def play_all_movies(self):
"""
Public function that plays the original data, rigid corrected and
non-rigid data file side by side for comparison.
"""
offset_orig = np.nanmin(self.data_orig[:1000])
offset_rig = np.min(self.data_rig[:1000])
offset_pwrig = np.nanmin(self.data_pwrig[:1000])
cm.concatenate([
self.data_orig.resize(0.6, 0.6, 0.2) - offset_orig,
self.data_rig.resize(0.6, 0.6, 0.2) - offset_rig,
self.data_pwrig.resize(0.6, 0.6, 0.2) - offset_pwrig
],
axis=2).play(gain=1.5,
offset=0,
bord_px=self._border_correction())
return self
def motion_corr(fnames, dview, opts, disp_movie, is_3d=False):
"""Perform motion correction"""
# Create a motion correction object with the parameters specified. Note
# that the file is not loaded in memory
mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run piecewise-rigid motion correction using NoRMCorre
mc.motion_correct(save_movie=True)
# Determine maximum shift to be used for trimming against NaNs
border_to_0 = 0 if mc.border_nan is 'copy' else mc.border_to_0
# Compare with original movie
if disp_movie and not is_3d:
m_els = cm.load(mc.fname_tot_els)
m_orig = cm.load_movie_chain(fnames)
ds_ratio = 0.2
cm.concatenate([
m_orig.resize(1, 1, ds_ratio) - mc.min_mov * mc.nonneg_movie,
m_els.resize(1, 1, ds_ratio)
],
axis=2).play(fr=60, gain=15, magnification=2,
offset=0) # press q to exit
return mc, border_to_0
def reconstructed_movie(estimates, fnames, idx, scope, flip_signal):
""" Create reconstructed movie in VolPy. The movie has three panels:
motion corrected movie on the left panel, movie removed from the baseline
on the mid panel and reconstructed movie on the right panel.
Args:
estimates: dict
estimates dictionary contain results of VolPy
fnames: list
motion corrected movie in F-order memory mapping format
idx: list
index of selected neurons
scope: list
scope of number of frames in reconstructed movie
flip_signal: boolean
if True the signal will be flipped (for voltron)
Return:
mv_all: 3-D array
motion corrected movie, movie removed from baseline, reconstructed movie
concatenated into one matrix
"""
# motion corrected movie and movie removed from baseline
mv = cm.load(fnames, fr=400)[scope[0]:scope[1]]
dims = (mv.shape[1], mv.shape[2])
mv_bl = mv.computeDFF(secsWindow=0.1)[0]
mv = (mv - mv.min()) / (mv.max() - mv.min())
if flip_signal:
mv_bl = -mv_bl
mv_bl[mv_bl < np.percentile(mv_bl, 3)] = np.percentile(mv_bl, 3)
mv_bl[mv_bl > np.percentile(mv_bl, 98)] = np.percentile(mv_bl, 98)
mv_bl = (mv_bl - mv_bl.min()) / (mv_bl.max() - mv_bl.min())
# reconstructed movie
estimates['weights'][estimates['weights'] < 0] = 0
A = estimates['weights'][idx].transpose([1, 2, 0]).reshape((-1, len(idx)))
C = estimates['t_rec'][idx, scope[0]:scope[1]]
mv_rec = np.dot(A, C).reshape(
(dims[0], dims[1], scope[1] - scope[0])).transpose((2, 0, 1))
mv_rec = cm.movie(mv_rec, fr=400)
mv_rec = (mv_rec - mv_rec.min()) / (mv_rec.max() - mv_rec.min())
mv_all = cm.concatenate((mv, mv_bl, mv_rec), axis=2)
return mv_all
def run_alignmnet(selected_rows, parameters, dview):
'''
This is the main function for the alignment step. It applies methods
from the CaImAn package used originally in motion correction
to do alignment.
Args:
df: pd.DataFrame
A dataframe containing the analysis states you want to have aligned.
parameters: dict
The alignment parameters.
dview: object
The dview object
Returns:
df: pd.DataFrame
A dataframe containing the aligned analysis states.
'''
# Sort the dataframe correctly
df = selected_rows.copy()
df = df.sort_values(by=paths.multi_index_structure)
# Determine the mouse and session of the dataset
index = df.iloc[0].name
mouse, session, *r = index
# alignment_v = index[len(paths.data_structure) + step_index]
alignment_v = len(df)
alignment_index = (mouse, session, alignment_v)
# Determine the output .mmap file name
file_name = f'mouse_{mouse}_session_{session}_v{alignment_v}'
output_mmap_file_path = os.environ['DATA_DIR'] + f'data/interim/alignment/main/{file_name}.mmap'
try:
df.reset_index()[['session','trial', 'is_rest']].set_index(['session','trial', 'is_rest'], verify_integrity=True)
except ValueError:
logging.error('You passed multiple of the same trial in the dataframe df')
return df
output = {
'meta': {
'analysis': {
'analyst': os.environ['ANALYST'],
'date': datetime.datetime.today().strftime("%m-%d-%Y"),
'time': datetime.datetime.today().strftime("%H:%M:%S")
},
'duration': {}
}
}
# Get necessary parameters
motion_correction_parameters_list = []
motion_correction_output_list = []
input_mmap_file_list = []
trial_index_list = []
x_ = []
_x = []
y_ = []
_y = []
for idx, row in df.iterrows():
motion_correction_parameters_list.append(eval(row.loc['motion_correction_parameters']))
motion_correction_output = eval(row.loc['motion_correction_output'])
motion_correction_output_list.append(motion_correction_output)
input_mmap_file_list.append(motion_correction_output['main'])
trial_index_list.append(db.get_trial_name(idx[2], idx[3]))
[x1,x2,y1,y2] = motion_correction_output['meta']['cropping_points']
x_.append(x1)
_x.append(x2)
y_.append(y1)
_y.append(y2)
new_x1 = max(x_)
new_x2 = max(_x)
new_y1 = max(y_)
new_y2 = max(_y)
m_list = []
for i in range(len(input_mmap_file_list)):
m = cm.load(input_mmap_file_list[i])
motion_correction_output = eval(df.iloc[i].loc['motion_correction_output'])
[x1,x2,y1,y2] = motion_correction_output['meta']['cropping_points']
m = m.crop(new_x1 - x1, new_x2 - x2, new_y1 - y1, new_y2 - y2, 0, 0)
m_list.append(m)
# Concatenate them using the concat function
m_concat = cm.concatenate(m_list, axis=0)
data_dir = os.environ['DATA_DIR'] + 'data/interim/alignment/main/'
file_name = db.create_file_name(step_index, index)
fname= m_concat.save(data_dir + file_name + '.mmap', order='C')
#meta_pkl_dict['pw_rigid']['cropping_points'] = [x_, _x, y_, _y]
#output['meta']['cropping_points'] = [x_, _x, y_, _y]
# Save the movie
#fname_tot_els = m_els.save(data_dir + 'main/' + file_name + '_els' + '.mmap', order='C')
#logging.info(f'{index} Cropped and saved rigid movie as {fname_tot_els}')
# MOTION CORRECTING EACH INDIVIDUAL MOVIE WITH RESPECT TO A TEMPLATE MADE OF THE FIRST MOVIE
logging.info(f'{alignment_index} Performing motion correction on all movies with respect to a template made of \
the first movie.')
t0 = datetime.datetime.today()
# Create a template of the first movie
template_index = trial_index_list.index(parameters['make_template_from_trial'])
m0 = cm.load(input_mmap_file_list[template_index ])
[x1, x2, y1, y2] = motion_correction_output_list[template_index]['meta']['cropping_points']
m0 = m0.crop(new_x1 - x1, new_x2 - x2, new_y1 - y1, new_y2 - y2, 0, 0)
m0_filt = cm.movie(
np.array([high_pass_filter_space(m_, parameters['gSig_filt']) for m_ in m0]))
template0 = cm.motion_correction.bin_median(
m0_filt.motion_correct(5, 5, template=None)[0]) # may be improved in the future
# Setting the parameters
opts = params.CNMFParams(params_dict=parameters)
# Create a motion correction object
mc = MotionCorrect(fname, dview=dview, **opts.get_group('motion'))
# Perform non-rigid motion correction
mc.motion_correct(template=template0, save_movie=True)
# Cropping borders
x_ = math.ceil(abs(np.array(mc.shifts_rig)[:, 1].max()) if np.array(mc.shifts_rig)[:, 1].max() > 0 else 0)
_x = math.ceil(abs(np.array(mc.shifts_rig)[:, 1].min()) if np.array(mc.shifts_rig)[:, 1].min() < 0 else 0)
y_ = math.ceil(abs(np.array(mc.shifts_rig)[:, 0].max()) if np.array(mc.shifts_rig)[:, 0].max() > 0 else 0)
_y = math.ceil(abs(np.array(mc.shifts_rig)[:, 0].min()) if np.array(mc.shifts_rig)[:, 0].min() < 0 else 0)
# Load the motion corrected movie into memory
movie= cm.load(mc.fname_tot_rig[0])
# Crop all movies to those border pixels
movie.crop(x_, _x, y_, _y, 0, 0)
output['meta']['cropping_points'] = [x_, _x, y_, _y]
#save motion corrected and cropped movie
output_mmap_file_path_tot = movie.save(data_dir + file_name + '.mmap', order='C')
logging.info(f'{index} Cropped and saved rigid movie as {output_mmap_file_path_tot}')
# Save the path in teh output dictionary
output['main'] = output_mmap_file_path_tot
# Remove the remaining non-cropped movie
os.remove(mc.fname_tot_rig[0])
# Create a timeline and store it
timeline = [[trial_index_list[0], 0]]
timepoints = [0]
for i in range(1, len(m_list)):
m = m_list[i]
timeline.append([trial_index_list[i], timeline[i - 1][1] + m.shape[0]])
timepoints.append(timepoints[i-1]+ m.shape[0])
timeline_pkl_file_path = os.environ['DATA_DIR'] + f'data/interim/alignment/meta/timeline/{file_name}.pkl'
with open(timeline_pkl_file_path,'wb') as f:
pickle.dump(timeline,f)
output['meta']['timeline'] = timeline_pkl_file_path
timepoints.append(movie.shape[0])
dt = int((datetime.datetime.today() - t0).seconds / 60) # timedelta in minutes
output['meta']['duration']['concatenation'] = dt
logging.info(f'{alignment_index} Performed concatenation. dt = {dt} min.')
