def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
warnings.simplefilter('always', UserWarning)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_topo(picked_evoked_delayed_ssp, layout, proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', 'inaxes')
func(event(inaxes=fig.axes[0]))
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
warnings.simplefilter('always', UserWarning)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(
evoked_delayed_ssp, ch_names)
fig = plot_topo(picked_evoked_delayed_ssp, layout, proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', 'inaxes')
func(event(inaxes=fig.axes[0]))
def test_plot_topo():
"""Plot ERP topography
"""
layout = Layout('Vectorview-all')
# Show topography
plot_topo(evoked, layout)
def test_plot_topo():
"""Test plotting of ERP topography
"""
layout = read_layout('Vectorview-all')
# Show topography
plot_topo(evoked, layout)
def test_plot_topo():
"""Plot ERP topography
"""
layout = Layout('Vectorview-all')
# Show topography
plot_topo(evoked, layout)
def test_plot_topo():
"""Test plotting of ERP topography
"""
import matplotlib.pyplot as plt
# Show topography
evoked = _get_epochs().average()
plot_evoked_topo(evoked) # should auto-find layout
warnings.simplefilter('always', UserWarning)
picked_evoked = evoked.pick_channels(evoked.ch_names[:3], copy=True)
picked_evoked_eeg = evoked.pick_types(meg=False, eeg=True, copy=True)
picked_evoked_eeg.pick_channels(picked_evoked_eeg.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp,
ch_names)
fig = plot_topo(picked_evoked_delayed_ssp, layout, proj='interactive')
func = _get_presser(fig)
event = namedtuple('Event', 'inaxes')
func(event(inaxes=fig.axes[0]))
params = dict(evokeds=[picked_evoked_delayed_ssp],
times=picked_evoked_delayed_ssp.times,
fig=fig, projs=picked_evoked_delayed_ssp.info['projs'])
bools = [True] * len(params['projs'])
_plot_update_evoked_topo(params, bools)
# should auto-generate layout
plot_evoked_topo(picked_evoked_eeg.copy(),
fig_background=np.zeros((4, 3, 3)), proj=True)
plt.close('all')
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
plot_topo(evoked, layout)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
plot_topo(evoked_delayed_ssp, layout, proj='interactive')
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=['y', 'b'])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
plot_topo(evoked_delayed_ssp, layout, proj='interactive')
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
evoked = _get_epochs().average()
plot_topo(evoked, layout)
warnings.simplefilter("always", UserWarning)
picked_evoked = pick_channels_evoked(evoked, evoked.ch_names[:3])
# test scaling
with warnings.catch_warnings(record=True):
for ylim in [dict(mag=[-600, 600]), None]:
plot_topo([picked_evoked] * 2, layout, ylim=ylim)
for evo in [evoked, [evoked, picked_evoked]]:
assert_raises(ValueError, plot_topo, evo, layout, color=["y", "b"])
evoked_delayed_ssp = _get_epochs_delayed_ssp().average()
ch_names = evoked_delayed_ssp.ch_names[:3] # make it faster
picked_evoked_delayed_ssp = pick_channels_evoked(evoked_delayed_ssp, ch_names)
plot_topo(picked_evoked_delayed_ssp, layout, proj="interactive")
""" ================================= Plot topographies for MEG sensors ================================= """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) print __doc__ import pylab as pl from mne import fiff from mne.viz import plot_topo from mne.datasets import sample data_path = sample.data_path() fname = data_path + '/MEG/sample/sample_audvis-ave.fif' # Reading evoked = fiff.read_evoked(fname, setno=0, baseline=(None, 0)) ############################################################################### # Show topography title = 'MNE sample data (condition : %s)' % evoked.comment plot_topo(evoked, title=title) pl.show()
================================= Plot topographies for MEG sensors ================================= """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) import matplotlib.pyplot as plt from mne import read_evokeds from mne.viz import plot_topo from mne.datasets import sample print(__doc__) data_path = sample.data_path() fname = data_path + '/MEG/sample/sample_audvis-ave.fif' # Reading condition = 'Left Auditory' evoked = read_evokeds(fname, condition=condition, baseline=(None, 0)) ############################################################################### # Show topography title = 'MNE sample data (condition : %s)' % evoked.comment plot_topo(evoked, title=title) plt.show()
