def inspect_summary(self): """ Plot and inspect the local correlations and peak to noise ratio. It's not explicitly mentioned how to optimize these parameters, but I believe you should be adjusting them until you see only neuron outlines. """ self.cn_filter, pnr = cm.summary_images.correlation_pnr( self.images[::2], gSig=self.opts.init['gSig'][0], swap_dim=False) inspect_correlation_pnr(self.cn_filter, pnr)
def on_cnmfButtonFilesPNRImage_clicked(self): file = self.cnmfListFilesInput.item(0).text() Yr, dims, T = cm.load_memmap(file) Y = Yr.T.reshape((T, ) + dims, order='F') print(np.float(self.cnmfMaxCorrImage.text())) cn_filter, pnr = correlation_pnr(Y, gSig=np.float(self.gSigFilter.text()), swap_dim=False) # inspect the summary images and set the parameters inspect_correlation_pnr(cn_filter, pnr)
fname_new = cm.save_memmap( [mc.fname_tot_rig], base_name='memmap_', order='C' ) # transforming memoruy mapped file in C order (efficient to perform computing) else: #% create memory mappable file fname_new = cm.save_memmap(fnames, base_name='memmap_', order='C') # load memory mappable file Yr, dims, T = cm.load_memmap(fname_new) Y = Yr.T.reshape((T, ) + dims, order='F') #%% compute some summary images (correlation and peak to noise) cn_filter, pnr = cm.summary_images.correlation_pnr( Y, gSig=gSig, swap_dim=False ) # change swap dim if output looks weird, it is a problem with tiffile #%% inspect the summary images and set the parameters inspect_correlation_pnr(cn_filter, pnr) #%% min_corr = .8 # min correlation of peak (from correlation image) min_pnr = 10 # min peak to noise ratio min_SNR = 3 # adaptive way to set threshold on the transient size r_values_min = 0.85 # threshold on space consistency (if you lower more components will be accepted, potentially with worst quality) decay_time = 0.4 #decay time of transients/indocator #%% cnm = cnmf.CNMF( n_processes=n_processes, method_init='corr_pnr', # use this for 1 photon k=70, # neurons per patch gSig=(3, 3), # half size of neuron gSiz=(10, 10), # in general 3*gSig+1 merge_thresh=.8, # threshold for merging p=1, # order of autoregressive process to fit
def main(): pass # For compatibility between running under Spyder and the CLI #%% First setup some parameters # dataset dependent parameters display_images = False # Set to true to show movies and images fnames = ['data_endoscope.tif'] # filename to be processed frate = 10 # movie frame rate decay_time = 0.4 # length of a typical transient in seconds # motion correction parameters do_motion_correction_nonrigid = True do_motion_correction_rigid = False # in this case it will also save a rigid motion corrected movie gSig_filt = (3, 3) # size of filter, in general gSig (see below), # change this one if algorithm does not work max_shifts = (5, 5) # maximum allowed rigid shift splits_rig = 10 # for parallelization split the movies in num_splits chuncks across time strides = ( 48, 48 ) # start a new patch for pw-rigid motion correction every x pixels overlaps = (24, 24 ) # overlap between pathes (size of patch strides+overlaps) # for parallelization split the movies in num_splits chuncks across time # (remember that it should hold that length_movie/num_splits_to_process_rig>100) splits_els = 10 upsample_factor_grid = 4 # upsample factor to avoid smearing when merging patches # maximum deviation allowed for patch with respect to rigid shifts max_deviation_rigid = 3 # parameters for source extraction and deconvolution p = 1 # order of the autoregressive system K = None # upper bound on number of components per patch, in general None gSig = 3 # gaussian width of a 2D gaussian kernel, which approximates a neuron gSiz = 13 # average diameter of a neuron, in general 4*gSig+1 merge_thresh = .7 # merging threshold, max correlation allowed rf = 40 # half-size of the patches in pixels. e.g., if rf=40, patches are 80x80 stride_cnmf = 20 # amount of overlap between the patches in pixels # (keep it at least large as gSiz, i.e 4 times the neuron size gSig) tsub = 2 # downsampling factor in time for initialization, # increase if you have memory problems ssub = 1 # downsampling factor in space for initialization, # increase if you have memory problems Ain = None # if you want to initialize with some preselected components # you can pass them here as boolean vectors low_rank_background = None # None leaves background of each patch intact, # True performs global low-rank approximation gnb = -1 # number