def test_io_layout():
"""Test IO with .lout files"""
layout = read_layout('Vectorview-all', scale=False)
layout.save('foobar.lout')
layout_read = read_layout('foobar.lout', path='./', scale=False)
assert_array_almost_equal(layout.pos, layout_read.pos, decimal=2)
assert_true(layout.names, layout_read.names)
def test_make_eeg_layout():
""" Test creation of EEG layout """
tmp_name = 'foo'
lout_name = 'test_raw'
lout_orig = read_layout(kind=lout_name, path=lout_path)
info = Raw(fif_fname).info
layout = make_eeg_layout(info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir, scale=False)
assert_array_equal(lout_new.kind, tmp_name)
assert_allclose(layout.pos, lout_new.pos, atol=0.1)
assert_array_equal(lout_orig.names, lout_new.names)
# Test input validation
assert_raises(ValueError, make_eeg_layout, info, radius=-0.1)
assert_raises(ValueError, make_eeg_layout, info, radius=0.6)
assert_raises(ValueError, make_eeg_layout, info, width=-0.1)
assert_raises(ValueError, make_eeg_layout, info, width=1.1)
assert_raises(ValueError, make_eeg_layout, info, height=-0.1)
assert_raises(ValueError, make_eeg_layout, info, height=1.1)
bad_info = info.copy()
bad_info['dig'] = None
assert_raises(RuntimeError, make_eeg_layout, bad_info)
bad_info['dig'] = []
assert_raises(RuntimeError, make_eeg_layout, bad_info)
def test_make_eeg_layout():
""" Test creation of EEG layout """
tempdir = _TempDir()
tmp_name = 'foo'
lout_name = 'test_raw'
lout_orig = read_layout(kind=lout_name, path=lout_path)
info = Raw(fif_fname).info
layout = make_eeg_layout(info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir, scale=False)
assert_array_equal(lout_new.kind, tmp_name)
assert_allclose(layout.pos, lout_new.pos, atol=0.1)
assert_array_equal(lout_orig.names, lout_new.names)
# Test input validation
assert_raises(ValueError, make_eeg_layout, info, radius=-0.1)
assert_raises(ValueError, make_eeg_layout, info, radius=0.6)
assert_raises(ValueError, make_eeg_layout, info, width=-0.1)
assert_raises(ValueError, make_eeg_layout, info, width=1.1)
assert_raises(ValueError, make_eeg_layout, info, height=-0.1)
assert_raises(ValueError, make_eeg_layout, info, height=1.1)
bad_info = info.copy()
bad_info['dig'] = None
assert_raises(RuntimeError, make_eeg_layout, bad_info)
bad_info['dig'] = []
assert_raises(RuntimeError, make_eeg_layout, bad_info)
def test_io_layout_lay():
"""Test IO with .lay files"""
layout = read_layout('CTF151', scale=False)
layout.save(op.join(tempdir, 'foobar.lay'))
layout_read = read_layout(op.join(tempdir, 'foobar.lay'), path='./',
scale=False)
assert_array_almost_equal(layout.pos, layout_read.pos, decimal=2)
assert_true(layout.names, layout_read.names)
def test_io_layout_lay():
"""Test IO with .lay files"""
layout = read_layout('CTF151', scale=False)
layout.save(op.join(tempdir, 'foobar.lay'))
layout_read = read_layout(op.join(tempdir, 'foobar.lay'), path='./',
scale=False)
assert_array_almost_equal(layout.pos, layout_read.pos, decimal=2)
assert_true(layout.names, layout_read.names)
def test_io_layout_lout():
"""Test IO with .lout files"""
layout = read_layout('Vectorview-all', scale=False)
layout.save(op.join(tempdir, 'foobar.lout'))
layout_read = read_layout(op.join(tempdir, 'foobar.lout'), path='./',
scale=False)
assert_array_almost_equal(layout.pos, layout_read.pos, decimal=2)
assert_true(layout.names, layout_read.names)
print(layout) # test repr
def test_io_layout_lout():
"""Test IO with .lout files"""
layout = read_layout('Vectorview-all', scale=False)
layout.save(op.join(tempdir, 'foobar.lout'))
layout_read = read_layout(op.join(tempdir, 'foobar.lout'), path='./',
scale=False)
assert_array_almost_equal(layout.pos, layout_read.pos, decimal=2)
assert_true(layout.names, layout_read.names)
print(layout) # test repr
def test_make_grid_layout():
""" Test creation of grid layout """
tmp_name = 'bar'
lout_name = 'test_ica'
lout_orig = read_layout(kind=lout_name, path=lout_path)
layout = make_grid_layout(test_info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir)
assert_array_equal(lout_new.kind, tmp_name)
assert_array_equal(lout_orig.pos, lout_new.pos)
assert_array_equal(lout_orig.names, lout_new.names)
def test_make_eeg_layout():
""" Test creation of EEG layout """
tmp_name = 'foo'
lout_name = 'test_raw'
lout_orig = read_layout(kind=lout_name, path=lout_path)
layout = make_eeg_layout(Raw(fif_fname).info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir)
