def load_stim_file(subject, args):
stim_fname = op.join(
MMVT_DIR, subject, 'electrodes',
'{}{}.npz'.format(args.file_frefix, args.stim_channel))
stim = np.load(stim_fname)
labels, psd, time, freqs = (stim[k]
for k in ['labels', 'psd', 'time', 'freqs'])
bipolar = '-' in labels[0]
data = None
freqs_dim = psd.shape.index(len(freqs))
labels_dim = psd.shape.index(len(labels))
if time.ndim > 0:
time_dim = psd.shape.index(len(time))
else:
time_dim = next(iter(set(range(3)) - set([freqs_dim, labels_dim])))
T, L, F = psd.shape[time_dim], psd.shape[labels_dim], psd.shape[freqs_dim]
if args.downsample > 1:
time = utils.downsample(time, args.downsample)
colors = None
conditions = []
for freq_ind, (freq_from, freq_to) in enumerate(freqs):
if data is None:
# initialize the data matrix (electrodes_num x T x freqs_num)
data = np.zeros((L, T, F))
psd_slice = get_psd_freq_slice(psd, freq_ind, freqs_dim, time_dim)
if args.downsample > 1:
psd_slice = utils.downsample(psd_slice, args.downsample)
data[:, :, freq_ind] = psd_slice
data_min, data_max = utils.calc_min_max(psd_slice,
norm_percs=args.norm_percs)
if colors is None:
colors = np.zeros((*data.shape, 3))
for elec_ind, elec_name in enumerate(labels):
elec_group = utils.elec_group(elec_name, bipolar)
# if elec_group in ['LVF', 'RMT']:
# colors[elec_ind, :, freq_ind] = utils.mat_to_colors(psd_slice[elec_ind], data_min, data_max, colorsMap='BuGn')
# else:
colors[elec_ind, :,
freq_ind] = utils.mat_to_colors(psd_slice[elec_ind],
data_min,
data_max,
colorsMap=args.colors_map)
conditions.append('{}-{}Hz'.format(freq_from, freq_to))
output_fname = op.join(
MMVT_DIR, subject, 'electrodes',
'stim_electrodes_{}{}_{}.npz'.format(args.file_frefix,
'bipolar' if bipolar else '',
args.stim_channel))
print('Saving {}'.format(output_fname))
np.savez(output_fname,
data=data,
names=labels,
conditions=conditions,
colors=colors)
return dict(data=data, labels=labels, conditions=conditions, colors=colors)
def load_stim_file(subject, args):
stim_fname = op.join(MMVT_DIR, subject, 'electrodes', '{}{}.npz'.format(
args.file_frefix, args.stim_channel))
stim = np.load(stim_fname)
labels, psd, time, freqs = (stim[k] for k in ['labels', 'psd', 'time', 'freqs'])
bipolar = '-' in labels[0]
data = None
freqs_dim = psd.shape.index(len(freqs))
labels_dim = psd.shape.index(len(labels))
if time.ndim > 0:
time_dim = psd.shape.index(len(time))
else:
time_dim = next(iter(set(range(3)) - set([freqs_dim, labels_dim])))
T, L, F = psd.shape[time_dim], psd.shape[labels_dim], psd.shape[freqs_dim]
if args.downsample > 1:
time = utils.downsample(time, args.downsample)
colors = None
conditions = []
for freq_ind, (freq_from, freq_to) in enumerate(freqs):
if data is None:
# initialize the data matrix (electrodes_num x T x freqs_num)
data = np.zeros((L, T, F))
psd_slice = get_psd_freq_slice(psd, freq_ind, freqs_dim, time_dim)
if args.downsample > 1:
psd_slice = utils.downsample(psd_slice, args.downsample)
data[:, :, freq_ind] = psd_slice
data_min, data_max = utils.calc_min_max(psd_slice, norm_percs=args.norm_percs)
if colors is None:
colors = np.zeros((*data.shape, 3))
for elec_ind, elec_name in enumerate(labels):
elec_group = utils.elec_group(elec_name, bipolar)
# if elec_group in ['LVF', 'RMT']:
# colors[elec_ind, :, freq_ind] = utils.mat_to_colors(psd_slice[elec_ind], data_min, data_max, colorsMap='BuGn')
# else:
colors[elec_ind, :, freq_ind] = utils.mat_to_colors(psd_slice[elec_ind], data_min, data_max, colorsMap=args.colors_map)
conditions.append('{}-{}Hz'.format(freq_from, freq_to))
output_fname = op.join(MMVT_DIR, subject, 'electrodes', 'stim_electrodes_{}{}_{}.npz'.format(
args.file_frefix, 'bipolar' if bipolar else '', args.stim_channel))
print('Saving {}'.format(output_fname))
np.savez(output_fname, data=data, names=labels, conditions=conditions, colors=colors)
return dict(data=data, labels=labels, conditions=conditions, colors=colors)
def calc_labels_data(elecs_lookup, stim_data, stim_labels, hemi=None):
labels_names = list(elecs_lookup.keys())
labels_data = np.zeros((len(labels_names), stim_data.shape[1], stim_data.shape[2]))
colors = np.zeros((*labels_data.shape, 3))
labels_data_names = []
label_ind = 0
for label_name, electordes_data in elecs_lookup.items():
if not hemi is None:
if lu.get_hemi_from_name(label_name) != hemi:
continue
labels_data_names.append(label_name)
for elec_name, elec_prob in electordes_data:
elec_inds = np.where(stim_labels == elec_name)[0]
if len(elec_inds) > 0:
elec_data = stim_data[elec_inds[0], :, :] * elec_prob
labels_data[label_ind, :, :] += elec_data
label_ind += 1
# Calc colors for each freq
for freq_id in range(labels_data.shape[2]):
data_min, data_max = utils.calc_min_max(labels_data[:, :, freq_id], norm_percs=args.norm_percs)
colors[:, :, freq_id] = utils.mat_to_colors(
labels_data[:, :, freq_id], data_min, data_max, colorsMap=args.colors_map)
return labels_data, colors, labels_data_names