def prepare_for_extraction(op, input_file_name_list, dimensions):
"""
Prepares for extraction by filling out necessary ops parameters. Removes dependence on
other modules. Creates pre_registered binary file.
"""
# Set appropriate ops parameters
op['Lx'], op['Ly'] = dimensions
op['nframes'] = 500 // op['nplanes'] // op['nchannels']
op['frames_per_file'] = 500 // op['nplanes'] // op['nchannels']
op['xrange'], op['yrange'] = [[2, 402], [2, 358]]
ops = []
for plane in range(op['nplanes']):
curr_op = op.copy()
plane_dir = utils.get_plane_dir(op, plane)
bin_path = utils.write_data_to_binary(
str(plane_dir.joinpath('data.bin')),
str(input_file_name_list[plane][0]))
curr_op['meanImg'] = np.reshape(
np.load(str(input_file_name_list[plane][0])),
(-1, op['Ly'], op['Lx'])).mean(axis=0)
curr_op['reg_file'] = bin_path
if plane == 1: # Second plane result has different crop.
curr_op['xrange'], curr_op['yrange'] = [[1, 403], [1, 359]]
if curr_op['nchannels'] == 2:
bin2_path = utils.write_data_to_binary(
str(plane_dir.joinpath('data_chan2.bin')),
str(input_file_name_list[plane][1]))
curr_op['reg_file_chan2'] = bin2_path
curr_op['save_path'] = plane_dir
curr_op['ops_path'] = plane_dir.joinpath('ops.npy')
ops.append(curr_op)
return ops
def prepare_for_detection(op, input_file_name_list, dimensions):
"""
Prepares for detection by filling out necessary ops parameters. Removes dependence on
other modules. Creates pre_registered binary file.
"""
# Set appropriate ops parameters
op.update({
'Lx': dimensions[0],
'Ly': dimensions[1],
'nframes': 500 // op['nplanes'] // op['nchannels'],
'frames_per_file': 500 // op['nplanes'] // op['nchannels'],
'xrange': [2, 402],
'yrange': [2, 358],
})
ops = []
for plane in range(op['nplanes']):
curr_op = op.copy()
plane_dir = utils.get_plane_dir(save_path0=op['save_path0'], plane=plane)
bin_path = str(plane_dir.joinpath('data.bin'))
BinaryFile.convert_numpy_file_to_suite2p_binary(str(input_file_name_list[plane][0]), bin_path)
curr_op['meanImg'] = np.reshape(
np.load(str(input_file_name_list[plane][0])), (-1, op['Ly'], op['Lx'])
).mean(axis=0)
curr_op['reg_file'] = bin_path
if plane == 1: # Second plane result has different crop.
curr_op['xrange'] = [1, 403]
curr_op['yrange'] = [1, 359]
if curr_op['nchannels'] == 2:
bin2_path = str(plane_dir.joinpath('data_chan2.bin'))
BinaryFile.convert_numpy_file_to_suite2p_binary(str(input_file_name_list[plane][1]), bin2_path)
curr_op['reg_file_chan2'] = bin2_path
curr_op['save_path'] = plane_dir
curr_op['ops_path'] = plane_dir.joinpath('ops.npy')
ops.append(curr_op)
return ops
def extract_wrapper(ops):
for plane in range(ops[0]['nplanes']):
curr_op = ops[plane]
plane_dir = utils.get_plane_dir(curr_op, plane)
extract_input = np.load(curr_op['data_path'][0].joinpath(
'detection',
'detect_output_{0}p{1}c{2}.npy'.format(curr_op['nplanes'],
curr_op['nchannels'],
plane)),
allow_pickle=True)[()]
extraction.extract(curr_op, extract_input['cell_pix'],
extract_input['cell_masks'],
extract_input['neuropil_masks'],
extract_input['stat'])
F = np.load(plane_dir.joinpath('F.npy'))
Fneu = np.load(plane_dir.joinpath('Fneu.npy'))
dF = F - curr_op['neucoeff'] * Fneu
dF = extraction.preprocess(
F=dF,
baseline=curr_op['baseline'],
win_baseline=curr_op['win_baseline'],
sig_baseline=curr_op['sig_baseline'],
fs=curr_op['fs'],
prctile_baseline=curr_op['prctile_baseline'])
spks = extraction.oasis(F=dF,
batch_size=curr_op['batch_size'],
tau=curr_op['tau'],
fs=curr_op['fs'])
np.save(plane_dir.joinpath('spks.npy'), spks)