for idx, row in df.iterrows():
df.loc[idx, 'alignment_output'] = str(output)
df.loc[idx, 'alignment_parameters'] = str(parameters)
## modify all motion correction file to the aligned version
data_dir = os.environ['DATA_DIR'] + 'data/interim/motion_correction/main/'
for i in range(len(input_mmap_file_list)):
row = df.iloc[i].copy()
motion_correction_output_list.append(motion_correction_output)
aligned_movie = movie[timepoints[i]:timepoints[i+1]]
file_name = db.create_file_name(2, selected_rows.iloc[i].name)
motion_correction_output_aligned = aligned_movie.save(data_dir + file_name + '_els' + '.mmap', order='C')
new_output= {'main' : motion_correction_output_aligned }
new_dict = eval(row['motion_correction_output'])
new_dict.update(new_output)
row['motion_correction_output'] = str(new_dict)
df = db.append_to_or_merge_with_states_df(df, row)
# # Delete the motion corrected movies
# for fname in mc.fname_tot_rig:
# os.remove(fname)
return df
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
def create_video(row, time_cropping, session_wise = False):
'''
This fuction creates a complete video with raw movie (motion corrected), source extracted cells and source extraction + background.
:param row: pandas dataframe containing the desired processing information to create the video. It can use the session_wise or trial_wise video.
:return:
'''
if session_wise:
input_mmap_file_path = eval(row.loc['alignment_output'])['main']
else:
input_mmap_file_path = eval(row.loc['motion_correction_output'])['main']
#load the mmap file
Yr, dims, T = cm.load_memmap(input_mmap_file_path)
logging.debug(f'{row.name} Loaded movie. dims = {dims}, T = {T}.')
#create a caiman movie with the mmap file
images = Yr.T.reshape((T,) + dims, order='F')
images = cm.movie(images)
#load source extraction result
output = eval(row.loc['source_extraction_output'])
cnm_file_path = output['main']
cnm = load_CNMF(db.get_file(cnm_file_path))
#estimate the background from the extraction
W, b0 = cm.source_extraction.cnmf.initialization.compute_W(Yr, cnm.estimates.A.toarray(), cnm.estimates.C,
cnm.estimates.dims, 1.4 * 5, ssub=2)
cnm.estimates.W = W
cnm.estimates.b0 = b0
# this part could be use with the lastest caiman version
# movie_dir = '/home/sebastian/Documents/Melisa/calcium_imaging_analysis/data/processed/movies/'
# file_name = db.create_file_name(5,row.name)
# cnm.estimates.play_movie(cnm.estimates, images, movie_name= movie_dir + file_name + '.avi')
frame_range = slice(None, None, None)
# create a movie with the model : estimated A and C matrix
Y_rec = cnm.estimates.A.dot(cnm.estimates.C[:, frame_range])
Y_rec = Y_rec.reshape(dims + (-1,), order='F')
Y_rec = Y_rec.transpose([2, 0, 1])
# convert the variable to a caiman movie type
Y_rec = cm.movie(Y_rec)
## this part of the function is a copy from a caiman version
ssub_B = int(round(np.sqrt(np.prod(dims) / W.shape[0])))
B = images[frame_range].reshape((-1, np.prod(dims)), order='F').T - \
cnm.estimates.A.dot(cnm.estimates.C[:, frame_range])
if ssub_B == 1:
B = b0[:, None] + W.dot(B - b0[:, None])
else:
B = b0[:, None] + (np.repeat(np.repeat(W.dot(
downscale(B.reshape(dims + (B.shape[-1],), order='F'),
(ssub_B, ssub_B, 1)).reshape((-1, B.shape[-1]), order='F') -
downscale(b0.reshape(dims, order='F'),
(ssub_B, ssub_B)).reshape((-1, 1), order='F'))
.reshape(
((dims[0] - 1) // ssub_B + 1, (dims[1] - 1) // ssub_B + 1, -1), order='F'),
ssub_B, 0), ssub_B, 1)[:dims[0], :dims[1]].reshape(
(-1, B.shape[-1]), order='F'))
B = B.reshape(dims + (-1,), order='F').transpose([2, 0, 1])
Y_rec_2 = Y_rec + B
Y_res = images[frame_range] - Y_rec - B
images_np = np.zeros((time_cropping[1]-time_cropping[0],images.shape[1],images.shape[2]))
images_np = images[time_cropping[0]:time_cropping[1],:,:]
images_np = images_np / np.max(images_np)
images_np = cm.movie(images_np)
Y_rec_np = np.zeros((time_cropping[1]-time_cropping[0],images.shape[1],images.shape[2]))
Y_rec_np = Y_rec[time_cropping[0]:time_cropping[1],:,:]
Y_rec_np = Y_rec_np / np.max(Y_rec_np)
Y_rec_np = cm.movie(Y_rec_np)
Y_res_np = np.zeros((time_cropping[1]-time_cropping[0],images.shape[1],images.shape[2]))
Y_res_np = Y_res[time_cropping[0]:time_cropping[1],:,:]
Y_res_np = Y_res_np / np.max(Y_res_np)
Y_res_np = cm.movie(Y_res_np)
B_np = np.zeros((time_cropping[1]-time_cropping[0],images.shape[1],images.shape[2]))
B_np = B[time_cropping[0]:time_cropping[1],:,:]
B_np = B_np / np.max(B_np)
B_np = cm.movie(B_np)
mov1 = cm.concatenate((images_np, Y_rec_np), axis=2)
mov2 = cm.concatenate((B_np, Y_res_np), axis=2)
mov = cm.concatenate((mov1, mov2), axis=1)
figure_path = '/home/sebastian/Documents/Melisa/calcium_imaging_analysis/data/interim/movies/'
figure_name = db.create_file_name(5,row.name)
#mov.save(figure_path+figure_name+'.tif')
mov.save(figure_path+figure_name+'_'+f'{time_cropping[0]}' + '_' + f'{time_cropping[1]}'+'.tif')
return
# use one every 200 frames
temporal_stride = 200
# use one every 8 patches (patches are 8x8 by default)
spatial_stride = 8
movie_train = movie[::temporal_stride]
t = timeit.default_timer()
estimation_res = est.estimate_vst_movie(movie_train, stride=spatial_stride)
print('\tTime', timeit.default_timer() - t)
alpha = estimation_res.alpha
sigma_sq = estimation_res.sigma_sq
movie_gat = compute_gat(movie, sigma_sq, alpha=alpha)
# save movie_gat here
movie_gat_inv = compute_inverse_gat(movie_gat,
sigma_sq,
alpha=alpha,
method='asym')
# save movie_gat_inv here
return movie, movie_gat_inv
#%%
movie, movie_gat_inv = main()
#%%
cm.concatenate([movie, movie_gat_inv], axis=1).play(gain=10, magnification=4)
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_rigid(save_movie=True)
#%%
m_els = cm.load(mc.fname_tot_rig)
bord_px_els = np.max(np.ceil(np.abs(mc.shifts_rig))).astype(np.int)
# maximum shift to be used for trimming against NaNs
#%% compare with original movie
cm.concatenate([m_orig.resize(1, 1, downsample_ratio) + offset_mov,
m_els.resize(1, 1, downsample_ratio)],
axis=2).play(fr=60, gain=1, magnification=1, offset=0) # press q to exit
#%% MEMORY MAPPING
# memory map the file in order 'C'
fnames = mc.fname_tot_rig # 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=border_to_0) # 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)
for i in ds_list:
if 'mask' in i:
m = np.load((dr + i), allow_pickle=True)['arr_0']
print(i)
"""
if 'IVQ' in i:
print(len(m)/16)
l.append(len(m)/16)
else:
"""
print(len(m))
l.append(len(m))
#%% Video
m2 = m2 * 1.2
mm = cm.concatenate([m1, m2], axis=2)
mm = cm.concatenate([mm, lcm1], axis=2)
m = cm.load('/home/nel/Code/VolPy/Mask_RCNN/videos & imgs/neurons_mc/403106_3min_d1_128_d2_512_d3_1_order_C_frames_20000_.mmap')
lcm = cm.load('/home/nel/Code/VolPy/Paper/pic_paper/video/corr_video.tif')
m = m[:19871,:,:]
m.shape
lcm.shape
m = m.transpose([0,2,1])
lcm = lcm.transpose([0,2,1])
m.shape
lcm.shape
m.play()
lcm.play()
lcm.play(fr=30)
def play_movie(self,
imgs,
q_max=99.75,
q_min=2,
gain_res=1,
magnification=1,
include_bck=True,
frame_range=slice(None, None, None),
bpx=0,
thr=0.):
"""Displays a movie with three panels (original data (left panel),
reconstructed data (middle panel), residual (right panel))
Args:
imgs: np.array (possibly memory mapped, t,x,y[,z])
Imaging data
q_max: float (values in [0, 100])
percentile for maximum plotting value
q_min: float (values in [0, 100])
percentile for minimum plotting value
gain_res: float
amplification factor for residual movie
magnification: float
magnification factor for whole movie
include_bck: bool
flag for including background in original and reconstructed movie
frame_rage: range or slice or list
display only a subset of frames
bpx: int
number of pixels to exclude on each border
thr: float (values in [0, 1[)
threshold value for contours, no contours if thr=0
Returns:
self (to stop the movie press 'q')
"""
dims = imgs.shape[1:]
if 'movie' not in str(type(imgs)):
imgs = caiman.movie(imgs)
Y_rec = self.A.dot(self.C[:, frame_range])
Y_rec = Y_rec.reshape(dims + (-1, ), order='F')
Y_rec = Y_rec.transpose([2, 0, 1])
if self.W is not None:
