diff --git a/.github/workflows/lint-and-test.yaml b/.github/workflows/lint-and-test.yaml
index 4f2f841..60a52d8 100644
--- a/.github/workflows/lint-and-test.yaml
+++ b/.github/workflows/lint-and-test.yaml
@@ -17,7 +17,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.13"]
+        python-version: ["3.10", "3.13"]
         os: [ubuntu-latest]
     steps:
     - uses: actions/checkout@v5
@@ -41,7 +41,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.13"]
+        python-version: ["3.10", "3.13"]
         os: [ubuntu-latest]
     steps:
     - uses: actions/checkout@v5
diff --git a/README.md b/README.md
index ca7b690..75aa9e2 100644
--- a/README.md
+++ b/README.md
@@ -36,10 +36,16 @@ The public examples cover several realistic analysis shapes, including
 SMARTEOLE toggle data, Kelmarsh turbine data, and WeDoWind challenge-style
 pre/post assessments.
 
+## Methodology
+
+The validation methodology that `wind-up` implements is described in detail in
+[**wind-up uplift validation methodology v3**](docs/wind-up%20uplift%20validation%20methodology%20v3.pdf)
+in the [`docs`](docs) folder.
+
 ## Installation
 
 Install the released package with your Python environment manager of choice.
-Python `>=3.9,<4.0` is supported.
+Python 3.10 to 3.13 is supported.
 
 Using `uv`:
 
@@ -146,6 +152,12 @@ When plot saving is enabled, diagnostic figures are written under
 `PlotConfig.plots_dir`; CSV results are written under the configured assessment
 output directory.
 
+> [!NOTE]
+> On import, `wind-up` selects the non-interactive matplotlib `Agg` backend
+> unless the `MPLBACKEND` environment variable is already set. This lets analyses
+> run headless (CI, SSH, batch servers) without an X server. Set `MPLBACKEND`
+> yourself before importing `wind_up` if you want an interactive backend.
+
 ## Analysis features
 
 `wind-up` includes utilities for the parts of an uplift study that usually need
@@ -210,7 +222,7 @@ uv run poe jupy         # start JupyterLab for example exploration
 > stop it. Use it when you want an interactive notebook session, not as a
 > one-shot verification command.
 
-The GitHub Actions workflow runs linting and tests on Python 3.9 and 3.13.
+The GitHub Actions workflow runs linting and tests on Python 3.10 and 3.13.
 
 ## Project status
 
diff --git a/examples/kelmarsh_kaggle.py b/examples/kelmarsh_kaggle.py
index e4134c4..9e60f2b 100644
--- a/examples/kelmarsh_kaggle.py
+++ b/examples/kelmarsh_kaggle.py
@@ -9,6 +9,7 @@
 import logging
 import re
 from pathlib import Path
+from typing import Any
 
 import ephem
 import matplotlib.pyplot as plt
@@ -732,7 +733,7 @@ def flatten_and_clean_columns(df: pd.DataFrame) -> pd.DataFrame:
     X_train = flatten_and_clean_columns(X_train)
     X_test = flatten_and_clean_columns(X_test)
 
-    automl_settings = {
+    automl_settings: dict[str, Any] = {
         "time_budget": 3600 * 1,  # 12 hours was used for best solution
         "ensemble": True,
         "task": "regression",
diff --git a/examples/smarteole_utils.py b/examples/smarteole_utils.py
index 822eaba..93f5acb 100644
--- a/examples/smarteole_utils.py
+++ b/examples/smarteole_utils.py
@@ -8,7 +8,7 @@
 import zipfile
 from functools import partial
 from pathlib import Path
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING
 
 import pandas as pd
 from scipy.stats import circmean
@@ -17,7 +17,7 @@
 from wind_up.constants import PROJECTROOT_DIR, TIMESTAMP_COL, DataColumns
 
 if TYPE_CHECKING:
-    FilePathOrBuffer = Union[str, bytearray]
+    FilePathOrBuffer = str | bytearray
 
