def initialize_movie(Y, K, gSig, rf, stride, base_name,
p=1, merge_thresh=0.95, rval_thr_online=0.9, thresh_fitness_delta_online=-30, thresh_fitness_raw_online=-50,
rval_thr_init=.5, thresh_fitness_delta_init=-20, thresh_fitness_raw_init=-20,
rval_thr_refine=0.95, thresh_fitness_delta_refine=-100, thresh_fitness_raw_refine=-100,
final_frate=10, Npeaks=10, single_thread=True, dview=None):
_, d1, d2 = Y.shape
dims = (d1, d2)
Yr = Y.to_2D().T
# merging threshold, max correlation allowed
# order of the autoregressive system
#T = Y.shape[0]
base_name = base_name + '.mmap'
fname_new = Y.save(base_name, order='C')
#%
Yr, dims, T = cm.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')
Cn2 = cm.local_correlations(Y)
# pl.imshow(Cn2)
#%
#% RUN ALGORITHM ON PATCHES
# pl.close('all')
cnm_init = cnmf.CNMF(n_processes, method_init='greedy_roi', k=K, gSig=gSig, merge_thresh=merge_thresh,
p=0, dview=dview, Ain=None, rf=rf, stride=stride, method_deconvolution='oasis', skip_refinement=False,
normalize_init=False, options_local_NMF=None,
minibatch_shape=100, minibatch_suff_stat=5,
update_num_comps=True, rval_thr=rval_thr_online, thresh_fitness_delta=thresh_fitness_delta_online, thresh_fitness_raw=thresh_fitness_raw_online,
batch_update_suff_stat=True, max_comp_update_shape=5)
cnm_init = cnm_init.fit(images)
A_tot = cnm_init.A
C_tot = cnm_init.C
YrA_tot = cnm_init.YrA
b_tot = cnm_init.b
f_tot = cnm_init.f
print(('Number of components:' + str(A_tot.shape[-1])))
#%
traces = C_tot + YrA_tot
# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])
# traces_b=np.diff(traces,axis=1)
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components(
Y, traces, A_tot, C_tot, b_tot, f_tot, final_frate, remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)
idx_components_r = np.where(r_values >= rval_thr_init)[0]
idx_components_raw = np.where(fitness_raw < thresh_fitness_raw_init)[0]
idx_components_delta = np.where(
fitness_delta < thresh_fitness_delta_init)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(('Keeping ' + str(len(idx_components)) +
' and discarding ' + str(len(idx_components_bad))))
A_tot = A_tot.tocsc()[:, idx_components]
C_tot = C_tot[idx_components]
#%
cnm_refine = cnmf.CNMF(n_processes, method_init='greedy_roi', k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, rf=None, stride=None,
p=p, dview=dview, Ain=A_tot, Cin=C_tot, f_in=f_tot, method_deconvolution='oasis', skip_refinement=True,
normalize_init=False, options_local_NMF=None,
minibatch_shape=100, minibatch_suff_stat=5,
update_num_comps=True, rval_thr=rval_thr_refine, thresh_fitness_delta=thresh_fitness_delta_refine, thresh_fitness_raw=thresh_fitness_raw_refine,
batch_update_suff_stat=True, max_comp_update_shape=5)
cnm_refine = cnm_refine.fit(images)
#%
A, C, b, f, YrA, sn = cnm_refine.A, cnm_refine.C, cnm_refine.b, cnm_refine.f, cnm_refine.YrA, cnm_refine.sn
#%
final_frate = 10
Npeaks = 10
traces = C + YrA
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \
evaluate_components(Y, traces, A, C, b, f, final_frate, remove_baseline=True,
N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)
idx_components_r = np.where(r_values >= rval_thr_refine)[0]
idx_components_raw = np.where(fitness_raw < thresh_fitness_raw_refine)[0]
idx_components_delta = np.where(
fitness_delta < thresh_fitness_delta_refine)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(' ***** ')
print((len(traces)))
print((len(idx_components)))
#%
cnm_refine.idx_components = idx_components
cnm_refine.idx_components_bad = idx_components_bad
cnm_refine.r_values = r_values
cnm_refine.fitness_raw = fitness_raw
cnm_refine.fitness_delta = fitness_delta
cnm_refine.Cn2 = Cn2
#%
# cnm_init.dview = None
# save_object(cnm_init,fls[0][:-4]+ '_DS_' + str(ds)+ '_init.pkl')
return cnm_refine, Cn2, fname_new
bl=None,
c1=None,
sn=None,
g=None,
**options['temporal_params'])
#%% get rid of evenrually noisy components.