ssub_B = int(round(np.sqrt(np.prod(dims) / self.W.shape[0])))
B = imgs[frame_range].reshape((-1, np.prod(dims)), order='F').T - \
self.A.dot(self.C[:, frame_range])
if ssub_B == 1:
B = self.b0[:, None] + self.W.dot(B - self.b0[:, None])
else:
B = self.b0[:, None] + (np.repeat(
np.repeat(
self.W.dot(
downscale(
B.reshape(dims + (B.shape[-1], ), order='F'),
(ssub_B, ssub_B, 1)).reshape(
(-1, B.shape[-1]), order='F') -
downscale(self.b0.reshape(dims, order='F'),
(ssub_B, ssub_B)).reshape(
(-1, 1), order='F')).reshape(
((dims[0] - 1) // ssub_B + 1,
(dims[1] - 1) // ssub_B + 1,
-1),
order='F'), ssub_B, 0), ssub_B,
1)[:dims[0], :dims[1]].reshape(
(-1, B.shape[-1]), order='F'))
B = B.reshape(dims + (-1, ), order='F').transpose([2, 0, 1])
elif self.b is not None and self.f is not None:
B = self.b.dot(self.f[:, frame_range])
if 'matrix' in str(type(B)):
B = B.toarray()
B = B.reshape(dims + (-1, ), order='F').transpose([2, 0, 1])
else:
B = np.zeros_like(Y_rec)
if bpx > 0:
B = B[:, bpx:-bpx, bpx:-bpx]
Y_rec = Y_rec[:, bpx:-bpx, bpx:-bpx]
imgs = imgs[:, bpx:-bpx, bpx:-bpx]
Y_res = imgs[frame_range] - Y_rec - B
mov = caiman.concatenate(
(imgs[frame_range] -
(not include_bck) * B, Y_rec + include_bck * B, Y_res * gain_res),
axis=2)
if thr > 0:
import cv2
contours = []
for a in self.A.T.toarray():
a = a.reshape(dims, order='F')
if bpx > 0:
a = a[bpx:-bpx, bpx:-bpx]
if magnification != 1:
a = cv2.resize(a,
None,
fx=magnification,
fy=magnification,
interpolation=cv2.INTER_LINEAR)
ret, thresh = cv2.threshold(a, thr * np.max(a), 1., 0)
im2, contour, hierarchy = cv2.findContours(
thresh.astype('uint8'), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours.append(contour)
contours.append(
list([c + np.array([[a.shape[1], 0]]) for c in contour]))
contours.append(
list([
c + np.array([[2 * a.shape[1], 0]]) for c in contour
]))
maxmov = np.nanpercentile(mov[0:10],
q_max) if q_max < 100 else np.nanmax(mov)
minmov = np.nanpercentile(mov[0:10],
q_min) if q_min > 0 else np.nanmin(mov)
for frame in mov:
if magnification != 1:
frame = cv2.resize(frame,
None,
fx=magnification,
fy=magnification,
interpolation=cv2.INTER_LINEAR)
frame = np.clip((frame - minmov) * 255. / (maxmov - minmov), 0,
255)
frame = np.repeat(frame[..., None], 3, 2)
for contour in contours:
cv2.drawContours(frame, contour, -1, (0, 255, 255), 1)
cv2.imshow('frame', frame.astype('uint8'))
if cv2.waitKey(30) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
else:
mov.play(q_min=q_min, q_max=q_max, magnification=magnification)
return self
# %% inspect movie
downsample_ratio = params_display['downsample_ratio']
# TODO: todocument
offset_mov = -np.min(m_orig[:100])
m_rig.resize(1, 1, downsample_ratio).play(gain=10,
offset=offset_mov * .25,
fr=30,
magnification=2,
bord_px=bord_px_rig)
# %% visualize raw, rigid and pw-rigid motion correted moviews
downsample_factor = params_display['downsample_ratio']
# TODO : todocument
cm.concatenate([
m_orig.resize(1, 1, downsample_factor) + offset_mov,
m_rig.resize(1, 1, downsample_factor)
],
axis=2).play(fr=60, gain=5, magnification=4, offset=0)
# TODO: show screenshot 8
# %% restart cluster to clean up memory
# TODO: todocument
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=None,
single_thread=False)
# %% save each chunk in F format
t1 = time.time()
if not 'max_shifts' in params_movie:
fnames = params_movie['fname']
border_to_0 = 0
else: # elif not params_movie.has_key('overlaps'):
fnames = [mc.fname_tot_rig]
int), max_deviation_rigid + 1)[::-1]
for num_splits_to_process in [28, None]:
fname_tot_els, total_template_wls, templates_els, x_shifts_els, y_shifts_els, coord_shifts_els = cm.motion_correction.motion_correct_batch_pwrigid(fname, max_shifts_els, strides, overlaps, add_to_movie, newoverlaps=None, newstrides=None,
dview=dview, upsample_factor_grid=upsample_factor_grid, max_deviation_rigid=max_deviation_rigid,
splits=splits, num_splits_to_process=num_splits_to_process, num_iter=num_iter,
template=new_templ, shifts_opencv=shifts_opencv, save_movie=save_movie)
new_templ = total_template_wls
#%%
pl.subplot(2, 1, 1)
pl.plot(x_shifts_els)
pl.subplot(2, 1, 2)
pl.plot(y_shifts_els)
#%%
m_els = cm.load(fname_tot_els)
cm.concatenate([m_rig.resize(1, 1, .2), m_els.resize(1, 1, .2)], axis=1).play(
fr=50, gain=20, magnification=2, offset=add_to_movie)
#%% compute metrics for the results
final_size = np.subtract(new_templ.shape, max_shifts_els)
winsize = 75
swap_dim = False
resize_fact_flow = .2
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
fname_tot_els, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False, resize_fact_flow=resize_fact_flow)
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
fname_tot_rig, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False, resize_fact_flow=resize_fact_flow)
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
fname, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False, resize_fact_flow=resize_fact_flow)
#%% plot the results of metrics
fls = [fname_tot_els[:-4] + '_metrics.npz', fname_tot_rig[:-4] +
'_metrics.npz', fname[:-4] + '_metrics.npz']
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)
for counter, mm in enumerate(mc[:num_frames]):
print(counter)
t1 = time()
new_img = cv2.remap(
mm, mapX_res[counter], mapY_res[counter], cv2.INTER_CUBIC, None, cv2.BORDER_CONSTANT)
new_ms[counter] = new_img
# cv2.imshow('frame',(new_img-bl)*1./fact*5.)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
times.append(time() - t1)
# cv2.destroyAllWindows()
#%%
cm.movie(np.array(mc[:num_frames] - new_ms[:num_frames])
).play(gain=50., magnification=1)
#%%
cm.concatenate([cm.movie(np.array(new_ms[:num_frames]), fr=mc.fr), mc[:num_frames]],
axis=2).resize(1, 1, .5).play(gain=2, magnification=3, fr=30, offset=-100)
#%%
pl.subplot(1, 3, 1)
pl.imshow(np.mean(m[:2000], 0), cmap='gray', vmax=200)
pl.subplot(1, 3, 2)
pl.imshow(np.mean(new_ms[:2000], 0), cmap='gray', vmax=200)
pl.subplot(1, 3, 3)
pl.imshow(np.mean(new_ms[:2000], 0) - np.mean(m[:2000], 0), cmap='gray')
#%%
cm.movie(np.array(m[:2000] - new_ms[:2000])).play()
#%%
from multiprocessing import Pool
pl = Pool(processes=5)
def my_fun(X):
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)
import psutil
import sys
from ipyparallel import Client
from skimage.external.tifffile import TiffFile
import scipy
#%%
from caiman.motion_correction import tile_and_correct, motion_correction_piecewise
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
#%%
m = cm.load('example_movies/demoMovie.tif')
cm.concatenate([m.resize(1, 1, .2), m.resize(1, 1, .2)],
axis=1).play(fr=20, gain=3., magnification=3)
#%% set parameters and create template by RIGID MOTION CORRECTION
# params_movie = {'fname':'example_movies/Sue_2x_3000_40_-46.tif',
# 'max_shifts':(6,6), # maximum allow rigid shift
# 'splits_rig':56, # for parallelization split the movies in num_splits chuncks across time
# 'num_splits_to_process_rig':None, # if none all the splits are processed and the movie is saved
# 'strides': (48,48), # intervals at which patches are laid out for motion correction
# 'overlaps': (24,24), # overlap between pathes (size of patch strides+overlaps)
# 'splits_els':56, # for parallelization split the movies in num_splits chuncks across time
# 'num_splits_to_process_els':[28,None], # if none all the splits are processed and the movie is saved
# 'upsample_factor_grid':4, # upsample factor to avoid smearing when merging patches
# 'max_deviation_rigid':3, #maximum deviation allowed for patch with respect to rigid shift
# 'p': 1, # order of the autoregressive system
# 'merge_thresh' : 0.8, # merging threshold, max correlation allowed
# 'rf' : 15, # half-size of the patches in pixels. rf=25, patches are 50x50
# 'stride_cnmf' : 6, # amounpl.it of overlap between the patches in pixels
#
# total_shifts.append(total_shift)
# start_steps.append(start_step)
# xy_grids.append(xy_grid)
#mc = cm.load('M_FLUO_4_d1_64_d2_128_d3_1_order_F_frames_4620_.mmap')
#mc = cm.load('M_FLUO_t_d1_64_d2_128_d3_1_order_F_frames_6764_.mmap')
#%%
mc.resize(1,1,.2).play(gain=30,fr = 30, offset = 300,magnification=1.)
