Merge branch 'main' of https://github.com/mne-tools/mne-python into e…

…poch_reject

.mailmap

@@ -220,6 +220,7 @@ Mikołaj Magnuski <[email protected]> Mikolaj Magnuski <[email protected]
 Mikołaj Magnuski <[email protected]> mmagnuski <[email protected]>
 Mohamed Sherif <[email protected]> mohdsherif <[email protected]>
 Mohammad Daneshzand <[email protected]> mdaneshzand <[email protected]>
+Motofumi Fushimi <[email protected]> motofumi-fushimi <[email protected]>
 Natalie Klein <[email protected]> natalieklein <[email protected]>
 Nathalie Gayraud <[email protected]> Nathalie <[email protected]>
 Nathalie Gayraud <[email protected]> Nathalie <[email protected]>

.pre-commit-config.yaml

@@ -1,20 +1,19 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.11
+    rev: v0.1.13
     hooks:
       - id: ruff
         name: ruff lint mne
         args: ["--fix"]
         files: ^mne/
+      - id: ruff
+        name: ruff lint mne preview
+        args: ["--fix", "--preview", "--select=NPY201"]
+        files: ^mne/
       - id: ruff-format
         name: ruff format mne
         files: ^mne/
-
-  # Ruff tutorials and examples
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.11
-    hooks:
       - id: ruff
         name: ruff lint tutorials and examples
         # D103: missing docstring in public function

doc/changes/devel/12348.bugfix.rst

@@ -0,0 +1 @@
+Fix bug in :func:`mne.preprocessing.maxwell_filter` where calibration was incorrectly applied during virtual sensor reconstruction, by `Eric Larson`_ and :newcontrib:`Motofumi Fushimi`.

doc/changes/devel/12358.other.rst

@@ -0,0 +1 @@
+Refresh code base to use Python 3.9 syntax using Ruff UP rules (pyupgrade), by `Clemens Brunner`_.

doc/changes/names.inc

@@ -386,6 +386,8 @@
 
 .. _Moritz Gerster: https://github.com/moritz-gerster
 
+.. _Motofumi Fushimi: https://github.com/motofumi-fushimi/motofumi-fushimi.github.io
+
 .. _Natalie Klein: https://github.com/natalieklein
 
 .. _Nathalie Gayraud: https://github.com/ngayraud

examples/datasets/limo_data.py

@@ -190,7 +190,7 @@
 # get levels of phase coherence
 levels = sorted(phase_coh.unique())
 # create labels for levels of phase coherence (i.e., 0 - 85%)
-labels = ["{0:.2f}".format(i) for i in np.arange(0.0, 0.90, 0.05)]
+labels = [f"{i:.2f}" for i in np.arange(0.0, 0.90, 0.05)]
 
 # create dict of evokeds for each level of phase-coherence
 evokeds = {

examples/datasets/opm_data.py

@@ -114,8 +114,8 @@
     )
 idx = np.argmax(dip_opm.gof)
 print(
-    "Best dipole at t=%0.1f ms with %0.1f%% GOF"
-    % (1000 * dip_opm.times[idx], dip_opm.gof[idx])
+    f"Best dipole at t={1000 * dip_opm.times[idx]:0.1f} ms with "
+    f"{dip_opm.gof[idx]:0.1f}% GOF"
 )
 
 # Plot N20m dipole as an example

examples/decoding/decoding_csp_eeg.py

@@ -92,9 +92,7 @@
 # Printing the results
 class_balance = np.mean(labels == labels[0])
 class_balance = max(class_balance, 1.0 - class_balance)
-print(
-    "Classification accuracy: %f / Chance level: %f" % (np.mean(scores), class_balance)
-)
+print(f"Classification accuracy: {np.mean(scores)} / Chance level: {class_balance}")
 
