event_std_thr=event_std_thr,
peri_event_dur=peri_event_dur,
corr_len_thr=corr_len_thr,
corr_abs_thr=corr_abs_thr,
corr_std_thr=corr_std_thr,
is_cosine_similarity=is_cosine_similarity,
distance_metric=distance_metric,
linkage_method=linkage_method,
distance_thr=distance_thr)
for nwb_fi, nwb_fn in enumerate(nwb_fns):
print('processing {}, {}/{}'.format(nwb_fn, nwb_fi + 1, len(nwb_fns)))
nwb_f = h5py.File(os.path.join(nwb_folder, nwb_fn), 'r')
plane_ns = dt.get_plane_ns(nwb_f=nwb_f)
plane_ns.sort()
for plane_i, plane_n in enumerate(plane_ns):
print('\n\t{}, {}/{}'.format(plane_n, plane_i + 1, len(plane_ns)))
bc.process_plane(nwb_f=nwb_f,
save_folder=save_folder,
plane_n=plane_n,
trace_type=trace_type,
trace_window=trace_window)
nwb_f.close()
def process_one_nwb_for_multi_thread(inputs):
nwb_path, df_folder, clu_folder, params, columns, save_folder, t0, nwb_i, nwb_f_num, is_overwrite = inputs
nwb_fn = os.path.splitext(os.path.split(nwb_path)[1])[0]
date, mid, _, _ = nwb_fn.split('_')
nwb_f = h5py.File(nwb_path, 'r')
plane_ns = dt.get_plane_ns(nwb_f=nwb_f)
plane_ns.sort()
for plane_n in plane_ns:
print('\tt: {:5.0f} minutes, processing {}, {} / {}, {} ...'.format(
(time.time() - t0) / 60., nwb_fn, nwb_i + 1, nwb_f_num, plane_n))
roi_df_fn = '{}_{}_{}.csv'.format(date, mid, plane_n)
roi_df = pd.read_csv(os.path.join(df_folder, roi_df_fn))
clu_fn = '{}_{}_{}_axon_grouping.hdf5'.format(date, mid, plane_n)
clu_f = h5py.File(os.path.join(clu_folder, clu_fn), 'r')
axon_ns = clu_f['axons'].keys()
axon_ns.sort()
axon_df = pd.DataFrame(np.nan,
index=range(len(axon_ns)),
columns=columns)
for axon_i, axon_n in enumerate(axon_ns):
roi_lst = clu_f['axons/{}'.format(axon_n)].value
if len(roi_lst) == 1:
curr_roi_df = roi_df[roi_df['roi_n'] ==
roi_lst[0]].reset_index()
for col in columns:
axon_df.loc[axon_i, col] = curr_roi_df.loc[0, col]
axon_df.loc[axon_i, 'roi_n'] = axon_n
else:
axon_properties, _, _, _, _, _, _, _, _, _, _, _, _, _ = \
dt.get_everything_from_axon(nwb_f=nwb_f,
clu_f=clu_f,
plane_n=plane_n,
axon_n=axon_n,
params=params,
verbose=False)
for rp_name, rp_value in axon_properties.items():
axon_df.loc[axon_i, rp_name] = rp_value
save_path = os.path.join(save_folder,
'{}_{}_{}.csv'.format(date, mid, plane_n))
if os.path.isfile(save_path):
if is_overwrite:
os.remove(save_path)
axon_df.to_csv(save_path)
else:
raise IOError(
'Axon dataframe file already exists. \npath: {}'.format(
save_path))
else:
axon_df.to_csv(save_path)