 SCADA_FILE_PATH = "SMARTEOLE-WFC-open-dataset/SMARTEOLE_WakeSteering_SCADA_1minData.csv"
 METADATA_FILE_PATH = "SMARTEOLE-WFC-open-dataset/SMARTEOLE_WakeSteering_Coordinates_staticData.csv"
diff --git a/pyproject.toml b/pyproject.toml
index da7252b..2c13e4b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,17 +1,16 @@
 [project]
 name = "res-wind-up"
-version = "0.4.10"
+version = "0.5.0"
 authors = [
     { name = "Alex Clerc", email = "alex.clerc@res-group.com" }
 ]
 description = "A tool to assess yield uplift of wind turbines"
 readme = "README.md"
-requires-python = ">=3.9,<4.0"
+requires-python = ">=3.10,<3.14"
 license = { file = "LICENSE.txt" }
 classifiers = [
     "Development Status :: 4 - Beta",
     "Programming Language :: Python",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -25,7 +24,7 @@ dependencies = [
     'eval-type-backport',
     'geographiclib',
     'matplotlib',
-    'pandas >= 2.0.0',
+    'pandas >= 2.0.0,<3.0.0',
     'pyarrow',
     'pydantic >= 2.0.0',
     'python-dotenv',
@@ -57,7 +56,7 @@ dev = [
     'types-tqdm',
     'types-requests',
     'ruff',
-    'mypy',
+    'mypy<1.19',
     'requests',
     'pytest-env',
 ]
@@ -74,10 +73,17 @@ examples = [
 where = ["."]
 include = ["wind_up*"]
 
+[tool.uv]
+# Dev/CI-only constraint (not part of published metadata, so consumers are unaffected).
+# matplotlib 3.11 dropped Python 3.10, so an unpinned universal lock resolves matplotlib 3.10
+# on the 3.10 job but 3.11 on the 3.13 job; the image-comparison baselines are matplotlib-version
+# specific, so keep the test environment on a single matplotlib (3.10.x, which supports 3.10-3.13).
+constraint-dependencies = ["matplotlib<3.11"]
+
 [tool.ruff]
 line-length = 120
 show-fixes = true
-target-version = "py39"
+target-version = "py310"
 
 [tool.ruff.lint]
 select = ["ALL"] # https://beta.ruff.rs/docs/rules/
@@ -117,7 +123,7 @@ max-args = 17 # try to bring this down to 5
 
 [tool.mypy]
 plugins = ["pydantic.mypy"]
-python_version = "3.9"
+python_version = "3.10"
 exclude = "build|tests|venv|.venv|__ignore__"
 disallow_untyped_defs = true
 
