all_masks=mdend.mov; else: all_masks=masks_tmp all_masksForPlot=[kk*(ii+1)*1.0 for ii,kk in enumerate(all_masks)] plt.imshow(np.max(np.asarray(all_masksForPlot,dtype=np.float16),axis=0)) #%% extract DF/F from orginal movie, needs to reload the motion corrected movie m=XMovie(mat=np.load(filename_mc)['mov'], frameRate=frameRate); m.crop(max_shift,max_shift,max_shift,max_shift) minPercentileRemove=1; # remove an estimation of what a Dark patch is, you should provide a better estimate F0=np.percentile(m.mov,minPercentileRemove) m.mov=m.mov-F0; traces, tracesDFF = m.extract_traces_from_masks(all_masks,type='DFF',window_sec=15,minQuantile=8) plt.plot(tracesDFF) #%% plt.imshow(tracesDFF.T) #%% save the results of the analysis in python format np.savez(filename_analysis,all_masks=all_masks,spcomps=spcomps,fx=fx,fy=fy,fz=fz,traces=traces,tracesDFF=tracesDFF) #%% save the results of the analysis and motion corrected movie in matlab format import scipy.io as sio sio.savemat(filename_analysis[:-4]+'.mat', {'all_masks':np.transpose(all_masks,(1,2,0)),'spcomps':np.transpose(spcomps,(1,2,0)),'traces':traces,'tracesDFF':tracesDFF}) m=XMovie(mat=np.load(filename_mc)['mov'], frameRate=frameRate); sio.savemat(filename_mc[:-4]+'.mat', {'mov':np.transpose(np.load(filename_mc)['mov'],(1,2,0)),'frameRate':frameRate,'shifts':shifts,'templates':np.transpose(templates,(1,2,0))})