# But check by visual inspection to have a feeling fot the threshold. Try to be loose, you will be able to get rid of more of them later!
tB = np.minimum(-2, np.floor(-5. / 30 * final_frate))
tA = np.maximum(5, np.ceil(25. / 30 * final_frate))
Npeaks = 10
traces = C_m + YrA_m
# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])
# traces_b=np.diff(traces,axis=1)
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples =\
evaluate_components(Y, traces, A_m, C_m, b, f_m, \
remove_baseline=True, N=5, robust_std=False, Athresh = 0.1, Npeaks = Npeaks, tB=tB, tA = tA, thresh_C = 0.3)
idx_components_r = np.where(r_values >= .5)[0]
idx_components_raw = np.where(fitness_raw < -20)[0]
idx_components_delta = np.where(fitness_delta < -10)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(' ***** ')
print((len(traces)))
print((len(idx_components)))
#%%
A_m = A_m[:, idx_components]
**options['temporal_params'])
print((time() - t1))
pl.figure()
crd = plot_contours(A2.tocsc()[:, :], Cn, thr=0.9)
#%%
#%%
final_frate = 10
Npeaks = 10
traces = C + YrA
# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])
# traces_b=np.diff(traces,axis=1)
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \
evaluate_components(Y, traces, A, C, b, f, final_frate, remove_baseline=True,
N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)
idx_components_r = np.where(r_values >= .85)[0]
idx_components_raw = np.where(fitness_raw < -40)[0]
idx_components_delta = np.where(fitness_delta < -40)[0]
#min_radius = gSig[0] - 2
#masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob(
# A.tocsc(), min_radius, dims, num_std_threshold=1,
# minCircularity=0.7, minInertiaRatio=0.2, minConvexity=.5)
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
#idx_blobs = np.intersect1d(idx_components, idx_blobs)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(('Number of components:' + str(A_tot.shape[-1])))
pl.figure()
crd = plot_contours(A_tot, Cn, thr=0.9)
final_frate = 10 # approx final rate (after eventual downsampling )
Npeaks = 10
traces = C_tot + YrA_tot
# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])
# traces_b=np.diff(traces,axis=1)
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components(
Y,
traces,
A_tot,
C_tot,
b_tot,
f_tot,
final_frate,
remove_baseline=True,
N=5,
robust_std=False,
Athresh=0.1,
Npeaks=Npeaks,
thresh_C=0.3)
idx_components_r = np.where(r_values >= .5)[0]
idx_components_raw = np.where(fitness_raw < -40)[0]
idx_components_delta = np.where(fitness_delta < -20)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
#%% plot contours of components
pl.figure()
crd = cm.utils.visualization.plot_contours(cnm.A, Cn, thr=0.9)
pl.title('Contour plots against correlation image')
#%% evaluate the quality of the components
# a lot of components will be removed because presumably they are not active
# during these 2000 frames of the experiment
final_frate = 15 # approximate frame rate of data
Npeaks = 10
traces = C + YrA
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \
evaluate_components(Y, traces, A, C, b, f, final_frate, remove_baseline=True,
N=5, robust_std=False, Npeaks=Npeaks, thresh_C=0.3)
# filter based on spatial consistency
idx_components_r = np.where(r_values >= .85)[0]
# filter based on transient size
idx_components_raw = np.where(fitness_raw < -50)[0]
# filter based on transient derivative size
idx_components_delta = np.where(fitness_delta < -50)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print((len(traces)))
