def first_pulse_features(pair, pulse_responses, pulse_response_amps):
avg_psp = TSeriesList(pulse_responses).mean()
dt = avg_psp.dt
avg_psp_baseline = float_mode(avg_psp.data[:int(10e-3 / dt)])
avg_psp_bsub = avg_psp.copy(data=avg_psp.data - avg_psp_baseline)
lower_bound = -float('inf')
upper_bound = float('inf')
xoffset = pair.synapse_prediction.ic_fit_xoffset
if xoffset is None:
xoffset = 14 * 10e-3
synapse_type = pair.synapse_prediction.synapse_type
if synapse_type == 'ex':
amp_sign = '+'
elif synapse_type == 'in':
amp_sign = '-'
else:
raise Exception(
'Synapse type is not defined, reconsider fitting this pair %s %d->%d'
% (pair.expt_id, pair.pre_cell_id, pair.post_cell_id))
weight = np.ones(len(
avg_psp.data)) * 10. # set everything to ten initially
weight[int(10e-3 / dt):int(12e-3 / dt)] = 0. # area around stim artifact
weight[int(12e-3 / dt):int(19e-3 / dt)] = 30. # area around steep PSP rise
psp_fits = fit_psp(avg_psp,
xoffset=(xoffset, lower_bound, upper_bound),
yoffset=(avg_psp_baseline, lower_bound, upper_bound),
sign=amp_sign,
weight=weight)
amp_cv = np.std(pulse_response_amps) / np.mean(pulse_response_amps)
features = {
'ic_fit_amp': psp_fits.best_values['amp'],
'ic_fit_latency': psp_fits.best_values['xoffset'] - 10e-3,
'ic_fit_rise_time': psp_fits.best_values['rise_time'],
'ic_fit_decay_tau': psp_fits.best_values['decay_tau'],
'ic_amp_cv': amp_cv,
'avg_psp': avg_psp_bsub.data
}
#'ic_fit_NRMSE': psp_fits.nrmse()} TODO: nrmse not returned from psp_fits?
return features
name=("n = %d" % n_synapses))
rec_plot[t, c].plot(recovery_grand_trace.time_values,
recovery_grand_trace.data,
pen={
'color': color,
'width': 2
})
rec_plot[t, c].setLabels(left=('Vm', 'V'))
rec_plot[t, c].setLabels(bottom=('t', 's'))
if deconv is True:
rec_deconv = deconv_train(rec_pass_qc[:2])
rec_deconv_grand = TSeriesList(rec_deconv[0]).mean()
rec_ind_deconv_grand = TSeriesList(rec_deconv[1]).mean()
#log_rec_plt.plot(rec_deconv_grand.time_values, rec_deconv_grand.data,
# pen={'color': color, 'width': 2})
rec_deconv_ind_grand2 = rec_ind_deconv_grand.copy(t0=delta +
0.2)
#log_rec_plt.plot(rec_deconv_ind_grand2.time_values, rec_deconv_ind_grand2.data,
# pen={'color': color, 'width': 2})
[
deconv_rec_plot[t, c].plot(ind.time_values,
ind.data,
pen=trace_color)
for ind in rec_deconv[0]
]
[
deconv_rec_plot[t, c].plot(rec.time_values,
rec.data,
pen=trace_color)
for rec in rec_deconv[1]
]
deconv_rec_plot[t, c].plot(rec_ind_deconv_grand.time_values,
