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