#%%
m.resize(1,1,.2).play(gain=10,fr = 30, offset = 0,magnification=1.)
#%%
cm.concatenate([mr.resize(1,1,.5),mc.resize(1,1,.5)],axis=1).play(gain=10,fr = 100, offset = 300,magnification=1.)
#%%
import h5py
with h5py.File('sueann_pw_rigid_movie.mat') as f:
mef = np.array(f['M2'])
mef = cm.movie(mef.transpose([0,2,1]))
#%%
cm.concatenate([mef.resize(1,1,.15),mc.resize(1,1,.15)],axis=1).play(gain=30,fr = 40, offset = 300,magnification=1.)
#%%
(mef-mc).resize(1,1,.1).play(gain=50,fr = 20, offset = 0,magnification=1.)
#%%
(mc-mef).resize(1,1,.1).play(gain=50,fr = 20, offset = 0,magnification=1.)
#%%
int), max_deviation_rigid + 1)[::-1]
for num_splits_to_process in [28, None]:
fname_tot_els, total_template_wls, templates_els, x_shifts_els, y_shifts_els, coord_shifts_els = cm.motion_correction.motion_correct_batch_pwrigid(fname, max_shifts_els, strides, overlaps, add_to_movie, newoverlaps=None, newstrides=None,
dview=dview, upsample_factor_grid=upsample_factor_grid, max_deviation_rigid=max_deviation_rigid,
splits=splits, num_splits_to_process=num_splits_to_process, num_iter=num_iter,
template=new_templ, shifts_opencv=shifts_opencv, save_movie=save_movie)
new_templ = total_template_wls
#%%
pl.subplot(2, 1, 1)
pl.plot(x_shifts_els)
pl.subplot(2, 1, 2)
pl.plot(y_shifts_els)
#%%
m_els = cm.load(fname_tot_els)
cm.concatenate([m_rig.resize(1, 1, .2), m_els.resize(1, 1, .2)], axis=1).play(
fr=50, gain=20, magnification=2, offset=add_to_movie)
#%% compute metrics for the results
final_size = np.subtract(new_templ.shape, max_shifts_els)
winsize = 75
swap_dim = False
resize_fact_flow = .2
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
fname_tot_els, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False, resize_fact_flow=resize_fact_flow)
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
fname_tot_rig, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False, resize_fact_flow=resize_fact_flow)
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
fname, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False, resize_fact_flow=resize_fact_flow)
#%% plot the results of metrics
fls = [fname_tot_els[:-4] + '_metrics.npz', fname_tot_rig[:-4] +
'_metrics.npz', fname[:-4] + '_metrics.npz']
#%%
pl.imshow(img,interpolation='none')
#%%
cc=scipy.sparse.csgraph.connected_components(img,directed=False)
#distanceMatrix=distanceMatrix/np.max(distanceMatrix)
#%%
m=load(fname,fr=30).resize(1,1,.2)
#%%
shifts_,xcorrs_,template_ = m.motion_correction_online(init_frames_template=100,max_shift_h=5,max_shift_w=5)
#%%
shifts,xcorrs,template = m.motion_correction_online(template=template_,min_count=len(m),max_shift_h=5,max_shift_w=5)
#%%
mov_path=m.apply_shifts_online(shifts,save_base_name='/tmp/test')
#%%
m=load(mov_path,fr=30*.2)
m1=cm.concatenate([m.resize(1,1,1),mh.resize(1,1,1)],axis=1)
(m1-np.percentile(m1,8)).play(backend='opencv',fr=100,gain=3.,magnification=5)
#%%
#mc=m.motion_correct(5,5)
#%%
#m1=mc[0].resize(1,1,.2)
#%%
(m1-np.percentile(m1,8)).play(backend='opencv',fr=100,gain=10.,magnification=5)
#%%
final_frate=15
is_patches=True
is_dendrites=False
def run_alignment(mouse, sessions, motion_correction_v, cropping_v, dview):
"""
This is the main function for the alignment step. It applies methods
from the CaImAn package used originally in motion correction
to do alignment.
"""
for session in sessions:
# Update the database
file_name = f"mouse_{mouse}_session_{session}_alignment"
sql1 = "UPDATE Analysis SET alignment_main=? WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=? "
val1 = [file_name, mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql1, val1)
# Determine the output .mmap file name
output_mmap_file_path = os.environ[
'DATA_DIR_LOCAL'] + f'data/interim/alignment/main/{file_name}.mmap'
sql = "SELECT motion_correction_main FROM Analysis WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=? "
val = [mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql, val)
result = cursor.fetchall()
input_mmap_file_list = []
inter = []
for x in result:
inter += x
for y in inter:
input_mmap_file_list.append(y)
sql = "SELECT motion_correction_cropping_points_x1 FROM Analysis WHERE mouse = ? AND session=?AND motion_correction_v =? AND cropping_v=? "
val = [mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql, val)
result = cursor.fetchall()
x_ = []
inter = []
for i in result:
inter += i
for j in range(0, len(inter)):
x_.append(inter[j])
sql = "SELECT motion_correction_cropping_points_x2 FROM Analysis WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=? "
val = [mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql, val)
result = cursor.fetchall()
_x = []
inter = []
for i in result:
inter += i
for j in range(0, len(inter)):
_x.append(inter[j])
sql = "SELECT motion_correction_cropping_points_y1 FROM Analysis WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=?"
val = [mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql, val)
result = cursor.fetchall()
_y = []
inter = []
for i in result:
inter += i
for j in range(0, len(inter)):
_y.append(inter[j])
sql = "SELECT motion_correction_cropping_points_y2 FROM Analysis WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=?"
val = [mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql, val)
result = cursor.fetchall()
y_ = []
inter = []
for i in result:
inter += i
for j in range(0, len(inter)):
y_.append(inter[j])
new_x1 = max(x_)
new_x2 = max(_x)
new_y1 = max(y_)
new_y2 = max(_y)
m_list = []
for i in range(len(input_mmap_file_list)):
m = cm.load(input_mmap_file_list[i])
m = m.crop(new_x1 - x_[i], new_x2 - _x[i], new_y1 - y_[i],
new_y2 - _y[i], 0, 0)
m_list.append(m)
# Concatenate them using the concat function
m_concat = cm.concatenate(m_list, axis=0)
fname = m_concat.save(output_mmap_file_path, order='C')
# MOTION CORRECTING EACH INDIVIDUAL MOVIE WITH RESPECT TO A TEMPLATE MADE OF THE FIRST MOVIE
logging.info(
'Performing motion correction on all movies with respect to a template made of the first movie.'
)
t0 = datetime.datetime.today()
# parameters alignment
sql5 = "SELECT make_template_from_trial,gSig_filt,max_shifts,niter_rig,strides,overlaps,upsample_factor_grid,num_frames_split,max_deviation_rigid,shifts_opencv,use_conda,nonneg_movie, border_nan FROM Analysis WHERE alignment_main=? "
val5 = [
file_name,
]
cursor.execute(sql5, val5)
myresult = cursor.fetchall()
para = []
aux = []
for x in myresult:
aux = x
for y in aux:
para.append(y)
parameters = {
'make_template_from_trial': para[0],
'gSig_filt': (para[1], para[1]),
'max_shifts': (para[2], para[2]),
'niter_rig': para[3],
'strides': (para[4], para[4]),
'overlaps': (para[5], para[5]),
'upsample_factor_grid': para[6],
'num_frames_split': para[7],
'max_deviation_rigid': para[8],
'shifts_opencv': para[9],
'use_cuda': para[10],
'nonneg_movie': para[11],
'border_nan': para[12]
}
# Create a template of the first movie
template_index = parameters['make_template_from_trial']
m0 = cm.load(input_mmap_file_list[1])
[x1, x2, y1, y2] = [x_, _x, y_, _y]
for i in range(len(input_mmap_file_list)):
m0 = m0.crop(new_x1 - x_[i], new_x2 - _x[i], new_y1 - y_[i],
new_y2 - _y[i], 0, 0)
m0_filt = cm.movie(
np.array([
high_pass_filter_space(m_, parameters['gSig_filt'])
for m_ in m0
]))
template0 = cm.motion_correction.bin_median(
m0_filt.motion_correct(
5, 5, template=None)[0]) # may be improved in the future
# Setting the parameters
opts = params.CNMFParams(params_dict=parameters)
# Create a motion correction object
mc = MotionCorrect(fname, dview=dview, **opts.get_group('motion'))
# Perform non-rigid motion correction
mc.motion_correct(template=template0, save_movie=True)
# Cropping borders
x_ = math.ceil(
abs(np.array(mc.shifts_rig)[:, 1].max()
) if np.array(mc.shifts_rig)[:, 1].max() > 0 else 0)
_x = math.ceil(
abs(np.array(mc.shifts_rig)[:, 1].min()
) if np.array(mc.shifts_rig)[:, 1].min() < 0 else 0)
y_ = math.ceil(
abs(np.array(mc.shifts_rig)[:, 0].max()
) if np.array(mc.shifts_rig)[:, 0].max() > 0 else 0)
_y = math.ceil(
abs(np.array(mc.shifts_rig)[:, 0].min()
) if np.array(mc.shifts_rig)[:, 0].min() < 0 else 0)
# Load the motion corrected movie into memory
movie = cm.load(mc.fname_tot_rig[0])
# Crop all movies to those border pixels
movie.crop(x_, _x, y_, _y, 0, 0)
sql1 = "UPDATE Analysis SET alignment_x1=?, alignment_x2 =?, alignment_y1=?, alignment_y2=? WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=?"
