MAINT: Check and format with NPY201 (mne-tools#12353)

.pre-commit-config.yaml

@@ -7,14 +7,13 @@ repos:
         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

mne/_fiff/open.py

@@ -263,7 +263,7 @@ def show_fiff(
             tag_id=tag,
             show_bytes=show_bytes,
         )
-    if output == str:
+    if output is str:
         out = "\n".join(out)
     return out
 

mne/decoding/tests/test_search_light.py

@@ -63,19 +63,19 @@ def test_search_light():
     # transforms
     pytest.raises(ValueError, sl.predict, X[:, :, :2])
     y_trans = sl.transform(X)
-    assert X.dtype == y_trans.dtype == float
+    assert X.dtype == y_trans.dtype == np.dtype(float)
     y_pred = sl.predict(X)
-    assert y_pred.dtype == int
+    assert y_pred.dtype == np.dtype(int)
     assert_array_equal(y_pred.shape, [n_epochs, n_time])
     y_proba = sl.predict_proba(X)
-    assert y_proba.dtype == float
+    assert y_proba.dtype == np.dtype(float)
     assert_array_equal(y_proba.shape, [n_epochs, n_time, 2])
 
     # score
     score = sl.score(X, y)
     assert_array_equal(score.shape, [n_time])
     assert np.sum(np.abs(score)) != 0
-    assert score.dtype == float
+    assert score.dtype == np.dtype(float)
 
     sl = SlidingEstimator(logreg)
     assert_equal(sl.scoring, None)
@@ -122,7 +122,7 @@ def test_search_light():
     X = rng.randn(*X.shape)  # randomize X to avoid AUCs in [0, 1]
     score_sl = sl1.score(X, y)
     assert_array_equal(score_sl.shape, [n_time])
-    assert score_sl.dtype == float
+    assert score_sl.dtype == np.dtype(float)
 
     # Check that scoring was applied adequately
     scoring = make_scorer(roc_auc_score, needs_threshold=True)
@@ -195,9 +195,9 @@ def test_generalization_light():
     # transforms
     y_pred = gl.predict(X)
     assert_array_equal(y_pred.shape, [n_epochs, n_time, n_time])
-    assert y_pred.dtype == int
+    assert y_pred.dtype == np.dtype(int)
     y_proba = gl.predict_proba(X)
-    assert y_proba.dtype == float
+    assert y_proba.dtype == np.dtype(float)
     assert_array_equal(y_proba.shape, [n_epochs, n_time, n_time, 2])
 
     # transform to different datasize
@@ -208,7 +208,7 @@ def test_generalization_light():
     score = gl.score(X[:, :, :3], y)
     assert_array_equal(score.shape, [n_time, 3])
     assert np.sum(np.abs(score)) != 0
-    assert score.dtype == float
+    assert score.dtype == np.dtype(float)
 
     gl = GeneralizingEstimator(logreg, scoring="roc_auc")
     gl.fit(X, y)

mne/epochs.py

@@ -1537,7 +1537,7 @@ def drop(self, indices, reason="USER", verbose=None):
         if indices.ndim > 1:
             raise ValueError("indices must be a scalar or a 1-d array")
 
-        if indices.dtype == bool:
+        if indices.dtype == np.dtype(bool):
             indices = np.where(indices)[0]
         try_idx = np.where(indices < 0, indices + len(self.events), indices)
 

mne/fixes.py

@@ -223,7 +223,7 @@ def get_params(self, deep=True):
             try:
                 with warnings.catch_warnings(record=True) as w:
                     value = getattr(self, key, None)
-                if len(w) and w[0].category == DeprecationWarning:
+                if len(w) and w[0].category is DeprecationWarning:
                     # if the parameter is deprecated, don't show it
                     continue
             finally:

mne/preprocessing/_annotate_amplitude.py

@@ -249,7 +249,7 @@ def _check_min_duration(min_duration, raw_duration):
 
 def _reject_short_segments(arr, min_duration_samples):
     """Check if flat or peak segments are longer than the minimum duration."""
-    assert arr.dtype == bool and arr.ndim == 2
+    assert arr.dtype == np.dtype(bool) and arr.ndim == 2
     for k, ch in enumerate(arr):
         onsets, offsets = _mask_to_onsets_offsets(ch)
         _mark_inner(arr[k], onsets, offsets, min_duration_samples)