diff --git a/tests/pp_analysis/test_cook_pp.py b/tests/pp_analysis/test_cook_pp.py
index 10d7ef7..04bb1fa 100644
--- a/tests/pp_analysis/test_cook_pp.py
+++ b/tests/pp_analysis/test_cook_pp.py
@@ -49,7 +49,9 @@ def _make_pp_raw_df(
             pw_std_col: [50.0 if p is not None else np.nan for p in pw_means],
             ws_std_col: [0.2] * n,
             count_col: counts,
-            pw_sem_col: [50.0 / max(1, c) ** 0.5 if p is not None else np.nan for p, c in zip(pw_means, counts)],
+            pw_sem_col: [
+                50.0 / max(1, c) ** 0.5 if p is not None else np.nan for p, c in zip(pw_means, counts, strict=True)
+            ],
             ws_sem_col: [0.2 / max(1, c) ** 0.5 for c in counts],
         }
     )
diff --git a/tests/test_backend.py b/tests/test_backend.py
new file mode 100644
index 0000000..0006671
--- /dev/null
+++ b/tests/test_backend.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import os
+import subprocess
+import sys
+
+_PRINT_BACKEND = "import wind_up, matplotlib; print(matplotlib.get_backend())"
+# import pyplot (selecting a non-interactive backend) *before* wind_up, as an interactive
+# notebook session would
+_PYPLOT_THEN_WIND_UP = (
+    "import matplotlib; matplotlib.use('pdf'); import matplotlib.pyplot;"
+    " import wind_up; print(matplotlib.get_backend())"
+)
+
+
+def _backend_after(code: str, *, mplbackend: str | None = None) -> str:
+    env = {k: v for k, v in os.environ.items() if k != "MPLBACKEND"}
+    if mplbackend is not None:
+        env["MPLBACKEND"] = mplbackend
+    result = subprocess.run(  # noqa: S603
+        [sys.executable, "-c", code],
+        capture_output=True,
+        text=True,
+        env=env,
+        check=True,
+    )
+    return result.stdout.strip().lower()
+
+
+def test_default_backend_is_agg_when_mplbackend_unset() -> None:
+    # importing wind_up must pin a non-interactive backend so headless / SSH runs don't
+    # depend on an X server (see wind_up/__init__.py)
+    assert _backend_after(_PRINT_BACKEND) == "agg"
+
+
+def test_explicit_mplbackend_is_respected() -> None:
+    # an explicit MPLBACKEND must win over the wind_up default; "pdf" is non-interactive
+    # so it is always importable in CI
+    assert _backend_after(_PRINT_BACKEND, mplbackend="pdf") == "pdf"
+
+
+def test_backend_left_alone_when_pyplot_already_imported() -> None:
+    # if pyplot is already imported (e.g. an interactive notebook), wind_up must not switch
+    # the user's backend out from under them, even with MPLBACKEND unset
+    assert _backend_after(_PYPLOT_THEN_WIND_UP) == "pdf"
diff --git a/tests/test_backporting.py b/tests/test_backporting.py
deleted file mode 100644
index d387640..0000000
--- a/tests/test_backporting.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import pytest
-
-from wind_up.backporting import strict_zip
-
-
-def test_equal_length_iterables() -> None:
-    a = [1, 2, 3]
-    b = ["a", "b", "c"]
-    result = list(strict_zip(a, b, strict=True))
-    assert result == [(1, "a"), (2, "b"), (3, "c")]
-
-
-def test_unequal_length_raises_value_error() -> None:
-    a = [1, 2, 3]
-    b = ["a", "b"]
-
-    with pytest.raises(ValueError):  # noqa PT011
-        list(strict_zip(a, b, strict=True))
-
-
-def test_unequal_length_non_strict() -> None:
-    a = [1, 2, 3]
-    b = ["a", "b"]
-
-    result = list(strict_zip(a, b, strict=False))
-    assert result == [(1, "a"), (2, "b")]  # Shorter iterable determines length
-
-
-def test_empty_iterables() -> None:
-    a = []
-    b = []
-
-    result = list(strict_zip(a, b, strict=True))
-    assert result == []
-
-
-def test_single_iterable() -> None:
-    a = [1, 2, 3]
-
-    result = list(strict_zip(a, strict=True))
-    assert result == [(1,), (2,), (3,)]
-
-
-def test_multiple_iterables() -> None:
-    a = [1, 2, 3]
-    b = ["a", "b", "c"]
-    c = [True, False, True]
-
-    result = list(strict_zip(a, b, c, strict=True))
-    assert result == [(1, "a", True), (2, "b", False), (3, "c", True)]
-
-
-def test_non_iterable_argument() -> None:
-    a = [1, 2, 3]
-    b = 5  # Not iterable
-
-    with pytest.raises(TypeError):
-        list(strict_zip(a, b, strict=True))
-
-
-def test_tuple_and_generator() -> None:
-    a = (1, 2, 3)
-    b = (x for x in ["a", "b", "c"])  # Generator
-
-    result = list(strict_zip(a, b, strict=True))
-    assert result == [(1, "a"), (2, "b"), (3, "c")]
-
-
-def test_nested_iterables() -> None:
-    a = [[1], [2], [3]]
-    b = [["a"], ["b"], ["c"]]
-
-    result = list(strict_zip(a, b, strict=True))
-    assert result == [([1], ["a"]), ([2], ["b"]), ([3], ["c"])]
diff --git a/tests/test_rolling_circ_mean.py b/tests/test_rolling_circ_mean.py
index 4d14b44..7834ef1 100644
--- a/tests/test_rolling_circ_mean.py
+++ b/tests/test_rolling_circ_mean.py
@@ -33,7 +33,7 @@ def test_rolling_circ_mean(*, range_360: bool) -> None:
             else (
                 input_df[col]
                 .rolling(window=4, min_periods=1, center=True)
-                .apply(lambda x: (circmean(x, low=-180, high=180, nan_policy="omit")))
+                .apply(lambda x: circmean(x, low=-180, high=180, nan_policy="omit"))
             )
         )
         assert_series_equal(result, expected)
diff --git a/tests/test_rolling_circ_median.py b/tests/test_rolling_circ_median.py
index 22346cf..a9c53a3 100644
--- a/tests/test_rolling_circ_median.py
+++ b/tests/test_rolling_circ_median.py
@@ -44,7 +44,7 @@ def test_rolling_circ_median_all_nans() -> None:
 