of background components (rank) if positive, # else exact ring model with following settings # gnb=-2: Return background as b and W # gnb=-1: Return full rank background B # gnb= 0: Don't return background nb_patch = -1 # number of background components (rank) per patch, # use 0 or -1 for exact background of ring model (cf. gnb) min_corr = .8 # min peak value from correlation image min_pnr = 10 # min peak to noise ration from PNR image ssub_B = 2 # additional downsampling factor in space for background ring_size_factor = 1.4 # radius of ring is gSiz*ring_size_factor # parameters for component evaluation min_SNR = 3 # adaptive way to set threshold on the transient size r_values_min = 0.85 # threshold on space consistency (if you lower more components # will be accepted, potentially with worst quality) #%% start the cluster try: cm.stop_server(dview=dview) # stop it if it was running except: pass c, dview, n_processes = cm.cluster.setup_cluster( backend='local', # use this one n_processes= 24, # number of process to use, if you go out of memory try to reduce this one single_thread=False) #%% download demo file fnames = [download_demo(fnames[0])] filename_reorder = fnames #%% MOTION CORRECTION if do_motion_correction_nonrigid or do_motion_correction_rigid: # do motion correction rigid mc = motion_correct_oneP_rigid( fnames, gSig_filt=gSig_filt, max_shifts=max_shifts, dview=dview, splits_rig=splits_rig, save_movie=not (do_motion_correction_nonrigid), border_nan='copy') new_templ = mc.total_template_rig plt.subplot(1, 2, 1) plt.imshow(new_templ) # % plot template plt.subplot(1, 2, 2) plt.plot(mc.shifts_rig) # % plot rigid shifts plt.legend(['x shifts', 'y shifts']) plt.xlabel('frames') plt.ylabel('pixels') # borders to eliminate from movie because of motion correction bord_px = np.ceil(np.max(np.abs(mc.shifts_rig))).astype(np.int) filename_reorder = mc.fname_tot_rig # do motion correction nonrigid if do_motion_correction_nonrigid: mc = motion_correct_oneP_nonrigid( fnames, gSig_filt=gSig_filt, max_shifts=max_shifts, strides=strides, overlaps=overlaps, splits_els=splits_els, upsample_factor_grid=upsample_factor_grid, max_deviation_rigid=max_deviation_rigid, dview=dview, splits_rig=None, save_movie=True, # whether to save movie in memory mapped format new_templ=new_templ, # template to initialize motion correction border_nan='copy') filename_reorder = mc.fname_tot_els bord_px = np.ceil( np.maximum(np.max(np.abs(mc.x_shifts_els)), np.max(np.abs(mc.y_shifts_els)))).astype(np.int) # create memory mappable file in the right order on the hard drive (C order) fname_new = cm.save_memmap(filename_reorder, base_name='memmap_', order='C', border_to_0=bord_px, dview=dview) # load memory mappable file Yr, dims, T = cm.load_memmap(fname_new) Y = Yr.T.reshape((T, ) + dims, order='F') #%% compute some summary images (correlation and peak to noise) # change swap dim if output looks weird, it is a problem with tiffile cn_filter, pnr = cm.summary_images.correlation_pnr(Y, gSig=gSig, swap_dim=False) # inspect the summary images and set the parameters inspect_correlation_pnr(cn_filter, pnr) # print parameters set above, modify them if necessary based on summary images print(min_corr) # min correlation of peak (from correlation image) print(min_pnr) # min peak to noise ratio #%% RUN CNMF ON PATCHES cnm = cnmf.CNMF( n_processes=n_processes, method_init='corr_pnr', # use this for 1 photon k=K, gSig=(gSig, gSig), gSiz=(gSiz, gSiz), merge_thresh=merge_thresh, p=p, dview=dview, tsub=tsub, ssub=ssub, Ain=Ain, rf=rf, stride=stride_cnmf, only_init_patch=True, # just leave it as is gnb=gnb, nb_patch=nb_patch, method_deconvolution='oasis', # could use 'cvxpy' alternatively low_rank_background=low_rank_background, update_background_components= True, # sometimes setting to False improve the results min_corr=min_corr, min_pnr=min_pnr, normalize_init=False, # just leave as is center_psf=True, # leave as is for 1 photon ssub_B=ssub_B, ring_size_factor=ring_size_factor, del_duplicates=True, # whether to remove duplicates from initialization border_pix=bord_px) # number of pixels to not consider in the borders cnm.fit(Y) #%% DISCARD LOW QUALITY COMPONENTS cnm.evaluate_components(Y, min_SNR=min_SNR, rval_thr=r_values_min, use_cnn=False, decay_time=decay_time, fr=frate) print(' ***** ') print('Number of total components: ', len(cnm.C)) print('Number of accepted components: ', len(cnm.idx_components)) #%% PLOT COMPONENTS cnm.dims = dims if display_images: cnm.plot_contours(img=cn_filter, idx=cnm.idx_components) cnm.view_components(Y, dims, idx=cnm.idx_components) #%% MOVIES if display_images: # fully reconstructed movie cnm.play_movie(Y, magnification=3, include_bck=True, gain_res=10) # movie without background cnm.play_movie(Y, magnification=3, include_bck=False, gain_res=4) #%% STOP SERVER cm.stop_server(dview=dview)
