Bump ruff to v0.3.0 (mne-tools#12477)

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.0
     hooks:
       - id: ruff
         name: ruff lint mne

doc/conf.py

@@ -1194,21 +1194,21 @@ def append_attr_meth_examples(app, what, name, obj, options, lines):
     "carousel": [
         dict(
             title="Source Estimation",
-            text="Distributed, sparse, mixed-norm, beam\u00ADformers, dipole fitting, and more.",  # noqa E501
+            text="Distributed, sparse, mixed-norm, beam\u00adformers, dipole fitting, and more.",  # noqa E501
             url="auto_tutorials/inverse/index.html",
             img="sphx_glr_30_mne_dspm_loreta_008.gif",
             alt="dSPM",
         ),
         dict(
             title="Machine Learning",
-            text="Advanced decoding models including time general\u00ADiza\u00ADtion.",  # noqa E501
+            text="Advanced decoding models including time general\u00adiza\u00adtion.",  # noqa E501
             url="auto_tutorials/machine-learning/50_decoding.html",
             img="sphx_glr_50_decoding_006.png",
             alt="Decoding",
         ),
         dict(
             title="Encoding Models",
-            text="Receptive field estima\u00ADtion with optional smooth\u00ADness priors.",  # noqa E501
+            text="Receptive field estima\u00adtion with optional smooth\u00adness priors.",  # noqa E501
             url="auto_tutorials/machine-learning/30_strf.html",
             img="sphx_glr_30_strf_001.png",
             alt="STRF",
@@ -1222,7 +1222,7 @@ def append_attr_meth_examples(app, what, name, obj, options, lines):
         ),
         dict(
             title="Connectivity",
-            text="All-to-all spectral and effective connec\u00ADtivity measures.",  # noqa E501
+            text="All-to-all spectral and effective connec\u00adtivity measures.",  # noqa E501
             url="https://mne.tools/mne-connectivity/stable/auto_examples/mne_inverse_label_connectivity.html",  # noqa E501
             img="https://mne.tools/mne-connectivity/stable/_images/sphx_glr_mne_inverse_label_connectivity_001.png",  # noqa E501
             alt="Connectivity",

doc/sphinxext/unit_role.py

@@ -24,7 +24,7 @@ def pass_error_to_sphinx(rawtext, text, lineno, inliner):
     except ValueError:
         return pass_error_to_sphinx(rawtext, text, lineno, inliner)
     # input is well-formatted: proceed
-    node = nodes.Text("\u202F".join(parts))
+    node = nodes.Text("\u202f".join(parts))
     return [node], []
 
 

examples/datasets/hf_sef_data.py

@@ -14,7 +14,6 @@
 
 # %%
 
-
 import os
 
 import mne

examples/decoding/decoding_csp_eeg.py

@@ -20,7 +20,6 @@
 
 # %%
 
-
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

examples/decoding/decoding_csp_timefreq.py

@@ -21,7 +21,6 @@
 
 # %%
 
-
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

examples/decoding/ssd_spatial_filters.py

@@ -20,7 +20,6 @@
 
 # %%
 
-
 import matplotlib.pyplot as plt
 
 import mne

examples/io/elekta_epochs.py

@@ -15,7 +15,6 @@
 
 # %%
 
-
 import os
 
 import mne

examples/preprocessing/css.py

@@ -75,9 +75,9 @@ def subcortical_waveform(times):
     labels=[postcenlab, hiplab],
     data_fun=cortical_waveform,
 )
-stc.data[
-    np.where(np.isin(stc.vertices[0], hiplab.vertices))[0], :
-] = subcortical_waveform(times)
+stc.data[np.where(np.isin(stc.vertices[0], hiplab.vertices))[0], :] = (
+    subcortical_waveform(times)
+)
 evoked = simulate_evoked(fwd, stc, raw.info, cov, nave=15)
 
 ###############################################################################

examples/preprocessing/eog_artifact_histogram.py

@@ -15,7 +15,6 @@
 
 # %%
 
-
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/preprocessing/xdawn_denoising.py

@@ -25,7 +25,6 @@
 
 # %%
 
-
 from mne import Epochs, compute_raw_covariance, io, pick_types, read_events
 from mne.datasets import sample
 from mne.preprocessing import Xdawn

examples/visualization/eyetracking_plot_heatmap.py

@@ -24,7 +24,6 @@
 # :ref:`example data <eyelink-dataset>`: eye-tracking data recorded from SR research's
 # ``'.asc'`` file format.
 
