def run_stim(self, model: sciunit.Model, stim: float):
stim_start = 50.0 # ms
stim_dur = 500.0 # ms
stim_amp = stim # nA
model.inject_step_current_soma(current={
'delay': stim_start,
'duration': stim_dur,
'amplitude': stim_amp
})
start = timeit.default_timer()
trace = model.get_membrane_potential_soma_eFEL_format(
tstop=stim_start + stim_dur,
start=stim_start,
stop=stim_start + stim_dur)
stop = timeit.default_timer()
self.run_times[str(stim)] = stop - start
self.traces.append({
"stim": stim_amp,
"t": trace["T"],
"v": trace["V"]
})
try:
result = efel.getFeatureValues([trace], [
"Spikecount_stimint"
])[0]["Spikecount_stimint"][0] / (stim_dur * 1e-3) # (Hz)
except:
result = float("nan")
return result
def test_error_types(self):
from sciunit.errors import CapabilityError, BadParameterValueError,\
PredictionError, InvalidScoreError
from sciunit import Model, Capability
CapabilityError(Model(), Capability)
PredictionError(Model(), 'foo')
InvalidScoreError()
BadParameterValueError('x', 3)
def test_ProtocolToFeaturesTest(self):
t = ProtocolToFeaturesTest([1, 2, 3])
m = Model()
m.run = lambda: 0
self.assertIsInstance(t.generate_prediction(m), NotImplementedError)
self.assertIsInstance(t.setup_protocol(m), NotImplementedError)
self.assertIsInstance(t.get_result(m), NotImplementedError)
self.assertIsInstance(t.extract_features(m, list()),
NotImplementedError)
def test_fake_test(self):
from neuronunit.tests import FakeTest
from sciunit import Model
observation = {'a' : 1, 'b' : 1}
ft = FakeTest(observation, name="a_b_test")
m = Model(name="a_b_model")
m.attrs = {'a' : 1, 'b' : 1}
prediction = ft.generate_prediction(m)
score = ft.compute_score([0.9, 1.1], prediction).score
self.assertAlmostEqual(score, 0.09, 2)
def __init__(self, project_path, name=None, **kwargs):
"""file_path is the full path to an .ncx file."""
print("Instantiating a neuroConstruct model from %s." % project_path)
self.project_path = project_path
self.ran = False
self.population_name = self.get_cell_group()+'_0'
Model.__init__(self)
Runnable_NC.__init__(self)
ReceivesCurrent_NC.__init__(self)
for key,value in kwargs.items():
setattr(self,key,value)
super().__init__(name=name, **kwargs)
def test_error_types(self):
from sciunit.errors import (
CapabilityError,
BadParameterValueError,
PredictionError,
InvalidScoreError,
)
from sciunit import Model, Capability
CapabilityError(Model(), Capability)
CapabilityError(Model(), Capability, "this is a test detail")
PredictionError(Model(), "foo")
InvalidScoreError()
BadParameterValueError("x", 3)
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
def test_Test(self):
pv = config["PREVALIDATE"]
config["PREVALIDATE"] = 1
with self.assertRaises(ObservationError):
t = Test(None)
with self.assertRaises(ObservationError):
class Test2(Test):
observation_schema = None
score_type = ZScore
units = pq.pA
def generate_prediction(self):
return 1
t = Test2({"mean": 5 * pq.pA})
t = Test({})
self.assertRaises(ObservationError, t.validate_observation,
"I am not an observation")
t.observation_schema = {}
t.validate_observation({0: 0, 1: 1})
Test.observation_schema = [{}, {}]
self.assertListEqual(t.observation_schema_names(),
["Schema 1", "Schema 2"])
config["PREVALIDATE"] = pv
self.assertRaises(ParametersError, t.validate_params, None)
self.assertRaises(ParametersError, t.validate_params,
"I am not an observation")
t.params_schema = {}
t.validate_params({0: 1, 1: 2})
self.assertRaises(Error, t.check_capabilities, "I am not a model")
t.condition_model(Model())
self.assertRaises(NotImplementedError, t.generate_prediction, Model())
self.assertRaises(NotImplementedError, t.optimize, Model())
self.assertTrue(t.compute_score({0: 2, 1: 2}, {0: 2, 1: 2}).score)
self.assertFalse(t.compute_score({0: -2, 1: 2}, {0: 2, 1: -2}).score)
t.score_type = None
self.assertRaises(NotImplementedError, t.compute_score, {}, {})
t.score_type = BooleanScore
self.assertRaises(InvalidScoreError, t.check_score_type,
FloatScore(0.5))
def test_backends_init_caches(self):
myModel = Model()