# Syntax is `epochs[condition]` # In[40]: epochs_data = epochs['aud_l'].get_data() print(epochs_data.shape) # epochs_data is a 3D array of dimension (55 epochs, 365 channels, 106 time instants). # In[41]: evokeds = [epochs[k].average() for k in event_id] from mne.viz import plot_topo layout = mne.find_layout(epochs.info) plot_topo(evokeds, layout=layout, color=['blue', 'orange']); # ## Compute noise covariance # In[42]: noise_cov = mne.compute_covariance(epochs, tmax=0.) print(noise_cov.data.shape) # In[43]: fig = mne.viz.plot_cov(noise_cov, raw.info)
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()
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
plot_topo(evoked, layout)
def test_plot_topo():
"""Test plotting of ERP topography
"""
# Show topography
plot_topo(evoked, layout)
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
colors = 'yellow', 'green'
title = 'MNE sample data - left auditory and visual'
plot_topo(evokeds, color=colors, title=title)
conditions = [e.comment for e in evokeds]
for cond, col, pos in zip(conditions, colors, (0.025, 0.07)):
plt.figtext(0.775, pos, cond, color=col, fontsize=12)
plt.show()
def SensorStatsPlot(condcomb, ListSubj, colors):
#ListSubj = ('sd130343','cb130477' , 'rb130313', 'jm100109',
# 'sb120316', 'tk130502','lm130479' , 'ms130534', 'ma100253', 'sl130503',
# 'mb140004','mp140019' , 'dm130250', 'hr130504', 'wl130316', 'rl130571')
#ListSubj = ('sd130343','cb130477' , 'rb130313', 'jm100109',
# 'tk130502','lm130479' , 'ms130534', 'ma100253', 'sl130503',
# 'mb140004','mp140019' , 'dm130250', 'hr130504', 'rl130571')
#condcomb = ('QtPast' ,'QtPre','QtFut' )
#condcomb = ('QsWest' ,'QsPar','QsEast')
#ipython --pylab
import mne
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mne.viz import plot_topomap
from mne.stats import spatio_temporal_cluster_test
from mne.datasets import sample
from mne.channels import read_ch_connectivity
from scipy import stats as stats
from mne.viz import plot_topo
import os
os.chdir('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/SCRIPTS/MNE_PYTHON')
os.environ['MNE_ROOT'] = '/neurospin/local/mne'
wdir = "/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/"
# load FieldTrip neighbor definition to setup sensor connectivity
neighbor_file_mag = '/neurospin/local/fieldtrip/template/neighbours/neuromag306mag_neighb.mat' # mag
neighbor_file_grad = '/neurospin/local/fieldtrip/template/neighbours/neuromag306planar_neighb.mat' # grad
neighbor_file_eeg = '/neurospin/local/fieldtrip/template/neighbours/easycap64ch-avg_neighb.mat' # eeg
connectivity, ch_names = mne.channels.read_ch_connectivity(
neighbor_file_eeg, picks=range(60))
connectivity_mag, ch_names_mag = read_ch_connectivity(neighbor_file_mag)
connectivity_grad, ch_names_grad = read_ch_connectivity(neighbor_file_grad)
connectivity_eeg, ch_names_eeg = read_ch_connectivity(neighbor_file_eeg)
# evoked 0 to get the size of the matrix
fname0 = (wdir + ListSubj[0] + "/mne_python/MEEG_" + condcomb[0] + "_" +
ListSubj[0] + "-ave.fif")
evoked0 = mne.read_evokeds(fname0, condition=0, baseline=(-0.2, 0))
sensordatamat_meg_mag = np.empty(
[len(condcomb),
len(ListSubj), 102, evoked0.data.shape[1]])
sensordatamat_meg_grad = np.empty(
[len(condcomb),
len(ListSubj), 204, evoked0.data.shape[1]])
sensordatamat_meg_eeg = np.empty(
[len(condcomb),
len(ListSubj), 60, evoked0.data.shape[1]])
# define statistical threshold
p_threshold = 0.05
t_threshold = -stats.distributions.t.ppf(p_threshold / 2.,
len(ListSubj) - 1)
# compute grand averages
GDAVGmag, GDAVGgrad, GDAVGeeg = [], [], []
sensordatamat_meg_mag = np.empty(
(len(condcomb), len(ListSubj), 102, len(evoked0.times)))
sensordatamat_meg_grad = np.empty(
(len(condcomb), len(ListSubj), 204, len(evoked0.times)))
#sensordatamat_eeg = np.empty((len(condcomb),len(ListSubj),60 ,len(evoked0.times)))
for c in range(len(condcomb)):
evoked2plotmag, evoked2plotgrad, evoked2ploteeg = [], [], []
