#%% compute spatial components via NMF
initTime=time.time()
space_spcomps,time_comps=(m-np.min(m)).NonnegativeMatrixFactorization(n_components=20,beta=1,tol=5e-7);
print 'elapsed time:' + str(time.time()-initTime)
cb.matrixMontage(np.asarray(space_spcomps),cmap=pl.cm.gray) # visualize components
#%% compute spatial components via ICA PCA
from scipy.stats import mode
initTime=time.time()
spcomps=m.IPCA_stICA(components=40,mu=.5,batch=100000);
print 'elapsed time:' + str(time.time()-initTime)
#cb.matrixMontage(spcomps,cmap=pl.cm.gray) # visualize components
#%% extract ROIs from spatial components
#_masks,masks_grouped=m.extractROIsFromPCAICA(spcomps, numSTD=6, gaussiansigmax=2 , gaussiansigmay=2)
_masks,_=cb.extractROIsFromPCAICA(spcomps, numSTD=20.0, gaussiansigmax=1 , gaussiansigmay=1,thresh=.005)
#cb.matrixMontage(np.asarray(_masks),cmap=pl.cm.gray)
#%% extract single ROIs from each mask
minPixels=8;
maxPixels=200;
masks_tmp=[];
for mask in _masks:
numPixels=np.sum(np.array(mask));
if (numPixels>minPixels and numPixels<maxPixels):
print numPixels
masks_tmp.append(mask>0)
masks_tmp=np.asarray(masks_tmp,dtype=np.float16)
all_masksForPlot_tmp=[kk*(ii+1)*1.0 for ii,kk in enumerate(masks_tmp)]
len(all_masksForPlot_tmp)
if False:
loc_corrs=m.local_correlations(eight_neighbours=True)
m=m.IPCA_denoise(components = 100, batch = 100000)
m=m*loc_corrs
#%% compute spatial components via ICA PCA
print 'Computing PCA + ICA...'
initTime=time.time()
spcomps=m.IPCA_stICA(componentsPCA=70,componentsICA = 50, mu=1, batch=1000000, algorithm='parallel', whiten=True, ICAfun='logcosh', fun_args=None, max_iter=2000, tol=1e-8, w_init=None, random_state=None);
print 'elapsed time:' + str(time.time()-initTime)
cb.matrixMontage(spcomps,cmap=pl.cm.gray) # visualize components
#%% extract ROIs from spatial components
#_masks,masks_grouped=m.extractROIsFromPCAICA(spcomps, numSTD=6, gaussiansigmax=2 , gaussiansigmay=2)
_masks,_=cb.extractROIsFromPCAICA(spcomps, numSTD=4.0, gaussiansigmax=.1 , gaussiansigmay=.2)
#cb.matrixMontage(np.asarray(_masks),cmap=pl.cm.gray)
#%% extract single ROIs from each mask
minPixels=30;
maxPixels=4000;
masks_tmp=[];
for mask in _masks:
numPixels=np.sum(np.array(mask));
if (numPixels>minPixels and numPixels<maxPixels):
# print numPixels
masks_tmp.append(mask>0)
masks_tmp=np.asarray(masks_tmp,dtype=np.float16)
all_masksForPlot_tmp=[kk*(ii+1)*1.0 for ii,kk in enumerate(masks_tmp)]
len(all_masksForPlot_tmp)
