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_con_colors(con_values, high_low_diff):
M = con_values.shape[0]
stat_data = utils.calc_stat_data(con_values, STAT_AVG, axis=con_values.ndim-1)
# con_colors = utils.arr_to_colors(stat_data, 0, 1)[:, :3]
# con_colors = utils.arr_to_colors_two_colors_maps(stat_data, 0, 1, 'RdPu', 'hot', 0.05, flip_cm_big=True)[:, :3]
from src.mmvt_addon import colors_utils
red = np.array(colors_utils.name_to_rgb('red')) / 255.0
blue = np.array(colors_utils.name_to_rgb('blue')) / 255.0
magenta = np.array(colors_utils.name_to_rgb('magenta')) / 255.0
green = np.array(colors_utils.name_to_rgb('green')) / 255.0
if con_values.ndim == 2:
con_colors = np.zeros((M, 3))
con_colors[(stat_data <= 0.05) & (high_low_diff >= 0)] = red
con_colors[(stat_data <= 0.05) & (high_low_diff < 0)] = blue
con_colors[(stat_data > 0.05) & (high_low_diff >= 0)] = magenta
con_colors[(stat_data > 0.05) & (high_low_diff < 0)] = green
elif con_values.ndim == 3:
W = con_values.shape[1]
con_colors = np.zeros((M, W, 3))
for w in range(W):
stat_w = stat_data[:, w]
high_low_diff_w = high_low_diff[:, w]
sig_high = (abs(stat_w) >= -np.log10(0.05)) & (high_low_diff_w >= 0)
sig_low = (abs(stat_w) >= -np.log10(0.05)) & (high_low_diff_w < 0)
print(w, sig_high, sig_low)
con_colors[sig_high, w] = red
con_colors[sig_low, w] = blue
con_colors[(abs(stat_w) < -np.log10(0.05)) & (high_low_diff_w >= 0), w] = (1, 1, 1)
con_colors[(abs(stat_w) < -np.log10(0.05)) & (high_low_diff_w < 0), w] = (1, 1, 1)
# con_colors = con_colors[:, :, :, np.newaxis]
return con_colors
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(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 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_con_colors(con_values, high_low_diff):
M = con_values.shape[0]
stat_data = utils.calc_stat_data(con_values,
STAT_AVG,
axis=con_values.ndim - 1)
# con_colors = utils.arr_to_colors(stat_data, 0, 1)[:, :3]
# con_colors = utils.arr_to_colors_two_colors_maps(stat_data, 0, 1, 'RdPu', 'hot', 0.05, flip_cm_big=True)[:, :3]
from src.mmvt_addon import colors_utils
red = np.array(colors_utils.name_to_rgb('red')) / 255.0
blue = np.array(colors_utils.name_to_rgb('blue')) / 255.0
magenta = np.array(colors_utils.name_to_rgb('magenta')) / 255.0
green = np.array(colors_utils.name_to_rgb('green')) / 255.0
if con_values.ndim == 2:
con_colors = np.zeros((M, 3))
con_colors[(stat_data <= 0.05) & (high_low_diff >= 0)] = red
con_colors[(stat_data <= 0.05) & (high_low_diff < 0)] = blue
con_colors[(stat_data > 0.05) & (high_low_diff >= 0)] = magenta
con_colors[(stat_data > 0.05) & (high_low_diff < 0)] = green
elif con_values.ndim == 3:
W = con_values.shape[1]
con_colors = np.zeros((M, W, 3))
for w in range(W):
stat_w = stat_data[:, w]
high_low_diff_w = high_low_diff[:, w]
sig_high = (abs(stat_w) >= -np.log10(0.05)) & (high_low_diff_w >=
0)
sig_low = (abs(stat_w) >= -np.log10(0.05)) & (high_low_diff_w < 0)
print(w, sig_high, sig_low)
con_colors[sig_high, w] = red
con_colors[sig_low, w] = blue
con_colors[(abs(stat_w) < -np.log10(0.05)) &
(high_low_diff_w >= 0), w] = (1, 1, 1)
con_colors[(abs(stat_w) < -np.log10(0.05)) & (high_low_diff_w < 0),
w] = (1, 1, 1)
# con_colors = con_colors[:, :, :, np.newaxis]
return con_colors