val1 = [
x_, _x, y_, _y, mouse, session, motion_correction_v, cropping_v
]
cursor.execute(sql1, val1)
# save motion corrected and cropped movie
output_mmap_file_path_tot = movie.save(
os.environ['DATA_DIR_LOCAL'] +
f'data/interim/alignment/main/{file_name}.mmap',
order='C')
logging.info(
f' Cropped and saved rigid movie as {output_mmap_file_path_tot}')
# Remove the remaining non-cropped movie
os.remove(mc.fname_tot_rig[0])
# Create a timeline and store it
sql = "SELECT trial FROM Analysis WHERE mouse = ? AND session=? AND motion_correction_v =? AND cropping_v=?"
val = [mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql, val)
result = cursor.fetchall()
trial_index_list = []
inter = []
for i in result:
inter += i
for j in range(0, len(inter)):
trial_index_list.append(inter[j])
timeline = [[trial_index_list[0], 0]]
timepoints = [0]
for i in range(1, len(m_list)):
m = m_list[i]
timeline.append(
[trial_index_list[i], timeline[i - 1][1] + m.shape[0]])
timepoints.append(timepoints[i - 1] + m.shape[0])
timeline_pkl_file_path = os.environ[
'DATA_DIR'] + f'data/interim/alignment/meta/timeline/{file_name}.pkl'
with open(timeline_pkl_file_path, 'wb') as f:
pickle.dump(timeline, f)
sql1 = "UPDATE Analysis SET alignment_timeline=? WHERE mouse = ? AND session=?AND motion_correction_v =? AND cropping_v=? "
val1 = [
timeline_pkl_file_path, mouse, session, motion_correction_v,
cropping_v
]
cursor.execute(sql1, val1)
timepoints.append(movie.shape[0])
dt = int((datetime.datetime.today() - t0).seconds /
60) # timedelta in minutes
sql1 = "UPDATE Analysis SET alignment_duration_concatenation=? WHERE mouse = ? AND session=?AND motion_correction_v =? AND cropping_v=? "
val1 = [dt, mouse, session, motion_correction_v, cropping_v]
cursor.execute(sql1, val1)
logging.info(f' Performed concatenation. dt = {dt} min.')
## modify all motion correction file to the aligned version
data_dir = os.environ[
'DATA_DIR'] + 'data/interim/motion_correction/main/'
for i in range(len(input_mmap_file_list)):
aligned_movie = movie[timepoints[i]:timepoints[i + 1]]
motion_correction_output_aligned = aligned_movie.save(
data_dir + file_name + '_els' + '.mmap', order='C')
sql1 = "UPDATE Analysis SET motion_correct_align=? WHERE motion_correction_meta=? AND motion_correction_v"
val1 = [
motion_correction_output_aligned, input_mmap_file_list[i],
motion_correction_v
]
cursor.execute(sql1, val1)
database.commit()
return
# makes estimation numerically better:
movie -= movie.mean()
# use one every 200 frames
temporal_stride = 200
# use one every 8 patches (patches are 8x8 by default)
spatial_stride = 8
movie_train = movie[::temporal_stride]
t = timeit.default_timer()
estimation_res = est.estimate_vst_movie(movie_train, stride=spatial_stride)
print('\tTime', timeit.default_timer() - t)
alpha = estimation_res.alpha
sigma_sq = estimation_res.sigma_sq
movie_gat = compute_gat(movie, sigma_sq, alpha=alpha)
# save movie_gat here
movie_gat_inv = compute_inverse_gat(movie_gat, sigma_sq, alpha=alpha,
method='asym')
# save movie_gat_inv here
return movie, movie_gat_inv
#%%
movie, movie_gat_inv = main()
#%%
cm.concatenate([movie,movie_gat_inv],axis=1).play(gain = 10, magnification=4)
B = b0[:, None] + (np.repeat(
np.repeat(
W.dot(
downscale(B.reshape(dims + (B.shape[-1], ), order='F'), (
ssub_B, ssub_B, 1)).reshape((-1, B.shape[-1]), order='F') -
downscale(b0.reshape(dims, order='F'), (
ssub_B, ssub_B)).reshape((-1, 1), order='F')).reshape(
((dims[0] - 1) // ssub_B + 1,
(dims[1] - 1) // ssub_B + 1, -1),
order='F'), ssub_B, 0), ssub_B,
1)[:dims[0], :dims[1]].reshape((-1, B.shape[-1]), order='F'))
B = B.reshape(dims + (-1, ), order='F').transpose([2, 0, 1])
Y_res = images[frame_range] - Y_rec - B
mov = cm.concatenate((images, Y_rec), axis=2)
mov = cm.concatenate((images, images), axis=2)
#%% Select the row to be source extracted using current versions of cropping and motion correction
selected_rows = db.select(states_df,
'source_extraction',
mouse=mouse_number,
session=session,
is_rest=is_rest,
cropping_v=cropping_version,
motion_correction_v=motion_correction_version)
gSig = 5
gSiz = 4 * gSig + 1
#distanceMatrix=distanceMatrix/np.max(distanceMatrix)
#%%
m = load(fname, fr=30).resize(1, 1, .2)
#%%
shifts_, xcorrs_, template_ = m.motion_correction_online(
init_frames_template=100, max_shift_h=5, max_shift_w=5)
#%%
shifts, xcorrs, template = m.motion_correction_online(template=template_,
min_count=len(m),
max_shift_h=5,
max_shift_w=5)
#%%
mov_path = m.apply_shifts_online(shifts, save_base_name='/tmp/test')
#%%
m = load(mov_path, fr=30 * .2)
m1 = cm.concatenate([m.resize(1, 1, 1), mh.resize(1, 1, 1)], axis=1)
(m1 - np.percentile(m1, 8)).play(backend='opencv',
fr=100,
gain=3.,
magnification=5)
#%%
#mc=m.motion_correct(5,5)
#%%
#m1=mc[0].resize(1,1,.2)
#%%
(m1 - np.percentile(m1, 8)).play(backend='opencv',
fr=100,
gain=10.,
magnification=5)
#m_denoised_now=(m_denoised_now- np.mean(m_denoised_now, axis=0))
#m_denoised_now =np.diff(m_denoised_now,axis = 0)
#m_denoised_now = (m_denoised_now - np.mean(m_denoised_now, axis=(1,2))[:,np.newaxis,np.newaxis])
if baselinesubtract :
m_denoised_baseline = voltage_imaging_utils.moving_average(m_denoised_now, n=baseline_window)
m_denoised_now = m_denoised_now/m_denoised_baseline
#%
m_mocorr_denoised_now = m_mocorr_denoised[startframe:startframe+framenum,:,:]#.copy()
#m_mocorr_denoised_now=(m_mocorr_denoised_now- np.mean(m_mocorr_denoised_now, axis=0))
#m_mocorr_denoised_now =np.diff(m_mocorr_denoised_now,axis = 0)
if baselinesubtract :
m_mocorr_denoised_baseline = voltage_imaging_utils.moving_average(m_mocorr_denoised_now, n=baseline_window)
m_mocorr_denoised_now = m_mocorr_denoised_now/m_mocorr_denoised_baseline
#%
m_now = cm.concatenate([m_orig_now,m_volpy_now, m_denoised_now,m_mocorr_denoised_now], axis=1)#
#%%
#%%
m_now = cm.concatenate([m_orig_now,m_volpy_now], axis=1)#
#%%
m_now.play(fr=900, magnification=2,q_max=99.9, q_min=0.1,save_movie = True)
#m_orig = cm.load(allfnames[0:3])
#m_volpy_now.play(fr=400, magnification=1,q_max=99.5, q_min=0.5,save_movie = False)
#%% Szar van a palacsintaban
subject_ids,movie_names,frame_times,sessions,movie_numbers = (imaging.Movie*imaging.MovieFrameTimes()).fetch('subject_id','movie_name','frame_times','session','movie_number')
for subject_id,movie_name,frame_time,session,movie_number in zip(subject_ids,movie_names,frame_times,sessions,movie_numbers):
frametimediff = np.diff(frame_time)
if np.min(frametimediff)<.5*np.median(frametimediff):
key = {'subject_id':subject_id,'movie_number':movie_number,'session':session}
fig=plt.figure()
ax = fig.add_axes([0,0,1,1])
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)
def play_movie(self,
imgs,
q_max=99.75,
q_min=2,
gain_res=1,
magnification=1,
include_bck=True,
frame_range=slice(None, None, None),
bpx=0):
"""Displays a movie with three panels (original data (left panel),
reconstructed data (middle panel), residual (right panel))
Args:
imgs: np.array (possibly memory mapped, t,x,y[,z])
Imaging data
q_max: float (values in [0, 100])
percentile for maximum plotting value
q_min: float (values in [0, 100])
percentile for minimum plotting value
gain_res: float
amplification factor for residual movie
magnification: float
magnification factor for whole movie
include_bck: bool
flag for including background in original and reconstructed movie
frame_rage: range or slice or list
display only a subset of frames
bpx: int
number of pixels to exclude on each border
Returns:
self (to stop the movie press 'q')
"""
dims = imgs.shape[1:]
if 'movie' not in str(type(imgs)):
imgs = caiman.movie(imgs)
Y_rec = self.A.dot(self.C[:, frame_range])
Y_rec = Y_rec.reshape(dims + (-1, ), order='F')
Y_rec = Y_rec.transpose([2, 0, 1])
