def compute_all_asymmetries(filtered_data):
""" Loop over all properties to show asymmetry."""
# Asymmetry index
out_data = PINGData(dict(SubjID=filtered_data.data_dict['SubjID']))
for key in filtered_data.get_twohemi_keys():
dest_key = get_ai_key(key)
out_data.data_dict[dest_key] = get_asymmetry_index(filtered_data.data_dict,
key, mask_nan=False)
# Add one property for total asymmetry
n_subj = out_data.get_num_subjects()
for p in filtered_data.IMAGING_PREFIX:
good_keys = filter(lambda k: (k.startswith(p) and
is_ai_key(k)),
out_data.data_dict.keys())
good_keys = list(good_keys)
if len(good_keys) == 0:
continue
# Compute total asymmetry per subject
total_asymmetry = np.zeros((n_subj,))
total_measures = np.zeros((n_subj,))
for key in good_keys:
values = out_data.data_dict[key]
good_idx = np.logical_not(np.isnan(values))
total_asymmetry[good_idx] += values[good_idx]**2
total_measures[good_idx] += 1
total_ai_key = get_ai_key(p + '%s') % '_TOTAL'
out_data.data_dict[total_ai_key] = np.sqrt(total_asymmetry / total_measures)
return out_data
def compute_all_totals(filtered_data):
"""Computes total surface area / volume."""
# Process & plot the data.
out_data = PINGData(dict(SubjID=filtered_data.data_dict['SubjID']))
for rh_key in filtered_data.get_twohemi_keys():
assert which_hemi(rh_key) == 'rh'
lh_key = get_lh_key_from_rh_key(rh_key)
dest_key = get_nonhemi_key(rh_key)
dest_key += '_LH_PLUS_RH'
out_data.data_dict[dest_key] = filtered_data.data_dict[rh_key] + filtered_data.data_dict[lh_key]
def is_sum_key(key):
return key.endswith('_LH_PLUS_RH')
def get_sum_key(key):
return '%s_LH_PLUS_RH' % key
# Add one property for total asymmetry
n_subj = out_data.get_num_subjects()
for p in filtered_data.IMAGING_PREFIX:
good_keys = filter(lambda k: (k.startswith(p) and
is_sum_key(k)),
out_data.data_dict.keys())
good_keys = list(good_keys)
if len(good_keys) == 0:
continue
# Compute total LH+RH, per subject
total_area = np.zeros((n_subj,))
total_measures = np.zeros((n_subj,))
for key in good_keys:
values = out_data.data_dict[key]
good_idx = np.logical_not(np.isnan(values))
total_area[good_idx] += values[good_idx]**2
total_measures[good_idx] += 1
total_sum_key = get_sum_key(p + '_TOTAL')
out_data.data_dict[total_sum_key] = np.sqrt(total_area / total_measures)
return out_data