File: test_ica.py

package info (click to toggle)
python-mne 0.17%2Bdfsg-1
  • links: PTS, VCS
  • area: main
  • in suites: buster
  • size: 95,104 kB
  • sloc: python: 110,639; makefile: 222; sh: 15
file content (320 lines) | stat: -rw-r--r-- 11,634 bytes parent folder | download
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
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
# Authors: Denis Engemann <denis.engemann@gmail.com>
#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: Simplified BSD

import os.path as op

from numpy.testing import assert_equal, assert_array_equal
import pytest

from mne import read_events, Epochs, read_cov, pick_types
from mne.io import read_raw_fif
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs
from mne.utils import run_tests_if_main, requires_sklearn
from mne.viz.ica import _create_properties_layout, plot_ica_properties
from mne.viz.utils import _fake_click

# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg')  # for testing don't use X server

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
raw_ctf_fname = op.join(base_dir, 'test_ctf_raw.fif')


def _get_raw(preload=False):
    """Get raw data."""
    return read_raw_fif(raw_fname, preload=preload)


def _get_events():
    """Get events."""
    return read_events(event_name)


def _get_picks(raw):
    """Get picks."""
    return [0, 1, 2, 6, 7, 8, 12, 13, 14]  # take a only few channels


def _get_epochs():
    """Get epochs."""
    raw = _get_raw()
    events = _get_events()
    picks = _get_picks(raw)
    with pytest.warns(RuntimeWarning, match='projection'):
        epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks)
    return epochs


@requires_sklearn
def test_plot_ica_components():
    """Test plotting of ICA solutions."""
    import matplotlib.pyplot as plt
    res = 8
    fast_test = {"res": res, "contours": 0, "sensors": False}
    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)
    with pytest.warns(RuntimeWarning, match='projection'):
        ica.fit(raw, picks=ica_picks)

    for components in [0, [0], [0, 1], [0, 1] * 2, None]:
        ica.plot_components(components, image_interp='bilinear',
                            colorbar=True, **fast_test)
    plt.close('all')

    # test interactive mode (passing 'inst' arg)
    ica.plot_components([0, 1], image_interp='bilinear', inst=raw, res=16)
    fig = plt.gcf()

    # test title click
    # ----------------
    lbl = fig.axes[1].get_label()
    ica_idx = int(lbl[-3:])
    titles = [ax.title for ax in fig.axes]
    title_pos_midpoint = (titles[1].get_window_extent().extents
                          .reshape((2, 2)).mean(axis=0))
    # first click adds to exclude
    _fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
    assert ica_idx in ica.exclude
    # clicking again removes from exclude
    _fake_click(fig, fig.axes[1], title_pos_midpoint, xform='pix')
    assert ica_idx not in ica.exclude

    # test topo click
    # ---------------
    _fake_click(fig, fig.axes[1], (0., 0.), xform='data')

    c_fig = plt.gcf()
    labels = [ax.get_label() for ax in c_fig.axes]

    for l in ['topomap', 'image', 'erp', 'spectrum', 'variance']:
        assert (l in labels)

    topomap_ax = c_fig.axes[labels.index('topomap')]
    title = topomap_ax.get_title()
    assert (lbl == title)

    ica.info = None
    pytest.raises(ValueError, ica.plot_components, 1)
    pytest.raises(RuntimeError, ica.plot_components, 1, ch_type='mag')
    plt.close('all')


@requires_sklearn
def test_plot_ica_properties():
    """Test plotting of ICA properties."""
    import matplotlib.pyplot as plt

    res = 8
    raw = _get_raw(preload=True)
    raw.add_proj([], remove_existing=True)
    events = _get_events()
    picks = _get_picks(raw)[:6]
    pick_names = [raw.ch_names[k] for k in picks]
    raw.pick_channels(pick_names)

    epochs = Epochs(raw, events[:10], event_id, tmin, tmax,
                    baseline=(None, 0), preload=True)

    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=2, n_pca_components=2)
    with pytest.warns(RuntimeWarning, match='projection'):
        ica.fit(raw)

    # test _create_properties_layout
    fig, ax = _create_properties_layout()
    assert_equal(len(ax), 5)

    topoargs = dict(topomap_args={'res': res, 'contours': 0, "sensors": False})
    ica.plot_properties(raw, picks=0, **topoargs)
    ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
    ica.plot_properties(epochs, picks=1, image_args={'sigma': 1.5},
                        topomap_args={'res': 10, 'colorbar': True},
                        psd_args={'fmax': 65.}, plot_std=False,
                        figsize=[4.5, 4.5])
    plt.close('all')

    pytest.raises(TypeError, ica.plot_properties, epochs, dB=list('abc'))
    pytest.raises(TypeError, ica.plot_properties, ica)
    pytest.raises(TypeError, ica.plot_properties, [0.2])
    pytest.raises(TypeError, plot_ica_properties, epochs, epochs)
    pytest.raises(TypeError, ica.plot_properties, epochs,
                  psd_args='not dict')
    pytest.raises(ValueError, ica.plot_properties, epochs, plot_std=[])

    fig, ax = plt.subplots(2, 3)
    ax = ax.ravel()[:-1]
    ica.plot_properties(epochs, picks=1, axes=ax, **topoargs)
    fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
    assert_equal(len(fig), 2)
    pytest.raises(TypeError, plot_ica_properties, epochs, ica, picks=[0, 1],
                  axes=ax)
    pytest.raises(ValueError, ica.plot_properties, epochs, axes='not axes')
    plt.close('all')

