def extract_wrapper(ops):
for plane in range(ops[0]['nplanes']):
curr_op = ops[plane]
plane_dir = Path(
curr_op['save_path0']).joinpath(f'suite2p/plane{plane}')
plane_dir.mkdir(exist_ok=True, parents=True)
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.create_masks_and_extract(curr_op, extract_input['stat'])
op, stat, F, Fneu, F_chan2, Fneu_chan2 = extraction.create_masks_and_extract(
curr_op, extract_input['stat'], extract_input['cell_masks'],
extract_input['neuropil_masks'])
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('ops.npy'), op)
np.save(plane_dir.joinpath('stat.npy'), stat)
np.save(plane_dir.joinpath('F.npy'), F)
np.save(plane_dir.joinpath('Fneu.npy'), Fneu)
np.save(plane_dir.joinpath('F_chan2.npy'), F_chan2)
np.save(plane_dir.joinpath('Fneu_chan2.npy'), Fneu_chan2)
np.save(plane_dir.joinpath('spks.npy'), spks)
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)
def run_preprocess(f: np.ndarray, test_ops):
baseline_vals = ['maximin', 'constant', 'constant_prctile']
for bv in baseline_vals:
pre_f = extraction.preprocess(
F=f,
baseline=bv,
win_baseline=test_ops['win_baseline'],
sig_baseline=test_ops['sig_baseline'],
fs=test_ops['fs'],
prctile_baseline=test_ops['prctile_baseline'])
test_f = np.load('data/test_data/detection/{}_f.npy'.format(bv))
yield np.array_equal(pre_f, test_f)