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[pre-commit.ci] pre-commit autoupdate #15

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677e416
[pre-commit.ci] pre-commit autoupdate
pre-commit-ci[bot] Apr 6, 2026
04c0ccf
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 6, 2026
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8 changes: 4 additions & 4 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
rev: v6.0.0
hooks:
- id: check-toml
- id: check-yaml
Expand All @@ -19,21 +19,21 @@ repos:
- id: requirements-txt-fixer

- repo: https://github.com/codespell-project/codespell
rev: v2.3.0
rev: v2.4.2
hooks:
- id: codespell
additional_dependencies:
- tomli

- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.7.2
rev: v0.15.9
hooks:
- id: ruff
args: ["--fix", "--show-fixes"]
- id: ruff-format

- repo: https://github.com/abravalheri/validate-pyproject
rev: v0.22
rev: v0.25
hooks:
- id: validate-pyproject

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6 changes: 2 additions & 4 deletions aok/core/kd_utils/Kd_analysis.py
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Expand Up @@ -4,9 +4,9 @@
import logging

import numpy as np
import pandas as pd

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from scipy.optimize import curve_fit

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Type of "curve_fit" is partially unknown
  Type of "curve_fit" is "(f: Unknown, xdata: Unknown, ydata: Unknown, p0: Unknown | None = None, sigma: Unknown | None = None, absolute_sigma: bool = False, check_finite: Unknown | None = None, bounds: Unknown = (-np.inf, np.inf), method: Unknown | None = None, jac: Unknown | None = None, *, full_output: bool = False, nan_policy: Unknown | None = None, **kwargs: Unknown) -> Unknown" (reportUnknownVariableType)

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from sklearn.linear_model import LinearRegression

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# def log_model(z, kd, e0):
# return np.log(e0) - kd * z
Expand Down Expand Up @@ -172,7 +172,7 @@
# })


def find_exponential_decay_zone(hist_df, decay_threshold=0.0):

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"""
Identify the depth bins that lie within the exponential decay zone.

Expand All @@ -196,7 +196,7 @@
Filtered rows with a 'log_photon_counts' column added; only bins inside
the exponential decay zone are kept (photon_counts > 0 and log finite).
"""
df = hist_df.copy()

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# Apply decay threshold: exclude bins below threshold fraction of peak signal
if decay_threshold > 0.0:
Expand Down Expand Up @@ -612,9 +612,7 @@
)
return kd, e0, nf

raise ValueError(
f"Unknown kd_fit_method: {method!r}. " f"Choose from {KD_FIT_METHODS}"
)
raise ValueError(f"Unknown kd_fit_method: {method!r}. Choose from {KD_FIT_METHODS}")


# another solution is to calculate kd without hist
Expand Down Expand Up @@ -849,7 +847,7 @@
else:
expected_nf_per_bin = None
logging.info(
"Hybrid: insufficient noise floor estimates (%d), " "skipping pass 2",
"Hybrid: insufficient noise floor estimates (%d), skipping pass 2",
len(valid_nf_all),
)

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6 changes: 3 additions & 3 deletions aok/core/main.py
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Expand Up @@ -284,9 +284,9 @@ def run_pipeline(args):
["relative_AT_dist", "lat_bins", "photon_height"]
].drop_duplicates()
unique_photon_dataset["relative_AT_dist_center"] = (
unique_photon_dataset.groupby(
"lat_bins", observed=False
)["relative_AT_dist"].transform("mean")
unique_photon_dataset.groupby("lat_bins", observed=False)[
"relative_AT_dist"
].transform("mean")
)

_closest_rows = []
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