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 get_rois_colors(subject, atlas, rois):
not_white_rois = set(filter(lambda r:'white' not in r.lower(), rois))
white_rois = rois - not_white_rois
not_white_rois = sorted(list(not_white_rois))
colors = cu.get_distinct_colors_and_names()
lables_colors_rgbs_fname = op.join(MMVT_DIR, subject, 'coloring', 'labels_{}_coloring.csv'.format(atlas))
lables_colors_names_fname = op.join(MMVT_DIR, subject, 'coloring', 'labels_{}_colors_names.csv'.format(atlas))
labels_colors_exist = op.isfile(lables_colors_rgbs_fname) and op.isfile(lables_colors_names_fname)
rois_colors_rgbs, rois_colors_names = OrderedDict(), OrderedDict()
if not labels_colors_exist:
print('No labels coloring file!')
else:
labels_colors_rgbs = np.genfromtxt(lables_colors_rgbs_fname, dtype=str, delimiter=',')
labels_colors_names = np.genfromtxt(lables_colors_names_fname, dtype=str, delimiter=',')
for roi in not_white_rois:
if labels_colors_exist:
roi_inds = np.where(labels_colors_rgbs[:, 0] == '{}-rh'.format(roi))[0]
if len(roi_inds) > 0:
color_rgb = labels_colors_rgbs[roi_inds][0, 1:].tolist()
color_name = labels_colors_names[roi_inds][0, 1]
else:
color_rgb, color_name = next(colors)
else:
color_rgb, color_name = next(colors)
rois_colors_rgbs[roi], rois_colors_names[roi] = color_rgb, color_name
for white_roi in white_rois:
rois_colors_rgbs[white_roi], rois_colors_names[white_roi] = cu.name_to_rgb('white').tolist(), 'white'
return rois_colors_rgbs, rois_colors_names
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 write_electrode_colors(template, electrodes_colors):
import csv
fol = utils.make_dir(op.join(MMVT_DIR, template, 'coloring'))
csv_fname = op.join(fol, 'morphed_electrodes.csv')
# unique_colors = np.unique(utils.flat_list(([[k[1] for k in elecs] for elecs in electrodes_colors.values()])))
# colors = utils.get_distinct_colors(len(unique_colors))
from src.mmvt_addon import colors_utils as cu
colors = [
cu.name_to_rgb(c)
for c in ['blue', 'green', 'purple', 'red', 'brown', 'yellow']
]
print('Writing csv file to {}'.format(csv_fname))
with open(csv_fname, 'w') as csv_file:
wr = csv.writer(csv_file, quoting=csv.QUOTE_NONE)
for subject in electrodes_colors.keys():
# for elc_name, _ in template_electrodes[subject]:
for elc_name, color_id in electrodes_colors[subject]:
color = colors[color_id - 1]
wr.writerow([elc_name, *color])