mne/preprocessing/artifact_detection.py

@@ -599,7 +599,7 @@ def annotate_break(
     # Log some info
     n_breaks = len(break_annotations)
     break_times = [
-        f"{o:.1f} – {o+d:.1f} s [{d:.1f} s]"
+        f"{o:.1f} – {o + d:.1f} s [{d:.1f} s]"
         for o, d in zip(break_annotations.onset, break_annotations.duration)
     ]
     break_times = "\n    ".join(break_times)

mne/preprocessing/interpolate.py

@@ -163,7 +163,7 @@ def interpolate_bridged_electrodes(inst, bridged_idx, bad_limit=4):
         # compute centroid position in spherical "head" coordinates
         pos_virtual = _find_centroid_sphere(pos["ch_pos"], group_names)
         # create the virtual channel info and set the position
-        virtual_info = create_info([f"virtual {k+1}"], inst.info["sfreq"], "eeg")
+        virtual_info = create_info([f"virtual {k + 1}"], inst.info["sfreq"], "eeg")
         virtual_info["chs"][0]["loc"][:3] = pos_virtual
         # create virtual channel
         data = inst.get_data(picks=group_names)
@@ -182,7 +182,7 @@ def interpolate_bridged_electrodes(inst, bridged_idx, bad_limit=4):
                 nave=inst.nave,
                 kind=inst.kind,
             )
-        virtual_chs[f"virtual {k+1}"] = virtual_ch
+        virtual_chs[f"virtual {k + 1}"] = virtual_ch
 
     # add the virtual channels
     inst.add_channels(list(virtual_chs.values()), force_update_info=True)

mne/preprocessing/tests/test_realign.py

@@ -158,7 +158,7 @@ def _assert_similarity(raw, other, n_events, ratio_other, events_raw=None):
     evoked_other = Epochs(other, events_other, **kwargs).average()
     assert evoked_raw.nave == evoked_other.nave == len(events_raw)
     assert len(evoked_raw.data) == len(evoked_other.data) == 1  # just EEG
-    if 0.99 <= ratio_other <= 1.01:  #  when drift is not too large
+    if 0.99 <= ratio_other <= 1.01:  # when drift is not too large
         corr = np.corrcoef(evoked_raw.data[0], evoked_other.data[0])[0, 1]
         assert 0.9 <= corr <= 1.0
     return evoked_raw, events_raw, evoked_other, events_other

mne/proj.py

@@ -151,7 +151,7 @@ def _compute_proj(
                 nrow=1,
                 ncol=u.size,
             )
-            desc = f"{kind}-{desc_prefix}-PCA-{k+1:02d}"
+            desc = f"{kind}-{desc_prefix}-PCA-{k + 1:02d}"
             logger.info("Adding projection: %s", desc)
             proj = Projection(
                 active=False,

mne/report/report.py

@@ -2271,7 +2271,7 @@ def add_figure(
         elif caption is None and len(figs) == 1:
             captions = [None]
         elif caption is None and len(figs) > 1:
-            captions = [f"Figure {i+1}" for i in range(len(figs))]
+            captions = [f"Figure {i + 1}" for i in range(len(figs))]
         else:
             captions = tuple(caption)
 
@@ -3143,7 +3143,7 @@ def _add_raw_butterfly_segments(
 
         del orig_annotations
 
-        captions = [f"Segment {i+1} of {len(images)}" for i in range(len(images))]
+        captions = [f"Segment {i + 1} of {len(images)}" for i in range(len(images))]
 
         self._add_slider(
             figs=None,

mne/source_space/_source_space.py

@@ -2408,7 +2408,7 @@ def _grid_interp(from_shape, to_shape, trans, order=1, inuse=None):
     shape = (np.prod(to_shape), np.prod(from_shape))
     if inuse is None:
         inuse = np.ones(shape[1], bool)
-    assert inuse.dtype == bool
+    assert inuse.dtype == np.dtype(bool)
     assert inuse.shape == (shape[1],)
     data, indices, indptr = _grid_interp_jit(from_shape, to_shape, trans, order, inuse)
     data = np.concatenate(data)