 # plot CSP patterns estimated on full data for visualization
 csp.fit_transform(epochs_data, labels)

examples/decoding/receptive_field_mtrf.py

@@ -102,7 +102,7 @@
 coefs = np.zeros((n_splits, n_channels, n_delays))
 scores = np.zeros((n_splits, n_channels))
 for ii, (train, test) in enumerate(cv.split(speech)):
-    print("split %s / %s" % (ii + 1, n_splits))
+    print(f"split {ii + 1} / {n_splits}")
     rf.fit(speech[train], Y[train])
     scores[ii] = rf.score(speech[test], Y[test])
     # coef_ is shape (n_outputs, n_features, n_delays). we only have 1 feature
@@ -212,7 +212,7 @@
 patterns = coefs.copy()
 scores = np.zeros((n_splits,))
 for ii, (train, test) in enumerate(cv.split(speech)):
-    print("split %s / %s" % (ii + 1, n_splits))
+    print(f"split {ii + 1} / {n_splits}")
     sr.fit(Y[train], speech[train])
     scores[ii] = sr.score(Y[test], speech[test])[0]
     # coef_ is shape (n_outputs, n_features, n_delays). We have 128 features
@@ -272,9 +272,7 @@
     show=False,
     vlim=(-max_coef, max_coef),
 )
-ax[0].set(
-    title="Model coefficients\nbetween delays %s and %s" % (time_plot[0], time_plot[1])
-)
+ax[0].set(title=f"Model coefficients\nbetween delays {time_plot[0]} and {time_plot[1]}")
 
 mne.viz.plot_topomap(
     np.mean(mean_patterns[:, ix_plot], axis=1),
@@ -284,8 +282,10 @@
     vlim=(-max_patterns, max_patterns),
 )
 ax[1].set(
-    title="Inverse-transformed coefficients\nbetween delays %s and %s"
-    % (time_plot[0], time_plot[1])
+    title=(
+        f"Inverse-transformed coefficients\nbetween delays {time_plot[0]} and "
+        f"{time_plot[1]}"
+    )
 )
 
 # %%

examples/inverse/compute_mne_inverse_raw_in_label.py

@@ -49,7 +49,7 @@
 )
 
 # Save result in stc files
-stc.save("mne_%s_raw_inverse_%s" % (method, label_name), overwrite=True)
+stc.save(f"mne_{method}_raw_inverse_{label_name}", overwrite=True)
 
 # %%
 # View activation time-series

examples/inverse/compute_mne_inverse_volume.py

@@ -56,5 +56,5 @@
     index_img(img, 61),
     str(t1_fname),
     threshold=8.0,
-    title="%s (t=%.1f s.)" % (method, stc.times[61]),
+    title=f"{method} (t={stc.times[61]:.1f} s.)",
 )

examples/inverse/evoked_ers_source_power.py

@@ -34,12 +34,7 @@
 data_path = somato.data_path()
 subject = "01"
 task = "somato"
-raw_fname = (
-    data_path
-    / "sub-{}".format(subject)
-    / "meg"
-    / "sub-{}_task-{}_meg.fif".format(subject, task)
-)
+raw_fname = data_path / f"sub-{subject}" / "meg" / f"sub-{subject}_task-{task}_meg.fif"
 
 # crop to 5 minutes to save memory
 raw = mne.io.read_raw_fif(raw_fname).crop(0, 300)
@@ -59,10 +54,7 @@
 
 # Read forward operator and point to freesurfer subject directory
 fname_fwd = (
-    data_path
-    / "derivatives"
-    / "sub-{}".format(subject)
-    / "sub-{}_task-{}-fwd.fif".format(subject, task)
+    data_path / "derivatives" / f"sub-{subject}" / f"sub-{subject}_task-{task}-fwd.fif"
 )
 subjects_dir = data_path / "derivatives" / "freesurfer" / "subjects"
 

examples/inverse/label_source_activations.py

@@ -113,7 +113,7 @@
 ax.set(
     xlabel="Time (ms)",
     ylabel="Source amplitude",
-    title="Mean vector activations in Label %r" % (label.name,),
+    title=f"Mean vector activations in Label {label.name!r}",
     xlim=xlim,
     ylim=ylim,
 )

examples/inverse/mixed_norm_inverse.py

@@ -137,7 +137,7 @@
     forward["src"],
     stc,
     bgcolor=(1, 1, 1),
-    fig_name="%s (cond %s)" % (solver, condition),
+    fig_name=f"{solver} (cond {condition})",
     opacity=0.1,
 )
 