     for col in input_df.columns:
         result = rolling_circ_median_approx(input_df[col], window=4, min_periods=3, center=True, range_360=True)
-        expected = input_df[col].rolling(window=4, min_periods=3, center=True).apply(lambda x: circ_median_exact(x))
+        expected = input_df[col].rolling(window=4, min_periods=3, center=True).apply(circ_median_exact)
         assert_series_equal(result, expected)
 
 
@@ -73,11 +73,7 @@ def new_method() -> float:
         return rolling_circ_median_approx(input_df[col], window=window_size, min_periods=min_periods, center=True)
 
     def exact_method() -> float:
-        return (
-            input_df[col]
-            .rolling(window=window_size, min_periods=min_periods, center=True)
-            .apply(lambda x: circ_median_exact(x))
-        )
+        return input_df[col].rolling(window=window_size, min_periods=min_periods, center=True).apply(circ_median_exact)
 
     new_method_results = new_method()
     exact_method_results = exact_method()
diff --git a/tests/test_smart_data.py b/tests/test_smart_data.py
index a544034..ac59f33 100644
--- a/tests/test_smart_data.py
+++ b/tests/test_smart_data.py
@@ -8,7 +8,6 @@
 import pytest
 
 from tests.conftest import TEST_DATA_FLD
-from wind_up.backporting import strict_zip
 from wind_up.constants import TIMESTAMP_COL
 from wind_up.smart_data import (
     add_smart_lat_long_to_cfg,
@@ -40,7 +39,7 @@ def test_calc_last_xmin_datetime_in_month() -> None:
         dt.datetime(2020, 2, 29, 23, 50),
         dt.datetime(2020, 2, 29, 23, 50),
     ]
-    for i, e in strict_zip(inputs, expected):
+    for i, e in zip(inputs, expected, strict=True):
         assert calc_last_xmin_datetime_in_month(i, TIMEBASE_PD_TIMEDELTA) == pd.Timestamp(e)
 
 
diff --git a/uv.lock b/uv.lock
index c105100..e61cb67 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,45 +1,45 @@
 version = 1
-revision = 1
-requires-python = ">=3.9, <4.0"
+revision = 3
+requires-python = ">=3.10, <3.14"
 resolution-markers = [
-    "python_full_version >= '3.14'",
-    "python_full_version >= '3.12' and python_full_version < '3.14'",
+    "python_full_version >= '3.12'",
     "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version < '3.10'",
+    "python_full_version < '3.11'",
 ]
 
+[manifest]
+constraints = [{ name = "matplotlib", specifier = "<3.11" }]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
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+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
 [[package]]
 name = "anyio"
-version = "4.11.0"
+version = "4.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
-    { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094 }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/b5/001890774a9552aff22502b8da382593109ce0c95314abaebbb116567545/anyio-4.14.0.tar.gz", hash = "sha256:b47c1f9ccf73e67021df785332508f99379c68fa7d0684e8e3492cb1d4b23f89", size = 253586, upload-time = "2026-06-15T22:00:49.021Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/ba/16/9826f089383c593cdfc4a6e5aca94d9e91ae1692c57af82c3b2aa5e810f7/anyio-4.14.0-py3-none-any.whl", hash = "sha256:dd9b7a2a9799ed6552fde617b2c5df02b7fdd7d88392fc48101e51bae46164d9", size = 123506, upload-time = "2026-06-15T22:00:47.595Z" },
 ]
 
 [[package]]
 name = "appnope"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
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 [[package]]
@@ -49,9 +49,9 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "argon2-cffi-bindings" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706 }
+sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" }
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+    { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" },
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 [[package]]
@@ -61,33 +61,23 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
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diff --git a/wind_up/__init__.py b/wind_up/__init__.py
index 85c5673..e2a2775 100644
--- a/wind_up/__init__.py
+++ b/wind_up/__init__.py
@@ -1,5 +1,20 @@
 """wind_up package."""
 
