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
#%%
dr = '/home/nel/Code/Voltage_imaging/exampledata/403106_3min_raw/raw_data/'
total = len([f for f in os.listdir(dr) if f.endswith('.tif')])
n_blocks = 1
n_files = np.int(np.floor(total / n_blocks))
for n in range(n_blocks):
file_list = [dr + 'cameraTube051_{:05n}.tif'.format(i + 1) for i in
np.arange(n_files * n, n_files * (n + 1), 1)]
#%% Select file(s) to be processed (download if not present)
fnames = [tuple([ff for ff in file_list[:36000]]),tuple([ff for ff in file_list[36000:72000]])]
fnames = [tuple([ff for ff in file_list[:72000]])]
# fnames = ['/home/nel/Code/Voltage_imaging/exampledata/403106_3min/datasetblock1.hdf5'] # filename to be processed
if fnames[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']:
fnames = [download_demo(fnames[0])]
#%%
# file_list = ['/home/nel/Code/Voltage_imaging/exampledata/Un-aligned image files/403106_3min/cameraTube051_{:05n}.tif'.format(i+1) for i in range(1000)]
# mv = cm.load(file_list)
#%% First setup some parameters for data and motion correction
# dataset dependent parameters
fr = 30 # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
dxy = (1., 1.) # spatial resolution in x and y in (um per pixel)
# note the lower than usual spatial resolution here
max_shift_um = (10., 10.) # maximum shift in um
patch_motion_um = (64., 64.) # patch size for non-rigid correction in um
# motion correction parameters
pw_rigid = False # flag to select rigid vs pw_rigid motion correction
# maximum allowed rigid shift in pixels
max_shifts = [int(a/b) for a, b in zip(max_shift_um, dxy)]
# start a new patch for pw-rigid motion correction every x pixels
strides = tuple([int(a/b) for a, b in zip(patch_motion_um, dxy)])
# overlap between pathes (size of patch in pixels: strides+overlaps)
overlaps = (24, 24)
# maximum deviation allowed for patch with respect to rigid shifts
max_deviation_rigid = 3
mc_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'dxy': dxy,
'pw_rigid': pw_rigid,
'max_shifts': max_shifts,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': 'copy'
}
opts = params.CNMFParams(params_dict=mc_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = False
if display_images:
m_orig = cm.load_movie_chain(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=60, magnification=2)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# note that the file is not loaded in memory
# %% Run (piecewise-rigid motion) correction using NoRMCorre
mc.motion_correct(save_movie=True)
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)
# intervals at which patches are laid out for motion correction
strides = params_movie['strides']
# overlap between pathes (size of patch strides+overlaps)
overlaps = params_movie['overlaps']
# for parallelization split the movies in num_splits chuncks across time
splits_els = params_movie['splits_els']
# if none all the splits are processed and the movie is saved
num_splits_to_process_els = params_movie['num_splits_to_process_els']
# upsample factor to avoid smearing when merging patches
upsample_factor_grid = params_movie['upsample_factor_grid']
# maximum deviation allowed for patch with respect to rigid
# shift
max_deviation_rigid = params_movie['max_deviation_rigid']
#%% download movie if not there
if fname == 'example_movies/demoSue2x.tif':
download_demo()
#%%
m_orig = cm.load(fname, subindices=slice(0, 100, None))
#%% play movie
downsample_ratio = .2
offset_mov = -np.min(m_orig[:100])
m_orig.resize(1, 1, downsample_ratio).play(
gain=2, offset=offset_mov, fr=30, magnification=1)
#%% RUN ANALYSIS
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
#%%
# movie must be mostly positive for this to work
min_mov = cm.load(fname, subindices=range(400)).min()
mc = MotionCorrect(fname, min_mov,
def main():
pass # For compatibility between running under Spyder and the CLI
# %% download and list all files to be processed
# folder inside ./example_movies where files will be saved
fld_name = 'Mesoscope'
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
# folder where files are located
folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name)
extension = 'hdf5' # extension of files
# read all files to be processed
fnames = glob.glob(folder_name + '/*' + extension)
# your list of files should look something like this
logging.info(fnames)
# %% Set up some parameters
fr = 10 # frame rate (Hz)
decay_time = 2 # approximate length of transient event in seconds
gSig = (3, 3) # expected half size of neurons
p = 1 # order of AR indicator dynamics
min_SNR = 1 # minimum SNR for accepting new components
ds_factor = 1 # spatial downsampling factor (increases speed but may lose some fine structure)
gnb = 2 # number of background components
gSig = tuple(np.ceil(
np.array(gSig) /
ds_factor).astype('int')) # recompute gSig if downsampling is involved
mot_corr = False # flag for online motion correction
pw_rigid = True # flag for pw-rigid motion correction (slower but potentially more accurate)
max_shifts_online = 6 # maximum allowed shift during motion correction
sniper_mode = True # use a CNN to detect new neurons (o/w space correlation)
rval_thr = 0.9 # soace correlation threshold for candidate components
# set up some additional supporting parameters needed for the algorithm
# (these are default values but can change depending on dataset properties)
init_batch = 200 # number of frames for initialization (presumably from the first file)
K = 2 # initial number of components
epochs = 2 # number of passes over the data
show_movie = False # show the movie as the data gets processed
params_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'gSig': gSig,
'p': p,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'ds_factor': ds_factor,
'nb': gnb,
'motion_correct': mot_corr,
'init_batch': init_batch,
'init_method': 'bare',
'normalize': True,
'sniper_mode': sniper_mode,
'K': K,
'epochs': epochs,
'max_shifts_online': max_shifts_online,
'pw_rigid': pw_rigid,
'dist_shape_update': True,
'min_num_trial': 10,
'show_movie': show_movie
}
opts = cnmf.params.CNMFParams(params_dict=params_dict)
# %% fit online
cnm = cnmf.online_cnmf.OnACID(params=opts)
cnm.fit_online()
# %% plot contours (this may take time)
logging.info('Number of components: ' + str(cnm.estimates.A.shape[-1]))
images = cm.load(fnames)
Cn = images.local_correlations(swap_dim=False, frames_per_chunk=500)
cnm.estimates.plot_contours(img=Cn, display_numbers=False)
# %% view components
cnm.estimates.view_components(img=Cn)
# %% plot timing performance (if a movie is generated during processing, timing
# will be severely over-estimated)
T_motion = 1e3 * np.array(cnm.t_motion)
T_detect = 1e3 * np.array(cnm.t_detect)
T_shapes = 1e3 * np.array(cnm.t_shapes)
T_track = 1e3 * np.array(cnm.t_online) - T_motion - T_detect - T_shapes
plt.figure()
plt.stackplot(np.arange(len(T_motion)), T_motion, T_track, T_detect,
T_shapes)
plt.legend(labels=['motion', 'tracking', 'detect', 'shapes'], loc=2)
plt.title('Processing time allocation')
plt.xlabel('Frame #')
plt.ylabel('Processing time [ms]')
#%% RUN IF YOU WANT TO VISUALIZE THE RESULTS (might take time)
c, dview, n_processes = \
cm.cluster.setup_cluster(backend='local', n_processes=None,
single_thread=False)
if opts.online['motion_correct']:
shifts = cnm.estimates.shifts[-cnm.estimates.C.shape[-1]:]
if not opts.motion['pw_rigid']:
memmap_file = cm.motion_correction.apply_shift_online(
images, shifts, save_base_name='MC')
else:
mc = cm.motion_correction.MotionCorrect(fnames,
dview=dview,
**opts.get_group('motion'))
mc.y_shifts_els = [[sx[0] for sx in sh] for sh in shifts]
mc.x_shifts_els = [[sx[1] for sx in sh] for sh in shifts]
memmap_file = mc.apply_shifts_movie(fnames,
rigid_shifts=False,
save_memmap=True,
save_base_name='MC')
else: # To do: apply non-rigid shifts on the fly
memmap_file = images.save(fnames[0][:-4] + 'mmap')
cnm.mmap_file = memmap_file
Yr, dims, T = cm.load_memmap(memmap_file)
# images = np.reshape(Yr.T, [T] + list(dims), order='F')
# min_SNR = 2 # peak SNR for accepted components (if above this, acept)
# rval_thr = 0.85 # space correlation threshold (if above this, accept)
# use_cnn = True # use the CNN classifier
# min_cnn_thr = 0.99 # if cnn classifier predicts below this value, reject
# cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
#
# cnm.params.set('quality', {'min_SNR': min_SNR,
# 'rval_thr': rval_thr,
# 'use_cnn': use_cnn,
# 'min_cnn_thr': min_cnn_thr,
# 'cnn_lowest': cnn_lowest})
#
# cnm.estimates.evaluate_components(images, cnm.params, dview=dview)
cnm.estimates.Cn = Cn
cnm.save(os.path.splitext(fnames[0])[0] + '_results.hdf5')
dview.terminate()
pl.ion()
#%
from caiman.source_extraction.cnmf import cnmf as cnmf
from caiman.components_evaluation import evaluate_components
from caiman.utils.visualization import plot_contours, view_patches_bar
from caiman.base.rois import extract_binary_masks_blob
from caiman.behavior import behavior
from scipy.sparse import coo_matrix
from caiman.utils.utils import download_demo
#%%
fname = [u'demo_behavior.h5']
if fname[0] in ['demo_behavior.h5']:
# TODO: todocument
fname = [download_demo(fname[0])]
# TODO: todocument
m = cm.load(fname[0], is_behavior=True)
#%% load, rotate and eliminate useless pixels
m = m.transpose([0, 2, 1])
m = m[:, 150:, :]
#%% visualize movie
m.play()
#%% select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
mask = np.array(behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
#%%
n_components = 4 # number of movement looked for
resize_fact = 0.5 # for computational efficiency movies are downsampled
# number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
num_std_mag_for_angle = .6
def main():
pass # For compatibility between running under Spyder and the CLI
#%% Select file(s) to be processed (download if not present)
fnames = ['Sue_2x_3000_40_-46.tif'] # filename to be processed
if fnames[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']:
fnames = [download_demo(fnames[0])]
#%% First setup some parameters for data and motion correction
# dataset dependent parameters
fr = 30 # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
dxy = (2., 2.) # spatial resolution in x and y in (um per pixel)
# note the lower than usual spatial resolution here
max_shift_um = (12., 12.) # maximum shift in um
patch_motion_um = (100., 100.) # patch size for non-rigid correction in um
# motion correction parameters
pw_rigid = True # flag to select rigid vs pw_rigid motion correction
# maximum allowed rigid shift in pixels
max_shifts = [int(a/b) for a, b in zip(max_shift_um, dxy)]
# start a new patch for pw-rigid motion correction every x pixels
strides = tuple([int(a/b) for a, b in zip(patch_motion_um, dxy)])
# overlap between pathes (size of patch in pixels: strides+overlaps)
overlaps = (24, 24)
# maximum deviation allowed for patch with respect to rigid shifts
max_deviation_rigid = 3
mc_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'dxy': dxy,
'pw_rigid': pw_rigid,
'max_shifts': max_shifts,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': 'copy'
}
opts = params.CNMFParams(params_dict=mc_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = True
if display_images:
m_orig = cm.load_movie_chain(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=60, magnification=2)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# note that the file is not loaded in memory
# %% Run (piecewise-rigid motion) correction using NoRMCorre
mc.motion_correct(save_movie=True)
# %% compare with original movie
if display_images:
m_orig = cm.load_movie_chain(fnames)
m_els = cm.load(mc.mmap_file)
ds_ratio = 0.2
moviehandle = cm.concatenate([m_orig.resize(1, 1, ds_ratio) - mc.min_mov*mc.nonneg_movie,
m_els.resize(1, 1, ds_ratio)], axis=2)
moviehandle.play(fr=60, q_max=99.5, magnification=2) # press q to exit
# %% MEMORY MAPPING
border_to_0 = 0 if mc.border_nan is 'copy' else mc.border_to_0
# you can include the boundaries of the FOV if you used the 'copy' option
# during motion correction, although be careful about the components near
# the boundaries
# memory map the file in order 'C'
fname_new = cm.save_memmap(mc.mmap_file, base_name='memmap_', order='C',
border_to_0=border_to_0) # exclude borders
# now load the file
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# load frames in python format (T x X x Y)
# %% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %% parameters for source extraction and deconvolution
p = 1 # order of the autoregressive system
