def constrained_foopsi_parallel(arg_in):
""" necessary for parallel computation of the function constrained_foopsi
"""
Ytemp, nT, jj_, bl, c1, g, sn, args = arg_in
T=np.shape(Ytemp)[0]
cc_,cb_,c1_,gn_,sn_,sp_ = constrained_foopsi(Ytemp/nT, bl = bl, c1 = c1, g = g, sn = sn, **args)
gd_ = np.max(np.roots(np.hstack((1,-gn_.T))));
gd_vec = gd_**range(T)
C_ = cc_[:].T + cb_ + np.dot(c1_,gd_vec)
Sp_ = sp_[:T].T
Ytemp_ = Ytemp - np.dot(nT,C_).T
return C_,Sp_,Ytemp_,cb_,c1_,sn_,gn_,jj_
def update_temporal_components(Y,A,b,Cin,fin,ITER=2,method_foopsi='constrained_foopsi',n_processes=1, backend='single_thread', memory_efficient=False, **kwargs):
#def update_temporal_components(Y,A,b,Cin,fin,ITER=2,method_foopsi='constrained_foopsi',deconv_method = 'cvx', g='None',**kwargs):
"""update temporal components and background given spatial components using a block coordinate descent approach
Inputs:
Y: np.ndarray (2D)
input data with time in the last axis (d x T)
A: sparse matrix (crc format)
matrix of temporal components (d x K)
Cin: np.ndarray
current estimate of temporal components (K x T)
ITER: positive integer
Maximum number of block coordinate descent loops. Default: 2
fin: np.ndarray
current estimate of temporal background (vector of length T)
method_foopsi: string
Method of deconvolution of neural activity.
Default: constrained_foopsi (constrained deconvolution, the only method supported at the moment)
deconv_method: string
Solver for constrained foopsi ('cvx' or 'spgl1', default: 'cvx')
g: np.ndarray
Global time constant (not used)
**kwargs: all parameters passed to constrained_foopsi
b=None,
c1=None,
g=None,
sn=None,
p=2,
method='cvx',
bas_nonneg=True,
noise_range=[0.25, 0.5],
noise_method='logmexp',
lags=5,
resparse=0,
fudge_factor=1,
verbosity=False):
Outputs:
C: np.matrix
matrix of temporal components (K x T)
f: np.array
vector of temporal background (length T)
Y_res: np.ndarray
matrix with current residual (d x T)
P_: dictionary
Dictionary with parameters for each temporal component:
P_.b: baseline for fluorescence trace
P_.c1: initial concentration
P_.gn: discrete time constant
P_.neuron_sn: noise level
P_.neuron_id: index of component
Sp: np.matrix
matrix of deconvolved neural activity (K x T)
"""
d,T = np.shape(Y);
nr = np.shape(A)[-1]
A = scipy.sparse.hstack((A,coo_matrix(b)))
Cin = np.vstack((Cin,fin));
C = Cin;
#%
nA = np.squeeze(np.array(np.sum(np.square(A.todense()),axis=0)))
Y=np.matrix(Y)
C=np.matrix(C)
Cin=np.matrix(Cin)
Sp = np.zeros((nr,T))
YrA = Y.T*A - Cin.T*(A.T*A);
for iter in range(ITER):
idxs=range(nr+1)
random.shuffle(idxs)
P_=[];
# perm = randperm(nr+1)
for jj,ii in enumerate(idxs):
#ii=jj
#print ii,jj
pars=dict(kwargs)
# ii = perm(jj);
if ii<nr:
if method_foopsi == 'constrained_foopsi':
#print YrA.shape
#print YrA.shape
YrA[:,ii] = YrA[:,ii] + nA[ii]*Cin[ii,:].T
cc,cb,c1,gn,sn,sp = constrained_foopsi(np.squeeze(np.asarray(YrA[:,ii]/nA[ii])), **pars)
#print pars
pars['gn'] = gn
gd = np.max(np.roots(np.hstack((1,-gn.T)))); # decay time constant for initial concentration
gd_vec = gd**range(T)
C[ii,:] = cc[:].T + cb + c1*gd_vec
Sp[ii,:] = sp[:T].T
YrA[:,ii] = YrA[:,ii] - np.matrix(nA[ii]*C[ii,:]).T
pars['b'] = cb
pars['c1'] = c1
pars['neuron_sn'] = sn