assert_array_equal(lout_new.kind, tmp_name)
assert_array_equal(lout_orig.pos, lout_new.pos)
assert_array_equal(lout_orig.names, lout_new.names)
def test_make_eeg_layout():
""" Test creation of EEG layout """
tmp_name = 'foo'
lout_name = 'test_raw'
lout_orig = read_layout(kind=lout_name, path=lout_path)
layout = make_eeg_layout(Raw(fif_fname).info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir)
assert_array_equal(lout_new.kind, tmp_name)
assert_array_equal(lout_orig.pos, lout_new.pos)
assert_array_equal(lout_orig.names, lout_new.names)
def test_make_grid_layout():
""" Test creation of grid layout """
tmp_name = 'bar'
lout_name = 'test_ica'
lout_orig = read_layout(kind=lout_name, path=lout_path)
layout = make_grid_layout(test_info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir)
assert_array_equal(lout_new.kind, tmp_name)
assert_array_equal(lout_orig.pos, lout_new.pos)
assert_array_equal(lout_orig.names, lout_new.names)
def test_plot_topo():
"""Test plotting of ERP topography
"""
layout = read_layout('Vectorview-all')
# Show topography
plot_topo(evoked, layout)
def test_make_grid_layout():
""" Test creation of grid layout """
tmp_name = 'bar'
lout_name = 'test_ica'
lout_orig = read_layout(kind=lout_name, path=lout_path)
layout = make_grid_layout(test_info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir)
assert_array_equal(lout_new.kind, tmp_name)
assert_array_equal(lout_orig.pos, lout_new.pos)
assert_array_equal(lout_orig.names, lout_new.names)
# Test creating grid layout with specified number of columns
layout = make_grid_layout(test_info, n_col=2)
# Vertical positions should be equal
assert_true(layout.pos[0, 1] == layout.pos[1, 1])
# Horizontal positions should be unequal
assert_true(layout.pos[0, 0] != layout.pos[1, 0])
# Box sizes should be equal
assert_array_equal(layout.pos[0, 3:], layout.pos[1, 3:])
def test_make_grid_layout():
""" Test creation of grid layout """
tempdir = _TempDir()
tmp_name = 'bar'
lout_name = 'test_ica'
lout_orig = read_layout(kind=lout_name, path=lout_path)
layout = make_grid_layout(test_info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir)
assert_array_equal(lout_new.kind, tmp_name)
assert_array_equal(lout_orig.pos, lout_new.pos)
assert_array_equal(lout_orig.names, lout_new.names)
# Test creating grid layout with specified number of columns
layout = make_grid_layout(test_info, n_col=2)
# Vertical positions should be equal
assert_true(layout.pos[0, 1] == layout.pos[1, 1])
# Horizontal positions should be unequal
assert_true(layout.pos[0, 0] != layout.pos[1, 0])
# Box sizes should be equal
assert_array_equal(layout.pos[0, 3:], layout.pos[1, 3:])
T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=2)
significant_sensors = picks[p_values <= 0.05]
significant_sensors_names = [raw.info["ch_names"][k] for k in significant_sensors]
print "Number of significant sensors : %d" % len(significant_sensors)
print "Sensors names : %s" % significant_sensors_names
###############################################################################
# View location of significantly active sensors
import pylab as pl
# load sensor layout
from mne.layouts import read_layout
layout = read_layout("Vectorview-grad")
# Extract mask and indices of active sensors in layout
idx_of_sensors = [layout.names.index(name) for name in significant_sensors_names if name in layout.names]
mask_significant_sensors = np.zeros(len(layout.pos), dtype=np.bool)
mask_significant_sensors[idx_of_sensors] = True
mask_non_significant_sensors = mask_significant_sensors == False
# plot it
pl.figure(facecolor="k")
pl.axis("off")
pl.axis("tight")
pl.scatter(layout.pos[mask_significant_sensors, 0], layout.pos[mask_significant_sensors, 1], s=50, c="r")
pl.scatter(layout.pos[mask_non_significant_sensors, 0], layout.pos[mask_non_significant_sensors, 1], c="w")
title = "MNE sample data (Left auditory between 40 and 60 ms)"
pl.figtext(0.03, 0.93, title, color="w", fontsize=18)
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
print("Classification accuracy: %f / Chance level: %f" %
(np.mean(scores), class_balance))
# plot CSP patterns estimated on full data for visualization
csp.fit_transform(epochs_data, labels)
evoked = epochs.average()
evoked.data = csp.patterns_.T
evoked.times = np.arange(evoked.data.shape[0])
layout = read_layout('EEG1005')
evoked.plot_topomap(times=[0, 1, 2, 61, 62, 63],
ch_type='eeg',
layout=layout,