@@ -159,7 +159,7 @@
     src_fsaverage,
     stc_fsaverage,
     bgcolor=(1, 1, 1),
-    fig_name="Morphed %s (cond %s)" % (solver, condition),
+    fig_name=f"Morphed {solver} (cond {condition})",
     opacity=0.1,
 )
 

examples/inverse/read_stc.py

@@ -29,9 +29,7 @@
 stc = mne.read_source_estimate(fname)
 
 n_vertices, n_samples = stc.data.shape
-print(
-    "stc data size: %s (nb of vertices) x %s (nb of samples)" % (n_vertices, n_samples)
-)
+print(f"stc data size: {n_vertices} (nb of vertices) x {n_samples} (nb of samples)")
 
 # View source activations
 plt.plot(stc.times, stc.data[::100, :].T)

examples/preprocessing/define_target_events.py

@@ -100,7 +100,7 @@
 
 # average epochs and get an Evoked dataset.
 
-early, late = [epochs[k].average() for k in event_id]
+early, late = (epochs[k].average() for k in event_id)
 
 # %%
 # View evoked response

examples/preprocessing/eeg_bridging.py

@@ -384,16 +384,7 @@
 raw = raw_data[1]
 # typically impedances < 25 kOhm are acceptable for active systems and
 # impedances < 5 kOhm are desirable for a passive system
-impedances = (
-    rng.random(
-        (
-            len(
-                raw.ch_names,
-            )
-        )
-    )
-    * 30
-)
+impedances = rng.random(len(raw.ch_names)) * 30
 impedances[10] = 80  # set a few bad impendances
 impedances[25] = 99
 cmap = LinearSegmentedColormap.from_list(

examples/preprocessing/ica_comparison.py

@@ -55,7 +55,7 @@ def run_ica(method, fit_params=None):
     t0 = time()
     ica.fit(raw, reject=reject)
     fit_time = time() - t0
-    title = "ICA decomposition using %s (took %.1fs)" % (method, fit_time)
+    title = f"ICA decomposition using {method} (took {fit_time:.1f}s)"
     ica.plot_components(title=title)
 
 

examples/preprocessing/otp.py

@@ -79,15 +79,9 @@ def compute_bias(raw):
 
 
 bias = compute_bias(raw)
-print("Raw bias: %0.1fmm (worst: %0.1fmm)" % (np.mean(bias), np.max(bias)))
+print(f"Raw bias: {np.mean(bias):0.1f}mm (worst: {np.max(bias):0.1f}mm)")
 bias_clean = compute_bias(raw_clean)
-print(
-    "OTP bias: %0.1fmm (worst: %0.1fmm)"
-    % (
-        np.mean(bias_clean),
-        np.max(bias_clean),
-    )
-)
+print(f"OTP bias: {np.mean(bias_clean):0.1f}mm (worst: {np.max(bias_clean):0.1f}m)")
 
 # %%
 # References

examples/simulation/simulate_raw_data.py

@@ -55,9 +55,9 @@ def data_fun(times):
     global n
     n_samp = len(times)
     window = np.zeros(n_samp)
-    start, stop = [
+    start, stop = (
         int(ii * float(n_samp) / (2 * n_dipoles)) for ii in (2 * n, 2 * n + 1)
-    ]
+    )
     window[start:stop] = 1.0
     n += 1
     data = 25e-9 * np.sin(2.0 * np.pi * 10.0 * n * times)

examples/time_frequency/time_frequency_simulated.py

@@ -150,7 +150,7 @@
     power.plot(
         [0], baseline=(0.0, 0.1), mode="mean", axes=ax, show=False, colorbar=False
     )
-    ax.set_title("Sim: Using S transform, width = {:0.1f}".format(width))
+    ax.set_title(f"Sim: Using S transform, width = {width:0.1f}")
 
 # %%
 # Morlet Wavelets

examples/visualization/evoked_topomap.py

@@ -111,7 +111,7 @@
             colorbar=False,
             sphere=(0.0, 0.0, 0.0, 0.09),
         )
-        ax.set_title("%s %s" % (ch_type.upper(), extr), fontsize=14)
+        ax.set_title(f"{ch_type.upper()} {extr}", fontsize=14)
 