gnb = 2 # number of global background components
merge_thr = 0.85 # merging threshold, max correlation allowed
rf = 15
# half-size of the patches in pixels. e.g., if rf=25, patches are 50x50
stride_cnmf = 6 # amount of overlap between the patches in pixels
K = 4 # number of components per patch
gSig = [4, 4] # expected half size of neurons in pixels
# initialization method (if analyzing dendritic data using 'sparse_nmf')
method_init = 'greedy_roi'
ssub = 2 # spatial subsampling during initialization
tsub = 2 # temporal subsampling during intialization
# parameters for component evaluation
opts_dict = {'fnames': fnames,
'p': p,
'fr': fr,
'nb': gnb,
'rf': rf,
'K': K,
'gSig': gSig,
'stride': stride_cnmf,
'method_init': method_init,
'rolling_sum': True,
'merge_thr': merge_thr,
'n_processes': n_processes,
'only_init': True,
'ssub': ssub,
'tsub': tsub}
opts.change_params(params_dict=opts_dict);
# %% RUN CNMF ON PATCHES
# First extract spatial and temporal components on patches and combine them
# for this step deconvolution is turned off (p=0). If you want to have
# deconvolution within each patch change params.patch['p_patch'] to a
# nonzero value
#opts.change_params({'p': 0})
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
cnm = 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)
# %% plot contours of found components
Cns = local_correlations_movie_offline(mc.mmap_file[0],
remove_baseline=True, window=1000, stride=1000,
winSize_baseline=100, quantil_min_baseline=10,
dview=dview)
Cn = Cns.max(axis=0)
Cn[np.isnan(Cn)] = 0
cnm.estimates.plot_contours(img=Cn)
plt.title('Contour plots of found components')
#%% save results
cnm.estimates.Cn = Cn
cnm.save(fname_new[:-5]+'_init.hdf5')
# %% RE-RUN seeded CNMF on accepted patches to refine and perform deconvolution
cnm2 = cnm.refit(images, dview=dview)
# %% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier
min_SNR = 2 # signal to noise ratio for accepting a component
rval_thr = 0.85 # space correlation threshold for accepting a component
cnn_thr = 0.99 # threshold for CNN based classifier
cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
cnm2.params.set('quality', {'decay_time': decay_time,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'use_cnn': True,
'min_cnn_thr': cnn_thr,
'cnn_lowest': cnn_lowest})
cnm2.estimates.evaluate_components(images, cnm2.params, dview=dview)
# %% PLOT COMPONENTS
cnm2.estimates.plot_contours(img=Cn, idx=cnm2.estimates.idx_components)
# %% VIEW TRACES (accepted and rejected)
if display_images:
cnm2.estimates.view_components(images, img=Cn,
idx=cnm2.estimates.idx_components)
cnm2.estimates.view_components(images, img=Cn,
idx=cnm2.estimates.idx_components_bad)
#%% update object with selected components
cnm2.estimates.select_components(use_object=True)
#%% Extract DF/F values
cnm2.estimates.detrend_df_f(quantileMin=8, frames_window=250)
#%% Show final traces
cnm2.estimates.view_components(img=Cn)
#%%
cnm2.estimates.Cn = Cn
cnm2.save(cnm2.mmap_file[:-4] + 'hdf5')
#%% reconstruct denoised movie (press q to exit)
if display_images:
cnm2.estimates.play_movie(images, q_max=99.9, gain_res=2,
magnification=2,
bpx=border_to_0,
include_bck=False) # background not shown
#%% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
# intervals at which patches are laid out for motion correction
strides = params_movie['strides']
# overlap between pathes (size of patch strides+overlaps)
overlaps = params_movie['overlaps']
# for parallelization split the movies in num_splits chuncks across time
splits_els = params_movie['splits_els']
# if none all the splits are processed and the movie is saved
num_splits_to_process_els = params_movie['num_splits_to_process_els']
# upsample factor to avoid smearing when merging patches
upsample_factor_grid = params_movie['upsample_factor_grid']
# maximum deviation allowed for patch with respect to rigid
# shift
max_deviation_rigid = params_movie['max_deviation_rigid']
#%% download movie if not there
if fname == 'example_movies/demoSue2x.tif':
download_demo()
#%%
m_orig = cm.load(fname)
#%% play movie
downsample_ratio = .2
offset_mov = -np.min(m_orig[:100])
m_orig.resize(1, 1, downsample_ratio).play(
gain=2, offset=offset_mov, fr=30, magnification=1)
#%% RUN ANALYSIS
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
#%%
# movie must be mostly positive for this to work
min_mov = cm.load(fname, subindices=range(400)).min()
mc = MotionCorrect(fname, min_mov,
def main():
pass # For compatibility between running under Spyder and the CLI
#%% Select file(s) to be processed (download if not present)
root = '/Users/hheiser/Desktop/testing data/chronic_M2N3/0d_baseline/channel1'
fnames = [r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M22\20191208\N1\1\file_00003.tif',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M22\20191208\N1\2\file_00004.tif',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M22\20191208\N1\3\file_00005.tif',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M22\20191208\N1\4\file_00006.tif',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M22\20191208\N1\5\file_00007.tif',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M22\20191208\N1\6\file_00008.tif']
fnames = ['Sue_2x_3000_40_-46.tif'] # filename to be processed
if fnames[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']:
fnames = [download_demo(fnames[0])]
#%% First setup some parameters for data and motion correction
# dataset dependent parameters
fr = 30 # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
dxy = (2., 2.) # spatial resolution in x and y in (um per pixel)
# note the lower than usual spatial resolution here
max_shift_um = (12., 12.) # maximum shift in um
patch_motion_um = (100., 100.) # patch size for non-rigid correction in um
# motion correction parameters
pw_rigid = True # flag to select rigid vs pw_rigid motion correction
# maximum allowed rigid shift in pixels
max_shifts = [int(a/b) for a, b in zip(max_shift_um, dxy)]
# start a new patch for pw-rigid motion correction every x pixels
strides = tuple([int(a/b) for a, b in zip(patch_motion_um, dxy)])
# overlap between pathes (size of patch in pixels: strides+overlaps)
overlaps = (24, 24)
# maximum deviation allowed for patch with respect to rigid shifts
max_deviation_rigid = 3
mc_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'dxy': dxy,
'pw_rigid': pw_rigid,
'max_shifts': max_shifts,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': 'copy'
}
opts = params.CNMFParams(params_dict=mc_dict)
#%% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = True
if display_images:
m_orig = cm.load_movie_chain(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=30, magnification=1, do_loop=False)
#%% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
#%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# note that the file is not loaded in memory
#%% Run (piecewise-rigid motion) correction using NoRMCorre
mc.motion_correct(save_movie=True)
#%% compare with original movie
if display_images:
m_orig = cm.load_movie_chain(fnames[:3])
m_els = cm.load(mmap_file[:3])
ds_ratio = 0.2
moviehandle = cm.concatenate([m_orig.resize(1, 1, ds_ratio) - mc.min_mov*mc.nonneg_movie,
m_els.resize(1, 1, ds_ratio)], axis=2)
moviehandle.play(fr=15, q_max=99.5, magnification=2) # press q to exit
#%% MEMORY MAPPING
border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
# you can include the boundaries of the FOV if you used the 'copy' option
# during motion correction, although be careful about the components near
# the boundaries
mmap_file = [r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M19\20191219\N2\1\file_00001_els__d1_512_d2_512_d3_1_order_F_frames_1147_.mmap',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M19\20191219\N2\2\file_00002_els__d1_512_d2_512_d3_1_order_F_frames_2520_.mmap',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M19\20191219\N2\3\file_00003_els__d1_512_d2_512_d3_1_order_F_frames_3814_.mmap',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M19\20191219\N2\4\file_00004_els__d1_512_d2_512_d3_1_order_F_frames_5154_.mmap',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M19\20191219\N2\5\file_00005_els__d1_512_d2_512_d3_1_order_F_frames_2677_.mmap',
r'W:\Neurophysiology-Storage1\Wahl\Hendrik\PhD\Data\Batch2\M19\20191219\N2\6\file_00006_els__d1_512_d2_512_d3_1_order_F_frames_3685_.mmap']
fname_new = r'E:\PhD\Data\DG\M14_20191014\N1\memmap__d1_512_d2_512_d3_1_order_C_frames_34939_.mmap'
# memory map the file in order 'C'
fname_new = cm.save_memmap(mc.mmap_file, base_name='memmap_', order='C',
border_to_0=0) # exclude borders
# now load the file
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# load frames in python format (T x X x Y)
#%% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
#%% parameters for source extraction and deconvolution
p = 1 # order of the autoregressive system
gnb = 2 # number of global background components
merge_thr = 0.85 # merging threshold, max correlation allowed
rf = 15
# half-size of the patches in pixels. e.g., if rf=25, patches are 50x50
stride_cnmf = 6 # amount of overlap between the patches in pixels
K = 4 # number of components per patch
gSig = [4, 4] # expected half size of neurons in pixels
# initialization method (if analyzing dendritic data using 'sparse_nmf')
method_init = 'greedy_roi'
ssub = 2 # spatial subsampling during initialization
tsub = 2 # temporal subsampling during intialization
# parameters for component evaluation
opts_dict = {'fnames': fnames,
'fr': fr,
'nb': gnb,
'rf': rf,
'K': K,
'gSig': gSig,
'stride': stride_cnmf,
'method_init': method_init,
'rolling_sum': True,
'merge_thr': merge_thr,
'n_processes': n_processes,
'only_init': True,
'ssub': ssub,
'tsub': tsub}
opts.change_params(params_dict=opts_dict)
#%% RUN CNMF ON PATCHES
# First extract spatial and temporal components on patches and combine them
# for this step deconvolution is turned off (p=0)
#opts.change_params({'p': 1,'rf':None, 'only_init':False})
opts.change_params({'p': 0})
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
cnm = cnm.fit(images)
#%% RUN CNMF SEEDED WITH MANUAL MASK
# load mask
mask = np.asarray(imageio.imread('/Users/hheiser/Desktop/testing data/file_00020_no_motion/avg_mask_fixed.png'), dtype=bool)
# get component ROIs from the mask and plot them
Ain, labels, mR = cm.base.rois.extract_binary_masks(mask)
# plot original mask and extracted labels to check mask
fig, ax = plt.subplots(1,2)
ax[0].imshow(-mR,cmap='binary')
ax[0].set_title('Original mask')
ax[1].imshow(labels)
ax[1].set_title('Extracted labelled ROIs')
""""
plt.figure()
crd = cm.utils.visualization.plot_contours(
Ain.astype('float32'), mR, thr=0.99, display_numbers=True) # todo check if this is important for the pipeline
plt.title('Contour plots of detected ROIs in the structural channel')
"""
opts.change_params({'rf': None, 'only_init': False})
# run CNMF seeded with this mask
cnm_corr = cnmf.CNMF(n_processes, params=opts, dview=dview, Ain=Ain)
cnm_corr_seed = cnm_corr_seed.fit(images)
#cnm_seed = cnm_seed.fit_file(motion_correct=False)
#%% 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)
#%% plot contours of found components
Cn = cm.local_correlations(images, swap_dim=False)
Cn[np.isnan(Cn)] = 0
cnm.estimates.plot_contours(img=Cn)
plt.title('Contour plots of found components')
#%% RE-RUN seeded CNMF on accepted patches to refine and perform deconvolution
cnm.params.change_params({'p': p})
cnm2 = cnm.refit(images, dview=dview)
#%% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier
min_SNR = 2 # signal to noise ratio for accepting a component
rval_thr = 0.85 # space correlation threshold for accepting a component
cnn_thr = 0.99 # threshold for CNN based classifier
cnn_lowest = 0.1 # neurons with cnn probability lower than this value are rejected
cnm2.params.set('quality', {'decay_time': decay_time,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'use_cnn': True,
'min_cnn_thr': cnn_thr,
'cnn_lowest': cnn_lowest})
cnm2.estimates.evaluate_components(images, params=cnm2.params, dview=dview)
#%% PLOT COMPONENTS
cnm2.estimates.plot_contours(img=Cn, idx=cnm2.estimates.idx_components)
#%% VIEW TRACES (accepted and rejected)
if display_images:
cnm2.estimates.view_components(images, img=Cn,
idx=cnm2.estimates.idx_components)
cnm2.estimates.view_components(images, img=Cn,
idx=cnm2.estimates.idx_components_bad)
