def convert_to_firing_rate(self, calciumtraces):
firingrate = []
denoisedcalicumtrace = []
for ii in xrange(0, np.size(calciumtraces, 1)):
fluor = calciumtraces[:, ii]
print 'Deconvolving..', ii
deconvolvedresult = constrained_foopsi(fluor)
print np.ndim(deconvolvedresult[5])
if np.ndim(deconvolvedresult[5]) > 1:
print 'Skipping..Cell ', ii
continue
firingrate.append(deconvolvedresult[5])
denoisedcalicumtrace.append(deconvolvedresult[0])
firingrate = np.asarray(np.vstack(firingrate)).T
denoisedcalicumtrace = np.asarray(np.vstack(denoisedcalicumtrace)).T
np.savetxt(os.path.join(self.WorkingDirectory, key + "_denoiseddata.csv"), denoisedcalicumtrace, delimiter=",")
np.savetxt(os.path.join(self.WorkingDirectory, key + "_firingrate.csv"), firingrate, delimiter=",")
plt.plot(np.clip(firingrate, 0, np.max(firingrate)), '.')
plt.savefig('/Users/seetha/Desktop/test1.png')
def convert_to_firing_rate(self):
rawdatacsv_filenames = [f for f in os.listdir(os.path.join(self.WorkingDirectory, 'RawdataCSV')) if
f.endswith('.csv')]
for ff in rawdatacsv_filenames[2:]:
print 'Deconvolving File...', ff
calciumtraces = np.loadtxt(os.path.join(self.WorkingDirectory, 'RawdataCSV', ff), delimiter=",")
print np.shape(calciumtraces)
firingrate = []
denoisedcalicumtrace = []
cellsused = np.array([])
for ii in xrange(0, np.size(calciumtraces, 1)):
fluor = calciumtraces[2:, ii] # First two rows are fish and plane number
print 'Deconvolving..', ii
try:
deconvolvedresult = constrained_foopsi(fluor)
except ZeroDivisionError:
print 'Skipping..Cell ', ii
continue
except ValueError:
print 'Skipping..Cell ', ii
continue
if np.ndim(deconvolvedresult[5]) > 1:
print 'Skipping..Cell ', ii
continue
print np.ndim(deconvolvedresult[5])
cellsused = np.vstack((cellsused, ii)) if np.size(cellsused) else ii
firingrate.append(deconvolvedresult[5])
denoisedcalicumtrace.append(deconvolvedresult[0])
firingrate = np.asarray(np.vstack(firingrate)).T
firingrate = np.vstack((calciumtraces[1, cellsused].T, firingrate))
denoisedcalicumtrace = np.asarray(np.vstack(denoisedcalicumtrace)).T
denoisedcalicumtrace = np.vstack((calciumtraces[1, cellsused].T, denoisedcalicumtrace))
np.savetxt(os.path.join(self.WorkingDirectory, 'DeconvolveddataCSV', ff[:-4] + "_denoiseddata.csv"),
denoisedcalicumtrace, delimiter=",")
np.savetxt(os.path.join(self.WorkingDirectory, 'DenoisedDataCSV', ff[:-4] + "_firingrate.csv"), firingrate,
delimiter=",")
plt.plot(np.clip(firingrate[2:, :], 0, np.max(firingrate[2:, :])), '.')
plt.savefig(os.path.join(WorkingDirectory, 'Images', ff[:-4] + '.png'))
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)
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, _ = 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
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 % 10 == 0:
print str(jj) + ' 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_
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)
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,_ = 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
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%10 == 0:
print str(jj) + ' 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_