pars['neuron_id'] = ii
P_.append(pars)
else:
raise Exception('undefined method')
else:
YrA[:,ii] = YrA[:,ii] + nA[ii]*Cin[ii,:].T
cc = np.maximum(YrA[:,ii]/nA[ii],0)
C[ii,:] = cc[:].T # if you use this should give an error that we need to correct
YrA[:,ii] = YrA[:,ii] - nA[ii]*C[ii,:].T
if (jj+1)%10 == 0:
print str(jj+1) + ' out of total ' + str(nr+1) + ' temporal components updated \n'
#%disp(norm(Fin(1:nr,:) - F,'fro')/norm(F,'fro'));
if scipy.linalg.norm(Cin - C,'fro')/scipy.linalg.norm(C,'fro') <= 1e-3:
# stop if the overall temporal component does not change by much
break
else:
Cin = C
Y_res = Y - A*C
f = C[nr:,:]
C = C[:nr,:]
P_ = sorted(P_, key=lambda k: k['neuron_id'])
return C,f,Y_res,P_,Sp
def update_temporal_components(Y,A,b,Cin,fin,ITER=1,method='constrained_foopsi',deconv_method = 'cvx', g='None',**kwargs):
# b=None,
# c1=None,
# g=None,
# sn=None,
# p=2,
# method='cvx',
# bas_nonneg=True,
# noise_range=[0.25, 0.5],
# noise_method='logmexp',
# lags=5,
# resparse=0,
# fudge_factor=1,
# verbosity=False):
# """
# update temporal components and background given spatial components
# **kwargs: all parameters passed to constrained_foopsi
# """
d,T = np.shape(Y);
nr = np.shape(A)[-1]
A = scipy.sparse.hstack((A,coo_matrix(b)))
Cin = np.vstack((Cin,fin));
C = Cin;
#%
nA = np.squeeze(np.array(np.sum(np.square(A.todense()),axis=0)))
Y=np.matrix(Y)
C=np.matrix(C)
Cin=np.matrix(Cin)
Sp = np.zeros((nr,T))
YrA = Y.T*A - Cin.T*(A.T*A);
for iter in range(ITER):
idxs=range(nr+1)
random.shuffle(idxs)
P_=[];
# perm = randperm(nr+1)
for jj,ii in enumerate(idxs):
#ii=jj
#print ii,jj
pars=dict(kwargs)
# ii = perm(jj);
if ii<nr:
if method == 'constrained_foopsi':
#print YrA.shape
#print YrA.shape
YrA[:,ii] = YrA[:,ii] + nA[ii]*Cin[ii,:].T
cc,cb,c1,gn,sn,sp = constrained_foopsi(np.squeeze(np.asarray(YrA[:,ii]/nA[ii])), method = deconv_method, **pars)
#print pars
pars['gn'] = gn
gd = np.max(np.roots(np.hstack((1,-gn.T)))); # decay time constant for initial concentration
gd_vec = gd**range(T)
C[ii,:] = cc[:].T + cb + c1*gd_vec
Sp[ii,:] = sp[:T].T
YrA[:,ii] = YrA[:,ii] - np.matrix(nA[ii]*C[ii,:]).T
pars['b'] = cb
pars['c1'] = c1
pars['neuron_sn'] = sn
pars['neuron_id'] = ii
P_.append(pars)
else:
raise Exception('undefined method')
else:
YrA[:,ii] = YrA[:,ii] + nA[ii]*Cin[ii,:].T
cc = np.maximum(YrA[:,ii]/nA[ii],0)
#C[ii,:] = full(cc');
YrA[:,ii] = YrA[:,ii] - nA[ii]*C[ii,:].T
if (jj+1)%10 == 0:
print str(jj+1) + ' out of total ' + str(nr+1) + ' temporal components updated \n'
#%disp(norm(Fin(1:nr,:) - F,'fro')/norm(F,'fro'));
if scipy.linalg.norm(Cin - C,'fro')/scipy.linalg.norm(C,'fro') <= 1e-3:
# stop if the overall temporal component does not change by much
break
else:
Cin = C
Y_res = Y - A*C
f = C[nr:,:]
C = C[:nr,:]
P_ = sorted(P_, key=lambda k: k['neuron_id'])
return C,f,Y_res,P_,Sp
def merge_components(Y,
A,
b,
C,
f,
S,
sn_pix,
temporal_params,
spatial_params,
thr=0.85,
fast_merge=True,
mx=1000,
bl=None,
c1=None,
sn=None,
g=None):
""" Merging of spatially overlapping components that have highly correlated temporal activity
The correlation threshold for merging overlapping components is user specified in thr
Parameters
-----------
Y: np.ndarray
