Add codespell support (config, workflow) and get some typos fixed with it

.codespellrc

@@ -0,0 +1,7 @@
+[codespell]
+# Ref: https://github.com/codespell-project/codespell#using-a-config-file
+skip = .git,*.svg,package-lock.json,*.css,*.min.*,.codespellrc
+check-hidden = true
+ignore-regex = \b(ALS|nAm)\b
+ignore-words-list = numer,reord,datas,fro,ons,shs,evnts,ist,aas,coo,ser
+

.github/workflows/codespell.yml

@@ -0,0 +1,23 @@
+# Codespell configuration is within .codespellrc
+---
+name: Codespell
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+permissions:
+  contents: read
+
+jobs:
+  codespell:
+    name: Check for spelling errors
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Codespell
+        uses: codespell-project/actions-codespell@v2

python/libs/edfrw/headers.py

@@ -95,7 +95,7 @@ def dob(self):
     @dob.setter
     def dob(self, dob):
         '''
-        If DOB is not known it must be an empy string '' or 'X'. If it
+        If DOB is not known it must be an empty string '' or 'X'. If it
         is known, it must be entered as
 
         (a) a string in EDF format 'dd-MMM-yy', as in '30-DEC-1999';

python/libs/edfrw/reader.py

@@ -110,7 +110,7 @@ def read_record(self, rec_number):
                 self.header.number_of_bytes_in_header)
         self._f.seek(pointer)
         # Data are saved as int16, so the number of bytes to read is
-        # twice the number of samles requested.
+        # twice the number of samples requested.
         nsamples = self.header.number_of_samples_in_data_record * 2
         samples = self._f.read(nsamples)
         samples = np.frombuffer(samples, 'int16')

python/libs/edfrw/writer.py

@@ -74,10 +74,10 @@ def write_data_record(self, buffer):
         Signals are allowed to be acquired at different sampling rates.
         The data are saved in data blocks (named 'data records' in the
         specification). The total number of samples in the block is thus
-        determined by adding the sizes of the individiual signals
+        determined by adding the sizes of the individual signals
         (`signal.number_of_samples_in_data_record`).
 
-        Each data block holds all data aquired during a time interval of
+        Each data block holds all data acquired during a time interval of
         `header.duration_of_data_record` seconds, and the total number
         of data records in the file are `header.number_of_data_records`.
 

python/libs/mne/_freesurfer.py

@@ -100,7 +100,7 @@ def _mri_orientation(orientation):
     Parameters
     ----------
     orientation : str
-        Orientation that you want. Can be "axial", "saggital", or "coronal".
+        Orientation that you want. Can be "axial", "sagittal", or "coronal".
 
     Returns
     -------

python/libs/mne/beamformer/tests/test_dics.py

@@ -429,7 +429,7 @@ def test_real(_load_forward, idx):
     reg = 1  # Lots of regularization for our toy dataset
     filters_real = make_dics(epochs.info, fwd_surf, csd, label=label, reg=reg,
                              real_filter=True, inversion='single')
-    # Also test here that no warings are thrown - implemented to check whether
+    # Also test here that no warnings are thrown - implemented to check whether
     # src should not be None warning occurs:
     power, f = apply_dics_csd(csd, filters_real)
 

python/libs/mne/bem.py

@@ -1131,7 +1131,7 @@ def make_watershed_bem(subject, subjects_dir=None, overwrite=False,
     if op.isfile(T1_mgz):
         new_info = _extract_volume_info(T1_mgz) if has_nibabel() else dict()
         if not new_info:
-            warn('nibabel is not available or the volumn info is invalid.'
+            warn('nibabel is not available or the volume info is invalid.'
                  'Volume info not updated in the written surface.')
         surfs = ['brain', 'inner_skull', 'outer_skull', 'outer_skin']
         for s in surfs:

python/libs/mne/channels/interpolation.py

@@ -64,9 +64,9 @@ def _make_interpolation_matrix(pos_from, pos_to, alpha=1e-5):
     Parameters
     ----------
     pos_from : np.ndarray of float, shape(n_good_sensors, 3)
-        The positions to interpoloate from.
+        The positions to interpolate from.
     pos_to : np.ndarray of float, shape(n_bad_sensors, 3)
-        The positions to interpoloate.
+        The positions to interpolate.
     alpha : float
         Regularization parameter. Defaults to 1e-5.
 

