MAINT: Remove code using vulture confidence 60 (mne-tools#12575)

doc/sphinxext/gen_commands.py

@@ -1,10 +1,12 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 import glob
+import os
+import shutil
 from importlib import import_module
 from pathlib import Path
 
-from mne.utils import ArgvSetter, _replace_md5
+from mne.utils import ArgvSetter, hashfunc
 
 
 def setup(app):
@@ -106,6 +108,17 @@ def generate_commands_rst(app=None):
     _replace_md5(str(out_fname))
 
 
+def _replace_md5(fname):
+    """Replace a file based on MD5sum."""
+    # adapted from sphinx-gallery
+    assert fname.endswith(".new")
+    fname_old = fname[:-4]
+    if os.path.isfile(fname_old) and hashfunc(fname) == hashfunc(fname_old):
+        os.remove(fname)
+    else:
+        shutil.move(fname, fname_old)
+
+
 # This is useful for testing/iterating to see what the result looks like
 if __name__ == "__main__":
     generate_commands_rst()

mne/_fiff/reference.py

@@ -25,33 +25,6 @@
 from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj, setup_proj
 
 
-def _copy_channel(inst, ch_name, new_ch_name):
-    """Add a copy of a channel specified by ch_name.
-
-    Input data can be in the form of Raw, Epochs or Evoked.
-
-    The instance object is modified inplace.
-
-    Parameters
-    ----------
-    inst : instance of Raw | Epochs | Evoked
-        Data containing the EEG channels
-    ch_name : str
-        Name of the channel to copy.
-    new_ch_name : str
-        Name given to the copy of the channel.
-
-    Returns
-    -------
-    inst : instance of Raw | Epochs | Evoked
-        The data with a copy of a given channel.
-    """
-    new_inst = inst.copy().pick([ch_name])
-    new_inst.rename_channels({ch_name: new_ch_name})
-    inst.add_channels([new_inst], force_update_info=True)
-    return inst
-
-
 def _check_before_reference(inst, ref_from, ref_to, ch_type):
     """Prepare instance for referencing."""
     # Check to see that data is preloaded

mne/_fiff/utils.py

@@ -301,31 +301,6 @@ def _create_chs(ch_names, cals, ch_coil, ch_kind, eog, ecg, emg, misc):
     return chs
 
 
-def _synthesize_stim_channel(events, n_samples):
-    """Synthesize a stim channel from events read from an event file.
-
-    Parameters
-    ----------
-    events : array, shape (n_events, 3)
-        Each row representing an event.
-    n_samples : int
-        The number of samples.
-
-    Returns
-    -------
-    stim_channel : array, shape (n_samples,)
-        An array containing the whole recording's event marking.
-    """
-    # select events overlapping buffer
-    events = events.copy()
-    events[events[:, 1] < 1, 1] = 1
-    # create output buffer
-    stim_channel = np.zeros(n_samples, int)
-    for onset, duration, trigger in events:
-        stim_channel[onset : onset + duration] = trigger
-    return stim_channel
-
-
 def _construct_bids_filename(base, ext, part_idx, validate=True):
     """Construct a BIDS compatible filename for split files."""
     # insert index in filename

mne/_fiff/write.py

@@ -414,21 +414,12 @@ def write_float_sparse_rcs(fid, kind, mat):
     return write_float_sparse(fid, kind, mat, fmt="csr")
 
 
-def write_float_sparse_ccs(fid, kind, mat):
-    """Write a single-precision sparse compressed column matrix tag."""
-    return write_float_sparse(fid, kind, mat, fmt="csc")
-
-
 def write_float_sparse(fid, kind, mat, fmt="auto"):
     """Write a single-precision floating-point sparse matrix tag."""
     if fmt == "auto":
         fmt = "csr" if isinstance(mat, csr_matrix) else "csc"
-    if fmt == "csr":
-        need = csr_matrix
-        matrix_type = FIFF.FIFFT_SPARSE_RCS_MATRIX
-    else:
-        need = csc_matrix
-        matrix_type = FIFF.FIFFT_SPARSE_CCS_MATRIX
+    need = csr_matrix if fmt == "csr" else csc_matrix
+    matrix_type = getattr(FIFF, f"FIFFT_SPARSE_{fmt[-1].upper()}CS_MATRIX")
     _validate_type(mat, need, "sparse")
     matrix_type = matrix_type | FIFF.FIFFT_MATRIX | FIFF.FIFFT_FLOAT
     nnzm = mat.nnz

mne/_freesurfer.py

@@ -506,23 +506,6 @@ def estimate_head_mri_t(subject, subjects_dir=None, verbose=None):
     return invert_transform(compute_native_head_t(montage))
 
 
-def _ensure_image_in_surface_RAS(image, subject, subjects_dir):
-    """Check if the image is in Freesurfer surface RAS space."""
-    nib = _import_nibabel("load a volume image")
-    if not isinstance(image, nib.spatialimages.SpatialImage):
-        image = nib.load(image)
-    image = nib.MGHImage(image.dataobj.astype(np.float32), image.affine)
-    fs_img = nib.load(op.join(subjects_dir, subject, "mri", "brain.mgz"))
-    if not np.allclose(image.affine, fs_img.affine, atol=1e-6):
-        raise RuntimeError(
-            "The `image` is not aligned to Freesurfer "
-            "surface RAS space. This space is required as "
-            "it is the space where the anatomical "
-            "segmentation and reconstructed surfaces are"
-        )
-    return image  # returns MGH image for header
-
-
 def _get_affine_from_lta_info(lines):
     """Get the vox2ras affine from lta file info."""
     volume_data = np.loadtxt([line.split("=")[1] for line in lines])

mne/channels/layout.py

@@ -970,61 +970,6 @@ def _pair_grad_sensors(
         return picks
 
 
-# this function is used to pair grad when info is not present
-# it is the case of Projection that don't have the info.
-def _pair_grad_sensors_ch_names_vectorview(ch_names):
-    """Find the indices for pairing grad channels in a Vectorview system.
-
-    Parameters
-    ----------
-    ch_names : list of str
-        A list of channel names.
-
-    Returns
-    -------
-    indexes : list of int
-        Indices of the grad channels, ordered in pairs.
-    """
-    pairs = defaultdict(list)
-    for i, name in enumerate(ch_names):
-        if name.startswith("MEG"):
-            if name.endswith(("2", "3")):
-                key = name[-4:-1]
-                pairs[key].append(i)
-
-    pairs = [p for p in pairs.values() if len(p) == 2]
-
-    grad_chs = sum(pairs, [])
-    return grad_chs
-
-
-# this function is used to pair grad when info is not present
-# it is the case of Projection that don't have the info.
-def _pair_grad_sensors_ch_names_neuromag122(ch_names):
-    """Find the indices for pairing grad channels in a Neuromag 122 system.
-
-    Parameters
-    ----------
-    ch_names : list of str
-        A list of channel names.
-
-    Returns
-    -------
-    indexes : list of int
-        Indices of the grad channels, ordered in pairs.
-    """
-    pairs = defaultdict(list)
-    for i, name in enumerate(ch_names):
-        if name.startswith("MEG"):
-            key = (int(name[-3:]) - 1) // 2
-            pairs[key].append(i)
-
-    pairs = [p for p in pairs.values() if len(p) == 2]
-
-    grad_chs = sum(pairs, [])
-    return grad_chs
-
-
 def _merge_ch_data(data, ch_type, names, method="rms"):
     """Merge data from channel pairs.
 

mne/channels/tests/test_montage.py

@@ -87,10 +87,6 @@
 locs_montage_fname = data_path / "EEGLAB" / "test_chans.locs"
 evoked_fname = data_path / "montage" / "level2_raw-ave.fif"
 eeglab_fname = data_path / "EEGLAB" / "test_raw.set"
-bdf_fname1 = data_path / "BDF" / "test_generator_2.bdf"
-bdf_fname2 = data_path / "BDF" / "test_bdf_stim_channel.bdf"
-egi_fname1 = data_path / "EGI" / "test_egi.mff"
-cnt_fname = data_path / "CNT" / "scan41_short.cnt"
 fnirs_dname = data_path / "NIRx" / "nirscout" / "nirx_15_2_recording_w_short"
 mgh70_fname = data_path / "SSS" / "mgh70_raw.fif"
 subjects_dir = data_path / "subjects"
@@ -104,10 +100,8 @@
 fif_fname = io_dir / "tests" / "data" / "test_raw.fif"
 edf_path = io_dir / "edf" / "tests" / "data" / "test.edf"
 bdf_path = io_dir / "edf" / "tests" / "data" / "test_bdf_eeglab.mat"
-egi_fname2 = io_dir / "egi" / "tests" / "data" / "test_egi.raw"
 vhdr_path = io_dir / "brainvision" / "tests" / "data" / "test.vhdr"
 ctf_fif_fname = io_dir / "tests" / "data" / "test_ctf_comp_raw.fif"
-nicolet_fname = io_dir / "nicolet" / "tests" / "data" / "test_nicolet_raw.data"
 
 
 def _make_toy_raw(n_channels):
@@ -1108,17 +1102,6 @@ def test_egi_dig_montage(tmp_path):
     _check_roundtrip(dig_montage_in_head, fname_temp)
 
 
-def _pop_montage(dig_montage, ch_name):
-    # remove reference that was not used in old API
-    name_idx = dig_montage.ch_names.index(ch_name)
-    dig_idx = dig_montage._get_dig_names().index(ch_name)
-
-    del dig_montage.dig[dig_idx]
-    del dig_montage.ch_names[name_idx]
-    for k in range(dig_idx, len(dig_montage.dig)):
-        dig_montage.dig[k]["ident"] -= 1
-
-
 @testing.requires_testing_data
 def test_read_dig_captrak(tmp_path):
     """Test reading a captrak montage file."""
@@ -1444,21 +1427,6 @@ def _check_roundtrip(montage, fname, coord_frame="head"):
     assert_dig_allclose(montage, montage_read)
 
 
-def _fake_montage(ch_names):
-    pos = np.random.RandomState(42).randn(len(ch_names), 3)
-    return make_dig_montage(ch_pos=dict(zip(ch_names, pos)), coord_frame="head")
-
-
-cnt_ignore_warns = [
-    pytest.mark.filterwarnings(
-        "ignore:.*Could not parse meas date from the header. Setting to None."
-    ),
-    pytest.mark.filterwarnings(
-        "ignore:.*Could not define the number of bytes automatically. Defaulting to 2."
-    ),
-]
-
-
 def test_digmontage_constructor_errors():
     """Test proper error messaging."""
     with pytest.raises(ValueError, match="does not match the number"):
@@ -1966,7 +1934,7 @@ def test_montage_add_fiducials():
     subjects_dir = data_path / "subjects"
     subject = "sample"
     fid_fname = subjects_dir / subject / "bem" / "sample-fiducials.fif"
-    test_fids, test_coord_frame = read_fiducials(fid_fname)
+    test_fids, _ = read_fiducials(fid_fname)
     test_fids = np.array([f["r"] for f in test_fids])
 
     # create test montage and add estimated fiducials

mne/chpi.py

@@ -1085,12 +1085,6 @@ def _fit_chpi_quat_subset(coil_dev_rrs, coil_head_rrs, use_idx):
     return quat, g, np.array(out_idx, int)
 
 
-@jit()
-def _unit_quat_constraint(x):
-    """Constrain our 3 quaternion rot params (ignoring w) to have norm <= 1."""
-    return 1 - (x * x).sum()
-
-
 @verbose
 def compute_chpi_snr(
     raw, t_step_min=0.01, t_window="auto", ext_order=1, tmin=0, tmax=None, verbose=None

mne/coreg.py

@@ -760,28 +760,6 @@ def _is_mri_subject(subject, subjects_dir=None):
     )
 
 
-def _is_scaled_mri_subject(subject, subjects_dir=None):
-    """Check whether a directory in subjects_dir is a scaled mri subject.
-
-    Parameters
-    ----------
-    subject : str
-        Name of the potential subject/directory.
-    subjects_dir : None | path-like
-        Override the SUBJECTS_DIR environment variable.
-
-    Returns
-    -------
-    is_scaled_mri_subject : bool
-        Whether ``subject`` is a scaled mri subject.
-    """
-    subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
-    if not _is_mri_subject(subject, subjects_dir):
-        return False
-    fname = subjects_dir / subject / "MRI scaling parameters.cfg"
-    return fname.exists()
-
-
 def _mri_subject_has_bem(subject, subjects_dir=None):
     """Check whether an mri subject has a file matching the bem pattern.
 
@@ -1479,15 +1457,13 @@ def __init__(
         self._scale_mode = None
         self._on_defects = on_defects
 
-        self._rot_trans = None
         self._default_parameters = np.array(
             [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]
         )
 
         self._rotation = self._default_parameters[:3]
         self._translation = self._default_parameters[3:6]
         self._scale = self._default_parameters[6:9]
-        self._icp_iterations = 20
         self._icp_angle = 0.2
         self._icp_distance = 0.2
         self._icp_scale = 0.2
@@ -1869,10 +1845,6 @@ def _has_rpa_data(self):
     def _processed_high_res_mri_points(self):
         return self._get_processed_mri_points("high")
 
-    @property
-    def _processed_low_res_mri_points(self):
-        return self._get_processed_mri_points("low")
-
     def _get_processed_mri_points(self, res):
         bem = self._bem_low_res if res == "low" else self._bem_high_res
         points = bem["rr"].copy()

mne/decoding/time_delaying_ridge.py

@@ -40,8 +40,9 @@ def _compute_corrs(
     assert X.shape[:2] == y.shape[:2]
     len_trf = smax - smin
     len_x, n_epochs, n_ch_x = X.shape
-    len_y, n_epcohs, n_ch_y = y.shape
+    len_y, n_epochs_y, n_ch_y = y.shape
     assert len_x == len_y
+    assert n_epochs == n_epochs_y
 
     n_fft = next_fast_len(2 * X.shape[0] - 1)
 

mne/epochs.py

@@ -4483,18 +4483,6 @@ def bootstrap(epochs, random_state=None):
     return epochs_bootstrap
 
 
-def _check_merge_epochs(epochs_list):
-    """Aux function."""
-    if len({tuple(epochs.event_id.items()) for epochs in epochs_list}) != 1:
-        raise NotImplementedError("Epochs with unequal values for event_id")
-    if len({epochs.tmin for epochs in epochs_list}) != 1:
-        raise NotImplementedError("Epochs with unequal values for tmin")
-    if len({epochs.tmax for epochs in epochs_list}) != 1:
-        raise NotImplementedError("Epochs with unequal values for tmax")
-    if len({epochs.baseline for epochs in epochs_list}) != 1:
-        raise NotImplementedError("Epochs with unequal values for baseline")
-
-
 def _concatenate_epochs(
     epochs_list, *, with_data=True, add_offset=True, on_mismatch="raise"
 ):