def _calc_activity_colors(params):
data, t, fol, scalar_map_big, scalar_map_small, threshold, do_print = params
colors = utils.arr_to_colors_two_colors_maps(data, scalar_map_big=scalar_map_big,
scalar_map_small=scalar_map_small, threshold=threshold, default_val=1)[:,:3]
if do_print and t % 10 == 0:
print(t)
# colors = utils.arr_to_colors(data[:, t], 0, data_max, scalar_map=scalar_map)[:,:3]
colors = np.hstack((np.reshape(data, (data.shape[0], 1)), colors))
np.save(os.path.join(fol, 't{}'.format(t)), colors)
def save_fmri_colors(subject,
fmri_file,
surf_name,
output_file,
threshold=0,
cm_big='YlOrRd',
cm_small='PuBu',
flip_cm_big=True,
flip_cm_small=False,
norm_by_percentile=True,
norm_percs=(2, 98)):
data = {}
hemis = ['rh', 'lh']
for hemi in hemis:
data[hemi], _ = get_hemi_data(subject, hemi, fmri_file.format(hemi),
surf_name)
if norm_by_percentile:
data_max = max(
[np.percentile(data[hemi], norm_percs[1]) for hemi in hemis])
data_min = min(
[np.percentile(data[hemi], norm_percs[0]) for hemi in hemis])
else:
data_max = max([np.max(data[hemi]) for hemi in hemis])
data_min = min([np.min(data[hemi]) for hemi in hemis])
data_minmax = max(map(abs, [data_max, data_min]))
for hemi in hemis:
colors = utils.arr_to_colors_two_colors_maps(
data[hemi],
threshold=threshold,
x_max=data_minmax,
x_min=-data_minmax,
cm_big=cm_big,
cm_small=cm_small,
default_val=1,
flip_cm_big=flip_cm_big,
flip_cm_small=flip_cm_small)
data[hemi] = np.reshape(data[hemi], (len(data[hemi]), 1))
colors = np.hstack((data[hemi], colors))
np.save(output_file.format(hemi), colors)
def save_fmri_colors(
subject,
fmri_file,
surf_name,
output_file,
threshold=0,
cm_big="YlOrRd",
cm_small="PuBu",
flip_cm_big=True,
flip_cm_small=False,
norm_by_percentile=True,
norm_percs=(2, 98),
):
data = {}
hemis = ["rh", "lh"]
for hemi in hemis:
data[hemi], _ = get_hemi_data(subject, hemi, fmri_file.format(hemi), surf_name)
if norm_by_percentile:
data_max = max([np.percentile(data[hemi], norm_percs[1]) for hemi in hemis])
data_min = min([np.percentile(data[hemi], norm_percs[0]) for hemi in hemis])
else:
data_max = max([np.max(data[hemi]) for hemi in hemis])
data_min = min([np.min(data[hemi]) for hemi in hemis])
data_minmax = max(map(abs, [data_max, data_min]))
for hemi in hemis:
colors = utils.arr_to_colors_two_colors_maps(
data[hemi],
threshold=threshold,
x_max=data_minmax,
x_min=-data_minmax,
cm_big=cm_big,
cm_small=cm_small,
default_val=1,
flip_cm_big=flip_cm_big,
flip_cm_small=flip_cm_small,
)
data[hemi] = np.reshape(data[hemi], (len(data[hemi]), 1))
colors = np.hstack((data[hemi], colors))
np.save(output_file.format(hemi), colors)
