def read_data(size=3, sel_label=["1", "2"]):
# Reading one scan( both raw and smooth) per subject and their DX label
sub_info = get_subject_info(size)
X1 = [] # raw
X2 = [] # smooth
y = []
N = 148
for sub in sub_info.keys():
rw_all, sm_all = readSubjectFiles(sub, "row")
t = 0
assert len(
rw_all) == len(sm_all), "Size mismatch in sub " + sub + " " + str(
len(rw_all)) + " " + str(len(sm_all))
if sub_info[sub][0]["DX"] not in sel_label:
continue
else:
if sub_info[sub][0]["DX"] == '1':
count = 1
elif sub_info[sub][0]["DX"] == '2':
count = 1
elif sub_info[sub][0]["DX"] == '3':
count = 1
for c in range(count):
X1.append(rw_all[c])
y.append(sub_info[sub][c]["DX"])
X2.append(sm_all[c])
y = [0 if a == sel_label[0] else 1 for a in y]
print(collections.Counter(y))
return X1, X2, y
def calculate_psnr_all(sub_names, p=0.5):
noise_rw = 0
noise_th = 0
noise_sm = 0
noise_d = 0
total = 0
for sub in sub_names:
_, connectome_list = readSubjectFiles(sub, method="row")
# connectome_list = threshold_all(connectome_list, vmin=0.001)
connectome_list_noisy = add_noise_all(connectome_list, p)
smoothed_connectomes_noisy, M, E = optimize_longitudinal_connectomes(
connectome_list_noisy, Args.dfw, Args.sw, Args.lmw, Args.lmd)
degraded_connectomes, _, _, _, _, _ = initialize_connectomes(
connectome_list_noisy)
connectome_list_th = threshold_all(connectome_list_noisy, vmin=0.01)
for t in range(0, len(connectome_list)):
noise_rw = noise_rw + calculate_psnr(connectome_list[t],
connectome_list_noisy[t])
noise_th = noise_th + calculate_psnr(connectome_list[t],
connectome_list_th[t])
noise_d = noise_d + calculate_psnr(connectome_list[t],
degraded_connectomes[t])
noise_sm = noise_sm + calculate_psnr(connectome_list[t],
smoothed_connectomes_noisy[t])
total = total + 1
noise_rw = noise_rw / total
noise_th = noise_th / total
noise_d = noise_d / total
noise_sm = noise_sm / total
print("\nNoise raw: ", noise_rw, "\nNoise th: ", noise_th, "\nNoise d: ",
noise_d, "\nNoise sm: ", noise_sm)
return noise_rw, noise_th, noise_d, noise_sm
def entropy_eval():
sub_names = get_subject_names(3)
#sub_names = ["027_S_5110"]
hub_count = 10
rw_en_list = []
sm_en_list = []
for sub in sub_names:
rw, sm = readSubjectFiles(sub, "row")
rw = threshold_all(rw, vmin=Args.threshold)
rw_hub_match = get_hub_match(rw, hub_count)
rw_en = get_entropy_list(rw_hub_match)
sm_hub_match = get_hub_match(sm, hub_count)
sm_en = get_entropy_list(sm_hub_match)
rw_en_list.append(rw_en)
sm_en_list.append(sm_en)
print("Raw: ", rw_en)
print("Smooth: ", sm_en)
'''
plt.bar(np.arange(0, 148, 1), rw_hub_match)
plt.ylim(0, 2)
plt.show()
plt.bar(np.arange(0, 148, 1), sm_hub_match)
plt.ylim(0,2)
plt.show()
'''
# Write node file
from utils.readFile import write_node_file
node_color_rw = []
node_color_sm = []
for h in rw_hub_match:
if h == 0:
node_color_rw.append('1')
else:
node_color_rw.append('2')
for h in sm_hub_match:
if h == 0:
node_color_sm.append('1')
else:
node_color_sm.append('2')
'''
# Write files
write_node_file(rw_hub_match, node_color_rw, sub + '_hub_rw' + '.node')
write_node_file(sm_hub_match, node_color_sm, sub + '_hub_sw' + '.node')
with open(sub + '_signal_rw.txt', 'w') as f:
for val in rw_hub_match:
f.write(str(val) + '\n')
with open(sub + '_signal_sm.txt', 'w') as f:
for val in sm_hub_match:
f.write(str(val) + '\n')
'''
print("Mean rw: ", np.mean(rw_en), "\nMean sm: ", np.mean(sm_en))
import pickle as pk
with open('hub_eval.pkl', 'wb') as f:
print(pk.dump([rw_en_list, sm_en_list], f))
