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 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 flatten_data(data, w=0):
M = data.shape[0]
L = int((M*M+M)/2-M)
W = data.shape[2] if w == 0 else w
values = np.zeros((L, W, 2))
for cond in range(2):
for w in range(W):
for ind, (i, j) in enumerate(utils.lower_rec_indices(M)):
values[ind, 0, cond] = data[i, j, cond]
return values
def flatten_data(data, w=0):
M = data.shape[0]
L = int((M*M+M)/2-M)
W = data.shape[2] if w == 0 else w
values = np.zeros((L, W, 2))
for cond in range(2):
for w in range(W):
for ind, (i, j) in enumerate(utils.lower_rec_indices(M)):
values[ind, 0, cond] = data[i, j, cond]
return values
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 compare_coh_windows(subject, task, conditions, electrodes, freqs=((8, 12), (12, 25), (25,55), (55,110)), do_plot=False):
electrodes_coh = np.load(op.join(ELECTRODES_DIR, subject, task, 'electrodes_coh_windows.npy'))
meg_electrodes_coh = np.load(op.join(ELECTRODES_DIR, subject, task, 'meg_electrodes_ts_coh_windows.npy'))
figs_fol = op.join(MMVT_DIR, subject, 'figs', 'coh_windows')
utils.make_dir(figs_fol)
results = []
for cond_id, cond in enumerate(conditions):
now = time.time()
for freq_id, freq in enumerate(freqs):
freq = '{}-{}'.format(*freq)
indices = list(utils.lower_rec_indices(electrodes_coh.shape[0]))
for ind, (i, j) in enumerate(indices):
utils.time_to_go(now, ind, len(indices))
meg = meg_electrodes_coh[i, j, :, freq_id, cond_id][:22]
elc = electrodes_coh[i, j, :, freq_id, cond_id][:22]
elc_diff = np.max(elc) - np.min(elc)
meg *= elc_diff / (np.max(meg) - np.min(meg))
meg += np.mean(elc) - np.mean(meg)
if sum(meg) > len(meg) * 0.99:
continue
data_diff = meg - elc
# data_diff = data_diff / max(data_diff)
rms = np.sqrt(np.mean(np.power(data_diff, 2)))
corr = np.corrcoef(meg, elc)[0, 1]
results.append(dict(elc1=electrodes[i], elc2=electrodes[j], cond=cond, freq=freq, rms=rms, corr=corr))
if electrodes[i]=='RAF6' and electrodes[j] == 'LOF4': #corr > 10 and rms < 3:
plt.figure()
plt.plot(meg, label='prediction')
plt.plot(elc, label='electrode')
plt.legend()
# plt.title('{}-{} {} {}'.format(electrodes[i], electrodes[j], freq, cond)) # (rms:{:.2f})
plt.savefig(op.join(figs_fol, '{:.2f}-{}-{}-{}-{}.jpg'.format(rms, electrodes[i], electrodes[j], freq, cond)))
plt.close()
results_fname = op.join(figs_fol, 'results{}.csv'.format('_bipolar' if bipolar else ''))
rmss, corrs = [], []
with open(results_fname, 'w') as output_file:
for res in results:
output_file.write('{},{},{},{},{},{}\n'.format(
res['elc1'], res['elc2'], res['cond'], res['freq'], res['rms'], res['corr']))
rmss.append(res['rms'])
corrs.append(res['corr'])
rmss = np.array(rmss)
corrs = np.array(corrs)
pass
def compare_coh_windows(subject, task, conditions, electrodes, freqs=((8, 12), (12, 25), (25,55), (55,110)), do_plot=False):
electrodes_coh = np.load(op.join(ELECTRODES_DIR, subject, task, 'electrodes_coh_windows.npy'))
meg_electrodes_coh = np.load(op.join(ELECTRODES_DIR, subject, task, 'meg_electrodes_ts_coh_windows.npy'))
figs_fol = op.join(MMVT_DIR, subject, 'figs', 'coh_windows')
utils.make_dir(figs_fol)
results = []
for cond_id, cond in enumerate(conditions):
now = time.time()
for freq_id, freq in enumerate(freqs):
freq = '{}-{}'.format(*freq)
indices = list(utils.lower_rec_indices(electrodes_coh.shape[0]))
for ind, (i, j) in enumerate(indices):
utils.time_to_go(now, ind, len(indices))
meg = meg_electrodes_coh[i, j, :, freq_id, cond_id][:22]
elc = electrodes_coh[i, j, :, freq_id, cond_id][:22]
elc_diff = np.max(elc) - np.min(elc)
meg *= elc_diff / (np.max(meg) - np.min(meg))
meg += np.mean(elc) - np.mean(meg)
if sum(meg) > len(meg) * 0.99:
continue
data_diff = meg - elc
# data_diff = data_diff / max(data_diff)
rms = np.sqrt(np.mean(np.power(data_diff, 2)))
corr = np.corrcoef(meg, elc)[0, 1]
results.append(dict(elc1=electrodes[i], elc2=electrodes[j], cond=cond, freq=freq, rms=rms, corr=corr))
if False: #do_plot and electrodes[i]=='RPT7' and electrodes[j] == 'RPT5': #corr > 10 and rms < 3:
plt.figure()
plt.plot(meg, label='pred')
plt.plot(elc, label='elec')
plt.legend()
# plt.title('{}-{} {} {}'.format(electrodes[i], electrodes[j], freq, cond)) # (rms:{:.2f})
plt.savefig(op.join(figs_fol, '{:.2f}-{}-{}-{}-{}.jpg'.format(rms, electrodes[i], electrodes[j], freq, cond)))
plt.close()
results_fname = op.join(figs_fol, 'results{}.csv'.format('_bipolar' if bipolar else ''))
rmss, corrs = [], []
with open(results_fname, 'w') as output_file:
for res in results:
output_file.write('{},{},{},{},{},{}\n'.format(
res['elc1'], res['elc2'], res['cond'], res['freq'], res['rms'], res['corr']))
rmss.append(res['rms'])
corrs.append(res['corr'])
rmss = np.array(rmss)
corrs = np.array(corrs)
pass
