ysa0QxiHNcI1f/vY53vD1Z/j1u07z81dXuX4mQ2+QRxQkSOB7y5s0/AA/7pPRHfpBh++ea3P1dJqUKrKr0qac65FTVa6ayvCGy6u8/zlTHBxN8devmAEgLxk4rsKvXDeC5UWEScjXT7eQZbhywuKKiS5jozYpIyYOVBpWirSasGF3GIg2/3p2nkpaIav/vz+M33J1jYlUgU5/gq1eBhKRy2uj/PJV43zwRTNc7Ni8676TfOiphxBljx2ZNLomIMshB0cVjo1meMnuEVKqQFq+JLNOaxI7Ri1MTaBfN3nhzirfOdvm8UaDoZPQsEIWmwqNpkpnKNLpSSSEvOXIBFIk8awdGc42m9jykAvdPp99tMtC2+EXvzRPZ6tKVU34lSvXWWpWGLoqBbOLmRmQzvUwjYQ9tRQfe/FexnMKiDEfP3kWXZVREpXOQMGOBiTEFFMSggDLnQhNSdCkFFEcMYxsshpkNQEvdvACneXtHMttgbQic35tjJHKOpIkMpr1KKX7jKRM0qkBmwOFlJKw0BAYDkv4UkCqEKL4Iu/95jr9gc7FtsuXz9WZGzpcO1Lhvc/ehd0u0hxGLLUMGkMB29fQlZidpYSsHrGzKGD5EXGYxaPPRAGe3PDZWfQIhCaVSoDtxaS0hEoONi2Xv314neVmxD8cX0UVJdoDjUNVlYVOwkRWpJyxECWbiX1XkttRxugukpIlfu3YDIQZ1EyXJ1YcTFmjFw1Y7MJ0MaAXtdgeJEShQdPr4UYhV0y4JHITM7fJbCkhN9rgN24cpyBluLhcohc4fPjhVaaUAono0xvmKChVzm9VcQM4UtN56xU1alqeONSYzedQpIRt22Gx18cRt2gFFvetb/CiQyY6AvMdn1NbPqPZmMMjIqYGLVtiZ16hKFcJ3RxNB2QRpnIC77p2ir+69RArAwvbC0kClZT2k3SOn/B/F6e3HJYaPsQCH39ilbQqcmHYITaG5DSYKooM/ZiNoUc5K/HaoxXONi1etL/IDlNjR05ivavS9nzkBAxRw40FtvohqVRMVU7hBTGJ7PCvj3Z453XjTBcMFjoOnzuxge+LnG31iFJtCmmF+jAmwmWmCFsDlc1BQhio5Ex4yd4qn3jVHkq6wXIrptHSKBoKwzBi6MZMlEOCEGw3Zrog0unKbHVFdDHHxgDiJKZkKGwPE4JIYtTI88XTm3z+6XWWuwGnVlU0UqT1Pm1bIqermLkG42kR5JhmV2EqK9L1PUZMEVkKcHt5DlR1UmYPQzIRFZtSCkISvn+ux9FJk1ceLvDtiw3ars8rryjynht28r5r9iAFOq+YmqJpC/QHKdIpl4wWYg1kvn5qwBVjaVbCHqLpYUqws9bmYDXgzGqKgmZw78Y2v3HHHINwiJyotDoaUQw5SSJtDrhqSqbjB3iRSn2ggxiyb1zgz286hB4W8DwZRJco0LAdiZfsGiWrp5Blj4GjMGXmGE3r/MEPlvj1O+b44I9WKKdFnreryJ+9cDctJyYKVU6tjdH3YqIkQhV1sorIT+8ZpVPP8OWnXN5x3QglU+cVh0d44eEM59tDLN9CVYe4gchUPiZviOiqjyS5dLoyIipX7upzorNAOtslEm16fsyOkspauEGnrxMGGpqUEIUJLSweW+3z0n96hn8+uc1EXuPyCZMn1gY8vLpFKW9h6iFzvQG37ahyVXmUNxyc4dhYnqKhsNULuP1CHcWI+OMHzuNGMQMv5N7lFtWqy2jO4+B0DzE9xLEknjs+zv4xnYwu8MyWxWwmixilsVyVkaJDqeiiqDEzRYMggr4XM1mT2D+WYn9N49b9Kd50dZl+EPDJRzb53vk2/3Gqzl8+tMDHH1/lsdUBIxk4MiJzcsumP5DQBJlfv7nI9ISHKl7yx4xjkZWmznJT5VRjyGcervPeu+dZ6FisdF0++cQ6kLC/mmLKKFBQRCRkXjpbpW/BiTWbiqHx7z9ziP35MrumBNqOy2orpD2M+eBtuxhP69TSMs8eG2M2PcLLdk3x0GqfuxYbPH9vjrrt0vMdvBDGcvGl310o0BvICIlEEGQ4vZnlmS0NVZYopUQemB/yB7fs4A9u24HlRfzMF87x7m8tstZ32IobyHLCWC6hZcWIYkxRlxl6MUNfISsLnK/HfP6pbWqmQqslMzMiIGVtZDXh8tE0N0+NklJUum7CntE+0yMOw6FAKZ0wDANGsuBHPqosIso+sRHwc4fHMVD59vkGNd3gSC2FF4fIgsZmP+bqHSl6UcANkwWsIOCySRM/BkMWsJKEoasBEtO1LhoSFztDfuXYThQZZssp/uHluzmx5bDQSDMIIqYq0PIuJUn/5rNHyasiR8d81vsBMyPwluuq/O53FzlYNXnt4Sq1PHz5mTpzmzGz+RSJ6hMnEaWUSCLbFBWVnTmTt14+xr890SSNykY3JCWpJLFKlNhUzJhXX2Gy6fZJvBSmqLC2HfHXj87x7u/Nc8uuPDdN50mEiA/9cJ3zWwJ7iwqeL7E2DHFd+Pq5bXrDBDuI2FfT2FnQ+c7pLoIQ8bavXeBsw2ahZ/OSXTWOjgt85OFVLm75TKZTXD1e4th4mt+9ZZIXHMyhK5cGo5ok8MWnt3nNV57BCkPOr0bUCiKHxgy+9PN78Yj5xPEN/uahNQTXpGWH3LnQ5rWXl/mb587+d5bQn8CP+erZcz/3DXRtlDjSGAQRmhpjagF5XSYSXHJmD1H0yWsGHVum7do8dqbKWEFGVSScxCatJWRVhbKZsDEARUqYLvgUTJ+uF3FxO01gZ+j6Ie02mJmIoqYTpxr4Efzigf380+k1QqGPqUhcPhFRzm7gBRLzG9N4scczc3luvqxLLuUgyT5bnRKWpzC/rbBvLOTtl+3naDXP3zy4wvcX2uyqyrx4V5XPPb3CaLFBTtMZBgF0FlGULJtihZwi0XclTKNPf1Ah6VusuXnMjE/LCZgpKsRSk6ycxo19JAFmSiFPrWlk4yxnt0VmKgKp9JDQN+i6Ajk9xIsjqlkHVY64WDfRZGi0VCbKIaYe4vgqhhKTS9uMZjy+fTpLljStoM/Ld02y4fVZaScoUgiqhWunudAKmC1qKHKEZcmMllwiuYeMRlqVQfSQBIU4hAdOG+wYA0OUWNmU+K3njPPFM9u0bA+vr1CueUSBSHcgISdQG7MpmCFuILLeE7AdmZeP6AzkLI8sD/nS6/fz1WdafP6pTUbyIalUxFipheNrdJ0EOxDIqworXYmxnIDlSBzNjTAzKvKVJ9tkNYNIHXBkVOdHizbECo4D5VyI50tcVi2S02Q6bLM6HJJOKsSyxc8fnOHykRJdJ+Cv7l/j0JjC95eXqaXShE6KJLXGVMkiTlwefvIQb76xyNcuLhImAWtNnYygcGwWFrZhXyHP4dE0i3WPX75+lM8fr/PvJ+q884YxvrR0isDLUjQUGnZAzrQwJIGBo9N3ZK6uFjDE05wf5rFjBScUkOOIXFzmH1+9C4AoTmhYASMZlT+4e4lI6bFtWyhyREkr8v4b9iOLAm/5zmPIssdGPY0hi7z5qhHqyaMMfZcHL05j2yr5tE/WjFhv6jhRQEaDzT7IgoIeSsiixL4xg1MbFoYqsGOsR3OoMZYfsFLPISkhdl/nxl15Xr1/hI+fXMQQL6V4rFo2raCN5aQwBJWhFeOrLmFic/kOh/ObWSo5D11UsQNo9yVGsjGBGLC2UeBvX7KLQeDxmR+dZt5JIUgxmgrlrMvAldnoCUwWIlxfAyFCVRxUSUUgoueoJKFCWVV5es2nmBEp5kO2BhEZHQwtIG0MGUnruFHEdl+kmAI36eE6Vbq2TNEMEBCZr8e8ZMckX5lbZ7aWUG/oqCYIocCElubZ+2W+u3qRJEohqT1SYppIsJFjk5Th4vkGKc2lmvYQhIjljoapwnIvJnYLtII+RlhmxXaYzoo0tlIYJYsXTo9y93IXNRHpehHFbEBeh31mk65istq9ZFquJjqNpVOYuUmMTJnVXszeUYGVTkKQ+EyXBETRI63blE2Znhtj+5DTNPp+RD7lICUGC20NXfExFQlREFjpCgw6Jqog0Yxs4oHKsy7r89C8xuxEA5UU86slViybkZLH515wNSlFxksC3nHnExQ0HVXvIyAgIhNHKVp2jOMoqLLAaMHn/LbIVKVLxzLQBJ1hPGBhW2N3zUYMyiSSiyR7zORN3nvNUT5w70Xmmh5uBFP5df7t1S/7L5VIP/yz/7WrZ9f96//3ZdI/4X8+P8//20d5/tEx7l5tImkufhyR1yQGQ41IsbluIk3gyzy9kvC+W3egqfCuOy9wuJDjTLuPmR2Q1UOyqsKFdYMjhR4nO3kMVaZtJXz+lfs43ejyyQebzJZM/vplO/HCmF/41hnmu32qmZCCdikWXpQiahmfohnSdDwcX8Ma5PHiCKcvc9lOHS8QeHLFJi1KZAshq+2YnC6xf8KmljIR4xQbbYHrp3J843SDVM7CY4gmSiSRziDwkQQNMRHZqiu88lAJ3xUopWQ8KeT2+XWyKZ+RDHR9F8fKgxCy3Re5vujxSE+makogxFieyMv25DjVW8W2skiKw8DR0ZWE9YbKLTtzfP/CkJt2pnn/zTv5yI/Wef3lFSbyOrefafCZ49vEUUJkWIwVIjb7IllVYm1b4qeOlLh/vYWuRASxTyEl0O7JCKJEybRZ2MyiSpBoHlKos90LGSuLSIrD246Nsic7wmRO5y8evMi5/hr9QQHLBVyF3TWF822bkbKP5wmMiRZrThE3hFj1qeoGL99b5YapLPcv9fmnJ7aIlZBd2QwrbZuhk3Bkp0THcWk5CdW8Ty0d0ejrvHz3FB9/qMX+ms7iwMJJfEwFDhSKbA985rs2qZTPTbMd7ECh3teY30zz2kM1zraHXD+V57tnu6xYQwQpRBIibhgfoWdBzwvJaDK/cKzGUsfjT+9ao5SLkHWfZ02UKSomy62Qy6cM7p5voPgtNhKF+lDG8wRkJI5OGmhGnUEQMCFM88P1LjMFg41WSKL6vP2KCX6w0KMfhEwXJS6sx9iJQ8YICZMYXQ3pDjXsgc5kSSGRLbzEw/IVFDHGD0WO1DJsdyKqGZWfOTyCocicWB9gaiKltIIqinTcgLGMxmTW4I6LTRZ6A0YzApGn8YPFLsMgIKO7TBU0pnMFrhot8eSKzY82tql3Y3ZoA8RySMYI2e4bdG2ZJBbQYolXH60wmsrynjuWuGJ2i8mCQmzPMp1N8c2LGwy8iN2FNP0gwPUFZjIGT2wNuG26yDmrx0hG4mg1zd0XLPpuxKFClptnM5RNBUkSmGu6/MfTdS40LX7qsjKn6kOOlNM8vNGg5UQ4tsRbrhqlYmj8/aMrpDI9VDRK6QBDtxn4IhvtPFeN5LnQ8pnOabx8tsqFbZvvPLGAVAItnYAwYDwPzX6Kbj9BM2HoqvSdmIwoM3RUYjXiPVfv4thEho8/OcfmMGB7kFDWFTphEyeUSGtg+T55Q8X2PUZyMYIQcm4rhykl2HFCWcmQ1VU0JWRp2OIFk9PceX6IaSYsDQa8eu8odzwz5DOv3k3RUPngvcvccabDtTMm918cMFoR2Du1QRik6XoxeU3n145ehiAIPLne59G5BcRMjnvnHCbLEUgecSLgBzotO2CmoPKmwzv4+CNr7Nuxxf2LaapyjtV2yGRe5lM/tZespvCRx87z/fkhng05RcHCx9AjZko+J5ZTvOXyUZzo0pplJlFQUjGdYUJOl5gcsWjbCWlFJQlTzHUH6J6BbkAvcUnLIodqBicWYkh5vPu6afYX0kTAr37jArKaYHkJuhZg6Aqv2VvlkYU5fuuWY/z9Qw0OTih89oktZrIGf/WCPbzqc+f54MtH+cijyzS7EmGc8LZrRrlvscvvPHuKmYKBHYSc23J5//eW2U5siEVee7TCL14+xt8+tsp0XudQOYMiQtf3ed+9C+iJykt2lRnLq/zdkyv8x4tnqJWL/1f2VT8uPdWPtaJIE002GxILDR83DMnqEeuDkIvdAUh9pnISBc0kJkaXIwpyiX0jKaJIwg9DSobK7kKGxa7PnU9KDB2RYspneTCk7nZo9EzERGGrF0ESc3BCoWgqRETkpQxvOrCb+WGLatZjOptlIn9JdWC5RYZOlZPzMqcuFLli1sUXu3jJgJ6VpesP2bT7HJq0ONcMeOddT/Lbd1/gcw+2mdv2GDVNrp3Mkstto8sqS/0ej335m9jtLnExYTQdsdwVyGYa6KLJujVARyUOwA99ZksqDTtAp8jQkwl8E1XM8PRqnqycY2MoMZK6NCgRBWg6lwy3t9oKgmRRNkOeOptFjlJ0hzI7KiLZtM9EIWR2pMVaS+fitsqJ9RS7KwFaesCImuU7pwa0rQhZ7VLKWRhyRCHX5VeuqZDPDEgpAlEEK4MIQ0wjyz5hAjI6jWHAMxezlE0dx5IIhIhMFv74R8v89L4KYxmdTErkr249yGjFoVaJKNVcMqkWS00ZIZGZKUREgcjp5pB7F7oEYcLbvnmRLz5Tx3IT2lZCyrBo2yphpKGIOoak0R6aBJHCdl+k7yTcv73Kv5zcZM23Wem6vG7vDAtNSGkh/lBiOADLSfiZQ2PsKOrcOJMhiCU2eyLZ7JBnjU/w9XMdPv/YNl852eLwpMpnn6qzsp7m1HbMot0ipYLj68zXq/zFKyZ4dN6m64QgCIzkBGQz5MElm2HS5anONh87vs63z3T4xTue5mtnNymUPL5ypk4S6YSeSlZUKZg+ihhxdsOkOZC5YjTHL10zymIrRcPS8CIBQYgQkoTU/18i2GrP5bfunONia8h8x2a+16eYcfBDlfm2zV0Lm7z2608gyyGDYYaX7RvhA7fs5mg1i+MLHF8eJfIUagWfnz52nmq2w2ipD0JI0TCYLRrkzYTJCRdfCMhrMn/+gp387JEaVxV3sdGSaA4vmTyGSYIV+9y70iEQfB5d67HaD/jW4iZzbZuNYYQbB/SciEJGIpdKeOmRLo1+gZ0Vj+ZARZY8gthlqykzYpb45UNHuXYqQ96Q2Z3X8GyR8WJAJQ+i4hKLFpIoMlsBO0joeB75VExK1tHUAeeWdK6plrl2Mse6HXD7m/ZjqjIb3YRqRkYUBdb7ETldZRj4aHJE1ZRxI4/BsIqm2uiahamCKHrctk/nLddW+cJP7UXXfP7gBROUTAgVB1kLsZIhkzkVKwgxxSxe0mFXIeHg1HkMCeqOjSyIqIpDWhPQZQlNEtiR1fCTmFRSYb0fsq+gMt8S0YyYWs7h9oUGn3v5Ptq02JUz2OgLLPc9lptZTm57WK7IWkflmYZLvyHx8D//DT2hiylLvOngNDkt4dC4xXLPIoxj4kTiXNMjZ3ik9ZBNu0OURPRslYV+nwQbVUpY6vdZHQzRBJGlesiZlk1JU8hUPJ7ciNk/ZmEPR9gaqNhRyGUjOjkxx2dPbDGSUbmw7RP6GTYHoAgSoxmR9X7MptOlPUjYPTpgrmtzsTOknLVQkgwlM6DjJqREgytGU0hxATcJ8WOomhLVXBMnHvKDpx0CdcBYPiKM/w+cufxk9ewn/G/wc8cqvO5YBQsXUxHJqSo9N6EbuVTSMQXN4GxrQNcN+ZOHLnB8s0dsKzy42iOIYyqGRhJrnKkLxLKH6xqIXFrFKecdvnFxhY89ucFA6eNom3zmkS1+6845mu6AEVMB38SKbKr5HrtKAbWcQzrVJ6+phL7J2iCk31SYHVN4esvm9PaQqikTqzErzYSZcsxYyaFpB1yogxOFpDWZb1zYQNBcvMSmkhLp9dJsDEKSKEVW93Eij+fsyfCWY6O8+9Zx3nRNjYcuWkQDDVNVOLmgoaPjRjEbbZWyrtByIwqazDCQaFoSaUVh1Z+n3s2z3JFZa2comhFtO2G8HHH3YpextMSvXzuF7cfcv9Dn7d+6wD8+cZFvLJ1Fy7oYxQ6jeVjviuwoxMiqxWQ15va5TXQ5Zq0XkdVk3NBDFmU0wyKdSshkXOJIQEFmvCTxqsuz1K2Avdkqn77Xo2QotO0AMTSoCZM0OgL5TEy6MqQrtdgz7oLg43gKhmDylqtGSWsSaqxy83SBb55t8Qv/cZF/fHADMZK4ulTiIy/eRS2nYxoSp1YCZM2jlk+II4mFpkhroPK5J+sEccShqokkxGRlEV0JeHijxbJlk9UksrJM15YQEp9uL89bj0yRUhV25k0+9sQKZzs9xgs2BR321gJKuQ7vvL7GWKnLEwuXFDC3zOR5//OrvOpyjZwuc+9in+/PNzEzHh9+dIlTmxZO5CErQ2IipFBGk0W6YRtZcdhTtnii3uLySoZX7a1ydDSFmAh86sl1zm85+EORbkulqGrcMFHElEyiUKTdSxF7BhlVYqXpM/AiBARiT6IkFHjBzAgNx6cvDJA1n29e3GR94HDnfJd/fGqRD9wzz2//4CL3r68jS5fWw148W+Y1+0fZV8xRMjSeNVVgXzlDt29yfDGh1U9IqxKvvbyCP1QvxaynE9JagCTGyGKCFEoEvkwoxXzxzDZ/dO8i6RRsNEv84Jks37vQ5B8eW+WWHRVed1mFOBJY6wX81IEyrzlS4UV7C2zFXQ7XFJ5Z8/jiuXm6yRb7qhqnW33++eQWv3fvRX75G+e5ejLDX794F7903RjzXYvpnMYLZ0t84sVH+fBtB/jUK/fy2EaXby4sYwoKqmhQyDoYeg/LV7GdNJYl8v2zQ0Y0gySGv3r8LP92boN8FjbqaW4e2cm1E1UsT8XDJZeFvq1RMIccHPNQdJ9SpcOYqfKhB1aJ4oSfOzjD4pbAzx0cY70bcbg4RVHKs1RXcewcyx0JVZYY2jJnNrKMpkPW6gpyrLE8sFAFmZlsBscyeXBrmzcdq7DWDXjbkRneesUUX3vDfhwv4U1fPcu+qs7hcprHF2zGa9CxY7qDIm7kQpjiiSWNfz3R4G1fvcg/PbVNzTB4cqvP0WmHSLDJaDJBJJDWXSZyAgvNkK/NXWQg2jy5XCCrqMw3PWo5iW3b5T33PM2j6z3uWuxQNkN0SaY1DNmZMalRYHsAu0c9PvP0Cref6vCZl+8lSrl4ScThCZXnTJZY2EwhhylePL2DjhVRJs3v3DJF3064brRAgsjqsEOp7DNR8Pmj+5eY7zj87cNr9NyY/gByuoRna6w2Y750bpu0mOIdd51ntqRT7yUcHc0wlJu0fYdP/cxuZnIZbpqqISoxO02Tiqbwnhsn+d2754nihE+cWGTR6hLHCbdNlvnm6w/xruumyGgSN+/I852FNYZBwO/euYQhS4waBgM75vCYyT890uTl+/J8/tTSf31h/ElP9b/Ej8lt/ufYfsj7btnB4bEUhiphByI7CzKX1QwmcgI93yYSuhArhEnCU1seQ6lOJedRziYooshLZkscrMhcvkMjZ0YIYsxUzkQMR0BysEKXck5krAgnt22arkUmFbPYFLh6rMzxNRtFFHlmO6LvhuiCie1LbPVFStkUMyMxmYzFmGmQU4rYgUBaTnO0ZqCrHodGQnZksjy4OGRmMuDAmM54WuMvH77IuFHEiwOiLZ+1408i7ziCFo3RsTUKukheLtL1YHfOJKUlzExZ7BtREGWfMVMjiAVE8dJQYLEVoooydtwFR2KrHeImAfWBwoHJTaIoRaxbjKRTPLSQpaBnib2ImZJMwwnQBIm1gcXFrTLdeEDZlECIkFDxPR3Lldld01kdhNSyIpLawlAUQj+DpjocLKdpdlV2FXSOjg2Zb4noQhpVClhsi2iijlIYoJjeJa+X1JBQGTCakRnGHr7YQZMF0qrCb15xGQM3QRASZDLsqjq07EtDoKysoIkyNUPjD583TdN1qGUkrtuZRtdUepbBrnKHotnHUAdoqoWueRBLyKhMlBLUxECMdCazAgfHZc40Byy2Q0wK5NIyByZSHKqW+KeTm9y5vMX77pvHTTqkFIm1XsjHnljh8RWLOzfn+e5Sgx/OdymmfUrFmNATeN91Bzi3luPRxTRZxpjJp6nbIU1LopaxGMnYJLFAEKpkVJ2scWktTkkHnFsLsH2RXakSt+0pcvVoBT+OqHtdwgjatoYXhQRJxLK1xYY9oGdJeD7cMFJj4MgsNzVW/SH3LLXxw5hPPrqJKMKnn15itWsjCwE5PcT3JdxQ5DNPr9BxYxodA8eTiOKESkZmvjfgzGaFJFaIRI8gDvm3x6ZJqSGqpCALOrftrBI5Km89PIHrxySKixOHXDaWJhISvnq6jiGJdLt5nreryHuum+G2AxJvPVbmcyfWqWWgbftUMgJZM6Gga+wohRyc7vBrN5R47YEqCT6tYYIkORwYa+CFKpqgM1KEbWfI5aMZPvCcaf7x8VX+6OETjFRdNrc06h0Rx5ER4xzljE/asNhVa3N0/JJqbXVbZeCmeP01Jk+sOjyw0mG2dslkctWxGMlBLMQIYkRFN1lsithhTF4PccKAakpFEhK2BhKKZ7I+tGm3Miy1Hf7Hnaf4+tkebztykM+eWOX4skfREHhmWGdfvsZYBi4fj9FUh6qRp+sF1LvTtDyLPSURRV9DE2W2nT6mFuFEDqEPcSSTCCGTJQUfn8NjCkiQVwxecVCgpGsc1CbY7ke8bu8ogm/gRCFSf5S5TQk1kfm9G2eId+7ENHKsz22x7jj8/RNLoLfoBz5FzWSuKbDVl9hfEVnvQxRHjKfTzDd0Bj7kpApeoGP7MVeM6FQNHSeCA1Myl++QEfWAvC4xmx7BCWTWBzGllEAxF9OnTdOzWLWbXPeZJ5guaLS7CS/aVaZpx7Rdm9lKRE3LI4cG1lDmcFXlWbUd7CllaDgRac0nKykstkQe37RZ6tkogkjFjBDlIX0/5jsrZ/n5m0SOTQagugjCT9I5fsL/XXz+zBp//OBZdEGm7blsDyIIVfZWRBoDieP1RZpthVox4VnTGd54eBxNlHjrlaOMFGFjENPoS1QMHSkycGKHfL5LUU8wVZEfLLXpDxKOTYRcWDT5zJMbmKmAmaxJu6WQiDZjWSgaIoLcp21JDKwi691L6sG3HRmjklV55ewogquQU1UGUUjgxxwajzBMm2HgIScmkWhxutllcdChVBhQzHeopSUaA41E7VPUNRIh5NyGRlFJU1R0/vreDUQB3vGVBbZsj0wmYakVcdlOF0UNGHoJk8WY8dom1VSC5UhYHZmaKdL2Ai5uVvBcERyJMVPjmUWdrKzQtBKKKei7Ib/87dP87ePz3HzAIK2LfO9cAyHWOFxKU1MqbHRERtOQ1ocoUQpNt5kuCKytq+zK5JhvSPSaJdqeixDrnGuIFNJQqHYx9YTO0OXeeYucoPPQRo9rZjV+6rPnuX+xx6svK7LkdhmvRFhBQC0Tk9F82m6C76TxHZGX7ClzoWkx8ELGcgr3LHT4xMtmGc/qVDIKgyDieXtzfPNcg3XbQjJdihmBds+gMYwoZ0Jumarxol1VYtFj5yh8f3UDlwAvCZHDHLfMpCmmQzx8fHzOrpU5s1lDVhIe2+rz7Kk8t19sosQKe6oRpXRIPu1yZtOAoEg1ZfIbV17J268f5cMPLvPue47zb6cX+OrZAYtNgV++YhpLGPJUY5s9FY2fvzrHeDqLZ1fJKAo7ChrPnSmgJgadoc5iK0s249HyXU7U6zTZwNQCptIZ3nHdOM+azfLBl+zg1VcUeGyry1rPYWArRELCTEXlBXuKqAqM5wN2VQZcMxPzhitrXDaSI+wZNLdMHjgb0WnLFBSN2WyK91w5y5uPTvAXt+7m9EbMu++c477lFms9l4qhcahS4LmzRf79mTqvO1zjd26d4O3XjyDHKllVIYxiVoYOP1xvoCcZ3rzvGNdXZ9juyPTjGMsX0HQPyxUIHIlYjMlraTRZ5ZoZld0lkcdW+vxoucuWb6PoDt9YWGRXSecXrxpHjlK8cnaKQ7U0OiqyJLPmrWErbY6OGuzQ0lw1keNDDy3w+/edRVViZFHgcMXkbN3hkZU+PSdiMqfTaAo0egmhahEmHuM5C0XSEBOBvgdXjGa4eWeWfujxS8fG2WGUedMVNRI5RMh6fPHMFk8sajx2usDQynGxbqIqLraXZqGhoaghQmJy5YTB51+1jzCOee8DJ/ila8t8Z6HJkfEUj283acctDk5YaJpNMRWQ0wMUzUWPVc4smEyNJjhxgIzKmcaAb57qYQ0k5tZk/unkBu+8cpLbT7f4/vk2giBQMhXkWOQLZ1Z41kGF62bTyEpANR/jxwFSkiJlDBjLh/xwoY6qJvTDgIwmcVmlyHxdo2LKIA4w0BjYEjnT4+CERyD0ecFuHdtVsRyZiZLMr1w7TjYlc74R84+PLzNpZOj2BcrFkJv2ZVjzexyclOgMUzTsgGdNpRFDmWpK5wPPmoVYYN1yePY+k/3lFGudmMfXBnzypfto9CK+dHqT371thET2OVYzUUSFphtihwmjusQHHz7P8dUBxyZNVB0a7QRJS/jZQzX+4Nm7We65xJ5I3Q54ujHktpkielzgnV9aZTx36WCuYXm4fZk3XFnlU49uccfCNrfN5vjb40u8ZNco353fputFnNi0yOsSP1ru8PavzxFFAqvthC+eWueqWpZPnVjhwpZHNgVzTZvpskrL9ZjKmv/dZfTHnh/rQdGbj07zskMlljoeuCKGErEycFi1enSdiJxiklZS2EHCwNG5ckxjZy6Nbvj0/YDI0/ju4ipr1pDIXGHfSAtNhuWuix33KOgyGSXNSEbj+TNlrpjQGMuksIKE8azERx9fIhEihoHPtClDrDPfdWg7MY1elvP9ISc3AkwZhqHDhtXBVEJEycVKmgwclShO2HYGTJY9chmX5a2IOxeabA4irqvO0u1nOLcSMvW6d6AIJqfrUO/LJLHCM/WIMFDIpDtkM11yesxSb8jQVphrghtbTBZcHr4YkRIM1oYuw0EWIe0xtqNPJSMymh/gWDV0xeVQRcGLQgqqgYXD3hmLftijYiYcXxOwhlXOb8KYmsGNfKbyCrrmoIoyXT/guXvSTJV8EsHCskZZaipMZvK8Zs8sN4/NMpnWOL7g0BnmKOgyCy0Fx9PouwnDMKRiaOwoWRya2ERKNDxLp9mEuZaFiMrK0GXLcvnkE1uk9YRSpsVGX6CWjtlRCDmzlmUYuwRRxOVjJn91/yqqKLDV93lkq4vtxGy3DRw3w2Q+IqtJ9G0TTUmo5bqEicvADyhXGmgpi81hwobT57w9x9COON8esjZwuXkmz/GNHqru0HaHCKLPfFMmrYcocY6bR6tokkizp+NHIQsdi7UtFXyNj7xwF3UnRAlSvHHfTm6aLPKXD19ETrcp6TJz9SIuEZn0gImix95iCVVKKOf7GCmfrKbw9y/bxYv2F/n6uS2e2faIpYCOn+CF0BqokCTIUoIhi6y0fUxJ5Ruvv5x3XDvJP7/sEKKS4AsOf/vIKueaFusDn7dfMclf3HKItKohRzWc9k56DgjElEwwJYl8dkgiudxxtsU/PL7Ao+tdeq6I5UnEJEykNV5+xEOVIYwiRMnnwc1zeILFFy/OkTdi8prC/GbAZs/ni0/XKZf7FEstLDfmO/N9Hl8N+P7pmFZb4lV7R8kqGl7ksb4t0bESVtsJQRwSCgPe9b0VLvZbPDq/m0omYUzbwcX1KbzwUtLYVi/Cjmzmun0A/vDmXRwoXvILK4x0MWSZiazK6/ZPoip9MjrEUY7GQEBQBxTzMWOpAvevWthRiBjJbA9c/vjRR7iuUqRvxXSsgLSiABFpIyEJ0wzjAYV0QNbw0FWPsp6i7kW8dd8RUhkXZIeM6TJWSPA9maVGwq9eW2EYeGhJij968CLPbBosthMsV2PoJzh+nrYtMZ5Joas2clxjyw7JKzn8pEUUZjjTCrEGCpreI2JIz9bZGFqMVT2MdIOlnsO9q9u4PkhKwldOtinKBjoqthfxgj05Ls/nObfmM1vOsOeN72RX2eSFMzJtzyenZKmkZLwooqrkaDQLDN2EJCgRxRqKOqAsG9TMgM2hQ0oGVRFwkhaGaiGLAud7Q5b7NqacsG37/I/rpqg3TSYLEYvdiEiwGU1lMAWTgR8xVQr5+FOL5DIihqxxsFRGTgrkzT4Nt08oe2y205yuB/zH0x3eeGA3O4sJjaFBItpkJZljoykOVnW8APquiCGqbLWLfPfikH89YSErDRRBYmhn/strlCD+114/4ccMIaA+sAldgbSsk1YVunaMH7sYskRsl9BSERZ9znTXeNf3znDrbI7XHa3Ssn3wdfKmSMuCYRCho6EmBi3XZ3FFIavIHJrwWOkl/N7zJhkvSuzLlbjY8tk5NWB3Dbw4wk1sVFHF9U1Obyr0bY2DhSLfXmyxPHD45JMriHpIxwu4ppZjX02m4cRsd3XyWopEGrK2qZOSVSTVQZUccprGVl8iJsAUsmy0BbY6EjVdxRX6nBv0ecnBAu+/c5nFwZBS0afv+8wUJPw4pDmU2FGAcrGBEKXoOApmxmb/jE0h08UQVIJYIJO2OTSh8IkX7eVQLcNmT0ZXBFQtJlOwMbSIp7baLHQcNvoBhmSwuK0xYeY5UMyxv5ymZSucWqwwsERGsj5BHHPtbpNbJ8qYgYEVxmQ0lYEfUdEVFlsJnpNm2woxzZBsxuJ5u4p85iX7eNNl47z1+goffnSVP7pnkZ4DkXDJ+HmpKZEkGnEk4tsKN07lWO/6xJ7CO6+e5EO3zfKGI2P84yNb/PVLp3nZwSIlU+a931/mqxeWGCv4JEGCpHscmRQ4NpJlu17il66YJaspvOHoOIIYEXoiZTnDsWKNphXywKqFG4gcGnOZrXpUiwNcL+TWiRpCJPK+O5dRQwlB9vFjl7mGznZfx0Bhbjvh3d+7yOv/4ywbA5f7z1us90JajojvK9w2XSaryXzgWbt5+c5RrshXuHVqjLQskTgSZmDyyZ/ew3P3ZWg6DvPrBktNiYGfMN/yuG+5x8qWQqNh8Ce37eCbC5v8YHmb379r8ZJ/USfGSWLMjMtkQaSUlhgvi+SyCQVthD3ZvewtjqHJAsfXBmz5DqKYIKsxZ7odPnD/AnP2kE8/WudzJ9f4twtneP6uEgM/4m8en+fTT2ziR5fUpl0nIIgi/u74Aref7vD8nSP8yo2X1F7n6ja5lMhVExIt32OlDX/7YJ+s6SFFCZm0w8p6CstL8IWYopDi7VdNcuNMAT+J2bBgceCyMQgIcKlmIgYDmV/4xmk+cN8c+0opLD/gvqUOimoxXe4TJwJRpHHHxQYntwYUTIH95TQXGj6fPl5nYdvnMydW+dIzDQ7XUpiqyEcf2KTjuTiJz2AgkZJlNns5VtoydUsg9jW27ZATrQ7LPY/1gc9kTictKWQVBU2UGPoh51sOGV1k6CSMFwfYTpqeEzOSd/E9A9sLeLrR4/azDV71mXPcNraDe1YbXGg4WIHPq/aPYaoSiRCRNQN2llxatkAlBRMjLQrliCiWeNvlU2SNGN8XuGVHnrdfM8Hzd5X5yPP3s97xEUWRE5sWAM80B6z4PdKxSdt1qKXh+vEqUSTT7Bu0LIVKJiKl+fzec6a4djrDVMnnya02uhxTy3sMPAj8DILqUJNzeGGEKkcYksx8v8mLDsigOEixyMeeWKXrO+RViX44ZM0ZMp5Lc7SaZSqv8EfP2seyU6emaWTlLA+uD3nWnhRv/+ocV4/lefd103zihYe5ZrzA64/U0ESFH833eWy9z1++aowWLe5datPuxTwy5zBwBJ47VaRnqQxim3JKpWQK1LRLaWSFMvzus3bw7XNtTq3bZAWNrp1wz0KXYxMGf/nQKm++bALB8Dm3bfN3x5e4c7mBmg5pOT5vuaHKd+YaRHGELsM7vn+Gix2XaiXgsprJPx2v86GHV3j95WUWWy7vvGaSM42AOy90+eWj0/zmzaNogsqbrxohIeHm8SolQ/uvL4s/6an+l/gxuc3/nBfsrPLQ0uBSwQlFBq7MzqzBgWKKjCaxPgywQoe0KtCyReTUCgudAVktRhd0GkOfuhUxZmY4UjXRVZCkSwloeTVN3fIJpRZ7Rrf44eo2p1oD1odDKpmQRg/m+k1yhk9WTLFR12nWdaZzJlKSwYvgdftHeOFBBStMiMMUZa1MJmUTRhB7VQTJo+OGTGY17Njj5IrMSElkoWfx+IrPqbrFfXfcT78/YHr3DE9v+0SRhN1PsTYYoosmgmzRtAQkUvSCPnKcodlW2FkeUNENHl8xODiqU80N2JOVSRsipXTIWEbFDWLkOMtyR2Ism/DkikTX0qi7LtWijyh5lFIxguSzq5zwpiPTXFMrEgUCeU3FjTwsx+DsVkBBkfmTO9eJfIWhYxLGAtWsyEOrPb5xps2uok7dDji236brJmQ0gIRz9UurIAdHexSzFmOFmBPrWZxkyC2HOhzZM+Sx9SE9W6SWUTjTsFkcNBkrDEkrOpdP9Dm3rXF62SRniORTMqKU8IONOv3Ex0xHlMs+h8cc8tkI10v4zskCp5dNHl3I4YcSJBK7qwG17KU1uIyqUk4nFDWZOFRYa+SZGnHIazKHqibzvQ75rM94PiGfEjgyYRPFCn4kMd+1uGuhhYdPORfxov15jtWK5DPypdOZb1zk7x9bxlQk5loen3tym4c3Wmz04S3HRtAkgb2VDpaXoMoxT231LyU3eAJ/9uzDvPGKMQDu39hCUXw2+h5dK2aq1GZnpU9aFjAUBUMSyYgl7jnrMp5W+db5BisdDz+CjCGQ0mLaw4DWIKDv+3xlfpk/efA0hhrStUIe2RxgeQK3jo1RlPIYgcHiRoY/ummWnz5Q45bxMR5cHrLVkTGEFB941mHeeeww57ezbPcF/Dghp0nMr2UxjJh2W+MFO2skUkgr6fH5kxsMAw/bNehZOXIZeM50noiQv3/RfrY8lz+9Z5UxqUg4TDNaiklrApN5KKYSBnaOfeMuHbfPoYlVegOJx8+J/ObV09hBTK+ns2fSR0Tjh0stADqOzw/mBnQaBVwvTcrwiOUeXzh3ltlKwFTBJm/0yWdsDFmhmHGZLvr828su559etR9dlBjPGKx1dFYcC8lwuGH3AC9yyGVCdhbAix16wwJ53cWOBlTSIjEhh0dy3LyzwOKWimXnON8MuH91i1i2uapcZNBTedXsLKok8q6rduKGPoMgQVd8RHmIrrg4QUzbkqllQnQ5gihH25IJ7QlUxWYymyZShwR2AcSYlO6T0SQ2LIf5zTHunYfPPr3K7zxvjJWBQ6kU8YfPm0KIRTwx4r7NbX752QW+fr7Fcn9A7GicOHWG7vf+nSumLOpem8WWzlRpwIltC0EI+dG5CndeiFmspzi3WaQZtnnqQpkrp1x0o0VzCKFfpW5pDEOfW3YJHJuMiYgYS8v89cMLBLGM5wvsKWrsq4UE4gAvjBBjlX7gMGevYHk+d5xucef8gKW+zXY3z56SiiSJuEnClJlif1XjoRWbnA5+CLmUTKL4PLVlsel0yRgxr5qd4kJdIxE9TCnNZeMpzqzuZrMv0bCj/7Za9hN+wn/GX966n2uqo4xVE2IupZRlJAHPNZGliLWehChGVHUV19V5dMlnuevyF/cvocYpUAck0gBTiSnpMn1LYmMAr9u7m9GRBBeXjZ4EVoUvPLXNyiZMZDVquYRCKmK5rmL1dEzRZHsoYscWE/kAVZR4atPCHkbsqgk0/QA3cdlfMDk0kqaq59htlshrKlvDgHbfZLyS0PFcjoy3KZkKMS5F00P2MixsiqT0kJGUQjrfRJYkdqZTfPrhOpEQUSoIDKOAgimz3VZ50cQBqqbCxTqsNUo0bR1Dd9hZSChmO6w0Lv0HBlHI3lyGk6shb/jqBebqHlIM5XSIIDro6PTsmMQ1WeoPSSlg6C5ThYhX7i/z1NaArpUgJQGGDumcTUoRUOUQOYHPPbUJaowgxbi+DKFMw5LJqhLDMCIlKfQ9j0O5Km+9doRPPbXJN843uXEmy0t2l/jgc3Zz04yBqjjYgYghKWz3BUIrhSwJPLLdxRVC3nx1hc1+yObA5y8fWObOxSa/dPsFPnd8m74TMjoSoGo+y5sKia9zw+goH7j+coJ+hhum0nz/fIfvLK/wbye3GDNSpPWY5Y7Hw5stzHRANZOgCAkdL6TvCgTdEd50aDdn6jaXj5p84LYx3nC1yWQxQhIMxnMhFdMD2eHMYJsnt3rops2yt0rGFJBiA02SSXyJexe7/PuTDXK6zLcWN3n1kRIS8MS6zYHphNdfUQZgbzHNT++dJp1JMBSRa2vj7KiEVLMe2UzMSw/ncYKYv3/+QW6dqtEYXFpj//LrDnK4mudFszU+/NyjvPu6GcqaQWcY0w9smk7Ci3aPcKCS5q1XjfIbz5pkoqATRiICAoNhxEt2VnCjGEX1Od+IWW3EjItp+g2d99+8A8uP+PXb53j/D+ap5EJ6vsW5zoB/eGSdhxb7/MvjDZwwZipnUG8b9Prwucfq7CgIFNKwY8RnNBtxzW6ZyydMirkYJRXzJz9c4dbJEj93YIY3XpHlZfuyOF5AnPjUByKe4OJJNic3+1yo23zkvm0KKYn2IMPF7SwNS6JjQd+OqY4MuWeuwzfPbZDRQwIXPvGyWd597W5+5kiZXhjyt4+u8K3TLfwQ4lAnl0kQxJC+51PNJBR0mVJuQCdsE0UCQizydz/a5LHNDv/45Ao+Hoo6IG8kqKFKKHkY+qXNAif28UOVZj+DJNv4gUq7H3P3xgWyxZh/eXqbg+UCz5pOc77b5V+fWafeU1lomKRUCVG0KZkSCy2D1tBkR2kAQsDuQoZBR8FIRXz1TJ0ntnq866ZxymmFN1xV5Tk7C/zmjeN86pEt9uRNXndwjPWBx+PzAl99esBGJ+YV+yrkUiKK6rG6lWOpnuKbZ7pc7PbpeQkpXeChjQ6yIJE2XEzN52BhjL9/2V7K4jhOGBJEMkuNHN887TBbTuglLi/eVWQsL+BhUzQEkkRkeWix3A75t5Nt5tseI8Iki5bNrTsq/Oz+Cb76TIth5PPu7yzwgtkiYQTfONPk6GianSWNnTWVtCrxqcea5JMSFzs9tjohoyMhDUvijlM2r9xXQhZ0+kFIJuvxxEaPp9dcNgc+D651+a2bJvnU45sESUApJeArPleMZskaMZ9/ahU15XG80eHm8RrjGYG2H/DFM3X2FzOYvsmoYfL9xW1esXOMV+wcx4nh4JTCA0sDfuvqHXz4kTXeeKzGi3ZVGDNVTE1kqqDz+sOjl9YkBwF/8Pwp/vyRRS4fyf33FtGf8OM9KEqShA/fs8EwckiZMUmcgLrNxe4AVXYoajoFXcWLYgJfB2+CvWUTN0roWiH/48YRfF/DTlqcali0hiJCrNGwPVZ6MSVDZ3fBYBh5aIrHgZLJ4ZpG103QzJDZkkxzqFH3+5SqA/bPDHl0zeexlRjB07ltd5675wOSGCQRTmxCZ6gTBBm6XshM8ZIf0TCIMSSVHbk0ATFFXeF5u/O0un0G93+NK685RELEjpyJLmlMjva4ZkoBEhQMykaGviMSOqN4gUqxEJA3DM5tGhQ1HdVwaVkGW22TtutSMGMaXp+0Bkt9h6OTbbbsiImKRUoLEEl47o4pQt8kpWi8YGaUnzs4w01TZSIhQkoNscMICGjaCUd2BHRxOTItstZWkQUD24vx/ATJV9hTTPEvT23zvCvWqWRFCrqAGw8YzQUQK+yubaNgEsUy882EnC5QTsH8lgaCxEQ+4SO3HeBPn7OTU40ORTPmYkMin+5RTSfMVn0OzGyz1kuIgWpW5Ni4wp/fNkOYRCBazFS7VHI+syMCr9o3RlbcRUpMyKcHrLdkzm5rjBdaKIrLVl+g3lGxg4RA8FE0n3ZHRpMCTre7pNM29b6EE0ZUcl3mGynyhosqShRTMbuLKWp6ihftGGO+FfLISh9VD9EUmKpIPHc2zztuGOU3nzXBb908wWUjKSxf4OnlEMcHy9WYKiRockRK8egMVa6abPHhB1b4maMVTm1bPLI65Kf3jaNrEVlRw/VNdEVgrOhxKJ/G92TOtYa85GgORRH5wuPbnNgYosoix0YzjGVEUrrAXz8xj6i7zLV7nO+0UfSITCrAUOHDz9+F7SS4sc+BWhpDFnj/PatMVzSevTOHGGgQyEyYOn937waLbZeFJvhBhumSQ84M8FyJhg2ZXMDdK1tkVBEl0uh6AUqoczBbobUlI8guP1roEcYh73vgBJWcRSSHvPe2Sd5wZYV+V0aUAjTNxvZjtocRYeJSt+CxlQxmOuDGvSYfvGed0E3hRxrrTY0ggtcfnATgw/duoCoxaiogEi12jTapZqCckljvSejykEo64cpxn1Kmz9kNmR+eF/j8U9tU0wpeIPCqPeNMZQ12V0Uuz41xfltjraXiewpx4lEzJSom1K2QtGqw0YvwwpiW4/PnDy7xigNVfv7AFKKT5/yawvvu2OY1l5d4ZKvL7nyKy8d1jtfrhEnEsbGInbVtqimVrmdxcMSh6Q5pDzUSqYcsBXhxxPl1HcdLYwchtXSKvp9ghHlUUaCgSRypiYiiz55clp4b8fEnV/mj50xhxwGLPZuXHCiR1kV25mQ+deZJfv6qHKUUxIrFZTc8lzsee5ojosaeQoqhK+IEGjftVBAlyJsJx0ZTJIKIIkjsLZsgi7R6CtVUjrQq03YCSrrMbMknllqIko3raZxv+pzp1pHFBCeENatH3YrQkzzvv3UCJ/HJKQYVtcJUNSJIIipKmpyYZ6Gt0Q+GyMRkjJi5ps+pus0/nVwjsGp0LZ3VjkBGg2PjKfaXTAQpZCSroCcZbhip4SYuJ+sWS10HU4GZwv+BcvoTj6Kf8L/BREHneGeTtxyZIfIVcoKJrMZsdBPeengvOUMgb4gstRTOr6rsqAq8/HCB+xaHOEGEjI4UZWja0Al9stmAy6p57phrc7EZ0+no4KdouR5rA4cPvGCSBzfa9HyHh86nIZbJ5eDMtspS3WQyI+PHES03JKMlZHWRth/yG1dPMWnkeP3lNT725CpNx+V8w+Xm8RFG1CJCqLHQiTg6ZmMHKTquT9c2sKwMTTdiasylnBERUh1838B1VX60POCmAwZ2FDOtpSmqKn1H5BePTXLf6jYbQ59SRkQQJMLEIa9LRDjMbZZIIoluW+S5M2WmKwKTNZdhECDoHplswkJT4orCDl4yM0VWSTMcCigJjBRdhnHC6w9N85avXOAVB4tIRh9DUxhGPrVMzONzKTq9LD9a6ZFNw8ANSEsqw7aIkqjkUjaSKJEElw6tBv08s6Usv/G9izyw3OPhpQHfn+vw8Hqf9917lmawhSFqGJrDSN6ilgtxZZtQ9ZEFuHIky9rQ4961Np96epkrp1MkUoQlWuTSAokSsd0XKGmQNeHN11bZbsG7b1/iVKvL187X+ejDGxAkTNW6PNVsI2oe1+ztsXt0QC41QFVsgkAh9k1agxSNpM/fPLTOk9t9vnByi08/c4IgcfGjADvwObdl0Bxq+IGBjEpGUclKJp6XYTSrY8c+suIShCFKyuGXbqwyns6TDMq889vz/P0T5xgzNbTIJGfKdOyAP3voArfPr7IzmyEvF7h7vs/AkTFUCQGRuxfb/N4P5vj8iXVSmoAd+ZxvW3z0iXWCJOTBBZ9PPLHOB+5bYEdB4xcun+TtR2dpNC79cdYtj7vmWpiizE07cuwtGeyv6UzVPD59cpG3XVflb557BLev88D2Go7q8as3jvC++8/wr6c3+JVrxxhJX1KOJ57K0UKBd1w7zvUzWV5/RZklq8+rDhdIKwoJCdfNZMiY0LFktnsaG12d01shcy0HIYmYzIlkUgJ/9+A6J1dtTqz1GcZNxnSTyMmQVgSKpoAhR8hxzAMLfcxCn586UMFxdNpdg6yY5x1XTbG3nGW7nsJKAiLZRpJDqjWHd//wAg+td/jWmQ7NfsTHXrKPd98yyU07C+zKGdR7In1bJa/LOIFCw4nwI4m8JiEJMAxDygWRtCzhO1Dvi0iRgRqmsaKIJFAomyI9L0KTJMbzPl03QpUk9o/3CCSPtKqQy7qgDXlwrckr9lX55AsuZ3cuw56iwWgmxPJDem4GSfJ59uwm1YxP3w+ZKlr8+nfP8Ymf3kPZUNg743PRqvPGr57ivd9f5EP3rzLwQt5z5wJdz+XNtz/NQ4tDijmBZthnvOpzfKPL8Y0+L945hoJKcxgxUxS4YsxEFSU0OcEJYF/Nx9QtrGEWN4o4vtbjNf92lj0Fk5xsIkg21UyIrsK5TZmSJvLF88vEMUzmIREdSgZIgsC59pCCJvHxJ1eZrkoUJYOPPrLBHRfbfOA50wzdhPnugF+4/Qw/WGrxtdMNPvjgac6surx8tswnH95iseWx3HNRohS+EtENHEbSUNZUduZTpLSYlKSiKg4DZUjZFCnoIo/NOTx3d55XHM4SkuDKXbRY5IM/WqFtwWo3Zl9ZJKcH/NkPV9moi8yYKrNFg/GciufB46sWhp/hy89sY9sSzxqvcNeFAb90bY2P3LfJVaNp/vnkFqIoMLft03ED3ve9JX7ui+cYy2iMZlWKhsKHnrMHL/w/sNL/k57qf4kf69Sz6z52N3m1iivbRK7MrqkhpiJQSEW4sUPOsOjaKs1eGR+XjX5InIjsKSoEwxovPVTgHx9fJacJ7Ko6+Lj0HImMkiCKMq3/h73/DLotq8400Wd5t739vDnf8SZPeg+JywQkvBEICeFkClGyJdHIlywySCWEHCphBBIgQAjhXSYJpDcnM4+3n/fb77WXd/fHqa6+HVERHRWX2+oueCLW771nxFxrjDnmGO/rOwRJiphaCJnKiXWBesFntqCRZCKl/BAhldBEk44HkJKmCpGnMvBicoWI8Ryoeo+S6dAeNNl1MnachGfN+4hixtowJArKbHVybHkeZVVDEWT6sUO+s0ri9Whev8CEaTCMEraHAi857HKhkxGGKnGSI6WL1qqxa4UULBcpLXKuHTFXuKodo0gxg2GZ81sx103JbIcudV3DTxJmKw6NfMrmUGIyL9IPYmynzHJXZKGe0LAk3rD3EAebJtt2SJgF/MnjJ5H1HQyhxDCIsN0yaZpSNGPaQ4OdUcjxqZC1YYDTaRIGIkY+4oYZkWvHilxyL9BzRC53JAqyRZxl5NSAkgkpHjnDYejUGTgqmuZxZafIsWKFn75tnHd94ySFQszQk7htYRVJlFnpVtgZavRGEmIm8/o5hcf9Fk19iqVhF0u3SYWIoVMAIrZW6xxfkHlmy0dXY4g1dNWnWXOIE5m+l2LKMprmMXA1NDkjy1L2NgM6joiclVnqpFhqxvHpbZ5YnmAQRExYCZd7IpM5jZiEka+QuAqeGFHKx+T0EUIwxo9eW+cvvr3FPYeKLG0nvOoGnQ892WJ7R8YWfcq5kOvmXaIYltsyI9tCVEWO10tEhHR8l61RyOHJNlWtwpX1Kq7Y5fb5Dl87O8lsWebsaopiZPz8LdN84sGLbKUWb791gmMTJv9yfoOn1gO8LGC2aNFyHVb7AWNajtm6zuqgQ5JIaBgMggzVGjJftDi/JvO268d48cEqZ3dc/vH0FuveDqaU59bJMl9bbKPKMY2iywsOXOHRpTleNncNj+/ucq7bY9uGmUpA2RzQUA/wpsMH+dL5Lh88tUbOSBClhKqhMAxdkkTlxw7P0XJSjtQt3nP/Ot3UpVoaUbMELqxb1PIStZzNWHnIcDTDPbPTrHUCPvjYDmrB55duXaBmqRxt5EmzjCTN+M37LrK12eGlN83w2YsdCpaDHYWUzBFVU0UTJXTVZtsRGA6biEmR33vOXoqGwh9+Z5UvXtylVkxRRZUoTBhGCePFFDsJSGOdnKKQN4c0CwmK7LExlBgMa/zSrXv5r4/tcqUdYAgyeUNkqedTycPaIOa+txwnp0qs2h5/e+oJhp7OeKnLVCllewhxbCJIQ0zZwEn71DSLbtinouU5sSkSRwUqeYdTqyXkwGSnk/HG2yp84VKXhVrA4rpOLMUcnQs5vSHzy3fM8a2NLW4dLzMrRrzvREgn9bhxvs+VrkTTLNDt6mwEA45UNNZ2XKZn+2x0xtj1QyZLHtfl9/Bga4tBGFCQczQru3hxhJJW2BwK7JvYpDes08gndHyPONao6AZh5rG4m6OoqZhawlZPRpMTJioZkuRyYdPiTddM8KXFNpG0y3pXZ7Ig4tkWV7ohmp6wty6iyRK7PRFJENAUMBQJMzdgZctC0ELygoEjuLiJD2TM5TR8t8DuMOFIw+LOgykPb/TpuhJuHBA4Pe5763fX9ezRt313Xc9u+cD/+g4d3+f/2D+/9ZWTLHowXdD56qkRxZrPZDGiJNfYGcVc2IyQBWjmJWZqKq84XOXvnlnkNXtn+dzlTXo+OAGYashcJSPupihWjZwpsdwf0fdTEtHnWFNmQm7wM7dP85bPn2W3l1ItpPSjiIIeYkg6lhaTSjZeKBP6RUQ5IE4E6qqGGBV4w7UNSjmB3/zmZbptkYmZIVVqnN+JyFSPo1MBjeIIOwxJozwrnQKIIaooo8kpIR5xZGJ7Mk4Iv3zLPO97bI1RT2RmPMXI27SHOuW0xGbY50evG+eTZ3bZHMQcbmbMqAN8U2CzX6LrJjQMk2fvUUizjK+eglYvplrLGK+3keMKP3PTfn7rG2sMEp9hEHJkIuVKJ+G5c2X+000HudT1eN8zJxmMZLZ6MnvHPII0ozcy8EKBkpkSpSmmIvDChQaPrTl0oi6GrLHShr2NFFFyyKImv3jLLHlN5huXenzg8R0myzJbts818z6ZkDBwZIRMJSBgshix1bd4w8FZxFhio7vMpxYFqprOW28Y56z7DI9eKTJwwTQDZsoxihjx+JUSBgpjVZmltscrD9Z4ptXHy3x6I9g/7RAnKu2hRs502FsfsmvncQONvO5TtRy+fqrJeDXDEjWOViq0RhEPbfXImQl7ah5ZJiKJAgNPojdUcW0JveTTG6noosBL91e50PXpJwOSVCCORbJM5J6FKm+7doqHV4Z8dWmHTtBlRrV5qlsgiK864nqeSC4f4ocSSSJQtCLm6w5OKBEFZZ49U+PEps2lnkuGQNEIecFcE1PIc6rTwUtCTq1mFAyZe+YqvPZ4HV25ehJc7vl8+OlNLEmi56YcnFCQRYFbJ0u895EV9uQt1gYxfgDLYRdDjdFQuWOuwtBPuG28hhPHfOTpHfbVZDpdgWfPl3nd9fX//s7+6WMXefSiz68+Z47d3V0uj0yeGQ5w6WCqIYIgsLiTIyenaGZElog4rsarD0zyzydbVMt9Uiz2VfKc23WYb3Zp2QZhKlFSTLY6YMkiopJw3XiBb6/u4sUZmpaR12IEQSIMZRxHu3ppLgdoSkwmC5iSSJ4CL9hbxVAFnjdX5q2fPU+UhahShKT56GpCs5CSJiLbtkCcwHRujKOVIh94fJOxukMtkuiIEm5oUVIlBrHP8/YUqeUE7l3aZaWbsb8ZosjghDGbrQqQEosBzbzAwBfQFIEXzU6ysptwaeDS9UcUCzGKAAcmttBlndU+qILJel9iOCzwoj11EinmoY1t9oz1sF2TMxsGBU0ip8nMmjke3m1RURW2BhllTSZSPfKyhJcmWErK0NVQ1JC5asC2LTKTN4iJOVDRGHS3uOiWEOSMwUAiCw1abkLFlFENF0mJuW7a5pEVnZysMfAFZCkmsYvE1pCxHBT0mK2BQBJZyErEwMuwJA1BHzF0VAqywtYoZT6X53l7Snzs3BqSKKCJMi03QvQkpscFXr0ww3u/s8GeiQg7i0iJiLw8040+LS+ju1tmPK+z5njkcz4aKgMfEkIqYg6A5x0s8OZjk/zOVx/j9EgjEQJC3+JwU+f6CYvXHWnyO488TDqa4nS/S2irpGTcva/MG69rMl3S+duHtnim22VxI+O/vHwPfpLwkSd3ecdtE/znL69y2XH41x85zB9/Z51Tay5hCIEesFDU+MCrjvx3w5zBYECpVPp/ZF71vZJTyf/ef+DfE0UUcWWfFDgw7ROmGSv2iNURHBkLGc8p7NoGhuExl/eYLhRwQoG1vsh/vKHBeE5FReZib0Qn9rh1xiVTy8SM2BzkmCmrhJFF100JE4Ebp0WKho6XOIwX+8gU6HoZD68G5LWMumGy6QRY2gDdKqDLGb3IZdKUEJMKix2NPYUCt08GtKIdbF9kLm+wFCkUcgFlK0fHFnDDhLlki0G4w97bjjJTktgYxkhixs0zEt3QZ7Ig88xKCcPsENlXNYY0OUFKy5xekZiuydQKNkGcIpFjK4k42jQh85kydRAFxso+U0WRvpdgSBY7jo2p6NyxcAl3eJwTay6HSyZvfPQSh5sGl3sex/a5KJpN08izYyeYUp5IcajnBE5viahSzHg5QlYcSmqZ2nSL4e4YPU/lscWYTXcLLxNomgoqOrYHmqITJDFnd1KOjisEYY7NXYVKQWAsH3Fhx+XRNQVbcMhkgVMrGs890qJiKKhyxpW2hyJHNAomSSRw/6LHBb/IR181xZ+d6CMKAqasIujQGSlcf3SFvmfhpSaDHZVqNcQXQtojhZKZUbJiFEFkFKrk9Iw91ZgHF3UaXsLQz+iPIjZaKgUzY8Oucft8h9ZIZm2rjpTFDD0ZJ42Zr9h0BIXI11ko5cjcKku+xz88tcMvP2+ME9tDXnVNjdsmCjTUEv/wxC4L1SpfW9piLN+i56romsjLD1T56JNDnli3SaSYHzrU4F+f6fPUBYPpMZsos+m5Kk4MmhZxvhOBBrImgJhgWD64GrIo8HsPniETI1AFxFDlJfvLnN7II8YDJkyDt17b4Ffv76LIAmOazvteMsdjm13e99g6qRyzGwS03YjPne3QCjoYasbPXD/DN5eG/NULD/PmT1+gUXR4aGmSOJH44weWyRSfmplSz/mkicHAK9DqhgwXYsqawh8+Zx+//uBZZop9MhT6rk5Jkdnqp/zzuV1ULeXWqRJP7owQBQE3ksnnA958fD9PbY344rkNDMXl8fVL+FFCviShKhIfPL3MTx2f54n1AX/6xHl+/Y7DrLfh5kmTz13epuPKzJYLDPwRUSixE8ckmUDZzHH72Cy3H58hpyr89YkVRkHKtc0cp+0hTUtgsZ1wdM5GlRTWBwliLKIS0/M98roAmUCYSNQNjSTp8BvflPnj5+/jFz+zQmYk3Lm3wOQg5mJvxLPqFmGc8XPfeZqNYYQUlJgY36HrKiRpRNs2KVsRhmRiB1AxC0RZSEWrMQw8JswCJzZFLDnPeNlnZOvMCzo/fuMEbzg+hhek/MrXz6IUBwzimMNTFr/1nQu889Y53vvIJvsMm4WxWdrtLg9csjg67bPtb/Lq/TfwV496PC0MuOHy/eScOb4W1zg2I+K4RX7pB2cpPC7x+aVN+kHMNBaHJ0cstn1qaY682ETMxWQZ7CnlaTkpGwOBibyGnOisDF1mqx7VXJ5R4nG+ndC0ZAqyxHsf2MLMxzSMModqGaMoIRXgYN3AsiLW+imSHFKwJBRkdgKbi2smU2MiNVNkEMtXuwEThX0lFScQ2OwJuF5ITknppTYP7+6iGCNWt8aYL4scyEvc9+8d1L7P9/n/4tF1m9mxCv0gxbIypksCXiJwsWdTtkL2jus4to6W9/jLHzjO7397iaPVAp84t0OQOViaQM0wKORiduwELZCQDbjUHWHoHhO5ECE1uH1ikq8ub/Lp8wpZmlLMJWTKiAkrpawrXN7NqBR8hEwnQePyIEb2DV54POKJixqOMuD9j8WUyj4bvRgrD3Kic2orQrZC9jUhIeFSW+a6sYzzLZFtO2HM1ImVmMEIcmoeP84YhiG3NZr8zeNb9HoC45UMMz+k5ygkvsGFkUsjb/CV80M2NgVmJyRGsU8kisSxxmo3o5lT6Ychnz0Tcaxe5K79ChVV56tri3SHObxA4GLXYyqvcemKx8KUxMVNgf3jCd88HXBq7SyiFhGTETsGP3tLnX84uUkm+WgSzE11aPdq/PCxCT5+eptPnm6hSAKZoDEYqOQQ2WqBLBrYfsRHT+zw5OaIqbLGG66v8p2tNW4dj/Ayj8ArIksxE+UBUibw9IZFOjL49KkOkjGgHCbIksXeusZXruyy6Rm8at84/3K2jaEGbAwkCrpEPZ/xyv11Lrd9MnHEeW+ZfiaSxQYz9RBJSEnFkOvKk4SSwnpPJEoz8vqIZiFma1CiVhAREoE7Jsd40w1N/vTRCxR2FQRPJo585hp9FlsmmiJgmAJpLCMmOuOFlDDK+PKVLoYWoygxvaFGTErJTHjtoTF++rOX2VM0OLHpEIUqE9Mp1aLDrg2aLDN0BWxX5gcXmnxldZOOIxF4FZ5zeMS4lfKBh7aYLBvcUK9xrtflpfvGMQWLKIIXTI+xY0fMGyEvOlBhaxgiCnDfpT6feabLjbMWEwWVTdvjymjE6mLKTNHk28t9elGAnQ2ZzdeYbai8+9j1fO18l7sPFoky+OK5Dh96aosxQyevwum2y41jJepleNtnzmKaGVN6njUn42efPcHnL3d4zazGv6wOcNOUTFSZqHkkmYw/EghCGV36bxpZesi3l3e5edpizXeRpJATqw5ZLLEsFAkikVGYUalofOCV87zrC8t0QpdvXO5RLWXM5wNsXyZJBYqmz8ZAwY0lCAXyJZEoMbAEGTcK2eoHXG5t8aIjJf7pqV2ySOCWvQZfvZBwoCCS01NUKaYbZEiSxWQxouW0+LflXSaaKtfWGsxJKe8/F2BKCe00ZDAQ+LI7YqyWIqo218+K9Owix6oFvrkyIMkyerZAybLYaidMj40gk/nEmXUKmkGUQBRqZEnGqpMQZ5NUCwMONTyccIQXmcSeSBRlmKJC1xEQN2v4okvFEuj5Ac2Cx7cupszV8rzumjrLfZc7pyr8/v0rLHd99o2L9KOY6YrPUlei56hUrYBY6BEmBk/tjFiQVBJCrKTCsCeQiAm3zVmghCy1NHIFn7W+yrHxmFE8RLHrdF2JRHGQnQK72ESJStVK2Bz4uKMcpumx0Y+5e7zMqcjFS31qeo7VbsjlosdsyWB56NK3Iw5OxOw6EUt9hYdam8zNekiqw4Sk0Pck+vTZ6OnkdQlVj1gbZBQ0Fbsj4RaHGIqGGOaYa2pkI4Un1kYcrths9FNCISEv5rlrrsgvPXuat37uFGvtlOfMHuaPvrNGUVOYnXK41INPXgi5sOPz4dft53zb5eUHxviz7jJ/8sAqH3jVAT4hdDi74/KGW8t4QYWxnEbRlFBkgXv2F3m4s42NzXufuMKv3LafMEl54+ee+fcOo9/zfI80Tv2PcR2omilxmpKIDiXT51hD57oxg4aVMoxCtgcyuigx8EUydR3TWscLdNRE5tyuh58kHGvI3DxpoCkKiurghzpTRbjcFvjm5YhU8EhTmVgY0A8H7KkG1M0cqRATxRKz+SI146rd+5FGTEMv4cYpu07E/lqCF4p88oQOaczxpsWJzirL3YycCsX8OnfuuUjFEFgbhPT9gPEiPPXR91KeGWeupCBJHjOlgImcSs/LiIMSWWphmh4TloUvgGUm1E2DgZ8xW5FJMpnNvsm4VcSJInKiSaoEmDmPoauwOQwoaykhu7TtAuu2i6nAxiDm356e543XN9hTSdgcBbzzBRMEqkOjEaKrCZqoE6egiDJu7DJT8RGUNs28hKb3MESVS9tViA2GnSrbvQxVzHBFB131mS1K6KqLLstoqkiYjRAymfFCxKYNQZTDMER0s8+FnTy6YFEoubQ9B100eNsNY3zrUpmvnavjhgp76/FVbSVbICXCVCXunqnxO99Y554Fk62hwOWWiiRGHJ4coMoKpipTt6BUTsjnY3Q1IUjAj2DgQ3sksNZWWNrKocczlE2XMBYZehqyINEopRStjMNjEWGscHGljJcmBC6omsdMxWeyEhMFKoVczHNmSrzl1goLYxl3zVuImUqUeTzWWWK1H7C3bvCu50/y3P0FrpnVSLMEN4qJE42vr2zwgrkK1zXz+AF8+OE2MxWVmYpIoxCgyRGBL7M5VNBVH4QMKdH48A9ey4FintQx+JuXHOBIw+LF09Nst1VSQvpOxkeeadEPQz7wykPsbxj83FfPYLsCBcvjfMtFFgRaborjCsRpzL+d7fDBpza5OGpxw9wut8z1+fvTp3l4o4sbpbz6uiLNfMze2oAoC5hqjEhSCScw8SOTnifScyROrmZ8/kyXDz6zwZrrsb9YJolN+o7JdMXF9VM+u3yJKI1pjWKWRjZ+miCIEX1XoOsK/N79a6y469QsyClQ1QTGSxmVXARpxpV1gV/7wgb3XhjSa+s8sNqjaAoMvJTlfoobR2y0FaRMASEib/Zp5AIaVsq5Tocf+tBF3v75i2QJPLQ64CMnNygqGppmM1kZQJwnTiPiRL+q2YWLH+rsDFVaXoQhqSRpiuuXSGORt/3TIjfu19jJhnz+0i6Lwz4T5YhUGvCrXz9JqyOSxCK1qksmJBiSSMuN0eSURBhiqAGLXYm2IxCnGV4UsDus4CUpN04U2LJlpKDEbY0az95b4I2fvMBffHuTbtqjPtanZmhMFyVkOeT2OZl/vrDGdEGgJGs8udNizCgyVU1wQpGpXIGPnV7FkCSqVMg994f42ic+xB1TCaYeULMEtp2ABxYdFEGgImsEmYsqD5ktR6z0Ip7eCrCUlBifxUGPKE4RM5mnNgX6YcCUYaEKJqISUdY0DlXzFA3wk4yxnEYhtbi8Aye3A4RYQjcDFt0Bp9ZCambGnqpI3gxJpZj5oskNcxpiprPZh5EnULQEMjVg0+tyZjuglAuYLsnsnXPQTZc4MjGzJvMlnVR0CcXWdz9ISd/l5/t8T/Ebd87z48cnGHkpOVWg6yfEWUhBFpEFhZCE2HCIYoE/eWiJB9Z6PLNrEyYBsyUJSVDRzC6tEVxfmaGiqzyx5NH1fJJEJifrbA5E3vfYOqFt8qVLLXw8TNNloiDTyEMkDFDVAFWSsT2JKzsqP3nNLMfmI95x/TX89F01iopE2w04uZlQNFUaObiwJjE1OWS8AH4aXu0MNFSWBzJrfYmyqhOJEVt9kZyq0HNFWl2ZMUPnBw/U+e3nzLOnpuEQsbxjEocmAz/B0gQENWRt6PDmW0sMhhJhLOAFBo+t6IznRcI0JY4y5Ezm9Haf9aHNnbNF/viu66hoJVp+wqXdgHc+Z4q5pkRrCHvHfTZ6GpEWo1sjNroxcawTaj5rdgjKiEZBRLf6eH6eQLS5d2UTQR2iITP0E8RYJfThl541zV+9dB+WpGAoAmMllYWqzlLL4xvLXZxIJBMTCrJEz48xZREnyLjYKlAyJCLFI9VGiEJMz8t41kSZd9w6ycbIwetbfP3CkK6dEjkGoa/jRTGynvDZxU1O9LeIxYC8lvKCvQlSJNAeGswXTF6x5wDn+i4XtgOqlk/RgKqVstnXcKOQMBB58cwkLz9S4g8eeYoz7TZDLwYjpO3o7AyLCIJBkmqQiUzVEvI5h4GfMbBFUjHGcxU0GarFhLwukkQK73t4jXv2lhkRIssipVJGa5AnR5PXH5rlRw/Mc9d8HkMTeGR7mxfOV/j4K6/jtcfHOJg7zKPnyhzOV/nTFxxgIqcyigI+fXaLh5dH/PWDW7znwTUe3xoy8BOWuwE3Tuf564e3WO0HvPPuBh9/ZpsHVvqsd2PymoLryey2JVxXIE0y4jTjkfUBz58rocoC+xsmiiShCiKHqjl++sYp3v3ief78xQd4z937eeDSkD95YJlNx2d9O2F/Xeel++p8c3nITEnlgfUOiuHjuAlxLDP0dbYHOqNEIJBgb7FMz9a4pjqOK8Y48jpkcLxW47XHq7zjzgne/dwjvGR+nKKiomnwhXNdfuymOjfOGJi5kN2+wtkVk8VtnWeNz1BN95CTCjTKKeVqhiillPIhf/bivfzUTVO8+2VT/O6LZ/jZm6cxBYUtO+abFwLGyxlxJrJti+zYMkJmUtFTEDxymocqaISewpXeiLGcwRuPNRmrBAxsmWo1o5DzuLgi0BlW2B0abA5iDlWLvGTPFD0HSoaMombkCz4iCk4YM1cPyZlDhrbA3QcKdL2UibxIliVMFQP8SGEUwJ5qiKl1uWzbvO7aOr95x36coU40LBP4Cq+6psswSDm44HP3wQJ/+fAGD6y1yGkSP3fnJG++vsEoyqjoCplkM1WSeMuxeUQUUiFjrDDCkEW6HpiKgqGnHBrXSTNYHHqs9TMCySUOdTYHKstdHTHNUci1GS9kaGaCR8RNtXE2eiqtocZkKcVOfAJfw4pVzrYH3FBu4rkGrWFC2RTICQopCTVTQBNktuyYuimxtxGyPBwiAK6XQzf6lHMeSlTAS6DnZUyUQTVStjoJhpGhRwXcOMMPY67shCzHfd5y/QR/8/AGx+o5fmBunHY/4+kNh5GfcHFZ4Itnuty32GWyIvITN01wblFnfyNmb11ipR/wsSfb/G/PneKjJ3cIXIklx+G/PLzOZE2gkhMpGjL3rW/zzSt97poqEYoJb799gpfvH6Osa5zYHrDYd1AlkZcs1P8v49z/NN/Pqf6n+J4ePbvn7+6jVNHIqSHV4tVkRNc7tEcqJV2lqit8+3IBU02pWDENC9xQ5MSlPO+6c5b3n9hAMT26gU0573Kk6SOmJS61FbxIxPEMmjkBWQ5wIshpEVPFlK7vE2YRZU3i8nadp9dT9ozZHKwpbI9k3ChCFxWmSjBe7nJyo4QQ5wjTmChN2VMLyekRqjKioIIbajy0otMbmlRNkcvLq0jn7+WlP/EK2v6QmimhixqndmMmcypxJuLGNlWtwMPLBtW8wzFN4ttd0IQczlBFyo14/bESz5ua5Ze/fpFBmKCqPjVDx8u6NI0cu77PRO7qwVEWoJpLOXW5QCKavHhfnWfabRY7MUdrBZ7eHqBh4Et9JvIyh6eXGLpVEG3EzCSMM9b6BSpGShClLOTnqOk6nzrRQsuNGCUwVYKVXsaz93p4ocS5bYOqCfvGt7nc1pkuyriRwPZAQ0JmFGb0/JiiprGnGmAHGZfbKoZoQJZx11yB7WiRet5jtVMiihWCKEayRf72tbfwrq8v0WwsIoohy90cJSPkqXWLiXKIJUsMA4EwVNBkiYEnEPoKugphfHXG2vMkzHxAyYypFkaEkY4bJ4SRyGZbJ04FmhUXQRDxApkoVtFFGVX1iYSAupWyPZQREhNFzjB0FzeUWKhCQ57isZ11VDXm+uIC77htgl+89xRZdNVadGH8EgeqbT7w6FGm8gVMNeTESoQkCRhGSBipFPSYMI25+8AWJ7cqnNk0SEOdJBExJIl33jXBBx/doiTayFqDZcfnTTfWeGjnGbwwZdCb5e23TnCl4xOG8KM3NPiRfznJdH2IZQx47OI4L95nMVa0OFa3iGKF959YZdMd4Ucpd+3rU8sF3HuxhJrUON4oMGapPNS+QMEIWd41UWWZYRhRzUXIUoKpgB9ptPoKP3Z4H2NFmd/+1iXcJKZiZZhaxLHJLj03z8VNiyhVySKBPTWLS70ROcOnpMPu6OqAcVFLsYyrnUbL2wV6YcRkJaQ/MrEdkZ+8ocGPXDvNX39nix+6rkoYx7z7G0+ykmoYaoqOxd66iKAukddT+p5CUQ+40iqyvVNCz3t4qY/n52hoIrLu8aOH5/i3Kyv8h+NH+OzSk+z2DSTNpZ7z6Tk6Dy6rzFUkxvIjdDXGDwqcXjO4Y6rGvSttJqvu1T1kC8yURAq5FjXd4v5TRUo5lVK+z0I9YWuUYEg6SH3miiInd1P2VWS8tIMqVNi0A/p2lVrR4eeO38ZfPnWS+y5JvGp+inc9d5p33XuFM2sB77i9wacuL7EyCDg2DpvDjJwWIWOQiQPUVpFLkUakuBxsSMjykFEoUNF0NkcBy8tVFmYSeq2LtIcH2beQ4DHgvXfdiRvCD3/qFOM1j+miRq3Uoqi7BFEdQwnw45SBm8NUYLEjM3A1xgyT5YFPz0mZqkXMlGO+cUZlz2SPzKsSxjJrwwgNievHc/RiDz/IEOWUeimj78jsDqCQHzJfg9V2DjvpIwBCUKFaACeKkESQpZTZSsrQk0hFh1OXCtTrPtdPBzy+atDui4xXI/KqQk0c8XevvOe72yL9U5D7Lpl+jAK45f3/67dJf5//Y/+cXdnlnffv8M5nTfMrX1piZipCTDPaXoibgCWr1EyBXUeg514VsC+pBvWCj6z49D0BTTCoq1X2mjkCu8OLb5rnn89t89BGC8cXMVOVnAVbOyJGZchYMaNqCfhxRNH0cXwLScgYRCHDkcGN9SncNOC+SyP21CFDYrkbU7YixDRHTETPTqnrEuNNlzMXTWZmPDzPIpM8JDlGEzW2RymNooeaWQz8DE2JKCkKG6MMb2DyumvqHBzT+NDJVUqGxPpogJAqeK6OrNvMlvJsbSkU67tU5XHWtnYZYDI57pKkISO7jB3FaLKE78sEccZrjlZ5aLPFrWN19pYt/u7EJqViH8tw2O1UudJJmSwqbG5lTI4LqFrM269ZwAvh70+sEsgBhiSQijF1KyEhYTgyEaUQ1zXxEvj1O+a5ZbpIxw35ic+f4/bJEhc2IlqJT8kK8dKEnCowCBL213RmrRKL3mVsN89qD6qmhAAoso/jWszKI1a9Cn/+sr0s7gY8sznCSWMeXrZ58y0lvra2ysgX2eioVAoxJSPDMhyGI5V5fYKmZfCVc12GYkgll3CgUGXD75MpNqII14wPCWKNrYHGaksjThRunS5yedhDV0IkKSOMM0wtIMsyskwgSiSOV+Y4UitTz4l8+WKPp9rb+JEImYAiR/iRgqoEtHsmaSggZQJjDYdQzKiYIcUw5pRdJApkxnIasTpEl2NcR0cXDcYqApe3U/KyzDtum8DQMv7qiXVarkez5LK0a5IFKi+/RuNfz3iQQrOUkdNE3DDjJfsbEGj869ISN09WeMcN81xsuXzq1A6SkrE69DneKFDUJb61sk1rBP/yw9f/93fwsTWb//rINrIEBUNiaxQiqRF5VaVZEDm7M+KleyfQ1JgHF11SIUVUYrquQyEOKdfKrGzKvOBwgQ8+uotqhchyiiqniGmOVx8u8dAln0E65LYDazihzIMXxkhDnWsbBX7rhTNsD0PuXxyQtzK+eHbAu+6a5u+fXmN51AEEJEFAFlP6tkYQCKBEJEA4VLluzqQ1ivnhaxo8vTskCGGpF3Bs3ODcZsDWMECQEgQ95k3XNPnMhW1UKSEjQ5JddFllaKsUNI3L6xLjDZtyBLlCk9VRwBuvafKPJ7ZJrA4NK0UUYNcVCCMdNVF54ewknz6/g5/GXDctkCHS9jskSEiCyHpPxEosXrK/yVow5OTuEEX2mCplyMoIS5a5sK0jiBI7PQ0VnT978R5+85tLXFsp8qz5Imt+j386u4yYWOw6MVXzqnTEjGmwPozZVzXouTE2HpqSEiQprz04wWcubHJ0wqda3EHLLOyWxGpskiEzcjJyhsI9+zQeXJY5txEjaj6z9ZSS1aLVr5AzYvzUxxnW2LEFjtQKvOZwnc8vbrDUdyhZESc3RcqYqFbIYJRS1DVCUtIspKZqSHrCMLKZMutc3Elx0pDZIni+SL44QBYlolhHVvqoSLh+CVHxCJKYLDUoGxmv3b+XP/rOOgVVYOCk9AcShWLGG66t8aHHWizoLgf2NClqBv96eYt/eNlR/ubBbU60+8SiT5DAdEHDD0TCzENRE6Qkzwtmanz68hZvOTbJl870KRQTnljzCByRw3MSbzwyzQNbm3zlZIAsSLzxhhqe4HNpK2Uz2aLvKrz+yBjvuGEPvf6ASvm7PHr2Xcqrvldyqu/pjqJ62WHk6JSrLU7vJqx0VZS0yZRVhlSj5QhMlDzqpsjAE3lq2+PbS/CnL1qgaIm0nAg/SThQk9hbLGEqOl4SUs15FBWFoZ+xMurzzpsP08zFDH243I0QMpmZXI4s00gigYNFg9lCkbYr0nckcnIOWYJM3mG5q6IIMs9se5xcg1ouoeMFtDwb1zdxY5HNERRlnbKugOBRCdo860feQJDA/opBWdfxE4GpXImCLpDXIiZzZZa7BrPlhMN1hSBJmS0YkKkoVsSv3H6A+y+L/NLXryBIAm85PsnhWpFMCBkzynixyJipYSoSUaQjpHlOrBSJohLr2yJXWkP6UZcskfj2JY8XzkxwueOTU1REtYUmFDizLWOPGmzbGk5QxgsVOgMLrzvJL9wyjx9nLNshgW9h2xphIjBbllnpqrQdA12LqVW2cUOLZ+8ZULFGaKJMGIokwoiRe1WbhCxFlmMGns5s1aXjRLzl2nHummxwYanGhZZGSoTrqRhplXfdMYMsiYzlFRTVZRAolEzoOwWSyGBtq8xGy2J7KKOpHpkQ4iURE7UeXhLTaktIkk+p2menJeEEGbsjCJOYgu6gSCmzDY+pWogqaQiZjCyKuEOJF+0r0/fAHuXojSwcT8X2BILU5YX7FMaVEme3RW6ZzOGFErfUphiMEpwg5i1HZzk+pnKh7fDIYpVTu2MYSoKf2lzq+ESJRNmQKEoWlhrRKDjMVAd87UKNrqsROSYSInkrIyHhA89codRokyQJ/STkj1+4Bz+AC5tVzi2OUZQVvrx6jq8u7bLp+PzKvefRBBEpK/HQM5P84rM1ROsinz6/wUfOXmayqNH2Igp6ylgxwdBcDCVCTA1unSxzqttjlERc3MrjuRPISoYXp/iRgh9pJIlBJsRs9lUGXYt7DpR5et0jjTQUUQEkNCViFEqkmc+esRZRkrA1SLnY7dEs9dnuSQz8jCgLUWSXtZbK05eKXN7VUeSUkplddUerJNxzSMZXdum7MbWcwsBP+IWvLtIoRBxqOux0ZC62fDZHI9wgT8vW0VSbTVvjUF2jnE/5iWv2kyUGUiyzuK2wulXg6TWBsys6v3TfWZz+AqcWdUw1JoxMwiTl6Fh6dc9qA7JERdM61EshlXxMHoWaVGUYCOyripzbFvC8Km5kM96I6IUusgIJI0aeSccBUzaQ5IjnL0SYms+EVSJIYhpmgT2NLoag8e6Hz3FLc5b5isADWz0USeQ9d+/lnkMFfvvra2wMEgqKwUNLMJ6DhXqfXSdAE3K4gg+xzE9ft5dL7ZiiplAxBJAGzBR1Di6MCJKAxthBtj//++xs9riuPsbPfeUcHz+5y13zVUTy+GmAlOWQxTyC1KXvZ/ihQSp1+M5yStmI0TWfX7x1DtcVODCWUck7OHHI9TMSJbEOqUwUSeyvmNRUnTdfN85uXyCviwz7Mr6wzbY3oqSK6GKOKE3J6QHjRpEDlTzvem6VOE3xooSZckCzNODJjQBF3yWJde44FBJHJkNfop5Lma9oFFWdy+2Uxzb9f++Q9n2+z/+JsYLKJ3/4EF0nRrUy1oYRZs6mlpeZKeqoasKGGzJXUrl1skBeNpDUPkHmYkgZWlbgB+ZmePiCz7d313nVNVWapsav3L4AjkUeA6MQ0PVjiuWQqbKKJGVs2CGqnJDFORQ5puOl2KM8ebnErXMya+4283WwfTi3FaNkAkJs4kQBNzRKHB8zUbSYp5dUGmMR9sBCNYeU9ZSyqiCrNgu1EXpm0HJjSqbHeEGgWu4zU/a4bdbiJYdLfPD0Mu+8dYETmwGaJGGoGUbOpp4XubyTMFL6jCvjPLrooQsC1VJI10kwJI2ul0EkoSkpiu5QyqXct77OW46P85w9JebLOqoSkuAgZQadIGSuCr0BjDVTqjmBNx+b4b3f3uaP7l0nDAVUKUYURSqmhywKBKFAs+Qx8hTqBYGPvfIwn7nY4jvLfd762QvkRJWbJws4hBi5PgoZMgKlnE/ZkNgcOjy00ePSepntocyUYbHRkUhihVubczhhjKrAs/YZ/MGDi3zxQoevr29z72oLSUv42OltRNFj6ClU8yIDV6LrghCXCAKDW/bo3L2/xEuPVLl7oUB/IPHEVg/dcDBkGc/XOLVZ4Kl1E1HMmK7GzDQ8doMuZlTg4qbB9rZFwxKwPRNLlbBUmYGn829nu7z/5Bl++esXeWx3k+2eRKcvY7sibVvB0mOKRkrFyKiVYvKFlPkxB12RSFMVLzAwVQFRkHjbdZNIqUIawYsPVEEUKMkGojbizn06f/zoZf7g4YsMg4CbxkpUxClKpoBmhXxtqc90Y0C14tDzMta7Kd2hwKdP9vmns1scr9d4w5EpBn7EO+89z7mtiP4IDlYL1HSdxU7Mbz/n6P+pSLTY8fnq+R63zub5vRfN8avPmeFZ8zn6fkzkibQ7Am+7dobHNvvcv7nMij1iseWz2HVo9SRuGqvwa3cc5IOvP8CtkyXuPlBCQaWiiqgoTOU0vnrJJkhhT02k4+S4sFMjjVVKlkRAwFcudHh8p8s3ltpM501euFDh8ZURbz0+iZyYhJGAQEApN2QQpWRKTCkXM1mKaTRj7MijNfT568c2+ekbZoiSlD15k+fPVXj3i+a4ed7ibbeMYUka//BYH9+TscMUQQiRMLHkFFmJWOpmHJoDP1bRDI/tZBUj1+efLl3kjgMKlpQjSgWGUUKaaIShiJP5nO53eNF8DS/OuLCVsWEPUJWUipEiSwG3zTtU60P+ZWmFlpNg+ymGItP3U55abnB6Lc/OUMd2dDIpYKI64Cc/dZEfOzrOfRtt3vPIMiQqRpZjd1fkcPOqK+RsFd54Y5O33FRjfTfhYEPHTyLSUGE0UvjapS6WotDxIEvyqOoIVZBZ3DUYOCrH5oaIissjG0M23B6NSkjOSFhtC9huha1RSm+g0xuWsYOIG8YKrLg9Ht/ukoxM3DiiPVCpJBbb3QwCGU1RGSURNUMhp0k40hBJDAi8IruewwvmS1xXKVFTS9hDmTDIszkQsIOQvGIxWQ0IcZGQCQILO/TZHmR89sw2rz6uY2FQKUGlmuEkAfeedrhx2kDT4KGddf7ldIs/vGsfXzzT59vLQ+JQ5EV7mhR0gV3vv+k4hhaDvsV/vHGGz15scctEnk+e6PKao3V+/VkL1C2JQ3Mp/UHC7967yq1jDWbqMkdmNLbsiPtXWzxnIY/sl5goQMpVw6kn1+1/p+j5ff53vqcLRQgZshnSHTSZLViUDVgdemy6PQrGEAkVJxTYtCO8QKIoNXjF3glunM7zXx5fo1AMGC+krA4yPn82ox+4hKGJJglEgk/NTDhgjPOV5VV6voCuwHju6oH0bMfjgSs6kaey5oR8ZzGmkfPJySo9P0aRY3Qa6JKB74lMiEWunVTwk5iinlKQy3hJiCgNqBkiS2sKa7bN0te+TG9tjfVRD0tJ6HoxK8MRA09ny45pux5ipjMMXRQ5YLKUULJaVMwEQUjpuhFThkHPSdkdhSRpRpS5nNv1uNK72grY9VIK5pCcltANRkSxSifskaUyttyhVA6x5RW2+yajOMHIRL6x3mW8mpE3QmaKKgeKM1xfn2Ctr6MIGqu9DFOCZ1bh1j0WfS/mi2d6TFZFYiXgloWARn6EJGXEiUg/cCjpcGqliR8ltO0Ki60SJ9c1Br08S7slBsOMhlBG8Ax27AxN9bCdPHuqCqdXQ57aGJHPhZBY5PSU2bLBe39gP4oo8A/PbBGR8PBilSttDVX2QfTRtZhmOaRWSmhYGc18xlQxxlJF3Fijlo/RVLi1Pk42qqKoEMcaNTNDkSQ0WcUNNURRQBAjBCFiuyUjCrCvYRBmIaknIwgZThQxUQrIqxl+pLHSrrAd2oyXInb9EUmkM2MW6Lox7/rWGT78zBJfOD2kYnhUcv+7cCSstmO21k3u3lMhSTQOVgtUCzZhGvHUSoOunccNVPZWDI41DSr5EE3JKOZD3EBBM2Kum7T47Jk2Dy4OeNYegVsOCCw7Dvv0eV441+DsYJOLWzGvOFzFjjzK1YivLO+gqy6KkLHeEzi1M8RJPOJUIo5FFqp9tgc5BDFlvCAw8CMe313neUdWOD6R8KajewGJgp6xa4vIUszQVamYGa++scA/nNjm2JgBQsqPXTNOXo8pGSkCOs18RssuoSsihp7RKCRoMhyYtGkWQnKqQK+TR8kUauWM+VpMzoCJYnx1rj+ApY7KqxaO8ZZPnue6SZP3P7HJyE0ZeiJJFrN/wqaiqGRZRJwlpMQEgc6YlXFrYw/1XI5Pn+3yq7cdohOkBMS85miD5+8tkMtlvHbfPDdM5vF1l4KasmUn9D0LQ5Ypmj6RX8fQHE6sVknSjC8trdBouNw5m6em5pHEhONTKSNf4bGLM6gZ1Es+vi+zNpDY1xiw2AVdDthxUvw4xo1AV0ZM5GM2+iLrnXFGUUKWifztky2aeo4Pv+IgLTfkHV+4yL+dbzNZ1CirEpaesr9m0vdFSCuUDI0ggbpukAoZVzohB6sWiBFxaBFHJYLUpW5BLR+x2ve59mUvZftLf8XrD01TLiSc7G/whcstwiTCUjPWHZutYQJxmY2+CoJDGoxxuCGzaWuYsszfPLGCJEq03RQVA0sWWLEdnl5J0M2YibEu53sOvSDkH06uc/NMnufONDEVmSlhnqJXptsXWOnHSMjsGxuw7fd5eDHh85fXmCtYFA2RTEgo6SLXNAw6vQkGQUAk7TIIA7ojEz8SSZQhbTdjqqBwoJL77oco8bv7fJ/vLbZHPsMgZsWzefP1df7wOXtJIpO+l3HjpMG+sspkTuZ5czWeXHexPZHAKdG0FC63DC6sSbz/wQ5zkxGvPTzOjh3xAx86w0985gJI4EkObQf+4w2zzDYENvsiyy2DybxEd2Sx2E9ZbBnookWayfzkDRN8/OwSQmoiSD5ukjJfFzAkhfVRiKEl6HrErufiBTBdk3BDib7goggyjmswVe8wU0oQYovTWxJxJKFJKk7skyQwcAo8tNXn8a0hv377fg438vz8jZPIiBRzA26dyrHTtxi6GVpkcd+iS6pEVAoJ+8bXOTIe0XUFJioOM2Mh5VyfRl6gkh9RMlI+c67D5sjnTx9Ywk8dTFln25Z49t4+upowPzXAkjVeOj/JJ0+3+KHrqtTKGcVqH03UGPoxCjo7o4wosljuyEyUEi5uwqMrDnfPV/jLhzbJiwpvu2GM9357i1RxKegKmTFi37hL18koWz4FIyMioGZCxQq4ZiJPRZfouAkPXnLJPA0nFMhjcCRX5mxnwFgl5NDEEM0aEkQyfmAw3+xx954SCzX4D9fPICQW+0oVBkOVKIE33dzkSiumWMwoFiM2+wqbbYXhQIfYREhUzq8UOL1ssjVQ2LbBU3rkFZFqXuWusUNs9BTajkB3BFkmUC9F6LLIgfERCw2P/eMeY9UITYOimWJKMR1bJZViUCMENebURhFTDlGkCFWO8T2ZkiryiXPrZALktRwL5QIffMVhXnN4DC0u8sjOGnuaLfxQ4Fdv20fHG3G0mqeohextuEiCzFwlQJUSIERRYzRZYaqgghjz5G6bX/7SFc61XF60p0Hfi7jUcej5AXfMF3jJgQq/+q3z/MjHz3FqZ8i/ntnhPQ8usui1ONVvE2cpP/yxc3zu3A6zFYXfuWeOqaLGP57c4cJ2wOKWydAHwQww9YT5cZu2byMKAl+73L06FrfTY7asIWc6qZAg6NsMY4fLLZfZXJ2HLlS4uTFJRIhs7dDK2lwzqXNxuEZZVfn7Z1a4a0+BIEn50wfX6Ixg5CoIgkKa6hydcKjnE2QBBq7E0I250oqQtJg3XlflxM6APTWZp1ptntpweO/jK3S6GY+tuOwvGkRZgm46FI0MS5XRlQgvDRGkDEuGtW5MP7iaqxiKjGX4DBx4cL2HKDkkhHR3yyiJSXck0jRLmKrEN3e2WCiYeHFGz5WRM5UwSRgvRMhSSkrMZClhsT+glk8I4wzbv2ou5CYxNzZqhEMNU9LZtEWmp1ze+8AGP3FsGksW+fzSImIqcdNBga2uzm17Bpiay5/ft8OUkeOf3riPpZHN4bqGkwU0iwLLQx9NTNgZKqx1TS7u1On6UDNFNrYVLm3WiBKRQQAFVaJc6KGJEq6n0HVACguc3lSoKUVcO4friYwbOb6x0ubc7oiSUGZ5XSGIBfbNRvSTGF2BsYLADx4oMVEUmS9nyOqQWt7HD1U+f7nDpY6DrPr4usdGTyAnmXS2i2y0BfqjPPO1gDU7JibGlPIMBjpPL6Y8sOTwU3fUefdzDxFkEQ3VYnsU8fiKiyRm6EIOw/B4/4MtXnSoyMuOFCiUfX7s+CQ/cXwOK80jiikbrYQ0i/nHJ3f5gQNlntzp0vUD/vn0Dj//5Qv0ByLtUUKxGFGUJf7w4WVaTsJSx+euuSKyl+PvnlylM4Q4FOiNYq70PH71G4vf9bj4/Zzqf47vkWX+jxn5JlUrZGXos+V1SBjRMCz2VyxEDAQx4ujYiPGcQpwonNz2+MijXc63RwRxjCQldNyMySI8b49CGtVQFA9JDBBTDc/R+MZZl0+c7hDHMhUzYcMOOLVRQI0bNA2TUEqoGzIvOpgx9DUCweHQmI0pSywNbR5ZTenbOkM/IlJ2yVIRQ1YYhh5j+YQJq4CmBOyddzlQMUjOP8Y9r3kdE0YVPxbYHOTQszrrPQlFStDEMobZIq9HWLLGhU5I182RkpLJXWbyEk9t+3zmykWmcjrrI4fUy/OlCwN6IwWBFFXtk8Y5BvGQkmqAZBMGRfwYJEGllM9wQpW5isBkySdfBFl2mKtA31VwA4M/e9BhcTBgFAUUDYdqzmWlJ9KsZsyWdP7zNy/Rd2PqeZgoR+i6z9CzaHkOQayQUwWCJOSWWZ+hZ/LAlTLbfYvtXYXMsNlbj/nVF4zza8/ZT93SEBIFRfb+m9NKxqPrQz78xDZXthT6I42Rk2fXTskyeM9D63zyZIvH1kcEnkVRFxEymWMTbaaqXcq5EXEaUc2POLlWYKNvUDEj/FClYsVU8wKvONbk7c+qUSqGOKFA15VY3NVpjUTiLGKzZ1AxfZJExbCgbMUse33umCxzzf42z5nPscesk3k1GgUXU415bGPEwAM/FPnKlXWCKOU7a31O7YzQozw/ce0+XrKvyU6nwOXtCk8uVRmTqlhpGVEUeGDl6k1xoC7RLHqYWoKlwYKV42hD43kHirz+2jreSCYIoOfK3DzW5GitjCpnPLjeJ0zh1QuHedOxBe7am+dKz6flh9w1NsMwjvjU+Q5dW6cmljHFGD+SyQSQpJRUSKjnYmw/I40l/CjHYyt5TCXhc5fXKOedq63mI41HllM8T0KRInJ6RNlIkAWJga8QJAlfX1nlK4tb/O5DV8jlYj52bgVRjBkGGbYPGSG6GpCRIksZI08lTSUauQhTzUgimTSWQMwYDGQ2+zIpMREhXTdju6+SkeDHCdce8PjYuWXOt21+5vZxBr7AardAFlf5oxctsN3TGPpQMUVEQed8S+Mdn9jmympGmiRcP1bkfS/ex2RV5s65Ar/75U2uaxa5Y7aIQEZOtHhs1WLggyxkbNoJJUPACVTObZU4MpagaSP8bo0rOzIfOb3KVgvaboLnqWwPJG7Y4zHT7DNu6Qx8AVMssTEQOFwXeeCyyXROZBSNiCODh9c0Oo7O4ysGSy2BF88cZnMY8qr9dR44naCKEjlFYtMf8vYbx1h1XRr5EC8dguhQMkOu9IfEkcj5HYkdN6IgKHxxeYkLuyFeoLI0tLm0rWEpAr24j5CJqBj4Y3uZu/XZvPvByyy3RUxV4vkHRKq5FBGRolRn4BbxkoA4KuEEJg+uCCC4mDKsjxye7m7SLAZUcwlXuhk9X+DmSZV94xKjUEQTFd5wvMjeqsGi0+fhtR6OOOSvX7OHn79tPy0nZLqZcGwiwdAC0lTiWMPkeQdTWm7Mt5YG2EFM1za5tKvQT7o4sYcu5qioVZ6110cQI9xQoKCoLNRCFtsxVwbfv/n6Pv/P4re+fYWXfOIZvnnepqro/NuFHotdgZLlsmOndP2QhJDposrfv+IA19QtMlJ2bAE/hslyhpaPyJQRf39ikw8/s0leEznTtxF0h2ZB5NfuWOD2uSKnd2Km8zrHx1R6nsDlNRU5VRnLZ6SCiy6HfPTUEoIooukt+r5AWZcxtRA5N2LCkpAEmUe2dhj6AqWCiG2LdLsiFUVjd6AxxKWkJyiiiG7ZzFYTSobEIPQY+TrbgzL9IKJsiMShyJil85NfPM0nnupwZklBdKdpag0OVy1+/PopnCSlXnWp50WqxT66pBPGUC3Y1HIZRWuAIUv0HIWdoYrt6Zzc8vjoqVUctUvZSqlaITfObuEEBo1iG8fNs+X6/NdHt1nuB3z87CZWwcZUJUo5l4aZsdbTCQMDhIQ9tYiOI/Gs2RJbo4BvXOphZyELDfjLp64gCRnNvERJ1glcHTvIqJgiGwPwQ5X5qk0oJkiCxDdXNkFOyMsy9xwp0GiEGAocalz9FspazL6yiRA3GdomguxRMBM2dsvcv7TB+gD+7old7IHAb79gnh+6rkZGxrvvXecPXzyPwlUhZSE0SDOVFx4scdf4FKln8bbrpvmFu8YhhThJ2B5KiJpPN/b4iwc2kWKVrXaJrmsCCUXLwYtjzmwaXG5pbA9l2m0FQbiq97hraxhagiBetQxPUrC0iFlzEkUUMJQIzfD5s5csELsq7372IfIU+dQTAz51ZpePnd6kUhmRpgqapGAoKRf7A57ZyTjWNPGGBX762mPcMlbDDVSOjLu87ECTHz1WR44kdp0hpZxPVc0RZBl/8vAV/uVcm0SNKWoqri/yu/cv8jv3L3HzeJHn7ytxYmvAqV6bRiVBSAyCoY4qCPzcXWMUiylNU6eeU9hwAg5ULH7reXMkkYyhyrz9ugVubk6y1i4xtFW+cL7D3pLGeE7jv77kMFEoMa4V8EYGO/0cZSskZ8I3Ljq85boJLnRcDF0giQzumS/y6ZNtNrct7pwp8957DjOIYh7abSHIMXkroVEO8MKMnYHCRl+m40DHlRHFhLIFlRzUiymfvrDBP51Z4kx/m6myRdsPyCtX3erO9Dpsp+tYOZ/QNQlDlVGQsTMU6dsFTFkgkxIGYYwiSDTyMUGUEkZwYMwjbwQosouIRrESUDM1xksSLdfh8miDW8crjOKYSEhYyFe5tHH1MsaPNLp+xsgzcYMMU4uJEhlDc7htboSujJguZ5zctPmxm5uUVA1VhjSWkawRHzu/zlonJIoUUt3m7vkSC004t1HjSktH0RN++5srPLkxQkkMrm9UMVWRmJD5GgzTgOlSxkpPoDuyCBOJWEioNxI2B0Cqk5GgSCndUZ7NYcZEOeXSuoUmqvz5i/byVz94iB+7oc7F3pAzGyElRWJips0rj5SZqWj88nOm8AOJw4Uix5t54iTly4tbZMo2cZqSk01Mw8VSQ0qygp3FXOgP2VfPULSIQRjSrCXEmcpT6yot22DCkqkXHNwwomRKVMd9zm5l/MnDi3zu8hZvvr6BHYRoWkqzCq86MMZONyWvKYykPpf6Q+7baHH7ZJV33f80F3sOsiiSOBZzsxGSkrA6HJHEUNRk8pWIV15TYxBGVC2RKFY5t6QwzEImNJOSovCLt0zzB1/b5FWHG9QMjZwisbipcf/ZgIWywXMW/tcd6fp/C9/ThaKuGyFIMQeqGvtKBSw5h6YOSIQOURowkQ/p+LA8DKhaAj9+ZJaXH6ry4ZOb2H5K4CtUdJEoSfjapYQvnpXxfB0/zLPtgJ73+YGbQo42LTQlwo9TsrjI/rLFYxcERmmHnBahGBFO1iHBY7YgMV6MuXZmlwMVldl8EdEIaYwHFOUyipwiqT0mijH1XMDicMBT6yYDR2d5e4XiW/4zV7oxZ3YTNMVDzAwGYch4MaCsGXRcaA/qyBTwk5S8VIJUI01Fxs0ihaLHHQc87pqtUDFk9hYKiCJMl0QapsZ630CMm2wONIpSEUPNyBITU1EpWyFVXWOrL5KXDUZhgOcbbHsBzULMDfOXWaiPeGoTZqsRu77N0bGIth9Q1hUaJY9cpvG+72yR6V2u2Wuz0smIPQU7ctDVgJliSjnXp6hpHGx47Kn3USWZ7UHC9rbG/ukIWcrQ1JjHttv8h68+w1DeZqOTw/NKZKmEpLhUTIE33l6kWffpD2WWdnT8gcyWHfC6ww1uGy/jxyl9L0OTQRQlNgdlVndqDD0LRU5wQ5Eshc5IZLGtEMYx59cNHE/gr0+s8u4Hlhn0ZW4aL7LVt9jTGLGn4kOiIqQKOwOLJEsp5Bx2hwpZJPC15R5ifz+SZ3GqPaBWHZHKEmUrIUkjClZMlCjYUUwuF7IVdrnh8Ca78Q5/9K01xsoyP3nTBDc1q6RejtccmeAXn9fg1gMmB6oFbB8Q+piyQMVIkUSYnfC5sTZJz0lQBJVESCiWbFQ54NxghYsdl8vrCbEQMVfT6QUxlzoBG8OAsYpA34t4YNFGTmW2ehFDP6YXhrRck5ZTQJZjfvzaPazafWQpIaeoTBTy/OszM9wzOU+7l+PWsQptW2OyFDBwDDZHPh86tULfuVrgqRdGeFHGbNmlpGf0Byauq/Drz5ojzlJ0OUVAQUSh71o8uVJHk2JUOWJ+rIftCCCG7DoishTiJhFYLoIW8VO3TdD3JQQhw/V1cqpIXhU4Usvz1E6fV+3dS+SavHC+zrsfWIZERgxVtrsZf/bkKRxfABQEBGQ5ppZzmZ1zCdQRraTL3z19ievHi7zr9jne9tkLWDo8tTPgnd98hid7VyjlIlojFUM2SCWX+foAyGi7EYpoYvsimmgwVQ8YdTSuKYzTFR3sQZFLuyl37usgSH3ObZbxYp+JUnS1kOGXGQYS+xoybVfBlGoEaYI9nObBK0Vums2oFj0e3GzxjuOHefCKzfMO5jEVkd1RSKuf8fXlHe46GDASPI42JbquRBjLqFkVOwqpKzlm9ByvO16nKdZoGAIIAofrOfJWSpqoNPUSw0Amp6UcnxCI52/j0Q+/DyNTWBvG9CMb19fwsRnGO6gijAKFjZHDxa0cewo6m/0CQRwzly8yX8qTL/hoSspCVWHgKaTKFttdUDKJp7cEHtne5XXX1CiJOSqllIfWe3zqwhqfPr/JsUMeI9FmsQOaKDIMMu67kLFqOxR1getmRMYLIkEKkqAxY+X4hVvGWO0nrI26dL2IRk7kjYdnWe1ouKHAvprE4bL13Q9S4nf5+T7fUzh+xqxhsbab8rsPXWTd22FfTUAVdB7dHPD0msRwYHF6PWKmoPFka0jLFvBci4qhESoukeCQxDI1zSJOUlwCKrmrxZi6qfHChTq/9uUVyqLBPfN1Tu4GeL7MQlOinA/wM5vNjkkUmrhJyPZAwnYrFDWZrVHI8pqBmuQII4n1tkDgm6iiwkpHIPBExosKg4FMZ5hw67yDJidc2FUR0WjkE7pByGBUxPMNrrQhTRTiWGSl5/O/fWmJkpmwFo2YrWq8+liNL61eRBEjLvccND1AFzV0bYAmiuT1hEquQxDmGYYpQ08nTBRE0SWvygyCFEOVEKWQgSOgSzKdIOTplWm8JKA9rIIQoYUKopLQLHqoasrOUEVIJC6smkSBRs5wKeoZSRjjBRrtnoplujzUucJYJaGajygWA66r1Nk/I/Mbtx/m9KpI0QJNCUhSAWeYY6Mn4Tg5rp/bwNJjTE1ClENevFDn1sk8na6KM1T5ta+scGo9QhNU7j8LZ7cicnrGXD1g6CdUij7FfIYsJ+yvyfz5y/dwZsdhc+jxoZMbvOxYCVEQyIQMSQ7Q9IAb60WeXHP46voyh6YlXn1NjR/c2+Rwvg6ZynQ5YLzkEyUJijlC0XySJEOTMyZKEWGkE4YmY8WMkp4RhRrNSsBCzaFkxBh6QskMmas5TFVc9jYd0kxm1d/GCyX8UGNSK3Bx12eyoPLhx9o81R5ybsfhH59Z5andHbZHCR1b4cxGCUWCL62sYooCD6z1uGnB4tHWOUZxl8C30JWEOHb5t0ttfutFk+yMRJQsx0/d3OAvfmAfP358FlNJmGl6VPIZngOCmBGIMaErk7dSBlHIybWUKb3EL905zX+8c4w4gQO1HLeNj3H7VAWAt980yTXNPLW8zKuPF/nRa6vosshKz2O8MqCf9fm7E+t87GSbjhMRJfA3L9/HCw8Xub5exJQMhp5GKsV40pBPXFhhyjJRpAw/jfnmcsC9l2x+5rZ57ChGEARmiwZ3zza41AmwJIMkUpiqOBybGjBZSqjloGqk5HRIM8ibLnEa8YoDE5R0iWl1nKqucq7d5sR2m2iokSQyfqgzU46oV110zcGPoWiIV0fP5IjeCO4ab3BdpcxWR6PlKtiDPAU9QFd8dFlBkzIyUq70HWwHJNnFdnI8stGh1UuRI50jzRxvvH4C2y4iiD5hpFLWA7ZHIvvqIabmYMgKy20Lz9cZjEz6fsLHT2+w0gsQUoUtW+RIrcJCXeHuazQaSpEDxQanW306XY00gaO1PLEYYskiv/etZV5/XYkvL7YZy8fUSiPKVsiBpkOWhRyoJ/S9GFmQSIUAS00AmZYtoAgqljFguuyjiBLrbZHZqsTfvGwfmZDy10+d4bVHGxyelCkWA9a3ZYTY4D0Pr2GHHh98eou+I7FhR+QTg9W2iCT5+H6Flb5E35MgtdjZ1RiOEibzV4upseAykZchMgkkF0NJWKjCW44cYOBD7JdIYomNnQzXU5ivwU5H4CuXW2z2A2rVmB0/pBeGbHkOMw3YcRPaHZWPP9ki7Bl88XQfJ4x4bGPAz9w+QT8JcByBsimTKfDRcxts2D6WDCe7a9ieQCuxIZB45fEqf/OyA/zDaw6xM4z5/IUuBxo6H328xU7gsTEIqZsKeU3kk0+3efuNU9/9wPj9nOp/iu+RZf6PGTNVVEHGjoc8uuHx+BrkFIm8XMDSJNwENEFloazxwvkmnzjR4bMX2nzrss3tkwWQEtbtBEmEmyc1rh0zWR/orPVlShrMVUM2vSFbXhdBiLAkEy9QeP7MBDMNmfliEVHJiBIRMamiZEUqxS0UuYOQSjy9obHjdZiq9NEkkW3Ho+tk1I0cZDIXWgXavXkMKUfqbbPxkb+gphbYHsQcaIQMnDKF3BZVy6Vn53lq26dkjYgSONN2yNKMMA352gUFEZEdx0eURlhqyhcu7bIVdAlFl3ypjSmBkd+mYcVc3s2IsghNDVkdBPRHBiEtdFmgFw4omjEbo4ilHYs4i5htjKjmHXZGV8fuKiZIUsaxZkreiCkqeSQ5Ia8Y9IIYKx/RCXy8RODY9ICup1I3VWr5PnVLQEJno6/hhiYPLOY4MrHNNY2QYRTRGugYosXAk9h2e5hayOpGgf5AIIqvfnw6jkxLHHBhuM5cPWZhwgUx4i13FvmNBy5wsevw6y+Y5vefuxcpg4VqFze8OhctSQKWljJVDNntFxEzlbyRcXRihC6LFMyUmYKOH4dUzIznHDT4refu4ZpyDUkQ6HkqMTGZFLHS1lAEnVub04CMIqg8te7xy8+bZKamUi8GaPqQSq7LmCVwuJ5HISVMoK5XKZEnIWK9bxF6Gjsjn796YoP7VwZYRsj4WI933neOP3/kCtc2DXYDD12L8SKV6ZJPIx8SxwJPL8d8/Mwau3bMb399GVGJEcQMQ024uJ3jBXM1RnFMnCU8uDJAFgRuHM/zmkNjXD9W5KZmiUZBQdUz3v/yA3zo5Ud50/VNmgUf29N4/d4DXN+o8MTyVX2u4SjjSDVPUVE50V+nUrbZ8RwcWyNOwAk1TAUOjA85Mt3jcLWC7ZuEscSl3TypELJ/ckCzlPGhRzrYIxlNudoOvzWUkKWYmYqLoUg0CzHdUZ7jszKWLJAm6n/bBwKqIFHWUr6yssSN0zYHmh7XjeWYy5e4YVLleXMl/uXiIu/6xmVumspxZTDizrEapixQKveAhF5PZm4swA8FLHVARffJqxqWnpIoLqMg4yvnXf7iwXUO1SwONGRsuY+g9skQCRIHWQ6wkhyOrZFXY0gsVnoZexsuzWKfPY1t5vJFavk8v/HiSX7+WbPMWQUMPeGW/UMkMWNzqKMrGYbZx/YV1oc+EyWbzWFMmGRcbKlsDWXKRkAq+MxWI+LMRxNMFgcDHm+1SYwBy7bNaz9+lm9c6vMHz99PPDJpdw1GTpE4DTk+kWEZHlkmoEs628OAYRgxXlIYxBF7jDHcKKHjO1wzOSSSOpRzWzTzMbLZZmMUMFsW6XTgyjOPU46L7O5W6TlgUebacY25+pCCLrKvYjAIZC7vpmSBgR3IfPtSgpeOCEKVk+sKi72AvCKzvr4XTVZwAoFD5SILVpM/+fYGi50IVYqJYpkvXd7k/Q/t0BkJzBRVxgyZ3b6KpQjcMS+TBjVW+xnLQxs7CKmaMYbicmpV4qK9zg8dGiN1Znlu8xhBLHJ0TKNswMDVCAWHS7vJdz9Ifb9Q9H3+f2C3n/Lx1x/itbdpHB6XGHoKhrnLIEiZLUpMWCrtnsBnzrb5+ye3qOoy+6smK8OEYRQwUYhZKKsMPIW33jCBqUn8h1vGmSqY7LoRD51N+f1vLzIKErpdgb98bIWxXEaSqkTSiILuIycFLE1gEIW89uAUdUtEV1IkZYSeyoxVZbrDDD9MmK6ISKLMZi9GDkRKOYGRlyAbIcdnUzJivr1kMVOOaeRdYiJMdORMxs9G5BQwFIgVhwc2uxwfy/F7zzrKDeMaP3vnOH/0yEWSyOKJxYTHd1pUTJmYiIW6R5YpLPYz1jrj9FyRji2jyxKXWypJZNJzJKIIdEnEiXxyskY/TGi3x5FEhSs7JWRBZautMN+UGLgZC/kifpSiSwp2lHJw2qdR71PLgayGVEsZmx2Nsi7wTHuIoYhc6G+TN+C+SwktPySHwdkdj1tnC6y2dAZ2ga2hSFkxeMPhMQQ55czGOGEaMFEMuG3PkDsXLD5+fg3FDBDVmLsOGPztK/fypmsnqRQUJDXE0AOETCavwWSlz9BVKUsZzyxnvO+pK7zngVV+51uLbLYjjjVzfOlyh5zps78xQtN9Hm+36HgRRTMlzWL++KFF3vPESX7l+VPMVGMUOSXLZAo6WLqAEEvkrRBD83Gj7KqoLgEIVzWmxgouXfvqOI8kpKQprHdM1ns6A1dmoTpAEGK2BxI7IwlNDVgbjfjj7yxzbtTm8W4XWQTDyhBjjdDNI4zyHDQrvGx/kx9cmOKaWoH9dYkruyFEIl84o1Kzirzz9iOUxXnuvxKykKvy+E4PWYuRtR7/dPoS73l4kY+faCOjYrsSu07C6sBnpxejyilfONfmi5fbDN0UVQnZCHaoGiK/ce8S775/jY88vc07bppm3NLZtgP21Q321nR2RhHzRZMvXejx7vtXOdUesd7KEYQqh6p5rp/ME4o+P/5v53lkrc+WE3LW3aTt20SBQBxIWFrKdB46TsLP3jyPLhi0nZRSKeXexS7Hxsz//j1YaYfc2DQwTZucFRAlKkNPJskSWraIJPtsD2RabY213QKDXo6HLtuEozxfvzDgdKvPDbMZmRSzb0rgQDNCEjK6I4NW14JMRcw0HF8ikgIEYmYbMXYU8hvPn+WHDo8hJcbVMcLNPEU9o5aL6Hswcg3+ww2zGLKM7RlXu/NLA8bHHUQx4yuXO6x7LX702Dyvmb8Z17c4t2NgSApOKFDLheT1lJ6f0e4X2egKHJ130NWYZ8+UkUWRu2YqtMMesbzFxa7NqXWP0zsj7j8TM0xCZC3i5imLD77sGsbzKq8+VOdLi1tY6lWToYKi0PNikthkodGiaMZ4ocrIEzEkjUQKuGbGplF0KZgeEgaJ4DBbjRC0hDDJ+OkvXeBd31jiO0seb/zcM5za8RgrJszPdrmyprCvJoCWkMkjiqpEIHkcmlbZVzU5vWwQJyHTBRlRjAnihANzLuXGkI0WTOQlXN8kzAKaOYnEzpHL98gEl6+vbPLaI2V27ZTJkkixktELIgY+NEoyQaDyTH8d25HYP5mhItGzBQ6NxVRNEaXkc89Ck6IhYqQGixs660OPvz+xzo/dlOOjrz5GMe8yDGOmLI2KprM7FPBYp1lKGMtrvOQGgd98/ixHmxaGIvGVtx7lb1+xj7YTE6QJRdFiYgy8KGVjEPHBx7ax1P8/WIt9P6f6n+J7ZJn/Y07vhnipg0yBPYUyx8dl3FhkbQhLbQNSmU1bpBd1+JOHNqnWEq6d1HnBQgVJS5AEmZqmMvJMInmbVNlgodHHkhX8JKLngSkWmLUqCGg8a2qGz772Wv7tTIeOm/L18ymp4FG3EgaRj2G2mSgoVLUSTiywv+Fz46SBKmpUCgMaOTjQyNhxh2yOfMhkFrsxg6jNmU99grmX/giDLOBn72xQNEQiIjSKyBSQ5IgbJ2TqpooqJ0xbRSRRYbtjcrhismOrDD2DvVURy+gyXxYZKzlY5oiGqRGqA7IoR9eVyOdcpovwyKpC4NVw04B+r8m+Zo+JQsLhMQ9Vgoqo024Z5GWLjqNSUi1qOZd91QxD9qnnIK+PCCORpQ50bBE/yTByQ0xRZhAGNI0G180PiGIV2yvTcyVEQcFNArw45siYi596eILMwkRGFIIqpZT0hK0R7NgilYLA/KRHmCbU8w7ECsO2wckNjYIeM1kOuXa2zx9+c4NWW4D06msxVpDY04Tzm3UmikO2hhI9LyWIU5Y6KpmYkKUZji9xcaWKqWSMl31uW8iRhRp5yeRHjs6QZRmX7T6TpQGmGnFgbECzEJHXRMI05jvrO0RJjK5mzNZ9/urJZZ7qbJFmKVEssNErcmbT4MlLMZJbJ0uvWlUOEg9JzqhZPoVSwAsP5vnka4/w5z+wj9un63TclNl6jG2b7C0VqJcGZELG+e06Xiix3s1dTTbkiDddO4EsZfTCmL5j4Ic6PcfEkEU+dXaHnXRI3VT4sxcv4KUxUZrxtw/ucKyeZyu9wOHp0zStkNmiQdVUeNZciVfsPYIcy3zkyRav/ehZXPpIokCQwpcXN6laAj84s4fffdb1JCggpZxYLFM1UnZHIgMvT2dY5MkNn7GcxO5ApZlTyTKJKBXwhSFX+kOEUEYTNZr5kCSViWKBUQiNvI0gxPRshXFTQ5Yy4uRqR4wkikxXrzqdiFLEtq0SJQnDMOHpHZ97T8X85ldXcYcl3vO8Q3zmdId+MmB2qs8rDzW4tGtSqYaIkkp7oFHKDXlqo4wgiAgIZFlCFEsogoYlaDx6xeHPvr1B18kYRgKHmyFVI6LlhpStiFLZATXAkE1qls9cJaLv5Nh2YnaHFYbZKmc3I2bLBiVd5vgkjFVsSqrKKJQ41gRL9ynKJSYrPRpmjk074/BYRiQMmaukbIx8nrg8SbW4g5BBkAbkrR5RLPK5i21sV+fUdsBcXeYTl1fIqRK/8vwpHrmcEgUyMT6p0CWOdLY9hyRLODjrMRJsvrnYYaE54rF1m6OVcXLq1b29UMwhiyYVy+VIzWAiLxBkCTe//q0IrXU22gl5BY5ODxmkHc7tikSxwubIoRf2uGPGYGEiZNdJkGKVW+cVyroMwtUOiKZucm5V4cxOgCxmqFrEiU2Ht107QbWc8cJrHKaqHpIYs1AxuXW/RHegs9QL0KwRpgrDMGbTtYmFIQ3DZL6Yp2zKRFlKwUjJGzr3XtD5m8e2ONUe8Y/P7PBjR2b5wKmL5C2PlIySrpCTzP+LiPN9vs//vcxXVP7p6W2ebLUYBBGHJ/ooXBUXHgYJowAkJWMyr/D1K30aBZEDExJHJ3z21VIEMSHEpZIPWR043DZV4nWHJ7ixWUOJ8ty2x+LmySJLI5dKPWSypNB2JBZ3BCpGxGBURlIiCrkR81X44pVtQsEBfAzBQJVVfuaWSSoVj5IlgRTjJxFjBYlGPSExXN50S4OyLtF2BR5fKjOdVxj4cGFXQkEiiyUGDhiigS5LbDshWaBRzUn0RjFv+dfTHKzl+MunLqCJKmmi8PE3HuCuuQKtnkTJDLC9HKMgQcZAElPcOGK2nLIxECgYGRkibpRwy3iVIEqIvTw5y0dC4B03zrHWj1Bii/98x1Fee2iMX7h1D3/3igPsKVaJYxlVuVpQSJEIUwFFAlGMUdIKTpChmAk1y8N2FWRRYOBKaILMTVN5VrsB775vjcu9AWNVm76jEYUqtuRw/0aH62ozRIlMmqioqstiK8d/fuA0p3cdDFUAKaUs5fi9r2/wldNDXnesymwVMmLcKGUUGFzZLaFpPh1XBSXhoWWfX79rlrdfP8vv/8Acv3DvKT53fpehI7LSLSGjMF72kPWAyzsaz6ymfOeiw/klnV/68hJvPXSItZZJOTciyhLcSKZSDpmu+UwUY8o6mIrK/lIVg5SyGeOlEpqRMZ0vYnsy9UJAo+gzWfbQ1YxHlpsoooipiZR0yJA51igxVTDpj2RCP0OUPZIkoVod0Syn/KfnTfELd03y+LrNh09s8+hyyE3NBgsTCpdGXYpGyr2XhgShwMp2nv21MkGcsr+So5GDgSdwYVfhctvnldcWuXtfjeFIxckCRDUhAyQxAzWj34fHlwNcV+PMNvzSfaf4qVsn+M0XzGKp8J++dpFf+Pxlfv6LVwDwooQvXmxzcSdgqxMhyTGmIlIvx1hyxo7ncaCmcedsgTcfH0cWBT5+ap0s1Xj2XJU/vfsIkxWVumngjUr8pzum+MgTHTptBSdICDKHz1/aZa6s/fff+/rlLpcGLptDGLgpXUfk0lYRKSkxXgoIo6ujo9VyymzTo5yTeOuNE1w7ZfCKo1VuH6vzzbM6OhYPLftcUx9DEDMcL+PuuQb3zI//N8fEgJ2eRWTP8PJ9M/zu3XsQRYHZisFCySDOIkRRwQk0+l6GSEbgyLz3wVUiP+GGxtUR2JEvkyQyozTEHqU8sRrxhw8u8Ymzm3hxwpHJEQsNFzfK0MSrJjKGLGLmfKabLoaaoZoOphVwy3iVK62QkZOShnXummnyvEMFbF/g1r1FRiHs0ap84uQuv3ffChe7Dpd2fXY6CmnmgwhxCrVc9v9h77/Dbc2qOl/88+a0cto5nHxOnVOnqAgFBYUWRbBAFLFBaBXbNrRli3pb29DaotKYGxMItgQvjQHJEosqqEDlfHLY5+y81155rTfn+8fppn8+P23BW/faffXzPPOP9a455zvX3s9aY7xzjvEdRFlI127iJQFzpsytS3UEYKkSoqseh6cCDM3B0hNOr0yTEtA0ZJJA5oapEtNl2B7lVAsh9ULCjp0hCgbFus/uSGCuJKLLMboWs1Ay+NWvrvOK/TX2TkG1EJDmCUVFwU8y7DhCFUyqjYiOnSKjYMgSP3hTi7mmwK3NI8SZxJmew/pIIM9Vvv/4HiqFmNccaIEg0PUjVEnA90qg+7hxTKOUUzDgmd2ISnHIgYrFnz/b5Q3XtphkEdNFkaZi4mQOj+1uc99Gn4JY4sC0S5CnuIFAvZhzdnsKy3QZhRnPbHuc2J3w7X9yjjzP0WWRzUlIcypFrvg4aUpKTMkSqFdzYjnifY92/rHM5z/z35D/sRfwj8mNCwplRWVlkOHEDlGSMV0GMdcYBxJFPcWUJaJwmr1VGS/zuNST6cUO+xoSYSLy7HbOdXs99tZURBT6XsbWWGaqFDFTEDnVFnn4DFhYvOf2WR5cdRiMct76oim+eLlNpRAhyRMcX2bG0hCEAYloEyZ1vFDDzwcU1IwFS+f0rsIo9Hn+lERXKZLh87w5ibFTY+elryc2Fnn7i/bzp+fPsuVELJQ0hmlC13cpSiX2trrcd1mjqRexw4yNUUxJMfEciW87UODeSR9Lt4m9AkVdoGxOKOsVOnbOoco0fhrSncQcbqooqse+qoYTwIyeEQUSG0ODpqWyr7FL6F9FO4ByQWR9CPuaErkwREZitrqLkIvkYkzPLrFhJ4RBiVSwaZUqeFHOjCWjyALP7EbMlmW8OGGqlNP1EpaqHqJo0PMSorjK2G/Qs2WaRYisjMtrEgf2Seyr+fT9hLmSR83KOLOl85VzJcxUolHOMIsZQZyzNjSQUfnNVx3g85fXeMVSlUdWJ/zSvWscXXbxI43L3RqbXZNrFlzqVsSDKxUOTo+4HGiMhiJW1SfKBEaDOi84XuLZDZ9fvu0wqiLy/lOnecGBbQauSZKp7E4kVDEnF1P8UGDiajRKCboe0HZiNroirz5U49ntDC8cEycZupoSGDkLNYvtNMBUSoQTlYkf4no1NCXks2dsVGWLzW5Ks6DwY9cd5o9PnEdUYzqByySKUCWDLJV5crOKoST84I0zbPZzTmxHnO077Cnp9FIPS4PFUoGT6wl7pzNcP6U7UHnPY9tcnNjcMFdAyET+6JEtTjsxt+yLqVcTHtoYExNw7XSdd3/FwVNkvuO6IsvFCr/z6EnKVogiWMhyxIktkUu9VT6/Y7PZKXO0UaTvxvTtHEkQcSYmAz8hTnNef3A/37qscaI74czkPLIkXTHaDZ+JnfPCqXnub29gKeCFOq2iQ5yKaEpGrexx30aCqV4pt9sohCSCgqWGbLaLpIFGqeSx0jOIkxhDyaCSsthIkMWc33nS52UH5vnkSsht83v48KOPo0RVFiwNrSxwaTIhzyUOtQKCRGDoZ5S0lMg0UJWQs9sCf/ptV7GnovMTX/TohC5uUCIlZmdYYq6SMV1rY/tldsYae+td1vpzDF2BhZpGlMpX5lQzqrrIOIx4w1XL/Px9NppiU9QTZDmlpGs4gQ6iiSrlSKKOLmeUFYMoyaioVfpZhpK1kA2baVXDiUJKakZRh6Gd819ed5Bbl6uc6E54eMXh6S2HGxYL9DMHQ6gw3xiwOwkxFYPNcYYlSxgFjYWmiuwXcGYc7l2POdhUkCSfIB/heFPM1XZ4YtMnjZqc39HoBimzx28imJyja+yj0ZS4qmXgxCl2CFN6DQ24bW+RL1zyCUORgBHPdhSunxOYeAp+NmSnbXJ4XsJPRCZhiigK3LCg8NHVJxiEBm3Pp6IZxDmcH3jMlGPmamXmywo9V8Yo+Yio7KlYhFlAzxvgugKmYFHWFVZ6MpocAQLXzV3RObg0iDEkk04wQKGIkslsDiGJ/efcRj2Xgon/VIQX/5n/wcYo4k+e6JBg8vz9DqYs0/Ngpa0wshUaJcglmfO9gCCAOAVFGVK3EnqeiCqHkBXZUypxqZdwyISfvPsJVvsqTV2lVlR4+1fWWW5kDGyZjpvy0pkG45LDVrfMbhhz/XKAkJcYugJhHmBpArNFi+1JRihGvPPhdRZKFU54DndetciHT21hKCHDKEGXDD5zsYNoTJBClbmqyOWejqy4XD8fc2LbQrZsDjUEciT6gU9ZKdAoRZzYSdnWEqbKAl+4MKBSBEmNWR/H/O4jG5zqBhxc6mCpKlEaYUmAKJJpI+aLTU5t69SKLqassDpKWKpK6KZHuRQQehbPruu8cL7Ir96/hibK2HbOj3z2DHIic9Yd8pKZFk9vuhyoaaxOfMqmwshPmS7K7NgSy2WD7iSjaIqYus3x2iwnB5uoisrlzZwX7DW4e2VIzRLQ3Aw3hpIkXUnjiiyGnoBLwn2b21zVKDAOTHaGOaIImqig6DY6Fq+ZbfInZ/vsq1lISs47720zM5VT1GXsKEWXQxqmwMZQQ9c9Qtci9WV+5OMrTDVdZtUmu25CEIFuJAhCSI5KQS7w4j1zTBUV7l8f8tTWmI6fQCDzoZM7WLqE7emoIuSyT8EKgQxRSnBck05fIS/ZFJUWr12aorc74MCiztNdm5pc4fb5Mn91pk2YuGSZQG8kY5gxXiggSwJ6JiOLLq+9aom/OLNCpRgzpVf5toML/Mo963zPNVPUNJVPnN0FxcGSJL7r2Dzvf6SDLYQU1Iwps8BNMwVEQeTJLZs/f9MRzvRcPnWhS5qJiIKCmanoRkaQRgzCFEuP8GKFqnZFz6des7mcWsTOldQvpAx7rPAvnj/P9jji4XTC/lIJOQv4lTfv495LYz59ckDRFKgV4J6tLcpFFU0Wmaq6iFLC8/QyZcfizk9eZKmh8LqrLOI85wduanGmG/DqfdNUTYGeH/KOb76KQZDy7z9/iaIs8cL9BU52Q5JcQlUFfuGuVW6cK/Gdx5v81zceYaUf8JEzHc50JlSKKRUro2zmJCGEMehKim2Dn0p8254GQy/lzFZIP45YKmv4Idyxb4pMhFlNZcrSKageD2zv8pbGEpc7FtVihCVJRFnK7ctTKJLIr9+/xiE95FdvO8APfy4kE31WuhqtUkqOhln20JUANygyDCV2Jwp5YoEQMV0NMZSMly9O838+OWKlGzHdcMlSg0QIOTgVECcyJd1HFRVO7ghUDQk7jCmoEqc6A/qTEMOI6Pk6fprg+gHfuq/Oo+tjIlfhtUdqBGnKe150nHfcv0bBEjjT8ZmdEtjx4Lr5lGfaCi3TIhNd8kTm7NY8opDzeDohkSPiRKNVdBj7JpaqkTJh/7xOzxPxk4RyNeOTp0NaRZEjcx4DP2XKkojdnLYNdUPFdQQEMSVNdQZpzHfub9H3O7znyXUMFGSpgWVMOL2jUDc1LuwKiGLOi5dLnNoN2BmnNEsSv/PQFnGe8fIDMwRJzr3bWzy6u4UoVrjYi5i4IrXlhKMNi1Ndl/YgpVERaBQN7DBlkng8tRuxVGyyNbZ5+TUKfzWK+PMTbRRBIFQC8kDBknS2A5nf2l5HE0UONnQEX2MUhChGSJYYJH6Jf3V8ht//yi6/JZ7nyHSDT5zu86KlEm/52Dne9Lw6HTsiVzI2XbDEgDxU2Dsrf22j87nkufKr/qn4VP9EPubfzmNbIev2BImcwzWdg3WdNJcYBTJNM0cVVPxE4IHLIbvBCF3K2d9QmK2IIIRoEly7CLMFkUnsshv0CTKPmWJKtRAyW3X4gWvnuGFR43XHG6iyxDsfWadiifz1hQ79pMs46RPHMm6oMI49JqGGKbSI4yJ+IjBlWeyrmawOZZLc5ZblEdtjhTN9h1M7BrIkcde7/zO2r7BQMtj2XdqThKtbJrIo4kcK81aNVinhkcsNtGwKLxaxo5zZgsHQS9mJHL6yNqbt2+xMFIJYYLZ2Hk2SEeQBfT9kwTCJ5VWWmwHl4i5XTW8zcCVMaxtdzdmNRti+zvl+yidPzPGtB+f4jW9bJMpT7CihNzF4ektn6Jt0XJEDzZAkFek7ReLYoGalTFsqZSuiqulsdlVqVo4sT9jT8Ok6Mm4coYkKiC7TpZjjMzHHZnss1xPmqj67dkqrkoCScX7NYGu3jueWyfKMkubiuhpLDQ9fjGiVZUq6CAKkeUzfi3nXU5cY+/D0lsPPfWYNq2ATZymL9RFbY5PpWkirmNGwUl6wPEJB5vjcBBWJ1NHIEZmuivzsXRfwFY+n2jbf89EzPN0ec2q7iqnEmGrArQc2aRQ8Qk9DlURqVk6t5CIKCXYgUTAEvro1oGmp9G2N5UaEIsfUqy7PDiYockKW5QiSiCbl6EpMlMooOfQCjxOdEY9edvnMxV32NSaoasREvgdNzikoCUEk0LEV1noaQ0ckU1K+eGnAnTcscnDa5NuvqjJTVImzmCwXWC6VUKSEqiXTCwJMWcZUct79nfsQVI+js30WyhM6I3hmy+UvTrZ57xObRPqAsS/z1XWXT13cIckzwqjEyw5UmHgqfioRkdG3FRbrNqN8wOuulSEXOTw7wg9zbj9s0dA1HtwcsW5POD9w0SSLMM7JSQGVLFH4+MUdXjg7R5IJZGmOLOVIoowkCAi5ii4JTBwTXYaNQQHX0xEFkUIpQhBl4lhluhxxqGGyv9ygXkwQxAQ/hvUeHJ7WePtL9/M7923QmWjsreusjG2O1MoUdZHj0yFJKtDxIc0TZEkkSWTGtkxBzfjrs31EUeDXX3aQoqoy9kW8UOPauYiBLYEgQy7jJh53nZ1j6OeULYeSAV6U0+tNM/QE/o8vn+A/3n+SH/3cGZxBga+eKpOmGl9ZMdBkmfsvgaWkjJIRhpLyvMXzFNSURzZUhp7MZk9lZyQRxiqjIEKVFMLcpqLndEeQZQI//BTMDAwAAQAASURBVIkLDJyMREo54XXp2TGNYkjbcwhCBVPv0fFCmoWYbjggSxP+8vQunzhrY6opAiJPbGbImUZNV3ETn4EzRdMoM/IVZsoK18zppLLF+qffxdE5Ay+OGQQORTXjYk9jyx1c0UqJBR5cSxhHIXsrBi/ZC7KcoyswXyxRsGS23BA3DlmoRGSCy8XJhIv9nBftjfC8KhtDHUOGq1smsdug7aZohQ36ExUnUEGy2XJC4lTgUMPimimdWiFhGMYkaYYlSaR5wpZv0w1cSnrOnz67SV1pUTITum6KH6fsqz33Dg3Sc9z+meeUd7/73Rw/fpxSqUSpVOLmm2/mc5/73Nfef8tb3oIgCH+jveAFL/h75x2NRtx5553MzMyg6zpHjhzhs5/97De8PjnLaBRhTyNjHCV86USZ2+cPYYkGNUuiH4XsRD6KKDBblalVfMbxhPtPF9AFCUsuEiQ5rz0wz8O7XbbcEdvjjM1xRC8fUpQUqobAJMj5P25eZME0uHV/idNbGXaUc2g6RcTEjgOKpk1dUZFzk1cv72N9KFHRBA5WiyiGh5GJ/NnJDqLq4mYhNUNGlRNGqY3nKxRUmZGXMnEUnjebkQoZYeYyX06R5QxB8qnrMvVigp97lHWRWlHg266q0ChCUUsZ+il6XGC5rKCZIwQMgjQliESEXGHHEVhrT9PuWQw6EgoGbRuuXnQIs4zTvSFKpqMXHJbrGZcnDgXTQZEFJsqVCCO9NCD2ZN735A7d1GdzkJHHOuQ5hpohCFDUQ4ZBSMAAQ0vod8o8vRuyMS6yPtCp1xLOTwaUizG+6KDrAiMnI0lkUHJUNeO1B5t8+4E5fui6vcxUfLZsl5IuoMoBrXJAEutcGgjcszpikoScm3Q43RtjWBmqlDJXFlmqxiw0HEzdp2JmJImC5wssV1WuXrgSLft4e8zIEaiVY4o6iOS0Kg4XetAoSbziUI2b5sp829VVysUYUQnI1A3sMGG9U0IUJJZrCWUV0kxhd2QRpyDrEVuTnKe3fH7mM1v84jct8PSOyw9fN8trDzX5o0fbBOmV9PWikTFVi8hTFV2SqeoCciqjqRN27Ssaj6tdhVN9l3tXxsRRzpfW+5zve3zp/AQ/CTCLPh890+Z5yxpV0UAUUwJxxLbn8PtPXESSM976xVOEecCWv4Ubwv5ynfd+xxFed2iO7ZHID10/RxbriELG1YsySw2NrWGBRiFgz/yYRsOjXPF4zdEam8MIP03ohwF/cXabhzfHmKrEqw7XeM2xGu96ZIvzwz6tYkzLUnjJ7AztgcV6X+Hxts2K3UdTBXw8PnrhIu/48jofOrnBiY7Lw5s2miTxg9fP8XsP7/DeJ9cZpwHrw5An2kNKlgdhihdm3LRQZLasIosCK0Of3753E0nJUM2AW5ZK9O2clX7KrFFFVUPODTwkLcNQMrw84KX7qvzbF88SpjFf3RogaAFP2+s80tnlPU9uMgoDJoHAOM74yzM7TFUCSkqKGGs4gcCXV0bkwMhLeWrd4xc+v8E+s4IdwGzNRREVVCUhJ6Wgi8xWHTbcCVfP29TLPp4nEYYWjq3yJ/eN2GeZhGlKZ1JBVTJ0JaVnW/ScnL5bIhcSSnpOkuVsjTT69pWqbGUzIfByDk77+FlE3w75w8dXkWS4f23EKIjJM/jQiV0KJhS1nET38cKIxbrHSqdARZPwk5QoLPHkZRMTmcAXsX0g0YiziJ5dpuelhKnE+qCOm0TIGChKTE5OsyrQH8JgbCKLMs9uadR1mbGj0J+IVAoit8xPM1USMQWVv7xwHsSAqYKCLOZ0RiJhaFEq+KwNYKEqcGg2ZE9NZF9DwhRldt0YUQqQBZk3/NVpVEkj8Uu8+fABgizimf6I2bLOl1Z7/ItjTSwV5ssqSSQxDCIkJF6zvMzEh1MDB40CnzsTcmnoM8gcTB1mSgKTLGXDiRAziRfNVwGJi+OU7iSlaeiIbgXEkNNb8NFTXV5zdY2KVOSulRG/dv86VUPmV29b5qEVjy07YKMNU7pMoyCTJSLrE5/bD1W+Ybv39/LPPtU3xNcVUfSTP/mT3/DE/+E//Adqtdo3PO7/TW6cUzENk1CL6HgJ6wOZODUpGwlO5pHHGaZqcu1cCUPTcOOYTHFwowRF0MgzkX40oLujsFgpsad+peKSjEtBS4iCKvd0tnm2nfPo5TbnvB52KHFpEGMW4Oo9BeqWwa4Ne5pjCqpGTQ+5bDus2TlVQyLOEtYmLuOgxr6aSEbOpV6JhUqGF8lsrm8Qdroc2T/HhbHDbz9ss6+ucHTuJI2izVfOXs3GJEEIAnShwsW+iB/qHGrJzBZlXjLd5J6NNlHqYslFdDEkJSSNFynpAVkuURRq/PVKh8Vmk+WGw1zZw4tqHJ3bQaBAkjnMRQX8UGXiSZiKTs+N+PylAUEAt++f4YndPodnTVTZ41BDAjKqmsGx2S6m0uCxdZ3FqsTADqkaGrESoCkucVDlZbPHWdBtPn3pInli4gUL1Is+e6pQNhxOtRNyNGJivnl2P8POkFceqvLV9iaCDJKU8MSWwVTDptMvMmOq3LyccP96jheqTBdDLgUqlzsZSs3gbOhTrsTsmbri+D21UeYHr5nnY5fOokoRJc2jPAWPrk7h5SL79k5ot4u8fGGG2/ZVef8z6zy7HfC7D23iJimzJBS0iCc3KhRU6DoyZAa6CANPYO+Uja4kZJmOH0jkec7EyanWBQa+RpgEvGJvjWf6OwRhzGqnhCaJaMqAspiTIvDSpWk+n3d5dsfn175lmZ6b8/sPr7NvTsfz4NntBl5oUdEKrA8CZF8lSXO6bsKZSR8xk/mrC9t0vIieZ2LoBo9vuMSxQJZBlMDP3rrA7z+2TiUzUVMNRRR541WLfHL9LD3PZG9DZ8O3ednSLKPE4XsW9vK+rw6YM3We7e+yr+VjBxn9UKWQVOi4AUoxIs1Upio7NHOTj55JecH8LI91UiI5oWwmvOpogUEQ8Ffn19FFiYbsUNIFdiYaUSASxDmLtYxLI5taWmfD9VgfGnhRjKkkIMSMxkXKhRBNFijpCQgZW8MiOSLHZm1a5QkXO2UujhPc0KFgpcwbDs92pygVYt527ypLNZ1EH7O/KPIjt+zj1R84zR/stjm0JJIiIkkpLS3EV2Q645RM9PATnWZR4PLE51V/eoLf/ZZ92EFOIkaQFlAkn3oxZsZU0HCJMpkszfFSh+P1iK4n4cYWZAKaNWF/pYkoCPzIjWVObUScmoTYocTRWo1rKiVOlTtsjnKW6wlh6rE7maZkdHnVoTKPbihcuz/n1G7C4WaLi/YOQq6w2S+RCzENTeInPnuBH7h+jgN1g1ZBQZFz3vPkJgXHYqHm0HEjSmqTgy2X9X6VaaNGOk6p6iKvPNSiqIec64U4kcDlEexvihxsRJzcTSmpGlfPj/jrUwZFWeUNL9zDr911kBcoHR4UCriBiCx67K9D156hFwkMvZzb9ots2B69MKApQ5yU8HOHnZFCVdHZW5bpupALCXMljZlMJshcRrGNqiVMJhX0cp+2D7KmcLhs0u4tU6tFTAKJxZrOtKlyupOTiD2yVKSuF5BTnXVHIcojdFniQFVBFHOe2k7we9sMJjqvPKxx9ULC6rbBvoLyj23S/pn/l5mfn+fXfu3X2L9/PwAf/OAHee1rX8tTTz3F0aNHAXjlK1/J+9///q+NUVX1fzpnFEXcfvvttFot/uqv/or5+Xk2NjYoFovf8PrMgsDWMOKOGzc4tz3NC5eLfPzcLqu7oCki1ZKOYUJ/krHtR+yvhmRhlasXE0ZRStuVKGkSHzp1GUtPWXMmbPWq7GvB9bN1vvDUBKEU85375/nrCz1edaTCOx5eoVSNaRRkNCWl72fYvoxuKGzuWkRywm8/epmZoogdB5zeCfjF2/fwm9tttn0HOZHYXxcQhJCRL1MzFIREYscNCV2N48shshIyCWC5KjMOcjbHKXVDR5FyxmGKqVjIgszp3ZC/ELcJfB0/jomdCmTwrse32dMwmYgRXixzqJEwsWEwLhAnFne+YIY/P7XNKFTwoogw0gnSmKOFaVYHDkgClp4R45M7VWwiZkvg+hnjXpmqETDTGnOgPIcoZJzvBuzaOWVdZ5C5lHUFRbEp6xrrIx9NlTmxIlCt6JQbPUqKys4EdkY5w1BguhSwbGastk1qRYXvu3aecZDzXVe3+KNnTvDQRQEnSSmqMlsDC0mJsVQRXck53DC4GCdosk4QiByumwS5wXAs0I9FrpkPKBVHeJFKoyBx+FCTMFK5f2uXYjHHdiXiJENVfPJcQxBU2gODgh7wRw/v8KKFCt96VYOeU6asqTzYucilTgFRzPFcgUIhwY1BkUGTYeSqxImMJICqiLQslYai8q6H2xyYUvndr+7wvddN87yZIo16m3OdEkkCmhIhiCJlPWZvc4xkmBybvppPnxryHcdaPLU7YmMY8rFOD10WeeFCkWe2fPbWDbbCkOXmLjsjkfWRgGqFVIoxlzs6iRmxM87YXy5x2QnIE4WdnkmzHLPp9PjuT9i84egUu07Ez3zlGfYv2Wz0iyhSShqrZIHAlleiVYpZqLvECew6CT976yJbk5B3PXUaSYtYqFW/9r0Mk4zpmsiUWWXDGyAGImESkacKNa1GEgzw5ZwoyhgHOTNlgfpCxjjKOViV+OzJAU9d9vj3t83ysacH3DBXYnvSIzUCkkjGkmuYFYlb9pS4ea7Gg5sjvCTmz8+cZ5RKjKIJmqzwlbU+siyyVNbYGSiEqUHdDLFHCmKkc8/ZgO84ElI3Fd7/uqP82Yk2X9m6xEbPJMsyokiiOxGwA5WpUkKzHJAIIUGSMj0bc7x4iPNdn/vXLvD0ls/1TZvbDy5SsuDEYylpqjFd9Oh7AqZmQiZwasNiecYliFSuWegwUypxbq3K/lqJ1ThC0SEJFDQpxgkTmopE1xUBDV1JGXgypiqwPgBTkZkECUVZo1UdI2kaaQYlLSfyBV5/1TyfOtehXAu5MBrx/OYMnznX55uWqlxVURkqMSf6LrpuEQsBx6ZCotylO2rQEYoEac6rD9U5c2kbLzXZzHJmahEZFpcHIdMF6HogSSEtrc6xKZXHdkeoasZ2V+bYksBaIrIxFLlqNmFtIDH24HPnxtx5417e093m9sU93Lu9jj1WKZfh8o7AWgZ7p3WCckIEhKHOl9dGXGqLHJjOkAMTO0xQhIiyLvPk+ATrfpHzfZPXH57jo2e3SXORpmbwti9sEyGiiSmLVYW1DgzjlJOVCXurKq9cbvGJlW3edHA/ZWVEzVQ5ORizY0e8ZLnMsysSiuLwsr0lzuyEvHihyhfCMZM4JvFEbqjXEashgyjhs5d6/PhN86wPY97+smUUSeTmxTITL6HzREAgQa+bozcmoFoUBIMf+usT37Dd+2eeW76ujaJ3vvOd3HzzzX+vg/PfeeCBB/jRH/3R/+U3ijYnEZvrRfbMgqVoNEzwkoSykJEFFRZqIee7Oqtjn1RI0SWJw02JKBVI0hQhkCkVq1SsHEMfsWUHrPQMrmrmVM2Eey741E1YrNQoTQsM/ZhYyKhVBaaqKYo6pBNEIFrUDNhxE0Q5RcqqLBUN2rbI2FGZKphogsqmM+TsiTrzZXBiGy+NyZwxz3vz95LIY/Y0dExZIRcCaoZKGi0wVXaQJRU/KqOpI8JMpSAUeGodTm7EXGx1yPUOsihiJwUeWMu4eTFj15G4PILZos6OFzJVjYkTja4XUVIqeFmME5TpOQbbQ5GDDQ2XMbMlk0s9+NjpHrE84fBskddeI5OudLC0nI4Lmhzw+XM1DjQjnt60kIQUQcgQpABd0amV+tQyi3O7OtfM1Hj7gyv8/u3HWR15+OI66/0qJzYtqlaf7c0mZzsyU5aIgca77u9Q1hUe3elhFm3Ob1WpFhWmig5PXG4ihjqlQsIzvRGvPLrJR57cR6uo0yxAZygxmeTc8cIizvYKtWKCImUoUsIHTl9guR4SJAJfPDfFoZkBzaLN+XaNasnBrIqsuxo/8OldpEhGkAW++4YiD+5eRBQV0kykYqZMPJP52uRKdQJFJc9EXF+nasZESUapkGBPNGrlgC03IE4j+o7Jx7suS62cVslHEBTsicJSI6HTrlKs2dy9uUEcGJRLMT/1xUtYmkAqinzP4Wv53d0dTHXEUn3CorrAU+s73HqoQT+8TM+OqZkCWwmc2vXIycmENt5EpVEO8BUdBYMsy/njJ3fwJgKHD3gcLFf5tx9f4duvqbIxLHCxV+T0es5vv2KRjz8zZN+cwlfXRogSdO2MJIeBU2BnAhtZxLGGSWW6z8A2yRFoT0rUrYi5okovDLE0gV9/6UFaBZ1ffvhBLFnn+sUhuiSyNpKwQ508k9GVnLGcst1XyUKR337VIr/64CUuD026PZnlaZtOz6RVS3j5gRq78RZdt0ySyow8gaLuI0oJF7smoyAninNUNSVOBS73C5CKVI2cfY0hl3omYmrxL26ugZAzXZZwSBm5Iit9FUPOmStDJuWomkfPMSnJApKQMl3KaE8k3v7gBRLRZdwpc/zQiCc2FQ5PJziRyCiQmLIkUjwkEYpaxoldhSgo8tabF3jblzPOiR5/+bpreWBzyN3b6yxUMs7taFS0lEcvDTk0l1G0QhRBJRMjRq6FocmUtIyj0+AGCYslCy/QmVGnObEbsliDWiGk01FZSE0+dqKLLAr8yM2z/01IUODRVZc8bjBMJ+yM4dp5le1JiikLHLBiTg5HPL4R8Vu3Xcvr94v8+Jceo2wJXBxFLBRlVKHGKIioGnDrssxT61fKUH//T/wcH3x2jeJ8yB5VRJVEOlGfUZTyksVF/ssTbfbODqkZGtK4iSaP8MOE5ZLJLhq9IMBJJtTMApDx8EWJ5ZkxVdUkjZqQX9F+sEQLOTMYZw5WaZ0vnZjh5r0pupIRZiGC7DFXtCgJ04RijBN7hAzwvRaGqmIVJ7QdlWYxZblkcLhWJZgKeOOhRX7jiSdoRynP9P4fMFLPpWDiP+n44f9neM1rXvM3Xr/97W/n3e9+Nw8//PDXNoo0TWN6evrrnvN973sfg8GABx98EEW5svm4tLT0D1qfpPtc35Lojqbws5QndkckiUqrKmPIEk6ckCoeZkmiiowkGAyilEbVYzhSqRoiXiQRakMUUUXKVRZbEAsxAy/lI285yG8/epFPnB5giy6z9ZQb5zVGUczlQcxCJUChhC4pnN0UePvL5/mdR1cp/7dUniwXuGZBg1SlPY4plCXmaym7To4bCRxspIxtlfUtFcmQODAb4qcB59oGBxopaZrScaCs6Yx9mYJpM1XQ8GOZXPE4tpAxcXSczEecWBh6Si75HDHBVGP6fsx8SebsrsnN5YyyXOGamTKfuNDmhvkCd69vM6tonN40OFg1+eblBr9yzufl10zx6JpNnOmIckLiCWjFnG4WU9d0JNMmCEosLqfcv9FHUSEYG0wVA2rFiK2hiSAYTBVzkkyg1XRBE3HGBfq9MkI9IAx07CjHUhU2d2TmZ66UCQ8cjT98dBMxVvmm5SqvWNrPfRfPEUUiFztgyCKSlCHLMK2lPLIRkmNSNVQm8pidnsZbnt/iDx/boFGNCBOFnUmZpcqIk7stPtse4ScphhFCpqAoIkIucWmjhKBC1YqJXJ1yOaTRSlBlgV+5a53HNsfMzfrEuYqhiaRZjqkFqDJUjJCRp1DQY+ZqIc+s1djbsrm4W8YOIoLCmJPtIotNGU0TWWqInHuqz/lRhWNzIy4PSsSBTKOYIksCfafEjOzzUOciO67BiU2BY3Mmu05AbSpkuqjgximHphRefWiOz1xa5zMXIra7GkkW0zAVylaJijHAiTNkQeHWAyVmejp/eaZNsxqgUYDIIPB9Pnqix7FGAUUJEIWMPFVYVBt4ePzcSw7wBw9t09JlXr9vL2Ek8WenOnzsbIeBnbHRl/BjC8+JePNHTvIjL5jhmlaF33vFVSRZxs/dk/PdL27wmw+t4Gcqr5qv8cjFIUkgUbNkcinl4o6Crkb0HZ2tTooEXL/HZMvx2T8n83CnT73oc6krU7auRORtjwt0H5foDUW23ICPn1knzUERBVZ3KuhmynzdxY9l1ic+ae5RMmJSIUXWU37/W45weRTixRl3XexypGmyOnZwQg0RiNOM4/t3ubBZ49CUR5QqXO6lNMoZ0+WAczt1vmp3+c2XHeL80GG5ZnPuYsjvP7xBre4gigo7I4WdkUS5EFPWMlqlCabm4fhFEALO75aJAwMnFLgUdonQOT0ZUy+CH+eUrAwEiYXakIomszWR0RUFSYgp6yL9scnRxQ5qbvBsW8OUNCLR49UHZlkqVfjExcukYkLThERwWXX7+FHK+ZHLH33rQR5ctzne83i002Z5qk8MKIKBIIfsmS7wyn0NHlq1OTZtsq0oXB6peImNJWtYqkCQRpiSQSQGhInNvWdLxFrMQi0DMWZtJHNsPuZSXyKIY4qyxradU22kRGnGX7zxKk53Hb560ac/DsnEiOmqQCaHDD2RebNMxRC4d9WjaWn82Aumef8TO5SKKfOlDCSbnZHK1m6DKRUSR+MVV1X58moPP01501Vz/PHj2xRlmUyz+cGbDvObD6wxr8CJdsh0FrCad3nHrcf55IkRB6sFTvYc9hWKPLQbIUsqixUJQ63xuw/vMpyAY0vMl3QmiU87yYjEjEM1E5+Y2/fU+c2vbKMpImd6PldNFTjZdvnVu7dATchzqBQV8Cp4QsqOE5O62T/I9v1Pea78qn8iPtXXrVH08Y9/nFar9XX1/YecfP1joIs6h5oaspqhqxHfc800962PmEQCXu5iWuvoTp2X7rcYeRUmQU6YxhhqQBrqVKsRJzdzlmcHJBONa6ZV9pZVSuaISagwX1TxfJ1A9Nl1I2JP5+CMyGgkkEgOW8MqR6euhFx2XFAFAUM02PVzEHew4zplzWIY+rhpTEkqUVEVnCjg+qkUe5Dw+NYW6aFXUFIl6oWAC4MJL9hjU9YyTvTGHJ8J+PDj19JzY8KownWzGi/Y43O0uMiDOz30woSSplKPBbb6Ag2jdKXEeRxw06LCrpNRKwTkicWFScRyVsfxA4JUQsl1HjoncHBG4XuOLfG+089yeUfi7d+0h9965CK6MmCv1eK3Hz+FJZZYZ8SsZfCBxxVuXEgIMw+RJpMo4FArI85T9jVSZFlEETNKusyclbPrS9x1ZsT9FxMmeZk9jQAJk69cKPCS/QE3LXnsjA2Kuo5QT5k2VHRV5eRaFSdM2R6alA2NmpnjJBGWFWIpCRd6RWYrE3bHFQ7VC3z/0TkqBnzh4gP4qOhyhCblnN1pMV/zUCUZVU7ZPzVm4GkEoUHBCLnUMWlaMY9cDkBIKFQ80kzjExfaNAoyVTNn4EhUzYjexOLJtSqWIFNRU3I5Z+zopCRUzZzJyERRU/wU8lREkyVMJcCPBa6eclAUn088sxcFuLRW45olmUh16O5WsAoJuhaw0NDRhJhRoPCfvrLNTfsSnFQiyXIqZoqu5zy61aegWoh+jJZrTJkisZbSrHeZKwVsTorsDHSuaTZplXLuaASc2c4JXZUfvW4vj63bLJZVDjcsPnO2hVXrUC3GtJ2Y77quwUPbY0TZ5dp9IhcGHSQppTvWyVOIMgFRgt2RxchRWGrGHJ/r8dD5WSZhjpOOWLZK/NQn1pmrq9RrNfrhDlmusKcWIaAw9mTiNMEyBTRPpGjm/OCNU7zlU2cwtZSyGdGa8uhMTBQ1JyPlY08FiOYstx3aZn1URFEkqmZAlCoggK6mpMaV8vIbQ40gVJmu+jQKMUUjpGzm/JvjhxBzn1+47wJunpILKZrhMPJEFmZCskxm4Mn0nSuCsFke8fTZIoLUY6ZcYcuLCOICZkGiZ+v4gYhIzCgMWahmrPcrLNU8NMkgSARmCxpbIayOfCqFlMFYIk4zdFHiDUenuGf7IntaMj07YKahsjrKOaTJnLNTbliI+PQJk+vnNVZHLkulnG0nwFAVHtrpc+28jSiW8bMxSaqxGdssF4pc2s354FO7DFOP7zg8zecvdvnAa48yiVJ++p6TqEafS8OMl+wNeHRdI8lgwSoy8TP+4ME2v/aqPRxv1Vl3uxTlKn3fJcsTDEnlvhWJ65d6zFan+LPTbT702mN84t3/EeXGW+gahyibMoeqRRp6wuFGylqvQtuRiZI+5y4KHNlnMYpyEsGlogbMFwrMlFScOEEVM27eY7I1quLLA4ZRjioU2D894eSOiJxnqOhYtLh+USBIBIIso6QXEHIZNx8TBjm2W+b4rEGaC5h7XVa3C5QrKZaiQh6yMgjY2ZV5+2tm+OjZHaakeQZqxDDt/2ObtH/mH5E0TfnIRz6C67rcfPPNX7v+la98hVarRaVS4dZbb+Xtb3/7/9SX+tSnPsXNN9/MnXfeySc/+UmazSZvetOb+Pf//t8jSX97rHsYhoRh+LXXk8kEgKIq0gsiRo5Js+ijmCJ+IOD4Ca/cW+WulQGpq2MaOU4gkPoJC/UxaVxAE1TIPcpGQBpLGKKCIcYEQYAX5WS+wj2rPZ5Yc+kHUNRk8khhxxkyCUQaVoKEhZ8mjANoFFU+dHIdSfboeinXtop0BJlrpgpMF0VK5RhVFnCjlDQRONa8khq+0cvZv2wjCDKZ4OMGEjfN+7Rtma2hwrQp03MkNscpz68IRDEU9AkzFXB9HVezEXolVO1KZailushUMcCJfPDrnPcEenbKVZJCN5lwT9tGE0U+c8FmoZIx8lOSSOSl+y2+aV+Z2dcrqJLAWjfFFcdo2piKWUSQQxaKKkESMxoatGoxH35mSEE2GPQFbt5X5NQwRBYtxpOMQ7MpK12V6UKOJLoU5TJZecTRqYiNiYCmGozHOfVyhigFNKwYd1CiUh8gZlXe/s37GIY+v3zfBew4o1aErbZEYTrCVFM0xcP3LZpqxMpAQJQCjjamWG6Z/OGjG2RiwnpfQxQzWlbKpa7JkWqNR3dSZDHBCUUOLUzoD5osz2ZMFVR8T+fR3R20oouXqIzGIj/z+fN0o5DXXlPi3s0x9rCAWgqo6hmqnDH2FSaOhSymxLHAKJAwlZyebXBweoJjW9yxbx/3XBghKR7dQODnv3yOPRUdz9O4sCvSLAZ0IpWdvkZJF5kEIJkpa4FCsRSx46Y42wGBp5FmAr4rIYYhh6dzfvqLF9hXVxi7EkVdJBVSIjHgzMhnWitSEERW3YA/fazHe193kI2JRycQeP9TPYI4oVrKyPKcg60qJW0ZNxE5VhIxJZF/eX2TpqVQlEW2Jz7veazNtFLgB6+dpe/FqFnIrKmTpDnjNMSJA/7gkUscr9eRhYzXXz3Dr33zfh7fmlCRSjjYPLE9RpFjpi2FPE8IMp+UGD9SuX7B4HmNKh8/3eHezVX+6rTBL7xkifvPrjExFXRBIgpFtiONFy+U0NSYz53v4rrwqkMt7uu0Uc0YHYEkStkeSsS5QFGN0CQYewJTVk7mSZzpejx/vkiWw6sPV3lwdcLFTkCj7NNDQpRELrfrlKwML9SoWWM2BxXssAS5ggg4kce//KvT/KvrZmnqBvmUQpoO0NSUJM241MtwfI299RBNjZn4IhIyphZgqD4SFm0P/uXzmmyMAx4Z27z56hn++syA3iRBzCpYU31Gjkkc5dRMB0tSWBlIlHUDL/LIEh3D8Dg6E7HZE9ncKfMlLyBPO/zwi+fpL0Y8uNHn6Us6RitD10ScNOE/P3qloMfD210yaYTvG2yME/Y3oD+ocetilY+c6jOwYxIzZiX3qBhgSAqIHqqgM/YhiUXIS+SCR6ZpVMyUvpuwWBY4F4l4EdR0FTuMSaKUw7MJv/qSq9kax/zcl09ypm+TJTKNsoRuTjAkkSwu0nNhNQh5540H2Zic5txuxNOdMa8+1ORj53cxtYCqrDFVSFnvpNQKApqe8kOfPYUsxZiiwYee3iUQffyxzkuPNXnHPZtEckpJ82jkFt++WOfd5xz+67NbLBdN/uzZXayyy/Mbe7k4EVkdjVn1UvRA541HWnz6bJ/Hx212+yIlC66a1hkGHmechJIu8K2HDnGi7dA0ZV5zsEaWZcwVZQ7MiOx44AQxXiRCkmNaAt937Qwvm9dY+MaTmv6Z5xAhz/P87+v0wQ9+kDe+8Y1o2tenwfDhD3+Y1772tViW9Q0t5h3veAcf+9jHOHv2LIZh8MIXvpBf//Vf59ChQ1/rk+c5b3vb23jve9/LcDjk+c9/Pn/4h3/4tdO7r4fJZEK5XObl77+LKCtTtAIWGxnfsnSIX3/0DPsbIRIyR6cTgkSk7yeMgpyBK9IwNBqFiBNthfagTFHN2ddK0ZSItpMxVZ5Q0TNGbpWzHQEvAhmVmaKK64vIWoAkRhRUGUONsdMxcZZxqCZjapALLnkmoYoFtkcmTpxgKjGSYLA2EDl7WWFhJua2I0M+9+4/oXLkGioHXw5iQpTlNEyBgy0bRfYIY42JZ/H5swXIoaio/NKt+7lhtsQ77l7jSfc0ZSMni0oovseWN8VcXeCpLYfbjgaUjCuVxfKkSEJIGGuMXANEj7KmYocpVzeaPL3rEmcRQqyx3pO4dllgmIxpGfDMSpnbDxd5ZMPBj1JUI2e5MWJ/y2MUivTsAqYSIksCspziuArDsU652kcRrpTpdUY1tgcCi9MR33Zonnfd16VeA10NeOnhDnFisjMROHW5iSRmaKbL2DOYqsRULIdWMeJSu8nYVwjCmIPzAxYqCVEqsj3WmfgaE0/j9Qfn8bOItfZZ+qKKLIMdiayvlVhe8FhqBMxXbNwYOhOd9riErob0HBnXM7BUsCcKL9lb5vHOgISEmZrPcs3n2c0ChhqTZwrbfRU10/nm562y0jPZGZWQ5AhTkdEVD0USaY9VfMfECWC2FeFHEi85MODktoooKNiewQ21JhejNnua23ixwdbI4MapaWxfYpStkKQ63zJ/lLu3NygX1hiGMrdMHeJ3v+KiSClTLY9rq4t84kyfctlFkSBIZBpWRMlM2NytImcy+/UAYzpBFipc3s2440iDedNkT13nV+9Z47Y9Ff56/RReLNEZG7xkvs6jnS0EQeD1h+b5wLNdIhfqzQi7b6CUPfAtmo0JO2OJop6jCgI3NGf54uUBThYzbaQMfZkb52o8ttNj75SHoUaoEqz3dOpqi9WJh6FHmKLM81uzbEwCnjdn8tnLq5jGhPlyyLntGlbcINB8Yl/jp188w68/doaiFhMkKevdAlOVGEnKUOWIgWNStzLGvoQgJIhCiiSJmHLM6sAgtjV+6UUl/supADvMueOYxccubHH90hghFzHUmJVuiTxXMJSYgZ8hCAm93Sm+8+gUX949w82LTXy7zNmOi1EIGScjoixipihQtoac2Z7iW6/e5tENEzEv8dTFAkt1g3HsEoQytyxXqEg6WZ7TqiZ84myHVHIpqTJ+GnDNbMiWDTPFDC/OiGKV6+f7fPF8izAyaDsp0wUVU+8xZamc3tWREKmYOYoo801TB/nk+S43LRb46uaAnh9wvFniYMNk2w7phT38LGR/wyGKdfx+zNPjKroscWpd4PPfd4yqKfL9X/wSi+WcpiUyjALETGHkNBiHKQIZCgavOVTnxNozfPCXf49X/OKvEOQjWkWXOGzScSTO7soYgsJCTcKLU8JYpGZcERJ9etcnyCJef+02flxiZZCCkDEYVagbGoIUg5DSKPh0hhX6toRc6OCGIsemY1y/zsAT0JSYopagiDKWmvOdB/fzh88+Qd8VaGgFskwnIyJDIM1EZDRKssSlcYST2vR6FvVSzl7L58/edBvj8ZhSqfQN2b6/y0Y9/tNQeI6kj5wQbvgN/kHre8c73sHP/dzP8da3vpV3vvOdwHNji/+/wIkTJ7j55psJgoBCocCHP/xhvuVbvgWAv/iLv6BQKLC0tMTly5f5hV/4BZIk4Yknnvg7/anDhw+zurrKm9/8Zn7kR36ECxcucOedd/LWt76VX/zFX/xbx/zSL/0Sb3vb2/7/rv/rD3yGSCgxV4uRpJyRLyIKCYOxwvNmyrTHCR0vRNZj3nC0xX3rHTQlY8fOQUipGzm6rJBkGTuTnCkpYZybNA2Tw7USHz3VQxGhZEmEUc4kSqiWAixFxVQzwjRm6IvookoeyRRKHnGWEYYG17UqfGV1gqZBVZPwkhgnjmkVM8g0njdd4O4VD0tNKJspshyQpgpCLjMJc+IsYbYEOdBxZCp6CojEeYDnm5SNnHEgYDsqmijjECCnMntmfAq6SxKrrO2WiSSPqiYih+CqKYaoMnBlRHLKhZQdO6Ommvzg9TNUDJVLvYD7V8ZESoisDXCjK5pvuRCioDIMMoqqRNXy2BkVyHIBRYRjrSK6LLIyCNmxXZqFjJiYq+dceo5AzYA4S1npVonSHMfVyWKJ77l2it0g4ME1m4NNjdWJw80zTR7cGOLnAbIoICshQaBTsUIUOcIPdeIsI0okpqWU2doUj7UnPH+qxgPrQwwjJYxkFDmhUvQRELF9DceRUbSExbrP0NZJYp0fumkOQxYRRYG7VwY8tD6iVXMIEonxRMfQMyQxJ4hlClqGjsFu4FI2E7JMRJdFTC28Eq2cichyRpyIjD2NPFHwfYn9LZV9dY1tf8KzqylIIOYSmRBSr/iIAviRTBzLaJKOH2ZUxIARKoqckqcCgpAjCjDxFUQB5msq33N8hs+fHfJsf0QaiXzH1Q0+fqZHwUjxk4Q5vcKliYupp7iezOsON/nsSoflqTEVw2foGWwNC3zzUosvXRpS01UmqU2ei7xwepqbFot84dyQoiWg5BIPbHRIc5l/96IrEYCXBz4jL+X0wGZzHCLIIVJsICNxywGRa6enONnxeHLdJU4ydtwI3YipEtHL1StalqmCKueEkYEqShxrlnh8e8h8WWHbCdFlibEfUi+FpJmIHyvcNNViZTym7cSEiYAkCEhSRpIKSMhXbIrokWTghjJiJlCwYlxP4ap6jdv2VdEUkS9fGLExCbGsjOfPVPjU+Q6Ok5HJCbICaSyhqilX1ap8dX2MrqXUrARLSxDFmCQT2O4VuX6mxvPni3zmxBqdHHLhyubbeKKypykS4VE0PPxIRRQk4iwlSUUkQWTXlqkaGUGoExCiopBGMt92pMoX1zokok/TyrE0D1USAIG1gQa5jJ9kLNdinCglz3QGPuiSxvWNJg9s9EhDjZ94yRy6IrI6uKID9JW1HikJmqhy96UxjWKGZQQkiYSpJQw9CQkFL8yQcpVS0UN0RYaoJEKEJStEeYwsCAiCxNAVaJoqliqwOoqo6DKaPkJCJk5UYlLiUCHNBYIsom6CjMJmR8Y0ciwjJSRAEyVMFcqmixvqDAYWRsHju4/t5961EWd6Nn4MN8+VOdkb42chNSNBl1XSPKNtCwiJzoyl040cohTyWEHMRbIMfullS/z5Mx0uDDxcQrIMnl8T2QxVUCasb5s0aiklA5xIwE0SDDlDlzQaRoE3H53iZz+3ilFI0XKF43MmL9/b4G33X6CoXflf3rJQ46pGgXc9scZL55uc6tlcHHlEgYBlZtQNDVWCXd/D81WuaxW5bUnjeUeP/C/pV/1T8am+rsCp7/3e7/26N4kA3vSmN33Dm0QA9957L3feeScPP/wwd911F0mS8PKXvxzXdb/W5zd+4zf4nd/5Hf7gD/6Axx57jOnpaW6//XZs2/6G7ychoCCj6BGjIOO9T1/mhUsZRaVIoxjyxE6EZV5mX73HlKkxbZlUrRBByFFyk9mihK7FPLnjs+OOsT2VhZJKmumMAoG6IbBUMmlYEpdGPmvOmMlAQhZUxpHPuY5CTS1yuFpBlQVEIaauSRQUg9VRyLY/pKR7VM2cVBjiORbff3Odkqbz1ZU6m+sp177wFpw4oW2nCNmV06x7L2dc7BoMfZWTvYjZcsBiRcRLU/7DPZd535ObLC/ucvUMzJUM4iyB2ODFCxXODUKuXozw4wxZblNSoWC2ccKIgavQCQaYSk4iTIiyEBmDk+2Q0FO50MlpVAP60QQnBFMLyBORF87V6Q7g+GyRja2M/S2HDTvCkjN0rcNcxaPjuwwcg3+x73mUihGX+ha5kFDWROamR8xPQd/WyUOVF+0rUDITFFHjU0/P0bGvPMA5eCBnDEdFCrpPUYvRRJ0klagVbRQ5Zv/chDTPKOkezUJCWRdwfAU/SslTuO/ykIaV0Swk7GuOGboipYbLyJUp6QEzxQmzRR9LA13JiRMJUZQ4NONyfGnI9UfarPi7WGZEq+zSt2X6rkLFClElgZqZMlWPKBY8HrpUZ6nhUDUShmP9iiaUwJWSoRMNURT4sRfM8R0HF/jPr5olSQt8303necGeDjfMazzddigZGZcHRQpqQs+WufvSAD/MGXg6fT/hHXdvcVVxio2xhSannBpMKJkZopJRs1y+tLLLNy+XEHIJx9cxFRBF8EKZq2tlrp0poqoZZSNCESRiyePzF3d54JKNJovMlFX++ORFzm0VCBKBZsXn3vUhdiCxr9jk3jWHpq7xbVc3GY8VBCVjruKjKQ5ZJlCQFQ5WyszUXe7e2iFJc2asgHIpwrJiTu96GHKOHYj0bYORp6EpGUV0XjnXQs4FBCnmprkiz+zaPL3r4IUiZSPh4m4BXU9wtQ5u4oEy4k+ebBONLQZjE1USWWp6lM0MUxHQZIE9TZt6waNpBTiOgqZFTJcmLNY95goJsppz79YlBv6VKnKfW+kxXcrxQ41RANsTkSABXc7JcgFVFKkaMlMtl+sXDMZ+kTO9CYt12JzE9L0IQYTpokIQS0iCxOFpn2d2NAxZYRIkuGnCMHAoFbtMl30+dWGXNX/AwWmZ7zi0gBAW0HIDL87Y18hZro9YqghIQsyeKhhqTJAUaBYidEFmoSgiSyktS0dRA/bWYXuoEacS5y6ZRFHGsZbFh5/pEGY+aSbwqZMTPn2mx2PtHTYcl1Yh4XQXBl5I04JKwcbSA/a3NP7TlzcoaxovnbmKnVGZSSBT14okOWw7Hlmq0HEkHlwNuXdzk910iaNveiMpAfurBq43zdrQoKKLvHgvmGbIpaFLnmdIosB77zjK910zT1mSef5ijh1amGpAQy8QOTPkQsLjWxGp4JKlCmfbJivdHNeDWaPKYrFCzylgxy52dEW4UVUi1icBa+OEL2+sc7gyz/6qRd+X2fU8VvsizUKC7cn0RjIvP1hDVzKWC2WuaphEropD9xu2QX8v4nPc/gE89thjvPe97+X48eN/4/pzaYv/d+bQoUM8/fTTPPzww/ybf/Nv+N7v/V5Onz4NwBve8AbuuOMOjh07xmte8xo+97nPcf78eT7zmc/8nfNlWUar1eK9730v119/PW984xv5+Z//ed797nf/nWN+9md/lvF4/LW2sbEBwG5q4Wg667nAJgG2nmLLRVKjzrE9C9y7KdM3BMqNMvcMXO7eNrino9NOCnhSmbGqcN9A5qSnQUHFFizWUoPDc0s80VMpN0v0VYOHuzJnxjpxQSfULfpKQlSeMJA0Ir3E5UTlohThGylbSY1zToH3rTisJSrtxOJsJHDG1xjJFn1R5/GJzAfPh8SWwkBWORVltJHZiEo80K7w+KCA0ZA54Vrc39cYCDoTVeNJR+OEXeeiW+Whoc7JfgW9XOfmqxb5qW+6jpFi8oStso3BWNOIagH1uZyJJrMWgqvqUA8R6j49scC5WMHTTO649gA/fu+Q7dhgcW6K1kKDM6HI+UCjL5pciCSyooxrZiQFuBybfOHcNF3RYCMTsMUK50ONZ8Yalz2T3bzIV3cqiGUBsexTbOZ4ZsJQ0TGnbDxdZUsUCKQKv/p0n8wy+fVvv55//eKruWHhIO854dLTUlZCFUeRWY8tLoQqtqrimTmOLuKbKruiiC2JfHIt5YxtoNfqlMtTePEUq5GB3kwQKxm+ldIWFCYFCake8li/TEfWmJ6Z4t62yL+9t8vTDjzqJEiNjIGssZHoeMUMz0oYqRIdUaEjylzIEjZTi1OTMutRic2gQT+bwzdSxmqOUg3oSSKOnmPWTdqqyLE983zggs3dnZQbjs1z8/49iDWB5UMOjqZx3q4zUTUa0xWuPzDLpVBlgIhsldk7tRe1NMXF0OL02KIblvm3tx3j373sGt5/OuLYvllW7CJjS+LD632CQg4lg11M9HqJuJiRljKO7C2yvNhiR7ry9xxIGlRShrKOq5RZC3RWsgizFTNW4c83HH7qq10+vhrz+a2UL20KvPjgIUS9QavVolytszw7DWaNt952nDWnQCw1ecOLDnB8/xyPjQs8MBA4Oza482VX8TOvvpo9y3UmisBWoNJGYT0p4RsCE1VArxmsBganHY09cwucdFXCUsDZKGc9KbIeV5HKFXylxJ+t29y3qXFyZHF+bLE6KTM71eC8Z3LRU3h2rNHPGxzft8hYMQlLArYmM9QlHuoLzM9O0Wq1ePGxJX7gpUf44Rcf5yM7A24+Mo9jqri6QlLMGRs5z7oGH930GCgqPVnHNkR2BIXTbpGLQRFHV7mYRvzKV21WQ5kuRS4EFpdii1p1ipccOES9Mc3FsMB6UmA1VrgcawwkjWfGJpc8g11B42wksBabbMYWpyOZSK/RTqqcaje54BQZaxCZIj05w9U12oJMVzBZTxXUekpaSlBqIYGR8Il2G9Fo8orr9vDbj7uUqg1uOrzIVzoitlDjC224bzIkKcmsxDJbaQG/4OMZMlu5zFauMdY11iONzajMSMzoywpDWWUtF9jOSqwjEpvQXIg4FeSEao2VYYkb9i7iKBZeIWEHlQvjKg9tWexkChRV1vOctiAT10TWxJQzYwtbMrmQSLRRaFNipMW09QTDWOQ/PWGTalW6gs5WUOBD6yPO+Apy2aAtmJzyRZJiTicus5KK3NcVUUsFNhODx4cKZz2VgapzdzfjX916hHO+jiuVCTSTp0bwlJ9yYdwkqmms5honfJHIzOlkFS5FBdbznC91UjythK2UuBSaPD3Q+HI/plKvccuRPZzxFU76OR+5HPKhiw5nRkV+4zGXT17OmCgmvl5iNTH48pbCNx3by1CyEEoan1vP+ckHt59Tew38s0/1DfJ/K8POcRwmk8nfaP93+PznP89b3vIWjh49yjXXXMP73/9+1tfXeeKJJ4Aru23vfOc7+fmf/3le97rXcezYMT74wQ/ieR4f/vCHv+H7ubGAUQppmCK6qLOv6XCmF/DsrkuGzfUzAlN6E1GwEKQQWbGx1ITVbplBELHpONhBwo1zMjNmnUYxZXMssDWSKKkZgiBwpueyOsj5wKuP87LFKfJcwwlE8sRkb03AjnLODW3OdTNkQSZMYcuJqRoSVzcKVAyZtq1wqT3FTYsWl4JVqtVVxNFFXvrjP04q9ViqD6lrRaJUIMsFGlqdMCqzPZaZKagsVnNmmuvMVseUlZAPnT3HfetdFBFSoUvXkZGQ+MSJIUVRwlIlZDmhqjYw1Yw0sajqBRQ5pqGVkCWRld0SqlDgkxd2aOoa/aFCHojEocZTZ01Sr8inT5WYLiv84hfWqRgSZ8dj9u712JyoNIwMNxaYLubsTHTCoMKFtsF/vH+NI+UDHG+VGLgaC2UoqSk3z1d5zf4mH36yS9eLUVWHZtmmbIV8+XwJP5JZagTEJFw/b1HWJY5MD4lTET9UWOmrTFfGVI0MQ5FRRIGCElI1I5abDo4v8Z5H24ShiCZnqGrOwLvy8LzY8Ng7Y1MxPA5XeyCmlPUAVYnYtRW8QKTvKaR5xiQQ0FSHLM/p2Sp5LrDZK6CQ40cKGx2LLM/45qPbSJLAareEpmZM11zCRCKKVCQhZr7uEUfgRhkXnR0e6j7AM1s573rgKh68MM2XTseEqksujhAQcUIF8hwhV/ipW5ZJEgsEkSMzOk/0L/OGq1eYLYRs9hOKlkCQghMKHNu7S0+8jCJBxUoQhYzdscZ23+DmPUWGk5xbFmps7NR42fI0am7y1hcsc2hW42zP4ZUHy+wtNJiugB8LVK2AWs3l+r0Tbt0rMF2SmbN0CrrMu19zhINTMoZU4qa9JkUrpFVxeOCyz3qnzNiTSBOJq2Zs/Eigaqa87qoyJbXETt8iSmS6tspWt8BOPGGDM0xVPOJU4FOnBtQrY3rBkJftaTKwCxhKjixm9D2JsScyXXMRjR38NCKXUgQhp2sr9B2JMIbpksvIh44tUTRD9s6O0eWUzZHJ1sggFRRaZdhfaFGs2kSJwJ66SNW4cnLbKPjMlgOKWs6eZh9TvVKdT5MlNDXinU9e5I/vOMZP3XgNv/WlHrkaMQojpooO5FcqfKVJEVXMsRQJXVIomQFXLyQYloeQWRQLLjftHTCIuvyHe1b506d3OLXuk8UZLz54AUPxeXxLQxJ8vDQhzTymSg47js+RaRu9sMVO32LHsxl6Ipf7CpPI5YUHh5zvB5iFhF97eJ2HLjncMFvix29cZKqQMVtR6bugUSIKK4x8mQNVg+lyiiKnLFdlZusDgtxnww64PPS5bbmB7ZncdbLAyiDCdmqkSYG+D3srBb7nuimWykVGYcie5UV2PvJbhEKbZtGlqCXY6RBJ2eWa2YSrZySKZoIsJXzqfIf/emaL7V5Kx/OYeDqrA5Ndb8KlfkJFsbh5UULKdaI8xpAlGrqGH2cM0w5rYxtNlJm2dKqGTJzlyKLI/qpCFhs8ttvjU+cH7DohSxWoKWXU1MIOICFClGKeaU/o2jln13ImaUhZlSnkX19q9v9OOI7Dm9/8Zv74j/+YavV/iLE+17b4f2dUVWX//v3ccMMNvOMd7+Caa67hd3/3d//WvjMzMywtLXHhwoW/c76ZmRkOHjz4N9LMjhw5QrvdJoqiv3WMpmlfq7z23xtAx0nZDX28QKdqlElSnTU75btvaPBHT62yMAclHc4OXVYGEccWA+ZLEq6js+PltG2N5VpCxZCIcpGRJ2HHGR84sU2llLMT+qQklK2ckimi6zGZlCBIInlaQFNhx8lolQP2VCUmQZHVYYao+bQKKpaos+XEdF0wJBldEXHTmKnKlShvSYkoFHxKis7YqXB6U2K5lfGiPRZDX6Hj5lR0lSBTONNR2VtP0XKV4VDkh44dYLmp8YKlCv/12S7vfHAbP4tYqHrUzJxBkLPc9JgtJyRCRKskkOYiq0ORMLGIxJg8E1kulXnXE1v8zEsX+OUvbfJdf3mWz690yYQIXZWJM4GKlaDKIk6SIAoaiiLwuusqZCREoYkTh2w6Q1bGLq48RBRF3nrLNJYusm2rXOhViEmxQ40z7TKGlrFcD7jtqiKmLPKBJwb87JfP8t7H2jy54/Haa0vMFuq87cXH2B0bhK5B5qvUSgFThZy5+oShqxMnCkNXJgokcgQmQcJKL+aF+8rUSiGamiMIMn6kULZCFhseM0WdIJV4ydwMVzfKHJ22uHFZ4vce22R1FLFr6/iJRqkAaS6z2S/QthUaFYcgzZmp+xTNFESRmAxPtFl3bcaBSS5InN+tkCAhKxmXbIcgEPmDJ9ZR9RwhU3h2M+Jz58asdFWCUESUMiahwLQ+zTM7CZkccN2iwZ66CrLEq4/UOdv1eOF+WJgPSPSADzy7y4muyziPWLE93vGKveyz6hxtWaSCyNokZM+0zWOdDrcs1HFDice2Q84PfL776j2UsgUC5yDfte+beO8dNyIgo+g5S1Mu40Bjc2Ci6xGZGKFpOf/u5j1MV1Ue2h1R1RX+4MEdfvmuLX7hy+voqsjP3HOaI/s7RIrNL395naf6EyZByoFqgR97yRxFQ+Fz69usDMdsDw1kSUaWReL8ih9qaNCobFEsjXjo8oQHLjrcvr/O2Knx4iWTZs2jKRZp9wQW6wJVK2Z+xuPAvEe9lmBVXO46HyKEKpoukooCHReubdWZqdkUzQAnhhSBvQ2df/nhC3zmfI97tjb57a9ukeUC37K8l6c3Yka+hKrHNMsujbKHQE6eySiKgCRCRn5Fc9HXcUIDRYWD1RqFepc8Etlbtgg9CWessBvEfHp1k69uTzB1yAWBQSCRouJEMqNARkC9cthUDLmxVeMP7jjEO19xkKfaQ37rFfuoNnJ2hipuWGDgqwSJSpKJjEOBopFzerfAwCkhihGSqOOlGUFscamT8MLlMq++qs6vP3KWHIHvu2GGH7hphutmSlzqadSLEQkZlhlRMQVyEhRJJ0hTPM9EKYSkqYIoquj6hIVagq6GtIo+RU2lUbaJUhXH1XimO6FSknjXU1v0JhqXNme40FbIBIFaWUTQXARBRKBA2wE/kalZEoI5wYtUpgsKshKSZCmrnSm6nsxYGFFWNO7f6nCpmyNrKZZUQJLBixJ0RSJKVXp+TLPsce10lVZB43WHlpmtJsyYOuQSoyTgUxfb/Mxn10hzga6TY2kJhiwxUw2IsoQ0gYoukKQykywjF2IMWSHLdfw05jce2uDotEGjkLO4GPLdx6d5/6nLLJpFSpqCmCpsDQRODAdYasq3H6tRMRXCPGFjHCHkEktTAr933y7tSc7ZTYFCMWRv6RsPOvlfmf8dfapveKPo8uXL3HHHHViWRblcplqtUq1WqVQqf+NDPxeMx2OAr4liX758mXa7zctf/vKv9dE0jVtvvZUHH3zwG55/qpDRKMQ4sUsnGDEMY2YLBtfPGMwUCmiayxM78BdPVXF8FVXQcKKEjYHCcjXjSFPn2IzEJPHZdEfM1rYxpCsPeIIUUFRjZuQyb33+PPMlg0e3XNqBjyLl2J7MluPjRhkLxSJL5SIDX6bnqShYqKLEljtmx52giTkgcv92GycU0dImj7/rnexpjJHzMp7fZHUUUyp0iGKVURRwYeRSNWLyXMBPXaSsyJ6azPH9HV5+xGe5qtL3BHaGLRqmwshLqdY8VDnhngsQRhKS7NHxJziRgaH3qRcmhJGMl3jMFnMOt3LecvUC1QLUp232zAqgJtxwICENZKpJASeNKDUcTCtBUzyOtFIUMSVJylT1gGlLIUxyCqqC44m0TI2Pntui64d0bBlZCtgatLh9ucYDG31+4fZ5rp8u0iwkvHDvOovVmJcc6lMyEgqKRsPKqFkCQaJy36UKRWNMkossVV3KZkiaS4SRxZndGmd6JmXDxlQT9k2FtKo5v3z7POe2a0RxxtDVsRSRgiIx9nQ2xybd2EQSchByurZMo+Qjyym7PYOvnqnRtnUMJadRiHj1/kXGPYs8ltnaKdEZaShGRBgLbI4trp0f0XF1JqHEVr+A42v4scgrr9rkO6/bYGZ6xF1bO7T0MtsTg/may017Bgw8mcMNk+lKTBBL1MyI09tlosggjQR+4ksn2Jkk3DgLe1sqvbGIm+rEmcKCVaE3FtHVkI1hmY5doqyl3Hpgh1bJJU4hzuD4QocPndzg+1/QpOv7TOjyi1+4zA9dt8hC0aKiy5zpuHxqZZ0oS9gZCgwcmbWuSRJJbA1N3vPIkEe2B5zdDUCMUJQcP5Ro0GSp2GCzb3J+rcZ02adWdNFkyJKcS4MKZSuiVQwYy88gqx6SmFOSCowdiflGSrXUY6VncHyuT1nVedmhCmWtyC1zTQYTmUs7Rc6tV+mOClw1M0EQRUQhQ1ECju3fYbriUjJDCnqIGwnYoYQdKihSxvquxcBT0GQRRbgSpl0V53nx4hT76wafP+fQs000LUPJTYIkZ7NvkKUalzst3FBm4OdoSsRM2SVJU4JYYLls0DA1NFFm/4IAQkaYJ2wOK/S8jEmUcnKrSNdNiFINJwI/tHBcnZKco8saQSIhZiUkKWfG0Pg/n2lzwzGfUsVl4MzhxAJR2OBC1yKJapzYLSGkBezAoD0uU1AsFloOs0YZhIyZgkkQGXi+yaxVYBJIzBU1bj9UQVUSfu/RDv0gwtRANwIujyJKWsIjazL3X9JY75UYByGGOqaiaRyYHRALEb941yo/c89ZIGO5anJ+e4oTOxKKIBOFMj95436e2BmwsqsR4pDqKTubAXPp+IqTpaco6TTtcQPkAZpqUzMTtp2YP3xyjefPVDi4JGKIFnEmkCUqptDkl29b4mQ7xqNDQaiQpVd+5+wwo2EpzGgNDrVEnMTl4tCmbPgEjsHQE4mJkKSUabPMjbM6JQN2/DEr9ohMiDlQmGGqKFIphZzqTyiZIlNFFTmXSawBz67+vVnc3zCCAIL4HDXhG7//nXfeyR133MHLXvayv3H9ubbF/18iz/O/oRf0/0u/32djY4OZmZm/c/yLXvQiLl68SJb9DxHP8+fPMzMz83UXFPnvNAyFGb1InKec3k3ZauvMFRX+4mQb2xOx0wA3FqnqGZqooCswDGAUJ+jylYheQ5bx04gL2xKWFlPXFaIs5xMXuySCz3QppSDpjLyc9REkscBc8Up6yTBImK24aJKCE0pMogyDK6WVnUDAFyKqqkRdUcnEhNWugJLqtEyFVPCZq7XRBInQ1+mNREwjp6EVuDByGLg58yWZmXKIafW5abmPJucMo4xiJeOjZ3p8//Pm2FOXKeopq56LpSdMFURk2SPKMlpmxq4j4IXGlTR80aeqC6QZKIqHpYg8uxbj2DlPtW12fJ+ZVshgItDv6mSZjJ+ktCwI05Q0MdBVgX97/RJP9ga02xolI0fRHRRBRJNi4sAgE2Lu2z2HqXnsjCuIgsC88jyeXKsQuAXO7pjsjqqEMagqlKs2lwYRnzy/y55pia4j8MPXLHHWblOuOOhaxsyMx8gX2XUsLnVLGFrMobpF7KokYo6uwWfPDlAVuGA7/MC1+3lmo8jEV6lZKSUjwhAilLTMUt3nQ48MWbddOkHA4UaR2/dUEWMRN4KxC1EkMl2OOTDjUiskXG4XkaWUWxcKqIJJnmckiURTrNHUr/g3JSPFjzT8QEXIJaJYw7JyxFTAd1VEOePSeMJ339BARcId7uUHrr6GH3v+Il4+JMsEuhOZ012X9kTgRQs15io6x2pVFpUFxp7EVBW+/UiFDzzR5lhLplV22QpH/MQti9x/nv+WiiVz4kKFJNK52M+4rjnFj924n7pq8rI9dc5M+pzo2vzUX6/z45+4yCD2aJY8TDkkiiVyT2XiqpT1GEmN+PAzO9w0W+RfP28WXZZYrur87G3zvPn4FB94rM3aMCJOFaoFlyMLNvViwk4v576LNj/9mUs8eHnC6d2Mvg/z1RhBTMkyENBJkRl6Es9sleg7KromEGoOf3mmzX+85SBPbyl0xxa72ZCr9mzTDfoMbJn1gc5q1yCMJfaYTfbUdExV5NalGk1L4T2vPswnL50FIeX6uSGWkiKJOY/ujvBlh/c/u8FXN7usjCf82TNdBl6MIgnkSsTIl/BCib5jYCgCkgJBfOWwa+jKpJlMzUqIU0CM+NTKNqoERiHk/KiHoCUszPpkis0k61DQQsgFiobHfDVAEQR2ewZSLnCobrBU0fE8k7VOyp2fO8W7HtrkgtPlPz70GGXLp9WyyVKJ1YHOpW6NsS9jKhLNQsDVcyO2xwbjUKbv6FzqGcShQldw+dFPXeADj3S5cabKLz1wivM9l+/6+ElMVeSmvTZhkjNbzgmTBMe32J7IkEiEnkGaZkSeybfsm0KRRFb7RUQppqCpyErAdDHiXKeCKPrMNWJkNeDWvSXqisrGlg55zrV7HJzUY3/NRBcN3DhlqRpwx9VtwszBDxVkwSIVA9IMxl6B832RJE946fw0q8OUCwMbApOCJTKJYmQhw5RVLg9VRqFPzRBQKODlOSc7E/bVdN7+4CW6HiDElIsZhprRDyLaoUejIPBD18+wsqUiSgmLVo1iJWVjnDOJfZYrGqagI0oRlwcxcZqyWNDZ6cbccaiGk3lIZNx13uFjj4f8u7su0h9CyZT55v0FREFCr0xQrZC9FRM5MiirCqkA/SAi1hxuataZa8h0BjrfeeTvtpX/UJ4zv+qfiE/1DW8UvfnNb2Y4HPK+972Pu+++m3vuuYd77rmHL3/5y9xzzz3P2cLyPOcnf/InueWWWzh27BgA7XYbgKmpqb/Rd2pq6mvv/W2EYfi3Rj5FDLH0MS1D5rppjVlLZ+iCnQzYdsdc6hgkUZWGoeOEEjkZ3VGN5WoKOTzbdtg7fY6qHnGkrv+39IqUJMvpjYqMRiViKeAFs0W+6YNP8PpDLfZVdexQpF6J2FeVmC+q2IGHl+0SJhFiptB3EjZHArpQZLlQgVzACwQqsgqpzpc+fS/zL7yOLJfZnSjIaoealdLQSihSRhypHCwVsFTICeiNK7ihytHZMyyWu4jkrI1cntkoYIoCXuozXYtoFGTGI5XDDZ2WKeBHIlreIoqBTEQTDDLRZmgXEcSMz54P+PSFHYbRhKrhkykOQeZQKzsMlR6GEeHKA3QpZWcQM19xkGSbR1crDLyMRmFMjo+u+kz8GEmAn37RPK2ijxP5NK2YBy4ZzGgmP/6FC2Rxxi98dp0HdzZoGjlkCjUjwRBURq6I4ysEccZ95x2WihZZLHBmx2TkqFzVCqnqKboUkqWwOTDZ6qs8vFKhNzY4PO2wVJ/w+c2HcYKIB85XcH2R2XJIUUsRspyVjsW9F2YRkdidGDQsl5GrIuQChp4giFDVIiz1ipbKfZsbvGC/QrOkUSrLyEpCWYuJE5kL7QoCGWUtpCDHaIKI517JFT63W0aRYU9jQtma0DIVokgnTXIeuDBFnAqsuCOaWpEwlNDlnILic6xcxLJS1nZTQkfm2ZUp7l3v0w0hy0SKckpRkfGcjHBsYABClrMxMLFDhV5f5daZGiUtIElzJkHGD33mDF4cocoSAzdlypJ4YmPCuR2fgZfSVDVGQYih5uxtBgz7CoOhQn9oIKs5JUWnWoDPXWjzsRO7rLczHr1sc9flFaaKDqqWEDoV/uWxJVRJQLcinEBGFmIGtsrjayZZFlC2QkxR4yWLFV483+C6xjw1PSWJJRZrG/zm/ZcYBR53r6/i5COqxQBFj9GVmN2RSdWIMOQUU8643CmzM9SZ2FfS2N5wcJ44FNnqF/B8lVbVxQ9FzGSaolRHymESxhywypztjKmUIvbWfErFmERax/Nyers6T15uoUkCMjk7A4uCBnEKgmizPVR4YtPnL0/s8oePbZDmCUKeU5BUQvdK9Y+WKRClCWfaFnmaMfA9FDFjvZ9xsVuga2eYaoAkhIy9jCTNcKMEx5dZH6qsbJR4dq1MkuREiYgdQBYrKJLHtJXTtmMUQSDObYII0ixiEgT4gcLdZ0Xqpsc4dRmHHp9f2+F7j08zWxmgyQIxE+ZLIq1CTpxm3DQnUdZEJp5Knlbo2QZhklHVVOw44NyOS0WLyaQx5zs+NSNHjlS644zFksrb71shF3IuD132lDTSROXQ617F6uUuS+WAgtHm/2Lvv6NtS6/yTvi3ct55n3zOvefmW7eiqlTKEpKQGzAYySYZGUxqMEhYgnbbH+Bh62O4wbjbbmiDwRiwoTEIk4xIypRKVQolVbyVbr4nn7NzWDm87/fHoWWrgfG1LKmd9Izxjj3WWnu8Z+0x9j5zrmc+85lXhxFJbLF9tME8cdGUgnsWNU42VP7Jw9tshVMOeibDmUVWzXl+X/K+az2+865VlsuLvHZ5AQVY8EBVJbeTkOd6KQjBouNzqu5i65JpVJHlGkVm0w8VdqYxURGio3Ou4XNX12G5E/LVFzochQmOXlAVBr5VoNoZRQlLnslrN78Ila8vQuvZ/z0W/kWkxrvf/W6eeOIJfvzHf/zPXPtPjcX/reGHf/iH+ehHP8rt27e5fPkyP/IjP8JDDz3EW9/6VsIw5O/8nb/Dxz/+cW7fvs1DDz3E13zN19DpdHjLW97ymT2+9Vu/lR/6oR/6zPH3fu/3MhwOecc73vGZNrUf+7Ef421ve9vnfH+7Q8n2vsJs6vLVJzY5vSKZpQq3hpKckoVajqUamIrJIJTsTCsabsKFZUHN1phXMU/sqXg6rNQdKqFxtV9gSo2vPNtgo26wPdKwGj1a3ZT1mkngSIQ+pDdXWPRMmo7BrbHk2QMVTehsdhUymaM7UwxN4ioGqpmTy5yNVsnmUkS3PmIpSBFVi1zAsEixHEnNMnl+NGacZiwGCrom8J2YZU/FNgwGcUGRKJiKyuGs4O/8wW3+5ad2EGZKLgsatkYl4RO3WmzUBDtTjWki6XoCKHENqDsKhhVSsxRMo0JoCX/9pXX2jyRLzZIstZiEFc1WRW+u0XQqDA2QBQu+QNXm/MzjN8llxsJSjOfmzBKLeWyTUNKuF2wsjFltJChqiq6q3BrDXrLNd9+7SikNZnONOFH53eu72E6BrqgstULOrSTsDjJu7he87fef4yNbA6pKY3Vxzkot59phwK39gOHEYzi1yBIHRQeplARORVArGOUZzxzM+Kcf36bmpfRCE4nCPNW5fFTjRjjjYOJi1yve/WSff/fEiHP1FkfpjFo9QxQKlVDYPfTY7nms1FPuWJqz2sqZzW20YpUfeOlJjNLCdwr2swlCH+DaGUUpaQYxiiKYJBrtIKLmxTSbIYqmcP9SnbZm8xtXdji9MeQgC/nDFyI+sj3j9aub1A2b91+dohslgV2xM035xx+7zve/dgnTUEkTi72R5F9dvkaoTDisrvPs+ArvuXaTn/jILqcWbDS1xDcLHEfhq08tkZcms8hhOTgmUgxNRaY2SmWjKCo3oxkPXQ3Zn/g0nJxzizOWlubkhYZpVNS8nMI55Jee2eInP3EFRSj8lUttao7O6083+Jm/epYFx8QzdTQNTtTr/L2X38HPf91ZPEvBcDJ2sn00SmRqM0w1PEvhbfef4VvubaKrBVmpMw1dKqGg6QUPtLqsWi7/7KEjoqwkTiVJqfK+J9foTQKE0GkHOV9+skNgS77u3joH84REFlw+miOJ+YVPHZDMGtzTmTKJLPLKpG5XLNVTFhsFniXwRZ2L3YCP3Bzxoa0eD12fIivISo15ZlIJDaSGqkhMvcQ1c5LcRFcVam5Jtx5zNLGwdMkwchGA72S8dHNIzck5v9rHNOBEKyfJjycs9qc+ValS5GBpOjeGGW1ZoxNonFzSuKNdY7Ghc7bRIQwtVLUgSXWySsMzc2xNYho5tq4wShQaDrT8kOGshmXknOrmaEaBlTvMi4RhnvH+F0Ou9QS/8fwButB4/GiP/tzDNGI6XkFaKAxCk/VmiW1lnAvqrLV0mvWcn/vUAdMYVmrQDxU6XklRGccEclDxyI0ahlZwfjHlk0e36TRTvvN1PoVU6I8CPFNlkpWsuz6WITHMGaPIwzV1DCNmwbVYUBa4vK9j65K2bRG4GQv1lK6n02rFfNeDS3jSpOZY2F5IWuUs+xqGaqCoCUIoRJHPIIIP3xizGpj87fvPovgZoyLF1S3OdyXf84o2pRHyh1dHnGp6hJHBq1fWsDXJZsMA4fHa9VVmmWDJ1zjV0Ykylau7EtWO+ddPHKLETdASHt0fYukqG3Wbt967zG5f5eOHI1651sZWXD62O+ZbH+yQiALfUDndVDAVA9MU3HVCQTNKzi7ZXOn/+fnJ54Uv5VSfE/4fTz37v/DMM8/w+OOPf5bB9BcDb3/723nmmWd45JFH/sw15f9G40kp/8y5/xg//uM//ucaPd5lmzQVk7AoqFU9epHBgnRYkjZuYTJNNLKq4A4vIstNGqVgMC1BOR7f/ZYNqEeLrKGSJQnzqKKhStTMIp5WtN2YrzvX5pcefY633+Hz/E6Pk/YINHBVHS1PSErJulfSUgIyoVBkgg0NpLDQqpzZMKXfD7jTjxEIiqTitS+9n0vr9xDPTfxUoisBl5yCwWFJ3clYthzKvECLQsx5i1ah4KcF4eEKngnGHFaFTadZoCshpzsV0dhnlo1Z9U18oZNO5vQmAZ5dcc4ZIrKAfiR5XTdiZ2SSZyWvbcI0STnjV9Qt8IIhSJPbI5tXNSVZERFmCk1VsLI2opZY7B6YnDcFTCXlqEFcliwUFhkZ55Ztfu6h59nwBHklcE2VmqYQT4bc5UruWFAoy4qjIiYbGVwdtTjZmnCY1qgXBXalkOYG73xZwG89PWBBmBSiYtHKqOVgFZLhUOPO5oCbcY3hQcBfvtjiA9dHWLqLOnK4uGRhLx2hjxoYKVhSoquSB5ojBqHJvOfy3NBBKRTu7M7wU4U4t9H0HK+RsVRLQWiYasxu4iDkGL2q6OoFjmrQqo4rtUlucvN6DVEqaIbCV68rPHWQ44iS4YHDQE9ZFBVpIvDSlDVR4+Z2TtcoKcho2Bpf0XV4uCwIxyqXPI1VL+LKEbS9hLgEvYhY1gpqXsXRjoettnhsd5tTDQXfFlhIDKVknOpoYcVJN+SZG5KGrhAd+bxuoeJKL2XvUPKSZZ+olvA7n77OWkPjub0ZXdthK0poUdKpFVBCrZszjwVfcbrD4wcjFjyTq+MJLaCczPne+ywe2dsnSzXiwmDTnVNVsH1bY0NJ+KqLXZ4b9+nPNVpqSZgpiEpS13KysMcoK4lmAs/JeP3SjCLR2bodsG6GtPQYW2myoBUcxSV3LY2QwqCUksOJg5ce/+4XRIGmlpCqGFIhn05YVnLqSsY8NXCsAkcvmEwGCKGhlRqWEvOHzz/BSatkyag4zB10UWKGFvm+yffcbfHsZIKaFxRzi3YATmQCJUGqcrefkJcZf/x0zLmuyYJI0XOVpqFQyoqWJmgVKqae03AFtbKi4wrmcckJO6HtKHjODC8zsM0pjhGwo87YaCokkeTN5zr88YtjOqrG+LDgREthEuZstkDOM2Tm4iZg5SVnTOjnc8pxjagquBAYNNo5blbxyrpJLlN8S+VffegKzUaBY2koToYQGWu144c+3yzYbFYczQvURGezKZnOCtRC41UtkzCIaSgl5xoqIzMlzhVWGwapkHznpQ4//6k9VgwF3R+TjFQuLgg4fzfjm88RH0nOawG0Km4OEnQVWp7GaGoiiVj0JUvLLmlqkFYlSlpyomZTbydUaZ+HbtX4G/ct8NjtGyzmAs0qaNgFD7QlUlGZzTKisiCwJWs1jWA1JMzAyEvOm4KurbNcC7k2giqXBIXJguby1M2bvHkl4CgZEsfgWSXrToVwEkpZsVn/r8ObZ319/bOO/+E//Ie8613v+qxzOzs7vOMd7+D9738/tm3/hXt9rrH4vzUcHR3xLd/yLRwcHFCv17n77rt573vfy5ve9CaSJOHy5cv8yq/8CpPJhOXlZV7/+tfzG7/xG581GXZ7extV/Q81u/X1dd7//vfzAz/wA9x9992srq7yjne8g7/39/7e53x/J1YKAj8ny1yWmoJXeU1+7YkZvl2wWBdU6NweVXhOzoqv0PQcUDOuDyKS1KbjBVzsVhxODA7mOXevOrwmcNhoSR7enrFQT1hsGgSmRZHq7E4lSWXSdAKatolQEmp2jqe1WAssGv6MQuaUiYtVuJRCYTdPWPVKljwLy8yY5RFmpePqFvNMYadvUGY2pqlQ6gmGJlhzbWZJxe6swjEtFnyYpgW9mY/vaMyKiqqQtOo6GTmVzPmGiyd4aKfH7MBi2deoewVCThnM1slyhWjosHouw7Ujksqg0hSe3lY5s1Sw4lt841e2eNt7YvpFyv/65g1+58UjSmOXhmVQUbLcEMySkjgG2xTY1pQwbrA/0dBLE8vP0BQdVS3JCpOtkUYmTJaDEg2Thy636aUHnFzI8b2KMLYJLKgqwUYrZRCqzHKTrWiAb2p0m8d5qY6KrmWo0gSpkRQKKzWbwIWbwzmbtZyIgjDxSFIw/ZQqMem0cwK7IhOCWWJi64JuLWU4cFBUFc8QCF3jwQ2Pdz18gyTSqHsugZszntiYaonvpmyPXaLMJEo1TMXhkVsJt4YTfvRNm/z8Jw+4Hc85xObkQkhSqhSlgqpXKJXONDbR1IosCvj6O5s8dGOCUE3WGwaW1mBHZnzg5ghHrejHc3zD5zXnW+xkW6TTGDMvyElo2gav3Ai4NmyhWwVXJ0esBW2e31a468SQe9anvHBtgTfc0eAPbvTJqoq8ULg8OQKtYhDDQ9tjxjPB+a5DwzBIhOCOjZJP7QsWa7A7r3hsawFFaESFQtM22KgZ3B4W7E5MKi1nGKvc3f4PLaOP7825fzXgZKNOVFWEecWT05gPN4541VqXg2LIsBjzx8/XKEXK//LVSzx8+BgH2z4/9dgNFgNJzRYcjU3yUqLrKkLT+OjWFEdTWWsJikTDVGCjkXO1KPDthFHok0Q2N5SM//nBC/zLZ25w71JA3TT4+NERVaXwRDrHlhpPDk9yohmiKBXD0KLuZqSFRllKhBNTqiWq5vATbzzD//boFk8dliiVZH9Uo1tPiNOSQigUAtLEounlCKEwnJmkpUGeqVzqLvDY7pxYz9iKPI5mAZ6TU8gSXVEZhQYSiW+VKJT0pibLSyGaZlCGHqqlUOU5T+/NKXWJLBXuqNU52TI5zGJqtkIpBKqq0fJSSpkT5yYNN6HpCIaRZL2lsD+TJGmAYma4nkmhFri2wY3xnI22yzQVCCpu9VzW2oK0DHD1GEX6TDNJdtihkgmxmBJOdaRusdSEKBFoUkGRNlmRslKTXB8qdNyCl52oEEqGqZmYpoqixzwzOmChXbLfd7EUl6Ms42iqcO9ZwSNXOty5NkevjlWUsRfzdx5c453eOjvhjF96ZpuaXfHRvQNcXcU3bP7FM9eJhEpTVbBVg4NM0PEz4sQn1xOePtI40cpYrTloRsKoCDnbOsU5u8OL6YzdsaTmFvzJzjaaYnKYZJBo3NmR/NbVLV67tsavPNZjsaHyq5/uMS0dtIWYShY0bJO/8ZplfumJQ1IxhcxBrXwuLBXcPigZTQV/eGXEG87pfPKKwe+/OMSyUlzV5b03Dzi/FhEWE77x4kV+6pN7KIXL7z0bEhVw55LNN99X529/fmH6i47/1nOqz5koeulLX8rOzs4XlSj6/u//ft7znvfw8MMPs7a29pnzS0tLwDHz9h9Lt3u93p9h4f5j/NAP/RA/+IM/+Jnj2WzG+vo6TyYF9zlTNpZSymIBQ62RKRpPhhrRSNDxYizNJjAEVyYap5yKzNVZ8A1KoTIyJmzNSvJSUjd8ao7KLBc8Pahj6BVfd9cKH7494NOjku941Qn+xTMv8NqLBntTi3Go4hsmF5ZTFLPkRppQKTlVFVCzVbbGFUVl42kBemCT2iFRDnr/BjtPPs3d9z/AC1tNalaNUkBYZFw8lVMUPmFe4ZoVsWhxK/c5SgtO2AaeVqAyQA0slLLOLJpxbiEhjOuMxwqJGaCZJvtlQVJ2aHiSF2YFi9UiaaFimFPGWY1DmXKimzCIa+ww56Kvcy0V3LOQEZglaV5QFi328wJN97k1MDjTbrMbC2YipQZs1COstkCtSiZTlTzpsFvoDLWQCJNKVTGlZC/1kEqOTF2evCy582yMUCQtN6WsDH57q83FhQpZlyhVytFOnf/9mQlvvqvNR2/mTPKQpeaEF1Mb1yywuzHPTD1ixcRqNPjkRONW6vGSpstHj6b82s2Ks66J1SjoRz5nOhMuDzyYO1SVju/AHd0Jr1o9YnvqoVaCKlEohWQ3ChipFk2vQPUL9scGipmSVzqTzMI2NDqNDK2sODoKyDMNVImJZB7OObu0yqSK2RmUtPKS0wsDBsMOP/PcmLN1n7ldMk9dLCslLg3+2XMhF+ub9PMU4UVcGc25u7OGKm6yv9fCcWcYKiTSJY8VXn7yiOneMlFl0Qs1VAkyzxCqwo19l/VmSmM9Jc11NE3hmTgn1k3O10yeTUOGus/zoc5NbR8Cj+98xUV+9P3b3EqnBIZCXhr8Dye7fOxmypvu2URx2timwu/v3OJEw6XZafFTTx9x5+mKqlLZuhVg2QoXliUfGvVY3Uj59V0FpTQpNVArjXkBpq6xEGREicpX33GO9744ZqlzjTMbE14Ytjl/fsxz+y2enQd0gpj9wTJjM6NIfVxL4tjHSbZIAjRF0BMGdy5POJz47B4F3ExjLixHDGIP3YBebtEwU25OLarCRBFwslsxzW3SskTXMka6jmaafO8r7uN7fvc6/+bZgrvXVlC826TkRI5KIW3yqqLQVGLD4ihVuXMzIinaHCQxW4nLqivQrTl1W2esC0qtwPMMZgaM5i0kBhtrdfbDGcJwyXWdearT8TVcvUBXHKwq5fkiJa4pnGiUzHKFmW6hmBqHiiSnS6qqEOjMRUpZ+tzIDYrE4WvPdvmOB5b57n9/lavzHlVi8zfvWeJXr1+hZeg8eqvBymKIqwX0k4xA13F8g6EeYpmwsKyihTmZq9JumDx8U2cU6xiYzN0j9ouKE20LR0+4fmQzmy7yi1fHZHXoz20u1GxSOWJfm1PkHlnvOg8/9Cwbb3gDobWO1XQpybmZaqiaiaHDoTrghV7Jgmdg2gb9gcmuotHxK3QWURfgn10O0e2ctmghc8FunNJSclZqFQtdB4mCYcw4KDL2Rcks7fLX7+rwS08eEXsphqFgeD5VZRDrEY/uSq5UQ37wJS/h372Y47QNdvo6iiiQakoc2+h28oUPhtqfri/UXhwnLP/xhI4/b1DF448/Tq/X4/777//MuaqqePjhh/npn/5prly5Anzusfi/NfziL/7iX3jNcRze9773/f/d46GHHvoz517xilfwiU984vO5NQA0Q2C6EV9+ao1/8fARd5+AViBR9RzFnFFkJveuWygYxEXFQZRQpg4idFhuFfhOwu7IYD528FWBSkU/CxkPMkaxhWfaNBwopGAcSyohcDSFji94oVdhmQWWqlA3PIZlySyDaVRjwbUYRgaJmtH2FHzDIBc5bSdm0TcAjUFUEBcFruVjKCqjLMPVVF691uDR2xGjuc6plRBL14iriP2JT9O02YsAMyawjtufwkzyHfec46P7BwgtQiltak7COIGq6nLUD7jVq3jzHSNMsyTOQZMOqRDcfXJMmtX5uadu8HOf6CNymFbQdSxcr0+ca5QSRolG3QJNzZHSp+UlFEJDQSGOVVaCClVPcQ2LaQquaTNNBBoeT0/B1mA413F1EEqMIgxcJ6HKHHx3ThjbXL9eo7NcoGoV9VpCyyuYJSWHowbbI4+OJ2gHBcORxl5fobISNjohLb0gqnRee8Jiex6zN5YM5hqKkISpRd1NiHMDMAlDlWFiUvMqdF1gOAUf3B7ylWdd/vD5mDefWcP2Q375mR62UdEJEq73apxZnBPHdeZ5xTP7CaebNv+fD97ARKVCxTcktn6sdNU1BaVSGGcaWanjmDp+EPHbzwpUrcIAGjR49GDA69e6/NGzMYanoUiDtbbGlemMUOQ4QkW1Y17cgV99eptHbiXcvWHx0a0JcWwzclLOLlgcTF12K5O3v3aFn/yTHpmpsNqomEwEz/cFdbfC0I74reciiljlw1s9dE3h5esBlawQZcV4XtIJUlS14HbfpawE/+ANTT54XeUontL2LKoqJ4ot5lWOkJJJUvLTn9rh7C2Dq/0Cz59RcyVHU5NffW6f335xQFZNmUU+33RpmTdf7PJHt29yrR+QR8fkTzuY0QpSSlFnMHf46s1FPnhriLtYMoslXS/gh7/sBI9uTXjflQk6U7aOGpxfLZCVTlrmXJmOOJoXjNQpWSWQKBQSbCPlDSdX+fRozsXlCQ9dW+KO5QlXjmo0nZz9mUc3UHn7S84zTit+5tN7VMoMcg3Tq7CsAg2Ja5XYZkFeaQhRkcQ2XiMmiTxamofup7RqOl93d4c/eWaGYUl0QxKlGg1PoemUZKWkFJLDiYdvSepOhm5AnihcPJFRFYK3XFjio9tj5pHFYTYnsyJevebzoZ0BuqJQiIKl4FgRuTf1j9VAbomiKAR2RVUJFoKcnTSgNzWpmQJdkwgpqDk5pgX/4NX38kMfep7Xri/ywZ0hjqXw2G2djmUyTASnWiZHPR3Nz9BsyXCs8qazLoNRj61SRVQm8yJBUXXWGjlxUZLkLXRdZZoIHF1FqiW5ECi4VJVGoQianqCf5Vw/cjjdrdid+AhRsVAT9FL4iUe3+Jv3rnA0qTiYqOhNm5VmxjQryHMTNXc5szzkqR2H17Qt1FaJa+Xc6ufULIeOD5NUoFJyIpD4XsHv73ySln2KM8sJ/UghLAtEAr5uUNqS3XnOLC0pTJ2nj6Ykocb1COqeoKGb9Pc1UidEkxUpOec7DmMiZmrMOFFYqsFKp+RgpDOeCbYbQwyrga24FKXNIBL8STLjVFdgqz6/frlPWBZMZwqeqnGia7AVz/i+99/8vOPgnw2MfGHyqv9OcqrPufXsF37hF/iJn/gJfvmXf5nHH3+cZ5555rPW5wMpJW9/+9v5nd/5HT784Q+zubn5Wdc3NzdZWlriAx/4wGfO5XnORz7yEV75ylf+hfv+RUaP6zWTFa+Do7eo1JLDOOf6sGQ0t1jwNUxT5Sgy6IUedy5XGKpPzZUcZhP20zEHM4O62WI1aJJLg16schQbrHYyap7g0oLHdtJn3WzydG/KHauS3lRjb6hS5jrLjZLtWcqNaUxSWKz6DVzTRKrHnj2O5nMUGjw3jNmZFeS5y/U//k3WX3YnJxo6p9s1DEPh7NKU15xOcAyNrXnEXpRjaBo7U4UXdhU2W6AqLvszm5pZZ8l3McyUO5ZTyipgUiS4dkQUWUTK6LilqFHgez2QFmHmkSNZDjwMQ+H+jYSG5TFNddZrHtPUZZbaJFkd1JA4aXI0czmYeGRSoZcU/NBrTvHiVkmV6VwfleiqQiZnNJySg6igkJLr04iNTsitcUnLL+mlJRudhG5jTiUU1jdChDTxbZ20VFmqCe5bS/HtkrXGcSua4kgc20CvPLZGBYoiuTV2Ob/YJy0NRmGAgortCHpJwTP9kEwp+T8vj0kqgaFLkDorrZxOPePetQGbi1MmqUk4t7H1knYt4VceP4uugaEdy4lVvSLOVRKh49klj291WG4m1P2KXmRhqArLzQRVhcVaRifIcb2SN55usFAvmeU6VyZT7loyqDdinj9osjXxSSqDeaYxLCeEwgBFISx0vCBjEhd8YifmICoIWMS2Kmbk9OMlLFXnvoVFmp5gHhtIReHhmw3a9YLldoqiKMSZyqvOHXJ2IWK5XoKiERcma80UXRPU3ALXLkhlSphrSFmxn0RIqZBmOsO4ot7KsayYeWwwi3U+eKtPVsIPf/TT/O6tfT56cIBjapxddNmZ5nRrkrqXE+c6J0+GGE7Fwczk7MqUs0tzBBLDygGFaWKhagq2VbHZCUFRuDmUuLUR96xOiCqDrhszS03ajo+QOvtjh9uzBKGp5FLjdHfGwdgjTA10LWehkXJ2acbNQUDDLzixnFJ3Km70GzQcwXIrIRMGN/s1okQlKSR+kJMpCYVUKCqTXKgEfoTlTnisd8A3PmBxYlHjdecavNhzWWnGGHrF7ZFBVEJS2Bi6glLpHM58XhzG7I98FltgmiWdes7JboxlR5gWZDLn1lijRPCxGxqzUGWcZ5zszCllzJmFkExkjBOLw7mC65QMsjE1r0CzZzSDHKFNWaillMqYYWSy1NhlmknqXkRchWiKyvY8ZRKDpml894MraHGLeaTw6O6MptkgLHRWmw6TaYs01/CUBrv7Pr25hq7q7EUJ47RA1xQyxkyLgrrR5G+95BRCVpyo+Sz6DqaV07BtFgKDrcOSnUlGOyjYjUq+6eImjqFyvqPTCUru+8rX8Nj7XqRm+QyynN15TtOpGEY62/OMo1lJwwr4iosC11KIZcGp5eNqvuUOeHQnZ5yWrLc1XK3G/ad9FmyXlUDHqgIOIxXV2SUWA7LCI4mXWQlsNFWw4ATUTJN55INakIuQa4c6tmHwig0TU7T5/vfe4uo04uZ8hm5nNAyL9bqFK12mafV5xb8/D18wf6I/XcCfiYV/XlLzxje+kcuXL/PUU099Zj3wwAO89a1v5amnnuLUqVP/SbH4S/h/F5KCeaIxTlJWmhr7yRRhDKk5ISu+zWrNQKo58yLFNCpc3cRVTEy3pOaHHM414kxnoVniWSov9FL6UcZOz2PF1/Ftye684Okdnc12znrDQDVSHt/XWKpFLHs2ldR5+8uXsdwZBhaOavJd963xT9+8iatrtKxjZWClJASmRFdUSlGSFCpZHlD3MgxnzmKj5ExQ43Y4RLFm3HNqxlJdMM0rZqnKSk0wLyRvPF3nQtvH0AUHcY6p6DxzNOfmOMPVdBxN48ZQJcosxqFPLxcstAWeXZFUGUgDXQPfijEVC9dKkEqFbeb4jRkty+RfPbXN65YuIoXLE7d9ksRiEJlcP/JJE5v9mUaUeDT8kHtOTel0InRdIS9Ukkyl6VRoVUmRm+SVoEhNNC8haBTMUpVTnTmzWKNRy0HCzYHHwkJCt5FQcyv2pxaH04CbhzUUBVxDoKkFZakyy3RecSLg1CIYesE81enNXRy1zpvPbtBWFzjRsGj6OXlpsj9yEUKSlRq2I2nVM7780g6eU6JKBaeyuDGJsK0Sx5ZEqYOuaLS8jGFsc8/6iL2RQ5xoCFlx34pHxbESe6N1rKKYzByK3OBUJ6RhF1RSpR1UxwSSIdC1Cs9NyUuQesbtfIc4gffdmnPnqs0Daz7DbM5jhz2uTsaoiqAoVLLMZj4X/MH1Ib004f3XxiShzlrd4RsuLtFSGkTjRV65cIpP7c2wgpA4M+nNDRa6KQ0vpW1b1E2bk0sz2t0CTRj8k790nhuDkmeOMlqeRqqlRIXO1lGAqUkMReXjuwdM5jrffvcmf+9l5/jr50+CLvjo/iG/8Pg+P/GRHe5p1vmBl21SFFDXG6RhjbLU0dWUcRyRFiZogl996ohff36Xh7dCDiYOga3gOyVnuxJTB6TA1ATXRjMCJyNKwDByLu9HpLng37/YozD7CAGa1Fl3lkCo3FGrkxYagQWzuUZRGpSFQScoOb084X23Dnn1Wp1Hb6zT8krG0zVUJLnQsJ2SXMaoquR8x+Urz9SJZUK7m5BnGmVhcG9ngeVmhG8VeEZBYEl0tSLLA1ZbFqE4JrSeGR6gKDmaAjVLogJFqTANTaLMouZkLAQpS42cqFAZTV1e0lwlr1T25nP20wGvXF7i+qGKbSgYqmBrkvPw7ZiicBBSkhY6o9jm8n4dR5dYWk5W2mSVpGFDXin0pzX6I5PNboFpFCBNDKtgqVmg6TMe3T/k8MjgodtTxkOFMrZRSpMiM/iypS53BA3qTUkqSlYDk26n5OHdAaqiM0tgODPY2m+jqDFH4xqeqSClwBQmvQjiQuLpBk1/jqlV1HxJqiegHe/3Ny5uEsUGtqYj9YRMKQk0E9NK+fXnthiXMbqq8d33nSDKLFZrGlLJGeUZR70mTdsikB1ONg3izKftFwymEltXaLowigX9NEeRBs8fOnxgr0/g97A0FUupcWPHZ5SHJDk8uOlwouFxayS4Pp2wviLZWILKSrg9yFBUyYLpo6cWv3r5gLfc0Ybc5rUnahSlxeVDjbTKaAaCoCboH9ZpdmfMi4KskLxqs0bbtlHRuDoU3J7OaRk2965LvFrOrVHJG0+3mH0R6m9fyqk+N3zORFG/3+fGjRt8+7d/Oy996Uu59957ue+++z7z+vngbW97G7/6q7/Kr/3arxEEAYeHhxweHpIkx98URVF45zvfyY/92I/xu7/7uzz77LN827d9G67r8s3f/M2f89+TsuLGWPDpAxhFOrp0aNo2lYAKwTx2Wa0JVus50zxjPxozLxJctcaFZp0Fz2V/JpmXcwJ3jCJ0DEXDsCc0ayF/90PX8GWT159u8Hu3bvDkUcbuVGelpZAXkt2pzh1LJXd0PNabCkk1xXYOmacqg0mNq8OcqCh4w5mSjaCGbeWYaxd4+UtWGUYesewzzsc07Iq0VOjHOabqsebV6EcmniV5xbmSaWzSS2eEmSQvNRLZZ3+m4hs6mn7cyqETUKuldByTM12DQaTw5K0VNGnyQq/kZGvOOFG5OQ6PR8JrUzxniqmCbkxp2rAzsXlmd4Wuucq733IfX366SZprXFx0GGQFv/MdFzjfCmipNrpioWGhKhYdVxDmFTVLECYBf+3eIUtBxd3LOWvNY4miaQqaWpNu2cGWBuuNgkliYqg6i8EMXS2pEJhGSH8q+elP7LG4MMS3S6LE4T2XT5DkFrpmYGsaulahihJDL7GdjDLVcK2KN5zr07AFqgqdWsyLfZ/ALrhnY8KlUxO2hx4vHnnsDGx+84kTHE1tXDNlFBvUnJLR3OTW0OO15w8wVEHLzTnZjgmcgllioKsluxOXMwshdSdnK71FzUl4y717VHrM5dEeC80ZYa5yY1BHUyvu2RwzL1XSxKAS0PQKprFOlRgseRazmcrH9ga4osEkgjXPRFgR730mZnsUgAZXD3yS3MKzKvpTh9dstPm5v3qeuumQpCZ1rU2Ax62eTz8yGEfG8XdqYnEQpiRlha5XdBoRO1OPrZHKLz+zy/a0oEx86kFCYGdcbC5wftGhxTK9sOCFo4qzSzp1u+TEYknNzRjNTfozBzPvMEsUUiVGRTCODFp+TquWYOkVp5s2uqKx3pwT5jqrzZD3be1SyoIKhQW5zxO7LW70Fvj7r7yXDbtGNLVxXMkbTx6PL98a1bANgWEILOvYO2qe6uxNTPbGFqZRYBgC06g4FaywPz42i224OUWloZkFd60fMQ5N4tRio5njWyUdL2IaK/zzP5nz4csqg7jkF5/awtZVdkdtskqn4YWUFTS9BE2P2VgeESYq01DH0jVW6gUdP6fratiGwmq9oG1rSKkQ6DYHE5uXnJowYUwlFJ7f6eDaMXvjOpcPbBRMDueC/qzO0/sOaWbz2M0GYeJRswS2Lul6BnUnA9ml6yW4WoCGgatrfNMdy3zDfW0+sj3ipas1zizAj7ypw0EaslyfEfhjUmWKLRSOkojhTBA4Kv2oIspMTNlFU1UsHcJond7cZnci+Oj2kGmZMS6mGApkVcnVYUrDqXjlXQV3n55h6BVn6hb/6JEbRCIiqTKSzObFmc2b3vU2ZvOCOxc0TncqNL1kLdA51zGxnJTHD1KScs5qvcS3JLXWDNfK2Ajq/LV7cvJKcjucMk4y9uIB3/qyDplSIMjpmA4y26DtGgyzGc+PZly+5bLWKvnFp7dAPVY9xKmFazu4nqDhH3GYRAg1Yb2usea1uNSxObE45Km9hKNkgu4mVIX/ecW//5IQBAF33nnnZy3P82i329x5551f8Fj8JXxxsOhpaKrBXYsBYzlhuZbSNALarskwyamIcXSBb6ooUkdXQHVj/GBKlIGt2Cw3YZAU1Lv71P2CZdfFMVW25xn9SKBLn/s3YJorJGLGoh+zGhiYmsHWRPDUtsc/eP8+euHTD1XKQufV6x1+5tF9qlRlZ56Qpiqnm8cEzfasQFczLMWl7RWERYJnZSilzeE84fZIsugrLNRTSpng6hLfcAgLsO0JqrNNXOUkouTsUk5V6jw36VG3S0xps3tgU7dyisJmZ6zzsuUaZzsmSQFpriGlTuDMmMQ+pq4ySysapopq5dw6slD0giv9jL1pxc7QYZaCpavsTnTK0iRJFYTUsQxJmElEZVHKlNncYzbXWG8WjEKdqHBo+ym2UFFNyVIzxbRKylLh+iDgZDehPzOJMxtdL3ntGR/HSGm4go6X8w3nN6nhI4XA0o8nTWW5hq4pfPDGmKMpzBKDolLQVYXffWHEpVabMwsGwj3kYGKi6yWGrnA48Vith6w2Yk52E24Na/h2yr2nh5jBjNtDFUWaTLKKD23tsFg7HpZyshVyo1cjTAwMuwC1Yi8LKdQC28lI1D6LrRmWLrmy1+G5rS5X9+tEqUpewrnVEUUlUFVJ3c0wDUFeQm9q0KmXUMFqq+TxQY+mn9F0SzbaCb3QQgKf3p2j6QrdQOPr7+5yT6eOZkr2Zhn/9tl9PrY94+svLfDEXsgfXNtnmsFbLjaZRiZnOhMarmCUxoTFsYfh153Z4FvvX2Je5OhOQiYKerOStptxujMnsEtEoR4XOpMmZxsez/bmbI0yTjQdTFsQOIJnjkKO5hlXkj7v+uizoMe8cAhbY5UvP9mlnDe5VG8xmVi86eQC33r3Mnc0m1w7kszmFnXD42/ecYGjySK9uYGugmuWPLFVMMslGzUTS7XJtIQff+Qm06xgPLfJKhWpFggK7l6q47s6f/D8hLv8JZRCx9RyHKPC0ATjyKFdj/jkwQHrrTGjkYdTutRMj9HIwXcrslLlucFxS/UjWzO2Dl1moUmjHhN4IZdH+2y255zpzjnVSai7ObpV0HISvvPuk/zjLz/FvQs1bNXlt6/0ObdosNxMsPWCmldwopOgaxWzTCOwK0xVYOo5nqfxupMLqMJiGHr0pgr/4KOX+c571ojmBt904QwNV+eOjkuFRFUrVhspjlFh6hVRbrBUiwDJwdTg+a02lh6jqiqGmzKd26AUNFyBqhRMYptn9zx+/bkj3vaGBWxLsLIs+K4HVvBMjVzLuHuz4onDOVLNkYnDICl4x4OrLNYiolTjRLtgmlTYhsJDzy2xOzI5GLdIq4rbhy4XFnIa/oS0MIiTBvO0YrmRcNdqRCoyKiH5rRcP2B5oGJpg2Vdo+XNansTSTKSW8eJsj4Yt+fGP7DGMJLcHDou+pEQwzSW2qvGxvQlfefIkUGCpLo26YGeSY+oZ96443Fk/wSzPGUYqy3WVp7fW2Zuq9EPByaWKrz19mhK4MYlYrRs0TAtRGeR6TCkULiwqnDkpMOop26MCzRGcrOv8y0/tsHWg88mDKf/jfass2y7z1KafpMQyRjc0dFwwIM4FjmLQdXXmoUfDtJCFx95Ysh9G1Ey4Z8njU9cy3nH/6f98AfQLiP+ac6rPmSj6ju/4Du677z4+/vGPc/PmTW7duvVZr58PfvZnf5bpdMqXfdmXsby8/Jn1G7/xG595z9/9u3+Xd77znXzf930fDzzwAHt7e7z//e//rJ7//6eoZHnM7poupdSwrIyaW7DQKDBVjaKS5GqfUpni6SoNvU3LquOYJUfpjFnVpz/TadsOpuIzTlUso6JmmOSZT1aVfHIn4Z89sss80bhzwWWxoeK7CWfXZ2y0CqTQwdhHKIdcO2pT5SuMZwHnax1es9bmFSdLukHIE/spVz79CRZf9pdR1IxMxpyo6dzRCYgKk3mukOcBhqJzbZxwpV9SCZ1+DMOkYMW3CQyXQaRTFC3ONI8Z7r2pxa1ei9sjSSKmfzpqWnC9p2NrJkklONetaDo2a82Q15+SFJXN9thHqVo0vONpAzW74qEXTJ692eVrzneY5iU70ZBLJ24itYRv+90X+NUne/zzrz3DN9/XISxzZpmJZ0XYumAwV3FNwTwS3L04YZoKXrI6oRAJaaFxcWOfrUP4xPiQJ7d8PvjsAjuTiqRU2ZkZ2HrKbi9gd2whK4W7TsaUpc5SPWe9USGFyZVDj8HMYpxoCApW1wY4TkmVO3zHfUuYwuGFQ59CSISANFcJTIGOSs2qGCcaX3X3LT52rY0oj2XltwYeR3OLSWgymVo4umSr5/DiYZ21RsZqPeXS8pBpZDIKLWyzxNYrXtgPGMUWw8hikjh85PoCQqrEuYGuVZiqZDK3yUqVQeggJKhCQTdLzi6GuGZBzdE423K5d81B1eHmJMMqDV57ooljx3huia7CpZUhmlYihI6qwIOrAX98fcyPfWiXcHIGWxdc6trcGhi4ZoGUkvHIZBIrVKogjA0coyCwU156oodCieNkPNObMYhLLq2YVJVGtxVhN25waxxxaxLjGBVFpDGcwce3hySxznbfoT9p4DkFcXxMkCzXYzRNsjVyadoly7UEVdH4tnvWaToqG80IS5MoqmSzO+fswgwNwdODVQ6iGqauszvJcVQTv5Eyj1Q+tt+jFJJJbCIVnbVmQpqrzFKTW4cNqBQURZBVCmFiMhzZvPuxGfsTm9VmiG9X1P2K73tgma2Bz2Rgc3YxZK2ZUFYaB+Ma12+2KRTJVJlQkBPHCpYusTSVqtRoupAXNlGm4xgJbS/DMQtmocAwCoaxSiZztkcew1Dh2d0GhVA4mNpMEo+0UGm6OjWnRC99wsRmMOtQtysuLha0vJJTXYGKwsWFiuVA52e/4l4cpc7twQL9SMNUdQoSEDBMBVsTnZqXo5gDHu8N+Uv/5mn+t4/d4q/+u6d5fD/kE0e7kEsUpeRcx+Blp2asdxPOt1wsp+BES+fORZtJHpIUFU/vtOjHCUWRc/uwjWHGXBtG3LlkEiVNpqlK13GwVJ9BGhOVOcOZx3OHcH0S4+oCixqjyCMVkkUnoFlE/Jv/78+TKyMcU3J9u83bH9jkaFaxXnd43aZGXnmkVULHy7FUHaHNGeVjUhHzshOCuxZtlhowL48nHZ1oaEijJGTKwTwnyhzON33uXrKJIosX+gXjYkpVqQSGwvY44ObIoVIEumyyXrMAie0dsDOPGEQCUzZ4+SmLfLBKInKe7n0RSl9fBDPrLxS+kLH4S/ji4DDOmOcxP/3ILht1nShzuD4SbI91stwiTDxE5VBWOrkouNpT0BRB23JQpEs/SSnVGUutggXXxTVha5ajGSkdR8fVLHStIikzLEPQ9aEUHpUQ1N0Y16jIIg8B7PcspkfHBsf/5JFbvHAUI8yclUByfjknrSokJWs1iYLH1Z5JP5bYqotvWMwihe98YA1LsTAUnYMZjBMVFZM4VxCyZDlQOZjDKMlRpYKnm5RajuckmJqG6SQsrs1QFI+9icHuvkqhRgzSDKTA1S3GqcLj2zUcDTr+jKVAYhsqO0OVmq8gVUFWCJ4ZjCllyaXV4+q6ruf4luTB83ssegWFUDBVjd7QZXfsUPMKao0Zw9BhFEkWGzl7c5OkslCUAikMDL2g7sG5pTGmprDcmrFYS0kLuDoQjGOLrzi5zk+98eW84USX73xlB8dLaXsFpnasdvackrXVKVmhMQhdKiGhUJGpyrf8xhX61Q5fdnKVtfacrp9i6IKGl1JJQVLogIJnFeSlwgs7ddq1nKaj8y+/+jyP3BqDnjFObBaCgoZzbH7suwVxrvKV55u4TsgrT3qYRoVtlTTcjKX2nNdsOJzwV5knJqpacdj3OJo4bC7MSQud4cyhZlUEboltVdhqyV0nh9wOR2zWapSVxtnFOUdTB88sqNslnlPw+tM2gzn83nNjnjwMMc2CwKn4yjNt/uo9bbYmOW881eDb71slygS//VhEXhiEiYmuVLT9Y8+lJDc4jDMcu+Qff/Q2rlmx2Ul4YNlnFB2Ti6UwuH8poCzg9izhMMr4yNWQa6OYjqvxDZe6VIXJpEgprZh5XjJOKuqOw1IrpOkX3BymxDLF9GJcQ+FqL+XSikvN0mg7JoGpcKrh8Ec3+zyxmxOmNi87OSCwNP7aPR0sHQbVFN87JmgeWLP4+vOrvHGzi6YomHZOKnI+uLXHx3u3ec0pj6+/r8OrTtfJMgNNzwicgnFkMI4tAqskL3Rec8bnqy62GPYspKqy6rZ45coir9voIoSkyFRUQ1KzNequimeXaJrk1iBgmjhYRsWCH+GYFVePbIZRTtc3OQiPCzSGLjicSXZ7AcPQpBAK13sNDmYWeamSCxXdEPRnFm84Ued//dgOca5i5wHR3ODyls6vP3fEm+9uca7l8i13LfGxwz00BU60ZwS2pBCC9UaKa+akhUFglTTcgqV2xNVeg4aTk5YKVa4zHgQs2h6OLlAqBZHaVCLnpz+5y/fcv0zXV3jFCZ+vOtcmK+Hj+3u02jMkgpanMkgKnjoosFWTOIeDiU+7EVM3LE52j8kbBej4JdKZc/OwhaGaZKXE1BR0Q1IpCZ6pIarjVlTdztBMyWFYoqCRJHVes7aMoSm0HI1pYqJoOYbUOBq5bE9KejOHRctG1UsOpyWWXvHPH7vFzsBjfwKurrLRBqFIRmmCUsH+zhLfdtc6lVLhGSpNV6LpGVGZM09zlnzJfKbx2P4Uy6xYrQs0VGwL7l/xMRRQSotuuyKVgn5a0GnHfNMDDe5bbPK157sMJoKNuslmw0amLmNCbk0ymp5gva3y2Pacbzi7TpgoTOIKS1FYaai0HJthAo/tznnT+QZ/9ML4Cx8Yv5RTfU74nD/m1tYWP/ETP8HLXvYyTp48yYkTJz5rfT6QUv6569u+7ds+8x5FUXjXu97FwcEBaZrykY985DNT0T53HJvSlRKixKKqLMJU5yhKGCQxjlWxXnNpW3XCNOAwinjiIOYwSkniBm1jgbptsDMxeWrHoyhV6ragF8K1cYJh5LzpYsWDmyaOoYM+Z5RGqEqFpWrszype7Fk8t3OS8fQ0CJdP3LZ5oS+5ephwbRiz3NoiLDIe3BDs//5vkqgBH7u5wCRuMok9MtlnL5yjKypJoTNOc1q2yelmQFVpuGbB689GGKoklgO69RG+IZHqkKt9g8m8w+2RB8KgYzUoKg3XhNMdg5o/pxkMWWtmbE0yTGNKLzR5tifwrYpKVsRlRce1uLg84a416AQWf/8Dt3n304cM5wb98TqrrYivuDRlqZVTlhVPDEbsTVUWrZRnjgyEMBklOZqq0myMec/zJ+jWBjy73yIXJaOhy4t7NUoEF1cSugsT2jWJY4CjC6RweHyni1BsHCy6zZSGJ2n6FZf3AsJEoxQlgpxeqHM0tbnVd+nNXBxN4a33LKIZ8OrTdZ7er9EPddpBdjzJBB1DVSkqhTBX+N2n11hsZtyxMeGutTGGnnOr52GZgkpVGYYWllnhaCWmJnGNkq2Jj2lUSKkwjQ2KSiVwSwytoipVJpFOVanoesXByMXXKhAK93Q6JJnOcpBSswu8WnU8KWoQ0LRKvvOBRW6OUibKgKZTkJYqhyLk3sUa8dwnzQ3iVCVwMtq+pJIKt45cSkrOL9h0mhpCUbjYdXl4v0dpRMzGNtN5i6bRhMrDMeGexRph6iIwuLzfBCFISkmUQ5KqSCpKYdJ2St73VIML7RpSE7iGJC4qbHfKJLH5rRcHBLWEE16TWaQSloJ71hPuXRuw0Si4uddA1RR2Jy5hYvKPHrlBpzZjnJgYmiDKDFbrMaoC73l+jT++eZ408nnXqy7y9993g928x+vOH2GYJVWlcDS2qSTHfeClzmZ7jqpIqlwHqZKVCtNYo0JiuwLVkRhGwSxRUBWJrsLvPLfL3l6TBy8e0Qki7lvrY6uCF293abRLHjw3wnZKTq/3ObfZpxPEaFpBP9JBKjRckFKj7SmIysPUJV6Q0/QkTT9md+gigSf2HGapxSCWrDQnKMqxinGaqDxxS2dzecy4ikhyFcOYcLY7A3XKNDG5PalIS41n+zG/8uwWf3xtjqZIHrnmYBsFrgnzQnC+I1CUBISLoTos1iu+5f42eSlYaSUstFRGaUGrDtdHCoYWsxzETJQ+YVFxcW2C1djinoU6atlgnilc6gaYyVk+da2DqSk4uk2j2eNwnrDRToiqjCcPSha9AlNpYBsSRRUsuXXu2cipjDHbI53b/QZ1Z8jOfI7bXcZIQvyqwDIyLF3wm08NWWhNudwLuTWqsDWdNPNZb4d4VkrXruGoLa73bUZFn6M4Qi9MojjgN1/scfeyg2OoBFqdWeRxdZRzYzpH0WbcdTrnUtdms62SKQXXBhVJprIUFJxdnLM9MQlzgWOoNJQ2bculQPCB5zUOk5CUgkXL5+4l5/OKf/+l46GHHuInf/InP3P8hY3FX8IXAzY1PNFiKkr2w5xuLebiYowiDQJLYxhZ3BorXO0L8tzkXMdEU3ReGFSM04L71lO6jo1vKaCHoJQs1CrU1KbtCQohiIqKrYFFzQRLMxglkp2ZYJ7ohGELVZf00pzUTOi2oFIF//7agKkRUrMl623JvBAMkwINhZYD+zOdi6tHtBxBzRZEBbz1JctoikKc6bzY05GVScexUNSc7anA0TSSzGV/qpIUCqutEtNMuHttxOkm1N2Mqz0FA5WsNEAt2VitGGYpujXHVFXizOK5PY+3nD1B3Uu5PdSPfUUQtIMSU1foTRU8SxJVc1bbc9abBY6VUjMVTnanJKXANWAaSarCRlCxXK+QSHYOAxpezpnFGbYmkbLEQINKEKU2aS440UyoZEmcWWhqiaaqvOpEn7I0ORgE/P4TOd/16zf4rt+8wu9du0nHPzb4BpVSCO5em1KUCp6Ts9ZIaPspll3SbU2YhBWfuubz3hsD0lKjkseFhmlocXvgkuQqmiqRUmU+t+nWYwylYjDL+dt/dJ0pc/pjh24QUXdyPKNEQcHUFBbrKX/84ozdvsvFRo2mreBbx4SZRLIV77Ff3kRqFYcHdQyjYpYZHIxdGk7OYjPDsgWOWXF+KUEoIIGwkLw4SBlOTZ7YqbPZmVNVGodDhzDReXhrTrse4uoJuiI40/bY9AMe3ZoT54KBmNJPCl66uMAPvfwcJxsu33hhibwyONWdIQW87qzDjUOf33thyK9dPqJp6Rz2bHbHBodZjzQ3qCRoesUTgwm+L2m6YNkZ9QACW+Nnn7zOe2/tUFKy0dD5kVdcYNVu0FIavPn8ImlmkucqZ1suP/mXL3BtWFLqFQ1XR0Hh5z6xj2kNeclJmGQFr9losGLXqIRkf+ai6Akfvn0IEhz92EBaMQRPD6Z8xYUGDd3mr93RxTMrZuxSCsksNJCFxt/6g6v0i4i2beE7FZNIQ1UEX35imTLdoBdZPLIz42eevIa0YxRNcphFrHke//6FI37841c42bExc4eGL9kbOTTVZYaRQ5ybDEKbw5lNnNX54Zffxz/9itPcnof83BPbrHUEW+keTS/FdSPOrQ7Z6M5oOAWeVaDIiqoCIXSKEjYXQj5wc8pb71wgj2Emct58d51OO6EyYtqOxr9+9ga/9cIBSeTgGiUHU4vdsU8hFJLCIC4UJDqFlFSlgW0WZIXBIDJZax5PeL7Y8fjue07yQy+/D6P0OL0S4dgxC42In316m6Zp8a2/9zSzMmG1WfLCnomhZyzUEy406viqjmNUvHrpAmlqABW22uBnvuYCC5aPoUM/lJxsZpzohpRKiaWZqFpOLkqWfME0NejPdTxVJy0Fipaw1hK0XIVpKjE1+JVPH3FrnDGPTTquzjSTjNICX9NxNJfDmYrUC+q2St0vCNNjvye10kHPGM4krq4TxQ43D1Xef21Mq57y0PYBZ5eOOIoTArvifEclr1QePthmOFVZa2u0vZJUhuSE1C1YbYXcv9AhzHNmc3AwqJnHzxizTNIKMraGJf/g4edYWsp4+Vod20wwnYqu6XOypRJ4Y46SGPSSm5OEtU5OMjOYxwrjWFBVCicbOm+51GFUpnxsa/afN4h+EfFfS071ORNFb3jDG3j66ae/GPfy/zqmkYtvKtia5Oak4kr/2FTNzBoEWp1FT6EfFdyYjtkNIzTF4GI7oGO2iHKdawOF7QOVWazjGhpZVTHLS0DngVWFiwsKs0yyHU9p1rfYaMacbpnYBmSFwoOt03zbpYvI0uEjtyQP36wI5xrLrsMwLqmqkihvExgN0oPrBCdXqfSYui2Yp7A/L/B0h465QFGajBOVLHNRpcl+mDDOR1SlwdnFCZfWt7lzQdAwAxwzx9ENXK3OraHBMI1pBQWGOeeZgwyEimMk6HrEsuegKBpLvmA8W+XFgwarHtQsnXFa8Pz2EiuNMZ/ak1iG5PtfsUS3FuKoBocHCrZR8uSOSVrafOLwiG/8t1fYGVeseAorXoyvmwQmtD0NU9OwVAfXVOhNu2yNGuz2l1EN8AyHV23UOB0s0nEFJ9oZLVey5KdMY5/AyUFIRKwyzSRHY5O2F3HX+hE6JcOBS5LpXFoZs1wrKRMPkZk0LRjOSx66OWWjbqNpBYOJw9M7TdpuhaqoDGOLR290iFIVXZNIoZJUBhKF1VaMaVQk+bFqRdMKwsTg6n6Lj15d5pndBot+TJIdEwe90KXt5XhWcSyRzUyKVGUwcGm6BZ5T8sdPrxPOdD65P2KjE3KzFzBLDM60dHRVEuUGt3sB964ELLYlr1xZxbcKbARty+LJg5CG6dNsJZxdmlIIjZaXI4H19hxFGRIVJbOk4uphxR9dUWm4U+pOzsXNnEpY9JOMlm2z6OckuaBrNNGEwmJQomk6carx4IrPubbLX7/jJK9eXeT2oIOlGfSTY5+YWWxTqxXkUsE1cjbrHnZV4+mjOYYmqJSIhXqfw6mLCphuyiS0iDIV16yQako/Mmg5GcPYJRcwTU2uHNW43q8xmFlMIo0oryhKwcXlMX/y7DJCaGSZQc0rmCQadTenbhcczDx8O6HdTFCFxrmlCVIKfFPjJcsBji0wdIFhlGSVwNIF/aHP0kKM40g6fsxzPZe19pS1lRGvO3fEUr1kuZHhmQpnuxFRakDaIssVntquM5jpbI9Molyl5RY4ZsnZ5YRTnRTHgPWmgmsoLHpQpD5HfZ/+rINtCDbqgqwweeX5Eb4JJ9sZJ9sZe1Ofw2mDMLFxNB3fUHANlZPNksMo5N4VhZoluP+EQpjp6NJHVSWGppKVJlJJMVXYns+wNJXBQGVeRqw1Ey4fQpJJTjQ0KlEgpcN6rUYzyCjzGio277l2QCU0Dgce33PPJueW4dzGFKw+682Q+1dUNjsVsoILHYN51OTaqDo2AY9sMiZsTyNEaXPPisRRYTizybJlirzJLJvx0r/5ZuIYhIBQpDx9EKKlXS60Ag57dXanKaqWc+2whW2HJGLGPMtZ8TzI1khTnxv7klvDgqtHGY+8oNCbqoxi0FTJSxYdlr2AYaSynw05yoYUhcpaoPMdDwasuF3meUEmUnxD4JkCVS3YGevMYgVP1znXtdlsqChuzI15xDD9L3yM6xe4+vUl/JePq7twa1qgSoWVAPLCQgqTpIRrA5WtoWDBgc2mg++UXB/l7E5U3nJukfXApqo04rz40/9jHrqisGK7BE7JMM0xzJCGqXO2qzIvSzRjQJTpnGsbJIXDi/uSem1MR7foWg6xmnMwL2hocLJms9yOScSUhhNzoavS8ioO45wrBzayalC3Ja16n5t9nXCm8LHtkJxjxcBCcNymEheSjnOsnrk1VHFMyWZTxTJymkEfS1PRVIX9qY4hHYTUqHsTdBR8SyMuKjbbEXF5XPDreho7UY+jUKHtCdaaEQ9s7NF2FcpKQTcKFuoFmp6yUs8I7IK2HxHYCqNEZZ44RKXGemdOUhm4foIUJmkBjq4QpgYNO8BSj9u+G/UUoQlss8IyK1yjpDezccySwCpIMo3Lhx3uWzFw7Jz1rkatnXFURJSyZBSZCKngmoKam1P3ClzreDS5pirM4zpGWeOoV0NTwHIzAjv/U1VyQSkUGn7OcjNBIikqlYOZg2IINF1wMPOwHInjj0lyjdecaPL8foMPPL9CWqn4dkleqiiqimXC99y3yv/xyCEXFlNmiUl/ZnF2YYbplGi6oOWluH6O6xdUQiUvTOapxXBuctC3ORr6HIybNPyMaWyioCCERDcFdatiEDkMZhaqDjoKnlvi2QVSBdetON1waTRKDGfGqzYCDucZT09v8BuX+9y54vGqCy6Bo1IKk7pVkpQajx+EfO3FJpfaHpNYMIgzTEfQ9nMCJ8fVK24NPCxNglB57coC33PPJtcPJe98cJPDWc793UWqSsXSBP0Z/MrlQ77qTIfvfvkK7352D8uq6LZCrgwSAkPhzk6H002by4chP/XYTSKjz6vO9kmUI14cTckKwdmuzWJQ4lkFhiIw7YJcKESFgZSCWWywP4B3P9nnsd6AnWlClBv0pi7nF+e4dsbHeruoWkGcSzBK5rGFoUJWKfzWC33mcp/AKlBlgRSgWQWGVnA0UHnPMxO0yuCwp3LnokfgqWyNJLnIeXK/BKGzWo9ZbiT4ZkFFwv/0npv8Lx+9ysf3+/zJrQk3RjNcU9D1C042HAxNp5Qqjlmx0Y6JcoPtYUBa6MfqxMwgSiU/+/ED9FpOsx7zu1ePaHgFi/WQf3vlU1zZk9weFTQbIZomSAsdS68I7BRNVYkzhaLUSArYHtc4mJl8451tpIDRJGDN8bmwavH9v3+DF45imgsjCqFiGyW6JnCtkL14iqdoPLY7JM50TDdBSAUhFG5GhziKxrufGRGJEE9T0fWclUZ57JeUaHRcyXCqMMs0znVKoEClomFDUmXoClS5z95EIy4lpmIyjhVMvcDXNcaJQlJlOJaKU3ocjTTCVMXIHSw/o1BKTDTqboXUpyiqYL2tIKXCtWGFokkWXRvPK0nkHCkld6/BYktQawy5NizYPVym4+lkokDRIgJTQ5MBqZAcxBmz0CJQA+q2xrWhQi/K+cD2TU53bSpVcK1fkWSSdbdFNKrxO1eOeGG34tmjBE1Vef+1CYPpsRr7cFKQZlDTOrx2ZZHRFO5a9uiHsLko+epLAasthf/hxAbDpOD+ZZuHtvrc0f4itPR/Kaf6nPA5f8yv+Zqv4Qd+4Ad417vexW//9m/znve857PWf02YVRHzaoxpRJxtwUbN5bTfotQqPnGz5A+eV9HUEkMscbpew9FN+nHBlUHJOCkgtWnbBoWUeGZO14KGZbBck+TKjJvTMY494WxbZ7PexLcVAjfkaKYwjmy+7lKXJbvOp6/qWMLj3q5P07GYlyXt1T5nV2foCvSSKStnbf76//xtnG25JIXGKLa5e6UkLx2ujXI+sVswmFuoaOzOBFVh0bYWWGsW9EKd7UGXeery4jBnnEVoaERFwUqzz6tPChrO8YjUV5/SaHmgGSkt28SzUxTjgJ2xz2EkMZ0e3aBinAiywqLlajx/VGez5tPySv7xx69wR7fFL18+4IE7Yu5cHfDlF/oMQosnrpukSkYspjS9nI/1atzRHTPOclS1ICoyFDVHN0JW6nP6UYmqFYzLlEqteHR0wJP7MYoSU8kcIQuePjSRVDx+O6Bm2EhdHrcUOgWnWgkdL+Or7t3j5GKBllsUhUnTr9B1hWmkc+/CEoYfsdGBf/v8LrOpxZ1rE2xD4pqCONfYGjssBBHdIKcb5FxanbPeiJimClmpYKgVlqbg2hUtP0fXSyxLIoVC4OY07ALfqjjVdFGk5KmdNuc7c9aaMWcWp7QbGe1OjKWVSAkNT3JmVeM1F3r0IhNdV4gzk0WzjqlLDFXyVRe7XBsl/PCrT+EoFhe6Ne4+NWBrPuH3nxtxNCswNXCcjLpV0vYypFD41LVFHnqxzsG45NYk5NY0IkolcVqnLEzqlk4aqVh2SsGIhpMxSFLCFLKwiSvOUndU6qaOsG8zqQZ84MqcS0uQlgprTYt+HvKV52u89kxGu5nhGjmq1GjaOnthSrOR86qzcy4tu5iayivW9rhy4JEWOjePAg6nHr2hx0u6i1SVwvOHdeaJASg03fR42o5V0AwyXM3C0TTOrBqYRsVLN+F1Ky08zSUvdQypECUGYWaSFgajyKZTT/C8kp1+jaxw+dHXnqfMdJpuyVIto+EWnF+cslgPWVyKUVXYGda53qtzqhUxz1UW6yH90ERIialXSKFye9DmUreNqubMhw5xaDGNDeJS8Py+z/bEZLk5ZBTpmHrKzkTDM3NUNeVkZ4ah5zhuShRb9GYGYeozi3XmcY2Wn1IUJkmhsVxPySpJO0jxnYSlRsI4llzfaRAWCYdDm0efsbGNgsA9oBAlJ5pj5lnOer3kWl+n65Ws13WeGx2wuaQziz3A4OUnYDg2cDSY5SYHYcw00XCtCfNyyvZRnboJnhuyEGh8/x9c4/GDAaZqIso2rtvD1CNMVVKoY+JyzLmFjHtXBLoq6HgVNa2DY5hcGRRkRYVlwz983Vk+dhuaLkTxGmfvvsTH3vMRTFWjhss//cubPHarYHtSsd6RtGyPjXZCLgSjaRdL8dCNiCePErZmCQueyqUNha5j0pvDziSnblqsNjJm45KV1h6ZSGlZAaeCJqZSJyXksb2ER/d6nGnpzOZtkrRJLlQykVFUJb4N33THMmEOqSyJS4VFX2U9cBDlXzzy9D8ZX0jTxf9Okpov4T+g5Sksezqd+rGPxyDWOJhL+nPJoqtxcdEgKgWHYYxWtvmVr7mX1cDClA5XejBOFAI3YnMhoW0Z6HrFO15+iu958DSO4hDoAaqe0qgfKx0MGiz6EkHE84cOa4sFVR4gSpVJkYMicRSTjcWcLBXU3IR5HOCbBnEuuTG06M9rLNcVZpnCc4c2L+wt8lc217hzxeZ9V0a0XFip6UiloCDE1VV01WJ7rGGbOS9bXMIyCsJMIUmbSEVllmdkqUnD0tD0nK2RCVIHCpbrMaLyCDOFsCzJM42jKMEzBJpi0I/ghaMGWX6sQlqoKVRSIRc5ngGFKJHCRFFUXDPFVDUqWXC736Rbi/HtkqrS6M812g343vtOszOoc2siuWfFwdFVPAsabkFV6YxiC1OXaFrFYO5yezcgDW1yGWGbko9sTdjwfdJMYZIadIOUupMjKg2F4zwuTA0GUxuZtPmuezbIhMSpFahWha4JhqFDmNpsDz2EhAvLUxb8jAtLE+p2RlHoICXXDmooVHTqCYYBdSfjoe0hL1l2+ZsXzvHY9QWSQj82Ao9t/sqFBb78XIuXn3bJqjndIKETZHzqVpetXsBez6dCpd1Ij1XppQpS4eDQYTY3cd0CIQWlmINybOhsGSWLrZimlxNmGkWhYNvFca5d1ylKlcHUpgJmsc4n9o9o+hOyzODjBwNGoYKNj6tqhGlJYGi8MJqxPdaZxCaOUVJ3JM8Oh9yaR6zUS3RDoBtTUCS+lXNiccZ2r8YkNBGlwkPXx/wfTz2Fak34qU/usN6wiMsKJKSRzV8526buFjy1H/LQ1hAdnY5jomYB3fqIH3vs42zHB/xPrzzJxWWLSMzJxbGSy1YlsjKOp6cpkie32zy7s87+yCPOdTRNIKWk5ZZEuY6p6XzrSxdZNn0e3Y7IUgUhJbomWW+HLNVSKgFbg4JCiRFSME9NdBVaXsL1/QZxbJCVKopWUXMKDF2wvlDx2rsS3nS+zpLnkhWSmYxZbM7QAV0RbDY0otxEConkuOjl+wkPrrSIyhIqyXxmcDTokE7WuDkuOemc5nDPo+3lqErFeivCt0pAISk0NrtzVtoJb7nXQ6QKjq7hOwX9qcsoMqj5Oa1G9qeG6eoxqTXzkEhqZkHHy1hvRXhWRZQpnGqH6HrO5d6Erz27St0vwCx5+8tW+TffcIbffGbAUzsW81xlZ1hHSsFavURS0qhnHBxZLLs+umJh6AUtV1CrhywuzFloJPze1QMwS/pzGyEFf/uPrvC1d7SZRhbLbYVhrFJWBnevT0Cb41kSDYekOn7G+K67TzAJVZqe4GQTdCPmaGyx6KlsHwSUSGxN5Z2vWGM4VTBUhVM1Fd1OGCUVZW6z4hvMs4pJJLiwWvC2l64htAxVLzjVKTBVlVYQ4rkJuqpye9Cg5qUMQg1D0RiHHi8c+seElaZT9xRMdNJEYzBR2enV2GxLRpOA29OQSR5yaqniTFvH91K+4Y414krgqT4byxU/+PJTHM0rwrxipaGiSx3dVkCBK4OMexcC1pcl//rZ2xSK4Nok53o44lTNY2uQkmYmP//0TeYi5JmD+Rc+MH4pp/qc8Dl/zL/1t/4Wu7u7/OiP/ihf//Vfz5vf/ObPrLe85S1fjHv8ouGupYIFq4Gp1JimFntRyNsePMnOIWw2HF53ykSTTcaJIFcm9OKIV50cstnUqekOYayheDlVoeEZKqeWQrZmMbZzRMspWXJadOwmgZNyY1QxyUJ6MxuEi2MqPDce8PPPf5LXXspYCGDrsGRUjtC1lNXasdxZUHLGT/n9H/kXaLqFUAfYzoDFIKVup+zMc5AGq26bwDSwrJT7N6as1CGpYmx3G0srGYY+n95xGM2a1PQ2ugabnTl3LkPTi3CdITU3RSoVsRzhGgLHBM9OWHECsswny3XWAw+BhqIlbDRKgtqQg3nF3StDEjnk0sqUC4s+nh9SlDqf2q7Tn7cxdcGbLtU4uwIbDRXP0jjVzEmrgMHc5uUrS6gKTMM6o8hgGNboBAm+qbLRiViuFZxbnLO5PKVuK0gpKSuHotJRFY0fec0qnXrJ0kLBmaWM80tzAkvFNTT2hwGzxMINUp67HdCbGahKTq0e8UtP9/nY3hjDTPE0k/OrCZ1ahm/lHM5MolxDCEleGUSZhWfBue6UU+0xjlHhGCWb3YSWn+HoGYOJhWdKdA0kkkevd3hqr4WqKYzTkpt7dRxTggJZpWEZFa5V0HQLtgY+49BizfMpc+O4xczOMY2KwBEstFOabomhCz64tct7rw9RFPjQrSEyaXN1r4VhSC4PxySVZKU+Z7MRE+YW48gkrSo0RXJy4bjC2qynBG6Oqarc1VoARaE3sZF6RVFBITRu9z1sTafjWNy5ZvN1d6xwsdVkteZwMAnQ8fnuly3RNALCBFZrJpUi+fC1OUIPWa7n7Iw8umaNFwYRjlewEMTcuxBwmM5pGR1+4bFLjHObV589YrUTogqJYlZ8xZkWalHjRj+gP7MBSX/uoSmS+dSm46W0Wwk/9unHef1pj0/fXOXxGwYf2h7yVy4sEJgWX3uhRVKAb5U4ZolnFkSZilKojAdtvv8lJ/mHj7zASzcsLBWajiDJLB55fo2GV5FXFU0/oxOkaKrGi0dNejOPnVFAzckIU4WaFXM015mlOut+m7PBIl91n063JQhcyVJQ0nBUNGAUOTx4cswsU9DUimFsMkwkk9hCNzKmGeQZ3Oqp1J2MYZozCE3mecZCPeXZfZ2tQQPXnjNPPA7noKs6cVlwZjVmlmp4jsLa+vFDh6m2adgFk2iBW8M6RWlRtw00LcE3BdfHFXFW4ds542JKmis0F2fcGqlURcC1wxXiTKduNliuKVSyAumgGwlbo4z7111uH9ncnmWUmc54vkJW6YDOUf8U8zRgP8zJSxVDKzm3cpUwj9mowys2SzQNFCS/9fwRinTYnkWkcsrtQZPe7QOuX9lGqjmf2An55pcFRLHCc4OYtMyxNegGEZcPbHZHJmrZ5VvvXOPiQsWNSciYEZY0+MZ7m7zhTo0H1gJKKVhummR5jaO5xpP7FVeGFbYmaestHlixiQuV37q2zeN7MUdTjTx3ieImo1GbUQKbLQdbU2h7FWmuoygCVStJCuU/c0T7Er6Ez0YtKBnFAqHmzPOSqhQEmstmW2NrLJmVc3pjG0dzaWo2nz4asTcV/PtrewiloOVA03YxjJytecY8hQ/djPijmwdINWecJWia5GDQZcFVmCTHJMkTt5tkmYkmbUoUZmVJ4KS0HIVOTTBMBIo35XDq0rA1xolOWOY0nQRXMzF1yf4URqGOUnp8/Z0L/Isn92g0M9qeYBQr3B7aJGmDhivIRYqvl7RpsxMNuTaU1J0KQzGZZ/D0dp0qtRmnEKUqDcukECqJiClLkzg3iUIdO3NwnJJMpFiaSlEZGFpGy4XJ2ACl4HQ9AAU6LhRSZZpVjCOXOIeytHBNSVqWLNZz1ppTOl6JqQvUUmPN8/hHj2xxMM/Iq5xZJJhkFYamoigVi7UC0zhuOysrmMQ2XjPE8zNeHPeOyaBK4cYkxXJKfLuk4VSsW2cZzAL6c4ejuY2iqPRGDg+uN7i8HzMKSwoUWq2UkwtzPLskyg0MrSKwBdd7dSqpcnvkc+WgSX/ocjSzaegWzapOGBnoakUpVWxNo4pMPnhlRCkU5qmBb0j+0evP8xWnOvyjh29zfRTx+E6HYWjhGCVNP8dzSmy3wjMr6naOrkhsq8B1Uky74t5OgxNWFylVJpGDoQjyAgxNkJYqmgqGVjGZWpiqwum2hW7kFKlOnBnYhsDVBfPM4Ga/jufkPNMfcLETcGdjgT+5NuOnn7jFx7fGfPJGSMMpmKQmpgbzFK7eMjnTcqgZDVZrKqcXI8JU5/HbC+yMPS62fV665hAECU4tQiglhqOwWNfYGqX8yZUpX366wYlllX93ecTXbK7x4njMh6+N+Etnmgxiwe5cwa/1MA3BYA7vvXnEXjinqFSadsHZ1pzdscNCDR7ZGvDp/gEPnDzk1SdMvvbCMpoqiHOdqtIJcwPHENy7YfK/P7qL0Co2mwbtQKflZmSVeuzJU6jHrYl6iWNUXFoLWagluFbBdOxQ5SqZUFmopxjGceGrECoaNq9cOs3b3vMCf/XeOj/20S2qDJJM59RCSF032ZvFlAKi3EDTBGVW56vPbvCx7YR33n8ey9TQdIMffPk6mpXRMn1O12sojsTSJKPQZhybLNRTwtSg7aZcO6ojhOSPbu2xvBRxympTVQqrzZQwVfj4lUUCO6Plp4SpSVTorDcywkSjUhQsvUQg8I1jxdyzu016M59JVvHzj/exSo93vmKdj+9N+PbfuUZEgYhNbK0gsBWiQgU0wgzCXEU3YJIU1HUXEPRnDpVQMYyM5XqFoh7/vi8sxbx6uUuuJvzM01fIqpKGIxFCsD/TSHObg4lLnoOtadwYWkRlyfu3DrBUlbQQ5KWOqigkMiHPDS6sJ8zKjKQo+b3rB7xivcmq7xKIFmZUJ5uapLlEU1UeODUjLCrmCdwYzzFUmGQZqprTdk36U5/9eUGzNqXraSSxh+1P2Z5UOJpC07I41Z3wffdvHNsRNCWGKcjVjN5EpShU6p7gcBowDk1KmeD5EbJwePeTQ+5Ycrk6KkjKjHc/OWDJcSmVnLfeeRKhZdRMlTy3aLiSf/3ELvMiYz8MqZsG64GLrs/5w6dDHt0bUGQamjDJc4cTJ4r/3GH0v3t8zkSREOIvXFX1hR8P/MWEpmrcmuYMsgFX+iWXFgT/6vFdznYdNL1gksdsTRNsK6ZhmJxu2Sw1EhwrJcwF9UaKJjWEIvCCfWZFxYm6hl51iaMO0zwhlD2u9CsmsxqW6BKlNpNEZZLCH988YK2RcJTPMLw9zp8Z8+pTGusNk3kOUGFrCp/+wBOce/VFBtkUUzVY9wNqliTMdJq2iSZ9SiIsd4eGO8dSDTxnxnozgmIVIWt4dsSZbshLN0L2wpJnhwlIlcBKiaoCVXo0LJtOkHDPssA3jid1qGS82Kvx2C0VoSRIoOn16ToqK42c1VrMy9cF1wc1uo6FEDb/5ultdFxuHTlc2/LZPvJ5/IZKlEq2ZiGaWnIUSq4NXaJC0vITLi7PWW6EpJmJbwqafsnJVsJCLeNU61jl0bQVktz4/7H338G2pnd9J/p53pxWDjvvs08O3adzt6QWakkICZAQ4DFBZsABBxyKwYzH4yrP2HA9zOA75t6BAWYGcCLYYmxjQCQJodCSWuqWWp3OOd0n73N23nvl9eb0vPePraHGdevWRbawwOZb9VTttd5Vbz1vrdr1+67f8/t+v8SFiypAVDrjwEEWNj//6ojrRwmmVqApBU07YLXu07R9RnmF5cSoekK/P2cy18kKjcHcpmZX6Arcm2YERYKsBH6q8JaTB4wilbRQoFJxzYqOW1Izc6SUBLnKqc4cTVEIkuMEjk4t5tHTAx7bmNB0ctpeSpBoLDciul5KUMRIWeFHKp/b7FFVgnlssdiIiXMNWSm0aym3pzN2/Iihb7HWCbC0gjOLQ17YP0QWHkpmo6sF43TK9//eF/kLT3S4Pcr41sstDJHz5LkhhlVwZ7dGWqhQwTDQCUMLVVXIchXDygljh/MLFkWs8dp+RJrDrWHMJI6JC4Gk4ITdx1AU/sZbFnlhd3Zs8h2YPLrsYaoVF/san94eYqgaXcfgaF7wQKNJoaRIYg59A0OovPtik/eePU6s69cjTLnODzx2kfesPEVZGCw2Yg7nNvPIoO4WnOlnqEJh4EtcIycrVASw3grIc4OmKcmkoNPc52Am+OC1LWaRSqOustQtONXRUQqOi7BRYJsSR5eMApPtkUulS548V/Ezn9/j3auL/C+fHBDmElnB3YMGNdVl56hHGBhcP6hxb2yR5DAKbQQqG53k2JfJydDUkoVmxNbY4udfOWKlo/DcdkbHEchKoqkVy82MxUaKpanMohp1q8LTj2VjG+2Chq2y2k6p6RZUGt3KIglczMpmvVVQVSZZ5iKkyTyoc2Ovh61pUGoseCVv2pjRcAtsQ6CoCf16yuZEcjCzeeWwQlMTMlnw0o5GGFqERUbdKnh4Cdx6jKdZeKqLosLbVxcJpk02hwZhqqMYR4zilCC1QAvRlJxFx+HsSkHN0nhosQ6Fy9a4IMkVglTlc1sl4yRlPG9TMwwmiUIlXeJ4nUpIZvmMoqgYhwY7QcZn90YsWS4Xaud4dWuZUSw58bZv5eanbzNTfH7upT3ec7LDA8sqS0aDmllRigykwbed7/HyDY9n76g8t+VzsbnEg32H7718nqSSvHW5w61hgtBmlFLQ7PgEuULHNnl8RaVpVtydJLxwXxLLgLrqcMJu8e6LJVFZ8PmdjGtHCaZa0jYNPn53zsOLNeIy5yiKOfIFlgatP4SBoj+Rnv0J/kOgagWL3QBbr2gZDraucXe/4v60QBcKVdJkoSmIEoVX96f801d3ECKlawtWawZ+XrE5Tei5M853FBRsambFfjJgpRXT90psRWUWWdw4srg9ULm772AKGyF1/DxiN0hpOpKeayIpSJgRpyprdQvPhBSfSVRwrgMKLp41w7YmeLrGE0ttRlPBnWnIOE5pmTppKalUnzi2qKSJJGcQgoZJkCXcGVWsNkuUSuPmkcn1HY+WZqFokrBION0LEUJlqTXnkdWAllNya6iTI1juFSAySqngGpKmM6ftCPJCJ7cjbF3Qs00O5wqmqrE9sjmYO8SZRlml5KWBbebUDEgyne2JRlWp1OyItV7GUTblrz3RwdYFo7HFUZSzP1XIc5U4FySZgabkNJ2CYahhqJCWCqouEUJSs3NMJ6VUA958ekRZwut7bX791iGqVuDZOS0nRUVgmAW3jyKeP9wDcdwk6NVSpqF+nA6lVJiaZLUZcmHR5wubXfaHDnGos9AN2egmfP3FOk+ctkAcNwhMrcAwEt44CjjIIuZzg+2Bzd0Dl0/fCXhhZ87rOzE3jjKC0EBVoO3m1MwCQy2xNUnDzrl7VCNKFKhgMLOpOzknuw6lmeLZBWVpMPBN4lzD1EuC2KTjpuSliuEUqKrk+njOt5zr8uiiwZsWuqiVx0MbGbaRcWMQM4tVhJLy8sGQX7t1QKaP2RyEdGyHH3jLBmQtbuwukE1W+b6HLrLS1rg/LBkXR6wv7mFqGYqoON/T+PZzK5zqOjy61CQNLLJCRRMGb13pseyaXB0HnF20eP+p03zTyRXe/0CTsChIMp0kU/ml1wbkuUFNGEzi42Tljmuw3tAxjRi10vAjlxe21qCoMZ1L+rWKjhsTpDq/diXld68HeGZO3cxoeQlhqlFV8MXtgNcGE2IRM0xDVD0glwqzSGd/7mAaYOjlsbF8quJHKjW7IM8UKg10CzxRY+gbqEISxCrzwMBPSxRR8U0P2fyjz2xhoaAZgrFvcmO3xeMndCzhUUoVSytJM4U3Dgv+2eePULWISpT89Hsv0KnpXDsK2RxUWEbF9dkQBcHVnfbvS9DqZoEiKq7sdKhbBd1aynzosLtV5+p0wty32B+5KMBTZ4agVJhahaZI/Fjl0Lc5mhtUpUohYRy6fO5ul9e2OxRSYAmbb1g/wWmnwapbw1JVqlKh1cj4rkfqnFoL6DVjdEVg6ylRpjIJDWYzkz/zcJ+hn5FmBWkBmpYx8l2EklCWOknqUpQV89Dkn129z3iu0fMEDS+G6tgI/40DiyTX8aMGw0iwNXKom4K0yCmqnJNLIaoZ4KcSXZgsuIIgKxj6FpeW4M8/vkCQF9yaTRkoA/6bd6zx8AmDMxsJiUiRIqTMLfo1HdP2eX0ypGYp1HWbu0OLvAp4fMWiaTpoaoVujum5BlXa5txCzt6sJCkTRvMW/8dnd5mGMEwjDEOy6NqYTkWu+MzzDM9Q0EqbtFRR9BShR9ydBax1M/qWgSx1XtyK2BlKHl+qcWcSICuDSZYcexDlBs1GxZ+5uI6qqMRlytY8YTJ3+eZHNf7he84yTyR7Aw0/qUjyPwTi8iec6svCl/2Y29vb/z+vPf/88/9Bm/mPjS/uVbiagqv0eaDvkJUqn9+dcZBFJCJCSIsF28HTdcJCQVEyNicOCgqmqhBVCYezCpnrmKJJ03TIS5OX93N+4wWDA18lj5bomx2mocWzbzT585fOYGYmhlJxFAcM45zz3YInl0zOdlRKEXIYBSgiomULclmRex28R99PQ+1QNwwUbcokCxiFOrJU2JxluGbEubZDx3aYpgJdrYhTD1XN2AvHzNOctaZkpTGlbhYsWR0cQ2cYaeSZiSoUdGPAMEqopMruTDJLBH7UQxFwfjmjbXqsNQNOtzM6DvTqhxyFGv36nGuDY63+9lTlZG9Gz8vxzITverzi6YUuf+ktLUIZUjNhqSGYRDpRUuPOyEKWBp/eGVKUCkvdEaaeAAlhCoMAggxQQppORK8+oG1H3JsqjELY2rV5sr+MrGIePb2HruXMYo1XdmuMQoMnFgYUpcCzSk73MppOyZnlgIVGiS00ilhHFgJVKbCNjPXuGFvP+ej1RVpWziS0STIDRQpsM6NXC7g/tbl+1GCSOORSR1Mr2m7MvXGNE+2IvFQ52/c53Z9iG5JZYtJ2MgwNntiwcM0CxywIE4GhlcwSk44b0q2ltL2MxW6Max/r99/Yb7LYiBiHJrPIYGtcUFQZSWrQrQu6Vg3PsLg5irjQWEFLOtw5bNBrRKQq7PseWalyqXWciOGYOVuThLmv8MiyQSQnrHV03nWyQ8fL+bYn79EwC8pMYzxooeqwH+dsziIebFl834du8C0XO4xCyb19h81JwXPbU37hjSuY+gg0ybWjkEtLEVGuEmcaZ+oGd4YJV/YiLFNw+/4ao0DjjekBwzjnRKekbh5PiJiKhqnotF2Fj94b8OCSzYlOiKtVlLnKjYMG71hfZGMlxNQku2MXSy+JE4M3rR1PnAyGGj/1mQNGUUXHS2m6MbKCKDXwLMnp/pyyKrkfTCglxInkoTMjRlOT5261WWhGhEXCwbxCMys6tYymlaIoOTcOHWwjQ9fA0AR5qTMKXQ6nNb7+woxnTrj89PNbBGODttpk/0jlKFAIUkFFgq6lbE0MZrFkpRUQFxVv3djHUAuWGlPSAgotpVBL4rLg+9+8RM/LOAwEipJxcvGQIi+YZxnzuKTTmDFPc7peTMcdcaKd0m5MMVWVtVbC/rhOQ69zFDgsOBZ1S+CagrPdClP3WW0lmNaMWZYyjQw044CXDwJ6riCvcmqmoGF0qJkSV4f1tsrSws5xnH2i8ZubW1xYsDjTUvk7X9vF0BOK3OOtGwqPbAyYZykNq+T6gcXzWwV7M0HXdjjdUXCtDFPVWLQ9srLic5sBj/Yb9F3J2HeZ1x+j8eDXseF5PHUuoW2rHCQTYllwNKuzedTm/tgmzFTec7F+nExmHPFzrxyy64e8dHjIqabF//DcdQ59hVeOxkDFRjPh6m6NnZlCVBTULcn5bo3H1isO5jpXj2KubxfMEo0LXZ23rJuc6ync3Fa5MZ3xq28csag3casaXb2OIgw2xyXbYfCVL1KCrxyh+SMw8NRut7+s1el0uH///ld7239sEQQOXUcjygUJPqXi02tLVmoGtqExTSQoKbosUO0UV9NZburszQTjzCfNJaeaOrOoAxQEmeQXruySFwIdnTRz2Tyy2JkWtGsBl/oVdcskVyJGWUxZmizaLoYJ29OS0dikyut0XYW8zJEkjOc1TnUAJJoWUqJQFi6eqXDKrZNkgn/0mR0ud+pMk5IwExiVh66p3BkpXNl16ZgWS90IvTHGNVMapk5W2PxXj5+myi1cu0CzUxq2Qi5N4rzg0tKIRVchzhUkCYvNjFyUpAUs10HXSw4jwfX9LltT+7jZoah85JqPAGxdYmgpp9rHDfID38DSKu4ObUppkeYqN48alFIQ5ZKlZoxrJvzarT10rWB9KcBQS04tBGSlyu7IRBWCXAoGcwdbL3CtEkvPEQoc+RZHY5u6UxxPW1sFa20f1zyW2p/o+NSMDFUpccySPFO5Pptj2wWL3ZAly6WQgn4tZqEe4Zo5g7mFqhaoouTxtRHTsU2hVIx9iyRXeHZ7i2e39hkPneMU1lwjzhS8TsZiz2d1xcfSJGc7KlMZ8Pm9Cb/wHRf460+tYqmw3gwJUoNJYJIVKr3a8QFYmhnoekXNLhCKJIlt9sKI7fmc0y2Hn//Wi1SZR78eEcQ6ulZydauBlAYXOx5+rKFWOs9tSr73sTN88WDKQueQtAxZb8FKzcBQJAczh0udBqgZnWbKPJZ85NaEf3PrkDNtl0dWK3xlxs+8doOvOeOw2FaIco22lVE3C0xNchhFfOL+hLFf8JuvT2m2IxpeRlFl3BkWKKVKLDPujVOK6lgOVQGOrvFgx6NQC/7Cw0v8+YdXubxuULNysqTJd188zd2jgqIEWWg80G2yO2qjaRUrPbi5B6ZasT22MY2cfm+PlbqFZyr4sX6cymsX9FoxbcPgYHTczItynaO5xSzWkFLBNXO6XoZhKKy0BEUlSAsd3QDXzmi7CegJl1bmnGildK0Wb143+N6HlvnJL97gE7czvuvyArKq6KoevXqCreV84vaciIg0FzSdhKab4roFnW5IRsmPPnufj9+e8f1PrPDhrW0sO+TeLOC14TbdZsR0ZpJlKjUrx080HLNgveuz3g4wqXNuVef7nl7mA5c2kKqk10w4vzJjEpnMYoOGk5FLBUOT1MwC2yrYn9c48A06bkaQqbhmSaueMEkKvnAwYi5mfHEw5q/9+k3SvOSbNlb54BdHzAOHqoSOl2IbJdNEpW5CmknuDFOeWm+h5x4Nw6FSCh5amdO2czpuwqlehKYn2GaJP2oSJ4LZyGV/cmw0rasK7zo/QlFL6lbJtd06R7OKNHTIkzpxmVFIQRTXKSrJ2DeJY5Od8XGjpG82+OBrA7bGJbqeUZUG3/urr/P8/gShlqw3FGYzF8mxT+tw0qJCIEWOZxasNEqGY5uzCxlCukymLb7l1AOsNnT6rsossFlvSzQER0FBqsestxVIaqRlyf40x9EFDb2GpYnjMKcqZUlvUiYNxrHGrEy4MR7Tqqd8zUqLr3uk4G0bTd62tMjn9oa8eanBybZOGWvcHUj255JZnmCkLiK3aWExSiruxUe0TZO3rjRoOApabPDtF5e+8oXxK8Wr/jPhVNqXu6l3v/vdPPfcc3Q6nX/n/eeee473ve99TKfTL/eWXzVseB6GprA7V4jKhMFURRQaS40KTTe+FKOuk5Y6G62EhVoOoiCWAZpp0LIEy05B3S6Ii4yb45i6WWKINk+d1WnWWtw9kowDQZQXLLgqB/GccXgcj3i+JejbFq5Zshf6fHyzgaMZrHj28Q81mRAMU+aDlEuXG1wfwHLnkMdqMUHSpZQKkZ7z4EJFy3aw9Iiw9Lm+s8BjyypSydnzVZZqbfq2R6+2j6pkJFmTQ18nYczpVk6Rd6nylDS3qWstDC3g/ILPNKwT5pJQztC1LpO05IUdUEQToRT4mc3jS5K00LBEgzv7Dg1nxtlezvYk4MLakJq4yK9s36ET6nhOgiJy4kzH0hMMTaPvhSjYRLlktSnJy4ys8IAEVXgMA4u66bI/lbz3nMV2tM9qM2dvarAzdmi3S/7l1X3evLLIwM+wdIUwNVlv+Vw57PHZe33uDzy6tZKWLampXQ7nCg1HUhKxd2hzsG/zlkcO2Z/r/N6razzZn3LypM9aK0VWJqoiaLoRtp4hZYmiaDx9YsK1/SamWrDciNmdWlhGiWdIdAWe3jjguXsdskLh7qBOr5ajqhK1MCgqjUkoMLSUvFComSU97/g049D3uLw44pXtLjLXSDP1WG6mFBiKgqEey3VGY4vXdyoWLMGhnyHUkv/l+XsMy4KupqEpOWmq4uoF57oTfvELdVxbUFYqQqkwHMkkH9LxCo4GBZ/a32F7ZKGoHTrtnBO1GisLCSoJD9Xr/MTnb/PW8/swWOH7P3yLpXbMkyfa3BzGPLPY4hM7JXcHCe8+q/C5ecQ5u0BKh+lc59SCy3rL4NXhNgKV3YnFp+7N+R/ffQZFQJW1+NCLKoWicrqXEqQl81mTH3j7Ov/tx17l8KjJW04nvHnhIh+8eshB5DOJYH9cwzCgqCR5prBQ01h2W/zyawPmzgxRmnSdDD9VmYQqHTfHNiAtKjr1BEVUPHna4F6xRc0pcewKxyooAWHmPNG3OIxjWk7KcsMnzhVeut1iS7GZ+ho1CxyzQFFKTndD7gzrTKMpq/2Cs62UycSm71l8x+UWLw7vE+U6TTvjwaUp277A0is6XsiNocck1Lg1XOA9J3t88KUJlg0VJb/w6haOYvD2y0e8tmsSxE0Wewk7By1UJ6DrWuRSkmUNHCOk4wBCsDcyeHg949n7Km+5kLEbxIjSpiBH0xLeOOhQMadlCc53dG4PYLFh4GeLBEXOxVaHtjbkKArIC4dbAxvbCln3DBqOwTDMqVsCJWzyKzcOoFFy/cglyHP2w5wTdYueafLEySllofLoisVhYPDSjsrFpZBp5KFqEUKJyUuNJbtBYwF++qV71JyIluPyrQ8v8gN/+yeZjp7g8bc+wu/dnvNQc4NPT3zuzzO+pm1haQm3w03ef+4C1784YtlxcZYNrm0rTPv3+OTtNc6vanzj2QbP71VAgm1FKMKh5yisNH0OAghil7zQWK2b9G2DrCj4ka95iB/85GfpuVDJGpfWNN50Yp3fuTXhf/zwEW4nwchMNlYETdvg8eYyf+OrW9L+yGM6nfLjP/7jNBqN/7+fraqKv/7X//ofu0nlP0rwI4VrexqmmbJct7E0ncwuGUYFLRtqxypoHN3AcQWHQU5UpqSJR7fm4qg5pj7h/tRh5Fu00pxeXeIaBm8cKOwOFRxD8pbTObZuEBUxiZCYquBsF6aBTk5OFOkUhcZiJ6eQOnGRougFk7lNzVBRFMHOXGFnanK6XaEpOrOk4Gev3mOlqdMzXUo1ZRxJLEXDqlXUnTkrWsz2URcUiIqKKLZxhEaqaPzU1z/GD314i8uLHp/fL1ntTVnwTEQFZxcPMBRBUYCq5ZzuqJhFxYI9YuDXqVvgZxWWojJKdYRIyDIPRdX4W+9c4jc3bzGJNcoqI8lsKiqeWJsRpi5ZWR5PU8UaZBpK0WASDzjfi5nEElOvAIgKQa7ljCKDqhAsNCOEULh90KDlKGwdubzj4pTNkcbJTsSL9+rkgc5EmrSciqoSdNwURy/xM+h6JWlRcm2/wTzUKQV0GwmyAlmpeF6FqCTj6DjFTlEqHLMkzg00I2MSmdS7CWlkoSHR1YqyqggylV43QtFyqkow9S2SuUaaSx5cCXC0gtFMpaYrnKm7aCqM4xTHLrg38lhqhszTOt16xv7MPQ4Nl5Kxb1DkKovtCE+4PLbQRBOCv/TEModxzGJnzGItwVArVLVit3BIC3jjKEIIieek3J6P+KmXMjQzpZAq49Bg7BsoqcZSq8FRGvDyXoBXg7xSMZ2cB2pNfuAt69yfJGz6CvvhHnf2DE49UON2uMdhovGF+10eXJ2SJQ6LnspDvRav7sfszFMqxaXdSMlynYPCRzLHrAy+87Ee//SVXR7pNXjlfkrXnXJ+/Xkmos8zGxdoOSZvXm3wS68oXAlHPHvL53Z8iG1VHAwlVydH1BWXywt19CRCKhXDcQ9ZRMxSDVcs8uJ2zoUFC0dRSbISTRRIrSQxp0zGFqZicaLpsz+XFFJHVhldL2Ea6ZRSkJUSVa1QKbF0ha6bcP+ggWokjEITTREMg5Rbc42Xtzap2Rp/5nKLX70yptae84NPXOTv/HaEZmfkqUoQa6w0jw8ybTNHVySVEFBWtCyLiV/yW8MJfqxwfjGhXkgOpY6qwoWNKbNIQRWSk92I1w/a5JTHYTrmjP2DNm9/pkFVwVsOU9IqJ80VVpsR46gCUdFyciaxiqtD00lp2pIw03j1bouTSxNKqSKUkqWm4OY4JM50NDTee6nFv7q1wwW7y7dcWOY37hwhRMVKc87myEFTMhqOSs0JiYTNajPk6oHGQmlRSR/PEhRSp1OboSouVZlzmAgatYrxpCIqdZZ6Ba3GFEXozCMHP8mwzZy//MQi//bGPkmu4dolUS6pGTkdUycrde4PFFo1wUrrOKjlUs/i1jjiLSemFBJGsxYbSxHXDxWGM4W1DjheiZ/nFCJhoWGBIrgzkgiRURcWtqnzz1/MmAY5SzWdwVxhlM7YSXLUysREZzJxGJUZdbuiaeX0GhXC13Fs8NOSbKyw1FBp2QrbE8F3PLDCP/j4HXo1k0LCJM7w7IBKd1Glwrc/3MTTLb7pwhN80z+7iq+U9Jsqb651GYox/+b6AVXoUqtXND14fHWBa1OFP/t/XmceCFyv4mRXwU+Kr24R/SOO/xic6stuFL3tbW/jPe95D5/85Cep1WoAfOpTn+L9738/P/zDP/zl3u6rCk1LOJg3WaiVUKkclhphLlBKiW1KKk3DEBpJVVAoM0yjOCYRhk5u6bS8lJf3QzasGdHU4vKiSp7byMxgc57xt56+yF/60HX+6uMr/Pr1IVQlz+7t0uppWKpG266I5JyDL0W7ftsDOduTOodhxfUjla4nuP/RFykUg5vDAj92ubzYxdF8DCeibvlUwwa6qKNoPkKLyJMOp1sOkojDQMfRKu4Pa+z6OV/YbfKOUx5RbrI91Xn6pEHNsMFJiIeCUVZnFCh43hFv6lfcLE1UNcBQm+xmGrIqOdm0mCUQ5AGPr8REmc71QUmQtnnjro1EoaUFHMx1XL3Lph/S0gzONmvsJjmurmEZEY4tCbOKF3cbNEyFdm1AVjS4tLTL//niQ5xemhFmdVBiwtRhb6pwNDExnYRpYlBzMqphgWfCwFdIypytiU3bKYGK7anBm9amfPx2j0gWnOkndPUz/NzrU/RY45FTJrfmCou9gCQz2Zt6mHrO6eUQR5UMQocFL+NNa3tszx1uHHm4huRcJ2Y/cHl9r4Wf6cwjFaFqRJlG20vQFFhrhUSFoOmkvPn0kLuDJkNfJ84F4yjEdARhqVKUBpooadgRAEmp0bITnr/b4+Kyz87URVMyVusRzw5bVChEqUZeCFAVECVTfP7xSwlPnXDIC40VB6Z5yu5cIguTLFd546jJQj1nHpvMZyornYCy0smlICvBT1Pm44oT/RRQCVMNKSKOdlKalsaZRRPPzlmo5bgWOKrCf/XYZT56Y8IDbYuP3Ts2a1xrGbiaQd20COcn+aF3nuaH4te4PpjhEyC1EtcsESOdt67X+LUrI5KiZBiUFBWYasm9gcpCu+BwnvOjz97jycUOn5X7bA/ajKczvvlci4/cmbA/d7i8PGZ7XofSRTQj9sOIV3cyHl23qbScQ7/kYO4S5cenqOvtmPujGp6ZUrczRr7Fc/dmnOwXmLrK4+sFr+/qTEqJrpVsjis2+iGWDoqqMw8MPK8izjSkBmGmYekZZSXRtJSS4xSeipxb44qZH/D0apvfuD6h09QJYpuDSYOWU6JWx+k2MzRe31ukYzn8r+86z69cG2NzLL/U7eLY3DmDa3sLHMx1bK2ilCmLPdj3Szy3QjMnBJnNNIC6kTAOXMZJyWffWKOQGWHqMA3qWJpkyVUoRUSeazRdjXke0XcMCqHwrWfW+MVrd/jZr3+MpmXym3d0/skru6BnrDc0bEuSlyFFqaNpKYlySCKXaNoKuZVxdz5isV7DDzUq5Yi0UFmqp9yd5qzWBpTVKu8+V+JYGdd2SkxRYzyHKwcSUR4nQi7VwDQFYXpsCv/eP/2n+Ngv/yLPLjzCF2+N+OB3naepGnxqZ8SG12Y/OeLAL/g3r4yptDnoIVHQ4sxiDb1c4bHzObuznD914Rwfvzekbum8urWMZ+XHEbJKQcNwyDON3emxoa6lFhhqxW9dH1GvFrm9WZBbM6KpgW3FfN3pFi+IiHedXeCXXh7wxjBguSlx5Fee0Py+aeJX6F5/FPCBD3yAfr//B/rs93//9/8h7+Y/bVQi58lVj0pRSeWEpEyoOzorrZy7Ry0Wa5J5rFFacyKZcXahpMxrjIRg4AuCHCrVxlBzllydmqYyUnNeumvR6yScW3bpegUlGXfHKp6ps97KsTWFI9+kUjLSsoBC48RihawEeTlnb67zoGPQdXUmcclhkKBisVwzSPKCnXnG/sTjXFeQJwr34ylLZkDP7VCzcxreGFXJsFUXTdWohM/ZfoSZnuPTN1K+6/wKW9OUW6OIRzYMFtohS3UQ5OzNVB5uWjh6yl6Ssz3xONuRNI2UuzOdjlfS9+ZMBiZUDr16yN2xgiw0WqrHFw5GZFKQJCBLA01R6DVCRn6dMFMJyxxV6hRlgWc4TOY6daMirwRRrnK25zOKFarA44K7wJ63xda44HTPBywO5oKOlxEVCvtzB1VN2Bk3+PtPP8BPvrDLYSIIEpVRYNNwUrpuwUPLE17bW+Tagc080liop+SyRAHSRMOycw7DGZ5dIVAopWCpEVG3UjyjIi8FUaEiK6jVU/yJxdGRTauZ0rQLHl6bsjlwuXfgISuB50lcs2QWGwzmFqt1m5Ec8/XLG3z/v91kxpT1xZBCqrx4b4G1VkC3HjOPLBQh0YqKg5FHuxORlQaXFpv80pUd/urj6/z4Z3eZiR0KqTJNLJJcZbkesX1Yw9Rzslyj1zz2bYlzGEclsrDZG2r0W3OyUiVRJYNEUHMzzjXaXJ9M8L+UeHn7KOXvPfsG9/YUvvuJNqvNCTuTNrvFS0S5gaEK7o8cuvWUnqfzPZc3+OFP3Oe9Z9os1XQmZUiYwlgK3rrW42PbOxRpxc9+fhe0CtcoeN+lDr+7c4fHT0BRqfzCSwOGxZye6/DK4RhFGDywZvDKNUHfNjjRNhjEOYMsJ5AB7SKjZno8tFTjymCOaZa8tm3xaL/BtcOU73yow8e2Bqw6Djuhj6pUaE5BGhVMAhdNyQnj4jg9FrD1grKSHPgqtm7ScjI0pcI2SoRRMg00FHH8+onlGs+Vk+NgFiH551ck71pe4sJilx/52F1yqdC3Dd7/0AK/u32TmlViaJKs0DH1kjQTGAIGSUyr1uIvP73OP3215PndFFvklIogSHV6bkiimwzmLkczF10TNJyEJNPY3/eYzAX/6POvc7pZ58qWh4pkeWFKWhjUrJKtkcNaO8NQBSfaMUFesDdpIpSUolA5mJmstI4P8lwjRRU5h6Mm//Uz63xuy+cfvuMiP/LsJlfvhDRqUKFQVBXn+j7X9l26XoafSo78hHhPRzgBh6GGberIKsXUKm6PLE7WCw5Th8OZha1XoILUcjSh0bZVLC1mFHiUlWAeK2iKgip0plXKxVpBWekc+QqekZOEJvVWgqao1E2BkVf8yq07rDQ0ZkGLqIwZxxl1W6FjWVxY3ee517tc3hD0HZMwEWQE1HWNSwsaR6HF3TE0rZLlukrfkxyFKf/6jX3ettHhXGfAi3c09sg54ViYmUMqYu4elaz3Mnq2SlfAfiiIMjgKVFy9oqHo/Pjn7yE1weY0o2/qNM0aaSx5rRzixxpN8x5Pd09jqSq/8J3n+bHPbfHRuyMGw5APPN0kzeYMEkGaGPwXT9SpGSov7Ous9CUtyyS1AurdIZ619hWvi18pXvWfC6f6sh/zZ3/2Zzl58iTve9/7SJKET3ziE7zvfe/jH/yDf8AP/uAPftkb+GrCsyrujRUMY4qq5lxYTFjqFqw0CyoEbxzl7IYRiiIxRY2qbFA3FKZJwWe3Eip1xNtP+5yoNXho0URRCu7NYz53v6Cg5Bev7PLEcp1JXBAXklrTp+1klLnJNEu4PoDNYQdX6bJac7GsiI3FO2ws3KPnljiazY0Xb5Cvvo84btFzDBwzYZSMONvdpGsJPMOkVwuxNQW1aiOlw2EYc2MYUzdT0kLn7kDh8nLAm9Z0jvwGW1MVx0iJMhNFnfDCdkWYOJS5SjrTeXTRBDyEKBiGdWSlkYkZUglwjIKwiFiuZySZy8u7bXR61CyVxy4e8cBGyedvdbhxz+aVWx2Oopgw1fiOCyfZnkBeKry265BnLo4p0XFJcpP7gxWywuRTt04i0Lk36KAqgvHcRtcEjpWT6/fpuRELtYin1ofoZkKcKMQpvD4MmEYwiRSEInHMjFf327xj6QzzUZ2n2k/wa1cSqCRLq3MSZYBnFjy1vES/ZuDoFUWpIkSFpec8c3qfm2OPu1OX5ze7FFJjvZXQdCUPLMzo1hKOfB1NlRxMXbLCwNYqilKw2vA5DEyeWTvgazYGvPfSDrpW4pkFwkqJYwNTL5lGOpPI4sZ+gzDT0EXJ9qjGdGIz9A2KQuNMo49a5bz71H0WawFqpXJ5fYJnFbh6SZypqFrMYXLEjfGEo2ofR1dIC8EjyxYNvYukZKMT0KtHXOxaVFJDyopJpDKZq/R7Pq1agagESaKTZhp+GR5PzZiSj9+akqQaqjhOZuk3Kg7DjJN9kz/7xAK6E/BnH+1zsV0nkRWaOQN9yN3wBUxsml6AZQ35609dJY10Hjs34dr8Br/4ygG/u7vLNM8RWsVG0+HJfhdL1Dm9UHLHn7JYM+k2Yk60dXQ9459f3eX+JEOroNdIMdSKkpKdicF2MKZbV/mRrz3Df/vUZS63u8ekSRUsN2IOfZ20FJTSQEFhOKuTRw6vb9eZhCZBHtOsRySJThjaTCaCKDOBnDtHLpPQpOvmGKrCbO5RVRpJoaMogttHDS4tTri0MqKkYm9mILWMX782ZM9PUJWMtjfFTyuuHTgE+bEhq235KKIgY8Zf+JWbfOTuEZHu0/Qiuo0RUV4xzVImsYqhz2l7GWl5nLLXr+U07ArKGqqi0bZcqBR0LHpezlovwatnaFpEWcJBFDKJBTtTk8Mw53DaIM1c5onOyabK//z8bdIC/s5Hb/L5nTlfd2IJpVJ57aBkmhQcTVtMwhplBX3H5olVyElJE4W1RszNScY4DWk7FUnSwNZzNKGy7DTwU4dSOaKQCfeHDW7sOtwdSjR03n5K423nMxIRM04zaqZkFKr86s093vuWR7nwzm/kWx+AxX5JXFb8za9Z5VKzQdcxeHm3YhgqHEQxfbMJZRNVC7kx8dkJAuqmwgN9nb/90VsEmcK2H3F/mmNr0DAVDmY1JBVhLtGoaFsqpgrzicpv3z5id1yhqwoPLZqsNTQO0yn/+sYeh3HK/aOSZ041OdusYeQNXtyVX+WK9kcfUso/MKEB8H2fU6dO/SHu6D9ttFzB+872kNohTSehbZkIaeAaCrkU7Ptwaw9i3+J026Bm6OQkTPOCrICiEDQthaWahm6mVNqcrMzp1UzqpotQcqQSULNylpspDRMsVSEtJXt+SV6VdC2T5a4kljE1b8CCV3GuqxKlOkKbMgk15mEXVTEoZEYlUlxVZbEhqIREmBFdN0dUDkKCUBMO5y6WYlNVglZtSs8tqPIW94Mp4yznp1/c5oc+cYdJkvOvXp7QtCpsTWWeVpxfGiEqhTgXTGOd5TrkBQRZRVV5bLRiPnff+5LMSiPKFNTKQlMrXp3N+MJuxIlmzHc+usmJTkZRSuLsWGK+eeQyGDUoSo3ve/QkhZJxkPsUGISJhqEXJKVKy46JE51Pbo4ZRyoPrY25uDBFV0seXp9QMzNaTsk01Nke1dibmkyyhLlMWLE8qtRBiGMCv1SLSQuVjhvz1PoYVVSUUmAbkqRUUfXjiQVLtfnu849SN238WOfq7nHd69cSPDNHVgLHkCwbTWzF4B0nu1QI0kJlEuskhYprl7iG4B1nqy+ZBgs8u+T6UczdA50bg5xB5rO2MKOQCpPA403rKjU7o+NknOzOEQJst2R9bYaWe3z7hRP0vONAhp/74i6vHU4B0BS4eXAcmz6MLBQB88RgqRPStFNkpZDkgv1JTklJVZiUUuHJZYfVdkAp4GBqc2s+xqokplpyqtEi1SLSAv7Lp1p84s6cu6M6RqlxeyxB2riKg1aZbA1rHEzg97Z3SdKKV49mvDSYseLUGEUls0giywoNhW9/qEu/pvOWxTae4vE1Gy3WGiqaItGEgdALHmp30NWMr1lcZM2s8fOvbLHc8illiWNU1IsGjgZxplC3czYWJ2xGu5xpLpKhcKZv8fgJjUfWVJ7b3yUup+yGAa5ZEkQaaaJTFAJFiXms3UdmBnsTh93RsadnJQXR3KThZNhmScNKGfgWlax4ZH3MSitk7Bv87q0ATYWWk7E1PP4ff2Uy5Ke+eB+nnvLD7zpJrfL4Xz+zz9RXcPQUTZHoSknDzjENiZ/pmEbOfpjxtz58C1PolBVQVaiiwnMymnZG3cox9ZJKwGozxDEK0kJFK128RoyrOmzPMv7rt6xSSpUgVYhij65ygll6PCXVqyWEucpiPeLC4gwhKpZ7AVFmsuEtcjB1uH1Q4+7ARa0q/v7H7jEKM6SEt220uLhmkhfqsflzpDCNbBZqCWl+rC2KshJTz/i+R9dYaxpkucH2VGOxlrBSTxjHGkuNFNeOkJR8+yNdVlsWi57NNLLYngnqdkqa1hj4GqWsON2LWG0lWHqOpSp4VsEgKnHtgiVPoahyyiqjZUtczQYlxzASitRmo5MhlJymk/DGbp+lfsIkUnDMAl0cJ/wqSo6mFiy4JVVoU1SSrCyx9Yq2o2CaGb9328dSTC6fKGgrNn/qfJ80BV3qXF5RqCqVuYwJZM5yXdAxNVQtRYiEgS+IQwXXUFh0Vb7hbI/DGSRFhvTb6HGDf/Vqwff/xh1+4tk9DsOYhbrgex7u821vdvm9exP2woBKKenWJK8d+vzciwfkVcFhmnOUpZxyGrx9+RQ3J6OvYgX9o4//GJzqy24UCSH44Ac/iGVZvOtd7+Kbv/mb+dEf/VF+4Ad+4Mu91VcdB3PYaEuqvMWCpzIObMbZjMN0QpSnPLpasuR4LLoaNSvlylGMYW2Tlznf8mDOgtVCwwVlTigHfOyWjqOrPLVqsuCZXD2KeeMg5rGlGo7QWG2VyColDCsuLKQ8vipZdDVymfB7d0s+e1+lyGu09S6GqnJjV/APf/x/p985Ji33ZgnTLGbRsdCrFfLSxLPmHAWCge+RFiX74ZxZrLDsNjEUm4ORyV+8vE5dr5OUJVeOIk73hqw2KuaJRlaYLNotFKFhOQGnTw8ZBia3JzlZ5TOPHQaBSpqanG4aJIXOai3HVGzeOLK4ui84mFoo2pRBLEGTuK05K91jLxStsjjZcvjxF3b40+fXUFDxrIxzvSlvPXmHxXrAvWnF3lRnLxBcP3IJM0mUqth6zumFhLIQqFi4JhiqRceWHM7aeIbKej9jtVew3Mo53ZOQm6iVwe6ow7LTJY503nPZ4GP3t+m0Zpzqp3S9jIZdoGqSj9+dsn0g2BkavOnkHp4dM4l0fvfGAqqouDlooamCvNR49uYKv3VthbwAx8h4bG1IlKtoCrScHCFAVhWVqLg/cvn5L57n1rjBamPG6d6MrptQMwvWGyaitHmy10ZXJJZRcmfQIMx01tsBj56bkZca47nBndmI25MGn99bIZeChU7M5lGdmp2BKjnfM0FqmJokKyVFoZCJMautFFWt+MxtgzxzCVOdk50Zlb1HLlUQKooQBKWGpmhoasUk1JklOt16xCw0KIREt0ZEMmGtO6dhHhs3Iw0GccLV/YB/88YR//Dtj9B0NAyhcGs846kTR5RqwIfvTJjkMWmps9qM+bkvPkBWwVonQJEdbK/koU6brCrRMosfevs5eo7F0ytN7gxTLtTbXB3u0jL6PLcZ8spBRJwobPR8PvCWTeL8OE5VKAmagGng8D+84zS/e3+Te9OEN2a7uHZKlOjcHdQIUx1HT1ltBegqnGg5/L++6RSLrZw4r0hLwVIzptcsONE2+N++9SyDUYsbRzVcK8FPBWEqkLIiT6BuZfTrBac7KQ2rYhy5zCKXpXrB4+tT1loZrVZBzZYMfJebhx2WGhmeXjANbUAljC1O9yJ0VdLvRHQbJY+uxSx1Urq2SpVaWIVHS2uSpnUUobFQO5YubI88srJgHBnM0oRprCD0MZks2TzS6dSnPLwao4iSlg0mTYLEoGVZzGODWaLScTP8YgYiJZew46c06j5dR8fSVN682OUDp0/jhw2ysiKITa4cFQRZAJXG06cnuFbK1G9wvu5y57BJLks8Q/D8vQaH8RwhYkTVIIoWsU2JEBVLjk4wN/ninYL9IMHRNZ45k3CilVNWJWtNScfJ+cdXbjNLhjz/8StsNOHHPr7LL3zhiB//ltO8c6PJYq3E1Eqc2pxIJvipwmrN48kVncV6wu1JwE4wpZIF6w2Ncx2LE23JJEk4TKb4qcql1gK2qhIFDlkBQVFgqRWuDQvdmE4vwLEjhJajKwqrdYPlTsme2OXTW1PmWUWUC1xN/coXKfUrvP4E/1lha1byY5/aw1Y8PMNka2wySSTbMxXdmEOic66n029VVGJOUSV03JyT3ZD1dsliQ6VuBxxEEbbh07U1KBoITXLkS8qqQlYVhiKo6TqjUOfzWzovbtqsN1TahkkpckJ8luoJcdimqnQGvsre0ORg6nG6K1mtWeRlxb6vsVIP8QwFU68YhZK8KnBNFUtolEXB6zsWng6KonMYqEiR0qvl7M4Fh3OJYWeYds44yhFWxumljJpZIcnwrIyqdNGUiu05UOmYqsqhrzONNEQl2J5aHM5cGrZC3Q0RSoKtKnhuxoWVCTU753R/hCoqznZjdDXHTyzaTsKJ/oxvuWgxnZj85Bc3WekEuGaIzHT8+Dh5aBgYpCWUEmpeRP4lT5mX9/rME40r201evtemIZdYalQ4CizZDv/vFzZxzYIPPO6wsXqEwrHR6/XDJlcOm4wji1d2W19KUi2QcOxhZBR07AamqvDSwZwyaqMpEkuXbI0cXtvtcG/iUlWCvBC8fhATZCXP700IEw0/MpiEBqoiGcxNFD1ncz7jodUZUaKRl3Cha3G5XWOjG/PYuSFr7YDLS2NUNeEgmqBpcHPQYBSaSARBqqApJZUR8POv7fBPXzigArqNjAdOTDG0EkWR6Ipk6FuUpcAwSgyjYji3qCqBKkpMoWDYJVkhiUiYJzrPbyestGKWmxGmWhEHOpf6dRyz5NZ0iqZWoM35lev7xNoUp+zxrtNdilKhW/M512limxUqgjCRNDQLWygMowJdi9lO7oBSsNLOeXy1xlPrgi/sz7EqjXecs5iGkp9+bo/BxOG5zT7bU3j/2QWKXOFyr8NREvHAkkFBxd7YZXdqcXOUoVoldS8lTDRMrYQKxjOX+4HPqufySLvBCa/JTA6Qis/bzhzimAWHMwNVk9hWgWLkTHyTfk3nRNdkEmkczmwOfYeyEJxZ0ilzEw1JUmgkucLJXkQuNQ5nFq5V0nAzdKXkaG7jWgVLzZCF9h6WLvhvnniYmqVydxYjtBJVk2xN6yhC4UuKQlpOTreWEvs6m8ER92YhH7qzx2hmkeUKUa7jmQXj2EDXKgxdcqrjY+oVlYRxYPHmE01mgYWlGPx3bz3Lv7x+wGI3QxMwT0s+dW9GzzkOnxGVZOzrXNtvMosNluoBtinRFMmtUc5gZmJokrqTUqsnZKnk1d2Yn/zcLhf7Dmpl8DefXudy4xz3hnX85PjzCIlEpe7FlKXkxU3Bf//mR3B1m7iQTGKLJPOYxSqjwOBsPyCXkmlS4KgaV7ZjolTHNQx0rWS5GfM3nlzil14eEKQaMqhzOBfERYGp6tQM5bhpqBecW4gJEkmaqZiq4MjX2Z1Cv1GgYXB10yUhZrmZ4GoGqClBHtGwC1brCld3PfaDHCEkZ1dSIt9EKiHzBDxNkIcuf+/tGxRSZ8F2sM2KX93c4pHzkp6nMwrhVKdgwdWY+Rq7QYhmJJxyerQdlTAvjv3iItCEwqfuTWjYAlUVbO5LRnPJWkPnWx6uc8Uf8Td+7QbPbh9ypu1wczKjLesYZZ3xHA6yiPvzgIYNi7WSjq3yPU/V+T/+1AX+zMUznG7WvvKF8U841ZeFP1Cj6LXXXvt31htvvMEP/dAPsb29zXd/93fzzDPP/P61P05o2w4NC7b9jFF2RL+9zdOnQk7U6mw0bfLc5DAKuTub8toBrDY0HJZYrXtICm6Oc25NAlzTp67VeaDn0bQEQo3Z80vmSU7bU1io6aRZjlByysrksTMBJ5oFQvHZi0bszeEtqxWPLLiAxTxPePWOwo3f/gi/8au/QZQqhGnJRrPiXMvANXSO0oT7YYClpmRFg2lsEBcKF7oKjy6rjCOdT98T/NDbz3JlNOX+5JgovWmtYqNp0XQShBpxZ9hgZ2RxZ1zy+Vt1PF3nxqDB7rQJxQKWIihJWW5mRDnkYgBKzijSmaUlj61WSGnyxZvL5H4fKsFfe/Q0F5YTpDNlZ6pxe5DxxcMBm7MZWZVyslNgaIJh0KJpq5ztJqw2YRYaWJpBt5bwzJkhQSb4xgvbPHV6h3OLI37napuL9W0+fL2Hy0Va3oQoq7i8ssdDq0ecXzri1MqIphcxTSo+djvjl67u8+l7Iz55DcKsZJZW9DyfTBakVYFlJzQ70XHBmxyfJrq65HBS4+6gyWDqMA0tVKViGGjcObIZRAaTyMA1KnRxbGpsahX3hg7jRONgbjJLLApUtsYuu76HqsByO6HuZBwkIcMjjZcHM2Sp8LZTB1hazqV2h7V2cpygkrs0nAw/0bBUScfN2B55FKXA0EvecnKAqpRM0pwwVTmY2ZSlwI8NducCRS0ZJSl/7+0neKj+ELuTBjtTh5otiVOBIgree/YuXTMniByKQicrFPJCkuSSPBXoWsHtveOEtZ1Dj92ZQV4K3numzyg+Ngxt2yoHYcrtSUiQlsRVwiS20NWCm8MaDy/UyPKSjhszS0xqdszL99u8fBBwvm8wSQPabo60Ev7RZ+/zzBmXQVRxqd/mxmHErUHKp26nlKVCJUDVJSvtkN2pzZX9NlVu8hcePMmldpO/+7YNroxG/NPP+Xx2e0pFhawUVEWgKIJD32KaGBQFWLrGD77pNNuzjJmvU1UghCTMFDwzY2+S8zc/fBPN9nGMiv2pQ5TqSAFZLrCaCUkpCdOKIKvo1XyCRLA9sQlSlUPfRRHgmCVVpdEQTVbdJhU6S82M9VbKajPG0CDKTFw9Y60d0fEigkxhFiuMYkHbNhlMTL7/8dMEmWQYVrx+4LEzValZMA5tljx5fGKuarhKDVsXdHWXvhtTMyXLzQlxWfCnzy3zyp5kb2DSdksMPaYoHE40dCy94lxP8uByzChOqMSxhvnbLy3iqDp/5bEVsrKk40DfajFPTaaRYBR4tNsTPnW/otMI2ah7/JNv+BqeWGxwsqWzNTjBtYFKWMzJSoXn7/TYnQlKJWOjq/H2yxWuVfD6UUZFhlIpGFqKYwUYesETqwpnTq1x89Mf4YsHMXtByGujKQC/fnsbw0zQVcl6S9B1cxx7xCSNyaoMS7RYddpM/TbXNgUv7kdM05S2pWLQJgr7TGOFx3obZPgYVomnajzYLTh10meaVsyykChT6Dol/fbx/iohiauCXMTULcnb1mostjKa1h+Cl85/wqlnqqryzne+k/F4/O+8f3h4iKr+Z8LA/pDx1HKDXh0Ow5Q3Do8bElGmUJUmfcuj4RXcmydUeogfOxSlTZbZ1EyBaUUM04g7Q52a6qGJBrKC/XnFtz9ksdausFRBwzCpUJjmIY415ZGVgreeyZnFKjvTCtsskKVFFHuMpg6Hc52kyinLguWa4MG+x36QMc8iFj2JKjwW23Nq3hFn+wFrDZ1uLWFahowzONlRyXKDw+BYRrbRUJlFDodByTQv6dV0kCphlWDbEW0PgkxnGEsUBKBSiRJdkXScgqSUWMax55qhl2yOVNK5jaYo9L0QS4U4UzG1DFOxONWLiIqCq4ctKlnxxkGdMHa4vtfi7735Cb7h5CnaXs6lVZ+iVIkLgchq7EzqpLmJrlRMEoOmm9GtR7hWzjAwmUX2sUmvobDU1BjJCYOZQhw51LyY5VZEmUl+/gtj7u8vcHunwctbbeaxhqNJ8lLF1ktO9yJaTsKpdkQQ6GhaySAJmOYRv/rajFtDn43aAoNhjazQkVKy0fZZbQYolQClYmkxQahgWQXnlo+9UbYmLrpesNrx6ddiolzFMUsKqRJkgu97epF/8fo+o1Bjd+rx7O0FRkMLKVUsreDS4pSsVBn4Jt1ayuHMJpeQRgrnFwxUIdicFszTYwl+kGi4VkFRKty82+FEO+BMf4ZEYX9qU1YqJxZmuGZBVQry2EAWGpbQaRslBhLbyLncb3A/SFloxqy3fVwr4+nlZZZqCmcW55xsW+xFPnU7p6Sg50pGowooOblQ8fL9HMtLMKyYshJkBVh6QRS7/E+f3GK57lC3S4RRMQkV3rpR52TH5BtOLlOlLgvNiL/7sRvM84xfurrJ5txnnBUohUZVKRShgmdJkrykqipAOeaASZ8g1tmalWwOMv7FtR1++oV9ZrFEEZKtics80lHKJlLaaFrJ+bbLn764yPO7EyqRcKKXcGl1ShIrPNBzuTtMuVhfJy+6pLmCqUluH9bYGztomkAISd0qiHONvADXKphHBpPQIowrfufGmF9+bZ8gkbz1RJ20PJ5km8UqSa5xb1xHVmAYBb1eyO7A4K3LTdpOyp9+oIfMbU7WukwjnWloICpw9ALPKClLhSDTKXPBnUlEzc345L0xrx76qKnJ33zyLFlu4ZgFvVaCqhzz4go40Q3QVMmlxRmj0MVPDCoB8zzmqbOHGLpEVgJDl7zzsuRtp2o0HIUffe4OwyTiH796lwLJQ91F4kJlb+bSr89ZrqeUhUqJ4Nl7E24OY/7Sw8dyqklkYqgSzwmQlWA4b9DxUq5PhvzFNzU5szFEUypkaZKWOUlW8tH7+6iKyt7YJFMqJpHN1tDFER5xYnIYFfTqPjVD43Q/Y5rHjAONjm3Q9SSKNmMy11nvQVmamIZPWkKV19ka9hglBUFW8dYNePtan3Gck4iEpY6kZ1tME8lhlNCrCz50Y8C9acyvvZoSFCVNSzLLAhrtXbrNkDgv0LWCpm7QEDVsN+XPPNxlPDd424WU5V7BNC4ZzCv8tMQxM8rQod1UqAzJOPd5bTjn/ed7NDydOFX5Z58bstbQeX0/Qi0Vvv5yjY5psjNWGcYSqebERcmLOyFVVfEPP3eTX7hy+JUvjH/Cqb4s/IEe85FHHuHRRx/lkUce+f31zDPPsLOzw8/8zM/8/rVHH33033sjXw0cRiFH8Rw/Nll1Gyw5barSJS4Tbk9iXrlTkRcqJ2oNHui6NAzBjl/x8p5kx0850cq50HVpmg3KSoISERURWQlrdaibGt26yofvjllcifBMBV0o5CJEsw5oOikP9nQe6JuYmiAop8TyCMjpuy2yW1+gcektXOy5nOzkLDckJ5sDNudTDnwoskXivIusSiaRIEgM5rGFZw85CAIWPcEHrx0xLSastAPy3MHSFRR1yhvDmN1xnWng4TgJl7p1vuMRk5qV49lDPKNgb2bwxMnrlFKQFxZZKVhybfK8DeI4BQkUWvUBKRmn2nUKAn7iuSHTWMFQBAueQbuZcbanMs9iut6M/YmHrBIymRGmBW8cqkwiFU0LMdUSz4pxDJWVRoKmGkxjj6at8a2PHPDs1lkebJ1mzWszCerkhU5WGhSlQKDQr8V4RvElDbakqmA6c3HqOW9bPkFRGLSsClODrzk1oeNWPNhrsNFwmEXHHkerrYSq0phHOqqWoygZmihoOBnf86ZNbg0bmCpMI5vlVoZn5qhqgWUUBKlJkBwT5+NTvZIv7jSRUiVJDFabKZpaUJk5SaxBeWwqeW7RZ1KMOOGe5G2nd0hljGFUPLpYJ8p1glT9ktklpFLhyl6TtVbIPJFYZkEQGei6hAoUpaKh2zQtg5+5+hLP7R1RUR3LtAr12BRbK/nXV04TSQ2BRFcMLnfbOKZC007pGiZds87ppZD1bsKZVZ+kNAh8jR/71Dav7A84t2DwQM/j/jykZemksjyOgxcWqtRJc8FvvTHFDyx+79oKBrA7dRnMHNJURy9sZKaQlRVRqHF1z+effH7AOCrR1JJ2PybIPL7uTB3XLXGNgqV6RMvO2Zl5LDYi0FP+xdUR21FIRcVv3LnPo2s2L+wfMQl0dg8NWnbCJLBZbcSc6c0w9ILruxY/9akj/vkrm3RaCaOJx86hR1YI9qcmX3uyjWlHNJyUvNCIM4NerWSjk7LSTbiwMCcrFPbGHkmmUlaCspLoakFelsSFpF/3ce2IrCi5s1fwmXsZmwOD3ZmCboSMQxXXDlFFztn+mFFoUrdjlpoxtp5zoplwbiXgt/78JW4ME5brFjXd4mKthyx0NEXn69Y22DloE2Q5cVnykTd6bM0KTC/mc1supYTdyRK6gIEvec/JHlmmEYUek6REiIRZpJPKEbLKmAZ1BoHJ//7iDr/++pCNtsXXXqjxsZvhsV9YNSbNBYezOoe+zsy3mM/7uLrDKJYcznP+ym9d4Ud/K2Oe+XQcyYrn0HePU9uW6wW2qnFnnnJ9EqCpBWfaKk+t52TlsSeKLF1qZkFUzoiKKSunFug1NL734ZOcXi/44mjAZ7YG3D9Umc5t2rXjNDlVERi0cTWNVm3Alh+x72es1ATvfaLkWy+n7M0Fsdhnez5HVApdy+YffHKbm1uL7Pk5d4MZr2/rNEyFk23oOg6OKSnEjEkxIUkqFr2KtTqYikOzmfDN5zuM0im7sz8CERh/jFBVFWma8sQTT3D16tX/r2t/gv9wfNulHrcHUBYWs1kND4WGoYMomZVzTiwd8OhGQtfRMFWVOFMZxoKtWYWuRbx5LWfJs/Ezwb5fEqUKdTvlo9eOf9gJobAfZkySlLap0nVsHLPEtnyyskKIClmqeIbKdG6yPy1JZIyr6Kx2VSqR8y9embPSGdDQ6/S8nLsTONOZcrFXUrdUXDPG0SOSzKLrKKhaSVRIxoHFuX5KkGocBsdR8p5uYKoCqVTopQa5SwHsThV0TGxdo6xyHH1KzTJICuNY4uFE2HqFoOK/PH+W5cWMa/sez97qI4SFaRSM4/JLMfYa1/Y77E1qvHJQo18PWW5mlKLkL/3KJh/bvsfp9V06TklFxXhu0bFMwiyn56VYuqTnFKx3QoRSUkgFz0xx9RLbKEilxSQq2TnQGE8tbDtmEGQkuYaoFP7sQ+vEc5MnVrqomULjS9PMulZyqjdDVSsOfYuTzjksw8aPTXRF0nIiuh2ffs3m7i50XJNOLWV7YvPs9UVmqYmmS3Qr4/6RgmMH9OsxpRQ07RzXyNlYCLC0io6T0q8laEpJlAmmYcn//PEd2naMa+R0nIQk02m1U5Jc4Vxnxul2QNtNqVs5s8jE0iVnFnzeemmf0h5iORltLyWIVUahRc/L0JGYesGlU2PuTRz6tYS2nZJLFc/MyXKVJFUpS5XvenCB77y0wvc+VbE59njbqQM6bo6fFZhmxv7cpGaVBInGS7sxp5pN/Fjnw9dHpIFFURh4RsHV8RjdLTG0EqnMWevH5JVkEqlIWRFmOoZweKDZ4PySgakVOKpOUAVcOZzh6CrfeL7NL76+xWjuMg8tGl7Ip3YOKMuSt68s89pBxJMrbRbMFoamstBI6TZ8LB2eWDnms9thTJxL1r2IViPC8TLGYU6YaBzMLI6C48bisulxoW5gG5KTXfjo7TEFBftziaNljAODTjNhL53R9GK+cLRPFdW4d3A8qVG3U2yrRFMqmm5Bw065tDRDlQqeWdKtZXS8BAXJ1cmQg/gQTc156WCKY5REuXrsv5WpNO3s938/1+ycpb7P46t1WmaTV49GqAq8dDDH1CpMvSTMVJpOxjTRUNUSpIpu5OyFY9a7AQstn5997Q1Cxadv2zzVWcGfmSzUU5ZbCX0vxjJKFmsJJ1oRs0RjpRmjS4GrHvPIJIfFeoyplkSpwct3jqfv64YGpYpqxBzM4DeuT3npaMA0Umh5OXGuUbdKlloxTTdC1Qv+7kc2udRp4lQbXN11mMU6qlCppCTOdIahSl6o/Kub90hSj6YbsVQPmUYWm2MH18w4ueQTEyNFRcdR+LaLi3zvwydQCp3zvYqikmhqxvawQZ7a5BVARVEabI5qTHwdTQgMReHGUQ1FyQEQ1XGjeJ7AOCkYBhqOoZEVGmGesz9yONutKJIa33i6z0E0Q8YOGw2TyFdJlTFFlaFh03EkQSGJsgpDL2mYUKQ2f+93dpnmkllxbINwqqvTaVT8P969TqGkSJGjVApNT+GxXh+jsHio12CUZNhCR1gZX7u+TK1VEmYV7z3TRlUkjy07PLmiszM2EUJybyR5cdfnVN2jJ7yvUvX844c/LE71B2oUbW5ucvfuXTY3N39//d9f/19/37179997I18NrNcV1mt1eq5KXhVszSSzPKBuhXQMi8fWTVqWSVFlROXx6ZqfKFzswoWOg2OAqUVM4oJ5WtBzczq2iaWDaSWoCuwclnxua0bTLkhkwN2JxDUq/GARUTbwLMm1Qcr1cchrOy1MFqiKNo+vu5x993/B9ThlkA9YaoVkzNiLDBTZw9O67M9tcjlhtbXNQi3B0gTjWPDqTo8l18PA4Y3xhFwdsTUxaFgFBz586vYiJosouOzMdA4DhVkE42KEoh8gMEmyGoZe8oV7p3A1jzhTWGkekuYq07TgKAqwNEFRQsMqeGRFR9UK7h8cm03ePDC5tulSZoLNcYkfC4qqYNGLoTyWsDV0B6FoeJqNoQkOpnVWmzHLrRlHgY5pzAgzSYXP6e6IVKZ85I1lfvG1Ab+7uQdITE3iWhWy0jE1gatXbLQTLixG9OsFNa+k08nQdMGn74+pUovPby0yCmwMvaRfi3ltP2I/9hkFGh9+fYEjX8cPdSyjxDULTvQCJqGBocEXtvtc7Ec4usTWC8aBhhAFuppzNLM53QqwdWjZOfNIpe8mZIXKwcyi6WakBSy2IkxT0mknZKrgE7cWEFWFa4Z86l7O89cfYjpxGM10dqIBJYJz3YA0V5mEJkjYmzp0vRwqSRAcj2KXhUpRKti65O6Bw9aoIJn2OUp8psGxfM5PVXJZseLU+YYHjriwEOIngv1JQd80aToKh7MFVpYS8kxSShVZKVzbbfPRa6u4VYu6l6HmNX775ohXDmYcjCp+7oVdFhsKZxp1BnOXvZnDPFaOjTQNgWnkmGZFzzP4nkeW+cBDTVZbkoNixsw3eOjUmE694OFll5Zu8OL+lPecXOId51TmOSBKTENy78jjo68vU0qFwdxGxi4PdB3qZsHVwzlRYvDukwu0LZu2KanVC4bTFkvNmLpTQKWxPXWJpcIoTFloGJilh2NW1GqSlUbKWifk86N75LJib2rSqwcoyvF4eykFPS/F0ASy1FhoFGiKxjisISuDhVrJequk6xXMIhdN6LgWrK4ELDcLTvVSDMVg6jdIc4ssc7B0lZe3llh0Ja4hSDIHhIKhCV7czfnz//oGP/XpPXbGGq/va/ztt64TyQyQ/PL129huyO54kSAxeHQ15cJiymKjxFBt/KziaG5xf57xO9fn/LUn+1i1EVIKHl2RPLA8ZbFRUTcaOGZM280Yz3U+vXPEL7++zV/9reu8PphzYzrFEm2WawqSglngcn3H4/m7kqOZwqLh8GTnAkmioKuSJy5IHDNllM4oq4JR4KEoJZ26j61pPLxoc3EpJ5IT9sMEXRWEqY2s4CjQyAuHjtlie1JnN5qy9oE/x2dv7rDouJzuGPzYizf5nesTosSG0iJKNfZnGke+SlJokC+w4unEsmRczMjVGRUVlxdMwmCDrgfXRwnbwYRBHHJhQeXd6x2eOSXxlYo7U5+rhwGqmtG1VbSqg04Lwwt5fRQwK0f4KbiqxT/5/CFrXo0g+8o3iv4v08Wv1PqjBCEEv/Irv8L73/9+nn76aX7913/937n2J/gPx5YfsdERCHTCRCEIBa8fqByFObawUMomjs5x0mQqyKqMrjel5+hstEzKqmDbz5jFKkdjlwutBdquziwv2fMzNicFTUtQ11wcQ2EaV4T58fStZ+ZcWCxp2BCVCZo35sxaRF2ro6oVuTZHUTOe2kjIM4e8lBh6yrn+lPvjGoezOqYuQYn51M02HafCs6FSUoQa0LSgbqocBhpCmdMyJVql881nV/jfvuESAg1NVRnNVSpySqmT5ip+qjKKGsxCg7yUtJ0Mz9SZhDqbE42P7tyn42U0nICT3TktO8fSCsLYoeOlbI8MgtQAdBQh6XgJy82AU/0ZT5w74nawj+TYP3ASGjiKxp1pgBAFk8hE13IsHcL0WILTchLujx0KKZiGBs+c8MirEtvJKHKICpX51ORwv85wbvJjz9/HLxJeOBzQ7UXEmTiuS05GkJrMIoN7hy6/fmNImHhARVwYWFrB5e4CSzWD9bbOxb7NQysBbz09oOklKBU0nQzHKvlTD3aYZwZhYqCqklGos9iI6dZS0kIhznUGc4MzCzNWmyGzuODyqsXI76GrkkFgIYGFRowqYLmWEmQKDTMhyY+TV7NS4fpug3vjGlDQq6WseR1GU4etwxq3d+ocBDZVVWEZOY+sTnj9oEGJAhVMY4P1dsAz5484vTzjY1tDbs3mXB3tsD21eH6nz+Z2k24rRKgxTUty/aBGnhtEMahqiaHAQ2sasYh40/oBlpLjFA0M9bhZFZQKgTjk3NKUjW6AqgrmoYWiZDx+wuMbT/Z57m6FKk2eWVjhrz65wZWhz09/cZOv3eiS5JJZpOOYUPNyXLvk/nzK0aSkbWv8/Xeu89SGRUdd5HyvxzzW+MJmQZJqhKlGVQh0U6JrBQ8szfkLbxEEqUkYWgznDlIq7BT77JUTTrZ9glzlzRsGjpnxFx86wf1Bl3uHdaJUx9JSFA0WWzPs5h1OLAQ03eNo9sHcoigrWlbC/sTlcG6z2I2OOW5oILNFLiw55LnK4dyiUhSeXmmhqwX3j1wW3IRZZGLrBad6AQ8vj5BVRVxUvDGZ8T+94zLvXF1mXuRYZgUI/ERjHBpsDuokhcYsOpavCaWi5aaEqYFlFNSdiFxG/JUP3eQTW0cs9wL6XoyulFzZa1IzMu6OPZJcJcp0GnbOas/nKDiWS84SiyDR6NQKFKXCLxU+tT3FUBUsXWIKC0UpEYrkYKqRZCq399uICtJC4URnhqVnXD4xIygT/p+f2sLPI+4PDe4OGlzZWmIY6oSJxvdcOkHb01AwabkJUZGy2gx5aCnBNWIGgcBW4akTAa6VYWiS3757yNYsRlElaqVzf2Jzf6zRczVO1RssNMfsT1RMVcUSFpNQMpgXtJyS9YZCXBRkhCSJTl03ca2M6wPJtj/GVjUadsE4qpiFCpais9DM+dd37jDPFMZTKClZ61X4sx5RWXBjWKIr0DYcSqmi2QHve6BJJcFSFL77gQU8w8DPCwZphAr81p1d8tRGcwMCcvyk4NYg4c89ssAvXr1Nt5lyZ08yjDI+uzVlpQlLDY0Xj2Z814Nr3JtHvPNEl/WmRldzcE344c/c5Cdf3OH2/ld+UvtPONWXhz/QY544ceIPvP44oeHEOFZM3QlRqgpPV1l0DXTVALWk1ZlSc+ec7k3QMGkbDqam0GsGuFaIrqZYeklZOjRMBdeKiHJo2ilFoWEIlZKKzWnMi1sO13cWqGsuhnDxU5VBOubqIEMVKk2tz6LjMQxVRknKr37yN3GXPBqmSrPhs9z2WfRgZ9Kn75YMU5+tYH48xhys0/MEqlKQFQqG0AhTlTvTBNMZ0TEtzrQV1rs+j65OeOuGj65U7PpEeLfrAAEAAElEQVQRb+xW6JXBPCkYBBphvIBaeUwTmCQxhYQoK3n+uoFr6ijCoJQqC46HqekYesLmqMb+XPL8wSFBVLHUCTjRH/PEyYw1p8a3XWyz1oK9icO/feksiILntyz25xq3hnB/bJLLnHmsUbNLisKi75Ws1sBQDDbakryweXVnAaRKvx5zdxrw/o2LKKLizsBiuT6jrDKuHXhcO3SIi4Juzef88oAgq1CUDIwpf+6xLuf6Y2ZxyZW9Gjf3PZr1kJP9Ce+6uMtqO+Rg6tI0LGqyTduyGc5dFhoFy42EKNGPiZIUdLyIpWaAH2tYasVqJ2Z75rE9c+h6AQ+tjujXIwxVIITCyzttJBVHvoVhyC+ZURa8dWNAWWmkhcJRFNDSXCQKlplzZ6vOwDf53P0uA9+maWUYasU8MPn4q2t03ARFKTGMlCLR0XXJLDEoyVlqF3zgoUV0oWNaGX5i4JmSdj3CLyaYWkGQK2iapF5L+M2bQ5ACG5MFu4FSgZQCXZUs1lOWawlff6mOKgR1V2OYJLzvTJ8v7PoIM+DaeJNreyGmJqDUCQMdqZRkqcrmQQ1R6Oil4CN3Rry2XfGmlR6a9Gg6OQPfpCpsNqchfpZiGgVBllLXPa4NRgggzxU6jZRu/ZgkCFERipRruznDqWQ7mrBs1PmXrxzwzo0OplWQZTq2IxlFOmmuk0kNWSqYasFeHmCjc7JjoeoFbTdDVIKOU3J+IeKBZR9bwN0jj6YTs96Z0XBi/EQhlyW2nYCSoKkFQuQ0nQjbyLD144agpMLUCwytwDBzFhsBeVmSlBWaniFEiqLGzJKKnZlge6azNQNNzZjFJVf3WsiipLIilhcL1usGj69aXJ/O+cgH3sSbVhwajkDTM073Z2zPS2w9wTCm5DKjbpXHKV66wlrdwnESvrAdo4smKMej24dRzCjMAYWalXC6O+epkzltyyYXKaM05J9cuct25ONnJYdzj2ksiDPBibbG6UWdREoOw4xvONPm3373A/zlx1YQisSgzUZTpaSkkBXbR20+9kadl/ZykjJnra7xyKJG3RTM05QgVem5JXGec+1AYxJXnO6onK430Ud3+Mgv/WNe2BuQpCp1XePEcsU3X2pSljpZKSgSG0UpOZgLbhyaxDmc7+psNBwAbo5yhknIMCyxFY+nVm3Uso3jzhhXB3zrxR5T3+DNJyocFnnnuYigCHjtIGaWBzTtgvW6w6NLLsNZF0Wb8MJ2xhvjOdOZ4EL3D0Eu9Z+w9KyqKlRV5Sd+4if4sR/7Mb7zO7+TH/mRH/mTaaKvID52f8S9eY6mp7RaEZWdcrorWXRqKErFrp+wP1cxtBzPSlh0FUylgaElXDksjpsw0kSg0rEc5nnGXlCSaVPS1OBsR0UTJmEhSWWBZWbYespqTbDWqrCsgHES4BoFruKSZzbzVNJr+CzVUxxN0HUkmTwOZQiyjMGsydG8zd5cY3uWEqcGC56JoVUM45SnTx1womGw3MyIi4Q4K6mKFr5fxzMVskLywWtH1HSdSVqQVhkn2wJLr8hkRsMJ0YRNkmtszWAWGWhCIJSKeaCSlpLtsUWcaTRthb2ZhaZHLDRKECqIlDjVcIwC28iZhTVGkcHXnb/P6V5A08m5c9hkFpvoWobnpHhuSRoZxLmKq6eMI4M4UzEUFUs7bjYFiYamlnxmK8TWS7qtlP5SSM2NObk+waqneE5JrZ7gOTkn2j7LzYjrh3XSXKNfS+g4KZoKpqLwt960QVaNkQCy4sZ+h2kEV47mvLAZ8vLBEVDgmDlr7RhFFUwinaKAz2zOWXc8slJhf2qhKvDg0oyiOJZyP3+vy/2hx/WdDrJSePTMkKPyLqLK+cJmlyAxWKjFaEJSVvBvXlvHVGFnWqNmZWS5QgVkmcI8VKmbOUUBu+GMhxYtTrQtVEPF1EpKFO4MPXIJtl6iqRWKtCgKwev7TVaaAQ+uTPAaI5Y8nSRzqBk5d4c1MAv83CDNFXStpKYrvHmpTaJG/N4bITe3emxPJdt+QVoefx91UyUJBf7EwFAr7g1qdN0MmWtcbDq0vQRJwc+/ssuz22O6zYiP3pxxedHhk7dnnGlZjIOSu9OAXBTU7Jz7ey6DscX2gcfVQcSjqzV+684BP/qZ22wfqly5l/LSwYCiEEgEl/oeSzUF24a9qY2lV+zOLD50c4xrprQdla7h8uSKy+MLDdp2ykozZm8843c3R+wFGR+6u03NKtF0iYIkKzVsraDtZMe+hrHOJDTRNXi41WU4t7kzaGDoGYqQhH6LqmyhCjjd6KKmFqU6xXNiHDtjN9kmCkxmkxqfvLGAJkr2py5xrpKUGnWzZOxrfPyOz5UDn1++sQ1CEqdwOHIY+QaaWjIObAa+jSIkpg6zucWdI5eiFJhagWsUpLlGmBcIreIotHjpdo9XttoUUvDaXhspFQwV6lbJ3tQmKQTrPR8/1jFUyTwxub5dx9BKdDPHNAs+dzDioSUVlBDPSag5CXVboiklLafgxsDj/thie9JEUypMTaCbBZ+4MyGWIS2npCwFeaIyDxR6tsFHbk3Z9UOu7mcYKhiqip/p9N2Es4s+h77K7qTGnYM2UsTIqqBp5/z0F3aIq5idQxtTWAxnLtvzglXP5InGJd7UXUaROoZW0mkoCFPBzwqyEhqmSdeFwzEMfYWqdOnaBqUaEuQZDVNwqqPQ70/5joun0PQSR9OwtAqtETINBbqV8nCvTsfSWa6XzPOUcZxhayoaDl2rzlEgmYclQ1+hyG2QNram45cZrx0N8aySvmPjaQLFyJkGMIkL9scqPaPGP3r/Bj//LZf5jZuHDPyKcZrxluUWn9ud8MzCAj/yG2OE5vPYQo8grNCVihOuy7sf+kOYKPoTTvVl4Q/0mB/60IfI8/wPfNPf/u3fJo7jf+9N/cdCVZroQkOIiqQKcJ0ZmlqiKbBUz3A1QcNUsM2EoCgYhjq5VLg/aDAPHag0/CxHioSOm4E0ENIgzWzmacmdeUyzpvLCX3mcrz/j4ugG01RyazJje55SNwyW7BZr/x/2/jTYljWt7wN/+eY8rnnPw5nvueeONY8UUIgSFBI22JoaYRQttSxHy+3uUFiNJUU7bMt2R1u2CFluNFggtywECFESY4GAogpqoOrWne+598x7HtZe88o5832zP+xqRzj6g5FcqADpH7E/7L3XzvXmytj5PPk+/yFy2J8YfOVQ8XNv2RyOIka//stELZ8k1Tid20yWPmnpkdQFSlxQVDo3WwEnsy2ejB2GWcE4q/EtmOc6JQV/6LkxNzs+HU9ntX3OOEswhGIlzFlWBZ4heM+2ganZ1E1FXCiq2kCScmv9Mbvthka5WKbi3TdKzmZd6qZklBYEdklWF7x+YmBgEbqK7VbD7a0GoUEvKFEIXh7PuL98wvWVMR+48ZibqzG31ub8h+/fQzUOgxA+ci1mo51zdVCwKDROFxqnC4NxVjJKdO4NTQy9YLdb8903VzCxCOyG/+GlE5aZTWTpZJVLXblojUVROczTgJVAskxduo6Nb0NkNfzE3SMej1pc7Rc4us5mu8YUgrNZRF477HZKVqKSbvec43zJgwtolOB8bnGjN2Wru8A2c3puxnvWLtjtpHzsxhjbBM9sOJ5dTkYXhcl2K8M1Gt63M6YfFEROwSQz0TTY7sRc6cc8uzXj5ZMOtn4pG5NK47cuLnAcSZLreFFJWug0XDayZaNTVYL3bLp85KaDVKBrGu/bsOj3U3peTNet8LyC0zjlv//iMetOSLJwsQVstOaUtcE4sakag1kiEChMYdJpV7RMhecqTCPHcxo8w6DjFFhWReAW3B2l1FLnNJ7zXK/Np96+wLQVz2yM+cStE773XRUt1WGZa7RsH8uu0SzF2qCkrE22BpLhMmGtbfC5gyHvvXYIjSLJXe70Qz5/tORJOiMvDX767pRfeGeMY0sqpTD1Bs+qqJXGaO5hag27fkhjL3nfTcWNnuCgHCKCC740fMB2L6EXpiRlyiLTqZUCBS1PcWc15vrGhKU25u1DSSnhZrTO/WGbXBqMY58rwQbPt3dJUo955pLkNqbWXG7A5A5rfkpZG+xPIh4NA+6ehjwc+hwuXHZ7c3StZLI0eXptzPnE4tpgTuiW3F5bsBLk9IL6MjUirAlsnc1OzdMrFe/eTLjaq3AMwSxxaQc1D4cZh+cVv3J3yY++9pjv/Imv8Ieub5IWJqZR0XHh49fnVLWDSRsQ9Pya0EuQTc440VlfPeJnHpyz1srpt3JWfMUy2eRoFpIVJqfzkKyZIvQU366pRYIQNV3H4v07Eteq6LiKtie5t1cTy4zIaVjr1DhexS8/GfLf/OY+/8lnHvBgEvPgwiCwGrpuSSUVjqX40BX4E+8d0RhT9pYxs9TANUxq6VHWGrKBzchgParZm1ecJTGSkhfe8zwbi/vc7vvcH1ecpzF1rfHLd2OeDF3OZj4XsUZaGASmwVo7J6klpplQqhrX8Hh+1WY7UizLgreGinlxOdHbDCLWvJC/89YbpPqEtJkxL0qSPGTD69DTu4wWLvNqwUmSUciclg2rXofbAwfZGBzmNVJLvsEV7fcu/uyf/bN8+tOf5od+6If4/u///m/0cn7fYL7Q+fbNFWazgFD4XO045EryZJaR1hJTc+n7koEnCG2TTNa8cS55+8zDEz554WNqBk1jcpoUfPlkjqwFkR7Rdg1MMyepKkrZUFSKli3oODZKk2jmBQ8vbNqOi6pDDEMymumYTX3JrA0VW+0aw8wZpjl5ZbCIVxklHkmd0vZjbN0nyUMQFfNccWs14XzRRgjFstColMZi6TOaeVwbeFi6zj+8e8y7V0IEJZu9GNvQqepLX77QLmiUjVSKupEEpkAXFrNMZ7jUeWazRNcEi1yx2VmiNzppqTOMrcv7fqGx1clZiSS2WXD3xCNObQxN4+55RFEL4lyw3qrY6GQ8tznCNBt0q8T2JLYBr550OBz7dL2ctLqU7222M9AaQqdAEzGRW9Lzaz585YKP3TonLY1L1o9VE6cGni85mAa8tte/ZIpVJvNMAzRqqdOJFD/yxiFJ1SClhmw04gJeOV3gaoL33q7pt3POlg53T1poQNOAbdWkmcWzKz5FpRiOfRaZRT+4rLsNgn6Q0XFKyvkGL0TXiScrjIbriGKDGxsXrLczRrFFVussSoutTsw4tvjZN7dQheDptTmRV+GZNSv9gpWoxLMupf3z1GBvuWRSLfHcnFoZbLRTpBK89GTAydTjZOYy8DVQJmVt8uW9VQK7glrj0bDBadbYO45IEgMa2PUDnmoPmC19eoHNeTnjmXBA5JVYbooyUqIg5Yt7fSzR4YvDEZg1ptXg6ApoOJgGWI7kwTRhnJgUtYFh5rw2XGJqBsJW/NO7Yx6k+/yDN49YdQIez1JaYY5hKD5yPWI78vnvvvMmVu3h2pfXadWOaHmCja0LilqjKA36rZyTuUTWJS9smjy9YjCJLfJSMFza1ErQmEsao+CNo5xhecwfvHXELLMozZTIK2m7Nd+yPeB8bLLWyYj8Cte8ZLTNcoOLpUvo1MSFTpyZlNRstBLyEkppE3mSSVXyzjkcDQMu4oLM3aMbVgRuTdMItt0t5tMu71tvs9GSLEsToUuS0mSWusSlccnU9pd89vCcwKlQtUZZ6JiWwtChlBodP6fnF9iWJMkFjq1hWJLQlZRSMEksOkHFaufSrPnbdns0QifLDOLcpJKCuDBBE1zEl4Nc1YAhFEWt8+i0x7sGa0SGzzzVKSqdjcgga5b87Cslpi7J6ssN27V2Td1ouFbJU+tj1ls5PVd+zbD+0utSuBVtV7HeLQj8mGtrS1xHcKVrYVk5SWLS8lMMIbCEYJJV1Ao6jk4vKMlqia4JLuYtzhb2pfm8Iymry8/mYhTQCSsakfDaaMyTRcEXR2e8fVihFNiWxnY35WRuUMmGwFa0XUl/kLIodJQ0oNFZ8QXdoCKtSxyrxBEuTxZzaqVoiNGURddxCDopw0TS78a4usuHr8wZztsoDGZ5hSLnc/tj/uYn7xAMSn7q0QknM4FoNHxTw9Uc6iqg1mKOFjmB27DhWbRbFZ96bcrTqxEvH0jWI4uzLOP9G33KzOY/eM82v/4g5uE05faKy0rXII4jPnNyRtE0LHPB/VHOt19vf2OL6O9RfD17qt/WRtH3fM/3MJvNftsH/eN//I9zenr6L7umf2W4P8lJmwsso6RjO0TG5Q5pUZvEcs5JmnBlMGaRBRiNg2sYyNrgrXON39gzOI1N4izE1S3q2iWrBS+fSI7iSynFrYHBmmvwM3fHvD6a8/p5Si4zNnyXLb8FqkVSlZylc1zvnNurOd92XcfULIo0Re93MLwcWVsIXVKqhCudhDgbYNDhIrF5bVhxsFzi6tCyAjQuJyfPrMVUlU/gpBwt58zyAl9vYQqXd4YBeeWwHmnoRo2yhggrZeAJTKFh6TBwAlA+989NfuOhzXBuk1aSZ9YOudWDovKRyuSFddA1nb2xRlI0BG7K3WHDydRH6JLQk/Q9G9/S2GlJbq7m6EbOK8MOkROz2Tpjo7VgJZiy3c6QSudkNuC1w1V+5qvP8vrhKqGlMc88vvvq08wKxZ2tEXUjyZuceaxx79zi1aOQk6XOVm9GrWpso+RsYTHOLD66FSE0yWY35drqkq5fcDzz2GilmIai5WZ8cHfI42HA6cLBNgs+dO0Uy8x4bmuMb+cgFGmlY+oNe9OAw1nA/qLDuwYT1oIcy1A4ZkGtmkvTPCH54t4KD8YtLhKH86VD282w9Qat0RguPJRSWIbk/dtjxrHD43GIwMSzYKVbcaNzaQyoCXANiVQavlkReBWrnZKOq7PMDVphSZIEaGj0W5eSxzg2qQodz82xhYkpTM6XJm+dRiSpjVQGR1MbUwdZufh2jatLdEzOk5pfv1/x5z+4xQe3WhwvLEZLg7OlhW3V5JWGoTe8fLbkM0/GKK1io51SKsFLh20aS5JXJrdWTBoFT/U8vv/pa1RS8dJjjTuDHjU5YfQaWaWz0414fiXgQh6i65JZprjTj0Dq9FoJUuoIIdjppXSCkuHcQTPAsQVtT2e9kzMwVvjc/oyWU1HUDbqoL2nIvuTFnSlCg+HSxrcKQDItbYrKZJlDYc0oKo2vDk8pm5oHQ4cHpy53oiv8+Y9sshoJaqnjWoqsMiilSdeXoFlsuQOKRR9NuhSZS9PorPkNa4HCMgS6cpjHIb4LcRpAY+AaGlvtAttQzDMD1SimmYZt5LxxamOKnEVR0PYTdtfG9Nwc127Y3Mh5/mqObzc4ms4/e2tGsWxR1Ip75wGZLKibjP2xQ9sfY2gNw9kKntmw3Wk4m6wQlxWyqRjnBcsyZ5oYnCwE81zQ9Uwu5htklUFg6+x0KhI1Y7JsmCUOV1cmmHqJpnw+/JxEiJpS1kip0/Jj3rMe8GAxoW+53Oo7SBRx0WAJndBLEdaYpM5J8hZtBlRFyN6s5mQOrtGw1YlptJJSVmiNxbrbxjd0DpcpT5Yx7/m+72PD97jR9vAMm/Ok4p3zDMso6XoK34PhzGOW6dQ1lEmLs9M2o6Mu37p5jUrVmLri6RWD926V2GaNsBbEVUVaSWxDYyeMMFTAdn/G20PB0VwjKS83zNfcgI1AJy0EjpMyzxsKqahVQ8s2aTm/A+Ol38eMot3d3f+VweK3fMu38KUvfYmjo6Nv4Kp+f+FK3+KN85TJUud2p8fJrEEXGS9uKPquiW1cmvfPCmi0GSdzwW7L4GpXo+XWnE5Nnpy6/JnnrlHlgvXIoOeZjBeQyZy81skq8ExBz7Mpa53fOhTksuTR6SZt3UUqgaJCo2QwmLKzkrERGgz8AsMco5HhiYAsixill387XAZUtUPLbmhoaAUJrp1jCpOycohLjUfThjgzcQwby5R88kafeT3lW7f6/OyDc8L+kP2jEEO5NGgsq5K8NrH1y4fLeVFg6Q3L3OXRyKdSDYamCJyCj9445/m1hEKBrlfcHpTcWlmyM5hQK43QlpS1wWzh4DmSs4XFIg0x9MthTNfL2W4lxJXFWrtksbDo+hl1rbEaFMhG56uHXfanHrouqaTOM+uXhv+a1mDoitHSJqsNXF1i6YLR0qXhkuX7nt42PdWlLE1UA+thjm9XWHqDjk3HclhtT9E0Dc+SzFKDjW6K5zSksuI4vmQeuUbFSisnr0y6fs5OJ8EyFV8dzvmWKz000eDbkrISJKVJQ8P54nIdwyzjU/cuGOYVM5ny5mTG41HIaitnvZ3zkWsXGDR89cEqWWpya2VO4BWYGmxFS1ZaBbqmGCUOSWEyjW36Yc611QXz1EA10A9zbq3M2eikuJbkVsdlvVUylwnrgcUfuhXw+CxinlqUSuOlkyVfeJzieBKhN7TDnM+fnPGr93KOhgFu47OoU3otnes9j4EdshrkLDODSuks6hm73YTtlRxZGcS5wXYnZZZapLlON0z55ltDPLvCMHPeuwPLssE1FK9enHOenbMRGnziZgtETlJonE887l5MKfKGn3rwmA/ftLl3mhCaJrf7Ho8XMW0/xzElgVvRtgISlfLx544Yl2OeTHJkI5gsbTRNY72VMQgK2rrPnQ2dD+xeYOkNu+0lO50CpRyOJxZ//8sjLL9gnhmshjmLzMQQDVLpbPdSFBpb7Qxbq7k/GZPWDjdWU3y75GDskRUCWWm0Wzl3F6fsDQNOZg663nB1sOTWisUL10vO44wX+ju0vJqdTszFwmKameSlgWNJFkub18dDTmeX6w/DgvVuwjMbCzbbOb5dYuowil0OLgLev9bFt0rWowzHUKy2EjyrAU3RDgvuzvaxnJwXro3Z7CQMworIKTmY+HS8nKzS6XglZW2jGh2ZuvyR21v8l99+jXThoxcm625Akls0bsGTswhN6cyWLsfHAfNUUCuNqrJouzWRW2KZJbU0+INXV0CBqdeETsW1/pzVdoGrwa8/nOK6CVd7Ls8NuvhmwVYnR9caGk3gmZfngCjxLImrawwXinECt1Yy+hHEZoxl5ESWwMLhOC25P5siNJM/8q4VRucWlbrs99+7k9JrjcjqnCR1udZVDOcGf/SpXWqpoymLnl1yfy8CanQh+cX7C2TtIDGZlRlxCluRiVkEvDE+ReonjFOTwBT8gZ1NstQlKQ1+63TM8bTkE9d7fNsNk7/4gVuM51Aj8d2G3W7DJ65s0XI0ZmrJwYXE0GA1Mvi+5wZEpsF/9Mtv81c+9xYf3uxg2w0H+QV3F1N+8nvexc++M2Y/Szmc1sha0AsMdjrwvbcHnM1+l4eE/C7C71RPZfx2XtQ0DX/qT/0pbNv+bR00z/P/XYv6V4Xtls6q00LTQNdyzrKErK44iduYBDw3aEjSHstcMC9zRmXGq4/WCC2d3VBgKA3LKjlNEuZFTaNs1kKB3vgYAqYpfO5+wi88mtIJFM+tWWy3NZJCZ1nmnOYVLSdl4HpEtsfU0lhvnzNJfd79F/4SaWYjpE1JxSS1CG0NpRS51NmfV3z6ns966PHciiAwaw7ngsOpwW5vTs+XHM8lobegrgeMZl36YcGDcc7RTNB2StqeQtNjOm6Fn3gkLiR1hVD1pRG0SJhX8NHrgiI12IgKBBGWYfDVA4tHk4Y7K5dpYs/tDCnSPu/aHvHsRsWD4QoHE4tF0rDINWa5oG5a7LYb7p5G/JEXK85jl2Xh0vfhZrTkzTrknXODWtZkpUEhCh6eBnz18TofvCH57q0+k/yI41mXp9ZzNiLFmw9D0kXIzKlw/Zh5YqChUUiTrDaRquJnXl0QdQy+krW4s5YyTgVX+1NeerJCURp801Mz+lHKSpizN/a5mPksjte5MkiZxA6b7ZyLGN466/G+nSmzzCSVJrXSaNk5DxYRlVSME49r/QXj1OVw2sIQDXFu8+6n9jiaWTweRXiWYiUqOJkEaDRc787puhXPrsw4mAY83W3z5klOkqecZEt0zSRyMiaZhY5ivLRptJrP76fc8Hw+cmPGvfOAN8czLGFyr+yw3ipQCq6tV3TMKSfTiN2ew9vHkltrC4o6xzUVcWHQSJO27pAUOT3dZMdv82BxRCes+Yu/9oir/TnXbKiljmoaatWw1VtwMo5Yc20avUS3zrkaTvilB7sMk5Q/966n+bduavzDtw7YGqSMEp2fejsjDJa06y4/8OIaP/z6a1x3Sn7pnW1kU/AdNyw+dwCBMAnclIU6Y2dFUNSCotRYa6fUUmOcOhh6TV7pxJlDq1PyfG+TX358gi40aqkxnHms70yYpg6upS6lZobiqdUlXT8nkBVf2fdwrQr3a1HC53OH86WLa5ZUhU+WaBzMcv7nN045WxSsdEpmuUBrFHbd0O+lnCwdfvVexp2tBZO5jxINtpVztjA4XgRYukRScRDrmFrFl560eXZnRKUM3jj1+di1OR2v4HzZ5Vo/JS1tum7NK8frDBcGninQtIKNQc5Ley6mWfB4KLg2KHnX9RG/dZhRNCGPT9t0bYtFYuK6EscwOZh0uLOaIWXDcZHz/u2UNw/W0c2KZeay6glOZiaTZcBmtKRkiWOaWEbD0aLCNQ06js03X1/ypXuKvLCYJy5vDxWBpuE5gtVIY5nWBKaksW3+ws8e4HfHOMUahfRB1JwuPa5ZGaueTd2Dzz2uGaaw6mSsuSam6TLLdOKiYLMFw9jht/Y8LNGwEkp2Oj6WCHiyVLwynvHqz3yKwTOfpCMCbvQr5oECI2deFwhabHdrGk2xTGwuZiZ1rVgJMr58YPH2OGKlldLxCqDBqWycxkUpQC8xREOpIK01NgPoOiYrQcmTyuB8YSMpMfWKuGyIHIFtCOxAshZVhF7Bk+Ovf436eurgf7fp6Z88efL/97MbN27wyiuvcH7+O5B08q8hPn84o9+J+HMfWuVTb8/4t57x2EstdD3jeNGw261AqxnPXDyjRWT6tL054wwWhUWtVbiB5CfeGLLW0oGSxxc2Aw92VhOWictqy8AwJNMi5WDuY2ku57MBrqGxWBgYToamF4xTg6dXFbXSWFQ598YC32iRVy6Gpljm0LEUw6XJTien7zfEpUatLVnGJtd6FkVlsDdtuDZYcLUdkWQOvfaCrK75rz7fYCgPHTivR4yXXTYGkrKERQEvrhSYQkfTFKZRsSos9k66WKZNXs35wErJq/OA7WjMo7HPrYGk7ZaMM1gNS4raYbKwqJXJMhfUDVxdrVlmFoaVscgNannp3xPYkkpB3025mLdZ6adMM43AkuyNPNLCB01xa21MWblIdSlFX48KjmaCyK0oap3HFyFF6fPJq7f48y86/KXP3qNuNOZ1yX/4TRt8+v6Uz+wPyapLWZGpQ1xmzNMap3T59tsHXJwE3O5MqDGZLV00Q9CuO8zjGqddXMrEohTZQFnpOFbDZK7x/3ntlCtrC5LCYm8cMEpsLEOii0t2qO+mZMpE1RpZavHijSGbrYLz2GYQ5cwzm81uimtLZrHF3ZM2w7lHIxr6Qc5KKycpXHTR8HAYcmdzxiS26fglV/pLytqkLDXGSxvPqtnupsgmo2MoNsKEg/0Aszwl9Lt8/vEqgVVybW3O4dgnzQ0sSxI4NUdnLtcin4N5xkunS262Qgx0xkXFJCmReYhnlzw8D4hzHdeqmc4jvve5HtfWHT79+IBA7+DpNW+fZ/h2zTTWMQ3F60clvSjHtmxWw4yHF22yOCetD4gLk45dE9oxN4MVprng4SGMmXIt8rHKhuP8DN+UXCxc8ixAKw3OZMLTfs08NXEtSTgoOZ87vGu9xf3ZiN1+TF4I7i4vKLOSF4TCLs753MP3c7u7yx94ts9//iuP0G14uutxXkw4mnk4ZoVhwCK/lB2dTHxiT6fbKhktXHTpMF0I4kwRuQVJFhIFDTvdhHdOOrS8gn50yf6ZLG1++DfmhJpFIWJ+8d4M03WplUYvqEhLncCpKWuN7mbB3YMWVaWju7DZSWi49Acy6oZK6lTSYn/kstGbcz8uKCuDNw5aXF1NeHQeoZRJVjaMY0hKC9dqmMxXMKwlfb9AFzWrUUVSaqxHBU0Dji5pKviB9w74e68d8ief2+Bm3+TWOnzpaM5Of8oo0Qk0yclMkFcaldSoK52qNpllPgN/jiE03jzqYKDzehKjGSV5pdH2L/2ifLMhCAtu9pcYusd4KflsuuDD1yV5abMoTe6NMhyhs9uRaNR0HBgvPK6tXP7f6bpkvVVSNwYb/QWO0LmzJfHHFotcQ1Maj5Mh//437fCP7z8kLy2msYfn6Pi25GhSUyURN1dLfuiLBygjwRybXOm1WOkrBq0pj4YKTRfoStA0OrfXE+4f6SR5TRgJhOzywsZjXjlxKFRBXum8uB7w2VlF5BT815/Zx2mVfNe1Nf7Rm0P0ysOxp3zvrVV+ee8xVrOOVvu8fy3kbl5zMFsQBYqOZ3Cn5zHMG3q9ksCrGNUZXzxdkpc2D8cpPc/gI2HN62eCo3OdKJKYRc7OjsGnfwdSz75efdW/Lj3Vb2uj6Ad+4Af+hQ76fd/3fURR9C+1oH+VsA3IZIlsSpZVxXARsh12uNGyUTQczmruDo1LH5HE47m1gBc2LPLUoGhS3roouEh0Pnkn4WbXZxR77F345FrK/kyx/yRkZbViVwtptWs8K2NRxdwfG1im5FoUcK1tY+kV06zkywc+7XHA3Z/4FLsvvMBy/QPIUkM4grOpz86VKYUseePcJM0Drrc9Ol5D5My5yDLOYhNDa9N1DAoJG+2UWdqizEPuzRuOkwV5ZfK+rQbfqrlINLpeg2hsXKvk3kKRScG7Nww8q8QQGh+7JtHQEf6IB9MKTQ+gkbhOzHNrPkralNKgqSN2+wmToqKqJZZZMM0MLM2maSyu9BZYwuRw5vH0+gTbqLneHfLm0ONkEeFal947a1HOg/Mu61GFQsPSMyax4u2TiNc2Eh6eV+wvXFZaNS899HHNEr+bECeXGzzvuTXH0Cuq2mS1VTJJAowWdNoVQms4mbVJKxjHIbfXUyKn4Mm4h21Z3OnPMRvBfBrx8e0d7iaH3No64fWTNgfjy7j6148ivvnGkF98ZwNdhGxGCbZRIxvByUWAUoKbg4TSq3jzuMON1RlnqcfxxKfjVvTCGpqGRVLw2pMuy9Lg1iBlJch4enXKK8cNV7dydNHwYOjx/EobzRwxzzyK2kKWBo5bsdFdcu/c4Ls2t3h+e5+q0fCbDk+WMVvuGqIrEaWN6c84W8bYhk2tQd8pOSTAMTKy2mQ898jLgjU/4OFFyaQ6YauXM156bPouH9h6wt5hQFFaKK3hYtlQ6Rq1bPjT797kPCl4eXbOFw5WeDJusRrpHCcZUgrOsyW31hIOdUmaemy0KraciM89mRE6Gb/+YINlbPOffPMWP/XgHW60+hxWiquDOSvhkrvnl4btvlPQdQwOxjY10A4q+kHFo7OQa+2AH/nSlF4bKulgGDWmMIgLC1OvSEqDjidou/JrOneLgZ/R8QuyUmO49NDjy9cLqWOZPjeigKOq4sffPMbyciIfZrGDKTQ8uwBNMUksssKi3644mrk0tYEhFbudhELadL2ctluQpz7DWcjm5oy0NJnHIY6pY2gFaekgNA1DaDQKyrrBsgqySiOXkjj1OZ26VLWJKaCRJh+8tsDRBVd7FY9ONMpakJuSi7FJZTWEnRlP9WoCp2KcOKxG0Gt0IrtmEEpeP9TZDA2ORzpOsKTMwDZ9ttoFu52EYRxi6waRf0pcdBinAVFrQZZFzOIunoAfeH6Hf3J/j45XsDe22TVjAifiO551eOm0w/e/d5V/9MYpShPkVcNv7dt807UFLSfkhXWXskkYp3MsHDRps9ku0LSKRkh82+CpgeDw2OagmTHP4bnNit22h76zxat/70fINz7Bk7rmPTdqOrbDMmuRU1I2krlcUhU+66FGkmms95e4ds2v7V0+3IS5Q9fNKSToAhY5ZFVFy1PYWGSFYCUosXSNSimWMmWSCQa+hafr6EKw1S45W+rMsgxNr9GVy4u+x799s8fPfaOL2u9hPH78mCzLePrpp3/P+R3+bsW7twtSreHRJGc1MEm1cx6MS671K9ajBoFGyzbIfZuHF4JRWoAuCCzF/syh7+lojeDJPGU0bLj2tGKrq1hv5Zi6TWkKSpYczQSeEVDnLQytoapqkgwMK+XxhcULmwpdGCyKAteeQ+Oj42CIhlpeJplhZpR1xVZkshpWZLLg1eOI7bbOWqCRlg2KnKwMGC87JLmDb1egSc4Wl5sJSJNf3Z+gDMVqqyAwNbQwZ5iUXCw9Nls1o6XOK6c+oW4SqhDbVrz/6gi/MoDmcqBjSs4WJrZRMfBM8tpiWcIkNfAMg7wSjFKFZ0Dbq4lLDdtoaFC4ZoFsPA6nNq+dOFDZrHUyispBNIJCKgZhhmNc+p5stmLOkobhMmKZm4R2iZQGz6xP+c13dhkvDV53Ur50NqTjp6y0Kh7MCv7uV3L++h+6zbwZkxQGJ0sbU1NkpY5nN6xFCbPUYi3ImFgaB1OfwKg5m7gcW1MkguODDjc3EiaJTeTmvLXfZZIbCCS3NhecLRxsQ+FYNfPEIi0u01zbfs1gUDKaCj60u8Htgcffe1VxPM95/+6Qs4WLZ5VIKVDKQTYaWW0ghGK1nXE4DUgrnSQ3UJWBZkgenrV49+6IrXaKBnz5kYdrKWwLphObaWoTeRWzVGMWG4Rc+gdFdk2Zt5lnGb4d45k1sdAxbYVv1Wy1LTZDGztIOctqHi8b1qKKD+34fPrtGXlh8Oy1GffOWpTSQCvhI9tdXj2LeXMCV1Y1xqLkjROF2Rh07YivThz8VsFW5BPLEte6TAdbljbKltwdpkSOjhQNK2HFYTIiVKtklNho7I8LGq3hlROTUaq46vSpiBm4HrU945nVBb912sW2ayK7Yi9vs7JiojlLXL1mWrlc7cfsdBOOZiH//OzdDNw2m77L//SVc27uLjkauTycZlTKwbYkaSm4NoixTcVbe11UBULTiEuDrBRM5mBYBlv9BEtXuE7Nu9d1Xj8MWQtTntsZoZTgi/dXiVOLsJVh6ikt6zJ1DxqKUie0aoraQErFdi+hqASG3mBYBW234DzxQG8oSgPV6KyECYcTA03TKGqdODEInYqk1IkLm+e3JsSFw6ORxywx8SxJ27b59p0N/qfXjxnOPV7cGWLrDVJdMndO5g6OLWm3Un7ynSMqpfGbBxMCUzA5m7NIAmzH4Eo3w7NK7l10uLW2oL/tYDPg3mxELhseXwwoE4PTpcbWIMMwF4RUoAlmqcNu75TjaZvAgLRwWBQ6nXaCJhSj2GEllNBo2HqFoVn0/IqTucb9wy7PrNkc5Bm1lpOWNVK5fPjalLeGgrWgRimPjagmlRVV4THPDLpBg+9KZmlOXhvUmUbgJnT9kuNFg2w0rq4V7F8EpFXC0bxhEJXM41U8q+BoltJzdAzd5XzUwfcLjocBYZhRa/CFR7uMFzYHZzp//oWQXx2d8/wg4qtngn6vYhLb/INXxhi64oVeh1bUYifyOJw6/OhoSJLCMst4ftNHjF0eZAu+9x+9xSfvdDh95KDUgp+7N+b5NYuLvCIran769SlvjWMsdG4MYO4KPD+mUg3/4LUTzueLb2wR/T2Mr1dP9dvaKPrRH/3Rf+k3+N2M+0OP65bOig8on8ywKVVFRcrLhwGOVfIX3n+dT907Jnd0KlnS6DEHqc44a7jitfjQjZIVr32ZqiFmvHZosbux4NlBm48NBrxxsWA51ziPM1SZsB5Jbvd8TN3AtS4lJrmsGSUGO50az3D4woO3uP5d/wfmKfSijDJzqWqFMBbMcotvuZLx1cMuRQGzMmFNnNHxXPSmTVnBKDGxrZrhvI2FzXJpU5gT3tVrWGQB2+0JvjPjaBngag6eVWGoAt/yGJguu+05mpaz3irYqwfM84qnB+dUVYtGuqy35ozigEli0WgFpj3hWl8R2oquP+dotsaktmlbGsJuOJ1ZPJyEfHA3Z7c3ZG9qMUlsFDpp2YJG583TTbYjycFUxzELukHMMNYYx5eJDc+uOfy1zx7RbSuubxYI2eXvfvc1/oevPkaaBW2vpJYF3UBxPNcxTJ1R0pBUkl53waC15K2jLh+8dsFn3ukjlM7Ch1lqUdaKd05COnbCa8MIvan41ONHoGsYlsGy1CjqhvfunnPvrMUvvL2G/rVY21+5t87VQcxTvSXns4CyMihqjeHSIqkEy9xg6Zq8sD3n/nlEXjU0TYNl1dzYqLiYeDxsDN61OeMitnhmbcaNQcbe5HIq9ub5gtWWg2NJAlWyKC8p0tPEph1mvHrksNKzANjsKqZ1xeN5iWlPmRc642GLd11d8pXH0O/kvHMREdolGg2Wrhi0Mk4qje0g5KlWyBdO5uRFhZKCcZ3iuJfJb+uBxuFC5zBJGYQGoWtyb5rwiRsd9ks4Gwt2OwHDtOLHXj9mlku++06fs/ICU2+g9FlrthBKZ57nzEuDSO8RhQl/86sP6Pom/6cXd/j5d6Y8SF1KVXNy4VJLjW5bQrbB0y2Tl4YXBE5J16kYmTX//MkJH77W4a1xzlp7QVw4rIUaWX0ZWV9UOuNYZ5qabHYhsCRZYTOOXeaZSSU1XKsmdApOMoMrjkvdVPzXn9zhb758n8Nzj+21hvMkx7cVA7/mqplwLw+YJB6WeZngYXsVpmiwDdhoLZlnJnsTjyjI+dDGBguxx4NJi1naYBkS142xzZykVMwzQaUEhi4whMV6WNM0FrGugZCsRiWD1pj7Z6uEYYFSOv/01Q1O53Crk5MWBp3NkpbnsWRCL6hBy/knL4V8y1MZLT/j0cji9kYCuseNrs+LKxbDOuP1kwShC76y12YlOmeSdFD1ZbpOz6tYCRTvnNvsLRLWfQcdm//+y8f0ghLXEHxgpyYdW5zFM/7w9i0Okyn/8LVjdKO6TBXRPdrtmAdTidAW1I2D3oQ81Y9BpLxzKgichkEgmWQaDSWOnbDR1/EDm5qUl44avunGPo17Fc91eW7LYJQrXHfK/aGLyNqs9wxMXTAvOthWxpNFwSwRDNomtit4126OouLRhQNGQmCZuLrH5kpBUjXEJYySikcXgm++FaPh8dx6ydkyInJ0LtISzZozSzxeWIfQ8Oh3JWfLhrM04zf3G/7yd9/hz3y9i9TXk978u2T6VVUVf/Wv/lVefvllPvjBD/KDP/iD/Mk/+Sf5yZ/8SQCeeuopfuEXfoErV658Yxf6+wDz3KIySz5/MiWea7wxarjatclzl81OTF43IEoWdcyTsx5roU1Tm2RKUdYWjpVyNNP5rz5+jb/78kNMYaBrBp6Tc7LQWBQ5Zu6w29VYZg6uYXMWZySZTbJs2Fgz2fJNRktBKgs2OzXjSY/IUfhmQa0EBzONeSYh93G7Lra5ZJhUCASbEdS1RSkuN27yWkelJqWrUVYGwog5HWtsReBaFcO4opI1H9wpyUqHZdlQ1hVXnB0+tGHxT94+5ywGXVmUpsbxoiCbZ3zPhoEsDbZ7Uy5SDUuEnCwDsjpju1VQ1D55CRexSc8TGBpETkNV67zvyilFXVOqAN+SaKLgaBLxcNiiqhraToVtlSSFw8CXrDo5pq7QhY5j1JwtfF4+8HnPzhRNSA7GPtf6BUWt02nl/LvP7PLzb43ZCSLK0mIiTXp+SdaM+NP/+B4zKv7g7ZieldMIh1pp1I1OqRqGiUde1LyZROSFRd3UtFyd923qPF6MWG8Lttsxk8QiKWxSpSMTA69dMkssnt6Yk+UmD44j0sxga21Bg2B/GND1atZ8k6JuOJhn2HbDYm7x9mmbjVZCWhocTX0eXwS0nJK8FHSikl5YoGg4m/vooqEVxUxSj8nC4bP31nhxe4pr1TSaIMt15pnJZidBAcvUIMssArtk1U3Zn7UoCh1Dz+m2csYLG9nAzRWTw7jkZB7w7VshhqnjFgE5C6Rm8WCS88076/yzZk4vLJANDMKU4cLDsyVxOecsgztui1cOeoR+TCfQaNsOX3risB05nNUSxJIXw11eujikrDUqCUmpcatv851X19iLY756NmY4Ngn7DYtSAYLVdkZWa4wWBjuhw72LgsARFPaYWyszSqlh6JLzpcc0qzGNit88GrLeMWg5Bll1KQPcG4e8cxqhpGDdcumbNYU5xiw1lNIoSgvfL0gKg51uQi11fKui3844HgZstWNOFx43+y6Hs4qaElMohGhwjIrPvm2ysVqxuxpj62A7JdfXljRaQ8cvOZ75rLdSKgl3j/pc7Vi8dWoTujWTwmTQSvHNmrZf0QtT7LIh0EvS2rxMAx755IWOaZWs2jmnc4db63NodJaljm3C0TzAMSRxZqAaDdMQNCLmH987wLMrQkdxkVisRxJDrwhsCUKhaw2qabiz1nD/xCEMR7Tcy/Cc0ATLgH6QMc98rqwtMXWDcV7xeDTE0CxczeP/9pFtfvT1Byh3QceTbESSJxOLjp9Q1RZf2VvlRr+gWOi0/JInE4PNqOFqP+HnXu5zc6NitV0glU3ZlFiaTWQbPCpKXjloaHUFedFipGJ8u+TheYtn1macLQRxrigqDyF1RkOdRSj58ddGUPeo6gVvnzV89FqJbViXoSXtCUK30LBYa1d817Ub/Mzjh9jWkrxwiSyT1cDgjcc23V5Gy7DpOgZav2CeNzgaDGPQRcN33G7z1750gFMU/JmPdvnqWYxtakQuRL7CdUr+zx/qkZQNP//wnO+4vsrNjsffeXWPZKrzi/cnbA8Ut7oRtZYwkQc891Sbugn4+VczdlZKOr7Jmi95ko/5wzcH/PTDY5JFiSobokAncCUfXov4+deLr39h/Hr1Vf+a9FS/S07zG4Ok1EhkSiJrpplB20vRaMhLhxXP4OleSF43jFKYFxVnccn51GTdt3l2EDKIDGoKjhaS4yQjsjKuDyyuRF1eXBtwVhTsL0p+6HuuUMY2u2HAlVaAbUoKleHYE3Q9YZm02Qo9en5NIyp2PvZxas0gdOFW32WeNWSZjpI2O5EkLjxylVNoQyw9oyo3MZoeXa/gxvoBtn3BPLEoKo86j/jOO208w+FoskrLzVjKMcOkoGU5tByDjVb2tYc6G1uX1Ey4e9FgYuGZlxP2Vc9iGkcscsXBIuF8aXE+txjHOm0rxLdMXFvxeNwjr3SeTAS5lmBaJcOJSZ6F5KWP1lj0HItCGXzpIGDgJ2hahiIhLmGSmqxGkvWoxjNsqC3y2CFtYlbWpyi9ohPOuMgW/B9/7m2mecqTkUtVCfpBRceVPL2aEpg6SIudjsQzNWapx3ObCUlpcqWfs94r6QcFjiVZiwqELvmFuxtklY7h1lxfW9D2cu4et7jZX/L89pxVv+LZjSWapuNaGtPEIyltJplLKXW6Qc7Z3Obx2ON6f0Ho5Eilc72b4JgNu72UvQuXSWyw0Y6ZJDaGCtk/C/nNx1000TBOXc5jm0oJbq0vkI2irAXLXGc0dQiCgo5X0fMzdtopuntG6BYUlcaX9wtAJ62X2GbFajdhmlhczNsoqdCU4HzucTGzqZSJUjp1BUKv+NyTBW9PZlzpCJS6pF87TkWV5eSlIC4ELb9iIzTIKoO2YfPSyRPeHJ/x6HST02yNIjcoFjaztOb6+ox780fMMpuHxxFNo/PmxYJRXhKEMXGh82iWYogGBbx/rc+vPYo5SzKurEwZxj7Ckhii4eDC5ZuuRvyJF1ZxdR3P1ojTNXqdFF2HtydzdKNmlhvYhkTXx2joPNtZIS0NDiY+rl1SyYa3z1oME+fyHM0GS2jcWl3y9NqCpmk4SKYs5YT/9AtvcWtg0uktqLQZm90ZO70paDV7I5e3z3w6wZKWn3F7c0ha1Rh6SVw2HM4NHo8DLmKTpIKL+m123KepShutsRjOI7IspKwdWrZAKoPzuc9Ga0lop7jWJTPPMjPissbQa2aJR2DC6XiF43GPyNV4/40Z2+szLkqN0p6BuEwe8qySwDS5sSIZL31alsmTmcX5UmeRw6feOePefMTppE2tfCxDcLWv+PzjbR6MG1RTcjazeTxpKGqNq134xFMVEkWpFI2Cw7nG0aKk4+X0XJNlusJf+40T4kJh2jWpVJwsJPNCEtkGW0GbeRLxhb2Gk2VOURrkRcCNla/5enFp9ng2C7i5doYdTpFNSdcx2Q46nM4HbKwccOvP/jk0FtzpBTTVGjd7JrM5vHURc5bkuJpO13Hw6g6eozEuY+5PElpOQt8TfGC3xNRalEpwGC95MI2J7AJLc7GbFpHeQjaSXDvnySLGMGJWfZ2rLZ8Vp8NaVHK4THnzrGac1dii4WorYCfyGMa/NyTX32j84A/+ID/8wz/M6uoqP/IjP8L3fu/38sorr/BjP/Zj/PiP/ziGYfCX//Jf/kYv8/cFzpcaXV9iCklkW1zv2eh6wxsnglxVOHbOw7EJtc/VjkXLlxzPDd45DMiqinFaE2oOv/j4ENMA2Ug0o+R42YCWc7Wtc2ugMU8NTKHz4asavVbMrY0FV1YNdOlwNGs4T2r6gUJJi8DUiByFZeakckmtGj62vUbPczge6bxzblPWFsvcw7fA0uF4IRjGFXLpE3kGuqg5nUiu9xJurwpWooKyqej4BbdXFRoakZujGo2LZcAfe3bAJCm5GDvkuUDLdYRQ9NcSXrg2RyoNW69Zb2f0fUVo58wSnaudjFLaXCxN9icOdRlgCphnME5M1qOSojb4wl6Xg4nLOLEQGJcm+d0l79odcWMlQypB28vwnYKWW9BxFVe6CQ9HARdL62sSFJ1FZpMWDUUtAIONMOcX9/Z533aLV2YjVjsxH7k+uby2o5CJWtINClKpY5gWsmlwLQWA0C4ZNZaQeKbENCRCaNhRzN3xjNCtsQ3JWewyThyGi4DvvLqO4TR4dsVaO+Vad8FWZ8mHb14gdI157BLYNZqmkSYWmtDYz8dUWskih/dfG9HyKjRdY545pLVJL6xwHHh2c8adtTFZJaDRLlPjbEk3qtCbBsO6ZP1mtYGhN5g6XG2HPBmGhJbiZn/B9dWElW5Op1UBCg34Q0/1ub0zx9BBCdCUy52+hyEUBoLjtOK7bg0oc4M7nS62t2SYJPzd3zrHaATbg8sAkI5XUyvBO0c9vrzfkMuKV0dDzpMaTYakpWA79KA2cKMhkVNgUvH2RYpZd/jw6nNEloagIVZzPrM/4RPXO6QVbHcchnXMd9xo8f7rE3RTIWoD1zA4TgvW+hn/zrsr+qHC1BqoaqpGJ84MLuYes6XHC4M+49jm1YMui8LCNRXncxvXUKxEGTfWbK70TJ5atcgrh1tRl+0wREmHgeGzzEzS8tJEW9MabLvi/lGHHX+FT1xb5amuw1qroOMXdNyStXbB1nqMZUnOFy7D2KGQOhXQckuySmBokqOpyzzp8v7NiMfHJk0N8+TSD+lgHHE083hqY0otDZqmISt1fEsxiDJMu0LoiuNxwN7Qg0bn0UmbQViiNTCODfpewTI36AY1m90UpQSHZz5SwsmFxzy22W5lBLZkPUgZJRYtNycpHXyrZl7HTJOKRWYilWAtKlFKw7FKjqYu+6OA0GqIvJL9CxdZa6y1CvKm5D/6mT0Wdc5w7mIKjdCuLv0vCw3bgGc3LhOwN9oxZWVjCh2pGmql8eKuYNXpkxUGRa1zNnfIKgulBEZt8afet0ZZmpfsqqah5WfoQvDyXh9dl2iawjMMLF2xuVYgpME0q1gPLCLLpK9bXOkNkU1JUXgEhsc4VcR1St0I3hyPOJt43Dv3yKUiqyCNbd53zeE96y1GRcZZrOh50HctzucmDRLTzHlq3UBW0DdNxnPImoKkzr52f4Eid/mhl+5zmpR8Zn/OG+dLVj2TjmPy0WcTvvf5Pr6f8++/b415VV1e16Giilv82y+ECLNgb1ZTNiWLKkPTGj5xtcOmb5NLjWFaYxgl798OgH+Tgvq/hd/pnuq3xSj6/YorgwWW1qOROr6pYwjJ6bLmIslxDOjaq3zhQUpTCfqtmrRwmNaCSlMoWTPozIlLSdfT6dkBumjQrYrDmeATOwP+x9N9BoHGT781pOsbVCpDGAlp1tDxJbKKMEyIC4urgzGOUhy+coq7+SyOcdl4Pd3tMXi+YNA+wjAbciU5TQsCR/FNHRgueghNsT+rMcw5euLQsgNKw0V4OZO05GcfLjGFwbIo0M0Jk6RFz7HQNZtCZsR1Sla20bSGipy9SUgjI4Ses92ZsigqzpYuJwuTBxc279oWZHmbNDOZZyaqW3HiFywmDYs44JtvzLm1AveO+qzZLoVf0NiSw6nDg3HB7ZUKqSTPbyzxTY87V+ecLCxGicbHblywP+nwW3sRy0LDpqa3MsVzdVRi4lsapuYghcHHbsxYbVW03CWPRh3ePgt4/86cuxcupgY3V4e8tL+KEDWqgdwUBFbJSliSlJd+N551SaOPXEVgKY7GIU1dkuQOL2wsEJrGP39zg8irOQpK4txgs5Uyyy9lTC235mp7wSQ30YVEpgbBes3eNKTrS1bDgr1pRAPIpsE2SjY6CU9GEddbXQq/xKoSzpYuq60M36x5MIx49+aIRnnsK4PTuU3o5nzszjmvHgzIa421MONw5rHMNTxTEZo1K37FLDeJc5PArNEaQdfPyWSN4xhkpUXLL0Ev0QClBFmlsWq2qbyYWys657FACEWaG8ga7o572LJFpeXkhUmV6BS6TtNUzGOXT79T4NuCqgZd6Jh2Q51qCE1jJco4mPq0o4KyMLjeczCNhq8eCK6spjwZaVjC406ri8ptYik5zkYsxyHLzGS2tFhtVdxpb/LOsMCxUjY6HsdTidEd8WQU4hgWO4HFUTojTm3maKyEDdOFzfEwx3QET29O0GiQUpDVGo9GHkmp45iSflhQlBazSqdlgaEJ4tRi0E6Z1WNWI5P9YYj0NXZaOS8f9XjKNFmzGvKyQSlBooeshxI0DV1zEFrNWlhzurQZLy2udSt+ef+cJxcR77ky4a3jgBVRUcmcpJF0vAX9UGecXkYy11LScwUdW1LWDUlpkssK28q5mFjkseB9t5dMUghMnfdcTdE1iWCIoQwMfUmjLK6unvLwdIM3T7rkuUYG3F6pcC2dYZJhGxXr7SW5rLjWMdnuKHQ9Zzj3afKAg1jyhSzl3RsVu1HFrUHGm8cOLReuBIKXHvZYiR4Rahot28VYK0krHZTOVluy2uR0ojOOY5/bfcF2y2O88BBaxihPyYuQ0DFIywZLb/AtiZI2supyOvUZtMfMK5NG09HqiDoP2Pvqy1SHX+Yn/tu/wV/57Jvs9Aw++qwkazTePG3YPy24vZuw0XO57TbUuLx2ZPLuKy/zC2+vEtkmHadNP9TR9YbPPozYs84ZLTWyNMA3A1pGh7br8M5xgPJGnCYOvm4igcCKQBPoXY2LhYmNwtQ0dFfy2kX89S9SGl+/cY72dTrO/0781E/9FH//7/99PvnJT3L//n1u377Nz//8z/Od3/mdAKysrPB93/d93+BV/v7AjUGDa+jMaotK1GCM2eqk3FrRKaRFVXr4wiUWGsdxzvODOS+uPsWv310iTJ1nVns8GUsm1QTHatBrDcdM2WiBo5toIudgLjicuVRVwzhb0LYd0HRKUTKrKtb7DX2vRBMGrtHQ8jIymXJvKNhtO2xGBm3L4HiSYzqKyMuppEnXvTSLf3brCe6ohajXsbyGWSlRueLjz48oahfPrKikxiIT9IKMpg6Ic4tMVrx1YrAWWPzC3hG6fUR3xWBH2OgKFlKSyQxHFwQWOKok0zJ808PQazwnpeWmzLMWaWmByNjoKHa6NWttSS0lAhNNU3Q9DSV1LKPmbGnjmrDTSVgUNZYQpLXNtX6FawkkDYFTopTB3nnEc9sxuqhpe5cPiitRQVr4rIQx+5XFeqvg88MLtEbntb0We65GWgueXnFZNDlCNKS1TiENFrnOBzYiTrMRrx20CXdHuKJhtVXSDQrqRkNrGqpGRxMajVTMMhuay/Tbl8/HRK2ctU7G9f6ctbBgntvcPWkTRTmGLnlyGqFrEs1WPJkLWl7JZw9qfLum1BqeW5/y6nGXQmokqYlj1QgaBmFB26twrZRHwwDXzKjUpczx1uac85mDsjTePm6hb1f4Ts79iUFV29w9a9Fyc+pGsBqlDBcui8xGKY1fuj9BtwPed+2CvXELL5ry2iTmatjmKE64N5rwI6/nbA9cHp54DLyASiakZY6nNHpuQa0upXlpIRCaYtAyuUgUz27ovHGoUecWdZ3yqw8zNClY5DZ922NvaPDffOcuf+Mr+1xtBTzTWWU7WrK/KPnIVR8Dk67loiuNddfl7ekx/Q7khUHPibjW8fjyecG1QcKW8xTWikFVXePe/E163csB4KJwWI0UH9rs8pWjKUqraTRJZ1Awjm3i3AK75KTc57V3LKrSYZZY0J3zvTfW+On7CZqVYGuX7PC4MpnEFg0NV7seZSn5zYMhb5xKttdguPA4u4h4fhsO4gLNkHT9kvOFzbwwyQoDz5RYhuLmSszDUcDDM4PuusV3PGfz6cc5dVPhWopBkBO5BWll0vZynFQjjQ16YY6uaQxaBaOljUxNDL0GAYZbEhdAo3E8cUgyE9sq8G0NKXWuD+ZMA53TmU+7VeK4iri0yGqBKQTLwuJ84RJYAs/KqJVBqxOjaZd9qaLh1rrBlx/16bZyfBuyssHUNbphwXhhEboFw6XJ//2bd/nhlw7RasX+RNHzKy4WAXFmcm2Qoxtg6ZJQCHxb4qsCz9ZJCofTuOR4NOej11rcP7Upm5Qkayhyl1anQmgarluwiG3WIxPXmjJPLba9TR4fWYyrgr/4oXV++v4hz6+2+PwDyf/1Iyv8+uEF64YgC2MOlz5FrdEgqWsdFx+dnNNlSZ5p7PYbZGPx2jG0bYUhBcWi5vuf2+ZwuWSWSR6NJeuhYsVziROdxMz4J/cPWZQmKij5pccjnu9ZxFrKqw8lja7IM4N2IPlnb47oyIijeMp/+9JrLJKAflhwq13jzD3uDS/ZevfOdEwlOR7P+Z5nurTNAMeHH7hznb/+lUf89N0R/9nHd3nl4oINL0DmNnvDml/UF/zUH32O9p/9OhfGr1df9a9JT/WvNaPI0QJ0TeMiy3jrImVcLihrk43AZret0fUrjpcZR0lOKmNmM53bW0s6nqTtKtqOhmgssrqhbCSNVrPTm5EWOr/yYEbkXD6M/NbphI2tMb1owXDu0XNtQtPmydjj3tCj5ccczRyUtHn7lz7LbCm5mJsMl5JPvTXkYFwyLWJyFeOYGWueoGO5LLMWmp7SaT3mq4cm64HHVnSZrJTJnFFa0PVTQrtBNTW9MCMybfK8wyKzqJhRMWbVBYHNICjwjIaT6YCuXyKbhGG2pK4daumRlyZN5rJt3GLFiTgcV1RajhCKLzzs8/reLiY+T0Zt3jgKqZuG53od/tEffZH/4F3X6ZoRF9MBJ6MB58uAWlpcho9mvH7mcBYLHowsHFOx0y1pWwahIViLNLLSwDJrNFGxyCUrrQn3RzZfPQhZ5jqhLbk1yElKl7r0GM0jXjrYYK2lSAoTrdEJrYJxaiG0BttMmWca09TA1FPWoxmNkswWgjrXmS5DLKOkbmq67SXCzFlWMMl0hKh5fB4wiw3eOY/4pXurBHrFxcLFsBry0mS4CDhfeKSlzsHUIasuqbyeA9PEZ7NVsD9b8upBAw0ETklaGHT9AtVofGFvwDizec/uhMAu6bgF2+0UQ2sQmmKW29imoh9WLAoby1F0gpTIrRhEKYvSRNMqitpE1xVSaURuRV6JS0lRVOKYNbVq2Fvk2HrD3iIlrnO8ps10YZDmgsnkKlUtaLsN//EHrzIrKrZ7Ca6tcAwYLivuXiwvja4lGFbGx+8cst1J+Or9HmlmIpVJiSBwLN636XNrYDDwEwxNYQrBQTzhKF1wWJ+z0R1zPnM5G3s8tz1nmZs8jEfcPU/5wGabP3ilz5rd5t5RD9eqMUTOo2mGQJDXJu9d6XA08pikghrFtZUUS+h0fUXbLQjsmtUw59nNEZ5VcLYwWZYwSzUsp0A3az64FbFrbfPyvU0+uXub79y9SlGEfPbB6iV7q2jo2B0WqUHkpoxiC7Sa0I3JK8U0g6Qqsc0Coec8Gjt4Zs6femGNb1t5Lx1TYGgW0yxEKp9KWvQDxVZ7BlpFXNa8c2ry9rmHY+fMUpuOo9EUDoZy6fYU49jjYOpxEQtqVXG4ULS9nMDSiTOXtAhQsk07KJiMHYTImeQZO705L24v6fg1a1HNWmDSoJFUJacLi0+9vMZFDHfWLN61pei7LgpJUWt0g5zN3hnz4jKl5M6GIE57xGWDVJJv3R6QFw3HI0FRwoqvM3A61AousimTtGSzXeHqDuezHpNUZ7TUqGuDd07b/PLdPsdzwdHMou8buHQRGsyqmKNlxTwTPPveF8gePeQvffYulnfBMJuRqgRN2dzu+fyB52purs3wwkNSLjD0nGtdjTcPn8bXO7wztHhnkpLKBaWsuTOArrkKVZdcSU7mBbMyp5A1PUdj1e3yVE8jciWmVfDo2ONwfBkjPk0lli2xTZ22o/Gzj/a/7jXq/2e6+PX6+t2Ak5MTXnjhBQBu3bqFbdvcuHHjf/n9rVu3ODs7+0Yt7/cXxIzzWKPSaqZpRd/R6doeReWhGp1xanAaVzyaKLYGBXESsX9RXEqOHcUnb/YRzoSOW1PUNoYQbIQGodMQ+hdM8orQanhhc0bX0/BMk3Fq8OaZZDQVdIVFaIPQTCxdo24KZkXC2Txkp+UwSQJcO+fTj2ZUeoXh5BiNi2foFKpmmJaMkhae6aBpDcIsKHOBo9dUUiAwuIg9Pvt6i0Wqs1z2OZ8GvPTYZ/+4x7eub6CkxuFyTiElz6+VbLQrBt05oVvQdzVCR1HUDaPE5mTWY5lfPrQOwpxR0kY1iqRsWAtrrvRKloUgKytoLpm5y1KjaWqeWZ+x2crRNEVaakglyEuXSgpWogQJ2HpDWRkcTG3OYotuUCIbnduDmPUoZiXI6bqS6/2Y45mPY+bMUoutzqX05b1XZtxpd/nupyLO8xm1KukHOU8uQk6XFvNc5yQbkUnFna05rl3imoq8vDQN1tDphyWbrYS2XbDIDeLUZnQRYimPdjhmLcpZ5gaHs5A0FzzdnWFYktV2Rj8o2F1PiHxJnluUmY6sNAInY6sdozfw9llEYFfMUock1wmckt3ekrjWuHcesCx0ssqg0XR8t0KjZrudcnM14V07EyxDoQtBaFdc7V6y3CxDkVUmugZ1Y9DzUyKnYnOwZLW3oJKCu2ddVsIEpRrGscW9YU6eCxynIsl0tsKQu+OU77xyg/E8ou1XLEqdJzOfUmq0jRZNY2C5NcfTiistg1QtaAc179uM6PrwLU8d4ZkVpmpxvqj51hshP3n3mL5b8p995gmbYcDeSGex8PjC0ZT/8bUnSG2MQrE3LrmxPmOe6KwGDZuByyyrsYTO8cka49jmVt/GM3U6bsV4aVNLl5Ztc6Nr8HC6YBDV9MISXzM4mXvcam3w1HaMbjSMYxNDKNqWy5WWx2wu+LFXJgznGg05bT9HKo2qgt1+gmMpJvWMeQqRVbPRn/J4GDKNbXwv47XTmsaAwFVEbo1sdE6nHi2vQDVQ1BqPJwG10vj4DcXLpzP+6ZvnfHhllfnUpSx1oMHWJbrWcLFwMXXJSpTz7PoYz67o+xm1ElheiVKC6z2DZ3s6tdJwLMmNlQW1gjxzCO0Sz6oZBAlXezHr7ZjdXkE3SNm/iJglGkfzEKGBLgS1hO3uEteEaysxvn35GbXdnPM5RHaDBiSFhq5LQKflS57dvUA1iqaB3zgZMojgT7+3T9/PmGUOm+2MF6+cIJsGQxM8GbU4XVhIKdiMFI4pv8Yq0rENjb4dYhkN42XAk9MO47lNFBV8YfSIb7vao6HhdG6zN+pzNPb5tusdbFNjLYS/9co+07RhWsxoWQY/9OUn/OI7MbMyZSuSzOOIvYlOISu6foljKwwhMHWT4VCnrjXaXsOzqxqBozi+aMil5DP3l8jKpkl9TGFzOG8oVEHbhQgPDdjsx9h2zeN4wcNzDV0FbHQNSimJK8U0hZMspSphO7IxhMu8bDicWvzm8RlFk/Er92dcb0UIHCa5wLYVP/b6mNM4Ick1DueKtmpzbTPh//kbezweWuzPChaJhqoNnlyU/PBX977uZfHf9FT/YvhdcprfGLx56vBrDy5ZGUbTwZbrREaIZUikPuE3T/ZwevtcH5S0zIDr6wrR2MwyOEyW3B8pslqgo+MKQSMtlLK4iAWf3Z8jNYksLrccDU0nME0WhY7QS47nNq5dstVO6XsaUlpMMo3pwROuPbNBO6xZDyuEIXnpOOYL+x555XM2G5BXEYtc48E042hRMlqu8KEdE1vYuFbC3jzFcyf0XJ3NlsCzJSutHM+UvHU84GRu0vLPud6dsOa2ScsOea0xL0oWlUIXEsMoqRTE6TqO4VKrhoKYfi+maDT+5HMbhGHNdmizEim2+gnXBgloNZ9+LWL/zKTOTL64t0RoGt92ZYW//ok7/NS/8yzftL3Om6cOdVMxzwzi0uF6V+LqJrL2WeY241Sn0nIMN+NsqUhLgaEpXBM2W3B7NedDV2J2OjmvHq3S9WJ6fsIkUzy3OWRR1Lxn94y8vNzAyGubtVbO8+tLhGZgaDrPb6T0fMlwETJwG9YCiVA6n7jdJrRtvry/wsnCp+eXCDRoDJ5bn6OUya31lNVWTdtRnM8ifvLVmxTSQgiN8cSnHyTUdUOlGpJCxzUkIC6nbpXOk/MWvfaCzdUZhyOX87mHqWvsdHIMoSilxpOzEEM0mEKjLNf43KNVnt0csxIVtN2SRWYSFxZKarTcguNZiKZBUQt6fsXR3MU1FU0V8a6NiNvrBTQNg7CklIJnNsZ4JvjeZUTuNNNptIywdcwzu1P6rZrKXLAsC4Zzk1dPMwKvpq1HpOVlQxyr4tJfR7X5/uc3WQt87l+EyHKduHZxnYpAhPwX33qDP7C7yj+9f8xxMiEtdaTUWRYp671zljIhlUveOW2hi4ZuK+d4bnNjfUleKdq9Kb/44IS/9dIhjlfxnl1Fz88pUVRKkec2H9tu8yS+oMhMbAHtqGSRCS6WJqXUWA0rup4kLW2S3MbSYSUquNVf4FoKXcB2Z8m0ecBvnp2TSsXf+vKIKwPB9dVjtnsxSS3wW0NeOZ1yc21ByyvYbC1ou4pBULEWFmyEcL1X0nIE2+2aphG8dTzgJ16Z8P9+6T4nacbxzEI1KblUXCSwSA0mqctaqMhKl55vsBbVDOcB51Obz7zRYRRDpirSSrHZSfmeF8650i1YJgFF4XO6cFiPFpj2DLQCXSj0RoEuaFsu1wcFpqh5PAy4tnpEWpXMEwvyFgcXbZrGJLIsfuOBzj/fPyOvG/q+pKwFRW2Q5hGOEdAYSxblEk3UyNrFEAaV0nCEy61VuL075iRNmOYlw3lAMt/kdLLFW+c2F2mGZxZ0vYZAF0xij6QEz1I8tVoic8HDkUFc1mQVdB2Hmx2XeGnzX3zTU8TU3P6O7+Cj15Z84prO+7YK7o8aXjutOFvmSCnoWl26Vo9GWTyalSzlggcXoDUGV8MWV6KALzxY4effClhUOWldYuqK2ysGt9d0Di7aPLzwSOuGeaEhtZJxWmMLyWq34qn1glsDybruMC8Ej5IFb11kzIr0G1rPfq9ASolpmv/L94Zh/K8iXYUQNM2/oZt/PVDVLmVh4+k6t1d1Wp7iPCs4WDScLUwWRcl45rHmerQtiyTXeTBKcVpz2tGQ//Qz+/Qcg0a6yMrGEA2ZzLg/ltRSwxIOlm4QOgLHVjwaSzKVElo611YUvU4NShCXGseLklLmFEWXrUj72oaB4mxucT4Dx1YMvMtwjCeThidjizurBY4u0BoN15E4booTztnoSgzhIJGcxSW9XsXHNjdZxj5FZoPU8f2cR/MpDeDbFY5Z4Fk1nqU4nnocTqFWOqqxiAuDcdqQZhaVtFgLUywjYT3M6XqKQZTg2yl9P8fRIS5N0gqKUme4CBgnHpXUKGuN42mHSun4VoHvFLhmw/E05HxuMQhStlo5K2GFbUhKBVWt8WTS5t6wRVJp3OjPsPWKSRLyuTfX8fSatSBhd2WJ5UgutHN+/fCCKMjo+iWa1rDVzUhLjZ1Oiq5X7HRS7qzNOZm5CE1jkphMUpPHFwEqu4olLEzzsheZZya1lZNUNVXVoa5hkdr4ds3RwuMn3rjGfGHx4pqklD5Gvsb1Tkg3qDE0RVIY2KbifO5zf9jCMhU3+nNe3Jzw1OacyK0wdYWvw3qUMlw62GaNZxZkuclmqySwap5bnzAIShy75P55i7gwLuXOmckksSgqQVEJen7GWpjR8koapbHZTelGJU0NNwYxXU9ya21Kt13iODWTpclbZwmffjCkF+j8nVcOWfVsZpmDUgJV6Vxpp4zyJYaoSHKdQTdjLpeETkFV1Pzi3jGzzODJNMRwK24MbPyw4NuutTiI56yvPCCRGf/knXM6nuDdWwFa6bJ3LtgfRaxEOropkeUG7x5c4dG5g2lJjspTPHfKwbjh1dOU6dIk1A1eP+iwfxGwzASSJftDG602qXMTlMY3b2/i1buUquHJRFAXOkmho5TBKM/5yHZIUWs4YYJjVGy0EyaxfykpjHICR7LTj+lHJbWVMqzHCAPetRrgOTW1ELx302enG7PVSdjtLVHqUlL2ZBRwMvfJawNDuxzAfelIYbsFg27Kq2dTnlv1+ejmCrX6mk9aLVhrZzQaBLbkjdMuT4YtrgfX+fjGCmWh44WS82LJO2NJ26k4nTnktY3v1Bi6IJfg25LzpYVqDEJXklUGbxyGqEoQFzpJ4XK+MDF1hecUvHPawTKg4+U4luQP3+yRFT4f2G7xn3/8KTp+gxCQV85l3xkkBHZB36+xTcmv3c+QesIv7R8jG4NRbOGYNb6tuD5IeH5zQsu/tCzpBRX7k4DThcYkubxeWS74lf0LPKvmSr/kr3x8HWXWLBObB0cu1/0+BgZCWrxnsM6gnfIrB4+40rG52ktpeSXzVKOQFcqMiSyHP/H8CucLnZO5TstVbIQ2t9fHSG2ObVyysm1dw2hVPDNokRcWRa5hKI9OS+cHP3SVf/r2GFnYBKaNa2pc6Tb8mfeusFQptp0SGCamZiOVwDYFwxwO5yW2VdNxXP6951dISpNK6YySisN5iWtqXO/ptL2G+dJmsvR567jgtw4SAktnp2OjNwabPUnXM9g/Fvz4m2NWHZ881XAs+KYrAR+9ZpI3kk3Pw6hc/tmDfzM0+t/C73RP9S8kPauqik984hP87b/9t7l169a/9Jv+boFeWwwCG0e3OUhMlrFFXilyofPhq5IbXY8k95kJg6opeTwpOZ4L1lsVThmxtXaZDOZbJZMiIakLnkzWCCwNUwk8C251W7wyHTMvU3QDbq3W5KVJ35NAg2UWZLWkUgZabfL+/8v/i7ry0YTCDWacjwPeeyvFETpl5ZFVGro5Z9BecHVgsT9epawuDfyW9YzGWGBoW6y4LWJNp1IVobegqGCeBhxPHRqt5hfevMmVTknXlzTRlFIVSASR6SJ1eHQRoggITI20LnjtTLBIde6sNJwmKT/++pCOZRJ6FW+fmhwuGt6zKfjwtRmrLZ3Hpz0MrSYxhvx3r79D37zKB1Z2QWt4PEkYnnX5TOZxay3FNlNCO8cwTd57ZcjrRzsITHq+hqkJGmw6riStGyw9ZyVcUKuC10/6fPT6MS9sCuqmoeOAEDnnsw6rUU1dO1xfWXBjteELD9eZ5w0n0xbfdeeAt4Yt8trmY9cO+dyjVT73ZIVr/QU7Kw2eo5gMEyJPo+tWtNwSS1ymm+S1xav7XWjgW54+4bWjLm0/Z1kYRE7KqWaR1TB53OfpnQlXuwlnhmSWuXTdkrYjORjZpEsDqfnc2ZzS9msMTWM1KCiURt/PyfKA9W7O3bM2ZaXY7p1wNHXJy0uJZK0aVsJL+VDgKO6dh9h6Q9NA26uopMDQNCzhI6TNG2cLnlrT0Q1JUpg4Rk1Dg2NDIhscs6ZFF8mIZWFRK2hHBYZyERl4juLlkyl/+OmAt0YLbq+GGMLk9fMJK+2KOjf5+y+fMG2myNLmQIeWl5OXBh3bxtJ1/tHbBxwMG57bTXh40WK1lZNUgtePOwRWwYrTZ5EnCL3GNis+dn3Il/YHfMftlNeP2vyD10/w3Jon84prgwVCaHiG4n1bqyyqij/zri3+H7+e8ckbLfaXc/ZSxTw1+MjNCwKz4mTpcD53qJXAMTSWmY0GjJIQQxcEriKvbHy7IjAbPCfj+atjvjQ+YRBIzpcmtm7g27DeyVmkHr5T0vYzlFKXPlVuxiQRHM4N8qqmbuB8ITBMSaoS+k7DepQyT0MeDle4tZJT1BqlNBjGBiuBou3NMA2LaRLimBWyFDx3NcYWBuXQxrNzbKPiwdigri5NOJeVgWs2GMKh4+TUVsnJ3OU8K3GcGKFrnE49NluK6/2S/Ysdjmcadeni2iXnWQFCsBLWXF0t0YwUqxE0mqARGa+emtzsVfiGQ9/ysYwFmcoopY5sBGXlUZQaaabz7m3FJKt57VRjGhv0nQavcfE0k9Opor05pMgF6x1JjUZaGnh2ReSVDAIPwzaZZIq0kPRbOabhc7Pt8De+eEZda/h9g175FbzuFRQhL6x6XCTwzoEORobvVfimRtv2yHOXyFtynhQ8mboElsQxJF3Xoa9Z5IXBZnuKZ5cYmsYibzidGbSDBkvX8fSGtPRRic2FWDIplignJVIG19YdLCNgJnPO4xrP/B2Qnv0+NLMG+KVf+iVarRYASil+9Vd/lTfffBOA2Wz2DVzZ7y+oyqGSIFXDoD1DiRmislnEEZZoMIRP5FicTRWuX7Cct+h1F1h6TWj43OhrVEpjnisOpiZtpRhsxLTqHkq1KEoTy6iYZA2uM8YQLewmQhMaQZCSVwJFwTxXtE2BI9rklUATGaOFSz9c4ivFahjQChWFrDiYGqy1BKFd41mwyCwkcDCr+ND1lI3QR2gCQ8+oGwmqSy0txqlkNbCZJDXHSX55j04ESd6wP2vQaLHealiNYoSx5L2G4Mm4wzQVnM8j2mnDxLHo+CWakOz60HNjKqVzzagppUNe65wnBkWl4fmCea7z8Mzhxd059y46LI9apF9jH1m6Rl4KRrnNMrbp6SXrUcbp0iCtNDQctrs5DRodL6eSDV1X8upJn6QUaMDuasL5LODVR202+znzXKcV1PyxWzf4mb3HKFUyTuCZ9ZS4snjpyENrKvbHEXkQM0sCzmswjYZJcpnidryc8O7BEltv+Oanhnz6LcF87nMiS0gadC2gF5UsM5Ok8Nmw1pFmjVnucDQ+IXJHuLkkrT0+cC3g4XxMWRp4XomhNVzvJezNArpORZIbmL7izsoS16y5fxFc+tcYMM9MpNKRTcMgyKmUznYY88felbE3DfnS3oCChKRyaXkwSUx867KGrEeKtDQ5m/tMFy5RWNJ2WpjU7EZTHk56CNXg25KmFuQN3BsnDNoFMg/IjCUhPsqW7HjrPJnGbHeXOJaLTEJKM2Oemjw+i1jpF0xSE1MoGqmTlSavjS4wGp2feOuMs0WDYyl6keRmR8NSAR/cCPnS2YjttkdVh+hGQz+8wDEF79nO+NxhxYPkkG+9dsIX9jd55soUXVf89S8suNK3EbpkNbLoGh3G9QJhJXz+LOU7bnb4jfOceaE4yMekYwvTMLl5ZcTrx22sJmJUlvyzR6f8xx+7ykpg8vdeOuaNQ8VKK6PrVxyMXHxX0XJrDoYh06nJJA1oeSXYp6S5R1XpvHNasbVmYYicN447IBqEoVgsLRpfYuqKllORVRbv3h0xXNrcO+6i6TmH55K3popndi7NkffGIY5Wc9VJ8YKCySygF1R89lHOpFgSeZKtQcIi06lFzTvnHdpeiWfBJNWxjQzPrEkKnX6gkVcCXVNMlyY9v8C0DBwLktykVBVC6cwSgUIwSytK5RLaFb+2f0GpDO4PK/7wdYuPbVzhx++e0jFzZomB0AXrrQbXrLm1PmGZdXDRWCiJpRc0jUlZ25gipusnXMQeT68WyIliWDgsFw7C1NE1k1aQ47gKqQSOWTJJPH5x74z3bbm8fpRzbX3Gpx64/NQfe4a/8HNPCCwbVUe8fFLgNEuur1tsRJKLpeIkron8GqUUX9yPkc2lusEyGyyjZp708MyUvXGJKm18R+DXBr9xMGI2c3BNnd21BF9v8SOvHyAcxf5pw7/3gTaf3s/4/7L3p9GyZml9H/jb7zzHHHHmc+fMvDlW1kBRAxRVICxjIQbZkjAGtzWAjN1Gk7XcS6stud0SVneDsFoStAW0JIQQWIxSFVBQM9RcmZV582bee/MOZx5ijnced384LC3Rq5elorOgjPRf6/0QJ+KcvePEidjPeZ7/sAptfuqlOeu+JK0zUpnSlAFDtaLn1pS5QVHZaHpEnFgchBnrrsk8q1C7kii2OdZXiEZn01AwVQWZWmyul/wf3rTLD370lO96a5effPEcVy0xhMFTV+Cbb3T5oY+eY0ifpKnIlDnffH1EkSc8N3J4mJ4Sfjkccv6AmVnDl7em+pJeAV3XuXXrFkJ8hQjz/v/EKwfQ7evMVn0cS+I7Na4FKBYt26SqS9KqQNETTmfQdxV6dgffsMgswSKVnMYld+Y1jhZwtQsWPs+PAh4uE+6NS/7m+4a89tED1n1oGgtFXkTT12KJUEps3aCoTSxF4+ilW9z9win2O/8IhlWx1VNpGRqerlHUgllW4JgrqsbFUtoojQ5S4STOGScRNzciNNHjUtsgK+HBvOFoqfOO3ZyuZ/LKiUWWK7gW7HZyaqlw51yhEiXrtoGueygqRGXGTm/K/mzAIjXJm4s39vWBTo1gXMyoSdgaaoyCkmd2I+LMIkp9fuvBEFVfIfSCQSujEiFlrXI3mvGB11Lesb7G4bwmkRlV1vD62CKTLk+tFUSpw0EzxNJVel5FXjWkueDeROF6T7LdDxFSxzU0PvNgSFM4vHYyRBcaqh6xFtR4doTML/HocMW1/pJXTlo8NowRAh6N17g2WOEZCs+MEtKyYJmbXOqlWHrFnTOfcVjy4f0lCMHAj1GUC3Ngx7jwChJCsNYLeXji8csvbrLVjzlZmlxfWzINbbZ7EYYieXAW8OA8wDUlPbfgaGmhKHCyNFnOL1hAb+15nK1ytjoXh6BrZJzPXXS9xrIqhFTpujGTUOdgblFUGscrm1E7YRZZbHVSTkKDTTMmLwxaQUxg1ZwtDXS1pgZOJgYb3ZpBu+A8alj3dRqpcLKySUqFrpOSVbDmRXxmZpFPAtZ6KUmhca1XcxaXXO4khLlC2/Rosg5R0mBaJl8Yr9jsFKwHErU9413Dp/nxWzP6nZyDiU7Lbxh6CWV9zj/8vELIgj/x7DYvT2vmq4bxQuf65pQnNubMFkMWWcmNro1qnfHE2pKP3B1yZRDzmUOLeKVCrRAmBq5ZUzVQ1yp1Y/OfPjsiryv+uw88QncUvvttQ/7mRzM8I+bJrSlJoeIYJctEwzUL9ucujxKDwCppGsHxUkcocHM0Zn/e4sXX+/Q7EW8ZbvDxuyrbg4gXVj62XvLM5gnhuU23FXISufhORZgq2EaFKjVOQwfZqGhCwVAFjq7Q92rCVEFRSsJSZ9NMCDODrC64e2YzSSAwdSwMLK1E1xqq2sQ1GlZFQ5TBqlS4YnUY9FL+0KVrfGR/H9VI2eykKKKgXWS07Jxp7KGqNRoKa35NmNt03YyeXfPrd30+dt/gyRHsdlOi1CEVkmc3Ew7DhmVeMp05POU1bLUzXKNkfzxAU108o6btHXH/9BI311ecrHTW+zFhYqMWglXU8D9//Jie33B1TbLZEpS1zW8c+RzWEQIFU1fZtCRp1sI0arKmwtQlhiax9ZKN9pLTmYkQKk+tl0Q5fPHQY6et8vaNHv/ywTm7ayr5ecEnfuUul7/5CoGZU8oCWzO5MTTYHcbEucLRUmXNb1ikkr4PV9oqDyYGi0gjyRVaXgVUzELJWkeyShwMoWFrKpd7NfPEYFmkhHVGVbj0PB3XcNnt5hwudFZyQd+Z877ddX7y1gGqohBo1ht/SP0BbRR993d/9++4/T3f8z2/4/YbXWP8hb/wF77k7/mrf/Wv0u1239B9/F7j3pHClWs514dTaqlD1bt4zypwvDQQQFRnlAsLRbboBhXbbRDCoKhLVvWK+dyjrhwaStTGIk4t2l7C/anJKlUIHEle1Aw9g6qlMw8hT6DdqZBKxXhhEmg2ml6zTEtqWTOODHQ9pqxMBi6MujlZXVCUCqNA/rZxrKAoTRwj4jDUeNulElNxQUr6bszRyuI8Muh7BZPG4uXzmMkC/k/v3uanX32IZzccnsOoF3GpmxJlNmVdcfu0jWkkNKZEFw0Hi4D/5MYmv/riA8pSwzEzqqbA0XPq2qJjVdyb6qSFzWarQUESF6AJDaUK+O6nBnxucRdDqwlMeHy0IMocCglh5rBMLHw3Q9FyXp8EWHrJ3tzB1jSSXLDZySgqhfX2ikkYMI18DhcqI78mrxscL+eGo5KVFZ4DcaHy6w/nPNMf8CA6xbATrg8WeGbJp/eH3Dvz8ayKF4/bGOK3Ta11yVprzsuHHRQ3R0NiawqvH7cY+CWWEVNWgo5bMJk71LXgZOWwYQfMlhJNj/nY0ZTNXoWhNBiGJCkKPnkQ05Qmz+3OGLQT7py1+c0HQzyn4N5pC6HAwdzl4cTjPdfO6dglb9qe8dm9AciGnpeRlSqq2nC4dPi1w3VujJY8MVqwzA0moUaaa/TVDqZfcxiHeHbNwcKhh+RrHzvjbLHFgzkkJHjzNtCQ1YK2X1CWKno7R9YuSaog65x5Vl1Ie/oTVmWL01XDQKxzGoVcG67YE5DEF4bkbTMhzk2kVAgTlUwF38pYxCbDVs5Jcs4wkLhaiaY0dEyP184Tkkclf/6rL6EIwd/5rQNePZ/zpstTTpYWv3wvp2+3cZwFL5wOWeYaW72Y6aLDTldnHme4Krx7Y4NrPZ/fPBa8Nl4w1DwexIcsYptfPD3FNBX6fsR7b5zy4nEPWevYXo2W6Py5N29zZzHmtanNrBnjWxBlJpOwQqKy5oXElcGTow7vfcsmP/DJlzG0Bt+qUHoRp7M233ilx0f2alZZjaU1jEOLMtUwdIkmGnwrZy1ImSY2R3MHQ63ZHazIa8EZDmUFB5OAtdaFZHEW6ShceLKOWgnLxEJz5my4YBkNHTfhfNFlFRq855rHF6dTVJFiaJJJqAMurpUjUJmnCjudhLRsGOgqj63PSCs4D1UWS5sUFceMaRpYpNqF2kGaPJhU+GaDYs6wtWt88401fubzS/7j53b4mbsPyPMSMNjulJgavOXKkgfHXbYGC14+7PPeqxYZE1468nl+q0SicDg38TIodANVqhyOJTu9BlNT2OyEjNMKTTXY7uSsipRX5uCbDnFmczhO+Q9+5DYf/L6n+PTJOZ87T+jWCp6ZUtQCWzZc6TVoWs3hPMfEoqxgzZfkpOQ1IE2KSuWP3Njg/vmU/amgaldcWssICxN7lJDXNZ6XczZ1yJuarbaKtxby//p8juk0DCyDQC94qrPGbx0vkCLhwamkO6gRhslau6Cpbd6x4/OB+2N+9fU5wwA0qXFzaDML4camwWdOxxwsNapCQZQVtiWxdZVvfErnF16c4PsJZSOAkGnicmca8w3XOzyKpvREl+7aPv/wi6d8zWWb733bJv/5v9zDMao3/mD8A9go+nLWVF/y0/yu7/oufuzHfux3veBXEp7c1NjtqrRslWVeE8ox+2FOXtfUjUKS67SsCksx2Q1sAsukkiWZzNkLS87TmMudjC23zZW2S1E6zPOS73rTiM+fpBSy4r/94D2udlUs1QIqDuMl03zJ3bMOQ8fE0w0UaTNLBC986gs0/R3+67dt09ZM6sLFNUFRV5ynKySgi4CeaeJoCnGZM6smzFLJja7GwOlQ1SaWEfLZgwZNC9lwTBzdwzVrTCOl117R8Qq6bs52d877Hj/B12wsRSev4DgsuDvNeTTuUZYuSW6QVxqq0MlLgRQJrnPOutew27nQwxaVxlpQ8PTmGKEtgIqtdsF2O2fNUfF0DVVpGKc5H3p9xXFcYTsZN/oWT6/pRGmL05WPoZWEeYmupmy252QVlJWOo1gUlUWWe9i65Hhu861Xr5DGCn/y2lvY1K/xxVd2+dS9dT7y6lWiXOHZXYtP3NtEUyRJqbNIJP/BVYt5JvnZl9a4O7EZJ4K8EqSlwmno0HNq+u0SW6to6TYjt2bdq2ganWfWIp5bj4lSC0NRuTzK8S1IU4fNbklZWnTdhp7b0HZqnthecWmYsNmKOAkNHCPjzonHLNYRWo1mNLx8WOIbFavUIM4VHs591oKcslK5NohIUoVZZNDzc6JMo6oEW92EKNNZZiaLzEARgrPIxtMLxguHONN5fmvG1d6Sy90Iz1tRN3O6ds40NmjrI7JKpWoksrmY6JWVwrI00KTKxjBBVRocVWOZWawSDd8o8KyC26c502qOImrefcXhUm/Otd4MXWk4nBv8+K1XMfQKWQuudTqsBzBPDTacDlstg0ezCyr5/sxENAqel9H3CmazNhtOi1ymLOopm+2Y1493UIUgzlUenXvksmLUTdnshthmwf7Uo5EqVaEyzSK+eDqn62n8pbdf4r//jde4v5xTNoIs3sTTHMahh2vCKnMoSp2RJ7nR2mAYFFzpxVzrragajawI+CvvvMbpmc+/vD8jcBXOxn0udyMsTeE88limNnHhk6Y2SWZyY5DTsiR5pVE1KpWsUZSKT77WRlMzxqHCJJLkxYXPj2MYDIKSgaORVwZX+ymjVoRjRtSypKxzLL3A0CuGrmRnkPPtj2/x97/tcQytoWeaPDixeeXIZpko9OyK5zZyHENnt5Mxji1KmTGNFYpaUtcNA7fkbbs5vqXzaG4zDg0WeY1pLlhlGiNXJcs7qAKiVOdw5vCpPYuoyljEFo8PIS975E2BZaakpcbx0sK2YkDStxV2WhbLhcVnjitUZUXXqfiapxKe35TsnzmchwW6UuCZDa4Os8ihrCs0tcQ1Gm4fDqgrSc+PcMyEoS8YaB7fdHmL733HGt/3zhGqlnH52WvcfWGPjukQZRcSVtSCXFbUsmEjaLjWbWhZFTc3F2x1IkyjYJFffL4YiiAQLu/bHdC1VV496XD7vOEzxxnjJGMjKHn7TskzA4vtQKftLRiXIafLirIp0YXCyPZJ4oDPnUwZ+AVPbyyp5L/T2RD/1mia5t941XX9hq75t//23+bTn/40L7zwwr/V9Xf+zt/5A8Fs2uhqbAY113s1irQ4j+H2pMLULxIns6bkTWsGX/eEhW2CY+fkTcPDuWSRZwSGil5ZOLqgbV7UAi+cqNyZKCwTi7adQmNwra9jagqFzBBmTG8UE2c10SrA13ySROfRmc4yF+h6QVVXtA2TjqVgaBmjzhghFCxNkkUWjq6gKILTuKQRBar0kI1F00h0raIWFXmT07IEganSyIZJUnFzS+XDBzPW+hF5JdjdWHGlv6BjFwRWjSJgrTXG1gocTWW2cpjPdX7qpQm7LYeWXeAYCc/2V4S5z0lkIUWJrE2ywkLIipaTcW2QE+c6T/R9/tndR7TsAlWBrhcycFM0ReHVkw5noYqlOCi1T2CWTGOVtFTpOxW+WWHqDcsc2k7NLL6Qu3bdhPVWwvncJV4OUKjY8VxsqyKvNBoaTvM5L05OCOyYvDKom5r9uYcqShxDUFUCpMJaK2K7k3ClF3KjH/L09gJTr9iyluyd32DXvk5e6tQVvOvqOZd7ETe2Fgz8BF2V3J5k3FmE7J2pNI1NnDkMWxltKycwcwatlHde9jkOLxKrbq4tmKcGD898prFJmmtoQhKYFwlrr5y2iUqDwCpouwWVVDheOYxjgztnPh03YxabvHLSJi8FA7/EsQsezhNeOc1pKsHj/SVDL+d6xyHKHWoxQ6iStbbKyE0oa4WiUkkLhaSE3U7ILCnJ6oLzhQVceNM8nDhstyRrvRTZBJiqzyI1kI1EolLVDYFdU9USIQVrng+KgioUNp2L8Jq2k2FqFePYpigEx7OasEj59psDfvn1U47DFEtRkUpGWphoKtwd6zycF6wim0VqUDcKn3vY5cG84LmRj2WmNMA//uIZP/3KEbaucbXT4iCb8pk9F1ex0FSBY1U8tzXl1bFPVikEtkqg+fzhKz3uLs/42OsJL53NWMQGpzObtpNzvLBxzRJNhSwzGIgW//OnHlA2cDp2WSUaeaWw4Vt84N4Jnr+gKDVOVg6BU7A+iNkYrLCMhmVikdcautIghGSSWKSlhqk1DP0MQ5V4ZcDpwqcoVTbbCY5ZMvRTykrjLdtT+l7NqJUgREPTCJ7bTfhvvnqX910a4ZkqZWMxngaUlYqvBRh1B5UGhZqXDvr4jiQtddKqAamiiZpvuNTHUxz2jj3qRqeRYCoCU8vpeSmOLngwVfmXd8esspof+dZrtAwDS5X4bkZeGswih7unLg9ObQyjYZXZNFR0dJ/zcUDPlRwuLMq6oW1LVonFPFGQes1av6HbzgichEqq6MJEihrLyOk5Fc9vp2hWyizR+bobNu4w4YXTJS8dpyhobLQKPDvEMXMURbAWpKSFSZK7TGIFW89oWZIbPYVFJrh9ppBXCp/ZE2SpSbvbkMc6vn9BRujaGh1bo+WEaGZGVUsc4yIw4PkrNX/9PTtghGRVSccUqFJA7rPV1ohywdGqplIq4rqgqRUsfC71BBU5aVGz63mkTclWS+N6T6dl6GiNxp/5OodcyXmi5/HcKMCxoWXoFLlFnrZAlPzyq0uKEtp+xUEUYTDgsb7OLE354z/zOR4+CgjL9Pf5FP3Kx5e7pvqSK9uiKPgH/+Af8MEPfpC3vOUtuK77O+7/wR/8wd/1Zn6vUes5kYhYlCWrQvC0pzO0dHxLYig6s1pSiQWHqw43+hV39vp4Qcp06bLdLhn4BpoqeTi5iI09jyAsCv6rf/6Q59ZNhFQx9JKkTmkZZ6xSk0vthoFl0zUUDKGzLApujSW39wbUssflm8/y6/fnyFqjViakMscyc55dM2hqB0Np0LSY82zB1W7Ey6fXudE1WQtydDXntYlgtz9mq72Dq7dZ1Tr3xgLTKFG0EKvx6TrQSEFa6RwvfNqWJIwUPnNmUqAwci704UUl2Js3iJnJ1T7EhULRGPQ8jzUPPDMHZYWkQFNcDpY2QhrcXEvpWBmlrJAiJS09HkxMihKkcUFl9NyKnpVwvNIRWohv5pyFPnvzLjf6DZpouNLL0USNpmssE4fPPQxohMPI1ng4f0iro/H/+MwDvuctm7z72jaLJOUfvTjht/aWvOPGGNdyqKVg6Gc8u63w2iLEMyWaovHaWY+NVsJjwyXjRHK8tNGVCtuMOR5bdNseHfc+n3jU55ueOOEsNLncmnF5oDNPDKJMR9M1troRUW5QlQFbgY3kAfcnAS0nJ6/gLDIZeBn7U4+yEQgBl9dimlrlak/h1kGLQX9F38l4NPPoWMVvG0GatPyMeWLg6hXPbC6ZxSZ7U4dLgwhDl8wTnSeGcyaRh23XBF5OUujsLTwu9RLefemcWeLSyJpxaFFEOrfFGCENAlcSZVA1DbpSUhQaNKCLmkoKzkJB0BQUNZyHOqUuKUrB7dkCT3X4+bv7xJnDRivlerfNIio4X2rsDBYcnrf47je1+fDJEUWlcBJHvD5Z0jQGn99P+brdLp84fYCuwf7Y44nOBnejU/p+gqkXqLJNVXo8P+iwFPe5vjFnthxg6xar8mL6GqcKLatB1yTHoaRn+rz3UsNf/hcPSPWcm5srkkRlw3dorDm+LSkbhXnUoqoVoOTWeMpuXyCFgqJI8lrj6y65bHR0fFMjyxX0WkOaAtuoScsaRVz8vu7NLeqm4WSl8k4j5dXzLo8PJ6iKwapQiDOLq2sls8jhyY2cWVITZQZtN6auG8rmomh4NNPZ0WCVGVzuXqRolLVgmQpePTF5YmNFz19xa1kAuzzf2uD/9uFDhv0S3cpISsF5rPBwatN1GnquTscSeIak5zYMgxCprKikh5QWjw1i9uY9wtSjKjRq85y9hY6GzSI08awMTYMk9/ljVx/nsP41fvnFx0gLl8fWa959dcXRwmGtldPImr2piZc1VCLD9wWXPY2W7uJaB+wtVAaBx5aq/DaFXFA0FYZeYaoXFP6qkbTsEtPIyRY2eWXSyJKzUPBEz2fT8VhGCv/k1jGHyTHzRKPVD/jWv/bfUMsKVeh4us7ZxGGzn7DIDC4PQuapJC00ytohyx2i/KI5mCY64zzjwXnJ97x1yG8dzLnUvpho1uR85kBiu2NsS0WEbZraoK3bpIpNIgVhXrEILdp2yf2JwmE8xjZKrvd1VGm+8YfUH1BG0e8Hfv7nf57hcPhv9Vjf97/Mu/m9QdpU7A5O0HWFkoQXD12eXq+IUpfNTsl0afNYz+PW/IRIapiNwjxt6Nmw5tmczBzqQqVSa7K6ZCZLBu2CTV9wVEtUYeMZFzXJOJXoWoeeXlPUOkL65BikqWC8qOn2K7pugmMoDFxYpioFEWpdsIgDhn7C6axNx734/Av8OU2tkmYBvtng6jW+U6KrK/ZmFkPbuDAuTmt8JyZKXU6mMDETbm5DXDZkTYKbmwSGQsfJePnUom070DiYSkFeWbzjSsAXTqckMuHyMKcfZHz2tMtOq+E08rE0wU53Tjk2WaYOp0tBXAhkpfLxgxnbg5BprDFwK+axx9HSQjQG89iibCqcRqU/nND3cs5WDkcLA5SEutQpCp0nRmOizKaWBrrSMI1Mel7EKilY63l8/sBj4S2YRT4qGrs9k+MkJVAEgZWxCF2ySqdl59wbW8wiA11V2ewkbHdCwqVB0MqQQmfdT8ET/L1PP42k5HovpKp0el7CMr+QVxlqzbV+xjwzKCooKxVNK1ikGoFu8PpZgGtWXFuLeDAWvHS+wHMaXjpq0XFzZG6x1hJIJaHjhtw+anNtFLJIdK72l0wTi1oq6IrEVGrySsXXG0Z+xnnkcKkbcrJyEAg0peRSP6HINcpaIFV4+aTFpV7MyXjBeucqX7iXUygVoZZxkjhsBhmrVCfJbbJaIcpyXKvhbGqhCIGhlJSlikAyWzks5hqxNicrGi6bGkWt4qkON9dPCOyKg4XLKtW46tis8hhV2Gz2Ja+dWVwdRbzwyOf9L7dYU13urCZcstv8gxcO+EM7a/ytj+zz5IZOx3A5neokMmW+0lAUwfHSwHdLNDT+8I0BUZWxv8yZJSrrWslGJ+bx7ojpquLVcMJ3P7vDi+dzXjqtaLkZaarzsdfX2OnFLBONjuIySSMsVTKPcr5mt8e9RYolCpxAcj5rs7s2pu2UhJnBWWhytJzT7SooscvYzHHsC0nZJJxhOSpRpjMMMmRoIaWg4+Z0nJI7Jxa22ZCVF9JxiULfyVDVhnli0UiBogqwJvgq6HrF3sQn8CuySENTJZ99OERTKmpFXEj3G40sqfmpvROaUmElDJ4bdXnPUw4fPTjlJApp2RXnpx6uJWm7OS2nZpGaTEIbXdWZpYJ/cRzxl79mkx/6zH1ko/CmrTGeKTmPTNZbFVlecvvI5B+9fMBHDyZ83W6ff/rKAYausNVrmJdw58RlGUks1UDXJP/xU9vsnUy4NV4Qi4S+nuPpNarQmEaC9faKRTTE0kEzMhwjxdIMkkJHiJS9qcNWIFGUmuOVgW3mFGVFKSs0VfI3PvkKy4XJY2seilaRly32ViV9R+KbNq4hUURFq5Ux8iWKonAyt+hYknkkWBYJr2aCG32Poyl4u2ccL3zivMKzKxxhoBPQaJJC5HSNikUsaBqNX3xlysjWeOUcfvHVc6LSRJQKrVbNSNOYpiqHy5q2WfPRgzOWicvVQY2peMRayS+8fkLHbfjw4ZK6UnEMjWHP5nMP4NmRx1/7lQO+551DpuUxZgxl0+CrCuueg1qrHDcHzKYmUl/x87cV1oKaQKgICZujgnTVf+MPxj+AjKIvJ77kRtGtW7d4/vnnAbh79+7vuO9/b5I0BYkiNHYCDdESuEbDtKiYZgV1tKTnpUzDDpdaBuEqoOtLFoXO0bxh2F/SdmtsXZKUDecLG60xebKvcjIR1KKilgW6HnISFWy3bd68bmKqUNNwGudM8gvJU7K8zHc90+aDqzeBYfDKWcI4LdjdTrjiGPhagK02HGWgmefM8porbUlTD9nwdfKqJm9WFJxzdRBgKX0GtsM80dhfNCR1wdMbBR3bodAUDL1gb25QNTotU5LmJdlMp9UM+Lqb8NnjFasMHs0vImIvtTQM1UC1a1wzYaNVIlBQlJhJWrHluyxTn4OZy1orYiOIGXg5pr7i3qTNnZM2hqqy0WoQsiapGpr8wrS536oZeBmP9wsutUpujmoWmU/VVEwTyfVuyZWeIHIl1/oFk1hgajV3D03aQcW3XR+x7fn8pQ/dwjYE33h1yM1RTlrq/JW3b/Ljt+4j5YytdsijucOtwy4dt8TWSl46XGOVmvT9BFeH85XLKjIYKTmHyQp30uHaIOU0dDiYO7x21sG3CzpOhq5e+PqscoMw0/G0kpOo5CzuIWuFJzcX6ELS9wp6do6KZJkMEMpFUzGtFA4XHsNuyN2DgECssSrOOY8dLncSHs4Fk9jl8dGKvanHoJWz3ok5XVgUtcJakFHX0HVrLD3m4f0BVwYZriEZuDHHSwff8njT1oxP7XfpeRmWLpknGnEOZwubm2saq7RBtxY0jYJqSW52zvjY/hrrnTlX2j2KNCCJIwo0+q0EWy+ZxgJNhcDUeDBp87kHNT3bwtErhCLRDGjbcM1b426Z897tPuPZFMM1+D+/9xI/8KlblI1BFkuqSqVtzdkdzFhlCk3jYNDlPM7x7BDDlISZziLLUI2SnlOyH/n0WjlJFvCmDZ9/fOs+tqJiSodWN6StVjwaewRmyV56zFcPYl4+bTOOTFa5wNRrHmuv84o8wjUybC3HMWvu7fc4XowJ84pES9kcZHzN+k2e2rD4Hz85pWUXjJcuTipwLYlrVVSNzp1xB1tXiEuHoZfz6onPNNVQtIJLgwlFaREWJkKYTEOXRFexjYSWVeOZFZPEJClKHsw1dFUjrzTaloanqdh0EFLjhUcNd8cJf/TpLj/74AGeK3GsCkNVmYQOTdPwwr7OTqfm6e0lcVkxDBImsU2Sa0SJTlFpnEYWhj4nTQestSpcyyIpBNOZhSIkuUjJmhpXsfnFOwuOwzexzGoU2eBYNnlTEtgxnllT1SYnsxbpquSFuObqKMLEI0nb7E12OVpo7PbnKI3JuicR2LScHNfMCTMNz1QJM4skh1pClqtUVFSNQttqeG3c8A2XfP7+Z45RjYLNXgpSo6ktPvQjP8bXf+c3oPhbxFXN2UKwvVZQFD4H0x5ZJXGshAdTSVhkDG2FTd+jt2WgCvjUnZL/y6de5GTh8LhasamX2JrBjY5DR89YZRdN3iQ3URU4WwqmVYZh57i6haPp7HgGuuVxnKbcnyecnen/5kPn33H80i/90r/1Y7/5m7/5DVv3J37iJ/6Vfv/fBj/6oz/KaDR6w9b//cJ8DkXpMInB11WeHhnoqkJYNLTtlNnS5Z+9POHKKOPZTZ1FYtK2dVRFsMwrXt1XsVXJ9371iP/l83ustcDraUhqykqQFIKWF7KaawxsjaPQZXcUcr4yiUo4jXP+yldd5u9/5oDArenaKpYusIyQeS5RULE1hfUgJy8sAqchM+fIsmHLr5klFkV1kQKlaxWlXFFXKpuBgWOUHC8VLL0hyi2KRrLuKVzuubx8lnMeV1zv6ZRWhZQxp5GBrTU8HLdwNJWi0kEPeTUcQ9nGdwo8L+HVc51n1koaCXF5kUj6+YcbbLZL5gnUzYVfZM8pEGqNJhquD2Mco0bKClOt+cyjTbJMYVUqCLMkKHSyhYMmKjzjIsK7bCqi2KZqDBoESaGw0V4RphaLJGCeQi3nWKrKZi/m6lrN5+502Qh0TDtGoPCxOyNubqw4XjgIVTKLXHpeyNnSQxU1VaPTtnIUIbnIAdU5i1xMbHQn5CDM2XEGnKU1Az+j5+S8dtrhWKsZeQlPbiy5r0mOFjaBF2NUAb12xizWuH0UUDcqLS/nmY05tl7y4mGXspEczzSCQGEa+1warLD0mgqFpDS40V8hJcxiC1MrqRqTV85aBFbF850JltoQZgbnsUVWCaL8IpnTti+Yp2t+TpxpKELygdcibM2kpCEqYDy3WKU6Ya7TDzL2TgJms00sfYquwSComYcC3a5opErKkkDtsu34PCwXRJnGRivmtTOTQSlAEUghcKyaF88m7HQtVkXF5cDhlcMSR8u4OlhQNQaO9JmOYW+V8V+8ZYPPH6yQRkIhD2gZA3y7zaf2JHVpIK2StNCpG4O3b5u8//45b1sb8vyWymFYossaRXb44tkK3SipqPnIwYKyyiiaC1uCjl9iG8XF4KsRjLOSsm5Ya4W0hM4njo+52nPYMJc8OgtQtYthVcfNySqVwCqZ1TMEDqahYMuakaNxGNb0vRRNVTg+7nJnabGzFtL3MqSERaKz1k6pGkHVaExjk66bkRQagV3QSNDVmq1WSloI1jsxWa1SegLHqDFsQVKqxKXANFS2nIyWLdifuWS5yaqJUYRBk8NplDAK4D97apd/8tptLL3h0jAmy03KpsTUBJNIoe00zOKauta4vL7g/Xcdeq6B50wopeBwESDJiQuNa/0V8wxmoctZmvATt16n7yioquBkZWCpAqVScb2cpimwDXh9kfL//CPX+ZUHJ3zqJEI2Bq5VoFGziDvkiYHTLjDNGt+u6DgZq1QlrwxUxcIxMs5im7apstPJOU8uhtKL3EJInUYUVHrOutvmzryiJMcWOjUJR3PBqCW40muY5yqzLCIwHHRNMg0Nhl7FNPIIvIhGk9zc8smF4N4Yun6JoVccnJv0A8F2S6WSFccrFd+oGS9VIjWlbelcaSnszyGrG5xGo4wqRl7NRqDTqXVOzlUywyFPVEp1gZR9RpZLS68Ii4qupfPZRzaBneOYCvO8JkpKrnZN/vlr+1wZpdwfW8ha4SQuKESOpbWgNFFzC900+VPPdfl/f+GM1G3IZgZ2b06U/ftgi/8t/F7UVF9yo+jDH/7w72qhr0RMsowos9kIVFq2ZJ4qCDVj6JX4hkfPdCkzh1Vec3dccxg3eHbF4zspz4xKVEUjKTSQOicrFaWpubkdcRapDIKQjVZKUVpkhY+tK3hWyCoHXY1QVZ/rXcHxfJ2bI5tXHz7gzod+msGf/POYtuCH3nedjxzVtO2MMKvxO2eousKam7LpO9SNQ1wooMR89KFD4Nj8Z90WW67HJDFY5TWfOxK4ZsSGZ9EyA0y1RsqM87ghKxVc/YIZcz4PeLuno+gGn3i4pKRBMyL6Hriqx04nI8wrUBd07AxDNckrBc+UpEXAPDGJC8FL+yot36BnOqx7FXuzHlHqUVQKq6zhwZHKyNPQjIpLQ4GnC3a7OW9dP+HFszXe5r/Ky82QJ4Yl08SlajSK2iDKdYRSoQlwnRxP0xh0K6LM4Tfuh4yrJarUWKQl/+L1U6YTlW962xH/7a9lXBqlGFrNJGqx3cpRZMbezOJ45jLwDPYPh9zREhStpOtmeEZKry7Z7pUUtc7Dic3AU6lQsXWJlBp1rdB3Sh5mPh2rpq4qZkuNLDXwWgVprPHwpMPXXD9B02CamiSFjta4pEWB5pUkhUBTl+y2MlxPYVyOsY2GhzOPvBDs9CLCXOU0tGnZFXmhocqaBpVlomCgsEp0vnjU4vogpu2U3DlucWM95v40YOgWvHzUZqsTIaUgKQXHCwtLU0HWGFrNe7a2+bufPOStj10UXlXdMC4dHFPS8xtePU94PGihKCrXei3uTWNUUaEqFQgFW7WgNgm8GaLuYRkplmgoC8kHHx3wF9/6LP/3z9zj8+chWa6z6Rr86Gf36dkmT7SH/Iu7J7T9hNfOa27qCg/HPt9x4xo/c/eUJCtAHxMVCueRyUYnY+84YPf6CmUEQjQczgw+/GBCWuq8qe/y3JbDbx5kHIY6PbsgSm00TfKhexusco1VbLHdixj4KWdlRMfWWAsyXh8HdJySRmqMQ4OPFyvWHJXJ3OPTLPnF++dktUVLb2NKgWlOUAT0vJD74xZF1XC5v8RQFJaZQNcVOqJhlRjcORkxCU2ujRaYypKjpc35SiBUg+1OyijICWMfGomtqQilQFMqslIhCOZEZYsk1/CE5F++NmNV1LiGSlE3nM9UfEulyE0UpWKz06AqOh1L4fbY4HovRxUK80SjqY0LI25dpaZkmTao6Cwyk8BO6fk521aDbUnuTRvWhnOWmU5bV+i4OZZeIxGcLtpcGpwQOGcsoh6+5TLYqJClxlmacTDWuTJUOJ63uNqNqRuH/VCw3Y5/m0lQU9YaPbeklhFhBoaqkGQWw06OUGrKWkFSsMxLXj4NsQyJYcHJ3MUyc+ZZgTsacnR7j6vv3mHown/0dT0+dT7j1jjhKa3EUV1czebJkeTe2OJ/+rrnUFH4r371Nt/6+ICTxRjPC7DQqauc73z8CX7slVskZUNa1yRlSdvWcXQdR4eeU3A411HUhFf2Kzr9GdNMp63AmuUgpEtjvvGpZ29kBOtXQpTrt3zLt/yO20KI35HG8a8Pm95I+dn/t37/34Tv+I7veMPW/v3EE1uClqVyGmpIwDAXfPHQhULHtQrWhyscU2O7a5AUkoaaQlZMVuAa8PHvfY7/46/d4gvnpxcMaqVEqCuWucZT2xPujjX2z7q8aTdjsjIoC4EEVD0kTlS2Ogr/690DPAs6TkNS6uj6jL2Fwm67xlAkhq6gCJU4lwhtjpA6b91JOVkEaApUtcYXj0zO45qvvizpWBehDGUNWaXwYKrQSEnfVdnwDN65HfCF6Sk9S6HtZkSpgWwpVHVNTU3fMSkqnUcTnfNYcmPYoPoNk1inTDWudHK6dsUsMWlqlU8/6pPWgqqRF/K6WmCbKddHKYvUpGguEtXK2sJQa7544pFUsNYtsLMKQy2oG7B1jaEXo2s5L70yAqmiC8HhwuYdl8YcLLscLwKkKCgqA6SKqlQMeyXTSKXnaARBzqRYMAwytloJ662Arr1iq1WwLDX6Ts0iFXhmQyXh1eMOVtpQWuB4BY8NQi4HPs88scHr2W1eOBJ84TTE0gxM3SPKNdp2ziK7kBkWDVSVz+W2gm0vQZwT5RfprW27oWpUPLNkGhkUjYVvVbz52Yd85M4avpmTlRZIwW4nIa1U4lxHE5I3rY1ZFBavnLVRBKSFjq0VHC8dXLNmux1zHpu0rBxNuWAzr/kZQz/HtSviTGUSmay3SxZFwbbqc57VCLVAbRqEgDBRuLQe8od3NvnHt5ZYRsGm1aURM0bBir1pQJJqKOYEt1wjTyxqt6SoNaoC0lLHMSpEo2EoOYanIISGpincmyR815s2OawfMo4t6lxlPagpGxXHSvgHXzhk6BrsDJY8Pkz4rYceTaPRMW1warLS4MqazvlK8lsHETtBi4yUVH0Z2+zjVQ4bfZWPPYhQdMlO4HPnLKYV5KBc+Cv2g5R7Jx1coyJwJJ2WTsstma4MYjHBMDXCJkRIWOsleEbJ/bHPpX6Eq1d4ekWUK5wlkjCxUA2FBzOTZQpVDd9+/TJ/+r0t/sIHbzOLTIZeRpxr+FZNVoFWt3l0bBDVBbPQoOPl3NxYoCqCB2cBIy9hGjvcWF9RpSqXujEdWXBcW3ScggcTj4GV0UiFdT9jmlh4VkjHVzkau7Q9hVpd8YnDkI89iNh2RoTyhGlksd1e8XDaom4UtrshaaHTyIauI1lmKrdnc66tXdT+TW3xaGKj6w2BqXG8cqlqgaY2jFoZ45VOXmo8NphS1DBZtXCcnKIsaVsCx2p4ft3gL//Gi+hYdNs1D6cm2+0MW1PZ6MTkjcTzFU7nLY7mMEkqeraOb5VUTYktG3RFEhUauibwdINGK3jLyGFvvsQSFwz1X35lzs5QYOgq57GKjAW7/YzZykJrVEZWQK2HmNYjatli6AlqJJZxMbTcX2bMjIyRr3K53/BwYjGLa5aRwcEixdMLdGGw1WqYpQ2qJlmkglKUbPiSoenzvitdtroGP/jxA4omZlnGuLrDWlcQZypfdbNGsM1/eHOD/+snDnjHlssL53MWsc1mYBA4Jbf3Yr7jmTX+yZ2IaXqK3VisjUw6moFpmdxcc7gXHvG9z2zyP35oj/Nxjek2FI3kbZst9hYp3/JVHi/P59xJvnLrqn9XaqqvgKf5+4eOU/L8Fuy2Beu+xNEUHB0acqJSkpQ1s6wmrjI8P2HDtnms67HlmwihsCgSHoULPn8Irinp2ILzZUDfk1iKwdDLQSmZpYIXDz325yqGWhClPZ7oeZiqzSTWeG0x5ROf/SwbNzd5ciR55yWHz58uyCt4tMy4v4w4CxVu9nW2/Raq0KjJebiKGKcJmy243g5Iizaq0rAsQxbVAg0YGF1Gvk5ZK5RErHcPGXkxN9dqNoKL7vd/+myXiBV3l0tOIzie6yANNgODK/2MrhtyuMpxjJDtloKrq1ia5OHMYp7pZFXD3lzHdXJ0Ah7OuvzUC5f4tdcG7M9N8qrGsVJ6bRVD0dhuVQgp6dgxcVHzS68PWOUqZ+o10rKDgkHVqIxjBUPNkJS8cuIQZwG2aNOxJF9zLWStHVGYU35j75yagj/81AGukbG+VvHaaY+dQclakOJqCm0rYpkprLXmPDZo+GOPbfDn3uLx5pv73FgLURodXdHZbOXkuUVeGWx2Ytb8nPvnHq+deJxHKqpa4hkZiwwCK6HtxJQVbA1DVLfgP7zW55mrBU/tjJnlOq+du5Q1LFKV02VFXqnMQgvfqXDNmntnHr5e0XEqfKukbeU8Wrh8+N6INT9j5Kd0nJztTogQEl2pqGuVZaFzuvCw1QYpBSMvpecVrEKd05lHz03p+xlFpZKXCnGhUVVgaDHbrkdZGnz8UcSzowCkQMqL1LRa6qy3C+LcYLMXcz+aIGsVQwUFFSEupKabTsBm4OBqBrPIYz/MsK0KlZqWV5GUCj/+0iPirOLN7SGeoROJGGFEfOfNqwSGxs4oZN1PCCydg2mbJNX5iS8e46rQ8gX3zgIeTVqojeDBmcfTV8e879o+79iJKcrOBQMJFVdreHWe8IuvH3Mal1Sp+G3D4gjbqPCsElFoXBstcYyL1Le61tE0yaOZdyHDLGGZaRTVReLFxmDBfKFzezwnKUNMLec9u20eX1cxtQqBIEw9dto5bQf2Zh32Fx5lreMaCULUXBmOSUpBlEsmS4+DWYDe2PT8mu12wyxRUdSaeS7p+HN8M6Ntp7iGzjL1OJt3mUQKjnVOXoIUkpfGM3QEhiq4Pqj4S2+7iow9Hp3qICLKWnK09NjwICscBm5Fy06xrBl5VROlBvOwA42OIlSiuc29iYo0FgROxizyqMsuYW4hRElSCxyrYNhaYWsFApWT+Tofu3uFDz00WZUJqtZgawae2sHRVeZFyvGqISk09sYDqF2iwsQ0VyBzxpFNlDloaFwaJNSipKHENyv6bs3ArTB0QVnXfOD+BFNT0BSV7U7J9UHB9X7O4KknSdWcSZoz8nM+dbggjLo8tQ73xgZ70YqTeEVZ13RtjR9/8ZC//pFHhFXO3/nMMYoTUcicpKoAya2DCtGY9F3JKgmI8xZrnRXTYsZxvCKrU7ouDC2Ptz+uIssWvXbFn3vbJkkieHRuMM6+DKaL6ht8/T7jX9fM/9qv/RrPPfccH/jAB1gsFiyXS97//vfz/PPP8yu/8iu/31v9A4FFlfDxhwYvHwUsM9DUgr5psdFScDSNUbuh56ecxgkFMbv9iGnSsJi6PNXe5p9+YUpYVpzEOVGRczCGk6VFXeuEmcaGa9KzdbKyYZI1FFrN3XONsql4x26Bb5poRoFsJIae8eKB4GhhcqkDmtCxdEAq5HXBcVQQZwZdS+FoFrBMbapG5eUjjXtnOkHtkRYeYa4R5xonocFJWJJlFiNPRxPwwkHC//Sb+1SVQsvN0VWFrNK5O9FJq4INX6XlSLa6S57dHtNz6wsvnVRDQeE8NIjrC1/DWWJz68Rnb27QsyRNo3ESaqSFwhOjlKpR6Dgx08jlC3sj5onGw6mHlAYbQcaoFfHE+pzNdolnlpS1JG8EmiJYa+Wstee07ILrg5xSSuqm4XAeMA5dVonKtdGKpm54++U5j68tSauGvDRZRDZFZXDrtEWYK5yEAQ0wT3XmGawHBTudlCQ3CTNBIxVmqcX9ox6/8tImItvizjjhpUOTm+sRIz8lCXUOjl2OJxfef7rakFcKq1RnERc8OmvYn3nMI4uq0TCNmr2px/7YYxRkPL255PmtOWcLmyK7wqiVo6iSx4YrDpcuj2YOpihZphrjxGDNyy6YQlJBU+RF2ERqcxY67E8dPLNERXK2sqmkgq1LZpFFz8nYDiJaVk7daKy7DjQwzSMMoRJYFbPEoK4V6kZlmhj8wy+c8J4rKtfXVtxdLshTwXjpotQKm214emfOSbLisTWfw7ML7yHHqogKjWlsIQuLPDdxhEGg2xQy5M4s4p/ePgIJy9ghqVQqJDv9FaLy6OgmqyLjq9b3+OJJj7M441se2+Ivvusyqp6y1ptynoaU6pRrXZtve2KA1Pd47dRnEgWsewqvz+cMHYd1P8FxzljvRZS1gqmUmDosYhtLrzD0mjCxCQyNw5nCo1VEWQnGCxOkpKhVHK1EEZLNVsLIS9lup4xjm5ZT0PVzHtueAjXzRCIbE71o8fJxzN/65D0acREVf7YyyGsFTTSoSB5MG9r+gqaBwC5xzIZ7Zy3KWkGoDfsLD9usuXvqowDjyAYEj42WqKJmo52Rlxqz1ObFoxZhqnI89fCNEikFnpvQIBkGKZfXxrw6D3l47vD6XoffurtOXhoczVWi3KCWoKkq14cRR1MTQ1OYJTaz2OD2qc8iVug6CrvdGFVNMTXYaBd0nJit7hJFwPnK4Wju0DXbGGZDxwbbKvGtgn/y4gm2Dtu+w9HUpa5NjpYuQlQM/QzHEMwiHcMsaNkCU1VYJA2mVmPpKq+dtnhl3yApVBQUjpcWrxzbvP/+OVLCKjQY+hC0S2qRIpFsd2tMQ5BVJaczi7KBRFkwWRrsjddpRIZhhThGg4oGEnoumHpJVGXkRcO6L9ls1Rh2xTR0maU1eZOBVAkMk0FQMeqk3FiLOc9KclIeTHMehnPW+wWuriKEIK5DUpnhmQq6nvGurRE/d+8+HX/BB1+dY5kXtdjpoiHJbDqe4Ml1g67XMGjBQSI5XDQsU8l5nDFyDZq8wy/dOcF3KlqOTtkofMfNDa71HM7CmnXHJc1sOsqXQQb+72uqLwm/K/fNz372s/zsz/4s+/v7FEXxO+77uZ/7ud/1Zn6vsek5KHpDVGTo6pyDpcn1QUFV2jy2loNUcFQdR5NUhc5CKsySikWmchBpeLbExONK20FtNBA1y6xhUSQUpcHVkaAsdcpKJ7BrHM1ESpgmJtcHS45XDqoW0nVV3vr2G/RbNzE9lVWW8ShN0I0IV8l5925D2/QIrJqiKYiqCM+KmCebbLg+Te5SNQ33ZwppU3CyMrnR09FLE0urabshh2HJN9/Yg9rHwKRtZWR1xPzY5ke/cMSbgwLDMHF0yTIVTJYOWqskNzM6zoL33ZCYqkNgKOR1zsFKYBgpatFGV1VcAxzNxNBgFmmEVUY/SLjeM7h72uFwnuGaYGk5Jxm09Jr1QHKjW2AbGa+c+tydGAy8MWGhoAiF6/2cMOugCpOdbonWtHln7wbXhgU/+Ol7rKqCrpPTciDOK47nPdZaOapSoakxeWlRygvKuhQ6G0GNo0vGC4ePnE74jYcKwmjT9TMaWZOWJZpW0A0kUq95MPGwNEFSWOiqpKwspqFGYhYM3Jy8Ekwilyc2Vtw/Deh7Bb/02vSiEdAHzyi40ot4+aSDYza8ZVfnLBQkdU5TqtSqxDVKArviUmfJ6zOPvpuhKwYCQVMpgErPyThLDA7mLoYGN0ZLNFFzMLMIM42Rk/Gxu0MeX5vx2mmfLL+g8r86NlBEQ1N4hElGL8hx9ZIrLRXbFEzTFF3J6DQKq7gCKTAVl/O0xFZMdJHhmwKn1nkwrZhHJi0vx3ckcVlzbzHDkwGXWybL6qJgOIk8tNqhaBacZHMWScDHzo5QLMk4DanrgB/73AG3lhMeHxUcz0a0hMejeI5t1bQ1m5M458ooZE01eDSTZKXFk9tzntua85tHm9w7a3E01lhva1hGhW8WLLKCs7kCqiRolQRWRs/PuXPSwrEqht2ItFKIc40Gwc32gNdW52g0CEXiW/D4aE7VqCxTg3unbZxWBagUiUHfTnj//SNUpWCkSMpS0vUiwlyn7USEWcDAi4hzl0Wq03MqhDQpS531lsJ6N2F+rHN05vL49RmWblykfqgqbadBSJusahg4CcvERIqMUaBQFS7z0OOPv8VhkRXY/gwVhUFQsHc+pKX2+S/e7PLTL51TN0sUUXEeCcLCYsPXUYTCmldh6yFhPuf2SZc/ceNx/tqvH7LeU1gfVNSKzqX+KQomWqxjagb70wCBwmYnx7cEeQV3xw1Dp8TWFa71QY590nrFPIVK1enbJqVtgVGj6yviskRRdBxD0DYupmTzXLLKKuJcxzMlllpxuZcxjWvyur5gIwgDIU0eH2U0jct/+eYN/oeP30FS03IVytLn5pNXufdSxv5CY6tzxoOFia91MC2bx3oqvtNgmBGnS5UsNziYrZCNytDVGFqSxbhFt5/iqALbaPi7v3lGZyNCQeGJoUrX0uhbPu+7UvPRexaLusDTY5Jao1YrVnlDkpcsEhVH1xgGCjeDIZ/9fT7T/veE7//+7+dHfuRHeNe73vWvvvaN3/iNOI7Dn/2zf5ZXX331DV3v3r17fN/3fR+r1Yof+IEf4D3vec8b+vO/EmEaBVWxTstqcFQDU9VpapXjtOCJdshmpyAuS754uMZbL+U0FbR0m8oyCQyL/+UTZxgBKEJlltS8+VJGYWuYek3VmNhmxfGsJFchSk2SULC7DtstE9vIiYsay6hR3YZHMxj6DV3bvkhfNQuqRnB/BkmhcqktOVlYRKmOLmwWSc7DuUCIiscGGnkJq8hANgqbG6cUYcO628JsBLraoJU2ipGxmirsXKro2iplBXmTkycqj40MNEUSZhpHqUNcFdRNQZm2CeyGVaJwUho8vqaiiIZZqpGXCpaqMfAXpKWJpQh0TRLmEs8QrFKPo3OHdlDSyy00teRN2ydEWYtV5pCWGoGzYBF30FSd6/1TTsOArXbGJFbYHo2ZJQZ55eMaJV0vJ8kbNEvBNQQbO+cYikQTDgBNqbAQCmmhcKUXERcC15CkpULLgLNc4WxlYJklHafgeBZwvTVnUShoeo2Jx6/eCYmLCqlrLFcjnh10kMoRLacgi3Vcs8TSLphhjplzMrcYDSIQFw2B44XNycJhrZUw8BM++bDPzY0FgV4zWzh8ulLotWxGnQmvnXSpSsEyMXktN6gQvHYa4JsSxwRNgYOJy5PbCzyzYO/A5fpgyWlko6sNuirRtQt53yI0+Y1Xt7g0WvHWzQltO0Ox9nnzZclnHvXw9AYQ9IOKo6lOnJi0Wgmet+TlowHboyWLpGTdaXN7nPIN13t8/vSYV0567AynvLQv0Y0KTalJCp2+n5MUKikJ19tdigJenc/p+JI4FWy1Vgyd9CJ0o9B5OEtxLY2ttsV/8tSIz57scToVhOkQR1f4Z68csMhUtjsFhhExXphsux3WAo9bpyHTRCeNulhaxUmS8lh/iClbHGZT+m7Cg5MRUoPHegrzyOJslTNoJ4xaOfcPTe5NE6paQUoVKQWOrkIT0JCwzE1cvaRoFMJcZ7edYKhwFtlEiYIQFgKBI32isuZtGz635jPOwwbfUwgzHVuvWQsSDLVif+ah6A3HKxNdq7G1hkvdCN8oQAiqWtAVW0j7IY/mDkmhX7AcBESZRlYbNI2CBGgqWnbNNLYoGoOTlc3OMOQ81Hl8PUHScDx1KcsGS6g4Vk3ZWKjqjMv9nDR3mCVwdRgzSRSe2FpyPGvxDbsjPn2kYRsJqp/zYOJwc33BIrPJSgNVKYkTA0fTMYyao6VKU5u0VIGh1XhWCI2PocGf/aoR/+jWIbWoGPkVh1FNWqgsMhspCxwjZSkN+n6BFAX3xxq7nQpdbShLlcdHKa8+aCFlyTLVubmW8PHXXaZxRddSaSqFtSAlKSxmoY3vNFgaDBxB0VhMs5xRP8d2oBIViIaqDIjrmLrO6ToSKQWFlJwtHTY7CeOwYjMoSUuXtqlQcWEXoKkRk0hiKi6mppMjKHKXn/2jz/DnPvhpPnxnwY26RFcgT3Q0R2Oy1JgsGt52vWYz0KianKOVxMLFNXQEBXVlkyw1buclHaPkcFFiKipN4nOlLcgleF5GIzV+5s4hYWSRVQWqJnHdmsc7Dn/rIwe8eByjlxp/81dPqW3Q4n+n+SxfEr5cNdWX/Ar89E//NO985zu5ffs2P//zP09Zlty+fZsPfehDX5IHwFcCVC1nldUM/BU7AXz1lkZLbdOxDQytIG5m3F+uOE9qDL3BtCqkFIhK5xt2ttjxWpiKQ1HXTFYlr5yVHEUZi3GLZ9YUZO2T5R6WXrPIE35rX+P2mctZaPPJh0OSQqNn6VzqwMnH309mtIiqikymhEWF1njc6AT0HYGt56yKiJNkRZhLFtEGgd4hL80Ladw0Q9PH6IrB8yMLWzXpt1J21w7ptfd595UTDiaXifMuAsjqjP1VQVk3PNav0BVB1ylZby+4NJijaw2qWjONDLLKYpF2SXKLvG5QlJDHhydseRpDDwxVEuUSVS9xzIQsFbxpZ8nltoZjmPT8nH5rQVaXdLwYKQWTRZ9XT4a8//YmP/HZXXQt5RMPOuQ1pJWJqepcboMqGlp2xtBfcBAu+Xuffcif/ZlzHh32mUwH1I2KrUkgBVEwSwWX+3MEGnltEJgar553OQt9ZqnCr9zu8cp5wju3AiptcWHWvPTxHOg6gqyy8a2aGo3zlc3+3GCy1NHUnEYW1NTUTUNeNTw5mtHzMh6MfUqp0nFyNK1BUSoejX1enwQIVAKz4movIWpmXG7pxLFBUgjeeumUd145Jy0Et88CdjoJ08jhseGK7XbGK2cdbh21OQsNFonJE2srikolTA08veCJ0ZLDScDLZx3+0BP7FFJnpxezOUxogK5VXHjBGA3dTkktBQYNXzxdcjgDoS8Jm4plqhAlLoFbcbCoSXOduKqJK5Uit1gkFUdzgaKX1JVH00BcJxyNBXenGV9/dUiNYLMVE6YmpjMnLQSnK52WH6OUGk+tQ9erCRzBXhLSdWs8DZ7ud1hwhmNW7AYXf/+KVnK0cHn93GARWax7GuPQ5sWDLrPEJBBDVKVikWX80RtrHMwt2qLP4+uCtlvRdkriwsLUFdbaOXmpcTAOUIVg5BckscUr4yWGUHFNwVftLFlv5ZSVwcnSZbOT0LJLntyccmU4w+tEHEUqipbR9hIC4yIFp2VLepbkzuHwQiZZXkRKl7XkNIJJrDNZaFDZnC5cPF3H8Svq2qSsG3w7ZOCFIDKS3OTRNOB02cdQdSy9ZC0IyZuKhxONX70dcpSc0rVVPMXgZOZzf57ynT/3Mk+MbFQ9pGpM+l7DVqviUi9kpzu+kFJVCq4h6bs1hqrSdVXM1pykTrnes7E1BUd1cHSFnpvTsgpcKyGrMoSoWSU2B2ebnJz0+NyR5DQs0dWaNQ8Glsf50iNrYgwzYnOQcLVf0HcVRq0MqSQYWswqM6hrg1Xqk9UGs6zhJBTsTV2a2mQjqAgMBccqOIlzlnlBxymxGosf/cgEVzUxrZiXz1NenxWkhcIH//7Psen6HEwuoVsJ/927LnMWXiSbOTr0zRaXWjprbkmTq7hOiRQ1hci4c1RzFlXYuqCIfZ66UnNzqNNyCqZ5yCzLKJsMSY5jKLQ0B1MxSWIXKovA0FjNPP7SB/f49P2asyxlHH8ZY1zfqOsrCPfv3///WTO0Wi0ePXr0hq/3p/7Un+Lbvu3b+OEf/mG+/du/nar6MrxeX2Fomy5lpXIe1WRNykkoOMlSKimJUp/zlYeJz5WuTlULVgUoWk6sRPyz1474nvd5bPZS/sRT6zw+1PEtgW1kLHPJWaRyf9og7JIybqEWOh1LxzU00rJmkafMEo3ThcZkptExHdq2Rd3AwcKgkjUPFxJTK9huacT5xT+UV7suoPFwXqOi0DYtyuoiySovdCqRcBw2dCyLuraxjIqdwGK9r3EWV/yFr13HqF2EaFDUHMdouNyNUKg5W7q07IrADTG0FFO1MQzJ5a4KtUpLFWhqiWeUdOyKtXZM4MQkuYGQClvdBe+4eozA5NHE585+mz/29AhdVTlZGHTdFBUdgSSvGm4MYzxDoWwUHL2kKHXM3x4Q2XrJpU5CXupEhUZe1yxih0XikdclLTun58E41olzjXms0w1ytEbnpX2fu+dtHA1qKZilBnfObHpuQ1arhJnBPFG51Itp2xVCwGRmYSsKwlmiuyk7wxWnScyL4ymiVilrlZ21kFGQsNGKURVoGoWWUxK4OaZe03VyArukZZdstRMmoc3Qy3j9POCTD/sYbsna4JxK5OxPfQZeyjM7S952aUINVLVCXJl8Zq93sb/OCtOseDj1kRI6dkFRX5huX+pFAJwuXByzxrJqzMbh9l6HcWxhGDWKchH+cHN9zjJvWKQai0SjrBX63YidboxrSXT7FEROWsJrk5CiqHnhbB/XzmiqmjBtXwwYgYfnLRyjQlEkuiaxDclr5wklKdsdm6Iw0DVo9Jo0rzFUhcCR+KZKVBhMFhWfPznlLFL4jf1naaRGk6m8ehYSBAdISu6dtQhDG0PTGEc1EQuqWiWsEtK6QFMULns9AgMGwYK9pU+tgKzheKHx33/tDdY9D09X0eiwNlwxzXNUI8E0MqpG4cralGVe4ok2s9BgmRnoas08Nbl1GrCIDapKwVAVntyYMWplTLIM34u4FZ7x5h2FS2sx6+2UG6OQS72YvFK4fRygqxVdL+WJrSWeU2GZNVUt2F96fPG4Q1IoGO4jhuaQqhKchwbTSCMrVRy9Rko4XVkMg5SOW2BoDQOvQFEK1topNdDUJq8eetw9DlgWKu98YorrF7Q7CVeHGpbesNGuaLkxy0RnErpEmU5WGZRS8i9eP2LkmTyaWCSVwmar4nN7PY4WFlWtcDhzaCoFQcXQjxl4NbomeX2Scr5S6Nga6+2Eg5nOD358jCIVztKIWSpxNQVFXJitp4XOOPQJs4vGtGwMNlo5YV6jq5AVJtdHC7Y2J3TcAtuquHXY4s27Mbv9HE1P2WwbRKnL0C9ZrSRCkZSlSt001KXFelsjp2CZVlzuJ2x2ckytBukghSQsUgIrQzQGWWZyMHZ5ciNlltUoaoipgyoNOk6DIgwWscfevGSZViiNSpg3CBSetHf5a1+/y7IsWGY1eQFQ07EUNl2bw3nNL74s+dEXjjiNC06iBkUI8simbcPuSPD2KxGJLPno/ZCmMrh/XFM1FYGp4JsaZDZX2xpP7yyxnJhVqjKXCa8vQ7quzppv4JsqT+zUvPVySdDJ3/iD8d/XVF8SvmRG0d/4G3+DH/qhH+L7vu/78H2fH/7hH+by5ct8z/d8D+vr67/rjfx+IDAVylqlZZcoaknbbHjxsMciVVlVMHQVnhqq5IXz2wazClEheXgKb93ocffOAUVds0gkeQNX+hJTdhEO2EbGcdjwKIxwzZSeq9GzBQomOQZRLlBaMY2acGWY8E9ffon1bxJIqWGoOgNbx9QkDTFFk3EcNRhazeXA5DzqMosNzkKDg1VDyznnud7F5LBnCXyr4DyqOAwLDiOFndYQxdNJCoOivmAkHSw0fN1mK9Ax1ZJsqqDpSy71pvimwS3VZKudoGolnz/o4JuQmRWuVTOPHd61XXC2koybCktPcB0NS2+xESj0PUlZ6owjh0kIqrbAM2O22zajoEZXC/KqxNBUzpYG+wuDr9pRUNH5wsEO1wcLBp5EUvK5Q5u2pbM36+Lo8KarKxSl4DP3hggJJ2ENKBSVwm5nxdDXUQQkucMigSu9nKEX8sLRGl27pJEWNCof3JtweZix2c6YJYIsV3HsAl1pCLQcwxaklQJodA2DZzYjnh6d8IXTAfemHlJW3J34KKLh+e0pr5y2uHd8YRh4HhoEVkbT1BwudKaJRlSCppW8PJvz1mshn7vf4tWzNlutjMfXltwdtwkzgyQ3OFtV9LyLqYFnFCia5BvX1jgtX+N4WTKJDVq6yuPDJXszj1mic7xcwzZLdtsxxcJib2nj6xcf4r6/oIo1AqeglgJJRVIp1KlgFWkMvZpMSfBqSCqDuhLoXsIs1FDLhv/ocp9/+CDmTZs5d0+hStvkpDRSpWMq/PPbx1h2g22UICQIgapL1q2MMDW5l0WEUmOn42EIjcd7AbemK85inb3zBY3Q2WhZHC0Lhn6OY5isVha35zlPrlnsrxKGnQrPalhlCkfZgsvrEfsLn3ka0lQS1Up5tEyJS53dboJrZBxObI6mFoYu2eglqKJhf+bScjM8qyCrBc9tT3j1pI2pVRdRur0QRysZBjE1OrIweHoj5GThsshMZqGO1qRkTcPhQmdopzjFk9wenzN6IkagYWkqJ0uX85WFYVXUasLhxOTyMMcPLhg62+2YqlH57OsDzkIQUuPGzhk7nRVZ5bLKTaraZ+DX/MkntvjRT815cN4wW5isuQ6PD00sGbLRVRCK5Dufucp//8mX2e1WLFMLXZe4ekxlVgy9iJfOFKrKYugqfOR+jCMC2n7MTuAgjX1WmU5WcUFJtgWPtxOOopzPPOhzpS25OoqxdItKEbxwZGBoEZ5R0TQWA7dipphkTY5UBFc6GWWtcaNb4hrxxfurlDQ0RInN8amHohfUakVc1NQIBq5NWhhoqoapCkwzJJUrzqMeV10XIQzaeou1YchHX3eIygjhukzOZ/j+GrbS5e9+/iFFpTDsRfhOTlqpFLKhVjQMVTL0JGmu0zEl9jMht09UHhQ5rlTY6Vd0WwqXWi6aWnP/3EMVCWmhAIJpKkirlIMJbPmCJ7cbntyqiWuDvpvwyoOAO+XsDT+j/qB5FP3reOtb38r3f//385M/+ZP/qnY4PT3lL/7Fv8jb3va2N3y9hw8f8va3v52nnnqKMAxZLBb0+1+GRJWvIJysBJOkRjN0juctmkZhzVXxDIGp5mSlSmrUnMQZy6omLzxUJadjl9iKwxdOFpiaxiqXNKKkVkIKqbDMXKJM4mkuXzUccTDPmJUNilFxFhc0MVzqOKy3coRUkI5K26k5WUp0PWfUKpmkNYo08bULz5CsEthmydWgzU99csKgJ+gYFpIGoUvUpsHypsxjwUYQ4Bk5miqJCpWe0ed/fXjOqCX5jYNTdL3Es3IkEkWJOVq4tCwdU2tQtYyhkXO6GBClOp4pMBWHr72qceLcpmVK8kpHiorH11Y8mnSwzYr9mcUoKFnlHrNEYBsVvW7KP7+ToiC4uV6A1DiNNGaxRc+RqAJmqULLytBVwSceDXluY0LPVel7DQ9nOvtzi7oMULWS06UGhYHUdcrSYBTYJAXcGCUoTYBtS752fY3b4YS9ecMy7TMMIh4b1ozaGevBik1f5YtnHZ7aWDGOVMYThzQ12Ohn2MGc1eqCPSJ/u6F1Y/0UTZW07YKDhU9eahwuLLLC4OpgSZwbLOKLsIowVxn6KXFusDdzccyKYZChqZJxYzOe6pwbLl997YxFqvP5Bz12hgmfeDDk2mDBreMORaWheBJHbzD1mrrQLgYEoYmqSHyz5GjlYKo1qtIQOBlJrqJLn4iKXivjYGGza6bcT33qWmGjHTPyEs4jm412ytM3lvzGK2voqiQwCqaJRVYKOnaO3y15NPVAAVk3jDo5UV5hGjZ5KWhoGLZKkDBfaai1g1BzdF3SVDqW3rCMNKYrhzvKkwg1o6P4PDivMB1JJWteHUsWiaQSxcV7TDT4Tk7XSThd2FhqjWrUGLqKbwr2VxpDPyPKFZ5srxFN5mRlwV66x6NVi8nCpOMVRInHtbbH0bJkp2dyPO/xMJ2yTH36jkGclVQNZOKilowSlaOm4dIoZ+AnOEaDbCSL1ABqHENiqjWz2CHOdAK3QJQ+g0DwYDpn3VO4f9LGb005WVi0VJ927TFngqIUTFcatlbjWQWTyGKtHRHlCsvY4iSER1WIqHp8/SWTD++dIYAw1wnMgoGXkJYqs8jG0CpmicZjayumkYVnVHSDhL0zl1ErBUXhcGGz1lsi0HGsAxapQUOFqTVcGZbMY8EidQhMuD5akVYa9xYrtroZLRvaTkpRV2S5w7W2w8deEyy1EiEEm505o9aSVw/XWKJgmCW3DgOe3QnpuDVhBpUUpJnFoLMANaPvSYTU0LUKUwMhLaaxRlyAouqoSB5NIcoMqqbH8VJjtxfhCBPHgLsnPmndMPJr3rJl8+nTOZYO13dS4qqkrHx2ehWeFVE1krBQuX1s8rZLR+zPBijorAcRhl5y66iFroZE6YXXpWkoCKBubPI6YZ4ndC2TsjSYnHm03BKZ2izyhHTesLEh+enb+xRazs+9FkLpMs9z+lpDJXPCQtJ2fVzb5WRV8NRmxfc//yyurvIt//TzhDMbt1vgGylZ6rMZKJw1S7b6MakCh6cmV7ZzolRjmZQEmUrPd4hCkydGAqVu8/GDGR87GfPLf/x53vt3bxEtMx73ayz1jQ/J+oPkUfSv48tVU33JjaL79+/zTd/0TQCYpkkcxwgh+PN//s/z3ve+l7/+1//673ozv9cI6zm9zoq+B6IxERi4ukJda4wcB1s1KVTBoi6Iy5xMXfJzf+wP8dFHC37gEw/Z6dtEkUtgwrIBoTRM54K19YjzpOSlQ493XV0Rp0NcpcYzMsaR4HNHFXEh2ejV9Bx4eGTQ2d7FN1Rq2RDmKmUt0bUSVUtIigoah65p0kg4WFqchILTsABR8M7tnKQMGIcGkHF3WnN3WhAYknXPwdFVslLS9Ra0nDMOFhYb3jqGcmEWCDnLWuD7CT1HZd2VKCzxrISHc4WeJ6gqk6bR6Fo1xwuPpDLZWzoYek4hK77+as6jqUtRGRR1w72JTriyuLaeo2HQyBZr7RxTay6yN4yInY7Cs5uSWlYczgM8qyQtJV84tmgbBtNoyNHYwVwveG4jphY5p0ufs5VLKnKe3ijQ1Jo4t4gzm+MwZeQV1E1J3lRstArC3ERTFCxNEucuqlJwfS3l9v0+h4ZBXEgGfsEicVlkDu+5dk6/qpjoOq28ZBYLdPOEZVXyky/t8GeeeRGhXEJVVEZewYOZyyxxOJ35mKakLjVaZsGd0y6eVjPypwRmw0vHPTaChMfWl1wdrLCsAl2pKWuVSoKtXaTIDP0ETWs4j3TWgpRXT1rcPWtjKHt0bA1NbSgb0DUVVdS86/KEjz/sc2UQcTC3uX3aYauzYryw0FolvlVRVJDmF7TxgV1waki2+3Mm0YXReFoJRkFBnOnQ1PRd/cIHoXQYOoKP7E+xdY3XZ4KicFhlJY2qsNZS8DVBKlJ0tWFvGhCYDUliIpWSSOicLG36joKhX8TBrxvbvDQ9Qddj6mpIy9CpjYS6kejWikYGXGt1uJXl6EbFvVnEbsdEipqz0GAWWWSxw/Utm8N5SWB0eG675rP7S0xD5bG1FYFZc7ZycWUXTYuoa4XThc2VYcFGJ2KVGCSFwVsvjTEVwaVuzr2xT1qqeHXD0crHUksejS0QgkHQYFkpj3VW0EA6tvB0A7e5xKfux3TcOZYLr5173BytiIsKx1TZ7BbI2qKuVN60m3AW6kSZRminfODVDr6p89hGxbsdj08dJLimuCiC9IRa1gSWSVw4/NTtA4ZdlUY0vP36MS8+2OX733mVopY8XGb84xfOeXrN5bmNmigX6KpElgpHuomjaRwvOzw1HPPr9xpGXkxYrbA1GM9dfl2c8dhI4cHY5v5MMPJgpyMxVI3dFqzWlzRS0vMFadlhmVq8dTfjNDQxFDCVhjWnRLEU9uYtNC1HVWMszWCRmago+IZkzU84WWmcLQRlraCVDqpTsN0JOVrpTJIcQ+hc6pcYikGUB6hIjk8Ff+/PbPBffuA2faDn6Dy/YdH2Uprv/OPcDwO27ZI015DEjHzwDANfL0hQmEUWRS0ZNwlmvmS+6vL4sGG3peJrBqqaMs8KThbmv5K+FbLk3jRH11M83SMwNTxdkFUuRazx2O4BPQ+Wqk+4csCIOZrbdDrF//aB8+/xO/DjP/7jfOu3fiu7u7vs7OwAsL+/z40bN/iFX/iFN3y9P/2n/zTf+73fy/r6Ol//9V//B75JBODoDVf7AkVVcc2KeaKiaZJpkeKIgmTlc7BQmcUqG4FCo0vS3Ma3G87mDVEVsT9xuX8+ph0kmAqsapem0nE0haLQuTryeD0+pesIalkxXVn88afWeWF8hqwNXLvC8hJ2+ifszTcoap07pxZ91yGwFJKiZJUrKErDoLXk/fsRb7rmUzcaeQrt1kVK4jjNUSKdzUBDV2KaRtDzCx7OdH74t07o+yqWMChFxlonJcp18iZGNjq6YqEoNapaMgltFAUa2WBqoNDwhdMF9/OM/m6bvhNSNQJdkdw5baMqNQoNSA2h1HSdiEXsoysS14qJSh3fUug4Ba+eeASOzrObU6LcwtQkZWXiGQWL1MbRBKqiMEsEmqqhKSBlQ5oq/OV3P8kv3Dnji8cxu6OQVaoziRziTPLgzEXFZLEU/NfPBvznaxusspIPPVzwicMzXj8/4drowvdnmRkkmWAWuVS1gt5I+q2UUgriwkBRwDQqLDXnq55Y8cXjC38Y3zJ509acR3OfoV/whT2PgaeTVwrTlcnupZj1IObueUBgFViGRlYo7AQh60HCz+91udzyeXzk83CaYBsFW4MUKSVdNyMrddpWySIVTGOD97+yyXoroZQKk6XNtl0zTSxcq2ISmtxYW2LrFWVjMF7Y9IIFlbCIIouWnaP7Met2zGunbX7z3oBnd+bkhUGRByR5xuMbCyopWWYGtl7RIHCtgrxSGHgZeS1wbRiHKsvU4UbPJKtXnCc6Kgk7/RyB5P55QTcomJegqzmOKhh2UqLUYlmUJInga691OFyd8sQavHKYMZ9IGgGWrvMnnt3h4wdnrMSYtOjy+plByyv4mst9rvcc7s+WSBERmAVlGXCc72OrDS/PFcbZRbiDqglUFYadBXeXJU8lOtNlCVSMbI+WM2caVaSlSdd2CKya1450QKHdTtDVmqQw6LkRutLw/2Hvz4Ns2/K7PvCz1p6nM+eceec3DzXXq1KpJFAVJSCAYgp1SOpGcqMmusPYGIdCjcCgQA412A7kDrXaJdQOoAADFqYBMQipNJRqUM3Tm+98b96cT555z3uvtfqPfFYH3Q67SrzSgPhGZMQ5J85Za2Xmjr1+57t+3+/Xtg2YC6PbtLbZ6uZUrUQ2NhvdEx6c9Vgturz3asL/7vmYXz4cXxhD21PuzWp213KG8YU0z3MMg6jieCHpeS0LX+HIilXlMF0G7LoB/+b1ObZrkVY2iW6ZFQ6WEAyChrKxmWUuZeMwTX1OFgFNKXEA2xUUrc1TW0uEUJynISdLH20UW52SvHLIKpeun/LVe5tsDxSdcMVWd8nRvMeTm3PGKw/XUrx0r8fasGJ/nPCS0gy6OaNYcZY6DFtD0yb045pCaSwJoSdZFhZZbXjH1oC0SVnaGY226Ho200xy79Qn9msSq8Ciom4DdGNzupQ4dkPX86gri6KGrm9Iy5DQKXEsxZP9IZN2ymlW8PL0PjdGFbUWHMwGtK1HoRsspyDyIKsEljRsdOGrRwllbfAsydmywyyHUSenUT4HK8NaUGC0xyxNUMqmIWY9KTlLW4aeh+fbJGFLbFnM85g6KZmXmn99/wAM5E1A6FhcHiqaM4kqQ7LMprM14X27j/FPXzulbGvmheJLhxnXNiyOVhmtslmagvGyJbRiXK9mltt0hYeKHMbHLp1ehe1rzquSWkhmqUHaNd+2s8687CCdJT/wj28SdjWpgUczwar4bWAE9DsE36ya6hsmigaDAavVCoCdnR1efvllnnvuOebzOfk3wZ38m4lAxFxOXBzBG6ayDY9WKadLl16sOZn3GUQ188yi1YLIJPz0lw8YZy0CzeFC8+X7DbVtWBc+8dAgk5rY1SwXMdd6FpGjeDRxKVRJphpeO7V5++Upy3SDvcQhqx1iv+Qt/8cfIGuXvHU3BePRtJKDWUJVh2DAsy8MHydzya1zi9MV1FXM42sXBEOjBFgpd+Yt9yc+Tw4FG7GFlFzEQrY50j6nVQ7rYRdaGyEafHfFq2eGK7FmaXWJ7ALHKom8lJsTQ6tsdOuxkWi6fsnhykFaDfuLgOOVz0ZX0/MblkWALQ2zWnNeXDwfxpLdXs16knGaaoRwkNaFT4EyFka71K1iVsJpduHp8+zWjIezi26YRkEcV7xzbY+v3K85tx5yZVBxZQD3Jx6ubeiGikle0w0la2HLnfOYS30Hg4Vru4RORuA2+K7i9rjHeOWw2/H5vveMuLV6nUv9lFtnHUqtGQQ1r5x4vKPTkOoLA+iHk4TtXkVReyzziM+NbxA6ilppvnqcINFcGsy5N/GJvZbXjzqoUvKua6ecL0LSfIs4fMAgKhHS8OUHAxzRgjRI21C3EDgaZQznmYfvaKoMRnHNrPDxpWGRupynLo0WGA22ELw+7nJFZ8xyj4045974YjNY5BbjVcD5ysexSh5fH/PVwyGDqKZoXcYl2PaFHjy0W4zSNMYhtAwLLelGOeeZxCPhLdse43TFhueR1i6GhlSn1MYlkobIhnEGkR9QScXk3Ec7EHqajaQm8RseTWICf8nxosdOGPPzh7fxXYNFSJpr0qpmx4qZ1hVbziY31jWzespZLrm8luHpAVkhyBsXy1K8ZafkpSMX4SzxHZ9fuDehqipaZRHamirb5uHswvW/YnZBhnZKOmFD5BnujGPesj3jZOkjDZyuXNbinO2+Yp65zFKPwFMEQctG56IlWimX2SoizVvS1ObbBwndJOHK0Ea4t7l5OuRSL+f6oMB3JJ7tIYXGdzV5VVBgc627xsEs5R07DplaMQh9+mFJHI45KWLmeZcbmy3GuNRa4siQSe6QNiU7/YbD8QZvv2JB8wydaM6PfPomXztJcWqfyHL41QdzeolH4Auk1Ly0n/C+Jw94bGTwLYuT1YArA0VVN+wvUzaHKX4u6IcWriVwhccPv3CFP//zdxCiZacnqNuQnid4tNDMqynaVLiWRewJHClJa814YdgRBhEYOj70oxaNTSdcMckirvUrznOf2ydr3J967B+GaKfhsSTECRS2Ceh7mv2ZTxwtKPUp0hqy3bVZiyTvflrxH/3MLf7h9z7Jj3zu05StwnNsPOlhVz5q/4s0g/cReC1rcUVWC06zFtcpcUVC0wR0HMPTaxbC8phZGdqqaHHRQiOMoh8YysKmyBI2N1JsS/KBJ3L+5Vf7vOfJY6bLPoF90akYOi4dK2K6cNnqtaDhZLrGzl6LY74J+5/gzWtvfvMP5v6dcOPGDV588UU+9rGP8frrr2OM4emnn+aDH/zgv5XU8WbhR37kR/jgBz/IfD7nO7/zO9/08X87QmmJ7yhWVUEQZwRezCQTzAuHa47LMDSUtc/6SGMJQ2NqThcxaWk4Lyq6QpK4EAcNlzoWWsfEbpdZDo5leHBmuDndZxh67AxyWmXz1IYHQtAomGQWla4J/JInbc1b93KOZjGqCKmdhoeZJm0silWAl2S8bdew27GY5YZFrul0c+6dB7QUtGnAsC9pFWh9IfM5XMLRUrDuuUSu4V1rXW5mpxhhcN0leeax3bEQ5ASeoqhtKt2Q5RZVmuCGmrXuiuOFy41BysxYnGcuSrusJSvedXnC7bM1pnkIKAZBw6IMSIKKvHTxLMlOtyJr4NZpTFoGjFOHw7nFXk/QqpzIa6iVIfIMdWt49TTBkhaDQHOycqk1rA1LfvHhMV85KulFBU3jM88Eka94x5WMeS756j2L63sF/+DeF9k7d+jJXTq+S1NLXj9e4/6k4LkdlyfWlwxjRcdLeeWkh+sIXKdlmXrEboUTWFhCszvIWFUXvn2/76kTzlOPblCx2UqOZxGjsGEtqdjolGx2Ak5XPraE914Zs6hcloXN5mbNlw/W2eimDIYZ79ve5GP7j+hFNludgkG45Gzls5mUfO7+GtOlx9uvTjhahOAYlpWD5zTkwubV/S67azlbcc7SdTlLfTY7BQ/vd0kzh7JxeGxrhtKSrlcSeS21LQk8za5TcP88xhKGQWfJzbOEnU4OSHb6BbPcY6eT8+ppD9dWeJZiPak4X7oss5DrnZjb05S9EUgUrXFIq5rI13hOwygpOZuMMGaJCFswNrujlKaOcW3Nl09m/L7rHe4sl7xwqcvL4wWjZEVRu3x6f8Wt1QxLh6wch7VugUuILSwOZzUvnWRsDpd8db/LRlIwyzxCq2CVe6SFwLIrNns5252CWeHwtg2bv/flE6QLZW3xZGeBqyF2Y/7Y9SsEnuGLR3Pef8Umr1teX12kkj2/N8fG8GgRoluLopVYCPLCYe54hJ7CdRR5Y+F5NQQSJ36VrywEsSc5WSV4ds1bLk84XESsKsVaUiMwNEqw2Sk4SwOMETiWomw8GqO4l83YHeakleRs6XGoHXxPsqodjpcBiVsRuQqtLYTU5IXFeqdCYrNYegSJwJKKeRGQ+BXnmU3HN1jS0A3KC0J3nHBlo8S2Gw6mPuvxhQH30dLldJYwcaCyarJKEvuKfrygE9R0fUPguZwuIvb6BVXr0zY23aBC+gW1dpmlNr9vu8u/up3yrsdWzIqGR7OIougSeC2TTLJchSSbmrJVJN2KVvqMVxaPby5oq5Bx6rFqGkRl2EgcLnU9Xr7f4MUuw3jFtz/2gHkxIK19LGGwtMQTDovCYIwkcGzmqc9mZ8W8CFkWII3Cc1xsaTHPBNfWZ2x0Ss5WhpN5j2nmIwHP1qzKmMXERfVXDIIU34PQbbkcD3hQtkwyzTCZU1QBh4uaCEGvtXhs3eJzZz5r/QaUzf/w+j26gSRrCxZ1wT+/ecYgDBg3NoGAUa/mxdTlh791h59+8TYnWUuZSSJsLg98GkeRhDBPE6armshXTHLJ33nxhP/zO3b5xf0J58uKjZ7FwHOpSovf87jkE2/2xvhm1VW/S2qqb5goev/738/HPvYxnnvuOb7ru76LP/fn/hy//Mu/zMc+9jE+8IEP/IYX8luBSaG4bCpOFwGrSnK4tJmmPjcGLqtVQt/TSCPQRrOqFZMUJukZSkkyrZlXFl4guBQF9ByBaEKioGFVN3z1TsT25Zp75x5SNuz05rTa4rkNi42oT8/RFA2c5iWnH/8ZDupd3vWh92IZn9NlB99R1K3FWWrhuYKerzhZehwvbYrKo+fYlPicp5qfe82m1jXjvOEdeynv25N4tiR0G5SpqcyCyGq43nM4WgwRWPhujbTmlCZlK0lI6gjlSColeLQypO2Ksh6RF0N2ug2bSc6kaHjtzONqT3E4T7AsSdHYlG2A0BaVEtRa857LKWnWZRi2HMxCbo4lLSXPrGtcSyKRpJUmrS3KBqSdI2VDUYW8frLJZndK5LXszwJ2B3M+f+zzh56+wv/j8yvWwgW21AyiDN9tyMqAw0nMc+sJl8WT/NrJQ4oyZ5LD2cJiq2cQcslWLCirhrKKePnA4c54grECznPBMCywpM9Wt2L/POFz04iJ7eC7CsfS7J/7dIKc0Cs4WLmAYDOq6Pqa02XA/XMX3xJMlhFPbef0PYdXHw24urbkC/sVjrVBqS0uDzOe2JzzYBLxbY+d8eWDIa0yxL7DVjdjkoVsd3I6QUNeO3g25JXFmlOx28tYlBdxrfvTkLyyGIYNrtWyLF2EFixzF9tRbMU509RlVUY8nBWM4gphFEUrCWxN7JRkdcQgUtw8SrAA323RpiFxW+ZLi8ooRqHLrMqpGklRSlLh03EVQ8sjiSXzeoHrCBwHBpHH6dRCKU2W29SdgkeTiLVOyekq4Gri8Uv3CnY7MbNUMeovuBasMWtq9rOSP/bEFuO84LMHBf1oQmtCssphWrQoC57bEDyaWMzTkK3+hF+5FSEsePUwp2rh8lbO0TzkYaYY9Ct6ccWysJBOTaM1jlUzyy32+hmrymajW/C14z5b3ZxV5aNal7LxMVwkvdTKIvFrPKvmYBqzP3HxbZtIxRwsc26pgntFxXrXohPWtEqjZY1B8WDSx3c0Pd9Q1g5b4ZBPvWJDrPjuJ5/kRz+5Ik0Dzo9jitxjY5Cz3ssoa8HK0vzCK5s8tV3z5MaSm2OXfiA5njc8WM5YFSmijRifWCR1l8sDj7fuhPyDl05oHMUV32IUNXzw2ZRuoOmFNR1XcZK6nKY+Wrm4luT5rZSXTwV5K1kUPtcGNs9vdHhuvcPt05QbazmxqzHa4/mtjKL1aWko2paR2+DYEqUs6tqhDkrqxtALSxZ5yFObK/qB4eUjl93ekkezi5MtR7j8qfd1+NR+yl/4ll3+y898CcdxGYUGT7pcXjO8fjpidzAjcDSW7XJ1PQdj8+OffcSqiLmnCtbjksYI5spj/0uf48a3vkAvALTPKvNpas3PvTRgY7AgL1p2OprEM5i2w2N9haU9xkuLVklq7XJ1bc5rbc7rDxw63QUdLyS0XZ7f9hjYQ5RncTr3CX2F7SkaFL900+U7nhuT1oaylcS2Q0znt3hH+50HIQQf+tCH+NCHPvSbMt/73ve+35R5frtgkORYgaGTlARWxNkq4Hq/xRt2OU5TsAtWFQy6GWnTEogez651+dLZ7A3/QRdPS4adlrq12V8q/tjbr/GFw9t045Qd7RDYIVpLLO1exNzXJde7W3zhLEcZj0Gy5FKvxRYxiWtQyqaqbF47gkw1XBoK+oMUgc0g9FkVFrZVE/mSRSmodcF6rMGTKHVR7N6fxFhWykuHEd9xtcPBDKaZQvoNI6vkeKlZTySbiaBoJHv9AozgdHWR6hqIhPfu9vns6QwBtFogrQYpLCzpIGioWoNrubiOYZZLhnFNx9M8mAm6bsU8jbl7ZrE1mHFtWPPiow5aK7aGNRtJjmNB0QpiV3CeCRqtCB3BKNZ88WGAs1aiWpfQ0bSm4Lw+5a1XNXXbsNureOseWLLl8w+GDOOWZ6/OWYsrWi0YFwWfPT3lRrTGg1lNGGlGocO9SUw/zDnNbCoVY8uWQdhyXsTUrcPh3ObycEU3bLCQ7B9eZTWref3RgNunDUm8Yq1bsus+xVGV8fn7NuudHGUEbRNiGZvzrGQUlWzGBXemCf2wInIMqhV8+vg2yrisxxdEjm8rhv6KLxwMGcQlUmi+eG+NKGhJgppe1FJaFntRzjJ2OE998lriSM15FnAwC1mWNnlucy3yWRQZ/aghDFqUEjRS0A8LTqYxo6gmbyweTlwcSyMsTZE7tFoSOi1GClAGKcFxNTaGyNfMl4pxVrI3WHIyD7je7ZCLU/Lao6xbkrBlGNYcTjOq2gYLujLhaJITBzlbg4qDqUVgxZg2AE9hWzXdsGSzI0DNEcLgew00CbtrY6aF4kZ/g39y65hvubzJp/YhzyuUtHnr7jmL4wRHgu1ontqaoVqHo0WEa7f8q5ehH3kUqmQQlZwufBDQdXx2BjX/6sUSz3N5fiPk7794jO1q3rZtKJXFWSUxSlCqCzJwI3ZZ755jSU3kt5S1JDCXeDgp2R7BrcMdbFtRiYLH1uacrgJmlUetLgJnAquhUhbL0saSAhvN2dxnZ5DSKjDaBqlYNYK9YYqcW2TKpS0VSoE2IKRF2xo8p8GzNUmgWY+6fGB3nV85PmZk7fDF+4Jhp+bJrSWJb5CyoWoEaeURui1GXZCh290UWybcm4RYomEtMWAqJit44cqUO2drnMxsokATOBbaKF458nlqKyV8Q9Hx+NYZZ6vBxeFq09CqkL//xTFS+5wsbfYGOXXbcFg22BiWZUgvmJGVLr3YMMscoGGYQGgbxqlhq5txPWg4mGvunvb4ve9Y57rv8AtH93l8rWReehyvLkK01hKFbStWmaDjm4vD7X7Fdl+w1anYn2vOll2y1MFxFE9vWSxKw+G0Q1pf3B9rXbOoawaBjWcZ7LCh6XkEoWTR1pxnDr2g5a17DqcPGp7Y0FRtgBCSZ3dKbp443JkaXrgmSFYwUxBZPtcHNacLi36omdUZr06XjHLNMo/wbUVeOlwZWPzMK6f0w5Y/+sSIv/npitO65j03El6ZzqgaTddPCI258I70Nf2w4e9+/oy26fKfvG+bt13y+Euf+BonK4+3PTP4Lds/fyfim1FTfcNE0U/+5E9SliUAP/zDP4zjOHzqU5/ij//xP85f/st/+U1b2G8GthOJZQI2Y4NWHiNfopqAWmkOlxaBX3O40Gz3c9Y7Fr6l8S1Ic5tI2KyFFkVtIwycrAxKtsx1xV5fkXQgEi6J19K0ko7ncJ7Z9KOaFkXWurx+36cTzTm9e84H/uR7sdzowrNn5hCGU44yxXtGJethwzBqef00xLcuOnEUAmmV5K1gll+w8duJzaWuYhDUlMowqyomRYtr+az7Qw7mkrSyEbLivLyInvTYoO/bCAG+3XB/rjhMLX7vFYvCD0mslqvDBa6z4LRQGL1L6GjKxqJVgodzeNvWCmNCTpYB988uvixFnmYtKcgbEO2FznQzMfhOQ6MapFViVMhW5yKG9HB60ZGU14JFrYmcgnfsGe7PEt6xAZ+5n9INWl69FxB2Da4dIaQicGyur7cE0QmdtXP+0gcvcbLY4xcfnnJWzpisuhzPEkZJyXThcXljwlcfDLHcmmc2SoyBFx9tcXW0pGwsdodzHrcLfvlsyNG8wyK1cbD56r7De66OiT3DwSKgcC/085PU5mTZpazhPdfHzHKPk1mPyxsT7p/2iRzNM5cKHk0dHowTQkfjuorPP9xgq1syzRyK2qaoPVAaWwokhq7bkLeCpzcXnGfORcqBkswLl27QcqlfMM49On7FUxtLPnM/YFG4XIlW3D2PuTrMUMbibd3b3Myv89JRh9BVbHdLzlYeri1YZBabw4KeGxO4J+xFCUdLm363JJIO9ycljTC4bkO5sAkCh2d3xxS5Rz+I+PIx3NiYcTWu+dk7m3SjmnnpIlpBXQpWlUvXdkncGsc2bHYadjo9kuCM188i7EHKo1XGk8M+n380w3UbFAVnq4inukMKMUP0FgytbT7xwLDVyynNDA9B4BhOpiFe0JI4DSfTCBuXd12OeVjNSEuDrSzK5qJzzrEvTkdf3h9QNBcyh80o5pVHBWGQsdfPeH77lEeLhKq1aFuLyDG02qcTSK5vFBydhMSRZFw2LCsHbPALl17Ysp5o9qc9FrnHWSpoGpsr6wsSJ+TRuKayKto04ie+/DLnqwTXrfmLH3icv/vaa9hWS+I5BA4czAVP7U4ZBGAosYTL0bzLzqAha3yeWLdYswe84+3rvG03wpIXhcEXZmOUnWFkQNGCJyQnqy7byQrcClC8emqzHTu0ymJ/0qMflLx2ahgvHfqW4G+Vh8RhS79ySGuBazWEHggjuHs6xHdrytbCoBhFBafLAXvDBl/AsjH0ooplbnMwS95I6DN8+XDErVOH0DFUreDzjy78PF6fpGz1Sj73UPOeyyVrnQCjPW4MbNaSinmV8+oYHH+JcC0eLuFqr8PTG0N++f6KVWZTihFVYQiCFcd5S2x7IDweHzUkdkjeDrjWn3K8dPE0HJ8H7KwtWRY1rbLZiBx87ZLlfZ5d03Rcw6ywyGqFK22ypmVWtUzKmrOlw9MdTde2mE03eP/1mtVqHS0rtGlJU59afhME62+mYeJvAz39T/zET/Bn/syfwff9r+v9P/VTP8X3fu/3kiTfhIjc3wUYhdDtaloTUDaCyRJubLcsFza3zxUvXE9JAo/zlaSsurxrb5tR5PEwXeDZkmlmCIIFlmV4+TBkTyp+8ku3WY9rpPS4MrBYFoLzFczTkH4v48HY4r842ef5Szb9sCFxfNBQNTlpLQi8Eh3WyFJyObAZRjVg4duCVknSRuE6LbW2OTyNubFTkeUdkIpOeBGd/fqZxe5A0/cdHp1rMt1i2ZKvnc3Z6Oe8fS8lrTqshQUPpz2kkJS65nTlka/61ELyyK5RjUVe2qx1Z/R8hRe2ZJVF4ELsQuIXLE77BDYMwgrPKanbAdoBKVvA4XjWZ56vSKKCTuBd1EetwbU0vuWQlpLXjrt0fHCloNWGvBLcPfXoBg2Nsgksje/WDMMa3y4JXADJJHXxbBhFNaFXobTG0QGHmcc0M3xhuSBJGuKg5Gq/yyvjirz2EBruTXz2OjVuo9kbZgySmoNph/vjHo7doBrJejfDjgyN8vnhb7vKlw4X3Jov+Ge3Zzy2M2cy9+gEGsdqKP0GJTUHS4/XznrsdlIuj1Z8deGzKkN6oeb+ecilJOba8IgH05CnNxYEdstjoyXHaYDWgqopWevVVI0k1FtgjSlrG8fV9OMKIS2KRqINTJcua50SB83mxoqjsUPltRSly2ktCYYNng2DbsXp0uVyPyf2HObFRXJsL6qZpD6bg5xF7tKNK5SyQAgcW/Hu9Rm3g4p75zFp5VJkNi9VJYPIxbFs0kLQTUpO5i5tZbExrJllksPG0A0FZS1RviTNBR9/dMJOEvD6bALG4lov4LVTydHS0I0rTsYd/uTzPT59ck7dBvzSo1NOl5Jb44rH+z1eFYfEfsPrBwOej0JeV1Muj3JsYhbpBpdCQyH36XVKYidAqpZGCValQ+w3SCNxRcRjI4u0bvnprz7k6saKZepRq4AonNH1NffOXU4XPv2oJmshfiPNzrI0rfL4nicu8+DsgAdnKTtdh6qBq5sLVpWLUhbTlUPROCgjeW5rwsNZxI1hTqUMaWXjOppZ5nNpUHCeKfLSYqdTgZYM4opb5xHX12vSSnB5kFE0AtuSnC0sagw3tma0teEDj4345TtzPjmd0e0JXFtTNB5nSwfHttlKWvJGE7ktG92GQZQTezVpFbCW1Nw7j9mfWKSVoBsqHpz32J9cyP609nkwkXz3Mwn70wnjZQSm4u175zyaB8ReTd1KpplLEjRkdYttW8xzj7deWrIoHd5ydU7ZgDkP6KKJkxIpLTwnRCMI/RmLImKyigj8JfPc5lt2hvzixMLVHqm3ouNLJqmDlDGuA1lhqBpBxxGspEDKlqe256xKl7ZtOVnErHKXXtCSlxZVZfGhvQ0++uoBG74kchu00FzzaobxjHHmsEhHWBIaA3Yb4kuX41NJM6j4uy8f4joloWdoVUhga+rW4x2Xaj7zasQn7uVUieI8lRhsHGHhCBtpHP7la1NiE3IwbglcgxvUvHqm2EwMpdRs+bC/WnB9rYPtznluw+ErY0kvWvEnrzzJ333tJg9OfLqDlqsbGtW7SF7+tdMjfm2skMLQjTWTQr35G+ObVVf9LqmpvuFfczAYsL29ffFhKfmhH/ohfvZnf5Yf//Efp9/vf6PD/ZbCthSrpkbLMa19ykmmqRUcTn0cu0UYm72Oz+WkQ9+L6AWCWiteO6sYNwsKXVErzR99ZkASg+sqdoKAkdtnu+viWYJS1XzpsOKrx4a7Y5/1pCB0ajpei295bPh9nnnnc+xdX+POxOULB/CFA8Urhwl912foW8SOiyMtDC3HqWCcCjy3pm41G52UyHJ4ah06gY0jFa6zImtT1qJzHBOxGXRYljH704A7E8nDZUZawjNDm9j1SEuPrIZSr3h8/YidxOFstUnsGp7amhF4cyZFQ1YO2IwdIlfRjwqQSzx3xZ3xkLIJKRqHzX7Ow7lNo1uEUKx3Wq4NG96ykzKICgZBygeu3uP3XDvn2qDmPEv46v4ar+7HaGqWhaRtAs7zLgKby72Cm/NTpuYBtYZWW3gOXBrU7PVK8kYzzwV3J5JfvLvil49e4RNnL3GWKnZ7BaNkDkLzYOLS7VT0A83ltZrntyUCQeJVPL51Ro2ibA33Trp88dE6iyzmg1e2eMdOiBAK36556SjhYGaTVYIv7g946WhA5Bv+0JMuL2z1uX04omtt0NorjmceRrTsrS3JlttMFi6jTk6W2YycAecrm9OVgzaKshY8uXHOdr/kwSzkeBlwuPRBGR5MQ87TkIeTLo7UGCNwHU1rJOtxRqsFD2cxW52cQQhnaUA/alBCIqXhqN2hyhvmecCidBmvAupW0vErkIZC2QTeiqyyOSkMsSMQKDY6GRknLAvDZtBhrxchZIUBHs5avnByhmNVPL0x4/NHmxS1oB/D1R4k0YU3VNvYHC8VG5HFwWrFMnO5uzphf34RwXswz+hFFUdzRW3NuTOpCV3FH7zxECkMD6eS2wcxu7GPpz1qJRjEJa0WrHUbOoEi9i5IUtc4/F/fv8fbtju8sDlkIwbXq2m4uFZdu+Ro7lAow1W/j7RaptWSRmuur+VErqFsAhwp8GxFN7xISHk0dUBWhF5BEikmy5bnRx1u9Ducz2KWhSStBV9+GPHaQZf9SYAjXKh8xvOEtUgwGJ6xPZzTTZZcH814/lJK4CqM3eJ6hi8fBKwlBRtJzvVRRt93udyv+PitIScLi9YojhcO17t9vmXrEmlbMEgMt89y/qfX9/nBf3mXRabxZIBnC5TWHC8lq0qxqByksDhPezyxkfM9jz/FIjfcP+swTROqqo9EMClrvny0xHML+rakLAOEuLgWPKe5kEKmAVJotAHbMghhyEqHunRwrAvJm++VeE7B9WHK9WFJaFv4lsvpCg7nFcdpQZbCL7y+ZLlaRzQd5nXNsPeISmcorTHaJ7IGXOlIzlcunltjiYa3rI34vmceIy0VYEgclxf+T3+RURCgVZe7M4vzcoUfzNHyIlY3sD3cNiFybPbWGhzp8sLlizb43Ew5zAqOV5pCKbq+xJdd8tWQ+aqDbzk8Hl/jn3z4O6gWLsdZSqNX7E8khWooWoPREt8WbA9a+v6bXzX8z6aLb9bPbzX+/J//878uXf968EM/9EOMx+N/53kHgwHn5+df9/svXbrEw4cP/53n/a2GtFZYzhzXzRH2FB0W7CU9xvWS3YEG1SV2Jb4V0rE69F2PS32HQimOV4p+d8ZmojiehoxCzWYsSTzrDfIAfLfgzmnLom4QTsv9sU3QerzjckU/MMSeIKsCXj91+ditDq+dJNwaX0Rlr4U+gS2JXIg8gZQN87pkLV6SljZKa3zbR6uQuobdYcpWL8Pz5vSClJPzNbY7NbNSEQeKJCoIgyVV6+BaNtvdlMg19IKWs1QwyWx2nSv0IofAVxznOaPQIQxy+r5CYzMIKmwJ20mFEBWurVkPDQiNweBZFk9uLFnVmrxy0EaQNwbPbghsiP2KvJGMV31eOenx6nHEw9NN3j24QuIZlhUo4+DZFqGXgjBsJC27g4zNZEnsaTzHpm4181Jy/3zAIM651J/yzHrBlX7FWWY4WUT0QoFxamoFXb9hXJ7xzstTvnXviGdHEz50/ZjNZIVrg0DQDxr2hkteuHLCdq9i2K04GEu0Fnxgd5ef/MI+P3P7iGnV8tyWy1pg8Xt3NhmvXEZRyTAsuXeecPO4wyy1OJ1f4TO393Bsw8E8YJHbWBYsmxlfPejiuA3Hy4BP3t/gznmXjtuw2y+4NMpBafLK5eG05GDmcuuwS9sInt+Zs9PNeXpzRj+4kJSXjYXrKu6MPXbWLfZPO8yzDRa5gxBwpZ9iC8XVYcZZGr7RWXJhlBw4iq1ujpSGpzaWpKVH4LUMw5LzNODmpM/lfkY3VIR+xfaaQaCYpwG+lRBYMZ5jsGywHHgsPqVRko3ukhc2dvGEh1YJ24OK0C85zJa4xsOxJb94y+XhSUJTt7iWwnY12rR8cO8qHRmx4fVILI/vemqTpT4ntkOOz0MqJfG9UwZhjtQ9HEbcnxRY/tEFUWg3zKoCW2ra1mKYlIRWzLxq+a9/7RafPz7lUXlMJyo5z3xabThYZdSN5O55wsE8wrdhLwnw3YrtbkarBLY0HM0d/l8vvsZKzNkYFPy+G31euDTk5aMBZ6uE+2cJGsGl4RKJ4ta4Q8dTF9JKu+X1kwGuaxjENVIqRlF5cX0sQu5PYpSGa2spi9KlbGzuT2LGy4iqcXGclrQO2PJ3ee3I4XP7K57aDukPShK/pNFw/zxgnocczjq8eBzRDyq09kiCnPPM5ubJkFoJjhc+w7Cham2SoMa1FGnuoRQkYU3kKRxp+G8+eXFwmhYeq9LjzrnHKKp5eislcGtunzjkWUS/X1MbzcEs4M7ZiEXhY0tFx9fYVksvzNnq1Gx3SwZRBgLmWYIQCter0cYmdFs++bDg3Vci/tvPPOJnb50xL6BRAa5siZ0M39EI2XIpSthIDFXr8rkHXcpGMi8tTlcu79hYZy1I+L+8c5vAMfzdV+8yXrU02rARu4S2YZ6FCGIezRJuji1Olw55o0lrcI3N07sNFhaidmmVy8HSsD+HWkHRwngRstmzGUZcpOBaDj/4wnUOZoqyVpykkmVbkkSGrYGk40PPt/DKkChYEQYlpanYTyec5iVKC6ZlRs8XbEYe/+NLh8S+ZmOnYVGAkhnSKzmrcvbTOQeLhrb2cI3Nl4//3ff//1/8h5rqG/ubfl0dRcvl8usesNP5ndN+Py8lmWXzYLzNtfWGaz2faWbjCZDS4mBVYluCsDCM4ppF5bBsKmzPY80PkMpHOzYHsxohwHMFeS45TTWqrZkvYH09w0oH9DxDJwIHl1zZ+M7FF437U4d1y+GlccJ/+o6nSaTPf/T/vsXOULHVrXAtw7Kp6URn9JOYJNvmud0VjoTYcfFsiSMtNnorbp1a/NqDGC0kg6DlUq9D4voUjWZeCNI2p1QVL2zWrPkBgSMZpxdt0bYoqFtFWQ1ZZQNOpw4feOwQS2akdYtSCYEV0GCwpMKSKUgb38R0AovFG6lJ8+qCwOoFDvM8BlEziEqG0QqtHRalR9F22J+GzEqfaeYiZE1mJFkliDyBLTxm2YXxpgTWOzNUR1Fon8c2SpRpKBqXVtsoU6FaH0h45TDiOG5ZlRZni5q3XMnJpgHf8cQpVesyyRxePRiiaTmYxFzfnIB2kboHusC1arYHJfZKYJTLl8aPWBUWlzZy3rK7wGAzz1wO5wHX1pdEbsPR0uczBwsuRQmVdlgWDovcYRBVdPyWvPGx9ZK0dFlWLq6nOM3njJKaWepxY23BIvcpG49v3Ztxe5Zwlgnun3e4PFiR+Jq60SDg0SLG6Iv0hdBpGMQKWyomq4hlZWEbC88umWUenq15684ZN8c93rFxyKvjXVwLHswirg5XOLamqC06XstWb8orJwldXxP4FxIu+gWJGDFrM27OlhR6iKSi41c4jqJtHc7TgEnhcpxC4DUYZROHc1ZNhNaGrm/ROIq8MZSVJAlzYrdhuvB4bMNCy5x0tsWfeG6NX73j4/bv89TWnPuLHidZRVULispBtS7Cz1iUF+bEgVsTuwp3t2aZDUjokWwZ7qTHfOv2JR7cmlK2Ds9thrw+mTOMa7LSp1YOrhDcTVckiWSaXiRWVG3L/XlE7CuWb3QIBW5LVjk4lsU8i7Bly2iwYO6U3DwL+X1vX+e1z+akZYRKHXaDmPttyuWNMXu9Fr95mi+eNXztpKIXBOz2l5yvYkoleOmhz0a/5R9+ZczasKFWgo7fcmsi6fkXJ3v7s4jLwxbPFlgy41N3Y25Ox5yVC9LC4T/7uTtUmU3UqZlPXJSr6MQ2qnKogcYUDJLZRRdB7nKln/PqOOS/+NU7OEjmuUU3Mez0K8Yrh+1BxaOzC7+ITtew2b8grsarDnkNW70c12lo1UXnYV5brCUlpoUm9fiD1wd8ZXqHwG2J/YJ+VPLS0Tpl7XFt1JAXMbOlw6y3ZJnZNLpid73l2R2BI0YoVVCTIbVmWmgcaWMLjyu9hknqcHzi82Be8t988Wu0suE4bainmvSf/wi7P/Jn2Qh7XOlZzArNJPUYxAXLApYV3B4bzpqMjchjENgIHfPCnoUyCVVpYdEyWboMOg1HC5vXH4H0K9aCloNFw9mq5dueiDnKL4wXLw0q8jSgtlpWVcPdU3hyd0pV/jYTrP82hDGGD3zgA9j219fMXBTFmzLvfD7n537u5/4Xo2P/lzCZTFDqm3CS+ZuMRWmx49icpTYODu/aVZxNBZ6tAYdppsAqyBuHP3hlyE98ZR/fAVdKEq9hJ3ZplUPfB8syOFozq0oeTya0yuc8N+wNAx7rbvLa7JxAOMSOIPIuDoqUMTyaK4Rs6AcW3aCkqGK2uhKlNI3WaATa1HjunKqxUbZH3dqENsS+ZFFI4jhns5uyai4i5rXqI41F4CqiUJEEBXvDMeM0YJaGPDhPeOHygqy28d2Cjl3z+QdrrIozytbl8lpDXinev9vnX99L2Bs2FI1maGuujzLW4xWrmov7u1HMcofACSmalNgt8KwOjbJQ2hC6hrIJif2c9VAhSPCshkppRv6Ijh3ymYczpk3AKGkZRhV16/LcdsH9CawlFaOwIWtsKl3T1h6rckBaK86WglXR53jWoR9nDELFMo/x3ZKTs4Q6s9jZWyGkYBBXF9L0VZdRnHNe+FwbFtxbWiA0e/2CgwfrzPOQbtByYzTlyjDnK/sd/sc7r5Gplq3IMCsrDkufK3bDzdUJ774yp2otemHL9bUVZRWTrjZYCxpOsoYHp10qLXnH5TG3D3uMuiVPrC+4ed7lqIG6cXl8Y06tLBwLpDCMOiXnmYvnZjgi4qk1WFWCZWXTaoh9OJyGbPgJwhoza136UcHRXOI4Dq43xdKGW6cdpIS9XsaqttntrWiNeCPQIsEYmKYBnt1iyTmXBinjNCArHLa7Ba6tyBqXrJGo1uJ4JokCjdv6ZKnNpW7AQk3QSBxHk1khvbDBshp+7fgRsWuzESbs12OUsnCEYLcfsul1+MX7xyhh6AY5gWzxpWF/UXG1m5DVmk4csJ4o/und1/mjNx7n84dzhHMfW9c8XHjcTH22OxHHjcbzK147sZGWxdk8wLU13/Xcfc6KPnfHI14+qRBColub3GmoVh7LDCxj6HZqLKk4nIdMlj5Pri+pWkklVnQDwSx3yWqLeeGxLBzyquHJzTmffnmTvzM7wfcU/Ujyp569zt95+TZpLbCEReQr+mFDWlsYJHkTcWmYYbTi3nmHeeqx2a2xMDQKbGneSHVuyeuAbtDQ81ss2bIsbeaZhxCGF8dTaGJ+6d6EO5Mc4bcsCo/3XDbcOuzxaFZyaWNJ0zpo41C1LYEl8SI4o+BkHgIe81YxywxRHVI7giDIeHqvpm0DThYOm/0ZkdNjM9EgcwbhioN5D5uCxljktc1jWylCW5RNgC0FFTU3TyOuDjW3TzusdXOur+Vs6Jq7hcsogsCBq6MZN09DlHZZ713I+IWwyJuGf/DiKZeHDkIIQr+i5ze0OqTRJcrUdIMG3/ERYkFZJXS8iqJVRG7N6dJl5Ic41Hz84Zxev6YXtMxKl6NlzbdubfKF7AhLGua5z3ObLaFzoezwXJezuUvganxjEQWGb9sZ8NmjCQ9OIpRXEHkGpRWLTOALB9eSaFvQiwR//7X79HsNBxOL7DzGcVMapenYERLwLc1W38InRhtDlifcXERUVYtrWfz8wyPmuUXgGlpREEpYTwRbiWGVdXCMoRdqFpWmzGxoJCk11TdAgvxuxG9GTfV1jdzr9b5uI6TfScWVMA6thgdzm71RjmXn+F6JcCr+4jt/D3/kH73Mc7s1/cBgC4etRBBkCX7fxbM0yxzO05aXx2OMMbzlSk1ROURuS4NNPDBcHfokjUcgNaGbc2emmRYZ77824QWrpiht/uHf+FXe9Z/9Ef772YQ1L6DnWygURqYsmgWXkhlnqc8qH7EZCfJaglBoUzHOHAqtOZi7NKrlW6+e0CiXok6oGonGsKoMWFMaraAdsR0rAhtAMYonjEuJU1sgEl4+7DDNfU4WLqO3TjivJUYFuNJHigtTuUrV9Lyab71U88VHA1zboBrFqjZ85xMPOEsHHM5CssqQVYJMaVzb4t3bNY+WIY122OuuqLWkNT5VE/LBJxfcPR2AFji9lkVleHbznHvnGwSOz+EkxDYtUirun7tsdDJ8GbIeWeSNIl25LJcO3/PULn/v5Uc8vrXiZBlhWy43zxxcqShaqExFXkTkTsHhzT3euh7yh57u8/P7X8WYhqN5QFwaKm3jOxrf0Xi2xdcOhjy9NePa2ozr6zP2ZwF56+FaglpIvng0wyC5NynJSo/zlUvk1VxZT9mfubzvyWNeO0k4mYQ0WtLvCHxX82DaY545nKcVv/LAYT3JiTyLvV7K0TJCcHEjWOUW652MR4sESyuUcqgnNpudnPdeOedX729gTIrnKFAdzpYKuW3Y6+e8ON3hmd1zPnd/nbUkI6skQ/9CUtT1NKvcIvFaHiwdlpVPqxpOV4bWmeJLF9sEDLor8pXk03cHeLYiMF1GiWKWuwjLoBE4Tsb9c58k0EzmHkmYY2mbus3o+wGRX3LnvENd2DQmwzYOH7w2pFQNlzdzAv+c09TndB5yqdcwTuHG9pyvThuurlXcOezgSYtN/z5V49OxfKLQY1Gn3F3mvDy1+fTtB+RNydYQjrIpg6ilH+b0/JYv7g+ohaaiRTaCtrXY7JbMi4S15MJPYRStKBuJa7ccVS7rccH+eRfVuvi+YjYN+RNPRfzrm6do01KvHH7wQx53Fgesr2runEa8emhQ5pAPP77Lv7j7kH5cY1mG991IqZQhDitsG65u3mJZezy+LhgELZ6lOVxGTFLJY+tn9MOAognJaounNjMGUYslFMsMRs6AnasOD7OKwjX8l995g//ua69ydhZgBZoKm41OxrJu6bU1woaqdhgEik7YcjqzyWsBAiKvBgShb3AtiTGwqiTHqUPolCjtEdiKYdQQeyU73YxpHiCEJi8tmpnFJ24pRhuCpNMwzSOuDla8durgSYu3bnaZ9io+dC3hTz3/LIFt8exHPkNv2DDwbWxLvnFfKphm8PJRl0uDilkp2eouUCm02hC7kvPCoedGrG+nvGalZGubPLh/wOBqB8eucGwLgcVaqKhrh1YL3n614ZXTgK/N4X2PLVk2htpArTRV4zCIIHYEo1AQ2hKhbfZncLrUfLld8N9+6hDhGQ6ODUd1yXqokE2PQSKoqohLN2rOsx6daP7mb1L/nknPfuRHfuQbev+HP/xhBoM3x6Pg+77v+96UcX4n4XLXQeuQprEJPIXnZPyrzwQ8diPjcNxnVhmSeY/Idvg3D4743mc2OViVHOUrPKfFkoLTVcvu2jl5EdETGe/sVpRVSNrYmMZlKwm4Ny8vfMWkJm8bvnJkEdoeaSVB1GxGLk+upeSNTR5D6CpqVaIaiIOKvGloGo+ntyoqZeFKG8eG1rQUjSIrfb5ExCjKMLqLa0FtBLM8QErFnbHF5ZFhmV10zLbKI619DhcOl/oLTtMA1y3p2zZ+5yLQwbMrfv7BCd04QynFTrchcKAfFCzfqOMezi+k4J4tmeUWD2chlYoRUtNqRc+HzX7BvGx539VTFkUXx16xHpe8eppw5ywjdlf4kccmhnlhOJg47I0aYk/x/M4EW9p0PI1VSMaZjRCGTpCTVhGWLShbyCsXjaYfpjzeG/Lx+0tsX+OHimljEZsL6ex6lHNz3OFomXClN2eS2wSOZjNquHseIUWDIxzWzOPsOTX//P4RvUATeDmtkSDgcq8Bc9Gx4siWStmMooK0trk+WPLKwTqW1HzloCVObIZJweEyZFF47K3nWKLh5rhD1ViczEOEZbg3TogDRT8s2eqWHM58ntpacu+si5E5d84Dntkt2Yhr+kHJq6ddOmGDF8DNcULopwgtsCzFVq+h71c0pcUoKjlcRjStRCPpeBWT0uXlk4StbsVON+fWieBsFbAVldw67dEPG24fd1kPCgadhvPCQzY+45VhPakZRopEDnhlPGehDJajoLWwhKFuQmzLEEgHK6pIM5d5rpnmIYmv8dyCZVFxOkvZTgIy1SJszaNFjKUtHmWnHK7GtKrDg2LOUkxoyw4/+9oJD9MxG0lLq9Ypi5ww0LTKMK0rLg0bXCF57SRgs2N4aveUe6sBd096PJoE9H2XnUFNXsHJqqU1LYFvCH3FZpKxP42wpGHQqZgWPkVj0bUTMrNkYjRrUUMnqIn9lvHK4cEkwYk0jlAoZeE7Of/PL93j+saKW6ddHKvBtcGTLblxGUaae+cOrtPgOOBYgt2eTT/JefF+QuC0rK+npIXkvPUJXINtQdZI+pFBaYdRVLKsHCZzjxeuuSzKkrBzzjBuOVl4ROYS3/fcgH/66iknTYbntNyfhoSOJnQaPKfk+tBwpVfxlYMRz3Qvc3I+QcgWIyq2uiWjuObWqUfdwuGkixGC2K9xHMWDSYwAauVh2w1HC80oFJTaJQlLxhMPN7SIfQNC4blQNpL1eEk6j2mNw6P5hSfQRke/4ZfkELkOrYK8EdxYK1mW8ANvvcQ/eP0hgWPIGotRR1M2HR7MYODnvDZZgrQoG0kvkPTjlkdzSdXA335xH1tIegHoVUgmDIlj8HzFT37inKeuG0aRJq1s1uKat2xpPr8fY4Rie1gzzRWVhsAKqYxFXdmMEosP3rjEk+sO//fPP2Cjn3PrQHA9bMgbSVEZYk8z9B1U4vKtuwO+dF7w8qHNZsdjY0NzfzlhmdkIz0ZKjWsJ1qyIwfaKfDrgbZdjvmV7jb/0sXs8vluwqFtWRcNaZGOkRGjJ0LdwcOl1S85XIe7KwfKaN39j/PdIevabUVN9XUTRr/zKr/z64wcPHvAX/sJf4Pu///t573vfC8BnPvMZPvrRj/LX/tpf+4Ym/63Gpx5YDIculnHJioDQBjyBzjt89KunPL7m0PMUyjRMqxwpC25PN7FaQSBsNjbm5BZYacKf/ZYRnz4+htpiLTHsTzXSKdAyp7B9Nv2UjIqqKrna13ScDrXjc/ZwTry2wenE45HO0XVF0FNYqmI9mRC7DY4ZIIWPb7nMa0ljKu5OBNu9Eq0HJF6LIw17/Ya9HqwqOF21KKMp2ob7MxdBRCg7PLudX0QmygbfWaJlyyTdZWRblLXHNAsQVoMymvvLiGXl88yo4t7MxndatGk5WoYoYJI6xG7DvPRYVoL3Xzul79lMMxetYbJ0sJyGRRnxBx+fEzvqIlZVFmx4Z7wyjhnFLY5syGuP6xtTZlnIWpzSCwzjLGYzqbk37pK3mv/D85f4pcNXGIQOG7HBkSXzwmf/OKARDSKw+Yc3H3JprUIKl5Mzm++4vsFR3vA9T+3wj18946CZ0AtbhknNWapYyimb4Tb3JxHv2MvZ6y5ZnjoYI1mVDv2o5HzlcX2t5nQxQpkpvpCEjuZoHiCEYZbaKDTb3RXLwsfzBIvcI3IEbePR8TXjVYjnCLaGFWUjWeQ2G52C8Srg0nDB7dOQJGw5mtuUymEYN0ihSGuLtaRlmrocLhK6fsOycPCclshRaAVHSx+BYrdXMk59Qm9Ft7V48WSN73z8iHunfZqii9GKNI2pa0PgCDa7BaeLLqW2iD2N61WotkUIF6Fa5vlFB5RtXFQ7oG6nDMOSw5lN6OZYsuXTd9ZwPUM/rFGtxDcBq7LFtVtUK7i8vkRrwTTX3D3rYRC894kTTjKfdCX55+cnSHfFWq9kcbqBUYI/9OwjHsxi3nLVwZaK1w48jA2laciaFa+cjZhmMaFf05oVSgl2+ym3zrqsmgxfeNw6r3h+u6YX15ylMWdLQatsysLjyiijHxeY8jIzVdL1F0h5Uaw0ymWcBhS1hWu3GCNYlYLtQco87fGn37XFr966DaFmb83mxsYp//SmTdZYZLnPMnOxLYtKaT76lXM2B/Dyocu3XMsQQhB7iu959ykHi4Ar/Sm/dnuLW6sAIQu0DsiqiKIRnGc+trRJC0naSLR26HqacepRi5KJOCJfxWz1GqRUHBUVf+5dz/ITn30ZZUFjJFq7+LagH2ZMcpusUURBQezZWP0K31P0whXLWjNe+HiuS9XY9AJDP1C4TsVeP2OedQCX0JHcO+vTC2oWueT2WYJvtUipefms5L1DSa0CpqnHr95xeDCOsOyK//3Tm7w8vkuuDP/i1Rn/4pUpnm3xcF5hDUoiVzItbJwqZhjl1B3NMNRsdTKyxuLhIuSwXvGh69f4K584JfEFPc/mPXvw4ru+jWnV4UZUopEIocmaGqdd8WjlsOZ7+I7F27cljYJV1sPtLfCDhnlhk9UOuV7iWiVuAE1z8f9bjx1aR+B7MNcZjrlIVpoVXe4WOcOgoCfUxT0wzrg18dkKvgknX7/LiaI3C1rrb8q4H/nIR/jIRz7CgwcPAHjmmWf4K3/lr/AH/sAfAOD7v//7+ehHP/pvfeaFF17gs5/97Nc1/j/6R/+I7/7u7+bDH/7wbyje1rIKXj0LULqlXYVcXz/j+rUFad6lE7WsDTSWuJBRSAHzouUto4SXbo/Z9muUSGnwyMqQspbg2qyyDhudCqUclHA4XjYcLFp6IbiyJmtajHJYpgFe2HBpYDGMW3xX0WgYxhnzqmGW2bzv+gpHFtwZD3CsAC0aHMvgOlA0DdOmxhY2RSn4z596gk+cvs5J7iIwoOUFoT2G7c2SeR5RNDb9UOHbkvtTj5OFz/HSw7JSen7NTqflYG6zLGxCFxy74d7E5luvWCReSa0FeW2hERwsFQeLiLruImnxLEXoCmga+mHBWdRlq1fQDwt2ey2fuLNDLygJHcEki2iN4NJowjTtsdNf4VoKaWmOFi4dr2RZelRtiBYFe0nFIDRsJSknuSQrAxLvwvctdiqaxicOG1qtOaqm3Nho2Z96bHUzFnnMo2nEg3GEa6/x9r0pd8Yh0LCdlHS8hm7U8tJxD9/WZKrmteVdvjb2OJwkGFvx7OUZnmMxCHJKZXFttOTOuE9aWezKnPvTDhLDzZM+D6eadbfh0saEshEsCo8bowW+rfAdMNowyy6SP4Vx+JZLB8xKl7y10Bpip32jo9ol8BvGswjLqbAtw82zDk9taCJP0w+WfOZOyNVuws1JwPNXJmzEFcdLj+NlwLZdM859ntuec+csZpwG6KFhPQzpb8155bgHBt69d8a/eX2PzzxY58n1JWe5z7OXxihjEXgt1SrE9kt2HCgVFJXgOFtiDOzPcjaGhqNpyLBbcPf8gng7aQRtE/DMoMftWcp6t6DIY9JSMVMlTeax23coVc6zGylffdRnGKdUtUXs10TWnLVgGyVhzROc5xU7g4rQCjgqGixRsxEOOc8KkqDhdOGRlZLEcVhVBfcmEYOgwLc9oqBEyZon+hu8elKyF4VMzBmB3VI0klrbNK1F2lysvRfVTDIX9IIGyRPrKbFnUFpztohYT2oKr2VvuCItXYyW7E99PLehbl1UfWFrsJuUvPRgmyhaEbuKwK04XsRUtc2DM59uoDiah6wPS5Y5uLZit5fjGM3XDno0yufSWsFufaSftgABAABJREFUN2dh1axKH4zFVjfgy/daksGKuowZH9sov+WfLSd03+JQ1C0tLqMkpROUxJ4hshtWtaRqDWVro7Qksi2SuOHZDY+sbcgqi27gMIxLiqZ9g0wJuTsOWRQQWjahr3g0Mzy1VWAJj1opisJmFNfsbs0I3IKO7/LUesr+XFG3krLxyHOLcR0BNpVpOZw7XFlbUCtFqyTGePhOi2tVkA/4v/2bEza2NVIYlrnHvlD4jsK14PUzyZODCqUjSp0xSkoc22MtbimyElWHGFehhaLfg7RxaZUgm/S4tDvHdjJa7ZBXHmllyOuAvm8xTh2KVrJaOKhkzipV/MvZFB+FtOD+POdoaUPjkJsef+Xbt/iVWy9SNCGnCzBWzqZ0sK2aRilM47MT2zhhyZPJJndnc4L+kpNFF9/XtKbCrT3ixqIwDWXtcprX/MfvvMI/ubuPBgI3xXcLGh3geguOZh2qyiPwC2pt0Qs91sPfxt6Pv0tqqq+LKPr2b//2X3/8oz/6o/z4j/843/3d3/3rr/2RP/JHeO655/jpn/7p31Gndlc7AXEUI4HAFWRNw3GW88pRH9lM2R601Lpm1J1xJZwzXvbYS2ykMRgjSFyHvuNidVxct6ExF6fgfjQl7BhGgc0sC4kdQSgFw46hamPW4xZHlhRKkwURe3/ij3Na5/yF997gv/vSQxxh4QpJ1wtJPHXR4mwM55nis/sOcdCyHhl2EoGLogkFQmgsW1G0gkUueeVRxNbagvNM4OFjdETkGXxXMS1h3Z3hOyt24g6XOjZOKShaC9ctmRcGy7b57IMtOq7gqeEJkVPTSMNOr+Bo6XF/GpGWAYOwYllINjolGJd5ISlrSVpJ5plFHNlcGdSUjYclMlzZENkFx3WPadFBKRsMHMx8GlresTul4xtqZZHWglkOWQlrnYJfOvoqgwj2egtcu8USBWsdm4OZj2sZzpYQ2xah07I/s3jL5XMeNue4zoD7qw7feW2dmyc5lZwxyUFrKMoOd8ctW9aIj70YYrsNV70lKml5ZnvBOI14dssjFl0+c2CR1g1XRwt0K6haxSzz6Ectm25F5DWsJ5pF5qK0YNPr8UcfW+O/+sw9XLvhifUpljS4Fjyax4xXAVvdAoPF41sZB+chfqxojODh+UXcvWfBIvfZSHK0kVSt5NowZVVbSDSXO0sOVxfX8OfvjXjXlQWBn3O2GHI5SvnCoyGe23D7OGIYO7TGkJcJJ/OcJzZLilazP42IXJey0gwjjdu2LCuHJKjJSptuYPGoBEdaYOBaN6KoQOuaUZzz+NqAV8YZ215MbV8kfsxURWhrHuv0+eWHKao19HxF1gjuTmL6VpcrscNSpVztjnhtusCxcjaHGR+/u4lvN5wuI3xHM+yuOFrGrPcKDqcRnq2x7JZl5oGSPLa7YFn6BCYgiFpcqcmMphs11K2F1ppH05DtYIg7OqPS8GCckDgNuQqYOTFbnQzfrZnkNk9unnN33MG1BfMiYBBplkXIMod/9rUpG52Kd20/xaN8Rt4uqVofoyXptIsOGj5wbcjH7p9zLepwsGxJ4pbYNWR1g6UsDlsfx2q4fb6OtFy0aXn9NOJqv33Do8fibDFiENV0wpQqg62OzTBqL6J9Hej5mrySdIIaRzl86mbOL1gTnt0Jee08p1EFCkVW2jyY9FhVNgJFFC7Y6wqWRRfbEhRNSOykvJhKtjspRRsTOoL1yLBsXIomI/SWVE1C0wbY0mKaJuStwbUvyMRB0KBbxbywEELSKoklAoaRxXnm81994UX6QcOLpzF6KGhNy7PrDsZ2OFnC09sr9qddykbwwtUlQhZUzUV7uzQufTdg4Ll819+6Q2+7xnIEiWkQQtPZ3mB5OiErN+n4mo6jsHA5mTscLQStalmLNJ6tqZXi4dTn0pqiLTt0o5a37miSwOIsMxzMPBJPE/k1y1JwnkqWy5rTAtZ9gSJgFAtGrouFYK+bYinFemjx1LqNa745ZMR/wG9f7O7u8tf/+l/nxo0bAHz0ox/lwx/+MF/5yld45plnAPj9v//387f/9t/+9c+4rvt1jf3w4UN+8Ad/kPe///2/4fUdpw7DUNG2ERiDUR36fkTbSOxAEbiKeV7SDxfM0g6fOav5uZd8/tPvuMzP799DaIfLPRvfghUCqR3yyiZXK0K/4MFsyL19yTNPzKjrC+mIbTk0VYgWBs8VDCJF2TYcLkEbST+a0WLx1IbAtzSh4xA6PrPMYZY5xF6LkDmZUuwNBN955RIjp8+/uH1AJ3aYpw6VEdhhydGpoD/KGUZw+6TLZqdBKQ/pFvh2Q9lEvH7q8O6rS7YTC6UFdSswSKa54DzTdD1J129pDeSNQyfQTAvDx29vM4oURW146+6cqvU4WfpsJHPuji/21ayStG2EtAxXRxMSV3G6GnAwc/HdlmGkqJqMXtjy/NZFcunTG+esKpvTVf+N7tUAW9akjWYzrHh1vMUgWPLY2hmh20MZyaNZw6OpR1X16EQZnq25PKyIPE2jCvaGNWuxQumWvLYRwuZoGTHNXKISDvdDFrXNWpzj25q8cVG2IkgqLo0KtJEczHwc2fDkxgopBFprur6i0Q4SwyAqeDQL2exULJYWJrMZJjX9sODuWYe3Xxnz9MaKRenRGEnRWNwYZSAFT69NQQpO85B+VFMqmOGxSD200LSlzRceDIgdzeV+yTIXjLM+W4OMnd6YjdFFeEpRQddvWWQ+lqfx7IajZUAvbkgbl/MiYK1T41sXsnwhNMvK5/3XTvniwZCTlc8stRnEBmXg4Txir5tStRchNYOgZRTXHC1aytLiJA1BS0bdJWXrXMRhG58mV/S9iIeLlKJt6DuGTMM8C7A9he028IbsbJJ5+Lakblwcu2SWuWS1A7ri+jpseAMid8Fh2bLKQ86WDc92JQflnGGY8PhgwN1py6tpipbQTS72s4N5h9nS4r2XBV86tnjr+pCP3b2L5+TEQcMwbpgXLuVyRFm0+F5D0wosoQntBiHhXXvnjIKKw2XMzdMu/bChVhLHkpwsPKpG8vjWCsdRJF7NovDQAqh2+PhRwV7XYV5WhLmmH1R0w5q7mcuNzZy2tojiiknqYlmSWepTlQ657/CeyyFfPCjoBCW2NIziFikrVqUhLwVu4lBri+3hgu76Hm9b66OlYlEYvv2xIZ8bT8lagVVGlE2F26mQuHT9iv0jm51ey4vnExap5PN1weNbFZa0+fz9ECkMmx3FTm9B1jS4tss7r6ScrSLO0xjXbnjx0Me1QOAx6uTMckmjBU8MSlzZvBFZrwjcAq0tysrj0dTHx8GOaqJgxSuHEYKAZ7amSFFTKxfXLvGNwwffHvFrJxXCtESexGibsjZM04COX/K105JBoEl8Q+i2F5edDgkCRUWF0TZKtNiuYuA5dAJD1TQofCI7J2sb0spjf+pQtZLAgcQzZMDlUcO1zZqsSbl71me7V/CZr/UR3gpbewgB/ajhU8dn+JbLRlRSK5tZJVm1NZ6j+Z9eP2M7aXGskKI1VNaM2A5wXYGvbZ7rD3g5e8h//nt2+Kv/5hGTpuLw9Yxat3zPU5f5ey/adDsKQcxmsqCuXHYSl/uHPjaCu0d9Op0VrTG0ovoN73//AW8OvuHUs8985jP81E/91P/f6+985zv5gR/4gTdlUb9ZOM4qApMwDFsQBZZd0A8t3rZ9Ie9wLBvfcglESN815E6M7RjqtuRoKTgoC6bnDm+9PuNvfbEiiS604IPAwbMuomJpPZaljeOU+N6MVRPTaM2y0kgr5cEXvkoQ91gbPsfP3zpnqyMpKxBSs6oNiV/zaBEjhMLxlvQDxY01ySCwCG1N4ytqt+LeuY1CMMmHWEKwNUxRStKUXULbYZG5WHHKomw5yWsOVgG7SZfdDkSOQJfQtC223TDNuwQutK1LJTWuVGzEBUYIWi0xRuPbit31KfMyIHQFq9Jn6tRc6ecI2VK1grVeze6g5MZoxjAsUEbRGsNZPeA8d4gdxd2FQ1rapKUkDiT9N0wybanoBiWeU+JZXaZ5l6qxOJo2XB5eMOanaUQoA/6Td13ntfMZ//r2lFlq8dhWhW1JAlsRuxJhziiaHX75geakWtHWDr4NsWdoRcv/cOtVNrsrnoggzRzKpUVvqPncvW3q1qKuJT3PJjM5b+mnHMxjLvVyntnMOVoorq9nnCx8Xjzs8J3PHDIPfRbVgEflgv/6szn9sMW2BK+cDFmLSkp1QabljeG1gy5P7c7wrJYwdBinzkV8q2w5Wfh0w4prwxXKCKoWtjsFm52Cm2c9hIDjIqEf1VxdG7MoPO6PO1Ta4HkKJOSVTaUt3rrR4ZXzgm4EndBimkbcO3YI/ZSicZhkFmibvPKBitYIdpOUl2Z97pUFg1GHZV4z6hbcPSmJvZTWuDQ64d5shi1s5rXmej/kC2cpPb+ibHy+NF7i2BcpY2uBj12VNAqMm7GfBaxWDq4sUaLgyY0V8zTgfBWwkQjW4gakxhKC9aRmWTikhYsINZZy2erYzKol08zi0TjmDz825Kh+yKIpSWchp6lHq2zyxsaxJb3AkBqPZVVi2S0PFzkoge1IqtChUTaRZ0A7LAubjW7JMCqZ6ZC6ctkbtbxz3WVHbPORV0/YGS6pa5/Q15zMQ3ZGHvtz+Jkvz3FcyYN6iUZeeENNYrZ7BavKphOsEIAtXI5XHt2gZKPTEroVdycS1fgs5zHzVYjjemz0FlhScZ7ZF1HsbkNVBwhX8u71S/y11+d0wxO2I5gueswXFg8XDsuq5ca6xVNbJRtJhQoEvtewbA2uW2LjEjiC+5M+kd3yaFGxk5RkdcAkuzDYfjQdIayM89RhK9YkQcU0CzAYEl9RNRpLaSxXMy8kSgvaVpD4kvddETRtyGcPDK8fV1jWnLNihWdiAmHw3YsvIMss4fGNlEnqsigD4jDl4UKTBAWu6FJWNmuhzQfePeCz+3P6wYwrw5RV6TAWM178xM9S7/zHPL7W0HV9TOsgioRt10ZVmkkDGoFtN5ja5ta54PBA8q6nWhxhMKZECofANrjCpR9oLAQd18J2DI+OXNZ356SFxrUN01zwcKEYlTNWSpI2LbZsSKTz5m9S/551FP37hj/8h//wv/X8x37sx/jIRz7CZz/72V8nijzPY3Nz8xsaVynF937v9/JX/+pf5ZOf/CTz+fw3tL7QcrClS6EskDkvn/isxRUnqWazVzFKFI0BYSw6rqBtJBt9i187nNMPNca4BLamUA3SbjCNxZ2J4bQImC77+G7Jc48t6Xg+ftQyyTwc4TBeCqywBGlY1CVxkDMMBEXdMAovPA9d2SARFI2FMYZl1TItBTfHCS0VZRHh2x6fuJ/hoTlvWlbKYbq0ERLCMKUxHns9mE0DtCVolOBs5WJERdEGhH7KILa5NlTMCp+uV2NbcLWzojUlz+9W3DkbcpJ6jHyNE8DxyuZ45VLWPi/NoesZJmmIMgJHthzM+5SNZrOzIHZdbClAtKyFmkZLlGmIA8NarFhVFtudlKyJsWWDJSWWdDnPfWa54tqooB9ojtMA1yqY5WtoAxoHIdRFLVW5WMJmVXp4TsmNtZRK2RijcSzFbv/ChHaSCWLPJi09bKtmLSpRRhAqTV5Kup7Lt6xf55/f2metv2Kn3zJNHRyp8BxFN/B4bq3PZ/dtrq+nlI2HbVW4VkW/Bw9nEZOVy3oi+T3Xu4zbc4ZRxt3zLpHf8nCaILHQRpM3NkfziCqsUCbg0Tzgqa0Vfb/hznlIqwRXhitWlceysihqw043p+O37J+N8IMFid8yz2y++GDI2y5NaZFMspC1JKcfNlSVDbbBYFHVoBWcrnxGQYllBRgtOZ6HdFzFqrJJ/AvipB/XTHIfyxjyokNsnWEQrHcqlqVNriwMF94+k9wlq2wit2WlYLebcefUI7IDHlsPuDfN8B2JUgGLViOFxBfyotZtJUUbcj6TlErjuZKtvqZuNbHfMqtWTOY9Prs8xrdrYtdhMjMkjsesKNntD4l8i1l7xmFqM+xXDEzErKxp2oDHRoZjuyXVBVIG/PdffMQqNzSBZFl0mM19Ik8xrVM8V7LWrWgxaH3hE3NjbUFWXRAJ650MW7Y8miekpcUgKugEAitsGc9j9icetqW5EQ+5kVisR4IwPuD144TLaw1v2ZrzaO6wzEP2+iWr0mapba7b2zhxSi+oeP1MkAjNICgpxQrPSej4mvPUY5I5ZI1D5MDl4ZwvTEa8+zGb40WX15ZTvnaSUhQ2bQn4imHHxvUCJrqh4wVc7teU6kKm2gshbyoeZQueuVSjjcSzG+ra4tt2dvn4/TnTLGen73Lv3CF0G4omxHcEjt3gWjWjWFLWhlXuEPdblGpplcF3bLbigoNZxFavZJwZfuH1NZ62bfq+w2PdiAOVsSwD8rbAtzW+K3FkzcitGIUVv+Rn/MpNxR9+R8TNeUbsVQhjM80DHNkihU0nUAySGXWTcDiLsC3FeBXSCyvmhaQT1gyimmUhiN0WYwI2+zn3zzo4JmLg52zEM159tMHmcMx4GV+kTwpDrSSLNCIOLDaThsNpl3DQEIc17xlt8q/vPaLWJYdLw56UaOUyCAXfurfNF4/PMablmZ0CZSqWBcxLly+eHpC1PZA2qjUYu8EzCf/g5WOElAxxSO2Um/Mz/uy/rJlNIJM1sSMp2px5GdArHSLbxnYUvUjy/is7fOzOkryOf0N73/8q/j3qKPrNwDdMFO3t7fFTP/VT/I2/8Tf+rdf/5t/8m+zt7b1pC/vNwMCK6QQ2u92awPbo2IZU2pg3IudnueHhqmaUN7QS8trgOjVlq3l2e4UlHCaBJHF8wp7BsiyyUiKFoDGKohLszwyeN+P+omFS9fClgysMfT8jtC3aRwd0336ZhRCs6oLWycmtFFdoQremVtYbiQQ2m5HFgd9nEGR0vAtDvYNTQT8+53i1x7NbJaOw5nCeUNUBSgnyIiT0W2rTgpXyHY/dYlY4hFaHReGxLCVgyBuD5eWczBM2kgZHSt6/s8ev3D9jUXsIAKGwZM1Z7rOsJGuBZDNugZqDhcf4zGMUKWxLEzoCY2tWpcNXjiI8J6WNXAJbYgsLR8KqMixKxfF5jytxiB2XGMCRNVVr8+xGSlobXlIdPKfEkpq7Y4dxrnAtF1f67Mbr/NIrBbfzlDio2Ozn3Drp4QiL22PJM5szjhYhZfdLvHr2LN+ytc2riymlaliVIHKbfnfBTi/FsS3SuGVhPB5MA96+NuTV+TF23LLKulwZGPanXSxpuD3uoDHsn8RkjWC3X/LkVsrn7m1SK4frA0Gultw86FFnEbfmHpYFs6XH1qBknvp045q6hqzyaLWNJxQL41IWkm5cozQ0jQUS6kpgWdANGu6dJ+x1M2blhQdEWYUXckAtWDYG1UQUpuKJtSmNL2hSF+3k+H7KlXiTLx1OyStIIskwKnj9xMOyGkJPU7c2TRUS+ArBBdFm64zYqylWMMs8lKkotcAGAj9nllpcG7pYsubmfEnoKR5bM0zziw6dXlgiheJ4Dr5r2OrWpKXk2zbX+JnZKa+caN7zRMprx12K+YhVrel6Gi0akuAi6aLRkra02Q1CCGeYVYJj5QROiZAWz+8IPvbglF5iYbVrdN2GfrCkqFt6vuZ+ldBWNte6m3wpf0RdO2zEPknQEvkFRmu+dtzhrbsLjnOPyJE8OOvzJ57cZGl5fHlxE6M6/NL9M677KaMk5P2bj+Pi8JFPHVPbipW3wtE2Tw9j/M4KbU95+bDLrVOLy2XEMGrpeDVGuwziiq8dG95++ZB74zWO5glZE9Pqlu0k4sVTm84wpW0lm7FASJvTleBwKQmskLoJAYlvdbi6dooyFVtDwelMYHsO2wONXzQYoRivHLphzdEsRtoOW71TNpIpk3zIMFIYY3Ash1XTsKgaprMYh4DIbdlKGjQ2o2hKoy78TuZpghSCVrn0g4ZIwKWgYLNf8uJhRFo1LAqHG2sZ64Hh6Y2G7Z5EWit+8dUeQ6/Faxt0c+Ft1SgoK4fEkzjCxzJwtb/i4dJhI8h5MJVI0bLRtXEsQVq6jJcdLCm5csnic7MJRRrylbJm4LmIKsA0gpSaQjWMYslax8J3bEaXNJu9gJ5l2O5WuBZIoZlXDqUuOF7U7AnQLWSNJnEU0hJIbTMMBbuJx9T36LglUvdYpj5t7rO3lV3oFt5kXCRrvDkm2UKaN2Wc/4D/ZSil+Mf/+B+TZdmvy/IBPv7xj7O+vk6v1+Pbv/3b+bEf+zHW19f/V8f60R/9UdbW1vjTf/pP88lPfvJ/c+6qqqiq/+/J6/8cQPJo1kLmsz+zWRU9Lg8Vq7xht6MYhQ6eEGwGF7LzhbLASJTdcHfW8NT2nJcONnjh2hmvnSTsdSRpo7nUKVlWXXa6LRiLju8Q2ArHLikbF9vKOc01XmMx6jU8nHp827WUsowvUhOVpOtqFrlFL2i4Pe6wvzDYsuJrD4dEfgomYq9rmGSSVJdMSkON4Ty10LYirzWzo4hLu5qmsSgLl6SrWOY2nl2wnuRMMpt+uMKTXR5NOniOYq1fk5YFrl3wubsj+kGD1pr1uMUWAt9uePFRDykEaWUwNGgT8uJBF4kh9jVPbE3w7JphBE2rsIQmcAxa+5ymLUI0dFyBUhpfAI6DMQV3zj1Cx8GWcHCe8NzOOdvdilnmMc8iBqFkVmrKWhLaLo1UNI3mdO7TDRr6QUXilzyaRghh2BtkBI6iVS3SOBcdLH7NeHVxoLI/Sbg8nBO5DZf6KaumYjO6yjy38CyPVe5gSYP0DAfTgLKW/H/Y+9Mo67KzvhP87eHMd4wbc7zxzkNmKlOpIRMJCzAgYxC4ljCYpsEYmcLtNouCpmivBtO4jFcZ4+WualvLLjcURoZq01DLBgwGI0GDhEBollKZynwz3zffOeaIO98zn713f7gUyyzsKsskhi78/xbn3Nh7x1o3zn7Os5/n9z/fifA3Vrk5LsiLOSczzXAW0Ypy2r5hs51zMk/4N7Mh51c8Hp6t4hxc25jie5aiDrg3DDmdh5S1YDYL2BosuLI54fZJQjeo8NXSnfWFh6t04oJIOC6saGohuX/cojKW7YFmbxgRB44kqNifJGx3FsxywTRbtjwHpmEjKWlHCw4XIbt9Qzf2WG0V1EYyUh6DpObmYYdLqzOssqR5jKklSVhRGYdxlpacc7HT8MJhlyS0eK7mXK9mOF/yZpTTrHcWOBezKDw2W4ZIBhykh/RbAeNjy1EBT22dkeWbbLc6fPjBCc+cv8XLw8e5U6aYOsDUBmcdWeHjScuFfsyt8YzH+qscp0OOJz6PDSJeHU15Xb/NEZZXRhOEqNhsecxKkPEp67bL/VHDKE5RuuDOUcwsVSx0TuA3pIVHYiKKIEOYCukUhZGczkPedP6E0SJkknrcsi2MbePpnINxzVpS0gtzpoXHcO4zaNV4yrBfGEK/Zjxsc2Qtw3rGyGa0Ap/VdkHLK3n+IGGYJowXARfXJzR1wHrQ4fnDnNpKrq2HTGZwOYq4tQ/nz41pRTVFZRnny5azp7fHzAvBq6crbK7mHM4NntZsqnV21xOe2op5407CD/7mPRZignGS3ZWSxsLpAp7ezpjlmuOZT1VLytJjoz1mnMWcTkMqI/jTmyG3OpJXx5rRoqGlfTbaDcezmNpo0hx6KymhCjkzHqWqKGrDICy5nybkheDIJIxzzWmacDILUEJjheVcz+PNF2IWByW+BusaisqQVZLVRKAwnC08/vTjQ3715Q4//5Liyc0WeVYyTjVFFaD9nAtrEzwFz+3FBNLiyxAhPJKgwhpJrBSnpx6edGSl5Gxh6HiO0FO0g4q81AwCn6xUJEFDVbZonOXemWO1bUizJXDbuZBANrQ9SyNi0gw+sH8f8JmVMJxb1jqWrDLUVjDMSrq+Jm8ciRQczhJ60YLESKZZQGkzsnnCfNIwqo5YCUMMJSttQR0GxDLEph6DlmO121A7h68N0yzByZKH44BpbqirklYgafvdpXmKr/5jtuP/Vb1WcdWflJjqc04U/YN/8A/42q/9Wt73vvfx1re+FYCPfOQj3Llzh5/5mZ95zRf4h6l55ghaNZWdc3+64MtvHHK2WCGtNd/01C4fP77Ppx75aDtANjmuCYiVwvrQjwsaIxnqhsw0KFkyqUvmNuA01XQCwZ2TmF5c0YtgvR3hS5/INxS1JPImDIsa329Qq2tokdLvT3k4hfP9lOF0i7KSdLwa4ZZVHu2gYVFIGlcR+gtGuePpcye8/9Z5bqxCrAKMscS+4dE4IM016VTTSlKubKV82bV7WBswCBOs86mNxmHYm8KlaE6lPeZpyMZ6iZAN42rI0Szg+aMeTSM41x/Tjy2rcY5yilHRolooRpnGkjPJDc56zLI2K0lNoA37M0FaGhSOQeiIdMG81GQ1vDrUJF7JTivmya2EvcKwEqec5nCxWzEtYualR9evGBpBK2j4/MtzprmkNJLTiWBeSObePZxquDSoMdZjkTpifwmLvHmcUDSK3XaLjY7HzfGMVmIwZUVaat64FXOUpfRiqBow1pHVgi+5sIlFsVkHaL9irT3jZJowyQL8oGank3M0C2m3HWkZMFxIEs/Qi0s++zDG5IJCdAhDw5t3p2SV45n1y/z8rSPmhaTdKhjnmqyWPL7qM7cjZpmmqCNWeppxWRL4LBOGnsOTFmvh7rALDhpXUzeK0zSk5dXMi9/ZSJRgqiy69hkXLVbiOZnyuXm6ZMFMcsPl1Zi9YYVQFQfzFmWj6XkF43lAN9Js9X3SyhDhU1UBq23BS2dzVkKDtj7CCTzhoXXO0ShBC0GW+hiRMi3gylbOMFeczgWJJxgEjhePWixKj5Ukp2p8djoBv33ykLdeCqjEiEenLcYLD61LXr+ZkFrBvNTLjbbQmMbj83bbDIucG4MNzMDjIwcLPu98j/P9lLPyjNxFfNPjT/KTL91GiIppqulEDbUN6XmWB+kEK7ocnrV543m4eya5tOqwUrLaqnhrNOF45jNKPQLdcG2tzd3ZFJN3WQtWOcjmaF1RG8e48PjE4Rhb+YRtQz8UDLwWN/dL9vWEcBjzl565wWQ2x+SCk0nJfn9K7Eec75WUTcONtZRZuUyklaZkRUBtfFi0IZozqyvO9y21U5yMI+4PQ07mBmE0sfZYaTccZlOu9GJujwWHU8hrQa9V8LrejLyxFGUXW3c4mtUUtWQlFNw62mCzdYSxNWUD1kHV+OTlCjf3JE1h8FG045CVWLHesdwfb+B5U5So6LemzPI2UkjqRuOrmo7XsNutqBrNy8chJ2nBnfGcrXZGEnjkdcyiWOFSX+F5FbUQmNSjwhHHGXuLmrVEYZ1PqD081WUlsKzEY/JSoqXgpz99hkxy7p42HKYV19YUG4niy77zO5iIACsVRe4hULzxcsJhVvKpezn4Z9RSUFYJK3FDaQtKZ0hLhxdq5lULaQK2EhBG0g3mGN2gBTgpkXHJS2eOzVbKY+sGbI+XRwusrFhUHq1wWUWBDP+ot7T/rD8CvfDCC3z+538+RVHQarX4uZ/7OZ544gkA3vGOd/B1X/d1XLhwgXv37vE3/+bf5Eu/9Ev55Cc/SRAE/87xPvShD/FjP/ZjPPfcc//Ba/ihH/oh/vbf/tu/7/p5qaiN4+JqRlYFdMIG6yyednjOYssaWUomY01TVXSsZGu1xvNyhkcJV3RNsPB4U9LgOUnoIFaCxrfU5CxK0AtQfkplJDeiObPScVkrNvoVb9npcnNU0CkiirkmDitErYlVQV0IBn7F0KRktYc1ERthjiclvnJ0KYmVxVOWs3IJpBWBpEDxNU+t8a9fGRIKh64sUZDjG4unSsapx+QwZBDP2YwtqVq6GkYR6IVgzeY89+oqF2TDfAr90LGDI6gNau44J2oejUOu+Bl149EN5kxTn07s0BjWTEULgZ9aZqVDK8m4DDhxlsZCJ6hIgpKi9inrAK0aBn5BW1lmUyis4AvXJqzohvlYkC80A9Ow4QoCt+RHZqfQCQU6i9iWJfkCNkXNulfjyIn9hp60+MKw25sxKhMeTmKmRwGL44idpKQuJG6qEMBuUlCLkt++/RmuhppsJklaS9eqLJV065pVXfORV6b04oKeSKhswmP9MdPMJ59pSqW42lkQl5adjkKqnEpbKqNRc824VFgr6DaGJwc+jyYF87IgTA3z4wCvFoxHIVFg6ciGxzclr5wsq3e0rGisYV2WrHQqQjtA6hnWSOQsopEF4yxkW1VUTrEdpdTzADvVnIx9NA4BBAVMqwgtG7q1gVryZHvKYq4IgRvJjLKKKFPohzWdMGM6btFRBXYqqTKQbUWJILGwITJiTxLnjjBL8bQk0HNCL2eaG1TteDKJOMxn9EzDbL5gL53w+n7B9CwhGy+4FpfouGZeOZqpYKU2BKHBKxasNoZsvkdgPNaw1NkhG0iGs4wUx4q0PD4YcTK5iKgNYuxzoeeY1g2r5FSNpBVbxnWAUBZjNButgK+40eKT+5ajomDmStJGsxHnqLnCzjXnjKROFUKmmNxxaW2BLQW+MoTOoTS4wjHOPDZ1wXrccJhCbCrefr7F+x+WREmDVIaTSURZK4R0rMqK+X6Lq2swrQ5IEocSio4oeF3bZ9ULGPQD7o0UV/spLRra0mEFNCPNdB6xRkVeKq6thqzGAYdiwpvWPI5mE/7pnQaX5azFBqVq5Hz5EqutZH7iUdQ+2+SsJedozjJOH3XRuiLMDS2l+MVP36LbqriER5BKNpMUXUgkFftzy7YWrBlNKEqaxkeWHm2vJJaWZ/sZrQJ6siRUltNMMMk0F4ShZWpOT0d8ZL5AKIkrLE+dW0AiKHIP34JUklDVjGaSL1pLuXsS8Z03zvE/v7xPU9ckJUTasu0MB8OQq55jpTXD2ZRx2qYrKibzgGtxzTBRbOqSEoX1K87SBqY+a14GjYSxQjaaVetY8dr8X54e8N+//4Dz8YKDWYty3BBg0RIwJeutGiQ4LGljsS5gK675gtUWr1QnnGUNJ8cNsSfR1hJZwVXfEEhHHVb4ynGWKaJgjmgLHh5p+u0pHR98L2d/nCCsT11I4qSh44HFURSaRAWEDrRquLyx4PjEo5fM+MTtkhXga8+1+We/b2f7z/pPqc85UfSVX/mV3L59m3/yT/4JL7/8Ms453vnOd/LX/tpf+/+7iqLL2ym5CrAyIJaKpgJTR9gm5ngiEQJqqxlnkrMMRpljnNeMioa744CVyCfSjrR0zIuQfqzZjGJWAoO1gtVIEXiCwAMhHKWt8VzJpILMDQn9kDd/9VdyWK2z5ResxOAJH+c26AQeOEXW1JyVOY3LeePunMe3hyxKxTgXWCfQrse1QcA085GionaGMJiTNl26Lcks01S1pbGS7bbkJO0SaEHVSAKv4NWx480XD3h0f5vMRHz1E13OijHjsuDuZIEUXY4mEeNC8NyBRyuoWY0E19fLpQWsbTicwytDTUsrjhd9TucxlVH044yjmSYOFLG3fKGcl5qXTlsIVdCPG5om5MJKwN4ioxCC0khG2dLKd7PdsCh9ZrXjNDOM84DEa6GFw/dq1rtjPnnf48ndnJeP+vSSjMgDIQRfuHOOn/jECUEoWI1CLkWbfHR8xlFRITLDarxsnbkzWXBjsMLtgw6FHbHTHTNddPnQ/hGeVLSU5uN3VvHDGu1VzBchrTZUiWWQGAwl41Ry9zTirZdOSJuIrX6FABJt8LTl7ong+lrErz26x+k84C1XJ0SeZZp7DOchn97zuD64iHEnhH7O8aLBClhtFSRBxaISXBosOJ6FHJ/GdOOaTzzsUdchvbiiE2VMFj5OLN0GTKVIgop5GdNPFvQiQ2ur4Oahx8lsxuNbPn/2+oCPnzwgLwSdsGJWBEjl029XPDvosZc1PHdckASGaTOhanrM84jKCISyTIuG7bhP5EmoPO5Ncnb6gnZkeHA4QPs5hYNeq2RcCLRn2dI+deWzvbagq33W2nOkWEIotRRsdDX9VgaNQhYb+PIEWAafvrZ0vZDDLCUzFV//+A5vv9LmZ++8QtfbZMdb4zg+5Nf3X6awBmsN/ZZlmiuE7VLQ8Bdet8n+vOILr6zw2QPDmzckJniOeemz2Uq5PeyxEtf4SmCsoxWfoihYmA65MSjtsZNUdGvDhj+nEDPuzrpshW2+/HHHy9MpT1/d47durzMa+VzsrPD1TyRstXz+8W8fcDTt8vpzQ/KmJvRgUSZ0o4qzRUVlBYEvuXfWZ7zIsDhWE0cSNNw+jvHp8ve/5Ar/8LceMK7nDLoj4rDml15sGC5CVnrQauVYW7PRnTIvNVWt+IKtC3zNjV3+61//LcYzDycMxtglY0MbemGGFA2rrZI3bq4gGs3zx1P2F5aVhWBRRCRhwzRNGGVtclMSeJZBBL2ooqh8Aq+hn9SMs5DLg5LraynvfblHaSyzekYsI2rr05iQbiToxRYhwZcGhOPmfpfdzWOCcMTpdIXKKHoW5kUAtstGBzpxQ1oIShPwhp2SwhYIZVBa8uIv/yydP/NfU7iKppBs+B7ohtetJmwEAXcnmjCY0I0KAI6mbbZ6U14+VWx2ciaLGGNASsO0cKzZhlDFrPQERZ7gd0uy2mOnv+ATD0Kcm5N4ksaE5FjKcMyo8FDFH8LpkpTwGlUUIR3wx4ej9IEPfIAv/uIv/qNexh9YN27c4LnnnmMymfAzP/MzvOtd7+I3fuM3eOKJJ/j6r//63/3ck08+yTPPPMOFCxf4pV/6Jb7ma77m9401n8/5pm/6Jn70R3+U1dXV/+A1/I2/8Tf47u/+7t/9eTabsbu7y0SFzJziXHfOeOKR64I4LFmNBZGf876XNnhyu6BxBbaIecv2Bp84PiV2PqkXcqkXUwYVtZEcLhRJU1IHgi+5dI6fuX2LUZ4waOV0pOP6Zk1WeYxOW9huwsSreZQlnIiUWFfcKjWRk2x2U8aFQOKxPw5J4gppQxSSy71VPnYwJZGOSlUstKNsPB5UhtDT2DymMvATt0pUEHK87E5Ga8FkLthaqxiWEXcqxVddHLKXdVntl5TOx0ifh67m4bzD/+3tb+Sv/+I9RmLMlUjhOguoHcGqRQiDsRVCOGKZM8w8TkWICiummcds2gcERVMQ+5BIjfEExklWooa1lZRumHI4GzCc+ASeIwg193OfTx222GoZvu76IaNSURQ+mRHkdc1Wr2JNOO6OQrSoGDmfk1IxaJfUWUBawqT02exINtdqYj9jlAW4rsdiLpikgkeLhKotuFMtuThKGoqiYWIV/bYhXEnZm65yYWcJ7j6xilorSuExyxUvztrsdDTTssaKmk4siJKK7XjBMPN5ddLhtAp5MJTs9uCZ7R4vDjPGJsUFYJ3mZK45TlMaobl8vqBpYHd7wSvHHTSCOCo5mkUczSty18Pz4NaoJopKAm3JjKQxBY3UnC1CtIbNrmW/jOiEDeN5QMsvuLG1YOgp0jRkXngEyjK3glwKahuBslRWI31Dq11hjOLeSZfIt1TS47jwORdn9KOMuVK0Vg3DLGJaSK6sp8xzhdEQxCmjKmSmQnSQMV1EbPug2gEvnWie2vSZO480kEw0tHWfoH/Ey2cxI6nJsQhpqbRgUkb0EnA65E6WEceWQkpO522eOj8hLRIeW9/lvL/gt2b3eWxzyG+8uklXhdxKBef7gqGWTELFXtVnrTflQjQmWemTZecpm5yZaPiNUUXuPCayTW8lx9YpUbtmUvrMPcGwEAgVUpWatU7GPRPjgFAIlNcQ+zV3Tjt0Y8OdYUASNdCzjNKI9fUNju7lDLOYN263uLZjeH64h6fAd2sczODFfErs+1zcmOOMZW/S5qQOaaqS1JUMVhtaKxWzUpJKiXOKUJe0ogLjNMOR5iFT7i58DschD1LDzZMa4SRbnS6TRjAa+TivYSWueGzNcLuMeHxjwgOzxicPCmTkOChCzq9aJsajE1pGJqBSJU3HsNLxkK2Kg7lP3K7xpaWTNPQTS1prIj/jlXSFJ/pP8MjeJEoaSiFpr5Z8/FYXJ0F0a/ZPulwYRCTBgA8dTLiwAYe1QuQ+V1cNzlluzWIGSYNE09nIuHNnjTtFyJ1K81JmcJ5H3mjaSiKbDqln6HRq9oqYXjznrGyRETByProUhJGjbmUUlaCxIQ/niqARxARo3ZDlPrWJcNZwaj1eTVuYXoePnQbsbGTcernNibcg0h7juWSzX+IRUeaSS/0+jTSstyWfPB5zy60Thxn/p7c8wd/78B2QDZOJ4Op6TSjgrNBstB1VqJk1Hokv2LrSMC4i5o3m5GiVaW54aruFtfBIHhAZTWUUj041g64j8iQBDZttiewlnKHxA7BK8T/fr/4Au/O/R69VXPUnJKb6nBNFsAQ4/t2/+3df67X8J5eSAisrbk8rWn7DubljlsNLRx6/fDjk2sWcUdZit79sofKUw5eK160ZuqFCCkdjBKVRVLVCScM8h1FhKXKBVWNePPT5Lz//hMbE9MKKUebz2FrK8WSLrTb83N/+f/AV/+1/hxIWMAhZ8OrxCoNWyUlmeKJ7hlIRodTM8jYbLcu80HgyIs08ImXwlCOtoZ3MEGpGZRRarHCuV9APZuz0F4yyAGMjitqjqC2GhptDwW/f6+Kadb5oUFHbgJdODMpzVI1iuIjIc839k4h+b8J6p+Rk0uOZnTll43GWaqQs6SQT+qnPPFvn0bgiqzSzQuGEZJDUvHE3peU5ZqVibxaR1YKTSY+8iBhlHvv5lBpLEIQYu0ljBT4p/bjmYBLQjTMeX8vRouKl41UOhm2mueDy1jGBX3D7YEA/hKpRpKXBior3PXxEpyspyoDDmeHdH3tEbSymgkEc87ruKjenZ1wb+Nw8dOgq4Mmta6xaybW1h0z9hIoUZWOUavjyS+s8nDa0d6YclwWLQuN7hvP9CXnVBQ+mRZvVJCeUKS8fJZxPcma5RxI1fOLQYzNc5UJH8vCk4tLanNWkph+XzAqPgAFv3Drl5z57nmuDmnneQsgpWRmileHWcY95sWzZO1kkrMU+zl8yfZRos9KqGWWSs3lE4tc0lcdxbVlLNKutlM/sBfQjibGG+yclwo1pTMx2f0TVOKpG0glzZkXNR46P0MQgwPccdaHoRorU5AgbUJkGT8FxuWAt7nJqG/q+JW1q5ouIQRgyzh1X1zPCMOXRyQptL8bKdJlQWPS4mS7AaMLwDCUElVlyaM4Fm1zfDvno/pAPP9Lc2Jjz9c8MefW0w341R9k13n5hm4+d3qHjBTzWOc//91ZGVR1xaE6pCji3WuFpR6gdBeCMIZtpLnRb/ORLr/ChfcNjK32e2dzgIycrjDLLL70UkJUe89xjs1ez1i7xVQbScf9AsMhiOknNILb0vZoSwaNpxGZvzsHY8aF9w9M7ku34Cc4/scI/+PVT/uYHXySRMULlvOWSoBAluXFstuecpW1gmSxZjSWjszbD0jHOwQ9yBh3HehuE8NC64mS+4OZwwULO6HcbhFJMihoRTgiaFdbbNatJTehJdrpzfutuj7avOMuWyYtIRxS55Mgsnew+9WCF1XbD4xtzalPTlfDx+5bFVNCuWoxswazKKJuY41nATrfgjecy1ttT/s0rq8wLj2GasNtbEOoaP6zJqwhnBWkdsduRzOs+03zGy5OIC/0S4yRWKArjqAqJEj5aSlbbEuoBK0FJk6ScTnvkjeLRMCYKHSdlhh8aLC1CrZA25sbA8OpJSOnnTO7f5fFByqsPOvzo11ynlwi+5wMvsB3ustOR3DxeVkl5yhFqn0sDgyThiy4W/ObNc3ihoRcZPG34P7/hEj956zm+9y2v492ffo6r7fN88OgBxylErSl3Jpq1wKcdC7p+QD901I3HtUHK4fF/1Hb6J1Zf8RVfwc7ODt/yLd/Cu971rv+kB0137tzhn/2zf8adO3d497vfzfr6Ou9973vZ3d39XbbQf6h83/9dmPUzzzzDxz/+cd797nfzIz/yI7/vs1tbW1y4cIHbt2//e9d1//7938M++l8c27TWvPLKK1y5cuX3/V4QBP/uCiWvZLNzyvGixSgL+I7HHudn73+GB3nIpcGE62sSSUw3lGwnLZx0ZLUkrTXCWb7qep9f3LvNomyxMDV15lEYn3d/eB9PtdnuOkLtca7nUzWKxjgq5+P5hrNM8IsPFmzvgvKmjIqAgdL85qsr9Fo1q5FHKyjpRHbJzmhqNAG1UTg157RwbLQqHoxgnoXEQcj6SsY49VmNQ+LWnLtnAk87pKiJ2zWdpGFR5+y2MubF0pBgrbVkFo6ygDunMbfv9XnX7VcZ1xUXthQ7/QW+NvjOMUxDGudQSiCFJG8a0koT+o6sEax1C956ccydYcA4N2jlM1potAJnfeLulNpqtPIZtFIeTQMcmqN5xCtHCd24oB033B17bLUb5mLJi1HKcJIm7HQXOCIcMeNMcnVtxIVezb1xm+f3u8xyH6kM5/pz3rCRcVdpWt6cy92ceRWRNRlSeYRBQFE7tvtzmHp02g0vHHXoRRVPb7RxfsqDUcC5fk1jGw5GClsHYJfP516nIPAcD067PL7RIc88Nj2fz6ZnWAfnVzP2pyHdac7nbVzgU8MXuTiYceukQzvSNFYi5dJ1K1AJ07Jgu1sQ+xWfOVxHSkNWCqYmI1KWIJKMFiG+buiEJZMqoJtU9GSNsYLRvMXu6pzTWYDUhuf3VsjjBUUkyBsP4wRZqVlrFYyykLZfUjqNtWJ54ABc6qWcpgHSOdqRoB71OJsbnt4e8bGH63iEHE591juSo2nDTr/kdBqyMCErcc7BLGKRRqS5QPYXjDIP4YVsJl0+ezxhbxpSG5+FqDiZe+xPW/RbGWFgmc4iauEQEhosoT/D93xOU8E4DXlsa8SskgwzQVMOeSRPqWP49Ts7/Olrx+xPUp5qBWgaHkzaVJVP6Sx55vi1g/NEQYOvR1jXAJY7D8E0miCqieIF3diRGZ/caMZ5gJRwcW1Olq5h9RJpoJTAWJhmHpN8aRAT+YYagacFfb3OWVXyM7cfst4rWG/nVPqUm1PJyaxF7QTztGKtHWDqAKFyJJJaSJ44N+axbUmwgIlS3Bq1Wa0VxnmcLkJ8ZYg6egnKLzWeFrRjyyRzxLHhJJ/THxTEnuMtGxfY6Qz4iU8+wI9hWmqy0nF5taK0Plmt6LeLZYeAcHhacHG9ZJg6UBIpfTwJvqpoRw4nZhSNYrMbMMtDkmjCMA84nis8nfOvXxny+AUPEzcczxMWleaFgxZPbfqsxZa8U/Ollzb55QeCBsGkUKy0DA7ByVwzSCQWgZCOrU5F1jgWwJ97os//8Mk9agdbnWWV2Sz1CauA9factBZ0IkcrdEyKBkdAHDcUlcQ4hcNDKWiFlkjFIHOiuEYKx7TKyUsPYRSVaXjf/RNiX6FmAaOpYm1dsDOoefUw5upmygsHAdv9iumkzX/zRRvcmxd85nBMYQ2ep9Ai5gMH95DGhybk3GBKagTa05zm4AcViW+4fepzrleyHQgWZUg3akiSEK09alvS6ULThGSNpCk92u2CtFD0QoNVOdMq5HXnQn7hlTmBZ2h7PsNJ/jntx3+S9YcVU/1HR7ZZlvHw4UOq6vdm+17/+tf/gRf1n0qDzhTnGmJbsRIHWBMyzVq0A8XFNcG5dhvfeSSewfcd88LSWItQFUFgiDzIaoetC4QfcWm94mjiWOSa0jlaWvLkuiOSbQwexvisJxXtQDJKQxqXY52jtAsO5xbrHFcGS8tEwZJ34azPEwMNBCihiDUUqmZSOJ7bT7i8mrOoYFFXfORBzGNbFW2V8MxuSdl4RO2GXlxzNI84mPv04oy9mWZWLe1pq6LD9UFD5AmywudsUeJJzUrHcDb1idtLyPdKkrEeW1aDmhtrC54/6i9LP2XGS/sdijJCGp/hQtCPKzxd0YsrVuKaQWT4xH4fKZZQ2Y7vwPkcz1o4VzE3c9I84LyKsWWPh+OS+6cJn95L8T3JhYHjct8xLhrSFKIgpdtyDGKNJyyXOiEVc1461FxYm/LcwxV2+haJxFhLmhvqGnxfkiQGEZTcb6Zc25nxymEb7TuOZ23OHpTIB7CjHGPpWFkpKVxNq+Mz10esDTxePM34gqtnzHPNwSTkM3tr7HYiPL1MsjwYd+j5NY+fm7Iel+xNO3jS0YnmHEwaJkWARPDg5Q2e2pL0e0fktWJUjVk0T7PqVzh5ArrCl4a08YiDhlnuYYwgiRxNbjma13QjxeO7Q2b5MgD2hKAT1GRGsdGd82jc4v6oxc7KCTfWDQezjEke0Y5rTqYBtbOUpsP5wYhOlBNqibMOIUvSwqeuBY0uiJRhURYEOkSJEKzkXNtwWijOFmAxKOuT6Ba5LMnMjCfOjamt4njY55ndDnfGI/Jak+cdGkYMYsHRJCASlkgbilIwThsenY3wWKeoFTc2F1zpOfJ8l/0JrCaSnXabzSTgN+/HfHA6Y286JRQK4yrwNLlRDFPHbn/BolRktUZT044rfvbuJ+mHIQ0NH79f80XnBPdGisZpppmHFJYkrDkY+niqJi9bbPUKOhFcHYwZlZoLKzOmJyGl1FxbzXHOYNIBP/hFT2It/MQnjvnFW8dIKVHCsp8teHI7ZcGyl35exJwsBFqVtIIJnhZMiw6eVNTOsdaZgBBstgT92JCWS/e/xpWMUoOvDStxiZCO3/zMGqku2OmXgGC4CMlqya+enefVM0NfRfyjLz3PZ0/nTDKPp6+OuHXqs6igE5bEvsQ5vXSRsxVO54Rtw1YL1kSFoaG2C/JasdZuiH3L4ayPh49Dscg0ZzJit1dibE3jDJMyxJiAq2splRUoCTvtksoCSGwWsDfxeTgydMOKKwNNO4B2ZMiKFWgcLV+RlgGxJ4k8zQ+/4wm+94MfZ5x6tD1NbmHdVhRGk1eOlQuXaTLL+S34f33yEUZljKaKT+v7HE59qtASi4bPHGo2WjX9JCNrPFpiwcNhj3NbJYEStCN4dTLiQrvHv75zh0Hc8PLwjMhXJKlHVSf81Duf5v/+oY9RVjEhDVKXPL8XcHHzhHH5hwFdVP+7rSg6ODjgn//zf86P//iP8wM/8AO8/e1v51u/9Vv56q/+6v9gZ7D/GP3Gb/wG73jHO3jb297GBz/4wd9lBj3//PP803/6T/mX//Jf/oHGd879Hl7Qv63hcMijR4/Y2tr6d95/7LHHeOGFF37Pte///u9nPp/z7ne/+3MO/HyhSHQAYUCVeXz00QzpYkojSCuP0GsY54JZVdHtdHjxbM6sLjBNzHo35b//5EusRB20gKKI2IlrzoRmxQ8II4twlk6cUTaaw8wjqxvGOUxHipSa7e2Utc6cV0/a9MKlW+CFvsPahKwQ9KOK0IO0qnk4CfnUZEinVSJ0xhODkkg5Ho1WuDwQCGeJAkteSgat5d6ktCb0LEE0oysc0ywmXXhculjiSR8pSjzpUALSEoyDL3gs5EOPJnSaCmV9fF0TKss89WgCx8NRsnz+hHO2OimBDHEuxRhBK3QMM59P3R3w1MUxBxPN2UJSlAk7/ZLGWkDjS0UYZUBD1WimuWCjU7AoQ8aF4f2vrvOVT5zSDTIirelGlvsjw4fv9/G1RgGtoKETKoYZFFVDXnpIodGyIvYaDJJIV/hKsJd2OJ4rGiupm4hFJXnd1hm9yGIKw0GtCD1HaXxS75CeV/GG3QIpGgJpSDxB3unxwnFGWik2+zWnaciTO0OkWlAVA75w53FuHTTsM+Qs1fjKUNVtXtyvePWsxwsP+wSBYb2bsdMqGaUhq9GAOk348G3JY+cmGAFK1pzNArotw7rOqQ2sxIZXjrp4psXFZJ3D6TFaOp7aHhP5DSeLkEdnEaudikA1WAuzYYDRluEiQCtHEtRktWa7m5PVilVv2RIzzDTbyZxxGRJox93TLq/fmWJ6Mxoj+MTDVWKv5sHxCquRgEYynPQRzOlGBY/OYs50RGOgF1kiH1pBwasnCdtdySxdumMlfo1fx3i64mDS4cZAsb+IicMQtGJBiqUmcgFxWLE3tLQDRd04hqnPbtxF+Abfr+jYFnfKjGtrEz52f5XQr5mXHqHSxGGNwTHNNIuyT1lDKDqkdUY3aWjqLldWQmqmnKSKyFu+txzNEsoy4elNzcvDlONZgDAO20TMw5puVBB7De1ew7TwKGrB2SKkFxtGmSIj4+ogwKIxzsd5C05mIaVRrIgeubJ4rZo1P+TupEZ7PlbMSQLLOAtZjUuGuc8JAVoK7o1aDOKSlu+4edChcSktzxIHDavtgtW4xBOWblQwyQxSOjwqPnRvitKSL77e4UN7Q7qh4HDWonYeWjqMcWx050Ras6g0xlrqRpCVIZ2owTkfJWDQWjDOBEkglq1TqcageTiOmJYWKX2M8AmSiqwKOZkLJpkkq0IE8PJZzsUVQ94E/NbeGc+datpBQ147OqoBAvany+T5RreiMeBrw8kixlh430tzwtChf8dpNfQk1sCiUMR+SNY4BknG8bTPvPJobMMg1kjVUDQwmkfMU8nuRsr5lYokLDicBYzTiH4yZpoXuDrCYLk3Tdlsg+d7hH6Dj6YXaC6tlbRCyWZH0AkLip7PgzTnXCvkh/dHbOuSRa0IfcW8LvBUB2cFHgnr7Rwt4cpAcjhrMc0krlDM64p7JwEnUw+9M2PaeJSNwswrdgIIPYdSjlpA4kLifs08jbAmIq0VV68GXF0zHM4EjXNLTutrrdcqrvoTElN9zomi09NTvuVbvoVf/uVf/nfeN+a1B3r+YSnSPqs6ADwiLaiMYV5ZDtOcomnolhVSSYRnmdeG47whrRWvbxdgPYQVGKPZTCo8QqSJoF5yMEDRCWsEUBqYlRZEwRObB2hlubRacTDtcf5L38HRVLPTW3A0XaUqHf1Asdt1rLQs1sb0o4asbqhMQ25yTgvLdjtHqh7Hs5CqkdSN5NmdjLRaIwkNK0nJ/lgzLARWOW4exVxa9dnuzgm1wjlD2++w1db04hpjJDiHVBXDk4gbO2MejiK+/vHzvOfT+7xSenQvLbi6tmBaLKuJhKy4P+4S6yXsVeolGLcT5YzSkM12w0a7oBdVsJLT0Za7kwFZHdEODOf6GdOioZVkHI/WKQpN3Gl44tyEtDYMs4bNjiGvFS8ed5kWGucZnt3Zx7iYri8YZiGHxwVzkXO2iPD9FZSNOT21GCwNAqktykkutSP+1Lk+k6biUycn3K5q/vSNE7LK49W2AyMYTnye3Qg5FjFv2Fzjp148JA4LHs0asrJks79gb5yQBA03NnOubRY0VvD8Xouy8YiDmnkVIxeSQPq0gpKz1GOaaoRwvO3KMdPM5/6pIDcVt19dBgGbPYNpJPdHDeeUz9W1GZ6yvHoSMs98ktBwOvOpjeTJrVM+dn+NqoZXDjusdzKe3B7zcJQAkkHYsKg0rajEk4YX9nrsdpfQ561uxnSxQhxOOZ57DELJwajLpbUR82LJqTG6omgaLvRnjFNFXSgqbdiIHfnC42LfY1ItkDZgNfIoqIjkMpG5udVilpVUVUTZ+KyFPZrGw6fL/sJxafOUh2cttnspg7Yg8SyjTFI0ktHco6d8bk3G7PYDbu7t8uFpilAZ3/i6i5wsHIvKcrxo+KbXn+N9d4b0Lks+9SjloydHbIclkpCNVsVmt2aWO9KRJnc1oWcZpYpW1BAqx7MbHlI0FKUgiisur5WstXL2Jm3yammdezDzubQ2oeV7bPdLxExwPA8RtUTFJZVVaKn45rcEfM8v3eXl0RwvdMjI8fR6l6e32/yr5+dYZmiZEsiAxnjcH7bZ6DieWh9zVkT042Jpe69qTlMfKZe940I4pHTEnmKz7fH//sQp3b6mshFaWv7LL+jwazdbNMExUVBgjUc3gmcuDuk9Ckm8lN/eP+P5k1OsyDmZxwgBP/C2y/ytD90k9CX7k4h+q8I0krwWVGYJTY+15u7ZCp937R6DyDLO+1R1xNGkxTj12OzNly85yrLRrrhdxezPIS0t60nN+ZWMuvGZ5AH9qKEdVDwUAYnXEHuOee7zuvP7tAJQLkEJOJ2HaKnJKkNeKloBXOhbfvwze3R0m43NHIemHRjKWnNxbcL+VND9qr/IQe6TRBWnqSUMKiqrsU5webDkdxnX5YlrI149aXEylzgn+exel6Tb0EqmhJGhtjEvjk8oG8vLozEbnQW3xz4rkSYOLbGMEQg6fkjuAjQa24TsqghlUyL1hxAwiNcwUSQcUL82Y70GWllZ4Tu/8zv5zu/8Tp577jne85738O3f/u1827d9G3/xL/5FvvVbv5Wnn376NZ/3e7/3e/k7f+fv8N3f/d202+3fvf4lX/IlvPvd7/6cxvq+7/s+3vGOd7C7u8t8Puenf/qn+cAHPsB73/teFosFP/ADP8DXfu3XsrW1xf379/m+7/s+VldX+fN//s//7hjf/M3fzM7ODj/0Qz9EGIY8+eSTv2eOXq8H8Puu/4coqx1BYyjqhnEz51++6Hj8vCVrKgojaQeC/RnsDdukaUnWOC52Qs53W5wUGUkgUUKR1w4lPASG2tXM5iEyanBqxnFmGB71GWaG3ZWGtFC0ewtWpOTqWk3oC+Z5RCAFeSWJ/IpRKhjOJY/vLGgsCJXTSSoaoxilDiEizrU1zmtoBR46cBhb4azEk/DyaIL2HEJARYGyBl/EzHKfzX6DcCGVgdgTLKqAwGvQWuErxfteWLC6MWWlFdEOcgKvAamYFT4yrNjqlkxyQ+jnDGJD3dQoYdgb96lNxYsHHdZXStIy4N5ZgBAOW0uSMGe7W1A2HreHMdsd6IWOhxODry1Z1cJaiyJgNdakpaAbFoReSG0EFo8rqw0XeyNeOQs4mvU4mApevzXk0TTGChi0p7QCx0osmNeaysBJ2aJxMC987p50aaqAVmtOO4TYN5Se5Xrfcq5f83AcMl4E3Jn36CcZQgTMsoh3Xr3C3ZOGB9ECwdLhKVCWaR7yxOaY56chnzh+hBGCtNJoI0A4XjrOubZ5whuvFWgJH73Xx1eSs7NdHswy7mIIZMZGv+J45nM0C+hFBikEF1YmzCuPSRoyzBWtqEaJER98WGKMZJEqPj5ZY71bMk2X373b+zHr/Zx+WBJ4DY/mIU4KilqQ1yFXN+YYB4O4YL2dMcpCVmPLwaJNN67Y6eTsjVo8/7AFIuDq2ilSO04WPldWFa8MK9Yin5NZzsP9Pk9eOwEJpbF4IqA0JfnCI1hVRKFgXjmKpMQPSpSqOa5HtLSjHXkc54pQWkIfNJbRoubyoOBwajiYKAadnHHWphuX+J7hqMzo0OXFg5Qne1O2VksOjjd5MFFcWJ+RBAYpHJN5AFaQLSI67YKO6BAGJYvGkNU++cJjLjOi2NI0mnmlMcBqp+TOfsJGnHBvNifyLEeTZQWKs47xIkRKQytomBcekyzkTZuKB2cJrc7StCQKG/ZmM0IZEbrzjEYLUqOZyJRFqrm06jF2JxgRMUsVD067XFuf0FiLcYJ+VGO05e7QZy12DFOPWekT2JDjseVO5vPstVNCZdibRdS1T+jXPBj6vOV8zDwLIB6y2U35zfsrTPIA3wOHZJIp3nR+RFG1yEpBN8ro65qsBONBqwyXJhyuYn8Uc/u0w2YnxRUBvna0gorIr7l7liBFgRCOc/2Gk6kiiQrqRjPOJSJv+JZnPD5zWnI40QySmjsTQykSespRCMvJXLDTg+ubC/ZGbbaEI/Qsw8xjkfk0jU8Ywlec3+YDxw+I/AxBRC9pOBprDsYxWlsOjhPqwsM5y35dM54vq7siryJUDofAWctquyDUgqwEaxyBTBgkhoWqSQLD3qHHztoM3yZ0YsswhbLRWCsZjUOurRVoJWn5JT936z53H/l4bYtwEqkrtN8gpWVap1RFCycF7bihrj1aAWy0HMbC5ZWSg2lCW7T4wXfu8jd+4xUkNYmnWFudIbQgVJo0M5zmLTypcDplWmg6gUdROw5HS/fZi/2GvJQ8eTniY5/zzve/odcqrvoTElN9zuZu3/Vd38V4POYjH/kIURTx3ve+l5/4iZ/g2rVr/MIv/MLnvIA/Sh0uCpLokMxUIJebaFpCEuQ8sZ5zdcVyY72g5Uk2EsXjg4A3b/u0dELiBQSepDICrRosNScLuD8WHKUle4sZ0hujdMn+NMBYw/nehLQIyYtVTmarjCeaOOmx1fEZRCGx8shLD4QhCGZs9x/y6SNBbmrGhWBWWY4WllkR8MrRBXxC+qFkJZZEStGLBI3RSGmojGFclnzskeT+MEAUinawdCPqBrDd8lmNPDY6NVKCcRblpVzeOOXS5eHSzryd8v69W9TG0U8kWmpCzzCrJb14QVZbOoHl8Y2UxLf0WgXdOMcCu/2Clbgm8AyvjgJGhWW3N+Wx9Yz1dkHsN4RejZKOuvYZxBXzhWNcpvTignZYc3HgCBQ01vH4xpi3XDiiF1oWVZftdsG8FOxPQ77lmavsjzusdpenIjfOTXjHm/b5v/7ZT/O2G4948/kxT+xktFeO6fSnvH9vyOGJJvQEe+MOizLgXD/nrZf3ubQ95KXhhE/tz/il+3tcWRNsdwx5A6FfESqNQ7PVLnnxoEdZOjxRc31zjpKKaR6hpaGygrPJCi2xRYDhZNRhsoi5fdylFdZ85esf8uVP7nF5Y8FG3GJWQmpzvuDcAM+scfd3XNW2+3OurE+pa4uUkk5cMylirqxlaCWQUhIFjpN5TCR8+nHFrPCYpBFp8TuuXkHD4UxT1z4PRzH4U4o6IY5qrBW0YotWmrRWGHImVcOiyrk98imNBaGJRY0m59KaoDaKogzQwjLKC/pRxGbbZ1jkzKsFT56DmC1GC7g/n3Dz7AztF/g658WHfSqzdB24vp6RVYo072Ernz9/bZO3XuxyY9DhbGqI2yPKwjCeBLz37itMxMfwwlPe88lDXjnNeWqtzYsnI+5MJ+AUZ2nCdO6x0cmIVUPiW5zzWBQ+w3m4DFyOIg5HHZ4/nfM/vXSL2mmudla4c9TiEw+6VI1gs9uQVyGrrZragNIVaaG5sJJhjIdEIIWjFSy4N4z4kY/NeZCnKKn4tjfv8peeXmc9UfzmKzVNIwikYlK0eOm4zfFMolVDXkecLHqczgOUWuDrjMJo+hGsxpZ2UGBdCtT4WtIJ4OpOykoro7Elpwu4MzJ815/aYaelsE7wcBRwOA24edTj4eGAL954E//dR27z4uiUVlARasfX3OjzDz/+iJ9+57P81aevcvc04d5Jm7tHXWYLn9oIzg8yhPH4wqtzFnkXZMlLx5I7w5D7J/EyqSwkrcDy+ZdGSCTrCVxesZRG82hmsCKlMDWzamndXFtH5Blib8leujKQ5Nkmi7xNWguO5hF7k5A/d/kazx8Knj8qmZYVjWuYVilSKDqB4vpqyZVBzePrJStBxGYr5PTXfp6zm5+hagRKG4aZZD8tePGkxOoZuakYFwZLRW0aqloSKsHVVcNbzhu2oi626YONUM5HuohEx5zM2nTijMYZkrhgmAvSxnF/mnFvXDEuGk5Tx8nUclosGJf/uUT6P1ZveMMb+N7v/V6+/du/nTRNec973sOb3/xmvvALv5AXX3zxNZ3rhRde+D2Jmv9Fa2trDIfDz2ms4+Nj/tJf+kvcuHGDt7/97Xz0ox/lve99L1/2ZV+GUooXXniBd77znVy/fp13vetdXL9+nQ9/+MO/J0H18OFDDg8P/8B/179Lz+1FnC5CRoXi+z//dQx6jiwLCAiIVUKVJwyCmCudkF4kGUSCjbZHWjUo1dCOChpKJlWJcSVHY8VLewHt9QVJmOF7hvGszTvOb7HihWz6Gzy5ntDxfba6grV2DU6R+I7Aq3GyWFYn5IbpxNCNc9pBwbXVMaux453X+7xptyHWMWkZMV1EBEqAgEE7o5MU6LCkkQWrnYw4qCgaR6QCTqYtIt+xkjTMS5+skktWW6OQoqQf5aSVYmPnjHP9irV2ReMcZa0xVlIaaKwgVA7rLKF2rMWGtWTO2SJAS0srKFHKgdPcOWmTjtt0Asnu+pAvvb7Hbi/nfG/BRlJx96yPdZrNdoavYZoL+klOJ5DMFwm3z7r81r0dDuc+tS0ROLJa8dxhn/1Jj8YIDmeKYSoReFwcLOhGDWnlcTgXeMJysVsjnGWaC5yTCCzf/uwFcIJBVJJX0PYs1kmEMOSV5uJqztPnTuknFUngmGSaW2cznhs/YqOTcn6wYJzFeEKSVpLP7veIvIbjacmsNlwe+LTjEl9arvRbxNrSDQt6QUkvbmirFc71fV6/Y7mxOeXaRsXpzOd0FlOUmvunIWmluX3aZV547PRTDkcJVdEiLwPaSc2zuw0bLZ9nN1cZzX1a0TKZp5TlfEfTb1VIBLsdgXSOTtQgaYi9mn5UUxnJzYM+kWfxpeFsEfFwFPPicQcpDVHQUNSG/VmbxK+JtOXOeEHsVSjjM4gjet2Ks9SjqBTGOMapwXcddvsDFnlIR0li0eJgMcGTiixLUFpiak1lGqxpkESczEruTKesdQtwGiR0EyhqzSxdVle0PdjtCmqV8paL2dJZ97DPQQo31gISHVMViknqMZoFeLbLWy6HXF3t8OyFNpV1vHlH8SWX4U2XM25sRlzprXBl1aGlJdaOlgftwPLZ44wm6xPYFSIR8cZzjpVWxY2NCa2oYZJHGCfYiCNqmxCKmNVQ4eScu5MJZ6nlaO7ABBROs9Xx2YzbrHQaRkXDU6ubPL7eoiu6PDxq8WjaIq99AlkhgU5g2e6lNMaRhIJ+1ND3IkAx6GUIYThetLh33OfmXpeP315nkcYczBZc6QVYKzmcRmjfsrVS8uzlAy6tz7ja7zJMexxOQ9LK49OPOkjR0Ish8QS+NswKj9tHMcOJ4mzS5sGwj5JmedhuoagrzvUykkCzfOwIVloN272U7d4yuWaF5aXRKYMkZdAqMRYkkiisKJylmMfktaRsBGke0Q4aRqmHc4p54REHJWXpkxaWf3X7iMoobh52GaWC2GuIgoo0VRSFR5X5tH2Pt+x2SBqfjX7Kerekcg3Kn9NuFzQ4nKsZZ8GyzdI5JJqWr7m4UhBqRafXsDfx2Ohl/NU3XuBkUTEpYJgJFqXBsXxG1I1ks+2z0w/wbYCPRhGzKELGWUg6jZiVhvFCMikNj+YN06KicTmZyUHWBJ6lH3rMcsu1lYjERvQjxWK2jm0SPOuzGmuaUhNoxyiL+G+++AoXOwnKk/zYb48Y55bS5RykJWHwh8Ao+hOg1zKm+pwrin7913+dn//5n+fZZ59FSsmFCxf4si/7MjqdDj/0Qz/EV33VV32uQ/6RabulcHWftdBHIAiVR+MJAi3oJ8uTr8YsXXIWZUkS5nT8gONFC183eCZnXjleOQvY7ZbEHjjmHEw9nlivUM0AV4d02imx7+h4MbWT1MZjXgQ8uH/E7Y98iPCJa4RhQW4SSiuZ5ILmVHJ/ssaVQcP+pMsgbvC1JpARHR1yknVoB452WHOWKSpneDRV7A5G9OMK42pqPIoyYrbosdU3OBuQ18vTFikkvbhhx6ZMM8Fqa4oOU57cKrhzOmBv3OHSSs4o12x1NY9t5HSjBiUsLa8iWckZ5n1muUfROAKv4XWbEyojaUzAalJRGcnts4RRXmGJsaZH7GnyWjMvFVIY6iYi9uHpQYe3v/UKP/jBlxlPFQsT0o4MVjTMi4IXjxXX10resDMlb3xGWcLDSURlSjbiiP/iyiZDc0TNmLQ2fPBuh4886OArxWMbY+6c+ay0Mj528oDLnXN8wRN9fvlBRloClHSCnPvjFsezLl4JShmOxoJdr0vUndOWa7x4NuUEg7GGB6NN3rzZ4WO3PAob0uDYGeRcX+lymI4Iqm1aieU4LVFSEyjDWivnaOaTVm1unbaIlCAUfTbbXT65Z7i3yOlFit21Ba/flBzPtjktJuR1QKgdQtaUteW4CPCU4emdIb/56hr3jmPWWgWZ83llv08SNgwiwbg2zPKQ3XZKLZanN4G2jGceUhVEvuNk5rjg13x2P6HrG0K/4XQSsNUeMazaxJ6HrS1OSWpqttoJnz08WVZpyQI/crxuZZPjrKTjaWpjyRYb3BkfY4XgysBxPGt45aCFQ/L6nRkbnREPZy0+s9cm8SIurE+52l0nQdJVmrfutvkHv32ft+1e4APVHQa9OcfTkBf31/kL187zNW/v8yuvjvmiCx3O5pJMFJxrR1xZ9bg1HXMwjSgM1I0gLyJ22j5nWcpqq6EyFXuTiOHYY6WtudDzuXnkAMHuIKUd1FjnMUoDVuKGoorwpOGzRzGR1wYTc0GXrPVOOM08Lg2mPDiL2OhkaAWe1/Czn5ogMo8LO4L/6o1b/Ju7hrP5CC0rWkFFJ1D4GkaFD9S0/QacxlqI/WVy6t4w4dHYR1mNJwOkEjgceRMyzCussYzymldOly2DkddwGlS0w4pHo5jd9YIffe4+SWCJ/BKJx0qr4EF6Rj1PGKWGv/qTD9hdTdjuWzzP4qymaiAtFNe3pixKTVlFfPjVkMsbJ4xGfdIc2knDcO7TTQqKRqCcwFlNqCSbScBzjwJG2ZSOX9EKFIczzbRqwKW0/RpPRWx0FL5ucFKxEpW8euRDHfKjn9onMDE//XU3+PZf+yQHM03glUxTH6tm9DszYhFhWVoMr8WSne0+B9MJLe1jrSSMDZHz2d0YMVpESAmxb3EC5k3BZKrY6Sg228ug7N5IM84auqEmUhFSCGwTshI6KqXRUtGLa+a5x49+6pBLvYQveHyXX3z1kCLzSbrLqq+N7uw136OEVK+JjetyrD9+Vq51XfPzP//zvOc97+FXf/VXeeaZZ/jH//gf8w3f8A2MRiO+53u+h6/7uq/jpZdees3m7PV6HB4ecunSpd9z/dOf/jQ7Ozuf01g/9mM/9u+9F0UR73vf+/43x/jABz7wv3r/x3/8xz+nNf3b+rY379LrRyzqBk8JdrrL/+/QA2slJ2NN0slxjWXRzHHCcFYqnr/b5omLBU+0M07SiMpoNjoFAsmFxBCogEBZfBWhEo/jcs56p8FTIaUtqB34usG4glmpWW8bYlmCVqS6S+5qzp1vePlwjcuDGcb1SLMun5oY4g5L99LCUBmJEJJ5CadpgKckeVNTNY5Xj7r4yqCVpKkFgXKESrDaMghhCXRN0XgEfolUNUWTUBjDeqtgraU4nkqaRlGaAOtqJA5PCbphzqwOqE3ASVYtoc6h4WgqsM6y3q4YLQTrnZw01wQaPFWzGpXcn/QI9bIFyiGI/YaTRYvaaL7hiR3ev/cymZEczj2QMdoTfHAaUZo1NtsN290ZOJ9u5Dic+uS14O6wz8vHMc45tnsWLRwt3xB7DYfziJunLTzlEXgO6yQ3hzOkFBwtfPJKARVxJ+ezx6v0YsPx3GNexbhGE+iGx7dHfGZoyWuByTSx7+N7ltWoxXMPfJxWXOiH5GiOpxmHM8PllTa1KFDC4TUXmc8ccbCA2nCap/gyIybhhQcRgQetCBZVSS+2bCeG2jr2xiEOgTUVOytz9s5arPUqWtJylkPsJzxYzImChqfPDckqj2m+5Ai+qX2Zu8F9Dq2iH8VUouLKeklZCdq+oWoCFrXPZw9i6gaECbiyMeLeSYClwTmBJxvyUjHJl9/tSPrcPfE4yOe8ccenJTVHc8Fap2BvFJKEIOSCK6s9bp3sUJkFjThBlAm+Z1mUHjjDtJac6/ukdczBPKPfbgg9zeSszb5IaUcOTcZpGrMa+3iupu1rjNEob8Zx4RE1kn4M11seK5HiZLzGxO5T1/DmSxPGE7g3rri21uJPXYx49vw2t8dzXNliVVd8aLZHr1WSG0WkoKh8jqeaJ7dWcLCstol8nl7tMpYPGeeKs0XELPepaksncsTBKUKXTEQXVS2Zrv24ZLObMUwrbs0WxLLPU+dGPJgZ3tIxfOrVDQ7Ggi++tMpBr+LemU9ppqz2a4SSZDUEyrGW1Bxb8ATcPFjDuiHaSdZCw/2zNsp1+I5nzvNjn75NEKb0Ysu4kPzkp2cMepp+UtA0DYN2jcBjkSX8/S9/gn/4yc/w2XsNVQNaST71ULPbz9nu5gRexoWuR2K63Koy8krjpOSVE0c7Sjma9OjHGbXxkMJDSUMSVAxaNcdzzUZ7xhdfy/jtewPKRlLWCpB4ukYqRS+sMMLQTcTvcL9aXBrM8NSynVVJycEkJPQqVjoVcezYG0sGQc16u0Qqg6cFq23BZNHQEglfcKPLzdkZrxsMWHvK59fuwtq5MbHvMFiypoZSsigUi8JDiCWzbacVEWrBtF5wKV7ns+6MeSWpGvje9x3je4ZJoTBOoKKUaelQQjPKHL3AcWM94DMnGVIsq8uVAGcd59Y0/4fHtxmVJb/ysGa9NeHOUZ+nzk9xWcNwESBUxde9fsA//uAhGxuWk1nDumexKHCWaRrQ68BWT3O1F3NrmPOztw45yxs6sWNnp2GrW7KoAi6vpvzK7dfeTfa1iqv+pMRUn3OiKE1T1tfXgWWZ0+npKdevX+epp57iU5/61Oc63B+pMjvh1lGfC70GrQQPJ5rGpTTOkhufdiAoG4mUFYIG5WIeHK9wbSMn0NCJBC0dEGiBlpJh4y8BrUhiXzBd+OQLzdZ6zv68YrM/ojIR1sRMK8toNOO46vNsUBJIReKDEkuLS1OtcK6f0dZLcLCvLFWjmFea2gpGmeLC6hQvmDKfKSoXIIQk9htaXkXglXS9Nhd7CcpottbmZLVP3FQUzfJhHeiK1VYF0mCKiiuDjOf2N+n7YgmBbJcoCW2/Yq1d0PYMG+2MuxNBZeB0ntA0mpv7Cf1WQTcs0EpRNIJ54aNVhZbL052NBK6uZEyLZZk1TnBxpWSSJXjKomjxPb98j0cLw6PFCt1IoCeCTlyxtWKWWXgjcSzBy2mlaayiGxd827+5zVffWOXXXopppOBtNw744p1L6PjDvHrWpTI+5/sLXh2GeFLz529s8NG9BWtyg73xkAoPKWNaPvja0W01RElJWinuLnKuxgEHxQm7g4Zbx20wgvkiYlHknFurkbJhkUlGc83do5rd1YDNtmLczDBUXF9N0N4ZgXa0s4C80AgrmRQe1qYMBm2216eYqsMwT/n4A8WDUUU7noOTuBqyQuAQNCbEOkWYFJykMd2kYTLzGTpw0tBrWTqeJmdB7JbJobTs8IadEzKjOZwErHcrZqVCSbiyM+ZoFrCa5IwXPhUgqQh8AbmkVoamkeRCca7lczApMKJGWE3stfCDho8dPWTgDegncG8keKjm7Aw02y3Db96DzTbUpeDx8wsyCj70YI0kaBgtAnprI/YmMYfTCed7Q77m6tM8dzzH+ikfnz7i8c01Pn0MvsrJ85hfePkUUGz1LL/44DY6OqSYtHlQQyFSOmFF5MEwbWGsI45njHPN9Y05CEXZSDa6c1456LAaxAxzwdt2u/zq3VPW9A5hdJeDuWOtXZBWEsTye9ZPGm6fhsRNSBylPDoYILWhaZbcoPODBbf2+ry0X/FX3rzO3rxkVM34px8+xiqDF/tEnuLBWYtFLrmyNadsHC1fstOtEDimZUMryDmaxkzSFmkWcSlqc+uoRHsC5xyFM+xs5tw9Shhmltdth0z2V3ll5HE2b3jpIMTliu0Vnyh2BJ5kq5Nx+3QFW2u0XmF3AN/1ay+hexXXrw55y27J0bTDp19dYyNMOJk2eNrRCg2riUGiORivcDjx6Xsli8KxSCP83YJbZx47CGolaIwCG9ANQoqyw0aSM0yXVVJR4DheFGz1hxzPVggDiycdaS2YFZbGObRwCCfYWRH8yGfucb4nKWsPZ2CrU+H7im5U8mCY0A4EOEMSpHSvb/LizYRwUdHxfYRcOhrGPvhSsNaqqIxmEIasiRUavyGUDWfTJavFiRLncj71Uo8Lu4ZIS6ZTj/M7U/amDWtJRSsypE3EK0czVroL3vKmHj/10iOk9Il9kLaNzx+G69lrzSj646Pv+I7v4Kd+6qcA+KZv+ib+/t//+7+nvSpJEv7e3/t7XLx48TWd9xu/8Rv5nu/5Hv7Fv/gXCCGw1vKhD32Iv/7X/zrf/M3f/JrO9Uett2ytcG6jT6w0nzw9pB3PKLA4JcjLkEep4cm+z7lBl1+5abl+aU7oOd54acx2XzDPWmACNmNFUXp0wxJ8SWUdOIEDPK/m5fGYS2sL7p0ajEg516uQSvHZo4DVVoPFcjr1KOoWvdDRCQuqJiTyQAnFLItIC5/RrOJSBALDKHf045JJFqH0gu1+SSeU5E1FXvgoYclrmKQR1kracY2zgm5cEXkFZ6nPhf6Mh1PFdsfRWEfgFVRNQNlI0lKiJAgnCFSzfKZIkKqmHwka4/Hb9/qstzNM4y/nyj3WWxaLZbef4ynB9Y1Tbp202Z+1gILT1KdufF4+Tsgqj+1uycks4H/YO2G9nTBOFYaaSeFzqbNgM6jAaK6sTXh1GOArQW0aiiZguoh5IDTduMRTlsBruL62PDy6dbbCqFAkvuHl4y73ziICbfjlV8do5fPcvR1q01BGU5QJkE7w2GCMEfDxB5s8nCtwAk+DNXC50+a0nOCrhmmteLiY0ukHjBeOmw8LglggfIsSDYVoODeY8+ohWFORLTx8HdBPFJkr2V6ZcztN2Rx4fMPrV/nXN095bDDCOsHR1CetQna7EZYJB7OQtm+5tjUFYRktIgbdisnCEsgeD0YRv/yZXR7fsGytHPKw8PjQ4Zgt10apACVzlHKklUZKyKeaWaYJlOMsFZzrlfjegv2xD6pGGcmi1ARBha0VD8YdPr9zwkQ2rPUEVlrujyw0PhsrmrwxbPYymkZggZsnJyhZE/sBo2pZWb3IJZ4KMa5ktV0yK5fVKe1AkOiIRxNDO8zZ6qS0gqVT1bl2xLie4qmYk0VJZUvGacSgk+IpyzDXOONxJV7hnhnSjSretF2Q1iFTr2I7XONrr+/ygYeH7M1Lbp2VBLah9s7IqoC8Flxam9NYQW4dvcDn0eKI2LfsLwQ3jxO+7dlV3v/ZwRKwXOply7uDwJuQNz6+NuSNpEPJtfUGKRqyShBqx/WNjHsHbb5g63G+4XrCuz94TFYuuDOe8OJ4zNX1BdurJYWxnM58fGkIPEvsFYymHY5nLWpjGSQ5rdDQi3IsirDyOVvkvHySInUFwjHPNSfDFp1WTicqUUJQOsXRVDOaRXz5lXXy2pCXIZsrI9JKY4RgUWimheXaesX5vuLjdy2zPKWbOJRf4fsNs0LRDR1/+toJkXZ84tEKi0IymkdcWB+BqNhoWyyOw1nM1UFJAwQK+rGgqCwud4yzmP3pstp8JbEkgWGSKXqxQ0nL8cwnqzyUMnz5pQG/cv8UhSCtJIHnuH0co8TymSJUg3WGXteyOHXcyQ7Zn2v8uCGvPLLSY2eQoWTGesvxcx/ZYDAouTIw1FbwxOqA4yynETWuNGgpcbVmLXJcvhZyZ7bgZCa5vJbz/H7AU9tH3Dtd4/5IEwSnbK8niEnNLK2pPUlWC9JSEPiGvVlOGMITqxHzpuD8imWedskLi5IOD8Hf+LW7fP8X7fIjz9+hEJpSZfQjTeYKDoY+O0pwnOd8y+t2+djeAxZ2DI0mVB5aG2DZoqZlTM97jeKff1uvKaPoj4/+sGKqzzlRdOPGDV555RUuXrzIG97wBn7kR36Eixcv8sM//MP/XkDjH1edTFdoqg6hzjibR3zJ7jq3Z/sUNWy0GzwR4nnLMuNI+VincGgQloYSI4dk+PRDjbU+t8/W2Z9YDk4kjVRE0lKrhrKxdKIF8yygHfgsTMhmbJjduE64epX10MM1kkD5eKphmivmuaB2jsYZVuOU/bnkhcMlFPF8z3J+MEXJmhcPE6racX0VPOGBtWy1CuZ1QDdQRErSNBLfrxlmAiss9yYtrg8yulFG0FgcJaNpl7ToEaqAWanoBg2xZwk6JScLDxwgG4aFw1Ml98ddIm2R2rKfg1INe7MWW23DJA+oGsFG2HCawePrC1ZjSycwNLZCSrMENgrFIKkpG8kHb485SCWNrlFaEQQCl2uaBhqrWI0lZ2nMhV5KbQSPJi18XRFIHydL/tXth/hxw/nelEgX/NRLZ/yZKxFtFpR+xO1hi6zUlFXM//M3H/LnnlhlVoLnSi4PFlxfW/DZwwGno5C8sZxMNa6OuLHS48O354ShprUxxtMVfmDZ7s9ZVB5lJbFIDkY+OMH26jLxdnM0pLQOv+lw59Cw2eowdzM6UUNZKpSw/NkbDR+41eE4m1PTZrgAFCRBw7zSeLqmF9eEGnylWVTLfu1xKrh/FvLUTsF2e4ExCQpJ31ccLxy1SulpyQxLHFT0As1Z2mI1KckLQV5oLqwuOJiEuAYGSYFA4/kNi0KjdM28iPB1TVFLtkLQvuF0HpF4hkVpudQN2Wop7mdjtIpZa0vyJmazO6frBbxusMkv3ruPpOZoCl6UcpJB4Ad0fcXZQtFr1VR1l35cUpYBh8OIXzFTdlcsdV1xPGkzaU8QNiKW67ztYofJvOEX7+6x2Yaqlrzj/Ofx7OYpW/GAn331ERe6PZ5YbXNvdsxzp2Mao6gdPL/XQaPZXU3JSo/Ht8cs6hmRi3g41/iB4cFiwpVIUJQeg3iBw6MbVeRVwMEkJlTQUoJ2lNFpCwLPcW+YEPqW4Uyz6Q04TCs++plTjLUYa4jCmsiL2Wl3OcqmrLQKBu2aju8vg5GFx+WVnHPdOU8N9vjYyTqOFr3IUlWWv/imDb7/F/bITIP2LMqDvGzR8RRRMOff3DnBswHngw3u2hnIEasbOes9jzRf9vRb5wGWMKjJ3ZRO37ChDO9806uMc8N227E/bmMcPHsh4sUTx/6p4PJOSi9sKJuA65slB+2aXjQh0JKP3d7keLaERVplkU5QGY/53KflWyZ5Qi9fwvj7UYNzMbbR7E0VwjmKRnKY+sRhxice+RiTEqJpCY3nGQ7SAqWWpd++gs2OJdQhfa+H7Ev6YUXZaIZZSKV2uL65z9bOnC/dfpz3fPyYc+cmDIuCoilY6zUIQgpTcTh1uKgE4fBEQKwVpZGshRq7EtDWBldpVhPIp5uc7yxYa+ccjntYK7iyVtOgUQq6keXPPdnnZ17ZZ2+mOTn7Qwho/nesl156iX/0j/4RX/u1X/vvBS1ub2/z/ve//zWd9wd/8Af5y3/5L7Ozs4NzjieeeAJjDN/4jd/I93//97+mc/1R61t/7hWeeKzh0VCzEku6kWA27NCKJErBpRUQVchhVrHb89loBQSqwQ8lWsI4U0sXVWWZ5tBSNUFQoSzcHfn0QoEQhkfjgJPZgPVuTqhq9oZdrFWAYSWoUa6g5UuEDfBVTSuA2hgQjtpIRpmPEQ1BXNJYQyee8WCk2ewaGtewnaREOkRgaZqYc13LvLRM0phQKyyWRFtCzy1P12uPlahms5OiPcujSZdH44iqsWy2YZJ5LHKfa5sZg1bBolIMopqFUvjSIYUiCSq0DJks2tRGkzYQasfxPGDQzgmUoKo1x7MWx5MupZmyEqWUxqdqaia5ZDVpcA60NKwmio6SmMQgbYee53PzoOaN52se35hyNAtJy4hSCqa5Iy2WWIO0Muz0KqzVXF+f4quGtm+wtsHahMJGdOKaja7CVw4pLSdnbXbbEMQTyB2TPGKtXbA3j6iNT2Ukn3fhjMiHtJa0w4qz4y5OS4YLzXThsRpJPFnjE2Bih7DwVZcGjDLL7emEReHxpU8ecTzzGKUhtpJ0gh7OxVxZ6fPpZkrh5vz8rfuEkWFeajbaBU9tl+Bm5EZy67CHUo5ZIfF1w9WBINAVo1SSFh5+e8rbrmWcTEOKRvGJe+sI6VhNQJQJJ1nFRrfm8sqco1mHwoCUjtp5rAYL2nHDwTjEOI/dlQU4OJ5GDDrFEr4roa3NkleTLCu2I+3R8jSH04yThSAMPC4NMk4XHrZOMC4nLwKcmKJNCysKVhMPaWJW2yF3p3O0Cxj0HMIqjFE8u9kitXugQ87mHltxF/wSlXcovQln04hzqym+UEgpWOQe49IjkgF35jMeX4/4+MNNPniWs7MieHa9zbSpeTQp+LzNTc61CzbCBZ89mTEuNRrLWiujHzdLjtU0oNEpsW+pXU0gEp6+pLh9uiAtBO1WzXYnY1oo5qVPWkbMS9jqpPSTkqc2JtwbJ4SewNMKJUoaK/mS6yH/6LePGZUlvVjjRYb/6pkL/H+eP0WLOb4uEWiOMsmd05hne1OiwJEEBVfXoDGOrFGEWuBJS9lYnCdIfMlPf/qMTs8jDiW+hHc9dYlf2buP56eUzfJQfLMz5x4S3y/55y/e5TA/obRL58L/4tImLwxPOJ33WBQ5jfMQUpAbg1ZLnq6oDG86nzLJPO6c+QynLUaZ5vzajKqBC/0xQljO8i7zIkFJwZvOzRinHtNCs91LSQs4nUeshxmH05ind09IgnAJXC88RJagcAznglZkudDPeP+9lCRsuL5dI2WOc5LHt2dMMk1VBeSVppI5v3CrxBjFnUnJSmII6mX3y3a3Yjzv04oyPnqnTbvr2B3M6SaCcab4xbv3iDyftXbBK8MTPOmRBI6s0jyzGfPKKCT2JFkR02kinJ3T8gVvOl8yzzX3zhy2jiiaAidqTmYBi8pxdaPgX79ySjuBr7re42NHAovBWoFWGi0Ng8TgScOvvToh0R4X1yVh4COdpqo117ZzHh71kLrib/36XXpdwRM7Yz79sEtTagZJzmFqCaSktobj+R+vZMwfZ/1hxVSfc6Lou77ru363n/5v/a2/xZd/+Zfzkz/5k/i+/wcqkf6j0KJWzAtJWln2p5J3PbHKB49uUv0OFO9wmuB5Jaf58mErXYswKLh1EtGKGj6xt8GX3zhhlreZ5RHCKXzr89RuRTeM0CiidkPbD+gEXWKv+R0Yo6BxgsOXnmeRlgzX30JWaqrGYEXNw3FNL64YpoJ26DiYeZxllvV2TssLwYaEvkHgcb5r8QREviSvNWVjEVgmmU8rrJBqCWm+deqzNxO86aJjmrYYLVrs9hccTX0aY/HmEcXI4tCUjeLK2gSHYyWuefXMoxOWOFERe3M+utcnVoJe5HjD6hY/P8/ISg0ojIW80owzTTtM8aSl5SmslfiqYCU2LGrHKO9TNT6x31Abj42NOaXv82ceP+DTD1c5nLRQsiZQFeOFR+Rprq4UeGIJB0eUOCt45WCdJtOEEUwXmr0zjxceDQi15JdevoizUGSKv/LWFZ58coU7o5TaOh5f6/C9v3aLa1sznj0342QRsZoUOGvIh4qrGwt6yYzT+ZRzGxpwnGXLdjmtlsnCdlQsgbm9KePJgJ31CmMU904iLqzlLMYh3/LGNX7qlbsYDRfaGZFf4ZyjMYJPHbbZaEW0wpybw5Iksmy1PGZNQaANlVGMFzHtqKFqHLMcBskSjOmKiJv7A3bXFqx3lm1+kdkBpigjaFSNqRXTIqAzaLi+NWa48OjFkr1ScbZIuDiYce80ph0Yagt9ryI1EWeFpecvT5MCXxGZlLMyovYl2mlWAsmg1bCf52xECW/a2ODl8Yy0cjw4cXzhZc2H90cczxw31jr4YU7slzw8azNdpGivZLNXMZvHPEodmz2LoyKKNO3I8dzZkP/j49f4Vy8fY1yA0hApj/1FyUfuz/krz55jmNWciYwf/vQrnO8F/IXXDfj+t72On3nhlF+6OefmoeTi2iZlPaUyyw3S0xXr7TmVCSlqxTSNaSzcTc/wlWCzV7I3adOJahZlyFZ7zrQKCHSNEiF5GVDKOdc8g/YNe9MOgbbYOuDeIuJ//Kpt3v/qlMleytrGiNpKulHGi/sBX7Cb8MHDfWqjyKuQwK2QxPcZZyG3zmI6qSJQCcfzkINpi0BZBu2Cn3z5Fmvn5jgM87xNLxKsJDUTETBop3zyYMZfuL7DT3zihO7/j70/j7Ztu+t60U8vRj1mueaq9trl2fvUOakJSYBA5BkK5XKRxnveh1eCYLM93vOpQAQeChKhERKJedcnsYaoeC+KgooiRSgChCQk5+Sc5NRn13uvcq5Zj3r03t8f8yRPBL0GToh436+13dqea609+px7tTH6r39/32Kw4M+8+jpXTyIWxYDWCKQT3DrtUDWSRlp8VVPWglGnZbwaMK9LfuW6ZbLyEL7hA/t3+fIL53n2qCArFZVcMwm08Njp1hwvYq5sZox6FScrn8hpnj2RuMpgTICsBN1uyaJyHC4UnoTUb+gGLUeLlGW2noZN85CqUUghkQjiOGMzbHnDzi4fPLwBsuLJA0UvythOJFtUGFqcaMlqhcRbMxGKhHo85uCDj7N9/osZ+j6pp8nyPuc25lRtAyg85ZhkMW//yl3+l489ySf3Y+7tebzu4pCfuXqEU4abpyV9a4jrhDCx5JWjp0qklzMrEgKtSLSkH2geP5ozDDWLqqDja9qmy7n0s2Bq+N8xo+h7v/d7eeMb34jWv70NaduWD37wg7zpTW9Ca80Xf/EXv6Trep7Hj//4j/P2t7+dxx57DGstr3rVq7j33ntf0nX+W6iHd2LOdRpMLYgDh5I+nvTACmoMSrccLy1lU9E0llHdMqta9hce9wyhsS2T3PDQTs3lMzMmd2OaWrPVmzG7tsv5CxWIlth26Xccsg1JQkkgPKxzKCnWKUOywDq5PsRbQ2UNncCwrA1PnawHEKXNGfUKzg0shwvB2aEk9Hzu25qTVzFaSnzZ4AmFEAIpBdYIpBIIaYi8GofmaCW5vFkwTFpuL+HZ4y7TrEtlIPUtSnjUjcBYiScdoW6Y5ZrQSYhbjlcBVePxwknInUnCI3tLJnmJEuswiWGcESiLwWNeSoxJ6EiHddCYkEuDmtuzlu3enMQLCHVDLzb0QzhZBaRGMMuXHKw0m92S0GspGkMaFpxXcGOScs8ow7qITpQBNcZJLgxmVAbKJmFawH3DGRcGJbemCk8ZfBVQG8VG4jgjesybEy52VtRtiA0aGrv2NHzZ5hQtGwyC0Gu5NY3ZCFIe2h7w+HTJZm/FhVGOs4J+WIGUFDUcL0NmasEziz4bKkJ5C+5MY7Ss2OtVtE7xkecrtK65vbK8cifhpHIs8gDrchojWFY+k2WHybyhk7b4XkvZ+Egh8F3C4cTn4lBz1Iw5nCZkTck805wbZOx25zx1uEuHLe6uTuioiu2gxwNDn0mZEgVTuqrlaBmT6IZ5rRkmhr1+xuE8YpGFpGGDL30cBcYma4sG49jyW+4uFUUZsCwktalRWlO2jiRyHC1SLA1t27CwjrZynBvBfGLY7Dq0bnnVSPHLL9Qo6WhZoGufP3LlLP/qk0coWfHK3XP88tUpDRUz74gq95jkPqLocHY4I/FaFq2irGLOpDVfcO8utRX0YsG/f3rKw3uGR++0eGrOE7MxZ+Jtrk4rpFR0tce0zDhaFWx3ElrXIhQUFTghENKjqASz3KGIMMBv3l7SiZZspDGe7XJ7OuP8qCANM6ZFwGbH0AlajK24u0hojGJZJGykBaNext1FyL99egkCfCX5M686wy88P+XdHzzgwVHCaZVw8xRas1YnFLVkVmgWS00aVAhKGu0T+jVNK1iUEVktqVpwVnLuzJLagrU+s1pwpufh3bGA5GAe0ossq2qEs4oP3Cgxcs4wKmlCyVfcO+Dnnlzw7V/wAH/zN27yC4+dxQlJ0Qpa0fDg9oplFjLqNdw67ZCElqa13D6OGQzW/csje8drQ+hGkvqG1vjsT0OsXeIpgacsJ0sfrVrSoGIpLKPU53DR4Wzf4EnBySzGxhEayX1DzUExYVqEaH9O3SqubC3Y7MyYFQGzPECKFksNeBQNGAVagBAWxxoIl0KQhg1ZHVNUEZc28xd9SAOOl4JV6eH7OZNc0FoPqZbkbUo/seyvNJPcEimPujVkhUYqgUYx6jTMsoDTLCZ3a6uGyHPMPbiyU/HMgU9ZKe47v+DRZ7r883pKFHjUrsFX4Kwlbyr8wGDw+fCNJZsjQ92sZV7CKHwfmiZks2cwrQehpS59zqrL7Mc5WhmUjDieenQ2KubLCNWUL/3G+N8po+iz1VN9xkDR13/913/676961au4ceMGzzzzDOfPn2c0Gn2ml/uc1vVxRNAGvKC7NC18+797jlfe59CBYLLqsBEZahtwsSsYZx2WRYixio5viHXKmXit29TSI1AKJaBsHINeTWMDnGyIVMHNecnZwZRxFRHIAOsE1yeSxcl1jss+D/o1VeVzdBzTKIl1lvMDy0ZkuTSoySpF3fpoGVJpj7qFYdySNesJn1aOqvFpreXGxMNJzYduprziTMFOnPI3/8cH+NYP/BJ//Qtfzdf9yyf5ypfNCZVkUaTklYexDlP7NIWjE7Q4HKlfMi0VrQXrLLUxGFfy/NSj41nmeUpZBfyLj5f8X1+xx7+5ukILSdkIjIXjRcBGqhBScWfucWmQMS4UsV+Q1SH9sOVoJehHFZ5qOVp4XNwoWVUp54cNxUJwmFv2NucIF9EYQdX45I3k9rTDspScLEIir0V4DavWEYY1vVQgheK+7emaJi8MTx4k/OLtFb91MGNc5JxPtvnlO7eQqmVRxNyeOWoj6AYlO+mSJ2YbGOPz5N2EwDMM0oplDpc2CmItOMkilHDMc0lRrV/fe7bgxklKP2kY9QqaqmUjVHjKp6MTZk3O4TzgwZ2Ceam5sllwulI8eStjXhqUFkwaHydqhrFlvgqp6h6VK1hWFYF2aDSRZwlQMCwwznEwDdlKNKGWXJtWRHFLHPoM9QarPCMKC/SL8bKRZ+lFNfNSY0zA/iRBWMm80CAMeeshMSSeYVY5fNEyKQxh1FKYmsnKsLflSNOW1UqhNAQi4JPHC0aJ5qmDGedHCmM8FvWUy5sli1Lz8mHKx2+mmGDCKNY0rmG+SpEu4mziuL4f8pYrAx4fL3i6rPgjVy5wbhCz1XN8/G5D6mtKV3K8lFwaBVyblJzdaDgzsrz58iXuLGqeGZc8eifjsMi5nc+Jey1/5N5dnjhZsWyWDNOKk5VCK4GhZbGK2OkVCGCWd5isFJu9knnm44xj1CupnU83qOhHLc8d9RCNh7EeT95p2b8bEPlw79aSzU7Ba3b2eP/1KadtSTf1uTOJSEND2UTkVcs/evwOge5St4rF3OekP+fc0KFkAwKE06SBYWAMH7+t2RtUNFag9ZJBYrk+jhnFDRdHNalvwQmeP+4hRMYnF9d511c+wo8//QJ3p70XpZ2WppUEylG168d8L6pwTuBrAMcoMQzimkfvJhS5oaoto7Tlpz45psQwXmp6iVsz8RpN6Fse2l5yY9pjI22ZryI6nsfW5pJ//dwmj1yuGPRLAq9hXrY8O3ZcGpYkfrEGx62jbgWTlY9zCk8obBtxsd8SeCGbCXzdg1u8+WKHTiAJlMfX/fRvsqoVx0ufM4OWyM954jBCC8k9Q8v1ieKkiTiZrHhsv+RP3GeoWkvTgkQQakmi13IGjeaXX8hIzBb3DgyJFtS141W7KYe5Qe05hBCc2xHct9Hlt46OeGG/x41xyDAGXyoshiSqePuvv8DesOC0yqnqiMlME/u/eyT6/79+93rzm9/MwcHBp6Xsn6r5fM6b3/zmz3qC6uXLl7l8+fJndY3PdfUCj5NiSW5bEiW5NRPotGI567OkJggMDp9SVdS1Zll53DrxkcJyUCf4nYyNuMVaH4+WQSiZtgGYlL2eR1VoOknBMBWEviMrFMYKtDIgGrpRTdn45KZiVmo2kxVaL/GU5NmTIVGwJG8UbavRIqdqQpq2IfYsSoREGsbLDr3YEmpDGlicc3hq/Qw8s1Fy+zQlDmsOZhG20Tx4ccLBIuRMdw5YplmCLwXXJzGvvbTAOnBOMOi0HC1ihLRsd0qqXBILS2sDslpz6zThylbJTrekaEAKja9bkqDibK/hYJHQGsG4iLiyveLuPCEeWdq2wVeCB7ZyjCsRNGymjqxaR4t3Q9hMF8R+xJWtjMQznOmuuDNPqI1jWlhOll0S3/LQ1hStHXnlc1r4xLqmdQrrDJPSp2iDtXzWeOz0Km5PfDAJWx2Px68FXJuf5XJYcOs44NLWkm7Qsqw0w7hkVsbcnq19aayB+7Y1T46HPHHd59WXxuzFm4TRDZ4+7qwTysqIZWn4hpdt8fGDmoPFGV4opvhBS+pZpDLEsaUTtpSV4tnDgss7IZG/4PrYo2wCXsgVaWK5uJWv5UTTgGECtfUIVELRFjwzyV9kFs0ZpA3PHaUcz3xuTxOk02ykFq3BupalmfNbd2tGXbAuQKDJC0noW2Z5QlFVDOKW0DcsCp+ikRR1i6JLP6qpXcWqUNw0HR7YPeWmVVB660h10zJIG1a1wvkVqyzAUxbrBHGwIq8MiC63xhWvPC+YFZKVmdEPA7p4GOV4+njK/Vs+NycNz4xzdoagheDqSQDWY6Mj6KVTTpYB4yLANop+WjLP4WPT53ndxv3sdCJGg0MOylPu2epwZ7VO3zRFwFfeFyJ1yfXFKYfZKUJEFK3A91d40lGagKxep671OpZeXIBtMcLj9jii5yWsKg+pHKbVDIOIVizwVAVOcGPSAQF1A8eTAdsdj9ttw6zo0zrQXstev+DwpMuybNnqKM4OQ26eWGwdM20NoVfz3FGX1GvIOyVlprlvqwZaxrnHXqfiYzd73Jl3sFWX2dLQOotS0NKw0WvIKp/SGC5vDDkuBMa03B17qMYn7GXUbc4rzpfMMg8NPHcITSN414evosKML3/kLuPM5xM3d1FtyvHMcWZYIZAsa0VtFfPCYW1F21iePhoQexbr1kBWbRR1o5llAZN83au1TiFlgyct5zeWOKMJPRDCYJ2gbSOMkdyd1cSBYKcr0bVhvAxRqiL0GnrB+hmUN2vm9MXRimtjmOfrZ1nWWgaBIY1qylaykZY4Z9es7MIjiQ2ny4h5JmhdQ1srqsZnknk0jSAYNCxrSRwUbHUNt2+H/KOP7/MNrxzxqzcXzDOBFIZlA5IW6wJaLKdzxXDguNSPcWnM40cFlzYsy3lCmWs2Uo2rJZ1ORWUEUdDStJrAb1mUmrz0ma8EMgBTScalw5ZwZstwewZnhxlFHiPDjHzVx7ddiipHacsg9hjFEduJYjsMOOMUH/3cbqN/aOqz1VN9RkBR0zTcf//9/MzP/AwPPfQQAHEc8+pXv/r3tPjnunaiGCUlnudhtKGqIPYkxkBlBImsmRWWNFxwZxEwnscoIYjDhvFJC1huL3q8/uKcOCrodgpWxymzPOAbHrrIjz5+gNCaz7tnzLWTHl94KefWpIexkkBqAn/AQ3ubDAOfYhUwTBRGBjgcO2lFJ3T0QkNtBFK0tE4SeiWeUuwNphzlLWXTcrDYQAtHHOS8cBrz8U+MOJ7GKJuwmNR87U8+RhJpvu39Vznf98nKDo22LAuNcQbTCjwLnrQMk4KsdYySJc+cbGNdy2Z3RRpmPHW8NnjseAnzImZV+DSi4aGNlJ94OiCvWyRrQ2QhDDudAq0kxklSr8FXhrw1aN0wCCuUzBivAgxwpt9gncY5hRaCN9874AP7B9ydRnzJfXN20hV57bMRGj6xn+BsyEYiyCvNzkZBZRqKWgA+iV+zEVcEumVRC3qR5dqJz5FrEEJxczJhd1DSiyT3bi1xArphi1YNs0Jzz2bGHRtxYWOFpyyP7JzwwqRDrC2nWcIorvjojQEtLf2wZbKKuTwQhIR4OofKY6vvWE1T/ukTd7hnZ4EuKqSwPHOUsswTmtquNyhRMkwsURiwKBucgKNFTCRSZmWB0pZ+KEh8w+lCsj/z1hMX52hqTdt6VErRiyM2ew1Hc40TOZIOSgbkmeDpuy1J7HNlc05ZS2zdo5+sOFpJXOvTYteUZwVl29DxfKZ1TSUk92516FsNJsQaj8Y5TFtRiRotKq7NFtTGpxcMuTKMuD5t+PXlXTo6YkufYyZmvP/5nLOdBO2nLDOfi/0dHhzAs8c5QrYEUclBueKeETx6I2dexvyNXz/kICuIVcgrNzfoJoJfv7pgKwqoGkNCj8Wy4CceP+TBTh+nC7x4xjAV3GdSylbyz5/cpypjjOigbcqr9iK+cKfHzz6/YqgMz97OMNJRtZJRt0EIy317U5aFTzdsiLTBIXjmqMNk6fOaCz7CSMrVMa+7cEysaq7Oh5xOEn7teM5Gf59ppkhiQydYx+KmoWOaSWpjsa6mF0PTgCcF+7MBnbAl9mpwiqxRRH7BPdsnlLVPL/JojGan0+KrGpyjNZL9ccQkC/B0w42JRxLO+L//7Cfppy2+t0HZrAGZQBmSACLf0lWGblyzP4/ohDVawryIGMQNZ/o1B65hMbccTXz+Ly8f8k8enZIVLb00oxuBc7BGtNYMoE5g2OrXaBFgWsfOSDBKDKOkoRdV/PJzA+4uLQ/u7rPVybh62mdWGVpT8/xpQD8UxEIglGTYWf/fhzrjAzdOCOOCu9d9vvzKFptiQL+TM4wsOzGsigFfcd+SO8uQrEw40yupz20ye/NbGfZXvOdjz1I3KR3Z0riaZV2gvYLnxymJ73h0vGK+chReQSwLQutxz67moGjwPYcDnLDrdBZfcc+momhjHtkJOVgV0PgsFyNesVNTm4CdtOZkqXntfR2eurPxku9RQgqE/IzDSf8z17IvyXVeqnJuDcz9p3V6ekqSJC/pWt/6rd/6X/2z7373u1/StT+XlSqP37raR9JyaTjFtREGH+VJBlqzNzDUTbNmAG9YqjpkL1U8shNzdbIkChXdEKpGIqOSQIG0joNFgMVyOk0oXEWYZOS1ZmkNrnA4K+gmBQPVshGW7HQX7HQKTpYx1jmeOxxijUdWdNn0R5wdVFxfOC72Syrjk/gNEolzgqzy6UY1UrT4yrCRrFk4NyY99qcpWS4pWoVW0NDQupatjmVVw3gV8qozKx6/u8Fmr6Fs/LWswyg6gWNWaBojEEAaNByufI5XKbWBvUHORmK4OUlZVusDcdYotlXNVlJxkvlY56hbjVD2xcGVY19YRknB2Z5hWgjuLgICZTjJNPMSzvZzUs8yydZpa1o2VCZCioBpEXKmV/PCYZ/IX/DCJGS30zIvNUWtObO5ToLshQXnuxWnuSUNBIsyYJL5RJ5kVbT82u1jOp0SiyRSBlYKYz0Sv2XVSM51CxrjcXsSMUwsBzN43yevI4GLWxWR1/Dh2zkv3xEUhUGbDl1/g/HK8GOPHvMll/tcm7ecG614YHvF0VKxzH2mLqa1Lb3QEMiIj9wwhCpmc1AzxbC7syJvQlblWqbSGo/SNDhybixrtBJ0RY/fmHj0Ise0WBLrkqKNuDQ05FWAo8aTHqu8RgaSFsWibIlUQxRIeonhNPPoRzlH85TGGu7ZmOHJlrL2GaYB4yJf+zPxYhJnKilKj80wxzQxdQPDuKK1go7XUDuJ71UsGoV06993Vmg63pJhaFllfeKooMgFX3y2x83VDKUFjREEnkcnaEmVRxwkfGT/LokfoLwVVkiOlx5aSIpG4mlLXoSMwiVHuc+TbcG4mSGc4fa4w3ZH4Nk+bzq3ydXTil+5MebKSDHJAi53LvHH75OEKuDavEfdwiTPaJgxaw0nJwHWxVzcnINynBmWtKbEdzHzvMOwU3F9ZuiGik7U0hjoR5ZZrrk5Twg8ix9OSbyG7XRtP+Gc43TS4Q27e/z4Y8eE6YLId3i6RAcdHtjocFQcstUt6XkxIg94/ihkI27Y6a7o6jnjskNuPDphzrA7YKwcx2VB5TRaKCKvRNmWf/Lkdd54ZofnJo6+KtjdOcZTmrzW3D71mC7Dtb+NrJiYOWfP1JTG8NDuik4gmeQeSgt2erATbfPR/RMu71UEOicOYJAY9gaOzdTS2i3Gqw7GtES+YZ4rqiIk0pbbs4jNtAEsvi5ZFB6uThFxS1b5THOfTlRTVBGd2DDQKeN6xX4xeZFFDYkPG2mBFI5BbDnrF1RtwP4iomg8zm2s+Np77+Wv/9IhSdCgpUXS0k8MrZHUjWIz9pm1LVXls9MPuTbL0cJiW41nNa4UnC4s/W7GMFlx/bhH2wp2+hmLuiHyHN/zpRf5Gx95jjtzRWsdkWrJbM1yEuLFcHY3hniDR90ttDbEcQkIgtAhjSH0oDURpoCtQU3VRmSVot+Fi/2MZaMRtuY0V5w5s2CVh/y1L7qHt//SLS7u1nSTgj/5mkf48O0ls5li2Tpe87IR3/2VFz69hx2fTvn7L/G++FL1Vf9H6ak+I6DI8zyqqvpd38gfxro1duzslRgsyivojTJ8f8VeN2d/1mUzyVnWgjuziKNZylanZbUKyJYRrgnodywX+jW+8HBGc6FnCS8b8tLjsaMZO12QSrEZp2jho4BuALNyLYXYfs3rGfUEDkNpDYu25SRvEA288cEZtfWpTInnVewOGn7p2UtsJoLdXsk4l0hZIqWgFxkitY5HbJqAS8OK7VCxm1qqDJQ0WOPRtpZeBMYq5oXmcOYRBmsJV182dKMCIWtCr+DJsWYjzbg17bPXz6msZSetqNqU02VMtgqpGsGZQca7fvUOLR51K/GSmnmu2erlbHcqBIraaGqrGK9CNpOCw2XKSnsAzCtF6wxVo9jrNsR+zdEi4N8/NWPexmz2FDdPJfdvZHSCmsTLGCQJRevRC2r2bUzkGeZ5yL3bS8YrTdsK9udrxofFcLqKSX2HEi3TzCObK241MXsbLVePuvTjhkGSkwQG4wSLLOS0DblnO6O1lpPcJ9KWT+yP0BKyUvPAxpDfvDWjFwqcg1lb4scV81xxdJJwetphb1jT1opbMzg3KMnbgD3fpzOISYKW06Ilyz0WrSVNKtpWkQSOc6MZh/OKV+6E3MlaqkazLHz6SckkU9SVJKTLGy8FeMrx3FHLuGwJkBgsiywljHPuHSTcFYZ5a/E0nCwDilri6wUr0xIrSRVKvCKldCtAQivRYYdvflWP1545i6c1x8fHjEYjTpY1K1Pw8X3JR+5OEM6xv4SLm4rr84q2leR2hodjVjRcGVgud1MOdM4orXlwuMW1Wc60zHjuoMQ0gKf4S6+/n9Mi59E7Jf244mOn13jD3gUaM+S5WcEzswXV2LHVbdlfWh7e7PLR0+vcP9ilcRV68CzOxIQqxrQpVSEIFFzoDNhvLXfn0PV8NrwB9/e3uPiqlv/pf30K7Wt2B4rAabK2oDU1dVuRBg1ZrekEayN2gPt2VzT1kKdOp1wONLPSZ+XBKM65IboIBIfHPYx1VKaiahzW1Kxqn3nh0Y0zlLQ0JqCftighSKKGSSY5P5xSNB7jZYRWivtHS07ziMP5kOkqZplHbHZzPnG9h44MzigQsDiOeMWlu/zGCx0SCQE+3VFB90VjyDuzDnGwpipvpBVHi5DYa0iDGl/BsvKY5glHMwHCUntQ1h7v/dUJf+3LLrDdk/zNj/8W/aSldVC1gsS3SNXga8FOtyT1G1g4Rq2gqCIqXzExAS8/UzOYaZ452Obqkca0mryR7G1MGGcSnGHVhkSx5ezWisOVo9+e8vc/FnHv2Ypbd/tc6XcoKomuLGngCD3DqgrQoSWS6zSeRAuKjuT5T/wMD73mT5JXMfeeHfLoyRRnErbjdQpjViQEynBuqyRN4dL2IcerEETNx0/itT9A2XJrqrkxFxwtarb6FYvWcGMC/8uX38tf/pXHqVcxSyMYDnNuTxVBUDArA7qRYlF9FpoGqeAlAopeMgnb77P+xJ/4EwAIIXjrW99KEASf/p4xhieeeII3vvGNL+majz322G97/bGPfQxjDPfffz8Azz33HEopXvOa17yk636u67io2E58Lg4DsDmeiNZNsoYgtPSShqyE09wjDBrujkOMddy/5XM1y1iaihjD3XmC9mr2wpZIVTx7N6WpBd/w8g1+5NGc7V7DeO4x6hdgPTpRja8c1voM4wwlfOo2QkuPrA5oyg5aOqo6pBKQ1YZIgacEjV17k2npmOYarVsO5wFFY7k17fCqs3NuTD3ypmVeSKQqWLWWDV8w7KwY5wGjqOE4i8krSVOvpfpVrcAp5plPXmgEltBbDwMibckLSa00w6Rmf65REnwFg7hgb7D2qpvllsfv9IGCWa4QzudbXneWX97POBttcnN8gOc1LApFa3zGueLuNObcoMTQ4Ml1yqVUJdOySzeCUVyTVYLTLMCXjuNZn9ftbvCJcUMnWPvqTF+8zmZaAQYtBM6tZciT3GPqNMtac2lzyY2xRuw0XNpYMl4kTO/4DEc5SkLTQuRZytbn9jzGAIfzmJNZwDITnNmumBYed2eb7PVrbmcdpLY8fhTxso0EkU55oL+1fu6ZBVuBQThHGlhCr2JalvTCGis0bTtjb8Mx6ljioCFYKhrjsxGUHC88dvsN28MVvjYcr0L6oaE2mj/5sm3+2TMv0LiKUVoxSFp8VaCF4fakQ1ZXVKblrAbfK4l0y9Gsx0YakrclrZWEvkPLkEFccLwIuSV7dDzBRmKYzgOccGx3HCeZT+RlLHLF3rkSaS1WCG6PY3KjsMaA8bAY4sDSNR6zvEV4PkkU0JiGqvGoW8vZVHFpx3J1ukJKxX39TXY6Pr91Z8qqNjw4CvjY0TGD2HF5EDItIsZ5gaMF1RJ6hrxISVVLtkqRYULeVkib8qdffpaffvYaHz9c0YsCPn53Se4cZ5KQtrWcHTaM8xU3F5pXbScE0ufqfMFzpxmv39tG9Q5RqmTUqUn9NVPqzjRCCegnGaeZJMTSCQy3TlIePLekH1U4Z9nrN9yadDg6UWR1RBpprh4LRp0aXzguDXp83sUYLxjyG4c5lvVA9nghgYZVnZJVAmkUr93KOT+Yc7jyaV2HUZxxcxpjrSYKGk6rY2QHjPEY+hX9pMHTFafArLJMmhnb3Q5emzPoF2SVZl6mGGM4ytbKjtBvCHSDlIKOBk/BnXnCONMsCkESjnn2+Q6tB3enEeOVZJS2PLi7om4dZe0z6jUcz3y2kobjSUyNYaOTE+i1gf6duc+V0YJOuMITCafzLi9MPco2xhoFrKVpg8TyZ162xa/e1Gx2LMtS8Zv7YxalTxoptKqYZCHPHHbY7ZVcO+6xP5UcCcX1gaIoBQcLyaBv8HRDawS1lUwLzctHMe+/UeEMnI87HK1aKpejPYsGXrvd5+nllLKIOGg8rFOkHkQy5PosQwrHjz75JJKAg5Me/d6CuvGp8oDaQIPh3zw34ZOzgpedryiMI+4sKKuUWa3XNhylZJZJTONIuwZr1+oMJeT6/FUKlFx7Ttk2pKM1z04nfNl9Pa4tF/iuywcPJvSjgHv2DAcnHldnCx4/nvPeR2/iHOzfzl/6jfGl6qv+D9JTfcbSsz//5/88P/RDP8Q/+Af/4Hfo4P6w1eUt6HegNhpPxWitiERAVVk84WNtQig9hoFiM/YIpKOUiurFxA9frQ9YpYWqdfTjORt9w/5tyfUZ9PslWdtwe7VCCY/9leTKxhix8Fm08NRP/ySv+dJX486/mp3+goNFAsrys9/wAP/b1dsoXVPUGmRLICXn+g5fSuZZik4k4BFpRWYSpoVHZRSpr+j4iq5v6MUNUdiihGNVK4aJj1QZe/0F1ycxDQ7tCnwluX97xtZORhLk1C7n6ZMuyzykH9aEyrBq/HXUrFybeoeeo3WC2khuLXP6qWCShdStwgmDFC135ymv2Zlxe6WJPMM9vRM+dDiiqAMmWUikDaGfc7rSbKcNFk3RCFaNQnsGbRXd0CJRfOJwCydboGJZKbY6OVJIWuvj6Zxh2rAsfKrS49LWio5fMykUZ/ol41UD1tBagcklQigubpfcs1GihWVeap466K5NtoViZFuubC/Rynz6UD0MW656dh2HKFv2izvEost0EdGNNBvegLIeY1xL2mnxJXRjzcvOKK7NG3Z6DdfGMZ1ozgvHaxaLpwWBDqgbg5AtceiQsuXJuxtshDHPTgsaqwk9w8WeZlGF7MQhpCXTfMlv3ioJtORNeyOO6yUYj4YeQjsO5xU353PODZZoUpoqwhctZwc5+7OAsghxzmGdoRdViCakbFpefSblgXrMF1x8FQDWrg+/Ukp2BzEQ82u//hHu27mXa5OML7kUUbaSWVlAOCWoUl6x2yNQHkoqFoXhtZsdPjE5JvYciYbbdcb9G122ox6+Fnz4xoK8abi9Knh4J+KjhxGnfcfXPNDnzLHHr9w8QvslN7M1qyZrEq7NHTdmV1Gy4enbKYFOaVrL2UHNcZOzHbUczg0df8gfe8RyduOA4+KAf/zsR7k+jQi7KWnZZX+ZEXkVX3x+kzu5pGqmTCtFGjiOBXwKEz9ZCVblCd3I0NSSg3nKpc05TljOby0JgoaR2uXlWz0EjsNVwy9cO6bXa0h8SH1JP664Po5IfMe0lGjtGKYZx0vJfaO1rNE4QVZ3URICvT7IzPKA8a0+X3P/Ln//0UMubSv+2hdd4Z2/eJdPPLNNIBseujgnL1L6YQvKsdWpefZQ0QsbAm3R0jFKa5a1wFhBL63XByS/4eq4S+gV7HRL9noLvuKeXf72h5/jY7cqvuzlS+ZVB79R6wS1XoWnJVopWiNRsqVF0xrHNPPxpeDssKBsNReHLVmleHBnhkDy5H6fO9NNHtppyUvNqFvx0Pk5V8cJWhTcXSZMGslTd7qIRpK3lp956yO85h99ACccr/Mybh4N2OpVHGc1xhq6nmRWdTh5/knGRcl0pfnSPZ/fOm6xzuFpw+nMJ/AcoQIPzVbi2ApThsE6FSXR4IuYUWjY2G25fuIRew7nQi4OLP/zy87y9l+4TSfR3H8l4PA45GphuGdY8NaHXsm7P3SbT07ucv/eIf/2c7GR/SGrXq8HrKdfnU6HKIo+/T3f93n961/Pn/2zf/YlXfM/Nm9897vfTafT4X3vex+DwQCA6XTKN37jN/JFX/RFL+m6n+v62OEC5yd8/etG/P1HPe4cwGhk2O0vSeMVvdhwtEppnOGpF4a0QUO5FPzY40f4nkRKySmaB7YnzDKF8MFayV63ZZE5/s1zh+x2HN1A4fUsmx2JsWqd1KoDdjo1oVJIF4DzqBuPVelR1R61WEeyB17Foqzwg4ZpoenHOfNCEXkN104jxkuPMz3DSa6wzpHVQy5sHnNmkNO0AVfHmu9548t41wdvcroUvOXBA/J6zWxxVjEuI0xrSUNDoAVlragrxVJoLmzNiX3DTq9gVmgCrXAIQs/w+gunLCufojHcmqUoKUjDlmf2OxzNeusD7Inmn3zyLn5guX1aonTIqGNYFB1Mu5buO6sJteXCaEVZe0gpOVx0uLhR0fcbbi8CrPWYF+uQhztjn1dvC5z1WBYRwrV0AscocWynNVnjiLSjaS2NE0gBZ3sZxq79fnZ7BQhDazSdsObCTo+PFgWBbrmysUQoiYelbR2r0qc2gk5asLvhME5StYLNTst92zM8VbOofW6OA37uhVO6sWWRHTHo5GzELb2oxtMNQ08CAiknHC1iukGFwPDynSm35ilHi5CzvZKnDyKOpj7DtGae+wxUws1lxUbS4CmLXgz4x0/sc3Gn4HjZUreap+7GRJ6jF3jkNiMJLKkM0XXBogGTd5BojpY1g0SipWM891GywdoQ5cHhxKeNFKlIicOS05njeOWQJqUxIYerjJohu72cslIoEeLJOSerCG3VWk5ka64//ihXHnkFxlpEpbmn71g2HpoUJywHTzzOzsOvxTaWX755ncsbQ5RWtEbwgf3bdFWPQCZ89E5BoiXDrk8kukg0hoqldtycwG7a0O9pbk9ajrOSv/WhG+S2ZTca8shWh8eOZqSBJPIkm0FKY0s+eTjh9WdCfvXOCzhRUYuUi4OUx8anaJcyzVsWc8uf+4IBJzkc1SV3VlNat05S7nqWJGi5Z2tO0wqi1GGt5bnDBGsVb/28Lf71tdtc2igYhkv2VzFzE/AbNzM+efoMAkknaaha6EeGKGqY1zWpD3Fo2D+NmGQe0ypis5ezqBSrOmUrWZtsLwqBMRD7Dq1aBIJAw/5BlwUWXymeOj3meNIQhTW3V+uACU9qthKL77f0YkM3LBlnAZHfvgjEQOzXKOHQsuboNOF/eGjEr91a0vMl0XBF2ypA0Ytrbk8UG2nFNJNkLZRWkEYlnbhgFLf8xtUOWlveeGEtSbszDUmFIKtDjAFpJCunubiRM0gMv/TCmHObLUfTkC+9uMUvPz+j0137NloHZ/sFvlcxzQPO9GumRUBdwwcOrmHaCKPseshoKxaVx6oMuHaieP7OjG5qsbLmkydTNlPFrClpfUNVa7aGjmdXFqUFWe3z0HbA/nLFcpkgqRGsvaOyQvHl9/V44mT9OUUhSXzHhWGJmWi6iaOq1s/tu+OAUepoi4C8cLRTTTIqaHXN3ZnPhY0leWZYVREow42ZZhAbilpwUhiGSvJRd8j/49UP8QO/McUIw/WTGX/04hbzqqJ2lmkF+4uKg0VD4Fkq136ut9H/5uuz3VN9xkjPhz/8Yd7//vfz8z//8zzyyCO/g870r/7Vv/o9v5nPpH7kR36Ed73rXRwcHPDwww/znve85zNu8hqZozzB6SpkWrYYWm7OPYaRwteO58cJ87rCuQZpK06mMcYYZpnFi0q+70se5G899gTTXFEZWLYSX603WycskW7Z7mXMs5jLmy2pJ8iqiKwI2UsLAgS9KCL0JFnj8/WP7PH+m8f85HPX8VTCZlqzlShmZYqvWrYSyCrFsvApfUNlNV4DszxgkgVIadC6ZNUYtjo13ahh2FNcPfJZNZbv/oJL/OjTj7OoHU6UnOY+QWt50+UlUjiKdh05OQgEgfQZ9hs8uTZym1cB8yxgr9fSDSw2rVG1YFl6dFKLNQprBYvS49JmxuedO+Z4FTOpQkZRha9abqwGdHzJyTKmajTTzHJ2kJEGJa2N6OmGkyx60cStQUmfLzqX4oXXGMUV16YRlRFY47MsAxywkTbMSs1XPXibo2UXZ322kwUXhgtOi5C7iw6p79bgUrdgfxLSG5Y8uDOnqH2EtHSiCqVqrmzk/MrTe1wZ5DhpOVl5aC3BWSaZIglaumHFtXHIMvPZ6FVs9Cq2/D1O2wnX9mMu7xXsbWtuL3MyV/KBawGtCLg52SNQjlkUsNHJmWSavW7Jk/sbDNKGQBsqz7ERZ0g0j+zU+DJkmSsUXZ44KBDOR0eC1sErzy2ZFLA/iXhm3rJqDPeOYr5sb4tfuHUToQSDQDEvojWoppeUteSx6z1ivwGp2eotuXkcMOzC2U7AqNvh5r/+Z3zL29/OO97xDr7jO77jd9wzP/RDP8R3fud38g1//tv4Y9/8F6jalluLFa2BntziSy8NeGZc8cqthJO8YJI1XFssKVv42WvXuJRucCZVTAvJbgKryjFuZ/i+4k17fW6WGa/dGXCQ5/zDj9/iN2+tsFhC3+BrSVVqTiNLbmpeOejxwWuWTuBxKmoeGXW5Ncl5ZEdiXEzTVtxdzrhzUyJuR8SBYRivTVB7oaWk4t6wRy0afuHqhFGnIo0lea1JgxpPWVK/pRs6prnPspQMorWEaykV4yzmoe0x0yJECfjkeMWqdtxaZhgqkmRt8Bf6LatScvM05U33HTPNUjztc2FQcbAMkKJhViqsk7RWcnsSs9ldsdtbcOM4ZZ4pygbu30oIkxWrSvK9v/o4F88u+corFTfGAZ9/Ycr107Vhs23hYJ7SiV4EiZQj1OukD+scnbBmGFXktQan2O02VMZx+yhkMeny5K2Wb3rlJa5OnmGzs+T2OF1POxsoWkM/mlC1CfMqZFn6hJVjmluEaAk9wTlalFTEfsNWkq3jlouE0HNEnkArxWZsqIuQUCs6nkLImDgQFANJ4hseGSW84XwHKQTnepp7RitAc6bXcGfex5BTG8F8lbA/60G6yePXBqAb/tYHD3jwUoETLZ1wwY8/d47NbkESwnwV0u0WzM0SKRpOyxG+NCzrhqJt0XrJ7UXATkcg0Bhn+by9Du/8xbtcPlfT9C2egtZa6tZgWo2UlqYRNPJ3T5n4fdXniFH03ve+l/e+973cuHEDgIcffpjv+Z7v4Su+4iuAdUPyfd/3ffy9v/f3mE6nfP7nfz5/+2//bR5++OH/3Wv/6I/+KAAXL17k27/9219ymdn/Xv3wD/8wP//zP/9pkAhgMBjw/d///bzlLW/h277t2/5A389ns0SjEELygWsVG2KA3p0w6jhGnQYlJTiNMxE7qeF125Jrk4hDXZP6As+DTujohI5Ix7gIfFr6gaBpNdbAMrcEnsBTjsh3SCkItWEdJu0IdItxhkW59i68OdMUlSRvHFZXSM8SxwYvnHG2n3EwHzBKT7i92OB4lVK0DQ7J8WnIrNL0ujUXhsV67SrhtRdO8XXK3/zN24z6NQ6LEyEC2EhaWgPOGQol6AQNZe3RiRvqxuNiL+Rs/xjk2lMk8VpUAk8d9Qi8hkkeIACtoBtYjBMcL0Ok8EjDhm6cczDrMMsb2kIgqLg4KsibdWqVABCWe7cWXN5ckvoNLrCsKo9FGbCd1kgl6YeWrBKsqoCrt1LO72X8yvWGcSERKsD1DYtS0Y8rhlGBeNHnKDdr6dvBKiHQLavawzkw1hF7LWc6NTdOIx4/KFhGARbBxw963L+Zo6XkzKDECUVWGia5JgkNuIZlrvA8ya1pgqd8/BdDb4dJRagl49xjToDoKp451MR+QOpLPG2YlxprFYNoReC1TIqI0yzk7jzhcJrQj2ImrWW60jgMvVFB5EuOTiOWK4+9DdhLI64dSx45W1MZxWYU0JgIHAwCn7vLlt1eRluyBiHSBmcNs7ylqTUNik5UUxuf+UxyZhjy8EjS92OePF4gnKIfhpwuGja6M4aRD1LRWrk+/DqB7+XktST2SnwSWiP54D/9cR7/yfex963fwd/5wbf/tlQhay3f8/0/wD/83u/hTV//Z3nwq9/KsJsyL1tCL8fSkmqfUFku9/vs9EKcabgwSLg5qzjIVrTGsBP2+cJXDGnnK375pGYjCLm2GvN1952nMT7XFkseOxmzN7QcLh2pH/L47A7ShqyaJbcWOec2+niix8GpJfIcMRGnS8Grz0EnqFCiINCS28uGO8cJo5EDI9mNE+bNCUnQ4lyz9i51mk7UolXLv3l+jJOOo6kmPaO5sLEkqwIyo4ibPoOuJA4dz+4v8ZQlrz3O9ZdkjY90isgzeNIR+4atTsnZbs2NaYrvNWz6jtbGTDLFyTJklFScTgYs2pbtnmKIx51xxX7ZJ45Lstzn/rMLjLNo4bgzS/DUmi01LzSBrulHNZ4yTAqNKTVCarqxI1AVt+cLXn0u4rSqsarlaLlWWXSjAv9FK4BXXxjTOsO11rIoFXnt0w41924VnOY+v3F9Cy0NeSnxDUihsEhCzzHoVVgnEVg+djzn0RNLXdU8OOzxurN9PnI8w1hJqA3jLFqfMSWMkhpNSLdbIYTm+7/iLG//1ZssyoAkLJgWiv3TDp6foYKGC5sl09JRuBXjRcrl7SllHXJqOvzyrTs0TUAndnjG455uwmE+JxERi6UlTVo2wog7M4fSFWcHBY0zdDsGXQnmeUxPtfRCzd5GhTfxiUPNhVHBIG5RsmG2CjH42DIgDWtGCVSV5PjUJ9A1gxgGUctev+Hpmwl15Dg57vKXf/omZ8/VFI3kaAI/XYwJI4VKCho85oXj8BR2NlsW7WcrJOQPnlH02eqrPts91WcMFPX7fb72a7/2JX0Tn2n9xE/8BH/xL/5FfuRHfoQv+IIv4O/+3b/LV3zFV/DUU09x/vz5/+rr7PUsxnlI5/MXXnuen37hKheGFZ6ylC+at65Kn8NJyvZGwy0nkTi6vsJTip9/bsGDww32V0sC4fCcJpGW2CXoRBF5AX2/JVGWjte8aCwrcGhiH+77/FfQHw0pkJS14HVnOvzc9bu8MKvYSiX3buYkQcadpSSVhk4omZU9ZpViUhvmpWNVJPRCQS/KuTvTdOI5WaN4pFuQeIKdnuFwGXJy6vG2X3qG80OPqtH4umS3n68N/qoQgopZHrPbMzw/3qEXwCRL8ZUj9S3GaHbShlFimeUAa98cJVsi31HXa+lZHK5ZH8YKPOmY5gFeCneXPrfmPoOoASR57TNKV6xqn/2lpuv7RF4FNAzjkjQEJR2/edPny17WsKgl5YvPi5fvznj6aEjrBGWzpl8fZz3yJmBV+VybJvzarU2kbIm1pBNWWAutgWF3RWt8POm4tkhI/Zo4qFlVil+7tsOq1tyZxEwWHq2w+NLQtobYs7RmLUM8Xq5pfQ9vrlhWmqPqLrUVvPziksb5SH9MN9SMOiVpkPPgTsZvXN0hyzyKKmTl1YSe4bQIGaQl3ahhMg+YVJLTZYAvJc8ct4TektCveOEkQkiP0LdU1YA07PCbVyVV5XjjxT6BJ1mUho/fzvn43RtkheDK7gpfBIyXGi9Y0fEqSk+QnqtIVIgwfYyL8YaOuqn5vHN9zI2rfNPb3w7Ad37ndwLwtre97dP3y6dAIoD3/a0f5pVvfBOXHnkle/0ALRRFYxiXJXsDx9PzI8YZXB+3pKFgs+MI5YjnxxXnuhtsBYZnxzlbsc9DW12OVhUfPZ7y8Eafq7OCW6scT7ZEPvheTRxYOpHDGtA65su2zmGl4xs/T7CsLcvGIEVJKxUfv9PydQ9v09RLjvNTilXAK6+cMkxasjok9lqaRnIhHfB/fmCXv/JzN7i4HaODJY0VPLA9BSGojI98MbXr7KBgGDf01S6pnRKGilEnZyMqeca2COH44iuWk1lLYy1nR0uWZYy1ilWxPhwN0oLbky6D2GKMx8EioROVjOKK7U7NtdMuWaMQsmVSWG6OE4xouLxTMCl8/u3dD5AEHRyK0LdsxC2B73jDpTmhsggBxgqkXIPHoWcwOBzrCGopDTu9itRbN1RnewW/8vw2N04jZgtB7ELect+ADx/f4cc+ckAgfX7xyYtc2aroBA3nh0uccDx5FOOcpqkBKwmpmZSOrf6MeRVTuwZcixAFFzePuXrS5+p4gDFQ1BatoKwtvpCUjaMXVRgcxjqioAXnsSgFv3hjSl3AMFrL2rKyYRCtTWxnRZdby5S9jkd3J2Xzz30bWeshpCLRjs0ElmXC0Uzx0JZCKUXiOYyvCZVHUw84XUnO9mtOVx5VrehEOVor+rFhvOzwpvN9rk9aVqXj177lFXzzf/gNPnSrpBOuiKKWZd3yLf/+OR46YzmcB8z47wcoOnv2LO94xzu4cuUKAO973/v46q/+ah577DEefvhh3vnOd/Lud7+bH/uxH+O+++7j+7//+/mjf/SP8uyzz9LpdP6r1vje7/3e39PH+P3WYrHg6OjodzRfx8fHLJfLz8l7+mxVN9AkPY/NOGC6LBiG0Iscqe+ojcK0iqZV6MDQ79a0JwENlroVtDgCvwUcB4t1bLnVBidbZpXG0lC4mtVScUY6jleW23PJK8+VOJFT1IqWhropuT3rsMpTnrrrgZM8dHbJSW5ZlQEHc82NaY8XTrp0A8nzp3v4UqLQZLlGOIVSjs2u4Vs+f49fun0DSYQmxDqLJzVaW9LA0o0ayjokDVr6geFwFdKPa7xKkASOqvUYpS3bYUAvadDa0glrxIsG14G2DJOKu7OQ60bTjdbMxaNFyuEiJPBKtrtrr6Sz/YrWrDiY+GAVf/KRHk/Nx5xmkjgQTDOfi5tzLo1qelFO6jUIPJZVxDAp6YaGeaWZZCGxB7FnuXS2oDUBKyOxtULJljvTlFefP+HyMCdvBZ5u2QhrVo3iqPCZFR6TLGYrzQh9QWNaYs9wtIpYVh5x3KLDLpd2Zty/PSOrY07mHrNcI5zE91o6Iez1CqQwzKTPlZ0VL9sec22WcLjocDQLX2QBJaiw4ZWXTok8GEUZ4yLEk3D7NKK1jnu3luwvAlInONNt6AQlZ/sWT0lOlzWj7pqp9uBwl+dnxzSN5N4dyf4qI4kX3Fh6DOKGT+z3MC104/rFIUTD7WmHzX5FFNT0XMupXJv+DjuOftAhbxTHU0HsOviq4uz5JU7NuDpJCGVGP9HsRCFKKx4/qlmVAbdOWrYGOXHiWBYRWWFAaUKlqE1Law3cvs7jP/k+AH703T/E/VuD3zZIe9e73sUPfO/3APCBH//7fNGbv4S49zLmVQP4vOHcFtZ57KU++6uc8bRhWa8YVxnn04iNsCFUI7p+wOO3cza8ORf6G0htEGWfX787YyPU/Mq1DKFabswahPBYrUpqVXK261Am5PE7kmvTmvNpShJNCD1DIiyNlDw9bfG15ZlZxjAxWB1xcbeDtD65KHn+pKLbUWSVRkoPKRp6UUMSGG6M194/Zwcr5mVAVvlsRAVbvYKi8VguawIdcXW8wqiW6dIjDgwnK58ksGx3a6TTaCydyFI3mkVt6YY1ZeMxXiXs9dYywWUWMF2E/D8/f4cfffI6tlL0tmb0+xnL0uPK1oSb0y7zyqeqIQks3cggpCCNWnpJS14pBrHBkxbjDM4KWiuxTjBb+JjIcGnkcSOfkpceO/0Zh/OEna5jtzNjlnc4WgZktWVV+SwLj0ArUn9JUWuujApGiUVJw51pSHYcE2uL1ZZQSC6OMj55p0cntATBGky6NFJcGkaMeopnl8eEniP0LKu5hxASYwW3TiPKJqSoLYFS/H9+9YSdXks3tJwftvzUozsMYku/U3NjPyU6mzNIl1wddxl1CqaFxtcViJgoyjnOFaF1/KXP3+Uv/4frnNtu+abXbfDXf3GfMGp51egSv37zBs9OJ/TjFUUdYGVDbgSesXSxlI1BeTVKBwjjEGKtrGgtKG1RSHb7EUelY5lFtEaTRI5AajYihcOjHzh2Yo/NUUlZGxa5puNblNN83pUKKQXTWUiJYqdf8cT0Dpe2LW0T8tDOig+/1Bvj5wgo+mz3VZ+tnuozBoo+hVx9Luvd73433/RN38Q3f/M3A/Ce97yHn/u5n+O9730vP/iDP/gZXKmhNo5lYbnQSTitSi55GaFWrJoQJ1csjOWJ2ylftTunaEIsLYcryyqXrOo53/jaHh+8lVG7hltTzf3bGXdve/S2KlZTR2YqLmxMWTYBlfVpjWBaGbS/YKO/ZOkkkwyWhaFpPT5+Q/PaewtAcZgpfuvukK+690k+frqNs9AJK870G54fa25PIjYTy4VhTl75xJ7gTfeM2e60rMoORRuikURa8+BOS14FL5pIwzC2TFYJlzcsg7DBOUWkHF1PcCw1zgmqdi0FS4OGrm8ZpRXOeZSNxzz3kFKgpSH2DYO4pW4UVSuYFJplpSlaj+NlzP4ixArDNNdMsoSLg5LtdEHZwp15RC8wCKeRokFLSaALdnoLymrt6fLMccIgzunFc4pGEHqWvcGSae5Tt5rd7oJOINifpRwsknXcbK0o6hgtFeeHOVIKKqMYpi1VI1mWPttJTj+pif2Srq+41gQclprMalZWIbXDU5pVLVnV0BgoG1guQjZHBfvzaJ1UlRY4Z3jF7ozH9zeZFyGhb7l5NMLJlqPIoIUjrzyE05wsYvZGKzr+Wp+8kdZs9woiVdMYTeJb9mc9eqHmg1dj4rDCmIDzsQ+9BXdODX0/5oFLJavqhLIJKFuPL78yQknDE+O7NC7EILl/p+DWJAVPcFoM6SqPRrZE2idSPlc2FPcOQ47rOV/1pV/CO97xjk+DQd/5nd+Jc463vvWtvPOd7+S7vuu7Pn3nvOMd7+D/9rV/jI/sH3JSzzjfTymqdJ185xru6W5w/0Dx5VfW5n3SaZ4cTznXiwmV5rjM6duYZdtw407JtCrZ6mj2+gFfeLHHez9saWXO7qAlN1DUglUOtunw4HaPSdnyx+/b4Fdvn7IZJPzKzZtcGt3lxrLDpZ2Mf/OcpuOHxL7k8vk5gQe1EQhhuXO8TVFLnjRz/rfHPSov47Bw3JNYfGHXqTLW0VhJYyQ4xcVhzvFC85vP5fyRs1AbiRaQtz5aSQZJw7SZUuqMfupRG48LGxk7ac0kD9hfhCjZMEoX7HUbfN3gaceyFBysBKXV+KrBWFgUIeOszzTTiLrDmW7O2V6BtEOEk3hSMMt86ianMgotDYGE1DfUcYOSlmWpmZeK3V6JdIqqVcSBZSupKFtY1Gtz1Mi3vO7iKccrycGsy75bsTms2d4ouIeG01wxWXbwWDcr925WzDqOZe0oMFw9SulGhjODkq3+mrZ/ZxpzzyhjUSuULFm2Lc+fhGA95lOPd/+xK/zPP/4c//DrL/CvbxxxZrBiUTmeOVKsGkNZ9Ll1XPDL1zNmx4K9S3PGecB2z7CZNDQ03F0onjlMSJRgJ/L4ib/ynVz6lr9BYxt2Ow5DQ9FqKB21rfGEoKw9rLM0VlDUGl9ZfA2BdmtvlCagqiU938dViry2dEPH05MZ1059EpUw6vvrGGqXs9dxRDZkI6qptMQWwX9mn/nDV1/1VV/1217/wA/8AO9973v50Ic+xEMPPcR73vMevvu7v/vT2vj3ve99bG9v88/+2T/jz/25P/efve6rX/1q3v/+9zMYDHjVq171X/Q7fPTRR1+aD/Of1Nd8zdfwjd/4jfzwD/8wr3/96wH40Ic+xNve9rZPf57/Xmq8agi3lwS6y81yynaY4XmWRVNzdxZyz6Dm5sThhMSx5MbYsFpqzAb0ejl3Z5Kbh10KSv6n1x0wubvJc7N1bHjHN8wzjyhqWRQBRdWy2atZlJpzg4LB0OKJgINFSFn5nB1kL0pWPTYSx8kyYDdKeWhHMG7mDELLsgrZ9Qx5EVBWmp2uJC80obdOdn1iv2Y77nF3KVBScjiXLBcRoe/ASSLP4mu39hJsPBalz0ZcIUPAKYyRtMbjDXtd3n/nOj2jOddfkvgtS6lAOgJv3etcG/coas3xUpMEjm7YkFWa86OS0KvphpYHtlcY2+Fo5vOzV0+JwwhjLaOkZllK8hpK4zhaaXS34mgZULaGae7RCxry2tELc7qh4IVxRODVpMGapbXyJNZKAm2oW4lAcrwKGUY1z09i9lceobLUrUJJi5IwLQKWJez1V0hhOdPJOFwIjmxJa1oST3Kw8BFYXn12wVNHfRwGTznSoKFsHK0WPHnc5/GjLhJIA8e54ZJ5HrPRKdesg8Bye7L2nvEVHBchN6cRHo5pFuPphoXvM88DAt0wKWKaRjHJNa/Ym1E3kuP6Kts92OxIxpnm0kbFsvTZ7Tfs9Ss+cj2hbcC1Xfx4gROOMxslg7jmZO4jjSYTikw19IVhxZKT3MMFITrwoVIcTrsUNmI3FGx0IrA+z44LnKwoG8vF0ZRBuEkQga9LYuEYJB6uVWwFHXCK1gpGlwdc+P4f4Hv+yncD/79B2nd8x3f8tgEawPe9/Qf4wi9+M6u6YlkZDlYFhakJdcVHDipM61MaiHTEbhhwdZpzT2+DWMO4aLi4EXEymbJfrtiME/qex2MnYw6kw/Na4mB9WMcUhH7E52+fY6ev8C44nj4qOFiVNN6So3nLG89uEoiGeXVMtdQ8eHlF20JWKzwcgYY/fs+Qf/qJY6TXsNtdUbUekd8wyTy2uw3DuME6WKy2GCSw25sTaRjGDXeXjtA3fOHLVvyHZ0qCsAHjaAxUtSKvAkJdIHBEfkuZCzzniL2GrtdyUEZktSBvJM8c97h5mHB+a45A8kuHJ1jXI7M1107gLS9bYqzEAZ5yVLWH1g1Z7ZEGBq0sWaXphg1bnTmJ13Km23Cap3zw2iam0eQrzf/w4ICPjO/yL5+o0EmEryyBp7kwKOhHLTdnCcYpVkVI6xyJD6O0YFlKqkbTi1s6geFMt+DDNza5Owvpt5bdJGW8bNnueWg54cJohVaSbtjSGM08F/zjTxyQZwarYJb7DCLFMK4YZz7XT1IeHMUsYs3KZLStwvgtlzYLFkXM9fGQXiTYSFtOFj06qaGoIu5MYrZ7GacLH6UtUZTjdWp+62af1JM80BtRtor3fNUlfvgjz/G2X7jF3kaOUD7f9Qu32NmouTuJOC0aWquIlOV4orny0ClyHjMuNau6RgQlq4VjUtTMy4CDhSQNGnyl+Auv2+Iv/dvr+L7j5rSlG7WEdcvRCiBg0Fl7ZHZHBY3wGVctFA5rFEFQop2ibHwWtqXXWCZyRe0UVaMI/ttKoP991Wejr/qD6Kn+0JkM1XXNxz72sd/2YAZ4y1vewgc/+MHP6FrLSjMuQupc8Q0/8RxeJ2C/6+iHkrKRJKGhMZILZ5c0reIN5wY475CvvnwfX/9Pr5M3ln/80Tlfee9Znlrssz81eCIgTg2x75AIfOmxFQsuDUuOFwpfW4QL2I41P/vvnuDMm1+J7XRopeOvv/8Gex1NJAP6YUNeptSNx8HqLDuxZn+e4IyHJxTne+A5TVGF+FLQ7xkGkWErivGFYxAqXK6oTUSkPNIYXNwi8ej4NZEnGESCC8OKXmCJGovvW5QUCCRlq/GVI681s8Jnr7tECCgaRWMlZbPeQGPfYRE4K5isgjVDIqk4ySI6PmylBTtpzl53yf/6+Hli3zGMG5oXZWqdwDJKWrK6pW49tHQYF5AGmt1Oya1JyNWTHm+4VJD6lnGWMi9rPNFirEfewBMHXSQeyyImL0PSKGezOyP1Lc8fDbl6knB5c8ly5XFxI2NZWSyS0yJk1SgWZcQq92mbAC9o1xOsoEFps05GyUPO9AsElqcOEna3plzcWMdBPrff40HdcKZfcJglDJOKODA8cWPI+WSDvU7ETz0d44eGCxuKpcm5e1czTDW9AGJfMM09djoNx6sA6zwOF4LxyuO0HWLqjCiBVlr2FxmZcbz2QsH1/RFPH0oeGW5wz8hDScGjtxfM25JJ5dENYVxaWmlJ4gWWgDNBSV5G6DbhXDdgGHucFjU/+8IBr9hea1w/NSX71P31Xd/1Xbzzne9kOp1++r75lCztcJlxvCw5E+3ywnHNZkdyuCq4Pqk5s7HiQq/DteMlN8Y1lpBGOLqeR+gp7uZLtkPH2WTAKzZ7/MbtCbfnBe9vD9ka+1hlaa3EWZ+qthS15csu7fArL2RUtePN93f5l8+c8PAo5hPjI2h9njkeksY1kyJABwviyPCajQHzuuYjNwv6KmEpJiArlA9Ra7la3qIfK7phxE7qUdolkVeyLBW73YqsCjheBZzmKZcGU2oDea3QgeP2rMNTh12uHUe0xmeQGHpxxTAusLJlp7fEWc2y1DR1wrSs2evPGMQtlanJKp9h0tAPHVkdMowcZaOp2oDTRUQ3rnnFXsxO3OPa/JTY73N56w6+MqzKlmkZMM81R/OQJKipjccorViUPoO4ZjOt0RqO5yHOSR4+U2CtZH8eAppAOwJtKFqPZaW5sj1nnsd0I4OSllUtkei1h9lmRuTB/rxH1y+Z5QmxJ9nqWLTRbEaSrSjgyrk5TRsQehYpW5Zlj8lyQM8OeMeXX+Ltv3iLb/13V/ny19/hr/6C5RX3aFLP4ivDA9sFh8uIsYzpDGtu3uogOy3L2mevZ/nkwYDXnp9QtY5x7nG07/NbuSIKZ1jnCMOCo1PNxc0cC3i6IWsdfrigrDSTkxH9nuVoqciN4sr2hNKCUIrGSa6fhFw79djpFVR5yEebFXHYcHV1zNO3IrYGhjeeF6Q64oXlBOeWXJsq0rSlbQWmfukp0kKqlzD17PdmvGiM4V/8i39BlmW84Q1v4Pr16xweHvKWt7zl0z8TBAFf/MVfzAc/+MH/IlD01V/91Z82Wvzqr/7qz0kwxt/5O3+Hb//2b+dP/ak/RdOsf2daa77pm76Jd73rXX/g7+ezWWnPIPIemHXSaseXBAoCT1BGcLrsEEqfQacg0oJOKkkiQycW+MonIuW1DymePF1xZ7xDT4B2Pl1PUVY+f+plm/z8zRPiwLIRQ90kDMOCSHVJdb2WoomYQeQR6YbLG4ZFCVXjERDiasXBzNDpeGi5TtHpBIKmlniRpbYer9hKcLR8/Jbl6qShFynOdra5vpxR1x5ZFhB2W+pW0VrFVmeFcVC18kXWM4R+zdXjAatScbLwedQUZKWH7zWMs5DrpwmvS08YmwiJI/JatjolJ8sQ62Crm7MsFZ3I8gWXTqgMjDOPVaVRUnJuo6JufLSQ3LO1Iq8iepHlTM8Amm5QMQgd0wIqK5mWEXcXip3uHI1hkvcoWrh2mLDbtyjVEAcVZRWjMVgHB8uA2lgmhebKIOfauM/UhFweraVNszzCV4bEd1jnoSX4XstGWqJ9w/VJRDfqsNMtWFUSJR0XhituTSNCf82YXpQe18ZdUr9FiRqH5uY4oBetvSx7XsOl4YqiUYTakNcerXSUhaYqNCq2SN2ipUBKR+00wrbgoGwke/2MeRkwLzSjNGMjbQl1RRIJ7px2abMNzvUDnr57TGskiedxe+IYtiHbvdWaGSscD+1OSWtBFkpOMs2tySZZmRLonMZCoSv8qGAj0uR1h9AL2OooToqG7TQgty1nhyXP7ndJooxYgHaOxo0QRQ9PNjReTM8XHOULiibkr373/wtfq982SHvnO9/JZDL59P32qd7o8f1Tbs0PiH2fV5wZrANWFg0P7/gkyufqYk7iJdyZVWylHXxf88Fbh5zpjEh9CUZRG8UwXYOAHHmUsuDhXcX+qmQ7Cbk1kXzeziYP7oUcLdcpXJ5Q7C+m7HgZo77g1++WDIIeDwz7lL2Mw9MunmeYFZpu4DOpMv7Jk9cxynImCZBIenGDcHC2X7KqfBqz9hyTcsntacBWv6VsJHnT484kwgEf2a84O1pQNx473YxBVHK06vLMYZfIN1waLmhawfzIp9CaxkoOM81WWnJy2OHWaYw1mjSSvHxwlpvZAWeTsyzLE1qjQTQ8f5LirODCxhKtWs4NlxQ11FbRDVukMBwuYnphzTCqOc1jVnVEYyVXtldIIZnmkqeKJTtDw+Ygx1rN/jRlkXe4ZzCnagP6oWVRrhmIJ8uA2Df0IsNePyfUa+mcpxx543E4TbjYGeLMikkZMuyc0tqQstFc3Mgoah9fWRZlihUrPnHX0DjL7qZgXvg8f9zlnlFOWXvMT2OeqzVFA0EEEkcctHQCuw5HsR57g4p8FeGJtYHmeKnZ7q7oByA7LWWruXG0RRpAKnyksDw/nXOnOOHwNCSOfb77TXv80ydvspnWHEUBg0jQetAYD1P6BD1D0nE8fzTkZUnJbr9hUYYs8xBjaqpW40rFMClII0PTRPy/H3uMV1wxbCSO65MejYHWri0/trsNdStpnENKw3jms1gI6lLyp1+5zb983tBLakzQ0OSWk6XiSlKAVszKiNNF9Lvubb+feqn6qt9rTwUvXV/1B9FT/aEDisbjMcYYtre3f9vXt7e3OTw8/F3/TVVVVFX16deLxQKArtZUvkRFitgTBKllGFhC7Ui0o+dF7CWK2PmEUrARSGoRcnWy4NzAZ6evmRUti7IllIrNWDIIGpoYUg9A0PMUqfbRtPhS4glQqLVZtpGgPIzRaNVSNBZfCDAaLWoaI4iUZV7GDF6kU+a1pmwlnmiItWW2kmSlZhCWeKEjbySVgV4AEqhqgRKOjleRhBX7kw6NWU8UNpKcM90VvlB0pKERjlmhWJQKKSRKWASG2kDrHL6DfrAGaVK/xvcapGwp65CykTStWP9pBEFsSTzH3VnE0WKAw/HqM1NCH4ah4XAVUdaKw0XEVtwSq/UU8PYsYpqnRF7NMKjp+i23xilPHQ7Z7vgkgcHadTrIZmyZZoLdtKFsBR2v5OELOxxXJWFYsix9LmwU1I1iI14zKTbihryOWJUe41mAIWBZAG3AxU7IuF0yW/o8cvaYx486fNVDR7z/2R3KGhq7nj5GniKWAyZ2yUanoKgFWb2ONzVGUzaa1lmW8jYfOYHPv2KZZQOsPGHgQ7ulyXKPmSfJS49ZEXAy96kbxwO7BdMyYjPQTMUMtMQ1KedGjqr0Yan56HMBjVvQiSUfPxjziQMoraEbt0SBA2dY1JJIePh4eK2kBqa1JPRyVq3h125N+JOPXOJNoyEPjDrcuxF/2rj6bW97G865TzOI/mOQ6Ad/8Ad529vehrWWomz48K0pTkrO9gJ8EVKy4tVnN0k8TaJ89nZ6zLMpr94NefSwQKPZjBVb4RafGK/YjWvu5DlONWgsdyYtB8sVfdVDYzmYNLQovub+PT5y95SOinlyvCDX17i9NHz8SHFlS+L5IVLW3D+a0VjHtZMOzx21HC+PWCxivKjm/MhnUqY8fVwRhy2zzCMvFUnoiHTBUVbQDS1Fo7hno+DXr20TepBXmiioefKwj0GwLDSDQUXWeFStJqt9BuFapmiMZFV7PHJmQV1rynoDVff5ygsDfvLxOU/cNOykR7RGoYTDk4Lnj/r0ooZV4VG3moc2Uk4ONF96bshbLm7zU0+N2T/u0Bvus9HLXpyYr41Ie3FNNyx5/ihltoo4TczaY0GvJ1EHk4T9ScJmp8YTazbNvPDpBJaiVqxKzf40pBMWOCvwlcUT6whW20pmkxjtQ55rdnoFvjKcZD6zVQxWYRtF4Dnq2icvUooKhlFLawVZEfHJO318BUlY8c+fvMErdjv48ZJIRpwfGXqBIC9j7kxjtnpzfNth4K8nhKN7VxwtfAYdwekyoS49DsaCKKy50JHIezRvONvnickp5cP386VXSp4IOzy0DYFyNGHDstLsiYA7pwlxotnsVNTt2icpcEMCGuZFilaOM4lAo8D52Mgj0OAqj9ZG/NU3jchtw8eP5+vnvR9RtCH/p/tKwCMrJVXtvTSb3Ge5PrX/faqCIPhtKRmfqk984hO84Q1voCxL0jTlp37qp3jooYc+PZD53fbgmzdv/hfX/o+p0X/tr/213+Mn+P1VHMef9ji8evUqzjmuXLnyB+6V9AdR5wchZa0Z5zWBXMtpJIJIC6T0qY2HJyWxL5BCobVgt++TmwqlBEZoAr8m9GCWh6Sypmp9PA1F7YGySCHQAjxtqet1k7qsPLphi3aCvFEIsWbpb6YF1kkWhU9VKmpjKa1jW3t42iGxSOEIvBaHQBYBX3Ah5pnJDK0Evhbcnlqu9AOaVuB7FhUYAs+sPYGwpH6DtaDDlsOFRgjHovA5XkSczAM8r+WJ/Zw4UfjSsao1rYGjPEL5a7BkknuE2tGPao7nCbM85PxgQWvXrB1PtXRDw7Wxj3Sw3S2RouVgHlI3Pr6Gl+8tiL2arbQlqyUnWcyiiBCipRuAQCHxCXXDKDEknuDshmG707AoJbUJyWqfjjJIoQi0QUnHtdMu3UDw8JklWkInsOwvPF7IIgZRQycs6fiGa5OEaZZwXq6Hd88fdfjN61u84tyUQJd4ytL1W7Rsacy6f6laeHDks93R1OKYrA45WRh6UbkGnrSlbGCaxRzNI4Q0NI3Hpp+ShDlYx5WNBatK8Yqzc5467KOlI+6UjDoVozDmOC85XQZEyuEpg7ES064l0n6S8+x0wWZHUrSKYW+M8yJWKw2pxFMh18aaO9JnTzbMfE3TSjwkbesIRczFDckyUxwfRXRjh/QM+7Oco7mHE7ARLSkF3DnS9MOGOPBQQrKqG4ytqcWEJosZ9Bz3DHtc6secH6bA7xyk/W4gEcD1acZjN3Pu2YnQwnJtcUrqdZgWmr3NCH9Zkldr8+Zlu07k3et2WLYVrTD0Y5gcVfza9YbQs4ySDkXb8OTdht3ugK4I2VQN//6FY56e51yfV6ShzzAKMFoghKTr1yx8yd1Vzqw1mCJm0HWc6fqcZitWbUHdalYldELI2yWptYQ4dns5H709QEvJotQkfsV0FbCqNBc3JrTW53AZcnMc0gkFwzRnvAjY7Lb4HtyabtDkm/zph7Z4z6/dpRtazg0XGCGIfcPxMsb3Gk4zxbz0eGgwZH8q+LY3neNfPj5h/3ibaf+Asxs5OMnRwmeW+4zSho/cHHEyjRl2Gpa14Gy/RArHk3d6SCFJ9hZUbcDRKiX1DVklOFokeAJGnYy8XXsDZbVingWYVpJVPjvdgtooVrVifxozywN83WKcXLN/4pxR0tA6yazwuTsLyCuPeFSgA49TmyPDnPECesB4FVA2Pg6QwpKElu5uw0muCbThdBlyYkKSABqjSBLBuX7AtcWcM70C4xRaGSLPst2tyCqJdY7lQqIEdOMW6zR161EbwzyL0LrFU5K80nha4oykyCXZMuR/fHCTq8sFH9qfoaqQvJLcf6YCBAVQuADpK3qR5RV7FbdOUqyxXOhbTrMU7Tz2uobYh7DrWFYxZzoF86XGuIRYNiwyzeUNiZQQKp8HNg39BMom5MpZi6g77PYEvlr7WXUjx1YcUbceoRTs9VrqSuMJw8CTDIYJwbB96aVnL3H91/ZU8NL3VX8QPdUfOqDoU/WfombOuf8skvaDP/iDfN/3fd/v+PqmXCJJmTvNojWYhUV0Kja6DWnQkGeWjmnRsiIsNSGnHC8qnrr2AqEN+PrLu/zr5065dbTE0NBrLVtyQZLWeBJaC10WVPOSF04jhBNoJZhOoF0IvuDrvo441ZTNmLwWiFaT14L6RBAmJUOv4HIn42QGq4nm+FSjhGNeglItpmnZVS3Fqc+dZUioS44PQrY7DStfIoXlnF4Qacm98Yy8idYo9HGI8SRJWKFXLd2gwa/WLKpl67Grc+Z5TGsEqgahBEIInIRGCM7IglAZArUGs4KwoShTlkpQVZblccStmc+BlCjxIuX01MMYhxdaipVmNvNJWse9fsHiKFob7UoIC9iTFZ4z2LliaBsu+zl6KWhbj9qT1GVLHLQkrmWjbQlLgzYSkzuieAKLljLzuHXSYRi1NJXieBXhCcvtPME6SeIMD3fmbHUL8kqwrDwuDzSHi5RqlZFPQrql5MlnBoS5ANYTtDRoKXOHlhnbBJi6ZkN2uHtVEXiWwLP0k4Y37R5zZxLiowhLwwW9BJdye14SV4IwqFELy1DX9D3Y6+bMCsWmadDS0bYlu0lNrgXTIsUtGjwUqpVseg07YYoO5hSBh7WCWe7oK5+iskTCYWroBgavtRhTYluFrAxawsgzfNGZDi/cuslHK8swVCzHKfdvdz99b3xKbvYfg0SDwYC3vvWtHB8fA1CsSh5IJMuq5T4/ZjaZsYNgeTpmYqBtBVqDq2qWs4BmkWORDOMe3cin8GqSZk5WVCSNoNc0eK2hrcF5EzwJ276hKBVPXd8ny2vSIONsErA6BVkItrWlPPLwbQ3asX8nJvZaohKuREtmi5A/fi7ho3dbbt7I6HQzXt4DJS29OiRz4CvHQDT4GVyMKw4zj8ZJzoqSovDwrcXVcM9gRdKBrmupTzXWCPzScU7ldKRlNyrZ7pbcPI25eytBOIikx9b/l70/j/Ztu8s64c9sVv/rf7s9+/S3y73JTUcSEhohQUCIyAt2SAUBrcrrS2m9ygAElEZGDTSlg6ZGoaW+YtQSFAegghIJRRKaJNzcJDe5ubntaffZ/f71q19rzvn+8UulDKLDwL1Bq+r554y9zt5zzr3HXns+85nf7/N4De1yydil5GXD6lTTDWpwgsA1vHAe4CeKulm3HV6PJM8FKW/ZGODXC/bCOcfRlJEqqReKKGrIU8GlqCCQLaaEIHNsNA16JQikxQnHrI1pc48LuiAxhsO7CUqFeEaQuJZAr82Yk7ClaARxAapy9JqY0lloa75wJDhvMpLGYucSqxzNStOvLcpB00BPGq7JnIiGKHcoAx3d0uaSLVsjnUDIBYvZkp2wYk+lJKKiGy/xS0eAJS7WFQEb1pG2FUltiLwWpWp01dDWHqGUxIXFlA7f1IxFxst7EaezjJ0/9JUMmoyRESRlgS8lToCrNWXTEhSCGMeGrUgbiXIVTd0gjCMoFIF2SOPwc0djFX3pEUloa03WGr5wPOY4N3w8X2Byn043J88dgc7ARui2IW+K3/vG9tshXkSPok/tj5cuXfqMxz/wAz/wOxKMhx56iCeeeIL5fM7P/uzP8s3f/M28733v+/eG+8/fg38nfOu3fitve9vbeMtb3vL7UlmUJAmvfOUrP+fzfi7xtQ9s8dffu2AlMxaVYTgsKZYhQlfcOI9pG8msLunaDMqQk7Th219/jR957Hk2RnPuupbf3Ffcm0sGcUMoJDfnFqUEo6jiF27eQUlYNT5nc7g8mrOqFU+d+GRtTawVL5wLtvs1o05FJ1qQtS1+JkmLACEd2gp07tgaVGwmDWlliSNLVXtIJTlctbx/PyVH84eubPCumym/8MKEcRe6UcPmuCD2BK0RDOOCeamY5QHjOOdgERH7LQjD7jDFCbu+5FiGlKVeX8Z5hke2VixOPea5x6LwmaQBkefwdct2v+RkEZOWinFSME0TBnGJVuvULyUND2wuSauYW6cdzggZROsQgZ1uSdd35PX6kD2Ia6a5Jq/WLXgdvyKXCl8pQr9lSzV0QoGvDdPVkK1hzN204PZ0fXBF1hjrsM7RGkc/bCgqzd15xOXhirbV3J4mXBvlzAufXphTF5KqCNjsFRxMuywLHyUkW8mK0GsZRQ23ipiqERwvQ956f8jNmWNSJ5ylHayD2jikWB+w55Ug8SzXtpZcHswp2pCs0nQXe5y3KaXRTPKA37wZINqETrKgdbA3LDheSkaRx76As+lFXjjOuThe0VrH6y9PuT2raPGQnsefenSXdz3nUTcZXuA4LxS+VzKILS/bnODnivsGloM0JAlSXqYszx+NKFqDCiz4Ai8M6YWOom3Z6gmOlzVSJXjCEcqKvGoZxGvfp0nmscxDOipi0DG0Tc0vPHOHoQ+Leptr4/9TLPrtlUSj0egzPIvq9pyNro8vfSQeW35CaRucCfiZJw5JErA45lnF5X6Xi90Bw80uHztZgRXMCoMWksoYnNWcL3M8D2yrOZ07up7luK548wNj9peKge9TFHBvqSmkIGsUUiryWnJhkHPzqMubLm7w3HlKXZcYYUmURYl1Guhmr0RLRzdouD6qmJXry5rjZYR14Em4b3vFwSzm3qJHVgS0JmCQNAyjmlddnGOxPH82pKwVR/OQRFqkkOAJTlYRV8ZLLnRzXKL40M0RlzYqfBlxunB85cObvLs64yOHK954PeCp9ADfbzBGIBDUxvGy7RXLIqQsI6pWUFSS2UrStjGR0qxKy6hT88T+kMYYeklD12/phzVKhrRWM8kdq1RzYWOPp8/nXBrBblfzwmTFabauwrs7jUgbRbHyqCLHsNOQhC2BbnHCsdfNWBQJvbhkY5jx/ByGpUZ3FVeGKzq+oXGCUBluT0JevtHnSKaYBuKkZiQNaRZyvTug5yk+dg/8wOA7zcPbIVN7inGC7e5auIq9lshzCDT7s5jNjsdeN+G55QmBatFq7UFZtZLlKqLTLUhTDyEcvm8wpc92HOOLtTB+mC+JQkFeKaRzICVagcNi3dovN/TWUfemFRQ1VBbAYtdukiShIGsMdbsWjg2WRSWRgK8dRWNRQmBcQ+sMaeWRNy3GSgLPsqo1ynMs6ppp2uLkuptFqpqykqSV4Tx37HmKxDcv/sb4YvGqz5JTwUvLq14qTvVfnVC0sbGBUuo/qB46PT39D5S4/wPf8z3fw7d/+7d/+uPlcsmlS5eYiw5lGCK1xC41TrfUocJ1MgghdI5FGhF1oG09btWWmQyYSY3uO/7tScpEJhA4yjYkq2CmLL0RaOnoe4bIFxxnPUobEGlJ7Lc0saKJFS/86vu5/vmfTxNfYWo1Z6FiXimSUcmzRQ/bdXhhjtMlAsP9gwlPHozxkoJnTzSvvHhG0fR47qiDLOHSuOCj9xI2q4aOChnGlm5UUAQ1B7phkFgmVYc7QrPbWVF7GYsoZWtkCZeaExHQ2AahavaLmFHScq+KSXOfNMpZlCHXRw0ToWkTR+okcVJztOyxmYzZ7mturk5oEGxulRRVSOQbWgfB2OAaRydpsU4yL9YJJr2w4CP3RnR9h6ccJ5WHQ7LKBKPE0AsNU6UI/BYXNiyAK5spSguqSlE0Le9+YUzXFwy9AX/kwja/dHTIaQ5x0GK0ptcxSGUR0nA4j/E9uNCtEAJ0x/LK0ZJ/+vgV5kvDvTPNazcsq0CS1YLfmo7piYSrmzW3FiVX+w1WWKa5Y9xZcbwY8vy5ZlX0efMrj9kKG86KiNxpdi/nfOK4h9A93rx3hV+7c06VVFzeXfLCeZ/Ic4SeJPRaTBf8uKE/bFCV5Tz3aYSjUBrtBSxbQVVpelspN4+65GHOaprQotjuWEzgc2YivLDi5nnLTq+m3wOUI6sitkaG/WnEUaYJjORsUlC1LViPJtZ0VcgXb219+h357SIRrCuL3vnOd/Jd3/VdAHyyvME9uSKIIp7NC+4bj6icxZUZu124OS2ZlCFhH95zpNCqwyPbPQ6M4onjkkZBLxJMSst57Zj7ggqf1+xssj9rOMgzYtOsWxAin0uDPmkl+OOv2+Off/KYLChJM4UVNWd1xeWBoLKaiZEsmpieaDk3If/iOcPucMDLxjHvunVO4Fs8ZXEYjDK84tICWBuZmp5j1Ck4TgPKWHK2DBCeYxiXNB1HWzuKWjLF58ZJnz9wbcTB5A4qUpSx4sDFFInEefDy3QW//nzMKpO8IpIc+pq8iRjbll2vJNCW/TTm45M+X7wz5+HoEu+6MWV7NOC5quE7HrtBT474oqtdbpslqVUMuymDzYzD4xCSFum3fPxelz/w8H384o27PLiVcuusiwVaT/Hw3mUePznB+A1ht+HJ22O2t5aUThJYSD2PzCh0UlFHjvPGY1mNqWSNkXO+5Q3X+bEPf5yJr9btq9oyKRzLwKCt5CQPUF7FqRMMvZpOUhPHhiBumJ8IdJxxb95hpkOUk3zwXs3ORsord05xTZeD1YBrnYrDleLDdxL++de9mr/0757i+kaJcYpMlxwvFcW0y3ve/kq+8103+fV7M2SwJD2J+JrXDph4x/y7H/lxvu+f/LccnfmMgoZ+oPGUIxWO99xLSHzJr/6pN/H3PvY8//LZKS/bnHE009RSMhEBs+OEvd0Jy6Tl9vM9rl9vCKRmUYd8/5dd4U/8/A3Gw5YH9gzxqOA083hm6fH8ost4KNjt1ZTeS1BRJOSa1LwoY63/2d/fp9f7P0Xh/9jNl+/7nzZdfN3rXseHPvQhfvzHf/zTB6Lj42N2d3c//fn/qT34d8JkMuGtb30r4/GYb/iGb+CbvumbePWrX/1ZflOfPd785jf/J0nUr/7qr77ka/hc4aeePOIvfun9fOx4ydG8wdYliyZEySFV3uHadkk3lAwj0MLyvV98iXd88AW2ei2v2M75kusTnjrapjIeZ6cxeqtAGslGv2CjI7k7kVzayBEWBnHK5Y2KrOjSLQ0fuTPkTzyyyQv6hI3OgoN5lycPA4zV+Eqzszej48GtgzGLytHYFusMUVjw3MmYq6MCuzL84s07zNIIQ84v3rhH03iU1jDNFLb1eODijMRTDMIWIWpung8oG4+qNUR+vU7sigpaa1gU6xh6v1sTacOs8DhahGxEBc5BP2xIfMNOryDQ63Ypv2Mo6gBjBQK4fdolikJCr2G7V+Prlkke0vEN/U6Fc4Ze1JDXCufWok5lwQlLYxyzTFEZS9Fo7kofX7UMQ8coytYpTXmXZaVYFpKNJKDj1cwzzcV+Qd0aDrKArPHoBCWBdtStAifXlUFCcqFfUhlFog03z/pc9QqMJzDW0U9qACoj2UwqFOvEuqujjA/e2qBOhxwUx8wbRVZrOn5D4LV4qiHU0BjB0TLivnFGVvssipisCbg7TejIOQ9s5lwc5KQjxROHY/yw4vZRh/6wYpoZDhceSlUEnmReLmlaj8JonDP8m0/scbGfcTSNuDoO+eVnCnq9I0oihICzecRmryQMLVWzbs+SWKxzfPjWiO2eRaG5N1Ek2mNv2HC2UtRthu83LKqWTuiRVS2lTGmMz0anZRgXLCvJVqdCCEPT1hwsJZHqsp0EnKQZv3HrLq/f67E76PGOd7zjM0QiWFcWveMd7+Av/+W/zOF8yZPHDeeFoREH3J2GPHpxzCrLSeuC3bEh8gbkuWa4oZBoJlnOwdJwUuRE3iEDT/IlD/ncPdtGKkmgLR8/XnBpZ+01lTWCL9jd4Isvj/mnnyiYZdAPfFJTE8iWbtCQ1ZJe0FJUHkj42MkBr7gQc5oGRDqjdRIjQEhDWynGw4K88bgx0QhhSUuJsxB6JWklqYxGSMOq0mRlwJVoyEIscK7lt+6MuTpMwQku9DOOl30uhh1+8uN3CTzL2Tziw3eGXJQVaaoJg5a8XPt/WtPyM8/dIWsMRy9MUXWfFYp51uHQc2AUr7l+ikMyyUPe/por/OQnnufisCSteiij2BvEXE40p+aEwLdMziJ2Bmvv1cO0xzAytNbha4snS379YIbyKiLl0Y8cQVCTlZK9YcblUclGN+cFetw+iVFW44uWVS3Z7RfcmkW0zlEUHqtSMc0Uo6gg8A0naUBa+rROcDAJSSufaVni6ZZQG8ZJhVcqFAonauZlSGT7KJlRtwohDBudklWhSfwW3xkcULYKnKNqBFfGjvt7XW6kB/TClsoIppmmMTBZKXr9hkA1lM7hZIuVki97MOEnPnjA5e0K7aeoWDGd9ziZKcLEct9Gzax0NMZSryRKGeZlRaUsz5wrkqCkH0hmZYPvN7zl6ib/9sYJZ5kgbyxWNMwqjwv9hv/h817GX33f82jtyJoWl0uWBRwvFY9cyHEoVrkHRvJrd4+YzUN29iqWywgVWCyWO5OIxUrzr/7fD/Bjjz3/4m+MLxav+iw5Fby0vOql4lT/1QlFvu/zeZ/3ebz73e/m677u6z79/N3vfjdf+7Vf+zt+zX+sDGx/JegPS4Ko5vQ0ZmYNMjKM+w2erXGi5uY84PK4QKmEk9JjXmhuzxXdqMCsNHUZ040NeSuZF5KsWd+eeUISeAYpIK8DjA0QTtCNHJ1Q4auQ/OSU0OYEoaBoPPaGglD6DBKDQgARgU5oTELkGY4XXSI/ZLtnwXmYdgtnAnZ6Yt3P6kX0TBdb19hAIkWNEj7j2DBNh2xFGaGGS8OSJLTcXUZsFBV50yCdhxWSwkg8r+Xu3KfqrJViKxznpaDrZ2RtwN4w+1RUrGZeaCorOMsti1ZhlWDZ+EjhWFU+DkNt1qWavajESbBGEHmW0ijmy5hhN0cLTew5ZrnHc6cRnlfQZpJVqUBZlLRMsoDWrT2OpPBorODl2zmNVXz09gV6keBXDj/ChQ3NRusoW4FzmiRo12aQwnBhuOJj+1tsJA3zPOB4GfD4/oC8kWx2G7745Ud0Ssujezm1NfzajR0kNc9PQtIyZNGNkK7lcBHTjQxJ3OKkoaxjJnnA8bILTjBb+UReh3szn2ubll+/vaRRE3wPDlcxnmw5m3fYHeU0ShIHLfdOu2zHS25NukxWAa1R3L/tmJeS/VlEIiIujhIujRzTwjLolCglMRZa0SADhxVLekEfqVuWpqSva5LYsp8qjgtFPzIY46Fll5fvBZwua54/XTFdHfFFl0Zs9zu84x3v+Azj6uFw+GnR6Hu+53sQQvCd3/md3DivuXcaMooFm72WD9ybcW0ck9aC1bm/7q/2NNgd4g1DFLcUdUs/cDzajfjIacUHD2fsDRpetltxOb7K43cCzuqS7aFlJRo2ezk9P+TOWcuVfpe3PtDjYNVS2Zaz+drY8XBpEJ6kE7RMc4nnwaWNnKrxULUlSRaU2hL3Ojy8K9D+krMsIC0kdaO5e94j9loubRY8eTzkUn9BY30WVUBlFb2gYV5p7s0HDBJDYzWLJkAGlhfOKnrhgKJJuTtL2OnnhH7DWRZgnOBiL+QN25f5uecPQBVUi4jDeQet1TouGcErrqWcLSMWy1NGXcFvHB3Q760N2WuR8UvP1rTa49kTTb4f8t6nLvDoxZTdfsOilhytPF73eT3etW8ojKJ2gtlZB9kpiduG113s89HTBSoLwTdEvuHqKGdVhuubJGHJaklpJZ2k5u75CqUNcdDyzDTDScu80FwXBiMaNvs5QtekRYe8DTEYHnumz+e/dsq4Nex5Dc9OYgprmOea27OQqgrBah6+MqWl4anjIdZ0wIVMsphbZz4bnZy/8fiH2BkFhKFgt19wkmp2+i2Hcc5/94ufZD5T/JlX7vErh5bWxMSB5eIAQu0w7Yg3XJT0w5C0DKisINGCB8cxFwctP/LBuyzqmkd3G7Tqs9eVFJW//h3tChI/5rJXs/WAw/k+Ugpk33FSp2wNJN1YoKWPVBZPKiJPglVI6fCUIncvkqDzEqPX630GqfnPhXOOqqq4du0aOzs7vPvd7+Y1r3kNsPYOfN/73sc73vGO/+zx/vW//tfM53N+5md+hp/6qZ/ix37sx3jooYd429vexjd+4zdy9erVz3qN/zn47cSpaRqeeOIJPvGJT/DN3/zNL8mcv18oTYPSJa2BQEvAwxgfayz9CIaxW1fYeh44xfOTnE6g6IctxnTAKkLtcXWoCI0gDgS7fZ/Ed1zoVcRKEgc+sE553QgNrtV0ZMC1keWJ4xQtNGfzEVfGBf3Q0rRgraCf5Dx1OGZzWBGHDZ4U7HYr7sw8tIC70w4b8YJYOzwBo96SvAo4XXTZG6WME4tpPe7fmDIvOjx72uc07bDKY0axwdew0ytZlCG+btiJaspGcZZ2ubq5ZJaFDJKKZelhnCStFbYQnCwjrmws2EwaXjjrcrYMiMOWyDMsS48891CeIfQteSMJPUvRCECw10+praF1a+P8yhrKwuM081gWEmNCpGzWrSORIVCGL742obU+h8uA88xnp1fTDSQ3DyUfz1bsbnmMAsPdWZ+qhWFSMM19druSul2nD4W+IZANeIpFoRAYzlc+TSPodUsWIuDmSQ+tAaf4wutHgGFahiwrzc2zDtc2M5q+JPELRokg8hznWYC1kkXtM3fQiyzjpOWF0z4Cn3tELDMPD8Hu7oppoREiom4d24mi4yWEuuIkXdsSDOJmLdRpS+u3dIKGyDPkjSP2ag5PdmhsycW4zx1xRD8okcpStR6eNGzEHoWpuTPrMzANRRsxW3j04hLTwjJzXN0swUUEfsp9nZqOMghluXEegxF42iMKWnwhyY3kE6eKjdgSeoIrw4KjuSDswUmbM1t41JWmNRU/+dg+4vF/zV/5K9/76fdrNBp9WjT67u/+boqqwf+SL0HYmu1EcVY1KOPx2O0Jga/phgLTKvYXJyR+j7ZqWbQLBkrRqIZQdDlLE5yVHJkOW52cw6whtAIhLYvCI9Q1vnIkkeY9N2Z41uPRCyHzNmVT1EzSBFV3uH0PjGoIAsfVzYxEDfjkecrVvk8v2OC5yRJjDaNI01EbaHWL2DcUtUfVwqoIKRqNVhqt1hdkl4YL8jrg2TxkKSc4q1HSIS0M4xatS5xzzFNY5iekq5iXX1ZcG8acV3P83BJ2SrRuyaoAqwyvurBgVUacLKJ14Ii3YNtzNI1ls1dTNYpBXJJ4a3/I//Ujtxj3aq6PFxSVZStpeP+tTf77117m73xkxiBsGQ1LNjo1D2+u0Gcx0yzmcBGwmMZc28q4fycnrQTzpuIklygUd6c9JkXEpcGcj93eotcp8azDtnDzLKabLNifhoy7GVoKplmEpxsuDnMCI2iU4enDPlUV4IygbeDhSymoAk/5OCE4zwLOV+skuIWdcuOoy6VOTFEqnF/x/MxQOknRSrJ6HSbUi9fWGmtfsZbDbMGHbtVcv1BgUSxLx8E8oeNbUgqypmU8WDKZQF4FdMOaf3rjGWTskxpF7Oza5gBHLRyRtLxud5ebxwuGgxW4gCSyNKVF4kg8y2qe8MADC545Cbhz3OG9TAl1y3TV4/K4ZJpJsnz9t+J/+sBddjtwMumytbmkNR6x1ox9jXLgaceFkWSnX9Eaj6t7DqkCVCQJw4DdbrBOCk4K/tYHb3C+Cn8fds7PDr9bTgUvLq96qTjV70ooeuyxx3jve9/L6enpp31N/g/8yI/8yO9qIZ8Nvv3bv51v+qZv4nWvex1vetOb+Ht/7+9x9+5d/tyf+3Of1Ti7XUOvs45/HfYkl6KCfgy+XN8Qn6YKX8Nzxz0GocfV0ZIjHZMbybWtM9KixxKFRfDA7pz3FyHXtg7Y60pOVn2ePY05SQOePg54/ZWM48mINz+yRGrLjbmhs6noJwtstOTGRKF1ymGZkBQN40hysFBcGrUcp4rtbsWd+QZarOOktxrH3VlIY/U66UNZcBpfeAgg9g1aKhQeoW4JpKFoOvRC1n22RlNFit3Ew1OSrA5YuoBlY3nhNGG3n+NLn6aVbHZaGhxfev8B1iZMsy5naYzvtVxNSg7n8JEbAyK1Ym8LxkHDM8d9jPUpaseoU3GwjNnqZhjnqKwg0IbGOgZexSRX1HbdpnY896kawVY/pxe13D3fZL7qEIcRvbjga16+T2M9ijrgcBGzyBVKtLzi4oRIduiENVauPVaWpUbiqFsf5yT92JLXHjv9nLvTDtZKtDKsSsn5wqcXOQZRS5s78gnMK4XG4+sfeIB/8dzzBKFHVnlsdGo6UcXRKqQ1IIVgY7juu782LjhPI3ACv9kkUCl/4PI2Hz45IfYbQgFdYThLAx7am6JliHWGW5M+VSt47F6PRAuWlY/vGW6cOGap48GLK5yB87zk0vaScSN5aDTlzrzD0bKLc5JEBDQiYLOfkbYtR7OAc+2x06+IvJZHdgvmK5+5sThV8IF7krpOaCyUq5Z/8sQB7Qd+/jNEor/+1/863/It38I73/nOTz//7u/+bj5+fMr1r/5jXNxwlKbi7lwicDxxaPC14HLfMikMkd8wyU4YBn3unK6Y1TVD7RFHNTgfTyQczCSr3DDvGhp1Rt14ZK2gaAT3pgHDTsWFUc1ZveIjRxLlJA90B4yiFK0zdvKcQPRJaw+KDZ6f1vhRSxIarm6uCEXMrRl89HRFojqk+QgtV7RGsjcwXI0vYYNDNpMZq0KRNR7zPGSeBlStom0dm/2SqUno+jVX44L5QjFJPVas8FtJbTRxUNMNWvpRQVlLWqu4MbHcmt3g9DykP/B45GrJV9835kPnC/pRDtKQ1l0aJekHBt+zXBCORSk4mccIATuDnKKRaFWxN5yzP+txZVwwiGoCY7kwNPz5X36Sh3crHt5ZopXhjZ/3Av/wt66ycjlXhgPefydjt58RJ9CPasZxvTaHz33O0oRZpkkLjwuDimG3pDWOytT8xEdv0A8Uy8ynFwec55ovuL7Pzz9xmXGokI0k1C1tC7fO+uz1co6WPltJzVbS8JGDHhe7Gtlt8HVNbSI2Y8cnpwOaKkArCUYwkIIvuv+cRRlStwKFZpF3mCx9xp2SQENRFtQuorGWppFIv+HWvKAyjgf/8NexrFuWtUFLx7L2kMIwKRy3ZoYgLPjAfsTFUcX13SU3JoKshtnKZxhZ0rZB1QWZKdnoLpmtRnRDRy4N/9tzz3JsPHwJy+pTHixNw0kFvrB0neBl4xEfOn3xhaK16eKLM674LCqtv/d7v5ev+qqv4tKlS6xWK/7ZP/tnvPe97+Vd73oXQgj+4l/8i/zwD/8wDzzwAA888AA//MM/TBzHfOM3fuNntabBYMDb3/523v72t3Pv3j1++qd/mp/8yZ/k+7//+2nb9rP8Dv/z8KM/+qO/4/Mf/MEfJE3Tl2TO3y8EYcEvHzzN/tEO8TCjyVqka5BlQ7c/pxMbjlYx+wtH7As+un9O3jjGvZIP7cfsDVqMccRhhvDXnoVG1MxywYO6pt+Bul3/DdAaPGVpjaQyHsOk5Hzp8PQ6GasTOLqBJauhrD26gQK7Fp32+o5xBODTD+FMKXwtGHUk20nGxw5iGuujheZCr+E491Gi5IGdJavS4+40ZFV6HJ4GlGjGnYYHts5obIhFMCsV3bDmwc0Fd2Y+WjkujTPGSYavBN2wwWjHvSIh9A1CQOyvDa3vzUOkk/hhzaL1sLol8FsklsbA6SpgUQW0rceFfo7WDYFuCXVNR9dMyoBAgacFN056hL7h8mjJay4sOF5FSKGwVhEowSBqOZh3WeQ+YdBQVoLzhc8rL53haQk4agOJ3zIvNIPYEDjLXq9ap7JW0AksH783YLqKiKOS1q6Nq7f6LZ2oZBhZikaSqIpMelwZGGKv5SxLWNqCF0426MRzkqChsc16nWlIVllOVgPiwNKLSg4mCVkD3Wjt7Xd9nHK89KlMwDgpmeRzHttX0Ho8slczzX3eeO2E09Rjr1+RNYp5JplXCcsywtOG3F8SKfjIySFCKBblGOEMDsV86VHWBUHgYYVFOEvdajwdcbE74Xzl0wrF/jQkChvC2meeRbz2wphPnpwjMAjXclouqacdtKoY9S09D3aSBRfHGeOgZq/bozQVt843aMqAVrdol/Ar/+zv8N5/+BOffrf+D0+ifz/17K/9wPfxxrf9WV73J/4UvrPIVjOKa5zo0Alb9mctnUgyCrrMK8G429JtY24uGiLfUBr48utXOTw658BqpqmkMRWLokUpj9ZKZrniei9EypbDbEVVaaSRDOMuttHsdgS1MVwcV2AipmXOLJeIsCDPfcYbG7wwLQh9Q1vCMGk5mObEPcuy9KlbSS9qkRK2BymKBk97KNFymsW0jWarW7IdDTlPNUcLQ13Ch5uAMKzo+iUP7C5JK5/taMiXXRmjpEDPDU/cnKBNw0YvJ9Dlp84va1/NQyfY7OZgPc5nI/7g1U1+c3+G8xo+escy7Dj2BjkXRimBAiWhdh7HqwGeififfuOAi6OWylo2k5w7k4iLvQVnqWSaaSrjsFjKZn25s9mtuNCrOUlDtjo5kzSmaCUHiz6bgwbnBN2+pS19POlRtSFF2+JJaDAoYRl2liSewc1i7k5CzuY+SSj4moc2+NmnzmmsYJr77PRy0iokqySb3QJjfYRsGPQqStFwOvfo9yo+eaK5POpwNgPTaKa54OUXG6S03Jt5IBzDpGF7M0VIwyTvsL9whF6N1pokXvuYHS4SJim4ymcjrnFSsNNvyAqfQbgeu+e3DCLDILZsxILt2GPQURzPA/J8SMcHn5rQhlzYa9jpWibnMa++EnFcnTPNevjSJ9YNCwK00EgsVS3oR5I4MginsK2i5wWkztJaEAaMa3DCUFnBqrT4viQtLYVtuDoImBjHaZHyv99q6OoXv6X/xeJVnw2ngs8Nr3opONVnLRT98A//MH/1r/5VHnroIba3tz+jhPtz5TPwJ//kn2QymfBDP/RDHB0d8YpXvIJ/+2//LVeuXPmsxjGUWBHgyXXVjNYWrQRKCny5vkkbhxGe8xgljn5oadt1ufPYHxC6kACFQJDomC+4AttRF2clVR0TiIDA+GyFmlXm8fnXF+x2Gia5hzdqaf/I17A9tvTCFYtCc7gUvOzCjAv9ht2uxJcwiHMmpeTDhxHT1HKx36BUyZWNKfM24AM3dhlHPpeH6403FTXUhj2/AiSVkXitRgrH4TIg1oI4rMmdZBBC7EliZZlahacEHa25VUacLDR7/Zaro5LNJGVWtdRGkNeCXtgwKwzCChal4GCp1xHuRUheabqqJfQdoSpRQhJ7LZ1gbZjYDwuKWhP7NQ6Hkg37S4EWFR8/2GJaesSBYxCvy+HvWJ/Nfs0oqYj8Buskm1HGvVaz2clZFAlCtCwrQdItGOmLLIpj7t8oiHyPnW7NeZrgKbc2mmwFUhjGiaUrxjx5tiAKDKFn8VVLN6xoMp9pHrHby4i8OX/tPYe8Yjdk5CnOCkfiW66MVnhaMAzXKQAAaS3JKkUYNuwGLUdT8ELBv3zyHD+uebRX4ylYlD5NI/nY8QYPXqhZ1pJB1OArGPZKlrnPfRtLXrE34yyNuDONOU09NpMpQnjcOOmSVh63Tkb4XsbZMkALRVkbfM/HGMe4E7DRXaKkxVjLTrfk8jDjXjjksm0pjEGKPnNZIlLJuBdx58mP8L/8eyLR3/gbf4Pv/M7v5PT0lO/6ru9CCPFpQvRTP/YjfMPuFV71Ba/FGEs3rtBWo+jge4K76QzhfIaVR1lJbmQpo/6cTa+LMnIdUV7Cy7diPnHQsigNzii2Bz41M6yAfrAWbarC5yQf8JbrPf7g/WN+7AN3OFqVtLTEvuOR7Q1+80bJRrzDVz845n235jhtOCkybpx2+er7Q564V/PQZsC3PvwQP/ju29xYOEK/QfcNH9xf8ODlHJUGtNbRWLGOCo4Ve0HNtPBAQOQ5mlazaHzO5hHjbsYfvLbJJ88cB6uS+dLnuarLoCvoxmuzxFG/QArHWerxN7/sFbz33hGbcYdsOeCTtwdc3Dkn8ktsqcgawX2bK5wwzI96FLXm2uba/HW68ujIIc+mIYGOaZsTjKk5X0k6fkNt1qaNxlnyRvDrdwcgLacLx3tvTbm0mRJ4hutxwcG8w4V+g3CSspXsz0I8XbEqIopOyzDO2Z97aF0w6tT0/ICLo4LjZQQy47E7O1zdqOl4miaxnE57jBOfSHYJlQEckW4JtEC5iK2uxZcVjVGsSs08C8kLH+EUHV9TNpDXLUqV1EbhcJRGcJLBEweGa5slWihaI5jVDX//w8dkrmFnUPHzz+SMEsXdo4rjrOFj94a8Ytuy1W1R0nB4lKCUpW58XnN9xiAynGcRxghso7nYX6dTdnzL3tDyzPEQWou2MbZyXOg2KB2yFa6rE2PfEnkGa0LecMmBFfQiy2asGUUvQeuZ1PAiCUV8FgkdJycnfNM3fRNHR0f0+31e+cpX8q53vYsv//IvB+C7vuu7KIqCb/u2b2M2m/H5n//5/PIv/zLdbvd3tbSmaXj88cf5rd/6LW7fvv1ZtbC9WHjb297GG97wBv7W3/pbn/O5Xyr4IsC2AeNhy6jrSGtJ2QiE9enqkvNlSD+yTDOPXuBQynG5l9EJLQfzgMIIOl7DJPW4fRIw3ijxg5JVKfjAzT5hUAE1j+yWTAqQumV/Jrk7FcRRSdFESFUz7C4JtUFKg3GatApYlh6NAeFD4DkCZbFW0gvWPCxQoPDxVUPkgWw9JNAayWZkuDoq6YeO1mg6vuNgEtK2ik5S0w1qfu63rvLglSWBCtjs5vz6zQHXxy2zTDNKMrRQJH5NoGBZegTa0FfV2iDWSD52MCTQDgGMkgIpIQ5qgsBjnJScrBSTaUAoBCepo6Ph4d2Ko6VHayJ6ARgCQiVYFTHzXBMHlk5g2OuVNFYSeWthO/RqmszjYOEhZcPRosN8qfE0WOc4z2Jao7nQr9jtpex0a+7NI25PwZefijv31hzNOsFk5bHMFBu9ltgzdLstyyogCRq0EkzymKOsQ974dAODdesM6vNVSNm0vKbf0BrJOK5orOTBjRVt07DVddyYdFkVimnWglX8Nw9f45ns40yyBCccu52M2/OEtnG0rcACt6ZdRp05t2cdFrmmE1hmecjxyuPZ/SFeaGlqR6gcb7p8hqcr+l7De25eIfJbQr9EfyoJtetnCCTUCk9aWlkSBi1NGuCphnG3AWGZLvqsCsFH77bgezy4k7LVrZhlgsP5NtomnFcprlYUkWK5ECzD3rrtKvW5t/TQTmGl4YUn3/8ZItGX/dm/yBu/7hv41588Zu/Lv4KvuX3ML/yvPwbAB/+3f8B9b7iP64++inw5xOKhgGUjuThIcE5Q2pZ+knKw6BNKw/Vuj5NSILXlvTemfOkFn1dsbJIEgl961vBcalEaNodz/HbMyqZ86Dilrnx6ynGSLbiX1XRFhy+/fpFfuHWLWQZOlozCgPk8ZrzT8vBuyH51g90tQ+wXPH5nk0VtGfUnOCRtq9GyZp4H1K3gfBWSBBLdCnb7K6bpGGG73DxomI6WOKd49YWIV252eD57Bi1rCqNZFj6bUZ9WePz0J+8wyxSDOOdSv0SoPSbZIZ9/dcY0l9hPJRRe31giheUTdwf8iZft8r4bc1Q0ZZorurHGuZbzVNOLSi4OahIvx5Md6ioijAuUlcRyQF2nnLeStzx0hpICTwsuD3M+drvH9nCFryXLyrE/jRh3avpRxnnms9vPmRUaKSy3ziJOFwEGQdyx/JlHd/l7H8/Jipa8gt1BzvXNFXdminGUUxWK+4c5aa3wpObffHKGEI7CSMZRg5Zrw/u0lOv06FqyLBXbg5KmWQuCda3xPcfXPXCVH76zz0HpkdaGJ9oR2+Oag4Vk1G3xvZqmWtOC7V6BUJa7RxvsbJacpz4C8KQgEB79kcNXitmyx7jfMI4dzkYknqYxan0ZVgV87LgEZRGsOU5j1jYpwoCpQSpLa6BuHeNEcyu1zEuLtS2Nqzhe+ti2xeqKg6mm02lYGcjz9Xnqz7xqmx+9t0/cs2StITOSZSVobc35NGZ725AvFKXv86Qq+fNvuMyP/taMJLT47iW4MHqxeNVnmXr2ueRVLyan+qyFoh//8R/nJ3/yJ/mWb/mW3/WkLwa+7du+jW/7tm/7PY3ROvBUyzQNaJ3lmROPb3zDXXwZc5JJPFWQG8ek0lzdyokDQTdccVZ0eH7aYl1Lolus9ThcOraH56RmRlt1mJUdlpVgVUiyyuF76whHazVaKgIZ8sGf/ldUb3o5r33jg/gyYhxJhPAYhBU7cY0U4EzCbmwIheEp49jo1EzSGCcU56mkNi3GKRDrtKbUtDhXcpq1bCaS0ijOMo/jVUDTKK6NLb7XUrUKpQwIia8tILBOYYzCmZC2UcR+zWZSg1iXqydKkyMAi3OOtPJ46rhLXimEBR20ZIXH1dGKTlxgrSIJQAnoBi0naUTXL0n8mtbC2ad67U9Tjz/04DFP7l/Eq30G3YJZ2qPrL+nFDZHnPnUrY7m7WCc2dYOcUPu88WrO/iLmPIt55jjg5nnJ3sBnf6E4zxRffn2f004fYxXjOMciOMs9mrbk8dsBoYZ+KHhga0JWrc38jkxEbdYRtkXliIOaF841ez3JZici0DP2+ituTLuM45J5GaGEIy1DDucBx7MOG/E66rwXF2xtZpxMEp64N2IQt3jaIYRECMuyrOiGDnCcrSKmi4CiUvT8mIN5TFFp0sxnmFQssSRRTeS3iHaDUSRY2pzLGyWJV5NWHieLgLoNQBUY4SOc43U7dwm0ZUPX1F1oGsfTp9vE2sdEDk3LvPIQl67yum/4Jh7/Z/+E7/2Bv8Z3fMd3fMb78mf+P3+e37h7yC/+7f+ZN37jn2b3la/luKhxjY9oeoR+TT9q8L2WbhuQNTVW5gz7LT0bMUx2uRAO+KXnzvBjB60HaF5/XbMRbPLR0xOcDZhnEaepxFOgkOA3dLyGX7k94cOnc/bnOVeiIVHi6IWCJ/ZLLnYivubhET/y2E2E01D5hJHFCw3PLI4RdsztScN3/LtncDrjNfct2D8fEgeCS5sGLQ2ebiibgCRwSAHzlY91sNNfsSwCLo9zbCs4Oo+J45qy1PzyzSVlFWJMTIulp0ueOxnyir0lPb/m6nDJ4SrkO950gf/xN5/lmx69zLtvHYJcJ47N0w5Rv+LisKI1gkmhuDf3SIKMYTdklBTcvDfk5YNN/ptX7fI//MLz3HfN5+njIUfLCK1Lro3PkdeX9AKLJ2uEaHnyoM/bX34ff+eDZ9w+FoRhh6RTstV39OOGQDlWnxI2k6BgkftEYY0xjkmj6URLLo5n+MLD2oRhaNHSUjaWsomwhEjWSUiv2JhzYGK+7NqQo7zCmJbwUx5pvlfTDQy+ckzSACtqDhaKk5UhiQxv3Orw5HHJwVnFU2cQSsM4tlgE00ry5keOmWUJHV0zS32+5NET3vvUmLj12Ula2tbn0V7Cbzz+69x8/VfSEZrJxBJS4PstiQrpdC3bcUMnMEgUo8hhjSWQYNp1+lxtLZVpOMsajHMUmcH3HKUsyfMCrYC2wwXdUjX+zB4AAQAASURBVNeWF6aCfpLiS4+OEgjZcnde/p72o/+S8A/+wT/4T/6/EIIf/MEf/D2nbLznPe/hp37qp/jZn/1ZjDF8/dd/Pb/wC7/AW97ylt/TuL8bfOADHyAM/8svc/9sULaCtpYobQk9R9FKlJD4SiAIqWofLR0dpXHGMQx8LvYbYm24NoLED+mHBk8oRokmVIJE+uRZwGirJPEdvbggr0KyUhLqnMA3jLoVN89iXCW5tp1iqRh11gexZ84DTANnaYeqbdgOGs5yReg7RkmLFI6mFTirsKwv8gQOTwmEWAtF/cjQGp9hmNM4yUmq2Oy0GCP5s6+5xPtPn+GBSyWP7szo+B5aldyexnz83pDXXT0j8lrGkeCF84Rnj/q88doMv1A8l3cJPQmV4HwVcGlUstvPGSUFZ2kXX7e86tIpvlacrHw8bcnrmL1xzrWNnMgTdH3FoxeOEfjUrU+oDYG2SAFXxwuM1YBlmsNOt6A0DZM8pGgErQVjJcPEkGYBOjBcGLWEngHfYLEsS4lEoOTaU+TaIOU47TEtNIvCI/EtcWSoyhYpBb2wZukUr9o7Z15o7s46XN/I2Ytm2E5EaXwEgnGyoLWCZanwVY2RPq2TPHuWEKiEVSWpjcQJzST1eXjUYWfoeP/pLUJP0w0NXd1ymikizyI8RxJVXBot2evnbCUVz51cobEpTx71kVLgSUu/W/HgzpJxXBEqw8f3R0jdoRM0PLA1Y1kH4CxlZen7hqrxWWSKoTWEXksSzrk76TBdhQR+y2kak4gQoWY8ujPmvExp2pB5GjLPNELEBCIirxvSbH0wrtqEG02HXTsh8NYXh+PYcPOkJgxa3vQF11h985/i1/7RT/P6t72dq1/1NfyTTx5CY9nbtGx/xdfyhWXGb77z7/OGP/U2Xvu6B6ldw0Zvwr35mGt9n7YKyNqI1+zFfPRwQV6FJH5DVSgKr6FsHJFnMaLgY4fwiBrxicmM47wkDls2uzW22iE1JcY4dofn3G66nBrYHaUINM8defzDD9+iFynedKlDYSyNM3zF/Vu864UpjpbadqhMQWtCLo0LylqQ15JlNqJsLecri7M+XtBiSke92OLhCyHFsmUgNc/NcrZ6IZlpMQYa4/g3N8/QsocQNa3R5I3ixrHiwY0FaSopS0HladCWx/czLowCzlPvU7/v61bEnU7K4bxLL675+VvPcnEjR6CY5CGX+xnXNlbcnMRorfCE4yTrYJ3HvLI01FibcLyq2RmvmC4jaivwNFRNSBw1XNvJ2ejWgGB2lGCsZpJ6dML12aIyjnGcUrUBDjDA/VspziqeXx2CVawKxVZPEihNqFrKJiTwKjpJyaQSCBy1EUS9lK/avczjk7uMOhWD2FC3iqrVeGot5rZ2febytCGKGqraIwxq7i1KwqSmbCXGWIpKcroU5LXikt/gaYOnNDifZeExWfoMu1Abn62Oo+MJjuchG92WUWJIS49xt6VtfAb9kqqR9KOGo4WirNeC0dNVzioX+CFUraEwLYFnKW3DSS4IY4dbCo7bgp97qqXTcXTjFc7GrGr4oS+9nx9+//OczSJMA6awfPub9vgff2nClYsF/+LGXbpaIfEQKF5/OeVs1mPZSv7yl17ECyyr+yX7ackz83MeP5xy30iwWEY40/n93URfRHwueNVLwak+a6FISskXfuEX/q4n/C8JsfaRLiBQARc6PhuxImBApNYmcEr67MYJtD6xgqKSeNLR8XycVbQWYL15PrC5IA5qPNlS0xJ4NYfLHrNMUtgSzxRkTcl7fusSj95/zslKomLDYzdrug8J0qZlUXokUUVpWqRscM6jbNZO/J7UJJ4i9hSL3AdiNkPHtX7IlY0CLRTCOa6PLYPuirzS7E97VE2Ia+EsN6xyxxvvP6Y2PsfLEK0E405L1dZ4ypDXimUZkATriNrIcyT+OnY27Fu09NmOW5aVz9EyJvRqYs9wdxLiSs1mV5IELadpyLTSpIXmD9w/IdQOJde3lk+fdnDSgVtHIHZ8w+Wh4WNH20zTkAiFr9YJGRtJyTTPaVoPIVsEloN5zNWh5hMnY+4bVTRGUzSauvHwVEVaBwyiAusEW92UD59s8fLNjMYIPNHw/KRDVmlOlppVLqhtwGs3hwwTS50ccbjw6IcV9+8WfOx4jBKWftRwstSclw2r1uL5IdPcZ1UJel5N1vhM8piDs5jzUqKVZVoqYt3yxssnBMrwJZfu8XOfvI/ZKmTUq/GUZW+zwjrLMMmBtUjXtBCFilEy58HtBe+/uc3GoAAniAPHLN3gfGYpTcG1foQLJA/vrog9y7JumRcBr9vp8qF7Ct1dkTWSs7lH5TwOE4m1At22FI3jaOrTCRuEhMakXBxnfPWfezuXXvmFiM/7PN717CFWChbnZ1T3TlhVkof/yB/Fv3yNB17zKKiUfscwzTyUB8M4IC8NdSuJlCTwPLTTWLFCiYqn7jXc0Bmv3d3k6ZkjCRUfOcx5ZKPDfT3Na3e2eOVmwv/8WEaApOdLep0Fe8mAm7MCVAA47h/28QQcF1PuZutEmy+98jCTuiDUEmstbVSxGXV4/qxgHEpefUmzrFuubx8QqAopYZIa8sbgeYt1q1WjUBIibZmVHlI7ZrnCunV852YnZVYGXO0n7J9bTs99RpsVnm54xW7At77iIX7qhffwqktngOY8D5mkEdf7Qz5yXLJqCt7x/tso6bhve8ZopHjj9n0cVM9x/zhnVvh4uuLmeZ+Lg5q+P+c8jdlfKt7+eWO+71232d5aMK2hGypCz6KVYZp1GYaOvUHBIKiRMicJFf/y1i26A29tBrkz5PnVCSeziK951RG+cixKzaqSPHLhnPd9co9LowIhHcY63vP0Jv+vzysYhT5VHZGKlmujgrPUoxdUfPygT15oWqsoG00UOd50cczf+fCcPGpoXUlha+JwgZKKftRya+oxSOZcEpC3Hj1f86vPLikNjIcNtu2wPbL0Q0fktQxCSW0G9HuatAzZSMATPlfGhnnm41qfUMOGtnhRzNlSon1LXgvCyNDkhidPajpRTRi1SC2oa0E3WlIgMCLkfLHBpXHJUepjtaNlfRBBQxw3VK3i5HTIfXsFKA9fOUCxnWhCr0MnEBS5z0cPa6LIvfiblFQvYkXRizPMi4WLFy8ymUz4yq/8Sv7u3/27fM3XfM3nRKj5+q//+s/42DnH0dERjz/+ON/3fd/3ks//uYTv1cxrReQ1zCu4M4OmtWwklqNUspgERL5i0VaEoaQbWMamRBpBN7JUxiJVC6xbLyVrPw9fajYTSy8yxIFmmvp0PIW0Q2Kt6fo+nrOMxjnSBQzCnKLReFIyCAsOywgQFI2ldYaikJTdit+626MXttyc+IxiR9dIVlVIpC1CQiANyyKgqxxZ5ZE1mlAb+qFFjQosgsdO7qCUZLfbIKUHTvCh/R1unkW8ai9lFFe0xpHVARudmo9UimnuExQwzwJa66MdjAYpgTZ0wzWvi/2KrW6NtQ0Gj42OoB+XLDKPQCva1gfXsNevqVsfgWIzrnj6LGGWe1SNJB4U7E97bHYdPb/AF5YHuinGBOzPYqz1WRU+fbnFgV6x01/SjwyBNljXUtThutI9ybG2ZjtpOM9iAt2ihSLyKrI6ROF4/YNnFLXHrAjxw5pJHlJ/Kh1JC8NRtUHHK9lKKiBgkmm6YctmUrCoPPJao8Q6dt5TDVJAqCBr4NWXFrRmwc1FiLM+Fk0ctERK8NAg4yw1dALD4Twk0C2TZcQzJz2KeYfTymfUTdkbZQRKsAhr7k4TslLjnCQMGs5XCcOoZrqKuH93AVYy9RuePerjBZL7NlNEWhPElkmVMM0jtvsgVMsgSDlfDOmEhryGvtfnoJ5SlJK63mDZWDa6BQ9uDZm2U6yFyjp6fk6FIsJwd9HjeBkS+4pIgpOS+/7wf8fgodez+bLPox9VLKoMX3cpioZhbPgDb/sWrj36KsYveyWTakXnU1Vyoyhlmg/YSBoeHA34yOmEjY4kKwLuH3V4dpayP2/4kmvbZIXl3qwi1OdcGQYUKmZ2L8OImvMsYJoXOGPoRZaDu5tYa+hElroJKFqBkoZk0NJaxa/fnRMrj1dv93nsRsvrt7uUas6qiHjL5eu8MG2ZlSVPF6dUxuK8itdtXeDXmhO6QcaFTsCdRYtwU27OFY9eGPDcpODBLcXrNjf5hRcEC0oeHm5ylN8kCiqcg8CzSK8F51O365bCBy5NiT1Dz9Z8ycsPyOqEvWTFstUY53OSBZznIYeriIZ1Ku3xJOEk9dns1UyLkOpUkfglG3FNWkuMs7RG8MD2iltnMdu9FqUqDpcxvajlycMe270VG0nGovT5xP6QYXfd3ZCWPq71EFYTe5LLg5xVJYl0zdEq4fBo7edTjxQb3YIP7Hu4NqARjqr1aUyLcZbEd8yyGFaCp847IARaNxyexPxycY7wNc8e93hkJ8MhuDJMmRcxy9IjKz2kFOjAEgctWgvSWvB3PnhM0hEsSkhTRW/D0o0aHu3mbCWCXmC5NlxyZzoEB00d8sBWxKwwFE2FlhVeYKlLje62eLqhbh3OWTxdsigVPdVynvksUhgkLbMCmpUmCCRnU8Voo6R1isY67tuyvGbjIr92cIBtfOLYMYpbNjqGs6WiNiE/+9xNLvTg/o2MulknOo4Cn+2RI1QK5xmiscP314loWaXJK0lWWx7aiIlDSbCl+PCR5BOzil+7t2Kz03I6S8hfkoqiF4lX/d+EU33WQtFf+kt/iZ/4iZ/gx37sx37Pk/9+4ySr+NjZLheGDcYVlK7kI8eGh7Yq+nGNVo7cSm4tDBuDFXk54NKgwTjWL7gAYSXaEwTKYxAmbMSCqQgoy5ALXehIwZdeu8ST01sEMuDBvZxHNnMu9hzLL7zCY8dD3n9jgzdem3NtY46nU1aN4Vdv7FLVHd5wZYoFnj6FRb4mKiertb9OLy7pxy0bScOqUIRacd9GSzfymOUKWoUJHGkp2ejXxNGMYZRyuNhit7/iymhB40oOU8uunxM7n/MsYGewNnpTomVdeC0YRQ0QsCxhUfkE2mGsz3Z3XSFwtAjYGBo2eiWDqME6x2an5DgN2OnUOCfxtaNxgpeNzjnPE4om5DztoIHarg2dqTTGCUZxSesE922c0hiPSdahbj2ujAqudmuuD46Zll1muWQ7KZgPBLUzTLIWJUE6gXM+J0uPuoFFuS7dFlKgheN81SHxHN/y6g1uLpY8c1ax1YtZleAbw7Nn3XU8t3RYqxh1Kl53ZcooqhmE8Pj+Br5qefK4h7EegRZ0Og1x5NgbZnT8ksuDgsdvbxNHNSdlhwd3U5omZZKHOKcoCo9LQ0NWRTQNFMaRBJZLw4yyltw6GzAvfLpBzaoIeHDcZbLM6HYtu55jtXBsxBGfPJL0o5y8iRn4Mftpvb7xXIZEQrF0PbJGMSxztpOUwoE1fYQsWdYOLFwdrtAhHByH9B56OdOy5bGjfU6mhl3dMA8dZRHS7yquvPo1CNnSCQWBFAyCBdaV3J32uDj0KaqA00zyJ1+7zbueP8FrE+6ljsC3hCLgIyczjKgpK8Nmt+TGasKl9EEGQcDPP31IKxs2epbKCg4nXU4XJa+4UPPCqePq9jkfvr2BFRB6NUO/4e6sy99+7B60ir1xl9FGye1Zy7lZ8YYHNGnRp2xbztIG7Wuych2Z/NDOkrM04HzlIRUs0h2EXvLhWx1qKwmDlsU0pJJ9vvTlIedZwyI1LGcZp63CDyH219Uph2nJD/3GU8yaAZuxIfDAky1ONLz7qZZ+7x7psouvHcbC2TKiqA3//EMLHr2qqS0YZ1lmPru9FmdDLg0KXjgMGfVzfugDH+XBvZROoJmmMVJYXr03ZVkKZmWIEg4lJKdZQO1aFrmi9QQejryRvHBe0h+WHE9imlbhq5aiFawax31BzSuuTOkFBqEEd6cel8cVt052uHB9hVOC1mqsNWx1GqzVdIOWg8wn8Stq0+B5BXcXOUdTj3nc0CqL045HNlNuTbYAzaMXZjS2oWp7jIIAX0l2Nw2v39rgPbcE20nDVidHS4fDUbfgaMlqj8pYWmM4L0oOipp51iMm5PJ2we7OHo/+uT/Lx+7VXOg5XBsyzQJqISgrxW5PsZk0SNbrPl2OiKWjNTFbfYlSAbtdST+K8YYRdeXhJY7QVyxLxW7fIW2Apy3GQmUaDpYtgW/Zdh5p7jGvcu7NXvxe+v8rC0Xf//3fzx//43+c4XD4OZ231+t9Rpu8lJKHHnqIH/qhH+IrvuIrPqdreakxjgV9LalaRdc39KXPSkq0FITa8P/9iov8/SdushOlCBxbHUknSklrTddXmKbm6dOY2SokczknqeHUhayKmutqxajT0lpDYz2cENRGrA+LyuHF0A3XPCFSPhJB1Xpo4dP1NcJIPGkpa40UghunXRZlxJVRww/+gQf4B099mO1ei6Pg1vkGkZZcGS+5cdalF4F0jk8e97k+ThlGNZ4SZLVHFBRYJKPE0PEElVnLW0UV0I0mJJ6lkIZQwcG8x6WNjMhvqJcBSWBIK0uWBmwOobFQG4mUoKVhIym5t4JQWh7YbNhfROSVY5BYrowKrg1KaitpjUUIy6LUBLpmVTnSSpKVgmFSEnuShzdKFrlmlkqeOhlybxZytoyYpoKroeCBYY/nZjV/8jXPUzY++/OAZV3zwDhlK8lwQpBVIZNSsdupaayHsZJJ5iOk4rmjLjvDnON5wsfu7PAlj5xirCTWhnvziCv9gkgbVo3EuvXPSWEZRIZIt7xQejgBFwc5BsvlvuXGeY+tTk5tJAfzkFnmE2mBrwU3ThM2u4bGCNJKkNaWTrg2/DZGcjJLQCzZHjWMo4Ze2OKsoxNopHQM45qb5126AXz+9ROqVlJZxeE8pjWCs8UmO4Oau6eC56oOb9g6pVawlVRU1TqZKct3SOUpZas5PY3ZHZ4xaRSRJ0jrHj6OnUHKZNHlGXvOTr9kmmoWucftsw4XhivuoOiqlvs2V0wWO4RhwcGyx7jjGL36jUxXlqUUeFqS146tSFGXCZOl5aHXvYL9GQyFx/50HaTiexYdLDnIfPaXFl8oXLM29X3XC3M2wxhnNb9+a8blDfATqFPJdlfx5EnIte4mN2cLplnDuGO4kCQ4ZziVYHWGMZLThc8gjPja6xf4l8+c0uqcQadBU/PcsuJlnR3uzQoaAWd5yS+2R8xTyG1DP0jYSjzGgeHGieBsGnGuJKfnEa+71qGyFdO85hOnK7Y7miujiF85epYk6FM1kt+4kVPrtdF27DlCryV0hsPC4wt3hnzFfR7/6uaC89JnFNecZwnGKAqnWDURZaPoeA2HywQlYJhknC47FI1PWWnqIqAJZxhjUBJmRUQnKJmlMZHfktaWflxiTMPzJx2ub+VIodDSAhqtBWcrRRhWtFZyvnJIZfA9xVkWsNnLaFqPXlDzgdu77E+6nNeC3Y0VoV/haJnmCqwgDhpO5xE7vYaeq8BKnj7YICpLzpcdfDQqXBGpmsgTtLKlaBStlThh6IcNxrWkdUURCUzrUbWWxiqqRjAvHF5cUbUeWSH40T98jR/+1Xv8sVdd4Z1P32AjqqmahnkRUzc+k5WPVpDXgmUGVaXJk5baNbTOo2igNiWzwif2DGEww2RdVpWHETWDviPSPn/li67zt594isnpmCBuGEYK0/jEuiU3EZ88LOh5HmFXMe425GmXXGucE+zEEedlgZISY1uMcNROMC8MpShYVFCamm7oKK3Pqm04OIzREqYLwbf9m2fxQ5hMoD8w/OlX7/Kvnj8krzW7uynP33gJLo/+LyoUvVSc6rMWir7jO76Dt771rdx333088sgjeL8tEvjnfu7nXrTFvdQY+glSSXzhEwQG34PKaLp6wc3zDlpaZquAopKcZR6HkwRjNEK2WNGQtQ1HM80rdgsOVx6eV7PTPyPPQlaNxPcdroq51NM8tWhw0tBLGjqBQwqNGVyjcx4wGMJGJEh8j9b1GXolu13DU/d85lnIqgm5O/F5/iRg3Gk5Xiguj3K0l3EhXtA4Sd5uYYxib5DhqRbrYJ5BoBukC+gmisiPEbbBtD4XBxnTPGYQ1fTjjFv7MXNpCbyKqhFY64iDkqIVYDUbcUXPM5S14vk0ZBg13Jz0uDfzKStL2C2ojU/ZSCqtCbyWqlGsSsE4rok8S082RGHBcaowrmFV9fCURIh1NceoWzHcylACFqWHVoZJFnJxWBB60LYenaDAtoLSSQbhnFEgmZUdLgwajlYBHd8hhGVeeDg0iddQNBpfGfImYLYMETh6YcufeG3Iv7v1NMZKtro1jRFcH2dsu5o7NiQtY673U85Tn8oorgxS/uXHL/P6SxUnp9voaIIX1FzpLdlKap49GZCWAYO44Yn9EXfPG2oEz93ZYKNfsdXN0dohhePhnXNmZcTNowFppRDaIrB0ZMnxQnN3OsBaiH1LL27pR4ZZKXj9feco+kRc4EP5gkG0ZC9K2Z+E0AQQWKxIMfW6dFvQsj/rEocNViiK1sOXGUoo8taj41Uoben3Gm6eDanFilhL0IZpqekOHEEjGISa/drQOAnOo6h8hF4wimsEiqxZi2plFTAOPR4Yxnz86IxHdwOeO1L80UdGzKuUxw6nXIp7RAG8+dqA/9+HT+iF8K6nV7z1YcW8rJmlAXndcnFzxZVkzLwqeeYwYNyxfOT2mPOVz85oxaVRxihpEVpzxyocDX58ztC/wOsfCUlLzWu2+vyTJw55drWkqQWrYsyDWwbbhEyyjNaVhH7FvIjYjGBZR9TG0U1q2jpib3vBzYOAd3+8YW/oo5Xl0tgwVn12uz7/7tY5pbH896+9zt/60NNcHJV0A4e1jlkeMs8lTjm+5NIF3tOcsyg9LoxTikZwfaNm1ZljtOT2eYgRLUJYhBB0Q0NlNIXVDOKUykocgqN5iJRQW8dGknNvMaIXGkK9NpHNGsU0jRh3GkKpmacew25BZjKWswAtYX+ecGW4Yl4ofFlxaxbj7DoSNfYbnjvaQErwcYRqXVkziksOlj47cc3xp9J0skqwN14y9hbMm4L3HeX4YR8nHJudDF8G4DwC6a3bAR2kZcIyHZD4gsYIrIVh4JOWLWllcFRUxpG3LZ88D7jYLzhLE8Zxw7PHXTaHBf3IkHgtPa9CKcWP//zHOHjq/bz8K/8omACpJB3fEUeWrdgSeppBJNCipWwtRVtTtIJ5bgl0Q41hVlhSt2K2FIhWshtXlLbl2XOPnX7G8bzPyy4WLErNZj/nvm3Lk3c20e3aBL9uNUnzH49E/X/wH+Ltb3/778u873znO39f5v39gJAVQvnUNQgcm7FPvXIoIeh4AcNAk3g+m0mIr1si39EPQyIlOV8ljDopedGhzUOkcmz2U4QUdLqQt5ZFKVmWglF3Qtt26EQrPnJ3ROI5On5FViuM1TR23aLSWokxPq2R9EJLX1q0siihOJ0PUBbqRvKO9z9PN5GUtUJqwbWNM/anWzx3FjLuTalNzL15hC8dsR9yadBgnUUC/bBlVSmUkIAk0patTsuD2zkfvbuBaVcEfkFWeVzstVwZpTgLEGAsbPVSJspRtYKyhmFsyWvJtBB0Ag9Bw/WNJauij0KyyDyuDGsUAuvWibVWWp446LORNHxkv8vJrMPYh2BXrz0bPUvR+qAld6ttzlLF0UJSG7iyldINSky6h7IBp2m4rgCtPAZhSmUEjx9sAHyqtd9yZ66ZZD4Iy1Z3xcM7KxSOrILjpeYVl+YMwgItJUeLDTrRksoITtIux6nkQr+i41skNWm1fj7LPV55YUFah4yilLNcc5YHbHdb7s0ifA3d0OIwhLrlDQ/NaawFFI3p0vEralPSWo+qadgZNJRlTOtavuC+Y87SGCEty0YzCC15o6kbhQ4ibs8s46Qi8ioq06FsNFKvuDUJiQKF75W0Fj5yZ8wrLy+4N/G5f6dkWrRs6w184UirkMMFXN4sWGZdcDV+ZPG9gEGvYLqMMCZks7sOUfAkCCd4ZGuOE/Abz+zS89cx4xtdx2QeMogzrAhJvB5lsw6UKBqD5/UxeYkQBqECVoVlc1CgJTgM0rP4dURjG6xqOU8TRlHAKFSUbcO1sWSeS07SFR1PMpYeP/nEXTzpEXiaRVMiPMVJqkg8mDUFL9s2TGY9bmYN/aQha0t+7eQ5NoYRHRVjUDjV8oeu7hKHjhfONU/PVvzhazG/cTMlq+H6liShxwvTJV/08B5f9YDPM7+yxAvgcq/h1jyndTCOQr722i4/8/QJk6VlshyQ1YZ+BLmfsiw83nT/DGcFB4t1giJYnj4tuJevUNIReA3CCQQCpVo+ejDm4qBiM8mZ5AGLwiOvFK6KyAufV2+OedOrhnzyOOdXbzdc22vpO9jqZDx/1sFXjgv9kruzmG5g2T8PAceq9LgwzChawdkqASEQQqKtT1oqOknDsFOwzC1vuXyBJ2clUpaUuccs65AE0O9YQqW5N1MEXo6UBtdovuzyDr9665RJFhB7FRINQhD4sDdsWBYCpQV/+gv2eM/hC1wcLbhxMuZ0FXDf5oKzLCarPJwDgSSt1jHtdauoW8cqlwS+ZNAxXNwu+Ovvv43Umn/0oXO2LzjGSUPZehhjmeUB85VPJzRkZsmi1ERKsCglRdPgh4Z7M0VmfK6Mar7uwYv886dqlqVH6kmauY/uWtpa8c6PH3GhM+JyIrm5sATSw3mgjMSThlnW4HyBxFDbhoOZT4nC0fL2V93H33zsGcZJyccOQy70LYtc8Tcfu8GD2yUvHPYY9VuKokPoWx7dbdlVF/ilpxa85krOa7aHvO9GRm/LopTm7jLnwQ2HJwOWpeKhzZYP/n5toP+V4aXiVJ+1UPQX/sJf4D3veQ9vfvObGY/HnzMD65cCt1c50s9xdUA3NMSiZH/ueGAnZ5IndHzNIAjZ6yo2ggTRkwxCtfYxCX0udQuqPKLvGxaFR1l2uHW+i5QZm52MjcSgleOfP/cMQbDio4eKL73vjLQR1NbwS795xic/fshb/9sNtCrxtaOtNVJ49EJJN5D42iOymvtGCh/NvYNNFJpoKyeQgrL2SDoZUqdcGq7wtGOWS5wsKZ3HRgRFo/AkJL6kG0ou9BoCLRiEgq4XYY3hZBnxQh2hVcClYYptJefFurJnlNT4rmSoM04Zk5YBrZEs8oC3XBnzrmdqnk6XvOVlh2xEAZGWSAFprTAVJH5J7EHitcRBS2UkodeylaQ8dxpgrSKtA/JasddfURnNNJcMQ00vEmSV5mieEPuWee5xU4/QXsGFbosWPmd5TN16DMKGne6Cu7MYh0XiUbYhsV8R6oKtJKcaBlRNQDdo+K2TnLxVzFOfrPa5PMq5cT4gSea8bG/GcVaDEHR9gy0FR6uEdOlzZ25oreDwtEsvrmgbj8N5w3kaoITm6cMBr7k0Z16sPRRC4dgclFyIap46HPLqq1NunSfsTxKEbul14LUXGw7mDXUj2BvkdDyfTx4k3DcumZaSjl8TiYrbky6TlebVWxXdsOXJgxBMxE43InMlaQF/6PolfsvMWNSOraSkcJJuVJKWiu3I8NTpJRalTxLWdBnQ6Z7x3OmQZebTT1rq2idWOdMiIPJzJF18LRkmJR0dYN26D90azWEa4guIfItUFdNmxaqxdMMdbq9K8gZSU/GLT59yfUMzDrocLAtePox59wsztiKP2tV8wQOKN1zs89R8Rj8qGPcKnNV8bLqgdorNuObGaUDrAmLdsEwTbhuffW05nyc4WXN1Y8XN8w53pim/fiPni692+V9eWPFFV3u8tjtDCo9Y9nl4uMtRWvLY3SU3ihmFKRjHNQ8MJPurCONmeKrloQuS5yeaV98/wTnHwE+40I35+PNz9i74nC0FV/oRJ+WSm+clEkviOUCS15JutMLhE3s+L5w19P2EQXdBHK9QteLSqEKJikXh88jWnFnlcMIxySCtA+5ME7pxQ208xhFM5n3uzSJ2x0vuTCI+eHeAFJpNr0SLdaJEqBusGXChX+NLwXSWsLmVcec8oRM6ukHLrPTxViGNa3nZ9oxfe+4SR7MendgSejWDpKUqQ3wLrVUYJ4i8tSHjqvI4Sz0OzkJOZ47DzGfn6jo2/mjexxUBJpQcTnd4ZCfFmoZuAGmluHUeM8klZwsPEAhhuHki+IFn9+mNK8RZzCBuqA2EQcpXPnTGeTpEOThbdumFmr4X01WS1nqUnyJc+eqUqsyprYMWcI66KliJFF/XaHykLNkbZPzazT6deMnp2YBVqfmC+0Y8e14xmRZsdSuCviJf+JhSsTnMeO0eFHWfrZFmFEk6PoQqYuhLrgx8tAStoGhhktYv+h61Tuf4XYWT/g5jvSjD/J7w9V//9bzzne+k1+v9By1gvx0v1aXT9evX+dCHPsR4PP6M5/P5nNe+9rXcvHnzJZn39wNCNvi6pRcXRCG8bus6f/tXJqxMi7RrH6C7Z6B9y3Y/QyuFEA5PGpaFTz8K0NZnOwnxQ01X56QuAKcRreJkLjDG0bYeW72GWAse2Z3y8f09Qs/RUuJ7lpaak7xlI8rXUfCLiAe3SyLPEvs1vpJkRZdipQl1i4zBVz5VA1d6DfOqZatbcLTQTHLJTrfGtoqFhfNFxOk0IEoq8tJHigStDavSkPgejbPsdAsO5iG4FuckWRlwdxHw0OacbiA5WgWMOhXDXkHZKhZljK8E/ajhoY3F2uPRGLRqGWrBoogoGo/zVZedfsX9GwvO0hApHGm7Dqy4Mprz1NEGUvj8sYe3+VdPzzhfdXjlxXOME9xddKhdTdGsRbOXbcHzZ1A3MbUqOJpVfMH9gpNUs90pSDxDbQN8WeHwmGSCUbSkG0KoK2ZFyFbS4smarW5B2QYczBOGccnz1YBbU9jtZmz3cs6WMb+yCulFjnFUcrpyvGw7xxBwPg/xVMvlUfOpSoSGyqyDHoZxSdV6hH7DyzZTPnpvi4vDFaFumRUhNycRdavp+A2TrM/LtudUbc3N8z7DJOOgsaR5wJNHAzxpcUIwiiy+qjhbeTgEp1nBxdHaTHuWBTS2Jc00xg2QsmDcmbEsfVqj6caG89Tn+oUUB/STkuOlYrMjeO3FksoFBCpCxw4/jDGy5GTpaFVNN1RY0TBNfdKsA7pBiJDaZtyZdOn4DUZouj60lc+FwYKjVQzSo25LOpHPdtBh2ZzQ2ClOdFHKZxwWVG1I3TiWxrIZl2SlT6wCTDKnqgJ0MmcYd/ngXcv2oGU/q2iNorWaIFixaj3aZoudseLDBytinbA7qFDSsKjPGAegVEGY5Lxq4JDlRY6LFbURdAOH8i2LVculpMfTJwseO8y5f8NnUfi88yMFvUDwRZe2efPVTaSQPLYf8dRhwUfP7rEZeWSyYGVX+J6jzTZpXMCv3VkwTy1nokQ46AU+9+3mpGZFjcei8DlfeXSjhmW5NmX3kxkHWUUcWpRwZJXPzmDJsgmoWx9jASxSSDY7FW+579Uc5i3OOO6cW9La8M2ft83z2RmJ33JvHlLWgod3UrJ6fU6YrEJuZgGe1xB6a/+yzU5BoFvO0oS0doSeQipoWslWzzKKW2xreWrxDPdvZpxmHpEG5dZtiIPIIYxH61qen/Tpx2B0zv++37CqfS6GFzhetTRGY53FmrX3W9FK0lnCj/36OSqKuTv1GcaSvDEcLmJ8KUlrRdkIzpYR1hl6UYvR6zavILTsDtZm1/dv5eSF4pXDyzw5vcdWr2aaBZwsfHqRZVko8kLRCRvGnYa7U+glLbVpkdqSlwlB0BDLhq2O5V98fImTPn1fc2GYYyqfMITWOHLTsOd7tLbCIdAoVrakMjVGO04XiiqwjGNJnYL1a5aFh+/X/M0PPUledeiFlmtjgy89vvTiDh88mHEp3OA0LOh6CaFQFJW3LrwQNQiH1g6EY1msWx+18Xj8aMnljRWPDi/wnnPHt75ym3/0ou+LLw6v+r8Lp/qsf1L/+B//Y372Z3+Wt771rb+rCf9LwoXI56QJ2O3X5JVH0wQYV/P4fpeyDLg2rnnzpU1acY/czonilMDX3JmPCHTL3mjGC5OEB3ZzsjqkG2rKJmYQCboRZE1I1/MZbzQoFWNcQy+Q+NJRNR5Jt0O2LOlEc/JWcWVY0jif/YVk1czIXICQJauqh5KWXmiY5BGdCCKtiXREqD1iqfGFoh+U6/Jh4UiCkmubR5ynA1p8lLIM4pxRXNANKhTrMlEpNHXb4/LAcra0nGeKvNL0o4ooyFhWltfvrjhfBGS54e6iy7L0OV5EZFXAuz9Z89XXN5nmDb/+/Abv+JrnKOp1m9jdecw4brjYy9YeA0IyKzXTImJPFywrRWMFrVXk1bo08vH9DldGS5bFgCfTDlc2Vjy0O8dhqK1hkiseGtfs9Q2lCclbQeTXLCvACUahxRtXPH/mk7ZweZCz0ylwzmGM46N3Oty/kZNWknkRYFuNcusDspIt87zDvTbhrotIG4evBEpZWqPIKo+4a3j6TkhvcM6lHeiHFeOkphe2fPzI0QkMwjmqRnM8i1iEHq+5PGFWeDwzjUk6FZPcZ1HGKNEj0AW3zxX7k4C3PHxM5LeAoKwkr75yxiT3uBhe5qjeZ7r0KFrYHZTkasWqjuh1HYtlSNq0bAxW3Dge8CvP36PTK9gIWu7MYxLPcOtkk6tbCx47uADGcGm4QCjJ8ekIYwPqxuO+zZKq6XNU1Ix9i64NqzxGm4btDcUsT7i9qnhoGw5mNWHg0ZSSSjsaZ4lDQyiWnBYhv7E/YzOBs7wi8EFhePxeyUPjHueZ4/HbFbujiovDLu8/qAknUF3k/8/enwfbluV3ndhnrbXn4Yx3fvfNL+eqrEGl0oQESKIRwgLR7W7joN1qg6Fx2KjDWNHC2IEkTFgRxiC3MA1EGyncQIiOBkyDhEoSGksqqapUQ2Zl5svpze/O94x7HtZa/uNUq8EKd7RElkpI/kaciHP3OXHWfve8u9dv/37fgV1nwCiNeGu+4GRt8ZyeHV+TmikyWRB5AbO6Q6JxHEPdSr73997iu3/0CW+1DsOkw7otrrC8ucp4aXrIrz5a0ypFL1q0POPl3Y5PvFujopKhm3AznRAqgW9dXrm3pvcCHCGpcp/n9jWLuuGibDizJU9XLc8MPQpTciEvmZUDam1546zCwWFR+jSd2DR+XUXkCt6eKV687vFnn73BX/qlN4iDnKyMWZQghOVi7fHSrmLdBhijEELQa8PxSpIGDWNfsChCWuOiJDw4HhKkK4yFxNfUnYMxkAQF8zwidl0cYSgbwcu3z3h4GVF1iuvTGsnGcLEzhtSruCgiHAcOtypAoI0FIXF8gXQtdacwGIq2RynLK0fbvH2WkpWK4XRNHDQMfJd15+MKhfYhDDRl6QMFrRZAx/Eq5PMnDqvSwcfFMy7poKTIFL/vg0c8OtpnJ9EUdcK1aUZnYlLVIUOHvrX0UUvTa7K2QxtB1wvyWtIay1p2NKM9FqXgq/YilKf5hdcjfuh/9hL/2S98gfF4w/CaF+qL7KUdRk6MF3vkjQEjSKWDNR5YRacNuusRdclFISiqiGlouOaUrCuHyybnIg/AqfFcSW8E/+ff9wx/zf2XvPNl3dF++2M4HP7acOn/WwL2W4WHDx+itf51x5um4ejo6Lf8fL6UGPsSJ1T0Fpreoex6rl0BFTScLyL+/L94B981bA9qZpVke5AxCi1vXbgYlfHKUcLpKexNW54b+hS1JdOKCI+82hT6SnzRxLVzMb7CBXYTQ+I63D/fNKh3k5qLSpE3glnpIlTFuu2oOpc0LNlJNWUrmBUDsJbI37D0ul4QOpJlGdJqj5vTnkcLyaNLn0nUcTHz+P27z/H//OwToqGg0w7zImVnUNIHPUpGaKOZxBvZfm8Vq1oxiSwv7s545XSAT8DhaI3vaPxA8nSlUFIzDDsORwXTuKTqBW/NA7zOEDkhRat483SAtZLUbzjLnQ072ytZNiGHA0PWwPVJQV4n/PTDM9JE0xuJxOCqmlYH6F5xuo4wxufGKOL++SaB7OkiJCfn7pHhI88rLotk4xnp9pxlGy+i53Z69pKWiyKm1QpPdkRey0G68WhstcFRCmMNWQUfOMxJ/A63Njy6SJFOx4GXsTuoWTcuJyuPrHNZNz7rSjKKe6ZhzVbcUHQukStptEHRc3NSEziGg3HOtWHJz767jzWGF/Zy3r0M6Y3mcNhwnkU8WQb4SlOt99lVEUJVvH484QOHK2a5IvQ0kRMQOJY8V2jhsioixnFFYwRX4og0DFgw40pYgZAIUVG1IKzhyTzl9taCR6uErahCOAGBHCBExKN5ze+7ekAvBK9erGgweJ6mqX0mScdlbjDCZWeywLFDQjegrCOaLmQ0KNEG1pmHcDu2og69gt3Qo3MqYkfQ9JbAG9KyQhc9WaMRvkbQbWJfrMtJIUg9EHK5qQ2FwLYKb7Li37lznTculzjKxwkqWlOjmwHDoOHNao2TDUgDTW1yXj8N2PVDVtphe5jzYBYTuS1RYJhxguN4zNcuTd8xaWMOB5Y/dHuLz1zMuTZSLPUCx6356I2GkyzmMxdP+MLyhEQNeTrr8YIC14F02BCLHkcJjBbo4IJZHXIj2eY/+dotAhXwj998ypWBZBp53F04OErSaIuU0LSbZu/+yHCUBdStg7WW29s5KreUvYuvBOOoYhSW3D3b5mLts1pGfMczAz5/eslxWWJEQ2ksvjPh224f8i8eLbl/HrB1o6JqJfNS8fByyDRM+SM3dvl7rz2k0ZLO9jTa4TJPNobQpmMn6XjzoaSVPaOgJ/Y0SaiIPShaj0WhUHHPzjhHSY2vJCeLmFmh2Iskw0HNlcklrzzZRjmSn39UMk19xlHP+68ssGvFch1gGkEUG/bHFW/fC+j9gDYBIXv8K4aq8RjFFasqIvE71o3hcFLyeK5469Tlxm7F9Ynhs48GDHzLfOHx7nqO61qGQUdWCbLaJXIUoWsRveRy7fAfv3yHT7/7LlLCPHOYJJowgvksZjBqUaJhe1hwvIxwlKDtBBiwFqyo6Y3lad6wqhtW5ZiXwi1es+8wHZQ8WW5z4+acrArZijasyc53aCqP7aTDdV3SQUKjNesmIHQkulZ0vWKeeSyKDCN7PEfz1onHcWFQLJjXlm7Z8e5RQe9uCAl1ozg5j5gkHT93f2Pd8F986umXexv9bY3fiprqN9womkwm3L59+z0/kS8HFm3F43XHlcnG9DDxFDcGEZ7jfXESIbl7mROIEKXGBG6LIwQ3xg3nucvT+ZiXDmdcFIqzXFD1PmlgOK8MHz48w+oRi9rjxb01eStRaITtcaRH4Ciee98dZHqLrolo/YrTtcuqcfFkTyQ7bmyfo/Gpes1l5iNUTemWeK5DbTqcHjxHYK2PI3ystWAVu7FhECqWpYu1LQ8ufKIrBUJ0aNuwnTQsygHgsao3N7ZVKZlnCXUeMrmyxncsiWdIwjWuXCKDKzyu98hrl6r1KRuH3sD17RUyDDhMIj533pO3Gk92HK0HgCDyDOvG5d7MobMKgSXyBMeZz6NZRNtvLjaTpOJg3DJONNfGa46WHkfaZxj2hK6mNz33z12++npOEkiO84SqEzR9i+c0jAKLxWNRJZxlHscrl/cfLDnNhhStT+h0LOsNHdZiePdyRG8kjmPZn3Y8ngeMopavu32KzBRqZFjXLteGCx6tE07WIUeriMjTuMMBrS4wtuSrb5xzXoSMw5737/ecFRFW9zxeRVRaMVYBXziFa+OCNOhoOrh7lrLMQrZiQdV6XBu6HK97skbwytGY3bQlDio+/2SL3WHJa8uS5/djtsSET9yraDqF77QkQU/ieVjHxfcbtB5xYzenqWMW64A4anhuZ8l55pOGHUoIfLfn+asL7l0mHJ1PSfwOS8d00LPIUkZphe85PLj0ORx3aB3h9Tnz3GF30DOvQaKwsiNvFFcGBtdvKGufUVyT1wGecqhkS2UMVeYSh4rnpzGn+Zx53fLhvZiTrKHRitOy4MbQ4Zl0ylurcy77hqN5Q0eHtopeWK6OBtycKp5kLgOvJ8oC3jprsRIcYflHbx7x8i3JduyxKFKeVjOktLx/eJ1/933bvHq+QimPkIgfu/eUpb3H3m6MQnN6mXB6WXFzO+I//PoDvnDS827eE7qGUCnOM7g2UfyeaxPqesLxrMNvV+xHUz5994zdOCIve8IdWM5crm+vGE1r1jVo4+GqjSn8ohD81U/dRTozaq24NW0o6oBVLbBUfPY45NbUUHeSyO04yV2KrmeU1MzyMasy4NogZOo4fOcfOuQ//sevMC97lDS4smMYNNyfpYSu4ca0QlvL07nHiwfnfP7xkMiDsnVJgxZH9qxri+fCogjpjUvXuxhrCd2OYQjQkTgRxno0JmPV9sxKn8fzGCUFytXsDXqyMqFpJK3xiTxDXVvGocPRvCNvBYOg46KS/PjnRmzvLZnGiqHf0TWSMJB89zfv80sXBV99p0Q4llXlUbQS1ynJe01tWoSCJC5oM8nlymPgbRgNbS8pakm4/Qw71wL2tyxaVawrxe7Q4SffWrOXOEzjGq0FVac5zSvOcp+6CZC245eerFnllmwBbaCYxJCMKpaV4PWjkNAVPLNt+MBuStMH5GWLkjEj6aJ7F2UN0gjeuChInN9cNPz/IN5Tj6Ivgdn2bxA//MM//GvPf6slYP/sn/2zX3v+Ez/xEwyHw1/7WWvNT//0T3Pjxo3f0nP6UqMhA0I0hryVPFnVxCG4riQdhURTh9NqxcBz8aWDZzuqRmJ6j0UWUK4Crl5ZcLTqGNRL3JXLk3nAH35xwj9595TJpGDge1S5ZFk7aLthNaZBzyjQ3JoYXMewHQvyOuHNpxG29qj7js+fJlzZ1bxx6tP1lkHg8OLBjNeeTHn5aknROihhkUKgrU/iaIa+oWkG/AfPXOO/+vw5ven53l96l63EYTYLub23wpqYonEQEvq1A1iUtAQuONJlXVliD66MLFVfkzgwiTqenqe00qHunI0psN8x8A15I1k3kitJT2Mcei1542SbReEihMRzDVcHJ7iuJO99hkHF7WHNp04OMMYlcHt0IKl7w7KCvHG4OSo5zRV3dmq08Xm48Lg7y7i1X3CR+Vysp6Rhx9Urm5S00C1Z1Q7LKmAU1khhuZJ2FJ2iaA1ZHWBROLJmFHTkrcescNDGsC4ieiMRWM6yiN7Ch6+d8+bZBG0FSnQIPKrOJXI6Gi3IGp+6VxgjNh6YtWLdOHhKcJn7LCoHJV2KBubZmHUV0HSWd08E1rEMQ4vFUHUu46hnVSiazuWF7YBFu2aiWpA9H7ma8XTl0xvJyTLhA4cxT7OCrNV0VjAILG5wzjRu2ZHwdJEQui2t2Jitv7PwGEcdeesTu5tgjptbGceLjjTsOUh3eHuZcbHSaCdnK1I4NmRnumBWW4JI4coeKS1dY8lblzhImSYrytqnQ7DuNdcGDVhBYwS5MdjWxdiOvKr4/TcVnzzxGAcCRM00MJwbhSs0TeMwDSSBKyhrj0UBo7Sllg4PZwHXhyWOUSSR5Mnc4+a2y1q0LOqOK+Euj5YZYaDpewdPGqaJQ2QspouJvJatKGDVZNya5GAFJ8t9diKfxs54sJL84CcN3/LciD/5oZv80sMlZ+Zt5rXDNKw5Wl/hPGupowytYFV5jOISieFg1PFkFhKIHaRsWfQ9t6cuP3n/iKxKubMVEkiXj71ZsjV0NybSs4QPXlsyTTKeLlPKxuNoAZO04cWdgklc8niRsCx9dtKawG2oWo+6Ubx/MuGnLzRHWcN/8pVXft117LLqePRkjFU1Bo9GtxwOCo4WKffmFffOj/nq/R2OunOyTnL/PKHTkv1RRVH5vPJwQt4LtqKWvUFOoy3go4Si6Qz7g55aO+wO5mSNj7AhF5cerRRcGTYM45pZNuT9V3LO1iWXRci68tgelLiuIQwsem25tqUQboWm5wMvnbGbbBrB68rl6WVC30vyQtG2LqOdjIvM5XQdk4QVN3ZLprFLVns4QrIqEprS5ajvub3T02toNAR+z0evjPnDtw740Xcv+K/vnvA9H7/LB29VHC9SZOtjO0sY9kxGGteVrEuXQVghERhjOF1J2t6QZ4bRuCSvI4QUHJ+mfPjZOR+6NuDtN2KaRnJ90jErBgwdRep3m6Tc0DLvDEUj8azlha2IT5wsUGWItJK/+/oF4VDz9LKnqBWO73Kx8Lm+v2QQOPR9xyS2DENJl3QUrUvsuJTGwR8LfJFQGYWUmzTa9xzvmUfR746a6jfcKPre7/1evud7vocf/uEfJoqiL8U5/ZbheDnicDsn8DeR46FrmBWCX3wrwQ0anuktiV/QG8sg6hnHJYnnE3mS0PWBmNQNUFLjpIK89gmkZTmLeegJzkqXzljung65Nl1xmkMclMSmY+A3JL7Lk1/8aco/8CdwHYHvevTaxVMOo1Syi2Ze+uylPfuDjtAr+ZWHHo+XDp6juLPVsh+ULGrFou4Z1hpPOSSexZMeIz/i3nnKza2Gr7sx482LlNRvOS8tUuTEKuGN0ylDv2e19LmR7PKZy5rzdcyzO2si1+e14322opZ13WO1oOw3jIfAs8i+R7k9j/MjZtkWz+3WfOrJlK88zLAY3C8mSD1ZpbiqZFkbBgEsq4DtuGUv7ZmXFtfpuZHkpH7L1xxWfP5kyukq5ObuCkdC0wnubJcMIw9jFW+cpxtPFqsZBZKrw57XzxKmUc+8dCh7yTgCTznspBmjoEcJxesn+8RBS9EoGt1ze9rwcDag6V1uTiu+7trFptg1kqfziMfzlKfLCCs6Pnz1lCfLBFcFXDs44rOPUwQKV2kE/10ihY+ygoNxQ915zHuPrNmYokdOR+MG1J3LQZiwFc45Xnlc5iGB23A4KbmzVTNbJ6SeptYCqTquTQs+uYq5ey6Y+AX7E0EQF7iOxZgAZSJuHKzIKoXndMwKxUXWMfYjtCm5yEKWhY8yDmXV803PnXCc+ZwsY6pWspXm1L0L7RAtMpq+pSgCdkdQNA2uBAfFrOqYNxESxbqGZ8Yt9+aSvHWxWjAJGvLGY5I0rOuQrchh3TYMBh2JdHmybtDaEDqah8UMz/W5LGqqsOVqtMXrixW/cp4Rqphb04jXzjW744JV5fGppxVvLxeM44ZO1qzrFNd3cUSIUB33Fis6BKUuuH8+4vnJNnsjycsHEf/y4SmryvB4VXFenfLyvuWto30cPD50GPFdX7nPTz084Q/fOmDV1uyNLA8LSKKWUTJDiIKffzDiVx9XRGKGlAJRL7l3XKFRdE3N3qTlSTNjkLggWlptyKoBSkpWlcMiU4x2FVbGrPISQcOiFDjK8uLeiqdrj5P1kMApuDFpOMlCms4SKJ+LZUoSNNgeiqXL93/rDcrOsOUn5JlmN13RaMsgqNiKXYyVCAHHa5cwKHnlaEC+ikimGiV7dgcVxjY8Xbm0eQB2Q9V+9DClUZr9qzW7ac+q8nl26nJUCFZ9S2t9zlcjlFAMkh5rJMJ6KOOTNz3zzkUqzSyH/+M33OEv/NRbnGfeprmuWv7wVx7z4GKLrVhgreSyl1SlR15vPAWmSY7WlsdLwaxt+ODhksfLIV0fULSK88zj6UXEdgqTYBMhHTuCTAkefPzH8G88i7P7IWZtyZO54PjS8mBW8pUvVURBybxymDolZ8WQ80VKrHyePcwRwvLGUcz7DlsQPpFnsdbSdw43x5sI6sTvSFyHp8uSty4blDIERuJYBxxLURh+YPGQkfwSsGN+hzWK/lV84zd+I//kn/wTRqPRv3Z8vV7z7d/+7fzMz/zMe7ret3/7twObCNrv+I7v+Ndec12XGzdu8Nf+2l97T9f8cqPpBK0ULMuQo5XD20XOZKvifOHyp14aY4zl7r1LjKhBOFS9pOg8tAmJlI8MFFtRgBKKQFm2hx3n1Pzs6SVJbFnkDqGrGcUtjxeK01XEnd2MvcGSOJC8dFgxKzwcaQhdiSsdlA9ppHClxGpB0CnqriVHUHcu0qlAlSSBz7xUrBtwlUYJTaclXRfwiaMlnid4btjR6JrDUc0n39rjwcxnGBi2kg5H9mwlLafrhLxxCT39ax6TTW9ZVTGOrDkczbE2otWSs5VL0UT4XoenOq4kJZUxBE7FUTvlbB2SVxvmYVG5uF7PqnQ4zSLWreClvRxH+ryzClDyi/HXqqftPVxH48iGuneodUriWxqtSIIe5VQss5RxCMeLIc/tr7FWkHoVsddwNakpQ8Wi3chX9tIMT2rCUFD1Dk+WDlIoZqVLMwIlLaPQsKw6xuOMj17bWB7sDyruzYb01uC73YYxLQQ7ScY43DAvA6cj9jdSorePXTxHI6xiWSmazmFv0NBbQ9VoXKlYtx5bQUjmrhFK0OjN+sOg5/wi5OEs4cqgJrMzPj9f4AU1o7jjtSdj8kqxkzTkrcf+pMR31qRdxOGk5dq45J2LiN44rGqfxO1YFB6DaUVvAk4XiqvjnGUxQMkaicMw6LAGtpOG4yxC9JpZU9H1imejKR/ejfjk5QNm6wBjNHd216xLn7JWtLZmkqy4Oc34zKMxrnJIwpq9AZxnMVZIQs9hXTZcmVQsypCmBS0bhlFHUQmGIVyUDjtRwWkWMg5qsjYg8DSBX+GUPlnpMI0F86bj4aJkd6vkfBVzsF3yeO0w9Dfm342uOBhaLsqevXHFIh9xXGQEPpvvRFXMapd5HiEArS03JhD6NctGc1kG+F7Lx95Y8bG31mhZ8adefpm//9o9grBme9QxaCJOcodaN3TaspVufIQ+/XBE30m8TrI7iujbiryR7Pq7PJd4lLbj+tDjw1uC47Ki6FfEngGhaTvF7WnJJx/4bA0bYheWteDRYshQw6pwORxn3BjVGwa2hh99tef2rkdea/7iz73OfhSy5cd86ErILz1d4TmGr34m4TNnJfPcwVOKWT7BlRB5HR4xQWRYnGyu5Y6yKNUgZU/WRCwrQSAUSSL4pfsTeg1l42P3Mt6/XxF7grztWVaKK4OGe5cu2u0wVcTNnUtGcUnZuWS1w43tNZGvGQSCNGhoe8XZLEJrxVcdRGTqhKcrMFbSGonrtKzzBFcobu4tOJsnDMKeVSWIwh5jIG9aHBUSOgIlW27vwDTpyYqavNtI7C9LybpSWGP5x28/5p/df0TfReyNc56cDlkVLqFj2BppYs9SVYow7HCkYFVbwrCjbQ1OsObBRcrQU9igxlOG3jpUteRw19K0CT/9dkvsgDQOZaNQWDSWRdlTdDCJNLPWsL50mQwcXnt7yXDb5T/8gGIvivjj75vyd19/F0HPOvdQfUwQa3aigL5XXBkGPM1K2m7DUsw7S9s5LOuNL67NBedLg+drTGfe+43xd1Cj6F/Fl6qm+g03in7wB3+Qe/fusbu7y40bN36dmfVnP/vZ39SJfDmwFXpshYpYKZRwmMQt2yH0teR4HbE7yPClodGCWR7zvv0z7l5IrqSGJIDL3OM0EzhuyTQqePDkGlEoaVqPZTnAERKHEGGg7y0Dx9JrcL2CeeFzc6fh5Atf4MYfqSl0xluzgGlk6TqXO9sNidsyKx1GUU/ogBQu14cxsSt4dmfJTqL54MGCR8sQKyQNFVJ4NCZg3Ui0kbSdz96gxFiHvUSzFYGSkkcryyszxUevPuFX7l0lFJrrQ49Pq5pZHuLuZ4wCwft2O96+mNCaHk9qemN4drvlbhdSyoaL3CeThhd3I64eFFgXWi3ojGYYapQAV0qa3udXHsT8sZefMk4NwwCKxmcS9XhOQY8hDXrOyiFFF/L8XsbdkxjP00jl4EmFI1wenfk8mrU0veJDV3NWVcTAFxykOffmAXUvGAaG1LOUnUPV9fTGReLyzM6CsnfI65BRoLk6WvKZR0O++taa0Om5KFPuXwZ0S0UXW+7srrjIQg7HPUo4bMUtb50NSbwabQTzLODn3j0k9CvGQYOrGq6NW+7NU/I6QOAwKzVXRobHy5B17bKuQlxVEsgY20cM/IIbuxnvnCbcPU0IgpbjdcQ4brg2qViUHrfGhidzhYhyWhyuJZamHqH8knXRslqOwNE8yXviIGcQxCS+QUvD5dLFVR6tqDhIFG9cjqhaB1cIdgabi36nJbFb8Ozuii88TUlizaqyDCPJR3aHPDwuCVxNLwpWdcRWYsj7Esk2UmYUVUTjaFK34XiRMHZjKrtiHAl6U5CbHoFi2SqcLif0W+aFJnQ1vtScNmfoesKV4ZAPHUT8/S+cEeKyGw54aW/FvUvJdFBQ9YrAgcmg5uruGlPeZFZVnOuCkWM5SPcYB4K8KzDdiF+4f85rZw23xjGLtiavXD79GJTo+fdf2OYP3tljVfXMlw7/t184Yn8gUFIwCARdb8jaFnDYDSIOwoS9ocNPv7Ug7gXWOvhKMFAuB6OWSrTcn3nc3FKsSh+LYDspWJQjkqjiteweEslWAoEL57nE9g1XRcsg2EzLAw8EDdpsGp0fOnD4o9depuw3SX4PLjv++iee8n3fdIM/83u2+ZdPFjiy5lcebFG3kpf2C8rG/eLkWfLxu1OmqeXaVgM4RF6HozpSv2BWDkn9js64tH2PDRtCKYl9CFxD1WnuZUfEwTkvbuWsqgnpVstiJalbj8C1pK6lQlI0kmUDq7an8Sq+++dfQ4uIQ/8GHsccLRvqXhE6Hm1nqTvJsrRcLHrefHzCdMdh1QxwVU9vG7rO52x5QOIoqs5HCkNdhuymm5ufNOg5XnkUXcPDpaFYnLH9wQ8T+BUHk5InqyE/+ade5q988jNcH2vWRcrjy5jZOmEgHcKBT1s7tK1LEvZkTU/c1whrQELewN1Tw86kYCdSuI7mn9/N6URH2To8u9uw5fdUjUtjBJPI4roB207HJ77Me9q/Tfi5n/s52vbX+zrVdc3HP/7x93w9YzYF582bN/n0pz/N1tbWe77GbzeM/Yh5H+HjMnIllXLQVYzoLT/8mWO+/YMDDscNj+aS53ZrXj/xKFuHogxZNx3oiokxCGHprcFxIIkhr2HgOewoyyipmUaGuhXE/pL9tMUITWdqfM8liXLemqX4smeYlKzLjQF8aSTv20q5KGc0RjMdlrgSxteXvLA759Fim1XtUvQVo7DneBXSaY+hHzCvC6ZpxyjUWLsZ0h1sV/heyZOlR9VvYrPzxkUKy6ryMaZFSosnFAfDCmMcDgeawO3AdlSth3DGLHPLnYOa0O2Q0nCySriWnvErjw4o65DLtc+tnRJrBWX3xdAP7XBluKDTkqGvCJyOqtVM4wZtWzy35MlszJP5mMB1OBx0TOMKR9a0OmFdBAihuMxThFa4quFkFRP7EaFnef0iptYuRau5OippOo+ig7Hfsqpc+h7yVmLtgNcdGAY1k6ChiB2O5i6vHg9wXIsvNEW3eV/gtoyjitCzDLwWJQ2rJsQaSdFaVqXDi5OUn3sj5vp2zqLcmH17jiWQmxtQYzuuT1tOFpJt1SKFIRUCV/V88sE2jZE4UvJ0FXBru2AStZyvfV57OmRvUDGKNMOopegUjhRklYeUELgdd88ShJA8OBuipGJ3sGQY1JzOEwZJw1C0PFwdcLTu8ZwJk0FB0ymGUU3oGaZxxS/fV4x8w6ySnBRLXr1cYugYRi3Pb+e8exlt1nYs1wY5nhQ8WqYMop7Aa7gzySl7yaO55dmdjNefpNRtRxDAnYGhaxsaVTANBmQVlK1kHPUkgUavXYoOBoGl1T1l7/P8wZJ3ZmPKzmUalVzk8HDm8+xORq1bro8jml7gNILWbiwq+kayXAd4CozXMQwtXR9Sdy29gZ1hiTGSphNIkXOxcHE9h+1Bw7JIqETHn/7AHj9z9ISPPf08rYlYLQJuTzVnzYKDkY/bDclMSdUJpknDwRCOVzE3E4835iXv24v5ljtT/rOffBch4KMHQ/6b1465ulOzPYkRdcvhqKTVivMipDcdL+yvWFY+RaM4WsUUlc8oWoPRvHY85p3TMVVrub5t+fqvGfHxp8f8jV/NuTkO2J/GOEryqydrHq8qHqwylOpoG5c2FvgOTJOS3cGK3m4zy3M+9lbNwDNcm25qxUURgLWsCkgCeGmccmnXXJsW1K3LZx9E1J3hovC4yF3SsGErNlwWIb7bEvgtvis4zhwaE+E6UHaCVAbsDzqGYc7RakAgnc3vLTacVkuEZ4g8wTz3KbueRR4wHXRYsxkaB6EmCmtWtWR/2DAvIQkMEocb0xX3LlLSADot8HyNZxWNBoTGIJgMVnxgvOB4NaXvax5dDNgfW7LSZ3dYEns9W2nNG49jxkFD3Spcr2BdO0wnBa8fR+wMLZHX4boSl5Q4kFSlQ+pvvLLurWqGEXRSc38h2Bk1zPOEWeHxzP7mewbLIBZ8w5VtXvUXNL3PK6c5d63BFzXSOhga3C82ZEpdMit6tIE//aEr/JVPvEFVJMyajtiHUWSJo4abqcaTPtO4pmx8bk7T/39d9T8SX6qa6jfcKPrvpnK/E6CchlILXj0z7KU9V6cZsau5vzA8eJiyO60IYw/H+kxCRaKm7IcBnrBoPA4GGiUcHs0HyHhBYTq2/CGKmM886OjdnHWpSdyAlo7U7/Ean9bTaBMCLtu7U66mPr0dYUXHwKsRfs+rx2NCryAO5rjS4rkRrnCRMmUQCEahZCfp8KVH5Lg4NqLpDGXbo+KWvA14MPcRskOolsvSYRxqQiXxHI/rA4dRkNEa2ButuMwH/Ni9M4xSNFqjLXjKknqSso0xJuO1Mwdh4MbAJxERnWwpakltJef1epPK9SKcrhPqTnB1f815luBKixDwTc9dsD9oeTAbkbgdUoKrLJHfc3N0wbzawlMb+ZwUULRwe2fGdiQZh4KHs5S8c3Fsy/4oZyetwCouCo/ENwSe4Hgd0GsYRZrdqGcrtiReyduXEVdHDfdmU6Zxx15akviCG1sFz+9c8rmjLX76rX2e3V5xmJa8VqS827nspiWutNy7HICFK2lDdnEVt+u5LBsusoDno4p1ndCZntcqH4lBWE1VugjH4c0nAZHX47gdrqP5d64+wycerzBqwdVRhRKCw3G7STMwhou1S68tNyYZb1+M2B1kSDei7UO+4doer5xf0Gi4Hh1yM1F84SynbHMGQU/ebGRE062OzzwOGMctUbiia2MkitidczHbxqo1vq+JjMNR4WO8CqkEUllGbsRCrtgbCl59qrkShBjPABVrryPXhqILUW7J1PcRTs6y8rACegO5zQm8jUmktS67o4xnB1NCx+eyANMHmwIaAdInciWVaTldCz5tLeOgw9iWdxYuTy8H+IHkZN3S9YJlHiPFJt40609obUrf+MwKhSN6hqR4geGPvDTlpx41eLOWmb4kCDRfdyVjXvo0veaXLxZ8/OxdhmqPs7ml0i13F4Y0qums5vq4JnAtgbRcqkteu6wYxDv8wRdH/L7phH9xWnFc1lwdBNwaxfzjdwv2Ry3rKqDTctMYqxU7wxnD0ON8PUIKjZIdvZVcGRYYC4syYhx2CN+wFTe8ejrgycJjvRrxNQdTPnec86P3LzgqMiJf8/w0BOBbbu3y4++e0sYnXK4k28+3NJ1D2TkcrUJmucMv/qkP8vGnl/yD107peoW2lrKFRTngfB0jhOVrb11w92yIblyigSF2DaHbs5Qwr0v2xwUPZnvsxZJKe1zbKtFWow0IK5Gqo2jAyhLHbfj6G2vunx2isDxeNYyspOw8RmGLK0sucpcHM5fzeYd2NTd3BGPPoVjE9K4GFeLIHlfUrGuJEJp1o3nrvGNv2nIzbXEVCKV556nHte2CyTd9Bf7+AQNP4QmfLTfipx6dMgpcErdnVVqeLjWV0ZtGmHXoCsXCSP7Wt36Qj33uNYIdQdc5YA2RB3emEtlPSZyeQNVcn2oucp/DFAaeoe0lnWk4zRWVrkgDjfCL93yPEkIh3iNGkRC/PaZfr7766q89f+ONNzg9Pf21n7XWfOxjH+PKlV8vP3iv8ODBgy/ZZ/92Q20aXjmB3XgTz971Bs8KUldikTxc1OzGPlK2SOuxFfmc9ZLcQupbyr4D0bM1qDGm42KeUpqOeSlwHMHANwgLoQuT0KXtXRxZoqSDKyrWjUunXbaSkiezkKqznGSaUdzheZLbo4BKtkykQSmJ61jGkSR2XKwVRK7iteMBHzzMuSxcwPKhvYRfPV+RBi2uY3GU4Ow0Zjqs8ByJ62SMos0gYisu+IVZQtWEdJEg8DWddpFCgOw5zQZclB7r0qduXb755lV+pH5A7Glc17JuJVtRy/3lGIuhaF1M5fHuqeRw0tBbmOeSb31pjicF60YxChq0VYyCGl+1FG3IrFBcmxScrlI67fBk5ZH4FZfape0tH75a8LmnHpGqQcS88XRA1zsErsRVktjPGAY900izn7a0WhJ6PU9XPq+dDJC4jKOSReUxrDz204aJX3DhBqS+ZpQ23JsNib2ewO0oWoErHVa1S+x1TENBqxWdBSMEoSsZRw13LwXP7pcMwp5Z7tH1CiUMLx/OKVqfde1ytIqQqsYYl6204Wwd0PYSBDhSMw4b5oXPsvI5WiUkXsdHr89Iwo7I6YndnqvDinnto2g5zSSPLwecriM8pRnEPUUlOF0k7KQ1t/bWPDxNqLOIcy0Yhx6CjotVwJ2djNDRmyCHzmUU9Swzn+m4YxRUVNrh6qTicFTxZL6DJ1twNbenax7MU7peEQcd06gj9jvSoOXp+RBXCi4zn/NSE/kWYQ2+yKhRZJUiaxsiN2YUaM4Kj0Bprk8y7l0mnGYusSsYRQXrOuC5nRWvH40Q1iEQDkFoeXg5ZG96gTEVWbHFM0nDeZHjSI8rW5rAyTia73FnEvHWcsmAiDSxtGSghwi1QkgHt93mYDQj9HMezoYkcUHq+Pzc0SNOso7b2w0HU8288LhoJDuDnnWjOVv1PH9lhRSWa6MWO6hInRGrOqNsLZ8/Lrh8ruPlvYi2k8S+4ltvX2FZac77E04XIbtpQ9sbRlGzSWytJI7qORy2vHM6ZJk5iNjwvivZF8NPWt46G3ORezxd3EcKSRIJnhYF785CPnW6Zjos8ZyOQSjpjWKpDYHTY5AcLTxW9WATnFMbrk4qUJJ54fB0GYJxWa09lHDwhcunnqz50199jdfzNwlcQ+RYLrMEKVyEaHhfpOmMQooea0K+6uaKty98TpcRt7bqLxraS4pWkWeWu2cxs1XMiwdrrk4KMtcj8FrOCpfY1bxbesRBQxpA38M4abhYJXiuJvA6ktCwqlxO1pYPDiS7SUXdCQIHFkWA5zZU3ebvPPAzTlc+u4OSNCo4Xo3pupC6EwwDy61xxJunm0CT3WGJowpUwIaNLixvPxgxCV22pjnXJxC70PWGYdyDVZzVhqoWRNqyqnvOyp7eOvhCEkcdApc//eF9/t4XTnBsTN8JQukQelC0BuVodAfnZc2jc83QF6wa8ELNnThEhQUVay5XY5TUfNePP2YnDUlHHde8nqr2CXxD24cMPI2SmraHvFIcpN57vi++V3XV75aa6jfcKPqe7/me3/Riv90wdHw8kRB6PX2reeVxTNNLLjKHb3n5lJ/6wpRveGHF5ToicC3nhea0zEFq6nrEi7sNvqM4SIb4WHYin3mpqWXL0A8ptMPW1pqmNzy6DLi1U3J+HvLKiWUYNtyeWv79/9P/krzryBqPQEkGfo8QDlsxDH2HNHT41SOfwHXYjQWajlWtmNctd7bXIBVSSgbBZkqkZM2bFzHzLGZVe7jS0JiWrK2IfYnF4EnYCiH1HX71KNncmClN4LccbDVktcOy3kgwYtdykFhGoWZe9Zzla165sDhxy6BX5G1IVoFwaiZbOUXroaRgGGjOcpd3ZiF7aY3vWq4MO5TwNikc85hV5TKJag7iDmsDduKaJ6sAJXsezBL6zqVqE3ynYF45KNUxjBusddiON3G789LQaZ/IDTFm84f7ZOlStZJx0GNbTei4KOlzkUPTt4wCzXkecbIWTOOWB/OULxwNcGTHRRZQlD6Zq7izn+Eoy5vnAZOo4TyP6RoXT8F+FHB7lPKLD2fc2rFM4hptwHcMnRasKsNWGHDWlAwHm9S3deWhe8NPPHjAwXTNYdiwbhya3qE1cGsr4zSLGUSGYdSTtT6eC7V2GMc1k8Dy88cPaRqH33t1n1lpOclKSjFjEATUvUPq+ky3ch7NPfYmNUOvZxC2OO0hs7qncscUpuLOTsEiCzkuII1KFpWk7sAIiRA1Qge0ekElFJ0pGXkTCluQlz6XasXBVs+l9TkqG0JP0PSKWeFza9pQacuDs4DYs/RSM1+PmAWW54YD2n5NEiryVrCbFqybnsU6oWgMA1/wNF9zkETk/YLaCMoeJmrIfL2DUGuiUcsgqDcm6cOSTmd8th+xbnxmdcODouOFPcub5xVftb/DR/csSmn+7qt3yWtL2UkcAp6eD4gdhUhzKtUzKyWjQc3VScGz2wVZ4/H60zHLMiKJM27uLnhradkJHP6biwoZHnJeLrk3L/m41Vzd7TjPIqrWYX+UMYkzBIpH85S6c3CdfjMxrRVfef0pqW95styiM5Kyq7k9mdNoF2E9Ui9BR7AzCPhj79vij71vw3yo+p687X/t+vXo0jKrdhDSZZYHDIKSvbTi/syj0z7/0//6NbwuYDQRTAcVZStYVQF157CVtgg6Pnc04nQZoYRi6PbsD0o6q5lVmqIzGD3gMAVrN2lIe4OMvAeJ4HgZsWxAlpKr1+Y4yqdtt7iz01A2mtOVpco6eqt5/5U5k1ATei0/8LM3SaKAm7uCxFWMgh53a8WigrPcodKwKDSnmcMz+wvuPonYnpRsRQ5d57MqPK6PK2LHkvgBT94piG4OaXRGXrS8fdGCd8L+0OK4Jb3sWWnB9bHmH3zb13KRt/znn3zMcdnydz/3hG96UdPLgGXnoXXPuq2YVRrTKFSoGcQtNT2vnUW4UhL4EgofGbV0teCZg566SfHFrzdI/jeGdDaP9+Sz3puP+TfFBz/4QYQQCCH4xm/8xl/3ehiG/I2/8Tfe0zV/8Ad/kD/zZ/4MQRDwgz/4g/+D7/3O7/zO93TtLydWZcLesOLqIEF0Cb90NOMrri94/cEOxzOXRdfw/lst22lL1TsooUh9SV0pHBsTuj26b8BRLIseVUm0L5Fuw/FZQHBN84UTRRpXVL3PPHfYHsDQV0jhIayDsA5Yh2nSshX35I3DRSG4OA/4vz+8IBoEXJtYHGl4bm/FJOrxHReFInY3JtEDr2ZeuCjV8f++94AXdjSx3+MKi5AOz09H1Kqh6iWT0OPZ7TOmkeb+fEBnDVkjGUQKR/ZYA4NAkDWGUHRM45a88ui05VPHC6xRlB3Mi4imN2Qt9NpnO2lp2orW98lKl3pQIoXiuf0lZ7nDy7s9ShiernyUlHTGEHs5H94v6fQ+1rRUbYs1gk8/Svnaqyd0xufOtOXpctOASaKar7hzjCd97p9HvDg+5I3FEz5yXXNl2BA4UPcw8QtK7XFRDFgUPpOk5s3jCWnUsq5rqk7RGJ/Ub5lLH9+xvLR/Sd4Em32ogbPMZdRJtqOWp+uARjusKo+DJCdSHc4I7l1a6l5wcjYELONByaPLhFHQ4jqGspWUjcvV8RpLw/6g4AMHay7zjYfcneEej+YFiVeybiSJ8pkGmmvTnLKVzKuAqnMZhjXGWB7OUrAuZyuHMGiJfY3sxgSq53B3zY3pgrtnY1aNw9RpeGEX3rlUjOOK/a2eq4OCty4GbMUNZ3lEj+BgK8dqQex3aLtJ2LpcbDOvWyK35840Z135XJ8UvHk+xJGWTisc1fF0HRG4Bm06OusxjRt8XzOKWspWoZTieOXiO5o0WSOkIfAURetQ9Q7DoMVYQVH6bKVqM+BqXK5MCx6dJ0ySBtd1mVqJNAorDK5fsC59FIqs2kj/Vzbl+t4p9y9TDqKI9+8lPLr0eX3Zkvg9qyql7qDpK8IyYJoYmlYgFbiqYOA1HI6g6SSzPEQbQeS3eE7LQFoCp2WeByRBy7uXIdoKdtNTxoOGWbXNzVHKKHA4nPZ87myNzab8rz58wE+9s+D1I4/dUNC0LVrWeJ2g6wyrzsUTPg9nijhouVz6YDbDn8Bt6LQLSJ4fT3n1ckbTKzynQUqHx+0pe5MeY2GaaGK/QtCBnRJ5MA5rztYThqHGd+qNlFLBg4uIF/Y7dgeaooYHlwF9NWaYwrM7IdtxxOzMp+vh6l5O7BmEaOj0xh/SUwZjXVy5kfA/mcdIq3j3MsBVlqwKKGqH/dTjm3Z3+S+PjpgViisJnBeaainYGVimccsL+zMqLXBsTdW15NmYoQoQ3jmp36Ok5XRt+OC1gkngc7xyuTlpCRyYLzy80OAKSa9qOt0zjj2U1KzLmGkkWRQWhaVtJZEIqfucyFiypqfRcHO74niR4Lod3/KSQ1l7rLSi6T18v6PUAjAbI+uFxDEQdBbl1Dy3D0NXElWay+UEf6vl86crEk+QBpqy1QgBnYWffHvGcFzjSp+TvKPuHL7txQk/8dmSuhNc0pA6NYGb8jVXY06yioFSDALFSV7jSklvBatmM8hVjiXwMvLWZV4k/IOzk/d+Y3yv6qrfJTXVb/o39ZnPfIa7d+8ihODFF1/kQx/60G/6JL5cWNWGy7mHCTpKbYgcl0ng4inJwE14ccdn4oeoBBwpaLsh10cld08lN7YXRJ7g6WLCt1y/yg/8wjGztmLVgWt7Ek9ijUvs+gTSwQsFqafYCjcU1Ccrh2a44Ef+/F/nW/7y9zMrJYae7dGCsR/gO4rIg8QNmQYpTWe5XCvWRcDV8Zpb04K8qzivNOdFgO+0VK1Db0JMH5EXG82sUYYnFxP+6DOfopQC342Z1wMO0w7bOaSey5PFJk2p0YrXn46Jg4p5CcY2DDyBKxUKj4PU4enS4XhpeWGnZi8SrCsPox1cvyUOS4ZhSdtHTMKesyxmGNbspxkIgSsFWR1TtS7rWlG0MEk0RecwCX1mtc+yUoRuQd7EHIxatiPBug7RtmHVWP7Y+x/zk29ep259puECJS2LSvIVBwUnmc/JkwmjwKPTPffmLh+9WtBoHwGcZymx1/GrjxNubeVo43GRJ3S9wFqXrIbl2uXb7hzz6npC1XgEMUSe4DIPaXvDw7MB/4sPjAl9wS+evMU3fjgjdFqujwryVvLG2RSB5LKI+JprLeYMVrVEoDkYr9lNK6wVxIHFGMnZOmUSNUSuYVm6HM0Deq1YiwDltOymGVJKQqfjorD4SnJtt2JWT3ntcoXEpexd0rDlxsjHyDkXmWIUNawrD8/5ImW/O8PaCKMEL+wveDKP0EaxP5A8unD5ylunFK2P6V0eZpbUV5StT9MpCivZ9lPeOi15drck8HtWa59nttfcmw1wRU/kC1zHUncxWQmus0mxublT8PBScbR2eNou6I2k6XpOFoa+97izFVK2KwZhwHaQcG9dcpSVdDagbxXXBwNUNCMyKdf3Ms7n2/hBwv35goHncGVYYXqXkR9xbdSxbNccz0P+3vwRz09i/vdfc5u/88lTbqRbuEFN5Dc8ON3ELud1ydo29Mah0wrfUeRVxOefhtycLgmDhoN0TCwG/Oy7OTe3AtraEqH55Scr/qOPHDAQAX/tE0+wSjGKWh6e+2wlGkcI0qCFccV5FnFx5qNUw/N7BduJ5N5lQqMFq9Ln6mjJohUEStMbl5EzoOpc/t5nL/kjz+4yCjeX6U3S4ea5tZb37SXcO/G4OVnguS3nuUMfasrWJXQUbWj51velfPXhFf72K29grWRdBbS9IJUNRlhefXdM5Du4Hij1RdaBrJiVHs/vWLp+gO9CZySJV1NrjSclWeNydJRS203SSeIFOMondCD0WqClNhWnS8u1kWRRhhjbc3Q0IXVCvv3DE542R2B7DD1BkGFsj2uGdG3M2ydD+h5++XIfTyh2fcPA6xDWIBBo09IYi+wtn/uxn+DOzT/ESakYxxqnd3AE7KU5yyqhbz0+cuDgqwG/fHzONPBZdi0necHjdcFHb9TMs5SzhWCloRewO9A4UU/owuky5db2kvfvupg2IA47snZjkhv5GzlTp3sK8yXQ0v8OxIMHD7DWcuvWLT71qU+xvb39a695nsfOzg5KvUe+TF/ED/zAD/An/sSfIAgCfuAHfuD/5/uEEL+jGkXogJ0oRLJpNN+ZCDxcAk/yXd+0x3/+y09ZloooEEhRcX+WMopL5o3AyQbI0KJchzTo2YocZG/RwmUYKs47SbEI+U+/apt/er/BkZrtUc2jleZrb+QUzSbavGh9Br5H4hrmRchu7NCajoWG3Z2Gk0zyVbce8/E3D3FUTezVgKLTiq6XuDLibG2JvQ1r2FEFyJ5r45aro5KfffeAK0mFaSWpX1Abl6KNgBJjoelA01G1ktCVJEHDKOhJPENvGh4sUs7WCVthw1l3Rm8DjAVP9YwDg5QtioZ1PUJrSS8MgSvJa5dRmvM1V2eEbs9lEaKtx/HaZyddo4Sg7D2GQc9e2nJeBFh6orDnQ9fWPFgN2Ik67p4P+cJJSF5LXtzXzAuXzvT4rubV2VOMtVyWKY4SSNFxbdjwYDUha+HV45ittGUU9hwOznjzbELVOcRuS9FvZHe7aYEOJKd5hKCn0pJOu/iuYRhWaCM5L0IQGlf1eA5E/sYTKgk26V0HowveOZsQOZCkNRelx9dev6TTitAz3BitGYaGqgFj4bJMiAPNu6tzCqPQ2mErSLDJAqzldOVzddSwqi3npQv03D0dczqPuLPb88LVOcfzmKx2+OCVJyR+y6z0mRUug6BGTXuCHM4qxdc+e8npOmQ/rfjc0Zhnt5Ys2wBHGoQ2YHyKBqaRR1WHZJnl2tZ9JjLhMnN563SLrbjmKA8AQVZJrk9bPvNwmw/emCENnBUp21GG5wt8RzIIDNoqjmYO13dqXGm5yD3WTc/J2mV3ZClbyzTtkU7LVtISSIdSKxAbf6hhrDBostawHV+yLkOM7EBokjgjNikDBdAztlc4uwxR0nBS59w0kkKUBK5lnOas2whfKT4ySXhanCEQ1LVid1JhxEbK53mGeekzCDqK2iVwDNcnLbPcUjoSA6RhT99LxmHN/cuU3sSkUU9hVvzQq0+oTcEs63lQX/LWxYhZ0eGGa1AdjtMTOobQa5lEPW4hKRswViBxUcriOprU61nVEalfsygdjhZrtmKLp2ocBZdrl9CrNn5lSB5eKMax5NrUIKTh4dyj7feResjd2ZIPXasIHQnCMIlLPv94EwiThjWm98majko0fHBni3cvG56eDxkPltzcqljXHtI6jELDuu25MS4ouzG+sxkajzyFFT11p7HW5dZgwq9mFV8x3eWTRyu6SvBoFpDWLl1suTYuSQKDI1ssPifzIdoKbm+XdI1gWfVsJ5aud3l37vLGmUPZxvTjlsjrqdqQWeFjjGQkR+DNaVWJlA4XqxBXbt6jbMfZIkAYQa0lP/HWktaAlQ4necD1MThiYxNQtwFXr4z4XF5tlCKOptUlszoAqYjCEi1dBsJhdeHwgecqqjak7y0Yie9D3UheLQoCZWlNgec3pKQ8ugxQUY+nPJqVz/O3cvLE8iQrmA4tQWRZVgZtDE2juDWIeeV0wX7q02vB/XPBy9MJt/YKXr9U3Nluma0TqlrR9z4fuFry7oP3nlH0Ow1f6prqN9woOj8/54//8T/Oz/3czzEajbDWslqt+P2///fzD//hP/zXTvC3O7Zji1GSTvjkFw4X2mAGgiSAeQm9yjkq1kBNKHzOcsntEKaxRyAier2ROnz88SXPHcB5ZXj52jmzfMBB0vOr97Z4MJcs65bb2zUjLQhdNp46SYiHYTAeU69KBtEYqRzeOBnw1TcuyLqA65OSunfotCFvXdrOR0oXXwUMVcBe0tCYnCRY8s7lHlWjuMhDtIayFQgUAx8iX/Hq+R2e5i0fPDxn6GWclQGd9kh9Q+BIUBZPCq5vVcR+jQGEbFl2LoGSSAFDX3JnCvcuJVvpOffOrtJ2koNJjedoBkGLpWZ/0HErbRFCsSgdlNAE3oZVMw7BVZKzzGVZSeKywxEu14eaUMEw0JzkgvddOebmWJB40GqYlZC3Du/OIxynR6qe42xjkjgJK3xnjRVDtpMOYRXzyuV07vKmZzFopNx00ptecWerxODQac0wymm6AGs8YsenaGrun6fUjsF1ahK/RVqft44ThkHHrYMZ//RegRGWvUFP0UjeON7h3mXP3iAjcDqU0nz4esGqc9lKA65PW57byrg3T8gbn7JT9DRIeqoGiHrqJqTrQ3wJz+5UzMsNFXxVhuwPK7SWDDxN3TqczLYozZrGWNA9X7G7xQevhPzjdx4SOQ4XRcDVcc7uqGRZBnSdQlvDNF3w8sGCX364zapwORhaFlXH9a2aTz/cYXvY4CpwPXCDBUopequRGHxvziQtuVi47G9LlGswVjOKG57OI7aTzaYrgo5hUlDohLpRZFVKHBvO8w7ZK77yVsGrT1Ou77TkXc/bC8hqhzipmdc1sduz1PD8/prTxZC8L1nMBTcmBSdrD+UtWbSWVRaSTCynecz2wEebgtNc0yGZZx57I8OjS8v/+kfvYuj45jvbPJnd4KJ7Sic7vu5WyLvnLo/XLp7X4zkWa+FwsubuScpPv3mA0pbHreT2jmCSKl7Yjni8bHn/eIgKAkzvEMaKrz+c8vpFTumeEzoWkFhcfGXZSUteOYoZJQ2R21K2Do+XA86yGN+tOF5rPnAIt0eWealQaN54rMlNz//hG65yd5bxY/efsB+mXE1SvvnWiD/+336ev/4HnmdvsDHke5wLHswUh6MOa4KNqWdomC88Xt4a8oOfeYC2ilRZpLT4XovrbvyMwghcrXCkpcfwSw9TzkuXQHocKcWVGytujgrOS4W2LWXlMolbys4jGjdkpyHjgeV9ezVlt0kUPMkEvn/B4XbIpBry/v0azxG0fcDj2YBhIDZeUqpkVkCpDedLje8oEuWx6ANSV/L8zTl3n6ZMoxYQrBpLT0OtGwrbgvAIPXCV4fZOxTBsCFSAc0Pg+QqtFWWvebJuELQYI/iRuw8oO8uDGRyMCi7mU+aZT+QIrmx3pJViK/FoGg/P1yA0q1wzqzJOs4BUCapCsMwMs6YnHlQoZZHW2ZjcvtcQavN4Tz7rtwdN+vr168B/7xn0W4F/VW72u0l69sp9n9vPWOZtxsVKcjC0CLlp7tbG8vwkxvN6QhkxK2K2kwasi7Y9hawYW4fHM8tkkLEdW/raYbEK2J9YAl/iKcuvHC1I3YCtZI3vWlZ1x0kOY6/HdVrqWqO6kMjrOFkPGXqW3vh864sjPrvocIThZB0wShuk1Dxa96Suw7xwWVcuZQfvj0dIWxO7JYnv0eieX7i3RewZlOy5EBWLSvHR6zWnGfR9RGlhXjj4rmBdayK/IXAlid/iOx0BgsvSZTfN2UtaTp8klKpGyo3cbBKVRG5P4GkezEOKLzJIemMZDkuaVvH2ScTDg4j379QUrUfdK8ZRx6xw+cLpkBd3W84zw0kW0nYOH756wWsnKca6m9CMlcOThYsxDhcXAx5vNSzKgEUJoa956aDidO1Tdz3TqMWRhqHX8kYtuMgDtBa4AkZhS+y1fO2tEwIX8t7B7TV567PtdTwuHSSaJLBc5oKyFYyjDiklWas4HFbEbo+vDMd5sGHBtCFGG9LQEDhfNPX3a4x1cCW8cjLm/fsLnttek7otnjJkpc8bZ2MSf8PWqqUC63KxjHi09tm/4qGNxHMgb3uGYcfJKuDtYkzdOeyMS8KwZpF7jOKOZ9OMVlt2BjWRr8kbRa/BaIk1htjXXKwCro4qHs1Dbm+tqY3LO2dDhEkomo7ntgNeXTfcW7YEwHNXTjlvIpaZT9mCkoKn85RKt1yuErbShtOVz7WtHGst53nEJCopOpdRaDhZeoxCQVG7GKG4XDtYYfFVT4O/MTHuYBxrtuKKo9k2ranQjiXx600CnZF4bs12XHB/NiTyOvLao6vG4M7JOg/1Renb6SJmoS8oKp84rOmRPFie4QWaXSWQYvNdBm7N40XIaRXieYZnDzKmaUNvoO8FK+sipSGrN6nLgVvw8Xe3uD4puMh9xmHL8dJn5PWUjo9Es5u2LAqf2InwUVyce3Sm5fZezturMz513JIMJMo42LAnbyTDENa1Q+A2PJyP0a3D6dLnYJrhOZZF5TGOG05WIcNgIxHdG5a8ez5kJ62J/AolIfYN68oihIuxivPMZzut6bTg0XlCrTNePKxpe5/QKxkEGl+FFE3H04UDswlSCa7uZVysfT5ztObtVUfdebjC5XStCN2OsouJvJKrwwZfyo2UShpcxyMNDPdPA9oq4g8+N+H+SYNp4EfvHbGqLX/+m3b55MVreLli1QUMI0PZbuRjWZ1we3vBr9zd43CoqU1D1jkMeoe6kxydRuykFbOzActTy+GVkjrWXOYOcSz433zVVf7LN455bmdB06YsC01ehbRNwKmRVEWAJxXrtqaymmGgmC8ddrZ7rEl5fOmgEFirWFSWh4uO7aEmSNa8de6TuFA0Dp2JuTlRjIOaWe7R6wBhN/WW2wkaGu4dC9IBqCIk0xW+GiCsz4euZVyuExwpybShNz69ETxeljjKsK4lTzNNJyB0JP/XH1+BHTCbWr7z6/bQvAnaI/QyYpFg641a43i9uQdF5pyXv43rqt8lNdVvuFH05/7cn2O9XvP666/zwgsvABtN3Hd8x3fwnd/5nfzIj/zIe36SXyoId02RbRGFkv2hwnN6HFewM+yYhi7TSLMzMFyUFReZBOmgZM+V0YJXTz2g5/GqR/QN01HLNFakXoAJNwkU17YMvq95shS0PfTUXJ1WvHM+RLktb817nvvmr6RuLcOBYhI6+F6KJzr2k4bE62ikZVG6vH3p8vBE8cxBzd7QYK1CyYC+S7iWZHzmsbsxlq48Aq9he3rB28dTxgND4Fqk8Hlxy+DZkM6UJJ4mcHuWTcreoCFvXHZVzbIJyKuIo0WErzRff+OCDsM4tAgkXRezH7vcn3t89onPs9s9id9RNC7ChhhT8DjTnGVbaOOjjSBvA5AV9xYuN0cK7GZzE0LxzkVM1mj20o3nySjo2U8ln3s65WINN6Y1AkPodHTdiLypMRTc3poTuT7jQNNbTdX7HC0SIq+gMQ5N63M9GXIyGyDcc1rjMM9dBqFL4hvWlUPZCbS1DMKa2zs9bx9v88KVFc5akA46Il/zxklM3Xuk0YaxpZTlpcOMUdDw2ccjLvMEbSSuhZefW3FRRDyYRcRuxZM8oGwcxrs5nztJSTzDonA4WqTEviZ0e25MN8ZwjmvR2qKF5lMPE1ypsViu7ZQUDQwiS9do7mwv+cTbHo63kf/UWvHOYsEgsoyClkpLro0yrm+VvHY8YitpOG4C7uzmCOD+IkZJzTO7NcOo57rqcaWmsYIP7F3w2snNzbVPuiRqzThwibyKdZ+zXB/guTWusihfbzS+vcR3DIvaYye2nKwkUkTc3lrzWj3ZFNdyo+vHKXh7Zrmzd8aqEvTVkHHcsSodVtkEP5qx5x3QtWccz4aMfI9CZfhG8exwysePDFJ0SGmJ/Ja6g/uzBFdURK4mCDb+GwcDl0ZLWlHx7724w1uLFT/78ISDOOTJ+YhRpHhCA47m99xIiYTLF85ztC1YVx6TWGOs5mLps+sr3plXTIct//OX9vnk0zX/6FNvEw6mvLHOqWzG/qBnd7rFaa14/uqK/YFmXbkcrx2e3cp4dnfJuoqou5C8gUdzByzc2DJUjcc///xVbkwa9gclqwa+48O7vDEr+S8+84Ci8Pi9N1J2opBfebLmxZ2QYWD43NmSz55dIqRmWRlWncNXXsuYhoo0WHG6dnh31vPnf/YNJq7P9ZFkHFUUreA073mcBXxoVxMoh2sjj3nVsT1ckpqM0I+5WEwZ+CVNr8g76HSHVD07iWFVRUSeZRy1rIKAQdzRakESlMwqzXHuMY0DXphKsD1CKMpGUfU9Z5kgdVp+4v4pd65ofL/nnaMB1ycuwoSgfAahoWw0qDWZkdyaVJwuUiK3YRKuWZYh10YhRe1zOKz5d7/3L5JhQXQUvSTXHaHW3JtbonBN3o64PbW8sLOg6V0uMp+hL6nqLZKpRBvoTMtp2XCeKZQy9K3iyqDkdK3wwzWtUWwlCl+BNgqRCHa2FLEfEjgG4yuK6su8of1bhu///u9nd3eXP/kn/+S/dvyHfuiHuLi44Lu/+7u/JOv+5b/8l/mu7/quX5faWlUVf/Wv/lX+0l/6S1+Sdb8cONypeWF8hdfPK0bxEoPPsjE8XHZ84SKnKAWZ2zOKLdp4+G6PMC6HqcdKSW7HCdEoR/qbQY+b9FQrn8tSUeke7XQ8WjmMY0EaQFYF7MaSZR3wON9ICvK2IhhWNFqBzFk2HmUdk+73iCVEjkPbJlybVASOQ9crPn/pY+l5sogIXNgLfXzVE7o9UkgEAbe2lxjj8u5lzLddu83/6/XHrGuPadBynMX0xsOKmmenCx6fXGURdsS+QVuHsnU4GFSUXUDXwLzShK4lUIZrkzm3JjWx21FpQ9Z4jEODNg1Nq9hKexaFJAw6bu40/OL9fUbROUpIwDKNckJH8TiwPLiIKdqAVmvGSU6nHb7qes40arFWcF647A8zlKNwZMFnHo2ZpgXG+hwOK25PF3z0sOPN8wRfaa4mJbXZBBNEvsMzOzWBWzEvXWZFzCCssFWPNRGPlimerPnIYIUJFVcHBUd5yCDQDAPNvFTkjct2IniykrywvST1O8baY1UZHNkhZEDVaoZBg5AGKR2mYUNWK7SRXOYhReMSeA0Cyar2sAh6bRBSEPk9kdcTCXjumSk/83DNs/trPrw/47IKOFpFvLi75iwLyCqHcbQZ/O2kNY6wGCwSwSxzWDYBA6/n2qTgovARDqwrh2Ha8GgeMYqaTcqchFHUsSh6vuIwYlY1DAcVjurANtw9TbE9SCkI/Y6u2mLZrfBcxThuGSYVl3nAM7s95+uYQdCwrgJsF3Iv6xlELXnt4SjBKO4YeZrT3EcKTdXDdlrjuzAMGurO4er0kkeXezw+gxev1TjSontJ5HVYqUiDlvPCYzooOe9d8npAbjKcqOWiDFFKspeWnEqLQNB3DvPaZ0RLqwW9FmylDavVFNcFtzY0LdSd4P5FjEDw/E6FES1559Bbw7yCRZVu/LSM2KQ1VwGO0CxrjzAouMwSVplPUQesY8u6WPH+nSFbrcc7ly6myznYFnhuRdYoQLCflpSNQlvBIOzYHxRkjYsWGiU3TVY0PF4kzMqA0OkYhht23/4wJ3A109gileGVJ2OazkEiWFUeoVvTa0mvJV3rMUoMjy4jntldkQaaVju0WvPilRXryueNI8sw6njhSsaVScvrj8c02jIdNvh+j7EuO+ma0zV0RiCEx36y5t68wVcKY3rKXjAaGdoQPnI9YX/o88aF5LjO8VTPj917glISv1Q0rsfjecggbEB4DPyGs1yxv9uyrlxc1SFxyGqX07nDf/q11/mh1+7xZ79hB9d6vLJ8zL1VSd1KrFvzZ//pG9y41vKy63G29IhcS9dJ9oOQRns8dyXmKG/4cy/f5C/81AOeP1xymfuErmIQ9GSVi7YC1yp+6t4F88JhVaasSsNeYggCyXRU0/cOvQEhu036WQsXRc2ydBg4giAqmIx9Jp6PTCq2EsUii5mMCgQOUgpq3bHsO7qFomgNYVCzu2V46+0R41FFGri0veI/+ugWP3N/RZR0PFln1J1PUbeooGJW+ZxlAX/lD17lL/70PcKk5+nSw3V+m+i7/i3Al6qm+g1/Ax/72Mf4W3/rb/1akwjgxRdf5G/+zb/Jj//4j/+mTuLLhd4I0qTk216I+MgzNX/ww69RyXPu7D+iEyWeXxB6HYPAEriKQHoIG+GSMnFH+CT8ha95Fuu0bKUV24OMJ2vNebXgSV7TiYZx3LE/UNyeBOzGDluhZS/R1E3E8WzMRXSb+eqYy7Ih63qGQUmnXRzhonXAPB9i+oj/4NYtbu9blFuwNz5CugV1D8vaQeLiSgerQ6T2OD8Zo3sPLSou6gzHLQm9jt2kJ3Qj9iOfyOkZBgUDv0LKnoHfcTAu8VSPRRIoRSAlVmwuIK3pqHvJKNAMA5dVvkV24fDOmUPdCeZ5wOlywBunV/iVB9u8fuozL1w8ZbE49Dri+qAidmvunifU3YaOPAwUjnR4OPcRomPslySe4MXdmr1Bx2VpuTeX/Py9KyS+pu48PCn5/PEOVQeLWrMTlgzcTbrFm2cpl7lLbyxn7ZyjlUbrEE/CMvfIakGjW77q+jkfubrg2e2SvdQwKwyVzVjWgrqXPLhImRcRXRvRdg4Xa4/TzKUzlmWlOFrGgKKpAl46WPPStRnvXIYUjeWF3TmnWcgw6Lm1VbIuIy7zmHvnQx7NEq4lEVZYbmwt+dX7Y45mEZHXsj9suBFtkzoBVetzkMbcPxtgcbgyKHlpd8007ri+WxBIA1qwHZeUpuPSvIO2hiuDkq+5teCtp0OEhVnu8dLeirJVlPWIpvcRQmClYXdQsx1WvHsZk7o9F1VM3YFULUpYvECxO+45HHd4ruHOdoaULq60dFrTaMUwrml7SVk7LEpN6HU4DsxKnxeurAAInH7z/8oq8iJC6m1GsSaNSiK/Y+q7eNGCuoqYdRmOa+jKmGWpEEKRBIbPnFwQOx1psDFQHUQ9Uknu7OREnmFduaxrQaUNUXpGx5rtAewNBE+LJc9fWeElF+xtl+R9xVm5ZN5kHDUP+RfvXKJp8RyB78L+sOHlK+ekcc3/9uv3yBuN1w35f3zihF9+ska5hoPU5cO7KdlFhGwTvvEZzSTUOAq0bTjJPK6OcureYS8R3NkueG73ktDtvmg6b+l6nw9drfg9z5yzNci5e54wjGr+yaO3OO8WDEIYTxp+6fKEf/jpBS6SH713TtEI/v4rZ1zmimUuaeqAayPDW+dbvHI8Zla4aKupW8ntccN2aHE8g6Msdd9ytHQ5TAUn6wmuFYx8l6zaJLFNIsM33K64GmnO5yGnK5fXzgLemcVY61B1AU3v4grDKOy5vZeBtHz+acovPRzy+eMIX3kEYkzbp+SNz/Ha550Lw1HWEIc1X3Mr4Pc949Frj3Eg2U9cTB/RGUVjepZdzsIseLhw8F2XVRFzOO6YRiD0kMSJ8KXEVR3Fxbv85D/8bzmpcrLW0vQuSgoiz3Bz2jB2hrwwjbg9lkghqXvDSZ5zkhdclpqs05wuJcLJWNeWf+8rzrm5bbm2rdlNLDcnmqEY43W7DENJ3cO8qnhzVnFU5iyajlkpKHXPyVy895uUlP99lOu/8eO3V8H1d/7O3+H555//dcdfeukl/vbf/ttfsnW/7/u+jzzPf93xsiz5vu/7vi/Zul8OuB6crgzbbkIsUxxc8mxC6jh87klO7mcIp0U5a1yn5v7ccpzXLAtJj+Gi6BiFitSLCJWLsh4Dx0UKTRp1ZLVg3ZXMS4tSOa+fuiAsbeeS1wFtGzENo00KaeNxZyunEw2zEv75mzPWjUYKhTRDBIq2C+j7MbrzMPRcmdZYWfLW4oJab/xYtBGMQ4FjfU5XIVXl83deu8sz+8ebei1qyRtF1vgoXE7zkMGgZlUGNLrHdzqa3qXqJAKDwqfsNtL/qvUQWMZRTegKJKBExb3LMZd5zDgp2R7WRL7BczdpU1ift89Silayl2xSzp6sPeo25J2TMedrhyz32R9UNFoSOBA5Bs8pwfa8fb7H48uErBEM45ZV5XMwKhj4GovBCs0z2xVbUc+iU3zmJGVRJqwrF6VqBoFlK+lYVvCJezu8fb7DaeaxFWU40uFoFbGVVEjpMAoMgdMSBzXrIqZpN+a/88LhaJ1QdA5W9JxnCYnX48qW3UGOkgJHbAZ2UvRspR03J2tSvyb0ezwJd7ZyLJZVqRBCsK42zZ+dqKXu4VNPl7x/N+Xto31+8eE2q9pnEPR84WTMmycpW2lN7Bsa7fJwHlN1gncuEu6eDfnM010uc49aS147GtL3kpHfUW+CKumtwlOWR8uIzzwZc772Ob10eO1ixdGlJHBalBAUzSYhr+hdtIGX9jNGw0uEhI/cmLM9qKiqhFs7q41Zd6N4cS9nFFaMByt8z5KXEXnr0/RwOo+ojSByO6xV3Nwq2Rs0ZJVAScmy8piVG0ZY6CvO1iHz0idrXeZlyOUqoGgV8yxgvnaxTosflEgsWe0TOxpsgGsDjN0YG7//cMZeUm8YZVairaRqJCeF4SzryGuHQaBpjEPoaxoteftsxKJ0qVt4drvgmeE2ByPNMDS89WTIwB0T+x3P7GZ86OolxsL7Dy+Jo55pEvIN1yOu7S4xwQNOmjUD36HtXRZ1jUEwDHpAk3ceD84HHC8Shn7FNGm5Oqp48cqKZ3dnLEufiyLmZB2Q+jWDqMNRhqbXpIHmcFTjqJ5l5WCMwlH6i4mpkkWVcP885enZhK/a38JUCY6NePs0pWwDRkHN87ubWldJy62djshxsFYT+h1/9P2Kq5OGeWm4zDxGPgSOZm+Qs59W1L3gPPdZViNcx3KWQdk6CKNoa8H3/ewj/sGnZnzTzQmojlFiWFeKyzJACUEatiRBwzjseeN0SKt7rPUYBBrPbRkEPWmkaXvDxFd8YT5DeQ3/1RfO+cWzC0qz8fSJXMP2cMVke821ISyqgGncU9YhReHy8k7Cn/3QDf53X3vI93/zLf7Cv3jE7sjSakXTBjSdS9n+d2nTlj/9kX0uMofnD2qeP1xyY8fwwtWWD+8lYAUWyErLZW6oOgOqZlk7HIwgDjSp53Fz4uCiGEWaqvEoGjCUXJaG86WgsTXaKLRRmNrhcCBx8Ll52DB2Qqx2GQcOk1RS2Y55t+QfvXnOWaa4dyo4z1wCV9L28H/5+UfsD8HHYTf22InfI4/GfxXvWV31u6Om+g3/K40xuK776467rvtbSiV/L9AYiyMdni46VB+zyq9xJUl4MpvgK8t22DIINNCzbDPemle8eSEp+p68A41i1VZsx5LtCGIn4PrQY+D6fObtIYN4RWc6BlFOEi3J+hWusyb2WyQee3FEslqTvfJZ5rnLNMnpjWVVC147d3h30fBwpWh6Sd4IvmJvymESUVQBx4XCio69QcbDtYe2PWVnEULgSQ+rU26MHSSGn7/ncG1ywigqcaTAkT5tn6CERMmWK8Mcy8aYcH9U4jsdoWeZl0NePdql1ZLLxkGbnlvTjEHY8+Juy9d+cIbWlrtnPkWlWFUe18ctf+CZNR88nHN9MmcnKUi8DldJym7AW5dDHsxDTtYK320ZeNB2EVK4+E6D7yw22nIhCBxL2fi4Cp7dmX8xja3HUQ77qaU1YmN+h+a08Git5WBguDXtcZyOe6c+dw7m+F5Doz22EoEjJEoqArdGqZZpXKE1eI7HONokbk3ikt1BRujVXFYuSWB4+XDN8zsFbae4fx7zzumQg3HO/+SDD7k6LtiKeg5HLaPIoI1gO61QUvPg0qdqJYuVx/kq4HBScdFmLGYRrz7Y4yO3F3zk5oJlFvHLd3f5xJMG34NBaHk4axkGDeOkZlG6XJY+T5cJEomvLLujDuFY3n+44P7JgL1BwYt7S87WB+yMOowQfOhgTmddeq2odYmxmjuTFVXn02lotCT1N8lzvYHLTNNpSfL/Ye/Pg21d87pO8PM8zzsPa1573me+59y8N+/NmSIpSCABy4EWtRC1OkrUpg3BMhRtkyYau9qoaFqqS9ugkA5Lqw2jQCzEtjQFGYREyEwgk7w53Pmce6Z9zp7XvN75fZ/36T9WSgRd3aFJZFYC5TdiRex4V+z9vGuv2Pv3W7/fd3Baul7NMm+YJh5pZXGWKhAwcgsuFj6rwkMq2O7mhG7D5SqkrCWubNgOC1oj2O0ueDr3caVg7AX0woZPPjVktSL2ap5MHa73OvSiiszUJHWFZQtevHXEojS8q3eNrAhI0ohbwR628VECqLfQ2lDWknUW85/cGnJ1UNL3DOsyYBxX1K3mn919StkYpNx8qNntJWz3CobdmoNBRVp6+L5mf/scWzbUbcPN4ZzYE7xvL+KHPvMajm04ShLedyXgbWOP9x/ETNeGd+zE/IUP7HOrO+ZXHxkeXHi8deFTNpJxWGCMT61tjuYup0ufwM6ZpYoqi3GlxbXhmsNByiAsuDo6Y7ufUNcRgS0pzRrLroGaulH8Z+/r87n5nFcmlxwMM4bdCXvDOYOoYhS3+JbNlf4S3ymZpopVYXN92OI6FlGnRlqaSbIxe9S1x/3jHSaTmDv9mNcmM5ap5r941zsxbYfLVYxyW4Rdk1Say7XLIGg+bxoPXa9iknp0g4rtfsYsUez0ljwzntBxXGI7RGuP06XPReLw8YeSj74VMorgP76RsK4L5lVCoyW6FdiqptCb9BWjUk7XcK2vaYsxV2PF1X7NICxpKbnMGtZVg2PnBG7GW/fP6PgNbTVA10PaJqIfCCwJmIZl2YJMUU7K4wWcpTnHSxdL+mzFgm+60eew56KrLl9zXeCKGJC0ImdRrTjNVrx2Co/ON/9jNRplFVzdLrnec7iz1bLXMRt/O/eL39AIaX1RH7+dcHZ2xu7u7v/s+ng85vT0S2Bg+XkYs6mT/9/47Gc/y2Aw+JKd++XA0YnDR19r+MzjmscXiotUM8tsfNsm9iWR49B3QtomYpUF9ByLv/TOF1gngqo13E0S/s3DjLzWFK0mbxSxY+Pis9ttuTGUvLBX0IsyxmHNO66ckjYJ6wrOE8EsM2AsfNshdloC28fk4cac1mpBVVzmJc+OYi7XLstcIYwidDw8ZVFVNmnuM8ngzacel4mLlA2xW+HYFl3PsNcreNdBimNVdIM5y8IhcmuaxrAdtbxnZ843PvuUq8OUtLTIG40QhnnmUTSKUiuu9VoEmsdTjyfzLkW1Md012Lx60efx3GGZ+QgEZWM4HKYkhcsyU6wyh7NVzDxX1FqzEzTcHBRsxxmylaS5IjcVtfYpa4/YKSh0y7LwSSqXpLCQQtDohjvbU4ahIHYbTtebEIFfe7zLrzzucbKWOFKyH9dEboZvtex1aiKnJC0kZeNQFYplLjhedHj5eIwxm9jytrUInZzYLej7G+aU61T045Jb4wTHAmMaskZRNxau3fBkEbAVV6wLn8Cu+arrE/Y6Oa2RzDKLN847KEsQ2jWrSrEsFTf6CXe2Eg67CbfHCV+z+zxn02cRliIKCl46zfCshrPJDk8mAWUDX3X1gm7UMIxq9roZoVNyo5+wrhzGYcVWJ+PGaIkREoPh2ihhOy54OguQRnK+8rFR1NpDAi/sLbkzcLixm9PzChpRsSpdhN70zjeHGbYUfPDZc45mHvfPPN53eMnLT7usKpvIT3g061Lpjffe07mNZcN+t6CsbKpasOW7WLIldBr6wUYOZVkNe+GKVgg8B44XDrFn6Lolw05CJ8zwbMHj85hZ6lJnHovcQrcWppVU2MjG4LcBVbOxfLhYO/TCOa+fBbStwFYa00LZwk4nx7M0UhjWpWLguOx2JUpILEtzpZ+yHeVI2ZK2DWntYltwunQIlEO12uL1Rz2EUQSWQ+wqRmFOKyQ9v0EgEbJl0JnzM2/lrAuXpoXbB+dMs5KhGxIoxdnSw7IMq8IhyRXdqGCrmzFNfRy1YWdrDfcu+nTcGleVdDxNP6j53KMeeaHYiTWWrFnmilFYocxm2HPYT4iUYDeCne6K61tLsDTP7DqooOCrd7axheQyMex1cprWQ6kaS1X4lsswFhzNXZLcYmWecmWQ4zc+nt2wqgSXSYQSPuNoTcfLOFnHdJ2G46VLUllYqiVJJd1eTexIirZGSUGeW9wa+JS1IlvG1A00bUPTWigJWQVF7XN1kOPaBVtxRWsUsVswiEqEn/HP35iTFDZV0/J4ueRkCdJIdodruuGS60PNbqdmmdlMk4CstlgtFT/26Sl//ife4mv//qf5sz/9a3QjSZlbXF506FsBaS64XHm8a6vPumr4np97wChSBLakaTx2uy2VllzveaxKwSzXPDpRZI2DZcHlIqBLRJtHCCTN573OlmVFqguOV4onJ/DJRz2SzEdYGtkG7PgOB13Nzd0aq7VIipbC1CwLzeNFw3e865B//OZT7DBl4GtC4aKEZOy6dFTMQa+lFwuEhMZoVkXJstScZc2/s859ofgPPdUXhi/4VX7wgx/kL/7Fv8iP/diPsbe3B8Dx8THf/d3fzTd8wzf8lm/ky4HLFB5NDE9PGoIA3CAGWdCs4D1eg29LXOky8BwOQh+rtbjaXxDYmnVhIWXDD336TSyrphstOHq6y1XPMAgcvuFmj46d0bSKyG3oBXBjuOatueAyzbjMKpa5RXe4S/qrv8i2q+g5isDZ+BG9uJNRtoZZkfBwEVMnCbd2JRIL2g6iyXk481gXiqRWBG5FGEx4Mu1jKhe/chh0DMMw5L2HF7w+FXzgygJDTFa75LWDLQVpZXHYSbnVX3G2sggdSAsLYxpOpiFdr2AvrJhmFrdGC6rGoW4EtgXXRw37/XNoBW+eDrnT3+bRpKJ3kLPdKeh5GcII5oXNlSjfbFFSj7J0cZyMTz1x8VXMOw5zitoirV06tk3slhzNY5Rs+Y+uzJnlAVI0nKwqJpmP0T5lvYka344aHiwjjpZQ1Ia9Xs40rXlvCNtxy6oQLDKbWkvqumWnZ3h02WUnKlnmNoU2PJ2HeJbmnfsLVoXNjlPz7HbCrJC0RtAVh4w8xcemU8ZxReA6eEQI7TAIKs4Tm6JSPF367HVTHLmR0a0yD2VcHpwGKKdmp5+hGx9ZWzy76zAePuXaYM2vHW1zkVp4UbGRLRWCrLYZ9Cr2ujlvG8z5teMxPb8hq2CZW9QGOn7JwDY0jeL6zoq8dmkKwRvnAscWPLe9QCPwnIorXsu9yxjZwqTwuNZL2AsSPnmyhZQtSglKvaFej7olSsCqsGmlQsiWo3mEFC27/QUXmaIfN0R2TVHZFJVF7NdUGubLANXPWJQOSm22HHFYsVi7jAYrrMbmZicksqcY09INSuZNzr3ziCCoWWUbT4PHFwPeff2YXzlPCMyY8UjzwWeG/INPlcx1iaKibbvMq5a6gH/88oqdvodvtUDD+cphGGfUBm5uFSwLD9sSPJ11qLWkpzJsVSGVYNBpWBWCdekgpeTf3A/oeDVXem/xngBeUgMCJfiqKz1+6q1zFvUcv2c4XYd88+0Rv/pkxa8/0nQGkq872OHVRUvgNtyfGHy7ZZlDpRWTdJ/INcyVpm4cHJmxrCyu9xtWZYwlBFt+HyUSnixLFqnNKBIMEPzr4zc5GG001WlpGHUyep5D28K9M5ftKKPrzXnl9Dq7nZq2VZi4wVI1WekxSxy2uporg5qkanFVy3waEjgKoVoOBy4vX6QkuU+CxHIK9qISS2maduMVVreKRjfUWmCMxLNrHlzEGAEffzDmHQcpgeWQlTZJKZgXLT/wdS/wzb/2Bs8cGFTbRaJZ5IZp1tBQ4XkVu32otSSrJLUWfOWhQbbbeJ6DZc3RckXJisoE7HQCxlGBawlmqU/PtvDf/jYyy6YqW06LFsddkzc1YdAQeS2OglXm4SrDKPBwjIfWirwRHMYB/93pEZ5fsTtMmCQesZ9StII3zyL2uvD7nrOx2pBJXaIbC9VECLfGoDGiYprDq+eGYlp9eQva7zAcHh7ysY99jOvXr/+m6x/72Md+o7f4YqLf7/9GMsjt27d/07BIa02SJPy5P/fnvujnfjnxNTe6vLmwMbLl2UGfn30w5713znl4PibVNcXE4qYNjm2RlIpRIPnwG3P2Boq3j3r86vEllamxbc08FywXhrNKg1GMeyWurfCUw7ZvqHXIbtTy0YebgIjYa4iDFUljUzUQeRVJ4fPcVoe35pqkKrEbDz9a8K+PVvjKoRdVOHZNo31s1eJZLbe3WqpWcyrWJLVhGJXYqobKouNJfKcGo2iagE+fSA46OWkVo43gxmDFPHHoWBXrymAJC1dpfLugaDxKLRA0bEUVS+UzijMuk4BH8w79IEXKmpEHC1fT6obQNhz0Ukpd88mHB4w6mq6bcdhP8SxBZRrOUhfTujyZRawyiW1JDnY2/h8fvHXB2M/J2pZPPDkkrQL2+glPZjZKONSNT9Nqun7Oooj5hXu7PLoMeW5/wSzz6Xsp47DkNHG5Mkh5sghYpB6VFlyuLbpxTcdrUHITmhH7Ake1BE6FxKLnajyVkdeCZeExz1yetC0HvYxCe6Rlw1aU41gRdetwuhbcGSZcpi7bcc3jxSb9CSPY6+Ukhc0st+l7ObW2OYgThEh5tAxJCsnd5DUsr2E1jZktXUwjEKKl47o0eOzGU2K/Yb+XYVqJJQyfebpF5FWMwoLIbdiL1tyb9un5GwlPZDckpUKz6VGElCSVRimIvQpHNTyqViglMQi6UUlktmhNxXsOJHfPIg4HZ5ynLrVRDPs5pbHwXMMik5yVAV2vBCGJnZyT6S6tKknyAikbbBWyahfs9Ut2uiUtNltdTdUY7p6GdN05SmxRtBbLvMGObNLaxlGaRaEYd3PWqc9hr8tSZIR2xUS7GAPKbdkRHXxX8Lms4tpowZsnPZ7bTynailvdCfMi4nTWIUlt9rYyqkawzH1WSc317TW7Q4sGyZOFj29rbowSBIZGW6xrh3XlE/Xg3jQHuwUBnzxb87W3Vzi25oqXcG/SYVW6NK1F1Rbc3E5JK4VhMxgy0vDKZM5Wr2HobtLJfKumF2oeXHRoTck8c4jdhtjVTBKbpFBYXUPgCk7XgpeO+mx1K2wHFgUINu9XVimEatjpZmwHIxauz3GSoJo+XlBS5pKP3F9yPhP4B4JFYWNZMXcnGV2vQusexzOfwF9xa5TTaWxacny7ZJbscmVb8WgpP89oM1gq5yL1Ce2WtIa8kYS24Oqg5HOJgy0cdOkiZcvOfsrf+tgRWkm+8doub83vc15aGKE56DfYSiFlQ8/XlI2g463RrWS67pGWilGcUTYW5yu4sZ2wzvrEXk2WK+rSohvn9MMZ79pbMc8En3rSR2uHvLLRbYUnA9510+FC3+Uzj7Yo0phRt6BpBGjJu8cdfvzeCkso/tnLC5xA8MyW4HCQcjx3ca0WaTc8XRj+wfSYwFYImROGPgPPYr10cP0GLRXrrKGJN4sFqyno9msULnGQ4+657A81q8JCtRaebJHUNORIy/Dm0y7j7RktHg2Gv/511/nhlx6y321Z5y6jQKJzC9cVTDODkRWXWcFlpQiMzUWliS02RAH5HzT9/774UvVUX/Cg6Id+6If4lm/5Fq5du8bh4SFCCI6OjnjhhRf4kR/5kd/yjXw5cBhGOMbFsQytlGz3ahzVcJY2+O6CwCuwbRttIpLaZllqtKmxpE3HsQlcQc+FlhYpG77mmSfshi0/d2+bb39vj//u1TeYpQEv7hhE62DTY+BYmLrlowuXyG9oO7vc+IN/grxNWTUrur7EUzG+5ZGlMZ6QbHVyCo746HGXcQR+plDWJr7RUhZj25BUNp60WRVwfl5wYBSjfkPoGN6926CkxVmiKJoGiWJd+AhhwChMW+BaLc9uLTlbbybX+92MReKy28k4SyIcWfNgFnO8DJmkFjfGFYaWeW6DVBvtNAXzXPJwHuA5Gmlsut6mqK+qmrvTgJefDHAsGIct9v6CT93fYV1IOo7Cs1pOsxG+JdjtJug2J9cuaWUzDgq6HqSlYtE0aLFhx7xyOkSKBm00thJ49gIhArq+4DKtWeUh48iQlDVxryarN2Tz1896fN0zZ4AitDfFwVIVN4Yp9czBVppJ0qGoPHz/jLsp7IxqVqXDuLNgmZVIp+bRIuZqL2WW2mRScboMsS3N04XPy0cdvv6WRy9+SlrYNEbS1A2eU+GHEyZpgGUJBkHFdlxwPPPxVAh+zosHl5/3D8j5ubcO6PkVw6BilTsUteTG1prJyme5UuyPaspaEXuK184lL+yfschdLNGyLgeM40vOViHGwG4n52zpcrmWPJzv8cLekiezgJOVT2Br+nGNoGU7rpjkDnkpcWLDbpBTthZVDYNA88Z5hKsSPLsk9lsEhoW0ubK14qCruXfpstPPCfycVWWRFB55XhIGJY59xjyzmCYuYVCx1hfcGHfJ8hA3SihqqMsOrz61eXan5s3zhJuBh5A1yl3h65bG5FSVousLBj2PTFfMM59FLXE9zXanwBjBna05jTEkuc/posMozlDSELktfb8kdDZU94tVAAIGYU7dwPnKZVXEuEqjG4tGwX/1sbv8xwc9Tmc+n00rfuWtM7759ojfd3uI7RacFAseZWsGYcl2vKbUksjWTNIORtQUlYU2klerGmMk50mHUZQxSX1Cx7AuFEeTcrOpEA7DuKbVAZ4FZSNYlBWxq8hLn9jJ8G1NUhjySvB0qcjbPh/YvcovPrnk9tacRSlRVkZb2HgWSGPx9c9c0vFzfvLVQ86qkF9+PGevG/N3v+U266rhl35+geesGUUloVuR15KOr2laSd0Khr6mqBW+UzJJHN46j3hPf82BUljCIasFl4nFo4ngf/jDzyER3Bp7dJRhstasi4IH84LzteIb79QcdDS2rPjovR3Cz/slOJbB0wFpDes24+mpxfM7PpPFPp5SOBQQ5GRNQ/fqiMbb4vSRpLUNJ089nn9uwWHXYuTFzFMPo1uSylDojMrUNDgsCo9FLvnenzymkYKdMGS+9hj4GtqKrtMQOz6YmlFkc7mumFUpr164iLXLrZuGonI5Wyh6QcFuFOAeah59sYvUv6U4f1F+1m8vxu93fMd38Jf+0l+iruvfiHT9+Z//eT70oQ/xV/7KX/min/e3//bfxhjDn/kzf4a//tf/Ot1u9zeecxyHa9eu8f73v/+Lfu6XE9OqZKsTcFHk9LsVz2xZ2AQc9CsWmSL3U1rAd2vmhSItJY/SAiuq6PuSnmdjbKjLChrFtzwz5P/6sZLxSPMrb3SxgwqpJLFbMUksduKancDmZGXT9Wpc5bAsK968CBhFBVVbodqGO70eD9Yz0hwGXoTnLrg+mjLLOhiRI1TCbN1lFG0k71s+eHZL6K6ZZZuELkflrMsusdcSORWrwuH2oOYirRnFc64MHKTQnKcxkWdjCYtV0XK8iKi1xV5cMkliaq15urRxS0XgGuq2YVnYCBmwLDXHc4t1HiJp8QYrhlHFumx557Uz2jZiK6oJnXIj9xCKjtuQ1xsZz/u2Dvj7n1gCHvvd9WbAZSpO1yOmq4DAr2kRDMKapoVZ5jKOc7puxTDQJKXkcLQk9kryxiOpCgbOnJ3I4+nSZ1VYzDKbG6OEonIYxim3txZMEx8pNyyholas1yH73YS0cBgHG7Pr68MlZdNlGGqOZjGe29C2AdoorvUSFqXF/UmHSeGT1orzNcRuwyisSEubWWETuzWWMOSVzzhMOU9dtIHbnTlFN8Sxai6TkLZtudJV3D+zSa0aIZcMLUPZKF457xK7m0HLIrd4x/6MWW5zbZjRtUs+drTDTpiR1oatWPN0EWAkn096bZFSsEotpNRcrEJmScCL+zMULb/yeIu0DPCClIOeQBqbSiwYxTmXqUfXsWiaduOF5TRsxyX3jiMiT7JIJVpusypKwiinH5UUjcPD85Y885hS0PdrjqYh3ajimeGaXz/a4nTZZ6uT4bYa2zJcrBVFadMPc3xXczLpYlmGe4s5N3Y2vV+DoG4lSMF5uWJkKnpxxjzz6HcqFmnIaRJQNw7dIKeobKpWcTL3ORwkIEp8t8F1NjKutrVYFS551ZAUFre3VtweLnjlokOre7x8uWSvZxFGKQ8ufZ4Z2pwvPfa7KdPCIXAb0tpiGJTYVktVGxrtcraMsLwEU0sOtxPWmYU2EZ4JKMQFbqPphQXr0kIaGAwShkFJ0cS0bUHsljzMAta5S9dvuDJckVYWl+sOWeFzvBQMoxLdWlyuXb7hnVf4rneM+DdvLSmpSZuab/n6HV65TPn1J2ue21X8QdnlYXLK0axL1UjyWuE7Gokicmq6XsVl4vPx+0McVdHxz3n7nuHJwmWeNWxFEku63L0QSKkpmgBHVUwTm3Xu8oErfT59Pqfrt9TGRXspsaP4R6+eUVSSfj8lEnBnvGJd27StYqdTIkXKySpGtD7bnZRl5mCMjaFkvfJx6CG0BX6Nwcaya5ZVxb6RvHGxTd0IksLDs6BqNOtCsCwqfvLlgsDfZujYOHZDiyEINgvm/9dnnzLut5SlTb9bY0ofnRvSoiCvBZFfcZnVGGlx2Gv531y/wocfHGH3BUJAKwxVrVhmNaUusW3Nam5R1x1GvkWnv+mNhJRoWlZFi64aVpnk+ZunPJz06biCWraYJqJJQgKr5VePlzQtlE3DugCk5tF5y9/6I1f4pScTfuVIU2vBjm/hhxXvH+/y0vkMY9WE1pdC0v9F6qv+V9JTfcGDosPDQ1566SV+7ud+jjfeeANjDM899xzf+I3f+Fu+iS8XFmXLIikxUUpRBwhL8L4rDa3pMPZqpMkoa8G6CJiuOzRNxadPPJTUCJPSoOj4G1NCo0OqCi70hsb5Y68/xBE+3/jsI149G5I1Hr2opdYNUlr0fYHvbDxBPvU//SR/4C/8UcZBTUVON1zRthFpNcKRLgcdqLWhKAwCw5VBStfLGAYrJlmEI3yyKqTjeuxGLu71jMcXNs6l5l37NSO/xFaCi7xH25ZMCsMqg514IwlbFCF+rXmaeGS15OZ4wcki5s7ekr1OwdEyYDvOuDeJWOUBZWXTNhJUQ1KyMaDul5xmM5SQaGpeObd4+zb4FnRd+Jk3D7GsnFVp6Pua0NUc9AtG/gzLajGm5izR3Ogb0srlIK6Z5pBUgkkqsVVDVgUUVYD3+RjOgV+Ra40lalzLBQNJGeBZNmcrgzY1b9uZ0RqY5wGTtbdJZjOayEspGkPsFNwZb6bm795JMa3maCWYZZK8bekGCWerEEuCa9lQjdGiZhDOmGcKT1XMM8m8UEwyxThsWGYejy96jKKWs2JKLxCcLyJqDV//tjMCu2JZemxHCx4vQkzbcn8W0vMqHkw077+5ZJFZGCGQtOx1Cl7YmfHZiwEYiW0ZTqc+geqxanMuFx6B7LCuFjx75ZjTNORkFeJbDUdzmxe3+zxeOOS1ID11uTle8tZphxeubDTjpd6YDEq5KRLGKBY5hHZNP6pY5DZ+UGNZBReFz5NVDyENs8Kiqw2BZ3CshsgzeLbmMlfs9kuyWrHQNr7VsLWz4DRRXCaSrahksopxnRZXFShL41oXaBngtoZhbLBri2vOiJeOz2jQPE5m/PNHZxSVQ20U775yya8fjVkWFj23Yb22eGZvSqUFx5cRtrUZaBR6ky5zbVQiZIYve2hWZKVCasMgzniylAzCNdpI0sLCU4Y72xmvHHdxLIi8GiVbvv2FHf7LX7rHLXfFzcgmrxv+xZuXfPZpzlKdo2lJmwak4OkyYr/TkJSCSWphsHg6g0GksYXN9lbCdqwJHcmyaLBkgyYm0wVbviJwBJZq8J0Zn3085HBUkCcWkQt1Lbl7PuDZ3TPWZcveYM1l0uEy6XCfFb2woRWai7Qlqj0OupqLheHN0y6feOxh0PR9C9sFy4InWcY3/P3PsTM0rHPBTschHiWsS0laWwixMWEcBTWxo3k0tbBUQ6kVz2yn5AuP2nZJlOGikPz599zge37uLT70rx6yH7vsdxxaVXK0MDyaS144WPF7nytAB9AGnCc2WW1oZYMxgsARvL6YMUsV77pq6LkBdd7Bam0ybbg/0/h5Suwa/vnf/B957k99COXCH7y1w2rHMLHm9P2KUsPRUiNkyb1Lh0Hg01EdsiRGIbBVw80tgTAC19GkhUbInPMipzUtJ4uYXuDj73V4PE9ImpDfcydlvnRppUdhBKXWTPJNtPKw+eJTpH83D4o+9KEPMZvN+K7v+i6qasPG8jyP7/me7+F7v/d7v+jnffu3fzsA169f56u+6qv+f0rof7fhzfkcgQduzqcml8SxzSSxGccVHWvIqhZEXoFsHfrWJm3rO796wI++esQ/fO2EvZ5GaYvLRCGMzVvTkr2ei2krXrhS08oUISoMNk9XLVKVuE5N23oIGrquRd9r6QRzBILLzOKlszlbbcjOluRxW5AVDtsdH2VKrvdXJKXFNDecrzU3tzMeTiIcVbPbsfBsGyE3/j2O1dAYSIuY0LIoa4lubSQOV4czXj3ZIXR8WuAydfEsm9NCciJS1kUXTMJl4hK5mhuDNZ0wo837HPZLel4JCLbDhnnWIsSa/a4hsFtWBYyCmrQrSYqCrHIQwsW11zyYe3S8muO1j28VfOLkCF91SQuFrRosCVnr8Iv3dmiNhdOUvHoaM44EulGcJOCrkMk6ph9kPJi63BoVjMKWStfcnUbMihBBxYOpz8A3jIM1jxchHTFklWvuXXZxLc3rj/tc2UrZ686Y5ZKk7BG4DVUjSEtJZLfYCixZE/ogkVztZ3hWjWe1lG2ALeD+uc84rjkrfK70Mmot2eqmBK7LLPMQGHynpmjAUpKBW+I4FpNMkVQ2z20vEKqlIyXrVnK2sOlGBVjFhhGVeaAF771+wUXqkWQWRWNzuvQ4agPma4emNmzFBWltczgoSHKoa5tBvySpFU0bkNcWnlMjgaoWPJ13yXKbwMtJdct0vstRfslOVAOQV4pVbbGuJMMwQ7GRtqEU08RmOyg5yxZcGa3oBDVVbXG68JBKs6403UiibEE/LCm1Ylk6DIKavqwoSsluL2VaeCyTkBcPpjyYd1mtFYEI2ek13L2o0EjyBrpezSzzmCxcdGnYGq0JbU1l2ySFTeRNEFmPjhrz4CwHWXF1K2VVucxzl62opNItY9/loL/gUw8HdMOKrFacLjYSqcNujidadkKbRb2kdQRvXUT0PUPCDGrFo3mEwXBntKZsJLVuuVj7uHZDP8jpWR0q0TC+knJjmDJJfU6mkm+5s8/PPT2hNQpjNkbetgRhLJKqRYmG66OSuNF0nZqjZUud21TaxlWQNJKv2r7JPWfNjUHIaXnMfmxzdznln/zLC0q5oigsinXA88OYT56d8+nJksdpxJ2Rj22nXAnZxLA3UGsHR7U8mHgMwpan8w7TeUCvq0hKSezWbMcJSWUROhpBS912OF84G4lq4XI86fOH7vT58KcTRrsFkad5PLPpeDWeJZiXCeO4xFLQ1yWFbng4GbHbLfHtgmWx8Zr1vJzYhWXm8+DSRUnJaADP7c45nsbkpYPvNSxyze6w4cHFFp6t6fstthQgSnJtY4ygdGoGwuMvfvUuP/7aBba/pmwEJ1OHSVkSxptggcrWRDYsGljnLU2iGHUTXj31kcbhxcOaVeZy0PUwtUvkWMyanExLqkzxA7/3Jt//0TeoS5dBx+BISVUJKt1iWgjdjIczQV467HYaVqVhtuojyw651lwf1TTaYZZA5jT88tGchhppJG0WoMn56//JAf/tx86IHEVgCYwMkVIDhlA5pG3OdNXioL/4hfF36aDoS9VTfUGDoqZp8DyPz3zmM3zTN30T3/RN3/RbPvi3Azzp8cffM+bXL+AzJxpUSWYalF1QmpInSYajKipt02iboW/hWg5fczDgn7w+Z9BPCJyGpLT47EnAvKjxbMNO2OAqh6aO8cUW1zqKrN5sS1ojOF54SDvh/kISByXnbz3AFwpfDRiFKx4uNbqt2evPGMc2dy87tCj6fsPVQUlsC/qexODSDxJePW+oa0WtfWJb4cuA3Zsln3nosMwEz27bvGsro9EhdePRd3J6zpRPnQxI8pBr/YIwN6Ta4u4k5FteeEzgNJyvYurWQiA4W3sklUEbTZ473D8foJyCuxOLVWl4cbfl5lbBIrUpW5c7O6fsxQrdRGwFDVtRwGUaghFYosRWOQjD1WGOLUHKEiVrZpnPNLN5ZlhQ64CsDLg5TNEUTNIAREM/yFDCZlVGjMIVjqroOTmtNDxd9rClIXIhrTwmWYFnt5S1pmgaDgeb6FqFz6ef+rxtZ8Z+nDKOSmyZc1nEnK8jnq59On6N72kwBWcrj8vE4ekJWMLi5lWHpHQZhpshyc3hmqwa8HDSoWkFrlvR8QpGUc6jaczucMlWVHK69tiJW26Pllym7ucbtYbdbsYi83n+YMHx0kdJuDFa85mTPu85nPBgHvJ0EoAesE4bdns1277PvFwzqzW1XHNzNOV4HbHKbJJMUloOe92SBxMf26mYL2K24pz1aoTtVARey8k8JLRqcquhbixir+F07hH7mqzUPJ643PJS5gsbpINnN8hW49qG+drh8EqB1tAaC2ks1qWNsgRS1niOwm4056lDx68YBPB0HjGIKqRYUaZ7nCxbQqvBaMH+OAUDd09jLDVlufboxw1Z7bLKJNtxzaivsS04W4d4tmEUpoTCYeII3jjp49gtX/nMJaU2YFqy0iZvNokcWSU4ShpCR2FbmkeXXa40DrPMYegXLHOPfliyrgyqFDhOzZX+erNFkzb/8OVT3r+1zcOnmiyCcS9B2TmfO1/ixRahC5FraLUk9Boa05DULlvxkpeebBG7hgdHXfa3Szq+ZlkYssqmHy15a9Kh40rsPlwbzDlexBwvHPYGKbv9DM/apMpVtcK2G1Yrw9FC4UrFMDD4dsPLj0KkrRl1KpaF4kqvoesZel7LMnVxrIyLiWHk+QhHsT3MSUvB8bnAGENtNJb0cCyYFwrfKUgTB0s4bMUlo6hkVQoaoXFVjTEx/dCQr2C6sHjwpMv3f/NV/quffszQ8omjksuy5uuujfjlswtu7R1jO2NCOUC2a5pWYFspr1xIjDVFtQFJHjAIKjqewGhJWfvY0nA8dbG9gofnFrPLkEHQ5flbBeWqJIxH5Gt4+zikdxV+9F7Kdi/n8dxHKUWS++z6AaGSSASe5/B4VnJaFXQ8Rd+xKLXAMtCqlnU+ILQcrvQVNIJaw8W6pJYFFSWrtkE3gstEsCprPMfjal+y59T8ype7qP0OghCCH/iBH+Cv/bW/xuuvv47v+zzzzDO4rvslPfdrv/Zrf+PrPM+p6/o3Pd/pdL6k5/8vie3QpWgNKI/p2nBSC8ax4Mmy5i+9Z5f/xyfewvMqdO2w13XBtHzufE7HsQlHFbG/RggX2654NDXkhSFpFFIodv2KnmczigvSuqauLY4WDqvcsK4M9Qp6YUXHkcSOxTSzoPWQ2ubXHyXsreD5O3NefzziYAiN9ilrSVI6jHyFq+aM4hVNa1gXNjtxjhQWLYbYWTEMG05XLmldc2/SQQDzvMSSLVkR0QtXXKYhUgjyWiIx3Bg3lFpz70zxzBZoI1CyYZ75TJY+/fGayLEZeKDRnCc2eRVxsfawZM3NYUPV2qzKlMNOg+y1fPbYomg2gSdlYzHNNCB4sgxQVsr22KHWNotCcbRS7IQRnmVYVzVvXHToeJK8EpzPHLb7Be/Z7vKrZ1OKwuG9V1ZYqmVdQFJGXOlnDPwV2ki+8faUSmvycuPrUSeC0ioQwvDZxyM8t+ZwsKZrVWhbsa5awJA3NgNP87mTHherjZfcdqdgv5tyECdgDMvaZ+DVBH5F0Xg8mHTo+CWz3MFRG/lx19f0vRzH0uzGNUmteOuyw61hy0Xib4REUrIsHbQWzJsV48hmr6cRUpAXikfLDn2/pG4Fb1728OyacVzgWRmWgKfLgGFcYklN2tjc6q+YF5s03Vnm4rmaqlWkpcU88dnv5zyztWCabPNwJpFoqgZWacDUzLm5U7IT55ytA5QQdKMMmdnEnmGWSG5tr9FG8HQaktQ2W72Ma4Oc88RivhpTVy3XxwGV2ZidR45hJjyyyubNcwffhq3OkkUecbyMgJZRaDHJXdoWIsdnmdVEpuT6uKEsfSqnRrWCrHQ46BpmC4cb4QG/tJxyvR8yW9VMGsV2ZHGtF7LMBXYwZb+Xscxd3jrvYMuWZW6zUDn5OgRhmCcO292S/V7GdlTwTz93BQXYWuI5fXx3SeCWbPcSMm1xe2vN3fOQrHGIHQ1ITpcB/bDgfOWRlhaRO2OdO+z0Gx7MAg6CEd/23uf4/n/9iGBkIaVhsvY4HGyYivenHld6IEXLNHXwhcZxDcPQUDU1r5x0GIY1y5XLJ18/5+a2y6dPT3jhUHA1GvGv3piivJxxJ+Pl8z5umPN///XXSIoNC3KxKDnNJbHn8uKtU0rtsShsHs4aBkFDpUMeTuHkNOJPPL/Ps7se/7dfvs94vAQcQrdEqpxV1kGgGMcZ52ubLI3Y8SL+6WszytLCTF1WviZyNAKbR48d3v+OBaEtmKwlq8LijTcOkMIGDLa0uUgtIm+BpRxOFyGPTjfG57fDMY6fYFkLuqHhYi6Akqr0ma4jBkFBaIOSihbNfG2RV4r50qYbN4x8w93lEstZM4xzThc2Zxceg22LrqtIE0kYaqraIm1qWrcgFIKXn0a8fa/hTnfEG7Mpdenw0YdrJgvJ931wn7/50itc261YpD7Tdc0wVMhKE/uada64zDMiVeJZLveOPZ7Zy9COwrWgF7QI41BYNdPCEPglx0vJMtVErmDczTmf+OyNNG+/HnJSFrx6uabUNSeTAmk1uI6iVTn5yuVfVkf8xa+4yX/98SOE/hIwin6X4kvVU31BgyLLsrh69SpafwkmfF8GPF0ZlNMSexZdXxCoFl9pglgQWAFKSO5OWuq25t6ZYNQxICz+8K0hl+mCgz64ykbZFl1nk5Dz8qnhaAH/+Z3b/Pefe8CoYzEIU7ImoKg8HKXYjiRlZjNbKJ6sCnq3r/PqwxmuH7PbUWz7HmltaFpDXisca5N+Meyl3NhaIAk2niE6JKsD9js5p+s5//KVIc+OC5oqpOu3vPuaQDgJP/bpEfb7TomdmkoHdN2Ndvh4VTPRJU+WCnvlkXgWsSO4TFyG4YqOVxI4oITFolAcdDOerEreebPgc48PiDyXrluxzCu60SU3hi3+luDp0ifJ++xtn3KRWVi2wbcrxqHCVSW7ccZZqrg7dXl+CwJHfn7wM+Zo3sWSDba1JK3BtzWDoOR4FRBYioWouEgNjnQxpsJ3bGzhMAqm1KblRGjSukW3Lq0x1NojdhpsZZBC4Vgp27FkkXs0rUujHS7STazr8TpkkVrIUmL7Jb7dAIJ55uFIycCBF9/msjIpr5/6vHh1yjfdPuYic/nU0xHXBwmW0kgEb57HDELNVlxzpX9B0ShaI+l6BaGjWeUep6uI64M1BvhMEjEMS45nEbv9HN9ueDyLuDrIqBrFXqfk5aeQ1St244ir0YhKneF7DQESKdbMU4ezlU/XKbAkDELN+WRIa6XoRuG6Gw3766ddbu7kFKXFg4nPe69UpEuXPO0wc1Iir0EbiaUMttoMaF7NOnQ8TdMq2lZSa8PVcYpnNSxKh6vbS2xV82QWMV3Y7PQ1g6DgsrG53kuZZCHjOAVSktyi0pLTNONKv8PDZU4/LDYbYWO4sZ2ga0PPr5jnm39wllCUtUVaW6jaUDWSq4MVdRPwyfsWtW4QliTyaj7xcEDHr7kyzHn34YK0tni6DNCtgwRsJcAovuLGhGVhUaYuTwufdWZRtZphWGNayeUiIpSSrFDkZYzUhouy4Lkt+OmHPuepRVmdszV0CL2WQku0loROQscvqLSi4xV0PcHbdlKWuU/cqUirkrK1GXVrHGlzvhwxS22UkMReRegtKXTIVgx5EXPQLwkdw+eOPd66UDy3W9E0ISPfYhBW1I1NazTTumTkbiKLd7twbZjhKDhfRdhWyQvXZuyPbE5nHq7SXB8vuUhLSqWYz7ZxlUUU1Ri74v4EXtyvuD2SrIoQX2laU/Ps9pLLbERoW+hGsMoFrdL0xgvOdcALOz5ff6PLvUWC61XM65L/5ydOefHmiizbJpQxmJakFFzkNe8bHLPf36Guuhi98YgqK4u6rVgUBrkStNrlj9w+4J99dsaNrTV/9oUrHK8qcuecG9/8R7k3KThdS/4PP/8aPcvmvbd8QrvCaJ9xoGhri3UlWBSGLIP/5vfu85//0zf4kT/yPH/tFx5ye2/zN9YYg8ajtSEtW2zLMFsq/tFrjzmeO/TdiFfu9/ClxdYoo3Rsbgw0lXaoNRT1l8Cj6Hcxo+jf4uzsjNlsxgc+8AFc1/3/azb9xUKWZXzoQx/ix3/8x5lOp/+z53+39DcASd3S2CUuDtuhQ92CJQ3v3dvjh3/pHOPVTNY+5zPF/+WD2/ytX33E5y4ErmUYhIqd2EG3LbM0ou9U+CanSiJEVPDWpOY9hxWRo7GVoZAWxnTQjWFqNOO44XhtQGS4tkXZbD7Qv++gg28shDdlEDS865kTYrciqbtUrYsSLY602I0disrQmI0XjKtakspHa59Fboi9BduhZpE39MIFjy62WYY+W3HK02WHvp+x1jXSWAS2orAblqUitB0Ohwl73TmN3rTAGsONQUo0LDGixhWSZekzzxwslYOJwGjW5cbzRxuXZWU4iEue30lZVw2hlxBZHo226LgFV/sVD2ddBlsrJILIaei5DZELz+/NOV27BE6BLR3OlyG2W9GNC56WTxl34Gxu0Q8q8kaxF2WEboInGyZZQItk6GeM/ZJRt2Tga/72L44x9TZN21BriZcrXj0dkFg+pV/j2pKb45RlLqkQnK58FJKiMkCLZzesSxfHaojtiqfLGNNIpBIoqVGyJckVoScR3kYq9tx4RVo7RFbNZergO5o3LmLubK2J3Zq9bs7TZUBr4GQR8batBQ1wufI4mcV04k1S1cN5zM3BiieLENNKbNHweBlxtgiI/AohYS/OGAQVq8riMovYinIqWyKN5qBf0w9tqsYirW2yJuOd+yWfejogyzaG66druFh2machWWXYjVOMEHSCmrcuY/Z7Oat8wzJ8dn/BKncxbBjBeb0JI/H9FatiSRg0OFbNJLGRMiMtYq6PEhptg7EILE1rYCcueLIQNJWFbiVZ6dDxLYQxCHtFICVlI7GES6AEFyuPYf+C48wwXXcYh5pbw5KXjl3GXsAbkxW2f47WiqR0uNLLaAFJi8k8dLXFV+/u8ovHDzgYzMgqi60o5zJx2e+lJIVimkqyEkIp8F1NIwUjP6dpDS8crvjcUY+H0xBHGiK3omkFbSvRjcvRMmDY2SwHH016zIKMzz15SBOsca2GspE41ibQZRhWlK1AY2iNYhjW+FXLwmiWmWKnV/LCuMcvvqL45ucH/OPZJQ9mOQY4z1bcn7R03JB5o5ESBqMKYwyDKMO1FVXj4Qc1RVMzzwI++nCPqtX0vZZR0PBk4eLamuPpgPde7fKHXhzwnT/5Bt/6Qp9/cT8hclqujgqOlx2aRjCIcqpGMUttZgXsbdWMS8NEZFRCESiLWgsCtyAaCjwLlKzZ6bbsdnNcXfBkNmSWhliyodY5AgthbO4fxeS1hePUfGJ2wf6gZLaOWc59DmKXQmj+0+d2+NmjY8Ch0YJ51rDINp8Vz2cW1w4Sup5NWRX8ynHOdjdDiIo4zPjKd9TMFj3SWqE+H1F1kVUsMsmw57DOW7Yj2ApsOq5kunZIUslPXCy5fnXOohkzCuFsHvDMuOUXn87Iao2vGxa54Hyh2RtnfOX+mDfPW0aHCbb00bKmaluyWmCrlEoJWhOhLAOV5MaVhlo7uNJmu7e5r3cfeLzyasq9xw46SDCOxTAWzOaC0ArZG2sit+YTT1LGrs/puvjiF8bfpYyif4svdk/1BUvPvu/7vo/v/d7v5Ud+5Ed+xyeEeFLyM2+ucb0Kx5a89NhCuinPbdc0rUBjMfR9qkZyfdQihMRzND/8qQc8uzPH9ha8OQ046Ap047ETGPbiiLyy+NTlU17cXzH0a3p+y+lS85FHHre2Ug47DuPYYncdEwSKm//bP4ZkiaUy5oVhK1xRJi5V6SCwCSyLqrbouy2R2zAInuIpi7IZ0RqXugoRreC5vSWRl3PabHE273AwKnEtn6oSPF3a7HcLQNLzK8BGEZAWLh99NeYD+wknuccLB2uMCSirhu3ulNB26Lk+eR2RVzGPLm2KtsSmREhJx4WrPZttvyJvWq71Gly7oW40lnLpeYbLzOHpUnG2tnlue6PzHvgly8zhwcyh1YLLtUQbh2Eg8N2aR/MeoSPYH6x4/dJjr9Niy4rLLGAy79HxGh4UUOiA/U5N1vS40tWEtuB07bEbF7xx7vHO/RTdthgRMEthlkQMwpqeXxK5KU+XLmDjWoKduOFtWyWvLTq4lmaWuUhhuLM9wxLthorbJLz55i5RBHlj89FHu/SDhsNuzTJzyUqLp4uAUVxgTMs8kyyKDs9vLzlZB58vABbTzOb6MOP+LGQ7SDf649xDtoZZpohcMAi2ooons5A3L3rsDQpeP+5ihzVvXOQEnoXWBt+rqBs4XXvo2jBtXXp+DS1c67m8Ns/YCmq6bsnRImIQlnS9kqJRhG7FVpRx9zLkbQdPefOiz7pwqFvBYS/FVoJVYbPfyTBScLEOiLyaZ7YSpuuQSaqInZymlpytQpx2hGkzHKV5MInQxlDaEilaskZxsgyQGN6+u6BsBJd5SZ66CG1xc1zxxmmXwPTZ3TrmMrPp+DWdQcLxMuDhJGCvn5GULlkl8R2be08GvPdqyesTwTKBQsO1UYotWz7xoEfILkfpOdcGa25vVzyedXg8DQlszS/f3caXht1BQSME18Y5q8JGCcEid3CchnkJT88i3ncTVvqcxdpHNx5//qv2+cjTMy7WDVtxSS9asipcWqOxlcWqCDie9Ij8nO7egsa0BO6C5w4cTlY2wiim6yGEKavCpWgatjtzXGWTVgHPba8w2AyDhFkhyMqAVaHoRTV55eE5kleOh+h2M+AGQ7eK+AOHfT5ydEnq1Xz2OGTcKfGkZq9bM808Yk9iegXnC5eirYiDjPdF21hbEXcXC7LaoMUCx7LoOi5dz1A0Gk3JK5cW13oNZ8sBt0Y5jq15fOxj5Zp5IxkYn//+pTNqDe/Z7fFzjzX3zwzvfCbBaocII6hrQUvFx4+gF1XcPd+nKYd0XENSQVqXXC5LbEtiWx7GKKgc3j4K+KngnI6r+P1v73MQu/zVn3qdRboxcnzbWHFtKAgsjSWh1CVvzlIsYVEbiw/ejPmud93g/qzget/jf/+eHX7urTm+A5adcncmEErjKpvtUJMULhjDX/jKXX70ZcHeXkvgtMzWMFs2NMGah1OPQTenrkPqViLMb8+m4bcrptMp3/Zt38ZHPvIRhBDcu3ePGzdu8B3f8R30ej3+5t/8m1+Sc//qX/2rfOQjH+GHf/iH+ZN/8k/yd/7O3+H4+Ji/+3f/Ln/jb/yNL8mZXy5ETsNoa8kkUYS+YpEpTlY2x6uKYeSQKZfnhn0+kab80CeOmM4Uf+wd2/zEKxNyvebGdkbdCMqlz9VeFzdPOUoNcSRYFoJfO/Jw7YqD/oqLtcRzBJF22I4EbevgKXg8g9Ol4uqgIqkaqrJiFPlk0kYKC1rJ2UoSeyv2YvAVPJpJBoHGsWJO5iEPNBRVwDjOaLXDMLRQwrCqBFq7fOxuRJZZ9EKPQVATOQ154+BbmshdQetT1l1KDbFf4liSp8uAi0Sw2zEMfINsWgw1tvQ5Szzeuow5XoRM0hbbKjnsp3Q9DRQUesO+zGtBYOdoY9N3DbGb02gHW7YUTcswdDcm1VojVcYoMBS1S+yWNLpE0WOZQWUqtroCW9iMwgU9P2ccDqi1JK8aLpKAG15O0Wp8u8GWENkt88LnaBkyyzy2exXLvGax9Lg1tHE8zTS1kD3NTtyw3d0kYPUDya8+3EFJ6HgZlZaUjc0kCbnazWgRLCuFpwyR34DQbO+n6EZw97JHS81WlDIIKwrt0BjJ69MOjxchy8zm9taa44XHna2StLSptcG3DO/cn3G68nlwERH7NpbwuFgKbo/X3BqumGc2jt2yLC0sqbFEi+80ZKXNizuXXAsmzHWHX7y7j9Vq7ADG/ZyqVbz8eIjlahzZMEs8XEdjEHhCc3UnIwpzngkM9yeGupaErqYTNVysPW4N51w4AbO1zSgqCHWFEoKikhwOMqaZy3Qd4CpJLQR5A28fJERWxccfbTOMMiwL1pXDdOGxP8zwHc0is7nMXHy35Xga4ikfW26Wn0dzwTO7goezkK1OyulS0A1qtJRgACnpqoCj5YK6tum4FbmYUxU+W/GAnCVlrXj9osNenHJv0sUShsqsuXvWw7EKxnHJutwM2ZLK5ngeYStNL6xQssVWmsN+TlYrXGl446LDM6MUIwXDKOd0FaIaRezXpKVCig1TujaQVTah3bDOLbKyxLNs7p/0CcKcW9trBIaen1NpyTzz6PkZTQvz0uUoi6kNPJm5TOc1z+7G/NLJBdKr6UUVHV8T2DWPUkOtc8ax5MF5gO9trl8fZpwsPCapwVaKfqA5WVb0/JLWGNaljTEWu92CJ7OI2Gv52is9/sRPfI6B6/Iz95fUWtEYOFuEuG5GPygRxmaebnwWD4cpGTnXdhqCxMJRhlXusUxctrsCZIFr54zDlry0uJhZ5JZgHDW0puF06TDqCrp+y7owCMcQWppKFhxEOYPA4e2DIZ9tlvzvvmKLK/E1Ki346NkjboxyjmY+i9xhPvH47q+6gnI1P37v/ueHcYrQ0WhqzhNYJOPNks+riYNq4yeWNFzbXhGvBhSZxahv8cxQ8niR895RyOV8jXQ1W7tL6irkoydPWaUugXIom5KXzuaUWtN1LIyjCaXHHe+Ab7uzzfc+eoSjHRIaAm9FrgN82/B45nJ1YBi4LUXps90XWLZgvqxpsprAaXl4ofiHr73BOunwJ9+zy8+ePCBLfCzZMOoYJIKsrinajNceLfkz79zn704vvtxl9HcMvlQ9lfxCv+EHf/AH+eVf/mX29va4c+cO7373u3/T43cSjlY588wQOpLtwOaPvn3MyB5yuQ45mnssCsXpyuE0MeBc8HCVUOmayJVYus+212cn8PDtCtva0FPP1orjdcOnLyb4XkrkSpQJ2Itdbg9tbKl5vCx5lDQ0VsEik1y+fp/X/6dfwGKTxnOy8tGtJKk1p1lCoTepYHUT4ogOnow4zwzrZgJixXnikpchu0FM2/ToBAsuqwzLTigaiasc5uttjucRHX+GZycsS4ElFV3PJXIVxwvJk1NJ18+RKKQMKaqYjn3BrMxpMVyuImI7xBUe0l6RVhsK8jPbKT1/I4O5TB3KRuM5ObNScJqEPF12cJRNxyto2obteA3G0Atgvo6ZrCLiMOX2zoRRnFLWkBYBnrRxrM1gaxBkVKZiHC2QYhMperXXgnE5WwuuD5ZIWdGainUekhQO1wctTWthKY0rS9Kq5vb2CseqiVyFkpJ37We8uLtmK8zxrRzfTrGtmsvUYhBU5LXDm+dbPJwMuXt6yK+9ccCNrYo/9fwVXr6/w+NpyP2Jh61KLjLFydKj51dYomUUFZskh1ZykfjcP4/BWBwvIxaZzzxz8S3NZ05GhI7mymBNCtRacaWXoFvDvYuQVggaLXnztMNs5jNZl9RySVpWHA7WWLQUtU3TKCpsun7New5n7HZT3ljMGMUVX3nlEktJusrjcJgxy2yWuUPfbwhdzVZckjcOjlVTNRA6mmXhElgWi2TjY7SJ96wYBWv6vqZs1CYetLZ4MO/Q9YuNoaFyOVmG+LbGdzaeTbNVzDJxUQKMEbx23qHr1USeZtwv6cU1d89jQk+Ti5R0NaAxMEkcikbxwt6CW+M1k7WHZzVEToOSkm5vzf2pYrW08D3DMKo3Wu7GZqdX84mTgq5veLrokxQeftvhW+/sca2X8e5rM7JC0ZW7FLVkt7PmsL/ghb1LhkHBB585I3YNV8c2O2FMx3V597VLEn3JP7l7j+N1xW5gcbIwTJKIRlt4lo0BBIKHM0nZCpTM2I0rtqKW0DW87zChH2SUteRsDZepYBxpJBa2VLStx7KU1FqTNYbLVJKUNrFrEdkWWSW50Q8YdpZYdoKUgqHb4WtudvipuwueH3c4nTu8duZw0Cm5OdjE0S4LQa0NgVOyH/nU2qLjOCgJ0r3AswseTRreuujQdwJiVyBFS64r9nsndJ2WNy/3CW1JY+Ay1dyfGUJHY+uQbrflI28t+fk3lrywFeGEU24fGGIroGkcLleStE3IzIrn9xLev68QbQ/TKlZly3m2YK3XHPQbbg0UNwYtu3FL6BckTc20TjhZaf7Uv3iVP/z//iT3X/sM+vKSd+0pbgxrmrYhCCbcm5XcnTXcHpfoaoDJO5zM4Cc+OyXNNbU2bPsev/BwQa1yThLN+TLEMX06Voe9YMjId6gazXPjiJN1jXRy4mjNoqm5feuIvi/5Lz9wjVB26Lg2tnZp2y+4nP478cWMcf3tFuX63d/93di2zdHREUEQ/Mb1P/bH/hg//dM//SU798Mf/jA//MM/zLd+67diWRZf8zVfw/d93/fx/d///fzoj/7ol+zcLwcc20I0HXxCdCPJch/fafjFezn30hWTouZdWz3yWiKMYhhZfPJRwpWRwJJwslQsC5uKlGWZ0nddtruKyGnZ8n0GtkdrWh5OukxzRdKsSeuW0KtptEAYl4OOT127nCxtzqYxv3RX8NmHOU2jKCoHWkWoQjqOwJJLQjfn3YeX7HeXtEbR922Mdnly7vLa4yFna8nJQjHNInoeHPRyxl2NH1UImXHvMsKzJI6Es2XAybLDRapYV5vwjmUS0JiWV4677HVLXKsidmsCW9J3K1pK1oXHw0mHdQlXBhlfeX1JUgmGQYFrSRxrkwInpGSa+yyKzbAe4yAwpFpzlkQIoai1YZbbtNplXrgsK5imDi0bGRey4XIRIlFUWnO29hlH4FoVaW2wpMO1QY5vZXRdTd8DRIMRgrxWRK7BdwyGBmkke8OK0p5xnuXEVkDf9RjHGaHdYAlYlQ63xgte3J9SaYtRWLHfTRlFCZ6tKbVECkXXr3nb1oJhWOMqzdt3luz3Ujy7JLQbFILTxOXhwucicTibBwz8mlXhEHmaxlhMCodF6ZLWgldOepxPQxyr5XRuyNqCdxzOmKYeD2chB72MRWpxJc5ZlTaltoi8im+8fcr17pxMBHz2bMCL+zPCoEEIuEhc7k86SLvlam9NWjlAy9V+QisMh+N0k2AGSMtg2g0rKvI1tmh5ZjzndB3RasF7rs6RwjCMa6Q0XBskjALJlUHGTjdj0J8SuRW+s1kSTkuffqhRysJRLTudmp1+QdkKtqKM2GvoeBpbNXTCmousIa1ypHtGJ8q5N+nQcWoCt6FsHM5WDm7r83AS4TopV7Y0edYjq21Ko7g2WvK2vacEtkXeWDhWi2sZLlOPrLZotMB2alb2EYMwY5o6LLKNj1TXKzHA1WHKZOVysfC4f9zjjbMOo6AkrSz6Xs3rpx1Whc3jaYwjW8ZxTlJY9MOSoqmpDRSl5Eo/Yb+fcavfoR8YlFLYDjjWhn10Y5iw3y0I7JKOV9Iai5OFR1ooDvtr9rolt8YpF2vDyxcJy6VgFNfs9xM8pyGtXK6OSt537RIpGzzH8PzOkvdfm5FXbBjjgwrHLVlXil6gsdVmmR+6JYOgZLKOOF/ZlNrwUw9P+X3PxjxdlZTasOMNmKWwLi06rqJpoeun3BylPLtd0A8S8hqa1sa3Dbe2LqgaidKbQJezmYMtJecrn1pvgnT6vuHGeI7naIaRJisFj2ceR9MevbhmNFyQ15IPHO6RpTGVWRNFBX/npTf5L37hl/iun3wZaSxsVZNVitCtmTUl/+3L9/gHn74gcioCt2a3mxN6CZeJZNcfcb3bRbcglaYbFPjujKoBSUzst9hY7MWKsta8/ETytz92QqdTEwc1Q9ujzgIWecX50uaZbpfzxBCFBbsdC0uCZ7mMu5Kea/PgsuZs0nI0Vdx9YpNcXOEHvvadlGmH/dDlRi8EqcmbhlrkTMuMVaUpSp/Lict+B644+4x8n2ldIIVgtjacphnYBYumYV5oji8G/IFnh3z8bMozA/+LXhf/Q0/1heELfpV/6A/9od/yYb/dMIw0syZht23I6pCt2GKxslhngnEkCSxBYDn0LImuQ5oaPnMsuD4seZKESNumHxa40kIqB2lJDjotymoI3TWVyfD9BY+mW4wDTehEbMcdkhK+7sp9Xl8OuHs5YN5e4finPsyirAgcgy1d0JLYgqCbcnde8Iv3W671HW6NXQ46Abt+wOsTw0tnNidzw1bYoozLwBc4FkxXDa+fgys1Te3h2wFbUQ2moDZT7vSfUGlY5zZDv8NAWpyQcZmniLbPTVszcB3SZof78xZHZCxyhSUFfd9lGKWsColpNzrf1857fODmJfcuN1uwfjjn6uCM2LNIK4vtCLR22YsLAqshawzLAmaJjWcZLpdDZitF28Le4IJrwwW2jHCk5mZf0LQuXbfh4TREiJrIX7LbKyjrlkVuc5n2iNyMRWFhyYInS4uDbk3eGHQb88x4QexBYFU8LR2q2rCuQiJ3gqfUJiVMKpalYjvaFO9xVDKOCl6Ifg+fPiv5tfSMm1cnXKxs/s6nnrDXd2nLMamZcLZquTPKudEteelkyF4vYxg0ZLVhHK84mkdcHWYEjuawr8krSdXauKriK6/NOF4GnC4CmsohqVtO1z6BCukGcy4zhzvbSy4X27xtZGG5Sz5+d5thd7P16ds2aWp48dqcV4/73BglKNXyy29u43kVt0YLfvq1A+KgRokVy9zmZOlze6tmWTQcLT1GQYGyDA9nIVkFN7sdPnuWMwxT6rql49VcJj5FY7jerVGO/HzCBgyjBgxcG6xpjWCq5vhWzZtP+zhOzahbkKQuJlyyTG1cu6XvVlyuPG4NDa8sXYqy4XCccrzweW57xicfjnAtRSeqGEUF08TFt1s6gabWFq7VsMxsbDz2gpB5tiTLLLxxzZXBmrxSnK0HOFbB2dJmf5ChteDBXPCf3unwq29d5eF8je1Ajear94cs9JyOqzlZxvTChtO1Ta4NnXjCL9wPsR1FYxz2+xXrvOLRxGcriGlVypOFx+FwycnC49b2mlka8tz+mtgzLIsetqzRwsZRKWfrkDfOPd62m9G2Dj23YBy2fPTuCNep+H3Pn3F/ISi1izAud4Y5b00kArg+vuTRpEtgBhz2Gm6NSuaJxcNLi299fsxLq3O+7fmr/PpPz3nhQCONR+CUrGtFplMOQski6fC2HYtXpg6Ntgiciu0ox7NdnGbIrT1D6Bou1i5SrQi9GVKEXO3ZnC0DLFXzZKm5O1HcGjXYpUfkCLK2YZpu0jf2ezZN0uNwJMgrOFnWTM4thgeGO90A2/gIkTNJFA/nNc/snrNtWxx0ekS2YrJ2kFbNokjJVMv/+ZcMge3hWxLbKEK75sGDY5yd61h2yna/4NHMQ4uad+415OUOZeOAsVgkhn95OeUnPrGiHyqu7JdYysK3HWTpI1BcjdzNNjrT/NFnt/iZew9ZVzV/5p+/zlZk0XVaWq3Y70CoYmI/oGo167pmmkreui+Iuv/OkvMFwwiJEV8c6ZkRv70kVT/7sz/Lz/zMz3BwcPCbrj/zzDM8fvz4S3bubDb7jfjYTqfDbDYD4Ku/+qv5zu/8zi/ZuV8OXKwr7s1jdruKjjRc6wYsSkU81hhVYynBf/Prd6ksg2ldlpVknsN4ZBjGLrRQGzjslHz8nmSmSt51fYdTLrh/EeLhkpcdAruizGMeLAIqWaGMSycouEgcQlfSDxTrMsZ3WkaRYlGUPJxrBrGFJTej9Z3Iom46/PJDj7fvpgz9CiEKTNvB1AGRLallQ13b+L0FjdFIIZAoxlGJ1nC6DAiclknqsBOXBBYczz12uxUnC4+r3YjLrCC2NOvKMIxWVHWMFC2ObHGFxbKw0dRIAa5SzHLFbavEkhbabP6OFIpG2xzNbNLKxnPh15+O+Pobcy7TiKxqeDQN6bgWHW/DMsgaQdMqigq2ojVpFdG2gn6QczBag3E5X4SEbsZb02jzIb1Un//91FTGRjeKsTfj0XLMrz0c0qB5bnvFdpzx9t0Vb5528ZwSpSQ7ccUqK/jsseROFNDxNxL8wGrYDhuSykaIFVmtOFu5CAwnS03Xa1iUFo6l6VstN/or0trlIvO5MVyiJJyvQxAFrtQ4wvBw0sW2DdeGKbZsuD/rEFgaX9UEliLFoW0Fw37Oo0nA9a0UWxmOZiF7/ZzQagHBreEaDSwyh7Te3NPp2uHu5S4ni4CtToVQNZbSCGMABdowm3jYUvO27TmR3+Dbmqu9BWepS1lZBK5mmdn0gwqDxbqweWa0Iq8kWRnQiBZDyyioWZU2O2HGpPA5Xkt6niGpFK0WnEw63DmYktcSW0r2Oglna5+tOOPeaYDlwG63YJH5DKOC44WPb238SXe6GaOwQqkWfzDHW7sMA4vHi4BxnHM+D8hVgjSK44sRZVxwZzzgOK3I1hGntqYf57TmjKLxSAqJbYGj4GLhEfmapGoZhBWLMuSwl7EsPCK3Yb72GVtdXjuR+I4myy2ubSVYjuZTR0PWqYVQhqJ02O6nDMMCR20YXUIIylqx3ctZpB7rXPFr93bxnIrK5AhjeGEw4N5qiaHlbOlTakloN9iWJnRqJIKuV5NdWri25nLhklch425NsXZpWrMxDfcNjyaSvV5GVRtOVl1s0bCsLHS7CVDpBy2XKfT8hp5vEKwpGoeXHvd4di9jHLRYKscvHW5uGY6m0Fhr7q1cXDvghXGHXzmecHsvJbBhK86ZZhuD66xW2FJytI5QQnC6cIn9go+9dnXj8wgUjcPt3Yy0bjlfegS2y5Ze89qpwyiy2Yoa1qImrww7HYVFiZJQaou6VXzs6YxuaHj5zCEpbcKgYLHuYRrJdr/gcuWjWzhdGN55Y0Xfs7lcGgK3InQ1s3wj8xRtxHt3dvhX96ZUjeBk4bGsC1brDnu+R6Mr8sLG9RqerNe8+tgnCiW+uwlVabRDYHweFxVP55JKw73iLZZ5xH7XpajA0YbY0SzLgv/xcw0/eW+BEpKD8ZIyjbi9V/Gv7y8pK8W4W/L7r+/ztz71GtfHhs8chUgcDjsWljLgtziW4D07fX7wpSMm1ZphXDNPbK4NJLZwcGqHuCOppMV79yP+1f1LvmbP+6LXxS9WX/W/lp7q32tQ9IM/+IP82T/7Z/E8jz/9p/80BwcHSPnF357+L40tN2Tk+3hG04lb3lycU7YtgQ01CbnJeLg45NaoxFIht4abSHlX1WSqQUlB4Gh8t+AiL5GyQYshoWUTuyEYn7O5j++s+aUHOziyYVW0DMKcoV/hpxYWDt3+iOc+8JVc65fMioa6TfFEByU8Rr7PMnd5ca/izYnmX71pcXNYoPAY+gEHoU+RN8yLEtMKro9TGu1yrWdzunSYlhVtm9GJUoQw2MKnqgacNnPenDZ8/GEEuYXfsXhxt+aN0y5//N33cEWHYaC5zGKq2uXViYXnrqkbH60tllnMMCzJSsBITpcOf+9Xt8iSLZ4/3FCcizpnHNQUfspb0x7XRkvA543LIWdrxfna4Hs5ZaPIq5ZBoHBtcJTLXiwwJqUhI6lChl7F7X5B2zrQVpwnPucrwThaMgwlJ0ufW8OCm33DPDMUmcey2Hj9rAqJpUo8y+KTT7bp+Q1Kwig01I3NupQMopR5YbETFwRhw25fM88dlqXH//D0FaDGcSWPJh6r3EHrll7/gpPTKwQ9j2e3TjhLHK70VkwqSVYKDIpH05iuV+JbObYlGQQ5SWVjKZu0bJhkNhepS1EpnkwDstwmChuOZxG+a1jXLh2/oR8UrMoZR5Marwy4sbekLCXrNEB5AaPOmmR6G8ElPT+nbiRhUBG7BTeHGW9eFCwTH98tmScB436KaxvcRtDUFpeJg2u1WEJQ5RFvLS7oBg03hyuOy4iqbrk1XpBW1qYJESfACNeqKWqPjlfwS29tMYgaPNWwzB0Cv6DRLtPEp6sEaWnz7P6aJ7OQZeHR9SteeTLi+XGHJ8WcVdZybZBwbxLx/JbNZZXwB669ya9c3CTyWt66jOn6JVcHKz532sNTLQ8vBIvOnLax8f0KBbx60ifyKu5sLQBB7NS8edlnEBYIVfHDn3rEZVLTupqiVPz6ccKyTXjvlZSu12Apl0XuMUsD6tqlEA2HWxmSFq0dniwdGtfguzm//LTh6ign8iQ73Yaen2EJn1Lb+DbkVc6ThUvPr9iJCz71tMcyC7jSa9iJNpHH54lhcuFRti0H3TVNa7EfGbLKIITkjYsIqRJGMawLj6TweTlfM2pi0rrl+ihntY5xhU2ycPmpNxd0Q5v7FzF7vYrANZwngr4Pi6RP0xouqkccLS3edZgSWRaBo3nHbkhH9vns+ZKqrknqAtcWPL/jUTcWGMmyAOSERenwtrFHXSmqc4GwS44XIELNu65I/v5LTwHJu7b7fOTogllZEfkBdwYOO1GFFJplVfB4bTHuZuTZFu/cL5hmkmUODxcFjpuAsrDrAbfHAt3CIoNJmpOIlOAr/zjjYMPEuznIeftWysN5QFJuzGXvnjs0Juf4UvKPvv05/utPvE5st6xLl7SASZYzWRt6lsN/tB8Rdwyvzif8vY+fM7A9DjsOloSWhsJkHF0ahGyp1hamNTyYnOM5NuOwRm87hFbEZ75Mtex3ItI0/U1br3+LyWTyJTW0vnHjBo8ePeLq1as899xz/PiP/zhf8RVfwYc//GF6vd6X7NwvB/7G173I93zsmKyqydce3/l7dvnun3zAwdjgSjDa4loPTheKsjG4RtKGJY/nglq1pI0CI3h2p+HtuxK1hm97YcTfe3nFuprTOhWnK4vdXkXVgmv5eMpmXRg6Xs0gLHi83Gy086qD6zRsxYaOqxiNpswLxTAQ5LWNEDUGn9hx+LVHDgZFXSuKRpAnPrfGioezjHXSsgh9zteGSaI3CbI2RF7D2dJmHFXMMslO3OA5HgIHw5rjacjX7mxznjwmy13ef3XBPDcM/Iy6tSkbw7pReEqTlC57/YTThY+tHH761V2e2y3YjQtCu+aybHj5uEfXE4zjNZEoefs2vHbRp+sVPE1cDnuaWWLTsvEQ2o4NHaek51acJDGn6wCoSSvFIBAIkZFWNnXrk1cNwiypdbwxt3Ubum7L/VnAT73+Nrpey9HCYhy3G5lT4RDZmkUpGdsOHadgr5PwRIU4ZpO6qtSanThHAW0rWZUWQmh8q6U/rLCkoDWCVWVRN9D3NadpwGtn3c8zwEtOlx2u9DM6bsU7RhccJTGvXPS4OlqTlQ5Plj7CbKQKl6nLXi9DSqi1JPJrtBGM4wrH2pgvj+OSuhasGo+3Ji1fdXjKh9+8zv3zDofDhMNBzjxzCZyWF/bnnCXhJo3Sq/BqTWqgbgWDQUZW2DxZdLhhLdmLcu5OOhwtQ0K3xs0NQkLgaZSV4ds1T+YRq0KhzCY45KBXsigsum6NlAJVavp+g+8YdroZTy46dOLNsMR3sk2qWKUYxSVKGnyvIa8cjGnIG8Vk0SFyCgLLMPYleWmx359v0lhrxfnaR1kZW3HLJHG5Ns5I8w6X9cbf6Gwl0F7DVsfQhoKvv3LIx87vIoXh6jDj3nmHndBhVXjoosHyG5QyZLlkVbn03ArPbrhYueS1xSg4xWn8jaxPKTy35ko/pUHysTe2uOb1kd0M1ysYRyWTzGO6Chj5FpKCVWGjTYuUiv/suT0+9aTgnQcu/+TuEZ+dT5CiZew3jKIc0yoezWIsqdmKM0ZxSV5JpDR89skQ03r4fo5rBagQXHfBOCoInIY0FiAEl6mPJUsq41I3Nr96b5fYL+m4Gt/WXLlxynkSEjgNjjFsd2tefhpwe0vSCRSuqgidhiw2WBJ+/9VrvHU8Yy9wsZBsRQVSaEIbSkfycO7Q8SRJEXB8GTAvJEPfpqpsJlnLzmhF13ZYFIr93pzHkwECRaMrGiPpepJ55nw+RU3QCyB0C2apT89vuViB66VkRYipFItC4Do1Ixu6QUXPK6kal0ULSVPw7HaJaQOezFxGcUXdKh7PNaW2Ca2QtJa8NclZljXLxGKeaN7VlwxHkNcNTQtGQmUS7h/73NrXVKlLFJb4DlxMFd/4ni1em75F3TgY07LXlfRcQ1bCKjcMrIbAT1i3Lv/H3/MM50nNy+dLDjpDTq0192YVP/9mw++/OeL/9MHn+bp/+Cn2Ol1OZoZIuzjBJuk7aUpCD3phya1tG1cIaHxCq+HK0KHntsxSyBuNpWtOl4ARtLnNzz+cfFlr6O8kfKl6qn+vac9f/st/mdVqBWyiZSeT3x1v3PVej6Rq+dQDyaSccZ4nOErTc332wx5Dr8NBt+Cj97r0wxTfK+hGKxY6YVZlCCFwpIdDzJUoJMs7FGZFYZbM84Z11VJoj8iKOewKbgwztjoFz4xT7q76rKuUkySn1BqhfC5mHSIrZl2GnKQ582pC6KyxpOKg43FnEKKE4mhd8C/vhlRtSWBDx+3gix69MCH0pux0LxlGKZFrcXtk8ZXX5oyic5Imx7VLDDa+3edax9CoGad5QVkLAkfy3LbFedLjWvcRtSlRouV80Yc2ZBg2hE6NJRuWucRxMgK3ZhAYbo01W52aeVXi2Tl5rZhnIZX2GActh92Cui34+KMOT+ce1/oZtD5KOFwd5HzzC/e5tT3hymhG7OXMM59KO5jWY+C3BHbOyf+HvT+Ptnc763rBz5xv36x+9/u3f31zupwckpOEBARiFBMoroJayrCKxlvltUqtKiz0CgpXLAkWcGsM645LrjIUwSs6BOxKkAyDCZBASHdy+ubX/3a/9l7t2zdzzvpjRYbWFUmogGjuM8YeY6+93zXnu9ba+53P+8zn+/1kIETLhf6Snm+QOBzMB1RttLrAWDXnBRhRM4pqkspj6Eu2uy0PZ0POc5vdfkprKhqlCZ0cxzKkpUfV+GxFhoGrsYwkkhWTwiF2SiJvyTBKmJcQOCWbQUQ2DfnUa1ssVcO1tYjjJCL2DOOsS1l6bMYtV7o5VeUwz0OWVcxmXOPbgqzyEMJgWxbKWPyey8ds91MubyVEUUU/KtEIIq9kq1cTOS3Hy5C0tLm8ntAPa2aZz8gd8J6Lu/SilG98bI/TZsrFUcq09HnppEc3bIgCxYfvbjKKG7rxKpFx3ZqmlZwubax6jwenl3BlhO8rXEuz1gXftlDaYT+NCJyWODScZQGeLThahswqh0Vu0Q9XlJll6fL45oyssjFyJb3aGZUEbkNHdRl1bWJf0yjYCHI2exm2DZd3FlwZ+lRakdWCWklOJiHLdo5tGz4xucqNtTnLXHI4CXj9YY8XDkfsfc6Y0fcUvq14560x/Ugxy4PP7TJLjHHwbShaj5trCY2yuDHo864LA7RxkDg4rsUodIhdhW1JjLFwpaBsBJ7T4jolgygl9DLKVjMvJMO4whiLWRLhSJtpFtKPBPfPOrx+EnOewzBMeG0/IKtd6tYiq7q8Nh7Q8yWNAoVgXBjGicV5ErLIXd58oaDrS7La4WgZMs5aFqVZUYVqD4zL27f2+O/f8ybed3PEKJK8+HrM8XTAE+sxf/1jD7AtwdWhh+VVPJoabp92uH06wLMsJssRR/OItALfn/LM3hm7XYMjLcZJyD97GV6bnfDk7jnLWgKGiwOBUh1mucfREuZNwt1pl4vdiO0OtK1kmRoeZQXV1GfXiQgdh9uTnEnZ8sSoC/aMN+8ILm5C7CkaU9GKhK4/51Jfc2MQcmWoqRScphW/8lAwKxWXeh5Prvv0HAeD5u6k4qVxgeOXVMshd//O99PvuNjCptY1tjPjPIdH84BXjruAQRqXmxccPnxnQeRqTlLFvani9aOGwWjB226VPPtYhQwakjZDGDhJa6ZFQ9ooLLfg6vaYT9z3sYXPM1su60HEZiQJXJgXFfvLkknZUv42mCBrxBf163dTfNVXfRU/8RM/8euPhRBorfmhH/oh3v3ud/+2zfvt3/7tPP/88wB813d9Fz/yIz+C53l8x3d8B3/hL/yF37Z5/1NE1/V4etNn4Dvc2nL54L1zoriiUhXjpctrB5JcpYAkcCSxJ9npOVzb1HznOy7h2pppLvj4I4GWJZ2g5efunrBMLS73HXxnZVbtSEUUzpiWLa4U+JaLUR5d1+axzYKkKfG9OQpDLy6xhaTnRvR9wUkiWO/knKSSk0RSNR511eP4uEej4OFpzLgpuTkKcS0H37ERwuBbcHk0pxsuaZVkFEq6vuG51wZ4juI8d2mVoesJHp6tUS4VrgWqjZkXLt2g4XQx4mAeUrSKspXY0mVagSMrXGtlF1M3mvF5zP6kQ6M0rTIYfN5++ZxhmBO7ivMsZllD16tQxuJ0HnC8dNjqFWxEK7CDEJpWSxSSwGnwbcU0C0hKl6yCVw47dHwYRhWPZpKk8lmLFNNcsiwdzjOf0+Vq43CcSjCSog7QbcsnHo547qjHtbWSwGk4TTw++sYuGHBsGHZTIrclq2xK5TAKC/pBzcVezlkWfG7zqqXRkNUWg7BdyZtEi2837PQydnsl7711wo3Rki/bOOWsjrk96bEZZ5S1zSTzWBQetZYIaZgWLgfLgEnqEHs1kdtgNOS14Hga4JjVOlgrycV+xldeOuOj+1uUjcUfeGqfWxvLlTQPSd2uiJVZaXN9bc5ap0JIwyTzuDDI6XmaZWFTt5qkcnnuaMjtsy6x05CVFueZS991AYlWknnuU7egjMWshIu9jPPcYZ7b/Nr+Op/cH+EIw1pYcr50eTTtoBAEnmHQaZjnHlUD98YRwhiSwmYQKh7bShBGklQerYK6tbAsxW6nw7z0mOUeLxwMOE99fNswy3wQcGM9pagDplXNrc2KUpeETklpKiZZyYNJyYuTY5I8pqgttIKdQcbrR5JazthaS+nHJY5ekV49S7HTL7jYz7AFTOc+j6YhrqVJCo+ysThb+CvpotOwPVS4QUIUnXD/rMPDSYxuNGeZ4CRtCTzFtbWUNPf5iu1N9noR3/v79/i1Rzl/8pk91nsFl9ZTbGmIPMW8sMEYekGDbYE2q47otLSplc1Of0WiTZsCJ5jhuqv/hXnuYVma8dLFkXBw1qOsNV9+/ZQLw4SBNeR82ufBLOTFoxE9v2UrrpnkgrW4Ym9Q49gVttCUyuLeWY9H4x6vHgX89CtTPL/iH7z6CMupmaYxReNQtJqNqGTgS14/ibk/8bGl5PJ6xaCTUzaCi+sVW92Gxih8V3H/fJ28kSwrQeRqpBAsq1VnZN06dPwKTcXJooMjJbVqOV16ZOcDirmP59b4tkFpeP00wBIWeeWzLARYFX0P6tZj/ywkdjxsaXOyVLx02KWu+jiWpmhsfu1oSa0rzqqai5stoe3RaEnZCDCwrHK0VNzcNkSuwHEVnqPJa8nDU8Xfe+mA33+ry42tjGEoyYoQQ8WDmSYtLaYLm0eTAT/wVW/htFygaljvgdI+rT3jwWlIiM1ryTmfOlyy27ERAtIc0lqR65LDucZ1JKEDgS34n188pit8XEtRlIKSmpO8YJxrhJFMEot+R/Op8YK9kcPFwRe/KeV/zam+sPi8Oop2dnb4mZ/5Gb7u674OYwwHBweU5X/YifzixYu/5ZP5nY4LsYcVuzzyS5K8T9Cb4HqGvVHDOIlwHU3sl1jYdF2LxLVwLI9rw5JxltHIJa+eR9xaX9GL9roeeRPQDzK6fsLBMuJCL0Ubg+81+K5B6ABHeBzlPmUtEFg8XFQcPvc8pjvi637vDn0n4rhweH1W8dxhQ89rcWVJ7Dn0A58s32YUZ3xs3wMtWOSCwLa40AnwpMd50bDWO6dUBozPWuxiyQ7jWlHqBYaYHoKe5/OVlwx/7N3X+Ev/5CU2KtjowI2BBNHn0UziWi2LwubiaMEkt9jsVoyTla/JJJNokxK7NZiAyIp42+UUz1E4VsNxInjL5jlZPST2BI9O1+j5iqO5xXmu6QYJTdPj5kZK6HosLZvAqVg2BUIaHs0Dtroua4Ekrx0soWmUhWsnCBHQDXLmC59JGuLZFXfON7gyWHBzuKpgnyz7HGdwloQMwxVtxBibx9cyTjM4TwMCRxO4DYcLDylqTnOXZe5Q5AHvuXDAcTVAU6GUYBIqjuYRTQbShrBTEfgNB0nLRVuyrAO6XstmN+MgCZmVNm/am5LWFjudksu9lF96uEnbCiLfIrA1F3o5P/f6ZcBwtvB488UZ+7OIUVBwuggIPMXZ0sW1NG+7fEZgaV487mMB07rgZx8e8o2P9/i1o3O0qBh1KpSWXBiWTOabFHVNN2g5Lz0245xHJyPedLng0STmQi/jPG+RXsqjRPN03NALK45nDReMR6UbLniKdOmSL23isGEQVNhWzCdPbiC0wpUShSGrBYeLEAxEdkt/1PDCgxHrsWRhpjxcWNy6UBM4igfnIbYWWMB52vJifY5BcWtrycHc59J2gs+Ah4uGslZIDI0WBLailIKitkjKgFH8Of1/WDMpPMrW4FqwLK1VIdM23Npa8uzeKa+edThL+syzhBfHS57aGtK4B7y8v5JlHC08ntg21Bo0mmHYMM5s+r7BkTaqkXiOhdMMOZ3PmAkLW7Zc2ZhyuvC4dxjyjY9v88snD7GFxHdcLm/lYFymuWY9znhy64SXT9fZ7FfEjs1aJLhfWrjSxXULOv6KzBJ5FUezAdO8g2/bn6Me1iyykDf1e1wdhDhYvDaO+DN/KmJS1Dy53qHVmo8/Snhs3efjU83/8A3b/Jv9ByS1y2vnDq2CrW7yubZ5i2sDidaSk8TjlVNDN0zZ7Vj4DgzDGq1tsjJgY5hxXlicJAJpfNb8GKM0pwW8PmmRpeQv/54n+XsvHBC5hkUhmJkE5Tb8vz75Ough/bCllA2FKvjKS/c4WfaQesTVATiy5iSDpao5XPq89/ElWdGnVhZ53XDnVJPQkiwdro4ctjo+pWx5VOcc5xleMaE0FvenO1RNjG4DLvQqzlLJsm5ZpC0/+9ocJUK0V2CVFk9d1vTCPr6lmFeCN/YTuqMFh2cRW11NbNvIxqGc+Sxcw/WRRdevKVrFea7oRQmTSvK33/d2/spHn2fgCJzmi0Qn+3dCm9Uu/xdrrN9N8UM/9EN8zdd8DZ/61Keo65q/+Bf/Ii+//DLT6ZSPfexjv23zfsd3fMevf//ud7+b1157jU996lNcu3aNN7/5zV/QWB/4wAf4wAc+wIMHDwB48skn+d7v/V7e9773AfBt3/Zt/PiP//i/95x3vOMdfPzjH/8Nx/zRH/1RfuInfoKXXnoJgLe+9a28//3v5+1vf/sXdG4AD+Y5d2YFrmwY1w2X3S02I59WacJI4dkWL+9HbAwyLFzSpsEozcBzaJXN117a4YWTijA0vHaecimu+ZWTRzw67nD9gmavZ2NYXbMcS7LVOeazh31U08EtPWJfMYx8bo4UpZoTOjP2ZwO0K4GWQWAzDBrmpSQpY85Sl2nq4xgXWzhkqeC/eds2f++FA/7xayd0bYthDK5w0aKibgX9sKExLX0XhqGPdSHHsROKZkVrFEIxXfSIOvCBTx7zX791gw8fVIwXEa5dMMk8tjv2inZmNaz7LtKUPJwNEFg4tmEwXKKMQ9V4lELy8qnHW3YKrgxaDuYWjlOtaEKZx6OZw6NpxIW+wLcLHLvAkzApAn71UchWJ+EsHZDXkv2FhWp9PHtVlIq9lqxysaXF0aJH0RRcGaYsyg53U8l57nFxmNP1SxrlcPss4Bfv7yARWNLQGljvZFwYWNw905wuYrp1xbDjMs0DuoHmifUJsJIG1cohdhuUAVtqrg0zitrhvHDpeA0n+cofJGtcvMrgdxTKWDxKe+StRa3AlnLVie0o7k0iHNliCcHVtYSzxOdSP+cwCQBJ0VgYI3lib8osC+mFBdpIbqwvePGsz/60wyAqcaVhXNiMlyEIzWa34ixz6QY1uXKYJR5WIWiFwHMUttMSOIadfsXl4ZKP31tHaZfA9jlduOwNz5lVLYsiZBiVtEoQOIZFaYj9BmUkWkFjHHY6GcdJRGtWXlTdsKJbBJS2IXIrXGloLZvjRcgsj9k/h2cuT7k9jmm1jTKCupUoJdDaoVGStc4+vt/lYBZzPguxLc21zSUncx+DoDGsimFCc7I0bIU1D8+HbG0umKUBw8jilbHi1lbBtDDMCo+ksLi503LvPKIfpgyiCsKao0VIVkjy1iZNHSxb4fstrmXwXU03KHCsz3k5aovDecTDscdap8DXPqNOQa2gVAFSGNJKYlsWD89Cdt0u/+d3XPh1gtL733uFB5OS0+MhB3bFpfUU327Z6JYYwLI00jIEjqJubUKvpSsK3jiXjCLDVj+jNYLAaVa5uG3QRmFLzb3jmJHvU1aSe2cGx9K0zim1FVNWkoPTDpu9lGFgc2WYs6wcHkwiZoVN1zMgGiyrIvBcHNfgBUf0iClKj+nYxZUWvhdyOK9YixpKpdif+tg4uFJzNHZ57FJB1brMU5dpLnhyp8ESFa+eOlSVg2/71LqgUgajHYrWJRqlZM0K9LNQkFRQZyE3txPGict6KPAdgeumTIqG33M55+WDTdJS4Do1kyRgWTr8757Y4OcWU24OYj54uya2Am5uaJLCISsdArfl0blAenBzXTCKDFkpSWvJMpOcJqv7jAs9TaMExmjCoGacGv67dz3GXzq7i6tsPr1fE4UBrqPYnzVIIWiziP/9W9a4e3yXjunywqOal0/nHJ0n/Im3D/mGq3v8b3/6nCvrAtdqCFzBB2/PyXXGeSrpeja1NsRScWtLMoghq1cbsW9MSg7OBHuXM7Ql6PSW5HnIhh+gC8mo1+JaDk+uR/yzV854y+bv3rzqSyWn+rwKRX/lr/wV/tyf+3P82T/7ZxFC8La3ve1/ccy/xa/954SW/clPn/G933SVf5AskMql63Tpe6uF4FK/otGSg9JH2CWT+pxZ06CLDo9tSEInou9b1LpEUBD7JZFXkUxCcqUYWhoLG4yH0bAZKpJGM6sTDvOUs2KP0LFZDyOEtlhcfJzPfvoNoqt7PLFRU9QRu5HFRidhEJ7z8cOIzUGOb1kYHLYCgaVhf1kQBoq1EDZ7DcZE7EQVZ1mD5Uyp6i5Z7dDxQi73Gu5Pejy7d8jD5XClEY4EP/LZ13CNYH9eMYw0vm0wIqJSIb90bw3LWl24Xzzs0gsbRj7YwmY822Kcl8wyw2ZsOF549KOCjtvQ8QTzMuCnX9tiq+PiCHg0j4hdyYVew7Lo8o1XbvIjnz7mJLFIqpC1EF47WEOLipvDKRrNg7lDXknyRrHba2m1jW97dDwbEFzo5BwmFi4lV4cpjW4ZRS5lIliLLO6Ph+yNCk6TFcGsH6TMK8nlwZx+4PNo1sFzVm3h4JA3go6rsCxBYVxKZXgwc3lqYw4i5B1Xj3HbDj/9YkFeS7bXSnpRhRZwvPB5PXF4096cy/0Fx9lqN22Secxzj0lhr0wstaBoFZdHGdPc4cbajFp9zhAxLul4LQeLiF5YsCxsAlshpWCeuZS24f55F0dCa2o2OjBZbPHKeE7HawkdxZ1JiGoFVa3Imw69IEUaTdFYtLXk7lnEwG04XPh0vCkXOjnb1orqdue0i5CKSa4ZdhoabTGKKuKwYV64FK2FNoJLoynjNKIbtChtaFqLSRpzdWPJ/jygTl36Imbdlfhuw8FcYAtFz2vYjGuWtcUi96hqaMyYwDF4dkvk1SxKFxmeYJI+V7s9jjLNyRICV3F1VLA/Dyjq1U7dxbUlnmWxLFx2BwXHM28l2SstJguHuyLiyiBnp1PzuuWxtQnTquCNxQxLD7kQWyybBYHdcvusg2sZQq/h2QtTnjuEoNvwwtGAjt9S1DbrccX1Xpc6m3N1PWNvmHN5VJMUBf1gi3ddTnllLGhUg++0hK5hBLTKIq1iPEtwsWfYiOfUysL3Eo7mPk0p2B3AY+sVs9LnnbsbvDYuuTKIuT3NyeQD1qTL86cJNid8dP+cVOXsf9jj5lrMD/1XAQfpkg/u3+GN2S7/zZvexC+evEAnKPFUQy1LunaEZ7lUbcvRvMdwK+PBNODRzHC0sOl7PmkhUKpiozfjeBFQK4FnNwgjUCokyWP2hiW1VvzKfQ/PEXzrm7f4Jy/NWevVHC8Nr58ZdgYV236IZ4kVAdFN6DoNrldR1iNm6ZBeUGNbOa5bkS9cbOHz7K5F5MF5Yrg/EUhL0bg5lyOPy1dbzhYOSSmYzI5x3vwVGFlRF+uclwHrcUPTWhwmJaPulLTt4VsBKha4WvBVlwccly6vqILSKAKt2T/3SHWN9jUdO+Ydl21caZgvLQ6WLWdzxa5QXBk2VLXP7cOYK5sZrvS4OehwlOZYxmZZtdRF9Z92QfvPLJ544gleeOEFPvCBD2BZFlmW8U3f9E38mT/zZ9je3v4dO4+LFy9y8eJF9vf3+ZN/8k/yd//u3/28n3vhwgX+xt/4G1y/fh2AH//xH+cP/sE/yHPPPceTTz4JwHvf+15+7Md+7Nef47ruf3TMj3zkI3zzN38z73rXu/B9nx/8wR/ka7/2a3n55ZfZ3d39gl7bTz1/QtXYXNjJKdqGf/zKCesdSAuPtbimG0g6ro3jGr720jb/4MVjjqc2x6nNh8Il73/PVf7y+Day6REhOE9y5pVNv9sg3SktFoHt41uSwBZErsNBoPjMQwtrT2FZFr5T4Vo2N4Y2xynUSrHRq5jXDWvdCkd4LIqQw7lEIygqi3nb0LEEl8Mub0wT4jbgW95h8anxQ2IXzpc9NmMoG5dFWSCFZpy4hG6DbVmkRYet/hnSxBgT4zqG1hj6nuLvf3pCGEnOo5B+LHDslKIx5I3DOAs4zgRnuUvoaNJSUzaCYQix11ApTVL5uNJmfym42J+w0/WIvZRZEbKsbO6dDrBljcGsKGOBInQ0hpznD9eZZR6WJbl9ErE7SBn1c4RocGyXyBFMEsGFUcKD8RDXXkEQxmnBaKNmPe+QVitc/e2zEK0EtmvhOgWeJfjKy6ec5RHjTNLxG0ZBy47qMHVKLg0XhK7iNAsYhgnzwuc4cXEsg+coQFA2Dp+z5cazFJudmtMkJi9X3kYnmYcycJ549IKWrV5NUrrMS0HRCG5sJKxHJVIYIkeROoZl7eDZhrIxnC59HttcEtqGqJfSGHh8NAcJy9JGCs3NjYSqlZylHoczj41uycgvuDPtYAuY5Q57/RSpJMZd0Wk1kpvbEywBdb1GUrj4jsVrJw6upegGLa0R9MOaqln5K7ZaU1QWF4YJWSUxQvLwLOTKesaeldDzayaFT9fTWFaLUS5lbYOt6PiK41mfzchhc1hQt5JOUHMyD1hWLrHbEvgNo7himYdsRAVJd2VzcHGjZZy5SAFxoIjdmrR0cMOMkRaIcoQtDxkNCw4nMaPQBW2w7Zq755KOb4j9lgcnHTqqw9t3LB7kKaGraLWgG9Rc6FW8ctBnvVcwDCr0QFA1EmMk2ijK1iJ0NFudgleOBjx96YxF6ZKWEuFIWiNY7xZMEh8wPDqLqBqLXq9Ga0OlNB8+OGDoBzz/QPF/+vJt/tbzd5nlLq8eD7k4XBL7LXUrcITiwSTCtxSmlhyMOzh+Sz+ukBLq0qLn1XS9BttqyduYi8OctPSYLV32NpZI0VI0LmeFhTBgW4a0ddifdjFmiRAOD6YhZ8uAp3bHzHKfvI4RssJxKoZBwyBURG7O/tRDxCWomFDWLNqVF5hnWRgDm4OKOhmx1j9h/zzk2kZKIl1mjaFWGlvCemw4UhAHBZNlQJK7JBbYaJLSo2ha3jjzuNAXBDacTmwaBFtdhcQhqw1ZI6mbiJcPAxaJQxw1uE7LeSHYiSTL3MYWhp+/M6NuLR7frViWPlIIBIJONKM4GrJOD7wGSxYsPtchZfsZF5wI0QYUVUXVCjy3pRctOUpC3v+LB+yMDGnWUraAY0hUzukkZLtjszZquDuuAMGDccNfv/OAzW1Jv9/yUy+kTIrbXO10OS4SpFtxOPN44eGE3d2KN/VtphOPnd2aVntEXs00ryhbSSdsOZ1qdrZbtgKPKGi56Wzy6VSRtBXLVLDZafkjj+/y9TfWOZtrfvoTj76gNe9LOX67cqrPq1D0p/7Un+Kbv/mbefjwIU8//TQf+tCHGI1Gv+VJf7fE0Lf5Rx9fsLWboZXgwXlM6CeQLtnrlThiSN/3eNOGzYW4S8/NeXWsOSscNuMSJRSBqwltn57TUrQSW9hMkogXDwc4dornL9gMQ1ods9upkSZknFicFSnZLMQWOVJ0eOu73800d9iMJnz0gU9gaa6vVWzEFYgu1/o2jSlQuqUXgDEu65HDNO2S1pLDNuPiMGOaddixLTYji51uwavnc144XOP6yHCxL7Flj6O54LRIWRQla76F5zRsjySTKsD3TjnJVobYdQsvH8esR4KTRZ9RaDEpS159I+DGJU3f7vDUxT53kzEdv6JoaiJ/judWGCLKJuTxzSW2UChj+NobD7FsTeSE/NrDIT/56gOuDoNVUamA80xyMLd49saMftAQOHBvGoFQhO7qQjMtNdqANooH05DQsdmKC64PKxxp8J2WRgtaYwi9jGXlczz38Z2GsnWptcYxhnFq4dgNF/opD+c+b9k9QynBOHV5NI/5fZcmnNcrP6a1SIN0eXZvwavjPsYccXPL4yz1aFtnZdY7iwHBLPU4XShiT9PUHpHXsNNbshbXPJxGxI6ibAR5Y3Oe2IxiRd0KduKMg3nEa+MBo7hgnruETkVRSdbjilHcIIXkxYMeoVuTFAFx0DBJQz4yOWdnVLLXKxEYXGGIwpYmSLhzGqMEXOhVpI3NzUsJp4lLEChOxj5VpfmyyxOqxmJ/1uFCb8mDaYdFYePZNkO/RBuBNBGtUYwzj7dfqLg3F9iW4I1xzLX1JdMsRBiPvLbISoe2knRHh3zm4QbXNjvsxA2L0qNqLGK35uG8x3a34NX7W9wY2kThGZPcxmhwMCSFT8dvePG05Kn1TZLimO1BjmsZtjpwnvp0HcWytDmcRrz7+pJxDlkVMAgz4m5D21FYGD56d52royWnU49wTdJUJVvdnOOZoZENgdPgSDhPI7a6OZaQHC8jGuVwmsR0PYHSDoNQMcky2qal06uRlsF3Gj79cIBVbfLeZ7v8zKcjBh3FopAUTUBgp+SVx85g8TmMsabjwRsTlyuDFmFsOp6hqAMeTmymmaYXTvn0Q3hhbPjjf2iEZ0u+eu+raYzmn752yD949R4XBi2h03BWQ1cZ/s4LD/jQvXPWYsm96TH/02f2eXov5ZmdmrWoouOtWr9nuSDwEk5nHue5zdE8YDn3eFd/yB94POAnnz/ngJJBrNnqKvr+qn21bC2maUTPb0gqRdPWJIt14v7K4+kot7nYnZFqydW1iL1uwCLx0aLk4sYJzx9FjEIfqSJeP2mZFwJppfSjlH/x/FXetFPR8RVSKlpd8vzhgEUOG2KDr78VcJyd09ByuDSclw2das7em76WDddHoqlVy0mWcm/qYDuQZut0bQ/dOGjXcD6v+HPv2uWTx0vO25fY6NYscx+3cdjtG8pmtTOWtS1WUHD7qEMZFVy9ZLHX9zHaJnAMoSOZVAmqsNn0JW/MFxyfBPyFr7rE//zSb9wl8lsNg8R84XDS33Cs322xtbXF933f9/2nPg1gZXL94z/+419QoegbvuEb/r3H3//9388HPvABPv7xj/96ocjzPLa2tj7vMf9/yWs/+qM/yk//9E/zC7/wC3zLt3zL5z0OwEylCDUidgWOdLnQtVGUvOUy2k0EAAEAAElEQVSCz+JsxN36lMvrJW0bIe2SYSQILJc0aPn0QYIlBe9/9w3e8bc/w9bAZs13GIYWSeZytS/Rco4jLSoV4EiNJQ3bXYebFxRRmKKNxdHSo1GKm2sVQvf58q09PjM+IXY1lWqxnIqiiXEtByFaQkez3a8Ypw2ZNLRtjHRb/ulzitbp0A1btjoJrXIYhA5Nq3nh0OfaWgtidRNtS8mjyQ62nVG1LY4tiAT8iScv8f/8pUN6wkIIs/J7GYDRBtuqyRqHnbgFUs6WXSLXoI0kb1saZfPK8RqalshfsNutaZWD79RooSlan6x0OE0kG32NJVckqrzRCBwcKegFBYfTDk/s5Dx7eUJaSba7JZUy+M5qwyVrHFxpsdGrkFJxlnl4dovBZSPKiVyYlSGbcYExksCr8a2WxzYzfFeTzGy0AUuAY9cU7YSOW+HamtuTmFkWUrUWi8ylMrA/iXlsO6HntcxKG9duGQbVSrZgBNvdgrQWIGBR2jjCsN6pmeQ+i8Kibm0cqXEtQ+w2ADyxNufFsz7jzOPprRmlssFobm3CvPbIZg7DsOLKMCNvLY6SCNcxbHRLNv0EadvcOesShy0X1zLGucc7Lp7ziUdrzHKXQeDSFQ2+Zdjo5GSlzaNZl6ywuG/D5aHLnWmLkC2O03LnrMNOp6BQgo1OzVnqYwvFMvWouzmTwsfWmkrb+HZDWnosKpdZYXOhW7IWVbTaphs0BG7L/fMOgZewLBqu2jUaCCxFJ1CMlw5VK1Da4/paiiM87p1tcGl4RqFsbh/7XOxXnKYe77p6xrJyuDPuUDY2jm0IRcN4GVCKgL2+ZNfv89x4xlOXMo6WMUprQqdk0K14lGqIM7SxmC4HrPXmBE5LqSy2hzknk4gLl885TkIcCZ0wp6rlqlMrKkmWN5kvS4bbBZ4DrRJEYou7s5S88OhFq256qXz+wOU1Pnx3zvf+/COe3I74o2/e4zt+/hUcJC9MFZv9grPEBQyNtqlbi75f4khFYK8odUIaOlHDonDQRrPbTSlDi7zxWBY2iypkPSpJa5f1TklS2ixLwdHRAGELLMusCqWWpK4g6QYsgppL/Rxb+AipOFn2+Ppra/zs3XOurqeUteTmZs60MLz8KMZqXSpdIbwZhYb1qGW9s/IzavCZztcJg5T7M5+NqMR3BZ31JQ9u91n4Nv2wwrMrtno16XLEZmyRVguevJ4SuwUPJyGNbri12bAZN0wyFyqf6XhIYOUIYWFhscgV3/0V1/js6YwHbsGsyXht3+Yt1yqOpyHzvGWW2Lzncp+XZhMQNv0QlG64c665sWXz1F5L00DVgpCaedlgWQ2bvkdauqSZYK5XPm+NEBRtyGYcYOkCIxRlI8mV4eTYYrtvcWtLELg1jo548SzjZr/kTDs8fk0xjCArLRy/xTQht89zaqulJx1KFNd3a6pijSheMpGKhpZSN8yyBqFdtPLZP9PcWJNEoY3j5GAkTtvlYHaGtg1v2Rig7Zafvz3j3rjlcFkghPmC1rzPJ75YedWXSk71eVPPOp0OTz31FD/2Yz/GV3zFV/y2mk3+TsVZrrgzL3nPRsN27GIGsBX55PUmr4wXzHKLtTBDSUOtNeuRTdX3sKRhI2pYVjaNEgg3w/GmzFuLWod0PQ+nZ5FUfSZJTdkkNEqz2y+w5Dpde4AMBXNR89p5g2elbK3l/Pxf/jvc+r4/xzObmmVTosn55YerZGUtbNnqOLgStntTHsxCjtIBeSMQ+MS25GjeUKqSbqDpODZDz+b6wKZtU87KhnFucbVv07YRF2PB3UbzkXsOV/oZ/dhlz/e4ezpkt3vOKDB4NlwcTRDCcJps0PcNlog579VEYUlZwH6mmKQCmg5vuTKjNg6PZoauK5GyZlFrrvQb8jrAGA9FS9u0XF874xMPfd6xtc6r5xGLYkVjWutkdL0WpRSnKUBJ1TrUKiR2NWs+/MqDTfLMY5xrbm6VCNlQa9gME8DhOLfZiQXLXHBrK+fOaci8DNgWM+rWoxOW9IXAtyFpbB5bq8grj6FfMQgEqQW/drRFqSWh09C0mnkuuD+LsMTqBj0ONE9un3Ne+OS1Q1a7bHRyti8vOVkEJEc9bq3PCVxojeB44aG0JGtchDGEtqbja/p+RVY5vHDap24hcFr2egUnSUBdO4xTf1VssluycsBZ4dIPKkKvZpZ7ZDl0ghplLCptMZl0mJUWXT8jKyRv2p0wTgLmOuKtl055IHocz1e6+M1BxZt3x1hi5dNEs87d04b33HC5sxzTDUpcR+FUmoaMe2cDfNvj4VSz29P4tmYzznk49RFCcmmtIG1hs1vx+smQp8NdpoNjNocW07Igq2zCjkKZLkrZRDJGWw2BDdPZNjNzyDdcuc5Pv3aIZdeEruT/9uwlfurlQ0LPouNrtrsJ/jJgUbjsraWcLCOur6c8fxSCEPSjmvPEZxgX9KOSGxtzPnJ7i1+6u0apKt6YKt61s8VzZ2doFP3uyvj72sYSgeHXHqyjFbzrek2tNJNMcnUt5SQJOV/4NI1kIyw5SULCAD71MOLtux2+7clb/IV/cZ/1bottVxzOA/JaEHkNlgMfvTPk+mbNxX7CJI+IHJBCEzoGIQv6seD22OHGmuG1oyHzRYnvwnf97EM6vZpfOrpNU24SGY9SrXTmbeUzCl2GnZLnxsXqZkaDsAQbPYVuIyaJxc5ujm1r6qbiPANHZgz9kINJl8XS5xtu7PDNz2zw958/pawVN/cmeFaX0LbwnZxaldye9LiynmAjOEltTiZD/qvH1zguZkyOa/YXitvzNS4MHTb7mqpVPDoRbF2c4Tg1N0YDeh6UreLVsc28FEyriAvdiEt9gTGrDrlHU4/ztA+5z+W+pqN9HCwcN6dSDa5nc9EOuf8v/jk3vv7/yKL2CERB3pQMQsXNYYRWPovMJasNd09qWrck9ATf9FOf4fqG5Opw1f06VjUPZzXrfktgWywSD8tR+LbLzpqN68WEfsOyajhdWgy6c/7Hr3sn//W/WtJzfdrM51P3W0Zdxa0Nl3n1xe8o+i9NevbCCy983sc+/fTTv41n8sUPpRQ/9VM/RZZlvPOd7/z1n3/kIx9hY2ODfr/PV3/1V/P93//9bGxsfN7j5nlO0zQMh8Mv+JyCeEboOqRtRWsyDDv4MiCrWpyg4emRZmc05cUjyb+6d4KyalIV04kcbKP5v/yr1zida+JAsd47Y+RqDpMBQq7W9EsDxaLWjAvDZgc+fegjTc2g5xLYLtu9BGk1lK3glXPJ/TObbd/GkxFpabHVbUgrSSPm9CMfpQIEBiMlHddiWuXsHwku79VkmYu0BWdLh0vDgsjNmWQBnm1zqW+4un6MLT2apkPo1pylEYHjUskWIyssXP7F3RP8qEa4isksYhDVzHOLwGnwHBCWQYoG8Hhyc8mnH/rUjUXbhkxqKGuBZ0tGsYNr5eTtSvbftC6VMthOy62dnLPEoVGaWgvAYZL71A1EXs0feGIMgBQZtQpptUvgrja1DuYBniVYFD53z1y2+yF9H0ZxRt067HWXVCrEaIteWFAom6SUXBm0tEozTgL2+gmz3OP1LGCceHzNxpiltKhbSVYH9MOSvLHZ7BV87N4GRe2SVAWHywDPchmG1UqC1trkjc0sd+j6NVktqdvVez/0SupW8Gg6JK8shICdfkpa2yDgs+MhrRJ4dotAk1eC2G/o+Q3KZCt6nBacph6XugVnqUfgtEROy6OkS6Mtnr0w4d40xiiB62g+fHsLx9I4gGMpJlOfrX7CvAixZcsgLtHawbM1s6KlF2o6XkvdWLh2SydoePv6nJM8pFIwXfhc3ZixKDyEhleORtzcnRE6is1uxQsHA7YGGQZB6Gr2+in3zmLWOgZPtgyimp1BwYVezn4ScrKMiIOa0K2JbI+kcXk47WJEwXzhMerYlI3Nre0Jv3JnnUFYsShttALfNbhWQ9tKsqbgRrfgYi/mvZeu833/+oDHt3q8elTQ7zRobXGyjGmKLk91OszUPqOw5N5xl3nVshaXFI2FbyssR/Hh1zZ4fHdB3TicpSHDoKIXNiSFy2eSgqIxPDyP6XdqLAuWacvANywyC9u2udYd8Yfesc1zDyp+360ef/iJTbq+zY9/+oSSjFSviqlfvZ3y8bLHonTws5a8sXGkzYVBw8N5gNAtm58j1oWegy1gnHoMowZXNRgh0FpwfxJzOO1gWS1Z5ZNWNuvrJVFQcjaPqVqHQadhhkVSwzTzGYU119ZS7k8CArflYycHPLW7xHcMY2PxcBpz5yzCsQpiT7ARQaWgRSBES+zUrIean3uuSxzl1KXHZrfh2saUvIpplMu7HzulxaVVDovCISls3rY1pFWa+XDMXn9Fse2FNocLlyo3XB01zAuPnb05B+cBy1lEt9fwvptb/O3n9vnVhzmzynBz2OVjJxNuXICzZUhV2LxUpfzxJ7f4lrdt8JOvNLyRzCnbimzucnldUNYhu72KWSZojeQ8q+jHEkt3KBqJRlHXFkpoLmzOkASoOiRLJLZfUdSGZW3QCm5tSlxbkNeGrZ4hMiF/8GaHn3vpnDfvQloEoHLSwmbQTfiJX7HobVRcGyrqFtZ8gSt63K9qDvKKuKs5PA3Z3VziNjFd33A8kwhlMxwl3JnBWqdhed7j08Upw67gTdsRW9Lnl07OKWrF7XGFcWr+5Z94nMGf+YKXvv9o/JckPfudyKk+70LRv41v/dZv/S1N9Lsx3nkt5kCXhEFGIeZ0ugmuY8iakJHXY91fGXrhrLxQNIZumJE2NQ0ZyqyBDqlrh6K0sEkJ/FNePx1yc73leLnNbn/AduSwvxD86r7h6mDK0XIA2qbje1wfupynkrsTB5WekeY5t3ZrjpMYdMxmnHN1NGcYaGaFu9Le6oh53iUpNcLKmOQ+VwKNLTqM/JxFmSJEQp0GbEYtj617XG41lj3BkhpHDLClzbWBYRgs+dTDLptBwRWvIvAqXLskdBoIfd68rTkvUh5qQ8cPaHXMrQ1NXvnc2EpJC8l6b8EbJ2t8pVehK5uba4rdboIwgqNEcpLW5FXE1WFD5FSsu4rzyuf6qOCfv5TTtDFnVctWDKd1wGflNtOsRZkViWMQLJBWwXkWo3RN2doEvmLdEri2xTAE1xYcZx6RK+j6S147W+dwIfFsUEYyDDSW8FFaYlqN8AoUNk9vnlG2DutByevTmLY1gGJ/afGmrYRR2KIMSKM5SkOSwuH+eY+3XzqjUA5VLbl72sF3FTu9iuZzWND33Dzlo/c3uDhMWA9L9hchJ0uXpJLsdHMit6UftGwEFYetQBlB6NUo7ZDWFqOgQUSG2F+93nnpUDclX3F1xqcfjZinK/zq1W5MZs6ZJB3yKqRqFJ7d0AtWJJTGuBwtI66tJ7wx7tFqm+ubC7LGYS2qmBUBolDUyma8MAw6BffSKeNZxMVhwjy3GClN1bY0Ct58IUEbiSXBkhX3pxGXhilSNNw97xC6Fto4bPgOLxxlbAYjlDhCGUM/alDaUBnDWlyxP+swGizo9Cbcnm6Q1i7Pn054aqvguWOHxTLkX7wx5qSasNWvmOc2mpCb64vVQigsXFszy7s8M9hhsy/5Nwev8mV7CXdOOmx1Mk4WEQO/pe83HM4t6sbitcUY1wbXFUhjcbFfktc+F/sZj21mPJpETJKYvLbYjFuS0kcrG9VK0tzluAoI1issqXl41qFoEj706kss6pI3r9W0StALBZHXYouQflByfaOh4xUsSkljKrbDjEppjPHJa5thaPiDj/vc2e/wtbs9fj6dcpa0zEXBdmnR72kaK2ct8OlkHoGTc16C9lJqLelFBesaHCvCtxUCSasl+wuXVyc9up7NXt/Q8yum+ZDAtjmZO9xfar7+8SFnec2njhb88WdG/PPbBT0f/H7K/XnNaeFwpa/peIK61WSVRWVsljphWWdooxisQd9zV6S31rBQS460z2OeS2QNcSNFN0iwnZxGBrizNQIZk5UtWlRMCxjGGZYJqOdDAleQ5YoZKfNxzjAUTJMRA+HT6Ss+c3zCH/ryN/PP7x3QaJvtToQrOxS1oDU19088xonm6qWEzdDHlg5VI1lUJX4wZlELOqHN17xFsUh7FNWKZJe3iqOkoagUfRuqQtBoC9c2XO55fPRwzLVeh4N5y+G4ZqYqnroxYVpfYs3/gpfTL7l45plnEEJgzH98l/A/Jxn7iy++yDvf+U7KsiSOY/7pP/2nPPHEEwC8733v44/+0T/KpUuXuH//Pt/zPd/D7/29v5dPf/rTn/dm21/6S3+J3d1dft/v+32/4TFVVVH9O4XKfwsf6dghrYTTyQhtlfzFt13hv/3/PCLplaRNwjOXDC5d1jyLs8ym7ymOlaIpGi4OFLfnij0/5sZmj1QXTJKSum348psTnj/w8V1Dx3Gwtc/DMYznAdPM4smLx1RtRD9QRK6iagT3Zj2SUvDZh1Oe3lVo7aNUBtrh+qDmUwcGV5b0PY/QU0RWy/YgZTzM0UaD6WC0ixsoFrkiclug5Wge0gtaHGLWwnMmuSZ2XWxjcTzzyZqVDKppW6q2oac9Lg7OaFVDVkpubMC8tMDJcaWNNh5nqUveSFq1Iq9VqqIX1KyFNq0G1yooaps1X3Ow8NjtZcwzD18atrtzRqFEqYiO09C2Lb5sMZZN31U0CmK3wRLQ91teGTs0KqDvKcayZXs9J3INF3qK107XmeUlZeOz3VWcpiE935CUNVnlEFgtwpP0/QLXbug5DR/fX+eN0yFlY3jX1XOMhsDRxF7Lpf6co3nIZlTw/OGIeeriCmhbwUHuETkKz/qc6bYSFLXAsRpun8YgDZdH+YreW7ksS5eiAq0MSMEsc9kbFgz8hv1ZsPoMbcWyclHGwrTguoqisrjVO+NhNqDr5Viq4mI3Ydk4jIKKvHZ42+YhdxYbFLVFL6xpG6hqSaZs4rDmwbjLRXtFLru5ldK2htNlQOQrYrdBm5ZF4WAJwzT12elmrEUlp1mwogEnK//HXNtoszJg7oYls8RlErssS4dFYWNEhKpzssrl6d0Z7Zphr1fxy3fXwUgC2ZDUHjZwnnpUreDyIOE4AduqcOyW9bjg7nHEIneQ0rAZFWz3M/bPu+z0cq6NFrwuuiwKj1v9bR6VD1Eq4KyY8L0fFrxpL8CVNb2iT12dM+wV7J/2GLgOG32Lw5nFKNJsr59TNjaTpcd2L2NeuUyWDldHioGr0H5DUnQ4nfv0o4K1uKVWLV0loBnyyoOKOFRokTKKSq4NK159NOLdu12u9yOuPxMBsD+v+MDHD/j40Yy1yGPo+mRqySuPdgidOb2wYZauTJU7rsXds5iiEJwlEcIpyZRNNyjISoeNqAADsVfT9RRdr+blwyHv2hnxy/fnQEtd22i/5nwW0u9kbPc1tZJErsdpJuh7NS8ddul4gsvDjK1uAQgGXoWUHrqVLGpwpaFsXAYxTHOLXEue2S3Y7JS4VskrR2vEvsUgyKhbwbJwOJrEbPVaHkwjOtuaUBiOEof7px556fCaSBlnOdfClqKyKRsfS9Ts9peEdoDSAtcqCF1DL4hxRE1aGj54e8ozvTWe3Y74Hz8+5X4yYVY4qMZj5El6ccO3PrPJX/nZA07ykpySHEVeQ9P41EKxFrY4FihVMa0Ml0NJx3XIyoZ57dC2YKKMOGhwTIwrBSeFIE9smkSSy5qukPT7kr6bcZQJtDa8cmjj+QcsGhfPcmhQLDND3ThoqyKvW/Z2Wja7UNQ2rTGkueBPv3WLH16+hlAhgdsS4KCrLkoLJlnKOLX4I4+N+JcvgYwqbsRr7DcNu6HDaZ7xFRc6vP/nx3TWGtYHFQ/HPmuE/D8+fO+3sCp/6cTvRE71JZ3ZPrUZcjJWDN0hkVdxntgkZU7o5mzELYaVnjtrBLtG4EuHo7xLXml+9Y7NhbUzzpKWqyOFkD6bsU3P9ui7Lv/4EwFCWbTGZr1T0/d9tOlRNhU9r8B3WpT2SOsIzwnxLY+n3vOVLCcz0vWI06VD6DaM4lXLuFI1nlWTtzYPU59Z4dO2Lotc0piSzf4MYxx6PlhEIOH+oiFwCrY6IfMiInT6xP6Uo3QKIiB2fYrG5dqoIigF/X6DlIbdbsqkqhl4ht2ug2+HNK0mcDPO0gDL1lSNomrhylpJUnvcWj9gVgRIobg9cfCdlktdzShcFT6UsQksxUGqaEKfSRaQtS7DQUtTSurG5Uo3YNIumeYtGx3FLJd8xdWU9ehzhYzU5aWjLlVtEcYL2tonb1tujyN2ujU7HYknBYsqAix2+g3nicPF0ZKtTsHBPOLhJOTmZsOW3TAMahAOsWt4fTpkVvpMUpuwgfVuQ1F6FLahbB1qLQkdTdctwUgmecA48VkUISExRbPgLA14cmuO7ximuc/lYUHZemgaGmWTVC62UFhS0w8qLF1TthYfem0Py9JcGCSMohSkpudXvPBoxLy2WOuuzBcPFwGOrQjcmlQ6bA5yPGuBq22WZUXoVCRlzFfdOOeV0wGOVPhhg+e0HE0j3nzxjHkR0vEbZKZZlgHrnZaP3h0ROApLC4QlOV10eGx3QT/SvHEUI1RNa8NGV5E1NvdPO2wPczY6OVUbMM5CAkux3U84XUa01TrTKmO34/PGWcO6ttjqF5wnAZ2gZppbpJlN1ZRc2io5XgTsRWu8UE44LCfk5z5FZRNGBbVyUXWENBXCUkwyh48mm9zYSDmeh8wyl/HC4qt3JP/kuTlXLzcEjuLmTgZYzAuPo3nEelwwimpqJUAYtqKSjU7JJHOZlDZNIxF49IOMfeESuBUPZz0uDDKGfsPRMkAKye+/1mH/OKUVDkdzj40YeoFimeWErs0r90ZsbyT0omIl83IbYn9JrQwnqcvlQU2tJE0b0mrFo8mAutWMl4bPPGr5I1e7eB6c1gW9gaBraTZijW/bHJ1bnC2XWF5K2WqWlcswbun6gqvDGlu6NI2mNRU3t085TzZpWsmTayVGd6hbh6JR9MKVRPE8jbBrlzuTkp+7e8qTmwE/d3tKUUt8u+Usb+j6Bses0fENaWl4/sTlfOHxpp2ccd4irYZLOy2eton8guMF3JkqvuxixbtvhGzHDr6dYUTOg4VG0mHg+LSBRVJpzjIb21Y8vpkhTAe0S1KCai2GGxkb4Yqi5DkuTeFxMYr5m99wlV/b/QC3Ht/g5x8es9PXZDVMcsnpwiU5i3n/1+/x2fGS11ONJRqSuuJ4adONU4IqoGNHVHUAGGY53J/lKKHo+4LHtzR0GgwWy8ymtAylrkmalpM8gyLmeLZERhVv32oYBev86uEYV/42mC5+EckavxsIHffv3/9PfQpf9Lh16xaf/exnmc/n/MzP/Azf+q3fyi/+4i/yxBNP8Mf+2B/79eOeeuopnn32WS5dusTP/uzP8k3f9E2/6dg/+IM/yD/8h/+Qj3zkI/i+/xse9wM/8AP/wXZzK1N0OwW6FXQsyS++eof/w9MRHzte4kYN8zGMtKKnGiwDI6shih3SEtatlMiGr9mOuTFy+Od3GjZ8Qw/FWqO47sD5oU/mVjhowsbhuldTIYnyiJaKuEoIlEME5LpFeg79TkO9bJBaI5cWgRZErs2bI03RVBycw4XNhr0QXG2I7Yo3TrusBRnaNFjCxilDxvsNgatYMw1ubnBtQVaMqCtB7bd06pZ1z2AFBXcnPpc6JZ4tOKkbWFrsxA2h0Ayamq4w1EsP11IYoFdaJAuXDTJC4ZHVDlVtQ6vZilpYRJzONXreErqKedHFyw2ubIiFZlL4eFZNMbWQToCSLk2r6Vk1ETaUFqEruHcQcT4LGIQNxtZYmeZCV1EWK7+eQV2zpUusxqacWNhy1SHhiZbL3YplVdEIGy/X5HXAi2frTNOALS/H9uHwYYRyXJxuw1J4rEUls9LwymsjAiG4YUlqkcFC8GUbS8rWJiwU40OfrajAzwxNBWu6WVGUTn0OEuj5DRSSx8KM8dJnsvQohYMRJWVhcdXNOU9WkrlNp+KzpyFaQ2krro+WHJ4OaJWF7bVkpktT2FwKM0QJW27Fr752kbyRbPkl2czlPHG5GBbEccPhLGTdb/ALQGrWm4pZ6fFlnTmzwmXdrzhLfbb8kkpJdvo2kxyqqc20cPClYcsqOZ0FdLyG7ahkkTo8ES0JHAWpIGwNF2TBplsynvvElsIsJVGjOVwGbFDSqVpcpckSm6ySOKnBlILM81gX0IiSYVPSrVquhxntTDKKa8ZHAd1accNvSM88MuGyZUqC1qFIzuhpwci2uOjnyM6UZGYReoqnhyuiVllrhmh8M+WlR2dsxCVubrgeZEwbD9tVnJ/7NLXNFbfggulw916Pa5tTLlgFlbJYzm3ioGU3TGmlRWEUYV/gSIXSkiq3SBOPt/QUGyblR//NGe+50eeDt6csypZE5TzW0XSChmniseG2+GJJx29w0Ay8Bm0EXdESeYqTumEUNUSNwkjwlCa0FWZpI1xNHMBQ1JychVxxU86nR6wZm6+/OeT2NOd+0nC9m+M5NZ5tMGicKONUeox0w0634HTp49qCuILA0pRTm9DVXJSK20uBV2s2o4xZKuhXNkO34ro3pyksimVEPnO45CZEoqE0DutORWwVOJnHWwZLqlyilMFJBO8dxTx7MebHPnvMda/hotdiEondakIlGEYKX+aoxoLWRlawFs1JCh/hacaLnCaQfOylhOvxklqU9CyP0AOjoG0054dzvmxUkC7GzMqawLdIpjbXe9nKSkALnLzg8U7JVT/EwmKWQI+WdT+n0Jqy8hkEoHVNoxRPxvBnv2ab7/vIfSzAsVqqRhOojGuuwygq2R936VoCp6qwq4a2MezZUOaCve0SiUfqVLhtRZV69BwQJXzkzvN8ec9GaYVpDMbNKZYK00CAy7de9vn9exK7MOynLTcczVmz4MnukI+eV/zCK6/y1q2YN29HnBQ5l9cVSZHz4Hj6hS/Kv0l8sfKqL5Wc6ku6UPR3f3lM77LLvDRISzGrQUvNumMR2RC4kt9/PSOpK+7PLUxb85nDLhf7FjtRgNPs0Hcabp/WPJxq3n4po2rAtS0eW/fISouNTsnHHw54fCtlFNScZi5dVyC1w6K0aZXEsVrisOSx3/t2JgcLVOtxoVfg2A2DsCCrNZ4l8CyHcW5z+zzkxYMBjmXoeobH1iuWhcNTO6cY7SHxaVXIla7BsTI+Oy7pOgLfdnBlh+2wIfZzyqblYDnEczROq3jjrMuVtQalegTOnF98IHl6M8WWHWLX42jh0bQusdew0ZvxaObwzstLqtbl9bOY3V5NVvtcG5QonXGeB4ROTdfPcS2FZbl4OLw87hI6DS05RRuhpIUVQa9fUGcWVRuzExd87fXXsB2HZSkZBCW1stkdtMSewyQLeXyrxBIttRK4smFegsbQtOA7GaKMiP2SC4HgaBGwGUFSGgQWdetTtZppYSHQZI1hkklOli5qLnnrZsZh5vL4+phEBUwLGyktHkwintmd8Pz+kNuPulzYyZmVFa0RTDOHaW6zFee8fNonbyxurKdMcx9LwkbcoPXq0vKu7UMsofns+Q4emrNJyDzzuL614IJI+MSjAY4wXNtIKFvJ6cLnmQszNsOCeb7GxfWMQdjg2i2eqChqwSJ12exVPJpHDIKKOGhYFqu2XCkklbJxLYUApqlPawRbnZrHtjJOFi6RZZEIjesYumGDVpK1uKSeW+SVTdXYGCO5vJZhWQpXKoZRyyBs8CzFtLA5msZs9JdcdgIs6XB1rWV/GrLT6ZLVKy29VoJLa0tcBjxceIhuzXY0ZrDoMl4YLCEYDXIEhkk9xpYDNt0rPFT3uDbKSCqXtpUsCodeWLPWSXn1pIsV1KyFDY1yKWuJZa2KckIqznOHjteQ1zaBq9mfBUReDcJwa23Gr93f5HgeoAhIKpdF6dIPK8rG5uVZxNnCwbMVH9mfMKgscmHY7KaUyubFBx0c6fI1Vzp89PCMw2kPxIJLw4qL/RIpLZaFgyUkeWux3Vvw0tEmcbAkdHOSRlK3AWs+fPjeglJU+DZs9iq0XBL7NsYUSMfmchfunQeczB0ksNUpubGR4doptfEJXcEsl7xxvEPXc3Gshr6vOZoLFBXzqqAX2IyXHbqB4evfGfHB+2ckbcm//GjC9q7ivVdHvHA+YT4d4K/7bIQ1y7qlMgvWOx1ujiTKSCwrZz3OaScePVkSBBkvHPUZ2BEdy6KobBQJnnvA8bzLXidEGY9FIQmDBdiaWdFhKxII45JUikwZ7E7NXm/JdBESu4bWKHY7EVefsKjl63zbj/wSez4cRRsgMzZ6KfuzkDa3uLFRcqx8doY2p7XFK2mDomVR2nzFtTllHWCJFRnv0WzlH9KNJrznyQaXLvMspOsZbs8kpVjw4Cjm1p7gO9+xy99/7pw7WjDJakaxoaGlMYp7M4WxD3n+5Et6Of284hu/8Rv5hV/4BQaDAX/tr/01vvM7v5MwDH9H5v7NCjPz+fy3NK7rur9uZv3ss8/yyU9+kr/5N/8mf+tv/a3/xbHb29tcunSJ27dv/6bj/vAP/zDvf//7+dCHPvSbtox/13d9F3/+z//5X3+8XC7Z29vjpWWEVXcQomFyYDMcGv7QtZipVa58QxzNJ2cOi1Jyc6Nh2hhOKoGjYwSChS35sTcKapMRhJKv20nxPJ/Ec1m6HtMqoKwqPDfl0aTDtc2KpHG52m1xpc+dJsKomrVAYg0qTpY9lp5H2YQcpC1HJ4avuJoxqz2syHB4PiDeyGh6S8Z1yHYsKW0L1dW8NOsQuJqmdvHtAEsLXHvO9mDOMh8irYBLwyVpYVgbFjSRxYPzLo70GesOd0/hDzx1jjY+aeVhuRM6A8GZCIn8AkvmRGsN86qD0ZrG1LRNRIbFvbMQ2UkZDTSNC2u9JdPC5skrOdPCRosMafkcpzGlquj3K5Rx8TuKvF0RrYKwZavTUinJslTcz2ImOuJ+7XK3jlYGzd2Wc+mj3Yq0dml7hlfKLtvdBt/OGYSanU5G0ri00mCHLc8f9Mnn3dU1qdNyuvDJc4/rGymB3dDUhuF6RaUEry8jEuFwpEPS1OaZKyXjhWCjp7lTRez2S5QDXqgoXUE/LBGFy2dv9/FdhRRwmntc9hOUL9nsFCR1yKR0VzCKtYZLw9Xa3VENk8LltSzm2PjYluK09Ugzm8Br2OyVHGQB49Sj4yuWbci19ZzTOmBnL+VkGfDU1px/9sou7sAw1zYzZXH1WsJ44TIpXfqjlrnrom2DJTX35hFNBG6vRUiDLxTHi5KrV3N2ewuS8wGthjdO++Stw1pkOC8dGmVzfW3JS6ddhmGDtA3n0kNYcC48IqeibVYek9qFyjLM6oBL3YTjxMcPG/LMwvM6nNQprm24uVVynIT0ujPspuZoFmN7moFfEQY1n3zVY6OveOd2xcx2eGEe4KtVV89B0TI5HCACgww1jg+nVsPA7/Cpex6Oq5BtRNybM5EuidHMGptx4TGIa+bSYVYHfP2tIQ9nmgM9JxQBa6OSO4cx3W7FWesyLR12hhlvHPhc21qitM2Xb1zgo4cHzJYe984cdAR3y5rH2pCXizEXts6ZnMaMFzFXXcH6oE/g1zzKjjmrXCK/pjEW2hhyJHtRzrK2yRAUtctrsy64hkFcsWZXXA4VF9ZTXhh3OdU+rTZ0wy2O5xmPX91ltFlx+5WXWNtNef2kz+m5y7OXp2jRkjc+DRZ9VzKWDh1PM29cLkQtPa/hMHNocHikfRzX5SS3KLTE9hxubCSUoUSgmKeSOoZBoDhYWkzLgI7fkCjFVq9lsJYzLQR3xl0elV3e+/gu3/dLh0gRsxVPkXHJYFNytLB55SBEFA6XBtALSl5YuJwsOtzYVCyERFo5x7pDheD+XLCxoRlEGnLJpJKcTkOqxvB7bgx57zM9fuHelEdK4OoU5XoMuoLj0qJRJWiHLB2wHjWkpUNeWdSiWHmyWTHSdUl1g2OVtFoyKxT/11+YUMkQaovUymiBi3LVAfjqfETQhYNGM8lKtluPmecy6jakVsDcEhwnMM40G52AaRawtZFjej7HyqfVNctGoZSPsWpKbRN4AbWU1EEPOgOWwZznxppfe21Jzwo5UAW1G4LwqWzF3tYmv3J7yv/msU3+7mePubL7v+ZV/7H4ncipvqQ/AQV40sKVDqoVhFIQ2DW5Skn1grbxuDkEpbtc71scJZJGKR7ONZPzDrFrMxwIdjoWsdewHi5WzvNlzcnSZdlYKAOusFGtiyVqhBHcn0Z0g4RWG5SyUcrjwWmAJ6d8+G/9Pdz/7ru5tV5iy5Xp2VqUsxFUzCoHYxzaJqCoBd1+znqvZqdbo5SDrWNylbG/lFwZNLR1F602cAycZSnDMEUYh7IJSWqHrJbMSxcXw7KwmTcZadUyL1yGos9WDAdL8zlaWEHR5nz2oMtux6dRm+i25tG85vKgQkjJvVnL0PcZhYK1SGIJQ61spqUFaHqO5DRfteZmVchm6NLUAkxLHGaMC59CGbIyoKy6vDje4E1bJbGnOE36VMqmE5SsxxNmBwFZ7RD5FdfXFXljcaknWdSSwLOJTcNunPHG1KFtHd60NccYSdqMKFrDKMroeDXj1KNSAWepyyTzqRuXL784YZwFbHRqZk2PR4uQSeahtcG3LT74wmV8R3FhK+PhWYQjNe+8PiNwa46XIbbUvHn3jJeP+3TdgrKxSUqPrl+y2y24PMgRwCuzDV486XNle44dKIyRjOKSV88GrMUVTWvjWvDgPOTCMMGyFHenHfYGCWdpSFJ8rlMnizhPPaaLgMtbOZHXsN2peW0cMUl8PLfhq66dc7IMcN2SVltcWs/Y6eSU1TprcYZra8rmIaKymRUhBsO8cGjNSkKVS4teVLPeKThPPZaFBFwuDTNmhcdJ4tEPKy6vpbxxGmGZmjddOkcqsL0ur5wpLg4afK/izlHENLOIhEemBcO4xvWnXF3b5NcOGlo0aaG4sbng1nrGx+5bzCrNvHDp+Q1p7ZFXmqKRlK3AtyVO+JCOcLAtzcnnSB1CSrp+xTLv8/TenLMkYLIMcIVEGbgtbL7q+gkPpn3WOw3jpY+UYFsWee1jNAyiHMeC2NPUjc00cQi8ljDUXB41zHLDm57s8Nok5xf3Z2x3HcZ5xXo3ZV74PLm9xLdgUQpa7aGUzfGiR+QaQtthfa3G4HNpdM6HX14jqTMuuzFTM8VxamxbI4yNJma3b1gLFTU1eREiRc1bLj3CMV3Guc/lvsSmwbUhbxxa1SCtOYta0/FbFJqtjiAvI9LKZpw1fPB2Shy0fNtTF3lu/4DtKOA4NWS1RsiGspaUqqal4uYImp7gPFWUOueVsU/T+nRqh86gQiFZC11U6lHWFS8eaawgwUk26FoxdbvyIRJ2yl6ccDrfhIFACs2iFOzPuhS1zZVRhW0pToxmPfQxTZ9JeUhRVQSew6d+6cMcv/1tfPITB4S+h29J6soHo5hmDbks+b9/6DZtZfHMlRbfUQw8jUaRt5pWWXzzY1f4Hz5xTOhA3+/hmBotWg4WMDQlmSxwVMBT6wGPb8BnjhJOl4JXJjOKmceFCw27PUWdRJjGwbQVkdBf9DVKG4k2XxzDxC/WOP//xKuvvkqWZQwGA77v+76PP/2n//TvWKGo1+v9pr//Qs2i/0NhjPn3ZGD/bkwmE/b3939TAskP/dAP8df/+l/ngx/8IM8+++xvOqfnef9BKZtlHHzp4Bgfu98wnms+eLBPWng0RcNezxC7IR1P0Q0qOp6mEyz5xANITl2ubhiMAiGhH3hMC587C4dhJMlbw3lic2VTMc3Xcd2awyU4FlS6phcqHBkwiDQHS83RIubRzAct6EaaN+0KQr/m4ULTNpLHNmsQEqMDLFqubUwInYZFucswkmhqWtqVlG4W4yDZ67uMQptlmdGLaixp0/U00xKQJf045HgWkBUuFzYWnOU+WhgurFUYE+A4CaeLgKCKuekppCxIKqiUy+VBiTJTmiairGzGqY3rJGSNyyg29IKCg8RlM1qZSd/TFg8nIVntcWsr4/HNjLxWlMomdiFyWoTV4gqLfthiWZpXT10OT0M2Roq1rsK2KqSw+fi9Ie++NSOrDVJUlE0AEvqBxncVntMwKWB/0SN2W47nfSI/5WjRYXtzyVanwpIGbQQhDd2gxbdbZkVEqw1bwxSGklQZNnqKrHTxHcVJEiDlam19drdgVjmktUMnanEsg2MZqkqjkFxZSzle+uzPYi6OUkZhSW0ER6mH0YKNuKIflKSVZBCuunw6XsNp6hNWDm/bmeFYBsuCaeaRa4vTFFyhqJTNk+tT7s275LXHVz12wJ1JTOgoXj0doBrNutcwTn32FzGPby5RDdzaWaz+7hGM01XHaN44nOcOWnQxAhrj0CIxFijtsN3NaHWNtA2Rp8kaj90OyH6Jb7Vc2aiZFz6PJgGNdnhyd8ZpErM7LNgblpwkHarWQ1oa5SxZ81qOFz7aSAK35XDRIascKiV5+XBAx2+wLc2lzZQ08/jYg3VurCdc2pzyaBIjKpdIVHRHDUezmO1hzv4kZmeYk1Qpm90+rogQdkauBY7QLCsPIyqkbXBc8Ft424Uub9ve5LMPTz9nFaA5XfoIS7LZr6iblofnMeMkJA5bHk46vPWC4FOnE1xHsdYvCHzFZ44VT1yo+OTxEtfPeOVggJHgupqhN+DqIOSFU8VxEvL47oJx6pNVFlfXlsxyn0pLThYRZWmzSYEVaOrMpQoUW705rRG8eDKgVTZlKzmaRWgmbPVCLvVD+r5Dkg158UiSFqtb1nkRoalWfkVxjuNYjDotoavJK7CtmvuLHg/PffaGFZc3F6R5hN84NLriZOrzaB7QaIteAMeJWZnKtw6OJYh8RTeq8d2K7f6qQ/DDb3TI0i57UcCPfeqMyq3YsEIGsc163JC0PsKCy+sV3aBC4JCUNossph8bumGJbUseHK2AJ5YlEZbFeOlRKckwBNnGKLsmaTSfXjziSbdPKSqcYEG+HLG3VnGSuHTDgoHbcrL0qSqXaetRaM3Bmcb2Ara6XVTQsEglW+sl88Jjt58ghMes1tRaMU0VVy7WdHxoWwtLWNguaGfJG496rHdjIqcis1YWD9pSHGQF51nIxYEgS3221xSWcJm3hqO0pMkiAstidzvFkjZpEVDWiuOi4mfeaOjEFh96rWYU+SB9LK+mlYaqhaO0pmkN//0vH4PT5WeynNgJedt2j//pN10Bv7D4YuVVXyo51Zd0oSgethwsWpwwIXYk66GLIzpc7EmE8XjuQDAr56wFGWshHKddLvVcysam8gTCbpgmkq1+hu+VlKomkjZGu2zGLRjBeRIirYo7s5b1WBHYFqFtsyx6QMurZxLPbpAqZGu9i64aIkfgOjVpbVBG4Qobi4Z5uUKTNkYROJLAthgnFldGKRU+jfboeTYmrrk/r1iUOVfxUCogdhxePO7QuTSmaFuq1mVaGF4/DdnttgwtzYZrc5ov2OhmpHVI14VuUHCW2ijpMAxh2D2nUF3KNKCobA4WFZHjcmVQc5JqpMyRsmZaGkDTdSwuRobnz2LuZxHjXLOsE24NS1wXHtuqeDjeYLfbUlTgdFqO5jmfeBTx1ms1dyYOHb+l0Svz5MfXK5alw5ft5qRFzKQIGKctxhiu9RsQhpO0izKCG4OSrbglsjKWTci0sHjLhYxFITlJYtJa03VKGg155XOyCLkySliPKmqjeTQP6PrV5+RmirSyeeOox+7akrRyKGqbGxsJrq0ZL0M8xydyW2rtsRElPHQNsaeYZB5JZXGWBggBSsOi6nP/rMN6Nyd0G5QyhE7FJAkwWnD7sI/BwHbGl12cErg1rlA8vTNHogiclqZ1iOyWymnp+JqqVswyh82OptGSxzcT/vW0i20rZoXNIKyZFpJR0PBg1uEs05RNhZSS08RHK3g4Dri4mSNp0cKmVTZ9v2Y/C9jqFXzsjXV6cc6FXkWjHDbinEXp0GiLrPTIlcV6r6AXKFxb0wsqmtrBdQShZXN3Gq12sPa7yEFGqOHVe2s49pK+VfK23ZC7s4SjScBdqVHaJnAr7iwMEoktBZbQ+G7DyWzIRr/gbBHSDxcYBHfOOuwOUt447XBllHGWhjy91ufVQwvXaRnELWW1MvJsNXz4jQ1cu2WReySpzWhQUDWGG5spWq3+/qdFjOdojhcuYZAjCsGdccSD84C1XsGr9ZzIaeibNb7+2pCfePEBjyZDHAkvHm3Q8QTKNISuJHKWnOUhA3cle+v6Ged5zNGiT+xKNqKWoXCxpWKz24JouTe2eWJ7jDKKV4+3UBS4Fmx2NL6MWOQBgZ2DEAgU3Shjy/aom4CHU5/j6YArawVl2zCMSpIyxPdmrHdteo5F5Cr+3st3WIsiLnUDfnl/zubwnMc3HGJbYknBhQh6rsNEaRrmPH8cElqrmz5tGrLC4o0Hm2ALmlLzaNmyOdTsxh2KMkDbDa+daTb7KdNkjTIfEvoKx6o5SRy6gWJvULLIfWy7ZlFYKGPxYKboBWNarXk4b4h8aIsCb/utgEM/zEmqhtfOGkaxRddzedvVhKpx2J94tCajpWKt63CehiyygNA1XO32aPU+lVaUiUOLRdEIqrmD1w3Z60jSpUtDzWGe8fydkKVpWA89Hr8uiXwXKRRHS8l+UlD5FfPii7+c/pcmPXvmmWf49m//dr7yK78SYww//MM/TBzH/8Fjv/d7v/eLOve/i6j/YsV3f/d38773vY+9vT2SJOEf/aN/xEc+8hF+/ud/njRN+at/9a/yh//wH2Z7e5sHDx7w3d/93aytrfGN3/iNvz7Gt3zLt7C7u8sP/MAPACu52fd8z/fwkz/5k1y+fJmTkxMA4jj+Dd+r3yjy1GJZQ1MqLm64PLklKIXGjxVv37zAa+kD7owr+oFECIMQFkPP5o/d2OHT84dsdCRbWx6fPlnQGpvA8um7NsdzQdFYKKM4yyqy0mW765I1hsDNuHfcJb56RscucSzB0I945/pV7Msj/uq/fkTs5fidhsj2kI1H2JtyllkMOznLPKRqbaTxOEl9zoqa83nMrc0SW9bM6pRHSwfb+OSFS1F7XB8WLCvJdidDmZDTZJ1hfMZ2/5Sy3qETRAhjKFRJPxBII2l0S2AX7PUdxklAVkt+6cEaF/uGol6hqQdRSSp81noFx5nPg/MOT+8W1Erg2BXSOCxLj3ulBNHi2YYk9TjLWi5WBaGn2O01TAvBSWbhOS6eLclrm3Hi0irAXVGi9hcuT2zmuLbh1naC0RJLVng2ZDV82XbKvPSplSB2FHnjMy8CPKsi8hR3z0LWY8XN9QUdTzArBIGt0dVqvmuDnL53yC/cvcjhqY+FzfVen1fGc65vrnDwXX8lVR8ENXlr0fdbsqjmaNkSOKu8Q3kS19EYA1ntEvkNkVvj24q8tlddNG6NlOBIgxSCReUyDFeyoW5Q0PFaFp/Dot+brTwey9rm1ZMO2thsxDkdt0Vahqf3Jrx8OmB/EbIR5VweJrS1oJjaZJULwEni88z2jLPcQUqJUAZbKo6XEY9vLUlrm5OFQ9rYxE5N1do8tT3FkhplLASGsrHY6hW0WlAbSEqPXNr0vZKycnD0gO3RMUprKiXxZYOv5uTNFrY0CDSnc4+nh5dw+g+JnIbnD4ZsdnPWu+WqY+NoQKNaHMcwyXxGVojnLDnLfJLC4bGtJW+cdPA8TY2FUoajmc9aXLGsHZ7anPPyYYhycjpugdvYeHbLZBlhmR6aKfPMxpFwdzbjaL7Gf/vuXf7fH9c8miqury+5vHbKq8cDHFtzc3tOWjiAgyU092cGIQyRq9jsNbyUeVzcSJku++w3C/bWGywpeHDaxfMbtGh47ihBSpACKiWxpWa90+C7hse7M+5NuvSCiq5b02sVgVSMeglVY3HvvMOTW1PA4eEk4PQ8oNdpmS4lj1/o8isPF3Rcm+985xX+2Z07PNMdcHEQ8WMv3ufCmqETrHx6arWyc0AIYk8QODXH85B7pz7SODy5m3G+ZJVrV4ZepBjGLZdGczquROOTVy7agGuVeI7HRrcmrwyTzOJ0GePaFqmyOEhKWlnjK48L/eD/y96fB8m63ned4Od53n3JN/esqlPL2e49d9duS5a8SbYRNksbtwncboiBngZ6GmJgjCE6YoY20QRt9ywYBwQwDQYNzISNATdesLxJCMmbtnulu59zz1J1as/KPd99eZ75IxV0e+iJQR6BjOEbkX9UZlTmLyoq3+f3/n7fhcuFy1tVm9J3sa0Sw0pwDJPAasiqhpySoWtiG4LzTONGCaHhoZTmdK0opE3oWIyvTL7rhQGfP7zk9q6iIeGn3lCMlwa9VkBT2qRlQ+BUWGbGLLUoC4+2B/HM4kqsuDGSCGEhZE2Rg6bhdGlzLSqxpQcYzLICyysJQ4808zHEJrHwItYUOqYpXPaHBpahcFD4VslVUrGuSwaOxX5kEZgNnucS6zVHU4N+Z02gA25fSzm+aOFZgmUmOFlltIKKO9uC2djhmW7Ata6A2uAoLej6OU3mURsZviW51Te4Hvj82sOCRdFQlg37wVc+Yf13kvTs30VP9WV3tk3T8JGPfISPfexjjMdjlPrNW9SPf/zjv6VCvhoYBQa3OhbtsEWtl9ybSopGc2sYUzUGXTdEVUMuK3h4Ba9ftKlLk4uZw7IqCX0IpIXEYbp2GK8DDFlS6pzAXuJafXTj0A8UtbBZFAmTbEHdNNR1l2EokEWb//T2Hj97+gZX+ZL3/8k/jisNWraFpMExC4S5ptECxzCYxhtX/73eiry0MYXPW5d9SlJCN6FjQsvxsNc9vnjk85qdcLOjeGYET3Th1w73uNVfo7WJUpKOv+LuxOKb+w1918F3PExqSlXjGoI3LkMu04a+62JLkzt9k6JJqWrF/bOAs3XJMKy46Wks6dF2K2xp0HJMVlXKPO/y2nmXZV3x8tGAG4MVPXeNZ0net5/yy/dGnM5tTK+g68HNfkzobdK8XnpwwDc/M0Nrxe1exjpvOJxbuEbD2crmdneN5zQs0w4fuLHmZN2l0g2WMOj5FRdJi3Ficb50kNLg8dzivdfn5A0sC83bR2M8Gz7+aMSwtUKKCiEbHs48nN5GTnWy3GzBXKshLk38ICdrNsbTF1OXvRszZonLIjUJvYbQrrl7GXG7s+J9e1f84oM97o9DQqfkej9mkRlkdYhnNvRbBb4Ddy/brAuDfpATuoqDbsLFzGPYyQjdmkYZxIXFM4MlrhR86qhP1y8xDcHJMuB6N0cpxSDMmaU2Ha/itfM+NzoK3zLo+SV77YQ0dwlsGPop08xmnVvstdecrVrsRDnns4DAgbqQpJVBozSKzSZyK4ypleRdB5c8nHcJ3YxHE5uztYukZitUzFIXWzR4bsONfopSGik1Hb/gcOZxZ7Sm7dosMo+ndpfk+ZCTLMZr5TjNkPvzjPFcMRyW/L4X2pRasVQpB1FMXkYkpcHJwsdAMl7aNI2mrk1Mq+F04bMuDLq+xlx7hG7DW+M2qhE09QppakKnpNQCVMOtwQLHbLiKXT7/sI9ZWXz4mRZn1YJlZuObG8P1WWrSNDarzCJwFF1PISqDwFX83pv7/JOXZ7z/ts/HH86RRsLf+0KCaRXsdDLaXsYstTmaBfSCNcNQkNUWPRe6QYnA48G4T5kEzHPNqFNztNacZCve88IVoWNQqYL9Xkpae0xil5YjSSqLReJytpC8eWVwa1Bzrb3g0XiLr39iTrFyEdqlH1S8MbYI7YrHc4Nndq6Iiz7doKJSEqdtE9gFs7Tm+97zTn7gly/4nmd3+Pz4gnfvGnQ9Tctd8+nHHmBtJJp1TlKa3Gj5nM0CLqqaG/aa+5OAWktcq8KQNb5n8o7dGM+ELx5LaiMjKzxu9/qYvktSN4RyxfHK58lBTFb4mGaDaRWkpY1SBrudglVmUouUydoizkK2ghUf/C//KEslOZ7F4K7JrkL2OxbDUBM6NXldc5am1KaNxqKufSopqGubsm6YFwUXaUHTSIrSwRUGXqfGFIoLWbEsFHVtkitN4BWcX7V48pqiblzyCpTMGaeCwwuTf/af3eFP/tRr7Ict7KjhC1/tQ+23OT7ykY/wAz/wA/zsz/4sQgg++tGPYpr/ehsihPiKD4r+beDy8pI/8kf+COfn57Tbbd72trfx8z//83zbt30bWZbxyiuv8A/+wT9gsViws7PDBz/4Qf7RP/pHtFqtf/Uejx8/Rsr/eTP5N//m36QsS777u7/7N33WD/zAD/CX/tJf+rLq+/anO5xlFsqLeX265nufvM3febGg38156TRmd9RmO0qx7TWVgnVZk1UVR5OcG9sdTpY5Z8uU3W7BeFUzTiqEb7IVmqwLwUxrlllAQ47nrcmqIY7hEfmasrSZNzZpFWPQ4PUs3r/fwfGOODt36LQL1pUmy132uz5apLx5lRG4FWVtk5Qey9RgkXhcrg36LcVWaFIVIe2g5GymkSsHx27TbRlIkTLPbSypMWVN35N89qRLUTdgNFSNz/HE5UQ0RLbJs9ca0soDMiJXogsHaHhjbPBEL6VoHIq6oNYl3aAmckzysmSeSgLHZyessKyGk6XPNHcoK5eb3YDzWcM6N8hrm46XgWjYb8fktcvrF316fo4SGbWWHPRyLKNklrRAN1yuQoatEsessc2GyMqYxl2WmU1cmKwKTWgLHi99LlYuaEVaGRiyphdIRq0c29gMSTQ2cSnpmZqb7TWyTjlbDzCNknXsgIJzd0xTOhsmZzfm6dGC87VLUpoM/QLfKLjWgqwykEJTNZLztUfPLojzjY/SMMw56KaklckytZknLo6hQBWcxB5ni4C0NGkaCZoNS1ZoJqmPZWp6XsWjicdBPyEp23hWxrCVoxHUCiKrIuzULFILx2oIrJpuK2dc+sTNJrJ+urL55P1t9nopiJq8skhLg8CqkbrkHaM5i9LncO4zSx3abkHVmFytLYSAd1yb4FsN92cRLXfz+4GlUELweBHS8Wta4ZhVatJxMwK74vGiRaWvk9YWZSW41o5hpTnJx7i25u4kYhSlCKGYxS7S0BhGQzco2W6nhG7D0VgxROOYFaHbsCoNvvbGhOWlg0Sy3wp5sMhwuymLVcjnTnr0/RTfbkBoOn7G3bMIw6i4Oy/pRwKJwW7bY2nmfGG84CwpSJqEa52Em/2Yee6QVQarxIJG0o1yhFFR1zCJPSxbkdcGcWVhmgrQPDcMeLTMyUqDYSdh5Ge8+XiIqAxSuUALTa9doJVmK9rEnm+3EhplsEhtlolJsnJ4phPz/NaCComUgkZvDNqXmcMsNul3C3y7ZKdX8cWrhp+/O+Ub9tu8Oo2pGsVlNOGlyQQtSta5xY3dOS27YVYILCSzzGCZhpwvuzzTHfJQxnzDQZc3lhnL1GF3e83x0sfQCgPJxSpgdC1mv7MirTwMYXI0F0RGji0bOu2SR5Me60JiYPGBmwFvLmf84Wf2+ZufO2ec5eyNTA7PW4Sjhi1Xf8lXMmeRuSxSj45n4pgNRdNQNDZDXyGpmaYGdgscbZIUYNYmd6cp//SPPMVf/JWXmcabofD2YE1gmQgnx7AqTHvBqFVji4D1wqTVWfOf3N7nnz1ck5UmrtlQNhJtpdRCMPAsdGNR1CnH8xqtXUwcWkGD78A6N7ioMrJSMvBaBJYgzSWlbpBOxbTQVLXFdmDxwtDjpcuEwDawlMfr45i9bsFB1+bxxKJROYkqMaycOJb0A4eDjkk3LCjXkl87nzMvM1w3p+W30HnAsF0gvqQGQBZ88/4WH7t7xH/9NTt8+IkeP/3y7zxfw68k/l30VF/2oOjP/Jk/w0c+8hF+z+/5PTz//PMI8dWfqP1WcXgG79xaY1sVpva43TVxDIVkw7BoNLwxDojLBsssqRsDW5o8vx1im4JfP1lxFDcod40rQp4cSnYiWGQuZ3HFslryeOrzDlOz37UwdJeOVXGUau5dZnxdMEczYpyWyKrN0dzlunPEy5/451z/gx/AMhySUvJoajMIEywjARHQdjTTRKNlg0BxvugxbLkoFfN4lbDbSnCsPZ4ZGhTapxvMSOuKW72KRSExjRqlICm7jAJNUTVcZgWWo9h2G7bDhu1wxbJosc59CgVZsySvumgteTyzeX4nJet5CBmRVTMEJa6lUGKOb9sY0sQ3HV4+6XK2CjhZVgzDjQmvNJa8cTXiau0wSTxujnIGgU2cucxWimkClqi5szdBCxMpSiaJiSkl1zsVk8Tj2WHB5Tqiajbmxa9cBtzp56SNQjUCQUWtFI+mXc6XJl93c8WTgwWvXnS4FtV8/cEc3zSJSxuUzTIXNNrGVBV5JTmb+bgWoBueGaU8nAUoZdDxFS1rkyi2PSiYrH200tzZXjJZu1hmRaMa4kbiKIu8lriGRmsDz1bcilJOFj49P8czFa+e9xkENU+N1swzi1nicDQJub294s2TNsdItjol7zgYs8xdEkMySxzQBt2gYCvIOJy1qRpFUUkGfs5V4lLWmqusQsuUZ3eWpJXAsBS+hrPEpeXUNI3BKCx4NAtpew1Kw6BdkJYms8Tn5mCN0IrF2qSxBUJLjpdttoICrQWhW7POJeOVw1arwjEhy1ssY013b8FFYjOehSzWNpWSnCwCtloxcwxCr+RiXnGjJ3jr3OZyVZM2Gf/F19ziMCv4tdOUv/qht/FXX/osl7Vmvx+zTD3uXbS4Fnj0RMhjtcTQFVqB7VSUhUlimDwzWjPNPAxDkxQmw9YScpOLlQcIRGHw4EvJdb6Vc9EvSJcBjaiZJR7X+zFSSJSSTBYhtzotLqwTlonDIrGxUoHWFs8MWvzhd1gordjpTLHthrSAdrCJTHZNiWPYFKXJedWmbkq2oozAL/nYvS4Sm5O5yzN9i3ZzgSs7fPutLT75+JyuY9DzNeO1g1ISdEHVlGSlQc0mnbAqBcm8QxPAJx522R/lrHKDZV5zu59gGRktD0zdZp6avHxym2tRg2MVjFpzDqcOl2solGaWSrYjkx9/dUyZB2RFSdtdcLyO+dqDmDjv0ShBowST1YDpIiQuS+6Oa0S7hRv6RN2a7cGcR1ct6sohzlMu6ozHs4D3P+nQcSRlI8mblFfPbe40Jl3bwTc1SlVMEpt1bnDQLbh3EaEomSQN7SJCNTZ9D85feYPjVz7N9u/7z7mxJVnGfYZRRVwY5PWSRK15dNXFliHPbde4FiwzwSxfcW/csBVqRBnxD149ZKslGa8Ex5McM1rScS32diVCmwShYh5Laq2Y5zVeUFOXLoZV0w0L6kXArZbHZ07X7PUVFyvJOMm+4mfU7zTp2VNPPcWP//iPAyCl5GMf+9iXFRX/2w0/+qM/+v/1Nc/z+IVf+IX/n+/xiU984jf9fHh4+P9nVf8zepHkxauKdwxDsqbml84e0OqUmKbBg9OC0zznRl8Qmj6OLMlLg9cP26RqyaOipu9beKaBxOSgW+MkmnO5JHQ1LT8kzjvQSHb6BavM5WSdM1TQ900sw6Lnp1zEEUdXLn/wxoDXrxI+eL3LZ04nzFKBISyEVtTURLbFza6NZ884XbmMY5+mMalKB98pmSYOj6c2kavoOJLWdsXRlaAabxKWAtfiF15v6ASKb31yhSFMhG54OA046KcsU5uqNOhFG5aTIRtsQzDwKy6WaiPTMjWjMCZvAjyzYpJ6oCXr3MSxNyxvrSVaS9Ja4VgaMIkzm7OljVSaP/G1Q37x8QINzHKDZW5zo7dh5LS8jKw2+OzRLrttxSKXXK5MHAmn84C+n6J0zSCoabuwFeYczQwcK+fTxyOkUZFVFY5RMoldun6GbzfMEpdluhn4RG7Nq5c2Tw9j4krQ9UpA0GLBTuBxkdj0ooIktVnOHT7w9BVdf8OkmSYOjxcB16KUy8TDlLAuTCKnxDEbFpnL27YXNBrySjKUGUlp8tZVm45XsttOGIQ5jyYBmSfZCjO02sSgV42FbWwWbqvc4cl+QlzCurRxbChqk7ab0QtqtsKSvTDmNAk4WfjkjWS7kyG0xrEVL572uSZyAivndNFivnbw3U1Ut2tLBkHKZeOjDEXkK+aFS9srMaSPQNEJatJKUmqDvlfgO5q7ky6SBqU0Z0uXshY4lub6IGUZm2x1Co5nHkntghb4ToHWip0oxpKarDLxPdjvXBIXJrXasH2EgnvjgPffumQZGwROTeQ01E2JFi2OJi12ugbjtUfk1mid4loN08amNKYY+ExiD9cseHIUIwS03I3f4psXLUbtjHVmMYgKLqY+cW5T+S4HbQ+hNX/o+QHH1SGdMMGxGooYbg0TjucenTDFkYpWIDhfugghKAqDvV5CWpkEdoPWis+cTXAtRdvasO00gsqu+eJlzM6wYOB1NvcQpmYauwzCnLh0iAuJFBB6NQqJ7+Y4bsXA2ciNGiRXa5fLpUM3KLANwZ3tJUXl4JpzFnmfT58t+UPPd/ni6jHjlcmu1+No1WCbknkqqRuLpNJ0XU2S24ysba5KRWS7mCrmFx7MCT0TrRuukoBvvzXk449i0loQVw6PpiaDYM4ql6SZzencY9iqOJ55REFFPyiolcEfurPD3/6NJTjwI5875ttvj7h/WvHiecINw+AgClnWMXXlEtmaSWxzf2JhIdlpZ2R1TSM2HrZVbVArzWpis79bsxs5fPvtbf5vv3HC+/Z8amUwjxuCcM5uy8QyFJO44CLWGEmfPINaCZSsuNMd8E9fG1M2Dp6vWK8tYrlGSput0CTNJJWZcR7bOKbJte2cqrbxLMUsa7iMYdQShE1IL8hY5IIkV1S1we/9ul2K8wsUkskK3r3d483llEmuGS8V17smgTS4XGWM05xG2KQK0sykrUNqIdHGjHGimDYlP/PWBWXmMgpt+j3FLKtpRE1aKna6K/7A/tfyV/7FMZ2o4fXTjKcHBZ98/G/BzPp3kPTs30VP9WUPin78x3+cn/iJn+A7vuM7vqKFfDUw6goCG5LCxrMKLKOm1BWXWUJW2+gmoKkC6tQgrgvOUs3FrKHfmvPH37nLg1lG39PYRsiyLLgqVjwVLKn0Fts6ZORpDAWffNnnfW8742Zf07Y9rgU2Rdvh5RPJW48z/vIHb/CrJwu2W4qRf5PP/L0f5/Vv+iZMI+eFLUVgGXRsg2VhIGSJZWocGTDLTTQ5j88siqHHc9tdttsmp3HCZT5jt1eSFhGB7TJNXN6a5gyDEq0NXh4bPLySPDuCpwcN7UqQhSlFbREXFgur5iwuaLklyzxgvG5hCUHdGCxWNp/XMUM3p+XAeNVirw2RA1dJj+NVxtCHo6XB4RLK3KTjNeSlgyk1dRmRlxbHcx/TXBP6Ng8nJiiDs1mfnf4S3xKEtkXb1RSNAyhss+Z6tGCeGbTdhLemLbTWHHQyXFNTNSaL1Ga3nZPXDrZRst2q2WqlmHKTxPDNty5ZFxZbzpzLIuQLFy1cs8QwK16e9XENG1emWEaBbWri0uKNsc8idWi5BbpRNKYiqwQaQVFYlJnJ0hEYBpwvW3zX207ouxn/9PXrxLnkA0+eYmKQNRuvokVmsd1KuUwcTKNhu52xzAzK2qCoBaFXs9dOuH/cQVqKwClYpDZNA2nlscpMbvQS7o/bBG7OC0OHt+YJCoNZ7mBJuFq7CCRSaUK7YZY55JXJW+M2k8Tm7btz0FA3grOFz/FMYCMQluZaJyMuLJLcISmhnps899SCV846bEcFoVsjZUNee5SNQVraNLqh56e8rXuDf/rmjNNVwL2xT9+v8PyK+SLANgqKGm4M17z5eIvGKBCy4eZ2TZxJEIJfPj6kpmKVO/w/732WRWrjuQVGsUloGEYZrx45PH39iL2Rg2tXLFc2hlSgBcNWjWMpbvorlkmP6cwlKQ3QEDk1i8TDNEFqyapwadkV3aDgnUOTk+IR1yLFFw477PZTDnox92KD+3GMLeFrb4z556/s0kbzf/yG22xHNu/eC/ieH3uT//7Dz/DDL72IaW0MtRe5SScsSIoWhlXS92sit0Jre/M/2625WAR0PQGyIc1ccho+cXYPLT0yVXBv3MaxN4PsVebQcQXTRYCWBs/vZti7Mb9xb5vb4TU+Pr9AeBbmhceNfkzbhrNVm57rEdgFp8uGZ4cJLRsu05rJZJs8jViX4DmKH3v9lEmW89nLK57oS2apSSfQGLpNaDU0jQKx5lcf7aBrg3mq6HXXfF3LZU/DiVZIe0lcVrTchrqG46VEyhZPb1tUlYlyVxytNqy33cij67SwzJK4qplmJqvMILQFy1xyvhA4dYtASK5vJRzPFVdpyuGvfoa9r3k/18IQXVu4piRXE14693h2tyCPtxnYPrf6Bkmz4DyOefk0IHTavLDNJlVNmZytUxZFwfHUY7ut2A0DpLbJtORq3TAtlqwyj61QcGOkCGyLQgiUUKzzhrOlwtU1D1YJo7bDoDemiP/N4s6/HOivoPRM/zagSf8v8f/JRv6P+Mrjf3rrGOm2+ak3Nb1WzZMDzcA38S3JO9/Z5eceXXC6LpHa4pkdjSElkS9p45AoOF427Hdq4lIQOYKOadBzHEpyTlcVpS6Jcw+JJnIcPnAz560rmJcFQQ47EQhMXNPgv/rpe8R1w//1W27zhYs5Xc/moHfFtd6SRrnkpUVRu7hmxH47581xSV1boG1aloEpV8wyi8jZeOVYwO9+xmAaWxzHJR1f8PbdirwpeOPK4tmhInQcnt1e0fJyIqdL3bSJc4tYVozCHN8WNFrjOjFkLarGRYqY0IlpGh8hLF4+twiszQC5KE3Gcclup6GoPY6XFlUtOVvYnC09DrolP33/HNOsiQsD3zbouCWr3CCrNJFTExcWhtSczx1WuUBK8G2NY1cIbTGObd51LccyJOexR14LPKvGNSRdL2Fd+PQ7MXdGa2apJK9c1rmFbULk5yzyDYsULdgOc2Sl6TglsRwiGollwPVhzPFlBy0qagV5bbMuDCQa31EbOU8NZ2tv4yNEw9HSBy0pm43/UFxKFplLXhv0vILIrtiPEg6XLd61N6fUm8CJyK3xLcUkVSwzG5SmaTT3r3yWpYtt1KxzE6VASkVSCiBjXpi8dRUxTW2eHc2ZZi7rQhLPPda5RWLUG6bRuUtaCiIvJ3AbLlcegWMQeoprrTWLzMMWNbYFj2ceoVNT1gIpJI5R0fVzZpmNISqutXIeLyM6fsXF0gNVMY9tfKeirBQ3eilniwhVdSnlgq3BEiEkWggeTULqShBYJUlpItCcL1zabgMCXr/sI6RmlVo0jSQpDepGkQqTWWxhCM08NWEgWGQ2s8IiigRIgdCKll/zylmbrl9SqZBBkKGQOJamahSeXTOMDLYCi195mPMhP+S7ntrmR185ZLY2GUTQKEHHLcirhqJscfe8y7PXFpytbLQS+HbJMnd5/bxDXUluDGN8W/HEzprXD7s0Aupa0G8VmJam1SrY662ZpRU7nQLVCB5fRszWLneuLfDtmm988pxffH2XrldgGZqr2MOUBUprpNj4UzVasMwc7mwvOZyEZKWD1g1tV/H27QE/9+gCz5GYpuLpLYvzrCFe25wuQna7m77yrWXA4cTFMwr+8jc9wS8ezoiiGtcsMYWgVJLIzfiN0zGGsVEdzFLFPDXJq4hx4rBaBfzBZ/b4qYePqGoT08pRjcEyE/zQJyY42oTGoKklT3cjLuMLylQjpcI2XHRp0HZrslrweO4hsRi2Gtp+wnJucWegSHIH2yrISpu9aw2OBb/v1g7fervPSxdr/l+vjNEi5+Z2yjxucTbbMHIsq2Ir0uSliWOCUJpMVfziWxpH22x3K966MKjtGDNrE5iSVd1QGwU7rQYlDZK5jdtRZLlAO3NOHm/x7O2SwGs4XkHeVAQerMqaCIvfOFmihcE0y0kam5946x7TlYtnSgaBQdkUfO7IphN5dKOGdOay22ko85BGZjy4MLECg6p22W7XPNUe8vmTlKxcs6DAMl3mqWS1dniic8Dr081QKMk1nzha8O7rAa+Ok6/4ufiV6qv+Q+mpvuxx2P8y4ePfdxh+zEWacLYu8G2IHJOu7XE23ePF4zY/+4bBJ+4rVnWF1YT8rps9vvmJFsiKH/q1I+5d5UyzClO4PN332QsjtIarbMlpnGDbCT3P5uufkhy0HM5XCmGecbLO6XgVge7z7IHNH/+pe/zlD97i3jRhScRwq0/bDrGFx7qqaMQMW9ZILJ7sbaQfcSWwDJOu6xBFa87SjMMluIZE1ttEVosnRpfsdk9xzZKtVsleJ+F8bVMpH1EPaLTkU4fguwmWFLQsMKTm0bzL8XKXB7OA3zg1SCrFdO3y4klIVpmMIpO+E2AaNRLNJJZcJilFpdlpKcraYJ4GTOMtDGDQStnvLnhy5zF53fCpRz0eL1wsaWIKD6EtXtgpeHIrBqF4ODEZr00uUs1bVw6fP25zlTh0XE1aRwx8sIRL29E4xkZfPvAyDtpzOl5GXhW03YSHC5NSVcSlAErO1h6hU+JZNcdJn7i0KWoBwmSZedzsx5tY8HVAXtqsc5O9TowWgmGYkdUmoVeyLAyUViwyg/3BgqCVMYwSXLPm5iDBsTSfPh0SZxbdIOdalFGjyGvN6cJHCIN57jBLLVpuiWogLTbU8uOZh23WCEMyGiYEdkk3rBmFOQ+u2ozXDq6tOZxFzBIbz6g5S2KuYodBUOFaNaahef/tS0wT9vqrDTVdQFxBx6vYbhUUDbTcktCseHZnzXY7xXQK2n6FbzcMWxnzzGK8CsgKi9N5wNt35yhgmm7+br5ZMFmFmIZgntmcr11+/eKQwKkoG5Bas8odumHOU9sLGiUQ0gQUO11NVlgoKlpeQlpaVIkkb2pmicNeN+WNseBaKAidiqySJIVNXhncub7g8SxEyJqWV7K/s2RVOFjmJt61bAzuXgZUSmPYFSbQ8WtuDjOe3l1ye3fBMIpJS4Vr5rx22Oajd5ccXYWMY59OWOJbDYdXEb1AoZWFKW1eO+9TrD2+ZtTlhZ2QrZaNEIIf+56nud0PsNNtTiddTuZdJIKOA/2gIMlMQrui5eZYRoEQJVeJxrUzonDK8SqnqDWjVs5WVLPbqTDxiFzBzV5D11WMVxafe7PPMhY8fORxPLWJi4aDYcavnF7QaxtYwiQvHRrlboyoWfLkYIohU0yrYrpuk5UuJi6jQNJoTaZWuHaGac0JgiXXQxuRRSS5zYuPBxxO+hzOHMbZms+dhgjtkxUOXb+kWPfYCjRVU3N/EXM0s8jzHj1f0QkystKjqT3i2OJoXhDXc7ZCk+utNo500GQsihJkQcsy8C1JXjdMYpd33Yy5sZvT6RYUYs5pnHBzVPCNH3qe3/9tNzCE4NWzhtN0zt2xy0EUcOBeJ5It5nnJ9zxznY/dNRif9vmWJwzu9FyUFtyfNnzxPOeVU0Fgerx33+H5LYtKSY6WBa+MM14/kSwXQ/aCiK3AxZaSi3XMa9OYcZ5j6gHf89Qeo7ZAlYLH84KrtGCW11/N4+zfS/zDf/gP+cAHPsC1a9c4OjoC4Id/+If5qZ/6qa9yZb8zEFoWgwCe2tpcR5dViutkLMuC7dBiK7B4dstgt5tztlI8mLiUImVZNPQ8k6cGFmjJ2VIyS0yuUg0UGDokskLKWtHvL1gUkmWuaDkVd4YGI9/kya0z4jrHNiuEMPAt2I40Z8WCFTnIEqUDLO3jGjG3+lNCN2cSu8SZj2f4LDOT01VDozWqaTMKK167b1PUDUo5nKwL7s+XPLk9JXQqBkFJ19dYUnO+tokLycPLAW+cDbGtAilrhHZoOQYPpw6GqGm7kp7XAIq0kNy93OIqCZhlG/n/IMjxbAVIDFGTV+BZgp2g5GZvQsfNuTkoaDk1plmhSKkaE9sUCAGhk9NxKkJHYxkgREMvTJjFDmlu41kgjYbAaRh5barGJK1sJqnkMnapFXS9mn5QIIQiqwWr0mQryNlvZwhg1MqwzQrfrlBoOm6NEIpB0JBWkqvC5zT2mZUOVW1hyYasgUoZTGKbaWLhGA2NNljlJpexyzx3KGrJwMt5OI94PGuxLCzmmU1WSV6/bHMVu8xih1dOeux8KeHzlbMuh7MWVWUg5eZGyjIq0A2+WeLZNfPMYZwEFJXB42mLdWESuiWmoSlrwRuXEfcmbY7nPqvM5Gwd4Jg1ngUPxi3KxvjS4kVSasW1XsZBP+fRxGeVGtS1JHJzPvt4wCKzEeaG+SwQOGaDIaAfZExijxcf97GFIq/MjWmvbnCtgr3eCik1N/pLDLEJuegGBYGj0UaMlDWzzGVV2JS1gSUVVyuXL5yMcA3N86M5262MWgtM2VBUgn6QIgWscsk03pyj0mgolcF4aRMnNvcvQ2xTYdkmb+uPKGpBrQx2o4TIrZknHkrD0TTiqdGSJ4cLrnVSTucetrfgOFnw4Zs9Xjqf8nP3Lyh0wjg2OJz4WFIRuordTspWO8W2FMvCZhBWeI6m41U8OVhwq7+m62fkucA1FXfPW7hBRV4I0txgsbbw7YqntiymSUjHz/GthnVu47klcSmZxi5aCwwheH53TuCUaA2B3VDUG6P1Wll0vAKhBbBh7uWVwfnS5nASkpY2v/hwhmXBNHZpaofQbJHXAsMtySuL0M5RGq5iyaidEYgG35a8vjrihYMpUVAipcFz15aYUjG+chhFCevc5d5pj1lso0XDMMzYG2T8k/tvEdgleQMvPxpwMo1YxDbaKgmChsDefF/fmiYcxgsOuhWWAXdnl3huwnZY4xoQejG3Rit22jme0bDbnXO7v0YKzbuG2wxDiWvVyNrlcFbxB3/8NV66XJA3JYUC1y4xBShlcLKwePFhgGcZ9H2N76Qczmv2+xV9XxK1CnKRUgrNXmSx0y+o0JS6wXYrprGNKy1aLU1WZ1xmOY/Ot3jn0wUtv2K6NNBWydHU5mJuERJga5vjVcZ5XDJZtjClSeRo2q5BWUsmWcy6rBn1Bdd6mm/a77PVMViuBed5RiUq+i2bdBnR1DZPdEIu4pqjWcnJ0qKpIorMxWk8Br7Jk1GLjx+fscwavv3OiF4bfvK1K653/4O2Uv6y8W+jp/qyB0V/7s/9OX7kR34ErfVv+UN/u+B62+CaH/D0KGFdL1DmKedpjsbkRtvjiXbEVmjw2qXm5cOGVy8U794JeWHY5nc/6fOBOwnvfOKIyzQlrguSUiJVn+utNhKXN68UlZiTiRQtLJ7uh/iiSy9I+MxJzasXFetMctA1+cFPHGPWPr/4usU7/8R3UyRzQsvD1G3G6w7rqiCucoSWaCURokaICi3gxsDkqb5kp33JG7OYq0yhmpBFPKLlSIQsiZwGAzDMOV+4ECwyl64V8rZtqFSNRtN2Gwq15teObN687GDR5mQ64P5VG9c02YliHsxyFBWBrRiEKSC43jX57KPr3J8E9L2cRwu4OzGwjYZ+K+NaJ2cUmBxejTClQVb5XKxNhKzQMmGnPSerNZaR8dR2Qsu2icw2qg7YajW8c3fFtSjDMXMgZ5opDleSlpswTyVS1pTaJK428pS08kgrl4EHEpOy8lnlPpELSemSVhYXccjxKiJyawKn4Xq3YBDWbEUFB92M692c/U7JXrvkequgbmzu9DOGgUIog6w02W2n7HUSrg9XrHKDnXbMQWdN0RjEpcM689iNcn79aMib44jPPhxwuXSxhKbrFjTNhkF0OA+plcFBJ2HULom8kvOlT1WDZyt6bkHHKwHF5cKnb4/w1BDHVFzvJxS1gWc2JIXFKEhwzAYhNUIoWl6DQNM0At0YdPySllNT1AaDMOHxOkIjaLQFGmwjY1kYSFlzufS53ekSuQrvS6ykWgmEqDlbujRshje3+kt22wmRk/POvRmNgi8cdVisAmaxwzoz6bgVeS2I3Ia0NDiJK3Z6aw7HEWfLAM+tqKRAaRCq4XzapiwDVs2SuDBIC4tFZrFKLL7r5vOM3JCiNLHNkrZb89TWkpZX8XAccRWbDFs5twYruq0Gz25wTMVbVyFJCUUN5yuXUVjw8lmfXqfgxnZKr9VQK4MnhwlpZWMYME0cZrGDUjWzzCRFsdex/9U1RGuNlILPHa85ntX40mKRmDhWTVqaWKbia25OcS3J4bRNUYdEDtzoNrRcwelVjzR1GbVyBmFOXtU0qgKZ4lkpZyvFw6nLXi/l9l7CMzsuf/JDLqErOJn3cUzBolAMe2vSSvGZ1wIuVu6XhogO56seJ/MOq7hHz695+dTilTOP8dqkMRKU8hj4kmFUUVYBCJekavjcieKPPfcMcW7zxdOQ1852sfRg492jc44ubYpSYlgZq6qhRchTbY+WqxAyJfBWbLc3xuDncYOUBov1Hsv1kKuVxZtXGY05B2VTl12y0iKrNgafceKC3rA4a2eGZxu8/5bGSCGZL0nFik88aPDDghuRzzMDH88UvG9nxBfPSwrW/HeffMidTos7u5pZqjhd17x4AgfDJd/4RMMLwxBX2JQ6Z1Gl3J9WXC4dnuy6vO+mRd8ziKuSqyzhwSKlQfN11wXPjxye7oZ8+njN42TNRz57xavncLUY8tz2V/6MUlp8RR+/nfC3/tbf4vu+7/v4ju/4DhaLBU3TANDtdvlrf+2vfXWL+x2CtFIsi4Kiqdlr2/QdH9+s6AUpH/niJefrgqLJ2G1D3/VpSoMitxHAyaogUxmjlmQrdLClZrL2uXvZItcLrhLJtcim71rsdlaMooRc1bhOjGcKFvEuTWPzyplkkVf4QU3oan767hxTWxzPLc7WNdPUxRQdDucRSq+YZXC6aFFUNkKbbLVLfC9llWuE9rh50HCxFkyzCsvQXB9ohKh5fvcxLbcisiWOUfNoLkgLk7xwqBtJ3my8b7ZaNaMw4/7E5CrxEJj4VknHT3BtTdU4XK4VRa2RWORViwdXNlWjGISQlSbLzEQYmlcvugzDmp0ox3MzBIpRq+Gp0YrDiUdVg2NqAkfTdSukaDBEw+ksxJIS26kI/YJlInnlfpd78zV7nQTbUNzsrPAtjWtqpBYgFOvSRqCwhOZw0WKSuIDClAopGuLcAaVRmKwLk9OVT1YbZPXGoPh05XK29LlY+lhmg+9olrnDKjVYFTaL3MKWDbcHMbd7KUlh8NE3r3GxdkgKi0W8YW1/8axLaG9kWsvMZjvK2PZz7k99NFArKGrJ0EtZZgbzzEFjbJZXvYz9bkLkFWSlgTQ0g6DANjSetTHrRUgmqUeSG+y0K3ajlLqRLDKLt4087mwtWRcWy8Jk2CrZ+ZKk/+iqhRYCBawyB8uAWWLz+lnE+dpl1EqZrF3absmtbkzHKxm1ciptME1c6sZAAGnuMvQrrvdShkHFdjsHIcgqizfOPaYxlAouVg7L1EApzclis2TqhyWP5yFHq4BKm8SZRVLamIbmZj+nFxZYpsHtUYxG0tSSqpHcHMXsj9bMExffrtmNXE7WFaY0QcAsc7nWSakLg6qWxIXJVexgCIEpNsmmh+OQ+Srk9zwzwMDnxfOYodkDqVgXNq+etzmeB0gBH7g15hufvGAQpIROiW+VVEqS1iYtt+T2doJlQL7aBQQol5E54GarhylNFpnN5x/YPB57TOc+ltQc9Nbs9DN8AaMgJ6tMHs0CVrnJdO2RFxZtL8e2FJ6tOF3YG68tIdnrrhmFKXltYZsNlqFJS+gGmoO2wdNbMIlN/h+fnnC9a2NZNa6leXgV0dQGq8zcGGpbBT/82UccTgwulj7L3KTnZ7TshnVh40c1Lz7sMlta9KOctl8zXbtcrB0Ct8AAprGPb1W4XsEytslTl5bdUKKQds7NUc5lNaYbJCwyA99WtP2aJLepFGhq9joF++2SwMlpqKibFp+7e43Llclf/5U1ZWEAJt/3ddd5uu9xEHj40uJioVGqZp30qGuTRqRYVsI3PpWyHVbkTck0EzwxgI4rQdZoWfCBawe892ZFXZmkpUllp4znkE1b9ByLRmku8w2j+4UdxfVhRaM2ZtnrRuNHMX3XpG25CFlyvizw3YqW43BjWBCa0GjJxbrk4YWmqX2e6FvcHBRElsX3vfcJaquitmOcOkCmAzxbsyw0WaX42p0Bp+mCv/9dd3jbIKLvaXRjkDYN5xPF3/78OWkS0fNN5mlNVWkus4zjVfkVPxf/Y0/15eHfaFT3Xd/1Xb/p549//ON89KMf5bnnnsOyrN/02k/+5E/+lov5d41pmbAtBL4ZsevXLDN469JjnkoOug1VpXAMixdGFrot+d++c4sf/I37XFN9vvV9Ji+NayK3RTPYRMU+OTrl7rTPVqui4wjadhsha7ROeGNs8a79nFBqRn6LO72AQ0rO4oKsFEwL2PYdnthSHL0y49FnP8MH/+R30vZMOo7DZTwgrRUnuYHSEFkGFwuY6Iqurxl4Bmm2T6kz7k9ht73Cy03etpXRdiq6bs3pqoPRePzG3S5ekDBwfdq+hSUloVNhCosbbZOD7pLPnXR53/VNpOvLpz5fd7Nkvyt51/6C00WDZRh8+kFIt53y1AACy8exKnzT5NufKKl0zjQJSaqCu1dwo6PQEvLKYCeqsayMyHJ4bmvFVljzG8d94hKWmcmt0SXPtHu8Nl+hhWDUSlgXNvcnPZ4eLakbn0VqkxQ2O51NnKzSAikyKgWmrHDNzTbt3pVHXsNVCi23YVnYIBvyumboF5QNjFOXWepsaL8orpsZw6igaiTLQhDnklI1TLPNBXiVO6S5w05UM00CDKnoBwW3Oit2o5xfeTwidEr2+zEdr2GrNefRLCQrDM6XAV9zc8rZOkQINgaimcmd7TVxbuNbFXFuUiu41ivo+RmTOCQQW6RVhmuVfPpI0HJrDgYV5ysf09CMWhlp6ZHXm+9jWpjUhSS0FadLC8s0OZwFjFoZ/aDEswsWuY1taBRis+WpLGZJgG2UDAcFF17OWxObPpppanJna0FRax5OQtaZw3ZnM7zynYb7ExfftGl0zQu7Uy7WDrZs8WBsMln7+KbAs2vqRjBLHLp+QVpqvmbXJZMrrtYm/ahmsgq4M/AwDY1pn3J/2uJ6f0lglcSlzf2jiH/82piDyOIobwidhji3WWY2d7YWOFbNp+5tsdNLGAQ12+2ctDCRRo1danphxqOrDu+5PkFrk6KyuDWMERLuX4U4Rs3FetM4395acrH0EHbNFx53KHNJt13ScgzysqFQiv/x1VfZCyK27S5/4UN7/PBnHhG6GS89jpgmHm/bveJo7lFVJreGE8rK4ZMP2jRo8txmsTS4fi3DMhRVY6C1i2WYhLZNx9V4tUnLTqmajFkS8OppwkWe8PQo5SqTpKWPlIo8aeGYMByCicXJ2sW3Cqqm5tEC4jygVjWBB75q4Rga3xL0h4pr3RWXScqdnYyXD21So6LnuHz0/tXGo0DWRI7EMnIu0oatKOfDX3PA56/OOJt5PN9PkEVNL0r4pXsWz+9VtO02tpDUwJPXCprapizh7gWY7hLb9GmyPVxDcziVZDX0w5yhkJyPLa5SRT9UNMqjrizKWvPpT/0qxWLJ+t4W7xh5dFsNQlQcLko8N+Uv/pzkf/8N23z0gUmlBKu8YW97wWJmYkuHOwOHyGgYryWLlSCrJXZL8Ny2JDItrgwTRYE2ai5jl6qW7A1D9jwLz6mwzDXj2OZX7s5ZFTXPHwh2nC/5VbgZ8/h/PRL9P+J/HX/9r/91/s7f+Tt853d+Jz/0Qz/0r55/z3vew/d///d/FSv7nYNO0DDsafIGsGIeT3q8c8/EURDYAikdHk8UihXj2KbjS2RiYFga07Com5JONGaRRASWgWlL3nt9l0+dC65iQctb41UeO20H18zphCtOFxGWveZoHnLQDbnZtfj8Wy5BO+XJUc14EdELG0K/IM5dTuM1CoHWEVJDO1jx6ftDDromHReuYov33DjjNWWgGxutYdtSm0WIn5HmLo50WWQhtpmTVDbDQHK+cjmcC4paE+LyeLqFb6c8uZ2itCRwNFepZhgKRoFNrCXXojXnK4+6DnDMnFnmUFSCWSLZb0tCt+F0rTmceWxHAV9/fcqqcBHCph9UtCzJVphjmDkIzdGsQ+RV9FzBrKiplWCe+ex2CxbrnHa4wjIkUkvafklWGcwSk+dGKfdnIbc7KYvUBgEdJ6OqHZ7op0xTm9OFS1ZJtNb0goK8Nmhqi8u1y6gVM0ksTKEJa01cGOwFG2bT2i/IS5PTuUnLzbjWyUhKScvJiRyFYynOlg7XOzmGsfEoskzF7eGKtDQ2S612TFyZrHOLO1tL8srk1496LAubG92YRktMQ7OsHLbaJW9eRpzOfdaZzbN7cxxDkRcGhlAIBJFXEzkV48TFEJrXTrpkpcARDtvWiEl8j0VhY0rF42RFnkc86a7oehWTxEcLsI2GblgwW7qUtUUdxgip2WmljFcej+cergG3R2vaTsVnjwdcrlwMwJQbuf/pyiPJLcpmk5wVVz6e0yBlRqM0p/OAwC856KwZtTKOFwFxIUkr70sDvgbL0sxTyaunXXbaGUUjaJqGrp8ySU0aBWjF2dInzk32+wmB3SBkQ9uu8MyKq4XHq3NF4OW0bBNV+dy/BKEEz92YcjILCJ2Ka+3NAM13avY7Nt+2d4Ofe33FP3rjlOeGLT79aM1PLifc2E1Aayot8eycL5522W7nZKVkkbobZruWFI3AFJrz2KNSJqbRcFUnoC3SUrDlGvyB57r8k/sL0nVFSollKB5OAx5dBrSiEq0EWIrXT7vc3F5xoxezKmzGTsN2kNLxc8rGpmgkltnQ8QQtJ0cBtXK4szXnpaMeRW4h7YZvvu3y2uIKQ9T4Tpuq0FwlNYFTsduueWIw495Vl9CtqZWkaBpiPeG5vZwsd1mmBs9sZ2jUl27sJZ7b0De7vDpZctBruNapGK83SaaBl9EkLpG3McGfTQVukKKUgy0hqyAtLaYLQa7b1KWBdlZMY3hqRyCRLHPBbnfJPOkjELx6HhAnbZaLAO3l7A8LdN3i5sjhnz8851ffyvnL37bLf//rc9phQ7pqc1lqbLtiq1NwtfJxrJRaKSyjwXcqFrHHfC1pVMPJwkLVCwQ267XJxZXG9m1+13MhJ9OaXK0oq5DvetrjE0cr4lyTlQLfqThauAxCSdvyiW3FpMj5wK2Sj/z+r+N/+OUXEVpQ1lCLnLhKSPMtntuFohGUKmGRmnxwb8giq6kKxUHbo8hLHo9r/E6D4wv+m/ffolHwLddHvLAV8sbVmvffNnArn5cXC3qR5E6/y+E0Z1oU/NpJSRmb/NB33Oa//bmXv6pn6L9P+LfVU/0bMYra7fZvevyBP/AH+KZv+iYGg8G/9tq/T+i7JklV0Ig1tc4wjJqtwOR216HtGCjV0Api2mFCbZdUWrHbsskp+Gcvldy9DHjzMsB1ckxD0agWLUuwzuDe2CFvGhqtGPkOdwaC04XHS2cml0mDkCV9z+e5QcB/8947HC5y7l5lxJXimbe/jezhA661DDKdUegpjdKMfBgFNV2/RAvFdstgFNikdcbxesXR3CLNuuyHPlthg2WmaAUKyaIsWJYZlTZ4esvEI8S2c9JK4EhY5OXmYlKb7LY8hoHgbB5yo1fxnhsTlvWClpsAEtfZHGzPXZN0XYNKGex2KtZVymVasyol53GNa1a0bJvntlKksNhrG3j2JmJ1kVWMojmdIOXhUhCXJpUycAyBJVzOiyP6vsHQV8yzgBdP+hzOfF4fu9wdu6zzhntjh/OFAeQsMsGyEMCGPqu0IKts9rs513s5HVeQVw6WtFGVQ9dTLPOQRRaySgKkFAyDmshRRE5DVQneMZohNUxTl56fs92KCd2M0Mu4MZxzsba4WnugDfp+Q65sHq8D8lpSNtDxM7peylaYYxoGs9Tlud0Zl7FNz634phuXLLIN7XsaO5toT7fBtRqeHi1ZZyZfPO5yPPH4+IOGUWBhiYBeuwBDc5XYvHLaRWjNOA4wDU2jJHFukecWptWw/tI2a5lamIZkEBQUtaSsbS7XHtNU0nVLWs5mO9sLCva6ORcrh7QUGPaKeWKw1405Xbo0WnC9X2BJwWTtUtaS8drm6a2Y61GHNy66dP2SQVAgyBj5OXvtjFnm8G13zjfSvsTjYuVxFfukOuX1sxbTuYdrWfQ8wQuDHvfGBbatGLVyxmuPQVgCDYNBwqyOGdenGFJz96KLZWjafkWjYbze6NwXS5etVo5SAtsqQGgO+imPpy3QBo9nbWbLWxRZm6KRSNFgGQ3r1CFfXmNdWBxP2zimplYmhjb4zqe2CYn4mden/PVfueAff3HC9737BV6/KPmxV874+QcTPDenUTb9EHqeZpH5hLbJfrfEkRauaXHQy2nZgsDRBK2GdWqhhWYQJux3pxii4P5UMokN3ryyETSsc4uer9gfFfR8g8OzAZenWyyXAYvEoKjgaqmYjiXnC4MHVyaeKQlsCC2X7ZZBXNj84WdvYmATBFN2+2O2o4Tt1oLtFsSrPqLZGNfeHFbM6zlYFzh2wXaU49kZz2wVdIwBp/mSq7zmrQuX0K1RImeSFQxdB08MMTCpdcZWe+OFFccWlyvFllfzbXc033onp2wUZ+uESqx5cpRy0NGUBVS5xfS8S1kEFKXF0Tzn1w4VzdEb/Mh//b/ha3b69ELFOIv55BsGO901HSviPfs+x4ua8RL+d+/ax3ZLhHbZbzv4tmKZadJG47gV26OY/WGzuX5RE1caadZstVOS9cabqOtYrDOYJ3C5FhwvbKrG5ImR4N0HFgaSONVMioqXTzWni688RVohv6KP30549OgR73znO/+15x3HIUm+8r4E/yGiqASuLdgJBaLusRXFFCqmUA2NLBgEklHo4sk2y5XNH33uFlmhqXWDbcFOaNN3fXrBmmWZY1kFe602LcthOzQITJtJWvPaWIGU6CYkrxyOpiFKwxdPbZRu2O4auNLnlVObo0WDbTdstRQGBl84HPDpwwEvnXrMM4u25fOB22s6rQlRsKTrS2arAeOVppExLbdBNg6eWTFLNnHeaIlnKdaFQeCUNFqgtUGjJP1Wjm4gtBSTlceN3pq9ds5BJ6FRNVVjIpG45ibGfreT0/cbjhaCR1MPzzR4ejsjzjeMlw/dmXKjn3C58jha2owTE4FkGCgKpbjWSRj6JQe9DN9SvD4OeHMS8uCqw/nKJ7AaGhqu9WLUl+p+dmfJXm9FWkgUml8/GnK6Cvns6YC8BsvUWEbDO3bWSKHIKwtD1NiG5tZwiWdB5Jc0emOKfLl2iQuLUm1YyUpD3NjYpqZWmrOVTy8o6QYbqV9gNcwSh6KB6drm6f4Kz6pxTM3NfsJOlJFXksCu2I0S2l7FQTvhZn/FLNkwjY+XAfPUQRiawNnI4JapRV4aWLJh1MqJ/IqLpc88cThfeQgpsA1Fyy4xDcVOK6HjlhwMYgKvZplpPvZmw+E8JLArstpEKYMnez5CwHjl8szWgpZT49qKqpZf8gEyuUp8fKsmKW1AoRqDUglqJTY+S62MQavgie0FpiHwHMX5yudsHTBJPPLGwjUK8lKQFZJ74wgtFfvtGMtUJJW1GRwuIrLK4novYRgWGLJmr5Px9M6MnSgBLWlKB4XBPHWZpR6zxObtOxO2ogxTbFjWttQUymSWeHQMH7Qg8BJMK8b3KqrUpt8qsQ1FL8jYbmfU2mCRW0R2w+tnDj/z1jmJqvjUozVnk5oSjTAbDBoMCRdLn4tlwPEsoGwkk9SlVJJGbewV6kYySz36rZKWW2JImC5s9tsKRclYX/A3Pv8WL/S3eTrq0wlLnt+fst1PqWqLtluxKkyWqUO3VdAPMspacroIN+bLQFlvQoPKWtL2KkAQujmG0AR2zevnHQwDlNQYumZcH+MaG5mhJ222hg37vQW73ZiunxNXBlWzSWVteyWG0VAWBq+ftvCdhpuDFM/aJBGCohuWhKZBrGoMR3M88XnxKKJsTNpuTa0ljl0R+TlF6XJzO8ZxcnqtFN+wiacR37q3S0+2WCUCQ5mkhck0lZT1xobh9iBFKxdD1Lx67jGJuzTKJtM1wojZammeGJm8cpbzq8drvvudIf/i/DG1SJmnBr5loLVgnleUteYdexvZYVLnzDJJVbZQSnI4ERhWzE5okpeC84Xkct3w9U+HvOOmzR9+2w7ruiSpbP6Lt+/w4n3NM702aWkj7DWXqclBZGNjEeclyyZlGGreOm/zD9+4T17XaFFwETebhWQ2YOBY5KpinFaMY4/zmcdnLqb8lU8eEXolR/MCJ1yxc62i4yt6nuRiWfPP3hzzwYMB/+JoQS9qcJ0VV2XGMDShFvzBZ4esVE7LF/yJd+3xve8a8MpFjO9/5c/F/9hTfXn4N+ps//7f//u/5Q/47Yy267A2BJ40NgkXa4udTkLZmEwSSdKYdAyJJQ0iV/P3XnpMWRjkZsJianJrRyCUgWvC3vaKs6ULosS1ILIlTwxiNCXjuMW1KEEtQ6QIsci4SiuyKscWBp87X/PNT4RcZStevW+gzYL3/ee/n8B0cQyLrlNT6RTPromLNvudmMcLE2kKbFy2zDbjdc3nT2vi1OLDz2ziX22z5uEqoOOC1j3ismZSTGlkgGd5JJmLahTrIqBnmJyswLUSkirk2Z0pPX9NXvXZjiCrSxxTgDKZUXF/VuHQsMhMtCixtIdvdng4y9lv1yxzm53hgqKOmBUlaaHYDjMUObpps982saRivO5iy4KbvQxprEhyxc+/2eGFayk9vwAKThcdAjuj4wpOFhHXooqXz9qoxiMuDB7PBP1wQ0ffixpcq0ICjZKcL3wGoSKySxb5ZiN1rZVhmpqkMjhfhcxSm6hSX2IhpUwXPr0o58GyQ1Z5FJWLIUquBSumscOzWzNsA16/VGgNWSW4WLnstjcJS6vMxrcrLlcO791OePniGqFdcXsYExcW16I1s8zlU4+26Xo1K68irwSmEPhWSdcvOVkEtP0ShGaRGLSdnIu5jTIKbLPBNRWeVXPQj9mJCiIvxzE060Kyyl1WqUu3vTFlP1s4TKcBe9sJtgkNNdPYpqg3NOu+l+DZsN/VTFObWeyjhEAjGUUp++2Mg3aOITziQrIuN8OlcWrTbRVcrHykhKZeE5oK2ygBj06YcTy3yBKPYSvGkBvPBqU1tmgwHcU0b7BNwe3tHLPZ5937EX/zV8c0wuDwKmK/v6BqPC5WPlorFgufa6GNIVNCK2dxtQtdjWWmXK1tTpcON/sZrqt4+biN71WgIM4sHKm5Pcw4nEpWmUFXepws1/R6DZFbctBNOBOSO4HD6Vs+hUgpCxNhap69ZvLe/RaferCkCBR/9pt2cCxzo1NfLTGsnM9PHJ6/tqZSDVexS1zCfi/mi2d9hkFFN6iZxQHr0mC7tUmruViaUEtCS+Bbgq4nSOyKLV2xykNMNOO1yeEMej68eWHwxFbF+VzwjqfPOLvq0dQWlmPSjTJGoaSuHQZhQl65FCqnGyQ8Tj0++9igaq7oBiU9r8E3fRyz4ngR8PrZFlJp7p8qBluK0C04WQjMbIv33UpouynDVswXjp4Aao7WC6apxe96fmPmea2XUtQ2t++sOZzbzOsZRemxFXhktce+H7E71FzkFwyCkrRZc3faZbdtMbJ9IicBUVKbLjduLlgufcZxQ9zUPDPw+fZbPR5e/34+8MIz/I8/92n+xre9nR/49V/nfU9KIrPDw4XBfFbw4qOC//PvP+A9ByG3Hpo0WmGZDaGrcSyNZwrqXJLrikJJHEMgtMUobLhYmcS54HRV07FNdG7i2nDjWsoqNyiVZp7VdD2Fa2mu1hZITShMgjZQfuVpyAq+YmbWv92so2/evMkXvvAFrl+//pue/+hHP8qzzz77VarqdxbOJgZbI/Dac+KqR98LsbTDVaZZTBx+75MdXpocUWrFfs+lljm2KbkzKnh+2+AX7xeYsqJtR6TCZJVrvvl6m5euWkzSOZa02GlBXBp8+nHGVktgaIfAtCnKirzcDFs0Ob5p8lyrx6gd87mzAtcu2WobHIQjHqwmKFVwPHO5Pcy43c+5SCxeP/cRImOW2Dy3XXM0c3hlCh4GIyI8PyFTa94+SEmLCFcWrPIUrTey9XVm8/KJQc8TbHUEt0cF06xgK2go6hDfgo43QWHSdQS32jFxqThbhBsPRbmJFUZrvvaJU+KsiyUNtMhQ2sc3DY5Tm6FfUeuSSmlMIYh86AdXHC99XDPm/tX2Js7cjdlrVySNwyLp8MzWiqS0cayaG/0Mgc298x5v28uYrh3mccDTO1fMU5eygazaeOgEdo1rS25FKaMg4TI28GvFeB7hmRVxIXHMhp5fYBWwGxUUtUFRW5RK0nZzukGJY9WUtYmUgv128iWJVMPZOqTr51ysbR5etdnrJlhGTcut2A0LtBAIYKtVYpuKZWLRCwuSwsQ2GrLS4NG0w/3LDpFXMpv77HRMmkbTaU+wTLjeT5nGNpKN5HyS2thS0+gNw73lGaxTG8eQFLXBInWZrm1cSzHO53RqC+FobvVWHM5DGgXdsOR06hOaCt8quYo9dloZCHvDvkFS1YJSSrZbBdPEpdES1y45W9pstWPabskqt3g0adH2KnajBT2vohZLThabRK+uU2Jb8OplyG4nxbFqTKk4nLWI3AbThNCqyWuLYZThOyU7Uc75wqGoYbeb0nJr3rU352df2+e9Ny9pORWny4BOULBYlGjdY7r2Cbya677F1KrwnYJZ7LDbTZinDpPY5tYgZpLaBN6ax5dtMHIMDTN1Ra8t2OvFOFbF/csOT3a7vDXO8S2Lzx0a7HfXbLVSfLum7VZETsFuO2aSeFyuHJ7YigE4WjrYJvhuQ1zCp06PEMql0pJlatP3cwZPFZzNAkxDUJSCnfZmoBiXNpahECiWqUUWQ1xZ+HZDXksGrYKOmzHPbBaZwyyx+MDOgM/kS5RQrDKTyM2Yr0POJ4pbuwtOlz6jVoV2G0J7w2oaRCWRp3k8N2iE4KmdOcNQY5oli9QnK8G1KuLC5KCnuVxumBKOW7NI4NmdGY4heHZrxSSWIEq22ymuKbnu5cySFu99wuMXxDm9YMigr/k/PHvAj754gQZu9CqUslCi5M0rk5G/kWvWuqLnw+XUQ6OROBS15ihbEXiKP/2OG/zS0SVZkxB5AqMxKSuFYWb0XcF+B0I3Y5Zqjqc98iRg5leUqiS0bZbLPr6jyEpFpQRPXs8o8alVw3boYEiT7//aA3Z8nxd2Kv6r913j4Tzj+//5A9pRwyrTzFbg2CGm29BuZ1wtNS9dzKGsebS2eWoEQvsUpcPlsqYdmRiFRJomH7jhcBLn/MtHczqdiusDsHCJXEGSS1aZ5v/yqVMsV/ONN9b89N0JjiGZJZJFWaGNmh/6jjv8zFtnvPu6i9W43Ow5vHQa86FbXZbxb9++6j+UnurLXoF+6EMf4id/8ifpdDq/6fnVasV3fud38vGPf/y3XMy/a1wuBXMiDDnHdpdMck1kBdjKpu9t0gtKpbk/kyit2W/ZtCyLfrfiyFVkjeZ4XeF4DdfaJpFjcL5qMU9Amxm1Tul7BlWtGPgFV7HLOrfpegZ7kcPVGu5PFIv8ksg16AYm3/I8LJs2stvip3/qV/n23/ce9iyXRjcs85qsaTANE8+0+eRDD9ur6HgWQ8/mWltQ6phZoahUw65fgUwolUlkefhGl7apyKIVqy/Fed4cnZKpElN4tK0Wkwx8uya0bUwpGbROMFSHjqd48cLh2X7NwPV5I4moTJODdsajuWaysrk9rBBmQq802Qo1Z7FP1zXY9j3GSqO1zTQReIbiWjuhIafnNnhezCtnIV84uUZZBzy/HfPCdoItYVHYDH14+cLnoBNjmgVlY7LMNaWq2OtOmeeSdGHTDxM8U/DEIOd85ZNWksBPWeculbaZpya77TVCNvS8nHFsI4Rmnkg8OyNObHY7De2o4CQxmeUunqXx7RIpNK9c9TEMTVlbNKrmPXsz0koyCgpeOhtgkRGInNfLm2xFOQdmwq+c7PHUYEUn0Ag0q9JAAYZRkzc2Z7OIeGXwxJ1LXny0xe3tJcvMohs0G711LQntGtNq2O0tqBuD06XDMMyZxB7jlU9gKb7m2pjjVcD5OqLjxsxWQ7bCjMs4YrtdMosdEBrXaui4OXHuUCsD11RcpTa1MvEtRd3AKNqwjhyzoe+XmLmmVAZx4XB3HNL1K6Kg4uWTNu/aMzEsQcddczyruDv2qUUP31bc7C15MPZJcofTuctFz8E1a1p2Regqji4iMiXwnZKWX3LDd5imDc9uhfzRd4/4i594QKUaNPDczpxfem2Lbzjo8vU3Ovz08RzHzEnkis8d+nRaghvDNWUtqBuTRktOJx7XR2siryB0G2apT1ZtNpdawxvLS9ot+OJhn0GroheWxLHFW2XGreszDi9CbC/njz33JF+z06Hrmfzt775NvJhgGZKqVvx3H3uME9Zo5aAak3eOBvz8/TVaGyxTyecej7ANRVFZ+KYisxXT1OCl4x7vuzXlamWw18u5jCGrQrbaOabYsOMiuyauJHHm8ujMI28LAksTrz2CqCQvIzy3IQgl7VbKrEyQssW8LFlOPaRoeGZnhiUciqLmud2Y0DbpBWtMM2W86vN4WXEZu9AIhFny/mdb3Op6vDHTdIOUYQC+nbCs54TKo8hdWtGaPAu4Hm0kYVdlidPPuHfRY+f2Gb2oQeiQLAuZxya/+84W//izKx7nFXuDEowxhvL53U9rlonFybLGceZfGraWNEpwmCuaLKDfr3GFxxdfepGf+cmf50+/929wOXX5a585wiailjl3r0xOjk2euJ7zLdd36PkWHcfi3Xs2J2mGZwpmK5+srjhdl3RckycGFeOlh9YC1TQc5ynTWJJWIbe3QRcmylXM6oxxPkNrH137GHFIU0HpVORFxemiIHJNrrdLSvXba7v02x1//s//ef7Un/pT5HmO1prPfOYz/NiP/Rg/+IM/yN/9u3/3q13e7wj0QgutoKlC3rY74d6VQ6FMUC3ed93nxccZ771xk594/RzPXfJLjxu+/sY2dxclr8uGnajmZGlRNBmW9Gi7Nn/185/ml+4ZhIHGFxVZbtOyTYqqywe3tvgffuUCz9HcGtgMAs3pTLI3TBmvAsomZcuLaBmCw4mD62RYdcGtfot5XXF/UnNbpriWYrdVY4o5cQmzJKJSArDxBUilmOWCbdcmTge8er7CMWArsHhmAIfLikVWMgwlH37aReqQ86zYpLBpD9ssuIodtK55fRzScSzuWClCVLhmim/7pFmL47lmKzSpy4h7tUHblUSeoFACU0uK2qHjJkyTFjtRQlpKylry8iJCA4Y2eJAOQJsEdkHwJXkX+AT25sbONGoCq+Is9jCEYBjlmwGKKKgawWcPhzy1laF0w8AvEKKh5WhuD3LyysMxFb5d85mjPlJqemHFTrTGNiRZZdChYZHbGIai1hJLblhY69zGt0uGYcIwrNhuZaR1jkRiSs3JKuBy5SFlQ60EhpDshBmB3ZDUBlllcrFyOLwKcGzFDS/m1mBNnFuscouOV2OjSQqLQS9l1E2plSQrJaGdg4BeoLgzXDP0Ms6TgDgzWBY2LacE7TOMMgwz5Vo7Y5FZLFKHwG1wrRrLqAkdqBvJyTJknVlc6ybYpuZk4qIQWHLjHeRYilo5oBvG6xYSwbKfELkVXb9gy0v5+IMdIlfT8Td+o7bZsMwtXh23GQYFhtD4liLOHVapxXY7RyuDrBIUlcUytzGk5njub7yynI28X6NIK5u3xgFZYVM1gsavWOUWCkkvyAmdhr5XcLryuFoE9FTBdj+hqiI8aZCoNc/upQCsCskksbFNhdGIjTxeasZrn2473QwnpEAZitujhLqRhHbFVjsh0QlBYOO7NSGadWFjmYqyFlwsA2oNLz/og6lwnJqtMCNyK2yz4XzhMoxSthC0xQEfexgjkVRKsuVnnMxCbm+tWGQWRW3weOGjELS8mrKBq6X/JblnB41Cmop3HiwAjRaANnjjrMXve2LEg3FFK0iRQjNLHMpGcToNGfQTSmUQuiUttwYEPa+iUpplajNfeMimIuzUdL0ChMXJwmW+brEuNEVug5L8l9+8w//08E32tlJOp30aJfn1+yOe3VkhTMEydRm2MrainKSwuFg5zNcBP7uc86ff/SwmBr9+OuH9Bz3+2b1zsqJhFbt0/YpKSQ46JU1tktYmHU/S1CZVpdjbuuL5nQalDN68MjiftPiXR3N2fJ+LfM69iw4uBlUBV5OAbluzimoWueDhxMHUNo4BSSGY5zZ7nRpTCmyr4jKGli9whc3lumClU37/P/4c19sOtmmy17X5pw9P+am7U7Ycl+f7AXfzKVlhYTuKUa/GFAbrxGae55wmkveEJruBiyEyzlfwe6+POJqcQWazG1i8bd/jfjrhz773Cf7Kr73OVstCKU1Wa+JckZZQq5qDyOM/eXufB4uEw0XOKKr58M19Hs4usWqfn7l7wrc/uc3//aUjvnG/zV/85JusU5M//w37/J8+eMDv/modoP+e4d9WT/VlD4o+8YlPUJb/urlUnud86lOf+i0X8tXAuEhwnS6B4dFUI251E05WMa+NFS1bsBX47AaavagGmSDNMZ+836MrM/r+kLrZsH1QJcfrmFqtqdUIqXz22zmu6dDy1sR1gZRQljaztcXZ0sYxBLapeXZkUinFq2+ZGE/M2R0UmGmPne4Bn//RH2P94XeQNlM82cK3BOO1weE8QArNM9sms1xylSbUVFy3bTzDI7IylIaitlG4vDXTvLBzwtk6pO2adB3YChcskoiO3caWV2RlimVYdH3Ng1lJ5FoYuo1oTGxbI7WFKw2+cNnw7DClVC1e2DmGpsM39gqEOOHV0x2k6nIRF9iG5CJp6PkJszSkZknTDLjZLcgqSdu1ca0aQ5jcu/S5fxVxOvcwTMXb91LSKiXTPhUpe+2KRi4YrwcMWylfPBkyXrp42BxOurhWgzYl07jDtp+RVD6hY5BWNq6E0yJkmZoEbkmjbAyhOFs5LDMfz5SMIsXVss1uL+Fs5SNJeXov4QvnbSyjwJSQNxrfKJlkPm2nwhAGx/OID+wc8hvjPYZBimlqIqdiFOV4FnTdktLLea57wWvLHVaFxSKzudFbcr7yeHwR0o4qOtsJr5x22G5DXNrcHszoeput3KvLiK5fYRmKohKklQ16k5I2Xjo8OYw5aC15c9bZeOtkDlVjc60Xk1cG45XNMCwwrYasspgkPnmpuFxZzBIPhcAxa0IrZ7e3QgjNqFXyheMOtTLRXkGlDPJKUNQGZW1wFQu+/tYldy99tJNxbxxyY1Tz9M6Kty4ClBY0Cmq1MdseLwX3Ln0UJj0/xTA6HE893re1w738iKoxOb7qcGE95N5Jjxf2bH7irVOeGQQ8WGfs9ROOZiEq97nMSj55OOMyk9zqa25vr7la+5hGydFVwJNbax7PWkxXNm/fXzNobfyHprHJLN4MkQZRQtkY2KbElJLdoMcblwXzhYvpV1yUK25EBXcOcr7wcMDJvObDtzfeT5FrkkvJ3/m1Sx4kMXFTYcchb9+JKMsZv3zPQknBIKiIvJq7lx7CBtdKeetqgCFMLhYeEsFnH+6w3W1oeQKFy34np+VuaPo3uxtNvWdpXj5p8Re+YY+/+9kzSp2SxBGxUdJvaS4WJs/uJrgW+LnAEjVHU4v37K1Y5iafeGuLjmtjOxN8t0IrF2msMERAaCtmmcO1wGUKxFWD0Shevch5dVLxvluaXlDi2Rmq7HH3YkjoFQRuQWBrlgn88puC/+zZjKRsc61dkNcmPdfhahVyOJG88ljwcHpOxzYRRoVl1tRNn6IISCuYlTNubK2YrLbZaytWecnF0sXzoN2uyErNp45WfO2bL3Lnaz/EZ47X3OyanMQrJonmYmWya3m87VbNqO3TsU3+3M894M9+/R6ffVwThTZzUs5iTeTYDDxJ5IIhGk7WGVnTcLtfsxV67LUEs7lDRcpL5w1hVLHd1riiR2BbxI3JotI8uCoQtibyDJ7oSXzbYOg1HK+/8gEPSkuU/soMoL5S7/OVwh/7Y3+Muq75C3/hL5CmKd/7vd/L7u4uP/IjP8L3fM/3fLXL+x2BuGyYZya2afPE0Gbo+ZyvG156ZPKhOxV66tJUglFg0FQRd8cF/+n72/z8L0+oadjtbuLoEYpHE40rV9yftnjummCSmLjmirPUZ5FqHAm/fHTFczsGb41L3hg3vH2vYKfTQiiPqm5wrIqXJjW6ttjvKi4WIZY349OnDs9uC9570CCl4NeOTW73NH3fYJnZFE3CyaJDZBkELpwvG5rYwhEugyjDMWquYouOZ6CxsESDaybEImCyqvnep0b8t59aE9qaovI5X6ecrSWODPEbA6FjTlOXdR4irZzDmYdGst0u6LqSx0ubRa4oWyW9wMZ3Nulni9yk71c8vTuh1A5xlXEetziee7x+1uKgq0jKhu2opFEGZ0ufkV+wE+aM1zaBXWFIzTJ3WSQWs8Sn7S2pGovx2uGgn7LOoeNXCAFZJTd+Nkgip6bvFpRKIoVmq51ztgjYaedYhqLn1ySFZl2YxImgVILtsCTaSgmcinXuEDgVTw9WHM0DLhOPvVbKwE+5TFwCu6YfZoyCgqN5i2Vp82jWQgjNncGaRWZytvb48O0+//JozeXSZZ46DMOCW4M150ufg+0VdS0JnYYnB0sezlo4Eo7mAU9vrXhmuKRlNwhD0HYrPEuxnG56m+v9mMNpi6Qwef2sjSEFnt3QD3OS3GLewLc8+ZjTLMKQmq12xjoz8O2K/YFmkdj0woJRlLLTyogLkzcvO+SFSWTXhE7FO3ZmjFOX+4sONZKs3jCNRFfwYLJJ+fWtkrLe9GZtt0YpQdFI4tL6EuNMstUqUNpgv5PT9ivSwiArBEptZJEdv6HtlaR2w+FVSN2YFMogzi2e2V6y20o4XgUsE4fxysWSmgk2vShmktl0Tc0qD5FooqBCouk5NZ8fR7S8HKUltlmz24mJC5er2OK5a0sCp2ESO+x2c2xT8WgS0SBYpjbPX5ujteZ0FSKFxc3eikYLqqKNWwz41uct7qcT2n7GPLV45/7GkwkUnz1L8b2aJ3dWnC89fvXeiOujFNeqiJTmwSpCCMUo3CSSBU7FwXCFlxnoNOCbbkW8mdyn7xecrxyUNkgriw/fbvGNeyMerx7x4f0z5pmNLQU/9eoW1f+bvT8Nln07zzrB3/rPY86Zez7zPefO98pXki3JQrIxGPCADc1gMLbLjQkXlGmguitcVLe7C9xNF0FRlCsKGjAYmjKmoGws28LyJAkN1ng13PGce4Z9zp73zjn/87RWf8gLHQQfCsMlJBu/EfkhY584uXbs3Hu9+b7P83syA1kK+n7DE5tzLH2NmZgkFp5Vs9mpiVLBqhB0vJIvHAzxLI3VwuGduy0+v5pTNTroNT/76Mt0g4b9icWolRN4FV1X8cWjNnEBfU+jweNKv2C3nXBv0qXrZ8xSg5++f48s8mj1LvjcySZZ0yByg6UwOVnAdjfieD5ACEVSlURpgCkMtkYrNN1gmWsIJQlMxVZbchRFHMc6L2xc5difMplLZkXN1paGKXSmGdhWTtv2SBMTx1+S5IqbI5PrnTZ3ZgtqFRPoI1yhM141nC9qXMthaJhsuzaGEli6xk//H97Gz78y5ZfeWPDYZfjcS4JLWxGubmIIj6hZcPvMpu/pDGoH21hxvqrRbInr1PzsaxPatoEnDL71iT7/+MED/o+3bvA3PnGCb5lEaUNcg6GD4+RI2caUHq6T8Q2Xh3zmOEZrErJC8KGXajzNZrJq2G/NeffO07x3b8jLFyt+7f6Yx/yQolFc7jpv+b34VvVV/6n0VP/Og6KXXvr/A6Vee+01zs7O/vXzpmn40Ic+xM7Ozr/3Qb4SNVnajMsS3Ul40i2QVYtrHYUuHVZVxKceNjyxnfLMVkZdtRn5Os8MbV47MfDcGYvUpWe7tBwfWdpEZUVcVty9KHh6q2S7bRHnHaJUp24KpllN1lhc6lRkNRzOLXa7GaEQPH3JQRBwFmXsT2P6NxS9rs+er1PVHUyrwLMKTN1FSsXHHzhcH85pqS6W6rHIS24XBX2/4UpX597E5fZYsRkINgJJnO2ghCIqGnxL48FMo5RLHMulI9pUTUHTSEzNZDuocKwJcdZnx5O0nBkvHl4hK0J0csq64EpHkJUWrpWR1zpoCy6SEdeGJYXMqaWNb4Y8mFU0jcd2O8fRVywznaSJGPoajVjy2mSbX7vTQ9M0hmGFEAV3zwcIPeRKr6aWLqpR9J2E8crkY/f2MKVNGVu4YcMq1xmFCXvdnHEKeSM5Wek83ltwWNtUTUPdVPSDnEY1nKxMaiUJrIy8UfS8nLiEC+mwP26x3cq5HBTMMpdbg4y7M5+um3O5nbJITcoa9lOXF7YWeKbgTrSFZ0FZalwfLfjowR6bQbJmDWmSgZPxM/duUTYCQ+hc7cW8dt7meOHywvUxhg6ff9Sl7VV0tYBXxhpff/WYRhkUms7bLk0pKpMbgxWfOehzpRPzpSzEMCRv32xzsszoeD7TxMMyC3Y6CV8+bhM6JQqN6xtLzt9sHq90I4rG4mgRopC03ArPqIgKg1UcMGrl+HbJ8cKiAQZBRlLphFrF8dLmPLI5XTi03JyXTkJ0YZFXDTvdAk00wFpynVUGLa+mrEzK2qaQgq/ZaHERzVA0bIY55xceqVxbA3pew++6bvEwGTNLbM6SBMcuudqx+LqORyomPJp0kHrDrEx5MFO02lA0OnvdgrS03gQVKl46GlDXUEnF141u8TMvT9jZvMC1KxANXT/l3lmb5y/NSUubvIJcxrTDGqkkCEVZaeyPWwROzTfdbDHwTD7zMOKdlwM+cm/JfLpAszweTeYIAzrtmM+cJozaDYqIulF0/QTHbLg0yEkyn7JpuDGa8MbZJqEXU9U6uqZRVjWGDr67YFU4ZA1st2osYw2FbKTDdjvjp+48oJQ+7xhtEOoWX5jlZIXOVn/BwG/wrITA0XC0lKPYpJKCTjBjo2VyvjJpOzotU8cyaqq6hWNpCC2m7ercOwtYlCmXehlnk4qTucGlwOXxfoZuVESFRVLY3J8JcjVjoJsoIdDNgt/1VI4hW+uUG1YUjcISOnfHirPjgJ/7nuv8nS8e8mAZYVklgd1Q1yZHy5KTWPH11yraZo/caihlzlnkY2jr99TxXDBPNTZCwRvOBu979zv55y/PWNlTdFkRWB2e3TBp6pqcmhdGW/z8FyPCUJLWFa+ME3YqizTusOUJArcmbyruzlM2WyVfd02jKtvowmWZCc7yhC890PnaZ2Y8d7VLYK639RcxrMqY/anFi3/qa/ngnRk/+eopqtCpzYwHp4rcmVGQv+V31Fqw/1ZZz766EjoAfuAHfoAf+IEfYDKZIKVkNBoBcHx8/Juun/hqrLZlEjoJEoPTuCIqCzZ8h7dfbnjjIuMH3rbFX//sQ1p+jijabLUVD1crhi3FoixgqePZkp6jCEwboxLrYAgiBCG6bPGHnxzwM3fOuDRKuYgtdGGy0zVISpNVlhL652h1yF7XZZEqzpOKJ7cXtFzBMrERSqfrlQg9x7NrHEPD0jReOXG4MoiZxR5V0UKn5Ciq2PA1rvQNsiZnkSvMQmfPtAlthWEsuEgsLN0mzg1KldBx2/z83Qnf/rTNR99QuJZHX/apG0GSmUjVcLHyqXSoAwOlgWXFJPMNLFPjPVubPDrK2ezWbPVXnK16BJbg8a1z2raFwME1lmiNhsIAFB1HvjmwMTD1hu12jFQGu50M14CktOj7CY3S0LSKSexysrJYRQ6HU523X10xamXcPx9yfXPG0GuwjRQp10qSqDBJKp2nRwWTTCetNCxD0nZzlNABsAzJXidhceaxUhaGXiBRaJrO0M9ZZg5Hs5CWXdLzck6jgMOlR1KtP/wEhuRyO+Vg6TEIUrLC4I1Jm4Gf84UTk6w0ePmwy9ZVE5Vb3ItbvOPaFMNYKyYCe80HKhFomuRw6dHzcqLS4kovJS1MSqkzyXSSykRKgW/V2EaNdMA1GwZBhm/rBFbNydIltEoWmcPz21MGTcNR0iatNIRqKOs19+9yP+HuOKRuNKJc5z1XV5hajcBFCBi2SizZ5guPdHQUq8KgajSu92JsS/LyaY+WXaKhuDVY8Mxoxr882MK3akCxyk0MQ0KtcaUXsxWmmEKyrKw1T0qTbLZrPnlvyE43pdsqUCJnElmEjqTtVxzPHcaRybuujWkajZfOulh6Awhcs8Y1G+qFRW5p9L2ctl+gaWId4pLrdFyde5M+z/VHfObkjJZfogvwHMnJyuT6KGGaOixzxU4n4f44IMlNHoxDNAnvvDHmqc0V9yYeh1Mf26xRKiR0a37npR4je8Tzuz6jhcHf+/J9lins1wbP7814+aiL7dZ0w4qDmU/ZGOgmuFbJ2crjcOrx3t0BL8/POI89BA2N1BjHLo8HIVFp8+HDczY6gpOVx+XekoNZiCYUnz5e8OH9Fc/urX+PNTReOQvIUotRYPAtj/X5Fw9OOI98fKtknq/Dh7baFXmlk5QGtWxYZDaWodjppHz79V1+6ksLvu3xDX76tSm7nZyuW6NripujJa5p8tKxx9kywMg8NG2t2pskOkPfRkqJqwu6/nqAXMYenVbGK48Clsl9GqmQAgIbTlYOqzqkY1loQrLIbWYXbXqDJY5VYgmXSQR3LjQszWLgQSEbDo8spqsVoa9QNfiujip0zE5Gxy3Jq5C+XzKLdWRh4mkGSWRyebfHZ44jqmqIyG0WssAwazYHoJc2zw/aDHyDP//Ld+j7Ji8MutRKcpylfOTohN2Ox61eyMEy4z97do8f/nDBhmfTciT5QrAqJaIyWaWCkasQlkGkx/y5917ib33pAd90eZOHi5zTOKcBvu3GDp+4tyQYxMimTVlJDhc5YUvxk6+c8WiRMzAtbN3kpfME6WZIS+dtgx1+7NPH/LFnRnz2eMFkqvGGXPJtP/EqT2wv3vJ78a3qq/5T6an+ncdhzz//PG9729sQQvCN3/iNPP/88//68cILL/CjP/qj/MiP/Mi/1yG+UtUzfZ7b8tCbHqtc8miV8mhZgVbQskJ+x1WTJ4Ya01SRyAnLKmHQitnyA7bcASPX5t6k4hMHFa+dN8ja4VrH4RtuKFaZwYN5yv4qQirByBO852rCY6OEbhDR9XLatsHJ2Gare0bQnZE1OgO7yzfdUNSy5rt+9A9SFBN8UycwTJaFQyUlgaXz5IZgnipeOs94dCpoOwZPbSh6bkPLzQlMm3fvrXjb7h085xzXgJEHjRQ4ZsOVjk1o2TTMmaYgpeD2BO5MNULLoml69P2SunF47WyX21PFy8c+d89DfvwzHcaJRlW1sLQaoSwW8Q62AVJaCEzOkgahFFLM6bhLjhYuthET2PBw2uN41eJTD27yxkWPUHeoUhtD6CwzA4VkkTc0ssa1l0wzjc8euYwjg9vHNmUJf/GbNjHdindcOWea2nhmhVLQsms8o+E8DbnSzWg5kkoa7I9bJKXJl487PJgFRIXBItMw9JKBnxK6KUkpaVRFyykIrZLNMOOJwTq5LK5szhOPOxddqkbjl+9u4pklWaXIag3TNHl1usk0NTmNLMLqHIHkMAm52kuoG3hmY0Jda+yP22y1Mu6chxzMfJ7cWmIYksK8YKufcpE4KAVJZWAb63SMV87a+GaNZTW84/IEz2zw2nd4ZmdG3yuwzYa4sDiLfWaRyzw3OVoELFMTy1Bstgr2ZyG6ptht5zy9tcDWMyoFs8Tl8jCjKE3OVy5RoWjZBYdzm61WSl5pHM4ddrtL2l6JwuBk3qPn2ThmySDMGCcWvtXw9N6KKDdYJLAoDLpuRl4pXp0tOZh4eE7FMrO4uqFxVFww6kQYssXf+pjJyUXAKjPJ63UE7zgvuNw1uFiF2IbkqW2NUhR0OgWjVkJZr2GljlHR9ko6ToUCslLn2qjgZ++esVIl09Si42WETsV55OOYiv1JG98quTFcoJkroMbQaq70U7qeQduVLCOPj91NOV4V/JVP3edHPvYGStR8ZnzMRw4OkaWgrt+MyawctuwhSuhst2qK2qOsHSzNpOsrep5iI4DHhhkbvsQzDIrSoalcTmdtVnGLsrbpOxJDSBQ6Xz5pcbL0KWTDH7x5iaSQ/Ndff5kfes8WtSjZbK3Y7a2IC4vzyKNtW/i2ZDewUcrENSDKPQ4XGknVME81plnE2lldIUSDajokuU1TeqyiIUkhWDUFtt2w0R4zKxcM/Yai0ilrwY2uT9sxOFoYTBITKU2WWUOqxrz/+oLdUGOV68ja4fvfM2R/UvCl04TXH0DPK+m5UMoCz9EY2F2W8YikrljWM9KmQDUeeWFT1jVNZfPkJuiLBZlh8FNfWPD6Ys71vsZTA59BIFmWNcdTg/MLmztHNUpIpkXF3/rsKa706TkWFhaJjNiPFpwlip4d0DGHFHmfVVlxEEX88huwSG3e/YRgYPdRjYVhRnz5rCZVC4YBvPeyyycOl3z2ZMWyKrl/UXJ3UdAbVWy5Pa6EwVf2QvtNXIPBgNFoxNnZGT/0Qz/EjRs3vtJH+i1Rb9sMMfBpW5K6chFCI5URtjunGzR86tEKozZZzF2+6VbAo6nBL++f8sygRdsIqCrFSSSIa4WuFzgGjDwL1QQ0Ws4X3zC5v0i5EoRUpUnZSC7mJlldowGB6XG9C8/unTLsnCGEzl7bIc4t9heSUXeJjoOGgxAVgR3jGDWeKQltxecf9jieO6SZy8hXjIKatALTKGnbArO2GLQSpolBYEFRuSxynawRJIXDS6+N2GrZ2K7kXVvbjBPIa0FZOrhWSSec03Vrntio2QxX3NqYcf+8z92zDovUIisN/tYXzqisDMPU0AT0goh56vDJ/RGV1BDonBcGk8zG1Axso1lb1p2SZb62bL923qLlrBUWlVS03QLXbNA00FHktYFv53hORaMkLx12ee20i2NJklzH0CQtu2DgF1xvRWyHGeeRS1rpNA3kpc4qNdGFWiu1NUVo11hC0Xdzen7B9W6GZ1Q8moU8mvnEhUDTFKauUED4ZurY6+MOVSO4yCymuY2uQdsu2e5kDLycstaZpxavnrXRDcUv3r9gXhZcHiY8nPnUlcAxajZbGbc2Fjy7Naes9DVMOfZ4OAmJC5240jleejRKEOc6k2Rty8pLk3HkoVBstVIGXoln12y0ctLKoqo1JqnNRWJjGw2LwqRSOtvdHNusSXKBb1UMwwRLV3z2oM8kdTiNXNDWoRWKFbu9hKjUmcQuQig8p0EXDX2/wLEaBkHBzWFE2qzZlj2vYJ6ajGOHi6VNXgnKRqPtlGja2r51f9Li7kXAlw66NI22TsktTDSp6LnrHsM2GjpeQT/IiQuTnVaMqTeMgoJ3Xp4y6ma03JJWK2fTW0O7Q6fG0NZn0zTFwdRHQ/Hxo3N2gj52PeDmaMXhzGenE3P/rMXBxCfODU6WHg9OWqyyDi2vYrOfEFU6SamhC0VZazRSYxI5PJx4/Pz9Mf/jpw7Zn6V88I05aeajNOgFOVGprwephmS2clklNoam0HXJo2nIxcoG4ONHY5JcRxOSwKloOwWXu4r3XWvz+5/tIxuT0KnY68SsMousNrlYOeSNxHUqNF1hCMXdcZvXT9toWsPIt3h+s4MnO7x00OFg1iYrNdLSopYaQiiGYYqSiijTeWprwSq1+VufOyerGj74cJ/tXkw3SDlerPmPmtB4+bjD4dwlzlyeueQxatXMVxbnE5e8FrSdnPfemBPYNVmhU1ca88TgsZ2MlltRUuO5DUoKNsOMpzdyQrtif6YTaD3edbXDw5MWDy5aZKWBVJKttsSs1/yiWSp59rGEjl8htBS3pfgz79il5UPLWxAXBnudEqkEgbcEIfBNyGr4Gx87ZTF3GdkejgfLrGG5MnAbm8dHNv/l+7f4wMEBm92GLdfmI0cTfvqlKZ12jau1kJXJuzb3uD81+J9efIOhp6NJ2Ahsnrii8203RgS6QccVnF9Y/NB7tvBtjduzJdt2wGeO5+y1bVRm8p8/f4kH44yNgULTTG5PagoqNjsGbc1FR/BwWvD8ZshkpSj0kmt9nT3fZOi4fHR/wd998ZS/86kJmlew4zjsDUzquvqK3qG/Weut7Kn+nQdF+/v73L9//1/73vb39//14/j4mNVqxfd///f/ex/kK1GTsmZeNIxjk7LosuX7+IZJy4146UQna0ocs2S35XCt7ePpOvemEse74OEcDM3gbRsBV/2Qs1OfTzyQHMURvp1yo2+z5bW50YF2MEHpZ1hmQcvSSHKfo1mLZaKhazqq7lI1AsO+4I1JhWNW7IYB9+51+Nv/zU8yK6I1m6c2UapC09YRk0+NLH7HNWgoOFxmBE5OUZn0gzlXh2Mq6WGwhaXpSBEjEBzMPD5xIDiOGq51FbuhxVZrnUq249sE1pKeP8PUABFzFFXktYus1vGTtubyzY9n7PUXWIbkcGWSNCs0rebS4Iz7UwukxzR26XmSG22H672cm/2GZWHSSIgLg08+DBBaSeBfsDeYs1Q5cQHXNxa0vZKdsGTgVzw5WPtmdeEROIpL/QaEzgcfvc7jo4yHsw6uoXGybNN1ISpcNGHQdxsqaeDogtCSaJq5htaGkiz3mOc+G0GDZwrmmUtZG7TcGk1TQMPAy9BETddLCIySi8jiaBnimZKqMYkyk88cjziJPEZugqYa7k9bFJVFWlt8fHyTVWW8yRhquDWK8J2KCrg+WrHRzukG62Zhs5WzEaZMU4dBmKNQPJr5PD5coYt1HGbHLuh4FVUDfbdECo1FZmGJmrZTYuqwEeQUpU5TaRSFQtcUk8giLgWhVbPITV478SgajdNVwDT1KSuNQZDxYBJwkbpIKTA0A9MAhc4rxy0Ua6bBRWxhGIqBp7PZUizLmH5QUFZr7k5cGJS1xio30TXBMvOp30zUaGqdXljSsyquDVe8ciq5WAnOlzaNOebpqye8cdHisc0Fu/2UG6MVQgkmxT6np20mic2rFxVm6bHRyllmJrPU4mjucfc85I2zDgqBrTdUhclfeOF5LvstZK1hiIas1gGFLgS3NmM8s+Zw5nISWdw/C7g6TLixkfPCaId3bQ/Y7sZ0WylS1kyKDM+r+PYbQ37iM2O6Ts3brq3wXWgakw1zl997Y4PvfHybvDRZ5iazxORkbnO2MllkOuPYYH9mM8904kqg6wUgmU5NigoOZi5RpvNo3kKhUVQeEoui8hivQn76tTmGpfjwowU91+JWt0PHL+nYklli8NlHHoqaVZVwqb9kkddkNXhmQy/IsLWAvLawdR9LczhaOjya9ZgnOhdZgueWFE3Nqom51BdshA1JZbPhdonygP25wdsuTbjSjzhclZzGJaGx3p6bhuTWwKWq24wzxb2pRlqYvO9Ki//qFx6ymAqeu2SwEWj4TsJjg5itoMIxC14ZZ3zwtZAs71MXGyziNZBc12GzU2FbGZ/90AeZJSU39wye33NQysL35pwnGb0gpWOtbYR3xzn/7E88yQf+8PM8v2fQDUrO0yUP5zGbnZQtr8NTQ5u+J7l9UfGxfcnhUhJFfd624dOzPBpRcLyqmeQp+xOXZzbgZtdHKYNJVvLf/soBH7qzoC51LvdNnhraOKagVCUV6Vt+R0kl3tLHV0MtFgv++B//4wyHQ7a3t/mxH/sxpJT8yI/8CNeuXePTn/40f//v//2v9DF/S9QHXpoxzRoyKRGawjcMhOywSnxqbcGv3FkgFbiGxccfLXHQmc0Ev3JvxjQrsISPr3nMY5NZqrhYGRhmwo4fYqmQP/07Q+b1hJeOU1aRT9dwsXTJyZlBWkpKCRfLgHncoigdTCtinja4uk3L8HntxOUwKhAKPC2gljpRnVA0NcMAtloKpWd899Mb6Mrkyc2Cjh9xdXSOZylanoaulTyaC4raxNEt9qcmZZPhmtDrCZ4fDPnGSz1+6vVDntxRJFUDoqLvCm70IoZhytAvyRuNcaJzrZ+jYRAVFbbRcGNQE7jrRZRUAkVJ6Chk43B34jFNLV49C7CMBtlYVM16ELAZpnS8lDRzOB4HHM59PrY/pJICKQ06TkloSRyjoe+vFxkdr8SzFVKu4+Uv9yOe3FywFSaEdoNpKHyjxKLGMWvm2VpNO/QLTL1BKhh465TPotbIap1GCXQhOVi6vDEJuXPe4sXDAYvMwjYkulgHf2SVjkCgoRh4FbbeoAmBrq8B07thwrVBRM8r6QclO+2MUaDz575uhysbMUWtMwgKOl7JZpCSlhpJqZM3GpvtjIvI5nThkVcabafi5iDCtyrScm1sKCrBNLUoa0HLLVlmJvfGLc4jG11TaMBWO+aJreV6mGPUvHjcJSt1DKHIKx2EYLNVsN3OyKVJ4NbcGC4xdAUohNQInIpWmLxpj9foejk9vyQpdG70Y670YnpeRVbrHC0d9ufrBL9a6swzm/HSYZq6GJqi5xWME4tZ7vDqaY9lZgM6s8Rm1M5YphbnC5uG9VDJ1CXXhzF7/QxNCBa5wReOBxzP/TdVDhJNCOaRjVt2eXpbxzIaLnVSBn7J0cIlKw00A662+ggNDqKI+3GK0BSjMOfBpEVSaThWQ5rrXCxcTlYeO36bkdOlqA0sreY8cjiY+1zuRygp6PsZeWmwKAXSXfLDv3yfX3+jJmtSkIpRkBFYNT2vQNaCaGWiiXVy2U4nYbcbkVcGliEpUpOuV/HGecgqN3n5oM8N5zJtx+B9V1v86a/dwLfUejBYawz9nKuDCNS6Z7G1kvPYpap1tnspl4Ypp+mCv/Lr99kOG77+1hmguHfR5nTsc+ekQ9tp6HoVXS/HNhsqCYaxHji5bkNgS9AUtrXmUj2cB3zpaMQqM9/s++HTxzPqxkBVgt1BhVQKpZXrND+huDwo1331VHC28BBC8fT2ArM2KGtFVDYIYeBZFUWpYeUBv++xPqrQMBuHvFa4zhJHFxgWSBref+uCwKnptxLi2CLUXZ7b8jhLSurGZyOAoqk5SxpCy2UzANPMOcsK+i2Nd13x+YYrXR5FKW3T5H2X29hewa/cjvi2f/Aam75BWRp8y60uNTVPXKkJsYgzycFE5y995Ig/+sQ2oLFKBd98bcBfeu9N7k8K7k8ybK/i4bSkUjq3Nlze1u/xaw/HfPJoyWSp+Pmj17h6OeYojTnIIv6zF3b5h9/6At/37BaXWgaiNFjlDf/yaMqqkHz4wZzAk1wb6mRFxb2x5B+/ds4fea7L77nR5Y8+32fD9hmXOfuLnEdn/lt+L/52T/Ubq39n69nly5epqorv+Z7vodfr/VtU7d+M1bYFHb+h5ydYRkVUmCg0hpZg5K03RfNU5/HNJS1LEVcae60WmoAsSKnJEcIhOu2z1dUJHIPdcEVULziJJcN2Sl622PIrZqmHaWZMyyU6AUL5lIXOYGPFNNPoOBpbZkBWZ5ymS646OVrrMumygtxCExWKBqFaSKUYtg+Jc4+rTof4msvp0iIuM3p+jau5VHKOac24SPrstU08LWccVzi6S575HM5XXO4tCHWNSVojspJOaOLQwtJntB0Jss12GHEamURZlxdvm/T78NiWTuAtKWuBZ7i8fNzlXdcjssql604RjU/LSalVRi0lln6Brm9Brdhf2Oy1JZ97ZNF1DKLSJTBNvmYvZ6uVIgQ0cq12MPX1psazMmrp0nZTBp0cz7BYFhV7nZo7E59L3YLXLkx2uzpC2SR1ytCrqWVFLjUqWWOZOZaZ0uQOjTIpSsWlUUqcO8T5OuL+7ZdmoBRFbXK88mlYNwJttyCudALbxjEMJonLlWHG2dLDswo2ruTYpiSXGobmUtTrCFaAqoG8trjWmnGaB9w5C9kIS961N+OjD4Z0vZKXT7qMY5ONMGMS22SlJK8tZmnC2crlci+hUgIDyGudo6VHVupEucblbsYrFx0cXbIsTOJCoduKqNRpeRVb7QpDb9B16HsNy1Sw5fU4iBY8t7PgeGnjWRmWaXA48SikwY1BTFwIDA2e2kow4watMRBKYuqgGRloEks3WKYWG2HMPPWYZ96b26mG22ctQgdMrWKVOFwaJKwygxdPhlzrx3zdY2PunHSJMpOWo1jlHsNOykXkMghr7p632O6U3J26eO2Ip/ojPvEwYmMjRSKxTcEi1SnqhmE7xdIUUb4eMPyBJwZoSidLBbre4DgVs8QhLk1a9lrOnpSCo5lDtzHY7uYcTUMGYUUTmnzm/JDAVrT9HM+u+OyhxhO7CV84XbGxOWO68pjnBkWjIYTk0bTijz/d4X99aUycQ1QYPLmd8nDiUTUN1wYl88wkb1yEtDiaOUSZwSCUlEZFGbuUjsZme0HgNLx6bhNaGkcriS0aXj0z2PBT8kbwbY/1AbgetvjwYcxG2yXLTa52apLCo1YaRRWy0VrRcSxWWYVtKnxb42BZs9cvOV7ZvH6hGDg+vSDl/TdrJssemlYQOBqThcVJtCJ/FLDT0sibFKlKfNMiyiGwdJ7btLCMirwp6PglrtFweyL5l3e7eLLDru/wwdeX7G6Aoyy6YUUip3T9GVEypFIK24l5oQOBJcgrnVfOJa+fKm5tlwy8hnGSMF/ZGGev8+7v/y6mkeAojkmXNVczj1sDg/nSp9YbEFA6KX/7pS9wLdjhtfmY0FGkyx5fsyeQ1YCsqikYcxbpVE2bS36Ijc9SNkwiiBtAt9kKdEaOXP/dsAsuIoetzgrHcIgjF2EoihxUqyT0czqapK5MPEP/it1lv5nqL/7Fv8jHPvYxvvd7v5cPfehD/Pk//+f50Ic+RJ7n/OIv/iLve9/7vtJH/C1TX3vNRymb148i3vt4Rk1BVDYUZZtLfZ1Vr+G/es9V/tKnbnOeWOhI3rXVZ78Y4zkJWbPiYOlyc+gw8AWyXNu337fnMV1a3Jkt+a4nLvP342MeLSQsDUahzs7lkk57St5IjpYthObR90p6QcQDXeN06TAKBYFl83i/xcMjyLpLitLHNm0uCklZZ7iGgYfPoqzpdhfYJnzNNsxyi3mZUWHSNCG3RjGvn4V0vZzNQOdk2WYWazSa4m9/8T6HZxabA4nnKJJU59w0eGqzpKptBq05uvTxTIP7KwNdq6kanUZIDF3S9STzrGCeujh2wDKT+KZOkhl8/K7O73+2oeMWnEYOUuoYRsYwqDledqhqDctQzFK4e+ETuIIHQbNO5tRqatlwEhnsdtZW5YlucTRv4TkNntmAUDyauwRmhm8paBSfON6h7685IFWj8CzFtNTQNEXLrTA1Rd8tKWqDpbIQOQh7bfMZxw5ZZRDaNZe6KY61/tBkGRKlFIFVYxo1L5502QhytsIUgSKpDSaZQ1LoXOokPFz4bIYZUV7xT27nDFsZe92E08hhmZucxg47YcYbsxZSKgy9oajXmICHkxZFbXC80Gi7OYGj8O0KhSAuDUwdnt6acbp06Dg1q8LgZO6z1U65iF08oyQrHfpNTZIb5LXBNHK4tbFkFGascpPz2KHv5QR2BQpqKTBNiQIsTeHZFZahGEcOu92EtDRwjZrjlcfJykUpMDSJpguKwmDgl8SFgRCCshHUpc7hLODxjQipNIpao+MUrHIdREPRCGxT8vZLU86T9aArK3XiQufZ3TnlQvD0VsLLpz3micGoVWBq65CNq/0V95ctCmfOSbngxkaJZTZkKwOpBKYOOjW/dC9CoNPGxQ9nRLlBUhjstBMelAHjpcVWp0QpxdOXZtyeZQx9B1CAwUUi8BzJJDEYtXMaNAatHNkIJpFLnCmcoGYWOfjOejA5DHI0FEG9g9bSeRDFTOc2739qxu3zFoa+tpkpXeNSP8G3Sz5yZ4No4fDL8ZxdR+cXXj6k147YaudrnqXScTSFLiR7/ZRbw4TzZK0q32xlJLVgGjlomuIP3NrkZ28fYUuf0F1DtvdGPZ7ZDPnU+YK2UzPNfHou3J8EbPsuRrPmVp0tHJ7ZTdlqRZjCISk9NkKFQtLMbJ4Z+PzKgxqrFHjtAsPQsPSGaexQNwa6nuOZPoVfoeew1S7QtILzpUfXVYyCiNCrSIt1arZr2rQ6JaOWzv/5G7b5qdsPWSQmB7Nt+p7A82Ke25uwTNrEhca9CWxbXf7Q00P+2qcPWM5MFp4g0HMOxi5XtlcUlUVRSx4dh/iaoG0ZfP1un594+S5//t2bfODOnMe3Le7crdneACE1GmomC4u/+qlDtjsNuhAUZsmyhE47xyhDpnnOwUQjrip+4aUF91cRTm1xVJzyQ+96hh/7/AHf/cQm//LBkl97OEXTBJapCAyJqF2kXvFPb08wa4tJmvKXfnXOvXjFNGlIE0FlQpbr3Bo6NKViKhecTnVCzeHSpqJMLNqWzdObPm/bDnnf//czWKbBtz7RRY8LPvMVvke/2us/dk/1GyIxmabJBz7wgf+gF/xqKkc0aFZMohJqBYGlMfIlde0QejnjBBb5Oj65qE1Olx55rZgmOhvtmkttm51OTWbFHK8KLiJJXLhcDXvc7K2TyhzngEU1JS5cAt3nhS3FtWHMqigoJLiGjqLENJcsipqdtuBSMKBtuTTanMf+yB9ilSrujjvMc52skRSlT98aMPIkk2IJ1gLD0DhZOLSDFVFpsum1Meji2xG1LJkVPtM0J2oWJJnFyPc5mnVwLMVO6HGpXTLOcqJSY5oKasZUSNJqDUm2DJ3rWxYKwfE8wBAWpjGlF8RYdo5uTjA0SWDlaEbJsxsNPa+mkS6lHCClTpR3iAqFY0gcJ2GeCY4XGmldMQhWtNwFrllzpTfG0hXTBDwT3rZ9wTt2I1aZRV54lKXHezcf5zMPt3ANwShU7LQrfFMwCmr6bo3Eom1L9loloOObFQKNa6OIxzdnaHpDaGXMUo1pomPoFbZRUKv1ZvI0ajFNAxaZy+HSJ7BKpJLkFQR2wTDIudJN2QpL7sxaXA4W66/XAoHOReTwif0hn3nUZ5q4zMs1BHCnF+NYNb98Z5t5ajNPLQA0FKFT00jBwdxnmVokpclOJ8HUJJoSbIY5s9TmzkWXllvyzM6CaWJjCMH+xKNl1aSlwUar4MntiJ5f0gsqGqlY5RquUdPyShJ1QtkoLEOSlhY3hxGLxMdWPXpeiaZBVhjogKsVICV1Y7DMbJaJzmRp8uCshVd3sc2SN867zFOHSmprXlQvo6htGiCpBJeHK87nLgNrg7Q0WGYGFyuX6xsxLS+n7eXcHK0wLMXF3GaZWqg3+QKhLXnbzpys0tgaxJjWknnmoGmSKteRRcDBeUDglJhGQ8dL+exBwlZo8vjIxcLh4TigqDSeGCW03YL7Yx8pJW+/MqWoDITQEAJWheBO9AaPb8wI7JrHhimNtLm6kSIbl9C2OI0FvTChVJKyacgL+P63j/iRX31EUymWicXNjRSUgaXr7HQbLncL3n4pomlcGmUxCiU3RzkboWSnC1sbBRshLLMAS7e4OSxQ0gbpsFy0eWaroGNr/F++foRj6uSVpO84/IW3P45n6hwdOXznY7t8+cjmeGnSyJLDBegClKjYa2vYhuCpjYqWaRMXBmk8Ist1ojJj6BecxZJHUYptKLKqwjAl13s6ngECj8f6JovM5NMHJqFdkVaKSTFnr5tgYK6BqXnIY10fU+i8/LDgJ185xXEki1jygy/c4Gi8wdHkKvPMJi11Bp7AMXRKuWRaTuh6GU9t6wx8MIwKTTNoWRXv+i//n1yUKa9c5CQzn0tel57jcbI0+JZbO7i1hWWA0DI+cXzG/+uT+6isRyC6+MIlJuKj92tqbYGt+2y7AwLDYxYr9g8NjleKK1sxI9ekY+kUjWQxt5BSYx65dFxJXWucRg1RXRP6DRvDNc+tqgzmac24yJhmb/0dJdHe0sdXQ33wgx/kJ37iJ/hrf+2v8XM/93Mopbh58yYf/vCHf3tI9BbXK7OE9+4NcfDJ84CmsQltUHrOYZTSDmq+dJQRCIeGkkJVjHommjDX6YA9jd1uxmmScxo1dDzoGA6fPx3z//n9N9lpuVi6yait88eeGfHd72yTS0WlJ7Rti6tdnad2LmiF57hWhqdbbIQlgR+xKmtMXXF/lqDCBXcufGbZGmq7TEIOJiGvn3okhU5RS1476XG6NHHtElOYdGxJlAmKUqPnN1zqr3j5xEMpnbq2OF4K4lwQuLC9kVPrMQpFzzXY7sQIUeDbUDUGllGiCY3zyOVXbvfQhcZOW3E4twFFlThEhaCqTBxD0KgK25Cslhafe9Tn7qTPy2frhDBHB4HkWi+n42XrhU13/aH5sUHKJDHJCoNJZq4t1aMlrtmg6zpSalwepHS8tUIotNdg6qLRWBUWJyuHjleyzA2kXHM2pFJIBdcHa/VRUhrsz0PyxsDWJY0UCMF64FMYaBoMgpxGCtpvJrpevPlBfCMs2Apz9joJvlWTVwbT1OV06XK6cqmVhi4k13sxl3opz2/P2AhjnthYcPciZLeVcWOwAgRRaaCheOW0xxcOBth6g2VINtoZVSPw7YqiMckqHUNAYNcEVk3LLbl93mJRmCSlRtstMQ3JRexi6TWeU3O+cjldOsSZRZyv993DIMe11glv55FLUWk4ZsPpyuNg7vPqUXedMublDPyUuDDQNEUjIcotDF1RNoJKCopGp2WXCBS23nC6cng4CZBSklYCz5Q8ub1i6Jc0SuNwGrA/CRAolonNXi8lsNYqtb5XUFQ6vl0yahUcznwaqbPI1wlR17sOx5OQeWpxuAh46bi7ZugYa/umYzQcztf9o202oNahKc+MPOrUwmtlfMu1a1SNhS40Nls5o1ZJ4NZcafWZJhZZ0WbD8bhIcmaJxctHbZbpejD83N6M53amhHaFrNeA7Y6bY1g1G52EG1tL8szgxf0Rd8473L3o8Np8yUlzQru1pBWU/OqrmxSVQBeCd+2FvGfPZplZrHKTq4OYa7srTDfll+5P8L0Fvl1wPAu4fdbi0/eHnK5clJBc7sZIBZfaCdvtbK1YbwQn5x6OLtDRGE9s7p+E5IVGPyj55N2Kf/5qzNHc43QZsNXKyCuNydJl19ykTA2EhO98/oDLvZRFZr9p21SAYJrYxMuAly9mtLwc16oRWsGVfkLbrWjZ64HRo5lNURuYds2NyymuqVCUGLqOY5UEboEpDC5WFvO8oePVvDqN+a5/8hp/7wtnuEHCtc0xb7+ccLkfI0XOKneYLFzqRuGZBnu+w5WOwxvjjI0NhVDw0qOA/+I9GxjCpW4EXzqwqKRGYBl8zWbI//D5feapxSIxsMyG/+nTJ+SVhm1YuG7NRVJjBTn9QKBpBklTcHsCMnNxmpCkqvmFu+fUEnY8Fzesub9Y8Ufe0eexTpfPXjyiSR1mcc3f/uIh/6d37WIWDh3doswNzrKcV881OrrN8zsO//z2Ob9yb86sLPiuZ/fY3KoYhQYdD77z8QFLfUpVmWy1bK5tlaAgLiXnccl3feCTfPv/8jIbvkPX1fmOW0P+i6/ffcvvxd/uqX5j9Rv+Lr/jO76Dn/3Zn/0PfuGvhtIbl03PYeS5PJy6PFhkLJopjVpDoK90FI5uMs00FmVK0TToaMxjh9ApuTpY0A8Sbm5JdrsmVQ2ffqhxZ6ro+Am6GuCITTTp8vJFzd3lklo1VJXPlb7E9yRJpdCFg2xavH7icJKseDAv8Qybm12Hd7/7Gh/+Zx9hnOokWUBgx9yb58RVRsf2uNb22Awq0CtszWIeuxxEK6J6iavrVE3A6+MWh8sAT+9j6yYHk4aylmhaw4fumkiV4hom79hJcK2aabxBlA5Jq5q60fCsgotsSSNqHLtiUeTM0xYaLSqp8VhfkOQhviXJyw4vn9oEVkXZFAgt4TwyafvnICRfOgq5iC3atkbdaFztaVzupVzuCAauRDYeD+cdfFNnr1OiSYOWXtK2G5JMI81djpcmv7I/pq4VSFAKdloRV3opu50E31RU0qCWOrU02OsUDIOaYdBgYHCy7DD0JUfLEIXGXi9hpxujhESqmqyCcWTQNOsm0tQED+dtpolN0YBrFuxPPPJGQ9cFmlKcZx7TzFx70M2aZ7aXxLmBZTTkNUwzF0MINoKcvpfjmJLZmzLrd16e8mfedZtGQuA0tOyatldysnQBQVyZ9Pyc89ghtEuKWkM2kqoWDIOcSWLR8jOOlyFFpdP1ChyzJsoF50sHz5JrdZRfca2fARLXkpwuAwyh8crpkEY1nCYJbT/HMteya8NouEgc8tpcb+JMyTO7c2yrYrfl89Slgo0go641QHI0dynkOl7W0CSrxEATAk03qDXB3WjGXifldGWCEDQKemHN7dM2r5x2sAxJ4NZczB2GQYFCYGiKVy9CFvII3y7pOCWbYYKtNdi2xBEdHKfhteMeZ7OAk7mL3T7lxz5xyvtutPnet22gC0UjNYZBxiBMmMQWcWlxOA/wLIVr1rS9nJ6f8tJRh1dPehS1Q1rYOGaD0CqiUucffOGcUSvB1BsCFOXK4YlLMf/o1Qd8za7N9lDQdmGeOtTNusG19JKogrKW9PwYqWp8o2SaKmapICprNCsjcAsezgX7U4eDheBgZjNNISol00xQ1g0/9skZ//3HD/jPP/AGX3c54N40Q+YdfvFPPcmjqY5qDHzDw9QVV3o6daNwzQTPaEjriFlmcB4rvniskZUGDyYWadFimbbYaec83ndRjYUwSvbaa47RM3u3eWpzhmVmLKqYb7iREFoGllFxo2fRczyUqPiV2yHL1CUqG14/LXD6Be+4pqE3FkJpfOTOkltDg4/tm7w+rbCNGkNA3qSEjmTHb2OLDllTk9QZ8wyUNPn0P/lJ7r30OQa+ztdesdgbGSzKkpfPFHceuHz+eMUgNGm0gl6rRNYdBp5F26t5tFrxxaOch2OHb32q4fmRC1Ln0QIeTBr2tuZ857M9LrU04lQDvSbwa1qeYtRbDx7P0oyoKpDobAQ2m46FqemcrgpeOsnYn0uSPCDU2xjqq1si/dUikz45OeHJJ58E4Nq1aziOw5/8k3/yK3yq35q15Zv8zU+fMC4TPv/QQylwdIObwwpX8zhZCp7f8ZnMdEauQ8ew+Lk3Tnh4CtNMgVbScwy2AxvPVFjWgrjJMBufv/2pY754vOTvfvGYkT3CNnU++yjCNQVn04DDVQOipGV5mKrFYSS4P6+pGoOB42MbYNgzeuECIU02QkkhU05WCqFs9jrQ8mMmWcWdWcqWZ3LnYcC9maJREFgWpllxkRW07ZqbA8VON2JV5bTthpbhEVo5q0yx2ysQyqBWEpQOUuM0kqDAFN468VGrsHUDWWiYhqTlSHb6U85WNoGnkUUmWqN42+6c737761weJPRCwXRucbb0GQRr+0/fA1uvaDsVvlMRumuo9ZXBEs+CzTClaDTadoFrViSVyUnkcrYKAcWTm0seG81puzVSwnNbMW27xDMaPKshr0w8s6Zv52SlQVzoJKVJUelcbiXsdVLed+WMKNc5jx2kEjiGYplZdN0SW6+Zxg6dNzlJnrVWIg39gqFXUksN15SYhiIuDM4jj4czny8c9rh70SIqDea5iSYUaWPwe26csCpMdnspmibJKpNxYpM1BsvcYqedEjglLbdiHDs0SrDRyuh6FY3UALVm9PgZoV1hCMkkcdaLwcYgyk1MvSHONXyrYhy5DML1UAilSHKTmxsLotJkGttru5ACoQkEcBr5xKVB6Nakhc79acCdix5CQMctaXk1jVTMUmv976yKstKoG4NFYhEVGnHhMEscLuYBm17NoJXS8zJkLSlrqBVUtUZa2axSG99uUBJOVw5RvoaH10qnbuBgGjCObUwNnt5a0QnPeP/jJ2y2Uwy9ISt0UAJTE2s0QZjR9QoQsNdL2AhTLiKf662Q7bbD5VabuBDcPQk5Xdrcn4TcGC7YbBV8ebykkRqBk/Morohyg55XsNnOGAYlg6AgSnXuT0NMTZHXGv1WwbBdcHkjZrcTU9aCRgqueG2ibIPJuEVd6GSFQZ4LdvoxTWNwsnDJc4tn+hs8M9jk5Yc9zpYOgVPi2zVogrwSKLm2ph/Mfc5WHtu9lK5TMk1txonFnYsW+/MQ16wBiDITPyzpah3+0efHOH5Jx12HsNw+beGFBba7RAjQdck4sul6FZdHEZa3RGvnCEPyi69e4sE4JC1N6kaw01m/X02jpN0qUZricrfENmsca410UKIkLlxWuc0fuPE4mtCYRIrQUThWg2s0XOqmbLQiThYeli7W6cq1x3hl00idb39ywG5vzV4Fg5ZTUasG3zQ5W3S4f2Jyf2ohSg9TaDy94fNjv/cxHut65ImOKjT+7sciDs5d2lbAB/7os3zH4wOiuuRnXpsSWhp9X7DZEszynL5r07ZMyqbg3hh2W4JnthShpbE/L5nHHn/g8QGeZnAUFaQVeCLgm6/3yCvJsilYLB2KQuPZjRYvH0pmM8Hf/NwpVAafvJfQNIrjNGVcF7Qdiz3XQ1Pw0nTFUVwjbEVgK47jlKJSRDLBsFdUlcCWIS3bwrYKSlFQq5qwHfOJgzmXgh5SKA6jjPOV5Ic+eJ9PPpy/5ffib/dUv7H6d7ae/au6ceMGf/kv/2V+/dd/nRdeeAHf/zeb4z/7Z//sW3a4/9j1pf2SuSd419WCrcDHNTU2vZK8iXjtvMLSoSXaCBlSFhaTyOF0pXF/ItnrGyAt9qcOSamhWQWWZTF0Q2Yrk4t4ySiM8ewS3wx47yWdjZbBgzm8eFQz9CocXTCJbGaxwDEle20DTxtQW3NO0xl14xNaIfc+d5ub31xStz36Wo/ntyCtS2SxoqoHOIYLSMYrnZ2uw8gdMktmKDclNO21rDEpMLDYdEO+9+0jfvHBBaOw4Wt3Siwjo8k1dHQsc8yH96/z3is2rxyFPLWZIJouL+zUxMWC41kHU4+JygbPMjgtF+jK5EqYUaocpxlhaoJXL0L22iaprOk5Bl8+3iXOfb7uck4tBU7lEmsJhmbhOwsWeUDbTUHzETKhlg79YEFHK3lp7lBVGkrPmUVd4lTx/I0Vy1ULy2koKsXvun7EvGhzsPQppUbHrehYKwJLkJUmniXQtIaicnDNGktXoMA1NJrGwrMaunZNYCouVgHz0sJ3CraNmoOlT14ZJLmLEGsJtWvWLDKTllPRduBLZ0OeGK54YxKSlxa+KegHNa5Z0XYL2k5FVhsITSKUoOOWbLXAsyqi3OT1qst2J+F4YWIZgt12xiuHfS4Si1uDhAeTAImOQNH3Mlyr4qnumAyXzlbFq+cBIHCsEoVklTqcrhwudUqeGa2opOT2pEdR6ThmSbGqMXVwbYFUa45SZ3vBwdThya0FgzDhfOWyTNvccmKScp0oIURNIyQn6QQxLdAY4LkNvl2T14pV5uAY6/dz6KZc6cUYukZaSI7mAXFhMAwz8tpmletc6aZc6aasckGUDwi1EN2O6IQlD6c+r5y0CZwMqTRCo2KSOAz8gpembRxd8eokRmkGj2/HWGUPrxWR5W18p+LnXp/y8sWUS4OEvU5Oy6m4P22jazCd+Ty2s6LllpSWIilg/6JFz6/RNUFSWGuAc6Ow9IaWpfEXf8cl/t5LCVe7S25einhiN+PTr23x7qsdPn5yyt6wxrIVHS+mljo9XyGMjI0gx9INfn1/SN3orJYtklxybafk+955yMtnHWxDo5AVXVvj0Txgnlp4doNhpvTDEsdKsRcev/Soxi89fvijL3N8YdIKK77vFz+NoyxWTU5cGATeipv9hmmmQR2i6YJl2qapfV6a63zT3gafO0z4Y89s83r8Gq+fCY5WGr/rZspLp4JaWqBMzuOGh5PLXO5ULPOKr91VZKXL3UmObpY4lqBiwaqw2W4ZSAQ3uiEvfL3Da9Mlv/vagP/58ycYHQPf1vjgw4wkdbnR17C1DF3P2elUlJXLopJ88TQhq2wu9SxCp+FhIpnffolv/Z4/gcThNCu4N8uxhOBq12F3x8LQBf/gu27ywx/9MiexYhFrnKQRW52SPd/g8X6DEBoNBdKIUIbEEptcCm0utwI6pk4YVawKk0GrYrUymZJiWzWBZdLXPWx0LAruzxVvHGaYQcMw0Hn3LQ1Ds5mngrMoYbl86xlFvxVLSolpmv/6ua7r/1Yf8dv11tRZnPP0dpdFaYAZsT8XXO42dKycwDboeyZ/9cPHpFXN7YuKnbairdt4o5JZbPPE5oL7K4eum+JoLZK0w2wW8Gih+EO3fD555NJqF3z2/ITmokYWHlpjEVgahxcdtlpThmGKrDxGjkWkwTRtOF8pRr6BZ7mME8WiTHms3WCoDkKUPFiVbIeCrdAkjXU+eT+m3VJsdnQOTzdx7Yxuq2G7rSgbnZqKltOw2zI4TTSiUsPQNHqtkvsTk3Zu0tItOq0xjqGTFj7P7V6QVgZNY2DoBrNMp+NKtvsNJ0vBdssEzWOW6PRd+L1fc4RmNJgGfPFkgGNWBK5kXtQUlSDKfN62NUFKi0ZKzmMbS5OMWjGG5nMw8/A2ChoMXFNyGrt0nDX7xtJqXMPCNhKUElzEJgOvwTZKykbH0mscY92zRKWBrWuEbspH723Rdk0EBk9szHmw8LnUXidgPbe55LXzkLLR0IQkq3TyUufKIGaRrXk5upDsBBlDVxFXJpXUsLQGS2/Iam39XK9pW6AJRVZpRKXBXjtlkTl0nYJXJl2WmcM0NfAGFdthimfVaCi2WgkvHfeoGsGjuU9oV5i64mDu45gVXafEsyS11Lk99ljlFtPE4dogJi0N9ichpt4ghEJD8PlHQ/a6CbutBFeTFK6gYj3Yi3Kdu+d9Nlopfa/ian/JMrdpvWkfTguDqtbYaCs22xmhWXEaOyyStfplt5vRcSryxmCzvVaedB1JUmn4dsHjWwlpbSJrDdtoyEudGgPZaOSVjmVJbrW7vD6bU0vFPLc5X3mgwNCg7RScLj10c818fDgLUEpya7Ri4BWc/ytVdrvAlw62aujaJZoQWIbC0hUPpz5KKbJa8Mv3p+R1gzj3uH+xAKVT5AbDrSWny4Cr7RGrLGLka2zaO7zjcY+ffW2KEAWjVs5GKyMuDEwTdtyU8cphkpi0/RKFYtTKsU0YtnIkGodJxR97/Dqv+xFfPswpioadS0sejUOkJrm5FVNW8I9vV/zXX/c0f/jpIR+4t15sBnbDwM8wlERpDboGPb8gLgS2XtNyCy5iA02sGabnqzZbnZhL3YIbwxWfXQ5JmoZGNeyMYjbbEYvMYpF6WEaBpZtoWkmS22y2U4SyaaTGJ46WWLrGdjemnLmUjU7XrdgMCqKypqoaskrHcRrGkUATOlf7K1y7YJH6RLVCNTYbTp+nBwM+pGWM7XPi3MTSS+Zpl91WQZ5Iuo6kRhKVgu2W5F5ecTY3MC8JbnZ9fvrlDhstRRymvPTA5tKOoO8ZFLXkex/f5hdeWXA4L/nvP3nEtz3e42icczHTGPXBN21eO1ZcRDV/Tz8GCXkJN/dS0tyjETX/650HbLU0eobHo2iKY8GTWw1XhylKeTw3cojeyCgzk+3A4cNVhGmDZ0CoG9jCRJolZWyyPWgwDcGH74959cii28vpmyZlLPiVRxO00mR728Q3BGUtOV3V2KaOpen80SeG/PL+GCkF/+LunHaQEBoefWPIz74+4SLLeHwY8tSWzkceOIjGwbckJ2nC//2Fx/jRk2P++DN9fu31DMswmFf/EaTav8XqP3ZP9RseFP34j/84nU6HF198kRdffPHf+JoQ4jfVoOjZqzaeB5962NCohMA02OgIBk7I8yMT01xwsJrwykRwudPg6CFd2+Xp7ZLjhcc4WV8AA0/g6g2mFjNbhESFw+WRhqlcPnqvIiobftetMTuazobrMXQCZqmGMCOs2kNJAVrKeWrgxIJnNgM2XLhIakq15O2/551Mb9/h6u+8zCyz8A2Hnt1lmSuiSrEsYOALLlTKq5OUUavAwEUXDUKv6TomSdHi9liySBR/7f0DPju5y4NFxfVBQ5R3UYmiIySh0eeF3QVxZaEanw/fVfiWQNYeL53DhldRpyEQsdfWuRp4REWFb9psOgVfWAk2fdifC4o6IK5jHhtI5qnFGxPBkyOJay+I823OVg5ZbnMe7/A7H1vw5aNLPL6RUMuAYVDgCJ9VqfF8MONB0edSW5IkDTNbkqxavPPWhN1OSqNMHi4HIG2OFwG7nRWz1CCwSuJSRwItu6DrFTyamzyzNSavG4pGp+tBUmosc4MHsy59p2QjKOj5Ba9ehOhazVMbU/bnbW6MFMdLD02sf+7LHM4ii6vdhL1OxjS1MDVJJMG1anpuSd9L8ayGnVZKUpocLi3GiYVCZyPMca0K2xDcvmitN5ICNE3w4mEfG8EqNVjkNv0gRkmDs9ii5Upso+Gi7IMoGQUNptbQaDU7nTVXIatsqkaw0Y6Z5C6vnQcIoRj5DavcY5a6dNyUtDK41ks4WAQEhkSp9RZwmesM/JSmEVRK0fFTrgzWqQ+eofH4lQjUGkRoG2vVzcmsRaVMvv76ORcrA9uoURiMgoz7UwcdwenKwTJ0FqmDbaQgIC40aqVzOJOYxpI60ziYeVim5NZoSdk0WKbO9d6SLx73mMQeg7BglbsMwozAyzk4DekEOd+z8xi/9GCKbkl+6B07/PUv7KPpKX2vZpxaBE7BXk8xtcDQJaZeoQlJnLtYpqKoLFyrxrdz4lxHF9D1S168L/mZ5jZtV9JIjSg3sExJbqX8zMsVTijZ9XtcREvuX7T4+hszHk07PLGZEhc+jbRwLR0hBVpLMuxl7PZKFoXPICyxNZOkkuhCp6gcQqfB1A02wpjtTkVaSdpWySJx0K2K9+6FVLslj+KEReKTpRa+0GlkhSlMFrnOSVQQWgZCm4MYUBQed44b/m/vGvC/vTTjJz57xt6uSUPDja7F+VLjOx7b4vVJxqKYk1QGHcdns32AZ8MiHTHNGiaJQ9f2kE3MLGmz4cJMa1iWK77v2Sf5b37pgLFM+dFPPOKZUQu7sfmF+ye4WodBR+BZCZM8Qkob13BI65Kz2KFldNbwfy+ibCrqpuFr/8h3sswFZ1lEmQfc8kJanYqkVPyh5wb8+BfOOI4yDqOUB9Maz9D5usuS0LKpJdweFxSy5uSRz7uuONi6IM8tbLvk2cEmv/DKkrJwqLSMg3lFkxu0OxZ910ZD42TZcJauKGsNQ5i84yb4lskyF5Qq4f5EY54prg8U7Srk5bf4jlJvobxZfZXIpJVSfN/3fR+2vU7IyfOcH/zBH/y3Gpuf+Zmf+Uoc77dU+Urje9/R5y988JCdnsVeAE1Tch43SCnJVcYiN9lsa7TaAt9fchZpWIbOpm9S1z59r2QQxGvOWyXwXQ3Pgr/xuX0avaDMaq4NNYRSvHJakKoSJ/LodAVRalOpnCjTudytsTSLsjIpa4llldjm+vdJ0xrKRtJxdGrp4mhwvDQxhaDXqikqgyzVGBcG2xsp89LAKDMsQ2EIk7xSnMca1zuKotF5aV6TNhVhY/P0VsrJ0qWMTRq9je/G7LZhkfp4zoxtXzBLPVxDUpQNvqMoVEnRmISGiUBRqZqeJzGsiKzsME8CxrEGUkNWgkY19FxFUTeEpmReazSq4Tzq4FgputDQ9YZ5ZrHbjtgMc+JSp+OUCAwOFhq2rpgmJmnpMY6h42Tcm4R03Zihl+Gb8Jg15+40pJIWd6ct4twgLR2e2Ul4/TzkHZdm3Jv6DANJ25K8sDMlGts0SufWxpJJ4tBxa5LSZJZY7IQxpqhYSYNVrpPXNpZeUko4Xa1hvX2/ILRLvnDU4Wjqc20Qo6QGAh4u1kmPs0Rnnjl8+chEKUHglIg3F1Av7E3JKoM3xiFRbqIUhE7JlW5CI7U3/7YqDA0so2boZ7x20iLKLEK3RAiBQGHqkq5fcG0Q81g/IpmYbPUTXjvvcRE5jFdthpZLXK6V8DeGAtNo6AcFHXc9cFtmJm2nYpWt1UJFI7ANRSUFaaHx/FbCqxdtXLMhMGM6dsXD2YhS6mSagYD1EtIt6AcV92YBhqG4uRnx2ongoj5jp19zPPMInIa2m1M1gsCtUVJyuvKpasH10QpNrIdvSakTW+ugF11IosLkIGpIffjUwxHvuXrOMjPJax2kQiqNXpBxNrFQmuTBLANzHV7S8SvqWsMxJK+dFywzWGYV02TMVXvA1+w4/PqJSc8r2PBzfMsgqwzKSuOzb4zAUpzMHera5doo5vpgyXnscZgbOKaOqQnujwsGHcn9BRzNWsSZyQ++sM0/uS1puwlPdob8P375kMpI2RtEbLVSUBrHi5COX3KWmQggyk2iHLbbKfPUomx0ymxtk0zLNW/plTOXotSwpcvDiwrNz9ag9mYNJQ/shGkq8G2YxDaX+iXj2MLQNNJCp+cV3NjIURKGoY5rNLQcxVnkEDgp48ihbCSWBq5dklcmUkkejHtEhYZn2bhqwJeO4YlejGM1tBydUZhRqgLftlhlBnZlM+zW3J2aDPyKpgGlKZ67XPKZozl3Lkp6rk5emZw+ChiMSnQ0vuX6gMudOeM85U997Rb/3UePyI2MditCBhF/5ukr/PLDMd94uctxecTvuNrmYw9XZE3Gpa6JbBxqSh5d6Gz6AZdDi7isEGbC0OrSczQWWcZFmvG5eykt30GaFX/3C6fs9RVPtPrcnxY8nBX85OmE568FBF24N0/4v374AW8LJVf7JpkUTLOCPLK4dVmS1QqBYJo0SKHT6ZZ0TIfHOz0u0ozjqGYZaYxCA7vuofSKzx9mdDyFZxv8sad2+AevPEDWJpNlQ38QMbvo8jc+ccoTOxaffRQzjQTf8kTIP3393lt+L75VfdV/Kj3Vb3hQtL+//+/1Ql+NNY8bwk2N612XjaBiEM45TTPQGo7jAU8MTR7r2EwSkwfjmv15TZRlXOlq7LZ1VG1hWSm6njPLoGubvP+pHv/bG8ecrKBSOn3H4lq3ZpF2OIpOGSclummzYzk8sZFwFjUcTj18w8f1JZcG90lVTVxZXOlYjBOXt/2+93N8FBMYIa5XsKjHLCudrB5xPvdYVZDViu3QpcbkYL7A24goK5uzuGB/kVHnAa7wwa35hy+f8NSgy6yc8mBhcLUtsbWCL1+ktG0Dx9Co1Iyu7zJNhmRljiUc9gJ9rSRyNXxTcW8meedew9X+GW3LYp46vDZWjDyDRjZYZo1Zd4jzJZc7AkvPMDSHsu5SSYOBb+AYFY6h8cWTNhtBQVZV6GiYekJgZIReStPYqCInbzwCJ+NsbNH4CsPI0LWauoGXz1u0LZ3HRzN0oVE0Nq9edOk4FZaus8xdDpZtAlOhVIlnNaSJjpSQ1ApdryhrwcDPcTTBuXJ4bBgT2jmz3CKpBC0n45JQzFKbWum4ZoNvFlykBkoaRIVOUgryRnC8XKchjVOXq84KXy8pNIPTlY9t1PTe3KBEsctWq2KRWmiaZKed0XUbHl4E1Iag9WZ6yqvnAZe6FaMwIcr9tYzcy3myt+Ro1SMqTaLMwjEb8hSGQY2jm/ScnMAShHbFTjtmlTuUtUPTmKSVS1kLziKP7XZOxynwrIZFbpBmNm3LouOv6NYNmGt4XVKYNI1G1y1ISpOkXifF5JXOsztLPvNwxDhx2emkHM5b9DcnPJw5KCkQQuN06bLZrolzg4X0GYagGzlNbnF5uKJuTG6v2nQ7OZqAw6mH5xY0qYmheVh6QyQFuia52l+hpOLLh12+/douv/Rowv/88SX/7997g5fGS4q64c6jHtcvJygqBm5O1TikhUZRrUHUs8TAt2oUgtBuKGpYZBpCCVaZzeVBQlU7vPPajKOLgFliMeqlHCYWnUCihMRsVXzLsycczAtGLUHgZBzMWkxWPnf1mr6fYBqKJzamnCwtpvE6ia4bVExTk8CZM467OE7Bw4XN8dzFdWPqMmTUO2GZeRzF4BuCeZHjS4f/5dMZ73k6ImtSPMvhe564xN98cZ95KmmnLeqmpFEewoLXT7YI9T5G5vPn3hHyoQdzWmEBmkZcpRS1RVkK5vWS0BHsLyx0M+epkcYiNfnYvUuMfA1JxefvdPl9twa8ujrgn33J5fGBz/s3pozTiEUy5B99YU5c1YSOTdew+Y6bG/z+myP+xAc/ySJyOU0qfvTWs/zU3S/guYKiSXn1ZIRQFkLpxHLMyJhSlSOSz/4qW+95H9KAG47LQnjMsopUzPg73/oueo7NZ44iPnsyZZULntpU9LyaZa5xFK+oVUPXd+g7HlueziwRFI1G2MqoK4dprHj5NF2r6Vo6nmNj+esG9TSe8WhmYQnB269YuLrFNDLRjJSLTHLv2ObdN3P6zRaXhhUVBdNIvuV3VCPXj7fq//pqqO/93u/9N55/93d/91foJL/1Sxcm/+ilE0aeSdZE7C8KntwosBoPpSs8U7I0GxK9RKtrhrrNlmdxGkkqmWDFNUPPpKo6fMvl6/zTXz/EC/O1dcmHwNFw7ArPTbB1wROqy3hl8XBWMBlrmJbDc22JaCruTRVJZrDV0jDRaHsrLiKH3U7NNPaJq4JHC8XvvrzByWzBPNHob55j4HFp4LMqJJgzTmcdbu1VJJXDWVzhWybzuE/XXzHLG+pGY+jrdN05numgpIFp5lQ+mJqJrdkUzZqFgnK5P5c4hknVaERljmM6DDUN145wDQuEIJcNd847XBk1CCUpap2Hpza+22AKm6KOUcrgQ7d32W7X2FrNaxceQ39tgyoaDYFG241Z5C4HC4VvaRwsNfquRmgVNIagEZLDWY+DqUbL1cmrioO5YJkLvnZnRdHorEqH08hhf+IRZwajbkNRGTy5OSe0Gra8FSeJQYJG16lIK508N5lnFscLDyVrel5CWhnMc5sH84CtMKLtNNRS0nEqloXOMMg5WvhcrDyKWlA1Ou+8NqaqBYYusTRJ38t48aBPWhloqsG2ar503OPaIOJqL2bkZTxaBjhmzSAoaKQgsGvyUsfUBZfbEWltchY7eGZNy1Ysc5P9qcb14ZKLlcdsZjHqZYRugSgs7o5bGJokrCsuFmtlUlb51KpkVlagaQy6FWeRTdutsc2GViA5Wdo4lqRuYJ64lDWMY59+mNJ2a/pBziR1KCuBqwsKaXCe6QSuRKmaKLfpOGt+0yIz2Z95dN2aJzcW3Bm3GYUpcW4xLV0ca20TDO2SRWZzf+wziV3GMwfZaHTdnLZfM1k56P2GvDZo2yXjxEVH4doNmPDMzpy4NPD0iovSJs5N8lrDMkwcu+ZG0OXzUYYlagyz4V3XJ8wzi4dTl9DSsWud9+8O+OiDFW8kGXfnDaVp8GgWIpWGhsLQFB99bYt3XfO5vZgRuiVNI5ilJpPEoefl+F7Fn37+GYpSY1FVyCZDNQbRwsE0Dd63t8GjSc2/PD3maDbBEhqXhhHDIMO3KgxN0PfnzE5NZGEwTiyWK4dRL8KzK6QyuNqPOYts+n6Caype6D3Bl8cRh1nFX/3m6/yZf/4AoUtCp6DjNsQF5JVOkms8u7VCEzb9oAZcDo87CDNnqz3Ht2CSaIyCgrRS/Pr9LUI3Z5F0sTT4Qze3+DufXmK6MboyeDjrsxXCKodyHnJu1Czrin/00jGmHeMbOlmp4zqCpl6nTmvS5GJpYeo5iyRA12ue3J6gmoDDmU4rEHzzlQEffjRBswUdR2OZSX785Uf88Ltu8Ev753zqYsp3PNfibnLAL9xrEFWLV2YR4ylsPqPTcQVD18F1z3CVwc4g5Y0Lh6/d6TKwFA4ad6cJ33Jtk3/xoORSu8PP35+B6jB0dTwHxnFNvxWxGbb5ric3+akvzvj73/kEZS35pbszVlXNT989ZBBWuE2Lll2zfw65aLA0m63tGsMQxAk0lU6+Mvn+9/b57Omc913p8g8/vmBpx7x9K6DpaczShofTjHc/5rBpOJyUCY/3PT5zFHG0aFjlCsOwsFWHUWBwECccrgS/98oGVR7zjY8FfO7oNzym+N+tt6qv+k+lp/oP+gkotY4UFOKrw6f3G61OIDCNnKg0+N2XjlFK0HJdNDTOBHzqSPLc1gz0kJ7bxsRjbCqSMuEkXXKwbHGl5bLVLVnEBraVcWtTcGVsgShwrIiNzhINBxofmh18veYgV/SCc4SwGXg1rx4rsloS2AJV7nBpOMMw5syKBFMPyCuPD/zYT6J+8Ft4x7Ob1HKLeVETlWt5sKcLHiUFL04F77wE/a5FYK/lqW0r5Gq74dPzhunKYLdX8bmLA7qxxtfsCBolUKKg7Zi0w5BcNijpoBqNk6ikqBVHZz5zWfHUBthmju9WPDfc5F50xEXkoYtNpnGFb1pshTpdN2NDwFY45bDeQNfWqVqbYcVRtLZ/3RnXOMLmZFXw/hs5rj2lKPtEleTZYYxtZaTSIy8lQrrMC8Feu0SpmLt2l0xTHC5sXF0nqSVdJ6PvadyeONzoZkgqTCNfe711uWbiSIey0TiNfbbDFE2YzHKXi5VLIxse31ri2QVNZmHpDZt+jqZLXj3z2GklrAoT2yqxSgNVrVOvHkwt4jJHoVNWFqZesiwMdAosXRJYFZbWcJJ43Ju2uDcOeWJrgWdX2KVANA0Ppz6mLkkrnWkCB7MQWRr4QcpuN2Wc2IROQ1rqyMLhcjelZ0ekjc/HH21wa5hxvvC4MkippE5WChqlYRiKVy9a6Bg4psQQGo9mHm235FJPsshsAq/ENWvyWucidhkGBWeRT5X2iTWBZyY4ZkHHA9tYc43SNxkEAz9jkStcC1ZZyFmkoWlw+zTAtwuSUuPLR12y+l/5eRWNkhSVi2k0yGotfZ9GDkfjNqXUuNqXCLMhjW12N5acLVtoVU1SOJRKkmQWvi3x7YpluoZa39hacrc4IW8kSSP4p3eO+B17fX7oVz9PpyOx9QbTaIgKjUudlKqyuV/ofG6/y3OXphzMQyxdYJkNEkkW26zSEIXBVl0zDHMmiUEmFMquuYgcrEC9ySES9L2ao2ULx6wpa4FlrBNSLuY6eWNjWTEjq+RqN+HFgxY73QShrbe6XgmuYbPbLWikxWFjMmopKmkStGpWcZdUWJzPTTqe4olRxZOtFl86yxhHBau8zzCQNFpOJ6j50hsOu52G1IzoWC5nccPDWYCmCqZZxn/3LVf50lnC50/O6AYp6ALLdrDMnJ2uwf6xjW9n7HR0zlcG92cNf/C5BYcLl88fGfTakm6gc3YkeOeejqnBIjfZa2nIWudf3J6TpxqX+gaZWXEl9PiOf/wynZYJekXb0fnxlx7SCSCu5+hSsNPJuT+BSVHxNSONvaDHWVly5yO/xtY3fDMjT1IWLh85j+m1Gryiy6unJd9w3aVRDX/902c8u1OQ13CYLHnjpEfXGXK5B6bWEFcF8xwcq+RGT2MSW5xnDf/slSlbPcWNjYrzhcusTLh3rKPHFoOgz1MbGoFfoIuYs6TkzgOD4Zbg5kDH3zVIly2SAoZuRCYVujL/9y+d3y5+4id+4it9hP9kymxl3J3r6I3DKHC4sZFzGgtqldFzoeMERI5GENRMIp1lWbLKSlq2TT/wKKqCl88blDTRthYMfZuXxjqrEkyzoWvWOIZBYApaTgFqzqrscrlr49o5aQP7i5rrXYVl2Ey1iqNVzcgXOHrA5W7JeSwpZIahXFapxY9/YUpe6vSxMGQb3Ui5KEt0YbPTMkidFbPMZKddMwoz0kojyn1WRY+8Sakbk55r4jk1k7TAxKXvGrx995zPPtxCRi6BE2HoOUibvpsSFzma8FG1w/XhnKQIUZRklWS3q3O8gl5QkhUeJ5GiLA32RgV39z0u7RYIveEiNnAMyfHK4FIb3nf9nEXe4SwyqKQkK2zunDVc7VfcHgeMfB0hMmxdYhklWQFVI/Dsmq4vmMc2t7YWNMrkPLI4XPnoek5oNjwoBLLR2e6nSAVCQFIbLHJBYUiGXk4uHRqpiCsdU0ie2liy4eektU1Vg0LQSMVOK8EzG0qpEZg1mqboOBUSsHXYnwn2Z+sFl0RDaJKy0d98XYHnVCS1zpVRyoaf8WAaUNY6y8JCSoGjS84zh3HkEhcWdaPx/N6MeW5yGvVACDQEjYS2W3IWeQhN8XAScqml0fILaqmYxTa2KRm1CvJa0AKWhcU0djic+Lh2TV1rRJGFqUs0BFVd4lgND+frs0wii6w2qSuFQidwCspK58ZgSd8vKWqdWhi8duFxbZARWjkdtyDKDByjYRRmmLpi4JcUjUZaaTiFQdlomLpiq5OxzGocq2GVWihlUEqdrXZKVRt4Wys0DYp6HZqw0UnZn3R4ZmeCpimySif0CoZOxYuLLuM4RROKa52IO1Md26qJC5eqFuhC8eJJjNOpaXkFl7oZobO2jfUDnfHCpcg0Xpos+N03+nzz9SH//OUx/+KNCdqbfJXDucf5ymWj7fLKNMbQGnQhGbUz0soitCsWuUUt10tVQwNNKmpNMWxnPLvp8w3bV/jRjz3kjemUvLHYaGcM2zm1NFBK0HYKLmKfs1XApsgYtgqqpsYWAt+uaSRIxHqJauY8GLcIjRa/+/qQb74+4K/86jE//KGH1HqJoytMvWae2FSywtIVv/PWGS1bIKjZn7aYrNq0AodlVTDPLGopEPp6SDmPdM7HPveL/x97/x1sfXaXd6Kf9ct555PPm9/ut6Na3QqggAQoYA/IBAcGexjK14EZYGwz4+tIAWPPxWbKjK/B2HPHXAeCwZggYaIkkEASSt0ttdT95njy2XnvXw5r3T+2oEbDBSO5MaZGT9WvTu19Vv32qn32Od/nrO/zfR6XyIc37vT4Dy8mSFWTTEPwap44N6GqfA5HDiUNtcwYtBSuXtOJYjQVYFspLQeGteTjdwb8+Ud1fmWUMV2EdFoV44VCEz5SmiAbvu7yLn/+6U1+6rkJ2x2LwM55YsPlm544x09cvceXb+/wo8+OeOTRmvcdCtZCjTUz5PnTOW961OdDJ8fUtcYjA5f37Ov0PMnJUsdxYuIiYid0mCQ1/8OrzlNJxZbv8ku3ZmiNRmQYaFpJ1w54bKPk2QOdwII4g/Gy4Zt/+ib/61eeRzcUP/WpE9qBxDcNEiTLWFCZNZam03EUptlwf6KxKGouRj5Pn414di/hm199lme2I4om45eul7xqo8VPXT/l7RfWGE0kMndQesPDPY97o4b3LI+IhMuGYZJRcn9WouXwXz3R5+qDEunGrLcMfuHeEXsT9YddRv+Lxx80p/q8dFP/5t/8G5544glc18V1XZ588kl++Id/+OXe2x842i1BUoGp6aS5i5I1LXfJRrRgENRsum2ybMCDicm9ecyLpyVpKdgOQkKxxvkuPDhSvHiaMs9Xowk/+uIDSpWz0YmJbIeBE2LrDfN6zM1pjFISX4+4eXCOl446VKriTEehKYNZueTX70mWpSIwW2wHHi23xjBqXv81f5z3/viH+OQw58VRia3pRJZGy89wrJrzrYBH+zqzsiKpajqWxLWGrLfvE1qKRzYqHLtGKZNXbLi0bUgrSWCaNLIAbYZjNDSyIG7muLrGdgvafko3tFl3HRqpsRE6KAVbvotqHCo1QaqG0wz2FqtI1kJOsY2KYWLT9WPmWZesgo7rcLGbEtgGT2/nDELFF50tAJN52qflSgaugS50FlnEPHeYZgGL0kNJk1qu9tDzDUxDIJSLVKuOYVmHNMqgrk1O4xbDxGIU2+xGGXtzkw/fb3Eam8SlomgUWQnXT3ocz0NajuRoGjBeOrTtCl0o3n+zy6dOQg7mLrvtilnmsh4ULHINQ2vw7ArPlrgmJKWJpUuS0mCRuzhGQ8/PKKUgLm0+fH/A0dxhbxpg6JJJslKV6KwMEPfGAePUoucVnO0mZKWOHxYMwoKq0RjGLsOFxXDh8PDakjvjkF+7s01cGjToHMc2hiZYFquxqEvrCXGxioufJw6v2hlSNqu4+9CpgBWhca2KyK1YCzI0KhyzJnRK1oOMXiej1k7JCoN5blPWOnFp4lslcabzif02dycOD/fnnG8X9PzVrLdtlhh6RehILg8SkkqgaSWy0fGdCk1bRfqeTmwWC49PH7RJCoO1ds5WaLAbReSVhmZA5Jac68XcPQ6xjZpJ7ADw6MaURzfmnO3H3B95OGZN1iwYRBpfcXbAteGEk7hkI1LsT02uHrZJC52+XyAQrEUZLT8ndEuWuclWu6QXFGiiQYiaN1w6YfszfgVlBYZWsT9zGcUmXa+kLHVOxiFlaRO4DTtdneHSxrMSIi9nmhncH7lEYYFUFkfTECkNhLB546UZ/aBGFw4Dv6bjVtSNwyjRmaYOpl5gGzXj2GSRg2ko5oVCSp07Q4Ojmc/PXB0xyzOmmSCwNHw9YJ7pLArB5XM1VW3QsV0iW2O0iNgMDR7amPNEyyMuJf/m+os8fmaO446oGoukLOm2DslLnVtHis32grIyuXrs4dDicNolciSvO6ux1S752Vt3ONermSQmH92r+PT9kKOpx4NZRadT8OR5gePV6Aa8/8GMViBR5hgpcmxdMSpmlGLCbqSxG7oIFJutkjddLNkIBFkzZf/6h3jzO54ksjQMBF96ZosvOi+4EISI0kITitf+789xa7ogtGBelNSqYTvweLjt0fMFLx6X/OpVjbypONtRDFydebYaOXx4rWIQCiKv4jif8MIwI840vvRhwTOXTSJfY1rnHGUxV4catmbyyBnYCCGuBOOFxl5qcvncHNvQWMQasape9hrVKPGyXl/A/71gCY2H1xMcq2Z/ImlEymZoYtPm/sTn3qxinDQ0jUnHNdCUSdtt8JyUWsT07IhAtslSn/fcmXOUZpiGyaZnoymJbc+wrQShLJRyqaWilDW2kxM6gtAUTGKXj++1yOtVsmzf09jtxSByHAOSJOJ45jLOBFmhM/BM/vund0lUQ1KaCGwqKRkvBEVhEdqw1h5RyprAsihrh3vzmuO5xXS4zr2DNiczQVnbmEJHiQLfhKp2UNqS47HD7b1NPvKgz4O5ja7p3JmZuGZF21t5+O12ZqyHDd1ghmPUXO5V2OYqhMKzGhY5LNOAxx4arlLNopJcNmy3cy4NJmyEOX1fshYkFIXF7VOLutK5ehzy7N01Pnm/x63TgKtHHY4XFrYmMY2anVbO+cGctifxnQrPUrScHM9aNZKSYtX0Od9fsNvNyEo4nnmcLh1mqbVSiWgVhdJYFgZHiUPLrlECRolJaNeA4M40pG7gxrBNWmlkjaCsBJ5VUdSCyK7xLEXV6KwHGU9sTTnbjTGERCqN+1OfRgniQsPQFBthRs8tSEoDTRPcGgfcOGlxfxaS1AYmDaOlzTw1qRqD06VLVhoMlzaTpcl2FPPMzpRGarS9kq3Wij+djlp8zfnHkGWPxzvnuXnQYRRbWPrKkNY3S47nHq6ueKjTpW4E6KtAjbUo5eH+DKEUtq5Y5iZFZZIUgpbt0/d1HLNmq51iGxWL1OLu2Od45iIVPJi63B2HCCSWWbHTiXEtRS1XaiNLV9wcthhnK48V14JG6hgavx2eYetwPDUZLW2GS3vlA7Q08a1V1Pw8s7nS6TDNHO5NQ3yzIs7NlZLaqpmlFntTj9+4t8bd/QjPloReRb+VYZmSrZ7g7Re6GELQ+gyfSWudeeogmtWo+3im87PPz/nv33mTm7OEra0Y06woKkEvzHGsiuvDhuGpTpmZXFmfsdtZ8cPTxCW0auqm4e+87zrf9cGrGP6C0K0YRBm35gt+6sYx+8kCzZZopQAhCZ2aC70Fk9Thkwc9DucueSnI6xVXvjvyEQocvcazGhQrz6esNDhdONwcKv7cT77EX/mla5RujOYvkQjKUmd/6qFpgklmU0vBMHZ58ThknPrMM4N5JjjfdmjZJlLCLHHYn/rcG4XUQKedEbQyXKfho8MHpCrmkTMjXntlRJMrjh7scjZq8+XnNtAMSZYY9PyaXhRjaCaBnePZFYGlePuFdX70T11BKINJYvGqtTaLWGc9lJS1zbyokLqG5VT8z++7y/ZOxtMXNW4Oa146SfjEaI9bDzS+8xf26Yc6/8fHZywzhzdunmVS5Pzjtz7CG7fXuT7OeOuVkH/+sSN22iWHM4EQEpuAjuny7utTjqYV06XEFTrf/aUX0QzJINQIo5zDE8E3PrnJp48kmhAcLkr+1af3cA2dyDL4ng/e5PufvcdWN6dlmyxTmwU5hif51tdus+U6NGbC/angLz2zycWOzTc8uca7ro547m7O33/3Pn/v1+/wwQdTDhYNP/KxMbIWvP/eBMcWtB2Dyljyq3dmzNOKM5GLIzRunxQksqKNQ9cxsYXGUsV8YC/mTzzW5RNHBd/79osve138Aqf63PA5K4q+7/u+j+/4ju/gW7/1W3n961+PUooPfvCDfPM3fzOj0Yi/9tf+2h/EPv9AYGOQ1z66XXFranC2o2hpJrPUxNJyxpnPKHWwdMVOoGM0OrU2Zlwp9oYdLvUdHj+nsShaLETNrXFFaEPbNomcnIfXJsxzD1O3CC2fYQI3Jzkfu1fgCpt+qCMQOHaOZRhsBRG6VvDSUAd9xMNdE8/Q6XpzNp84j9RMLLmDacXEVY6pORSlxUf2dBw9pqNH+LrLIq/JapOu3eNoJml5M7Jap+VU3FtkXDY1LvdrHiwki8zhTRcinCqh7ZaYmoeh1UyyCqF8nr/roukJHc9AGmNGSUSVdPj/5vd5dCDo2B6GXvH0Gtya6fQ98M2I7VbC0XxletlzBItcUdYWtZKYekqjbEZpje8IllWJb2gcLis2A43DtEZHp6FkzSupFdyfe0yWNidJAzQoUZCVGmVTM0xMItMkqxVtt2YzavDMhFlu89zRJteHLlUNuRBM0bDMhtNFxKJY/bHdn/iAzpVBgSYMTmKHKMiwzZK4gIHbIITN4Tyk75fcG9sEtqLnlTiGQVZLTpc2ggZdW83lW4ZCFyW2IbB1uD3xaZSGLsCza4pGwzYle5OA7U6FayUUlc7exMc2JL0gRxM1p0uP44mL7xdshiV7cw9Lq1mWHndGLrah2MsNntyZMEwMyhpMFE2jI5CAxm/c7ZGUDg8PFniW5HRp4loaa2GJktDyJONcsMg1+r7k+bsdkloRuhaaIXFLieHUmIbk6lGIbhb0g4yi0jmOI3QNlrm+OqzINbo+nO3ELAqNRlqEjo6rVUxyga0LitpEtyp6gaKsoBOUWJpAq1yuTqYIpWOZDaBYCzJCx6eqBG2n4N4wYr7u4NsFSsFaK+PWcQtD0xi4Lr6nkw513n1nxHd/ydN8x/uvkzYlN0chrcSi70t6fsYTO2MsrWZvHhJa9SpuGJ3tdsrp0uPCIGWZ19zYjzgYeVhejS4Ez/S3eX4yRTU6SV5RoTOb+tTGgk/vt/hzrzngV2+t8eSZmKqxqVKHR3fnHMcu9yYex0vBLId5bBAFkrZfUCkT314yjXssCsFwruHakjP9MRuh5PZIx9YUy0LimgaTRcOZQcm3PX2ZH/rUVd57U/HgeISKFhhawIvDBoRHvzXDMnQWqUklGjyz5DcPJpzMDQahouVY6EpH81I6ZodbM4/t7YLj0eZnEipgfVDwwfuCjhtwrivRnVN2WiGTRRtExSu2dLqF4PmhR1Pq7GwU3DluUM6SSz2d833Jr+yfcLGlozUeQuTstBt8rYulZZxmS6RmstlyWWYaoyLlgq9x4fKTaE++mReOdY5nLtfuTwhaDc89KHn+W57gxeOUKx2PVEtICkFRBmxEknlS8dF7Jf21DPF8xgABAABJREFUhO2OzjNbOp5rMM0ltQTT0DCFIK0rbs6XnO827DhbPPOUw/VJzKLMuTEHoVdESUhX79DyCiQNtV4zXVqc7xVsdkruzRtSUpazEKTFld0ZH3+Za9TLmazxX0pCxxfwnw9ZDbpwCWwBSuOl2xtcPDvk1tRi3TPphBa2KHkwl5zvF8wzj0EgaGTNzeOIr364xbXbDQ9t18S5xzm34advSapGsmu4tMyAc/1jFlXDsvRZD2w0KpbVgqOFz4YvUHjkVcWH7ng8vF4w8DVkY+Jac+6MungWeIXNUVzjOjEODrfiY66cG3K41CkXIV1LI0ZxfQivPgs2IbmqGGUaSJ26sMCsMQHX1Li3ELiuIrQcttsT4ipnkga0bI2Zl1Iqj0Xscb2puNDVeHqzYHZisBmldFxJx5txuhygCZPDBQw88OyU22MHJS0cp6DOHY5HbS5sT3FNhWbE+JaO0DTSCpLKZm9uohuKjp8T5xHf8swGv3BvD6MUHI1ceu2cu1MHxyrZDJfU0qZqYna7FkUFAgOpCixtxVVAkFY6B/NVgllcKJaZxfXjgFkhmaQBnpHxiu05HbvgYKljSIFn1Hz6JMLWNO6NW2haQWEZnOkssYyGRgpcQ3AUuxSNJLBqcmmw1co5WZqMMwPPaigawbpfsB4U3J36dPwcz6qoG8HR0mEjKIgzE0dXaEJQNwLfqpHKIPJq0olF5FYsC5Naavh2jWDlQbRuL1C91Wj/OLMpSo2yzvnl+zkHC4mlJww68Sr1Va0OsU4WLr6dM88CnrufUtYWQSvHdyRJtTINP1y6zBKL9ahkc3vC8dylkSmDsCSwqpWx9DRktnRJc52sNAldWOtOaYU1D6YBpq5IigpNaLTcglrqNFJhGZLR0sJzGh5em2IIwfMHERID01RYZs0rd8c8mLaoGw3bkmhCcTL0yCuNyczhSF9iiIBHtmN0u2Ga2EipUVaC06WLrq2MyPv9nKLSViNupUWjFIuy4TTN2PBcJmnCdlRxsZNz+7TFWqSx29OoZMMLDxShZTPMCi7vpiglON9bcmPYYpo4lKXC9hukoZjlDoFc/YzT0uDGaUCcOqQTE7tdE+cOF9ZifLtGCcFpekKNSVMJrLAk8iq2WgkHUx8lBfPcgRrOr8cUpzbPXe+heyWWU2CZENgV88zEsRpunUY0jUJrIE/hTY+2eGGxz9Gxh+42OHXALFG03ZRX74yIK5escrg9tKlrj/unFue7FqezBsdZ0KDRDzOGxy5JprPeBdsoOE5cRKORpB4P7aTYpqQf5HzJK6ccHbyCS36Hf3FrD01ruLyTUVQGa60Cz1B4dszN0x77hc3QsvjFfI/J4YKmCrhRlcwLjTCw6QUZBwuDK+sp73uwT9GAY9rsZwf0Wia7/YpfvjdhZLo8dN7g+VHObK6x0bZIqobTfME/ff46o7lBLir+3KNnOEk+ze2Z4GgJkWWxiB1+6WDON796i1vLlH/3/JDQ1Xn64YTdTsPxsuFkITizDS+O54yShlqUDAINQ5nMEsnzJzO2ug3nQgdVF7w0Evy9L9/hBz+6z+Wu4svPd/hH7zllY93iyTWH990fMc1gmUtCT8fTdVxb8Z5bE1AWm52EfO6g64pp1pAUkrF2A10LORP6TIuCZQZ/8ZVn2JsVbLZ13v3phL1lyn+4MUbYBZ60+cUbE/70lQ0udOyXvS6+XLzq/y6c6nNWFH3/938//+yf/TP+4T/8h7zjHe/gT/yJP8H3fu/38oM/+IP8k3/yT/4g9vgHhvfcXjArc1RjE5oOw2WPeWGyH0MjJC0H5qnJzRODw4WglIJ1L+BKz+fJnYTr44znThMKkbPmmTy2rnFlPcdzU7JK0NQBtlFxY1riWCcYuqJt9LjU8Wgk7M9r6sbAMxQ7g0Ms+5jIy2mbXUQzIK01TL2mbbtshClXDxb8xq9+mMAyUMphVmiUtcWjPZ+W6WB5M2opqRqLF4c5y3qKIVZxzraZcnFQ8dS6h2MoXKth0zd58cjgYF4ipc6kiFk0SzxD4zTR8KyUx7c1DGHhWbDph5SVhqEZuMJFiVXXLLAmeFaKUDWHccqyMNiPU9IGLvamNCLH0A0ezGEct/jQ3ZCep7CMEkNY3DyJ6Hpwrp1SNS7j2KZuXOJCo2MLGiVJ8oi8FiT5SlUikXTdio/dC1gPYzbaCzZaE2yjoO8vqakYJxafOoqoGoGp6wSWhmc1TGOHflDziq0p2605gZuy1kqwhKSROovcYrjwmCQWplHT9TN0AUnlcLIMOV14+FZFP8joByXrQcl2e5X0YeuSjajEFA2lXJG6Fw/bDJc+x1OXpwcbbAQVF7pLThc25/oLdvwuWakjBGS1Ri/IaHklHbfCMRp0XbLmFURuyXDp4FgNvtVQIxgmLsdLhzvjiNvHEaGd0fMrXnP2lLIWtJ2Gc52UrpuAFMwzh7ZbomsFtlGxFub0vYpH1xYMgoKsNoiinJafsd5ZrKJfpcLSKpRs6IUJG+2EflBTNzbXTzrcHkaYRsPF/hTflmy2SoRQPLo2Z7ebYumKQglmS4vAUthmxSt2p1xZn7HZThjNAu6Nba4P55y11gl1m7g0uHbU5vppxO5gSVHrHM0DHLskL1eSb8+q6XoVjTJ4qLNGXeh8w+Pr6Ng8GEr+Hz99jVQmCCHI6lWn73RpY4iGJzZmZLVBz2nQtFW8rak1JIWNlMZKVq9VrPfnvOVSH9fQ8GydONdZt23+2hvX8fx05XNUlKS5wWga8u4Xd0CZXFlbolGSN5L7Uw9JzjSDjlcyTQTL1OBCN2ctSGg7EkczGCcuobkafT3bkQhhMkladBwT31asuxYagnc82abjavzU7U+Tlibneiav2Ax4ZX+briv5onMZj2/mzDOP0LaRVNBE7MUN/9vH7xC6Je/6tMfVwx7jNGe7M8YyCvaXMUluM0wENw4D1h0b1yr5448uedVug0JiqRBDeIzznHEqcAzJcVIwnjQM2hXDWOIFJY8NHBxd55237xGZbRZJm+NlyjgvSIuV4eJxtsQ3dc5GHq7R0PYrzrUcDu42/Nt/8L/zr97TZZmDa8C0SFmUine8IuTjJ3P+9cdP+JOPrjOZGwgh0EXDrUnG/kLwzCXJa7ddOq6B5c7RzSm6VhFagkpWLKocy9B4ct0m1CNev7XGjcUBH9kvuXEnYEcPOOdGGOhMUoFl5cS5was32hh6xaxKEVZGy7TwiHB1FyVK9uPFH3JF+wK+gM/GX3rFOf7CE4+QNTVVLRDKxDctvuTCErSCwzhF0+FK3yRZRDyYNFw7FSSlwU6k8f5bC8oK/te3PIFpwKvPBKyHBpvbOaafcPNUIy8DQj1ESMFpmrEezenaHp7ucDCXHC5LBCZnWiaTxGBaLEkrDUu00ZXJ9ZFiFCs2PIueYxHXFdcnCTstxYZv4Dgpkxg6vsZWq+TW0MY2FYZmcXtkUlOzFmjUlUXtzNGF4snNlVn3g4nFsvAJ3QxJSV25RK7C9efUtUFgaXz6xCMrXRwDbDOl5SiOFgHnuzNajk5WwbWhx7P3t9if68SZTcsC18kAjY/dC7BMia0ZjBKTs2HFVrgkLUxMvcTSayJXkVeKf//SKb6T0Qsr4kqSZRZ1DoGpSGtBWliUEppGZ6u9QBcaUtYMgortKGbglXTcgldszojsmofWZ6xFJS0vZ7xwOZ27SAGWBvNC56HeEteUFI0isGqqRsezcny7pKgMktKkaARxbjLJbBaFQc8rsU0FUqAkOEbNTjvFNhp2opRL/YRtf852O8XRKqQStJwax2iopcbjG3PefPGEL7l4woVejIZGVtpMEodaaJSNgUCAEoCgaqDrVdxatJjnBh2vILBKXrk742w/5jSbs92dcX8eE+cWQoMGGCYrA+SNds7ljRl+mLO5kTAIMwSKtLB44bjNPLVpezUdN+d06dD2Sna7MYam0PWGmycd9sc+tZCcWV8waJUYmoFsHPaGuwyXFoHV0EiN0dIhLQSL3KCsBKiGZWZgaBJXXzW32u4q8W1/6jFcmsxLh6qGQZSyFsYYukJZEs+SCENSFRqXNxMUDTqCZWxxpe8xjx3qGmaxiZl3eWZwhtCpidyGc90Ftimpa3io3WFR5ayFOfPC5uppRJYYtFTEttMhzi0CX/KnXxmx3qm4e+rRAD13FfDy5M6Ex3cyXtkP2WjnLHOTB+OIaWIRFwaa0PDdmsFaRlVqdIICISTT1EQpha4rLgwSnj4/5qkzE2yzZpFr2IYkcmueHPSoEdw9XaXe9rsZ2+2M3XbCU1szZrlN5JU4ZoMAoqAkikrCdsEL8XXWoyWPPzSmG2Rs+Ba6FGy2U6a5Q1YqRrHD4czFs2sMo2Fer/yffKfhye0FjWxYDxueOjtEUxaVrNkIa2Qt2BkUdMMMXRjMM4MP3d3kOJ/z/c9dpxfkoBeMZgGuVbIdNXS9gryRZKXNrVOL28sZ+0lM2E556yMB//Vrevy//vgml9ZiAifjjReXhHZN4M/xLY3QzekGKV9yYYGuV9wb66jCoRKSstBYa+soreHdD+6x2y4pZcGozJgcB3zvbzzg2ihnuLS40DPwmzanZYo0Jcuy4fE1j9O4YqpSXhxmTDJBVmn8hae2Ec0q8c80GzquhS50JmlNhWDQ0mkyn2lece/I4c8+tslPvjSkaWCaF3Q8k0fOWbxix+XOLOcgrvjvXr3Br+4fY1vw0MBBr0zk0uaPXW4hSo/CyDg8EvztN+7wFa9YYCqfaV5ye1pS5Cbrjs177p/wpvMR3/SKbWy/oZSKtbZk3XEpEp0PHU35ukfW+bHnhn+YJfQL4PM4KDo6OuJ1r3vd73j+da97HUdHR5/3Rqqq4m/8jb/BE088ge/7bG1t8Y3f+I0cHh5+1rqiKPi2b/s2+v0+vu/zjne8g/39/c/rNd94ER5ZT3iwSDlMJ5zGkOUBadZlmrSJ3JRaljiGzsBvQJR8Yl9ju3eKqZt8yTmLZ7ZNBCV78YyDOMExAOkAOssKstLmoY6HagJO4piPHGQUTUXPMYgsmzujgGFsc77lcrkraMSUg3RCUkHTmBhaxTBVnG8L3vSay1z/D7/ENC+QSmc499lbLvnEYYVnGnQcA9euyAsLQ+2g4/Hw2gkaNnFhcGe2QJKx28lo+0cEbsKrdgWHi4hJZpHl2yxzn1lhYGsBL57a+G7MxbWE9d5dfCdjvb1EC4doQjDNFXenNlXT4SP7IY/0Gs53VqkCZyOPs+2EjuPz6NoSTXkczSKmScBWS7IobNYCBSg6rmAUW4zTiKTUMQ2DRgkcU6NoTAKz4Vy74uG1GW3HpKhNdiKwTYOndmOaJsIQoKTBWqD45LFPYFZst1N0vWBeKC72Fmy1loBiq12xGUgUqyjyfpjR8nI+etjlNx90UBL+2KPHdNxVzOy1YcjAz2jbNWVtMUkcfu36GlePAyaZwSRx+dRel6LWQayIghAC16gJrRJQPJi4zGKLO1P45INNPn3cQtcUk9ThbnzCvbFPXgnOdRMkK8KY1RqwkgcfjkNe2O+QFiZlDZtRznaUEFgVaamzyEyeOrug7RXsLXx+8aVdklJjWejcmbRp+yWfOolICpOk9HB0sLSCohaUtYauCXRdsD+NEEqw1S640E15cnuIkpLDhcOdSYteULMd1Qz8ml5QkhY6RQNtN+XWqE0jDTyz5mRh89G9NtPEomo0pqmB0CWRU7LbiTF1cMyGlpeDrogzjeOxySenQ6ReYkmfeWZzMPapGoNLGzN8u2ajlXGmlxLZNbahsHWHv/u6hxCFyY1hzC/cPuHpjQ51Kcjrhm5Q0A0ymkbRSIGmST58v8MH7/YpK52704BlqSOlYp65mBqMFy73xw6fOgiJc533PTheGZaWOncnNXFZMwgcksJHSoFnaDi6huM1fOpBxI29gJ//1CYfvt5iUUlu7ncpK5Nz3ZieC+f6NRfWE7qejikMul6Nwsa38hUR9FM0raaRNY4d88T2mFlWkRU6w7SgVCnDfEEpJQVLAlvj3TcnXJ/MKVRCVRusRRMe31gAv2XUDZFf8ORWzUYr5Y89UtDxFP2gwdZaHM4dzncsIluSyIw//9QWk7Th+qhhFHsMs4xRVhE5kqxO0HWDJzcVulbTMi2evgBJITAMiYfN8Vzy8cOKO0ces7nJtdOKqnTZ8ENkA32/om9HVFVEPzzipeHqH5nThc7P/Zufw3nl13F508bRXRy7wNRN3ry9y5ODFn/9PS/yK7dnPLqts9WtMfSC2cKhTvr4WoRSklk95rmDGiUFnh5haQJdLzE1E1VFJHHASZLz8cOMf/Cbdzmdt7gcRmwGNkkBi6bkb7xlA98CpE5awmt3IjxLQ9RdVB2hGsmomvKxg5TTVBKa0edd/343NFK8rNfvF9/zPd/Dq1/9asIwZG1tja/+6q/m+vXrn7VGKcV3fdd3sbW1heu6vPnNb+bFF198ud+CL+A/Ae+8NqTMDLqmzzhpmFFyslwZCQ98wZpnIZWkEHOSUnGxY7PbEqAlVNoMy8nRrJpv/rmr/MnLO/ziS3MeWfegdukGNYdHEb9ybcCdqUPPF/iGRV7rmNYSQzQMPJsra6vkwWXZ0HF0ItPG0GtOE0XkKi70JIbRMJqufM5CS+A7Kf0gZrtdgtSYlCUNDaFts94qmBYp/SDnXNvgzqnPKFt58GnKYHtriGtAxxeE/oRKNiRph6KC44WBo+kkJfh+Rss2MDTBrYlFLQ3iUvLJg4i+r3Fj5GLqJZ4pSKuSZe5yvlNwcW3KIJD0oxnrvYS+J5mlAt/SeGgwRqFh6AKh1WwHDRvRgpajCPyEdpBzMjfwnYbdtZjNXsppagHgGw2LwuD+OMI2Gj6x1+dg6nF3GjLLbK6PfCppMsk0qsbkQm/JNHUYJy5JqbHTKdG0ZjUeXpl0/RyHEk0DoUk2wpytVsbj22MeXV9wcRCzyDUO5h6Ro9gKc3puTsspMXVACIoGAqvAMysGfkVWWdRS48Vxl+f2Otyf+bSdCteUXBnEPNxP6IUls8LiE4dtPrbX5+df3OQDt3vUUnG2v2CZaUxiE0NTRE6DqWvEpUlSOMxyi/tzBwV4Zkle6TgGtL2Gyxtzzg0WPLExpWWvPIj6QYqtSy72Ura6KbomWWQuV48ipqnBjWGLuNA5nVnoGpxpxfSCgkFQc647xzVhp7f4zPiZ5P4oYlrozIuMo4XJsplxfjBnqx1zrpPgOxUHs5Dz3QTTBKFprHVydE1QSJ2sNrBNyXBp4RoNgyhjK8p5bHtGLQ0QOo+tz+gGFYMwxXcVhlUT56vY9pOliWPr3DypKDONKv8Mt7Vy7s0X9PyU15w55UwnZbe7GnX/lb07zDJFkttUtYZnV7h+yQdPxqRNQT+QGFbBT3z6kOXUo5EaoVHS1Sd03ZpZ5nBvaPP8yeq97PslpgFCaFiGoOtXbLRyulHBRjclK0xCu6TlFlRSUDaKuNCZJj5pYaCASmpIJLdOQt7/ogRNYRo1bb9kvV1i6pLDucMsMznXTnH0hjg3KAub4cTh9MhBlCYb4cp36YmtOa5bclgusL2crNRwDTD1hqLSabkVQkgG7RrPLbg5y0jK1WfBd3Ke2JrysdsDLkRrjBIdXRl8/WMbFIWFUjV5aXN37GHqJraX88hWhmlWnOkt2epmpLXkcB5xb+Lxqy9dIK8Er758QCdIKSmQjcP9JOMfvO8B/+7GLdbCOY5V0XEkQitpWSaDsESKhGXhcTALeOnIp64j2mHO3VObr3lsDdMUxFXNfN6iqCxKmRK5Db1+w835km955jKepdO1bULP5L97xXm2fIc/9/Q6SSV586MBrqm4MVSMFnCha3B1POf2UcO/fPYEXzexdcGyLHGcAt9v0IXOXJZMU41vevUay7LiKM75b57aYDEz+V8+dJOdtsFprDMfmsjS4uo45ThuiFXKx49iHt1y+Nqnujx3nHBnVqEpeGjD4Vfu7eM6E25PFG880+GpLY/1SLIoK06zkr/+i3d5+489j2FIHlqzWDYFNx4oSrvkTz3a5yc+NeSj+y9/A+4Pg1PBH11e9TkfFF26dIl/9+/+3e94/id+4ie4fPny572RNE157rnn+I7v+A6ee+45fvqnf5obN27wjne847PW/dW/+lf5mZ/5GX78x3+cD3zgA8RxzFd+5VfSNM3n/JrjoiZ0TF6zY7Ccn+XqUcg7X7L4yEFBUoKtWTy9m3FpPaETJPT8ms3QJM0dXjhUjMsFUmRsBj4PdUK2I42DheLBPKNudBx99QuhtJhlZfJwN+JsGKJpJYfLlKxqCOyGBzNB6A050415pBfwynUdqRTzQmIIm46T8txJhtWVnH3kEkYDB3ObuLC53G7z9JmCg8XqH8Jz3YRXnH+JF09rplnA3YnLaVpjah5PDhyK2sTRDQZuSC1LPCfG1DRuTSUvnmocz32eO5JkZcOGb7AoarY7NTZ9NCF5clPyqu0cQwdXBIwTOJyF3B6HfPLEIy8tsmaGQLHVOsQzUxZlwzBr6PgZJ2nBVpSSNTM8swF9TFEZfHxfQ0qNl04VTQNCm4OSZE3NsgLHSvnQvZUXizAWBG5JrWoWucAQkJYCJR2KyuGLdqbYuoWtw3Z7ST/IsU3JOIPAiel4OaFdsx2mvPrMlCRzSXKH0C751KHPvDBIKo22q1j3a9LKxNQltZQEdsn5fsJalHO8sMkKwf7cZbOd4dsFtlHSditcU3G2m2Lpkn5U0gszhC55/nbF3ZPfSq5YfWbrRudtF/ogNI4WPoamONtZ4js1ldQYLi0cpybPNfLG4HjpUNQ6lRRoAuLcpKg+87XRsPSGhzdirmxMCL0S22zouTX7E4dhbHL7NCSpbBAaQpQUjWCa2dwfh+iaYquTUzUGw6WHIQxMXWeaBPhm85mOZoUmwLUqzg3m9IME16qpa5Mq08kbg2mukVY6dW0T5wav3B0SOSA0xYOpS9WsYodPFh5rfkC29PBdRTdc4lk1rl3QNmtCO/9tefhOJ8MxG+aZyd7MJ7RqDmc63/m++xwscxpN8a+eHVLVitpswJA0zUqmPktNThYeJ3OHaWpzpp0yz31UA0Up6HoVoV1zd9RCGDUtr+BML8HQYRm7PNJ3cMyck2KBF834jvc84G+/9lF8AgK34srunFfsnpI3FXmmsxGWOC5MU5vdQclW1GDqEJcWcWYxzRR5rYgriWnEFBW47py4EGhazTRtcAyDtjfj6kGfpFaYlqSpNAzrlIFXoAtJmts8tdZia6PiUj9mq7PkUj9hXpZUjc0oKzA0g6xOKRpIy4ZfeNFlklgs6jm2IUnrkrguMDWNhpQLHY2juMJwa9p6l+O5x7WDdTY9H9vIUULQcXQse8R+XKHrkGszhDNjI1RkpcZoGPD67g4PhW2UJmnVEV3H4KXjhpeGkrLWGSUQN2MMzeCpDZNx7PLiUYezr30bl175xZzkGdNyhm2VbLUrXrnR5p98dJ8n17r8T1/W5+fu3qcUJctCx3UVeSF5/jjhNK1pGT5f9cgqWahSFfNCIy1s6lrn2rDkxqiiHzQ8ueHyUCcgTXWOZg37acZfffM6G6FJ27FZNhVxafInH+3xa1dT+rbP/XnO88cJmr9AVl2e2fLZCXwco/i8699/aXj/+9/Pt3zLt/DhD3+Yd7/73dR1zdve9jaSJPntNd/7vd/L933f9/EDP/ADfOxjH2NjY4O3vvWtLJfLP8SdfwH/Z9ydJfy1d93irRf6XOib9DydWQ63RhaIEtNo6HoCXXrsbiS4wYS0rtgKLay6zVedeYjcmXN3ueDDBwve8XiXD+8vOF7WHE1czgwUstH42D2PYarQNR3VOExTk0JVWFaFbxmshyUbnSmWleLZAk0aJGXDKJH4lmAjgsZJuTORNLWJakKeO/Bo+UO6HgwshzhXjFJJ31NQu8xzxdluTMvSKeqSWtR4hkNdO0yLkpZdc6kn6QcLDB3ansasKPDdnHMdRcdL6AcZHVfxYArTzCIrIq6dujyYhFw76XF3otN2VwrefpTQdjWkqtB1yTx1qVRNx1uFkYwTl1vjNtfHHlltMc8VaW0zzQW+KTH1VWz82X6JEA0dt8Gxc774whRDL5nnATeHPv2gISl1Oq5kf+pwNPVZ5L+VymZRSoXA4GBpsuaVrIcpoVuz08555swJstHJKp3bE487cQ8dCQpkY+BbFRc6KW2nIbRqzvdj2k5FowRls0p97Vg1JpKOU6IJyaK0MIVGYEp2WimLTHAS27x0FPG+6xvcGYfktcEwdWkU2EZFIxWnS4fIWTWjFCt/xKIUGHrD+UFC4OTYeoEQDY4usQxJXJp0nYp1P+PezKeoBErJz/hAKjZaOU8MRsTFamxtmrpoQlJKQWjXqEbj4tqMx7bmxLkDKNbCnLSyuTdxOUlWzSDXKFjkNpPUQknFlY0FFwdzBlFC18sI/Qop4Vwvoe8pHkwdnj9osyxMHKvC0CS+vTrwyEuNk4WDrTdEdsE8N5FK4VqKrDIpag2Q1FKxEeaYZkXbLbkzCoEGoQwWiYWpNWy0cl6x0aLVKhj0cyLLIilMZrHkk0c5R3OPB9OQ09hB1xt8t2SWOozmHh+53SWrBa7ZcK6XcWlzyYvzI750d4N62SLOdPaW+Srl05OkMsKh5N7QR1OKVitnURhMUouk0FiWOqOlxb1RwN6oxyj26IcVZwcJZaPh2Q0ttwAkd49DHox8TBOK0uD+OOA3rg84GbnUeo1v10RujqbVKLlSVntmRSU1amlyMPe5feIjNAWlSY3C1x3e/+w5rh9FzDKHXlDQ66S4huD2aUAtdfJap+WVtD1FXsFuJ8PWClw3ISlWwRWT1ObBJEQJuDGJkY3GLNb5ket3sT6TCqiUhiYafFNh6AUdt6EXLOl5UMuG47kPCC63tvlvnthGiIZp0qaSCsvMsMyGlqvx1LaPRsP9mcZuVONbOb5d0TQ2w1jj1nGXybzN1WOXntXjYsfizoGDoXR+83DMPK8JHDi/VoNW0AiJQ8Clgc2//bpX0HUtZKPxwnHMxw+XPHe45I3nI1qugW/o7IYuk7xBx6Br2syWDS8cLGi5BluuTS1KxmlOzxK4BKBX3J8XmFbD432Xu8Ocdz845oe+8gmKQgOz5uMnI77ttbu88UzEm654gOJd10dMpxAIj9ef8/nQ3pw//9p1vvqxgCub4OJyki8ZlXM+fGcbUYSkSYvTNGG+1FiWKy47CA1sTfG1Vwa8tF/Tl13edKlF3zG50on4P5475NUX/N+jwv3Rwh9VXvU5exR993d/N3/mz/wZfv3Xf53Xv/71CCH4wAc+wHvf+97/vwdIv1+0Wi3e/e53f9Zz3//9389rXvMaHjx4wJkzZ5jP5/zQD/0QP/zDP8xb3vIWAH7kR36E3d1d3vOe9/D2t7/9c3pNR4c7k5TN9oJJsU5khgzCkmXhMc9ilmXOIEzQRZ+ickkKncjPuT/1eGZHQ8qQG6OG+0OFoym+8mkNW3hcPD+n52csK0XLtlESRmmGYy4Y5y6RGbEdadR1gxIJF3smND7LakjgTVDZAL1yudhNKKRBz7V4at1jkhf43/JW9l+4CduvZpzPwJCsux5f/vCEo9jGNjS65hpP72QsCkVgBtSyxHX2MfQezlJDmEfYhsFrdzRO04y9aY9u6fBAQi1NzgQGW+0Rk7RNWnqM8pSBazMvcpZ1yZmWYlzc5/bReZ4+O+XBUtK2Hf79J/t85WMxaRmwP69Jym1cq0CTLXp+DrLH2bNTIsvE1HU8q+T6SYumtlnza5LC5UyrILBMLE2n7+nUSoHyaVTNmbbk7ljSskoMAc8dmpzrKHY7CVdPLR7q19ydupxrNziGyTRzeDCJONfNmGU2pr7qjK37q8QPRcOFdsJBO+L6SZujhUbg5BiiZJGbGLrOMnc5mNkcL3z2JyvTxFJ6jOKKM4Ml48wgskvi3ODmscdGu+DBzEXQcPoZI8m2UyJbDfPUxjE9Ht0Kcc0jlrnJ8dyl60lemByw0064Pery+nOHnG1l/PKtDbZa2cosfBrRixIiu2YUr0jk0cJFE7DZLjC0msCpMTS40p/w8b110tJGUBI4ivvzgEe2pxwvfO6ctJkkLk7U8GDh8xWX91gWHnctn7QwabkJ57ZjIqfmeGEhhMDQ1KoLHeTcGrVQKCK3pOMuUUonLm18q+ZMO2UQxBS1Q13bBG6NZ+V88c4JJ4uI4cLmXG/OVqvkYG5TN4LjZUmmLCJTcmM/ZGe9pKRktAiw7IJOafPU2gnT1Of+3OJ44dDzY4apiRQ1UssJQhMtlWSxzgduzXHdilZYEzoFy2L1ud4b+TyyUXD7yOaXM5sLazmeLclriyc6CxzTXMnBRUOjJPfHEVc2lkzigg8cxOSNjms0GIZgrbPgez50i9ds9fjA4RIhGu6NPXq9jDPtOaNpm7YrSVON0CoJnIZrpytl33DpkMucpFD0A51rQ3MVuZr5KARvOJfxYGZg6zbP3t9FSZO3X3T5yF0N14JaaXQ8G02U7IYOv3J9wdm+opACq25h+AmR6TEsNCLbwfFqHKvCjz3u7Dl8ydmMdlBy2S9wDJ15URMYbWqVUSkLW2j8+KdPGUQa00RxN8453/Jw7DmLqmQrDJgaMZ6pc6XrYGcZhqO4bGncHiu+88t2+PFrN/my7S2+5Hybb3znC6y3HJ6bpWy0BE9tWWS1RNNKQrPDJKkZZ0vuzl0Wz78Ht9fjxiwG6TNw2kRWzlw1/C/vu8crNuGbntjgf3v+WQopWXd9itJmumzwbMEbd3Us3cQypwT+mLgUzJZrFLlLTA4IHt8UUDrUhcneYtXh14TgQs/gimORqZqLXY/X7EQ83AvotxRvO7fOV/3yDa7+rVcyK0p+4GMPuHa6YN4U7I0rFrnEz4LPu/79bng5DRM/l/v80i/90mc9/pf/8l+ytrbGs88+y5d8yZeglOIf/+N/zN/5O3+Hr/3arwXgX//rf836+jo/9mM/xl/+y3/5ZdnzF/CfhsfabQ4KnX/+7AG2n3AmyCmLNj03JykLlJYRuQXDhQeVQ2BLrKAk8DJOEsk7rw758vNdbuzr/PtPjHnTWzq87oLHzeWCQqXsZQaB9Li0Ickrm8Adscw9NNnFFhZ3xwUtr2AjMNGKaOUVlCf0XZ11z6GwK1KZUscdLvQsTicGx6caeRcGYYtfvRrR8mpMHdqmYBgr1iLYaemMU4dxltONUsppAGTkjSIUOq/YHbEoTPquybKwGCYNfQ8Gnk5c5fQck3OdmrO9EZ5mI3CIxzqjrMV05vGCAU3jcrTUeGwj44mNilLGtGwTTavYmxUs0nWmWUHHMQkcCyEqXF1jnNXYugsqwzUVj66V3Bw6tF2beS5YC0FRcXlQUqsMXTjsLw1mcZtCSgZeSdtLKEuftBTMFj6LPMU1TTStoG50Gk1giAbHE+z2FsyL9upwyVdcHmQsi9UBRa7pLIoG1y+wg4pZrvPicYvz3QKoOUocus7KJ2iS6ZiVSdOAFBa1lMSlzjyzCe0G32hAW43U3594WHpDVdrcOO4wz9zf9lVMSgNHr9iMEtJK46kzYyaJRVZZzFOT3W6CJmpuDdsopdF2M+4vHJa5RWDWuJbgNAkoSoNP7/dIa0FgNeiGw6W1mJfGfQzRUCtBXgsO5xHnejMuDWZIqagak8OZRTeMsXSFQLDZiVnmFpvRAqUaytrANiSWXjMpPJwqo+dUaN2MYWyTFDaPbpwyy0yKxkBKhW9LlKxZ5CvFe2hVxLlDxy95MHX4wN01vmj3hHOdlLQQFLWFoSSZgm6gETg1lYT5IqBqILAlvrNKVEtKk7uTgHliU5cpQaYTVzqPniuo5jXLXNJUJmVjcBKbPDxYUkmde6cOhl6z3atYN/ucLgsu9FIsveJwFlHlBv/iE3fYGVgczHSUU5FUBnGho4RkVjlcXp/x0kEbgaTrZWyEGXkVEGcmeQ3HU49XbDpMyiU7nYTjuYuhaTx/c41cg77r8fe//Cz//Pl7zDOb7XbK9XsButSJOjmN1FDKwHckvmpoLEHRWEReyt7UwRAao7mJpUm6UYZl2pwOTd56pcPpsuBaOgMlGPgV86yhqm08J2GaWuhaRVGDlDq2lSOEzm5vdcAjVc3+NCKtPJLCpOXXSCoGgWBRZGx4DY0EpUxss6BY+gRWReA2OCbcGdt0XJNJLul7kjMtgS9c9pYnDMIFJ7Me01xysVfhFyWfmowYeAa+WXCxV2EZGnkTs8y2OFnaJGXN+X7Na9Y2eNeNQ87ZPr95Mke3BR2/YbiAv/v68/zTj+2RVwnT3EXVLv/Dq7f5/7z4Sf7yz9VsOD6TkUarqxGagmvHOc/fy3nhdEmWCb75dX1aRxldo8fJKeyPFH/lDWf40eeG1HrBTidmHPtIpZOrAt+TbFceoQvjmeD1j3sMwjMEps6/ffGIdtNQmy7/9Nm7nCQV33Bll4/9Qs6FdZ2LuxEfOlzw/ttLTHRePIn5pZszRrHimY2AvVjg6BmqcElqyc88u0AFkhCdx/o2x/Oa0yIhn7p8/68PefuVFl/9WJ9ve+dtTAfujQsGnknfefnTZF8uXvW53uOPKq/6nBVFX/d1X8dHPvIR+v0+P/uzP8tP//RP0+/3+ehHP8rXfM3XvKybm8/nCCFot9sAPPvss1RVxdve9rbfXrO1tcXjjz/Ohz70od/1PkVRsFgsPusCaDsNbaOHp7pUhc4//NJL3JwmKLFEq0Me6Tk83hc8snnKOEkoG0lgStJCYJlL4qJhMzB5zY7D+YHOtRN47ijlNFbUtcXhLCApoGxgNwiIjB6XBjPuTJeMEokpNJq6xrVnTDKdjtkjz7a4cRoyKypmWUijJK7xgJohHVtnK9T5+X/2UxhlwtmgDVXIyVKiCQ1bwKIacZLGGEpQNjUdfwiNT1ls0LJLzrQaukaHeS4QQqGky3brkJadsExNro1ialnRdRukmLAeVqy5kov9EVd6BrthTVVbnAlCtloJi7hHWXjEmcXZyGSaCCwchrEiLWyyXJEU0LIqDC3F0RVr4ZSOUyFVig4E7gzfbCiaz0ieVUlkKzyjxNY06tpgnjvUtUtolmwHGm234c2XZlzql/hmzlsuDWk7NQ8NjvnIYYe9ucXhEsZLndO5S17qWEJwuV19RrZcgCjZW9gs8lX0qSkaOm6ObOBCd45Qkkls82AUcvUgJM4V94Y2b9i16biK7TBh4Kec6SxYizJefWHI1zx5j3U3QUpBaBZklY6lCQ7HPqFdkomC0/KQOLcYxx5VDeO5iSEq9qcBoVVy4zTk2mmbrNQYLmxco6TvZZweBaTFyqARJKYA2UhcXaJRc2/kcfsk4sWTkGmmsxasvAhso2QW2zSNzrqX8MzZU5RsEGoldx9nHlmteNX2ERd7M3ajBA1FWmpEdkPbyegFGWkp0ABN1PhmzjQVDBceVaOTlwab7ZRZocNniFicrRJGFA1p7XGhM+d4bmFpkqJarVsLKwbhnPVWgmwEUaBIlzbP9LYZtHIe2phjGxWfPGhjCEXTCLajhPUwo67B0hvyTOfqSYEmatqtnEEvpRutzOQju+D+2KNj2lzpmeRJhGlAYEnKUuNCd8rAS4hzk7zUaJqVL8PeqIWnQ2BUPLo1Jq1WBq+eUaMDOhqmEfMr9/eZpoL9U5entmO+6tETdCGwrQrfklTU+M4MHbkaPQsWnG1nyMrhwThkb9IjSX3i3GQtgI4fI9XKv0uyGgPYn8C1PYuDdI6hV7x03GCIkrJecOfYZ5alrHeWPLsPZQ1HywSloGaMamCeJ6SZ4MGpRm8wYqM7J2vmtGzJMK45nrQoSoO6lvR0j8d6Pt/41DqXuzWDdsLZIGK3nXG2u+R8qLMeTlgUKaEJcRVTNgmyNEnKlKQueNetfaapxnvuTPixF/d5x+UBSpOs+zodWydvZozTBZZQxEXD/jLFwGbbD7n77p/D3HyYDb3NpTVBXC741HHFWhDTC3Iq5iAUZ/wBb9juUZSCLFc4toaLwTwpuTaaY5gzqrJDma1zPHcYTmzKPEDUHlVT85s3Sz5xnBPagtecMbjQMQm9gqNlyQ9/ao+8qnjf3TG/+WDO33/jQ5xvufyPbx3w7186Rkj4tVsLnruumKSSzUjnUssh+ANofEn18kmkf8t48f9aC4viP66Ems/nAHS7XQDu3r3L8fHxZ9Vi27Z505ve9HvW4i/gPy8ubbirMSvNpKeFdD1oRUM0c4zQTHq2R1XpXD/WOI4VJ2MPS7PRlaDtloxI+c2DOd/0qjUe7nt8dC/mQ3sLlqXkTWd6bAQmbpRRsEpK7Fo+qrE5SWoKWUHRoixb7M9M7k0VsvaYLXvcGfuUKscxBJZyQDRIKjpRzV987RrDheJg6HA2aKE3EXfHNa7dsGF5ZKXG0aIhshQt28CxEgK3wjcNGpExiGYssw6zxKOhxNI1ytrg6tCk5yqobaa5oqxNhBLYdsxD/RxTaJzEOk+ut7h73OFwalIWITeHNqAhlcVpCqM4YJIpykpjIxI0NPT9hkE4o+cXnG2l6HrJMg94MLO4Mexza+ggpcF6a0nLa9BEg2dW9P2SuDBoKo2XDtsEVoVv1xiiQQGb7RjbgA/e6PLCwYD33+qRVQ6LwmBRmMwzG6kUG0GFa9XYusIxVqrUyIGW2wASqQzy2mBZgGs2WEKyGa5GfpLSoJJgaDWWJlmUHsfLlV+RLiCtTPZnAWmtE1gVhq4I7Ia8smj5Nef6MWkhUEgco+Jid8l2O8e3VnzOEIKNMGcjytjqZKSlwdXDLpoQmLpiWdjcGYXoWoNjlSSloKwVZaNxZpDy2OYqhMOzSspKcWfsITHIS4MznYSdzoyz7ZSNsKQXlCxzQVwYLFKXWupk9SpAo5aCGnCM1dj7ojDYn4WMlibj1GKa20gp0ZBcHizQNYlvS1pOTsctUWhMM4f1VomSikaCJhRxbhLZOWe7MXfnEWmt0fZqLg+m7LRyFBoPJj6+VZPXGkllshFldL2Ms+2MblAwz3TujdrcOWkRlxXCVPRaJQfJCKlqTKGjSTjXW/LY2pJlYbNIDfJ6paQSmuK4POWLz5+Qloqrxy1mExfLkSSloNBmCC1D0yuErvDsiry2eDBxuXnc4uGtGRvtnLO9mLZbsB5lnCwsXLHG1z2yzsf2Ymxz9bNZDxMOJh5+WBOaNjuWz99+712mWcbJ3OK5+z2WWKz1U1yjoufnrAUFriFRCKAmr3SUFBhGSdkIfEfj4tYU2yzxnQphNXz0YMFGy+JgGHJv5LM/bTEIUs60NEZzn1FiYhsa53tzHt+asRbl9P2Ent/Q9zOWucetocf9YYtb+y6ODqZVEDiSWtYElsS1V5Mi81yjLC0WuWC0DDmcmfSDVTu565UYuuJdL1m889NTRsUQ3zJwrIZXnBmy2SqxzYyBL3Hsgr2Jy/Gsg6aVPJj2GC895nlFaGuUlcG//fQBTWOz0YNJkbPRrpHUBGHOj127C5RcveswUF3+2MU1/uVHR4xmLQ5mNR+7m/ONXxzhmyZfeamP9FO2tzOOljkHWcJ3/vI+ddriu950ka97ssdu5LA3LZgmJZYXMxr10KWJZi2Z5zpfdrZP4DZ88XaHUpP87LVTzvYMfuBj98HM6QaKvqdxdRTzeD/kXfeu0woUO12TyLapM53/6fVneN25iA/tLThYlmBU7E3ylQfeqcu4zJlmDVfWXa50WhRS8nffeIF3/tnH+adf8TivuSyoqPjOt57lNWdCfvabrrAdCXzDYFnWWPrnFc7+e+Ll4lX/KZwK/ujwqs9ZUQTwzDPP8CM/8iMv914+C3me8zf/5t/kG77hG4iilffD8fExlmXR6XQ+a+36+jrHx8e/672+53u+h+/+7u/+nd+YiVW62cLm8TDnl1+4yxtbFplcsD+bk8+WFI1GzzX5srWavJ4wSS3mmES1QVM1jNMCMpuH1ioMPHSrpp7qnBQ5nqawpMlOe8JJopMVHmeFS3dTcbEtuDqsKRudjmxw0oJyvsCqffpVSKss2H/g4OoeVjfALmtyWeE2Nm/7uj/J7fe/QOuJ17IV6CxTiyUSW+mcsbvUaQUIQsNncdzgyAVZGeJISTFfoClBXIdcXcBmICirbepEsWnDGccnqlKaSUAvFyxjyUAYLE8sNK3CqEzGSYYpQnpNSppb9GxJWo9QVZ97d2wu9ms6vkVal3RbNsu0oO8ZrKkF2tKnVpKwFlzbd2gKCz1peGgzJ7KnqCagmhfI2uRwlhOYNrNM0qFhVhbISqEvTMpcEjlg6oquDq5yEZVEobNlVpye2kxPLM6bOV6loKrpGg311EXXJU2tkdcCVZlkpwZaU+PakguhxHQbHtzvUNQ661aCtGEmDRy7xIsWLGYNr2jpzCcuXS/FqDV6sqJrF+Rjk5bdkBYN88KkrjVCq+FhLyUrdC72x/hOhioMHDLqzKHjFXh5g2NITCF5vD1mf9nCyxS2XMWoJkph9yratcS1ChZDl66fMUksArumlIKmkVS1zkLatETN5ShhkWucsZe8P/awNQ1dSPSmYbl0SCoTGoMHRYRj1gilSFKNTFuNKK4FObqmKJYGZaHTdiumJwakisM4ILBrfK8m1GA0MYisirwUHN2PaBlgVDWBltOuGo73AppC54ye0ioVfbMgzx3imcGy0Dlr58SNJDBsNC3nYHiLRwKFJguENFkUYKSKQV2TjS3K6YrwMTd5NFog0EmKz3g6GSWRl1LNDaKw5FWtBb41YZ55uEbJky2BZysWscm8MbE1mMYOSilEBsenLdY6Ja4l6TYSnxqvP+Vo4jKwKzaNkrYBhurxscUSKWDHLPBSjeGozZqWfeYgy+S8pZieuHhFTWAZWJkOc52HzYbFyCRNJHntslQVD69XrJkZxcRi16n5wO2QwLL4zmd6/PrdBX47JbAE88ykmUrqasA5d8lT/ZpybPCmXkVHFFCHDOqYOG2xISXjzKCZepxzlnR0nTBP6OgGy1OdxUzHkibHcYMrWtA0vP2hgF95cIPL4YJF6jMtBccHEm2xxfl+TE3NGQLMOOMhu8bFYmmkvHQUsq3rTIYLvuXJM3Rsgx/42AO+/YvPsakE776fk+QVTuyxFdmQVGx4BbZuMU5MhsdDXvO2t3E+hJNiSWDOeXiQo+GyyDWEkVPGPu+/ep0v7Xf4yMkB6SjkklPSNJIkrTELi7Mdg14mmGWSSaJoUdCxNdBzTpcavqf46isFpqYjKRklDX3RsKlL2rqgVAWj04yfnI54647Br1+9y6ODgI/cvsNp3PBjv2rxZM/iTY8LToqGsshpTMXZ3oI/Ckcku7u7n/X4O7/zO/mu7/qu33W9Uopv//Zv5w1veAOPP/44wG/X2/X19c9au76+zv3791/eDX8Bnzfec2NKLVyUprh3LAhDh8e2JIGtcU+bM8s04rzNWy74XB1W3JxVzBVousPAh6SVc5LW/ItnTxj4Jo3I6bdqMlljGxE92wVjTjcs8CyJVBXrvsbdccSdoYWoBbphoek6J0OdmaMYhALLMLh5WnO2H9PxLM40MMwSapHxwSOPS+FnRjwPc/745TaHkzmmmWE0GiY2LW9B5JeoJuKwsonrAk83oQh5Yd/g0c2MwDKoZU3eCHquw7W9NqYhiByTrjfhJJV04jaGMSUyS3q+xElS7qVTLrZDvvKhAf/i2QOSOuT+tGYtECgS9uch9058Smp6uo4bxDhWg6kZJLWk52gsq5R+YHCaSOapgyYkhYQ49xFU9P0G0yixNB3PzvnEgzV8N6eqbZIiZ6tVkBYJJ3ELDIllKdLCZFk0BBac7c5ouwpd5EilU7s1w0RnEFhUTUlaaugY6BQYCpaJx9HSwdCLz3hAJSxKjd1WTl5Z3Bo7RI6GpWsUtckwNei4Gec7GS2nZp4L+n6Kb1ZUjcFDgxTb0MhKnWuHAarWqCR03RxTS/EMRS0tstIir0ApQc8vWGYO88zi4mBBWhqcxg5rQcFakFM0BnUjyaoS26hZD2M6XsaNkw7dsMQyJKYO62EGNDTKpKos8trgEwc2vlPRSMFuNydwam4ctEhLje1OTi4bbAOkNFiWFWVjEpiSuhEMwgTT0JnlJkUl2GxVdL2EG6MWceZQewmeVdNxC2aJjiY0strA0UsWhclOK6OoV7YBG9ESQ7BKUs0MlDDxrIZF3jBPXRyrwNZKpNJxrZUaZj0qsa0F+yOXTgQdzcVQCUrpmJpOo1fMGx3XURSNydVhiGuW7I0DitxiKnW6YUro1aS1RmgpNlolTZnzcHedvfgIzyo4yH28MCfODOa5yd4yIJUGT5yZMo5dhAaT2CXqxgyXLucGKXdPHN5bjNHwOF7YgGKmKYQuyUqDOKv52HxBpxMTuQVC01BSgNBICg3PLlbhOWZF5CiMuqExdNaiOQczj932ghdPXAKnolE6w8RkPSiZBTo3xymOJfGDkrRWbLdr6kYnMluUTcL9oYanC9Ky5my34kpQUjUKic0H73RJcp/h2CZ0NdAaNE1hCEXgVDy+nWELk0UpMaXN060LvPN4zHBm8JpLLs/uLeloOn5nTlZEdMOMrgaqKIkrRdcwkKLCN01mmU4gajajnGGu2O0aVFLw/ls9NkKNB3PBVqsitBrujBRbHR3HyPjg6DqvPGdR1honicAXHlg5o8Sga1u8brfFn33lOifTmvlRwSip+QdfsckPPn+P0VJxc1Ryoe0SV0senOj0OzqerfFl5zr8tz95nXc83MMwFP/jm7YZMefaXou5bFBNRU/3eHoQ8eJoxKKp+aUbBpGvUKLh779nj0u7itdsdhidjPj6J7b4fz97l1HSENCitpZ87cOX+Hvvu8cbLrR57W7IP/voIZMqo2+4nG+HXD0qiewFC+nQs126us43vmrAX3/PbZ7qRZztrEKJQldxUCQ0lsULp0s2fJufv33KcAprgcH5zQStbv9hltDfFz5XTgV/tHjV53VQ9HLgR3/0Rz9LRvWLv/iLvPGNbwRWxtZf//Vfj5SSH/zBH/yP3ksphRC/uwTsb/2tv8W3f/u3//bjxWLB7u4uz89drvQM0sZj3Rrzk/tLPMvgbMdnfUtH8+CFJMeTCRtBBnWfXFksGo3bS48aMLyAQhO890FJVsPOuuJS28X1YFpIarPEM0I6a4r5rOFTw4x5Ljmd+Pypxx/ib77nBo/vSh7qRChf59bQ4GYKj4QmINGtMVDSjUJkBe+73cZ7OmBn6SBtwWEF88onLA1O8pimk7LmOoyLgiyHV/dtNsKKyDkhLkMeDnVujU1cW2coW/zKNZ3H1ioGFNxeFlzsmyyEoNambPd9TK3izjjiyQ2NpF6ywCdtfO5PHELLJmzP2Jt0UQT43ZJe6CD9nMp2wSuRgcFGK8e3FXolkCrHEC5ZKVnb1Ll5Kgg8E7ObIiwXS6tJK5fCa8gbg0p3ybSYNc/kbHhCPW4hRE2txWwMoG5KTlKHcy3Jur1EKI1a2gRBSYRJZNecxrAoBK6loxsKU6tZa1XMlx5xrqO3De6dWvSFxCx9WpWi9Gs21xcME5tKlUTtmjtjnzfvTskqi6bRwILryxYXBjEbQUnXL9C0EkMXtIKSeK6TFBYTZeB0c4bTkLK2yBIdR1cUtc3SEIRhw17uYEvBQ505C1fwYGlTuALTaPjkNGK91VCHirmmUVoGQRjjuYqHLo85XUZ8cj/iYOYSWQLHyVkLM6L1ktnE533TNaaGgWGYbLcq8hxy2fDppEXklPhhgWYJ8lojimo0auKiZiGgqHQKNG7XFlGpM0vh4iCFVk7dGDSmYKlpjA2DptHILY2dVompSa4fBERILLth3jgUQrJ7fsH5rYRhoqGrhrunPqcLHd/WiPyc2k0YLn18zWZYaBxMW2y1a7KqwdUFZ89O+fjeGqamkWYm+42DjeDyWsHJ2EEAa92atX7CS0c9rqwvUFFDWhicFibLxKAdlFhhibQqri48kDqPbMRIJcGpeP3mwyRlzYIxubeg6xWkC51L/ZgX9yOg5ubCZJaAYXhc3pwyK0yGM5fR0mV3fcRGa06WhSwSj2UjONeZYtugxQ1HS0k3VCyX8NJRi7dciria3qG7mXB3EtCYJdgu3lrDq9d2ed2VXT6R3iIyjzDMnHPC5JevdTDw2F8oNjRB2SR80U5JZWZEtoWMEkTjMc5KLMfk1rGGa7iUAl44XWM3gpOZYtPpccqShQkpJqkoedfwkNYg43gRkBghB1rN7pZiUQj+9Y2Isy2LSQLnN2e0jJAz1oKxUVJ4HQ4SQaUEv3Ca4DcuqdNwR5X85kJxq4Fs0UL6GfNqyE5nQSa7OFrOA1lxePCAwRv+GHdSCNYqTNcncTRsa8yNOMQJLO5nOgfJjOdiwdvPP8L1eM7NmYasBZ1uQ1JoXFvE9LSKc52aQg6YxhqG1RAFGqHrYFmSa/MKqad03JxUWwdlcCMrmGUuQnfwDR1Ha7gzK/ibb9lhLbD4q1/m8PNXZ3zoVszuZosjdcwnxjYPD2qSXOPO7OWPSpVqdb1c9wLY29v77eYLrDpWvxe+9Vu/lRdeeIEPfOADv+N7/9e6+x+rxV/Af14IM6dvtHHCClVJTpYW0Tzjia0E3+gxLAKqpuarr6zzkVvHvPGcy3GacjSvuDNWPLSWMPAHlGXBR/YnHA0Vqm3QsV1+7tqC04nGoGtwvm/TcTNKlbNsFDvtjKp02ZvU3JlKLvYlrz3X4v5ywd5I0u80tGSHewcBB15C2y2JLJe2XjBNxzR6n//2qQ1+bu8+P399xkakUeYu0yqjJRwGTRtXH+K6M6qmhaYJPv5Ao22aDDyTSi5Ia52w9ul6M44XEZttycFCcZrohJ4LToFlVtg4+PoU1/R5ZEPw0mlNWTf8xK05li9pKo9ZphHYksgxWRQ1vi1YTE2qsEATDnkzp6xtpIgZJS61grwyaRoN2yhZDzWSckYjPeJS8uRWSegk5EXELIe8tNmfg+gZFHKEIXRqKh7qp4xiG9dcGWCbekHVGNyduFzql5haTtdVTDPJo2szysblKNZ5YjCjaBzqpuZkEnCUOTx/0MI2JVutkusnPm+4eIxCR9cbrqwvOJgGnMabaMpikY9AuejoNErnYm/KPNdR2OhC4VsVgZOTNzbb/YTDsUfo1Nw46TDPXbaiFEMHy5AIFFIpskph6DVX1pccL1wsQ6ILyc0Tl3FicKaTMs9t8tqglho9L6WoFbu9BdPUZplbDIKGSeqRV4INI6ffTrl60maaesRjg66X0w8yfKNB1zTWgoTQTgEHTQl0URHnOodFwMFk5dV3biDo+SW+WWDpJq5ZoxRUtQ4oHEPiGDDPNHQB+1MPhWQ7avDNGql0Qqfm/sRCSY9aCeLCZbLU2erkLDMb186xjIZF6pDVisO54LUXJgRWTVYXJLnJkzszrp+2KDFpGkFW6pRSwzI0NDQkNV0vIS0NDmYdGgS6USMVlLVJUemcLgN6fkrfz4AKXXR5uLVJ6b6E2MkI9TWeP4q5M2xx67SDQCI0ViNXdknbK/n0UReldE7mDstEJ41buF5B161YDzOmuY1AMpuZCE2g2ZJGmjSqxtMqHFdyOvUZTXx6bcWZ7QkKjbZTkic6UsFL+y1uHvT5iN5HGCWPbGW4pk7olABsd3PGqcG1cYKh2XT9OQ/1Yo4Tm+f3hqA8NLFKYnVMjVo2gI6hF3z0XmcVYrMwCXxJmeqsd2vyWmIaLhe6R8wyj6MlLJct7pz2+OKHShoq/MDmHVfW+Muv2uH/+R/u8fbth3nvwR4Ppjp916DXXjBPAtIcpGp4MGrzYKp4MjDQ3YyH1jICw2KYKHYth6xQbLYSdDxmRUHg1rRt2GhNGScetiG5tlB0XBPVNNwfGwSmja4p/snze/zS/og+DtO5ImoJfugTh+zPKkyrZlTkLMqc46lNbyCwDcnJVPHTV0+5tGby4eMxV49rvupHXsTRFaVWYTkFqnYoMo2r8YIgEHz9I+f5yH7ColnwurVt0uWYb316h+/90E2ipc6PfvKIyHD52itrKCU4nB/y6/fm6FLnq670+K9/8pN0bRPH8Kmk5E89tsHfG9/mjdu77E8aPnY8Z9v1ee+tOQPH5HVnot/mCRdaIX/hsYv8/LUZT6wF/MWfu8ZesuAHv+YJnjuZ8fYzO3z/f/jdRSCfL14uXvX5cir4o8Wrft+aLk3T0HX997wM4/d/7vSOd7yDT3ziE799vepVrwJWh0R/+k//ae7evcu73/3uz3rzNzY2KMuS6XT6Wfc6PT39HSdw/2fYtk0URZ91AbzybMF2t8SyCxaFzXqosxVVjMoU1zumFjlbkYPLFot0kzszkzvzkkbU2DrMU1hUJblouLDm8PSug65cWtGQRM5BGHT9kmGeYFtzbMPmSj+k73lcP9JJU3h4zePqns9vPGhY1As6rkEjfV48iZjmPobc4drJOX7hepdpabAWKW7OdD7wC+9k/6Mf5k9eusT+qc7pyOPRtYCD8QbjzOBM6PLFZysm5SryfGAmbIV3WAvnmIbkk8OCeQVnOhqZSllkLk9s6iwKDUsLON/y0A1Jz/W53C+QKLqux6JqKBuP/ZnL/akOymRrsI9EZ14WaGZB209QekyjNG7NCtLa4jf3FRIB2pgGwUlW4NkV46KgaHTGRUWjNDR9yXZUUKsSKX2yRuDqLm1X0fcVD/Ulhi5J8s4q9rxRJJXL3kJnVko0TZHJlXFg39cwdI3tzsoH4PaozZ2ZybSQDFObvLHYmwcMly3+wZc+yoNRxCh20XWIvIoGQVGvTC6LxuTVZ+fktU3gNJiGoB80RK7EtRr6YUHXlXimhlQajqnYbuWgCeLKIql1OmFGIXV8C+a5S1IZrLdTEIKNKKftVhwsXLaCgsfW5lRKQzcUO72c8705m+0S18k5nIXMc4t+UHB7FHFj6GOaGr0oQ2lgaBqW1iCVQmmCqtFx7ALfrhglLh1f4pgrI80agRSCtDKY5xZNbZDVDpUyEUKn5Ta4tuLLHhrx5Y+e8qqzC3wbXBNCp2GWO4wzG9OqONefca67SgDxnAalNdRKY5avPku73QWNMjlYOtyeBnzquM9i6eC7K2XdwcRjbxoiNNBNRS8oubQRU0idx3cWzHKT5/e3aRm7TFObdpCx2cq4tBGTVQbzXOepMyMcq2FvFrAeVSxrk0Wu8cn9DtPUpNLAtWq22ylnOktsQ9ENKjRN0QsKbEvw75+fMVsa/JVnnsKzTI5jh+NFi6tHfR7fmZPWOgWKXDWcGWRU0maUmlwbm0StmLWwwdAsru53KasuQlhktWKcg9I0TLvgwdTi1Wvn+btffoYZKbsdk5PEYSNUtF3BydKmqEJsZfF333uLj5/e49L6jFlZ0fFjsgb25xKkQZJrYGgILWGS2+RNyYOlw/25RSuIEZoEZ4YQOr3OPj1P8oljgTThL71qhxdumjw16HH1tEA1goI59xcrrwjMOV98cUrLgSBIuRAE5LlDN1Q4YoPAlXQ8haf3MKyCdhgTWiYvjUf82K19HK9hnrj8zNUpqnFwbIP7c4Oj6SZxco5JYXOwCIgPc55957t573UNz5/i2zArcz592vDrN86hy3XS0sAwwREdnmht8As3p4yrOdLOcHSNUV5wXOdstTVeuRFgihbLSjFMNGp0lDLYixMm1ZK//tpHWXPaOGKNeGkxTDVmuUWv3SBFw6cPM+4d6Py9NzzM933wgF/fm/JoP+Jvvfkcp0XJB4+nJHWBbTRIoyAKS77y8uD3Xf9+v/itWfqX6wJ+Ry38vUjNt33bt/Gud72LX/u1X2NnZ+e3n9/Y2AD4HUre/1gt/gL+82IR6xykOe94uEe/LdgKbO6etvjJ585xd+wwzRRN4/JT1w957198kn/0lkdYFoqWY/NQf+UjuBfP+IqLfS50DLo+nE4UL+5LLF3jyTMr9cKv3XTJZIGttWhZDkldUesL1iKNTc/mdGTz5ee7WLrO3/7SM7z9zA7jtGEZ26jS4YWDkHsTg7z0cXTBP/rjZ/ivHu1y2euy7jdgLdDNmq7t4Bk2l1pdFlmLZSGwrCmeqfHQQOK6GZVSxHlIpVJGicnd4SY3Tjwa0dB2dOK8oVawFWqYWoZj1eTSI7CXmIZGyzGwrIwntxY8sjUhCmLmScAnD23a5hrDeUShJJudGsvIaKSBTk3bzbE1g16wJLANKlXgGhoHcwNTh36Qc7634OFBjKnXtCxFrRRNbeHYJSK1OZl4fPTuOsdLB4HCMiRvPH/IdrfAtVK6vqIXJHTchmFsU9QalTQpmob9eUhR67hGhWXqWGaDbTa4psSzKg6mFpPEY5KtDLGVMnF0OFma/NrNde5PIj501+A91ySW2dBxaoaJS2hnHMc2A7+m7dQ0SiOrBX0v5//H3p9H27bfZZ3wZ/b9nKvd/d6nv/fce8+5XW4SQhoSiPQQwBA0NILIqwVaolAKUjYlikWURiOF+IIaEUhEAhIigRDScm+a2zenb/Y5u1/9WrNvf+8fOzLKt6qGQW+NiOYZY46xx5przPnbe+6xvs/6/p7v89hqQccuuGclZMUvaDkVtlaTljJ3pga+lbHsh7hGQZirWHrBYWgiyzXTVEWWK9baCeutEMc49gRtqIGazSClFhodS2BpsN5KEU2DqacYaomuCKaRRd8pqGtBz404WljMU5srhy1cK2WcaOzPAkytZq0TUTcKotG4dNenagRr7RxZgt2pxo1Bi6xUmSQKi1yjKBVcI+f60Of2JKAUEit+xMMbY7ZaEUehwd7U4drA4+7EoqyhaGSeutZjHJmkmc44VvGsAsdoqJqawC5wdFgKYmSOR/SbWrAoZAK9xf/0WJd7ezLnWh6O7OAaNWEiEecSS35JVhnIEsSFYB5rdP2SU/2EOFOYZxLTRCLKFS4dBhwtPGqmXBtUPL/d49Jdh0/ejbCMFEHDcKFyZ2gQphKOmdF3Y/Lq+GdBySJSKfNjDtzUMpoq0wiNslawjQrbK1C1CqluSCKNMLQI7JJJpHEwNRBCoie7DOcuppYfj8BVKrsTj+e2W5Q1+Kbgoa0Zq60FJ9oxm60EIZUAtC2BlOv0vIxXb43oODErXg5yg6WXUBmEBRwuPK4PHQ5Dg/e/sMnNgcONgz5CA1XSaHVjdFVCkkt8o+LSwObu2GeSKEznHt/+6h5PDibETYPtFPzND97i7/7OLouyIK8bDucSZWmSFCrXDm3yUuNwDqaisD9XWfZqJCRePLSZhi2uDE32Fy5ZqWHoKXXt4pkZtplhqceqzFFiIKNxd6ISRR6uIXMQVeiajOs0xErJd79ilUc7AS8MEhoBji4xzmIMQ2A1DjdGCWFW8lA3YMUymOcNP/KmDTxN4SiLOcpTTm9V0AhGaUa7FRNoFnKpsj+u6bUqvufBE3zl6WUWU5nvvv8efu/mlGXX4PVbHTZ8na3lkris8Q2dR1Z9JOC7X7HM+6+MuX/J5ZdeOKBlacxCCd+R+Q9/5kHe9eSAvm3wFx89xbJnoOQ6f+uNJzhKcpIc8krwlT93iT//69d4aj/kiRsp33R+ia/85ef44i2HP/XAKr6h8nNPDjBkG7Ndvex18fPJqeCPH6/6nDs7v/7rv/7/eO7xxx/nne98J0J87i06z/PwPO8/ee0/NomuX7/Ohz/8Ybrd7n9y/hWveAWapvHBD36Qt73tbQAcHBzw4osv8o53vONzvvd/xH4S0xE+W+2KG0MLRRFUCLYCnbYuIaSYRTXnuaM+LUPjVEthMPbYjyU8t8A2wNFU4rJg0cwoMoOepdNU3eMPO6khK2U6ps/houSZgwrPHKMrPb7pwS7vurzLNzzg8b4mJbA7XB9KZFVzPF9uCPailLsLwemWzgP9gs/cbXGym3G+24Yv/gae/Cd/g/hPfgtn1hSEdYhpz7HLFbanCjthzjdfbNhQGmpS4sYnL1oMExXPULi/azNMQiZRQJ4HaOocTWnYbCc45gxdUbi7KLg5LViyVQoxQhc+N4cdorLCkDWu3JRR5ICWo3DPyiFP3V7nvuXbGLLHg+t3uT1eZs0qCYyaMy2FnbnOwysmUZmh4ZLVJZuBQEhz8sKkEQmGIug5CXaVsj830JQJMj5HUQloyNJxlKUkTO5MLR5aKdCkkmlecXPUo2plFCLBkHV6dspeqKKic3E1Y5rmXBtaTGKVVQ+SUuXyvseGJ/OTT9xGKjV2pgbnrDlZqWDpMlvtDFlSiTObODcoG42iSXH1jEHksBTEHIXHCqP7l0OuT1yKUqVtCzS5wJAFQqvpuSmjWGejHVHWNVudjKPQQtAwinQuri9Y9xNWvIg7oc+zey3qRiHJK3RVphYNVS0zj3WEkPCMktOdhI6dcb6/4HevbuFaDReWOvzuDY3dlsGtybFPjiLLnF8ak1bHO07DyORgHvDA2oCLSxN8teRXXjzPapChaoIwNlCUiqLW6Zgpq/2IyJSpKpkwNxBI6ErNqU7EUagxSVVcs8LVZVS54XCuISRY8iLCzOXKnknbizh/cUpT19ydOWhKzlY7Js4kBqGO7xSUI52uYWBZIwKnpGpUJomBqzeME42ilPDVgFGcMgkNqkYiKy0CK6ESgraTM4ptispAUNDtx+zNDcq6ZpEZuHrJbGzxmof36Jg1Lx45HExcdFWlqGKK2uXR9YL/45vOseIZyLLEN574Mn7kY09Ti4pppHFn7OIoJZO5je/UZJVEWqhUtUTdCOqmIS01bgy6qBq84YTDJ/bnhLlCVMKaK8hyj2RmkQUK/+zaETgJZ5ZkxrHCshciyxqXRxl9T/Azz99gzZO4Z7XgmX0bQ1X5yE0FLfVAKviB16/z4b19ppHJrVGLnqNwa5xiqSZCSnCUgJszi5aq0Ug1u6MTGCq8/uwcT17nJz+9w8NnDI7SlFXb5OETM0w9Zt3SqUTI6V7M1aNVntwDR2uh5xZ30gFnDIu704KDqKbfMxnXx/4LW4HOS5FgNm1xvg2Hs4JfWtzi9WdhZyizl6Xcv1qQ5wZXxjG1UFi1XT7+7n9B701vx3UcpEJBNec0uXXsaZSqPLNfUdcq9y03TBLBr1yd4rXgnmWfJFaYyg1q6rLWq+j4c47SOQcLg/M9GVW0mBcF00xGyzr0PJN/d/mIw7jk7ljBSWXaSw1ZVTMtamTJ4Jsu9Pmm+/v8wqcOeSGa8L7tQ9zM4csueNiaTC3Hn5XDwxvXN/jU/pQvXvvvp0EihOAv/aW/xK//+q/zkY98hFOnTv0n50+dOsXKygof/OAHeeSRRwAoioKPfvSj/PiP//jnY8lfwP8NZFXia851efOJZd56fp1nByN++qkrZJlNkcNhKEOpMEsr/sVzO7w4DGlEjWMm1DTYqoYtG/z8c7eohYQpCVracdJj20sx9AJLUfFMwa2hz8Qu6do1S7aCXFlsLbf4wPUpvq3xk0/cxXdSntlP8PTjZNfduKKqbc6t5cyyku1pjaOa/NNnnufFA5VoYeDKDt/54Br/6IlDTvVqhFzw3kslfV/iXD8gLioMOSNKWuhazkFY0jZ1HL2NYi9Ich1HVRmlGVJustGGokkIU53Nls6deU5V+Th5QVYX2KpDy4rIa42mNpHlmLZjMQh1fus5nxPthhcOU052BQ+uL6hFhCFL9N0pndpDUVMUdG7mHkLIpLlJI0K6tsDQYvp2g6nFuIpMQ44sBQxjibc+2GfdM/jnz8C7FzbnV4992dqWxKofc310rJhtWTFtSyYuJcJcIy40HK3g8uEyqZvwyvUFs8xBUBIXJmWlcGuhs9nLqSuNUShzpi8d++24IWWtoysyu1MLRap4+8U1PjXM0KSUi2tD5pnGiVbK7anHppcgKzW21pDXCj2vZGdqYRvHG3gr/pSqlpEkCKwcSTTosmDZrTBUuHTYJs1lVEXg2yUHIwfPyQlVMFSJWcJnk15NPlW3j9PPpi6zRKXjCCxD4GkShzOTQD7mD6oKq+2Cw6lFVUsEdoIsFezNWth6cdyUkRqiXKGsVQ4inTBVkRSFtBD0vISHN6bUjUaUS0xSlaQwqITMIpfpuiVVrVBWEo7VEOUa2xOLeaoyi010VUVVKtp2wd7UQTca8lLCtGrKUiKvFVyzOk5fyxXaNhwtHNJSZzCzsHTBajvkMMl4cXaE5cjsThxWOw47oUKSWnh2TstOkKSSUeygSAotp8Q1cjpOzKUDl7KR8AyBrhSsBjl3D9uY1goH6YgUibKS0CqNYayhuSlFYWCrFZu9hJ5doMkwy1TuTlzmsc5XnOnxGy8teMNGm6dnQ7JaIEmw5h/7r6iK4CjUyTKVE1aPzZUFB4lJ2y5JWinDBbR9l0goTGKLcVhzdKfFbh7wzff3EbLgTrGNJOnMYjDkEl1VGYQOSg2nzIA/+UU9fu75yzyzu4xrR8Spi62B0BK6nsokVji/NKcUgr2ZiSKrqHLKlr1MqsV83ZkV3nvjDqIpce0GRa156e7ycfCLojJNGn7p6g1co+I1ZzIOZy6Tmc28zHnroy2uTEI6foil6lw70mgbFrKc0w8q8sqgH4RYkkGVldiKyY2Bxp1pw7qrkbsLbMOl55SYRsJ02qZlx7TcOZNFj+FCIYo1ug7cGcs0tULbFxxOS867HcqqYckxCNqCtpmRSTnLbkGcBkyHCodRTcd1OLNpEe4IPvCt9/JbL06I5YSWniCVHSyhMVcSWm5FtvCZ5wJZqlhpq/z5iydpaSZv+ZeXWG2rvPfSgMOwYCIi5lmBrUrMpgqHRyo/8JZ1fuOFMd9wscvb3/siilaT1RWDpGA6V1hemSAlq/z+rSk784xvf3SZb/jXl/m6+1u8ct3jfN9mN8v4kTdt8O9eGKPpgkujmJap8mXnAn7lhQFv2Gjx+3cGfO/D5/g7H7rLe956P//rB28TZi9/o+jzhT+uvOpzbhS95S1v+b+8duXKFX74h3+Y973vfXzrt34rP/qjP/pfvJCqqnjrW9/K008/zW/91m9R1/UfdtU6nQ66rhMEAd/93d/ND/zAD9Dtdul0OvzgD/4gFy9e/MMUtD8K2rrPfqjhmAvmpWBJM4lTgyV/jmXkCKHgaB3EksFz+4JndxWiuORky2TdrRgmMmEOgSWzZgSMFzJJPWfeLBCVx0YAaVUThSqbQcP5Tourew77M5lvfpPDJ3ZDHup2+ZFbR1w4PecBV8XTQGtFyCis6RaemeKaUwKzYHu8xae2VW6PBS3F49RXvJWffvwOhuZwzg/QRc2J7pww9ZmmFi8cSNy3lGIrDYN8Tlk6IBSiasA8XwahkTQjknIJH5vHt0tefzpnzdFomzOmySr39zOySgbRRyJlM1CYJCoLqcQ5YzBKVHS5xZUjDd+qmcVtlv2MpGjjmSmrrkLLmJEWq9yINZLSJcpLJGz2Zgq6bGLoIZamo6sNbcsEKWTV0aiaEE1WWHbmfPpAI0o8FG1OVnnkZY2tJ1SNRtGApyvsJS6XhxKnOyWj3GGSQS1qepZK300ZJxbrrYqmUZimEi/sORhKzU6YUBUajl0haxk3hjanvRBPB1WBrSBH66aMIos7EwtZlsgrhb2pxbIPDSVhAQeRSc9OeOmwjamWuG5FWNgsEhvbyOnYNZWRkdcFWWXiaBn7CwchjhNHykplnCqsuAVxIWOpsNmKmaYWi1yjZZfMc4klr+L5nQ4dp8I1Gi4f2agyFLXMtXCHja6CqteUTcPRwmC9nVI2OvNURpKhqhXuX5nj6AJZlrg8C1hvLQismjRX2Z9bBFZ2/CXezYhzDS8oCAsFIQl2pg5VDZePHK4d2Jzsp5SVwiCyCcySwMzRtJrAVklLiXtXZ9TUxLnGViuhmpYIobHZnSMo0dQ2daNQtxtW7Q4TZHRFohE1JzvHM9vzxKQodGIlYpJIrDktpEKmH+wzjA1OtHNUReXyXhvHqnHMgkGoswh1DhOLwGpoOSVdP2NnFmB0F4CEhIahNohGQpJkntqxubR7jXRu8U++8Qx/7/HLqE1ALkYUdYkk1+iyTM+XKGrB/lQhL1UsU7DqN4xCB8/IKGsZVa34l88dsOTWLPs6G36Mo8OpbsVr1hW+95F1fuYJifdcjbh3NeaR9ZBKxByG6xSFxnNHLmc6FaWosRUDyyyYhA7bQ4dGlnBVjWVX5889eIof+Z07RPOApBvR9yTmWUUjqehyhe+E6JLFpYGgrTm0vQNMxeIDV2LqSsMzwGDBPesGlRQxWPRoGQtadsH+dJWdqYGjmNwT+Hh9FWNuMl1oTCKdR09KwIiLa3POlm2e29e4MdbZ0GS2wwTLjnh0TeYwlJlXgvNLGlJt0fdDzi0L4kzj+Z2S1W/4Pr7uzCl+Z3vKxy7B6qrDybZgkIZElcHpwAahUjQZVa3zJx7NOJybxPWM7cTib7/uHL/+zJSdcoQtcgLN59Sm4CCUsO2MRlaRhU5Sl7z/ZoqUK2x1bC52dMZhTV6BJHSsXMOwCr79kSVOdk2eSw4519dRKp/DWc0nb0VIZo1jQVNrLLKcN2yu8fPPjPi7+9f/i+vf/xP+o2niy3WtzxXf933fxy//8i/z7//9v8fzvD+sxUEQYFkWkiTx/d///fzYj/0Y586d49y5c/zYj/0Ytm3z9re//WVZ7xfwX4/XnIGvveBi6xK/eWXE00dDyrrG8w65trPEZkciyWQOF/DPPj5kebnAUXWqSmejc4QiKyz5MlXTIkwbwrRiUVU4moKjyiz5EZZekxY2o2nA1ZlAMyLaFgRmxnY4YzSt0ZyKQDcxZYtXb5r8g08cECg6j5xM2J01xGVO11ZYVitmaULWSPQ9lbKJOZoYvHTosOkbTOKErlfgaDqyHHNtoLPV1imaklTE6JLORtBwsKgJU52sbFORYOk6amKTKzFN6nJ2o6SSCvJKR5FqGikhzl10vUGSC7LCY5CUeJrK4dyjoeZESzBsZihaxb3LEqZRkpcmp3oHlKXHPDPRZIGoPIqmxlFlJCVCkY7NvM/1EtZbC8rKpqwlbix8rg9NqlrB00v+YHdCTIzc2IwimWlqgJyjySZlU3GmmzJNdMLMgEZmXlaokkZWNtwet9FkCPOGvTAgsAqgoWk0jmIVUxZ07Ia6SdDVmo59PLI0iDVOdDJcbc40kZmnFu99ccTmiuB0d0qYywRmgaGApTXcmNqklcSSm+PrgqKSCMyCtpVhqgWT1MDRGwKjYMPPOIxskHQ8vWBn7lI3CnGu8cD6jKhQkeSGpNBRFUGsNeSlxonOgq5dUTUyoGJqsD83uXvksrW0YKYp+GaBrzS0WjUv7rcIc4WmaWg7x890FDu4ZkbZgKtXtK1jFdndqcbOkU07qEkzmX6QoikVk9jGMQq22jFJGTAILVa9BUexQVWr9JyEaaqzyGzKWmUcKcS5QtctUdWKslZJxDG/8t1j4+u8VMlLi5YzJ85VOq0cWa6ZxDWrQU6Yy5h6TlHrLJLjuPusTBiHGpZesx2XyEisdmeEuUlRGzhajurHdO2Yj760wVKQcjBv8aUnfa4Nao6mEYGlsjczcMyCzwwGmHZOExusdTRet9Hhg9sD8kLjFad0tqcNqpBxTUFaaohGp203jBcav3ZpwmtOOFwdpcynGoauEtqCFb8krTRErUGjsxwkXD2KqRWHQk44vVSQlxV1k/H4XcHpvoSpadydONhahalm7CYZt44ycAUnuxKv2JqwMz9WgyE0cgqOmjH/24dSKklDV+BgYWOqNQiJlhPTNCYneyXzQmAqKqWoONWL+MSlDkNtyndeOMmnh5dY8msEBcuu4Nl9k/WgZJhWSMJilkC3o2AbM+LU43CiYMkK5zsOOTG72ZyssJGpce2SnifT8UfkWZtFnnK6WyKJknwkYdYq33DuJP/yE2OSLMHzK1xdYRA3ZIseltqwN1cYR2ukhcZ3PHCKf/7kHou0Akmmp2mkVUTba2h7cG0+5x1vuo+P74yQ9ARZKYlTH00yed09Dh+5cmyo/i+ePuJkYPDM0YJbaUQ2t8DQqDKNf/72+/i5Z+/w8dsLBmMoJXj7Y12eP0j5D8+HfPJgjz/5cIcPXp/y9EFEOxBsuQFP7kQ8ewe+894uD5zw+dD1GW9/tM8f3J3Rs3So4KXxlL/3hvv43z9yk3NBj4dPdflbH72FIikYssTX3tfh96/O+atvWuXrfvVJvuxUj/dfnfCZ7QjPVvjAd13k310a8eLdlGePIq6OZb76nhXe88KQLz/v8MJ+yq1ByWone9nr4svFq/6o1/jjyqv+i+zE9/f3+Z7v+R4efPBBqqri2Wef5V3vehdbW1v/xQvZ3d3lN3/zN9nd3eXhhx9mdXX1D4//s9v3T/3UT/EN3/ANvO1tb+O1r30ttm3zvve9D0VR/sj3HCYFnmZgNF0CzSKtBKO0IikMVFkhLXMOk4hKijnRUXh41eGvvWGVtmISFimuWdAyGk4tH5CKCaWU09HarJod8tKkaEpsTUGuTYZJSST2mdYJgWpzpu3z1efa/MqVXb7xos+yZROVNUlV0rUkLCPCcW7jGMfE4jDSONUfoDYtLvQtDK1h41WvgSf+PYFdcJjENEKhqEtm1RHzFKaJxXODijtTgzTt4Rk5vlHT0ft0nZLAhLNtD0stEI2Ggc9hBLPU4dbU5SjJ0GWVuF4gyRmCht0wwtAaonqC7YakZc2Lhw2+XmPZu6z4BT3/gLquyeqcwJgRKAviKqRuYJBk6HJN2x2gSIKXDhUcvaYWGYbSUNUNshBosoIuC7LSY3tmoRNwbeTz+M3147+tSPD0GlkuGKfH6pesAt9sONmS2AhSJKnCVmXmmYathZzuLtjwS0xVZsUL6XkpjlHzus0WpwOLwE8xtZpz/ZAlt6Rt1gihoakFN8YGHSdiJUiZxAaXDgOySkaWwDMadEWQlCqz1GTZj9lb2Nye2qhyRV4fE6qdmc3uzKWsNRSpYZZrrAYRJzoxugJrrZRhovL0Xpe6UVgJEioh4RkNW62QFS/BN0tkpeahrTkn2yFlJXOmF9FINb7RsN6d02tHBHaFbzYEToJnFBSVwutOTXnV5iGne1PySkaRFKRGYW8ekJYSS07E9bFJVgnKRkaVGgytRJMrokIhLmSuDzyyssExU2apzol+hqYqjCKHeWIgSRWulSBLNZYq2GgvEHJFWirshw7bU4srRy1WvBRFljhYmMiSzNGhiVzb1EpKVQvWg4Q1P0UgmCUaEiqy2vDo8vJnVWcae2WEZxdstRP2pj7XDn1KUX9W0lwzT2x2Zz73+CtsuV2GC51xpKLIFVktUJSCtheRFHDz0GKeyoxji52wZL8K+d9+7y7jkcainrLIFTw3ZSVYIMvQ9wq6Tsl4YFNRstaKadlwsldwe6wzTWp8o6SJLaaJhoKEb2hcPvTJC5c//9AFJEniL37xOj/0uk1KUZIUGpO4jabUnOnCUjtDV2R0pUZXE3QFrg0FkiQzqUMco+DP/sYVfvfWmN2jggvrIS/sqwznHk/cNpiOA27PYNOXSOuU020Zxwo51am4NTQpmwZXVokJWfcl5mWKpVdIAv7DVZPPbK/yidsaVwcqEvCeF6Z86NaMoiq5Mc5pqRJ5k5BUAk3yyaqKri143aljtYCdO6wZHea5jGcVvOlcRlaWPHW7IS61Y6VXfcTt3/h55N1t/swja3zHfeucbzssmyaWnnOmCw+uyhyMFe7EEWVT8uD6gowZmTzlRAte0W/z5ae7nFkxCFQfT/EJnDn7yYLtSYOtQF7Coozpr8Rc7Ns8uG6BDN9ycZlaq9AbBUkIbg1TZlXMX/nN2/zIB7a5t2OwyOBbH17G9goM81j2fhBl7EwEs6nP3/y9O1zstuk4xX9x/ftvDT/7sz/LfD7njW98439Si9/znvf84Xv+2l/7a3z/938/3/u938tjjz3G3t4ev/u7v/t/UQp/AZ8/fPnWFr/w7G3e/u4X+IXnbvC+l2KWHDBEm7VOye6iZD/OONPVWG/rVI3EblhwFDcUZRtLNmg5M0xrgGWULPsytiqRiYSbE1CwkRqXadrw5HWNWWQQaA6KJDNJU/bClIdO1Wz4gqioGc5l3vPiIct+Sb89p0hUVm2BZWQo2hxNVllxZQ4XDUWT4mkm92xmVEpGz1VZ9RsmSXU8DqGZrAc5e3NIcoMzvQzHntJzKlY9ibiqGEcmVemQNgmSULEVA7+7oGo0+nZJVKYIYbLlZ9TU5KVD2dRME4WWKVOT4Gkq3/fICWoBhqIzyxo22iVQM0pUqtpCUnLS3ObSQOfJu31ePHQpBWSVQcdNmcQeN8cmKhpCKhlGAZ+86zNYBDy/Z3OqW9AYMyy9QXVTVoMGQ61Z9o59Y2y9QJfh0c1D7umnnO1PWfcLokwHKiRUNDVBV2ReOuzw0sBmlHhIUo2h1iiySssqMdQCVZaZJAZ5dfwFVUGioaHnpty/mtAJMuJCcGPsoEgwjh1eGgSUdUXfTVGlY0+luNS4MbKompp5LhMVOq5RI4SMLCsUtYoqC8JMcHeusUgFuwOdjhfzwp5Dy4p57NQhbz5/h7aTsOotsPSEuzOL/YXGNJG5NbGJMp0l93jjI0s2uDPxqRsJUy3YnRrMMpWq0pBllaKRKSpwzITALAhMAaiUdc3NocPVgwDXb5CAjf4cVW1wTcFGO2FvbvKxq2uM5h2O5jov7bsMFy6LTGeaGhQVdJ2Cvpuy7MfHv2sjMZobxKmCrlScCXw0SaNjJ6y2MlaCktev91hkMkUtU5Uqm62IjpPQMkssXaZuGjwrp2sXCFSiQqWsG6JCJc5MRqOAIpfx9JS9mU3bqmiEztdf1NkeBGyPdD61FyKAe3seuxOfC2sxmlaSVA0PBktIlUb9Wb665ht4Vs1heojvTUAuEaJClguysmFvaiAQyCjsTcC3ZJbbKovU5ObA44V9n64ds+wvcPSKReLQyBUvjSKmscrOsMPexDu2pegmx+pEIbHsV0hCxjJz7llWeMujOqf6M3Qt58akzTzTuXzXwVEbvmjdJik0llsZFBpyrfNnLpyiaBoCJ8M3BbMU2lZNXkoIAUtuRVHXLLdL9kcmLxxmPH8o4Rk1rpFT1HCqm9BxE5LMJC0gsASKUnF7v8eNA5daUpC0hqfnEy6P5iAKHE1h59BAKi0WacnOqI+sVHS9lDgzaESOEBoP9To8thzQIFCsBR1LwTISPCvHNwXDWGIxbzGe2Nze07gzPfa+jCOJNQ88LzlO3U08np0MuTsP+dGP3mJ7WHN1u81issRgpvPU7YKP3IioGviq+9rcv2Yi1Josh9+9MqftKkSl4H96/Qp/9Tdv8ZGbCxZVwde/0uGr7m/z5F7EU4cRT01m3LdqcuUgozIjLFWmaxrcOCz5sSdu0DV0hnHBNz/YxbZkfuj92/yzJ3d440aL7UnJG7eW+bWXhpxeF/z1197LWx/q8kUbLj/8hnXe9cyAky2DUx0TVZW4p2vxyqU2zxyFdPsSFzZMHt+Z895LI/7hW07x2CmTtq2y2lK4HU1Jm5JfvnGDr72/xbIRfF5r6MuJP6686o9kZj2fz/mxH/sx3vnOd/Lwww/zoQ996A8NqP9rcfLkyc9pdM00Td75znfyzne+87/+prXB1XECCLLM4UtOtNgeZnSd41ERx1DRFYtxLBNVY7YXJcO44K+/7hzv2z7Cd2IakZGWARuuytEUDvKQbm+AKq9xNPPpOYdMc4mOvIQjQ5w4TJqQp/dDnr9b8onhkKiAV53IUYXNZjCkEBpbloKjdSmbhLBJ0YRNXDXEYkRWGtiGRUHE/PAaSwfbOBtnKEWGrdi0VINIaYgzkyWnj6oM2It0VCXgxixHNAV9R0JWd6niLogeN4clqW6R1h32FjNetd7lvpV99kOP8x2N2/OMjmHzqs2MMG9AtHh2z2A90DCTmrQ0uL9nomslG46Hpia0G5uGhEz0aIRMy1QpK4lMMkCKWfNL5vkEhEJgD6nrgP1kiq6YVFKMrmjM85IrA4+6djmY6gSWgiQldEyFtHS4PY/wjQZZKpGUObVQGcc6B7HCut+QFxJhkZBWDaos6Lsphppzd25ztluwPXY411U5KK7TKTXKEuapTRWmrLoxHTPn9txgxYuZZxoSMk1tIKNiqtBzGjZaMUkpoysqi1xmOLfp2SUNMnsLnfuWppTNseGfqlTcnbhstUtOdSIGsUlgZ+hKiSzJVJVEUsic7M7Zn3VYCcYUtQSoCCRmiYGQFO5fnjGINGRZ4mhhsshUXD9nGNokhUTHaQhzgzV/iqZIGCrMUpODhULfLZhlCpqqcVW08fSGtJJ4/rCNb+Vc2na4OXC4uDnmyTstHmtN2ZmbyNQMQo2qUlnEGtNMx2kLltwMXy8REoS5xDBq0TZrNjsZVVNza+TQMhuiTGNvpiNLgrzSUOSEc/2UZ3YcJKPkNZs+oTwmlypco0RIgjs7y1RNjRA1geZwa5YxS1WeTBY0ckUtVDQl/6yvgUzdSEi1xPWdLoosk9WCp4cDfviLzvMLLw5o5BrfzLk2cHj+wONkO2GRmTx6ZsilgxY9NwUaZpHME/sSLVtGSyw6QYhllNiqwLIybs8M9sYtPFciq3SSvGa9FeNbFQKV6wOd3bFFt91w/8aCSwcBg8hCNBWfeEHhV1sjvvFCl0Ve8dzkkNNtQSMkXF0DKSWwSmpUHr/e4RUnK+ZFys3xKroWs25GPHgiYnvQ5qEthdNtkz/1JQ1CjnnzvQ53JxbnlxTqUsOSPZASzvZK9iYKi1xlOO8TOAUXFJM745KO4RKmFat+yIpt0LZmbHVNbg5U2hbct1RzdQDf/5pVnh9OuDHRuXdJQdci0qrGsnXuzEO2ZzZn2oLHpwlHkcxGS+fqJOekWnG+pzKcQZ2rfMWDFaZRUAuFnqQzunoL9bEHeXYvISsFR4uKUZJxMvVAxMyLlEIxubiks9mZE1cSRu1wpqVzZU9nfxZze5ry3GBOlBcM8xlyptKSfWpXZVE0LAcFdWUym+vcHGbIVskvfNWD/MaLQwwFoqLmi065dM7D5SOF24uaD9+e0elFUAS8tJ8S5w26lkOpshVY5JXOflXzzEHO+7/tIt/xa9v/9TXp/79E/Z/m4F+Oa32u+FxqsSRJ/J2/83f+s+keX8DnD++9tken7bHdVNyzWrDZzQiclEFYIEkuK5bOrJb40rVlfvGFAxxP5b6lBtcSqNqCRVWjSS59UyeOalJRotsKVu5TKxmXBsefp5uBxkNnM7JGgJLh6hJJaeEaGXmtsxRIlKnOXMQcxBq+amJ5JZMiYbIX8OA5l6YuSKWKjlURmC5Hi4bxkcLpLZPnjmKSJkWXdE50VPI6Ia0Uep7Cic6cYaKS5wFF3aDLGS1LYXtocnsKW75Hg0lSl6xYx8bAlwegqy5ppbPk5mhym7aZcyv0iBKX3bnACQNmc0GpwiCCG/s2S0FNnNlI8oSH1o5Hb7cnbRZFwYojeObuElmhYqs+qpZzz3KOoaac6UlcH5rc0/ex9Dl9p2SjHTKNZLK6pGPXnOrPiJI2X3XiLO++tEdSHiuqizrBVhTGqYKlWzi6zH4IUeYRVxVh1GIlSCibklWvoagFti5IC526zug5JbO85u5MQQgdVdLoenOGsckJvTjeNJErVvyCcayhWzmyVKFJEsPYQ5Yb2kaErjR0zRpNLlnkMlcHNlkJWanjmw1pLmPrNVktoykVtyYOYaZSVgqWfjyOJSkgCZn71iKmiUNllAg0ltyataCgqGoOI5AQtK2Cq0OdZbPPrWFGxw/xjILpAjQFFPl4rG8aGay0UjbaCxRZAqEQTjyEUnKmN8UzCq6NAsaxzWYnA6mhbSVIskLTCDSl5urAoqhkFNkAoSNEiWPWKHKJY9SEmcLJzoJVr6Bqju/RsisQMsPYICsEtYBEHNEPYCMI2Z35hInNp4+GLPslPTfj7sQlKQwCKyHWFBQJVoKKKFNR7Zy7U4dGyHhGiZTWrLRTllZdbhxK7M18Vlshilyy5C+4cnhA4NmomU5VC0ZFQp2lXNicoSsVkqzRDyI+ehdMVWet0/D+27u0rJLBzMEVOl23ZL01J69lskolqY7TY5NM4nRX53BY0agxr9zweGpXB73GNzMUBKZeIMkZtVA509Ho6Dbbo4qyKjD1iJUgx9QqZqmCkGpErSMqFdFU/PbNPS6sx2iKRkXJItXZHnkYVsEsM3j+wCBLM1pWxatOu0znDh+4uc+aZ3B1qLPZVrDkioOJyWY/xDcXlLXDh64EuOi0PMG1vYLJPOCaGrLZkplVFbpicnuqcN+KxLO7sLVUUIiCwFYZZjmurDFd1Kz2auY5RKnPt11Y5dHX+vzEk88TVTmGrGJpC/anS3hWSph5eIXDbzwX8vT+LeYi46SlEVgxqhLjqD12xoI4tjEkha8522c7n7IzKvi5t57hN28MeOJgn8Opga8rGG6Mb6YsFm0uzUaYLehj89BKmyeuJywbJUVTMUsgLErKUnAwbPjxj+3wyJLHk/tzHj3p8dTenFuLmFGdsx5AmsuUTYOswnpbQm4knj2MeMWyx71qmzvDmj//ig3+1w/fIMDi9ad8ruzscXWU8vitEKHm3B3AP8+vs95z+Lp7erzruUN++s2P8v95zw2+6KzLoqi4tljwXa9p82tXB/zEl5+lY2u8ev0ih2HBtzzY5wPXprxwlPDYWoFjSPyDj99kf1YR1hU///wuLcmib9mcaXk8cSPi1uTl34B7uXjVH/Uaf1x51eesKHrHO97B6dOn+a3f+i1+5Vd+hccff/xlaxJ9vlCUGqd8ny23hSYpfNO96zx3G7IC0sJkmkrsLmQ0tWHVbnPKbTGb61iaTibNEci07YJGCslFQs+Rua/rookuHTdiEAuywsUzSl4cJEyrEbKksOSo/PNnb/AH2xHLusdDyxbjUGd3UYEwkBrYCeegHKLKGrpSc2koI0sKJ3yPIrdoJMGG5/Il3/F29p77JHWjMotsHr8Le4sKXZL48DWJ37tuEGUBhhZylJSs2g6yBIKGrt5nFHr4Fpzp60hajSoZWIrHztwgK7rHJEqp6egme2GDo6k4ekzLijnRyhnEBat+jZBDqlplby6xF2pkpUwjCp4/6vP8yAMcakoUPHStpqnbxKXCmmsiJHBUH8csMSWbsNC4NrKRhMrjO8fz87emEi07w9QK1ltT1v0aRRYEmkzfltlfKLh6jaU0PHWgoyoFribRdQu6dkFWuUxTi2l2nBzi6uDoMqf7I37xpVsUdcWpTsJjWwt6bkYjV8wKqISEjMo8sTFUiUYc+zFstBKiXCYqJD59p8PO3KSsBYiateA4LjcudE60C871EixNsNlK8MyCrhcxzxQkjuNa80pjd25xa2Qxji0QMk/vdLkzNlBQWPEy5pnMwcKk61RstGZ0rBJDF6hKgW9JaFKOTMNGO2OrE5KWGqok6DhwfWgziXUWhUpSGTy52yGvG1wjx1ZL5pnOklehyzJNY6JbcKKXsDdzsPUCTytQECDJnOoltOyG9W6BazRU9bHh40oQIys5uiJw9Yas0jDkgrKCe/ozNlo5tRAMQhPfFJhazTTVaJkFbbuklgWXFgdY1oCz/QWKLJinFpZagmgwVZm7s4xLhwWSBBu2T8cuyUq4PnTZbEf0/QpNUUhrhW4npBtEXNgasdaK+CdP3kTTCmQBt0YtilrlwbWQUeSz6qdIskzfrWhZJWkOo0SjyiRqquOd0KGDInQMRadlFthawrddWOar7+1wrutwNHLwrZCqKZjGDm1LArlBkaBtwZsfuMPuTHDjoIVm1YDgH376Cn/7Dz4FyiGaohBXJUVTcBTaLPKUJLN49dl9Lq7MUHCRqTnb1fCtgjxvkVUSS7ZDnMGzO/D8gUleyTjWBFOR+dmvvMD9XZdxPkcS8PTBscrrxrTkYO6xSFRGQwlbL3hqr0SVJVR9QMdKmMQyYT1jxS8oxIyykdBkhaf2SnxdQVJL0kaw7NjUoqERNic8h6IWrHs6F08qJHnDwxsFp1sakzTlTqQQeBDVMxbVFKmR2B5U9L7xz7GyVpPJCd/+yj7f9UV9vuSkjqvD5QOPyXyZs31BK9gDdcg80UlKmU/dLYkzlZ/5unO8OIqYzhsQJnW2ioHDh140EPoATa6hkblyVHLzUKLr6Lz13DofujbnVjbgL792ma2uwpm2zvtvHnIzmpEaKefXZTZdH7VW+J3bI7peTc8FWWoYFDNeGsWgZNwbuPzKpT1szJe9RjUNNI30Mh0v+/K+gP/GcbioeWm/oDJiZmlDxy0QjUGaW0wTSKqGConfvjGi22kQUs2iKCkqFU/VaRqDg4VGWDb0PVBKk3Cq0WgJdW7QlCZ1LXEQSphGRcuUONldkDUp68GCvqNhqjVps+DE2oSvP7nFu77+QVRdcLDQuKcnOHd6xmEosYhtaDRKYpaCKRstlZW1lKRJMPWKM52aC5tH7E916txnEClsTwWabPHQasR6d4wqaQwXHo/fsLANONPR+M4H15FqHUuTUfSYliUIzIq7MwgMFVsvqSlRZQ1Ti5FkkIXOUVryuhN9/vKrtvjMfkjbUlgUDT23pmpKDKXidDsjq0ou3V3jMzs2lirRSDmqKjic2bx4x+f6UYe9mYajK9yc6JSNzTAVbPo1DSWbQcWlgcVoHjCIJX7+yQPetNXB0QUtsyAuZfLGIK5q0tLkxYHJpYNlXthvcRhadKyGo1DmgaWEllXy2PqQriXQlAJHK/HUBk0t0fWKotJJqgbfzNFlFYTEnalGVmlsBDmvPXnEm87tseJWaKrCIlWp6opaKDRCo6g1JKkgrxrySmKw8MgKC1k69lEcxgrnumMsrWLJzfEM8AyBrQmKysQxBfPMYGfiEeUKhlZxe+wQ5YJJojJNNUytwlAFaa3RdyteGi3QVUFV6wxigIZpohJmsNaa03Mz6kYmLy2qSkcI6DgxF9cGrPkZi8ImK0zW7Q7heImdwxanghXi3CDMVMaxQl1L6KpMWDXszktsI8M3YaMd04gKUz9uCLasirPdiNUgZy1IWfYz+m5O4JS0nePkW1WWGKUWk0RhsxujyDJJofLE7TZ5rYCkMEssjhbmsdm1WXGmnxCYJav+gmU/QlMFp5cyZrHNUaiyF8lsuV2O5g6D0CMrTKJUI8lkHlw/5DVn9ljvzKgqiTDR2WqV3NNPiTKLC/2AriuYljM2ujFlI+O4x3H1QqjcHHQoS5MVt2TNL7AUmWXDQS4Vuv0Jil7wyYMpspESWDktq2ZvHnBpd5kqX6Ko4SDOeGG8wLNkDqc1hpZTixrPqFjza1a9hLRuaHk5TWNSVOCbMatezlaQU9Y1qiyIZh2iTCcwVL7m/DKP9U6w7CpM0hxZOLztzAPYSsX5fsVr15ZZ604JTMEwtlhkMludGs1MWdIs9sqUhzddrh9aXNlt89J+QFGYvO3es5x2l9DkYy2dKlQ2+wtOdDNkSaDpAlEpDAcmX3tqha89u8TPfnKXosmYTbu4Zsozd1fxjJJBJGEpJq/aUji5lhMVJZImWG+VNFLM0bzPlQOXo4WKpcoklPzmnSMeWgp4cRZSIbg2yBhEAltWkbWCRmoIZx0sK6IQNad6NZNE8NuX5jy4ZqM6FaHI2VitaRs6B2HB6mrNa9Y9XthNUU2Jrz3X42O7M3Kp4c89ssam5/L+60NuJAfkdYFoZP7Ka7f4pT95P3/19RvcHBW8/lTAv/n0iEf7LdICvufVy9y/YvHLzxwxinNMP+Knv+4Mp9sB3/LACo+uefz0V57l9jjnsMgITJXLt2ue3c/omTYPrzpYmszX/eIL/P3fv8skKfkLr9ggiuC7H1rh1jAnrnPed2nOP/3qe/iJrzjH15xe4rseXeEdj2/zvksLer7KP/gTp/4zVe6PjpePV73sS/tvEp+zouiHfuiHsCyLs2fP8q53vYt3vetd/7fve+973/uyLe7/bQRuxM1ZjSYrgMe/eWGfL7pf4u7UZ8mPCCwFuTG4MRA8vaci8oaeo/PJ3TmzxTqGNKXjRHRMlyhTeWm/RtVDTjXwRSdAiIzLRw5dt+DiEuxMOyRVQxhVnO5IJFnGVJozDVVOBAarrQTTSNBqjSWtg4JOI5eosomtOPzykzp5KXFPD+7O4TDOef2pJTZf9TXcurmNdG6dM4FKXJUIIXjlus04FDy7K/HQusY0K1gPElqGhq5OyKqCZU/npaMMH5dlr+QgK9jSZfIm5/Yi4aRvsJvPcJRjIjLOJFypxQeuSfyJ0zPGWUBaKyyZEqo+wqXNKBvj6RYvDGru6QjyJiSruihKSFxZ2E1CUWu4ZoqCS1zmhJnNUSlxsgWmEhHoLmGVsu6qIGAUymy1ZUxjzDTV8M0SiMgql3mu0LEqll1oSClp8HWVqikZpSqKcIkLlaTMsE1B24QlJ6MWAkfPqRuwNIlpbDKOVC56M5RWQtmo1I2Mq8s8tdNhe1oRpgbrrRhTz3AtmbjgWMpaShyGKl27ILAT7k50TLVAkXPuzGyKWlDUKrYGWSUTVzJ3phaOUaErBbO4zTyBRhQ0jURZ6bSsGkkuEUJClVUmiURTywh0JqnEMLLRFcH2yEdRCrpuzKqbcWdmM40Fa0HNKDJQFLCMlKICGZm80ln1U/KqxjMECMEoMjkKTWy9wVIFYWJxsrfgvuUEpZQxFJmXDloczVz6rZhxaOMaDatBSi0EKjWu1pCVNoYqCKyUQaxxY+RycTUmLo/TOM4vxQzmDotsziJT2Zt2mabgWRUNCk1TomoKOzONSghkBSz92Guq7dZs2T0uj0LavYpvOnUfHz56ns12wYp//BxHkUxdGnzF1j28+/IeqqJwv7vCpXLIaGEhyw07E+h5x4TpwtocR885CnW6zrFHwiIx2WonHEo6bTfFNBOu77S4M3DRZJl1OWJ77vGpO3c5u5zjGAonVwS/8/waD54+RFEqXF3jlasOlw5CDD1jltisdxdMdZmTywq/dHmH1XbOWqugkRJ2ZjaeafGvn+rzmpMFilyCJHigr1CKgq1Wg6EtUCWNiprr+w6TkUlapryiJ3hpW2Wp00ZeHnK2K/jAZ+B7FzdJ5Ip+sESWlTy2UTCJLBaxzRtWl/mDywlbnYqt3h5ffl/B1ZFLmHgshMVRXHHCN7kzkUmrFmpu8mvXb3G2bwAZ+1HNa7YqhLygFiWOYvH08FgFmJcqw2xOq6fhGhKLMsYzVLYCk7TMWfVVPL3Fpy9XbH/wQ7zhO7+dvWnO3/roTX7qYwN0oeCu7LMZFKzlywwjCUWPKfI2qtYmzjVso+JUy+Ki3+G1mz6v/LkbBA4oisQzBzm3BgF/7ys6fOwgYz+XOJxZ2Oj02irDrODr7+nzZ37rBdZsg0WkcXsRc3A5xdds2obCRAGhxuyEEo5XYMgGSVkyy0tUTaNOlzjvqURJw2GR84Fbk+NG1RfwBfw3hKv7Db22wqk1A1ur2Z4JHK3ENeBca5m33rfM//JrexR+RkuDlqEwzwSVEzFMYMWViFOVZ/Y0ArNk087pNS6SUfKDX7zJD/3eLU7KLVSRE4mYRdZwqmfQNXUmaYFhjNCKLnnR4vpE4sHzDU/vx6ilwbCKmKTQc1SiDCR9xs4k4FE3oKhyNC1ixTepmpqDMEFRZFYcjYEXcfugRT+wqOuMpE6Zxm1Gc5WDUHCqW2HrKfuhgi10JmVCo2foksZwbhEXKafaMvO8Jq1rusImymOMRtC1ZY7iiLbdZjE1uJlMGd4qePEwJjBVFBUkqcBSNPajjBXXIikFLS/nYBoACpvdCkmKcXWT53Z0NjoSw0hgqAY3hxX394+baGnu8kUnBkySgKSqiRKfo5mGrQr+2VMDHjth8vRexVZQsx+rnO2mgIqrlySqRJgLOpYOUsyJdoqhSghS6kZDU2raVsHePCDIq2MfF1mhMQuSAprGQADTFEBmkZmoSoxvxJzyJXx9wrMHAVGuIDBwNYWkLFlxIzyzxlRqDOU48OPmwEGKFE51Eroy3Jn6OEZDnOu0zApDr5BFTWDHPHW3h2iON/3WggRNaeg48MlbSzy0OUGRBT0np2VmCCQOM42sAk1NURQJTy1Jaw1bF0S5xaVhC9suOdNVuDFRSAqdre6x+mme2UiULBKFSSLzhmWP7WFIkiv81rPQ7ykIUeJaAkVW6LsJy37MNFKYJBaO0TCKTKIc0hw6Xs3+3ESWanS1RjQycaWyFqRYCbTtijizmKcantmgKA13xg6qBK6V0rFzlr0UW5fYnTpIQuaB1TG62nD1qMM8NnDMHN+KWadgqzNHQXD5sMVy0LAfjakR9J2Iq8OA/aGDkBXmqUdLyggs2Ggv2PBDZLlif+FjminPDmcst1OW3Yj1IOEaAQeTNo0k0XVz7k5Nrh9anFyKcfWSlVbE3lxQA6ak0nYEeVWiaw1JBVnVMI0d+voyqpDZOwRNS2n1IpYcD0VRiGqTmpBSFOiKRssE3ywgc1m1A24tQp7f93nsxBwalcCu2J3VPLSlsz2vGWYLLi6b/M61BbO44tsurvL8fspvXN0DSeV3Lts8tHmEwEBXY851Ia9jbg/bXD10qFo5PcNAUuCbz6/w61cGtG2Dp+82XNmbkIiEzRboZsyJlmBnLlFWBmnR8GC3RcvS+eY3LPFvnx3ytvdcwtVlDsIWbUPh605e4N83N1gUJeeXaqZxzZXpnHV/iU3L4m6SkpQVZeOwN3UpifCkDosQGlSWuyaf2U340w8us9Uy+ZE3nOAvvLcilQrqusJTdBo7Y5apeIpGXMakUk7fd+l1Jb4k6HIjnHHrQPD/fXaPvmZCCk/EE2Rb5a+9dot3PLFNKUqWTY/XbwR8en+GW1vY6vFmmcgVFqHg7EmLQVSQVA37cYpva/yDrzrFn/63l7g+znjuMMb2Ld7/3Q+wH2fszysOwophXPFDH7rG15xZYs3XmJHwG9cP6PUE3/PIOm1d5923J1zZTKibml99dsSvXR5yxrd4cNnlA5fn3Fyk/M03bvHUbsypjsUpLF657vNvnj3ClUweWDb5C69e44T9BV71+cbn3Cj6ju/4DiTp5ZHA/7eCjVbNsuqgSDWJOOR3r7XpdTJ6hk17SWOaykSZQlkrPLSsM4s0bk0zvvxsh/dcvYvUeCx5GZP02FTstaclFqnPeueImhhDM+laOg0xoyLlIOpzsq1zMLJAiji5XFKVbcKyZBA3BI6MqC0mWULgHlLWLXRVpmHCJLM53/NYFA3dICawTSRMFlmDqUaM3vsLnPmBHyasCu7vN2RiwfufX6Yttbg7a5jmgi8+oRwnYdQZLdlFKAkXVo+I0i2SskLC5Q0nC5Kq5KElKCqPoslYNjsgJ0j4DEMDhMyFvskLRyYXliGpEhTJxJJt1joJjmazFYRIUouWFRHnLaIiI8n6+P4BCh6VFKLLLU52diirgEUZMR92uTlt2AxkHL2kY8X0bJ+dqcSFFcEklZnGDltrKXkT4xsgUTDOJTYciZ5TsSgq7u9Jx89EStibr6CjMUxVVMngbF9GklKW3BmTVCctLR5djRmmCqd6Y57Z6RLmOi3RkFUqQggUucZQExalzKlehKJoHC0s2lZBz2nouzV7cw3frDE0iTTXWPYK6gaOEpln91pUTU3bLriwEhEWBo4ORQ0rfsbR4rhpNgwd6tIhTDUsO6HtpGy2aiw149mDHodzh8CUONmbkpcmVWNweyxRlhq2AXszjXlq09CgKgJVqfHNisOFQ1rEyEAjBGt+SlFLtKyaWarQc+Z8ZmedJFdwFJjMDDZXYu5dCunYFfFQY5qaxIWGqghkSSIpFbY6c1pOhkzOIrdoWxWuHjKIbDwj4ebIpWPXjGObWSph6hkn3E3myZRxaqIrFbNUwzEKTL0kL3TaZsOKl3NtZKEpGi0rp2kkBDobQUWz0GlinVthRLOtsj/tcZRn3G3H9NyKtVZCVki8+9IeeWowlwXX1V0yoRFlKr5TMo5Mul7OhZUJs9QlLhuGicxwZONYNfeuzjmaOjiaTMupyWsZzy2ZjDxuyxK11lDHFmvtGsdsgApDa7hnteLZm6t0g5pTXZ8feNV5PnznkL//iYZHtzJcXeB1YyahhyzL3J1Cz6sQksWVgYZUd/iSe+Y0jYKrWRhKzDRVuT62OdedU9U2kjrDVFo8d6jwyFrBX3z0HP/Hx8a4mowqSeyP+nhqwkoHMimlQqDJGopU0NQ2RVNxb1/i6lHKl9zv8dtXJjx9e41AH9G242O/kbxkq6WTl9D3C/LUZzusWHVNVFkwjFTu7Wo4xpTAlAlym0jJ2fB8FNElShcYwmXFSxlnEcuejKZIPHeY8I0PjRjMOxzGc8Yf/RU2z7+Kg7DkzsThkRWX0VQwLkqW5Q4ttSaTI1RdRsNAVTNeGJRcutziwj0l22PBje0Zf+V1G/zJB7p8fG+Goeec92we6AV88ugOzx9W1I3BmzZ7/MaLM3YWGXbQ8DOf2uOkZ7FIBUdRyRtP2bx00DAvY25PJTS1Ybm2WbMborqhEg2yZNPSFPIKdsOCm7MaYVWcdGx83UVx5y97jarF8fFyXesL+B8Lm22VVK5plCl3ZgYPr6vUjcrOrOHWXsqPf2mbN5+PeX44Jytjjo4M7j9TItctwixinscMw4rzSxLjhUtcRhxUIaeNml+9sotrKNw8bOi2JbpOwMXVKdsTC02pMFWLSWTSkJPXGi1L4z0vHXEQHtFRdJYDh/miQJJCGhGgCYczKxMWucayq7GgImzmnOlJbLRU5nnDJFVY9eDmsGB3KnNG9UkywdFco23KrAUZjjVjs2XiGgZXD3Ou7dfUqclBUbHVk6iRGCQNLcNEl1MGsc6ptkZZZEgY1JWObYWc3RDcnkrYWs1Kq0FRcgxFQ1ZyLKPCUCRmWcOarzBNFrzypMYTt22EkFhkGqok0fOPx7o3OxVlDcs23J6rPKA5pFWFq+sURsySq9OxJ1R1m92pxLJ7HERxbiVmnhvEeUPdmKwFY/zCQZVC1nzYnbe4fmRyYU0wTRuqxqBtFvSciGFso8sNe6FGgUVWH0e/P7Q2p6xVbo4tytrm/FKCox9vTsxTB90tyRq4OzFY9moMVWa9NaMRFaNEY6slYaoy5zoz8rpiECoIobA7CbDNhrRUUaKSwMwoGuh7MabasL9wON1LiHKVjtVgqTKDyKGsZLY6OUtexSwxaJqalgkNUFcGSW6yGiS4RgUStNSEqJQ5mGjMypqoUZiHLuPM4JETU0StsxykdKyEsIS70x7xvMW7B2NkSaBqDULPOYpUVEnF1AtOtCIMreBwoRAWFrbWMIoMTK1CVxq84Djta5bptIuYo8hEQiFMDTJdRlEKRpHKPDFYDVJ8s8BUFXZylbV2QdnIeAacbEccxeZnvSVjBpHJS7s2YaZAZdBSA3ZHM3xvzu6RyfWBT1HDmX5Gxy1AyDx+e5VZrNENUkaJziCS8O2CcWTgGzWq3HB36jFPLRapzWpQcW454VQ35aWDNklm4DsNHQ+q2gChUdQyN/Z97l2fc2Ngk84NvCCjFmBrAkM75sJZrvBI+wyvvr/HH+xO+KUX9tBdhdMrKcNQ4kM3Q2yt4eRywwPLKdMEhFRwbeRxNLXQFhKP3Wszradsdo6VaZbaUFYeUS6zHY5QVZlZBCfdgMd6Oq99zOeVWx6aOuCffnrKyb7EQ1szTK3CVGQ2g5C9RZtLhwGjWGG5J/ipLz/HkmOgqzL/8PFtJEVQFQo6grab8EBvgSrJKErFPNOpKg3XyPkT93Z48+YqDyy5APzVL1nnyX2PX3huFzfSmCQF77lyk7AQdD2QAEnOyXObq8OcKElxfQlSgyJ0KOWEZNqjro/H25c8lVuDgteYHt/ywBLf+b4XmaUVpSYYJiX3+Cq6npHkEqZko6spl7a9Y18pt+G7H1njPc8PGG7LfOdDS3z4aIfdA8HuSNAJdN50OuBXXxpSTHW+9EKLT9yY8y8uX+bPXryXn4l3+a6Lm3QeNfiJj+zz5nuOvX/+zu/d4Yu3PD5+d07X0RjGBd/20DJ/63fv8je/aI3c8nl8Z8EHb4751O2Ed3zNSf7hE3eYxRVPXMv4s69coi3Z7M4TzvZcXrPZ4oNXZ3z/G1eZljlpXfMtj/SZ5SUfvRyRpRJf9pCJvi34tUtDfuKrz/5hvcrrmt+8ekgjJE73Tf7n993gPv/lr4svF6/6H4VTfc6Non/1r/7V/4vL+PzgxsBEcgqWPUHbsnnjOZWwsEFNGKQRolFRsNCUnH4wZJyusO5Z/MQf7LDmKcwiiRsHJmtdmCZwdRoxDhtM0+KB1ZyWlTGMJLLCxtZc9tCY5SmNkI8TfMSE69OKDV9lzVPJ64ZcFAS6T12YpE1JW6mw5D6OKvHCUY3vaESJymG64FS3ZhF3uFLaZIZPtbvL/W+yeGxzRFp0mCxqXtor6as+lqYR5gvuWzJRlQRLS5AKlzDLsfQYzzXYWyxoECy5ObmYoyk+SZnStxsqoZE1ExRpneePwFQqXrE5RpYr7o77FAyQJAPPHCCJgJ2FTF4LLh16tG3BSb+mpS64Nl7BUGIOZ6tYWsMibXOulXBrofAVZ0Z86I5BnDtM8zmurnNleOw74+gVB6HMqqfjGiGOBj1bYnue8UC3RqBT1AVJ4dM0EroacmPURkVmUdRMI52WpXEwU1j2FlyfuJwMaixVQlcbVryU37/d4/5uip2oZJXBYSyz7occzD0cQ9CyU4aRS9sGhEpSqKhyTN2o1EIB0RDlxzu3mgxpaaAKibZdE5gRBwub5/fbGHqCruZcOvRpmYK8UgnMiq3WES8dduh6CaYm0bIyrgwcduddTrYq+l6Ca8A4dslKWGQKm52Uum4YhSZlY4BUkBca660SWc5ISwNLq0hLE6QCTT42ttwfO2iyxN5CpeMkmHrCJHLpeQUPBAummU1UKjhKSSbJuEZJ3824HhtMYoVppJOkDq8+GzJJPQZzh4c2QvbnBmGhMowtikphkdrIkuBPP3qbSwML3bjKl50v0JScu9MutYBr+w6PnpCYE+GaGQ0GLbsgzGQUuWSjJXHl0OLWYYfvfYVHDXzVfRuc9m3+5w++hCyOPQMCs+JUd8GTd2zyqkKoMvetNXzshourC0yjoOsWrHgJgZlxEDpkpc44VljxYhxNZmdq0FITLp6MmMUutp4T5jo7Q4uTazn/y2vP8dMffgGttqjjmoksQ5NwbmPG/mQF39CxtYQn70Y8sTfm1hDKzCKtCpZNgaaWXDqoMNSCLNMw9IiiaFGVAY5eIhoL15gDgoO5wyCOaWqDmzOFVaehERYHM5P7lxTGE5UPX0uZNDFntmpKEbPeFjyxbbLqgGIOWTEtjuKUjUDlyqDiZKchaRL8fsonDgJyM+X8suA9z1o8uuGwFAwRQqPrLXjmUGHNcfjk7ZKtTkaUG3jeESd7LcZpiaZPKSufoyTlMLGxVYevP3OCf3B4HVOFkoJ138TUaiw95dsfKzkKO6SVzKnA4ZYcIN3zGPtDlzVP5tsvrPK9H7jO9z66xuNHU3bikDVPoS4dJllJXDrYssnZDZlhrGA2GvdvHBO6H/mSUxyGOX/+vVfYHzdc3Zvg6y1OLsucXlH46687zV99TcNf/u3r3BhlPLkbYQUxvmWiGwWf3p4yXNh0bZ1Xb8qEqcRwLrOyMaasFe7xtvjArQE3xoK2qfO3X3+aX3z2iKxoyOWGUQjf98gGH3+Za1TzMnoUNS/Tdb6APz4o1AS9cSnCPjIFV/YrTiyPMOUup5bgH39in6tHGYdFwYZnce/5hEkGQo6YT3VOtWWqwuCoqYjKFB+Nnmwxi3OM1hDLcDGEzjRVyUpwDRtLE9wZK0hKw5Kt4ug646ombyLatomp6oR5jqSX1AuPLK0YpyWWLtDxsK0Zrh2SFH3alsTRouGRtTl3ZwFpFZFLBqc7DqgRs9jAy1zSvOHmQOahjYaWqdCQoKsWW92CG9MjDMWi6+UMYol138DVQmSlxlBMyiYlLmT0xqBUY1S5TVwWKHpB13aJywJJGExGBq1OSlm4NOzjmwrb84aW0bDsNxzMVN5wLqQSJYs0IM4XlHWXSsSosgbkICmEmcrOwqZtLz67WVVS1SqTVOL+9ZCoagh0lUpKUSSNYSQ40a4IrPR4XEYXlE3Fsg1CmlHWLk0jU0s5rqFydWTRoLPiNiR1TZi6xIpB4ESEucs0sRjGx3HvgQmzVENVMppaJ5dglhW0jJQT7ZJJbBHlFbcmOmue4Gy3RBISslwwyhUOFh49u6bnZDxxu090IHNqbcrNkcdmR8ZUYaxL3NuLsHUJKZK5uDplkWkMYwPXSLi28HGMgnV/xv68z3orJq4aZKmhaEoWGaSFwVav4fzynI0gxVLhctVmb+Kjyw2KGuNLMlXTsB+ZdL2QWa4SmDVdS6Lb9jjZNvn9u0fMypy2m7PyWU9HTRGMEoW0crh55CFLx6ljeSkT5Q33rYYUtYGp16z7CfNcJ8llbo1arPoFqiSTRBbzVGFvEbDezhiGFlkNdw5cltsjAqvGM0vySkeTBQ+uHxEVJsPIpBdUyKrMcJFTaxGGaFikGhkKp/sZpl4ySxR6XsgLBx1mkUIVezx2seH54ZxxZHH1UEeVGhZ5yTC2cPWc9SDEN2LyWmNRwMFCZZxKFE1N4MTE+fHooWsW5JlJWepsH/XpaRpbZxVuRSlLQQwIbo9cykJnzTO5rxfwk5++yXM7JUWm0emkjKcGnl1xcrmgbEpkJedoYXBPJ+Qz+wHTVP5ss6zi3c+MOLtZossSuqJwFEkIGh5cj7l65CBKBZE73BgUPLsX8+2vWOLKUcL7XpyiVgqLvOFg5rPSmtK1C8aJw6VDG08/NqYOZJ8V1+AgLHhiO+It55aomporiz3alsw4qZlGHo2U0jJt8jrHM1QGMwkp1/iLL1zlb7z+FH/iTAdDlTnhWyjI1GrB1nJMmOvoRoSl6tyYSPiqxXAuM5k5+K3j/1kil7SuSMc+pg7DWc2ZvonlCf70Qx0meck//ORNKiICG0oppu266FqOkFMMpcU0lhkNPd5yX4tXbLi840P7vHiY8PHbIe/5Uw/wjj/YZhg1/OCXrvOvX7hFmZtcWJP51F7Koyc8XhxGxHJNt14lzhpWDIdP3A5R64Rve2WHt/7ak2zpHdZbOn/zS0/wzf/qKv/oK06w4hl8/X0G1+ZTZEXwox/ZZlgU3Of7/MibTvCLL+6zuweWpfCKEzqjpGIyEdSmzp+9sMHHbs148z0B3/ZvrzCNGh5bafE9r1zlnY/vY3dK/vobN/mpT24zWsA3PtjF1mV+9EN3+JYHl/jw3gGDsUAxBR/bnqFJMn37jx5U9Z/Dy8Wr/kfhVH8kM+v/3iAr4BsGrpbz4lGDpU+ZRQ6PbGgYUodazlnQUNQ6mgjQZYmrewXXBzn9lsz9fYlp6rJ3VeORe8ZcXHKZuiq2MSWqF9iKSWBUjNMxdxYWS4HD4Tjgwukxt4cBa36be3wd151gWyWitnHViEreJ2lULMknLkO2Z4K+p3J6WbA3LxknBqdbOlFaEecyj25IvP3H30ZBjaMbbI877MXNsReQ1HA7iThtaFRNiaztYshtBAWldMQgPEFBgio3bLQ9PnO34cvvlRhHbXruDEO0uD4N6ZoKtfCpiTnTaXFpmGEqKrdnGuNUYSPQEVJIVfko6hRLdhHSEbU4wa35FCFscjFjd7rCIMkIdB3HGNAIk4M0Z8XRQUl59brE80cSCi6+sWCRV0hNi7yecbZfUNUaDSWGatO2ZgRWRS0M7sxyAgNcrURXC7JSp22ppGVF0QiWXAUknbSsiHOTC/2CUWzQMmNGqcFB2EOuTW6MdR60QpbdDNtsEEKg+QUyBs/s9nHNgmlyXFyLquL25NizytQjNLVEIJPWDTfHHlmpsTPVeOWJY0+irNDJK528MlkNSvICnrob0HEjTnZyVAU2WjmzXODoggaJLLc4mBqoUomlFZzrLUgqmf25htOYXD/ySXMZT9OwzYy1YE5ReWRVQ1IqLFKZ0cLgoc0UTSkoKoGh1XQdFVmCltkgY9B1M051I4axzijS6LgpYSYzLxVkuWYY6dwcWQi54su21vnVlw7o+AWypDAOPVRFMEs1TC1j3Q+5OWlBo2LrAknOedeTm2wENVZvTiNgb+4wTRXWWglIBrbcQ3VGtE2Joi5BUjBVibiU2Z06VI3A0Brec3mP8VywPUqplAZF1tGkiraTU1QNT+50eKjf5YOLCeu+zMOdM6inFziyye/dPkKXM3xDYKgKeaViKDInOjm6LHPj0CfOQGnLjCIdWyuYFw2GmnF2JaKsVX72yX3+yitP8qPPTJmOFaxKcPF0yIv7bSaJxNmlkI5bENgNP/nkS/zS174O22p477WSvMzwTJMlvyApc1Z1F0m4SFLN6W7GYViTlDXLPpQc+3tElcIgkiBdR2oWnG5XrLUjJKHzqZGKZ8uEccNqf8HJVsgi1ek6GoEXoSkyipLTtlQGC4MLqwtEYzEvFNLcZhLDYydUJmnNQ6s6HSdizZfp2RWlKLi41GZ7rHBPX0bGYF41nLU6WHrC6X5GWXY5jArWJYs0dvnWR85gKApSaRNJA4h8eu6Qj9/WONMOWAmmXJ/ULFkOlz7+BP2vegub7QpTEuzPG37p+RHf9dAqu5OSu3tdVvwusTTjqcsObrfE1XTulhF7d23+7ldv8i8/OeDTdyIe35nz/FFCV1OZL2R8Q2a9DyJXuLGfc2WSsSgu8Y1nl/jAzTFtEy6eFBSVS1qX/P7OAZuBxhs22jx1OOJuGJOVcGJJoWNJzDKFh1ZtPj0AR3Z5bM3nl58ZsB0lpKJmzTVoyTqBoX2+S9oX8AX8J+i5NYdZQUWDXBk8urLE47ctwlKgNjKT+Zg33i/Q9nNkLaeWcwzVQsFgkCg8M3Nw3ZJAU9DkDEtLaXKbthIQxoLl9ogiN1FmffKyYW+SstRJ6No+XTfDNOaMIp2u7VDUCrfGMktuwalWQFhGXB81nKHDW+9XeWL/gP0QTmk2ksiYlzmBrmGpOsM0Jm8KqqrFYC64eyTz8BmNPJc5iipapkLfaxinBYbq4xo5fW/GIl2iRGFQ5DzcFxxFDVldsWFqyErMONHpOTpZPaMRGpPIpyLBkF1ujSVW/YIwthiW0LcFB1MVRZL55M01HtwcEWc+tjpksWhz80jHlBQ2exN8Y8Eo1tDVBVmhY2gxrqQwTmTq2meWZjywJCGrCWnuEpc1hmwgiLiwltAIKGuJKDPpuSldu6ZtRlTCIioqROOQNwXjWEKXZe7OwbcsHLVivTWnLG1GicqyndPtRzyfNNwcBBiKwu2RSlLBqXbNPctT/uBWG0u3iHOFVX+GbzTosk5glTx5N0BCpaw16jpmyR1RNgJVUkgLB09vyBt4es/FsyKK2mR3EqDKMgczH88s0dUSCQPXzFFUlWtDl3v6C+pGIswNzvYy6gYuD/pYqsQwDDBbCYFdsNUuacSCeaof8wlZYt3LOYgM8kqiFgXDWEeSBH3/OLXTVOC+fsqSlTLPNfa9BR8/GLDsL4MWca5z7MEV5QaekaOrJYpccqqdU5QGZVUhSxArMq6REuUGJzopQkjMs+NEMk0RPLY1pUHmcG5yGJnc3rewbEgKFRlBUQsUs6ISIEn1cWqeKtAVQVRKHEYaklwioTGbWaiyoGhyNjoh5USj56V03WOfn5ZZkpUS9/ZiXioVduY5H7ye41gNUSqYRRrU0HFSdFUAEmtBzo0ioC5UhgubWeQS5wq+XdIIGVMV3Dm0ETJUiYokCb72gsVTo0OmouH+9RRTFaSlwiJt2MsEX3m2y+/fmrFiudzSFkhOTpTrnF0dokjg1CZxodN1Y6Ky5urYQ5Z0Vrwc4SUkhx46KtePHAI7x9Y0ek7JJM2Yhz6gIGkVLUlie5bRdVUmecHHbi/4wTeu8yvPD/jE7Zq2FYHQMdUFaWFxojtHRqdj29w8TPnB33uR527XBI7GLzxVcXZVARQ0ueSB1YRZXNHzcrJSYbDvU5QqaS0xTWIcQ+XhleMNqH/16SP+9TNHBJaCEyzIC5flzpCWoTCJa0xFJsscvva0w+huhFAzejYMFhUNYJsymR7RXzL5lke7XFi1+NGP3WBvLDjZy+m4NXGZ46gGg0RCaCkibDNdyMhSwTe9osPbH1jhL//2TX70qzb5mU/v85NffYYP35nwiZ0pSabyq5e3kZSC6/OGn3miwVN1ntlPUCqFJdfhDWc8/vdPX2JZ72DmOmVe8fc/McCWbG6nGf/4LWf4+U8fcm5ZZ8M3AHh6b8HRouQj04w4EfQ1m7dd7KMogitHBastDTPI+eYHlvnB/3CblRWJrz7d5+9/cJfdJOOVGx739xyWtjR+49qYqKx5eN1B0ivec3mX/aRguaXx2tUOP/7RHX7vyoLXn/T5yK2YB1YcbsQxh7OKLBM8rYWfr/L5BXwWn7OZ9X+PMBWFvhdRCjjXVTnhBWwGEpI64uZswaKosRSJWaLwmbsORS3z0EmDi0suaq7z4h2VuKwxNZnDBVybxITVnLzw0Qk4ikwCK+d0y2Pd8bgz07g7r5BR8a2CJ/dzHr8Mi9SlKDSEvODywGfZ7HDKP05ZaOke647DIFaopZI1X6Focq6OEzQZ8rri5nzBaq/mU//6V3n68SdoEORZm2FcoSsSW65H2dQI4aI2y6w4MYbsUZVL1NQoTR8ZnaqWubdncXNsI2qbNVehayes2A4dK8PVdMLU52BhcNK3eOJOhzzvUVYymmwyjduAQs+06TkFZwOTV20OOBNYFE3ONFxhdyGI0y6WMeH2uMteWHFn6jAvao4iFVOtuLA0ZqsFVSN446mSs8sTOnbD2W5MXC9QUIiLGleJMSWdYawhCY1x7KOrEWHRkFc6EjVtK+dMtwFJ5ZO3TeapCsKkETJ5U/DCwKIWCpqscRTahHnF9swgrRVMWUXGwFAbVv2YN5zdwdAjJrFEmCnM0mMjzI0gwbdqxrGJpcDos95Bsiyw9Jo7E5un7gTkTY2pZ5h6wSQ2WPIbPFPidDdhzU9w9Ij7luacaGWAxuFc52DmUuc2sqQQFRp5VdEyagxV0HEi7luJ6bolup7RtSoOFy6GllI3CqqsUTUNaaUiAxeWYlq2jETO6f6Cx9aOMLWaQWiT5Db7sxaT2EJWFFb8Ba8/tY8q19yYWLTshC8+u0/HSfjU8DavvmeMLGmMIg9dEahSzSKDtpVRCcgLk3muI6Sa+5ZnLHk5jlGSlQq6mqJIKp5RMcsEuizx/H7M5QObq0OPwKwwZEFSwCg0sTUJ1yrp+Cm70wrDrhikJZO8ZD/MSQvBINJYD3JaapdFqtJxBIui4BdfvM25js6XbnVQFMjrhrad8NDKAR1nQWAltMyIjzy/wt7QQZYktocddkZdwtzigZUFZQ17A4O7I43bo5LfuT49Tu+66DIsCj59q01T2bxha4lZprAzk9FUlb/xmov85d+8xfVRSmA1+HaIocUsOYKm1skqiaK0sLSCpMoRwkDIMYZasTteZV5IyI1HnKuIBspaZlaUXDloM0gTXFnhn3zmLsv9GtsKsWQDz8x5ZCNHV8DTDXamDoZs0/cSVMlhlgsMyWCelbzqdISsRfS9FF0pGMTHCru8mRNnJrO0ZJI1aLJEJiafJe0plhGRFzY3xg3zxCUvTS4fmHzoSsHZtsfX3NdmzQnoOgWarPCKVYuqkbg77bBk9jCyXT727t/ldMeiagQ3Zzme1fDmcwY/86ld3n99gmU2vLRb8ZFnPTq+iqGoBJrNn3lwnb/yZV2evpPyLQ8t88Z7fH7h04e8et3hA8Nn6K5m9FyZG1HE//rmDVb7MudXDa4OUv7x07e50HV4xabMzbHCII04mskUqUGY13zjvSvEZcW9HYOHllzW2wVJJWgaiV96akiYSTy3l/Puy7vslwmPbJqc69pQyyRlwz/68P7LXqPqRnpZjy/gfywUlcKqJ9NySmJSfuCLT/KqtR7/P/b+O1i2/a7uRT+/mVPPzr1y2DmcnHUUQUIBIRAZk55BNr6+GOxrsH2NAdvXmGccwML2RWDAgI0sjIwlwHCUUT7SyWGfnfdee+W1enWeOd8/lsqv/P5493HrlP2HNKrmH11dPXt2qBqjvnN8x3BNhe+9b45Yjniu77HYyqjrEkkGURmTlCWnmiaGDDujirjykCsdU9YQesT/8ugCg5ENeYuaJYhkDy8t0EqXtWbBQnOMJOXIlQlAKfmA4GSrQhIpN2dDHl3ostCrqKkKf+H8Gm3DJk4lrg+PXTANTafvKxwFEhO/xYu7NW6OJAzZom6XXNs0UWUJW4O4CpirFcSxi6wEbI1cZqHNzrSESqbnlOx6BSv1iodXJ8y5Y7qWTFGlDAKFnWGPW2MVW4X5WkpUFHQdFS/OmG8k3LM+Znl5yP1rEbkUUpU6N44cNocWn72+ziuHFmppcflQ49nryzy1aaMIiY4TYOkFlppzpjdDEyV5UWIqglGk0vccklIiTLVjnpZKulbK46v73D0XYmk+K27GxsggKUzKUmCqOW07ZWsqIzC4PTQYeAbj0OLGQEOUBm07Jc0hyiwqUVJSoAiFQ19hGKgsuwULjRmX9m0eWZ3StVO6tSmrzYglt6QCorwgTySSxGZ3ZHH5wGRjopLmOpqs4Gg5C+6UWSw43/UYBTXqpqDpZORlSSVS9mcyF+amnOpEqJKCH1nsDJs8u71AlGuocoGoElQlpWn5aEqCpkYIKQISkiJnrjZloT7h7FwfXU/oBxpX+zUmoUqW60ipRs8taduCKDVQZMGXN9tcGZhkRUbX8dHUjE9sDXF0UITEcfnQcXi7pWa0rZKy0miaOW0nYaHhsVCf0XMSWnZKWUpoSk4BIMFKM0ZXS6QKTLXgruUxZ1enzLV8JCkny2R838TQJI5mOvujHk/e7PHZW/O8clDnYOoyCw0OPZVDXyVLBapaHmdIpiametyiNg7hwJO5NdR46aDB7WGNJNMxdMHdrS5r6ipLWgMFCcuCI9/g2c0WR75GkMEoktifaswimZ1DE6nKWWl6zLsRtlHQrOc8smyzPi/Ramc8cc2nKmGuPmOpMSMuCjYGOv2hiUgNLm1nPLnp88SVMYkckhbQsiuKUmOWKBQkjAOZqlSYcypUNWG+PkVTBJv9JmEGipEyXz+OPQjzAi/SaBnwyNo+826MJZuMZxW7QcztQcTf+vglnt6bcKJhcHMSUbNk9n0FL1a5fNimJKFtSKhyBJVEkQtmMwXdFDy0YvOL7zxDCwe1VOi4E061I+5b3qdnpXhJiR8ZbB/JVJmKJCWs11Xq+rGL5T2PzfG/vW6JSk8JAxfbCLBU0JWCnJwwsnl5t+STt0dIcoSlgWtmaHpI0xBEcoyjSUiV4Jefuck/+eQ2sizxN14/x1JbZpqUVJVJlMjoVkgWdghHJiNPUHdUrMrhuz5wBVOWeHjZ5V+96xS/9vwmv/TUHVKRstguyUVElBf0dANdqXjfu8/xK+8+zUpX5Y0nXT548w6vXZjjIIr4+lMu18cBllAZZyk///Z1dqYJH3ylzxd2Zvz60wdUVcU//uQOP/nYSRqWTENX8fOMf/zxXaRKcKZpkishbznp8tvPHvH4uk1BiWNKPHLW4O45i++6p81Pv2WV9zw2z8+/dZ2jMOW1KzX+5MqEKwcljy00+SsPrPLjH77NZ2/N+K77Wnz4ep/vONfj5UHAf/6uu/i5t6zz5vMuwyh51Xnxa5rqz4ev6kHRrpdTAZpUUXf6bM0ikHKqrMdb15c42+gyCKGoKro2tOyQwtzl6tBH0SpO96CKTXrdGVnmcKbpYEk2QR4yjCMUITMMJPbDMeN0ylJN8MiJkFJkdGoJDy1aPHi+wM9gGpk0VJuWPePyMCLMBUe+yn48JGVMx4KsyhlEAWsNmXsXJJKipGGoXGg7PLvd5lv+yjfz1O88weUD59jdQZ1wVjIJKnqWxHJzGy/LudJvUkkHpGXJ5sjgyiBAIOiHEcM4Ys5OKeRdwlRmPygopSFZoeFam8y5CbYxwDKmTCOJy0c5XmhxaxISpxq3ZylxrlBUMWUlceirlJVM36/jJQZyaSKh0PeanOtGzDsBF7sxW7OcojIoGNK1CpbqQ8axTlzITCMNR7U48ASOYrExdKhbQ+5MmwwTAVVJWbokBYwilao0uT3RuD2SqSqTObtgpXVEzSwoChUvkSmrgjhVWa2X7E1t/LTiZGfCzthm6FtcPWiz69UYx4JKREhSQsdOuG9hzOlOiK3KtG1BVamYmuBiO2KhlmKo0LFy2nbOQi3jbDdmGDhkuYkfOuweOahSiaqmzDUmWHqMoeb0nIKmCZpScfd8n4KCSWSiC5Nuo2JjIFMUCuPYIikkDDVDkNMwch5cPeSe5T5ztZy8FPzhi/NsDFxmocIkNCkpGMcSBSqzWCXOFO6MLK6NLcpKIEslVSnQZFh0U040Y7JC4+bQZXPi0LEyOtZx40peyKhCZuTbxLnK3sSkoiAtZOpmyDQ2SHKTUaDSH9sEacGNoxqWKqFIJUEmc3tQJ8phZ2oiVTJJLhFEMgv1gjhTuHSoE2Y6WaliajmDiUJZQhobaEJGrxT8JKdKBXImc3IxZrmRMAwMxukRX94bsT+TGAQyXizzix8f8c++dIWszKmbOXEus+vbyAJqWgYlDAsJIReEeQYio2HH1PSULDc40804u+JzfjHi37zrLN92sYOpV3zs9pAwMHCsiCiReXypyXvuPYUkp1zZ1/i1p/cY5B5XprssNqa0TInT3TF5Ca9dj+i5IcOgZGPQ4pUDnTAVSFWdSuQsN0okIZFVGeudmOtHBaNJnT+7UWMW6vzg+ftw0FjVbcJEQuQtohwc1SIrYwxFBXnCwytTgnzGnJuy66UkicLmECwjoWaG1K0RhiIxzmZcnMuJyxnPbi8h5IKntx26psowDpkMeuyMFV451Lh1MM+nbipsDesUhcLbVtdwDY37Fw3e+B+e5JOb+4yzKZI2IM10NmcBQvFomwWT1OPJD32UN//Ie8jISUvB42sSC/WU3728z8U5i5W2TJDAwrxgvaswLH0CPJByPr11yDvOdriR7/DousWnNyZs+R6/ffUKjtTk/nmX11wQPNSq8cS1CZ4I+d9ft4ZtCGy94EY/ZCvwMFTBmWadmqoy9mWCwOEL22NONFS2Zhk/9tBZnrvT5GDssDkqeGk/I04kXn9a4u6OQ8sp+dbzXZJY8MMPzLPW0NmevvqCpqzEq3p8DV9dCCLBMA64tG1wulPy71844G++folxFvPHt4a8drFNzwbHGhGVMwxV4cF2j8HQ5jvPr4Aes+pozNsKy50xszSloQv+9ZM7ZFXB7QMVAaw0SqxaRH8E09Cm58AkLrgzTVElndMtONMbUkkBjqYiVSq/e+mQFBiWEX/pT5/j+f0UW1OYt1T2PQlNyShzk1tHGl+8Y7Bck7EVKKsIWWhIGhyFCUVhkGcO25OKeTfFklU6tQlhprLklhyMC2Q01pspdWdCw5AoKkGY52jC4NZAZnNsUxYaXpayUi9pWSG6kkBlEVcZDVNgqgkLTZ/7l0IU1aemqijaDBmVOC3J4uOiiL1Qxo9qPHWnTpzaLNRHqLLGczs2dVMQZAVNs2AYligovLBrURUat/oScWqjyDF+4tC0Ah5a9qmqkvuW+pSlRJQnzGKHfiBwVJhzQ1wrxBAaX7zaRKBybeCyOVao6YJpIpEVObqkkJYVbSek40AlKoqy4FQnJy5kVhoTzrYDFu0cWSow1Ig8V1lsF9TMhGlYMV+LiVKbQWjy/J6Drfi07IIznQhLh6WmT6s2I8lz4kTw4NKM9zyySZTo3Bk5+GmFEBlZmeLFBa/suXzpVovntuc4nFokmUlNrxCVBpWBn9g0DdifNvEji7I00GUZQ5YochWETJZKRKnEfe0ufiJQlBjXnDGKYHvicH3UZs9zObcQsdYJMNWSpCjxYsEsqihKwTQxOfQcvFg91pCByvZII0wlJEkhzlQkqSLNZJJMMPA0+r7O3tTATxVkUeEoKR0nYr42o2UnlAJUo+BCx8X3GswCGUOL6Xsau1OHvm+hKQJKDUdLcJoeiIS0gHGoIUuCONXJCoOiVPBig7Yd0LQDkjKjKCEh44XREZthiO2kuHaKqZU8uDJlFNrUNBh7JqDxuhWXTithfS4gSmUGgcqdgU5JxvXJEXfGKWECyAVVqVAUJmmmIyqZ3ZEJagJmwBvPOvzqt5zlu++v41oF9y4afN89i5SlzvlWG4HGvSv7nO6OONkMONnKqCi51depULB1QE2wjBhbUxgEGhUZW8MaH7u8ys5EZRoXBHLCVjBlbs7nO84tcKHlYuky73vXWWq6RE1T2J9K+LHJ1b6DnwomkU3fV/mHbznBP3/naWqqwmc3Znxs84jnvSG2mbBSz1BESpJb7My+4iCUUhqGgqRHiMLkIPL4iT97gatDDyEE77jQpGlJtGopQopxFJnNCTQNlZqV8IvfeAJDkTia6LgabPVlpNwmKQtcu2TdqDPySxxh8z13zfH3Hj/Dp+9MebS7Rl3uomAxS0rCSZM3LHbwq5JTizJFrLM+V/DoScGirSNJ8A8/e4tP3pryE4+coG1q+Fl2XHmfOFSioB8W3JlEXB+HTKqUH3l0nm8+vchO6DMJSt7/3BFlJoEasNoRXDsM+bE/vc4gSznR0fi2u9oAdHWdSZzxxc0Z+0HKXQsO77qryWvXXS7vx8iYvGV1iZMtg/4Y/EBCEoLXLrTY8RJ2vBQvzvnEtQl7Qczf+pMN/uJ/vMGiZWBUCr/wljO8/WSHexcsfuYtK3zg5T7P7M94fi/gfe8+w7VhzNvOtXhgzuUbV+dfdV78mqb68+GrelB03xyokqDpRIjS5UxLQZEqtrwZf3Kzz+sWljEknXTkMPYFPTdntdbgwVUFu+aT6COirEKVZKIy4PJRiJ9luIpNf9qjpMDSJFpqD0d2UbUASUnQvhJCvRuOGCcJqpAJi4jbkwRD1ll3DdICglylLi+QpU2GgcLJhsx6S6aQAmQ54UQrJSpHTLMZRaGzlRm86yffjVIpCHnMF68qLHThKMq5Oa5Isg4fudrm85tNnrx9gU/dqCOjcaEjI6SUjl7HUGS8TGW15vLsbg1DqhOlNoeBzu5knaKMmLNsNDlhrZmxN3TZHKmEYYuX9mX8oMPHbyvIGMQMkITGlVHCJDT5/B0FW60Q0oyOJagbPq7moMoZ51sakBJmTdISbo4FRSmx6x3vzm+MdLLSYrleYBk+g6DDl3ea3BqVmApYWsyyG9G1NSpRst46pGsXDAONz9xqcWl3nrOdjLuXj4iKCFurONmqaFsZu7OKl3ds9qd1FmvgGjm3jjRGoYStJizZEZYikRUWaWFjaCWynNOyfeK8oKwykBIcPSTKCuIyox8IGpZPWiZkecEbzmxTNyLWujP6R3VsTZBnOppc8cWNNp+51eIokBjFKjuzJnIlocsFXj4lThMsTdB2UgahYBCouLrCSjOkbYdEmcnOpMbWRGMc2Sw2MoYe3DpycXTBXN3nbHfEoadTliAknRMtH01KkeWMthPQsGLyQqJCMOemtMycslJI8+PfbNGN6Vg5p7oRDStDBqoCOm7MSnPKQt1jEmnsTS0u73eYznS8mcLh2MFQK0aBydUDlyDNCTLr+PNXKZNQxdYhETnzdZ/5ekLDqJCoGIcVUaIjKTJRKhEWJfPdkLWmjSUrdNwCTS842R6z2hoxDI+zkd641qBtKfwfbzrFhVYd15ZxJIfYN7mx3+LIc9id1BkENaaxzZ++fJJ7uzZvPGXRcXLmahHjSOL2SGbPE8iiQlcq5vR5Fmo6v/7sAePUQ4iMli0YHnQpc4nTLYNTtRrBYB5LLdmfpDhGxnIrQ1I8uk5ETbU40ZmhKRG2DkGeEOcltrBpOhGrjYQst5Bkn7VGTI6Ha4Y8vB7yYw+d40fvu5uhp+GoBo+u1jB1sM2IuhliGQF5mTCJJXanAlfPsLSIeTehYaY4eoVdtigRXOyWOHqEJdrUrZD753X6M4PPXD2LVJp8/vo8DdHg7k6Th9qn0A2omRVd3eXQU1moWfRsmb1Dm//00oi8KPiXz2xy0rWRtRhXtajJHRQlpGvp1OQaR2HKzkbM+Xf9EMXiAlcPTESpE1ZHdFp7NM2S3SBiJ4z5O29awVYl9oOMmmawaDkcxSEX2i0+dmef77u4yi8+vcnfevMcb7mYchRFPL2T8MRVjy9vB1zeS/mWe+u4qsYzOwGqmpGV8Nb7U9ZqNUy15AfvXial5B2nm8RlwZtWOnzxloSS1/njK1OoYGdgEkUNVtoaKjq5csRTWwlpUXGqVsNUBD/04AK/813n+aMfvvA/m9K+hq/hv0MU63Q1mzefbUJl85vP7pJkFXc3Gjyy5HB5p+DOCFq6wXpDYb7u8diyi1rp/LsXtpGQmZubkJQF8zYs1lM0PaCQPVxDwdCP+bxmTznVhsWFjJ1pRVL6nG5XtAydqkqZJoK6oVAzMhpuH0cvaGsGMhmRFOBqGktuRVLF9NyIuq6wM1E48AtcxSCcmrxwx6Cmy0SZSVLECElgZQ5/9b5TFIWKKkyiPKeoBEtujmlOaDkZD56MQPEoSwMvstgYyRiyeuwkmpgsWi63NhUOxjWCyObSocG8I1FUGXUzxTXAS3N6NsR5QtMseHRthCynNA2VuIppOzI4MxxZJggrKCQWGgGjQMVPDE62J9y7GDBXn3K2lzCKJFRJA5FhqRIHM42q0nj2ToMv3ekyjlUu9U2KSmGUFlSVTlImJLlJkEJRSfiJybPbNRZqFbqeQCUY+zZZkaKqEraasVifkJcGlp7TcQM0SUUSJZe2TUZ+i9fNX0CpGry41+ZKv83lgUVJBmmJqUJa5hxNJRoW7E0b5CUMgmO3y55XpyYXaHKOrkac7ozoWBkXehFvOj/GNHIyUdKpTZlEgr2pg6mUqGjUdJleLWOhkZPmClUh6PsK+57GS3sOz+3ZfOx6i+2ZQpDlSEqCoQUkZc7tscIshSQrSVOV1Z5MJQqGvk5Ng6V6zoVeTNsqWXASVhs+jpZQVhnjSJAXBS07pm6DIiv4scbI1whSlVkkiPOKpNAAlaaZ8uDSiBNNHz9RefpO+7hgol/n9pHLlYM641hjEhtoCpxoB5zqzFjrzGhZcOjHmFZEIUeUomK9PWKx7uEYKXEmo8gSaWZQ0wQ1M+dkb0yQFiRZiSoXHHk6/ZmNqUDXLpl3Ss50Yk4teNwcHw8y5hohdTNmue5x31Kfbs1HlRNe2KtRs2LSLOfS0ZSOGwIQpDpUCqutFAmNstBp2CVtxSGKZYK0Ynsi84XbHSazVe5utLFklbpyrDurquKPXwwZDRxuHVZ8fPsq2xOJp25VuHpOVRroas5hCH4iyDODtU4IIiZLJUTmsHXoEKUVmtA49AySMmO9HdNxMnQ1YrkbYwiV65sGT1wKeXythhCCO+OYSRZhaCWqrIDIONEqqUTM0LeYDEwORzl//cM3Ue0pZzoaP/LgEl+30uH5TZMXd7rcHOtsj2uEuYQfq8wSlYWWyjeeWqZTz0nLkrJKuNz3+f0XBiRFiSILJmmJLllcG+iYio4gY+PI4NrAYxonLLdKdiZQSRptN8TUU1aMGuOsoN0AkUsYmsw/+uQd3rLa5fdfGfBD9y1xbb/kQm0eW9L5w9v7rC8FvPtsj2GU8MmtPq+bX2AclPyfT+2yMZnwN1+7wAev9vHzlKwssFE5GKpoSsrjJxT+zZd3aOoKZ2sONV3lbSfmaJYNXrfU5Afvn2dUJsiVwXQm8RvPHpLnsNJQ+Qt3zfGD//EGk7DgNesO/+JzO4R5wQ/c1+V1K3Wuj0L+2Re2+OaLTX7w/jmKquJPrw2ZJTk/cG+P77k4z9mOwcmmwQ8/MM/f/8gmf/8jW7z383u87WQTSRK8frXOH/zgRX76E7f50KUhIPjjS2Nct+LbL8zzU29c4zN3Jjy1MwMgyUve8/CrPyj6Gv58+KoeFB0lM6YxNM0CLylJiTBUWLQadJQ2//Azd3jl4LjZQkEhSgt2vCkbXoQm66y6BudPeBx6Cj3L4LEVjbNzIak05SgoGHkOL+7pfHE7Zc/LsRWdjp1wGJZossE9XZtvODcBKUcrGzQ1B1nxySofWej07DHb/ghTS0hyCdPaRpJ9lmsSJQlhEfHwksKcLdF2Z9hSB9F7nI/+y3+NnLrct5ozHGncu6iw6FZsT1RONjUADgOVhxZL7l7ap27mVKXCOImQKpOdkYKfgKGoPLOrMgnqzJKKvBSowiSrUmRRZ5bCmY6gphfkucWDKzGKDI7i8vxBHS9cQJYi5kyLPa+kZehYzhBNlpgkEV6qMYlz9n2dtJTp2cc5JKLKkaomSRFzoZ1xfu6Ic/M76HJFXiXYqo6oVM70jmibKqNIYOlTKlQ0qaKmJTQNgwcWJ2jqGEUuMRQVXc3Y8SQUoZDkGmEmkRQpbTvG0uTj377psdwcUXf847rZyuIgqKMpBZDR9+Hyoc4klDmYGdy3OKasZC4ftnjpoMOR5zKYudQ0iSRX2Z/U0RT40kYXQwI/l5ibm+AlJdNYRhYlpzsJsiShyApJJnFnpLE11ZgEJoJjW/rGnsr2UMcLa7y822R/prE31dkYuaS5RpyXHE5VvMigKARCKbD1Ej9RuGfeo6okhIhJSplJaHJnVMPUBK6ekpcpeSWhqhkrjQBJgKOn1PQCXa7wU5W0kAnT40izupWiKRlzjQlZUbExNI5t80JnsR7hGhmr81OWlwZcXPLxI4OsPM5D2Bk1aFkRipxTlSq2UWGqCa85NWR71ODAl7kzMbC1iqKSEUBJzMHQxFAFXiS4MvQorADDCXng7AHLzZi8MFlsJMzZGbcnIUFa8ivPbVBJBb/5Hef4ubeeJEvgYCCjqyEFCZtjjRe3bYIcjqKU5w+nWCrIksHZXkJNk1hwY0bxceDivd3jlohzLYsoVImrlIXOkHfeq/ATj59gGGb8wO9dJVMTyGusL0yxnRFVKXBUg4YRkuQSA7/Gy4cqihxyugMr7TEnehEdWyIlIi8EQVYxjnN6ts7z2xZr9jKPLLu8+1yP7z2/wMZRwp/dGbMTB2iSTCmPCCOXPa+gaSg8sr7P61dTLC3jXAcu7dZpKHPcNe/SNHT8LCFI6szShLyUiIqCIEnxPZ2PXlIYDlxuHwoebJ7iz25NeWUvJ5xY3BxGnJob4RoRd6Y+VIK9IMavQtp2zp7v0a152HqJl49x9ApTLbm0qyBHK2z+7i+zfeAT+/N0DYNxJCOVHYpokZKMrqrxD19/mnedazMdSXR1nbpdcMfz+UdvOs0P37POf721w8VGm8STuTAn2AkipErnG880aFgKcVnyE2/t8Ec3d5l68MB8jSgFV68IyhnXxz66FvHMvse9cw4NS+O1Cx3SouKxVYeDccX7X9knDAV6JTNMYm4NU/7B161Qpl3uWTB583qH33h+n2lQ8qMfukGYFrj6qx/5V3wldPHVOr6Gry78xYebTMOKz930WdIarDV1mpYCpcznNqecamuAQkFGy54h0PnNZw4YVQE7swxJSGiSwsnukK1ZgaMXLDoqrp3hSx5BURCGPeadkpXugGZjRtdUuTNo4ecpqlRiKho7U4XdIKZlQRi1sYyYsMhwNBVHk/irD65h6bBoyyBSmrUJhqJ/xf1aUNdV5hvVV1Y1JFwbvCJCd2Le/8pNkCLyAsrc5Cgo8TKZjiGT4VG3CpbrkFUJilCZhBa3hiq6pOGnOW9YnWd9qWKW5mz22+xNbLamCnVdJSdGVAp7A4e8ylmpC7w8oW5IuJbHWivmgZUxaVFSRi6DSUXDlOlPKhxVIScmSk1uDlVcveRka8RCLWUWwygweWarTlEYfMvpJc63G+xOJMYzFz9NMSSNYVSSZRZJAVmhADKGGtPUBZKIWaiVpLlEy/U5txwwimF36pJmKqYWogqNohI0TY8LvZCzcxMOJ4L5esn+TOXDty5TSj6acrwSrkoZ06iGnym07BmmkrHcOW5PVZWIvMzIM4tZVEOSS/yi4kTTx5AFmqzQsgrWW1PO9w7RlZQr/SafvHaCWeQgCYkCwVJnSs30cHQfTY1x9IQbgxq395tc2e4w8mqMPJeeA4as0LZAERp+7DCLHDbHLeLUxNQlTi+EnGyr7Ac5qgTj0CDNZTQlZa7mkRcZZxtHxHmOqWZYWokiKdQMibqeo8sFtpZTMyNGoURagSyVOHqFKglUuWKWKASpoBIl05HBnaFLkqsYSomt5Yx8g5d3W+xNHD5zc5HLBw7jUMHUMla7Hqd6Hmd6Ho+vTVhsxIxjGSFiLsyNaFkRS+2QpVbMcjNlZ9hAkmSiTCHJC2YJNG2PexYPaZkhipzhJTJpAZpS0rAKilzFjyz6nsPNozZZobLcSKlKjbJQoVSxrZRzPY+qqph3pxhqxKVdC1WN6DUCFtoe51YSbE1iyexgZcsEkwbTSGa5I1MIGUvYHE5zfv9yn2+86JImUMk+YaLj6BUnV/rISsgkUtg46rAzbnJ9oKMqKY4GmiQxncp83XqdU72SjYHBy5suVWYyV0swNB9Nljg1F1CQUbNKhFoyKSL+65UhRVnxXy4NkEqNsoqpmyXne2NMJUUWJncGGpYJv3ntOlZ7TJzDYezxnv98jSc3fBxVZRTC4aTNtb7G1lGLUVxyd1dnlORcme7RqB/xyKrPLCnw44Kcgp/9zDW+9/wC9aJOFDqoco6tVux5grZVsZG9xMn5hFEoqFsFJ3shk7gk9GtcGQYcxhGikBhGOb/6zDaHQcTvXL3Gm044TJOUriv4y48s8dBpmHcFezOFX3l2h7oleOPiCr/9fJ8ffX2PD700Io5MPnrN563rLZjWaWo6z96wcGohHVdia5ry1rN1PvDckL/4YA+AT94cM4pT/tk7TvDEnV16qsosylhZmPLTb16mV5eZ+AX/70/u8NrTNnVTpuOouKrG99/X4/ObHqt1gyyCrUlMXkKQFfzSUxucbzhkZUmeyXzvBy/zwVeO+GuvWeSF3YBpWHL/isVffmien/6GVf7xO1dRFfjVp3f50i2fE02DH3/dPFtewJtONPiJx1dI8pIvbnj8lYcXKcqKH3p4jh/6kxdfdV78mqb68+GrelB0z5zGWisiLb9CIOhMA8GnbxU8ebNAiJJ3nmrTdAVpUZHmJicbNVYdg81BxY4XIEk5PSdhks5QjR0MJadrmKSpxdW+xSxos6B1kHITP8soK42OKaGoI144iKjpKaZc8OTVik++YhLFbbZGTa4PFcK0yZzZgkqQ5iotrcdDyz49d8qJusq8U9GPEnT52D0gKQdUlHTvfoAP/7v/hG6lWHaIVx3StlPivMAr+/QsWO/s0XT3WXFLjuIZRQUj3+QonhJENd73mTnujFQOJzWuDGMOpi1GyQRHj0nyjCAPmHdiYjGkaZf4xRhdESCmmGoFZEwSkygzONVKeHApYsHNSVKbNDeYRTJhqjPnHgEKinRElOlcG8IsdWmaCRImJTqG5rPgyKiqx9ZYZxZpbI1kLNmiYZQgInShMQhk8jKkYyk09Iqn90yaZsFaM8ZUAww1Q8MiSDX2vJJpUtH3W6jCZL0zxtI9zvc84twkzSx2xw47nsWlvsGeJwgymbzUsBUJCQ1NVglSkzDVGQY2hlxQUPJ1p/apGwmHnoprpSw1YmoG5NEcP/7Avay6dfzIIstqbPRt6maAa/gYcoajZaS5jSFDEmmMItC1HFMc3+Fab3v8zce//JUWEYXVxohRoNCf1ul7FlVVYYoWZaLTdWcoUo6tChy9YnOio0gVfiJTVRKzyCHKVOacGFPN0NWAYSgxDhXCTAJRMYpUxpGCrWU0rYSiKggTQdMJiNLjoMXFRsbh1GQWGbTNlJoZEmXSV95LQpIEHcfnpf0ammgyCkz2phAmxzbyaQw3jixkSdA2M0Qlsz9T2RlrzLkFFAoNsySvUqYTFVkqma9LaLJMEJkEqUSYFRx6MpIcc3OrJA8l8kjjrFXnH/zpFv/007tYTsm9Jz2S3CTOTGp6ySTVCBODotAZTiSyImG1NaBhBSw1QsaBy3q9oGUl/Munb/EX/+AyszKg10lxdRVNqbg0HqDpBR+/PaLTS6jZEVVZkBcKSaagKCFVpbE96RJkKbO4omMplCKFCgbTHjsjhVkgk+YK/SglSV12Jzr7YxtR1vn49ZCf/cRtPr895hMbR5zqaBRygoFOTsZwukhOSUNvEBdQUx2GcUFRWcySAE0RPL3n8YnNIdMkZ2tsk+QpV/sGfqyxNckQlcPWJGW9pZEqIboG733qBoo54xvvTRh5JVrgkocO47jge86uEZKjKNDTLVx7wGtPhVDoCHnEsqugyjktd5//9eElvl5s8B1veAy7eZLxTOaFw4Qkk8mKhGf3E5JCxtFVvvlsh4/fHNOoV8w7Km9ZXeSM06NtGsw7Bg9Zp/jlz+7z3fd0+ev/dZ+jcRNDqfhL956kH8V85/l5Hpx3+fxun/XFiqP4OAMprUr2+gvcO2dS5Bb3dB1e2gl54sqIv/boAvcvOPzWu+/ix1+3wJqro1Yylh2zpBn85KOr/P7VXa71S+7MfD55e8KW5+PHBS8fhHz8zoAf+9jl/8mM9jV8Df89ZnHBcicny3L+6IUZfb/it587JCGmUjxenBxRJhZDv8koVPmz6ya5HNJVTFomSIrH9iwlTmqc7UCSwzQNQQi+81yPopDZnckMAotTzYyL80c8cGKDnl0RRC0GIez5GW1TYup1SIqcga8glxamnmK5R7SclD+8fQ1djZHVhKHX4umNFjcGJW1LpmXJZGpElitQqjTdIyQl41tPLeLnBZYzo2UqBDHsT1SCsM5TmyZpCRR19v2COTemZ5fIanCcUyOVeFnGcrPio9uXMLSEti2QNA9TUnl4uc9CY4atqlzrK4SxzRdvNTnwDPYmTZ7actCEjZAmrNVLTs8N0KsKzcrJpQJTk3n5jsOJVkanNqGpK8hySENX8LIETdLYnYJc6byyq/Dhl4bc2Cu4y3XxU3hhex4vkdga2zStnJ2pgiJpjOOKvUmL60cWUa4iKom7Fw6OXSbzI77l7i0W3YxPXG9x6WCOSXR8E8lQjjNXMhLuWw1QFMFqe8SCm2CqCVmRUJQyZWkgREkiBHmhc24uRVFyDMNHqwQ7ww6GXoAUsjXW2Zm4bE5cDnyTthUQZAVdO8bWZDpmhYzA0CKevNzipV2bK/suN3ab5KmFrcnHDickWhYYZoZXVDRrM1Q1JC8TsjJFkmNkKeLGUOdjl7tcOzC5NTCZRgJZmzHI7nCUHJFVGWFa8NyuzZc2W3z0+jyHocMkd1mul7RtqBsFaaZy5OmMA42iLDnTHdOyQ/ZGBvsjF1EpWFrBJDI4CkzKSjCNVUw1wW0F5JHGwcDkaGAxmelUQtC2U1p2wmI9YKU141RnSl4l1OwpdTOlbqVE2fFnfWhpxFozQBIpk6RkHBznRo08kypos6S3iUKdg0GbKLQJIoNJZOBnCi/tOuRFCSKlIuPOkUJcZhxOZEoySpHy0r7FjSOTQ09ic6Qi5JS5mk9aCFQFbE1iuZHz0JpPlhkYKsxCjWG2T5jlnG2bXOkHTNOCURDw9OEhshBEIuD9L+/xX67t8cWjAzrdlKOBweZAZa9v8spGjxe3FhmGEjszGVWuWHAFlhYzCAQVMr1uysbMIy8FjqYQlxUbI40Xby/y/NY8mhLSMgtcI2OaSDy4WEOWS54dTPnxP7nOkxseCBgHFtO4oqwMFCUhzhTatZxmw2e5AdsTmaYh0XSnKErBP/j6NZZtgyiTSIvjdmJZSnnNiQn9ZMpce0jL6dNzSuJc4i/fd5aPb0z5recO+K6zS/zzT/S5b8lmVkY4qsaTty2k0mG+PaIqdNJMopBy2nbFKMzRhE5a5BS5QtuomHpw34kYozZGcxI6tsa/f/GQX3nqDg3V5Mm9IU9uxcxiwXec67HsGBwFUFMV/sGb1zjVsvjhR9sYWkXX1Hj9ShPDqri+paDqFfN1lSAxePPyEmEgeHTV4a55h4NZwr/43DZbYcTvvtBn47BEsWb8na9f5Ix5grna8QbGW062ODOnc9+8zTf85st86MYBP/eOVfpxjJdk/OaX99mLQ+4EAWuuybWjgHs7Nodezt978wofvdPHMQR+WnJ7HLMxSnjPa+Y4nOXsjXL+xh/e4vdfGvDQss3Hbo/4zz94gQeXbf7BR7cYi4D/7dE1AP7l53b4oQd7bE8Svv/919mYRvzwvSv/8wj0awC+ylvPYgZIxTxabpPlFVcOLEy15MEFiSyX6YcJ7zjd5onLMxYswcZIxTY9vKSkZ9WwRBNFzFCUgjM1Ha2c49KRDFJKKQSWqvL8VZmYgoWGyWIrY3eSU5QZi67Lg4uCOyOH5VbEo6dqHEwlPn+jzvlFn9VmTMve4VJ/jrNtCdcasReG3F8TJHnFrDhEV2qcbkFa7ZEXDlRtkjLhsW96jOSDX+b2vslDp3002SSvUtYcQce2keUZhlwnTRMkM+JUvcX2VKZhKHhBDy+QuWdBpj+RMRSFI1S28ooH5RNYyj6nWhKS5DGKBKWoeHYzZb6mExYHrLoKN8djeo6Oru4xDpbY8wMs1cDQpzSrFptDiZbZZXs6QOIUYRHy0FIDIWI6jmCSmAgiTLXOzqzCUjrIUsrTm03aloquHhHmTXZnkFcZpmxzGICmjslKi6o6zn16ZCFm3zewtD5INVQsojxHyB7DQGaxJlMzc6IsoyClihpMwhppLqFpIVujBnvTOr16wGq9QJEUulbBsxsWNaMkrSr6QUVdz7C0jM2xTcMU5FXJqdYYcNicmOSFxN7EYD+N+Xsf2eWXvvEuPn/tJj/26CK//OVttkZtTnZm+CnkRU6WF1y647LeUjjTXOC5/hRhZDjmjChX+aNb56lpMAwUbo8cWqbMejvmzkjQn5r05iTm2hGGGqEpglsDlzBymWWCWaJhaDlHnsU4spBFxnxNUJYK87UpDSMhKwzGUUXXjlEVg52JiTLKmIQSqpyQFzphomFpFVmh0jILDLViFip84to8O2MZQ5FpWhXz7gTXTAhTGVORWK5pTCKdqoKaWRDEBSPfpCwURiLl87fr1DSdsoLUVxCNAEmrWGrIHHka7U7OG5bnuKvj8L4XbnJ24dhObasKilMwDdpc6FmkckqYyfzRtQGzJOcb2i2WOhm6nlEUxw1q5leyocZeznCsYisKriHTtgrCNGLP03E0wa2hxZHnUNclojzilSOfcSWYc6FpqiR5wd/95E0sVdCtQeDViOMcoYQ8OB8hqLi0b2PIEmFqcbVf8eiJBFG1GCYFUHJ2ISJjxrILpci4tm9T0xyGQUFZQJjAx69PeHbXJ1Jn/NtLCX5cMdeQGPkOTSOiKAVxWpGUgrhI+PStNm07o+81mPgKQaDz828/yS89c42eLegHBue6Ci9sq7RMjUQJectdCeOwIMoEql0xLiJW6jp1e8Q7X+Oxu9ti1J/nb72jxeW9AllMcVSFUZFgKA6GkjFJSs60dJBShKhIo1N8/ek53rcTMrp/iccWGwyMilsTGdcYE6Yad/Ukrh9KCHnMX/voSwxmJTNlCJrN04djvrhZ8ne/8CneuLzAx24KZknFbhzgGhLTKGfR6vG57SHfebHDX7xnjfd86CpzpsNffWCdn/zTTR480yeKukyinH48Ynvc5cMvTTCETNfS+MCzI24MIn7mrSs8cfuIPS+j2yiQFZlbO/DIao1/+tQdVl0dU1cJswI/hrqmcr5n8gdXdrk1jl51jno1AxO/WoIXv4b/D/7dZz2aqzIPn0nY2jf43vu7vGG9wb/90j7n1jTm6jG2ccBhoBD5NS52ZG4fWYRhjlIWrLQt1jtTmnaAF6vUZYmmKtibFnz5VsGji3U2plNuDQQnOjFnWzKvHLa53jdICom2aXG6M2Z7erxylKU1/Ehw61BhtV2iVTbLrSMGfp2WKbM1ljnwoKFbrDTC4yG37GIZEkMvYxIZrLU73B5AGHicbLTYHQ+x9JzllsIsixknMnXd4tM3ZHqOhChN9mceq80MKomJ8JAwkaSYaZxwsgn15U0mux2iQsfQp3iJyyiScM2cpioxmFa4pcv13RzL0jC0gJ2pxkm5TiwFx82Q8h1ubq8ySFN6jkJaxlw7aHDX0iFrrRlpIeNqMxQ6ZGVFGJlMcglTK/Gqgr/zunkGccivPTumP61xecfmwdWE3UnC6U7GIAyIE5UwUzjVHrE17pCrAXszl4aVYWsxRWFg6SENQ8OLNWzVZ7VeojsFy+qMW8MWUhUziir82GCsKMRFTscuUGWZuMx44cDEUR2iVOXGQKVpBUSJTSlVbAwVTL2i48jIQsJQA3QlpmPnZIXE6VZGVmoc+gpJUbLYmLI1rnF2zcfSSpp2hK5k1I0YXSrZm8nMUoM5U6MSOlpRkGcWpppxMKsR5wkdJ2KpGdI0Q0SlcTBqoIoSVYrxEpNePUVXCxoG2LogLRRkIejaIVmpcr1fJyslwlQhywWVqOhPFYLIYLHnYWgqmixx14pPEDQp0zluHXic6UKcavixjKrmZKWgW4s4HLtoSklaSZS5RJWUUCrEOTTMBFOCvi8RpDJDX2WultAyUwylZBjqxDmYaoafKPTcmJt9jVmoEiUKLX3CC9s2d1sGb7+4RlDEPHGz4IauEmUl5+Z8skJmb2qhUNFxAtJcpmVHGJpEllrEqaAsoaaYnF8zuTr0kSWJWawTZzJHQUmvFqIoAi8zqcUCxyioSpOWIfGF3QMuzDV58SDgcCZwc8GDPZfdWcL2LCMGsrSibdg8uKxxy5/Q7U25vutwohazXC+YxAp5FaBIJbvjNpNQZ7E1pBrZZGrAVl+iLBU6hs7hsCByEt58YspKa8bnb7aY+i1+4rFlPnh1n5YLdzUbPNefYrkVkzjjTKtgpeUTZCVrusSNI1AlGVOPMFSFh9ciwuRYb+Wqx+9d3uXqLGDJtXhlP6NjKvSDnLZjcm7xkFnQYpLACbPioxsWw9mAv/26VZq6ynJd5533BFycU+mXBdMk5eETMUGskuQyrqpzY0tBt02ibIgkDNISwlzQcStcd8JjTZWKkv5QY70O33nmBP9u1mdON/g/3rbG//ncJqOwAiRe6gfsHZb8zJvXuNCzePdvXaZpKtRqoKsyP/vmNf7Ti33ecabJJ7URng/fd2GJf/WlbW6OfO6aM/jPz0+YxQVf3pnxC28/SVIU/PQnNogo6FDniSs+7zrbxZEV7qu32JiF/Oa3n+MoyHj6YMq3nu+yO0156k4IlcmYMboj81OPn+RffOqQfpTgbCqc6Gp88Nohh0nM33j8DH/wypDvuruLEIJJlNFrCT63OaHjKhh6xj/+9BZvWG5iajLvf6HPmITTRgtDlnl2x+PP7kzouCof3RjwM9+wzNmWzbzSe9V58dXSVV8tmuqrelBkiQ41M+HAh6gQWDIkWcUr+xmLrRBL0/iPlzf5unumhHnF/sTEUZtImcTWLGeUZTxkCwyl5IWDjPuWphjqApZscunIoOVm3LWukMUWsRKwO1OZdyRUOWOhucvNgYNOnf1pyQ1vCqWNkRvcHntMU8GJVo9HliJGkcKJpk7LMrE0n5pWMQqbDMICL1FoW20se8KRD6bUZBQmrHz9G7n0e/+Ry8a3c9eaT02TScoxQpKQgUk6xpTqTGOfG8OMuqzhFVNKucGPPLLMh+/cpFuTuX2QoUoydSfg0KvoOg0iecacbTOJC3qWzOluiiJkbKVGkGYs2DWEmOAl8yjqBCN3+dK2xIVuyVEww1Qb3DgSXFAdNsocRRhcP4po2SOWHcHlWcSibWJoI8bBAhOhsTetYcoOXuJjaCanOgGmNkEWFpIElhyxN+uQ5DmqMqIsXSQVVCVm37dQxXFoXl7InO+U2FrJ1b6BLAcITKLExNYqDj2ZthKyfdikZRW0rQohJEw1oygr8irDMn2K1GJpLiFKTMpCJc8rFuspdaOCUqdjp5TNDFOV8BOJaDWkKArSQvBvnr2Nqpa8/8ouC90JWSkoidHkgqv9Orqq0G0VpFLME5cLDFXgNktco0KqZO5dOOTGwKHvuShCwjF8WmZGhYkkEhxjTFPJsbWSNI8YRwb7M4ckUTkKJeK44q4TAUt1nyCR6fsuQlQkRZ392XG4u5BirvR1vEhFkXIOZzpNu+B0Z0bTgud2jnf1ZVEihODc3JiKko1BnUl03LpCobPbd9mQQmxDIotMnvICvMTi0RM5PTvnZiIf361DxaxlZIVCUeUMfJluJyXOdNpWDAI0LWAwrvGFvT770YS7uxZBZjIOI873Jjy902BnDCe7hyzoTe7MJlSBiVQIvrw3o9EIWbUDylLjrvk+X9hYYmugsT+DlpGimClhKhiFNqpccrqdMYtk9j2FcZhzuO/SrpcITcULVVZbY4SQkITCPYsRN440ZpGCqpS02jmQUdN9tkbzGIpglCT4Y5fvuK/PMBLc3mkSJAqjyueiJjB1CyEGFHmTulVw5JV4scmOF9OSbGYBvPvBDn0SnrkhmKtpDGc5q3WTlw8j7l/UuLB0gzSd4yjO6NgSH3mly+mGzPZA5U3rDn/8yoh5OycuMtYbMv0goqaZzJKERSsjLRUuH8os1TRe2Mk4v6BROCOSMqGhSswv3SDPWhiY/OvnX6ZrSRyGIYsLglkMunHEcr2ObQ748maTutrgk1cEoyd+iU+NTWbdN/LKwZS5msrNaUJPUjnfKzn0E2qmzImmxObUAx0W5TqOEeGngnfeW9K0TD59J+DkSbCEyrUjwWBXpukq3NMz+a2Xb5AWsOt5x2syTsprl1r84MMDro4bHPkKi80cZit01Iov3Qp577eu85+uHvLUnRnPH4T8nbd1cWsDzpQ1xqlgGEmcWCv5s60jVl2DWZYSiwgZHV2BnUnGMAtYbGesGhavvMocVVTHx6t1rq/hqwurDY2/+4azPL7q8q7ff4F/9/w+wzTkwnpMveZRycehsic0mZv9nEvbdeRSMNcu6E8USsknyiWq0mIaCqIwJzRL1lsKd448njmsyMuKMx2T6/sLTCOfKJXo1QQ3+xJPb2u88xGZlm5w/fC4BbPtVMf5K0GBqYMp29yzOCRIBUfeIv/rAyv808/1AYOGUbLtCfJMYrldsDWOubZpstKV+ba7m/zelT1sxUKWPLykYr1RIWljBp7JxZrEvpfjaAV55nDozejYFXUdRnGAJlnMuwGmntLUdKZSTNeUMbSCowA0oVGIiOWWy3QoE8YlhlKnUifoism5xV0UDJpGga7MmMRNxq0RslcnKELsyiEXAxxV4fYkY7Uu+NKew7xToCghdy1JXDpQqWk6ijLhN16+jZ9H3L9WEmUz5KLDUSjx2lM+hlIyiWUcRaKtjRhHdVzLQ5MqskInziOyvMatgUx/ZpMXGrYxYBzY6BScmh9gyRIdO2V7IjNfU2lYKVcOLWZBnV2twjY95msyS/WctIC5+pBZ0kWSZOzmiElSoih1bvfnubML9eaUJCs426sQaDy/59AwI9aaHreO6ghJsN4MkYSELKe4ZkCvduz+UBWFooS2DVGag5gRZQKhK6RFhq5FHHk2FdC0coahiq7kJKmEoVYohUKVSTRaCbZeYCr5sT4rFebrJV07pGVleLFMXlbYakleyQwjmbbt0R83UeWcolA5mNZpWhkD3yTNSxRpynwjQUgqs0imaRes1wMapoShgiSNSTIFWQJbKxBAkMpYWk7bSVmoZxhqwaX9JrJQaNkpFXDoG1DBfuCgyAVhJrHkRjiLHs9tNfFiQSxyzHrAsmvwu1evU6YmcWDxzY+c46c+dYtu3adpBQxDhf2pwyxUWGjGCKlCkRI0vcLSMkw15+mNHrd2JXq9hP1xj+lEMIor1ucDkuw4HHm+VjGLTe5ZGrA50imlkHGgcXZuyF1KztaRQxErfGZjhmZkIDS6liApVb7tQod3nmvzw//lCoFXcddKwJUDE6QcR5do6ApVFaCpPsvNEi+y8GPBRl9DU1S+8WKd99y/zD958jKzckbXnSALhUfXx7y4X/JfbibsBfCtpxZ57tBjmsbYqkJHlXDNCWVp0LIOmIQqsiwzCCsahkIlRbSMis2hQ9fJadoZV26H2ImOiCXeumLx8ZtDFjsqwyhBkRrY5hRdmHz4JYe6pvPjD69iaxKGejwIeGZrwnOTCW074+zclKoweMkDJ29w/+IS44MNLvsF090W3VoBckzLkEiqmP7URQOGaUnTUHj5psyd3UP+zbed5FTL4ks7E/7s9uR4G6UmcWkvpN6WGMcZP/ChV7DrJaKQ+Uv3L/MfXjzk5jDiMMz44s4EUUA/i/iFJ+9QN0veeWqFDzx/xLlWjTAt+a3vPMf7X+jzgZcPCJWYeUMjjAoujT3emDf5w5fHPLpc479+dMyvfXmfH3hgDlUp+fmP79AxVPI0Z5YlrFoS54wmv/3cET/1liVuDxI+cHmfvKr45XecY2eW8BvP7PO/v2EFIQQfvNxnZ5rwyn7Mb37HWc50DH7yY9fIEnji5oh3XmyiK4K6otLUjsPbXUPhtet1FlyZw6Ti4pzFz3z0Dn/5fudV58VXS1d9tWiqr+rVs13Px89kFh2NidciLSvyQuPueYM11yLNC14eTNj1Q0wFTD3k9jRg46hkualwvqNyMDaR5ZILbYssXiTJS8IywNYLpEpmWg25dBCgSlDXcz5+TeP6QCJNu9zVhUrkqLLOfXM25xdiFD3GVep0TJ2NiSArZWxVoRQjhDQkyzUsY4hf9qnpsDsr+cStkoOpy52xxsevg6vLx/Wm3ibDSzd4abtGUvm4qsvtocBPKy62ZXb9hLI0WXIzoiLhRF3n9lHJf7x8QK+WMcs91noVz22WTDwLU/MR8pSsEPjFEFGpvHCYYqqCQkypKoGhJNT0kuVaRcso6JoaLavgnecmJIXggcUpS80ZNTsgSCVe2D7OC9LkkKZmchBIPNCVaBgRXixjawXjJEBXZCqRoUsKflhnc5ojSo20qDgMcgZeA4GMqXjYioIsCooywlIVupaMqfkUVciFuYhFp+B0bY+lxhBLFdR1iWmkEmQxXgp5afL6EyEPr4z4+tO73L2wR9P0AQjjGkVmg9CIUgPXzIkziIuMYQimGlKIiI2pxs5MsFCboisBj6xts9QcU+Djl1Pm2wNGUcGR52DIKmWlMwwcJqFMXpTMNye0awFzvYzF+Yium1OQEpU5g8DGz3RaTsaJzpT19pBBYLBQz1moewRZRk3LMVWZl3Zdbg9UNK3i1OKMlXbE998/T39scFf9LM/cbjIONCwtI81k/NikqmSKUidKTTRZwjUqTjZTBMdOIS8+tlDX9IJZIhFmEnkFL+/ZbB86rLVyum6GZcakoiItFFRRMItUqlTm3MIEWRS0rBhVrrh/sUKqBDtjQZSaTD2HwdhGLjrsz1SEECy6Gcv1DNeq2D9S+ct3n2OYBLScgDCDzUmdvNA53ZRwiyUcGtzdXGS1qfKeRxvcu2Tj6hI3Drp0LKipGm0bTvdiLi6kvHm9haaUFCLj0JfYGCncHhqAQdOAotDJpQIvrggTWGum2KpGlleM44RKFJztlZxf8FnuHdCseSy7MkLoRHlO3UxpqAaByLhxVGd/3GVnVqFZEWc6JZo242IvYhY3yauUppHj5zFJVnB+vqDZHnFqRfCloxvklcfa8hjZCtCNkvc8tERbsnCsEUm6wDTJceUml3ZtTrdU/CqlVYMv7A3ZjGY0mgesNGMkdYSQIEhT7lo9QFQyceVxoSfY6pecaOusz03o2DlN3eDWUOX2/jmuH8X85CeucKEn4VgRK67O3rhG28k533KR5BQJh47R4Pkti4XK4/f/5CMo597IQ3flrLU0fuwNi/z77z3DyXaBbh6yUM84Pxcxyo7o1qYoQubWxCOXh5zqRiBP2Q9nSJWCXCpM8whDkjkzZ6Cr8PHtXe5fVLh/weTyICGTCpykydPbMz61vcsnrmqYSklWlDy7kTEsIpZ6EtdGAWFW8Oi6y0PrFr93ZYMgFdyZlGSZzCPzNQZhzDesd9icRrQtiZZuQgVxXrHUVrm4aFL4LqHk/0/ls6/hfzze9773ce+99+K6Lq7r8vjjj/PEE0/8t+d/6Id+CCHEf3e85jWv+b897x/8wR9w8eJFdF3n4sWLfOhDH/p/dH2pEXLg5Xxx02PVNpnvjPnCwS7DCCh1zrQTgjxHlyPm6xUdp2TmFwzihF4rp4hdbh5aPLcnUYkCUzYYzRwu72tERUHPkXhwdYque6SlTJ45RHnJnh9ja3DXWsEXr7U5CirqpsBSBDeGJbaZsGgZ1M2UwwBEpVOUKpI64VNbB5xzHcK0Ajnj1EKOaoaM44SVuspPvHmefpzy5VsRpArTJKcqTWy1YGta0DNl2lbCLMs504GmFaIoKWVhM00K+r6JXOqERYCMgSZn6GrM6VaGa42p6yWmWhIVJfuDNqtGj8fWa7z/u8/hOhqiMNB1n+GsjSL5zGKHqwOLOJPRJAhDQeU7JFVGHNS5MVJYrkkMowKpdLg+lOnZCoWIuNDLsQwfGYejsMRWVIoqo6YL5uoB44HB5qBJ3xdUucnzezppatG0Akw1x9UhrXJkDAwlRaICVEZRxsBz0VVBWWXcGbTpBzrP7xm4ukLDTDnyBB27wtJLkjJm5LkczVz6vowiR9iyxutWBzi6R80I6ZoyNb2kFDG64zGe1bkzauKn8lduUEUcTLt89uY6o9BkGgtKEhbrE5pmQZQ6+LHNopvRsz16zoxRWNIwQww1xVKhY5UsNCoMWWW9nWJrAi826Tkxqw2fdn3G/jRns59SyBlXDmz8uGB3JnEYqPQDqKqEkpw4z4mLihV3Rj/Q8BMdKo1J6HB6IaLTLNEkUGXBxoHLaGoQJxJn63X8yGZjeJzreeRZPLPtMo4UunbCibbPnBujCKgQGGqBoZToyvHAahKpHPgmlp6zPTJ4Ycfl87faXD10eHG3ydObLS7v14kzlVFkoMiCk92I5UaKqAwkIbg2DpCBmhOwtjzgF597hbyq8GIZIQqSrEIWBYaeUjMSmlbM+bmQCz2fU62IJDnOdGw0M07UOzy+2OJ8t4Fl5NzYc3hl32IaF2hKQFkWXN5rkmY2kmzQa2U8vw1eluM6Pj/ymi7fdLrHXW6LXtfj4bM7NI0QUQp0ReLn3nySpDAIvBYmGn5sEeYZspShSTprzQxTnXEwNpEqldW6jSvpPDbf5G987Co3J2McVSBQqJsTZCExDgz2ZyDnBv/+qSH7XsrbTnYwTB9Hz49XzyLozzpIcowqCVaaATUjwFJlShHTscE2ArzQJJEy6q2EVIn53MaMMx2bqlIxFJVJLHFta57b+z3+4evPcaYnM4hC/s2z1xECnt/zGeUxEjJ9v2A4bbI1FVRZndHIZM9LkdScWi3FkBQOpxVtUyIpM7JCI56YbI0VbEXDS0v+7luX+cV3neBUy+Jy3+fnP38Ty4pZaRcMvAKlUpEKwW++vIFtZJBL7GURv/riBn6ZcGPs8+Joj1MtlQXTZLGmoKoZN/sVv/7cHr/4zSd508k6L+0H/OxHNvjtS1uklCzZBvtHEqOR4IFGi69bbXC5H/ALn92i14azbYu3//olXtpNyPKSN59u0NJVvvOB49a+rcOCu5s17kwDfv/aPsIIeO83nUISgtW6wT96ywmWXB0vyfndlw/4648t88QP382ptsGdccyLgwmL8xU/97Y1ejWVbzzbZjyp+PtvO14tO902uHoQ0dR0usLhj64MaRoyf/2jV/8fcd/X8Orhq9pRtODUiDOZWRLRDxUamk3oS7yyn7K8FNOyBS3T4O6lCXFRMI7qnGvrHMgSh2FMIYfIlYMiKvzqkHFokmdtNGESFRXeTOFEt87Z+3PSKqXpJLx+Xcc04PoATnZKJFSe3VIpiLl3QeAYKVtegmkm1KIaz4UeJ9s+HcPlel/n8fVDDqZLzFsZJQELNQcVk8/cMJhzCr717hmVlLIubOZ/9C/xqQ9+gpOP3I8tW9hGSNM0WW30WaolPDDXZhgXOJoOtmCcZLxm3abvhwzGDuttn7yMeNM5m7yK6B+ucO/CVUy1Yhy0eX7X4EQDvryd8eCCwTAeocl1DP0AWWg0jD5F0aSf57RMjQcXx9wednhpv8ZaHfbDgJZRR1K3iQuNqJix4pbYWkWVV9Q0C6HM6Fku+7OEU50RquQAEeOoRlb57E5jLrQlxlGJq8csuQqSnNMPI+ZsHUeVUByPMM8J4jkqIiw14uXRHDIaSS6Iy4xOLeVUK2YSqNTSgtAMSSvYm5mMAo3NkUSQmCRFyTgpOdmekpYpt4cqPbuibuZ4iUaaG2iSwTix0KWCQaBhqhLT2EGRFFShIKswi1VcMyMpY2wzQRIRo/D4Oy8Km14tZ29a0qyleHFGlWioioEoBV++4xIXKVmhUDdUqkoAFVEqYWoSS/UYVdaYxjJ5olIqFXEGGwOTqoI/uXFEr5Xyb5/fo+FWtOyCphXQtGDg1TiYWay0ZhhKwTRUQVRcH9S4OO9hKCWDSOFkx+PSgcv1PZuRmyJhc7I7pG0fi34qwTgGU4/oGRKuUXJ7kNOyY+acCkcruHZk8vSNBhfaLnpV0Z/ktOZjhmOL8w2H77u3wce3d2nZPnO1mGe2XWSlYnk+5D9cuU6UVZiKiquHeKnB/kzm8FAmTgPe921z/NjHX6KuCf7z9Snvfcvd/Itn7xDEEgUpsyxlrTlEokaQ2+zNYkxkWqYKlUScFSw1xniJTVGWnOx5OJrEYj1ka1MmK3NkqQI0XCPnyr7CYydmOKpETomXTtmctKjpNXq1mJ1ZxWO9U0xnIRUDhJRz91pMUHjM1VTKwmbJ3cNUXYZRQVimLNeafGRL4rRsEXoaNSdivZciKRPWLYtWrc8rN87wMx/b4HV3jVlwFKJMwtU1Nocyaa6B6rHgljx7R2WplfMD98zz6y9FvP3iFtNE0DYEaxf6bPbnmcQFrtZhJhV8xwMue56PrkJN15mlEY5S4zDLWF6JsWSFURrhKiZ+WBDk2fHAUxxiKiYH0xq3xzl3LVUc9XPO/qUfpd6pmHNk3ni+zUc2Bjy6Jjg9N6BlahwFBVkJa26dI0+lYUestAQN08E2JgSpix+4bOzLLC3MSOImWqHyiu/hGoJ6qTBKhoSZxHrLZOI5XNmJ+TufvMnDq/M01jJGScI7TiyzrCfsRTGb2wX3vrHGt9/V5fvef5VbgccBIbbk0DFUXFPmXMvluTsJv/i5PVYbGtOkoCSjrivIcsnlvZidLEeOdJab9qvOUWUlKF8le/NXS5Xr/0gsLy/zC7/wC5w+fRqA3/md3+Hd7343zz//PHfddRcA73jHO/it3/qt//YaTdP+f57zySef5Hu+53v4uZ/7Ob7t276ND33oQ3z3d383n//853nsscf+XNfXNXV+5ZltCiWhmlksLNks1BOyIub6kUSvrtEzVPZmMq8cKgySiMWehVxYxHnIfpDRthXKrM7l/YJ77BBDg1omc+DnHExLdNXhRCsmqwJujDTWmzJn2nBrEHMwcuk5gr2jGhEJy82Ccx2Nm30dKRMYSo39ccnNI51HVn3UyuHKUUEWh3RNC0mEtJt9jKjBJII9P2VjmPGeu5f5necOqDkyVSSzEcd0ajaqnHIYeCzVFDS5oB8IWqaJIuf4iaBmmDy3rWNpNR5eH2CbI0RlM0tmuFJOQzHx8gRR2ly53UDW4VPjCX5Sor+UsjuN0VUDxgUHApJ8gV59SJHV2RgJ2qZCrztlOmny/fes8NFXQoZjlS+XI1wjRQig0lDkEktRCMoCU1ERSkJRKnRqKUNfQ5Qat7d0vvneFvcvL/L+VzbQtJDh1GZXlzg/H7LqSBjajF7NBEqSPOfB5ZwbwwhDbVPTS3anEnM1iWkmuLZVQ5d0NoY6C40JbVtlFJa0alM0SSNOE4QUsVL30RWVcVIwTgrmnRxVlrjsSXhpzrn5gllg4EUlYZ7TNmOiQuWeXsrqyYwPXQ8RhUMuBHvTJtNY5WimkVc53VpMywwpKpWyEpzrpcwiBS+R0ZSQmyMX20ywjYRpqGFrFfNuhCJL3Dhq4wcup7oVgpSaELhCwotr1M0ERa5omimuUWBrkJU6XmTxyaMOXizTtDL0WkqQqOwNbWp6SbcRU5QCSSqpKkgKiWf2p1RShRcppHlF0BLcuxgyjnV2ZxpRIqPLOYpSkOUyqSRRM1P8RMZQjrVNUSrosoFcKWzeMfi+x5o8ceuAXiOl4/rUzQJZrmhaMR07YckNkETFxlDCjxTmHY2aVmNjFhCkkCYSNQ28yILyuHE4SgUnnYy6bnBzADuSzL1LY8oSGnZCflAiKyV3vD6fu+YgZJi3bZYaBdNMJUkclpoBVIIFq8WbVzv8/OfukOUyDQO6NdCkkt/44pj/9IPn8bOcf/vyPorQqLsVbz1b5zMbY97/cp+upnPfosV/3dhmoZ7StMHP4FrfoutYhKlMWGa8ab7BtUjmxJzKz37uBqZa4cUWC43w2D09ttnzJDRJxo9UDCVlvqMwGCh8Jp9hWRZbRxI9V0VXS1QlQMKmqBLmawppGRIkNgdejSTPyIMal7cNzixkbPQrTKHzyLJFVzf40I2QruXgT20e69n86GsXMFWZ1662+OdP3uDrlhf5jWf3+Ni1ARdXU24OYN6tsedFrDZlZlJCpAk+vxNx0VJYthf5yK0Bp+ZzXCtgrSfTH9Q5IKVpqpi6z08+eJrXLh2vUlVVxd//1AbTpORC3cRLZyiSg2JWzPKUhqUQzmQyJWa9ppAUCWlZ8nuvbCFpES8NKwyhk4ucH7x7mT+8POHvvXGVAy/lt545JMwK9sKEbzjT5aXDgB++Z4F/PdxHtyXesFbn299/mcfWagyrnEMPCkreeKbGZ3bGnOtYPH7CoVY1+JaHT/EjD1RMo5wf++PrzPYSRCHxUHMB/r80RZQV1HSFX3nHWX7vxSO+/4Ee//jj23zi+phm2+afvPkMN4YR3/eBa8zrBn/vG5ZxjOP8tLSoeGzd4Q9fGvO/vHaeexdtZnFG10r44z8X6/3f49XSVV8tmuqrelCUMmIWz7PimtzVU0mKmDBSWG+a1DWVYRASVzPm45A110CWYl4+hCQtWXE16qZEzfLxU4WleoM1p+KztyoOR4KWKZOpFTdHAattH1M0qaqAnXDGopKx6upMQo2cgEXXZd7lKxN+mYcXTOpOQlGV1NQ6ZaHglSmSsBl6DYLCw0sNGprBvh+S5hULdYl2Y5dCFliySb88wmnqPPqtb+PLn/1T5r/lMUDiRG+TSdwmFwlxNUEWTVq1LarZEiQqB9GM5abKrWGCk8gICirFZ9kRbM88oqRBxz5CUMPVFKaRxMWu4KU9A1U0mARjznfn+ZObHm9aa3NrGtFSGxxGE+ZNBz9LONGQ6QdwsmGSVVPSpIEQQ9IyJ69kimKMKbU5ygs6hkWpxwjZp2MYhPkRVA5pIbg11DjZFMw5Q6ZZTEM3kYSEIKahu6SFQIgQ0FGFTsueUVQZ/cAhTC0+e9um43jMOypr9YgkV7CMKQ4KhSIx8S0OE4dhILE1EYSRharAxfkximQxCV1sPUORCly9JM4qdDUlLTMOfQVZylHkijgTLLoheSFRImEbCV3XR5VSoqLgYi9GlQuuHGoUZYWjR+hKyom2h6l02Kx0FGQGszqB7qMpJdNYQ5dAqSo0GebdI1xdRpNlEDll6RNkTR45PWF3ajKLCwaega1pKGrFONCpmSHdWkbLDrFVjzB38FIFIQosNaPrFIwCFVdPqdVThqGGLGVUVcHuzGYcyix3x6w1Y1xdxlArumXAy7smll7w+pN73Bw2UDCoGTmdWsTQlxh4JsvLUw58jbUFn6bmcv0gpWtpNHQFpTtmJ4r4L1dSSrXiTA9aZsnJdkpV+dw4krk18RlFBc2pTpxJnO9lnOuFuJrOrUODj9854NvPd3hyb4JRVfz0J7aIozat7oxVJ+GUNuRL3mku79d58Y6NqUS0WxE9d8TEb+LoJWHiUtPAT3OEHiIrBmEm0bFgM7Nx9CFhXpLnBqsNidtHDUpiDEWlbZksNxJyEsJY5/ZBk88+O+PeUwlNCy4fZpzqRKi5w96kpOvIXB+0WWwcYuYNdqcGL9yqc/e8YOdIxhIyW7sGJ7sjXE3DMQakeY9ESzixKDMIFdzAxjbGyEIhqWI0xcAxEw4nLmfmfWQMfvmZO9y1GDOKTBxNptkYMwub7AzqfON5kz+9tUOZa5xc7PDB50IeOmlRStuMZ0vsTivSsmJNUQiVAat1m6QY4QiBpji83E/Jqi5pLvHEJYd7FhXGVz/K/s19Tr71/8W1o5irfZWdyT5Xt1NuRWPefEbi1tRDQmKtZnDpKCaqJBqayySJGUQ5Rb5AVZVsDGF5Lqeh1JAdi7tOOCi3K+KqYOwLVKXLiU7K5jBjnMV0eiYrdZ2/cv8q3/9HzzNnWcxZBr++OeRfv+sUvyUf8uhajV97Zpd710wexuJWMGZrVCGrx4PDz2ztsj2AG9MQU4Wz8wp5pVKJnLSC06sqhjAZxxkjr3jVOap8FZs1vlpEzf9IfPM3f/N/9/jnf/7ned/73seXvvSl/zYo0nWd+fn//yt+3/ve9/LWt76Vn/qpnwLgp37qp/jMZz7De9/7Xj7wgQ/8ua7v5e0Mx7RZXDJIpJKtccUkLbk4VxDEJncGLgMjJMpKll2F2DcJ8pIsFsyZGg+vezjWhCzXyfI2uixzZyxIihhXs/i2cw2+uLfL5kRisa7TNSv2ZhWWWrHWkDkaKNwYlBgaLNdVdCVgFsF6W+GC2+SpXZ9JktKwNT70vEVNh66t4EsFXlyyUGmcaE7wazsMZx3y3OSVgc/f/tYLHCUzPnljSp7rNByVzWHO2TmZKHG5EqecbFXc7NvsjOFsLyOTM4I8Z6UpuHxQcPXA5d7lglwOsVSbrIzRlJThuMmhJ2EZCodeiaXnWJrMkztj5usVmj4lzRzCNGN/ppOXLcaRzNluQlpNMNQaUTbhwzdKtgOJb1ho8MT1ipVmiayGmIpGrz7BUiUOfQlNCIQUcXFecGMo0bYFN/oZ3//gKt9+X5u//Ynr7PsKEjYaOkd+RMtrsuj2CVMTUQmW6mMs2SCpQpKsRZKm3BoovOOcjxXnaHaOoIGQfGQ5IohtTnQm1IwAgcH2RKEkoGNJDKMaSZFzshkziWrMSoGje5xszXA0l3GgsuMJDDWlrsMrh210NSYvS/phn4eXKu5pweakx4dvDBgFMA41arpM32/wypFCywxIC8G1vo4iSdSNDEuTePzshLwU+IlJ3Uxp2x6DUGNnKhgFcPtA5V+87Rzve/EKoipo1yMkBHkBulphqTlNK2bjqM7toUNZCaJMIUgl8kLG0jOyRKJVi2nZCQuNgK2RxXRq8JZzLk/tDkmrgqIoMfWS5VZKmOrsTGuMQoOmlaCrFUGs4mgZR76CLUqqkuMMouBY6zTNnPaCxNNphiLn/PGtA2wtpSJlf6bjmjFLjRmuUWCoBWEqs96eMQgVitwgLFO8MoXqg/cAAQAASURBVEFUOt5ERzNz2o2QE50JWVWxMTZIy5JZAlEqMYkzSgGbY5kwVdke20S5jKVWGIrCUrfCkXT8tKThTiEuqDsxt4YWf/uRC9QUjc1Jwvfd2+UPr++xsa9xZ6Dy9pNd/sJ9BroicXkQ8tS2gqs7uEqLf/vMAR+7PKXbFnhVxqf3ppyaS1lv9Zl3c3bG9WMXoT7GT5ucaMKt2RHPjWKWnBpZIqEoIbamsz920ZQRYWRSlRJXDxRsSSHOBGlZ0OsUzIoSVS64sFwh5JDVBtyzOMSPLBYdmShPaBsKT2+aaMJAViIOJip1p8AwQk7M6zzW7rEZBMSpxmtXmvz9r1tFliX+1ef2+dSNKd90scXuNOELd8a82J8SFTH3nKiQpJiVlkVZphiqwFBLuvUQQ5G5NZB40NGpWSVzzYK6GZPkEKc2sRSz2NaYRinTSOP2UcnjixVCCHamMWFe8nWrLV63VuP9V1KkSmKurnGPZfDsTognctbrMkd+jqwU2JJFpU7YHduc7xpc7WeIUubFwwHfdLbDP3vqCiQ246AgIefx0zYdW+WxZZePXp3QcmUu78f80ud3OLuk8+CKwfODMX/1kSXedrbF2862+PStMYMw50TTwFl1AXB0GUeX+em3rPJrz+1wMKj40o7HF7dnvOtcG4CP3hjx0ZsjfvEdp/jZj9/hb77u2Cl0qqPzzL7E2081aRkav/rMDUxkDEXw9gtNACZxxqXDgOcPPT74XRdBCL6wPeF1Kw1qyqs/pni1dNVXi6b6ql49s0QTS4Wrw5iNWXjcEGWUDKop1wcpklDo6U2G00X2ZjrnehF3dXR6usm+n7E5jREIqCqePYhxnU2WGyl6pRPmIVGRs16v4Uht6nZCkFi8blXlYq9A1wMcTaammIzDmEnq0bIqaqZPwAGKnCFR8dxBzI6XkWUWX7ij8rkthSJr8NRWnUEacr6jYigFhfBw5SaaEMiST8doMfRMhKHgff4jTA92OfRq9CerXBkoTMM2eWYyinNm0TKjqMI1Shq6wiiJeXCpIGWMbVREWUUmpjy0PMUwd0kykz++qlEhMwpzsgIsrcKLVbK0wTBUWXNNZkmFqzQoJB8Vm2vjAketkNTZcd2qnPPoisfZbso4UqCs8fR+xY6/QFwESAiqSmIaK5ysqyzWh2iKwNEz2vaAEw1BWQn2AkFH13DNgJoyw5Qs2lYG0pQo16lQcPSI+dpx60icC/oB7I5NruwucXvg4qcVt8YyICNQaZslkhSzOxE0zRCpkmlYgoYp0GSVigpTDTjR3adm+iBFVKJAkyoUJGwVotRkycm5pxuwO20wimxMVSYvZQaBjZByGqbPueYBW1OJujljFh9nvVSYeEkNRZagkpirD7m4NGBvahAmOiv18jgocKYyihQ2xy5eoqErFUUJVwYOFQJVFqw3A5abIXP1CEvPcK0YiYqaJjjVjslLiUsHTS7t15nFME0qGpbPIFBw9IRRpHylDU2lYwWs1MdMIkhymTQ3CBKDQ88iTDXyUhz/75sTappCzy7Ii5Lrhw6KLNBKgyQXREXG3fMj1uomrVpJZ3GIpGeISmPi26ilwc1hSH8mc+hJ3BzJ7M9sTLXiVKckzXSSwOH2XoM0F1SiICWlVfNo1Me8ND7g7ScXGPQNDsYyW8ME5AQvLVC1nFtxF1GU+LFFTdFYaRWYqsaVgw5BKjj0YJpk9H2dI7/GSxvreJHF3kzQrk840fGoSoOWVVIzJiBlFJnG/lGTfQ80NSbNLDaGdT70Qp3p2GShKSPUGETC2W7BgSehKjmnO5CU/xd7/xkt6XaV9+K/N8fKecfO3ae7T85HByWQhCSwskA2EphgMCbYxibIyFdYYFkY7AuYZIJBBEkgEJIQOsrnKJ0c+3Tu3b1758rpzfH/YcuM64u5Hvjq/u0xpGfU+lCr3lrvqg9Vz6y55nyeaD/Bl+qsjXKCsAyyhBMIqChszHzKpszHHulwabPBzK8RpgKnWjG5tF9V86V1yHOTq5OIhqmgqi6WZKFrEQeqEqIcsVzJ6ToxY9di153Tc0p8aVNEUhL+8uouQSrgezr/8akrnFgERRuTxy3yPGW5krNS0FDUiKMVG0ON6RQFqrrO5lTFEmpkqcrp2iI/ef8qKi5f+N0Pc8c3vwZJjDnekLihpXD3YpHX3VTlUMWg78qcqOocr2ooqk/b1qlpFXpOxtW9MoHXZnuS88yWxpuPL6DKAQ+dE7g2CPnsepczXYed+X5rZiY6DPwZqiKxaJVYLhn89rfcSEGXualZJifll7+8w6mOwa8+usP33Ln/5/1w2eD2VpH3Pzvk/MjDUKEs64RpTH+WsVBVuaVhc6AuMXSgUZog+BpJLLE2cXm25zCNQg5Wpf+FbPZ1/K9Gmqa8//3vx3Vd7r333r+ef/DBB2k2mxw7dozv+77vo9fr/T+u8/DDD/Pyl7/8v5l7xStewZe//OW/9T1hGDKbzf6bAXCyrfOmuxWQHMapT0WTWTBVtrYtaoURY0cl9Qqs7ZY5vycjKwklRWal4SIoE/ZmOWVFo6bDcq2LJDmsFKFlyownOVGSc7pexvc1Lveh7woUJJPtvRK7sxxdj6hrEq3yhFniU9UVWlaOl0R8cWvI0YaA66fsTFLaukLdThi4KRIS7VpALM7R5P09BEnISnWKJUOaZvyDU8s0ixKxOieTIk63E4LUQ5MT6rpIf56zUnY5WPaJU5+yLjJ29jXpWqbKlZ7I1V6BybzI+Z6IJiiMfIgS0ESZSPQ4UE85tdzlaNPjJqvGdlcljw32ZjlSvu/4FEUKNzSndAoOTUMgySNKksLWBIqyyGfXRtRMka6TUzdEfC+m16+wM86wJQlBcJBRkWSXjpXRn4MUaXz0Sp8feuA5cmHOidaQxVKGYUxRUOk7Ig9daTJ0DK6OJXZnTfY8ie7MJklkLnVlZvMiT28V6DsKHRNWqzPWBhYzrwhCxMbIxPFtdDngjuUdyrpCb1zmyXWbLJHwIxM3yhAIqGoibStFEn0k0eeuJYfFcoyl+aSEbIxtHL+AKgjk+Hzy+oS16VV0QaRpQcWMKBpzDlUHZGnC3qzAzLO5qe2zUvEI4wwBn5yAgSNiKiFFNUYXJAqySEGRcQMTRVR4xwPX2ZtDlOREYYwbJuxMZboz6Dpwsafz7K7O89csNocyg7mAHyW4Yc7VHZMgjbENB01xmAYgiSGl8ogHrnYZBRm9gQYIqJJInCjcsjimboaU9ZCaEZOnAkU9IYglskQgTgS8SCLNBGQpw/FlKnqMqW9xpD2iacccrASc6syp6imrZZ8sE7jat4iSnL25SM8RGDgCSZLQKQ2wVZetqYibJDhZRNmactvyHmm2/1m9EHZ2TLa2a6ztmXiByXhus9GvIeYqh6shLzw0ZKXqMppLTOYSihZh2j1axQm3L08oazl1I+PXnrjA93/0Ar/01AVeulylpktUiimdksyxus4bbqzw+MaUP3h0gBjpyGkZ0VW5PPD4Ny9f5ntv77BUNHDjiKkrsTFosD60kWWPoh4wdqq4QUZ3KpAmMrYqcqXrUrYTxEznkFamaWpc3GqyPQVNynjVyT6nVna46+AIUpFxmGEiEyUZkpixWkoZuClnt1s8v6exPTHZnVqc7+poGAy9FNcp4CYpyxUf8gRFjPmrjR0ev+bx2KjHzU2LNM8ZuD5/71SZbz5eIssyPnp+wE31Kk4gEHomEjGSKGBKCbYWYEoCAycnCky2p3Dbksc0GnFlvsaBcszYhTAw6c4yFooZihATRzmCq/Ibj+xytuuwN/X5gb+8yL+8e5nPnp/xKw9tk4YCexM4VjF5bjNAlSKO1FLizKdTjripWkNQx7huifsXCryss4Doq7QKKZvOgI9c3aKpFfiOm5toRspCRebFqyX+/NIu02TKQknid95whNPLKgfqMu968UFesFjm9cdbvOVUgyRNec+XrvKfH92laci85vfO8cCFIV6YkGUZWZbxwNqAv3+ygyTBv335CqcaBlmWMXRCfufxHd56c5Pfe3abEx2VY3WNp3bG3LpokgkZ33tri//48HU2dxMmkscvvGaZs3tznCDmxz5xmShNecf9K1waePzmE1s8uTMjyzKe3Z3/z1Ly1/FVwtd0RdE4nhNmOofLBkcqGUM3R5QTlkoaYqKxPYp51g9pFGNk0WQ3izg/9Bn3JZZbKkVdJkqnLJYjdLnMztACIUNRQizFJiPm6jhAkTK+8ViCILkgzwkynaKs88y2wA3tHofrBxBFY78dxoLVismxZpehp6NIDZxI45orcaxi4Eca5/shMibdsYSsBCyUdEpqgGm4ZJlJnEqseQmqZNGdC6y+5YdZ+8znePH31EjJKCgmTjymqmssFRIEfBZsGzdOKOoSUWLRc1JWKkVkecZyWUEQIM1z/LDO9iTmhrrEIBxyy6JOmO2RpMvszTOcUMdUfGQxQ1dEDDllGtqockxDz9Bkny1HIMsUhqGPJIjUC0OaFBgHKTXVZhz6yKJKxUiZhjOqegknzpkGZRYKMddnU7K0higmlPQA0hKp7CHiEKQlMgTmcUocl5lHEikJilDEVhImYUKaJ8x9i4WiwNltCTdUCZIF2kWP3WlAkkacH5VQ5ISqkSMIOqcX54zcgDCReHZHo2WJHGkGDJwKYq6xl9hUzJh5pFHOZAaeQkZAkkOUp0RpxhcvlxjPFerlBEONcEOdA1XYdhNKqoqtxQSRwNqghKG4+HFMuzDhwnYbQczJMp3bVlxMNWbsGnQqM56+3iCKdQxF4PpYJM5yihpUNJFJKGIqMTXTR5VU/Egkx6U7k1mqZpiKjB8rRGlOSU9xAgVVStFVlzDNaVge/UxjlEislmbsziuc6ZYwNY2GHZLnGl6kUNAypr5AmIhEmYIfJzy1ZXHjYoIs7ieYMiGgbAiEoYGQyYw82J2a7M00hpKLkiskUsrtnQLP9md0DAFRyklimyhNKYg+B2ozdmciT260iZMcUY24a7nJU30PJ8jRxZgoldFFjdHQ4le/vMuLDpWJc5vPr82RVIFOMWPqa9xdu8J/Wb8dSQpptGb4UQFdiGjZIbqk4Mc6EgJRkrM9hredXuaz21sU9ISiAOcGKlGWYoUGY6/KyFOpqCplE3TVwAlBVXz0XKGS2Vi1KWJq4QcmSWFMycyomQbbc5mMmI6d0fcCssymbQl89rJBWRPpRgF5KlK1FH7iJcuc2XH53O51wlinqM+J0xxdMXhuR+HW5Rgn9KmqJTIhoqrVmEchLVvGSSbUTZNh0ON0R+D5rkhRbRCpcyzJxIn3v4sdW2EohdxlS7ixhyqWkeWQWiHDC0zmuUvbgJQ5gigy9w38eYytAuKU04t7vLx5N7M44dOXxtz/vd/FWIxwnCKyHLMxj6kaEld6IVbR54WHcgTBZRrlVDWDsSdyfk/AEAsUDQEBjbfd2OTxTQ8Ekb1elZuXRRIpwPUlvvuWRc4MJ8j6hDiRkLMKF7oSv/aqA/z+hWf50MVtXrBU54ntGS1sNDPl6e05aq7w2w/3IBc4u+cz9GJ+4EUmj2xKbPdh5AgUbJvTdZNPDsakA4HFlYDjbQnX1xgFUCol3FVR8V2NSPDZ3Prqny593fXsf3+cOXOGe++9lyAIsG2bD33oQ5w8eRKAV77ylbzpTW9idXWVa9eu8Y53vIOXvvSlPPnkk2ia9t9db29vj1ar9d/MtVot9vb2/tY9vPvd7+ZnfuZn/sa8HM5YEgrcqGdUA4E4dynEGasFjVJosDnN6E0FDgoCC3WJ6/0YJ5sjBHD7Mpiahz8VUOWEdqYixhkr6oRxbNGqGNTjgG84WGEyHKAICobikUQpK4qAlUHoRyRJzAm5gINPPfUQFIGCZtKNYhJ/wN31IncvFvnM9TGFNOdYNSRIYwQkJN9itBuR5zKrgszmWETIBvyHT4UcKltYXsyNhkAuuVS1GEFWCecpJStDkwQUeUos6shixjyKsBSDTtFhdxJzoCKSeSF5olLSc8ZJQqcookgzJkIBU5VB8GnnEr484mBFZckSuDqPubOQsjNTOKmLhKOcILcRy2MaakySi6hCiKSLSIrLgmkjijm7bkA8yThgZkz9HCtUaahzBo5BxXLJJypKCK+q1XnMd0jFIZYikRGhRwrzScKBYo4qOQhhTtlMub6ucqAqkk089mIbVRIYexn1UEEMI0ZbFvNqzpaSYWsJJ7WAp7YUSrpKxY7JopiSmmDnGqfNMTtxgmlb9Mcqbhyh5hKKrDMKIiaILGkZRi4gix41zaFh5eSIlBWPvqPgZaDqFnIiE8QiwUjYjy+EmHBucX5gUbBC3DSnWQxIRImSnlFLRIREp1X0OFIbszvTycOMq47GasWnoKaUY51XLKa4Tkoq5mSxgJSLtMoBE18l8BUmrkLFjFgkpd0Z0Z8akEKxGJDnOUrJQ5YzsqnCoaU5cSKyE6oc1GJMV+fOAyJbzgA3UuiUAgpqjO7lTEMdDVg0fZpGRN/VSWIJIRFJxjKaklLQE4w8gwhEGWwpY0lI0E2HIJZwhxp5KmNJKZkv0bYTtjcq+LGEoaa0Cx6yHjL1VUpCyj3lOZaWEaUChpJSDCW8SEb1dGqhgmVnyFJAKiQ4UU4yF/E8mSTLqNgeTqjgz6CVRFQqLpq4r71lhQLdgYUXiRQ0DzFOOamZHC5rrG3t8salEn+4N0CKQ85e3aIuzPjc+hBZ8KgJImYKR6s2k7nI+554niCGZkFhJRdI3YSONccKIPJM0iilqbgYuUEma9xcknB3Yg634KZaGV0WuW1R488vxuiixErVohd2KScpHStma5pzR3XG0FUI3ZSKktPSptgxpJHO7lYOmcU4SKmbIkGaoccJrUjGNOccrQgM5jmWopEQsoJEYMX8g5MdDlcz3vfEM7RNC0vUMWIDgJcvwIcvuNSSgMVKiJYFiJEKwgwJne4sQhcNpn7KjVZEKY0oyilts8Y4zLG1lInvcX8jY2toIc4kXlRTOL/ncqKmogYT/s1ndvjRUzX+y0MXua0gUy4n9EYibzksUwp9lpU51VJKmMTMAp0bTJttv893ri5giAp/8GSX2JlyU0UgzH1wdW5bNnj5oSK//MhVjhgpjiMQTkfcbAVMBi69eMYHHnN4xYLIQxfn/OaDZ3nViSpH5JRf/MRZbl02uHilz5tPNPjI42u0pYjLuzHjYRlH3j8E+96jFpuTOW+/02I4HPBXT8344fsW+fPn+yjhjGGvz5W1CT94b4ff+MIZnr4a8IoTFd56VOWhs9dxB2Nee0hGUhWmoyG/+Lkt/vU3LvP6FZkbjIh3fWaN77mzxY1WiiaKbO/u0R/0/2do+f8RX3c9+7vhazpRZMtFaoWEvuejKhGybKErFqkwYduxkGKLxapKGhlc6SfoisaxqsREEZhlAZtzAdPQ8KKEbWeMn0gcKuuU7YSnt6BqpdzQlOmU5vhJxs3NGC+qIwgekwBuaCh4fhsnzBBFkZatk+GSSRPIFZYKDgPfJ0jqDOYVvnRBpl7JOdmG57shS1WBu5ZF6oWrTPwCeqoTC3vUbJVoq8KnL4rctpSxcqRNdvDFbDx9mRe8tMbFgc0xzeBPn5f4xsMpEiIVdYiattl1PGqGQqsYMPByDtcUTG2EJtk4YUaWSVzptomzjLtXcor6ECeosVjdxosO8elnRYp6nc15n3uWdZwooqAqTAOBhqmz5yYcrQg8eg3uWlZ4pjfnBcsKkjQhSgsggCKIzIISQd6nopdBHHOhV0MVFeyox6JtcKGfI0kBZTS8JCMINKK0TZKHBJFKSVPRFIetWU5FN1FUWLAnaIrIxqjByNO4NpJpVubImYUXSkwDl1wIaCgSty1GJFnCyJ+xNrQpmy5LJZ0gMahbHn6sIYs5ndKIS/0mz2yZLJdhFgqMfAtTzlgp+gjIPLldIs0UDtZ8dFlk7mpkiYRi+7ixxOWhQkFLeWzLoGXFDH2ddihzqObStGOSo1Mu9AymrgXE5BI0rRRT0Tm95BLHGkU9pFkYoIsgiRYjTyWM4Mt7JktljSP1iCN1h8sDjTDNsPWIhZLPztQkjAUWyz5COWTiS3TnRQR8ZqFMJgjUrZi+azHxdKboHKqNKWoJgZ7ihSphKlGz5/uOI4UAP4aGGTFybfxYYbXqkgshDTujPy2wPdWJhTpRoLI3yflHdyzwW0/s0SwnPDu9xtFWThAp7IwFaiYMXQFym6VyQm9WpKypaIUAScw4u+fjJyKm6lOzZ/zFE0fZcURWKg5X/RndFMa+SrEWkiYG8zBHkXJ2siW++fiQz13S+dK1CgvlmFoxpGA6rJYS1idVIk8nTQ2EVOV3n7+GLmcsVCbEjs6Ljs45t1PkU2dtKmV4zfEW9y1WMDX4d489iSrIyKR8/NkiR1ZcxNwmlgMSQiqaxs5cZLUcs1JK2Zql1KwIXSzx/J6ELesUSgmGOWBRVXl6K0B2KvzqY9vc1LIRUoNx2KWoSwz8CFk0WCwnOL6GIlaxtJiSLnDRD1msxIwDHxOdemXATYspTiiyVCwzdiU2Bk0kJBYqM0xZYWMaUNZMZskUW1Px4ghR8ZAym4evJ9y9KjGJpoiKQBCYbM9CbrMUrk4CQAb/Nn78C4+S7Vxitnae7UPfzvFAolmaoCohByoFzvdHVCo5yyUNSekhSTFHKxpf3IBrI4miouMxI89TFrQl3nd2i8tXdO7/exXSNGPNm1JQZFqmzDcdKvP+ZwccrJZQ9ZArM4ET1SJXR3P6c/j1h7v8bjrl9nKdXjYjzDN+6N4F/uK5EV9em/NQr8+qUuAHX1jjv1zcYnNQ4ddfcZzf+FIXP0+5ZanIxX5EZzVGL7hsjxVyJG5YDtibC1yf+mztwHJD5RsOWDz6v5DPvo7/NTh+/DjPPPMMk8mEP/uzP+M7v/M7eeihhzh58iTf9m3f9tfXnT59mjvuuIPV1VU+9rGP8frXv/5vXVMQ/tsANM/zvzH3f8VP/dRP8c//+T//6+ez2Yzl5WWmss57N4bYWkwoy2RJkVs6ZX74ruV9UdT5Gnpm8QuvPIggCLzl/RcoFhJkY8pjE517D7tkqc4sS5j6EUVBYU6Fbiqx1jc4O8n4e7c1eWYwQNQDTlkZmp6T6XOGaU5aajAc6FxLBE5Uq1yeDpimIYKQoBgmqLA2A2kiUqwbXBxGCJHKPQfnrI+yr7Rxt8iFkO5co2HKjJOcc/2Yci/kB+85zH8+dwE5L9L3fMpWgCkbXA0TDC3jhrrK1IswFZEjxZBZPOXaoIkTSqy7EkdqNgeXNiA1CDyZ7SSjUpQZJjGZpTJwNHZnKY2CzmcuJrxk1cJquESpgipLbM8ldFlnb5IykmQKxhRdlvniRgVTl5FEj1kS4UQitiZT0D12Qx3JjBEU2MgUKrWIUEwQ0HhuaHJmmDJyNA4t5PTTgKNNAVF2kCmzEyl0BwbL1QQ3n9JowlTOKNg5eSIiqDOmUYvrgkKkCdQ1kTVfAVEhUWUuJwrt5ZAgkVkPcyqazuVYpq5OuTArE0QGrpti6RmapqPJCdXClCRT0SURJ81YmwkY6Bxt+shazCTIEZSciiVg6AKJkPDotSIiMom2r0UUJbA+lTHVnFpq75uthDrHOiO2A4VKyUeQMjYjjdg3sKyMgWNyblahm3ssV6dczxQe3MiQ84ATCzPqcsYwU5j6FotVHylN6ToGfUlHtTJyckQRNCWl69gockZ/ZlAwQjpln7GsUSnErFY8tsY6azOZR2YhmmQQy/sW8/e2HbamJpcGJSQRYkugYsTMFImzThFVyUhzkZIeMk11kpkJMpSMmHbBQ7dSZDWFQMR1ZFxPRZK/4pSWKFhaxKGmS8VImPgyu75FL9BYkn2SQk4o7hu6qHqKI8hoZsLVscEkKFDWUjaGGWYhZbHqsjctICJQ1mUE2WQUGAxyg2mkcb0rINgBdx7sUSz4mHrGxnaVJLCAHNs2uOfoQf702QHdyGdq6ty7VGZ9NudXL+9yqDmlIKrYCKz3DHzN4oZGmQ9d2WXu54ymCm+8scrTk2t0c4s89alZKUZRYHem8czQ5q5mhQd7fdYzWDBSPrrjE8YSH9ydEgQaluUxDkPaRYWrcY4hKAi2yKWpjCjrJMWMvmtyeLnFh844HGuLeLhMfZWymfC0k9IwJXoxLBRTZE1iIxZxRZ1BGmMqGheHEvcuVPmpL0z51pMSH78isGznXB/O+PPvWKBsKvzhM3v8+UbEiY7EWI+pGhqzKKZTlMnxkOQCz29YJJHCDZUQ1xxws13nUgRCYch1V6dSTjg7KpDrEcuVKudmCd9wyzIlTWGxU+OHvqnMj/3lOj9y33GONXVWyxqfvTLlvU/0eNFxm4nmEgozxrMKfqDy3DjlULHJvUcO8oY/OM9yx+KlJ5b5N59dp1lWaKpF3nbvId756es868j8+P1HuLVl85Ofu8huntJQa5xYKLDtiZiqyMKCwRf6e3zqMxP+xf1LfN+Ll/nY2Qm9KOZzOwJPdBXkcsKP3HGATruFKIr82sO7xHnKt59u8tYPXeRfv3iVnzh5AEsV+YGXNnmTG/Nbj+zxtvuP02mYvACDohny4PaMb7+5wWJJ5a3tJj/0l1e472CBX3zGodVssLLYYWURfvfxPW5YXSA3ihyrqHzre89xU61Au9z8n6Xmr+OrhK/p1rOntgV2ZypHqwodvUqYWECKLRucqCu8/Zs7bM5CLnRjhEzkbacO89hmwvmdjLop0BANojRh4KgcLBe5qWUiSzlFI+LmtkLblpmEEUEWYkgyj26YXJ/NQIgQBYHHtmLODwIaVs4syJgkY64PKii5jShNSck4UTWQxAQ/zXnJcYPvvnGFi7sWy1aFOChydWASx8vIWDixSBav8PRmh4oZ8uJDKvVCDLnKjZ0Sj/3Be4nGGfcvR5iay8uP5IwDiVmocaXf5sooxZSKIM+xtAhLzdl2XGqGRC7s4EYqwyD4ioVoTCZEmLJOSoJMk1pxxr3Hch5dU5nPO/z+4yX8sMQoGCMKKrtegCoaTMOcbzoaE+YRLcukYs5ASBGEFEVyqRoyZdPFEMv0vP1+bEvJUOUpLUuiboaIYkCaFJlFIaqY0DAVZNHBiyQkUSHKYqahTE032ZpqXBmK7HolDElHUedoypyGJSDmFp36CEObIyBSVg1kETTZRVanxFlG3QRLNdBkqFkukjxDFD2GnsjnLi3j+ianOyFFTYFcxQ0tDAUmkUKQwEplDHlKkKUs1mYcWuhh6hEIKSCwPiqRZBqVr1RQvOTIhJWKT7uQIBDihDCPUiZBgqXlFFQFWwcnVFkfGoyDiDTLiVMbRVIIUwE3krjUL1G3Q0w1ZervB71ZLvGCg1NMNWMSCNTtOUuVGSAw9VWcUKVVjFBECU0CMZeQRJE8VWgXQw7WXMJExYkVTDWmU/JYqTikQsTuTOLMbhVZUPFjlbEvYmoOS2WXqiljKpALEd15ysgxmXoGmpbze2fX0MwQ03RwoghJkNFFiYKocLLj8cKDOxyojlgfK+S5R5Bm5MQokkgozPjGE0MOVwPO7XZQZZmCYJDlGlJqUTJEDtcS/GmRZjElzSREMSOKUv7w6TaPrFucWNyhYnu07ZC6IZLn+w5ym1OZqlriHfcfIkt0GpZAmOioss/2RGLgqNxxLKSsS0S+yI89cIV/9dnLOL7F8zsaT18vcexAhqn7FC2XA9WYkhUhSHMOlnO8SKHvRqSpRZLn5HnM4ZpAlAfUix6LpZjNacZKUSeSU9pFhdefqtF3ctZ2Ojxw0aSkFOhUZqxaRSauTJRIDL0UQ/G5ZaVLzXJpWAnHGnNMRcWQZOahzXBe4PyuievqnNuGPFNYn81oFwS2HQ9TkTFVH0MLaBga53sSR0oWQ0dg5jaIoxKamnCkUiDLUxYKMm1L42NrY5KZzZd/87c48eK7eMnRjEZxysF6yGJBZxaltG2JxYLC3lzkaneJLK2wNVPIkjJVtQBiTFkxqakVLo1dkkTnDXdbPLQ+xSrF3LmoUdFlIsHl5x+/wMmFlKu9hItrGq8/ssC/vG+Zt//VDhf2VCRB4Fo/5Ce+sYOhpbQNg4NVndzyufPGhNNlC0FL+E9f7CPMFvnpFxzkvgMl1ichYQhnd12uj3y6SY8nzhcREx1Fyuh5GXWtxJtPLHLHQYU3nWyy4YRfdY7KcuGrOr6Orz5UVeXIkSPccccdvPvd7+bmm2/ml37pl/6713Y6HVZXV7l8+fLful673f4b1UO9Xu9vVBn9X6Fp2l87r/3XATCIY+JEoW6aVO0MJ/P54+cHPHBpzKFygd9/zW1kocRHz075+S9s8/tvPsF33tXA1AVWa+DGAjtuRJRJFFSbKFG5PFSIEWhVRH719Yep2hrfcqJGVVfZnYvsuhmaZFDUVYqFMXohpalafO7qjHM7MlJWQEZny0lplwPuP+zjqnv4wpyVisANCxM25glVS8XximzOIU5MyobI9ZGEmEtUTdjxIn7lC12kuEihMCSKLLzQZnMqUjZFklTkylClbimUTRdZiahoKpnk0CmlyKnC5jQnCCscrPdpFULKes7zPR1L1nGTmIqd4McK5zct2kWJ3bSLKqlYuk+jFNMsRahqgiiorA1MrOQUH3tyCVtXEY0ZqqThpCGWKrBSH3Ci7XNiaY/FWoxpOLQKsFwbcctSiKp5LBRFJm5KuSAwDOHWFQ9BjNgYlpEVGEcCtqQyjXJMRaVecGnbPqBwqN6naqg0Sh7H21MO1GLcPMBWoefYZHnAUjnmat/G1BxuWYi5oTNFll2e3l7gwm6D53YMxp6OIurEecosElkbNbk2KpERMgo04qTA2rDER87WuDywkXINQZBwk5RdR6DnGhyqpeSCxCTMyYSIIFFYKMPNyyHffNymWJhSL0/J8pxOMaFuC9QMkVwQ2JhqXBqopEKKpkUgSYz9IhVDoqCoaJrC9qhEmkMmpARk9FwRSQ2o2R66HGPqEXevDnnFyR2OtOYcbDpkuUjRjqgUEgw1Y29ewI0V4kwil2Qq1YgwUYgyhaVaSLMY0vd05oGGF+8LkJeNBENNyQSRw60ZjUKILOdsjgpc65XY7BeZ+ypRIrE3M9mbm/uJJCulYiVoWkrBELF0GVEQMBQYBxqzSCQT/msLm8g0VHBjGS8WifOMVnHG6XaXRmmOE2cM/ZDLPQFVh8VKQqcscP+hKS8+OsLWcgp6iqIkiLLHJMhxSSnrMbYqEsYWAjKHOy65GDLycrZnGb/8+CbXozk9L8VP4Eo34so4RhJUJAwONiaIUsBkYvD4tYT3nd/j5150HFKF5YbCn17s8fSWiKbFnG7H3NSZMwtSBElkoQCP782JIomWUuGWymH2XJAUgRtqVcIsoWZlFM2IzYnK9V6HK3tF4ixnqZpTKU32jTV0ieVimTuWiwR5zBtOrHD7ikzR9jjZSdmZCdQNCduaMQ11NscW33J4kZZdYn0s0yhLnHMGLLVSPnG9S1FVePNNTe45YtEPUn75kR3+7EqXThUWGj1OdSI0bc5iJaVqubiRzvbEQjVSvuPWBmlu8oL2cc50EzbnPrbu0SjFuKHGLM4xVI1z0yFJAg9vzalZKn/0XB9LUThWsTjU1HjbBy/yjk9d5ze/3OWNt9R4cH1Gdw7DYZU003CyjG89XuPbbmkiiiLLZZ3vu3WVn/nsJrWywPZA5Fwv4Cc+dY3HB2PeeFOd63OPsqnwr+8/RiVu8wuvOMHJSpFpmPGjL1yk5yUIrs0vvvowdxwo8Po/O8MHz/fx0xTLkLn1gMErjzRpFjSujUN+/nM7fGZtwl+cmfAdf7jGqVqB0y2brpvwg39xFVEUqVoql+Y+b//sdfpOwo3NEv/wnjY/+4oDvOhQmeWyzslWgfd92ynedtMC14YRB6omoigCAs/ueVQNjY+enTCPc1YKBm+7s8nj6/7flZL/h/h6TPV3w9d0osgWbZLI4OIwxs27RJlHRd+3/wwY8xvPbPLmE00OlwycUOCZHY9vPdrg5KKErvmEcQixQc1MkdVdxtEQUchxQ4ktb0LP81FynfVBiY1ZSruYUpZr5LkCZNy1qCJFTTaGBmpm0dArHKjP6bkKrt9GyBUQXZZLAlUjZd2ZoSkpP3DbIv2hiOfLCJnE2X5CkEgoYs65foQbalzasxhGMyxFpFoYk0gBb/mJ7+Jj7/1LklzAkiVk2aFharihTJJKWEqRNE8pKzW6MwtbVVgpqMiSR12voGpDNvqLuJlDd2IyC2IkyWOxNAfBIYxMnCTjho7ENBRYLRb40jWTKGqxXNliuQCKPEQRJQQxRBM13EBjzzGQ0Xm2m+NGNrPIJckMJoHC0DWZe2UUpU9F2+9dn4cKJ2oCnVKfjbHJLDAYuindeYU8M3hqR8INJWxZpWo5ZIJP0xIpahmy4lLRU25oxazUHBqlAed3imSRiRNYXBkJBHGMJMrYooWAyjQUeWpbY20kMw1UhMzC8UuMnSIn2nMsLeDKQMJLIm5o7YEYEmcplhqTZDJxotIspJxo+Wiigh8ZtEsJS5UAUZA53piiywlRmhGkKV6csjWFCz2DM3s2sgw3NBNOL/gogoTjqcyCBC/KONKcUStEpJlCFGsMPI0g9mnZUyRBombKGErKPEroORK6DE0balZKnMLWVGASCHixSNEIEcUMW4sRAEmENBeY+DorFRdNShl6OpqSocoJFcNFkhIEIaGgJKzUHAQEnFDFCQ0WyzMWi0PGoYytJmSZhK0l3HGoj66EZFJGFOcoUYmyLpKmoMohOQlO4iMpKbIEFwdVtqYNLAW6/TpCJnF9ZBCmOaY5x1R9un5MmCY4mYcXJbz5+BK6FFGxB8zmoFpzpoGIGwr4icKlYYu6JXHfUQdTUQGReZQR5z49R8QJJU62IjIh5BeffpaRk+BECf2pjh9ZuJHBKErYmmaIUs4je30iIrzMZaUaslyNKRYmFC2HoqYjyxkhc6q6QEGVEYQAXc6RJYF6cQ9DMjnW8EEIqVswieaM5jYKJnGesVjJ+dL6jC9tjLFKPrkYc7wu0i7PqGsl7mi3kCSJxzYzhExnx0mRsKjZPsdqOSUjoqIJnOlqrPfLPLYuoOYKz2+HdMoSXjZlqWDixhEvOBRwsBpS0GClqHO8UuPO6hKDmcR0UCLyNb60ZrC5V2N9GjAPZWa+yCCaUDVSLl4bcug1r0UvNtj1BziRCHnOlcm+eKcsu+z5Y0p6SorLxy+JrA3KbE9THtuKqJgxfuKzNnEw1BxRjnliuE2kb9Ipxex5DkEWY8k6ohiRiDHLJZ2XHi3xI/cuc6hq8LKjZeqWiBfDj798gT86t8XGCM7txkyDFFOBTWfGmc2ILBVI0ozr05CVos6P/uUaiyWFsinzf77uMP/4m8pUaGHpAiM/QxJAyw3+4U1tPr21Tjca8tlLU4ZB8FXnqPQrootfrfF1/H+PPM8Jw/9+0nA4HLK5uUmn0/lb33/vvffyqU996r+Z++QnP8l99933d97LjQ2blZpLlPvExByswsF6xL/9zDY/+9kNuvOIX3jNAX7p8Q0+9tyUn/jkVT5/OWJzz0CUx+hyTkUxuLxX5JFrFoIgYAnKfhX1LOTdD6+RZjlOljCNYkq6jCbIPLMjM3AltLzAqbrFe15xiDs7Fd7zisO856UnGTgGJ+oyF/ZsZlHCajnFCVQu9MBQReqGxDz2aZcDbFljY0fFmahUVAknjlHViJPLMaWFHUZBxGDUYHeWkSMgoXFm26BoCLQKMX4WkOcWthrixCkHShKbs5RqKaVmZjy1pTF2GyTEHKx7rJRjBNlFlXSe2dSxNYgzGE80OnbOgUaPhUJOyZywXI6YBSJpBiXV5LU3LHDzAY0339TAEit8540rSEGNgRsi5CZVe8Dhcs6J5jY3NFMsY8LIrTJwcyxRxTJn3HRA4ltvqFC3E5xQ5FB9yqGaz9TPaVoipcoYWxFxY+jOFSaBhZ+EDJwKbpxjqxG6LLIxy+gUc0pmxCxIiVOTuuVxuD3BC4s4SU6WhxRUjSgFN04oqxLkEl4cM3BNskxlFmSIQsz6pMrU17iwpzF0FTRJ5+x2nXMDjb2ZiibJlA2RVsEjx8OLJAxZwdZEdDViFsX0HHi826WsSZiKxOV+kStDmUfWC5zZtelOi2iigipqpJmMrYdkeGxOBDZGGpKSICIgShJJolHQBVbLMUfqAUUt5VB9wmp9hCQFxHnE9ZnM2Je4OjAQpZSiHkCeEcQSlhrhhAq7Mx0/lLD1hJN1japSZHdskGUS3ZnJxni/6nowN7g8KDL0NMp6TNlICGKZHAFNSakUQ9pNDy+W2RiaKHLKqfYYVcrYm2rMg31B8ltrB/nGhSNs9PfNMdI8Y2eu8dhGhWtDDVkLGDkiEz9HEkPahTm6kjH2DZ7ebBG6NiVJ5WAn5EjHo1MMgZx6wUNRAtqlEUkeUNI9slTjdTdVePWxOm5o0HV0RmGGpTtYqs9qOcaWVA7VJJAjdqcphzpDTi9O0cs9WsWAnZ7C5kjiC5cXOLfdAh0WLB08hZ97cBOrmHBXu0ScZpxcSCjrEU4U051rnGpHLFUmDDy4a6GMLAq8ZKXG87sBp6tFvuemZYazhHY1xksj3MBAFkR0bcahVoAi5RyoD6mbIlsjAVn1eO8TW3z5ischo8V//NyY9V5A1QoYODIH6xmV0ow0KTJ0El53pMWdCxWujiIkEXQtZKESMndFFmyRhJD3XVzjid0x/+JTF+nYCmKgoMo+YayzOxVZKEZkWU7PVdgcFhClgJtrNT6y1if3Vf7osZDNrgSCyCPrDYJIw0tjbFnh6kDAGZcYuimXpx6/+/QOv/94j89eHXPrAY0f/9wFXn+qznN7Hr/7liOc7fm8+kgdI9dYtmz+5NtP8m9ffpCffOkK33KqysfPT/jh+xap6DIlG65PEoqFlGo1ZhRGvO/1N9K2dDRJRBAEHrgw4adessLuNOK9T3X5Z9+wzzuvOlHlu+9qc89qkYEXYaGyNY7w4ww3SrnqeNQthSjJ+LnPbHL3qoUf5/yTe9pYlsDTezPe9EfneeDCiO+7q8WXrk959R+eYeLHvPxIhX/4gcu84X1nSbOchq3yq4/s8HtPdgFo2SpxmlM0JFZLOhMvQRQFfvqblvnsdo+3v2yJP39+wCtuKHOybfLLrzv4d+a9/xG+HlP93fA13XrW80N2dgrceqCMlJYpyBmKHCBIIqposJ5E/PFzPUJHZGlR4PPrI7Z9n8RTuOOIylIn4coARE3gznKZlWLCxA+YegpHKwXGnkh/LqDJOodrMPESTG1GRRDIMomz/YCrg5jVmsLaMKK/EfLiIxIl3eHR3Yg7F3PalsQ4GrFYSwijBX79mavkQYFXnND51MaMxy8bvOEen57rcMz0aZlNntwOGU5tjnVyHtuKuW1RJ0kjjNUab/6hN/G5L17hpS/sMA8qjMIJJa1ML4lJlISKYbM9j1iwNUytv29dmBsEWY6UNNmeKrQLVbYI0WQLRRqRZAYLpTnTUCZMdII4YbkSISCwWJCJc7jcW6RsOiiSSM2esb5VRiKnVZoh5DZ7rs/LD8Rseg7zwEQUEsa+RhgXOd/LcaIV5lFOnIfUTZU4jylqEje1fbbnMTOvSVHTeWYvom6m7M5zNGVCz6nQNHJkZY6Q+/hBkTQTkKQxi+WcVipTNR2ev1KiWouZB0UGksS0n1EtDAgzH0XUWS3l6IrEPFAoWwPcoUUupgRxQs1OuEHIcQKTc3tNDCVm7InMQoG6lSIJMXGaEucxZUtkFkOUmnTHMsvFlM2ZjClrjFyJY82ANDV4dqPGQjnF0n2KWooqZyiSy9VuGd2McEcqy2WRwaxKnsc0zBlBIiDlAk6kcWmvhSKI5LnM2BPYGIOlphyuxwgI+wHfjo2m+ZR0n5rp0XX2T0uu9cv05hanOzPiVKSoRchSjBtLDB2VLE+Q5Zg4EQkilVlQRJMzZr6MqiSUzIScnEt7ZeK8xGo54EB5xixUuWlhwu60QKES0nVjoljgjtYKX94OGTg2S1WFLJXpzkXKZkqYRThJRBxpdOc6Tq6Q+wqmCOpX7Om3p3Ua9pQjtQSBlLjs8r6zXVbLBnO/RKea4o90FEkgF0TO7FhocsA0CJgGOtsTnZYtIeQFSvWIK1OToQOSmLLdj7ENicPtlDAxsI0ZpupjiTInmvDstSJODoqYc6iZIYsiouiBIFHTNeaRwOZEplnMqRcEosxBEhVahZhp3KNmGjixziefLXO9XuJCX+JIe8BK0WRzkrBSBUFIeXIjp1qw+Pdf2uZER0MSIxRZwAl1rg0THovWeW5X4ECpxNpowl1WA10eUlZ1knyCLJg81/W43j/AtbFLU2rRDRyOlwtkooOU1VFllxcsOMwDlR1HQhFULnZj5r06f3VlhGVl5JHMr77yJP/luR1+//MjVlp1GhUX3ZpgUeHC8+dYjS9w+MWvwok99LzBUmnM2tBETm0SacqBWshyYuMmc8hlVksKs9hHoMxLjkhY+pQkNymqJvPIpVqIOdlMudiX8VKHimozzwWeWs/o1GS01ODKTsil3ZBQifiuWzp84vyUgiazWlc4WFf4lafn1E2VlbZBlKfkQFOpondkfBKEXOC2SoFffniT77tjgX/56Sus6jZ/dXHAH5/dZTTQWO1AWZMI0py5m/PvvnyNxWoVOZAZxBGm8dVPFH0d/3vj7W9/O6985StZXl5mPp/z/ve/nwcffJAHHngAx3F45zvfyRve8AY6nQ7r6+u8/e1vp16v87rXve6v13jb297G4uIi7373uwH40R/9UV74whfynve8h9e85jV8+MMf5tOf/jRf/OIX/877O7cdsbxqUDNzRkHKOI3RZYFDCwnPDMb8vf8y4c/edgPfc1uHj5wdsD6b8vxAwkIhDMtccAPyXKJdzEgjhbEnsD5MKWkFlssZ23OX9zx8hbsWC2y4MYfbO4SJCrQYzyUujUxqhZAHrvX4uZcfxA1TfuuJXWxUXnW4zocv79Adl9idRdQMKKsqThzQLGToks3uPMJLUu44YPHUhk82F6nXdLzYZRhn3NRRQZhCJnHEMNBVj4lXQs51HrsoctMhAUUy2BF8inoRxBhbn3GqaXNtPEURLJpWyCMbGi+o5Kz3Eo435nzwmSWmoUBFU9ide7Qr+wmQuztLbAYzCuaMJC/x+IaCKioUC2PiJOb7P3aOgqDz8qMyozDmj89u8pYbF/iTcyGPb0joSgdF8bHkOpszmbqhMQ8yrvQWUOWYvZGNrjt8sTthtRJg6z5+ZHGi3aOoV7kyitgbl5j6IlGWokoi4yCgYVbZmWUca4zJUSgYE063aswDgTTPOVhPmEY588jClEV0ecKiHSKJGlfHIJFhyfK+452jYEk15qHPzPO5cTHAVlOiNGDTs1mqxKSZiBtlVIwIERNLDUjZr5LOMhFD0dGVlO0JlMyIuiWzVIay6YMQkWQ5i4UMRcrwI4Xu1EaSXCxtRuAZ1K2EOFEpaCKKKGGrCYIQ0nMUxo7KPJJxJQExg97M5HKvzMGag1aZk+fQtHIkUaSgJvRnCSXTAXL8WCWMZTQlwY1zshwkIaNecElSid4speubGEqGLO27bFUsmTQXIYc8h3mgkGX7yaFmwcVQVNYHBfI8Z+IqpCksNTwUOWN9bLM9NaiZHkO3wLVuzt58A1NNuO/EkCARyUkw5BRbFxBQMdSUSjFjkCoEoYgb5AwVmTQVABFdT5nMZUZjFUv32JtLlI0ASYxYLcd0HZu9UZ1rewWSTOSJZM4P3rrMJzb2OHutQKemM/diJBGGnkggxgzCkCSyKZsZ/VmRVmWOJIf4cYFKAUajCjISTh7wLcfqyEjYhsSpjsYfPevziQtTvuV4mYtOlzQTyTEoGlN2pjrrgxpiqvDg9THf2IDFqsp/uOUQgiDwrz+zxkzaY7kcYWs566McBJUT7QBFcREx8CKNjaHCkYrBa441+O2He5TLKaYkUpQl5oRMXIvjnSFCrnB9aBNlKS2rQM2WedcXLpEKDpZiM/YFCprEQtNBEQVScT/Bc+tCgY1hxn/+chdJzplPLExZQLdAmxQJCYnDArnkkAU1vulgncOlArok8vFr29xWKPKXuwmqGHOpG1FVNWLRR0x1RCFjPlN42WmLjz8343hH4hObO+yNJf74TadYKOjcs1pgYxbyr166wjs/eZ0wj7jgRvznZ7b53lsW+fFPrHHvcpF/cEeDqZ/wAx++yPY8pl2UKJkRGwMZSxCYhxmvPtLg5z+3zcyP+Z57Wgy8iB/6yCUu78X8s4+s8763HudbT9b+miN+7ct7jNyUf/KCDn/y7IC1uctrjjY5WjL4g6fXudCDX3h4k9943VF2nRhTF/iuO5eY+ikfOzveb/2vaMRZSslQeOvNTfww44aWiSTuJ1LKhsybT9f/+p6Xhz4/cvcS7/n0Di8/XuZ3Hu2yFTj80N3LaLLIG0/XOVDRidMcpaT+TzDz1/HVxNd0osgUdPSCxMPnTSo1h4IZcaisszWWqJXGFG2ZW5ZX+Ic3LrMdTPmTc3sc1WyK7YSprzDz4XBVoVWQ6LpjVHWOqRapFVxQhjy912LFNhEkjx13xoFqBmmZjcmYQ7UBB0qrSEspO1ORhaLCQtliHsyZhyZ3LYgs2i6mEnGyWudcX2OlMaWgl9CUMZcmBsdrKn1N4HLf4HRHJI4lRCnhQKkCccpopnOyrjDzA1aqGhVDZGca8/lf/3M67R+g0k5pGyWe7+YcMARiKaFoDanENs91PVpFk8XKGmQFPF8mFubUzSLrI4GKXuBSL+JwtYkgzMkzi+XSjEHTpucKqIpAyfAJIpMsAy/SKRkRMhmaPGOhkKNrGwhpnY1ZxOm6SrM4QlUVNNnhmT2NNDL49PkCrYLI5V6Ipc95enuR0wubmJJJmCqsjSNubgk8PBEoskpJmpKkfbruHFGooEkCBxobOJGKF1tk5HQKQ9JpjVCWuTA2GAUBdjnG1mQy1Wd3DmUzJ6WILMjIokiQunjJfr/77rTIsUZAu+Dx3G6RWSAyCUN6U5HdmcRiJeHO5SldT0ESJLpOhdXyvt28E6ZkuUyS5uiKQMtOmIU6QaKyWIpQhARFFjjcHOJEFpf2dAqmy4FqjiKnBJnAsWpCyYgp6SGzINkPvHKHKFMYTEvsTE1Gc4VGIWbgKiyWXU7qKWGi4MX7gaMiJpTthKadUdYFhp7J2BNoFCeEsUxBz5HEGFkSmMfQ6+kcqk64PtSZBRJubHNtYLJSzpn7BstVb7/81AzZnasMZhbNQkIeK8hiTE5MRkSQlUhymWbBp++aZEmB5wY9RCkkF0XiVCEXctqFDEWSqRoptibQj0WCJMPQAiLRw0tFRh7EeYoqQy4l2JLEifYmj11b4adecJBPXZgS2JcZO1W+7+ZD/Moj22RiysAp4ARlBHJsLadm+SxWXBTZ5dEtHU0SqBgCu5MiJApXrwvUmyFL1ZhDtQx3VGCUSWw5GYYIZRu2HVifxJxoiIjo+62MQszeYIU4kNnG42g7IU5MLGWEIAjkSZGzo4zrG8uknsFj11walsjYsbA1l2MNjZ1ZjCJJHGm6PLEuc7SlMprn3LQCp9o7yOJ+q+Vj1xQOlHUUJeRQXSEXZszDIqLcp6CYzJIJ13snKdh92kGHCQHLWEhSgGVDlKZ05yKH6tAPXRShypm9gKh/kH/26gZXox67A4VZFvHn53v85H2r7PYyDtYVhDjnjKfR91wuvvc/86qffjsbIxVTLhBkAZd6JRRBZ+AFFHUbU5FpV/qsqBLXxzaxMKOoGjSsiD1HYB5W0OWUaeZRMmTads4kiDEVkaJqsDkBW0+574jKzlQijODoiook5pwfuTzTm/MNh4pc7vl0Y5ef/tQ6LUNhFDt8aSfg6tinUkh44pqAoPgcrem4c5Gnu3MKlYgPXk5o6gp9P+LnH9qmrVQ5cCAkIGDPFZDTArYkMU0nzIcZf/G6e3nfmR4PXZ581Tnq62LW/3uj2+3y1re+ld3dXUqlEjfddBMPPPAAL3vZy/B9nzNnzvDe976XyWRCp9PhJS95CR/4wAcoFAp/vcbGxsZXyu/3cd999/H+97+fn/7pn+Yd73gHhw8f5gMf+AB3333333l/FStn6OU4cYwmSayWJXIitqYekZRTrRT5Z584xyyOCXydSkmmVY9Yn6ZEuUdB0REFiYEb4yQ+HVVlqZYzjjxGgUpN03l8d8Knrg1ollJ8v8ZStY8XjUEokYbwxmNtPnD+OtcGEadrBd7/+IhSKecPHp8xSSUMLUPPTDaCkJ+4Z4nfeyblWi/iWHtKzTTw3YSWYnKik3PrgsVfnB9TNWUalk/Xi1gqKCD4eElA5BfxZzJpBhVb49owxtYgyyy8ZMzJZsQ4EBgECSslhVZlE1XQ6c5U9lyBqVDh579Uol4RiCIBJ8w41pFIxBnz0OIXvzhludLg9lWBgj5jsVDkS5dzDgllJlFAxVIJE58gTrD1GNv2+fNrDkYhJ/U0zu0UWKnJhGpIw0jYmZuMHQVJ0OnNNHIRBCEmCAxmoc+pVo4kjVFFnVwI8EObIAGBnLlr89goYbVu4AY+9x/uUdZhbZwi5DaGFrBYdjE9jVjzMVURgQwnnmPKKroikuJyuAJ+aHKhm9IsZCiljFceavHBCz00SWISRKRZjCzq1K2MoRtTMhIO12HsS7QKA25oePiJRpxKjAKBPSfFkhMO1/bdX/0kJUshy1UOVhMMNaFsBIRZwifWFyibKZosUtZklkouqhwx9gRUSSBJVbJcxFBylkoxZ3cVrnclvCgn9gWmgYipRIxcCVU2yHOIM5mha6DJGXU7o1GI2ZzoDF0NRUzZGGu0swxdTjhQ9TDlFEXLCFORjl1mz41wIxE3UggiiQMVh2PNOQfKc4Q8YxQaRKlInAoEscxybU53ZuCGEs2ShxPKaLLELJDJyXBiiWbRJc1T1voGdx/eY3dmMfE1bCWhPzf5pgNVHr4eMIs9NBLmoYGkJEyCBEHMMJWERsHhomLiJAI/dk+V33gMVpoORjEmS3VkEcpGwkJlysWuyiRU+Kf3rvD0jsdNTZP1cUS3r1Ky+1Rth5otUVBKyFmTsS9xpCLzhd0JZUvmZMenrDs8dlkhl+FbDtf57NURn9/pEkQppDJnBiqinPPGm+psByP0bIUz22PqtkvfaWAoKZLiY+oqaWRyreczjifcuVTmqZ0ZZ0YTqqbFlb5Gnmc0bQizGEFMmHsl+n7KbFzjlnqHhq7y+w8PmYseWqDz8bUhcZJzSDBplTcwZYONiYAip+hiztkdgfeO+oi6jyFaJOKMQxUFJ/Goyhrbc6ibOW6c8k/vXOF3n+rzyNTDSRLKusy1scpyJnM9nlHTizy2odCxLVTd5UBV4Rcf3mQWR1QEicMHdE54Ap+4OGG1XmKeOFQMEUGPuenUNqv6Yd5y4iAvPzjljy6d5doE3nRzk5al8ci1Gb/15C7/6VuP8rGzI54f7dKsJ7iByZ883ScNRS4NPW5sW7zroXW2+zGXJj4tQ0OWHUQp4+XLbS5OfD53fcSj1+e0NZ1/8Vfr/OC9HX7zmeus76V83z0tTnesv8ERv/DKg/zyo1t86MyImRTyrrsP8oEzA7779haLUgO9lHGqY3GgajANU372ZQc5XNPJcyjpEr/zWJe33d7iXQ9e554TRWRR5J/ev8RPPHCVYzWDg1WDOxcKvPWPrvAn33UcgNeerJOkOX/wHRbP7rg8tDblvX//GEma86mLE152vMxDVyd88MyAn3vJV1+j6Oti1n83fE0nin7kzkO885E+zRoUqSMLPpe6IoqcIac1NCnjrXfXuGehzHPbMn8Sjci0LtdcmbapIKNzcRDwT+86xu+d9/B8E1ufUTIEpLzGnUsSThSSJCo1tY4fOMhGiCIUObPZ4HxfQZMymqbEtVEEUsgpW6JqOYjygDDTIc4R5B0kqUMSl1ibuNyx7HG4ljIM5yTzFl6o8NmrPt94WGFrWOZcP6Rj6VTtEZszmQXLIMm6KFJMp2Tw3e96M5/4vY/wxp/6h6R5zKJdYNgT0CsaRd1HNF0OVmX8bI6YVhHkKWlWZuDo+PkcMaxzpp9w82qRC/0xx+sF8tzHlIvcs9LjYr/BIPDJcxNDH1E2VLZmCYXAQtDnINQZhRGSv8Q0mXKorJALU/JcxpAlojTkYEliz9tBkzrszQQKukq7YLHrTLlXUVCkhJEvksU1PnoOrnR1THFKksHtRxyWihJXhyILJY+hU6eoB1TNAVFapOc0CNMMTZmxXMqxFZWJErBQ8ujOoSop6KqMJEac2ZO5oeFRk2P8yCLOZAQhxU9Snt7VMJU5YVzClFQONyIUKcFWBbxEYbnkMfYMUgKO1oY8tF6iqEYgBkxcmaLh0vc0CnpAGnsoksC1icDxWkiQmgzmCkeaIVf6BcI4YuyWKFsJcSJxfWSTInCoHlDSY6aBiq0mnJkUSVORVtkjIyTJ4Gp/v+S3aEbMs4SNcczuXCVJ93vk1/tlBClFFmPqVkxR92kVYrJcZGNsIc5zJoGIiEqaiyiiQH9QhlTEixICX8WPIypmxDQQccKUI60hjl+mYiaUrIQglYjSDDHPcEOIdZt/fd8JrkznXJ/kfOx57ysnig5Pb+mIuczJhYAgBTXVMdUMWTBR0amWXDQpY3dmYCoC5ClZYuPEKk9cr9Ep5vzhmeeZT5tMByvokszPb21SK6YYWoQbwcgVGMwkDrdSOpWQspGjSSqSKOAFKWM/ZndQRAUqZRE9NxHEGbtOREsQ0RWNxULOlSDAiRXUXMJIC2x2c+zVEQ1DYK1XYxwmkIi08hKPrmfcd9Ajz026rg9EOM4SPUdGIKFiSRxauUbTsFgohThhyFLR4PLEQ8nL1EyR3FVZsGNKhS6HKinr8zllvYUqC+hyTuCrOEGEF1skeUA7qGHrc57bOMXYFRjPVxCkFEUSSNIYRQ8Jk5xI9lm1Bc52c8S0xSx2sWjw2psbvO/iNkEiIqo5qwWJ9z3T5+E1l+vjgC1P4pZqynO9CNuZsPDKb+dyWGFRUXjsesRdh0PGc5tzE4+7VnJ2pxJfWM95iVRhLQrYnMCRSoXF6hbTUQkha4MyIBFjVFp0XZ9yocdOf4VWZcxDVySKgs1K0+HJHR8xVzhQgvN7IZom8MO3LVNRVZ7c7mFJIm1dZxzHhKRUlQJVSUI3PZyZyVtOVzg7mtH3E37shQt88MkhQzHm2cGIaaBysKLS60nszj2OlaZIaQVbUhi5GXoh5MUHyrxkpU3d1LiwGfOqlVU+9FXmqK9mH/zXSj/9/z/xO7/zO3/ra4Zh8IlPfOJ/uMaDDz74N+be+MY38sY3vvH/zdYAcPKYgqzTNHXCLEKUXUwtpOA2CRyZiZ/huwLNpoQqJmwPMk6U5xxvmJDaxOy3AquSwUoZXr9S45cv9ripXsNC55HtMaKRUNMVDlRHIIdMA4u6ITHzfFJV5APP94gyld++3uVwecrJBY29dEzP1agUFGZ+zLaXs1LVeM8XNtkdSpyoV7mx3OHDl7qQSMRKQCxFPLA+52DJYt2PEGWFdjFlGvncvhCwOzf5wo6IpolkpPh5QkvS6PbBKMZIswqP+XOWKjmaINL1UhYqBXR1hKkVyVyVdS+nU5XoBSElUeXYooumhZQllUevSCwVFAxjzO7MxE9E2uURN63IDL2EmmbQ9XxsweY3n72KhcHYjahZkOcJFTsmSRP6c4NKIUUSMyzNJ4xkrlxXeO3NVT54aQdV2ndxEsdVPhUqvO50RJiF5OiMXZUwhIpic0vT4oneiN15ykFFYHdawFIG1DSNK17G1FdpWyqiELJaSXHinLN7KpZSoViaIogBWWziJ9AuTLhxQWVvpjAJcj5xdZvjLQ9BipAQaFgiFWuMTE6WNJlFCue7Eq+8YY+SAZPAxEti+q5OnmmkicHuVGO7p6FbcLTpESYiOzORqj3HiwVmQYntqclKNSZKcww12I+FMp3BvIAiZahSiK25jAOVLM9wIptmIYAMlqSEsSIhSylxlrMxlpiGMivVGEWKcUKJoathaymLJY9jdQ8/0IlSiSwXcAODcQqKlHKg6nF5ZDNydEzZZeTtC29LUoYTgK1JIOQIAoiCwN7M4OmdGmEs4kUSopijShn1UoAkga7GiGLC1sQkSkR0RcZWfW5e7DHxOoxdi6aVEEcaSaJRtTKeGQyQNRHfU0BIWKk7LFf39UJlKeV4bcrIV+iULMjhL9b2aJVkSlpO1YyYBDKaAhd6GpsTiyMtn6mb86fnd3H8jB0nJXChWA3QpRxLUVCljND2+NLlGb/96pupmgqNpyW+tBXTdRycACxN5g0nFqjoEjvPxBxaDKhYMv2+jEfK6460eMnhCg9fl/jDx9aplDUMRaRdHuD4CjuDNi9cbPGnO0NKFYm33FrjZx7YJBMzXn20xud3NmjZInXbw1DnxEkBcnjsSpWObdDzI95yS4P/45MbHGnohKLIZXeEHKvYxQzDmmNKBp3ikLJRwEmmPHV1AcWA1x6uc8UR2AlGtGwBJ3I5XBWIGLIiVjizJ3J7o0nbMlEyCSdNuHnR5Jlth3pZ4/o44PSixSx0OdZQuLAlUReaPL0REnkZtUrC5ihlz4v42Vcd446VAjd1LGQ15Z0fXycpdJm5NT6yOaLnP82rl09wacNiZcHnoxenHC2O+L1HB/zmG4/x5fUZ//6L6+imSupqBHnMoY7EjhMwmqZ8cm3INx+u8f23d/jHf3GZzVmEFOh841KDP31yjKrnfORCn94gp3FY5/WnapxsmvyHbzrB5h0hP/6X17mhbfJLn9/hH9/XRpX3Dyf+w5c3OVo3ed70eeeLjvKOz67zx284iSQK5MA/u3+JHIE/eLzHi44W+c4/vsyHv/sGirrMbujhEfE7j3Qx9Jw7WkVe/3sX+O03H0YRBQ5W953kntpx+LFvbP81L/3p+T1eebhOxZQpGxb/5IUtnu86fOTMmFZB4R/95QVOlQu86+UHIPb+X/Pg/x1frbjqayWm+prWKLqtU+RITcf1RJ6+nnBt5JNmObKUcbYX8Oi6wAcvX+anPneO7//4RUZDGT1o8+pDHWauQSYkHKiK/Paze+zNBNbHEMQmQiby6XMyl/sxhgSzMOHhzZDrEwlDhj13xsVxgC4qxGnGs1sp9QJUDYXLgwRVSoiiBpeHIo/u2JhiDS8SGQYpt7QVBq5OtTCiY5U43p6ha3NaepmPPbXIm44do6EX6XkBA9fgDTeOKRc3SZICncIcXe9x+MgBvvOdL6d36Rx7c4XL4xBVlMiEMZKYUTFiSuacG2oyTduhY5jkucBiMSWJTOaByDtfeJAzaxJPri/zx8+aDJwim/MISTSpWXPqWhEvjmhbOrXCDjd3IjKxT5rp7Mwz2qbJ7lyAuIooeGR5SpQJBGkClJhGEb3pApnkk+X7gtyXewoHShrTSECXZ1iKgp/m7E1MGgWVSRRy54qM4zd4ZFNnuTqmoCpUzIC+HxElNqYEtrYvCCjkOqaaszVPSbKQNNOpmxKWFpDlIV4ksloCS90PBE52tjnd6TLzJUZOkb1RnSu7HWQRrgxl0jwkTF3KpkPd8Jj6OmGikcQFHttYYrEgo8kKSVyiYYMmmmiSjCZaDOY13KCALFg4ocWpVsytyzMcX0eVcmQxoqBHeEnEua5M2fCwtBRFSlFEBU0yeH63xnQmE0Qw82SSPMOLRE525qxUfDQpx1JjJCkkTkPgKz+W+w9kSdgneDtl5NrsTQu4oYkXGOS5ydizKWg580AnyTWEXMILBVQ9RBRTkiynasWc7OxrLxVNHz8RuNyzOL9TZ3vUZmtcolaYk0rbfHjzQa66z/Fsb5fjLY07FlXc0EQIdIqaSMl0MNUp3bnM2hDWBhK65uFEMSu1KSM3xwkkpsG+Fk2j6GLqPhu7Ft9z842oOlwa5VwfCQxGOYOeiusquL6JkpksGAqL5YSinhEmCdcn+9bDQ0cji+t8y7Eq5ALDcY6oJsiiQEUzqVkxiuJQtuac7sTUSwnNZo+5K3BjW0USRC4OMtIclmsCqRryj+46wMmGiiAIuIlHmlh86ImDXLlus2IbSIjYVoAplAmSDDfav/bqNEZIGjhhyr3tKr/09w6hWC55ZnF15hNHLS50DWqGjCxF6ErCKMxwwpzjTQdNTujNGvieRkGRaZoq33VzBxGR/+MlB8kyiaGfsVyUiQlw3WW6joQu1BiMFT56ZY8LwzGiPOdgPWAvHGPqAmd2XOSSz8++bJXn93zag6fpb2+iHX8BNcVie5Zyz0EQMo2EnFccF8jyHEOVONWwubBncq27QFWqsjeT2ZssYNBgqTbhaF1Cz5tYps83HPSxhBZFM2DteotjpTKNcsDe1ORAochqTWCWJCybJj//0kO8/8kRFV3iaEchIaGfjzjYGdMpxpzdDTl7PWYy0YhjiW873WJ7F/7FvStUZJX1uYdtJJyomRyqKvieRKGccc8xGZ0KYx8Gg5xONcfWBX7w1qPc0axy3y8/x7bn0ypJ/2sJ7ev4Ov5vuKlpcqgxo1EaAwJZYjCcmVyfJcyijIapoIsyu6OcXS/mwCJomFTtKQNXRJNk8kxjY5qwMYu5OHQwsiKlrMBPvmiFBUtjteYzSz0u7VUpqQYSEdMwo6RLtIsJg2zOPE1YKMjszCMuDz2auszpg1ME1aFdlHnnS1bpTgXKBY9OWSIk5a2nDvL6E22QY768O2HVUnHcnDVnhpYWCLP9trSqofF8T8NP4GgtZTaBPJQpWzmi5nCkrnCkbKGoHiVVJ8kCZFlgobAvCqxLJqIUgZDTLgjI5pilksRip8/RlkPVzMgEnxMLGY44QxN1JMlDlSDPBIqGy+FqhqK7tG2FQPBZLklESURdlVgsD2kVUsIkoVHIaZRG+IHBxb7G3C1wflelWErRBZXjVZ2dSYaBSncuMppW+OOnFji7W8cJFZZqDguVlGE84ULP580n2xQ0UBSR7anEzC8ySxL8UCHPFQZuTt3M0eWAIAbHL3Nm16LnamSJSUF3iVIZJyogyh62HtOyBVqVGSeaYxaLOUkmsFgeUdEyTFXCNByeulQmjk0+evYAv/b5JYauRBhZjB2bh9eLPLVuM5+bVAoSpp6gaw6SFFLUBOLE5FjDQ5Qc9mYyY09HkxSEXCNHZBwkJHmIKicYyr55hyXL9Bybc7s6l/sK9x7ao2IEiEKOiEQcGRQNGT8y2JvpbI4l+o7I2BMQxYi+IzMJNDQ5oyBUOGq1mUwtLFVAFhX25jaKJNAuzQlxUZWA7YlOb2Yw9jQGjsq1ocXZXonzwyIjXyXLIIxFVClDAEQx45blATe0RxyqT/FiCUHI6fZ11nctwkTl6qBKxYSha3Fmq871fpn1fpHNkUl3ahAkInGe48dwdctk6Atc7FlsT0ye3WlyqdegU0y5adGlrCf0JjJbU5mntmz8JOOhy4s8s75CFFqIgkTFDimWN5kxQxJiKpZIQVCZBAZPbhQZ+DlplnKkFvHOL1zktx7f5ctrHnPHIHDbfMPCCX7zW07zhlMNHu1OefXJKifLy8y7Fb731hU+9pabuX3R5p98cI1f+tI2pQJYRoSfJAShyda4zM4soW5JrLQUDjVVDlZ03nRbhang8IWtPTq2yFK1S4ZAlBTpTjMePbuCHOrULZWXr9bRZYkffsECb7ityjDvcapiMZ/LBKFId2rRc0UGTpUkC5nMyzihxY/fs8onrnfZdKccbbp0SmNuaMdU7ClyXmAQJDRUjYcuT/l3j1zgu+9q8+IDZXxXYKWR40QJq9WcjBAnLLI1VpBFmXke8JvnLlOuB1jGnJcdD6iY8FvPrvH6m+o4ScKzWwG784iyprMxTTlQtnjrDTewN4+plzKGTkxHLPDzD26TJNB3I971mQ1uqBeo6hrDGajZvivwIzszOvWM9bHHx9Y3+Z3Hd+klHu/8phVuaRZ4w6kmP3x/m6K67wJXr+d807ESD12b8pY/uMjn16cca5r8+Xed4EvrM061TN7y3ks8sTXjV7+8zUMXHT5+acS339TgjoUSH/v7N1ExFB6+PuPXH94lz3P+44Pb/OHF6zy9N+UXXnMA7StJpjs6JQ4XbHZnEd5MYmcS80uvPcjWJGZtNwb2Nfs+tb2DLOcAPLk742NXBljKfrwUJBk/+emrdAoar7uxRiQm3Nq2+dH7FykbX9O1LP/b4Gs6UfSGDz7Hub0ASUk50lFZLZRYqnjIYs6BssVtCwqTMOTcqI+iJBTtnIvDiNcfWcUJBUQkuq7DQ+tjvEghS2y2ZzHnBgqvvtHBkm36nkBJFzlZtzEkhaGnslwscryqI0kJEga3Lyr4c5UwFTha1XF8m5yIomphKyoXxxJVU6FmqAiSS8MU6U+bXB3JRHERGZsoj2hXEj505QK5EHG4bLFaDciA+1ckjjTGCHmBjmnRKMxomnU++J8+yvqFLWq6ynO7AkJaQlYGCEJCxfAxjS5OZDKMfCxF5drIpKjL3Lta4L5DNkfLJpf2JFynxl9d1Jg4Fc70YjRRIRU8WpZJRkgQLCBLYwypyCCYI+QaSR4g5jrrwwKmDAeKEVuzgCzTGAchTlAjF1IqRsjBRoxl9znejvETiatjnb15nT3PZ6kUslrN6bs+RqJTFgvsTAWO12MOllNyYUZJS1guqHzmWpnHdzSmQYmDpRxbn1DUPE429wmhYTvE+NTNkIVCzko5QpHnuGGMJKRcHTS51GtyuO6REVEtjmjVhnixRElQ6A3rTN0aG+Mqn1trMwlEDHmCn8RM45BZGLE9z7g+Vnl83WQWCFSsCXXbQ5NdDC2kas7Yc3Ke2izy7I7E0MuJ0gRTHzMLRGwNjjRdqlbOiUZKmioMXRU3lPEDgUniMXAgiBXypEjDTknTjCgVaNk+fizzzFYLLzJIspyt8X6AXTIDNkc6WS5yqV9hd2qxM7HwQxVNBi/UKRkxIOBHCpKcgyAw9kVmUcy5XZ1ZIOOHBgoqRQ3KRkAuuMzDHDdS2BoVGLsm80Bnd1yGTMRUHCqlHQbpJj3PZTCXsS2BmpUjixIyNu1iRruQsFrev+dCKSBKJBQxJUlURq6AIoITiohI3Lta4d8/tM04nnGwKrJYzTneMnnhgTKuaxDnKS1DYyZ5GKr3FWewjLYt059XcGOJ892M58ZTDrcl/vA7jtLQTQZOztYsJskkVssZdStiZ5bjRgKuV2NlIecdLzyOIcuUFYs7D/Txoghblvg/n75A3wuYejrXBlV+54tVDFHFLkj8ymsP8muvP0CzGOMlOaasMfQidCWmZshMwwglK/HFaxM+vzXg+kDn6lDisavHOd810CSLiDmdYoxk9qlqEncd3EMUDBRp3yWoXJqjqRHPbwZ8+PIWv/iKQ3zk2nmK1pw7FgUqpochlri0JxEHFlvzGC8DDwdBmXComlEqbrFa0umPMiq6yk/cc5hffOwaB4o+z/3p73HzXSdp6RqGFvLCwxGqEhGmsFKGYeBjqilNK+JCP0bKS6SRzMNrIl6o40QR16YeeaawOcm5PPFRJRE3TthxHZKoiJjL7Hgz+jMdRZTZdQOCOKVtSRQUiVjIuO2IzCiJiWSH20863LMsU1AhygS++44Gtx8wCHMIYvjZB6/zoy9c4HUnmty9WmCpLiIQ8sxuwOUrMt9ypElMzIWBz7l1CSsyaZRFnCinoZZ4eM3j3Z/ZRDVyRn7Cez6/+VXnqP9aIv3VGl/H1xae2AiZ+TbtgsDB+pjNWYwTw9GqSKuU4+YR4zjmRKnEMbvAD925CEqEJJgca3kgzYlx6FgqmmAy8GNONEyeGY759vef5Uw/JIhE7lyMOdaacKmvIAo6h6oBqjIjymJuWsxR1BhfDGiUcw50Qo6s7NIue/utYJ7HclHnJatlru2YX6k6jnnXF8/zD25Y5UWrFUwl4+PP+zSLAiICe1GAkNhEscw0DGnoKhtDg0QMuPkIvPhokamr4Lk2u7HLkaqBG1io+pyVssiJVo9jzQkFRQHRYdUWEIQUN4KqrrAxzZBym4XSmAMVj5lrsjvP0bHJccjSIlkeYWsSCyWfldqUgiLhxQJ3dcoEacwth1xuPbQJuUGc+5xqB3ipy2o5p2wPWC7liPKclqnSnWV88Eyfq5OQliXTH4gsl3LecrrOZGbx/EadmS9iKyKaNmG1luHrXX7rsS6Tqc7GSKZpCey4KaN5gSSTeGatSG9WIUhT8lyiruec7SoogsD2pMDmTCCOdRQxY2sioQgKDTtmtbHHQjFBUwQWy11efGiPgghtfUTPFRjMStTrEdNgX39xoSxyrlvjw8/X2Rzr6IJKs5QwDDNGUzBEcIIi2+MCYy/C1HxGvo6Um4iCyCzMWRtIbI0MRk6VJLHxYx0vTlkbqXzxapnndypc7taIIxs1URkHEnGeIEkhVcvFlGM0MaeoCzieycQtsDmyCVNwo4yS6eJFMPFFSqqMqcjc0LSYexp7kyIz38BUoKRDUUuZ+QJJmjKayphqTJrnTH2ZWSATxiKKnFDQQgw1pWBE2FqArQUEicAslHFCkduX+ixV5thGjCzA1NXpOzYlI+V0Z8r9R/rctDLmUGtGp+QzD3IMzScnomS5LHambA5K+JGJkCuIooiqQJrl+EnK0M9J5YCpL7MzLONPT+POa+SJgGmE1AsjMjFg7uu0CxmVooBVDOmNJWZBQsWMCEOLsiFyYmnA7ijmW49X+eO3nOD933aS97z0FkqSzdWhz3M7Hj9570HySOL1J1r8/j84hpelvP5Pn+OnPrNGqy5iqSJOHKPLOa1CzvWxwtAVuWPR4rGrHi86VGR7GvGP/uos/+7xMwjyjKWaQ9/NOb+zzMCR2JrIhMECrzpVRip6rE3HfOTZEetDn0s9n3/7havoeZmeK9JpJCCm3L464nhzzjxJmfpFvrhW5P5Ok64XUi26nOgMaVsumpyxM1PxQ5OBKyIkNte7Msc7Op++EvDdf3aRm5dMUiPkLTe3Wa7opKSMXZPhVMEd68iCQJDnaCg4cUJR1fDyKV/c6vLNBxb4zOUx/+nRbZBy6o2MPSdGSErcWV/m+z/+PL/2xDUGSUASlVkb+0RCyvd/Q4Mndx3uOmDxK689wu+//gRvvrFJSESGwEIlYZ6GlC2BezsNLnkDfuZFh6mqCs/tuvy7hzb4k2eG/OMXtDlcKuLNJf7VA9c5N5wxS2O6Towfpzy1O+Nze0O2XZ+XnS7wwx9d40MXexzqKJxsm7zuVAMA8Su6QkVd4m237bd9qTq85nibVx9r8HNfXuPK2KPvRNzeLvHNN1S465DFa05V+bZbGxyuG0z8hF987SoAv/b4Nvd06tzeKQNwS6vAr3/zDQjC/n10WeRP33QKU9nXXfvElTE/cPsin7o4Icvy/0948esx1d8NX9OJoqWiTruosFjMccQx50c+fpJSUHIktcuWNyXKMpZKEogpiTmlaih8cm2MrYpMXIm2bXFLy8RWc6I8I0tqFCWbbQck2aWiiRhqyObcZRoITByTRzZyuv4cQxbYnadccaZkichi2UG1Nrg2ESCX2AvGhNmcKDLYdWa42ZSpU+TaJODsIMGSRT6/BhcHGYYM150pkeDRKfpo1hZRKhOlKYrSx5BkRkHELMrZGtWZhBnf/64f5Mk/+jPaxZAjNZnzPZ2CWOdEfd+RaOCUEMQhz+8s8plrOQUV3Mzh2mzCj3zqDJg+y2UJJ5T+f+z9Z9Bs+VneC/9WTr06hyc/z857z54cNTPSSKMcEAKECEZGIFmA8RFgQGQBBpMPhgPYFkgyYCGSEkI5zChLEzRp5/Tk2DmtHN8PmxcOr131HtfRseuU5le1PvSH7urqXrWuu+7/fV8XhyoK/SDElAxWJwFzdkQqjDEVjXGUI+Y1Bn6KLhbwEo+nd8qEac5CxWVv3OB836Ck2kR5lzgx6HsCfqQzVxTYGgeUFBVFcqlbAYeKIuNkyuFywlJliqp6rJRVXDHm767ucaiWslh2udDLKOkibqQjiA7LFYc0s9gcGWxOEhRRRRA0ICHKppS0hJO1nGGg4cQxEHDrbMBcCfq+xZFah6VKj8tdEzew2O81aBgis0WfqRBQLQ/YdRIMNcTSYiR0yobCbbMRx6oJqmTRtFKahRzH19jcr3LxoMzjGxU0NSXPU2pWymJlSpToVE2BkpkxW5SYBiVAYbEMcaIwDRQUMadhOyyU2qR5wsHARJahaMVokkjLTshzBTcWKJljaoUpUSog5DolTeZY3SPNQBBD3DihoLuMPBUvkokSkYGjIQnXTR1nyy6dqY4oZohSiqVHlC0fQUpojwWCSMDWMiRRpGhkFI2EvqthqC5VyyFORSQ5JRMyvFBFFQ3STOdav0R7YiICm0OJYqFPszJmGAh0HAFNyTGUmJoVcbTuMxobCLnBeq/MPYslXnOsRRwatIcVeo5JGGtcdfbpxh6RENEZSuxNMr7n1gaPdUcgx2SxzNE5gZauIeQ6mizQ83O2xznjIEEWRIqqRtcRqBZE3vi+q7QHOUpqIGVFUnLCNGDk68wXZRCgZLdx/ISf/sw6X1rVkdUxQWzRKsVk2gDPV0jiIk4cEKUZx5oqxeKURAh528NbfNs7r9Ib6ZBrNOwet89FXBtIjD0NKS8Qix7zzYjf+fwBGiKScP0eKEizbI8yxNxkHA9ZsE1UbUIc1/DjnDRVyaUx86WQJ1cVjizE/PDtR/n7tauEucvppoCpj7BVgWvDgBsWPFzGCLlAHAnsTmVmTZ0gixgMV+j0VGaKKncvF/Bx0TWX9t4BJ7/rhwlEnUE6QVfBzXsEWYoIaMYWi6WUQ40uF3sJC0WNn7n3MAf7EoumRiYNSXK4cz7j0S2J/bHJoqWzPoh5cqvMcDzLm24+QidOsOIKYRKxPQ6oCQaKlDGIR0ySmL88t8tb7lnmBUtlZN9kGnlsTkds9HWCUOdbTja5PJ5gpAqCCFd6Pv/5sR3ec26XD1xu0x7BdFLlaLHI99/T5Eeft8DNrSKzRpGSLLE/jfnFB1coyhqvPV3j5z66yYeu9vnBu2bQRIFhEP+vE7NneZb/DjVdQhAiFHmALKfctRBT1EQmUYqf5pR0gbc+f4F3vO4Yb7h1lsfXUiKvyJWuxCiMaVoSLUtEUaeMA+i7KV/b8OkFIb7s84rDFQaORkRAWYcj1ZhpFDGNE5bLAnEssTWOuHU+51uOzpFFKeWiB6mBQIiijbjrsMe//cRVPr424FDF4DWHDjEJM/b8Ph9YvcJbbj/FLfMyy3Mx3TBCVWHRlhCUCSUjp25KXB0IHKlF7PVtrgwD0EYcnRkTpAm6oPK+833CRCBLNCZxhC7pqFLMQnmCF5QpWkMWSilFKyQTEu5eCjCNIVvDGbqewGItQEam78LIqaOqDmJu0fcEosQkTETmSg5LZRcvkBH9Il1XpD0t4aUhflDkfEeipulsjXMOVyQO1bvIFJikEWVDZjgGOynw7afmac6FpFLA+y/toIg5iuKzuV/n8S0VL6iwPxGJIgOUjFSOKGgyW+OMRVtgpTpBFCOapYyvXCnw9PoMT+9V+YunZ1guZyhKwjiQOLs7x9MHFk9s1WgPqjx2pcL+WCWNbXQ1YHcqE4QlvFihqrQJYp2qDkg+shRzuB5TK8QY2piS7nPHQoCu5FjWGFlOaBahXEkZRQm66jFbCjEVHTdU+dxqhQ88OcvINdAFlbIuEGQpYVBl4hXoTiyc0CZNdBBynjuzghMIkOZIksTQsZEFmblSSKs0oVkd4iUZgpBhqSlCrFLUBeZKCcuVECdUuNTWOBhqPNMbc3ZwwL4/QBJTwkRGRMSQc5Ls+mHX0C0QZwKG6eElIqKY48USk1Cl5+l0piZerGJrISdbY5rFEC+6bmo9X3IJEpEDR+fqQRHf0xFFCZCpmjGqmDL0NRQpo2R63LXcYb4yxo1hv68ydHV6/RK7nSqjqU27X2CrX2RnZDBjT4izkLN7BbJMYamSsVBJsI2cM70hW8OEEB9RiPBCk4YhcrLpslidMlcdMF8bcvcNXW6eCzlcDyjoAZc6Ep84P48kSfz2V9f53g9c4Dc+t8lr33OeP3x0m196aIOtYYAoCoRJxlM7Dm6Y8hfn95ByKBopT7R7jOKAuSLMladIsssdiw4vPtnhK9su6/6E3YnP3jAhSDIKioii+KztlBn2ygyCmDjRmfgGaxOPp/rb5EKOFtm87aWLnJ61+OLOgCjPOfA8BNFlHAYsmBkDXwYEjlV9rvWv/yZf6azx8M46ohSiCgp+BoooUNJ9xr7Cdttmv2fwkuMV3v6q07zuhjp+mvI3T/Z54+0NHm1vMfJivEjEC2RSIaXVCInEiKIO7QGIocXOWKQ/bpCHFjVd5UsbE164VONDF/rsTTOEzGZ/IPGfHm0j5CKO4GIJBmEWUijHHK5oPG+5zHo34MfvW0SWBBRJIhE9dEWgUkjJpRA9MyFVWbQL/Px9J/iDzx/w1r/f4qXHSjx2MKaT+fzXp9u8eG6GWcPgRSeKTNOEB48XMVWRN37wIm/5+FXurJT5q/Md/uKrA775VJX5isKfvOYEP/+8Fc62HQD8KOXPH+tQ1GT+6EsHjP2Enh/zr+9c4Je/sMrrb5pluWjwXX95ia9sjvjAuR63zdjsTaN/1J27lgu8/r2XGHoxa8OAN982/w+TSXvkeY6h/NP09d4komoofOd7LvHzH9vi3z94iE9eGvG+Mz1EUSBKsv+5ovks/w3f0HNdshRRLQ+4dLXFHTcEZKmOafhIQoCSlri5KbEzEXCJKRsaRVWjj8u9y8v8zaWMFTtjfxox8GJqpsjd1ZjNic/eyOLGlsbJlk/Xm5DnMsdrJfamCU4SISQVbCnAyQLUrMxMUUOrjxh4KscaVezKhEkkMKPXqBgeF3sxplQnkURCw2U6anLH/Ij9ScTNM3Vec7zCB1fXOVE1GEwjblzokyUVLvciimONlVIBN+sh5xWu9HIud2Vumymymfu84m0/zdlHnqTYuhNNKHO557BcqSLLAZYicr69wlN7GrpQ4aNrGc8/kvD0dISV2aSihyYrzFk5AiLHqlxP9FBlhkHIbbMRfppyRFcw1ZQotVgfKhS1CCdU0TUXXVHYHguY6gwHzoClYhNJniKLCrKokeQJd86LBAzZnZQRxYyi7lHWbVIcDqbQsmDixxya8XDHFTpOzGrf5njDxYkiVvsGJ+oVJoFI1exxMG6SxlWCdMz5Aw1NydFE7frkhuAgCAaaqKJKGV7iEWUBFT3nSr+JJMjUzYxzPRG7MOCpHYt7VhxeeWJE2RS4eT6jM7XRZI8ok/jips7hSkqcZ+Sphp/IKFLOTClgNC4x6TSo2AmWnlHSHYRcY+AqmGqGG1qQZeSyhCRAtTBFFiQiUhKuN/1KRs6T+yXWD6o0yhlhrKGpIabi4ycSJ6pjZDnkzJ7K2X2LOC7gRyr9XCaKFQaejCKDG4Z0XYmb511kQQUk3EjEC0RGscylvslsNUKTU+YtC8czWR33KWkx86UEx4fnLEp8YUPAjUT8WKJuhSSpTA7sRilenLM70HjJDQ6SoGBpCXEGUl5GEEV0WWC7U6OsiLhpTl6O6DoKLXtAnIuMY4u33L3C2z63gaymXGiPuKcl8XPPW+GXPreBl084VBtzYd9EFCRevbzEJ+MhPS/l46sD7lmw2HcUNqYT1iYHoAVEuYBETlEVEAsSq0ODSSDSCwKU0MDC4CXHNDpOxDUvwR3nVBMbxyty49IOlw8UDKWA4zUJc7g8mFIt5AynDXZ7Am4kocsGVmVKqZBS1hS2JhkDP+WQYSAWXH7gruPc0NBYjc5hyhJubPDBC3C0onFht0SjGrJUFfm7c3CseX1NZC7TOd5QeenyHFvTCZreZ6mo88yOwOlZg1HgYcoKZ7sh9ywIXOp53HmsyNq+xlc2Xb66H3KiqWEY28hRhT1vyumWzcE0JggaiGqGKkgcbmZ0HBU9s7k2yimo4LoRD+13aAsZ5z/+l9xz5wM45RqrOxorTYGdyQg/msc2PGbrA2RamNoQPypyomZy90yNmq6xXNXIjDEzZoGG5ZDmKfcu5+wOdboeKFmNLMnpOBIfOT9mXrNoD1JKRZOFQg6Ci6hK5GGDgZIQE/H7X9mAUKM/EZkG87RqCR03JQ4FfuGTWzQEi24U8buvOMQfPbZLO3C4pVnkDe+7TF1X6SVjDkYCb3vuMX77s9t8dXtMlgmcWFa4pdTifM9jFCT8xpe2OL2s8sChMg9dmSLZPrOpwoWvs0ZlX8cI1m+Uffpn+SfEXKU/FdjuzTIIUwpaQtUKINV4xeI87/pSnz+Y7vCqEzVedrLCS0+Uef37JhhqQhBCkHXRdZE5ochwDOMgY5hEzBckugE8M+nzA7cd4sOrAnE8omQm1C2N3kRnj5CaHaGKBfY6Nr/97fOstn1yXFLBo6JJkEOhvMe1uoY1tplp9Xjt6aNcGfeZpCHnR6vUdnV++u5b+YUvfwVtrLE1yAhCgVtXNEy9x0I5QBUrfPTpKqYsszjjc3k4YtbOue9ol74voyhVplFMx9GomBauOoaoyPZIJSMnSWaI/Ji6AWGUEmUZNckkzjwmXpXzexqGJGDkMpuDnJJRIS1MKGsyijxEk3UkYUqOSJhcRY5KjKY6GTmaoNHxBBaLCsMwYKUi0/ESJEpMwpSZYsrIc2nMqAwTgT95fB9VlTAUAVkWaJhjPN9meX4XZTBDeyqgCiJhCqkQUzZEZGVEs6DSdkMGbhlLEQgJ+dH7jjI+MPmrrwQcWojoRilpZlJQcorWiN1hmTiT6AcRRVMjTRXOt2PaTgtLSek4E47XAI5gaS7bE5mZAtStIY9ulLh5zrue7CrGDPwMUy+wNyowSSUUbYrnW1QNhfbExlR9Xn5qDze0EAUZIQ/Y6Eus1ELsf5jk0ZVtDnomkpQiEeInFoOxxQd2puiZRWZ3GfoGwZ6NXs6YbyaYekaj4NK0fTa6NkUzo2SZ6EqKqeQMvQKbfYt+zyIVMqYOeMWII/WIipoji1DSI2w1pSan7I0sDjd8JClms6+RpwmaHDFXDrG16x6RTqCgyym6mrHZL9AqexSNgDARuNguIIg553YqjKYmORKWmiLL4EcS9UKGJiccTFXmyw59X8JNMo42HCRBRPNSpEhEzFVsTcHIZCZxh2Ic8dCVOp1xkbKRslj1qZoBm0ODiS8wcVRUJaKoiyiCTpQm2IbH9kRk4klUC1DUwNZ8BCEmjDMGnkVJV2gtTQgzgbsqJ3jHV3tMvRjDDkGIGToqqhEzcGPKmsyfPdPmkfYIMZEYBBGaJmCpKtWSR7M4olVMqegxPV9k7Fv8yHOavPLQPG/+yDnqdsYwhoI5RYgLTEMR1AA5tgjTEWFicLieMggi7l+oY6gxv/HFDZ7ar7PuOKhijq7mpH6Juxo6D292eWFdQiCl52rcNOvhRhO60woX2wIrFQVJGmFKKjtTgTA0WOuryIKAm8KLj5b59a9eQRcV/utrT3Km7fKuJ9v8q1tOcP7yASVNYTedUDMkxnHEQllhYxhTL6kMxvCqpXkePRhwT0PBUEVsS+Aza33+5FuP8xtfucbT3T6NikCayUzcnLJh0g5TTFVCTAVW9xJ+4u/XuTCa8IXNET949xxPt6ecHYyRhJRMSCiIKs8/1ODRnSnDacLhEzV+/9VHcKKUoiHx+K5DlOVc3o15e7/N77x6haf2HZ7Y9AiilD/8/D7feWudsuTyqpM1Xm80+fCFPp9dG/Hef3EDT+05PLE/ZXMc8OtNi5/+6CYvOV7iJx+6yo/c1aSgyfzKiw7R8yLKusK3nWwx8hP+wzcd5ic+tobvw7H7DV53yz+lml3reXz/bTNUTIXffdlRAP72qR5BnPL9H7zMj9+3iIRAkmf8+ue2+cNXH2HGUnnp8TI/+sEN3nRPk1eeqgDwWw/tft118etVV32j1FTf0BNFu8MMRbC5+VDGxWsFeo7LOIxRJZFpGDJKD9gah2iiRP9A5Vw7QdV93v7kFouViH03446lgNtmDZbKGbmQcayq88CKRJoJDD2NOdtmtqCgqS5FVULKyuwORbZGIqakYZgRW6OENNUQcpkLnYxpJOFHJmc71x35bUVifZjS9yGNTbZHMd2pBJHJzjjEixRMyuyMZRTBRM5tZkoj7piXKOoiw1DGFFoEaYosmARelc+umhA1UZUB60+dQ1z/EgfBiM6kxCAco4oCBSWgbgScqOfoWsB9KzIr9TYvOJJiWj1O1nW2eiJuIIAYkOQiuiRjqkBuceAqVPQMS3M4mIrIYkacyGwPm5yabTNXENG0Nq1CzigMKcgFticKTqiyUAoYRwGGrBPlGWFYommHpMKYMLHZmyqMAoWGKZOLE2aKEYcqGYnWQxIhjE3WhxkHUwMBnXEIp5ttaoZKqxhSL3bZHRtY6vXJDFntsTs2mPgVdElkHKbEWcD2sMTOsMHjm3NMg4Sel7Le1ylpAodqMF/KOZgqPNPWEAWPrZHI0caEB49cj2u/Yy7EUmR0WaJsRFzr2jTUJtvTnLlGj5+4f45xlCCKAZrap+OIfPJCiUkUIwoRzdoBs+UhYZqyPshpTyXyTGS56lI2U4KogBdUKJsJppoyWw6oWhFVe0zNHiMIMSNPJYxtdnfnubexjO/J+JHE7shEFkCXIct0Ts2EVM0EXUlQ5ZBm0WG5PqBqOTRKI+r2BEWKuDp0mOYHHG953L7oYSsiU19HyFOONvrM2BOyPEVXYLaYUzVz6nZE2fQ4MjPgYKoSpgmPbFjsTTVkMUdXE6JE4FgrZrkRcvvymKKiUzVBlTWqxvXElQ9sPMWRZsx86fpp9+Whx28/eo27D0948bEJzYJARZeoWPDxjR0mPvzaiw/xgiMGH7864mTJpmr4yKLMUklivpSBKNJxc/an8MS6QZQozFgadhE+szrk764dcLypcWejQugV6TkijXKPuiGyXEnJiBhHLmVD5EjV4MquxMuWllkqllCVmFwZM2uLpKnCruMzYwkcbaSMgutG9z/10FUS4ywNU2YUZvS9nJWywsOXi7SqKZIS8ryZY9wxL3BodpdbllM291SW9Dq/9dh5BMFnPKmxO9Y42sjIhCmmImMae7zsWEjX94n8WXZ3NU7WTBZMg8y12JxMWevM0w08LKlIQfUhtZkppgiiQK2cEogTbAUeX0spaDlOHDH2oWFKrJ57nNH5ywRGg1etHOPOJRtFiThRN9GUhJlijJIX2RjmdCdFBo7K2W2Rr12FswcOF/susiIyTUbk0oiSmrM1demFHmIq89R6wvqOyIOHSnzHjXUu7ga87pYqU8HFldr4qcLuQOCZnYBy2cVUMj5xecxj4x3K5ZhRGLPaEfmBm5awkNkZhzhpyvGKweWBR8eL0ROLdz3axtZkwiSnoRV46z2H+M2HdvnAxR6GqHGkJvGWOw7xV+fbnGoYWLrInhNypuvyrif3mCYxCiJ9J/26a1SWCV/X61m+sThaVegONfa9lCBWiMIiq+0SG6OMP3vigNffa6FLAj/x8VXyPOfjVwdUSw4oY8IsYjxtMJiUCbKYetGhYgosNxyUYo/DMw5yLvOZjR6Hi3XyrMzuoMyFtsgwEMjjMk9vlLjaltFMl+943znO9Dz2OgUOhnVW+wIdL8MPa8xVHMZZQHuk84MfeZozGwmCYNKfmnxs8xIfWVsnywQGYcDxmYQT8x5B7hEGJZ7ZrdL1Up57zCXOMpwg42g9RVJcJAlm7JTZSh9bVjjoWTx6qcruoM5jmzbTwIBc5mpfwZJVKsaUky2PipGyMVSQc4P9iYilqKRCTMUOKRsJVzo6awcN1oYSeWZh62MUSWOh3OVwzcc0XUwjpj3QGXkKFV1EkmNMRSEnZt6OWawMOVzzsTSPKJFBBCEVkZSciu1ha7A5AC8oMIxDeuMW0zDFUASSPKdRdDnciFAUH1PUeWrLwAkVKkbC1khhpZLxkWv7nB9OmLE1bqjVeGChyQ0zAbbpUNEVgiSnUXS4f1nlZceqCGLIclVCliL2pgLDaY2ndmoM/BgvMpm1U8Is4OJ+GVPW+Op6nVGQMG+HLJcF0jwBISRJZNLYwtRidicZrYLHkVpEmKicbauc27HwfZWCbIAgUzKmzJaHKHJOo5CTZRpfXW/w6BWb45Ua9x8p8oJTBpKgUSoGFOwJSZbRdWXGvoqQyxRUhSxXiDKoWGNqBYd6wcFUE0w1o1rKsDUBVZSRpJy9icxuX6XvSkQJeLGMGymsVFyEXMBUco40fKrFmGrBZbE0Jk5z1ns2O0OLKBUp6jEzZR/IMZSE5eoYWcoYeSq6AsdmXeZbHgUrQldSwkSkYkaUdZ+mPaGkucyXXA6XA8oWBImKKIjMl1T+4rWn+U+vOsEPP2+Ol84f5pnLK+x1apTMnJlSyFxpgqb45EJInIImi8wbJe6dqTIJBJ7eNrjc1tFlgcUKkMvsjlS67nUjdQGdODHZHhqs9RU2uxpf3hnzvCNFRD3A1B2ONCPuO5Lw9if2+Pa/PIeqZ/zY/XOcrFiM/JS5qoRtgGm5LNfH3DwTsVByOXAzHM9iOG5yslrhdx67gixH+FmMIMBur8b6OKVeTijqMHBSRqMSspRiqS7HGnBxvMcXNh3majmKlqAroKgeUSxilqacGR1wbCbn86sm670qk0ggTnMmgc04zFiu5OxOE3YGdVI8CopCzxOpGgIOMSU75tcfucrzF+v89H1HmC/pPLBS5p2vPsVtc0V+5mWz7PguuqizMcqwJZ1cnFJRFKZJws+8aIG3PneFP3v1TXzTsTo/8eENNvohRxsGO9OAM70RYaCiS1CzpyxUwbLGHG+FFI0YlIhGPef8aESldH1D5ImdCQeeTyZETDyZVkFka0dDFUW6XszbH9/nhz58maWKSq0g89ZPrjKOEqaCz2xFwpRFjtZ1LnZdYilCUhNecKTEt95YY5jEVAoSv/vZPd53rsfRGYlrfZ9f/dwW33yixmtPNvnFT69zYkbj4Wsj3nDzDH/+RJsD5/qkUN1U+cnnrPB3F3qoksAtcwX+6JuO8vrbGty9ZP+jOfZq3+fPH+9iKf98DuXbb6nRLCrc2rL4Dw/vM/ZTnCjl7d9yjNmixp+87hhn933+zf0z/NnjXW6bL5DnOUdq+tddF5+tqf7H+IZuFJmyxFo/I9MPUMoBSlih16+zN5GwVIWm3uC5K+DFApVyzhHLwhAtrvZDhn5O3ZQ5u20ziCYMw4Q0VRmGLpG4j6mFpKnIF9YTLg9CFEFmHIU8sppQ0RW8ic2nL4r03JwbZhNGsUPHC8jiIhc7Ko/t5DRNhWEg0iju0LAkpoHIl7czaqbCYxerfPJJjaKicqpm8dW1mIIiXF9L60BRzZivrqNJKV/brPHwmsHEq/HpCy0GnoGYVHlsrUR3eIhTr309V87uc9/shF7ocn5/hU+sSmSIhJmPokzQpZwEn6HTIskTjlVMtscJ73j1CXLZJY4VRMEhzgTiFGwNkthg5Jt85OIMPVfnE5dLgEiaxaRJjTB3sdUSM5VNlksJqhJQNUQkNErGgKOVnHHoEscmcSry1LaJKZrsT1NahYSGIeJnQ2qmSMWImIQJTcNEFBJ0dUoUV0gzBUvvocsCblhGFMdYSkBv2mR/YnO5IyMKOZ3RPGEiM4kStscKiqjgRCW2RhojP6FVHtF1qkz9EmUjZX8qcqEtUNADilpK01TYnVQRch3I2HcjIKDtpohizIzlY8oCS5WEUegza6qAzvuuXkETBFy3SJqZlPSciqbg+BpFXaBmyJhaTsOSqJsqqiRxeiaiaUrIec7+uICYydQsD10NqRdcSmZIdwrrg5y9icrAtambArMzDl9qb4EcUSl4HGsNaZU88hzSXENGZamUcKTq0bQSqmb8D6doGVUzw491JEHiaMPlUM1HlkTSTCZOFU7OTvngRXhsW7ve+BJy+q6JJEJRu94EyjODuqljKSJZnmJoE4IoZ640omT2SIUIVYbbZ5o8dr5Ge2qx3jcZ+QqmnLNUcQARQRAYugq2liPJCQgZfV/iwAWBgEiYEiQJl7dlkiznV790mb+7csBSLeLxboe9fokoEdgc5ejKlChNKSgSzYLIc49NmQYiB2P4t/cssWQbLOsm50Ydvrw2RTVc5kopiqCzNsxRZZ+GlWFIFkme8NLDFY43VYJI5PJgSklPOVEDTXVRlZi6YZAIHtOJhpLqjCOHmuXRdVX6fkRZF/BiiQsHJnNFlX4Usjew+aXPb3B1V2Z7f5F6QeMVp0rcPFOgpKp8+/FTZJlGw0oZjHPOXZkjzMfoYpUk9yirZQ6mArMzGRv+lL1pyLmDiP5gDnBx3Sb9qc7BpAqCSN3OqaomW96UeVvkbC/k/htDcjFAFmRmKxKWFWJ4Acvf81McLZd4460LvOEOm4VKyt7UZRKCIqZ89KJEzRTwUpfHtzWe22rwma0eP/+FVWZrIp4wpmoqVFWNUeKj5kWEtMBmR2ClpfJjz2+x5g95pjvirS+a4y0PzHPUrGEJDfZ7kCQKdx8RMdWQSx2Zu47mKLmBUepjiiKvPV3j9x4+YGcYM9fIIYWn+hPumitiSzI7/YiPXhvwC89fRkGgFwTcUC+wn08oFSAmYhBk/OaXtnn+4QKP7I6JSDjaUHjF8TJ1XeNf3NYkyxWaRfV/oZo9y7P8t1z2d5EiGTktIiKyM87punCsXKIkK/z1M0NmmxNGUczPfXSTX/rkJl+5JlLVNEqaxDRKmAQCcWCTxRKmMcZLJWq6zupeAS+POJhGbAx97mms4EYarlPj4laNJ3cV6jZUDIX1A4WRl1EpgmZ6bI4SdvszDKc2X1gzINM43kyZpDHlUoBpCRxMcqaBiuMW+a9n9plEMStlkf1pgiDkNA2ZURgQxBpRbHK+I9Eo57iBysWOxGxBpGq6tAoOM7aAJE0pahJBqvDoRgFNVMkJiFOJViHDTxIMRUMSfep2hKXkdD2wVZGBl1CULV54tAm5jK0n7E8ENtt1Hr5WYOxV+PRVi55T48ldg4VShm2EzFZSkkCm3TW5tFVmd6CR5QKTUMRWU2QpJUwUBEHkRYdtbm4WyYWIJNW4sC+zWJYoFsbcPJuiqQ5CpNPvaCRpjuObjCY5p1oRh1ptbpj16TkG632DuULO8dldjs52KBoB96+UkBODz53NuNoukMQGpjHinpUhhys+gbDLueE2SarzlbM23VGBmiVQKQypWQEdp8q+A0MPGoZMxfK50pWZK4b0nTKfXatwpasydm2yVMVLYoYDmdg3KMkKuhISZQn74wJVpcJ9zcNc2zaIk4QwUmiPKnQmFiM/Y6naZ7niMV/ImDUt/u1zVvjwtS5f2uqwUA5ZqsTIgsokUNidCAwDgY2hyf7EpKBDEJlEqUJR9/DSnK4r4ccgSzmyDAUrQ0xURCEnTHJMLUJTYkQhRRRyhp6GGypEiUSUqJCLZLnMuXYZXc443BxTMUMmgcLOwOSJ9RpXOzaQ0Xc0TjVH3DE/pmwkqHKObcQUjZiiHuPHEud2bc61bbaGJn9/boXHN2fZ6NWIvCJKXCX1CtzcKBGlGa9+7xO87Qvn+PT2Bq2mS7Mccqg2IkhSup7I0JOpGlDXQREk3CQlFxXWOzqtQkLdlEgSFScUGQURPVeCzCKMDAQxI8l9WsUpM3ZGowCbE5dHOwdUrIgkLhJN59ntVpCkjFol54vrDku2wUeudLjnkIEaGlR0hRvrCpooMokiDiY6QVhmbaAxjCf86blnuDwY4qcJliKTiRkrzQnPXbIZTGX8KKdZFJGVADkXiVKNXHApKhI1O2Tgx3x5p0+ceyyUM2YqDmtdAXKN9X2ZJVvBTa43X5sFj2bBo2Zm7I1FiqrOidld8txi5Eccrsb4acKxRkoQJXzz8gJzpsXHLw4Z+wlfXBsD8Oj2mC+sOujo9NyEqmqwui3R7zWISPnFB5a5ZcZk4Edc7E3J8pzvuKXGH7z6GD/3/GUWbJ2WWqRZDMnFCENS8RMfQ5EI05zjrT7zpZS+F1Gycu6aLfOe7z6FqOasTUY4Y4PlWsKrjsxwy1KBxwd9fvi+GX7gOS2+7VSTV73zIj/2oVX2PJ+3PrBAQ9XxpgI/8uIKf/rMFluTgHd/+w3cP9Pk86sT3n92wL++e44bWxa/8c1LHJ6Tec3RGX76Y+v85HPn+ezaiLd++grn2i7bvZibZwo8sFThB58zy0JJ41Lb481/e413PLLPRy8NMJTrrYPf+ewet8xbAHSdiIeuDfnlhzb51/fO8K03Vf+ZFp3Zc/nIhSGWqHDnoslsUeGBQ2XqlnL9N9+c0nFivu3mOu/93hMsVTQEQeBf3Nn4nyeYz/Lf5Ru6UXTrYgFb1SkrVW6cTRBLI0Z+RhLpxLHIOO7j5300OUG3AraiAZNkRM3MOVT3WWptMAhATsrEkc5qT0DICtSUGjfOjjjcHHOiZlEUazy9XSTxy9x7WOXuRZMXHa5xum4xU5AJspxTdZXTLYVMyLAki9tnVXQ1RhV1smSWZsGjpMucqhYYeCKtGvwf33KI1988x3/82i43z+psDnNO1GUO10JWRwklxaZmdSmYY7badb60dT3hSZKndAKPlp3xxIaE61ZYesXr+cLnOyypCQdTiVNVg14Qk6PRKHj0g4iaEXOxlzB2bM7uqZiywh9+eZ9XH14kETzqFnTckDCVGPgpcabiRgqqaPL4doE8s7jSj7E1gUkYsVw0OT2zQ10vcGpmF0lMkcUcQwmpayb7/oSGJRJnCVNf46aZDElpY6vXV5vOHBTwgxaaMuFofcDLj7s8eHIVS/ORBYM4y0nyECGzQbi+RhYnNpokIksuUZog5AZ7oyIhQ3QlIs8tLFlkpjSiqg85XAtYKOc4foGCPsGNQzpTjYVqikaBg4nN2T0DVZmw1pNJc3BCnTypECUmTUtEk3PaHqiKwzgI+OJGRipE9N0UP5DJ84xMgElgoCsptyw66PL1dT4nsEhTkMWcuVLI6dkuKxWHIJHZc2SGPrTUKmvbLR4+W+D+2aNMfZUotlgsJ0yD6/GwkiCQpjKur0Ge03czkHz8JEYWE8pGSndaQBYElsoxZT2lWYgpahlz5QhLlWlYISUjxdZSslzACRXiVKRoRIhSSt32qJk+j6w3cEIZQ41pOwprQ52Rr0KuUNFTjtQ85ksRhys5R+oJSS7hxRIrVY/NgcpHV/cZiyFHykX2JhnTKGboZ5w5gLWeyjSKec7RVW5eOmC23EYkQRRiTElkzk44VgURnZefqHDTgoJlRMxXHHLZYRzHxIqHLKYUVZ0r7RmGbom58gRFdmk7CXIq8pKlGu9+ogOFDoE+YOSLLMzkKKKCLAeEaUgYVXh6Y5avbZRI8xhDUvjrS9tc6ns8tjdGz1UEyWMSZpR0mbrlYGgDbMmiUvERtDG2WGIQiDQKLicbEUVrl4YpMleEzhCkoMKuk3C8ZvBdt7Y4VS/xLcfn+eDaNr/+8DZvOH2CtY6EpfmsVGK++4YT3HBsi5WShBNn+FGNnpsxV04YRhNaUoG/uLTPTz44R5znXNteZG9osDcxUEWF77vhBm6rLvLorsupusLnLuq8/FiKoQccrUNZlQmTiHPv/AOChbuo6fO8/uYZtkch73xsnz13xGRU50h9zAfPVLl3WeBaT0AXDd5w0yyzC22ec1SgoavE5Byv6DSsiI6rMRzP0ZkohLHAYk0hzBP+/OI+s2YBU9H4+2s9NoYBB75H301o2hoVM2eShkynZZZsCSdKaJYDmnoBURH5+3ND7lmxufWQSpTlNGdjXnq0xN8808HLUk4dhu+5qc6ffaWDI8b84nOP8PYnt+i6KbvTiH979zJVTeN8x+ex7i5P9LYhhV0n5PdfcZx3v+4kJ5s6Ay/DVr7+m9zpP4xIf72uZ/nGQk0KHD08IRF8oux6qtebb15hmk34/jtnqagaU6dAN3Z44Q0F7p8v85LTDoo2RZEjqqZEvZDQDyO2eyoyBiIZT1wzuGHB5003LvGeb7kFpJT3XdpDzEUubEoEroIl6hx0ZcYjaFWgXo5IRQ8nNFiugGUO2B3rPH9+jq+crbHWU7AFlatbNoMw5dZmi9998E7ixKKqFlGlFE0fcbgqslgbk4kuM5ZGQsTVjk7NkHFTnyQTiSOTh64UGbol4lwkygMO1XPmLJ37FkvIkc7lHR1Si4LmocoRRQ1U2UGTNMI4paAn9CYFBkFGvSCwNQ241JnS7cgMuiYFPSPIA3RR56l9mRN1ON+RUIUCPUegpEmsd1U0M0fQQyRJBCEhy0WCGLJcomSE2FpOnmV8cdXnzhmbomix3heYK4m0Kn2qukSYhaiShG45rMw7NEsprfKYZknkWj8nCG3KpkuWQc3KOdbapW4oLFUmSGrIxekujww2OHJ4iKX71MtDTMnACQUSYk63Ukzdw9I95mdS9ocKfVfA8crsjXXCLCJJTIJUJcVBQGapknJhz+LlhyyWSzDyTb6ybnBpr8jJYp1DMwp3NWs4ocR6u8lqp8onLlb59Fmds50xP/HgLLfMlhn5CYYWEiY6IgZJJqAoU3IhIRVjfvXz63zzsRr9IOfAERi4MhUzYb4cIaLRdTQEIUCVrntDlo0YXZJIM50522N7pDH1RcYTmdOVEjfMaCiyiDe1mK0GGEqApgaMQwE3EqgVQk7NjykZCVkmULMi2hOdJINhoBCnErKU44cy6x2bwUTDDRR2RhYd12B3bJLnXE9HlXLCRGLo6mzu20RpzrWezlZfZ7VTxgt0ehOTzaFNe6oyclKGfsKu5xDEKbfMZjTtBFMHQU4Jspyndm3O7hZZbVcZuUWiRCWVc8aOwKmKyRd2BqzUI+p2yiQQ0TUPRfFRBJWCouH6dT5yrsb5tgm5jqlI3DA7oFrqMVOe0ihAGNi85vAyP/XcQ3zfzXOcsur8wUtP880na/z+I6u0ai6P7Y/Zmwb4U4VHNmQOplBQdBbLAYI8pWkn2LqApE5YqQYslFJyKeD2WZsHF46w6YxYaQYUDSgXewipzkHPJM9zRk6FSRwTRipFA/ZHOaakstZT2DiokSUKghDwnBNT7j2+zt0L1wNmynqGKsWs9yyiSMeLMh67doidcYYmGVzqitRMCS+KqJoi7z7T5lc/vcntCxb/5bE2//7hbd7+6C6/+KkNrky2mcQRDUtm5CfcedjETXIsUeP9Vza43PX5Nx+5zI/+/TqfXR1ztKrze5/bYxIk/NAHr+FnDoMw53AtJsFDEnUUOaJiBQSxwfZI5NBMhutK1OQCv/blq3xxt4uY6dRKcKUj854nffbjA77npjqvPFbnTXfMcbphMVdU2Rh73DAD73pyj91RwtE5kXc9vcGnLnr86bedQJUkXny8woOnC7zoeJH7l0q89UPrvPVTV1k/SPi9L+zx8y9a5KcfvsoHrrW5pWUxGOcIQK77HDghi2UNgHd/rcMTu1Pee77LH776yD8aUf/n1x3mvkNFhn7Md/zVRWRR4D+/5hinZix+6+FdPn5xCMDIT/jxj6/yv90/w2PbU1RJ4q+f6v0zrbp7qcAffdthAEr/p7Szgff19358tqb6H+MbulF0xLZIs4xLfQcnGVNQBFYaKbI25MK+gUqZulrB1kMqVsKphs5SoYxthiiyQE0vcVML9kcinq9S0UxUOWHfjZiGKlOvwPogpB+6tIoJsuZwoefzTGfKMB2zHYw5cBOCQOdCJ2aajFBlAT/1GMdDslQiSAPG8ZCiliNJAQeuS0GWCaIU3UgI44zNQcj+NGG5rCFJIQVVpqYXGYUCtlpGF01k1Sd157nYFjlUi1gspxxZvMJcVeArGzlpDl4q8/5//zsoqGyPFRYLJrOFgIYh8LITAyr2gDlbYM+bIuUqFzoJvTDivedH6EKVL66XWLQVrvRTSppILEwRUYjSnIpusTNOscQSft7jSD1iqdpGzMs0rAkH0yKaFBNnPn5sszGRmLMKbE0yJDHG1sFNIhpGETcJSbKYVkHkYlek61i4kY4iTzlRTrh7cYKfTNEkgbKmcHmQMg1VJIqUzAmK5LFgi9w449EodUjylOF4DkkQ6XsJsuLjBAWi1GYUwNBPKFkOqqDTNEUWqy5hFjNNPepWwH2HplR1jdMzDltDg4+crbLvpmyNJGQxxZBzNLHAwCuhigV0SaJiCJQLAboSIqgOmuoyDlPe+blZ2hORY7PbJHh4cc6VLkyjhGkIYe4RZz625uFECVvDnB+5Z5lXnaqiSCK/9QkXEZF6QcALyoDEwJOZq3icaI5ZqjnMVAKaJZco9bnalWk7GtcOCghCjp9IeLFISU+wtZgklYkTidsXh+gy2HqELGXsjAwmvogTSuyOdYJIJIx0XL9OWZdQ5YyyMWVzlLEzuh4j27IDNCXlUsfiic0GPadClGjIooApKRyrpxxruEhSzokmTMMMVVQQ8yK6LGNKGiVJRcwUkrgCuY6h6Ny2FHJqxmWhnFIyIgpaiF1w+NR2m8ujIc1Kl4btsVTKaBgilcwkSBQEMWK1n7HaVfjCtRkGTgWFAnYx4FO7B/SzAQUN5m0NUcjJxAmm5iELKoqk8sSORoEGv/ui4yhemb0DmSDIuW1O4kjRpjPMyZMCmiQTZz5hrKJJJqrmYCkZimhzdZiwVIbtocWeE1HVSrhxgihAo5zyMw8s8b03zqArKfcv2Txw2OYXv3CBulzih+5aIAklbCtEkXLe/oU6X+6dwQlN0kxguZRSMUbcuThkrZ/hDGusuWMWTYONicetM0XiWORgJJFlCZ+7JvKbX9zgPz6xxamGwjjO+JbbfAqmg4LEwBOYJh57H3kXk8JRvv+2U/ziixf59c/s8JMfXeV8OyecLJGLMS3lNC84rOKnOTc0BZJU4tHOBn0vYBRI/Mg981TNjEjs0HYkRp7Kw9cybCMmS+GZzZAskBFSgTfessAfP7XLH7z8KCsVnd97+QkIDC53fDphhCkpnN/N6MUj8lwlJ2TDGSIaProo8pYHZnim4/Gyw3XGQcQnLk94eHuILgrsjUPe98yQruhwqKKjygKX+i6HqyrPXy7zHafnuG+hyo1Vi6Nli9HEIJqo3GTWcMOM5YrOiZpFTVO4e9n6umvUs6lnz/J/ByfPcEKZki6xUPI48ALe9WibH7j5GP3UJ9R8JlGKKSj8569t8bMvm+f1Nx5jxlIo6QKy7GHqLpaiohjgpyEgcWSG66lGsce5tk/kaXiZx8404vShhHtOHzBf79Oq5dRrGYrmIckJC0VYagzYGEBJNZCElA9f9njdrVW+++QcPR90QcLMFcqZSdXQeMcrb+Y/vvxG4qhKElno+hBbFanoAm7mMmsJ3LIwZnucU9M1Fhtj5qpDKrrM+lCk75gYskjXTymXXL66P8D3wVIVLmzrTLwiQSwgyxHkOrtOTpqaHExFoiwn8AxExWG5IvOylTkaloZe8IgzkbKeMwkz5myRaRRxqgEFc4ytKKz2RGqWSHua0bJzrMKUqWvRcWQUWWFvolBQIMoSbAP6Ycj7zwz4ntOznKpeX+1JYwsvjUgTnShLadoZpULEyZkuFV0glyeoWDy6XmTkGagyNEpD4rRI389RRVgo+UzDEEuU8bIJJ1pDarrEzvi6we/ELxCmARUjo1W8fiiz1AypWSmNYp/5UoSIiiy7SEKOIZvMlsY4ARiyzG8/ZPOer84yLx3jSLmAJMHBSECVI64EHUwrIBJ8LDXhWDUjC2V2vT5/dWGD9cGUVhG6rowfh7RsnzQ2GDoldjo6Tu5yZsfhoWsDGrZAb6ITZDGqEpKmAjUj5VAlQlcEwiyhPZUgTxEESFKFJDU42nBIhQTHz7ji9NieOjRMiaYpA8r1qSEESnqAIiWQgy5lmErOTDGgbMbcs9zlcH3KjO0hkJMDppZg6imilGNqCe2pTr0QsFL1mC8FrFQdWnaAiEAUC+hmwGAiMXWvx6zPlWNOz4850nI4OTMkFX0SY8y331jnWKXAzz+8xs5IRhJzaraLpoSkeUJJTzg14zNXilGVjKlXpqXV+Je3tlgbBjTFArc3m/RckZI15VjN4dYZn6VyyF7HZK+vkgYazrhMlMhsj65fU7eE4xfY2jPo+D5P7I95dHvCu59p88P3zvK+pwd8aWPMTuiSCDEtS0BSYGPkoykJNVOh56esD3RIbLwYKuaEw5WcRtFj4OdUNYNdr8uT/bM0rJxUCFmqjcgTm66f8YY7G7z+1Gn8JKKkqZi6h61dTwLUlZxT1gzOBDQl43gr4KaFHRZKOZd6EtOgyIVOga9cnaM/0Mlygee0FthoGwjecc7sGRAX6XgRTVtEMyes1DPmijoNS+HHHpjjY288zb+5d4GffHGRfhJRMkRiIuaLKr/64CH+5d0SQR5xtSvze1/eYdsJaJUEPrs64i/PdDnR1HnLB9b49htrtNsG80bO7jRj5JSJs4gkE1GQmEQpsyXYact8/+1zXBsPeWh9gimrvPZkA1URqBoig2SAMy4iZf+0frVY0Xjndx7l++6pcqWXsu9ELJYlhmIPUxf4y+86wQcv9Pn5T23wjkf3efjakE9eHfLZq2MeaY9Y68d87x1NfuoFi/z8xzdYsSx+8LYFNicxf/7dxzi+IPOeRwb84N+u8tfPdNib+DzWnvArL1/kj7/1GPr/yYj6/7tu5scZf/jqozz/cJmCJhHEGWme8/KTZQCu9n1+4cFl/v1ndvjtbzpEmGZ8/93Nf6ZVgiAgif+8RknSnDd/8MrXXRefran+x/iGNrP+L0+2MWyLVqFETTPYHIATScxrBjfNZkzDCD+NUUSZspEj2Fus9iyk3CJJJGJhF1k3sfQV3EDg8Z2QG+fhSE3jqV2JPPdQpQY3zoyw9DXO7DY4qZXY78h8671zrA4DBm7OKPIQsxJiprI+SlmuqmRxjdXRmBlLZbGoccXx0FWZk02NS/0Oo2GLP/raJuf3E2YLCnIOl/seN6kG0KftgaUaXJ4EiFLG8WbKOBghKRUmfowswmC4TJg7HKoVcSOP8smT+Af3cvapJ6g89yacZAdbLdJ2oaILRLZMSe1Q1Gq878mQQxWdK12PEw2NcTDleEPlcj/FEg323Q73LuRI8h5hssjeJOammTIH3hQ7nOEZd0xJt3DEmKWiSdMKCLOYOClwrh1iyHW2xylFTUJXEsJ0yrytcLGr4AYqEjm6ErJUUnCTHFEaokkmXurhpz6yUGfkKawNMp677FE1YqZJyGyqUTUkRmFAxYhZLBmoYsTFboQkRtwxn5GORS73FCQJREHF1mSWq0NMuYcq5zyx20DMLQxfQRQj+qFPmGrYWkbdFCjIQGoTpwIHU5m54pTFUsraqECeW9y00CdOTYa+jq4JzKmQ5yJSLvDgTX38yGI4WeTBhUXef3kXUfbJc4cbWh7boyIXOjkiGmPHoqRJ/MjHVnHClJqpkEQ5BxOdspEhCNAsBEhiyCgQ0OWI2UpElMDOWGPollBEhdmSh17zSDOV8wcl0nxCSYs4mOrkWUZBSXFCibJ5XbTzXEKUdDRVYLHmkGRFxq5+3V/ADijqMYvVKbLkM1uOaI+LVIwptUJMSs7AV2n3LLojgVIhpGB4HKoFaHLO0FdwAoVWMUNQejxvrs9qt07XSVnvFHFDDTdO6Lkm8+UBoqQgiQIpI5JU4EubJud2ahiKRMuUmbFjqrpOmkqcnBnwmZHNUAiwMkhjFTEzGDsSu72E553qstw84PLuIQ5XJJJMZG8kEAsjmpaJLmuMPNgaJlRNiQW9RcPS+FcfuoKZSSw2BGoNh0ttBVeYMjcT4+cOZTOnoKiM8il1K6IzabE1lPiZe07y1MGEj65vIcsi86rCgeOzYJe4FsXsTVR++/FLjEOf5bLBX1/a5JktkdSp8MuvXCKO4W2f2aBVSRhmKTcve/T8jFlboFGIkYSEMBV4bMdGQiUVU157Yp40g++5rcHmOOBLm1cx1JSrezotU6E9zLj9kEQqTliupLiJw2Rc4EJbpmx51JQyr3vtS3jNq1/NfEljteeh6Tkn6zozocKT6z4FxeBwUebR7oCaZuLmHSxDoIjOtaHPyabC//6lLRJ9gj2dpWwPuHCgcu+SyIUdmdRVOd1UGPgRma/wmw/t8F2nW8yXdN5/qc1nzkyJyTi9oCMpAUGa8uKbUia+ze5QZrGmo8UVxp6AYAU8vNXn1150mE9s7qL4BV5x3OSRzQndQU5FK1KpR+wNM371BYv89pc3EbKcyz2fC6OM3585zwcueuzuQnU25c03LfJfv9xjtRdQNq4XTO8732WSJHzoyvB/qZ49y7P8/3K4ZLHtyuxmE24/NOFQ0kIqJdw3X+e+ebjSC5HUAV+6EuOnIm/7/GUUKUXRBJq2T5iYuIHIKPKZhgWIyrxmZZF3n+lQryR8fmPEux+77t3SKhZYqk7IxZAoVZi1c0aeg6FkaDSQxJS1scOxhsrzj0740ppN3RKQhZT3X9vnxobN9986zzu+tkcaSlwZueR5TphkvP2xXc7v5JSKKvPlBn13ykI5wBBtavYBu8M6c5rI7jjh1qJArRCjKz3CqMQ0DJkruwwckysH8Obb5/jjx9oEYki5oHB2X+Z4VMIuTNBNh4ZuMY0HHG9UuNaFHU8inZSIJJd3X9jlzfcu8GeP5bjSBE3JOTHjoCkBi+UMVR5zrdPiS2sFDlVhL/Q51hQQpSmtosLQHzGYlpHzIcdbMl/e1FkqxWwOQo62ZJL8gC/sxtzRqvO5jYwL7QnzdoXVrkKzIGAYATct9KgYOUnex/LK9JQJpqbhJiKtgksUWmw4IfMlFT8N2RqZaIrEKPJZNkCRoagNaRZ0/DTFNlxUQaXnRlxrN2jaAqbhcbw5oaBCypA4VajpEkEak+YpSyWVO1cGfHm1SOTZmJrIe88PuftEn1ceP8qxSo2NkcPmKOaJgz6ClHFuX0YOdUwT/GmRbp5gmAlmUMDUUlwZBt6AmVLIjAgP3txnbyxzdlfglrkK7YnKDRWJUkFgvecyLwTIWo4XKUBOloqM/eu+S4YkEKUaYSyhiWBrAlIhIUhiokmBV93W4uiMwsdXc/YnY3pqiq+HbHYtmsWUipFi/YMvjiQmjHyNWsEjjCRUJUESU4JYJEoELCMmzsCLRAaextjV0GQRQ4EsF0kzgV5PIRYkEHIWqj5FLSWIFNxAY+rKFIse9eIUPxb48MYWt5kyQaqiqQkVy6NRmKAqGot2TJxdP7wTpZSvrtdoGQrrw4CO0meppvP4hodlahTUjIYFQWxwpatz6cBEtyZkskK9qKDFNgfTECHXWG3PUtADplFEvXE9GbGfdfntL49p6TrTKOUtD8ySpBlv/pgLscbdCxZ/3w6pWwpxkvO5KyZH6ipBpFKyxwi5iBva9P0x+yObObOMno2Q05yJZ7M1zilrEmNPQdMnzJVq/NXXhphFj9mqQsUcMvULrA9M/InMv7njEJvDkPNdBy8CWUi5tD+DIPsUVYWe79LrNRhMZW5dhvubS7z9K22qmkyvr3JjeZ6vdXq0qhar/QCSGj985yIvWK6TZTkfPNPnm09fX5d6zbGjkNn8+fmrtHSV9Z2cN334PK85rXJzy2RzMqZiuyxlGlfaEnc2Nd5/pY2Fxvfd0eL3H9tC0eHRDYPFmsUoDqiZMmmS8ETHoGnKHEwz3nrfEu97esDVUcBSU6DjBXzuYJ2RZ9IZwXzVZBznTMOUdz5ywL96zgwA4zDh3WcHKCjUDAFXdJnRivzuC2/CVGR6TsKSrfPei/t88+kSLztc4Xe/uIuqwEsPV3nD7TN897svM2/r/MCds/z2F7b5m+8+xWdWh3xl0+VtLzzM2x/Z5eK4x/d96CIvPtbgjvki3/PXl/iNl61QMRWON4x/1Jm5osZcUfvH17oicseyxZ98bZ8fvGuOuxZs0izn5PcYOFHKRy8O+KH7Zv67mhUmGdo/NKA6bsQP3j3LZ/6fkcdn+b/IN3SjKEgSWnrKk2s5zrKDqQqYqcbONGWu3CVNVE5VNTZHOYo+JU1KvORIghsP6boGU2eBWTNnW0kxkGmqRdRcZux7qIJFQZWwG1NycmylxEoF+q6LYUr8yVe7dIYykpzTKhWIMp8ohaNVFdsMkeSEilimZY0QxJiFQpHtbpkohxsbFruqR2cqc9uCzjTyqRUCZuMiotLFT1RuaHoMoxEVvUouDNgcphwqa+yN+6iZiSymjKOcxaLJ7jiiImtIeszya5+Pv9/nsc89zJH6HZSsA7K8Spr5VHRY6zYJ04w3PGfEZ64aHKmXaEcjSrHFvtulrBUYxTFHiiWG0TYLmk07GLNSLZBk+1iDBk/vw23zGr2gjy3r7DsiNSOnqElsuj5l1WZtlCNikmcpj67b3DqbUrd7pJMqnUjksa2c5yzJuIJPq+DjhSXa+NQMFVtVONvOuKERUzUFnGxKFZlZS2VjknKi5qKLRVplFzfqszk0WCkrrHYt1roRup+iV1QyIadgjAjTlM2hztGqTt8TaE9laoUhqprRMHVqlsDBVOVSR2N/LPHCE1tESQFbgqqhMGPJaLKEJuVcPiiQSSnztsDJuT3CqEjZiBj5CgO3wKW9ChVTIEPgT57sUjFljjVSwlhjb6xzvDFBk322hgo1rch0rNELYnQlx5AzBDshTKAzVagVIsihbIZoisd632CtX0XKZKJUo2TEnGjGeJFBKiYslh1yBNIsZeBBmKa0Jxp2kOKYMabmcODI7AyrHKp5qJKIIoAuQSpDvRziRTJZllDUYpxIxvWqHK7EZKR8bbvMM+saBTPFJOdFx5r86TN73LCQ0HclzuxabA1ydDnH1DxiXPanIrsTkfNtDdeVePCGLd77ZJWZIGdzJNAZ69hqSkmTSTINVTCYL4ls9TRqhZBGacwdizt87OIy5zs1XF9luZRgGylBLNDPQrxQZerm7PbLBGmJkaOzvy9QnXWYK0rIkokXKowCDz8UualS4Eduey6KJPLnZ7c41MwJkwSrNCVOc0yhwBevxAxTuP+Ixkp1yM44Q8Hk7F6VnqMQhjo/+5k1mqWUuSKYuoOfxrRMg+0xjF0dTQVJGbOgldjtwGvvXuY7jijsjkPuWyoB8OLNEn9x5oB7j2q0SvtUNIMoS1HEFMSAzVEdN9Ap6imZrPEvbmn+o6DLosBCWWKzK3CkIiGqPsfnc8zCACmzcNIBQVglyq6nGyqdTZKvvJcjP/ubzP2DH8+hqs6P3bfAVC3w+g9cpClbdAKfP7t8gCEUGcZ9DhdM4iSjEwrYUpOnLilkosSsUUIQHfb3W8wYsN+BgqgiWgJ9L8YyRMRUYH+Q8MZbZ/nC1oDf/PI6RVWiYAmsjSLqVsbJmYyO57I9sGmoKmMv49pexvGGwWxF4IfuWOTzqyO8qYKtCHzrjTbtaEA41uiOcgRH4puOV3lyf0J3mHHnzAxpM2av0aesl/jod53gxz58jZeeLvGdN8zykTNj3vqCeZw4QZckfv2hHX7gvgZ/9uj4665RWf71S9bI8q/LxzzL/4t4YiPCrGeIXpmvXSowzRPmKi5/8NhVjhRrXFiP+fmXHyfKL3DCXuTjmzsUtAQjs5kvxQxinzAXmC/YCJHCPTMFHl4LWCoa7LkxmRBRKWoUlIT1ccK0r3LHIiiKg5fkzJckdoYmVwcRb7lrEV3Zw7LWSISM5x5W+MzFIk4gsFQVCUOB152a5VzH40xnwFY44Pcf2eLxrSnrg4iyKRP6OmtpQMsqMdXA0EastpcYBTGDLMFMixxMfXJBZc5OuOL5KJHF1jAnxqNsuvyXR3t8101NPnBln91pwuGaTMnuUdEDRrHJmQOV5bLB9jjHVEXmSgHtkUZBNegELu96aptBlFI1ZWZLE3QlxpABMaTrlFgbwYKh8V3HF/nQ+lXKxQ4yFqMgoaJL9HKH9rTCnuOxXM55bFvCknQ6k4TFiszGdIQowpvuWOTp/QIYA2TJRxQTSmaALAqEaUxJldEKHabBLLEUIeYqXqSjSmPipMzlbsxSGcpGzIVBipgWWes73Kj51C0ZXRrR9YqkucK+K1LSRG5e7LMxMDlciamaAWkOZVUkx6ftyRRVBQSZsx0JWYSjjYxzmwk3litsTl16E5sHb5/jD595ioF/3a+maLiIKJyaSWhPfMa+gC5oVAUZUUpoVXvIgooui0z8MoguaZqQZxJVE25b8pnECTctKlztqWz0dCaehJ5LpKKHqkU4foGpp9EbmARZhhfk1NUcVRQx7RzNCKmZOXEuoloOf3dtG2MbdB2QRXoTi5EnoyoZWQ5BLJCmEkkuoEgCopjQdw3iDNbaZe6caZJMYM1LyNWUjbFCLkdUjJAZOyVOBYQcklzA0CIqrZyxL3GsOSWJVQRBwo9F0iynUZlSNANWymOe2p4jkCV6fkgo5whpQsmYUjFibmoFgMD2yGK9bXFtoDB2NKZaBJlM7kqIRoYvxey6CeWCjCDk7EwkVrsaQaRTMTMUOWIiJvzKS07wn74moKvwyM6Q3kBHEBVUK6ViD/AiWKzZKGj85EfX+bPvOEbVVPmdF52iaihc7niMBz0Wqip/c6bLyXmVjXGI60g04wpuJHK41ccNLLxQ4cow4AZDYCc0WGqOmbV19iciFTOEuEg/iFENkWYlxLaGOEGJqx2VyFU40lC4/1CRx7b2OFWx2RlHfO2yhF11uXEOSkbvuq3ETAfLKnFur8T53W0wIQ40fuDBFodqOj/9kM/VrkPFLLLXgQ8/PWHJKvAbn97mSi+gbCi88Pj12uo1J1oslRWGXsYfDPeZehkfuuxgqhDnCRIK/SDFym28KOIXnnuIv3liiB9l6ErGJItYaaq03YCGJQEx13o6lqiz1kk41TR59yMDXn9HnfecC1g9SGlVVfb7Mm4E33y6yCfOevziSxZ57/kuP/qcxX98rpd1hbc9d4Xf//IuXSfmtkaN33rBYb6wPuaha2Oe2J5yvG7wwKEyzkTiRz95ieculbk2hA+eHfDoqkuSZPzKSw8RZBl/9u0nOLPn8aFnRrzzO4+hSCIPrfeZTmXyXODOeZtJmPAbL1/hRz+wTsVQ+OibTyH+wwTQv3tokx+4a4ZWQUUUBfI851PXBvzaiw/xzq+2uTRw+XcvWaZqyXz33z3Nn3zHDSjS9fe+46tt1oYub37OLE1L5TXvvMQH33SCoi4zV9R46bF/7nX09eDrVVd9o9RU39CrZ4aRIxs+NyzkFJJZpLRISk5V06gqVVYqOaY+ZcaOKcgWcZIT5WM0NaCoihhGn144RlUi5soe/aTP0wcJeS6xM4JPXM4ZBzkFJWMUddmZ+lSNjILd4aubQ1QpR1JCLvQcRr6ALqf0oj5tN8NWU1ZHU/pBSJQo9MIRqRCgCCI7Ux9ZTLBVCTcJKCgak0EVJ8hpGEXyTMdSYxpajVGYIKJxqCLw5U2RFxzzWGxskeJTt0IG0YiqLqAoPgVNRBV1SnNVrvz9Zzl79goTb4ZMHHDgWPT9FEH0kbjupXTvcs4giLljVmRj4lBSdc61Y8q6TNkc0zIq2FqXIyUDN+1R1gVK1pgbWwLjIKMgq6iKx8AT2J/aPLZrU1NtNp0pTmCyNdCpWymnZhIOfIf2pEJ7KpNnMFNQ2XcCdFlkHBQIsxFhbBGmEqY25qZmjhNJbIxE4rDKMNABl6OVCEkwEMWI9alMEJeRRYmb5tcRhQRnWkFGJskDLuxrTNwSeW7gxSJhGlI2HIqajCrprFQidMXjXEfAUBJMVWS+JDD2SxypOWyPU1TZoRvERKmImCssVxKUrEyea7RHdXQ1Zrky5flHr7FY66PoLlcPcubKI6ZxCFJAnqYcTCWSLGLky4SJyDS8fp+95nid//DyI4gCJKRYWsyRZg9NG+AnMWGW0Xc1ntyt8shmlb2RSiqISCJ4oQa5zHwxoqAK5LnMcsUHAcahxoX9IutdA0OJaJgelpKiCAJ7Q5v1bpn22OZSu0pnYnHQN0lzKJohRTPAj2V0Seb0TMBcKUEVBeZKHnce8Tg+A5aVc8XZ5VBriojO1FdRZI+F6pSTMxPaExVZTPAjkyiWGfaK3DZTQMwr3DQLQm6w1WsgUsCLNAbe9f9+udqlYk04sbjBbH2IJMbsjBo0TNBFk9mKh2kEtJ2QaZAzW4qYtwWqqspG1+SxVYlXHGly86LKjCXRKvoMwglxliGkNmloX4+4n7r84EfP86FzEy6tiRhqiJKZjDyNRHRoVQNOzaZME4+dkYklGVzugx+JWIpGOwgJ84ipK5PgUlQFTFli3xFRRQ3dGDJf8SC12e7K2KLNXFHllz+5hSRef2x/4uKAv366yxHbYM+Jcb0mkyhDl0CUpmwOKmz2LWxVQVcF6oZKq6CS5zmTIGFvFPOuV97ILQsildqEmXLGpuPjeCXO9wJ8v06lMMYLShwtdPn07/0xy698M7/02XV+58tb//gc1WWR+YLG4ZLBQexy42EfMSli6B6HigXGvooTXk9jvLqtkOcqopKwM/WJIo3NHlztB2ShzpbjsdaNqRkKO17A77x6heedtBgFMT/72Qscq+dMkxDd6nLzXEaj5OJkI4qaiSHJXNtLOdgr8ZylIj/3wDKPXorZGYX8wqc3+bkHDvHXrz/Bnz61y+V+zG++apnDywHICW+8o8EHVq9iyiKvv7vKp7a6bI5EtLjEai/k3HbEgmXyx4/uXzdTz1z+5fvO8guf3ODFx8r88N2LPL9V/+8Lzf8N0vzruHr2DbJP/yz/RMUSmbUzyvUebU/Azi3edOMpHrmS8auf2eZ1t1f4P754wLccPcKGv8/9s3WEUEeUPS51VZZLMJ6W2HMSwtzj7GDEqj9mw/GIIxVL0OkOM1LF5WhDYLGcsD4UiFONNCmwOZTxk4yqKfGHn9vnJXNLDKezOF6RMHc42go52kxpuyl7vsPbPruGrcp4sUTFFPnY6gG9OGC+LFKrD1CVlIausDPMSFIBIStimH0sVWSlHNOfRiR+iZEv8flVm5P1DEMbEacSg0mFzXFKpRLwXx5tMwkzWqrCOAgZjCuMQpmEmJtmEq70Yc6W2R7I9F2D04suiuozU5BJZI+aCVPnHw5wRiZxmjN0TcZRjJwaXBzE/MkT2+y7MVFY4mCaUVQVzuypjCZlLq5qTEYVRmHCyUaKLHvI6DxyvoAc2zxzEPDOp7b41NqAM2s6mwOVka/gRhqLZYfZQkZBHxEnJc4f6KSJTsXyKeouomCSCwFparDW0+lMNFZsnd09me6gyCOrLf72iRkuHMzy+dUqA1ehqIj0g5ggUtFFmSd3NHaG1+uUjbGIqQiUNfF6wlaS0TQlDNUnyWMOz8QIWkhF1ekMDX78oQuc2c8Z+ynLFRdNFhkHKbXChBtmPY61QsZBRhGV1C2wvdtiY6CBEJJlIoNJkbVuFUHySbIEVcwpaDGPbCdsTx1mNJ2apiPmMnlQRaLI2T2deb1FVVMpqgIn5n3MSoBVm1IqeJT0hGNNl+csj6hXQ1A9UjEkySIUGWp2TMnMONackOcJ40BhY2BxrV3k8l4ZP1YpateDPk7Pjbk46LLr+FQ1nRN2hTSQMNQcP1EomjEtO0SWc6IUdDVClRPCUGDoGlhaRqMYUrFCTC1AVSJmbIehX0BTBDQ5RyTn2GyXQ40hFTPHVCXSXMJPRLYnIl84U2c8tTAlhTm5xO+//ASLNZVmq809xybMVqbs9nQOphIbPYs4tJBimf5EQFYCVBF+8tOX2B2l/NS9R5Big7Ja4N6ZKrsdmSgS0CSF+ZrL5tQllmN+60tb/NDfX+YzG/u85ePn+cmPr/HohsPHL4w42TBpD0SiCA63QlqVCQXDJ0kKPL1aYr2jUTc0TtZs6nZIUVcoGFNuXxxRt3L8OKSiXm+QNOwxhqgzCFJMQWJlYcyG4/ArD2/y1gfn+a1XH+I/vOYQxXKGKkmMwhg3LGGoUwxBp+tDloq4ESSxyL2Hi5xqmVzue7TDMYcbCS9YLvGzDyzRd1L++skuG1Oft718ntOzBr/2mW2cMAHgtlaVy+2I8STHkhXqpsRMuc98MeXMnoYzKjHwUu5ZLPLioxVuP2Ty3vMdBlORkqxj6SnfdKQFQoilx1Q1EWcg8sbbW6ztJYziiPecOeBaL2GlruMEEOcZrz3Z4Iu7fWbqIn9xYZufev4ipiYy9P/Jr+cVx+r89XfewLccafGaGxv8zKc2+JszHXZGId96Q4OlqsbYg++7u8Gb7phlb5rw+ttb3L9Y4sCP+JVXLXNqxuS2uQItW6XrxPzH1x5GkURGfsKVkcN0IqD4Gjc1CpxomNy9aPO2lyzyzu86gigKfHT1gHef2UUk5999doPX/+UVoiRDEAT+wyuOQg4fu9LHSRJMReTXvrTGD9++hK3JvPCPz/L7n9/lA+d6fHlrwl891eFXPr3Fz79knqL+/+wMy9etrvoGqam+oRtFQZJhShrjgcrjVzM2RyGSIBClCc90PLad674iQQy77oSYgMG0Tp6K7Ls+U1+nqRfpdjU03eVETeZkQ2VrJCMKEnfO6xiSRM+XaJpl7l2CSZyhyyZ3H1MQ9YilhscNNQtbT+g6AstFC02U2XNSbqgrqKKEk0zQhSKKlLI99SkoOl4aca4dogkqF7Zy1vZz4lDn6Z2E7aHOer9MP4gpqRbkCnEqc6xisjZQKco2t8yNkZUppxo5T+2FCMgcOAl9T0DA5Lk//pNcebzP9lhESOrIskPHsei517+bIAjIss9LTvrM1oY8b0Vgfexx71KMrB5gyipxHjDyFrg8SJGyOleHAYYkoioeBcUgSCBJBVrFAEWeUtUVrg7hjlmZqt3FUhPa0wJhHFCQSlztZ0SxgibJvOjkebK4RHuisD3UyZI6k/h6gldJMUnlIbtDkzQucmUgsDeScKISQZrR9UOSTEOTdK4NMqqGy7ndw1SsGF0f0Z0o+EGRsiFwuStw8cBgs1dlZ1QnSiREySHOrqedNSywZIOMnLLVplqYMvJEdkZlCrKFFxUxVXCTjFHsUrAmKKpPnmfUrZwHFvusFDOytIwhymiCiWFAZ2KiSgJJ5qMoEQNP5Fhrm5O1NnVzSqPUZpzt8I4nd/jxj10jUtoUC2MkKaTrGoyC61MZUZohCDJHqjktS0MTZcJYJMlAkVNMNSVMJVp2QMlIiRMZAYkwUVmsRNyxPCbJVB5fW+LDT5zmM08fJ/BU1H84DXBDiTjLCfIMVQ4p6g5pBpIgUlChoGbEqcgklEkzFVNRmYxssljmYJJT1VUKiszIu57EcdNszk1zHt90eo/nLvucbIacmp1yaiFiMtG4tGNSNhOmocg4yBhOZbZHMXHuk2QhbVemZmWkiU0YlVnr1Hhyq06Uh/TcjLWBgBtomLKBmhscjEy6A4Xtbspz5sq85FCJZ/oHuNqQQJggChk3NGQWKxGTOCKWPO6aK/LUwYTXnW7iRTnzVZnJpEAQBixUHW5fSNHNkFR0WCxqhInEsfoWTUPFUAVyacJdiwlJKjB2wJmWWR0K+IlIoyARMmHWktgf6ZhazGI95pm2y89+Yp2uk3DXYgE3TPndz+7zE/cvsutFlKnQdnLSVKcTuDhBEYSQu5Z85ipTyHLO7gd8+lqP5/3xM7z5b6/ys5+9wu8+skUn8NFlkUE85GRD4JmDhGNVg3GYkiUlCnLI/sjmxPf+Ip8fXm/ivOHW62PDUZLxSx/f5q0fX6XnpdyxLJKkClXbZ74osuuEZLlAnGZsjENecaTBbugRSg7H6wJf201R7JS6WGB3GnG8JbJcl3njXTM89uY7eNdTe3xyo8/3v/cKRyoSBTPg7qWMpllk6MoYokFVKTP2FORQ5dZ5lQePFnnnt57gke0J33RDhfec6fDgsRKnWia/8+Vt9qdwW63Key/tkMoR9WrOHz6+ReYWmRLzUx/d4qhlk7sKtirxjnOX+d7nVPiJT6xy+3wBTYO/OLPL2Y2U91/o8bMPLrI7Cvno+cH/Sjl7lmf5b5DMIQeugCnqzNsCvUnG3gD+5HVH+dH75/njJ/ZZG/n8xdeGDEY6D12d0J0K+K6FJqic70gYhkNRMWhZKVujCFVNOV41SbKEXSdhpWSi5WX2pwl9T6JhyKx3DS61VYqaTJao7A8zFFXg3U93+OJFlSsdnfZghrWexs4ITjZTikbE2X6HMMp4xbEi0yihYYvkocL+QEBILBqlEJ+AmxYyymZIudCnquos1PqIasRL7xxhFvvMlWKed2SMm7oUFJPdkUou+pQUi3916wqH5jOKCrSDCCEosTa8nqBoSNd9mI7X4MBJWapkZGnOJFA42QrIRQ8j19j3UkoFiSgNKeopexON/YmCJiiERDR1hc40x8p0ED2y1OKRDYX5skQvjDiynDBNUtxpGU2ZYmsKXjZlcTaj66cct6u86FiZlbLGd9zUoKAICAL0HJUzuw06XoIiFpgtDbhz0SXKE9xAQhRkwMdSFPYmGaOpjSor7E1Unn+siOsJRLGELCs8dK2MHxQ4t1/hka0yvXGDyx2TvZGCkNl87mqZp3dKKBisDWXCNCeKLZxExFQ9jlRjHjzWpu+GFCSF199R5vvuqNC0JE7XquyNbHYmAhIZsyWwjRjbnKDJOXP1nHIDvu22CmVdZtEySFKTiwc6ZzbKjKYm1w5arPZNZkojZksuN827HK7FnBl3udLxEcSMsaNzeXWW6djgk1en3L9cYqEiYmvwwJEetUKIXRgzW3aoFwIMNed0y8c2EvqOjBOI9B2V7tjAVKCspxhajJ+A6ynUtQJF1eTyTplzezXO7pevTw1rEQUrZpS5nJ/0ObQ0pFoI0ZUEL1SIUhFTSZgp+tQs//oamySgCAJhrNAZ64x9kZX6iIWyR5gYTAOL0VSnN9BIxJQozSkbOUUFylrK7lhldyqxPTI5vORTNgTyHKbSkD965gxXxx2cMKVp+0hSyHAosd2t4AYF4gw6UxiMFG4tnuANNx7n+25b4I++6RjvemaLpYaIT8JPPbjAm+6YoaHNI8khbVfkcD2jasc8suqyPXL4269NWN2F77+jya99yyI/+uAMZwYjytUBdx8es9js4wQqBVUiziOqdsShgsk7XnWKGJelmk/F6rFciZDlDDe+Pp036KsYosKZ9TmGUYwpKeRqwiCQma9en85/dGsKQMtW+eXnH0GWEqTcYhr77A7rnDkwGI0r+CHMl0V+8YWL+EnCj376KVbKGr90/zGutjUu7OW86HiZlTmJthPzb55f49e+usqbP3yRj17r8YPvvcbuKOBnP7zB+892kVX47tvLaAqslApM4pCqKpNoY25dkrllxmZ3EvLZzQM022WunjLwM4pykU9fG6DLMqokcuAKvPGBGrc2i7RKUKlm/O43HeE1N9b43+5pccdyxslSmVuaNkZUYBB6XNlP+bvzPX7gg1cI4uyfPd8rhsJ9h20+cXnIy49XCMn43195iI+v9vjU2gCSHEtR+I4bZvmRexb4V3fM8e9fucxdixZ1U2F/EvKGv7nMF1fHfOvNNQqaxNWBy4fO9pmVC7zwSJVvu7lGy74+QS4IArcuWLzpA1d4Yn/A313e47tOz1I1VcIAXny0/I++RQCGKvE3rz/JH7zqKH96Zg9bk3nJ4QYPXR0i5PCtN9V47/ee5M13z1K3VMqmxAuPl3HClN1x+D9LJp/l/w/f0KtnJxoSP3T7Ef7dFy+yklT46XtvZZpPed+VTRa0AhXFIErHzBQThEmJMLluLp0LCvMFi42hwnqQUSvm5GGFThCRZilVQ8bSItqOR4KIpZp84ZrM6fkhTdPg8Z7G0E0p6Qp5rOPlHoYicayeICkOCBqGUGbP8amkNi3Lpu2I6JLK4bLB/4e9/4yyND3L+9Hfm+POqXJ1Vefu6Z6gCZogzSggWSiAJJIJEgiDEEGAEBgDAozBBBEswAhkDAgRLCEJyQqjUc6TelLnXF25aufw5vj/UFg25tjr2Ec+PmdJv7XeVfXh3bXftWrt53r2/dzXdW+Pc8KoyH37cp5agZm6wEzZ5VxbYEoucmHHZ9czuG9R4ctrIt9wKOT0tRrbE5FjMyqXeyF3HEloWSptN+YVN8l4/ZBj0ymKFHGla1Iq1+E5L+LDv/MfeeVPfBOtao0sTZgqhAx9lThzMRQDN3ZZskXWU58ps8S5zog750xscx2VKikeNzWKnOskTFsWl/sxrlvGT0RKhoWAyGy5Q0m1CZI+UVpnczLgrpmIK3qbL12bRaTGo1dzbl5QGOZwdMZhpbPIpuvRLAlUNYGLvZRv2C+gSRmS5FCW5xGEjI2hTNWssZLE6Eqfgm4g5CKJGLMyUDlQFel5MtuuB0mJqiFAMWQ3VNgaWNi6RhBlVAptynrKONCYNmW2HYkHL1W4fd5hujQijMv4QRNbyxGkCVEmkQoCbcdEpMCBesRN9YyhI3Dz/ICdkcjWWONLmxr3zMUEiUiYQqOQosmQpgqzRSiZEgIqswUZGQtDjWjoPm5kIYsCmiry8mMFPrE2wtZ94tjEVgSqzRxFDtDlnJImYKkZS9WIxQoUjID1gYkiZkDOvqqHqaZEqUiai2hyykwxJM1yRDK6jsyJ+THTtYDT16pYZsQkEIhSia2extH5AVc7RbpezjA0KKkCFSMnzxMUKUUkI81VdBmGjoog5cRpTknJOdTyyHOB3YnCkakBYaRjqQmmFtB1bPwYdhzYmKhYah/kCaN2AS9P6E1kmpZIRZARsiJRktCyAkRirvUUvDBjfRLTMHWudess13IOF3zCGLpewvZYZ+BJHCyqUBO43HXIzA5lOeRwJWOp6hEmGivDgF2nRUFQmbGK1AyN3/7MBvsLJrYBw3SMkOoUSzJhrJJmPoYsE8UWQd6lKOk8ur6fXhCzUM6o5CJr4z5mVkKRJc50Yu4v6/ghGFJESdHY8R0O1nV2x0UkMeN1t7d4zmKJU5suH7q2S1FWmG1IfM+zmnx5c8SVtseSpjOM2pxsKPQCn4pmYqgTVvpFnCygXBT4d09dwJJKtKOIhqXy+NWQWw8ZPLzV49kLBiVrh9fdUcBJIm5SZR67avH3/+YPmXnuN6EfO0Adk+sjnx0nomVruGFKzZb40k5CsyLR8R3CSOVEKaITTJgrlticuDyzWuN1J6d5z6U2dx6IEAUJQQ552TGFa11wnYxDcxLjJCFUY3Zjj89vhFwa95gr2rSHY6r6CDEvsu04JJlIzZLwcTi/YlLLbCZ+hFHr8ZrbD/D+013e/vgWj/3Qbbz9sS1+7u4Frg08Ht3tkuQJp9bHdDOXGVvj4k7ClS2HfRWDfQWVx696iCLcvGCimRFXexFPb7S5b6nEk5sTdoMAApmjcxIvOlClZir8wEevcGhe58JXWaOyr+Jkja+Whe3r/P8PZU3lpUtTvPeZIbKWUzQlBKCgy3znbQ10Dd751Bap0cYJFXTZZsaWeXzdY9+cR9UoUDIi1t2Y5ZpDFhfY8QSev1yhM8hJpZh/+6J9hHnCrz98mSBJ6HgpuizSKCT0vZQwF5ip6LzhjmlONkq8/kMXGY1tVjehWoQDLR9RdimZOtnI4BPnR9y/VOb2RoNxHNFzPCqqytWeQLOgcLgVIKtDZFEmTWwEaUJDVxCyCFEKuGnK5kpHpmw62ILJ+a7AlJ1xYauMEwf88RObrG1JKKLMkUWXzZFDXdO4smNRyBT8VMdLU2xJ4+KuSqOQUtLDPcu5CVccnxIlJvGAem7Q8wMWKhmykCFJGSemfdb1hKsrU1zZFHCDFjtxxEJJoe87nJiSUeQetlZG1Yf03Qq10i41q8zZnYCpksYzvQnbvkIsj/nTRzP8XKZRTXBdjQtZjqVWUMUxhlxE1cboUpO+J1M2A5aqOaq8jcw0X7xa4AdvnmUnsXnrpzepliQGA5DklIYB6+OUmmqyrznm0PQ6T91Y4vKOyiSE7z6xyGJV5F1n2hRlHy9LMfSQ5XqIIk4o6g62rPDqWzt8/FLCnzxpI0oiTpRQNnuUtSLOuIgm9znYcLEUiTiFjZGIRIyQZ/TcmNVOzGwqMyvVKOgjvDDG9yTKmsj+RsA4VLBUH1EQqZsCjaUx12wVv6PiCCF+lGFKEkkGHTfhajdjuSEwiQxsVSDKRNw4o2GluIHKKDAoaBmimNEZqcxWI0w9IU0lBr5JmAiMAwE/zRhGIYoICCJDTyVMYRTEKIJAveRRskJWOiZxnjNfcQhiDSdU0GRo2AE5CT1P4dJ6AVWQGTsycZqS5AklOyHPc5xQY+Rb9BydcSxgl10emK3yYFsmz2IkQWYSB+iKRxzKzJcT1qOM/kDGLI+Zaw4xFYHblhRW+zZPnm7yutuaPO/5Jh+73qadDMmllGIpo6gLbAY9nlc4zCcuDvnQMysYqshvfMMRwiQjA4ZRwoMXfBZbJs1iRmecs9kXaFUSKoWIjcnefX99epc0FTgyrbG/noEokAsBI8emaGTYegcJg7VY40bb4c0fvYaWZ2TlFEu12ZlkuKFIGFpU1Qavf3aZd5/tsOPFlAYNfDyKmoAqwVvuvomPXx5yueNhKRI3TZv84WfbPHdplu3sDLZssOn4OKNFXrTY4Ho35BdeuMDp9ogVv8dgJPK562M23ICSpvCCAwXe8OBptiYRx6dz/vx0xnRRYWM3pWaJrI8C3vDeFZIsRUDkFUcrnN+OOFQu89CFDFO2cHOfqlLm0UspfzDYYiPuI9sB9UJKnPkUDYOrg4RjCwFplpMLGScbOlIu8UdPXWVmKuM3HriF9UHE49c9Hjhg8ov33ExJU/n5B1fYjR00dGxN4MHTE/7Ta45QMZV/ssZf7fn89jcu4ycpyyWT2ZLGn3/LYd70oes8tuHyn053+JG7pznaMAH4m6fbPL7qce1wwN+dbXNh2+fnPr3C29QD7Pgev/nlVW6ulfmdVyzT63ZoNpv/5P3e8oJZ3vKFCxTFEjcGIc/sOPzZtx36yjS0/xZF2iscffvRKUxFJM9zPnKjy8+/eJbF6l5I9/fc1mLkJxQ0iTzPecPfX+V1d7SYLWlc2HG/mpIIfPX2VV8re6qv6UKRnwVc7Lq0TJsz7RAhF3n2dIsPXdlhZejS9mP25wJyUeVcJyAHarpOkto8vCZiaiELtoknxHzujMqB5QRbMRnGA0ZJzkJZ5+K2xkxtlfb2PHNOiZVxyM5EY8YymaQjro8zDtU05mqbSJKPTBVB9bjYjVgoqqz2JDbGLrMFharlcqmTUVALREmGkyQ8a5+EbY2RpJyZssYw7GO7FiVd40o/pq4W+PAZDT+JKMsFTneHlOIKo0s5M+WAmYJMkvkULIMdN+LOfWP6rs2Noc+RhoJz5zHe9fN/wrf/2hspGAZXuzoiIllmkEpjbpmWmC4OUaQZrvZcHmjlPHhVo2k1GAoDmnoBRXbZVyxyZZChCybnRzlFVWJtYLNUi3lmY44TM6vYSplrcURJLXFlOKHnqhT0lM2BxJFphbWJxwPLHnUr56ktkW84GBBnKY1CwlRJpWT20AWTcVRhnExwnQZabnB+12O5LuCUSnTdEYIYMm8bHKjF9PxdRsEcx+oiN/ox13o6x42UqmbgxRFRGhLGNrZQYF+1Q8f3udJW2BppkFhc3LZplLoUVB9VSakYOkGiUTFy3BBq1gRNHxLnEoPAZL7iEsUq88UASUwp6gnjWODsrsXnr+mYEmxtmrSaHo1STtGI97zzMpzbadB2fI7UPQpqzslpj/Yw5FMrKY2CREEV6QQgCyKKICPlBkXVwVBS4lTZKwYlEpokslAJSLMMXUkZhgJ+ImGpCWm293tRS0gQyAWBKBEp6SGynBEvDhiHEmGSEWUpG5sGHipRljEONCyxwtl2TNf1KRsBihJBntJxdMhkojRnOJDIbQ+EnBtdA0OPaHsZVUdFl1POd8BQREaewcZQoe3m5Cn0hipvvPtmzm37XG77GJaPXchwQ4OtoUzfS7n3gI8TpmyPRaRMoSgrpIGBGmZU7AGm3iXOqiiSQLOccetskSTOaQ+GbLomVVHDKohUDIcoH9P1mozcGpFn0xlIXJlMOF6MWLB0NsYR5VKEKejopkSUB8RZRku3GCYplzoiR6cMLD1k2/V51qzOOBB5elPA8+ewyiG+l3K8JtHxM2bLEmGqszF2eM5iyq47YmdiYEgWf/5wlye2HdqDFLMY4sQpv3r/UZ7Zcbgx9inaIheGAfdU6jyxPeG2aRVFHiMJEqI0wJSqbI0UjpbLrIshB+oab3vJIVb6Ad/+zos8cJtOvbjFUkknykboqcHaKOTxv/8Exvwx4umTNDMFwRzzolaD4429CV8VS+GH750me9Lnw5vbTOsW1eoILwtYKpmsDgPErMKsrfL+c0PmKxJ+LKAqGfOmwPrEQxAlVFnm2mTCnK3geBlvP3WDWamMaCv80gNL/O6pc4x8m27uoAs2kOExYTyosaSYXG4HVJoxz6nv58feu0Jf9HneYhldFvmpe+f4mU9fZGccMwgiCkbKyQUdQc7YdhLKkoleFfilBxa5a77Ih8/1+c0vrfKT983xU597kpctLfDX59v8wG3TvPq9zzAr22y7EW0p4gfvmOZc2+HM7gTH9f6v6tnX+Tr/PWsDibIVcf9ildPdEe0k5Ni0SdeJkSUBWRQZeDnSyGC+EbESO3zLycNsdHcIXJHddESUC8wUc9rDEsU8QhJj3n95ix+/f56f/fsdfvpj11hs5SjqEDc2WCyDJE9AEFisiVSMiI1Rwh8/dY0DxRJr7RRRy5hviqSix42+yJGmSd9jr8u0IHFq0CPr5zxvsULJnBCmLk1Np6CHDIKURcOgWezjRBIyJlE2oWqLZIlByISmXeKJ6zPomktV1zi9KoOcoWcWK+2MSkEiCHPWBzJ3LbnYeodSIDNUcq5sKZR0ldVJznQ5Q5Q9okzm2NSQrYlEw65zfuTRCkp0JgOOTIMmxegyPLNZoWQGSIJCoPhMV0xWXY+Zoo4Te8yX9ywex6dFdG0bhQKC1CNNSgzDiP1ViY1JxLFZg2tbGbpYwg8zzFKGm4joVoCEyupAZmM4Q9cPmSko9D1oFRMkSUCVA9LUohvGHJny2PU8hpnOXF3l5ccqfH57jSDLuDGQWCxL5KLHwBPpjhso2piXHW0x8kS+tLXDTGmOd778BBd2XH7tizdwQp+56hg/UvBjk4oxIU41FitwPkxwQxkFjfZQZexnzFVzCppKmPrEfg65TkWTOL+uMi44HGsW+P57anxkbY1LfQlJ0CiaCVV7RFGLEBGxFAkvNojTEFuJGQc2RV1komRMHChZMYYtIEhwbtzh+26bBsHnma6EF0foCpjqhIIWIosZqpxjaTJ+HLHakXFjAclXMJUURRSwFfAijanSmCgxCVIVWcmpWAlV24UsY6qYoYgiQ18lyXNkOcSLFUT2rGO6kqHJKbIUcKjhszY/ZuJbxKGKO1IwyhFL9QmWmhPEAraSopVdFqoBT66XeWSrgxsXcXwDJxQ5NuPQcywUSSFJVKI8J9MD4kzAC23iSKDrqmz0JbJA5PpuyvV+m3XPJ8pkqoZMSc9pliZsejk//anTyLmCH4k4HZEoyTBVkT99You/u7CLrQoErsWVccZMY8LdByNsPWRjKNHIDALBYqufochwo53Q81TQBPygwXRtwlx5QEWTGEce2xOJA02L1aHLspohZxa7noeQaXRGJsO+xcFlgyv9gER3OVaLqJQGVDWRMM05UjzGu86u8unrPRpKhZIu88HzHSRzxEWvjyJYPHLZYqZQ4BX7m3zsQp/vvr3Ff3ximy0npGUZ/PMDLf7kiR3CLCPNUj62uoJpOdxZF4jziFsXFB65XqJYhHLBoWSr/PSzD9CdxIyChDsWC/zMp87x8DqUDAUnjigpZf79PzvMux7f5bFrq3iqzPFaxuo4o6gYXNqROTwTM/ZBEGW8kcnBSoFUCHHiGHerwJV2wIVdn39+S4PnzjcAOLUx4bNrA0qmgeunFHKDv/q+g3x5dcL7zvT45RctMFNSv7LG/+Bd0wC893SXd5za4uZpm7miyr6qzpvum+W22QJv+NAVVFHg977xAN9/xzSvPF7nlz65yo/dM4skCHScmB9+3zWmKjLPnqrxy89f/CcTyOI04/Suy/3LZYIk5aVLC3zbsRYNS+Udrzz0j+6NkoydScxC5b8GXFvq3vCP3/3iBvfMlviG/bV/9JqSsVeOCOKMf35Lg+ftLzMJE37h0ytfHTH8Ov/bfE1bzzRB4g9PrfPYpkcgxfzJ4xt8/0dPcXY3QUJlvmATxhq5ENAwDKaNKjsDi+s9m5unFA6WbUQhJfNNbjuooGGRCymmUKapVxl7EvtqEXk0x/MPjOi4GgcqJjc1NSaOjCEWuXNWY7Y6RJdVmkYNJ0qRxIxDFZNzWyoXdk2KcpFh5KFrYw7UcjrhgH4Qk8QqobxNLiZoSoyo9pgu5jQKLlEGM5aFofuUNJOqYrDRyWlqBa4HHtsjjUvbRR7bzDBlgZI1pGmLPLE+RZAotCwLRely8tn3cPKV9xEEEk9tqpDJBJFOnktUdJ22HxEnBkVzh7lCRppHfO8tI1JhTFEukAl9nMDCTxPmCzJL9Q5zxYSMkJUti/VBhb6fszOeYscfU9Jk+r5ASVWR5JTNEVRNEd2YsFSWGIQiSR5xU0tgdRJxtDFhfyXkzplryFiYSoIqudhSAcOKEBWXaUNFQKRgjhh4BcZ+mYtdmYrmMGMaHKxNMM0O+2oBBxsJSCk9NyXwFY41BV5wZAs/H3ChbZEmNrNFgThL6fgJUZqwM6hzcXuGp9dmOLVu0fYiciFmppSiyQol1SSMRTQl4PPXFK71dJ7c2JuKJos6RQUMyaRmqKSpgoDIxY7A/fMNep6AkzoYussoUDi9ZfKF1RpP75QZ+CpLzQH1go8sCgxck1wQiHOIEgmQ6LoF2o5OmOakGYwCld2xSZQo5Mh0HZOBp2AoIZIYkRHR8yR6nsgoFMnyhCTLEMUYJ8rJhZjZah9N9ZgEGc35IY6nMG2rXN8q0dKL2IbH5hCu9xNWBzkpOWmW44Q5ojyhJw2YuBljX2B7IrLjBMRJjBuKbA5Veq5Md1KnbmXcNj/mSE2iYoi86ECVJ3eG3PAmtFMfRU9JCBhGAUVjTEyImCsUVJGyYpBlCo1ijpvFZIUuVctFEw2E1GQwKSFLMVedFc7suui5Tr0okmYKO05ARogi2tiKxEpXY3WcoBdjXn1HkefsK/GXrz7KycWU6ZKAqEZ8900zLBaKWLLCMIhRBPi2WwYs1joUtZA7Z3NGYUh7opEmJUaxg6VBpZiQym32lcW98O8o5HBN5fFNgyu7VbIMntl1WJiSWHM8GrUI0woxYwtZELhjrshPPHuOtUHIQtFgcxxgS0Wu9lQ6kwIro5DuqMnORODN987zx686yGxF4dGtCe891+E1HznNiUMJljVhytQQpRGqqLIyDLh6poNwy7dw7NWvYK6gYRU8KorNmfaIf/m504zCPb+8Ion8wovmOdm0qVSG1AyRfSWNvi9R0QpEWcKh6YRbZgyuDTy6AwNd8ekEA8b9GqMwYtMPON5SWd9QiCOReb3Ev7hjmo9+++2859w298402WdNsd+e2pv4IsgYSY2bGkXuP1BieUZBylXOb8e8+i6daVtm6KdIosDfX2rz6IZDkkq8ZH9j72Q3jtFknYpsI8g5ISlPbE8I4oxrjssd+2xSIeabDs4QiBFNU+ZtD69TFyy8KOfEUsi33lRFQOCnHrpKKqfMldX/qd7875Dlwlf1+jpfW8i5zCDyuGUR4kgiTeBXPnWDV/3FRV77t5d5zkKRIw2dSSTSdlOqVsTp9oiBn7I9SYmjIhVN2svQUSJUUeIVy/OMA/jDJy9z31GVTc/n9K5LnljsrwcUrSF1O0JGpesBUshsUaDT1bg82mW2mVEv5CTSBElQmLJF1obgRBoNW2QcZshSRkETeHx3G1VOKSkSuuYSpxKGpHK5o9AeFzlYjZmrdDBljZ2RzdW+REXV6Dgiuppi5NM8uaERxzoyEqEQMl2USIgoWCknpn0yIcBWZEzV49jUhOce6CJKPkvVmEalw2w52ctNUSNIDdwkYLGoMI4i8qTA1lCmoEHRCFmsTVjtlDizpfN9Nzd5+zcdZF/JYOgn2IqCoY9YrqdsOzFZUmIcRQhZATeJyFObp7dkCoqOojvM1VMmYYpmZXh5iq1CUU85ON1htpyhKhOWKhJRHiLlCptDhaoRk6QaCR5L5YyVgcSFfo+ZKrz4WIGnOx0miYcXSJR1kWvbAklsgJCjyC4LRfjCRpunrkfc15jm9z6zwxs/fp4PrWwjW10KZkxnVCXMY+IkRxE0BDHh3LaGKiroco4TZhhqTqMo0Hdz1joa/UmZJJM4tWpRlGxumy2wXLH59mNTbIZDpospup5imB4FLcYQS9w3fZCrXRVVjlmuhByop+w4ClEaEScyFStnoemj6D6iElE3BZqGQSeY8MqD+1nva/QdlVFgMXBtOp7GbGHCQmlM3QypGClTFQ/b8JgqTajaAQC2lnNyts/dy23uWd5hsT5GEmBnaHCja7M1NnEjGS+SCGIZRcpY6RTYHRvIQk4QSwxclYoRsFwaY6opc5UQ20hJhBTNCsjyDD8UCWIVId8LzTZUn56vYMo5QqYg5SKjkUScJ9wY2DiRTRjLTJUmtEpjjs30qBsC3YHNMzfK3NhSsPWU+amIq+4OK6OAcCJSlmUsVUDXAjZHe4eIB1oBB2Z7DJyMlxys8IGzfQZ+gqaILBQM5FRlpydTLQQUdRFRjDi/oxHFJnGqYRb7FC043DBZrKs0LJV7Zqv0A4GxJ7LSbnFjqGCrEYcbMdc6IkU9xdIErvQEtrpVLq5Xub6pM1tWeeh6nzvnDZ41I7B/apv5Qk4mRLQnBh+6ssN7z45RBJNBPuFvLq1yarhLpdpFl0WeXNexdJlzOwHvenIXTZV44ECRH7pjhn913yKqZ/PghRE/dc8cP3H3DLMVEVn2MWUZRfYpaRLXOhI1K0PXAlxfoz2R+fC1Nm/78hZ/fHqDn/zYRdZ6e1aqcRZhKwpHqhZDL+X0rstz5st8x5EpHr1cIE8MgnzCwemEjhcTJBqdXZOdvszxWYnXnJznzXceIQxz3vyR61zv+3zHrXX+9tw2PT/i9IbHTXULxwUZhV/+xjk+eqXPx670+WeHKnzXe87x7vPbpP9NkvJ/eHSbU5sTlss6z6y5fGa1y23TFs+aK+DFGZYsctdcgTjLeOj6LlVT4Q9ecYCbp20KqsSvfXyT19/b4ofumubCZvCVkOr/Qp7n/NCHL7Mx3vuM6LLEy/Y3ePN/vkGe5//k3h95/3We2nT+iR5d6/lc7Hi8/vaZ/6Fm6YrINx7ZC7D+8KU+P3nP3P+O9P1P+fqe6n+Nr+lCUZBB3VA53jA42hIYiR4CEstVlcOtEEMfEaWQJjbdicL5doafZgixzvYoYsfzUJQEQ8q42AlYd2IOVQoMfYGntmImoURBS4jFDqIyoVVIcWIHLx/RLCaYukcv8MhTba9LB4eqrtAe1rmwqzNTVCnmBS7uWuhClcBvMlcecLhmslzJ2Jik5GETL07o+QGGZJBL27TKIwp6zJbrUtBEFDlE02KOLQlMYlgum2SRyCTO0Cnw5Y0cBQUnccnynMVKQMN2yeIWBbPLwWfdz+UvPMTZ932Qnh8RxAJXd6vc6BUoKSYroxwFk4X6KmGqs+uDIVWYJD7tSYvHN8ENDbq+yNawRdn2yHIRz5MQxT0LzNYkJY4bTOIxLTvD1mLyTGCmmOGnPjVDY8fJcAMFMStxe3OKlmHz+FaBszsma6M5RGlClAvIgsGms9dO3CgI6NYINxSJIptxlDEIUvy4yKVeiXEyIkxl5KyMEycsNTrUjJz5aoZhO9RLXY40XH7w1h6zBY/HNgVGgYiOyrSpgpAyiQLGYYQoKBiyipAV2B5LrAxEHl3VudEvcL1XZuRVuXfJJ0dksd5BlEZAxJaTk+X/xSKWUinDPsvg1M6YOLaYLkSUDQdTDbh5bptXHn6CKXuELCaMw5Qo95GkiJIZkWUZ2yORUSAyDiT8SGboGWyPdAY+yFJElOa4oYQuZyxVHQ43XUQBrnVtbvQtRCGm7Yic2TL5zJUqQ18kSDOOt7pMF8fEicBiJaBuyzQtlWlb5hsPlpmpxFydbLA1UhlHKaNAIggNZCTiRAYho+uILNcjDs4kHJ9xqJgxiqhRNkzc0MbWYKkiUDNFJFHAjzQQFBLgizs7nOkPuT7y2PEdWtURquJz61zA7fMu33x0TNcpsD1RGYcSO27Atx5cYn/T5Y65FDe0OL9dJ8hixqmLGwlkURVbkwg8ja1xSJgm6LKJ45dYHSg8tWHghyrLtsjBlscHr7UxFJGBn9Btm2wMMl5zbJlTuwMe30z4zkMnMZI6lhGy5fqYkkbVjGm7Y1qmTFFP2HE9ZmyD4URlZ5KhpHWudWWiRCSILP780TK9UYvdicI4gIWSiC+47G9CkLu8ZGYZGYlbZ/e6eqqGwr6ayq7vUlHL7DgiqigjqR3KismlnsI3Lk7x7SdafHHF4eKGx+GayE0tixcuVdgZy/hOGTdx2B3XeKYdcuZza3z8P7yfellH8C2CLMOUDK60Y6ZL8PhWjx/46Gmyf9gkfODSLr0gRqKIpqRsjCCMdPrhgJoZ8r3HD/OL9y8xoxXI1AlOJDHoz6OYLiem4MSUzN2zVb7ptgoVW2LsZawNIt5/vsvDNwIWrDKnd12+7cgiQaAxdDIev5ay2Y/4T+e2ueZO+Jl7FnnR4QKf3rnKv33hMnfPFel6Ef/qU9eYKcEvP7CPd5/fxU8TNEmg60/4woUMJZJJEnjeUpm73/EUkzDhZ+9Z5LapCj/2rAN0JjGX2yFfWnH58XunKdgZ/SDkP58b8oq/OIeAgChA2fzqN+h+1YKs/+H6Ol9bZKlIewDvvrjOC48oaMD1TkQc55ycNnnHI7tc2g2JYwlLKLPeN/jApS4/ef80c7bKJMxYHSjkmY6QqwyDnL85s8PLl1ps7VhcdraomAJlScfUJ6R5gpCbGEpMlEeogkwcG1zpiaAlKGmBQPAIiSkpBnPVPjuTlIqukKQZ7UlO0RTwPZWeB2Ju0huqbE9AEzRsLSImomVJXN41udIzUDFICWkVfcTc5DNXy8yXUzxf49ZGleWijSKHyFJKUZVIZJ+KIWAXfAQ5omEJFM0xliohSxGz5Ql3LI6pFHuUNJXNMRiySNcxyYSYGVskFUOKskYi+MyUUm4MVJJUwtZiZmsjjFzjTx8e8fOfucauE1NSZNx07/nGUUDL0kDyaZkSPS9GEzVOb0hMmyprA4mdXpUrXYlSKcNLUyxpLyOoZIbMlUJU2UcVDVRlyLQNsuqxWIkYBgqq7FPVVUZRyskZBydxeMdTN/jw1W1yfY2WITEYqpi6z73HHHRjwkwpRkBhqtTjjvkJsTbhiztdnn/QpiGafP58yGRUZewLtIoj8tRiFIlsORKGLHPz3Ih6cYgui0iixChImSm7HJsJqBU9dscC59aauJ7J5o7Csxct7mhUeNcTbQ4XW9xcm2G2oDNw9oK7x0HIJ1a3qRcSNNXF+4cpZIebISUzomSOKBoRM5WMhWrIzkgiT1RO1irISYHve/8FNsYxopSgSgm2llMxEsJM5NrAxIklTDVHRKbn6rgxJKlInoOtphyoe6wOiox8BT8WCROJMJHouyqKnOPHIoIIipSxPdortOlyRJIJqEqKrUcMA4nT7SoPX28yDvY6K0Q5J8xzVDnDUAQMGUpmzFxlxCQQsdSU2VJEyY4olTzqrTFlPUTJVaJYIswE/Djm5pkBC5WEhZqPZXs4ScLhxQlHpnzm6y537N/k+GKPk4fHzLZ8CsUxowCmi7BU94nzGC/S2F9RWR/GfPezGhiyxKOrDmkKf/lth5lpCuSCyuW2SpxlnJiKqJgRbTdl4hVoVHNaNfj+26b51y9eZN31KKsqcWKiqBMONXwEQeFzVy3qtoA71kGI2FcBy/CpFh0OL4RsJl2qhZC3PbxBN9oljEoIUkiWyVTMiIEH07rGdhvEUOeWcp079o3QlYxrPZmZokjXyWiYMr005JW3lAEYBSkDP+GuxQKWJvPkpstHLgy5tp3T69fZHGqcuTzH9Z7KTElAU12mSzGjnoIlaISBQKIEWFqCbLjM1hMUa0RBVtlxM77paJVf/+wqEy+j5yRsORGGmSGrHi1bo+OFiInFyAdPSmnWY/74yS1+6aE17lsq8UffuswLD5XpRCE/8uAFbgx9CopEnOa8/ZWH+LUX7eO2OZPPX5lwftvnt1+6zAMHinz7s2p0nJTffvi/DhRpexF1U2a+aPBz3zCHkin8wZd2+Oz1Ebos0nUT9lUM3vzpcxiK9I80wo0y3v6ty9w2Z/NnT+/wmy9d/EoG6X9BEAR++f59HK7s7Tn7fsyvfm6Vf/3ihX9iN8tyeO3tTb7ppn/cMQSwv2bwjlf+Y4tamGR88fr4/6V+/fOTTe7bV/5/S+v+V/j6nup/ja9p65muREx8gSe2I2abDjWthKHChjNi18+4fzmloGacbgso2phS0qLbEzgfpOh6gfuPhnz+msJys081bFArhXzmqYzfefVBfvfx62xPQjIx41jTIssEitYWhAWWRZHV8S6Op9EQTK70B3ixzIsOGJzrJCTpiAOl/ZzaHrK9nbBvxuBax8EycjS5wu4EVEng9jmBDA9LMTjcTAiyAX23yZHGFrkQUA0bXB+NiLMKbqAR5Q5V1aIzyrBVlX2tGESHpljgxqjPzYcCxrGHE0nsszS2nRjHm2IQhtz64pfx2C/8CeHHH6dy+/PoOApeUMUJFE7MyJzajmgZU6w7PjebElkeoFBmGHucaJlsOmNcr8E4lGnaGXXbZ7uts9W3Gfshdy9F6Oo6y0aZjAGr/SZpllEp9BCFJgMvYcpWubStIcsTDpYFXjx3M3+/8jQdR8RLHVSxRtOeoMfrHKge4+FJys5ARMrKOHHE7iRnumihyAFBEhAkKod1jQ4+o8jhkFEiz0RcweVAPeRgUyTKFJ7ehZVRgZKioQgFum6GbHYQomlsVaRuQS44nF4Xeea6wl2HFHzBRhIE3FBkpQfX+nDvvgQnsrAUkKkgCw5hkrHhBIyChHGcsbIl8YKDNncvFvnieoeJkKCKNiXN53DdxQktPrO2xEwpwdZiTFVgc5KzO5bZV01pFEMmYYqhxiSpzcTVURSBZiGg54p4EWhKjiSIlM0cQ03ZX3EZhhK7E5NzW0UMNUEUM8aBTBCJ1BWf650KPdcmz+Fg3UMSQJdCtkcy9VqZT10foxsZfUelWXJZqo8p6iJ5pjDyTVQZ8gSkxNw7uVFzslymqKdMfJOaHVPSQy51TIaqybHWiLI1IsuhoMXUi1s8eqOC71ZRlZBbZ1Py1KBuBCxWUpxYJBdDen7GxgjqVoAX6vzV2XWqBYE4E5Ekh0lkUdRFDlQkumMVQYjwGVNSKkzbBYI0ZhK6XO6odCZTfNdN85QXdX73iSt4XZXXH5vDUCRWnZCdUULFLvLvH97i9kNtSnqRtz58jcX6AE2OKYg2gtRnZ6JA1uCGo5BlCc8/mHN6C7LYoKIWeOy6y0tvFomSlFEU8dLjOZoyYuyZSJJM34UTsypnVwSUpMLvrm3zpudNfyU08HLHYyuYcLypE2YhrYIASoeibDOMAsqCxYNnPdR8g3ee2ubEwZBn1lS2PYd/+8Ah3viRK3xpNSKX5ikV1yGocuGTf87zf+oX2fUUykWZBU3iR++c5nceO8eZdsS9+wSeOz3Fv/rIKt91RCbJYKmsseYOSYYSQq4SiWPumtX40DmDp25c5a45kWJljB7aJGGKqEYsVkT6Xkx7lDIqyLzv4jamJtJULF58tMRr33uJN9w5xY989Aof+s6T/O4jq9xoJ7ztpUd53+kOT016HJwNOFJY4G1f2CS3HN707Jt566d3eMmRCr/22TWONlReebjF2x7b4IVLZc52XFqlCYP2DPvLMlZ5Qk2DPzy1wk/eM8vHLg75YnXMq27am2J2eU3gTXcucnLG5F99+hoDN2UqrtLQElYdj9SBxbrES5aqvPv/pqB9na/z37Hc0On6Mq4v8f7LXQqlhKpRRNJ8vMzilx5YYBwmvP6eKUxV5Ds/8DS3z5T5wy9sE6kxjaJAHps8elmjbEfsVzOKisShmokkChhxlUZrFwGBbr/G4bkBayORKLWYsmFrHOJHMtOWSlfI2AoSlusi/Z5Gz/eQFYuZokiYexiqTt1KEGSPfGKiigr9YUahmKFGKm03xFI19ulgGhOKicVqt4KhdNBEhVEoUTAdBEpc6+jkUspHLrfp+xm2qjNbG6EqLkFkkTIkjg2GroGfeOiSjZpPyHKZvqcyCWOKikXB2KViltmZiIBIHJsMgpyFRsLl1EUMLZ7ZzHjVzSPO7BYo6zFhZCCqEf/seI3HOx1Kssm2E3PAMBCkNgdLkAkDMq+Cl42ZK+3ZfO9Ykrm0JaAlOuvjjNmKxDBIqRVgEqbcVHWYKrtIYk7ZgLnSBlfaUwSpT0Eps+um5LmJKmYs14bcMZNzY+jSd2sUsoAolYjjIrP1NrYZ0nFlmsUBNcvET2JEMWd9UGVtLDFdDdkd53x516WgKCiFDEGOUCWdy7sljs72mVIUaqbHOMyomjoICUEypGgYpIhoqkfLTlBEmTib8OR6FTuwGTgpb/9sh7kZn2OVBu5uzpnthCDVWShnRJKPYXi07BRVht1RiaI+oWVllGWZsg5dI2TXlegONcZeESOHs6sx17sdLD1D0TMUKWcc5DSLAQICI8+k66p4cU6eiahiStOKub5eYUvJWdnQOdRKaFRdokxjqdbDjTT8UCYORcRIo1Y2OXVNp2qnLNdDalZIo+jjRDlBKOHL8j/kMsIoKLE91ogTCVMVUaSUKFZwQpEkU1jrFYmKCbbmI8l7ey4x14iQyLIMP41RJIH1fgVDEZkpj/HjlNlyTJwZFLScoZ9y97xJp60RehW2oxBR9hh5JeYqCUXdJ0wTBr7MwbqErmSsDiSeulbmtmaZuinwWy9d4ofee5W2FyHqGX/56mNYqsTr7ijyF6eHzJRiLFlmFOU8s2qzr2TynSdbnNkMODltcbhu8tjGmINVlX7SoWR1mSvmmGrCU5sFlmoZG72USSJgiBJz07s8s9lE1ySGfkhJMhhNJFqNEVlSZByPKEQqsrh3sFhTDe6abfIFpc+mExLoG1zbKDJTG3GsGYHcY3+9xI4HjdDgdx65znue6LHtRbziRJEPXR7wXcdafHFjiC2qPG+pwskpi4+cG3A9G1FMdDJG2KrFVk9CUUS+8+Ym7z63SxKL6HqMhEQqpEROmTAQ+cUHFvj1Ry/x/NkZpgyDwwWPn/vCEFmNmCkIuHHETMGi47m0xCJZovFdN7V45+Md7lsq8FMfv0ynm/OqE3XSYcKhus0rD7XYmUS8/t4psiznIysdpisqtiDxY8+Z4u1f3ualRyv80G0LfH51wHMWygA8vDbik1eGvPhAhfsOlHjugSKfWt9FTXWet79MexJxpGbyu4+sMRmI3Prcyj/SCF0Wec1fXaFVkfiuWxq87u+uctO8xs/cN0fhH+55Ym3Cnz3W4d6lAkdaJj/64GV++u4F9v1DxtB/iyQK3LtU4Gc/tMobnzv9j2xy/z15nvMj773ON5+ofnUE7+v8H+FrulBkqCFSmrBQ0Viwy1Qtk1unNT61ukPNyBGEiFYhBAqs94r0IgFVFTlgq5gFl1GQc6SZogoVnEJELubsm0v4y3NXQMyoWzJNvYwXDVFljemCipDnOMTsK1kEJkRZTEUqUVVFLnVdSooAssbpzoSfuesgf/D4Gitjh123zNEZjy+vO1hyTpxm9MMxNdXiRienVsiZLxokicfAL7NoC6znE549J9P2+jx4tk5Vsnl6I2ahpjDxU7pjODSt40URBaXA2bZAxYw5XJ8wCEfY8hxPtjPqZoHTWxnP+5Gf5RtnGvzeZ84gVBe50RVYroMoprQMk1QYoOYt+uEW07JFmMbUNAsnGdMwSoyDMWW9xMDXaBV8OvUAWQ3wozJrfY9bzQaatkkUNkGMmLJBEFN2tXUseZ5HrinM13xGrsmj2+tslrZI8ChpMGtLrLsxYWZSESUUtY8lLRDoAuN4zFIRssygaq/y8I0WR+o5dWudbpRRN0GVM87ueuSZxUJRQitEyHKXgdsiz3TOb++1Fh+f2SVLK0yZOnLuMwwdaqqJG6kcbAici0UutnW2diT2z4h4sYAhJcyVHEqGS9sDSYjxY5U8F/AiA8ct4QYyuiRRa/bZ8AV6gcbDm2NaBXBDlYIWECUiU1bMobLDlmdwc33M5miBuULO0E/I2BtxbygZfpKyNYo4WPfRZYmeq5OTkWQpQRCjKzmm5hCmOZsTBUUCAUgDlXGsYGgJuphjGTmWBJtjGz9OMZSMZzYKe51y2T+MmZRHHJyeUDDG+JHMvqqPreX0XBUvVZgt+VR0n0GgUDP3wgl1OeHirkXZDPFjFTfKcGOBMM1YLnocau3SC3xcv0LbUbnatanpJqKeIEsJeaYzDgRSRJy4wHMObKLLGroMtlIgTTXiSMas+MxU1xm4ZQRB5sRUztATubRj0Sr3KaoCmiSzXI750vWM/kTgV559lHc9ucuMInJq0+ObDxlUDI2GpvPOp9u8bHGavzvf5tW3VPnAM0PKLZcgLOHGGeXyDvtqMm4ksuF42L0KTgTXhyE3NQUQUh5bT5D8Bnkk87MvmOEdp69ypa1DqqFSwGhdxZLKJLqL2ylx+0LIlbZEswRP77osNhTe/0yfFyxXaBZUfu8LWyw2LBAn1C0ZUdnmeF2gH/hEUYu2I2LoGX934zo3H4aOl3JkyuAPH97iybWIz6yMONhU6YZdxmdj1tfPcfuP/jq74xxRSvnnx+u85TMroPQxzA7ffMLi4q7MjdGeFaQbBnx5w+Mn7jzIWx66xvowYGlqyImaTseLWCjoTJXGmJZPKyjySDtluuYzU8rY8R0mTp3xWONvr6yzUDXZ7WeshgFv+fgKEhkfWr3ONx2vMFNQKZvwwCGL5y6XuO728ddVHF/gk70hBV1mf8Vm7OzlkSzWVH734TEiIqc3I670PH7u2UtcWFtlPIk5vwn1qkOzkHF9kDERPb7lgI4sCLzy+N5p2IOX+pQNkTfcM8P7L23RNCSkRKZQ8LhzUaUpL/DeSzv4Ifzpo+2vukalX8Uw66+VUa5f57/yb+5f4nsfXGc4kDk8nTH0dCZCxLQZsOEP+atndrnY89gaRTx7sci/e8Fx3vSxa/zw3dP85VNtDlUlxFzhVDDmtkYd39ui4yX8wSOb/MW3HmIYRfzqlz2WahHX4pQLOzpLtZiuYxCkPqYqMBhZrI9yskxmuS4QZy6lqk88riEIfXI5IHWrLNZ7OJEISZHQURlFMD+d0vZE8iyhnlvc2rD59GqXmxdyiobLxtDmwtYUG0OBipmyXPepFPrYus6FbR0njakaCr4QsDPRqRgqiD6HGgm50GEUyAwnZT56UeOFTYkrXZlaIWGhqHFt6KM5c1QLQxYr4EWAkCGJBbp+RLOo7AVhKyqfu1ZhrhxxcafKxJfIU4VHr0T8yHMP8DuPrTKtauz0YqbLNjvumLJWAHGI5zVYC1KkVOdMV2a+BOc2UpZbYJkjFhWBVAjQJJU4y9FFqFkh40DACy2KhkMQmYylMWJa5XIbeq6KKORULQ9TMmgTk6clJDGl42QU9cpet5coc6NX5nCzj4jN2W2F+XKIJUt4UYiQ6eSeyk4EzXqCqgkIooMk6uyOLXLGmIqFKkVEWcRwUmPiqZStMbqk0TJFRDGm6ylEUYmRq6PoPi9ebtKdCNyxUOfxLY/X3d5gpjiHEyTseiGfXW3z0LUcQ+2hJQqyqNNLJIZ+gCRIkKt4sUAYCWw5UNJTCpKIKEg8b77KF3d6KGlOWROpFkO2xypuKFLSFQp6gCpHlLQJ5CqGkvLCWyIudjT6JFiGRZbshY4jxFxpFxBzGQRIxZTuSKJopFhqSkmPmC35LNdDLvcMJo5Kmkt0JgZXXJs4TfA9if3TPpaSkiFg6hGdgYFui8yZVeQMPndxjG6ELFY9bFVCkTJEMWF3UEATZExNRBRiciFm5Cls9OuU9JSMhPWRzMUViyNTBqM44NowZKmeMlXMKegJgxBkBGaLOWECqwORKBXZP51wbTyiXJjwxo8GLNQLfMdCg3v3FfHClIeuj7lzcYr/6HZRTZ9cnKAKsFRN6LsZD+yr8vkr6zy57vKbn9xE1nJULcSwM+K0RhD7JHnMbDHjc+c0uiONmUaMoIQ8udaiYOTYeh9dLrLrRFTqCZmgMY7HzBoGkygAIaOoFfit557g97+0xcpOQq2Wc6Etc2Cqy0whZ6bcRxd1/Gyd8rDOkxsSRU1honSomxlf2B6hqCIfWFknTEMWCjpvvvckf3RqlbsOmAyu+PR9nzAt0+0piInGoarJvoqG4+YkOaSpQLHssNOu4qUp331bnf/wxC4/fs9B7potszkM6PUcphoRXp6z4whYoskgTBHTIr/2wBHObYf81RO7/M7Llnh0a8jTqwFve/kyR5sW3yzWGPox3/2ei7z2tinqlsIPfugSLz1U4ztuapGkOb/4yRt84fIYVRb5gZrBcxcrZFnO1b7HXXNF7l6yaY9TLrY9Pna9yzNbId91ssq/+/wWZ7ddLgwdvu14g5tvK5DkGb/yqQ3e8vy9bqCBF1MvSKwOQv7mTJtDMzJndz1+8zObfOdhmb88u0mYwhufM83hlgHAu7752D/JMPpveceXd6ma0v+wSORG6Vcyi370vinq9j8N6f4/yVdrX/W1sqf6mrae+bHITEFgcapHpPR4ardHnIbo2ohuPGBjnDFwTL60InGx5xOkMYaecqHnsDIKSFORenHImjug56eYsgLWFjthD1XK6A0FTq2H9DyR3sRk2x0TZC5ippAwwMs8bFXY+4KdBigYCIhEWcpCOed91y+BnHKkZnJgKuJK22TetjjU6pGLPgWxwKeviFze1rnernK2G5AjcKVjsDaGhmFQMV2WqwkvOBDT6cDBlsZ2GOCT0huYXO8GNKwcWfJQMVksJVTNEFWo0IuHVA2ZlYGHLilsjxQe7/us/Kd38DJpk6KVcWpLoe+aRHlCmtZplNr0nRa9YEySaGy7AgNnmgsdCSkzsY0+N02NaNkRdyz1eOGhLkdn18gw+PCFKiudfWy6PqooIggC/QAKio1ltTkxF+JGMF3pI4saw9BBRafnmThhzOGKjKkFjDApKxZ371+hYHjcMgN3H1hl5Ob0nQYvPLxJP0i51qnTc0r0g4QzOwWKqsVy1SHFoaDkkFZJ8pxndiQ0SaAoq3z87AIrfYmPnK0SeCYnmgIVM2DXd0izhMMth5mSwC2HHMZRjpNNQIxp2DECMk1TZ+TrhGnGOx6u8/mrZTZHFlsjE9c3seUCBXvA57euMVUdIsoDojTm/K7Op68W+My1Jo9tzwAi40SjZUfMFKGoSVzeLrHZK6KKBvurMnOlHFML2RwaXNku0HcMGoWEg80J4zBjx0nxY48oD/HihJrtUS15SGJGmEhErobraISxhKHmFLQURcow1ZSqHXB8ashSdYIodzBUnzDWMBQZJ7QIIp2qkbGv4lIzfZqFAHKFLBcJUwFRDFntqCiCTX8i7WVUJC4bfYUwzbnQMZApMHCqXN2pIWcGrUKMqUVc2LKZhHBuFcIkRRJgpTNFZ9KgasXUrYi1vsjqKKRmejRMmYWSgC7AoWYfUemiKwkiJhVdQRBjdvw2u37AzTMWp9Zc1vsJX1odM4h6/PWlSxhaxHP3m/zsC2Z4z7k2Z9pjHlzfpVJOCSZF1oY5rqfgjVt86XqZR27YZFGTix2LawOJYw2Fpu0wCWKaZoHdOCdVQ9762ArbroMmSQSJhGANuLazjyc3Da62y0wXYpwwY7YMnXjCgqUzGUnEecqPfeQqlzs+S2WdfpSRRDbrY5+ibHN9GNMwbfwkoWzmjKQRh2pwuSOhZiUsY0LNSnn/mR4vPlDh0mpOuh3yqT/4fWYOnWDbiWiVQvwk4Lcfv8qt+0IW6lscqOrsOnCwUuH2VpPHuwM+eLHDT9yxzK89tM7r75zl3iWdhqVxdstiMKkT5R6a7uHHKleHExolqNoTDL2NlDRY78iYhYT9hQKeK1JTdA7UFWLFZd9siqFIfHl9wms/cJqndkY8ueNyoevw0LU2z+z6eLHIrK1ybivkc1cjzuy4/NZLlrlnocTrbm3iSxHveabHH73kMH/6yC7XfY8nLxtMVSSazSEPrwn88C2H+K6jC/z4B1f4+efPIwgCfpzypo9e4+nhmJ1JyO88co1nVmKs6oB791n8i5PHecnhKuM45mTDZmccfdU1KsuEr+r1db62KBsq73rVMX7mvjly0UdVfDRRQRNkhmHMf3himxcfKnOjvzeK+FjL4mWHqkyShFGQ8Pi1gDzPiUOBL2z3ecHCFIeqOqv9kNe+7xIbY48fvGWJlb7EUj3AUmTWBgrtiYLnF3nkYpWtkUBFUZmtBriZiy0XKCo2mdUniDWyoMQoDrm8U2LOMvjmAwsoVkzJFtmZZCw1Jiw3IyJtyLYT8Z0nZ3hmy8LxS5BonFnXUUSVzV2bU2s2YWjT9QQOTg0oqTKh7FHQJGbKDgk+uqjhJj62mlHWQJDHLJdEzmyYtMcFzlyeQZncxInqLGtDkdVOjUmYo6sZTSuhbPeYLQqEuUtV06nYE5ZqMeNAYbE2Ic8lOqOcew+Z/PsvdXjZcgtXDqnZIuM4pmGJdP0QP6jT83MsUaXjJdw9b9P3JRbqIqkQYMoKB6e3WKpmzFSGHKz7SFJMEIrMFiMkKUaXJVLBRcmKrHVldFliY6hxeqvK5d0GF3oSBVXGTxN2uxaRb/HYxSKdQYlLbQ031Li0W+dSR2GhuFegyIlZruQUChOqdY8DCx5+GlIzEg7UA3LRw5AELFlBFCcgSFT1HD8LyRKRJLZI8whdjahoMguVMZd2NRZtEyspcWOYcvtckXM7Cbe0iujK3rp0ruPwIw+e54nOOvP1kDDWyYQQTfbRFJEo0VHVhK4r0x9bpKnCXNVhutqnWfKYb+QcnTH4rpPTyJIASGz3S2x0bG50DbbHBh3HwgtVLrVLuJFIlApkiNw0M2G+7nNtOOLcTkYSVDhqH2N1tUK7q2OoEc2KQyz5tMo+VTtElXOSXCBDwFYTNC0lSGRGnoLjyww9FVHOaY8t1no2I0+kWXQ4vtSjVY640BvzVLtPJqR0Bzo3OmU2BhZhmtH3NExFxDJAEnOurOtsj3SadsZcOWCmPGQUwsZ2kVbBoGUpDEYSJTUjTSzGgcIwSClqIQ177zB3awJbwwLz+iLfe/Qw/Z6C59iAwIXhmGfWHX7uwRu89UsbDIKYN33qDM1GQKEwZrVrs9Wv0o9EXn60yAcv7/JLL1rgVbdUmZ3KkS0PJ82II4uVnkaW5QycMp+7UqbvqlSLOakUQi5zcGrCTKW7FxExkmkVAqom+HFEVVcpmWNUMWWggskZAAEAAElEQVR3VOZSW2THDfmJe2e5ZdbiW47M4EcSZCatYo84tvHTiDTd+2mrMF0fQS6xMzYZBwIlIycjwlZUnlqFn/jkM6yHa1xyrzIY5iwoLRTFZ6oq8ub75/j9Vy9z+1yBB5YqlMsBVTvgxlaJjpvywgM2T9wImC8q/O35LdpuxPe++wp/+cQuW2O4vKUSeRarI4E0ttnuZ/zof77O739xixvdiE9c6VLQZO4/bBGn8IK3n+XPHt3lFX99jiTNqdsC3/mBp5izNV56sMYPvu8Kv/H5VT632ecnXjDDDzx76itr+4989DJfXh/xLz92jS9cdrh9wUJScr60NeR1d0zz1OZeeL6fpfzyCxb58fvmODlr8i8/coN7Fot8619dIE4zbF2i6yUca5m84d4mou7xTYdr3D5v8ZmVId9+a41feNHcV4pEsNc1NPBj/uVD17+SUfTI6pj3neny7ic7nN72+Onnz37lfi9K+aUH18jznAsdl299z1nSLEcQBB66OuC9Z7r/58Xwv+Hre6r/Nb6mO4rWOyVCPaZsiNw6C0U14unBFdxIY66gcn7H5Ho7o6AlLJR0rrWh7+Xsb4moWZnr3YCjmsotUwrdgc13Hm/y9qdD9lVSLndiFCNnQbDoTuDSjsRtC1MoUsqNQUZZt7lpOmLb6eL7GtOFnB2/B/leAOzGeEBBh+WqxbWBh5nL3LEYcmCqR1mDOFbZGgccrpUZ+/DYdY0XHm3x8dWUgpEwnxbZnoy5bUYlzzMK9oT7bjJ4elNjuaqCEHKjAyWvRiZfoGZZlOwubqygCzpV06UgTvOZDZizi5zrRJQklcduZNz02l/n/X/zG/jPezO1KZ0v3ghYLlkkQkJBnSMRB8zmZSStQzBZZMeR0IQiiTCg7xZp2H1KqkJRj2naDnXT4As3XApqmfOdMXfOFuhEATMFkaKecb7jYsoVcimgYakUjZxGsU1Bsdj1RrRUCVnSObUVsVSxkASJjXHO7niOOJXYdUMm4QJhrLE6dMmEKtPFnF4YoCgiaVZhFCWYiocXaqSZREEbIikhqlTl3kWX1ZHI2c0CpqiSpRYF02PgC3z6UpWbZjJmrJDVnsJjN+CN99T4/KpI3RIo2Rllc8LaQKLvy+yv5hxt+IiCwj37Is7tWAiZxChMOVCPKOspupRjWRmiZ3KjK1MxAnZGCqLcx9QiBoFEyZCQ5YCiHjAJZUo63LYwxFD3NiZjt0G95PKs2Q4nWy6fvrzARr/AertO0TaRgMttAVWO0BSXq70SiiiwMpRxuzqCDLVSRMGISTKBKAM/EZguBSSpQJjmbI1VtkYGurw3Qa+kR1hKRpxJZDnEWYYkxSR5hhMapLnI1lAnymIeGZcIRgq1vMLI6bG5bdGqFpmvpiiiTMsUSFIbRYJDM30EIefydoWza1CwAsJUolqGpq0zXQwQRIXrPYOL7QwZBUHIuGkWyDWGvk6QiKw7DqpsUDEE7trn8eiqSc32ObelccdCxl0LEavrKZ9Z22BGt1isaXRHOahjxp7Gp3OHq9sD9hccVE2gWZDoDgVW+xkHlQKaGvLEtZwjcxrlgkA3HHGoJlMxEoaxSylRSTOT64OAm2dlhr7Cm24/zlsfuUbPzXDCFKnXYtfPma5rhNKEO/Z1mXh1nlxPqUg13vSCBd722ApuHLM5yvnxhy4xdnJmaypbjsORlo6mX6VllPCSiDxXQetxvGUwCFL21wpY+hBDFvnsZsxPPq/FdxybYxidpnPhGi/4qV/kRmKxWBGpl3YQxRqmEpDmMkJeJGbCtFXhpYv7+PlPX0PMJL7lyDQfPTNiPfA53Z3QDiaIYoah5Oz6Q2Ztlb4H9cIA2d3PRJlgSCYTp4SsRNyyqLPRVchzn2PzGRfWRAqNIRXV5JG1nOlKwm2teR7d2WTcLvPu1x7iDQ+dZjiR2FfV+Ym75vmdR9e4qW7xPbc3+bYTTX7vC5u86qYaV7wxc0WVV9zW4mDdZL6hUC83OLvtEmY5y9oh7j6p8MzW3gjbH3xOlUZhTxZ/+3Mb3LtY5GBT53e/uIklFLjtUMpLjyzw5aspn9DGrLkuP3L7PO8/0+eHnzvNm/4v6tnX+Tr/PVGSMVvV+I6bZlDllI+ubDIKXDIpY6qcMxE13nV+jczVeWB/iamiykLZ4APnu9y5YHG6N+GRnSFZJmNmOn/9VI/X3neU7ngLVQJDknn3xTUOFitsuR22uwoLUynuROPSJEfWcqqaRLO2y9DTKIsm17dkfvH5y7zr3AqXd1NyOaEomYiqz1M7GVm+wY/ducDbTq1g5hpjLyUm5UAj4fTKmEB0+Bc3z/PJlQ7fuL/K33kddiYBBT2jpCic201pWCojNyaSHMy4QCxPuLJrc9fShDjvYika4yBBEiOm7QKn1uFIQWBjLBOnGc/0Brz19lvYHJ7nyXaHIK5x19IuLin7KwpuskOezbDSC+gOS/iRy1Ld4+xGmVwIaJZNPnmjw6sOz/D9t09zqe/zyA2HLKuxaa1ztAnn2gFiWuKJTYmFsshnL4fcv1zms+cd5qcMtiZjzM4MguixVJPIRJcgLLDjxgiSQJxKdDwBRTQ5u61gqQpxOqFpKewMNRq2R1VXMASHkzMS5yT4sZtv5kYv4o8eX2dpKma23KbvliioEm7msaDL7I5kuo6EkBjc6MrUCjk1O6VW7lLVRSpmgqE4xKmJHxWYJCGdUYUsM/HzGH+sYagaWT5krhSjiEWaJZ+Rp/OWFxzkSH0v5+SDF7rcGHt8ZmMHIc8xFJmKCVc6OsdnXapGDoKCpk4YeDJ+aLIzNrm8rWPKEkeMDAmRqj0gTjLCKOU9l0LWNgxUK2W2pO9lDmkh9803eXh1SBjKlG0RQ5uw4yiI+Z41bGtUQBBjBmMFXRGIsogPXPFAVfFjkUmgIsoRU2WPjiMTRDlxpLHWtxh6IrO1Eb2JTpbJzBcV7rnJZmMocLq9TdmMKNsBghgwDBTqVo4fJvQykYkvMBnqvOymCocbGnGW4IXwnH0Cf7uxxY4XMPBEUlEiinSSLCfJAh5dK7PeK1AvZ0yEHh9dkahLFkKu4ccJQpRgGzBlC7SsCWsTiZIuIdgycmJzz0KZD39fgUtdnwu9CX/2xC63NnJ+/nnzfP97rnJma4IsqgzCmCCxKWgSUe6yvxBxupcwZVb47r8ekmg+iRRg6zE1U2OtJ5Ihcm27QZAniDk0KynV0pCSJlDJQyTDwQ/LDHwgh9V2BTdKqOgKF/wQXTHZGRW43tb4ufuW2BjG/M5nNzk2bfHhK9vMlEK6XsZKZ5p6sYuZGwyjDMe3EaSUnYGJH+0VhhuFjAu7KrYi72WQTkkUrBE1U2RtGPPyZzv82C0neGa3yZGaja3uaf/pLY+PX+1Tqsj4IUzVXSqRxkeuDJgyFLY3ZF5+ssx0QeO3XrbI2uYO588GzOgKv3DfIh+5vsuRaoEPPD3k7kWLtXDA1U7Ip9tXmXI07plr8rtPnmGmXGa6rFDRZDYCjzc9uELT1Piuk1NcaPv8wJ1T3DZr85IjFW5pFf/R2v5b37CfX/nkGg+vuvzCC+Z5znKJ17zvIv/y7gX+5uku33GywYcu9Hj1iRp//MwNjjYMfvzvV/nlF8+zNY749ZcsoUgi2+OIubLKv/umZXpBwKuXl/j1L6xzvKXzy3fMMFXREQSBLMv3Yi0UkZ/8+xu4ecyP3zeDIAgEccZvf36D77ttig9c7vP2b93/j3KIfuWhDV58pIwgCHx+ZcRvf8MBJFEgz3OWKjqvPF4jy3J+4N3XuHPB5vX37hXEHl2dcGWz//8dsfw6/0O+pgtFZBINzWK6pJLmA+p2TJraGLbE+khAkVMUUaeoA5KDZRiUVZ1E9BlHEVKu4k6qXJgk5GnEMzsBeaqwPclo2AqqKBLmE9xAYa6oEMYxI09lsSjQdQS2xy4LZZEgzimYQ4ZBA1uPiBlS0Yscbo2Q5B1EaYaOk7MbuFQnJuuDiHHoU1RLhEGOGCvsK6hcvCFzYv+QjUEBQdnBiKvsehtUjYyWKeFXN1mO59ieiGiyzJGqQqSO2O4sYhjbHGjF+ElGmPkoVIjylGfNSHzivECrYLPrhZQ0me+4ZYpXv+6veM2/+1OuXjGZPXiCj6ylfMOxkEGaUDWrXB20WSg2WRv7HKjq6MqYK+0qktql51hkZoguqQx8i4s9mX3lhGe2Q2aNEivjTW5uiYhChqlkVAyRU1sd5kszXM9C2mMVVahyNYDFCmw7KftKIQ3T4MntkKapoYs6q6OI+ZKAIKg8vhEzX5AZOXUq1nVkwaKqmVhqn+mijyLpJLmKpg4wLY2eV2LHi8jyvcWsaaosNTx2nBH76jmHlRxLAVXtslxIeOjKNBUDjk9LbDsRa+OYQmYg5BZZqpGlEh1ngi5nzJWHVAyNNLfwooT+qEQQxKwNXGZKOZkY07ByaqZKaT7AUlMUUaLnFvFjl+Waz/ZEZdtJ2B1LkMuIQGei052okKpYWkbJSriy2yLNUmbKDpaisaDN8YWtHSwRTsxK7LgZc6WEmZJD30uYq+3y6KCFIgl4XQ1VFciNHKkS0pkoOIFOsxhRNWMUOcMNdfxYwQvBVEJyJaagRgxDib4PS0bAONQRxBxT9RHknPVuThiq7J91+MJahhRL/Mzzprh3ucC7z27z9GrKxDcIQ4GlqS4Fw6U7qVAwXO49GqCKNlmW0yqlGLLAlY5IJuwVZOJY4/b5IrKgcKE7wTT6bDkRmqDTNE0kUWLaHnJh22DXjdgX65yYiREEeHxdYEafcJOlcbhS5COX+khSzqxisK9cZKmh8OilHseLClGg8EQn4Z55m7tu1/nCVodaacSzDlgMXQXT7DClmviRQir2WCrqbAxNTm+LHG3kiGLISh/+8Mmz7LgSCjJ1S2V1PefQgohm9HnR4Ta2IvKZ8zYaFv/mhUv83qlr5GJIUbX43uc2+I0vrzBTMxmEIUsNAdPaJI2nqdV3GXsFtpyQkmLS9mIWyyqKMiJPJR7fhNvmEv7i3Coff2KVh/7oV3jBz/w6F5yQaUvnYtvF1hos1cZMAos4FRjHLlOKwh8/ofLE2jV+7r5lbmvZDHpd7j1aJ1fguUsqV70IL0nRrV0OS2XG8RhDtNhZm6UzkmhWK+R5hzgPkbF4YjtGyiWm6hHDdMSxZRMBeHI755ZZiW9eOsQfPdLjFYv7mbvJ4HjT5t7ZOo9vOIzCnP/w9BZJIFGXVb7tRJOPXOjxV0+1ee/pDjNVCSfKef0dM4yChFObDr/xkn38xINXKMsq33q8xXe95zzP2qfz03cv8ZoPnuavvjzkDc+Z4r1nezz2o7dwpevzox+6zE4csDYWMAnpejHffuIAX7yu8fFLQyxF5HtvbX3VC0Vft559nf9PUGWRcZAgiwK+q7G2pVGpZWSJjiiOUO0QxS/x4lsK/Pinn+HPXnorB6o6rp9xteuBBLfO2fS8lMs7PmUj5y/PXMfXQ7qBxDsflfAUgbXRGCkzWWomrPUkTCNAkUzKlRGdiczQKWOoMbtuRKmc8QePrPOGO5f41Z01CprMxjhCDRUWqzKfOCNyqb3O8UqZM/0eOil1Q2YUecy2Jmx2Kryz0+Hbbq7wwcs7eITYmEyXRyiqR1W08FMPPxVpWhrX+j5TksyxuSHbE4GlioUgjDFUiSxTSWWPY02F3Q0RVc7R5IzzoyE//LFnOL0ds1AxqbXadD2FfeWEXHAxFQO33CVM65zdyBhPimxulzCsgGZBYBx7mLHOux7t8ZkrY64OAvZPKeyMQy6vztOZ9Dk5E+CGI6aVKdZ2BVpFmeNNg6sDl14UsmCZOMmE5aqAZQxJ4yK2PUJEpe1IaJKEKkJ7AgdbEWHqkUYWu37CiemQuh0wX3KZtC2GYcpMMeffPnKFFy5VmKrm+HnA1qCJn8ZMFwIKioIk9bh11mbXlfnkBZ2aruCEESUzpjeqUtB2WCillLWUtbHH+XaJJK6w6+SoCMihgWa63DQ3YRwYPLZqUjZSyprEyPP42Q+s8m++cZF9VY0TLYu1XkgaSFwZjzk006VgWKiqwMAXMOSEki4goHGk2ePJdZU4sLBlEUXy0dQIwxbQRJFMjBkHGpaesryQ8dpjB7llxqJkyHz48g7//sl1HFegiYClSzSLEKYZO30ZWZIpGQm1osNMRaSk5ZDJ9DyZ3Y6CoCSQC4SRRCQpHGiO8WKJkiagSTmz1RhRTKiaKV6U0h+n/NkTAZoWo0kCaiIhhTLNYkxdTAEdVQoRxIShryMJGYtFgzRPeOfZdfojkVOljJ6soGoSfQ8qdoKQSww9hdlKwMF6SJYYrHVNdvspfiSyPCUzU6zyiZU+yy0JMTfYHsW03ZxJoOIGNmGe8LmtHeIvpuiizHff3uR3njjPsWmTiilTsRTu3VfE1gWe3nFpewKDNAHFY7GeMB6XWVmz+cFXLbI9WON8L2W2GlNQRc5v7Fn06qaEKZiI0d73iUAeMlWA+fKI3pZCMKlxtS8zckziZC+HbNq22KdbDLOAljjL5ze73DlT5M/PrkCW4Kcyr11scn6sMExiFgoCkjLGknT8LEYVZVQlJHEKTJViLH3IyLNZ7avsq+asDyPKhkLPi7F1E4QRJaXEZy5nDNur/Mo/WwLgzx/bZWcS87nrQwrliPn6CFGQSDMQxZClmsY4SHnhsSrPXiwSxBknp2ziicpNDYVf/cZFDEXieGuZdzy6xbedrPHXp3eItRG1qocpy/QDn4d3Vmj3K0RRxN+c3qUdhFR1BS/MmHhwsG6gySJXuz4fudTlT8+s8XPPPsByxaBZ2LNzFTSZX37hAmd3fQ7UNF7395d4/e3TvPWzm8ipyH96qsOVbkA3dXnLffuZKxm85zWHSbKcE9PWVzSibMh0JgkPr0zQFZF3PtLjjXfP8M1Hagx6Xd71ZJtXHKvxKw9t8Kx5Gy9MOLU+RjRyGtbes+iKyHu/5xi/9alN3vaqJWRJ4O+e7nJyxuRw0+TXX7Zncxt6Ce95fMA/O7CXSSQIAuQCv/rJDe6ct7naDfimf8grcsKUn39wlT9/5f94Qtr/Ll+3nv2v8TVtPSsaGTu+y5XBmMvdvXbeVnkdw1qn7ea8/ECDMxsJpzYyJCGHRObMSsLGOGaqnBLi88jFnDCG7znR5IPXVrjQiVEEGTfxON/10OSYxdqIYdohJqSoZwziAb0gJo51Om5ImMeIWYFGwQFyalqZA3UXRcqpaTVkKaVg+OwvWQwDDz/NaJgKq84Y1QoQYuEfWscVuo6KKqcUpSon5rawFYv5Yk7Z3mHGkvGlLrK8NwnCSxPqchFd86gbKld7BoIYcKVfJWTAlAWy4vKCIzGpGFI1JeqlMe+/tsqbP32a0sFF/I/9EfQeZX9d4lw3YbkWMlvpUdeLXB/FHKzLtH2XJDFpFsc0DZ0dNyZHICXnUruCjMkHz5eQ0MhEhzxu8timiCaBgEiKx7Rl4mUOC+Wck7M9zu0oTPwy73+mhh8adFybHTdjoaiR5NAoDLilJZKLMQuVHrfPiLTKY3LR45lrB7i0UeZSF/yoQJrpKGqX612Ls1vzOJFMkPk8udaiPapR1B22nBxJhOWqxlTJQxAHxFlKHFfZdKvohsskDqlYIYNozMGaQK7G9IOUJHew1BwVizA0WRuUuNJLEcWA9ligaorcP1dC9W1KhkCjGOCnAxrFEZoSI4kx08WAEzMht0z5mEqOpYg0LAFNm4A4YhLmtEoecpoxGoMfwcBV6Tg22+MSV3crtB2BJ3ttCkbMYsOnohZw/QKaaDJbUDk5FXDX4pBb9/WZKnnMzgxYmh5SsX1MJcbWEprFmFYhwlBFdFnGUASiWCFMZARAkRL8NKNkjOm6EmIOm0OTnYnCdLGPrY8oqhJTtoFpiNiFmG++rcCF8Yi3nbrK5YHL2NP5gWO38/SaxaVujq3mtOwcQxaI4xKOr9PzASFClDwkdULNkJgvJ9y77NFzMz7+dESch5xaLTIZz7Gy0eTptRo9P+d8Z2+CyrGGxCRUSRnjxSlVyURXFIaeSKm6wlQpYbYkQibwiZUhX1ofcGAegjCnO8po2To3Txd483PmsWUVZ1TFtPvkUorr1tl1MuI0Rc2qjKKIJMs4OZ2y68b0HA1LUfnsUyq2oDFbUnnnN9/EG19YwSqOuWt5hCJl7Loy9x8cIZhr/MFTz9D2XA7VbN509wJ/9NRV5osqfhoyXRIw7U3ktEGj0CdNTZzURRYMrvZEpm0ZQUzQpZxtL+JgNScWPGbFmA//xps59so3cmlT4hv3TXFhNeNgdc8eOfYL+HGCoQ8pawLvPFVif8nkNSdmuXuu/JW19My2z189vs2fnblKKow40JiwVFRxkxRLqnKwUuV5hxoUiinndyPOrJVpT1ROt30ONyXiPGLXyTGpsztW2RhUOVgx2N42edtn+hytm/zsA4t897OafH6tz8eu9gnjhJKRcKY9YcsJeeCYQdsJefOnr9CqQH+S8ksPLHG8VGR7EmLIIqYm8OtfuMFwlHPDc/nXn71Os5ryg8+a4y0fXeeWQo0gzVgs63z2B06wPYr44Q9ewRECjjcVrFThzKjPVhjsTVT7yBqvOlnlwmrEHz+y/X9HyL7O1/mf8NSGyw+8+xrfcrLGobrJ0FG52oYbXZspUyWLRD76pMe3HZrjFz95jS+vjdjxQ1qmzm0zFvctVPnjlx7m+IzIzct9zOIOzWLM0VaKUN5ge5RSNWRkOWdjINAqZAiJxiSLGPsyR5opPQeutjVmbZPeUGPb93jH0ytU7QQ/c5kpihRUka4fMV1PSdKUx3YGCIlBWYervRwyiyBWGGQhaZrz/kvr9MKAsqaSGQ67roQumchKH1sFSxKZxAENQ2Hgi5zdKbJQEhlHMWlmcbVd4uKOSRqbjMOYmWJCnELfz6noImvOhIOtvW7sOFZIUp2LXR1N1FiodKlqMtd7IgVdIAwkwhSkXKPrCizXYlLVYXmfz5rncvu8zV+96jj3LBfR7RGjURk/DVmqQj+PKFvQSya8/1KX7ijFSE26bsR2r8ojKzqdUZN+mGLLCmEWMlsASw3Q5AxdgatbJmFosenEzBUEvCRla1Dk/G6RoSdT1ETKVp8DNY8vXwkRcokosFCUEVUDMnLcWCDLiqxOoOdYzJZSvufmJocqKoOJim24KIKJEyc8vlFmY1Dj4q7FhdUGg505rmwbGGaEmOs8eqmKKkXsK2dcWy/yo7fcxg/ffIgDDYNxmPCOZy5xarfLII9wJY9WOcPSUyzDwVQzxFwlzjNyQjRJwIsNdNUjFTNsA6p2hqZGdCcqVzt1Lu1U0bWQMIGbmjr37y9RMmSe2JxQ0XUemKsj5TKVost02aeoZ8Be91JvoKPkIpIg0LB8lmtDDjTG3LHQ4y0vPYUoZ3iBQhAqyCLIUkaa7o22zxGYLgYU9BhJAMdX2Rnr6FqCKscU9RRFTslyqBgxDStktugxW/ZATFmoO9RKCeujgFnLpN/XmCul6EoMQoITZrRKHlUrJ4gUrm6ZfOj0LI+tzCFlZb5hvkUmZ7zoUJnX3Nbkh++cRYhF+r0qT96o875TLT53qcXmoMraQGO9U+LZM3U0RWJl4lPUZN5821GaQonvvaUFwE89b5bX3z3DL75ggUNTOh4Bigiru3UutVUiJeZnH7rG5dEQXcwoaiKlwojDc2Nu3ueQKS6vf3aN26ZLfP/t0xyuq+hKgKkE1E2Ico+amdMoJgydnJMzFt9+osX33tHgrS/az/tO96lqClvBNvO1EaWiw/37bD6x0uH8rksWFiiYY2q6jCA5BLHO0xtVTt1o0HZ0VjoqZ9emWR8ItIoZHSdBzU1udAXkzOTsmomVHKEhzfO9R4/y48/5r+PX71oogJBhFxPKxTEgM10ak+QwnBjMGHXe/crbeN6BEk9sj3nJX5zhXz20wlLV4K0v3/eViWIPXt/lvVc3+JOn1mlnI+JEIPJ0hkFERZe4uK1RLEY8e5/NO199mO+5uYXnCBy0i3zgNcfQZBE/TnnDB67w+1/e5t885zC/9JFN3Cj7R2t730/4mQev82N/v8LP37/AsaZJ0RDo+wmrw4jvvLPKj92xjzd95AZvf3iLlX7AK/7jBdww/crfuNoLqOgyz9lfxFIl3vXdB/n2Ey3ee6bHQ5f6nN/1MBWBoi7xqhMVPrcx5LV3N/iNFy8xV9L+0fP8zAtmMVWJD57p8ftf2uLPTu0CewWhMMn44fdf4zdfvsjiP4RgZ1nOpY7PW75hnpcer/K3rznEy4/vhW3bmsSDP3CMkvG13c/y/wt8Tf8HCrZLnteomSXKpgfCmIqmU5BtbppKuTEKeeOz5/jiRocsTVDVnAPTCvWSSSKEzFXg1obN1a7PI5dD3nDbMh+90mZ7kjFTFnnWjMaus9euelyz97z6pR2iQY1jUyFXexml0OJYK+SRjYj5coIhK9wYO0zZcNgW0JQhi5UifmSQCQ4VDMa+wuWuyPGaxHMOHeWH3r3Kt95W50Prm+ROzsnpiFRySFObpuWz61gU1SIrw4wjNZFPbIhMjIC5gshu5FNIbeLMZ7GU0fcriNKQ9f40lwceBbXCIPG4eVbHCX2cSEIUBJ7cTHj+QZPveetPcvWJNc6spxw7aNGPdikbJp4QUpTLWPoNDqo1LnZDZm0NN/WoqAZrI4/lioil5pzaKFDVJC53c4pahWp1nTwtkAtbSIJOS5N4qJ3RMnTaTsY4rDFbjlnpZMwUVL54zfp/2PvvaNvysswf/cwcV447733yqVPn1KlcUFVEAUGCEkREbFEwIdoqKihmQUVbfmrbxoZWQQykIlNVFFSiikqn0sl7n53jymvNuWae8/fHQWya7ntHX/nd0ffCM8b6Y8+xvnuvtcYe833X8z7v83CkkTCRMxnp60znTGRBYbq6Cp15JFHm+MwFNrvzxPmEpV4f0yuy3TNZ7nZ47YmEKJ3GT8CPAtJBFS1JmK5s4HoTtJ0Kc4WUM2GfrWGVOK6wv96nYPTR5AgvLFBWLbpqRpQ5eLGErdsc1CMsNUHWRnieiCSL6LLEQjmjascoYsLmMGOr5VPWI/LFABEJb1whERwQAuIswfVDCrpA3cyYLERIQkKgqViKQJoqfGnJoutoIGTU6yMwPWpmSMUSiRMVIZXRJJEguTwVqeR8Vnsm53ZCZqspAjJeYCGJKmEsMVNMaeZ8RoFKTomZlyM6kkzLUSkbGSUjI68naFJGa5QSJiIbXYOCOSbKBB7fLGJrJlu9PKvdImQZhxt9Fspj3EglDSX2hjJDT0ZVfB5r7+B6GhNll1OrBoYQcs/WFrKcUVQULNlETCEDgkjCjaBc2EXM8siixMGSTDM3pmCMOLVZoBcNCDSVk/USrz82y5mtmN+5fZ3UShGzEs2iSTWXsDv26TsitZyCgMpM0+HCjo2oBKz3JRbqPvddkDhZzvGcWdgeRCz3Hb77sEthVmJ7pPGGq65ElUR+5vo5fvZTS+hGjqIqcHoPFkomTTthprZMQdFJ0jFLA4eDlQqXWjKLrZR9TRVnJPBnLzmEQMZDe9s0cimjMKFqBxxviKwMOszkGzywklGQDU4tZ9x58SxV2yAiZLok8fiOx9X6BIXSHo5fRJM6pKmBqfc4VK2CkJHTYky9h9xt0I0yZqU+XVnl5p/+Pda8HG++Oc/7HxxwbFqj5/lURZuh3CaKVQRR5b6LBY6Xi/TjIbqS8anFLe5Z3+Xnjk5QtSWOzQs8vZty7XSRqtEhTEMsN8eZTfD7JmvhJu3I4+S8zsPrKUXP4tBUxOqOylxRQBAVljcVcrqII3jsjVL251QuDCN+4/lHAWiPQ976+bMcLKsM4wEBIvNWmW445i/vbvPFlS5TBYlhnHDFfo2/eXSLcz2XDz61iyoJ9IOIG+t5DuZzPLTbw0kDbEXnzvND6rbKX756P39+/w4HqhoffbrLB5/aolKQMDORoQsFVSGnKkxXVOq2xg9cV+UjT3YwdIHuOP6m16hv5h78t8o+/bfx9Xj2gQKXuj4/9MkncdOAIBVp5CXccca9T9jMzI4QnAp+KPLQukcWKByoalw9rfPj187xc7cv8viay8VuyIINC2WBcb7HwJcII5vr943p+SmTVkKnX2a1JZPFEvmciCqmBKnD4QmJlZ0K37Uwxd5wEyETcNIR5XxENbOI5S6rHQtbMtnrpFRrLnNFg+XtlGVB4qrphDM7AoIg8LIDJT7xmIcw1pmdCthxAoqGAiFs9kMmCiUMbUx3LFI1TJa7AUKmUzFSFtsqC2WROHGp51Iu7lS5dynlSC2iNY6RxJS6LdIaxxysSNhWG0XUaOYCRqHAwUqMn3pc6tS5NEiZymcs7YIogyKIjIKIiYqApAyYyZd5ejNlriZztjXiP372AqcuBZQKZUZJxCPLMwhCgK1JxJnLnC1QsXYo2DnWuwITapE4ERmFRR5Y7rO/ptJ2XI5UFdx4TG+cZxBkDBydJJHY6gks1BME2SFNLFSjhxdYqIlPyXTRFYmtgcPJfQUMKc/GeB1FlolSB0tRUdUxD1wqosgS20OBvCbxkcVVeo7A/lrMes9EV0ZYik3OcHC8HFXD5thkkVNLIS+Zn0JVBNZ6IftmdW5f7HHzPp3jN4jMFkw++nSXZknm/ksjdgWXYi5ht5/Q6mUcmYjIUh1BCFjrKEwVYmRBJhN98nqMKorEqczeCEpGiiyL6ErK0IvZXws51AguDzl382woGb9z9wqvP97k9O6YnX5I18uYrkjsdnJEiU9e1xkFEAcyYzmlPdboRZfXBo9PXEIgI0wkNsZFdC0mjSLiVKY31Gg5JeJY4FAjwlRj8npIOJZR5ISyFRCl0PcT2m2TNCciqTDyM/peykzJp25lQMLuQGW7bWLrMjvBmH2lBvMTIyxZxJYi6mrIniuTV2VSJWbkiXiOjInBQBRwI4+2f54DdZntaJe/P7fJz15zDTNFmUgMMawhcaIxkYeUhCQTuHYupGEHvP7ILK/7+3P89h1rrHY9+n5K240xVJH33rvJhT0PWcv4rsNFzvf6JImEn8a8/sQET++6PLrtUjdhohgyW2tjSAkz+ZjTexpyXObdX9zm+4/X+eTSNqM4wzIE9kZFCpLDXEHBj0c8sCxycELhdHvEhuNRWJYIA4nXnqyRMzKe6reYKAzQxxoX+y2uq81zVTPHUr/H2k6dcWmAJtVZH+j0HJU0ldjtSDQtg93E4WhT4lI7paaXONcPmSkq9L2MV58o87zZOves9PncWosVd8zPP3OWxY5HThO50HLZP7WNrSoo6gDXs1jaNcgShdfcVOfX7lzhfH9E20mZLsq84ECR//rwOj/5HSVKhsZfnVrhnx7uYasm1VrCFcUJPnu2g1h1yQs2D6zo/OC1FT58tsM1h3KIoshP3jDNrdMljjVNxK+aRN/2dJcDJZNff8EszbzKJ9985GtJt/+K9X5AVVV594vnmK/o/Nht51ntRExPSPzRCw6yr2Lw2OaImZzGy45WaLsR/+11B7G0y4TWOEx49Ykqrz5xOeH1pvnLOWdP77jcvzLgnTcV+YFGg3ffuckXLw44teHyhhsrvOZE/RvqzNndMRkZe8OYP/ziJjfO2/zud84DlwmhX/3MKq+/psZ1s/bXzoiiwK++YOZrP08WVH7mo8t839VVnrGQQ5FEvG9aJfw3fLP6qm+VnupbWlEUxQpypBLGI9p+SNsx6I8NVkceYeqyNnL45JM9zu+F9PyU6WKIke+yFzkkaUYWqrzmaBNZy+hGHu97uMWuE5NTBYLQpuWmFA0BSyygyz45HfSsxlQhIG/EHK0Z6ILGOEqZtEzOb9Xphy5NS8FUIlrBABGZWq5HIrbQJZE0GyPJHlXN4K6nqnz8bI8feEaF3/7OOd558348X+PUdgaxxf2rIn6cQCazPhSomzatkcKhaRclLPKV0woFweLRFWg5Bm6o0rAC4qDBnucxbRlEQgtDFQjiBBEVQ1bY7MlcMRFyup3iixM0rruG4HO/zsNfvMjm3gKP76Qkscl6X+Hi9gHuWFI4WkuRlCEHyiL1QpeaKSOILjU74GA1wE8j6rZIPwjpORO0Q4cnNid5fDvPHZeamFIOVWmxrxwjih4QM1uKyFk95ooSji/hxTF5pUTMGFmKOVaSyZkdGrZHHFfYG0docsLRmkq10idXGDFhXPavWahscqQaMFfQWO3onNvNcXbzEC3HIKQLAhyuihSNGEVrYcgJqqhjyCqHKmv0g4yiETOZ07DUjHEYsdGPkQSBNCphqhJRmtENRiAkpKlInGq8+JgHSoQzBi8N2RpAEMvkNYXNXgEJHT8yaHkOCR4dT2Gpa7DjKGyPLFpOiZopUjQjeq6BKolfTXDTeHJbYmsgsb5r0RvpBIHKhNqklC3QH8vU8mO2Bjrn9vK0xzopEpO5kANVH0sRUEURx7PoOiZxomIqkGQyUSIRxiI9XyZOBeI0w41Tbj9T4osX6qRZRsuxGY41/FCiWfDpeCpfXJpGxOTYhMtV0z1sw6PrSrQclZEvcK6VcWhhh+OHAr642sbQUnb7JbJMIm86tL2EUZQhyz6GZNKwBeaKPtfPdjG0MXv+iLWuRRjrzFdklnspb//iac72HIrNEVO2QRKr7I1Utp0QW4uZzGWsdaosthUeX9eoWQlzRYnHVyzcJOS6A0NOLEj8ynPneOZMkRdeJVEwUybKbdq7CvddGgFwy0KBlx0v8X2Hr2CnL1HXNEwtY3eU0h40WOrkCFOfSdPkKysFdkcSB0o2v/LcOZ6zv8Dq0ON5H3gEP5Lp+ymSAIak4UYhYVDg7ksyFUMhiAT6bkROU/jp62c5USsyih2uqClsOT6DUZ3NoYiAypYTI2U2J6Y6FI2I9jjFC02cOGNS2OMD7/gDNjYzNmWdYwdC/urBLlNFlZONAjdNVOiFQ5RMY3FP5bHTU/zMdQf4q5ce5bVXTGEpEp9d3uLtNx4liFL+46cvkSoOV9RVOq7IV5abLO7a3LecUdI1/uOtE4DAlXWJxTYczNtU8hm6GnDNQkA38NntCYiJxNmhA0JKPQfvesF+vvyTJ7FUiTTN+KXPLzFfkhCVgH0VAzPL43hQNXQm6wLdaIyUiNiizPpwzOP9NrMVeM5Cng8vLnNo0uMl+8t8/Ok2oeBjqXBmJ+BDZ3Z4z0vn2BlG/NGXtnjJ+07zkadabAYuj2+OCQIFO9NZbkWcXY35qWtmuftin/90zyb/eGYXoxBz01z+/1W5+f8I6Vcl0t+MR/otIpP+Nv4Np3dHxEnGG29oMFc00ESRZi4jE0fkCw6NWsYrFi77hn3s/B63zuZBTaibOp862+PX7rzE6W2HZx/McUWhhOOWWGx/1a/OVpgq9hBkl6oJkqDgpiGWFVPIQSj4jOOMIMwhCCnILn/xyCY/fHISWb0c/rFQijkwvcbBWsBCOWPHCbFyGWG3zMU9qFVDpmyBUeRytBmQVyQ+9eSYH7y+hm4mLLUk5gsiGTGa5UFYpOMmdB0DITF4alWnqVtYWoSXxEzmQRRHlM3LK2aTlT1qtkTbDygaGQU9QlY9FsoiISEyOseaLaaKDgUtIxOH5BSN7XGILppsDBQsI2PfzJCJiR66IrIxyOgOaljmkP2VlD0n4mDD4ExnTKmSoukuliDT6UlkmUIl76AJCn6Y0Rvb2PqIoxM+ASOONhR+7LomTTvPpY5CZ1jlQidjs1vjiS2b0ThPyxOYbzgImcDKho6S6UyUOliyxjjxkUSJOBVZH8ioksm9ay5ntgJm5QNc2CrRHhY4tWGy0y1hGSEFc0zNEgiSEEXMKFgJPT+mqIs4oUTPB1M20DWX2UqHDXeJ03seH72wyb+c3+KzK7ssOyNeeKTA+x/uMWfmOLU94jsOFinbMq3YJ3RKbH71s6vkYL0vstbR2e0VKdljVNXHVkFG5iNPTPKlxRphZKEpAh03ozfW6I4sRBHyhksUS2z3Sgwcja1hQEkXuONijx+8psEvPm+G3/+ufXzge47zidefJEwk2iMNd6wjKyl+FlG0PObKHvuqY8JEIkkz0lTEixSun++CkJLEAkkkk6QSuhyiKzGGkpClGUEksNUzabk6USzgRwpJrHBFqcyJSpEozjhUH5AmKud3i9x5bpKXzC9QwOCAmWdKM/m+jz5Jz7FIMhFDi5iv9tlXHVHLRZQtBy9NUYwMVZQJQ4FRACU7YqoQkjdcTMPh9x56HLMw4OjsNgs1hyumA5xQoj0WqNkRO+Mxdyy3+JHPfRm7MmQ9WUepbvD2F5V5qu3wq3esMFUSmWqkLHcDPra0jK1Ba09GJuWO1T1O7bhoMsiCzHJL5dx2gTjRceKIKLSIBYGuG/GPZ3bYHAVUcy45TaKR76HLIlXTRxAiZvOwNUxZqMbMN0cMw4Afua7Km6+b4NJom3EMyx2TS60qplhh14m5dbZE3TDoRRlLuzkWWzkMOaZspfhJzL6GzMuuVriiBsMw4Q3H5zheK3CkCYV8iyMTHvdsbvLu+8/xt4/t0h2mHCyb3LXY44/v3+QTZ1oMlD380KZk9dFFlXO7FoaokKYJv/HlM1zodxn2YK6U0nViPvj0GpfcAb917yIAL1pokNcVXnK4yltOLnD/1jZHD4yQBZltJ+MVV5Z51lyVKcvkxqnc1+7Vxyetr5FEAI2cwrGGyXu+uAnwDSSRFyX85h3rjMKEZv7yCtifftdBfuDGKn/x0sPsqxhsjQKunrT5++87wnRR4+SUzcRXn3v34oA3fOAiaZp9Q914YNnhRP0yiQUw8FIO13S++3iZ257qc6njf93zl7seb/3EEtuDkL9/ZI/Zssa7XzL/tffz9k+tcqhm8l1XlP+XteqfHmvzl/fvMF1UecbCv30ue6P/B0JCvt1T/W/hW5ooOrcjcr4Vc2EtjyVYfPZMji8u6eQUENMCp7cT1kKXfGZgyiqdcMRsrcuUreFEMZvemN+/b5U/e9EVnN2O6EUeampwYU1gqevTH2vsDmVWRwNEOaCkx6yNHLp+hpiqOOGYhBQ5y7PR1xmNTcS4ScUak9MzbLFKP4iIkpSmadLy+whCwOZOmYdWEyQ55ckdh89e7PIbdy/xj2fXSUKVqlrk8R0Bz89zfq/AMO6RJhpn9jJUWaZsCjTqQ245KvOLNx1kRq6w2tV5bEPjwl6FXiAgZXV2xz6GWGBzFKKpEYqUYKgxx5sZkuIyl1d4Yieh2y5Rf/mvoDz017xhOsfImeTUdoKU6jy2bnJlTUVSdmmaIgVjwP6iykRhj4omkdcdDtdHnJxyyMQRBV2g60VMGDaR0KXjmnixQsdNWG5N0w9CqqbCOAZddWnmFHRjj7odYakCR5srFDWDUSDzeEtmylbRlYht5/KXSScUsIwemqTRcRWcxMdSJdzQRpJdrp1s8ZorM6ZKI0p2n8l8yHBc50LL4PPnCqSJjuOVyYSYyfwOmiTR8+vMFsdkUhtVDikZIjPFjHbbIMajZDrktOiygkpTsHWPQRTwlZU8rX6e/bWUg3Nj6sWYlUFE283wQgldibCUjAMVOFQSKBk+huIzDOGpHYX7V8rs9E0sVWCqEFIwQ7a6edTAZF8l5sTsFh1/DNoYUY5AgEuDPk93uugyXNrN4UcgSzHHmx3iNONC2+bCno2hBihygqokZFxO3siZEXGaIQATOY8ghj1Hou0K9B0YtUx+5MpDtAY2qgDzNY/p0pgoEqiZCYerPvNFj4WKgyCHDIIYUY4RiHnD8SYkKqFfYejmcBwN35UIY4lxnOAnXQpGxHQh5uTkmPmCTMfVeGpXYscBS/GpahpXNkNyWoymhAR0uLAp8qVLfcTIZjZn0YrGOAGomcaELRMLl1UgP3fDAZ493WDky2z2RI7O7DKVl8ipGZ98/PJM4x0vmOZlByYx1CFhaPOcE/DCI0VWeh4/85mL3H1pyIm6zZGGhqKPESSPfeWUWPBY6mgst5p8adkgr8fMFmXecfM8f/7YOpKe8dO3n2ehpLDelRBTAS/SWBvInNme5MvrAvtLMh1HBiGh3gzZ3kt53wNtHtocoGLz0CVQkyJOlJFXUracmIYlYqoxcSpTtkZYSsKeo1DWRL7y8bs4+oO/zFCf5mgz5tyaylxO56qmxbWNHP90qkdeLvDFJ/P8wKGDvPHaCV55pIkqS7zuijke2e7x1msO8677lnnvfeugORQ0ndVeioBKJxgzTkOun444vRfznq+cw1Qjrm82uXWqihOH5K2QvO7jsMuBasKgBzvZkIMFg5w55mdvnGeuYH41wQY+drbN4mCMG8jkFJW9sY8TB+gKbHkOS84YQ5ZZ2kjZCYdMFUSiLGbV7fFrX1jmaD3izF7K7zxwngNTIggZb7lujmfOFPjDFx2goMv84meXOdzUMEWRXhzx5qtmOVI3+JGrJzFFleMzOm9/wSS/+fl1fuWeJV59dZm5pkDPzRiF33xF0bfxbfx78PjOmLd9fgkvirlhssDeQCTGJwh1bFljlHp88Ik2v3DTfn7+mdPM5kweXfZQBHjxfIMN34VI5MNP7/ETN03whuMzOOMaS5tNHttQcUKVhiUSxCFOmDJfioldjb6fQKaiJApdL2XgqUzmRUbDhD+4e4Mfv3qSfZWUfpAhpHnWewa6NuJIPcMfqrSHKWVMHF9mZSSQlw0EIaJW7DM3GfF3T24xjgWOTyYcnNzmxHSfhiUi6Q6un0cRQZR9DjZk/vwlx3nuXBMJjTD1sDWVnpeRENIwJWyrgyaYRAlEmUBBUfHikKoJOyORlV6OxVaejp/w1PosX7yUo64W0NNJChpcMdnHNsYslEPmJvaYMFWiLGTkFNjyEiaKsO0N+ZHrCtx8ZJN9Ex6V2oDJ+hhJSDHUjPnGEF1NWKj2sdSMqjWmYMbcs9Lj757c5Y+ed5JnTTXY6ic4XhnEMfvLMYMwZCovULDHNAo+qZZybsfk0m6F07syxDlWOgqX9soMPZWVPZ3ZnM2fvOQwB2s6sizSHqnookQx12euFCOIMSdndjhcj6jkB0zkIyq5hHKuy2QuZRRKjIIYMbNY6Stc6FhUKilunCEpETPVhM9c6DFdVPntF87x6bM9fv2OFd79pRUO11UIJcqmzNARidOQqWJEQRewDJe8EVK1RA42ugiCR5joaFLGOAoJYpHpYsxkxUWVfDQ5o5lPWO7qDH2RJJHYX4u5oVFie5hyqefzMx+7RBD/27pOexzQ9wQs3adW9KkVEq6Z71G0x1hKQsOOGIUKQSzgBBK7I53uWEPTElQ1JW/KvHC+TqebZ71ls9a18BMJVc5YqDlMFjySTKC7a/H8w5d9Lu9dGVEveMSpRC0XMF0a4Ywz7rngYRgSFwZD7trcopzzaBQCLDWl60icWisSRCbjQGIQiMS+iC5JlE2V58xVsJSEiiFwoDYgZ3iImYwpQRBnuKGIkGgUjBBTH7FQFKkZRfaGEn6cUDJTagUPJ0qJI42/fHyF/3LqLOc6Az5zocupbRcpFZCDIhNqGSMfUTFtvmf/FLoCZUsgIuJgY0TFErjUE3lybYKOq7PTksgrCn0HZisOR2shuurQdw32RgaPblicWp7iqW2Nqh2TNyKW2wrXTuT5wBO7PNVqc3Vxlp2eybRxgFcdmMPx4XOLbT56fpOVYUyaSgSJBqJPf2zRcgT2lTNWHYdPnXEop3Xedt1xPn5hl340QFWHlHWFjh9hKgqC6jJXBif2aHsOf3D/OtfO6bSTgIalsTdOWG7XObcnc+20gywFLDQDZBFCIWGy6bMzzDg2PaYXhRQEk2sqZV7792f5yU8s0h/By46V+MNHlqkVM/wooWBG6GjctbrNu+47y9rI40Lb55GNEduDkN+9Y50gTvHChPsuDbl1ocBmL+TnnjVBlmV8+myX5L8jdQxF4q9ffZCPv/EoT/e2+K0HH2LL9fmPN82w3An4+NkWr/7np/mD+9f+p7VBFgX+/gcOfh05BZcDELww5c3PuKwaumdpyGxJ469fd5BrZixefaJCIyfzo59+mk+fa7M9CnjzbRd45/Mn+c/3bfOfXrHAB3/g8NcRWz9yY4M3PaPxv6xTD6wM+NiTbX7kpsbXpaUBqNK3NE3xfwS+pVfPDjfA0nNMmCoPbV3+IpWXTL50pgJiymQ+Y6GoMh77DDsmB+ZS5kswsjroXQtPt2jqFvvKBm9/3iR3rm2yti1SMmUmrAKbo4BiIcF1mqymQ/LWkAk7x9ObBS5lLjNFsNWEdtQnZ5n0RwZP7CRomsFsMWGcRthSAU2MiYQIWyqx1dNYGsXkkjI7kcd4HHOwrHPH6hZVU+LIpICXJlSEHF4UsdLVkZjhdDti2pbQ1SHjSKaZy7Ped/nQhRU8I+JHj07yrsf6PLQWcPN8yuZQ5lBNxkmHTOUlljsyCBH7y1AwAoZukVhqMZcr8+DFhFcfm+Xn7/oM/+3RFe7+wkPccOsNPLAisr8qc6krULYmqFdWIJ3AjSNmLZ2KdYmeN0Vqjhj4BZq2jKH2mCsZ1At7NFOb0+0hTUNHFODxzZBmzqJe3MPWdFquysiPaJg2g9ClbAqsdSdp+2MOFg16gX15YlGwKRkeYdplWizT8eDCjkDDBjew2RyOMBWJLDW5e81nIt3j2gmdW5vXcqrV5861VYTUYHcoM/Yl8rrKartOz4PpQszAFyA1MCWNlquQMaSo5njxNQPcSGEUhSSJw4RloskZjm+jK2BZu5xaOcLbnrHA6cE6m07M8WZMRsauIyFLIfvLAXUrxlRhHKlc2CvSciR2nZC9YUpeNqnlFPJGQMUMUAQRW07xM4krahkPXoILl2T+5DVzvO/hXXI5D11NCBIZVY5JMxh6Cp8716DnSzTyDrI8ZsdV8MKE1sBElTNEI6GgxEgi9H2BcRRTtRzmSgbfNT3H739pCysf8iePLKNKEjndx4sUMqCWj6jbCaaSIUspURpTscdU8hV6wwKmmfGV9iWqukyW6ASRgKCOKRghc40xquzTGpbJKwZplkFaZsIu8niwRcuVuHrSZDcUGfgq2yONx7cE5ooZeUPm6KRAmiSsbAs871DCqY7CbDGjbAcMA4mfueYYVzfKZFnGTM7k+2/boSzZrO1MsKNkvOu5+/lINKJkyvhxwnseXOHmUpGWJPH58xF2usFnznUZJBF5SeYnP3sW2/TYVx2xr5zQKDhsDUTKuoKuDTlUyWg7Bi13yO9/5QxtX8YUCzR1lWHcp2rnWe9ruJnCYUyWugIF1eSBSwplQSEC9rwRs3WNkTCiYctsOEOunrEo5IZMFWJqhS4zBZ+lroKpQEqKIIRsDctsL24xPHUPR179gyy3VOaKPpIUslCxOFbJc/OMza98coNXX1/me481KD9XZSKv8uEn2uyOQho5le444v1fHnG2PeJ8K+amkkddSxAUj6l8ia2xw3Re52Ctx3JX5vmHY87viViKySsOTNGaCtnwu6RSl3EKOTHP0/2EIwciHNfAy1KaSpUT1crX3a/3l3UqhsLKyOXPvvNK3v3l05xviUjmmP9wYpZPXNhirZdyaF5BlDWcMGMmr+J7NqWiQ9exmS8o7I5S3CRjzs5zdvdyLLisxPzRAytc2PXIEoFKUaZgSxytWXxxecCEoXGxOyYWU/724RaWAZYm8rxDNqe6HU40LF5xtPI/lph/N769evZt/HvwyHafuUqN9z5ygSPlHJMliSDIk+BxviVQNqGkpNw8XcLWZObyIaM45FPneqSRwBtvbPDh0RavOV7nN+5a5dkTIbteQJSIFIMSj49yKJrP4YZLGOls9FOa9QFW0mTV79MeKVRsg0wJuNQXMfOgZ/AP58/z2sOzfPK8xOpgcHnCGxZZH2RkesgL5is8ue7jOTLXHQyoFTr4kUaW6vRcl0rRJHJ0Ok7IZJDjyskOW0ZI0cyx1otZ7ZrMFBMW+wE/87lFdvsR22OJNKpxajXipSe2cQKL0z2R+SI8OcioKzBpR6SijxKZVHJb6KrN6a0iM+U+YVBlYyRSkUvsRFVWugGlQpeWK3FyckzLhaKWYzsNidw8m6FH0VDZ6ovkdYFHW8t4YY5n7t/hQF1krVei7UiQCTghHGo4SFJMlkmsdE2mCmMEIWa7K/DmT51lr50SJXn2zAEVW2dvHJFXbJbaInm1xDgLeelhm1OtDiNfpFmEMB4zactsJjHDbpl2S+H3n7cfURB4wYEKj6+Pue10l3ozYqNTZr7eom7oSFKELEXkBYmvLCscrAm0HJsw8pguCiRZRobPTN5ioxtRLiTogUoiRCiSwEwN/vD+VZ4zW8MhxBFCSETec88GXSellI+ZLIgUbY+ea1DQM8JUppRzUUSV9qiApbsMxlDUBS7t5nnm4T0sVSTNMp7ckBDFmKqZYegufU+kYoXU8wr3rWR86HuuQ5MkVrs+P/PRZQQ5pWjKOFFERsbatsVEKWa27FAwYoIwRRIVXF9BFFU2fI2yftl/SFNhZ6CiKBJRknDX2pBSQaBox+jK5bW1uu2RJgJZGmKpClki8NL9Ne5c7rHW9zC1BEGQ8SOJnYEKgY5YTrHVGJSIQuGyIsxSEyQxYeyItAOLklLg6okiJyY03he08eOU07suu0GXk7Me9XxA10/Y7Bdw3Dy7fQVFlLhmQkcXdO58OmNAwIFSjm4GWy2YrUUEUYwgjpksKMiizPbIo5qD6XzGnitgBBJXTeooscHFPY+5nM2l4YhPXLjEbElkfSiTJjIbaQk38cnCPLIgohgutbzJwEuwLZ+cKrE+kkiSEqacEPQzPK3ERl9BETKcnsE4cthXSRGNC+yfUvnjR0Ysr1scaRa4a3HE3TgI0piZcoYoxSiiwchViUhRxAKZOORAVWTHEThcLPMrtx5kOq8TpylvOjHPnz66yGRepuVmqILKIIixFYlcpYvmW3zgyS4zeZkJW+fejR12HCjJeVy9S8OSGcUjpmsGa12Vaxplntge0B9nPONgn2EUYUlNInHEpjuiZqk8p17gutkczZzKM2dVHt5tUdBlVtoakhJRlUSWWxJXTir8/v1LCHJGGZutXsyrT1Z49x2b/NANdT7yRJv7lkdcNW3x5N42DUtFFEpfd3+fLWn85aObLDt7nNrK6AzXeO5UlT+8b4ODNZ1bZ23eeuM0n3i6w/6qwbGm+bWzN++7rIC+sOdxqG4AcP+lIc+Yz/HTz54gTS8TrLfuy/OVTQcvSjg5ZXO4bvD2L17gydWQtdFFfiTZxzufM80fPrTMf33tcYrmN9IKhxvG/7JGndsb87OfWObqZu5rQ8Esy3jXnRu85eaJ/+nv+/fi26tn/3v4lqbqRAEe2RzyH65uIgsiSSTw4FLCxU5EmkjsDeHRLY8+YywNZmtrIO+iKx5FI2LDcfn8pQ5LXY833tDEzvtUiiHVYoIreBRMkRlbp5p30WQFU6wRxxJRItMwCuRUSIQAIaqwvFtgsRMhpQbLXZWLvTFZKiHhEmYjFEFlc6ASJiKHKgZjKcISFG49mLLSDlEyg8FYph/3sBSR7UEMSOQUi1SIOFTSma22aBYH5BQJnw55Fb5yKSYYS3z47C6tUUJVM7g0HCOnCoubMrJgkiQSmhyTUwTOthJmixH7Gi0MsUovcrlyRuC7jpX40s6AL+3tEj74CU7d+Rlm6xmSOUCXcty3rNEfT7E08EgSk5afEqVNImFIlKpUzZCtvsmTmw0sbURJtShaW8zkLk9AHF/jhlmXYeQxcMsMwjFH6kOubLqs9SNMyaIzlnl6x0ChRCb6gEhe1ZAkl6lcxFw+xYnGKILMbClhy/U4OjlAEgw+dSHPclenouvIIhSsNVb9T1Oxz9PxQ3TV5VA1oWQIhIwQpAARg5FvI4guqRAw8jXyqsBUIaFodZgq9DlYHWFJJookcXJ6h0TwiNOEnltmvTVF4mqEEXzqbIgbSNRskclCRDOXUDBiLg0CdhwRL5bYGdg8vlxhZ7eCIeYZR6BqHRKiy75V60U2d01CBOYrPlFkc6KZoanwnoeexCju0feh7ymEsYAhp0wUQooGXDXpsL/qUjYDRCFk4CccrO8wWeowVRogEOOFIJDghiL9QGMUKAiiy33dU8zNjJBUSGKZih0y8FSGnoQfaAjIRLGMqUbYmkvHc5EEj5yiUjEESEXcMCNvp2haTKcncG5doJ4bc6jmkmUyaZoHJJZaCk/uOHz47OVmeKGksjcq4EQCbpSxOciYKQYcqCbU7AgnlHnjtROYRY+9YJfnH+1ycKLD2mjM3sDkoY0B7390m+v/yynymsRz5wtU8jGL69D3BH7psxscqBjkdRldlnjeTJ3Vnozjlvnoq4+zMQjY3Iv51VvneMmxIrthn4qdIksaSH16Y5u2m0dWeyiixubQQpQ8JnIyvcgliOD2xR4X2mPqhonrSGRZiiWZbAzAllUutDMatoBipIwFj/mSTKYOKedEtlyPI1URRR9RNAS6XkbdCjHkjJKeYigJijRGFBS2L1ziyb/5U+af/xKGvkScpOyvD7A1mdnJEUdnE97z8FnedEuNTz85REFkIq+yOQj4z/dvYyqXy4UqCVwzL7DpxNRsmVj0SEmIE5WVoceVNY2UAC8oUTMFnNhHlyRWByPe/LnH+b8eeZo9b8RCwUTJZFZ7BpO2TZKllApjqlbCU7sur/6Hs5zacgAY+DG/eMcS77h5nusrZd7z5YsEBBwsq/zAsUmCJCKHScPQSMQQU4upW5eNLFuejyKJXOomdMIRrz08ieulPLY54v72Ob7jsMZv3nuWv3hklx+8tkZAzOLY4deeO8f7Ht/id18wz09/8hK/85JZ5nM6m8OAxZHHn734CP/1iQ2WezG//Oy5r4uD/Wbhm7V29s1MT/s2/n8Hk6bOF5YG1KUS9651eNPJSWIpwA1hwtJwxgoXtzN+6UtPkaQZ/3Sqje8LXD9jUzBEcpoEicz7HtsmiFJObY+QkWiYCok6pD0SSAObtY7F3jhByFRGoxyKLPKCfSXq+YwQj5Yr0LRECAUCOWLk5rhtcZ3FHQlvUGG7VeRiS6JkyNTy8Phwj7c8r8bN+3ViwUPAQBYkJNljfzXB0iLGcUo+KzJjNji/m6eii1RzXWYrl9en1wcwWcjY9ts40mVSfW2UoKUq+6wrqdsCTUvlyS0TS7ns4SbJPiVNZNcNuLjbACEhp/ssrk3y+LKNHOk8tSRwasvF0XdouXBVc0wmhEwVQJQdypbAejdBSk1s3WeyEHFkoo0iJ8zXhlxs2+iSwv5KlysmXFTZY3+hwOZAxw91BkFCQRfYGuqMfRNVCxgnI6ScR6pdjv/+0lNV+t0iozDg2ISPl4QQWXzmwpgjFRXbCNkbaASRwd5IoD3MsxfFlPIZP3/HBV73d+f57c+vc/fagGpBpDvOKJk+tiKRiQ5bvTqPbeTojBWOTkTY1oD5YkDFjuiOEzRJZbroEmchxydCRMljpjJioRxyzdw2C9UR9bLD55Z3+NlnTvG2Z8wSZAG6HjJbi8jZPvPNHg0bRoHARLHPfDm6POwZCICMJooM/IypksMzDw3puAprPQ03MJgpeyDEpFmGIspkSLhRRlMv84K5Kd75xUX+8qEt/CTlT161wB+8dIEpW+d7jtR530uOUyo7TFZGNPMBWZax2rNpjzWCVKY71pjMDbHUkAwJx1cZjHWcsUgaCdQLY47Mdrlurs3xZp+y6SOKCaIAQSiytGOSCZejxCdzGrqksNq2uP/MBE9cnMHK5vnsjxzncE3H0iNq5SF+JKFKIqaWUjAdFDllfzXET3v8+YM7/PUDbbJQIvYFoljAUCRGY4O1vkx3rDJVTNjfGFAujTje0BH9CQpygw+87hg/fd0+rm8UkRA50JSZLxuM05CSGXG4PqZk9clSAV2SKeYcpisONUPg/tUxty91ufVAnh+8pkGWCHiRxeKWQU4PmasmSKqLhIokigzHGQXJ5juPlNHsgIIpszGQMOWEyWJIx4s5UMrxzptOUrUzJCQ80eVYM+TI1A45LaNoROw5EtVKytm9y8pG0+6z0AiYKLmESUY9HyPKAbOlmEqhw9VTMWEWIMU2N0/VGfmXjZo3BgF/9/guSSyx3tfR5ZRKziGnQNtRiRMFQ/WwM5lDZpE7l/r82s3HmNeqrPWhqVVJoxpGcpDFPSilBS7sRQSpwPG5Proe4gc5CsU9Tk5o/MLN+3nbs6doOQlXT9mIgsCVhQlip8rDlwyisYqlJfSjhMNTAf2kRyGXsZDP8devOsin33SUgzWTn3/+BHlL5JZ9eZ53IM9tF1vcupDnZ26ZQhAE4iTlzJ4LwI4TMAhifvdZV/Mbtx7mkJ3nvfdtMFtQee4hFc3w+cjpFp862+XLK0M+e7YHwN88tMOnznb46wd2+ZN7tsmyjP/rS5t88nSP/7GNef8ju9RtBUORyLKMN336NN99qMHvf9cs73neFTxzush779vmL158jIb19ebW/++wPQj5nc+vc/VEjt972dzXrn/g0RaiAKX/B0gi+Ob2Vd8KELIs+8YFxf8/x3A4pFAo8J1/+wk8ocqhosHj2zFxAKoOVVMmU3u0hga2ojBVGrM7Trhios+JRkY38HhqO09BE1nbmeBDrzqBoUj88f1r/NWjW2h2wJStYuo+HS9mvpxQN0U6QUBBz0jDAkHmIUkiOVllc5gRRDKel2MY+Yx8uGE+xBd6qGLC8/aHLPY8NnoVSHKs9YHEYq7sYRkeRHlO5g7w7i9scnBfyMpojCmaaCI4UcZN8xEThQ7zFQ8nkHlyT0BKytx5QaGkmqgZzCo+LdlguR9yrJkhKi0GrUlKZY9KLsINVBKxjynaaEaLKdPmsQ0bTVC4/6zIgQmFQeKxvyJRNNuc+shniG94FYdrKm4AB+ohq4Mxt87HDKOQhYKCl7aZzau0xgmLu9M8sS2zvpPnhv0DJkouJyZG5M0tzmwf4bFNA0MLsBWRNE1QRQXbanFNM2V7pPE3D5Z59myDp9s9Ts5uM5M32HUdZFFh0paYLK3Sc2psj/Is9sdMGDn2XI2EMXUrI0sV9lwNQ2tznSkhlTyWBwIqBm3XZLVr0g3G5KQS89U+C5UR66MANasSphmGGiPLHXy/SpSq6MplI+qHVjSunRYx1JCinrDYFZkv77HVn2Gja7HXyRF5Cntij0P1kKIuEaeXm58kFYgYMFV0sBWNlU6OtT0bCQ3biuh7KS0vpKinJK7NZMNjsuxQMUM0SSRKJQZjhcW2yvk9kd4YrFBnbjKmkk8xFZjIe+yrDYGMnZFKIz9isaNiamNWOzY7vTyH9YjQElBECUXKKFkhFTMAISOvB+w6Ki1H4+KOTaPoUzBCwkgmjFUUSQBEqvaYWq7PruuTZaAKBdqjPBkimhzQdg3WuzlGfsbFVsT1Cy63LgToakJvLDPwDC61De4/pXBizsCyMwZhgKkKRHHK7MQyNcOkZEQkqczuyOaOsypapjNdzShaIYYaMVnaIk1Nhp5MFOVZ7sXsdgpoesirrqjz7OkKP/+F8zg+XFHL89PXzbCvbLDeD3jvPVs8vN3jcM6jVq3zlYshL7+mwJuvaTIIY37xziUmCglboxhf6LNQUDjcGOImPXShytZIZmcUU9QK7I4S2gOVkimz1YaJasbOCK4vV3j5CZNfuGuRoyUDTxwylQ/ZXx3TdVUauZj33tHgxLRGe5TQyEfs+hFHarBQG7LjCCSxSCPnESU5osyDTOH02S1EeYJEFFD0MrtegCmrHG72SVKDcQTLLYXYVUn1gLffeIiXH62x1B+yv5gnTbOvSZTfefsyXpwyJuTBtR63lhPkiV1aoyI1S8CPQRUuJxi2Rxp912AYuzx7X0LLC9AkhSmryIWOjyTK9F2ZceLTNBW2nYALWwYHLIvloc8HXnuUayctXv3Pp3nxwQq7/Yg7zg0oFEJyBlzcFmlWJWZshXsuONQnHSYtndWhi61nPH/yAHdvL7HSNvnlm+fJyTK/85ldMiPi6LRPPwpxfJ3vmKuzv5Djk2e6rA8Cbpkr0I58LvZHLJThkFXnbbfO8Yu3L5LEMF3Q+NmbZ/CimEe2HW6ZKTAYDCiVSgwGA/L5f59f0b/WqO/56H9Gsf7X07j/HUSux8de+VPflNf3bfyfjX/9/+n3+7zzSy2WWh4dJ6FQ8XnjtRN88MkW2/0MMRW5etpkdSfi4KzMn77oKB97ssvLjpV44z9dZG8c8QvPmeTaqRyvfP9ZjhbHLAFBmmKpEkUVljYVFCXjVdeYVKQif/PQHnlFYHbGYeAn9EYGqniZjBBDhelmBMqIai5CzHSub9S5+6zIrj9mEMYgpzRsgWdOVvjJ6+b4xS88TaLsIIspWWqy2JLJGwlX5Jt87imfmw6YOEIPPxtwqBqiyD5ukuKPy6z0U3bbBb77ijKfujgkZ0QcaHQQxYwsbPDETsh0MUEUfA7oDomp0o1CDNFgYyCTZSKBZ7LWlbm+UeCRdpcJ0WYkRqSixw37hlTsAQvFBEVxWO3Uue3pErqg0+3JlIohV0x30dWQkpYxjsFUBHK6Q2tUoh9EqJlFltZQpYwvXHK4aiJhFEUIqcEwDiipKjE+vmexvq1xZSPHmb0xghlh6QnPPrhNx7HZdVMUwULTHFRBY7kjEUUa9dRHKkXsDQvsOTFlVWcQxdQMkRtn8uwr5PnEuTZbjsOhpk/B9nhouUAzL2KbA2xFpGiFNHI9dElnEGac2ykQhyZuFHHl1AhZgkGQQmoSxjCOU7KwwPkdlXrJYaEscbGVoSgBqqAz9EQ6bZViHkIyirZDNZfghyodN8Ub6TRKGbo1JK/5ZGmO7aGME2YUdRFNTJgQHYRiSEFXGEXQcmTWNosUlALPmi3y/ke3ma/pVAoiT+71ERH43iMT3Lqg81enH6FsZOiKyDhMcEIRWZDRZRFFHnOs2SGIDYaewfmWztntHLYqUDITaoUefiRgKyKmmpEKPggZPadAZ2hybtPghnqRWk4myBJWHY/Tuw4v3l8hp0p8YaXPh7/3Sn79vvNc6o5x/IQr57pUDBFJirE0l+6mTUuw6flwaSOHKYtM5XUSIaWb9KjZIqoSkwk+VStFlwWGYcorFo7z0IWEKIUrpgxuu7BDnGa8YKbBLfvyrI7GvP+JTRS9RUEVcCNI0gRF1Ol7Asend+n0p5kxJvjHR7rcMGPzPccr3L7YY+BFPL3rYebGNPMCl1oimiqQVzS29lIsHfK2xI9fPc3fPrHBQHCYLbsUDfBTHw2dtCPw1mc9k8dbI/7b2TOUdAVZGXPVxIBRoPPEpk4zJ/HUlo6cqWRiyInpAVU7JEhS3MDm/K5IQdGZq7eQ0XhsU0NKLQQpJh5rjAY6zz2c447zAxI1RMwEFqaHzBR9Jgs9Nvp57jvfQMxEMilGyBRmTINnzhV447VN/Cjh4Y0R7713i+cfzPPPj3c5UjF4YndMJQdafoSpClRNHy+RyCIFYQS/+9LrKP93REmaZrzl44uc7g45XNdItR0GY4OqGVMrDuiPKhzKz/KW66bYdEJuX+pyvjvgwUtjKprBDRM5XndNjamiivLV9auuG/Hjnz7PG65q8rLD1W+475/bc/mlzy2i2SOUxOL66Sp7bsjP3TTDXzywy698xzR/8uAG7VHMb71gnvV+yHRB4be/tIYmifzSs2a+NvBK05S9vT3q9frXvIoAhkFMXrtM4Ow4Ab921wpvv2WWfeXLfUoYp9/gp/S/wls+fImfedbE1xRN/4o4uZwy7YYpaeBSKBT+j+yrvlV6qm9pRdF0TiGvi/TTIY1iyDXTNodrJr0ggcTg5BQcaPYZxxIzeQUhKbM2UBkHeQ6VDXYHRVIx5B13LvFn921jKyoFWWefnUdVPfLmmPmCjh/KdEOHgiZR0SMydRtFCakZAXt+j4SYkpkyisaIyByoyPQ8kcivUNbybAwz6qbOfCmkGwypmiJ+5rLnj1FFFScdklldfub5Vdwk4WBJAxJWuilVU+HhjYTNQQ4vkimaAVfWNILM4VBVBHGMK3hcP5VnNmdyqKJxqQNlw8RTXTaGEgJQsl2UtIymd8jCKo9uJywUbA5YTfY3NEK5y1ThcsJJmuXhujfgPHwvj33oL9EkyISQo3WBe1YkZEwEwaGh21zo+sRxDj+JiL0cgzjh3LbJRq/I2igmihoUrDZlXeSxtRyjIMNWFdy0T03XMJUIXR7z+muGnGhKnGwKLO5V6Y4TZPIMAhEviVncnUOVA2zNZV9Bp5JzaeRbnGgkHKyOyOQeVTOlNSgRJjFhLFDVDTIh4HxLIU1VpCxHy0347KMN7nt6nv6oyoOrFh0vuzz5I0+ER3ecoYkJ+4opP3TDFpLkEacBu0ONsqbRzMFMsYeXhHz/VQWet6/IT5yYZTBW2ewpNPNdTNVBFkHK8pzbrtN2NYIE7NyIKA1wXBVbljjWTMgZMQvTXVQlpO1oKFKErY2RhYQglikbAhOGxQe+5xD7Dmwjmz2myx1mKgOquTFpKtAdGwiIzBcjnjU/om6KFHSRup2RJgoD18IJdDIuN89RIqHJKaKQMlscosoelhHg+BJbPZutfoGea+H4Kq2RgqWN+NQZm8VWHtdrYCsyk4WQcSiy1rdQ5YSy7fCi4xd5yYkuJyZTbC0lyxI6Y5GOK3J+W6ZWlLl62kBTM/wYOj2ZS5sCaztNJEEnSS+bbD+1JZOOFY5MxExXBuhqQKOwSZLo7A1K7A5zl9VdQsiEJWCFFqYscqRa4IBRRvZ0zm8HvPkjF1nu+pzaGbAUbNCsZNi6wPK4x++9YorAz3jlPz7FWz99noNlnTMtn54jYJGnVugSJQlJXGPbSZHFhKO1jMWWyL7i5Yj7Vk/kcEPnl59xmDcen+K9L9/HoUqBK8s5ZowyB0oyopQgiBmz5Zgvb8ALrwxx4whLkjmzolC3RRoFj7zmYyspw1Bgpd1kqaWytFPjjtvu49Tf/B0jCiA2We7EXNFMQIxw/AJumJFmKeNAQjBj3nHzfl5+tMZju23ece+jtNzg66ZMP3fLNC8+XOSLSwMmbQ0710VOi9iagKbEGLLM+XZMz7FZHwjsugm3LKTsOBkTtsH1jQmuKDcoaBId30XTBuwvx9xzXmLcqTKn2nSclJtmClzZMPmDezc4XLYoKyofOdWjbin00zGPLYp89/Eyv/XsfXzxKY+TBxJUUcTLxlSkMj98xRGWRwNeNLePX755P0+sRPz5Ixscm5epFGJ6gYAaFTlYyPFLz9yPJovsuQE9J+Gecw6HKwYCESs9lw8+1eJPH1rn3rUBrz3W4OOn+tx2uo2hyORlme//0HmWeu43vUYlqfBNfXwb31q4Z73D+d6QrpOhKSJyZPKH9++QiC5TeYWmrdJ2El53ssnKVsrv3LXKK6+6vEL508+a5AOvO8SffmWLxa7HM/fZ9McJ3a7MTF5GV0JMq89EI2Y2Z/BjVx3i+040ODllklkjRnFExRKIQ4muI1DUFZ53KEfXySBTcFyLnpfw4M4Gs5Nt0FxsPWYql9IPYu5c3eP3v7hOTS6ys1tlu1vmkVUTMpVh12I7GPCKY0XefM0k33/FAmFQ4JOPV7nUMVGxWO6nNPMZ5XzMR851eda8yVRlgKXFmLKIk7apWpC3+ph6gJCquGnAvqKAoY6ZKETU7BhB8cirMo/uDviNZ+8nVw14/ckyFV0iTGKEzGDLjRmOi2y7KddM+/T7EoYqktcz1ns6QzfHjiNTMzOGYcRqp8auC6NRkS88XaLlRFxTb3DTjMb6QETMdAI8ZvISlXyfhg2VUpfDCwF6ccjRuQxdC6laIRtDE031sVSZVHBJY4Oel7C/GlPO9TlQ85irOrzo2DIvOtrj2Pw6B+sRku7w5bU+f/aVTVaHHkVdZ7WjsbQ2SezprLVEeoMSF9s6YSTR93KUzD77iyHPWNih64Pj5nj0fJlLezlcz8KNfFxPY6eTx/BrPGNeRBYzhtGIw02PZiEkTH1KdoqaS+gFEQoiA89kZygDAoakMBip7PVVdrol7r0wweKeiiRKTBZDKrk+RSu8bKgsZ9haQNOKqFoBsZowTMb8y5ldKg2HSO3SCfrUczFeIPLwqsMHH+uxMzDwohRdSpjIXTam3huKdByVlXaRfz41z3Hr+Qz7V/DUWhlRAFONyWkRBT0hZyQkqcjA0xh4OkNfZ+CqDDwVUU54YncAUoymhSz1RuiCSNsLkVR48cEyUZJSUQ08X+LKaUgTAzdU0GQPiZiSkSKJKZc2c0hShm1CJ3BZ6nhUTSiYAeM4Yb4yQhZF1ro2V1tX8dDFlEwQ+IXnTdMJAtIsY30Y8I/nLvHeh8/yz+cXmdBMBoMyF1s2fpxSsy97cvZ8gc3WAZ43u8BbbpzmqimDjWHA51e3WPdGrAQDipUBFfuyqbmRqVyVL3OkYmGpEiCw2vd47wPrrPUSymZIQRfpeDFSYnBxzyAj5R/OX+RFB2rMmHUu7imUtJgg1lBkh5op89CqgS5JpGLMNXNtZkoeshQShBb3PF5CTS1QXAZujnESUzUF/CxCVyIyNeJPXjXPiabFS4+WuXGySKE4ZCLvX07U61V5fCPHgYaHpAY08zGTlsp3H65xTcPmJ/5lkR3H4+SkzVtvmeBziz1+6dlT/NmrDnByyqDnpSSRSckaEaQSOx2Ti9s6L91fvZy8+N/Bj1PEVOIVR8vcNGfjeSWCOKPjSez0Kix1M1YGI9706af56Tse57NL2zyx63Dr/hL/9P1HeMcLZpiv6F8jic7vjXndBy7wzlvm/6ckkR8n/NHDi/hyyJxV5hkTNdZ2Yn7yummqtsovP3+a37t3jSBJ+e0XLgAwU1RJM4Frp2x+8daZb1BFx2nKzuDrzaT/lSTaHYW88bazvO3mma+RRI+sObzu7y7wP+pPlv8H8+t/xW+9ZIZ7N3rfcF2WBP747m3+4v6d/+m5fy++3VP97+Fb2qNoxXHY9Q2qpkLNTLl7tc8nXn+ct37uPE4gcO9KyHedaOH6c/SiEU/taty6P6XnFDjXSlGkkKZp8vBOl3sXR/z28xe46YDE+f4ILdHJySo+HqIok5eq+GHAQErZX5BouSmS4nKsbuInfc7syUxVSiztCZzvxpychAONNj1Pw/fLCKlH3Rpzspnj9K5LmJhIaYEL3TZVQ+HvHx3y5pMVbpq26IW71AsCDbvA+bbPTMFkuedg6iJHqjYJPpNWkVPnVfxA5MicwxfX9zg2P8VW4HC0obDZUzBkKJseT+6I3Dov4WtjpKRGqmwzI5f51BMRrz8hERNT1jX8LKCigqaM2VfP0X/Oc1j57B4Pf+i/UXzTD0A2xpZNemGbvJdjxwkx5RrroyGyUKIbwOFKxkZfZBwKtIZVMrbJy0V6fsaV9ZjFlkmapkjU6IZL7LoFBmHEQxsqZb3NjusyWcjIMpVxPGB/USOvJwyjASO/hCoHPLYlce2ETV7v0Mh3ITU5UglYao+4eiol9jUe24IszSFRAGS6vkdO1VEEmU055pKTYo7rCFLKwPXYX91m5FVQhBRLzdgNHMKkTDNXYL4YsTYQ+OeHitx8KEGWGlStENfX+ZUv7nBVU0PrXpabazKc2S7TKO8xXYppDfJ4oYLjZxgKiKLP9lBkwkjIUo0wMBHTmPMbCrVygCRmGPJlubwmJxhKwmY3x2og8RePX6BhS3zlkkUaebzsyjZlS6Q3Nhj6MmEssDkwgJS8FrI3zCNkAkkCsgp5I8bSLhv2bgwMxJF0WbUUGNhaQtX2iGOdrmMw9jVKdsD2UCVvDvn403mqVoQt55nKB5TNmK6XomuQCjIIMFkcs9QqYysqhhzjJyBKDntOgZ2uyS8+c477Vwc8vjXGFcfISogi6VQUkYqtECcBe35Aa2AhixL7JwX6Q5Vz3ZDrZn16bomKITFXafPhx2uUjTy6LNCshfzes67id+5Y5486m/hJyprr8ZM3TvKcAwV+6/5lnGSXI00RIXPZ2Yqx8xb/5bEVntWYoFQIEVH58qqDG8i86LDFarDG0FNIE5M9r0dZs1Alma9seDxvrsZV9Txvu+MSR2sqrtjmt+7y0LIc1VzGRy9scrYFJ04k3LeVMVuJiROTYdblynqRla5DSTVZHkVMTkT0+ia9XMK5rTzLg4h9JZXNvoStayztbROcucR1P/fr6LLOxS2fZxyKGcUu+0plVjtgKjqDxMXWBH74xDSnt0IWcg5povIdEwd4wV+c4bVXV3n7c6cRBIGzrTG/8PllGrZEubRNUTNZi1IsVSZJY8Zpj6smbL6wJFBMikxXhmz0dPZVYop6xvFKk7956knGmUNVq2AaHR5bq3HjrMQtEzVasccT6x7jOEEEnt4d87Zbpvnlz61Q1GXe87IFNoeTSILAr35ujZqqc9XhGEkN2W+JtHt5drsyf3nvgIOTCj95zT7uXhrwgb0Wvu7xUzcc4p13LNGLXHa2Ur781sO03ZCf+9wiGRlvu2GOWxYKfN9tT3Gw4RP5JQICbpoq8OiSx3++d5t2EPC7D53jsxfq7A4jfuk5M8za3zaz/jb+z8KtMxU+vTFkT84I5TG7HQVZlrEokqkRP3T1NP/lgR0UI0YwXe44l1A2N7l/s896P+Btz5zlH15zlIyM+Zsn+f3PdcjyKhd2fK7e7yIkOSr5ARu9iJ/86CKvv7bOo5suumxSqI4p2h2mGjH7jXkaRYELXZcbpi02Q4eNvkHD0umNYxAcDtUl+p5MkiXM6CpxmvJQZ4sZqUYlZ3DLTIl/fGqXTHCo1Dw63TJLrHD7hT4vOGFwYUUkl88YOjZnAp+D1YwkDdH1gGqqccdSn7KVp24FqJrDtGQz8Ef0xiZX1IaUkwBNM6lYffK6wlpfRjFHaHKBu7sOBT3Hai/kj7/jOL959yVeOD/NbRckFvU+z5gXeWJDQUXjmtoMZwo9fuOWfUzkZd5xz1Os9wPqdp4noiGHKzJPdCRcp8iGk3DVtMG7nn2Ad335Ik/sRkwVRTYHGZN5nXE0omLLGFqHfJBnix7tYZk9N2KqkFErdpEzk5rt0x5ZuL6BL6bIokajuMVsUSfopUiyzDiRaJYcJBEWKqvsDcpsjkLssU3LEfnDFx0FIWPK1nn7nYs8sungxhEFXeLCns6xCTi7V8HUfGRsLN2j5akkqU7Xj9ECk1QwedncPPfEe2zFm6huRMmSyBBYais0bYWpksvOQKJiKWzFIr7gY4kanb7Jy6+t8MDGgFrDR1cDLFnF0sBNEhQJskRlqa8ShSovrMvcMFHm785cxJIVwljn+4/O4oYJZzsD1h0wVJ+iLhOl0HEUVjyXvUjCi23IBPpuiqnadL2U2Kuz4yR4cYysefzmlzZQRAFZyhh4ErqSktMltgaXVwo7Y504VkgEge2BjoJGHIs0SwO8UOTcyAMhRURF0TPcIOXCWoyTRbzkYMyzZypUZIPvuarIu+5eYrnj4qc+FV1BjhS2+jo/fsMEuhZxoTvgsZ0Rt8yNKJoSquzT285xfm2C4VijM85Y2djmmhmDQ02Fn7/zaQZhSCgGPOfIHiVDxtZDdh04tx7j+CaqeXklvD3S6Dgi3zFfwsbmXbdv8bFml51RSGeYEkgJshoRpyG2otMeyvzTq6/k1KbL7Rf6TNsq/WbCU/0ek7bCyE2QjZicJqJrPcqY7I0k3BBC1aSkFbl3tcdyJ6akR/TGNr1gjDOuMopC6tZlL6I0TFncbrA1HKBLOk6Q0KykrLYlJrI8rTTjJVd2qVkxu6Ma3YHN3ijjV+9YYaMfUNQU+nHEy49VOTfYQcFmrS9Q1iW2+xGGqDIIA15zsMifPrJCMZfgEfBLd43525cf476VIa+5osZfP7LLgxsOT7ddpmsZwyihNyqz6yRYSkrOyvjMpV3Ucp4X7Zv42r13bxTxumuqnJw2ectnTxOkPrMl6I4F9twMFZMHt4bkZZU01fjxa6comgrPmi3yh1/a5E03NqhYl8mnz5zt8jcP7vKel85xYtLm3N6YnhfzjK8mrbbckDd87Ay6nPKGY5PcudbiZF3iGbbOdPGyyuktn7rIjdN5fuiaJnGS8RP/ssSP3NTguhmbjz/VxZJlnru/+LXXH8Qpf3jXJmvKFn/50uNUTfVrRNLeKOQ17z/PXE3nYPnflDlPbbu873UHvo5w+vP7dlju+rzn5fNfV5uSNOM/fOwsv/sd+76hbgVxSpxm/PxzJxmNRv++Ivht/LvxLU0UlRSTfXWNSq7PxkBnrqrx549dYKo6ZLOns1/XcJ1JVG0IicDxhsKZrRKK4iNjUhBVLnbGjHyZQyV4x6fW+Oc3HuA3HniczkDhEc9noZpRVAV2xmNkQaJhG4hiC1M1qJgFanYHWUpRhQanNkOiVGK2oOOmewjSgIOVAsPQoReahHGZrZFAw1YpqCJL3QhbrrId+qyMAv7hzCrN+jqqZKBJCuWcy9WqRc8f03NVzq5PstJtc3Vd5/OPVjBkmWI15lJX5FlVkU8s7nCgojJZ7lLwDeIM2uOI66clNt0xV9ZUylaL9X6JRzdTaqWMDz+5R0FXsFWHOJBoFgdYakKaqiCKFK77YVTB5fTtt3PV867j2rk+g0BFV4bUbA1BapHXC3z+QsjhZsKjGzHHZhLcSCTNQBMtLnQTapaApm0zUSwQxDJVu89aex4h67HebuIGGZsdjZmSxHK/T8UARczjJjvEbg0kcPEYeCUqJpxvyVw7o+MEMknm4IYFSobIxmhIUcpx43TKwxsJK10o6QpZmhKmHo5nsb+iECTQzMckmY+Q5jm7E1PQYbowYrpgMI4lwnjEw5t5dFFBEiQKhYR+mHB6x6agX/ad2dIllnZSbjg0JmcEqKKC5csMxnl2hRGKomHrGoIAORWKkowiDAjpYwgFxqGJGIsoqsA4kDg+M2BjYKDIMYYSIUkBhq4xPz3CT0Q8P8/JGZ8ogXEM3lDm4q7NTt8mb8ZIks1MaUjf0dgZKkwXfBRZQJMFogRWWyatgYZieHixxMnZNoeqIVlmEsU6SQqqFBGlCnGWIkhj2p6PLkscrQlM5D2yTOTsns16X+DUcpFmOWS6FOAGEnkVCmZC1XZYHYZM5AKKGowN+MDpdWREUjXEiwY8c1JgHEr0xiI5IyRkSHtkImcGYqrQ62n03RTBlpHRmC+N0JSUT5zOsa8ioUkJLVfgwUsS/1LaJa9LHGoqfHajx8k5DcuEn779DNPFMcdqAoY+xJQjGpnJYt/kV561n794dAsVi/MbIdfPWvzcrZN84Owl8ggMxwXyxoB9JYG65fLohk5OLHH7xRGfudhif0UmwqekypwNBHR5xAefcnhsNePqGYlL7jpTpRE1U+bCnoIqTBGIHYSkRixGlIoJcSTzyqN1rp6c5y0fX+ZHn9Hki1urXDOd40N/+C6uf9VrGX7/T2BrFutDn6v2Z2yNXapqmfU+TNgm/WwDZVxn0JX4l6d69L2Qzy7tIUkCLS/m2pni5bS7rxb/nhcCCflcwEzBIAkDGnmZ7fGQuXxCTUh5dCPiQK7IRgum9UlEcwNNG/Ky6Wfzrkfv5urpITu9BmE2oDdqsFDK8OOQlaDDkzsBv3brAZ7addEUib//3iO847PLvPP5s9w4Y/PU7phbFgr8zufXuWU+j2SkbPYU5uWMlYHH+qZEIw9L3TFrLYXh8xLe+vFLkPP5+xcf5xduW8UzRgiexY8/o0HZlPneD53lyqbOdx2s8aZrJmm5IS8/avLkrsBNMwXe+8ImH32iQyOn8jvfOcvbP7/E47sCqijxT68/wMXemPd/Ze+bXqNSvnkRrCnfGtOvb+Pf8OEn2kyaJk+5Q0rFmLmGwthV+IlrZ/jVu5a5NBwxm9N57RUTDEKPOxMHJ4zJKQq6EvE3pzYwdYEX7KtgKSK/8KwZIj3PT99+gY3dGpVyB1WC7zxQ4KOPO/ynu2Jee1WRT5zusdQWUBSLsplxds/lx68/yDvuWuSRjsP+yYwjtYCVnoAiQxBYZOqQku3RdUp03ZggkaibIo+sjiAV6HkttschcqajCRqy2aMmyfhyyFWNJhcOjNjYzWgPZKbrFo+sCuyvuuiCyk4QYaIxjnxO7+kcrmqMkxFXNhUeXMrz4EjmOc0RsTqm59mUTYcrJ3ZZbjfIRJcbZxWKUZHlnsfPfWqJ7zxS4h9Pb7M7SKilRc7vdSma8OglnXPLIyYKGk9ujVntSyxuybzieIGtYB0Rm77XY6YQcyEaUvOK7Hh9/uqRHR65FCPJBmE6YqGaMAoTpNQgTkcEQZ4g85jI6YhZj9mKyjD0iIIi7Sgmo8BkuU8UV2l7Io1cRGtQJExC9kki66OMhbKMKAYognQ5wcseYmo6206AoZZ4y+dP8w+vOMmTO2Pm8xZBAI+tjUmKKaoi40YOsmCQyDEtV2QqJ7DSjjlUi2gWPdzQxQ9yfPD8CsO+ylRZJ1MiyvYQQ5aRJZfdfh5VKrHbFdGkDFvU6LkiipVgKTJ/98QuaahRsWXKuQEl3cFUJbwkpjNS8AKbKFIIk5TtXsr95xQG/SmWHB9VEXl8eQ9Vi5gqChR0gTATaORHKJKIqXks7plImYaqikSpCGLCyFPYaRvUDIkwTElTkUGi0Cj0sTWFghiTiQGyIFA0wNbHLLUtLMHG9zXOb5jMT0tAfNl/K1Cp5wPymkCQZByYHNJ3TWZLEm++apq+l2ApEtfN6Fw3k+PHPv8AAgKSnuCHMpdGNpU4JRNC/vrLWzxjf46DTZNyYQddk9GVgGZ+yFR+xGa/wmdP1yhLOvVKzNO9IY+2M2aKMdWcj6L4JJmGG4mUrD6ymKOSC7nYUrlp1iOvCWw7CTNGnbPbEefWu6iiwObIx5c86s0MXRWo5AbkVJmhn7DdyvGjHzvPSHSJYtiNTV5xuMrgQo/+OMG2I6bLKXtuiqkZWFpCzvA4IAskw5iCKvKhM1vsdRJKts165tCwLaYqXVQxQxYl7j5Toawb/OJN83xycYvPnXXJCSJuIJArhPghqFrKV5YnKNpdiprA+iiiYassDz0mCiJtN+K5+3LcfzbmyMw0D+2MqNgCm6OQKVtjc5hy/WSJv3l8i6ol4MYJJ+t1fuq6OR5YHdHMKTzRHvLuF8+wPvRZD7e5an6N3qjKmR2NhbKIog7QJBFjJCMIl02Y/7VPWmz7/OOpNsLpkMV2wDVzkIoeRUNj4Fq4fkZZMdnuRxypmLziSBVRFLn9fI+5kvY1kijLMp7YcvmuoyXec9cWr7++wrvv2iAvKXziTUeRJQEBWCipPLIY8H6nzQ9dW+PFh6r89u0bvPJEhXGYoKYyP3BVg3GY8NaPXOLaWYub5nNsD0Nefbz6dSQRcPmMmfKmK+f5oQ8u8Sev3Mf+qg7AUtunmpf5jRd+vTfjG2/8xmSzubLGj9/8jdclUeCfXnPsq2q0r4cmi7ztf0g/+2bim9VXfav0VN/SRBGZzMV2RCZlTBdiqlaXPUdCFAT2l1Nsu4cXylgoaOhcaCmYakpZUVGsjPZIpKbaHKsnbA1STFPi3XdusjlWObawy/LmJI43YrGdcnwiQxMzvnAp5aVXpFhKiix3iFKRJLVA9KnbNlKisbQjURHzaKRU82vYkUHXSXhwPaSkGbhxSpgGLJQsVloiI6fAiVqCoO/ieDnmywPavkjNKrAZO1QsFRmNXjDmkHKI933BoWSL2DkfRUuY003GcYeZfJ7Jyjq2LjJf8mm7KnNFmaW+y0JRw9C3KRsmo9DhirrJ55dlZDsmkmAUqDxr/w5LPdjrFwnTDDFVUQseRqzilyf4xDt/k+YfvImpWZtxpDBfcpEFhfPjIScnDbaHXZ45b3G67bO/YLHes2jm+8wWBC61PC5uLTDfXGJfxUaVY4J0F8dr8MCqiSHKgMRiO+T4pME4HpOkMfFokq4nc7TukdM0tiKHntMkTTX+4gGRV14ZYGkagtSn7zSZtHL0ehI72xpVU2S1J9AdZ9hqhixqLLZijk+ELJRjDhenuO3SDgfKGroCJTPCTx3iTEJCxwlU3EBhkI2ZKVhc0UjpuSqB4BFkfdLUoG6qOFrIyo5Nsx4gk1I2AUzCOGWqPEKVQsahhKlC1Yp5xdEOH7tgUjcHPLW+nyxTaeYTdC1AEqA7NikaIZKVESYwjlJiUvxIJclEFqoRKQmgkMQSqpihKQmWFjJf6VHQXbZGGlfPrxKFBkqmszfScQIN3xcIxxJSXqRZdtgY5NjqmsyVUgpGhqGkxMnleHo/8XHiEDFTOVxPiRODjptSsVwmCgNWB0UadoSMjB+m5PSYDBEyH03pc+NsjzSuMyrGrHczvAiiQMQTEk7OiJRNmCj0iLOAlmMhiwILJYXhOKLre/hJBTWXMFFQyBkOTuowcoscb6Zo0pj7LskImUrZVLhztculwYjblgUKioaYKHzo4S6qrpDEOQa+w4mZXcZegz1SFnczfuoTi0yUJT7w3cf4vn84T0TM6257jFnbxtBtDkyucriqUjF7fOlSnbKh03JdBFFnwrT4yiWPH7rusjfOly8tc9gy8JOQG6YtYnmEofgoks5XVmRycoGVrscLrtRpjWJKVkQ+y7iwK3B6z2W+pPO8Q3k+tbzFa4/O8v5f+XmOXH2c6n6ViUTi7FbKRE5GUtcppVMsd2SO1/P8h+MT/Pb9CRv9kMPTMqNgSL0Cm06K56u89dpp1p2Ak5Mmj22N+OzFLh9b3GKyLHFswmOi4CIPFTayAfuKOSyjw9LeJFJSws0SnnUox4sPlPjdBzv82QtP8pN3PMrReg5LdpHlGCHO0XIzcnpCkkh8eWuXCbXOz35uiT99yUF+755V/uMzpumNUrwg408f2Oafn2rxnPki95wf8d5XzvPuu1fZGYY4BExSI1JdNp2Mn7ppmmN1i4Ipc8t8nuv2N/jgox1EI6Km5uiMRD76dJvFoUvL96gWBA5VdE7tDLm6meflc/Osbm7xS8+Y5/yexydO97jjx65gtR/wiaf7TOd03vWdc+y4Ia/58JNUouSbXqKSVED8Jsmbv1Vk0t/Gv+FvHt7mF198mAtNl26nwNI4RCDiLx7eYq4sc9dFl9deUeftn7vI+a7PIIr5/MU+f/LSA/ynu7Y433f4kwfXkROJZ++/PL1+YNllt5shGSH0yszVxnz+4phfetYCf/voLlc0de7edkjdAk9cUphpjpiuO7zhHy/QG8fYeRlnbKEXu8yWZdJMJW+12WgXGDo6Z3ckLE2lbKY8uSlgaZA3Yy7sxeQNCU1LWBtk1PQSnhKw2xN436l1fAL0AhiZwkY/QREk/OEELzhY4UPDbcaSw2is0MiZPL0TUFJrPL2sIsgJ1zcaPLmZYVRNIiJmywkFNYetD1GkPPftCDzW6jA/4bIRwvse9Wm7MZMlkVh0GfkWbhhSznu4rsyaG3L3Rovff+EhFsqHuKJucr4zxc9/4WmuaBbo+SmT+ZSh2qOai3lw0eCN1zS4p7XIyJcw1TFTpQBLSfBimd44I80sltsWFTNBkV3qtsRUYZWhV2YQxIShTSh4WFKeC5saE+WIiULCIEgpF8HQHIqqRMnuE0YmJXNIx0mRZZ3FvYSKKfH62x4np2rsjS7X8MPzGZHgMVuMkWVIBRdJ0IhSWGrrVE2JzUGCJusYus9aS6Seg74AOz2RWlRgKYP5sk/FFDi7LWErULAyNroSLzuqUTYs7tnawJJlBp7MlpchqTFialDJtcipGQIKqpTy2HIBQ5YIk5RmQWFj7GGhcs1ckfs3ephahKzFPGuuzpozYqkX0RkL7K8knJwc0ciP8UOdOFEQpctD3lCUaJREKlKezihCEERSBBxfRULEUKBZGjH0VYa+xnLHYLuj8lvPW+DCbsjZrW0SR+SmfSVSMeEjTw4QExkhl2IaY2YqQ7ZEicV+nx/46NPEY5nDEzq5fEKYRchZninbwEo1Pr2+zVwpRldG6KJCrR6z7QbsTwq0ejmyKMWxYaldQ8gMssTic99/NYIgcPvFLv/w+B6OOMb3FR5rq5yYSxBkmdWuhKYYRLHOOElpFH1ENBLBZaVVZYTIRivlx25p8MBOl/OdIY1cyGw5Jm8NkAUZP4LNgcRMM6LvxRyqeVhUuXcp5O+jZUBBFAPmSimaPmah6kGmoShjcorBub2ENJS57eIuL56bpt9v4+IwWYwQyBgHEk5iECQBM9WUvdGQPcfn2dMN7j+7zhVVi4dbfUgUZidbqKLCxiCjmtkM44jpisbWKGS6LLMxiNlfkqiVQl7fqPB3p3c5WtdY7I2ZtGQWt0SmawKrTosDNYXVls6vPnsff/eVDrc92eVfzmzTrEcMA4HHdi0+fGabIBPZ6ZXoBiEHqgKTpS5ZpjP28hy36wixyiv/6Wn+8qWHqdsqeV3kj185z+1LPRKxh585WILJ0kBEFFMMNBaKBj94fAJZFvjhj17k/a86xAsPf32y2W1Pd7FUiVdcWcY0BT59scsfvHiek1P211LCqpbKD5+cZrO9ylYv4r99uUdnIPDDN9bZcyJ+9XOr/OhNzcsrXffscK7v8oM31gCYyKtM5Mtf9zd744gf/dQF3nqsSsEw+c3vtL5GErWdiF/97Dp/9b372VfRv+7cyE/I6RKfeLpLlGS86qoKL7ni69/Pf4//GUn0/w18s/qqb5We6luaKHpiS+DIvojBqMR6v89zD28zDKbpDkwebqc856hDTinS9gNG0ZihX8ISdL68lNGYcLFVmTCJGMQeOUNjvhTTdj3IdPzRBIIUo8kyh0oKq7sWBybbXDepQKaw7gRMCxZTuSGndsYUdY2BV2DT9SlaFnlVZLFtUrebWKrLZLHHdWmT1cEAPxSIgiaLg5CSLpPTDLbbMtO1GjcfW2RvnLJPKzBO1ymETTa6Bm6UcbRc5fe+4yDPmulhKCI/++kl3nhThdO7AXJkgeDhBxazE11MzaGo11DlPvWczVK/R8+tUjZaTOZMdodQqge85cRBfveBVcpWzDCQuXHa465LfYgmGIYZ82WRlb7D/quOs3/2rbR3HYapz5ULIm4kEsYy+8sxd1wMUESTpY7IwZJBKvUoyznW+jrHGiOunJTo+ZDFNZxoQFNVmMh53LcEh2sCj227zNs2qioRiXtkaZEoGzJwE0qGxrmuwJStI2QKT+0IzBUkJm2TxzZkjjQzJuwSguSx4wjMyBKW6pBmMjUrx05fYRTvUlTKzBQU9kYCqbhH39tDTgso8ojFjsghDFS5dnnHWo6JEw1D0th2MwZeRBBJCIJA2VBY7tnsdiVqZoqayGgaBKFBNxlQyyVMmAJxKhAmMkcaQ3YdCTeQMRSRjVGBsqry1I7B09sSVzRDhEwiilUGXkpeD8nrCZtDizgRcUKZKElZKFjM5E2+tJ4wXR4x8HTWu3kcX0fIBLwAzmxXaRYUxnGEKoe0fZNqmmEoESkiiiwg5n36nkB/VAAj4YVHumjy5cZq4KsESUbXzXjXrTcyk7d5w21PsNcPkEsepuax66ZsDi02uwUmKwkjT2boKahyhiZHKLLLXKGPkOXYci1WOybLWyqlcshE1SNnhkzkBAQhJcj6zBViTEVGFiAl/L/Ze89oya7yQPs5OVQOt+rme/t2TspIQgQJCSQRRDZgDBgDNg6AwRgwNh4w2DCAMWCDMQaBRRCIKDIKCEkoh5a6W51vzpXzyeH70TYeBpgZsGbMfMOzVv04e5+z96k6q+p9642Yaou7FstcsjOFHMncu9bikQqUEiW2FWEk08cPJZ60y2F2ZQutjsYVO1J85O4BI0mFoUKd1apOJi9hKgqHV2MuzsQ8tDxDs6fSb7jkM2BZMqeWQ/7xrg18KSCbVNgX5PFkm6nSBqMJg4EfsrShsKMY0vcbPLhSZjqn85u7xqjX10hj8nd3rDFlJnjsWIbHT6V5+x3Hmcx4uL7JgTWHLRmDlm1z8c4utb7IlkIfVY74woE0BdGk5lX5wtEWdxyRedZ0xEP3nSL/nD8gMWIxpCU4WRUoJSNEY5PQG6brBuwvZnj/ZTt4x49Osdi22FVKgGwxlYK7FiKGpCRuEPH9hQoDP+KbJ6tszRj85RNmOF4d4CkNvEDF8vq4ts/EsMZy30IUhlmsZRClkPG0wfN2D/PO2+Z51xN38dtfWmCqmMQKBhzdGEOWThtMR1IF1voWG+0keSHH8Z7FWEbjMwc3OLRm07diojhG0gLurdSx/YgvH67xg1ft48VfPMkHnj7DB+9cZrWp8+LHlpkpqjy0PuAPLhwF4PpDDSrdgJefMcJrlk+xYdt4XZGZpMFyx+HmZYtrnr2bvC7zm984yNeedw5xHPO6ry1y9Yu2IiDwrpuX+durpjFVmWrfYjpr8KcXj5HSZG5fbpKVNKzOo1+j6Nf8mv8Qoci1h9bpRj5NL8RAYSqn8LKzyrzz9iWKaYFQiHio3iOTcElhouo+d6w3+MQLt/GZ+6s858w8r/jiLLoisEWHi2dSfOxumZKpImgdNvoCUwWHr84eZ8VW+Oi9EZ945mOYr/v8yTcXsDsqh5ZlXvfEUT521zpNx2VdiJDEPOP5Jj4OtpPC8WVW25DVJfwowo4ddpdzvP6CMT559GFUJUOlLhISsH9YwRU6SILAtmyWfCrgeMOilAo4timQN2BjU6ajd+gfCVntuyQ1hbEkNHswklNZ2FTwBxrpbMRyv81jx5Lc2W2CIDFbKTPwYyYzaTwcEjpMjnXoByHbiwqLdYvd5RQDsYHn6rS6EAoaWUPEjQOymsqxVp/f+85RmnbA48azrA8snrdjlOuObaDKEEUuohhQ7cpkii2+veDiiwJTOYeBJ7JtKCKIPYbUGEkMWW5kCULQFR9diei7Il0niSg5jCRFJLlCRs9zaKPH1mETyw+Yr+YZlfpsdEyS1RTby232iWm2pNdouyVaoosXGGwbcjg4p1JKx4RKg3I6wUZbZqEvMlEUicUqOV2gH0i0rIiiKbEke6S0mILo40YeOCaGGdB0ZbYOwalVmUxBp9aSuadjk9dPdzgNY/t0VJgCB2qbTGQFsmZAFDtsSWiYmksu0SdnKkSRRBBZBIFGGMpYPhSTPttSDj3qbHgZElqamZEEY5kSfhRxrG4ReBq1SkyzJRILMV1bIaGermmTUT0yRny6mYwR0ApiImIeqDUpGhqEIs5AAdMnYziUUy4zQx3mGikcVyGKFOJYQBJEEGIQoae2ebDZI4hAVCXsrk47jAgli1O1NAlJwNB9Ilnk/c+c4qyRFPK/1p655tAKdy62mat3eNFZQzTdLt1WHz84XTw7Ejt8Y66HJig0+hKNXoKkEZA1RRqexYOrPeZqHg+u9tESPi/ZNcneYoq7lrs8WNM4tuEwWWriuiaxEJE2LSRBom3DYjvJJeN5ZtdiXnOZyffmqrTsELerIekibtQiZwSYksBsy2MiF2N7IfONFHI4zLGWhWH6SKKCKNtsy3rMFNskNQtFNFnrBwiRxqYtsdFR2a1LXLBtnGsP1qi6HpoksbSRQdV6aFEJV7UZSUlIikVoZ/jA7RucN5XgY8/byl/ctMT5Yynuqzdx3AS24DCekmm5DtvyEitVESU06UQBmVin1o44sh4jlDx+c1+eLx1fp5AIqfcFClkBD4udeY/DyyqvvXCEx09mkYWYjx1cYnzEp2cHlLUcn7qzgSIpnLWrwq7hDs2BgRf1MWSVUw2QQ5WvzrVYdT3+5unTlJIqfS/g7w+sEIbQCyxESWZYStIMbBJShuUNhYQi8o4nTfOBW9cxVJF/fvZ2BEGg2vOo9H0+cu8Kb3/yFM/ef7pmnBtE3HGqz1ueNMFk/icNNF8/1OAf71ul14UzhlO895nTrHYdvnC4yp1zPUIxwlBEHD/iTy4Z5aWPGaKUVH+myKj0PF523Qne+PhRZL/LmWOJnyhmXUjIfOXlO3+qbf2P5rp88LZ1PvLcLXz9UIN/fuHWR1GQ/Zr/TH5li1m/+tWvRhAEPvShD/3EuOu6vPa1r6VYLJJIJHjmM5/J6urqL7XHK88a5bLJERQ1ICmnaLa3EYcKmhYymVFJC0MkjR6yKDCeNBlKimw6DqYJSTfP3ELMUiNGjg26bkhfqJFJ9xktWDzSsNnsgRzLLLUD3FDA7hcZeCKyJDGdSmLIMQM/5pyyiKHYGGadclKkG/dp+QP8QGe+KdHzFGTybCu0kNFRoiL3r4BtmfhhzPLAYntR5YrdNkGksTOvk0nU0IQ0xys66wOH0XTEYq/L2364wN/ftcZf3THP47cl+cf7q7z8zBFU38DvZql2MhypKQycAmuDAZaXYK6hsDWrMtcUeXCtwN0LWWxfYVsp4JPHjvG8s23sqI+ETsOOecYuF83YZDR9Onx2ImVwrOaTG9uCNjnDt953Hdd+8jYWGmmqfZ2lVoKdQzZe6DGVh4bbYyIlIyk2CgkGfkDTktle9JlvuwhxAjsK6NkJ0nrIyU2JESNFvQedtkG9VeJYRcPz0ggiJPUeQ3qS2kDkeFVnLKlT74skNIu0qnJoQ2ahrWM7aRKahSw7zGRPd+w6sAaSIKEIGWTZIWX22FH0CQIFU+9y3mSDsVRMva+x0feZreURYo1YkDnVDJClkC05EVWWUFSXpNlAAvJqgkLaRlIcnDjA8wRi36RgqLScHmttmZwRsDVvk1BFhlMBgmzjx326towkKJQM7XQBad8nn7Ioplx0WcALRKp9g8VGioEnU0y4zAz1seNNbphrMHBFhtMDRjMDCimbtOERxyJ9S8dyDXpOgrGkxFhKJKFIyKKEgIwiiGSMmHLaR4plFEFlpuihiGC7KsvtBPMNg+MVmZVOyG9e/xBfPLLOWCZkKO0iSgHrvZiNnsBmO0talYhjCVGI6dsyohAhSh67hxost0sstots9jVO1mMsKyatxeiKQEIFJxCpWi4Zw0ESkoynXQQp4PC6yW2zQxT1JIoc8fJzy3hiyFACcprGwA9pODarrQyHlqapWgp9bZmvPVIlhUbXiymkalyw3eaMiTZ938cQJOrNHA+tytT6CqNJjefuG+Lbr9jLhTNJHqz0aEUt0qLCRidE1RxU0iBa2F7MSNJkPFcnDBTGMnCqYfG398yTHWrzB48f5s8unuS/XjnDiGHwF9+fI50YnPY2B30eNwV9P2Cq4OETsCXfQ5dF5poRZw1L+LJLOZHiniMyxc5BbvjQnzPblZlrZhlN6nQ9KCZCMtkaQ6aGHbgokc5CNWapZfOtY222ZDVadoiKzNqgx9kjEiPDVc7c5iCqA1IJi5QmcKJl88E7Vllu+Ah+gkEwYLmVYDzjsdqLGDLSLLU1SumQIPS4czbiMwc3OLkcc2DZQo5EhsQSo0aehbbPqZpJrZPhzkWBpJxFdnVsS2baSNB1Ag6s9Rk2VJ53Zp6TTpsP3rPE31y6nS//5m4+84KdTOQMbnzVPm6db7PeCchkYMeIyoWTGf7gwlEG3ukIn6fuyfHp39xGxw25ZanFsGFgIBMYDUaLAm+6cIoLx7JsyZpc/fT9TKR0BEHght/fw/7RBO+5bZnNns93j7XouyEXTaf51iv2cMWu056yzgA0QWY087MVr/8IUSw8qq9fhNtvv52rrrqK0dFRBEHg+uuv/4n5OI55xzvewejoKIZhcMkll3DkyJFH8d3/mv8ozz2jgJZwCWOf8ZxA2oTfPX+Ef7itwrN3FPnnp+3mpWcP89xdwxiSyp6pDRA9blqo8pmDG7ziwjI5Q+Hal+zgjBETgH4Q8hdXjOKJPoocM5GRUJQesWhzxliApHg4Ycz5kykuHE/xu+dOYks+1xxeYiMc4LoieUVjoRlx27ECQpDirpNJVtsSV2zN4rsi3QFIXpqFWkDPEdkib2W9IhEHAllMzimU2FfMEfoah2s9Yj9JHOvU7Yjzp/q0HJ9Qd0hGJqfqLnlVIakKSIZHOSGw0hQRFAHDiFlvRDS6EgerTYJQIa1J9NwYXVRZ7sTUu0lEIaTWlyhoKi0n4rxJmWK+w4t2T1LO2IwN9ZkpBPh4lBMKv3vuOPsyOZqtmFJC5o6lNnN1j28cr4AvQiRR6UkYkowbQkKWUMweKVlhtmaQ1WTuXzFRYgNB8FBFmXK2zo4hn64r0hkkma0luXdhmPsWixxYzrFQLzDfFNla9BnP1xnPBZjagDMKBd5y4W6G4nFuOzDFHfM57lqdYbkX4/pp1nsKrq8xVrYpF9rsGvKYKlXYN+YQhjAIoNnPYOg9tmQDxnM9LD9mpuCjqX2IFGqdJIg2pYRIEEfIgspbnzzGR5+5g2tfuI/fP2sL9Y5IShZo9Q1alnha17VM/NAjqUiosoAiWwxnQpqWQRD4ZDSROJapWwJxqCAKAtWegCoKTOYsdo3Xyeea3Li8yGLDZWcxwfP3DvGVgw2OV2wEQWS5luTYfIKNWpIwkAkFSOsuCSVio2Mgix45IyClB2ixjKFBJi2wKznMaiXHyfUshzczqJKPofhkTQ/HlnnX91b55qEmihrR6ih4joSh+AxnLa462+TC7QppPaCYsMknfbKmQ+CIOEH8YyPRa786z8mViLKaJnQEvnusyYE1lygGSZCJRQFBtClnLEZyDrLi4XsQ+TrdvsGFuRlun+9RTim86dIxPv60/WQVjbd8e4lLtmQ5p1im2lQ4dKLEnY+McOdchiPLJaodg7YjYHeSPDjnEYTw7ZMNHFtgh5mhnD2d4p9QHeJIp+v7lJMuRTNkpaPw5K1pJpNJmlZIxpBpWhFZ3afe19jsSay2SjywZvLg4jB3zJU5umGiCwa1XsTXT2yQzQT89pllHj+dBM0irekMJJuMJmIHMctVk1Qarn3hLt556QyTOYOrn7uD9165lYvGM/SsJEG/xIOLCYg0FlsKXU8jViJEX2B4uEsya9ELPe6vrnHD4hJOR6G6mUQSBCQ5YDjtU+mpZMyQf3hgmffedZJPHTuEKtus90Jk0WQ4rfC46STDY3U6tk4cxZRSfcpJkZodEvh5njQ+yUhS5eItGc4eTQJgKhL7ikl+95xR3v7EbQSuyuxqgn6nyGYvYktZ5YPP2oIVhBQyEm97ygSqLPLxOzf5y++u8OmH1rClNvJ/k9b10NqAy3dkecu3ljlVs4HTndU+eOsady30ePJMnn1jBh97wVZyhsy7bl7m4UWbd105yV9dOsWbvrVI2w4QBOHnGokAFEngXZdP8be3rpPQftpEIAjCTxmJANY7Hp/9rW2MZjU+/eLtPy7C/avIr3WqX4xfySd5/fXXc++99zI6OvpTc69//ev5+te/zhe/+EXuuOMO+v0+z3jGMwjDXzzs/18erHB+aZS0KhEEMvcsQ92OmcoYTGUFOr6DKEhMpWUkySGfDNiWS3LF7iyWEDGW1TijmCQWfAxVRI2GkATwxD67hgP2jLmEagU7iDAkkduPCxxd19jsZPjBqQRHK1ksd4ilrkYc6ezMpgiEAUMpm205mY5f53hVpWkZHNiEnqvTGWRZbursKakIQM2OOGMMKsIyOzK7GTgmVuCRVVM0LQVDCygbJhZVRtI+927UiOWQtz52mhfsLbOvrPD6mw/zxB0GhbxFLAhY/RFONAOkKM2JlosoKCw0TUZSIY+sFllow2otzcKmQUbTuWOlz1kjEQmjhSElaXk2T9/pImlVErLC0YpEXs5yrBoQBime9Y7X0tl0uOP+pdMFnBWPvqdQTg/IJwaUTI1NyyJvCIznK3TtEg0bgljivDGXnaV1xhISJ1shkigxU/KYa3iU0hINC6JQRhFlDm9AOSWSUCPK6Q3GUyKFVJ+E0eGC6T6rndPtWmdyCkndZjJfISmnCSPoeH00SWJ3yQepjybFpHUJQeqTNHs8dtJhOBWyaTmkdJun7R4wU+jTdQI6bkxCVjhrzGLgu6Q0B1OBhKzhxR5ubDOed9g37DGc8UgaNv3IAiFGE5JIosFKW+EHx4d4aDXLWtuk0c9gSBqC2EVT+mhShKEo7C0HZM0BCw2FzY6KF4ik9Bhi2F1us32oSznVR1dCDC2imLKQpZhTNZNTDZWeGxHGEYbmU0w7lFMOeSMiCDU0UWY841NOe5TTLik9RBFjNupJwhjGCwO8UGSxnWCtqxFGIVuKNWTFw7USlBMat66ukEl2Gc/30JQBptqnYEbM5ANGsj5J1SefHJBMeCzVDFabBg+uDjNwUziBRNP1aboeI0VwBwm6HZ2BKyFILorkowtZMnqEpnSZb2ps1FJ0GgVcR+WGE302Oj4FXWNhs8B9p3LctaCx0RhDFCQE2QLZI69m2VtO0qRHEMbMb05R6WrMt1wKCRgdiqg2RdqbabYYORp9gZuPd3jvrcusd1yu3JkjHWe4b8nm766aZntepucFrHU1RMEAqcH9S8Ns9lLcclillJBoWDGvO3sX3zrS4rn7iuwpGZzs9iiUW0xmBXStz0xW5u5lhamcz0imRynVRRIMWraPKaT40QmdF+8e50nD44wkbDq9Ck996z9RFQrMTDrM1XXKSZ+xfIty0uNkJYEhK4RiwMvOK/Dibx5k94iHFcQkVYH5tsOImcAXewwlktSsEFWU2eiIvOuSbUxpCZZrASlV5FjTo1Yboev6VDopdMmg7wWoyFSsPo5n8s/P2sb7nryD3398iY44IJOKuWhrgs/d5xK0CpyqCZzcNNk1pFDryIwXBQw5whoIvOqsURrtmIerHT760FFqnYjXnreFLVmTrUWD21Za3LvawVAkdhVN0nrMst3nZV8/yicOrNC2fS74+AH+5vZFNFkkrUu87ftLDCdVnChiZiQir6bwHYXPHajx7RMNFElktRHwxm8tEEYxOVPm4UqXBzZaVNsBXzvc+LHs0JXTojOOY65+YIPVjsvbnjzxy4q8n8t/ZtezwWDAmWeeyUc+8pGfOf++972Pv/u7v+MjH/kI999/P8PDwzzlKU/5dfHJXyG+8nCDZ4xNk5EMVloR1cDlo/es808vmOGCiTSv+cY8QRhTTsts9gUWaxlGU9Dsx3zukQ2+drQGgKFIqJLIDXN1Trb6fPCudZ6/YxQZjbrtE8Yy2wvgRhZVy+Mdtx9jod3nj54wwmfvq/GXl07QCDzG8hHloQBLHJAzBbbkdG47aaLrsN3McPnkKF958R7e+oQJBGREBN535xLfnG1gaiJjIz62ZPONo3Xumo2oWZDSJB7e6PGSPVNEdp6bjycop32es2sIWZAYSkk8Y3eCs4by6KKGkWzx2O1NCqZHux8yklKodiH0NRRBgn9Nn7HCAVGoEwNuCGePuQSCR04XOLQZ8bQt27jxqM3KRpKxTMxUucKusoegNvjM0XnK+YhXnTdC3Fd59dkT7BjxGC91mCq5FLNN0opKdSAwkQkR5R5bc7BnfJ2dpZgrp3awq2Cw2BE5tjHMPUsJ+lYR5AGT2ZB0oseWbMhKQ6TZyHH4ZJ4j82PMruU4uJLjVDXLZlcib8KBSpeuF/Cep87wnLNOG5VuP1XixmNT3DGfZmAlmK3J9AYpDq9n6FgyCVVgKFfncXvbSKpHu6fzwOIIx+sKAyeDqjiUkgN2li1azmlHzGI9cdrAJuicaDh88dAmn3pgk/WOz1U7hvjI0/dy6eQoe3NZ3EBkez7JVTtytB0ZQ/UwJImmrZA1HPaWwQz2cmR2F3cf3sGtx0o8tJwlpxm89Iwx6n2ToxsFlhop6oMYMY55sLnGtpLCGaNJvvbS3WRKfTI5h2IyRjZ9nFigbZks1FPcOZ/nZCVPMemQMgekDB9TjZnvOMiRzP5ClvdcsYW/uWwb541k2WimqfYSxPHpEgCJtIeYtJASNvmszTnb6mwf7TCe76HIMYIQ4Xs6J5ez9GydjqWyUDGIBKha/o+/n//wvBmeujfLWt8BBVRFQFI8FCliZ3nA/rEaTiBjyjJ+CI2ewq4xmSdtT/HMPXmKhsxYRuG2tSYxEf909wZfebjJp1+0nUrPp94LedauAmeNJ+gGEV5HRQ1V3Cik01e5cDrFF1+2m6t/cxu/tXuCi0bzzHd66IZDOjlAxKBuCRRMmyEjZhAEeI7GwWUfJ4jZXjBxQp/R3ABJUOh4EeutIQ5vJpirpglDlWoPSnqOF+4e5Y0XTfCaCyYpmDK3rFS5cb5LRpXpRx5DyRBJCgkiuGgyRbuuUun/+2elyiKCIPDXl+xgTzHJRjdEUQJ6jkJzoIAk0hlAKiHQ9SJ0RcKNBmSMgNk1Hc2MedKODI8dL6AqNqosIAsBXigT+gK3r7SxPRU7iNiW03nXJTvYlsyQNzRGpFFEyeZkrcDJWoofHB/iwOwo982rfPVgj2ftK/CHTygTERPHMc1BwJ89YYpLZ3IMmwYlzUCNJZodgSeOlvjkc3ays2Tw5h/MccWOLI4fAfD8swrsHNFQBYWrn3Y+pcS/Rw5dOJViZ8ngT580QiEh875bV3jVdXN89USVvu/zvZNN5lo21x2u8rG7NhhJ6Fz/yl08cSbLOeMpvvyynQyn/+fOrLyp8JiJFNe9dCcj6dOFsA9X+iw07f/hdaYu8O4f/nJBGz+P/7572qPFr3WqX4xfudSztbU1XvOa13DDDTfw9Kc//SfmOp0OV199NZ/97Gd58pOfDMDnPvc5JiYmuPnmm7niiit+4f3ed/syc12RZNIiCFTymsmdSz0+9fRzedvtj7DZisik66R1n4Jp8qNTLm98/gzHa4s8f0+Bzx2sIzgKUwWIhT6CKLMzKYLgYAcCtpcjg8HSBuTSInlD4puHVc6Z6rNYz+Fjsa0AQpRipSOQ0zQmsh65ZIVxV6VuKcy2fIq6xj/eNEw/DpnKylRaoAoiW/IxVtwlpSi847Y5eoHK8/encY0OW/ICzV6e+xYFpgplFr0+oSCQ1GUq3oBEoOJhcfa4zU0ra/SJSMpZFuoS+/UyxwYttmRV6o5DFOkMnCRh7IFfYHlgsyWdQJIHqH6BufYiF0+BLDXQZJNDmx5jyQS3zcFERmHT6mGGBe6Zd9k1PEzpWS/mgskBH33D+3jl219FSxCRoyS6ErBnpIPlixT0Frafp5xZJPDGiYUBcRziBRoRLk/bKnLj3AA/SJM2BJB7nDOd5LXn7eWv7zqFJibZ6HVIyBoNOwOxT9lUOb6mU05bPGufx/FqkqxZYzKlU0r3sTyPIMjgSDKLrYjJbIQfeTy0FmDICZSoSH3gosp9prIyqqQiSA3Sepb5ehpFVKm5LabRyOkaI6k+uuLRtSNKiSxDnkLd6ZPWJUwtwPMMgjDN0VURgQhDk5DjFIoYI4shXTdmoythKApOoNOyRRD65HWfjC4SI7PUMGnZUE56NPs6cSySNQViISKhucgiDCcHRKFEImcylAgJhAgngPVeRE6z2DHcYzrnMZkekFZt1iwDWfIJQxHXUkibPqoK1a5G2xVIJmyWmxK7h33iOCQCnFBio2dSMEP2Tg5w3ASFhE3acDlV1ag7CgVTodnN8ISJPKttgQdWB5QKA2pdjbTmEUYytp9g4LsgOsRCl3PHdNqdmKSk8cIzyvzFzWucMRkxlIoJ4xCEJh3PYjIdYUx3ObSeoNHT+dCV27jxeIezSgluWFklsjOklDQp3SJh1hmTRGxbwXGyrG5KYCXYOW1ysBYxmu9zTjHE8QZ89niKLUMhj9uRZq0SIsgutmjzo40+1X7MdYcDGpHNUyaGeaBS55yhcT57OKTgmwS+TdQYJ61LDAKbp50jMruu4Nrw7ps3OWc0xbP35dnoeRyoNEkaSR5ed9hVKDLbcimZYAcCfuygRFkqloDnFvj+AYN/fu42vn+oyr/87R+j7djJ+IW/waFWgE9IpZbkiWeuUUhEnKwDsUlS80nrIifXRf7rLesMlz2a/QQJLSQSe2wrxHQ8m9FkgtogIqtLnGwNuGQmxyVTeb4/1ub+2QHLzYBSXmPN6bJFKROqDRp9lSCOuHNBZFcxS2x08QWH99zR4ub5JqWEyPw6bL/UZFJLMldx2VLM4WgOET5FUwatwSXn93nmxEXsHxpmWzbBp48/xGpX5Wn7dB47kQJOdwD53qkGb7hwgsWmw59/f47dO6tsc8sstwM+dkcV3xMpJiR0WeSahzeodUIOVnsoikBCjukGFlld5LzyGN+db7A1r/HybzzC3GrEx5+3lYv+8SA3vWovv//dR+hVDN5y2RgpQyKp/WROvSAIXP+SvXzywAZ/f/v/nlau/1k89alP5alPferPnIvjmA996EP8xV/8Bc997nMBuOaaayiXy1x77bW8+tWv/j95q7/m53DOuMkTt2b4+P0byHFMQZdZ7Nn89udn+dgLZnjPldPIksDzd5c5Wbf40UoTK/DR1YiZnMnTtp+uXTHbsDjW6HFgzeLI/BKXjOe59tAq6bRHWlPRxZiOb7GrHJJSDR5e9nj9zQf59DPO433PnOblXzvO03aU+drBJk4YMT6ko6sBrzijzL/c3+TJW7P8xv4h3vqdRT7wzC08b3+J86fS/MG3j9NzIhKqgB/7NN2Ichr67oBKW2M8r7DeCwlcWGx5vPeyPbz+27OcnR/lcK3P0/Zm6IcOP1yuc/G0wqXbRvnqSQ/b7VIutEjqeVbaHgVNpJB0ua9jsss02D85Tz5R4OimTKOvkdF0jto2500OWGj5aIrIvQsWf/+Mnfzxt0+y2Gyxp2xiqF0mMgkeWQ25Z73LwWWf5+4u8Z3ZdYyURVKVqHgOGikc2UaNdUIabCuIdLwehiIjq02+esrnwHyK4bzK9uEGU1mVQeAjRAmSqR6hb7I6CDlr0mOp4VOW0zx5ZwYjLnHNoU2G8yIVO4KUS1bx+eg9a9QHAbOdLlduT3KwVkOIVeqDiJwBCVWgWRPJZlWWWhqGBoVkmyC02FVWeXBJIhWp1HoG46k2o0aMKPaYrw0TIbDZFRFcjTXHo6DHSKHChjPge/Mun3lokyFNYk8pzdkTCf72qVs5sNblz26ZpZACRdHwow4zBQFJstnoq3R6JgeWW1z9tN189fgmy0eahIZDEMPnD68jxBF5TWUzVKj3ZIqaQhxIpLXTf2zX+i7EKprmkJZBU0PKKR9NkmnYIWEEfqizWMsy8OC3dk/wmIzJ322u8+y9BVRR4pVfPokmCQQCLLdTzG8kOX88jamI9K0WaCCKArYrIyGiyTGiFHHuZIP1TkTPlblodxNZlBg4Iq2+wesuLJExIr4zu0l1EPC4iRyjaZVQDJkqu6T0CE3vMiK7CEmZrhdiuRKhezqFzw4jKh2NvhPykSun+aOvzrHZdXn+eXle+51TBAEkZYU3f2uRrCFyd6PCaCYijGGoEGCIMrLk0A8jTMng7nmLe6bbRLHAxVszXHekyu5yggOLIgkjZNmKyaUiTgYjeHHAwYUCW5IZdo+mOditsz6wCYIIUU9ipGtsN2Vyhs8jGwkKhsbxDYGdJZVnbCvzgR+uszfpcqirkEoKuKKHqchUOiCKIgk0Viohz9hT4E8eNw6P46fatQ+8EFGAcdXAstuMphWq/YCRlMxmK6aYkKjbHpNZHQ+XyVxMLFrsmZCZb8HBaodKO+bi7UOc6tTALfC0HXmqToeT7QoZPWbgiay1I1wv5mXnlfirG5b50ZzLeLHInB8ybJZZbboUsj5XbM3xlsdNcePhWf7y6wfZmU9Tq8NTd+W4dEeW8YzGLfNt3vHkafpuyErH5ak7/70e0LXP3cttpzq86evH+cLLdlBIKPzRRaNo8s+O4WjYPh+/d5ORlMKTd2Q53OxSlDWevD2HFUYs9QZcfU+VF50xxEeeO/Pjzy/3MyKA4CeLb/+3CIJAxpCp9uChjR6vv/EkMwWFf7nqzJ95/tcfqXPLbJu/u+qnO5j9snz/eItbTnV428X5//nJ/5fwf6tO9SsVURRFES996Ut505vexN69e39q/sEHH8T3fS6//PIfj42OjrJv3z7uuuuuX3i/mfxpoZHTNMpalqxs0ugq6FKCD92zzPp6go31BIP+CI49zG0ndUYnenz7SJNNv8ffPzTHHz92DEWMONbo0/ECTBmGMsvoWo+ELDNf1bBCiekhjaF0TKOncvaISKuXZq3nMr85zErL5JGai6mEBFFA03X/9Y+dgCaHlPQ0RzZNKoOYkmpwaMmn60ZMlR0Ms0cpoVFvZtnYVJhJmxyseqz1VKJYwNQ77B8BcPF9g5wmc2w94DsnanzsgVUEP0nfMYkFl7ym0/I7pHWRxaaMEmdo2BK5RA9REHDDiG2JEn/x+B1MZUxW+haqYNBwAvrWMBVrgCjIBKHPvjJ89WAGIZRptEWKUp71aogUmMzXJSK7yIrt88w/eAHv+6MPIndjVvsuuiST1Fxyhkc57TGRbRKHQyx1PSQMiqkmbigRhCqHahFb8wK6PqCcCpFEmYW2y4cOHCZldsmYNhJpBHHAZCZiJOUzklnnKbv65E2P2xc0thd8zhhrktD76KLBwFXZ6EWstoZo9Ey+czDLQyfKuIMyK12B9kCg3s5wvJJnueOR0QRSisFC08AKbbJGjCZkiEQbXXYIApW2lWHDBj+2Gcv47B0K0JWQth0RxS5DiZDdoyKy1kGVLaJYIopUFPm0BwxxgK5YZPUQVUix0ipwYE0AwWNLzmLPsEsxGdO0QVUt4jiiYxvUeylqfYMIjwPrSZqWTFY3SSpJenaKKDSp9zSOVhRiHNJaH0UOqToaR6sGn7pvgtvnshzfSPHwUpbZSoKmJWKHPsVMHUlyWGkZzFayrDTTBIGKJIjkzZi9IwOG0w6SFJFLdEknBgSBgiLIFJMh63YTT14nlbIIIxjJ2iSNiKGkx3jOwlAtRtNVtmRjtuUhn3aox+tcO3s/WRUIFIrJgJ4bI4pdNNKIqJyqqwx8H1UOeLDSZSyvcONsl15fpZSPGMo6jGTbjKY67CoKTBfaHG/0uXA6wV1/eBZvfvwErX6EKcmosoAgDzhjxmJb0eRPLphibeCSknTS6TqZtMX2vEjPjbD6MpfvyPHKM7dy/eEBnpPggVXwAh1FksiZTQwhwZ2nUng9naKiM99yePljShyv2PzOl06xPZPmZNNlIqXh47GjZHPeVAtVcgn9HF86kGJ5s8w9Syr/9elT7B9O8PVrP8XQWZdROOd5DOVC7Oh0ClahXEeMExhqjwsmbGSly8XbLNbbAqqk0LVDrpraTkILSRoehuojSh6lpEI22WMk7WPHDbakk1y1dYJDlQF3L/RwwxjFiFnrBBTEDJs9ifYgSd32cKOA7cWYk/WY7co0Fw6XKKs6MhGrTocrz06yr5zgiy/bzrnjSXYXk/yXJ0zju3B8KWBLTqLpeLzzrmPcvb7B47dkmJCmyakyx5s93nzLKR5ZH/Cn1y/xxefuI4hinv+p48yMibh2gb4fM53VuHxHBk0WUWOZgq7wsfvXedGZQ0wXVJabPic3Q1JxirdfeCY3HeuxfyjJn99yikdWXRRJ4KuH6/zOeWXSusLbL9rJKy8os9n3uWuxx+GNAVH0k16uHy62uXG+yTkT5i8sg/5nxJFA9Ci94kex8OLCwgKbm5s/IYs1TePiiy/+pWTxr/nfw4rtsNh2uO7FuyhJOpvNEAGBfFbgkY0Bb/7u4o+92cO6waAvkVVN3v2knUzlRD57dJF7V7q8+muzfOhHq5w1YmAGBnet1vE8BS1O4YQ+gyAiDpKst9OYRpvdYzY4KQ6tOmQ0ib94whT3VasIpseOITDNAYHg8ZlDK1w5k+O2Ez0OrQ8wFImP3rnBg6tdxpIql27NMpQC2w8ICEmrCsutmFrNYDwLg8BmsugyPRRy/Yk17l/r8rkX7mJj4JHTFD5/oMaBhYCMnOCLD0bcsjzLRk1ldjPJkK6RSXYopSMSpkco+Dx+pk8+VWe2VkaVA3YOtzljbMBmP8B1UhxeMUgoIqGj8O2DXW481eaPHzeFYA+z2FTQRJmmLZJP2yQVAUe2mW1ZLNYEfDdJOtlmdymg47qYQRZVkjm5OcwjmyaVTprNrkwcKZyq6MRCDELEajvJ9qEm+0d6PGvvImePtZkpNthXdnBCj6QmUWvH3L0w4Bsn6pw3mmC9C2NpgY4jkTUizGSLmZzOi/aOcP9GC12W2DZc4ewxm2yqS1oLCbWAM0tpfmP7DJ1+gdVmGT+GvGnz1L2bnDO9wu6Sy1LHoNo36bsGD6+fbgtft2Qu2ZajJKdZboiYivivxYkjCrkI1+hz0qnyxSMbSCLcMTdAsDQ6XYHQ1lls5Flty/SdFJ6vsNTUMET4rS8e5661OqViSBRLFAyNgq7hWDJDaYt9Y30u2NYgkeqRzNpc9aX7ePX3H+LulSbXPfscZCnCVCLKKYekPiClewynPJJ6wL5imvNLE1QaSa4/bPHNY02iGP7+3lW+dmqTx+9SqQo15ESD3VMdkimP+XaHhyodTDPA0ELShsee0TZJPcBUg3+taeQwkbUopmysQCZGwtBCclmXb8wv8bmjS3zswWW+N7vJ3969yGu+f4L5loMXKPT9iDgUaVsmJzbT3H2yRBhoBJGEqHukkj5O6HFs2eNVXzmOqQtsK5r0vZDphEGzHfORZ23l8TsS3FVrIEunZdV4ocnZk232T7TQDJvY1bEGEl035Dsn69Qtj4/ctcYV23KcNZLmMy/eybQ2wuyKyUYjxclqivsODfO0LaMMopDRIREpFpAjn/GsQNsLyWghM3kHU7PYVXbJpVqcO9NkomBx5dYiz9k1RMnQmChK2FFIUvcZzbp4Qcy+QoYpNU3KkPnmsSZHqxZzLZv6wOO7x5r4YcRC0+a51x3gDTccYa7hcuF2g05gsyUPHhYjuZBuELC1EBHLPSYyIZo2YGClWGiBGRlsdkIu353iVCPmvMIWnrazxN0rdY61qkxmIqptiVojQ7Mr8rYfzvKh+xa5s1InnYhZG8ALdk+wr5yikJZo2jGPtBq88aZjfOVwHUNUOFLpktZFvnq8xqHNAWEUU+8HfP1Inb1lky05ncWmTc8JuO5AjT//9iI3nGjz6Rdv5cBGj088sPFzjUQADSvgGbuz3LvZ5cvHKvz2/lEEAa47XGG2ZfPqc8f4s0sm+IPHjfzYoBNFMR/90QZuEBHHMZ9/sErPCbj6ngofvm3jZ+7z7SNNDq2frrm40vCYSpl89Mp9P9NI9IPZFt842uCDV239pdLNqj2P13xl/ieih7wg4jtHW7zrqZO/8Hr/KzxaetX/KzrVr1RE0Xvf+15kWeZ1r3vdz5zf3NxEVVVyuZ+sol4ul9nc/PneXNd1cV33x8fdbvf0enafWNGYykms9S08TyGjaGS1kOP1Ln6sc9Zokv96xR4++OAJthVtJDFLqNu8Yv8YP1io808Pn2CzrrBjQiWtxrTcLp31LGcMR9TCDXyGScp5Vts2mWzAeEbFtQW8ns6uYR9d6XD7sQJX7utyohphhy453cCN1kgokFWHmK1BwRB5/PaIE40+w0My5403WRwE5NCxfZtUQmIoYZA0OxiKhozLUtdmLKXR7HcoJDSGkiFrPY9zp5Ms9drkNBMplBGJKagqs2GbETODHVXRxSS2KyMIIr0whx+3SSllFqoW1x9osdztk1FNFhohGU3h2IpItbuVF56zTDkJJ2sJLtne4Oh6AdMQSYk6n3vhNO+8+yiSBMPpNrGfpbTV4Z2feSsrzRZzX7iL+OnPwpDziMoGIhq6EnPLrEHRkHhkU+CM4TLJRAMpyjFsQsOxOGdU5o6ghxyUSRsRq12LC8YlMqlVKt0iUWggSwuMpYqEccytJyVSSgohdmhaHnKUxgok5touYpglDAMGnk9GUZgZ8tnottDjNLGnsWt6g/lKmpIho8sRQRRx/1Ie2ze5ZznkzNEB+8un6+gkNI/GwEUmS15JEQttdFGm7moQq+R1mx/OS+wsOJw5GmOFPlHs4/sR5ZSIIgrYgcyBpTL5ZJeJtIChiAwbKgvtmIY1QBZ0xFigqAsIUcChpQRTQyEjqRhZEogijfZAJxZ6LLWK9J0OQiwThRJxZDKRCtjsS2x2TNKagCL0mUq1yEw0uWtRoe3KFDVIiQKxIBIJPtNDNot1k91DPuWkRRBpNAcaHUvB60mkdIfHTPQI0z1OVtIMvByy6GMNdBqxhiQJtAYhCCJBKGDZKuNZh4nCgLGsgx9CFHsU9BBH1KkL0HN95uoRe8gwNWRTSPdw/IgYOFkr4TgZjm2kEJUKe0cS2LbED5eXIYaWKzBmJLB7AusOjGR1enKZtBFzvOZTVERqPZ9b51s8firNxdMmh5dlsrkBnl1A9tNctqPAqVaf4Sx0ejFdUSVFiiPLApftT/DmiyYoJFQOrXXx7JhKP2QkK+E6UEhXaXSGeWAxzdVX7eChZYu/vWOFtzxpjCFT5vJ/Osp/vWqSJbtJDwFF7TKa9kloATUrZEsuYK5hMSJv5axiHlMasH7vjVz+zgd48ov/kFeeX+av75zHDi2GTJ1lu8OomMLQajhuCsFY46KxApWBw3g6wbFNi8/95l4+c2SBlAYBPSYyAnYQsqPo0LRkNnsevpflkU04MRzy0rNNXnP+GO+8bZF3XzbNB25bJRZ9ioaA5VuM5UQWOz5hBMNigntmHf7027P8cLbFyISN6OjccKLN07a2uWgyzceetwVNFhFFAfesHXxSP8iJWsh4coy87vFwtYLom9w4VyVXtMgpOn947hiv/voJ3nrZGENJiT+8fo7hIejbIpKoo+sBdWeAHbg8sNpjsenx/nsW+YNzxiiYEvObLmeMKWx0AiptgZtPdBkvSFS6HmYiZDInYUQKb33SGIc2B3Rsjyu257h8W8ybv73Ebz+myIs/e5wvv2wnhioxkdVoDnw+fd8mf33pFC/77IH/sAz87wljEB6FNq7/thb8u/z7NzRNQ/tXT/z/Kv8mb8vln2x7Wy6XWVpa+uVv8tc8qrzw7DQffXCRNz9uC//4G9t4y7cW+f3HlUjqCm+7YZE3PH6Mlh0woqj8znllHl4dEEcxZ5UzZFSFP79pjmZBY9+oieV43LvaoOUm0fwE2ZxLxQ6QXYMzxxwGgcNmN8FwlCMIA1x5wHvvWCSlyqw3Q15x3gR3y8tk0jXCWMSxkhxZFbi6tsnz9hS4cCrJY6dTfOKeCm/4+iI7x3TGTZ2NqkAqHzKcjJH1NrGUwMhFbPRACRLEukPbPh1N8ME7Vrh9sc27nzLDDfMNHqn1EMwGCz2BobTOQ2sq+bSFHOqs91zKSYGxiU2ObiRQQo2251BMGARxgBWISKg0HY8n7Qx4YCmi0sjS7kW0vIid4yI5QyaII9Ydl7SQJG/67Co3eGhxgmpHZEtB5VUXjPDYGYPrTp3AcRIk9R6jWYkTax5nGFkavS4rnsF4JiKtuwxsnS3DLTZrQ3hhyPaUhx/HlAzoBDFCpJEzHDY6KXIJF89XSeUduqHPvtEsD1UbJESF5bbHVEZAEDxyusY/PXKAyZRGTEA57WKqUDBbpN0ENbmJLA5xw6kW9630KKYU1lZSjBdklLEq23OQSwzY7ImUkvDdYwV0MUffEYg8ibyiMJE1cLHozgd0LIHptEmjE3H2DoPF/gBJq/Pk3Vn8MKbp+PzGeWluWltjYOkodo79+TG+ebxDYxBhRyEpKUZPRiSTXUI7RaWhU7FEdkxEaFoXQ3ORdAU5jLB9C0OKkAYCgtQla2ZZ6zmst2V2lx0KSZ+1TgLLMgl9k2fvLODHAieqDrEjUVVc8umAbVv6VBoGAy/gh8sNposWrpeg2jZxPRE5Ugk9GIQRUiJGFUUW6gm2DXUQjJCE6lHra/ieRN+RWW3lGEsJp9MobY2OFSBIYAkuexI5GraHH/nkUz6aouD4Imp3O5PFAduyOV62K03Hc3lgs8XJ7jI5XQQsGk6C0SGTtu0w1wg5WhcIQxASHtc8tEm152N1RCbKMoJko0o+jp+k5sQEgchU2abrhdAxWOiF3PNgk1fsnWBXyeRo1eKaByrIgsTOKZeSnuHWI6ef1+FOlVpg843ZDl7sks3IdF2ZkWwbVVJpOBaeb9K2JEazXVw3wzcf0plWq4hAw3VpWjo7JhukFJV+4HPmpMmG5dLrqbixyFBK5YH1DgdrffZmUnz2vgbLbYcbVupszyc4tOwznbNZ6tlsHYtxwtN6iB247B3xKGcclptJFpoKpiJTa6tIkYgnO6STMrcsNXnj+dNcvq3An91yBFfsUNBlHlqXKOo6jX5EMS2y3gr4ztE2+ZRNEEu8+byd7Ciq/JfbjmGHIVuLAs2BxWI7ZliJaPdhOpXGIeKTz9lB2wl5weeP0Q99XEIypsin76mSURVEoDmI+PMnj3H9QpXf+doJzhhO8vZLp37ub3kQxty90gUidg8ZfPDKbbzzxhXWLYeMKPPZ39jBTMH88bl9L+SN35zHkEUeM57mv3x3mVJaAUAgZqnt8FdX/LQh5v7lHtcfbvLR501zfKHFh+9u4ofwmftqvOKC8o/T7v+Ni7dkuWQmiyT+cnrKX3xnmT+5ZPQnjFCqLPIPzz0dneT+vAv/AzxaetX/KzrVf5qh6POf//xPhFJ95zvf4cMf/jAHDhz4mVbL/xE/L3zu33jPe97DX/3VX/3U+Dm6xNhUg2pfIKEp7Bs3edJkjr+5bZk9JQdXjNmo91ne0HnV1iJ/3z7ORgfW3D76QCPlumREgZlhCPyYnBpjqsbplKFqgCmmuaQQ8dBsn2lERrUIP6qR0GMelx1mw3MRQnjezAAt9NhrSCRVkY2eRSgaJEyRzqBDiSQCAo7UJZfRQfAwIo+nFKFidTAiiZyq0nZdir6CGgtkPYuylKTe7TElgyoGWL7EcEJltm5xbhJi+jSbGmeUXIqyhSmoLLUHZBSRkWSbnqsieCb1jktWyXGqEbA95eJbDd64u8gXHqkzpIgofkwxFROFAnc8VGJ3KcSLIkoi2FoPRY2xXJtP/KDP684a4vrZNTRbBsejX03jSSI5UcdMtVi45ktMvOSp7BxLIvcGBFGSGdllfi1H1hCwm23SboGE5tLqJAgGMj1H4ixDZK3TJ6sL6GmbnQmH5iDDwPGYyHok3Dz4PraX5MKcxa0HE4wOaTTXNT5+zOSJMx7DeY/1lgD9BANPZaEuMZxSELwYI9mhlPTJOTqF2CIbaMidFGmlR4kmbU/h3IRK0AkQdB85EBF8E2fgkpQ9hhQJOTbx/Ag18PAdlUGQpeTHrC4JPHGmix9BtS/T8WI6dkyISEEPyejg+iJ+28PUNDKCxHZZxbD7tHoCWT1iJA5IxkledWHhdJoBIYWsT8KB0E6SD1yKyQa+n0BCxw0VepbEsCiSkASStkTQ0lm1FDaEFD0fxmKZpqdgCoAUgRgDMVNDNkIBVFHE9SUGbkzs+bhtlSgUKA4FbKwZhJHCqOgSuwLtQZJyeFqRQ4iQhIiEHpKMBSQ1Iu5INLopEiMxPUfCUESONRJosoQaeJyZabJdS2I7Weotkc2uipCRKCY9gn6atVqCXi9gLGcwpEj4Yo/RjEu9m6aQk1HEHquNiIvKDlpfo+2F5IoDtsoiO0o9jlYsqo0k9x9XSTkBrb7DtlyWi7bn2ZXPIAgh9641+K2tOg8tdlDVPB0r5s3PLjCUVKjUqnzsBw0a/RDXszgvD6bmEftgdIost2TGRZvvHTqJZYtcOhExozm0mzU++vQ0Hz9wmKwOZmyzN9/Hc01aPZsJ3WRtUaHXGCfl2qhum9Y9N1FLevzzO15HKaVz4/wSk9oGZS3HPXMW5+cVul0fpWOw2eqzo1Cm5ofYdorGpsjVTxrnH289SikdogoDthZCgriLoaWJ7YCUIzEchwSiQyapcv/8SbI02ZlO8dvbVb597wLnlFycCPqOz45iyGwjZlxM0XYEYrXPaE6g2qtwyRaFvu+xd7pDRhhjpbrMp1YDnrl9jCXL56GNHt99pMOW0QhVlvA9l5GkybNKJVY7Lc7NBgTigNqGzCdvPs5UHGJ3G3znwQ52q8G0JlHMSTxc7SG0RPYWYaXpkZE9nj4jUu1FHFmd4wt+m30Fm21FncfszXP7YocXbpeZ0TS+d7JJT+gThrAvX+DY4jp/dcMSBU3hty8osXPI5E0XmNy1uMl7npTi7757lKfvyXJcE7nheJO0F/GJ24/xx2cn+b8h4Wpi4idrKb397W/nHe94xy+11n8vd/9nsvjX/J/l84fXkJI6b7nlGBePlNkYeGzJm0zkNHYUdd5/3yJfe8F+4PSzfMz4aUPN6799imOtPj4hNy3WuXJHgTdcuYUP3/ogpaEE/TDioQ2PpK6Q0QMaVsR4LkAIBQ4taxiKwiVbTTY7Ioc2HGbKJkcqNifXdSZGsgxlHCTVYvtIiBGVQII3fG+eginzpsdPMJZWSesSkihw73IPgpBe2GVY1Ag8mTYh0wWB1UZA3xPZmtPYls2x3mpydN3iqi8c5i8vnsCIZfpexNmjULP6WK6MLKgs92FCMumpbWYbGUqaxFpTQkSm0omJgS0FH1XtMpVJsNSW2Tva45BtUKlJjBZVrthS5vMPV1BMnxfvHeG6o+vMN3TCWEPTLB4/M8w9xwPe/oNFPvnsHfiE3LFxgo6tE0UKubTFwZrIIAwYy6jM1iLagxKe4GKKOVaaAuPpJMdqAkFc4pHIZns+zbGqwngmou2G2FaakxWVXeUQN3bo0WeqGCDEMl4UYtsafqhysqZhKAmCqMG5EzF7htdpDbLIsksQyYykod4bcPZUhkNrEYrl4ysehuEQhSoIfXRZR1ccav0s5bTHyQ0NQ9CoxRH5pMV3l45R72sUcgKSbvOMXcM8fdsQv/+NWSayJjuHdnLV1jKqLPLqC4cRRbhoPM8fXT+LIoZ88kCTtO6QMXT6LZHh8TaKoLLU0vmjM6dpTscc2OhzstYil9LpOg6yHmMqIsWkRxxFTGkKGz2JG+ZqHK64XDY5ygOVKgNXIZPqEoUi+bTOQlWkbQesdkNiI8ATAmJEvEBCVgMERcDxZR6cL5LTTxsEr3vJPgBunq/zvjsW2T2iERNxvCnRczXatoAgOay0E5S0Iban87z3CVv49H0VFpsu175wkjd+e46K5aDIPkt+DU0XGTUdigkfU3EJcag24XAj4t6TLmcVbabyGk/ZVuKG7/ew05BKN4mBwxsOrUHEM3fmqfYDHqy0GU67HGquUncjtm4JyRsRqjagYydRJIGE7pA2FMLIpzrQKOiwsC4RyQK3rrT54tEaT5ksMFt3aEYuF4yMctPxNkbG5+6NOopiU05Kpxu7yAEqAkljQMYQaNkxTmCSUiX2jdboWBlONVTSesxnHqrwuvPHSZPk3uMWU2GCoaEKO3Sw/QHfPTKMrEh4OOwci7j22Bov2T/GS/YN84w9BVQZnro7R1qV+drBBg3fo73ZxA1E0maAKCoYmkdCEygkupgyfL9exOrruO7pVLAjGzaXbDW55mCdjz+0xmMnM7x4zyTvvN0jCG3OLid44ug41zy8gSf3eNzWDIcqPpdMjfHlR5p84fhRECO6QchETmF+U6OUliDRIxlI/MHuUb55osnfXbmVoaTKjxZrrDs2aVPkDedM8PWHW+QMmY7vkMuFvPeKrZRMnc7RgN99zCjP2VP8mb/h1b7HUEKh74VcMJ7iO0faXLotx4d/tM6diz3Gsxr//JztFBIqJxsD+lbM2767zJN3Zjhet/jAM2Y4umHzpO0ZrtiVJYzg9755kosm0j9TVj9mMsV5E0niOKacVnnPldN870gXL4h53w/WeNG5RXYMGT8+X5b+Y/L+Ey/a9h+6/leB/7/rVP9phqJnPvOZXHDBBT8+/vKXv0y1WmVy8t8tnGEY8sY3vpEPfehDLC4uMjw8jOd5tFqtn4gqqlarXHTRRT93r7e+9a38yZ/8yY+Pu90uExMTFLe2kNMmCcNEsnUqQYbzdkxx0abIDxbqqIkB1cjk48c7fPjKPbzyIoO/ufsIxzdlfjQnMT0TYkRpFhs2caxyoSkjx11MOcLUdTZ7KrEUYI6qXDQ0yr8c3iSfg7wkc6JX492Pfwwfuv8Yob5J0RQoFWIU2cEoyvRdHWNoncwgxfqmwFwzYEtG43BDRRYFzi2IDE8s020YLK8VOVIPKeoJNLWBroKqZgjUJqqmoAoWg8DB6kwwW5fJZzU2fBeHAefusehLHdJBAlfx2D3kcqKSpGd2SaWzVPpgGgaNoEkrnmCuFpORZR48YaOmQtYqGcJA4LK9KlduG+E9ty+wst7n3MmIQdDFLBgcr3oMp5MsdPv8zT0qjcBg35hKMlVhU1DRpRjbVyg98TEcTO5gyU8TnryByy89E0lsc7g/xt1tncdmfXpODq24QTJl0vE7uPEwvhyz1g/I5HV8pXs6dDMTU8htMkQOUekzkmwTxmmObri0xGmEcsTdTZkdBRk3PWA+EjmyNo1AjGjbeFoCbbjL4WoaNRRJeAY1b41MWMCKYmLRZrjUJEoYpE2HO46KaKqMgEQrUEiHTcbkBGoaGoFLpJ1W2MJAoWmHPLBqMmyk8D2RU6sRu3f2MWSJSIJAEtASAWvtgFDREFDpuhIrHYvIN9gxJJMxPFIpBVWuM98exo41fNHgsyd9/vxpZ/DqGw6zVVIZSYekTRcjFTAIfOZqEbEvIwk6VqDg+zGa6RAJCoYaoyciJFEiCkCLYnQlQpOhJwnYnoKmxkQpgVpPJaWKpJIRGcGm29ZY7oosVDTijEpGlhBlkXK+T960iZsGXqDi+BJdR6bS05kxQgaOhC7HJLMesRjzcDeJqboUVBUjHeETMdsAVdbRjCT1QOZhLyKri7QHafYbGaZSCb52osZ4uYaa05ASLivtAXKcxtFV2kDPUdgIY1THRJf6+NIAJUygaCE7S01uruV42r5xrtpRRpYEjlUH7C4lCKMYyw9JqhJXlUq4foAuyly0ZxJRFGkOPN5/7wo/XGhyTjnNiy4c5v33zrIhCAhxh4lkhgd6Iisdk91Fg6edOc0NxztcviXFG74xz2NnEty+XmNrQUXSHVxcHmyVcCOfUlJlsReSEsd4yRNLvOmLP+DYfffi7Ho+UzvK7N82xZ1LHT50tM1v7duKKRkMUlVur3tcvivEHG5hRiJHBjGmkOZA0+Dl5+xix2QWda6Kn+gymfXx5CpqPMSRukZG1XiwGTKeVDh8IoeREEkAf35Plx1mzGsfP8mauIGoyVw8NMxX5o9yyoaulOdYW8FAYaPvEhAxnkuybttszyXp6R5Vv4bTz/POx5+N68X84XeP8KbHjVOcDKjhYPsyRV3jst07eNPds6yuSQhpj6Q7TC+SqfdiJnIqL33idh5c7+MnAt71zG2kdYm/v2uNa46s07BD8ukkgWizGIvoKQU1JTIxVuK+fsAfX7Kf5Y7HDT/qsGda4J239Pnsy3bzgQfmOLEacKIacet6i0hO0/QjDnVVnrC3RBzHnJp1aHZ9xkbLtBWVj9+zQSGh0g0j1BD++jFTj7qh6N9CnB+ttQBWVlZIp9M/Hv9FPV8Aw8PDwGkv2MjIyI/Hq9XqT3nEfs1/Ho2ews6ChiuEfP1YnTASePP3ZglCgd+7YIQDtwz4zqkGL9x7+pn95rlFbq9sMDto4iOR1STaUci3TtUIopDdiRwHWxrvv3yMP7npBBWnRc+VkEUDRfBQ1QGFrMSokuct5+8E4MP3LFFrxbzryine/O1FTjkDWlWdrUM+ghRystOmmFB52vY87/zhMvOtAe+/coYh0+DulRajk23aro8UJmlazulWzxi0BwM0RUB2TQ5tQDNr85aLJvjrW9ZIhBJvv3UBxJAJWceL22wtBtieRoCNoaap9mJkW2UyI7BQEcnFCo8ZHudzxzYYywScrMUMp4oQOYykLR5ZLeHKNqNDKerNiE/dWyE2HeyKSMVexg0kAjfFkQ2X6XxI3d/k1ZfMcPnUGKIocsecy57SbqLI4bOHu4wNewRhyJCqIqottiVUmnZIRlaZW5GYzGlcNCMy17XwYpuErLHccRhORRSSHRQpxUbYYc9wnr7vUjRlBNEip0qISgchNGmIfeyBgaDEiLKL6yWYrVv40QhpvUtRNtBkl9VqibaloegCLzs7x2fvabF1LGI438Fx0zy8nmexFVAwA5Y7IglFJpNw6bVUtmZCNm1IigX63YCE4jCcDvne0nE2rT5XP3cHf/6DeY7WLHRZ4s++tYTrR5wxZrLUtRBleOq2HFXH447lNiklJqPLrLcNxjIBM0Mut65vEAYiywOfSluC2ETTXLLJPqKkEsXQdmTkWGWxppHUPdpOn6bVZSjtoCugSCGi7NMYuNy7WWFUS3D2pMmFhsH99ROn6xsJPuV0gGUlcV2FhOFDqOK5Inctd7jhWJtq38PzJDpWgCQKOJbGpgBSFOFGJlEYs9H2+eGSRU6uoRswM3w6kuPCmRQ/WvFp+D6qoBJEMY4v4fgCEFHpJSEU8MIIQpHJjIYiiHzx+DIZSaE/iAhljUSkEjgyaSPkrrkBThCRK9jEsc5GS8JMDSiYMdnEgJ4jEPs6s1WRy/e2MZSAlhuQ1EK6gxAr9hEjHSsI6LXgxkGHgeBw5oTBrUtNWv2YYl6l1g3IJFQEBAI/pKAZNCKPONKJwhhJDknKGgm9RcdOUrckbE8iY8SsdHwebtVpt+uUczotRyClRDi+zmJLYv+Ihxu1CYIk632JV541wbN2lFluu3z8vg1WLIvXPGac6xarPLDRodL22TshgNolpehMFuuIsc5aN2a9OcxsUyCn6KxZEWePJbjhVIstQwLfXGhQSpq8cO8I7791jbvnesiywkvOKHHXvMe3Ox3+5sk7WOy2uXV1k1dP5bj2kXVG8xLrPYjdBMmkTXUQMz7kgeDg9tO0XYfPHdrkXU/eSjmlEscxz903RD4pce5IGi+I+cLDNeqxzWRRYBCEvOeHq8RiTM0OeHij/zMNRd84WueaAxWu+Y2dZA2ZK3fkKRgKH/zRGj0voJyX2Tei85VDTSIl4LqDVfZk0nzuJduZazlEYsSFU2kunPp3eS9L8JeXTLIlZ/zUfkEY07YDikmFMIyo9DzO31rinLE0XzxQo2b5fOnhOm97yqPfuOPn8YUHao/6mo+WXvX/ik71n2YoSqVSpFKpHx//3u/9HlddddVPnHPFFVfw0pe+lN/5nd8B4Nxzz0VRFG666SZe8IIXALCxscEjjzzC+973vp+7188LA1vvhezNS+haB6QOB+cF3nSjwO0LHaRYZNxMMlUK6QptPvLQPG94zHaesX2Yb7JOQkljiglqrkNKF5nIQjduYPVkdqkGkVCj7mbJaSZWFFEoRDxjd45H6i3WahL5XIa/vvsYJ6sx+4UcQ2YLT2igCQlyCRfEiJ5TZjrbpmb1kcQhjlQDHGy2ZTT64YCNXpqsKmGaA8pmkcVmyGwjw+8+tkMg9pElk6Yl48UWY+kM1UGNpDbChSNlRMnjodomFbfKeaMSc/MCjp6g6yaxI5soGEIw15nKFQmiASvtNK5iI8UqgS0ReRrP2bmTv11f5eySypLd5LOHPbp0yAU55ioDZoazHK0NmMqa1C2PkZTBLRsWu4s6dScmaeQYzR9DE0rUBxFLjQRRtshq0+ae609y0zeO83tv/R1umDXYXRJxAvBDk5O1AppkkVPTfOnBFDtyGpbQpJyymMpHuHEFKyiTMboU9TolXSArzLHin01OTXBPXUGQZKaLIbFsMWGqDHyJzYHDsJFBwMcwetQaWWRFYM3rsl9LIkopTHPA6maapJZkuatwZDNLVssycFXWbZU4FMnqMofWVd7/lDG+NVeh0vfYP96i5YRogo5MBlVyqNs+ZU3j0rO7IEY0bJUoijG1kBiBXt+kYflsL4IqquCr+FKPWJIppnwM2cCPXTb6Ia2azhmjGtmCxBtuPo4f+zRcgVwokYwlVFFB0Uy2DjksNzQIZRQF7FAkr9tU+wayrBDFHvmkSxxHbHZTbDR1Joa6pPQIVQ1oDhSOV7I8fc8qS80cmhKTMx0WOxHHFzIUCh4tJw1iTD7l0HYUzpmoYAUitZ6MLMeUszbr3RJZw0cWBSxPxrIUBgh0LIlhCRBEvNBjKt/AiSQ8r8BGS6dmB8SCR1LNIIkityy1CaMuIgqunYXcgEjsM/BMtuUlYlqsLSWZKbmMJTWII1ypza6iTClhs9F3uG22hOKn+drBLpfNDNGzQrbkDf75ngrXHaqxu2TywKLFD/9oL9fcX+PAqRq7pseYa1m88qsnGC/K5A2FhzdsTmwsI8Uag9Bhy1AGOwJFcbl4u8CBFZHXXr9AuSBwzeFNJpIqh1t99o8FpDQPx4Nmfxg7CthWEGj6TTpWiV7s8vsf+hiD+25i+gVvYUs+xwvOKvHtoy3edtMiIyWd7891SettFLPPVk0jm7JIaiFBbDEmGqy2RZpdnb+6ZYldo8tEos+IDp2wS0Ys0g0jxtIxvbDF7uIQt5zUGc4qZBMSf/mE7bz8KydZdCy+erROKEq8avcEL/7aKUYSaUYyPfoBpBWZuc2AkbLESoPTqRdDKWq2j1/PY/l9jsxK/OVjBZ79uWP84WPHObA54IFNn5F0kvWexYE2rLcWmO95bBuNcL0UnZ5IKhPScyOONH28EP7spkU+9Zyd5BKnu3i84QmTNKMB960McHEpJWC9GzPfdXHcNF+K1njjY3Zy2SePUDZVBkHEe+9e4SlnZvnkoRUeM5JnRhW4d6WLI/qgxOzLJ3nNRWPctdLh7+9dpel4fODy7bzgS0fYtZGmbvt0xIAvPOsMCqaKHP2Pu4L8qpBOp39Cqfll2LJlC8PDw9x0002cffbZAHiex2233cZ73/veR+M2f82jwEv2jfDyi7bw2pvvZ7jgE7oGp3oWhbTD1ffFbMskuHxLgTiO8cIYTRZ51q4yVx9aYjwTsDHwySQhDgzWOx4v25HiSNfn/XcvMJZIcqoxYKLgE4sOPS+imIDNdsSJboe33DzLey7bxusfO80n79nkMw/UuG9xAKLB1i0OdctHEmEmD686d5jxjMHR6oDrjlR47U0PcMnIJF8+ViUSZaayCrEyoJR2aUkZqgObKFQopyOWGxaaZnL5riwfvmeDv7h0nM8frrJhuxQMGT3ZIq1HiAIUUg1cL8Oq4xLFBr1elmbfZTLr0910+ey9Tc7fodKIOiQFHV3tsdZKsNbI0fEDpjNpTtVd0nmJvhfgtWX2zFjohkPe0DhZF8noMl5kM5z2uGFxhWsfqbOzYDKSSRKHcP0hhzc8YZQb51sEqT51p4/ommQzDQQpQRz32LtNZrkSc/txk5ecP42gLvK1IyENK8FYxkFTNEzFYt9IxPceCen2U/Qsl6miTtsKKZhpDq2rTKcFes2YdEJitaaRTwj0ZImC2WWuVqI1UNg25DMIfIqmwEIlZqPWQdFDBNmj1ktzoqIxldPwgh61bgoriJATHjPFmGWphx7leeGWPCc6m5w1lubLR12i2MSJPL4/2+KR9RO85/KtFAyVpuVz71qHnAlLyxtY7RQZXeCu+gISOplETC7ZY8hUsAKPlOFhWxl+NO8xnveQNRlJkXj52eM0GiLXnkgyUfbRVZ9KTyIhS5RSEaIYMJoOGM6GLDZU1EyIIogk1ZB6H1KSyseev40/+8EspxYHaIaBIkSkDMglHTBjiimZuxcyXDadxfUF3vLdBT7yrK0kZJnvHG0hSTF116KfrdAe6DR7Ip0gwHZFjFhi4Ht86N4lLp3O86xdRW6f63DPfJejVZtMTsbQBbwoRJFDUjoooksxJZBVXYqRw8B0mbN9ZFHnPZfs5d0/WuKe1Q4vmt7KZNrkgokMggAH1wdkdJF/OrjM3nwaRZS4ZbFJShC5/ZhBQhNRkPmdc3Pcu1BEFmUeXOqwpWhSSKrY7Q4pQ0XTBCYLAlNpnTO25Hh4vQtqn20TSXYUUtxd2WAkA35skVRFMuaAk1UTXVQ5VpEYSRpYmsNkIUSXROYaAqOmyf7RUb4zu8ltJ3rszgeUEj5W5HGqWqKUbZI3FKoDm1onQ9dRueaq/eSM03K90ve4aDxDc5DggvEMP1ys0/Jcpsqw1InZWjCJxR5CpLLeg/YgzaFVnYsm0pw1lea6IxUO1btMFiPWLZ8EaXZlM/SsiN88a4gdeZ2r761wwxGHP3zcMGeNJvnYHZvct9zlt84f5dP3tBgrplDUgPOHRrj2gQZ4aTJDXWIhpN9P0PUinlJIMi1mOLpus9Fz+eBda7zlCRNctu10IebPHKmgSCLbizIblk1O1mjEAU+YyGDZMW983DiHK3225gxM9d8bZlw4keYZuwpIosBdq23OLqcopkRSqZjf3T3OtQ/W0WOFa49t8LSdOZ6+ZQg9FfNH3zyJLks8c2eRV37hFH/99ClG/rXjWRzHP9NI1HdDXnHdKV52Xomn7Mjy6i/PcsV4zP6tp1PBXvqYEg+u9jlvIvVT1/7vIo5j7CD6P7bfL8v/33WqX5kaRYVCgUKh8BNjiqIwPDzMzp2nPVOZTIZXvvKVvPGNb6RQKJDP5/nTP/1T9u/f/+MuaL8IeS2BH7fp+RqjSYli1uMbx2pEAjxre5G3PHGSy64+xO6pkOXBCkcaGV64cysrvR73+j0sX2U8reI6MkFsk5FTXDBuU3caPLKRZU9O5vqjAhBy48I6CbOFamhMSRkiMaQ2UNk/EmPINr0gQA9ydMMOqmKRUMp0XJtCJDCT0XnYsZjOSUg5jW5QRRRU5usGScNhJKmy2vDRBJmposSm06Wgmww8H1WBrJyk7nQZSaqk1BpfPh6xLzfEQsVk+3SRB1ZaTOku7cDAjWJGkyaDqIk8KCOYTTK6QWugEccihihSGfjMFFVuPN7jxWcUuHl5lb2yQd/tkIpzkNgkDos8uBSR1lV+cDJk95DK2gBmCjKr1oAzhlWQe6w1ptkzusaomGclU6fYT9Lup8le/m6y/W9z86kUqc46cS5NUi1Qs7vMV4fo2w4NO2BI1zi2plLKFDnm2TQtl93DETUvwgmK2H4PUegjq1twYhCFmLPHLO5YcplKG4higCB3ySlpdpRbGGKP+nqGWiwh63XGjCJWqGAm2oiCTFoXCMQeW0s+spRm0/eZ3SgiRBIpPUDT15lKpxjOKfzTw8uUkiGGLFLrpgkZUPc8ppMGz52Z5B8eXKJQclmtZum4CqO5AWPZNrIgImBg7vG5dV6l5UQM6QaTGYW5jkYs1pEkFUWC3iDPSlOl59ro7RhjIDCZg7YfM9cQUUSRciokpYe0bJ3VpsFaw8CQRTQlxtR8Dq/p7BhpoyoKggi2L9IcKIzluhTMLmNGCBmB+kBn4MvUBnDfSgpNEsERmWsofP+RDJokkVEM8mbESNahlO4x35Q5UjEQidhWHJDSfR5cyRDHMQ8vFhAEkRiIgxgvAiKBTVfHCVxa9oB8IkIXU1QHCXpeRN12OXNUwpQ9TlZl3NDHCNIkjRBZC+j7Ea2Gye4hEVN1ONU63bVFk2R8rUNCi0hqHmKscbweUOtmCP0kAyLcqMtvfPkQw6bJvUtdZooaejLmWKvPJ160lYQm8VvnDOH1mkRxzJkjCfLZiIQsUQs75DMaCtAMupw/LlDK1kgoAglFpB80MTe3ESYinrK9yIjqcctyi23D4IcibuQS2EMcPhlz3h6Z1U5AJCQJWy0qB2/niU+6gGPnPpkLS0O8/JwR+n7IpdszTByGphMynY8I/NMpeWnfpFO2OLKe55Idp1htZakPZHaN9ajZPoZmIoshbScmJ+mMZVqsd4pUXYu+VeD7hxJsyyXYkjH40NO3UrFchgvQqxskBIXnnWvwoYMP8LL927m7sgKBThAJDOiyd6vMoc2YLUWdfl+k2hLIZ2C+1SWrZPjE83aQ1mXe8ZRJjmzYHF1xyRoC812LET2BJcUsdQP2lDXatk/PjjFVqPh9Is/gnRfPYCgS25IJTieInObLx9a4Y7WFIkmYusdC28ft5NlVUHjrE6c4ZzTFB25bQQhELprMcOmuNF8+VuXe1R55TeaJwyZfOL7J11+5k7/84Sw/Wu7xT08f5/XfmeWu1Q5t3yfnm3zhcAXd0fmDJ4/whSMiz9yb54zh04rTJ+589PPIw1h4FGsU/WLr9Pt9Zmdnf3y8sLDAww8/TD6fZ3Jykte//vW8+93vZvv27Wzfvp13v/vdmKbJi1/84kflfn/Nf5xXnDdCSlN46pYx/uVAlY7noSgCl06M861H2tgDn9+59hSlIYGlpsdXXryHJ00O8Y93VjnRt9k92ie0SjxlX5ovHV3hE/f2eaAn03UCHrtd5RVnD/PdlbnTjTiCBH2vSynTA/JsTZm87NqTfPKF23jVhae9pXlT5hMHVlnvh4xmYzR0VjoOL/vGES4cznPTqTaSKBE4Cb61sIomaOwtZ3lwY0AcZAhdH0W3SOsyIxmLEzWViYLImudz9X1VrtyT5r0/WuGymQxCXWCxZmP5Wcj1SSZ7iEKOuhVRSAQoQsjBVdieyfHIvMuU2eUJO0waPegHAWPDFdKGh+2YLHsuM3oeI1L5jZ05vnKkRiyHjJQENhsK40WB43WB4TS4fkRnoJEzYjKJLrGv88jKgNZQh9W6wgvPHuOjBxfYmjMJfJetJY99IzVqlsy+XIOQmMVaHtlwGDeTfPiuCi/YN4476JFSbI6uJZgwxynkPA6u1BnJWciCwkpb5VwtyVrY5viaRtqIWO/HTCYiOoLN+LCGHQaokcFdJ/K87FwJMz3HQmUbTxzN8w+3NtGNiJFyHUKTuisgxCqGErNYU0kaORTFJikbzNckyskYU4nod3scaXXZHMSc3BQRIgVd7ZIUdE71RZqBw8u/dRC6JudPpnjlecNISsR9azoPd/skNejaOrp4Oo1REESG012Gkj6SZHPPfIqMCcXMgNg3aKcUvvpQk5fsTvCF39jJYm/At47XOd7q0VUiDEWmZSvYrsdwOmI4OyChB2iSyHxPwzRthtSIl3znLpKKTDkD1a6CrfuYmkzRtKn0VEJE8kmPAxtViobO656U4R03LBLHEm3P56XnlJA8haVKgl1TXcqpiHSiTa1roEgRihmzUk1S6Xt86q46j3Ta5EwRWfdJKAFxJLNS08kkHJZ9nUpf55zJNrIo0h5IVPom06kElqXgBBFvumgSXRZ58VeOYKh1/umhADNMcOFkjhecVeCl+0e5d73O105VmUqkWGuJXLQlxVnFPLsKCd749SVmCjp//7wtfPNonW+faFBvDxjO6pysOExmNZ63v8B4WufD9y2xMfCQBYWcKbHptbhgMqbu2IyYNllDRJJs+o7JYjMio0HKCNE1m66VoR7bdAcJ6v2YSrXHYyfz3Ly2jhArrPdE8oZOKV0lpUYM8AhCjULSZzqdxQ3/Xa5vyRl895ENXnlhmb+7Y4Ubj/ZJJkUiwWJ72SdrWpSSPinNp9JLkjRshjMyhyo9HlofkE26TCUDVmome4ZyXDKZZzKrcdZoki8dqfIvx9e4dF+Gv3ziDHeudDm0PuBEzaKUVPjAD6u87NwSXzzQ4E+eNMGBFYvzRtO89gkjtIIuHz94ClWLePfjdiEOOlx91ObOYzVShkgpYeL5MXctdLloS5qzRhJstn3mmzIThsTcisDbrhzhO0vLnDeR55qDGxzcHPDcHSUeN52mYfvcv9bjhftL+GHE++9YpO357C6YfOTBZT5w5XZuP9Xjty8Y4kP3rPJXl05zyXQON4h403fnWOv6JMSYaw/UMVSRH8112D+SoD4IuOb+Kp944VZO1Gze9cMlPvmcHbScgD/6+ixvvniCx06nCcKY1z9xlGFp8ONnIQjC/1Ej0b/t+YoLy7zhUV730dKr/l/RqX5lDEX/q3zwgx9ElmVe8IIXYNs2l112Gf/yL/+CJEn/84v/OzTVQ5cTbMtoGFqHx8006NsTEKgIyXVKyW386SVDfGNugKzV+Pjh+3jXY5/Cq8/cy0O1HyCLOdbaIX1LYEpSKBZtJMlha05Ai2W+dzCHGUbsmgio2l3MKOKMckDXn6cxMCkLJSzfBcFACEqo0iYDX6bRHef2JRjPhciCQdUKSagyMhqn6jCWSZAyeiy2RMIox31zBhlDZDKpsTZwkDemEYbXQQyZSavISpeUriALfe5bM7hyX4+DGza51BC1rkRCN+i6cLwbcUZZpTIImCmY9IMejYFBrWsiyRHbCxKzrR7bJjK0/R4Z0eSrB3tMlZLMN11G0zoTI1VOrBTYvXODES/FqaqMqWjUrJCUplAbeOR0FUXtk1AMQmmDjfYQ612Zsqlxt2XxqrO2sz2V5J0/eBZWtcpDn/4UhR27GL/iJYzksuhGFYEiAy9kEIQUMzFzXYsLJjRW2jGNQYGxTIwr1Dl3OMOJpoiTFZEEkyNNn+lEyOU7+tw5p7K3HKFqEcOpNdpWjigKUJUeulgkjFQixWYsZdAaJGg6DppksGvIYf/YItXuMNsLMmLc52RVZbspIyga6z2fM0ZdJgsBc/UkKBZZXUCSJB5aE5mfhy35AWWShGIbJ1AY1BPEkUwh4ZA1m+gSTCkqV+5yWO8OSKl5cskEw8WYTQu6fp+OPUS7b6D4Kj3XYnFTYEtZJGXAlrxIOtEHAgRJQJdDtuYDllsaE0WbpCLihwqVjoou+miKi6naJDQFTYohIXDHok4p6XCqbyJ4AsNZi4n8AEWDpWaGgqkgiyLVnoIQquQMkXwiRhIFGn2VWBSYKW1SbWcopyQ0OaA2UFHkgK2lHqokEMciC9UkXiQgSjGmLJOWFKLYppDqsdws0uhn2OgYiEqbx0z0GU6ZVLsirjBADfPkUzFOHBASEEZQMGRkKWKhDbVumq1ZFaQ+KcVhyJTwAhVRsGk7AiMpFU2pIaoil0/O0O+bfOH+JsVsRCEXMtcakNVNvjVX48zRFAP3tGf6ymsO88TtadKyzmYvRNd1IlfG12zOHA0ppHy8yGLCSLPRd6j0sly21eAFO3Zxw0KN+xtNSnkPBRNNb6IFZU41BKanfBp9FUN38U48yENf/hJP+ePXsRZrPG9vkTPKOq+/8Qixq/Oicwu4YQRCiCSorNQUXnpGkWse2aQ3SPL/sfeecZKd1bX3/+RUubqqc5qePBrlHBAKSCIIEQTGxiIYTDI22JhgbAwmWDgCBiPAYHJOApQllOMojCanDtO5u3I++Zz3w9jCXKcbeK/vvbA+VVed89TpX1Xtvc9+1l5r8/ARGt1+IlxOH+lS9aqk2iPM/NOut2FUyJsCs9UMXihRaUvMVCSm8hKtXsgF40mOt3p8+KFjvOmMUXbkUjyx1uL3b13gKy+6gN/6+gJnbxym1VpDECXylstCPeakokGlG5FJiSxXQurLApeclOPuWYdm4COJAitdlwcX63zp5VtZ7tj8zm2HUI0Wm0dS9LyIlXpIQtFZa9tEgsembIKFcsz1dyxz84E6i06Ht/1khndeMIplSPzVAysMZERWezaKElGU07z20jG+f7DC3pUepw0m+P2LhtmYs3jhSVkeX2pz44EqbSfgYM3h+soyf3jxMDlTYd+CxxUjfbz7thnKNfj1UwfZs9plS97kjy4bZkBZ5WC1SxTDS7YVOV53OFzp8JU9/76hwv8swkhA+AWNnoX/g+s88cQTXHLJJc/8/c/j269+9av50pe+xLve9S5s2+Ytb3kL9Xqdc845hzvuuOPnmMK/wn897CDiK0/X0QyHgphAVWKu3VHkJVsH+MLjazy20mBpRkBNhPzej47xsas38YcXjvLxh5c4rzDM1/av8P3HZd54wRTfeewY9XqabFbh9P4EexuLvGzzBm47vkA36BCKErqkstIM+XGjwtvPHqPnRRiKRBzHfPPIKqsdn6yoYck+Hb9H2tBQIoG7j9exdJGxvIeoOWiBQseVCbsKL9ySR0XkG09XSZsyiVQPQepx1mjE7mWLRNZmc5+FKwSIaoyuSMzVHQYslT84d5gP3bvAyeMJjra6aKKI60k4kc2OIYdj8wq/c/YwySjJY2UVSwt4ztZRPrlLYMtQh5oNA5rJ31y+mXtmm3z8gWW8EPotmXbgk0uIHKsKKIFGpxMiaT7ECjXbJi2kmF1UsUyBQysiG/oCrtqYYSSj8NcPLeDHCpsKDgfXdTb3xay0RYYSEZrWZudAkqFUiSFzmO/uqXHmpMxsu8fmQY2DjVVmGglWOxojVoZXbhtiz3qTw6s+fdYghxsdipGEHdtIRkC9YTBW9BAlSBoNUgmNrz5q8ZZzL+alm1XWujZ/fMkEn9k9R7VrsG2gQSZU6bghmyyN1bZPEJi4gUrZEYn8BPvqPkNpiZVWxHiyj9/YovLQtMds3KPS0VFFGUsRedZggZW2y8ONFo9XyzxRPeEeW9RVIk+iaof0YZKXTAShRbltsZZwaDgmoqhwrKSQ1mPatkwQyOQNlbP7UgRRjyiOOW0gy75Fn4NyQEduYiqgiRFfeMFZfHb3HHfPV5HEBnlDYSRro4gRQhwSCRLLqxpZS2BioIEkiESRwqPHCxhqTFIRGcu3qbUsWiHc8GCEosQYcogiB9w8v0BSE5kcduhLuiQ18EOZGBnbE1lvKbx0ywCmrOC5MDzUBkQemRZpt03G0xZ/fFE/n39qCdcLCSKRIFQI4ggZeOHGAkUzw5VTeRRJJIpi5mouW/ssun7AdL1OIuty6+IiX3h8hVOHTS7ZamJXTdqRjJlyeeHUOKcNpgH41Es3MJQ+wSppugGHqz2uv3ySOIaHU03KXZ/vH6qwM5/kbWePc/KQxftumefRxTbffs5JHKrX+eKBI2Q0gSBuYTsqGcOlP+VStGSSZp21Rpq9qxqLa3kMQaXthxSHAo62ffrEJC27iaZHKApUbZEgMpivi+iiwXBK5toNY9zw8CpXb82zrd/kNd86QijF/J46yNVb8mzKmWwoCnz4kf0EgcxErkPbTnCsrFKwYmS5xpa8zc37C+QTULMjbNek15M43nQ4VrK5YlOGN/7gKBdtSDKUCdlbX+ONN3eZbfik0FlvBfRlBDpCwDePLFPviXz0zhVefVaBUtej7YYIgkHoJmm4Nn/3+BEsx+ewq7CpoHDNphF25jP8zX3LfOiqE1IqJw8msBSJqT6Dph3w8PIad6we4i2nncw77jhO1/c5q5jlvpkW508k+ch9C3zwsgl6XsgrvnOI3z5zkKu3niBS/P1VJxzBX3JKnntm63z2ms14kc83Ds7zG9vH+dgLNvLRuxcpmCqvPafIfdMtbj1U58U789wzvc7fvWQSQRC4a6bOb55a5Ip/3M/WnMHfvGADG/InmEayJHDyoEWp1OX/Rfyi6qpflppKiP+lJ90vCVqtFul0mjf8+LsMFk16UY+C5dNvKtx2sMBC1aI/6bE5k2Lfkkeu2CWtxfQleziewfvOPp9P7N7LT462KSRtcPrQE1UkyWdDViKOQZZdyrUsK9UMRrJD2nDJ6gKBWKcv0SPy89w/J+KHKlnSOKHLKZNNEqrM7mWN/lQHXVZYbvfY3h9QMAOarkitk2etqdBwQs4ac/nKLotcAgYthU5LQBUNfK1H2/W4dLPNut3mpKJIwXLwIgFTkmgGdSrtQZ5YtJAiDSlWGJCrdGQLSa+iCxlGsi1kyaZghRypCkhRkfWOgOvq7Ft3GE0lKDclFFEgm6khSRLtdgpZCQAfJJeiqdF0oG0rtLyQHf1Q6gbo8olEEfk6+XSNsUyXIExxqAQJRWNHegoFgY/evsZzT05ypFtB3nczfadfhNNaZ3BijK4r0peIeHpZ5sqJMW45VmbnWJ2kIjPb6GEIeTqBx1DK5rQhB8sokdN0Kj2Z5ZZBo6ez2rKYq/m8/BSbDX2rNB2RVi/P2gpMO0k6Toq6ExHEAUnJwMVhMNvgtAGBzf0r7Fos4Pkyrm9x62GJs8dDiomIWGydoO3XLZzAZL3rs1BT2ZLVqPZiDpUEfvzyk7lxZpGHl8vIEmiyy1AqZiB9olFkyD0UMUelY/D1XWnO2LTESCJFGCqIUg9JKbFSG6LV6qNUNVluBKy2fTaOCIxkBBKGjxt6hILNlr4OSU1GFCS8MOTJxTyjGQdJUPACETsM8SKbs8YqJxqTLYtHjqcp9xzOm1pn0LdwTZlYjAnokjW7OE4a10/StDUaPYmeF9PqGRiqiChCxwsY75+naWvs7JcYTfsUEzYdP6Bhx0yX+lhvp+m6EpWWwnU7RrhgIsV42qDpety1sMqXH2wQCgKJjI+pdxjOldmUkwGJ++dkZisGRSNFryextAIXnF5HRMINYww1QJE7pBQThJgj1S47iyq6AoLUptFN0OgatBwTWVvFktI4nsVyQ8Jt6JgJj1Smg6k3SMpZjlUCRqJxlpsuV0/CF+dszh3MctuBJoWiR1KXKTldzhyNyVhtdg400SSBxZbKelsiqwzw9tPOo9Jz+PNHjvLonE1/SqTtiAzne6y3RVRJAiIqq02E5YPUMiOcf9IQgt7D91XShkC1G1NrG/zm1klWnR5f2rvI6UMKpbbMGQMpbputcdqQRNl2mMoFDOZWyWppqm4Hz8twpCzQb1rMNlwm0hKGWWEqo3KkAranU+6A7aZxugplx2PbgMzbz57irMETmnCPLDaZyhrIgsA90y2qYZtDx5e4ZUVgPKeQNnsUUyGNVpoj1YAhS+PvrtjGcFrlocUmz57I8qP9Vf78oeM07ZD7Xn8Kr/zR0yT0CENQ6YQufYmAuVUVX+2yrU9l3/KJUUsrTrFWEoi1kL40HJqLkVW4emeG+9cW0GWRvGIhKD2alSRXbizyvQMVHC/i0i0pPnT5BLoiUe54vPCrB6h1Agp9MWak0myKvPmCAYayCn9+1yKq5bNj0EDoGfzNNRPcNV1nseVyen+KX/vKEXIpgQ8/f4wdfUme/Y9P88FLJ9iaDDllcohms/m/TEP+5xx17he/gGya/8s5DyDo9Xj0ta/7hVzfr3ACN9xwAzfccAPHjx8HYMeOHfzpn/4pz33uc//VsW984xv53Oc+x8c+9jHe/va3/4frfvzjH+eGG25gYWGBvr4+rr32Wq6//np0Xf/vuq5//v7882fdcgO+tX+V7+yv0OiFDOUE/urSbWwpmByp9HjX7dO0WtByQnIpiau35PnG/nVSusQfXDjIN/aX+MQVm3lq5ji76hI3H26S1GReenKSmfYqz9swyZcPTCMDTT/A9UVOyQ3yyBGPz167kafLDbYVkhiSxDvvOkIkdvDjgIlcSLMnU2or+I6BKEeYhk0hEVFuaKSTPYJWPy87ucANTy5z2ViOHzxdw9cCtg6G5NJ1okii2kxRaguMGUlC1aHpuzx/bJhvPd7grRcOcvVJOQRivnZwlqfWmrxo8yjfOrTAbDVAFETOzvRBt8n5pwzyiSePo8hw2caAp1ZCCA1qjgd2AkNWWO+ecFkqtyMG+gNEySGIZPrMgPUOnJId4kjFZSQrcHZfH199ooynOChGyEu3DvDThRLXbh7iyUWbg5UujbDLWWMONTfClGSW27Clz+XJRYvxbEDLExEDheWmSD4Z0Zdq4Xo6pbJBKh2wVNKwZBlTg6u35fnGwWU2ZJJ84spNfOi+OZbXV7ENg2bgsa0QEoo9lFij0pX5s/NPJ4wEfv+mGc4ZT3LLTJmxPChqh/FsSCcImcj06PkaMxWNbZlh3nDqFL9/yzEW2j3iUCatyPzaKQWmKw5XbsxxqGRz17E6s50OGV2m2ojI5UPiWCCUPPKGRMvzMdWIyNdYqShkLAE7CuhLdxjPCSSMBtV2giiSOV5WKegGyw2BSzZbXDaep2PHfOW+I4iJHC/Ynuf2+XU0JWTdbaKLEiBArOI7CqV2hJnqUrQk3Min5wskJIX5mkjgSozkY6xkA0UMyBsq3cBnPNdClzS6gc+xUhIii2MlmfG+HjIaizWJKJYwJIlOO2bHVJ20EVOzY8JQJqdbbM3288PdTYoJjWu3Fzl3IsGe5S6femCNP7limJQh8w+71ujPxMRCxI2HawymQmTZ59RUlTkvy+F1CyMwOHc8QTYp8LUnKkwVNDQ9xHEkMrrKk0stiiakkh661SX2dSo9kWZPYlPGYrUicM32PLYX88YLTjD7yh2XV33nKEMpjWPrNglDIJOQ+PyLt/DUaoObjjR405nD2F7MgfUuz92aw1BEfu27+7F7EY7gMN4noOlNTEkmFAIm8i3EWOPxxRRiYDC9JpPTFDbmDGzJYbUWUFTqxEmdkUKJkZRAJHTYvzjMdNnk76/agYTAm78zy4tPzvO1J8pYOnTwuHRjhnIVJvtUXnlGkb945DDH6m2yukI62WYq57FtcJV2L8PdMwlU0WS+JpJWZY6tymzJWHzimilqtseHHziOrAR4YhVdEhAFH0GQWClbDBcaNLopqjakZJn5JZWxvMo/XruZ1bbPwfUuP54uU/N6xFIXQ9RpejZb1Agrm2VnfoC8oXLJhuy/GZfDKOYN3z/Gtn6dt184wr0LJb62b4kja6AFKt/9za0UkurPndNxQxKa9Mz5/6272I37K/x0eYn3XbSVoqkzX3N4yw9m+fBzx9jRb6LK/75l/Z/cdpyDqzZffMUm0sbP80aiKKJUKlEsFhHF/3Hb+18U/ttc9otY6xdVV/2y1FT/1zGKfpHYv5pi15rJVSe3qXUKHFhvYVoVjI6CqgQ8tF6iWUtSyCssNBy8WCRvevzJw7vo+R5bCiJSnKMlthAFgaKR4HDJB8FjJGVSciLmWw6DgsJk1qE/06LtGAwYEnNej3PGBZ48lmdfw8F0DFRdRFG6ZPQYEYsw7jGe1khqXcqOz3DCwosqZKMEKT3NetfjgimH6cUcBzs9zhyVKHdamKpIQra45WmV87aIuGGJSLDxvH5ipYsppmn7NpoiYgoK9a5P7KaY7TgMZ1J4ok/DkRlJmdhhma25FDONCv2JPm6bVxiwNJZrMaoskE4EOIEEjkHdDTk5F9GN2sR+htV2iBUlWGr12JhT6Po2Jw2E3D8Xce6oSLXn0uklqRs9Yv+E3XzCbLK7ssD+4wabxiTOGsywxbW48rL38rmnH+OmT3+R1uYiF736VbiSzXnjeRzqTGZkKi2TA22BLUUZP3ZJCgIN2+JguccZwznqbg9J9MnqJt94SmVLzqDraByteohiH2PZGkdKPinVJC11WWqYJBQFJBc3aDGVUUklbUwtYP9KP55v8e0ndbbndAqKwHS5TUoLGc8JZHWfktoiIYkEkc5eJ+JISeYlW/p562kJPnr3MqFVx1QEMqZH24eZqkgYJeh6It2wyXDSQxZMLtlmM9fU6ToR2woBhixhRwkWmgEEMUbKZ9SAlivRi1o4oUnQ0XEdHSslUHc8UkaIpbjgR8hyj4WaxmBagFjE82XGC22IVZqOSds1yJnghQoyJgISsighSiEFI2YgKbAYyXi+SBSLdDwJQ/OQ/Yh8wiFjOdw/bfCsbJfiSIAhaYgCzNZVFNljrW3h+AbNjoYdhXRsgaG0hqVK/OjYGg/Od5hed/ERUCSQZQdBskkpOkEEiDaWDhtTaWp1ldW6z+NvP4UPPHKAw2WbajfmtHEPS1ERxJj5hgBhknpXwzBLTGUdCJPUejEBPtVGnj7DYNcMvOtZI9w5V8KT2iR0j6ya58LhIe48vIaUbVEWPS7eOMLFp0zwez+ZYbgocuFkH3fOVxhOgyZBLrGKKGQp2T0aPZPQT/L5XSJCMM2dx9fRZJgsyuxb89iQNpkuSQxnPQxZ5sGbb6f3yB1krnobWzaM0w4jJk0fQ3GZqaTRRIWyY3PPSonpeocd/SoNz2U8B09UFpjIJFnvRhAZHKnVyCVzRHoHVTQ50nDYmE8wVwtISAaPLzu89kyNbuCRUhPsXoYzhkSOlkJymYCoK/KnF25lSz7xTMw8bzT9zOPnbs/w6h/NEXdcNvelKDs+Rpil5HeZrUZM9mn85eVbmMicuLl99kSWPStd3n/7Ip9+6UbShkx/QuO3TxvmC0+s4Usu48WAktNC1rNM5QXKnZDN/SodR6Vku6RyCuuOTdn3+Z1nbeTcsRTvvmua8WySUh20hM2mvMljaxrnTyW5f6XGsZLHj46VefXp/SQVmdf96DC6GXN6UafiuPhduHRzitecU+TO6RrFnESs+vzWznGu+/pRvvx4ib94ZIGbrzuJtabPQFrGNjoIQsyHfjrPhy6d4GU7i/z+9/f8wnNUFAtEv6DRs1/UOr/CzzAyMsJHP/pRNm484dzy5S9/mWuuuYbdu3ezY8eOZ4678cYbeeyxxxgaGvpP1/z617/Oe97zHv7xH/+R888/n6NHj/Ka17wGOMGo/p9BSpN5wxmjXLutnzf+5Aiv2NnP7948zV9esQFDFqlVBXqqy3DG4I8vHcNURb61p4yuwN8/tcDbzpzkrbcdYavm8tsXncSVG4v89f1LDOtpvvxwE9dfYLmaoO569CdlEpLArlKVP7x0E+++Y4YaDYglfn3TOEsrUCwI5FMau6dVdC1i50jMkXWXZktlNJWl1msgag5FuZ8PvHQTuiwzXe/x7b0lBFli1FKo2T52mGA4E9LwAlRFYaHp0ujEnL0xyQu3FXjiuMNtx6q84KQsoiBw19MxhysStc4SnSggbQRkdImjvRbXTVj83cPrhLLItgEHXV/nrDGTuXqHxloWK+1Rb3ucOZzjYKtCsV/ADWKSChQSAYHQZTSd5MH5JucN5njruUNossi3D5RpdkX6dJ2dfRk2ZCwOVFr0Qp/f3DHEPQsVHj3axjLB6UiIMlTNgPG8jRDJxHFExxNRpJis6aEoEZYSYGp1as00A8Ue7Y5OI4p4aKHJG88Y5duHF/ndOw6yWA0ZlUTmmhHZhEbNqTOcVpHlDoaa4ptHjtNuazTdkKmMzrZ8kulGi/E+jY7foGjJZKwelicS5AMenm1Ray/wqRds4WjFZlvB5LajNe6dabKn3GT3WovXnTrC3794iiCKuParRxhKqnhOhKAGNDqQM2yKCYGmC7ogo0ogqD2KqkLKjBCIaPcSSFKA7ZjUbYVBTeflO1IYishQ0qB/QGGDPsaX9vl8+sE1bHyKaYFAlehFArqksFYXee5kBrJw6xEwB1wUTWQo3WXEGOXsfotvPlVFFSVcL4Er2hRNny39DZq2goBMGAXEsYAkxPSlHEYzHRKazUhfwHrTJAp0jgYK1205haod8JU9ZXYOO1iayJ5Sg4+/cIqR1M9uSM+bTHHeZIpPP7HEgfUu1QrMNEJKrkPS9IkFmXJXYjU28FUJy7BpVCQeXmjhiyFTGYsNSZ2e1CNUY9a6TS7YXiKtK8RCF9uVkcWYdqDypu3DTKRT3DFX41C5x1LN57fP60cUBQoJjVed0c+PD1XJDXTI6Qp26PLGm56k2lRpdgSKao0/uHiYmYrD+29d5K9eOM47zhlnqenywNosmtnADyFn+uSSLVKKRCS0GEgYzNc0XndaP6YV86OjVRKxxCt3DrJvqcO+TshUn8xSC9aa/bS6Gq2WxPX3LNJ0Qn7/0kGGsyJP1SLaQcxvbRriaMVhLahi223eetsadVtkIGESCB0cV2G2FmOpWWquwGg2RBIryGKKuarOxlSCV5yW47N7prlnrkV/IiKXrJHRFJq+R1qVKXUizt5UY7UFumZjeEnyZoKprRrP39qHKov86FCFe4/XaQQuUiiSSxi0opCRrEy56jPXE9Fihz+8oPDM5920AyxVesYZTBIF/vr5k2RNhY4b8OXdq+QsnfdfNIQox7zxtoP849U7yOjKM2v8c5No91KHP755ga9ft5mseeLW/atPlDhatvnYFacgSwK3H6rz3T1V3vasAd528zQnD1p86upN/2ZO+MR9K/TsmO+8auv/snPZ/234RdVVvyw11S91o2gy79OOdFYq48iKTRhk6bME6obHSltgIh9QFgUeX/UYyTt0emn2LIWkTIeRtIAiQqXrYaontIr8uEfBEonDDNVej/WOwFhfzFor4t5ZiRecJGGpLorSY2NWp9RRGSnWCbwB2orPgfWAyWyClN6g48OAqpE2OpTaSbYVPNphDS80MSSZmaZNv6XQ6Zn4os+mjMV7ztnAb996kJwu0RMchrM6Tx5PcmBd4KUnd3h8SWIqm6XWExAkm+2FiCdXOgymEggtH6GVY7rt48kOU30RbuSSFLOkrEX01gR7qhLj+YCsEfORZ+/kXXfOYAdgyin21xzOHY0x9C4ZyWC5W2ZQLnBsXmJMT3K02uTkosVTMxHP3VZBVGoUk0WO1Rx6vQJ2EHDakEMsVUgqGmmzx4vHzuJ7+6pcMJrhj26Zx9ETXPneD7L25A8wgjJyt4GTkuh4EouOS4YijZLAnNhhY79EoCwxZOXxYljvBBSsBL2wjColuXJrj10zSXb0uxgK3HnM4JyREbpBk2ZNxzNkVDViYVFirJDm6JpHUXUZyhistWNqrk1B7OeTl2/ivbfMMzIQgypRt2Psko4X6MSRzWhugbwzguupPLBb4Irn5fjpfJVZp0Fe9RnJdUhrMn5LZs3z6XkCOUsmo2nsWg4YNFRkQcEUZWKxjKkKKJKObacwVJ+lTpeUqCDGEhP9Mcstg6rsMpSCbBJyaY/BZIQqxASRwnpb5onjFmdOVrH9FKoooSshXVflSFdnS8Ehne9gaRpLjSyVVorIkSk3dJIJj21DDsdKBsVESM7oMOj1iEUV24/JJWJEUaDhCIRxzFNzIwylZZIaBHHIo/Mqw1mFvG4S+0mmknnuP9LmnZcMc95ImkdWanz68WVSVpdsTkBEIohg61AJN5SxXYue2mWtK5LVFIxEzHLLoewG/P0Ty5SaEjPrcOZUF0WUaTsishSgigGWmaTugKDZtJ0+vDDCCV36EzCYBCcISCRlbj++TM3rYsiwXOrDVi0+Nttma96k4rj0ugL3TDdJZRRadDh7MMePnmxz6lSCZ2/s40DzKFKcpdqVWOvKeF6S4004Y7LHqtuj3IXTh3QOlgIGkzKIIadOVhnq6nzv3v2MTp2MesEVqIKCbvRQBAnXt3DjMkOJLCu9FlvyaVa7XfpMiarb4ryxmKVaTLOVwVFDzFil7UZMjgiMZtpockhKbyGJCVYbJp4vU7EdXnRSm1DwCAKTqtfg8o0JnlxKMp6NeHoxIBsWGU7o/NntC7z27H6cIOLAapcX7czz7X1lZEFgqSSwMakw3QgYTyaptSNU30TXbT707K2s9zpsyJj86OgaF4/m2Dlg8tM37SBv/awQsl0JSxPRrAaHqiqbMmlC0wUkiqaCojUZzYc8Op3hcMlj84DCH56zmUsm+3jPXTOYekipLmDJIoWUx0s3nsr08gLv/OkRbnjuNqIYllsOf3PPKitNF80QKaRVrp7K88Xda9RaMa95SZb33vs0dx4K+d6v72A8o/P2G2eJgWJC4dZX7WQiq7MhZ/DsHQls3+DAsovtx6RNiS88scr9C9VfeI76rxw9+xX+c/y3Bhwf+chHuOGGG3j00UefaRQtLy/z1re+ldtvv53nP//5/+majzzyCBdccMEzugQTExP8+q//Ort27fofvr75usPOf9rprPV8PnDXAvPrAX/VXOQtZw3zzltm6bghH7pqghseWeV9V4xy6uAJmvtXXr6F13zzGErG4+sH5mmHLsc7Nr9722G+fe0pfOjySd7y3Vmu3trHd/dUUNIuxYRCFPnMrinksiGf3TPHclPE0nXOGFMwzIAdQybrHZG1Wo+hQpdON0WtqfDqU1PccrjOn1+yieVel888Oc91J4+iShIfeWCOmw800CSRsX6fdS1vVQABAABJREFUXuyhxhIbCi4d32ckbXBsUafbjZnM6WxPZShYKp952UZuPFzmrbce5TPP38rfvGiCL+1apxJ32F8OGMmIhFKb4bzN7cebvGjHFLYfcvv8GpLYx87hFS5Ih7hOmoVmRC4lcbhd5Te3j/HdvSUEo4cYazy9FHPmsEVCTKFqbbYN6Lztphk+8YIprt7Sx/GGzVNrbT547yztyKU/oXHNpn72L/co+y2G+nsYokLHiIh9ncW6wWQuoItLSrVYrwtkVYUjSzLbR2VasU1GNenGIWao0+1KDOcFjrda3DrTZKSvju8mERQFJZIQbA03jmmGSbp2RDENUezREuvkpSHef/kYl2zI0qDF+rETbqSyrFPviDTsIqWOSJ8us7auMqCGvOG70/zDyzYiCPDwQou65+OFMY4bcv29C/zVfQIjWRVfcxEUgWFNZ6brkTdEgjhGCFI0Oz6fffF2jlZsFnsN7l8q0XQVZLFL3lRZaSkguEz2CfSENe5bbVJtS7z0pBM34ook8sdXjHLPXIMP3jtPJMREgULHFkFWiO2YuxfXyJgyZtZjuNDGVAVqPYF9lTq6YPPaMwaYq3gEUYonahUM0afjA2JIwRDxYpmWo2GIJ5jPB0IYz8JQrsJQqoPjC8xUZRKayulDOZ4zVeQ9t87SFET+8qqJf/d3+ZYzR555/LH7l7hxv4+qCKS0iPFcF9WHjC5y8eAE0UZAjHn59hNNZj+M6Poh3967zqOHXZK6hhCDoshYmoQX+iyWMizlBV68JcNpAxkAul5IzQ7osxR6XshCw6HkdRlLChi6TV51CBwF2U3wsu0pnj11YlNoqk9nzXF4aKHJeiPgC/vmKaYFxFAjrUnMN3xShkJXiKj1LBRJ56Wbx9i1XGNxpYkSmMxXRb7QXGJMhnrLYK+kMVSsYCoqa2HI1kGZyyczWJLEPzy2hiC6dNyYjUWdL+1ZYaJYY+NIzHRZRxIUXrKhwH1zNfoGXQxFRpRcSl2DpNHG8RSOrKdpdhOUShJdw+drRw/T80QmcgGD2TY5MyBtVOk6GTpBly1FlYWGgC5Z7F8xGdJSfPIlW575jOI4puF6tCOXrCqTz1UYTPs4noqquAxFaV557gY25KxnznlwtsVH717ig88d4dShEzF1teMynNTpeSG/8ZVjvPH8ca7cmuWjdy9yoNHgU1dtZantcOt0heduKPD+2xb58PPGSOoSfhjzjes2kzF/dtv+m2cUnrFOD8KY/Ws9LtyQ5Du7q2wtGvzxs3/mIv7f4lVnFZ9pOP2y4b9q9Oz/Vvxyfkv+CQlZQFSrVLoauViiIGd5/GhAKuMynhIolVJ044icZnHdlkkm0hZ/8NODKHGKhL6KHXpEcR8jGRcnshFQyOkWs5UQ209xUiFmsRnQ8xQmLIvbDkpsHSpjqTq9wCckYiyjkcNgR6GfL+9eY6XsUbZ1Ltvkg1IlFmEsbaDKNgYGRdNiT7sNcY6luoQdhAynDUodnw8+speNah8PH+5y6kaZQPbIWgJ50+KplRab+3zm6yJRqDCQTDJT67KtT+S2gwIXFyBf9LBbARsTGm1/laxh0XJ7jGZNIqGHIuoEocXexZinB8ssVyMmizprbZ+d/QqSvkJCTdHxbIqGydFKl4GCiqC2GOuZZNJlclaKfSWBnQMZJKnHSNJgrlllay7BULaCGJk4XpuOW+ADj+1mU9ZiV6OJmGswpOgkE1UmrroSoh63fvKbyHLIKa96M1PZPOV2l8F+i9k1nWLSA7HInAubCzp2EFJ3fJJqFlmyESOLgXTIvUdl/uDiDKecupc9S+PUWkW8rsi+FYOThzRmjTYbUkO0nC7HKgrzLZGRjMLhqosRSpT6q3zqJVN89+gyix2fE9LlNqtNi4GUQakrk1R69JkqiRR8Zvcy12zqw9kYctd8hSPrAXlDpC8hsH2wx9GygKnpFBMmQ8k2TlDDUnIURJmKYzDXaHFSQUQTTWK/j4UVkTQSV+1II2gejyy7LJUN+kyXXCZEEgABGq6KpQj0mQKXbW3iCx6VRkhOl9C1kMNrJsPZHtWuTEo/Ifqd0HyOraXYogR0JBVJDSl3FNxAwAtVEmqEKHm0/R4tW0LFwnE0XA9yZkS7l2EtFqjKITXXZbEOI2mLZieB4xgooU83Cmi5PovtHtv7EkwUm4ShgCSquL6ApPjsW84ShQZhJGE1BIrpFoZs0owijpcjtqQSzFRtphttJofqpDUTx49JWi2kWENEptqFcjem445RqWnYQomkoiMJAmmrSs8zuWDSoO3YzLR8NqaH+K1zR9nZn2Cu4XD3dIOdQzpPzdkUtS6f2TvLeEbkyZUOXigSOSrvv7XCc081sT2BSzetYSgp5pt1MnoGWQwRpYCLN8qUOw4ZXSOp+0ylsgQP3snnb7qFU1/1ewgDaQaTLiI+tz+W4KxNEk1tBTPsx4kDNMHkWDsgLZms1uH0KZvp9QLHKxKyKCCJERNFm+VOl+durRPGIbIg0ZdsEkYJnjiu03FkDMXhsbkcp4+2sGOPvKaxd1VhMhvTDZr0mwWeXmnx2u8dJasoDCQULvnMPuwo5EC1y61H6vQnYiq+wzZJIG+oNFyfvqTKvlKXkwct/vbJfVw2PshsOeCTTyzwBaXCl649YfN684Eaz9+R4/v7ynx97wq21GAkzDKVbyBJEf1CGqQ2yDY5XeTBOZkwijhpVGJ1XWahFvGJ0iKPrtQxVE44xkVtusezvG16Gitb5wOXbGNH8QQbalPO5Pu765R6PlLoceX2LO++Y44whsvHs/zZI09zcNbiI1dMMv5PDKi/vnqCTz26wpVbs0iiwJ1HGix3HH5wsIzfEzl5IOD9V4xxzXf2kDVEzhqzeOq/Lp39Cv/FCMOQ7373u3S7Xc477zzgBH3/uuuu453vfOfPMYz+I1x44YV87WtfY9euXZx99tnMzs5yyy238OpXv/rfPcd1XVzXfebvVqsFwGhaJYoifnKghufHLNUdZDmmIGukZAnRF0mq8OUn1llsOLzlxmN87eVbmMgabMhrfOEVG3jPzXPMlDtkki55XcCjy95Sk2FLZ/OoyM4RnbuPylw0kuNNFxZ41717MYcCqi0RQfYYzEvYXY2CnGGu3uGs4STfeLqLkhAQQp1ubNNqOdy8D16xc5jXffsoz96e5OqpYT5073FefkqOpaaDaYbkDPBFmz49xvFiYqHDgKVw34KB6whcvjnF+y8f4/OPlnj0eItzxpOcUrAY0BWiKGKlZVPzPN5z0SRvv32aPYtddg6biFqHsT6PA/VZOr7G1RuL3Dq7SsftJyFLzFQlMppEredhKAI/OrZAzZG5ON9PRVpjY05k91LIcMrmU1ds4sP3LvGei4cpWDKWFXLvgQqXTiQ42qpixgJxEPO5x1YYyyi8eNMYt82tM7PucdKIC8kGnmtS6XqMpU0em5dJyCJVOySliSzXQ4ZzMn7UZSonc2RFIqFJtHyXDYWQ/mwVXdIx02sUrSSryxoDfQordYmTUwXmSi4DGZWnaw3+6IJxZFR+uLfGTfvq/PnzJxEjka/trSOJKZzIpdVMUunAqiCgK7Bad3nTecOcmGiJ+eBlo7zx5j2kFIFu6DNYEOhFDk3PQnQk1js+URq8IEaTBGpNFTMJf3D2OClNoe30uHO/x3rbRNVVOi6IUYwhB0iiTX8iJKDHSk0mY8FLv7mPd10wypQeE0UROwomv3/eMCWny5FKj90dl0AMMZNg+xKqFNKnO3h+gKUo5C2bWlvGE0R+MHccMZZRBJGU4qMIIVEkossuQaQQxlCraWRNiTiMyBoeoiBTaescLydRYh2npuJ7MccqHb65r8Rs1UaXJKLo592avrh7mfsXK/zWjgmKSYUtxRNMo4GUTKwGCJGE40PWCskoIR21zt89rNKXEnAJuHpjAU2WkARIqRLbCgZKwqfaSbHU9LhwqoUqCbScmO1DHea78NcPRpxUtEhqEn+76wjjGZkQiWpdZq3r05frkdFF1jsCjSiNIgr0HJ8Da212L3Z4zdkFJvM6oymN9brPJx5dYrQIkWATejLNKCZj2lTbCVbaMZ6TYHZd4UmpTBTGfPPXT6PUDXmytM6uRYe457N9LEbVbeJQI457iHGSiu3xnA0ZKj2P8zZY7C45mOk6rchCVUOycp6nVzyu3jDES7cW+dbTFSZzJpO5QZ4orVE0TeygRdzNMF1WiQOd8opCzpQp91yMRgq0Ho4vM55toooibVej5QR0bZPlQEAnya9tneKKfpVzx5NEUUSt6/Ox+1d596XDPGcqy9MrbTwcBpISknhCiH3PQpqcE3Ngpcdi3eOOY3XeceEwP9hX5uVnpHjPfYc4ayDD4UqbXkPloy+YYOdAgu++ZhOKJDLf6DLdK3PpxhyKAH94y1GabYG7D7Z407mDWKpAFEWcOWo9k1P+Jf5ZPUYU4C3n93P9vQtsLejcNd1g73KX/s0nNuV6XvhzjmppXfxXa/1LRFFEHMf/4TH/O/Bf/f6/wi+5RtEpn7yZqfGYvGYwW7b41BUnce2XjrJtUuDYqgeBRDYl8qItBd524YkdgEeX6xyttfne0XlOG+mC4OBGDqKgIIQG6z2XMNIJnCyLTRFLjRlOg643ObhqMpERSCRWmCo08d1+DpYkFktpziwO8tCxDm4c0ZcEIbnGWK7LSQWJWGqSUlUavSQLTZG0FvLATJqlJpw+qFB3faLA4FmDRb6+q0ZfSqAldTE0GE+rtNyA0axNO3BJmx1ySgZBdvE8k3uPqXRtiWcXHVYlGU0yeGrB54JNDhN9JRKKTiHRw4tb7D6+kaNlnaRkMntc5Iu/McUf3HkMWRZAqbOh4KKKIZqs0rB9eoGIKhgEsYCu9IhjkASdmYqMKMTsGHBoezF+KHHuRAlLUVjstJlKW8zUFI6tFJhvCOwYDCm7TbYXJMIoxDQa5JQ0T64HJFeOERhJSmttCltP495jFhMZlXSygyZFhJGKpFY4qaDhUqdgiOiSxHf3GxjxANM1l/MKg/TpAgcr69TigClZ5Pa1iBdvHGLnkM50s82etc6J8cL8HAk5w1I5yZ4jCjs2uwiRxuZ0lt2teYYTGkmzhaRUGLZONKUWWzC/NIIditQ6Cp4NwwM9MgmBai+kmC2xMW2iKiEr3Q5DCR8hzuNHIU2/iiomMCWLMI6Q1RU25nzKjWGOVlIsNn0ePGpy5rDJUFpiteOwtKqyaaLOaN4na9qk9A5hKGEpGqIo0PVijjeh1ZNJaiqqFBMJbZzIp9/Q8Pwky02d6YpEf7JLsmNQEkwsy6YdOGzurzGckLBU8COXhVbEkZUBDDFBGCjEUUAn4p/EuQNyyRbTdY+ECkOWhRBLHFrXWajGJASZ7QM6G/plVmoCR1pVBpMRuYSPKHkcXDGxFImEHqMpLnbgUjAVolhhpiqyPG/wqjMGOFLp8kBtjfMnQEJhvtXlqq1VdFmn2oN79g3xO+cMs3/d5fbpOsXiGmPJFFXb5tJNK8RRisWWyBPH+3BdmWJS47Fpjx9ft4NqN+BLu9b5yPPGuWl/jVv2HiMzpBKoNRZWisihjOsDhs1UEXaMHWQylcIObQ6uJYnDNPfMxrxoh4+k1FhrpfG9BLfd9AgXbyyypsVs3TyAH+o8OCfxnM0xPz2skZNMnjWZocEqhyo9hlIyHS+k2oGiabBvNWJzQWJ1TceURY5Ubc7bInLK5AITmYBe2EKKiyCVCP0sdxzJEDtZnlh32JjT6TgRWU1ioH+RcitN3owQRAHHE6h5PdJRnuV1nZed3MfvXDjEk0ttjtddrtiU4R2378fW58EpInU9OpqBEGh88UU7eOedJ5zCthVMzu/P8cWn1uhLicyt+5w8mGDY1JElgau2p3nPbcf5zIs38OGHjnLu5nm6bp7ZskalK7C5GFJ2mgiCQEJI0/QCus0kF470cfK4zKefmCcOZeaqEV940WY+/MAcpXbIhmLI+UM53nHulp+L+0EY86GfHuf+6jSNrs6AlqRWjxgf7ZLQJLZaw7zr4nG+uadEWpdJ6AJvuukoF4/k+KNnjXHpp/cjWSEb0jpzVY8vv3wLf3nfAi0cfvfsEb7y6Bzff815v9BZ+tP/4UtIvyCNorDX46nffs3/8/P0/7uxb98+zjvvPBzHIZFI8I1vfIPnPe95AFx//fXcc8893H777QiCwMTEBG9/+9v/U42iT37yk7zjHe8gjmOCIODNb34zn/70p//d4z/wgQ/wZ3/2Z//q+aNHj2JZCT754CorTY/nb8/Sn1T4/p4aDSfgZafk2FwwkUWBv7lvmak+jV8/tYj4TzoYNzy+xGKnQ2grDOQCclIHT48Q4yyv2DLB3z66hB2EjGgGJw8nuHAyxeFqjZtmVmjbKpakMprW2FvqoQoCApAQNLYPGCw5y2hGj9V6EtdV6AQRKTSmijpjGZ3d6208L6LRC9gxrDBb8+l4ITkrwtIcOr6IgkYQBUQIGFGCt5wzQhCCG0ak9BP7oLYX8sXHSwylVZ6/LcvTqx3umK1S6wUkVRlFc0nqHpO6R0MK8QOd42WTCDAlibobkjUFum6MLMNIxmWtI2LJMo2OwAUjeRKKxC0HGwz1SYyldRQJHpntcslUmgcWqyiah6WF6JpLTgc3DrEdk2pPYFDLcMZQmluO1ah0fZJGRNc7IShbsCTOH8lxcM3mSMUmaXp4gYAgCsiKT8aI8MOIRiNBzxEY7u9haB6mFhFGMUtVi1QUUXJVJFnE6UicOpTgeNtme9Fi33KPkwYNzhpNMlO3eWihiQA0fI98wsPSImZXTCbSOprl0bQDmm5ETpMZTSc5bSBJrefxrQMl8iZEeMiSSBAJdFwomBqn9mXZXNBJGxJ/+cgcKhLNHhRMmf6kynWnDeCHEbIo8IWnlplruAhiQH8KcokWjqfT9ST8KEKKNNaqEpvyBgZdlkKJX9s+QM5Q2F/qMlfvsW+thxCJGIqAK/qYkogoRth+TF8qJiLADyNMSSEQArqOjC5L+JGPgIwixaStHjkjQpF8pismqqDQ8WJajkLejFFkh46nIMUK1ZpMNh0jyTFNN8SQBCCm44hcMZXnvNEUP95Xo+bbLPcczurLs6FPY1v/iRv/2XqP22bLhFFE1/fJmF1G1AgSLvuXMyfGzesirzytSFpXGEnrPLnU5uHjTcqdgEJKpOa6DOUcZCmm6QrIgkqtBxcOFFluehyodUgbMZEAg6aBHbj4QpesGRNHEStNFUVQiAWREctkOKkjSTErLZdDqx6XbExzwXiKTzwxQxhHJ0xfJBU/9kiqAiE+jqfghxLNpkJClcilJMZSGottF111WGmE9MsecSImiEPiSKXjRsSBSa0pMpSVacc9MgbEgo/nqZRbEtduz3P60Al2kygIBFHEw3MdsqbI0+UGNa+FLHtIgky9J6DECtW2jC6K9MKQybzKKYU0u1ZqaIkGGR26XkCrkabsxhQSEt2ewPZCkis357BUCS+IuOlwjVrX59KNGZKaxCceWULXfcbyPWTFx3ZMyl2RwFNIuRHbR4vsWmnRcEKGEzoXbkjwdLXMSiui54MlKFQ7IUld4vVnDjCa0Xhkscnj62Vevm0ES5b5wq4SwymVhhvw6jP60f4DfSGAg2s9JvM6hiISRBHX37eAHflkNJF6Q2J7v8l1ZxW5f6bJYwtt3nHx8DOx/T9DFEU0m03S6fR/qUZRu91m8+bN/0fWVb8sNdUvNaPorFGfQBjCjzpIssd77z3KZTsNXnvaIK+7eS+5KMVq1+NT969zxcYc2wZMzh3Ocs5Qht3rTY6VY/oshdGsih8GNCObtKZCLNJTFwmbBfKmhi+00BE4YySiZgcEQQo1VqmFTYI4wUt3pvjq7nUCQWUwrdAMfeJ6Ac+yOFJbZXtRRIhDAtrkjCxNW0STIzZlLR44GrBjLECVwTIFfv9ZQ3zm4RUSRYd+M0HN7ZDVdMayHVSthedlaPoVzDjDXENhJBsgZ2PWmiH5ooIgNzh11GC9HZG0FPrNkKajkFJzdOM6A8lBen6X/iGN3/zaEf7suZP8/RML+KFJoxeR1EMa7QSu0CajSdihy9ZiRC+qEXl5eoHLpjw8ughBpJA1uxhyjwiwfYGMJtP1XYaSEgflKkOJfjy6bMkpmFobQ1I4UEpi9EVszEJiqJ/IL/DwPT/i7n/4AYVr3oIxNETRUlCNFTJKgabv05/qEkUqbtylYMm8+CSXuw6Z5JQMj6+1cGKHHdkUf3HhCF95dC9nDycpmBq3zy0hKRGrPZfNeY1KY4jDPZ+CLJFPS3Q7CorZY6YTIQohodBBQqdpm5w+WGW9lcXxJJACfE8ilxRoChILLXADBUnQqLQt4iAgqyap9xLY/jI7Cj2UyKDlZbh/RuPUQRlLFeh2R2j01kgqIUk9wOoapAWNblSn4likVIPLNqQ56nVYraq0uyrZtE9ad2k5Moqo4ocwlOxgSCGmckJcXFM8nKhHSrGJowhFdlntKOxe0jndjBB0H1OzaQUuad0ho6ukdIjxkCQJ34lodkSqHYluHNJqS1iaRDkQKPU0zp6qoZLAVFy8IGZvKaQ/H/CKLZNsyuQ5YyjBVd96gtG0RzEdYmkOh9dNRrIefVaEG4IkugyoMUKsM6gP8Btb8mgXyvzwYIVdyx0+9cId3Lmwwqpd4fTxNSazCpbaoumKKJLAZ3at44Qxjh0j2nnaqs9cLWTf8hirTZnVDpi+CRGsdTw+cuUoNx6u8LnHV/nyS7YyW3E5sG6zbUjmsC/QskWG0gJPzHkMpVU0SSWXLOF7fRwqizi+SBQmiQSHa3baWKrMejdGCE1+/MG/IDeQJ3z2yZxU7GLbOg0n5NKNEcfKOv2mTNXt8IqTN3O4qlG2n8JSEvTCNmeP6+w6lmRjSuFPL5rkm483uPHwOtvHBDYNrTCUiInFEu32GDVHJKUVON50ibo5qq7H+eMqbz5tii8+VmKh5XBa+iR+WD2OWM+QyVbIWD6SqNCwA86dNHnDeScEMM8YSXLGyAkK9V9dtZXfu6vLgRJcUpRYaEUkBZGG65LOzXGBNMKhisu9q8uEakDZD9k5luCqDVmcOGTQUHnnT+b59nVbeOXXj2JqCivVQco29ByVhNGh5HQZMi1ank/Xi9CiFJqmc8F4mk89PsexSkxCC3jL2YP83ROziGLE5j6FRlfgO0/2mEyWecmOn+kFyJLA+58zwetv6jLjOvSCiKGBGE0VQBB4x0VjzNVsPnzPAt/79e2855Y5pEDiTWcPIgmwZUih4gm87OQCF09k+NIT61R6AScPpei5J0YCfoVfPmzZsoWnn36aRqPB97//fV796ldz3333Yds2n/jEJ3jqqaeeGQ/478G9997LRz7yET796U9zzjnnMD09zdve9jYGBwd53/ve92+e80d/9EfPOKjAiYJ4dHSUQqFAKpXigy8p8v475rnhSJ3XnTHE+154Ep99pMQ3Dnf5NV1nQ1GhKlvsmwnYOq7x3G0ZAP7wOXnefNMRmq7PzGrE2QM+tmtweNlkMq9y3Xlb+OC9xznejHi47LAambz5nM1MhxEH1nwmrBz7yjbXnjVC1w35u11LJFWR6XKDXmhwkiHSUkVWXAEBi6ovc2qmwEPVDgvOCaMFRTKYOyKgZT0UyaTiumxId5E0g71rOq4vsKOgsKN/kHfcU+Mt5w3wl3et8keXD3PexIni/f3DA9xypMbb7l7nLecPsi71cI0AW3LxQo1CqOH3mqQKApWuQseAi4cnOHc4w18/sMjWMYNaN2auXcFO1hjJacxWYF1O8EDL4e1nb2C/o+BFIcVimqk+nXtrx/nKbId2x2ByQGbF9xkzVbREmYwq0+n2SCZ0dpd8NmsmM22HltijIEW0JJBDhaWui7Me8EcXbePvdy3z0EqZkUyMF7toosVyz6FgKjStAFlPs7cHU4ZKHHYRQ4s9HYWTlJj9LZXTBlLMujanmRk++pxtANRtj1d+6wjfm2mgWRFn9he4/UiDrcUkT9Z6pFSJchTyjvM2UjQVXvWTg2SMmGS2Qt3o8oW5MraXpK3JNEIwZZWuHaGLGiUnZNWVeXLd5ZrtSSxN4EjbYigf0BNDGlHEfCdi9wMVzhtN88Rak0i0UdIKJVskpXv0DAEt1SMTGczVRBodDT8p8cJzJ6hXKji1iA882iRryFTDLjkDSAlYuoMXmMw3VMaSPQxJQ9W6BJJIjMBKU6egCXSDmJlGim19JqcMJXmiPIeuQiPQCKUAS4XhsTZBqCN5MfuOFQiEGCEOWaskMBSFki1g+iArJwS50yZ4gUBkKzy4onDHbIcl2yVjKNhhxN31BgdCjXsbIddu62dyOM3MoRaaHNESInQroBcrrPsKWj4kjARcQeKnVYeJpMFpGwusLYU80GrxvM1ZSk6PpWoMsYQu9bCyAQlJoxymuH0lZqkdUUylsHIy890S63ZIq6ehqRKDocXhlZCEAZEUc+aYzLLnMddcQVFk5hsKO4aLpHJZBgeyjPc7/PBwjYtGckxkdG5dnCUrQn+mh+4rTK+pyKrOfF1iu2oiyQqLYZN6WaYvV0bXYlZEH4UED09bnDuc5neeNcSf3jnPIyWPqREJK9ljraEhKyI90yC0MuTyfc+IMu9b7RLpCom8zI8eapPNZDD0DoFvkE51kQSRpbpGX9bFjgQORw6PHxOxEdmWSLE538edhz3muh2eNWmSVROsdlzurHXZvW+NT11xCk4QsXFU57KN6Wdi9wt8gR/PzbCnkz7RtJR8giBJwwt5Vj7m60dtkvmIC4ZHkQWJb8yUqQshliJjuzq5bIqxbMC9x3r86UMtRvtkjtVtfv+cLcy7Al9/bJ03nbWBv39gjTecN8zoUOY/zBOffWSNmQp8aHvxmYbSyRtDfjI7z0xb5d0XbOCqLSdEtTMNjY+es/FfCWH/S5TaHqYqPaOJFEURgiBQKBT+SxtF/70GDr/C/3/4pW4UjWRdZjoNbFdjNKVQavscrIZ8Y/867z53I3/32CKKINLfLz3zA+t6Ie+6bZY757oM5MGUDfavCIzlHfK6wnJdZfvwOrsX+yhKBvvWXXaOBGR0EZ8yqp8kpWp0fZttfSAEJl98qs2Xrj6Z99wxw2K3Q9aUSZoSq02FQlhkWW2QSVQpGAmW/BZpU2Z70WSm1iGbtGi2UrhihztmS1w1Mcirzhzg9uMCC+0akzmJyb4SSDFpNYWgr1GILL78WIqEJtBnqKx2bM7pg1WxSl43KCZbPDhr0mrnaaUWScgZHjwucXJ/j58ccBlMKmSKHcyET8QQeVWnEwkcLTtsymSZr2oUTJ197RbP3Sqiqw36ZIOKt0LSH2Y57nD+RIK255I3Nbpxg66dwY99iqaGHbhMlzKUmiIp1SWNRjtaYVBJgFjn/DENN4xJEKEpIhWvx3kveRn69heTjGqs7HqIrRcN0FfIYSl1DFfHDwNiTggiL3Y6ZNUE24darHREBtU0e9dhqenz3nsOc36xyQs3bWLQSnKJa7Ha9vhsa5WGWyGlJLFthWVXRpVjfF/jyHzItiGRMM5y93GPC6ZiJooms/WAfrPHxrzKYLLGUkNkrpJnrE+j5enMrodctTmBbPjsWq2h50KSmkLSlNlfChhLCkixRUL1We/0GEykkAWRILTo0CTwksiiSKEvouXKOE2V80ZSvPc5I3z2aY+Zchl8hTDQqfQcDiypbCsq5CyfIJJQJRFTObGT1/OSpIwIEIiEE44ePVdHFSU0JSZQIAhlokigbacpoVDtgRcaJHUfSZQIQ4F2V6Jiy8TEDGZdsmmHAIfVah4xNAkjCVfsIUo2g4bKo6sV7php4z+ooEkxliaS0FwKqR6C4FFIQEb3WG2r5IwujV6Kw+sqlVaHm482SCoa5w+nsXsxn3l4jVMndPY5DqebGrWeym3TMvV2lmI2pOZ2ETwVAx3FT1Dp1kkpFmtNnV5comBm0GWHhmeT0RRuOj7DzJrBsKVS7QZcsyPPWWMWDx0OeWJvlayR45zhNE8caZCSZWa6HbSVPMeECC+2GU+rRHIVXTQpNU1GzBV++vkfoZ55NX/y0et5pNkAAtpdk9lazEhK5+F5l7NHXbqWy8zBFO+9+ThzfpkzRhKkzBZ5S+HGfSb9osbR9ZCxhIUg1MmlYbEec6pv8NSKjKUN4wUKOSOg6TlMT0+gmj7bh+Etp27lQ7cts9B2uGZ7nk/cUeb5p4wx3akSRxaztRaBk+XSyQzvPHcKQRBwg4jH5ts86590C9KazocuPJ333bePIO4xaOo8etThzbcc5HfO2chRRaLi2Hhihcu2KRxeU2n5IUs9m58cqWBGKp988SSvvfEwUyMS6/WIvQsmaStivt1jJKOSN0MOrBqk1Qx24BGEAn9x8Qiv+OZhYinmjEmFlXbA45UlNNlgrS5Q9X0GUzJN32NbweRv71vmDy4efibmf/iB40zXXDRBxsen5NjYyHzz6tORRIH+hMLLtvfx1u/NECV7vPeyUYaTGp9+dIVOELKtz+RVp/Zzz3SLe460yKdkXn/mAL/+rSPc8vKN/PgXnKPCWIBflEbRL4nw4v9uqKr6jJj1mWeeyeOPP84nPvEJtm3bRqlUYmzsZzoRYRjyjne8g49//OPPOKX9t3jf+97Hddddx+tf/3oAdu7cSbfb5Q1veAN//Md//G8W7ZqmoWnav3peEARE8YQT5YevmuRtt4T86EiFmw6vs1KWuO70AjcerLH0dIsgAllXeGqpw/N35ABI6iJ50WS+2yalyVw6MsZtCyJh3OE7h9bxj9h0HIWELoLu893DJY7UewiCzJLTYKmmcHIxwbnDafKWynzL5vHKcdabOgNJlafmdHQ94NQhDQKd3cs9fnSwiudHSCkPTVIpd0LcWORspZ8l16bSFUjIA/QilyAWKSZhT8Vj3V7jr1+whXfdc4TfOK/Itn4LURT5zCMr/GBvjU+8aAOn9Cf54E+XuHxTkqcqFRquyIYc9AIbVVR5ct6k64mM5SO+dXidzQWLL798G1/dt8SBSptX7Jjk4VWVur3GeF8XXZE4sqzzF4/M0HR9Ak/jibU2127tR5Wg2RMYKUCkugyaIsgtGt0Uh1cU8imX+XKSliNy59EGv332CdbmxLDPA/NN5usheVXl6UqLN958iG7gY0QaCauCKMj0/CYp0eLgvEYyqXHKgMpFmTQPrs6TNXWO1SSG0zGtls9oJsmHrprgjtky/7h7mf03tRkqCLg9mVCMySZhvRvxyFqVRELi+qumKHV83vj9o5wylGbnQIq/eWiBjgOnjnQw9ICD6wkkQcSOXTKWQqkT44cyfuyTTXaZTIhML4tcMpnmlKEEhYTMZZuy/PYt+ykmBERsYgTW2iH7VhVSks6e5RQbCgpOx2MagWTXIBZcJrI+mhphhz66JPO226a5rE9kITDpRRGm6KOKIEkeIhrltkjBihjKRsxUk0zkYpKCyEi+SRzpHK2Y5BJdJswuC7UE9aDHTxfruJFEvySQT7cIkXFChWK6Tall0fYkVAWIZXpdjYn+BIIEXa1FQhEQBREl9snqIpLkszWX56zBJBeNFHnbPfcSRwqlLsiCRCeKOTWVQoxPOJctr0kUM5DIBiiiBlGIGyokNZGQHquz/YQdkYcP1blwLMuVm/KYsszj6xXWvC6TRRs51nlquo8/etYol2/KIQrw+d2rfG3PGr977iiTWZm/2b2IFMPRssI5uQEOr3mEsoMoq7RDn5VeF1Hy6AUKgmdQWtNIBiFa1OPmfQ2OlG28UGH/ikvROiF+XvIrdHspZtd0FFXi9PEUF55b4PzJJJIo8Of3zfGQs85ASiAltchmodqKUOQkrTBkLGty3mQSMTFDLiEykG4znlW5fHgnpTr8ye3zzFZ8fv/CUeZqNl/fU+GyqQzb+xOcvVVirtXEVHRi0yahKfQCn51TPQ6XRHK6gqK4jI877F7SWK0muNmuk8u2GVUNHppzyek+A4UGF0zmuXrDFJoioylwxZbcM3H0pgM1vvJYl3M2j3LXfAUzNmnHLq4fM5QSqPtdLj7J4rdO2c5qy+edd86A5DGQNKi6Lrl0AHqHJ1c9+rMGDdtjsePxljNGuWZ7kYfnm3zzFTvww4itRZPhzL+O5f+MphOQ1mVef+4AivSzXHDjwQp3zbS4YnyCC8eSnD78M+ORK7b+2y5s/4wnFjr8yS0L3PCyDaSMn+lH/ix//Nc1iv7/eO9fVF31y1JT/VI3imp+i6l+l0MLm3lwJiRl+SQVlS1Zi5dvH+TO2Rp1y8ePbT6+a4F3nD/KH/74OMe6baRIxIwTPL1WJwgU8pZBGDXphTKL1X6CQMEVY9J+CimKWGg12dAXEgQR8y2bINZQJYlI8DlzzOaDd83zgYuneNOPj+L6Ad3AYSQls2OwTX+qS6kzwM2HT8wDj6cMVjoeWUNh51DAU3MKxXSSitPli08vceZAGlf2SMcWI9lV2r7/T1TSLlGU4eHjFog2l41sZNnuIAoRulZDjmS8ABQp5qINAQ8cjxDFUTS1R1qXueVggTgOEGLQ5ICMqvOlmad554Vn8dB8j+W2znI4x2SfjCx3GYsSyNosIglafoOiYVCJ2qS1FMcbTfr0JJWex0Aqw0rXJqtmmWutMZpMoSoeeSPFfF0goUbohoqpdiha0PLLWAyw2qsT+X0EUUQ+2eD0rX0strOMZ4b4wV98jh3nbOdFb3gesrSOGA9Q90rosk5CVlluh2Q0kZQWslaKmdCTVB2PbDJAjE3OGSjw5SNPcd/xDs2ezpmjBnvLIpLcY0NBp9WF422HSSNFIZJpR02mcgaC4mEYHuWWScIX0KUOjh8ixBp9iZD1XoW8KfPcwTxnXp7jQ/cuUyCL7fjsWgo4fzxGijLYQZO1TkjopQicNI8s9xg2TCazAoYq88gSDGQispqCpUikLJG647NnucMrv3YEPR2QMANSukijm6YViGiaQ1J3yFsRiuQwY1vEsYSlishCQMc1adoqnm8ionHBmMRd+xQSSpdAEKi3LYIoxA8ibF9EFGIajkRKdzDUEE3ziK0AL/bo0yUEUaPaMEGR8X2VQjKikG6TTS9wmiDRsQc5XoaVekQnrjGeVRAFEVOzkUSXHf0ueTOi1BPZkOvSsJOst3VWqjKi0kWXNDotgTuabfr7RAxD4EClS3/yxE5PKbBp2gIqFms1iUTGJqspDA6KfPiibVzz3aewFIly1yNjSVio7F5zOW0kJmuEtDybVEJiUybNjXtqbC+aTOU1NuUSvOHUBFdOFTAUiXplgdGcxmeecok9CV+vI3hJjq6INKMMU1mDjjPN/R/4a0562YuppDZy00LAQifi1P4ELdYZSSdoOXDycIOW3c/RSsC2Aag6PXYUBVS9TK+X5fbDOhlZxfYEfviarZQ6Pg/NtTEkg3rPY+/RfkhX0KU0L9g5jxCrPLok8axT19m3lKHcU3jl147S3x+gSBrffqrKja/fwsY+nbfd2eGhJY8BfYBGN2Z+XSCKoWUHvPprR+n4EX9xtchZYydYRWNpg/ddtIVvPLaX3zpnE+92ZllvhHx3f4/VXptQcJnKaYhSi6HUANOrIg8fb3NOMcPdhzqstTyK6Rgbl0Je4dC6QyuO2JDV8GKbeieFJsgsN0ISisafPXuCV351hrddPMKtS7NUGiZDSZWO0+V4JWQqbbJkNzg4L/HN39zGO350nFecVvi5mP+CjXnunq0zW3VJWALjKZPrL91MWlP59t51bj1UZ67uctZEAt1SUEKVl3x9P/mkyFrPY6nmc923jnJs3aFlR3z+1zZy50ydz714E2nzF88oimIB4VeuZ/9XIY5jXNfluuuu4/LLL/+516688kquu+46Xvva1/675/d6vX9VHEuSRBzH/I8qBdy0v8YrL/insQ1R4O+ev5Ewirn+0SP0gi5zrS5pTWauoTBaFFmrh9x1tEVKX6HqeqQtiY+9aJL33z7P688ponktLtie4XdummGp6SFrIsOFECd0CRBISxrTrRYdL0KJDUYyPueNpXjV947y+Rdv4tYDbVwpwVnjIk/NxwiSwJiVRg5U/uySDXzwnhkOt1eJXBErMhGMBoap0mck2Ffq8opTCjhhyI8P1pFU2DRkc2hdImdC1455YKHJ1685hU89sszv3TzDBVMyXztchVjlxoNl3nf5GNFdMfctlel2JTYOioRChz5TIOjFWKaNJqdYqgekNRhJaoiCwPlDeb73dJO/KS9z8RaRiwZ38LX9yyy3YKwvJBS6DJtQ7UCjJ/LQyiJlN2DDkEqrE2J30oTZJv0ZDU2EpidyRW4bVuSS1mResmmQHx2oMdlnsOd4iOqnwe3h6i0GM6ArTbJCAJGGpfnoUkjSiFipx6RyDpFr8dOjNv1ZGx8VRYhQpZg4FjBVgfVOwLXfOMibz++nP6VimjFT6QQ3VY4zVUzzmeedxCcfWeKHh2qM5hS+c7DEznyCUwdS2GKHzz41zx3HGiRlmXIvZlBM0OhJZHURIY4wNZsxLabnuBiqSbmtkFElshmPXzslx47iCWbX3dN1njc2yP5Ki6NlBUGK2Nxn8tkXbsEPI/av9hhJqxwud/mTu+YRTYGeqzCUrpFSRTYUOkhEZBIe61ULURMRPJmJhIir+LhxyHCig653EBGJIoWmLdKzVcrNNNWaRjYh4voiizUDs60iihGOrdC2LS7alKQXdSi3epw3XkNXJOZqFvW2wXpTpVM2OWnCYudmix8cWWdD1uA3d4whRlDtBHz/UBU5EdMLReygwtFam79+dJaEppJUIW14+LbCwqrIV8tLLHfyXDySJ5PxEbWYjqMy72vUXJeqKpFQVaq2wdkDWTbnTDRVQpUE8qbCkXWXomlwqNYl8DWCGMIAvvz0Gtc/uMjHnzfFKQWL2zWdth0xOJqgXhug6npUO7CXBulig1+bGuOcoQIfffwgUSSjaz1MRaXctdkyYnLN1jwZVeE7T1ew/Yj+fISaqnDvagtZhFfu2MK9sx3sTsifXD7K+ZM/G8H5+t41nl5vMZrv0bEVlv0E8+sZ5tZ0zhozUZB5/Q8PM9ux2diXZKZqo8hpmo7PRUVQJAFDh58cqLN/ucdczeOVpxf40cEKn9s3QyC49CdjvLBLVhPIJdtIiPgEnDSQoOlEvGjTBqZXZOaoY6UrJDWZ1ZaCIYtkDR8EmZdv3MFFYwP/bhy9bFOa528/0WxZqUfsrTcYSgmsNiOcKObC4QIvOn0He9a7vP+BAwzlArqeRCz6FE2VxWZMoxvg+xJ1XPKWTErSuWTDiTXPHz8Ro2VJYvhf6Aj9S8RxzBeeWOOu6QZfffmWn2sSeUHEQ8fanD6Q4HtPV7nvSJtPvEhn5D9oOP1L5C2Z775mC0n9337v/9fwi6qrfllqql/qRtGIpSNIaVCaJEyTrGzR8G3+4v4lrtrYR05XeXS5SSEZsNLt8ju3HiQSBAZVg8KQw3QpJhIsNuVEetECa02Fk4seTy4puL5I1gSnJbJas9g4CIeXBaYGV0i6WWTJY71no4sWPU+jFXf5+IPLJJMhFcehTzep2T2O1WTyZgpT7XLBpM+R1RFWe2vIUprVepJIiDlnKuRgyacXwNYi7F5sk0+oDA81qNsSW/MyJafBkKTTdDT6Ei62neGG+yu869IBdnkVNFmiqImsdjsE3SKzFYUtfV3uPqixcyzi5GGHU0e7zKz189RSzBmDGdZsl5ye5nd/MsOLtvRzdNXlHZeczqefPkzBkBjprxCEKim9R9NRiQWHgWTA4dU+VMkniB0kQadqNxlIiEyXRGIGkYUSfYbGjFBjNDnAdEkgiAfYGzU4fbhNylB4YsFkJBPS9nroYoZ1v4muxGiSyMCmfv7kC+9kfm2FXXfuobQ8y4XXvghNydD1AxwvQd2WWG0KbCuCKpe4dOAkPF/kH/cssEnTuGl6jW/utTl1SGK943K85dFnyNgBtIMefekEdR/WyiHFpM7RhZgkEZMFsOOYfKqFKooEoUbFDuk3HVROsIM8YZXH12xun59myQ/Zak6xwcxzz/4O7cEGlqYwnNC47bBBUdIhUjGB2bpLztKwvQRWFGGZK6T1gIylU7cFlAEbS3GRpQg36tH0DCRRQpcE0qpO1gxZamos1XWW2wKW3mIqHyKLIqJwYresmLCpdTUcVyX2swiuTb+mEIQGqx2bfL6NgIgqRZiqR1+yxVOLGSxFRpBcYikkjHQ2DYTk9B52GOKFoEkeiiRQtQM8kpw6GKESsiB5OKLHYCJmKB0iiiEDqTYJJTghtK14LDay7J8fYb2RJBRC/AgMTHKmwdmjKS7dnOK+5VUeW61xeA0u3phAVTocK8WIYQ5BgEirkNdlMobH06s2f/vgIleNDfBrJ/fx8h/upV0foNKVaHkecmiRSc7TquQoJuDJtRaxJ/OFp1f488smCaOYF27uZ2+pyxcfX2fPgk2uJDFm6vz22f3c8HREIwroS3UQn36Quz95C8//0B8z9GcfY7YWkxdVmp5PTkmw2orJpVREFXSlR0pJUvEdpjI6D89EnLvBx7ROzNXbbkTKctE8A092+OtdM3zyqu28/4pRzhhJ8I+PrfHD6RK5OEkxO0+5VUBUypw7ohBSRxzW+doTImMDIa2uhKkK/OQVWykmVT5033H2rXmkFBVHaHNSvsDjcx0+eNsiN+6vMZCV2VQwOHXY+rkYuiGd5PUnb+SaG48QdRUmhmSWuj3yhshKF46UVIZTg6yuqyQFlS//2hbSmszBc7t87P5VvK4GRo91t8OpIzrHKh4hLv2WyUJdwOmCIcrMtXq85yfzfOCqYW450iC208T4zDYcMpqOQMiHLpvgLx6Z5nmnDfGF/Us8e7vFq84s/tz1nj6U4lsvPYnfvfUI0wsh1507QrUT8Wd7jnP3dJPNOZOcIfOSkwp8+J55ppfKNMQON1x+Kn9y13HmSh6Hlm3SSZF3PHuUrf0mW/tPzLp/7Paj/7tS16/wfwje+9738tznPpfR0VHa7Tbf+ta3uPfee7ntttvI5/Pk8/mfO15RFAYGBtiy5Wf6Wa961asYHh7m+uuvB044qf3t3/4tp5122jOjZ+973/t44QtfiCT99xXy/9xQumBEfkbY+l/iWYUEUdfjrrklLhxN8fYzs1y8Ic1fP7bI3qUOR5dD8pbMD/c32WxFJLUe199ziLeflOCp+R7HyzUQI4pGjBQ1iTwFS9Kp2l3qbYmsJeG5AvsbMX9ROcSZfRkeO7bOP149xHvuOMqOlIlcjKh0Qv78ggF+fLTKd586zgV9Onc+KbJh0mGl0yERx2xOuVR7LRTJYPdSwEcum2LMiPiHJ5Y5vtphUyak3DIQhC63H/B5zrDOa05KsVKp8vmHVyimIvJ9ArtWyrzx+2WOVGy6QcBUn0Cr7SO0Raxsm6zSpdbLM9+qIccSnWbEm36wmz+5cIzPPVzhjKLOcM7iK0+skTkNkhFovsv+AzKoEuOFkHrXppiOqbVdhpOQ0LpIkY4ntyiXVaKeBEqXQT3iob11nrUhzZf3zxG6dWbqPV7aN8JP1ssYKZ+iITNfihjOBYhWgwFLwo6q1CspkGwSqkK52kMJdRZLHfrSAqvrIX2WyLrfo88Q6Xk+KTkEyWVAShC2HD520QRrXYfbZ6o06hG9sMnzbtjF1HgbS5GYkPqxW23e+8ACE3kF16jx6FwFUYSkLFGqBHQbIk7XpeEF6LKIbNZJKAqFpE2/MkkQWjy95JAMJUZ1gaMrJXKGyneePsbhNZcojklZAm7kc2afQbPZZKFlk5Vl/vD7xzDNCF1u4nkChgper4GpyYiRj+16JESNwUTM+JDKW87IUkwoPLbU4FNPzmEnwA51cokuaQ2SYh0JCUGWKDckRrUMLxkz+OaBVdK6REJpkk9E5Ex4YH+KfAJaMRyVwZRg/7qC50a0HR/kMgdW2tx3OOaaUzKUOj3+/u41shaIUoQUhUihiCk6iK6HKipszXXpMwM0OcaNArpKAlOOkexJ1kpN/nDvIilVwO4KZDNtlFhGDB1ETz7B4rZjPC3maFMg7CUYMvIU1SR/cF6GxYZDaDtsKxpsyOp8+N5jNFoiScPmr+5vstqO+OSV2xhNn4gDO7Ihj66V2WRm2JxMce8hg7ENPp+fn0YJXdqehxT6GKrE+nGZKzeJXDyk8r09FV6+TWffyhrdHqh6m7wm4scu33y6w2JV4hNXbmMyd2Ls1Qsj/v7xJQ6st1nruThdBT/22GKGlN02luKwd7XGe87byIZckUcWWtw2U0FXGiysKsyuG7x3fh/VikA6IeB2QDBCbK/NzYer1LshW4dDQtHGaRtoio2lhfTJNRKKyLGqTq8r8ZHzzsMP4fOHjxAobUw7Yt3ukBQ0Fss+acni7P4MWyz934yTfhjxwGyLSzdl8B14/09nOdqoUTQjWp2YZ4/k6bgBG0yZZrPJpCFwelpmuiRz7rDOvUvLtOoi40WFYyUbSxVpBwEr3ZirdgyjRQ6tlvOfxvNS1+V99x/jWUM5/u6KAexuB/tfvP6xe5d54HiVV55SZFwPySd8jNhmfq377zqbeUHEn9+zwO9dMEzeVIg9j5b3s9ejKKLdbqPr+n8po+ifP5dfQjnl/2PwSy1m/bof/oj+fhlFgqVKH73QYaVhYEgxpqzzngvHue7GQ2wrSiy1O7S7GhNZFVX0SaYdPNtAFmUi0aXUkHnuSXUOVHwyZg/HHqQXtpmd66MvobJhvEqlo2EpMJwv0/U9TNli13wS14fhpEGlIdFuK6TSIQtLMZmhOtsLKqOFZSxVwZR0lto+s+UUqhSzZ0VlJGEy1teg40p4nsmhisNIWqBVs0glA7aNuByu2Jw93oJYRhSh283yo70GCVUim+xhqbDTDNjneGwuiOxZFVHjNEHoc3RGY/tGn6FCk4yqcaRmI4UZltZU0qrCYtWnr99FEVVsXwRHpCO4bBsUWGi6vOjkGuP5Brqo04vKrNTGeXguiyQIFDIlhpMGsVhGiFK4gUytJ+LHMe1uEseXaa0nERU42GryrCmVhZbLJVMdVjoaxVSbkbTDWltFk2QqHZVOT6fhwXC6y5Zim5ods3D3/ey/bxdv+sRbUQSZTtTCEgeo+zXW6oPMVlKUOyLXbhrjgZkOzeYaI8NZDjbrDCclBtM2ghgxW1YYTKnoxgoKKZqdBLful3npxkH2rvRo+B7prEfajBjNt1ioQ9GwWG8LjPQvMGRmKHdlVnst9LiI56scrnbIR32M5jT2lDpUGyHvvTLJYqfNDw95/M0lO/ja3lVqtkNkrBEFGuNpA4QIN27jhgJTGYGBtM2+1SStno6ITN12qbkep49EZHUZUQzJJ5uU2wZxrNG0Yx5bEGm1dM7bEDJe6JJNVCmYLl0nR7ljsn/VZOZ4gmcVItaCBIsdl8DsMZLx2drvMtlX4WAlII5UdCFJx5M4XJKoNpNsyAvkkx6W3qRhnxBXBFDVJpv7evh+ltWWyRPLAkfnFV5wakB/xkOWuyT0FkVTIa2HSKLP3rU0i2tZdh/tw8jbeG2FLdkUn3rpFK/53hFEtUEu28HUbPbPZ5HRKeTrrLYgKWY4vqTQlNqcPaziyx3mawJ9SoZ2T+LWV5/MG75/hOPNHr++fYj7FupUuh7pgVlGkllCoclTcwWSkkbdjskoEmfmAqZDj7W6SuSpRK7A1n6DfeUuqgzpRMieo3OcmV/n6FO72HH1KzETKofmE6QNkcWuxxUb01w2keHGuYP0mTF23GI0abBn3cYgT90JmVkzGbRUTpo6ghjlWW/L+K5GLLeRoiz3HIQ/v3KCV5x8ohkSxTFvu+MQj61WOHskJJe0Gc3aNL0WoTvM/jWZejvF8UrE9oLKehO++vKtjGV0Hlio86FH9lKwQl66aQtXTPTzjhvn+b1nDfCnty9wxpjFZZvTFHSVt/xwls9cO8VEVieKIo7OL/PH9zcp9Xx2DFoIepNIW2DASPDQbBpCBdGTWXYc7rzuVAYSKm+48RjVnsN8w0dTIZNy+fCzt/G5x9ZoUsHQfALP5In9Gt9/3WY+ctcix0oOubyAFEis2i4DWYm2E1D3bTZaWUYKAQdWIopJiYwhccPzdnCo1EMWBbYUfl640PFDbj3UoOy63Hy0xllDFj/e3+CLL9vMu2+ewybgky/YxMY+gyPVDh++b5FnT2Q4oz/FR+5apJCS+fy1m39uzUPz62yfGPiFii5uv+GrSMYvSMza7nHwzdf9Py+8+L8Tr3vd6/jpT3/K6uoq6XSak08+mXe/+9085znP+TeP/7fErJ/97GczMTHBl770JQCCIOAjH/kIX/3qV1leXqZQKHD11VfzkY98hEwm8991XUtLS4yOjv4v/ne/wq/wK/wKv8Kv8F+PxcVFRkZG/pfW+EXXVb8sNdUvNaNouhoSaAZRoHO4HDCQjpnIRrS9ADeI+c7BEh++dIzP7ZkjpYuMWAoHZ8EXJM7YLKOoDjVHIKMJnD0ZoKgOJw9GuF6OtrJGs2pwwc42cw0PN/YYzwlkzDZ+JFPQDfauyRRSDTrtId56+ib+/JHD9EsyPVystEbK72PPWo18BoqWeGIURjMYSMastH22FQwOrTtYZsxERmC25rKtCMdWdTzBZlDRCOIem/sE1ptJOkGPHQWd43bApmLM0prAjCty+RTIskufbnHvdMxE3sF1A0DglC0BTy5AfyqBlqghhHnKTg8jxQnrdTXmNds2cMvSMpsVk986dZjrH5zBjbpMZFVuO6pz7Uk5RKnHcDLLQGaV8yYUHl2KCPw8CA3kKE/Nb5KSChwuw8asgiDGCLHC2y7t55P3r1NMynQDl435mJ8c0jl1wGT3soPtJJmuiuhySFpTeGo1YGtfgl0LMbosU0y12XjFBVx67aUcmp/lm+/5Cmdfcw4vePlFjCdVBqwKa02VtJbhZdsHOHmwgVNT2N0Lmcqm+Ml0BS/0Gc+J2JFD1gowVJO21yQmwWRRwFNdPvC8YfYsdfjOkRU0xcP1TBYrEouxzEBS5vBqgSOxzBnDEf8fe+8db1lZ3f+/dy+nl9vr3LnTG0PvvakoWBBF0cRGNBIVS6xRv9bYNRrUiL0SlCIiighIh2GY3mdu76e33ff+/THRSAxJzG8mqNz367X/OPu1zzrP55571rP22s9aT7spIInTKFEboSDzxFSVoJgirymU/IgfPBTQnU2RFi1edcN+Pvncfh6dLTDZinHcsn1Uan0QaYhySBBZqLKK5QksyVfYM5PFsRRCN47tVbhrt8aZw9Cb9kmoHlVZxXNFCGVSskCqLcT2FUYLcQ6UPNKxJv3xEEnyCQSbsqOx4Ll4MY8VnWVmGj5JQ/633gISWbNJUhEg9Ci2InKmSuCISEJESMCuOR1FCuhNCGTjFqYW4fpxnEDG8n12HUiyfqlLICh4oU0YeeSNiJwZHk6MRFBqGFRcBTNlYTUkZEFkxqvw6ce2U/M88EWWdFkYmsuyNptNexOgSaiCTtODkuXRHk9Sq0t86pLVfHPbFBvb05w7mOe7OyapS01c0Wd9v8HyDoMdsy32WnVkyWHHjMzSnMB8q05UjyEqMF6x2L9g0pM2ufaCbpa1GzTsgKtu2o019hj7br+epadsZN0lz2cmt5xYLMGCXWK4S6FQE/nys1bRl1F49R33ceagRCgtMBCTcQKbKNKIGz7TFWgXDapWyPjMEjrapkloObZXG+S1BE7gsSSnE1PE3217KgoC7z5tiNfdUaDph/QqAbN1mCx3oogy5brJbNNmaV5jTY/Ke87o49nf3MYHL+7ihSv7eHWzl3+4ew7VqfPc4S7ecl4nr//RIa4+pRNPdnn1bbsZTOjMOj7v+NkIX37+MOWWx5fun2GiIbIsH+OKjSk+9/gcZ3aajJQDehIS42UHJVnns2evpjup8fhUnRm7gi+79GR1di14xAyd6x6aY6LZpDOtYAVNtk6pvOz0Dla2meiiTE+bgKiJfOmiZbz1V/v5hzMH+eTjWykUUjx7WY75qscBuchY1WK6pHHXwRJvvm2E71y+4g98v65I5OIy/7prno+cP8iLbtjJbVeu4003jTDlN8koGuNlh9UdMda1J3njiT2c3n+4seWbzuqm4XgALDRc3vPLUT58wSBdqf/ZMu8/hsXSsz9trr/++j/q+v+sL9E999zzpNeyLPP+97+f97///f/rcXV3dzMxMUEikfijGmn/V/y2QfbExMRfXFD8l6ptUdefH3+p2hZ1/Xnxp6IriiLq9Trd3d1HzOZi6dkfxzM6UaR6OXYdNCkHHsmET0pTqAYLNHyDlGTy6P4mx2bTnNSVIYrg0ckGmaxIStA5uBARjzXpSkB32sEVW9QcgbyhoRp1zFAhrWpEwgKyBo1GF6YxjiAKqGGeuQYMZDwKtXamvCZX376T5/X38MPNRbo6RJZ2hIyWIyI3zq7RGOlVu4grOgfmFQQxoE2PM1pxqLd0Ij9OI1hgIGtwcCZJsyQz2CdTDgskHYW8GZFKlkk6KUJxmv5sGlOSGCtIDEhJ7nrc5YVrXaZsFYEAAoOa67IkrXOw7LGuX2LWapH1VE4YWOBAOaBYMWm6PkPtMh+6b5zXnJjh4ekiH/51wJayxTE9GodmBXK5BBO1OYZyCk2/Qlw9XILWk9TZP++S1mOs7R0l4yTQ5AKrGu3csVPh5RvyvOjEHv7hrhGqnk8cg0JRQci1DtcG10BTU9hBlRN6JXYXHcrNLoLAYaYg0WHkGC00masmaEuPM11O054c4uKPfISZu29nbqbKgekRjjt1Aycv28PusfWc+bXNLO8JWCLbHPRVvCAgY5gcKshkDZHuhIIfFYjJcbxQZK4hYkoSO6ZtPlwYI/QEcHXevHElt48dYHlHk7u3Q07SMPUkyfw4uhpjSVLANGbZOR1nVYeKG/k0mgEGaZYPhOyaq+MLMeKqTDLh8v5fjbO2x0BRDWqtJKJSIibmcHyFuOYSRiG2p+CEATHdwfMNUvEQ0VOZjHzqTkTNFpmpq0yUTTRJxvFU5Ejhtcd2cNP2EnXLJzI8oihCFxQU4fAqpKHBBSQrTrluMtFIYBo1gkjB9SNkSUGKEkSijCwJiAIUyiZRKFJuSByYE6hGDZblJDQ5pD1hY3siFcug2NC5Z79EKh7Ra+boNWH7gsWKTpcwMGm6AZEQMl/XSeohTrLJaFFD1SM0zaK/vcasLTDcJ9J0Q3ZOpyFSiMkqFy3L8mgRXBwUxSWVk0gYAdWohSTIvPe0FURRxENjdeYqEZ+/YA2fun+SjK4w1G3wgz0zTNVVYmZAWjPQVJ/94wLLDI3peY9Mu0A2J1DwLD700F56zRiXSCN0Fqew801O+9jbSKVjjBQMBpMKpl4lI0jEJZu9Ewl+M1FAnJAZSimU7ICMqWC5EsWWSF9CYtOYSbeS4o0X9vLRB8Z5YtLhWDmLJAn4voZsBmR1gy9fuJprbh5l20yL95x7uGHu3fvq6HaaprZAFOrMNByGcz6/2ZVFF+JYVYtXnDjAC9e18fhMlZUDNtc/usDydJbTe3pYlW6xMh/jvvEyr7ttDxs6Yxy053h4qkbekNk/57IiYzJT8bj6xoPsmWmxNu2RNxK4QcT+osUnztrAJx/dRd0SqHk+vRmRnfPwgdun6XihyT37a+iCwkLQJJaoc6KZYboWMO81iJsuFb9Bd1xHk2Q2dsb58K8nmHZL5HMh/3LRCdTsgJHJkJsPjvLcpX1cdG4vd+2r8NWHp5FjLlqgMdWyeectE3zqkiUc25P4A99fd3xef9MBfvpXq5msO3zpOcsYzpl8+fKlvOvOEcbmXT7wy3HmGi6vPL6TMwbSAJQtjzfesh/Bl9jQGedvbtlPpQqv+sk+PnZ2/v9w9lpkkadGFMX/309fn4pkMvkXdUP0+/ylalvU9efHX6q2RV1/Xvwp6EqlUv/9RYscNZ6+wsM/AZpBgBGTGIiZ9Cd0JLlOUhPoT+jsHJeYqLl89r4p9s0GnN/fRT4m058VsWSXuCaTUVJkTI9QbKLLEqoocKDSou74xBUBRAvHT9JlpojEGlum8shRml1Fi7lmiOeajBYFCGIMpwy+v3MBPwRDULCo0J2xOXN5g1ymwi+eWMMdO7t5fEomCDRGCgq1SoozhyNSqSlmK3FaLiiax5ohGJ0MsV0VXY6oOiGBn2UwP8VYKYUsQjZZ5YylDpMLHjFTZKKqYHkiQxmZ0arP8raIJe3TnLm0QUeqQnuqAZFEyVIYTOoEUoWOuIAv2nQnFL6zqUoYKEyWHWKuhh+5dLW51FoSD49k2TWd5kAxwZbJHJYnUanriFKApgTsnM0gSw6aHNGXKXBMb8DfHreEphOxb9JjacpgzG7SFpMotCKyhkwu4VAsy1RqCVKxAucvtQ+vWorrBGLEG0/s5rvPO4mrVq9ganodD27N8fhIN2UnzdBzziXWNsjY6CzvfOkn2fXrvQRyi7PWNDBUl3IrQhclIqmFJodoQYySfTgpqCkhkWCDH0cUIlqeS0/O57JVOeaLEaYs8qqb9vCTLR6+b9DV6WOpVUxVwnHiVByLUlNn33wfM7UQx4NjOkMS8Tp25CEGOoYmMt90QIxYm49zy8vXUHIsdDkkCpIcWEhRsnwkKcILZfIxG0WChXqCuWZAzQnwPAm3laA3KSFLEbIUQhQykJ8DPCJAUkO+vnUST7ZIJDwSikzOVFEFGV0WGEx7nNBv0dPmMNTZZGNfizOGLNpiIUQKRAo9yYCsGWIqAZriMF8X8WwJxw4RtDpJDToTIoZ6uAF2xRJpOAqlVsiyzhonLrMYaHMo2BaTVZgutXOgmGV/0cANPHoSHh2JFm4ooXsSsidhaA4Z06Ur4ZDSG8yV4tTqJnGSXH/JRiarLoEdEeLjhwF9mQhZdUjHa/z0wAjvvXOE1R/bzMd+PcHtu8qkNZlPXLSUoazBdx+fZ64YMFPl3z4jYqwk0aFrpNMWA8sn6cv51KImkh/gV3Vu/uDf8ZZv3E45vpJ6fh0WfRysOMRUgXyyTFeqyep2n21zEgPtAt+6v8pvJuaZqOqAjxAl2DYnEQRJfnNI4sL+Tq5c38Hf/fQgVuAiSyGKLBHhckxXgCi5XDycR5Ekzl+ts64txrcenQdgvGzzj89ewql9IMkNumNxbnikndN7u3jHaf28ZE0HL1zXxu37CrzhF08QBDrHdWUoWx4vuGEbs5aNL3q89ze7OXlQRDMLVPwSJSvA1B1yukxktGjL+OyYsrj61A4URWRlPsa1p/fymXtn+cTDe9g/o+AFIqoY8Oh4xHP7BwhDgb+/Y4Rfjc3SUhbY2A26KBIzW/QmBSSlTme6Ss4UKFo2x+biPDze4JHCFAMd8M8XHoeuSHx38wJ/f24P7zhxDZcO96MrIvmETD4tUCiLlH2P5WaCV5/UwbNW5DhYsP7A9yc0mftfv56hnMGZg2kOzrnsm2/Rl9J51cZOkrpExfW5eEWWphtQsXwAREHg7MEMrzmpg289Mc9w1iQZFxDEiJGCc8TnqDA8ssciiyyyyCKLLLLIM5XFmOqP4xm9oqjcjBhIR8hqEx+Blp0gHxOYqQUkYiK9SZmW4LCv7vB3t1uctdRgb20eT9DpMAxMs44fauiCTH+qhBe1yAkGEjJVv4yuqMSVJKps0ZEw0YU4335Y4filc+CleHDMRVc84pLBXBnaYgKxuMzecpNOTM5aUSGuObSnVI7tmeCnO9O06zG2TLeIKQ6reiJc0aFaz3HS4AJjFZGcqbOv6pNIiyh2hpHyNOu7RJJGhYVGBjWKs3m6xaqczmwroqsXPDei4gTEEgqzNY+VbRK75n1ESSNvCLQna0hRAuQSOhn2F22WZmQKrRodKQ1ZqlGpm1gNjTAI6MwHzDVgeV6iXnVYl5SouyGl6QFmrQar8zGaYYs23SQSqixNi8SNKrbdw4GSSEfS5xs7DvHswT7a0hKKDtdftoyv79pJXyTxxFTExm6dXNssKS3L45NpluZ8+rMeVy4b5q7RMn9/7y7eai/l+Ss6OKM/zTc2zfL5TaOcOKCiyB6Wr3D2i5/N8686l1rR58c33sITO5sce9lLcAMNK1KouxnGGiErOyWcUMQJPDJqnKoNEzWRmCIyUtfxfI+YUuW+a9bypl89jmHDo5MuxaZJX0JD1OfpTzs0HA1DK2AHKoZsEDcapGI21VaGtO6wfzqi04jojmlMiDU8JSJQJa751Vb++eJ1zLSabC8ajFOg2GqR0gISmk9MDYkil5XtEbOtGgfmAwxkBGRcZHw/IAxlZFFirCazf16kPxWwtL3BbF0mCHRkEWJ6QDbmo4sRcRUM1aHqCDhqSFwKiEQoNRLosowiRSCESEKIKgWYqo+i1GjL6yTFFE1XYmYyxWC/TVzzSRk2uuJQbukYikt7yidjesxXY+wpuEw3GkzNG/TFZBZkm1zCYrYeQ5d06g4U6grpvEPdEXB9hWrTpCZEjFckbE9GEQV2TDl88M4xxptN5l0PLVSJaQpOGLK0qw5IbC0sMFpW6O1WkFSPudmAxyYbnDaYYqrm8KuxIiNWA1OE2YUsRctleXeTdBfomoXliAQli4W7f0bp0UeJv/DDrH3dp1BkCTeI0LyA3TMR8ViSwV6XuFmkXU+wYNdZ2ynj+SJaPkITk4w3m6zBZKbRIK6ojBUV6sUEqeU6H7t7khcfn+HXk9Oc0uaRTBToiplU/TlO6DwBKdL4218+zkxVpkO0+YcL+rn21oPsrxSYk/cQEWLbGe7Zq/O2U3uwLYmP3DnJBy/u53tb5/ja4zM8f2U3GztTTBUiXnfLPnpSKm4Qsqs4y/JOC0NtkNeThEKV05fE+Mk2nRU5SMVsGq7PJ5+/nouXZZCdCq89d5D7J6oMdHnsHDXozcpU/TrNpsG3njfMiT0JZmsH2FKoY4oCa3MSY1WfNj3FfDmibnus7YOtswpdcZlWOcfyzjjPXpFiR3WcX+7Q0a0JPnHJIFsmWqzvjP2upKbQ9HjFjXuR1IDlbTr1usj6rjhvOL2Lew9WedPNI/z8tavoiKuI4r8vFc6Yh7eBvWlbgS89PM1Xnpjk2pP6+PBvxrnl5WtI6jLtcYWX3LCbIIz4p0uG6UpofPGypYxVbF7xr3v51avXs9D0qNs+z/vipqdjGltkkUUWWWSRRRZZZJEjzjN6RVFPQqFFkem6R4hPEMK2mQgnEFiSlQiFgKQp0GmaDHWETLvTzFU1VndGTDVrzDUCZGRmqiJ759JktDhr2zx6MmUcL8ZsNc2WWZ8QB89TmaoJrO/1KZT62DonsFCIk9EkxpsNQiEga4qIpk2XorOqs0XDqxOEMlmzRkfC4kXrm0hqhZRpM5iN0GMLZI2A1V0OutZiMKUyVrXRDY9OU6EhtmhUOvCjGrLk0h0XSMUreHYbt+828EKBj5w3wMcuHKI7oTJeO1yaE9cslrZXEUKdki0ghHkmmy2iwGShFSBgsmsmjuVFJNSI4a4Sy3tCqrZPZ05mwrboSqg4oceGLkgnp+lOBiQTC+R0g5LlIkVxDpZtbMfEp0rkZ5HlJsd0Rjw6CXNNi8GMxnXPH+bU/iTXPXEQhIiaJRFTZMbnJGrVDjzBZmlWRFWaKILIa3++lVpZ4AMnr+Kf75/lFbds5yd75xhzJ+nP+8y1XFqtTiTZwpBk8vGQeTHOxkueR2LFBrbe8B2SUsDkvk1kDRVJ9ak6IZ6dYqpiUHciVEGmakk0LZG2hMyc7XBmf4aC3cKTZ2hLepww4CDJNXRZoNHI8fxl/fSlQ0xJZyhfpDPRYn1HwGRNxfUjoiBJKNpIapNsPCSny4hKFVEI6YjpvP2+R1mZTSNHSSwnxkI9yVxDoe4ojJZ1+lJ1epM2vQmFsqVghS0ySYf2tEV31iEbdzBkla54yHBnkTXdCxzfN8u63gkMrQlCxEJDY66mMt/QKFsKti9RsxREAcJQYKpsMl4yiSKQJZ/HxhMIok1cdckYDdJGnZ6UQ1vKI6GFdGZD5Eih5cosNAWsoEXe9EjqHtmYR7lpEoUydSfg4EicvriJa2ksNAQeGTepNBOUWyr5mA2RgBdAyrSoN1T2TGS5b1eKesVEFSQsXGJpm03zJWqOj29JyL6C05QRCPACEc+XiQQPXfHoaXOp2yGffc4gUXg4efCuXx9kaU5noRqSFEzGpyViYkBSk7ACix076jx06ybm5pvEzA6e94//iJxOsbYrwWcuWUJcEwkcmVCISKkx5lsBgZfhiRkNXQ5Z09lkuqTSISVwcViaE9g7LzO+kGDvVI6JOYMzB1PcMVbgqhPaeO2xPZzY5zDQMc5gKkCWW4zMt/PgzDyfe3SEkaKCLIbIiRZ//bPNjDgVersLxFSP8WKC6WoMTfe4YUeBH26Zp+4GbOgxuWxVnjtfuYFrjlvKz7c2+eXeKpevaeNjFwxxxlKVZLJCXBaQBZmU2UKIRLbPOxzTpaCoDlGosXPC5FOPjfCO20Z5aKrKR+4b5Q137MZpxjA0mQXLQ4t0Co0AEHj3L8ZouiEXD2f40rNXsO9QHjmKEwk23emQWKLGznmfJWmTmekUvq3x0g05TunL8I7jN5ATdX5zoMYrb9hHUpc4b3mKyZpNGEUYssCGtjhpVaXZEhnKaXz0Of3snGvyxYem+O6Vy7j0X/bwjl/tIwifvHfD5qkGn31giv68SK0KH7triiuOybIsb9CZUBmrOFRsH9sP6YipNFwfURRYkjW4+zUb0GSRzrjKm348ygWrj/zy6CASjuixyCL/WzRN4/3vfz+aduR7cT3d/KVqW9T158dfqrZFXX9e/KXqgiMbVz0TeEbvenbuv9yJq5oszYccKgpEoo0sCBhRmoNFDzWSGMxLWFGAJEUkNB9NktgzG9HbUSUtJyi4FdKGj0aGjX2zLGtrEAQaRUtiX1FkphInropkEvPsn+ni7O4hRqo+j89UaU+4bJqA3oyHLqnMFyXakxJLuhoYegUx0hhsm6VqiyxNmQSRzYKlMVtJs2BXSagKcUUhpnkIkcr2+YCxokaXmmJsWiMdE1g6NMnDowYvP65BPlFFiHTuPmiybVqnTcpybn8ba7s0rrt7FyOBxsrOCEl2Gcw61IIyeS2NF7q0PIgATYKFSpZyUEHy81h+SN6IuPdAxNJkDCcMCKQQSbboTimcsXSU6YaC56YoWyGD+Rqur1Oo6+xZkHjWyibtiQppwyaKdDZPxxgvJpBFGVMR2TSucOagya7KJB1KnidmPY7vA1NtUmxoGFrEQG6erCERRA7bx/vZOZLkJevaOFB0eM7qJD/ZP8evR8qcPiRS9WoMpAVM1aU7XaYrpiIrFR4Z7WXfXIa98wGnGhY//JdvIFZH2fjaa8n0dlOqyeiKTCQXyWtxZmoa/3jOcupuyPE9cUDgrtEi77hrHxt7QsJAY7LRJC2bZGIhBwoKw+0OcTWgPTNHmwlCpLN9TqA7GVBvpRirQMKwSComdVvm3hGffzx3BRld5YSuFAtNj88/vp+Zpg2CQ1u8RT4e0p6cJ6ULxGUNOxDZVwzZPZvEEE0arsDqniI5XUSTBezAQRI9FEFDEn0qXpWbtywhqyaou2AFNsP5gKFsSG+mwGRNRionmQhi7JoXWdJeIa1pyGKEpM5w7pIS7YaBLNg8PJtgtNBBvZXk4IJOwrTpjPukTA9XnMP3IavlgQhDs2m5GrarsHUmRBYFEqTxI4HlfYewXYm4kqDhRvRkyiQUkbjmsmchheOq1G2R+/ZmGMiEJGMBzahGT1Ik8nTu26vQFpPIp6AW1khrMpKgUnVtNvQ0iakC5VbEb57oI6UqbOiJ8eFnDTBStnjHLw8yX/E5vT/N97fPcdaqkIZbZ/oXP6Q6Ns3ySy5ioHcD04JB040QXZ1ADKjZAm86sZ8lWY3PPTbKWLWFIUf0ZkJOHd5LUsmwec5mbn4pRaeFhIEsRUzXQ/rjOo8f9NnQq7F9xmEwrRER0dZZZigbIMgF2nSD+ZZAEBg8OpIkrqhsm3I4pj3GjNUkq5gM9i7QHg85ULbR6WDPfEBGUVlYUOhNq3z5hcNk/20VTRBGnP757Vx1fBuvPaWDkuXz9jv3MVHy6Mm6WMoCvWYKTW1QbOmMVyBrgC4abJ+EHjXGted2c8eeCjML0zzRhGVZjYIlMJCBiZpL0/N53fphXntcD59/YJJVuRhvuXmUS9amuHumSF8WUrEG45WAgaTOSCVAEzV036TQckhlXD565jrWdyR5YrLBe345SltC5k2n9qAq8PJbdvCTF23grT8Z5+zhJJm4yBcfmCUQQ37w4pW88Ae7+NaLVlC3Qj728AFO7Etwak+OE7qT5P7t7/Ci7+zi3ef08fe3jTGQ0Thk14gcmZO6k3zw4gE0WeTO/SV0WWJdl8mlP36EGy47no6Y/rv55Av3zjBZdXj3mWkymcwR3fVs6PPfRzxCu56FVotDb7ryL36HjkUWWWSRRRZZZJHf50jHVc+UmOoZXXomijCYVpHEGht6A0YWUmhywFjZIgpk+tpF9s07IEd0pxVagULJh7ZERIwcNa9OZyJAE1IcKLeY3xcna4Y4QYgXCgxlJGxbYvOUxrCfR5fhCw/N8r4zh9g+ZVGqRpwyZDFZ1JlsOnTnDpcQFa2IQT1BOl4ljEIGUwaSMouEQS6KIWVK6C0JPxDYOR9xTIeGIHh0JwPymsH+coFEsg03UNk3m+HKjUX2lS0iQUeXJHrSNoGb5r49LrZV4I5xj6QckZMlCi2LdV0imlYmqiewAwtNUliSddk255NQMoSCh13rZM6u05M02b8gkNRCfNlCFxUcH2oO9KYcts4kOX2wwUyjjihkmG2EdMdUKrbEcT0BZSciYyRwozIJKYEmO6zqlKjaCrunEsgCVJwyK9pcarUWqSjNXLPIMsOgHtZYmjQpWNARE8kaHoHUpL9b5Us7DnHRkjauvukgJ/UmkRsmE2WXbCxOLjFHXJEQBIWqa5EkTT45T0KT6Uy3ECspTnn9tVhWk562Flu/+Wk8WeDES15AUenn0QmBpUmV5399L887weSx6RjffrTIVcd08KNLNzDr1Pja5lnw4zQiyBKwvrdKynTpS7skdR8/Al20OLkfDpYl0rEmgmiwe0EgNHwUQSWtGrzrV6NcuCTLPSMVbttdQFRCluZFkqaHIEBKD1BEGVmqsn0miyyp2K5HzTJwRQXHj9g6pdGXEuhNCphagCyGuJ6C5Rs0PAFFlJAAIYIg9IlwcXyZ/UWNTKxM1YtTcwVkpUHRCsnqIbLsYQcKU7UUlVaciiOway6OFKlEiCiyy1RFIgpk5poCSzpgaVYmodgIosPWmTgxBRBcEjoYUgxd8mh4NrvmFLqMFE1LJhGr03IUDFFgriFguxKeL7NgBRiaQDbpk0oUMVyF3qSPps4SyDoqSSp2RDryMEQNy/fY2F+CMMGuGZWlmThDOQ3bFfjwswaoWR637SxzcNbhdSd18bK1aX7wrc/wqx88hHHBy3jRy19GMlFmumISawnMFCBjSnTmBF60YpALluSJgNfctpOLluT55vYRVneF5BIlTNmk4YY8tL+HdlNhx744Jy6VwZhjY7eBG1U4YWWEKvl0RS5d6YADMyJuJUkrWSItZUGapzOWYX+lxPJ2k+1jAr3K4RV+XWqS953fy6/mZynaDYQoR931aNMVdkz4rOmSGKk1+OVIgStWd7JrzmJNp8ktr17BFx+c5a0/HeXu0TJvPr2bH5Tm2TMnc8xAG9v2afS0mwTGNMf26NQdgQiLWJBgf8Xm4bEmm+ZKDOvQo1vUfYHhNpn5hocexJmzbDrjKp99cJzvbirSE6vzo1cu522/PEB/UiEeK9KWcGi4Go8eUjhxSGWiHDHr2HRlRHYtwBt+NMrPX7eGjb1xXrAmT0dS4SU37mJFp8wXL1rJUMbkY5f0s7Lj8KTvRyHHdiV4ybf389nnLf1dI+v29Apm6i7X3nmAX1+1EQAvCBHkkK8+MstZS1O8+7wePvvQBN9+oMTPm2XeeV4flhfy1p+P8JVLh3nfvft443FLnpQkAnjdqR2UWz77Fxb+D2evRRZZZJFFFllkkUUWOXo8oxNFWiAxWm6SjAksVRUM2UdVA7qTKivyEp5Qpi1UMPwE8yUfLdlEdGMYish80yeu66RkmUSsgINC5KZ5bKrB+u4KkZ9ntiES012O6Vao2xr7iyJDnR6fvn+SRuDTFVOJyxHr+sscmE2TiteYqkHCjzNZsfEik6Sq0PJnQAIhiDPdcDBlFVNSGGifwpC7mGl4mIqEQoon5kQG8zbTgUWxBB3E+NU+m1ecNMtDI70kdJekqjPdsMinknS1OdTcgDZBYUvBYUg2EeUCiqixpqPKaFVAkWK0fIv1HTK7CzV6s3FKzRpuwcSSBURfJmcEFG2PZTmNQ2WblW0y6fgCA6rIgYrDsoxMb3KcTePdNLwGw/k4j024rOuIsa9QZ7XYQS5T56wBhS0LTTSxh91ynaX5DBVHpD1p0NldwTQ8FpoCfdkCMQ0EIaIvqVOwfGbrXSiKjaEH5OMB+ypVlvWF1IQKg30KghJSqIvMlFMk4vOszht0pKZx7B7CUMPDIWeohI5DuxoiZ3QaYcBlb389ozsfpzaxic6uCls2j2Ef8wL6UzF+s91h2YBNOhUhCvCJX08zXXMJJZGuHDRDC92wiasRpuYyV5cxZA1TqyOLIjFBYWN3ndl6At/XyBsqjx2IMZDW6NBgbt7lvtEKA1mVfDai0PSxAwXJUelItrA9kYgU2+YERCxyuoYkyKzrarJrJIbny0w2bLbuinP26oDOdggiheGsQ0yJEAQFTdLxXBXNVwglid1TEYWKjB3GGe6ETlEgG/NwxRDb03A8jSiSiBkelZZBNdKYqsk0bJmUJiCLPnMNiZYbktFUXEcm3cgREwUCPSQSHZJ6HV0yKVkCshBDkyTCKGKm4dKy8+hpjbZ0lYoVklJVZMkGEXrTDg3Hoz1p4lQVZhYkDlYsluVlpqsiB6shZww6GFKDWbuE5+bIaC0sZtHEFHNVjft3G/SvbiOvhXzqiiW87+dj3D9SI5Adzox2sulr1/GaL3+Z55+zhtkrL0XTPWRFpOw4hE4nBxcClqRN3nTSIKMFj7SiIQgCURhx+coOrtu+m+P7AjRjgSXZEFPx2TmXYk0HzNZtVvSqTNXg5B6fpbkF7j+UIwx1HAIGkxo7D2oM5QVITqGJMcpuhTwaLh5ipDFXl0hHcV57TgeffnASTwj4/ug9zJfaMWMOGcMmkQ4xVRdBi9GfbNGIdL7w2CHu2V1n+2yLd5zbzec3TbA+l+QVJ3bQnhF48FCDZZkY15zWxdtuGUMzfcYaFhuzOnXPwpBy7JkLCCWJzz67n1+NlDEkiblqQLYjQai7NFyRes1kpuTz9Rev5LaRaZpewMpMjE9eOsj2+QaWDYgu/QZM1T3SusbGQYeqozCQivNYzeKDZ6zgPT8f58oNHVy/dRI1lPn6wwt86YVDDLWLvOWkQU7vz+D64e+SRDN1h89umuBtJwzwjnN7OH95hp/uLHHKQJxV+ThETW66fB1Z4/Bqos8+OMlCPcBMhfzV8W2Iosjla9t5cLLE+89cTtqQmazYXHNyNz/Zv8C2EZ8zO/5wGXbDCTj3SzsYTLhHfI4KQuF3pZH/fwmPkJ1FFllkkUUWWWSRP0eOVFz1TImpntGJIjleYn68n6E1LjOtCvuLEsP5kN6MRd0BJVJZnjI4OCHQ1ubg1VMIhs2eUYVMW0i7HmPnTIPlXTGWZQS2TnsUFzJk1Bh3H9RY1lFlKAOiWUdTFVTJZO+MRqUR0pZS8CSHQ1VY2aYzmG+SMBvkjRSBNM3B+SQxJcamhsvK7iz1lsKBqkVPIqLc0qg5Em4wQDa+QCiFiH4Hcy2H9niclh3HiSz62jWqdpMoMLljxwr6OkZpNXrYMmagSgF5WeZtJyzjJ/vHmJws0R4lKDsWI0WNjCEyUbdZllHYNe8gCxl2NByajkxTMjF1h6F2mGr6rGoXcKV5slYO06jQF5nYlGg6Osvys7QLHvN1jbhqkEtUaLoalhfheAaHChoRKu3JSYymQRi5dJkCT5QhYyjYXoO2mMqCUyNjJrHCJt2JDAcLBhu6Gzh+yI6ZGF0pkYmqh0KSlm/RriaZKyooegAKBEqV3pjJ5qrNgBPDkxRqDmjNPEm9xYqcwljV5d6JFMsUiYLtsCKv0KU5lOyINccew3SrTHEuT9qYZuvX38rZb/w08xMHOeG01eTicf5qbQ8AUxWHW3aUeO6aLL8cmefmvTNsGFxAESXmWjqllg5kyJhN0lqILBks2AHzVZko1GhGAXNNn05dpzshML3g0xUTUA2FjBFhqDa6LDBZUciZEV0JkeGciBtVyCs+gqgw35RY3Vei0kxguiL7fJszlgySNrr4p01jpHSfhCpTbEhEoYggQjom8ey+bm7ZP0NcC8koIcvzARnbps3w0KoiLQdMJcLxZfZN58nEAjRRYq4ZcdzALGEQo9gS8FBJqhqyIOETsTzVyWwtYseYj2iExBSZlKbihh4500VCYqLqI4sRAykFQ/Ux1Ba6GiCJGgtNhTCCum2yf0HhhNQyzu4XWdut8eGHmoiRgB3U6En6yBiUHRtZEOnPBsT1IltmdRTZBLVCMqmyvVZmxqrxlUcVHt38CIWKy4nZOnmxybs/+lFkWeZNL3k5V357P4MdLqQKLFRy9MRdxmoKL93QwapcnHfdtovOmMY5S1vcvq/IbMtlWb+NE2jMVxTadR1JOciSdIqMOcHI+Bqajkh/fxEZk+9sSrCmTcNWZkkIOfYXXYZ6AwwdJmsm7YZCM5CYkWIoikVSjfHC5QOc3JXh2d/ezoqsyaRbQ53upitTozupkozN0GGkmajXeMEaj92FOGu74aebTYqxGmrM49v7dxJhMmaX+Pu7SviixQV93bznzGG+eP8Mf3dGJ4/MVXikUOBgUSKnpxmtqASWxkLRZ7zs8vhEk6YdcVwHbCmFHNersnVCxHPhny5dwucfnubYHoOPnr0E8d8aT3ensjwys8Cj80VGyj6d8SQ1J0ARFVKyzhcvXkOh4fL87+3iLaf3EjcjfnTPAqIn87NXr6IjqfHTnuNQJJHrN83w2GSDL1+2jFLL4w0/28dHzxniuSvaALhtV5F3/myMoZzOT1+9mlVtsSf5/zee2EO54XP52nYuuG43d7x+JVf9dDP1msZkzWUw4/P9zQVu318kHRexWwKXrM4wV3fRZJG0cXj6/Pojc6QNmZptH/E5KowEOEJ18OEzpJ5+kUUWWWSRRRZZ5D/jSMVVz5SY6hmdKMrqKuduCJhseMR0iRMGAnZOaaj6PKIok6CDrVMRUijSTpz0kirbJgz6en3MKMZ7z1jKS27YRT6WJKJETyagWs/w4KEAUXCp1jrZ7RdpS1jkNImG5WLoGiv6RBqBxUIrIG3obJ92iGkya40M/bk5am5Iujdi13wV10ugTHdTcl1MVeWk/mX8YmYrqmQyXtawggRD2ZCCPUWnnCKtiRRaIbps8uBBn1wCju/z8KgztdBL0fbwidC9OHubNu/82SjXntXNds9hOnLYN6qwoTtClGroUYKHJm2WpB0KDQPXF0irOiNFibyZZn/Vokc3aYUtEppOomMKXTTZ2FdlrOqS0GJMVuOsa69j6y0cL0cklElrcea8kLUdAodKPknV4F+faOfydR6ZxAwtN0UuZnFwLs98U0QMYbbZhuiBpgQcLAp0JWPsL5bpTaSZafi0mTFS5gK6oOBHAvsqLpmUzIFpj+H2OON1iXWdNss7HMqWQl5uY75ZJ6HppLDYPCdhCDHGqxIr2iIG0z5Vp0FKT3Li4EFKtV48N8mUlaT3lBcSP/45nLmyG/+J2/n8679M76kX89j5z+WfLl1LT1rj4rUJHpqdY6Q1Rz7l0nBlnEBEl0MyZhNTEZEkD5GI+VqKghUSRgG6HLK2UyAUiwheng25BPe9fhlnf3sz+UjEVCNk0UOTFWYbEpYnIiCiqzkCQcKWIanKeKELiktfWxO9IdKwFG4/NElMC8gny4yWIwwhxsGCSibewnUEmh58f5tDMh5gqAKGFqLLAqocImoCCU2gbqlYrkKxIVKxBFqWjhxKjM4E7J/Pc2yviKK26Er6KF4cEQFZipgoqVQdl5JnM1WIc/ogiGIIkUgQCLQ8gc3jGh2mTjorM1aK8KWQhJTC92BJrkUUiShiC9/NcdfEAggC12216E8alGoRnuZySl+EIofM1x3a9AQ1W+GBcZOMlqRga+wr+SzLakzsP0jbcp3PvfWvOPWY9Xzub1/HqpUr2V9s8ZXNBYrNQxy3VGP5UItkvIHrh/QkFQ7Ny7TnwSHAVCXedmYvqzsM3nrbKBN1m1zSxXYTzNVEOpIZds1H5O12NEHjG/cMs7pDphHVSGoyCy2HpTnoTFcpuyGeLSAIAZ0pGK/K9CV0dLVBu6ojqSXEMMuDEz4XnqzRn9ZZkUhQqft05WQKbpPVsQhRWWAwLWEqFWKqxHitTkxO8eh8yJkrfULRIZOQ2V/26c0IlCwXVZNI6xGbpxv8bN8C3z90kFPsNI8cDMgZGeaiOv0pgdGqTMUK+Ne/Ws6GnjiiCJ+6bxzbFzih32KuphOXDSbEJkXX49iuOGs6dG7cM8tzhtp5660jLOkJ+M1kgbQep+GXmamJpFSDyQokJZnJqsOLv7GXU4ZSXLEuT4TAffvrXHtGL1lTZsdMk7VdMX64bZ5bdxf4zuWrqLs+f33LLl6ytpPnrmjD9UMemazx8QfGSJkSH3v2wJP8/g+2z9Gf0kjrCj/eN8/eWYdvXjnMa2/aT1KLc9HaNONFh7dsGaFouZQ8jx9fth5REBEEged/bxfvP7ePdZ1xupMabzunm6Llc9euytMxjS2yyCKLLLLIIossssgR5xm965kmxim7LhlTIi5mODgTJ274TBfa6YrFedtJK/BVm2Qy4mDNodqC9T0e63ot1ESVN99+gO9cvoKtsz6PTxhoxNg5G+H7Cl1GnPFplf2zKQI3x9YZkNUafSkf2xIwVZHlOZ32zBzxWJ2kKiKIddzAoCuWpBnUUJWAngTsXYhoNE3KxTauvaHOa5adT7GpUrFFWlaOuYaLJMgkFQlZrtOVEGi4HgMddbKaQtO3aY+LIDoMtpVpj4lU7JBeU+eDF/fxnjtGESKJmiuwekhgqtGk5ah0JBuIgsBcLU3TFWg3TA5WbFa0SZzemyFvSkh683CvHC1iKF+kL+XT9ARW5lQUpU5Ol5mzWvQmZBS5SVJNYoU1MrrIbVs1DEFnohzQoZvsXlA4MNfDroJPFJo0wgYZVefgpEFWTHJwOsl8JUOlpdBwBA4V8uyZ08gaKtl4mbiUwA58+tMBnZkm8XiLFcNN9ESF3piBorRYnhdImi65WJ20HmCoBaJQYzBTxfMjjul22TcrgZ/Gi3ySustIoR1JsskbMo4vkBQMlhpZpus+11z7Fj7wLzfwhlddxYX6LBdffDHXXHMNP9g0ydcfnWWkViZj+tQsHbDJGj6iKDJW0ijW4zQ9mUJLJvAMtk0e3lVtZYeLqdtEUh1D9fjJrgJfftZK+lM6ga8wV04zsRCnXEuxbUpltibRtDU8L8ZUOUbNUpBFn7Id/VviwaM77SMJEZats3usizdvOBer1Mn8lMHDu01MzaIz20QxW2RTdVKmgyr7VG0JiJClAEO1ycaaKLJP3YuYrUS0vIhW5CNm6qiSSNPWsByV5W0+K3saDHXV8IKI0XqVVmBRaEbIgU6pnmS0EEMWPVqeQrEl05OJ6M+DJESMTcFCNYYmy7Qn6qSMFoYcMNuQmS/FObk7w7tPH2RpPMGyvEIg1bBtHcdNMN/0aDMt+lOQipVQ5RAFmScOtvhrJWTLx9/Czlt+jFVJcdybPsf9q17ATTMGl35tN3/9kz0syeu85pQ2bh+ZQFU8YppDvZVk77xG2VZY0aawbb7CfRMlLlyRQZYF+jsEGnKdRExhyyRIiMzVNPrSdeJinEdGkgy1qRQsj4FMxFxdJK75rOtpkTRs0nKc6ZrEYFqhGTbJ6CKzVou4GqHrFRxXZ7ys0bLjVGwfSRR49wU9nL88zVVrBqhbCp4vMpRtcqhkUrclijaAhu2HdCYDRLWGZRs8MCIRl1KEgkObKdEKGkhegooT8S/bDjKUbyBqs6S0gD2TEZcsi1NqSaweULjrb9ayoScOwNKMwYo2jfZ0g0I1yVhBZ7xq8Y5Te/nplhrbZ1p8YdMoZ/dn+chdE+xaaDIyB01HZvN4QDxoIwxVCk2R0FPZMtXib35ygJgusmO6xcfvGWV3ocGd03OIYsRLv7uPa28Z5auPzPChu8eYrfnsLTa58ubNRIHAOYMZfrqryEu+v4cP3zvGvOvwyecNsLbr31cSPTRR5ZMPjJPRFN77i0MorsprTu7ACgNcwScbUzipJ8FPdhS59qxuxqsO/3rFKnIxlbQh8b5fjHHpqhy752z+4ZdjADw+2eTmHUUyunLE56ggFI7oscgi/xWbN2/mggsuIJ1Ok8vleN3rXkej0XjSNY899hjnnXce6fTh5u0XXnghW7Zs+S/tOo7DNddcQz6fJxaL8bznPY/JycmjqOTJ/He6vvnNbyIIwn96zM/PP6Xd2dlZrrrqKjo7O4nFYhx77LHceOON/xeSgKOnC+Chhx7i3HPPJRaLkU6nOfvss7Es62hLAo6uLoAoinjWs56FIAjcfPPNR1HJH3I0tJVKJa655hpWrFiBaZr09/fzd3/3d1Sr1f8rWUftO/tT9x2/5Zvf/Cbr169H13U6Ozt54xvf+F/afbp9Bxw9bfD0+o//jsWY6o/jGZ0o+us1yym0IHBVds2GBBEYxGiPS4yWRT6/ZTNfuXgtJSrUfJ9qM8Zsw6MVeAxkfeJawHVbDvDPz1rFYCLNK1YdQyT6mJLIXNMjk47oN+LsX5CoOzKuk2WsZtN0fMxQJ4w8dk/lWZVXSSanaPkOIhoLrYD2mER/wuSBsYi4aUEoUnYFMhkQIpWPnn46PQmdyWrE3tkO0mqCglMjrkb0JCus6mrSG48zVrcYL8UpNjR6MxatVjcHyxbxmA9iyDvv286PXraCK9a0c8GSHGMLESNTKWxHAQSO7RSZa1lIkcrOGZWYIrNzKqJsRfRKKfZNqCRklem6yNj8EE2/SV8yxImKyILKZFUgikwOlCAIZWqORxRJ7JqJoyoC4DPcYVO2odpS8SOXjA4JvcHG3haKVqe3Hb7y3PVsaMvwxFTIQFpkWec0vQmFgIi8IWD7MhU7oNyMcfcBHVM02TEjIQYpZhsi6/rLZE0XUaqzvN0hECyESAEkKk4AQZJHJmWkKE4oBYxXNOYqeWbrIh0mCELIZE1gWZvPmcsT/PNlw3z4vCHOGsww33L52tYi/1SOc8J7PsvKS17M9kaJPV/7B+780Kc58NgmupI+QSSB4GPIAnYgMV012DObYf9MgtlSHHyFphcRRCIDSZVMskQ9LPOve8Z5+ff3sXO2hiRC05LwfYG12SQ3PH8D26YECnWNastEQGGhHuNQIU8o2GyZNNg+laJhi5TqKmEg0ZkWedvd25jx66iJEJ+IYlMgiEJ68nPsnQ+Zqyvsm9O571CMrVNJ7juQY/e8zP2H4owuaMzVBG556Rq+cslyXrW+i1iosKojZLijyaquOpIgoCkRfuRjBQ6OA9WWTNSMc0p/krMHs7x4ZS9Xrz2ej5x2Ap899xi6kwEJXcDDox4EqFGSlieSNGxKLZ2paowD82myqYAtpQXedvd2zhhK0HIECrbLinYBJxBp+hZr22R6YxUWmg5br/sGN77j7ez+zVYSQxu576472fuLb/HXG5fzkjVdGIHOgbLFZy9bwgfOGuKXe8pcddM2JFdj58Lh3z1CSDbmsL6zhScuMG2V+PnBSV71/QO89id72FctM5CJiOsOuWQTQ5IZKwfsne7g1h1J/EBE0Xw62yr0ZyJiZoWBtICqzOJ4BvtKAYogcKigcWg+QbmuU2xITJZSCIJHpZFl/6zJ2T09PGtpB3cfqvCqG/djqgIfu3uU0wcDWkHIE6PLiGs1LDdBQAvfTzBbV0lpKntm4liejKn55JJVRCQmGwFxOUYrchhs90kk5ulOatQcmSV9NX7x+hW89cT1nNrdxT89azVt8cPJkErL520/HeFA0cG2kvh4rO1xyKRb/PNjszQii30LNiu0PHlD5b3n9/KiDXlesr6DWKiTEBResKKHEzvzeIHIB84aotvQeNNp3Zy7VkMxbO4tjPDFJ3bwlWet5DObNyNpDqYssrLNoN2QKUc2b7n9IKGj0mHqtJkyP9i6wHXPH+abL1jBV56zktP600/y+Rs74/zoRWu46od7mSh7vPPcHp69KstU3WEgZfDmE/r4u9sO8fnLlvCuO0b45HOW8LGHD7Jppsqn7pliuuJyzSldPDBW4wvPWwrA/SM1REHg7Wf1HPE5KoyEI3ossshTMT09zfnnn8/w8DCPPPIId9xxBzt37uSv/uqvfndNvV7noosuor+/n0ceeYT777+fZDLJRRddhOd5T2n7zW9+MzfddBM//OEPuf/++2k0GlxyySUEQfAnoeuKK65gZmbmScdFF13EWWedRXt7+1Pavuqqq9i7dy+33nor27dv5wUveAFXXHEFTzzxxJ+1roceeoiLL76YCy+8kEcffZTHHnuMN77xjYji0b9lOJq6fsvnPvc5BOH/3h8eLW3T09NMT0/zqU99iu3bt/PNb36TO+64g1e/+tV/1rrgT993AHzmM5/hPe95D+985zvZuXMnd911FxdddNF/afvp9B1wdLU9nf7jf8JiTPXHIURRFD3dg/i/5rdb5J37+fv59OXrufz7uwnEgNVdCtP1kLgW0B4HDZnujME5fXm+tX2SklvFD0MymkFHssmhmQSSoHH6QJZV7QZf2jTOKT0mP3isgR6POL5fwNTr2J6KIihU3CqBEGCEOWabHkldpM2UafgN8qkqXYaOLVQQI4OUalKwXPYv6JRraeabLnnDQFciFDHiJav6+ciDB+jN+GzsCWlLzpM1bWqWSjZmE4UZfjOiMlVVaFUziLLP8o6QQxWbhYbKkrRAy1HA0dg/7fKJc1N8ba/NlGWTlzVqUYszl7u0pSYpWiYHRzuZL5moMZ/xQshZy+LsLFYxfZ1UW5WsKWAaBZp2nNMG6+wu+qzKQ9U+XKqUii2gRXnuGYsYSMQYLauEvsJMJaIzqTJllTihWycWnyEpx1EVGz+QGa/IuK0O3nDsCj7/6CHu3+1y4ioHL3IRZY+8oeIJCyzLJEjERmi1BtlTaTAzPcSh+YhUKsSQBXKpMuu7IhS1yHA6IpJsZmsqQZBivObQbsTZveBSq3Sw1KzR0AUajgGij+UF9CYFWoFPrWXQtDX+36lrWPNvKxXO/dpW8jGFt5zSy3WPTFANHEabdVbmY5QLCwT2OO1+nUdv+BH96/p59l9fTkuKc7BgoIoaszUJRRCRBEgk51nbAZokcaBSx3djCEGWnWMGkStz3rIUJy2JM9qo8fhMnV3zNuOzIWt7JdJJn3V9c4iRiuNLbJ6GpqPQbuq4URNNEkgoJn4U0PQi7Hocy5YpujYxMyBrykw3m+iqzVDawI9cFloOA6HBvKATS84iRgopLcFUzcdyRZJyHMsX0FWLvoxPQouQ5Qam4iEJOpP1Fk9MJWnTElRbKpV5jQ8/q59ETODNd+7EsWU2diU5vifJ46W9KILCjnmL+arJMd0qqmqTS8wjE0eIQrZPZ+lJ+QRCjV0TbeQ0lZm6Rz30OLFHQBAKWKP3MXLbrxGlkNf/0yuZm0qxs9XL367fgB3Amf05vCCkZHlcduPjdBg6DVvgtivX873NC3z6nmm6kwqiHDHpVdnYGWOkWeGUgZCEblEai9HbNsC1Zyzhqlu2UPddZMkja0qU7YhWXcNyJWq2T1dcY8Gt0xePUQ6brO/2yCerRFGIJOjUvBpK1MbOeQvF7+TAfEiHodKyBBKxiEYQ0JXy2TUTccZghmtO7uZLj0zx2HSdD50zyOkDGd5w50OMViOO6/EQlQX6UwoJ1cEPBSTJYr7azd5Sk8jtwtCadCQCZls2vt3O3oKDqajEVHCDiPZki6VtJaIgx2yrSlZcyvtP2/C7oNr1Q3bOthgtOXz1kWnqWpELO11mUBlrOLSrSUp2iCLKzM2ozLkeP3/FOoZzBmEYccm3dnJgzuFLLxjiguUZgjDiUNHism/s4XOXLuGxwjyPzhbIGCH1oI6uhCxJtLF9JmC+rPL85R18dfMMuiLQnVSw/QghhE9eNMwJfYkn+flPPHSI5y5rP9zI+vcIwogP3TVOGEX8vwsH+c1IhbfccZDvvGAlb771ENec2sUXH5nGcwVOHNZoBB7/dOFqbttV5vzlKQxFepK9KIr42F2TvGKtSV9X/ohslfrbOarzkzcckW1c4fBWrrNvf/Ff/Faui/zv+OpXv8r73vc+ZmZmfhfQb9myhY0bN7J//36Gh4fZtGkTJ5xwAuPj4/T19QGwfft21q9fz4EDB1i6dOkf2K1Wq7S1tfGd73yHK664Ajh8k9LX18ftt9/+3950/F/o+o8sLCzQ09PD9ddfz1VXXfWUtuPxONddd92TrsnlcnziE5846jfpR1PXySefzAUXXMCHPvShozb+p+Jo6gLYunUrl1xyCY899hhdXV3cdNNNXHbZZUdDyh9wtLX9Pv/6r//Ky1/+cprNJrJ8dLuMHC1dfw6+o1wu09PTw09/+lPOO++8/7Htp9N3wNHV9nT6j/+KIx1XPVNiqj+N9N7ThJ5w+cJjk3zq4iWs79QZq9hUbR/Xk2m6EXtm4PGxFnceKlOoCyiCQW88hiRblByHrvYGliNyy84y++ddVuYM7p+s0d1lM5RUqfol7NChMx6haw1yMZkOLcmOeY+FZoQuqehamZUdNlqUox7WCKIQU9KouS1MRaAvoTBeCTAUGS/0sfwAWRK4bst+GpZIuymDVMPxTAqNJKs6ZwiDGH7UZFWHRU4zmXVb4GvsHDUoz6Y5tjugFTVpS4SoZh1NFLhpZ4FyI+CMngxzYR0l0Ng1brBlooucLtDeucBQl8f0HPRkZE7qTfLaY3qpSS3KTYVy3WCsZLCq3UOVXU7sgkio0Z2IWN4xThSkqXgex3aHLFhNOhMBgbzA8m4blDppzUTXa7TpMoP5cboSEbsLLglVoxIV+fq2cY7vjXPM8ohWEGHKCjVLQRFDIhR6UhW8IE6ARW9cwxU8OjMyYSDRkbLxA52SZaMKMYpOEzFMYAcBczWDQt3kod1pTDFByrQolU0m5xOs7J5gQ3cTy5MZL+s4rsFcyyEuiVz5nf3smmkyXXO47tJlvGRtO5++d4rdzQJ9OYfzVlRArrCkP02id5j8Met42ac/zNCpJ9GW9nn4i1/g3o98gK0/v5fqgkSrCgk9omaZTNWg2NBRSfHgmEnVEVnSHXL3G9bx3gv6uWA4y2uPGUQWRHKmRDot4IkuURQyW5cptSRato7vxElpAe0JiyX5KoZRYSBfYVVnhaF8jbjpEDcD4pKMJkhokkBHXOLEfoe2hI+hSKxo81nd1eTU4SK9SYGelEBcjQh9Hd+NIQkCGdNDlgRajkrdlkB0KLVkqpZI3YWhrEdHPKQ9aYERMNV00CSBquuQMSXGyg7feWKOhWqM6apI1VIZTKn4rkypFdARcxlIO3SkLEJf4NCsyWP7cuihSLxZ4OYXL6P6vf/HLz5wLbs3baF76XrOeeeHuOCD7+BAcZiK0oMXufzT5v18/IExjv30Zv7xnknaYyp96Yhje3Teflo/iiTyovV5fnn1Gt5xXi/vOrefFck0JQsEQoRI48FRBc82uWNXkx9vXwBfo1gRySgSulajXJVRZQHZaHFsn8KbT+1lRTLDRDWgIxHhRQEdcYuhjE/LAzHIsXdeJ7Dbmax5ZHSZmabHyb1JPv2sYf7qmG4uHR4gigTmygGv+Onj6IlpFM3hR3un+OiDI8zXdfrSdURBJKm75HSHMIKaKzBbSzFWVmhYSXS9RlyDB8cF5oudCJLN+i4BQW4SVxSswKPWUpmrptg2oTM63cOND4RsmWoCEIYRz/7aLm7fU+brj08zHlY4rlsgJCKm2aQ0aIQNkoqGIsh85tKl3PKyNQznDHbMNhFFgS9ftozvvHQ5FyzPACCJAklD5O8vbOdH2xd4cKSJKnl4YoneuI4U6bhU6IoLlKohX3tihvOGUhgauAGYooRlw7K88bsxXnPLAT5490Een61Sb0bcubfyJL8viQLvP7+fl21sY+tMnatv3c+NV6ziDTcfoFQN+OJv5hADgUDy2F2q8a6TD9/8PndNFoD5+pN3N/vi/TPcuqPEB34xfsTnqDAUjuixyCJPheM4qKr6pKe+hnH4d3X//fcDsGLFCvL5PNdffz2u62JZFtdffz1r1qxhYGDgP7X7+OOP43keF1544e/OdXd3s3btWh588MGjqOgw/xNd/5Fvf/vbmKbJi170ov/S9umnn86PfvQjSqUSYRjywx/+EMdxOPvss4/Y+J+Ko6Vrfn6eRx55hPb2dk499VQ6Ojo466yzntLmkeZofl+tVouXvvSlfPGLX6Szs/PIDfp/yNHU9h/57c3r0U4SwdHT9efgO+68807CMGRqaopVq1bR29vLi1/8YiYmJv5L20+n74Cjp+3p9h//ExZjqj+OZ3SiqNAMGK3WuXtyhpYfMtQmsCKvEYYhFddGNm2SusDD00UsPyCtqCiKQ6UZJy4lqTRV5r0Gthdw/8Q8440aCU2iLxGnJTZYqOskpDj1oIAguiRVlUBw6UkH9Cfj7F5osWkiRhAYyHIT20kiB23cuM2gYEHDMmj5IacNWSiiwETVRxUlQhwSusgZQxEVv0LNFgCZpieydXKI5W3jZIw6tpVgthHSm5QpliNsH3o6RB4cUeiOCyA20fWINct8yq7HQFbjUxcv5UsXraGzvQFCgBHGMfQC3XGJ8QakMxGOYPGNbVP8/GCRwpxCVlMJBR+30cFd+1XGijl2FiIEDETRYqaWJmlUyZk+XuijyhHjs3F2T6dp2gqlhk53yqJqqeyZT/HEVJaRooEkiGRjTfrSNtsma+ydjNg2FeDaOpFcpCcpY4UtMkqcbbNx5CiGHUiMlwyycZ8PnN3Pxo44Dx0SaDclAtFGlUXcUOSJ6QRz1TwHihrVajtzpRg7DuYo1U1EvU4uBg/s6aJiCaxod9i8X2JA6+W2F57LlauWcOpQgv6Mxs37x3nlzVu4bssEawdlmq6IR0B7TMGnhSiEaKJO3WuhqRJLN6xk1pG45O+v4fIPXYu+ZDmKO8rur13L3R+6lnBmml8/UGLXQYtSPc3yNoey5dIV//dtueuOj+uHvGBlF8vSCeKiQmfKZjDfoCNZJBIbWJ6E6+qErTgyEpqk0ZFo0har0B5v0JYs85qTt/GCDYdQTQ/LknFdCV2SkAUFIhERGd83kSWBhBqRM0ETJVxPYqFmYGoBmhoSRCGR4CGLAX7k0pUssDRXZzDX4Jguh5yuEtNCYoZN6IWMl10GUjECK0ZMksknW6iGgx8IVO2QFe0B+VhE0w/Yfsjgnt1LuWd/B7/ZGWNd6xAvG5RoPPg9xq9/K+n9v6Irk+DyN3+I4au/yHz6HCaaq3l4tI3bd3QwX0lzYEFlZCZDQszw4tVdrOrTscIAQRD43NnHI7WSHNuZRBIFUoZMb1rjwuUZThtIcny/iSfaLM2o7J3VGJlOICkw7jT4/o5Zpp0SaU0gEkMSms+pw03acyX60gGu1OK0gQRnLknz5ect54TuFJFYISbr2FGJpAoeNTKGQybZxLVkEqrMd164gjOGkqxsj5HVFP7xnkl6YzqiXuGsoYCiHXLagMK1Jw1RaFnM1hxCJ0fKrNOXMBAkC0PWmayFjC10cqhgIIQmu6YS7JzIMlfRySbqSGLEzrkQwUsxWRGIEWfrmE5eWIbdSvO+U9fwzy9aysbewytyRFHgqy9aSi4uU/M9VnfbFO2QpAaqVsWUdObLKfJ6ku9eupHhnME3t07zzp8f5NU/2cutuwr0ZzROGvj3lT8zDZsrbn6M6x+fp+i1aIktXFcB36TienTGVQp1icdnfTYsEbnhitV86bnLOWcgi9WEShlu/6s1v9uB7LqHZ9gy2+CRsRYfOHU5r/zeAbqT6h/4/k1TDS751i7e9/MJfvDiVQxmDNb2GKRNibrvI4gCxw0afOzslZz7te2875djRFHEq354gBu3FZ9kS5EEWk7Eu8/rPQqz1CKL/N9w7rnnMjs7yyc/+Ulc16VcLvPud78bgJmZGQASiQT33HMP3/3udzEMg3g8zi9+8Qtuv/32p7whnZ2dRVVVMpnMk853dHQwOzt7dEXxP9P1H/n617/OlVde+bsbp6fiRz/6Eb7vk8vl0DSNq6++mptuuuk/XVl1pDlaug4dOgTABz7wAV772tdyxx13cOyxx3Leeeexf//+Iy/kP3A0v6+3vOUtnHrqqVx66aVHfNz/E46mtt+nWCzyoQ99iKuvvvqIjPu/42jp+nPwHYcOHSIMQz760Y/yuc99jhtvvJFSqcQFF1yA67pPafvp9B1HU9vT7T8WOfI8oxNFri2RNj2mnBlCHHKqQibRJBMX0EmQFU1suQKSQ5shc6hk4wQhg5mIUsPAD2VWdYhkVZWd4yE1KySmCFQcn7iq0mckmLcsJqsGbabMRM3CDSSWZDQKTYeWo9KfUtk0FVCzJdpNlUBwWN5hc3Cmh62z4IcCTcckjHyWZBSqrk3dC+hLCiTiZeJqRFJRqDgWhiTTkaixfT6BQpJDVcgaEralULYCMopCzfFYkg+J6y7pRJWcLlMPHDqyISP1Bp9+ZJTvPlzg8UMqmbjISC3int3D3H8wjiiF9OR9FL3JivYA1aixeujwyh8p0phveUwsJBktGxQaBrO1JA1XpicuMN+EwDeYqUNSijFblelRE1ihy4p2n8cnJDRBJ2GWcN08JUtgSVojG2vQmxTp6CjTnfeIxRwG8jaSHJCLt1CkCEWtocsit+yKMV/NcN8hDSHSeP0vtoPc4oRlRWoOzFVyuIGLGGbQFJdKM4YfSLz5pAGetybLp541xEtXD9Gqpzijq5981Mv3f5ND8dpJZl2evSLLbM3lPb8aY1mbxvGf2camuXmOGYgYKThsmWnSkxQR5CJ1J0ZcEzhUiogpCqIg4wUWcUVkSbaF4wfoRgw1P0BuYIBj3/ERrvz0u1i1ugOzuo9d3/8U277yQY7prPHE977Ob35+Jy/91gMA/HhHgZOue4JrfjDKZNEjpkeU6yb1lsF8Lc2hkkyhKhN5EmVHZbwQY7qUZbrcjiJFJPQAQ63z6JzGnKMSj9exRIeQkIYjsX/+cE+rlnM48NfEw+WYQSRgqj4p06U728DyIixPYKYesmlMYaKks2dO5+79PUxU2ii1dExZQhFFSk2ZB7dn8aSAyVaLmCqxoVugLWXjCC4dqSJD7XV6sy064h7taZvy/ntxH/46U7+8kbHdE9z7mU9y868e5F82jXL8i1/ExR/6R7ZsOJeCZXPK0n7miiFruiBuulStgGO7ItKmRYjPq49Pc/YygZesaeNzz16GER3eQesXe8v8Yl+ZW3cevvmPoognJuu86ScjvPA7u5hvhLheRNM2qbsR67tNjumKEQ80IgEavs9wr0dX2zg9qYCe3BztSZfOlEOpIvGKG/by6HiDzzw8yt2HbJZkHeabCnMNne6US3+uwsoOl6oTMNwZUnBc3vHzUT7/2Bi37p3ne9vnWN6h897ze1na3ULRS6zINzHNBd521w6WZ2PEBAUHC8dV+PVBHduNsb8oMTrfy2QtIibLPHpIIB4lWGiEDGdVZmoqtWac0XmVeksicCVGFkI+edES3nvWUr78vGUM5XUemDn8tOu3LMnpbJ2t4ukVVrcHLM03UOUmxUqWzRMyq3JJPnvhMkbLDi+5cSf3jdTZWWwSRbAsb7J56t8bJR4oN3jBjx8j6acw9JBUHHpMjV0HVMTIwJQU9s9KNCodqLLP9hkfXRGRRIGPnj/Es4az/PS1q8jF/z0RpAoiA0mdG166mqGcwU2vXsGarj9cYnzfSBXREXnLGT0c0xXno/eN8cRkE1cMSMYEvvfSFXzmglXctLNIQpEZyursW7DoSCi8/tQnP4n+m1O7ePTa9WTjR6GZNQJBdIQOnhlPvxZ5Mh/4wAeesonsb49NmzaxZs0avvWtb/HpT38a0zTp7OxkaGiIjo4OJOlwuaVlWbzqVa/itNNO4+GHH+aBBx5gzZo1PPvZz/6jG5VGUfT/q0/MkdT1+zz00EPs2rXrf1T+8d73vpdyucyvfvUrNm3axLXXXsvll1/O9u3b/2x1/dbfX3311fz1X/81Gzdu5LOf/SwrVqzg61//+p+trltvvZVf//rXfO5zn/tfa/hT1fb71Go1nvOc57B69Wre//73/8Xo+n3+lHxHGIZ4nscXvvAFLrroIk4++WR+8IMfsH//fu6+++6nHMPR8B1/CtqOlv84khyxuOoZElM9o3sUnf/1X2ApCqIQctZQQCAWma+nsKwMpYZCFILVFOnt9JioN5mejnH8cEQyUaLcjEFg0qekOW9plvfdMUEqFeIqVWRRYUlGwQ88phZ0jlnaZF/RZrijSq3Uz0wN4rEAGZWyVyUSAk7okYgbVcJQxfc1frZLJ4pk1nR5lJsKsqAgiE32FgRWt2m0pyqkzZCehE8gzVNtZljTbmFqHpVWnIenfGKyxsRCN+OVACXSWKi5GLJBVzZgymrwrJUWB2Z1pgtJ+rQmD83orOzWma4ESK6EjU8yJpJMuszbDZYmk0wUBTpyDrlkkZyhMVMN2TdvcNFQF+cMtHPd4yM4kUVKE5ixapzaL1HzKgznQvbMZUgZNtumdXQSbD0k056NWDtQJohc8qZE3Q1puCJJVSUbn6HdNPn1AZO8KVOpt/PuU5bwWOEgu0tFGl6AIqg0/RZtpsqmSYGkHGeuZdOdlPCCgCBQcBwVRfXpSDpk4haDmTKGotD0q3huNz/dpdAld7FtzGFJu8yl/RKffdTmK5cvY3mbzsd/PcWVG9s4pieGIAhMVmy+8MgkL1ydR5Q9btg7zo5CCauZImcqLHjzDKUSaLLPnXskzl0moRs1dL1Im5oniCxmGhGakGSiqtKyZAzNpy3VQIoM6rZAPDFNTzyJ70XccNscx0ZzLB1awb17Zgk230J/dwf/+KnPcNejT1CXTCpqG1srLazQY7RikVA0dFEC2aErCXFVQJabnL9slrgmsmDXESKPhWo/X380Q6kmsSIXo9oKsESP7riCQMiJy8dYJoGR82mGLcZLWTzf5ImZEEMK0MM0927V6Buq0p9IMV7xWdtXod2UiQjwwoCGFWOyIrNrLmB51mA4G2NFOsV9EzOU56aoL2yhu7cTd36WTTffjoDIaW/7f/zipgfo6+yhe8VKnJhCJuYS0yBhOAihRtkKEOQWx+b6eXTCZ1+1wGn9OnGjzo45gb5EnOmKSIhLZ0Kk4QpctWYpLTviM/fNk1AlZKXF5av7uHxtF7oi8g+/HGXbTIuWYNOVUlnbqfDYdI1SXSCSHJZlDDKBze6mRqkpEovV6UpJtGdHSYod6OYYgtfNb8Yi3HonJSfkJWvz/HqshKbZtMV8BvIV0rqPF4YEIciSx6bxdixPYKESR5YP/99KCPzNxn5OG0hy2Y8f5bTBiGRiFCXq4PFJlSXxdmRBZbTaYMeYzzEDYKbGsJpdBKKFoQiMLCSQBZiZkbnm1G5+MTNGIBcwoxyKpJKSTHZWCgykFB6fdliWibE6leXWPSUycYGWH/HO0wbY0BHnU/dNkhBlHpgqcNKaBazAY2VOpjzvsKmSplSJIwgS5yxJ8v0tRQbbIPRU7MjlouEMN26qM9gNnzx/JctzMe44OMff/2SKpC5zxkqTS1e286Zb9zPU36RmK6Q1ibmCxo1XruHGXXPkTZVLV7Y9pV+/50CVd90+xtcuX8qhkvO7UrH/SN3xueBftrOhI851LxxGFAUemajyplsOUREcfvnyY+hP6cDhgPSeQ1XOWZr+AzuWG/DjbSVefvzhMVUqFTKZzBHtUZT52I2Ieuy/f8P/gNBuUn7Xi/7i6+kXeTKFQoFCofBfXjM4OIiu6797PTc3Ryx2eL5LJpP88Ic/5PLLL+f666/n3e9+95P6WriuSyaT4frrr+clL3nJH9j+9a9/zXnnnUepVHrSyoANGzZw2WWX8cEPfvBp1/X7vPrVr2bz5s3/bVPZgwcPMjw8zI4dO1izZs3vzv+2QeyXv/zl/4Wqp1/XyMgIQ0NDfOc73+HlL3/5785fccUVyLLM9773vf+Fqqdf15vf/Ga+8IUvPKnUJggCRFHkjDPO4J577vnjRf0bT7e23/LbZvOmaXLbbbc96fP+Nzzduv4cfMc3vvENXvWqVzExMUFv77+vKu7o6ODDH/4wr33ta//A9tHyHX8K2o6W/zgSHOm46pkSUx394tU/Yc4faOPBks1w3sY0ZgmikOFsyH2HWsw1DDK6TELWmJ6REE04cRgitUDdDehNu4wUBTbPicQUhSW9ImM1C7tucNYKi22zPp2JiN6cwXhZpj0eUq/18MB4iOIrnJMzmWi20CSF5XmRelAghk9MMWgKDmcOexyYy1KwW2QMEwixPJkT+yIqbonJqkJfSiJplpFFgYYFBTugVzZwgyYpXWa2lGWiCnlTQVLL6AkP1dUoWgIdpsZIpU5fe4WYrFEu+Jy41KDhRphmREdXmb1TKm1mjJF5iYEuA0Ur0d8eo2L57JlJcmI/CFKN4/p0dpQWmGlYvPf0YW7YM87u8iyuZ1C1bUxdYOt0AttVaNkKDcdnsM1lm+lhREm2781z8uoimmKhiAqW76FLOpYf0XR0utJ1FirdFJ0Gb7ntEBu7Uvxmyqcr7VHzLRJqFkmaZ11nHFmZI1bKUXUiNEml6QhkknVkQUXRSphygrlWQE8sQdlyUKKIs5ZV+em9GfqyKlNeme1FCUeFWw9NcYqd5b6pMpoisLrT4Cubp7l0eZ6N3Qm+s2We3xyqMdgpEFOSNCUHRRYRnThTNZ/epM7K3iKyIpM1ZHwR9s4r2F4SUa4ihQpNS8XyIqLQ4OEiDGVhaT7AMD12z0ekpBSZwS5GRJ9kZ4x/OOtc1nW+hqBVJZNJIzeK7H/05ySTSYSlx7Hp8/9IIGt4Z76YVDLO2NhBzNU6fccdQ8tp4AQhiq+iCCZjVRVTFnj+MXN884Huw4kLQkTZJaaHSJJHxfJxDRHfhR0FlelCDBGDaqtJJYiR0zQSCXjzxrU8NltlrlpleXuTZlVif0Fi876Q46LdDHsttlrHUX/we9x+YBs/znSy7vJXs/Wmb9G1LEPfhi4GTljJ+vM2EpNlrLDCyIXn0heLUSzLzJYc4nKMOdtjqK2KIgRYUZOsrlNyC8y5PmcOOQxmPOZaNr3xODVbQtcc8iaEuHiWwvd2juKGAcO9AqHgktAitFidR6ZNfrqrxGTV4cUnxPjZHoetYza+UEMUQ/xGklRCZqzewvUCKi6IiospG4wXBCx3kBOWLCCi0vJFpisyA3GBndMB9x6qsr/W4NylAoq2QH/aRZZcREAWFfYVRdo1mZ+NSRzTJeKIs6SlDnbN+5w6kCRvqly8QmK67mBXOtg9G2dlNsUr1/Xzs/1F3nnqIJ+Q9mKHPnalm0hy6UuoTLcq9KYSjJR9zlmbYDSYRhNgpqXx8o09aILG++87wHF9AYrS4IQ+k8lqjZFWRNMN0RoKLzwmz3NX5HjNT/Zx5YZ2ZhottthVxis6Jw/Y+JGHECSYrQvkJBVXdPjl+BSrug3mZlQGOgNmWgHffajCSSsiFiyBLz4+yhcuXMNZ/Xk2DpXIySofPm+Iv711L+3dBdoSMkKkMNt06cwaeEHID7YucNpAkuetyD/lk8TTliT47pXLeP439/CZ5y15Sr9/+54yChJJXea3pk7qS/HNlyzHDaLfJYn2FpqsyMf+0yRRFEW86gcHObbvyCRxFlnkaJHP58nn83/Uezo6OoDD5SG6rnPBBRcAh3u8iKL4pN/gb1///srD3+e4445DURTuvPNOXvziFwOHSxt27NjBJz7xif+NJODI6votjUaDG264gY997GP/ra1WqwXwBzv5SJL0lH+L/wlPt67BwUG6u7vZu3fvk87v27ePZz3rWX/UuH6fp1vXO9/5Tl7zmtc86dy6dev47Gc/y3Of+9w/alz/kadbGxy+Cb7ooovQNI1bb731/3eSCJ5+XX8OvuO0004DYO/evb9LppRKJQqFwlP2bTtavgOefm1Hy38s8vTxjE4ULc8mERIC060GtpsmrYpsnYvoyVYhUijPG8zVfWKaQG9Sp2zbeFaGDb02Db9JItYkDEXunywTCJDSVM5cWiWbmsYK48hhhvFqAyKDrkQKXy6TS8a5bKiba0/p57W3b0MURQJhAVONEMIURb9OTNbpSChM1+qUGnFKHghSQHdcZb7hYRghWVVlpNJElmNEYYqKU8P2cuyfE1iSkzi1o5uP77dQ5ZBlHTUWbJ8+RaNgzSApWWTZo2xJJKUkZTekMwFbmlU+cMYKHi/sYdtCg5OWxym1PFbEZKzQRpB8euIeBdvnmG6ZhN7Ajwymag1iapKdszZX3ryNe686iW2FHNdvOcRMTWOJJNKVniZyezlQtliZjyGpNs9dG3H3Hpn2lMYv9imcOaSxfdZnVbvB1hmZjngfTdNBlw73UDmjL817nr+S722b5Y4pC5E48w2PY5bbxMwmvqdSdWR6cgskrSQ+dQZyScZrLQYyIMgOuqRTckxqroMuxtk+G5FWOvASFpIiI3rg43HcoMwDoy3WtCX49LOWcsOWIs/9+m4cDu+Cdu1J/dQaESf2JvjqE9MsbYtY1VVivtpGX0pmptVClkU6TZPu3BgJqR07VHmw7LMsE9EKdOzAoSsd4QYRtquSj0TaUmXCME7LzjNVjcCUyCZCsmKSci3kDT/bR4es845zerkoL3HFFVdwxRVX4AUh533vUS7+f58mcItMlhRS7gKUiux5vMrwurVs+s63uHdqnGVrhrjgZWfxlbd9FwGRtc+7hNjsIQ5seRREha//0yd4+4evo1SYIzecYunzj+eub/4rVctl5ekn4zkyYzc/hN2IOPGV7yC671tc/a39pLr7WfvSK/nyGz6OGTPYcMlJCPYw26emaJInu0wmMfhSYs++AiNmY0gafVf9LWs6WxjxGLvnQ9pSdSIkii2BTlNCCEVKrsdXn7ecR6ZLPDA7gyIImGqLmCKTUBWqjkdMc0gqBnNNl+nav71XauEFAZpsMleXUESJquMxmHXRFZkDxRBD1rlp7xzFRpmMqrJ51sMWG7Q80FFpVFTG6i5D2YiS53BcNk1P5HBg2iOjC9Q9lw39dWJahBMItJod7FuIkVMV5ksCaU2ibkFSC+lJu5ixKmGYZd620cUMC02HTfsGqTgi3QkPTW1h2SazDYkPnTHMW39xgK89dwXPG1zJhx/cDcS49oRB3AAu/9FOVnbo3DE2ghiapGNQcQKGsj4lS6JQyZDUPVKqxN3jCyyNJSgWYnz5ijVcv3OUmu3Tka6hKzp+GFF0WsTlBA4Wa7t1dk0GVNwW949WOViyWNtpMn6gRVo1qbsNRgp5ZKmM5DosMWO4QovOWMQTsxELLZ+hvoiSHZJWVXLLC0xUsqxrT3Llyi5e9J1dVHFwGxIXnZhnX6nF5nKJkwZUHB9ihotdUnl0vsG7f/M4bqQyXnM4VLIZyuq4QcTPd5W5bH0OgErLJ23KdMRV/ubkTs5fngZgvuHSHn9yj6IrNrTREVM5a2mSyUaVH+zdwdXrTmJl/nDSZ67u8vNDRf7p0QnefGIfV23o+oO5ww0izh5O8rpTO47qHBWGAtERaph4pOws8pfLF7/4RU499VTi8Th33nknb3/72/n4xz9OOp0G4IILLuDtb387f/u3f8s111xDGIZ8/OMfR5ZlzjnnHACmpqY477zz+Pa3v82JJ55IKpXi1a9+NW9961vJ5XJks1ne9ra3sW7dOs4///w/CV2/5bd9Q172spf9gY3/qGvlypUMDw9z9dVX86lPfYpcLsfNN9/MnXfeyW233fZnq0sQBN7+9rfz/ve/nw0bNnDMMcfwrW99iz179nDjjTf+2erq7Oz8TxtY9/f3s2TJUz9YONIcDW31ep0LL7yQVqvFd7/7XWq1GrVaDYC2trb/tPzrz0HXn4PvWL58OZdeeilvetOb+OpXv0oymeRd73oXK1eufEqf+KfgO46Wtj8F//HfcaTiqmdKTPWMThT9bOwJ3nP2OXzq8RJhYDBS9tBlFV008X2JYuAgRAodcYN9C02yaYHlbTLtiTpeTSauaNRaHsWwiRnpdCYjAiHED5Kc0h/y6HSRnpxIq6EzUYloT5qsa1f53pYFXrmhiy41wf3TJbq8ND2pOImuA9SqcSotE0Hw2NDls2ncxBeqCEGGSLCI6woZNQnqDFOlDFnNIGbUcd002+dMWp6ISRs/uy/Op5+3mi9t3YoX+azIC+ycq9OVtpn0skyUDZbkRTbN+HSaMn6gsFCP+OoTh8inHE7ut6g4FaJCDkWIsWtU4qRYEitwOLbHx44qqLJKy4UVnWUcO04xYZP0Ety2r4Aeyty7XeaEpQpRfoKWnaRhhziewUgDsqZBKNjE9Qhdd7ArCo4Xsqa3gmtnaI+7NBwB0NgyFiOSRV57zBJUWeSvj+3mxO4kb/7ZAdoNldm6TZ+cRVUshuMScb1Gy5HYNiuzd8EgECVGQo+eTIa4WaArlqbh10grOp2pBqNzOeKqQCvyaFPjhNTwQhlNd3AdkZYXkE2IrFBFdiy43HVwgcdmF1ioyhiIWK0IP/KYL3cSii5xPWTIcNDlgLSq4QZQckMkIQ0CLFQkdMkglW0SkwUQPSaKEkP5kLLto8dc9Ejn+P4iCcVCFuKIvs4r1y4lp6lMVF2+/MAc9x6o0ZNVWNluoMgRVdehXE3geh20tbXoT3Wz4fjzCKnTYdgMv/tFeKGPKWZoehFv+urbEEKDpivg7zUIV66jZYd84vFxsseeSsyySGVcXBKsPu8cJMUn09aGj0E9Pky13MZgtpubf/w1XviDnSiyw3D/GMd95p3EFJmSU+XQ1mWsXHs2pwzF+eETC6AIlFwL0VXozUSs7bawrQxjjsq+hQhRSNARVxjM22iCSdmJqAQ277/nIHEj5JglJYayFulYgfl6P+WayU83y3S0BYwIKtnsLEMpgUpNYrohcdFgF0k1xvV7ZjHViFjeJQxVpqoeuiyhSgLzLYf+jITnNcnpGoYks23S4dnLTF6+voNaK+D7O+bZVmxyWpvG4+Nl9LiM60uAyFQxR8GxSEhZpsr/H3v/HSfJVR76/5/KoXOcHHZmZ/OudlcZZYFAiCAhYXKQyTnIJJucbWMMmGAwUQSREVEooSyUN2m1eSfnns7d1ZXr98fa4srY1/b3Cvven+b9etUfM1N1qqpnus6Zp8/zHIlcKkCRwLNEegydhutz+qhAKLZpWzkOzZuEoU7LNmg4AqoSYIgBpixguSJCkCRuWvz0+AGKxRX+aW/AP9zkcdn2Ll6yo8g/PjTL/iWLgbSGFbhk1DhTdZeMFmO21iajZrCCDsWYQCHZIgwVJuoaTxnL89rn9+OEIScVUgynTL61v4Uf2chCjOlywMaCiB14dKQ620Y1di8H7Fno0JJa/PrYMr84NomqtknKMVpuRFpL0J+ZJ9UVMllOs9DyGU6k8GhjORoiDgP9E6hBkV3zOkcW27xv7jizKxGvPL2L6bLD83bk+cTdx+jNtHACFUUUmW44mIrOyaMtji2k6I/rvHhzF5dcvY8/Pz3HrXttnDBCU0SKcYWXfe8ot79pCy/73hE6XsTmbhNFFnjNz45y35u2E1MfO1A+f22K6brFJx66g4MLKfbOHuArl2zmOw8t8/19yxgmtO2Q84cz+EGEKJwo5v0vNFnktWf96VfNCSMQHqfk8Cdekvmq/6r777+fD37wg7RaLTZs2MBXvvKVxyzfvGHDBn71q1/x4Q9/mDPPPBNRFNmxYwfXX389PT0nAqqe53H48OFHPzUH+MxnPoMsyzzvec+j0+nw5Cc/mW9961v/Lf/A/mfu6198/etf5/LLL/+j4rnwx/elKArXXXcd73nPe3jWs55Fq9Vi7dq1XH311VxyySV/8nuCP819wYk0Ldu2efvb306lUuGkk07ipptu+m8rtPunuq//G/wp7u2hhx7ivvvuA/ijpegnJiYYHh5+/G/kX/lT/c7+X3h2fPvb3+btb387z3jGMxBFkfPOO4/rr78eRVH+zfv6v+HZ8ae6N/iff378Rx6vcdUTZUz1hK5RdPIXr+P8bR71+THKyiSFpI8hpJiuKnihiO2K5DSVvzprlOd+4winbPQY7p0jpQuowol/ACcqEivNGJVSgqoVsmnI5oyRNuu6FplviSzVCkzUbSaXs4xkNZpOwFxVosdU2TfjoqkiYwWFWhu0eJvLtq/wwGwIYQJN0FlsQRQJ5JJ1BCEkIaWpdRzW9y5QbfWwZ15mbS6k0lKpuG3iYoorhoepdkLump/H1hbpTxisK9YJhRaTpX6OV8AQTI6XI0Slw9qkiWZ5VFWZ/uI8oZ8ipfus7xkn8PqYqUvUOypV2yGpGciCTzxWIiEWKdsdau0sqtoioajcfEQkqcTRJDg2ITA6JBLJLbb3WvgIyEg8OC+QVzPsXbJ48dY0p/Zl+MmhGVasiKwR4AltUkqM6WaDPjPL9btFnr0px989Y4T5ukt/WuOtvzjGnvk2quGhGCE7BhdJGhELDYGBdMS9Uyrr8woPTAv0x+No5gJZLcVcu8WOngg3tEhpIlXL4GgpgYCILHlUWjL9YkBLd7DcFG1b4GlDA4ShyLVHlkAA1wtRPJmOK1DobiAiM1xsYDkGM3WPnriAIEgIcpWMmqRm+wwVFlCjLo5WAh5ZMFiXjjHYtYSpeCgiTFUSOK6BFTa5fNskpeYAU/UIN7SJvCx3HUny4+dtoSd+YoWIybLNoeUOB0ptSpbHUtthb61MUtRRRYHNI0sUDAlZDhgvy2R0ATf0SJglDDGJGwpkYg1CL03LVTi0bCCh4voS5Tb0JUIM1UfX2wyJAWEiopBs0HENLE/lwJJOaT5DVk3wleet5fLvPoLlu7SUFU4bcDFkiUNLCnmxmy4zRjweMdMs4Qs2ir6IHCYAFUOzaVlJVtoyxyoOb9yxhj4zyZPXZrh3to4XBLz8Z4fY2W8iKBbbBxdJqBJ24LJcy1HrqNx6DJ48lOXM/jQXjcX4zT6LuYbLbdNlsqbKcEGlYnkcrTUYK7rkTAErWiGrpXBDj6mqzGDSYMkuIYU5PF9mvOxxeqHI9Q+3OWs4wbO3ZPjkg0fZlFVICwusBL0cqFgUdRMvChCEgLisM1OOGM5Bkxaim6Dh+QzlbFQFVMUibyq4UQNTSjHVsMlKXVy332Nrl87uYx6nbQFPLHPmkIcX2RxcyOC4JkdmFHpzEostDwWZ3rRDLFFDJUHTt1lcSaKrEl7ooykRvakAmzJDiTT3Twk8eWCQvzp3+NFn4FLb4fk/fwBd8elLSjQ9m5issdxUyegCnrRERk1yeD7OUjXivecNsHulScVpc/d0m34pxoa1VVpeiw2KynggIUQGBxZFvvC0zXzijkmcqM1Id4tO2ObYQhd9pkIt7PDB07fR6ET89kCNvpTGW87rIQgjPvXALmbbNeodidmqRiha9MdM5lc0XNGlPyVyuNJBCkzO6s5QbYd88/lj/O0tc1y2Ncv6osH7fjPFVNXlzed38fd3zvHJp61hbf6PV1VZark89Xt7eONpXfz8cIkdXSl25jN85q4ZKp7PD5+/nn2lKi/aPMgrf3iMp65P8/ztf0h7+5dimq4f8oU7F3n7+T3U6/XHvUZR4mM/RXicahRFdpvm+674//t8+lWrVq1atWrVqv/V4z2ueqKMqZ7Qq54VTI12J8mSNM/BQ3kEN8v1BxQWmgGiAEkVOoLFx+85yg+uHGWmY9HyfIJAZbntIQoCo1mRrlhI1XeRQoFnjYwy29A4UkpjiklyiRUypscZQy5zLQs/DBnNyDhSk0zOZTAtM9XuEIkh5w7EOa/7ZAYyHmGgMtOIiKsi/RmLSHBJqSpV26bj6yxXBvHEFWKqzexCmvmlGLgZDk/I/M2t80xZVUKjiu+bbOheQZYCDDGFoi+S0Hy8QCAKJPpicUKpRiHhMVYs0XIFhjM+KbPD+PIwLmX6UjZrclVCfAbiSSq2iGN3sdRxETEo2y3qlsrRpRgJ06OYCPEI2DAKFTtE9BP88PcZqrUUE3WfHX0ustokqSjceMziu3uqOFRJGT5zDRfP15ipwtxylumaSHeXz97lGpf/eBdXfOMQb/3ZOHdO14kZIlOtDjlTwBebpDWFlNlgqZZhumJQs1SSpktProYqKqTjNbYUI1ouJBWNfMxFFHxyMYdO0EFEIWG06U06bOlt051qUEi6/OPuGVqhyw0v2cHGoophhAjJOoP9bQwtZKy7TibWYbEhktYURooNkkZIraMzU4OsodD2IhabImldZ8dgiUK6SbVt8shinIVGEi/yMXSblGpwy3iKlbaAKiqogsq5fV185IIR/vKWY3zp/ll+fajMcE7n4o0Zrjq3n8gXePHWbh565Vnk0jb5jI0gNYkElyCQsRwNPxJJaApuECdtuAymLJzAoeH5tFw4XpJYrqo0mjK92Qa5ZJvuTJuzRxbImzbZmIMfBUhSgIDPQ8fj1G2BSscHQJVEvEDAsjUOTPay92gfOamPq85aw2vP6Gb3Up1I9IjENocXU3h+DFkKSRk+GdOl6bcYLVS4a3aej942xeuvPcyXfr/AK354nMGEge8J2H5EuWVSaskEgUYh4ZFLz7Oh26GY9Pn+7hVmyhGjXTofuXiIa563ided0osUiDheSFdSIGdoVDoePQkYzXVIxRbJ6jK1DhDBWN4jn14gIxs8smLjih5rChrP3JRjczbO0WWB+eUM9YZCTHcZygeIao2MoXKk4hCXRZZrAl1GjGdtSmPoPn5gstIWKZoicaOBLKhUOhHv2HkaV52ymZ5Ch4AIJW1TSDg8aXSRmAphJLCp6NB0JPoLIq2WQFHRyWQaDBcD2l5IX9qnakkM5QNUxScbCxnON+jPtCjGXep2hBokeMZYnrmGA4AbhHzxgTkyisl0WSUKVXTFw9BbmGrEeCVEDtMguozkQkJLQpckNEngy0/fypnZPB0xRPYLLNRjPKl7DZEfZ7IEO1J5XvvLwzR8l8F8B1VbISXHGcraxJIV3nnaCDv7UvSnNBw/4g1nn5iVIwCLyymqtQS7pzS8lkKXKWP5MNztopsdKn6NoUSS15zcy+eePcrVLxhDlgSuOr+HyUYHSRTY3G1y5po4r7j2CB976vBjgkQT1Q7/8rnI3TM1PnL+Gl6zY4jPPXUj5/flee8Nk7S8gA9dOMAn799HXBP5+7tmCSPImjJvvvbEsq9Nx+f8r+yj1HJ53Y/GaToBgiBw46Ha495HBaHwuG6rVq1atWrVqlVPVKtjqv+aJ3TqWTZmo6kK/WmRTWeIXDK0iVv3H0M0VUpNHyfwKcRhR6/Bzr4UHztnC989cIQjnRVUOUAUTCZW4ggCPGnMY2LC5JN3zPCD5+/gmwf2M1tv050Q2JiD6WaJ3rSOGhaZKXsEfoznbUzwtV1lRCnk9EGLltjgzvkm80tjzLVLaKJOMdkhZTq0bAMnaJOKR+RDjZLl02mnyagGD1QC+mI6rhdg27CuW+HBpSbpuMnO/hqqUiOjZYipZWwEGg2FUjNkOKfwyLzD+j6R3rxNxXDZnNRp+y10WWc4t0zZFtEFk4rd4uQeiTVGN6PJLJ+6Z4GN3RH5mMR8XWJ7t4YfuKSjFIHQohiLcf8jEX15lWo5QjUi5uoqjSDGcNqikHBIagFLLQnDcHEFm4yS5IFphYRiMFMNGClEVNoiKU3FsgScjgBqQC2w+MoVoxxfcfjY7TYtS2C2nEYNJeKGwq5Flb5EAEKHgYSKJDqszfvoqk/TBhWRXx1IsTabIZKXGUkH6FobXQhZsSKq7QipmaDpN0ioMSJf4khliR8e9rhjssZw2qAdiDRtGR+JuRWZ7kyShqWQjXVoutDoRKTUE3WIlqopmkE/3ZkqBSWDG+lMVkNSqkmtLWDb0HA9hnMu/SmfUAwpWR0yWpqWLfGVezqI0QLvOq+ff7xrkevadQQp4tapEg8tVrFdmf68xHlhgjWFBmldxIocWm5AQlVRFZcojBAiCV1SyOguuiJghzJh4BP6AYs1AS3rktTB0Ku0vAxtT8UODdZJIUEYMl6V8N0TgZrlakQ8KWITcmjZ4p3n9nFwxeIL9wcMp0z6Yjq1wGOx5bBeNzhcbrBJkmn7Chu7HHriDoJokTNbxGUBYUmkbRVQNJkwhMMrNnFV4hvPG+PqfbNUvTYebTqOScv1SGk+hiLRcgTOGYqRl2P8cL7OP96zyFiXzqfvmUYRBHpUk0s2pzl3zRC/Olpid2mJhl9CinT2LxpMNQQG4yoHKjbnrYW03ubqe3o4pz/O3mmbzZk4eUNl++d3cd6aJPW6zUk9CkfCDjlNY7YiUeloxOMynWZAukthf7nN+zeu5azBFDcer7JiOazt6mD7EYKrIEQqYSSwu7TAyzdt5ayufibay1ywIaDlR7hOjly6jGgVufGIRn8y4HitRcrMsmhbnJoOMfQmJ8UDdLnB+kKcpXaTvnQCK1xmfUFjuh7QtrqZquis71J46U8OclIuydvO7eWd100gGR5LbZctRVBVi76YwmjXBNWmyHfuT3FwTmcor7LUinjzUwp89aFFnre1gCpJ/P2zR7j2UImirtGrJfjmrhmWnCRVK+A303W2DCgousVySyZDnKYd0RuXGa/6XHXDHL//8x7WFgw+e/kf6kJ8/YFFds21cZEo6iIDfUsIQQY/9JC0OmflVH6xL8k7z+jhJdtOBJem6h1uOl7mR/tLuI7I8SWH7z24wq9fvZHnbMvRn9Iebf/OqRqv//UR7nrFDtKGwuUbi4/+bCwbo90JSZkSLdfnFxNHOKXQzWxV4HdTZb72rA1c9JVHuP7Vm/GCkFf99Cgv2VEkZyo8eV2KF52cZ+9Ciy/vmv3v67xWrVq1atWqVatWrfoTekIHio5VQ8R4RNGUaAVV9tQnecPZXfxmYprJZQXTBEWUuHmqzNr9c6xLJTk+qzM4oKAJJr87ZBIS0ZcUObMnw7nZHF+4a4GDyxYPzgcU4hJqlMb2myRUib54nJKzQCodJ2x3MV8X+NzTxvjpsUMkkrN4YYyJqs+81aHVTrBxuMlCK0SVdPJxi0hoYrsFSq06Mc0kLse56WEJ3Qh5+qY0uga/0pf5+AUD/MUNE/QnLCzfZrGRJ12oo0gCPWaMe/wGCVWlVpMQXAUtStLwymimQSBUSBoCUmgw09AYSKgcLUe4Xor7xyUMdZwXbBwkn2xh21kmOh47+2wWGzZpQyMTXySMFBIK9AyFPGtwhDWJNN/eN0tbdOg1NNLxKWJSlusOh4xlDEpti2w8TsuzOW0YlhsihbjMSPcMa8M0txyV6E4mObjgMJI3ONiq8bG7bARP5893dHPtwRXinQGqSx7pVIpWWyNmuJTbBorUwY90ZKnGoKrTnZxDpo/RYhmr3c99h4eQNzRIxjVafoPeRIKW18LxiyzWkzxvbT8LRYs7J6sEQoXTx5ZJiCnaXohLBVPIcN/BGBVXxiPk8JzGfLmXnlwbz1HZNa1SEDTiMYM7yyKb8iamqjBR9tlcFMjFXUzFo4hIItZElkyCKEXcqCIJMQRRxAsjxCDkRVcfZ3QAEqbMp++dJK6CgETVdulNq5Q7NlEkQSRCGKPluSQ1n6F8FcuJocpxGq7MbM2kL6nRG/N5cCJBGMqs63FIaiJJHVTZpztRQ5YkjldEFiKNqiOgaRUcT6TlKOhmxOU7MvSYBu+6bgLTgLLjkjAidD1kzm3y0KzNYErjrIEUTgCWrWN7IpoQ4voSVUfGCQx0CQoxEdGTWGlIWHZEWhNYbNq8+/oJ+tISldDD1ECRBLZ01XF9haoTEpezXDa6lrWpHC/YGBDXTzzSXvqL3dQ6IRlNZrHmM9WwmGpYnNOX49D+MvO1OM1ghZ64iqnZNF1YqBa5txbywz/bREZTueQb+zlpMM0bzurhKRsSfHvvEooWoekeecXCkJLMOAHDiTTnD+UoLZRpWwKCGPKtPYu86UdTbBtS8cUqKU2n7JQYNpO0/TLt2hCf3Wfz7BGbtz2pnzfftMJ0RWPLQIVIENg7n2QoFXHymiWajWEqLZW+roABs01PyqUYb6OrAQcXU3Q6MvNVlUKvz2jWYr6ps1hPsNDyWJMSWfFXWJctMpIx+OWBCsuVgKduSjNdXqHuwJakhSjXWah1cWDeZCipcmDBRww1LNvlqw8s8d5zB/mzrUU6bkDWVHjGWJ6X/uAwn37GENOzS6xJmrzhzDTvuGGcSl2gP6ZQsi0Gczp+IHG0JCL4Ra55zhiy+McTWf/85C7unqnySKPFhy8a40u7HGxfJKYqJHSXvYsKl6yL8aItRX57bIU9S3W+s3eZnrRHSk2RNHRee2Y3Lz+1SFJ/bLd2z2SDz989zy9fuJW0ofybfcG6oslYL0SCR71usKsV0pu1+fHzthBTJe55yzYSmsSf//wQm3t1Xn36iXosLz6lgO2FvOQnh/jy04Y5+/HtoggjASF6nIpZP07trFq1atWqVatW/b/o8RpXPVHGVE/s1LNYRLMdY/e8T8X2mW/XOdo5iqFKbOgL2NLtM92w6fg+P9u/zELNZVsxjdLpZ/d4ioolIgsKrZrMLx+p4qkNnrU5wwu2dnP1JTsRvRTXP6JzdCGB7+XwaDO+lKY7pjFnt9m7XONnhxbohG06nkTeEPBosSZrs7XXJ5IaFOMeumLTdmVUIYWulIkZbfqSAYlYnR3rGuRVma/vnicKBd56Rj9vuHEvT1ubZrkRIaKTUH0W2wJLjTR3ThqMFdv4tkzgC5y7NkE1aNDsKMxVTfYvJjGlOLrWRo7i/PaQjh/AUjXJkckEOSXBA/MtXr9tM1bQQhYF2q6PIgd0Jz1EyaUrptJ0Ik7th9/Nz3N3eYqFsEZCj5DUJvvnC7S9kN50By8EQwvoOCogkjc9hnIWJw3UEUUYyHR4+ZnHOG9smafvnGGkYFFMdVDEgNmGzS+PLCFJEVqsTSIZcWhOQUPi4FyE2zE5VJKQ0ajYMiAR1wR0pcPpgy1m6h4DGZnf3JNmYbGLW49mma0k8H2ZhycNwk6av/ltlbGMxPr+GgndQUajHTbIx0Tmqjq1jkwm51GTm6SSIZoOphZR60iIsseOIZ9WECFJIIQq++ZCys0EKV3BjxziaoCphsRUOLikEkQ2aV2kPxmhK20KcRfdDIjFAuLxCEesksvY9OcsskmbwXSE60p84fYF3nLtFMeW4zTtE4HMdfmIuBZQaurccTzOsZLJw3Mxbh/PUK4P88y+ZzBXTlJtGQiRQUqPSBsR3TEZBBUn0AkDA1EUMSSRRidDsxOjbcXJx2RuOFrla7vmeeYOle5siKz42A0ZE42Xb+vj6ss28oLNRWRJpFeLk5RUnrkxAZjUbYG+zBK9SY9CvENGF5ERma9ANbRJpiLiZoSpCXj49OVWGEwruAFMVEwmy0luP5pijbCRr93ZZMundnNguYPlBTz9+7uodaDphIx7S9xWOszH7t1Dy7V4z/ULOJ0kpaZM1RYomDLpWI2iCW1H4a/O2ETR0BjJGawvmjw43ebSH93PO24+zK7SChsGA+wopG6lMKQ4rZJOFwnunmqQz8q4gs9wymCu6ZBLR1Q6IT3xAFPrYHsSMhG3HenCjAuMrVvgBwdnKJoGOwpFZmtgtZKIosX2noClpsiR2REmKgqjWZWyFbChAKlYCRGdgysh7UaGXcdirM9JeGHAUqUfTXE5dcDC9cELFZodhZbkc9UFPVy2Jcv7L+5jIKuxqUti69AciqhRaissNw0iwEyU2NAXoBkWpw3IfO+5Gzi84PDj3Suc/rk97F2s0p1Q+f6L1vPSnx7mSWsS/N2lw2ztibMlF0MyQ+abMJrzma2HBG6CmRWVw8sOFcv7N5/FX7lniYmFgO88ext/fc9xbE9DEEIW2hbz5R4cx2DvUod333qEv7rtED8/Ns2GgkoQGBSTGt947jpUWfyjINH9001e+aNjfOJpaxjJ/nGtIgDXD3nNb/axZPkUjQy9Zoq3n93Pdbub8M8V/JK6zN/eNYMuC7z/vGEAgvDED3VF5NZXbGM0Y/4f90n/WhgKj+u2atWqVatWrVr1RLU6pvqveULPKMrrEoeaIEkiSpjkSLlGf8anNy0TBiIePtt6PSI/wUyrzF9d7/PqJ3WTMHXW5n1uOF5B0ywS8SqT413cMr1MuaLytmt94hmXA8cEhKRPGOiMlwMMNcFT1ro8XLKIxDhpzeBIxUGSMoxkRNxoAUU2ick6U9WQTivFzoE6ktwi9LL0JNu4kUVcKVJ3yxTjGhI6e6Y7xAUN2w15ynCRxYbPlkKC+6baTC9pHF2ROGeNxO8XTGxP5vTiCDe6NUYyJp++eC0tb4DP3fYgtY5ITE/zwFyZLUUZiDiyHCMmGJSsiO0jKp9/2la+uneCf3hgBlGCrpjI/bMm56wJ6XgOOV0jZS6TNk2OrUjENIXjZZ8wCulJ+iQTZTTSpM0OQ36Ch+dFUlqCwxWPrUWNfHqCAYpMNS0KiknLtdHlHHGjyRZTxHKXiBtJKp0mmbjOgSWRS0aK/OXZo0zWLQYTJm+47hDTdYFICEhqArN1WFMwOFwKWRN20/Y6xOUcitYhFsWw1YCWLyCEOoutgDGli784cx0/OTKLI8+y4CaRIhPLb5DSkjxcapHpEdnUbXNsWSYXi7PQFE8ErISIpYZAIR5j54DJI6UmZrZGIqExoqscq7fQVRlNFLBDhyDQmWpqiKGK48R54doRbpw6xmwz5OiKwuZ8mtduH+ZI2SZltDhQadFxQ5ACErqLEMToSYm0Qgtd9JldlkmokDJlypaMJsm4bkitFmMuVJmqO/TkO+yat7jiyH7cyEVHxfFkXE8m0kLKrSQZI4BIomWZuIZLW5CYraiIoYHrigxkLTQpou373DFbQ4p0VpoCjuCwb6nFTx6skc0IPH19lg8+eYgn9xcZzGi8aEeB1//8ELvnm+zYnGBdOkYouEyVE6xPJ/jac4ZRFJ87p1a4/liJwbyFGavgRQ4JKc54WeS+h/MYqoyHz6+PrFB3Qn716nUcrDS45vZJvMgmoQtsH66RNUJ8YQXPzSFg4YcZsppKw2vRm4GqpbJ7PsVYNsW6bIqP/GYBWQVNPhFAcF0I2yqB4qBJ0LQjNE9iYzbD31+8nqnTbO6fbeKFMeKqxGcemEKQI/yOSCIm4os+KcUkplU5qVvk+IrEhoLAeFlAoJvcmjj7llrsnu+QViKs0Md2Ve4cj1NqmCB6iIiEUYipehwvxVhoZ8loCSbrCnnDIZWO2D2hsLZXZKLu8uqBAEOrc1JXP/ONEDVKc7zk887rx7l9ssagZmJLHl4kMOCkGLdEZPIcXFIYyWgsWjaepaIpDtOWwydvmqUUtdjfhHRvjfffPsn3n5Ok2vF5/am9TJZKvPu3E9w6XkdRYU1RYrrpMVdO4YchohAxkJHYv+zw0buP8I+XbKY/8YegShRF2H7Ib16ziarjIkYKiC5pI2ClI9EOLJJKhrLlcctEhb64zkRZ4YodfciSwPM2FZmsOOxbaHPpltyj7d4z2eDVPzrGz65c/2itot3zLXb0xh/dxw9CXvyzAzgRDKUM/mxTFx+/+xifvQ2+85IxYtqJlVU+f+8cd07W+fmLtiAIAvWOz8VfPsC1r9xAd1LlH+9ZxGrW/1v6rVWrVq1atWrVqlWr/tSe0IEiWfbZ1ufQtpIsdxo0HJ2CGRHXazQcFTHUickJZq2QrkRA10jIF+9YJJ4JeM0pfaiiQLOlE5Fm56Ym0w0XKWbQnc+STWr0DVXQ2nHm2iW65ABVTHBkRWZL9yIJKcMt+33SCRjJKcw1RQpJnYKh0QnK9KdSTCxm2TXncHK/hyC6NFyFghEnk5vjvpksCVlHFDuct87inNxOvrN3hZYbcvZwkhf+9ADPXJvlgRmPQMpwJBEiCB6mmOBzN7j8w3M2s3/JIq7LfHPXAjO1ACmlIUt1phs6QwmFpudy2dYWt+7S0SSdhhawd7HNclVmcklic18CxCbrCgLzzYiMoaLJHnVbpstQiJs1+hIqDa+BupTDizrMlIp0Qo+mkyEZW6YvIzC9YpAzfVQZZqsxemMSedMlCg0iATTZRxZBFDyKcZ8oKqNKcRq2S38mxu9na9w2VeXC4Sy2F/LcTQWarsexGZ+B7iRHF31ichcWNTJGSCe0sV2ZgXSH44s+G3phpHeF7myM2YaH7QlcdetBhgwTQUrSiRzW5UUcLFQhThioHF2B7kQaQa5jiDFigsJ83ScXU3CdCFeS2TMTUbE0XAv0Yo2hvEJ/9woEbSQhwXxDZamhsdAUSWowGEsQF9PcdSxBw1Wo2BHVusw1D0yxrZBkrEvGlUKSWgtZCkjFLEJPYlOfwMF5HUkS8UWLaifAUDWiUKJUM9FElY+c38evji1TdVyqlkBdc1lya2TjITnTRHFt3EjCDSTyMZeaZRIEMhVLZ8lSmHYMmpJPShOQVZ+WL5A0fFBatFzIqDKaaWEKElv7Yvz6ZTsf8157z5P7KXc6vP53NyLFdZJ5FynUKbdMjq6kaNkyu+Y7fO+B/VyyWWVtUSaV7iDKAoutkFP7QlJak619Ne7Q+3hkMYlSNTFjIV5sgbff0kYXdeq+xXBGIBVvkTLtE7PSEiGELgtWk0LsRCChP79CSspgBS4fOXs7iy2Pbz1QQdADGh0QwgBf8sjmIzQ1RFV8MgZYfpusbXDP4gof+J3MPbMNgigko0tEvsL2Hp09KzXSeozllsspY22CyGWpoRFGoCseMTVipRlxRn+S523q4cO3jnNk2mW0x+D+cYHATfBIucUFIwEVb5luM8eeOR85yOH7Muu7Y9w720EVYhxblEkoMl6qjBgmEUWb+ya7GC2G5FMNjs+kqHUi0pLM4VKHHl1jObI4vSvFW08f4DN3TrEc1lmoyaTiPnONkMDN0ApcRtQUf3XGIH/5u+Mk4y5l2yUhpbliQzctJ+DS7xzg008fplMT+funj/Ct3Yv8+NgMY71lzJrKZA3WpmM0PRvDaHC6kcbyXX50aIarTl3/6N+GIAicM5LkjvE6n7xvnJgskTJd2mGL7T0mD07F6SgRcVljot2h0Qx48dYuXvzPtYqmqw7P+PoBvvLcxy69+rN9Za55yXre+tMp/v6yYe6ba/DZO+d45c4e3npeLwD3zzWpezZtKyJsKXywdpSvXLKF4ZTJI4t/WO418CN+8GcbkSUBxw959Q+O89onddOdVNk91+K28Rrfv7yfv36c+6hgNfVs1apVq1atWrXqcfF4jaueKGOqJ3Tq2VxdIqsLmEYLU9bZmFNZbArMNQVM1abaMJmohBiygB7lOLYoIkgQDwy+/tAiKx0PTRER/Dj3H4kTVyKM0OQXx5ZIKAqn9CTRc2VKsynUMMmu2YiFuk5kr+fe4ypm0iGva8w0PMoNk3arm7smJSodkY6rYwURU3Nd7J8vsmz5OJ5KuSPTsBXOHvDxhQoJ3Uews3zst4u8/Yxe7psv89k7Fnjt9l5un1tE03y6EzBREVEFnZlGhGe4fPi243z1gXm+eNcC5w6n8ByV129fx5VbtrCtS8enSXfCp+0FDA1X2DbaJJlZ5uu7pxGAlKLQ8h1ajsLGrg661qEYExCkBjJxZuoRhqQzXhFodHTWdi1TSLisybdJGQ66LFG3IW9INL2AtBKj7UXExCyS6NJod9FwIgIvhhtZGLJC07cQIoOa69CXFLAji9EsNIIWt02tEIQRH7l1kg/cME1cFLHCkIAASQ4opmqMFSxiWpu0ZlDpSJhSAhBImzBR1olpFv1pC1VxGMsHNEOfhGDghj6SEKArIdlYlScNBiy3A5ptg2MzeY6XBfKmQr0FpiTQX3QQ9DaVoELD8+hPA4JFyrDJ6hqaXqEr4dCX9vCigJgW0nRExEjk4i8f4C2nDHJ6dwHRUQk8ASWSmHaXmGo1CQP1RBFtXyOILOJ6m5FchzNGGwxmO/SnQ6zAAQIUuU3LAQGZ++ea1BoQjwyEQEcQIBf3kaQOCb1D3CzTk1mmL9NAU1p4kU9CU3n7aSNIyISRzF+eNcJgWiYUHGq2i+1FLLV9vMAhptts7a+TUFRKrYhv3rfEF+9c4NO3zj36ftMkiXsmZVq2yUg6geNkWG7qpGIt+tMd4vEGBV0mJsY5WpKJcInwKMYFDMVAlmz2L8sYskq3keQF2wo8bV2B44tpNAwiscNQrkw25uIHMou1JHvnVVxfx/Yl7h7PsCYvsNgMSWkefRkbNwj53AOH+Oztc0wveoiOjKAGmEaIqYWYmkjda7O+q0V/bgVFlDGVCKuucqhSx5LKFNM+8502z91p0AxrRIGMIAcM9bRRUGk5Aj0JMPUGxRgEQouhjM39Cyu86NrdHKo1MHRYaYj052xMs80FG5bJxFsUEx6iaLOwkqDcDvnO5Rt53faNeJbBoSkBMZBYavsIThzDaLE+J1Bq+yzXi9x+PEZvMUCIdUjGRWTNIzIdlEhmxanzzl8f49KNXfSYaTp0SGkKqhygmzZiJDNVt3j/7YdZcSzwDCxH4xMXrOOS0Tx7Zttcub2bz++aYbbTZs9yg28dnGRLt09PusZwoUFWF0Gy2VDwEfDJJFpcMlrk7aeso9R2H03dun+qySu+f4ysIZPTNeKmRcqMUESBmUbI2ryMbcPhBY/16RNB2YyuMF2zAehOKPzkZRs4ZyT16N/aZMXm5qN1vr+rxCWb0tw3X+eXB1fwOiLP2px9dL8nDaZ466mD1OsiB8ptaBtIocRbfjbB1+9dBmDffJuv3L1MwwkIwoiXfvcozcDjnLUJAHb0xfntK7agyo9/dxqGj++2atWqVatWrVr1RLU6pvqveUIHinxXZ7LRRpBa9CVD0vE2W/ss8JMcn+th92zIxIqIHMkcL0HCkFjbJePqHSrtgCgERfFxIp+EKaP4WfYve6xURX59dIGD5QZeaPCkrR6Hyy5e6KOhYvp5rjpziIFYkpWgQssJiSsKTS/C8zQWFge49bBGJIRcuinL7hmDI4t5DiyaHC8naVgFHlwQSekWhqTiiC5aMuA7Bw+imC1E3eYbD89RLmukYwKe5CNGJjN1CVPz2dwtcqTiYiZ85l2LTcUYH79omDP602zP93DDAZMDC0nmKhlmahIDKQVHsNnY3eHIooUuSDxzu0skdkhpMk3f4qQel6HcMoIg03IivEBmYqnASksjoagstE/MuhgtzLG91yNpVJBIstSGvpTHsWWJqcUkD8xozFRjtFwHUYBcokFMgWK8yeZim+5Ek5N6GySMNhu6WszWJQwF9i6Xeeo1D3LfchXJCJntNNk5ZlPMNjl92CFpOqiCTttTcAMRXQ44uKQhCREV28fqmDRdkcCJU23piKFMsyNQswTGj+eZrirYnSINF2xXo94RqTsBG3ojpio+/RmJ77xgmN6MTSYWIMk2hCqBJ6KqEeMrcRBb9CUFBtIemtwmY/rULAVNkPEjG10Xec15BZ7/vUOIgchLtnUReAJdKRkvCLhwKMdFw90EoYwuS5Qtg+7kIusLTQYyLUaKTc4csjl3rMlwvkkncIjUOprmsNhyCaMIXRFIaTJhIEEYp92J4wUSqhwyXlGZWInxkz1d3HkwxcR8kguHcgQhlJoeb7hmgo3xHJ84dzMz8zqlis5SNU5Gl4kpEWsyMJzzCYUW1xya5cbxFX55oMIF372Pl/1qDxf94F66jSSjySwjch/1RoK5mkTTdZAlEIiIx0MabsAVG7qoNTIcnEti2XGOL6f54Z4cNz3SRxDk+dalm3j9WT08Z2ORdYk4o8k440sKE/N97JtOYSguuhzQcnQW6mmWLAfH0+jLOsTiNQwpRcuN2DcTo9lK0mlpXLYlz6vPLZBJhDRdh+Fch3XdNc4ZK5MzIxbaDjFZou7b5IsegeSzsctjRyHJ8orE9Ueq7F706IpLuEKdwUyArFbZ0dfEClzSekBXokUQKniBQttWGUrrXDbSR1dMJRa3GUjKRIQEbp7ZmoZldTNbV9g+GKGYTa7eM4MiCqzNGOBJPHl9CuSQuCKSirkkDAdVColEm6QmgNwhqys0aZExJWZrIUM5kU29Mp99zgjfeGQOOwzpTqhUbJ+4rHBwQqUYkzF1F0ltEJdkuswYX3/6Sbzq+xNcs6vE+24a5+czx0ibDpoMuxbqBEHI4WWV2WqWIyWRjV0+64pVCtlZRjIGL1y3CdVPcsdknfO+uZtDKydm7JzUF+OWN27mtMEE63I6qZhDxWmRVGJ4vsGKFZAzJEIBPnHRCF+8bBRFFnjZjw8ThhGqLLKp+7H1gf7utjk+c+kaPn7JEOt7db6xa5FvXbGeW964mZG8/ph91+XiDBRkkrJEow3X7Fohrkn8/WXDHF/p8KEbpvnBS9fTm9I4sGhRcV0UFYbSf2jnTxEkWrVq1apV/70mJycRBIE9e/b8SdoXBIGf//zn/5+Pv+222xAEAUEQuOyyy/5Tx1x55ZWPHvN/cu5Vq1Y98TyhU88qjkdjJc55awJa4TK2Y6BIGpKgU3NEDDmkP6Uya1VxMNiQy3DKkM99i1VUOY5niSy2LBK6zEhOBNlm66DPjvQabl+aQBEFemIx5uoR3UkfNUwxUWvzg2rAOcNpSh2fjq+xPiuz3G7Tq0KXEefu/TI+IXJOQNdC3nPmML+aOMJ8yyaMDBbrJvONJBeu7XBwRUCV4Zz1FuONDr0JFStyEH2FTYMCi1aHuKIR+RoTjRabuxTmGiGJhI8iGNy7UOXPf9mmWS3zsjNkDtfafPvSzXzsznEOLDqIksCE0CEmK9SaGZKFCocaEvMlm+FchCK3qbYN1qRPpPmsS4scrrbRozx3VTx6zRiCvExeBsf3qDkacdlDlSK64j4122ViJUM2ZVEwTPYsRJQbGZbcFTZkE+yatbh4XZw5cYWRVIqe+ApyO4PteGSUBIWkhWunqbUk+nIOHQ9abQ3L0ZjpQDYus3l4CUOBktdEDWIIUomueBYv9CmaMoLsYUghtqfQ6gjkVJ+aHdGfhZWaQT4rMLMQp5P1qMwLxGWDLjNC11tIYQpRcWiHDb6yr0TFbjOUTJI0XFyvSSTIdKUcCimBhuuR0U/UlZpuecTkGKYqkI756LEa9bDNA6UUPVmB9V0az1hfoO0HLLVcbhmPOClXoG6HzMy0Ufs8HCnLbeMi/fEYkzXojkskNFAVBV0MWZMJWKz7tD2H/WUXU1DJ6AqRJSMnfEwVerSAvmRIKu6TVAM0waUvHeE7LrmUD4CgREhmwFhvlfurNr+a9cmmTERBQu0k6HQkKhKULQFVFJHkgGYQcsFYgsvWFehOqmR0GUEQuO7YMvfP1vnVkRUu3ZzlwrVJ3nFbmUxBQZcFiqmQFg3+8vYlevQ4jhtxfDaFIqo8XGmxtVuhZHUAcLyA4ys2l2/NcKja4hcvOIkbj1aZqTtM160TM9YSHqokEjPLnDNi4gUNtHpIx0kxUwt5z5N66bR1wmEBwwz53APTJESZcgm0npC5VpPtSQFT7eAHKl2JgIZjMZZLcudEwPvWb+WmqRJ9PS4NX0YkIhvzEbQWYzkVw5xFj7pZ6KyQUjIstFuEXpGJusNJPTmeu76H91w/zUKnw1BW4N4JlZOHI2arAWJo0PR9BtIBktSmU05xbbPJDx7ey+WbCrz6yh7eff0ExbjCkUaLtb5B3auwrqAQiA4jmTiq0sEUIu6fk7CDkLggMzUncN5AgWdfc4C/uWgEQQz49sMdJLlNx1PI5kSWrJCTMgIVz2JtpsA/PH0tbTfkM5cOc3+pzNhQQCC38NoJtvfHeP/Ny6zvSTLStUxfqk6AjGm0KMYkDlc1fN/h2n1t7jpWIaHKfP+KTWwuxgCYqHV41a8OMZYxmbdaxHUTQXboSB0KhsnhOSiVA15zRi/be+J89MZZZpo2v3n5FkTxD1N/f/7ICsWYwpOGU3zh8j+kocmRyDvOHCBtKqT/VR8wUe7wzO/uReloDGZMdvbH+cuL+tk13+R3x6u89dpJVP1EgBVga2+Mv33GMBsKJrL0p592HIUC0eNVMPEJUnhx1apV/2+68sorqdVq/6PBjIGBARYWFsjn88CJwMwFF1xAtVolnU7/j13Xv3b48GGKxeKjX7daLV71qldx++23c9555/H1r3+dWOxEH/u5z32Ov/7rv6anp+d/6nJXrfq/xuM2rnqCjKme0IEiIzToMhP8Zo/DOVtqJFR4YCIOYkTahJxqMFV1sTDY3ivy2p19nNqfwAseprQYMNMAT9F5y6ld3LU8ThBCWknwu4kaX3zmKXzuoQMk4vMYWpxyM8vRkk2zojEwKHLPTI2YAV1xnSZl5peTFAyJWQsyBZekH2P/nEXbK7OuUKXhesR1KDclEET6kgI3HIHBrEVCTfDIkkDMsCEU0Q2HU0YF3nbqej58z25yuky506RmxVlqeMiSzLPXdvGT3TX6ciKLVoeEEPHNvQu0vZB7Z2tUbYekLqFJCksNm81FhflWh4wuQdgGR6TjGiiiR94wkaRFklqS6Uab3oRMy27TmxIp6gpVS0ETEhytifQkNWZpMZxWmGxXWJNSqCQWGJOy1G2PrX0BpbpBvRVHz+SYXZG4zY9jJjSesm6JppembEW4gcy+JRlTjhD1CkU5iaKWKCQSTEoVCkKBOw5EZHSDXXMqZw8qxLQmWd1kphnDsjWGsi6u56HJClZYo0eNM1726TU0ZEni/KECtZTGMzdlmGs4/Pz4Ih1PQpEdJAweGo+xtQ+ymsJD4xGnDsfIxxrEVQcnhEi2WNct0/EUhlI+zcjFDVw0OUaER7Wp0u5IyAIIYoadY8fBF1FUka8eOMR3D07RXonxiWcM8d4LBjnlKw8xnNKouSFlS0TX4swtxxCKPp6wTFfawhRT2EFEta3R9jU0UcT1JSzf5RuXbeHGI1V+/PAKlZqMqUt48QAxa6GIKjFVIQpE4op4ItUJnZ/sKSP6Anqk0GxkMREJApdM7ETKz/ZsnKbWRlF80rEmthunEwbYYYimhGzqOjFQmas5XPtwhXtmayy3XCLD4oGSxw+PjbO+qCECfihSSDm0/AayFCOX8DEMn6GMhaFZ5IsRA0YfI5ksz/inAwiAqUpctDnOCzb34PoR/QmdV53aw1t/67JnsY6CxC0VE1FbQ4+RZs8M9OVsnCBiviHyvb0rVMsq/cWISbvO04Z72J5P8dHGDBXLQI77xCSTpDHDWKYLWWzQCCUkNSQUBG6dqPPgsYCR7jhxs87JKSgkK5yaqyFhoogizY5AqaUy18kw2ZTIqTJzKxIb4iovuuYI2wY0JpohaxI5Ds1VcNoJjk56nLpOJPTb6JLBiuWTTUusTcd43/kD5OMqtxyv8q5z+0kYIm+5eRdT5QRuFGdyMsHW0QhfrFAv9bN7UmS0X6DeEumLCXh6i3tKLj97/ka+su8YUzWPmbrNGjPNYsemKy6yuX+ZjisxPVMkFWvzvUfmeOpInp+MH2W86lGxHVJiN44fcf9iDUkQKBRXGM46pOMrZI0cbhgRCi55Pc39Mzrn9YpIRwWmVlxM+Q9dz8funKTeCdg4ZlBbanK87DFoFlm2baIoohDTWCkHnDOS4lv3L3NoqcO3XrQWVRYJw4h3/2qK0YLGlx+apr/H4dqBs1GkEzN87pls8LZfTPCDl6zj9xMNnrQm+eh5J8s2z/zqIV64vZebDtXZUDT46CUD7F9q8/wfHCQVaWzs0XjyWIbrD9Z4cLrFi08usrMv8d/XSUXCie3xamvVqlWrVv27JEmiu7v7f/oy/kPFYvExgavPfvazxONxbrzxRj7zmc/w2c9+lve+970ApFIpUqnUv9PSqlVPMI/XuOoJMqZ6Qs+Xz5oiTS+gkFaYmR/i3okkE8sC1XbE63cO0mUYJFWFtYkY+ydFXvyTQ9x4rMJ1e0SalkQmITKQ0PjUrUu8Y8fJGKLJXCNCUX1++kiFh+Z99s7FKcTB0BvkEyIbhkQOlRyqrYi8qtO0faJQ4dwtDlW/iUBEjxljpuHi2SK6oLLY8lEEhaSi4AptFFGg5UjEdR/Bz1BxLLoTHmn5xHGKIHLhmix/++CDPGWgn5ZnkY1FnDbWQNMsuuJwx3Sdn75kE2uMJG5DY2M+RrntnyjwJTiU6yIpRWGiBJpo4IUOuhKiSxKa6rOxAHtmI6xODEGqc/vxXiZWEqR1F9dL4oZwZn+Ih4MUGUw3LcJQ5rZjOofms/xuf4H9cxn2zWY5sJCkEBMYzdcYSMls6q3yoi0FPvWU9fzsudvQ3Sx2Nc+DsykOL3XR9gSkSKdiCbieyXJLIpOIkGSf3pTPYLZNV8IhoUeEhLiuhht6FMwIxBrr8h5OVGEoExFILZKaxO7xDLWWyWDGxw99VFHk63tW8FWP7X1xnrExx1efuZktqSxzSzq2CwlTYKkssqGgoSVsEqaLJivMND1kUSZlhojI1G2B2aqBHMZwoiaurxIFJjUnJG462D4UYyKq7JCJNRkrWgxkXM5eo/F3lw4xnNVoOgFbMgmUQKZWD1m2fDq2SmDrtDsqjpPknskEFUtCUzqIko8hi2zrsykmHdbk4PLvHuD2qSptuc3bz+rnaWuKjM/EOLoY5+BimpTuElN9sqaPLEHZ8vjS3Yt4UYAZC5BFgYTms7W/xlhXg0KmxUzQQJJ9RMllsiYSEhAJTepVhZXGH95rfhgRhvC8rUU+9JRh5qdVamUNUzAwRJWQkMWGRL2jUOmIbO32ycZtGrbIweksP7u3i8mFIuWmynd/3+H153Tx69ds4kdXrufVp/Yxlo7xhbsWOLh0Ip3ptJ4UMUmhXUty8KhCx0rgBgI2LppoMlnWcMoJhLbJFVvznDOcJeqYTC+GvPXaKYYKAZJWoyvu03Ql9i+bZAwZ23dI6xrzTYEvPG0d/bE4UszGJ+LAnE6jbbJrOs29x0c4VIpzYH4Nh0smU0uDHF3RCLwM8w2R3oRCT0Jl/XDE2SNxfvuSrWSSLkO9DqLRYmDAph3YKIJMzbWwHZ1y0OKtZ/aQj6sAXDia4cLRDKf2pjirp5/zBoucmh2iLQRUW0mOl+JEakgsa7NsOeQSLkJ6kUJMYqUd8sG7DlOymxgC1OZjrDQgowvMtzpUG1mmaiqnjLTxApGvPrTEm258iFZYpjch4HjQiQLOGUrxrLEip43KtF2FRxbitJ0YvtBCFg2WWwoPzihcMbKWyErQn9a58XWbGCv8Yan6zz51jE9dNMq7zh7mnaePYVsakysRmpek2YFjZY9fvWojZwwlWAls3nRB8dFUrw/fMIPth/SmFEa6Iz594fZHg0QAG4omP3zpet7w4wn2L/yhOPVk2eadv5zgC1esYa7lsrZb41PPHkIQBBabDqlI430X9fODF21kMKnzs4fLPGfrH1ZUW7Vq1apV/71uv/12TjvtNDRNo6enh/e85z34vv/oz88//3ze8pa38K53vYtsNkt3dzcf+tCHHtPGoUOHOPvss9F1nU2bNnHzzTc/JiXrf009m5yc5IILLgAgk8kgCAJXXnklAMPDw3z2s599TNvbt29/zPmOHj3Kueee++i5brrppj+6p7m5OZ7//OeTyWTI5XJceumlTE5O/pdfm1qtxrp169i6dSsbNmygXl9dhXPVqlX/557QM4pKYQMtlOiJx1iohniRQjEVkSPBpkyaiy/t4psPLTLbsKl0qigI/NUtx6lbEVlZZbg7pO6GaB2Bd14/wazrkFBF+kyD6brDZy/azN/fM8G1e2w29tps7RY5OdXHB+dn6SkIvO70Ar86rDHVkGkF85w01KReG2BmOcIQNAbyIgcWOwzlJYbzOp1ohYwhook2bVcBL8FSp03KCBlNR0zW2xQSHo1Ogtm6wFt2bOfPvzNFsWhw1piDpM2jyElq7Ta92YAP3H6EN5w6wP4ZkwcPz/D5p63jh8fGqQRV4kaCmGaxWQuoNBM0HIk1GZi36mTVBN2pRc4SZLYmN3P+SD/XTz9CzQ6Ymh5lue0ykFJAqpM2XGKKj6i2UfwU99Ql8mKS2RVIJwfZV/ZJJGXuHo8oJlRqjk9fUuFH40sIqLz1tGGufvFazv/2QzQWskythCQMDU0SsbyQI00JyzbZP68gSQXiskU+LhMEZS7aEWOy7BDzEjwwDdt7+wikEsPJOIa6Qt7w2KpVmFrMsm3I5lg5xkAqwUy9gxJXCUKH8YrF1bsWuWxDjpSp8MmnrWHPQoGfPVLm0EqbozWL4WSad565hk/ev4e+lE6dJq5v4oU6OaNzoph2dDJHFrq5en+Dk0dFmoHPSjtibdbkOSfluW1mkdsPryGmdRhKxXA9iSOlJjeP3821lz+FtCjzo5ds5Krf7WNFcmi7Pr2KjBILcUMQxASVpsReX2eqrnPRxipZE7zQpyVG5A2ZlZiCKSrQNjhjIMW5wyLnj2T4+oG91ByB6ZpMRhUQRA85ZmOIIZ7qoRkeSTFgbV+TpKri4lLtqGQ1mTedMsDHbgpZbruMjc5gOzGWaim6sxKdMHj0vTaU1XnLeT18/vdzvPOGeXJ5AVkLKSZ9VEWk7Xc4c+0iSTnDrjkdU1JouRGLzYANuYBze1w2ZXOMJNO86qQ0fhjy1YfmefraLJYT8vyrj/KRpw/wrC1ZLDdAE0WeujaNKEYcarRICzGOHI9wpYAgELHcgCtOTvH2M9YAULN8Hplx2VDUuW+uRs6EpmARl9KUOw5DmYC0YbFkecTUkKWqyj/8fpGzhtLsLGZYtFts7nXQVR+/rdB0Q6yySRCB40uEoYTtiSiiwcWjeS4bK9KfMpipd/Ol3ZO8/86DLDUF0rpJx7Ppj6u4QoOUGmOi5rOtaPCz+0X2zHdYkzP/6Fn2dxeN8d5bjvPrY2WypkjLd8kbBnM1ibyucHzFYyAlY9kSuhahSBF1v0naTzNZhi0jEk8bLSBrPt87eBTkkN54wCPLkNNUpjoeTUfCDRIg+KzL6bxy21rWp0yu+sFu3v70jVxz5AiOVGa2UkBVO8xX41idLHunRFr1OgVZ55svWIuu/CGQE4YR+ZjKxWMngjA5TWNTLsZ8xafUCBnKJ/nJc0fpiqt8bfc89841eNOp/Y8e/44L+rhnus5f/GaCX7zsJEayxmNel4wpE4QRbz63mz/bnue24zWWWx4fvG4GLRHyw30lDpUsWk7AO387zvsvHOJ9v5nhXRf2cdm2E9d04doU544kMVXp8et8/rMi4PEqmBg9Tu2sWrVq1X+zubk5LrnkEq688kq+/e1vc+jQIV796lej6/pjgjNXX301V111Fffddx/33HMPV155JWeddRYXXXQRYRhy2WWXMTg4yH333Uez2eQv/uIv/t1zDgwM8NOf/pQrrriCw4cPk0wmMQzj393/fxWGIZdffjn5fJ57772XRqPB2972tsfsY1kWF1xwAeeccw533HEHsizzsY99jIsvvph9+/ahqup/+vV505vexJOf/GTe+973snbtWm6++eb/9LGrVj2hPF7jqifImOoJHShqdwQGChJvOrmfz9yxRMMT+chThnEceNY3DnLXm7byqlN7iKIIOwi5d66O0wJVizDNgIpvkTBkNvYZHFx0aPsyQwmFw4sBSa3D/TNt3n3GOl7y/UNEuTilEPYETb542QgfufcRPnbPft575kbG6xE3Tpm4tgFKmXw+SSomc7TaIoxkkqrKQsNDEBM8a/MKx2tttE6OScukZCnkdYWm2+Kk3hazlSz7GxYPLVZ5+dYBrn/NVt574xHaQYN4qFKIe7ihjeQkuWehyfvvOMwz+/t4xWldfPbuKYxcjUbHRBV9Op5IRlc5sORgajrZ+Ay6aRETVSQpZGuXxLGVKQ4+mOH3UzFOHvDoz9VYaplEgcr+RZOzhhSqjkVai7Nsu5y30WViVkaQFd521gB75jvcNF4mlHzEMKTmOWRUhYgOEzWLsuUxWW/zlUs28OXdUzywUCUup9m/7LKpKLHiuAzEDeZaHn0xg6ZjkTcTFJOLxDWVSGhTbejsXVDoT0HJTjCWsSDSEaU2kp9hthERF7PMzAgUFZFCokYlBDnQWOzU+ekRm49fP8uFazOcPZLkWZszfPSiYQAeWW5x1e8OsbtUwRUdBlM6G4suGW0ZL1RoemBIGreOV/n4BSN87pJRzv6Hh8mlDbKJNl7kcPX+42hCjI6b5fC8TT2t0/RCYoGCQhZZ/MM/1lXbZV1eoBG0yOsRKAHdiQAZk7QXQuiTcGD/osK6rE7TEzij2MecDNPzbZ69OcsPX7SR2yfqpHSJmCpxYCLOSluj5Bu84bQib9ue47LvHaE3KVD1PBwtoupBIrBJCSI1u0NMg6Yr8NKfP8yOXhjNW8QUBd/TKJdNTuuNc0p38o/ecy/dUWRnf4x/un+KQLJJJmyKcVC0WXK6AJELUYpaR+FQyaddUyEWZ0NfLzvyCU7rTbLlb3azodvgtLUmX3xgjh/tX+aTTxtl90KLT9w0SyEp0RZszhyVGG806DZ1JAlWPI+d/SCpNmInYvfSHz5xS5syO4dNPnv3HIPFgIWSgZKPaDs6xfQKBDmm6wFLtRThik7YOrH62tFGjflOh4QRICKTNm0KsRP1qExJY7Luk9biHCu7pJQ4FdtlthRw8R2P8OSNMe6cbpA1ZKpOQEKVma5F5EyZSiCgKSYrjs4HL9jAqX0Z3nGaTzHx7w8ce3QDtyVhGBKS6FFpKcS0gJbjUUwKVO2QlJzADhyGMgqtqE2vKbHoaFRCm4lmjBt21dhSTKDJDSxLY7Ei8+ydOXR1ibLXZE02xnLHwQ88NubidMUUPvjUAV71iyniMYOeHpXb5iRG4kUWmwGVVkR/SuApoxmuOnOAZcvluT/bw2u2ryEta3zoxmlued2JWkMN2+e51xzga5ev4/eTDW6frPOVS9cR++cAzQs3d/HnJ/VQsXxe9O2jfPclYyR0ibG8yY2v3EpP8t9+bfJxhWXL5ZrdS3zi1lm+9WdjDPZKWE0ZfIFt3SY3HKvyi2Mlxksurzuzm+ftyD96/P9IgGjVqlWrVj3qS1/6EgMDA3zhC19AEAQ2bNjA/Pw87373u/nABz6A+M/jpG3btvHBD34QgLGxMb7whS/wu9/9josuuogbb7yR48ePc9tttz2aXvbxj3+ciy666N88pyRJZLMnVsn816le/5Gbb76ZgwcPMjk5SX//iQ83PvGJT/D0pz/90X1+8IMfIIoiX/va1xCEE2ks3/zmN0mn09x222089alP/U+fb3h4mKNHj7K8vExXV9ej7a1atWrV/4kndOrZUNykYjd4x60P8/4n91JUdB6asDhvNMWVpxbR5RMPWkEQWLQ7iFJELiYzkFSYabpMlmVkX6XaChju8tmU13CECp7sYqoithdy2kCCH71kA1MNm9fuHKLtBly7v8KnLtxCXI7z/UMPsyYbcdHAECvtgEj06E0GeJKFqYqcOgKZ7DQps8NoVmL3osi23irFlM1Ark1BN9i/ILB3LkmpXsAKG/SkLNymyrt/M0VKlzhS9hmfTyEGKWabLl1xlzD00GTIqjo3HK/yoz0lDtZsqi2FLd0BhewyaV1g74ROLu4iCrBrJo8SFlhqgRfEuH9WRJN9lhs+zZaM68SZrkmcMuDRl11hc4/LQKZEd7JDrSPhhRKuEyOWdBjp9XnP7YcRVI8vPXsdWwtxRK1K1pDwhQZDmZBjlSZP/fYe7pyp8PobHsahzmixQTFdYzDXRlcsUjGX5YqEUzaQI539sxnGKxLjKxkaTogqKhQSNcYKkEu0GcsohIRs74pYk20Q+QZNJyQAUt0NKk2RUiXNobmQYkzl8JKAKsqY2YA5v8b3Dk7x9K8/zIu/fwiA6YpLoywjehrHFkzaXkTLjuGLDbJmgC4LVFsGh1bafOXuJb6/p4QVr2MmPCRE7AAmyxItv0VXMmBzv4sguTTsEEtyKeQi7pqpUum4RFHE+QPd9MWSGKKB5YnYnoDl+2hKAJFEQvfY0iVgO0mmV2LcejjGM0YGOKU3jZlw2Feq8/f3TvPDvSW+dO8cr/rFIwiaRS7ro0siNx1tceHXDtN2fVwfSjURP4gIwgDHDxGEiLThccaaKjuHVjh3XZ1NPRZjBYfBdEBX2ma0t0U+a3G0Pc9zfnoff/7r3Wz9pzu5b7bGdx5e5F03H8eTPBzBQ1EsQlxW2jrLjS4mq3E6roTVUViYVelOi6wEFjdNz3DPwjI122ddl47thexaavLIchszVLh7osHmboOrX7yWi7bGSaY8pho2FbvD9tEW/T0rbFq7SG8moOOBHhgcmYY3XneYy364D4BzhlPc8ZqT+LO1o7zpjCGETjcPT8S5/kA3907lOLQUp9NOMhjP8u0XjvH9l69HBBbrPq26wQPHFERE2r7FunyTsa5lkhrEdRtDFnnSmpC7XnYG6woGAjA+H+JbMkldwtR98nGBKBBQMclpMc7M9NGxJa49UEWVxX8zSOT4IZ+/d46jJYvdU22KaYGG1MSUFWxapFSZSlMmoSoYSoAgOSSMEEFuUzA0JqsKvQmFhZrEgVmHWKTw5tPHKJUy7BuP84PLNuPQJhIdMoaPJLeJiSoPTmi8/6ZJgiAkJCKTBCcKmV/JUa+ZnNlbZE3KwEw1ycYFXnNyD/fPN3jjbw9Sbgp8dc80n/v9NJ9/zgh/deMkjh+S1GXuePVJnNaf4G1n9/HTF296NEgEEFMlJisOL/z2YV56SoGYJhFFEWuy+r8bJAL4+ztmue5whXxc4VPPGOatNx/l0k05Llqb4c925rj5WI3Rgk480Hn2phxXntb1n+o/ym2Phu3/xzv+n/iXXPrHa1u1atWq/wcdPHiQM8888zEBkLPOOotWq8Xs7Oyj39u2bdtjjuvp6WF5eRk4UQB6YGDgMTWITjvttD/Z9Q4ODj4aJAI488wzH7PPQw89xLFjx0gkEsTjceLxONlsFtu2OX78+H/5nKIo0t3dvRokWrXqf2d1TPVf8oSeUZRLN6mTIKWJvO3nk3z+2WO8/ifjnL82heuHvOh7R3j3hX2sKxj849M38pKf78MPoWJFZAyB3oTCwaqN6his1Q1Klo+aVDmpN6TulLllyoLb4T3nDvKtZ2/hzTccIvREXFfgi3cF1G2BA7MpZGGewFc5MpnlpHUyvlojH49IqknGqx2SgsLObpeYXqNkRYwvDRDKDfrTEYVYh2sfTqAT5/CURFevTEKMc7Tu01F8Vto+N71iG++64QgPL7oMdwm0bY0A2NYt8MhSk1zc4JTeOL+7r42QNQmFFeIKVO2A7rzN+tgaDjXrOK7JrnmLpKYgIRLXqxB0MV6V6EkJOFGHUi3OmYNLJLSQTcWItNmmx0qSMWew7V7unvZJqFkOHZNA1NFFmcuu2c/2XpOj1Tg7e0UmV0xO6g1xzCZPHRvgLSePYGoBt0wukzET9KarZA2Nih0xqsvs7zikEgYXjWbZ2T3I1ftnqLfrKGKDroSPJlts7m0SRBELdZNDy3HmtQBFkdg/G0cWYL4uMhTL87yxbh48Os2euk+/JCJLAp0gIB0TqHht+lM6Td8mkziR/rN72qLZiuhKh2QweHihwdbuFIHQwdY0Gq7AQkMnqcN9c3XmWxYpQyAQPEQRxtImumSzd0pk7WafgVQHx7dY2wvHSjE8WeDjdx9GFAOSqs73LzuZbzzcZsGWKMQs8nGI5Bp5UyBhOtQsk5QS47UnjfDrA1W8TsQZX9zFxkKchbJM5Nnsw+aMUQU/DKhLHsW4hywJBKJKQgopuhKyGpCKhwzJIT1pl2TMR5ZFUnpIVpZJKBICAt0JkCQII5EgDEGIqNgih8sWVcdlpgYbshI9CYUP3jGBEIESSvheRJMA24Wap1DzcnSEOBMlGYcARw7IdtsUMyZtL6C6rPGrksU19z/MU0ey9Cc0Th42ma24fHplgeG0wRVbCzz7+3txQo8A0KSQ7f0d8jo4UZkeP4kTREzOmHTFJYxYxEhK542n9vGF389x09EaH3jKIKISccFYilvHC9xZqlPQTMpWSGMqj4FGT5/NVTcf4+J1GaqWz0AyZLSrRUv2iQKdrvQcaSPNbKvGSC7JklXnlOEE480Gi60Oz99a5MZHGrz81AKfvP84s/WA7iwg1YlrCebrHm/c2cUX7lwkm4VfPFzlDad3EEXwgohaOyCI4IyhBB+5dYKHKyvcM7vExaP9nCqofOuRNjHNRe2AorcoJjWmmx5nDgosdNr0JeLIShvbVWl1FBqdkBwGtuDxrG0p3vibY7xwSxfvOCvNa355lI39Li5NNuZk9o6nqVgCaRnGW00+eucEB6dnsMI0ohIxZ3lcffkmfn6kxP5KB0UyKdseH/39I/zikQ64Epv7Faotn0Y74q2/PsoVW4qoksBkxeY53znAm8/u4RUn93Bs5cSMwicNpfinexbJx2Te++sZvvL8Ebb3x/jJ/hK/OVLmm5dv+N8+55+9Kcd5oyle+KODFJMSrzu1l3Il4oYjJW6bKhOJ8N5zByESOHf0sQU//SDiqp9P8KGLB8jGFO6danKk1OGyLVme8U8H+eDTBnj6psyfpH8CTqyqsbrq2apVq57goij6owBIFJ3I/fhfv68oymP2EQSBMAz/3Tb+vxJF8dHz/wvP8/7o2v71tfyvwjDk5JNP5nvf+94f7VsoFB6X61y1atW/8niNq54gY6ondKBIV11iusSRuRjlwOUF3z7Cr16zgTVZg809Jn/9u1ne+cspXNHjhy/YRNTSKUc1TE1ibVImVErEbJ2ejMaKZ4NsoLppajQ4umBw0qDPTceqLLc9Pn3xKP90ySYu/+lekopCYFSxWhHZpMB4WaOYcti6JqJlg6iE9KU8ZGGZlh9DE5NMNyv0IdMbl5hp1cGRkGSVcifiudvL3HxIxrJN5IV+dq+0KaYddvZkGUxrHCpbHGs10BQFxypSLM5gajl+uy9HqRFw4bYUF69Lccr6Pv7uoT3UOgYDSZm6ZJGLqVx7sMrfPnUN3zswx1Rdo69XRVJqSG6MDh3OXV/n2EIBQRQ5qTvEDh3SxKl786SiGGHUJKmFVBoiA1kL38liZC16SPDwSpurzunmpweXyckGJatDVzyk6VkoosZDpXG+uLfBzeNtdEmhkFpGlSQko44gxlhuewznYe+4wLd3L/HlYIHL1ue5bZfLSJdMf98MvXGdntQUipAhoS0gRgl+daCIIado2CqGIGE5ImeOpbhia45DM+NEjkCjE9Ct6ExOhmwfibGv5qNmBDb0OVy+3iSKIrb0Gnxp7SCf3nWY0X6Veyc0njKUwwllrns4OpE2JkLBlDjedtiQClkjBxw8KpLWNRTD5ORukSVnBt2IMNQIhABZ0MgmypiCRCYUiOkd1mZ0AI5UHBodmbGigyYqHFiJsWsxyeFlmS9c2kPZ8tm/3KTRCegxVUKtRTrbRMutsLJSJApUjtTK9Cdl1nc3ObaYQRZlwkAk9GVUR0aWRKRAIiHLWI7GomMy3FVHEQXSRogfqPihjONLOI7GgaUYpuajCQpHpmQGExqTZQU9a1M0TJ4xkOe3k8uUOi62EGAFEjONCKk5SFrVGR4MWJdOcumIyZqMynyrwxd2H6A3ZaPpNY7JaZaaMu84bYCpqsNPDpa49oBArROQSAQcbNRZaRdZmzTYMwt6wqPteyiiTBAJ3DOVYjSdpB0EbFu3giLJzJZj3LO4zI8OL3DBYJbPPmuUL903z9auGGf8wz6eNBwnU2yQMXQmZw3OGUyxc1Tl7oMTLLse98zblIMmo1mZ/UsKWSNElHx0CSxXxnJkDFEha7ZJiAl+9ECGbykl3nH2AJdvz/JXt44zmlOJ9UzTbaY4XHbZ1u9z97THrfPz1BSHQSMOisOvj5T56p453nrKAJ+/dYmhtM7zTk9z+1yVtCyzey7EoMquhSabhwRCfMayGp0gYDAXUshW6cvJzE3LHFuJ6E0mOFByKco6S55Nb5eL7Qv84ugSH75gjGetL/DR28aJpxpoqooqudx2LEWfKbHodMjFY7h+xG8O1jgpoaJkmuhSjJ89eSdxVebzD03Q9jyGEwLZmMORss9IKs503aXWFIhcjUXH5qvPWMf5I2nunKjzqdtniRSf7+xfICLir2+dpahpnDmY5I7xBklV4ocvX8dH7xlHeFCk1PL48Qs2/4fP+UrH40O/myJpirz59H4uHs3x3G8dZMl2iYsC37xiHWcNPzZAVO/4/N1tc0yu2PSnde4aP1GZ/d3XTfGLV2zkR7tXePWZXX/aINGqVatWrQJg06ZN/PSnP31MsOf3v/89iUSCvr6+/1QbGzZsYHp6mqWlJbq6TswcfeCBB/63x/xLnaAgCB7z/UKhwMLCwqNfNxoNJiYmHnO909PTzM/P09vbC8A999zzmDZ27tzJD3/4Q4rFIsnkH6fqr1q1atX/tCd06pmuAKJFMd0mrSm8+IwMz/n+Iyy1XBw/5N0X9nHGYIKEqHL3VIOH5zosLSrkTZD1FqaisrNHohHU0bQ2mYSHoLlEgsy2wZDFaozllsee+RZX3XiUvqTGj5+zjWLSQ9M6bOrx6E2GLHc8VpoqdUeh6chYdprDSwotV+C0AYdjZYgIcLwYu2c1/ECl29DxaZLQImzPoCdbpifvsFwDCYkL+7optwKef81BRCHC9RRUNMpOh+lKDMs2UHWPoZTGbUfafPreGfKmQhBp+F6cyaZFT7KDIIREms2XH5pjyXLx7SQPTmvcd6SI6xmUWjqtVp6DUyJuW+eBGYlSI8Z4VSeMJA4uFvn9dBbfzdP2Iqx2gePLEvlEgJau0IpWuHn2KPmUzVCxSUyNUEQZUw1QQoVD8wkOljwy8Rrbem2aXoAs6CTNCiM5n5HiPOvyHoFmkTdFFhdD7p1oUvM8TEVGEkO64yEJTUAWRVasBAeXUugKTC5ptNsKZdtH1HzumJvhqdfsohp2OH2zS1vwuHhjhutft4WTupL849M28a4zRthZLHJGbw+WG/K7Y3VuPtLiqtPGiGEgRxK75pukpTx25CFLIgiQMUNkzWG+DpEbJybrKKLEHVM1DpdajOYCDL1EUg3IGCG6BG1XIxJr9KVtVFlEl2T2l+rsnXdpdhRW2hqWq5HSVGTNpz/v8MrvrPDLB0MeOS5geyfSdcTAxHU1bNcglbLoKbTZNycxXjL43aEMTTckqalcua2fKzZ384LtBWIxH1kOQUEzNfUAAGN9SURBVBAIQ4EgEqh3VFbaKgeXkkxU4hxfMXlk3qSgdfGhs7fw0bN28qEnncTDbz2FHYNxEgmBvrhE2bb4ybFJ4kabga4VTl07xdahZbb2W1x+qsrpGzQaLZmpqseFa9J8c98MX907gyYplNsyB5cUDEVAkiK+uXuBmydqTLTaxGKgawKqoHDvMYsLvvYQM00LJdEhnwgpJm06jsn+RYn+dIfuhE023iRnnAjiJI2IpXYHTZBYagaossDlW3O8dEeRq184Sj4j8YaTNvDsoRECKeSGuRK/PH6IuNGmNxWQiYUkdAdV0qi5HhsKIpHYwOoUOFaRaHdydCUrxP45oGsKEreO1zn9M/t4YLKF2xaoWxGCEGFobUbyLTb3LzKUs2mHPnFFoO54fPnSUQ5UanzvORt55ck9PH1Dho88fRAvjPjRFVt5ymA3oiMxljMwjICmK2JqLoJcQRUFOmGbdTmRnlST80c8LFdiuRFjdiFOqQFZXeTIrMBQIkG9I/KDfSXCKOKdZw+xvqAwX5coVXqJayE7+wwEX6MlNOlNR7hRSCBGtCyTvbMR39g9zzvvuAtFa9CfCRDlJp6dpNIWkWLLjGZldvYkeePZPVz70s2cP5LmwFKbV//sCAerLfrSGmM5k5GUSUHRcJ2IR6oNUobINS9bRweflXqA40T8+iVbyMeU/+0zvtrxeNW1R/nkU9dwQW+OyzcWuPVYHduPSMclvnzpY4NEx0odvnHvEpd89RFuGa8x1m3wpDUJPnj9LCN5nZ+8fANjeZ2HKy1yyf+G2kXh47ytWrVq1f/F6vU6e/bsecw2PT3NG97wBmZmZnjzm9/MoUOH+MUvfsEHP/hBrrrqqkfrE/1HLrroIkZHR3n5y1/Ovn37uPvuux9dQv7fm2k0NHRiNcxf//rXlEolWq0WABdeeCHf+c53uPPOO9m/fz8vf/nLkaQ/9AlPecpTWL9+PS972cvYu3cvd95556Pn+hcvfvGLyefzXHrppdx5551MTExw++2389a3vvUx6XSrVq16HK2Oqf5LntCBovGywLLlE4u16ElHVOyAD18wxBt/dZRT/3EXB0sWf/OsIb72vLW8aEeRb75wlG2DEnvnPeZrIjI6x1c0SuUEpqiz1IyodSJ0UcawM3zpGetQNJACkT2THV7w0/38zX1H2T0hUKrK9CV9YmabnOnTl/JwQ4/ANzEkgXiszkI9xm0PF3E9mbnlPvbMCdieitMpcs9MRBD5pFSBvXMGvcmQRltAlAWGtBg/erDKhSNp0qbIC3++h6vO6KdpC4wvJwjdPCudkHUFhw9dOERa1BgwDbriBt96+uk8ZbhAy9apNIo0bIHtfQGzFR/flRjMO2iaQ8ZU2DUPfqCwf0EENcLQQyTJp1Tvpu3C7ulR9i2qLDcS7F7QcHyRtiPihxIKGlEU0h0XSMYajOQ8PKnGQMZnqdOmbiU4XA5RQ5W5eojtGqTNGheMluhL1ZBFkZYbsdSMs9SS6U13OHdNilg2QjIs0mkPI9Gg7ZgcXBZYbKQpW21yekSHKv0pjyYtutM+iuwTU0U0RWA038H1RWRRopipoWgughyyscvky/ctcNk1h5H8LKYiY6oin7tshI9cPMQ5/XkWy9Af03mkbHPPTIO8LhGTAmo1CVV2OXutjW6W2FQ0uf2NW/nq80b5h2ePsFxVadQKPLKQZ6oaZ76h4ocC63IhPh0kKaRhi+wvdXjXrfv50LljvGhjH7cdyLBQ0yGSiGk+G4oq175iHe+4oIc3nd1NLXCR9ZB4LMDQTgSDVFFEEmSSeojrRSzXIw5Mi3gdjRdv7ebFO4q8/NQi88syGTnGW09bgyRIlNoibc9HEkLWFpcQBQ8vCvjMk3fy8i1r2Nmd4u0/m+G9182iySIndyd4Un+KrKngE2ET0AkFjpcE6laMtiMxmI44Wl/mwdIUhyp1rj9aZsfndrMlneOaS3dCJ82xGQVN6ZA0I8RARQplZks+P7liM2f1p0mkXLryFkPDS2xdE2Im23TnlunPWYzkHHIxn0hokJQTLLUkmo5LQvdI6gFxEayaRsGU2LNS5dnX7OUTt0yz+W938b7fztAT17hv0uLvbpsnoYjY2HiewcElkZwuYAftE0E91WFjTwU/FKnZEEU6lhcwkALEADFKMF012dAr4PghLz8zz6cuXcPFJyXwpBA1SNOdbLO9O8L2BUZSMk1HZG1BYl1fjZ8cP8CBFYsfH1pGEAT++lnDbOw2sP2QjC7zujN7+O7LRrl1qkpPSqBs2+iiwUxNY03OI6YG1G0RP4Cm1yCmiCxVBTbkYrzwpCIv2jzAD56/gXVdEVlV5GC9zr1zdWZrLqX5LLIYImpVPnj2VhQ/SV9cI234nNxvsLXH4DUn97G128DUbR6sPkKjI2P5Ad2JkOmFFC3fY10XtGwJz5cYzWp89NZp9H9e4n5j0eQZmzKoOjxtbZbnreviNT85BkpIphCAbrFujYMqC3zq99P0pGV+8uJNxLR/e0JsFEX85lCZKIrIGAr3vHY7V/1iku6Eyi8eKfP+66eYbzp0J2VO6Y0DcPNEmV8eKvHsrx1iXcHggnUpTh2I8/6nDKDJIte9ZiNbemJs7jb50v3zlC2PZ23I/ek7qdUaRatWrXoCue2229ixY8djtg984AP09fVx3XXXcf/993PSSSfxute9jle+8pW8733v+0+3LUkSP//5z2m1Wpx66qm86lWvevR4Xdf/zWP6+vr48Ic/zHve8x66urp405veBMBf/uVfcu655/LMZz6TSy65hMsuu4zR0dFHjxNFkWuvvRbHcTjttNN41atexcc//vHHtG2aJnfccQeDg4NcfvnlbNy4kVe84hV0Op3VGUarVv2prI6p/kuE6N9KpP3/c41Gg1QqxY7P38BgP/TGFQ4uiwROjNefPIgEfOSWaXRN4McvGmNNKkkURXz2gXHuWz5OqSUQI4WkdohCjSCQcSObhusReiYZDKy2gCIJ/MMVQ7zo+0cxRYnuIkzUbGStw4a0TiFfI6n7tDsmdb8KkYlnZ+lELXTVJqvJzK7EqdcTvOeCPt55yxE2FCXankcoNRlMmBhqm4zpcO94DxXbIa/EWFqQWLJ8NvVoOIrDfCli3YBHy1GQkKgGdQbSIiMZOFh2+KtTT2JADOjt7uYDt0zhBiGvODnLld+bIp0IiaVsJucUhnM6tjFPVtfwfYnZpsOwmeLglMxpaw2GCwG7l1coJJqAhqk4iKJI0QyxoiZalGX/Shs1KjLb6JA2FAoJi7IVsjajYFEmRhdHqy1MunhwzmdDTqPut+kyNSSlybqCh6G2KZgRTS+kbsW48WCBnmSAJpmcks3z9fvKrBt0GO6ZgTDDfKvFYCKBK87Tb+Z4YMFiIJ5jz7yP5mVwAw9Ji2i7Cm5bok/q4GQCNNVGEhRiUpJ/eMpO7piq8dpfHeb95w3z4m3dPPOfDpDUZZ66PkVvWuH9t06yszvO28/p5YsPTHNoxcL3Axodie68R84U6bDIcLybnJ7mgak2UyshQSAymJGx1SYbihYxs4IUxQGNxZaLGMS5c1xhU7eAgECpFZE1RGyhxI4eGVXxqLR1Qt/ggXGVgqZzfNHj+y8f4/6lJR5YniChqgS4yGKAGBnsnVKJ6wpLts3iksr5a1J84MnDfOC30zw026IhddjapdMRy4yJKvfVZHTDZmtRIZ6YwxDjNJyIX9/XzbM3ZnnmphwdP8D2Q567ucjvZ+r84JEl2lEVJ7QZrwQMpUzmW202FGQUKaBhK+iCysNLIUaosdQI+ORT1rBouRxrl5lajLj3uMMpW5okFZPjkzHihsChRpO1ZoIwhESxgi5JHFuW2VCQaYZtRvNVUqZEx5VxbINbDso8c5vPqSMTVDoyLcegYSv4ocCBhTimFP//tXffYVZUdwPHv1Nvr9v7LiwsXRAQURSNvddoihpborFrTExeYzRNYxJLiiUao8ZuomIXsUBERAWk92XZZXu7ZW+dO+X9g2TjxtT3VYlwPs8zz8OeOXfOmR9378z+7jlneKfNYK9wmPZeCwuTcEgiEJDoSZqQ0tF1SGtZagIamtGPPxqkZShFY1QnbZpYBRmXLhH2xskX/IT9cYo8OxebThtuWvqCpHMyAwkfmuYwZ5TOB90JHEdFkvKcOKWXIbNATzxKLGfiVlzk7RyxoTDpgsxFezVxxOgibAceWdnLM5v7GF/q4WvTKvnxW610JHJ0xU0m1Zgoni78WoSiYC9lfot4KkRvLkVUK2FlTxYnX0rXgERWsjioOsJbOwaZXCWTMk0cU+OqWaNY3prl8RV9ZPIOBza5uWh2Dde/uoMiv8rPjhpFRzrFe119rOodwE6maDa8hD02G/tNipQgXv8QbQmLKWUeLPIk8wZ9fUUoLhu/rHHfCeNHLM5tWDbLu4aYXR2idTDHqY+tw1RNGopUsnaKtkGFRneIGAY5w+G08WVcfUDNRz7bTcvhwme3YNkOvz15LIq880K+tivNkrYkv3q7k9qQi9Zchu8fXE8saRMKwPcXtaDnPNx50mj2bQhw2+J2GiJuTpxY/JE2kjkTr6agKiNvEuLxOJFIhEQi8f++yf/LNYqrXgKX7/91rGH5NNx69MfSP0EQhN3B22+/zZw5c9i6deuIRM9/q4ULF3LwwQcTi8X+o6ewwc5RU8888wwnnnjiJ9I3Qfhv9rHfV+0h91R79BpFVSGZgOqlPZUk6LUZXRbm1InFaIpMImfy2NouLnttKb86dF8eX93PwvYBHEI0RPO4Xe30ptwotoJRkBjKKHg1C7/HYkeXQUB2cdCYIOc8vZXHvtTEjQvb6MynKQ5CsddDINJOf9pPqc+NN9RPfEAl6tLZOlTANH0otocdxiClkTSm4ebaZzu49cRGHtq8GrdLxSgEaR5M4la9VHoiDOZyZA0NUwM1XKBK02lPFIj6NKaOzqJ6egmZUfqHXJgFiYCq43LFqAya/OzdZvYNuGkYlDl+fJSXN8W4Yl4HvzxpNEvakvxm+c5HhqftHL1xN6PqIZm3GB1xoWkJpo6TeXeTQ7W7hB8fWMr8zkUMptw8uiLEAaNAUzuojRTQpDjjlAwtneVs6QgwZ7RMPJujOqAzkLXRlCLWDbooCeTZ1qlS7MvT3qOSyPpxVRrgUljfpdMWDzK5Ioes5HeuAeNPMzAYoaMXeip6UYM7p2q1DLjxSG62Jy2ibonOIT8J3YNtWRh2mvoilZXtQ0SlMD+aO4YlHf2ML3axvrmfP7Rn8BbcWLKBKds03fo+C86dwoZLZmHZDm2xHC2DOaojKs+2xDGzbpJZixRJLl7QTaGgEvFoFIeS6IpK2jAp87pJmDLLd1jgpIj4szRVK7y9VuXYcRFMzcWm/hi2olAUyFHi0UhmXCiouFw5/L4cEd1LUcDCttxsGvAwmE0TRAGpAJYPzXTx/aMbCHlljnxsBZPKPPRkFWbVmng0C5/bQJcUpjdm6BwMk7VcVI1ykZdMjntoLbcePYrbT2zgoF+vw2O6aBuwGV1UQM8pxIZ0utDIxm0qAxp5q0Cxy8XWvhw3LGxhn6oAlUEXAC39Od7emKGsSKUjLxF0F3ApNsVejYjHJuhNYw260RSLsmiKIjWM2wu/W91C0KvwtRm1vCEnWB3LUOEpYUKxn9MawuxV5aUiqPPoij7ypsMT29LkJZnJ4RDVAYUn1xcodpfQPWiTs1T6MxaHTS7QEApz/dPj2adOZihrkSmotMQd6iMSSUPnqVPG8+jKXjbtSDKp1s2kWp2NHQWsQp5w2GRr3KDOqzGQN6jTQJJN0gZ4VImulE2J20V7XCGZrkBRC/i0EN7QILblI5ZRcKPTnvAwtUpiZU+CdR0y3VmLKWUKrUMGrYNFpOx+StwyXWkbn+JidbuLg2pK+eHcscPD4t9qibOkLcmPD2pgbImXIcOkK5Omv5Bn79EGWRswSkAzUO0gBSuGpjhs7QsiFXxkDD/f3q+eJ9f38vr2OG3pNEG/QSxvUcgE6RhweMGXIF2w+J/P1XD9q22saTe5bN42rjywmqPHRemI5/nW053MHF/gnPHjeeCtjciyScKwCdkh0nkJyQoR9nbTl9GIunTWtOrUFVtIOZ1kTsawHBzH4VdLOxhb5OHIsUXMrt45BSxVsCgP6BiqSc4q4NU85OMSfaVZJpcGWdOe46xpf//JZLIEh44Oc/qUEiRJGl7PYlKFj8fWdGPaDjcd3cAzqwdYsT3Da9sHKYko6DkPd5w8iic2d/Bai5s/bU/y8yNH8eB7vXxln9IRbQTdn+JlUyxmLQiC8LF55pln8Pv9jBkzhq1bt3L55Zez//77fyaSRB9WXV3Ncccdx2OPPfYv61544YU8/PDDn0KvBOEzQCxm/R/ZoxNFAc3B686Tz+iEXRrzN2Y4d1KGYo/Ga81x8oZMf1eIHy9Zy5J1KuEwlIc0NrVpJG2VQyf20TFoUgAiXhVNcZO2Y2hBNwHbjSxLXHtQDTe83sr3PlfH1fOb8SkOOxIZUo6PGVUSWSuDW3IzoUhh40CcgNePnStmbYcNehB3qU5esXBHbK6f30m42GFUiU2kqIfifJ58xsu2XplkzMsFs4uYvyWFLpsYgQxGBgIeH44yxGDaRaVXwe0yKHFcdA6ZFJwADUUZBlJJerMF3t2ylm39bs6dXM/3D6vj+wva2H+ci4dObeLuVWtR9SzBIQ8BbyfFATd5K0s67yOdDUJgiOV9Ck/9IcMNBx7Ic5t2UOSL0RuPsLKzkqOboDebY2KZhtudYVSZhy19KjkrjFGWo2DL+HUHWZZo6y+mYEu4C0Ecr0WFW6VvSMKf9lJS0csR4w3Wd7rI5qoYVZok6rHpiRWorbWx8xJy2k8qksQt+2mN6TiWTkt/gWJfmK4hmagnSnsyxqiwTlNlPxFV5sL571MfCvLq9hTFBcibvj+P1IG8CkVFJtcvXstJY6uZW1NM2rAZW+km75gkDBm/ZlLISaTtPDUhme3JFOUhH6oqU+orkLIHqQu6yVkOr29wGBN1KA3lyWT9uCJ5jhkXZkZVmIvmbWXICLC2q4emyM7HwMsolPs0ljTL7FerUxKKgy1THZIoD6fwql46hwwsJYDmN3hhWzdBl0qpV8WxHEwLWmMSXk1jfGUSTdGwHRvTAU2WmeiL4HfL+Jwsm3uzdCYMrv5cJWNL3Oxd5ePmBe8y4C1QKkG538TvzSE5EmnDZlRtGpesk+xXOLAuzPHjijn8N+vwuSWS2hCjfSoVnjyYPrLGzrWnhrJu1nRq1BWnkJU006vjuNCwJTczyoNMKy1mv6oicnmZ9b05RnmDvLYmxc+PcxM3cry2PIFXk/ny9GK+PL0ElyrzsyWtrOlN8fwXp/D4uh7eSfah2iqDgzZjG0fzxqok878ynh3xAu+3pTiw0U9nxuD+FV28N5jgwuc3oaHw6FljqSvS+c6bG8koWcpLCkwtCfLzcY08sn4zXblumlQ3ucAANUUFaiI2rYko/RmJoZxE1rAZW5EjVTAJugr4tBhbB8rw+2wunlHNEyv7IO/CG81Q5SpQsDV0WWJ9l5e9G0yi7iR6XMciA06AdztjpAoWAX3nR/VBo8L0JkxOum8TT53bhKZKlLq8dKdMdgwGiATSuNxpKkIGljTE4FCYwZxMie6nx4DxJTq3vdeCaUlEPQ7hYJxAQWVZu4tzpxRzyRer0RUJWZbYkcjRWOFCd1R+eVIDUe/O9YB+8lo7584qJVOw+cLvt/DTz0WoMBwu2mc0azrTfPPF7fTFHQ6vr6Lb6MO2TWqK3Vw7ZxQhxUXBcjj87nXMnehlVUcWkDi8MYosS6QNi7P+sIk7jm/kiTU9rEn20jNoML4iwvFTIpT7JY5tChJwKXzhD+u4bm49E0v/+s2QLEt8Ya+diZ1k3uSLz6zmtsOa2DCQ4vXOHp75yl5c+HgLJ02J0pfPYWkm9x8/iZBL5ZL5G9jSYWIW8lx5YAWfv38zLl1idkOAsSUeALb0ZakI6vhdCo7jsLk/S1PJzicgtsSyn9alSxAEQfg/GBoa4lvf+hY7duyguLiYQw89lFtuuWVXd+vfNmvWLLZs2QKA3+//t17zgx/8gKuvvhqAioqKT6xvgiDsfvboqWez7n6ZhioPZT6VtliBeM4mmwkQUHQG0hZ+XaYiYrMtnUFX89Qq5eTVPLEM2AUZR7Lwh3vBcbFXOZiSwbI2L3UhhWUbNfyKi2l1Xha3JsiZNl+bUc4zaweQVYlo2AK9h7oikzKPjw19Fm7XEOWeMBt6XHTFPBxeF+WpVYOURm0mFgc5d1olX3t6K5NHpyiLJPHoJstaA7hVnWp3lJdWZ/j1CaO5b0UXKztTFEVs/LJKIg1jKgts7lMoCtqYtkPBydEYdePxdpI3FDwDJbzRoTF1jEG+AO19PkZXyHSnDCIelR2pJE3FMvs2bKc96SbiCmDKnWhOKe+27lxYeb/SWh5b3c/5M0qoCfh4fF0XBSlHLKXwrTm1BDx57lnVTNRn41gaW3rd7F0eJCt3UyhovN/m0Fii0hLPUOX3EctnKPHq5E0DRdZJZSVMQ+fyfaN4wutZ0eFlbHAUr6/Os7Y3R01FhnAojWNrJPMyChKm4zCUcTGYhbqITcJIE1YDtKaTTCl30ZFOoqWKCQaHsPUCsuVloN0h7/FTFEngVhW29fop8ipsi2cZ7feystlk/4YgU6u9HDYmxIaBNHcs6UJxm2ScHPtUhljdm6E2ZOJy52kqSRPx9xLSbTJGCb/+Uzn1EQj6k9iFAFsHHHq73Bw5NsKtJzQAYNkOpz68lv5sAdXt4NcUtvTnaSxRiRWy7F1dwKU6hLxDaJKbP203qQ+5MDFJZV34NQ9Zy0DDxfYBi6JQmqgXJla3ojklrO+DbC6EZauYpkW1P0jQq7Byi8JgysaQbcYWe6gN6cQGelmd1Jg8pp+wWyHij5PJ+elNQyyj4ZJcrOpQqPX4Wd9WAF+O4oBCzMgyudyFI2dIJEIULAVbyRPUVdb3Z5jTkMPlzlAwXXhkH+u6dco9Ht7dnmXReVMp//PUpHuXdXD7su00RlSyWdinrIwjmiLsXe1nfX+StQODLGmPsaE/gWpEmNsQZmatl1kVEUp8Lv7nhVbmrRnkj2c3Mb7cw0kPryeWLdBY4aLW7+b3y/v4/KRiZlYH2LvCzysb4zy5uYtUwaZYl0mlJSqqshw2KsqfOjcwSfcT9yaoDbhIFWy6EwHaYm40SSZPjsYim95siglFOs2JJNl0Lcs6ChxUWs5XZ5Zz+8pNLGsvMKZIpStlMKHUZsbo7aj46BiywAqzZcCi1l/CF8eNYa+yvw5pTWRNfv1WN5ccUIYkScz5zUo8ikJxcQHFFWNM1E2WHvap8DBgxKgP6CxuDWJToHOglOYBkxK3D7c7R2WwQHO3j1xW4SdHNvDG5iH2bwgwd1SIh1f28MMl26gNK9x/3BQ8qsLClgSSJXH7nzppiLjZMpjlyHFBejr7aM17+eHR9Vw1bztHjAtx9cFVONh8d/EK1vdlqPL7uePwaTy0tpt5a/rpNpKU+lxEFT/fOaCO2//UxR0nj0ZVJLKGxYn3b2SvSh+RsMn9f0pwxymj6DQHeKm5lx/MmcL33tjO7OoQdX4PDVE3TaUevv78Fn50SAPVIRc9KYNLXtnI0WOKOHtKJd95czPf2LeBIrfGn5qTbIileXhVDy+cMYmIZ2cCbFFLjGtfbKPPMAg6OsUBlS/tXUJPssDYUg9jit2c/LtN/OGcsUyq8PG9BdtpieW464QxxLImR9zxDhu+e/DHO/Xs8pc/3qlnvzhqtx8mLQiCIAiC8GEf+33VHnJPtUcvZh0bktAlnYyVJuiRKfe7cMuwNZFhyChQEbZwB1KMKbYIq0HWt+f57uyx1Ed1DqgLks1JbGsrotitkzRThDwFZlRJ7EjmCZUmmVbnAt3C75eYUR3gdyt6yBRsxkS8nDWhho0txWQzYTb2Kci4UOwS1vQaBL0ZJpRZzF+b4Mkvj+PcvWopd3u55OkWfn96I52DQTa1F9MR99AzJBPW3dz4uSZePGcidy3tpDNewKXK6JJKQTWpLIEPui2yWQm37SFlGpgFN6ZdIF/QcFkRBlMKblVhbZubnKFy7mwPdx05iallQTb35hnKqoTd8MqmIkq9Gt1DFrGMh4LlEPUXmFFtM7kuxg8OquUPa/p5eXsPujdBwGsypiLHHR9s5cEPYqzt1IgnI1wxbRr7VAV4pznNIWUT6Orxkx9ykU+7sG0Zn6bQ3u3GjZu4UaDUL+HzD1JXUuCa5wYY6JrJ2eNn0T6U4duH1LBPtZ/tqRyZvAvUPipDOSQtRpE/j6SmCHlMhgomDhr9KQk7HWZTp47bibAjZ+LkK7l+1v4cVV9LUNZwyzLpnIfBlAcbmzlVUR49YTKpvEQkJLEy1c3rO3r58pObeGBpP7Yp47bddCUkcimdc5saaB5QaO7xM5BR2NpTw7JuL22DAUJuCZeWxyJLV1KiOujmgdMb+ebBlcPvTUWW6I7ZOKaCopr4XQ71QYkrpjfwxInTWdWhMTDkJZnzADL7j0pRWxSjJpLE407j0Sy6e3x4dYvx1RlCbglVcvH6xlrWd4dI5m0sKYVLz9KRcmiLW8xbVmCwYGBqJpInT8JOs2ywA5cnQ3V5Ak01UBSLvrRCxpDpTUPLgEau4OH+EyZyw+fq+cPZYzmgyWJabYHGsIRHkkkN+ZhTE+GsKeX0xCTiQxIFe+eizy5Fxauq+Fw2357dyPfnjmL9ZTOHk0QAo6Ie8oaCT/KjqQpZNckP31/NtF8t4+Y3OlEkiUo5QldriBPGFZGTssypLqLEt3Ma3AX7lbHwkolMqPAiSTvXDovnTW46eDTX7F/L46c3URLQuHnxDq57vZWBjMmvj27iva9OZ7Q/xI5+i+/uM4VzJo0hmY6SzEmEXDkKmNgUiPiTlAazlIWHmFqVpDyQIpP3srk3xOtraokN+En3u8g5Fue8soqI5ieXksHUUDUDty6zvsdHb2rnQuYDaR1FMWmIKjRF/dz5TicD6QIAp/9+E69uH2DJjiRBt8rXZlZw05ENXLJPBRlDJm0oTK+OYZIl6pJZ0OynOiBTGR7k4MYYji1TWZTE7zZY22fiQiOesfnp4h3kTJtZtX6Oe3Adf9qW5KD6naPqbnlvA/vdu4I7VrTym/c66YwbxJwcllrg8U1d2LJFPGty3UttfPuQSqI+lYXNCRRJxmVHyRoyyzozXPX6Wu5b0cHWVIrqsM7+NVF+e+I4zn2imaPGRYbX/PHoCjceXcdPj6vnW/uP5oWvjmNxbCNd2Rj/s+8kTnxiLYP5HLmsw88XdlIZ1Dn3mU1MKfNRFdTZMpDh0PtXc+akSs7Zq2rnAuCfa6LEqyPLEnMbg/SnC8w/awouVeLZLTufLjO3IcJ1h1ejGip3nzaaZ88dT9aweWNLgsObwjyzZpCHzhjDpAof7Yk82+M5Ltuvkpl3fsDlzzbz4yPrP/6L1F+GSH9cmyAIgiAIwp5K3FP9R/boqWejQ25akylGF+fwuRSsgkbelvHICg2lCkllAMdUibhceJQULR0Bzn9iK1NGuZhU4SOgqyzcmmTJdpNoIMgRYy0KThpd8VHkVlnUluTmw0ZT5FVpjsd46ozxXPn8NmbXBDllcikNUQ9FPoXvL12OT9M4pr6WH72zaeci1knQI1m+81Ir1x1ay5enlZFTcpz98gpuO3Qyj6xtYfkOP42lQ7T2+bj6xRaOmxBhXX+GEq9Gld9FpzlIRHUhySbhQJ5iXcewsiTiHsaWaHQMqtQX6yxapTElWqA0DFvTOXCCPLcpzkE1OcaEgrxoJCkP6QxmkuxVlWB9TymKbOO2SonbWUZFTJa05TAdk8FUAp/iJ5GAQbnAuCKN5vgQlUEXiVyckqBFKuXn2AfWc9UB1dxzYg23LdnBtqEsY8t8fGVaKbe/a9OTMikuNsg74NJs/C6DAcPC77JxvHkkxaHS76UpUIZLU6guU0h0KSjoaKpDkdchnpeIuMFTniafV2gZ1JlbXYJP03l7xyDdaZtMysvAQJ7iapWjH17PHz8/gQOOdXPmvF6crIchdq53NG/1IH9cNYAhW5RHNVrTFn6fTalkYjpZLMXBwoXqKKwcjLMjlaW6OE6FX8OlWSiOi/UDfsKaTGmwQHOPD0328/3PNbChN80tb3WQylvsSOR542tTUBX4wfFVdKaGeHh9H36fH8VdoLcwhNNn0jYoUeJRkVQV29JIGSXYmGiyzIodCqNCOv0Fi1rDxuNRKPIbuGQTFIh4TMgrRHxp3LLFYE5CR6PPklAARZPp7ZPxFsvsSMvUlkp0FBzKS1K4NR+2lcClygQ8OVRZJ54tcOx9G5ha7iOVgbs/Px1dlfniQ5sJl7g4pSnIYY0RSn0q967dRJHPxWBBJ5eTKTg2RT6LeD7DJa+sZ9/KMMc3llMfdjO10s+Xfr+Z5oEsD546ljdb+3m9OcuGHpvN3RoNfjfJnM3T6xMULIuSUpt3uwbZNmAxrWgANxoHjg5SE3bx4Sff7lPro2Moz7WvtvBGc5yGsIe8Ab//fBPj/jyNaShv7hyx0xjg9e0xOocMplT6mVtTwoaWfgblKrKmjN9lYcoZIm7oTKeojagkCwkCaoDBXIZSLcyAmSdc5NDcn+PXR05kwfY+iiI2ealAkQc0LUPArRL2DtGdDNIR0zhz0l6c3FTDd15uYcm2FD1xk4Ll4OBgFyTueruLI8ZEmVjiZ+8qH35XiHlbd5DKW2zsaGRHXCPqkZhWv5Uqv4uBbIFN3RLTagy2DSrUhTwkMhkCbodoFH58WAMTS/1sHsgwd2yAMaU2T21pJ+hWiZuDzGl0s6XTRVkkS0fBIivbVBXJNMcsHFuj3K9x9+mNPLtukLe2JTlgdJCTn1iLrGWoDGrYUpaOdJqKoIrHzHLZ9EnMrChBV2UWXjQJlzryO4vpNTuH1HcPGVz8bDPn7F/MF8c3srwrQYkf8pbEm61xnjyridKAzr0nNOF37Xw0cd50uOeEMcyuDfH3SJLEdQfXkcgZnPvS+xwz+q8J2qkVAY6aEB5u/7iJUc6aWYpbk/mfw6qH61WHXPzu5Cb2vesDMGTOnVnOnMo9+nsXQRAEQRAEYTeyRyeKAqE4aTPIQDKIgoSiZqkqcih2B2iOpVFdLvaqkGntlXGpXuZMTvGVsdMYV+rlmEdXM6M0yFDewp12E424eL8jxtgSmFxh8nazAq48P327lZBLxtRtfvTORgp5N3e81U3WcLhs7s65wtfPmsFV89fzCt1cv/84fvbuFhzHptjr5hszKjn70a2cMSvC8v4BCpbEY5vWUhQZYoKk0zUQIS9n6cjD9+YnMWybsE+iICcZavUyda8Cup4jZboIBwZRJDdRV5CrZ43lrmWdXLJ3JRUHaNzy0jrWtdq4gxLf/1w9sZzBD9/ezD4VUe48bgw3vLGdHX1B0jkPhpSiOuhiwRaJhiIXJX6bbb0QVtxsGEhRX2EwMGQwMOjBW5mnNJjDT5A123RG15r05EzCboWnNnVyx7udfH5aCH3Q5MDRAU4YX8KqnhQvNw9S7PMxmLWIeoPIapLqgI4spTiwSeaUicXEsya3LOykM5OlMqzhFHQMO0d/fwQnZOJSZKoi3ViOTtdQnnG6nwOrxjK7NsriLRmGEg571XtoixdIphx+cvBovvb4Vq6c6eMPZ41j/uYEj67rwSjYZCyI+h36nCF0PYArr1JwcoR9ErKUJojDQCxMfcDFtmSW+hobGTeOY2FRIJuXwfKwaIubCWVeZpb5GcwZXP7qen52yDgu+HINFz/VTNuAwTH3rGVmQ4D1iQQlAYe8ZZPIWpiOzIMr+ij2qRRLXvIZmRUxN2OKFBxJI1eQKPHqeB2bbEojFsuSqTBQLYn2uIuaoER5IItPkwl5TTQFHFvBMLy4NYWwz2BSTQqX6rBCdhHwByFnkB5SaevUiQb8ZL1+Vna7GBX00pvLcM2+TaQNeNiI8fnJxZw+Zef6MIZpc+4+pUwo9zCzNsB3X2nhkQ/6+fVJ4xnIGzxrteLSVdKWQ0AvENK7iXoa6MtmuW1RJ6OKdTrMJKCQUiweWdfJtLIg184pZm3PEKbUythiB92dQFEKWNi09RXh0R1Ub4YXt3bRlXB4dqOXhRvTLPj6BBr/vM7MkaOLiaUcQrrMn0gQ8UNbJsuSrgHe6+vitc1p3t+RY+E50zh1cgmn71UyvJj0rLIKlq2MM5SOYHtzGLaJrkPKcaFKaVQ0dNVgYoXBgq0SjZU2a7apTKsIc/i4CPObB1i0KUOZ14usZSn1q4wrG8Sr22QLMhXBPJs6Izy4IsZJY6s5ZVIxXlnlnFmlfPXJrWxPZhldoRCzTP7nxe28tjXB8ZPClIU1Ovo9mI7J5i6dOU0xUoaDy/EiSzmy+RKSeY2Iz0KKSThylgAeknGZS/arojHq5Zfvb+c3yzuoCtu0ZW0yZpbxxT76cyaD6QwzRhdwtBRlRQE6kw75goM75yPvznHwxCClAZ1z9ymjNKRw0hOraSrVUZQCWwZsqj1BZMkgZRlct+9U7lray3uVBt86oPYjSaIP+/ozW7nygJ2LaH/QE+MbCz8g6g0wFFPJGRLpgolhacNJIoBJZSOHFHcnDby6PGIR6p4hg9N+v4nLPlfPKeOrAGiP5Tn2dxv4wRE1PLG6l1MmllAR0vlHEjmTareHMw8o4dgJUeLx+L9/8fl3icWsBUEQBEEQPh5iMev/yB6dKEoOhmgaJbGhzUtnzKaiLMvMKp3NuSwhxUWJz0N3KoniSlMoBDln4mT2rdr5LfWxY4v4w+p+gppGXanGgJHY+aQrlwu36uB3S0ioGBmHjpRDRdjNNQc3EFR1Tvv9JkzZGu7HNc+10dqvkMk6tAy2kjYLlHplwp4C1yzcyhPnjOea+dvI2Tpji6A8uhmf5qbcL/Ge1MXmzgguH3jCWarTXkIBi4Gcyt5NNpsHTOoiGqOKTBKFAmUeN+93Zjn5ofX88Ig6xhV76Yzn6MnmkT0qZXKQFe0ZjhgT5puzxnDfqjZWtGW49fAx/GFDF8+sHcTr9yFl3CQGDPSoiqx2MrHShUs38egmphGkP2kxtcbBxqLYq4GTYubUHPFkhIqiHCm3l4IDo0tlQpqba/ev554P2nh9+wCxlINpOaSyYCmgqTaDaY2akAlyHlUKcM2fVlHtipJ2Chw1OsqBY0I8vqaHde0Oe9eq5Jxeavx+LClN1OVhy6BBRFP46gtbOHlcCY9/cQKDmQJRr8Zg2uBP25M8s76fmJZmXZ/NqIEcp+1VzOlTS/jiQxuImwYluof1LQX0Chvd8pCy+qj1BhjIanhkleyQSnWpg8c0cbvyeFwOlmOzqcdDdSBHqc9DDoecuTORcnRjMVeX1jIqunOK1B2njGZVR5oT79tIS18ey1axPBYqLjqGCkTcbtI5hRqfh2/sH+WVzTHWbDdIBlKU+HUailP4lRLm1pTz+qYkLi1Pa0ylzNRAGcKlqpQEhrAsH/mCSr6gokgqZX5QHIfRxVAesPFpDuGxSXTJQddtQnk3Rcg8ccwxvNueoKWrlYaQRFMZLOnsJ6i7+eOXJ4z43dJVma/sU8oHXUM03bGUaUVBRoe8/PaDLpKFAgEvpA3YPCBjm0GSJjQUqZhWgbik0TlksDVpMbvCT1mxypiQxhut/TTHWqkMKExvSBF06WTtOAoBDAvKgzb9mSzzT9uftmSeRW2DxOMSKz05elOF4UTR5AofD63s4emNg9RW5/AGHdxOiiWdO9jR40a2VHy2zoLNcc6YXsqbWxL8YlEX3z+qhllVQZzZFSQ1P690rsGQkngkL25XglH+HH5PDsW0CbsSZIwwhuJwz6mj6TXS/OrdNgzb5sJ9GigNydy1eiPNfTaNJTpbBrMYuSgru2yCtsqarixXv7yNlzYN8syXJ1KgQFO9xVC3yaX71uOSFW5fuoPKsjSbC528vamMbTGDA+p8pJ1+8kaAwXSerQNBUgWFjqECVcEceSfDAaPCbOrxUexXOHVqOaMqHT7/zHvYtsy0SoWSSD+lXhc5OY/uMonixXSGqC3ysmlAIpbNoDrFDOYlLptbTi4xyDkzK0nlLa54sZmWeAZdt+mPS9QX+0gMGlQ3DKFIHtoHJX74RgenTCjmwn0qR7xnsgULj6aMKJt31gTkPz/ifktflpyh84WpVdR5/bhdcPq81dx7zARmVPz90UObe7Oc/LuN3PX50Rwweucccsdx+MJDm7hqbiUnjC8CoLk/x0n3beS2E+t4uTnG220JNvZnuP5z9X/3uEM5iyPvXs81h1Rxyl7F//Ja83/m/Hn7uI4lCIIgCIKwp/q47qv2kHuqPTpRlE7rDA5q1FWkGcx4CRPlKxPruTW7kbBL4pdHTKArk+HSBSv56pQy9q3a+UfFne91cP/ifmS/SYVHwe1P482BJOn43UlMW6YiKNGZ87O138bttTiguogppTv/UJl/wXhO+eNa0k6B6w5s4JYT6jn1/k1MLPOwbCCPYeoU+XYmXSZW5bn4lfXcePBYXt48yJ86ehhqKeaIMSYVoX4O9eYIayE295p4dRlFNzh/aiPvtMeZv70Dy3GQbR9LthWoKjKpUEvIWRn2a3Lx9Lp+nlzbz6+PGYVXVqnze1nTn+I373Xy3JYeNsaG+NbsBhrq/Jz96BZmTbIZ2zCAu1CMraYIelzkrALrO0NMqzIxbIemIpUNXSaRgM3WboVkzk9VOIvpOGhyiETBJiJrbO+2qQxrdBUM5q3vZ2xVgoYSi0RKJplVKFFc9CUson6NwaE85WGN7bE8FSGFwVwe2x7ggPIKbjikllFRD2OKvLzU0kGmBwqWjOLY5AoqWwcCFOkBJOK09/jwShqr29P8/M0OLpmzc0TXDW+08V77EFUBnfyAizZPgQv/2AxIHDEuwvpUkqBXZWyJh4PHBrl1aTv1RSrNcS8+PCQNi1OmVFA/Oci6eC++njiJXArNchFwZxhT7LCp10OVT6M2DF5PhpgywB82DtH1jsMb508bfk/uVeVj23V7c+G8zazozpO3HHTJQzxnEpBVEoMw4DZ4YF0buixTVmZTUzxA1B2gNa5S4s6zvHMHY/xRplcGOH/6WHakk/x61VbqQw5K2kLHwXbAsEGTVLIFFV2SyOTctA1I+F0OY8r6UTAZVWRh9euE3H8eUVMd4rkvTeG1LTEWbUvw9dkVVAZd//B3bFpFgE0X7zv885XPN7NsR4qMUiBtmIR9aYp8PvyOwtiwn7pAiLJJIWpCLkYXeYZH8qzsSXD02BK+/doG0imJnKmBA0lLoj6goCsWqYzEijaFtoTBxNIAE0sCOI7D/xxS+5F+pQyLvoxBU42GRZrRxVnCHpt1W3zoiszeVb7hJ115NZlvH1pJxCcjyxLvdyV5vrOfsRUuMo5CxK/TFk9T7gth2H34lCA9KYWJJW5qfVEe3LiNjlQaj0slE1N4dlMPG+MZavwBuroKrPX5SCl9jAqppPMOdUUW3oTMtEo/Z+5Vxu1vt7NxqAtSYb7zuTH8bFE70WCOkpIeyjxhVvbINBXr/PKw8fz6nXaqozlMDEYXOfTnspT4/BT5u/DLPppjKpu63eSG3GztMWkq7ea59/sIedz4XFl0V5Jir0PEl2ROQKE1KVEcyOBx54jnXfhVHyv7ZSoUD89+fjw+Xaa3d2dckpkCHakcg4U8dX4PoWCKkD/H3JCMiUm5z6a5K8jPjhjFtMrA8P+F4zjcu7SHx1f28/qFE4f/z4HhJNH8DTGuf66HB8+cwj41QV7eEOO3b3dw37ET2bs8yKaBNE1FPrKGxQvrY3x+6s7kzeJtSX77hUb2rf9re5Ik8dJXJwwnpRzH4fKnW/j1qQ08vrKfvlweR3K4dN+qf/i+9rtk7vr8aGbU/ntPnREEQRAEQRCEz5L/ukUVNmzYwPHHH08oFCIQCLDvvvvS1tY2vD+fz3PppZdSXFyMz+fj+OOPp729/f/UluXK0ZGQSMS97DNKoj2X4rQ/rOeKvZuQcm5+tbiLsRE/tx48jR++3suStgSLWmM8tr4bd8SkXHPjDw+i6wVKfDLTaxKE/DE0NU9uyEs6qeAKWly2bxXzNyb58avtOI5DxKPzypemMqNyZ+KoKuzi2fPH8fOjx3Dm5CoqPF66UjlyBagIOlSV9HDV6+uYVKlxycwGMuli1nZrbBsM4lhR3moB03aIuDX68xm+u2gj727P0tfuo9ztwpIylIcMMIqYXl7M1fvWsrXb4gt7lfCtA6spC+pceWAFlVGFeWdMZGqVG0uNoynwxOpevv/OZh48cxT1ET+pTABTzuD3FpgxLofLbVDuV+jO5ADY2OPGtFTceEgnVeSsh+5sCk2BgjNExGeRSutIjkyRT6a8dJDxVTksdYAin0VVaRtjK5OYrhR+v4Xl2MQG/fR3hdnaXkxLTzGxtJtRoQiPrBrkvmU9vLs9RSpfoDriMK3eoD2usK2rglXtbja2V/PKqhDbOitZsk0hJGvcePgodHXnosYAvzyukR8fWk/fUIHaahnJm6a+wqG/kGNl1xA+WcXvknixeYDHVveRtAwU1aY2YvGVKaUc11jOwXUlTKrwsnizxYzQGNoGVUxLI214yOX9aNoQulZgfFUWlytLXxoy5BhdKbGsM0na+OsIM0mS6M8bpO0CObIEdAW37UEq6CRtg0zOIZ1SUD0GDWXgVt14VCgP92E7BbKGg6OZTKzR+NFbzcxbN4jmuGjr9/Pi6kre2+FmfY8Pw8nhODYF2yRXkDEsh0RWIZnV6U56SOZ0hnIalqGQT7qwLHu4jys6U7y2LcaOeP4jv1fxbIF1vWlSefMj+0IulWTeRJIc8naBsaUmEV+BUr9JwShw12spfv7ONr70/AqMD7U3tSxEU5Gf0f4IkiyRyrhojpm4ZBlZtinYMm61gMtW2NifGBHLe97v5JynNzKYzfNqcy+XPN1MVyJHWYmFS1OwpRw+2c+2rgCXHljGT4+p58dH1bF3lZ+CZTO12se5L2zglnd2cNoDm1jVliUSLBDLQl8yTEfcQyzrZjDjoTOp0pEIs3BLMRRCtPTZbOuW2L+qiNMb62kKRDiqqQi3JpMsFCgvT+Pxx6kIGMTyBcoCDv25Ai+cOZEzppZx97tdHNMUZUKwFF9xjD9u3UpeSxLwgY1DZ0KlpTNElS/IRU83s6Iji88qIpWXcGwvmmKSKjj0DHl5t81Na185Lf0qPsWFo8KKVptkRmNlh0PEAyWBIcYU5fHrBRJWkkq/iSRlUYkwlNNxcHBnwsyqjIwY/WPZDpVBFxfPqgActvQWKPbJhD0ZSkIpqoImHQmJuaNCw0mizJ/f82nDZnl7ijtOreeS+evJFize3jHItW9u4i8P5exIGCy4aAL71AR5bXOc77y6lXZzgKqAzk/f2caZz6zlt+92cuhd6+mIG8P9OnffshFJor/4cN8lSeK588fx1JoBVFni/L0ruGa/WqJe7SOv+/BrPpUk0S5ezPrOO++koaEBt9vN9OnTeeuttz6BkxQEQRAEQfgU7OLFrD9r91X/VYmi5uZm5syZw7hx41i4cCGrVq3iuuuuw+12D9e54ooreOaZZ3j88cdZvHgxqVSKY489Fsuy/smR/77eHoWMbRBwy/RlbcJehaqwwgVPNXP5nCo29ma5490uvvPmNp44fRy3LW1Dlk32qQpSHtDRgim6kuBg4ndBa8yLbato+NkyaKPoDuftVckF+1fwyoUTqI3ow9+W/3HVABR2hr97yGDub1fxZnOcgiHT2m+zsT2E4ngZzMrURnPMGZXhqa1b+KC3D6+ksbbLzdbeCI8uq8C0ZIK6xkCmAI5MdUjGVNP4wg4XTR1DTchNhU9nQ2+GW5e2URty8+QXJ/DchkH2Kt/5x84LW3tZ3jvEWc+uR/H2oCowowZceh4jq3D205vIpXSaQiVs6fQRHwyRsQaJ+gzypkTaUKn2RWmLSXg1mda+AnqwQAGb3rgXXXaRMGyqg5AkTSTo0Jsu0JvwEPRlCXkNIm4IezI0Ftn0ZQuURKBtwCToUbj2c7WM9oQ4f3IjEa2IY+rG8vSXJnLjYQ28vyPFyxviXDd7KlWucgq2QsSt4NILeBwfiZSMlA+STcn4dJnjfreBUr+GIv/1l3x1rI9IUYYSt0R/TEXVHKqqsqi+HC5VpjCkkU9L1EZcyJZK1wBkhoJc9eIOWgZzVARceDSZc2eWkS+AJ1NET5+XTV1+ElmdZM5FMp/Bo9kE3AYza7M4Ug6w+db8bfzm/U5M66/jGJ/6wmSWf20Gtx82hb3Lwng8DpoGXj8M2lmycoHiwBAVoQKK5MJxNBxHJW+bWHKKNf39rOobQHFbuDwOUU2l2F9gfLlBXTRNfTRD2nBIpFXe2+ile1ClIwaRQILiYBoLg0RWZVlrkGRGI5kC24HV3Snm3LeCd9uTDMRt0nmblGEO/0EP8NCadr7x+irueb+dm19rH3FeQb9EUspjmxoF04UqubBscOt5siRptZIgO3hVlZZ49iO/s2PKXWQLEl0xL5u6PURdHlTFIJ6zKZBjXF2exZ39HPrgSn6xdAd/XNvHst4eBqRWLnp1OUu7+lkxkKCnkKaxpEDQk6EtppE13Ggukxebd3DZC1v5zistjL/pA5bvSONSZZZ/bQa/OGospmKSk/MEdYibKSIemZSZoj7skDIKbO4pZn23h+5eL++3WGxoz9LSU+D0pnrmjg6z91iHh7esoz5qoXpjTKqCnJ3DK4fQ1DSjIiqxtMODH3TjOA6n7BXlp+9tp9tM4vXkSRoWXl2i1OMll66lPlDK2+fP4Jo5dVw1t4or51Ry/UETAImBrI1H9mM7FrJVTM5UkGyFfL9OLOFQ6tJIFWyKtTCRQJ7upIuqYJa0mcG0fAxlZd7bVk5bzMv77dAYqOJnBx7Eq1+bzHcOqUZVJAqWzWMr+rj46W0ULJv3d6Qpd3kIRfLoeg5NgSHDIJUNsa4twNMr4zy1qp9fL+nkoHtXs6kvzXdfbkVXJM56fhUfdCe4csEmLnt5A0++neI373QDOxM+1eGdI9fidorJoyQeOm46YZfOih1pkjGF2xd3cc0hlVxxUOVH3jf/iiRBeVhldp2PS5/dxnULWnl7W3J4/45YfsR7fE/wxBNPcMUVV3DttdfywQcfcMABB3DUUUeN+OJGEARBEARB+Nc+i/dV/1WJomuvvZajjz6an/70p0ybNo1Ro0ZxzDHHUFq6c4HcRCLBfffdxy233MKhhx7KtGnTePjhh1mzZg2vvfbaf9zeb04bxdRijfLyFsK+FEUeGceSKLgznPn4JhrLVYp8Kkc3FnHzO62s2Z7nW69tYGalC68GWUPFslTCLoWcnUWTZfoHqukYyiC7skyqcnPV3J3TF1RZ4syZpcNtzx0d5Pr5bdy7tIsyv8bLZ0/mjnfbeWJDD25NoTHsoTWZJZVXUe0IhjRIcSBFRzJLdy5DLOGhtTtAMusi6tZoj9vYtsqosE5HKo2iWjSUWlzy4lbmlIzi8MpRyEkP1R4P9y3rxqvJmJbN5fO28fqWOKu645SFc4wpM5CVHONKDUrCOxhdGWdSfQq3P013LsPFM6ooCiikcjJLN5eSzwbZ2iORSIbojKu88MXJzCwPY0oSNT43qjeNkwmytVchlVcxLZmqSIrasMxQTKFEDWLYBYx8iHTBoD3uZ2W7H8OUsCwFT1GWgCZz9qPNTK/zcelzzewdjfCl32/l9j91EPWq3HxMPT94q5VvvLyVha2DKLJFwDtEWWiIVFoil4FptS7K6obQAmlCUZPHN3Vy/8pOkrkCtu3w/Lo07TEFSctRUz6EJWfxKi62D2Xx+S2+d0QNd39+NHedMJb7ThpFVs4QdKu4TZ2WLoNvzW+mc8hg7qgwVx1YxVuXTKEg2QymJNoGNKRCCM2VJOR2GMp56YlHKUgGpiGh2grL21JMvm05s+97n+8t3Lbz/Z4zCWk6maxMLidhmxIRxQWKjd9n816rm96Em+auIH1JD7GhIhJZlbyhI2kZfO6dC5m79QI+r0kyqxNLBbEtDzge6kIWpeEck8cMUhJNEQ4NsWSrj9VtARZsCNLS5yZjZ8lZFrFMgX0feJvvLFxPv5EmL+fAn+XGd7Yy+773SeUtElmTx5b30T9UQFcUhvIFiv0a9of+wG6I6tQUyVSGVaJeCdtykczbhL1xysIxqosMwl6QDRfNAztHqTmOg2U73L20i7U78sRjEmpBpdStMpCMsLnPw47+AFv6XaRzHrrTBX511BhUW+ZX77XTl5LY1udmanEZfSmVSbVDNFTEqAsbaPogRR6HeFaiMyGRMGVmj/bz5b1LeOZrjazsizHzllV8f8HOD/EjJ/mIBG00RUbCwaeqZC0LyQozkFHxy35ShkZ5SEaXZPw+mYoymcfW9XD4IyvpThmAhS6pDBkyqiJhOzY5S6ZvSKE/qZDp03lzU4qDf7+CW97fTE1xmoAvg2wG6Y172K+kipsPnsKjJ0zjhgPHEPXsHPlSFXAxsdTH+r4cXjmMIkPO0FnfEWLZDo3+AQ8tPRp1pTroNkG/zLfm1HByYzlBxYXPk6UnXsPm/jBvbyzh0SVjaR5USSQqWN9cRICinY+8//Pi02u705z68AYMy+YXJzbwm/e6WLQ9TqyQYe7oFBFfFmwPLX1BtvRplHk1FEMlbzq8tHmAgXyOS55qIehS+MrsCBNKdXA01vWlKdH9KC6bx9f2snBbDNg5aunnS7fwx00d/PKwKYwO+/jeK22YWZXqsIsHTx/L8ZOK/q3P/u2DueF/O47D5S8182JzL7/f2Iam2cgFhY29OxOVS1qSHH7POnpThf/4GvP/5kgf7/YfuPXWWznvvPM4//zzGT9+PLfffjs1NTXcddddn9DJCoIgCIIgfIJ20T0VfDbvq/5rEkW2bfPiiy8yduxYjjjiCEpLS5k1axbz5s0brrN8+XIKhQKHH374cFllZSWTJk1iyZIl/3GbD65qZ3Mmy2BGoT6aAcnErTtojs6kWo0v7VXOvSs6SeVN4kYafzRPNubn0TXtFMjj0VTqQy4UVy+amsGvaWyPqWzuiHJYo5tNnXlufHMHqZzJvretZk1nerjtqpCLN78+iUWtcb7+7FYqAho9hQwmJqUhSEtJdgzq2JaLnpRF1GtQ5nWRMkxsQ+fiWRVYeQ3VUPnqXg3kDBlVkqkr6WWv2kGqQw7dKQhH07QmU3xhWin3f3kUZ08vY0q5n1++20FnIU1zJsn1C7aTiOvohpctgwZ9KRcRr0GxP8voIotQoJ/G0hQ1UYmGqIcvTSqhoBiEdJXuIbAlhyK3TEcmwW3vbuOHh4zigZPHYsgGFg7j6vKEfAVUs4jBLCi4SJkFbM1Clk3a4x7csoeBjIugGqI7qVHq9jAQl3DSXmIpCxObY8ZFWXzBVI5oirL08ims6cow81cruXFhGw+f2oRXUTCGNDyWTmu/n66BIrKSw14NHr534FhunjuVnqzDuEqHuZNX0JzawOx73+PrL2yigEFI18iaCvl0hG1dOmHVzeCQgmxqXPPSdp5bNwDAW21DDOUdVN2iuNwkLmV5qz3Gk2t72TqQ4ZjHVnHj4hYKcpqI1wHZIuJWMSwdCYem8jjFfgNNkhk0c7j9ee49ZQwLLphIxsmyaSDBxfO2cvPiVo5/fCXL+/tw3EP4Q0PUlRm4FQ2PpJNNyeTyEm53FhsbTZYJe0ymVZtEPBaabLG+Q2HvsmK+PrOB8kiWqnAe09aRUZAlNy5FZlyJTW00z5SKPD6Xht/lcOreHUytiRNSHXQFVA3MvEo2JxN2Kfh1GF0MQUUhYHp58P1e7niri9+8080H200Gezw099ncv6qLlliO/szO6UAH1kW5aO9aygMaumrj0Sw87kE6Ezp9GZXRxTZRf55AMMcfN7Uz5pdLea8jiSzBs1t6WZtI8vTZYzlxUhGO6SaWdtGbsqmN2jSV5Im6JdZ1m5z24Eb+uGYA27GJZWw8msy6gRiregfpTnjpS0TY3lXMk29Wk0uU0RvXuWpGI9fsV0dQVbny+WaueXMVi3vaSHkyDMk7+3/kqBJkZHbEHYbixUyIlLFjwEUyL+FzD1ESGsK0HHLkUWyJTMrhvAk1LN2Y5VdHNtKWMBnoC6LoaWbUDIGcYygTZEunl/hABWWuImaN8WFJNhHNoTRo4NNlTEslZ1loqs1L23spWPaItXweXtbHxU9tY2l7nB+93sqydSoysD1h4FNVov40VVGQHYlYroDLa9BvJZm/uZubF3fgOC5sW2Ztt8aGTQ1s6PQyusjF+VOrOWFMDfec3MjGRJJb320BwLQcrn6xhe8cXMNXZpbhUmW+NrOCSdU25aVxknkf23r9LNqmE1JDpHMavYbB7afW8OrGOIps4jZcfPOgKhpKNL65cCXN8Z0JZLMA7fEC48s94MB181tZ3ZXimEdW8frWNLd9bioKMuc9tRnDsrl6bjVFms7eVf96KpjjOPz0jXbOf3Irtr0zgSlJEvVRHUvJk7CGqC5R+fYhlZy3bxmO43D7W508d+54ygL/+AlonxgHsD+m7T8YEGUYBsuXLx9xrQU4/PDD/0/XWkEQBEEQhF3u47qv+g8HmX9W76v+axaz7u3tJZVK8ZOf/IQf/ehH3HzzzbzyyiucfPLJvPnmm8ydO5fu7m50XScSiYx4bVlZGd3d3f/w2Pl8nnz+r2upJBI71zBpi/UTDil07PDTpxkYVh+K5KLG5WHrthyntL7LNw+s4HdrmnHLMhHZIe8yWbfOpqrSoNjvZfsOi44BN1MrLZa1JfFpKpUumQVrDG45dBwPvNfHNS9189MjKvj6w6uZd944VEUinjGxbIdfHlbOj99s4/Tfv8stc+v44eJmnLyJamap0FWGEgVcmkRLMsxgfoiIO8eMsgDHVtfzxdFBfreyk98sXcNdh47j+eZONmyXaSy1kOQ2JhXrtHRFeWTFZrb1dbO9z2JzLMexY6NcMauGi6eEuPa1rSyJ9aEoMpAkJGfwFwLs6MngdUnE7AwFO0cRCu9ua+cLLV048QCZtMHYmjw2JqP8Km3dO6cLDcr93PDSWsy8Q193hkjQpj2TIOzyYJtx0gkdTZJQnDgHj3azpR+GYhYJqQ+PrrJhwIvHiZFLuSjkDI4bHSCk6yyyctwyfzMX7l9GXcRN70CC7cl2vDa8tjHDvNXbCCsauulw0qhylnYWaO7JYtsGx4wLEI/HeWLZNgb6hqh3yTz7XpCp5Xn2qhzANocIqTKyo5BNO3i8Bn5XHrkQJiIbpJIWigOLN2X53PpO9h3lR0kWcKJdlOkKgzkDXdFZsCnJ4i1t3HBgAy9s7sNXyBLyZGgdMqnQXFhJh4wdx5EMUmmJYsliaYdDhV/nWy8vZ1ppiFtm1/HylgHeaNlBbUShSCtg5iwUK4dpGBR7vKRUHZ8iM74sSaUPUtYQEU1HQmJDV5iQS6bElyGX0pCzMs8s38EdyQLFEZugS6IsmELN68TiUO7PEJDz2E4WWXII40Y2ZNZvCyIbGqkuFSucpdxrINsS7nwBx7bIxm2Gchb1UWiqVEklEmiKhNffhdelgGcIXdMpCiW5dkEf/WmZP5w8jS2DGe76UzNRr0RWSpGQIWUXKPe7MKU8XtMCR0M2LcqDEX5z8CiUbIZEwiFMnvc6hjh/3iL8uguPYpPNSFhSHEcpoqnEx0ENEb63d4gfLGqlNZnijLHlHDoqzHffWYZcSNHdreCKaMyoDhJRXSwpxFHNAplsgd8uTeD3p6kOOMyd2EU+VcuK7RqXTq4kNmSzcN0OTNPGThbY3Fbg0jnVvLa6m0ZvBiOdQVHyZAt5fLbMtkSBYslhU0eBVc0y39w/ygPvbWPFthQTak08UhK/KeFR+hnnd7G4S6Y+7OKHc0pY2Znku/P7aUkYHBSN4chxZKucgXyO4+oaOaCqknnL2jisKTz8mXZoncwR9VWoisRra2Gp3U+iX2PDdg3fqBQpI4+pdlIdCBCzEpS5IhiFBAO5NJqis70nz4yiYr48tpw7kq34yZI1Ury91WB5d5JSr4qFjYXFWWNCqLLM46dUY9s2/f1JdF0HJMpMjY0xg65Omfqwjm7G2NzhI6y7sfNZ7novCSb4vA6PfmEC331hK31GjuIiSBhpcpJFhezhV8fX8ZsPthKLKfQkTC56YiW2alIddGOkkxz78Hq+snc5R4/xc/L9K7nn9NHDn+v/TDxTYEtHHw+fUkcy+df61ZpFGSbru2yuOCTE4aP14cfd33N8OZAn/nfW4/qwpZs7AT7eKWpG+l/X+Q+PlUwmRxS7XC5crpEL0vf392NZFmVlZSPK/9W1VhAEQRAE4b/Wx3Vf9R/cU8Fn+L7K2UUefvhhx+fzDW8LFy50AOeLX/ziiHrHHXec84UvfMFxHMd55JFHHF3XP3KsQw891Lngggv+YVvXX3/9Xx6GJzaxiU1sYhPbJ7Y1Nzf/v6+P2WzWKS8v/9j75vf7P1J2/fXXf6T9jo4OB3CWLFkyovxHP/qR09TU9P8+P0EQBEEQhE/LJ3Ff9e/eUznOZ/e+apeNKDr++OOZNWvW8M8lJSWoqsqECRNG1Bs/fjyLFy8GoLy8HMMwiMViI0YV9fb2st9++/3Dtr7zne9w1VVXDf8cj8epq6ujra2NUCj0cZ3SZ1YymaSmpoYdO3YQDAZ3dXd2KRGLkUQ8RhLxGEnE468SiQS1tbVEo9H/97HcbjctLS0YhvGvK/8HHMcZMWUR+LvffBUXF6Moyke+5ert7f3It2GCIAiCIAj/zT6J+6p/954KPrv3VbssURQIBAgERj62eObMmWzatGlE2ebNm6mrqwNg+vTpaJrGggULOO200wDo6upi7dq1/PSnP/2Hbf2jYWChUGiP/+Pmw4LBoIjHn4lYjCTiMZKIx0giHn8lyx/P0n9ut3vEEz8/TbquM336dBYsWMBJJ500XL5gwQJOOOGEXdInQRAEQRCE/ytxX/Wf+69Zowjgm9/8JqeffjoHHnggBx98MK+88grPP/88CxcuBHYmds477zy+8Y1vUFRURDQa5eqrr2by5Mkceuihu7bzgiAIgrCbuOqqqzjzzDOZMWMGs2fP5p577qGtrY0LL7xwV3dNEARBEAThM+WzeF/1X5UoOumkk7j77ru56aabuOyyy2hqauKpp55izpw5w3Vuu+02VFXltNNOI5vNcsghh/DAAw+gKMou7LkgCIIg7D5OP/10BgYG+MEPfkBXVxeTJk3ipZdeGh7hKwiCIAiCIPx7Pov3VZLjfJyPaPlsyOfz3HTTTXznO9/5h3MJ9yQiHn8lYjGSiMdIIh4jiXj8lYiFIAiCIAiCsLvYIxNFgiAIgiAIgiAIgiAIwkd9PKtuCoIgCIIgCIIgCIIgCJ95IlEkCIIgCIIgCIIgCIIgACJRJAiCIAiCIAiCIAiCIPzZHpEo2rBhA8cffzyhUIhAIMC+++5LW1vb8P58Ps+ll15KcXExPp+P448/nvb29l3Y40/HBRdcgCRJ3H777SPK95R4FAoFrrnmGiZPnozP56OyspKzzjqLzs7OEfX2lHj8PXfeeScNDQ243W6mT5/OW2+9tau79Im76aabmDlzJoFAgNLSUk488UQ2bdo0oo7jONxwww1UVlbi8Xg46KCDWLdu3S7q8afnpptuQpIkrrjiiuGyPS0WHR0dnHHGGRQVFeH1epk6dSrLly8f3r+nxUMQBEEQBEHY/ez2iaLm5mbmzJnDuHHjWLhwIatWreK6667D7XYP17niiit45plnePzxx1m8eDGpVIpjjz0Wy7J2Yc8/WfPmzePdd9+lsrLyI/v2lHhkMhlWrFjBddddx4oVK3j66afZvHkzxx9//Ih6e0o8/tYTTzzBFVdcwbXXXssHH3zAAQccwFFHHTUiybo7WrRoERdffDFLly5lwYIFmKbJ4YcfTjqdHq7z05/+lFtvvZVf//rXvP/++5SXl3PYYYcxNDS0C3v+yXr//fe55557mDJlyojyPSkWsViM/fffH03TePnll1m/fj233HIL4XB4uM6eFA9BEARBEARhN+Xs5k4//XTnjDPO+If74/G4o2ma8/jjjw+XdXR0OLIsO6+88sqn0cVPXXt7u1NVVeWsXbvWqaurc2677bbhfXtiPD7svffecwCntbXVcZw9Ox777LOPc+GFF44oGzdunPPtb397F/Vo1+jt7XUAZ9GiRY7jOI5t2055ebnzk5/8ZLhOLpdzQqGQc/fdd++qbn6ihoaGnDFjxjgLFixw5s6d61x++eWO4+x5sbjmmmucOXPm/MP9e1o8BEEQBEEQhN3Tbj2iyLZtXnzxRcaOHcsRRxxBaWkps2bNYt68ecN1li9fTqFQ4PDDDx8uq6ysZNKkSSxZsmQX9PqTZds2Z555Jt/85jeZOHHiR/bvafH4W4lEAkmShkcI7KnxMAyD5cuXjzhvgMMPP3y3Pu+/J5FIABCNRgFoaWmhu7t7RGxcLhdz587dbWNz8cUXc8wxx3DooYeOKN/TYvHcc88xY8YMPv/5z1NaWsq0adO49957h/fvafEQBEEQBEEQdk+7daKot7eXVCrFT37yE4488kheffVVTjrpJE4++WQWLVoEQHd3N7quE4lERry2rKyM7u7uXdHtT9TNN9+Mqqpcdtllf3f/nhaPD8vlcnz729/mS1/6EsFgENhz49Hf349lWZSVlY0o393P+285jsNVV13FnDlzmDRpEsDw+e8psXn88cdZsWIFN91000f27Wmx2LZtG3fddRdjxoxh/vz5XHjhhVx22WX8/ve/B/a8eAiCIAiCIAi7p90qUfTII4/g9/uHt78sQHvCCSdw5ZVXMnXqVL797W9z7LHHcvfdd//TYzmOgyRJn0a3PzF/G49Fixbxi1/8ggceeOA/PrfdMR4fXpi5UCjwhS98Adu2ufPOO//lsXaHePw7/vYc95Tz/otLLrmE1atX89hjj31k354Qmx07dnD55Zfz8MMPj1jX7W/tCbGAnSMy9957b2688UamTZvGBRdcwFe/+lXuuuuuEfX2lHgIgiAIgiAIu6fdKlF0/PHHs3LlyuFt6tSpqKrKhAkTRtQbP3788IK85eXlGIZBLBYbUae3t/cj3wp/1vxtPJYsWUJvby+1tbWoqoqqqrS2tvKNb3yD+vp6YM+Kx4wZM4CdSaLTTjuNlpYWFixYMDyaCHbvePwzxcXFKIrykVEQu/t5f9ill17Kc889x5tvvkl1dfVweXl5OcAeEZvly5fT29vL9OnThz8zFi1axC9/+UtUVR0+3z0hFgAVFRX/8noCe048BEEQBEEQhN3TbpUoCgQCNDY2Dm+hUIiZM2d+5NHWmzdvpq6uDoDp06ejaRoLFiwY3t/V1cXatWvZb7/9PtX+f9z+Nh5f+9rXWL169YhkSWVlJd/85jeZP38+sGfFw+PxDCeJtmzZwmuvvUZRUdGI1+zO8fhndF1n+vTpI84bYMGCBbv1ecPO0R+XXHIJTz/9NG+88QYNDQ0j9jc0NFBeXj4iNoZhsGjRot0uNocccghr1qz5SIL1y1/+MitXrmTUqFF7TCwA9t9//396PdmT3huCIAiCIAjC7kvd1R34pH3zm9/k9NNP58ADD+Tggw/mlVde4fnnn2fhwoUAhEIhzjvvPL7xjW9QVFRENBrl6quvZvLkyR9ZuPWzrqio6COJEE3TKC8vp6mpCdiz4mGaJqeeeiorVqzghRdewLKs4ZEA0WgUXdf3qHj8rauuuoozzzyTGTNmMHv2bO655x7a2tq48MILd3XXPlEXX3wxjz76KM8++yyBQGD4PREKhfB4PEiSxBVXXMGNN97ImDFjGDNmDDfeeCNer5cvfelLu7j3H69AIDC8NtNf+Hw+ioqKhsv3lFgAXHnlley3337ceOONnHbaabz33nvcc8893HPPPQB71HtDEARBEARB2I3tsuetfYruu+8+p7Gx0XG73c5ee+3lzJs3b8T+bDbrXHLJJU40GnU8Ho9z7LHHOm1tbbuot5+uuro657bbbhtRtqfEo6WlxQH+7vbmm28O19tT4vH33HHHHU5dXZ2j67qz9957Dz8ifnf2j94T999//3Ad27ad66+/3ikvL3dcLpdz4IEHOmvWrNl1nf4UzZ0717n88suHf97TYvH88887kyZNclwulzNu3DjnnnvuGbF/T4uHIAiCIAiCsPuRHMdxdk2KShAEQRAEQRAEQRAEQfhvslutUSQIgiAIgiAIgiAIgiD834lEkSAIgiAIgiAIgiAIggCIRJEgCIIgCIIgCIIgCILwZyJRJAiCIAiCIAiCIAiCIAAiUSQIgiAIgiAIgiAIgiD8mUgUCYIgCIIgCIIgCIIgCIBIFAmCIAiCIAiCIAiCIAh/JhJFgiAIgiAIgiAIgiAIAiASRYLwX2XevHk0NjaiKApXXHHFru7Ox2r79u1IksTKlSs/9mPfcMMNSJKEJEncfvvtw+WSJDFv3ryPta2FCxcOt3XiiSd+rMcWBEEQBEEQBEHY1USiSNjjnX322cN/+H94O/LIIz+W4/8lsRCPx/9l3QsuuIBTTz2VHTt28MMf/vBjaX9PMXHiRLq6uvja1772ibaz33770dXVxWmnnfaJtiMIgiAIgiAIgrArqLu6A4Lw3+DII4/k/vvvH1Hmcrk+1T6kUil6e3s54ogjqKys/Lt1LMtCkiRkedfkeAuFApqm7ZK2AQzDQNf1v7tPVVXKy8s/8T7ouk55eTkej4d8Pv+JtycIgiAIgiAIgvBpEiOKBIGdSaHy8vIRWyQSGd5/6623MnnyZHw+HzU1NVx00UWkUqnh/a2trRx33HFEIhF8Ph8TJ07kpZdeYvv27Rx88MEARCIRJEni7LPP/kj7CxcuJBAIAPC5z30OSZJYuHAhDzzwAOFwmBdeeIEJEybgcrlobW0lFotx1llnEYlE8Hq9HHXUUWzZsmX4eB9+XVNTE16vl1NPPZV0Os2DDz5IfX09kUiESy+9FMuy/mFcbrjhBqZOncrvfvc7Ro0ahcvlwnEcXnnlFebMmUM4HKaoqIhjjz2W5ubmEa997733mDZtGm63mxkzZvDBBx+M2G9ZFueddx4NDQ14PB6ampr4xS9+MaLO2WefzYknnshNN91EZWUlY8eO/Rf/k//cD37wA8rKyoanv9XX1/OjH/2Is846C7/fT11dHc8++yx9fX2ccMIJ+P1+Jk+ezLJly/5f7QqCIAiCIAiCIHxWiESRIPwbZFnml7/8JWvXruXBBx/kjTfe4Fvf+tbw/osvvph8Ps+f/vQn1qxZw80334zf76empoannnoKgE2bNtHV1fWRZAjsnM60adMmAJ566im6urrYb7/9AMhkMtx000389re/Zd26dZSWlnL22WezbNkynnvuOd555x0cx+Hoo4+mUCgMHzOTyfDLX/6Sxx9/nFdeeYWFCxdy8skn89JLL/HSSy/x0EMPcc899/DHP/7xn5771q1befLJJ3nqqaeGEyzpdJqrrrqK999/n9dffx1ZljnppJOwbXt4/7HHHktTUxPLly/nhhtu4Oqrrx5xXNu2qa6u5sknn2T9+vV873vf43/+53948sknR9R7/fXX2bBhAwsWLOCFF174d/67PsJxHC6//HLuu+8+Fi9ezNSpU4f33Xbbbey///588MEHHHPMMZx55pmcddZZnHHGGaxYsYLGxkbOOussHMf5P7UtCIIgCIIgCILwWSKmngkC8MILL+D3+0eUXXPNNVx33XUAIxaWbmho4Ic//CFf//rXufPOOwFoa2vjlFNOYfLkyQCMGjVquH40GgWgtLSUcDj8d9vXdZ3S0tLh+h+eQlUoFLjzzjvZa6+9ANiyZQvPPfccb7/99nAy6ZFHHqGmpoZ58+bx+c9/fvh1d911F6NHjwbg1FNP5aGHHqKnpwe/38+ECRM4+OCDefPNNzn99NP/YWwMw+Chhx6ipKRkuOyUU04ZUee+++6jtLSU9evXM2nSJB555BEsy+J3v/sdXq+XiRMn0t7ezte//vXh12iaxve///0RcV2yZAlPPvnkiPV/fD4fv/3tb//hlLN/xTRNzjrrLJYtW8bbb79NdXX1iP1HH300F1xwAQDf+973uOuuu5g5c+ZwHK+55hpmz55NT0/PpzK1TRAEQRAEQRAEYVcSiSJBAA4++GDuuuuuEWV/SfAAvPnmm9x4442sX7+eZDKJaZrkcjnS6TQ+n4/LLruMr3/967z66qsceuihnHLKKUyZMuVj6Zuu6yOOtWHDBlRVZdasWcNlRUVFNDU1sWHDhuEyr9c7nCQCKCsro76+fkRCrKysjN7e3n/afl1d3YgkEUBzczPXXXcdS5cupb+/f3gkUVtbG5MmTWLDhg3stddeeL3e4dfMnj37I8e+++67+e1vf0trayvZbBbDMEaM9gGYPHny/zlJBHDllVficrlYunQpxcXFH9n/4diWlZUNt/m3Zb29vSJRJAiCIAiCIAjCbk9MPRMEdo5aaWxsHLH9JVHU2trK0UcfzaRJk3jqqadYvnw5d9xxB8DwVK/zzz+fbdu2ceaZZ7JmzRpmzJjBr371q4+lbx6PB0mShn/+R1OgHMcZUe9vF52WJOnvlv0lyfOP+Hy+j5Qdd9xxDAwMcO+99/Luu+/y7rvvAjtHH/2zPn7Yk08+yZVXXsm5557Lq6++ysqVKznnnHOGj/HP2v9PHHbYYXR0dDB//vy/u//DMflL/P5e2b+KkyAIgiAIgiAIwu5AJIoE4V9YtmwZpmlyyy23sO+++zJ27Fg6Ozs/Uq+mpoYLL7yQp59+mm984xvce++9AMOjYf7ZotH/iQkTJmCa5nByBmBgYIDNmzczfvz4j6WNf2ZgYIANGzbw3e9+l0MOOYTx48cTi8U+0sdVq1aRzWaHy5YuXTqizltvvcV+++3HRRddxLRp02hsbPzIgtgfh+OPP55HH32U888/n8cff/xjP74gCIIgCIIgCMLuRCSKBAHI5/N0d3eP2Pr7+wEYPXo0pmnyq1/9im3btvHQQw9x9913j3j9FVdcwfz582lpaWHFihW88cYbw0mburo6JEnihRdeoK+vb8TT0v4vxowZwwknnMBXv/pVFi9ezKpVqzjjjDOoqqrihBNO+H8d+98RiUQoKirinnvuYevWrbzxxhtcddVVI+p86UtfQpZlzjvvPNavX89LL73Ez3/+8xF1GhsbWbZsGfPnz2fz5s1cd911vP/++59In0866SQeeughzjnnnH+5eLcgCIIgCIIgCMKeTCSKBAF45ZVXqKioGLHNmTMHgKlTp3Lrrbdy8803Dy/UfNNNN414vWVZXHzxxYwfP54jjzySpqam4YWuq6qq+P73v8+3v/1tysrKuOSSS/7f/b3//vuZPn06xx57LLNnz8ZxHF566aWPTC37JMiyzOOPP87y5cuZNGkSV155JT/72c9G1PH7/Tz//POsX7+eadOmce2113LzzTePqHPhhRdy8sknc/rppzNr1iwGBga46KKLPrF+n3rqqTz44IOceeaZPP30059YO4IgCIIgCIIgCJ9lkiOe+SwIwmfcDTfcwLx581i5cuWn1ubZZ59NPB5n3rx5n1qbgiAIgiAIgiAInzQxokgQhN3CmjVr8Pv9wyO5PilvvfUWfr+fRx555BNtRxAEQRAEQRAEYVcQI4oEQfjMGxwcZHBwEICSkhJCodAn1lY2m6WjowPYOcWuvLz8E2tLEARBEARBEATh0yYSRYIgCIIgCIIgCIIgCAIgpp4JgiAIgiAIgiAIgiAIfyYSRYIgCIIgCIIgCIIgCAIgEkWCIAiCIAiCIAiCIAjCn4lEkSAIgiAIgiAIgiAIggCIRJEgCIIgCIIgCIIgCILwZyJRJAiCIAiCIAiCIAiCIAAiUSQIgiAIgiAIgiAIgiD8mUgUCYIgCIIgCIIgCIIgCIBIFAmCIAiCIAiCIAiCIAh/9r89z8JcF32ZbgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
     "\n",
@@ -474,10 +905,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "29",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:51.010159Z",
+     "iopub.status.busy": "2026-08-19T13:34:51.010010Z",
+     "iopub.status.idle": "2026-08-19T13:34:58.618364Z",
+     "shell.execute_reply": "2026-08-19T13:34:58.617812Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x900 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "VARS = [v for v in (\"DBZH\", \"ZDR\", \"RHOHV\", \"PHIDP\") if v in rdf.columns()]\n",
     "\n",
@@ -494,10 +943,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "30",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:34:58.620322Z",
+     "iopub.status.busy": "2026-08-19T13:34:58.620194Z",
+     "iopub.status.idle": "2026-08-19T13:35:01.039847Z",
+     "shell.execute_reply": "2026-08-19T13:35:01.039226Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Filter and crop first — the plot follows the data, gate for gate\n",
     "sub = rdf.filter({\"var\": \"DBZH\", \"logic\": \">\", \"threshold\": 30}).crop_around_point(\n",
@@ -525,10 +992,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "32",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:35:01.041404Z",
+     "iopub.status.busy": "2026-08-19T13:35:01.041271Z",
+     "iopub.status.idle": "2026-08-19T13:35:08.973534Z",
+     "shell.execute_reply": "2026-08-19T13:35:08.972819Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1150x900 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(2, 2, figsize=(11.5, 9))\n",
     "rdf.plot_ppi(sweep=1, ax=axes[0][0], timestep=\"2024-06-12\", title=\"PPI | sweep 1\")\n",
@@ -556,12 +1041,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "34",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:35:08.975586Z",
+     "iopub.status.busy": "2026-08-19T13:35:08.975459Z",
+     "iopub.status.idle": "2026-08-19T13:35:10.272527Z",
+     "shell.execute_reply": "2026-08-19T13:35:10.271775Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 620x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "dt = raddb.open_any_datatree(sorted(MCH_DIR.glob(\"L_*.zarr\"))[0])\n",
+    "dt = raddb.open_any_datatree(sorted(FMI_DIR.glob(\"FANJ_*.zarr\"))[0])\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
     "raddb.plot_ppi(dt, sweep=4, variable=\"DBZH\", ax=ax)\n",
@@ -584,10 +1087,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "36",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:35:10.274942Z",
+     "iopub.status.busy": "2026-08-19T13:35:10.274612Z",
+     "iopub.status.idle": "2026-08-19T13:35:12.707187Z",
+     "shell.execute_reply": "2026-08-19T13:35:12.706517Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saved: /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive/ppi_example.png (678 kB)\n"
+     ]
+    }
+   ],
    "source": [
     "out = ARCHIVE_DIR / \"ppi_example.png\"\n",
     "fig, ax = plt.subplots(figsize=(6.2, 5.4))\n",
@@ -615,6 +1133,1153 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.15"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
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diff --git a/tutorial/05_demo_pipeline.ipynb b/tutorial/05_demo_pipeline.ipynb
index 871a2f0..93c648c 100644
--- a/tutorial/05_demo_pipeline.ipynb
+++ b/tutorial/05_demo_pipeline.ipynb
@@ -7,25 +7,42 @@
    "source": [
     "# 5. Demo Pipeline\n",
     "\n",
-    "A complete pipeline in one notebook: **download** raw volumes from three public\n",
+    "A complete pipeline in one notebook: **download** raw volumes from two public\n",
     "networks, **archive** them, **plot** them, and cut a **vertical cross-section**.\n",
     "\n",
     "| radar | network | format | reader |\n",
     "|---|---|---|---|\n",
     "| `KDVN` | NEXRAD (US) — Davenport, Iowa | Level II | `open_nexradlevel2_datatree` |\n",
     "| `FANJ` | FMI (Finland) — Anjalankoski | ODIM HDF5 | `open_odim_datatree` |\n",
-    "| `GUA` | IDEAM (Colombia) — Guaviare | IRIS/Sigmet | `open_iris_datatree` |\n",
     "\n",
-    "All three are public and need no credentials, and the cases below are only the\n",
-    "defaults — section 1 is where you pick a different time."
+    "Both are public and need no credentials, and the cases below are only the\n",
+    "defaults — section 1 is where you pick a different time.\n",
+    "\n",
+    "Two networks, two file formats, two continents — and neither needs any\n",
+    "preparation beyond the xradar reader that understands it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:29.520647Z",
+     "iopub.status.busy": "2026-08-19T13:36:29.520555Z",
+     "iopub.status.idle": "2026-08-19T13:36:30.408307Z",
+     "shell.execute_reply": "2026-08-19T13:36:30.407561Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raddb 0.1.dev5+gde6070734.d20260323 | xradar 0.11.1\n"
+     ]
+    }
+   ],
    "source": [
     "import shutil\n",
     "import tarfile\n",
@@ -59,10 +76,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:30.410116Z",
+     "iopub.status.busy": "2026-08-19T13:36:30.409901Z",
+     "iopub.status.idle": "2026-08-19T13:36:30.413624Z",
+     "shell.execute_reply": "2026-08-19T13:36:30.412998Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raw files: /home/erik_poschivo/Desktop/LTE_project/ltenas8/data/RADAR/tutorial_raw\n",
+      "archive  : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n"
+     ]
+    }
+   ],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# CONFIGURATION — edit these two paths to point at your own machine\n",
@@ -77,10 +110,8 @@
     "    \"KDVN\": {\"kind\": \"nexrad\", \"site\": \"KDVN\", \"open\": xradar.io.open_nexradlevel2_datatree},\n",
     "    # FMI publishes one ODIM PVOL per volume.  The ODIM code is \"fianj\" — five\n",
     "    # characters, where a RadDB radar name is at most four, so it is aliased.\n",
+    "    # Only the quarter-hour volumes are the full 13-sweep scan; see section 1.\n",
     "    \"FANJ\": {\"kind\": \"odim\", \"site\": \"fianj\", \"open\": xradar.io.open_odim_datatree},\n",
-    "    # IDEAM splits a volume across task files: SURVP (0.5 deg), PRECA (1.5-5.1),\n",
-    "    # PRECB, PRECC.  One task is one DataTree.\n",
-    "    \"GUA\": {\"kind\": \"iris\", \"site\": \"Guaviare\", \"open\": xradar.io.open_iris_datatree},\n",
     "}\n",
     "\n",
     "print(\"raw files:\", RAW_DIR)\n",
@@ -102,19 +133,35 @@
     "\n",
     "* **NEXRAD** — the Google mirror is complete for 2024; one tar per radar per\n",
     "  hour, ~10 volumes inside, so any hour of any day works.\n",
-    "* **FMI** — one file every **5 minutes**, so the timestamp is rounded down to\n",
-    "  the 5-minute mark.\n",
-    "* **IDEAM** — tasks cycle `SURVP` → `PRECA` → `PRECB` → `PRECC` every few\n",
-    "  minutes, and one task is one DataTree. Which elevations you get therefore\n",
-    "  depends on the minute you ask for: the default lands on `PRECA` (1.5-5.1°)."
+    "* **FMI** — one file every **5 minutes**, but the scan strategy cycles over\n",
+    "  15 minutes: only `:00`, `:15`, `:30` and `:45` are the full 13-sweep volume,\n",
+    "  while `:05`/`:10` and their repeats are two shorter task sets. RadDB stores\n",
+    "  one geometry per radar, so the timestamp is rounded down to the **quarter\n",
+    "  hour** and mixing the three would be refused as a scan-strategy change."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:30.414914Z",
+     "iopub.status.busy": "2026-08-19T13:36:30.414826Z",
+     "iopub.status.idle": "2026-08-19T13:36:30.417378Z",
+     "shell.execute_reply": "2026-08-19T13:36:30.416844Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KDVN  2024-06-25 23:04 UTC\n",
+      "FANJ  2024-08-09 12:00 UTC\n"
+     ]
+    }
+   ],
    "source": [
     "# --------------------------------------------------------------------------\n",
     "# WHICH TIMESTEP? — one UTC timestamp per radar, edit freely\n",
@@ -122,7 +169,6 @@
     "TIMES = {\n",
     "    \"KDVN\": \"2024-06-25 23:04\",\n",
     "    \"FANJ\": \"2024-08-09 12:00\",\n",
-    "    \"GUA\": \"2024-06-12 19:02\",\n",
     "}\n",
     "\n",
     "for name, when in TIMES.items():\n",
@@ -139,12 +185,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "7",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:30.418658Z",
+     "iopub.status.busy": "2026-08-19T13:36:30.418580Z",
+     "iopub.status.idle": "2026-08-19T13:36:31.820186Z",
+     "shell.execute_reply": "2026-08-19T13:36:31.819568Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KDVN  KDVN20240625_230458_V06.ar2v       15.1 MB\n",
+      "FANJ  202408091200_fianj_PVOL.h5         22.6 MB\n"
+     ]
+    }
+   ],
    "source": [
-    "def resolve_url(name, source, when):\n",
+    "def resolve_url(source, when):\n",
     "    \"\"\"URL of the volume at or after *when* for one source.\"\"\"\n",
     "    t = pd.Timestamp(when)\n",
     "    site = source[\"site\"]\n",
@@ -157,28 +219,12 @@
     "            + \"?alt=media\"\n",
     "        )\n",
     "\n",
-    "    if source[\"kind\"] == \"odim\":\n",
-    "        t = t.floor(\"5min\")  # FMI publishes every 5 minutes\n",
-    "        return (\n",
-    "            \"https://fmi-opendata-radar-volume-hdf5.s3.eu-west-1.amazonaws.com/\"\n",
-    "            f\"{t:%Y/%m/%d}/{site}/{t:%Y%m%d%H%M}_{site}_PVOL.h5\"\n",
-    "        )\n",
-    "\n",
-    "    # IDEAM: list the first key at or after the requested second.\n",
-    "    prefix = f\"l2_data/{t:%Y/%m/%d}/{site}/\"\n",
-    "    query = urllib.parse.urlencode(\n",
-    "        {\n",
-    "            \"list-type\": \"2\",\n",
-    "            \"max-keys\": \"1\",\n",
-    "            \"prefix\": prefix,\n",
-    "            \"start-after\": f\"{prefix}{name}{t - pd.Timedelta(seconds=1):%y%m%d%H%M%S}\",\n",
-    "        },\n",
+    "    # FMI: only the quarter-hour volumes carry the full 13-sweep scan strategy.\n",
+    "    t = t.floor(\"15min\")\n",
+    "    return (\n",
+    "        \"https://fmi-opendata-radar-volume-hdf5.s3.eu-west-1.amazonaws.com/\"\n",
+    "        f\"{t:%Y/%m/%d}/{site}/{t:%Y%m%d%H%M}_{site}_PVOL.h5\"\n",
     "    )\n",
-    "    with urllib.request.urlopen(f\"https://s3-radaresideam.s3.amazonaws.com/?{query}\", timeout=120) as response:\n",
-    "        listing = response.read().decode()\n",
-    "    if \"<Key>\" not in listing:\n",
-    "        raise RuntimeError(f\"IDEAM has nothing at or after {t} for {site}\")\n",
-    "    return \"https://s3-radaresideam.s3.amazonaws.com/\" + listing.split(\"<Key>\")[1].split(\"</Key>\")[0]\n",
     "\n",
     "\n",
     "def download(url, dest_dir, when):\n",
@@ -206,7 +252,7 @@
     "\n",
     "\n",
     "for name, source in SOURCES.items():\n",
-    "    url = resolve_url(name, source, TIMES[name])\n",
+    "    url = resolve_url(source, TIMES[name])\n",
     "    source[\"path\"] = download(url, RAW_DIR, TIMES[name])\n",
     "    print(f\"{name:<5} {source['path'].name:<32} {source['path'].stat().st_size / 1e6:6.1f} MB\")"
    ]
@@ -225,15 +271,72 @@
     "and archives what is there.\n",
     "\n",
     "The CRS is **mandatory to write** and comes from `suggest_crs()`, which returns\n",
-    "the UTM zone of the site: three radars on three continents, three projections."
+    "the UTM zone of the site: two radars on two continents, two projections.\n",
+    "\n",
+    "`FANJ` may already be in the archive from tutorial 1, where all three Finnish\n",
+    "radars were written in the national grid EPSG:3067. A radar's LUT is generated\n",
+    "once and then reused, so this volume joins the geometry that is already there and\n",
+    "the CRS printed below is only what `suggest_crs()` would have picked."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "9",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:31.821452Z",
+     "iopub.status.busy": "2026-08-19T13:36:31.821355Z",
+     "iopub.status.idle": "2026-08-19T13:36:51.061031Z",
+     "shell.execute_reply": "2026-08-19T13:36:51.060302Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KDVN: 21 sweeps, site 41.61, -90.58 -> EPSG:32615\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  crs         : 32615\n",
+      "  radars      : ['KDVN']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 1 archived, 0 failed\n",
+      "  elapsed     : 12s\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FANJ: 13 sweeps, site 60.90, 27.11 -> EPSG:32635\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "RadDB archive\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  crs         : 32635\n",
+      "  radars      : ['FANJ']\n",
+      "  filter      : keep DBZH > 0.0\n",
+      "  volumes     : 1 archived, 0 failed\n",
+      "  elapsed     : 2s\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
    "source": [
     "for name, source in SOURCES.items():\n",
     "    dt = source[\"open\"](str(source[\"path\"]))\n",
@@ -249,10 +352,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "10",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:51.063148Z",
+     "iopub.status.busy": "2026-08-19T13:36:51.062844Z",
+     "iopub.status.idle": "2026-08-19T13:36:51.079338Z",
+     "shell.execute_reply": "2026-08-19T13:36:51.078414Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==============================================================================\n",
+      "RadDB inventory — archived data\n",
+      "  archive_dir : /home/erik_poschivo/Desktop/LTE_project/ltenas8/users/giacobbi/raddb_tutorial_archive\n",
+      "  radars      : FANJ, FKOR, FKUO, KDVN, KLOT, KMLB, KTLX\n",
+      "  volumes     : 52\n",
+      "  time range  : 2024-06-01 12:00:01 .. 2024-08-09 12:00:03\n",
+      "------------------------------------------------------------------------------\n",
+      "  radar   volumes  time range                                           size\n",
+      "  FANJ         14  2024-06-01 12:00:02 .. 2024-08-09 12:00:03        29.9 MB\n",
+      "  FKOR         14  2024-06-01 12:00:01 .. 2024-06-14 12:00:01        13.3 MB\n",
+      "  FKUO         14  2024-06-01 12:00:05 .. 2024-06-14 12:00:05        25.9 MB\n",
+      "  KDVN          1  2024-06-25 23:04:58                               17.6 MB\n",
+      "  KLOT          0  unknown (no timestamp in the filenames)               0 B\n",
+      "  KMLB          1  2024-06-12 22:57:27                                7.1 MB\n",
+      "  KTLX          8  2024-06-12 22:03:24 .. 2024-06-12 22:53:30        59.3 MB\n",
+      "------------------------------------------------------------------------------\n",
+      "  load with   : db.open(radars=..., time_period=(start, end))\n",
+      "==============================================================================\n"
+     ]
+    }
+   ],
    "source": [
     "db = raddb.RadDB(archive_dir=ARCHIVE_DIR)\n",
     "db.inventory()"
@@ -268,18 +403,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "12",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:51.081217Z",
+     "iopub.status.busy": "2026-08-19T13:36:51.081079Z",
+     "iopub.status.idle": "2026-08-19T13:36:56.961574Z",
+     "shell.execute_reply": "2026-08-19T13:36:56.960751Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "fig, axes = plt.subplots(1, 3, figsize=(17, 5))\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
     "\n",
     "volumes = {}\n",
     "for ax, name in zip(axes, SOURCES, strict=False):\n",
+    "    # A radar may already hold other volumes from tutorial 1, so name the one\n",
+    "    # this notebook just archived rather than letting plot_ppi guess.\n",
     "    volumes[name] = db.open(radars=name)\n",
-    "    volumes[name].plot_ppi(sweep=1, variable=\"DBZH\", ax=ax, coords=\"xy\", context=True)\n",
-    "    ax.set_title(f\"{name}\")\n",
+    "    volumes[name].plot_ppi(\n",
+    "        sweep=1, variable=\"DBZH\", ax=ax, coords=\"xy\", context=True, timestep=TIMES[name],\n",
+    "    )\n",
+    "    ax.set_title(f\"{name} | {pd.Timestamp(TIMES[name]):%Y-%m-%d %H:%M} UTC\")\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -302,10 +476,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "14",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:56.964196Z",
+     "iopub.status.busy": "2026-08-19T13:36:56.964026Z",
+     "iopub.status.idle": "2026-08-19T13:36:57.056694Z",
+     "shell.execute_reply": "2026-08-19T13:36:57.056174Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae5becf1b1304b9c8dc6dd890b79139b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<b>Draw an AOI</b> with the toolbar (top-left): ▭ rectangle → <code>crop_by_bbox</c…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "kdvn = volumes[\"KDVN\"]\n",
     "selector = kdvn.interactive_crop()"
@@ -324,10 +520,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "16",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-19T13:36:57.061417Z",
+     "iopub.status.busy": "2026-08-19T13:36:57.061285Z",
+     "iopub.status.idle": "2026-08-19T13:37:10.077037Z",
+     "shell.execute_reply": "2026-08-19T13:37:10.075937Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nothing drawn — using the fixed line ((-91.3, 40.95), (-91.3, 42.1)): 2,655 gates\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "LINE = ((-91.30, 40.95), (-91.30, 42.10))  # fallback: north-south through the strongest cell\n",
     "\n",
@@ -354,7 +575,7 @@
     "---\n",
     "\n",
     "That is the whole pipeline: **download → archive → open → plot → cut**. The\n",
-    "archive now holds three more radars, and everything the other notebooks do —\n",
+    "archive now holds two more radars, and everything the other notebooks do —\n",
     "filters, label selection, the other three crops, RHI, CAPPI — applies to them\n",
     "unchanged.\n",
     "\n",
@@ -382,6 +603,1153 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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diff --git a/tutorial/README.md b/tutorial/README.md
index a67e311..cb9ff73 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -3,27 +3,29 @@
 Five notebooks that walk through the whole workflow, in order. Each one is
 self-contained.
 
-| #   | notebook                                                | covers                                                                                                     |
-| --- | ------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
-| 1   | [Archiving](01_archiving.ipynb)                         | the storage model, the CRS contract, `archive()`, what lands on disk                                       |
-| 2   | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters                                                |
-| 3   | [Areas of interest](03_area_of_interest.ipynb)          | bbox / point / polygon crops, cross-sections, the interactive map                                          |
-| 4   | [Plots](04_plots.ipynb)                                 | PPI, RHI, CAPPI, vertical cross-section                                                                    |
-| 5   | [Demo pipeline](05_demo_pipeline.ipynb)                 | the whole pipeline on data it downloads itself — NEXRAD, FMI and IDEAM volumes, archived, plotted, cropped |
+| #   | notebook                                                | covers                                                                                              |
+| --- | ------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
+| 1   | [Archiving](01_archiving.ipynb)                         | the storage model, the CRS contract, `archive()`, what lands on disk                                |
+| 2   | [Opening and filtering](02_opening_and_filtering.ipynb) | `open()`, `filter()`, `sel()`, computed columns, converters                                         |
+| 3   | [Areas of interest](03_area_of_interest.ipynb)          | bbox / point / polygon crops, cross-sections, the interactive map                                   |
+| 4   | [Plots](04_plots.ipynb)                                 | PPI, RHI, CAPPI, vertical cross-section                                                             |
+| 5   | [Demo pipeline](05_demo_pipeline.ipynb)                 | the whole pipeline on data it downloads itself — NEXRAD and FMI volumes, archived, plotted, cropped |
 
 ## Running them yourself
 
 RadDB is network-agnostic — any xarray `DataTree` with the standard
 [xradar](https://docs.openradarscience.org/projects/xradar/) layout works. The
-notebooks use NEXRAD volumes stored as Zarr. Point them at your own
-data by editing the configuration cell at the top of each notebook:
+notebooks use Finnish (FMI) and US (NEXRAD) volumes stored as Zarr — both are
+open data. Point them at your own data by editing the configuration cell at the
+top of each notebook:
 
 ```python
+FMI_DIR = Path("/path/to/FMI_datatree_zarr")  # radars FANJ, FKOR, FKUO
 NEXRAD_DIR = Path("/path/to/NEXRAD_datatree_zarr")  # radars KTLX, KMLB, KLOT
 ARCHIVE_DIR = Path("/tmp/raddb_tutorial_archive")  # where to write
 ```
 
 Then run notebook 1 first — it creates the archive the others read.
 
-Notebook 5 needs no local data at all: it downloads three public volumes (US,
-Finland, Colombia) over HTTP and adds them to the same archive.
+Notebook 5 needs no local data at all: it downloads two public volumes (US,
+Finland) over HTTP and adds them to the same archive.