CX_rois = ['PB','NO','FB','EB','AB(L)','AB(R)']
#CX_rois = ['PB']
empty_rois = []
h_results = {'h':[],'h_randwire':[],'h_randweight':[]}
#hpy = lambda x: np.random.randn()
reldir = '../'
for roi in CX_rois:
try:
print(roi)
path = os.path.join(reldir,conf.results_dir,'CX_study','roi='+roi+'.txt')
if roi not in empty_rois and not os.path.exists(path):
n,conn = fetch_adjacency(adjpath=os.path.join(reldir,conf.datasets_dir,'noncropped_traced_'+roi))
nlist,adj = conn2adj(conn)
print('adj size =',adj.shape,'non-zero elements =',conn.shape[0])
#fh
h = hpy(adj)
#fh random rewiring
N = adj.shape[0]
perm = np.random.permutation(N)
h_randwire = hpy(adj[:, perm])
#fh random weight shuffling
nonz = np.nonzero(adj)
weights = adj[nonz]
np.random.shuffle(weights)
adj[nonz] = weights
#hpy = lambda x: np.random.randn()
reldir = '../'
for roi in CX_rois:
try:
if roi not in empty_rois:
n, conn = fetch_adjacency(adjpath=os.path.join(
reldir, conf.datasets_dir, 'noncropped_traced_' + roi))
for th in ths:
print(th, roi)
path = os.path.join(reldir, conf.results_dir, 'CX_study_th',
f'th={th};roi={roi}.txt')
if not os.path.exists(path):
temp = conn[conn['weight'] > th]
nlist, adj = conn2adj(temp)
if temp.shape[0] > 0:
print('adj size =', adj.shape, 'non-zero elements =',
temp.shape[0])
#fh
h = hnx(adj)
#fh random rewiring
# N = adj.shape[0]
# temp = []
# for i in range(20):
# perm = np.random.permutation(N)
# temp += [hnx(adj[:,perm])]
# h_randwire_mean,h_randwire_std = np.mean(temp),np.std(temp)
neur_CX_split,conn_CX_split = {}, {}
for roi in CX_rois:
n,conn = fetch_adjacency(adjpath=os.path.join(reldir,conf.datasets_dir,'noncropped_traced_'+roi))
neur_CX_split[roi],conn_CX_split[roi] = n,conn
#main loop
for roi in CX_rois:
n,conn = neur_CX_split[roi], conn_CX_split[roi]
for th in ths:
print(th,roi)
path = os.path.join(reldir,conf.results_dir,expname,f'th={th};roi={roi}.txt')
if not os.path.exists(path):
conn1 = restrict_th(conn,th)
conn_out = restrict_max_comp(conn1)
size_maxcomp = len(find_max_comp_neurons(conn_out))
nlist,adj = conn2adj(conn_out)
if conn_out.shape[0]>0:
print('adj size =',adj.shape,'non-zero elements =',conn_out.shape[0])
#fh
h = hnx(adj)
print(f'th={th};h={h};size(max_comp)={size_maxcomp}')
with open(path,'w') as f:
#f.write(f'{h} {h_randwire_mean} {h_randwire_std} {h_randweight_mean} {h_randweight_std}')
f.write(f'{h} {th} {size_maxcomp}')
else:
print(roi,': skipping, result exists')