def write_results(seed_ts, fc_map, global_signal, hdr, input_type, zero_mask):
analysis_str = 'fc_map'
if global_signal:
analysis_str += '_gs'
pickle.dump([seed_ts, fc_map], open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
write_to_cifti(fc_map, hdr, fc_map.shape[0], analysis_str)
elif input_type == 'gifti':
write_to_gifti(fc_map[np.newaxis, :], hdr, analysis_str, zero_mask)
def write_results(input_type, lag_results, lag_projection, hdr,
global_signal, zero_mask):
if global_signal:
analysis_str = 'lag_projection_gs'
else:
analysis_str = 'lag_projection'
pickle.dump(lag_results,
open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
write_to_cifti(lag_projection, hdr, n_comps, analysis_str)
elif input_type == 'gifti':
write_to_gifti(lag_projection, hdr, analysis_str, zero_mask)
def write_results(input_type, spatial_map, ica_ts,
hdr, global_signal, zero_mask,
real_complex):
analysis_str = 'ica'
if global_signal:
analysis_str += '_gs'
if real_complex == 'complex':
analysis_str += '_complex'
comp_weights_real = np.real(spatial_map)
comp_weights_imag = np.imag(spatial_map)
comp_weights_ang = np.angle(spatial_map)
pickle.dump([spatial_map, ica_ts],
open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
if real_complex == 'real':
write_to_cifti(spatial_map, hdr,
spatial_map.shape[0], analysis_str)
elif real_complex == 'complex':
write_to_cifti(comp_weights_real, hdr, n_comps, f'{analysis_str}_real')
write_to_cifti(comp_weights_imag, hdr, n_comps, f'{analysis_str}_imag')
write_to_cifti(comp_weights_ang, hdr, n_comps, f'{analysis_str}_ang')
elif input_type == 'gifti':
if real_complex == 'real':
write_to_gifti(spatial_map, hdr, analysis_str, zero_mask)
elif real_complex == 'complex':
write_to_gifti(comp_weights_real, hdr, f'{analysis_str}_real', zero_mask)
write_to_gifti(comp_weights_imag, hdr, f'{analysis_str}_imag', zero_mask)
write_to_gifti(comp_weights_ang, hdr, f'{analysis_str}_ang', zero_mask)
def write_results(input_type, hmm_results, mean_maps, n_comps, hdr,
global_signal, zero_mask):
if global_signal:
analysis_str = 'hmm_gs'
else:
analysis_str = 'hmm'
pickle.dump(hmm_results, open(f'{analysis_str}_results.pkl', 'wb'))
if n_comps == 1:
mean_maps = mean_maps[np.newaxis, :]
if input_type == 'cifti':
write_to_cifti(mean_maps, hdr, n_comps, f'{analysis_str}_mean_map')
elif input_type == 'gifti':
write_to_gifti(mean_maps, hdr, f'{analysis_str}_mean_map', zero_mask)
def write_results(emb_weights, hdr, input_type, gradient_algorithm,
global_signal, zero_mask):
if gradient_algorithm == 'laplacian':
analysis_str = 'eigenmap'
else:
analysis_str = 'kpca'
if global_signal:
analysis_str += '_gs'
pickle.dump(emb_weights, open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
write_to_cifti(emb_weights, hdr, emb_weights.shape[0], analysis_str)
elif input_type == 'gifti':
write_to_gifti(emb_weights, hdr, analysis_str, zero_mask)
def write_results(input_type, pca_output, rotate, comp_weights, n_comps, hdr,
pca_type, global_signal, zero_mask):
if global_signal:
analysis_str = 'pca_gs'
else:
analysis_str = 'pca'
if rotate:
analysis_str += f'_{rotate}'
if pca_type == 'complex':
analysis_str += '_complex'
