def place_holder(fl):
import calblitz as cb
import ca_source_extraction as cse
import numpy as np
m=cb.load(fl)
Cn = m.local_correlations()
cnmf=cse.CNMF(1, k=4,gSig=[8,8],merge_thresh=0.8,p=2,dview=None,Ain=None)
cnmf=cnmf.fit(m)
A,C,b,f,YrA=cnmf.A,cnmf.C,cnmf.b,cnmf.f,cnmf.YrA
np.savez(fl[:-5]+'_result.npz',A=A,C=C,b=b,f=f,YrA=YrA,Cn=Cn)
return fl[:-5]+'_result.npz'
#fname_new='Yr_d1_501_d2_398_d3_1_order_F_frames_369_.mmap'
Yr, dims, T = cse.utilities.load_memmap(fname_new)
d1, d2 = dims
images = np.reshape(Yr.T, [T] + list(dims), order='F')
Y = np.reshape(Yr, dims + (T, ), order='F')
#%%
Cn = cse.utilities.local_correlations(Y[:, :, :1800])
pl.imshow(Cn)
#%%
if not is_patches:
#%%
K = 100 # number of neurons expected per patch
gSig = [5, 5] # expected half size of neurons
merge_thresh = 0.8 # merging threshold, max correlation allowed
p = 2 #order of the autoregressive system
cnmf=cse.CNMF(n_processes, method_init=init_method, k=K,gSig=gSig,merge_thresh=merge_thresh,\
p=p,dview=dview,Ain=None)
cnmf = cnmf.fit(images)
#%%
else:
#%%
rf = 30 # half-size of the patches in pixels. rf=25, patches are 50x50
stride = 4 #amounpl.it of overlap between the patches in pixels
K = 2 # number of neurons expected per patch
gSig = [12, 12] # expected half size of neurons
merge_thresh = 0.8 # merging threshold, max correlation allowed
p = 2 #order of the autoregressive system
memory_fact = 1
#unitless number accounting how much memory should be used. You will need to try different values to see which one would work the default is OK for a 16 GB system
save_results = False
#%% RUN ALGORITHM ON PATCHES
Y=np.reshape(Yr,dims+(T,),order='F')
#%%
Cn = cse.utilities.local_correlations(Y[:,:,:3000])
pl.imshow(Cn,cmap='gray')
#%%
rf=10 # half-size of the patches in pixels. rf=25, patches are 50x50
stride = 4 #amounpl.it of overlap between the patches in pixels
K=4 # number of neurons expected per patch
gSig=[5,5] # expected half size of neurons
merge_thresh=0.8 # merging threshold, max correlation allowed
p=2 #order of the autoregressive system
memory_fact=1; #unitless number accounting how much memory should be used. You will need to try different values to see which one would work the default is OK for a 16 GB system
save_results=False
#%% RUN ALGORITHM ON PATCHES
cnmf=cse.CNMF(n_processes, k=K,gSig=gSig,merge_thresh=0.8,p=0,dview=c[:],Ain=None, \
rf=rf,stride=stride, memory_fact=memory_fact,\
method_init='greedy_roi',alpha_snmf=10e2)
cnmf=cnmf.fit(images)
A_tot=cnmf.A
C_tot=cnmf.C
YrA_tot=cnmf.YrA
b_tot=cnmf.b
f_tot=cnmf.f
sn_tot=cnmf.sn
print 'Number of components:' + str(A_tot.shape[-1])
#%%
idx_components, fitness, erfc ,r_values, num_significant_samples = cse.utilities.evaluate_components(Y,C_tot+YrA_tot,A_tot,robust_std=False,thresh_finess=-15)
sure_in_idx= idx_components[np.logical_and(np.array(num_significant_samples)>0 ,np.array(r_values)>=.3)]