if self.b is not None and self.f is not None:
B = self.b.dot(self.f[:, frame_range])
if 'matrix' in str(type(B)):
B = B.toarray()
B = B.reshape(dims + (-1, ), order='F').transpose([2, 0, 1])
elif self.W is not None:
ssub_B = int(round(np.sqrt(np.prod(dims) / self.W.shape[0])))
B = imgs[frame_range].reshape((-1, np.prod(dims)), order='F').T - \
self.A.dot(self.C[:, frame_range])
if ssub_B == 1:
B = self.b0[:, None] + self.W.dot(B - self.b0[:, None])
else:
B = self.b0[:, None] + (np.repeat(
np.repeat(
self.W.dot(
downscale(
B.reshape(dims + (B.shape[-1], ), order='F'),
(ssub_B, ssub_B, 1)).reshape(
(-1, B.shape[-1]), order='F') -
downscale(self.b0.reshape(dims, order='F'),
(ssub_B, ssub_B)).reshape(
(-1, 1), order='F')).reshape(
((dims[0] - 1) // ssub_B + 1,
(dims[1] - 1) // ssub_B + 1,
-1),
order='F'), ssub_B, 0), ssub_B,
1)[:dims[0], :dims[1]].reshape(
(-1, B.shape[-1]), order='F'))
B = B.reshape(dims + (-1, ), order='F').transpose([2, 0, 1])
else:
B = np.zeros_like(Y_rec)
if bpx > 0:
B = B[:, bpx:-bpx, bpx:-bpx]
Y_rec = Y_rec[:, bpx:-bpx, bpx:-bpx]
imgs = imgs[:, bpx:-bpx, bpx:-bpx]
Y_res = imgs[frame_range] - Y_rec - B
caiman.concatenate(
(imgs[frame_range] -
(not include_bck) * B, Y_rec + include_bck * B, Y_res * gain_res),
axis=2).play(q_min=q_min, q_max=q_max, magnification=magnification)
return self
def play_movie(estimates, imgs, q_max=99.75, q_min=2, gain_res=1,
magnification=1, include_bck=True,
frame_range=slice(None, None, None),
bpx=0, thr=0., save_movie=False,
movie_name='results_movie.avi'):
dims = imgs.shape[1:]
if 'movie' not in str(type(imgs)):
imgs = cm.movie(imgs)
Y_rec = estimates.A.dot(estimates.C[:, frame_range])
Y_rec = Y_rec.reshape(dims + (-1,), order='F')
Y_rec = Y_rec.transpose([2, 0, 1])
if estimates.W is not None:
ssub_B = int(round(np.sqrt(np.prod(dims) / estimates.W.shape[0])))
B = imgs[frame_range].reshape((-1, np.prod(dims)), order='F').T - \
estimates.A.dot(estimates.C[:, frame_range])
if ssub_B == 1:
B = estimates.b0[:, None] + estimates.W.dot(B - estimates.b0[:, None])
else:
B = estimates.b0[:, None] + (np.repeat(np.repeat(estimates.W.dot(
downscale(B.reshape(dims + (B.shape[-1],), order='F'),
(ssub_B, ssub_B, 1)).reshape((-1, B.shape[-1]), order='F') -
downscale(estimates.b0.reshape(dims, order='F'),
(ssub_B, ssub_B)).reshape((-1, 1), order='F'))
.reshape(((dims[0] - 1) // ssub_B + 1, (dims[1] - 1) // ssub_B + 1, -1), order='F'),
ssub_B, 0), ssub_B, 1)[:dims[0], :dims[1]].reshape((-1, B.shape[-1]), order='F'))
B = B.reshape(dims + (-1,), order='F').transpose([2, 0, 1])
elif estimates.b is not None and estimates.f is not None:
B = estimates.b.dot(estimates.f[:, frame_range])
if 'matrix' in str(type(B)):
B = B.toarray()
B = B.reshape(dims + (-1,), order='F').transpose([2, 0, 1])
else:
B = np.zeros_like(Y_rec)
if bpx > 0:
B = B[:, bpx:-bpx, bpx:-bpx]
Y_rec = Y_rec[:, bpx:-bpx, bpx:-bpx]
imgs = imgs[:, bpx:-bpx, bpx:-bpx]
Y_res = imgs[frame_range] - Y_rec - B
mov = cm.concatenate((imgs[frame_range] - (not include_bck) * B, Y_rec,
Y_rec + include_bck * B, Y_res * gain_res), axis=2)
if thr > 0:
if save_movie:
import cv2
#fourcc = cv2.VideoWriter_fourcc('8', 'B', 'P', 'S')
#fourcc = cv2.VideoWriter_fourcc(*'XVID')
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter(movie_name, fourcc, 30.0,
tuple([int(magnification*s) for s in mov.shape[1:][::-1]]))
contours = []
for a in estimates.A.T.toarray():
a = a.reshape(dims, order='F')
if bpx > 0:
a = a[bpx:-bpx, bpx:-bpx]
if magnification != 1:
a = cv2.resize(a, None, fx=magnification, fy=magnification,
interpolation=cv2.INTER_LINEAR)
ret, thresh = cv2.threshold(a, thr * np.max(a), 1., 0)
contour, hierarchy = cv2.findContours(
thresh.astype('uint8'), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours.append(contour)
contours.append(list([c + np.array([[a.shape[1], 0]]) for c in contour]))
contours.append(list([c + np.array([[2 * a.shape[1], 0]]) for c in contour]))
maxmov = np.nanpercentile(mov[0:10], q_max) if q_max < 100 else np.nanmax(mov)
minmov = np.nanpercentile(mov[0:10], q_min) if q_min > 0 else np.nanmin(mov)
for frame in mov:
if magnification != 1:
frame = cv2.resize(frame, None, fx=magnification, fy=magnification,
interpolation=cv2.INTER_LINEAR)
frame = np.clip((frame - minmov) * 255. / (maxmov - minmov), 0, 255)
frame = np.repeat(frame[..., None], 3, 2)
for contour in contours:
cv2.drawContours(frame, contour, -1, (0, 255, 255), 1)
cv2.imshow('frame', frame.astype('uint8'))
if save_movie:
out.write(frame.astype('uint8'))
if cv2.waitKey(30) & 0xFF == ord('q'):
break
if save_movie:
out.release()
cv2.destroyAllWindows()
cv2.destroyAllWindows()
else:
mov.play(q_min=q_min, q_max=q_max, magnification=magnification,
save_movie=save_movie, movie_name=movie_name)
return
pl.plot(mc.shifts_rig)
pl.legend(['x shifts', 'y shifts'])
pl.xlabel('frames')
pl.ylabel('pixels')
# %% inspect movie
downsample_ratio = params_display['downsample_ratio']
# TODO: todocument
offset_mov = -np.min(m_orig[:100])
m_rig.resize(1, 1, downsample_ratio).play(
gain=10, offset=offset_mov * .25, fr=30, magnification=2, bord_px=bord_px_rig)
# %% visualize raw, rigid and pw-rigid motion correted moviews
downsample_factor = params_display['downsample_ratio']
# TODO : todocument
cm.concatenate(
[m_orig.resize(1, 1, downsample_factor) + offset_mov, m_rig.resize(1, 1, downsample_factor)], axis=2).play(fr=60, gain=5, magnification=4, offset=0)
# TODO: show screenshot 8
# %% restart cluster to clean up memory
# TODO: todocument
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local', n_processes=None, single_thread=False)
# %% save each chunk in F format
t1 = time.time()
if not 'max_shifts' 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
# else:
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
cm.concatenate([
m_orig.resize(1, 1, downsample_ratio) + offset_mov,
m_els.resize(1, 1, downsample_ratio)
],
axis=2).play(fr=60, gain=15, 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
def main():
pass # For compatibility between running under Spyder and the CLI
#%% Select file(s) to be processed (download if not present)
# fnames = [os.path.join(caiman_datadir(), 'example_movies/sampled3dMovieRigid.nwb')]
fnames = [
os.path.join(caiman_datadir(), 'example_movies/sampled3dMovie2.nwb')
]
# filename to be created or processed
# dataset dependent parameters
fr = 5 # imaging rate in frames per second
decay_time = 0.4 # length of a typical transient in seconds
starting_time = 0.
#%% load the file and save it in the NWB format (if it doesn't exist already)
if not os.path.exists(fnames[0]):
# fnames_orig = [os.path.join(caiman_datadir(), 'example_movies/sampled3dMovieRigid.h5')] # filename to be processed
fnames_orig = [
os.path.join(caiman_datadir(), 'example_movies/sampled3dMovie2.h5')
] # filename to be processed
orig_movie = cm.load(fnames_orig, fr=fr, is3D=True)
# orig_movie = cm.load_movie_chain(fnames_orig,fr=fr,is3D=True)
# save file in NWB format with various additional info
orig_movie.save(fnames[0],
sess_desc='test',
identifier='demo 3d',
exp_desc='demo movie',
imaging_plane_description='multi plane',
emission_lambda=520.0,
indicator='none',
location='visual cortex',
starting_time=starting_time,
experimenter='NAOMi',
lab_name='Tank Lab',
institution='Princeton U',
experiment_description='Experiment Description',
session_id='Session 1',
var_name_hdf5='TwoPhotonSeries')
#%% First setup some parameters for data and motion correction
# motion correction parameters
dxy = (1., 1., 5.) # spatial resolution in x, y, and z in (um per pixel)
# note the lower than usual spatial resolution here
max_shift_um = (10., 10., 10.) # maximum shift in um
patch_motion_um = (50., 50., 30.
