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'],
z_thr_rel=analysis_params['rf_z_thr_rel'])
elif response_dir == 'neg':
_, _, _, _, _, srf_on, srf_off, _, _, _, _, _, _, \
_ = dt.get_everything_from_axon(nwb_f=nwb_f,
clu_f=clu_f,
plane_n=plane_n,
curr_strf = dt.get_strf(nwb_f=nwb_f,
plane_n=roi_row['plane_n'],
roi_ind=int(roi_row['roi_n'][-4:]),
trace_type='sta_'+sheet_name)
curr_strf_dff = curr_strf.get_local_dff_strf(is_collaps_before_normalize=True,
add_to_trace=add_to_trace)
curr_srf, _ = curr_strf_dff.get_zscore_receptive_field(timeWindow=response_window)
if response_dir == 'pos':
polarity = 'positive'
elif response_dir == 'neg':
polarity = 'negative'
else:
raise ValueError('polarity ({}) should be either "pos" or "neg".')
_, _, _, rf_mask = dt.get_rf_properties(srf=curr_srf,
polarity=polarity,
sigma=gaussian_filter_sigma,
interpolate_rate=interpolate_rate,
z_thr=rf_peak_z_thr)
roi_grp = local_grp.create_group(name=roi_row['roi_n'])
ia.WeightedROI(mask=rf_mask).to_h5_group(roi_grp)
if 'alts_deg' not in local_grp.attrs:
local_grp.attrs['alts_deg'] = curr_srf.altPos
if 'azis_deg' not in local_grp.attrs:
local_grp.attrs['alts_deg'] = curr_srf.aziPos
else:
print('\t\t\tAlready exists. Skip.')
# ====================================OFF========================================================
localdfoff = localdf[localdf['rf_{}_off_peak_z'.format(response_dir)] >= rf_peak_z_thr].reset_index()
print('\n\tnumber of rois with significant OFF RFs: {}'.format(len(localdfoff)))