style: pre-commit fixes

docs/source/extensions/numbadoc.py

@@ -5,6 +5,7 @@
 
 Adapted from https://github.com/numba/numba/issues/5755#issuecomment-646587651
 """
+
 from copy import copy
 from typing import Iterator, List
 

src/pygama/cli.py

@@ -1,6 +1,7 @@
 """
 pygama's command line interface utilities.
 """
+
 import argparse
 import logging
 import os

src/pygama/evt/modules/legend.py

@@ -1,6 +1,7 @@
 """
 Module provides LEGEND internal functions
 """
+
 from importlib import import_module
 
 from lgdo.lh5 import utils

src/pygama/evt/modules/spm.py

@@ -13,7 +13,6 @@
 additional parameters are free to the user and need to be defined in the JSON
 """
 
-
 import awkward as ak
 import numpy as np
 from lgdo import Array, VectorOfVectors

src/pygama/flow/file_db.py

@@ -1,4 +1,5 @@
 """Utilities for LH5 file inventory."""
+
 from __future__ import annotations
 
 import json

src/pygama/hit/build_hit.py

@@ -1,6 +1,7 @@
 """
 This module implements routines to evaluate expressions to columnar data.
 """
+
 from __future__ import annotations
 
 import json

src/pygama/logging.py

@@ -1,4 +1,5 @@
 """This module implements some helpers for setting up logging."""
+
 import logging
 
 import colorlog

src/pygama/pargen/AoE_cal.py

@@ -37,7 +37,6 @@
 
 
 class PDF:
-
     """
     Base class for A/E pdfs.
     """
@@ -1518,9 +1517,9 @@ def drift_time_correction(
                 np.nanpercentile(dep_events[self.dt_param], 99),
             ]
 
-            self.dt_res_dict[
-                "final_selection"
-            ] = f"{aoe_param}>{aoe_range[0]}&{aoe_param}<{aoe_range[1]}&{self.dt_param}>{dt_range[0]}&{self.dt_param}<{dt_range[1]}&{self.dt_param}=={self.dt_param}"
+            self.dt_res_dict["final_selection"] = (
+                f"{aoe_param}>{aoe_range[0]}&{aoe_param}<{aoe_range[1]}&{self.dt_param}>{dt_range[0]}&{self.dt_param}<{dt_range[1]}&{self.dt_param}=={self.dt_param}"
+            )
 
             final_df = dep_events.query(self.dt_res_dict["final_selection"])
 
@@ -1550,12 +1549,12 @@ def drift_time_correction(
                 "errs": m.errors,
                 "object": m,
             }
-            aoe_grp1 = self.dt_res_dict[
-                "aoe_grp1"
-            ] = f'{self.dt_param}>{m.values["mu1"] - 2 * m.values["sigma1"]} & {self.dt_param}<{m.values["mu1"] + 2 * m.values["sigma1"]}'
-            aoe_grp2 = self.dt_res_dict[
-                "aoe_grp2"
-            ] = f'{self.dt_param}>{m.values["mu2"] - 2 * m.values["sigma2"]} & {self.dt_param}<{m.values["mu2"] + 2 * m.values["sigma2"]}'
+            aoe_grp1 = self.dt_res_dict["aoe_grp1"] = (
+                f'{self.dt_param}>{m.values["mu1"] - 2 * m.values["sigma1"]} & {self.dt_param}<{m.values["mu1"] + 2 * m.values["sigma1"]}'
+            )
+            aoe_grp2 = self.dt_res_dict["aoe_grp2"] = (
+                f'{self.dt_param}>{m.values["mu2"] - 2 * m.values["sigma2"]} & {self.dt_param}<{m.values["mu2"] + 2 * m.values["sigma2"]}'
+            )
 