pickle.dump(
{
'pca': pca_output,
'metadata': [input_type, hdr, zero_mask]
}, open(f'{analysis_str}_results.pkl', 'wb'))
comp_weights_real = np.real(comp_weights)
comp_weights_imag = np.imag(comp_weights)
comp_weights_ang = np.angle(comp_weights)
comp_weights_amp = np.abs(comp_weights)
if input_type == 'cifti':
write_to_cifti(comp_weights_real, hdr, n_comps,
f'{analysis_str}_real')
write_to_cifti(comp_weights_imag, hdr, n_comps,
f'{analysis_str}_imag')
write_to_cifti(comp_weights_ang, hdr, n_comps,
f'{analysis_str}_ang')
write_to_cifti(comp_weights_amp, hdr, n_comps,
f'{analysis_str}_amp')
elif input_type == 'gifti':
write_to_gifti(comp_weights_real, hdr, f'{analysis_str}_real',
zero_mask)
write_to_gifti(comp_weights_imag, hdr, f'{analysis_str}_imag',
zero_mask)
write_to_gifti(comp_weights_ang, hdr, f'{analysis_str}_ang',
zero_mask)
write_to_gifti(comp_weights_amp, hdr, f'{analysis_str}_amp',
zero_mask)
elif pca_type == 'real':
pickle.dump(pca_output, open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
write_to_cifti(comp_weights, hdr, n_comps, analysis_str)
elif input_type == 'gifti':
write_to_gifti(comp_weights, hdr, analysis_str, zero_mask)
def write_results(peak_avg,
hdr,
input_type,
global_signal,
zero_mask,
peak_ts=None):
if global_signal:
analysis_str = 'peak_average_gs'
else:
analysis_str = 'peak_average'
pickle.dump(peak_avg, open(f'{analysis_str}_results.pkl', 'wb'))
if peak_ts is not None:
for i, window in enumerate(peak_ts):
write_to_gifti(window, hdr, f'{analysis_str}_ts_sample_{i}',
zero_mask)
if input_type == 'cifti':
write_to_cifti(peak_avg, hdr, peak_avg.shape[0], analysis_str)
elif input_type == 'gifti':
write_to_gifti(peak_avg, hdr, analysis_str, zero_mask)
def write_results(cluster_centroid, cluster_indx, selected_tps,
cluster_win_avgs, window_avg, norm, vertex_input,
global_signal, hdr, input_type, zero_mask):
analysis_str = 'caps'
if global_signal:
analysis_str += '_gs'
if norm:
analysis_str += '_norm'
output = [cluster_centroid, cluster_indx, selected_tps]
if window_avg:
output += cluster_win_avgs
for indx, win_avg in enumerate(cluster_win_avgs):
tmp_str = analysis_str + f'_window_avg_clus{indx}'
write_to_gifti(win_avg, hdr, tmp_str, zero_mask)
pickle.dump(output, open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
write_to_cifti(cluster_centroid, hdr, cluster_centroid.shape[0],
analysis_str)
elif input_type == 'gifti':
write_to_gifti(cluster_centroid, hdr, analysis_str, zero_mask)
def write_results(peak_avg, peak_avg_gs, global_signal, gs_map, hdr,
input_type, zero_mask):
analysis_str = f'gs'
pickle.dump(global_signal, open(f'{analysis_str}_results.pkl', 'wb'))
if input_type == 'cifti':
write_to_cifti(peak_avg, hdr, peak_avg.shape[0],
analysis_str + '_peak_avg')
write_to_cifti(peak_avg_gs, hdr, peak_avg.shape[0],
analysis_str + '_peak_avg_gsremoved')
write_to_cifti(gs_map, hdr, gs_map.shape[0], analysis_str + '_map')
elif input_type == 'gifti':
write_to_gifti(peak_avg, hdr, analysis_str + '_peak_avg', zero_mask)
write_to_gifti(peak_avg_gs, hdr, analysis_str + '_peak_avg_gsremoved',
zero_mask)
write_to_gifti(gs_map, hdr, analysis_str + '_map', zero_mask)
