cnm = cnmf.CNMF(n_processes,
method_init=init_method,
k=K,
gSig=gSig,
merge_thresh=merge_thresh,
p=p,
dview=dview,
Ain=None,
method_deconvolution='oasis',
skip_refinement=False)
cnm = cnm.fit(images)
crd = plot_contours(cnm.A, Cn, thr=0.9)
C_dff = extract_DF_F(Yr,
cnm.A,
cnm.C,
cnm.bl,
quantileMin=8,
frames_window=200,
dview=dview)
pl.figure()
pl.plot(C_dff.T)
else:
rf = 14 # half-size of the patches in pixels. rf=25, patches are 50x50
stride = 6 # amounpl.it of overlap between the patches in pixels
K = 6 # number of neurons expected per patch
gSig = [6, 6] # expected half size of neurons
merge_thresh = 0.8 # merging threshold, max correlation allowed
p = 1 # order of the autoregressive system
save_results = False
cnm = cnmf.CNMF(n_processes,
Yr,dims,T=cm.load_memmap(fname_new)
Y=np.reshape(Yr,dims+(T,),order='F')
nb_back=1
options = cnmf.utilities.CNMFSetParms(Y,n_processes,p=p,gSig=gSig,K=K,ssub=2,tsub=1,nb=nb_back)
#options['preprocess_params']['max_num_samples_fft']=10000
Cn = cm.local_correlations(Y)
Yr,sn,g,psx = cnmf.pre_processing.preprocess_data(Yr,dview=dview,**options['preprocess_params'])
Ain,Cin, b_in, f_in, center=cnmf.initialization.initialize_components(Y, **options['init_params'])
Ain,b_in,Cin, f_in = cnmf.spatial.update_spatial_components(Yr, Cin, f_in, Ain, sn=sn, dview=dview,**options['spatial_params'])
if Cin.size > 0:
options = cnmf.utilities.CNMFSetParms(Y,n_processes,p=p,gSig=gSig,K=K,tsub=1)
options['temporal_params']['p'] = 0 # set this to zero for fast updating without deconvolution
Cin,Ain,b_in,f_in,S,bl,c1,neurons_sn,g,YrA = cnmf.temporal.update_temporal_components(Yr,Ain,b_in,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params'])
Ain,Cin,nr_m,merged_ROIs,S_m,bl_m,c1_m,sn_m,g_m=cnmf.merging.merge_components(Yr,Ain,b_in,Cin,f_in,S,sn,options['temporal_params'], options['spatial_params'],dview=dview, bl=bl, c1=c1, sn=neurons_sn, g=g, thr=merge_thresh,mx=1000, fast_merge = True)
Ain,b_in,Cin, f_in = cnmf.spatial.update_spatial_components(Yr, Cin, f_in, Ain, sn=sn, dview=dview,**options['spatial_params'])
options['temporal_params']['p'] = p # set it back to original value to perform full deconvolution
Cin,Ain,b_in,f_in,S,bl,c1,neurons_sn,g,YrA = cnmf.temporal.update_temporal_components(Yr,Ain,b_in,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params'])
traces=Cin+YrA
tB = np.minimum(-2,np.floor(-5./30*final_frate))
tA = np.maximum(5,np.ceil(25./30*final_frate))
fitness_raw, fitness_delta, erfc_raw,erfc_delta,r_values,num_significant_samples = evaluate_components(Y,traces,Ain,Cin,bl,f_in, final_frate, remove_baseline = True, N = 5, robust_std = False, Athresh = 0.1, Npeaks = 5, thresh_C = 0.2)
idx_components_r=np.where(r_values>=.6)[0]
idx_components_raw=np.where(fitness_raw<-40)[0]
idx_components_delta=np.where(fitness_delta<-20)[0]
idx_components=np.union1d(idx_components_r,idx_components_raw)
C_dff = extract_DF_F(Yr, Ain.tocsc()[:, idx_components], Cin[idx_components, :], bl[idx_components], quantileMin = 8, frames_window = 200, dview = dview)
idx_components=np.union1d(idx_components,idx_components_delta)