def main(): pass # For compatibility between running under Spyder and the CLI # %% start the cluster try: cm.stop_server() # stop it if it was running except (): pass c, dview, n_processes = cm.cluster.setup_cluster( backend='local', n_processes= 24, # number of process to use, if you go out of memory try to reduce this one single_thread=False) # %% First setup some parameters for motion correction # dataset dependent parameters fnames = ['data_endoscope.tif'] # filename to be processed fnames = [download_demo(fnames[0])] # download file if not already present filename_reorder = fnames fr = 10 # movie frame rate decay_time = 0.4 # length of a typical transient in seconds # motion correction parameters motion_correct = True # flag for motion correction pw_rigid = False # flag for pw-rigid motion correction gSig_filt = (3, 3) # size of filter, in general gSig (see below), # change this one if algorithm does not work max_shifts = (5, 5) # maximum allowed rigid shift strides = ( 48, 48 ) # start a new patch for pw-rigid motion correction every x pixels overlaps = (24, 24 ) # overlap between pathes (size of patch strides+overlaps) # maximum deviation allowed for patch with respect to rigid shifts max_deviation_rigid = 3 border_nan = 'copy' mc_dict = { 'fnames': fnames, 'fr': fr, 'decay_time': decay_time, 'pw_rigid': pw_rigid, 'max_shifts': max_shifts, 'gSig_filt': gSig_filt, 'strides': strides, 'overlaps': overlaps, 'max_deviation_rigid': max_deviation_rigid, 'border_nan': border_nan } opts = params.CNMFParams(params_dict=mc_dict) # %% MOTION CORRECTION # The pw_rigid flag set above, determines where to use rigid or pw-rigid # motion correction if motion_correct: # do motion correction rigid mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion')) mc.motion_correct(save_movie=True) fname_mc = mc.fname_tot_els if pw_rigid else mc.fname_tot_rig if pw_rigid: bord_px = np.ceil( np.maximum(np.max(np.abs(mc.x_shifts_els)), np.max(np.abs(mc.y_shifts_els)))).astype(np.int) else: bord_px = np.ceil(np.max(np.abs(mc.shifts_rig))).astype(np.int) plt.subplot(1, 2, 1) plt.imshow(mc.total_template_rig) # % plot template plt.subplot(1, 2, 2) plt.plot(mc.shifts_rig) # % plot rigid shifts plt.legend(['x shifts', 'y shifts']) plt.xlabel('frames') plt.ylabel('pixels') bord_px = 0 if border_nan == 'copy' else bord_px fname_new = cm.save_memmap(fname_mc, base_name='memmap_', order='C', border_to_0=bord_px) else: # if no motion correction just memory map the file fname_new = cm.save_memmap(filename_reorder, base_name='memmap_', order='C', border_to_0=0, dview=dview) # load memory mappable file Yr, dims, T = cm.load_memmap(fname_new) images = Yr.T.reshape((T, ) + dims, order='F') # %% Parameters for source extraction and deconvolution (CNMF-E algorithm) p = 1 # order of the autoregressive system K = None # upper bound on number of components per patch, in general None for 1p data gSig = ( 3, 3 ) # gaussian width of a 2D gaussian kernel, which approximates a neuron gSiz = (13, 13) # average diameter of a neuron, in general 4*gSig+1 Ain = None # possibility to seed with predetermined binary masks merge_thr = .7 # merging threshold, max correlation allowed rf = 40 # half-size of the patches in pixels. e.g., if rf=40, patches are 80x80 stride_cnmf = 20 # amount of overlap between the patches in pixels # (keep it at least large as gSiz, i.e 4 times the neuron size gSig) tsub = 2 # downsampling factor in time for initialization, # increase if you have memory problems ssub = 1 # downsampling factor in space for initialization, # increase if you have memory problems # you can pass them here as boolean vectors low_rank_background = None # None leaves background of each patch intact, # True performs global low-rank approximation if gnb>0 gnb = 0 # number of background components (rank) if positive, # else exact ring model with following settings # gnb= 0: Return background as b and W # gnb=-1: Return full rank background B # gnb<-1: Don't return background nb_patch = 0 # number of background components (rank) per patch if gnb>0, # else it is set automatically min_corr = .8 # min peak value from correlation image min_pnr = 10 # min peak to noise ration from PNR image ssub_B = 2 # additional downsampling factor in space for background ring_size_factor = 1.4 # radius of ring is gSiz*ring_size_factor opts.change_params( params_dict={ 'dims': dims, 'method_init': 'corr_pnr', # use this for 1 photon 'K': K, 'gSig': gSig, 'gSiz': gSiz, 'merge_thr': merge_thr, 'p': p, 'tsub': tsub, 'ssub': ssub, 'rf': rf, 'stride': stride_cnmf, 'only_init': True, # set it to True to run CNMF-E 'nb': gnb, 'nb_patch': nb_patch, 'method_deconvolution': 'oasis', # could use 'cvxpy' alternatively 'low_rank_background': low_rank_background, 'update_background_components': True, # sometimes setting to False improve the results 'min_corr': min_corr, 'min_pnr': min_pnr, 'normalize_init': False, # just leave as is 'center_psf': True, # leave as is for 1 photon 'ssub_B': ssub_B, 'ring_size_factor': ring_size_factor, 'del_duplicates': True, # whether to remove duplicates from initialization 'border_pix': bord_px }) # number of pixels to not consider in the borders) # %% compute some summary images (correlation and peak to noise) # change swap dim if output looks weird, it is a problem with tiffile cn_filter, pnr = cm.summary_images.correlation_pnr(images[::1], gSig=gSig[0], swap_dim=False) # if your images file is too long this computation will take unnecessarily # long time and consume a lot of memory. Consider changing images[::1] to # images[::5] or something similar to compute on a subset of the data # inspect the summary images and set the parameters inspect_correlation_pnr(cn_filter, pnr) # print parameters set above, modify them if necessary based on summary images print(min_corr) # min correlation of peak (from correlation image) print(min_pnr) # min peak to noise ratio # %% RUN CNMF ON PATCHES cnm = cnmf.CNMF(n_processes=n_processes, dview=dview, Ain=Ain, params=opts) cnm.fit(images) # %% ALTERNATE WAY TO RUN THE PIPELINE AT ONCE # you can also perform the motion correction plus cnmf fitting steps # simultaneously after defining your parameters object using # cnm1 = cnmf.CNMF(n_processes, params=opts, dview=dview) # cnm1.fit_file(motion_correct=True) # %% DISCARD LOW QUALITY COMPONENTS min_SNR = 2.5 # adaptive way to set threshold on the transient size r_values_min = 0.85 # threshold on space consistency (if you lower more components # will be accepted, potentially with worst quality) cnm.params.set('quality', { 'min_SNR': min_SNR, 'rval_thr': r_values_min, 'use_cnn': False }) cnm.estimates.evaluate_components(images, cnm.params, dview=dview) print(' ***** ') print('Number of total components: ', len(cnm.estimates.C)) print('Number of accepted components: ', len(cnm.estimates.idx_components)) # %% PLOT COMPONENTS cnm.dims = dims display_images = True # Set to true to show movies and images if display_images: cnm.estimates.plot_contours(img=cn_filter, idx=cnm.estimates.idx_components) cnm.estimates.view_components(images, idx=cnm.estimates.idx_components) # %% MOVIES display_images = False # Set to true to show movies and images if display_images: # fully reconstructed movie cnm.estimates.play_movie(images, q_max=99.5, magnification=2, include_bck=True, gain_res=10, bpx=bord_px) # movie without background cnm.estimates.play_movie(images, q_max=99.9, magnification=2, include_bck=False, gain_res=4, bpx=bord_px) # %% STOP SERVER cm.stop_server(dview=dview)
filename_reorder, base_name='memmap_', order='C', border_to_0=bord_px, dview=dview) fname_new = cm.save_memmap_join(fname_new, base_name='memmap_', dview=dview) # load memory mappable file Yr, dims, T = cm.load_memmap(fname_new) Y = Yr.T.reshape((T,) + dims, order='F') #%% compute some summary images (correlation and peak to noise) # change swap dim if output looks weird, it is a problem with tiffile cn_filter, pnr = cm.summary_images.correlation_pnr(Y, gSig=gSig, swap_dim=False) # inspect the summary images and set the parameters inspect_correlation_pnr(cn_filter, pnr) # print parameters set above, modify them if necessary based on summary images print(min_corr) # min correlation of peak (from correlation image) print(min_pnr) # min peak to noise ratio #%% RUN CNMF ON PATCHES cnm = cnmf.CNMF( n_processes=n_processes, method_init='corr_pnr', # use this for 1 photon k=K, gSig=(gSig, gSig), gSiz=(gSiz, gSiz), merge_thresh=merge_thresh, p=p, dview=dview,