-
 import matplotlib.pyplot as plt
 
 import mne

examples/visualization/topo_compare_conditions.py

@@ -19,7 +19,6 @@
 
 # %%
 
-
 import matplotlib.pyplot as plt
 
 import mne

examples/visualization/topo_customized.py

@@ -19,7 +19,6 @@
 
 # %%
 
-
 import matplotlib.pyplot as plt
 import numpy as np
 

mne/_fiff/tests/test_meas_info.py

@@ -350,8 +350,9 @@ def test_read_write_info(tmp_path):
 @testing.requires_testing_data
 def test_dir_warning():
     """Test that trying to read a bad filename emits a warning before an error."""
-    with pytest.raises(OSError, match="directory"), pytest.warns(
-        RuntimeWarning, match="does not conform"
+    with (
+        pytest.raises(OSError, match="directory"),
+        pytest.warns(RuntimeWarning, match="does not conform"),
     ):
         read_info(ctf_fname)
 

mne/beamformer/resolution_matrix.py

@@ -1,4 +1,5 @@
 """Compute resolution matrix for beamformers."""
+
 # Authors: olaf.hauk@mrc-cbu.cam.ac.uk
 #
 # License: BSD-3-Clause

mne/beamformer/tests/test_lcmv.py

@@ -589,9 +589,11 @@ def test_make_lcmv_sphere(pick_ori, weight_norm):
     fwd_sphere = mne.make_forward_solution(evoked.info, None, src, sphere)
 
     # Test that we get an error if not reducing rank
-    with pytest.raises(
-        ValueError, match="Singular matrix detected"
-    ), _record_warnings(), pytest.warns(RuntimeWarning, match="positive semidefinite"):
+    with (
+        pytest.raises(ValueError, match="Singular matrix detected"),
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="positive semidefinite"),
+    ):
         make_lcmv(
             evoked.info,
             fwd_sphere,

mne/commands/mne_bti2fiff.py

@@ -30,7 +30,6 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
-
 import sys
 
 import mne

mne/commands/mne_clean_eog_ecg.py

@@ -14,7 +14,6 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
-
 import sys
 
 import mne

mne/commands/mne_make_scalp_surfaces.py

@@ -17,6 +17,7 @@
     $ mne make_scalp_surfaces --overwrite --subject sample
 
 """
+
 import os
 import sys
 

mne/conftest.py

@@ -802,8 +802,9 @@ def src_volume_labels():
     """Create a 7mm source space with labels."""
     pytest.importorskip("nibabel")
     volume_labels = mne.get_volume_labels_from_aseg(fname_aseg)
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match="Found no usable.*t-vessel.*"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="Found no usable.*t-vessel.*"),
     ):
         src = mne.setup_volume_source_space(
             "sample",

mne/export/tests/test_export.py

@@ -78,8 +78,9 @@ def test_export_raw_pybv(tmp_path, meas_date, orig_time, ext):
     raw.set_annotations(annots)
 
     temp_fname = tmp_path / ("test" + ext)
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match="'short' format. Converting"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="'short' format. Converting"),
     ):
         raw.export(temp_fname)
     raw_read = read_raw_brainvision(str(temp_fname).replace(".eeg", ".vhdr"))
@@ -303,8 +304,9 @@ def test_export_edf_signal_clipping(tmp_path, physical_range, exceeded_bound):
     raw = read_raw_fif(fname_raw)
     raw.pick(picks=["eeg", "ecog", "seeg"]).load_data()
     temp_fname = tmp_path / "test.edf"
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match=f"The {exceeded_bound}"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match=f"The {exceeded_bound}"),
     ):
         raw.export(temp_fname, physical_range=physical_range)
     raw_read = read_raw_edf(temp_fname, preload=True)

mne/forward/tests/test_forward.py

@@ -230,8 +230,9 @@ def test_apply_forward():
     # Evoked
     evoked = read_evokeds(fname_evoked, condition=0)
     evoked.pick(picks="meg")
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match="only .* positive values"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="only .* positive values"),
     ):
         evoked = apply_forward(fwd, stc, evoked.info, start=start, stop=stop)
     data = evoked.data

mne/io/_read_raw.py

@@ -5,7 +5,6 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
-
 from functools import partial
 from pathlib import Path
 

mne/io/artemis123/tests/test_artemis123.py

@@ -97,8 +97,9 @@ def test_dev_head_t():
     assert_equal(raw.info["sfreq"], 5000.0)
 
