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# Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD
import glob
import os.path as op
import numpy as np
import pytest
from mne import what, create_info
from mne.datasets import testing
from mne.io import RawArray
from mne.preprocessing import ICA
from mne.utils import run_tests_if_main, requires_sklearn
data_path = testing.data_path(download=False)
@pytest.mark.slowtest
@requires_sklearn
@testing.requires_testing_data
def test_what(tmpdir):
"""Test mne.what."""
# ICA
ica = ICA(max_iter=1)
raw = RawArray(np.random.RandomState(0).randn(3, 10),
create_info(3, 1000., 'eeg'))
with pytest.warns(None): # convergence sometimes
ica.fit(raw)
fname = op.join(str(tmpdir), 'x-ica.fif')
ica.save(fname)
assert what(fname) == 'ica'
# test files
fnames = glob.glob(
op.join(data_path, 'MEG', 'sample', '*.fif'))
fnames += glob.glob(
op.join(data_path, 'subjects', 'sample', 'bem', '*.fif'))
fnames = sorted(fnames)
want_dict = dict(eve='events', ave='evoked', cov='cov', inv='inverse',
fwd='forward', trans='transform', proj='proj',
raw='raw', meg='raw', sol='bem solution',
bem='bem surfaces', src='src', dense='bem surfaces',
sparse='bem surfaces', head='bem surfaces',
fiducials='fiducials')
for fname in fnames:
kind = op.splitext(fname)[0].split('-')[-1]
if len(kind) > 5:
kind = kind.split('_')[-1]
this = what(fname)
assert this == want_dict[kind]
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave_xfit.dip')
assert what(fname) == 'unknown'
run_tests_if_main()
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