sn,
options['temporal_params'],
options['spatial_params'],
bl=bl,
c1=c1,
sn=neurons_sn,
g=g,
thr=0.8,
mx=50,
fast_merge=True)
t_elMERGE = time() - t1
print t_elMERGE
#%%
plt.figure()
crd = cse.plot_contours(A_m, Cn, thr=0.9)
#%% refine spatial and temporal
pl.close()
t1 = time()
A2, b2, C2 = cse.spatial.update_spatial_components(Yr,
C_m,
f,
A_m,
sn=sn,
**options['spatial_params'])
options['temporal_params'][
'p'] = p # set it back to original value to perform full deconvolution
C2, f2, S2, bl2, c12, neurons_sn2, g21, YrA = cse.temporal.update_temporal_components(
Yr,
A2,
b2,
#%% update_temporal_components pl.close() t1 = time() options['temporal_params']['p'] = 0 # set this to zero for fast updating without deconvolution C,f,S,bl,c1,neurons_sn,g,YrA = cse.temporal.update_temporal_components(Yr,A,b,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params']) t_elTEMPORAL = time() - t1 print(t_elTEMPORAL) #%% merge components corresponding to the same neuron t1 = time() A_m,C_m,nr_m,merged_ROIs,S_m,bl_m,c1_m,sn_m,g_m=cse.merging.merge_components(Yr,A,b,C,f,S,sn,options['temporal_params'], options['spatial_params'], bl=bl, c1=c1, sn=neurons_sn, g=g, thr=0.8, mx=50, fast_merge = True) t_elMERGE = time() - t1 print(t_elMERGE) #%% plt.figure() crd = cse.plot_contours(A_m,Cn,thr=0.9) #%% refine spatial and temporal pl.close() t1 = time() A2,b2,C2 = cse.spatial.update_spatial_components(Yr, C_m, f, A_m, sn=sn, **options['spatial_params']) options['temporal_params']['p'] = p # set it back to original value to perform full deconvolution C2,f2,S2,bl2,c12,neurons_sn2,g21,YrA = cse.temporal.update_temporal_components(Yr,A2,b2,C2,f,bl=None,c1=None,sn=None,g=None,**options['temporal_params']) print((time() - t1)) #%% A_or, C_or, srt = cse.utilities.order_components(A2,C2) #cse.utilities.view_patches(Yr,coo_matrix(A_or),C_or,b2,f2,d1,d2,YrA = YrA[srt,:], secs=1) cse.utilities.view_patches_bar(Yr,coo_matrix(A_or),C_or,b2,f2, d1,d2, YrA=YrA[srt,:]) #plt.show(block=True) plt.show() #%%
'n_processes':n_processes,'backend':'ipyparallel',
'memory_efficient':False,
'bas_nonneg':True,
'noise_range':[.25,.5], 'noise_method':'logmexp',
'lags':5, 'fudge_factor':1.,
'verbosity':False
}
#%% PREPROCESS DATA
t1 = time()
Yr,sn,g=cse.preprocess_data(Yr,**preprocess_params)
Ain, Cin, b_in, f_in, center=cse.initialize_components(Y, **init_params)
print time() - t1
plt2 = plt.imshow(Cn,interpolation='None')
plt.colorbar()
plt.scatter(x=center[:,1], y=center[:,0], c='m', s=40)
crd = cse.plot_contours(coo_matrix(Ain[:,::-1]),Cn,thr=0.9)
plt.axis((-0.5,d2-0.5,-0.5,d1-0.5))
plt.gca().invert_yaxis()
pl.show()
#%%
t1 = time()
A,b,Cin = cse.update_spatial_components_parallel(Yr, Cin, f_in, Ain, sn=sn, **spatial_params)
t_elSPATIAL = time() - t1
print t_elSPATIAL
crd = cse.plot_contours(A,Cn,thr=0.9)
#%% update_temporal_components
t1 = time()
C,f,Y_res,S,bl,c1,neurons_sn,g = cse.update_temporal_components_parallel(Yr,A,b,Cin,f_in,bl=None,c1=None,sn=None,g=None,**temporal_params)
t_elTEMPORAL2 = time() - t1
print t_elTEMPORAL2 # took 98 sec
ds).mean(-1).mean(-2).reshape(len(data), -1),
shapes.reshape(-1, 96 / ds, ds, 96 / ds,
ds).mean(-1).mean(-2).reshape(len(shapes), -1),
activity.copy(), 20)
# reconstruction and residual based on low-res data
denoised = activity_ds.T.dot(shapes.reshape(N + 1, -1)).reshape(data.shape)
residual = data - denoised
if plot_smooth_shapes:
A2 = coo_matrix(np.transpose([gaussian(a, 1).ravel() for a in shapes[:N]]))
else:
A2 = coo_matrix(shapes[:N].reshape(N, -1).T)
fig = plt.figure(figsize=(16.7, 10))
ax1 = fig.add_axes([.002, .512, .3, .46])
cse.plot_contours(A2, data[0].T, thr=0.9, display_numbers=False, colors='w')
im1 = plt.imshow(data[0].T, cmap=gfp, vmin=mi, vmax=ma)
plt.title('data')
plt.axis('off')
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im1, cax=cax)
ax2 = fig.add_axes([.332, .512, .3, .46])
cse.plot_contours(A2,
denoised1[0].T,
thr=0.9,
display_numbers=False,
colors='w')
im2 = plt.imshow(denoised1[0].T, cmap=gfp, vmin=mi, vmax=ma)