backend = Backend()
backend.model = myModel
backend.init_backend(use_disk_cache=True, use_memory_cache=True)
backend.init_backend(use_disk_cache=False, use_memory_cache=True)
backend.init_backend(use_disk_cache=True, use_memory_cache=False)
backend.init_backend(use_disk_cache=False, use_memory_cache=False)
backend.init_cache()
def test_backends_set_caches(self):
myModel = Model()
backend = Backend()
backend.model = myModel
# backend.init_memory_cache()
self.assertIsNone(backend.get_disk_cache("key1"))
self.assertIsNone(backend.get_disk_cache("key2"))
self.assertIsNone(backend.get_memory_cache("key1"))
self.assertIsNone(backend.get_memory_cache("key2"))
backend.set_disk_cache("value1", "key1")
backend.set_memory_cache("value1", "key1")
self.assertEqual(backend.get_memory_cache("key1"), "value1")
self.assertEqual(backend.get_disk_cache("key1"), "value1")
backend.set_disk_cache("value2")
backend.set_memory_cache("value2")
self.assertEqual(backend.get_memory_cache(myModel.hash()), "value2")
self.assertEqual(backend.get_disk_cache(myModel.hash()), "value2")
backend.load_model()
backend.set_attrs(test_attribute="test attribute")
backend.set_run_params(test_param="test parameter")
backend.init_backend(use_disk_cache=True, use_memory_cache=True)
def run_stim(self, model: sciunit.Model, stim: float):
stim_start = 50.0 # ms
stim_dur = (60 * 1000) - 50.0 # ms
stim_amp = stim # nA
model.inject_step_current_soma(current={
'delay': stim_start,
'duration': stim_dur,
'amplitude': stim_amp
})
start = timeit.default_timer()
trace = model.get_membrane_potential_soma_eFEL_format(
tstop=stim_start + stim_dur,
start=stim_start,
stop=stim_start + stim_dur)
stop = timeit.default_timer()
run_time = stop - start
self.traces.append({
"stim": stim_amp,
"t": trace["T"],
"v": trace["V"]
})
return run_time
def test_Test(self):
config_set("PREVALIDATE", True)
with self.assertRaises(ObservationError):
t = Test(None)
config_set("PREVALIDATE", False)
t = Test(None)
self.assertRaises(ObservationError, t.validate_observation, None)
self.assertRaises(ObservationError, t.validate_observation,
"I am not an observation")
self.assertRaises(ObservationError, t.validate_observation,
{"mean": None})
t = Test([0, 1])
t.observation_schema = {}
t.validate_observation({0: 0, 1: 1})
Test.observation_schema = [{}, {}]
self.assertListEqual(t.observation_schema_names(),
["Schema 1", "Schema 2"])
self.assertRaises(ParametersError, t.validate_params, None)
self.assertRaises(ParametersError, t.validate_params,
"I am not an observation")
t.params_schema = {}
t.validate_params({0: 1, 1: 2})
self.assertRaises(Error, t.check_capabilities, "I am not a model")
t.condition_model(Model())
self.assertRaises(NotImplementedError, t.generate_prediction, Model())
self.assertRaises(NotImplementedError, t.optimize, Model())
self.assertTrue(t.compute_score({0: 2, 1: 2}, {0: 2, 1: 2}).score)
self.assertFalse(t.compute_score({0: -2, 1: 2}, {0: 2, 1: -2}).score)
t.score_type = None
self.assertRaises(NotImplementedError, t.compute_score, {}, {})
t.score_type = BooleanScore
self.assertRaises(InvalidScoreError, t.check_score_type,
FloatScore(0.5))
self.assertRaises(ObservationError, t.judge, [Model(), Model()])
def test_is_match(self):
from sciunit import Model
m = Model()
m2 = Model()
self.assertFalse(m.is_match(m2))
self.assertTrue(m.is_match(m))
self.assertTrue(m.is_match("Model"))
def test_get_model_state(self):
from sciunit import Model
m = Model()
state = m.__getstate__()
self.assertEqual(m.__dict__, state)
def generate_prediction(self, model: sciunit.Model, verbose: bool = False) -> float:
trace = model.get_soma_membrane_potential(tstop=50.0)
prediction = numpy.median(trace[1])
return prediction
def test_getattr(self):
from sciunit import Model
m = Model()
self.assertEqual(m.name, m.__getattr__("name"))
def test_get_model_state(self):
from sciunit import Model
m = Model()
state = m.__getstate__()
self.assertEqual(m.__dict__,state)
class MyBackend(Backend):
model = Model()
def _backend_run(self) -> str:
return "test result"