def eval_low_range_consistency():
sub = "027_S_5110"
rw, sm = readSubjectFiles(sub, method="row")
th = 0.0002
rw_th = threshold_all(rw, vmin=th)
hist_rw = get_sparse_hist(rw_th)
hist_sm = get_sparse_hist(sm)
print("Raw: ",
(hist_rw == 1).sum(axis=None) / (hist_rw > 0).sum(axis=None))
print("Smt: ",
(hist_sm == 1).sum(axis=None) / (hist_sm > 0).sum(axis=None))
write_low_range_connectivity(hist_rw, hist_sm)
def main_sim_net():
sub = "027_S_2336"
rw, sm = readSubjectFiles(sub, method="row")
baseline_rw, _ = sort_matrix(rw[0], True)
baseline_sm, _ = sort_matrix(sm[0], True)
# baseline_rw = binarize(baseline_rw, ht)
# baseline_sm = binarize(baseline_sm, ht)
# baseline_rw = simulated_community_structure()
# baseline_sm = simulated_community_structure()
th = [i / 10 for i in range(12, 25)]
T = 7
recalculate = False
if recalculate:
psnr = np.empty(shape=(4, len(th)))
for i, p in enumerate(th):
print("p = ", p)
sim_long_net = simulate_longitudinal_network(baseline_rw,
noise_level=p)
for sl in sim_long_net:
print((sl == 0).sum(axis=None))
sim_long_net_int, M, E = optimize_longitudinal_connectomes(
sim_long_net, Args.dfw, Args.sw, Args.lmw, Args.lmd)
sim_long_net_deg = initialize_connectomes(sim_long_net)
sim_long_net_th = threshold_all(sim_long_net, vmin=0.02)
psnr[0][i] = calculate_psnr_list(sim_long_net, [baseline_rw] * T)
psnr[1][i] = calculate_psnr_list(sim_long_net_int,
[baseline_sm] * T)
psnr[2][i] = calculate_psnr_list(sim_long_net_deg,
[baseline_sm] * T)
psnr[3][i] = calculate_psnr_list(sim_long_net_th,
[baseline_rw] * T)
with open('psnr.pkl', 'wb') as f:
pickle.dump(psnr, f)
print("PSNR sim = ", psnr[0][i], "\nPSNR int = ", psnr[1][i],
"\nPSNR_th = ", psnr[3][i], "\nPSNR_deg = ", psnr[2][i])
else:
with open('psnr.pkl', 'rb') as f:
psnr = pickle.load(f)
plot_psnr(psnr,
th,
color=['r-', 'b--*', 'y--.', 'go'],
labels=[
'Simulated data w/ noise', 'Our method',
'Our degraded method', 'Thresholded method'
])
def compute_eigen_spectrum(sub):
th = Args.threshold # threshold for raw connectome to have comaparable sparsity with smoothed version
connectome_list, smoothed_connectomes = readSubjectFiles(sub, method="row")
connectome_list = [
threshold(connectome, vmin=th) for connectome in connectome_list
]
raw_spectrum_list = [
get_graph_specturm(connectome) for connectome in connectome_list
]
smth_spectrum_list = [
get_graph_specturm(connectome) for connectome in smoothed_connectomes
]
return raw_spectrum_list, smth_spectrum_list
def main(sub="027_S_5110"):
data, _ = readSubjectFiles(sub, "whole", sort=False)
for t in range(0, len(data)):
data[t], order = sort_matrix(data[t], False)
# Plot Raw
rw_links = get_top_links(data[1], count=500, offset=0, weight=True)
#face_color, edge_color = mp_ring_colors(data)
face_color, edge_color = get_lobe_wise_color_ring(len(data[1]))
plot_circle(face_color,
edge_color,
rw_links,
save=False,
fname='demo_cplot')
def measure_change():
sub = "027_S_2336"
rw, sm = readSubjectFiles(sub, method="row")
baseline_rw, _ = sort_matrix(rw[0], True)
baseline_sm, _ = sort_matrix(sm[0], True)
T = 7
p = 1
sim_long_net = simulate_longitudinal_network(baseline_rw,
noise_level=p,
count=T)
sim_long_net_int, M, E = optimize_longitudinal_connectomes(
sim_long_net, Args.dfw, Args.sw, Args.lmw, Args.lmd)
sim_long_net_deg = initialize_connectomes(sim_long_net)
sim_long_net_th = threshold_all(sim_long_net, vmin=0.0002)
sim = central_difference_of_links(sim_long_net)
sim_sm = central_difference_of_links(sim_long_net_int)
