[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -2,24 +2,24 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v6.0.0
     hooks:
     -   id: trailing-whitespace
     -   id: end-of-file-fixer
     -   id: check-yaml
     -   id: check-added-large-files
     -   id: check-case-conflict
     -   id: check-merge-conflict
--   repo: https://github.com/psf/black
-    rev: 24.8.0
+-   repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 26.1.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort
-    rev: 5.13.2
+    rev: 7.0.0
     hooks:
     -   id: isort
 -   repo: https://github.com/pycqa/flake8
-    rev: 7.1.1
+    rev: 7.3.0
     hooks:
     -   id: flake8
 -   repo: https://github.com/pycqa/pydocstyle

phys2denoise/cli/run.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 """Parser for phys2denoise."""
 
-
 import argparse
 
 from phys2denoise import __version__

phys2denoise/metrics/cardiac.py

@@ -89,22 +89,18 @@ def _cardiac_metrics(
         data = io.load_physio(data, fs=fs)
         data._metadata["peaks"] = peaks
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with existing peaks metadata (e.g. using the peakdet module), or
             by providing the physiological data timeseries, the sampling frequency,
             and the peak indices separately.
-            """
-        )
+            """)
     if data.peaks.size == 0:
-        raise ValueError(
-            """
+        raise ValueError("""
             Peaks must be a non-empty list.
             Make sure to run peak detection on your physiological data first,
             using the peakdet module, or other software of your choice.
-            """
-        )
+            """)
 
     # Convert window to samples, but halves it.
     halfwindow_samples = int(round(window * data.fs / 2))
@@ -384,22 +380,18 @@ def cardiac_phase(data, slice_timings, n_scans, t_r, peaks=None, fs=None, **kwar
         data = io.load_physio(data, fs=fs)
         data._metadata["peaks"] = peaks
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with existing peaks metadata (e.g. using the peakdet module), or
             by providing the physiological data timeseries, the sampling frequency,
             and the peak indices separately.
-            """
-        )
+            """)
     if data.peaks.size == 0:
-        raise ValueError(
-            """
+        raise ValueError("""
             Peaks must be a non-empty list.
             Make sure to run peak detection on your physiological data first,
             using the peakdet module, or other software of your choice.
-            """
-        )
+            """)
 
     assert slice_timings.ndim == 1, "Slice times must be a 1D array"
     n_slices = np.size(slice_timings)

phys2denoise/metrics/chest_belt.py

@@ -56,22 +56,18 @@ def respiratory_variance_time(
         data._metadata["peaks"] = peaks
         data._metadata["troughs"] = troughs
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with existing peaks and troughs metadata (e.g. using the peakdet module), or
             by providing the physiological data timeseries, the sampling frequency,
             and the peak and trough indices separately.
-            """
-        )
+            """)
     if data.peaks.size == 0 or data.troughs.size == 0:
-        raise ValueError(
-            """
+        raise ValueError("""
             Peaks and troughs must be non-empty lists.
             Make sure to run peak/trough detection on your physiological data first,
             using the peakdet module, or other software of your choice.
-            """
-        )
+            """)
 
     timestep = 1 / data.fs
     # respiration belt timing
@@ -219,14 +215,12 @@ def _respiratory_pattern_variability(data, window):
     elif fs is not None:
         data = io.load_physio(data, fs=fs)
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with the sampling frequency encapsulated, or
             by providing the physiological data timeseries and the sampling
             frequency separately.
-            """
-        )
+            """)
 
     # Convert window to Hertz
     window = int(window * data.fs)
@@ -288,14 +282,12 @@ def respiratory_variance(data, fs=None, window=6, **kwargs):
     elif fs is not None:
         data = io.load_physio(data, fs=fs)
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with the sampling frequency encapsulated, or
             by providing the physiological data timeseries and the sampling
             frequency separately.
-            """
-        )
+            """)
 
     # Convert window to Hertz
     halfwindow_samples = int(round(window * data.fs / 2))
@@ -338,14 +330,12 @@ def respiratory_phase(data, n_scans, slice_timings, t_r, fs=None, **kwargs):
     elif fs is not None:
         data = io.load_physio(data, fs=fs)
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with the sampling frequency encapsulated, or
             by providing the physiological data timeseries and the sampling
             frequency separately.
-            """
-        )
+            """)
 
     assert slice_timings.ndim == 1, "Slice times must be a 1D array"
     n_slices = np.size(slice_timings)

phys2denoise/metrics/multimodal.py

@@ -66,37 +66,31 @@ def retroicor(
         if data.physio_type is None and physio_type is not None:
             data._physio_type = physio_type
         elif data.physio_type is None and physio_type is None:
-            raise ValueError(
-                """
+            raise ValueError("""
                 Since the provided Physio object does not specify a `physio_type`,
                 this function's `physio_type` parameter must be specified as a
                 value from {'cardiac', 'respiratory'}
-                """
-            )
+                """)
 
     elif fs is not None and physio_type is not None:
         data = io.load_physio(data, fs=fs)
         data._physio_type = physio_type
         if data.physio_type == "cardiac":
             data._metadata["peaks"] = cardiac_peaks
     else:
-        raise ValueError(
-            """
+        raise ValueError("""
             To use this function you should either provide a Physio object
             with existing peaks metadata if it describes a cardiac signal
             (e.g. using the peakdet module), or
             by providing the physiological data timeseries, the sampling frequency,
             the physio_type and the peak indices separately.
-            """
-        )
+            """)
     if not data.peaks and data.physio_type == "cardiac":
-        raise ValueError(
-            """
+        raise ValueError("""
             Peaks must be a non-empty list for cardiac data.
             Make sure to run peak detection on your cardiac data first,
             using the peakdet module, or other software of your choice.
-            """
-        )
+            """)
 
     n_slices = np.shape(slice_timings)  # number of slices
 

versioneer.py

@@ -427,9 +427,7 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=
     return stdout, p.returncode
 
 
-LONG_VERSION_PY[
-    "git"
-] = '''
+LONG_VERSION_PY["git"] = '''
 # This file helps to compute a version number in source trees obtained from
 # git-archive tarball (such as those provided by githubs download-from-tag
 # feature). Distribution tarballs (built by setup.py sdist) and build