python/libs/mne/coreg.py

@@ -1777,7 +1777,7 @@ 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

python/libs/mne/data/FreeSurferColorLUT.txt

@@ -173,7 +173,7 @@
 169 Left-Basal-Ganglia                      236 13  127 0
 176 Right-Basal-Ganglia                     236 13  126 0
 
-# Label names and colors for Brainstem consituents
+# Label names and colors for Brainstem constituents
 # No.  Label Name:                          R   G   B   A
 170 brainstem                               119 159 176 0
 171 DCG                                     119 0   176 0
@@ -416,14 +416,14 @@
 # created by mri_aparc2aseg in which the aseg cortex label is replaced
 # by the labels in the aparc. It also supports wm labels that will
 # eventually be created by mri_aparc2aseg. Otherwise, the aseg labels
-# do not change from above. The cortical lables are the same as in
+# do not change from above. The cortical labels are the same as in
 # colortable_desikan_killiany.txt, except that left hemisphere has
 # 1000 added to the index and the right has 2000 added.  The label
 # names are also prepended with ctx-lh or ctx-rh. The white matter
 # labels are the same as in colortable_desikan_killiany.txt, except
 # that left hemisphere has 3000 added to the index and the right has
 # 4000 added. The label names are also prepended with wm-lh or wm-rh.
-# Centrum semiovale is also labled with 5001 (left) and 5002 (right).
+# Centrum semiovale is also labeled with 5001 (left) and 5002 (right).
 # Even further below are the color tables for aparc.a2005s and aparc.a2009s.
 
 #No.    Label Name:                         R   G   B   A

python/libs/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

python/libs/mne/decoding/tests/test_csp.py

@@ -261,7 +261,7 @@ def test_csp_pipeline():
 
 def test_ajd():
     """Test approximate joint diagonalization."""
-    # The implementation shuold obtain the same
+    # The implementation should obtain the same
     # results as the Matlab implementation by Pham Dinh-Tuan.
     # Generate a set of cavariances matrices for test purpose
     n_times, n_channels = 10, 3

python/libs/mne/evoked.py

@@ -265,7 +265,7 @@ 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))

python/libs/mne/filter.py

@@ -805,7 +805,7 @@ def filter_data(data, sfreq, l_freq, h_freq, picks=None, filter_length='auto',
 
     .. note:: If n_jobs > 1, more memory is required as
               ``len(picks) * n_times`` additional time points need to
-              be temporaily stored in memory.
+              be temporarily stored in memory.
 
     For more information, see the tutorials
     :ref:`disc-filtering` and :ref:`tut-filter-resample` and
@@ -1997,7 +1997,7 @@ def filter(self, l_freq, h_freq, picks=None, filter_length='auto',
 
         .. note:: If n_jobs > 1, more memory is required as
                   ``len(picks) * n_times`` additional time points need to
-                  be temporaily stored in memory.
+                  be temporarily stored in memory.
 
         For more information, see the tutorials
         :ref:`disc-filtering` and :ref:`tut-filter-resample` and
@@ -2146,7 +2146,7 @@ def apply_hilbert(self, picks=None, envelope=False, n_jobs=1, n_fft='auto',
         If envelope=False, more memory is required since the original raw data
         as well as the analytic signal have temporarily to be stored in memory.
         If n_jobs > 1, more memory is required as ``len(picks) * n_times``
-        additional time points need to be temporaily stored in memory.
+        additional time points need to be temporarily stored in memory.
 
         Also note that the ``n_fft`` parameter will allow you to pad the signal
         with zeros before performing the Hilbert transform. This padding

python/libs/mne/forward/forward.py

@@ -1184,7 +1184,7 @@ def compute_depth_prior(forward, info, exp=0.8, limit=10.0,
           prior dependent only on the sensor geometry (and relationship
           to the sources).
     * ``'whiten'``
-          Compute a whitener and apply it to the gain matirx before computing
+          Compute a whitener and apply it to the gain matrix before computing
           the depth prior. In this case ``noise_cov`` must not be None.
           Whitening the gain matrix makes the depth prior
           depend on both sensor geometry and the data of interest captured
@@ -1256,7 +1256,7 @@ def compute_depth_prior(forward, info, exp=0.8, limit=10.0,
         ws = np.sort(w)
         weight_limit = limit ** 2
         if limit_depth_chs is False:
-            # match old mne-python behavor
+            # match old mne-python behavior
             # we used to do ind = np.argmin(ws), but this is 0 by sort above
             n_limit = 0
             limit = ws[0] * weight_limit

python/libs/mne/io/base.py

@@ -1104,7 +1104,7 @@ def notch_filter(self, freqs, picks=None, filter_length='auto',
 