#%% update object with selected components
#### -> will delete rejected components!
cnm2.estimates.select_components(use_object=True)
#%% Extract DF/F values
cnm2.estimates.detrend_df_f(quantileMin=8, frames_window=250)
#%% Show final traces
cnm2.estimates.view_components(img=Cn)
#%% reconstruct denoised movie (press q to exit)
if display_images:
cnm2.estimates.play_movie(images, q_max=99.9, gain_res=2,
magnification=1,
bpx=border_to_0,
include_bck=True) # background not shown
#%% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
#%% save results
dirname = fnames[0][:-4] + "_results.hdf5"
cnm2.estimates.Cn = Cn
cnm2.save(dirname)
#load results
cnm2 = cnmf.load_CNMF(dirname)
mov_name = fnames[0][:-4] + "_movie_restored_2_gain.tif"
helper.save_movie(cnm2.estimates,images,mov_name,frame_range=range(200),include_bck=True)
def main():
pass # For compatibility between running under Spyder and the CLI
# %% Load demo movie and ROIs
fnames = download_demo(
'demo_voltage_imaging.hdf5',
'volpy') # file path to movie file (will download if not present)
path_ROIs = download_demo(
'demo_voltage_imaging_ROIs.hdf5',
'volpy') # file path to ROIs file (will download if not present)
file_dir = os.path.split(fnames)[0]
#%% dataset dependent parameters
# dataset dependent parameters
fr = 400 # sample rate of the movie
# motion correction parameters
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)
max_deviation_rigid = 3 # maximum deviation allowed for patch with respect to rigid shifts
border_nan = 'copy'
opts_dict = {
'fnames': fnames,
'fr': fr,
'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 = volparams(params_dict=opts_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the movie press q
display_images = False
if display_images:
m_orig = cm.load(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=40, magnification=4)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
# %%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run correction
do_motion_correction = True
if do_motion_correction:
mc.motion_correct(save_movie=True)
else:
mc_list = [
file for file in os.listdir(file_dir)
if (os.path.splitext(os.path.split(fnames)[-1])[0] in file
and '.mmap' in file)
]
mc.mmap_file = [os.path.join(file_dir, mc_list[0])]
print(f'reuse previously saved motion corrected file:{mc.mmap_file}')
# %% compare with original movie
if display_images:
m_orig = cm.load(fnames)
m_rig = cm.load(mc.mmap_file)
ds_ratio = 0.2
moviehandle = cm.concatenate(
[m_orig.resize(1, 1, ds_ratio),
m_rig.resize(1, 1, ds_ratio)],
axis=2)
moviehandle.play(fr=40, q_max=99.5, magnification=4) # press q to exit
# %% MEMORY MAPPING
do_memory_mapping = True
if do_memory_mapping:
border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
# you can include the boundaries of the FOV if you used the 'copy' option
# during motion correction, although be careful about the components near
# the boundaries
# memory map the file in order 'C'
fname_new = cm.save_memmap_join(
mc.mmap_file,
base_name='memmap_' +
os.path.splitext(os.path.split(fnames)[-1])[0],
add_to_mov=border_to_0,
dview=dview) # exclude border
else:
mmap_list = [
file for file in os.listdir(file_dir)
if ('memmap_' +
os.path.splitext(os.path.split(fnames)[-1])[0]) in file
]
fname_new = os.path.join(file_dir, mmap_list[0])
print(f'reuse previously saved memory mapping file:{fname_new}')
# %% SEGMENTATION
# create summary images
img = mean_image(mc.mmap_file[0], window=1000, dview=dview)
img = (img - np.mean(img)) / np.std(img)
gaussian_blur = False # Use gaussian blur when there is too much noise in the video
Cn = local_correlations_movie_offline(mc.mmap_file[0],
fr=fr,
window=fr * 4,
stride=fr * 4,
winSize_baseline=fr,
remove_baseline=True,
gaussian_blur=gaussian_blur,
dview=dview).max(axis=0)
img_corr = (Cn - np.mean(Cn)) / np.std(Cn)
summary_images = np.stack([img, img, img_corr], axis=0).astype(np.float32)
# save summary images which are used in the VolPy GUI
cm.movie(summary_images).save(fnames[:-5] + '_summary_images.tif')
fig, axs = plt.subplots(1, 2)
axs[0].imshow(summary_images[0])
axs[1].imshow(summary_images[2])
axs[0].set_title('mean image')
axs[1].set_title('corr image')
#%% methods for segmentation
methods_list = [
'manual_annotation', # manual annotations need prepared annotated datasets in the same format as demo_voltage_imaging_ROIs.hdf5
'maskrcnn', # Mask R-CNN is a convolutional neural network trained for detecting neurons in summary images
'gui_annotation'
] # use VolPy GUI to correct outputs of Mask R-CNN or annotate new datasets
method = methods_list[0]
if method == 'manual_annotation':
with h5py.File(path_ROIs, 'r') as fl:
ROIs = fl['mov'][()]
elif method == 'maskrcnn': # Important!! Make sure install keras before using mask rcnn.
weights_path = download_model(
'mask_rcnn'
) # also make sure you have downloaded the new weight. The weight was updated on Dec 1st 2020.
ROIs = utils.mrcnn_inference(
img=summary_images.transpose([1, 2, 0]),
size_range=[5, 22],
weights_path=weights_path,
display_result=True
) # size parameter decides size range of masks to be selected
cm.movie(ROIs).save(fnames[:-5] + 'mrcnn_ROIs.hdf5')
elif method == 'gui_annotation':
# run volpy_gui.py file in the caiman/source_extraction/volpy folder
gui_ROIs = caiman_datadir() + '/example_movies/volpy/gui_roi.hdf5'
with h5py.File(gui_ROIs, 'r') as fl:
ROIs = fl['mov'][()]
fig, axs = plt.subplots(1, 2)
axs[0].imshow(summary_images[0])
axs[1].imshow(ROIs.sum(0))
axs[0].set_title('mean image')
axs[1].set_title('masks')
# %% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False,
maxtasksperchild=1)
# %% parameters for trace denoising and spike extraction
ROIs = ROIs # region of interests
index = list(range(len(ROIs))) # index of neurons
weights = None # reuse spatial weights
context_size = 35 # number of pixels surrounding the ROI to censor from the background PCA
visualize_ROI = False # whether to visualize the region of interest inside the context region
flip_signal = True # Important!! Flip signal or not, True for Voltron indicator, False for others
hp_freq_pb = 1 / 3 # parameter for high-pass filter to remove photobleaching
clip = 100 # maximum number of spikes to form spike template
threshold_method = 'adaptive_threshold' # adaptive_threshold or simple
min_spikes = 10 # minimal spikes to be found
pnorm = 0.5 # a variable deciding the amount of spikes chosen for adaptive threshold method
threshold = 3 # threshold for finding spikes only used in simple threshold method, Increase the threshold to find less spikes
do_plot = False # plot detail of spikes, template for the last iteration
ridge_bg = 0.01 # ridge regression regularizer strength for background removement, larger value specifies stronger regularization
sub_freq = 20 # frequency for subthreshold extraction
weight_update = 'ridge' # ridge or NMF for weight update
n_iter = 2 # number of iterations alternating between estimating spike times and spatial filters
opts_dict = {
'fnames': fname_new,
'ROIs': ROIs,
'index': index,
'weights': weights,
'context_size': context_size,
'visualize_ROI': visualize_ROI,
'flip_signal': flip_signal,
'hp_freq_pb': hp_freq_pb,
'clip': clip,
'threshold_method': threshold_method,
'min_spikes': min_spikes,
'pnorm': pnorm,
'threshold': threshold,
'do_plot': do_plot,
'ridge_bg': ridge_bg,
'sub_freq': sub_freq,
'weight_update': weight_update,
'n_iter': n_iter
}
opts.change_params(params_dict=opts_dict)
#%% TRACE DENOISING AND SPIKE DETECTION
vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
vpy.fit(n_processes=n_processes, dview=dview)
#%% visualization
display_images = True
if display_images:
print(np.where(
vpy.estimates['locality'])[0]) # neurons that pass locality test
idx = np.where(vpy.estimates['locality'] > 0)[0]
utils.view_components(vpy.estimates, img_corr, idx)
#%% reconstructed movie
# note the negative spatial weights is cutoff
if display_images:
mv_all = utils.reconstructed_movie(vpy.estimates.copy(),
fnames=mc.mmap_file,
idx=idx,
scope=(0, 1000),
flip_signal=flip_signal)
mv_all.play(fr=40)
#%% save the result in .npy format
save_result = True
if save_result:
vpy.estimates['ROIs'] = ROIs
vpy.estimates['params'] = opts
save_name = f'volpy_{os.path.split(fnames)[1][:-5]}_{threshold_method}'
np.save(os.path.join(file_dir, save_name), vpy.estimates)
# %% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
for t in range(init_batch, T):
# read frame and append to queue
frame = next(iterator)
q.put(frame)
sleep(max(0, (t+1-init_batch)/fr - time() + t_start))
def get_iterator(device=0, fr=None):
"""
device: device number (int) or filename (string) for reading from camera or file respectively
fr: frame rate
"""
if type(device) is int: # capture from camera
def capture_iter(device=device, fr=fr):
cap = cv2.VideoCapture(device)
if fr is not None: # set frame rate
cap.set(cv2.CAP_PROP_FPS, fr)
while True:
yield cv2.cvtColor(cap.read()[1], cv2.COLOR_BGR2GRAY)
iterator = capture_iter(device, fr)
else: # read frame by frame from file
iterator = cm.base.movies.load_iter(device, var_name_hdf5='Y')
return iterator
init_batch = 500 # number of frames to use for initialization
iterator = get_iterator(download_demo('blood_vessel_10Hz.mat'))
for t in range(init_batch):
next(iterator)
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:
dview.terminate() # 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)
)
new_templ = mc.total_template_rig
def main():
pass # For compatibility between running under Spyder and the CLI
# %% Load demo movie and ROIs
fnames = download_demo(
'demo_voltage_imaging.hdf5',
'volpy') # file path to movie file (will download if not present)
path_ROIs = download_demo(
'demo_voltage_imaging_ROIs.hdf5',
'volpy') # file path to ROIs file (will download if not present)
#%% dataset dependent parameters
# dataset dependent parameters
fr = 400 # sample rate of the movie
# motion correction parameters
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)
max_deviation_rigid = 3 # maximum deviation allowed for patch with respect to rigid shifts
border_nan = 'copy'
opts_dict = {
'fnames': fnames,
'fr': fr,
'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 = volparams(params_dict=opts_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the movie press q
display_images = False
if display_images:
m_orig = cm.load(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=40, magnification=6)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
# %%% MOTION CORRECTION
# first we create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run correction
mc.motion_correct(save_movie=True)
# %% compare with original movie
if display_images:
m_orig = cm.load(fnames)
m_rig = cm.load(mc.mmap_file)
ds_ratio = 0.2
moviehandle = cm.concatenate([
m_orig.resize(1, 1, ds_ratio) - mc.min_mov * mc.nonneg_movie,
m_rig.resize(1, 1, ds_ratio)
],
axis=2)
moviehandle.play(fr=60, q_max=99.5, magnification=4) # press q to exit
# %% MEMORY MAPPING
border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
# you can include the boundaries of the FOV if you used the 'copy' option
# during motion correction, although be careful about the components near
# the boundaries
# memory map the file in order 'C'
fname_new = cm.save_memmap_join(mc.mmap_file,
base_name='memmap_',
add_to_mov=border_to_0,
dview=dview) # exclude border
# %% SEGMENTATION
# create summary images
img = mean_image(mc.mmap_file[0], window=1000, dview=dview)
img = (img - np.mean(img)) / np.std(img)
gaussian_blur = False # Use gaussian blur when the quality of corr image(Cn) is bad
Cn = local_correlations_movie_offline(mc.mmap_file[0],
fr=fr,
window=fr * 4,
stride=fr * 4,
winSize_baseline=fr,
remove_baseline=True,
