1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
|
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import glob
from pathlib import Path
import numpy as np
import pytest
from mne import create_info, what
from mne.datasets import testing
from mne.io import RawArray
from mne.preprocessing import ICA
from mne.utils import _record_warnings
data_path = testing.data_path(download=False)
@pytest.mark.slowtest
@testing.requires_testing_data
def test_what(tmp_path, verbose_debug):
"""Test mne.what."""
pytest.importorskip("sklearn")
# ICA
ica = ICA(max_iter=1, random_state=0)
raw = RawArray(np.random.RandomState(0).randn(3, 10), create_info(3, 1000.0, "eeg"))
with _record_warnings(): # convergence sometimes
ica.fit(raw)
fname = tmp_path / "x-ica.fif"
ica.save(fname)
assert what(fname) == "ica"
# test files
fnames = glob.glob(str(data_path / "MEG" / "sample" / "*.fif"))
fnames += glob.glob(str(data_path / "subjects" / "sample" / "bem" / "*.fif"))
fnames += [str(fname)]
fnames = sorted(fnames)
want_dict = dict(
eve="events",
ave="evoked",
cov="cov",
ica="ica",
inv="inverse",
fwd="forward",
trans="transform",
proj="proj",
raw="raw",
sol="bem solution",
bem="bem surfaces",
src="src",
dense="bem surfaces",
head="bem surfaces",
fiducials="fiducials",
)
got = set()
for fname in fnames:
print(fname)
kind = Path(fname).stem.split("-")[-1]
if len(kind) > 5:
kind = kind.split("_")[-1]
this = what(fname)
assert this == want_dict[kind], fname
print()
got.add(kind)
assert set(want_dict) == got
fname = data_path / "MEG" / "sample" / "sample_audvis-ave_xfit.dip"
assert what(fname) == "unknown"
|