for i in range(len(ListSubj)):
fname_ave_meg = (wdir + ListSubj[i] + "/mne_python/MEEG_" +
condcomb[c] + "_" + ListSubj[i] + "-ave.fif")
tmp_evoked_meg = mne.read_evokeds(fname_ave_meg,
condition=0,
baseline=(-0.2, 0))
evoked2plotmag.append(tmp_evoked_meg.pick_types('mag'))
sensordatamat_meg_mag[c, i, ::, ::] = tmp_evoked_meg.data
tmp_evoked_meg = mne.read_evokeds(fname_ave_meg,
condition=0,
baseline=(-0.2, 0))
evoked2plotgrad.append(tmp_evoked_meg.pick_types('grad'))
sensordatamat_meg_grad[c, i, ::, ::] = tmp_evoked_meg.data
#tmp_evoked_meg = mne.read_evokeds(fname_ave_meg, condition=0, baseline=(-0.2, 0))
#evoked2ploteeg.append(tmp_evoked_meg.pick_types('eeg'))
#sensordatamat_eeg[c,i,::,::] = tmp_evoked_meg.data
GDAVGmag.append(mne.grand_average(evoked2plotmag))
GDAVGgrad.append(mne.grand_average(evoked2plotgrad))
#GDAVGeeg.append(mne.grand_average(evoked2ploteeg))
# plot topomaps of grand_averages
plot_topo(GDAVGmag, color=colors)
plt.savefig(
"/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/decoding_context_yousra/"
+ "_".join([str(cond) for cond in condcomb]) + "_GDAVG_mags")
plot_topo(GDAVGgrad, color=colors)
plt.savefig(
"/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/decoding_context_yousra/"
+ "_".join([str(cond) for cond in condcomb]) + "_GDAVG_grads")
times = np.arange(-0.1, 0.9, 0.05)
for c in range(len(condcomb)):
GDAVGmag[c].plot_topomap(times,
ch_type='mag',
vmin=-40,
vmax=40,
average=0.05)
plt.savefig(
"/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/decoding_context_yousra/"
+ str(condcomb[c]) + "_GDAVG_mags")
GDAVGgrad[c].plot_topomap(times,
ch_type='grad',
vmin=-10,
vmax=10,
average=0.05)
plt.savefig(
"/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/decoding_context_yousra/"
+ str(condcomb[c]) + "_GDAVG_grads")
allcond_meg_mag = [
np.transpose(x, (0, 2, 1)) for x in sensordatamat_meg_mag
]
allcond_meg_grad = [
np.transpose(x, (0, 2, 1)) for x in sensordatamat_meg_grad
]
###############################################################################
t_threshold = -stats.distributions.t.ppf(0 / 2, len(ListSubj) - 1)
T_obs, clusters, cluster_p_values, HO = spatio_temporal_cluster_test(
allcond_meg_mag[0::1],
n_permutations=1024,
threshold=t_threshold,
tail=0,
n_jobs=4,
connectivity=connectivity_mag)
t_threshold = -stats.distributions.t.ppf(0 / 2, len(ListSubj) - 1)
T_obs, clusters, cluster_p_values, HO = spatio_temporal_cluster_test(
allcond_meg_grad,
n_permutations=1024,
threshold=t_threshold,
tail=0,
n_jobs=4,
connectivity=connectivity_grad)
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()
=================================
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print __doc__
import pylab as pl
from mne import fiff
from mne.layouts import read_layout
from mne.viz import plot_topo
from mne.datasets import sample
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
evoked = fiff.read_evoked(fname, setno=0, baseline=(None, 0))
layout = read_layout('Vectorview-all')
###############################################################################
# Show topography
title = 'MNE sample data (condition : %s)' % evoked.comment
plot_topo(evoked, layout, title=title)
pl.show()
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 = mne.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
colors = 'yellow', 'green'
title = 'MNE sample data - left auditory and visual'
plot_topo(evokeds, color=colors, title=title)
conditions = [e.comment for e in evokeds]
for cond, col, pos in zip(conditions, colors, (0.025, 0.07)):
plt.figtext(0.775, pos, cond, color=col, fontsize=12)
plt.show()
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print __doc__
import pylab as pl
from mne import fiff
from mne.layouts import Layout
from mne.viz import plot_topo
from mne.datasets import sample
data_path = sample.data_path('.')
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
evoked = fiff.read_evoked(fname, setno=0, baseline=(None, 0))
layout = Layout('Vectorview-all')
###############################################################################
# Show topography
plot_topo(evoked, layout)
title = 'MNE sample data (condition : %s)' % evoked.comment
pl.figtext(0.03, 0.93, title, color='w', fontsize=18)
pl.show()