gaussian_blur=gaussian_blur,
dview=dview).max(axis=0)
img_corr = (Cn - np.mean(Cn)) / np.std(Cn)
summary_image = np.stack([img, img, img_corr], axis=2).astype(np.float32)
#%% three methods for segmentation
methods_list = [
'manual_annotation', # manual annotation needs user to prepare annotated datasets same format as demo ROIs
'quick_annotation', # quick annotation annotates data with simple interface in python
'maskrcnn'
] # maskrcnn is a convolutional network trained for finding neurons using summary images
method = methods_list[0]
if method == 'manual_annotation':
with h5py.File(path_ROIs, 'r') as fl:
ROIs = fl['mov'][()]
elif method == 'quick_annotation':
ROIs = utils.quick_annotation(img, min_radius=4, max_radius=8)
elif method == 'maskrcnn': # Important!! make sure install keras before using mask rcnn
weights_path = download_model('mask_rcnn')
ROIs = utils.mrcnn_inference(img=summary_image,
weights_path=weights_path,
display_result=True)
# %% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False,
maxtasksperchild=1)
# %% parameters for trace denoising and spike extraction
ROIs = ROIs # region of interests
index = list(range(len(ROIs))) # index of neurons
weights = None # reuse spatial weights
context_size = 35 # number of pixels surrounding the ROI to censor from the background PCA
flip_signal = True # Important!! Flip signal or not, True for Voltron indicator, False for others
hp_freq_pb = 1 / 3 # parameter for high-pass filter to remove photobleaching
threshold_method = 'simple' # 'simple' or 'adaptive_threshold'
min_spikes = 10 # minimal spikes to be found
threshold = 3.5 # threshold for finding spikes, increase threshold to find less spikes
do_plot = False # plot detail of spikes, template for the last iteration
ridge_bg = 0.001 # ridge regression regularizer strength for background removement
sub_freq = 20 # frequency for subthreshold extraction
weight_update = 'ridge' # 'ridge' or 'NMF' for weight update
opts_dict = {
'fnames': fname_new,
'ROIs': ROIs,
'index': index,
'weights': weights,
'context_size': context_size,
'flip_signal': flip_signal,
'hp_freq_pb': hp_freq_pb,
'threshold_method': threshold_method,
'min_spikes': min_spikes,
'threshold': threshold,
'do_plot': do_plot,
'ridge_bg': ridge_bg,
'sub_freq': sub_freq,
'weight_update': weight_update
}
opts.change_params(params_dict=opts_dict)
#%% TRACE DENOISING AND SPIKE DETECTION
vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
vpy.fit(n_processes=n_processes, dview=dview)
#%% visualization
if display_images:
print(np.where(
vpy.estimates['locality'])[0]) # neurons that pass locality test
idx = np.where(vpy.estimates['locality'] > 0)[0]
utils.view_components(vpy.estimates, img_corr, idx)
#%% reconstructed movie
# note the negative spatial weights is cutoff
if display_images:
mv_all = utils.reconstructed_movie(vpy.estimates,
fnames=mc.mmap_file,
idx=idx,
scope=(0, 1000),
flip_signal=flip_signal)
mv_all.play(fr=40)
# %% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
def main():
pass # For compatibility between running under Spyder and the CLI
#%% First setup some parameters for data and motion correction
# dataset dependent parameters
fname = ['Sue_2x_3000_40_-46.tif'] # filename to be processed
fr = 30 # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
dxy = (2., 2.) # spatial resolution in x and y in (um per pixel)
max_shift_um = (12., 12.) # maximum shift in um
patch_motion_um = (100., 100.) # patch size for non-rigid motion correction in um
# motion correction parameters
pwrigid_motion_correct = True # flag to select rigid vs pw_rigid motion correction
max_shifts = tuple([int(a/b) for a, b in zip(max_shift_um, dxy)])
# maximum allow rigid shift in pixels
# for parallelization split the movies in num_splits chuncks across time
splits_rig = 56
# start a new patch for pw-rigid motion correction every x pixels
strides = tuple([int(a/b) for a, b in zip(patch_motion_um, dxy)])
# overlap between pathes (size of patch strides+overlaps)
overlaps = (24, 24)
# for parallelization split the movies in num_splits chuncks across time
splits_els = 56
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
#%% download the dataset if it's not present in your folder
if fname[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']:
fname = [download_demo(fname[0])]
#%% play the movie
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = False
if display_images:
m_orig = cm.load_movie_chain(fname)
downsample_ratio = 0.2
moviehandle = m_orig.resize(1, 1, downsample_ratio)
moviehandle.play(q_max=99.5, fr=60, magnification=2)
#%% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
#%%% MOTION CORRECTION
# first we create a motion correction object with the parameters specified
min_mov = cm.load(fname[0], subindices=range(200)).min()
# this will be subtracted from the movie to make it non-negative
mc = MotionCorrect(fname, min_mov, dview=dview, max_shifts=max_shifts,
splits_rig=splits_rig,
strides=strides, overlaps=overlaps,
splits_els=splits_els, border_nan='copy',
upsample_factor_grid=upsample_factor_grid,
max_deviation_rigid=max_deviation_rigid,
shifts_opencv=True, nonneg_movie=True)
# note that the file is not loaded in memory
#%% Run piecewise-rigid motion correction using NoRMCorre
if pwrigid_motion_correct:
mc.motion_correct_pwrigid(save_movie=True)
m_els = cm.load(mc.fname_tot_els)
bord_px_els = np.ceil(np.maximum(np.max(np.abs(mc.x_shifts_els)),
np.max(np.abs(mc.y_shifts_els)))).astype(np.int)
fnames = mc.fname_tot_els # name of the pw-rigidly corrected file.
else:
mc.motion_correct_rigid(save_movie=True)
m_els = cm.load(mc.fname_tot_rig)
bord_px_els = np.ceil(np.max(np.abs(mc.shifts_rig))).astype(np.int)
fnames = mc.fname_tot_rig # name of the rigidly corrected file.
# maximum shift to be used for trimming against NaNs
#%% compare with original movie
if display_images:
downsample_ratio = 0.2
moviehandle = cm.concatenate([m_orig.resize(1, 1, downsample_ratio) - min_mov,
m_els.resize(1, 1, downsample_ratio)],
axis=2)
moviehandle.play(fr=60, q_max=99.5, magnification=2) # press q to exit
#%% MEMORY MAPPING
# memory map the file in order 'C'
border_to_0 = bord_px_els # exclude borders due to motion correction
# border_to_0 = 0 if mc.border_nan is 'copy' else bord_px_els
# you can include boundaries if you used the 'copy' option in the motion
# correction, although be careful abou the components near the boundaries
fname_new = cm.save_memmap(fnames, base_name='memmap_', order='C',
border_to_0=border_to_0) # exclude borders
# now load the file
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# load frames in python format (T x X x Y)
#%% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
#%% parameters for source extraction and deconvolution
p = 1 # order of the autoregressive system
gnb = 2 # number of global background components
merge_thresh = 0.8 # merging threshold, max correlation allowed
# half-size of the patches in pixels. e.g., if rf=25, patches are 50x50
rf = 15
stride_cnmf = 6 # amount of overlap between the patches in pixels
K = 4 # number of components per patch
gSig = [4, 4] # expected half size of neurons
# initialization method (if analyzing dendritic data using 'sparse_nmf')
method_init = 'greedy_roi'
# parameters for component evaluation
opts = params.CNMFParams(dims=dims, fr=fr, decay_time=decay_time,
method_init=method_init, gSig=gSig,
merge_thresh=merge_thresh, p=p, gnb=gnb, k=K,
rf=rf, stride=stride_cnmf, rolling_sum=True)
#%% RUN CNMF ON PATCHES
# First extract spatial and temporal components on patches and combine them
# for this step deconvolution is turned off (p=0)
opts.set('temporal', {'p': 0})
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
cnm = cnm.fit(images)
#%% plot contours of found components
Cn = cm.local_correlations(images.transpose(1, 2, 0))
Cn[np.isnan(Cn)] = 0
cnm.estimates.plot_contours(img=Cn)
plt.title('Contour plots of found components')
#%% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier
min_SNR = 2.5 # signal to noise ratio for accepting a component
rval_thr = 0.8 # space correlation threshold for accepting a component
cnn_thr = 0.8 # threshold for CNN based classifier
cnm.params.set('quality', {'fr': fr,
'decay_time': decay_time,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'use_cnn': True,
'min_cnn_thr': cnn_thr})
cnm.estimates.evaluate_components(images, cnm.params, dview=dview)
#%% PLOT COMPONENTS
cnm.estimates.plot_contours(img=Cn, idx=cnm.estimates.idx_components)
#%% VIEW TRACES (accepted and rejected)
if display_images:
cnm.estimates.view_components(images, img=Cn, idx=cnm.estimates.idx_components)
cnm.estimates.view_components(images, img=Cn, idx=cnm.estimates.idx_components_bad)
#%% RE-RUN seeded CNMF on accepted patches to refine and perform deconvolution
cnm.dview = None
cnm2 = deepcopy(cnm)
cnm2.dview = dview
cnm2.params.set('patch', {'rf': None})
cnm2.params.set('temporal', {'p': p})
cnm2 = cnm2.fit(images)
#%% Extract DF/F values
cnm2.estimates.detrend_df_f(quantileMin=8, frames_window=250)
#%% Show final traces
cnm2.estimates.view_components(Yr, img=Cn)
#%% reconstruct denoised movie (press q to exit)
if display_images:
cnm2.estimates.play_movie(images, q_max=99.9, gain_res=2,
magnification=2,
bpx=border_to_0,
include_bck=True)
#%% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
def main():
pass # For compatibility between running under Spyder and the CLI
#%%
pl.ion()
fname = [u'demo_behavior.h5']
if fname[0] in ['demo_behavior.h5']:
# TODO: todocument
fname = [download_demo(fname[0])]
# TODO: todocument
m = cm.load(fname[0], is_behavior=True)
#%% load, rotate and eliminate useless pixels
m = m.transpose([0, 2, 1])
m = m[:, 150:, :]
#%% visualize movie
m.play()
#%% select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
print(
"Please draw a polygon delimiting the ROI on the image that will be displayed after the image; press enter when done"
)
mask = np.array(
behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
#%%
n_components = 4 # number of movement looked for
resize_fact = 0.5 # for computational efficiency movies are downsampled
# number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
num_std_mag_for_angle = .6
only_magnitude = False # if onlu interested in factorizing over the magnitude
method_factorization = 'dict_learn' # could also use nmf
# number of iterations for the dictionary learning algorithm (Marial et al, 2010)
max_iter_DL = -30
spatial_filter_, time_trace_, of_or = cm.behavior.behavior.extract_motor_components_OF(
m,
n_components,
mask=mask,
resize_fact=resize_fact,
only_magnitude=only_magnitude,
verbose=True,
method_factorization='dict_learn',
max_iter_DL=max_iter_DL)
#%%
mags, dircts, dircts_thresh, spatial_masks_thrs = cm.behavior.behavior.extract_magnitude_and_angle_from_OF(
spatial_filter_,
time_trace_,
of_or,
num_std_mag_for_angle=num_std_mag_for_angle,
sav_filter_size=3,
only_magnitude=only_magnitude)
#%%
idd = 0
axlin = pl.subplot(n_components, 2, 2)
for mag, dirct, spatial_filter in zip(mags, dircts_thresh,
spatial_filter_):
pl.subplot(n_components, 2, 1 + idd * 2)
min_x, min_y = np.min(np.where(mask), 1)
spfl = spatial_filter
spfl = cm.movie(spfl[None, :, :]).resize(1 / resize_fact,
1 / resize_fact, 1).squeeze()
max_x, max_y = np.add((min_x, min_y), np.shape(spfl))
mask[min_x:max_x, min_y:max_y] = spfl
mask[mask < np.nanpercentile(spfl, 70)] = np.nan
pl.imshow(m[0], cmap='gray')
pl.imshow(mask, alpha=.5)
pl.axis('off')
axelin = pl.subplot(n_components, 2, 2 + idd * 2, sharex=axlin)
pl.plot(mag / 10, 'k')
dirct[mag < 0.5 * np.std(mag)] = np.nan
pl.plot(dirct, 'r-', linewidth=2)
idd += 1
def main():
pass # For compatibility between running under Spyder and the CLI