scale_time=1,
time_format='%i',
scale=1,
unit='Patterns (AU)',
size=1.5)
###############################################################################
# Look at performance over time
sfreq = raw.info['sfreq']
w_length = int(sfreq * 0.5) # running classifier: window length
w_step = int(sfreq * 0.1) # running classifier: window step size
picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
eog=True,
include=include,
exclude='bads')
# Create epochs including different events
epochs = mne.Epochs(raw,
events,
dict(audio_l=1, visual_r=3),
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in 'audio_l', 'visual_r']
###############################################################################
# Show topography for two different conditions
layout = read_layout('Vectorview-all.lout')
pl.close('all')
title = 'MNE sample data - left auditory and visual'
plot_topo(evokeds, layout, color=['y', 'g'], title=title)
pl.show()
T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=2)
significant_sensors = picks[p_values <= 0.05]
significant_sensors_names = [raw.info['ch_names'][k]
for k in significant_sensors]
print "Number of significant sensors : %d" % len(significant_sensors)
print "Sensors names : %s" % significant_sensors_names
###############################################################################
# View location of significantly active sensors
import matplotlib.pyplot as plt
# load sensor layout
from mne.layouts import read_layout
layout = read_layout('Vectorview-grad')
# Extract mask and indices of active sensors in layout
idx_of_sensors = [layout.names.index(name)
for name in significant_sensors_names
if name in layout.names]
mask_significant_sensors = np.zeros(len(layout.pos), dtype=np.bool)
mask_significant_sensors[idx_of_sensors] = True
mask_non_significant_sensors = mask_significant_sensors == False
# plot it
plt.figure(figsize=(5, 3.5), facecolor='k')
plt.axis('off')
plt.scatter(layout.pos[mask_significant_sensors, 0],
layout.pos[mask_significant_sensors, 1], s=50, c='r')
plt.scatter(layout.pos[mask_non_significant_sensors, 0],
raw = Raw(raw_fname)
events = mne.read_events(event_fname)
# Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
include = [] # or stim channels ['STI 014']
# bad channels in raw.info['bads'] will be automatically excluded
# Set up amplitude-peak rejection values for MEG channels
reject = dict(grad=4000e-13, mag=4e-12)
# pick MEG channels
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
# Create epochs including different events
epochs = mne.Epochs(raw, events, dict(audio_l=1, visual_r=3), tmin, tmax,
picks=picks, baseline=(None, 0), reject=reject)
# Generate list of evoked objects from conditions names
evokeds = [epochs[name].average() for name in 'audio_l', 'visual_r']
###############################################################################
# Show topography for two different conditions
layout = read_layout('Vectorview-all.lout')
pl.close('all')
title = 'MNE sample data - left auditory and visual'
plot_topo(evokeds, layout, color=['y', 'g'], title=title)
pl.show()
T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=2)
significant_sensors = picks[p_values <= 0.05]
significant_sensors_names = [raw.info['ch_names'][k]
for k in significant_sensors]
print "Number of significant sensors : %d" % len(significant_sensors)
print "Sensors names : %s" % significant_sensors_names
###############################################################################
# View location of significantly active sensors
import pylab as pl
# load sensor layout
from mne.layouts import read_layout
layout = read_layout('Vectorview-grad')
# Extract mask and indices of active sensors in layout
idx_of_sensors = [layout.names.index(name)
for name in significant_sensors_names
if name in layout.names]
mask_significant_sensors = np.zeros(len(layout.pos), dtype=np.bool)
mask_significant_sensors[idx_of_sensors] = True
mask_non_significant_sensors = mask_significant_sensors == False
# plot it
pl.figure(facecolor='k')
pl.axis('off')
pl.axis('tight')
pl.scatter(layout.pos[mask_significant_sensors, 0],
layout.pos[mask_significant_sensors, 1], s=50, c='r')
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
print("Classification accuracy: %f / Chance level: %f" % (np.mean(scores),
class_balance))
# plot CSP patterns estimated on full data for visualization
csp.fit_transform(epochs_data, labels)
