def save_evoked_to_blender(mri_subject, events, args, evoked=None):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = np.array(evo[0].ch_names)
dt = np.diff(evo[0].times[:2])[0]
data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked[0].data
data = data[..., np.newaxis]
ch_names = np.array(evoked[0].ch_names)
dt = np.diff(evoked[0].times[:2])[0]
if 'Event' in ch_names:
event_ind = np.where(ch_names == 'Event')[0]
ch_names = np.delete(ch_names, event_ind)
data = np.delete(data, event_ind, 0)
data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
if args.normalize_evoked:
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt,
minmax=(-max_abs, max_abs))
return True
def save_evoked_to_blender(mri_subject, events, args, evoked=None):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = np.array(evo[0].ch_names)
dt = np.diff(evo[0].times[:2])[0]
data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked[0].data
data = data[..., np.newaxis]
ch_names = np.array(evoked[0].ch_names)
dt = np.diff(evoked[0].times[:2])[0]
if 'Event' in ch_names:
event_ind = np.where(ch_names == 'Event')[0]
ch_names = np.delete(ch_names, event_ind)
data = np.delete(data, event_ind, 0)
if args.normalize_evoked:
data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt)
return True
def save_evoked_to_blender(mri_subject,
events,
evoked=None,
norm_by_percentile=True,
norm_percs=(1, 99)):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = evo[0].ch_names
data = np.zeros(
(evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked.data
data_max, data_min = utils.get_data_max_min(data, norm_by_percentile,
norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'),
names=ch_names,
conditions=list(events.keys()))
return True
def calc_minmax(mri_subject, args):
#todo: merge into save_evoked_to_blender
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
data = np.load(op.join(fol, 'eeg_data.npy'))
data_max, data_min = utils.get_data_max_min(np.diff(data), args.norm_by_percentile, args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
np.save(op.join(fol, 'eeg_data_minmax.npy'), [-max_abs, max_abs])
return op.isfile(op.join(fol, 'eeg_data_minmax.npy'))
def plot_color_bar(ax, graph_data, cb_title, data_type='', cb_min_max_eq=True, color_map='jet'):
if data_type == '':
return
import matplotlib as mpl
data_max = max([max(v) for v in graph_data[data_type].values()])
data_min = min([min(v) for v in graph_data[data_type].values()])
if cb_min_max_eq:
data_max_min = utils.get_max_abs(data_max, data_min)
vmin, vmax = -data_max_min, data_max_min
else:
vmin, vmax = data_min, data_max
# cmap = color_map # mpl.cm.jet
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cb = mpl.colorbar.ColorbarBase(ax, cmap=color_map, norm=norm, orientation='vertical')#, ticks=color_map_bounds)
cb.set_label(cb_title)
def plot_color_bar(ax,
graph_data,
cb_title,
data_type='',
cb_min_max_eq=True,
color_map='jet'):
if data_type == '':
return
import matplotlib as mpl
data_max = max([max(v) for v in graph_data[data_type].values()])
data_min = min([min(v) for v in graph_data[data_type].values()])
if cb_min_max_eq:
data_max_min = utils.get_max_abs(data_max, data_min)
vmin, vmax = -data_max_min, data_max_min
else:
vmin, vmax = data_min, data_max
# cmap = color_map # mpl.cm.jet
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cb = mpl.colorbar.ColorbarBase(
ax, cmap=color_map, norm=norm,
orientation='vertical') #, ticks=color_map_bounds)
cb.set_label(cb_title)
def plot_color_bar(ax,
graph_data,
cb_title,
data_type='',
cb_min_max_eq=True,
cb_norm_percs=None,
color_map='jet',
position='right'):
if data_type == '':
return
import matplotlib as mpl
if cb_norm_percs is None:
data_max = max([max(v) for v in graph_data[data_type].values()])
data_min = min([min(v) for v in graph_data[data_type].values()])
else:
data_max = max([
np.percentile(v, cb_norm_percs[1])
for v in graph_data[data_type].values()
])
data_min = min([
np.percentile(v, cb_norm_percs[0])
for v in graph_data[data_type].values()
])
if cb_min_max_eq:
data_max_min = utils.get_max_abs(data_max, data_min)
vmin, vmax = -data_max_min, data_max_min
else:
vmin, vmax = data_min, data_max
# cmap = color_map # mpl.cm.jet
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
color_map = fu.find_color_map(color_map)
cb = mpl.colorbar.ColorbarBase(
ax, cmap=color_map, norm=norm,
orientation='vertical') #, ticks=color_map_bounds)
if position == 'left':
ax.yaxis.set_ticks_position('left')
ax.yaxis.set_label_position('left')
cb.set_label(cb_title)
