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# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: Simplified BSD
import os.path as op
from functools import wraps
import warnings
from numpy.testing import assert_raises
from mne import io, read_events, Epochs, read_cov
from mne import pick_types
from mne.datasets import sample
from mne.utils import check_sklearn_version
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs
warnings.simplefilter('always') # enable b/c these tests throw warnings
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
import matplotlib.pyplot as plt
data_dir = sample.data_path(download=False)
subjects_dir = op.join(data_dir, 'subjects')
ecg_fname = op.join(data_dir, 'MEG', 'sample', 'sample_audvis_ecg_proj.fif')
base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
evoked_fname = op.join(base_dir, 'test-ave.fif')
raw_fname = op.join(base_dir, 'test_raw.fif')
cov_fname = op.join(base_dir, 'test-cov.fif')
event_name = op.join(base_dir, 'test-eve.fif')
event_id, tmin, tmax = 1, -0.1, 0.2
def requires_sklearn(function):
"""Decorator to skip test if scikit-learn >= 0.12 is not available"""
@wraps(function)
def dec(*args, **kwargs):
if not check_sklearn_version(min_version='0.12'):
from nose.plugins.skip import SkipTest
raise SkipTest('Test %s skipped, requires scikit-learn >= 0.12'
% function.__name__)
ret = function(*args, **kwargs)
return ret
return dec
def _get_raw():
return io.Raw(raw_fname, preload=False)
def _get_events():
return read_events(event_name)
def _get_picks(raw):
return [0, 1, 2, 6, 7, 8, 12, 13, 14] # take a only few channels
def _get_epochs():
raw = _get_raw()
events = _get_events()
picks = _get_picks(raw)
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
return epochs
@requires_sklearn
def test_plot_ica_components():
"""Test plotting of ICA solutions
"""
raw = _get_raw()
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica_picks = _get_picks(raw)
ica.fit(raw, picks=ica_picks)
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
for components in [0, [0], [0, 1], [0, 1] * 2, None]:
ica.plot_components(components, image_interp='bilinear', res=16)
ica.info = None
assert_raises(RuntimeError, ica.plot_components, 1)
plt.close('all')
@requires_sklearn
def test_plot_ica_sources():
"""Test plotting of ICA panel
"""
raw = io.Raw(raw_fname, preload=True)
picks = _get_picks(raw)
epochs = _get_epochs()
raw.pick_channels([raw.ch_names[k] for k in picks])
ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
ecg=False, eog=False, exclude='bads')
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=ica_picks)
ica.plot_sources(raw)
ica.plot_sources(epochs)
with warnings.catch_warnings(record=True): # no labeled objects mpl
ica.plot_sources(epochs.average())
assert_raises(ValueError, ica.plot_sources, 'meeow')
plt.close('all')
@requires_sklearn
def test_plot_ica_overlay():
"""Test plotting of ICA cleaning
"""
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=picks)
# don't test raw, needs preload ...
ecg_epochs = create_ecg_epochs(raw, picks=picks)
ica.plot_overlay(ecg_epochs.average())
eog_epochs = create_eog_epochs(raw, picks=picks)
ica.plot_overlay(eog_epochs.average())
assert_raises(ValueError, ica.plot_overlay, raw[:2, :3][0])
plt.close('all')
@requires_sklearn
def test_plot_ica_scores():
"""Test plotting of ICA scores
"""
raw = _get_raw()
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=picks)
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
assert_raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')
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