def model_to_dtc(self):
dtc = DataTC()
dtc.attrs = self.attrs
try:
dtc.backend = self.get_backend()
except:
dtc.backend = self.backend
if hasattr(self, 'rheobase'):
dtc.rheobase = self.rheobase
return dtc
def test_ap_threshold(self):
from neuronunit.models.reduced import ReducedModel
from neuronunit.optimization import get_neab
from neuronunit.tests.waveform import InjectedCurrentAPThresholdTest as T
from neuronunit.optimization.optimization_management import format_test
from neuronunit.optimization.data_transport_container import DataTC
dtc = DataTC()
dtc.rheobase = self.rheobase
dtc = format_test(dtc)
self.model = ReducedModel(get_neab.LEMS_MODEL_PATH,
backend=('NEURON', {
'DTC': dtc
}))
#score = self.run_test(T)
score = self.run_test(T, pred=self.rheobase)
assert score.norm_score is not None
self.assertTrue(score.norm_score is not None)
def generate_prediction(self,model = None):
if self.prediction is None:
dtc = DataTC()
dtc.backed = model.backend
dtc.attrs = model.attrs
dtc.rheobase = model.rheobase
dtc.tests = [self]
dtc = three_step_protocol(dtc)
dtc,ephys0 = allen_wave_predictions(dtc,thirty=True)
dtc,ephys1 = allen_wave_predictions(dtc,thirty=False)
if self.name in ephys0.keys():
feature = ephys0[self.name]
self.prediction = {}
self.prediction['value'] = feature
#self.prediction['std'] = feature
if self.name in ephys1.keys():
feature = ephys1[self.name]
self.prediction = {}
self.prediction['value'] = feature
#self.prediction['std'] = feature
return self.prediction