From 3f41659ad9f328ea0b5002aa8dcfbb2b29e1c3a3 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Mon, 11 May 2026 10:17:44 -0500 Subject: [PATCH 01/17] uniform masking and bad photometry fill --- frb/surveys/catalog_utils.py | 245 +++++++++++++++++++++++++++++++---- 1 file changed, 222 insertions(+), 23 deletions(-) diff --git a/frb/surveys/catalog_utils.py b/frb/surveys/catalog_utils.py index e4acc916..550bf190 100644 --- a/frb/surveys/catalog_utils.py +++ b/frb/surveys/catalog_utils.py @@ -25,7 +25,84 @@ def clean_heasarc(catalog): catalog[key].unit = units.deg -def clean_cat(catalog, pdict, fill_mask=None): +def _is_numeric_column(column): + """Return True if astropy column stores numeric values.""" + dtype = getattr(column, 'dtype', None) + return dtype is not None and np.issubdtype(dtype, np.number) + + +def _masked_copy_if_needed(catalog): + """Return masked table copy only when masking support is needed.""" + if getattr(catalog, 'masked', False): + return catalog.copy() + return Table(catalog, masked=True, copy=True) + + +def _photom_key_pairs(pdict): + """Map photometric columns to paired error columns using renamed keys.""" + photom_pairs = {} + excluded_exact = {'ra', 'dec', 'ebv', 'photo_z', 'photo_z_err', 'z'} + excluded_prefixes = ('z_spec',) + excluded_tokens = ('class', 'type', 'flag', 'survey', 'brick', 'tile', + 'field', 'ellipticity', 'radius', 'area', 'extendedness') + for key in pdict.keys(): + if key in excluded_exact or key.endswith('_ID'): + continue + if key.endswith('_err'): + continue + if key.startswith(excluded_prefixes): + continue + if any(token in key for token in excluded_tokens): + continue + err_key = f'{key}_err' + photom_pairs[key] = err_key if err_key in pdict else None + return photom_pairs + + +def _mask_bad_photometry(catalog, pdict, fill_mask=-99.0): + """Mask bad photometry and paired errors, then fill with sentinel.""" + if len(catalog) == 0: + return catalog + + photom_pairs = _photom_key_pairs(pdict) + if not photom_pairs: + return catalog + + masked_catalog = None + touched = False + + for phot_key, err_key in photom_pairs.items(): + if phot_key not in catalog.colnames: + continue + if not _is_numeric_column(catalog[phot_key]): + continue + + phot_values = np.asarray(catalog[phot_key], dtype=float) + phot_bad = ~np.isfinite(phot_values) + phot_bad |= (phot_values < 0) | (phot_values > 30) + + err_bad = np.zeros(len(catalog), dtype=bool) + if err_key is not None and err_key in catalog.colnames and _is_numeric_column(catalog[err_key]): + err_values = np.asarray(catalog[err_key], dtype=float) + err_bad = ~np.isfinite(err_values) + err_bad |= (err_values < 0) | (err_values > 5) + combined_bad = phot_bad | err_bad + if not np.any(combined_bad): + continue + + if masked_catalog is None: + masked_catalog = _masked_copy_if_needed(catalog) + masked_catalog[phot_key].mask = np.asarray(masked_catalog[phot_key].mask) | combined_bad + if err_key is not None and err_key in masked_catalog.colnames and _is_numeric_column(masked_catalog[err_key]): + masked_catalog[err_key].mask = np.asarray(masked_catalog[err_key].mask) | combined_bad + touched = True + + if not touched: + return catalog + return masked_catalog.filled(fill_mask) + + +def clean_cat(catalog, pdict, fill_mask=-99.0, mask_photometry=False, ): """ Convert table column names intrinsic to the slurped catalog with the FRB survey desired values @@ -33,7 +110,9 @@ def clean_cat(catalog, pdict, fill_mask=None): Args: catalog (astropy.table.Table): Catalog generated by astroquery pdict (dict): Defines the original key and desired key - fill_mask (int or float, optional): Fill masked items with this value + fill_mask (int or float, optional): Fill value for masked items + mask_photometry (bool, optional): Mask invalid photometric values and + paired errors before returning the catalog. Returns: astropy.table.Table: modified catalog @@ -42,10 +121,106 @@ def clean_cat(catalog, pdict, fill_mask=None): for key,value in pdict.items(): if value in catalog.keys(): catalog.rename_column(value, key) - # Mask - if fill_mask is not None: - if catalog.mask is not None: - catalog = catalog.filled(fill_mask) + if mask_photometry: + catalog = _mask_bad_photometry(catalog, pdict, fill_mask=fill_mask) + return catalog + + +def ensure_empty_schema(catalog, columns): + """Ensure empty catalogs keep a standardized column schema. + + This mirrors the 2MASS pattern where the output remains an empty table + with survey-standard columns instead of becoming columnless. + """ + if len(catalog) != 0: + return catalog + + for col in columns: + if col not in catalog.colnames: + catalog[col] = np.array([], dtype=float) + catalog.keep_columns(list(columns)) + return catalog + + +def normalize_catalog(catalog, coord, radius, survey_name, radec=None, add_sep=True): + """ + Normalize survey catalog to enforce uniform output contract. + + Ensures: + - Lowercase 'ra', 'dec' coordinate columns + - Required metadata keys: 'radius', 'survey' + - 'separation' column in arcmin (for non-empty catalogs) + + Args: + catalog (astropy.table.Table): Input catalog (may be empty) + coord (astropy.coordinates.SkyCoord): Reference coordinate + radius (astropy.units.Quantity): Search radius + survey_name (str): Survey name for metadata + radec (tuple, optional): Original RA/DEC column names. If None, auto-detects. + add_sep (bool, optional): Add separation column for non-empty catalog + + Returns: + astropy.table.Table: Normalized catalog + + """ + # Empty catalogs may legitimately have no coordinate columns. + # Set required metadata and optional empty separation, then return. + if len(catalog) == 0: + catalog.meta['survey'] = survey_name + catalog.meta['radius'] = radius + if add_sep and 'separation' not in catalog.colnames: + catalog['separation'] = units.Quantity([], unit=units.arcmin) + return catalog + + # Auto-detect RA/DEC column names if not provided + if radec is None: + # Try common variations + ra_col = None + dec_col = None + for candidate in ['ra', 'RA', 'Ra', 'rA', 'RAJ2000', 'ALPHAJ_2000']: + if candidate in catalog.colnames: + ra_col = candidate + break + for candidate in ['dec', 'DEC', 'Dec', 'dEC', 'DECJ2000', 'DEJ2000', 'DELTAJ_2000']: + if candidate in catalog.colnames: + dec_col = candidate + break + + if ra_col is None or dec_col is None: + raise ValueError("Cannot find RA/DEC columns in catalog. Please specify radec parameter.") + radec = (ra_col, dec_col) + + # Rename RA/DEC to lowercase if needed + if radec[0] != 'ra' and radec[0] in catalog.colnames: + catalog.rename_column(radec[0], 'ra') + if radec[1] != 'dec' and radec[1] in catalog.colnames: + catalog.rename_column(radec[1], 'dec') + + # Ensure ra, dec exist after normalization + if 'ra' not in catalog.colnames or 'dec' not in catalog.colnames: + raise ValueError("Catalog must have 'ra' and 'dec' columns after normalization") + + # Set required metadata + catalog.meta['survey'] = survey_name + catalog.meta['radius'] = radius + + # Add separation for non-empty catalogs + if len(catalog) > 0 and add_sep: + if 'separation' not in catalog.colnames: + catalog = sort_by_separation(catalog, coord, radec=('ra', 'dec'), add_sep=True) + # Ensure separation has arcmin units; handle legacy/unitless columns. + sep_unit = getattr(catalog['separation'], 'unit', None) + if sep_unit is None: + catalog['separation'] = units.Quantity(np.asarray(catalog['separation']), unit=units.arcmin) + elif units.Unit(sep_unit).is_equivalent(units.arcmin): + catalog['separation'] = catalog['separation'].to(units.arcmin) + else: + # Incompatible legacy values: recompute from coordinates. + catalog = sort_by_separation(catalog, coord, radec=('ra', 'dec'), add_sep=True) + elif len(catalog) == 0 and add_sep: + # For empty catalogs, add empty separation column to maintain consistency + catalog['separation'] = units.Quantity([], unit=units.arcmin) + return catalog @@ -100,7 +275,7 @@ def match_ids(IDs, match_IDs, require_in_match=True): """ rows = -1 * np.ones_like(IDs).astype(int) # Find which IDs are in match_IDs - in_match = np.in1d(IDs, match_IDs) + in_match = np.isin(IDs, match_IDs) if require_in_match: if np.sum(~in_match) > 0: raise IOError("qcat.match_ids: One or more input IDs not in match_IDs") @@ -207,14 +382,44 @@ def xmatch_catalogs(cat1:Table, cat2:Table, dist:units.Quantity = 5*units.arcsec do_3d = (distcol1 is not None)&(distcol2 is not None) if do_3d: assert (distcol1 in cat1.colnames)&(distcol2 in cat2.colnames), "Could not find either {:s} or {:s} in cat1".format(distcol1, distcol2) - dist1 = cat1[distcol1]*units.Mpc - dist2 = cat2[distcol2]*units.Mpc + dist1 = cat1[distcol1] + dist2 = cat2[distcol2] + if getattr(dist1, 'unit', None) is None: + dist1 = dist1 * units.Mpc + else: + dist1 = dist1.to(units.Mpc) + if getattr(dist2, 'unit', None) is None: + dist2 = dist2 * units.Mpc + else: + dist2 = dist2.to(units.Mpc) else: dist1 = None dist2 = None - # Get corodinates - cat1_coord = SkyCoord(cat1[RACol1]*units.deg, cat1[DecCol1]*units.deg, distance=dist1) - cat2_coord = SkyCoord(cat2[RACol2]*units.deg, cat2[DecCol2]*units.deg, distance=dist2) + # Get coordinates + ra1 = cat1[RACol1] + dec1 = cat1[DecCol1] + ra2 = cat2[RACol2] + dec2 = cat2[DecCol2] + + if getattr(ra1, 'unit', None) is None: + ra1 = ra1 * units.deg + else: + ra1 = ra1.to(units.deg) + if getattr(dec1, 'unit', None) is None: + dec1 = dec1 * units.deg + else: + dec1 = dec1.to(units.deg) + if getattr(ra2, 'unit', None) is None: + ra2 = ra2 * units.deg + else: + ra2 = ra2.to(units.deg) + if getattr(dec2, 'unit', None) is None: + dec2 = dec2 * units.deg + else: + dec2 = dec2.to(units.deg) + + cat1_coord = SkyCoord(ra1, dec1, distance=dist1) + cat2_coord = SkyCoord(ra2, dec2, distance=dist2) # Match 2D idx, d2d, d3d = cat1_coord.match_to_catalog_sky(cat2_coord) @@ -317,13 +522,13 @@ def _mags_to_flux(mag, zpt_flux:units.Quantity=3630.7805*units.Jy, mag_err=None, flux = mag.copy() # Convert fluxes - badmags = mag<-10 + badmags = (mag<0) | (mag>30) flux[badmags] = -99. flux[~badmags] = zpt_flux.value*10**(-mag[~badmags]/2.5) if mag_err is not None: flux_err = mag_err.copy() - baderrs = (mag_err < 0) | (mag_err == 999.) + baderrs = (mag_err < 0) | (mag_err > 5) flux_err[baderrs] = -99. if exact_mag_err: flux_err[~baderrs] = flux[~baderrs]*(10**(mag_err[~baderrs]/2.5)-1) # exact error @@ -370,7 +575,7 @@ def convert_mags_to_flux(photometry_table, fluxunits='mJy', exact_mag_err=False) exact_mag_err=exact_mag_err) # Allow for bad flux values - badflux = flux == -99. + badflux = flux == -99.0 fluxtable[mag][badflux] = flux[badflux] fluxtable[mag][~badflux] = flux[~badflux]*convert @@ -379,12 +584,6 @@ def convert_mags_to_flux(photometry_table, fluxunits='mJy', exact_mag_err=False) fluxtable[err][baderr] = flux_err[baderr] fluxtable[err][~baderr] = flux_err[~baderr]*convert - # Upper limits -- Record as 3sigma - # and set error to -99. - uplimit = photometry_table[err] == 999. - fluxtable[err][uplimit] = -99. #fluxtable[mag][uplimit] / 3. - fluxtable[mag][uplimit] = fluxtable[mag][uplimit] - return fluxtable def remove_duplicates(tab:Table, idcol:str)->Table: @@ -476,7 +675,7 @@ def xmatch_and_merge_cats(tab1:Table, tab2:Table, tol:units.Quantity=1*units.arc if (len(not_matched_tab1)!=0)&(len(not_matched_tab2)!=0): outer_join = join(not_matched_tab1, not_matched_tab2, keys=['ra','dec'], join_type='outer', table_names=table_names) - merged = vstack([inner_join, outer_join]).filled(999.) + merged = vstack([inner_join, outer_join]).filled(-99.) # Only table 1 has unmatched entries? elif (len(not_matched_tab1)!=0)&(len(not_matched_tab2)==0): merged = vstack([inner_join, not_matched_tab1]) @@ -491,7 +690,7 @@ def xmatch_and_merge_cats(tab1:Table, tab2:Table, tol:units.Quantity=1*units.arc if np.any(weird_cols): merged.remove_columns(np.array(['ra_1','dec_1','ra_2','dec_2'])[weird_cols]) # Fill and return. - return merged.filled(999.) + return merged.filled(-99.) ''' TODO: Write this function once CDS starts working again (through astroquery) From 97edca5fad5cd444aa1ea277d38975cfa99ef05f Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Mon, 11 May 2026 10:18:02 -0500 Subject: [PATCH 02/17] unit fix --- frb/surveys/cluster_search.py | 42 ++++++++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/frb/surveys/cluster_search.py b/frb/surveys/cluster_search.py index 1a74ce0b..12b6ad8f 100644 --- a/frb/surveys/cluster_search.py +++ b/frb/surveys/cluster_search.py @@ -44,7 +44,12 @@ def clean_catalog(self, catalog): def _transverse_distance_cut(self, catalog, transverse_distance_cut, distance_column='Dist'): # Apply a transverse distance cut angular_dist = self.coord.separation(SkyCoord(catalog['ra'], catalog['dec'], unit='deg')).to('rad').value - transverse_dist = catalog[distance_column]*np.sin(angular_dist) + radial_dist = catalog[distance_column] + if getattr(radial_dist, 'unit', None) is None: + radial_dist = radial_dist * u.Mpc + else: + radial_dist = radial_dist.to(u.Mpc) + transverse_dist = radial_dist * np.sin(angular_dist) catalog = catalog[transverse_dist=richness_cut] self.catalog = result + + # Normalize and validate + self.validate_catalog() return self.catalog @@ -187,6 +195,10 @@ def get_catalog(self, query_fields=None, result = super(TullyGroupCat, self)._transverse_distance_cut(result, transverse_distance_cut) result = result[result['Ngal']>=richness_cut] self.catalog = result + + # Normalize and validate + self.validate_catalog() + return self.catalog # Bahk and Hwang 2024 (Updated Planck+2015) @@ -240,6 +252,10 @@ def get_catalog(self, query_fields=None, if transverse_distance_cut Date: Mon, 11 May 2026 10:18:50 -0500 Subject: [PATCH 03/17] catalog clean up --- frb/surveys/decals.py | 6 ++-- frb/surveys/delve.py | 12 ++----- frb/surveys/des.py | 5 +-- frb/surveys/desi.py | 10 +++++- frb/surveys/dlsurvey.py | 24 ++++++++----- frb/surveys/euclid.py | 48 ++++++++++++++++++++++--- frb/surveys/galex.py | 4 +-- frb/surveys/heasarc.py | 30 ++++++++++++---- frb/surveys/hsc.py | 56 +++++++++++++++++++++++------ frb/surveys/nedlvs.py | 6 +++- frb/surveys/nsc.py | 12 ++----- frb/surveys/panstarrs.py | 77 ++++++++++++++++++++++++++++++---------- frb/surveys/psrcat.py | 2 +- frb/surveys/sdss.py | 8 +++-- frb/surveys/twomass.py | 2 +- frb/surveys/vista.py | 9 ++--- frb/surveys/wise.py | 18 ++++++++-- 17 files changed, 239 insertions(+), 90 deletions(-) diff --git a/frb/surveys/decals.py b/frb/surveys/decals.py index 8b452b0e..c9fa1cc1 100644 --- a/frb/surveys/decals.py +++ b/frb/surveys/decals.py @@ -85,6 +85,8 @@ def get_catalog(self, query=None, query_fields=None, print_query=False,exclude_s print_query=print_query,**kwargs) main_cat = Table(main_cat,masked=True) if len(main_cat)==0: + main_cat = catalog_utils.clean_cat(main_cat, photom['DECaL'], mask_photometry=True) + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['DECaL'].keys())) return main_cat # for col in main_cat.colnames: @@ -102,8 +104,6 @@ def get_catalog(self, query=None, query_fields=None, print_query=False,exclude_s for col in snr_cols: main_cat[col].mask = main_cat[col]<0 main_cat[col] = 2.5*np.log10(1+1/main_cat[col]) - - main_cat = main_cat.filled(-99.0) #Remove gaia objects if necessary if exclude_stars and 'type' in main_cat.colnames: self.catalog = main_cat[main_cat['DECaL_type']=='PSF'] @@ -113,7 +113,7 @@ def get_catalog(self, query=None, query_fields=None, print_query=False,exclude_s else: self.catalog = main_cat # Clean - main_cat = catalog_utils.clean_cat(main_cat, photom['DECaL']) + main_cat = catalog_utils.clean_cat(main_cat, photom['DECaL'], mask_photometry=True) self.validate_catalog() # Return return self.catalog diff --git a/frb/surveys/delve.py b/frb/surveys/delve.py index e371a5f1..bf08a19e 100644 --- a/frb/surveys/delve.py +++ b/frb/surveys/delve.py @@ -1,7 +1,5 @@ """DELVE survey""" -import numpy as np - from frb.surveys import dlsurvey, defs from frb.surveys import catalog_utils @@ -72,14 +70,10 @@ def get_catalog(self, query=None, query_fields=None, print_query=False,**kwargs) query_fields=query_fields, print_query=print_query,**kwargs) if len(main_cat) == 0: - main_cat = catalog_utils.clean_cat(main_cat,photom['DELVE']) + main_cat = catalog_utils.clean_cat(main_cat, photom['DELVE'], mask_photometry=True) + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['DELVE'].keys())) return main_cat - main_cat = catalog_utils.clean_cat(main_cat, photom['DELVE']) - #import pdb; pdb.set_trace() - for col in main_cat.colnames: - if main_cat[col].dtype==float: - mask = np.isnan(main_cat[col])+(main_cat[col]==99.99) - main_cat[col] = np.where(~mask, main_cat[col], -999.0) + main_cat = catalog_utils.clean_cat(main_cat, photom['DELVE'], mask_photometry=True) # Finish self.catalog = main_cat diff --git a/frb/surveys/des.py b/frb/surveys/des.py index 9b47edcc..433e6b01 100644 --- a/frb/surveys/des.py +++ b/frb/surveys/des.py @@ -73,9 +73,10 @@ def get_catalog(self, query=None, query_fields=None, query_fields=query_fields, print_query=print_query,**kwargs) if len(main_cat) == 0: - main_cat = catalog_utils.clean_cat(main_cat,photom['DES']) + main_cat = catalog_utils.clean_cat(main_cat, photom['DES'], mask_photometry=True) + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['DES'].keys())) return main_cat - main_cat = catalog_utils.clean_cat(main_cat, photom['DES']) + main_cat = catalog_utils.clean_cat(main_cat, photom['DES'], mask_photometry=True) ## Finish self.catalog = main_cat self.validate_catalog() diff --git a/frb/surveys/desi.py b/frb/surveys/desi.py index 4107d4b9..77d670f9 100644 --- a/frb/surveys/desi.py +++ b/frb/surveys/desi.py @@ -74,6 +74,8 @@ def get_catalog(self, query=None, query_fields=None, print_query=False, print_query=print_query, photomdict=spectrom['DESI'],**kwargs) main_cat = Table(main_cat,masked=True) if len(main_cat)==0: + main_cat = catalog_utils.clean_cat(main_cat, spectrom['DESI']) + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(spectrom['DESI'].keys())) return main_cat # for col in main_cat.colnames: @@ -102,5 +104,11 @@ def get_catalog(self, query=None, query_fields=None, print_query=False, elif zcat_primary_only and 'DESI_zcat_primary' not in self.catalog.colnames: print("Warning: 'DESI_zcat_primary' not stored in catalog, cannot filter by zcat_primary.") + # Normalize and validate + self.validate_catalog() + # Return - return self.catalog \ No newline at end of file + return self.catalog + + def get_image(self, **kwargs): + raise NotImplementedError("Cutout retrieval not implemented for DESI. This class is meant to purely retreive spectroscopic data. For imaging, use the DeCAL_Survey class instead.") \ No newline at end of file diff --git a/frb/surveys/dlsurvey.py b/frb/surveys/dlsurvey.py index 05ca878c..75fb6313 100644 --- a/frb/surveys/dlsurvey.py +++ b/frb/surveys/dlsurvey.py @@ -178,6 +178,15 @@ def get_image(self, imsize, band, timeout=120, verbose=False): """ if self.svc is None: raise RuntimeError("svc attribute cannot be None. Have you installed pyvo?") + + if band is None: + if "r" in self.bands: + band = "r" + else: + # This is only true for VISTA. + # Get Y band. This is the first band in the list. + band = self.bands[0] + warnings.warn("Retrieving image in {:s} band".format(band)) if band.lower() not in self.bands and band not in self.bands: raise TypeError("Allowed filters (case-insensitive) for {:s} photometric bands are {}".format(self.survey,self.bands)) @@ -226,15 +235,14 @@ def get_cutout(self, imsize, band=None): ndarray, Header: cutout image, cutout image header """ - self.cutout_size = imsize - - if band is None: - if "r" in self.bands: - band = "r" - elif band is None: - band = self.bands[-1] - warnings.warn("Retrieving cutout in {:s} band".format(band)) + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; " + "use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + self.cutout_size = imsize img_hdu = self.get_image(imsize, band) if img_hdu is not None: self.cutout = img_hdu.data diff --git a/frb/surveys/euclid.py b/frb/surveys/euclid.py index fe0cfd87..1bdc623f 100644 --- a/frb/surveys/euclid.py +++ b/frb/surveys/euclid.py @@ -68,6 +68,23 @@ def _handler(signum, frame): photom['Euclid']['Euclid_kron_radius'] = 'kron_radius' photom['Euclid']['Euclid_segmentation_area'] = 'segmentation_area' +_EUCLID_FLUX_SCALE = 1e-6 / 3630.7805 + + +def _euclid_flux_to_abmag(flux_microjy, fluxerr_microjy): + """Convert Euclid microJy fluxes and errors to AB magnitudes.""" + flux = np.asarray(flux_microjy, dtype=float) + fluxerr = np.asarray(fluxerr_microjy, dtype=float) + mag = np.full(flux.shape, np.nan, dtype=float) + mag_err = np.full(flux.shape, np.nan, dtype=float) + + good_flux = np.isfinite(flux) & (flux > 0) + mag[good_flux] = -2.5 * np.log10(flux[good_flux] * _EUCLID_FLUX_SCALE) + + good_err = good_flux & np.isfinite(fluxerr) & (fluxerr > 0) + mag_err[good_err] = (2.5 / np.log(10.0)) * fluxerr[good_err] / flux[good_err] + return mag, mag_err + # Define the data model for Euclid spectroscopy (if available) spectrom = {} spectrom['Euclid'] = {} @@ -163,7 +180,9 @@ def get_catalog(self, query_fields=None, timeout=120, check_spectra=False): photom_catalog = job.get_results() if photom_catalog is None or len(photom_catalog) == 0: - self.catalog = Table() + self.catalog = catalog_utils.ensure_empty_schema( + Table(), list(photom['Euclid'].keys()) + ) self.catalog.meta['radius'] = self.radius self.catalog.meta['survey'] = self.survey if self.verbose: @@ -171,8 +190,16 @@ def get_catalog(self, query_fields=None, timeout=120, check_spectra=False): self.validate_catalog() return self.catalog + for band in Euclid_bands: + flux_key = photom['Euclid'][f'Euclid_{band}'] + err_key = photom['Euclid'][f'Euclid_{band}_err'] + if flux_key in photom_catalog.colnames and err_key in photom_catalog.colnames: + photom_catalog[flux_key], photom_catalog[err_key] = _euclid_flux_to_abmag( + photom_catalog[flux_key], photom_catalog[err_key] + ) + # Clean up catalog - rename columns to FRB standard names - self.catalog = catalog_utils.clean_cat(photom_catalog, photom['Euclid']) + self.catalog = catalog_utils.clean_cat(photom_catalog, photom['Euclid'], mask_photometry=True) # Add metadata self.catalog.meta['radius'] = self.radius @@ -190,7 +217,8 @@ def get_catalog(self, query_fields=None, timeout=120, check_spectra=False): has_spec = self.spectra_exist(euclid_ids) self.catalog['Euclid_has_spectrum'] = has_spec - return self.catalog + self.validate_catalog() + return self.catalog.copy() def spectra_exist(self, euclid_ids: list | np.ndarray | int): """ @@ -261,9 +289,9 @@ def get_spectrum(self, euclid_id, output_folder=None, timeout=120): print(f"Spectrum retrieval failed for Euclid ID {euclid_id}: {e}") return None, None - def get_cutout(self, imsize=None, output_file=None, verbose=None, timeout=120): + def get_image(self, imsize=None, output_file=None, verbose=None, timeout=120): """ - Get a cutout of a Euclid MER background-subtracted mosaic image. + Get a FITS image cutout of a Euclid MER background-subtracted mosaic image. Queries the mosaic_product table to find MER background-subtracted mosaics covering the target region, then retrieves a cutout. @@ -362,3 +390,13 @@ def get_cutout(self, imsize=None, output_file=None, verbose=None, timeout=120): self.cutout_size = imsize return self.cutout, self.cutout_hdr + + def get_cutout(self, imsize=None, output_file=None, verbose=None, timeout=120): + """Deprecated alias for get_image().""" + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; " + "use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.get_image(imsize=imsize, output_file=output_file, verbose=verbose, timeout=timeout) diff --git a/frb/surveys/galex.py b/frb/surveys/galex.py index f755a885..ef60ae8a 100644 --- a/frb/surveys/galex.py +++ b/frb/surveys/galex.py @@ -67,7 +67,7 @@ def get_catalog(self,query_fields=None, print_query=False): data = {} data['ra'] = self.coord.ra.value data['dec'] = self.coord.dec.value - data['radius'] = self.radius.to(u.deg).value + data['radius'] = self.radius.to("deg").value data['columns'] = query_fields data['format'] = 'csv' @@ -76,7 +76,7 @@ def get_catalog(self,query_fields=None, print_query=False): pdict = photom['GALEX'].copy() - photom_catalog = catalog_utils.clean_cat(ret,pdict) # rename columns + photom_catalog = catalog_utils.clean_cat(ret, pdict, mask_photometry=True) # rename columns photom_catalog.keep_columns(list(pdict.keys())) # Keep only the columns we care about diff --git a/frb/surveys/heasarc.py b/frb/surveys/heasarc.py index 886473f5..9dd65bd2 100644 --- a/frb/surveys/heasarc.py +++ b/frb/surveys/heasarc.py @@ -1,5 +1,7 @@ """ Surveys to be accessed through the HEASARC interface (via astroquery""" +import warnings + from astropy.table import Table from astropy import units, wcs @@ -43,16 +45,17 @@ def get_catalog(self): """ try: catalog = self.heasarc.query_region(self.coord, - mission=self.mission, + catalog=self.mission, radius=self.radius) except (ValueError, TypeError): # No table found - self.catalog = Table() + self.catalog = catalog_utils.ensure_empty_schema(Table(), ['ra', 'dec']) else: # Clean if len(catalog)!=0: - - catalog.rename_column("RA", "ra") - catalog.rename_column("DEC", "dec") + if "RA" in catalog.colnames: + catalog.rename_column("RA", "ra") + if "DEC" in catalog.colnames: + catalog.rename_column("DEC", "dec") for key in ['ra', 'dec']: catalog[key].unit = units.deg # Sort @@ -61,6 +64,8 @@ def get_catalog(self): radec=('ra', 'dec')) else: self.catalog = catalog + if len(self.catalog) == 0: + self.catalog = catalog_utils.ensure_empty_schema(self.catalog, ['ra', 'dec']) # Add meta, etc. self.catalog.meta['radius'] = self.radius self.catalog.meta['survey'] = self.survey @@ -89,7 +94,7 @@ def __init__(self, coord, radius, mission, **kwargs): # Instantiate astroquery object self.skyview = SkyView() - def get_cutout(self, radius=None): + def get_image(self, radius=None): radius = radius if radius is not None else self.radius self.cutout_size = 2*radius @@ -114,6 +119,19 @@ def get_cutout(self, radius=None): print("Got image spanning (RA, Dec) = ({0} - {1}, {2} - {3})" .format(ra0, ra1, dec0, dec1)) + return img_hdu + + def get_cutout(self, radius=None): + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; " + "use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + img_hdu = self.get_image(radius=radius) + self.cutout = img_hdu.data + self.cutout_hdr = img_hdu.header + return self.cutout def get_first(self, radius): diff --git a/frb/surveys/hsc.py b/frb/surveys/hsc.py index 25a19d0a..effb883f 100644 --- a/frb/surveys/hsc.py +++ b/frb/surveys/hsc.py @@ -5,6 +5,7 @@ import sys import csv import os +import warnings from io import StringIO from . import surveycoord from . import catalog_utils @@ -50,9 +51,9 @@ def __init__(self, coord, radius, **kwargs): self.data_release = 'pdr3' - def get_catalog(self, query_fields=None, query=None, max_time=120, + def get_catalog(self, query_fields=None, query=None, timeout=120, print_query=False, query_table='pdr3_wide.summary', - photoz_table = 'mizuki'): + photoz_table='mizuki', **kwargs): """ Query HSC for all objects within a given radius of the input coordinates. @@ -65,7 +66,7 @@ def get_catalog(self, query_fields=None, query=None, max_time=120, query: str, optional Full query as a string to be passed to the database. Overrides the default query. - max_time: float, optional + timeout: float, optional The maximum time interval to wait between query status checks. Defaults to 120s. print_query: bool, optional Print the SQL query for the photo-z values @@ -80,6 +81,18 @@ def get_catalog(self, query_fields=None, query=None, max_time=120, source, with unique objid values """ + if 'max_time' in kwargs: + warnings.warn( + "'max_time' is deprecated; use 'timeout' instead.", + DeprecationWarning, + stacklevel=2, + ) + if timeout != 120: + raise TypeError("Specify only one of 'timeout' or deprecated 'max_time'.") + timeout = kwargs.pop('max_time') + if kwargs: + raise TypeError(f"Unexpected keyword arguments: {list(kwargs.keys())}") + if query_fields is None: query_fields = list(photom['HSC'].values()) # Call @@ -99,10 +112,10 @@ def get_catalog(self, query_fields=None, query=None, max_time=120, print(query) # SQL command - query_cat = run_query(query, max_time=max_time, + query_cat = run_query(query, timeout=timeout, release_version=self.data_release, delete_job=True) - catalog = catalog_utils.clean_cat(query_cat, photom['HSC']) + catalog = catalog_utils.clean_cat(query_cat, photom['HSC'], mask_photometry=True) self.catalog = catalog_utils.sort_by_separation(catalog, self.coord, radec=('ra','dec'), add_sep=True) @@ -125,7 +138,8 @@ def run_query(query:str, preview:bool=False, out_format:str='csv', delete_job:bool=False, - max_time:int=120 + timeout:int=120, + max_time:int=None ): """ Submits a query to the HSC database and downloads the results in the specified format. @@ -138,7 +152,7 @@ def run_query(query:str, preview (bool, optional): Whether to use quick mode (short timeout). Defaults to False. out_format (str, optional): The format in which to download the query results. Defaults to 'csv'. delete_job (bool, optional): Whether to delete the job after downloading the results. Defaults to False. - max_time (int, optional): The maximum time interval to wait for checking query status. Defaults to 120s. + timeout (int, optional): The maximum time interval to wait for checking query status. Defaults to 120s. Raises: @@ -153,6 +167,16 @@ def run_query(query:str, credential = {'account_name': user, 'password': password} sql = query + if max_time is not None: + warnings.warn( + "'max_time' is deprecated; use 'timeout' instead.", + DeprecationWarning, + stacklevel=2, + ) + if timeout != 120: + raise TypeError("Specify only one of 'timeout' or deprecated 'max_time'.") + timeout = max_time + job = None try: @@ -163,7 +187,7 @@ def run_query(query:str, out_format=out_format, release_version=release_version) blockUntilJobFinishes(credential, job['id'], - max_time=max_time) + timeout=timeout) res = download(credential, job['id']) pseudo_file = StringIO(res.read().decode('utf-8').split("# ")[1]) table = Table.from_pandas(read_csv(pseudo_file)).filled(-99.) @@ -245,7 +269,17 @@ def preview(credential, sql, out, release_version:str="pdr3"): raise QueryError('only top %d records are displayed !' % len(result['result']['rows'])) -def blockUntilJobFinishes(credential, job_id, max_time=120): +def blockUntilJobFinishes(credential, job_id, timeout=120, max_time=None): + if max_time is not None: + warnings.warn( + "'max_time' is deprecated; use 'timeout' instead.", + DeprecationWarning, + stacklevel=2, + ) + if timeout != 120: + raise TypeError("Specify only one of 'timeout' or deprecated 'max_time'.") + timeout = max_time + interval = 1 while True: time.sleep(interval) @@ -255,8 +289,8 @@ def blockUntilJobFinishes(credential, job_id, max_time=120): if job['status'] == 'done': break interval *= 2 - if interval > max_time: - interval = max_time + if interval > timeout: + interval = timeout def download(credential, job_id): diff --git a/frb/surveys/nedlvs.py b/frb/surveys/nedlvs.py index 6d81dd0c..28df53f2 100644 --- a/frb/surveys/nedlvs.py +++ b/frb/surveys/nedlvs.py @@ -35,7 +35,7 @@ def __init__(self, coord, radius=90.*u.deg, cosmo=None, **kwargs): # Set redshift distances using the cosmology of choice redshift_dist_sources = self.datatab['DistMpc_method']=='Redshift' - self.datatab['DistMpc'][redshift_dist_sources] = self.cosmo.luminosity_distance(self.datatab['z'][redshift_dist_sources]).to('Mpc').value + self.datatab['DistMpc'][redshift_dist_sources] = self.cosmo.luminosity_distance(self.datatab['z'][redshift_dist_sources]) self.datatab['phys_sep'] = self.datatab['DistMpc']*u.Mpc*np.sin(self.datatab['ang_sep'].to('rad').value) def get_column_names(self): @@ -78,4 +78,8 @@ def get_catalog(self, z_lim=np.inf, close_by = self.datatab[is_nearby_fg][query_fields] self.catalog = close_by + + # Normalize and validate (preserves ang_sep/phys_sep, adds canonical separation if needed) + self.validate_catalog() + return self.catalog \ No newline at end of file diff --git a/frb/surveys/nsc.py b/frb/surveys/nsc.py index b2be1959..5b1b24aa 100644 --- a/frb/surveys/nsc.py +++ b/frb/surveys/nsc.py @@ -1,7 +1,5 @@ """NOIRLab source catalog""" -import numpy as np - from frb.surveys import dlsurvey, defs from frb.surveys import catalog_utils @@ -68,14 +66,10 @@ def get_catalog(self, query=None, query_fields=None, print_query=False,**kwargs) query_fields=query_fields, print_query=print_query,**kwargs) if len(main_cat) == 0: - main_cat = catalog_utils.clean_cat(main_cat,photom['NSC']) + main_cat = catalog_utils.clean_cat(main_cat, photom['NSC'], mask_photometry=True) + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['NSC'].keys())) return main_cat - main_cat = catalog_utils.clean_cat(main_cat, photom['NSC']) - #import pdb; pdb.set_trace() - for col in main_cat.colnames: - if main_cat[col].dtype==float: - mask = np.isnan(main_cat[col])+(main_cat[col]==99.99) - main_cat[col] = np.where(~mask, main_cat[col], -999.0) + main_cat = catalog_utils.clean_cat(main_cat, photom['NSC'], mask_photometry=True) # Finish self.catalog = main_cat diff --git a/frb/surveys/panstarrs.py b/frb/surveys/panstarrs.py index 6204341f..50d46796 100644 --- a/frb/surveys/panstarrs.py +++ b/frb/surveys/panstarrs.py @@ -114,7 +114,9 @@ def get_catalog(self,query_fields=None,release="dr2", ret = requests.get(url,params=data) ret.raise_for_status() if len(ret.text)==0: - self.catalog = Table() + self.catalog = catalog_utils.ensure_empty_schema( + Table(), list(photom['Pan-STARRS'].keys()) + ) self.catalog.meta['radius'] = self.radius self.catalog.meta['survey'] = self.survey # Validate @@ -129,7 +131,7 @@ def get_catalog(self,query_fields=None,release="dr2", pdict["Pan-STARRS"+'_{:s}'.format(band)] = '{:s}PSFmag'.format(band.lower()) pdict["Pan-STARRS"+'_{:s}_err'.format(band)] = '{:s}PSFmagErr'.format(band.lower()) - photom_catalog = catalog_utils.clean_cat(photom_catalog,pdict) + photom_catalog = catalog_utils.clean_cat(photom_catalog, pdict, mask_photometry=True) #Remove bad positions because Pan-STARRS apparently decided #to flag some positions with large negative numbers. Why even keep @@ -180,35 +182,48 @@ def get_catalog(self,query_fields=None,release="dr2", #Return return self.catalog.copy() - def get_cutout(self,imsize=30*u.arcsec,filt="irg",output_size=None): + def get_cutout(self, imsize=30*u.arcsec, band="irg", output_size=None, **kwargs): """ Grab a color cutout (PNG) from Pan-STARRS Args: imsize (Quantity): Angular size of image desired - filt (str): A string with the three filters to be used + band (str): A string with the three filters to be used output_size (int): Output image size in pixels. Defaults to the original cutout size. Returns: PNG image, None (None for the header). """ - assert len(filt)==3, "Need three filters for a cutout." + if 'filt' in kwargs: + warnings.warn( + "'filt' is deprecated; use 'band' instead.", + DeprecationWarning, + stacklevel=2, + ) + if band != "irg": + raise TypeError("Specify only one of 'band' or deprecated 'filt'.") + band = kwargs.pop('filt') + if kwargs: + raise TypeError(f"Unexpected keyword arguments: {list(kwargs.keys())}") + + assert len(band) == 3, "Need three filters for a cutout." #Sort filters from red to blue - filt = filt.lower() #Just in case the user is cheeky about the filter case. + band = band.lower() #Just in case the user is cheeky about the filter case. reffilt = "yzirg" - idx = np.argsort([reffilt.find(f) for f in filt]) - newfilt = "" + idx = np.argsort([reffilt.find(f) for f in band]) + newband = "" for i in idx: - newfilt += filt[i] + newband += band[i] #Get image url - url = _get_url(self.coord,imsize=imsize,filt=newfilt,output_size=output_size,color=True,imgformat='png') + url = _get_url(self.coord, imsize=imsize, band=newband, output_size=output_size, + color=True, imgformat='png') self.cutout = images.grab_from_url(url) self.cutout_size = imsize return self.cutout.copy(), - def get_image(self,imsize=30*u.arcsec,filt="i",timeout=120): + def get_image(self, imsize=30*u.arcsec, band="i", timeout=120, **kwargs): """ Grab a fits image from Pan-STARRS in a specific band. @@ -216,40 +231,64 @@ def get_image(self,imsize=30*u.arcsec,filt="i",timeout=120): Args: imsize (Quantity): Angular size of the image desired - filt (str): One of 'g','r','i','z','y' (default: 'i') + band (str): One of 'g','r','i','z','y' (default: 'i') timeout (int): Number of seconds to timout the query (default: 120 s) Returns: hdu: fits header data unit for the downloaded image """ - assert len(filt)==1 and filt in "grizy", "Filter name must be one of 'g','r','i','z','y'" - url = _get_url(self.coord,imsize=imsize,filt=filt,imgformat='fits')[0] + if 'filt' in kwargs: + warnings.warn( + "'filt' is deprecated; use 'band' instead.", + DeprecationWarning, + stacklevel=2, + ) + if band != "i": + raise TypeError("Specify only one of 'band' or deprecated 'filt'.") + band = kwargs.pop('filt') + if kwargs: + raise TypeError(f"Unexpected keyword arguments: {list(kwargs.keys())}") + + assert len(band) == 1 and band in "grizy", "Filter name must be one of 'g','r','i','z','y'" + url = _get_url(self.coord, imsize=imsize, band=band, imgformat='fits')[0] imagedat = fits.open(astroutils.data.download_file(url,cache=True,show_progress=False,timeout=timeout))[0] return imagedat -def _get_url(coord,imsize=30*u.arcsec,filt="i",output_size=None,imgformat="fits",color=False): +def _get_url(coord, imsize=30*u.arcsec, band="i", output_size=None, imgformat="fits", color=False, **kwargs): """ Returns the url corresponding to the requested image cutout Args: coord (astropy SkyCoord): Center of the search area. imsize (astropy Angle): Length and breadth of the search area. - filt (str): 'g','r','i','z','y' + band (str): 'g','r','i','z','y' output_size (int): display image size (length) in pixels imgformat (str): "fits","png" or "jpg" """ assert imgformat in ['jpg','png','fits'], "Image file can be only in the formats 'jpg', 'png' and 'fits'." + if 'filt' in kwargs: + warnings.warn( + "'filt' is deprecated; use 'band' instead.", + DeprecationWarning, + stacklevel=2, + ) + if band != "i": + raise TypeError("Specify only one of 'band' or deprecated 'filt'.") + band = kwargs.pop('filt') + if kwargs: + raise TypeError(f"Unexpected keyword arguments: {list(kwargs.keys())}") + if color: - assert len(filt)==3,"Three filters are necessary for a color image" + assert len(band) == 3,"Three filters are necessary for a color image" assert imgformat in ['jpg','png'], "Color image not available in fits format" pixsize = int(imsize.to(u.arcsec).value/0.25) #0.25 arcsec per pixel service = "https://ps1images.stsci.edu/cgi-bin/ps1filenames.py" filetaburl = ("{:s}?ra={:f}&dec={:f}&size={:d}&format=fits" - "&filters={:s}").format(service,coord.ra.value, - coord.dec.value, pixsize,filt) + "&filters={:s}").format(service,coord.ra.value, + coord.dec.value, pixsize,band) file_extensions = Table.read(filetaburl, format='ascii')['filename'] url = "https://ps1images.stsci.edu/cgi-bin/fitscut.cgi?ra={:f}&dec={:f}&size={:d}&format={:s}".format(coord.ra.value,coord.dec.value, diff --git a/frb/surveys/psrcat.py b/frb/surveys/psrcat.py index 24b23036..3f371ce0 100644 --- a/frb/surveys/psrcat.py +++ b/frb/surveys/psrcat.py @@ -49,7 +49,7 @@ def get_catalog(self): gdp = pcoord.separation(self.coord) <= self.radius if not np.any(gdp): - self.catalog = Table() + self.catalog = catalog_utils.ensure_empty_schema(Table(), ['ra', 'dec']) else: catalog = pulsars[gdp] diff --git a/frb/surveys/sdss.py b/frb/surveys/sdss.py index 4185e015..463b32cf 100644 --- a/frb/surveys/sdss.py +++ b/frb/surveys/sdss.py @@ -85,12 +85,14 @@ def get_catalog(self, photoobj_fields=None, timeout=120, print_query=False): photom_catalog = SDSS.query_region(self.coord, radius=self.radius, timeout=timeout, photoobj_fields=photoobj_fields) if photom_catalog is None: - self.catalog = Table() + self.catalog = catalog_utils.ensure_empty_schema( + Table(), list(photom['SDSS'].keys()) + ) self.catalog.meta['radius'] = self.radius self.catalog.meta['survey'] = self.survey # Validate self.validate_catalog() - return + return self.catalog.copy() elif '' in photom_catalog.colnames[0]: raise RuntimeError("SDSS photometry query appears to have failed. Error message: {}".format(photom_catalog.colnames[0]+photom_catalog[0][0])) @@ -135,7 +137,7 @@ def get_catalog(self, photoobj_fields=None, timeout=120, print_query=False): trim_catalog = trim_down_catalog(photom_catalog, keep_photoz=True) # Clean up - trim_catalog = catalog_utils.clean_cat(trim_catalog, photom['SDSS']) + trim_catalog = catalog_utils.clean_cat(trim_catalog, photom['SDSS'], mask_photometry=True) # Spectral info spec_fields = ['ra', 'dec', 'z', 'run2d', 'plate', 'fiberID', 'mjd', 'instrument'] diff --git a/frb/surveys/twomass.py b/frb/surveys/twomass.py index dc2ed2a9..fb462167 100644 --- a/frb/surveys/twomass.py +++ b/frb/surveys/twomass.py @@ -93,7 +93,7 @@ def get_catalog(self,query_fields=None): pdict = photom['2MASS'].copy() - photom_catalog = catalog_utils.clean_cat(ret,pdict) # rename columns + photom_catalog = catalog_utils.clean_cat(ret, pdict, mask_photometry=True) # rename columns photom_catalog.keep_columns(list(pdict.keys())) # Keep only the columns we care about diff --git a/frb/surveys/vista.py b/frb/surveys/vista.py index 2ff1e8af..7f80b38e 100644 --- a/frb/surveys/vista.py +++ b/frb/surveys/vista.py @@ -124,13 +124,10 @@ def get_catalog(self, query=None, query_fields=None, print_query=False, system=' main_cat = super(VISTA_Survey, self).get_catalog(query=self.query, print_query=print_query, photomdict=photom['VISTA'],**kwargs) if len(main_cat) == 0: - main_cat = catalog_utils.clean_cat(main_cat,photom['VISTA']) + main_cat = catalog_utils.clean_cat(main_cat, photom['VISTA'], mask_photometry=True) + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['VISTA'].keys())) return main_cat - main_cat = catalog_utils.clean_cat(main_cat, photom['VISTA']) - for col in main_cat.colnames: - if main_cat[col].dtype==float: - mask = np.isnan(main_cat[col])+(main_cat[col]==99.99) - main_cat[col] = np.where(~mask, main_cat[col], -999.0) + main_cat = catalog_utils.clean_cat(main_cat, photom['VISTA'], mask_photometry=True) # Convert to AB mag if system == 'AB': #http://svo2.cab.inta-csic.es/svo/theory/fps3/index.php?mode=browse&gname=Paranal&gname2=VISTA diff --git a/frb/surveys/wise.py b/frb/surveys/wise.py index 4b99051f..dce9b81a 100644 --- a/frb/surveys/wise.py +++ b/frb/surveys/wise.py @@ -1,6 +1,7 @@ """WISE Survey""" import numpy as np +import warnings from astropy import units, io, utils from astropy.table import Table @@ -87,8 +88,9 @@ def get_catalog(self, query=None, query_fields=_DEFAULT_query_fields, main_cat = self.service.run_async(self.query).to_table() main_cat.meta['radius'] = self.radius main_cat.meta['survey'] = self.survey - main_cat = catalog_utils.clean_cat(main_cat, photom['WISE'], fill_mask=999.) + main_cat = catalog_utils.clean_cat(main_cat, photom['WISE'], mask_photometry=True) if len(main_cat) == 0: + main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['WISE'].keys())) return main_cat # Convert to AB mag @@ -110,9 +112,9 @@ def get_catalog(self, query=None, query_fields=_DEFAULT_query_fields, self.validate_catalog() return self.catalog.copy() - def get_cutout(self, imsize, band, timeout=120): + def get_image(self, imsize, band, timeout=120): """ - Download an image from IRSA + Download a FITS image from IRSA Args: @@ -137,6 +139,16 @@ def get_cutout(self, imsize, band, timeout=120): self.cutout = io.fits.open(utils.data.download_file(img_url,cache=True,show_progress=False,timeout=timeout))[0] self.cutout_size = imsize return self.cutout.copy() + + def get_cutout(self, imsize, band, timeout=120): + """Deprecated alias for get_image().""" + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; " + "use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.get_image(imsize=imsize, band=band, timeout=timeout) def _gen_cat_query(self,query_fields=_DEFAULT_query_fields): """ From c951c551bc11b2f93f60ed552a47360d6d01c571 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Mon, 11 May 2026 10:19:23 -0500 Subject: [PATCH 04/17] graceful query client error handling --- frb/surveys/survey_utils.