) # patch size for non-rigid correction in um
# pw_rigid = False # flag to select rigid vs pw_rigid motion correction
niter_rig = 1
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, 4)
# maximum deviation allowed for patch with respect to rigid shifts
max_deviation_rigid = 3
is3D = True
mc_dict = {
'fnames': fnames,
'fr': fr,
'decay_time': decay_time,
'dxy': dxy,
'pw_rigid': pw_rigid,
'niter_rig': niter_rig,
'max_shifts': max_shifts,
'strides': strides,
'overlaps': overlaps,
'max_deviation_rigid': max_deviation_rigid,
'border_nan': 'copy',
'var_name_hdf5': 'acquisition/TwoPhotonSeries',
'is3D': is3D,
'splits_els': 12,
'splits_rig': 12
}
opts = params.CNMFParams(
params_dict=mc_dict
) #NOTE: default adjustments of parameters are not set yet, manually setting them now
# %% 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,
var_name_hdf5=opts.data['var_name_hdf5'],
is3D=True)
T, h, w, z = m_orig.shape # Time, plane, height, weight
m_orig = np.reshape(np.transpose(m_orig, (3, 0, 1, 2)), (T * z, h, w))
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
# NOTE: ignore dview right now for debugging purposes
# 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=None,
var_name_hdf5=opts.data['var_name_hdf5'],
**opts.get_group('motion'))
# mc = MotionCorrect(fnames, dview=dview, var_name_hdf5=opts.data['var_name_hdf5'], **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,
var_name_hdf5=opts.data['var_name_hdf5'],
is3D=True)
T, h, w, z = m_orig.shape # Time, plane, height, weight
m_orig = np.reshape(np.transpose(m_orig, (3, 0, 1, 2)), (T * z, h, w))
m_els = cm.load(mc.mmap_file, is3D=True)
m_els = np.reshape(np.transpose(m_els, (3, 0, 1, 2)), (T * z, h, w))
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
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
def run_caiman_pipeline(movie, fr, fnames, savedir, usematlabroi):
#%%
cpu_num = 7
cpu_num_spikepursuit = 2
#gsig_filt_micron = (4, 4)
#max_shifts_micron = (6,6)
#strides_micron = (60,60)
#overlaps_micron = (30, 30)
gsig_filt_micron = (4, 4)
max_shifts_micron = (6, 6)
strides_micron = (30, 30)
overlaps_micron = (15, 15)
max_deviation_rigid_micron = 4
pixel_size = movie['movie_pixel_size']
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 = tuple(
np.asarray(np.round(np.asarray(gsig_filt_micron) / float(pixel_size)),
int)) # size of filter, in general gSig (see below),
# change this one if algorithm does not work
max_shifts = tuple(
np.asarray(np.round(np.asarray(max_shifts_micron) / float(pixel_size)),
int))
strides = tuple(
np.asarray(np.round(np.asarray(strides_micron) / float(pixel_size)),
int)
) # start a new patch for pw-rigid motion correction every x pixels
overlaps = tuple(
np.asarray(np.round(np.asarray(overlaps_micron) / float(pixel_size)),
int)
) # start a new patch for pw-rigid motion correction every x pixels
# overlap between pathes (size of patch strides+overlaps)
max_deviation_rigid = int(
round(max_deviation_rigid_micron / pixel_size)
) # 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=cpu_num,
single_thread=False)
# % MOTION CORRECTION
# Create a motion correction object with the specified parameters
mcrig = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
# Run piecewise rigid motion correction
#%
mcrig.motion_correct(save_movie=True)
dview.terminate()
# % MOTION CORRECTION2
opts.change_params({'pw_rigid': True})
c, dview, n_processes = cm.cluster.setup_cluster(backend='local',
n_processes=cpu_num,
single_thread=False)
# Create a motion correction object with the specified parameters
mc = MotionCorrect(mcrig.mmap_file,
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(mcrig.mmap_file)
m_pwrig = 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),
m_pwrig.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))
m_pwrig2 = (m_pwrig - np.mean(m_pwrig, axis=0))
moviehandle1 = cm.concatenate([
m_orig2.resize(1, 1, ds_ratio),
m_rig2.resize(1, 1, ds_ratio),
m_pwrig2.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=cpu_num,
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
roidir = savedir[:savedir.find('VolPy')] + 'Spikepursuit' + savedir[
savedir.find('VolPy') + len('Volpy'):]
try:
files = os.listdir(roidir)
except:
files = []
if usematlabroi and 'ROIs.mat' in files:
ROIs = loadmat(os.path.join(roidir, 'ROIs.mat'))['ROIs']
if len(np.shape(ROIs)) == 3:
ROIs = np.moveaxis(np.asarray(ROIs, bool), 2, 0)
else:
ROIs = np.asarray([ROIs])
all_rois = ROIs
opts.change_params(
params_dict={
'ROIs': ROIs,
'index': list(range(ROIs.shape[0])),
'method': 'SpikePursuit'
})
else:
#%
print('WTF')
# 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:
try:
m = cm.load(mc.mmap_file[0], subindices=slice(0, 20000))
except:
m = cm.load(
'/home/rozmar/Data/Voltage_imaging/Voltage_rig_1P/rozsam/20200120/40x_1xtube_10A_7_000_rig__d1_128_d2_512_d3_1_order_F_frames_2273_._els__d1_128_d2_512_d3_1_order_F_frames_2273_.mmap',
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'
})
#all_rois = ROIs_mrcnn
# %% Trace Denoising and Spike Extraction
c, dview, n_processes = cm.cluster.setup_cluster(
backend='local',
n_processes=cpu_num_spikepursuit,
single_thread=False,
maxtasksperchild=1)
#dview=None
vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
vpy.fit(n_processes=n_processes, dview=dview)
#%%
print('saving parameters')
parameters = dict()
parameters['motion'] = opts.motion
parameters['data'] = opts.data
parameters['volspike'] = opts.volspike
with open(os.path.join(savedir, 'parameters.pickle'), 'wb') as outfile:
pickle.dump(parameters, outfile)
#%%
volspikedata = dict()
volspikedata['estimates'] = vpy.estimates
volspikedata['params'] = vpy.params.data
with open(os.path.join(savedir, 'spikepursuit.pickle'), 'wb') as outfile:
pickle.dump(volspikedata, outfile)
#%%
for mcidx, mc_now in enumerate([mcrig, mc]):
motioncorr = dict()
motioncorr['fname'] = mc_now.fname
motioncorr['fname_tot_rig'] = mc_now.fname_tot_rig
motioncorr['mmap_file'] = mc_now.mmap_file
motioncorr['min_mov'] = mc_now.min_mov
motioncorr['shifts_rig'] = mc_now.shifts_rig
motioncorr['shifts_opencv'] = mc_now.shifts_opencv
motioncorr['niter_rig'] = mc_now.niter_rig
motioncorr['min_mov'] = mc_now.min_mov
motioncorr['templates_rig'] = mc_now.templates_rig
motioncorr['total_template_rig'] = mc_now.total_template_rig
try:
motioncorr['x_shifts_els'] = mc_now.x_shifts_els
motioncorr['y_shifts_els'] = mc_now.y_shifts_els
except:
pass
with open(
os.path.join(savedir, 'motion_corr_' + str(mcidx) + '.pickle'),
'wb') as outfile:
pickle.dump(motioncorr, outfile)
#%% saving stuff
print('moving files')
for mmap_file in mcrig.mmap_file:
fname = pathlib.Path(mmap_file).name
os.remove(mmap_file)
#shutil.move(mmap_file, os.path.join(savedir,fname))
for mmap_file in mc.mmap_file:
fname = pathlib.Path(mmap_file).name
os.remove(mmap_file)
#shutil.move(mmap_file, os.path.join(savedir,fname))
fname = pathlib.Path(fname_new).name
shutil.move(fname_new, os.path.join(savedir, fname))
#print('waiting')
#time.sleep(1000)
# %% some visualization
plotstuff = False
if plotstuff:
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)
def main():
pass # For compatibility between running under Spyder and the CLI
#%%
"""
General parameters
"""
play_movie = 1
plot_extras = 1
plot_extras_cell = 1
compute_mc_metrics = 1
#%% Select file(s) to be processed (download if not present)
"""
Load file
"""
#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])]
#fnames = ['/home/yuriy/Desktop/Data/rest1_5_9_19_cut.tif']
#f_dir = 'C:\\Users\\rylab_dataPC\\Desktop\\Yuriy\\caiman_data\\short\\'
f_dir = 'G:\\analysis\\190828-calcium_voltage\\soma_dendrites\\pCAG_jREGECO1a_ASAP3_anesth_001\\'
f_name = 'Ch1'
f_ext = 'tif'
fnames = [f_dir + f_name + '.' + f_ext]
#fnames = ['C:/Users/rylab_dataPC/Desktop/Yuriy/caiman_data/rest1_5_9_19_2_cut_ca.hdf5']
#%% First setup some parameters for data and motion correction
"""
Parameters
"""
# dataset dependent parameters
fr = 30 # imaging rate in frames per second
decay_time = 1
#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)]
max_shifts = [6, 6]
# 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)])
strides = [48, 48]
# 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
if play_movie:
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)
# type "mc."and press TAB to see all interesting associated variables and self. outputs
# interesting outputs
# saved file is mc.fname_tot_els / mc.fname_tot_rig
# mc.x_shifts_els / mc.y_shifts_els: shifts in x/y per frame per patch
# mc.coord_shifts_els: coordinates associated to patches with shifts
# mc.total_template_els: updated template for pw
# mc.total_template_rig: updated template for rigid
# mc.templates_rig: templates for each iteration in rig
#%% # compute metrics for the results (TAKES TIME!!)
