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# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Cathy Nangini <cnangini@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: Simplified BSD
import os.path as op
import numpy as np
from numpy.testing import assert_allclose
import pytest
import mne
from mne import (read_events, Epochs, read_cov, compute_covariance,
make_fixed_length_events)
from mne.io import read_raw_fif
from mne.utils import run_tests_if_main, catch_logging
from mne.viz.evoked import plot_compare_evokeds
from mne.viz.utils import _fake_click
from mne.stats import _parametric_ci
from mne.datasets import testing
# 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')
raw_sss_fname = op.join(base_dir, 'test_chpi_raw_sss.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, 0.1
# Use a subset of channels for plotting speed
# make sure we have a magnetometer and a pair of grad pairs for topomap.
picks = [0, 1, 2, 3, 4, 6, 7, 61, 122, 183, 244, 305]
sel = [0, 7]
def _get_epochs():
"""Get epochs."""
raw = read_raw_fif(raw_fname)
raw.add_proj([], remove_existing=True)
events = read_events(event_name)
epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
decim=10, verbose='error')
epochs.info['bads'] = [epochs.ch_names[-1]]
epochs.info.normalize_proj()
return epochs
def _get_epochs_delayed_ssp():
"""Get epochs with delayed SSP."""
raw = read_raw_fif(raw_fname)
events = read_events(event_name)
reject = dict(mag=4e-12)
epochs_delayed_ssp = Epochs(raw, events[:10], event_id, tmin, tmax,
picks=picks, proj='delayed', reject=reject,
verbose='error')
epochs_delayed_ssp.info.normalize_proj()
return epochs_delayed_ssp
def test_plot_evoked_cov():
"""Test plot_evoked with noise_cov."""
return
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
cov = read_cov(cov_fname)
cov['projs'] = [] # avoid warnings
evoked.plot(noise_cov=cov, time_unit='s')
with pytest.raises(TypeError, match='Covariance'):
evoked.plot(noise_cov=1., time_unit='s')
with pytest.raises(IOError, match='No such file'):
evoked.plot(noise_cov='nonexistent-cov.fif', time_unit='s')
raw = read_raw_fif(raw_sss_fname)
events = make_fixed_length_events(raw)
epochs = Epochs(raw, events, picks=picks)
cov = compute_covariance(epochs)
evoked_sss = epochs.average()
with pytest.warns(RuntimeWarning, match='relative scaling'):
evoked_sss.plot(noise_cov=cov, time_unit='s')
plt.close('all')
@pytest.mark.slowtest
def test_plot_evoked():
"""Test evoked.plot."""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
fig = evoked.plot(proj=True, hline=[1], exclude=[], window_title='foo',
time_unit='s')
# 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 & spatial_colors & zorder
evoked.plot(exclude='bads', time_unit='s')
# test selective updating of dict keys is working.
evoked.plot(hline=[1], units=dict(mag='femto foo'), time_unit='s')
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
evoked_delayed_ssp.plot(proj='interactive', time_unit='s')
evoked_delayed_ssp.apply_proj()
pytest.raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', time_unit='s')
evoked_delayed_ssp.info['projs'] = []
pytest.raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', time_unit='s')
pytest.raises(RuntimeError, evoked_delayed_ssp.plot,
proj='interactive', axes='foo', time_unit='s')
plt.close('all')
# test GFP only
evoked.plot(gfp='only', time_unit='s')
pytest.raises(ValueError, evoked.plot, gfp='foo', time_unit='s')
# plot with bad channels excluded, spatial_colors, zorder & pos. layout
evoked.rename_channels({'MEG 0133': 'MEG 0000'})
evoked.plot(exclude=evoked.info['bads'], spatial_colors=True, gfp=True,
zorder='std', time_unit='s')
evoked.plot(exclude=[], spatial_colors=True, zorder='unsorted',
time_unit='s')
pytest.raises(TypeError, evoked.plot, zorder='asdf', time_unit='s')
plt.close('all')
evoked.plot_sensors() # Test plot_sensors
plt.close('all')
evoked.pick_channels(evoked.ch_names[:4])
with catch_logging() as log_file:
evoked.plot(verbose=True, time_unit='s')
assert 'Need more than one' in log_file.getvalue()
def test_plot_evoked_image():
"""Test plot_evoked_image."""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
evoked.plot_image(proj=True, time_unit='ms')
# fail nicely on NaN
evoked_nan = evoked.copy()
evoked_nan.data[:, 0] = np.nan
pytest.raises(ValueError, evoked_nan.plot)
with np.errstate(invalid='ignore'):
pytest.raises(ValueError, evoked_nan.plot_image)
pytest.raises(ValueError, evoked_nan.plot_joint)
# test mask
evoked.plot_image(picks=[1, 2], mask=evoked.data > 0, time_unit='s')
evoked.plot_image(picks=[1, 2], mask_cmap=None, colorbar=False,
mask=np.ones(evoked.data.shape).astype(bool),
time_unit='s')
with pytest.warns(RuntimeWarning, match='not adding contour'):
evoked.plot_image(picks=[1, 2], mask=None, mask_style="both",
time_unit='s')
with pytest.raises(ValueError, match='must have the same shape'):
evoked.plot_image(mask=evoked.data[1:, 1:] > 0, time_unit='s')
# plot with bad channels excluded
evoked.plot_image(exclude='bads', cmap='interactive', time_unit='s')
plt.close('all')
with pytest.raises(ValueError, match='not unique'):
evoked.plot_image(picks=[0, 0], time_unit='s') # duplicates
ch_names = evoked.ch_names[3:5]
picks = [evoked.ch_names.index(ch) for ch in ch_names]
evoked.plot_image(show_names="all", time_unit='s', picks=picks)
yticklabels = plt.gca().get_yticklabels()
for tick_target, tick_observed in zip(ch_names, yticklabels):
assert tick_target in str(tick_observed)
evoked.plot_image(show_names=True, time_unit='s')
# test groupby
evoked.plot_image(group_by=dict(sel=sel), axes=dict(sel=plt.axes()))
plt.close('all')
for group_by, axes in (("something", dict()), (dict(), "something")):
pytest.raises(ValueError, evoked.plot_image, group_by=group_by,
axes=axes)
def test_plot_white():
"""Test plot_white."""
import matplotlib.pyplot as plt
cov = read_cov(cov_fname)
cov['method'] = 'empirical'
cov['projs'] = [] # avoid warnings
evoked = _get_epochs().average()
# test rank param.
evoked.plot_white(cov, rank={'mag': 101, 'grad': 201}, time_unit='s')
evoked.plot_white(cov, rank={'mag': 101}, time_unit='s') # test rank param
evoked.plot_white(cov, rank={'grad': 201}, time_unit='s')
pytest.raises(
ValueError, evoked.plot_white, cov,
rank={'mag': 101, 'grad': 201, 'meg': 306}, time_unit='s')
pytest.raises(
ValueError, evoked.plot_white, cov, rank={'meg': 306}, time_unit='s')
evoked.plot_white([cov, cov], time_unit='s')
plt.close('all')
# Hack to test plotting of maxfiltered data
evoked_sss = evoked.copy()
sss = dict(sss_info=dict(in_order=80, components=np.arange(80)))
evoked_sss.info['proc_history'] = [dict(max_info=sss)]
evoked_sss.plot_white(cov, rank={'meg': 64}, time_unit='s')
pytest.raises(
ValueError, evoked_sss.plot_white, cov, rank={'grad': 201},
time_unit='s')
evoked_sss.plot_white(cov, time_unit='s')
plt.close('all')
def test_plot_compare_evokeds():
"""Test plot_compare_evokeds."""
import matplotlib.pyplot as plt
rng = np.random.RandomState(0)
evoked = _get_epochs().average()
# plot_compare_evokeds: test condition contrast, CI, color assignment
fig = plot_compare_evokeds(evoked.copy().pick_types(meg='mag'),
show_sensors=True)
assert len(fig.axes) == 2
plot_compare_evokeds(
evoked.copy().pick_types(meg='grad'), picks=[1, 2],
show_sensors="upper right", show_legend="upper left")
evokeds = [evoked.copy() for _ in range(10)]
for evoked in evokeds:
evoked.data += (rng.randn(*evoked.data.shape) *
np.std(evoked.data, axis=-1, keepdims=True))
for picks in ([0], [1], [2], [0, 2], [1, 2], [0, 1, 2],):
figs = plot_compare_evokeds([evokeds], picks=picks, ci=0.95)
if not isinstance(figs, list):
figs = [figs]
for fig in figs:
ext = fig.axes[0].collections[0].get_paths()[0].get_extents()
xs, ylim = ext.get_points().T
assert_allclose(xs, evoked.times[[0, -1]])
line = fig.axes[0].lines[0]
xs = line.get_xdata()
assert_allclose(xs, evoked.times)
ys = line.get_ydata()
assert (ys < ylim[1]).all()
