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 import data_transport_container dtc = data_transport_container.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)
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 import data_transport_container dtc = data_transport_container.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.sort_key is not None self.assertTrue(score.sort_key is not None)
assert seeds is not None except: for local_attrs in grid: store_glif_results[str(local_attrs.values())] = {} dtc = DataTC() dtc.tests = use_test complete_params = {} dtc.attrs = local_attrs dtc.backend = 'GLIF' dtc.cell_name = 'GLIF' for key, use_test in test_frame.items(): dtc.tests = use_test dtc = dtc_to_rheo(dtc) dtc = format_test(dtc) if dtc.rheobase is not None: if dtc.rheobase!=-1.0: dtc = nunit_evaluation(dtc) print(dtc.get_ss()) store_glif_results[str(local_attrs.values())][key] = dtc.get_ss() df = pd.DataFrame(store_glif_results) best_params = {} for index, row in df.iterrows(): best_params[index] = row == row.min() best_params[index] = best_params[index].to_dict() seeds = {} for k,v in best_params.items(): for nested_key,nested_val in v.items():