def load_current_fmri_clusters_labels_file():
constrast_name = bpy.context.scene.fmri_clusters_labels_files
minmax_fname = op.join(
mu.get_user_fol(), 'fmri',
'{}_minmax.pkl'.format('_'.join(constrast_name.split('_')[:-1])))
minmax_exist = op.isfile(minmax_fname)
# if minmax_exist and not _addon().colorbar_values_are_locked():
# min_val, max_val = mu.load(minmax_fname)
# _addon().set_colorbar_max_min(max_val, min_val)
fMRIPanel.constrast = {}
for hemi in mu.HEMIS:
contrast_fname = get_contrast_fname(constrast_name, hemi)
if contrast_fname != '':
print('Loading {}'.format(contrast_fname))
fMRIPanel.constrast[hemi] = np.load(contrast_fname)
if not minmax_exist:
data = None
for hemi in mu.HEMIS:
x_ravel = fMRIPanel.constrast[hemi]
data = x_ravel if data is None else np.hstack((x_ravel, data))
norm_percs = (bpy.context.scene.fmri_blobs_percentile_min,
bpy.context.scene.fmri_blobs_percentile_max)
min_val, max_val = mu.calc_min_max(data, norm_percs=norm_percs)
_addon().colorbar.set_colorbar_max_min(max_val,
min_val,
force_update=True)
mu.save((min_val, max_val), minmax_fname)
def _calc_best_curves_sep(data, norm_percs=[3, 97]):
if len(data) == 0:
return 0
try:
max_diff = max([np.diff(mu.calc_min_max(data[k], norm_percs=norm_percs))[0] for k in range(data.shape[0])])
return max_diff * 1.1
except:
print('Error in _calc_best_curves_sep')
return 0