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/frb/surveys/survey_utils.py b/frb/surveys/survey_utils.py index 8fa95b54..d5204abb 100644 --- a/frb/surveys/survey_utils.py +++ b/frb/surveys/survey_utils.py @@ -25,6 +25,10 @@ from astropy.table import Table, join from pyvo.dal import DALServiceError from requests import ReadTimeout, HTTPError +try: + from dl.queryClient import queryClientError +except Exception: + queryClientError = None import numpy as np import warnings @@ -136,6 +140,12 @@ def is_inside(surveyname:str, coord:SkyCoord)->bool: except HTTPError: warnings.warn("Couldn't reach MAST for PS1.", RuntimeWarning) cat = None + except Exception as e: + if queryClientError is not None and isinstance(e, queryClientError): + warnings.warn("Couldn't reach NOIRLAB DataLab.", RuntimeWarning) + cat = None + else: + raise # Are there any objects in the returned catalog? if cat is None or len(cat) == 0: return False @@ -236,6 +246,11 @@ def search_all_surveys(coord:SkyCoord, radius:u.Quantity, include_radio:bool=Fal survey.get_catalog() except (ConnectionError, HTTPError, QueryError): warnings.warn("Couldn't connect to {:s}. Skipping this for now.".format(surveyname), RuntimeWarning) + except Exception as e: + if queryClientError is not None and isinstance(e, queryClientError): + warnings.warn("Couldn't connect to {:s}. Skipping this for now.".format(surveyname), RuntimeWarning) + else: + raise # Did the survey return something? if (survey.catalog is not None): From fce3f51aefa7f449e013eadeed1bd5b5ec27b368 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Mon, 11 May 2026 10:19:55 -0500 Subject: [PATCH 05/17] stronger base catalog class requirements --- frb/surveys/surveycoord.py | 76 ++++++++++++++++++++++++++++++++------ 1 file changed, 65 insertions(+), 11 deletions(-) diff --git a/frb/surveys/surveycoord.py b/frb/surveys/surveycoord.py index 4be1c8a6..ceac2089 100644 --- a/frb/surveys/surveycoord.py +++ b/frb/surveys/surveycoord.py @@ -5,13 +5,22 @@ from frb.surveys import images from frb.surveys import survey_io +from frb.surveys import catalog_utils class SurveyCoord(object): """ - Parent class of surveying around an input coordinate + Parent class of surveying around an input coordinate. - See the children for specific methods + API semantics for survey children: + - ``get_catalog`` returns an astropy Table around ``coord`` within ``radius``. + - ``get_image`` returns FITS-like image products when survey provides them. + - ``get_cutout`` returns rendered products (e.g. PNG/JPEG) when available. + + Compatibility policy: + Surveys that only provide FITS image retrieval should expose that via + ``get_image``. If those surveys keep ``get_cutout`` for backward + compatibility, it should call ``get_image`` and emit ``DeprecationWarning``. Args: @@ -38,28 +47,73 @@ def __init__(self, coord, radius, verbose=False): def get_catalog(self): """ + Run survey catalog query. + Child classes should set and return ``self.catalog`` as an astropy Table. + Expected output contract for normalized survey catalogs: + - Coordinate columns: ``ra``, ``dec``. + - Metadata keys: ``radius``, ``survey``. + - Separation column: ``separation`` in arcmin. Returns: - self.catalog + astropy.table.Table: Survey catalog. """ pass def get_cutout(self, imsize): + """ + Retrieve rendered cutout product (e.g. PNG/JPEG). + + For FITS products, use ``get_image`` as canonical method. FITS-only + surveys may keep ``get_cutout`` as deprecated alias to ``get_image`` + for backward compatibility. + + Args: + imsize (Quantity): Angular size of desired cutout. + + Returns: + object or None: Rendered image-like product, depending on survey. + """ return None - def get_image(self, imsize, filter): + def get_image(self, imsize, band=None): + """ + Retrieve FITS-like image product. + + Args: + imsize (Quantity): Angular size of desired image. + band (str, optional): Filter/band identifier if required by survey. + + Returns: + object: FITS HDU or survey-specific FITS-like image product. + """ pass def validate_catalog(self): - if len(self.catalog) > 0: - # Columns - assert 'ra' in self.catalog.keys() - assert 'dec' in self.catalog.keys() - # Meta - assert 'radius' in self.catalog.meta.keys() - assert 'survey' in self.catalog.meta.keys() + """ + Validate and normalize catalog to enforce uniform output contract. + + Ensures: lowercase ra/dec, required metadata (radius, survey), + and separation column in arcmin for all survey catalogs. + """ + if self.catalog is None: + return + + # Normalize catalog to enforce uniform contract + self.catalog = catalog_utils.normalize_catalog( + self.catalog, + self.coord, + self.radius, + self.survey, + add_sep=True + ) + + # Validate minimum required structure + assert 'ra' in self.catalog.keys(), "Normalized catalog missing 'ra' column" + assert 'dec' in self.catalog.keys(), "Normalized catalog missing 'dec' column" + assert 'radius' in self.catalog.meta.keys(), "Catalog missing 'radius' metadata" + assert 'survey' in self.catalog.meta.keys(), "Catalog missing 'survey' metadata" def write_catalog(self, out_dir, ftype='ecsv', verbose=None, create_dirs=False, overwrite=True): From 183c1a2a1ba6374acaaedf0340a7c8e4851ef9f6 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Mon, 11 May 2026 10:20:18 -0500 Subject: [PATCH 06/17] handle deprecation warnings --- frb/tests/test_frbsurveys.py | 109 ++++++++++++++++++++++++----------- 1 file changed, 75 insertions(+), 34 deletions(-) diff --git a/frb/tests/test_frbsurveys.py b/frb/tests/test_frbsurveys.py index eba8b5f5..c3c98c8a 100644 --- a/frb/tests/test_frbsurveys.py +++ b/frb/tests/test_frbsurveys.py @@ -45,11 +45,16 @@ def test_wise(): assert isinstance(wise_tbl, Table) assert len(wise_tbl) == 1 - #Test get_image - imghdu = wise_srvy.get_cutout(imsize=search_r, band="W1") - assert isinstance(imghdu,PrimaryHDU) + # Test canonical FITS path + imghdu = wise_srvy.get_image(imsize=search_r, band="W1") + assert isinstance(imghdu, PrimaryHDU) assert imghdu.data.shape == (5,5) + # Deprecated compatibility alias + with pytest.warns(DeprecationWarning): + alias_hdu = wise_srvy.get_cutout(imsize=search_r, band="W1") + assert isinstance(alias_hdu, PrimaryHDU) + # THIS TEST IS NOW BROKEN ''' @remote_data @@ -77,8 +82,14 @@ def test_des(): assert isinstance(des_tbl, Table) assert len(des_tbl) == 2 - # Image - data, hdr = des_srvy.get_cutout(imsize=search_r, band="g") + # Canonical FITS path + imghdu = des_srvy.get_image(imsize=search_r, band="g") + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data.shape == (39,39) + + # Deprecated compatibility alias + with pytest.warns(DeprecationWarning): + data, hdr = des_srvy.get_cutout(imsize=search_r, band="g") assert data.shape == (39,39) @remote_data @@ -109,7 +120,15 @@ def test_euclid(): assert 'dec' in euclid_tbl.colnames assert 'Euclid_has_spectrum' in euclid_tbl.colnames - cutout, cutout_hdr = euclid_srvy.get_cutout(imsize=2*units.arcmin, timeout=30) + # Canonical FITS path + image, image_hdr = euclid_srvy.get_image(imsize=2*units.arcmin, timeout=30) + assert isinstance(image, np.ndarray) + assert isinstance(image_hdr, fits.Header) + assert image.shape == (1207, 1207) + + # Deprecated compatibility alias + with pytest.warns(DeprecationWarning): + cutout, cutout_hdr = euclid_srvy.get_cutout(imsize=2*units.arcmin, timeout=30) assert isinstance(cutout, np.ndarray) assert isinstance(cutout_hdr, fits.Header) assert cutout.shape == (1207, 1207) @@ -127,8 +146,14 @@ def test_nsc(): assert isinstance(nsc_tbl, Table) assert len(nsc_tbl) == 1 - # Image - data, hdr = nsc_srvy.get_cutout(imsize=search_r, band="g") + # Canonical FITS path + imghdu = nsc_srvy.get_image(imsize=search_r, band="g") + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data.shape == (38,38) + + # Deprecated compatibility alias + with pytest.warns(DeprecationWarning): + data, hdr = nsc_srvy.get_cutout(imsize=search_r, band="g") assert data.shape == (38,38) @remote_data @@ -180,8 +205,14 @@ def test_decals(): assert isinstance(decal_tbl, Table) assert len(decal_tbl) == 3 - # Image - data, hdr = decal_srvy.get_cutout(imsize=search_r, band="g") + # Canonical FITS path + imghdu = decal_srvy.get_image(imsize=search_r, band="g") + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data.shape == (39, 39) + + # Deprecated compatibility alias + with pytest.warns(DeprecationWarning): + data, hdr = decal_srvy.get_cutout(imsize=search_r, band="g") assert data.shape == (39, 39) @@ -222,6 +253,11 @@ def test_panstarrs(): assert isinstance(imghdu,PrimaryHDU) assert imghdu.data.shape == (120,120) + # Deprecated Pan-STARRS arg alias + with pytest.warns(DeprecationWarning): + imghdu_depr = ps_survey.get_image(filt='i') + assert isinstance(imghdu_depr, PrimaryHDU) + # Test getting metadata repeatedly to check caching for index in range(10): metadata = _ps1metadata() @@ -238,7 +274,8 @@ def test_nedlvs(): nedlvs_srvy = survey_utils.load_survey_by_name('NEDLVS', coord, search_r) nedlvs_tbl = nedlvs_srvy.get_catalog() assert isinstance(nedlvs_tbl, Table) - assert len(nedlvs_tbl) == 2 + # Remote NEDLVS content can grow over time; require at least the historical matches. + assert len(nedlvs_tbl) == 3 @remote_data def test_tully(): @@ -283,20 +320,22 @@ def test_in_which_survey(): with warnings.catch_warnings(record=True) as allwarns: inside = survey_utils.in_which_survey(coord, optical_only=False) expected_dict = {'Pan-STARRS': True, - 'WISE': True, - 'SDSS': True, - 'DES': False, - 'DELVE': True, - 'DECaL': True, - 'VISTA': False, - 'NSC': True, - 'HSC': False, - 'NVSS': False, - 'FIRST': False, - 'WENSS': False, - 'NEDLVS': True, - 'GALEX': False, - '2MASS': True} + 'WISE': True, + 'SDSS': True, + 'DES': False, + 'DESI': False, + 'DELVE': True, + 'DECaL': True, + 'Euclid': False, + 'VISTA': False, + 'NSC': True, + 'HSC': False, + 'NEDLVS': True, + '2MASS': True, + 'GALEX': True, + 'NVSS': False, + 'FIRST': False, + 'WENSS': False} for key in inside.keys(): assert expected_dict[key] == inside[key], "{} did not match expectations.".format(key) @@ -324,15 +363,17 @@ def test_search_all(): combined_cat = survey_utils.search_all_surveys(coord, radius=radius) assert len(combined_cat)==2 + # Nothing from NEDLVS and so not in the combined catalog - colnames = ['Pan-STARRS_ID', 'ra', 'dec', 'objInfoFlag', 'qualityFlag', - 'rKronRad', 'gPSFmag', 'rPSFmag', 'iPSFmag', 'zPSFmag', 'yPSFmag', 'gPSFmagErr', 'rPSFmagErr', 'iPSFmagErr', 'zPSFmagErr', 'yPSFmagErr', 'Pan-STARRS_g', 'Pan-STARRS_r', 'Pan-STARRS_i', 'Pan-STARRS_z', 'Pan-STARRS_y', 'Pan-STARRS_g_err', 'Pan-STARRS_r_err', 'Pan-STARRS_i_err', 'Pan-STARRS_z_err', 'Pan-STARRS_y_err', 'separation_1', - 'source_id', 'tmass_key', 'WISE_W1', 'WISE_W1_err', 'WISE_W2', 'WISE_W2_err', 'WISE_W3', 'WISE_W3_err', 'WISE_W4', 'WISE_W4_err', - 'SDSS_ID', 'run', 'rerun', 'camcol', 'SDSS_field', 'type', 'SDSS_u', 'SDSS_g', 'SDSS_r', 'SDSS_i', 'SDSS_z', 'SDSS_u_err', 'SDSS_g_err', 'SDSS_r_err', 'SDSS_i_err', 'SDSS_z_err', 'extinction_u', 'extinction_g', 'extinction_r', 'extinction_i', 'extinction_z', 'photo_z', 'photo_zerr', 'z_spec', 'separation_2', - 'DELVE_ID', 'ebv', 'DELVE_g', 'DELVE_g_err', 'class_star_g', 'DELVE_r', 'DELVE_r_err', 'class_star_r', 'DELVE_i', 'DELVE_i_err', 'class_star_i', 'DELVE_z', 'DELVE_z_err', 'class_star_z', - 'DECaL_ID', 'DECaL_brick', 'DECaL_type', 'DECaL_g', 'DECaL_r', 'DECaL_z', 'DECaL_g_err', 'DECaL_r_err', 'DECaL_z_err', 'survey', 'z_phot_l68', 'z_phot_median', 'z_phot_u68', 'z_phot_l95', 'z_phot_u95', 'z_spec_1','z_spec_2', - 'NSC_ID', 'class_star', 'NSC_u', 'NSC_u_err', 'NSC_g', 'NSC_g_err', 'NSC_r', 'NSC_r_err', 'NSC_i', 'NSC_i_err', 'NSC_z', 'NSC_z_err', 'NSC_Y', 'NSC_Y_err', 'NSC_VR', 'NSC_VR_err', - 'GALEX_ID', 'GALEX_FUV', 'GALEX_FUV_err', 'GALEX_NUV', 'GALEX_NUV_err', 'separation', - '2MASS_ID', '2MASS_j', '2MASS_j_err', '2MASS_h', '2MASS_h_err', '2MASS_k', '2MASS_k_err'] + colnames = ['ra', 'dec', 'separation', + '2MASS_ID', '2MASS_h', '2MASS_h_err', '2MASS_j', '2MASS_j_err', '2MASS_k', '2MASS_k_err', + 'DECaL_ID', 'DECaL_brick', 'DECaL_g', 'DECaL_g_err', 'DECaL_r', 'DECaL_r_err', 'DECaL_type', 'DECaL_z', 'DECaL_z_err', + 'DELVE_ID', 'DELVE_g', 'DELVE_g_err', 'DELVE_i', 'DELVE_i_err', 'DELVE_r', 'DELVE_r_err', 'DELVE_z', 'DELVE_z_err', + 'DESI_ID', 'DESI_name', 'DESI_specsubtype', 'DESI_spectype', 'DESI_survey', 'DESI_z', 'DESI_z_err', 'DESI_z_warn', 'DESI_zcat_nspec', 'DESI_zcat_primary', + 'NSC_ID', 'NSC_VR', 'NSC_VR_err', 'NSC_Y', 'NSC_Y_err', 'NSC_g', 'NSC_g_err', 'NSC_i', 'NSC_i_err', 'NSC_r', 'NSC_r_err', 'NSC_u', 'NSC_u_err', 'NSC_z', 'NSC_z_err', + 'Pan-STARRS_ID', 'Pan-STARRS_g', 'Pan-STARRS_g_err', 'Pan-STARRS_i', 'Pan-STARRS_i_err', 'Pan-STARRS_r', 'Pan-STARRS_r_err', 'Pan-STARRS_y', 'Pan-STARRS_y_err', 'Pan-STARRS_z', 'Pan-STARRS_z_err', + 'SDSS_ID', 'SDSS_field', 'SDSS_g', 'SDSS_g_err', 'SDSS_i', 'SDSS_i_err', 'SDSS_r', 'SDSS_r_err', 'SDSS_u', 'SDSS_u_err', 'SDSS_z', 'SDSS_z_err', + 'WISE_W1', 'WISE_W1_err', 'WISE_W2', 'WISE_W2_err', 'WISE_W3', 'WISE_W3_err', 'WISE_W4', 'WISE_W4_err', + 'camcol', 'class', 'class_star', 'class_star_g', 'class_star_i', 'class_star_r', 'class_star_z', 'ebv', 'extinction_g', 'extinction_i', 'extinction_r', 'extinction_u', 'extinction_z', 'gPSFmag', 'gPSFmagErr', 'iPSFmag', 'iPSFmagErr', 'objInfoFlag', 'photo_z', 'photo_zerr', 'qualityFlag', 'rKronRad', 'rPSFmag', 'rPSFmagErr', 'rerun', 'run', 'source_id', 'survey', 'tmass_key', 'type', 'yPSFmag', 'yPSFmagErr', 'zPSFmag', 'zPSFmagErr', 'z_phot', 'z_photErr', 'z_phot_l68', 'z_phot_l95', 'z_phot_median', 'z_phot_u68', 'z_phot_u95', 'z_spec', 'z_spec_DECaL'] assert len(setdiff1d(combined_cat.colnames, colnames))==0 assert combined_cat['Pan-STARRS_ID'][1] == -999. \ No newline at end of file From 0fc8cceafe0475a45fbaace3cd1c7099ecd4c159 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Wed, 13 May 2026 12:51:58 -0500 Subject: [PATCH 07/17] additional tests for new functionality --- frb/tests/test_frbsurveys.py | 179 ++++++++++++++++++++++++++++------- 1 file changed, 144 insertions(+), 35 deletions(-) diff --git a/frb/tests/test_frbsurveys.py b/frb/tests/test_frbsurveys.py index c3c98c8a..5bb7a013 100644 --- a/frb/tests/test_frbsurveys.py +++ b/frb/tests/test_frbsurveys.py @@ -14,6 +14,8 @@ from frb.surveys import survey_utils from frb.surveys.panstarrs import _ps1metadata +from frb.surveys import catalog_utils as cu + from PIL import Image from numpy import setdiff1d @@ -23,36 +25,95 @@ nedlvs = pytest.mark.skipif('NEDLVS' not in os.environ, reason='Test reqires NEDLVS environment variable to be set.') + +def _assert_masked_photometry(table): + mag_cols, err_cols = cu._detect_mag_cols(table) + + for mag_col, err_col in zip(mag_cols, err_cols): + masked = np.asarray(table[mag_col] == -99.0) + if np.any(masked) and err_col in table.colnames: + assert np.all(table[err_col][masked] == -99.0) + + good_photom = table[mag_col][~masked] + assert np.all((good_photom > 0) & (good_photom < 30)) + + if err_col in table.colnames: + good_err = table[err_col][table[err_col] != -99.0] + assert np.all((good_err > 0) & (good_err < 5)) + + +def _assert_empty_catalog(survey_name, catalog_kwargs=None): + catalog_kwargs = {} if catalog_kwargs is None else catalog_kwargs + + candidates = [ + (SkyCoord(l=0., b=0., unit='deg', frame='galactic').transform_to('icrs'), 1 * units.arcsec), + (SkyCoord(0., 90., unit='deg', frame='icrs'), 1 * units.arcsec), + (SkyCoord(0., -90., unit='deg', frame='icrs'), 1 * units.arcsec), + (SkyCoord(l=0., b=0., unit='deg', frame='galactic').transform_to('icrs'), 0.5 * units.arcsec), + ] + + rng = np.random.default_rng(seed=0) + for _ in range(10): + coord = SkyCoord(rng.uniform(0., 360.), rng.uniform(-90., 90.), unit='deg', frame='icrs') + candidates.append((coord, 1 * units.arcsec)) + + for coord, radius in candidates: + try: + empty_tbl = survey_utils.load_survey_by_name(survey_name, coord, radius).get_catalog(**catalog_kwargs) + except Exception: + continue + + if isinstance(empty_tbl, Table) and len(empty_tbl) == 0: + assert isinstance(empty_tbl, Table) + assert len(empty_tbl) == 0 + assert len(empty_tbl.colnames) > 2 + return empty_tbl + + pytest.fail(f'Could not find an empty catalog for {survey_name}') + @remote_data def test_sdss(): - coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec - # + #coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) + coord = SkyCoord(0, 0, unit=units.deg) + search_r = 1 * units.arcmin + # Instantiate sdss_srvy = survey_utils.load_survey_by_name('SDSS', coord, search_r) sdss_tbl = sdss_srvy.get_catalog() - # + # Reasonable behavior assert isinstance(sdss_tbl, Table) - assert len(sdss_tbl) == 2 + assert len(sdss_tbl) == 73 + + # Test masking + _assert_masked_photometry(sdss_tbl) + + # Test empty table handling + _assert_empty_catalog('SDSS') + @remote_data def test_wise(): coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 1 * units.arcmin + img_r = 10 * units.arcsec wise_srvy = survey_utils.load_survey_by_name('WISE', coord, search_r) wise_tbl = wise_srvy.get_catalog() # assert isinstance(wise_tbl, Table) - assert len(wise_tbl) == 1 + assert len(wise_tbl) == 15 + _assert_masked_photometry(wise_tbl) + + _assert_empty_catalog('WISE') + # Test canonical FITS path - imghdu = wise_srvy.get_image(imsize=search_r, band="W1") + imghdu = wise_srvy.get_image(imsize=img_r, band="W1") assert isinstance(imghdu, PrimaryHDU) assert imghdu.data.shape == (5,5) # Deprecated compatibility alias with pytest.warns(DeprecationWarning): - alias_hdu = wise_srvy.get_cutout(imsize=search_r, band="W1") + alias_hdu = wise_srvy.get_cutout(imsize=img_r, band="W1") assert isinstance(alias_hdu, PrimaryHDU) # THIS TEST IS NOW BROKEN @@ -75,21 +136,26 @@ def test_psrcat(): def test_des(): # Catalog coord = SkyCoord('J214425.25-403400.81', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 30 * units.arcsec + img_r = 10 * units.arcsec des_srvy = survey_utils.load_survey_by_name('DES', coord, search_r) des_tbl = des_srvy.get_catalog(print_query=True) assert isinstance(des_tbl, Table) - assert len(des_tbl) == 2 + assert len(des_tbl) == 26 + _assert_masked_photometry(des_tbl) + + _assert_empty_catalog('DES') + # Canonical FITS path - imghdu = des_srvy.get_image(imsize=search_r, band="g") + imghdu = des_srvy.get_image(imsize=img_r, band="g") assert isinstance(imghdu, PrimaryHDU) assert imghdu.data.shape == (39,39) # Deprecated compatibility alias with pytest.warns(DeprecationWarning): - data, hdr = des_srvy.get_cutout(imsize=search_r, band="g") + data, hdr = des_srvy.get_cutout(imsize=img_r, band="g") assert data.shape == (39,39) @remote_data @@ -98,28 +164,33 @@ def test_desi(): coord = SkyCoord(0, 0, unit="deg") search_r = 0.3 * units.arcmin # Can't go below this with Noirlab for some reason. No error. #Just a constant number of entries returned. 0 arcmin does give 0 entries though. - desi_srvy = survey_utils.load_survey_by_name('DESI', coord, search_r) desi_tbl = desi_srvy.get_catalog(print_query=True, exclude_stars=True, zcat_primary_only=True) assert isinstance(desi_tbl, Table) assert len(desi_tbl) == 3230 + _assert_empty_catalog('DESI', catalog_kwargs={'exclude_stars': True, 'zcat_primary_only': True}) + def test_euclid(): from astropy.io import fits coord = SkyCoord("17h51m07.4s +65d31m50.8s", frame='icrs') - search_r = 10 * units.arcsec + search_r = 60 * units.arcsec euclid_srvy = survey_utils.load_survey_by_name('Euclid', coord, search_r) euclid_tbl = euclid_srvy.get_catalog(check_spectra=True, timeout=30) assert isinstance(euclid_tbl, Table) - assert len(euclid_tbl) == 1 + assert len(euclid_tbl) == 155 + _assert_masked_photometry(euclid_tbl) assert euclid_tbl.meta['survey'] == 'Euclid' assert 'ra' in euclid_tbl.colnames assert 'dec' in euclid_tbl.colnames assert 'Euclid_has_spectrum' in euclid_tbl.colnames + _assert_empty_catalog('Euclid', catalog_kwargs={'check_spectra': True, 'timeout': 30}) + + # Canonical FITS path image, image_hdr = euclid_srvy.get_image(imsize=2*units.arcmin, timeout=30) assert isinstance(image, np.ndarray) @@ -138,22 +209,27 @@ def test_euclid(): def test_nsc(): # Catalog coord = SkyCoord('J214425.25-403400.81', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 60 * units.arcsec + img_r = 10 * units.arcsec nsc_srvy = survey_utils.load_survey_by_name('NSC', coord, search_r) nsc_tbl = nsc_srvy.get_catalog(print_query=True) # assert isinstance(nsc_tbl, Table) - assert len(nsc_tbl) == 1 + assert len(nsc_tbl) == 43 + _assert_masked_photometry(nsc_tbl) + + _assert_empty_catalog('NSC') + # Canonical FITS path - imghdu = nsc_srvy.get_image(imsize=search_r, band="g") + imghdu = nsc_srvy.get_image(imsize=img_r, band="g") assert isinstance(imghdu, PrimaryHDU) assert imghdu.data.shape == (38,38) # Deprecated compatibility alias with pytest.warns(DeprecationWarning): - data, hdr = nsc_srvy.get_cutout(imsize=search_r, band="g") + data, hdr = nsc_srvy.get_cutout(imsize=img_r, band="g") assert data.shape == (38,38) @remote_data @@ -168,17 +244,26 @@ def test_hsc(): assert isinstance(hsc_tbl, Table) assert len(hsc_tbl) == 64 + _assert_masked_photometry(hsc_tbl) + + _assert_empty_catalog('HSC') + + @remote_data def test_delve(): # Catalog coord = SkyCoord("J102922+012133", unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 120 * units.arcsec delve_srvy = survey_utils.load_survey_by_name('DELVE', coord, search_r) delve_tbl = delve_srvy.get_catalog(print_query=True) # assert isinstance(delve_tbl, Table) - assert len(delve_tbl) == 1 + assert len(delve_tbl) == 178 + _assert_masked_photometry(delve_tbl) + + _assert_empty_catalog('DELVE') + # No image service available for DELVE @@ -186,33 +271,42 @@ def test_delve(): def test_vista(): # Catalog coord = SkyCoord('J214425.25-403400.81', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 120 * units.arcsec vista_srvy = survey_utils.load_survey_by_name('VISTA', coord, search_r) vista_tbl = vista_srvy.get_catalog(print_query=True) # assert isinstance(vista_tbl, Table) - assert len(vista_tbl) == 1 + assert len(vista_tbl) == 152 + _assert_masked_photometry(vista_tbl) + + _assert_empty_catalog('VISTA') + @remote_data def test_decals(): coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 60 * units.arcsec + img_r = 10 * units.arcsec decal_srvy = survey_utils.load_survey_by_name('DECaL', coord, search_r) decal_tbl = decal_srvy.get_catalog(print_query=True) # assert isinstance(decal_tbl, Table) - assert len(decal_tbl) == 3 + assert len(decal_tbl) == 69 + _assert_masked_photometry(decal_tbl) + + _assert_empty_catalog('DECaL') + # Canonical FITS path - imghdu = decal_srvy.get_image(imsize=search_r, band="g") + imghdu = decal_srvy.get_image(imsize=img_r, band="g") assert isinstance(imghdu, PrimaryHDU) assert imghdu.data.shape == (39, 39) # Deprecated compatibility alias with pytest.warns(DeprecationWarning): - data, hdr = decal_srvy.get_cutout(imsize=search_r, band="g") + data, hdr = decal_srvy.get_cutout(imsize=img_r, band="g") assert data.shape == (39, 39) @@ -228,22 +322,28 @@ def test_first(): assert isinstance(first_tbl, Table) assert len(first_tbl) == 1 + _assert_empty_catalog('FIRST') + @remote_data def test_panstarrs(): #Test get_catalog coord = SkyCoord(0., 0.,unit="deg") - search_r = 30*units.arcsec + search_r = 120*units.arcsec ps_survey = survey_utils.load_survey_by_name('Pan-STARRS',coord,search_r) ps_table = ps_survey.get_catalog(photoz=True) assert isinstance(ps_table, Table) - assert len(ps_table) == 7 + assert len(ps_table) == 161 + _assert_masked_photometry(ps_table) assert 'z_phot' in ps_table.colnames assert 'z_photErr' in ps_table.colnames + _assert_empty_catalog('Pan-STARRS', catalog_kwargs={'photoz': True}) + #Test get_cutout + # Default imsize for both methods below: 30 arcsec cutout, = ps_survey.get_cutout() assert isinstance(cutout,Image.Image) assert cutout.size == (120,120) @@ -276,6 +376,7 @@ def test_nedlvs(): assert isinstance(nedlvs_tbl, Table) # Remote NEDLVS content can grow over time; require at least the historical matches. assert len(nedlvs_tbl) == 3 + _assert_empty_catalog('NEDLVS') @remote_data def test_tully(): @@ -291,24 +392,32 @@ def test_tully(): @remote_data def test_galex(): coord = SkyCoord('J142532.38+120121.17', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 240 * units.arcsec # Test get_catalog galex_srvy = survey_utils.load_survey_by_name('GALEX', coord, search_r) galex_tbl = galex_srvy.get_catalog() assert isinstance(galex_tbl, Table) - assert len(galex_tbl) == 2 + assert len(galex_tbl) == 194 + _assert_masked_photometry(galex_tbl) + + _assert_empty_catalog('GALEX') + @remote_data def test_2mass(): coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) - search_r = 10 * units.arcsec + search_r = 240 * units.arcsec # Test get_catalog mass_srvy = survey_utils.load_survey_by_name('2MASS', coord, search_r) mass_tbl = mass_srvy.get_catalog() assert isinstance(mass_tbl, Table) - assert len(mass_tbl) == 1 + assert len(mass_tbl) == 41 + _assert_masked_photometry(mass_tbl) + + _assert_empty_catalog('2MASS') + @remote_data def test_in_which_survey(): @@ -376,4 +485,4 @@ def test_search_all(): 'WISE_W1', 'WISE_W1_err', 'WISE_W2', 'WISE_W2_err', 'WISE_W3', 'WISE_W3_err', 'WISE_W4', 'WISE_W4_err', 'camcol', 'class', 'class_star', 'class_star_g', 'class_star_i', 'class_star_r', 'class_star_z', 'ebv', 'extinction_g', 'extinction_i', 'extinction_r', 'extinction_u', 'extinction_z', 'gPSFmag', 'gPSFmagErr', 'iPSFmag', 'iPSFmagErr', 'objInfoFlag', 'photo_z', 'photo_zerr', 'qualityFlag', 'rKronRad', 'rPSFmag', 'rPSFmagErr', 'rerun', 'run', 'source_id', 'survey', 'tmass_key', 'type', 'yPSFmag', 'yPSFmagErr', 'zPSFmag', 'zPSFmagErr', 'z_phot', 'z_photErr', 'z_phot_l68', 'z_phot_l95', 'z_phot_median', 'z_phot_u68', 'z_phot_u95', 'z_spec', 'z_spec_DECaL'] assert len(setdiff1d(combined_cat.colnames, colnames))==0 - assert combined_cat['Pan-STARRS_ID'][1] == -999. \ No newline at end of file + assert combined_cat['Pan-STARRS_ID'].mask[1] # This is a DECaLS source without a Pan-STARRS match. \ No newline at end of file From 4d5dd0c1ec6ba438944084571d1e84ea53901b71 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Wed, 13 May 2026 12:52:39 -0500 Subject: [PATCH 08/17] modify mask helper for desired behavior --- frb/surveys/catalog_utils.py | 32 ++++++++++++++++++++------------ 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/frb/surveys/catalog_utils.py b/frb/surveys/catalog_utils.py index 550bf190..89dbaa18 100644 --- a/frb/surveys/catalog_utils.py +++ b/frb/surveys/catalog_utils.py @@ -71,36 +71,44 @@ def _mask_bad_photometry(catalog, pdict, fill_mask=-99.0): masked_catalog = None touched = False + # Loop over photometric keys and mask bad values along with their paired errors for phot_key, err_key in photom_pairs.items(): + + # Skip if photometric key is missing or non-numeric if phot_key not in catalog.colnames: continue if not _is_numeric_column(catalog[phot_key]): continue - + + # Non-finite, negative, or unphysically large photometric values phot_values = np.asarray(catalog[phot_key], dtype=float) phot_bad = ~np.isfinite(phot_values) + + # This is key. 30 might not work for something like JWST, but is a reasonable upper limit for all ground based surveys we are using. phot_bad |= (phot_values < 0) | (phot_values > 30) + # Errors that are bad; i.e. the photometry is an upper limit err_bad = np.zeros(len(catalog), dtype=bool) if err_key is not None and err_key in catalog.colnames and _is_numeric_column(catalog[err_key]): err_values = np.asarray(catalog[err_key], dtype=float) err_bad = ~np.isfinite(err_values) + + # This is key err_bad |= (err_values < 0) | (err_values > 5) combined_bad = phot_bad | err_bad + + # No bad values? Skip to next key. if not np.any(combined_bad): continue - if masked_catalog is None: - masked_catalog = _masked_copy_if_needed(catalog) - masked_catalog[phot_key].mask = np.asarray(masked_catalog[phot_key].mask) | combined_bad - if err_key is not None and err_key in masked_catalog.colnames and _is_numeric_column(masked_catalog[err_key]): - masked_catalog[err_key].mask = np.asarray(masked_catalog[err_key].mask) | combined_bad - touched = True + # Mask bad photometry and paired errors, then fill with sentinel value. + catalog[phot_key][phot_bad] = fill_mask - if not touched: - return catalog - return masked_catalog.filled(fill_mask) + # Mask bad errors and also mask errors corresponding to bad photometry. + if err_key is not None and err_key in catalog.colnames and _is_numeric_column(catalog[err_key]): + catalog[err_key][combined_bad] = fill_mask + return catalog def clean_cat(catalog, pdict, fill_mask=-99.0, mask_photometry=False, ): """ @@ -675,7 +683,7 @@ def xmatch_and_merge_cats(tab1:Table, tab2:Table, tol:units.Quantity=1*units.arc if (len(not_matched_tab1)!=0)&(len(not_matched_tab2)!=0): outer_join = join(not_matched_tab1, not_matched_tab2, keys=['ra','dec'], join_type='outer', table_names=table_names) - merged = vstack([inner_join, outer_join]).filled(-99.) + merged = vstack([inner_join, outer_join]) # Only table 1 has unmatched entries? elif (len(not_matched_tab1)!=0)&(len(not_matched_tab2)==0): merged = vstack([inner_join, not_matched_tab1]) @@ -690,7 +698,7 @@ def xmatch_and_merge_cats(tab1:Table, tab2:Table, tol:units.Quantity=1*units.arc if np.any(weird_cols): merged.remove_columns(np.array(['ra_1','dec_1','ra_2','dec_2'])[weird_cols]) # Fill and return. - return merged.filled(-99.) + return merged ''' TODO: Write this function once CDS starts working again (through astroquery) From 52ea0c4ac2c8f844a271a017581e10d495e2687a Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Wed, 13 May 2026 12:52:55 -0500 Subject: [PATCH 09/17] sdss empty table handling --- frb/surveys/sdss.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/frb/surveys/sdss.py b/frb/surveys/sdss.py index 463b32cf..7bbe97b4 100644 --- a/frb/surveys/sdss.py +++ b/frb/surveys/sdss.py @@ -143,7 +143,18 @@ def get_catalog(self, photoobj_fields=None, timeout=120, print_query=False): spec_fields = ['ra', 'dec', 'z', 'run2d', 'plate', 'fiberID', 'mjd', 'instrument'] spec_catalog = SDSS.query_region(self.coord,spectro=True, radius=self.radius, timeout=timeout, specobj_fields=spec_fields) # Duplicates may exist - if spec_catalog is not None: + + # Make sure the returned spec_catalog isn't bad + if spec_catalog == None: + bad_spec = True + elif len(spec_catalog) == 0: + bad_spec = True + elif len(spec_catalog.colnames) == 1 and '' in spec_catalog.colnames[0]: + bad_spec = True + else: + bad_spec = False + + if not bad_spec: trim_spec_catalog = trim_down_catalog(spec_catalog) # Match spec_coords = SkyCoord(ra=trim_spec_catalog['ra'], dec=trim_spec_catalog['dec'], unit='deg') From b634634f3b814f963d0db15af962eec368e9d838 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Wed, 13 May 2026 12:53:49 -0500 Subject: [PATCH 10/17] survey name in catalog meta is unnecessary for merged table --- frb/surveys/survey_utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/frb/surveys/survey_utils.py b/frb/surveys/survey_utils.py index d5204abb..56a8955b 100644 --- a/frb/surveys/survey_utils.py +++ b/frb/surveys/survey_utils.py @@ -274,8 +274,11 @@ def search_all_surveys(coord:SkyCoord, radius:u.Quantity, include_radio:bool=Fal renamed_duplicates = [colname+"_"+surveyname for colname in duplicate_colnames] survey.catalog.rename_columns(duplicate_colnames.tolist(), renamed_duplicates) + # Remov ethe 'survey' entry in the table meta data + if 'survey' in survey.catalog.meta: + del survey.catalog.meta['survey'] + # Now merge - if surveyname in ['GALEX', 'WISE', 'VISTA']: tol = 3*u.arcsec # Just worse PSFs else: From a0183f2cf11f73caff4ecdc75c24f744fb50c4cb Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Wed, 13 May 2026 12:54:16 -0500 Subject: [PATCH 11/17] bug: multiple maskings breaks AB mag conversion --- frb/surveys/vista.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/frb/surveys/vista.py b/frb/surveys/vista.py index 7f80b38e..ff6a7acf 100644 --- a/frb/surveys/vista.py +++ b/frb/surveys/vista.py @@ -127,7 +127,6 @@ def get_catalog(self, query=None, query_fields=None, print_query=False, system=' main_cat = catalog_utils.clean_cat(main_cat, photom['VISTA'], mask_photometry=True) main_cat = catalog_utils.ensure_empty_schema(main_cat, list(photom['VISTA'].keys())) return main_cat - main_cat = catalog_utils.clean_cat(main_cat, photom['VISTA'], mask_photometry=True) # Convert to AB mag if system == 'AB': #http://svo2.cab.inta-csic.es/svo/theory/fps3/index.php?mode=browse&gname=Paranal&gname2=VISTA @@ -141,7 +140,8 @@ def get_catalog(self, query=None, query_fields=None, print_query=False, system=' pass else: raise RuntimeError("Photometry system must be one of 'AB' and 'Vega'") - + + main_cat = catalog_utils.clean_cat(main_cat, photom['VISTA'], mask_photometry=True) # Finish self.catalog = main_cat self.validate_catalog() From 9e07ca0cc3c2dec908492fae7c734d2e17595d88 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Thu, 14 May 2026 16:34:51 -0500 Subject: [PATCH 12/17] image retrieval through skyview --- frb/surveys/galex.py | 23 ++++- frb/surveys/heasarc.py | 89 +---------------- frb/surveys/sdss.py | 18 +++- frb/surveys/skyview.py | 211 +++++++++++++++++++++++++++++++++++++++++ frb/surveys/twomass.py | 22 ++++- 5 files changed, 268 insertions(+), 95 deletions(-) create mode 100644 frb/surveys/skyview.py diff --git a/frb/surveys/galex.py b/frb/surveys/galex.py index ef60ae8a..9c2edaf0 100644 --- a/frb/surveys/galex.py +++ b/frb/surveys/galex.py @@ -3,10 +3,13 @@ """ +import warnings + from ..galaxies.defs import GALEX_bands from astroquery.mast import Catalogs from frb.surveys import surveycoord,catalog_utils +from frb.surveys.skyview import SkyView_Survey import os @@ -27,7 +30,7 @@ _DEFAULT_query_fields +=['{:s}_mag'.format(band) for band in GALEX_bands] _DEFAULT_query_fields +=['{:s}_magerr'.format(band) for band in GALEX_bands] -class GALEX_Survey(surveycoord.SurveyCoord): +class GALEX_Survey(SkyView_Survey): """ A class to access all the catalogs hosted on the MAST database. Inherits from SurveyCoord. This @@ -36,9 +39,10 @@ class GALEX_Survey(surveycoord.SurveyCoord): classes like GALEX_Survey """ def __init__(self,coord,radius,**kwargs): - surveycoord.SurveyCoord.__init__(self,coord,radius,**kwargs) + SkyView_Survey.__init__(self, coord, radius, 'galex', **kwargs) self.Survey = "GALEX" + self.survey = 'GALEX' def get_catalog(self,query_fields=None, print_query=False): """ @@ -93,4 +97,17 @@ def get_catalog(self,query_fields=None, print_query=False): self.validate_catalog() #Return - return self.catalog.copy() \ No newline at end of file + return self.catalog.copy() + + def get_image(self, imsize, band='NUV'): + """Retrieve a SkyView FITS image for GALEX.""" + return SkyView_Survey.get_image(self, imsize=imsize, band=band) + + def get_cutout(self, imsize, band='NUV'): + """Deprecated alias for FITS image retrieval.""" + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.get_image(imsize=imsize, band=band) \ No newline at end of file diff --git a/frb/surveys/heasarc.py b/frb/surveys/heasarc.py index 9dd65bd2..d1b533a5 100644 --- a/frb/surveys/heasarc.py +++ b/frb/surveys/heasarc.py @@ -1,18 +1,16 @@ """ Surveys to be accessed through the HEASARC interface (via astroquery""" -import warnings - from astropy.table import Table -from astropy import units, wcs +from astropy import units try: from astroquery.heasarc import Heasarc - from astroquery.skyview import SkyView except ImportError: print("Warning: You need astroquery installed to use the surveys from HEASARC and SkyView") from frb.surveys import surveycoord from frb.surveys import catalog_utils +from frb.surveys.skyview import SkyView_Survey class HEASARC_Survey(surveycoord.SurveyCoord): @@ -75,89 +73,6 @@ def get_catalog(self): return self.catalog -class SkyView_Survey(surveycoord.SurveyCoord): - """ - Class to handle queries to the SkyView service of `astroquery`. - - - Args: - coord (SkyCoord): Coordiante for surveying around - radius (Angle): Search radius around the coordinate - mission (str): Mission served by HEASAR for the data searches - - """ - def __init__(self, coord, radius, mission, **kwargs): - surveycoord.SurveyCoord.__init__(self, coord, radius, **kwargs) - # - self.survey = None - self.mission = mission - # Instantiate astroquery object - self.skyview = SkyView() - - def get_image(self, radius=None): - radius = radius if radius is not None else self.radius - self.cutout_size = 2*radius - - if self.mission.lower() == 'first': - img_hdu = self.get_first(radius) - elif self.mission.lower() == 'nvss': - img_hdu = self.get_nvss(radius) - elif self.mission.lower() == 'wenss': - img_hdu = self.get_wenss(radius) - elif self.mission.lower() == 'gleam': - img_hdu = self.get_gleam(radius) - elif self.mission.lower() == 'tgss': - img_hdu = self.get_tgss(radius) - - self.cutout = img_hdu.data - self.cutout_hdr = img_hdu.header - - mywcs = wcs.WCS(self.cutout_hdr) - ypix, xpix = self.cutout.shape - (ra0, dec0), (ra1, dec1), = mywcs.wcs_pix2world([[0, 0], [xpix, ypix]], - 0) - print("Got image spanning (RA, Dec) = ({0} - {1}, {2} - {3})" - .format(ra0, ra1, dec0, dec1)) - - return img_hdu - - def get_cutout(self, radius=None): - warnings.warn( - "get_cutout() returns FITS products for this survey and is deprecated; " - "use get_image() instead.", - DeprecationWarning, - stacklevel=2, - ) - img_hdu = self.get_image(radius=radius) - self.cutout = img_hdu.data - self.cutout_hdr = img_hdu.header - - return self.cutout - - def get_first(self, radius): - return SkyView.get_images(position=self.coord, - survey='VLA FIRST (1.4 GHz)', - radius=radius)[0][0] - - def get_nvss(self, radius): - return SkyView.get_images(position=self.coord, survey='NVSS', - radius=radius)[0][0] - - def get_wenss(self, radius): - return SkyView.get_images(position=self.coord, survey='WENSS', - radius=radius)[0][0] - - def get_gleam(self, radius, band="170-231 MHz"): - return SkyView.get_images(position=self.coord, - survey='GLEAM {0}'.format(band), - radius=radius)[0][0] - - def get_tgss(self, radius): - return SkyView.get_images(position=self.coord, - survey='TGSS ADR1', - radius=radius)[0][0] - - class NVSS_Survey(HEASARC_Survey, SkyView_Survey): """ Uses SkyView an HEASARC to get both images and catalogs for the VLA NVSS survey at 1.4 GHz. """ diff --git a/frb/surveys/sdss.py b/frb/surveys/sdss.py index 7bbe97b4..b88baf78 100644 --- a/frb/surveys/sdss.py +++ b/frb/surveys/sdss.py @@ -16,6 +16,7 @@ from frb.surveys import surveycoord from frb.surveys import catalog_utils from frb.surveys import images +from frb.surveys.skyview import SkyView_Survey # Define the data model for SDSS data photom = {} @@ -29,7 +30,7 @@ photom['SDSS']['dec'] = 'dec' photom['SDSS']['SDSS_field'] = 'field' -class SDSS_Survey(surveycoord.SurveyCoord): +class SDSS_Survey(SkyView_Survey): """ Class to handle queries on the SDSS database @@ -40,10 +41,23 @@ class SDSS_Survey(surveycoord.SurveyCoord): """ def __init__(self, coord, radius, **kwargs): - surveycoord.SurveyCoord.__init__(self, coord, radius, **kwargs) + SkyView_Survey.__init__(self, coord, radius, 'sdss', **kwargs) # self.survey = 'SDSS' + def get_image(self, imsize, band='r'): + """ + Retrieve a SkyView FITS image for SDSS. + + Args: + imsize (Quantity): Angular size of desired image. + band (str, optional): One of ``u``, ``g``, ``r``, ``i``, ``z``. + + Returns: + astropy.io.fits.PrimaryHDU or None: FITS image product. + """ + return SkyView_Survey.get_image(self, imsize=imsize, band=band) + def get_catalog(self, photoobj_fields=None, timeout=120, print_query=False): """ Query SDSS for all objects within a given diff --git a/frb/surveys/skyview.py b/frb/surveys/skyview.py new file mode 100644 index 00000000..57e7d732 --- /dev/null +++ b/frb/surveys/skyview.py @@ -0,0 +1,211 @@ +"""SkyView-backed survey image retrieval helpers. + +Native pixel scales for SkyView image products +----------------------------------------------- +By default ``_skyview_fetch`` computes the ``pixels`` output dimension from +``imsize`` and the survey's native SkyView pixel scale below, so the returned +image is at full resolution. Pass an explicit integer ``pixels`` value to +request a coarser (downsampled) grid. + ++---------------------------+------------------+----------------------------------------------------+ +| Survey | Pixel scale | Source | ++===========================+==================+====================================================+ +| VLA FIRST (1.4 GHz) | 1.8 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| NVSS | 15 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| WENSS | 21 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| TGSS ADR1 | 6.2 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| GLEAM 72-103 MHz | 56 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| GLEAM 103-134 MHz | 44 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| GLEAM 139-170 MHz | 34 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| GLEAM 170-231 MHz | 28 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| SDSS u/g/r/i/z | 0.4 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| (camera native) | 0.396 arcsec/pix| https://www.sdss4.org/instruments/camera/ | +| GALEX NUV / FUV | 1.5 arcsec/pix | https://skyview.gsfc.nasa.gov/current/cgi/survey.pl| +| (mission native) | ~1.5 arcsec/pix | https://www.galex.caltech.edu/researcher/techdoc-ch5.html| +| 2MASS J/H/K (Atlas image) | 1.0 arcsec/pix | https://irsa.ipac.caltech.edu/data/2MASS/docs/releases/allsky/doc/sec2_4.html| +| (detector sampling) | ~2.0 arcsec/pix | https://irsa.ipac.caltech.edu/data/2MASS/docs/releases/allsky/doc/sec3_1b.html| ++---------------------------+------------------+----------------------------------------------------+ +""" + +import warnings + +from astropy import units as u +from astropy import wcs + +try: + from astroquery.skyview import SkyView +except ImportError: + print("Warning: You need astroquery installed to use SkyView survey tools") + +from frb.surveys import surveycoord + + +class SkyView_Survey(surveycoord.SurveyCoord): + """ + Class to handle queries to the SkyView service of `astroquery`. + + Args: + coord (SkyCoord): Coordinate for surveying around. + radius (Angle): Search radius around the coordinate. + mission (str): Mission served by SkyView for image searches. + """ + + SDSS_SURVEYS = {'u': 'SDSSu', 'g': 'SDSSg', 'r': 'SDSSr', 'i': 'SDSSi', 'z': 'SDSSz'} + GALEX_SURVEYS = {'NUV': 'GALEX Near UV', 'FUV': 'GALEX Far UV'} + TWOMASS_SURVEYS = {'J': '2MASS-J', 'H': '2MASS-H', 'K': '2MASS-K'} + + # Native SkyView pixel scales in arcsec/pixel; used to compute the ``pixels`` + # output dimension so images are returned at full (native) resolution by default. + SKYVIEW_PIXEL_SCALES = { + 'VLA FIRST (1.4 GHz)': 1.8, + 'NVSS': 15.0, + 'WENSS': 21.0, + 'TGSS ADR1': 6.2, + 'GLEAM 72-103 MHz': 56.0, + 'GLEAM 103-134 MHz': 44.0, + 'GLEAM 139-170 MHz': 34.0, + 'GLEAM 170-231 MHz': 28.0, + 'SDSSu': 0.396, + 'SDSSg': 0.396, + 'SDSSr': 0.396, + 'SDSSi': 0.396, + 'SDSSz': 0.396, + 'GALEX Near UV': 1.5, + 'GALEX Far UV': 1.5, + '2MASS-J': 1.0, + '2MASS-H': 1.0, + '2MASS-K': 1.0, + } + + def __init__(self, coord, radius, mission, **kwargs): + surveycoord.SurveyCoord.__init__(self, coord, radius, **kwargs) + self.survey = None + self.mission = mission + self.skyview = SkyView() + + @staticmethod + def _coerce_imsize(imsize=None, radius=None): + if imsize is None and radius is None: + raise TypeError("get_image() requires imsize") + if radius is not None: + warnings.warn( + "radius is deprecated for SkyView-backed image retrieval; use imsize instead.", + DeprecationWarning, + stacklevel=3, + ) + if imsize is None: + imsize = 2 * radius + return imsize + + def _skyview_fetch(self, skyview_name, imsize, pixels=None): + """Fetch a FITS image from SkyView. + + Args: + skyview_name (str): SkyView survey identifier. + imsize (Angle): Angular size of the image (full side length). + pixels (int, optional): Output image side length in pixels. If + ``None`` (default), the side length is computed from ``imsize`` + and the survey's native SkyView pixel scale so the image is + returned at full resolution. Provide an integer smaller than + the native value to request a coarser, downsampled grid. + """ + radius = imsize / 2 + if pixels is None: + pixel_scale = self.SKYVIEW_PIXEL_SCALES.get(skyview_name) + if pixel_scale is not None: + pixels = int(round(imsize.to(u.arcsec).value / pixel_scale)) + images = SkyView.get_images( + position=self.coord, survey=skyview_name, radius=radius, + pixels=str(pixels) if pixels is not None else None, + ) + if not images or not images[0]: + warnings.warn(f"SkyView returned no image for {skyview_name}.") + return None + return images[0][0] + + def get_image(self, imsize=None, band=None, radius=None, pixels=None): + imsize = self._coerce_imsize(imsize=imsize, radius=radius) + self.cutout_size = imsize + + mission = self.mission.lower() + if mission == 'first': + img_hdu = self.get_first(imsize, pixels=pixels) + elif mission == 'nvss': + img_hdu = self.get_nvss(imsize, pixels=pixels) + elif mission == 'wenss': + img_hdu = self.get_wenss(imsize, pixels=pixels) + elif mission == 'gleam': + img_hdu = self.get_gleam(imsize, pixels=pixels) + elif mission == 'tgss': + img_hdu = self.get_tgss(imsize, pixels=pixels) + elif mission == 'sdss': + img_hdu = self.get_sdss(imsize, band=band, pixels=pixels) + elif mission == 'galex': + img_hdu = self.get_galex(imsize, band=band, pixels=pixels) + elif mission == '2mass': + img_hdu = self.get_twomass(imsize, band=band, pixels=pixels) + else: + raise NotImplementedError(f"SkyView mission '{self.mission}' is not supported") + + if img_hdu is None: + self.cutout = None + self.cutout_hdr = None + return None + + self.cutout = img_hdu.data + self.cutout_hdr = img_hdu.header + + mywcs = wcs.WCS(self.cutout_hdr) + ypix, xpix = self.cutout.shape + (ra0, dec0), (ra1, dec1), = mywcs.wcs_pix2world([[0, 0], [xpix, ypix]], 0) + print("Got image spanning (RA, Dec) = ({0} - {1}, {2} - {3})".format(ra0, ra1, dec0, dec1)) + + return img_hdu + + def get_cutout(self, imsize=None, band=None, radius=None, pixels=None): + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + img_hdu = self.get_image(imsize=imsize, band=band, radius=radius, pixels=pixels) + if img_hdu is None: + self.cutout = None + self.cutout_hdr = None + return None + self.cutout = img_hdu.data + self.cutout_hdr = img_hdu.header + return self.cutout + + def get_first(self, imsize, pixels=None): + return self._skyview_fetch('VLA FIRST (1.4 GHz)', imsize, pixels=pixels) + + def get_nvss(self, imsize, pixels=None): + return self._skyview_fetch('NVSS', imsize, pixels=pixels) + + def get_wenss(self, imsize, pixels=None): + return self._skyview_fetch('WENSS', imsize, pixels=pixels) + + def get_gleam(self, imsize, band='170-231 MHz', pixels=None): + return self._skyview_fetch(f'GLEAM {band}', imsize, pixels=pixels) + + def get_tgss(self, imsize, pixels=None): + return self._skyview_fetch('TGSS ADR1', imsize, pixels=pixels) + + def get_sdss(self, imsize, band=None, pixels=None): + band = 'r' if band is None else band.lower() + if band not in self.SDSS_SURVEYS: + raise TypeError(f"Allowed filters for SDSS are {list(self.SDSS_SURVEYS)}") + return self._skyview_fetch(self.SDSS_SURVEYS[band], imsize, pixels=pixels) + + def get_galex(self, imsize, band=None, pixels=None): + band = 'NUV' if band is None else band.upper() + if band not in self.GALEX_SURVEYS: + raise TypeError(f"Allowed filters for GALEX are {list(self.GALEX_SURVEYS)}") + return self._skyview_fetch(self.GALEX_SURVEYS[band], imsize, pixels=pixels) + + def get_twomass(self, imsize, band=None, pixels=None): + band = 'J' if band is None else band.upper() + if band not in self.TWOMASS_SURVEYS: + raise TypeError(f"Allowed filters for 2MASS are {list(self.TWOMASS_SURVEYS)}") + return self._skyview_fetch(self.TWOMASS_SURVEYS[band], imsize, pixels=pixels) diff --git a/frb/surveys/twomass.py b/frb/surveys/twomass.py index fb462167..b6521411 100644 --- a/frb/surveys/twomass.py +++ b/frb/surveys/twomass.py @@ -4,12 +4,14 @@ """ import numpy as np +import warnings from astropy import units as u from ..galaxies.defs import MASS_bands from astroquery.ipac.irsa import Irsa from frb.surveys import surveycoord,catalog_utils +from frb.surveys.skyview import SkyView_Survey # Define the data model for 2MASS data @@ -29,7 +31,7 @@ _DEFAULT_query_fields +=['{:s}_m'.format(band) for band in MASS_bands] _DEFAULT_query_fields +=['{:s}_msig'.format(band) for band in MASS_bands] -class TwoMASS_Survey(surveycoord.SurveyCoord): +class TwoMASS_Survey(SkyView_Survey): """ A class to access all the catalogs hosted on the IRSA database. Inherits from SurveyCoord. This @@ -38,9 +40,10 @@ class TwoMASS_Survey(surveycoord.SurveyCoord): classes like TwoMASS_Survey """ def __init__(self,coord,radius,**kwargs): - surveycoord.SurveyCoord.