def spgl1_foopsi(fluor, bl, c1, g, sn, p, bas_nonneg, verbosity, thr = 1e-2,debug=False):
"""Solve the deconvolution problem using the SPGL1 library
available from https://github.com/epnev/SPGL1_python_port
"""
fluor=fluor.copy()
if 'spg' not in globals():
raise Exception('The SPGL package could not be loaded, use a different method')
if bl is None:
bas_flag = True
# kde = KernelDensity(kernel='gaussian', bandwidth=100).fit(fluor[:,np.newaxis])
# X_plot=np.linspace(np.min(fluor),np.max(fluor),len(fluor))[:,np.newaxis]
# p_dens = np.exp(kde.score_samples(X_plot))
# print 'esimating baseline...'
# n,bn=np.histogram(fluor,range=(np.percentile(fluor,1),np.percentile(fluor,99)),bins=100)
# bl = bn[np.argmax(n)]
bl = 0
else:
bas_flag = False
if c1 is None:
c1_flag = True
c1 = 0
else:
c1_flag = False
if bas_nonneg:
b_lb = 0
else:
b_lb = np.min(fluor)
T = len(fluor)
w = np.ones(np.shape(fluor))
if bas_flag:
w = np.hstack((w,1e-10))
if c1_flag:
w = np.hstack((w,1e-10))
gr = np.roots(np.concatenate([np.array([1]),-g.flatten()]))
gd_vec = np.max(gr)**np.arange(T) # decay vector for initial fluorescence
options = {'project' : spg.NormL1NN_project ,
'primal_norm' : spg.NormL1NN_primal ,
'dual_norm' : spg.NormL1NN_dual,
'weights' : w,
'verbosity' : verbosity,
'iterations': T}
opA = lambda x,mode: G_inv_mat(x,mode,T,g,gd_vec,bas_flag,c1_flag)
spikes,_,_,info = spg_bpdn(opA,np.squeeze(fluor)-bas_nonneg*b_lb - (1-bas_flag)*bl -(1-c1_flag)*c1*gd_vec, sn*np.sqrt(T))
if np.min(spikes)<-thr*np.max(spikes) and not debug:
spikes[:T][spikes[:T]<0]=0
spikes,_,_,info = spg_bpdn(opA,np.squeeze(fluor)-bas_nonneg*b_lb - (1-bas_flag)*bl -(1-c1_flag)*c1*gd_vec, sn*np.sqrt(T), options)
spikes[:T][spikes[:T]<0]=0
c = opA(np.hstack((spikes[:T],0)),1)
if bas_flag:
bl = spikes[T] + b_lb
if c1_flag:
c1 = spikes[-1]
return c,bl,c1,g,sn,spikes
def spgl1_foopsi(fluor, bl, c1, g, sn, p, bas_nonneg, verbosity, thr = 1e-2,debug=False):
"""Solve the deconvolution problem using the SPGL1 library
available from https://github.com/epnev/SPGL1_python_port
"""
fluor=fluor.copy()
if 'spg' not in globals():
raise Exception('The SPGL package could not be loaded, use a different method')
if bl is None:
bas_flag = True
bl = 0
else:
bas_flag = False
if c1 is None:
c1_flag = True
c1 = 0
else:
c1_flag = False
if bas_nonneg:
b_lb = 0
else:
b_lb = np.min(fluor)
T = len(fluor)
w = np.ones(np.shape(fluor))
if bas_flag:
w = np.hstack((w,1e-10))
if c1_flag:
w = np.hstack((w,1e-10))
gr = np.roots(np.concatenate([np.array([1]),-g.flatten()]))
gd_vec = np.max(gr)**np.arange(T) # decay vector for initial fluorescence
options = {'project' : spg.NormL1NN_project ,
'primal_norm' : spg.NormL1NN_primal ,
'dual_norm' : spg.NormL1NN_dual,
'weights' : w,
'verbosity' : verbosity,
'iterations': T}
opA = lambda x,mode: G_inv_mat(x,mode,T,g,gd_vec,bas_flag,c1_flag)
spikes,_,_,info = spg_bpdn(opA,np.squeeze(fluor)-bas_nonneg*b_lb - (1-bas_flag)*bl -(1-c1_flag)*c1*gd_vec, sn*np.sqrt(T))
if np.min(spikes)<-thr*np.max(spikes) and not debug:
spikes[:T][spikes[:T]<0]=0
spikes,_,_,info = spg_bpdn(opA,np.squeeze(fluor)-bas_nonneg*b_lb - (1-bas_flag)*bl -(1-c1_flag)*c1*gd_vec, sn*np.sqrt(T), options)
#print [np.min(spikes[:T]),np.max(spikes[:T])]
spikes[:T][spikes[:T]<0]=0
c = opA(np.hstack((spikes[:T],0)),1)
if bas_flag:
bl = spikes[T] + b_lb
if c1_flag:
c1 = spikes[-1]
return c,bl,c1,g,sn,spikes