# %% download and list all files to be processed
# folder inside ./example_movies where files will be saved
fld_name = 'Mesoscope'
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
# folder where files are located
folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name)
extension = 'hdf5' # extension of files
# read all files to be processed
fnames = glob.glob(folder_name + '/*' + extension)
# your list of files should look something like this
logging.info(fnames)
# %% Set up some parameters
fr = 15 # frame rate (Hz)
decay_time = 0.5 # approximate length of transient event in seconds
gSig = (3, 3) # expected half size of neurons
p = 1 # order of AR indicator dynamics
min_SNR = 1 # minimum SNR for accepting new components
ds_factor = 1 # spatial downsampling factor (increases speed but may lose some fine structure)
gnb = 2 # number of background components
gSig = tuple(np.ceil(
np.array(gSig) /
ds_factor).astype('int')) # recompute gSig if downsampling is involved
mot_corr = True # flag for online motion correction
pw_rigid = False # flag for pw-rigid motion correction (slower but potentially more accurate)
max_shifts_online = np.ceil(10.).astype(
'int') # maximum allowed shift during motion correction
sniper_mode = True # use a CNN to detect new neurons (o/w space correlation)
rval_thr = 0.9 # soace correlation threshold for candidate components
# set up some additional supporting parameters needed for the algorithm
# (these are default values but can change depending on dataset properties)
init_batch = 200 # number of frames for initialization (presumably from the first file)
K = 2 # initial number of components
epochs = 2 # number of passes over the data
show_movie = False # show the movie as the data gets processed
params_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'gSig': gSig,
'p': p,
'min_SNR': min_SNR,
'rval_thr': rval_thr,
'ds_factor': ds_factor,
'nb': gnb,
'motion_correct': mot_corr,
'init_batch': init_batch,
'init_method': 'bare',
'normalize': True,
'sniper_mode': sniper_mode,
'K': K,
'epochs': epochs,
'max_shifts_online': max_shifts_online,
'pw_rigid': pw_rigid,
'dist_shape_update': True,
'min_num_trial': 10,
'show_movie': show_movie
}
opts = cnmf.params.CNMFParams(params_dict=params_dict)
# %% fit online
cnm = cnmf.online_cnmf.OnACID(params=opts)
cnm.fit_online()
# %% plot contours
logging.info('Number of components: ' + str(cnm.estimates.A.shape[-1]))
Cn = cm.load(fnames[0],
subindices=slice(0, 500)).local_correlations(swap_dim=False)
cnm.estimates.plot_contours(img=Cn, display_numbers=False)
# %% view components
cnm.estimates.view_components(img=Cn)
# %% plot timing performance (if a movie is generated during processing, timing
# will be severely over-estimated)
T_motion = 1e3 * np.array(cnm.t_motion)
T_detect = 1e3 * np.array(cnm.t_detect)
T_shapes = 1e3 * np.array(cnm.t_shapes)
T_track = 1e3 * np.array(cnm.t_online) - T_motion - T_detect - T_shapes
plt.figure()
plt.stackplot(np.arange(len(T_motion)), T_motion, T_track, T_detect,
T_shapes)
plt.legend(labels=['motion', 'tracking', 'detect', 'shapes'], loc=2)
plt.title('Processing time allocation')
plt.xlabel('Frame #')
plt.ylabel('Processing time [ms]')
def main():
pass # For compatibility between running under Spyder and the CLI
#%% First setup some parameters
# dataset dependent parameters
display_images = False # Set this to true to show movies and plots
fname = ['Sue_2x_3000_40_-46.tif'] # filename to be processed
fr = 30 # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
# motion correction parameters
niter_rig = 1 # number of iterations for rigid motion correction
max_shifts = (6, 6) # maximum allow rigid shift
# for parallelization split the movies in num_splits chuncks across time
splits_rig = 56
# start a new patch for pw-rigid motion correction every x pixels
strides = (48, 48)
# overlap between pathes (size of patch strides+overlaps)
overlaps = (24, 24)
# for parallelization split the movies in num_splits chuncks across time
splits_els = 56
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
gnb = 2 # number of global background components
merge_thresh = 0.8 # merging threshold, max correlation allowed
# half-size of the patches in pixels. e.g., if rf=25, patches are 50x50
rf = 15
stride_cnmf = 6 # amount of overlap between the patches in pixels
K = 4 # number of components per patch
gSig = [4, 4] # expected half size of neurons
# initialization method (if analyzing dendritic data using 'sparse_nmf')
init_method = 'greedy_roi'
is_dendrites = False # flag for analyzing dendritic data
# sparsity penalty for dendritic data analysis through sparse NMF
alpha_snmf = None
# parameters for component evaluation
min_SNR = 2.5 # signal to noise ratio for accepting a component
rval_thr = 0.8 # space correlation threshold for accepting a component
cnn_thr = 0.8 # threshold for CNN based classifier
#%% download the dataset if it's not present in your folder
if fname[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']:
fname = [download_demo(fname[0])]
#%% play the movie
# playing the movie using opencv. It requires loading the movie in memory. To
# close the video press q
m_orig = cm.load_movie_chain(fname[:1])
downsample_ratio = 0.2
offset_mov = -np.min(m_orig[:100])
moviehandle = m_orig.resize(1, 1, downsample_ratio)
if display_images:
moviehandle.play(gain=10, offset=offset_mov, fr=30, magnification=2)
#%% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
#%%% MOTION CORRECTION
# first we create a motion correction object with the parameters specified
min_mov = cm.load(fname[0], subindices=range(200)).min()
# this will be subtracted from the movie to make it non-negative
mc = MotionCorrect(fname[0],
min_mov,
dview=dview,
max_shifts=max_shifts,
niter_rig=niter_rig,
splits_rig=splits_rig,
strides=strides,
overlaps=overlaps,
splits_els=splits_els,
upsample_factor_grid=upsample_factor_grid,
max_deviation_rigid=max_deviation_rigid,
shifts_opencv=True,
nonneg_movie=True)
# note that the file is not loaded in memory
#%% Run piecewise-rigid motion correction using NoRMCorre
mc.motion_correct_pwrigid(save_movie=True)
m_els = cm.load(mc.fname_tot_els)
bord_px_els = np.ceil(
np.maximum(np.max(np.abs(mc.x_shifts_els)),
np.max(np.abs(mc.y_shifts_els)))).astype(np.int)
# maximum shift to be used for trimming against NaNs
#%% compare with original movie
moviehandle = cm.concatenate([
m_orig.resize(1, 1, downsample_ratio) + offset_mov,
m_els.resize(1, 1, downsample_ratio)
],
axis=2)
display_images = False
if display_images:
moviehandle.play(fr=60, q_max=99.5, magnification=2,
offset=0) # press q to exit
#%% MEMORY MAPPING
# memory map the file in order 'C'
fnames = mc.fname_tot_els # name of the pw-rigidly corrected file.
border_to_0 = bord_px_els # number of pixels to exclude
fname_new = cm.save_memmap(fnames,
base_name='memmap_',
order='C',
border_to_0=bord_px_els) # exclude borders
# now load the file
Yr, dims, T = cm.load_memmap(fname_new)
d1, d2 = dims
images = np.reshape(Yr.T, [T] + list(dims), order='F')
# load frames in python format (T x X x Y)
#%% restart cluster to clean up memory
cm.stop_server(dview=dview)
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
#%% RUN CNMF ON PATCHES
# First extract spatial and temporal components on patches and combine them
# for this step deconvolution is turned off (p=0)
t1 = time.time()
cnm = cnmf.CNMF(n_processes=1,
k=K,
gSig=gSig,
merge_thresh=merge_thresh,
p=0,
dview=dview,
rf=rf,
stride=stride_cnmf,
memory_fact=1,
method_init=init_method,
alpha_snmf=alpha_snmf,
only_init_patch=False,
gnb=gnb,
border_pix=bord_px_els)
cnm = cnm.fit(images)
#%% plot contours of found components
Cn = cm.local_correlations(images.transpose(1, 2, 0))
Cn[np.isnan(Cn)] = 0
plt.figure()
crd = plot_contours(cnm.A, Cn, thr=0.9)
plt.title('Contour plots of found components')
#%% COMPONENT EVALUATION
# the components are evaluated in three ways:
# a) the shape of each component must be correlated with the data
# b) a minimum peak SNR is required over the length of a transient
# c) each shape passes a CNN based classifier
idx_components, idx_components_bad, SNR_comp, r_values, cnn_preds = \
estimate_components_quality_auto(images, cnm.A, cnm.C, cnm.b, cnm.f,
cnm.YrA, fr, decay_time, gSig, dims,
dview=dview, min_SNR=min_SNR,
r_values_min=rval_thr, use_cnn=False,
thresh_cnn_min=cnn_thr)
#%% PLOT COMPONENTS
if display_images:
plt.figure()
plt.subplot(121)
crd_good = cm.utils.visualization.plot_contours(cnm.A[:,
idx_components],
Cn,
thr=.8,
vmax=0.75)
plt.title('Contour plots of accepted components')
plt.subplot(122)
crd_bad = cm.utils.visualization.plot_contours(
cnm.A[:, idx_components_bad], Cn, thr=.8, vmax=0.75)
plt.title('Contour plots of rejected components')
#%% VIEW TRACES (accepted and rejected)
if display_images:
view_patches_bar(Yr,
cnm.A.tocsc()[:, idx_components],
cnm.C[idx_components],
cnm.b,
cnm.f,
dims[0],
dims[1],
YrA=cnm.YrA[idx_components],
img=Cn)
view_patches_bar(Yr,
cnm.A.tocsc()[:, idx_components_bad],
cnm.C[idx_components_bad],
cnm.b,
cnm.f,
dims[0],
dims[1],
YrA=cnm.YrA[idx_components_bad],
img=Cn)
#%% RE-RUN seeded CNMF on accepted patches to refine and perform deconvolution
A_in, C_in, b_in, f_in = cnm.A[:, idx_components], cnm.C[
idx_components], cnm.b, cnm.f
cnm2 = cnmf.CNMF(n_processes=1,
k=A_in.shape[-1],
gSig=gSig,
p=p,
dview=dview,
merge_thresh=merge_thresh,
Ain=A_in,
Cin=C_in,
b_in=b_in,
f_in=f_in,
rf=None,
stride=None,
gnb=gnb,
method_deconvolution='oasis',
check_nan=True)
cnm2 = cnm2.fit(images)
#%% Extract DF/F values
F_dff = detrend_df_f(cnm2.A,
cnm2.b,
cnm2.C,
cnm2.f,
YrA=cnm2.YrA,
quantileMin=8,
frames_window=250)
#%% Show final traces
cnm2.view_patches(Yr, dims=dims, img=Cn)
#%% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
#%% reconstruct denoised movie
denoised = cm.movie(cnm2.A.dot(cnm2.C) + cnm2.b.dot(cnm2.f)).reshape(
dims + (-1, ), order='F').transpose([2, 0, 1])
#%% play along side original data
moviehandle = cm.concatenate([
m_els.resize(1, 1, downsample_ratio),
denoised.resize(1, 1, downsample_ratio)
],
axis=2)
if display_images:
moviehandle.play(fr=60, gain=15, magnification=2,
offset=0) # press q to exit
pl.ion()
#%
from caiman.source_extraction.cnmf import cnmf as cnmf
from caiman.components_evaluation import evaluate_components
from caiman.utils.visualization import plot_contours, view_patches_bar
from caiman.base.rois import extract_binary_masks_blob
from caiman.behavior import behavior
from scipy.sparse import coo_matrix
from caiman.utils.utils import download_demo
#%%
fname = [u'demo_behavior.h5']
if fname[0] in ['demo_behavior.h5']:
# TODO: todocument
fname = [download_demo(fname[0])]
# TODO: todocument
m = cm.load(fname[0], is_behavior=True)
#%% load, rotate and eliminate useless pixels
m = m.transpose([0, 2, 1])
m = m[:, 150:, :]
#%% visualize movie
m.play()
#%% select interesting portion of the FOV (draw a polygon on the figure that pops up, when done press enter)
mask = np.array(behavior.select_roi(np.median(m[::100], 0), 1)[0], np.float32)
#%%
n_components = 4 # number of movement looked for
resize_fact = 0.5 # for computational efficiency movies are downsampled
# number of standard deviations above mean for the magnitude that are considered enough to measure the angle in polar coordinates
num_std_mag_for_angle = .6
def main():
pass # For compatibility between running under Spyder and the CLI
#%% download and list all files to be processed
# folder inside ./example_movies where files will be saved
fld_name = 'Mesoscope'
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
# folder where files are located
folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name)
extension = 'hdf5' # extension of files