if compute_mc_metrics:
# not finished
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)
final_size = np.subtract(
mc.total_template_els.shape,
2 * bord_px_els) # remove pixels in the boundaries
winsize = 100
swap_dim = False
resize_fact_flow = .2 # downsample for computing ROF
tmpl_rig, correlations_orig, flows_orig, norms_orig, crispness_orig = cm.motion_correction.compute_metrics_motion_correction(
fnames[0],
final_size[0],
final_size[1],
swap_dim,
winsize=winsize,
play_flow=False,
resize_fact_flow=resize_fact_flow)
plt.figure()
plt.plot(correlations_orig)
# %% compare with original movie
if play_movie:
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
del m_orig
del m_els
if plot_extras:
# plot total template
plt.figure()
plt.imshow(mc.total_template_els)
plt.title('Template after iteration')
# plot x and y corrections
plt.figure()
plt.plot(mc.shifts_rig)
plt.title('Rigid motion correction xy movement')
plt.legend(['x shift', 'y shift'])
plt.xlabel('frames')
# %% 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 = 2 # 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 = 2 # number of components per patch
gSig = [15, 15] # expected half size of neurons in pixels
# initialization method (if analyzing dendritic data using 'sparse_nmf')
method_init = 'greedy_roi'
ssub = 1 # spatial subsampling during initialization
tsub = 1 # 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': 0})
cnm = cnmf.CNMF(n_processes, params=opts, dview=dview)
cnm = cnm.fit(images)
if plot_extras_cell:
num_cell_plot = 51
plt.figure()
plt.plot(cnm.estimates.C[num_cell_plot, :])
plt.title('Temporal component')
plt.legend(['Cell ' + str(num_cell_plot)])
# plot component sptial profile A
# first convert back to dense components
plot_spat_A = cnm.estimates.A[:, num_cell_plot].toarray().reshape(
list(dims))
plt.figure()
plt.imshow(plot_spat_A)
plt.title('Spatial component cell ' + str(num_cell_plot))
# %% 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')
if plot_extras:
plt.figure()
plt.imshow(Cn)
plt.title('Local correlations')
# %% 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 = 0.90 # 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)
plt.suptitle('Component selection: min_SNR=' + str(min_SNR) +
'; rval_thr=' + str(rval_thr) + '; cnn prob range=[' +
str(cnn_lowest) + ' ' + str(cnn_thr) + ']')
# %% VIEW TRACES (accepted and rejected)
if plot_extras:
cnm2.estimates.view_components(images,
img=Cn,
idx=cnm2.estimates.idx_components)
plt.suptitle('Accepted')
cnm2.estimates.view_components(images,
img=Cn,
idx=cnm2.estimates.idx_components_bad)
plt.suptitle('Rejected')
#plt.figure();
#plt.plot(cnm2.estimates.YrA[0,:]+cnm2.estimates.C[0,:])
#
#
#
#
#plt.figure();
#plt.plot(cnm2.estimates.R[0,:]-cnm2.estimates.YrA[0,:]);
#plt.plot();
#plt.show();
#
#
#plt.figure();
#plt.plot(cnm2.estimates.detrend_df_f[1,:])
# these store the good and bad components, and next step sorts them
# cnm2.estimates.idx_components
# 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)
plt.suptitle("Final results")
#%% Save the mc data as in cmn struct as well
##
#mc_out = dict(
# pw_rigid = mc.pw_rigid,
# fname = mc.fname,
# mmap_file = mc.mmap_file,
# total_template_els = mc.total_template_els,
# total_template_rig = mc.total_template_rig,
# border_nan = mc.border_nan,
# border_to_0 = mc.border_to_0,
# x_shifts_els = mc.x_shifts_els,
# y_shifts_els = mc.y_shifts_els,
# Cn = Cn
# )
#
#
#deepdish.io.save(fnames[0] + '_mc_data.hdf5', mc_out)
#%% reconstruct denoised movie (press q to exit)
if play_movie:
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)
save_results = True
if save_results:
cnm2.save(fnames[0][:-4] + '_results.hdf5')
total_template_rig, 5), vmax=np.percentile(total_template_rig, 95))
#%%
pl.close()
pl.plot(shifts_rig)
#%%
m_rig = cm.load(fname_tot_rig)
#%%
add_to_movie = - np.min(total_template_rig) + 1
print(add_to_movie)
#%% visualize movies
m_rig.resize(1, 1, .2).play(
fr=20, gain=5, magnification=1, offset=add_to_movie)
#%%
downs = .2
cm.concatenate([m_rig.resize(1, 1, downs), m_orig.resize(1, 1, downs)], axis=1).play(
fr=30, gain=2, magnification=1, offset=add_to_movie)
#%% visualize templates
cm.movie(np.array(templates_rig)).play(
fr=10, gain=2, magnification=1, offset=add_to_movie)
#%% PIECEWISE RIGID MOTION CORRECTION
t1 = time.time()
new_templ = total_template_rig.copy()
strides = params_movie['strides']
overlaps = params_movie['overlaps']
shifts_opencv = False
save_movie = True
splits = params_movie['splits_els']
num_splits_to_process_list = params_movie['num_splits_to_process_els']
upsample_factor_grid = params_movie['upsample_factor_grid']
max_deviation_rigid = params_movie['max_deviation_rigid']
# newstrides = newstrides, upsample_factor_grid=upsample_factor_grid,\
# upsample_factor_fft=10,show_movie=False,max_deviation_rigid=2,add_to_movie=add_to_movie)
#
# total_shifts.append(total_shift)
# start_steps.append(start_step)
# xy_grids.append(xy_grid)
#mc = cm.load('M_FLUO_4_d1_64_d2_128_d3_1_order_F_frames_4620_.mmap')
#mc = cm.load('M_FLUO_t_d1_64_d2_128_d3_1_order_F_frames_6764_.mmap')
#%%
mc.resize(1, 1, .1).play(gain=10., fr=30, offset=100, magnification=1.)
#%%
m.resize(1, 1, .2).play(gain=10, fr=30, offset=0, magnification=1.)
#%%
cm.concatenate([mr.resize(1, 1, .5), mc.resize(1, 1, .5)],
axis=1).play(gain=10, fr=100, offset=300, magnification=1.)
#%%
import h5py
with h5py.File('sueann_pw_rigid_movie.mat') as f:
mef = np.array(f['M2'])
mef = cm.movie(mef.transpose([0, 2, 1]))
#%%
cm.concatenate(
[mef.resize(1, 1, .15), mc.resize(1, 1, .15)],
axis=1).play(gain=30, fr=40, offset=300, magnification=1.)
#%%
(mef - mc).resize(1, 1, .1).play(gain=50, fr=20, offset=0, magnification=1.)
#%%
plt.subplot(len(fls), 3, 1 + 3 * cnt)
plt.ylabel(metr)
try:
mean_img = np.mean(movie, 0)[12:-12, 12:-12]
except:
try:
mean_img = np.mean(movie, 0)[12:-12, 12:-12]
except:
mean_img = np.mean(cm.load(fl[:-12] + 'hdf5'), 0)[12:-12,
12:-12]
lq, hq = np.nanpercentile(mean_img, [.5, 99.5])
plt.imshow(mean_img, vmin=lq, vmax=hq)
plt.title('Mean')
plt.subplot(len(fls), 3, 3 * cnt + 2)
plt.imshow(ld['img_corr'], vmin=0, vmax=.35)
plt.title('Corr image')
plt.subplot(len(fls), 3, 3 * cnt + 3)
# plt.plot(ld['norms'])
# plt.xlabel('frame')
# plt.ylabel('norm opt flow')
# plt.subplot(len(fls), 3, 3 * cnt + 3)
flows = ld['flows']
mean_flow_img = np.mean(
np.sqrt(flows[:, :, :, 0]**2 + flows[:, :, :, 1]**2), 0)
plt.imshow(mean_flow_img, vmin=0, vmax=0.1)
plt.colorbar()
plt.title('Mean optical flow')
C = cm.concatenate([m_orig, m_rig, m_els], axis=2)
#C.play(fr=60)
pl.ylabel('y_shifts (pixels)')
pl.xlabel('frames')
# TODO: show screenshot 6
# %% play corrected and downsampled movie
downsample_ratio = 0.2
m_els.resize(1, 1, downsample_ratio).play(
gain=3, offset=0, fr=100, magnification=1, bord_px=bord_px_els)
# %% local correlation
_Cn = m_els.local_correlations(eight_neighbours=True, swap_dim=False)
pl.imshow(_Cn)
# TODO: show screenshot 7
# %% visualize raw, rigid and pw-rigid motion correted moviews
downsample_factor = params_display['downsample_ratio']
# TODO : todocument
cm.concatenate(
[m_orig.resize(1, 1, downsample_factor) + offset_mov, m_rig.resize(1, 1, downsample_factor), m_els.resize(
1, 1, downsample_factor)], axis=2)[700:1000].play(fr=60, gain=3, magnification=1, offset=0)
# TODO: show screenshot 8
# %% compute metrics for the results, just to check that motion correction worked properly
final_size = np.subtract(mc.total_template_els.shape, 2 * bord_px_els)
winsize = 100
swap_dim = False
resize_fact_flow = params_display['downsample_ratio']
# computationnaly intensive
# TODO: todocument
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
mc.fname_tot_els, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False,
resize_fact_flow=resize_fact_flow)
tmpl, correlations, flows_orig, norms, smoothness = cm.motion_correction.compute_metrics_motion_correction(
mc.fname_tot_rig, final_size[0], final_size[1], swap_dim, winsize=winsize, play_flow=False,
resize_fact_flow=resize_fact_flow)
import psutil
import sys
from ipyparallel import Client
from skimage.external.tifffile import TiffFile
import scipy
#%%
from caiman.motion_correction import tile_and_correct, motion_correction_piecewise
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
#%%
m = cm.load('example_movies/demoMovie.tif')
cm.concatenate([m.resize(1, 1, .2), m.resize(1, 1, .2)],
axis=1).play(fr=20, gain=3., magnification=3)
#%% set parameters and create template by RIGID MOTION CORRECTION
# params_movie = {'fname':'example_movies/Sue_2x_3000_40_-46.tif',
# 'max_shifts':(6,6), # maximum allow rigid shift
# 'splits_rig':56, # for parallelization split the movies in num_splits chuncks across time
# 'num_splits_to_process_rig':None, # if none all the splits are processed and the movie is saved
# 'strides': (48,48), # intervals at which patches are laid out for motion correction
# 'overlaps': (24,24), # overlap between pathes (size of patch strides+overlaps)
# 'splits_els':56, # for parallelization split the movies in num_splits chuncks across time
# 'num_splits_to_process_els':[28,None], # if none all the splits are processed and the movie is saved
# 'upsample_factor_grid':4, # upsample factor to avoid smearing when merging patches
# 'max_deviation_rigid':3, #maximum deviation allowed for patch with respect to rigid shift
# 'p': 1, # order of the autoregressive system
# 'merge_thresh' : 0.8, # merging threshold, max correlation allowed
# 'rf' : 15, # half-size of the patches in pixels. rf=25, patches are 50x50
# 'stride_cnmf' : 6, # amounpl.it of overlap between the patches in pixels