assert (ys > ylim[0]).all()
plt.close('all')
evoked.rename_channels({'MEG 2142': "MEG 1642"})
assert len(plot_compare_evokeds(evoked)) == 2
colors = dict(red='r', blue='b')
linestyles = dict(red='--', blue='-')
red, blue = evoked.copy(), evoked.copy()
red.data *= 1.1
blue.data *= 0.9
plot_compare_evokeds([red, blue], picks=3) # list of evokeds
plot_compare_evokeds([red, blue], picks=3, truncate_yaxis=True,
vlines=[]) # also testing empty vlines here
plot_compare_evokeds([[red, evoked], [blue, evoked]],
picks=3) # list of lists
# test picking & plotting grads
contrast = dict()
contrast["red/stim"] = list((evoked.copy(), red))
contrast["blue/stim"] = list((evoked.copy(), blue))
# test a bunch of params at once
for evokeds_ in (evoked.copy().pick_types(meg='mag'), contrast,
[red, blue], [[red, evoked], [blue, evoked]]):
plot_compare_evokeds(evokeds_, picks=0, ci=True) # also tests CI
plt.close('all')
# test styling + a bunch of other params at once
colors, linestyles = dict(red='r', blue='b'), dict(red='--', blue='-')
plot_compare_evokeds(contrast, colors=colors, linestyles=linestyles,
picks=[0, 2], vlines=[.01, -.04], invert_y=True,
truncate_yaxis=False, ylim=dict(mag=(-10, 10)),
styles={"red/stim": {"linewidth": 1}},
show_sensors=True)
# various bad styles
params = [dict(picks=3, colors=dict(fake=1)),
dict(picks=3, styles=dict(fake=1)), dict(picks=3, gfp=True),
dict(picks=3, show_sensors="a"),
dict(colors=dict(red=10., blue=-2))]
for param in params:
pytest.raises(ValueError, plot_compare_evokeds, evoked, **param)
pytest.raises(TypeError, plot_compare_evokeds, evoked, picks='str')
pytest.raises(TypeError, plot_compare_evokeds, evoked, vlines='x')
plt.close('all')
# `evoked` must contain Evokeds
pytest.raises(TypeError, plot_compare_evokeds, [[1, 2], [3, 4]])
# `ci` must be float or None
pytest.raises(TypeError, plot_compare_evokeds, contrast, ci='err')
# test all-positive ylim
contrast["red/stim"], contrast["blue/stim"] = red, blue
plot_compare_evokeds(contrast, picks=[0], colors=['r', 'b'],
ylim=dict(mag=(1, 10)), ci=_parametric_ci,
truncate_yaxis='max_ticks', show_sensors=False,
show_legend=False)
# sequential colors
evokeds = (evoked, blue, red)
contrasts = {"a{}/b".format(ii): ev for ii, ev in
enumerate(evokeds)}
colors = {"a" + str(ii): ii for ii, _ in enumerate(evokeds)}
contrasts["a1/c"] = evoked.copy()
for split in (True, False):
for linestyles in (["-"], {"b": "-", "c": ":"}):
plot_compare_evokeds(
contrasts, colors=colors, picks=[0], cmap='Reds',
split_legend=split, linestyles=linestyles,
ci=False, show_sensors=False)
colors = {"a" + str(ii): ii / len(evokeds)
for ii, _ in enumerate(evokeds)}
plot_compare_evokeds(
contrasts, colors=colors, picks=[0], cmap='Reds',
split_legend=split, linestyles=linestyles, ci=False,
show_sensors=False)
red.info["chs"][0]["loc"][:2] = 0 # test plotting channel at zero
plot_compare_evokeds(red, picks=[0],
ci=lambda x: [x.std(axis=0), -x.std(axis=0)])
plot_compare_evokeds([red, blue], picks=[0], cmap="summer", ci=None,
split_legend=None)
plot_compare_evokeds([red, blue], cmap=None, split_legend=True)
pytest.raises(ValueError, plot_compare_evokeds, [red] * 20)
pytest.raises(ValueError, plot_compare_evokeds, contrasts,
cmap='summer')
plt.close('all')
@testing.requires_testing_data
def test_plot_ctf():
"""Test plotting of CTF evoked."""
ctf_dir = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(ctf_dir, 'testdata_ctf.ds')
raw = mne.io.read_raw_ctf(raw_fname, preload=True)
events = np.array([[200, 0, 1]])
event_id = 1
tmin, tmax = -0.1, 0.5 # start and end of an epoch in sec.
picks = mne.pick_types(raw.info, meg=True, stim=True, eog=True,
ref_meg=True, exclude='bads')[::20]
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, preload=True, decim=10, verbose='error')
evoked = epochs.average()
evoked.plot_joint(times=[0.1])
mne.viz.plot_compare_evokeds([evoked, evoked])
run_tests_if_main()
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