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():