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.check_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
def calc_connections_colors(data, labels, hemis, args): # stat, conditions, w, threshold=0, threshold_percentile=0, color_map='jet', # norm_by_percentile=True, norm_percs=(1, 99), symetric_colors=True): M = data.shape[0] W = data.shape[2] if args.windows == 0 else args.windows L = int((M * M + M) / 2 - M) con_indices = np.zeros((L, 2)) con_values = np.zeros((L, W, len(args.conditions))) con_names = [None] * L con_type = np.zeros((L)) for cond in range(len(args.conditions)): for w in range(W): for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): if W > 1: con_values[ind, w, cond] = data[i, j, w, cond] elif data.ndim > 2: con_values[ind, w, cond] = data[i, j, cond] else: con_values[ind, w, cond] = data[i, j] if len(args.conditions) > 1: stat_data = utils.calc_stat_data(con_values, args.stat) else: stat_data = np.squeeze(con_values) for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): con_indices[ind, :] = [i, j] con_names[ind] = '{}-{}'.format(labels[i], labels[j]) con_type[ind] = HEMIS_WITHIN if hemis[i] == hemis[j] else HEMIS_BETWEEN con_indices = con_indices.astype(np.int) con_names = np.array(con_names) if args.threshold_percentile > 0: args.threshold = np.percentile(np.abs(stat_data), args.threshold_percentile) if args.threshold >= 0: indices = np.where(np.abs(stat_data) > args.threshold)[0] # con_colors = con_colors[indices] con_indices = con_indices[indices] con_names = con_names[indices] con_values = con_values[indices] con_type = con_type[indices] stat_data = stat_data[indices] con_values = np.squeeze(con_values) if args.data_max == 0 and args.data_min == 0: data_max, data_min = utils.get_data_max_min(stat_data, args.norm_by_percentile, args.norm_percs) if args.symetric_colors and np.sign(data_max) != np.sign(data_min): data_minmax = max(map(abs, [data_max, data_min])) data_max, data_min = data_minmax, -data_minmax else: data_max, data_min = args.data_max, args.data_min con_colors = utils.mat_to_colors(stat_data, data_min, data_max, args.color_map) print(len(con_names)) return con_colors, con_indices, con_names, con_values, con_type, data_max, data_min
def calc_connections_colors(data, labels, hemis, args): # stat, conditions, w, threshold=0, threshold_percentile=0, color_map='jet', # norm_by_percentile=True, norm_percs=(1, 99), symetric_colors=True): M = data.shape[0] W = data.shape[2] if args.windows == 0 else args.windows L = int((M * M + M) / 2 - M) con_indices = np.zeros((L, 2)) con_values = np.zeros((L, W, len(args.conditions))) con_names = [None] * L con_type = np.zeros((L)) for cond in range(len(args.conditions)): for w in range(W): for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): if W > 1 and data.ndim == 4: con_values[ind, w, cond] = data[i, j, w, cond] elif data.ndim > 2: con_values[ind, w, cond] = data[i, j, cond] else: con_values[ind, w, cond] = data[i, j] if len(args.conditions) > 1: stat_data = utils.calc_stat_data(con_values, args.stat) else: stat_data = np.squeeze(con_values) for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): con_indices[ind, :] = [i, j] con_names[ind] = '{}-{}'.format(labels[i], labels[j]) con_type[ind] = HEMIS_WITHIN if hemis[i] == hemis[j] else HEMIS_BETWEEN con_indices = con_indices.astype(np.int) con_names = np.array(con_names) data_max, data_min = utils.get_data_max_min(stat_data, args.norm_by_percentile, args.norm_percs) data_minmax = max(map(abs, [data_max, data_min])) if args.threshold_percentile > 0: args.threshold = np.percentile(np.abs(stat_data), args.threshold_percentile) if