+import contextlib
+import os
+import sys
 from importlib.metadata import version
 
+import matplotlib as mpl
+
 __version__ = version("res-wind-up")
+
+# wind-up renders figures to disk (see PlotConfig); default to a non-interactive backend so
+# analyses run headless on CI / SSH / batch hosts without an X server, and so a dropped
+# X11-forwarding connection can't kill a long unattended run. Respect an explicit MPLBACKEND
+# (checked by key presence, not truthiness) for users who want interactive display, and leave
+# the backend alone if pyplot is already imported (e.g. an interactive notebook session).
+if "MPLBACKEND" not in os.environ and "matplotlib.pyplot" not in sys.modules:
+    # use() can raise if the backend can't be set this late; never fail `import wind_up` for it.
+    with contextlib.suppress(ImportError):
+        mpl.use("Agg")
diff --git a/wind_up/backporting.py b/wind_up/backporting.py
deleted file mode 100644
index 13cc53d..0000000
--- a/wind_up/backporting.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""Backporting utilities for features in older Python versions."""
-
-import sys
-from collections.abc import Iterable, Iterator
-
-if sys.version_info >= (3, 10):
-
-    def strict_zip(*iterables: Iterable, strict: bool = False) -> Iterator:  # noqa: D103
-        return zip(*iterables, strict=strict)
-else:
-
-    def strict_zip(*iterables: Iterable, strict: bool = False) -> Iterator:  # noqa: D103
-        if strict:
-            iterables = [list(it) for it in iterables]  # type: ignore[assignment]
-            if not all(len(it) == len(iterables[0]) for it in iterables):  # type: ignore[arg-type]
-                msg = "All iterables must have the same length"
-                raise ValueError(msg)
-        return zip(*iterables)  # type: ignore[call-overload]
diff --git a/wind_up/models.py b/wind_up/models.py
index b86c229..9e9997c 100644
--- a/wind_up/models.py
+++ b/wind_up/models.py
@@ -13,7 +13,6 @@
 import yaml
 from pydantic import BaseModel, BeforeValidator, Field, model_validator
 
-from wind_up.backporting import strict_zip
 from wind_up.constants import OUTPUT_DIR
 from wind_up.yaml_loader import Loader, construct_include
 
@@ -506,7 +505,7 @@ def from_yaml(cls, file_path: Path) -> "WindUpConfig":  # noqa ANN102
             )
         tt_list = [TurbineType.model_validate(tt) for tt in cfg_dct["asset"]["turbine_types"]]
         cfg_dct["asset"]["wtgs"] = [
-            Turbine(name=x, turbine_type=tt) for x, tt in strict_zip(cfg_dct["asset"]["wtgs"], tt_list)
+            Turbine(name=x, turbine_type=tt) for x, tt in zip(cfg_dct["asset"]["wtgs"], tt_list, strict=True)
         ]
         test_wtg_list = []
         for x in cfg_dct["test_wtgs"]:
diff --git a/wind_up/northing.py b/wind_up/northing.py
index 17ccb4d..d4f7607 100644
--- a/wind_up/northing.py
+++ b/wind_up/northing.py
@@ -188,7 +188,7 @@ def _calc_wf_yawdir_df(
     wf_yawdir_df = wf_df.groupby(TIMESTAMP_COL).agg(
         wf_yawdir=pd.NamedAgg(
             column=best_yaw_dir_col,
-            aggfunc=lambda x: circ_median(x),
+            aggfunc=circ_median,
         ),
         num_turbines=pd.NamedAgg(column=best_yaw_dir_col, aggfunc=lambda x: x.count()),
     )
diff --git a/wind_up/optimize_northing.py b/wind_up/optimize_northing.py
index 8d2ee25..eb5cb54 100644
--- a/wind_up/optimize_northing.py
+++ b/wind_up/optimize_northing.py
@@ -13,7 +13,6 @@
 from ruptures.base import BaseCost
 from scipy.stats import circmean
 