         .. note:: If n_jobs > 1, more memory is required as
                   ``len(picks) * n_times`` additional time points need to
-                  be temporaily stored in memory.
+                  be temporarily stored in memory.
 
         For details, see :func:`mne.filter.notch_filter`.
         """
@@ -2052,7 +2052,7 @@ def _get_ch_factors(inst, units, picks_idxs):
     Returns
     -------
     ch_factors : ndarray of floats, shape(len(picks),)
-        The sacling factors for each channel, ordered according
+        The scaling factors for each channel, ordered according
         to picks.
 
     """

python/libs/mne/io/brainvision/brainvision.py

@@ -697,7 +697,7 @@ def _get_vhdr_info(vhdr_fname, eog, misc, scale):
                 highpass = [float(filt) if filt not in ('NaN', 'Off', 'DC')
                             else np.Inf for filt in highpass]
                 info['highpass'] = np.max(np.array(highpass, dtype=np.float64))
-                # Coveniently enough 1 / np.Inf = 0.0, so this works for
+                # Conveniently enough 1 / np.Inf = 0.0, so this works for
                 # DC / no highpass filter
                 # filter time constant t [secs] to Hz conversion: 1/2*pi*t
                 info['highpass'] = 1. / (2 * np.pi * info['highpass'])
@@ -901,7 +901,7 @@ def _parse_impedance(settings, recording_date=None):
     Parameters
     ----------
     settings : list
-        The header settings lines fom the VHDR file.
+        The header settings lines from the VHDR file.
     recording_date : datetime.datetime | None
         The date of the recording as extracted from the VMRK file.
 
@@ -963,7 +963,7 @@ def _parse_impedance_ranges(settings):
     Parameters
     ----------
     settings : list
-        The header settings lines fom the VHDR file.
+        The header settings lines from the VHDR file.
 
     Returns
     -------

python/libs/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)

python/libs/mne/io/eeglab/tests/test_eeglab.py

@@ -300,7 +300,7 @@ def test_degenerate(tmp_path):
 @pytest.mark.parametrize("fname", [
     raw_fname_mat,
     raw_fname_onefile_mat,
-    # We don't test the h5 varaints here because they are implicitly tested
+    # We don't test the h5 variants here because they are implicitly tested
     # in test_io_set_raw
 ])
 @pytest.mark.filterwarnings('ignore: Complex objects')

python/libs/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:

python/libs/mne/label.py

@@ -1512,7 +1512,7 @@ def stc_to_label(stc, src=None, smooth=True, connected=False,
 
 
 def _verts_within_dist(graph, sources, max_dist):
-    """Find all vertices wihin a maximum geodesic distance from source.
+    """Find all vertices within a maximum geodesic distance from source.
 
     Parameters
     ----------

python/libs/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

python/libs/mne/report/js_and_css/report.js

@@ -216,7 +216,7 @@ const disableGlobalKeyHandler = () => {
   window.onkeydown = null;
 }
 
-/* Disbale processing global key events when a search box is active */
+/* Disable processing global key events when a search box is active */
 const disableGlobalKeysInSearchBox = () => {
   const searchBoxElements = document.querySelectorAll('input.search-input');
   searchBoxElements.forEach((el) => {

python/libs/mne/report/report.py

@@ -2682,7 +2682,7 @@ def _render_raw_butterfly_segments(
         try:
             raw.set_annotations(None)
 