             aoe_pars, aoe_errs, _ = unbinned_aoe_fit(
                 final_df.query(aoe_grp1)[aoe_param], pdf=self.pdf, display=display
@@ -2021,9 +2020,11 @@ def calibrate(self, df, initial_aoe_param):
                         self.low_cut_val,
                         peak,
                         fwhm,
-                        dt_mask=peak_df[self.dt_cut_param].to_numpy()
-                        if self.dt_cut_param is not None
-                        else None,
+                        dt_mask=(
+                            peak_df[self.dt_cut_param].to_numpy()
+                            if self.dt_cut_param is not None
+                            else None
+                        ),
                     )
                     self.low_side_sf = pd.concat(
                         [
@@ -2043,9 +2044,11 @@ def calibrate(self, df, initial_aoe_param):
                         self.low_cut_val,
                         peak,
                         fwhm,
-                        dt_mask=peak_df[self.dt_cut_param].to_numpy()
-                        if self.dt_cut_param is not None
-                        else None,
+                        dt_mask=(
+                            peak_df[self.dt_cut_param].to_numpy()
+                            if self.dt_cut_param is not None
+                            else None
+                        ),
                     )
                     self.low_side_sf = pd.concat(
                         [
@@ -2083,9 +2086,11 @@ def calibrate(self, df, initial_aoe_param):
                         peak_df[aoe_param].to_numpy(),
                         self.low_cut_val,
                         self.high_cut_val,
-                        dt_mask=peak_df[self.dt_cut_param].to_numpy()
-                        if self.dt_cut_param is not None
-                        else None,
+                        dt_mask=(
+                            peak_df[self.dt_cut_param].to_numpy()
+                            if self.dt_cut_param is not None
+                            else None
+                        ),
                     )
                     sf = sf_dict["sf"]
                     sf_err = sf_dict["sf_err"]
@@ -2107,9 +2112,11 @@ def calibrate(self, df, initial_aoe_param):
                         peak,
                         fwhm,
                         high_cut=self.high_cut_val,
-                        dt_mask=peak_df[self.dt_cut_param].to_numpy()
-                        if self.dt_cut_param is not None
-                        else None,
+                        dt_mask=(
+                            peak_df[self.dt_cut_param].to_numpy()
+                            if self.dt_cut_param is not None
+                            else None
+                        ),
                     )
                     self.two_side_sf = pd.concat(
                         [

src/pygama/pargen/data_cleaning.py

@@ -5,6 +5,7 @@
 - find_pulser_properties (find pulser by looking for which peak has a constant time between events)
 - tag_pulsers
 """
+
 import matplotlib.gridspec as gs
 import matplotlib.pyplot as plt
 import numpy as np

src/pygama/pargen/ecal_th.py

@@ -1183,13 +1183,11 @@ def bin_pulser_stability(ecal_class, data, pulser_field="is_pulser", time_slice=
             "spread": np.full_like(times_average, np.nan),
         }
 
-    nanmedian = (
-        lambda x: np.nanpercentile(x, 50) if len(x[~np.isnan(x)]) >= 10 else np.nan
+    nanmedian = lambda x: (
+        np.nanpercentile(x, 50) if len(x[~np.isnan(x)]) >= 10 else np.nan
     )
-    error = (
-        lambda x: np.nanvar(x) / np.sqrt(len(x))
-        if len(x[~np.isnan(x)]) >= 10
-        else np.nan
+    error = lambda x: (
+        np.nanvar(x) / np.sqrt(len(x)) if len(x[~np.isnan(x)]) >= 10 else np.nan
     )
 
     par_average, _, _ = binned_statistic(
@@ -1225,13 +1223,11 @@ def bin_stability(ecal_class, data, time_slice=180, energy_range=[2585, 2660]):
             "spread": np.full_like(times_average, np.nan),
         }
 
-    nanmedian = (
-        lambda x: np.nanpercentile(x, 50) if len(x[~np.isnan(x)]) >= 10 else np.nan
+    nanmedian = lambda x: (
+        np.nanpercentile(x, 50) if len(x[~np.isnan(x)]) >= 10 else np.nan
     )
-    error = (
-        lambda x: np.nanvar(x) / np.sqrt(len(x))
-        if len(x[~np.isnan(x)]) >= 10
-        else np.nan
+    error = lambda x: (
+        np.nanvar(x) / np.sqrt(len(x)) if len(x[~np.isnan(x)]) >= 10 else np.nan
     )
 
     par_average, _, _ = binned_statistic(

src/pygama/pargen/energy_cal.py

@@ -5,6 +5,7 @@
 - hpge_fit_E_peaks (fits the energy peals)
 - hpge_E_calibration (main routine -- finds and fits peaks specified)
 """
+
 import logging
 import sys
 

src/pygama/pargen/lq_cal.py

@@ -221,7 +221,6 @@ def fit_time_means(tstamps, means, reses):
 
 
 class cal_lq:
-
     """A class for calibrating the LQ parameter and determining the LQ cut value"""
 
     def __init__(