args.threshold > data_minmax: raise Exception('threshold > abs(max(data)) ({})'.format(data_minmax)) if args.threshold >= 0: indices = np.where(np.abs(stat_data) > args.threshold)[0] # con_colors = con_colors[indices] con_indices = con_indices[indices] con_names = con_names[indices] con_values = con_values[indices] con_type = con_type[indices] stat_data = stat_data[indices] con_values = np.squeeze(con_values) if args.data_max == 0 and args.data_min == 0: if args.symetric_colors and np.sign(data_max) != np.sign(data_min): data_max, data_min = data_minmax, -data_minmax else: data_max, data_min = args.data_max, args.data_min print('data_max: {}, data_min: {}'.format(data_max, data_min)) con_colors = utils.mat_to_colors(stat_data, data_min, data_max, args.color_map) print(len(con_names)) return con_colors, con_indices, con_names, con_values, con_type, data_max, data_min
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_connections_colors(data, labels, hemis, stat, w, threshold=0, threshold_percentile=0, color_map='jet', norm_by_percentile=True, norm_percs=(1, 99)): M = data.shape[0] W = data.shape[2] if w == 0 else w L = int((M * M + M) / 2 - M) con_indices = np.zeros((L, 2)) con_values = np.zeros((L, W, 2)) con_names = [None] * L con_type = np.zeros((L)) axis = data.ndim - 1 coh_stat = utils.calc_stat_data(data, stat, axis=axis) x = coh_stat.ravel() data_max, data_min = utils.get_data_max_min(x, norm_by_percentile, norm_percs) data_minmax = max(map(abs, [data_max, data_min])) for cond in range(2): for w in range(W): for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): if W > 1: con_values[ind, w, cond] = data[i, j, w, cond] else: con_values[ind, w, cond] = data[i, j, cond] stat_data = utils.calc_stat_data(con_values, stat) con_colors = utils.mat_to_colors(stat_data, -data_minmax, data_minmax, color_map) for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): con_indices[ind, :] = [i, j] con_names[ind] = '{}-{}'.format(labels[i], labels[j]) con_type[ind] = HEMIS_WITHIN if hemis[i] == hemis[j] else HEMIS_BETWEEN con_indices = con_indices.astype(np.int) con_names = np.array(con_names) if threshold_percentile > 0: threshold = np.percentile(np.abs(stat_data), threshold_percentile) if threshold > 0: indices = np.where(np.abs(stat_data) >= threshold)[0] con_colors = con_colors[indices] con_indices = con_indices[indices] con_names = con_names[indices] con_values = con_values[indices] con_type = con_type[indices] print(len(con_names)) return con_colors, con_indices, con_names, con_values, con_type
def calc_connections_colors(subject, data, labels, hemis, stat, threshold=0, color_map='jet', norm_by_percentile=True, norm_percs=(1, 99)): # cm_big='YlOrRd', cm_small='PuBu', flip_cm_big=True, flip_cm_small=False): M = data.shape[0] W = data.shape[2] L = int((M * M + M) / 2 - M) # con_colors = np.zeros((L, W, 3)) con_indices = np.zeros((L, 2)) con_values = np.zeros((L, W, 2)) con_names = [None] * L con_type = np.zeros((L)) coh_stat = utils.calc_stat_data(data, stat, axis=3) x = coh_stat.ravel() data_max, data_min = utils.get_data_max_min(x, norm_by_percentile, norm_percs) data_minmax = max(map(abs, [data_max, data_min])) # sm = utils.get_scalar_map(threshold, data_max, color_map=color_map) for cond in range(2): for w in range(W): # win_colors = utils.mat_to_colors(coh[:, :, w, cond], threshold, max_x, color_map, sm) # coh_arr = utils.lower_rec_to_arr(coh[:, :, w, cond]) # win_colors = utils.arr_to_colors(coh_arr, threshold, max_x, color_map, sm) for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): # con_colors[ind, w, cond, :] = win_colors[ind][:3] con_values[ind, w, cond] = data[i, j, w, cond] stat_data = utils.calc_stat_data(con_values, stat) con_colors = utils.mat_to_colors(stat_data, -data_minmax, data_minmax, color_map) for ind, (i, j) in enumerate(utils.lower_rec_indices(M)): con_indices[ind, :] = [i, j] con_names[ind] = '{}-{}'.format(labels[i].astype(str), labels[j].astype(str)) con_type[ind] = HEMIS_WITHIN if hemis[i] == hemis[j] else HEMIS_BETWEEN print(L, ind) con_indices = con_indices.astype(np.int) return con_colors, con_indices, con_names, con_values, con_type
def create_coloring(x, subject, atlas, conditions, colors_map='YlOrRd', exclude=['unknown', 'corpuscallosum'], colors_min_val=None, colors_max_val=None): labels = lu.read_labels(subject, SUBJECTS_DIR, atlas, exclude=tuple(exclude), sorted_according_to_annot_file=True, only_names=True) for cond_id, cond_name in enumerate(conditions): values = x[:, cond_id] if colors_min_val is None: colors_min_val = np.min(x) if colors_max_val is None: colors_max_val = np.max(x) colors = utils.arr_to_colors(values, colors_min_val, colors_max_val, colors_map=colors_map) coloring_fname = op.join(MMVT_DIR, subject, 'coloring', 'labels_{}_coloring.csv'.format(cond_name)) write_coloring_file(coloring_fname, labels, colors) values_diff = np.squeeze(np.diff(x)) abs_max = max(map(abs, [np.max(values_diff), np.min(values_diff)])) colors = utils.mat_to_colors(values_diff, -abs_max, abs_max, 'RdBu', flip_cm=True) coloring_fname = op.join(MMVT_DIR, subject, 'coloring', 'labels_{}_{}_diff_coloring.csv'.format(*conditions)) write_coloring_file(coloring_fname, labels, colors)
def create_coloring(x, subject, atlas, conditions, colors_map='YlOrRd', exclude=['unknown', 'corpuscallosum'], colors_min_val=None, colors_max_val=None): labels = lu.read_labels(subject, SUBJECTS_DIR, atlas, exclude=exclude, sorted_according_to_annot_file=True, only_names=True) for cond_id, cond_name in enumerate(conditions): values = x[:, cond_id] if colors_min_val is None: colors_min_val = np.min(x) if colors_max_val is None: colors_max_val = np.max(x) colors = utils.arr_to_colors(values, colors_min_val, colors_max_val, colors_map=colors_map) coloring_fname = op.join(MMVT_DIR, subject, 'coloring', 'labels_{}_coloring.csv'.format(cond_name)) write_coloring_file(coloring_fname, labels, colors) values_diff = np.squeeze(np.diff(x)) abs_max = max(map(abs, [np.max(values_diff), np.min(values_diff)])) colors = utils.mat_to_colors(values_diff, -abs_max, abs_max, 'RdBu', flip_cm=True) coloring_fname = op.join(MMVT_DIR, subject, 'coloring', 'labels_{}_{}_diff_coloring.csv'.format(*conditions)) write_coloring_file(coloring_fname, labels, colors)
def read_electrodes_data(elecs_data_dic, conditions, montage_file, output_file_name, from_t=0, to_t=None, norm_by_percentile=True, norm_percs=(1,99)): for cond_id, (field, file_name) in enumerate(elecs_data_dic.iteritems()): d = sio.loadmat(file_name) if cond_id == 0: data = np.zeros((d[field].shape[0], to_t - from_t, 2)) times = np.arange(0, to_t*2, 2) # todo: Need to do some interpulation for the MEG data[:, :, cond_id] = d[field][:, times] # time = d['Time'] if norm_by_percentile: norm_val = max(map(abs, [np.percentile(data, norm_percs[ind]) for ind in [0,1]])) else: norm_val = max(map(abs, [np.max(data), np.min(data)])) data /= norm_val sfp = mne.channels.read_montage(montage_file) avg_data = np.mean(data, 2) colors = utils.mat_to_colors(avg_data, np.percentile(avg_data, 10), np.percentile(avg_data, 90), colorsMap='RdBu', flip_cm=True) np.savez(output_file_name, data=data, names=sfp.ch_names, 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