print((len(idx_components)))
#%% visualize components
# pl.figure();
YrA_tot = cnm.YrA
b_tot = cnm.b
f_tot = cnm.f
sn_tot = cnm.sn
print(('Number of components:' + str(A_tot.shape[-1])))
#%%
pl.figure()
crd = plot_contours(A_tot, Cn, thr=0.9)
#%%
final_frate = 16 # approx final rate (after eventual downsampling )
Npeaks = 10
traces = C_tot + YrA_tot
# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])
# traces_b=np.diff(traces,axis=1)
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components(
Y, traces, A_tot, C_tot, b_tot, f_tot, final_frate, remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)
idx_components_r = np.where(r_values >= .5)[0]
idx_components_raw = np.where(fitness_raw < -40)[0]
idx_components_delta = np.where(fitness_delta < -20)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(('Keeping ' + str(len(idx_components)) +
' and discarding ' + str(len(idx_components_bad))))
#%%
pl.figure()
crd = plot_contours(A_tot.tocsc()[:, idx_components], Cn, thr=0.9)
#%%
options['temporal_params']['p'] = p # set it back to original value to perform full deconvolution C2,f2,S2,bl2,c12,neurons_sn2,g21,YrA = cm.source_extraction.cnmf.temporal.update_temporal_components(Yr,A2,b2,C2,f,dview=dview, bl=None,c1=None,sn=None,g=None,**options['temporal_params']) print((time() - t1)) pl.figure() crd = plot_contours(A2.tocsc()[:,:],Cn,thr=0.9) #%% final_frate = 10 tB = np.minimum(-2,np.floor(-5./30*final_frate)) tA = np.maximum(5,np.ceil(25./30*final_frate)) Npeaks=10 traces=C2+YrA # traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) # traces_b=np.diff(traces,axis=1) fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components(Y, traces, A2, C2, b2, f2, remove_baseline=True, N=5, robust_std=False, Athresh = 0.1, Npeaks = Npeaks, tB=tB, tA = tA, thresh_C = 0.3) idx_components_r=np.where(r_values>=.6)[0] idx_components_raw=np.where(fitness_raw<-60)[0] idx_components_delta=np.where(fitness_delta<-20)[0] min_radius=gSig[0]-2 masks_ws,idx_blobs,idx_non_blobs=extract_binary_masks_blob( A2.tocsc(), min_radius, dims, num_std_threshold=1, minCircularity= 0.6, minInertiaRatio = 0.2,minConvexity =.8) idx_components=np.union1d(idx_components_r,idx_components_raw)
def initialize_movie(Y,
K,
gSig,
rf,
stride,
base_name,
p=1,
merge_thresh=0.95,
rval_thr_online=0.9,
thresh_fitness_delta_online=-30,
thresh_fitness_raw_online=-50,
rval_thr_init=.5,
thresh_fitness_delta_init=-20,
thresh_fitness_raw_init=-20,
rval_thr_refine=0.95,
thresh_fitness_delta_refine=-100,
thresh_fitness_raw_refine=-100,
final_frate=10,
Npeaks=10,
single_thread=True,
dview=None):
_, d1, d2 = Y.shape
dims = (d1, d2)
Yr = Y.to_2D().T
# merging threshold, max correlation allowed
# order of the autoregressive system
#T = Y.shape[0]
base_name = base_name + '.mmap'
fname_new = Y.save(base_name, order='C')
#%
Yr, dims, T = cm.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')
Cn2 = cm.local_correlations(Y)
# pl.imshow(Cn2)
#%
#% RUN ALGORITHM ON PATCHES
# pl.close('all')
cnm_init = cnmf.CNMF(n_processes,
method_init='greedy_roi',
k=K,
gSig=gSig,
merge_thresh=merge_thresh,
p=0,
dview=dview,
Ain=None,
rf=rf,
stride=stride,
method_deconvolution='oasis',
skip_refinement=False,
normalize_init=False,
options_local_NMF=None,
minibatch_shape=100,
minibatch_suff_stat=5,
update_num_comps=True,
rval_thr=rval_thr_online,
thresh_fitness_delta=thresh_fitness_delta_online,
thresh_fitness_raw=thresh_fitness_raw_online,
batch_update_suff_stat=True,
max_comp_update_shape=5)