residual movie after subtracting all found components (Y_res = Y - A*C - b*f) (d x T)
A: sparse matrix
matrix of spatial components (d x K)
b: np.ndarray
spatial background (vector of length d)
C: np.ndarray
matrix of temporal components (K x T)
f: np.ndarray
temporal background (vector of length T)
S: np.ndarray
matrix of deconvolved activity (spikes) (K x T)
sn_pix: ndarray
noise standard deviation for each pixel
temporal_params: dictionary
all the parameters that can be passed to the update_temporal_components function
spatial_params: dictionary
all the parameters that can be passed to the update_spatial_components function
thr: scalar between 0 and 1
correlation threshold for merging (default 0.85)
mx: int
maximum number of merging operations (default 50)
sn_pix: nd.array
noise level for each pixel (vector of length d)
bl:
baseline for fluorescence trace for each row in C
c1:
initial concentration for each row in C
g:
discrete time constant for each row in C
sn:
noise level for each row in C
Returns
--------
A: sparse matrix
matrix of merged spatial components (d x K)
C: np.ndarray
matrix of merged temporal components (K x T)
nr: int
number of components after merging
merged_ROIs: list
index of components that have been merged
S: np.ndarray
matrix of merged deconvolved activity (spikes) (K x T)
bl: float
baseline for fluorescence trace
c1: float
initial concentration
g: float
discrete time constant
sn: float
noise level
"""
#%
nr = A.shape[1]
if bl is not None and len(bl) != nr:
raise Exception(
"The number of elements of bl must match the number of components")
if c1 is not None and len(c1) != nr:
raise Exception(
"The number of elements of c1 must match the number of components")
if sn is not None and len(sn) != nr:
raise Exception(
"The number of elements of bl must match the number of components")
if g is not None and len(g) != nr:
raise Exception(
"The number of elements of g must match the number of components")
[d, T] = np.shape(Y)
# C_corr = np.corrcoef(C[:nr,:],C[:nr,:])[:nr,:nr];
C_corr = np.corrcoef(C)
FF1 = C_corr >= thr
#find graph of strongly correlated temporal components
A_corr = A.T * A
A_corr.setdiag(0)
FF2 = A_corr > 0 # % find graph of overlapping spatial components
FF3 = np.logical_and(FF1, FF2.todense())
FF3 = coo_matrix(FF3)
c, l = csgraph.connected_components(FF3) # % extract connected components
p = temporal_params['p']
MC = []
for i in range(c):
if np.sum(l == i) > 1:
MC.append((l == i).T)
MC = np.asarray(MC).T
if MC.ndim > 1:
cor = np.zeros((np.shape(MC)[1], 1))
for i in range(np.size(cor)):
fm = np.where(MC[:, i])[0]
for j1 in range(np.size(fm)):
for j2 in range(j1 + 1, np.size(fm)):
cor[i] = cor[i] + C_corr[fm[j1], fm[j2]]
if not fast_merge:
Y_res = Y - A.dot(C)
if np.size(cor) > 1:
ind = np.argsort(np.squeeze(cor))[::-1]
else:
ind = [0]
nm = min((np.size(ind), mx)) # number of merging operations
A_merged = lil_matrix((d, nm))
C_merged = np.zeros((nm, T))
S_merged = np.zeros((nm, T))
bl_merged = np.zeros((nm, 1))
c1_merged = np.zeros((nm, 1))
sn_merged = np.zeros((nm, 1))
g_merged = np.zeros((nm, p))
# P_merged=[];
merged_ROIs = []
for i in range(nm):
# P_cycle=dict()
merged_ROI = np.where(MC[:, ind[i]])[0]
merged_ROIs.append(merged_ROI)