sim_th = central_difference_of_links(sim_long_net_th)
sim_deg = central_difference_of_links(sim_long_net_deg)
print(
"Simulated data with noise: %f\nOur method: %f\nOur method degraded: %f\nThresholded: %f\n"
% (sim, sim_sm, sim_deg, sim_th))
def mean_std_voi(sub_names):
voi_rw = []
voi_sm = []
for sub in sub_names:
connectome_list, smoothed_connectomes = readSubjectFiles(sub,
method="row")
voi_rw = voi_rw + [
voi_between_community_structure(v1, v2) for v1 in connectome_list
for v2 in connectome_list if v1 is not v2
]
voi_sm = voi_sm + [
voi_between_community_structure(v1, v2)
for v1 in smoothed_connectomes
for v2 in smoothed_connectomes if v1 is not v2
]
voi_rw_mean = np.mean(voi_rw)
voi_rw_std = np.std(voi_rw)
voi_sm_mean = np.mean(voi_sm)
voi_sm_std = np.std(voi_sm)
return voi_rw_mean, voi_rw_std, voi_sm_mean, voi_sm_std
import os
from args import Args
from test import optimize_longitudinal_connectomes
from utils.helper import add_noise_all
from utils.readFile import readSubjectFiles
if __name__ == '__main__':
args = Args()
data_dir = os.path.join(os.path.join(args.root_directory, os.pardir), 'AD-Data_Organized')
sub = '027_S_2336'
connectome_list, smoothed_connectome = readSubjectFiles(sub)
connectome_list_noisy = add_noise_all(connectome_list)
smoothed_connectomes_noisy, M, E = optimize_longitudinal_connectomes(connectome_list_noisy, Args.dfw, Args.sw, Args.lmw,
Args.lmd)
# Compute noise in raw
noise_rw = 0
noise_sm = 0
for t in range(0, len(connectome_list)):
noise_rw = noise_rw + abs(connectome_list - connectome_list_noisy).sum()
noise_sm = noise_sm + abs(smoothed_connectome - smoothed_connectomes_noisy).sum()
print("Raw: ", noise_rw,
"\nSM: ", noise_sm)
def __init__(self, subject=None):
if subject is not None:
self.rw_data, self.smth_data = readSubjectFiles(subject,
method="row")
self.T = len(self.rw_data)
from bct import *
from hypo_test import *
from compare_network import n_comp
from utils.helper import get_centrality_measure
if __name__ == '__main__':
sub_info = get_subject_info()
count = 0
min_bound = 6
for key in list(sub_info.keys()):
if sub_info[key].__len__() < min_bound:
del sub_info[key]
count = count + 1
else:
rw, sm = readSubjectFiles(key)
for t in range(0, len(sub_info[key])):
print(key, t)
sub_info[key][t]["raw_data"] = rw[t]
sub_info[key][t]["sm_data"] = sm[t]
sub_info[key][t]["cc_rw"] = np.mean(clustering_coef_wu(rw[t]))
sub_info[key][t]["cc_sm"] = np.mean(clustering_coef_wu(sm[t]))
sub_info[key][t]["mod_rw"] = n_comp(rw[t])
sub_info[key][t]["mod_sm"] = n_comp(sm[t])
sub_info[key][t]["d_rw"] = np.max(strengths_und(rw[t]))
sub_info[key][t]["d_sm"] = np.max(strengths_und(sm[t]))
#_, sub_info[key][t]["bc_rw"], _ = flow_coef_bd(rw[t])
#_, sub_info[key][t]["bc_sm"], _ = flow_coef_bd(sm[t])
#two_sample_t_test(sub_info)
print(group_consistency(sub_info))
#diff_eigval /= normalizer
print(diff_eigval)
def diff_eig_vec(temp_net, eig_num=0):
print("*** Angular difference between "
"eigen vectors of the temporal networks ***")
t = len(temp_net)
n = len(temp_net[0])
eigvec = np.zeros((n, t))
for i in range(0, t):
D = np.diag(temp_net[i].sum(axis=1))
L = D - temp_net[i]
_, eig = LA.eigh(L)
eigvec[:, i] = eig[:, eig_num]
ang_dist = np.abs(np.dot(eigvec.T, eigvec))
np.set_printoptions(formatter={'float': lambda x: "{0:0.2f}".format(x)})
print(ang_dist)
if __name__ == '__main__':
# Input
s_name = '027_S_5109'
_, temp_net = readSubjectFiles(s_name)
temp_net, _ = readSubjectFiles(s_name)
diff_eig_vec(temp_net, 4)
# diff_eig_val(temp_net)