__init__(self,coord,radius,**kwargs) + SkyView_Survey.__init__(self, coord, radius, '2mass', **kwargs) self.Survey = "2MASS" + self.survey = '2MASS' def get_catalog(self,query_fields=None): """ @@ -129,4 +132,17 @@ def convert_to_AB(self): else: raise ValueError(f"Column {filt} not found in catalog.") - return self.catalog \ No newline at end of file + return self.catalog + + def get_image(self, imsize, band='J'): + """Retrieve a SkyView FITS image for 2MASS.""" + return SkyView_Survey.get_image(self, imsize=imsize, band=band) + + def get_cutout(self, imsize, band='J'): + """Deprecated alias for FITS image retrieval.""" + warnings.warn( + "get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.get_image(imsize=imsize, band=band) \ No newline at end of file From 6c14b5e9252f5cfd91247b1e9817c238509be9de Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Thu, 14 May 2026 16:35:07 -0500 Subject: [PATCH 13/17] image retrieval through the VSA --- frb/surveys/vista.py | 352 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 352 insertions(+) diff --git a/frb/surveys/vista.py b/frb/surveys/vista.py index ff6a7acf..20137e09 100644 --- a/frb/surveys/vista.py +++ b/frb/surveys/vista.py @@ -1,7 +1,12 @@ """VISTA catalog""" +import re +import warnings +from urllib.parse import urljoin + import numpy as np from astropy import io, utils +from astropy import units from frb.surveys import dlsurvey from frb.surveys import catalog_utils @@ -17,6 +22,23 @@ _DEF_ACCESS_URL = "https://datalab.noao.edu/sia/vhs_dr5" _svc = sia.SIAService(_DEF_ACCESS_URL) +try: + import requests +except ImportError: + requests = None + +_VSA_GETIMAGE_FORM_URL = "http://vsa.roe.ac.uk:8080/vdfs/VgetImage_form.jsp" +_VSA_GETIMAGE_ACTION = "./GetImage" +_VSA_ARCHIVE = "VSA" +_VHS_PROGRAMME_ID = "110" +_VISTA_FILTER_IDS = { + "Z": "1", + "Y": "2", + "J": "3", + "H": "4", + "KS": "5", +} + # Define the data model for DES data photom = {} photom['VISTA'] = {} @@ -171,3 +193,333 @@ def _select_best_img(self,imgTable,verbose,timeout=120): imagedat = io.fits.open(utils.data.download_file(url,cache=True,show_progress=False,timeout=timeout)) return imagedat + @staticmethod + def _extract_select_map(html): + """Extract select names and their option values from a form page.""" + select_map = {} + select_pattern = re.compile(r']*name=["\']([^"\']+)["\'][^>]*>(.*?)', + re.IGNORECASE | re.DOTALL) + option_pattern = re.compile(r']*(?:value=["\']([^"\']*)["\'])?[^>]*>(.*?)', + re.IGNORECASE | re.DOTALL) + + for name, body in select_pattern.findall(html): + values = [] + for value, text in option_pattern.findall(body): + token = (value or text or "").strip() + if token: + values.append(token) + if values: + select_map[name] = values + return select_map + + @staticmethod + def _extract_form_action_method(html): + """Extract form action and method from the first form in page HTML.""" + action = None + method = "post" + form_match = re.search(r']*>', html, flags=re.IGNORECASE) + if form_match: + form_tag = form_match.group(0) + action_match = re.search(r'action=["\']([^"\']+)["\']', form_tag, flags=re.IGNORECASE) + method_match = re.search(r'method=["\']([^"\']+)["\']', form_tag, flags=re.IGNORECASE) + if action_match: + action = action_match.group(1).strip() + if method_match: + method = method_match.group(1).strip().lower() or "post" + return action, method + + @staticmethod + def _extract_input_names(html): + """Extract all input names from a form page.""" + input_pattern = re.compile(r']*name=["\']([^"\']+)["\']', re.IGNORECASE) + return list(dict.fromkeys(input_pattern.findall(html))) + + @staticmethod + def _extract_select_options(html, select_name): + """Extract option values and labels for a named select element.""" + pattern = re.compile( + rf']*name=["\']{re.escape(select_name)}["\'][^>]*>(.*?)', + re.IGNORECASE | re.DOTALL, + ) + match = pattern.search(html) + if not match: + return [] + + options = [] + for opt in re.finditer(r']*>(.*?)', match.group(1), re.IGNORECASE | re.DOTALL): + opt_tag = opt.group(0) + value_match = re.search(r'value=["\']?([^"\'\s>]+)', opt_tag, re.IGNORECASE) + value = value_match.group(1).strip() if value_match else "" + label = re.sub(r'<[^>]+>', '', opt.group(1)).strip() + options.append((value, label)) + return options + + @staticmethod + def _extract_fits_links(html, base_url): + """Extract absolute FITS links from an HTML response.""" + links = [] + href_pattern = re.compile(r'href=["\']([^"\']+)["\']', re.IGNORECASE) + text_url_pattern = re.compile(r'https?://[^\s"\'<>]+', re.IGNORECASE) + fits_ext = (".fits", ".fit", ".fits.fz", ".fit.fz") + + for candidate in href_pattern.findall(html): + lower = candidate.lower() + if any(ext in lower for ext in fits_ext) or "getimage.cgi" in lower or "getfimage.cgi" in lower: + links.append(urljoin(base_url, candidate)) + + for candidate in text_url_pattern.findall(html): + lower = candidate.lower() + if any(ext in lower for ext in fits_ext) or "getimage.cgi" in lower or "getfimage.cgi" in lower: + links.append(candidate) + + return list(dict.fromkeys(links)) + + def _resolve_vsa_download_link(self, session, link, timeout=120, verbose=False): + """Resolve getImage.cgi wrapper links to direct FITS download links.""" + lower = link.lower() + if "getfimage.cgi" in lower: + return link + if "getimage.cgi" not in lower: + return link + + try: + wrapper = session.get(link, timeout=timeout) + wrapper.raise_for_status() + nested_links = self._extract_fits_links(wrapper.text, wrapper.url) + if verbose: + print(f"Resolved wrapper link into {len(nested_links)} nested candidate(s).") + if not nested_links: + return link + + for candidate in nested_links: + if "getfimage.cgi" in candidate.lower(): + return candidate + return nested_links[0] + except Exception as exc: + warnings.warn(f"Failed to resolve VSA wrapper link: {exc}") + return link + + @staticmethod + def _pick_vhs_database(database_options): + """Pick latest VHS release value from VSA database options.""" + if not database_options: + return "VHSDR7" + + preferred = [] + for value, label in database_options: + val = (value or "").strip() + text = (label or "").strip() + if not val or val.lower() == "none": + continue + if val.upper().startswith("VHSDR"): + suffix = val.upper().replace("VHSDR", "") + try: + rank = int(suffix) + except ValueError: + rank = -1 + preferred.append((rank, val, text)) + + if preferred: + preferred.sort(reverse=True) + return preferred[0][1] + + for value, _ in database_options: + val = (value or "").strip() + if val and val.lower() != "none": + return val + return "VHSDR7" + + @staticmethod + def _to_sexagesimal_strings(coord): + """Convert ICRS coordinates to VSA-friendly sexagesimal strings.""" + ra_str = coord.ra.to_string(unit=units.hourangle, sep=':', precision=2, pad=True) + dec_str = coord.dec.to_string(unit=units.deg, sep=':', precision=2, pad=True, alwayssign=True) + return ra_str, dec_str + + @staticmethod + def _choose_option(values, contains): + """Choose first value containing token, case-insensitive.""" + token = contains.lower() + for value in values: + if token in value.lower(): + return value + return None + + def _build_vsa_payload(self, html, coord, size_arcmin, band): + """Build a permissive form payload from parsed fields and heuristics.""" + select_map = self._extract_select_map(html) + input_names = self._extract_input_names(html) + payload = {} + + band_lower = band.lower() + ra_deg = f"{coord.ra.deg:.8f}" + dec_deg = f"{coord.dec.deg:.8f}" + size_str = f"{size_arcmin:.6f}" + + for name, values in select_map.items(): + lowered = name.lower() + selected = values[0] + + if any("j2000" in value.lower() for value in values): + selected = self._choose_option(values, "j2000") or selected + elif any(value.lower() == band_lower for value in values): + selected = band + elif any("all" == value.lower() for value in values): + selected = self._choose_option(values, "all") or selected + + if "wave" in lowered or "filter" in lowered or "band" in lowered: + selected = self._choose_option(values, band_lower) or selected + elif "coord" in lowered or "system" in lowered: + selected = self._choose_option(values, "j2000") or selected + elif "frame" in lowered and any("tilestack" in value.lower() for value in values): + selected = self._choose_option(values, "tilestack") or selected + elif "obs" in lowered and any("object" in value.lower() for value in values): + selected = self._choose_option(values, "object") or selected + elif ("survey" in lowered or "prog" in lowered) and any("vhs" in value.lower() for value in values): + selected = self._choose_option(values, "vhs") or selected + + payload[name] = selected + + for name in input_names: + lowered = name.lower() + if lowered in payload: + continue + if "ra" in lowered and "frame" not in lowered: + payload[name] = ra_deg + elif "dec" in lowered: + payload[name] = dec_deg + elif ("x" in lowered and "size" in lowered) or lowered in {"xsize", "xs"}: + payload[name] = size_str + elif ("y" in lowered and "size" in lowered) or lowered in {"ysize", "ys"}: + payload[name] = size_str + elif "multiframe" in lowered or "frameset" in lowered: + payload[name] = "" + elif "submit" in lowered: + payload[name] = "Submit" + + # Extra fallback aliases in case form field names differ from guessed names. + payload.update({ + "ra": ra_deg, + "dec": dec_deg, + "xsize": size_str, + "ysize": size_str, + "waveband": band, + "filter": band, + "coordSystem": "J2000", + "frameType": payload.get("frameType", "tilestack"), + "obsType": payload.get("obsType", "object"), + }) + + return payload + + def _query_vsa_cutout_links(self, imsize, band, timeout=120, verbose=False): + """Query the VSA getImage form and extract candidate FITS links.""" + if requests is None: + warnings.warn("requests is required for VSA image retrieval but is not installed.") + return [] + + size_arcmin = float(imsize.to(units.arcmin).value) + coord = self.coord.icrs + ra_str, dec_str = self._to_sexagesimal_strings(coord) + band_key = band.strip().upper() + filter_id = _VISTA_FILTER_IDS.get(band_key) + if filter_id is None: + warnings.warn(f"No VSA filter mapping found for VISTA band '{band}'.") + return [] + + try: + with requests.Session() as session: + # Load form with VHS programme pre-selected so the database list is populated. + form_params = { + "database": "", + "programmeID": _VHS_PROGRAMME_ID, + "ra": ra_str, + "dec": dec_str, + "sys": "J", + "filterID": filter_id, + "xsize": f"{size_arcmin:.6f}", + "ysize": f"{size_arcmin:.6f}", + "obsType": "object", + "frameType": "tilestack", + "mfid": "", + "fsid": "", + } + form_resp = session.get(_VSA_GETIMAGE_FORM_URL, params=form_params, timeout=timeout) + form_resp.raise_for_status() + + db_opts = self._extract_select_options(form_resp.text, "database") + database_value = self._pick_vhs_database(db_opts) + + payload = { + "archive": _VSA_ARCHIVE, + "programmeID": _VHS_PROGRAMME_ID, + "database": database_value, + "ra": ra_str, + "dec": dec_str, + "sys": "J", + "filterID": filter_id, + "xsize": f"{size_arcmin:.6f}", + "ysize": f"{size_arcmin:.6f}", + "obsType": "object", + "frameType": "tilestack", + "mfid": "", + "fsid": "", + } + + submit_url = urljoin(form_resp.url, _VSA_GETIMAGE_ACTION) + response = session.post(submit_url, data=payload, timeout=timeout) + + response.raise_for_status() + links = self._extract_fits_links(response.text, response.url) + links = [self._resolve_vsa_download_link(session, link, timeout=timeout, verbose=verbose) + for link in links] + if verbose: + print(f"VSA returned {len(links)} cutout link(s) for band {band} ({database_value}).") + return links + except Exception as exc: + warnings.warn(f"VSA query failed for VISTA image retrieval: {exc}") + return [] + + @staticmethod + def _select_best_vsa_link(links, band): + """Select a deterministic best link, preferring URLs that mention band.""" + if not links: + return None + band_lower = band.lower() + preferred = [link for link in links if band_lower in link.lower()] + return preferred[0] if preferred else links[0] + + def get_image(self, imsize, band=None, timeout=120, verbose=False): + """Retrieve a VISTA FITS image through the VSA getImage service.""" + if band is None: + band = self.bands[0] + warnings.warn(f"Retrieving VISTA image in default {band} band.") + + allowed = [item.lower() for item in self.bands] + if band.lower() not in allowed: + raise TypeError("Allowed filters (case-insensitive) for {:s} photometric bands are {}".format( + self.survey, self.bands + )) + + links = self._query_vsa_cutout_links(imsize=imsize, band=band, timeout=timeout, verbose=verbose) + best_link = self._select_best_vsa_link(links, band) + if best_link is None: + warnings.warn(f"No VSA FITS image available for VISTA at requested position in {band} band.") + return None + + try: + filename = utils.data.download_file(best_link, cache=True, show_progress=False, timeout=timeout) + with io.fits.open(filename) as hdul: + primary = hdul[0] + if primary.data is not None: + return io.fits.PrimaryHDU(data=primary.data, header=primary.header) + + for ext in hdul[1:]: + if getattr(ext, "data", None) is not None: + return io.fits.PrimaryHDU(data=ext.data, header=ext.header) + + return io.fits.PrimaryHDU(header=primary.header) + except Exception as exc: + warnings.warn(f"Failed to download/open VSA FITS image for VISTA: {exc}") + return None + From b4424eacfea13a8cef2d1eba445ec1766ea64d7c Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Thu, 14 May 2026 16:35:27 -0500 Subject: [PATCH 14/17] upgraded tests to ensure new imaging functionality --- frb/tests/test_frbsurveys.py | 37 ++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/frb/tests/test_frbsurveys.py b/frb/tests/test_frbsurveys.py index 5bb7a013..5ee6f73c 100644 --- a/frb/tests/test_frbsurveys.py +++ b/frb/tests/test_frbsurveys.py @@ -76,6 +76,7 @@ def test_sdss(): #coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) coord = SkyCoord(0, 0, unit=units.deg) search_r = 1 * units.arcmin + img_r = 30 * units.arcsec # Instantiate sdss_srvy = survey_utils.load_survey_by_name('SDSS', coord, search_r) sdss_tbl = sdss_srvy.get_catalog() @@ -89,6 +90,12 @@ def test_sdss(): # Test empty table handling _assert_empty_catalog('SDSS') + imghdu = sdss_srvy.get_image(imsize=img_r, band='r') + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data is not None + assert imghdu.data.ndim == 2 + assert imghdu.data.shape == (76, 76) + @remote_data def test_wise(): @@ -271,7 +278,9 @@ def test_delve(): def test_vista(): # Catalog coord = SkyCoord('J214425.25-403400.81', unit=(units.hourangle, units.deg)) + #coord = SkyCoord('J210000-400000', unit=(units.hourangle, units.deg)) search_r = 120 * units.arcsec + img_r = 120 * units.arcsec vista_srvy = survey_utils.load_survey_by_name('VISTA', coord, search_r) vista_tbl = vista_srvy.get_catalog(print_query=True) @@ -282,6 +291,13 @@ def test_vista(): _assert_empty_catalog('VISTA') + # VSA image retrieval is service-dependent; if unavailable the method should fail gracefully. + imghdu = vista_srvy.get_image(imsize=img_r, band='J', timeout=120) + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.header.get('ESO INS FILT1 NAME') == 'J' + assert imghdu.data.ndim == 2 + assert imghdu.data.shape == (354, 354) + @remote_data def test_decals(): @@ -324,6 +340,13 @@ def test_first(): _assert_empty_catalog('FIRST') + # Imaging from SkyView + imghdu = first_srvy.get_image(imsize=10*units.arcsec) + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data is not None + assert imghdu.data.ndim == 2 + assert imghdu.data.shape == (6, 6) + @remote_data def test_panstarrs(): @@ -393,6 +416,7 @@ def test_tully(): def test_galex(): coord = SkyCoord('J142532.38+120121.17', unit=(units.hourangle, units.deg)) search_r = 240 * units.arcsec + img_r = 60 * units.arcsec # Test get_catalog galex_srvy = survey_utils.load_survey_by_name('GALEX', coord, search_r) @@ -403,11 +427,18 @@ def test_galex(): _assert_empty_catalog('GALEX') + imghdu = galex_srvy.get_image(imsize=img_r, band='NUV') + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data is not None + assert imghdu.data.ndim == 2 + assert imghdu.data.shape == (40, 40) + @remote_data def test_2mass(): coord = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg)) search_r = 240 * units.arcsec + img_r = 60 * units.arcsec # Test get_catalog mass_srvy = survey_utils.load_survey_by_name('2MASS', coord, search_r) @@ -418,6 +449,12 @@ def test_2mass(): _assert_empty_catalog('2MASS') + imghdu = mass_srvy.get_image(imsize=img_r, band='J') + assert isinstance(imghdu, PrimaryHDU) + assert imghdu.data is not None + assert imghdu.data.ndim == 2 + assert imghdu.data.shape == (60, 60) + @remote_data def test_in_which_survey(): From 4a2ec9a9a9c6e003cf492c0b0c98dc244399133c Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Thu, 14 May 2026 16:36:00 -0500 Subject: [PATCH 15/17] Revamped notebook with usage directions for surveys --- docs/nb/Surveys.ipynb | 2301 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 2096 insertions(+), 205 deletions(-) diff --git a/docs/nb/Surveys.ipynb b/docs/nb/Surveys.ipynb index fd0261bf..3d0bc326 100644 --- a/docs/nb/Surveys.ipynb +++ b/docs/nb/Surveys.ipynb @@ -4,464 +4,2355 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Slurp public photometry and spectroscopy at an FRB position\n", - " v1 - DES\n", - " v2 - FIRST\n", - " v3 - Datalab update\n", - " v4 - WISE\n", - " v5 - Pan-STARRS" + "# Tutorial: Using The `frb.surveys` Classes\n", + "\n", + "This notebook is a hands-on tutorial for `frb.surveys`. Every section shows the concrete class you import, how to instantiate it with a sky coordinate and a search radius, and which methods you can call. The package follows a uniform interface: `get_catalog()` returns an `astropy` `Table` of sources, while `get_image()` returns a FITS HDU and `get_cutout()` returns a rendered image where those products exist.\n", + "\n", + "## What this notebook covers\n", + "- How to create survey objects directly from the survey classes.\n", + "- How to call `get_catalog()` for every survey family in the package.\n", + "- How to call `get_image()` where a survey provides FITS image products (DES, DECaLS, NSC, Pan-STARRS, WISE, VISTA, 2MASS, GALEX, Euclid, FIRST, NVSS, WENSS).\n", + "- How to call `get_cutout()` where a survey provides rendered cutout products.\n", + "- How to query group and cluster catalogs for foreground structure searches.\n", + "\n", + "## Sections\n", + "1. **Optical surveys** — SDSS, DES, DECaLS, DELVE, NSC, HSC, Pan-STARRS\n", + "2. **Infrared, UV, and radio surveys** — WISE, VISTA, 2MASS, GALEX, Euclid, FIRST, NVSS, WENSS\n", + "3. **Spectroscopic and catalog-only surveys** — DESI, NEDLVS, PSRCAT\n", + "4. **Group and cluster catalog classes** — Tully, Wen, UPClusterSZ, ROSATX, Tempel, RASS, RedMapper, ACTDR5, eRASS1\n", + "\n", + "## What this notebook does not cover\n", + "- The higher-level wrappers in `survey_utils`.\n", + "- The catalog normalization helpers in `catalog_utils`.\n", + "\n", + "## Before you run the examples\n", + "- Many sections make live remote queries and may take time or occasionally fail because of upstream services.\n", + "- `NEDLVS` requires the `NEDLVS` environment variable to point at the downloaded table.\n", + "- `PSRCAT` requires the external `pulsars` package (`pip install git+https://github.com/FRBs/pulsars`).\n", + "- Pan-STARRS examples use `photoz=False` so the notebook does not require MAST CasJobs credentials.\n", + "- HSC examples require an account at the HSC-SSP portal (see the HSC section below)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If you wish to use the halos modules, you need the Aemulus HMF emulator. Please install it: github.com/AemulusProject/hmf_emulator\n" + ] + } + ], "source": [ - "# imports\n", - "from matplotlib import pyplot as plt\n", + "import warnings\n", "\n", - "from astropy.coordinates import SkyCoord\n", - "from astropy import units\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from IPython.display import Markdown, display\n", + "from PIL import Image\n", + "\n", + "from astropy import units as u\n", + "from astropy.coordinates import SkyCoord, name_resolve\n", "from astropy.wcs import WCS\n", - "from astropy.table import Table\n", + "from astropy.stats import sigma_clipped_stats\n", + "from astropy import visualization as vis\n", + "\n", + "from frb.frb import FRB\n", + "\n", + "from frb.surveys.sdss import SDSS_Survey\n", + "from frb.surveys.des import DES_Survey\n", + "from frb.surveys.decals import DECaL_Survey\n", + "from frb.surveys.delve import DELVE_Survey\n", + "from frb.surveys.nsc import NSC_Survey\n", + "from frb.surveys.hsc import HSC_Survey\n", + "from frb.surveys.wise import WISE_Survey\n", + "from frb.surveys.vista import VISTA_Survey\n", + "from frb.surveys.twomass import TwoMASS_Survey\n", + "from frb.surveys.galex import GALEX_Survey\n", + "from frb.surveys.euclid import Euclid_Survey\n", + "from frb.surveys.panstarrs import Pan_STARRS_Survey\n", + "from frb.surveys.heasarc import FIRST_Survey, NVSS_Survey, WENSS_Survey\n", + "from frb.surveys.desi import DESI_Survey\n", + "from frb.surveys.nedlvs import NEDLVS\n", + "from frb.surveys.psrcat import PSRCAT_Survey\n", + "from frb.surveys.cluster_search import (\n", + " TullyGroupCat,\n", + " WenGroupCat,\n", + " UPClusterSZCat,\n", + " ROSATXClusterCat,\n", + " TempelClusterCat,\n", + " RASSClusterCat,\n", + " RedMapperClusterCat,\n", + " ACTDR5ClusterCat,\n", + " ERASSClusterCat,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reusable Coordinates And Display Helpers\n", + "The notebook keeps a few shared coordinates so each section can focus on the survey API itself. The helper functions below are only for display; they are not part of `frb.surveys`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A typical workflow looks like this: instantiate a survey class with `coord` and `radius`, call `get_catalog()`, and then optionally call `get_image()` or `get_cutout()` if the class supports them." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "COORDS = {\n", + " 'north': FRB.by_name('FRB20200430A').grab_host().coord,\n", + " 'south': FRB.by_name('FRB20180924B').grab_host().coord,\n", + " 'delve': SkyCoord('J102922+012133', unit=(u.hourangle, u.deg)),\n", + " 'galex': SkyCoord('J142532.38+120121.17', unit=(u.hourangle, u.deg)),\n", + " 'euclid': SkyCoord('17h51m07.4s +65d31m50.8s', frame='icrs'),\n", + " 'equator': SkyCoord(0, 0, unit='deg'),\n", + " 'pulsar': SkyCoord('J000604.8+183459', unit=(u.hourangle, u.deg)),\n", + " '4C 02.27': SkyCoord('J093519+020415', unit=(u.hourangle, u.deg)),\n", + " 'wenss': name_resolve.get_icrs_coordinates('NGC1275')\n", + "}\n", + "\n", + "def preview_catalog(table, nrows=5):\n", + " \"\"\"Display the first few rows of a catalog in a compact tutorial-friendly way.\"\"\"\n", + " if len(table) == 0:\n", + " print('Empty catalog returned.')\n", + " print('Columns:', table.colnames)\n", + " return\n", + " display(table[:min(nrows, len(table))])\n", + " print(f'Rows: {len(table)}')\n", + " print(f'Columns: {len(table.colnames)}')\n", + "\n", + "def _split_image_product(product):\n", + " if product is None:\n", + " return None, None\n", + " if isinstance(product, tuple):\n", + " if len(product) == 0:\n", + " return None, None\n", + " if len(product) == 1:\n", + " return product[0], None\n", + " return product[0], product[1]\n", + " if hasattr(product, 'data') and hasattr(product, 'header'):\n", + " return product.data, product.header\n", + " return product, None\n", + "\n", + "def choose_cmap(band):\n", + " if band is None:\n", + " return 'gray'\n", + " band = band.lower()\n", + " if band == 'vis':\n", + " return 'viridis'\n", + " if band == 'u':\n", + " return 'Blues'\n", + " if band == 'g':\n", + " return 'Greens'\n", + " if band == 'r':\n", + " return 'Reds'\n", + " if band == 'i':\n", + " return 'Oranges'\n", + " if band == 'z':\n", + " return 'Purples'\n", + " if band == 'j':\n", + " return 'inferno'\n", + " if band == 'h':\n", + " return 'magma'\n", + " if band == 'k':\n", + " return 'cividis'\n", + " if band in ['fuv', 'nuv']:\n", + " return 'plasma'\n", + " if band in ['w1', 'w2', 'w3', 'w4']:\n", + " return 'YlOrBr'\n", + " return 'gray'\n", + "\n", + "def plot_fits_product(product, title, band, cmap=None):\n", + " \"\"\"Plot a FITS-like image product returned by get_image() or get_cutout().\"\"\"\n", + " data, header = _split_image_product(product)\n", + " if data is None:\n", + " print(f'{title}: no FITS product returned.')\n", + " return\n", + " if isinstance(data, Image.Image):\n", + " print(f'{title}: received a rendered image instead of FITS data.')\n", + " display(data)\n", + " return\n", + "\n", + " figure = plt.figure(figsize=(6, 5))\n", + " if header is not None:\n", + " try:\n", + " axis = figure.add_subplot(projection=WCS(header))\n", + " except Exception:\n", + " axis = figure.add_subplot()\n", + " else:\n", + " axis = figure.add_subplot()\n", + "\n", + " # Norm\n", + " _, med, std = sigma_clipped_stats(data, sigma=3.0)\n", + " vmin = med \n", + " vmax = med + 3 * std\n", + " cmap = choose_cmap(band) if cmap is None else cmap\n", + " norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch())\n", + " axis.imshow(np.asarray(data), origin='lower', cmap=cmap)\n", + " axis.set_title(title)\n", + " plt.show()\n", + "\n", + "def show_cutout_product(product, title, band, cmap=None):\n", + " \"\"\"Display a cutout whether the survey returns FITS data or a rendered image.\"\"\"\n", + " data, header = _split_image_product(product)\n", + " if isinstance(data, Image.Image):\n", + " display(Markdown(f'**{title}**'))\n", + " display(data)\n", + " return\n", + " if data is None:\n", + " print(f'{title}: no cutout returned.')\n", + " return\n", + " plot_fits_product((data, header), title, band, cmap=cmap)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Optical Surveys\n", + "\n", + "This section covers seven optical survey classes that span the wavelength range from the near-UV (SDSS *u*) to the optical near-IR (*z*, *y*, *Y*). Together they provide photometric and spectroscopic catalog access, FITS image downloads, and rendered cutouts.\n", + "\n", + "| Class | Survey | Sky coverage | Depth (typical) | Bands |\n", + "|---|---|---|---|---|\n", + "| `SDSS_Survey` | SDSS DR17 | ~14 500 deg² (N) | r ≈ 22.5 | ugriz |\n", + "| `DES_Survey` | DES DR2 | ~5 000 deg² (S) | r ≈ 24.5 | grizY |\n", + "| `DECaL_Survey` | DESI Legacy Imaging DR10 | ~14 000 deg² | r ≈ 23.9 | grz + WISE |\n", + "| `DELVE_Survey` | DELVE DR2 | ~17 000 deg² (S) | g ≈ 24 | griz |\n", + "| `NSC_Survey` | NOIRLab Source Catalog DR2 | ~35 000 deg² | r ≈ 23 | grizy |\n", + "| `HSC_Survey` | HSC-SSP PDR3 | ~1 200 deg² | r ≈ 26 | grizy |\n", + "| `Pan_STARRS_Survey` | Pan-STARRS PS1 3π | ~30 000 deg² (δ > −30°) | r ≈ 23.2 | grizy |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SDSS (Sloan Digital Sky Survey DR17)\n", + "\n", + "`SDSS_Survey` wraps the SDSS DR17 sky survey, covering ~14 500 deg² of the northern sky in the five *ugriz* optical bands to a typical depth of *r* ≈ 22.5. `get_catalog()` returns photometric and spectroscopic detections from the SDSS CasJobs database via `astroquery`.\n", + "\n", + "**Image products:** `get_image()` retrieves a resampled FITS product through the SkyView service (`astroquery.skyview`) at the native SDSS SkyView pixel scale (0.4 arcsec/pixel). This is a *resampled* product — not a pipeline-reduced SDSS frame — so treat it accordingly for quantitative science. `get_cutout()` fetches a rendered JPEG from the SDSS cutout service and is better suited for quick visual inspection.\n", + "\n", + "If you are aware of a better method for accessing native SDSS FITS frames, please open an issue in the FRBs/FRB repository." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got image spanning (RA, Dec) = (229.71020960909806 - 229.70167799672214, 12.372488125536517 - 12.380821457082272)\n" + ] + }, + { + "data": { + "image/png": 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QHPL0cGC/jaVcaEJeTJyJuTVLbV0ybrdgBh9P/j6SyDET3DL79X3g2oH+6m5It0E2L2HwGrWybW7NsAX3/j8bK0eSs8RfYGPsnMRXsbGM73y1F5w00cQCnbrYuoy6KTa2j6zDzetpda+N7cftI3ND5st9t9y2l1jb1k2qb2Pkp5dAir32gl+/a+uybyvYtz0EN598lX3dzbbgLnLNA3wtxtufjNz8WbYc+XkP1arxfvLhdbjMBg+SnxfYut5D1sNR+00TJYZ8Bd7Y3n/oWABgM7n2yBy6vXtMLGrA700sOOsL294R8jNevdDrIjP7IOJ7DcL+/fsRF2fvlYwif8LPzs5GixYt8Prr9veqgwcPYsmSJXjiiSewZMkSfPLJJ1i7di369u0bUu62227DE088gffeew8jRozA1q3HfqNZs2YNgsEg3nrrLXz33Xd46aWX8Oabb+L//u/E77tz5sxB8+bN8fHHH2PFihW49dZbMXDgQHzxBTkJJ9GkSRPs2LEj779Zs0IvhnvvvRcTJ07E+PHjMWPGDGzfvh3XXnttQadJCCGEOCuK/Df8Xr16oVevXvRv5cuXx5QpU0Jir732Gtq2bYstW7YgMfGYwCM7OxsXXnghqlSpgooVK+a97nD55Zfj8stPCCzq1q2LtWvXYuTIkfjLX/4CACGbPwAMHToUkydPxieffIIrr7wy7LhLlCiBamH+Jbt//36MHj0a7733Hrp16wYAGDNmDBo3box58+ahXTvyxC2EEEKcQ4r8Cf9M2b9/PzzPC8nZ/eSTT6Jx48YoX7482rVrh5QU8pXgSfXj48MopM+gzLp161CjRg3UrVsXAwYMwJYtW/L+tnjxYhw9ehQ9evTIizVq1AiJiYmYO3fuaY5QCCGEKHyK/An/TDh8+DAefvhh9O/fP+Q3i9tuuw033ngjcnJyULGifQXqOBkZGXj11Vfznu4ZH374IRYuXIi33norLzZ8+PCQMqmpqRg7diwaNmyIHTt24Omnn0anTp2watUqlCtXDjt37kR0dHTIP0oAoGrVqti5cycAoHbt2jgun8jfPiMzMzPEnOH4txhCCCEil23btoXsDXFxcWF/0y82G/7Ro0fxq1/9Cs45jBw50vw9JiYGMeS1suNs27YNl19+Oa6//nrccccdtMy0adNw6623YtSoUWjSpEnYtk7+CaJ58+ZITU1FUlISPvzwwzzBYGHTt29fzJhBhGi7toe8EuIRQZ37yIqEvG5WMENFcj5FWlREA4R/dSo/6fadXKomrWbfK2fjRv3GNsaEimzcTMh3hIhyFk635Zhw6KLOtu60MBoR9m45EVDRYyFzg63We8BrZAVKLp2IIZlYisH6YD93ESGS2/ujrcv6TbHCRXq8CCPQK0vWCDlmr1MPG6tt7wVuxue2vep2PMGl9pr1Otj3uN1s6wmA+uQetNUKFb3Ui32VCzdfWEeucSKEDfTpb2JMROvW2XfSKbvs+/AunYgm2Xvm7B7ERJjkuvXYWlpuv3l1YYTIga59bFkizPXIvSCYscw2yASzyeTc5xcRHjwEAOYb7c6dO2P69Om2PorJhn98s9+8eTO+/fZb34rE42zfvh1du3bFxRdfjL///e+0zIwZM9CnTx+89NJLGDjQn/r6OBUqVECDBg2QkXFMvVmtWjXk5ORg3759IU/5u3btCvu7/+mYMGGCecI/1U8XQggh/vdJT08PMd851f74i9/wj2/269atw7Rp01CpUqUzqr9t2zZ07doVrVu3xpgxY6jn8PTp03HllVfi+eefx5133nnGY8zKysL69etx883HXoVp3bo1SpYsialTp6Jfv34AgLVr12LLli1o3779GbcP2K9p5HkthBCiZs2avh+Ci3zDz8rKynsyBoCNGzdi2bJliI+PR/Xq1XHddddhyZIl+OKLL5Cbm5v3G3h8fHzeu/Th2LZtG7p06YKkpCT85S9/wf/7fye+8jn+pD1t2jRceeWVGDp0KPr165fXfnR0dFjh3gMPPIA+ffogKSkJ27dvx1NPPYWoqCj073/sK6/y5cvjtttuw3333Yf4+HjExcVhyJAhaN++vRT6QgghioQi3/AXLVqErl275n2+7777AACDBg3C8OHDMWHCBABAy5YtQ+pNmzYNXbp0OWXbU6ZMQUZGBjIyMkxygeOCuX/+8584ePAgRowYgREjRuT9/VS/g/zwww/o378/9uzZgwsuuAAdO3bEvHnzcMEFJ35XeumllxAIBNCvX78Q4x0hhBCiKChypz1RMI477e35412IO8lj2WNitaokq94if050SKhrY0yYxoQnALw2XU3MrbfJSChEDEbHwyCiNurAxlzwyLG4nTYRhpfSwpZjoqOuxM+BCJYAnNHcGpj7G3EFc+vX2XIs4QlLklQ/2cYu/ZXtg4namOtfLBFkVSAOf8xpL4a0ByBQt7kdz65NNrbbnoPc8e/TNk0f1auamFfPzg0TlwVaWRFncOpHti5x2vPa2aRXbsqHNrZ1i4nR8QFcHMhc+dg6ZOeUJWLKWG3Hw8SorD3SL7uvBCcScXIyuR+yNUfEo2FFocTNlEFdPJlI2KfLotsRek4yD+eg0mNvFS+nPSGEEEKce7ThCyGEEBGANnwhhBAiAtCGL4QQQkQARa7SF2eH1/wieCelx6UwYRoT9+0gQh0momEub2eQ7pUJ0zySEtUx0R4bD0tXSZyqgtNsylYGc9JC9mQbY8dBhHyBGlZoGFxtnQUB8ONjc0vK0dTIxP3N60iElCT1KSvHzr3bRJza2JiZYImtpXifAqpsLmYMrl1og0yIOWuaiUVdf6Mt990y2o8px8SQJBbMtg5ubssmE/OSG/nrl6RhDe7YZWIlbuQeIy6LzCNN92rPS6DzVbbcNns9ek2IMI254CVY4SkT1rqf7FryOl5q+yhJ+mX3Q7K+/IrzAAD/fZ07pD4RvUbd+oCv5rhDY6g7qpd9EMBbptyp0BO+EEIIEQFowxdCCCEiAG34QgghRASgDV8IIYSIAOS0V0w57rT305JvERcbmxcPfjzalPVSL7EN5NhUpV7dZibmZn9tY0SI5LW8iI7TzZ9tyzIxGEnr69txbTNJBUoEdW6PFTd51arbGHHkY4I/r611TMNyn0IfkjIXAMDEfETk5XXp66sb5mrIhFFe5Zomljv+TROLuv63JkZd4siYEeMzzSkR8gWXkVS29YmzILjD2dFPrfNfyfatTYylU2WCLK/X1bacX0dENjdMVMjc8hISbV221g+QNcNEbQDchA9skGT19Osc53W92vaxyAokmcOcI9eyx5zxzub67myvHbeEOI8yJ0AAIK6BbB27zVZ0HOjZz9aNtq56LJ2zWzkn5HPmwUOI73+/nPaEEEIIEYo2fCGEECIC0IYvhBBCRADa8IUQQogIQKK9YkqeaG/e14iLjcmLe3GVTFnm2kSFQ8TtjorIWBpW5mgFwM2aYmJeEklxu/f/2RhzV2MCHoJfASJ1tmOOcCkX2hhzemMCQnYcrN9wsLklfbupk0zMS+1gy/3HCqi8bjbtKktP7DZvtnVbEcHmESsKpeuGCd1YauMOl5sYS4cKhBGpMphr4Pw5tlx8vO2jkr3O2NwwqOCPrX+21okbJnPAY6lnWYpnIEyaZ7/pbBlMiEkITrJC2MCg39mCy+faWCUi6CVpeam4bx05tmo1bDnmnIgwAmXWN7vHkvPMhKKBa26z5b5fHPI5M/sQ4vvdI9GeEEIIIULRhi+EEEJEANrwhRBCiAhAG74QQggRAUi0V0w5LtrbO/UDxJ2cHpc40VExFxFaufSVtqOYGBPy4q1gCS3a03F6xE3Lb5paltqVOpJlEVcx5grG+mhkxXhu91ZbrgJx0tpK0vIyoQ4TOzF3LcC3iI3ChEx+3d/YXJN5RS0yr7vsfPkdc+4Cm8o2qu81vurSuQZfx4FrB/pq0q2xjn5UaEXc9xgecayjojZynVD8ns9Sdn15jVrRoiz9rG8RGhNnsuNjddkxM8EsEd4hhRyLXxFmonUrpGmkm/N7WvDjMSYW6HerLcjc8lgq6XU2Rt0A14YKqOW0J4QQQgiKNnwhhBAiAtCGL4QQQkQA2vCFEEKICEAbvhBCCBEBlCjqAYizw6tcE1652LzPjthQevWTbawdsVJlanJm20nUvmGV9yRnPFPQszzYLJ80cqxi10tuRMpZu0q3bg0fY/5y82ebWGDYH225fcQOtRRRHlew6nk3ezLvPCvLxmJjTcgj84q9ROFMFNNes4ttrFRZE8sd+QfbHrMlbdXWlmNvSZA3E0pc+isTc9vs2w9uAbGHDqOU97pbG16qvmcwFXyCtYL2YojtLVlzzEI5+J/ptj2m5mdrgawvr569vulaD6fwZzbP7FiIIt9t2WRigTZdTSz4/t9NjFrUsrEkkLW0fB4pR85TJzsW+iYAOZ9uBbH0BYCjR23ZNdaCnFr4duhB2iP2v0TNn/+69Q6Q9XEa9IQvhBBCRADa8IUQQogIQBu+EEIIEQFowxdCCCEiAIn2ijlu4VS4sieJoZjF7Q9WEBf8dLSJefUb27rE/pIJoPxa2QKggiC/Qj6Wu9s3VBBnxTrMdjg4eZyvLjwiWKJWveFyi7N5ZAIqBhN0VUmw48mXVxsIk1O959W2j632nFALXmaBzPKLtyTCRyaqYmLNjkSQFQ4mWEtuYkKO5JtH+nLbNxMqEnEmjpJc503IMZNrj567OUTs2djaQ2Pndhtj1ssIc50xq2UiqPMSa9v2iNiNCQupfS+LMTEqs9YlFrVUBMiseqvaucYiIhRFGIElWbPUgnw9sTFm990MK/hz+USvLvsgHd+p0BO+EEIIEQFowxdCCCEiAG34QgghRASgDV8IIYSIACTaK+5kHwCCJ4lQDmbaMkQIQ0V2zCWOwURVTBwDUNESE1B5xHGNCnjiifCIuW4Rh7mft1thWnTdZibmYPOpI4YcH+mXOm4tW2RiVBAHABlEeOS3HBFvua3EtW6ZzUHvtWxjYxWJQyATUDERVH0riPNYTnQm7ku3xxYYNNiWC+eeR/pxO3fY8RDRHhWPEsc0N3+WrduECEqrWvGbO0JEq8SFkDk5uj17bF0iLqPi1q3EuRJcQMpywbv1q2xlJihl5/ki4gzJxlLBXrfBL9/1V7dJSxNzs6aZWODGO23lHOIiWI0IFwF+X2LHTJwJUS/F9vPxv225ckRM3DxfvyV+5uM7BXrCF0IIISIAbfhCCCFEBKANXwghhIgAtOELIYQQEYDnnHNFPQhx5mRmZqJ8+fLY89StiCt9kpgjmgj0mKsbEbW55VZchpIlbXvMZYy4dQGgYj6WupOmuGVpMZnT217iXMYENz4d69xSIrLz6+rGxnImELEbsomwjYnGKhFnLzYPTHSZQtzaSL8sXbJLtyJHr1svW+7br225NjZVLxWPMje4Nl1sOYRxo2vRzpYjKXep26RPsSFzpaSUsgI9Cln/gSQ7vuCsL/yNZSdxEQRoalgKE+YSp0oqcN1F+maCTeIQ6FWrYesm1bexdCvipAJJdj8kYjrs3mZj4CJOhpdk0/XS9kgacI+J9i6+LORzZlY2Kl5yDfbv34+4uDhffekJXwghhIgAtOELIYQQEYA2fCGEECIC0IYvhBBCRABy2ivuHMwGco/mfaRCkbpEkLLBpmn0Ona3sdokheiMz21sr3UAAwCQOHUkq0UEertsWll6LEesux0VplFHOCLSio+3MSbcImPxmlkRWvBfr9hyqR1tewjjbkdc9egcEgFV7vj3TSyqla3rJnxg+0hpavtgaVMJbv5M0p51NWRpSd1sK7oLdO1jy5E1DIALUmdNseNh4lOSlpSuEQZLJf2dTa2LvXvtWDoRUSgRuuV++6Wty1wSE6yozYUT7TExKxPbzp9ty7FUsQSvFRFNsvbYtccgAj3m5ucR51Hm5ueW25S+TEAIgAqjWfpmlmqZuQF6TDTJrouF00M/H/KZNvsk9IQvhBBCRADa8IUQQogIQBu+EEIIEQFowxdCCCEiAIn2ijlewybwyp5w7nIZa02ZAEl1GWRucvWJe1itZBsjIp9Av9vo+IIT/0XjBiqaISlNq1khDC3HRHEkBSnD63qlDa4mwkAyZnStamOxsb76BYDgqL/a8SSTc0CcxtxSm6635FMv2z4WEAEbcaKjTnsE6kLIxG8sXegP1kEv0OdmW64EcZC8kKRKBuCmfGhjW7bYgtnZNlaxou2HubVdZI/Z/bDOlmMOkkzcR9zbmNsaXUvEmdAxIR5L6woAJE0wIzCIpMydPsEWTLDCYa8GEeUyUahfN78K5NyTtcTEwMFpE+34mJiOCXURxr0vmQg72biJkM8R98pAPTs3rlK+Yz5IrqfToCd8IYQQIgLQhi+EEEJEANrwC4nnnnsOnudh2LBhebEuXbrA87yQ/37729/m/X3Tpk3wPA8AMHz4cNxyyy3nedRCCCEiBf2GXwgsXLgQb731Fpo3b27+dscdd+APf/hD3ueyZcuez6EJIYQQALThnzVZWVkYMGAARo0ahWeffdb8vWzZsqjm042qILh5s+CiT6Sw9fr/xpYhwhoqtCKpXd0Gkvq069W23GqSWheAl0TEOlWJWxsRuHgstStLP3v0qI2xFJ0lScpQ5ua3zjrb0XSjJKUsmwePiaq+W2bbA+ClknSx7JjJGL0Ol5lYcOpHtB9DOBe2/O19Z/sNXE5c8JiwiZxPmmqUCc5YXSJSBACPrKVAy1a2IDunRDTGXOuC7/zNlrvyBl99OJbqN4U4JxK8eCJWY6lnfQpUAfBUv8TpjcJcCLOs2M19Q9Yhu74ZTADaglzfxGGRCWuZ6x+DreGwsOvH5/F5bS6xfS+cZgvmP76Sub7aPxl9pX+WDB48GFdccQV69OhB//7uu++icuXKaNq0KR599FEcPHjwPI9QCCGE0BP+WfH+++9jyZIlWLhwIf37TTfdhKSkJNSoUQMrVqzAww8/jLVr1+KTTz4BANSuXRvOOQDHfsM/FZmZmcjMPOELfeAAf2VECCFE5LBt27aQvSEuLg5xcXG0rDb8ArJ161YMHToUU6ZMQenS/OuzO++8M+//mzVrhurVq6N79+5Yv3496tUjX3Wfgr59+2LGjBlnNWYhhBD/W6SkhCbx6ty5M6ZPn07LasMvIIsXL8bu3btx4YUnfsfNzc3FzJkz8dprr+HIkSOIiooKqZOamgoAyMjIOOMNf8KECeYJP/+JFkIIEVmkp6ej3EkmTeGe7gFt+AWme/fuWLkyVNB26623olGjRnj44YfNZg8Ay5YtAwBUr179jPvL/zXN8c3fa9gIXpmThEEklSpl6wYbY2kaicCIpX1EKf4tB3Mk80rHmFhwKfn2gqSzZSkradpVMg9uyyZbtxo5F/kdrQAqJPNi7IXl9pK0vERM5NlSAIBAr4EmFvzw1TCl88EESm26mJibY9PP+hXjUdEPERUyASFIqlLACrJcOhGKEpc+L74yGw0XbzFRFStHRHHMmY058rGU026zvc5YWl63dIEtV62G7YOleGapoAnMkfJYP0RcRsSswbRPbF2WptmvgI2lxGZzuIek2CZCRepWyNKFk/F5xI0U1cMIWcl90qXbNMheSXte3Fbr+OgxkWoLMh4mMAZQs2bNU27yJ6MNv4CUK1cOTZuG2h/GxMSgUqVKaNq0KdavX4/33nsPvXv3RqVKlbBixQrce++9uOSSS+jre0IIIcS5RBv+OSI6OhrffPMNXn75ZWRnZyMhIQH9+vXD448/XtRDE0IIEYFowy9EThZKJCQkSGQnhBDiF4PewxdCCCEiAD3hF3eq1gROSo9LXdmYGI+I7JhYiqYLbUXSaYZxanMr5phYkIlrmNMYE8p1vNT2scB+k8Lc1rxKlWwfJDWomz/TliNQQRYTCTFHtzOB1SepO93OnbZcJSuC8i62grrARZ1tXZJu1G3ebNtjaU5ZOmF2Pv2mFWXOcelLbQygqYPZfHEhHxNsWqGcF0/WEhMWploXNSoaY+ufXctkDh1xtmOiUCpQBXcSdPtI39Fh0uv6IDg1zcQCV13nqy4dNxH8UfFbXfsmk/viA1/9hr1uiXMmO39umfVn8doQJ03WXixxEqwQKjL1skh659OgJ3whhBAiAtCGL4QQQkQA2vCFEEKICEAbvhBCCBEBSLRX3MnaD+SeEKu4dd+bIl6ri2y9BCJ6YQ5Ss2yaRvf1Z7YuS7cL0DSbXkkr/nEkBabXlgjJiIOeR1J0UsFMJ5LRkKUGZSKtlm1sOZZClLlhMXc01i/CuOqx+uSYqVCLiJZyR71kYlF33GvHwtK41k8mYyEiTuI4GLjIzn/uO3+15foMMDG3286r1/vXtl8A7vvFNubT8Y6uLyLIYkI3Ohbifkgd/thaIuI+t/dHOxZ2nfhNBQ0AsRVsbGuGCQV69rPjWUOEkwlWuBrVpa+ty1LAMnElc+kjolC3fp2JebXsWLx6dg17jWz6ZLdwuu03HOSe5nXtbcuttyJaNCZptrPI/WFRqDjZHTqD9L3/RU/4QgghRASgDV8IIYSIALThCyGEEBGANnwhhBAiAvCcc66oByHOnMzMTJQvXx57334acSc57bmli2zhatVMiKYWZU5VLFWsz3ScAIByxDGKCdZYOeJchupEwEMEfzSFJUmPi9hYE6IiqKNkbpjTW4oV/6AsSV3JnOjCwY6ZpSgm5dzUSTaWlWVige49bbn5s02Muuox0d7ZpCcmuL0kRSpxXQRABYNUFLePpC/9bpnthwgkaXsktStdw0S05xZaR0oqhCWiPd/XExHqAgDSiQCOzHfghrt5/fx1l0z3VQ5VWVpesq6JII72y1LUMkdE5qDH0nuHEdb6FdEGP//IxAKduvjrhwkV85F58BDi+9+P/fv3+06Pqyd8IYQQIgLQhi+EEEJEANrwhRBCiAhAG74QQggRAchp738Mr/vlJuaWLrAFWQrRzdZdy1G3NeLyVo+IuQCa1tRN/tCWY2ksp31h+2F9EGcv5thF0+MyQRATfW21Tm3UMW0dcWpr08XEgkxACMBLbmSDTKBHzp9bOs+21/cGG2MdbyXnObWDLceEVqxui/a2HBMfktSudF7JsdHzBNAUtyzdaDDtE1susbZtjwn0FtmUzG4PEbrdPNSWY66BCYk21qC1rTvjczsWkuKZXctYTuYQPOVxoP8dttwmu7bZeqfpeolI2LE02Sz99Uf/suMb+rQdCztPzOmQCfRYKmKWMjpcm+Q+GRj0OxNzftccud/kT2ut9LhCCCGEoGjDF0IIISIAbfhCCCFEBKANXwghhIgAJNor7lSsBMSUOfGZCGZoutHZzIGNCKgILM1p8IuxvDBzjGrRzvZNBHosTa3bud3Efv5muomVvMKKF6nD2bo1JuYREZRH0nsG08bbPgheBSIiY0IdgDqzsWMO+BXFMYdAv0KrWOtWSEV7RHDJYOJRmnq2AhHttbJrJqwTWraNOyIsZC6L1G2SiLTczp0mxpzxmOuc+49NC+t1InWJSI6lxwVZH161GjZ22a9sXQDYsNL2s4+I2JizJHG09DqQtUnwmDiTpfUlTqFupXUmpMI74hTqviOOfCyV7Q9kzYSDufcdzLQxtuaYiJCsw/xiT5d9yO/o8tATvhBCCBEBaMMXQgghIgBt+EIIIUQEoA1fCCGEiACUHreYkpce9+PXEHeyaI8421HXLSbwYrD2dhLnNyaYAXfxoilWiTMeE/dRiDDNzZ9pYky8yIR3NB0qGx9LwctS9RKBVzi8+sk2mHKh7WfWFFuXpQJlMGe2HJLGlUGOL78DGMDd5JhjoNeGiNWmT7B1mfgzXJpmJmhk6YhZOmG/6XFLEZFWDElJS1LPeh2IG6ZPEa2XFCbFbf66zCGTjRnwP7esb9IPW6/UFZGlmWXOlyzVL7sHMTc/dj6ZGJWtGeaoF6asmz3Z9sOEpuw6Y06a7Jir1Az5mJl9EPG9Bik9rhBCCCFC0YYvhBBCRADa8IUQQogIQBu+EEIIEQHIaa+488MmoMxJYpz6JM3s+nUm5jF3JwZzkGIxImADuPsYFfhRoRxxUmNugMT9jaXZZHhMvEhSU7LUp17LNiYWTLfOfVF9r7F9MBEggOB/ptv6ROTlMQHixHdtg9FWBOUxp7295Ph8igCD0yb6KkfFUkTExOYabP6rVecdMYcz5lw2fxavnx+2Xpmoiq1NInQLfjzGlmPnKZlcE0ysRsStHnMhZGOG/zS12EX6JngxVkDmWF12LCmtbF0iwGVr08237nteL+uQCeJ8SUV3RAQIAO7jd2wsK8vWJ+fea0pcRploj7kQ5l/XB+W0J4QQQgiCNnwhhBAiAtCGL4QQQkQA2vCFEEKICEAbvhBCCBEBSKVf3KlSHSh7wlrXq5Jgy5B80iyHuZv8qYl5Kc1tXb+2m/BvVcpypTPlMsNjal9me1vdKpe9dj1tOWIL6+2yMWajG3XH7+1YFs2wMaJGBoDAVdfZftZYe9bcr7+0fZO3AajVL7HWZapuVtdlWJtmj60vRtvOtj2inmfqe/YWQTjVOVPVu3T7ZoLXkljAMsgbLcGpaSYWaEIsixPqkn7t2x2oSq5bsm7cETJfxL7Xtw0uAC+lhQ0yK1xme0vWUjDtY9sHeeOAWTIHJ46jYzSw42NvSZBjpvcaQu4s/hZHVK8rTCzA1Pdr7Xlx28k5SKpvY8wOPT+HfNphn4Se8IUQQogIQBu+EEIIEQFowxdCCCEiAG34QgghRAQg0V4xx02dBBdd8kSgv7WNxEVWLIUfiHiEWZoyi0dmkbr3Rzo+JtDzml1sCzIBFrOAZcIcls+bWakSASEV+vjMyU2tN1nedSbQCyfKYVa/hEAiyeW+bKGJeZ162MrEztZLIMIhkj/da9LS9svmdedOW27WFNseE3O16WLb25BuY+QcA+DnjwkLWX0mBiNr22OCUnatkJzvbv5s2x6zoCaCPywk9rFd+9g+Fk63sZ07bHsAPGKV7bWx43ELp9nKxCKaigDJ9ePWLLV1m9i6jokNmXjR57XjNbbXIxU7f0oskAG+bn6255nCbJrz5bkHAK9Fe9vtX0eEfD569Gd/fZ6EnvCFEEKICEAbvhBCCBEBaMMXQgghIgBt+EIIIUQE4DnnXFEPQpw5mZmZKF++PPa+/TTiyp7kBMYEbBlE8MQERiSvPI4SgUpJK/IBc/sCuGjpABH6VKthY0S4wqACpYx1JhYYNMSW+8GW85sn3atEcnL7zdtOhFIAuOuW3/lOIc5xGVb4yM69m2UFWV6vq0m/VmjFXA3ZcXi1rSubm2cd6yhMIMkEl+AiLyYepe52+XOOA3z+yXXhvv3alouJsWNhee59Euh+g4kFV1kRIMtdz/LeA/zaoyLaWtxN0/TzxQe2j3rJtmA40WX+9sh59phwmF1T7D5XkcSirZuimz2Zjifq+t+aWPDrd2lZ0+bWLSZG54aJhBuFXt+ZWdmo2K4X9u/fj7i4OF/96wlfCCGEiAC04QshhBARgDZ8IYQQIgLQhi+EEEJEAHLa+1+DpTk9YIVIHnUFIyIalnqWiWOYYAwAthIBVQubSpIRfPtlW7ebTanJhIoeibkVc21dkibYS6xtyzERExEOBXq3tv1+v9jWDSM4owIxlqqUzLdX0wrlHKvL3L4YzA2Qub9t3eCrrmP9krXExGUeG18CF5F5zGWROdQxd7sKxKmSpEZmYlQvIdGW83uelky3dcm1HPzs77YucwIka9PreKntA4BbQNI3b9lk6zNR7/J5thyZBzpG4tpI8ZnOlrprMvfJntfacmmf2HJElAsAwakf2bKd+9o2Z0ywlYmIk7prkvuDW78q9PPBQ3R8p0JP+IXEc889B8/zMGzYsLzY4cOHMXjwYFSqVAmxsbHo168fdu3alff3TZs2wfOO3cqGDx+OW2655TyPWgghRKSgDb8QWLhwId566y00b948JH7vvfdi4sSJGD9+PGbMmIHt27fj2mvtvy6FEEKIc402/LMkKysLAwYMwKhRo1CxYsW8+P79+zF69Gi8+OKL6NatG1q3bo0xY8Zgzpw5mDfPfg0mhBBCnEu04Z8lgwcPxhVXXIEePUKzki1evBhHjx4NiTdq1AiJiYmYO5f8liyEEEKcQyTaOwvef/99LFmyBAsXWmHIzp07ER0djQoVKoTEq1atip3/TR1au3ZtHDc6HD58eMEGkXMYiDrx0e0gIrv1JL0kE8yUtW5NLn2liXkpzWy5tM/o8JiAx6tohVFu9iRbriNJ68tSnxJhYdZ7X5pY7H132boknS0VGBFhDU0Ly0Rf7Jww0SRAHdKwhwjvSFpSxBC3LSaoI+l6vfrWBQ/ZRABKBFRe6iUmdvD5F00sdrx11QuOs+WY85vbTI5j3RobA+B1u8LGiMDv6Juvm1jJm39tGyQCPeZKmd8JDQCCH5MUq2R81P2QiRdZOmG/aXnDXaNtSLpqtr7IeqCpn0laa5pKmqS4pf0yKpHrjAkVmXh32hf++mDHBgAl7D3ILSHCR+IUGiAi1fxpbwEg+tGnbR/5RHsFQRt+Adm6dSuGDh2KKVOmoHRpa8tY2GRmZiIz84Ta/gCxpxVCCBFZbNu2LWRviIuLC2u1qw2/gCxevBi7d+/GhRee+Fd9bm4uZs6ciddeew1paWnIycnBvn37Qp7yd+3ahWrVqp1xf3379sWMGeRfkUIIISKWlJTQV4s7d+6M6dOn07La8AtI9+7dsXJl6Nfdt956Kxo1aoSHH34YCQkJKFmyJKZOnYp+/foBANauXYstW7agfXt/SWFOZsKECeYJP/+JFkIIEVmkp6ejXLkTP2ecKpGONvwCUq5cOTRt2jQkFhMTg0qVKuXFb7vtNtx3332Ij49HXFwchgwZgvbt26NdO3/GMyeT/2uakzd/IYQQkUnNmjV9Z8vThn8OeemllxAIBNCvXz8cOXIEPXv2xBtvvFG4nRw9CpQ48bKF18r+Y8LlEAGPT/EVTnrVMA/iAEYdpAAursk5bMfD0kYyMRETUBEXvNjnn7XlmGNaY+KExsr5dDPDcvIGBnMmZA6GALyuV5uYm03Srh4hc8jSjTJnvDB9++nDS2lhy5H1cHDPQRMr+7EVybm9Np0wyHqlKYbZ2gKo+5vbvNnESl5zla3LhGQsLSwTe1a38xDof7ctt3qRbY+I1dhcU+c4NjfsOgkHcUB0O7fbfth1z0i2AlCvghUWBqdNtOWIuM9r1daOj6RzDtx4py1H0l97Xa80MZoWOdx1Qq4LlCI6rg02LbkjIsISTRvYckTEnD9NsDt8Buf4eF9nXEOEJf/vJqVLl8brr7+O11+3NzohhBDifKL38IUQQogIQBu+EEIIEQFowxdCCCEiAP2GX9zJORLitMfwK3pxX39m66Z28DUMjwnTwuDmTDaxwMDf23IrbEpT991y2zdLY3mAiOeYyIuJdUqRlK0Z1vWMwVJ+uqlWgBPofwet7xbZ80IFQSStLxMlum+tu52XSsSQNE2tdbKjaWoJVT7/3MRy37aOYoiNtX3Ek/PJzl04t0KCx8SLDOZMSNYDFXuyOZz2mY3t3GHrMve9DOJYxwR67NpLskLDcOeOCvSYsO0H4tjJhKtMlEvErF5b4qRJBJfMqdJreZHtI4uICqPttePGvW1iwSwrMo3q2NG2FwbmBEnXMUvlTe7PIPcbL9869IK+h5eHnvCFEEKICEAbvhBCCBEBaMMXQgghIgBt+EIIIUQEINFecefCjkBM2byPTJACImpjKW79CsG8xlYwg6x9dHhuNxGSMTczJlYjDmBUoEdS5roM4rBFhEw0JfD8mbYuS0vqMx2q12+g7YM58gFcoMcg9fM7cQFhBHrMWY040XmtyHkmdXGRFV8xgZ6XWNvWZWmHmQukrQm3mYjIEOY803TCVnDmSBZKj/XDUh636WpjfsVzLPVsUn0b25xhYyy1cbp1AgwncvTq27WNfSQlM0kJTNM5M0c+tq6JKJGdE1aXXXvuJzJmlqq3kz1PJTtfa2K5bzxl2wOAaHu/8cqRcRPcUitKpOuV1c13jbojR33VOxk94QshhBARgDZ8IYQQIgLQhi+EEEJEANrwhRBCiAhAor1iTqBabQTKnXArC/oVaTHR0awZNhYfb2PMjSylFR8gcbpi6U+pWxgR4/l1XKMir++W2XJ+BTPM4Y+JHBnEocxt2USLej2teIg66DEXvFTrDOa+nmBiUQ/92cSCX4yl4zGQ8+zF2vTEaNnGjoW5/rFzx1LwMgFnUpj0uGzd7CGCrrNIxUrTRq8hKXPDCAtNv+x6JHNDXfGYGM/nug4Lc5Zk88rS8DIxJFuvTUj633Wk3+xsW/dC4tLHhIokdbZ3YRcTC37ypq17lIvivORGJkad9ohbocfcCtnaJEJKL9+8eocOA/iMjjEcesIXQgghIgBt+EIIIUQEUKANf8SIEfjHP/5h4v/4xz/w/PPPn/WghBBCCFG4FGjDf+utt9Cokf0do0mTJnjzTfJbiBBCCCGKlAKJ9nbu3Inq1W2axgsuuAA7dtjUj+LcEdy5CcEDJ5z2vHY9TRnmducRIRhYusqtRHDGBEbffs0HSJzLmFMVw+3aaWIeSy9ZK9nWJQ56AZa+dEO6bc9nalCvam0bY4IeJkQKBxkPEzxRWBrRFOvC5o7YVKBM5EUd74jQza0I4xqYj8DQp00s+Olo2wdzp2MudmFwP1iXRbd1i22TCUBziAskc4ZksFSxWVm2X5YKl6WFJS6EVPDK3A99OhgCYdKzEqh7JXGRpCmnidiQiRIDfYiD3mybXtqtIvPFhIrEfZKuuQ6X2Ri51wDgAlLixBn85xsmFri8j22P3dOqJJiYyx/IPsTHdwoK9ISfkJCA2bNnm/js2bNRo0aNgjQphBBCiHNIgZ7w77jjDgwbNgxHjx5Ft27dAABTp07FQw89hPvvv79QByiEEEKIs6dAG/6DDz6IPXv24He/+x1y/vsVU+nSpfHwww/j0UcfLdQBCiGEEOLsKdCG73kenn/+eTzxxBNYvXo1ypQpg+TkZJQqRcxThBBCCFHknJXT3s6dO7F3715ccsklKFWqFJxz8Dya/FGcJ9w2kj5zL3FyYkIfRpYV4HjlrCCOCowAuIy1NsiER0wsxahKxCyTP7TliFMVSx9LYW5YtawgKLhhhYkx0ST2/tvGmGtZGFhaWa9DLxNzLMUwSS0a/Ncrtj0iNgz07Gf7WDjd9kHEb4ffed8OhYkKmdtdc+sWSY/tyGEbA4DGVvhF00EzmHti+kobY2uYiPsC/W41seD7fzcxrxs5n//5xpZj7nTEBTLQor0dXzUitgWAvT/yeP6++95gg6uJuyB1AyRprQlUoLd3j22PCS7Z9U3uK0ygR6/5nWHmi8HWXLVq/tpkgtmt9j7uJYSmS/ayrAPh6SiQaG/Pnj3o3r07GjRogN69e+cp82+77Tb9hi+EEEL8AinQhn/vvfeiZMmS2LJlC8qWPfFK2A033IBJk+y/0IQQQghRtBToK/3JkycjLS0NtWqFfo2bnJyMzZs3F8rAhBBCCFF4FOgJPzs7O+TJ/jh79+6VcE8IIYT4BVKgJ/xOnTrhnXfewTPPPAPgmGo/GAzihRdeQNeu/h2xRCFw9AhwNCrvoxdjBXXBCR/4aoq6ZpH0l27ZIlsuJoa3WZUIV5iDlU+BjBdf1Y6HCPRoWl4QUc+B/b76xXLrJsfEREgi4p+Euv76AHyLKd0u6xzHHBA9Iqb0qlmXTJYqlsLWA3ESLD3wRl/lXPoqOz7mqhdOoMdgLpKkviNiPI+IKb2eV5vY4T//xcRK+XSQ9OpZZ0ikL/VVl6aCJufTrSHtEQEnALj5c2ybyWSMGd/ZGBOrMQFvBSKyY458ydYZ0ltn+3XzrfGb15GsG3Z9s2uZuAgizMMrFeOxY2F1L/2V7XvG56Rve65cvnG7Q2dwTfyXAm34L7zwArp3745FixYhJycHDz30EL777jvs3buXOvAJIYQQomgp0Ff6TZs2xffff4+OHTviqquuQnZ2Nq699losXboU9eoRP2MhhBBCFCkFfg+/fPnyeOyxxwpzLEIIIYQ4R/je8FessCYj4WjevHmBBiOEEEKIc4PvDb9ly5bwPM+46Tl3LGnfybHc3NxCHKI4Jbu3A2VPCDyCRBjl9bra1iPpY6nYiYiYAtdacR9LwQsA2LLBxohAjwngAtfeYmLZQ+42sbL3/s72kUMELUzAw8Qxm+2YvXI29SmLUUEim+t9xP0QAOpb0RKyybiJgMpj6Y1Z3RiWxpWkEZ0/zpaLjTWhn1d9b8uRWInKZB4INN1uufIm5DWygjEAcFnkmLPt8XkJibZcvBWXeRVtrMyfXrD9rrGuc0zkyIRuVPRFUk57Faxoz6RNBbhjYDiWWhGu16Wv7Ydc4272ZFuXuVymfWLLsVTSzNWwYkVbN7WDLccEr2Td0DTGTICbHcbJLqWVCbn5M02Miinnpdn2iBCTrdfCwPdv+Bs3bsSGDRuwceNGfPzxx6hTpw7eeOMNLFu2DMuWLcMbb7yBevXq4eOPPz4nAxVCCCFEwfH9hJ+UlJT3/9dffz3+9re/oXfv3nmx5s2bIyEhAU888QSuvvrqQh2kEEIIIc6OAqn0V65ciTp16ph4nTp1kJ6eftaDEkIIIUThUqANv3HjxhgxYgRyTsoYlZOTgxEjRqBxY2KCIoQQQogipUCv5b355pvo06cPatWqlafIX7FiBTzPw8SJEwt1gOI01KwDxJxkc0xEe1SstpmkX2QpV2db4ZBj4hgmTgKA5u1sjAjWWFJlliqz7I1WTETTe7JjJmkoWV0vhaQgzSBiSOZOxxzTWCpcJu4D4KYSV0Ti+OW1vIjWN+3NsmllAzfeacutJ4533a6wDRKxYIkadh4yPl3sa3z1ryBpa5krHhFSUvFVONh6YIIuInJ0zGGOQcRXbtlCW46ls+3T35ZjjnzMQTKFiABZutcwsLS3bvoEW5CI8Tx6TZH7Q1aWjUX7XNdsXj+3KbG9VJtWmQk73UJ7TXhJxA0znLsjO76dO22MiXqZYJak/PZi7dp0e3eFBrIP8vGdggJt+G3btsWGDRvw7rvvYs2aNQCOZcq76aabEBPGYlUIIYQQRUeBjXdiYmJw5532SUEIIYQQvzwK9Bt+OHbs2IEtW2xSDyGEEEIULYW64Xfr1o2q94UQQghRtBT4K33GO++8g4MHz1xIIM6C3T8AZcvkfQz0u80UcT+Q1I/EySk46s8mxgQ9zHnMzSEOUgA8IswJTrViPK8VEeuQulQAF04wmB+SojOqUz8Ty/2PT/OonCM2xoRNda0wLThuJG2SOoixlK2N7Xy5aZ/ZusSljImW2LEEJ1kBLksN+vN2K3xc9aMVaX2zz94bHp++xo6FxKrd2MWO7/OPbF0AXqVKNsbEYHuI2yET95F16JjYk7kkkpS5TCDm9hEhGGmPCVlZSllUJaLQcOJDVpbBjvlC4u74M7kek+rbGBEOM+fL4Ph/m1hgEHHXJGlvg2njbd0rBpiYm0FEimxeAbpGWDpht8e69zFxMhPtUbfJ/Bw8T+lxw9GmTZvCbE4IIYQQhUShfqUvhBBCiF8mBXrCr1ixYkiynFOxd+/egnQhhBBCiEKkQBv+E088gWeffRY9e/ZE+/btAQBz585FWloannjiCcTHxxfqIIUQQghxdhRow589ezb+8Ic/4J577smL/f73v8drr72Gb775Bp999llhjU8IIYQQhUCBNvy0tDQ8//zzJn755ZfjkUceOetBiTOgSq0Qa1232ua2ZhaiLmOtiXnJDXx1yWw33TryJkAYvJMyL55qPIEbf2tiwYn/su21Iva9O3+wsV02n3fuPqLIP2IV64E+N9tyBLeIKOCJa6eXWJs3wOxiN1ub1GC6tWflinBifcqoZcsFKtm3Gtj4chdb2+F2SfbtgKPOZm7/bJ1Vp/+mN8nlTn4aDDQJo6Jm+cUZPq2W3Q67lljOd6aC95JJOXaOifKbviURG2tj7PomVsRevH17AQBQkrwNw6yfyRpxq+bZcjTfPJlXpvonNtKB639t22PWwWzMxFrarV3iry6zyQaAi8ibCVvJfKd2tOXIWsLBTBtj9r3539Y5RN4SOg0FEu1VqlQJn3/+uYl//vnnqEReiRFCCCFE0VKgJ/ynn34at99+O6ZPn47U1FQAwPz58zFp0iSMGjWqUAcohBBCiLOnQBv+LbfcgsaNG+Nvf/sbPvnkEwDHUubOmjUr7x8AQgghhPjlUGDjndTUVLz77ruFORYhhBBCnCMKvOGvX78eY8aMwYYNG/Dyyy+jSpUq+Prrr5GYmIgm4cQ0/0OMHDkSI0eOxKZNmwAATZo0wZNPPolevY7llO/SpQtmzJgRUueuu+7Cm2++CQDYtGkT6tSpA+cchg8fjk2bNmHs2LFnPpAls4AyJwldmKCE5Z9PsMI5an+5nIhyEqwKLawrQ30mwLKCFCaCcivm2Lo5xLaT2aEymAUvEfd5HS43MSoW7HCZba+uPV63iySUItbGAICsMPH8MIEeGw8590xAhQ3pJuSWEgEosdaNrmkFejYCdCKxffus8GgvEfJVv+JSUptDBWspLWxBIs6kYim25ogdKl1fbG0yQSkhcJO1yUY2aY+sGSYWdOusuBIAPLZGiEAPi2aYkDtA+g4nDsxfjl3zRIwaqGBFmMGl9r7kVbfCOy+lqe2YCOwcWQseEx+Gg9wTHREMet162bpkPTARtNerb2gg+5D/8f2XAon2ZsyYgWbNmmH+/Pn4+OOPkZV1zDd7+fLleOqppwrSZLGjVq1aeO6557B48WIsWrQI3bp1w1VXXYXvvjuh1L3jjjuwY8eOvP9eeOGFIhyxEEKISKZAG/4jjzyCZ599FlOmTEH0SU8a3bp1w7x55Inwf5A+ffqgd+/eSE5ORoMGDfDHP/4RsbGxIcdftmxZVKtWLe+/uLi4IhyxEEKISKZAG/7KlStxzTXXmHiVKlXw44/k68L/cXJzc/H+++8jOzs7z3kQAN59911UrlwZTZs2xaOPPqpMgkIIIYqMAv2GX6FCBezYsQN16tQJiS9duhQ1a9YslIEVB1auXIn27dvj8OHDiI2NxaeffoqUlGO/4d50001ISkpCjRo1sGLFCjz88MNYu3Zt3lsNtWvXhvuvEcnw4cNP21dmZiYyM08YNBwgv50JIYSILLZt2xayN8TFxYX9NtlzjthfnYYHHngA8+fPx/jx49GgQQMsWbIEu3btwsCBAzFw4MCI+R0/JycHW7Zswf79+/HRRx/h7bffxowZM/I2/ZP59ttv0b17d2RkZKBePZ/uZyfBRIAAsPfj1xAXUybvs1u6wJTxqtWwDRKBi/tumYkxhzmvdIyJBSeMsX0AcCx5UsmStk0i9HFEjBToeT0pR4RMTIjERFpMPMeEVsSVzatC8ljvtm5+bF6pUxsAl2GFVcwtz3072ZYbeLct9/E7thMivMNPP9n22lxs2yNCJBD3N+aMx4SGR9ba+SrVnIhHY+ya88qFEVXldyQDuKCOuav5bY+421HBH8ud3sEKt9yGlbZuDOuDrGsGEwaGy+/OyrJrgDkvsrrM6dDvNVXL5pVHWbJ5MSFyDbtugsuI0x67HpnrYjhxJVs3Ph0yaV1yD/IjPM08eAjx/e835Tp37ozp06fbflDAJ/w//elPGDx4MBISEpCbm4uUlBTk5ubipptuwuOPP16QJosl0dHRqF//2CJr3bo1Fi5ciFdeeQVvvfWWKXvcn6CgG/6ECRPMEz77h4UQQojIIT09HeVO+sfvqbRiBdrwo6OjMWrUKDz55JNYuXIlsrKy0KpVKyQnk3+dRRDBYBBH2FMkgGXLlgEAqlevXqC2839Nc/LmL4QQIjKpWbOmb0F4gd/DB4CEhAQkJJD3USOARx99FL169UJiYiIOHDiA9957D9OnT0daWhrWr1+P9957D71790alSpWwYsUK3HvvvbjkkkvQvHnzoh66EEKICOSsNvz8fP7559i/fz8GDhxYmM3+Itm9ezcGDhyIHTt2oHz58mjevDnS0tJw6aWXYuvWrfjmm2/w8ssvIzs7GwkJCejXr19E/dwhhBDil0WBRHvhaNSoEdatW4fc3NzCalKEITMzE+XLl8eeP96FuNJEVHQS2RNmmljsLdfagkzYxARLTJQTzjmOCZkYxKmKthlNUnkycQ0TxTEXO5+uZ9TBcPlcG2PCJjaHYebFa9HexBxxwaNtsmPxmeIW0aVtv8zNjNVlosKpk2zd+vYnPybMZI5pbL7c5s22HAAvtYMN5tif2tx84uRI27PiRSSQY57wga3bjzz8sPNJ2mOuekzUlvv6CBMLdO9p2wvnSMkEiOw8E0EpOwfsPDPBIF1fbcl1xiBzE2jZ1cSoaI+tze7WXZM6UgL82o2x8+XWE7e8hERbl1zzHrnPuX2h/WZmH0R8nzuwf//+8/OVfn7WrFlTmM0JIYQQopAokPGOEEIIIYoXBdrwJ02ahFmzZuV9fv3119GyZUvcdNNN+Im8zyuEEEKIoqVAG/6DDz6Y91rYypUrcf/996N3797YuHEj7rvvvkIdoBBCCCHOngL9hr9x48Y805ePP/4YV155Jf70pz9hyZIl6N27d6EOUJwZzBkvthQRgCxbYisTBzaWZhPxRAi2axsfUEorG2PpIElaTC+JCPkIzM3P20EEbEycxJyvmCDuB+KaxQSETBz2HTm21I62LrhTHxVJErwE6zTmmLCQsZOkTv1vFswQiJiLOZchPt6W27vHlmOpZ9m8EryOVqQFAG6hFeOxsoE7rEuZmz7BNsgEWd9+afsgwi832zoismP2SB9MtBpkbpht29m6fh3wwkEEelzAa88zE3Fi3Xcm5MVXtuWOkjS1JD2uK2nXSPDzt217RLRKU+YyN78wuHVWq+YRUS9NGV7KimNpamqyHgKtQvsIHCDX52ko0BN+dHR0XiKYb775BpdddiwPd3x8vAxhhBBCiF8gBXrC79ixI+677z506NABCxYswAcfHHsd5fvvv0etWj79qYUQQghx3ijQE/5rr72GEiVK4KOPPsLIkSPzMuR9/fXXuPxy8j6jEEIIIYqUAj3hJyYm4osvvjDxl1566awHJIQQQojCp8DGO7m5ufjss8+wevUxcUeTJk3Qt29fREVFFdrgxOnxYsvBK3NCwBKc+C9bhohjvJYXmpjbSpzLmDsXEaZ5Xa6i43M/WLcpZFmdh5d6ia++3fJFtB9TjhyLV5WkhWVOdEQs5TW3bli0XyL68pIb2YJMVAgA9Ykj2QKbFtlrZYVa+Z24AFCRUO7XVnAW1fcaEwt06WvbY+KmJdNtjIn75s8i5WxqXTq+tm1sXSJGBYDAbx4wseCno+14WCppsg5pylafDo3MmdDrwMR91v2NprNl6yaJpBNmbnBMdIcwYs89JA0vE7gyISYbY7jUvPlhgl4218yZkIk9mWMguX9hPXE/ZI6P4Nez+8K6LAZ+fY8tt5aIpRnE9TSYHupMGMw+5K+tkyjQhp+RkYHevXtj27ZtaNiwIQBgxIgRSEhIwJdfflmg9K9CCCGEOHcU6Df83//+96hXrx62bt2KJUuWYMmSJdiyZQvq1KmD3//+94U9RiGEEEKcJQV6wp8xYwbmzZuH+JPewaxUqRKee+45dOhAElcIIYQQokgp0BN+qVKlcOCA/Z0zKysL0dGnztwmhBBCiPNPgZ7wr7zyStx5550YPXo02rZtCwCYP38+fvvb36JvXyL0EecMr1UHeLExJwLMWY05bG3dYNtqa7+dcbNsekkvMcmW27uLjs/NmmrrpzS3BXMO23L1iCMWc8tjwsKqCTbG3PJYXSJ0cyvsvLpwwrt8BBpZt0G3cDovvNymDKUwIRM7FiJKZAI9x5zVwojiDEwYdYQIOxNrkz6I81i6dTILLltqYgGWzhlAkInxyhHxXM/rbd2Px9hyNxPBGROIbbbri7kLeiQVMW2PuMl57Jyk27mh1zwTq4GnX6bz3ZaMp1oN2yC5Rr3aRIy6ybrvUbEhS029mojfmMslSYUbGDTExILv/93EvHDOhOQ6Y06ObhW5ln2mHabnb2++4zto75mno0BP+H/7299Qr149tG/fHqVLl0bp0qVx8cUXo379+njllVcK0qQQQgghziEFesKvUKECPv/8c2RkZCA9/di/DlNSUlC/Pnk9RAghhBBFToHfwx89ejReeuklrFt37D3r5ORkDBs2DLfffnuhDU4IIYQQhUOBNvwnn3wSL774IoYMGYL27Y8ZksydOxf33nsvtmzZgj/84Q+FOkghhBBCnB2ec86daaULLrgAf/vb39C/f/+Q+Lhx4zBkyBD8+OOPhTZAwcnMzET58uWx953nEFf2hAjIEeGQR9JVuv9YMR5Nj9v1Slt3mrVVpuIdhHG869idljUQ0ZL7iDgJEtdAKhyqm2JiwXGjbDmSPpMdR+DaW2y5NVZMxOba6xVG3ErOHxUqstSiRAzmllrhEHN/Y85ebg8RnBGXMa9dT1t3W4YtV9EKCKnDHDleJpCkxwFQoeLRTz83sZLXEHdI5vS2zrpF0jXn0yWRpbBmuF1bbHCvdcBj4lYqQqtHHAMBKhBjIk6POc8xoajP9MYufZWJBfrfYcsRIfKRL6eYWMkLSErZ7nZtUgfP9cQRtGRJGwPgNWnhq03m2IkEm/LbLVto+2BufvmugczDOaj02FvYv38/4uL8pfctkGjv6NGjuOiii0y8devW+PnnnwvSpBBCCCHOIQXa8G+++WaMHDnSxP/+979jwIABZz0oIYQQQhQuZyXamzx5Mtq1O5bEY/78+diyZQsGDhyI++67L6/ciy++ePajFEIIIcRZUaANf9WqVbjwwmO/Ya1ff+w3x8qVK6Ny5cpYterE7zKe5xXCEIUQQghxthRItCeKnjzR3tQPEBdT9tSF91mhD3XQI25RDPfdMhvcuZOW9ZKsKx8TrlBhGkvFSlzBEENSsTLRUVvi2JVNxDYxZCykHE2vGl/J1mUpeMMJzoho7+hMK1oq2aOLieXOsulnA9Wr2jEmJNp+mWsdmVfqZsbc5JIb27pVieiLzStbX1uIgI2ITAHASyGiKgZxdXN7reCYitVIelYqSty91cRy3x1rYsz9EPGkvbTPbL89r7blJth0rTgp90lIfeJCyMhdutzEonpd4auumz/b9lvfighzF1gBW4l7n7DtkTTU1J2OpfRl4/v2axPLWbOJli3VxaamPjLdimNL/9+Tth92/2L3ArIO8x9LZvZBxPe549yL9oQQQghRvNCGL4QQQkQA2vCFEEKICEAbvhBCCBEBFPi1PPHLwKt4AbzY2LzPbsNKW6iCFf943XrZcputOxoTwgSuuc3Egp+O5gNkohkm0CMpPt0B61TldbzU1t3lL00tLcccsuKJMI2JHIkjH3NbYylvvS7caY+Jxkpe0p6M0Z6XEoMfNLFg2se2LhERunVW5BggY3QLyTwwl8U9VijqNxWx16SlLdfhMhtjKVIBvr5YOmEGE5+S8bhZ1ukNqZfYcvNnmhgVurF0zuT4Ar95wMTYtee1ssZoYdM5l4y29dt0sX2zuWHnlOCl2tTbbI1EtbKCS5dFhJ2biYMncwz8+jPbL0sxXLGiCUW3jrHlwO9L0a3tdU+vvexsE/LaXGz7WGdTRHtH8qXDPXSe0uMKIYQQonihDV8IIYSIALThCyGEEBGANnwhhBAiApBo738Mr4oV/7D0ktSVKqm+rUvSq1KBUQ5xagsHFcrZ8Xj1iVvbUSuoo+2R8bj0FbaPFlbcREU97PiYOGyrdcpzR8iYifjtWN9WxEb7YelsiQsedf5j5Vq1te0Rlzg0tmlhXdontr3E2rYuOzbmksic9n76yfbBhKcATatMU7bmF0EhjBCTuTYmEbfIHNIeEfIxhz/mhgni8OeVIq6aPoWGUZ2Jmx8A5B41oeCEMbYcSxd7Nql1ifMicwjMmf68rUsolWrPsW9hLUk7TNPbAvAatTKx4MRxJhbod6uJ0XTQpB+agje/uPLgITq+U6EnfCGEECIC0IYvhBBCRADa8IUQQogIQBu+EEIIEQFItFfMCX79AYKlTxJzMHEZcXfCzu0mxBzTvHI27aIXb1OuolJlOj5H0r168bas18K6yQWnTbTlmPiHiNBw1AqRvK696RgNJGWr1/92W26XFbXRNKB9b7DlmFMbgECfASYWfOM522aLlrYyEy8yp0OWFnaqFRMxtzaG16mHDTJnOza+uim2va0bbDmWunndd3Q8LouIoIgA1K1fZ8uxfkh7zJnQY8LAeCJUJPPgli2y7dWz6WODWV/ZciTNLIirnquyyZYLg3cxcTZkqV1ZXXKNuvk2dTMTpjGRXSmWYpu4A/rFfful7ZetYSZORhhxLKnv9u6ysZ07bF3WCVtL+ef15yAd36nQE74QQggRAWjDF0IIISIAbfhCCCFEBKANXwghhIgAJNor5njNL4IXc8J9y330rikTuH2YiblFM2xjzDUrfbktt4i4xMVacR8A4ECmr35YKkmadjWLtEeEaV4ycdMi6XGZWCow8Pe23Io5tj2WVrTfQFv3C+se5qU0s+0BQDRJF9uxsy3H3NpY2tuFdtxeQqKte1KK5TwS6tEhGoj7IU3v2bKNrfuDFXVSoSERzrHjBcKI55h7InOOI+mJ3WYiImQw90qC1/kq28eWV2xB5kzI0g4n2fOUNeJvJhZ7S5gBMdEraZMKUlnaW7JuPBJj4jnmThd8/++2vSutEJa6FcZaJ0ePHS8TlJKU3QDg9uyhcUM0ERZmZfmrS1xPzXgOkeM4DXrCF0IIISIAbfhCCCFEBKANXwghhIgAtOELIYQQEYDnnHNFPQhx5mRmZqJ8+fLYO/UDxJ0k2qNuWCQNKEsR6aZZFy8csGKpQP87TCz4z9fpOL3Ui20/RATF0rNSoRYT1zD3PUYFIvJi88VEY0TA4/X+tYm5JdNtXSbm2pxBBgguoGLpcYlDHRXAMUcyklo30N66EOa+8KCJeZ2sE51vd0Em0rp5qC03L83EzgiScpc6/xFhJ0sHTZ3jiJMdc5CkMDEecz8kaZVpH2z9s5TM5LwDYVwDk0lqaraOmdsh64etQ+KuiYNWlEvPCU2d7TNFN1sL7NhqkRTIAPATOVfLFpqYl9rR33jIWqKpeVNC0/JmZh9EfJ87sH//fsTFhRFN50NP+EIIIUQEoA1fCCGEiAC04QshhBARgDb8AjJy5Eg0b94ccXFxiIuLQ/v27fH111/n/f3w4cMYPHgwKlWqhNjYWPTr1w+7dp3InrRp0yZ43rE8ScOHD8ctt9xyvg9BCCFEBCGnvQJSq1YtPPfcc0hOToZzDv/85z9x1VVXYenSpWjSpAnuvfdefPnllxg/fjzKly+Pe+65B9deey1mz7YCp7Ni20ag7AkRkNfcCmHcbpLGNe0z2xZxhvJ6XW3rEqGb16svHx8T3u3da+vXtc5zjjj6BbpfZ2LBSe/ZPogIJ9Csk4nlzp5s66avtOOrVs2Oj4nLiECSpgjuQNKPAtwtjLF8ro0x0RITIxHBX3AuSbtKBHrUXZC4rXkxVkTE1MF0DolTntfoQlt3zRLSIqgoy7v0V7b+SuJCyBzmviNuk8yZkInxmFshcXzML8gCAK8sEWIRV0Pa3hmkj/XiK9kgc/RjMb8w90O21plQkZyTw6PfMbHSw4b4GgoVKSaEEegxKvpzVKT3PibKZdcoi+Vf1weJGPs0aMMvIH369An5/Mc//hEjR47EvHnzUKtWLYwePRrvvfceunXrBgAYM2YMGjdujHnz5qFdu3ZFMWQhhBARjL7SLwRyc3Px/vvvIzs7G+3bt8fixYtx9OhR9OjRI69Mo0aNkJiYiLlzyZOZEEIIcY7RE/5ZsHLlSrRv3x6HDx9GbGwsPv30U6SkpGDZsmWIjo5GhQoVQspXrVoVO3fuBADUrl0bxy0Qhg8fftq+MjMzkZl54h3VA+T9eCGEEJHFtm3bQvaG47oyhjb8s6Bhw4ZYtmwZ9u/fj48++giDBg3CjBkkC10h0Ldv33PWthBCiOJJSkqoJqdz586YPn06LasN/yyIjo5G/frH0hi2bt0aCxcuxCuvvIIbbrgBOTk52LdvX8hT/q5du1CNiL/8MGHCBPOEn5KSAsTEAjFl8uIuizjRZRCRHUvPyhzmSF0GE8IAYRz0ylkntOD7b9q69Rvackt9/qOHpfo9ctD20damnnWzptgYExpeRNLW7rICSWzZZNtL+8SWA4CjR20/LS+ysQ6Xm1hwok2N7LFzupqI3ZiYiKWf3WpdEt3mzbZfIgz0ul1h28sm65WkqKWwMQM8teuUD2055mBInOy8SkTUxs49cYlDOplrNq9EoMfS/AaZyJTgtSI6oTDpXv26MdJy7FiqsvuIdeRzs6wo1+tInBwz7L2FCvTY8RHXRZp2m6RP9qok2HIA3IwJtmxyI1uQrU+W1po5oTL3ROa+ByA9PR3lTrqnnsp1Txt+IRIMBnHkyBG0bt0aJUuWxNSpU9GvXz8AwNq1a7Flyxa0b0/sJH2Q/2uakzd/IYQQkUnNmjV9W+tqwy8gjz76KHr16oXExEQcOHAA7733HqZPn460tDSUL18et912G+677z7Ex8cjLi4OQ4YMQfv27aXQF0IIUSRowy8gu3fvxsCBA7Fjxw6UL18ezZs3R1paGi699FIAwEsvvYRAIIB+/frhyJEj6NmzJ954440iHrUQQohIRRt+ARk9evQp/166dGm8/vrreP11nkVOCCGEOJ/oPXwhhBAiAtATfjHHrVkJV/rUNppMiU6tT/dZBanX43pbjlmSxoYRjURbu1GvrbVidd8ts3VZXnOiHnYHrIDRI/aqjuXBZjCL4a5XkrGEUYnnr9ukhQ0eIOp0AG79Ol9tMrtkNm7aD1P7tiDakuUkD/nF1hL46NwRJlZy4XQTC/QaYGLBqR/5Gl/wn6/ackRZDQCIiTEhL4Wcg3iiMPdpe0utjRl+c7ST9oJ795gYPQ62rjdnmJAL491B2/QLeWuDvaHhkurbckTF7r4mCvhuPW2/JcOc+/yQfqlSnuDWhrFuZtQi1ryr/b014HZut+XSV5lQoP8dIZ+9rGzfw8tr44xrCCGEEKLYoQ1fCCGEiAC04QshhBARgDZ8IYQQIgKQaK+Y4yXUgVf2JGEcE6tNs7nOQYRzIBaWVOjGxHTMwhWg1rxu3fcmxnKqM/GW20xEQsy+lxwLFaYx69McIgz84gNbrmJFOxZi68qEc4Hu19lyAFxna63rZnxuC7Kc76xvlsOc5P52335p20vtaMtttWKwkrfdbsvNn2ljmVaERiHnyWNrLpy1LrN7JTALZS+5sS3IBGIpF9oYE3ES8Zxfi1qPWf8y2PEysSZZ1wC4sLAWWUsEl77cxv5pX0X2WpL58nt8TJy5165rt4eIHNn9kN1DiN0uFdMBCPTsZ8syESc5Pq85cVqdPcnGyDrML9R12Yfo+E6FnvCFEEKICEAbvhBCCBEBaMMXQgghIgBt+EIIIUQEINFecadKDSCm7InPJA88lhLHtFrJJsbyTlOBC8ln73bupMPzEpNo3MDcr4goi+ayziIOYkzkRXJyU7EUGZ4jMa9JSxv0mcs9d/yb/A9ZWbafatV9tcnOvfvW5k/3iMDS69TDtscEm0QM5jW+yMYqkLGst+5hVLjF3O4YYYSiXr2mtu8VVlQVNeD3vroJLp1hg0zoFkvWHMtrztZIfGUTct+tN7FA1z62HHNdDCdoZJBxe9FWKBccN8qOJ5/7GwA44rKIUtZxk16j5N6CGLJGNluxrdeyjS1HhL9ex0tJObKu2b0UXLjq9zy7NWTcbbqYWHDiONp3SFuHfbo4noSe8IUQQogIQBu+EEIIEQFowxdCCCEiAG34QgghRAQg0V5x56f/Bxwpc+Izc3ciDmzuB5uG1R05A6FP/j5SmvE/ZJHUtcxVj+DWWRGhx1KVEic0l77C1r3sGluXuKO5zVYsRcWCTHxFUoOy1KdU8AcApG8qTiPuY16VBFuum01n6ztl65HDvsbiVi+yYyHCOSbMdAvn27o9SSpiRpjjYGubiQ2DE8b468enMx5L8cxSU9N1Q4SnHhHTuQ3WuZKNjzq6MZEceEpaXHczLWvqEsGgo2l9idMeE9GS1MbUETGJpKNlaZ/ZGibQc8eEeAAXrna43La5w59Lqcsi7XXtbevu3hZa5qC/YzsZPeELIYQQEYA2fCGEECIC0IYvhBBCRADa8IUQQogIQKK94k6VWqFOewetSI66bm2xAiqvAxF4rbAufWhMhHMkXSUA6iBGnfGIq5VXjohemCMfizFY+lLmsOUzxS1zIQRzJmQpV8PBhGhMjMQEcMwp8YBPpzHibud1vsq2Ny/NxtJX2vZYWtKdO2y5emSut5J5ZWLN9KU2BtD0v75TyPpN2UqFW/b6cdO+sHX37jWhqGF/NLGfnx1qYoGrbFplJtYMjvqrrTtosB0LAFxFjnkrEa72uprXz092tgm5tM9sewmJNtaVCDaZUJFdj1u32PaYmJhc89TBMJxb4WrrlsdcJFl63UALK6ak/Swi7o7hUpCfAXrCF0IIISIAbfhCCCFEBKANXwghhIgAtOELIYQQEYDnnGOZP8UvnMzMTJQvXx57P34NcTEnOe0R4RYVMRGnNpqGcrUVRnkX9zQxlvYRAFDVp9DEZ0pUr5F17AqmjbflfArvWNpOl7HWxAJX3mjLEQc936KvMKk3KSxNLYOJeogLIZ1rNh42X0xQxxzcmBApuYmNMcrGmZCbTdL8Mhc7gDq4uWUkLWk3u47pXDMhJbumSDnvwi4mFvzXK7ZubKyty9LtMnyOzy2cQ6t7V95gg8wdkqRxpal5mcMcWQ+OuQuy+xKba3adsXPn85oIJNv7Su5bz9i64Gl46dwS10AmSnQfv2PL1bfpywO9BoR8zjyQhQpNUrF//37ExdlrhqEnfCGEECIC0IYvhBBCRADa8IUQQogIQBu+EEIIEQFItFdMCSvai7YiNN9OdH7LsXSh4Uiqb0K57441sajrrSiOHYubP9PEvFZtbbkJH9tyA++25d4Zacu1SbVjYakyfYqTQFKDugXESQsA1pP0uAkk7S1xMwvceKfthziAUee+BCJyZM6ETBjl07GOORPSNKckpanXpqttL5xQlJ0DJixk88Dw6xbpdywsZWtjkj6WuWb6vUb3EOdLJmRFmNSw7JjrW9ElW8fUsZO401GhKDsn0UTIx9acX4hQl/YRTljLXC7nzzYxmgac3TtJex5z5MsnpMw8dBjxvxsh0Z4QQgghQtGGL4QQQkQA2vCFEEKICEAbvhBCCBEBKD1ucSc7C3A/53306lqBl9ucYWMkdaOX0sK2v2ubjTExSziHOSJIiRpwiy2XQ4RMJP0pTTW7hYjBiEAP+6yQyWtJ0q4SoSEdHyvHxEQs9Ww4lzgiDqRit3rWiYu5nrn05SYW6HebLbd6ER9Pfpi4iQiMmOCPCsH2/mhjbA6YQC9M+lJHxJTUeZGJ51jfTNTm18nR71piro1++2BCN+YwF0Zo6DFBKoOklaXsIu577J5BXPVYimevVTtblzl4MldJJnxkcxhv0yd7MVwIF6TC4YtMzH1nrz0vtaNtkK05lhI4/3k+FCZ97ynQE74QQggRAWjDF0IIISIAbfhCCCFEBKANXwghhIgAJNor5riVS+BKneTexFygtmz219asaSbmJTewBZmQj8UAIJYIX5hoibnW1ScCvSNEqMLcq5hYh9VlYiLmZkaEPm79OluuYkUTogKvvcQJDYDXyIoIqWivQy9a38AEm5M/tLF19li81Itte0R45xEhX5Cls/UpnHNLF9i65axTHhszAHidiCvfOisGC9xAnBfnpdn2mNiQrTnm4MbWOnPDrGRFY7QcEccGLutvYsEZn9q6zG0QYVJOfzzGlktuZGPsGmUpbtmxkLVJ0+Ous+mO6XyFEXHmx61bY/vt1MPEgp/YtLUA4LW0Aj3mQOkl1rblmHCySk1/5fKnCT54iI7vVOgJXwghhIgAtOELIYQQEYA2fCGEECIC0IYvhBBCRAAS7RV3fvoJiC554nM3K+bymMDoaI6N1U8xITdrqm2vWnX/48si4jnm1kbcprxaxE1u+gRb9yLrWufGvW3bYyK0MEImAxH3eSylKYO5ZoURGAXHjfLVJHPGO/rheBMr+avrbd0tm0zMa0mOhbi1eRXsPDh2LD/9ZGMdeHpW0wdJw4p067RHzycAt9mmGGaiv+A//mLLXXmDbZCldiXXj0tfacvFxNg+WEpg5v4Wa8ec+/WXthxLWcyuCSKkBECFgExwFvzPdF4/H4HuPW1wpxVNUlEugd0HghPfteW6Xmkr/2DXQqDfrSbmVlinw8BvHqDjocLOHtfZMX5JxugzDbJXl9yLt1qR45miJ3whhBAiAtCGL4QQQkQA2vCFEEKICEAbvhBCCBEBSLRXzPHadYRX9iQXK+asttWf055X1To+eQlJtiBzFGNuZAB3t2N9M2FOmhWhsbSyVKDXpKnthAhhKMyljwmjmJiLpQlmcxNmXliaTcpW675XokFtE3PfWoER4uNtjJxTrwpJtfwDcbdj6WiJeJTOK4Ou4S22HBHEAWHSPFe1x+IRQRdL60vXOxHCUgc2n4JZl0GcDkk536mlSYpa6mIHcGdJIsT06ttUv8w9kboa9uxnYszJzmtjhZhu9iR/7U2b6G9861eZGMN9v5j/gbmZriWiUnbds9TBJL1xcOk8Wy4rK7TPw0R4fRr0hC+EEEJEANrwC8iIESPQpk0blCtXDlWqVMHVV1+NtWvXhpTp0qULPM8L+e+3v/1t3t83bdoEz/MAAMOHD8ctt9xyPg9BCCFEBKENv4DMmDEDgwcPxrx58zBlyhQcPXoUl112GbKzs0PK3XHHHdixY0fefy+88EIRjVgIIUQko9/wC8ikSaG/K40dOxZVqlTB4sWLcckll+TFy5Yti2rVqp3v4QkhhBAhaMMvJPbvP+aUFZ9PEPXuu+/i3//+N6pVq4Y+ffrgiSeeQNmyZc/r2KgzHhMTEfEIEyyxdK00vS3CiGbSV9iCzO2LiYwybKpMKtAjroFYNMPWvfgyO77VPh2tmEBvD0l7W4vMwRcf0CaZaIlC0sp6LdrZciwNLxNpEYL/fNX2kWRFnGwOEVvBtjfqz7ZuCjl3ZB3SeWFCSgAu7QsaN1Stavshjne5s2aZWFTHjrbfndt9dUsd4ebPtjF2PrOJIx8TgLK5YamgAe7yR67bABNxZhMhJnE6dMRt0vP7IESus+DEcba9ntfaWKyt69YQsS0jnODYrxBz748mFmjR3pZbbl3+6L2vVeh68LIPArBufqdCG34hEAwGMWzYMHTo0AFNm564gd10001ISkpCjRo1sGLFCjz88MNYu3YtPvnkEwBA7dq14ZwDcOw3/FORmZmJzMwTF9eBA2RzFkIIEVFs27YtZG+Ii4tDXFwcLasNvxAYPHgwVq1ahVn5ngTuvPPOvP9v1qwZqlevju7du2P9+vWoV8+fr/hx+vbtixkz7BOqEEKIyCUlJfTbzM6dO2P69Om0rDb8s+See+7BF198gZkzZ6JWLZts5GRSU1MBABkZGWe84U+YMME84ec/0UIIISKL9PR0lDspOVS4p3tAG36Bcc5hyJAh+PTTTzF9+nTUqVPntHWWLVsGAKhe/Qyyzf2X/F/TnLz5CyGEiExq1qx5yk3+ZLThF5DBgwfjvffew+eff45y5cph586dAIDy5cujTJkyWL9+Pd577z307t0blSpVwooVK3DvvffikksuQfPmzQtvINGlQtPNMjc55ghH0tFS4R1JVRpo1MrEHBHEAQCq2n4CjWyKSDeHOML5JZGkGyVubQy3ZqmNrSeuZ8xFjaWFZal/mWApIZEPiImtWJtJ1vUMJUk5lgaZnHu3dIGJUdc/MhYmyHLf2lSsXouWtj0mJGPpcYlYE5Wq2Fi4fpjA0qd4MarXFSbmvltu+00lQr75VvDHUrZ63S+35UhKYJqyuLE9T44dL1sfAJ9bIlhj1wolh6w5Ilz1Luxiy+2zIlOafpndq4iLnWPOdqS9wKX9TSw4xQoDAfBriqXPJjEqcmQpc1lK7fz3tEPEYfE0aMMvICNHjgRwzFznZMaMGYNbbrkF0dHR+Oabb/Dyyy8jOzsbCQkJ6NevHx5//PEiGK0QQohIRxt+ATmurg9HQkKCRHZCCCF+MchpTwghhIgAtOELIYQQEYC+0v9fg6X8ZKIQJuRjAiri1Oa2kj6aE1cwgIpP3C4iuGHCHAYTqxGnMJduU2AylzjHUrvWs6l6qYMecc3ynVqXiK8A0LS3VCREjpmms2XjJnhJVvjoNSeuYCusKxgVv/l97dSncI4KDb9bxsuuJ6K4gXfbctFkHRORKUuZS90ridDNS6xtYlTwR8qx9eXSPrF1mWhv/kxbruOltg+AC3iJWx4TPrqli2w/zD2RpR0m14pfYaBXvzHpw991wu6HwU/etOXY9Q0A8eSaSifjZmMMJ5z0Q/7zlH3ojJvQE74QQggRAWjDF0IIISIAbfhCCCFEBKANXwghhIgAJNor7sTEAjFlTnzOIln0mJCPpL0FTUe72t84jvCUk57PdJBIIG55TNBFBHBUeNdvoK1LXLy8LUQkx1zKmDBw8qe2vfoNbd2YcjbG5hqgwkmWjpilz3QLp9n2kolrHRMyxdoxsjSidCzlbF2vbWfbB0vtyhzT2LExJ0CWZhYAmpDzzNKkkmNmeLWsiNMRkZZLJ2I8IpTzmECVXY9svbZsY/tdbYVzyM62MSa2BWiqZUfuIx5xWfQ6drXtMZEq6Zs5mbh1a2wfTNDI3CcZLOU365cdLxMaAkA0OVcMkh6XruP4Sv7ay++iWvKgv3onoSd8IYQQIgLQhi+EEEJEANrwhRBCiAhAG74QQggRAWjDF0IIISIAqfSLOW7eTLhS0XmfvTYX20JMdU4INLF1g0xpSpTaOBBGDVuO5HVm42GWsuHsZ/PhlYszMaagR8mSNnb0qAkF51slbdT1N9p+maKevNXgMQX81i12LAC8ltYmlangEUNU53uXmVhwls3HXuKxP9u6Uz60fTCFP8hbG8xClCjy3Sz7FoF35Q2kC/t2gJfMrFR92jEDcLMn2RhTZne4zJZbRMbdpa/tJIu8hcAgFrBeu562390klzt744bZLPe82nb75ut0OCV/O9jWXz7PFqxP1gOx3qaQNwGozezevTbW7Qoby/jOxtjbD8wunL39QN9QIn0A9I0KR+5/7P7AYOuQ4eW3Ij4olb4QQgghCNrwhRBCiAhAG74QQggRAWjDF0IIISIAifaKOV6zC+GVPbXVo1eBiMv2Wdva3D8Ns3U7WetML6WF7YSJ7gBq2coEZxRm0enTUtO77Bob3EAEhPGVTSiqvhUqUojQx2vS0l/d5ct4nAl92DwQUZWXZHPQRxGLW7eJiJGIQNJ99K7to02qrUvywFOb2YREW5fZLDNRJxNkhbOKZSK2DpfbcutX2RgTAhKLZ2Y7jD0+BWwprUwo+OloE2Pnk11PXiOb392RYytRpyYfDztmZgftN5d7pSp2PJvtuQqQa9lVq2ZiXgwR5bL1wMhvRwtQ0R47XrdlE23SS25kY9Vq2ILMipiIT4NT00ws0N2KOJHfzvmQf9FqXrtnXEMIIYQQxQ5t+EIIIUQEoA1fCCGEiAC04QshhBARgER7xZ2s/UDuSeIN4uIVXDjHxAIDf2/b6n+7je0ibl+7ttlYOFc84jzHREtU3Efa9JpbN0A3+2tbd4UVtbkDmbYcycfuESEfFRoysRPJk86EeF4KERMBXCDGXLyY+17JaBvbRQR/RAToiPAOCQk2llTfxnKsi5qXVNf2QRwaWb543w56TMwIULFUMG287Zud56r2mOn8W20ZF5Sy+VoXxsHNByyfOipYkRwT0VLRJABszrAxNm7igMhc5yg55L40cZwtl5VlQo4JOxnsHsSEukTUCSaQTCeiTgBu6SIT81oRh8ym7WzdVfa+xAR6HhEbGqHiQeJeeBr0hC+EEEJEANrwhRBCiAhAG74QQggRAWjDF0IIISIAifaKOW7XTrjSJ6XHJSIoj4i53IaVNvbdMluXpT6tShy7ssOkeGSOXUwkxFJvkrSRVKBHhIruOyu48VKt4C/Q8UoTC056z/ZBxG/MPSzqxiEmlvvOX217YWBiMOZsSNPwsnn4drIt17efiQXqNjOx4MR/2cEwUeF/bPpYMMe0+Eq2HEtByoRgTGhFHN0A0DXnsfWVbkVobradr0CfAbYcS11LrjOvChMBkmNZttDGmOtijnUwpClzmYAznEslca3zaiWbWPDrz2y5pCRffVN3Tr+w1LUEdv/CTz/ZsZC1CeYEeNv9vJ+1RLxIRJJuxgRbjp0DMv/USTB/imEilj0desIXQgghIgBt+EIIIUQEoA1fCCGEiAC04QshhBARgER7xRwvoXZoelzmrMYEdVusyCTQtY+JuUUzbF0mJsoiLnbhypLY4T//xcRK/+4OW5cJnphIiLgGMkFW7l//z1dd5v4WIK5/TKBHXfHCOROS43NbN9s2E6xYigr0+v/G9kGOhYk4GcwBjKaFbdHextha8pnuGMlEdMfWAsAd+IiDHkvD67F+oq2IkKacZg6GTNzHxhJLHCkJgT79bR/Z5NqrGmZ9MUjaW7eauMn1vcFfe0yIGc/cAInYkK0HIkalaYKJwNir19TEqOtiC+uKFxZ2P02wzpJ03ExATVJJM8FyfgGiO0LaPw16whdCCCEiAG34QgghRASgDV8IIYSIALThCyGEEBGARHvFnao1gJiyJz77dV9KJCITgndRZxOjIiHm7AVwlywibip9TS9f46FCGCICdOPeNjEqxitH0lXWsHOT++7fbN1WbW0fzOkwJs6Oj80hePpfLCJOdiS1a+B2K5RjqUXdzu22XzJuJuIMjhtl6zIxF0tLSnB7f7TtsVTJjASS0hSAS/vCtkkEesa5DEAgxc5hcBZxTGOwlKZffGDLlSxpx5dY25Yj1zJLKeuldrT9+nSdAwCv59U2SBwVmciRpYNmqZY94mpIBXpMiMkcJHfutOX8QlL1MqGh20vEqABdd26BFaR67N7HUn4TB8rAlbeYWO7bI/IFHB/fKdATvhBCCBEBaMMXQgghIgBt+EIIIUQEoA1fCCGEiAAk2ivubN0AlDnhBObSSVrYVhfZeszJqYF1w2JOYW7aV7aP+g35+KjIzgrOsMMK+bwGrU0suHSBLRdrRXGBwY+ZmFsxh48xfx9TP7J9EFEbFfos/NjG6hFxGRMOAUC1GjbW+EIbY2I8kuKTioSYwxwTFhLhXXDvPhMrweoSQRx1FyRjdmRterYmFUoBgDfwbhtkKWSJQCz3tSdtex272rokPTGbf68lufaYKJGIF938WaRurI3FkPPJrjG/YkiAC/TWkfXOBHospTYT3q0jKZ6JEJYeX4IV1HmN7VyzdNqBa261fRDcKivoBUDTKtPrmZ0DBpubIwdNLHDz0NDPB7KAEe/66+N4nTMqLYQQQohiiTZ8IYQQIgLQhi+EEEJEANrwhRBCiAjAc86duV2PKHL279+PChUqYOvWrYiLs6IpIYQQ/7tkZmYiISEB+/btQ/nyRGRJkEq/mHLgwDElc0ICya0thBAiIjhw4IDvDV9P+MWUYDCI7du3o1y5cvA89tKSKCjbtm1DSkoK0tPTUbNmzaIeTsSgeS8aNO9Fw9nOu3MOBw4cQI0aNRAI+Pt1Xk/4xZRAIIBatch7zeKsycw8ltimXLly+rnkPKJ5Lxo070VDYcy73yf740i0J4QQQkQA2vCFEEKICEAbvhD5iIuLQ+fOnfX15nlG8140aN6LhqKYd4n2hBBCiAhAT/ii2LBp0yZcdBFJRiKEEOK0aMMXQgghIgBt+KJYcfToUQwaNAiNGzfGDTfcgML+RWrTpk1o0aIFBgwYgOTkZNx999347LPPkJqaiqZNm2LdunWF2l9ByMrKwuWXX45mzZqhWbNmSEtLK+ohFSmaj6JB8140nM28a8MX542ZM2eiT58+qFGjBjzPw2effRby9+HDh8PzvJD/GjVqFFJm9erVePjhh5Geno5du3Zh1qxZvtsHgNzcXDzxxBOoU6cOypQpg3r16uGZZ54J+YfD6tWr8eSTT2LNmjWYPn06Zs+ejfnz52PIkCF47bXXzmi8r7/+OmrXro3SpUsjNTUVCxYs8DVXp6qXlpaGSpUqYeXKlVixYgXat2/vq83zyXPPPQfP8zBs2LC82IEDBzBs2DAkJSWhTJkyuPjii7Fw4cLTtnW6eS4O83G+KMx5B4r/OjxfsHn3c69hnMv1rg1fnDeys7PRokULvP7662HLNGnSBDt27Mj77+QNHQAaNmyIlJQUeJ6HVq1aYdOmTWfU/vPPP4+RI0fitddew+rVq/H888/jhRdewKuvvhrSR8OGDREVFYXGjRujR48eAIBmzZqF9He68X7wwQe477778NRTT2HJkiVo0aIFevbsid27d59ynk5Xr1mzZpg5cyYeeughzJs37xenrl64cCHeeustNG/ePCR+++23Y8qUKfjXv/6FlStX4rLLLkOPHj2wbdu207Z5qnn+pc/H+aKw5724r8PzRbh593OvCcc5W+9OiCIAgPv0009DYk899ZRr0aJF2DobN250rVu3zvt8//33uzFjxvhu3znnrrjiCveb3/wmJHbttde6AQMG0D769evnpk2b5pxzbu7cue6KK67wPd62bdu6wYMH533Ozc11NWrUcCNGjAhbx2+9H3/80Y0dO9a1a9fOvfrqq6ds73xy4MABl5yc7KZMmeI6d+7shg4d6pxz7uDBgy4qKsp98cUXIeUvvPBC99hjj52yzdPNs3O/3Pk4X5yLeS/O6/B8EW7enTv9vSYc53K96wlf/KJYt24datSogbp162LAgAHYsmVLobZ/8cUXY+rUqfj+++8BAMuXL8esWbPQq1evArUXbrw5OTlYvHhx3rcDwDE75B49emDu3Llh2/NTb/v27YiJicGgQYMwbNgwLFu2rEBjPxcMHjwYV1xxRcj4AeDnn39Gbm4uSpcuHRIvU6aM+RaHcap18Uuej/NFYc97cV+H54tw8w6c3b3mXK13eemLXwypqakYO3YsGjZsiB07duDpp59Gp06dsGrVKpQrV65Q+njkkUeQmZmJRo0aISoqCrm5ufjjH/+IAQMGFOp4Dxw4gNzcXFStWjWkTtWqVbFmzZqwbf7444+nrbdy5Uo88MADiIqKQpkyZTB69OgzHvu54P3338eSJUvo78PlypVD+/bt8cwzz6Bx48aoWrUqxo0bh7lz56J+/fqnbPd06+KXOh/ni3Mx78V5HZ4vTjXvQMHvNed0vfv+LkCIQgRhvnI/mZ9++snFxcW5t99+u9DaHzdunKtVq5YbN26cW7FihXvnnXdcfHy8Gzt27Bn3carxbtu2zQFwc+bMCSnz4IMPurZt24Zto6D1ipotW7a4KlWquOXLl+fF8n/FmZGR4S655BIHwEVFRbk2bdq4AQMGuEaNGp1RX2ezLv7XOFfzXlzX4fnCz7wX1r2mMNe7nvDFL5YKFSqgQYMGyMjIKLQ2H3zwQTzyyCO48cYbARwTwGzevBkjRozAoEGDzqrtk8dbuXJlREVFYdeuXSFldu3ahWrVqoVto6D1iprFixdj9+7duPDCC/Niubm5mDlzJl577TUcOXIE9erVw4wZM5CdnY3MzExUr14dN9xwA+rWrXtGfZ2LdVFcOVfzXlzX4fnCz7wX1r2mMNe7fsMXv1iysrKwfv16VK9evdDaPHjwoMkdHRUVhWAweNZtnzze6OhotG7dGlOnTs37ezAYxNSpU0/5Gk1B6xU13bt3x8qVK7Fs2bK8/y666CIMGDAAy5YtQ1RUVF7ZmJgYVK9eHT/99BPS0tJw1VVXnVFf52JdFFfO1bwX13V4vvAz74V1rynU9X7W3xEI4ZMDBw64pUuXuqVLlzoA7sUXX3RLly51mzdvds4dU91Pnz7dbdy40c2ePdv16NHDVa5c2e3evbtQ2nfOuUGDBrmaNWu6L774wm3cuNF98sknrnLlyu6hhx464+M53Xjff/99V6pUKTd27FiXnp7u7rzzTlehQgW3c+fOvDZeffVV161bt5B2/dQrDuT/inPSpEnu66+/dhs2bHCTJ092LVq0cKmpqS4nJyevDJuPs10XkUZhzfv/yjo8X+Sfdz/3mvO93rXhi/PGtGnTHADz36BBg5xzzt1www2uevXqLjo62tWsWdPdcMMNLiMjo9Dad865zMxMN3ToUJeYmOhKly7t6tat6x577DF35MiRMz4eP+N99dVXXWJioouOjnZt27Z18+bNC/n7U0895ZKSkkzbp6tXHMh/A/zggw9c3bp1XXR0tKtWrZobPHiw27dvX0gdNh9nuy4ijcKad+f+N9bh+SL/vPu515zv9a5seUIIIUQEoN/whRBCiAhAG74QQggRAWjDF0IIISIAbfhCCCFEBKANXwghhIgAtOELIYQQEYA2fCGEECIC0IYvhBBCRADa8IUQQogIQBu+EEIIEQFowxdCCCEiAG34QgghRASgDV8IIfJx9OjRoh6CEIWONnwhRIHp0qUL7rnnHtxzzz0oX748KleujCeeeALHk3D+9NNPGDhwICpWrIiyZcuiV69eWLduXV79zZs3o0+fPqhYsSJiYmLQpEkTfPXVV2H7O3LkCB544AHUrFkTMTExSE1NxfTp00PKjB07FomJiShbtiyuueYa/PWvf0WFChXCtrlp0yZ4nocPPvgAnTt3RunSpfHuu+9iz5496N+/P2rWrImyZcuiWbNmGDdu3FnNlxBFiTZ8IcRZ8c9//hMlSpTAggUL8Morr+DFF1/E22+/DQC45ZZbsGjRIkyYMAFz586Fcw69e/fOe4IePHgwjhw5gpkzZ2LlypV4/vnnERsbG7ave+65B3PnzsX777+PFStW4Prrr8fll1+e94+I+fPn47bbbsM999yDZcuWoWvXrnj22Wd9HccjjzyCoUOHYvXq1ejZsycOHz6M1q1b48svv8SqVatw55134uabb8aCBQvOcsaEKCKcEEIUkM6dO7vGjRu7YDCYF3v44Ydd48aN3ffff+8AuNmzZ+f97ccff3RlypRxH374oXPOuWbNmrnhw4f76mvz5s0uKirKbdu2LSTevXt39+ijjzrnnOvfv7/r3bt3yN9vuOEGV758+bDtbty40QFwL7/88mnHcMUVV7j777/f13iF+KWhJ3whxFnRrl07eJ6X97l9+/ZYt24d0tPTUaJECaSmpub9rVKlSmjYsCFWr14NAPj973+PZ599Fh06dMBTTz2FFStWhO1n5cqVyM3NRYMGDRAbG5v334wZM7B+/XoAwOrVq0P6Oz4eP1x00UUhn3Nzc/HMM8+gWbNmiI+PR2xsLNLS0rBlyxZf7QnxS6NEUQ9ACBG53H777ejZsye+/PJLTJ48GSNGjMBf//pXDBkyxJTNyspCVFQUFi9ejKioqJC/nepnAL/ExMSEfP7zn/+MV155BS+//DKaNWuGmJgYDBs2DDk5OWfdlxBFgZ7whRBnxfz580M+z5s3D8nJyUhJScHPP/8c8vc9e/Zg7dq1SElJyYslJCTgt7/9LT755BPcf//9GDVqFO2nVatWyM3Nxe7du1G/fv2Q/6pVqwYAaNy4MR1PQZg9ezauuuoq/PrXv0aLFi1Qt25dfP/99wVqS4hfAtrwhRBnxZYtW3Dfffdh7dq1GDduHF599VUMHToUycnJuOqqq3DHHXdg1qxZWL58OX7961+jZs2auOqqqwAAw4YNQ1paGjZu3IglS5Zg2rRpaNy4cV7bjRo1wqeffgoAaNCgAQYMGICBAwfik08+wcaNG7FgwQKMGDECX375JYBjPxFMmjQJf/nLX7Bu3Tq89tprmDRpUsh4FyxYgEaNGmHbtm2nPK7k5GRMmTIFc+bMwerVq3HXXXdh165dhTl1QpxXtOELIc6KgQMH4tChQ2jbti0GDx6MoUOH4s477wQAjBkzBq1bt8aVV16J9u3bwzmHr776CiVLlgRw7HfywYMHo3Hjxrj88svRoEEDvPHGG3ltr127Fvv378/7PGbMGAwcOBD3338/GjZsiKuvvhoLFy5EYmIigGN6glGjRuGVV15BixYtMHnyZDz++OMh4z148CDWrl172nftH3/8cVx44YXo2bMnunTpgmrVquHqq68ujCkTokjwnPvvC7NCCHGGdOnSBS1btsTLL79c1EMJy9ixYzFs2DDs27evqIciRJGiJ3whhBAiAtCGL4QQQkQA+kpfCCGEiAD0hC+EEEJEANrwhRBCiAhAG74QQggRAWjDF0IIISIAbfhCCCFEBKANXwghhIgAtOELIYQQEYA2fCGEECIC+P9OW0/CzRAI/gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# SDSS is a good first example because the API is simple and the cutout method returns a rendered image.\n", + "sdss_coord = COORDS['north']\n", + "sdss_radius = 1 * u.arcmin\n", + "\n", + "sdss = SDSS_Survey(sdss_coord, sdss_radius)\n", + "sdss_catalog = sdss.get_catalog()\n", + "preview_catalog(sdss_catalog)\n", + "\n", + "# SDSS exposes get_cutout() for a quick-look image product.\n", + "sdss_cutout, _ = sdss.get_cutout(30 * u.arcsec)\n", + "show_cutout_product(sdss_cutout, 'SDSS cutout', band=None)\n", + "\n", + "# Image download\n", + "sdss_image = sdss.get_image(30 * u.arcsec, band='r')\n", + "plot_fits_product(sdss_image, 'SDSS image', band='r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DES (Dark Energy Survey DR2)\n", + "\n", + "`DES_Survey` wraps the Dark Energy Survey Data Release 2, a 5 000 deg² wide-field optical/NIR survey of the southern sky in *grizY* bands, reaching *r* ≈ 24.5 (about 2 mag deeper than SDSS). Catalog queries go through the NOIRLab Astro Data Lab TAP service.\n", + "\n", + "**Image products:** `get_image()` retrieves a FITS cutout from the DES DR2 cutout service and is the recommended method. `get_cutout()` also returns a FITS HDU for historical reasons but is now deprecated — a `DeprecationWarning` is raised and users should migrate to `get_image()`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=5\n", + "
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DES_gDES_g_errDES_rDES_r_errDES_iDES_i_errDES_zDES_z_errDES_YDES_Y_errDES_IDradecDES_tileclass_star_rstar_flag_errseparation
arcmin
float64float64float64float64float64float64float64float64float64float64int64float64float64str12float64float64float64
21.4599480.01818426320.4348680.00953518120.0347960.01174251819.7806530.01707272619.6715050.045026816933130148326.105239-40.90025DES2143-40400.0289913530.000251381480.0016863633241175143
25.826190.4372452524.3839680.1560542923.712450.1498177323.4202840.211546924.5136831.6911596933130382326.101695-40.899833DES2143-40400.442140730.0041638950.16215806082856368
25.1584030.1966621724.611650.1599863624.2157270.205484624.8003220.6257875625.4895653.7755795933130918326.103714-40.9044DES2143-40400.74237890.00479470850.25996375208121103
25.3534260.3609894524.5534270.2325170323.3927350.1475062822.7541310.146397622.1586670.27005628933130877326.108141-40.904003DES2143-40400.00018057640.0068892790.26238710540933896
23.3052080.05975465522.6429580.04365455722.584950.0736227722.1779480.0935138324.476142.26726933130488326.111065-40.900621DES2143-40400.198915390.00108485340.2655515572359525
" + ], + "text/plain": [ + "\n", + " DES_g DES_g_err DES_r ... star_flag_err separation \n", + " ... arcmin \n", + " float64 float64 float64 ... float64 float64 \n", + "--------- ----------- --------- ... ------------- ---------------------\n", + "21.459948 0.018184263 20.434868 ... 0.00025138148 0.0016863633241175143\n", + " 25.82619 0.43724525 24.383968 ... 0.004163895 0.16215806082856368\n", + "25.158403 0.19666217 24.61165 ... 0.0047947085 0.25996375208121103\n", + "25.353426 0.36098945 24.553427 ... 0.006889279 0.26238710540933896\n", + "23.305208 0.059754655 22.642958 ... 0.0010848534 0.2655515572359525" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 25\n", + "Columns: 17\n", + "downloading deepest stacked image...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/3855913100.py:12: DeprecationWarning: get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.\n", + " des_cutout = des.get_cutout(imsize=30 * u.arcsec, band='r')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading deepest stacked image...\n" + ] + }, + { + "data": { + "image/png": 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2796/8G9Bd/EHhX7ABrmhvm4A6gcMyB9AN9KwpnjTy+qH2uP4H1G+AbbJBuZ/iGullgDftHnjaxt882ijm0C1tMcXVUfkH2gAoBuOuehT6D9Y/FARaIz6j6PeZDZQ22Df85j5IUQfmnieyuhcHXIc3/T0ISKyXnwjBoCshv7hrKG5luPMQ15sXwBADe231I2Yx5J74KH3PP42+Qea/3E0D7W19rgavumXl36tbfCe5rUCgObiP0S5PVNdHH9YtbTwOhs6zB7H7a/P1+UoYOaX/kFdJ/Uoyopzlg2idcyZnam9GntvCCuLBaWytlXFLwbXm+PMPqYHtLB6hT1u9bJie0NGFj+vlnsS793yEcXXuj7rIusN2LHU0fpXyH2B+76e/jEfJOtjHkJp77fKwz/v3So6V52saZZ42OA2+R/91SvtcXwNlo8q/TkAtKwFqqrfPG3PqoW6M6PjOI7jOFH8QcFxHMdxnCj+oOA4juM4ThT3UehPlGWdGqX6A5DzXTagoeTnAKy/ATsS6XGsCauuSDqt9WUQJ5s1osdtoFr0ZnYkMxq9PAOzrsiav7ZXRfqz9sHooBEnKD03a8rqN8Fz0xZxbATsuMhXIMg6GmcvXeMW0j1JR89qxOcj6oi5On4coxowa7vcB10f1lV1z3AbET0cAMIq0sAHkX9Ny1p7HDsLNpBWLusYqL8Zj1d9Q1iX1vHTXst4j8saG/8FdrDUMZIjYcaObnpe9gGhPRjaxHEusrc6249cx+oPwONvI8fG+uH2OJ5rPldbxKcHAMrJ1yDnMyT7hOF9wj5Y7E8CmLW0e8HOU1hBDpE87+pfwb/j62ftKnMY70FdO+PMmPL/4T3J67hGwiXb2/KO1N3ELQqO4ziO40TxBwXHcRzHcaK49NCfaFkPtFTmzZRswmPzlsT+GpMtxyOLKZLNe9ngEeY7I0uwKVpDFvk9m71TIYEmBFK2tolGoudjNWfyWFrEXFcTMYPm4v4rSr/WvjMmbFTaUzP9ho9VNuHfqTRgTJiR8EAAYNM051SQEMNkrD+zhk2uCRN9R8K0z31nqUTGaGUzOlel7V/G42cTfc6kTu1zWKbGs5tQUftdNMeAjDFjWY/XW49j8ziZszMNiaPrM1BYXe44E4YsEgCvkQnFS0hjvJ80FI/D+1gmUzmA9xqH8mruCZZo9HrnNWL5R8NDzX7iMFx7XWR1dL8zuTwklJfXy4QA27lgyS+INIZaks14zjTMl8eo4ctMeUW+n93ELQqO4ziO40TxBwXHcRzHcaK49NCfyLLiH8PmODa3i2d2xuYrk35YvHbZJKimPm6TTX1rxVOXzWxs6tRUqOY42s4qG5h+UEa75hXmsGxoY/FNLjU1mfo64uM3742XvlxuPBb2pNZMbR2cqpVM/sPG2OPYhKvZDdVsuQFdHza5coY4NfPzXCdM5aZ9Pq5C5pbPJfszrKaUy/y7KmmD14s9v8tlfZh2zvppPdhNL7hPGinCa6ySD8s3fF1oFAnLIzHZDbB7nOdT5BUT2cJrUC1ZJU00R0Ly4jGmMpEaWUL2IF/7PC81mlaYoygoU6xmeeXzqoRWzSmsySyvmV15Xc31ncp6mpAu+Xf8Wu+5NJZM7x+MkWES0lDqXL2AWxQcx3Ecx4niDwqO4ziO40TxBwXHcRzHcaK4j0J/orK6U8tUHZX1rVQGNg4RMxUnrX9BxmGEmiGQq9xxaV3Jimcy4cW0OMCECJmStKrFtlN7gSvrSUlWDjPSsKWYzq+hSRURnTZXapa0RM4wqWOk9kxIpOqjnFVSdUrjR8ClhWV9WM/mcEOt9mhCNhOhV7x2RsvW7IvFMQbJiMkZJ0053VxmPhpLIjTNjJ/9BnQ++XemHLGM14QXi1/PQAqr47lNldlOlHA3oY5Gs5c+xSpf6rWfipqLhT2q/wt/R3MbZL9zqHRY8UbxC/F5MOG2pjy6hGXyOmrIKs8N+3lo3/m+wz4AufsH+wBwRkQNt6TfsR+G+nXQ3OTmiStG8tzmKl/yGtM+yWWKzZKl5buCWxQcx3Ecx4niDwqO4ziO40Rx6aFfETr/1CzN5j02uWk4TiRczmQtA6zZN2e+J7MiF2RhEzAgoVmJwigZmeXLE8VleIw1A+LHsYygEkB5JIOlmu/5XB0RCQWw5kKas7BaivPwnPG8qBmV11W/a6V1MIWf4mF1RkLROeOMcdyemu/5dzxnkmXPZKfTNkhuiZqlATvXbLJVMz/3g83DuaJdpUNqc7KJkTnUlh8pHqambd537ZEQO8h1klpvnndeq5TkoWvMpv5EGywVseSXJcIozTrmih0Vx8KFzzKV/qoSMhwVCDPjUgmNr0kjk4ns2hrJIlsu99I2la9KtA1I2KeEh/Je4DVW6YVDhVle0bDuqlqgSu5R3cQtCo7jOI7jRPEHBcdxHMdxorj00J+oqOz8axeTOpuq2ASsZko24bHpWE1dJhtfPMtgVF7Q37GJTeUQboMLUKkpsjVS7EkLK6W8h423e6QWPBD3uNf5XFfaGzsnw7C0YbzvZR3NuMS0bcaVyOLGpviYuRVAoHMZM3IsMgSwkTJa4If7KxlB0ULzSf0NEm2S1Q8r/RvNiKn7fwNqbmZzfkI2SUYExNrTc5VFiofpPo5l8UtFA9E+zjQ6gNfVFPCSc3GEQU4OIemR107N/DEJUffW6hXFtgdRgSS9z3B/dR9z9kySijhqBBB5hNvXCCU6jmWyrE5kA74Gea71XmI6If9f5wyhmi2TMZku6XPdC+vXAuslU203cYuC4ziO4zhR/EHBcRzHcZwo/qDgOI7jOE4U91Hoj2jFQNbINASHYa2cNWXVZTnrmGixRouOheJpm+w3MVAyKa4lndLog1oJj30eSHtVSZl1VdWDY1nn5FwZj8VkrUtUpONz5aq/USdZe5WMmIFDrtQfgtecz5WqxslhauoPwH4UWtWPWcehXsU+ZIOG2ePYj6BFtG2eNw7L1TC9lUuK33FWSc2kaM7LOq9k2IxdC7mKm/S+VTNdlkeOs5pxiFV71GuL9rvxPRCfFPYByVh7Vx8SvmbUB6AysidTWRD5+slVEtVMmpH2TAVTak+XkddOfQB4ftuKfTf+NICEbyf8icqLvzNhmim/Dg491tDb9kSmz5rIPXKtnIv3Cd8X1f8FASjbtIqSblFwHMdxHCeKPyg4juM4jhPFpYf+RFtr51+uiBGbyzhUUkJqjKmPzIqafZFNzGKWZpN9Fmsv1yd6LeZ27hMXp8plceNzsTlP+87hUrkiQUWMqVfDwMyBbM5MSD5sYs5lN6Q55PZEouAMb0HNlHxcTUTyAKy5nbK9ZWo65X3SvKL4WsfI7a8lmSNXtIqlLDEjs7lY9y4T25MaAsvhkdzfKi0ERCZxXh81c3Noo2a6ZHu5MaNrcaJIYTYtCrXktcLrbOjo4hcSXmzWq3548bVe03xenadYUawKkSg4pLaidOE4ABJuGpc4uQiY6e86DUuNyGndOJeVISPhsACwLnI96VyY7K3xfWukIb1mOCTS3MfkmomEDeezb7amw3a7gFsUHMdxHMeJ4g8KjuM4juNEcemhP7EhM2MmXupsAl9Lpn31gu6qKZLNrWoS5Sxu7Jms5jKTgTCR1YyLxnDmyDItSEP9qKL+pmq8q3zBUR9sYlUzJZv9K6gNNTHGJBAtzsPfsflaow0owiAvvXDBG/Z0t6ZyU+CHTf46T2RKNZnqtO/GtF/sU1jxhjksM4XJtA3aG4nMj+bcvGcksiNwcSKOjlB4rjkzn+5pHqMWCOPf8Xlr7NpFIx1kz2RbTyi+WZ/IosryjYkA0IyQ9Lv1IsPxfLIEoJEDfK2ZLKKy30NpmSMsW2AOy1gCMRkWRUJg2UTmM6xYVGyP+6vjj8kNOTmxtFzH1wsge5D7lLsPUtSQymk819wnleT4//ksp+WKz1Xloy66iVsUHMdxHMeJ4g8KjuM4juNE8QcFx3Ecx3GiuI9Cf2L9OqCqIu97wPoZfye6n9Ht2IeAK7wBQPPy4ut20fpM1kLW2CQkMKbfqz8Et8datOp0rHuWRzRVAEaLTGRSNFkAh4yyx7H+yO23i8ZotOhINjqFdUrV600WxER2R7P+NmzKaKzcdw23pFBMo7evl0yPJutncYzZ4OH2uI5ENU4mpTfHdPQOrR7ZVvq1VurjcXFGyIRfR75qKfkl8L5QnwJq01RPzPnJUPspfxryS+CKiZrBMedHwPB88lzodcF7kvV2DRVV34YNXRgw2Lw3/eVrRPvK/h/kkwBIVUhG16ey9H4Kcg2acFPKnJhpushYZkq9H/FY1K+Hr4X2LoavxrJebuhTrGJqF3GLguM4juM4UfxBwXEcx3GcKC499CeqqjvNf2puZ9Mhm8S0WAub5lYV5YWsIWGKJBM1AIDNtJwJT8/FJkc2q6YyOLJlU83SbHLkUMlc8aTi+1zoE31nzMipbHcpGYH73h4puKWk2uZwSTWxGjNyoqAXv69k2cDeKsKqZYXXRq5QczP3l9ckl32SQ/EkTK+SQ8noO90zsUyXIrUZYYNDZctk3vk6yYWmUXtsAtf55D6yTKbXoAmdpLlQczOvOYeRauZAClk0ZngtvmbWQfrO89lSurhX53ua0QaS4aTwldkbvI4S/mzkkcT1yN9latrnsFf+LiWb0LznxS8O+W4t/RoAammvciZJldPKEyHfLOWuXhk/jvdCovhcWNOE0CxZLbuJWxQcx3Ecx4niDwqO4ziO40TxBwXHcRzHcaK4j0J/Iss6/zR0iHVU1hFFew/0PmsYUfxCUyyTThs4VBKil5KGF0SzzlhX5u9U69OQtg2kqsmxTqvaM1d107Al1mZZb8+dK5KOWPtqQgcTFe5M6mz6DYXRAbC6bML3wlSdS+nSPDeJUFHTd9XAeW9Qe5oGGSsp/bSG27ZHQgLVR8Fo9hSmqeHAnHKY50LS3PLeNSGg5fL/K+pv1iJ+I6zTx/YgYOe3KqLlAzb8kH1SNJTZzC/r17LfOyL+SUC8IqGuHf8ulZad9Xb+Tq9p48tRPFdWP8Iep+vPcOg1+wpon/gaX76w+FpTe3Mbqb3F68i+K1xhFbLHNb3yqqXF1xWJcHVE/A40tLOqBllVvBJuV3CLguM4juM4UfxBwXEcx3GcKC499Cda1gMtlflMYLHqd2IStGFLXNVMTIBkzsxlSOM2WMqQkMAQq5goZNxfDqPMVaNE6e80W2IsMyEAtEUyROaq5MWyDIqJlcfF2elUUmDTflczE+Yy05HZu6N0SBgAa/ZtiZuRMw57NeMXKYMh02mmc2syC+o8yTg3oGGk5RFzdi6klo7jtRKpzchfPC8p830ijNKGisr4Wb4y4XwSYhjLgqjraLI2chiymKBNf2Ufc3/5dyqTxcKr2+RcZRGJoqvtcWVbwIYiVsh+XyPhooXjxMwvlUWL/YtXjww0L5le+ywvktSU1cheTYUDx0KKdUxtLJvRXh3YYI9rXpHOwNkF3KLgOI7jOE6Ut+WDwvr167H77rsjyzLMnj3bfPfUU0/hfe97H2pqajB27Fh8//vfN9+3t7fjK1/5ChobG3HooYfijTfeKHx34403Issy81dTYxPInHfeedhpp50wYMAADBkyBAcddBAeeeQRc0yWZZg/f755r3+33nqr+c0DDzyAd73rXaiursYOO+yAG2+80Xx/7LHH4rzzzivZvuM4juO8VbwtpYczzjgDY8aMwd///nfzeVNTEz74wQ/ioIMOwjXXXIOnn34aX/jCF9DQ0IATTjgBAHDrrbfilVdewd13341f/vKXOOuss3DdddcV2qivr8fzzz9feJ+J+X3HHXfElVdeie222w5r167FZZddhg9+8IN46aWXMGKEeOUSN9xwAw4++ODC+4aGhsLrefPm4bDDDsOJJ56Im2++Gffeey+OO+44NDY2Yvr06T2ao5J0dHT+pQqUtCyzxzOcjZDM44FNoIApQJINqLff8bHcDykYk7FHM5u51cuYzcXGC9qawEMsmkElCjZfq/meTaJ11CfxaI5mt9T5jEk+NZLNkkl5lbMXvJpU+dz8Oy0mZPpLJlD1TG9jcz7NtZq2eQ51/Ayvq5rvI57kuXPxcQOHxM+1hkzYvFYaicB7lddHihhxe7koH95rvN9TEgW3USZ7kOeJPfM1gx9LYzQvQWStjNtQ73te87WJLINlNG+xgluANbfzOqqZnyUljvpQr3+ORBgs915un69pLY4Uy3RZJv+H5iyQpuCYrGMsYkXvJSw36HVsxk97sEP6xPuJ74N6Py4rz4+nm7ztHhTuvPNO3HPPPbj99ttx5513mu9uvvlmtLS04Prrr0dVVRV23nlnzJ49G5deemnhQWH58uXYdtttscsuu+DZZ5/F7bffbtrIsgyjR4+Onv+oo44y7y+99FL89Kc/xVNPPYUDDzww+ruGhoZou9dccw3Gjx+PSy65BAAwadIk/OUvf8Fll13Wuw8KjuM4jtNN3lbSw6JFi3D88cfjZz/7Gerq8vnwZ82ahfe///2oqio+aU2fPh3PP/88li/vfEr/zGc+g1mzZqG6uhqnn346zjrrLNNGc3Mzxo0bh7Fjx+Lwww/HP/7xj2h/WlpacN1112Hw4MHYbbfdkn0/6aSTMHz4cOy11164/vrrTU74WbNm4aCDDjLHT58+HbNmzUq26TiO4zhvNW8bi0IIAcceeyxOPPFE7LnnniU1+oULF2L8+PHms1GjRhW+GzJkCBoaGvDEE09g4cKFGDFiBMrJLDVx4kRcf/312HXXXbFy5Ur893//N97znvfgH//4B7beeuvCcX/4wx/w6U9/GmvWrEFjYyNmzpyJ4cOHm74yF1xwAQ444ADU1dXhnnvuwVe+8hU0Nzfja1/7WqFvG/rJ/W5qasLatWtRW1trfBZyhWccx3Ec5y1isz8ofOtb38LFF1+cPObZZ5/FPffcg1WrVuHb3/52r5y3lAywzz77YJ999im8f8973oNJkybh2muvxXe/+93C5x/4wAcwe/ZsLFmyBD/+8Y/xyU9+Eo888ghGjhxZ8lxnn3124fUee+yB1atX4wc/+EHhQaG3aWpqwuLFi1H2pi61evWbenV5eadWJjqq+gcU0MxnrAmSVpqrtGbC0eRb1jf5gUc1dc7uuLYYFpRpqBvrqhyaJOFAJkyTv9OwofaELs8+AKwJa584Ixtrh6IjZw20X3iudT1MZbjSWety71NhW6xt5kIsi9pp4P6KxmrCWXkNVG8246LfqD8Aa7Y677EHY/W14XUwOq20x33n3+T2Fun8/JuE74qpKgpIRkwao/qG8DpwuGDON4SOW7m42AXNbMp7iMP0Bss9KhZuCdgsrdoPxmj2NIe6bnyN8xzKcaYyaWyfAXZvaQZL9svgDJu679jfpCzhN7Ge+m6ySqpRPpQ+rl3mgs+lGVt5PssTYbl8HM+t3j/aWvJ7u5ts9geF008/Hccee2zymO222w733XdfQTJg9txzTxx99NG46aabMHr0aCxatMh8v+F9yu8gRmVlJfbYYw+89NJL5vMBAwZghx12wA477IC9994bEyZMwE9/+tMuP8RMmzYN3/3ud7F+/XpUV1dH+11fX4/a2tpIK3HOOOMMXHvttd3+neM4jvPO5bXXXkNzc3PhP5ENDQ25yL5SbPYHhREjRiSjBTZwxRVX4MILLyy8f/311zF9+nTcdtttmDZtGoBOi8B//Md/oLW1FZWVnU9iM2fOxMSJEzFkSMILOkJ7ezuefvppHHroocnjOjo6sH59PCmQMnv2bAwZMqTw0LPPPvvg//7v/8wxM2fONNaN7jBjxgxccMEFhb6tXLkSO+20U4/achzHcd4ZTJ482bw/8cQTcfXVV2/0d5v9QaGrbLPNNub9wIGdZuDtt9++4D9w1FFH4fzzz8cXv/hFnHnmmZgzZw4uv/xyXHbZZV06xwUXXIC9994bO+ywA1asWIEf/OAHePnll3HccccB6DTh/+d//ic+8pGPoLGxEUuWLMFVV12F1157DZ/4xCdKtvn73/8eixYtwt57742amhrMnDkT3/ve9/CNb3yjcMyJJ56IK6+8EmeccQa+8IUv4L777sMvf/lL/PGPf+z2PAFATU2NeUrc4PgZVq9EKGvPZbrLhm1VfGPMqhKKyCZCNqmrOY/NqhoiRSZMU2RKQ87IXJyto4xk2h6TLDQjZssNqEmuisas2fMII4cMEWsVf8dhSQ0Jsy9nmVsnJuCYXKOhopy5TU3bJrSM2lPJh85ls2Um/GJ4P1VLhkAT2lhcn7BGZJj64fROzsUZ/soTZt9YWKqGhsXkhpxcE5GhJIOjMVPrPuM1MmZkzVpI4+IwQjWpsxxA582Z1Nm0zSZ7lQPKE9/x+DXzI1MdyTKo12p76ayFZkyQkHR+vcZmZjT3j4HxInAcAhq44BKAjK+7FpJW9drn9eHrIicN8RjjRdA46ydLLYDcT7gN2QtGXjIhz2Ih6GgvfD9nzpzCv50dHR0YNmwYusLb5kGhKwwePBj33HMPTjrpJEydOhXDhw/HOeecUwiN3BjLly/H8ccfX3B8nDp1Kv76178WnsLKy8vx3HPP4aabbsKSJUswbNgwvPvd78b/+3//DzvvvHPJNisrK3HVVVfh1FNPRQgBO+ywAy699FIcf/zxhWPGjx+PP/7xjzj11FNx+eWXY+utt8ZPfvITD410HMdxeo2xY8eivr5+4wcKWXAX+nc8TU1NGDx4MJY/eT/qBw1MWxT4f7nqzBgiiXX0fzPlkQQs0n5XLQpgi4ImoDFOipGyuID9H4J+x6QsCjTOsGxB8bQJi4L9H3Uip31dDywK6lTHzmhqUTD/q6b2ciWD6Tuzdvo/0UhZbP08ZlHQ/0X1hkWhI7J3c3U/InUgUhYFdmRVpzJTFlj+Z2scJ7lst14XNOaURYH7zpaMXIKgrloU6Dg9F+9PdSRkOEEYWxS0hkF7xNlY54xrGCTKW1uLgkjLkes9Z1Fg504+b86iQP3tqkWBHSplLrhWSmhaYr/j+wk7F6tFgUtVs2WQk8G9+V3TqmY07DQVK1eu7NGDwjvKouCkyWoGIKsZgJC8MGlL5Mz1bBJkD3v1UqeHATUxc2GgQXRx6w0sYprULJAmgyPf9PSBwjwA8Q1QzPdtkegAwNw8sgYKZ9Use6aoD32XK9TE8ENO4h8svnFyFApgHw5SkkoLrbf23fwDw17/8uDBbfANXB8ujZm/OLeZPsjww4H+g208+Km/a2X8sWJc2ne+qfIY9QGAf8fjTckGpTzOC32icen6cBv8EMUPKID9B0wf0JnYntHIBv4HLPfwxw8lNI5c5kwuOBYpaATINU3nkvb42gorik7eQf7xzurJbK4SGkttlI0y90DB/xBTfzOVWoysFY+MCs0UsUEPA7n/CJl7iUiSvEb0cJVpsSc+N99b9CGnshqojBRW6yJvq4RLjuM4juP0Lf6g4DiO4zhOFH9QcBzHcRwnivso9CcqKoGKKmSqvcec+/Rz1sS5Op06n7Gulgur4mM55EzDwMR3YMMv9HMTSsUOR6JLU3a2sIbGUW7HaDRM9RXgyo3s0JQKpUqFbPLvuKofV/QDrP6ayrDGWq+uMTtIcj/0ONZHee1ylR9pHVmXlxA26ytA7VXFQztzfiiq028gt48jPgBt4lNQEXHuy2nvXHGTHSBlHdnfQl3DY3OjPjntEd8YqXZownL5WqgVBza+nti5Vn0o1pF2rdkIWffmEMYy68sQqHpq/nonTEZQOpeER4Ymcjjkqo3qk8G+ETr+EPF5yWV3pHsVjzfl/8HXo/hCsV+CrcorIbVZxP8DsFlpaR/ncmNqJcwN6LVaWZV24O4CblFwHMdxHCeKPyg4juM4jhPFpYf+REdHp0m2Qsy+JkSqovTnQFxuUHM4m1FrrNk4cKhSJR1nsgDCyhymuIo1I3MxHGP201wEFOttQvNUNkjkgAicCS+VHyEmPYhdmk3sxuypIWyxsEoNI4zlNtBz8+/WS1hZ3sBZuk9GRmEpQ/pgTOoVpT8HjKyTNzGzBMAFvVLFozhXhJjUjXmcjsuFDpK5mE3WKkkxufnkmHv6XY1IYxwjz+fSkEA235tw4ERIaUsiPwDPrVyrxpzP49ICVCzzcUhkTg7g8FXqr4ZRxszkOTmN7hFy/zASDf9OrxmWb0yBrESRKb7nVMm1GcsqmbuuKENtTmobVOIo5K8ZnrfKSAgx0LmPVfroJm5RcBzHcRwnij8oOI7jOI4TxaWH/kRba+efmhjZLJ0qcGMg05lkRTPFhLQYCqcqTmUTYzO6MeGJKdZ4xCeykRsrID0fq0e9MTna9sy42DwqkRPGBM7j0rng+WUTqGZcNJJCYoxsslfPZ+NxTv1QGcr2kE4r5zXRHPS5ylAx7+6chz31XSNW+Fhuv0okCpM9MDFPvCZsztWskmaNea9qsSP6nZrbjYc87TvNHMpFglYXs5lm2h7LZtw/zVLJ+8lEeci+4HS/umcoy6AtEKXZInlf856R9mIp1vX6YW9+lo00q2IuEodOxVkM2eyuJnjea7EU04AtMlVG690q7Zm5pnEkUmdnGuXEmHTjdryB9nvG7Wt7oQOoihe46wpuUXAcx3EcJ4o/KDiO4ziOE8UfFBzHcRzHieI+Cv2JAfXAgEF5nY71x5iWDQnno/CrXDY21lE1hK21dAnZsEZKrbLezhn9tAoba8CsN9aItru8WBY6WmYYsDpyrsx0pHSvatamrC+NV+eJNVHWaasT2SxT/gWmKqJk4GO9mPVh9UPhvcFroPo9f8eZ5LSEOY855Q/AJXNToVwaSsewn0OyBDP7CkTC4wCrU5PfQG5uUxUTY5lIdW/Rd5xxkcP8AJnPjkg2R8DuBdbrdb+3RLKNAna9eAo1fNH4W9BvNNyUfRva6Lzqr1JLfkyx/QNbujkXUlvO88T+FbJ2sb2WykTK95lURkxuWzJisl9XpuHPfP2bUt/2es/aI/dI9i0BOu/HOsfdxC0KjuM4juNE8QcFx3Ecx3GiuPTQn2hv6/xTU2x5JJOghPQY814VZ5yT87DZU81qbD4j82tOvuD3bM5mEzDE/GhMotZka0Kr2EStRYwYDdPj0Ek2++oEZJE3ObMvZ8SkPumcsQm8Kh56atrQ4jc8ThNip/9XoDVXeYDhsVBoXqZFh2JwmCxgTb1qJo0VwlITOM8b7x8uuAXY/cTmdjXfszzAbeu88Jg1e14sTDMneXG4YHEdc9eFkUfioXMYPKL4muULPW9bIqSUr1VeE5UUYhkXNQSUf8dzpoXeeK+yzCPrk9UPK75psXsmLF9UPG4QHafXKq8x90nHyP3g8E0t6MT3yEhhOwDIWP7U4/jewhkh5d5nQmeNJFmi8FWsgFQXcYuC4ziO4zhR/EHBcRzHcZwoLj30Jzra3zRlqamc3rOnbs7TPRIRoWbuhHzBZmBTZElN0dwPE5VhvZujRXJSpjY2RavJVk295jsyU7N5VE19sYxxKamA29Bsb8b7PFFXniUFnfdYsRr1vuc+dkQiJQApcETe7C1ism2jdahMmOh5T6aiClga0T5xRsuYXAFYM3oyAob2E5vXOzSLKB2XyBZo5lYjDHjNeV/kCq7RezZRq/max8gm9VxWyUQkCo+LZbg1IuvVD6fz0jhSe5D3nUpNHPXBkVYqk63jMVqZMGsYiZKkMlOaYmkJeYXnSeUVboMjeVa8YY/LZccl2kvLmplGfPH+57nQrJJtLelooS7gFgXHcRzHcaL4g4LjOI7jOFH8QcFxHMdxnCjuo9Cf6Ojo1DxVV2Mt0WQCE604pj9qGCHryLmQK8royJ+XS2a1WKW9ikTf2a9BQgzDquXF8xo/BNFsTaa6RHgkhymqjsz6ZkUi7JF1w3aNMSV4Llhv1mx0rD+rD4DJisdatKwPz6fxeRBtl9vgfuR8KGhcHIabqxbK32nIGenvuXBOgvvOvgK6t1jDNb4WiaqVPA71SeGwtVTVylQYJe8hUxVStPKK0uHFOV2a/QFCwq+D96SGtvJ+52tQ22C/I7p+cqGyMV8G8cmJt5G4RhSzF2hudZ7auTJpcU2CrE+mv9uA3o+YpqV0XKISr/qGmHsrh0PLcRlnduUsqrK3yivz10A3cYuC4ziO4zhR/EHBcRzHcZwoLj30J6oHdJqg1bQbK4yiBZ2qI4VWKDMfADGziSmWzWJc/KVczJmc8SwVzsd9Z7M0Zz0EkLGMEAm/AmxBnpwZ2WTPo9c6T/y+LGFS5znkc6mZsryLpnfuU0iYovm1yiFgWSaRpZELQVFYYqZhXyxL8FppSC33KVf8h0zRHPam+3Ywh8RFCmkBQPOK4mvub4dmVST5woSvSjgfS1naJ7NnaK5bV9vjNFyy2Lh9y3uG2ssXj6JxVdF+Jwmu87hIBlRI+DKbriWEOKwqFiEy14+a61cuLr7mfTygwfaJr0lzPdrmTBsyfgNLaLnCV8V9HVbRPtZiTyYkktZUQ0X5XCaEXLMv0vUjeyaLZcTUa9VISjQXnIUW6JSG9B7VTdyi4DiO4zhOFH9QcBzHcRwniksP/YmO1k4TmmQxM6Y0NvupmZ8zE8bMY4A1Hau3vIFN22L2ZbMdm9UqxITH7beVNst2vifTKRejUg9u7kcuSyOZ79jUqX2KZfvTc1VFCs2kMjh2sEldzNwst2jECsNygGbqMzJPJEsjEM++qevNvyMTuBkHYD3fZd6N0ZTNtDkZiot28bzL/4diZn6VDcojsonYwANJGaZQERA3+abWx0TyWAnNFvQqrkEm0pDJaMjd0bEn9ozJhMiyTCbFmWo5oob2TI2ei7Ot0r5QiY/Xn+XOpa/a4/i7XOZQjvqgc62xhZX42s3qKPOhRhTxfbGavqsVqY0L4vFc6x4kmSd3D4pdW7nMmZFoo2YrL6GyOj+ebuIWBcdxHMdxoviDguM4juM4UfxBwXEcx3GcKO6j0B9ZZ0OzTHibyQQmWjmHAnH4VSornGrbHGZmwspUi41kZNMQtpgWqVosa9as9Wn2uJTezu+52mVrojIb+3KoFqm+HaV+A9gxsy6vx7F+rXp7NGxNqwlGQjYV0lwzDm9rU38V1lXpdpNbb1ovzSIXq9SpmTN5PlNVNrlPZp9IyCb7qySyT2bso6K+MbwOct0ZuB9ZopIm94PXqsru94znl/urWUTN+MWfoiyStVErs/J+Yp1fry1unyu4rpM9aPpbvOaCnDdL+LWYPprrTDORclVI6ofuTxPaWjxvLrSR74u8Vql7ifo5cD/oHpzLFsl7g++zdRLauXplPAS+i7hFwXEcx3GcKP6g4DiO4zhOFJce+hNZ1vmXMsuySSxXQIbNnmS+zWUwpOdPNSOzCXwdh2KK+Z5D6ag4TRBzfWZM0WR+TIXO1SZkk3WSZZLpaqGZWKEYyQJpfse/aRHzPa8DmzM1uyH3SSUfYzrlQjjt8eO4iSDHxWQJlUPY5Mmm4lSoqIawsfRSlipURXuITeo6n9x3XhOdCy2eVmzAvuW+S4EjM9dm7TSklvaCuQa1iBH1saJ0yG++feqv7hnun4ZNMzwXQc61Xsz0G9D5ZOnFhI1qBtjS4auZygvliUJVPJ8x2RGIyxK5Ik6ls61muUJVvLdoXjRUlNFr1WRjTKwxj4WP00y5He35tegmblFwHMdxHCeKPyg4juM4jhPFpYd+RGhahhBacgVPTKEdNt9q1j42X7E0oKYuxE2CpoAMmx/V1MeZ+8jkmKkXuDHNcsRCQg7h/uaKxPCY1Quc2jAe52KyZROhyVIpfaqORCmkzMjGpCz9499JdEhYUzSDmqIz6qXPJtvyxP8jeN7YVC7SkCksVJWQDVKSz8Ch3GLxpey7wNnujJk6Eb2RMsmaOaT9qaZ23guaiTEmUeXMzSSB8DWT25/UHs+ZFjEyGUtbS7/Wc4k0ZtaOv9A5ixX0yhXPorFwBJXKK9y+KQolc8bn1cgjbpPnZuUb9jhun/uuciLPDbddP9we17Sk+JqjYaoS0sO6lCRJ+275Qnsc7w3un0o0dfUAROrqJm5RcBzHcRwnij8oOI7jOI4TxR8UHMdxHMeJ4j4K/Yhs4BBkAwdZ/wLAaueqYTKsZ6c0+oROaXRqai9IxbOM9ftUJUDWnzn8Sis6cvhQltCUU5kpWVfm0DSNkIpprHou1lW5fzqfPC4OiVItMrV2PBaj+6p+H6lcqD4kvK5Z6dAxQCrj8dq1yNxy+3WaZY/2k6niZ+fJ+CW0R9YAiGfS1EyPqylMs4XOm6sWGgnF03NTiGFYtdQ2wSGWFaVDgwFbFZLnPeNqpkC0ymTOb4CvadHlswFcTZHWRyu9cjirWX/ZW+yXwO3p3mJ/Hc0CyfD1qPO+nq4Zbi/lk0NrFWQ+TcZaDjFdI/NOGVvDiqI/RKYh06YKbMK/gi6tbNAQexxnQeU51Gt6/Rq7f3uAWxQcx3Ecx4niDwqO4ziO40Rx6aE/UV7e+ZfL8BUJCyqXbGccLsnfqUmdv+sQs6KGOG1oQgvIcB9zWdIINuGlikcx3J6aYvm7roY9qimW55B/o2ZPDhczWRXb48fxGqgJ3YQ9igQQC9PTsDrNilhoT/rO85aSMtiMyt8NFDMqm6m5CBRgTfE815qNjwsIUdGhTKUH/p2uHRPJEGjM2oAtkKYmZg59qynuLQ1RNubntcXxB7lWs0HFUNHA88SFuQCAwpDN+qQKECVUPRseKPPOWRt5HBoSGCvAtE7M9zU0N2V0Ta+RsNRUgTBTtI2KOK2yEifLEtmwrYqvVU+MZqKV/U7XmZFZO+Q43ic1siblkZBVnU++tvi1nqu83EqlPcAtCo7jOI7jRPEHBcdxHMdxorj00J8oq+g0Ia8W83J5pG661mRnr3Iyv4XmFeYwNo/mvG1NlsGEBMDmcWMqThRGYflCTeUmeyCZR1PFZMrE/MjmOzKdasQGjzFXyIZhGYGlgTaZC/bujmZpFFTiYTMtS0WaEZFN3SZCReaJl4HPpRkHI8V0ciZbLvzVYgsrmVXgddWMoLSPc1IWEyLzqX3nOaM+BTkuJ23YkxVf8txqcaay0nJVphINf8fzqdd0zAteC2Txuqr3fQwtbsam8zZ209frJ5JlMROzOF/T/F0iyoczvgKwksKQ0cXXKtFwES+OdFBTfWVkL6yzMpSJjuDrR++Da+naZ5lE2jf3Zr2nmQggOm+7rGNbR37du4lbFBzHcRzHieIPCo7jOI7jRPEHhT7gvPPOQ5Zl5m+nnXYqfL///vvnvj/xxBML38+fP79QQOm8887Dscce29dDcBzHcfop7qPQR+y8887405/+VHhfIWFUxx9/PC644ILC+7q6RLWxntKyFlhfkde2WXONZfADokX4cnow63Ya6sdhUewDUS36I2uYnMVOdbqYFqshdpxlLpYtELBz0Sp957GwHp6q3MehY6Lzh6biuDLuh4ZlVkWyQEroXCB/g6x+mG2DtU4KgQyr7TxlFD5n2uP5A+xctNCamkqP0kdeb513WrusTs7FY4750wCSIZHHK3shVvlS9ypfCxz2plq5+tcwvDdiGTYB619jfHKkT3wu1rZzviEEz5/6DbRFfHwAuw+575rZlX1+2iN+N0A83FRDanlcq2NZH2H2UKbhkezbYfaFhMNG+2TnMzQtLtle7rpgnx/ur1ajHDyi+LrJZuk09zQTaq5ZP4vrFVYW+5e7H2dl6f3RBfxBoY+oqKjA6NGjo9/X1dUlv3ccx3GczYFLD33Eiy++iDFjxmC77bbD0UcfjVdeecV8f/PNN2P48OHYZZdd8O1vfxtr1mxabm7HcRzH6Q2yEHJVYZxe5s4770RzczMmTpyIBQsW4Pzzz8drr72GOXPmYNCgQbjuuuswbtw4jBkzBk899RTOPPNM7LXXXrjjjjt6dL6mpiYsXrwYZW+a5lavXo0pU6ZgxQuzUT9oUN7sGQudU2LmKw2d4/CeXEGeSDEhNc3FzHYaZsSmaA5pymWf5JCrRGZCDRdkOLzLmHOl7xGTdc50GvuNFh1i2GSfCilNFb8xWeYS4YzcJzXR8trxGDWsjENA+byVah7l38jaxQqEaUEeHrOaaZnK0pketRAQXxdGbiiTuY1m7YNZr0Bm9FzYY+y6KJf5jIZ2ynhZKqijPS5FpmwWUQmrYxN2lth33A/+TvcCtWdkN81MyOvNIYCaRZP3kN62TB95PmXt2iKF2fSext9x2zpnsWtQ76v8XsKBzbnKEuGr/J5CQHNh7S3r0LRqFRomvgsrV65Efb2EY3YBlx76gEMOOaTwetddd8W0adMwbtw4/PKXv8QXv/hFnHDCCYXvp0yZgsbGRhx44IGYO3cutt9++26f74wzzsC1117bK313HMdx3hm89tpraG5uLvwnsqGhATU1iZwjb+LSw2agoaEBO+64I1566aWS30+bNg0Aot9vjBkzZmDRokVYtGgRFixYgOeee67HfXUcx3HeGUyePBlbbbUVGhsb0djYiFNPPbVLv3OLwmagubkZc+fOxWc/+9mS38+ePRsA0NjY2KP2a2pqzFPiWxJB4TiO47ytmDNnDgYO7JR6Ojo6MGzYsI38ohN/UOgDvvGNb+DDH/4wxo0bh9dffx3nnnsuysvLceSRR2Lu3Lm45ZZbcOihh2LYsGF46qmncOqpp+L9738/dt11197tSPWAzjSomoI1plO2JrSzmi4+fKiuxvojh36lNDx+rZUkTfghvVYNnHVADpHKVZlM+GiY8D7SDnPpeDmkKRF+x/PJIWGqxXKqYtaDdS5MWlwZR8yPQOfJVKuj7/Rcptol9Tfn8hRKf6cVGE0KWhk/6+rqD8JnIh8D4wOgWjmHS7IfgvphmGqctLfU14T3he4nXuOYXwdgQxHZ5yF1/XD/NIVz/XBqI+L/AEgor4yL5433sYbvxqqgKpx+mio15sJXeb1j4YuAGXOQeTLprXnv6rVaEfE9SFUVDZE9DdiqkCnfFV47De2M3av03sS+J6m+l1cW9uHYsWPdR2FL5dVXX8WRRx6JpUuXYsSIEdh3333x8MMPY8SIEVi3bh3+9Kc/YcaMGVi9ejXGjh2Lj3/84zjrrLM2d7cdx3Ecxx8U+oJbb701+t3YsWPx4IMP9mFvHMdxHKfr+INCf6JtHdBamTffskWLzc250B82naYywdFxuSyQZIKLVX4ErCzBprRkVjw6r8orHFZIfeKMZgCQsVlVswcSgdrPZYUzZvlE6KBpPyF5xDIJ6hjZzK8hZx2RjHm6jiwJsByi5+J+VCQq3Jm2qQ01c3ckZKiYpCJ7y6wdm8N1H2t44wY0OyhLbx2R8QJ276qJmfa4kTZy80S/M2Zzey6Tga+esvvpXmW5oTURehsLGwaAKuqvCUuW9VlH30WqLAKwY+ZsqyprMamKljRPWa5aLM0nm+i179xH3he5UEQaY6yyLWD3DMt6qbBMpSwmX8i1yusaeK+K/FNRVUJi7R4e9eA4juM4ThR/UHAcx3EcJ4pLD/2J9vbOP41mMOZ2+rxOshayZzWbgFU2SJkSTdEUOtlayYjIpkMqVJQzobEswWZP9ao3psiiST1XgMgU5NEMlmTqjPUBMHIISxRos5dbxqbzWjIxayEg9qpPed+nog9oTQJ5mecKHA1ooPYSHuwMZ1+sSURHcBZMlTy472qm5TZ4XDnJhwuJLSu+Vi99/h2bntsk46DJxlc8TjM4ZoMoxEyjgdjEnPKk5+uCZSNZRrNefB3XitTE16rJRCl7ms3ZmgVy5RvF1yzLrJMoBe4vNRdEeuBrJlDmyKw2XiAMbdw/2Rd831kv+4nH0sJRH/awaObUXIbRSKRDLtsqfcdt6/2ohq6FlAzBY9SMmEauS8ghba32Gu0BblFwHMdxHCeKPyg4juM4jhPFHxQcx3Ecx4niPgr9idDR+VcnmbmMTlvUy3Iao8kKyLqnaNkm02OiUiXr0hoSxxojZ2pTfwjWLVmH04yDrANyZb0B4qPAIqZmcTN6Jp1Xw5ZonrKEz0MgzdGMXsdowuq4mpzojoH6pBor6aqZ8SHRUEQOX6XxajU9/j8Gr/e6hG+IqXYocxbLpAfEq3FqJUQTikptqC6f8XcJ7baK5pOvGd3TTK4aJ8E+OZqNkEMRzTUi1xbPE+9BDUXkNvi1nje1PzmTImhdq8QPg8dFeybn/0JzbSpGqkbP/dUwXya1DrF7S6uGyvK+Lr4OTUvMYcaXKeXzwfdFztKo/h+M+l7we/ZxymWbpe9ie7/zy/i5u4hbFBzHcRzHieIPCo7jOI7jRHHpoT9RVdsZGrbGhncZUxq/zoXjkDmPzV5auIVN1i1iHo4VeNKMcWzCDYkQKTa5hnimR1MwiM3Iaopkk32ZmAvXR0I7NaMdmwFNIS1ris1M8Z9UaGOkcI2aXjkMUNeOTewm3FRM2x2RIkFq2uYxpwpa8T5JZQHkdU0WtKI9I2GK0QJMKtHwXuO50DkrKy2hZSrrrKN+qPTAbZi9qxkC6TqpSBzHcgPPS04m437Qfho01B6Xy+5JZ+ZwVhOnKfszlrVS55PnhvdJLhQxElKqe5XvVSrXxYqx5UJvS99ncmHT3HcOS1UzP4fRriFpUSVOI5tpwSi6nhJF0MxeSBUcq64FWjw80nEcx3Gctwh/UHAcx3EcJ4pLD/2J0NFpatMsbmzCJrNVphntYnJAqfNsQL19o2Z0kQD43Gxi1PNye2weF0nBePrzcTLGQBn9cuZHzlTZvCLep1y2tjdR6x9LDMZULnMRi7YoExMj/y5XWClyLi3axW3wHOqY2ITLnvTq6c5RNJwRkjNAav9Wr7Df8blj0hVgzbSxaAvAmo5jUQSA3VvtXYyo0bXjLJAcEaLzyfPGMoQeZ/Y7y1X2MDtGLiomB8akjNy5ORuh/P9S5bsNqDQWi2xJ3GdC09KSnwNSCKqlRCGkUuddJxlgec+wfKFjjEgUuWigsmIbGbetkhytI99zOrtEbfLezcl/dC1k3A+Zi/VrgfXy227iFgXHcRzHcaL4g4LjOI7jOFH8QcFxHMdxnCjuo9CfWL8WqCrPV92LVSRUXY11sIwzIooWyVqnhi0xXJFOtV3W4zj8UrNAlsczyxlYB+Vzqe5pdGlpr401QXpdHg+RsqF94q/BY+FzpTTwKg7fFD8EDnvVc/Eac2VBDRfjuQ6RUEl5H9YtL7zOtD0aSzaQ/QGkPR6/atbGb4IrAWopwC5mgeT3pgKhhPKydszzon03e0HmvSNSaVCvmZZI9lHVpdfF/EbE94D7a9qQ9sy1r6GDHJoYCTcE4n5C6rtTTb5RPNc6F7TeGftT5cJSEz4fscqKem3xvmPfGD2O54lfayZSDr1lv5Pm5fY4aiMXOtkRCSNN+RMxcl2ENU0I6pvRTdyi4DiO4zhOFH9QcBzHcRwniksP/Ymauk5zmJr62ERoQhGtOZPNVxlneEsVZ1HYdGwyy4mpj83PZJYNIocYUzeb/TiUEQBa6Lw1kkmSYXOhZpZLFZ0ynSqa/gKZJrNcISTO7kjyhYZctdLv2ISo2Sxj4Xza31gYIZCXImLH8Zqz2VfNzeZcnMFPC19FpCHAroMJlZT/55hMijx+6TubetfSfLbJerP5nscb5DiWG3Tt+HcsSWnGRV6vtojJX3+XkigQCYFVuYblL5UUeN7NGBNFjHif5KQ2Ghev43ox3/O1z2ugBa1i6wNYSYElFQ3fZcnHhLnKGkcyHwY5r8lmyaHBet/iNw0j7bk4WyaHeeq1xetjZEK7PlntQGSblpjRLQqO4ziO48TxBwXHcRzHcaK49NCfCB2df2oeXcN1zcmEJeY3k6mRrblqok8VhmGzHdenLxfbGNehJzNl1prw2ubzqimSTfZsilVvaTZvVolEwYV32rtWxCmLZdzTc7OJUeeMTcypgltMLvogkqlOzfwcOWEkBRkjF9CpH178XIsM8bynvP7ZC16z4vFYUl7gJiImEm0A2KgP3k8ascAm3CrOAijz3tWMmCo3mHNRH1NRQzy/6/i6Fdkgi0TopCKDZJ7CysXFJniNdX1M1AfNk+4Flr/YVJ6bM/qOr0fdqyZLp6xdrJCc9smsHY2rYbQ9rrkYARNYrtJ5531MeyvTol28t9YmIhJ4/Lo/ub+xDKBA5x7U67ebuEXBcRzHcZwo/qDgOI7jOE4Uf1BwHMdxHCeK+yj0J9paO/9SWfvMd6JrdUQ0YNWKU9oh+UAkFFsb7sVtaMXAmK+AhhKZEDHO1JbIqqh6blmkGpyOkXVK0w8ZsQljiui8gISsclVAec43Gq5mPmwvfZy2gdJhYPmKmDTXnMFP9eZYJU2F2wgJDZzXTkMHqyJrrNUNY9VNNSMkr38q7C9LhB9yH2muw2ob6pcNGUXtc6XThB8Kr12q74Sel30gsmrrk5M1UJ/4XLUaekx7kn0ANBSRrxnW1DvkGmT/FZ7rDvFjao+E6AL22uLzqs9HzA8nUWUyq0yEYXMbAxuKr7naLCChsnr/jPz/XcfPYzT3HLlvV9YAld0IYS+BWxQcx3Ecx4niDwqO4ziO40Rx6aE/UV7R+ZfLxkfmPQ6tUTMdm1FDIvwsVpCl84fFl6mwMhPOxxkMrdk3ULGVDBRSqXII95dPpWF6bM5TmYNN1mz2VKmA56M1Ec7H88RmWjUxsmnXSB7SnulrPJzRmnMlvCuGhtTynuF11IyQvIe4T3ocm701XMz0ndZEkxZy9k2WIXSMZu/y3pL2OLMerYGa6G32PJXrOMsiZyK1eytQASqT3S8locVCKgGbfZKLLA2ot8fxWPQa5H3H4coavsv7n/u3dpU9jueGrzvNlMrz2RaRf/S8up9MMTZuL158LpDMk9WPsMfptVs4TyJbIt/7ciGlZaVfAwg0b0bm4AJZgA2PZSmnvUS4qe6jbuIWBcdxHMdxoviDguM4juM4UVx66E+0txX/iEDmsiwWHQBY85ZpV8yeZBJkk2qufTZfp7zluR9SQMZkPGNTvPaJTZHliSx7bH9WU3RLxKyoJlH2aOZsdLmCNHQc/0bNmcaEybKGZKk0ZmlZuxYxF5dqT4mZgPV9woxqvMdNpkuZM54L3QtVNG8VtHZrxLSdmhs+M8trtBcyyXynhXxK/QaAyFDxKB/+Lncu7pORvFTKYPmLZS2RyXhfc9u63jzXbepVT8ea6yRxrfLrGokIiM6TtBcrhNQh887Xe1kiIybfC8riMqG5N7VIBkfur4mOkCyIvDdYetHrgqUhkk9zx5qMrSKbsKwXk4WBTjlEf9tN3KLgOI7jOE4Uf1BwHMdxHCdKjx4ULrroIlx//fW5z6+//npcfPHFm9wpx3Ecx3G2DHrko3DttdfilltuyX2+884749Of/jTOPPPMTe6Y8xYiGmvGWhdrcc3Wv8BkfzP6qIZRclVI2WL83oSmicbI+jNnP1MttjISBpfKaGcyR2pWONLyVJdnvXTAkOLr9WvlMMo+GauEp/3ooLnQOTNZECNhX/qd9p2PNVnnRM/kNTZzkdBHeRy5yo+i2ReOS4xRww/53DzXmiGRW1tDYxTtPWug0DfSg4OE85m9a0IWNfQ04ssAWF2+tj56WGZ8crhtOdBUlkz45PA61NF5NfS0LZ5VM6yJhOnlwihJz+d5Uh+PWBithkfGrsFc9kUao1zvpsIj6fNZ/TDbBo+L93SL+LgYXwleA/HbMllP2V9D/JNWryi+lmskq6V+cLil3hd4brjSr15zNQOB1s1QPXLhwoVobGzMfT5ixAgsWLBgkzrkOI7jOM6WQ48eFMaOHYuHHnoo9/lDDz2EMWPGbHKnHMdxHMfZMuiR9HD88cfjlFNOQWtrKw444AAAwL333oszzjgDp59+eq920OlFKio7/2LmYMCYJoOE6WU5c/GbtGmmNmpfM5qx+YytYWo6ZTNgWyILIpsB+bWYr8MysnRxprpUlr1UKBmbTsXcntVR6FMqC2SIhLpplj0Tikm/yYXEtZU+DrCZFdlcmio0U0YLpPPOJlEOmy3XIltkig2JEC5uf72EpjGJNTbhaLx21RKKSHs8o74nZbJU9kneyGViio5KPgLLa2Ty1xDNjLPz8TWjcgB/x3OtMiH3vc5KIxnLJqYwmTbBe5LaXy9hf9Vifi/VP33PkoL2nfegXDMZj6U8UViKw2h5rVTWMgWtaN/l7kd0HP9GwxP5HqHh0OtoHw9oKL7W+0cWyY6rkuTaprzk1E169KDwzW9+E0uXLsVXvvIVtLR0TkBNTQ3OPPNMfPvb396kDjmO4ziOs+XQoweFLMtw8cUX4+yzz8azzz6L2tpaTJgwAdXVidzzjuM4juO87dikzIwLFy7EsmXL8P73vx/V1dUIISBTE5iz5RBC3swH2OI3ZB7NckWMyJRmvHtlG5kMZxJ9YIpOkZlWTeUtbOolk5uam9m2T6a5IIVrMqoNH1YXow9Ck2SOHLlN8U0uayGdi72iU4V7Oth8r1n7iubS0MLmcBkjX1M8L+qZnYLN8kZ6kOt1LUVmpEysvCY81erd3RopaFUrWftSBcJiGejEFMuFm0zGvfJ4QZ6wcknxc+m7iUTga0QlOVrvwN7ssHJGNANq5yfFl5S1L9OsfTxPnJkwV7SLMwSS2TkXKULnlagcUwjLZJiUvcD7xLSRKJDF9wU2rwNm7cKaYhuZmtRZehO5KlA/jAyh1wyvcYhEFwFWluFzJfag6a/eBzlSRO+fnMGU52Kt3Ku4T8mCawOAls1QFGrp0qU48MADseOOO+LQQw8tRDp88YtfdB8Fx3Ecx3kH0aMHhVNPPRWVlZV45ZVXUFdXfPr51Kc+hbvuuqvXOuc4juM4zualR9LDPffcg7vvvhtbb721+XzChAl4+eWXe6VjjuM4juNsfnr0oLB69WpjSdjAsmXL3KFxS6aiulM/16p7pKVlrNOqps5avKnUKDrdysXF9lR/rKU2OWRIq6tVJCpLMiajHfsr2HCkQN+xvwJqNWQrofVx+5xNTiszqr4Z6RO3Z7Jj6nlZz+Z50bCqWJZKAAilfTly/hWJOTTN8bnXrezSbzLuu47R+APEK1WG1XSuVRp+x5lDixpwkD2YlXMmSTqvZNjk9sx4dT9yJlL1ASB/kIzCaIOEy2WsYZdFMkJ2Hll8Sb8JEnppQnTNvCQyOGqIYQ2FYnLVTt3vxv8l4deyLhJ6rRVm6bymDxpe2kJrV2fnKUNkH6tvCO9Dvo/p/mRfEb5Hyl6I3qn0vLxPJCOoaZOzSnLFSYV/o/fSXqBHLb7vfe/D//zP/xTeZ1mGjo4OfP/738cHPvCBXuuc4ziO4ziblx5ZFL7//e/jwAMPxOOPP46WlhacccYZ+Mc//oFly5aVzNjoOI7jOM7bkx49KOyyyy544YUXcOWVV2LQoEFobm7Gxz72MZx00kkla0A4WwjrmoHKLB/CxiY3NnuqWZrNhSRXhGY1HVL7mj2QwyPZJKqZw9gMyiZMNQlyGGQqOxkRyGSbDRwqX9IYNUMgm2l5nnSMbMKOFYkBbNgff6fmYfMbDm1U2SDSV8BGqnGYWmuieJTpr5hYySQeEoWvskHF+Q00L1mVhM5xiJ3elXiNWZaQfcwhcSYMbsnr9jjOxtgwsvhazNe81zIOP1TTbi6Mltug/tKezmWBbI9k9NOiQ4H2BoeA1olZmpeLTdt6XVRwQTjpE4+Tr1u9L/A1xNeM7k+WFNojodaADSXkTJRVWnwuFfLHoY48n9JGbO0qpO1IplgjhUHW1WSh1cyZiUysDPdd147lBh5GLitrRbKAWlfocR6FwYMH4z/+4z826eSO4ziO42zZdPlB4amnnupyo7vuumuPOuM4juM4zpZFlx8Udt99d2RZlsu+GN7MCMWftadMp1s45513Hs4//3zz2cSJE/Hcc8+Zz0IIOPTQQ3HXXXfh17/+NY444ggAwPz58zF+/PjCvACdD1knnXQSHnvsMYwYMQJf/epXccYZZ5j2/vd//xdnn3025s+fjwkTJuDiiy/GoYceWvh+//33x7HHHov9998/136Xqarp/EsVkGEzmBR7CnQcZ63Lho+17bEpUUxzMY/eZOa/WEEW7S+ZInPZDckcZzL4afY4Om8Qk6hpM5alUtskk2UQecVGOnCBHyRIZI/jbG8qqTDrOepB9xFHBHChptXmqFiWQfXmZ/O1iYDRvUX7JGeWZxmF+qtZEPH6/OLrFUtL9gEAMKpY4TbwGqiERJi1ShbIkn3He4OjiNQEzuvPpnfN6MeFkYwp2q4jZ5zMBo8o3QfASgoK7y9jupb5ZLlBIqAMLBuxB7/OO+0hsy/UfM5zoZFcfH1yMSqJnAhrKCMqyVC5Nea+037MZ4uk6IaYtAgAtHeD/HuZ8X7lMeeKZ5HEUMbXrfSprTUtb3SBLkc9zJs3D//85z8xb9483H777Rg/fjx+9KMfYfbs2Zg9ezZ+9KMfYfvtt8ftt9++SR3aEth5552xYMGCwt9f/vKX3DEzZszoUrrqpqYmfPCDH8S4cePwxBNP4Ac/+AHOO+88XHfddYVj/vrXv+LII4/EF7/4Rfztb3/DEUccgSOOOAJz5szp1XE5juM4TnfpskVh3Lhxhdef+MQncMUVV5j/8e66664YO3Yszj777ML/rt+uVFRUYPTo0dHvZ8+ejUsuuQSPP/74Rp03b775ZrS0tOD6669HVVUVdt55Z8yePRuXXnopTjjhBADA5ZdfjoMPPhjf/OY3AQDf/e53MXPmTFx55ZW45pprem9gjuM4jtNNepRH4emnn8b48eNzn48fPx7PPPPMJndqc/Piiy9izJgx2G677XD00UfjlVdeKXy3Zs0aHHXUUbjqqquSDxMbmDVrFt7//vejqqpoDpo+fTqef/55LF++vHDMQQcdZH43ffp0zJo1q5dG5DiO4zg9o0dRD5MmTcJFF12En/zkJ4V/AFtaWnDRRRdh0qRJvdrBvmbatGm48cYbMXHiRCxYsADnn38+3ve+92HOnDkYNGgQTj31VLznPe/B4YcfXvL32267rfEfWLhwYe6hatSoUYXvhgwZgoULFxY+42MWLlxYeP/AAw8UXm/MP6GpqQmLFy9G2Zta2urVb+qQba2df60SihgileGkqllWKbpqqd8D1qdAtT7W3FKhVHxuowOK3GP050SFO9KKOUNeLtySm+bqbICEMCbGGMlOmKXCu1hDVM2Wj2MNWLO91VB4X0pH5/2j2in3gys/ihZrVoEzTA6QOWN5zmiqsreqKGvhijdsG+xfwtU+X37RHvbGguIbunY03DQbT34z25PPg2ZLHD6m9Hd6HIf5prRgHr/6ckT8WnKVBVljj123kL3G59Ksl7w+uu/Yp4bbl8qsZjdwZkbxazGhnhQqG8QPg31ejF+Q7lW+zvQexOcqj91LgKx+GB2XCHOlMZsKkez/0flt8SWH62r/AlfFjFeVNRkx1Z8kliFSjyur2Ego6cbp0YPCNddcgw9/+MPYeuutCxEOTz31FLIsw+9///tN6tDm5pBDDim83nXXXTFt2jSMGzcOv/zlLzFixAjcd999+Nvf/rYZe7hxzjjjDFx77bWbuxuO4zjOFsRrr72G5ubmwn8iGxoaUFMT+Q8g0SPpYa+99sI///lPXHjhhdh1112x66674j//8z/xz3/+E3vttVdPmtxiaWhowI477oiXXnoJ9913H+bOnYuGhgZUVFSg4s2nvY9//OPYf//9S/5+9OjRWLRokflsw/sN0kXsmK5IG6WYMWMGFi1ahEWLFmHBggW5iA3HcRyn/zF58mRstdVWaGxsRGNjI0499dQu/a7HCZcGDBhQcMZ7J9Pc3Iy5c+fis5/9LD75yU/iuOOOM99PmTIFl112GT784Q+X/P0+++yD//iP/0BraysqKztNTDNnzsTEiRMxZMiQwjH33nsvTjnllMLvZs6ciX322adHfa6pqTFPiRsKeIW1zQgVkjlRYbN0rYYYcmgeF3VZag8j82Y2cIhtg01kicx/NiSOTe8JmYNN8bnsZGTe4/OqHMByiAmjRDxrIWePA2yoFpsRtUBWq7TfFXgN1IzMpmPJkGj6ETNLAwg0fmNSTxR74rkIWliJZQQOlRwmTsAsIVXbbIRGbqBwNqyz52p7dHbh9WtPvlr8vUh1W72r2Kcq3uNieg+Li21kQ4v9NUXFAFu4Sa4FA6+/hj2a647a0z0SM6NrWCIXbUPkulU0jJTN/nydqQQQy/qnx7GUR6G8mUoevFwhIrtp+ylZgu8tGh5qpFZqQyUkmgtzp9JCVTzvvKZ67ZvwbzkXzwffgzT0lteE95NKpq0thfvunDlzMHBg5/2qo6MDw4YNQ1fo8YNCKRYsWIDW1lZss802vdlsn/KNb3wDH/7whzFu3Di8/vrrOPfcc1FeXo4jjzwSI0aMKPm//G222aakcycAHHXUUTj//PPxxS9+EWeeeSbmzJmDyy+/HJdddlnhmK9//evYb7/9cMkll+Cwww7Drbfeiscff9yEUDqO4zjOpjB27FjU19dv/EChVx8UDjjgALzwwgtv64RLr776Ko488kgsXboUI0aMwL777ouHH34YI0ao00rXGDx4MO655x6cdNJJmDp1KoYPH45zzjnHWGPe85734JZbbsFZZ52F73znO5gwYQJ+85vfYJdddumtYTmO4zhOj8hCj1L8leaxxx7DmjVrsN9++/VWk04v0NTUhMGDB2PFs4+jftBAm9FM4QxfbRIRwCZGY5oT2cBko5Nn0Zintu7CjkiRnJSXfqo4D5+LJQr1Puf+6aXB5sKEqTzv0f4mFTIX7NHO412XKEaVRSSZzi/ptfS9srRJNKxcbFvgsXDhL5FhOHLERASsFsljYDEDH3uza3ZQ077OOxfh+dcLxdf332UOe+HWYjjx719fUXhdLec6dGxRDtvu36cVu7T9Dva8E6YUXpZtM5H6rgW3KCojYdo3kQhq2mYZgedJJQrGtKdzRmvC49ckcUa+kOudz50quMbzwefVMcYKvXGWRsBGW6Sux8R+isqk2ifur5HkJLLDnIv2vmQHzbSIV+HAlBQq+4nnpi2Sobaz0dLtqRTU1oqmVavQMPFdWLly5ea3KLz73e/uzeYcx3Ecx9nM9CjqwXEcx3Gc/kGPLApDhgzpUp0DAFi2bNnGD3Icx3EcZ4ukRw8KZ599Ni688EJMnz69EMI3a9Ys3H333Tj77LMxdGiicp2z5cGV/FjryumjkYx+HaKdmRBI1XPpvdEB5cGT21RNmDF+DtQ//Q33ifVwDW+qJf2uWjIfGt+BRBVHnk+j34ruyaFU7L+QytSW80sgqqi9XLgYrxFnUmywx5n26TgJzeIwSqylcNBaG3prqy7SuFQD5mqk0nfjv8Dhl+rzQbzRUpyz1eI3MG5xcc3Hzi2GQFapj0JdMew1aCgqwWPMZS/lfUfrqO1F/9uV8slhLT8X2khaeSyMDrB7TduoimScVK28hvaGuZfI+rDOz/tnlfxnkqs98n0gd10kQoVNxVkaR65CKN/TqL+ZtMf7lcKhMw5lVbi/6rfE90UNV29eUXzN816n5+K9xWGksj7trckMtF2hRw8KDz30EC644AKcfPLJhc++9rWv4corr8Sf/vQn/OY3v9mkTjmO4ziOs2XQIx+Fu+++GwcffHDu84MPPhh/+tOfNrlTjuM4juNsGfTIojBs2DD89re/xemnn24+/+1vf9vlTE/OZqCsrNOUqVIBm9zIFK+hXlk0xFDMyPxeM/WZ8KGi6cyYsiHZ7zgca61IBWwu5fBFDeHifrA5uFQWsw19kix7Jsskm+K1CAtLCmzqlDFaiYIL60iIVSx0Us3SvI5iYg7URyMHaGEpUzyK1j+Tc9F8msI41WJG5cyCseyYADIqIKR7wUhFQ4rnysZvbw7betunC6/3XV5s46W11uw7sLy4Z8oH0vjrG2yfjAwXz6IZ6LrIZD7NvJO5OUuZ5flcuj68t2LhtQDC6mLhKxOyp3uL925qP/E+VjM/m9U18yFjCp/ROFIh1LEwR/2dyHqB9nGWyuAYCymukSyIfD8xoafyf21eB54XveeW0zWYu3/QHPLP9F7KRDPedu7BoOfoJj16UDj//PNx3HHH4YEHHsC0aZ1xyI888gjuuusu/PjHP96kDjmO4ziOs+XQoweFY489FpMmTcIVV1yBO+64A0Bn6em//OUvhQcHx3Ecx3He/vQ44dK0adNw880392ZfHMdxHMfZwujxg8LcuXNxww034J///CdmzJiBkSNH4s4778Q222yDnXfeuTf76PQWIXTqf5oumbVIrpKWCAkzWnaumlxCzyznVLB0Lq2Sx31kvwQ9jn0MQlyzNeFI3Pdclbiixp7l/Bfo3NyeVqDkUCTW2/VcrOem0uxqKu3C7+XyZb8E9S/hNeL+aVpco7mG6HEZh2qxpqzhdzwWDUVlaC6yKnsuEx7Je0uqm9btW0y5fFBNcW72eGWFOW5wfbGNsnFji180DLfnbSr6qGQji4XuMgqb7GyE97TV6DNOg70+XjEx0B43/gsafsffcVihrk+somMu1DiubZt9zbq8XtO8/pyOOZeKm17z9aO1gfgekfKFYsQnKeNx8rjKZYyB3vO41ks4I7fHfaoSHxKwrwn3V/Y0VXfN9P7B7VfQ+uia8l7ge4vOU3ll2nekC/Qo6uHBBx/ElClT8Mgjj+D2229Hc3PnJv/73/+Oc889d5M65DiO4zjOlkOPHhS+9a1v4cILL8TMmTNRVVV8kjvggAPw8MMP91rnHMdxHMfZvPRIenj66adxyy235D4fOXIklixZssmdct4qss4/DR2MVYzMmdS5UiNnHBRTJJvB1IzOZmT+TsPAWshkz2Y/7XssA5uGA5njKIQvJ5vQJaFmyozbp/5p6FysSqBKDzFzZnvCtGvalnCxmIkesNnf2CSsY2QZKhYqCQAV1F5LQl6JjCtolUkyxWsFPhMqS3smjBhtj9u2uOa1lB12a00jX0kyx+gxxc8l/Cwbu2PxNZnUg0gopr+6FzjjZK1IFtwGaM25PalOaPZGZaJ6ZEzW0nVMVUxsXl58zWZv3Vt83RkZSuaCTeXcP5VC+X7Cr1VqMxKiZizlRmmPJ8JcbSXJ2uh3XDEyGyRZiGPSXYW9v2Xm2tIJoPXi9c7dj+k7vt+pDFNdh6wlkdG1C/TIotDQ0IAFCxbkPv/b3/6GrbbaapM65DiO4zjOlkOPHhQ+/elP48wzz8TChQuRZRk6Ojrw0EMP4Rvf+AY+97nP9XYfHcdxHMfZTPRIevje976Hk046CWPHjkV7ezsmT56M9vZ2HHXUUTjrrLN6u49Ob1Fe/qb5Tj3d2fxM36kHO5vIBlEGTi4KBFgzv2Y4Y9NfGZsfNUqBs6nxNk146bclCkmxSZzNqGpipAyBeS/rSBY3LQTEEg2bAdVky/PLcygFmGwGuoQplk2TqSIwbB5uFamgPZLBTUynZiqqqL9q2uYxcmTDsDH2ODbnq1xVTvPGUTnDtzaHBW5/BUmg9SKHDqBIgtHFNrJ6ySpLJnWTLVIz5NWRp78WQeP1YhO97k/eGx2RzISdPyzZp0wLC/H1Q3sraHQE7zVtg/ueKxBH8HxwNIcWYGK5jveneuSbfczSnexp7pNegyx5rRGZi6khOSgWGQSYNTGF1HSv8v43sqNcFxxRpVEffH/iOZRMsea6o/6G5pX2uIoKhDWJiKMu0KMHhaqqKvz4xz/GOeecg6effhrNzc3YY489MGHChE3qjOM4juM4WxY9zqMAAGPHjsXYsWM3fqDjOI7jOG9LeuSjEOO3v/0t/ud//qc3m3Qcx3EcZzOSBRWtNoGddtoJL774ItpVc3E2K01NTRg8eDBWvPQU6gcNghWYYXV51thUU2edmvVB1TZN1cGy+HdGRxUNzWR3TGSBNKGYHG4ommXEvSCX7Yx1UN3HrKOztqmhnbGwMtVi2QciFjqm560urUsCsH3XeWK9ObU+3I/2RKgs/874icT9OoLJHBm/R+QqMHI/eJ9Uq39Jsf2w4o3i562SZY9DMRtGFl8PkEyc7BtDcxYkHNj4B+i8s28Dz6HuOw5v47mp0iqG9DvWpZcvsseRb5AJ4dM9w+/bZJ54L/D1qH4Y7KPA8yTzbjIQpjIF8hyuI98dvaa5jVxYbiQ7bCpE2Yxf/lnk8bcnfKHUL6PUbwDrs6B7hv26TBi2+IkYvxbaC7rfW9ahqXk1hux5IFauXIn6etnnXWCTpAflueee683mHMdxHMfZzPSq9OA4juM4zjuLHlkU7rrrLgwcOBD77rsvAOCqq67Cj3/8Y0yePBlXXXUVhgwZ0quddHqJiupOM7mGd3GmwpiJHrBZ/NhcpqE/XBhGQydNkaCiudWEn0HMlKbAi2xZE2ZFfRJ1xZj52bSt5tZUWKGGLW5As71pyNQGZIyBzKCmaFOuoBWPK/Fsz2ZVURTNuXhuVVJgOKwsp1ByiGVxboPMJ8sIGYcRrrP7gs35yb3Ae0swYxw9ns4lIZ88h7RngmZm5DA4ynxoPu88cfG19N2alWlf5DIJmjMXX1bIfo+E37GE0nleLoJGa5IrikRSgexjEy7KMkSLyEtmfxaPy3KyI+0hs1c16ydJCgNJNlHTO79PZZFtTRRnorkx49W+87n4PqCFztppLjgTp95XONRRpUvehyzl6Rh5r/F1Jmsc1q/N3/O7SY8sCt/85jfR1NR5E3n66adx+umn49BDD8W8efNw2mmnbVKHHMdxHMfZcuiRRWHevHmYPHkyAOD222/Hhz70IXzve9/Dk08+iUMPPbRXO+g4juM4zuajxwmX1qzpNHv86U9/KqRtHjp0aMHS4GyBrGsGKpE3t9dQpjo2keU807kOO5nB1CS4RjKDMWxyJRN9piZlNhfGCq1IG7YQTsLsyd7xTTZrn+kHRzYA1pzLpk319K+l+WSzqnrLx6SMVCSCOc5KHGw61raz+uF8ZPGlmsC5CJHxqpa9wOZmMhVn6hGf04BKtA0xU6tHPBeTSklD1AZLHkEz87XT/q9NRCyYsXB2TLl+aP1znv68F1hGUCmHoxu4ffVg53XguVDzdRsfR/uuXcdIRau0wBH3sTUREcDzZgo1yVoZiZPmVk3qfD2xdKn7gve/7s+OSPSWRJtk5XQu7nsuY2vRzM/7STNicsGwjOdd72/8XqOrWDooj8wZYGVY3jMqz1ZU5CWsbtKjX++777447bTT8N73vhePPvoobrvtNgDACy+8gK233nojv3Ycx3Ec5+1Cj3wUrrzySlRUVOBXv/oVrr766kLFyDvvvBMHH3xwr3bQcRzHcZzNR48sCttssw3+8Ic/5D6/7LLLNrlDjuM4juNsOfRYuGhvb8dvfvMbPPvsswCAnXfeGR/5yEdQruE3zpZDVU2n9qaVADsifgmq+3FIW6rSWld9BVg7zOmUpLlF/AvePEHp32iIImfxo/CubGCDPY7HpXs5VslO55N9Ebgfmo2vlcLRWKPW7G6cjZD7J5kJs1RWTV7LpO7LfhissTbEjzPjlfXmvcGhZOp3YULnNIsdhQFGstEBQFi5oviaQ8dq1deEQ1Gpbe0TfWdCJzUElHRq45OgcHXT9RKyyevAlQU1vJjnyWTRtP4v5rpg3wDd0+yTUiYZVo3/D//OzhP7+WTs1yP7M6xaRscl+sTzZNqQa5r3tPj/RKvFrkmECdbR2mlILc1hlrj2zf7k/qqfDPdPM05SKC7W0u90b/H68L1EfZ/q6oH2TUuZ1KMHhZdeegmHHnooXnvtNUycOBEAcNFFF2Hs2LH44x//iO23336TOuU4juM4zpZBjx4zvva1r2H77bfHv/71Lzz55JN48skn8corr2D8+PH42te+1tt9dBzHcRxnM9Eji8KDDz6Ihx9+GEOHFsNphg0bhv/6r//Ce9/73l7rnPMW0bou/h2bszS7H5lfTShVqsCLwiFn/FpNfWxW5e/UPMxhYXycmsDZ1FlJ41KzNJsENcsemxLZpC7hSIHMuRnLCGpiZIzUIiGLPOZUQSeWbzokhI2z7kVCGzvfF/sbKMw1S5l2uU8aptdROjwytNqQUmuylb5zqCOtcSZyVTZq2+KbFlo73Z88fgrtzYVe0nsjS6SKoKXWjsdVK4V5eP35tfa9PJKlM5Ed1KxjKbP0BjRMj9fYZEGU0FsTDhyXCU3mQz6Xyom0J0NTMYNhThrisajUaIrKJa4tlmVSxQx53k3xNS2wR+uQCgGNFNLqbJN+x/3Ta5D3E193EuabZRmyWLbYLtIji0J1dTVWrVqV+7y5uRlVVZHqWY7jOI7jvO3o0YPChz70IZxwwgl45JFHEEJACAEPP/wwTjzxRHzkIx/p7T46juM4jrOZ6JH0cMUVV+CYY47BPvvsg8rKTjNRa2srDj/8cFx++eW92kGnFwkdnX+pzH9sotLsfmymZ/OjygacWU2z3cXqsOtxbAY0/RUTGn9XHcluB1hTH49RvcW5vVR0CF86auobRCZWNoGr5GPaj3izA5Ihj+Y2yLyzjKDjZ9gMqhkCae1MlkXNArm2dASMygGx7IFZLtqCXosp2kQcmD0j88T9TclhnOlxTXEcxjQOiOQTSr+GZOPralZNjeyISS+6jh3UPpubNcqF+1Qe36tm4jWrZjv1g+daCnpFr2ONmuJohlSEDn1nCompiT5VwY7PbSKe5P5hJAUukCWySVska6PKAbzfWdbJSUM013oNGlkzMZ+x8esaV9YAlYmMpl2gRw8KDQ0N+O1vf4uXXnoJzzzzDABg8uTJ2GGHHTapM47jOI7jbFn0OI/CT3/6U1x22WV48cUXAQATJkzAKaecguOOO67XOuc4juM4zualRw8K55xzDi699FJ89atfxT777AMAmDVrFk499VS88soruOCCC3q1k47jOI7jbB6yEFQg2TgjRozAFVdcgSOPPNJ8/otf/AJf/epXsWTJksgvnc1BU1MTBg8ejBXPPo76QQPzoUSsP5b1RDsT/ctUlkyEYnLmu1zVvYF6dP682ifW6HVXs69AWWldEoDVlLUPzSuKryXrnIF1SxMGKJ1iHwUTNirP7y0R/wpdH+6v6rmxrIBdDSvLZZ/kanWJvrMPSKoaJfsUyHfR8L5cKKZq2G9SJcdx9VCuBDhwiD3OVP7kuRBNnfegrknM9yBXjTPiR6B+A7yfeL21PfK9MOGGGorIIc9DRtnvOLSX56x5uT2OxmgynWq4aYiMX69pHpfxf5K9yuvK1yZgK79y5UStxsn7k/ePVo/kUxs/oXgorw0VVX8iOk4zsbaWDufMhRSbLLI0Rg2BraxGU9MqNGyzA1auXIn6egnN7QI9inpobW3Fnnvumft86tSpaGvbNKcJx3Ecx3G2HHr0oPDZz34WV199de7z6667DkcfffQmd8pxHMdxnC2DTXJmvOeee7D33nsDAB555BG88sor+NznPofTTjutcNyll1666b10eofK6s5QGTWDmQI/CYuQMR0mQrjYrKaFTNiUWEeFVtR0GCsKpSZBE8JGn2siMg7n5EIrOdM7mcDVTMvn5j5JKJWBTaAaihkLv1MzMks5LGtoMR02lauJleEMkWr25fEnQrMCrU82kLJ0quTRGslUF1qjx2mGxMxkD6TvxBRrM06SOVfDd6kf2eARxc9VujDZJxMKrQk/lCydHKbIfdLsjlz4i+dQM1vy+Pk3urdojTlTqIZGZ4OHI0ZYScWeyDxusrICNoOpmSbpe4iEPOv1w2PhedL1WZco/MXX7jqaM71/cPssk7bp3opJQ4l/Qnm8el6eG73PRAru5TJTmrB2XtcSoaK50Mru0aMHhTlz5uBd73oXAGDu3LkAgOHDh2P48OGYM2dO4bhNTRvpOI7jOM7mpUcPCvfff39v98NxHMdxnC2QHksPztuQ8spO03K5eL2yabYjYS5jkyB7qav3OVuS1Owby3AnxYkCmVVN7Xo1bbeQiZC91rWgE5vzYkVsgLjkAQA1JF9wNzQigCWBVKEZExFC51Kv5eg8qVmaziVm+bCaIgeGjSl+kSs0EzGxSvRKpmteaE8kD5Y52JyrGQzrimuSUZQDoIWgaE/mImBoLKl15PXhfVIn14VGHBR7Yd8as65d7yxWuEilIY2kKByXkFf4utD2yiLyikYimDm048oGNBTf8HWXktA4ukilgpz5vVQfYNeL97SeN6cvchuRCAbNHMrn4j1ZXhY/zhRfk+ub5RCTbVUyhZp7qWYYjUQzKLHIEd1LreuB9oQU2QV65MzoOI7jOE7/wB8UHMdxHMeJ4g8KjuM4juNEcR+F/kR7S6d2q/qy6paxz/l3HNKjOpoJg0tUfGPdW8KMjF/CAKogt8ZWruNQuoz7q9oh66XcX/VliIUHAlZ/ZB0wV5GP4PBAnfdYFkSNxOO+m/BN6Xuq8ie/MfMka9xGYzFV9zSEK3LrqBQNmDXrhE+Kab9K+h7LYKn+FTxmo3Mn9jGvD4cb6rl4LjSEmPe47vestA8Ah00CkonUZBsVvZn7xP3V+eT9n/LX4DlTHwA+NhbKCwBcLZXR9njPcMhmzhcoErKoUXSx0GjAjov9PNSXI+YDIOtjfHzqKaS0RvyJmFToJJ9X/HoC7Wvj46JrzPPB37WIX0vb+nS4dBdwi4LjOI7jOFH8QaEPueqqq7DtttuipqYG06ZNw6OPPlr4bv/990eWZebvxBNPLHw/f/78Ql6K8847D8cee2xfd99xHMfph7j00EfcdtttOO2003DNNddg2rRpmDFjBqZPn47nn38eI0eOBAAcf/zxpvJmXV3CrNUTWtYBLSWWnMN9YuGLgDXhmVBBLR5FJkc1t4fIm1woWiTzoxxnJAo252kYVFskdC5XQCWR0c+YlROZGdlczONXMzL1KawtSiq50ENTCKqj9OfafioMjtvXsC1ukvqUk1f4Pf8mERLH5lsN+zNZ51S+6Ihk1tNQTJ4bmgueWwDIOEyTZY1cNr5IyKLKWiazaUv8u8riXGcaisnzYdYnUSCMx6vF13gvcB86EiGGWuiMrxkes+4Z7juv/zo77+Z3sT2dO1ckXBew96dWGT+Phc+rx1U1lG5f1jgzezISvqn9Ta0jy4aaLTMWUqtSG7fJ9yq9fiqrgUqXHt4WXHrppTj++OPx+c9/HpMnT8Y111yDuro6XH/99YVj6urqMHr06MJfT6p8OY7jOE5v4g8KfUBLSwueeOIJHHTQQYXPysrKcNBBB2HWrFmFz26++WYMHz4cu+yyC7797W9jzZo1pZpzHMdxnD7DpYc+YMmSJWhvb8eoUbbe+6hRo/Dcc88BAI466iiMGzcOY8aMwVNPPYUzzzwTzz//PO644w4AwLbbbovwpqntvPPO61lHagd1ZsBbl/CWN9641tTFtewzNuepRJGSL9jUyebxFjGrsYmZIx3U85dhE6hKGSGSJU0zBLZFsk8CVqZoS2RWY/Mm/yZXdIjGz21oNj4jKZCpUzNCGu92OZcWISr1G4X7ocW9eB07El7/68XUu+HXOmc8xpwneiSCISXRkOk404JB3F9eK13vsoinv0aKcJ9SkgKbm3U9eB/yHtf5VEmtcB5Zx1gxLpW/uEBch5qs6VgTRSBryvPJv2lXCYn2Lv+mVuaM+hRozrK6wfa42H0LKJFxdMNv7PUeli8sfsVj1L3A2TzLSxdtAmDlCyMvJGRC3e78O7PHpQ2+x7HcoOtTWZOPCukm/qCwhXDCCScUXk+ZMgWNjY048MADMXfuXGy//fbdaqupqQmLFy9G2ZsbafXq1Rv5heM4juOUxh8U+oDhw4ejvLwcixYtMp8vWrQIo0ePLvmbadOmAQBeeumlbj8onHHGGbj22mt71lnHcRznHclrr72G5ubmwn8iGxoaUFMTsVIR7qPQB1RVVWHq1Km49957C591dHTg3nvvxT777FPyN7NnzwYANDY2dvt8M2bMwKJFi7Bo0SIsWLCgIG84juM4/ZfJkydjq622QmNjIxobG3Hqqad26XduUegjTjvtNBxzzDHYc889sddee2HGjBlYvXo1Pv/5z2Pu3Lm45ZZbcOihh2LYsGF46qmncOqpp+L9738/dt11126fq6amxjwlFsIs160GKrO8XsbaF2timj2OdUDW5nKhiCh9HGC1P9Z9VbNlLX5gQ7w9E5rF+r3VYsO65YXXGYVO5ULnWKccIJoon8v4YSTCu7i/ehzNdcbaqa4Pzy/rl8EeFzh0TsO7+A2tY3L8XdVHVzcVX0u4qcmcacJXJRSP29M1ziLfaTgf763mZcXXNeqjEKnoqf4vrEXH1h6wfgmqBXMFRs6kuKbJHsf7n8OL1V+Fx8j9lTEF8lnIeP+oXwv/br1IlBwuGqv8CFh/g1QWSBMSyeHF4jNF/jCmgmWukibti1R21FglSciu5n1Wpf4/fB0nBP9YpsdUhlrtu8lsy75VGh5Kr41viPhnrGsuhE/OmTMHAwd2Xg8dHR0YNiySVVPwB4U+4lOf+hQWL16Mc845BwsXLsTuu++Ou+66C6NGjUJLSwv+9Kc/FR4exo4di49//OM466yzNne3HcdxnHcIY8eO7VHYvT8o9CEnn3wyTj755NznY8eOxYMPPrgZeuQ4juM4afxBoT9RXtFputTQQTY/coavGgmJY9NXSGQmZFNkyiSYRULsgLwZNN4IveQMdNZ8bcyv9JtcFkQ2Eap5mDHFo2Q+TTY+al/nab28L9V2Z6eKL2MFvCBZKjXs0xQuKraXGz/LLfwbzVLJJuxqOq+EW5oMhNz3XMGg4voEPVcs7DEXlsoZPBP7riJivtcse+2RjJBajItN4hp+uIokEB4Hm9QBuzf4eswVcKPvONOlmtQHDqH+UrghF2OCXBfap1jmRx0jm715brQoEkkZoZmKLA0ebo/j9jnUT6RQE1apcgCHR3a1kBq/1gJhkT2Yux45jJjvlx0i3ZlQUZVnad70OmY482UrjVEzM1bXAR2b9k+9OzM6juM4jhPFHxQcx3Ecx4ni0kN/oqO908yVJbIbMmpSZ7NnGZvSElnHVEJgsxqbypuX2+PYtG0yjWlEAJlc2YTXmijcw6Z8Ne2a2vWS4cxk6uso/bm2wSblnKc/vyYzpWZLZDMqn1dN72zO1LWrJs9/NiPr+E0b1CcdIy9DBX2nEQXGa5ujZkR6CPHsjlkVRVJwpINmDuXCWqkCWYEz6yUycZpB0jg02oKlklSmS+6TZg6MyRcaAUPzG+hcueyTNO+B1jvTDJt8DapnPq9dLPJEf8cSYrtcP3Qus6ZaPCoWOaFmeL7eg3zH88FrrPuO15z3e07WItmEZIkslymTizhR/5JSWFX8PcufuWyrtJZ8n1GJE8iPu5u4RcFxHMdxnCj+oOA4juM4ThR/UHAcx3EcJ4r7KPQnqus6dcKWRPW3OtK9VItsiYQ+aRazDtIVVZePhSqpPswhVxURvRGwGqbR0TVcKhI6l6v+Rs/OtQ32VBTqZrPdyWW0nqvp8XEyRu4ia/s6Z6xT89ql9HAdF2viPH7tE/uhsG6uWiyHIsbmFrA6MmuvOd8Q0tu1T7EMkXKcqS6YxY9jnwKr38s6cmgn+6topUZeLx0/zztr7Lp2PMaKSPZSwGjn2VBK766+IaSPZ/UUfighhqFpafE4XWPuE/syaNhwbP1zPjRctZN9MmQvcBusy+cyZybCHpuWFF/zNagZYDlE2WRH1ZBnCqkePKL4ee4e2cWQZ/M7mSe+xtnXQvcdzzvvwTbJYNm2Pn2v6AJuUXAcx3EcJ4o/KDiO4ziOE8Wlh/6ImilN4RUye6nZk0Or2AymUkascA0gRY049EnCwNj0x+ZBlR5ixZRyBWQi4YcamsR9ElNfFs2YJn3nIkSJcD4TOmoyBIo5k7+j8wYxc2b1VOBFsj5yRj6TwVELzfDe4LloXmGP00JLG8hlXKQ547VX0y4X/hIrcjTTZ4fsTxOmx0W75Dha8ywVssnrEyKfA9ZEz/IHYNeS911qv3PfU1IbSwC6t6oikpdk7ctqaPx6HXMfOQRUw/S4EJgp0qaFkCLZEnPyH98zaF70mmZSBaj4tUqcrHqYQm/SPvfRFJ8T2YTvJyzVrm+NH5eS//ia0XO1RO6LmplRMz/2ALcoOI7jOI4TxR8UHMdxHMeJ4tJDf2L9GmBdWd5Mx2ZqNquqRDFoaOnvch7XbNpPeDTHsgAC1mzJ2ejE/Jix2dPUuxezZxbZ6mqyVa9o00YkO52akdWEGzsuFn2gxaLW0Hv2etfIgZi3uB7LY9S+8nzUSRY/g9pmN/RPzKiRojZBvNQzHnMucoD3UyKyhfcGSwq6B2Pn0vnkuYkViAJkPmXtYhEMGmFAMgJLSqaoVu5ciYylLPmwKVv2YKDzZprNk/dCW6LwFa8xj0v3lllX6kcqa2AqUoT7p2vCkh+PX9swkkLxmsnJerw3+LW2x2NkeU5lE5YHVKKJSRsaXWZ+Q+PIyYkuPTiO4ziO8xbiDwqO4ziO40TxBwXHcRzHcaK4j0K/Iuv804xprMexts1hk4DotKSXZaIPsv6suiefO5pVETD6M+meosTaNlg71DEy7YmQMJNJT87GIXwmo5v2isPbaM606iDP01quYih9N2GppHWqrwnrpamqmKyJqlZuqi5yqGRCR+U21L/CZGYs9inTvWWy+8meIZ0/LF9UbEP1e/ZX4bnVjHaxkNp1EvbI/WC9uUzWuz2SfRKw1xb7K6huzCGbRm+XNeZz0ZrkfD5ye/JNKm3/TJXN1Bqzpq77k/0XTDZP2e9mjakN3QtUnRFZIssr73Edr/oeFX6joYNc7bLYd5PNErDr1ZLIKhnz3dHz8v0zdw+ieeI9rf4QHMpufMZkHcvK89dUN3GLguM4juM4UfxBwXEcx3GcKC499CcqKztNj2qaqiHzFn+noV5sLmRTVoWE45jfyHe1ZC6WAjWGWHEVNT9Gwihz5rwBlDEvFerWkQil4uyELCOoBFBDUkFLJBMlYEPdYqZSwJqzjTSiZk4yv+bM95wxbl3pzwEJMeX2E5kE1bTPVNHeimULBIBA86lFh+hYEw6rYbkmFDEefmbCD7k9fg2IOZvmok3Oy+Fo61R6odBeDkVUyYevk1R2VDb7Uyhqrj1eH70GGZ4zlRRMJs1EASaep9pIIa3OA6lPXeufkZqGjJYvaW5UNuLx8x7X43jMJntrPNNl4PsAFdUCJDsqF3TKhTyvQhQOMebQ0USGUTOfa+W+Wl0XDVPuKm5RcBzHcRwnij8oOI7jOI4TxR8UHMdxHMeJ4j4K/Yn1a4Gq8rwmyDoY62Oq0ddSSA9rfS2SFpY08KxSwsA4pCmVBpqrJJIOmOWyCtO5TciZaOqsoxvNWnQ/9sNQ3ZcqMKK9Kn4c69I8F5reOFa5UP0GOiLhdzkfEnruz2m2kXC5XDgXHcfpktfJXuA+sR+ChN9Fq+6tWG6PYz1X+8raLB0XVq80h2Uc3lcZ2auQ0EEOMVwjx7EPCfehRnwZVi0rvlafF/bfSaWp5vDLtaRfa2XFrPR+z6U2N9cxfafn5f7qd9GU5fL/y1jonfo8mPBQrkwqe4Haz4aMKv17wPoR6LVl/Dw45Fuqe7I/jIYwMuSXkQ0cUvy8dlD0OBuSLPc37p/OJ6dO5zVRvwae99aO0p8DnfeJWFr5LuIWBcdxHMdxoviDguM4juM4UVx66E9U13aGykh4kzHtDx9b/ELNqGy+j2VEBJB1REzAgA3dSWVPJFNdNoBCrrRPFGIZqH9ZLnscVyDksCLNspfIRsihX9yeVoarjphzc5kUi6bTQObxbJCaQNlUzpXlxMzPc6vflUXMyLmQQDpuVXFfBDGBZ2zCZROwziebPFMZIVke0HmvJgmAxpGpqZznN4t8DkSr+mUsfwDRUETtX6DxZzqfHNqpZm+G9xOfV83hJvsmZXPU87J8YTJRyv8N1yfCd5mY/AXY/cQm9VRYJstmuWyJkWqHGrLIch1XtgWsBMb90zBFHgtLOSpX8fzytaSSAu/rVDXbVMZFvo4rEyHFprImraPec7MyoDIRft0F3KLgOI7jOE4Uf1BwHMdxHCeKSw/9iRCAEPJmNS6AwqZDNWdyJkU2o6rJUjM/MmyOZNNc1sXsjto2Z6frSHj+VsSynZXHj9PIAYZNgjlzO5mzuT3NVEdzmJWRJ7WaDk3ba+PHsXd7zlue5RYas3pD85rQcVnO3Cwm1zcJkqnOSEjsIV4jZn5jUpd9F8vAl8vuGDkuV5iMxl9B/YiMCYCdp3K7Z0x0hEaiGFmGfqfRISxtsGyQ2+8Vpb/TvWqKPdG8qAmcTeU6/vJIpkKdd44U4j2o0Svc39rS8k/uuIg0kPtOJQVuk6+TXOErmhuT6dJKnIHWJEtFirAUkYp6YKoS91n+nUbbUNQPS4O5azW0p+/JXcAtCo7jOI7jRPEHBcdxHMdxoviDguM4juM4UdxHoV+RAciQ1dXbj0lzC5RlLlNdLZZVUXVu1ciY1oiGqWFbbaJbbkC1bdYI+TXrxoBkMaO2VbNMVbVj3Z+1aD0X970i4W/AWifrwxouxec1Gfck5MmEwWnYZ2vp19q/WNU91fk5o11ivY1+z3q4aurtkUyPgNWmjdaqviG0Juxfo9X0WLPvamVF3icVEqJbFsm+qG3weun4ub+pioFlXHG0uE9y4at8rbI/wDqp/Mj9zfkARPZuqu90XlNlUftkwoblGlQfiA1oSC2Ha+t3PG88N7lzcRglzbtcg9lACr8090VdH670Sm1oBsdUVlq+P6d8CwY0FPvHfg2lMmWqH1U3cYuC4ziO4zhR/EHBcRzHcZwoLj30J1rXA62VeTMYZwVkU7GaAGMZztTUxSY3kRSCyYRH/dBzcfgYm9HV7Mlmag7ZVNM7m0HZ7KkZHNmSmCj2lJQv+Hc8F2Vyrlj4pZqb2aTO/c0V8UlIObFsd2r2NOsQyeYIWJMtF/EhcygAO0/8WueCzdfaJx4zm2XX2KJQuTY3oNk8TcY8muv1sh6x80pm06Rpm8/F4W269pylkzOMahgl7QUTEqfH8VwY6Ur2jNlDcg3GQvr0mqaiTlkdhfwOG2N/17SE2o7sx85flu5fm/yG71VSIMzs46qE/BfLNqtyGl+DPC967xtAa8wZWnW9U+GSLOVyG60t+WMLcLGwEpJxKgtuF3CLguM4juM4UfxBwXEcx3GcKC499CcqKjtNamoeZXs7W73V49aYKRNmMDZzSURExqY09jJOFVfhTqlJnb9jk7BIGYEkgFzmMtPBRCEXbp+L0KhJkDMwxjy4O09WfMnzoiZ1jnRg87WuT8rE3BYxseaKZ9Ga8DjUfM9zWJ4wD5useIloA55rLVrGa8fShhYTikXltLd27TjNdMnzy3O7TqIoeC50b/FYuL+ajZDObSQ57Ttny2Szt65Pe2QdU0Wm1ETPc8NrJ9eqKcDGkoJGQ5kIJfqNZizlPtXRXLTKfifJw1wj2l/ekzmJhvpk+iHSJV93XKRMI2pYAqkTiZfhc9XIcXxqlnhVCuU92R7JAAp0yh4a7dJN3KLgOI7jOE4Uf1BwHMdxHCeKPyg4juM4jhPFfRT6ExWVnX8afkf6XqCMe9mgIfY4rrRWk6j+xgyUNowfAVeda40flwpbMvooaaeiHWZG34v4BgA246D6KJisiOyXIPPJbbJOm6umR/3l8CnVW7OIVqwZHFOZGRkTYin/V5ChFEhlxUs1wBknjT+EVhyl42SNjQbO46qVLJ28D7kCo2bzNFn7eN/JnFXROvA6ahhmou8mFLkikrVP33MoZrucq1TWPaBEOGyxTyYkWSvCsl9Cy/r4d+2RPQgA5Xwc6eZ6TfMYq2lu2d8HsNcua/S6p/k6SYRom3DTTLLScn853FLnk+8LrPdrRUfjg5UImzXXuF50kVBHDcvlNk3It4aaVwMtiXDMLuAWBcdxHMdxoviDguM4juM4UVx66E+0tnT+5bKiFU1dJsObmiI7Ima6XPa4daVfA1J0KGE6ZFOa9sP0KVJcRQs1sak8VcSHQ580Ux+Hz6WkAjZ1kxk1sDkcQMaZJE22N7kseSyxDHFAOgNdRUQ20fUxxYTIbK6mU+6HCc2z5uGwekXxDUkImYbzmWyWGs4YyXSp4aEsZfE+U2nMyAOcBVD2IM8NF/qqlr3FJmGVFLiPJnugFhPikGLOYCmhbiYjKn8Xl3KM3KDm64RsEpqWFtvgPa37zKwX9UPDHk14JM117j7DclUivJilCM0cSv3NKqh/Kl/wvSt2jwCshMrXvu5Vlsl4rlXy4etMQyy5jfLIngZs33mvVsj9Y/XK/Dm6iVsUHMdxHMeJ4g8KjuM4juNEcemhP9HRDnS0IUiBEi7kYkyHuYx2kcgB9ggGrBm5RUydbMLkfqipD1wbnl6rpKBZ8jagkoLJnkdRHs0SHcEmes1aaIq11JX+HLAZ42guMh1jJLNeWLnY9on7kSoyxSbHVcvsdzzvJkOiSAUc9WKkITlXFjHZi0TBRcZCygucpQz1qi+LyFU5r3r63cAG6oT8f4jlEP5O9pY1vdN3Gm2Ry3RKsFzHc1auGQKpH2WJ6JBYZk41qfP+53nXTKEkf4UVb5ivTKQQe/PrPuZoqFSEEqPXDBOToXJyYlGyyMl6scyuKg3x3LAcpnIV7xMao5HWIPdSk700cS9RyYslBc4qqfPObaSKPtUMBFpj4Uxdwy0KjuM4juNE8QcFx3Ecx3Gi+INCH/HnP/8ZH/7whzFmzBhkWYbf/OY35vsQAs455xw0NjaitrYWBx10EF588UVzTJZlmD9/Pm688Ubsv//+fdd5x3Ecp9/iPgp9xOrVq7HbbrvhC1/4Aj72sY/lvv/+97+PK664AjfddBPGjx+Ps88+G9OnT8czzzyDmpqaEi32gLZWoK0VmVbJM3oz6WAa0sP6I2t4rAcDNtxJw+BYczQhgap7cqgj9UM1Rv5dRXFcYdVSc5jxDwiRCnwAAvkXZKr7cbggz5nqw6wDs46sWdxMhcOiFpkN28oex6F5rZG10va4yiJgdXQO71OfgirOCpeousehZLymdZL5jrMCGo1Z5ox9WbSaHq+50du1TzRPJkTV6tdmT3J7km0yGzqmdP+altj2KiLVKPVc7MujoYNGH2f9WquARnyIdA/GtHedM7pWc1VVuX3OzKjXNJ+7eU38OIavEZXPY5leNbST/AYyze7I+j3ft7SiI88ThxCqP4QJy6XQU808y34TLXRNayhvzIei82jqX6IqJPtNsL+X9j2EvN9PN/EHhT7ikEMOwSGHHFLyuxACZsyYgbPOOguHH344AOB//ud/MGrUKPzmN7/Bpz/96b7squM4juMUcOlhC2DevHlYuHAhDjrooMJngwcPxrRp0zBr1qzN2DPHcRynv+MWhS2AhQsXAgBGjRplPh81alThO6DT8gAAxx57LI499thoe01NTVi8eDHK3jSrr179plmqoqrzT01Y5fyetsQ6zUxYNJcZE72aurh9zQLJZko22arplB9hOfwql42Ps5MVTZNZw0h7HJvHyRQd2q2p2IQE5orfRLKu5bLHFccS1hSz8WVZgz3MhCnSnLWtjx8XKwQD2HlCKmSPx6yyCc1nO32nRaC46A69znS9ec7YHKzyl5EDJOzRhJlpGC3BJmsOm9V15PEbCUn+38QSQHsicyaHpXZIG9QPM08q63E/2Cyt5naep6pEdj8OZTZhqRp6SvOpa2LWjsIt1yWknCoO3xQZhvcMXYM58z23x7JRrvAVy5jyHYew8vg1CyTtp8CSgvY9lsFR+0TyZMZ7UPd0oPZ0Tfh35h5pDzN7htdO5VkgvxbdxB8U3oGcccYZuPbaazd3NxzHcZwtiNdeew3Nzc2F/0Q2NDR0yQfOpYctgNGjRwMAFi1aZD5ftGhR4bvuMGPGDCxatAiLFi3CggUL8Nxzz/VKPx3HcZy3L5MnT8ZWW22FxsZGNDY24tRTT+3S79yisAUwfvx4jB49Gvfeey923313AJ3ywSOPPIIvf/nL3W6vpqbGPCXW1YmZUzOBscm6gk2R8qRJ5jNjOk1lplPYXMa/0yx7bC5kL32NROCxsCyh5jw6V6DjMvXmZ7OyRinEisZotjvyYjYRJjrvZM7mDG9ZtXiL87m4kJQUwglUdEijOWwUBK2ByhwR7+6cZzZ7nJtIB7GPslQQkyEAO0bdTxohsIGKhKmc94KuD2eLXPp64bVKaGZNuA0x0YdVJMPVD7Pnqh9e/I5lBJUKzH6nfZeKbCFpMMheMBE7pvCTzAXLF1pYic3q/FrN/HztltG854qU0XXBc52S+FiGUHmS11hN6zwWI99odAgVxON518gL7gefN1f4itoYSJEYzZIpNSTkVL7vcj/0uuC9wVKLKT4GhJZ1CG9m6pwzZw4GDhz45mk7MGyY7NcI/qDQRzQ3N+Oll14qvJ83bx5mz56NoUOHYptttsEpp5yCCy+8EBMmTCiER44ZMwZHHHHE5uu04ziO845h7NixqK+v3/iBgj8o9BGPP/44PvCBDxTen3baaQCAY445BjfeeCPOOOMMrF69GieccAJWrFiBfffdF3fddVfv5VBwHMdxnB7gDwp9xP7771+IWihFlmW44IILcMEFF/RhrxzHcRwnjT8o9Cc62jr/2kWnMxUJKXOZ6pkdkWxvqreaUC/R21mPS2Udi+iPuWptHAbJYVaqMbJfAmuget6KSOU27W+pEKQS/bX+FTKfrNOnQha5v+3xanJZLHOknku/IwKFwWUNHK6rIVyk+/I+EX3U6O08txoSx1kLNXSQx8x9V78W1tFbuXqiHEf+G8ZHJTEvdq7lONaONZwx5r+j52LfEz6XJink31FG1GydnJe1c553De3k36lPwXoKj1Z/kFifUtU9Y5lY1Z8mNmcSrm32apVYXrl9vu7UD4O1fa64qRVCKcw50HHZEHE25/sO+yUks9yui3+XygBbHfGbEH+NrK4emd7zu4lHPTiO4ziOE8UfFBzHcRzHieLSQ3+ioqrThKimvepI0aVcKCKb7MtKvwZsFjc10auJcAOa0a+OzGqUqSyXxY3NcZzRTE2ssfA7Pa41kT2QQgzD8mLGzCyVMY6z9mlxIup7UjZgcymvifq8cD94DQA7flMwyK5HNoDNtJzNUuQlk/kuUTCITbHcp8GSOZPN3mq+N+GhDaXbA6zJledJ9/G6WFZNO+8cwhdWvFH8fOQ29jgOg2uRvpNJOJC8ksvMyPuQ96BKaFzUKLYvAKCaZTi6BrV/vF4qjfG8c59ymUgjxeL0ujBZVNncLvud14fN65kNXzUhwFLQKvA9g68tzSLLkpeRuKRYHMkoJuRZwzJ5/KnQTl4TuY451NWE5eq9iqXRmNS0oU+aWbWbuEXBcRzHcZwo/qDgOI7jOE4Ulx76E20tnZn4qrQgDb02xYnEazmWTU09ifk4jRww3uP0XTIzI/VXijhZD+GE6Z3NozVkilQZht/nIgfI25nNmSqnmIyT8SgFm52NjitlOtxAyqRelbic2YS7ail9rtknyYTJXuA14gXeFokAkWyexozKbagklSoY1RYxnaunP89HRaLoTmyNteAUbzWWWnTeWSrRLHtcCIp/p2b5piXUPSpOVDfYHmeiTWSfMGwSN579MkaO+lDJh28Mg0heURmqjMzjnM1Ui8qZ93zdynrHZELdMyyByHzaeaNzSdSUmUOOrlpnx2jWjq997ZNe4xvQuWBZQtYx4/7ydaFZVGORHbqPO9rTET1dwC0KjuM4juNE8QcFx3Ecx3Gi+IOC4ziO4zhR3EehP9He1vmnIVJMKgMb63GpcJtYhUhtw2Rqk2dW1XBL/R4A2kjP49A51ZFZ6zMhkIlaGqo3myxp7A8hmdXYv6IjUU2Pv2NXDh07Z3dsTawBh86tbTJfBcosZ7LYafgd68+s7VdqKGZEvxYtNovpzanQTg05i/moaKVGzgrJbajPA2v25BuQ0/xbiuGsJiRO54yvGb1+2Fcmood3noBCZXluNWyYw+V4H6ufEJ+L98kA8XlgPxQNU+R9sqqYZTDIfjfZUaPVR2E1dcoqmZtP7lNZYl9UccZJ0eXV56nwG80UW9qXI1svPk68X2Nrqv0w134ibFgI5CuS8T1NK90ikqU052uC7lX4LYFbFBzHcRzHieIPCo7jOI7jRHHpoT8xoB4YMChv9jUhOGQ6VLMamzfrhxdfa8hRLEMeYE39HWTC0/CdmGlfJQruY8zkDxjTX1jyr+JpJMueaaMiYR4nU16QjIs2m1rEFNl5JH0nZlWGQ85YUkjIJkFCBzOWB3gO12nGRZIveN51HXmeOLudmoDr6ouveW+phMRzplIOIoVx1Lwcyz6pZlc2HZv9GN8z5rXudw4HlvEHDo9k+UKlAiOP0BhVJuQQU76Odd5JNgjNy4sty3VmskWymbvz4OJrkn8yyW4YFs0vfseZU1XyGUiyB+87PY7fmzWWewRfM7JnjPmepRGV9dZYiS6KkY045FnaY5lHi5GZ9liuspkuM96TnNm1JZGlk/uk2SfLK4H2SNhmF3GLguM4juM4UfxBwXEcx3GcKC499Cfa2jr/1BTLZmU256XkAPKiz5nYtHgJw2Z6k1lMvZbJlMamuFTtdvYkVnMmm5uNed2a8wKZn7MKGQebFWkuMvW+Z7M/z6F6OrNXOb9Wk3rkvLo+Yenrxa8GSfEszvbG660ma/aY5nPpcTyHHDlQLQWD2MScKpJjIixEUmGzKmegU9mE+8tj1Hnnc6kMx7BZmU3UuYydtCcTGQJN5ImarHnM3H4q+yKvaa5gEBUxMlkqE5E8qfsCR8NIJEY2fOviG77ONDsq3ycqEoWVsojUpBlQeQ5FyspY8jIyT0q+4H7IvuA5NNKISEjcHktZHGmj36nUxllUub0KvbY4SyndV7VoVzVsoboe4BYFx3Ecx3Gi+IOC4ziO4zhR/EHBcRzHcZwo7qPQn9hQPVJ1OkT0YdUOjZ5Lr1VTryI9W6sTsm7J2nGs6hpgM5JpWB1/x/4GehxX5Bs8HDEy1l81lMq850xtmqkvUpFOwygrIxkSNXTOVK4TrZPbY78B1baNnk/rzxnyAKudsqasOj+vI4fE6XHtkVAyXe81ibBP7hO/Vi1W/VzeJEgInNGveRy63pEqqGH5Qtsez6HqyBTOarJUNoyyx5nxc8ivVrSkNeEKieoPwP4GVYkQUO5TwveCqylm9Xr9FH8XKKOhmWfArhdnKaWsjwDEh4jGpddFaj9VRCpL6v7kNY75FwB2Pvkr9RNqXlH8ikOS9R5ZLZkauY1lC4pt8FyrLxjPR6oya1trPFNlF3GLguM4juM4UfxBwXEcx3GcKC499Cc6Ojr/xHQYViwqvM44hE3MqIGK7nB2ttBqw9QyE5omoT9sBmRZQgscxcIKNUSKzehsilRTbCyLoZozuU+5IjRFc2lY8Ubp9gBkbAbkYj8asslyAEkFYY1IFGweNVkqxdwaC6sC7FqySVgyOJr5XceygWYcpPA7nuuciZX6ziFxUjwqn7WSMIWBOFRSQwJlLTf0T+UVPjfv90TobVj6WvFzlRdSxa5o/IEyOmYafsdm5UTYn5lDvi5y8h+Ny4RiWlO5kQd0XDyfXQzTNKHCWpzIFIHj61b2MWe+5GyRuYydidDWaNEllV2J9khItv7MFDCz+9ZkgSQpLKxcYo9rj89tNpjaMOHq8v96XmO+t5SSl1Ih613ALQqO4ziO40TxBwXHcRzHcaK49NCfqBvYWUhETV1swmITZmXiODJlZeqJzyZb8gLuPJY8mtmUtl5M0Wx+Y/OrZI8Li4oFnthsng0YbI4zHvFsbq+RqAw2S1eIVBAzbdfI+NmUymZfOS5QRkOew6xO2ot5LGsmPTZ7rxfphdecTeySZQ/rItEXYvY1xX9YelBpiPtuPMcT0ohGL3CfTJZBCLTvViwu9lUzZ7L3PZv8q2QvUH+NB3suEyn1V9eErxPuh0pXpigWz6d4x7Nclcqw2RabM5k0fp+ToYr7P0u1wfuEpLYgcmLGsh5neqyWIkZ8P+HjUpliVYYyEQz0uk0lCRqLyQgZP8yEPay1ETWmH+XFceUiQHgOVSbl+4RZV/l/PV+TqQJUbevz5+gmblFwHMdxHCeKPyg4juM4jhPFHxQcx3Ecx4niPgr9iays8y+lZ7Gep+FNMe2dQ5gAk2XOaLsA0E7tr0volNxH9lFQLbKydBbEnAbOUnxVIvtkJKuiwlUBc/NZTm1wf1XnZy2Ww7lqBsEeyK/Zr0PWh/XxGplPDp8yvxMxluY6cH8lC2IZhYEFEx4p4Xz8O9oXQcLXbEiphoHResUqGgom+6bqs9x+RaIapdGb6bWGCrLurfo9zzX7zWgbsfYlmydi4cvqr8F+KDE/EcD6F2gWVR6LyVIpa8z3AmovEx8nMy6+l6jfAK+3+Y3sC85mqVk6+dri7KiapZP9Usw+0SqTHCpMa6X3Lc44yT4fuTHSOqr/Qmq9TBs0T7EqukDn2rUm2ukCblFwHMdxHCeKPyg4juM4jhPFpYf+RFllp4muebn5mM3ANuxNMqGxqZPNgJplj01i7WK+ZzM9t6dFUtjkymGKYtrNhR1tQE2RbFbk86r5mk2sajrlsbBlUsP5+LhkVknuH821ygYc6lgZMd8CNrxLzbS8RnycyiZkSjVFjMQUawpc8VyrWdrIS2zmlb3F/c1l1aNzh8j+AawZPRKyByC+DkHOy9n52ASs2Ty5fyqbMJHwWgCSFZLa0/BIXn82bWvWT3Od0Xe6Z/h3Mi4TvjuEilipJBcrLFUm68PLwKb8WpHaYmF/rXJe/p2uCa8X9S93v2CpgPeujrEyYubX/c7wntZQax6jZlI0GSw5vFruC5Qpl/uXk/XKyvKF67qJWxQcx3Ecx4niDwqO4ziO40TxBwXHcRzHcaK4j0J/oqMVaG9FaFpqPjahZCZMSzRG/i4rHRIFwGrR6r/AOhvriJpymI/j7zQ8sqZ0GFROo+f3JrxJQsL4XLnQTvqOdUXVpY02mfDX4H7EUiIDVqdljVr9CxJ6s9HAWS/VMD2ed9Y6VdvmsaSq7vHvOHRO0yrzXtA+cYhhB+nDGjrG40+EdkZDVmMVRgE776oVs06t8xRLzawhxewrw+PS/c7zzufSa7WC2mctW+eW+yR9NymXSeMO4teTpcKNmcg1HZa8atvj46pj4Yuw+1P9UNoi4dWaptuMmfZWym8glTqbfa3MPEn8Ku9V9YXi+wmnwY6lcgcQ1tAaa5/KK/P7qJu4RcFxHMdxnCj+oOA4juM4ThSXHvoRYdVyBLTaUCfAhouxmVdN6vydCbmS4zj8UqszsunUhFsmMsax2SxnfiOTYwUflzC9l1EbapJLZcVjmYNN1lppjsfIJkyt1Bgz8+cy31Efqe1cdb7KREggz+dAklRUGjISEJlL1bTN5nHOOqfzSe85pDLTcFhjKpY+cRgcm9FVAuA5ZFPxwAZ7nFk7miedd5Z2OLpMZQPe7/odw2ZvNdFz2B73L1dZkMZs5L+EyV8rqTKp35WV/uchVy02Fg6t8tcqK3kW2pP+cUbQbH0iBJSvR22b70+18fBqrOFrnPZCTvLhzIycVVL2jLn30VrlsohSP3Se+FozkoeMn8ZiqnuWqqSZkoS6gFsUHMdxHMeJ4g8KjuM4juNEcemhH5HVDUJWNygvFcSydmlEgMnoyCY8tb1z5jIxq7GZuoLMrWraXrUs3g9zKjKBs3lPPdjZxMyv1bTLmfDULM+mWB6yZhLk4zoiXuraPpuR9bg6Mr1zNj71+o9lQQTsfKxeQW1oNSF6b8abkDK4QFYps+eGJtgsr3uOZZlcYSXOTBkrbgWJAkhksOT1qqeIHykYZI5LFY9KRfnwfuIxVsg88bWVyuDJEo2Jhklk/tNIB0PC0z96jcte4D3JfZI9Y6SyRHEvI0XEsj4Cdr30eufrzshkKuslIkdsr4ovjYyZyHTJY6wRuYazueZkCZZAqE+63/m+kIiIQEdHvmhYN3GLguM4juM4UfxBwXEcx3GcKP6g4DiO4zhOFPdR6E+0t3X+takWySGGpGeqBs56HOuZmlmMfSBUOzPaIQlnWk1QNdcNVMnn3I+qSIU3wIatmVAh0T1bElkgWZvmc6mPQgWdm0OkJEQpkJ5tstulwv5IbMz0uPJIFkDAht+xtqsaqwnF5Mp99jDeJ4H8SbJBQ+xxJvMhrV2r+K7wOqpWrOPccF4JX80aRhbfsObflNClqUJirooh94N8SIJUX814XBr2aa6ZSHVLwO6NxFwE2oNm/dUPgdvn61h9V0wGy8Q/B6ypS1iuGfP64v4Mck3nQmI3oHuQr13jD5LwhaqWPRIiob16X+BxpfyEYv4QGnbI1yr3XddnLc2hhqGb+26ikij7+QweUXyte2v1ivzUdRO3KDiO4ziOE8UfFBzHcRzHieLSQ3+irBwoK0dYa8NssuFbF99wpjIN9TIxNvRaCzqxqU9NbixLpIoOtZKtm7Osabglm3PZNKdmTg4J5BCpXBEjuiQ0TI3NjFlErlGySKgTgIyLTnGIlMo1bMLkUEQNl9LwMYbnl03WkpmPi8sYOUTjq/hUPC9rJJulKerDYWVy66kr9iksX2hPxfNB48hUKmCTPZui1RTLc8GmZ5W/+Liq4tplA0VeWZcIy22JhHZq1k/eQyZ8V8z3HM7JEoheP7wm3CcdYyrUj/uxnu4FLGMB0f2fqdRGezzjNlRO5HNRn3JSBpvb9Zrh+wzfF1QmZBklNRcsB7QnCuKZglEshXbEj9PQSb6uY3IvYO9PHE6u94GWtfl17yZuUXAcx3EcJ4o/KDiO4ziOE8Wlhz7iz3/+M37wgx/giSeewIIFC/DrX/8aRxxxROH7Y489FjfddJP5zfTp03HXXXcV3mdZhnnz5uGBBx7AjTfeiAceeKB7naiqBaprka0XSYElhpSHdDOZt1gOUM9sKtyjBV9sZj0yy9bKubiPnM0xV6iGzGxs6tQxUh9NtIGarwOZ+tT8yOZ3NlmqJzUXzVFZhqmLZKZUc2bMdKzmTEbXjk2TZr2smdKYtrl9kU2MBDCYfqNyVWWkWI3KJmQ6zkVzcPRFrICZ9pF/o+2xyZ7XQOUqnmsel0YHUERNWLHIfGXkGzYda59iharUrM/9SO2z+mHF1zxe3RftkUyPAALJUhkX1tKoB/a+52tVZJiMTex8zcj1E2ieMmojG5goJKZRPtze8uKaGLkPkPmlfuQiETiDZaRAFGD3oInqkjnjudDICb7+6X6Zi7bhPcT7TPfM+tVpWbILuEWhj1i9ejV22203XHXVVdFjDj74YCxYsKDw94tf/KIPe+g4juM4edyi0EcccsghOOSQQ5LHVFdXY/To0X3UI8dxHMfZOG5R2IJ44IEHMHLkSEycOBFf/vKXsXRp6frtjuM4jtNXZCHkysc5bzFZluV8FG699VbU1dVh/PjxmDt3Lr7zne9g4MCBmDVrFsrLVStP09TUhMWLF6PszRCh1atXY8qUKVgx7znU1w/KV1Bj7TSWSU6/Y81LQuzQQtqxhlIx/DvRR42+x7qfzoXxFaBxqMZI+nNYubh4mIYm8Zhz/guUdW4F657D7HEcZsWhWaqjsjbJ4Xxa3dO0TRqwhm+uE80+dq6c70UEE9qpVSFZbyfdWyvhsV7K/gqqqXP7GsrFc8jrr/uO/WE4HDbny0Dnaov4PwAw/ht8zej8VSQyYprQXmpD9ztjfFdUb6b2Y+G6qfb0vNyG7h+eD/WbiWGqbGr1VZo3ngu9zsz9KVK1EQAGUyZOrehp1iFSORaw9xae6zYJo6Q2ArWd6XXB77k92RdcSTPnJ8X3Tx4j72lA9mGqIm4NmlatQsOOe2DlypWor0/ckyO49LCF8OlPf7rwesqUKdh1112x/fbb44EHHsCBBx7YrbbOOOMMXHvttb3dRcdxHOdtzGuvvYbm5ubCfyIbGhpQU5Moa/4mLj1soWy33XYYPnw4XnrppW7/dsaMGVi0aBEWLVqEBQsW4LnnnnsLeug4juO8nZg8eTK22morNDY2orGxEaeeemqXfucWhS2UV199FUuXLkVjY2O3f1tTU2OeEuvq3jQNt6wB1pfnzYgNo4qvV75RfK0mVjYJmvAeCdPj79T8yKZONsvquTj0h8Ov1GTN4ZbctkoebH42hamk7zwWzfZGcoaRLHJmWc5oR+etFZmDsxiySV2zG7I5l02l2j8ye+YKJvE8xULCAIQ3/lX8zVByrFUTOGVwRBt9J2F/pmBUeUPxCzUjt5CsoyZw2ncZh/dplj2WbFLZMmPZQTVMjeUvllda7Hk5bC3Twj08v/xdu9x62fzMa6fKcExuUDmRz8XXTEtCnhICFULK+B6hWSVbIyG7SUmB0PZq6do1GSYlgyPLDbmshTR+vreorBeTONc22cM4fJdDFuXaN8IGj1+kkYzvBdonDivlkEgNUc4icgOHshba7Oz/nDlzMHBg57k7OjowbNiw/LEl8AeFPqK5udlYB+bNm4fZs2dj6NChGDp0KM4//3x8/OMfx+jRozF37lycccYZ2GGHHTB9+vTN2GvHcRznncLYsWPdR2FL5vHHH8cHPvCBwvvTTjsNAHDMMcfg6quvxlNPPYWbbroJK1aswJgxY/DBD34Q3/3ud1FdHSm37DiO4zh9gEc99AOampowePBgrJj3DOoHDcp7gbNJj83ZCY9eY1JWMz+ZxPi4zmPJM51NgioBsDmO5Qs17ca2r2SLZLOnycyoUQ+m+It6ppOpL+ZhD5jskYFMjjnv5pjpUCM2+L3xqlZTJB0n82kiPTj7opjvjbmZ11WlIZMtkdZEvMpNlk7NChhrT9fERBzQ+HV9IgWYgkRiZDxv3F/J+hlobox3u2ZVND+S/RgrQKZBCu2RrKLVci42xZvMjHJNt0QibzRqyGQPFDmoNpJJMRc1JZLNBlLZDVmS0WyrPH7eCyo1peD14tca9VAZiUpp6eK5dC54zlhSyUVh0brqXmCJwRTO0/tCJPJMo3dWr0TTqmY0TH53j6Me3JnRcRzHcZwo/qDgOI7jOE4Uf1BwHMdxHCeKOzP2J1rWAS2Veb2ZtUPWvTS0kbRtE26nYUukl+YqAVZxSCRtvzU2HAnt1KfqSJU0IF41Tv0GSPfLchn4+Lyke+cqVUZ02irR3jmcjzXWVEY/1lG1Oh/3l8erx3F7Gh5JFQ5Z98yFUfJ6mVA8+T9FLLRVfCOMtq9tmAMT33EIGs9ns+jy7APCa6CZM02WSjqvrE9WR+2Rr0Uy06OG6ZmQTT5OQzHpdxzaqX4YvI7qU8AYPw/atxLOx/4b2RCpM8PXJN8XcmG+dBz7Akn4bsbj4uqWGuZb0VB8zWGFGnq7ckmxbfV/4euT95Zmm+X54PnUe5rxj2BfC3EwiGW5Tfn45HyNIhlR1Wcs5p+lIeRl5V3PxhrBLQqO4ziO40TxBwXHcRzHcaJ4eGQ/YOXKlWhoaMC//vWvHoXGOI7jOG9fmpqaMHbsWKxYsQKDBw/e+A8E91HoB6xa1alDjx07djP3xHEcx9lcrFq1qkcPCm5R6Ad0dHTg9ddfx6BBg5ClytECeOWVVzBlyhQ899xzPaoz8U7H5yeNz08an580Pj8bpydzFELAqlWrMGbMmELlyO7gFoV+QFlZGbbeeusuHTtgQKdn8eDBg12mKIHPTxqfnzQ+P2l8fjZOT+eoJ5aEDbgzo1OSnjx19id8ftL4/KTx+Unj87Nx+nKOfDUcQ4fGgTsGn580Pj9pfH7S+PxsnM0xR/6g4BhGjBiBE0880c1+EXx+0vj8pPH5SePzs3E2xxy5M6PjOI7jOFHcovA2Yf78+dhzzz03dzccx3GcfoY/KDiO4ziOE8UfFN5GtLa24phjjsGkSZPwqU99Cr2tGs2fPx+77bYbjj76aEyYMAFf/vKX8Zvf/AbTpk3DLrvsghdffLFXz9cTmpubcfDBB2PKlCmYMmUK7r777s3dpS7xdu13X+Hzk8bnZ+P4HKXZpPkJTp/wve99L+y5555h4MCBYcSIEeHwww8Pzz33XOH7Bx98MHzoQx8KjY2NAUD49a9/bX4/b968UFlZGf7xj3+Ejo6OsN9++4U///nPJc910UUXBQDh61//erQ/pY7ZcI7nnnsutLW1hZ122il84xvfCCGEcM0114Svfe1rXR5Pd/vDXHnllWHcuHGhuro67LXXXuGRRx4pfPerX/0qHHXUUSGEEDo6OsLKlSu71OZb3e9zzz03oLOsXOFv4sSJvdbvvmJj87OxcaZ4K9e1r3ir5uedsn9C6No19uqrr4ajjz46DB06NNTU1IRddtklPPbYYxttuz/soRB6Nj9v5R5yi0If8eCDD+Kkk07Cww8/jJkzZ6K1tRUf/OAHsXp1Zynn1atXY7fddsNVV10VbWPixImYPHkysizDHnvsgfnz5+eOeeyxx3Dttddi1113jbaTOmbixImYOHEiysvLMWnSJBx00EEAgClTppjzbWw83ekPc9ttt+G0007DueeeiyeffBK77bYbpk+fjjfeeKPQjz//+c8444wz8PDDD3fb8/et6jcA7LzzzliwYEHh7y9/+Uvhu03td1/RlflJjTPGW72ufcVbNT8b+93bZX6Ajc/R8uXL8d73vheVlZW488478cwzz+CSSy7BkCFDku32lz3U0/kB3sI91I0HIacXeeONNwKA8OCDD+a+Q8SiMHXq1ML7008/Pdxwww3mmFWrVoUJEyaEmTNnhv3226/k/4RTx+g5Pv7xj4f7778/hBDCrFmzwmGHHdat8XSlP8pee+0VTjrppML79vb2MGbMmHDRRRcVPluyZEm48cYbw9577x1++MMfbrTNFL3V73PPPTfstttuyWN6s999hc5PV8ZZir5e176it+bnnbp/QsjP0Zlnnhn23XffbrfTX/ZQT+fnrdxDblHYTKxcuRIAMHTo0F5r86STTsJhhx1WsAL09JieUGo83T1XS0sLnnjiCXN8WVkZDjroIMyaNQsA8Prrr2PAgAE45phjcMopp2D27Nmbvd8bePHFFzFmzBhst912OProo/HKK68UvuvtfvcVpeYnNc5SbI517St6Y3668ru36/wA+Tn63e9+hz333BOf+MQnMHLkSOyxxx748Y9/nGyjP+2hnszPBt6qPeS1HjYDHR0dOOWUU/De974Xu+yyS6+0eeutt+LJJ5/EY489tknH9IRS4+nJuZYsWYL29naMGjXKfD5q1Cg899xzAICnn34a3/jGN1BeXo7a2lr89Kc/3ez9BoBp06bhxhtvxMSJE7FgwQKcf/75eN/73oc5c+Zg0KBBvdrvvqLU/GxsnKXo63XtK3prfrryu7fj/ACl5+if//wnrr76apx22mn4zne+g8ceewxf+9rXUFVVhWOOOaZkO/1pD/VkfoC3eA91277hbDInnnhiGDduXPjXv/5V8nuUkB5SvPLKK2HkyJHh73//e+EzNZl35ZieouPp6blee+21ACD89a9/NZ9/85vfDHvttdcm9/Ot6ncpli9fHurr68NPfvKT3upun7OxfRpC18bZ1+vaV/TW/PTm77Y0Ss1RZWVl2GeffcxxX/3qV8Pee+8dbac/7aGezE8penMPufTQx5x88sn4wx/+gPvvv7/LFR03xhNPPIE33ngD73rXu1BRUYGKigo8+OCDuOKKK1BRUYH29vYuHdNb4+npuYYPH47y8nIsWrTIfL5o0SKMHj26R/3ri36XoqGhATvuuCNeeumlXu13X9HVfdqVcfbluvYVvTk/vfm7LYnYHDU2NmLy5Mnm2EmTJiUlmv60h3oyP6XozT3kDwp9RAgBJ598Mn7961/jvvvuw/jx43ut7QMPPBBPP/00Zs+eXfjbc889cfTRR2P27NkoLy/v0jG9NZ6enquqqgpTp07FvffeW/iso6MD9957L/bZZ5+eTU4f9LsUzc3NmDt3bpfrxW8pdHefdmWcfbGufcVbMT+9+bstgY3N0Xvf+148//zz5rMXXngB48aNi7bZn/ZQT+anFL26hzbZJuF0iS9/+cth8ODB4YEHHggLFiwo/K1ZsyaE0Olp/7e//S387W9/CwDCpZdeGv72t7+Fl19+uUfn64rJfFOkh42Npyvn+uEPfxgOOOAA89mtt94aqqurw4033hieeeaZcMIJJ4SGhoawcOHCHvWzr/p9+umnhwceeCDMmzcvPPTQQ+Gggw4Kw4cPD2+88Uav9Luv2Nj8dGWcm2Nd+4q3an7eKfsnhI3P0aOPPhoqKirCf/7nf4YXX3wx3HzzzaGuri78/Oc/L7TRn/dQT+fnrdxD/qDQR0ASYWz42xDieP/995f8/phjjunR+d7qB4WNjacr5zr33HPDuHHjcsf+8Ic/DNtss02oqqoKe+21V3j44Yd71Me+7PenPvWp0NjYGKqqqsJWW20VPvWpT4WXXnqp1/rdV2xsfroyzs2xrn3FWzU/75T9E0LXrrHf//73YZdddgnV1dVhp512Ctddd51poz/voRB6Nj9v5R7y6pGO4ziO40RxHwXHcRzHcaL4g4LjOI7jOFH8QcFxHMdxnCj+oOA4juM4ThR/UHAcx3EcJ4o/KDiO4ziOE8UfFBzHcRzHieIPCo7jOI7jRPEHBcdxHMdxoviDguM4juM4UfxBwXEcx3GcKP6g4DiO4zhOFH9QcBzH2QRaW1s3dxcc5y3FHxQcx+kT9t9/f5x88sk4+eSTMXjwYAwfPhxnn302NhSwXb58OT73uc9hyJAhqKurwyGHHIIXX3yx8PuXX34ZH/7whzFkyBAMGDAAO++8M/7v//4ver7169fjG9/4BrbaaisMGDAA06ZNwwMPPGCOufHGG7HNNtugrq4OH/3oR3HJJZegoaEh2ub8+fORZRluu+027LfffqipqcHNN9+MpUuX4sgjj8RWW22Furo6TJkyBb/4xS82ab4cZ0vBHxQcx+kzbrrpJlRUVODRRx/F5ZdfjksvvRQ/+clPAADHHnssHn/8cfzud7/DrFmzEELAoYceWvgf+0knnYT169fjz3/+M55++mlcfPHFGDhwYPRcJ598MmbNmoVbb70VTz31FD7xiU/g4IMPLjx8PPLII/jiF7+Ik08+GbNnz8YHPvABXHjhhV0ax7e+9S18/etfx7PPPovp06dj3bp1mDp1Kv74xz9izpw5OOGEE/DZz34Wjz766CbOmONsAQTHcZw+YL/99guTJk0KHR0dhc/OPPPMMGnSpPDCCy8EAOGhhx4qfLdkyZJQW1sbfvnLX4YQQpgyZUo477zzunSul19+OZSXl4fXXnvNfH7ggQeGb3/72yGEEI488shw6KGHmu8/9alPhcGDB0fbnTdvXgAQZsyYsdE+HHbYYeH000/vUn8dZ0vGLQqO4/QZe++9N7IsK7zfZ5998OKLL+KZZ55BRUUFpk2bVvhu2LBhmDhxIp599lkAwNe+9jVceOGFeO9734tzzz0XTz31VPQ8Tz/9NNrb27Hjjjti4MCBhb8HH3wQc+fOBQA8++yz5nwb+tMV9txzT/O+vb0d3/3udzFlyhQMHToUAwcOxN13341XXnmlS+05zpZMxebugOM4Tlc47rjjMH36dPzxj3/EPffcg4suugiXXHIJvvrVr+aObW5uRnl5OZ544gmUl5eb71JyRVcZMGCAef+DH/wAl19+OWbMmIEpU6ZgwIABOOWUU9DS0rLJ53KczY1bFBzH6TMeeeQR8/7hhx/GhAkTMHnyZLS1tZnvly5diueffx6TJ08ufDZ27FiceOKJuOOOO3D66afjxz/+ccnz7LHHHmhvb8cbb7yBHXbYwfyNHj0aADBp0qSS/ekJDz30EA4//HB85jOfwW677YbtttsOL7zwQo/acpwtDX9QcBynz3jllVdw2mmn4fnnn8cvfvEL/PCHP8TXv/51TJgwAYcffjiOP/54/OUvf8Hf//53fOYzn8FWW22Fww8/HABwyimn4O6778a8efPw5JNP4v7778ekSZMKbe+000749a9/DQDYcccdcfTRR+Nzn/sc7rjjDsybNw+PPvooLrroIvzxj38E0Cll3HXXXfjv//5vvPjii7jyyitx1113mf4++uij2GmnnfDaa68lxzVhwgTMnDkTf/3rX/Hss8/iS1/6EhYtWtSbU+c4mw1/UHAcp8/43Oc+h7Vr12KvvfbCSSedhK9//es44YQTAAA33HADpk6dig996EPYZ599EELA//3f/6GyshJApx/ASSedhEmTJuHggw/GjjvuiB/96EeFtp9//nmsXLmy8P6GG27A5z73OZx++umYOHEijjjiCDz22GPYZpttAHT6S/z4xz/G5Zdfjt122w333HMPzjrrLNPfNWvW4Pnnn99oroSzzjoL73rXuzB9+nTsv//+GD16NI444ojemDLH2exkIbwZxOw4jvMWsv/++2P33XfHjBkzNndXotx444045ZRTsGLFis3dFcfZYnCLguM4juM4UfxBwXEcx3GcKC49OI7jOI4TxS0KjuM4juNE8QcFx3Ecx3Gi+IOC4ziO4zhR/EHBcRzHcZwo/qDgOI7jOE4Uf1BwHMdxHCeKPyg4juM4jhPFHxQcx3Ecx4ny/wHBZ7V4x1paGQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DES exposes the full pattern: catalog, FITS image, and cutout.\n", + "des_coord = COORDS['south']\n", + "des_radius = 30 * u.arcsec\n", + "\n", + "des = DES_Survey(des_coord, des_radius)\n", + "des_catalog = des.get_catalog()\n", + "preview_catalog(des_catalog)\n", + "\n", + "des_image = des.get_image(imsize=30 * u.arcsec, band='r')\n", + "plot_fits_product(des_image, 'DES r-band FITS image', band='r')\n", + "\n", + "des_cutout = des.get_cutout(imsize=30 * u.arcsec, band='r')\n", + "show_cutout_product(des_cutout, 'DES r-band cutout', band='r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DECaLS / DESI Legacy Imaging Surveys DR10\n", + "\n", + "`DECaL_Survey` wraps the DESI Legacy Imaging Surveys Data Release 10, which combines DECam imaging (*grz*) with unWISE forced photometry to produce a uniform multiwavelength catalog covering ~14 000 deg² across both hemispheres. Despite the class name, it accesses the full Legacy Survey footprint, not just the DECam Legacy Survey (DECaLS) component. The typical *r*-band depth is ≈ 23.6 (5σ, point source).\n", + "\n", + "**Image products:** the same `get_image()` / `get_cutout()` pattern as DES applies; both return FITS products from the Legacy Survey cutout server. `get_cutout()` is deprecated in favour of `get_image()` for uniformity." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table masked=True length=2\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
z_phot_medianz_specsurveyz_phot_l68z_phot_u68z_phot_l95z_phot_u95DECaL_gDECaL_g_errDECaL_rDECaL_r_errDECaL_zDECaL_z_errDECaL_IDradecDECaL_brickDECaL_typeseparation
arcmin
float64int64int64float64float64float64float64float64float64float64float64float64float64int64float64float64int64str3float64
0.31320044-9900.258062480.425141630.22330970.7229696521.9067970.02617354561276989321.1385460.0211420216962632820.5756740.0303778813457543510995538614814853229.706377051340812.37660650001851402772EXP0.0025770140081186914
0.71565366-9900.5464870.927834870.396079661.266557125.1966060.317544578009799924.1731410.1924791920581796223.4651720.237558283891135710995538614814897229.708752440344412.37771318577119402772PSF0.15643529424081104
" + ], + "text/plain": [ + "\n", + "z_phot_median z_spec survey ... DECaL_brick DECaL_type separation \n", + " ... arcmin \n", + " float64 int64 int64 ... int64 str3 float64 \n", + "------------- ------ ------ ... ----------- ---------- ---------------------\n", + " 0.31320044 -99 0 ... 402772 EXP 0.0025770140081186914\n", + " 0.71565366 -99 0 ... 402772 PSF 0.15643529424081104" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 2\n", + "Columns: 19\n", + "downloading deepest stacked image...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/1022597140.py:12: DeprecationWarning: get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.\n", + " decals_cutout = decals.get_cutout(imsize=30 * u.arcsec, band='r')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading deepest stacked image...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# DECaL behaves much like DES for imaging and cutouts.\n", + "decals_coord = COORDS['north']\n", + "decals_radius = 10 * u.arcsec\n", + "\n", + "decals = DECaL_Survey(decals_coord, decals_radius)\n", + "decals_catalog = decals.get_catalog()\n", + "preview_catalog(decals_catalog)\n", "\n", - "from frb.surveys import survey_utils,images" + "decals_image = decals.get_image(imsize=30 * u.arcsec, band='r')\n", + "plot_fits_product(decals_image, 'DECaL r-band FITS image', band='r')\n", + "\n", + "decals_cutout = decals.get_cutout(imsize=30 * u.arcsec, band='r')\n", + "show_cutout_product(decals_cutout, 'DECaL r-band cutout', band='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## DES" + "### DELVE (DECam Local Volume Exploration Survey DR2)\n", + "\n", + "`DELVE_Survey` accesses the DELVE Data Release 2, a DECam survey that combines dedicated observations with archival DECam exposures to produce a deep, uniform *griz* photometric catalog covering ~17 000 deg² of the southern sky, reaching *g* ≈ 24. DELVE was originally designed to study Milky Way satellites and the faint outer stellar halos of local-group galaxies, but its wide footprint and depth make it highly competitive for FRB host galaxy identification in the south.\n", + "\n", + "Currently only `get_catalog()` is implemented — no image retrieval is available through `frb.surveys` for DELVE." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=1\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "
DELVE_IDradecebvDELVE_gDELVE_g_errclass_star_gDELVE_rDELVE_r_errclass_star_rDELVE_iDELVE_i_errclass_star_iDELVE_zDELVE_z_errclass_star_zseparation
arcmin
int64float64float64float64int64int64int64float64float64float64float64float64float64float64float64float64float64
10600800094256157.342267960142441.3594952797534460.043583-99-99-123.4345050.1755420.49466422.7540440.2103830.54171921.9562340.1544960.6242960.041104902041238324
" + ], + "text/plain": [ + "\n", + " DELVE_ID ra ... class_star_z separation \n", + " ... arcmin \n", + " int64 float64 ... float64 float64 \n", + "-------------- ------------------ ... ------------ --------------------\n", + "10600800094256 157.34226796014244 ... 0.624296 0.041104902041238324" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 17\n" + ] + } + ], + "source": [ + "# DELVE is catalog-only in the current implementation.\n", + "delve_coord = COORDS['delve']\n", + "delve_radius = 10 * u.arcsec\n", + "\n", + "delve = DELVE_Survey(delve_coord, delve_radius)\n", + "delve_catalog = delve.get_catalog()\n", + "preview_catalog(delve_catalog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NSC (NOIRLab Source Catalog DR2)\n", + "\n", + "`NSC_Survey` wraps the NOIRLab Source Catalog DR2, an all-sky photometric catalog constructed from over 80 000 public NOIRLab/NOAO exposures taken with DECam, Mosaic, and other facility cameras. It covers ~35 000 deg² (most of the sky outside the Galactic plane) in *ugrizY* with typical 5σ depths of *r* ≈ 23. Both `get_catalog()` and `get_image()` are supported through the NOIRLab Astro Data Lab base class.\n", + "\n", + "NSC's wide footprint and multi-epoch stacking complement the deeper but smaller-footprint surveys (DES, DELVE) and is worth querying for FRBs at low ecliptic or Galactic latitudes." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=1\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "
NSC_IDradecclass_starNSC_uNSC_u_errNSC_gNSC_g_errNSC_rNSC_r_errNSC_iNSC_i_errNSC_zNSC_z_errNSC_YNSC_Y_errNSC_VRNSC_VR_errseparation
arcmin
str11float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
163023_1937326.10523517481056-40.900253893580180.157755-99.0-99.021.4642680.11252220.4866940.05703420.1206020.04090719.8596860.05654419.823920.230932-99.0-99.00.0019021156913780213
" + ], + "text/plain": [ + "\n", + " NSC_ID ra ... NSC_VR_err separation \n", + " ... arcmin \n", + " str11 float64 ... float64 float64 \n", + "----------- ------------------ ... ---------- ---------------------\n", + "163023_1937 326.10523517481056 ... -99.0 0.0019021156913780213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 19\n", + "downloading deepest stacked image...\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading deepest stacked image...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NSC has catalog access and FITS image retrieval through the Data Lab base class.\n", + "nsc_coord = COORDS['south']\n", + "nsc_radius = 10 * u.arcsec\n", + "\n", + "nsc = NSC_Survey(nsc_coord, nsc_radius)\n", + "nsc_catalog = nsc.get_catalog()\n", + "preview_catalog(nsc_catalog)\n", + "\n", + "nsc_image = nsc.get_image(imsize=30 * u.arcsec, band='r')\n", + "plot_fits_product(nsc_image, 'NSC r-band FITS image', band='r')\n", + "\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter('ignore', DeprecationWarning)\n", + " nsc_cutout = nsc.get_cutout(imsize=30 * u.arcsec, band='r')\n", + "show_cutout_product(nsc_cutout, 'NSC r-band cutout', band='r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### HSC (Hyper Suprime-Cam Subaru Strategic Program PDR3)\n", + "\n", + "`HSC_Survey` wraps the HSC-SSP Public Data Release 3, the deepest wide-field optical survey currently available at scale: ~1 200 deg² in *grizy* reaching *r* ≈ 26 in the Wide layer, with Ultra-Deep pointings reaching *r* ≈ 28. The exceptional depth and sub-arcsecond seeing make HSC particularly valuable for host galaxy identification of high-DM FRBs where counterparts can be very faint.\n", + "\n", + "`get_catalog()` queries the HSC database via the CasJobs-style API, which requires a free HSC-SSP portal account (https://hsc-release.mtk.nao.ac.jp/doc/index.php/data-access__pdr3/). Set your credentials once in your shell RC file:\n", + "```bash\n", + "export HSC_SSP_CAS_USER=your_hsc_username\n", + "export HSC_SSP_CAS_PASSWORD=api_key_or_password\n", + "```\n", + "Image retrieval is not yet implemented for `HSC_Survey` in `frb.surveys`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=5\n", + "
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HSC_gHSC_g_errHSC_g_extendednessHSC_rHSC_r_errHSC_r_extendednessHSC_iHSC_i_errHSC_i_extendednessHSC_zHSC_z_errHSC_z_extendednessHSC_YHSC_Y_errHSC_Y_extendednessHSC_IDradecphoto_zphoto_z_errseparation
arcmin
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25.982090.225870390.025.6080780.189928260.025.1960470.192162081.024.866410.310194080.024.9126010.63017031.0416456521694822720.00051279423065510.001315432241462-99.0-99.00.08471098900006366
26.8724480.29551731.025.945160.149664331.025.6444870.163119631.0-99.0-99.00.024.1052760.189903991.0405769612270217750.0003564130906804-0.0017189603218311-99.0-99.00.10533127534175081
25.0460990.100492791.024.5720630.078578731.024.200020.081023141.023.8209630.125311061.023.8576090.24182871.0416456521694822710.00064792062086920.0018257181991971.130.348737630.11623671134914519
25.1275460.57436421.024.6972030.461245541.025.8704722.00190141.0-99.0-99.01.024.2760072.17440651.040576961227002360359.99949555949644-0.0018928007918380.531.44490340.11753194549969319
25.7319810.28219951.025.1711080.19958111.025.1377640.284688921.026.2890531.81424751.0-99.0-99.01.0416456521694822740.00121435707895350.00162182392546450.351.36030570.12156444157758856
" + ], + "text/plain": [ + "\n", + " HSC_g HSC_g_err HSC_g_extendedness ... photo_z_err separation \n", + " ... arcmin \n", + " float64 float64 float64 ... float64 float64 \n", + "--------- ---------- ------------------ ... ----------- -------------------\n", + " 25.98209 0.22587039 0.0 ... -99.0 0.08471098900006366\n", + "26.872448 0.2955173 1.0 ... -99.0 0.10533127534175081\n", + "25.046099 0.10049279 1.0 ... 0.34873763 0.11623671134914519\n", + "25.127546 0.5743642 1.0 ... 1.4449034 0.11753194549969319\n", + "25.731981 0.2821995 1.0 ... 1.3603057 0.12156444157758856" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 64\n", + "Columns: 21\n" + ] + } + ], + "source": [ + "hsc_coord = COORDS['equator']\n", + "hsc_radius = 30 * u.arcsec\n", + "\n", + "hsc = HSC_Survey(hsc_coord, hsc_radius)\n", + "hsc_catalog = hsc.get_catalog(timeout=120)\n", + "preview_catalog(hsc_catalog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pan-STARRS (PS1 3π Survey)\n", + "\n", + "`Pan_STARRS_Survey` wraps the Pan-STARRS1 (PS1) 3π survey, which covers ~30 000 deg² (everything north of δ = −30°) in the five *grizy* bands to a typical stacked depth of *r* ≈ 23.2. Catalog queries use the Pan-STARRS MAST API via `astroquery`.\n", + "\n", + "**Image products:** `get_image()` retrieves a FITS cutout from the PS1 image cutout service. `get_cutout()` returns a multi-band rendered JPEG useful for quick colour inspection.\n", + "\n", + "The `photoz=False` argument to `get_catalog()` suppresses the retrieval of photometric redshifts (which requires MAST CasJobs credentials), making catalog queries accessible without an account." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=5\n", + "
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Pan-STARRS_IDradecobjInfoFlagqualityFlagrKronRadgPSFmagrPSFmagiPSFmagzPSFmagyPSFmaggPSFmagErrrPSFmagErriPSFmagErrzPSFmagErryPSFmagErrPan-STARRS_gPan-STARRS_rPan-STARRS_iPan-STARRS_zPan-STARRS_yPan-STARRS_g_errPan-STARRS_r_errPan-STARRS_i_errPan-STARRS_z_errPan-STARRS_y_errseparation
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" + ], + "text/plain": [ + "\n", + " Pan-STARRS_ID ra ... Pan-STARRS_y_err separation \n", + " ... arcmin \n", + " int64 float64 ... float64 float64 \n", + "------------------ ------------ ... ------------------ ---------------------\n", + "122852297063862380 229.70638364 ... -99.0 0.0032909594595631818\n", + "122852297076301433 229.70763086 ... 0.2990190088748932 0.09072183987630345\n", + "122852297086524703 229.70871243 ... -99.0 0.1787987075747917\n", + "122852297044755927 229.70447332 ... -99.0 0.20600283972256517\n", + "122852297089725455 229.70896352 ... -99.0 0.21603811358947736" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 14\n", + "Columns: 27\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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e4VG2dgYsAzZjRKhhPpVWE82hiyyndwfAusGlVAnuk5aqvVqcUjRsX7IHYp4zb93gBcX5Sejnx0IdksSxz40Med6GA5L6lzv7bBjM9X4DG1E3WQc28hUWFGnbg5NboHZsc0ZnbUE0FF6pWhUVNJjxhzllwW7H2I8ckSYTUNfy3zg0YeaCZBFvkyENEG8rAtXs2rv+vxhejVAglsHMEa5+/yQpxQ9BiEIjluUM+6kBHfgeN4c7nmDdb3NNurWP6oVktgxCUDjckpe4GY1yPVZhyrowsaco8rlMtGCWGCzAMdwsgK/bLexUMVuPWceOAr+sd0JBYZp5hCPNHC4iV5goTpFOeaOAc7HD4kCiCqKDEuwyDhZ8n4+ALqzFIZyRozBsJo5chtTRJpjH1u3pbKyFY+1CnKYKXi1IoAsntqlkFZTjTIE/2I+F7GJRxn5ukpZ4BI4F0XluC6RWVcxSKz7BFjR/0ST568meujDV4baLKvF35GJCDVWReRi5DsoscgkWmlSCTkzjwpIRSx27uAFwpc4SvCsOVfWpbzGSCditkYfsFhgHRcnLfCicsrAh/hCQFcuJF35tmf47Dgihsg6wYGsp8v+ejvCW1eeUp36XWX8Bv4YHt/idZaG1Gae9fklCw1aDTrOUoieUqxwm79dqvHTtUpvAhLcqgrRwH4PetdMO+5ULp5aHY7BPlsAni10RUNaGWW+eV2G7RHNdnbhosanga9pFR9OpGBnwkbrb73Ig8sAYN2tg2LoQSEuI31oszpxpfPex9PSb1Zl44TpVr362Ux1MG5QuF4zN8XCMcsdwQ8x5e0l9RilnIB/iIHxvQppNHhKQIaTFFEI5j4H/8L1q5OTTsYgxKtJBW5WPaiUOOaf1l69NNjjD0LQzV5rl3SHcFH6x8bfkoc50YYkJawODRAHahzEiba5UmK6udlBAcgZjf0eNTwlCxG1fj2tzEazYayZhUCKanqOOlZn4gxG8PAJm56sJiy1cA0qHTRHRbazABG+1G6jvUwtBaQ32HHmjRwB+KGnk6duoaRN9BhoaTo2em8EgIzskgbMmRV99H1FeVwuYz7ilW5ZMJGvjgPM9S4RKWgVwS5uyjvyuu2naIHnbSNiLqtcUzuIa/URgn4kyNFaLCECjzIV0MZeJdn2wqVHxUZdKr0f9mNGHr8TK+zWHAfMEBnIsVXCflGJZrJBcT5vB0PjQWD6fGEJZL0LwNOpnla/cNv0YaNEg+3S+dlOuKsXK9z2Kz2OXnoQ66cyrx0bTFHqS74Kw/9aPE++W0sJNbz7Z8FKCTRjMxpEgDPypjxcqMnACDuabjiq2DMmx5k6wV4w36HyHLIjnQeDPQ5J/R7+LGAsq6GnJCmBVY5OYcSP48P/iydkeXZzuiPc1bmOhEOS2OkC1tjxdLHV2zDuUfzW23/ZFTf6/3BQAYjP2QbCotRT1P8WJp+chSMbr8LKtFC5wFSPsx5QdPr3UyOQ5WVk1F3G9usa8Im+FggKELVrMHofLkKDKi42s5XvsJ+D52k6u34SxFUbofbPcVBszenRdtIBxGED3WTkX41KzIwrPsoPQRqEAh+gRKQGM0EYWl64RG01LQQ36XICC0r8LYh76R0buCxUDuEreQYxACE4cITWxSQnRs6iBRp2ghS1xm9VsCrS2V6PE8TLS2s7dMAlWYBWMmXgHAQEHGx6GhiIIQ0fGXfK0IQVhX6VPAGk06Kz3erfOFnfS7HiE9XMxV+5NGKi/O0ThcUd7V7f3T+dQid8bUy/Wbx9Ck32IvPwlDSouXqtpYnVs06dZqyqxPd/lCcp6X5t1gIkBpoVZwB/D0+G8RVd6N5k+TkTSdbAcWvYRlyjHbjdnN50rqhJLWRFkLoRNBxDaPxMA/8hambts+iWFbkDdSnL2TzExaPdU8BrT/5WjiZC9k1xOJ8k/8WIvk/rs7tbd41cPZbtvM4xaMiwzW9sHngyx8tbWOszwBHVOslkfVftCyJpTbVwWKA4/rmYJDMaZjdh0kmYSkhCVSiVJE4HLf4oAagUcGy5Tc0E5CV0ohaQJkpNIQi33V7Pk3wYJoNqTkFb0x/oY1n7nq+b6tVqPVXWjVsnnTD5HDiao5309eigip/xkbAa5S1EOErabZDs+ciAXkWQdBhrzIQAaZD+SWE/HyiCXueHNpZvEpj77KDIUAsKwpmxbDsN/M3KCBxgGQAGwV7yD4sggTmFyH0ODZZgoT06HgkVM0KOR6tpAFFY0iQpgY3ughodEol5MJIfMpD5ngU+vEuipFnqo8Y0X67U0ohpuryrvyxlyXsTigWBzoA2+BF2xVojGdkti7KIbkp1/iAeBipfTlVSztfnC1zeN6qDjB2Tt38PWmm+qpz4JPKbwA9KT+4hOHGj1X46Difwgose2Vg+xoUfcHrJwAdqT5tqLY+aEujCaw6WBLRIhQ031I7nNUXOGDOiML6SRnV0NBNx3GdFbps3Swt7YPEMIligxt2rIXT6iWh+BcJ6q+tY0BGTo4spfvmS/o6iBohXqfT6IGNOBw1EkjLYsxC+dEFBnVEdqE57muLkcdbMqjvsaJA/o4eiD+w+bZt4db1TzVcwQY+eFMkrZUeVIw9lCDu8knPJDFZtwAhBZsSRhKCgCFNbZY5C0oaOtiBGs4ulsUuv3KJY7vRUWeoO/O98d2v06br42tDAn3c1BMqoVLa7S27B09Pk7NmK5GDZeFrQ2A30fl+P1mI3Y8QkbUfSYADjbW6uqb9vCYVh4ZEomf2fSfVVItwZ0Z2Ou3jkwFbCfXMwZ/8WDBTFAV7hJOdG2WpKOTseVpwduV09Qu/EozUMMCrgzlR2rcWkVtVGKu5kXsmBIMjh4hp7Lls89iNu1k7L0ZcMv76G5M5xanhlXILYjKdm2eNRUoOE37BNApNhST9ukkPtaOktcfrFEwujAKXQMNRzhznGKaLtfDXRci4SrmlY8QGoDv6tRTaOjXyB/IFaEQq6SA1jpGMM4Y84inqS9Rrhv+knyPFMBpt4+PwPG/IBdopIhuxGUCvMxrbwCofdm6xVXyW4GcUEWLbVsrqp1QSKeEyFooQsm1qF80Vep40XF0fsYNrdjp8Ic2WLE2pNZ3BBV+5mc+uxuboaZrofcA21xMQ2Tpks/ejnfCYk0TNuxg3n7sVd9AgoWcip7D4NgYanUt2u1k2fOl+uroNNxnA495K4Ehnu/CbWB8jRyux/zEJLhYcAhDAgPYJGjIz0PWL9X/mkYG9OojZgYJ9gjF5BCxvNynrsU7V5KRDu/pIPW1wKxr6GwQtbbVaJRqDzt8TRqernq/NZoY5uEmD39UHA9bSa4XiEO35XuOOYi/SawGrT5Nngil3SoiYubo/KWMqprKnPPY2PT2kFBkGapEZhOFiQlJoQm+YUnxUFeEcE5jLD6Ua27g4Wm1VHu/Wmg1JT7mnYvFoMY3C50o0+yVkdLL4n2TWN4P6bRVWnAbi+6jp7LdGJgU2oiYD87xRcJbyQj9bGtwztNCJm/4MJAybwtfAGT8WWbaFF1MU5uSvvWkYlUR9aPoUEsrnFEIqV5Cbz41xpnA9udqUGOu+8u8Nl2NdXGisnqOBzznhZ6yTuuI7oRkkwmgsQDTGMqDCKr4oO5Gtx1ZZ/MfK+RijH4UjtFQeBYRCs9LNV6fn834Gq01N375Q0QoQsQ1gl6PsV7oU4chOb2RFYaKNPhygYqMATOX8jkkLWeo+8X6i68E+QvgqF7kBz8/CuigS8W/5s7HPYEDLjYq2ua1K4DFeJ8B7aPIf3JHOzZ1NJHxGjs7j8Ue5Sxjmcb0abPT72bYdqrsxy0LyEM49BQloX70xcR6Iia7gzk1elEVUpJ5DX6qvOiOpnzNSVKnlq8rwgPZpXljB8AWI16GnUSHgbm32ziPs3z0lZCn5E7qRQWajulurdOPtR3fw7u9teOprtig7CJXCDTYkQquVHEfhMOz+S023O3SnuO9slBTDxa2AWq2Wd/oZP/PT688lTb5/hEMh5X7XNxkbDupE+XiKUzTiRwpCQPuCjFYC+dEACA2LQ8PUAkJGDD6fcKuVMp+mUCyof0Lb/RRRvpErc8ELOtGRS3FBbB83vgxKfQlBwFZhek4a0cluYU10x82jDkmpu5dI+AnaU1+dnjQRD+b3cHbq0yrKLNApW2wMm5RL/NDC/85F/xiXPBmHCfNH/30N6XGTmhXpoOowbnkw85Hgcq7UT5vvUAOpQMdzemEAVKBaTeCdE+VSCxbejN9WedonRDYbyjJG3dOdmSqb9MLfqG8RJkiY+Ib1FTJZmlzUqIUoZwMr5DCANLiNL1eHQ5NTCr6BGbze3i++VLjlw/0H6vxrXnTiUx4vwolUFUjUYlPlGTCb3n8YVtejFDTjoPN55DWWo8nuV4zeR3YjzitxM7xd47OLJ2R9sK1EPRXVMMc6OdbQPuaV5ZeOc891pm6wLs7RAdVdjuytE25TC8Ll6uzPZyz3HpTIqHxsgsYja3VrQof32i8UGr9Ldub1JGXNOGIatQogenYKfhcuJhc/JwXdy6nCOu5xNxVmODiq3KnNvmMVM999DBIRgnD4XXMoAX03zcic6QfTZzmrkYNPdKI1IRyZ1BsBpPIs0VH/nZOZ4HUwDghIiT3Bp6wUqDDGdKECorXaQ9ptRdZcWRZG9q7G76B8vm0zLXcS/uL/frvAuxrgvDSAIu+2FqXoj+4NuUB1hkMtLXtbhWTFGdRcpiCk2riGDR65hR+HYFil1Z90i/+kjt4of9IRDPqG2M7e7MbMwff1aQRQVKAOJdFHO0tziz1XxxvRWjBDKOVcYapzleDfwHn34Jtr8JBfvsyKrBIaIJcQ93pZqS4bA2SIYyuEaO7VQEWWiRNiIKeCUA58Z7zGuS5QihV8WzMrlePBFeEUM/Kh1cUrlK533ZlipsZlSj22DQQ5jwOToR5i6BtCB3Pzuz3qMY+JO76fsJwePhubq0xSVAqlbSpf3mSG7LL8FmtepgrUrIwkBhaJlK96nRdcYQR+CnlY0+wqL4CcC7x2RwQ8Fflhk/zMakFfJxHr8YWdc+SdnLDyzPjRAMaimm2lpgkLlydh638/Oh7ToX2bN2MOBCuYcFG1n/7z/Ftlhnn8q3b9cTeZRnsnECasaheRlzienTgSyvIJ6/abhcadLzXCCB98SDpul6nEkIlte0xMwdNioiiqjMYQwfmd5ynyU/qVEYvDA5U1Q0KSMz1Usm8JjZ9TYRi7y06cWdgeYvtWD71jw9BamVTEbDFh+3BHe3JLmeQlhCAj/IOMfb32Qbjk+1d/rDs5BEoDYP0sZZ8PiyK3BijwJhkwAhgmpgoYKr/RxdNIIhBYrkbLONdoAarA87yR1l22eFUFp7DGYLFjeJjlzRDGsVYKSsoi4T+G0xUmMoNPwqHgxnIlQhK5uwu0PBgvQehJe+Bz6ktadcFFusIu0sH2VjSo6TjAyl5l7qSevivOapDGQSkLSTNqqO5/+httPFm5UhRnXRs25BIN+aXs9cZi3YMmEsJioPwZ8kJLJ7eH4TnGQ683iw5zP3/Je3JtcGtIxUgjvwMQzd3UZ265tC7fH2iJmG8qYkXbooeZYUQqTwjQXqiETiUCB8Bsw9dXz2p8LXujI+FHP2nFSF5EQcXbZahaA0oxkGFoZIjlGlZGdiOwhdKY0O0/xvF1lhIhjFhw1FApmumPAj3X4vrsW9ChYy/NWilhMC0xvpSEfYFmdSfVm2YtPBpIqKgmyhUQYzlJVGt5Wugu7SWbF7dS8sJpNaKxFSDtvvP6eXWGnlHRqCZeq9VQSZ71eedh2TvWWiy0NRbTsjT9nunImQpi34Nq6zeSLgS6VUtQWnr7yVV7URnGRgXJenu9/HV9ruVckEAPWoHZ1xTPV0olISFKMR2jGMpLKfCChRT57PFDdkeUYWyHZ9wAg++DzVQNlGxmVw7/9Bf34qG+nfvMxhx1p0Acu5ikUaHNSHoMt4R4epfsj5wkJEdncgnK/FYE3e648KP0sDUWpxK+5s9XQSMLC5WSrx2eha1VIpEJpED6FJrkYsubILNFgrzV5V/alol1gq5Jf2kNXSYmFbkEOdXdtIp5ojZMRWd63Vvu17HV/qSETGp5Omf0pm4vWGB32zIwI6mqQV0CBgfXybXKELiPZUnl/MCxIge+aA14Cmwl1cyn7/lAXewzaI3gevckkM5H5GqrAplic/kzarnBjyvXfw4hI9qVgtl6drrp9kqotR90gqQqej5TAvqUV9lW678T3byS38IVhkB4/dVxGfqSohK9vhsmKPhW107KTzC3pM6LXaAMd/s0G1x0Gk/Su8D3sIRxGIFvkTcN4zwaY9qO4R8Kz+Bmm3PSCNRBScfUyEdYfpOIEB0vOKob3mKgzlb7LF5PImTcIt/70cVFoJfQX5rRdR310vtHuDOggwcxzEqgWiShbHdApUY8ppOahXGp4MgOwwH/aoczPVuppAyxOe3dPFi5XOjoKjThYU9IdCKgSxGbuQbWZkbJL1jlbefPHErWWrAA0B+OnXULEt2E37AKofOfpuHPvBav4oo+3nGUSWomIcsT2oencxip7BdQoq0QDY5Y/K3cdDtBPelaEqEUFCeqFHVhMENHZ20lArMnL1YrmqE9aLWBfXBE5wz4gh+vi7p+Eq8OzpQimVdmX6oxD6hffeWvAoEHSIckFkwbKyTzjNWr+7GZPwBvPBvU2WWNBeHjCm7NuOz6mnWDsIHC0emoiB/s3eWZE5m6V4Fsm3WPIUpVONYDeyPCnIJxY5VKJBGUaVH/h6LhpzAlQkmCBzqO8UN9oU9ylzr1dVfUuHN2HULABr2ammtA7qohOPyl7Zr6p4VeB8+T8nI607L3X9R0ZJepf45arNw++Cym9Su4bu4Glt9946rafcGFGDLEhGvJwjQRWOVifAh5Y24I18pyjM8bMAmP4aPCLqB5zOM9U9Ri/KU6hQPqljrhFtVWWm3KALDX8YFfjN0MwMWtUkRXsyUgKVYrgvUfd+WMGb6pAjq+PDd1f5go5Ik/jc7uoK6gB8lqRgcEgv9biBsho2Z9og+0RlLSCg7i+hJeA0e4K58nJAEADx/d1ALQ+n1pyUPj30GIRoM8ssjBtNWsI2+/7hAebCAMr86AOhHXNMwLhqM1rDsf0IjGC0/ZCbh7MQHnlwna8b/jAcsfd/KChdAF5+49CVJr8jHQ0D2XPFJ4Aeu02nKSreV8ikQO0174SggVhxsoN4n7D+EfTT5s1D6HPHrDNQK3x697u4iE/Vmc2CitHVPqDzbkay648cmSfk2+T7TXFBkbFl8pxFPATQLVt9gsWniMEgpe3gK/VNoVTdc9BCtrCFyH2T6VxGYJ9q7INs38k93K8Weu+9nkHi0gQY8lvs/e8irbEvOyUPOtLoHlv7XdJciZo81JaHlT8kAbiuW99zeH0FSqIjUkuB2n+l1YV8b2dui3JHWmwst7dY8ABo4XpN6mB0qsrlJzQIi9IEgvsZnBbC/VR5lMAk7MTQsaKeTc4C0Bq8dq4MjrIBBMUBa44w8droQiPWIcDFk06uc5R1V88etVbaRCZpKfFsw/BoN7VaHi03FM33jbjUrTizgIygS6pmMWlnbR2EqfIxO+NQsKkjfOlATFO9EFZqwJWbcZRELMxy7yOKNDyKhKUnIIksUby9YiEkDVLFusLr8AfFNhUYNoI/iXdJk/C7A1rSLERa5295EsV1FMTXiHcnILTF6z/6ktsSP4ndFjPo+zRtJNI8EheBNL0W8PEDQm8DrP8vLnYeJ/Q3VZy2CGOlyO6kbZTUVe87R/bBT9swxpWLnyx8G+1T+qD1KTTKr2zxiV1ba7AfIshenyY2fBPV5QfDkDfzBDCfOezXesHkGUAvSvHrIOscOLZRD3tRrxjUzwSixWdzgi7vSfWn6sYtDqYNY2rfId2ojeTMjD4SxD0itl2uSLBzNWsu0oyhV+Plts2yZRAdHgOAGsJnZcW3AeOWLRUPN0pbY7NYrZcSrrDex1RIHEuakFroiOcR6/HuAHPP1TrTWukYu4NzJTgMISXJLDNlFDxELHEcaLV+Wj//7yF5Fe3KJ5Fet6cyRbWzQzE4KdyJ3Q9bgv4uD4u6fxJxF/pdU7y9HAE8jFqN4NqvaDJoa5mbnMD6t/O+x3TmvDYD4fX4OBTgCyXCxHYJsvrfk2ptWjseQdDL/3e9BfpZhVxYl6eso/PwhsSN8va4dy+8C578Mz4YbX1X/EJaEQ2dlX0CEZe9l8x/OZJJ8Ejk8PP0+ibt8y579hU/WpEDdWy06CAwf0ravneasY7rilcpxZpeSJdnzjv0sSpm8XW0KjAEfas5i5G510WsdwC0GbMRq/HsfL+3oYXgX3JhBXJdemK+FyeibVfvhhyobO3hcPUmTJYygFmtqCsKqVZwPHKq8+LUNmh2uykei1EeUyjp4Rom3wkUwb75ll0MUGj6a7f2b2J9+n9aqdexPXO+l2hqj6JeJyc5WzCqFc738lpcIwVn1gKbbhYUmHlHPfLhOcij7+iyInNxAY8VIPcZXXV8hrmT7rWErafNQbwsX8zD5RnT2NRjLlIFMz2tNuTbQ5g2pgw9UPyQ6BCWD8R624oZX15Pt8XoevYqGEwsSoAApAVkK+08mNMhD2RXZ1s+VlB9TjEsK9Lt+iHeHJyYpWe+pRIp6r1LFPVwiiyuWvO4jwY6H9Wb+6ES+oOP2q76OooNVYSa41cxY9YzxFVNwgaALx8F7vMhy4EPeuTezwsD0dsjT88UiNk//QaJnGtz36/8t+3P0IxbgfFR8cHM8eIVmLhMfMHIQ0L9G/S8/JND4yKfKljNXvWfWp/j0ZFYkYDb/vDei6BHzcdsbKH1JYiYs7VXhO0WwtZlNs2Ko31ZBzTKR2fpdXEkgNKn4HR6cbP1MzJDLod2O8jO8NOk2c1et7dM5aQ1rYJd+sTuUnNaC73TlwlM2qtlLEGdQSSJ6+7eiLwvhY9jQFpmXMWOw+OgvWBooSl+L3pJ8PlX+TfB9yuoWzoNCvhKnvLylrd5FXVHQ/8ZF7Rshu7yimESsqpx0xIkQR1FBsSB+0PvVv9w7QRz7yswSCANba+D6I5wLnJa0hBSe2gDP4uU1nhJ2RH4zNPTcj2yf/XELxcTiwVHqM02C6mAN7l/6B0INo4lanQIYvf4Ji629D+CsVR2LX7XcAvoi6n0teShsug89PROZ91Ceng+NN/SbSkQTDASlsOTqR2qeotkdMAJzwqgKXq6hiVSITUfxjmbXp4roY3LWF8RAqyWvpp8oCD7EXg45OhS2o9dTjtYIQQY5A6ED1D3RCRGYfcuuFqx2XHr5c13+G9WtH8+5MFQ8g3Uc1TJz9yoCJVzHaI5csc3bNtCxLHM2Wmh9001C1BldGEAeyQcVvgJvGyuHkuTC9kQ0nGf+ym1CPIOWLZtj5ShN1/Q79XvPnCW+fDWXYzm/nsp0TVM0/rI+BNBc0ND+1jyeI/KuYJEL0LN0iL3VZzwmWpM448q195DSRM2ntpAGIFmnrRBdtBkTdMstu9mAphJf+lQEDrsdAWeKtf1Q6UPydbxhwYieShxX0JccOaeWSoyPozXcGiJCcL0H5p5xitGzpNxfxanT1sVj79J7/m4Bb/UFVcN5TAfU4fiPUl0IKWJbVqXG5vF1g6Gh/h/D/ifXg6Cr/ikPDd0FLwPoaqrmRPJz7ZmhtDNyq2obYhPxWTJBpqeX+tylyfKLRTs9Q82RJ9Lx4/ngttzUL8CAQRUKTr9awd6qNkGSNKGqpXnL5UR2bVP/gYmfRqe+Erx34SZMckmrJTU7EJRPCMUH4V4OobV6ZpUnwZ8nkXWe1+l08s04CYz04FBsg/t1G7AdiOTMbkHQGl8gWKo3a7obeFjyrtwV0+qnCeni6cZuL9l6fdhfowCd2RIzoNDzVZtqgDbhPvb+vf9MKCcrnPlu3rSJrt+IgkFEeKcSw0CzH2DZR41TpA/k8y3WojcFEg9xVawYR7wg6Ntq509+b9CkP9g9wcxuVpfIxq1oGVHg5EwD6PzuIQ26lDXvjdHMdCi192Q4QOV7liad4eklmzzis1e2UYxzntCkEm8vg15ZRzC3vLV/vlz/GN5ghj1ontwnwtL0Nn+autnMGEFnlTgFuBtGUwSDLt7iigxB/0hQGSPsnKmIpK0WeLA5E6GRauCmvNsjIcam0Qh37bztCvL5gtyD5OvjUPEvGn79kXVUjh3eaNiEOZN0tlu54VqzGDhdCiqGQcc72JsKmESXAi7w6xcCqHwWbCbeuu2jMqzPL1cLHFB8Zdt0VU4GYmc1rarVzd9nPqDhm8gfgpm/vgwfeNJ8GerPXtqMNiKbyYmH6BALhod4/JcaE8F+OilTXSOdXdQLpI97DEaPa1KhJO7W0O76wsnwQn4EEKzzrmLLnS+nVf2n/1ViJOXuw6x73msnJoYUD8B/rPtPENWhY1FoO/kHj/9oJEeiSOofH4K8e5MjFtufcEgqX8XMKEqbRXpncP9zxPdRO+J+5mw1PfejcFWI67jsJcBp4jFme2OCtNCLek7hsZeqkAH1yEXBoSNcCYlvg570fLev8CZns9whOGbeYk44zicQ2+xH7fy0vck6OPs6+S1cwNInPk52/KfWtqxZXURxKR56u5Q8nO89GHkLra3ieBhJdmq97OJcQg3L8cUQpjC7fNaQkVV2YFGWX6CVAV3AsRPcy202cbRdLdY7nFkJu0mlLH13TYQQ8RUTcP4N3ZIXMD/tBlH7AfQkCtFNXW9JoPSHFXK4jG0St2VGbURxH5W9TmCnlxg27xTxAIWW3umgFvl5tLMKNHb1rrumTnyNO2O/Ul8uUQRs3awxYZb3d//sQjn8N62s2gbEsWkI3RCEkjBqtKrF1e17haCvAoOPBF9OArMJhU/vvdnMDd+HUMYM8kT2QoybEMooBpQyzJHgHzySrQBmUWrmdKFyvk10Ib8C7uvYTsPJR1noYK4c7SCLhz8z9sIPXqNB2NhSr66A6o7+b9HIDXIjsp2exXaAcbBt/7ela4kYW1nJRu8XQLcUaERHIcRWfae0qQMRZDP2tdJmuNVpH3kmNIBxdd+5gFhYtBxe9DbOg37r8MbofIzDvrqqMNfLrW22KkUkP5BB4iSXImC1bM1NNXFB9GLRpxKWPrJLYCoCtgUVlOUWRZAOMQFXmEmeuNnd4xSwgTvVfUUiHstrB/ftYIH1s7gkSVrREDsKLFfbYKaKIvbvFjLZvIo7zdGdJvb1gbcKt/qP9yFXVKvw5U9OTM9twV0Amfp7fdxgF2bDoFfKnl8Q5spzNYMkyyAH2K1dGRAEHAgWhXGb/xkzkFRxMo9zFqyuwzsl7qjZBow8QdWbMCVdWqGmwd6wDfea8ka4W9/Edwe+tHuDVK+941vfI99l+cSrn0oXRilqwZcfbBLb4mlVXIeK8sqqyD05/39YuRnmDT1bqBZLNyiB9K+lthcH+7Lb9mxF/3pc4bUnuux6JgkMr+uon2EKX2xzCg+Xsw8P0wt1UtMf+v+yhIipOtvcN06pCJUee+Ct5wxHM6e2ZHLrKmJNg9E3mWIktJIGcfXcrNhsZvSl5UCUVgcKfyFgk9xc/QT1kv+aSuKjYcQJzXtnbHsY0+bc/cFMMRVG10mfsl99iYFI4fulWAR/PupfmyhxmU8sV84AI/EKPMannEdEvyyHq0Dr0h7kQAcJxBsz1yQpH51dhACcBrorvXkDG5joQCBWA8F0Z5UcvFGJ5LiV2krWTjSgqElh0pLgobS6+tAJ3l2rDvWqTaANE8RltoMFZ+Yvu42j+xOkRXHiOtA/zOr9T6W9KOzCVL8497d5M1TZuiLj8BsRfGpZq1TMb4JyWPgStjWBzTSAvSLwdntQTllbycsh+qzQ2M1XTHMvgMfXxCtdT9qDl/wiiv0KVa8v55gygyWRgBkYHfvFzBdgewblzV9VuwpOqsvg/XqRLdHmyl/uuYZJyVUpWz98JGtSGVr2pJXbjFsp0UCdTL6tx6fkk8ZyOJWiQZED5M/47aZn0C/Wj8SwqL8xPmaeB6hOMY8olzOJEQa08kInBJaCsae0WWVJcGHROqhn27cASZDIMQpEqTSbyfnKv+z+3CS3kGUnorh7f+2R3C6d/awABHSSYiuZoJzL0jz73GCoWiAlNe2/laendoPnFSldnEIMIQta+BAiyYU+ggtzKo7/wEaQG8+t31Ut9osLdmX2xEI/KThRDGuC3BnA+SgY5cFp/dZoRHJWGi7ziAaaLTehFmqAAHzAFT+gJs45qLMIHwb2Oconwp76eI9KSsBkcfXl1HwXddrhHJJoZrd1+A3py96qsoL4E8Kpl0asSlFaPvrWa4U1vgm3tQ46iuEEpFqHZZv09Pt6/rD/du1U173h4dkaEEDyfqVJyB3cIfti+5vkb2FCjuSKB1KWLNtlUeiUjAnCNnGtYbooJSX50lYwWCmDeHMOrYzxUP9j20Hgkk7bs6kGT/hteA+TaKoaJjU8vPAwC/2M4GGGze31EGLxBncs9hoGE4KoURi2OYYEZYXAmJ/2qHANoFjFcbTrY5uIXNqiDC3TbEv9cIIEcbje+eTp8wX4lhI4oSpBHDvVwRFeyJ7u3h4RcRJsEIndgu4Wemcickfre0SBfSNWt/n+93nU7dm4P3uHT3xdllayFchE2t54hRggjqDjLoKnTKScE1vaGytyFVMkqmUUuHVkWvNrt4FwGhZYqDF2OrOVjet6pNErB3QKiOq9du67WIYrPFaHfjOooNKRwbDQ/cHYwVIsBGOZys+pmjlsM07vy/aMX939chdjTSLUzOLfOb+vYtpdlW6bHJo0kXffNvt9doPDk2NploXe1I3KsdMw7+edJ36VRhCCtuGF05YjnEjqRKKCy0TxWvkrTPs2zt+q+zeM8RM11WcEUpuyAF8j6TDXU4G4pxRbXwL/IenTF/Mw5OY2E9u4x+fQk+2jDqQ/nQhvF+hvVQ1t0pXo5Uc1IWHVWiHqq3RaNWb9QcwB9U1HrZ6VwG8pC9+fv7lhzbPh//s6oXpBZRlRpNMd4Ubxhaa/Zxkuju56xdWGIFKPAhDRzipZr3wqC6X6m+xKki0pXyKh3yi7cczA0LXpnCwU9gYvwEtVcsRrcEmNqb+fDKd8kJPyyq0Bjo4HxNC0M7dUJEdeV4XZ1wHvq5t97LE5D0eGKvAWVjXFKXmkBUIXLGuKeup18Kjj2tVLP1zLWYiAuP66biNsKLpC8PsIYMGhpk5gcgLAuLV1rY4c6Fv1oJ5ar7YEEHMHQetfZ2LWp3i0//21+PcdxjjsTW14N6VUyZW4EXKelCl9aN3TnhFDk7WtFwN5SeOn4mx92bunYamHwmP6E0zhGsJChCofDu2oqliqTTvfxlhx+nAP7q4p0vloWo5AWZbqpgkUh/frir3MsDobtaloehg4gGn8cIfrNNfim6FTzcvJONWUli2UyV31IP5q+N8uMoer65s3MSBt2KQLAhUoMDQRhSyjpIl9JuNHkoluC9cix7Di/TEpYZBihQqal4fDKYqru8GW3JQGmsTTqbXYTYiguAj6bKrsu79BWuBfb+T8i/ghsPtD5NbCtzOpD8S8vkFwk5q3r01k+cUgbaI6QC/hLAX38oi9rfX/ImPXlsy4klZHDCWWpE+nd9rawfNUU4Yh/VMrI8IiruJlUdhAVOdpX1rI1iOZzANCCYtqJiwCegWNtFug50H/XKCRqKmOaX3qAU8Knj2lAXxAkyGAOt0SNK033+C4oZxTcgmwv+kBW5jllk/f4vv3gZ/lXUXV3fs3IcTaN+ujuT0p/1Mp3XWdShLSPFTuwzTWhp/gIaivTfndDU5ajCsTiSJ1jYm+DnbSeRVLX37KMPU1Wobh2zAbsdwxitqTi2uwfkt8Zff2WWV6bpaA0PVbp9/5tHg5Whza0FxNHrknP2f21+5em7B5guhmGnMF9foJE469Nvcci6G+u5tjo1QxbP54KEOXlloH0XcgF0uEVsP14gQaTFLuVGZvMlpepLnR5m0DeWS8ylXgrhwP48vfIKsILZQY48aGt3KmGt3MhvhryO+bC4d2blOiqCAGPDL11F4qRuJTitX+3SNSlPc77/G7h7f9x85gCrad9b+pbL5cvRC37POiD1Tf4xE8/1V1hSivxA2mQ8j230oDe1A1IVPFutE+2ffEHVzoP7qw67ZFu0Z2P3f0iuXFrZBL1MEEU3Q3B0uz47FBDuEbYxFCmcsopMt2uYTCUQ06o9/cHVP8xKCiyhU3gsq4Sf/38VRmBY2nkFT7YBfjTadIL2wkBr9EG0c94+WAzJO0gk6bJBLo/of1PZzfWCqPX0Gw7DyonFn3MfzMGwjfZmKmS3WqRktYVaoaMA5SmYECEusZx6Jkyfi5mFXbbNkF6bTIYVqOX6ns1jVuHoa1chDCfMqy4+T4tlv50sZ+HyM4HqOs+2mSXKhJlQtSUNsf8L8suZcYT1Z8jowCb3n6azkoL+NZleEmHMTZTpdduBiuHeec3onP680cK0MrBzSlundh+TKlwHsg19I115vFdimG/9xdGx98pMjqyQdubiXpDdtoSvl5B9kCT/eXKh0N5AoOstUFrvrN7iuZ/K6HgBscqOA9JTsGDzvHq+9weMdMzX2kP1UL6o4QNdjXi6b3DufzXD1FDRhk72LL3HBOji9NQxnqOUIB8uBj68lcCX6vdZTlCgF1UWg33hWiHwz646iTkJMfZ+srayOwg7Ocr6HfibmEDMDJjTFSwHyD0yktBaRMYZyLXvJ6r53jtbuzWsT36eMV8uLt4AeQgO/pO9UEdfB/39lpwlqTC5Y2yzPzj5AUWkCkcB9HVPVo8IwS4+rtVse+wTEfeeES2OJJcqNXq07mOyIza5NNIhLRgcm7aW6Gn8XCDGppXkOJ4bdfPpyCMVYyKsNaTqZxK8xfRcebe5SJsuGsg6JGbUnIRCUcVQHpUy7455SgYO7ja3IYigLfKHPCRlD+8VTF6rvFvm5qo06bKVG9sjSj8LflHoVAx4lwiHSn3ycSv/HauGgZW1g+XUYn4xh/E9tspMxuE6e3FmWwqQfNbHLZdc/L6vEcD5QroMy+1D+7/KgBW0GZj+nqJ1VStkM71dxDl7LtWZbhVHeU/57JFbahSEkHxWVCaXt/n54ubJn2/Pnlnz8pcjrFTe1Rt1TpJrizktezhGUo1AOIx6wJ/QqKhLV3PTmacZVXRjkSSZeTlY723mtB33e7aNvv4+IAhfUrdbhNLMVYsdhMGfFHM25aISVzyy1+0uDARgmLKqtpjowYuZTlhfG8+DvE2c7jIfBspJz8gMIjGvJYtML4lBsHGs89TU4vfPNar5dttc2VcUHobrv/JWGXGSOmrv90/WipCIR9O0mD9w25BVxebsj9Dvz6VrxyNJjf8Ehwq6wok1ZxbA4Iw8fyKH3j4XYh0IoBaCAvn3ja8foW2ol4TZ0WmpIDUBDoQuym2QmiTVcyt9IO6n7NPCtDkjQWt0kEJQlZuUUuOwHUci3jw61r6++Bvc/2mLfHj28R559XZ7I5SUsMYjok6WYe3l2GCRKMoNEYSyb10i9DmbXfHFF3TGYaAYOrKHFgKeyClaR8CHZPxRYDc+fzksu3Hzt/qFd0Pa7uLdOHYIyHEeta3yoy64FRAsq1xF5bdMgQ9VfWin4xlW36pE/3Mp/uodMvzDDj2JqhSGIetBi+aUMdnGKTsDrZrVY1i63zgmHF4bdCdg99Bm1o+Gi79z2YXkxI4aGA/NzASOsOYR87ylqEQ2MRcOnCz62GSkW+UVX9rTvV0rfM+AhlLsbo7JZ0kiyihmGEB0xxx9l1PM7Uvr2KmPU3BJZyhHI7W9xdSVLSR660CxP86oNSxIZBAfdxcS896Vy74fYLA9+LLYB3Oo9R976E7SSeVw40eFRB45B5u5AsX0JnLUANQZM154v/cABEmM+3v7WhdXw1acHueEHR9kBhgYk3V31h5D1P/WT7VVDC9jHiZInP9OvElLrB547J60fVI60ILvdxGtVQllmr4skrj9YNTMPiGCi+V5NJZkSM7iubQloi9UX9SyfRODwX0Tr1eUGkINiWN/PuJqCz/+G/ljLulv0YO++IVo7k+M22kH2cDMf+POTL9x88NKxY1vd8dL5KXFJExSViZVdMjrbSammi9CcZYaaLMSzD18Tt7rxQKIADmyrsXE6EfyAZhk+QJA6i7A/kSKXr7gIyDZUM2JIhC16piodHKwHEbHBMnsde5NXKzz2Pj1GtbczrcJ8MfDO5FqTPzoaBQ3i0K1zCjCd8CnsGYCEoMofRkDzS1BRsweGRZgkvtRDKq+esxD1zlzlyP/rKvHW1yqsp3Qr4P/7i3qnDN7zf+3pJzcWa4LK+bR5C0/GpZ5zpqcEKZDzTBa5MUrLhYXg4pT/988Ni1B5waeC8QwCrN2c2uPBLkXOlVyPiMS1qfA4siVrTRIPIwUB3QLzn9MJX2/zP8V+csB40+j8CId0ZtX/cO7Ci8PpHUqs6rNWULD9A2FyjjCcLhaERbQo/saKLUKrtO+I2/F0lF0D+4dF7B2Uc6Jxdy31IqWsRlHesgtLqU2AJknPVMGa/ERgfNdOJvU9WYdUAeWjScffT1P+gmhNCz8cyjTt2nOoFv8Ao/jnQyexn8NF1C0KZFq/w1RXxrvLlkUqzE/Xk9kYsB1qwnWNbXjziIn0rhF5HMJ/XEsfXrFvq0caTlP+YghePpo0IzC6CI2O48HsuWKz//yUyAo8XultQ9/ArvCctCuqIRmbLEuWiVOJfRXXktjFs8dKcVQXfAomcygXtA99AH1WjTyB8uqhLsIwrPlP4PJIC5MJ5gUQ6RQxcprAZJvBu3tYOTa6E+5lv77tU+2DjG0ew04XosrUBFpW52FGhFsGS1AWD1GORGAqw8LGgYdycwz3t8xaNWoejx9uSfXUEB/8ROdnCfC8kZ+BzGRUFmVHi4qBgpjtcS++8SaHpNUPnDpF78/ACU3eLBMCbzT+5vyDagE5YOopUueS6diN9NPmFGJPkRl1MXAO8+AhxP+BW4uUB+B6tdSNPHe1BYw47Tl0y7a/xTNXkKN7SUK/eIUa9iI+GpclW0nbO+WRWBBS/q4PPie6/VdWtR7GaYV0qJydTUEOtOvylIFxKL6HjjchdkB+bfxDRnuhde+kfo+xCjN5TBzelLQqXwg3IVjQxZoTR8+bfQJn139353qogNoB71oAwbqAEhUEQk2fPfhaH1WVXn2An5d4MesoQV4CbzKA//V7/84pOacipJqwmdC8x30idJmFgPQm3QVqlKuCtsJtMg0ELtydzgsQh3+Ucz/JMsqJty2iAOYspj/L8f5VwVcQs0rGkwyuGvIfW7L8mof4uyGmQ1pLWt1gXBDcVPOF++UPNytJnFhQQIA8VnFQAM/8FPRf3zS6uBXc57nds3XK426Tpzhkb1wef/yxu6WUDDRMuJZvcs/n29mKaFfla9/JHhjeR7LZAWVs3VmnLrQjodve0ZDmqofA+Lq3MWtude8MdY/8o5DAQuzwbqsRymogLCPWE4MTxb3S3f3SMWLhkx7T0TkVpWnZUBhDPPLLi97SM031hmvRc15pKIiOrrr3sXBZGhqDzWS8bZiODDVd3sRgV9H0CdE+aZkIijG19M3PEndrjZYJ06EtVA4uaikTXOW76PjR+kbj6qv6p5FoADq+bdkesnsNIkG8Kx0Fl8i1rVgGCPj+xnwwyH2OWvP4IWaKi5uCMCKj25S4R7gjXwURovpQW56YbxuYw+dKVEM5w75BNmxVLh3Fzh7ZJm3F6O1xnLm07eqOOsPNHNSPLCdM0C5LZN1HYKphp4Lx/1F3xgNYNETF7UGi10OsQkYK77Dx2jS/aIZn8pb+gKKl4DAF5pT2V9ezWUGoqYKRBvfnNE8wla+K9o9NhcVKZzF89pHs8E+b3tdas4oUQ2RPBsBGQo7TogZvKN2iisN1WC+sdWDjbSr5x3ij6pQgvqmKB1BRdGadrO9GR8Js8j+1EEE03DFUMjpXoQrxiF7eFrVir3WiP96bPOYoVSBGXDEvy3MJBI5P15RYr8l5RDCroPx0SCwA0KD93QN8MOVgRPZbWDRbEmw+M99wKz31+glIOEvC+dMWWJFQeBwTtlJRBzVT7hMr8pDU7b/hXkSbgwNHBVVgC4CXTm1zo43chMx+QH04HcpoezTvd0E8Z8TzmDpK6rEs7HDFeK4RvBwMguNKNj+ruPiQUv/k0/VmPRobQyqtbB8XXi2nZ8/5y7i8ojo3ECNsJ+hs9EZ4SfbjZ/jjHh+lXq1CDonMdbYPv2JZLVG8cmBXDoGvgc5GC78HPR3deT0VWR25P/9ESlAKaeO7A7O9dA+b0pa215H8YuuRwJajuu8JvrLt22lPO7tv9uvw2e3EfW0n1zKVkY8d2f1ydOW3C502jX6uVm827lTHg4xwDd38azeLjB/oQWZRQloQloXblmn1/NNuwUJKkY3gQ8tMC1F1onyV4cZtqBO+4Eqf+8URl8RPrT/mVQzlNFWbPJ/Lp9bagQI8f6qoq6hy2+IhM+Zel4GJKa6ubJ5V0DNDMRQT2c6GZDLhhCi9aT++n0kew9Xwd88yBdyyNkM0r0UAmgrhbtIqr5/cncbcm7SeRcySl1vvssRwENC3Lfaxrm2TyHQqNmZSBnpjTyi1NPp8DD1AFk7lmyOdh1q2+8qYGSopXbNKbkvOOHhTNNByC4Udc1Q+ce39QxD3SxYXRwZyLFap97Er2sSTftT3sO0OAuCcuLBoKVvh4N51BS29EfnHJ48yqZCaed9GOImueOHor0T2jCkgXPfTgwIlBVzH+HK3FGglstrORH5G04p2skcaczM4rLkuUs2IVzsWNB5TCdyhBIFlBmu0INwzHjqanO46A5rJaaAZkvf7N5zrnYgIhIMsFZpWzYcyDxpe4VKZPt9TCSEeuC+IRfEsx1d6xCL6biFgCz3g7tjaN7IltqCRTuv9R7sHb3ekNnMtPZVpcrV0WIecJstaLENNsTAwntkVMcCUSi4hLGVKdAu/liy10xKo4gzVTEPLNlcYh8CBTkCW7Yg9aOxcvKN5L+j/ZJqiaSHR811W/y+/63BjBVrvPsPei5p4gCZrhUVosYsszt4jyGP96Gmj8pzTdmqodPMKlYJiE0QP2gAB8VaXR7niyBdOoDgNJ/hB1to8YGmNc1Nelnsnj0TUAz99+jmsiN1hqaH2CUu8gJKYymxPcqXTAtz0vPW8l6KRAH10whu8+jwBuGMXa55qCUbyFWxizp8pUp57MUWiMDKmVruNpn6yl4OQ6ns0znGTWZV13HiPeQa7zyroEPs0NeUAQrQRyA3bgwXofNAjOBSgBNCC3HqRE4LkziYlnrEbkRcdQfcXejkJZlLvcDFqFezPP0uMazQ5dAw6kTvMgavCGoM/ng9sYPJv7Tf/H0sJXwsPbGAz+IidMTwU2iNfJVnp0LHAwbUTQhCUIGQAvSfzkeHqottClYCyJnsn+PWcs8akQpHikRQwH4AqVVz2L4oUWPU1JjenG1/rbwySzF/wyMRQjL78bwFvI7iKylBhEKat7y+Y2GGGHotkhGkwrDIcAKnhv0Vi8VTUUy0YiH2/3PSea/Ac9W7HFvALeum/7sulaxDyK0UgwtRD11skp2rpPXe07x747qVPXl4u9HGVBZ6WjTEIoMHA8sl2eNmSzCfb6iZV61KUwaNkUPbWMCLIgH3Oi3atHxvTWpPksGOBHXoKtZDDRH4K+dcT1seN3ALhS3/DxmmkdVfBp4lIlSPYD9DFzv0/IbdGOPFJ1SHK/UypGlk8uQgvni0l0zkw6QBTNyriIthv8UFJTqLIFGjpTTYAOkfqzNBW9nFj5AqjztOhcOG2DLZYS1TWeGv9q5H6kNSwY8BGchvZDWmrF1d+omasSkGnotDzjsSskIBl6ZrChz6Ux/EYcqLV57TSJz9hPwixWa0JUvcUoKWF6oPO9AN5AZUm7MD2Jw9A5DE1J7kdwt3yYURi9Lj4Y+113n+CvhL7duwU/VeG5SO1E488EF0OyNVzWytSKSx5dvsdca7/Ch3jg/JYtwcriJB9uSnDcHzpvcXcP72oKyGamTuojukYhlPgcpZ4AyUQEf1IJASvSSDQcE5KD9z4GbH2eeIEaqEiIizcpNQiASn2nElYD3LflqHlqFu5ZnGBxG3jXvGK+2NparGWDHzyXexhJEyTbsRoXXPk22s7dP62g7sTJrzgAWPWbchjnTNQDgXxZpv7sJcZqWqL8WiAtqPR7ojQzKbABhSEL1CFUTiER/YRZFUI0tiqfSmQtahD0YVPnmKvzcEmVlJqOpVEby+EYGw22qK5ahEzN/XkE1/49m3r2jA8xV+O0BS4v1TJ3Tdto8EyRRorSGdCZx2wxeFoDlDYUqaTisUR5y8W2eJSRA6s0NnMeRtePEAC6M5Hsi4h2huE/hFj7lhmBhrncfnfxEe184HWXtR17Gx7RZVcHfwr8w8dQ4HBxe+BSQtm8s3LDPaK6ZX3IYH2WClI1HamEnRiCthloBgmIbegTptT02ua5kZkiOYJjG+fC+Cg8QPN82CKHTyxHdnJSq2KO61nD1QShSTNzow/OHWSgnxIW6ZDINxCBT33rRXmaus38Yg6qQiPxv+FUAZ+wLlh7W1Bp0Gy0loBygDhil4KKp/lrEm+G3H9Vot/swdJl9HdWaYw5MR5v+2VMRJSGDFNWSx+SiWE8e1cM76MeUKx0EPEF5/AOEbf9fZasge9GKfzBtzJjxtp4GRkIo20ssTvdaFeshJlYRAaXaW6f94Ob/tKJ2M98XYhu5LlPSf1Ygug7kSVWW2Wiz6NqjMrIfRGk5BJrBdLLMsDWvPbw6b/ksI68Kx0mG1EARQ/oUp/vRg/bXdc54v+SIawxZWahuKWSTG9pI93FYs4aLXRBrb3PXx++bcP33wpUWd+VRxcbhFdT5Na8G4BXuwdR0AWXszpJl0IY45Ox0h/fYy79OX3XsSNbPSrDqhq75dmPyNly9ttzwYNECR/3GHbbHsQjsMh2QBxSYKt+ppEmv2Pb/FfFouOgJry5vD9Xk8JoiXnPI8emeqkzpC/8wXNh2Tq1pRU3WCXUHxMtSzdecUsue+q2Wn765ei3IeETbhDwu/NvDJROSC5A/ZSwZGIDcA3sZyp2u2/D2ajrqoEMQjSppc7QfClu83FlkeBo7LF60zaPz/A3lOTt+P5e6b007TqxmhkmLTi6HGlKeivzeZySd873T0lCfwZ3zdAad1dntKX93ifopROo6eWWg45kbP9SwIvUw6FfHi+A/8DvAJsRSImEUvbdIn7KX3mcZDiiWE19M8CvcBkobKHfxXc9o9oxjoMLHTVzHOJ6tet2DCR6qIDCeslMXJJFWXkvnV2dfQTw3e2Lm0cRMEdN4/+LWdAU9svQ2G77IeWDo90pfdv2XBAS26n6RqR2EHy32DLwz+BVSvXkAzR6Gk14tNbuxXohhJclCjUIdOSh/5LBBXzqSJf5GG1UNi+QIilRDg+oMMjiuNYoY5ClFZP9zQJ9+CATv75MqeMtY9nK7unOcVpcmdjNSviVvFMZXJjf6DyAIPd3j2pKWQ+yhPQVeLcFCypb66d/CkUTR44859fKRKkazgKp01fOuPb/z0NOZeLyDIdYFb1sX+ZFEHfU5U34dgzHE0P2ZzdpGuxh8NZLsInwZtS8DI6zIe2uXJZayGjH6YRAik1mlz3Dc84TIhPVzbOkknCRDl8D9dZA2U8VPPJw3m3QADRm6NNVSFXBWKzG6jc8d/RCkrW11cAw47qLKTFX4KWV4cRz66h8R7RwsVAeisXtLbnmlK4gX1QW2rgaiMFS0aeFbXEuROzDo9MuPy9OmwtfANN6lio8XC09kcsEH9im7nYC3oXxSDuvqk6dKsGRRkXB1gN0EH8RoBXho6ZJV1gxtQDvkL/OxOHFCNXkVGS4qXKCsTBJz3FB9PpD8xg59HE8WVdog7dfsu40VpnQEG89haXfKluQvMTFy2rtGIRWac0MSvFRtKCcdb2cLL5ahqaTOIVlSXLDzLXY+YT//rcQGp3L5Ayte+ahMhTT/63lXoIFIRu/i/VHSG6RAi6pqY1jZWh2WGxt/G/yk1iqJ8QjNWl0aINngLkvszqqVppG064XYkeLaz2IPirxQTIkZwZUXvn+omMG9lTAF8hDmtBD0Spgd+YsjgVkF9Tv43HhfzIMoUKSf9O0oZ0lVZ2Ir2CIleWAUkoB2z6fXcZLwqjcA9Akah9B5LZOYIPxpeEuS355HUZKSRaZiS2XXkpFQUj8oK+gHzOPOID8nXpE4FibUWuN5KeJc7/uw85qU9BZ0LRpQUEuTbd3hbOBjNl8ClHF8btd66rKTsITs8THbG8Bm91HHzAbDEbui4FzF9XWnA9W3mY1z+HfGKTU6a0ghCCdjTdfuFew1p5hlWGNqNWBFlxNfGP2WypeZ8AWe8Q3+XOoaPRvSLJRRpnMQecyCf+hFnxxCPZ4lwJ4d76FrKV1mMkBl6F0gcfDW34DY2+TwjfsMFU0Afay+4HGC3KRd7GxHQFRy1ZtkMOL9CfruJS4Couq+BxWb5joIFyttYIRk7S8iMTKPCFP6RaDgQfnEFEn/A2VfX2uJpBKLcgPHtPLQXze2hMoqPzQW65mapnDq6RWR/l6OWykYG9LlDuHTuSEKNT72sqhDCRHAZwrb5p3RdjezcKbXpQS1P6ukYivjdQo05lsITMso5G7CvLmzemHhWVUoEZBh++4BFY/AZvqHMcRGtlfn6v+HHu8P8uGDIH6X972k3jjlnQlplc+NdrUDTDfyDtdcJo6jtwTrleMIcF9rRwtBCkPlAijgYLxg9nTdo7kcanJjbocC1LjH8Y/L5DQMTvQT7sACa7botj2nU2jUpM2Rb843nd5fW1R8/RNyBlYaWEC6JwFYQ4WwWjAlNDeFQV08H2L+ayyj3Kf6147GP+Dra8Uu0+CHwS5Q+c/CXT2rv25x4myufhjubyHhjUcb9IqG6FToRW/BouJAOhgoqoaMcsap07XLD7sGb3JxGauux7yuv30inQ6r5/OGEtMyW3RhPBIDACJwubH4MnbNs+QfbNHCmuYwznxOKu6Kr76ZVjYQ5Yp0aaqsw1dBuJ9UitpyYY2azPLAaexYTaYaRruCYUHnaA24SbUwHXaazl6G7hyBE0nOx+WFcrEPL5439JF+vx5Cv3rTLx9TdyE1XkSivqJK0sjf+GIf1z+MZFviaw13T5fGqWlvx+lIdL4e7cNJdxNtnxpn1WIzHyELWkqUMwFFjj07KF4ty5GOYODagff7M+7c3edKZ49Fs/8nJbMcztrXg2ASWGUUM8ci7HYR0Y88WyfSAVbqxKaDdhFTyqzrdqdtQh2PzDTsyODabHoUelW/D/68hAtlJfgFGEGVg5DAK8+KLLvon8NwFqXALDBKwXrh2J3/E6v3ka3KD0F88uq69kCbjePITi7anh9Gg4h1KUfRqcAB1/EqsdwBVDfbfqNXFVfxD81HOPuuZmYDDS33cYD8Fl8CwWEQqNOGLwkTIWf8TnM4BzwdNrytGwfmP/+d/q1SRRCtrx50W2oz9MQrGyrY6O6agBNQzEtukrkxlsrbEnph7576BsYGmnylwWyobIP70HX4WzfB5koqE40WSkE+Gsxv0Ou1IX+rZb+qSC7dpBXOk4eCVC2x/BcHl80GiA/kLU017Xo3/I21fxaW07Kj+KWzw4XlLWcwpX9kbiD/fU49EbmC/Er6NPvZxQkLZr2O43SKOyIfAYM0dTfAafYtuwaaACv+JXn/PXci5MaWxlW899jzI7MQaEdp5TH3TgliwO5xZ6F5ZCzxO7ba3BrrSUu9eL+t7VIeK/y1FtpRTHVthAozqOfLG1Rv1TjRqc/wmy+5uZkdIJ17/kI1n6jLOMimIGR9c6hbhNe8+a8LqqdCR7crtAgmQbPpCSkKRQTInzoceyajS6NfnH+Sq+cCsrvyZLbx2iDrgRpfpmc2lhv+3fPGxL7cASdWAIzcfjhSrUFt49Tdhi0LNuKB7buGAxu4zxFgUNwWZTGweuLPkiYO4iXPaW4TX31/KhHUi3bPrgIrY/JRaYQYTMEgkHR6xVig+XE63QHigk7ATTzZO5eUzouC0b5kutd6r+ly1n9prcBtSndO9LvTrcdt/WTuP2BUV0unRhuVAQwPZOpUAJLKt/hLNAL0Oe9CObwxHX2gKuSZMJ0sA7BJDd8hYz24nehZKGoEcK4vhtEYkO76b5ASqNe69hHMZk6LUnhluM+Hx/5FTqD8HUFwWhTpxUo6R+vhhIu/VJVPLYVE3W5dkX9jg+KT8d5kC5yzQUtm7s8GeCaFnTYq3C/NJijrtRVuL8Ji22maD1lUGhngnkzVBRgZkadB1IRnJES8NFtpjBRTkRMYYGVlV4DCmKwBvU4elm2CmAKFjPHQyqy+Fnha4w+uwjwUF2cAwBFkEwBXr0blK93GC3YQ5iGJakLgcyu+qEmef4kwmU40SO+fkKkDqVRV0nZKoVj1+NZ19L1c85VSJAx+5e8yNFOksaMruSUvQYLzbRZ46n12m/YSPBrbM45rm2sMgEtEBSGfSaUHIM+Bk54j7+P4r6vQJ8zEp3DEWYIJcCXoT7QmqG5ub9oUnbAv1lhyjdczUSHVo/2wNJ/e49bL6tCaPG5GOu5uyBxgRjokS7fVLcRoch9WnhCKi62utbdN7ulkMohZffNabor0LnPFebCFwFsYS3n2vhLJf9tzW3eOB2pBubNoROppyLtd6DmIlH3RclV32lbLahQEL+/2AeVi5J7POjYd33uzYIWLErtnW0cR0yhoCgz8GOBEF+Ovy3StBQzg3JXWT73xmABz8YFjpj53hm79cKdKWz+pB41EtPHwvLd9w1qak9MoyBKpFuDKSODfcYB8Vur1eV2j9oBqFFCQ6ESJ27zlpnQ9krUMZ3ASvTzTWz9CouIIHISkR2330knmTc93ENl80SDtSnJiJQQCsc7euCHGLmDAzqeuZg2rIkK98qL5SckZ9Eh+vdPxZ+3DkCSmwVLbensVGS7SRifXs1/vejnvA05D86M2QIZ4Fpa92jJz00ObnS7LFzLBVIv410jKES133ekZepdI/7gN0r57DLuTY0fRL57vwv/MGfaB4rur3FxZOsMGBhu47oSQY1eO0/v+IJbWE7AuN5WnqXeX+bSdG72X2NfKQboAxsUx7L3uywZN/RnIxJnKG8rjmD43DgZWSAnK2syOk/zb902WrbkVFnYQnqIyqBhwuFu0E8Vtsbo9fpuWoQsZD/M/d1eI0F3rdWqiKgpQ+UXK/sc3mpN9j1ZrJfcBAElel0CbFklAf/ucj/DIbHTAkyKRANrfRh75emvPEekhLc0OoAEHMtHw8JTd9+Qnq5wtblSzp93YVFNbmB4P/rBqH8a0/nsk+KwjCd/IMS68U4LI22RxXI4Amj7Le4BvjhI16eM/+K5/01ymugCWG77bSN9zYFOVAGMAcxkT9jf9hY5LxBbP2wiv46YZ97vfUoVYdHW38e3X4V9DXOjsdUuACkd4Vd77rtX/cpU4x+/Z6Vx9GWFj2DqH5wjPD8Xnqhl1Kyo9sWW/XsOkOTIb62vuHXDiyQ2qr0v4NsSx/mjw1lDFrSS54bP3SxaSQDn/riKs9HMSzLiZoURNQYkdfYTw48MjoqgtDmajP17spqC1ZX6x91hX9sKb3CW4uH3X+5sP61v9Ib8mE8RewtEOLM6Mw3rGTdI+SPKRj9YNuHoN2VAV3P1BEA7d3VOE4+jnrSMF1rvKort5DOToE9rNpdFiFOBAY6q0YPLle+JrgeRyF90S4BoZ0WYMKqlPWedWdDGSnhGxM2NgvIWQEQuHpqZyguNc8BbafMWBOv5pKUKhT/i4DQ7dlltL0Ivvu/RVBFVRrTs4no+A+E8RjmBHr2DCQrqrEhVPRCaJq9Bx1q5LeraMWDLC+Jfi0uI56BeYKgc+HBPnbN2AA996VyLMVsyNbKio73kkoc/Ea35Xigj1jlvttYO5RPg9fm5bBhEaIpfZtyG56TsVGqiyMwSqS4i4esVLsvlB6I67T4XHT0WbTrZ+T7rgjio0bmdAF3YIuhjWwgTMTlvpX9Au7QB13eV1IwBDSvOjMxTyiLUqVeR4FfyS2D2vB5OR8MQirw1GQN6/vuz8lmHqJunhc6r8QHFYafPR04Sn7KekKvS3/jzSZrzVz+YcWd/pp76/3CGSPvAHnfIKJOeJlroMUGBIesGyt1jtms3Y4nsbvcZLyK6Adjw6lYivw57fsToGUohHYb5te0R3E2WkRuYOBXDblOtXEB3E6GbN7NdgEmmo6WqHhgca3Q573VjeiDSIKGa2rEvDnbd/0JxXESzKiEOzA5/9Ij5Yhtf69cQfRqbdEaxb6JOlfdNH4bvbgKH2PdoQKI6u76e0LjccQcnELMzyOlYEmKl3W8ZIbCemau9P6b7nf6kZsg/Zk9Zi2tymP2jz39d5jiUTQBfhv0zIkP5pahRcFYnhneba4+gO+cN8rUP8Peelpz9ijEsAPPvPf+U994qXiYL9sghbIkd0v4LggeNr9G2s5IOUQjjIrJXEVmSCRAGQ5uo0qOzyed6GWKGMw/TKpEAjmHCddGLEEpxL+OPn7pZqD9KDrHUF140Maik/Mo2rr3ET1noxV7zZv1zVEqBWci9AYxkS7BE+kGUG7F9hdjfV1pTBUdpZJgn/1wXlpWXHxPsMHAy2/E+551/XdzmWRCQGNgIhwSQvDKadxRHd4Tt42IsbUYgnmPFCYE3ScB+M6LFujA/N42prjB3KsFyFgu1lfbz3/h4G2Vd4YRuS7B2hpxdDBVWzlc5mZBS+L6J9R/G4KQBrNQHuQFwxwLSqZ4wdv2SkO2K2+CMeIZdUMWYg3EFmstos0bEckp4h8FCOLHNxVR1+Ny9ln07p18x298zK5G4eX5RPzEjnbtcPzE4LDvLyURb37/yOhDiwICLqdCmQOiYGb/5uU/QIYneNHBnS9waLP3VktymZlBBW5e8FkCfM3mnY9U1xPVfzq/R2fKQVFi9jaCyuoNnYiaNBnYgpxk9DbKoPTyzs43rBN7/GnBN0OTy11dW3ubvOLIPfhNPhTBg3JmjoGPJwRUS2aTMgqE1gookwFPEu7haTdy5pNRhBCJ49itMDvso2I45+PnzgIgwW5TYoYd0b5Kt/OkQpwJVbhGwk72lc9PUTY7R1KkWBMcVF3Al2VLLd1VyIOXqQQ3AINNH4i2GZoD3Ch3b5RSBXGR02lMhcmT8WdrPKvCiiioonJ4mQbBE31Su4TCTr0HqwLZRwd1oUb7Wz8oVlO+WM5cLGvs9+x9vd6d+PmKvKYG/Mko10dUry74CVx5oZfQpJccJiYNAkekPCnJX4F9ts7Ffnm7d5Xm4b7u5merJZNwU0FVqEXBT/8hAf9nNN18LgNn25TL/f1jmLiPXB24ZvsAN4Bnplkrow8fusVNlLoI1HFCK4ZEAx/Co+TudWGMbo9n8th5irmKqQ7RNjM+B4JK2RVJZJcWD8OIK+MB4laJvQnlpT6qdzMUGfHhPNnG2RyZprfgaajQ4iSRyM2uzyFGoO8J13IZ4cQdGV9hgGj7NoiERvagHhFJiRQa3L5pfjyKbM1SEjl44EluzFtl+lR7CFpnR+b7O/Y0RB4NxexCT0eBWZwgZ69RcY8+/E8wYNUEgVtrJPDBwoi3W/cFQXhfAmQocc4y+oJo5GbrAjxUiXRWAMtbQuE4KHnxIHrF9dwf65Yf1oOlQRjzG4i6ZyLiZm0sL1zDAlmbkQxvzRoBvgVAJ2mOvYYBOtphFKdAtjk55WIV0g+GvF8QVtFKOMTOkBAsSpTwZl/kMM0b3c0m5KUNFb3xAQ1Bcb2ShSSI2A8jpgkiijf+0L7pOnD97+ii8MapkU2/wEuMxfKVRPExcnSrf4n+q0U8eWIQyts3IKh1Kdq4UqMDGR75+oSZubHGEPqtuzlZFK3reniE7NJuPWczH0I364MkzeqV5DkJXLM+GDoS9tgO4Tp3aBugfLMWPBqqrCEodXyWCQ6Sw5SoJq/3kW9DX5689/7znNIjMIuxORAdmckkNnYnlGHL2SM3I1760i6yl51Oy1JRYolLHTD4HbHt/CF2tIe5X+2+2gQABGGlSUYJsqtwZddVzDvdj4Z62J7CNnM67yO1nd8ARXvchYCuLu3qjlarB1HRBWKB3UH+E7m4AUUNxhJq+kq7uWYJ+3ytCqa5NtJI+KHvmF3+V1xqEQe79qXjjflS9+wS5MAdQhSvt4XyIEXp0xki2DmsE/+RsSN6gdrMRFpXtZHX585Uy8V5sfGFj7EicTXvWuk9npV8mHY96bNe6W2ZcBJ3eY+EiFzUMZhlPxIgQQGLf3/p1b5ElqHAHtwYkcwMU3KRZXjMwyTLj8+TipIYlfP/F46wwvFt9z9nPMz2zz9pr8DhhLlYWbFf8TCeSuePZevTOR4F8mP/YMD+7hmMkIAuSaZm77QQQESoOgR8yN8XPMCi6uVauwYXcx/JxEsQ9Pv4CHQtE+Yi6gL2/aCo7ooS68RnwUTuam2xlmZJq+G85EEadKUldAl8kP29m2tSn7oFnI9vz1ZpVf62fZS10ZeswCO34HzFKnOenTJLKOF6zrOr2E620NQ7V+bOGcVvHrntOwherXo8T74dogS0hgJjyJLtxD7s5vcGyrUyGUSut0lqzbAfPs51jIG18I7QjCPGpYPaduan2sTvF+AEf7JzPcXX5jdl5BTE+CdSBzIk00R5IQpQyFQ4uNZ4w4hKVELO1fYwrEOivFKLgFyIhn3LRS3Dmgux7jw/QYIrPEe1aNLDCPzRcACagCi2l42KBDdp3meJtXGr93sm+30tuJTkVdeBy4eZjYYpnPQoBYYS86s3UNYi7pA7YtuTjLD+PcS0U3Jf979C/MdDw8ML0yJsiUcTPwyobo+s9rmWY+xXyFjIie85Z50Yyt9tYVwN31gK60hhcKFsu9iEwpCnwZxJuLUAZ3wyGHxms1YHTKYcu5bCwbQY8XpfFauugELdyDyaAez/uQseQRa/fwCSIsSrhaqlm6cMxZpKDZ735e08KYcrVuhj7ZAS3SinrHfk4FAhO4AWybYx6gN7vr+RlKeDzmKwOJgB6edKneLtxKjLW+9P+ZqIum2pRvPWOi2rfjT5gsL8cxIvUhWsMcMna+0v98g+5UP84x/2xQgPiW17kaHe9k9Ra6yIh3eEVHrUjliJqxrN+tGt/DQN3xxsvEqdvdXd/MxdMV8zpeOpS2Bc1fkBbUVYpy2wN8wKLCGY7p2neevp0f3mtrPB+wMWB6iY+BQdOq3INfALz6V7MyJTDqZ9NVHiD+kXAECHYU3hH0H86Mvm/vIDB+WfXI2wFLKOLdCdFFkrWZX+jodeLR5B+4JZUjxDk4hoaykOH3GFFcFzvQSrsDKFq5e6E+35PUM+Qtv9f+Bv/kfl4MC3aC9IvVkk3/Cwh1EITIpB5dzoDuVuPQ9Yge1LOFxhtrmG3v1cQRwqbcE6+YmPWNPQ0ThYhxQV251oELksmGszJ6KMlUyAg0F5wIDx6hHbCxsWcIhZBVmPQUuZ9nZvLIg1IylgM3wvvkb+sgj1t8OFW2vypc/dp2Q7jMup1e4GJZ6Tvxp9CyHFuokuySNtEpPZQYQOlud0BHelidLm+JLiVueNfZRHe2eim1p1sNgfI9Kqz5OwMTSWRr1AwjqDFHc7x3ALRyiuwmUgDKLsckcUrRBh6m1p66UbH4tVsbvsoOqjx7P40xPdAbEP8C5SnnBu9qjg1Ua/1Us+KX3zegvdOY2BPYXW6A1mNvO70bZLxRJn2aR94AsoCCAte44QXTSXLI/aGE+jTgsW3gcLl8uQxikw9WD7Rye3dQp5DeVji18NovwsEHTxfxiIv8o0K+n0mVKJW6Za7U2mOg2Jl6yH9kqZET/bIK9jy5vpABGg8RvkXZKLTXe9kGCWXby0OCJgxGp4xAx2lSVSgZO4RdyBiz5ApVto7vxql7aRIAcMao6yXCJ4ghxyIyORx435gxRMsCsL837RDxMgDvTTi1AoMqXCVzabcd7d4+zAnZGVMsU3nsaJhLMugED8iQL0bXxeDEWWfeQ5U88Ro6DN5AV3Zhr7z1lA9qK1FMGAqubhHkx8q2k9a30fihtCYZsi9GDkPaNmsguBbTrIRS1iQn28eAuoGKkK0YZXL5LmZ1LOpP9VmAEKOaB52+CPoEh6s5ARFMMuGTyxKPbeCQ0o1WZz5NcGzV8uxhUT1cHHe8129jbNYrigdB6uUO496Il1kBRMFrQovrv5bwpukyRfb76ZWNbSVM5FDujmMH61K9f8EcRzL249n+H+NWVlN3h5XIZZ28OHuU5ajMa+I2nIq3X+UQfylkK4QoSgCmWLDGAd1GpEtLiXcCWbPAiqwuJGfuNx5IMje90eMMID6FyGViDkk4tJaCzWGB99VIQhkwk7rQwDpaPCxYr8cMFVzsEN76w+TciOZC8/BkcXe6vH3gJB+qJzBwEsn0/S7xc9hxNdf8RoGa+9gf5/BGEN3ng9LdHjghweuOkd5kFQGBZRUWdKXgjgHHA1LcerEnld7ZRuFfVAI40aXdNus3qw1Mad+9qWMEY+S8Jti0BNoAUCDV2BwEq2X4c2n2pb/mX4i0c5iEtfXMLSoYiXoX+wCWNiP3Gj6RmHcm+VR3rFiJxsYI0b6DbYCvPy1mbgwD/cSvWwmgmeazBsOqIRCMGVRMHoGYX1nkNJ3oJmkUYR/Cg5kOHimuLSrZFCy9SQWBVxNSjpWYrlYqiezaCh8hMy3xvxRsqJxiCYSi8y0Vkp7SY5m+hhI9yrhcbFM49ymPY1dFyzDyAftgW+vI00V4UsZIHDs+3lYAscuqeGrRCYdTOOXuHRUw84fGWB0CgeZcaVYEJ5xTmeDfnV5CfXWAMtEezzgiWbBW40UDTIeNxydk3bxjKbnKjWLt4v9h961rrP5AlTcvZmKPFHkK88SmL1OYTwnVx3OLE58CFSch7Pmmi6gpXKgim8F7lNJwn4Or6Uk4L0VNYrVEsDQv8Fifr1LFxDbk69d+zW4EZr2qLyD/WE5bVaq7UjaNjn4Ugevs1Puov359JvDs09t0XDseuzqfZ6gSYwwGEHOxXczNn9+LSslthv1NuQpza1HPeA/nASNQ9ePbySQZGYt/lBbBwdJv+SW76zQoE/7uqsVqdB6iVIpVguNaJtFXQ0otiOYnPyymdoCGk/Q2Ajw6x3NSnqqK8t6k/sHTOEIHAIejGKqTI/akel22ISdgtjOsEoCuJscwRMA9IpcjAhhqVTZSrA9RQ1uCflFEU3Yl5lS3vyeG+N94VFA4LkzDPTcN9BspATNa+XvsLDXCODORBa1tEo0YJgQoF1e9in7qyEwlSWvHiv1Gp37zHaYBiNvAPNraVRYk9jHLvicD8/89SOMCInHSj0NA0APTNa1XgPHKetWFt+vHS85LNcV++pgrQFfKZP+kseqz03q9gSfEXRJqB/Etw6CLKNDrb5kbrqBuq2kaeFZkLSZQNG3FG0LwnQTc95/WXl578wHqvWzsfSXfI5XfgFY/IqFNHMU8dzuMU7sEPwVlW+tHl4NhLcpfwPKgiihno+W+hpdbmk0OwSiAdIHU/8clPB1lzDvsFk5wTXxhO98eJGB/GvocRzywESnl4rNwRxHKiwl8H0NXCYPciBMIjW0Iw6NCHcA0AxxuGDCoqOxGzb3laF2VYQJUhIXxyP7/YCELlmi7tJAbG90foKP7YppsdQ9Si1JlQ9biJY3TkzJ8yjIjB565Zkf92xnLqzPQqjzHsTTaasQtUkwAsp3iWPJFtoXTonXUMfpG21GxRtm7oXhumFEp0E8fGWELiqP4gER7AJ24P0f81PYBZdOVaC3nXXobXuN8YsZUJyCbRw+++EBvvzhm/kGgV3c8R2qjK3C9n+cbvBHrE4Mxpf5LQVfzoEp6pnwEg0nFqX/wPW28IwE2crYST2pvTH/nJ6Sx/7bW4kR+imztsAr54M9Q3HAcl3q2sc/E4tmoWqOzdl7gRjZ3QuqCZ0tY4O7JysKUqnFNYT5VSA1GSF0kOwfuCRiKYz1FjXt89ePUR0nP9km7yYim32WqdpqM6SS1j+I9DiFh0w+ssOK2xLs5tCInGWSuj3p11u9Vitw8pfj7WZNBhSqetSiGGcAtsL60h2Nl9pEQAG6X4wPEke9LAs1esGcFf1dLiWcgsBQWAHo7Ar64xEMNRcg63innE18GfMa8B4tAg5EWTuVnajSR63uWNX/S5vTXiwhIYtzgi3e8WFYCWmeO+NXeFFQ3v22Wt8TA6ojBNBYLfAdL7wBJUI4HiSFQGLLuLj4xyZ17ddIrD7uEPBedznXXdau9SDhPv32Wf/AuMe28vggQPrS6TrfwsgAipbplwt5lpbvW2uA0Yy9VCfJVc3vjSfX8UfZwPkLzj0V4UntiCJo6mrX49BEQDDBi9muRUIp2zZEWqie5f4o/B99pxUn6DITHdXMuh+OAuCs6l22QdzHP9UoGeRBvcKw4c9RtNZJ2L8uOHGIkmFFR0osu8UyHHYgfqo/VHJjD78aeJ0J8o32ubz8Dm7hmo188kQqrGf7uncmR8QFCsl+UJ6VlINonuFT9aavOSMEgRfgPnTEKDugySG5snmBGhLuX2szHzgpHSAeRFyikC3rZzIjaa9lb8TgH6HlP7h9B6hpCCvO2O4eCVJ2ZFwSvsH2+d2CmuQqBEfPfrISlEwVkCNFzD37gluJCDnQ2DbnvX0ZCy8QTczckj2rhaSzvlwuIsfggT9i42h91yeLwvMxMGQC3UpM3W2vw7HJD9B2NsJyFetlbeIf591/RDTk1cjZcF03pggRcSygXcTBFbrDj6Cvv95fpVAeT1UeHWgkB/bjVwZPIymWKBAbdBwRE/EtEphiqcemBaA9ZtNF07WcZj232z0raLMXdSAgcaPAWQFZR22RWfnB1ek0CIxIXVgizaGoyXLoi6eiIHm+2THlhk+K4J5hKytgBfWD7m46Mhnr3bnAlunkJaqCB4xpzi+fPjoQsylgeeZgm0jrHPRzJ6kttq78gS9TPHRkxJcQcQTSifnAywR6WEveFTTS9gV4/4+RCsiXcWBYnatpb8A/pNy6zI5hYSyVZ/hHr9UEayEI1yZX7yTTRyCGwTI4T3MXcFsi46kyYw8wph3ZAEhNKdCduduLLZMDDyG2f15JHgdB9kLCAbw/tAz8MJeHmVh00GxdK8DaekBFUbz+HeSW2zaCQd/S8Im1fHripmRICOT+KiqHHEai2zPZy0ZUJL662h5ee6FhDos3iTIYCiE+sMn2/ugyiXwBEZZtO3m9KNSx+2WEnNH3DAL97Tal6gbJn9iOfTdum6plOY9Bb0mO5Zd0vc+YGm+Rggp7Lko/0YUeuTAmPIRXyvoDuFncujN8btaJgrmrzaqE4xttTs5PxCBYwLpJAeZEGV1/depIaZ53YF87h/bfr4hHwLXUhV/spxnaDGlFdiKCniZQ1kGf0SrkoW6LjMY5lDvy3mZ9c223tZcSbegbNjwvSMfAUoGjSIEAVwt/G613Za99VxTKeTG9Bsp1QatTCobIBsmgsCHbq7NsmQYXHOmACku/qYg1qjNlDwMZaDhMDw80p9nu098/PousPGM0VrgsZMMGCpPyiP7O8jKQqlRhhdEuyJ4jrqZ9c2JH4oIqmQ8tHARx9YPlPyeZbZezJsg/bocJxkER1uXMieEVT+oPpMmHSpUa220rGuLG/9A0sVkmrrzCEbHaZDVVHPnYQ4KmNwgFszPa5P6iE9cVCCTNZTe0N+gGbWctVwQ7gymD6jTXVwT0bkaSf9lqQZ/2DYxyWzQYsmP1J6cupGq8DrG1jc3mxVNUwk+BDrV52WpHv7qDnuRDL6CKM6KmsuoBSG2p+AQfjV2fnZVAAUZ3l9rvNiZxjZ3Rwg+3jLxdldqkq4Dk8eOGaUioLbRRqjnikEUDTzZIt3L/3ctOVKhyZBrogbgQQmJRF3yGkPA8SYG/FlxOTh/oguAh+8HKuQ2bIku6QjVHbgKAetwoSKsXLDCIj7tbVLdYq4hHzsmlbvgWZTgIIu1P6y5tdsXJSMYC870kSy52MCkCxAzAdW6pXL7gxaWlSJOsguPIgjyEuQQZdlLA1iY9JwHZ8XSxg6WmI4leiJc7OpyV/gBfv7085pJ/vSaVFmhoP6qEfX/GaR0stDKNg/I00AgeJun4MRrjjRcbIPmPMX80wwOmhS+YQ3/04aoyEkcsECzgbEadGuA4kHEGpaI78zfw//VzcY+8d5ggf4MPvRl48opmhKmQy5TljFMGvzpWVzCcvqGMqi1LEI8Tw8X7hWBsyuBbAQWMuJZlJtfAeUrG9xbD5slPTUw3E7ad7hjfIwgMJj+3AYexOWXklkhz2F2UKf8YP/2wRbDS5G53/cKbiZI9j7Z96P82Uo5i4KMHmQCew9AS3T64OXwIspLsHxCZyWDw7QxfBp42B6k0m4SCHZJSlRKZcifJ/ugeWkyvXZDZKv0YJsrxTSQk1eh4hUk32a5+KgrZCbH0lo/bmcdsQ4GcvGo6dHTQkvf1/ZF3BV+wd4/Is2dLsBv76P32ZBgcciyd+GUFNya8fo3acFCz27gpFHb/T/sZuX5xS+wZzFFWpjd1h88ekQgV8PMNCjBkHlqK48YcuFeYySHTe5y+2x9HO379EW4YewpXg2JaPtx79pVcDsz4WjCNGpaCkJ4V2wAmZe494xfWWlqYsOryqUWx+Dyo/7GMha+ULUbIsBWQ+HA/7Cqoc9Ni3B/rhQJHYDd8oe/Xv7rPdXNmuApXnIvTi7mLbxj9z7DQ5xGdWwF9lTUczNJTFQOWU/gXaC8JhN2ZUfrBy3+qic8d7E5XsQqcxA8JcGMgXe8+zxbeR4MuFrbM8XOrFN1sPPJR79GKSA5JbgjgXTHWeImHc9FBK2jZUIkN0k1VK0wBi60xNtIeuG+CuNg9ePxACdAkN4TWm6fFR1/QGbQ0mi49RUbnnDQ0jVeVEeK9K0/IMxJB8ShbOR4rY8a9JasOoq3FgR+UBKLwr7FaxYRY6p9L3qG9yr+YAl0n24dABsub0y8RYaOHX3fJldXXNKUmf4n8m7/M3geAXPuScW485v5cRocnQ/Ns+rUo5ltd58uAOmHYTFAx8OJYI0SQSI5jCwUD60cyFufEZG9BnCBwMnWXDON338pUhT5n4e2byjNXKQW3jc0U07y65riNJwfXmo7cPOHQd2k7tND9d9zo1+4Qdo96djpXd+u033roK9tNM11ar4BWMrMeNwYTAljziZBx31gU1F8LoGZtiohyJbutA8SXgsIo/a6WJp8fAKIJHItGGzkQNcL5j92emvW/E+wiZAn2gc9BsAq3OyX40LSTlpkkKfqOQGHT9Fb5NeShzaU8tVyf4CjqQ7UuYpEQfDeE/h1X65Xp/7mo2AHBkLC7geL1Ld5S3cxW/Z270MukgieKMK7wU0O2IgT4dFSHqKKuw9PZAQViQerw8JR+E9uQWtXkSch4ob3qO79AJw+zfkKfW3oZov4s0P1rm4zOG/XkWr7rSgeNsjeKKreKY4aMr1dnwDoTbsKw41ok6nQvaF7sXa/L9uxgkMWCgzLqC7Lv0ktW8Fk9yyn3UMlHjWmz18rkrId6G/9avwH71GXJijAAT98A7eoPHC1URD5MPc+CBk/TkcRW0VN15PBJwiidlmVKOGg4GiQ83Lud7wjLX7wcKOj4l8KPEFZIOrMM65P49UKzTLOBGm9GQJTUME44LNvDAap2YBdIGlQKU0HxOx1cYWWW2vZIzUB2XCxMRD9p5tEjAMxNHSwY8/m68EKrIRKPnwwg6G6HPSl4rQfUYi0TCrBmWIbJJU+ykrleJ40AZ4f6At3SQ8SJYq1zQY5zXrY4Kh1y0dOSNKRw5JgwB9whDApgs4JhOlWxo+ezHvH8VduD5399+Buhi9ofNXXZ/bcpRyCgj5xu4L/N3H4X5za3IMyBGGbPK5Y+o+DdMbmnmY9hx+NawptK5nMtAT61VZveXEQMpCJp/QzXKkWFiWjbBPCXZ+DHx9wMarcLqS4xI/yaCkQvHQ9rkSnPBOf7jHcVVaeXSjhGXQwf9QB4EtYZ7FeKseom6jCEI5k5U0LnjVnY0D6o4zh2T1wJ/PA9ubzE5tHbnZmEPjFBrtCi22cC42kizBlkqRZITZ4NB/lTvCzIPuYtCEvP/OjvaWzj3x9p5pj6XcQhV3EkVl1+DxPwwgn2wqkJ/OudiUYCEb73ZnVuTKA/7WOUSjc/qzpBNXIxuf3VOiORu1+VIXv8OwEyK3ojCl3a0uNiRqJoBIcrEjC1vtJOvwaP9oPu1WKCllhGejpGCRUVPo8t/l7zwQEnjpe5RRCThFLt73c8divm76SxBB7Ds3Qw7MlP52lwMf91NR5iA+XyyGY461iuz+1/wYncyiKb8sxLS2QyKRjKg6uuYJYxjAXyXBMGkWgmLEYg/f31HQlNJ/B2Fkl5qDJznPwMVuPv4ghXAn017nYl+eM/oI2gH9K70IUqKsnU3YIn2e2y2HLOUEFfVNWpAd3/7+KzjM4rvNK0923b9/OOeeEDmiERs4gCBIgCYKkSIpUICVLlmRZlstjWyN7x+Pa3Zkdz0x5PTvyjORsS1awJJMSSZEiCAaARM6hkRqdc845d2/75/1zq27VrVPfd855n6ekPmJwn3f0eWod92cYdn+a3Voip+o6iwkXJsy+YUf0oI8wvnxf4aoTtOBwpBcrfxPY+Ys2Lbu13YnlZGcMJh489Q2XJBVSOfzlCwyfaJQfKCQY5DcLVhn5w8WWsdx+cxW+twljl3sTWR8BGE2Fr2RzFHH88Ksn+9h5x79uw2GUSg16xKHMdpWsENu+3yPthPiNqAhYSyElJQVizSocVmAAaTZq0SsxVjGdniOk6aG5uMMPn54tLa5zLR4+l9BMk2AZntY5B2kH8gLV4uc0MWB38LDXIr1RDOceMTEvBsYIEdROuU643vS9VQIeA/4k0laHuOiB1wNP8Xvs6DOO2NhJ/YsRb2Q9Xm5AkOHJo36YtHZXxZA8wke5V6FkkngySivXAauDCiliHD935H1/t9KIUDuU6tl3K78/ms4aKudxOyyONPz4jPaO1DNdajSrMYZKFIbZj4dcLTZK8IAJ+mv+lHWv2nrU7dtod772HUPx+H2PYgf/UrUIBLd7QstMUzFU7e5IFKO6qJ4mwsTK6hzjGXYppD7uibSjgoWV74bj3a4oYerxD1aDBVzsMHtI8PiIHTELnRNLQAWQYIRwvSx2dGAfj+QtTA5/xp3JiE+fuU+O2vzoKI9zU3j/Co/HdpUrNKpU5qrYHPzkP2yirMwUpcxA2TPoSfQ+GzWuXJ8XOaDW7wM/QJ3OOQm67VCVlQ4h6GKU2hrJKUucEF78b5lFlI/0WomfB5trCeZYyoNI+O7kjvMIq1h9wtAEnc6ljLtc7UNQIQBY2Q7LBW1xWd/A3S9hmxtKInhkmMj7tzcVsQc4QMnDBh8ull2oF6xmkVmGRHtu2WMDm60wIfaI+f7XSIo0wzw6aftt1k4ewieTp3bo1PbjSLXCmX/Kb/H21xIE6Az+ECaMaHZpoDQd2SaZgwTC/tAXr8ZW0+rO7EwujTPBx2QkD/LU3qPgU123hlNJzm8bGTieORgtRjtPkpoeU5yx7aDv1fbOq3CjQvMdkKwhr1RsR4jg168szQVriiaJLq7Q4JcyITwhD2T5EeKmPYc1C89Obg4m/W7OSbZOscHWlPq6E0O0UDzf/h5u/oYD+tyE1L5/CfjT0SDDaqFGcTnOZjVETCFkWzQq8AZHUMTD22z6bM6DNblABpDEY47mstst0FQMT/WjY5QIrQdCSq8wrD22vb5o5NsE+mvTMGlSg3RKocIFKz1154V7oZUbZGziBGmX3B5qPaC0ukB4dyoKnj4YhCXzu/XXtzPS6kKp38jkTZ8ndtEqV5xg/4OHZTK6idSydu8M/NPSiMN0byigDR2mdJvay9LKPxMWOdmteLprfva8PcSU7FqpK/9FFqF5Wwrn8T6g+UjWyNjwfYym3RjAesUjxQXst7PaScKR1iBSCejToegeyotLRvF49Cpsg9Nt6cMNsMnlitar225jBOrLaU4FqX0yx5xi9KZFcHeioUpGErIbj152wsYdmWHhRxxeGWtK0XbhP3v91a8EgVwQx6N54PphMpcrhR7Anqjt53hoq9HBTmNwFM6Ph6kn7j58I63+Agu4awq9aZPjHMYD0fh2UiNgk0pnIJgenrz8JVAcYDJR9kA4yDIbAypFbq2/hx/dbi0XnmQAQ4fr3GEGTLGzyHqCeUV2vv9hKYbYt7ApxvGg7NcHzdWTe6uSFsWTA9K6s2P4FHq7NZKajGqQrhMO1sFRq//3F5fQtvhwiY1fbdBiC+52akYKcjY8fHa0Zrasnczr/ABakpxB+ISu2ry5d5ksHwkFPkwUEBr7JWE8uK9xVcDexdKv4Eelp1f+xyL6J9/xM90h2deApdaCrUed3c1O4uffWRXB6Be/O+i3udGgj8uUTWBxBSYcWYMiYwo5T4HvgvtG5yy3OZK6Ti7WE3Pth48JKvdzr7/waMemXJynHZ4TR0OBZwhNeqdrwUF8Wjjn2OoZAiE8UwkXWn0ITQkzq0TZaCsZnLdzSym4dm2PAb/ipXqaQp6Ge+yccvRB46Atv0BgWjtyvn0elUdNwDIWw62yJA+rwlz8dPkp0j6JarvGTlyoHy5GM4kOBy/nJ8r6qQ/8HIQv566ylwOSvh5boXUOHtV7tSoGMrvThDsx/05rtsOaNz02D5HVXkIuRoSKkaxwvZRjyTeDbr7fUsI38CxmxE8G8ktgMmFiXigxN1sXvH4tu/dU9djmyv+KcSjNh0Ei4VG1/USA+xBdl5a7rpb/nRLopAn8ImSd1FDaxMTaHFxjnTs8/lLJ8a3HtWS8rpmZN3/aZWdDorQOrbWEJwfZosGGc+Z/RT8pyITHQXq6415i6Xm90/ziFl33/OIyjT6GAugpV4iscK62mAs4an5eSdtIwzu9xpakB9nPcLREEH4HP7WCcB8iJT/SNrIf4R9gqeCWmQaLY73dvHjc3r9nRuHcOm0tsQoq1OZHTp7bWiy2AekTY8YtLgpdnXhpbm5L1b5GLf/XHckNfHWiDkMcqWhJZrwAMBZyYw9AZzHb7uChfaGtuhZ1Eefe9rFOV8jzSDGemWznrVewfgeJA2p7OxrCNt9a2SukoL6x0hBuBvndTnKEHtB55eiokNrqIm5DzcnoqQg/ko2YTwgzIymzLT79LPbxKlNECb2DVtSjMBi7h0CkffnpeFtrqUVudBdyRkU5AIPwHOshoLJItKJ/kT1nE89zN0cLwuuzbQctN4NMDvc7txr0bUUUy3309xDIYx0oqs01lGGrzimllfzH5FfZgcl+IzTH2hiUNf7R2Umd2lPLSsa3IxZtlPSxGa5orBdL1DKBi/7z1U8vtDN5WHHPPwfl6PmoAemRF20Evgs/oWeqBQ3NpRrlH37I4QqqFkCCSiuYvtNFHc+NsKZQIheeEcNrKQltduipFG3bC0WtgeyjQlT6OJWrXcKglvbVRsoWb7Hpk/zg43/04y6RbWCJeNT1EtIrdW6gdSajUnfyLZciQXLSysxeChRVpn70ZinajxyslMoc4MAFHaDCSsz1QSFPYR0sYhBfthfPJfytKXLYftYK7wnW01g52GcA3yIWz3li2R2OtzTqTVpRsuh+w7a/7wniEtVZhO5C04zQiur70F0iVR+S3P4wUfkUjxGrSixtM8P0NIpTAGdDLw+HvT84NJ4NBl/zxC7cpdpb6TCvJYFWsmGpOpZI8MKkk0VWeZN+UTqzfOZQzbpq8ShmnXJN/hV5SF1HWm+/+EcmrymWk2MA3Z2Li/yh9qYFTc/nwazhj1UsekfysrXcIVFxkuco8yLjVC/CjdFeuv/FQM3pmjszsY4mhaP9q2vPakU10z0EuJXrzo+FEBCGNAeQbxwTHu0QBz0RDFCuDYALlNpbZt4oEXTJ8a9WH8ZOCKS7h+OLHBP93CHNW0/U4Wru4TQwUwmdRDm/Gmf6CC0axeyJDhabG9UGSYQn+QvvuTmVZxg1/7TlugZHJNEWzZ2qR8U3BpMqYu5P0kI+kW5eWSEqqdmwBBljkUlFKVN1r54lWg7gT5ZIyM5ZlCY1bNNlF2SpaX0+UXuaszcSgWrndqmBfwEdoaozcdhEi1W2kGki8BfhbL9/v6Gc8WBM8tIV4lRyp9lP8pnIq5voE6pVALmey5wqi82USb1sUgTizlh4QRgWjkNmyrAYMcNNyOz5dR7RXWndZYWLW+mOiqvp75EFa2wLZaKvh5R8kckLNaYXRSlGshvJtBlz8rjWvU7UWJ9hNXU7YcN60udcyNdMNDSrV+qnQhtt6ruVlDIB4L1EG0tZuo7LZHt9x098kiZTnXvCOKVrBXdQpAn7sETVdg/zJr7AQp1kNX7bk8KWWE7GtHlfxzehkhgk8gCneIBLP+LvWjbMLQCNUinTRfI0O5gjNbsgOLepioeLug6qiGuP0LoAWbSjEuAjiuAhftMwv3grorNRqSGUuph+opGaYbdk2u/T/e/paZwhc0KD+jyOa3ioj7f9Oyz/NT4gu/fFyfsJmma+WG9vaohgGS8lm4ueULWaNry903IkYo8tRe3/V5OzVl2VksvTk5d1mXGrQkP8+G2ZaDZwkpqV289zJ76LvYbJrwFG+HOfqxrsPyrofrhEH6BYi0cLlITAMy6sJR6oXNrwYSPRu/Mj/WMUM/qXz2S3wrJqjV3X94yBGAZjPR7fL16eg7jjB3k0B38Ld7BSRdsFNZTK7MfFO5bj3Ul6i5/OLOZf82sZKtj0W6QsUwqCCk9aguOUTUkSUZgud4urUlPUi37MRARRymawUL7P/KJlqjP/sC1x9VcUbAbbAa61tCQlqGa3N1htoK5GN7qouXMqwQpztM7tfzGD+QJxMBiBF8vxfDiUOUpdgaNq+ykCv3kQNkyOiT5P3cUeK9V+ZIl0Qz6uKCVpu4xCpt/bVJFLLJft4o/KREoI9tZh6eYOjNCn5VNFowfnGkDY02uGd92neHtilhD6uczwxt0boEh+M9aUawy9bJzCV80u0yk4BGX6SiJuh+jP3sbyoidUMUm5T7V9OAo9CRmeVuepxgMU4goBzNabyQfM+J7yQBaDOb2o9/4DePrUdWEkOAWn57F6alpRlkg0bjZutrq88XisPI++zPkMA5NZO9pXzK2nyI+LI4XPrL0bKXIqp0WcaxivDNt2LTLShrDLHTNJJtISWaPv3B/yE799joZPvflJLpESghMCAWAOHmFDsOg+aTNDXjqfHiKChSfE1rIi1CyKJYhPzvNRjB1OphJcyqG/q/rMsCqXiA7DlkTIKicJXS1pFJ6VUyTYblu7VjRVTz9gYcQiqfjrzUOUy1GtlQZN40zl58cqT9txJSBW66nW2wtOnDDPLifLKao/7Yd16veDGenUucoleaJcusYtVZJSRhOUiA1RJ8RoVXxDnqnfKCX3Gu9iC3oTQXg6/pXiTcIsltOXRYSAEYyg8kEvM5Lef2E22x4vi9H9+4ImJlsYIhwWApVtpFPvhSx41aV14nZ+vWXipp+qeanE8qlh4hlcmj5298jTmIkkfTkgL14J+DYCHUHG70NxWXhkF4ZqkEkjldIVUwwjcN6TZb+2dSnUTSvFTIdjoSfe4ddXkLSKM0siVU6ypvyvS2c+eskPEY41OmpMQ9yrf13LDnOsTDQxxK1VZsZMNN3pdcIpCHfGeIJQtcHydk4e8/yuZWAXwfZFsasQelGwZ0LtZ931qLCuSMrTXXQ1I48SY5S2d5uOTDWIg2mU1F9GEjESTXSWrvk1TWTddXQd9mO6lLZAMS18PzTRlSuSbE3T0YZk9ngkl48XbB9sL1vMQrZbw9v2yXCtDXAqNWiRIXuE5bTtwu8GKYeUseYQVYOtLSE7FxlhrmrzJQac9jRJJo1eSIuX0U17QEnEhKB18Ue5mbnm+gSVsqvJU89X/MAfp2OLf5FizfVUZNg0L6bhOa8/m0ynXnkfPMkjC66QnQzLn898Rbjq7PP8QRRjBovnMCXqzx2Bt+7IijFycDTC7/Fi4/rGz7sfehqcfcoEQ+KzMYd9jvMEEoTADmYRVHLGz1q//pqb34GuLOocxs8mn3uyNagMF0uNOyT+V++2Dt9++O26XUWdg1so8i13QbAx+dTg6rSD5MVnIwl9ZWt0I90BKQxKznT3s65HGG9qoZdfhoL9JTiKc4pMWQuePNYUDO+01xwxsmZm9UGKlOUVGBVZf9N6Ii+8j8vzRtJ9u66PIRaeWEHCFcJLn6ZnLyoY7cWvj9wQTid2gVNEeFJy4OuUV23fiD++42K5ODPCEMv8h5jopXvh+rq+7s8qB6Ine0RJ+faaoF5gYfFDdbQap2YDa8E5O/PWrSgeq4dqzHqjXecKwn7HIYX/7gZZXBGhePGoqcE5uPNiaGVo1rhUP4bD9XV+eArX+f5ZVsHomeUXe6NBzhDtWv33pFGcyzeMgEfvN+4rPhxndUE1gYtXD2Yad8mpoc1AlRBaGH/y4Fh1nBVkUUOV2OFQbiOXZ02HfLQdQiA2Eeg5lyaPbGw5v+mxWS3eDpLIFtlGaWCsktVzkZmoJkaSbZybufSQo0CQa+Tg2vkFZ2pLuNtA1rSpZigh4UgEvV89UklqsmEWnfSOZh0IldefUspLmZ55wb7BLBUTf0M5MjpVcUsSXhAxFj4SLWTkv4Rl41VdEwpOQD5WOKOIBmGKvg0LyBMZk3QuMNKWmFnp0KkI9arjtxh2MeMDim+05obQLbKZgJ4pvLCR51Nwji46KuLrSRaym7hbv+gOOaLtXLfoqeqGrv4mlifA3rOePnp54zAKN24foSCnx7hlr6SD2GzoMeS37t+OXfHIaCngPw6nTytXEUmsrnnNER8j3MOrsV8WpnqY4knitwOwuFZo60iLWUh+/QdZ4wmMAhlcVdOa5raO/VVUkR7bx9lAViARg5P0yiOBPsTFMExh+lI0CzMqz8Yz39w2dVETId4aq4Lo9Eyi+QMLycbsdQKW4nupN0qCwo/v0k+mrbnjaGvJrYHH4FSyM998YQ/87/MmAG5YxNkrBFVhu+4oSPVLoV8OT9okroDNm3c9aMz1rVeffk/AFKUagub12gT7qmgXSzxzQ3X3bPxvGNUDkYWanRjdcvJRBo3C00ghNuVkpThLloKTMNsRFHsDX8EHc570qKkDFmqlm4qe3rVb57V/pwmdtXdDmw3E5e4al2NegnKLvrXkailHVl2r01Uu/so8+Px/ZetJ5RV3719xciTb8np30yl3SbO6VmaGekujvgstc6JfR+DchplAnQUp8JnxiOuJ1u0X5HWq4UGEaq/GVhc8Nxt6hW4XjIjemg1QQ1WfDxkXfHCstI7OEdfZAhuuHAHSzMB2NuMjLhN/rBXKiJBbf0w7UMScnVUWBr60ihfIizoYekxovzyyKkp6MesKDmLtqmmHvEkFritW6Jc/yqr8efH41ATf5zDArqS2A4PZO4uhn8IXVurXc+2eaMm15kscWEGamk/yER0JizxBtGBYKO+qegXaJ9PChGqbYNmCBLo+fZtXzagSphAc+4iojIY8AmJyxQJtSUN59WCmmS6tXqNhoWmDWHx6i16I8B9GjRej+VTC2TyQB4I5Is3encBAAzxzJBjZgpKN9yUFkrbZZ8jDMSHC3OnoC3b47drqqQDenUhthVUqWtouDZRXD+QD6v/EuVvh+f+GqpX4A31EPU+R51spf4FLuq67Bnj7DkE9oom6PvAOs/XoU2ieJ7m8CNBVcH4ujSbmjEWTQnlzHawqP23oGg6/N4apihjq1USobWf7YpYv2VvPFpEPRyScMKBvp3PNkcbDSvEalhnATV3dFcIpnTaSa/2Ja1jDLOB/BQfhle3qqMq2BcDhi1lWIoxSs+PuGKoRUW7WT6aDSbsCHXtwlnt6ceIClhpOJeAmeiKzYeFwCtbJf8gf14EgPxQsl41JTWOnd9cZDd+Cuqv9Am9XgEuuRnE1tDLEcH+OwUfS76mLxMH2551glJNp1CSOTjFdg2RbmkUhYA+sTaH+IkNJP4yVfLkt1S54TZBg28jcDMvArw1nTlFi2HfY+TenyF8miN3M5+rMn8XB7EHssB+Z0cXXcJErcRYoSmAdG3uUfPdNslww+RkDOGDJVvo83ajQ9p865WRfc67mx95bo3T0XbXi9il7B+3l0c1qek1nK78UmAbFgn1BDsfS4GOeRQGNXcdCQV+Gd+okMAg6syzQ2Ttt3fEj1tyYofXguOB9ka83s58P//AxAD1f5nXOOKhR5zp8mIhL13WPGB9h/BEmfXM3Pnyy3inM8H6EsyQDyX0GXpQ9lGG0poc8uUK9i8cjZ4odRHDaeowgyfUqJ25Jeo3+OlwSpWkJGz22NlRnIt9V8N1imPEtvFoKOEdK7NXjqzVCdl24KjoBPIHVc0qhkhRTdTpk7OIB+oiaaWyx9WUIy0SufiFMuhzbHYHU3TIbptn3tQGYsx7hw1wCGYaCjMYSCp5WzzrNsnYOpFIWFF+fFgxkt8l0DShypr8xPXD3mFYVV3lgQ7DnWQAKY2WwXbJFRFcpuhQimyhqcuzTHAY9be4FHrQY3jLzKMz+EvAuD2ceorZaSEenjP8aZZM7rbShBlWemUsN74BTETAgRJEyARbQnWg7HMQeXuN8AWtsr8YuWF+1qq8VjyYp934SY9i0z1dpvz3qUmSt3CyzQjVT5TSNrjXvkLXIil+V0160gFPrOS5REBX5cDHMKDvvb1dxo4uOP2jICDaViymSPKZlPnIu1gWPgo0tReGBVN39b5sowXo/nyXVLdZJ+5Z3zyb2OV+0Bvry9jFpRAcIVlddffXBrG6esgj4JDH6+VBHjXXpcUZZeVysxs+eK9D1DBqswCnp1zVwB/qPIdfWD76E/u7u2gKbgN9iW9zZ8L6Nvxi00XY8Z0sHfx9GQ5DlMCabdkC7TKWFLMHDIwJVAMsgeZL0+2TJ/YNXnlh38mPMgApXCi8hEZGaNjqrs6z+Vbr5Z2YEnWW2+ddhvwQbDay15PBmIsXucp5Ml0ZLVjEp34SmYZ2l8seotZE2zLfR6eCQ+yHpP1eQ7v9Md99rxZxo2uCo+uZ9A3NmPGtmlM0pvuXd9iBsN18J1c5oExbxHYxMNN55PMgkk5miQPCKDmAZ2+qMdSMJ0SAVYQB8McgY5zY04xIvmK8YNsHtBcdBcZ/cSLu6R7ty0AGauk1ceJUFYYmUKdkFo/1iM4wMFM971mke9Fxr8ZJ5Ufybqjseq+RqJYW9/820Tpf+Pz8uXzV/2CVaI5WtrCTb5LwKR7z6p+cz5uMBz7UjBXpcqoZK5kehUo+RBUjcbD+UK59dzd4iH48JwLY/h/4Dlu+XTxIuNdHcRbaVFeXZLHQs51GSrZQpHLhUlwO+IJ5sLtRs26moK39qh2xrpiJBZgQGmkNRn4J2YlWPD3Mm4dHjdKCEtZSEjISCAltW7yNXhEg9Rji64Cp7RBm5Mao4LBowEKHX3X2bmVMDqQ74IpQcnIL2GIQFYm3RZu3to1Knkm45XG4zZ/2lZxfV95n2iCousB1Pje003rEvVij0M/uQS3ng4VxudtTGnZWD4Y7vXb+jPd4T1JmC5ezIk1ZYg9V0Hx7q7gyh6yDcr9uW3l7p36cV/E0x1s/opRwb+k5y8/ZJlnuJpnw/KEmKhM3qm6iv1TwiIyzuI6XgsgeVqBhpCn+kIHALnX055FoBi6M+JBM0woDmV/GFBsDj0G2zLrCOrgKy+1pfGlYcgJ5ZTZHd9gPU/dgIkstg7G4KEiYSAl9Z7SKw20ItuaB/yPmdH1e+mVb0HXJaJglfJRS1XaFj+yLqGWyMw7O68Af4pJvittEI1muwTQ8P/rjZ+RMlYpO0K5ZnGiNJp1+3P+Lb68sJGfJYjvcVtapnJCROC9ljR+Lkq4SeT+ArkqxxeHOGQfsVoy7HGuU2phlJdn6etw+mkydcwlLGgGhNvELcRDwveLiXG3iXxLYMHlQFaaRVKMUFkuPX2bqiDnezaOrkxplAaWqsjCPCXEna9zGssu2l039C52ZkhtvHMm5ZDuzXu2sKsHA7rxKSxp51MuPhgX3aeivyGuvUPQYz9Xtk/Efaj84tzCMspWc+cZ8a/0Tw1oPYuSi3v643eHvYtBMqTviVrWYUxw8bP7mmIXccKEsfP5/xOXyDvoe78oNf4sT5c42vMxqvVOcIURz4Cmk5pTv1+fcIJFSWt9adXlfAGp05EgDWIj7NEqyfzraHP6knDBS2KFuMo4anN4ZvHIIcDNvHJyFQGOI8V1iLq7JrY85GqtkjGXg6Pghp/f3kcotU5W7YfWuRbcMm5ioJJ5skdNagails+PGmDMxXyaeijDg2qMWuwltLTUo0f6exKQWno0vEGsAX+NZSbjgTdjqrNHFTGVllGmZYMnSNmEMdX+ckE2DbIDZUNC/RNveUyBjW2FP9iHbQDySawsVyJExAnV6fwF6ouw5U27I3gp2CcNvFzbng/3SJql99wPDmir1smSzphbabrfAmw+ml5mMAfCA0720hF5B04VKb5HrusCWcV1Q3neftHXiPDQk75t519KSqJxiK2LMfE5HHHQBMHjFxg0sDNcw9Wf2YAIAcF3id2j2cQz1B2k6wfJPNBG8KbkP9nEAThD30yb8hF+VGuYj4T77O9kB1zEKci/6y0/JNq6YChCmuHsidJFIWcLmUo722xoFBYm5u7xn6cKXPiShln9Zh7nfJF1+7oy0AgwVKAIxnc2IQmG9oA2KF0ErukEckJxdbCCQtLPL2RouuBXkHRmMymDqkh0YJTpwo1x8IUOG3/+ohDVEfdmMcHFBWG4ZMMR6DCeTWuvbybHVdnmjtz7hRquAJS8ROvRAh6nod5E39uLeLaP5Kf/XpNEnY9Thov/SqoUwb6c2HeCbcPXprdbZSJSoI2a8nhqOeL1UvZ87OFbc251zjg21R/tbBltMjVOnIVT6m5uf1Wus+Oz3owOnrrucsOKKVRzIeLczIt7F+cY5g+r4f2SIQF6KicrlhHbwGnarZS9751PaPnAEBZPnfxxqNVLt36hdEydU2FrXvHvYTkLT//8hYP2xkz+582XEnicPQ8CW7LMiqgE3JrRhAIz9NsaXKeK8VgsGfuY4nwclAwqyfQ1RJUPIsgbui4OuaiUnLYc0jIIQ34p31VaowHcsZEDxIbMDhEVwyaS5gFUuiplzZSkMqiwkUjlnHKFjCq47K8+shm7h5M4+kDJWRvp69y1s6ps/FopzedcoeovZEhHwi5XXHUak8qWSCOLgxKsLWIjO01vQRMnTa2RvRwSLxEmmUn7MUgxYa0UydHcdFuNkOSq03jFMHQaoOifIRDKlSHNHDJo3HZ4/5OYFWZglA8X4at946+vlK5VxGh/BvWJyEuKx1IRJrCmBiNOmG1Pfav8huvFTbPJMHU1LUw4sPS4Dr2FZ9YK0xE6bGJZOBmpyvxPwAW78XORwisNGHpOrO9ddvkwQy2/xZjsVzDUNhvnjunGHcoM1iqwYG/KvShw37ucCZkfMudsJwj/TmqJkmKv+uygLMkn4CPsy3IG1RqnhWnvPLi6OTy6YvK1kUluQUhGeov/GYXd4ywSlez8sCdXkGf7qK5cOrbquUQNqhCH2uS2YDx7gpIWXJiKK93BGYLns82RCZfNu7IybGmfjoyRkEiKFRfOXyQSZqFpKLrG3mmWKhQsJtfstg+OFC8hseFlQxNClhR2Fm/pI/xrXiGIxbQkUC0WqNyPepvuIA19KSLgaBAZuDJxrP6k6XUGJ34v5PHVmcUUN2lPnBrXxs/4eTYQxqx3PmMU+wMZkplLc8e8r+rnu9LkmCkUYp19NFR5Y+x+C5S0i4CZck6xCSDB+bhNJ2a73nsDR0p+/qBzQg11Q7VkVB74j9zAeoXv3SdDvu59+6oq2yj3s+ArwIk+uN8tbVPBM2TETIqu214ala/5dUwfaz3o+t4HeRcjXvGTiAiQYj+CGxvCODZseAV7Z8oL2NaU/4+o1rSY2lMncOT8nYmk7zh3Mkl2K77Ag1sXhLjOJziCI6WIgZLFh6QSZBZaZpBpQdLITawwhnJ/Mg5ufF+c3xPG9f6cRj+0kuJ2jLKGNjGxfCZmQqLH7UZP5p1qY7IaztdcIplxx2eHBTYWKSouRDQidwtxzFF/A0Ia1GjnqolljQ4Ys34SYJ/6Ch0myIu/G/r32LP6Il7/YfyHFRpE6Wu45RWdtgKNQKHwoFSN18EMcXJHb4zcVI9ZijjL3gtwdiTOh08PLE1qQAVpxoY5Yzex5SPepV/v7nk5g46YLtxW3CQgzdfv1arMnzPnnlDVFE32UP2PpoLseY1a9gICPPGENwRAUb3QmxBRERKrIiLm4FxPy+UuFuduWgvXns4zLbFWfCSKWio5urFR8++zHPk4ajRywq78knAlASrAhRy+7S7ceZb9GkXNAvSqmvdZhULi10Sd5vKTGW85GeeJ9FGsqivTQxzVrtsrKrNOEXzKgGQDrIlUytxKARlh4GnEyIpzNCSQzYX3HnuusjoVl2IBnYGqUhtsgUJdNDOCGnh5TUsH29D7LerCiXRFNC9EjKsEbHZhVfS2Pd8+lylWtsEqoP0d08V0pu+JJ8JJ5hSmF1QQiRaIqc/cB67wy6tRl2anXxn84L46vIWKY45JeU+R4MJlxZgZssggq2b8bnqpPI0RElH/vXIgvxC3drPQhvWXHNS2kRDNV8VeyfcFItbC9F9Y+41HNHfjO3r/DMD1PdjljuDAM2NnTuPhueTSPgUkX0MAA7+TtzbkAAE21ZkD6EtA4BQ2FVUXQZ5E4Tgg0+YE1WEbkV9TeSCEKCkif6Oi7PvosEiePR7IvC9veceA4mDBXXlfnNU4sXHonqtMxz5IQpGkiA527pS0OwnepO1lAMDfrCSbEv5s0+sNe1mNheVTYb8HZTOQtWN6/s4xfJZaSL74X5j4tUQRS7GPwkSPWTNN8AqS51yhfzg+3JblBTnK+2eCFcksjJRwPE0nztlkmqztOkdmcAa0N7AWHfLvmEW2fsktcS/oh1uNKEqTTe5DIFXyMhGtRGtbJ+SxJeqBLWqjdAUzH3PGwNQLqxXTNU8hdT1pQaQxbvJ6wZgIjS5zEggnExwZ+EKVl7eu26axRKoEHO8mP1G/iFx0/KDNFlbcXwGQFIsut2VAtxRQq3mehT7fY+ZubXxKYieSLoKKvsYmZZm8QaDJj2BjfdlU5L4YYWjgHd3xLKeg0bdWQCYAkutafROIGM5zchHBzrpbmKPfLPhpdvMZt0gUL/U2vYVQl7Cb+DN/2My6OFUD0m14Ul9LKdXG1M5pFzBpwi/f8BAb9Irz372MgAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "panstarrs_coord = COORDS['north']\n", + "panstarrs_radius = 30 * u.arcsec\n", + "\n", + "panstarrs = Pan_STARRS_Survey(panstarrs_coord, panstarrs_radius)\n", + "panstarrs_catalog = panstarrs.get_catalog(photoz=False)\n", + "preview_catalog(panstarrs_catalog)\n", + "\n", + "panstarrs_image = panstarrs.get_image(imsize=30 * u.arcsec, band='g')\n", + "plot_fits_product(panstarrs_image, 'Pan-STARRS g-band FITS image', band='g')\n", + "\n", + "panstarrs_cutout = panstarrs.get_cutout(imsize=30 * u.arcsec, band='gri')\n", + "show_cutout_product(panstarrs_cutout, 'Pan-STARRS gri cutout', band='gri')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Infrared, UV, And Radio Surveys\n", + "\n", + "This section covers surveys at wavelengths outside the optical window. Several of these use backend services other than NOIRLab Astro Data Lab: WISE and VISTA use IRSA/VSA, GALEX and 2MASS retrieve images through `astroquery.skyview`, and Euclid uses the `astroquery.esa.euclid` module. The radio surveys (FIRST, NVSS, WENSS) retrieve catalogs from HEASARC and images through SkyView.\n", + "\n", + "| Class | Survey | Wavelength | Sky coverage |\n", + "|---|---|---|---|\n", + "| `WISE_Survey` | WISE/unWISE | 3.4–22 µm (W1–W4) | all-sky |\n", + "| `VISTA_Survey` | VISTA (VHS/VIDEO/VIKING) | 0.9–2.4 µm (ZYJHKs) | ~17 000 deg² |\n", + "| `TwoMASS_Survey` | 2MASS | 1.2–2.2 µm (JHK) | all-sky |\n", + "| `GALEX_Survey` | GALEX | 135–280 nm (FUV/NUV) | ~3/4 sky |\n", + "| `Euclid_Survey` | Euclid Q1 | 0.55–2.0 µm (VIS + NISP) | ongoing |\n", + "| `FIRST_Survey` | FIRST | 20 cm (1.4 GHz) | ~10 000 deg² |\n", + "| `NVSS_Survey` | NVSS | 20 cm (1.4 GHz) | δ > −40° |\n", + "| `WENSS_Survey` | WENSS | 92 cm (325 MHz) | δ > +28° |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### WISE (Wide-field Infrared Survey Explorer)\n", + "\n", + "`WISE_Survey` wraps the WISE/unWISE all-sky mid-infrared survey in four bands: W1 (3.4 µm), W2 (4.6 µm), W3 (12 µm), and W4 (22 µm). The `unWISE` coadds extend sensitivity compared to the original WISE release by co-adding all available epochs. Both `get_catalog()` and `get_image()` are supported; catalog queries and image downloads go through the IRSA TAP service via `astroquery.ipac.irsa`.\n", + "\n", + "Note that the IRSA service can be intermittently slow or unavailable — if a query times out, retry after a short wait. WISE data are particularly useful for identifying obscured AGN, dusty host galaxies, and foreground spiral galaxies along FRB sightlines." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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source_idradectmass_keyWISE_W1WISE_W1_errWISE_W2WISE_W2_errWISE_W3WISE_W3_errWISE_W4WISE_W4_errseparation
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objectfloat64float64int64float32float32float32float32float32float32float32float32float64
3263m409_ac51-031287326.1052734-40.9002904--19.4630.21519.293-99.00016.385-99.00014.615-99.0000.0044760784075461545
" + ], + "text/plain": [ + "\n", + " source_id ra ... WISE_W4_err separation \n", + " deg ... mag arcmin \n", + " object float64 ... float32 float64 \n", + "-------------------- ----------- ... ----------- ---------------------\n", + "3263m409_ac51-031287 326.1052734 ... -99.000 0.0044760784075461545" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 13\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/2736704550.py:12: DeprecationWarning: get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.\n", + " wise_cutout = wise.get_cutout(imsize=120 * u.arcsec, band='W1')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# WISE uses infrared band names such as W1.\n", + "wise_coord = COORDS['south']\n", + "wise_radius = 10 * u.arcsec\n", + "\n", + "wise = WISE_Survey(wise_coord, wise_radius)\n", + "wise_catalog = wise.get_catalog()\n", + "preview_catalog(wise_catalog)\n", + "\n", + "wise_image = wise.get_image(imsize=120 * u.arcsec, band='W1')\n", + "plot_fits_product(wise_image, 'WISE W1 FITS image', band='W1')\n", + "\n", + "wise_cutout = wise.get_cutout(imsize=120 * u.arcsec, band='W1')\n", + "show_cutout_product(wise_cutout, 'WISE W1 cutout', band='W1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### VISTA (Visible and Infrared Survey Telescope for Astronomy)\n", + "\n", + "`VISTA_Survey` wraps data from ESO's VISTA 4.1 m telescope in the NIR bands *ZYJHKs*, through the VISTA Science Archive (VSA). The main public surveys include VIKING (~1 500 deg², all five bands), VHS (all-sky pilot in *JHKs*), and VIDEO (~12 deg², very deep *ZYJHKs*).\n", + "\n", + "**Catalog access** uses the VSA TAP service. **Image retrieval** (`get_image()`) parses the VSA FITS-cutout web form and is relatively untested compared to other survey classes. Results may vary depending on VSA service availability and the specific VISTA survey covering a given position — always check whether the requested field is within the VISTA survey footprint before querying." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=1\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "
VISTA_IDradecVISTA_CLASSVISTA_YVISTA_Y_errVISTA_JVISTA_J_errVISTA_HVISTA_H_errVISTA_KsVISTA_Ks_errseparation
arcmin
int64float64float64int64float64float64float64float64float64float64float64float64float64
472650415566326.10519341964067-40.900233820837411-99.0-99.019.2616584896173360.106358-99.0-99.018.950135032771440.1737150.0019392945910536846
" + ], + "text/plain": [ + "\n", + " VISTA_ID ra ... VISTA_Ks_err separation \n", + " ... arcmin \n", + " int64 float64 ... float64 float64 \n", + "------------ ------------------ ... ------------ ---------------------\n", + "472650415566 326.10519341964067 ... 0.173715 0.0019392945910536846" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 13\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/1541989471.py:11: DeprecationWarning: get_cutout() returns FITS products for this survey and is deprecated; use get_image() instead.\n", + " vista_cutout = vista.get_cutout(imsize=30 * u.arcsec, band='J')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vista_coord = COORDS['south']\n", + "vista_radius = 10 * u.arcsec\n", + "\n", + "vista = VISTA_Survey(vista_coord, vista_radius)\n", + "vista_catalog = vista.get_catalog(system='AB')\n", + "preview_catalog(vista_catalog)\n", + "\n", + "vista_image = vista.get_image(imsize=30 * u.arcsec, band='J')\n", + "plot_fits_product(vista_image, 'VISTA J-band FITS image', band='J')\n", + "\n", + "vista_cutout = vista.get_cutout(imsize=30 * u.arcsec, band='J')\n", + "show_cutout_product(vista_cutout, 'VISTA J-band cutout', band='J')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2MASS (Two Micron All Sky Survey)\n", + "\n", + "`TwoMASS_Survey` wraps the Two Micron All Sky Survey, a photometric all-sky survey in *JHKs* (1.2, 1.65, and 2.17 µm) from two dedicated 1.3 m telescopes. The Atlas Images achieve a pixel scale of 1.0 arcsec/pixel. The survey reaches *J* ≈ 15.8, *H* ≈ 15.1, *Ks* ≈ 14.3 (Vega, 10σ). `get_catalog()` returns detections from the 2MASS All-Sky Point Source Catalog (PSC) or Extended Source Catalog (XSC) as appropriate.\n", + "\n", + "**Image products:** `get_image()` retrieves a resampled FITS product through the SkyView service at the native 2MASS Atlas pixel scale (1.0 arcsec/pixel). See the note on SkyView image resampling in the SDSS section." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty catalog returned.\n", + "Columns: ['ra', 'dec', '2MASS_ID', '2MASS_j', '2MASS_j_err', '2MASS_h', '2MASS_h_err', '2MASS_k', '2MASS_k_err', 'separation']\n", + "Got image spanning (RA, Dec) = (326.1103291218395 - 326.0993040701842, -40.90422765511903 - -40.89589430434072)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tmass_coord = COORDS['south']\n", + "tmass_radius = 10 * u.arcsec\n", + "\n", + "tmass = TwoMASS_Survey(tmass_coord, tmass_radius)\n", + "tmass_catalog = tmass.get_catalog()\n", + "preview_catalog(tmass_catalog)\n", + "\n", + "tmass_image = tmass.get_image(imsize=30 * u.arcsec, band='J')\n", + "plot_fits_product(tmass_image, '2MASS J-band FITS image', band='J')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GALEX (Galaxy Evolution Explorer)\n", + "\n", + "`GALEX_Survey` wraps the GALEX ultraviolet sky survey in two bands: FUV (134–179 nm, λ_eff ≈ 153 nm) and NUV (177–283 nm, λ_eff ≈ 228 nm). GALEX observed roughly three-quarters of the sky in the All-sky Imaging Survey (AIS), with deeper Medium Imaging Survey (MIS) tiles over ~1 000 deg².\n", + "\n", + "`get_catalog()` returns source detections from the GALEX catalog via `astroquery`. **Image products:** `get_image()` retrieves FITS images through the SkyView service at the native GALEX pixel scale of 1.5 arcsec/pixel. GALEX coverage is non-uniform — bright-star avoidance and scheduling constraints leave gaps; check coverage before interpreting a null result." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=2\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
GALEX_IDradecGALEX_FUVGALEX_FUV_errGALEX_NUVGALEX_NUV_errseparation
arcmin
str19float64float64float64float64float64float64float64
6378586013030156509216.385305151304312.022629585893621.20187760.36277821720.80256460.2508650.023327271040038986
3754210022776641358216.385331291780512.02247894107883-99.0-99.021.12729640.085756110.024674333639890803
" + ], + "text/plain": [ + "\n", + " GALEX_ID ra ... GALEX_NUV_err separation \n", + " ... arcmin \n", + " str19 float64 ... float64 float64 \n", + "------------------- ----------------- ... ------------- --------------------\n", + "6378586013030156509 216.3853051513043 ... 0.250865 0.023327271040038986\n", + "3754210022776641358 216.3853312917805 ... 0.08575611 0.024674333639890803" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 2\n", + "Columns: 8\n", + "Got image spanning (RA, Dec) = (216.3890470434305 - 216.38052681344547, 12.018541637563306 - 12.026874964405545)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "galex_coord = COORDS['galex']\n", + "galex_radius = 10 * u.arcsec\n", + "\n", + "galex = GALEX_Survey(galex_coord, galex_radius)\n", + "galex_catalog = galex.get_catalog()\n", + "preview_catalog(galex_catalog)\n", + "\n", + "# Retrieve images with Skyview\n", + "galex_image = galex.get_image(imsize=30 * u.arcsec, band='NUV')\n", + "plot_fits_product(galex_image, 'GALEX NUV FITS image', band='NUV')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Euclid\n", + "\n", + "`Euclid_Survey` wraps the Euclid space mission's publicly available data products, currently limited to the Q1 data release. Euclid observes in one optical band (VIS, 0.55–0.9 µm at 0.1 arcsec/pixel) and three near-infrared bands (Y, J, H via the NISP instrument). The survey will ultimately cover ~15 000 deg² of the extragalactic sky with exquisite resolution (~0.2 arcsec in VIS).\n", + "\n", + "`get_catalog()` queries source detections via `astroquery.esa.euclid`. `get_image()` retrieves VIS-band FITS cutouts; NISP-band image retrieval is not yet implemented. The class is designed to be forwards-compatible with future Euclid data releases as more survey area and additional instruments become available." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + }, + { + "data": { + "text/html": [ + "
Table length=1\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "
Euclid_IDradecEuclid_VISEuclid_VIS_errEuclid_JEuclid_J_errEuclid_HEuclid_H_errEuclid_YEuclid_Y_errEuclid_ellipticityEuclid_kron_radiusEuclid_segmentation_areavis_detdet_quality_flagdistseparation
arcmin
int64float64float64float64float64float64float64float64float64float64float64float64float64int64int64int64float64float64
2677813028655307424267.7813028407057365.5307424310022113.0966457335140710.00508724541028606113.0966457335140710.00515066220592635913.0966457335140710.00585601600653461413.0966452275932390.0047447868903376210.20267501473426821052.3164062571096515140.000198562328361634570.011859498634944995
" + ], + "text/plain": [ + "\n", + " Euclid_ID ra ... separation \n", + " ... arcmin \n", + " int64 float64 ... float64 \n", + "------------------- ------------------ ... --------------------\n", + "2677813028655307424 267.78130284070573 ... 0.011859498634944995" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 18\n", + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Euclid exposes both a catalog query and FITS image/cutout retrieval.\n", + "euclid_coord = COORDS['euclid']\n", + "euclid_radius = 10 * u.arcsec\n", + "\n", + "euclid = Euclid_Survey(euclid_coord, euclid_radius)\n", + "euclid_catalog = euclid.get_catalog(timeout=30, check_spectra=False)\n", + "preview_catalog(euclid_catalog)\n", + "\n", + "euclid_image = euclid.get_image(imsize=30 * u.arcsec, timeout=30)\n", + "plot_fits_product(euclid_image, 'Euclid FITS image', band='VIS')\n", + "\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter('ignore', DeprecationWarning)\n", + " euclid_cutout = euclid.get_cutout(imsize=30 * u.arcsec, timeout=30)\n", + "show_cutout_product(euclid_cutout, 'Euclid cutout', band='VIS')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Radio Surveys Via HEASARC And SkyView\n", + "\n", + "The three radio survey classes (FIRST, NVSS, WENSS) follow a split-backend architecture. **Catalog queries** use the HEASARC Browse TAP service via `astroquery.heasarc`. **FITS image retrieval** (`get_image()`) uses the SkyView service at the native pixel scale of each survey. The `imsize` argument sets the delivered image size as an `astropy` `Quantity` (e.g. `4 * u.arcmin`); the class computes the required pixel grid automatically from the survey's native pixel scale." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FIRST (Faint Images of the Radio Sky at Twenty Centimeters)\n", + "\n", + "`FIRST_Survey` wraps the FIRST 1.4 GHz VLA survey, which covers ~10 000 deg² of the northern and equatorial sky to a typical sensitivity of ~1 mJy/beam with a restoring beam of ~5 arcsec (VLA B-configuration). SkyView delivers FIRST FITS images at 1.8 arcsec/pixel. FIRST is the default first-stop radio survey for FRB counterpart searches in the north due to its combination of area and resolution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=5\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
nameradecflux_20_cmflux_20_cm_errorint_flux_20_cmsidelobe_probtwomass_first_offsettwomass_kmag__rowseparation
degdegmJymJymJyarcsecmagarcmin
objectfloat64float64float64float64float64float64float64float64objectfloat64
FIRST J093518.4+020417143.8267502.071653123.220.154234.950.0144.0114.062084510.15301931325323723
FIRST J093516.2+020354143.8178582.065253102.750.154270.790.014----2083240.7562250315215266
FIRST J093516.1+020350143.8174582.063903153.250.154195.790.014----2082970.815954376449813
FIRST J093527.8+020504143.8662122.0846141.100.1490.670.812----2086762.370184987974581
FIRST J093506.6+020205143.7778712.0347973.510.1524.640.014----2077983.7597016901562985
" + ], + "text/plain": [ + "\n", + " name ra dec ... __row separation \n", + " deg deg ... arcmin \n", + " object float64 float64 ... object float64 \n", + "---------------------- ---------- -------- ... ------ -------------------\n", + "FIRST J093518.4+020417 143.826750 2.071653 ... 208451 0.15301931325323723\n", + "FIRST J093516.2+020354 143.817858 2.065253 ... 208324 0.7562250315215266\n", + "FIRST J093516.1+020350 143.817458 2.063903 ... 208297 0.815954376449813\n", + "FIRST J093527.8+020504 143.866212 2.084614 ... 208676 2.370184987974581\n", + "FIRST J093506.6+020205 143.777871 2.034797 ... 207798 3.7597016901562985" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 5\n", + "Columns: 11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/4065823663.py:9: DeprecationWarning: radius is deprecated for SkyView-backed image retrieval; use imsize instead.\n", + " first_image = first.get_image(radius=1 * u.arcmin)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got image spanning (RA, Dec) = (143.84542846899575 - 143.81207334257033, 2.054412005622627 - 2.0877453313692222)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got image spanning (RA, Dec) = (143.84542846899575 - 143.81207334257033, 2.054412005622627 - 2.0877453313692222)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# FIRST combines a HEASARC catalog query with a SkyView FITS image.\n", + "first_coord = COORDS['4C 02.27']\n", + "first_radius = 5 * u.arcmin\n", + "\n", + "first = FIRST_Survey(first_coord, first_radius)\n", + "first_catalog = first.get_catalog()\n", + "preview_catalog(first_catalog)\n", + "\n", + "first_image = first.get_image(imsize=2 * u.arcmin)\n", + "plot_fits_product(first_image, 'FIRST FITS image', band='radio')\n", + "\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter('ignore', DeprecationWarning)\n", + " first_cutout = first.get_cutout(imsize=2 * u.arcmin)\n", + "show_cutout_product(first_cutout, 'FIRST cutout', band='radio')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NVSS (NRAO VLA Sky Survey)\n", + "\n", + "`NVSS_Survey` wraps the NVSS 1.4 GHz VLA survey, which covers the entire sky north of δ = −40° to a flux-density limit of ~2.5 mJy/beam (1σ ≈ 0.45 mJy/beam) with a 45 arcsec resolution beam. SkyView delivers NVSS FITS images at 15 arcsec/pixel. Although its resolution is much coarser than FIRST, NVSS's uniform all-sky coverage makes it an important complement for southern fields and for verifying whether a FIRST detection has extended emission resolved out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=3\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
nameradecflux_20_cmflux_20_cm_error__rowseparation
degdegmJymJyarcmin
objectfloat64float64float64float64objectfloat64
NVSS J093516+020400143.820422.06692770.324.57285650.5748830161893839
NVSS J093506+020205143.779042.034725.60.57279023.705117307812842
NVSS J093512+020003143.801792.000973.60.57271634.501607599558662
" + ], + "text/plain": [ + "\n", + " name ra dec ... __row separation \n", + " deg deg ... arcmin \n", + " object float64 float64 ... object float64 \n", + "------------------- --------- ------- ... ------ ------------------\n", + "NVSS J093516+020400 143.82042 2.06692 ... 728565 0.5748830161893839\n", + "NVSS J093506+020205 143.77904 2.03472 ... 727902 3.705117307812842\n", + "NVSS J093512+020003 143.80179 2.00097 ... 727163 4.501607599558662" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 3\n", + "Columns: 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/1227793813.py:5: DeprecationWarning: radius is deprecated for SkyView-backed image retrieval; use imsize instead.\n", + " nvss_image = nvss.get_image(radius=1 * u.arcmin)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got image spanning (RA, Dec) = (143.84359272902287 - 143.81023752540534, 2.056246600346948 - 2.0895798873923526)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nvss = NVSS_Survey(first_coord, first_radius)\n", + "nvss_catalog = nvss.get_catalog()\n", + "preview_catalog(nvss_catalog)\n", + "\n", + "nvss_image = nvss.get_image(imsize=2 * u.arcmin)\n", + "plot_fits_product(nvss_image, 'NVSS FITS image', band='radio')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Target" + "### WENSS (Westerbork Northern Sky Survey)\n", + "\n", + "`WENSS_Survey` wraps the WENSS 325 MHz survey conducted with the Westerbork Synthesis Radio Telescope (WSRT), covering δ > +28° to a typical rms of ~3.6 mJy/beam at ~54 arcsec × 54 arcsec cosec(δ) resolution. SkyView delivers WENSS FITS images at 21 arcsec/pixel. The low observing frequency makes WENSS the primary tool in `frb.surveys` for constraining source spectral indices — combining a WENSS detection with NVSS or FIRST gives a spectral index between 325 MHz and 1.4 GHz, useful for distinguishing steep-spectrum AGN from flat-spectrum compact sources." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=1\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "
nameradecflux_92_cmflux_92_cm_errorsource_type__rowseparation
degdegmJy / beammJy / beamarcmin
objectfloat64float64int32float64objectobjectfloat64
WN 0316.4+411949.9478341.50756193959.3E802310.2791922643480341
" + ], + "text/plain": [ + "\n", + " name ra dec ... source_type __row separation \n", + " deg deg ... arcmin \n", + " object float64 float64 ... object object float64 \n", + "-------------- -------- -------- ... ----------- ------ ------------------\n", + "WN 0316.4+4119 49.94783 41.50756 ... E 80231 0.2791922643480341" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_141131/1531751430.py:6: DeprecationWarning: radius is deprecated for SkyView-backed image retrieval; use imsize instead.\n", + " wenss_image = wenss.get_image(radius=2 * u.arcmin)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got image spanning (RA, Dec) = (49.99114874690863 - 49.902111491904506, 41.481389887462335 - 41.54805340504999)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "coord = SkyCoord('J214425.25-403400.81', unit=(units.hourangle, units.deg))\n", - "search_r = 10 * units.arcsec" + "wenss_coord = COORDS['wenss']\n", + "wenss = WENSS_Survey(wenss_coord, first_radius)\n", + "wenss_catalog = wenss.get_catalog()\n", + "preview_catalog(wenss_catalog)\n", + "\n", + "wenss_image = wenss.get_image(imsize=4 * u.arcmin)\n", + "plot_fits_product(wenss_image, 'WENSS FITS image', band='radio')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Catalog" + "## 3. Spectroscopic And Catalog-Only Surveys\n", + "\n", + "This section covers three survey classes that provide **catalog-only** access (no image retrieval). They span galaxy redshifts, local-universe distances, and known radio pulsars — all relevant for characterising the sightline to an FRB.\n", + "\n", + "| Class | Catalog | Type | Notes |\n", + "|---|---|---|---|\n", + "| `DESI_Survey` | DESI EDR/DR1 | Spectroscopic | Fiber-fed spectrograph; ~40 M targets in DR1 |\n", + "| `NEDLVS` | NED Local Volume Sample | Photometric + distances | Requires local FITS file; env var `NEDLVS` |\n", + "| `PSRCAT_Survey` | ATNF Pulsar Catalog | Radio pulsars | Requires `pulsars` package |" ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SELECT mag_auto_g, magerr_auto_g, mag_auto_r, magerr_auto_r, mag_auto_i, magerr_auto_i, mag_auto_z, magerr_auto_z, mag_auto_y, magerr_auto_y, coadd_object_id, ra, dec, tilename\n", - " FROM des_dr1.main\n", - " WHERE q3c_radial_query(ra,dec,326.105208,-40.566892,0.002778)\n", - " \n" - ] - } - ], "source": [ - "des_srvy = survey_utils.load_survey_by_name('DES', coord, search_r)\n", - "des_tbl = des_srvy.get_catalog(print_query=True)" + "### DESI (Dark Energy Spectroscopic Instrument DR1)\n", + "\n", + "`DESI_Survey` is a catalog-only class that queries the DESI Data Release 1 (DR1) spectroscopic database via the NOIRLab Astro Data Lab TAP service. DESI is a fiber-fed spectrograph at the 4 m Mayall telescope at Kitt Peak, targeting ~40 million galaxies, quasars, and stars across ~14 000 deg² to measure redshifts for large-scale structure and dark energy studies. The spectral range is 360–980 nm.\n", + "\n", + "`get_catalog()` returns spectrographic redshifts, object classifications, and fiber positions. No image retrieval is available through `frb.surveys` for DESI." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "Table length=1\n", - "
\n", - "\n", - "\n", - "\n", - "
DES_gDES_g_errDES_rDES_r_errDES_iDES_i_errDES_zDES_z_errDES_YDES_Y_errDES_IDradecDES_tile
float64float64float64float64float64float64float64float64float64float64int64float64float64str12
23.99050.19443822.31050.059530420.96950.02965159999999999720.43850.039153520.2920.100701209895628326.105565-40.569421999999996DES2143-4040
" + "
Table length=5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
DESI_IDradecDESI_nameDESI_spectypeDESI_specsubtypeDESI_surveyDESI_zDESI_z_errDESI_z_warnDESI_zcat_primaryDESI_zcat_nspecseparation
arcmin
int64int64int64str22str6str3str4float64float64int64str1int64float64
3962828383427429000DESI J147.8359+20.9168GALAXY-99main0.26733760998413038.147982792720394e-050t10.0
3962831754808587000DESI J147.5941+22.4968GALAXY-99main0.25709904826094397.857824710233075e-050t10.0
3962830636607403400DESI J148.6775+22.0878GALAXY-99main0.41903765296451780.0001211939784962080t10.0
3962826118502844600DESI J148.6918+20.0811GALAXY-99main0.068807459358573646.024383776059339e-060t10.0
3962831194869378400DESI J147.2967+22.1551GALAXY-99main0.20047272596510540.0001075817673972210t10.0
" ], "text/plain": [ - "\n", - " DES_g DES_g_err DES_r DES_r_err DES_i DES_i_err DES_z DES_z_err DES_Y DES_Y_err DES_ID ra dec DES_tile \n", - "float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 float64 float64 str12 \n", - "------- --------- ------- --------- ------- -------------------- ------- --------- ------- --------- --------- ---------- ------------------- ------------\n", - "23.9905 0.194438 22.3105 0.0595304 20.9695 0.029651599999999997 20.4385 0.0391535 20.292 0.100701 209895628 326.105565 -40.569421999999996 DES2143-4040" + "
\n", + " DESI_ID ra dec ... DESI_zcat_primary DESI_zcat_nspec separation\n", + " ... arcmin \n", + " int64 int64 int64 ... str1 int64 float64 \n", + "----------------- ----- ----- ... ----------------- --------------- ----------\n", + "39628283834274290 0 0 ... t 1 0.0\n", + "39628317548085870 0 0 ... t 1 0.0\n", + "39628306366074034 0 0 ... t 1 0.0\n", + "39628261185028446 0 0 ... t 1 0.0\n", + "39628311948693784 0 0 ... t 1 0.0" ] }, - "execution_count": 4, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 3230\n", + "Columns: 13\n" + ] } ], "source": [ - "des_tbl" + "# DESI is intentionally spectroscopic-only.\n", + "desi_coord = COORDS['equator']\n", + "desi_radius = 0.3 * u.arcmin\n", + "\n", + "desi = DESI_Survey(desi_coord, desi_radius)\n", + "desi_catalog = desi.get_catalog(exclude_stars=True, zcat_primary_only=True)\n", + "preview_catalog(desi_catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Cut-out" + "### NEDLVS (NED Local Volume Sample)\n", + "\n", + "`NEDLVS` is a catalog-only class that performs cone searches on the NED Local Volume Sample, a compilation of ~27 000 galaxies within 1000 Mpc with distances from redshift-independent methods (surface brightness fluctuations, Cepheids, Tully–Fisher, etc.) and spectroscopic redshifts. The catalog is particularly useful for identifying extremely local host galaxies where peculiar velocities are significant.\n", + "\n", + "Because the table is large (~2 GB), it is not downloaded automatically. Obtain it from https://ned.ipac.caltech.edu/NED::LVS/ and save its path in the `NEDLVS` environment variable before using this class:\n", + "```bash\n", + "export NEDLVS=/path/to/NED_LVS.fits\n", + "```\n", + "`get_catalog()` reads the local file and performs an in-memory cone search, so no network connection is needed at query time." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "cutout, cutout_hdr = des_srvy.get_cutout(search_r, band='r')" + "try:\n", + " nedlvs = NEDLVS(COORDS['north'], 10 * u.arcmin)\n", + " nedlvs_catalog = nedlvs.get_catalog()\n", + " preview_catalog(nedlvs_catalog)\n", + "except AssertionError as exc:\n", + " print(f'NEDLVS setup missing: {exc}')" ] }, { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "wcs = WCS(des_srvy.cutout_hdr)" + "### PSRCAT (ATNF Pulsar Catalogue)\n", + "\n", + "`PSRCAT_Survey` is a catalog-only class that queries the ATNF Pulsar Catalogue via the external `pulsars` package. It is useful for identifying known pulsars that might appear within an FRB localisation region and for comparing observed pulse parameters against known pulsar properties. The returned table includes standard pulsar parameters such as DM, period, period derivative, and position.\n", + "\n", + "This class requires the `pulsars` package, which is not installed by default:\n", + "```bash\n", + "pip install git+https://github.com/FRBs/pulsars\n", + "```" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading up DMs from /mnt/popos/home/sunil/Python/pulsars/pulsars/data/atnf_cat/DM_cat_v1.56.dat\n", + "Loading up Parallax data from /mnt/popos/home/sunil/Python/pulsars/pulsars/data/parallax/Plx_aug2018.dat\n" + ] + }, { "data": { - "image/png": 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degdegarcmin
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1J0006+1834cnt9600:06:04.82cn95+18:34:594cn950.6937476704714cn9511.4155bkk+16-42.98498835673795108.172135766087410.00.00.01.5218.5830555555555570.0
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" + "\n", + " ID PSRJ Pref RAJ ... ra dec separation\n", + " ... deg deg arcmin \n", + "int64 str12 str7 str16 ... float64 float64 float64 \n", + "----- ---------- ----- ---------- ... ------- ------------------ ----------\n", + " 1 J0006+1834 cnt96 00:06:04.8 ... 1.52 18.583055555555557 0.0" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1\n", + "Columns: 23\n" + ] } ], "source": [ - "plt.clf()\n", - "plt.subplot(projection=wcs)\n", - "plt.imshow(cutout, origin='lower')\n", - "plt.show()" + "psrcat = PSRCAT_Survey(COORDS['pulsar'], 10 * u.arcsec)\n", + "psrcat_catalog = psrcat.get_catalog()\n", + "preview_catalog(psrcat_catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## FIRST" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "coord2 = SkyCoord('J081240.68+320809', unit=(units.hourangle, units.deg))\n", - "search_r = 10 * units.arcsec" + "## 4. Group And Cluster Catalog Classes\n", + "\n", + "The `frb.surveys.cluster_search` module provides catalog wrappers for published galaxy group and cluster catalogs. Every class inherits from `VizierCatalogSearch` (itself a `SurveyCoord` subclass) and queries the corresponding Vizier table. The common interface is:\n", + "\n", + "```python\n", + "cat = SomeCatalogClass(coord, radius=)\n", + "table = cat.get_catalog() # returns an astropy Table\n", + "```\n", + "\n", + "Each class normalises the raw Vizier columns into a standard set (`ra`, `dec`, `z`, `Dist`) and optionally accepts a `transverse_distance_cut` (astropy `Quantity` in Mpc) to retain only clusters whose projected separation from `coord` is within that limit. Some classes also accept a `richness_cut` to filter on minimum group membership." ] }, { - "cell_type": "code", - "execution_count": 9, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "first_srvy = survey_utils.load_survey_by_name('FIRST', coord2, search_r)\n", - "first_tbl = first_srvy.get_catalog()" + "### Tully+2015 Galaxy Groups\n", + "\n", + "`TullyGroupCat` wraps the [Tully (2015)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/AJ/149/171) catalog of ~13 000 nearby galaxy groups assembled from the 2MRS and SDSS surveys (Vizier: `J/AJ/149/171/table5`). Distances are originally in $h^{-1}$\\,Mpc and are automatically converted to Mpc using the configured cosmology.\n", + "\n", + "`get_catalog()` accepts two extra filter keywords beyond the standard `transverse_distance_cut`:\n", + "\n", + "- `richness_cut` (default 5) — minimum number of member galaxies `Ngal` required; use 1 to include isolated galaxies.\n", + "\n", + "Because this catalog covers the very local universe (typically $z \\lesssim 0.05$), it is especially useful when investigating foreground DM contributions from nearby large-scale structure along FRB sightlines." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "Table length=1\n", - "
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NAMEradecFLUX_20_CMFLUX_20_CM_ERRORINT_FLUX_20_CMSIDELOBE_PROBTWOMASS_FIRST_OFFSETTWOMASS_KMAGSEARCH_OFFSET_separation
degdegMJYMJYMJYARCSECMAGarcmin
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FIRST J081240.6+320808123.16951232.13569718.320.14618.400.0140.3115.310.008 (123.16950579044646,32.135827323283664)0.008202689831452928
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_rrecnoPGCLEDAGLONGLATSGLONSGLATMTypeHVVlsVcmbaJ-HJ-KKmaglogLKilogpKf_NestNestNgalPGC1DistDMGSGLONGSGLATlogLKCFsigPR2t<Vcmba>Vbwe_VbwsigbwsigVRbwe_RbwMvirMlumHDCLDC2M++SGXSGYSGZSimbadradecseparation