-from wind_up.backporting import strict_zip
 from wind_up.circular_math import circ_diff, circ_median, rolling_circ_median_approx
 from wind_up.constants import (
     RAW_POWER_COL,
@@ -162,9 +161,10 @@ def _add_northed_ok_diff_and_rolling_cols(
         wtg_df[northed_col] = wtg_df[RAW_YAWDIR_COL] + north_offset
         wtg_df[northed_col] = wtg_df[northed_col] % 360
     elif north_offset_df is not None:
-        for ts, no in strict_zip(
+        for ts, no in zip(
             north_offset_df[TIMESTAMP_COL].to_list(),
             north_offset_df["north_offset"].to_list(),
+            strict=True,
         ):
             wtg_df.loc[wtg_df.index >= ts, northed_col] = wtg_df.loc[wtg_df.index >= ts, RAW_YAWDIR_COL] + no
         wtg_df[northed_col] = wtg_df[northed_col] % 360
diff --git a/wind_up/plots/combine_results_plots.py b/wind_up/plots/combine_results_plots.py
index 299774f..f8cc334 100644
--- a/wind_up/plots/combine_results_plots.py
+++ b/wind_up/plots/combine_results_plots.py
@@ -2,7 +2,6 @@
 from matplotlib import pyplot as plt
 from scipy.stats import norm
 
-from wind_up.backporting import strict_zip
 from wind_up.models import PlotConfig
 
 
@@ -15,7 +14,9 @@ def plot_combined_results(tdf: pd.DataFrame, *, plot_cfg: PlotConfig, confidence
 
