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)
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(' ***** ')
print((len(traces)))
print((len(idx_components)))
print((len(idx_blobs)))
#%% visualize components
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(' ***** ')
print((len(traces)))
print((len(idx_components)))
#print((len(idx_blobs)))
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(list(range(len(traces))), idx_components)
print(' ***** ')
print((len(traces)))
print((len(idx_components)))
print((len(idx_blobs)))
#%% visualize components
#pl.figure();
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
