(subdf['rf_{}_off_peak_z'.format(response_dir)] >= analysis_params['rf_z_thr_abs'])].reset_index()
if len(s2_df) > 0:
s2_grp = save_f.create_group('{}_{}_{}_{}_ONOFF'.format(date, mid, plane_n, response_dir))
s1_on_grp = save_f.create_group('{}_{}_{}_{}_ON'.format(date, mid, plane_n, response_dir))
s1_off_grp = save_f.create_group('{}_{}_{}_{}_OFF'.format(date, mid, plane_n, response_dir))
for roi_i, roi_row in s2_df.iterrows():
print('\t s2 receptive fields, {}, {} / {} ...'.format(roi_row['roi_n'], roi_i + 1, len(s2_df)))
if response_dir == 'pos':
_, _, _, srf_on, srf_off, _, _, _, _, _, _, _, _, \
_ = dt.get_everything_from_axon(nwb_f=nwb_f,
clu_f=clu_f,
plane_n=plane_n,
axon_n=roi_row['roi_n'],
params=analysis_params)
_, rf_on_new = dt.get_rf_properties(srf=srf_on,
polarity='positive',
sigma=analysis_params['gaussian_filter_sigma_rf'],
interpolate_rate=analysis_params['interpolate_rate_rf'],
z_thr_abs=analysis_params['rf_z_thr_abs'],
z_thr_rel=analysis_params['rf_z_thr_rel'])
_, rf_off_new = dt.get_rf_properties(srf=srf_off,
polarity='positive',
sigma=analysis_params['gaussian_filter_sigma_rf'],
interpolate_rate=analysis_params['interpolate_rate_rf'],
z_thr_abs=analysis_params['rf_z_thr_abs'],
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)