    # Test merging grads.
    raw = _get_raw(preload=True)
    picks = pick_types(raw.info, meg='grad')[:10]
    ica = ICA(n_components=2)
    ica.fit(raw, picks=picks)
    ica.plot_properties(raw)
    plt.close('all')


@requires_sklearn
def test_plot_ica_sources():
    """Test plotting of ICA panel."""
    import matplotlib.pyplot as plt
    raw = read_raw_fif(raw_fname).crop(0, 1).load_data()
    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.exclude = [1]
    fig = ica.plot_sources(raw)
    fig.canvas.key_press_event('escape')
    # Sadly close_event isn't called on Agg backend and the test always passes.
    assert_array_equal(ica.exclude, [1])

    fig = ica.plot_sources(raw, [1])
    # test mouse clicks
    data_ax = fig.axes[0]
    _fake_click(fig, data_ax, [-0.1, 0.9])  # click on y-label

    raw.info['bads'] = ['MEG 0113']
    pytest.raises(RuntimeError, ica.plot_sources, inst=raw)
    ica.plot_sources(epochs)
    epochs.info['bads'] = ['MEG 0113']
    pytest.raises(RuntimeError, ica.plot_sources, inst=epochs)
    epochs.info['bads'] = []
    ica.plot_sources(epochs.average())
    evoked = epochs.average()
    fig = ica.plot_sources(evoked)
    # Test a click
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax,
                [line.get_xdata()[0], line.get_ydata()[0]], 'data')
    _fake_click(fig, ax,
                [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
    # plot with bad channels excluded
    ica.plot_sources(evoked, exclude=[0])
    ica.exclude = [0]
    ica.plot_sources(evoked)  # does the same thing
    ica.labels_ = dict(eog=[0])
    ica.labels_['eog/0/crazy-channel'] = [0]
    ica.plot_sources(evoked)  # now with labels
    pytest.raises(ValueError, ica.plot_sources, 'meeow')
    plt.close('all')


@requires_sklearn
def test_plot_ica_overlay():
    """Test plotting of ICA cleaning."""
    import matplotlib.pyplot as plt
    raw = _get_raw(preload=True)
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    # can't use info.normalize_proj here because of how and when ICA and Epochs
    # objects do picking of Raw data
    with pytest.warns(RuntimeWarning, match='projection'):
        ica.fit(raw, picks=picks)
    # don't test raw, needs preload ...
    with pytest.warns(RuntimeWarning, match='projection'):
        ecg_epochs = create_ecg_epochs(raw, picks=picks)
    ica.plot_overlay(ecg_epochs.average())
    with pytest.warns(RuntimeWarning, match='projection'):
        eog_epochs = create_eog_epochs(raw, picks=picks)
    ica.plot_overlay(eog_epochs.average())
    pytest.raises(TypeError, ica.plot_overlay, raw[:2, :3][0])
    pytest.raises(TypeError, ica.plot_overlay, raw, exclude=2)
    ica.plot_overlay(raw)
    plt.close('all')

    # smoke test for CTF
    raw = read_raw_fif(raw_ctf_fname)
    raw.apply_gradient_compensation(3)
    picks = pick_types(raw.info, meg=True, ref_meg=False)
    ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    with pytest.warns(RuntimeWarning, match='longer than'):
        ecg_epochs = create_ecg_epochs(raw)
    ica.plot_overlay(ecg_epochs.average())
    plt.close('all')


@requires_sklearn
def test_plot_ica_scores():
    """Test plotting of ICA scores."""
    import matplotlib.pyplot as plt
    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)
    with pytest.warns(RuntimeWarning, match='projection'):
        ica.fit(raw, picks=picks)
    ica.labels_ = dict()
    ica.labels_['eog/0/foo'] = 0
    ica.labels_['eog'] = 0
    ica.labels_['ecg'] = 1
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
    pytest.raises(
        ValueError,
        ica.plot_scores,
        [0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
    pytest.raises(ValueError, ica.plot_scores, [0.2])
    plt.close('all')


@requires_sklearn
def test_plot_instance_components():
    """Test plotting of components as instances of raw and epochs."""
    import matplotlib.pyplot as plt
    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)
    with pytest.warns(RuntimeWarning, match='projection'):
        ica.fit(raw, picks=picks)
    fig = ica.plot_sources(raw, exclude=[0], title='Components')
    for key in ['down', 'up', 'right', 'left', 'o', '-', '+', '=', 'pageup',
                'pagedown', 'home', 'end', 'f11', 'b']:
        fig.canvas.key_press_event(key)
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]],
                'data')
    _fake_click(fig, ax, [-0.1, 0.9])  # click on y-label
    fig.canvas.key_press_event('escape')
    plt.close('all')
    epochs = _get_epochs()
    fig = ica.plot_sources(epochs, exclude=[0], title='Components')
    for key in ['down', 'up', 'right', 'left', 'o', '-', '+', '=', 'pageup',
                'pagedown', 'home', 'end', 'f11', 'b']:
        fig.canvas.key_press_event(key)
    # Test a click
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data')
    _fake_click(fig, ax, [-0.1, 0.9])  # click on y-label
    fig.canvas.key_press_event('escape')
    plt.close('all')


run_tests_if_main()