 # %%
 # More advanced usage

examples/visualization/evoked_whitening.py

@@ -84,7 +84,7 @@
 
 print("Covariance estimates sorted from best to worst")
 for c in noise_covs:
-    print("%s : %s" % (c["method"], c["loglik"]))
+    print(f'{c["method"]} : {c["loglik"]}')
 
 # %%
 # Show the evoked data:

mne/_fiff/_digitization.py

@@ -132,14 +132,15 @@ def __repr__(self):  # noqa: D105
             id_ = _cardinal_kind_rev.get(self["ident"], "Unknown cardinal")
         else:
             id_ = _dig_kind_proper[_dig_kind_rev.get(self["kind"], "unknown")]
-            id_ = "%s #%s" % (id_, self["ident"])
+            id_ = f"{id_} #{self['ident']}"
         id_ = id_.rjust(10)
         cf = _coord_frame_name(self["coord_frame"])
+        x, y, z = self["r"]
         if "voxel" in cf:
-            pos = ("(%0.1f, %0.1f, %0.1f)" % tuple(self["r"])).ljust(25)
+            pos = (f"({x:0.1f}, {y:0.1f}, {z:0.1f})").ljust(25)
         else:
-            pos = ("(%0.1f, %0.1f, %0.1f) mm" % tuple(1000 * self["r"])).ljust(25)
-        return "<DigPoint | %s : %s : %s frame>" % (id_, pos, cf)
+            pos = (f"({x * 1e3:0.1f}, {y * 1e3:0.1f}, {z * 1e3:0.1f}) mm").ljust(25)
+        return f"<DigPoint | {id_} : {pos} : {cf} frame>"
 
     # speed up info copy by only deep copying the mutable item
     def __deepcopy__(self, memodict):
@@ -362,8 +363,8 @@ def _coord_frame_const(coord_frame):
 
     if not isinstance(coord_frame, str) or coord_frame not in _str_to_frame:
         raise ValueError(
-            "coord_frame must be one of %s, got %s"
-            % (sorted(_str_to_frame.keys()), coord_frame)
+            f"coord_frame must be one of {sorted(_str_to_frame.keys())}, got "
+            f"{coord_frame}"
         )
     return _str_to_frame[coord_frame]
 
@@ -414,9 +415,7 @@ def _make_dig_points(
     if lpa is not None:
         lpa = np.asarray(lpa)
         if lpa.shape != (3,):
-            raise ValueError(
-                "LPA should have the shape (3,) instead of %s" % (lpa.shape,)
-            )
+            raise ValueError(f"LPA should have the shape (3,) instead of {lpa.shape}")
         dig.append(
             {
                 "r": lpa,
@@ -429,7 +428,7 @@ def _make_dig_points(
         nasion = np.asarray(nasion)
         if nasion.shape != (3,):
             raise ValueError(
-                "Nasion should have the shape (3,) instead of %s" % (nasion.shape,)
+                f"Nasion should have the shape (3,) instead of {nasion.shape}"
             )
         dig.append(
             {
@@ -442,9 +441,7 @@ def _make_dig_points(
     if rpa is not None:
         rpa = np.asarray(rpa)
         if rpa.shape != (3,):
-            raise ValueError(
-                "RPA should have the shape (3,) instead of %s" % (rpa.shape,)
-            )
+            raise ValueError(f"RPA should have the shape (3,) instead of {rpa.shape}")
         dig.append(
             {
                 "r": rpa,
@@ -457,8 +454,7 @@ def _make_dig_points(
         hpi = np.asarray(hpi)
         if hpi.ndim != 2 or hpi.shape[1] != 3:
             raise ValueError(
-                "HPI should have the shape (n_points, 3) instead "
-                "of %s" % (hpi.shape,)
+                f"HPI should have the shape (n_points, 3) instead of {hpi.shape}"
             )
         for idx, point in enumerate(hpi):
             dig.append(
@@ -473,8 +469,8 @@ def _make_dig_points(
         extra_points = np.asarray(extra_points)
         if len(extra_points) and extra_points.shape[1] != 3:
             raise ValueError(
-                "Points should have the shape (n_points, 3) "
-                "instead of %s" % (extra_points.shape,)
+                "Points should have the shape (n_points, 3) instead of "
+                f"{extra_points.shape}"
             )
         for idx, point in enumerate(extra_points):
             dig.append(