def process_one_nwb_for_multi_thread(inputs):
nwb_path, params, columns, save_folder, t0 = inputs
nwb_fn = os.path.splitext(os.path.split(nwb_path)[1])[0]
save_path = os.path.join(save_folder, nwb_fn + '.xlsx')
if os.path.isfile(save_path):
print('\tt: {:5.0f} minutes, {} , excel table already exists, skip.'.
format((time.time() - t0) / 60., nwb_fn))
return None
else:
nwb_f = h5py.File(nwb_path, 'r')
plane_ns = [
k for k in nwb_f['processing'].keys()
if k[0:16] == 'rois_and_traces_'
]
plane_ns = [k[16:] for k in plane_ns]
plane_ns.sort()
# print('total plane number: {}'.format(len(plane_ns)))
total_roi_num = 10000
big_array = np.zeros((total_roi_num, len(columns)), dtype=np.float64)
big_array[:] = np.nan
df = pd.DataFrame(data=big_array, columns=columns)
curr_row_ind = 0
for plane_n in plane_ns:
print('\tt: {:5.0f} minutes, {} / {}, processing ...'.format(
(time.time() - t0) / 60., nwb_fn, plane_n))
roi_ns = nwb_f[
'processing/rois_and_traces_{}/ImageSegmentation/imaging_plane/roi_list'
.format(plane_n)].value
roi_ns = [r.encode('utf-8') for r in roi_ns if r[0:4] == 'roi_']
roi_ns.sort()
for roi_i, roi_n in enumerate(roi_ns):
# print('\t\t\troi: {} / {}'.format(roi_i+1, len(roi_ns)))
roi_properties, _, _, _, _, _, _, _, _, _, _, _, _, _ = \
dt.get_everything_from_roi(nwb_f=nwb_f, plane_n=plane_n, roi_n=roi_n, params=params)
for rp_name, rp_value in roi_properties.items():
df.loc[curr_row_ind, rp_name] = rp_value
curr_row_ind += 1
df = df[0:curr_row_ind]
save_path = os.path.join(save_folder, nwb_fn + '.xlsx')
if os.path.isfile(save_path):
os.remove(save_path)
with pd.ExcelWriter(save_path, mode='w') as writer:
df.to_excel(writer, sheet_name='sheet1')
def process_one_nwb_for_multi_thread(inputs):
nwb_path, 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]
nwb_f = h5py.File(nwb_path, 'r')
plane_ns = [
k for k in nwb_f['processing'].keys() if k[0:16] == 'rois_and_traces_'
]
plane_ns = [k[16:] for k in plane_ns]
plane_ns.sort()
# print('total plane number: {}'.format(len(plane_ns)))
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))
save_fn = '_'.join(nwb_fn.split('_')[0:2]) + '_' + plane_n + '.xlsx'
save_path = os.path.join(save_folder, save_fn)
if os.path.isfile(save_path):
if is_overwrite: # overwrite existing xlsx files
print('\t{}, file already exists. Overwirite.'.format(
os.path.split(save_path)[1]))
os.remove(save_path)
else: # do not overwrite existing xlsx files
print('\t{}, file already exists. Skip.'.format(
os.path.split(save_path)[1]))
return
roi_ns = nwb_f[
'processing/rois_and_traces_{}/ImageSegmentation/imaging_plane/roi_list'
.format(plane_n)].value
roi_ns = [r.encode('utf-8') for r in roi_ns if r[0:4] == 'roi_']
roi_ns.sort()
df = pd.DataFrame(np.nan, index=range(len(roi_ns)), columns=columns)
for roi_i, roi_n in enumerate(roi_ns):
# print('\t\t\troi: {} / {}'.format(roi_i+1, len(roi_ns)))
roi_properties, _, _, _, _, _, _, _, _, _, _, _, _, _ = \
dt.get_everything_from_roi(nwb_f=nwb_f, plane_n=plane_n, roi_n=roi_n, params=params)
for rp_name, rp_value in roi_properties.items():
df.loc[roi_i, rp_name] = rp_value
with pd.ExcelWriter(save_path, mode='w') as writer:
df.to_excel(writer, sheet_name='sheet1')
# positive spatial receptive fields
srf_on, srf_off = strf_dff.get_zscore_receptive_field(timeWindow=[0., 0.5])
srf = srf_on.gaussian_filter(sigma=1.)
srf = srf.interpolate(ratio=10.)
print(np.max(srf.weights))
f_o = h5py.File(fn_original, 'r')
strf_o = sca.SpatialTemporalReceptiveField.from_h5_group(
f_o['analysis/STRFs/plane0/strf_roi_{:04d}'.format(roi_ind)])
print_peak_z(strf_o)
# print(strf_o.data['traces'][0][5])
# print(strf_o.time)
f_o.close()
f_r = h5py.File(os.path.join('repacked', fn_repacked), 'r')
strf_r = dt.get_strf(f_r,
plane_n='plane0',
roi_ind=0,
trace_type='sta_f_center_subtracted')
print_peak_z(strf_r)
# print(strf_r.data['traces'][0][5])
# print(strf_r.time)
roi_properties, _, _, _, _, _, _, _, _, _, _, _, _, _ = \
dt.get_everything_from_roi(nwb_f=f_r, plane_n='plane0', roi_n=roi_n, params=params)
print(roi_properties['rf_pos_on_peak_z'])
f_r.close()
s2_grp = save_f.create_group(table_fn[0:-5] +
'_{}_ONOFF'.format(response_dir))
s1_on_grp = save_f.create_group(table_fn[0:-5] +
'_{}_ON'.format(response_dir))
s1_off_grp = save_f.create_group(table_fn[0:-5] +
'_{}_OFF'.format(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_roi(nwb_f=nwb_f,
plane_n=plane_n,
roi_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'],