def thresh_diff(dens_thresh, thr, conn_model, network, ID, dir_path, mask,
node_size, conn_matrix):
from pynets import utils, thresholding
if not dens_thresh:
print("%s%.2f%s" % ('\nThresholding proportionally at: ',
100 * float(thr), '% ...\n'))
else:
print("%s%.2f%s" % ('\nThresholding to achieve density of: ',
100 * float(thr), '% ...\n'))
if dens_thresh == False:
##Save thresholded
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
edge_threshold = str(float(thr) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
edge_threshold = str(float(thr) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
np.save(est_path, conn_matrix_thr)
return (conn_matrix_thr, edge_threshold, est_path, thr, node_size, network)
def thresh_and_fit(adapt_thresh, dens_thresh, thr, ts_within_nodes, conn_model,
network, ID, dir_path, mask):
from pynets import utils, thresholding, graphestimation
##Adaptive thresholding scenario
if adapt_thresh is not False:
try:
est_path2 = dir_path + '/' + ID + '_structural_est.txt'
if os.path.isfile(est_path2) == True:
#[conn_matrix_thr, est_path, edge_threshold, thr] = adaptive_thresholding(ts_within_nodes, conn_model, network, ID, est_path2, dir_path)
##Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model,
mask, dir_path)
#np.savetxt(unthr_path, conn_matrix_thr, delimiter='\t')
edge_threshold = str(float(thr) * 100) + '%'
else:
print('No structural mx found! Exiting...')
sys.exit()
except:
print('No structural mx assigned! Exiting...')
sys.exit()
else:
if not dens_thresh:
print(
'\nRunning graph estimation and thresholding proportionally at: '
+ str(thr) + '% ...\n')
else:
print(
'\nRunning graph estimation and thresholding to achieve density of: '
+ str(100 * dens_thresh) + '% ...\n')
##Fit mat
conn_matrix = graphestimation.get_conn_matrix(ts_within_nodes,
conn_model)
##Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model, mask,
dir_path)
np.savetxt(unthr_path, conn_matrix, delimiter='\t')
if not dens_thresh:
##Save thresholded
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
edge_threshold = str(float(thr) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model, thr,
mask, dir_path)
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, dens_thresh)
edge_threshold = str((1 - float(dens_thresh)) * 100) + '%'
est_path = utils.create_est_path(ID, network, conn_model,
dens_thresh, mask, dir_path)
np.savetxt(est_path, conn_matrix_thr, delimiter='\t')
return (conn_matrix_thr, edge_threshold, est_path, thr)
def thresh_diff(dens_thresh, thr, conn_model, network, ID, dir_path, mask,
node_size, conn_matrix, parc):
from pynets import utils, thresholding
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if parc is True:
node_size = 'parc'
if dens_thresh is False:
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
else:
print("%s%.2f%s" %
('\nThresholding to achieve density of: ', thr_perc, '% ...\n'))
if dens_thresh is False:
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
# Save thresholded mat
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
np.save(est_path, conn_matrix_thr)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network
def thresh_and_fit(dens_thresh, thr, ts_within_nodes, conn_model, network, ID,
dir_path, mask, node_size):
from pynets import utils, thresholding, graphestimation
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if not dens_thresh:
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
else:
print("%s%.2f%s" %
('\nThresholding to achieve density of: ', thr_perc, '% ...\n'))
# Fit mat
conn_matrix = graphestimation.get_conn_matrix(ts_within_nodes, conn_model)
# Save unthresholded
unthr_path = utils.create_unthr_path(ID, network, conn_model, mask,
dir_path)
np.save(unthr_path, conn_matrix)
if dens_thresh is False:
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
# Save thresholded mat
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size)
np.save(est_path, conn_matrix_thr)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network
def thresh_diff(dens_thresh, thr, conn_model, network, ID, dir_path, mask,
node_size, conn_matrix, parc, min_span_tree, disp_filt,
atlas_select, uatlas_select, label_names, coords):
from pynets import utils, thresholding
thr_perc = 100 * float(thr)
edge_threshold = "%s%s" % (str(thr_perc), '%')
if parc is True:
node_size = 'parc'
if min_span_tree is True:
print(
'Using local thresholding option with the Minimum Spanning Tree (MST)...\n'
)
if dens_thresh is False:
thr_type = 'MSTprop'
conn_matrix_thr = thresholding.local_thresholding_prop(
conn_matrix, thr)
else:
thr_type = 'MSTdens'
conn_matrix_thr = thresholding.local_thresholding_dens(
conn_matrix, thr)
elif disp_filt is True:
thr_type = 'DISPα'
G1 = thresholding.disparity_filter(nx.from_numpy_array(conn_matrix))
# G2 = nx.Graph([(u, v, d) for u, v, d in G1.edges(data=True) if d['alpha'] < thr])
print('Computing edge disparity significance with alpha = %s' % thr)
print('Filtered graph: nodes = %s, edges = %s' %
(G1.number_of_nodes(), G1.number_of_edges()))
# print('Backbone graph: nodes = %s, edges = %s' % (G2.number_of_nodes(), G2.number_of_edges()))
#print(G2.edges(data=True))
conn_matrix_thr = nx.to_numpy_array(G1)
else:
if dens_thresh is False:
thr_type = 'prop'
print("%s%.2f%s" %
('\nThresholding proportionally at: ', thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.threshold_proportional(
conn_matrix, float(thr))
else:
thr_type = 'dens'
print("%s%.2f%s" % ('\nThresholding to achieve density of: ',
thr_perc, '% ...\n'))
conn_matrix_thr = thresholding.density_thresholding(
conn_matrix, float(thr))
if not nx.is_connected(nx.from_numpy_matrix(conn_matrix_thr)):
print('Warning: Fragmented graph')
# Save thresholded mat
smooth = 0
est_path = utils.create_est_path(ID, network, conn_model, thr, mask,
dir_path, node_size, smooth, thr_type)
np.save(est_path, conn_matrix_thr)
return conn_matrix_thr, edge_threshold, est_path, thr, node_size, network, conn_model, mask, atlas_select, uatlas_select, label_names, coords
def build_est_path_list(multi_thr, min_thr, max_thr, step_thr, ID, network,
conn_model, thr, mask, dir_path, est_path_list):
import numpy as np
from pynets import utils
if multi_thr == True:
iter_thresh = [
str(i) for i in np.round(np.arange(float(min_thr), float(max_thr),
float(step_thr)),
decimals=2).tolist()
]
for thr in iter_thresh:
est_path_tmp = utils.create_est_path(ID, network, conn_model, thr,
mask, dir_path)
est_path_list.append(est_path_tmp)
else:
est_path_tmp = utils.create_est_path(ID, network, conn_model, thr,
mask, dir_path)
est_path_list.append(est_path_tmp)
iter_thresh = [thr] * len(est_path_list)
return (iter_thresh, est_path_list)