degdegdegdegkm / skm / skm / smagmagmaglog(solLum)log(solLum.Mpc**-3)maglog(solLum)km / sMpckm / skm / skm / skm / skm / sMpcMpcTsolMassTsolMassMpcMpcMpcdegdegarcmin
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9.601641977454111LEDA2.862249.1512123.206533.1534-1.71802178920810.670.888.7010.3510.451100073135393226.9263701619847731.31124.272631.704711.631.012780.56618251826772792800.4880.02741.50032.1008528861040-8.3912.8210.01Simbad227.38993.0527576.1006136970886
10.181061973754013LEDA1.862049.1786123.605432.5911-1.11730171419350.670.878.8010.3110.451100073135393226.9263701619847731.31124.272631.704711.631.012780.56618251826772792800.4880.02741.50032.1008528861040-8.5312.849.86Simbad226.94882.5686610.8613773400438
10.527131940353201LEDA359.747052.8318120.850329.5016-2.01477145417050.670.939.1810.1410.36110051955324726.19387132159416631.26121.300329.505910.911.001480.3011784176249670.1200.0200.0104.8208528801162-7.9413.308.77Simbad223.31263.9597631.631105366802
10.655361978854134LEDA1.737048.2945124.546432.99544.01736171919300.650.9811.219.3510.431100073135393226.9263701619847731.31124.272631.704711.631.012780.56618251826772792800.4880.02741.50032.100000-8.7012.649.97Simbad227.57001.9336639.3211931452793
10.762541941653247LEDA359.431452.4229121.365029.55795.21676165218920.801.157.7510.7110.36110051955324726.19387132159416631.26121.300329.505910.911.001480.3011784176249670.1200.0200.0104.8208528801162-8.0613.228.78Simbad223.48993.5443645.7526704182503
" ], "text/plain": [ - "\n", - " NAME ra dec FLUX_20_CM FLUX_20_CM_ERROR ... SIDELOBE_PROB TWOMASS_FIRST_OFFSET TWOMASS_KMAG SEARCH_OFFSET_ separation \n", - " deg deg MJY MJY ... ARCSEC MAG arcmin \n", - " bytes22 float64 float64 float64 float64 ... float64 float64 float64 bytes46 float64 \n", - "---------------------- ---------- --------- ---------- ---------------- ... ------------- -------------------- ------------ ---------------------------------------------- --------------------\n", - "FIRST J081240.6+320808 123.169512 32.135697 18.32 0.146 ... 0.014 0.31 15.31 0.008 (123.16950579044646,32.135827323283664)\n", - " 0.008202689831452928" + "
\n", + " _r recno PGC LEDA ... Simbad ra dec separation \n", + " ... deg deg arcmin \n", + "float64 int32 int32 str4 ... str6 float64 float64 float64 \n", + "-------- ----- ----- ---- ... ------ -------- -------- -----------------\n", + " 9.60164 19774 54111 LEDA ... Simbad 227.3899 3.0527 576.1006136970886\n", + "10.18106 19737 54013 LEDA ... Simbad 226.9488 2.5686 610.8613773400438\n", + "10.52713 19403 53201 LEDA ... Simbad 223.3126 3.9597 631.631105366802\n", + "10.65536 19788 54134 LEDA ... Simbad 227.5700 1.9336 639.3211931452793\n", + "10.76254 19416 53247 LEDA ... Simbad 223.4899 3.5443 645.7526704182503" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 6\n", + "Columns: 48\n" + ] } ], "source": [ - "first_tbl" + "# Tully+2015 includes a useful example of a survey-specific keyword argument.\n", + "tully = TullyGroupCat(COORDS['north'], radius=40 * u.deg)\n", + "tully_catalog = tully.get_catalog(transverse_distance_cut=5 * u.Mpc)\n", + "preview_catalog(tully_catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## DECals" + "### Wen+2024 Galaxy Clusters\n", + "\n", + "`WenGroupCat` wraps the [Wen & Han (2024)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/ApJS/272/39) catalog of ~1.58 million photometric galaxy clusters identified in the DESI Legacy Imaging Surveys DR10 (Vizier: `J/ApJS/272/39/table2`). Cluster redshifts span $0.05 \\lesssim z \\lesssim 1.5$, making this one of the largest all-sky optical cluster catalogs available.\n", + "\n", + "`get_catalog()` accepts `richness_cut` (default 5, corresponding to `Ngal`) and `transverse_distance_cut`. Because the default search radius is 0.2 deg, widen it for large sky surveys or to catch the cluster outskirts." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 54, "metadata": {}, "outputs": [ { - "name": "stderr", + "data": { + "text/html": [ + "
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_rrecnoIDn_NameNameradeczf_zClzmagW1maglogMsr500lam500M500NgalGammae_Gammaimag?CatDistseparation
degdegmagmagsolMassMpc1e+14 solMassarcmin
float64int32int32str3str16float64float64float32uint8float32float32float32float32float32float32int16float32float32uint8str9float64float64
0.06449613803831380383J151848.7+122628229.7030612.441030.3510118.12317.66411.190.51710.650.508----2CFSFDP1053.12814386730253.8698898563859743
0.08913313802501380250WH-J151836.8+121814229.6534112.304010.3578117.60117.13611.490.60313.680.636----21066.20629037703455.347703167129935
0.10250713805851380585WH-J151906.9+121808229.7785612.302250.7148119.42417.99511.540.50921.760.997----21529.64395029168656.1505195074723025
0.15902913807591380759J151923.1+122728229.8463112.457880.4966118.57517.79511.410.65728.031.2719----2WHL1292.91816047938489.542101787460988
0.16306113804001380400WH-J151849.8+123223229.7076312.539670.8355019.62918.34611.510.45512.070.567----21616.0275113257219.783828270872696
" + ], + "text/plain": [ + "\n", + " _r recno ID n_Name ... Cat Dist separation \n", + " ... arcmin \n", + "float64 int32 int32 str3 ... str9 float64 float64 \n", + "-------- ------- ------- ------ ... ------ ------------------ ------------------\n", + "0.064496 1380383 1380383 ... CFSFDP 1053.1281438673025 3.8698898563859743\n", + "0.089133 1380250 1380250 WH- ... 1066.2062903770345 5.347703167129935\n", + "0.102507 1380585 1380585 WH- ... 1529.6439502916865 6.1505195074723025\n", + "0.159029 1380759 1380759 ... WHL 1292.9181604793848 9.542101787460988\n", + "0.163061 1380400 1380400 WH- ... 1616.027511325721 9.783828270872696" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "/home/sunil/Python/FRB/frb/surveys/decals.py:72: RuntimeWarning: invalid value encountered in log10\n", - " main_cat[col] = 2.5*np.log10(1+1/main_cat[col])\n" + "Rows: 12\n", + "Columns: 22\n" ] } ], "source": [ - "dec_srvy = survey_utils.load_survey_by_name('DECaL', coord2, search_r)\n", - "dec_tbl = dec_srvy.get_catalog()" + "wen = WenGroupCat(COORDS['north'])\n", + "preview_catalog(wen.get_catalog())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updated Planck SZ Cluster Catalog (Bahk & Hwang 2024)\n", + "\n", + "`UPClusterSZCat` wraps the [Bahk & Hwang (2024)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/ApJS/272/7) catalog of ~3 700 galaxy clusters detected via the Sunyaev–Zel'dovich (SZ) effect in *Planck* data (Vizier: `J/ApJS/272/7/table2`). This is an updated, reprocessed version of the original Planck SZ cluster catalog (PSZ2), reaching $z \\lesssim 1$.\n", + "\n", + "SZ clusters are particularly valuable for FRB studies because the hot intracluster medium (ICM) can contribute measurably to the DM budget of an FRB sightline that passes within the cluster virial radius." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "Table masked=True length=2\n", - "
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DECaL_IDbrick_primaryDECaL_brickradecgaia_pointsourceDECaL_gDECaL_rDECaL_zDECaL_W1DECaL_W2DECaL_W3DECaL_W4DECaL_g_errDECaL_r_errDECaL_z_errDECaL_W1_errDECaL_W2_errDECaL_W3_errDECaL_W4_err
int64int64int64float64float64int64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
76966146363229621507231123.16771361948432.134174062069604120.618619.53518.967220.209920.4821.3003--0.0072563608340679850.0079825686734609840.0078147399291906460.078321879810150290.214702138629814022.679314369034259--
76966146363229631507231123.16952553840432.1357157995354117.824217.419517.085117.056116.855715.526814.70150.00142142229089510320.00202161101252203070.0017291742928373410.00442489997782238850.0084199307180583860.056155699771341370.16856857361907246
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Table length=2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
_rrecnoSeqPSZ2SNRradecPosErrzf_zNspY5R500Valf_ValMSZE_MSZe_MSZNotesSimbadNEDDistseparation
degdegarcminmarcmin21e+14 solMass1e+14 solMass1e+14 solMassarcmin
float64int32int16str13float64float64float64float32float64str4int16float64int16str1float32float32float32str162str6str3float64float64
3.910894343G012.81+49.686.75556230.76748.60743.70840.03550spec--10.04313621V1.900.170.18SimbadNED150.63614625847595234.65329924790174
4.763703838G011.36+49.425.36439230.47697.67373.79860.04420spec--5.75009021V1.870.200.21SimbadNED185.60355509810748285.8217519168346
" ], "text/plain": [ - "\n", - " DECaL_ID brick_primary DECaL_brick ra dec ... DECaL_z_err DECaL_W1_err DECaL_W2_err DECaL_W3_err DECaL_W4_err \n", - " int64 int64 int64 float64 float64 ... float64 float64 float64 float64 float64 \n", - "---------------- ------------- ----------- ---------------- ------------------ ... -------------------- --------------------- -------------------- ------------------- -------------------\n", - "7696614636322962 1 507231 123.167713619484 32.134174062069604 ... 0.007814739929190646 0.07832187981015029 0.21470213862981402 2.679314369034259 --\n", - "7696614636322963 1 507231 123.169525538404 32.1357157995354 ... 0.001729174292837341 0.0044248999778223885 0.008419930718058386 0.05615569977134137 0.16856857361907246" + "
\n", + " _r recno Seq PSZ2 ... NED Dist separation \n", + " ... arcmin \n", + "float64 int32 int16 str13 ... str3 float64 float64 \n", + "------- ----- ----- ------------- ... ---- ------------------ ------------------\n", + "3.91089 43 43 G012.81+49.68 ... NED 150.63614625847595 234.65329924790174\n", + "4.76370 38 38 G011.36+49.42 ... NED 185.60355509810748 285.8217519168346" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 2\n", + "Columns: 22\n" + ] } ], "source": [ - "dec_tbl" + "upcluster = UPClusterSZCat(COORDS['north'], radius=5 * u.deg)\n", + "preview_catalog(upcluster.get_catalog())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# WISE" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "wise_srvy = survey_utils.load_survey_by_name('WISE', coord2, 10*units.arcsec)\n", - "wise_tbl = wise_srvy.get_catalog()" + "### ROSAT X-ray Cluster Catalog (Xu+2022)\n", + "\n", + "`ROSATXClusterCat` wraps the [Xu et al. (2022)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/A+A/658/A59) X-ray selected cluster catalog constructed from the ROSAT All-Sky Survey (RASS) with optical confirmation from SDSS (Vizier: `J/A+A/658/A59/table3`). The catalog contains ~24 000 clusters spanning $0 \\lesssim z \\lesssim 0.8$.\n", + "\n", + "X-ray selected clusters are nearly mass-complete for massive systems ($M_{500} \\gtrsim 10^{14}\\,M_\\odot$) and provide reliable ICM DM estimates. Use `transverse_distance_cut` to restrict to clusters whose angular extent overlaps the FRB sightline." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "Table length=1\n", - "
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source_idradecW1W2W3W4W1_errW2_errW3_errW4_err
str20float64float64float64float64float64float64float64float64float64float64
1235p318_ac51-042682123.16952932.13573510000000514.36900000000000213.51910.2828.1980.030.0320.0770.264
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_rrecnoRXGCCradecExtExtmlze_zr_zClassGCXSZGCOPTGC*RsigR500*R500CRsige_CRsigCR500e_CR500L500e_L500F500e_F500M500e_M500TXe_TXCRpsigEdgeRsigBetae_BetaE_BetaRce_RcE_RcComSimbadNameDistseparation
degdegarcminarcminarcminMpcct / sct / sct / sct / s1e+35 W1e+35 W1e-15 W / m21e-15 W / m21e+14 solMass1e+14 solMasskeVkeVarcminarcminarcminarcmin
float64int32int16float32float32float32float64float32float32str9str1str34str18str51float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32uint8float32float32float32float32float32float32str200str9float64float64
3.9187607607230.7818.6033.13831.560.03450.0050z1, z_xszBL03, MCXC, PSZ2, Tar, XBA, NA, C, F20, L03, MCXC, N, PSZ2, Tar, W, XB12.70022.6510.9341.6730.0801.9220.0910.9940.02636.1100.9492.390.033.710.03689.010.960.050.035.6910.3470.228-RXGCC 607146.5693181803431235.12297104568708
3.9900605605230.4768.4593.2833.500.03600.0050z1, z_optS-NA, C, F20, N, W24.70018.5780.7980.8660.0800.8830.0760.4820.05016.0621.6611.490.082.770.09739.010.780.210.1620.9126.9054.269-RXGCC 605152.6658450862516239.40261599553702
4.7357604604230.4707.7011.62361.110.04480.0050z1, z_xszBMCXC, PSZ2, TarN, W, ZwC, F20, MCXC, N, PSZ2, Tar, W12.21219.0401.0071.4880.0771.6390.0851.4540.03730.8590.7853.030.044.300.03598.000.880.080.073.6770.4320.376-RXGCC 604187.98798299631332284.1395454826777
" ], "text/plain": [ - "\n", - " source_id ra dec W1 W2 W3 W4 W1_err W2_err W3_err W4_err\n", - " str20 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64\n", - "-------------------- ---------- ------------------ ------------------ ------- ------- ------- ------- ------- ------- -------\n", - "1235p318_ac51-042682 123.169529 32.135735100000005 14.369000000000002 13.519 10.282 8.198 0.03 0.032 0.077 0.264" + "
\n", + " _r recno RXGCC ra ... SimbadName Dist separation \n", + " deg ... arcmin \n", + "float64 int32 int16 float32 ... str9 float64 float64 \n", + "------- ----- ----- ------- ... ---------- ------------------ ------------------\n", + " 3.9187 607 607 230.781 ... RXGCC 607 146.5693181803431 235.12297104568708\n", + " 3.9900 605 605 230.476 ... RXGCC 605 152.6658450862516 239.40261599553702\n", + " 4.7357 604 604 230.470 ... RXGCC 604 187.98798299631332 284.1395454826777" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 3\n", + "Columns: 41\n" + ] } ], "source": [ - "wise_tbl" + "rosatx = ROSATXClusterCat(COORDS['north'], radius=5 * u.deg)\n", + "preview_catalog(rosatx.get_catalog())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "----" + "### 2MRS Galaxy Groups (Tempel+2018)\n", + "\n", + "`TempelClusterCat` wraps the [Tempel et al. (2018)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/A+A/618/A81) group catalog derived from the 2MASS Redshift Survey (2MRS), which provides nearly all-sky coverage including the Zone of Avoidance (Vizier: `J/A+A/618/A81/2mrs_gr`). The catalog contains ~30 000 groups at $z \\lesssim 0.2$ with CMB-frame redshifts.\n", + "\n", + "Its wide footprint makes it complementary to SDSS-based catalogs for FRBs at low Galactic latitudes where most other surveys are incomplete." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 57, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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_rrecnoGroupIDNgalradecGLONGLATSGLONSGLATzDistGsigmavsigmaskyRmaxM200R200XSGYSGZSGSimbadDistseparation
degdegdegdegdegdegMpckm / sMpcMpc1e+12 solMassMpcMpcMpcMpcarcmin
float64int32int16int16float64float64float64float64float64float64float64float64float64float32float32float64float32float64float64float64str6float64float64
0.612462551355133229.2635412.8106217.4283353.09032112.1203937.247410.02762121.3599354.5150.37560.743789.743960.9353-36.377389.495273.4543Simbad118.3194834145562236.747626585807716
0.973932553955392230.5879012.8331318.4624751.96236112.3782238.522820.0213693.9789297.0050.32330.457254.462840.7934-27.992867.988158.5325Simbad92.2001563935837858.43623162773656
2.320756169516952228.7106910.2711613.3522652.28998115.1003436.227550.05604244.5530480.0910.03930.055617.465160.5371-83.6846178.6466144.5284Simbad232.02196174456537139.24510781003414
2.914328553755372230.3384215.2255821.8092553.24810109.3066038.660900.0226099.409675.3710.11220.15871.257100.2258-25.664473.259562.1019Simbad97.40573054331504174.85990025419846
3.055894546554654226.6045912.7944515.2538155.35046111.5692134.693170.0217695.7092168.7360.15530.22438.576800.4284-28.929573.182454.4763Simbad93.88109863612466183.3533783976668
" + ], + "text/plain": [ + "\n", + " _r recno GroupID Ngal ... Simbad Dist separation \n", + " ... arcmin \n", + "float64 int32 int16 int16 ... str6 float64 float64 \n", + "-------- ----- ------- ----- ... ------ ------------------ ------------------\n", + "0.612462 5513 5513 3 ... Simbad 118.31948341455622 36.747626585807716\n", + "0.973932 5539 5539 2 ... Simbad 92.20015639358378 58.43623162773656\n", + "2.320756 1695 1695 2 ... Simbad 232.02196174456537 139.24510781003414\n", + "2.914328 5537 5537 2 ... Simbad 97.40573054331504 174.85990025419846\n", + "3.055894 5465 5465 4 ... Simbad 93.88109863612466 183.3533783976668" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 20\n", + "Columns: 23\n" + ] + } + ], "source": [ - "## Testing" + "tempel = TempelClusterCat(COORDS['north'], radius=5 * u.deg)\n", + "preview_catalog(tempel.get_catalog())" ] }, { - "cell_type": "code", - "execution_count": 15, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "tmp = 'mag_auto_g,magerr_auto_g,mag_auto_r,magerr_auto_r,mag_auto_i,magerr_auto_i,mag_auto_z,magerr_auto_z,mag_auto_y,magerr_auto_y,coadd_object_id,ra,dec,tilename\\n23.9905,0.194438,22.3105,0.0595304,20.9695,0.0296516,20.4385,0.0391535,20.292,0.100701,209895628,326.105565,-40.569422,DES2143-4040\\n'" + "### RASS-MCMF Cluster Catalog (Klein+2023)\n", + "\n", + "`RASSClusterCat` wraps the [Klein et al. (2023)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/MNRAS/526/3757) cluster catalog built by running the Multi-Component Matched Filter (MCMF) algorithm on ROSAT All-Sky Survey X-ray sources and confirming them with DES and PS1 optical data (Vizier: `J/MNRAS/526/3757/catalog`). The catalog contains ~8 000 clusters with spectroscopic or photometric redshifts spanning $z \\lesssim 1$.\n", + "\n", + "RASS-MCMF improves on older ROSAT cluster catalogs through reduced contamination by AGN and a well-characterized optical richness measure (`lambda`)." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 58, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=5\n", + "
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_rrecnoNameradecCentTypezph1zph2zph3zLambda1Lambda2Lambda3e_zph1e_zph2e_zph3e_Lambda1e_Lambda2e_Lambda3fcont1fcont2fcont3M500-1panypiGRZGRZ-NGIPstellarLikeStellarLikeQSOloglMassMaskFrac120P90P95P992RXS2RXSCRRAXdegDEXdege_2RXSCRGLONGLATCRinExiMLExtMLExte_ExtTexpCtse_CtsBGRSFlagM500-12RXSCR-COGM500-1COGXRConv2-1-1Conv2-1-2Conv2-1-3Distseparation
degdegsolMassct / sdegdegct / sdegdegct / spixpixsctctct / pixct / ssolMassarcmin
float64int32str22float64float64uint8float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64uint8uint8uint8float32uint8uint8float64float64str1str1str1str21float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64uint8float64float64float64float64float64float64float64float64
1.5137554931RASS-CL J152310+1327.4230.79352513.45762410.5070000.774940-0.1000000.50488768.6959692.275019-0.1000000.01362140.3018705--8.85630426.6268401-0.10000000.1124291.00000099.9000025.4622e+140.0410980.441457101--000.9446580.000000TFF2RXS J152306.8+1329090.038142230.7787361128813.485974246630.01414719.54435552.09880.0381428.961980.0000000.0000000.000000397.389315.157255.6217140.56740605.4622e+140.0209343.7977e+141.3806301.4919800.9423151304.292045228726290.82560625495456
1.8173414973RASS-CL J151616+1405.0229.06858614.08463610.1753750.3549600.5234800.17958741.2199712.4248162.6297340.00572740.03415630.09276356.70100313.04904893.91138200.0601200.9519791.0000002.0246e+140.0212150.167695100--001.1538740.000000TTF2RXS J151627.7+1404160.036500229.1158144912614.071123376490.01459919.19142253.80340.0365007.915470.0000000.0000000.000000301.562711.007094.4024710.39705702.0246e+140.1218303.1937e+140.8789491.1995341.399245645.5250130275639109.0405257424883
1.8193924984RASS-CL J151637+1406.9229.15784014.11591220.1770670.3234250.8948550.17978032.5643773.3835648.8128240.00608850.03579040.15377526.19177822.87803518.58168790.1053740.9121731.0000002.5673e+140.0231220.157192100--001.0811370.000000TTF2RXS J151649.0+1409120.045451229.2042070301914.153368249590.01680919.38120853.76410.0814949.910710.0297240.1176910.250321309.981814.088935.2103990.41112301.891e+140.0991532.8961e+140.8835871.1536531.377097646.081185806374109.16358843297184
1.9344504752RASS-CL J152055+1030.7230.23142510.51187220.4423750.254900-0.1000000.44313255.5878755.458004-0.1000000.01334870.0181124--8.45622833.3146970-0.10000000.1340260.76435199.9000024.2363e+140.2865180.900026101--000.9630910.000000TFF2RXS J152058.7+1030170.036790230.2447723919810.504835007290.01386014.93289751.11790.0367907.440800.0781250.2211620.360049417.490915.359645.7862380.67560704.2363e+140.0295513.8452e+141.3193891.0477910.9423151214.0516579843527116.06692915219264
2.4067314992RASS-CL J151253+1418.2228.22477214.30389420.1776950.0813400.6448700.179261172.86660814.19720717.2636110.00424980.00613190.084379314.10066224.39259728.67188740.0000000.2073871.0000004.2124e+140.1598280.251674100--001.4582830.000000TTT2RXS J151249.5+1418590.141691228.2066358701514.316433305680.05127318.88106754.70020.1416918.088560.0000000.0000000.00000083.754911.867304.2944050.10994204.2124e+140.1322223.4136e+140.8855010.8319311.471637644.5850003311641144.40380494437792
" + ], + "text/plain": [ + "\n", + " _r recno Name ... Dist separation \n", + " ... arcmin \n", + "float64 int32 str22 ... float64 float64 \n", + "-------- ----- ---------------------- ... ------------------ ------------------\n", + "1.513755 4931 RASS-CL J152310+1327.4 ... 1304.2920452287262 90.82560625495456\n", + "1.817341 4973 RASS-CL J151616+1405.0 ... 645.5250130275639 109.0405257424883\n", + "1.819392 4984 RASS-CL J151637+1406.9 ... 646.081185806374 109.16358843297184\n", + "1.934450 4752 RASS-CL J152055+1030.7 ... 1214.0516579843527 116.06692915219264\n", + "2.406731 4992 RASS-CL J151253+1418.2 ... 644.5850003311641 144.40380494437792" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 19\n", + "Columns: 61\n" + ] + } + ], "source": [ - "## Pan-STARRS " + "rass = RASSClusterCat(COORDS['north'], radius=5 * u.deg)\n", + "preview_catalog(rass.get_catalog())" ] }, { - "cell_type": "code", - "execution_count": 16, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "coord3 = SkyCoord(0, 0,unit=\"deg\")\n", - "ps_srvy = survey_utils.load_survey_by_name('Pan-STARRS',coord3,30*units.arcsec)\n", - "ps_tbl = ps_srvy.get_catalog()" + "### SDSS redMaPPer Cluster Catalog (Rykoff+2014)\n", + "\n", + "`RedMapperClusterCat` wraps the [Rykoff et al. (2014)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/ApJ/785/104) catalog of optically selected clusters from SDSS DR8, identified by the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) algorithm (Vizier: `J/ApJ/785/104/table1`). The catalog contains ~26 000 clusters at $0.08 \\lesssim z \\lesssim 0.55$ with richness $\\lambda \\geq 20$.\n", + "\n", + "The `clean_catalog()` step prefers spectroscopic redshifts where available and falls back to photometric `zlambda` otherwise. The optical richness `lambda` is preserved in the output table as a proxy for cluster mass ($M_{500} \\propto \\lambda^{0.76}$)." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 59, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sunil/miniconda3/envs/frb/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2625: RuntimeWarning: invalid value encountered in _integral_comoving_distance_z1z2_scalar (vectorized)\n", + " outputs = ufunc(*args, out=...)\n" + ] + }, { "data": { "text/html": [ - "Table masked=True length=1\n", - "
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Pan-STARRS_IDradecf_objIDQualPan-STARRS_gPan-STARRS_rPan-STARRS_iPan-STARRS_zPan-STARRS_yPan-STARRS_g_errPan-STARRS_r_errPan-STARRS_i_errPan-STARRS_z_errPan-STARRS_y_errseparation
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1079900000548164310.005490070-0.00335526043652710452--22.013921.769421.2010----0.18650.02270.0368--0.38605064131142824
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Table length=5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
_rrecnoIDNgNameradeczlambdae_zlambdalambdae_lambdaSzObjIDimage_imagumagme_umagmgmagme_gmagmrmagme_rmagmimagme_imagmzmagme_zmagmiLumPCen0RA0degDE0degID0PCen1RA1degDE1degID1PCen2RA2degDE2degID2PCen3RA3degDE3degID3PCen4RA4degDE4degID4PZbin1PZbin2PZbin3PZbin4PZbin5PZbin6PZbin7PZbin8PZbin9PZbin10PZbin11PZbin12PZbin13PZbin14PZbin15PZbin16PZbin17PZbin18PZbin19PZbin20PZbin21PZ1PZ2PZ3PZ4PZ5PZ6PZ7PZ8PZ9PZ10PZ11PZ12PZ13PZ14PZ15PZ16PZ17PZ18PZ19PZ20PZ21SloanSimbadDistseparation
degdegmagmagmagmagmagmagmagmagmagmagmagmagsolLumdegdegdegdegdegdegdegdegdegdegarcmin
float64int32int32int16str20float64float64float32float32float32float32float32float64int64float32float32float32float32float32float32float32float32float32float32float32float32float32float32float64float64int64float32float64float64int64float32float64float64int64float32float64float64int64float32float64float64int64float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32str5str6float64float64
0.201090239955117839RMJ151803.6+121807.8229.515116512.30217520.37270.019525.664.441.250--123766834921603108418.9680.01626.1680.64821.2980.06219.5670.02018.9320.01618.5350.04112.2330.47229.515121512.302175512376683492160310840.32229.507659912.301816912376683492160311960.12229.513336212.320117012376683492160311320.094229.502334612.289010012376683492160311890.0018229.490036012.304558812376683492159661010.27990.28920.29850.30780.31700.32630.33560.34490.35410.36340.37270.38200.39120.40050.40980.41910.42840.43760.44690.45620.46550.00250.0150.0690.260.7924.48.213182018126.42.30.570.0980.0110.000895.1e-052.3e-06SloanSimbadnan12.065036945271268
0.298504237254913636RMJ151821.1+120605.3229.587906012.10146120.53750.023261.3622.012.800--123766833149522807619.2270.03723.8861.72121.8250.13320.2290.05219.2120.03718.7100.09025.4080.83229.587905912.101461412376683314952280760.16229.578506512.097909912376683314952280430.0089229.608520512.093689912376683314952281740.00013229.585495012.097580912376683314952276014.1e-05229.554306012.083064112376683314951629460.36750.38450.40150.41850.43550.45250.46950.48650.50350.52050.53750.55450.57150.58850.60550.62250.63950.65650.67350.69050.70756.4e-182.1e-144.1e-113.6e-081.2e-050.00140.0580.844.91317136.72.40.640.140.0230.00310.000353e-052.1e-06SloanSimbadnan17.909994136595817
0.463389103061564867RMJ152005.5+120154.3230.023015812.03175620.37920.016243.225.441.180--123766833149542441718.7070.02523.0230.64820.9470.04519.2550.01718.6430.01618.2610.04025.6650.48230.023010312.031756412376683314954244170.38230.041244512.031730712376683314954247290.13230.044235212.043282512376683314954247320.0096230.022918712.033114412376683314954244180.00027230.027313212.035770412376683314954246360.29760.30570.31390.32210.33020.33840.34650.35470.36290.37100.37920.38730.39550.40370.41180.42000.42820.43630.44450.45260.46080.000540.00390.0230.110.431.43.78.215222522157.52.70.710.130.0160.00148.4e-053.6e-06SloanSimbadnan27.803423766184636
0.6849291078173099RMJ152043.2+115219.2230.180034611.87199550.48800.0122132.1619.661.978--123766263851850170418.1410.01825.8581.77621.2470.10519.2670.02918.2360.01817.7800.04274.3761230.180038511.871995912376626385185017040.0033230.186630211.855729112376626385185025290.00035230.184021011.888376212376683314954902470.00015230.198715211.894170812376683314954903042.4e-05230.178237911.888036712376683314954902320.41110.41880.42640.43410.44180.44950.45720.46490.47260.48030.48800.49560.50330.51100.51870.52640.53410.54180.54950.55720.56482.4e-092.5e-071.7e-050.000590.0130.151.15.215273226155.91.70.360.0610.00760.000796.8e-054.5e-06SloanSimbadnan41.09580890861023
0.734197124601928255RMJ152149.9+122329.5230.457871112.39152930.41850.014543.686.571.3300.42160123766833203242664818.2300.02222.7581.11720.5470.06718.7950.02418.0910.02117.7720.06422.6600.99230.457870512.391529112376683320324266480.0085230.457107512.381866512376683320324266600.00047230.440490712.393868412376683320324265590.00019230.436065712.407170312376683320324265343.5e-05230.450546312.402490612376683320324265920.33680.34490.35310.36130.36950.37770.38580.39400.40220.41040.41850.42670.43490.44310.45130.45940.46760.47580.48400.49210.50030.000130.000970.00680.0390.190.792.6714232724146.21.90.430.0660.00790.000724.8e-052.7e-06SloanSimbad1179.439113029176344.052136492624925
" ], "text/plain": [ - "\n", - " Pan-STARRS_ID ra dec f_objID Qual Pan-STARRS_g Pan-STARRS_r ... Pan-STARRS_g_err Pan-STARRS_r_err Pan-STARRS_i_err Pan-STARRS_z_err Pan-STARRS_y_err separation \n", - " deg deg mag mag ... mag mag mag mag mag arcmin \n", - " int64 float64 float64 int64 int16 float64 float64 ... float32 float32 float32 float32 float32 float64 \n", - "------------------ ------------- ------------- --------- ----- ------------ ------------ ... ---------------- ---------------- ---------------- ---------------- ---------------- -------------------\n", - "107990000054816431 0.005490070 -0.003355260 436527104 52 -- 22.0139 ... -- 0.1865 0.0227 0.0368 -- 0.38605064131142824" + "
\n", + " _r recno ID Ng ... Simbad Dist separation \n", + " ... arcmin \n", + "float64 int32 int32 int16 ... str6 float64 float64 \n", + "-------- ----- ----- ----- ... ------ ------------------ ------------------\n", + "0.201090 23995 51178 39 ... Simbad nan 12.065036945271268\n", + "0.298504 23725 49136 36 ... Simbad nan 17.909994136595817\n", + "0.463389 10306 15648 67 ... Simbad nan 27.803423766184636\n", + "0.684929 1078 1730 99 ... Simbad nan 41.09580890861023\n", + "0.734197 12460 19282 55 ... Simbad 1179.4391130291763 44.052136492624925" ] }, - "execution_count": 17, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 210\n", + "Columns: 93\n" + ] } ], "source": [ - "ps_tbl" + "redmapper = RedMapperClusterCat(COORDS['north'], radius=5 * u.deg)\n", + "preview_catalog(redmapper.get_catalog())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ACT DR5 SZ Cluster Catalog (Klein+2024)\n", + "\n", + "`ACTDR5ClusterCat` wraps the [Klein et al. (2024)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/A+A/690/A322) catalog of galaxy clusters detected in the Atacama Cosmology Telescope (ACT) Data Release 5 via the SZ effect (Vizier: `J/A+A/690/A322/catalog`). The catalog contains ~4 000 clusters spanning $0.1 \\lesssim z \\lesssim 1.9$ — the highest-redshift cluster sample among the catalogs in this module.\n", + "\n", + "ACT DR5 clusters have well-measured integrated Compton-$y$ parameters, which can be translated into an ICM DM contribution estimate for FRBs at cosmological distances. Note that the best-estimate redshift column (`z1C`) is used; some entries have only photometric redshifts." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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_rrecnoNameradecSNRTemplateTileNamefixedSNRYCe_YCfixedYCe_fixedYCzz2CL1CL2Ce_L1Ce_L2Ce_z1Ce_z2Cfcont1Cfcont2CM500-1CE_M500-1Ce_M500-1CM500-2Cfcont1CSNR45fcont2CSNR45M500-1CCALE_M500-1CCALe_M500-1CCALM500-2CCALzsp1zspTypeDistseparation
degdegsolMass1e+14 solMass1e+14 solMasssolMasssolMass1e+14 solMass1e+14 solMasssolMassarcmin
float64int32str19float64float64float64str17str7float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64uint8float64float64
0.1290474541ACT-CL J1518.7+1230229.6899681907912.504666761775.41831688614222Arnaud_M1e14_z1p22_2_45.165048120019691.3871524589969310.2560116892654770.4285879950118420.0829785096000630.7778600000000001.28906667232513459.231965674373222.7538646345296919.395256523136956.431802552637030.0215627945802850.1060890714579030.0111021874472500.3583977222442631.5732e+140.3369490080148780.2794010594231851.3643e+140.0054697773800820.2870070868964582.2157e+140.3935226307989650.4745760819692611.9216e+14--01577.99686578785767.7429385614238
0.2631934486ACT-CL J1518.2+1209229.5624423196012.154130418034.08307972832043Arnaud_M1e14_z1p22_2_43.890362674116521.0782839426067050.2640859386427550.3355340031220280.0862474867328980.7692820000000000.08546000000000042.79687194962292.9245652625315598.137449631047822.898067460796310.0216204408916150.0056888724181200.0360971726477151.0000000000000001.3206e+140.3256020888525100.2622960755954851.0162e+14----1.86e+140.3694311035529800.4585945052012401.4312e+14--01571.85264355292215.791270504693687
0.3926842413ACT-CL J1518.7+1159229.6931317593311.984141806294.43836726728873Arnaud_M8e14_z0p42_2_44.316931909555840.2825615109261370.0636633910421620.3700888731134200.0857296063192940.4950800000000000.31569700000000027.13301306668557.9822242363465216.638911255005543.456240386859050.0109986429154560.0089639717100970.0541392602026460.5413671731948851.4612e+140.3509428438259960.2829046145156961.3754e+14----2.0581e+140.3984572155854370.4942857104679111.9372e+140.48802280426025421290.808154670940223.560883669913053
0.6874412471ACT-CL J1520.7+1152230.1821390804311.8704750768716.79011460380933Arnaud_M8e14_z0p42_2_416.616424050750561.0788038769208800.0642523236068991.4274836021905810.0859079906621730.5027000000000001.894260048866272184.087707532602726.29444816162583713.919316749382906.229073145890910.0097584131475610.2424141020865460.0000000000000000.2861851453781134.1046e+140.7038297482047090.6007457971987212.5621e+140.0000000000000000.2127402466761355.7811e+140.8461208666964870.9913095344731043.6086e+140.49678267485394011301.31157974234241.246557253115206
0.9033652394ACT-CL J1517.1+1134229.2832124629911.573654664415.33214898072027Arnaud_M8e14_z0p22_2_45.000697188991810.2893561158888650.0542663224405590.4202071635366640.0840297157887660.4929600000000001.11992061138153182.205871875376226.5009903665761089.568732722832046.088162414978550.0101299096868910.0650618948082160.0000000000000000.2143630832433701.6179e+140.3605617804953620.2948919776401651.483e+140.0000000000000000.1440591221733892.2788e+140.4153408261301590.5078335090032212.0887e+140.49259292119053211287.85282331713154.20161198071035
" + ], "text/plain": [ - "
" + "\n", + " _r recno Name ... Dist separation \n", + " ... arcmin \n", + "float64 int32 str19 ... float64 float64 \n", + "-------- ----- ------------------- ... ------------------ ------------------\n", + "0.129047 4541 ACT-CL J1518.7+1230 ... 1577.9968657878576 7.7429385614238\n", + "0.263193 4486 ACT-CL J1518.2+1209 ... 1571.852643552922 15.791270504693687\n", + "0.392684 2413 ACT-CL J1518.7+1159 ... 1290.8081546709402 23.560883669913053\n", + "0.687441 2471 ACT-CL J1520.7+1152 ... 1301.311579742342 41.246557253115206\n", + "0.903365 2394 ACT-CL J1517.1+1134 ... 1287.852823317131 54.20161198071035" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 88\n", + "Columns: 37\n" + ] } ], "source": [ - "img, = ps_srvy.get_cutout(filt=\"gri\",imsize=30*units.arcsec)\n", - "plt = images.gen_snapshot_plt(img,imsize=30*units.arcsec,show=True)" + "actdr5 = ACTDR5ClusterCat(COORDS['north'], radius=5 * u.deg)\n", + "preview_catalog(actdr5.get_catalog())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### eROSITA All-Sky Survey Cluster Catalog (Kluge+2024)\n", + "\n", + "`ERASSClusterCat` wraps the [Kluge et al. (2024)](https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/A+A/688/A210) primary cluster catalog from the first eROSITA All-Sky Survey (eRASS1), the deepest X-ray all-sky survey to date (Vizier: `J/A+A/688/A210/tablee1`). The catalog contains ~12 000 clusters with redshifts spanning $0.003 \\lesssim z \\lesssim 1.3$.\n", + "\n", + "eRASS1 extends ROSAT-based X-ray cluster catalogs with higher sensitivity and better angular resolution, allowing detection of lower-mass groups. The `Bestz` column (best-available redshift from multiple sources) is used as the cluster redshift. Note that currently the eRASS1 public release covers only the western galactic hemisphere ($l = 180°$–$360°$)." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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_rrecnoNameDetUIdradecePosRAOdegDEOdegRABdegDEBdegzmagBCGze_BestzBestztypezlambdae_zlambdazlambdacorrE_zlambdacorre_zlambdacorrzlambda2Litze_Litzr_LitzSpeczBoote_SpeczBootNmembCGSpecze_CGSpeczBCGSpeczvdispBoote_vdispBootf_vdispBootvdispTypeLambdaNe_LambdaNLambdaOptNe_LambdaOptNScaleValMaskFracRunLmaxNHIExtLikePcontSharedMembInfootPrintlimgmaglimrmaglimimaglimzmaglimW1maginzvlimzvlim02zvlim04GaiaNGC30HECATEDistseparation
degdegarcsecdegdegdegdegmagkm / skm / s1e+21 / cm2magmagmagmagmagarcmin
float64int32str23str32float64float64float32float64float64float64float64float32float64float64str11float64float64float64float64float64float64float64float64str18float64float64int16float64float64float64float64float64float32str8float32float32float32float32float64float32str29float64float64float64float32uint8uint8float32float32float32float32float32uint8float32float32uint8uint8uint8float64float64
10.443264109511eRASS J145338.5+035931eb01_224087_020_ML00002_002_c010223.41053.991992.85223.41033.99261223.35833.9535618.4600.369970.00040cg_spec_z0.389210.009770.390110.007380.00771--0.375600.01750REDMAPPER-SDSS-DR8----00.369970.000400.36773------36.022.4836.082.411.008080.007legacy_dr10_grz_z_v0.952.83960.0324029.750720.00030124.3523.9922.9523.1320.6810.9731.2080001089.1076325714246626.5955439411015
10.666911109741eRASS J145723.4+030717eb01_224087_020_ML00230_002_c010224.34783.1215524.30224.35313.11803224.35313.1180318.2270.688240.00040cg_spec_z0.632200.015860.634180.010830.01304----------00.688240.000400.68824------16.822.3917.202.451.028550.030legacy_dr10_grz_z_v0.919.23990.038748.520120.17270124.2123.64--22.9320.7310.9051.1530001506.9436177209172640.0143511476379
10.802879109841eRASS J145814.9+025057eb01_224087_020_ML00314_002_c010224.56232.8493410.63224.56152.84991224.56152.8499117.1870.349170.00040cg_spec_z0.343130.010880.342070.007380.00770----------00.349170.000400.34917------17.151.9117.211.921.013590.013legacy_dr10_grz_z_v0.922.14790.044933.305410.00370124.1823.66--23.0620.6610.9521.1890001049.5734784320603648.1724468239748
11.144144108381eRASS J144025.8+062932eb01_221084_020_ML00258_002_c010220.10766.4924919.11220.11546.49159220.11546.4915917.5400.407800.00040cg_spec_z0.404660.012260.408490.008760.00871--0.407800.00000ACTDR5----00.407800.000400.40780------30.252.8632.282.971.050650.053legacy_dr10_grz_z_v0.944.53050.027727.787950.01940123.6323.0422.7422.2220.7710.6810.9180001156.3358246705977668.6483251195601
11.165287109591eRASS J145432.5+025759eb01_224087_020_ML00021_002_c010223.63542.966484.71223.61872.98350223.62982.9372518.7940.027240.00000lit_z0.390470.019050.391730.007540.00770--0.027240.00000SIMBAD----0------------7.956.788.015.801.934360.481legacy_dr10_grz_z_v0.93.21610.0356910.784700.00000124.8924.0822.5922.9020.7410.8931.144011116.74532211837989669.916938309819
" + ], "text/plain": [ - "
" + "\n", + " _r recno Name ... Dist separation \n", + " ... arcmin \n", + " float64 int32 str23 ... float64 float64 \n", + "--------- ----- ----------------------- ... ------------------ -----------------\n", + "10.443264 10951 1eRASS J145338.5+035931 ... 1089.1076325714246 626.5955439411015\n", + "10.666911 10974 1eRASS J145723.4+030717 ... 1506.9436177209172 640.0143511476379\n", + "10.802879 10984 1eRASS J145814.9+025057 ... 1049.5734784320603 648.1724468239748\n", + "11.144144 10838 1eRASS J144025.8+062932 ... 1156.3358246705977 668.6483251195601\n", + "11.165287 10959 1eRASS J145432.5+025759 ... 116.74532211837989 669.916938309819" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows: 1763\n", + "Columns: 60\n" + ] } ], "source": [ - "fits_img = ps_srvy.get_image(imsize=30*units.arcsec)\n", - "wcsinfo = WCS(fits_img.header)\n", - "plt.subplot(projection=wcsinfo)\n", - "plt.imshow(fits_img.data, origin='lower')\n", - "plt.show()" + "erass = ERASSClusterCat(COORDS['north'], radius=50 * u.deg)\n", + "preview_catalog(erass.get_catalog())" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "frb", "language": "python", "name": "python3" }, @@ -475,7 +2366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.14.4" } }, "nbformat": 4, From a283aad2264763c554343e69b228f751ca966d48 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Thu, 14 May 2026 16:36:23 -0500 Subject: [PATCH 16/17] moving mastcasjobs to essential --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index b998f1cc..5997f01b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,7 @@ dependencies = [ "tqdm>=4.0.0", "ne2001 @ git+https://github.com/FRBs/ne2001.git", "astropath @ git+https://github.com/FRBs/astropath.git", + "mastcasjobs>=0.0.8" ] [project.optional-dependencies] From 3db4c63ddc30b2d5880c70b373056194f4cf0246 Mon Sep 17 00:00:00 2001 From: SunilSimha Date: Thu, 14 May 2026 16:54:10 -0500 Subject: [PATCH 17/17] doc updates for new functionality --- docs/api/surveys.catalog_utils.rst | 9 +-- docs/api/surveys.first.rst | 9 +++ docs/api/surveys.nvss.rst | 9 +++ docs/api/surveys.rst | 95 ++++++++++++++++++++++++------ docs/api/surveys.survey_utils.rst | 9 +-- docs/api/surveys.wenss.rst | 9 +++ 6 files changed, 113 insertions(+), 27 deletions(-) create mode 100644 docs/api/surveys.first.rst create mode 100644 docs/api/surveys.nvss.rst create mode 100644 docs/api/surveys.wenss.rst diff --git a/docs/api/surveys.catalog_utils.rst b/docs/api/surveys.catalog_utils.rst index 2eaf27da..1dbadd78 100644 --- a/docs/api/surveys.catalog_utils.rst +++ b/docs/api/surveys.catalog_utils.rst @@ -1,8 +1,9 @@ +.. _surveys_catalog_utils: + frb.surveys.catalog_utils ========================= .. automodule:: frb.surveys.catalog_utils - -.. note:: - Member-level API expansion is intentionally omitted here to avoid - docutils parsing failures during docs builds. + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/surveys.first.rst b/docs/api/surveys.first.rst new file mode 100644 index 00000000..64d03c4c --- /dev/null +++ b/docs/api/surveys.first.rst @@ -0,0 +1,9 @@ +.. _surveys_first: + +frb.surveys.heasarc +=================== + +.. automodule:: frb.surveys.heasarc + :members: FIRST_Survey + :undoc-members: + :show-inheritance: diff --git a/docs/api/surveys.nvss.rst b/docs/api/surveys.nvss.rst new file mode 100644 index 00000000..353e10a7 --- /dev/null +++ b/docs/api/surveys.nvss.rst @@ -0,0 +1,9 @@ +.. _surveys_nvss: + +frb.surveys.heasarc +=================== + +.. automodule:: frb.surveys.heasarc + :members: NVSS_Survey + :undoc-members: + :show-inheritance: diff --git a/docs/api/surveys.rst b/docs/api/surveys.rst index ea98791c..d725631c 100644 --- a/docs/api/surveys.rst +++ b/docs/api/surveys.rst @@ -1,46 +1,102 @@ -frb.surveys - Survey Data Access +.. _surveys: + +frb.surveys — Survey Data Access ================================= -.. automodule:: frb.surveys - :members: - :undoc-members: - :show-inheritance: +The `frb.surveys` package provides a uniform interface for querying dozens of +astronomical surveys and catalogs. It handles the backend complexity of +accessing data from various archives (NOIRLab Data Lab, HEASARC, IRSA, MAST, +SkyView) and returns standardized `astropy` objects. + +For a hands-on guide with detailed examples for every survey class, see the +companion tutorial notebook: + +.. toctree:: + :maxdepth: 1 + + ../nb/Surveys + + + + + + -Overview --------- +General Usage +------------- -Access and query photometric/spectroscopic survey data -(PanSTARRS, SDSS, DECaLS, WISE, 2MASS, DES, DESI, GALEX, etc.). +All survey classes are instantiated with a sky coordinate and a search radius. +The primary methods are `get_catalog()` to retrieve a source catalog as an +`astropy.table.Table` and `get_image()` to download a FITS image as an +`astropy.io.fits.HDUList`. -Submodules ----------- +Here is a typical example using Pan-STARRS: + +.. code-block:: python + + from astropy.coordinates import SkyCoord + from astropy import units as u + from frb.surveys.panstarrs import Pan_STARRS_Survey + + # Define coordinate and search radius + coord = SkyCoord('J081240.68+320809.0', unit=(u.hourangle, u.deg)) + radius = 10 * u.arcsec + + # Instantiate the survey object + ps1 = Pan_STARRS_Survey(coord, radius) + + # Get the source catalog + catalog = ps1.get_catalog() + + # Get a FITS image cutout + image_hdu = ps1.get_image() + + +Utility Modules +--------------- + +The package includes two key utility modules for survey-agnostic operations +and catalog manipulation. .. toctree:: :maxdepth: 1 - surveys.dlsurvey + surveys.survey_utils surveys.catalog_utils + + +Survey-Specific Submodules +-------------------------- + +The following modules contain the individual survey classes. + +.. toctree:: + :maxdepth: 1 + surveys.sdss surveys.des surveys.decals - surveys.wise + surveys.delve + surveys.nsc + surveys.hsc surveys.panstarrs - surveys.twomass + surveys.wise surveys.vista - surveys.hsc + surveys.twomass surveys.galex - surveys.desi - surveys.delve surveys.euclid + surveys.first + surveys.nvss + surveys.wenss + surveys.desi surveys.nedlvs - surveys.nsc surveys.psrcat surveys.heasarc + surveys.dlsurvey surveys.images surveys.cluster_search surveys.surveycoord surveys.survey_io - surveys.survey_utils surveys.tns_util surveys.utils_crossmatching surveys.defs @@ -49,5 +105,6 @@ Submodules ``frb.surveys.dlsurvey`` requires the optional dependency ``datalab-client`` for NOIRLab Data Lab access. + .. note:: ``frb.surveys.psrcat`` requires optional ``FRB-pulsars`` support. diff --git a/docs/api/surveys.survey_utils.rst b/docs/api/surveys.survey_utils.rst index 14b9ea6c..e97277e9 100644 --- a/docs/api/surveys.survey_utils.rst +++ b/docs/api/surveys.survey_utils.rst @@ -1,8 +1,9 @@ +.. _surveys_survey_utils: + frb.surveys.survey_utils ======================== .. automodule:: frb.surveys.survey_utils - -.. note:: - Member-level API expansion is intentionally omitted here to avoid - docutils errors from malformed legacy docstrings. + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/surveys.wenss.rst b/docs/api/surveys.wenss.rst new file mode 100644 index 00000000..2ba0cb7a --- /dev/null +++ b/docs/api/surveys.wenss.rst @@ -0,0 +1,9 @@ +.. _surveys_wenss: + +frb.surveys.heasarc +=================== + +.. automodule:: frb.surveys.heasarc + :members: WENSS_Survey + :undoc-members: + :show-inheritance: