[pre-commit.ci] pre-commit autoupdate (mne-tools#12988)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mathieu Scheltienne <[email protected]>
Co-authored-by: Eric Larson <[email protected]>

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.4
+    rev: v0.8.0
     hooks:
       - id: ruff
         name: ruff lint mne
@@ -58,7 +58,7 @@ repos:
         args: ["--ignore-case"]
 
   - repo: https://github.com/pappasam/toml-sort
-    rev: v0.24.1
+    rev: v0.24.2
     hooks:
       - id: toml-sort-fix
         files: pyproject.toml

doc/sphinxext/contrib_avatars.py

@@ -34,9 +34,10 @@ def generate_contrib_avatars(app, config):
         driver.get(f"file://{infile}")
         wait = WebDriverWait(driver, 20)
         wait.until(lambda d: d.find_element(by=By.ID, value="contributor-avatars"))
-        body = driver.find_element(by=By.TAG_NAME, value="body").get_attribute(
+        body = driver.find_element(by=By.TAG_NAME, value="body").get_property(
             "innerHTML"
         )
+        assert isinstance(body, str), type(body)
         driver.quit()
     with open(outfile, "w") as fid:
         fid.write(body)

mne/_fiff/matrix.py

@@ -113,10 +113,9 @@ def write_named_matrix(fid, kind, mat):
         if n_tot < mat["data"].size and ratio > 0:
             ratio = 1 / ratio
         raise ValueError(
-            "Cannot write matrix: row (%i) and column (%i) "
-            "total element (%i) mismatch with data size (%i), "
-            "appears to be off by a factor of %gx"
-            % (mat["nrow"], mat["ncol"], n_tot, mat["data"].size, ratio)
+            f"Cannot write matrix: row ({mat['nrow']}) and column ({mat['ncol']}) "
+            f"total element ({n_tot}) mismatch with data size ({mat['data'].size}), "
+            f"appears to be off by a factor of {ratio:g}x"
         )
     start_block(fid, FIFF.FIFFB_MNE_NAMED_MATRIX)
     write_int(fid, FIFF.FIFF_MNE_NROW, mat["nrow"])

mne/_fiff/tests/test_pick.py

@@ -477,7 +477,7 @@ def test_picks_by_channels():
     rng = np.random.RandomState(909)
 
     test_data = rng.random_sample((4, 2000))
-    ch_names = ["MEG %03d" % i for i in [1, 2, 3, 4]]
+    ch_names = [f"MEG {i:03d}" for i in [1, 2, 3, 4]]
     ch_types = ["grad", "mag", "mag", "eeg"]
     sfreq = 250.0
     info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
@@ -496,7 +496,7 @@ def test_picks_by_channels():
     assert pick_list2[0][0] == "meg"
 
     test_data = rng.random_sample((4, 2000))
-    ch_names = ["MEG %03d" % i for i in [1, 2, 3, 4]]
+    ch_names = [f"MEG {i:03d}" for i in [1, 2, 3, 4]]
     ch_types = ["mag", "mag", "mag", "mag"]
     sfreq = 250.0
     info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)

mne/_ola.py

@@ -318,15 +318,9 @@ def __init__(
         sfreq = float(sfreq)
         pl = "s" if len(self.starts) != 1 else ""
         logger.info(
-            "    Processing %4d data chunk%s of (at least) %0.1f s "
-            "with %0.1f s overlap and %s windowing"
-            % (
-                len(self.starts),
-                pl,
-                self._n_samples / sfreq,
-                self._n_overlap / sfreq,
-                window_name,
-            )
+            f"    Processing {len(self.starts):4d} data chunk{pl} of (at least) "
+            f"{self._n_samples / sfreq:0.1f} s with "
+            f"{self._n_overlap / sfreq:0.1f} s overlap and {window_name} windowing"
         )
         del window, window_name
         if delta > 0:

mne/channels/_dig_montage_utils.py

@@ -38,10 +38,10 @@ def _read_dig_montage_egi(
 
         # EEG Channels
         if kind == 0:
-            dig_ch_pos["EEG %03d" % number] = coordinates
+            dig_ch_pos[f"EEG {number:03d}"] = coordinates
         # Reference
         elif kind == 1:
-            dig_ch_pos["EEG %03d" % (len(dig_ch_pos.keys()) + 1)] = coordinates
+            dig_ch_pos[f"EEG {len(dig_ch_pos) + 1:03d}"] = coordinates
         # Fiducials
         elif kind == 2:
             fid_name = fid_name_map[name]

mne/channels/channels.py

@@ -1648,13 +1648,10 @@ def fix_mag_coil_types(info, use_cal=False):
               Therefore the use of ``fix_mag_coil_types`` is not mandatory.
     """
     old_mag_inds = _get_T1T2_mag_inds(info, use_cal)
-
+    n_mag = len(pick_types(info, meg="mag", exclude=[]))
     for ii in old_mag_inds:
         info["chs"][ii]["coil_type"] = FIFF.FIFFV_COIL_VV_MAG_T3
-    logger.info(
-        "%d of %d magnetometer types replaced with T3."
-        % (len(old_mag_inds), len(pick_types(info, meg="mag", exclude=[])))
-    )
+    logger.info(f"{len(old_mag_inds)} of {n_mag} magnetometer types replaced with T3.")
     info._check_consistency()
 
 

mne/channels/layout.py

@@ -103,13 +103,9 @@ def save(self, fname, overwrite=False):
             raise ValueError("Unknown layout type. Should be of type .lout or .lay.")
 
         for ii in range(x.shape[0]):
-            out_str += "%03d %8.2f %8.2f %8.2f %8.2f %s\n" % (
-                self.ids[ii],
-                x[ii],
-                y[ii],
-                width[ii],
-                height[ii],
-                self.names[ii],
+            out_str += (
+                f"{self.ids[ii]:03d} {x[ii]:8.2f} {y[ii]:8.2f} "
+                f"{width[ii]:8.2f} {height[ii]:8.2f} {self.names[ii]}\n"
             )
 
         f = open(fname, "w")
@@ -945,14 +941,13 @@ def _auto_topomap_coords(info, picks, ignore_overlap, to_sphere, sphere):
         if len(locs3d) == 0:
             raise RuntimeError(
                 "Did not find any digitization points of "
-                "kind FIFFV_POINT_EEG (%d) in the info." % FIFF.FIFFV_POINT_EEG
+                f"kind {FIFF.FIFFV_POINT_EEG} in the info."
             )
 
         if len(locs3d) != len(eeg_ch_names):
             raise ValueError(
-                "Number of EEG digitization points (%d) "
-                "doesn't match the number of EEG channels "
-                "(%d)" % (len(locs3d), len(eeg_ch_names))
+                f"Number of EEG digitization points ({len(locs3d)}) doesn't match the "
+                f"number of EEG channels ({len(eeg_ch_names)})"
             )
 
         # We no longer center digitization points on head origin, as we work

mne/channels/montage.py

@@ -341,8 +341,8 @@ def __init__(self, *, dig=None, ch_names=None):
         n_eeg = sum([1 for d in dig if d["kind"] == FIFF.FIFFV_POINT_EEG])
         if n_eeg != len(ch_names):
             raise ValueError(
-                "The number of EEG channels (%d) does not match the number"
-                " of channel names provided (%d)" % (n_eeg, len(ch_names))
+                f"The number of EEG channels ({n_eeg}) does not match the number"
+                f" of channel names provided ({len(ch_names)})"
             )
 
         self.dig = dig
@@ -845,7 +845,7 @@ def read_dig_fif(fname):
     ch_names = []
     for d in dig:
         if d["kind"] == FIFF.FIFFV_POINT_EEG:
-            ch_names.append("EEG%03d" % d["ident"])
+            ch_names.append(f"EEG{d['ident']:03d}")
 
     montage = DigMontage(dig=dig, ch_names=ch_names)
     return montage
@@ -927,7 +927,7 @@ def read_dig_hpts(fname, unit="mm"):
     out = np.genfromtxt(fname, comments="#", dtype=(_str, _str, "f8", "f8", "f8"))
     kind, label = _str_names(out["f0"]), _str_names(out["f1"])
     kind = [k.lower() for k in kind]
-    xyz = np.array([out["f%d" % ii] for ii in range(2, 5)]).T
+    xyz = np.array([out[f"f{ii}"] for ii in range(2, 5)]).T
     xyz *= _scale
     del _scale
     fid_idx_to_label = {"1": "lpa", "2": "nasion", "3": "rpa"}

mne/channels/tests/test_montage.py

@@ -1086,11 +1086,11 @@ def test_fif_dig_montage(tmp_path):
     raw_bv = read_raw_brainvision(bv_fname, preload=True)
     raw_bv_2 = raw_bv.copy()
     mapping = dict()
-    for ii, ch_name in enumerate(raw_bv.ch_names):
-        mapping[ch_name] = "EEG%03d" % (ii + 1,)
+    for ii, ch_name in enumerate(raw_bv.ch_names, 1):
+        mapping[ch_name] = f"EEG{ii:03d}"
     raw_bv.rename_channels(mapping)
-    for ii, ch_name in enumerate(raw_bv_2.ch_names):
-        mapping[ch_name] = "EEG%03d" % (ii + 33,)
+    for ii, ch_name in enumerate(raw_bv_2.ch_names, 33):
+        mapping[ch_name] = f"EEG{ii:03d}"
     raw_bv_2.rename_channels(mapping)
     raw_bv.add_channels([raw_bv_2])
     for ch in raw_bv.info["chs"]:
@@ -1119,7 +1119,7 @@ def test_fif_dig_montage(tmp_path):
     # Roundtrip of non-FIF start
     montage = make_dig_montage(hsp=read_polhemus_fastscan(hsp), hpi=read_mrk(hpi))
     elp_points = read_polhemus_fastscan(elp)
-    ch_pos = {"EEG%03d" % (k + 1): pos for k, pos in enumerate(elp_points[8:])}
+    ch_pos = {f"EEG{k:03d}": pos for k, pos in enumerate(elp_points[8:], 1)}
     montage += make_dig_montage(
         nasion=elp_points[0], lpa=elp_points[1], rpa=elp_points[2], ch_pos=ch_pos
     )
@@ -1398,7 +1398,7 @@ def test_set_montage_mgh(rename):
 
         def renamer(x):
             try:
-                return "EEG %03d" % (_MGH60.index(x) + 1,)
+                return f"EEG {_MGH60.index(x) + 1:03d}"
             except ValueError:
                 return x
 
@@ -1920,11 +1920,11 @@ def test_get_montage():
     # rename channels to make it have the full set
     # of channels
     mapping = dict()
-    for ii, ch_name in enumerate(raw_bv.ch_names):
-        mapping[ch_name] = "EEG%03d" % (ii + 1,)
+    for ii, ch_name in enumerate(raw_bv.ch_names, 1):
+        mapping[ch_name] = f"EEG{ii:03d}"
     raw_bv.rename_channels(mapping)
-    for ii, ch_name in enumerate(raw_bv_2.ch_names):
-        mapping[ch_name] = "EEG%03d" % (ii + 33,)
+    for ii, ch_name in enumerate(raw_bv_2.ch_names, 33):
+        mapping[ch_name] = f"EEG{ii:03d}"
     raw_bv_2.rename_channels(mapping)
     raw_bv.add_channels([raw_bv_2])
     for ch in raw_bv.info["chs"]:

mne/cov.py

@@ -30,12 +30,8 @@
     _read_proj,
     _write_proj,
 )
-from ._fiff.proj import (
-    activate_proj as _activate_proj,
-)
-from ._fiff.proj import (
-    make_projector as _make_projector,
-)
+from ._fiff.proj import activate_proj as _activate_proj
+from ._fiff.proj import make_projector as _make_projector
 from ._fiff.tag import find_tag
 from ._fiff.tree import dir_tree_find
 from .defaults import (
@@ -267,7 +263,7 @@ def _get_square(self):
         if self["diag"] != (self.data.ndim == 1):
             raise RuntimeError(
                 "Covariance attributes inconsistent, got data with "
-                "dimensionality %d but diag=%s" % (self.data.ndim, self["diag"])
+                f"dimensionality {self.data.ndim} but diag={self['diag']}"
             )
         return np.diag(self.data) if self["diag"] else self.data.copy()
 
@@ -564,8 +560,8 @@ def _check_n_samples(n_samples, n_chan):
         raise ValueError("No samples found to compute the covariance matrix")
     if n_samples < n_samples_min:
         warn(
-            "Too few samples (required : %d got : %d), covariance "
-            "estimate may be unreliable" % (n_samples_min, n_samples)
+            f"Too few samples (required : {n_samples_min} got : {n_samples}), "
+            "covariance estimate may be unreliable"
         )
 
 
@@ -743,7 +739,7 @@ def compute_raw_covariance(
         _check_n_samples(n_samples, len(picks))
         data -= mu[:, None] * (mu[None, :] / n_samples)
         data /= n_samples - 1.0
-        logger.info("Number of samples used : %d" % n_samples)
+        logger.info("Number of samples used : %d", n_samples)
         logger.info("[done]")
         ch_names = [raw.info["ch_names"][k] for k in picks]
         bads = [b for b in raw.info["bads"] if b in ch_names]
@@ -1182,7 +1178,7 @@ def _unpack_epochs(epochs):
         # add extra info
         cov.update(method=this_method, **data)
         covs.append(cov)
-    logger.info("Number of samples used : %d" % n_samples_tot)
+    logger.info("Number of samples used : %d", n_samples_tot)
     covs.sort(key=lambda c: c["loglik"], reverse=True)
 
     if len(covs) > 1:
@@ -1466,8 +1462,8 @@ def _auto_low_rank_model(
     max_n = max(list(deepcopy(iter_n_components)))
     if max_n > data.shape[1]:
         warn(
-            "You are trying to estimate %i components on matrix "
-            "with %i features." % (max_n, data.shape[1])
+            f"You are trying to estimate {max_n} components on matrix "
+            f"with {data.shape[1]} features."
         )
 
     for ii, n in enumerate(iter_n_components):
@@ -1479,7 +1475,7 @@ def _auto_low_rank_model(
         if np.isinf(score) or score > 0:
             logger.info("... infinite values encountered. stopping estimation")
             break
-        logger.info("... rank: %i - loglik: %0.3f" % (n, score))
+        logger.info("... rank: %i - loglik: %0.3f", n, score)
         if score != -np.inf:
             scores[ii] = score
 
@@ -1498,7 +1494,7 @@ def _auto_low_rank_model(
 
     i_score = np.nanargmax(scores)
     best = est.n_components = iter_n_components[i_score]
-    logger.info("... best model at rank = %i" % best)
+    logger.info("... best model at rank = %i", best)
     runtime_info = {
         "ranks": np.array(iter_n_components),
         "scores": scores,
@@ -1831,7 +1827,7 @@ def _smart_eigh(
     if isinstance(C, Covariance):
         C = C["data"]
     if ncomp > 0:
-        logger.info("    Created an SSP operator (subspace dimension = %d)" % ncomp)
+        logger.info("    Created an SSP operator (subspace dimension = %d)", ncomp)
         C = np.dot(proj, np.dot(C, proj.T))
 
     noise_cov = Covariance(C, ch_names, [], projs, 0)
@@ -2274,8 +2270,9 @@ def compute_whitener(
     n_nzero = nzero.sum()
     logger.info(
         "    Created the whitener using a noise covariance matrix "
-        "with rank %d (%d small eigenvalues omitted)"
-        % (n_nzero, noise_cov["dim"] - n_nzero)
+        "with rank %d (%d small eigenvalues omitted)",
+        n_nzero,
+        noise_cov["dim"] - n_nzero,
     )
 
     # Do the requested projection
@@ -2403,8 +2400,10 @@ def _read_cov(fid, node, cov_kind, limited=False, verbose=None):
                     data = tag.data
                     diag = True
                     logger.info(
-                        "    %d x %d diagonal covariance (kind = "
-                        "%d) found." % (dim, dim, cov_kind)
+                        "    %d x %d diagonal covariance (kind = " "%d) found.",
+                        dim,
+                        dim,
+                        cov_kind,
                     )
 
             else:
@@ -2417,15 +2416,19 @@ def _read_cov(fid, node, cov_kind, limited=False, verbose=None):
                     data.flat[:: dim + 1] /= 2.0
                     diag = False
                     logger.info(
-                        "    %d x %d full covariance (kind = %d) "
-                        "found." % (dim, dim, cov_kind)
+                        "    %d x %d full covariance (kind = %d) " "found.",
+                        dim,
+                        dim,
+                        cov_kind,
                     )
                 else:
                     diag = False
                     data = tag.data
                     logger.info(
-                        "    %d x %d sparse covariance (kind = %d)"
-                        " found." % (dim, dim, cov_kind)
+                        "    %d x %d sparse covariance (kind = %d)" " found.",
+                        dim,
+                        dim,
+                        cov_kind,
                     )
 
             #   Read the possibly precomputed decomposition
@@ -2468,7 +2471,7 @@ def _read_cov(fid, node, cov_kind, limited=False, verbose=None):
 
             return cov
 
-    logger.info("    Did not find the desired covariance matrix (kind = %d)" % cov_kind)
+    logger.info("    Did not find the desired covariance matrix (kind = %d)", cov_kind)
 
     return None
 

mne/datasets/utils.py

@@ -765,8 +765,11 @@ def _manifest_check_download(manifest_path, destination, url, hash_):
         if not (destination / name).is_file():
             need.append(name)
     logger.info(
-        "%d file%s missing from %s in %s"
-        % (len(need), _pl(need), manifest_path.name, destination)
+        "%d file%s missing from %s in %s",
+        len(need),
+        _pl(need),
+        manifest_path.name,
+        destination,
     )
     if len(need) > 0:
         downloader = pooch.HTTPDownloader(**_downloader_params())