idx_components_bad=np.setdiff1d(range(len(traces)),idx_components)
savemat(fnames[0][:-4]+'_output_analysis_matlab.mat',mdict={'ROIs':Ain,'DenoisedTraces':Cin,'Baseline':bl, 'Noise':YrA, 'Spikes': S,'DFF':C_dff , 'idx_components':idx_components, 'Correlation_image':Cn})
big_string = ''
for substring in my_list:
big_string = big_string + ' ' + substring
A = pcf.cnmf.estimates.A
F = pcf.cnmf.estimates.C + pcf.cnmf.estimates.YrA
b = pcf.cnmf.estimates.b
f= pcf.cnmf.estimates.f
B = A.T.dot(b).dot(f)
import scipy.ndimage as nd
Df = nd.percentile_filter(B, 10, (1000,1))
plt.figure(); plt.plot(B[49]+pcf.cnmf.estimates.C[49]+pcf.cnmf.estimates.R[49])
#%% Flag auto as False, how does dFF look
import caiman.source_extraction.cnmf.utilities as ut
flag_dff = ut.detrend_df_f(A, b, pcf.cnmf.estimates.C, f, YrA=pcf.cnmf.estimates.YrA, quantileMin=8,
frames_window=500, flag_auto=False, use_fast=False, detrend_only=False)
slow_dff = ut.extract_DF_F(Yr, A, C, bl, quantileMin=8, frames_window=200, block_size=400, dview=None)
fig, ax = plt.subplots(2)
ax[0].plot(pcf.cnmf.estimates.F_dff[49])
ax[1].plot(flag_dff[49])
plt.figure()
plt.plot(flag_dff[49], label='auto=False')
plt.plot(pcf.cnmf.estimates.F_dff[49], label='auto=True')
plt.legend()
raise Exception('Movie contains nan! You did not remove enough borders')
#%%
Cn = cm.local_correlations(Y[:, :, :3000])
pl.imshow(Cn, cmap='gray')
#%%
if not is_patches:
K = 35 # number of neurons expected per patch
gSig = [7, 7] # expected half size of neurons
merge_thresh = 0.8 # merging threshold, max correlation allowed
p = 2 # order of the autoregressive system
cnm = cnmf.CNMF(n_processes, method_init=init_method, k=K, gSig=gSig, merge_thresh=merge_thresh,
p=p, dview=dview, Ain=None, method_deconvolution='oasis', skip_refinement=False)
cnm = cnm.fit(images)
crd = plot_contours(cnm.A, Cn, thr=0.9)
C_dff = extract_DF_F(Yr, cnm.A, cnm.C, cnm.bl,
quantileMin=8, frames_window=200, dview=dview)
pl.figure()
pl.plot(C_dff.T)
else:
rf = 14 # half-size of the patches in pixels. rf=25, patches are 50x50
stride = 6 # amounpl.it of overlap between the patches in pixels
K = 6 # number of neurons expected per patch
gSig = [6, 6] # expected half size of neurons
merge_thresh = 0.8 # merging threshold, max correlation allowed
p = 1 # order of the autoregressive system
save_results = False
cnm = cnmf.CNMF(n_processes, k=K, gSig=gSig, merge_thresh=0.8, p=0, dview=dview, Ain=None, rf=rf, stride=stride, memory_fact=1,
method_init=init_method, alpha_snmf=alpha_snmf, only_init_patch=True, gnb=2, method_deconvolution='oasis', low_rank_background=True)
cnm = cnm.fit(images)
Y,
A,
C,
b,
f,
final_frate=final_frate,
Npeaks=Npeaks,
r_values_min=r_values_min,
fitness_min=fitness_min,
fitness_delta_min=fitness_delta_min,
return_all=True)
print(' ***** ')
print((len(traces)))
print((len(idx_components)))
# %% save results
Cdf = extract_DF_F(Yr=Yr, A=A, C=C, bl=cnm.bl)
np.savez(os.path.join(
os.path.split(fname_new)[0],
os.path.split(fname_new)[1][:-4] + 'results_analysis.npz'),
Cn=Cn,
A=A,
Cdf=Cdf,
C=C,
b=b,
f=f,
YrA=YrA,
sn=sn,
d1=d1,
d2=d2,
idx_components=idx_components,
idx_components_bad=idx_components_bad,