def __init__(self,
files,
random_state=42,
batch_size=32,
train=True,
var_name_hdf5='mov',
subindices=None):
"""
Create a Sequence object for Ca datasets. Not used at the moment
"""
if isinstance(files, str):
files = [files]
self.files = files
dims, T = get_file_size(files, var_name_hdf5=var_name_hdf5)
if subindices is not None:
T = len(range(T)[subindices])
if isinstance(T, int):
T = [T]
self.num_examples_per_npy = T[0]
self.random_state = random_state
self.n_channels = 1
self.dim = dims
self.T = T
self.batch_size = batch_size
self.train = train
self.on_epoch_end()
self.var_name_hdf5 = var_name_hdf5
def mean_image(file_name,
Tot_frames=None,
fr: float = 10.,
window: int = 100,
dview=None):
"""
Efficient (parallel) computation of mean image in chunks
Args:
Y: str
path to movie file
Tot_frames: int
Number of total frames considered
fr: int (100)
Frame rate (optional)
window: int (100)
Window length in frames
dview: map object
Use it for parallel computation
Returns:
mm: cm.movie (2D).
mean image
"""
if Tot_frames is None:
_, Tot_frames = get_file_size(file_name)
params: List = [[file_name, range(j * window, (j + 1) * window)]
for j in range(int(Tot_frames / window))]
remain_frames = Tot_frames - int(Tot_frames / window) * window
if remain_frames > 0:
params.append([file_name, range(int(Tot_frames / window) * window, Tot_frames)])
if dview is None:
parallel_result = list(map(mean_image_parallel, params))
else:
if 'multiprocessing' in str(type(dview)):
parallel_result = dview.map_async(mean_image_parallel, params).get(4294967)
else:
parallel_result = dview.map_sync(mean_image_parallel, params)
dview.results.clear()
mm = cm.movie(np.concatenate(parallel_result, axis=0), fr=fr/len(parallel_result))
if remain_frames > 0:
mean_image = (mm[:-1].sum(axis=0) + (remain_frames / window) * mm[-1]) / (len(mm) - 1 + remain_frames / window)
else:
mean_image = mm.mean(axis=0)
return mean_image
def local_correlations_movie_offline(file_name,
Tot_frames=None,
fr: float = 10.,
window: int = 100,
stride: int = 100,
swap_dim: bool = False,
eight_neighbours: bool = True,
order_mean: int = 1,
ismulticolor: bool = False,
dview=None,
remove_baseline: bool = False,
winSize_baseline: int = 50,
quantil_min_baseline: float = 8,
gaussian_blur: bool=False):
"""
Efficient (parallel) computation of correlation image in shifting windows
with option for prior baseline removal
Args:
Y: str
path to movie file
Tot_frames: int
Number of total frames considered
fr: int (100)
Frame rate (optional)
window: int (100)
Window length in frames
stride: int (30)
Stride length in frames
swap_dim: bool (False)
True indicates that time is listed in the last axis of Y (matlab format)
and moves it in the front (default: False)
eight_neighbours: Boolean
Use 8 neighbors if true, and 4 if false for 3D data
Use 18 neighbors if true, and 6 if false for 4D data
dview: map object
Use it for parallel computation
remove_baseline: bool (False)
Flag for removing baseline prior to computation of CI
winSize_baseline: int (50)
Running window length for computing baseline
quantile_min_baseline: float (8)
Percentile used for baseline computations
gaussian_blur: bool (False)
Gaussian smooth the signal
Returns:
mm: cm.movie (3D or 4D).
local correlation movie
"""
if Tot_frames is None:
_, Tot_frames = get_file_size(file_name)
params: List = [[file_name, range(j, j + window), eight_neighbours, swap_dim,
order_mean, ismulticolor, remove_baseline, winSize_baseline,
quantil_min_baseline, gaussian_blur]
for j in range(0, Tot_frames - window, stride)]
params.append([file_name, range(Tot_frames - window, Tot_frames), eight_neighbours, swap_dim,
order_mean, ismulticolor, remove_baseline, winSize_baseline,
quantil_min_baseline, gaussian_blur])
if dview is None:
parallel_result = list(map(local_correlations_movie_parallel, params))
else:
if 'multiprocessing' in str(type(dview)):
parallel_result = dview.map_async(local_correlations_movie_parallel, params).get(4294967)
else:
parallel_result = dview.map_sync(local_correlations_movie_parallel, params)
dview.results.clear()
mm = cm.movie(np.concatenate(parallel_result, axis=0), fr=fr/len(parallel_result))
return mm