def get_neuron_criteria(cell_id,file_name=None,observation=None):
from neuronunit import tests as nu_tests, neuroelectro
from neuronunit.tests import passive, waveform, fi
if observation == None:
observation = fi.RheobaseTest.neuroelectro_summary_observation(cell_id)
#observation = neuroelectro.neuroelectro_summary_observation(cell_id)
#
obs_list = list(observation.values())
tests = []
#tests += [fi.RheobaseTestP(observation=observation)]
test_class_params = [(fi.RheobaseTestP,None),
(passive.InputResistanceTest,None),
(passive.TimeConstantTest,None),
(passive.CapacitanceTest,None),
(passive.RestingPotentialTest,None),
(waveform.InjectedCurrentAPWidthTest,None),
(waveform.InjectedCurrentAPAmplitudeTest,None),
(waveform.InjectedCurrentAPThresholdTest,None)]#,
observations = []
for index, unpack in enumerate(test_class_params):
cls, params = unpack
#observations.append(cls.neuroelectro_summary_observation(cell_id))
tests += [cls(obs_list[index])]
hooks = {tests[0]:{'f':update_amplitude}} #This is a trick to dynamically insert the method
#update amplitude at the location in sciunit thats its passed to, without any loss of generality.
suite = sciunit.TestSuite("vm_suite",tests)#
if file_name is not None:
file_name = file_name +'.p'
with open(file_name, 'wb') as f:
pickle.dump(tests, f)
return tests,observations
def get_neuron_criteria(cell_id, file_name=None): #,observation = None):
# Use neuroelectro experimental obsevations to find test
# criterion that will be used to inform scientific unit testing.
# some times observations are not sourced from neuroelectro,
# but they are imputated or borrowed from other TestSuite
# if that happens make test objections using observations external
# to this method, and provided as a method argument.
tests = []
observations = None
test_classes = None
test_classes = [
fi.RheobaseTest, passive.InputResistanceTest, passive.TimeConstantTest,
passive.CapacitanceTest, passive.RestingPotentialTest,
waveform.InjectedCurrentAPWidthTest,
waveform.InjectedCurrentAPAmplitudeTest,
waveform.InjectedCurrentAPThresholdTest
] #,
observations = {}
for index, t in enumerate(test_classes):
obs = t.neuroelectro_summary_observation(cell_id)
if obs is not None:
if 'mean' in obs.keys():
tests.append(t(obs))
observations[t.ephysprop_name] = obs
#hooks = {tests[0]:{'f':update_amplitude}} #This is a trick to dynamically insert the method
#update amplitude at the location in sciunit thats its passed to, without any loss of generality.
suite = sciunit.TestSuite(tests, name="vm_suite")
if file_name is not None:
file_name = file_name + '.p'
with open(file_name, 'wb') as f:
pickle.dump(tests, f)
return tests, observations
def get_tests():
# get neuronunit tests
# and select out Rheobase test and input resistance test
# and less about electrophysiology of the membrane.
# We are more interested in phenomonogical properties.
electro_path = str(os.getcwd()) + '/pipe_tests.p'
assert os.path.isfile(electro_path) == True
with open(electro_path, 'rb') as f:
electro_tests = pickle.load(f)
electro_tests = replace_zero_std(electro_tests)
electro_tests = substitute_parallel_for_serial(electro_tests)
test, observation = electro_tests[0]
tests = copy.copy(electro_tests[0][0])
suite = sciunit.TestSuite(tests)
#tests_ = tests[0:2]
return tests, test, observation, suite
observation = cls.neuroelectro_summary_observation(neuron)
tests += [cls(observation)]
#with open(ne_pickle, 'wb') as f:
# pickle.dump(tests, f)
def update_amplitude(test, tests, score):
rheoXbase = score.prediction['value'] #first find a value for rheobase
#then proceed with other optimizing other parameters.
#for i in
for i in [4, 5, 6]:
# Set current injection to just suprathreshold
tests[i].params['injected_square_current'][
'amplitude'] = rheobase * 1.01
#tests[i].proceed=tests[i].sanity_check(rh_value=rheobase*1.01)
#pdb.set_trace()
#Don't do the rheobase test. This is a serial bottle neck that must occur before any parallel optomization.
#Its because the optimization routine must have apriori knowledge of what suprathreshold current injection values are for each model.
hooks = {
tests[0]: {
'f': update_amplitude
}
} #This is a trick to dynamically insert the method
#update amplitude at the location in sciunit thats its passed to, without any loss of generality.
suite = sciunit.TestSuite("vm_suite", tests) #,hooks=hooks)
brain_area = 'cerebellum'
neuron_type = 'cerebellar_granule_cell'
path = os.path.join(OSB_MODELS,brain_area,neuron_type)
neurolex_id = 'nifext_128' # Cerebellar Granule Cell
# Specify reference data for this test.
reference_data = neuroelectro.NeuroElectroSummary(
neuron = {'nlex_id':neurolex_id}, # Neuron type.
ephysprop = {'name':'Resting Membrane Potential'}) # Electrophysiological property name.
# Get and verify summary data for the combination above from neuroelectro.org.
if reference_data.get_values() is None:
raise ValueError("Unable to get the reference data from NeuroElectro.org.")
test = tests.RestingPotentialTest(
observation = {'mean':reference_data.mean,
'std':reference_data.std})
suite = sciunit.TestSuite('Resting Potential',test)
if not os.path.isdir(path):
raise IOError('No such path: %s' % path)
model_names = os.listdir(path)
models = []
for model_name in model_names:
print(model_name)
if model_name in ['GranCellRothmanIf']:
continue
model_info = (brain_area,neuron_type,model_name)
model = nc_models.OSBModel(*model_info)
models.append(model)
# (1) Check capabilities,
# (2) take the test,