nC = np.sqrt(np.sum(C[merged_ROI, :]**2, axis=1))
# A_merged[:,i] = np.squeeze((A[:,merged_ROI]*spdiags(nC,0,len(nC),len(nC))).sum(axis=1))
if fast_merge:
Acsc = A.tocsc()[:, merged_ROI]
Acsd = Acsc.toarray()
Ctmp = C[merged_ROI, :]
print merged_ROI.T
#aa = A.tocsc()[:,merged_ROI].dot(scipy.sparse.diags(nC,0,(len(nC),len(nC)))).sum(axis=1)
aa = Acsc.dot(scipy.sparse.diags(
nC, 0, (len(nC), len(nC)))).sum(axis=1)
for iter in range(10):
#cc = np.dot(aa.T.dot(A.toarray()[:,merged_ROI]),C[merged_ROI,:])/(aa.T*aa)
cc = np.dot(aa.T.dot(Acsd), Ctmp) / (aa.T * aa)
#aa = A.tocsc()[:,merged_ROI].dot(C[merged_ROI,:].dot(cc.T))/(cc*cc.T)
aa = Acsc.dot(Ctmp.dot(cc.T)) / (cc * cc.T)
# nC = np.sqrt(np.sum(A.toarray()[:,merged_ROI]**2,axis=0))*np.sqrt(np.sum(C[merged_ROI,:]**2,axis=1))
nC = np.sqrt(np.sum(Acsd**2, axis=0)) * np.sqrt(
np.sum(Ctmp**2, axis=1))
nA = np.sqrt(np.sum(np.array(aa)**2))
aa /= nA
cc *= nA
indx = np.argmax(nC)
if g is not None:
cc, bm, cm, gm, sm, ss = constrained_foopsi(
np.array(cc).squeeze(),
g=g[merged_ROI[indx]],
**temporal_params)
else:
cc, bm, cm, gm, sm, ss = constrained_foopsi(
np.array(cc).squeeze(), g=None, **temporal_params)
A_merged[:, i] = aa
C_merged[i, :] = cc
S_merged[i, :] = ss[:T]
bl_merged[i] = bm
c1_merged[i] = cm
sn_merged[i] = sm
g_merged[i, :] = gm
else:
A_merged[:, i] = lil_matrix((A.tocsc()[:, merged_ROI].dot(
scipy.sparse.diags(nC, 0,
(len(nC), len(nC))))).sum(axis=1))
Y_res = Y_res + A.tocsc()[:, merged_ROI].dot(C[merged_ROI, :])
aa_1 = scipy.sparse.linalg.spsolve(
scipy.sparse.diags(nC, 0, (len(nC), len(nC))),
csc_matrix(C[merged_ROI, :]))
aa_2 = (aa_1).mean(axis=0)
ff = np.nonzero(A_merged[:, i])[0]
# cc,_,_,Ptemp,_ = update_temporal_components(np.asarray(Y_res[ff,:]),A_merged[ff,i],b[ff],aa_2,f,p=p,deconv_method=deconv_method)
cc, _, _, _, bl__, c1__, sn__, g__, YrA = update_temporal_components(
np.asarray(Y_res[ff, :]),
A_merged[ff, i],
b[ff],
aa_2,
f,
bl=None,
c1=None,
sn=None,
g=None,
**temporal_params)
aa, bb, cc = update_spatial_components(np.asarray(Y_res),
cc,
f,
A_merged[:, i],
sn=sn_pix,
**spatial_params)
A_merged[:, i] = aa.tocsr()
cc, _, _, ss, bl__, c1__, sn__, g__, YrA = update_temporal_components(
Y_res[ff, :],
A_merged[ff, i],
bb[ff],
cc,
f,
bl=bl__,
c1=c1__,
sn=sn__,
g=g__,
**temporal_params)
C_merged[i, :] = cc
S_merged[i, :] = ss
bl_merged[i] = bl__[0]
c1_merged[i] = c1__[0]
sn_merged[i] = sn__[0]
g_merged[i, :] = g__[0]
if i + 1 < nm:
Y_res[ff, :] = Y_res[ff, :] - A_merged[ff, i] * cc
#%
neur_id = np.unique(np.hstack(merged_ROIs))
good_neurons = np.setdiff1d(range(nr), neur_id)
A = scipy.sparse.hstack((A.tocsc()[:, good_neurons], A_merged.tocsc()))
C = np.vstack((C[good_neurons, :], C_merged))
if S is not None:
S = np.vstack((S[good_neurons, :], S_merged))
if bl is not None:
bl = np.hstack((bl[good_neurons], np.array(bl_merged).flatten()))
if c1 is not None:
c1 = np.hstack((c1[good_neurons], np.array(c1_merged).flatten()))
if sn is not None:
sn = np.hstack((sn[good_neurons], np.array(sn_merged).flatten()))
if g is not None:
g = np.vstack((np.vstack(g)[good_neurons], g_merged))
# P_new=list(P_[good_neurons].copy())
# P_new=[P_[pp] for pp in good_neurons]
#
# for p in P_merged:
# P_new.append(p)
#
nr = nr - len(neur_id) + nm
else:
print('********** No neurons merged! ***************')
merged_ROIs = []
return A, C, nr, merged_ROIs, S, bl, c1, sn, g
def merge_components(Y,A,b,C,f,S,sn_pix,temporal_params,spatial_params,thr=0.85,fast_merge=True,mx=50,bl=None,c1=None,sn=None,g=None):
""" Merging of spatially overlapping components that have highly correlated temporal activity
The correlation threshold for merging overlapping components is user specified in thr
Parameters
-----------
Y: np.ndarray
residual movie after subtracting all found components (Y_res = Y - A*C - b*f) (d x T)
A: sparse matrix
matrix of spatial components (d x K)
b: np.ndarray
spatial background (vector of length d)
C: np.ndarray
matrix of temporal components (K x T)
f: np.ndarray
temporal background (vector of length T)
S: np.ndarray
matrix of deconvolved activity (spikes) (K x T)
sn_pix: ndarray
noise standard deviation for each pixel
temporal_params: dictionary
all the parameters that can be passed to the update_temporal_components function
spatial_params: dictionary
all the parameters that can be passed to the update_spatial_components function
thr: scalar between 0 and 1
correlation threshold for merging (default 0.85)
mx: int
maximum number of merging operations (default 50)
sn_pix: nd.array
noise level for each pixel (vector of length d)
bl:
baseline for fluorescence trace for each row in C
c1:
initial concentration for each row in C
g:
discrete time constant for each row in C
sn:
noise level for each row in C
Returns
--------
A: sparse matrix
matrix of merged spatial components (d x K)
C: np.ndarray
matrix of merged temporal components (K x T)
nr: int
number of components after merging
merged_ROIs: list
index of components that have been merged
S: np.ndarray
matrix of merged deconvolved activity (spikes) (K x T)
bl: float
baseline for fluorescence trace
c1: float
initial concentration
g: float
discrete time constant
sn: float
noise level
"""
#%
nr = A.shape[1]
if bl is not None and len(bl) != nr:
raise Exception("The number of elements of bl must match the number of components")
if c1 is not None and len(c1) != nr:
raise Exception("The number of elements of c1 must match the number of components")
if sn is not None and len(sn) != nr:
raise Exception("The number of elements of bl must match the number of components")
if g is not None and len(g) != nr:
raise Exception("The number of elements of g must match the number of components")
[d,T] = np.shape(Y)
C_corr = np.corrcoef(C[:nr,:],C[:nr,:])[:nr,:nr];
FF1=C_corr>=thr; #find graph of strongly correlated temporal components
A_corr=A.T*A
A_corr.setdiag(0)
FF2=A_corr>0 # % find graph of overlapping spatial components
FF3=np.logical_and(FF1,FF2.todense())
FF3=coo_matrix(FF3)
c,l=csgraph.connected_components(FF3) # % extract connected components
p=temporal_params['p']
MC=[];
for i in range(c):
if np.sum(l==i)>1:
MC.append((l==i).T)
MC=np.asarray(MC).T
if MC.ndim>1:
cor = np.zeros((np.shape(MC)[1],1));
for i in range(np.size(cor)):
fm = np.where(MC[:,i])[0]
for j1 in range(np.size(fm)):
for j2 in range(j1+1,np.size(fm)):
cor[i] = cor[i] +C_corr[fm[j1],fm[j2]]
if not fast_merge:
Y_res = Y - A.dot(C)
if np.size(cor) > 1:
ind=np.argsort(np.squeeze(cor))[::-1]
else:
ind = [0]
