m_copy=m.copy()
#%% resize to increase SNR and have better convergence of segmentation algorithms
resizeMovie=True
if resizeMovie:
fx=.5; # downsample a factor of four along x axis
fy=.5;
fz=.1; # downsample a factor of 5 across time dimension
m.resize(fx=fx,fy=fy,fz=fx)
else:
fx,fy,fz=1,1,1
#%% compute delta f over f (DF/F)
initTime=time.time()
m.computeDFF(secsWindow=15,quantilMin=20,subtract_minimum=False)
print 'elapsed time:' + str(time.time()-initTime)
#%% compute subregions where to apply more efficiently facrtorization algorithms
fovs, mcoef, distanceMatrix=m.partition_FOV_KMeans(tradeoff_weight=.7,fx=.25,fy=.25,n_clusters=4,max_iter=500);
plt.imshow(fovs)
#%% create a denoised version of the movie, nice to visualize
if True:
m2=m.copy()
m2.IPCA_denoise(components = 100, batch = 1000)
m2.playMovie(frate=.05,magnification=4,gain=10.0)
#%%
m_copy=m.copy()
#%% resize to increase SNR and have better convergence of segmentation algorithms
resizeMovie=True
if resizeMovie:
fx=.5; # downsample a factor of four along x axis
fy=.5;
fz=.2; # downsample a factor of 5 across time dimension
m.resize(fx=fx,fy=fy,fz=fz)
else:
fx,fy,fz=1,1,1
#%% compute delta f over f (DF/F)
initTime=time.time()print 'elapsed time:' + str(time.time()-initTime)
m.computeDFF(secsWindow=10,quantilMin=50,subtract_minimum=True)
print 'elapsed time:' + str(time.time()-initTime)
#%% compute subregions where to apply more efficiently facrtorization algorithms
fovs, mcoef, distanceMatrix=m.partition_FOV_KMeans(tradeoff_weight=.7,fx=.25,fy=.25,n_clusters=4,max_iter=500);
plt.imshow(fovs)
#%% create a denoised version of the movie, nice to visualize
if True:
m2=m.copy()
m2.IPCA_denoise(components = 100, batch = 1000)
m2.playMovie(frate=.05,magnification=1,gain=2.0)
#%%
#%% compute spatial components via NMF
