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