# read all files to be processed
fls = glob.glob(folder_name + '/*' + extension)
# your list of files should look something like this
print(fls)
#%% Set up some parameters
# frame rate (Hz)
fr = 15
# approximate length of transient event in seconds
decay_time = 0.5
# expected half size of neurons
gSig = (3, 3)
# order of AR indicator dynamics
p = 1
# minimum SNR for accepting new components
min_SNR = 2.5
# correlation threshold for new component inclusion
rval_thr = 0.85
# spatial downsampling factor (increases speed but may lose some fine structure)
ds_factor = 1
# number of background components
gnb = 2
# recompute gSig if downsampling is involved
gSig = tuple(np.ceil(np.array(gSig) / ds_factor).astype('int'))
# flag for online motion correction
mot_corr = True
# maximum allowed shift during motion correction
max_shift = np.ceil(10. / ds_factor).astype('int')
# set up some additional supporting parameters needed for the algorithm (these are default values but change according to dataset characteristics)
# number of shapes to be updated each time (put this to a finite small value to increase speed)
max_comp_update_shape = np.inf
# number of files used for initialization
init_files = 1
# number of files used for online
online_files = len(fls) - 1
# number of frames for initialization (presumably from the first file)
initbatch = 200
# maximum number of expected components used for memory pre-allocation (exaggerate here)
expected_comps = 300
# initial number of components
K = 2
# number of timesteps to consider when testing new neuron candidates
N_samples = np.ceil(fr * decay_time)
# exceptionality threshold
thresh_fitness_raw = scipy.special.log_ndtr(-min_SNR) * N_samples
# number of passes over the data
epochs = 2
# upper bound for number of frames in each file (used right below)
len_file = 1000
# total length of all files (if not known use a large number, then truncate at the end)
T1 = len(fls) * len_file * epochs
#%% Initialize movie
# load only the first initbatch frames and possibly downsample them
if ds_factor > 1:
Y = cm.load(fls[0], subindices=slice(0, initbatch, None)).astype(
np.float32).resize(1. / ds_factor, 1. / ds_factor)
else:
Y = cm.load(fls[0], subindices=slice(0, initbatch,
None)).astype(np.float32)
if mot_corr: # perform motion correction on the first initbatch frames
mc = Y.motion_correct(max_shift, max_shift)
Y = mc[0].astype(np.float32)
borders = np.max(mc[1])
else:
Y = Y.astype(np.float32)
# minimum value of movie. Subtract it to make the data non-negative
img_min = Y.min()
Y -= img_min
img_norm = np.std(Y, axis=0)
# normalizing factor to equalize the FOV
img_norm += np.median(img_norm)
Y = Y / img_norm[None, :, :] # normalize data
_, d1, d2 = Y.shape
dims = (d1, d2) # dimensions of FOV
Yr = Y.to_2D().T # convert data into 2D array
Cn_init = Y.local_correlations(swap_dim=False) # compute correlation image
#pl.imshow(Cn_init)
#pl.title('Correlation Image on initial batch')
#pl.colorbar()
bnd_Y = np.percentile(Y, (0.001, 100 - 0.001)) # plotting boundaries for Y
#%% initialize OnACID with bare initialization
cnm_init = bare_initialization(Y[:initbatch].transpose(1, 2, 0),
init_batch=initbatch,
k=K,
gnb=gnb,
gSig=gSig,
p=p,
minibatch_shape=100,
minibatch_suff_stat=5,
update_num_comps=True,
rval_thr=rval_thr,
thresh_fitness_raw=thresh_fitness_raw,
batch_update_suff_stat=True,
max_comp_update_shape=max_comp_update_shape,
deconv_flag=False,
use_dense=False,
simultaneously=False,
n_refit=0)
#%% Plot initialization results
crd = plot_contours(cnm_init.A.tocsc(), Cn_init, thr=0.9)
A, C, b, f, YrA, sn = cnm_init.A, cnm_init.C, cnm_init.b, cnm_init.f, cnm_init.YrA, cnm_init.sn
view_patches_bar(Yr,
scipy.sparse.coo_matrix(A.tocsc()[:, :]),
C[:, :],
b,
f,
dims[0],
dims[1],
YrA=YrA[:, :],
img=Cn_init)
bnd_AC = np.percentile(A.dot(C), (0.001, 100 - 0.005))
bnd_BG = np.percentile(b.dot(f), (0.001, 100 - 0.001))
#%% create a function for plotting results in real time if needed
def create_frame(cnm2, img_norm, captions):
A, b = cnm2.Ab[:, cnm2.gnb:], cnm2.Ab[:, :cnm2.gnb].toarray()
C, f = cnm2.C_on[cnm2.gnb:cnm2.M, :], cnm2.C_on[:cnm2.gnb, :]
# inferred activity due to components (no background)
frame_plot = (frame_cor.copy() - bnd_Y[0]) / np.diff(bnd_Y)
comps_frame = A.dot(C[:, t - 1]).reshape(cnm2.dims, order='F')
bgkrnd_frame = b.dot(f[:, t - 1]).reshape(
cnm2.dims, order='F') # denoised frame (components + background)
denoised_frame = comps_frame + bgkrnd_frame
denoised_frame = (denoised_frame.copy() - bnd_Y[0]) / np.diff(bnd_Y)
comps_frame = (comps_frame.copy() - bnd_AC[0]) / np.diff(bnd_AC)
if show_residuals:
#all_comps = np.reshape(cnm2.Yres_buf.mean(0), cnm2.dims, order='F')
all_comps = np.reshape(cnm2.mean_buff, cnm2.dims, order='F')
all_comps = np.minimum(np.maximum(all_comps, 0) * 2 + 0.25, 255)
else:
all_comps = np.array(A.sum(-1)).reshape(cnm2.dims, order='F')
# spatial shapes
frame_comp_1 = cv2.resize(
np.concatenate([frame_plot, all_comps * 1.], axis=-1),
(2 * np.int(cnm2.dims[1] * resize_fact),
np.int(cnm2.dims[0] * resize_fact)))
frame_comp_2 = cv2.resize(
np.concatenate([comps_frame, denoised_frame], axis=-1),
(2 * np.int(cnm2.dims[1] * resize_fact),
np.int(cnm2.dims[0] * resize_fact)))
frame_pn = np.concatenate([frame_comp_1, frame_comp_2], axis=0).T
vid_frame = np.repeat(frame_pn[:, :, None], 3, axis=-1)
vid_frame = np.minimum((vid_frame * 255.), 255).astype('u1')
if show_residuals and cnm2.ind_new:
add_v = np.int(cnm2.dims[1] * resize_fact)
for ind_new in cnm2.ind_new:
cv2.rectangle(vid_frame,
(int(ind_new[0][1] * resize_fact),
int(ind_new[1][1] * resize_fact) + add_v),
(int(ind_new[0][0] * resize_fact),
int(ind_new[1][0] * resize_fact) + add_v),
(255, 0, 255), 2)
cv2.putText(vid_frame,
captions[0], (5, 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
captions[1], (np.int(cnm2.dims[0] * resize_fact) + 5, 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
captions[2], (5, np.int(cnm2.dims[1] * resize_fact) + 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
captions[3], (np.int(cnm2.dims[0] * resize_fact) + 5,
np.int(cnm2.dims[1] * resize_fact) + 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 0),
thickness=1)
cv2.putText(vid_frame,
'Frame = ' + str(t),
(vid_frame.shape[1] // 2 - vid_frame.shape[1] // 10,
vid_frame.shape[0] - 20),
fontFace=5,
fontScale=0.8,
color=(0, 255, 255),
thickness=1)
return vid_frame
#%% Prepare object for OnACID
cnm2 = deepcopy(cnm_init)
save_init = False # flag for saving initialization object. Useful if you want to check OnACID with different parameters but same initialization
if save_init:
cnm_init.dview = None
save_object(cnm_init, fls[0][:-4] + '_DS_' + str(ds_factor) + '.pkl')
cnm_init = load_object(fls[0][:-4] + '_DS_' + str(ds_factor) + '.pkl')
path_to_cnn_residual = os.path.join(caiman_datadir(), 'model',
'cnn_model_online.h5')
cnm2._prepare_object(np.asarray(Yr),
T1,
expected_comps,
idx_components=None,
min_num_trial=3,
max_num_added=3,
path_to_model=path_to_cnn_residual,
sniper_mode=False,
use_peak_max=False,
q=0.5)
cnm2.thresh_CNN_noisy = 0.5
#%% Run OnACID and optionally plot results in real time
epochs = 1
cnm2.Ab_epoch = [] # save the shapes at the end of each epoch
t = cnm2.initbatch # current timestep
tottime = []
Cn = Cn_init.copy()
# flag for removing components with bad shapes
remove_flag = False
T_rm = 650 # remove bad components every T_rm frames
rm_thr = 0.1 # CNN classifier removal threshold
# flag for plotting contours of detected components at the end of each file
plot_contours_flag = False
# flag for showing results video online (turn off flags for improving speed)
play_reconstr = True
# flag for saving movie (file could be quite large..)
save_movie = False
movie_name = os.path.join(
folder_name, 'sniper_meso_0.995_new.avi') # name of movie to be saved
resize_fact = 1.2 # image resizing factor
if online_files == 0: # check whether there are any additional files
process_files = fls[:init_files] # end processing at this file
init_batc_iter = [initbatch] # place where to start
end_batch = T1
else:
process_files = fls[:init_files + online_files] # additional files
# where to start reading at each file
init_batc_iter = [initbatch] + [0] * online_files
shifts = []
show_residuals = True
if show_residuals:
caption = 'Mean Residual Buffer'
else:
caption = 'Identified Components'
captions = ['Raw Data', 'Inferred Activity', caption, 'Denoised Data']
if save_movie and play_reconstr:
fourcc = cv2.VideoWriter_fourcc('8', 'B', 'P', 'S')
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(
movie_name, fourcc, 30.0,
tuple([int(2 * x * resize_fact) for x in cnm2.dims]))
for iter in range(epochs):
if iter > 0:
# if not on first epoch process all files from scratch
process_files = fls[:init_files + online_files]
init_batc_iter = [0] * (online_files + init_files)
# np.array(fls)[np.array([1,2,3,4,5,-5,-4,-3,-2,-1])]:
for file_count, ffll in enumerate(process_files):
print('Now processing file ' + ffll)
Y_ = cm.load(ffll,
subindices=slice(init_batc_iter[file_count], T1,
None))
# update max-correlation (and perform offline motion correction) just for illustration purposes
if plot_contours_flag:
if ds_factor > 1:
Y_1 = Y_.resize(1. / ds_factor, 1. / ds_factor, 1)
else:
Y_1 = Y_.copy()
if mot_corr:
templ = (cnm2.Ab.data[:cnm2.Ab.indptr[1]] *
cnm2.C_on[0, t - 1]).reshape(cnm2.dims,
order='F') * img_norm
newcn = (Y_1 - img_min).motion_correct(
max_shift, max_shift,
template=templ)[0].local_correlations(swap_dim=False)
Cn = np.maximum(Cn, newcn)
else:
Cn = np.maximum(Cn, Y_1.local_correlations(swap_dim=False))
old_comps = cnm2.N # number of existing components
for frame_count, frame in enumerate(Y_): # now process each file
if np.isnan(np.sum(frame)):
raise Exception('Frame ' + str(frame_count) +
' contains nan')
if t % 100 == 0:
print(
'Epoch: ' + str(iter + 1) + '. ' + str(t) +
' frames have beeen processed in total. ' +
str(cnm2.N - old_comps) +
' new components were added. Total number of components is '
+ str(cnm2.Ab.shape[-1] - gnb))
old_comps = cnm2.N
t1 = time() # count time only for the processing part
frame_ = frame.copy().astype(np.float32) #
if ds_factor > 1:
frame_ = cv2.resize(frame_,
img_norm.shape[::-1]) # downsampling
frame_ -= img_min # make data non-negative
if mot_corr: # motion correct
templ = cnm2.Ab.dot(cnm2.C_on[:cnm2.M, t - 1]).reshape(
cnm2.dims, order='F') * img_norm
frame_cor, shift = motion_correct_iteration_fast(
frame_, templ, max_shift, max_shift)
shifts.append(shift)
else:
templ = None
frame_cor = frame_
frame_cor = frame_cor / img_norm # normalize data-frame
cnm2.fit_next(t, frame_cor.reshape(
-1, order='F')) # run OnACID on this frame
# store time
tottime.append(time() - t1)
t += 1
if t % T_rm == 0 and remove_flag:
prd, _ = evaluate_components_CNN(cnm2.Ab[:, gnb:], dims,
gSig)
ind_rem = np.where(prd[:, 1] < rm_thr)[0].tolist()
cnm2.remove_components(ind_rem)
print('Removing ' + str(len(ind_rem)) + ' components')
if t % 1000 == 0 and plot_contours_flag:
pl.cla()
A = cnm2.Ab[:, cnm2.gnb:]
# update the contour plot every 1000 frames
crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9)
pl.pause(1)
if play_reconstr: # generate movie with the results
vid_frame = create_frame(cnm2, img_norm, captions)
if save_movie:
out.write(vid_frame)
if t - initbatch < 100:
#for rp in np.int32(np.ceil(np.exp(-np.arange(1,100)/30)*20)):
for rp in range(len(cnm2.ind_new) * 2):
out.write(vid_frame)
cv2.imshow('frame', vid_frame)
if t - initbatch < 100:
for rp in range(len(cnm2.ind_new) * 2):
cv2.imshow('frame', vid_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print('Cumulative processing speed is ' +
str((t - initbatch) / np.sum(tottime))[:5] +
' frames per second.')