     # test with head loc and digitization
-    with pytest.warns(RuntimeWarning, match="consistency"), pytest.warns(
-        RuntimeWarning, match="Large difference"
+    with (
+        pytest.warns(RuntimeWarning, match="consistency"),
+        pytest.warns(RuntimeWarning, match="Large difference"),
     ):
         raw = read_raw_artemis123(
             short_HPI_dip_fname, add_head_trans=True, pos_fname=dig_fname

mne/io/besa/tests/test_besa.py

@@ -1,6 +1,7 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 """Test reading BESA fileformats."""
+
 import inspect
 from pathlib import Path
 

mne/io/brainvision/tests/test_brainvision.py

@@ -613,8 +613,9 @@ def test_brainvision_vectorized_data():
 def test_coodinates_extraction():
     """Test reading of [Coordinates] section if present."""
     # vhdr 2 has a Coordinates section
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match="coordinate information"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="coordinate information"),
     ):
         raw = read_raw_brainvision(vhdr_v2_path)
 

mne/io/edf/edf.py

@@ -924,7 +924,7 @@ def _read_edf_header(fname, exclude, infer_types, include=None):
             if i in exclude:
                 continue
             # allow μ (greek mu), µ (micro symbol) and μ (sjis mu) codepoints
-            if unit in ("\u03BCV", "\u00B5V", "\x83\xCAV", "uV"):
+            if unit in ("\u03bcV", "\u00b5V", "\x83\xcaV", "uV"):
                 edf_info["units"].append(1e-6)
             elif unit == "mV":
                 edf_info["units"].append(1e-3)

mne/io/eeglab/tests/test_eeglab.py

@@ -140,8 +140,9 @@ def test_io_set_raw_more(tmp_path):
     shutil.copyfile(
         base_dir / "test_raw.fdt", negative_latency_fname.with_suffix(".fdt")
     )
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match="has a sample index of -1."
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="has a sample index of -1."),
     ):
         read_raw_eeglab(input_fname=negative_latency_fname, preload=True)
 

mne/io/eyelink/_utils.py

@@ -3,7 +3,6 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
-
 import re
 from datetime import datetime, timedelta, timezone
 

mne/io/eyelink/tests/test_eyelink.py

@@ -256,8 +256,9 @@ def test_multi_block_misc_channels(fname, tmp_path):
     out_file = tmp_path / "tmp_eyelink.asc"
     _simulate_eye_tracking_data(fname, out_file)
 
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match="Raw eyegaze coordinates"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match="Raw eyegaze coordinates"),
     ):
         raw = read_raw_eyelink(out_file, apply_offsets=True)
 

mne/io/fiff/tests/test_raw_fiff.py

@@ -2120,8 +2120,9 @@ def test_corrupted(tmp_path, offset):
     bad_fname = tmp_path / "test_raw.fif"
     with open(bad_fname, "wb") as fid:
         fid.write(data)
-    with _record_warnings(), pytest.warns(
-        RuntimeWarning, match=".*tag directory.*corrupt.*"
+    with (
+        _record_warnings(),
+        pytest.warns(RuntimeWarning, match=".*tag directory.*corrupt.*"),
     ):
         raw_bad = read_raw_fif(bad_fname)
     assert_allclose(raw.get_data(), raw_bad.get_data())

mne/io/neuralynx/neuralynx.py

@@ -347,9 +347,9 @@ def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
         sel_samples_local[0:-1, 1] = (
             sel_samples_global[0:-1, 1] - sel_samples_global[0:-1, 0]
         )
-        sel_samples_local[
-            1::, 0
-        ] = 0  # now set the start sample for all segments after the first to 0
+        sel_samples_local[1::, 0] = (
+            0  # now set the start sample for all segments after the first to 0
+        )
 
         sel_samples_local[0, 0] = (
             start - sel_samples_global[0, 0]

mne/minimum_norm/resolution_matrix.py

@@ -1,4 +1,5 @@
 """Compute resolution matrix for linear estimators."""
+
 # Authors: olaf.hauk@mrc-cbu.cam.ac.uk
 #
 # License: BSD-3-Clause

mne/minimum_norm/spatial_resolution.py

@@ -7,6 +7,7 @@
 Resolution metrics: localisation error, spatial extent, relative amplitude.
 Metrics can be computed for point-spread and cross-talk functions (PSFs/CTFs).
 """
+
 import numpy as np
 
 from ..source_estimate import SourceEstimate

mne/preprocessing/_lof.py

@@ -5,7 +5,6 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
-
 import numpy as np
 
 from .._fiff.pick import _picks_to_idx