cnm_init = cnm_init.fit(images)
A_tot = cnm_init.A
C_tot = cnm_init.C
YrA_tot = cnm_init.YrA
b_tot = cnm_init.b
f_tot = cnm_init.f
print(('Number of components:' + str(A_tot.shape[-1])))
#%
traces = C_tot + YrA_tot
# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])
# traces_b=np.diff(traces,axis=1)
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components(
Y,
traces,
A_tot,
C_tot,
b_tot,
f_tot,
final_frate,
remove_baseline=True,
N=5,
robust_std=False,
Athresh=0.1,
Npeaks=Npeaks,
thresh_C=0.3)
idx_components_r = np.where(r_values >= rval_thr_init)[0]
idx_components_raw = np.where(fitness_raw < thresh_fitness_raw_init)[0]
idx_components_delta = np.where(
fitness_delta < thresh_fitness_delta_init)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(('Keeping ' + str(len(idx_components)) + ' and discarding ' +
str(len(idx_components_bad))))
A_tot = A_tot.tocsc()[:, idx_components]
C_tot = C_tot[idx_components]
#%
cnm_refine = cnmf.CNMF(n_processes,
method_init='greedy_roi',
k=A_tot.shape,
gSig=gSig,
merge_thresh=merge_thresh,
rf=None,
stride=None,
p=p,
dview=dview,
Ain=A_tot,
Cin=C_tot,
f_in=f_tot,
method_deconvolution='oasis',
skip_refinement=True,
normalize_init=False,
options_local_NMF=None,
minibatch_shape=100,
minibatch_suff_stat=5,
update_num_comps=True,
rval_thr=rval_thr_refine,
thresh_fitness_delta=thresh_fitness_delta_refine,
thresh_fitness_raw=thresh_fitness_raw_refine,
batch_update_suff_stat=True,
max_comp_update_shape=5)
cnm_refine = cnm_refine.fit(images)
#%
A, C, b, f, YrA, sn = cnm_refine.A, cnm_refine.C, cnm_refine.b, cnm_refine.f, cnm_refine.YrA, cnm_refine.sn
#%
final_frate = 10
Npeaks = 10
traces = C + YrA
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \
evaluate_components(Y, traces, A, C, b, f, final_frate, remove_baseline=True,
N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)
idx_components_r = np.where(r_values >= rval_thr_refine)[0]
idx_components_raw = np.where(fitness_raw < thresh_fitness_raw_refine)[0]
idx_components_delta = np.where(
fitness_delta < thresh_fitness_delta_refine)[0]
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components)
print(' ***** ')
print((len(traces)))
print((len(idx_components)))
#%
cnm_refine.idx_components = idx_components
cnm_refine.idx_components_bad = idx_components_bad
cnm_refine.r_values = r_values
cnm_refine.fitness_raw = fitness_raw
cnm_refine.fitness_delta = fitness_delta
cnm_refine.Cn2 = Cn2
#%
# cnm_init.dview = None
# save_object(cnm_init,fls[0][:-4]+ '_DS_' + str(ds)+ '_init.pkl')
return cnm_refine, Cn2, fname_new
Yr,dims,T=cm.load_memmap(fname_new)
Y=np.reshape(Yr,dims+(T,),order='F')
nb_back=1
options = cnmf.utilities.CNMFSetParms(Y,n_processes,p=p,gSig=gSig,K=K,ssub=2,tsub=1,nb=nb_back)
#options['preprocess_params']['max_num_samples_fft']=10000
Cn = cm.local_correlations(Y)
Yr,sn,g,psx = cnmf.pre_processing.preprocess_data(Yr,dview=dview,**options['preprocess_params'])
Ain,Cin, b_in, f_in, center=cnmf.initialization.initialize_components(Y, **options['init_params'])
Ain,b_in,Cin, f_in = cnmf.spatial.update_spatial_components(Yr, Cin, f_in, Ain, sn=sn, dview=dview,**options['spatial_params'])
if Cin.size > 0:
options = cnmf.utilities.CNMFSetParms(Y,n_processes,p=p,gSig=gSig,K=K,tsub=1)
options['temporal_params']['p'] = 0 # set this to zero for fast updating without deconvolution
Cin,Ain,b_in,f_in,S,bl,c1,neurons_sn,g,YrA = cnmf.temporal.update_temporal_components(Yr,Ain,b_in,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params'])