evoked = epochs.average()
evoked.data = csp.patterns_.T
evoked.times = np.arange(evoked.data.shape[0])
layout = read_layout('EEG1005')
evoked.plot_topomap(times=[0, 1, 2, 61, 62, 63], ch_type='eeg', layout=layout,
scale_time=1, time_format='%i', scale=1,
unit='Patterns (AU)', size=1.5)
###############################################################################
# Look at performance over time
sfreq = raw.info['sfreq']
w_length = int(sfreq * 0.5) # running classifier: window length
w_step = int(sfreq * 0.1) # running classifier: window step size
w_start = np.arange(0, epochs_data.shape[2] - w_length, w_step)
scores_windows = []
for train_idx, test_idx in cv:
###############################################################################
# Set parameters
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
event_id, tmin, tmax = 1, -0.2, 0.5
# Setup for reading the raw data
raw = fiff.Raw(raw_fname)
events = mne.read_events(event_fname)
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
picks = fiff.pick_types(raw.info, meg='grad', eeg=False, stim=True, eog=True,
exclude=raw.info['bads'])
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=(None, 0), preload=True,
reject=dict(grad=4000e-13, eog=150e-6))
###############################################################################
# Show event related fields images
layout = read_layout('Vectorview-all')
mne.viz.plot_topo_image_epochs(epochs, layout, sigma=0.5, vmin=-200, vmax=200,
colorbar=True)
title = 'ERF images - MNE sample data'
pl.figtext(0.03, 0.9, title, color='w', fontsize=19)
pl.show()
if not lacks_mayavi:
mlab.options.backend = "test"
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")
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, -0.2, 0.5
n_chan = 15
layout = read_layout("Vectorview-all")
def _fake_click(fig, ax, point, xform="ax"):
"""Helper to fake a click at a relative point within axes"""
if xform == "ax":
x, y = ax.transAxes.transform_point(point)
elif xform == "data":
x, y = ax.transData.transform_point(point)
else:
raise ValueError("unknown transform")
try:
fig.canvas.button_press_event(x, y, 1, False, None)
except: # for old MPL
fig.canvas.button_press_event(x, y, 1, False)
clf = Pipeline([("CSP", csp), ("SVC", svc)])
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1.0 - class_balance)
print("Classification accuracy: %f / Chance level: %f" % (np.mean(scores), class_balance))
# plot CSP patterns estimated on full data for visualization
csp.fit_transform(epochs_data, labels)
evoked = epochs.average()
evoked.data = csp.patterns_.T
evoked.times = np.arange(evoked.data.shape[0])
layout = read_layout("EEG1005")
evoked.plot_topomap(
times=[0, 1, 2, 61, 62, 63],
ch_type="eeg",
layout=layout,
scale_time=1,
time_format="%i",
scale=1,
unit="Patterns (AU)",
size=1.5,
)
###############################################################################
# Look at performance over time
sfreq = raw.info["sfreq"]
if not lacks_mayavi:
mlab.options.backend = 'test'
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')
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, -0.2, 0.5
n_chan = 15
layout = read_layout('Vectorview-all')
def _fake_click(fig, ax, point, xform='ax'):
"""Helper to fake a click at a relative point within axes"""
if xform == 'ax':
x, y = ax.transAxes.transform_point(point)
elif xform == 'data':
x, y = ax.transData.transform_point(point)
else:
raise ValueError('unknown transform')
try:
fig.canvas.button_press_event(x, y, 1, False, None)
except: # for old MPL
fig.canvas.button_press_event(x, y, 1, False)
mean = values.mean(0)
values[np.diag_indices(values.shape[0])] = mean
# set a common range for colors:
if vmin is None: vmin = values.min()
if vmax is None: vmax = values.max()
for i in range(values.shape[0]):
topography(values[i], x*zoom_factor + x[i], y*zoom_factor + y[i], nx=nx, ny=nx, plotsensors=False, vmin=vmin, vmax=vmax, colorbar=False)
if plotsensors:
plt.plot(x[i]*zoom_factor + x[i], y[i]*zoom_factor + y[i], 'k.', markersize=8)
if colorbar: plt.colorbar()
if __name__ == '__main__':
from mne.layouts import read_layout
layout = read_layout('Vectorview-mag.lout')
x = layout.pos[:,0]
y = layout.pos[:,1]
# generate some values:
# value = np.sin((layout.pos[:,:2]**2).sum(1)*10)
value = np.random.rand(x.size)
plt.figure()
topography(value, x, y)
plt.figure()
values = np.random.rand(x.size, x.size)
hypertopography(values, x, y)
plt.show()