fixes for latest codespell release (#11047)

* fixes for latest codespell release

* use URI ignore words in actions config

* FIX: Work around pandas dep

Co-authored-by: Eric Larson <[email protected]>

.github/workflows/codespell_and_flake.yml

@@ -41,4 +41,5 @@ jobs:
           quiet_level: '3'
           builtin: 'clear,rare,informal,names'
           ignore_words_file: 'ignore_words.txt'
+          uri_ignore_words_list: 'bu'
         name: 'Run codespell'

Makefile

@@ -3,7 +3,7 @@
 PYTHON ?= python
 PYTESTS ?= py.test
 CTAGS ?= ctags
-CODESPELL_SKIPS ?= "doc/auto_*,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat,install_mne_c.rst,plot_*.rst,*.rst.txt,c_EULA.rst*,*.html,gdf_encodes.txt,*.svg,references.bib,*.css"
+CODESPELL_SKIPS ?= "doc/_build,doc/auto_*,*.fif,*.eve,*.gz,*.tgz,*.zip,*.mat,*.stc,*.label,*.w,*.bz2,*.annot,*.sulc,*.log,*.local-copy,*.orig_avg,*.inflated_avg,*.gii,*.pyc,*.doctree,*.pickle,*.inv,*.png,*.edf,*.touch,*.thickness,*.nofix,*.volume,*.defect_borders,*.mgh,lh.*,rh.*,COR-*,FreeSurferColorLUT.txt,*.examples,.xdebug_mris_calc,bad.segments,BadChannels,*.hist,empty_file,*.orig,*.js,*.map,*.ipynb,searchindex.dat,install_mne_c.rst,plot_*.rst,*.rst.txt,c_EULA.rst*,*.html,gdf_encodes.txt,*.svg,references.bib,*.css"
 CODESPELL_DIRS ?= mne/ doc/ tutorials/ examples/
 all: clean inplace test test-doc
 
@@ -103,10 +103,10 @@ flake:
 	@echo "flake8 passed"
 
 codespell:  # running manually
-	@codespell --builtin clear,rare,informal,names,usage -w -i 3 -q 3 -S $(CODESPELL_SKIPS) --ignore-words=ignore_words.txt $(CODESPELL_DIRS)
+	@codespell --builtin clear,rare,informal,names,usage -w -i 3 -q 3 -S $(CODESPELL_SKIPS) --ignore-words=ignore_words.txt --uri-ignore-words-list=bu $(CODESPELL_DIRS)
 
 codespell-error:  # running on travis
-	@codespell --builtin clear,rare,informal,names,usage -i 0 -q 7 -S $(CODESPELL_SKIPS) --ignore-words=ignore_words.txt $(CODESPELL_DIRS)
+	@codespell --builtin clear,rare,informal,names,usage -i 0 -q 7 -S $(CODESPELL_SKIPS) --ignore-words=ignore_words.txt --uri-ignore-words-list=bu $(CODESPELL_DIRS)
 
 pydocstyle:
 	@echo "Running pydocstyle"

ignore_words.txt

@@ -37,3 +37,5 @@ nin
 trough
 recuse
 ro
+nam
+bu

mne/coreg.py

@@ -1764,7 +1764,7 @@ def fit_fiducials(self, lpa_weight=1., nasion_weight=10., rpa_weight=1.,
         self._log_dig_mri_distance('Start')
         n_scale_params = self._n_scale_params
         if n_scale_params == 3:
-            # enfore 1 even for 3-axis here (3 points is not enough)
+            # enforce 1 even for 3-axis here (3 points is not enough)
             logger.info("Enforcing 1 scaling parameter for fit "
                         "with fiducials.")
             n_scale_params = 1

mne/decoding/ssd.py

@@ -184,7 +184,7 @@ def fit(self, X, y=None):
         self.filters_ = eigvects_[:, ix]
         self.patterns_ = np.linalg.pinv(self.filters_)
         # We assume that ordering by spectral ratio is more important
-        # than the initial ordering. This ording should be also learned when
+        # than the initial ordering. This ordering should be also learned when
         # fitting.
         X_ssd = self.filters_.T @ X[..., self.picks_, :]
         sorter_spec = Ellipsis

mne/evoked.py

@@ -269,7 +269,7 @@ def apply_baseline(self, baseline=(None, 0), *, verbose=None):
                                    sfreq=self.info['sfreq'])
         if self.baseline is not None and baseline is None:
             raise ValueError('The data has already been baseline-corrected. '
-                             'Cannot remove existing basline correction.')
+                             'Cannot remove existing baseline correction.')
         elif baseline is None:
             # Do not rescale
             logger.info(_log_rescale(None))

mne/io/brainvision/brainvision.py

@@ -931,7 +931,7 @@ def _parse_impedance(settings, recording_date=None):
     Parameters
     ----------
     settings : list
-        The header settings lines fom the VHDR/AHDR file.
+        The header settings lines from the VHDR/AHDR file.
     recording_date : datetime.datetime | None
         The date of the recording as extracted from the VMRK/AMRK file.
 