-            # Create the figure once and re-use it for performance reasons
+            # Create the figure once and reuse it for performance reasons
             with use_browser_backend('matplotlib'):
                 fig = raw.plot(
                     butterfly=True, show_scrollbars=False, start=t_starts[0],

python/libs/mne/simulation/raw.py

@@ -564,7 +564,7 @@ def add_chpi(raw, head_pos=None, interp='cos2', n_jobs=1, verbose=None):
     # turn on cHPI in file
     data = raw._data
     data[hpi_pick, :] = hpi_ons.sum()
-    _log_ch('cHPI status bits enbled and', info, hpi_pick)
+    _log_ch('cHPI status bits enabled and', info, hpi_pick)
     sinusoids = 70e-9 * np.sin(2 * np.pi * hpi_freqs[:, np.newaxis] *
                                (np.arange(len(times)) / info['sfreq']))
     info = pick_info(info, meg_picks)

python/libs/mne/transforms.py

@@ -733,7 +733,7 @@ def _cart_to_sph(cart):
 
 
 def _sph_to_cart(sph_pts):
-    """Convert spherical coordinates to Cartesion coordinates.
+    """Convert spherical coordinates to Cartesian coordinates.
 
     Parameters
     ----------
@@ -1112,7 +1112,7 @@ def fit(self, source, destination, order=4, reg=1e-5, center=True,
         src_rad_az_pol[0] = np.abs(np.dot(match_sph, src_coeffs))
         dest_rad_az_pol = match_rad_az_pol.copy()
         dest_rad_az_pol[0] = np.abs(np.dot(match_sph, dest_coeffs))
-        # 5. Convert matched points to Cartesion coordinates and put back
+        # 5. Convert matched points to Cartesian coordinates and put back
         source = _sph_to_cart(src_rad_az_pol.T)
         source += src_center
         destination = _sph_to_cart(dest_rad_az_pol.T)

python/libs/mne/utils/docs.py

@@ -974,7 +974,7 @@ def _reflow_param_docstring(docstring, has_first_line=True, width=75):
 docdict['exclude_frontal'] = """
 exclude_frontal : bool
     If True, exclude points that have both negative Z values
-    (below the nasion) and positivy Y values (in front of the LPA/RPA).
+    (below the nasion) and positive Y values (in front of the LPA/RPA).
 """
 
 docdict['export_edf_note'] = """

python/libs/mne/viz/_3d.py

@@ -2235,7 +2235,7 @@ def _cut_coords_to_idx(cut_coords, img):
                      % (stc.vertices[0][loc_idx], dist))
         return loc_idx
 
-    ax_name = dict(x='X (saggital)', y='Y (coronal)', z='Z (axial)')
+    ax_name = dict(x='X (sagittal)', y='Y (coronal)', z='Z (axial)')
 
     def _click_to_cut_coords(event, params):
         """Get voxel coordinates from mouse click."""

python/libs/mne/viz/_brain/_brain.py

@@ -2268,7 +2268,7 @@ def add_label(self, label, color=None, alpha=1, scalar_thresh=None,
             (away from the true border) along the cortical mesh to include
             as part of the border definition.
         hemi : str | None
-            If None, it is assumed to belong to the hemipshere being
+            If None, it is assumed to belong to the hemisphere being
             shown.
         subdir : None | str
             If a label is specified as name, subdir can be used to indicate
@@ -2685,7 +2685,7 @@ def add_foci(self, coords, coords_as_verts=False, map_surface=None,
         name : str
             Internal name to use.
         hemi : str | None
-            If None, it is assumed to belong to the hemipshere being
+            If None, it is assumed to belong to the hemisphere being
             shown. If two hemispheres are being shown, an error will
             be thrown.
         resolution : int
@@ -2922,7 +2922,7 @@ def add_annotation(self, annot, borders=True, alpha=1, hemi=None,
             as part of the border definition.
         %(alpha)s Default is 1.
         hemi : str | None
-            If None, it is assumed to belong to the hemipshere being
+            If None, it is assumed to belong to the hemisphere being
             shown. If two hemispheres are being shown, data must exist
             for both hemispheres.
         remove_existing : bool

python/libs/mne_bids/dig.py

@@ -388,7 +388,7 @@ def _write_dig_bids(bids_path, raw, montage=None, acpc_aligned=False,
 
     if montage is None:
         montage = raw.get_montage()
-    else:  # assign montage to raw but supress any coordinate transforms
+    else:  # assign montage to raw but suppress any coordinate transforms
         montage = montage.copy()  # don't modify original
         montage_coord_frame = montage.get_positions()['coord_frame']
         fids = [d for d in montage.dig  # save to add back