     plt.figure()
     labels = (
-        [f"{i} ({int(v)} wtgs)" for i, v in zip(tdf.index, tdf["wtg_count"])] if grouped_results else tdf["test_wtg"]
+        [f"{i} ({int(v)} wtgs)" for i, v in zip(tdf.index, tdf["wtg_count"], strict=True)]
+        if grouped_results
+        else tdf["test_wtg"]
     )
     values = tdf["p50_uplift"] * 100
     yerrs = tdf["sigma"] * 100 * z_score
@@ -44,9 +45,10 @@ def plot_testref_and_combined_results(
     plt.figure(figsize=(10, 6))
     labels = [
         x + "-" + y
-        for x, y in strict_zip(
+        for x, y in zip(
             [x[-1] for x in trdf["test_wtg"].str.split("_")],
             [x[-1] for x in trdf["ref"].str.split("_")],
+            strict=True,
         )
     ]
     values = trdf["uplift_frc"] * 100
diff --git a/wind_up/plots/data_coverage_plots.py b/wind_up/plots/data_coverage_plots.py
index 59b8dee..946ed01 100644
--- a/wind_up/plots/data_coverage_plots.py
+++ b/wind_up/plots/data_coverage_plots.py
@@ -22,10 +22,10 @@ def plot_detrend_data_cov(
     window_hrs = 14 * 24
     rows_per_hour = 3600 / cfg.timebase_s
     expected_rows = rows_per_hour * window_hrs
-    xlims = [
+    xlims = (
         min(cfg.lt_first_dt_utc_start, test_df.index.min()) - pd.Timedelta(f"{window_hrs}h"),
         cfg.analysis_last_dt_utc_start + pd.Timedelta(f"{window_hrs}h"),
-    ]
+    )
     plt.subplot(3, 1, 1)
     plot_df_raw = (
         test_df.resample(f"{window_hrs}h")["test_" + RAW_POWER_COL, "test_" + RAW_WINDSPEED_COL].count()
@@ -37,7 +37,7 @@ def plot_detrend_data_cov(
     plt.plot(plot_df_raw.index, plot_df_raw["test_" + RAW_WINDSPEED_COL], label="WindSpeedMean before filter")
     plt.plot(plot_df.index, plot_df[test_ws_col], label=test_ws_col)
     plt.xlim(xlims)
-    plt.ylim([0, 105])
+    plt.ylim((0, 105))
     plt.ylabel("data coverage [%]")
     plt.grid()
     plt.legend()
@@ -46,7 +46,7 @@ def plot_detrend_data_cov(
     plt.plot(plot_df.index, plot_df[ref_ws_col], label=ref_ws_col)
     plt.plot(plot_df.index, plot_df[ref_wd_col], label=ref_wd_col)
     plt.xlim(xlims)
-    plt.ylim([0, 105])
+    plt.ylim((0, 105))
     plt.ylabel("data coverage [%]")
     plt.grid()
     plt.legend()
@@ -56,7 +56,7 @@ def plot_detrend_data_cov(
     )
     plt.plot(plot_df.index, plot_df[test_ws_col], label=f"detrend {test_ws_col}")
     plt.xlim(xlims)
-    plt.ylim([0, 105])
+    plt.ylim((0, 105))
     plt.ylabel("data coverage [%]")
     plt.grid()
     plt.legend()
@@ -91,10 +91,10 @@ def plot_pre_post_data_cov(
     window_hrs = 14 * 24
     rows_per_hour = 3600 / cfg.timebase_s
     expected_rows = rows_per_hour * window_hrs
-    xlims = [
+    xlims = (
         min(cfg.lt_first_dt_utc_start, test_df.index.min()) - pd.Timedelta(f"{window_hrs}h"),
         cfg.analysis_last_dt_utc_start + pd.Timedelta(f"{window_hrs}h"),
-    ]
+    )
     plt.subplot(3, 1, 1)
     plot_df_raw = (
         test_df.resample(f"{window_hrs}h")["test_" + RAW_POWER_COL, "test_" + RAW_WINDSPEED_COL].count()
@@ -107,7 +107,7 @@ def plot_pre_post_data_cov(
     plt.plot(plot_df.index, plot_df[test_pw_col], label=test_pw_col)
     plt.plot(plot_df.index, plot_df[test_ws_col], label=test_ws_col)
     plt.xlim(xlims)
-    plt.ylim([0, 105])
+    plt.ylim((0, 105))
     plt.ylabel("data coverage [%]")
     plt.grid()
     plt.legend()
@@ -117,7 +117,7 @@ def plot_pre_post_data_cov(
     plt.plot(plot_df.index, plot_df[ref_ws_col], label=ref_ws_col)
     plt.plot(plot_df.index, plot_df[ref_wd_col], label=ref_wd_col)
     plt.xlim(xlims)
-    plt.ylim([0, 105])
+    plt.ylim((0, 105))
     plt.ylabel("data coverage [%]")
     plt.grid()
     plt.legend()
@@ -129,7 +129,7 @@ def plot_pre_post_data_cov(
     plt.plot(plot_post_df.index, plot_post_df[test_pw_col], label=f"post {test_pw_col}")
     plt.plot(plot_post_df.index, plot_post_df[detrend_ws_col], label=f"post {detrend_ws_col}")
     plt.xlim(xlims)
-    plt.ylim([0, 105])
+    plt.ylim((0, 105))
     plt.ylabel("data coverage [%]")
     plt.grid()
     plt.legend()
diff --git a/wind_up/plots/northing_plots.py b/wind_up/plots/northing_plots.py
index 22864be..a313e67 100644
--- a/wind_up/plots/northing_plots.py
+++ b/wind_up/plots/northing_plots.py
@@ -120,7 +120,7 @@ def plot_northing_changepoint(
     plt.axvline(northing_datetime_utc, color="grey", linestyle="--", label="northing correction")
     plt.xlabel("timestamp")
     plt.ylabel("direction [deg]")
-    plt.xlim([xmin, xmax])
+    plt.xlim((xmin, xmax))
     plt.xticks(rotation=45)
     title = f"{northing_turbine} north_ref_wd_col={north_ref_wd_col} {northing_datetime_utc.strftime('%Y-%m-%d')}"
     plt.title(title)
diff --git a/wind_up/plots/pp_analysis_plots.py b/wind_up/plots/pp_analysis_plots.py
index 01914ca..d058283 100644
--- a/wind_up/plots/pp_analysis_plots.py
+++ b/wind_up/plots/pp_analysis_plots.py
@@ -534,7 +534,7 @@ def plot_pp_data_coverage(
     plt.title(plot_title)
     plt.ylabel("data coverage [%]")
     plt.xlabel("bin centre [m/s]")
-    plt.ylim([0, 100])
+    plt.ylim((0, 100))
     plt.grid()
     plt.tight_layout()
     if plot_cfg.show_plots:
diff --git a/wind_up/plots/scada_funcs_plots.py b/wind_up/plots/scada_funcs_plots.py
index 2233415..1bf56d4 100644
--- a/wind_up/plots/scada_funcs_plots.py
+++ b/wind_up/plots/scada_funcs_plots.py
@@ -9,7 +9,6 @@
 import seaborn as sns
 from tabulate import tabulate
 