mne/stats/cluster_level.py

@@ -479,7 +479,7 @@ def _find_clusters(
                     len_c = c.stop - c.start
                 elif isinstance(c, tuple):
                     len_c = len(c)
-                elif c.dtype == bool:
+                elif c.dtype == np.dtype(bool):
                     len_c = np.sum(c)
                 else:
                     len_c = len(c)
@@ -1634,7 +1634,7 @@ def _reshape_clusters(clusters, sample_shape):
     """Reshape cluster masks or indices to be of the correct shape."""
     # format of the bool mask and indices are ndarrays
     if len(clusters) > 0 and isinstance(clusters[0], np.ndarray):
-        if clusters[0].dtype == bool:  # format of mask
+        if clusters[0].dtype == np.dtype(bool):  # format of mask
             clusters = [c.reshape(sample_shape) for c in clusters]
         else:  # format of indices
             clusters = [np.unravel_index(c, sample_shape) for c in clusters]

mne/stats/tests/test_cluster_level.py

@@ -610,7 +610,7 @@ def test_permutation_adjacency_equiv(numba_conditional):
         )
         # make sure our output datatype is correct
         assert isinstance(clusters[0], np.ndarray)
-        assert clusters[0].dtype == bool
+        assert clusters[0].dtype == np.dtype(bool)
         assert_array_equal(clusters[0].shape, X.shape[1:])
 
         # make sure all comparisons were done; for TFCE, no perm
@@ -847,7 +847,7 @@ def test_output_equiv(shape, out_type, adjacency):
                 assert isinstance(clu[0], slice)
             else:
                 assert isinstance(clu, np.ndarray)
-                assert clu.dtype == bool
+                assert clu.dtype == np.dtype(bool)
                 assert clu.shape == shape
             got_mask[clu] = n
         else:

mne/tests/test_annotations.py

@@ -278,7 +278,9 @@ def test_crop(tmp_path):
     assert raw_read.annotations is not None
     assert len(raw_read.annotations.onset) == 0
     # test saving and reloading cropped annotations in raw instance
-    info = create_info([f"EEG{i+1}" for i in range(3)], ch_types=["eeg"] * 3, sfreq=50)
+    info = create_info(
+        [f"EEG{i + 1}" for i in range(3)], ch_types=["eeg"] * 3, sfreq=50
+    )
     raw = RawArray(np.zeros((3, 50 * 20)), info)
     annotation = mne.Annotations([8, 12, 15], [2] * 3, [1, 2, 3])
     raw = raw.set_annotations(annotation)

mne/utils/numerics.py

@@ -938,7 +938,7 @@ def _fit(self, X):
 
 def _mask_to_onsets_offsets(mask):
     """Group boolean mask into contiguous onset:offset pairs."""
-    assert mask.dtype == bool and mask.ndim == 1
+    assert mask.dtype == np.dtype(bool) and mask.ndim == 1
     mask = mask.astype(int)
     diff = np.diff(mask)
     onsets = np.where(diff > 0)[0] + 1

mne/viz/_mpl_figure.py

@@ -1847,7 +1847,7 @@ def _draw_one_scalebar(self, x, y, ch_type):
         color = "#AA3377"  # purple
         kwargs = dict(color=color, zorder=self.mne.zorder["scalebar"])
         if ch_type == "time":
-            label = f"{self.mne.boundary_times[1]/2:.2f} s"
+            label = f"{self.mne.boundary_times[1] / 2:.2f} s"
             text = self.mne.ax_main.text(
                 x[0] + 0.015,
                 y[1] - 0.05,

mne/viz/evoked_field.py

@@ -473,7 +473,9 @@ def _on_colormap_range(self, event):
         if self._show_density:
             surf_map["mesh"].update_overlay(name="field", rng=[vmin, vmax])
             # Update the GUI widgets
-            if type == "meg":
+            # TODO: type is undefined here and only avoids a flake warning because it's
+            # a builtin!
+            if type == "meg":  # noqa: E721
                 scaling = DEFAULTS["scalings"]["grad"]
             else:
                 scaling = DEFAULTS["scalings"]["eeg"]