Ain,Cin,nr_m,merged_ROIs,S_m,bl_m,c1_m,sn_m,g_m=cnmf.merging.merge_components(Yr,Ain,b_in,Cin,f_in,S,sn,options['temporal_params'], options['spatial_params'],dview=dview, bl=bl, c1=c1, sn=neurons_sn, g=g, thr=merge_thresh,mx=1000, fast_merge = True)
Ain,b_in,Cin, f_in = cnmf.spatial.update_spatial_components(Yr, Cin, f_in, Ain, sn=sn, dview=dview,**options['spatial_params'])
options['temporal_params']['p'] = p # set it back to original value to perform full deconvolution
Cin,Ain,b_in,f_in,S,bl,c1,neurons_sn,g,YrA = cnmf.temporal.update_temporal_components(Yr,Ain,b_in,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params'])
traces=Cin+YrA
tB = np.minimum(-2,np.floor(-5./30*final_frate))
tA = np.maximum(5,np.ceil(25./30*final_frate))
fitness_raw, fitness_delta, erfc_raw,erfc_delta,r_values,num_significant_samples = evaluate_components(Y,traces,Ain,Cin,bl,f_in, final_frate, remove_baseline = True, N = 5, robust_std = False, Athresh = 0.1, Npeaks = 5, thresh_C = 0.2)
idx_components_r=np.where(r_values>=.6)[0]
idx_components_raw=np.where(fitness_raw<-40)[0]
idx_components_delta=np.where(fitness_delta<-20)[0]
idx_components=np.union1d(idx_components_r,idx_components_raw)
C_dff = extract_DF_F(Yr, Ain.tocsc()[:, idx_components], Cin[idx_components, :], bl[idx_components], quantileMin = 8, frames_window = 200, dview = dview)
idx_components=np.union1d(idx_components,idx_components_delta)
idx_components_bad=np.setdiff1d(range(len(traces)),idx_components)
savemat(fnames[0][:-4]+'_output_analysis_matlab.mat',mdict={'ROIs':Ain,'DenoisedTraces':Cin,'Baseline':bl, 'Noise':YrA, 'Spikes': S,'DFF':C_dff , 'idx_components':idx_components, 'Correlation_image':Cn})
def process_data(haussio_data,
mask=None,
p=2,
nrois_init=400,
roi_iceberg=0.9):
if mask is not None:
raise RuntimeError("mask not supported in cnmf.process_data")
fn_cnmf = haussio_data.dirname_comp + '_cnmf.mat'
shapefn = os.path.join(haussio_data.dirname_comp,
haussio.THOR_RAW_FN[:-3] + "shape.npy")
shape = np.load(shapefn)
if len(shape) == 5:
d1, d2 = shape[2], shape[3]
fn_mmap = get_mmap_name('Yr', shape[2], shape[3], shape[0])
else:
d1, d2 = shape[1], shape[2]
fn_mmap = get_mmap_name('Yr', shape[1], shape[2], shape[0])
fn_mmap = os.path.join(haussio_data.dirname_comp, fn_mmap)
print(fn_mmap, os.path.exists(fn_mmap), d1, d2)
if not os.path.exists(fn_cnmf):
# fn_raw = os.path.join(haussio_data.dirname_comp, haussio.THOR_RAW_FN)
fn_sima = haussio_data.dirname_comp + '.sima'
fnames = [
fn_sima,
]
fnames.sort()
print(fnames)
fnames = fnames
final_frate = 1.0 / haussio_data.dt
downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer
final_frate *= downsample_factor
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
idx_xy = None
base_name = 'Yr'
name_new = cm.save_memmap_each(fnames,
dview=dview,
base_name=base_name,
resize_fact=(1, 1, downsample_factor),
remove_init=0,
idx_xy=idx_xy)
name_new.sort()
print(name_new)
if len(name_new) > 1:
fname_new = cm.save_memmap_join(name_new,
base_name='Yr',
n_chunks=12,
dview=dview)
else:
sys.stdout.write('One file only, not saving\n')
fname_new = name_new[0]
print("fname_new: " + fname_new)
Yr, dims, T = cm.load_memmap(fname_new)
Y = np.reshape(Yr, dims + (T, ), order='F')
Cn = cm.local_correlations(Y)
K = nrois_init # number of neurons expected per patch
gSig = [15, 15] # expected half size of neurons
merge_thresh = 0.8 # merging threshold, max correlation allowed
p = 2 #order of the autoregressive system
options = caiman_cnmf.utilities.CNMFSetParms(Y,