# save the shapes at the end of each epoch
cnm2.Ab_epoch.append(cnm2.Ab.copy())
if save_movie:
out.release()
cv2.destroyAllWindows()
#%% save results (optional)
save_results = False
if save_results:
np.savez('results_analysis_online_MOT_CORR.npz',
Cn=Cn,
Ab=cnm2.Ab,
Cf=cnm2.C_on,
b=cnm2.b,
f=cnm2.f,
dims=cnm2.dims,
tottime=tottime,
noisyC=cnm2.noisyC,
shifts=shifts)
#%% extract results from the objects and do some plotting
A, b = cnm2.Ab[:, cnm2.gnb:], cnm2.Ab[:, :cnm2.gnb].toarray()
C, f = cnm2.C_on[cnm2.gnb:cnm2.M,
t - t // epochs:t], cnm2.C_on[:cnm2.gnb,
t - t // epochs:t]
noisyC = cnm2.noisyC[:, t - t // epochs:t]
b_trace = [osi.b for osi in cnm2.OASISinstances] if hasattr(
cnm2, 'OASISinstances') else [0] * C.shape[0]
pl.figure()
crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9)
view_patches_bar(Yr,
scipy.sparse.coo_matrix(A.tocsc()[:, :]),
C[:, :],
b,
f,
dims[0],
dims[1],
YrA=noisyC[cnm2.gnb:cnm2.M] - C,
img=Cn)
import caiman as cm
from caiman.utils.visualization import view_patches_bar
from caiman.utils.utils import download_demo, load_object, save_object
import pylab as pl
import scipy
from caiman.motion_correction import motion_correct_iteration_fast
import cv2
from caiman.utils.visualization import plot_contours
import glob
from caiman.source_extraction.cnmf.online_cnmf import bare_initialization
from copy import deepcopy
#%% download and list all files to be processed
fld_name = 'Mesoscope' # folder inside ./example_movies where files will be saved
download_demo('Tolias_mesoscope_1.hdf5',fld_name)
download_demo('Tolias_mesoscope_2.hdf5',fld_name)
download_demo('Tolias_mesoscope_3.hdf5',fld_name)
folder_name = './example_movies/'+fld_name # folder where files are located
extension = 'hdf5' # extension of files
fls = glob.glob(folder_name+'/*' + extension) # read all files to be processed
print(fls) # your list of files should look something like this
#%% Set up some parameters
fr = 15 # frame rate (Hz)
decay_time = 0.5 # approximate length of transient event in seconds
gSig = (4,4) # expected half size of neurons
p = 1 # order of AR indicator dynamics
def test_general():
""" General Test of pipeline with comparison against ground truth
A shorter version than the demo pipeline that calls comparison for the real test work
Raises:
---------
params_movie
params_cnmf
rig correction
cnmf on patch
cnmf full frame
not able to read the file
no groundtruth
"""
#\bug
#\warning
global params_movie
global params_diplay
fname = params_movie['fname']
niter_rig = params_movie['niter_rig']
max_shifts = params_movie['max_shifts']
splits_rig = params_movie['splits_rig']
num_splits_to_process_rig = params_movie['num_splits_to_process_rig']
cwd = os.getcwd()
fname = download_demo(fname[0])
m_orig = cm.load(fname)
min_mov = m_orig[:400].min()
comp = comparison.Comparison()
comp.dims = np.shape(m_orig)[1:]
################ RIG CORRECTION #################
t1 = time.time()
mc = MotionCorrect(fname,
min_mov,
max_shifts=max_shifts,
niter_rig=niter_rig,
splits_rig=splits_rig,
num_splits_to_process_rig=num_splits_to_process_rig,
shifts_opencv=True,
nonneg_movie=True)
mc.motion_correct_rigid(save_movie=True)
m_rig = cm.load(mc.fname_tot_rig)
bord_px_rig = np.ceil(np.max(mc.shifts_rig)).astype(np.int)
comp.comparison['rig_shifts']['timer'] = time.time() - t1
comp.comparison['rig_shifts']['ourdata'] = mc.shifts_rig
###########################################
if 'max_shifts' not in params_movie:
fnames = params_movie['fname']
border_to_0 = 0
else: # elif not params_movie.has_key('overlaps'):
fnames = mc.fname_tot_rig
border_to_0 = bord_px_rig
m_els = m_rig
idx_xy = None
add_to_movie = -np.nanmin(m_els) + 1 # movie must be positive
remove_init = 0
downsample_factor = 1
base_name = fname[0].split('/')[-1][:-4]
name_new = cm.save_memmap_each(fnames,
base_name=base_name,
resize_fact=(1, 1, downsample_factor),
remove_init=remove_init,
idx_xy=idx_xy,
add_to_movie=add_to_movie,
border_to_0=border_to_0)
name_new.sort()
if len(name_new) > 1:
fname_new = cm.save_memmap_join(name_new,
base_name='Yr',
n_chunks=params_movie['n_chunks'],
dview=None)
else:
logging.warning('One file only, not saving!')
fname_new = name_new[0]
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
Y = np.reshape(Yr, dims + (T, ), order='F')
if np.min(images) < 0:
# TODO: should do this in an automatic fashion with a while loop at the 367 line
raise Exception('Movie too negative, add_to_movie should be larger')
if np.sum(np.isnan(images)) > 0:
# TODO: same here
raise Exception(
'Movie contains nan! You did not remove enough borders')
Cn = cm.local_correlations(Y)
Cn[np.isnan(Cn)] = 0
p = params_movie['p']
merge_thresh = params_movie['merge_thresh']
rf = params_movie['rf']
stride_cnmf = params_movie['stride_cnmf']
K = params_movie['K']
init_method = params_movie['init_method']
gSig = params_movie['gSig']
alpha_snmf = params_movie['alpha_snmf']
if params_movie['is_dendrites'] == True:
if params_movie['init_method'] is not 'sparse_nmf':
raise Exception('dendritic requires sparse_nmf')
if params_movie['alpha_snmf'] is None:
raise Exception('need to set a value for alpha_snmf')
################ CNMF PART PATCH #################
t1 = time.time()
cnm = cnmf.CNMF(n_processes=1,
k=K,
gSig=gSig,
merge_thresh=params_movie['merge_thresh'],
p=params_movie['p'],
dview=None,
rf=rf,
stride=stride_cnmf,
memory_fact=params_movie['memory_fact'],
method_init=init_method,
alpha_snmf=alpha_snmf,
only_init_patch=params_movie['only_init_patch'],
gnb=params_movie['gnb'],
method_deconvolution='oasis')
comp.cnmpatch = copy.copy(cnm)
comp.cnmpatch.estimates = None
cnm = cnm.fit(images)
A_tot = cnm.estimates.A
C_tot = cnm.estimates.C
YrA_tot = cnm.estimates.YrA
b_tot = cnm.estimates.b
f_tot = cnm.estimates.f
# DISCARDING
logging.info(('Number of components:' + str(A_tot.shape[-1])))
final_frate = params_movie['final_frate']
# threshold on space consistency
r_values_min = params_movie['r_values_min_patch']
# threshold on time variability
fitness_min = params_movie['fitness_delta_min_patch']
fitness_delta_min = params_movie['fitness_delta_min_patch']
Npeaks = params_movie['Npeaks']
traces = C_tot + YrA_tot
idx_components, idx_components_bad = estimate_components_quality(
traces,
Y,
A_tot,
C_tot,
b_tot,
f_tot,
final_frate=final_frate,
Npeaks=Npeaks,
r_values_min=r_values_min,
fitness_min=fitness_min,
fitness_delta_min=fitness_delta_min)
#######
A_tot = A_tot.tocsc()[:, idx_components]
C_tot = C_tot[idx_components]
comp.comparison['cnmf_on_patch']['timer'] = time.time() - t1
comp.comparison['cnmf_on_patch']['ourdata'] = [A_tot.copy(), C_tot.copy()]
#################### ########################
################ CNMF PART FULL #################
t1 = time.time()
cnm = cnmf.CNMF(n_processes=1,
k=A_tot.shape,
gSig=gSig,
merge_thresh=merge_thresh,
p=p,
Ain=A_tot,
Cin=C_tot,
f_in=f_tot,
rf=None,
stride=None,
method_deconvolution='oasis')
cnm = cnm.fit(images)
# DISCARDING
A, C, b, f, YrA, sn = cnm.estimates.A, cnm.estimates.C, cnm.estimates.b, cnm.estimates.f, cnm.estimates.YrA, cnm.estimates.sn
final_frate = params_movie['final_frate']
# threshold on space consistency
r_values_min = params_movie['r_values_min_full']
# threshold on time variability
fitness_min = params_movie['fitness_delta_min_full']
fitness_delta_min = params_movie['fitness_delta_min_full']
Npeaks = params_movie['Npeaks']
traces = C + YrA
idx_components, idx_components_bad, fitness_raw, fitness_delta, r_values = estimate_components_quality(
traces,
Y,
A,
C,
b,
f,
final_frate=final_frate,
Npeaks=Npeaks,
r_values_min=r_values_min,
fitness_min=fitness_min,
fitness_delta_min=fitness_delta_min,
return_all=True)
##########
A_tot_full = A_tot.tocsc()[:, idx_components]
C_tot_full = C_tot[idx_components]
comp.comparison['cnmf_full_frame']['timer'] = time.time() - t1
comp.comparison['cnmf_full_frame']['ourdata'] = [
A_tot_full.copy(), C_tot_full.copy()
]
#################### ########################
comp.save_with_compare(istruth=False, params=params_movie, Cn=Cn)
log_files = glob.glob('*_LOG_*')
try:
for log_file in log_files:
os.remove(log_file)
except:
logging.warning('Cannot remove log files')
############ assertions ##################
pb = False
if (comp.information['differences']['params_movie']):
logging.error(
"you need to set the same movie parameters than the ground truth to have a real comparison (use the comp.see() function to explore it)"
)
pb = True
if (comp.information['differences']['params_cnm']):
logging.warning(
"you need to set the same cnmf parameters than the ground truth to have a real comparison (use the comp.see() function to explore it)"
)
# pb = True
if (comp.information['diff']['rig']['isdifferent']):
logging.error("the rigid shifts are different from the groundtruth ")
pb = True
if (comp.information['diff']['cnmpatch']['isdifferent']):
logging.error(
"the cnmf on patch produces different results than the groundtruth "
)
pb = True
if (comp.information['diff']['cnmfull']['isdifferent']):
logging.error(
"the cnmf full frame produces different results than the groundtruth "
)
pb = True
assert (not pb)
def test_general():
""" General Test of pipeline with comparison against ground truth
A shorter version than the demo pipeline that calls comparison for the real test work
Raises:
---------
params_movie
params_cnmf
rig correction
cnmf on patch
cnmf full frame
not able to read the file
no groundtruth
"""
#\bug
#\warning
global params_movie
global params_diplay
fname = params_movie['fname']
niter_rig = params_movie['niter_rig']
max_shifts = params_movie['max_shifts']
splits_rig = params_movie['splits_rig']
num_splits_to_process_rig = params_movie['num_splits_to_process_rig']
cwd = os.getcwd()
fname = download_demo(fname[0])
m_orig = cm.load(fname)
min_mov = m_orig[:400].min()
comp = comparison.Comparison()
comp.dims = np.shape(m_orig)[1:]
################ RIG CORRECTION #################
t1 = time.time()
mc = MotionCorrect(fname, min_mov,
max_shifts=max_shifts, niter_rig=niter_rig, splits_rig=splits_rig,
num_splits_to_process_rig=num_splits_to_process_rig,
shifts_opencv=True, nonneg_movie=True)
mc.motion_correct_rigid(save_movie=True)
m_rig = cm.load(mc.fname_tot_rig)
bord_px_rig = np.ceil(np.max(mc.shifts_rig)).astype(np.int)
comp.comparison['rig_shifts']['timer'] = time.time() - t1
comp.comparison['rig_shifts']['ourdata'] = mc.shifts_rig
###########################################
if 'max_shifts' not in params_movie:
fnames = params_movie['fname']
border_to_0 = 0
else: # elif not params_movie.has_key('overlaps'):
fnames = mc.fname_tot_rig
border_to_0 = bord_px_rig
m_els = m_rig
idx_xy = None
add_to_movie = -np.nanmin(m_els) + 1 # movie must be positive
remove_init = 0
downsample_factor = 1
base_name = fname[0].split('/')[-1][:-4]
name_new = cm.save_memmap_each(fnames, base_name=base_name, resize_fact=(
1, 1, downsample_factor), remove_init=remove_init,
idx_xy=idx_xy, add_to_movie=add_to_movie, border_to_0=border_to_0)
name_new.sort()
if len(name_new) > 1:
fname_new = cm.save_memmap_join(
name_new, base_name='Yr', n_chunks=params_movie['n_chunks'], dview=None)
else:
print('One file only, not saving!')