@@ -993,7 +993,7 @@ def _parse_impedance_ranges(settings):
     Parameters
     ----------
     settings : list
-        The header settings lines fom the VHDR/AHDR file.
+        The header settings lines from the VHDR/AHDR file.
 
     Returns
     -------

mne/io/curry/curry.py

@@ -393,7 +393,7 @@ def _first_hpi(fname):
             break
         else:
             raise RuntimeError('Could not find valid HPI in %s' % (fname,))
-    # t is the first enttry
+    # t is the first entry
     assert hpi.ndim == 1
     hpi = hpi[1:]
     hpi.shape = (-1, 5)

mne/io/nihon/nihon.py

@@ -392,7 +392,7 @@ def __init__(self, fname, preload=False, verbose=None):
         # Get annotations from LOG file
         annots = _read_nihon_annotations(fname)
 
-        # Annotate acqusition skips
+        # Annotate acquisition skips
         controlblock = self._header['controlblocks'][0]
         cur_sample = 0
         if controlblock['n_datablocks'] > 1:

mne/preprocessing/tests/test_eeglab_infomax.py

@@ -76,7 +76,7 @@ def test_mne_python_vs_eeglab():
                                    % (method,
                                       dict(eeg='eeg', mag='meg')[ch_type]))
 
-            # For comparasion against eeglab, make sure the following
+            # For comparison against eeglab, make sure the following
             # parameters have the same value in mne_python and eeglab:
             #
             # - starting point

mne/utils/dataframe.py

@@ -8,6 +8,7 @@
 
 from ._logging import logger, verbose
 from ..defaults import _handle_default
+from ..fixes import _get_args
 
 
 @verbose
@@ -47,6 +48,16 @@ def _convert_times(inst, times, time_format):
     return times
 
 
+def _inplace(df, method, **kwargs):
+    """Handle transition: inplace=True (pandas <1.5) → copy=False (>=1.5)."""
+    _meth = getattr(df, method)  # used for set_index() and rename()
+    if 'copy' in _get_args(_meth):
+        return _meth(**kwargs, copy=False)
+    else:
+        _meth(**kwargs, inplace=True)
+        return df
+
+
 @verbose
 def _build_data_frame(inst, data, picks, long_format, mindex, index,
                       default_index, col_names=None, col_kind='channel',
@@ -63,16 +74,16 @@ def _build_data_frame(inst, data, picks, long_format, mindex, index,
         df.insert(i, k, v)
     # build Index
     if long_format:
-        df.set_index(default_index, inplace=True)
+        df = _inplace(df, 'set_index', keys=default_index)
         df.columns.name = col_kind
     elif index is not None:
-        df.set_index(index, inplace=True)
+        df = _inplace(df, 'set_index', keys=index)
         if set(index) == set(default_index):
             df.columns.name = col_kind
     # long format
     if long_format:
         df = df.stack().reset_index()
-        df.rename(columns={0: 'value'}, inplace=True)
+        df = _inplace(df, 'rename', columns={0: 'value'})
         # add column for channel types (as appropriate)
         ch_map = (None if isinstance(inst, _BaseSourceEstimate) else
                   dict(zip(np.array(inst.ch_names)[picks],
@@ -83,7 +94,7 @@ def _build_data_frame(inst, data, picks, long_format, mindex, index,
             df.insert(col_index, 'ch_type', ch_type)
         # restore index
         if index is not None:
-            df.set_index(index, inplace=True)
+            df = _inplace(df, 'set_index', keys=index)
         # convert channel/vertex/ch_type columns to factors
         to_factor = [c for c in df.columns.tolist()
                      if c not in ('time', 'value')]

tutorials/stats-sensor-space/75_cluster_ftest_spatiotemporal.py

@@ -141,7 +141,7 @@
 #           an adjacency for time points was automatically taken into
 #           account. That is, at time point N, the time points N - 1 and
 #           N + 1 were considered as adjacent (this is also called "lattice
-#           adjacency"). This is only possbile because we ran the analysis on
+#           adjacency"). This is only possible because we ran the analysis on
 #           2D data (times × channels) per observation ... for 3D data per
 #           observation (e.g., times × frequencies × channels), we will need
 #           to use :func:`mne.stats.combine_adjacency`, as shown further