pytest adjustments

Author: matthiasdold

mne_bids/tests/test_read.py

@@ -3,6 +3,7 @@
 # Authors: The MNE-BIDS developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+
 import json
 import os
 import os.path as op
@@ -857,9 +858,7 @@ def test_handle_chpi_reading(tmp_path):
     meg_json_data_freq_mismatch["HeadCoilFrequency"][0] = 123
     _write_json(meg_json_path, meg_json_data_freq_mismatch, overwrite=True)
 
-    with (
-        pytest.warns(RuntimeWarning, match="Defaulting to .* mne.Raw object"),
-    ):
+    with (pytest.warns(RuntimeWarning, match="Defaulting to .* mne.Raw object"),):
         raw_read = read_raw_bids(bids_path, extra_params=dict(allow_maxshield="yes"))
 
     # cHPI "off" according to sidecar, but present in the data
@@ -1080,9 +1079,7 @@ def test_handle_ieeg_coords_reading(bids_path, tmp_path):
     _to_tsv(electrodes_dict, electrodes_fname)
     # popping off channels should not result in an error
     # however, a warning will be raised through mne-python
-    with (
-        pytest.warns(RuntimeWarning, match="DigMontage is only a subset of info"),
-    ):
+    with (pytest.warns(RuntimeWarning, match="DigMontage is only a subset of info"),):
         read_raw_bids(bids_path=bids_fname, verbose=False)
 
     # make sure montage is set if there are coordinates w/ 'n/a'
@@ -1098,9 +1095,7 @@ def test_handle_ieeg_coords_reading(bids_path, tmp_path):
     # electrode coordinates should be nan
     # when coordinate is 'n/a'
     nan_chs = [electrodes_dict["name"][i] for i in [0, 3]]
-    with (
-        pytest.warns(RuntimeWarning, match="There are channels without locations"),
-    ):
+    with (pytest.warns(RuntimeWarning, match="There are channels without locations"),):
         raw = read_raw_bids(bids_path=bids_fname, verbose=False)
         for idx, ch in enumerate(raw.info["chs"]):
             if ch["ch_name"] in nan_chs:
@@ -1228,9 +1223,7 @@ def test_handle_non_mne_channel_type(tmp_path):
     channels_data["type"][ch_idx] = "FOOBAR"
     _to_tsv(data=channels_data, fname=channels_tsv_path)
 
-    with (
-        pytest.warns(RuntimeWarning, match='will be set to "misc"'),
-    ):
+    with (pytest.warns(RuntimeWarning, match='will be set to "misc"'),):
         raw = read_raw_bids(bids_path)
 
     # Should be a 'misc' channel.
@@ -1480,9 +1473,10 @@ def test_events_file_to_annotation_kwargs(tmp_path):
     # ---------------- plain read --------------------------------------------
     df = pd.read_csv(events_fname, sep="\t")
     ev_kwargs = events_file_to_annotation_kwargs(events_fname=events_fname)
-    assert (ev_kwargs["onset"] == df["onset"].values).all()
-    assert (ev_kwargs["duration"] == df["duration"].values).all()
-    assert (ev_kwargs["description"] == df["trial_type"].values).all()
+
+    np.testing.assert_equal(ev_kwargs["onset"], df["onset"].values)
+    np.testing.assert_equal(ev_kwargs["duration"], df["duration"].values)
+    np.testing.assert_equal(ev_kwargs["description"], df["trial_type"].values)
 
     # ---------------- filtering out n/a values ------------------------------
     tmp_tsv_file = tmp_path / "events.tsv"
@@ -1528,16 +1522,17 @@ def test_events_file_to_annotation_kwargs(tmp_path):
     )  # now idx=0, as first row is filtered out
 
     # ---------------- default if missing trial_type  ------------------------
-    tmp_tsv_file = tmp_path / "events.tsv"
     dext.drop(columns="trial_type").to_csv(tmp_tsv_file, sep="\t", index=False)
 
     ev_kwargs_default = events_file_to_annotation_kwargs(events_fname=tmp_tsv_file)
-    assert (ev_kwargs_default["onset"] == dext_f["onset"].astype(float).values).all()
-    assert (
-        ev_kwargs_default["duration"]
-        == dext_f["duration"].replace("n/a", "0.0").astype(float).values
-    ).all()
-    assert (
-        np.sort(np.unique(ev_kwargs_default["description"]))
-        == np.sort(dext_f["value"].unique())
-    ).all()
+    np.testing.assert_array_equal(
+        ev_kwargs_default["onset"], dext_f["onset"].astype(float).values
+    )
+    np.testing.assert_array_equal(
+        ev_kwargs_default["duration"],
+        dext_f["duration"].replace("n/a", "0.0").astype(float).values,
+    )
+    np.testing.assert_array_equal(
+        np.sort(np.unique(ev_kwargs_default["description"])),
+        np.sort(dext_f["value"].unique()),
+    )