fname_new = name_new[0]
Yr, dims, T = cm.load_memmap(fname_new)
images = np.reshape(Yr.T, [T] + list(dims), order='F')
Y = np.reshape(Yr, dims + (T,), order='F')
if np.min(images) < 0:
# TODO: should do this in an automatic fashion with a while loop at the 367 line
raise Exception('Movie too negative, add_to_movie should be larger')
if np.sum(np.isnan(images)) > 0:
# TODO: same here
raise Exception(
'Movie contains nan! You did not remove enough borders')
Cn = cm.local_correlations(Y)
Cn[np.isnan(Cn)] = 0
p = params_movie['p']
merge_thresh = params_movie['merge_thresh']
rf = params_movie['rf']
stride_cnmf = params_movie['stride_cnmf']
K = params_movie['K']
init_method = params_movie['init_method']
gSig = params_movie['gSig']
alpha_snmf = params_movie['alpha_snmf']
if params_movie['is_dendrites'] == True:
if params_movie['init_method'] is not 'sparse_nmf':
raise Exception('dendritic requires sparse_nmf')
if params_movie['alpha_snmf'] is None:
raise Exception('need to set a value for alpha_snmf')
################ CNMF PART PATCH #################
t1 = time.time()
cnm = cnmf.CNMF(n_processes=1, k=K, gSig=gSig, merge_thresh=params_movie['merge_thresh'], p=params_movie['p'],
dview=None, rf=rf, stride=stride_cnmf, memory_fact=params_movie['memory_fact'],
method_init=init_method, alpha_snmf=alpha_snmf, only_init_patch=params_movie[
'only_init_patch'],
gnb=params_movie['gnb'], method_deconvolution='oasis')
comp.cnmpatch = copy.copy(cnm)
cnm = cnm.fit(images)
A_tot = cnm.A
C_tot = cnm.C
YrA_tot = cnm.YrA
b_tot = cnm.b
f_tot = cnm.f
# DISCARDING
print(('Number of components:' + str(A_tot.shape[-1])))
final_frate = params_movie['final_frate']
# threshold on space consistency
r_values_min = params_movie['r_values_min_patch']
# threshold on time variability
fitness_min = params_movie['fitness_delta_min_patch']
fitness_delta_min = params_movie['fitness_delta_min_patch']
Npeaks = params_movie['Npeaks']
traces = C_tot + YrA_tot
idx_components, idx_components_bad = estimate_components_quality(
traces, Y, A_tot, C_tot, b_tot, f_tot, final_frate=final_frate,
Npeaks=Npeaks, r_values_min=r_values_min, fitness_min=fitness_min,
fitness_delta_min=fitness_delta_min)
#######
A_tot = A_tot.tocsc()[:, idx_components]
C_tot = C_tot[idx_components]
comp.comparison['cnmf_on_patch']['timer'] = time.time() - t1
comp.comparison['cnmf_on_patch']['ourdata'] = [A_tot.copy(), C_tot.copy()]
#################### ########################
################ CNMF PART FULL #################
t1 = time.time()
cnm = cnmf.CNMF(n_processes=1, k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, p=p, Ain=A_tot, Cin=C_tot,
f_in=f_tot, rf=None, stride=None, method_deconvolution='oasis')
cnm = cnm.fit(images)
# DISCARDING
A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn
final_frate = params_movie['final_frate']
# threshold on space consistency
r_values_min = params_movie['r_values_min_full']
# threshold on time variability
fitness_min = params_movie['fitness_delta_min_full']
fitness_delta_min = params_movie['fitness_delta_min_full']
Npeaks = params_movie['Npeaks']
traces = C + YrA
idx_components, idx_components_bad, fitness_raw, fitness_delta, r_values = estimate_components_quality(
traces, Y, A, C, b, f, final_frate=final_frate, Npeaks=Npeaks, r_values_min=r_values_min,
fitness_min=fitness_min,
fitness_delta_min=fitness_delta_min, return_all=True)
##########
A_tot_full = A_tot.tocsc()[:, idx_components]
C_tot_full = C_tot[idx_components]
comp.comparison['cnmf_full_frame']['timer'] = time.time() - t1
comp.comparison['cnmf_full_frame']['ourdata'] = [
A_tot_full.copy(), C_tot_full.copy()]
#################### ########################
comp.save_with_compare(istruth=False, params=params_movie, Cn=Cn)
log_files = glob.glob('*_LOG_*')
try:
for log_file in log_files:
os.remove(log_file)
except:
print('Cannot remove log files')
############ assertions ##################
pb = False
if (comp.information['differences']['params_movie']):
print("you need to set the same movie parameters than the ground truth to have a real comparison (use the comp.see() function to explore it)")
pb = True
if (comp.information['differences']['params_cnm']):
print("you need to set the same cnmf parameters than the ground truth to have a real comparison (use the comp.see() function to explore it)")
pb = True
if (comp.information['diff']['rig']['isdifferent']):
print("the rigid shifts are different from the groundtruth ")
pb = True
if (comp.information['diff']['cnmpatch']['isdifferent']):
print("the cnmf on patch produces different results than the groundtruth ")
pb = True
if (comp.information['diff']['cnmfull']['isdifferent']):
print("the cnmf full frame produces different results than the groundtruth ")
pb = True
assert (not pb)
import caiman as cm
from caiman.utils.visualization import view_patches_bar
from caiman.utils.utils import download_demo, load_object, save_object
import pylab as pl
import scipy
from caiman.motion_correction import motion_correct_iteration_fast
import cv2
from caiman.utils.visualization import plot_contours
import glob
from caiman.source_extraction.cnmf.online_cnmf import bare_initialization
from copy import deepcopy
#%% download and list all files to be processed
fld_name = 'Mesoscope' # folder inside ./example_movies where files will be saved
download_demo('Tolias_mesoscope_1.hdf5', fld_name)
download_demo('Tolias_mesoscope_2.hdf5', fld_name)
download_demo('Tolias_mesoscope_3.hdf5', fld_name)
folder_name = './example_movies/' + fld_name # folder where files are located
extension = 'hdf5' # extension of files
fls = glob.glob(folder_name + '/*' +
extension) # read all files to be processed
print(fls) # your list of files should look something like this
#%% Set up some parameters
fr = 15 # frame rate (Hz)
decay_time = 0.5 # approximate length of transient event in seconds
gSig = (4, 4) # expected half size of neurons
def main():
pass # For compatibility between running under Spyder and the CLI
# %% Load demo movie and ROIs
fnames = download_demo('demo_voltage_imaging.hdf5', 'volpy') # file path to movie file (will download if not present)
path_ROIs = download_demo('demo_voltage_imaging_ROIs.hdf5', 'volpy') # file path to ROIs file (will download if not present)
# %% Setup some parameters for data and motion correction
# dataset parameters
fr = 400 # sample rate of the movie
ROIs = None # Region of interests
index = None # index of neurons
weights = None # reuse spatial weights by
# opts.change_params(params_dict={'weights':vpy.estimates['weights']})
# motion correction parameters
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)
max_deviation_rigid = 3 # maximum deviation allowed for patch with respect to rigid shifts
border_nan = 'copy'
opts_dict = {
'fnames': fnames,
'fr': fr,
'index': index,
'ROIs': ROIs,
'weights': weights,
'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 = volparams(params_dict=opts_dict)
# %% play the movie (optional)
# playing the movie using opencv. It requires loading the movie in memory.
# To close the video press q
display_images = False
if display_images:
m_orig = cm.load(fnames)
ds_ratio = 0.2
moviehandle = m_orig.resize(1, 1, ds_ratio)
moviehandle.play(q_max=99.5, fr=60, magnification=2)
# %% start a cluster for parallel processing
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %%% MOTION CORRECTION
# Create a motion correction object with the specified parameters
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run piecewise rigid motion correction
mc.motion_correct(save_movie=True)
dview.terminate()
# %% motion correction compared with original movie
display_images = False
if display_images:
m_orig = cm.load(fnames)
m_rig = cm.load(mc.mmap_file)
ds_ratio = 0.2
moviehandle = cm.concatenate([m_orig.resize(1, 1, ds_ratio) - mc.min_mov * mc.nonneg_movie,
m_rig.resize(1, 1, ds_ratio)], axis=2)
moviehandle.play(fr=60, q_max=99.5, magnification=2) # press q to exit
# % movie subtracted from the mean
m_orig2 = (m_orig - np.mean(m_orig, axis=0))
m_rig2 = (m_rig - np.mean(m_rig, axis=0))
moviehandle1 = cm.concatenate([m_orig2.resize(1, 1, ds_ratio),
m_rig2.resize(1, 1, ds_ratio)], axis=2)
moviehandle1.play(fr=60, q_max=99.5, magnification=2)
# %% Memory Mapping
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
fname_new = cm.save_memmap_join(mc.mmap_file, base_name='memmap_',
add_to_mov=border_to_0, dview=dview, n_chunks=10)
dview.terminate()
# %% change fnames to the new motion corrected one
opts.change_params(params_dict={'fnames': fname_new})
# %% SEGMENTATION
# Create mean and correlation image
use_maskrcnn = True # set to True to predict the ROIs using the mask R-CNN
if not use_maskrcnn: # use manual annotations
with h5py.File(path_ROIs, 'r') as fl:
ROIs = fl['mov'][()] # load ROIs
opts.change_params(params_dict={'ROIs': ROIs,
'index': list(range(ROIs.shape[0])),
'method': 'SpikePursuit'})
else:
m = cm.load(mc.mmap_file[0], subindices=slice(0, 20000))
m.fr = fr
img = m.mean(axis=0)
img = (img-np.mean(img))/np.std(img)
m1 = m.computeDFF(secsWindow=1, in_place=True)[0]
m = m - m1
Cn = m.local_correlations(swap_dim=False, eight_neighbours=True)
img_corr = (Cn-np.mean(Cn))/np.std(Cn)
summary_image = np.stack([img, img, img_corr], axis=2).astype(np.float32)
del m
del m1
# %%
# Mask R-CNN
config = neurons.NeuronsConfig()
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
DETECTION_MIN_CONFIDENCE = 0.7
IMAGE_RESIZE_MODE = "pad64"
IMAGE_MAX_DIM = 512
RPN_NMS_THRESHOLD = 0.7
POST_NMS_ROIS_INFERENCE = 1000
config = InferenceConfig()
config.display()
model_dir = os.path.join(caiman_datadir(), 'model')
DEVICE = "/cpu:0" # /cpu:0 or /gpu:0
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference", model_dir=model_dir,
config=config)
weights_path = download_model('mask_rcnn')
model.load_weights(weights_path, by_name=True)
results = model.detect([summary_image], verbose=1)
r = results[0]
ROIs_mrcnn = r['masks'].transpose([2, 0, 1])
# %% visualize the result
display_result = False
if display_result:
_, ax = plt.subplots(1,1, figsize=(16,16))
visualize.display_instances(summary_image, r['rois'], r['masks'], r['class_ids'],
['BG', 'neurons'], r['scores'], ax=ax,
title="Predictions")
# %% set rois
opts.change_params(params_dict={'ROIs':ROIs_mrcnn,
'index':list(range(ROIs_mrcnn.shape[0])),
'method':'SpikePursuit'})
# %% Trace Denoising and Spike Extraction
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False, maxtasksperchild=1)
vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
vpy.fit(n_processes=n_processes, dview=dview)
# %% some visualization
print(np.where(vpy.estimates['passedLocalityTest'])[0]) # neurons that pass locality test
n = 0
# Processed signal and spikes of neurons
plt.figure()
plt.plot(vpy.estimates['trace'][n])
plt.plot(vpy.estimates['spikeTimes'][n],
np.max(vpy.estimates['trace'][n]) * np.ones(vpy.estimates['spikeTimes'][n].shape),
color='g', marker='o', fillstyle='none', linestyle='none')
plt.title('signal and spike times')
plt.show()
# Location of neurons by Mask R-CNN or manual annotation
plt.figure()
if use_maskrcnn:
plt.imshow(ROIs_mrcnn[n])
else:
plt.imshow(ROIs[n])
mv = cm.load(fname_new)
plt.imshow(mv.mean(axis=0),alpha=0.5)
# Spatial filter created by algorithm
plt.figure()
plt.imshow(vpy.estimates['spatialFilter'][n])
plt.colorbar()
plt.title('spatial filter')
plt.show()
# %% STOP CLUSTER and clean up log files
cm.stop_server(dview=dview)
log_files = glob.glob('*_LOG_*')
for log_file in log_files:
os.remove(log_file)
# @params splits_els for parallelization split the movies in num_splits chuncks across time
splits_els = params_movie['splits_els']
# @params num_splits_to_process_els if none all the splits are processed and the movie is saved
num_splits_to_process_els = params_movie['num_splits_to_process_els']
# @params upsample_factor_grid upsample factor to avoid smearing when merging patches
upsample_factor_grid = params_movie['upsample_factor_grid']
# @params max_deviation_rigid maximum deviation allowed for patch with respect to rigid shift
max_deviation_rigid = params_movie['max_deviation_rigid']
# %% download movie if not there
if fname[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovieJ.tif']:
# TODO: todocument
download_demo(fname[0])
fname = [os.path.join('example_movies', fname[0])]
# TODO: todocument
m_orig = cm.load_movie_chain(fname[:1])
# %% play movie
downsample_ratio = params_display['downsample_ratio']
offset_mov = -np.min(m_orig[:100])
m_orig.resize(1, 1, downsample_ratio).play(
gain=10, offset=offset_mov, fr=30, magnification=2)
# %% RUN ANALYSIS
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %% INITIALIZING
do_motion_correction_nonrigid = True
do_motion_correction_rigid = False # in this case it will also save a rigid motion corrected movie
#%% start the cluster
try:
dview.terminate() # 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, # name of file to motion correct
# size of filter, xhange this one if
# algorithm does not work
gSig_filt=[gSig] * 2,
# maximum shifts allowed in each direction
max_shifts=[5, 5],
dview=dview,
# number of chunks for parallelizing motion correction (remember that it should hold that length_movie/num_splits_to_process_rig>100)
splits_rig=10,
