def generate_prediction(self, model: sciunit.Model, verbose: bool = False) -> float: efel.reset() stim_start = 10.0 # ms stim_dur = 50.0 # ms stim_amp = -1.0 # nA efel.setDoubleSetting('stimulus_current', stim_amp) model.inject_soma_square_current(current={'delay':stim_start, 'duration':stim_dur, 'amplitude':stim_amp}) trace = model.get_soma_membrane_potential_eFEL_format(tstop=stim_start+stim_dur+stim_start, start=stim_start, stop =stim_start+stim_dur) prediction = efel.getFeatureValues([trace], ['ohmic_input_resistance_vb_ssse'])[0]["ohmic_input_resistance_vb_ssse"][0] return prediction
def generate_prediction(self, model: sciunit.Model, verbose: bool = False) -> Optional[float]: efel.reset() stim_start = 10.0 # ms stim_dur = 5.0 # ms stim_amp = 15.0 # nA efel.setDoubleSetting('stimulus_current', stim_amp) model.inject_soma_square_current(current={'delay':stim_start, 'duration':stim_dur, 'amplitude':stim_amp}) trace = model.get_soma_membrane_potential_eFEL_format(tstop=stim_start+stim_dur+stim_start, start=stim_start, stop =stim_start+stim_dur) output = efel.getFeatureValues([trace], ['AP_duration_half_width'])[0]["AP_duration_half_width"] prediction = output[0] if output else None return prediction