MAINT: Replace in1d with isin (#11994)

Author: larsoner

examples/visualization/evoked_topomap.py

@@ -148,7 +148,7 @@
     ("MEG 2411", "MEG 2421"),
     ("MEG 1621"),
 ]
-_channels = [np.in1d(evoked.ch_names, ch) for ch in significant_channels]
+_channels = [np.isin(evoked.ch_names, ch) for ch in significant_channels]
 
 mask = np.zeros(evoked.data.shape, dtype="bool")
 for _chs, _time in zip(_channels, _times):

mne/_freesurfer.py

@@ -249,7 +249,7 @@ def get_volume_labels_from_aseg(mgz_fname, return_colors=False, atlas_ids=None):
     elif return_colors:
         raise ValueError("return_colors must be False if atlas_ids are " "provided")
     # restrict to the ones in the MRI, sorted by label name
-    keep = np.in1d(list(atlas_ids.values()), want)
+    keep = np.isin(list(atlas_ids.values()), want)
     keys = sorted(
         (key for ki, key in enumerate(atlas_ids.keys()) if keep[ki]),
         key=lambda x: atlas_ids[x],

mne/beamformer/tests/test_dics.py

@@ -108,7 +108,7 @@ def _simulate_data(fwd, idx):  # Somewhere on the frontal lobe by default
     csd = csd_morlet(epochs, frequencies=[10, 20], n_cycles=[5, 10], decim=5)
 
     labels = mne.read_labels_from_annot("sample", hemi="lh", subjects_dir=subjects_dir)
-    label = [label for label in labels if np.in1d(source_vertno, label.vertices)[0]]
+    label = [label for label in labels if np.isin(source_vertno, label.vertices)]
     assert len(label) == 1
     label = label[0]
     vertices = np.intersect1d(label.vertices, fwd["src"][0]["vertno"])

mne/beamformer/tests/test_lcmv.py

@@ -193,7 +193,7 @@ def test_lcmv_vector():
 
     # Figure out our indices
     mask = np.concatenate(
-        [np.in1d(s["vertno"], v) for s, v in zip(forward["src"], vertices)]
+        [np.isin(s["vertno"], v) for s, v in zip(forward["src"], vertices)]
     )
     mapping = np.where(mask)[0]
     assert_array_equal(source_rr, forward["source_rr"][mapping])

mne/channels/interpolation.py

@@ -212,7 +212,7 @@ def _interpolate_bads_meeg(
         picks_bad = pick_channels(inst.info["ch_names"], bads_type, exclude=[])
         if ch_type == "eeg":
             picks_to = picks_type
-            bad_sel = np.in1d(picks_type, picks_bad)
+            bad_sel = np.isin(picks_type, picks_bad)
         else:
             picks_to = picks_bad
             bad_sel = slice(None)

mne/chpi.py

@@ -727,7 +727,7 @@ def _setup_ext_proj(info, ext_order):
         dict(origin=(0.0, 0.0, 0.0), int_order=0, ext_order=ext_order), mf_coils
     ).T
     out_removes = _regularize_out(0, 1, mag_or_fine, [])
-    ext = ext[~np.in1d(np.arange(len(ext)), out_removes)]
+    ext = ext[~np.isin(np.arange(len(ext)), out_removes)]
     ext = orth(ext.T).T
     assert ext.shape[1] == len(meg_picks)
     proj = Projection(

mne/conftest.py

@@ -167,6 +167,8 @@ def pytest_configure(config):
     ignore:mne\.io\.pick.channel_indices_by_type is deprecated.*:
     # Windows CIs using MESA get this
     ignore:Mesa version 10\.2\.4 is too old for translucent.*:RuntimeWarning
+    # Matplotlib <-> NumPy 2.0
+    ignore:`row_stack` alias is deprecated.*:DeprecationWarning
     """  # noqa: E501
     for warning_line in warning_lines.split("\n"):
         warning_line = warning_line.strip()

mne/cov.py

@@ -709,7 +709,7 @@ def compute_raw_covariance(
     if picks is None:
         # Need to include all channels e.g. if eog rejection is to be used
         picks = np.arange(raw.info["nchan"])
-        pick_mask = np.in1d(picks, _pick_data_channels(raw.info, with_ref_meg=False))
+        pick_mask = np.isin(picks, _pick_data_channels(raw.info, with_ref_meg=False))
     else:
         pick_mask = slice(None)
         picks = _picks_to_idx(raw.info, picks)

mne/epochs.py

@@ -503,7 +503,7 @@ def __init__(
             del events
 
             values = list(self.event_id.values())
-            selected = np.where(np.in1d(self.events[:, 2], values))[0]
+            selected = np.where(np.isin(self.events[:, 2], values))[0]
             if selection is None:
                 selection = selected
             else:
@@ -538,7 +538,7 @@ def __init__(
             )
 
             # then subselect
-            sub = np.where(np.in1d(selection, self.selection))[0]
+            sub = np.where(np.isin(selection, self.selection))[0]
             if isinstance(metadata, list):
                 metadata = [metadata[s] for s in sub]
             elif metadata is not None:
@@ -2849,7 +2849,7 @@ def _ensure_list(x):
     id_to_name_map = {v: k for k, v in event_id.items()}
 
     # Only keep events that are of interest
-    events = events[np.in1d(events[:, 2], list(event_id.values()))]
+    events = events[np.isin(events[:, 2], list(event_id.values()))]
     events_df = events_df.loc[events_df["id"].isin(event_id.values()), :]
 
     # Prepare & condition the metadata DataFrame
@@ -2954,7 +2954,7 @@ def _ensure_list(x):
         event_id_timelocked = {
             name: val for name, val in event_id.items() if name in row_events
         }
-        events = events[np.in1d(events[:, 2], list(event_id_timelocked.values()))]
+        events = events[np.isin(events[:, 2], list(event_id_timelocked.values()))]
         metadata = metadata.loc[metadata["event_name"].isin(event_id_timelocked)]
         assert len(events) == len(metadata)
         event_id = event_id_timelocked
@@ -3302,7 +3302,7 @@ def __init__(
             self._do_baseline = True
         if (
             len(events)
-            != np.in1d(self.events[:, 2], list(self.event_id.values())).sum()
+            != np.isin(self.events[:, 2], list(self.event_id.values())).sum()
         ):
             raise ValueError(
                 "The events must only contain event numbers from " "event_id"

mne/event.py

@@ -914,7 +914,7 @@ def shift_time_events(events, ids, tshift, sfreq):
     if ids is None:
         mask = slice(None)
     else:
-        mask = np.in1d(events[:, 2], ids)
+        mask = np.isin(events[:, 2], ids)
     events[mask, 0] += int(tshift * sfreq)
 
     return events