NCPUS,
p=p,
gSig=gSig,
K=K,
ssub=2,
tsub=2)
Yr, sn, g, psx = caiman_cnmf.pre_processing.preprocess_data(
Yr, dview=dview, **options['preprocess_params'])
Atmp, Ctmp, b_in, f_in, center = caiman_cnmf.initialization.initialize_components(
Y, **options['init_params'])
Ain, Cin = Atmp, Ctmp
A, b, Cin, f_in = caiman_cnmf.spatial.update_spatial_components(
Yr,
Cin,
f_in,
Ain,
sn=sn,
dview=dview,
**options['spatial_params'])
options['temporal_params'][
'p'] = 0 # set this to zero for fast updating without deconvolution
C, A, b, f, S, bl, c1, neurons_sn, g, YrA = caiman_cnmf.temporal.update_temporal_components(
Yr,
A,
b,
Cin,
f_in,
bl=None,
c1=None,
sn=None,
g=None,
**options['temporal_params'])
A_m, C_m, nr_m, merged_ROIs, S_m, bl_m, c1_m, sn_m, g_m = caiman_cnmf.merging.merge_components(
Yr,
A,
b,
C,
f,
S,
sn,
options['temporal_params'],
options['spatial_params'],
dview=dview,
bl=bl,
c1=c1,
sn=neurons_sn,
g=g,
thr=merge_thresh,
mx=50,
fast_merge=True)
A2, b2, C2, f = caiman_cnmf.spatial.update_spatial_components(
Yr, C_m, f, A_m, sn=sn, dview=dview, **options['spatial_params'])
options['temporal_params'][
'p'] = p # set it back to original value to perform full deconvolution
C2, A2, b2, f2, S2, bl2, c12, neurons_sn2, g21, YrA = caiman_cnmf.temporal.update_temporal_components(
Yr,
A2,
b2,
C2,
f,
dview=dview,
bl=None,
c1=None,
sn=None,
g=None,
**options['temporal_params'])
tB = np.minimum(-2, np.floor(-5. / 30 * final_frate))
tA = np.maximum(5, np.ceil(25. / 30 * final_frate))
Npeaks = 10
traces = C2 + YrA
fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \
evaluate_components(
Y, traces, A2, C2, b2, f2, final_frate, remove_baseline=True, N=5,
robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)
idx_components_r = np.where(r_values >= .6)[0]
idx_components_raw = np.where(fitness_raw < -60)[0]
idx_components_delta = np.where(fitness_delta < -20)[0]
min_radius = gSig[0] - 2
masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob(
A2.tocsc(),
min_radius,
dims,
num_std_threshold=1,
minCircularity=0.6,
minInertiaRatio=0.2,
minConvexity=.8)
idx_components = np.union1d(idx_components_r, idx_components_raw)
idx_components = np.union1d(idx_components, idx_components_delta)
idx_blobs = np.intersect1d(idx_components, idx_blobs)
idx_components_bad = np.setdiff1d(range(len(traces)), idx_components)
A2 = A2.tocsc()[:, idx_components]
C2 = C2[idx_components, :]
YrA = YrA[idx_components, :]
S2 = S2[idx_components, :]
# A: spatial components (ROIs)
# C: denoised [Ca2+]
# YrA: residuals ("noise", i.e. traces = C+YrA)
# S: Spikes
savemat(fn_cnmf, {"A": A2, "C": C2, "YrA": YrA, "S": S2, "bl": bl2})
else:
resdict = loadmat(fn_cnmf)
A2 = resdict["A"]
C2 = resdict["C"]
YrA = resdict["YrA"]
S2 = resdict["S"]
bl2 = resdict["bl"]
Yr, dims, T = cm.load_memmap(fn_mmap)
dims = dims[1:]
Y = np.reshape(Yr, dims + (T, ), order='F')
proj_fn = haussio_data.dirname_comp + "_proj.npy"
if not os.path.exists(proj_fn):
zproj = utils.zproject(np.transpose(Y, (2, 0, 1)))
np.save(proj_fn, zproj)
else:
zproj = np.load(proj_fn)
# DF_F, DF = cse.extract_DF_F(Y.reshape(d1*d2, T), A2, C2)
# t0 = time.time()
# sys.stdout.write("Ordering components... ")
# sys.stdout.flush()
# A_or, C_or, srt = cse.order_components(A2, C2)
# sys.stdout.write(' took {0:.2f} s\n'.format(time.time()-t0))
cm.stop_server()
polygons = contour(A2, Y.shape[0], Y.shape[1], thr=roi_iceberg)
rois = ROIList([sima.ROI.ROI(polygons=poly) for poly in polygons])
return rois, C2, zproj, S2, Y, YrA