-from wind_up.backporting import strict_zip
 from wind_up.constants import DATA_UNIT_DEFAULTS, SCATTER_ALPHA, SCATTER_MARKERSCALE, SCATTER_S, DataColumns
 from wind_up.plots.misc_plots import bubble_plot
 from wind_up.result_manager import result_manager
@@ -45,9 +44,10 @@ def calc_cf_by_turbine(scada_df: pd.DataFrame, cfg: WindUpConfig) -> pd.DataFram
         hours=pd.NamedAgg(column=DataColumns.turbine_name, aggfunc=lambda x: x.count() / rows_per_hour),
         MWh=pd.NamedAgg(column=DataColumns.active_power_mean, aggfunc=lambda x: x.sum() / rows_per_hour / 1000),
     )
-    for i, rp in strict_zip(
+    for i, rp in zip(
         [x.name for x in cfg.asset.wtgs],
         [x.turbine_type.rated_power_kw for x in cfg.asset.wtgs],
+        strict=True,
     ):
         cf_df.loc[i, "rated_power_kW"] = rp
     cf_df["CF"] = cf_df["MWh"] / (cf_df["hours"] * cf_df["rated_power_kW"] / 1000)
diff --git a/wind_up/pp_analysis.py b/wind_up/pp_analysis.py
index 71f7178..8e0ac88 100644
--- a/wind_up/pp_analysis.py
+++ b/wind_up/pp_analysis.py
@@ -538,7 +538,11 @@ def pre_post_pp_analysis_with_reversal_and_bootstrapping(
     pre_df_dropna = pre_df.dropna(subset=[ws_col, pw_col, wd_col])
     post_df_dropna = post_df.dropna(subset=[ws_col, pw_col, wd_col])
 
-    n_samples = cfg.bootstrap_runs_override if cfg.bootstrap_runs_override else round(40 * (1 / (1 - confidence_level)))
+    n_samples = (
+        cfg.bootstrap_runs_override
+        if cfg.bootstrap_runs_override is not None
+        else round(40 * (1 / (1 - confidence_level)))
+    )
     if plot_cfg is not None:
         logger.info(f"Running block bootstrapping uncertainty analysis n_samples = {n_samples}")
     bootstrapped_uplifts = np.empty(n_samples)
diff --git a/wind_up/scada_funcs.py b/wind_up/scada_funcs.py
index a114ab3..c556115 100644
--- a/wind_up/scada_funcs.py
+++ b/wind_up/scada_funcs.py
@@ -10,7 +10,6 @@
 import numpy as np
 import pandas as pd
 
-from wind_up.backporting import strict_zip
 from wind_up.circular_math import circ_diff
 from wind_up.constants import RAW_DOWNTIME_S_COL, RAW_POWER_COL, RAW_WINDSPEED_COL, RAW_YAWDIR_COL, DataColumns
 from wind_up.plots.scada_funcs_plots import (
@@ -115,9 +114,10 @@ def _filter_wrong_yaw(df: pd.DataFrame) -> pd.DataFrame:
 
 def _add_pw_clipped(df: pd.DataFrame, wtgs: list[Turbine]) -> pd.DataFrame:
     df["pw_clipped"] = df["ActivePowerMean"].clip(lower=0)
-    for i, rp in strict_zip(
+    for i, rp in zip(
         [x.name for x in wtgs],
         [x.turbine_type.rated_power_kw for x in wtgs],
+        strict=True,
     ):
         df.loc[i, "pw_clipped"] = df.loc[i, "pw_clipped"].clip(upper=rp).to_numpy()
     return df