nm = min((np.size(ind),mx)) # number of merging operations
A_merged = lil_matrix((d,nm));
C_merged = np.zeros((nm,T));
S_merged = np.zeros((nm,T));
bl_merged=np.zeros((nm,1))
c1_merged=np.zeros((nm,1))
sn_merged=np.zeros((nm,1))
g_merged=np.zeros((nm,p))
# P_merged=[];
merged_ROIs = []
#%
for i in range(nm):
# P_cycle=dict()
merged_ROI=np.where(MC[:,ind[i]])[0]
merged_ROIs.append(merged_ROI)
nC = np.sqrt(np.sum(C[merged_ROI,:]**2,axis=1))
# A_merged[:,i] = np.squeeze((A[:,merged_ROI]*spdiags(nC,0,len(nC),len(nC))).sum(axis=1))
if fast_merge:
aa = A.tocsc()[:,merged_ROI].dot(scipy.sparse.diags(nC,0,(len(nC),len(nC)))).sum(axis=1)
for iter in range(10):
cc = np.dot(aa.T.dot(A.toarray()[:,merged_ROI]),C[merged_ROI,:])/(aa.T*aa)
aa = A.tocsc()[:,merged_ROI].dot(C[merged_ROI,:].dot(cc.T))/(cc*cc.T)
nC = np.sqrt(np.sum(A.toarray()[:,merged_ROI]**2,axis=0))*np.sqrt(np.sum(C[merged_ROI,:]**2,axis=1))
indx = np.argmax(nC)
cc,bm,cm,gm,sm,ss = constrained_foopsi(np.array(cc).squeeze(),g=g[merged_ROI[indx]],**temporal_params)
A_merged[:,i] = aa;
C_merged[i,:] = cc
S_merged[i,:] = ss[:T]
bl_merged[i] = bm
c1_merged[i] = cm
sn_merged[i] = sm
g_merged[i,:] = gm
else:
A_merged[:,i] = lil_matrix(( A.tocsc()[:,merged_ROI].dot(scipy.sparse.diags(nC,0,(len(nC),len(nC))))).sum(axis=1))
Y_res = Y_res + A.tocsc()[:,merged_ROI].dot(C[merged_ROI,:])
aa_1=scipy.sparse.linalg.spsolve(scipy.sparse.diags(nC,0,(len(nC),len(nC))),csc_matrix(C[merged_ROI,:]))
aa_2=(aa_1).mean(axis=0)
ff = np.nonzero(A_merged[:,i])[0]
# cc,_,_,Ptemp,_ = update_temporal_components(np.asarray(Y_res[ff,:]),A_merged[ff,i],b[ff],aa_2,f,p=p,deconv_method=deconv_method)
cc,_,_,_,bl__,c1__,sn__,g__,YrA = update_temporal_components(np.asarray(Y_res[ff,:]),A_merged[ff,i],b[ff],aa_2,f,bl=None,c1=None,sn=None,g=None,**temporal_params)
aa,bb,cc = update_spatial_components(np.asarray(Y_res),cc,f,A_merged[:,i],sn=sn_pix,**spatial_params)
A_merged[:,i] = aa.tocsr();
cc,_,_,ss,bl__,c1__,sn__,g__,YrA = update_temporal_components(Y_res[ff,:],A_merged[ff,i],bb[ff],cc,f,bl=bl__,c1=c1__,sn=sn__,g=g__,**temporal_params)
# P_cycle=P_[merged_ROI[0]].copy()
# P_cycle['gn']=Ptemp[0]['gn']
# P_cycle['b']=Ptemp[0]['b']
# P_cycle['c1']=Ptemp[0]['c1']
# P_cycle['neuron_sn']=Ptemp[0]['neuron_sn']
# P_merged.append(P_cycle)
C_merged[i,:] = cc
S_merged[i,:] = ss
bl_merged[i] = bl__[0]
c1_merged[i] = c1__[0]
sn_merged[i] = sn__[0]
g_merged[i,:] = g__[0]
if i+1 < nm:
Y_res[ff,:] = Y_res[ff,:] - A_merged[ff,i]*cc
#%
neur_id = np.unique(np.hstack(merged_ROIs))
good_neurons=np.setdiff1d(range(nr),neur_id)
A = scipy.sparse.hstack((A.tocsc()[:,good_neurons],A_merged.tocsc()))
C = np.vstack((C[good_neurons,:],C_merged))
S = np.vstack((S[good_neurons,:],S_merged))
bl=np.hstack((bl[good_neurons],np.array(bl_merged).flatten()))
c1=np.hstack((c1[good_neurons],np.array(c1_merged).flatten()))
sn=np.hstack((sn[good_neurons],np.array(sn_merged).flatten()))
g=np.vstack((np.vstack(g)[good_neurons],g_merged))
# P_new=list(P_[good_neurons].copy())
# P_new=[P_[pp] for pp in good_neurons]
#
# for p in P_merged:
# P_new.append(p)
#
nr = nr - len(neur_id) + nm
else:
print('********** No neurons merged! ***************')
merged_ROIs=[];
return A,C,nr,merged_ROIs,S,bl,c1,sn,g
