def test_run_feature_no_time(self):
parallel = Parallel(
model=TestingModel1d(),
features=TestingFeatures(features_to_run="feature_no_time"))
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_run_neuron_model(self):
path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"models/interneuron_modelDB/")
model = NeuronModel(path=path, adaptive=True)
parallel = Parallel(model=model)
model_parameters = {"cap": 1.1, "Rm": 22000}
with Xvfb() as xvfb:
parallel.run(model_parameters)
def test_run_adaptive(self):
parallel = Parallel(
model=TestingModelAdaptive(),
features=TestingFeatures(features_to_run="feature_adaptive"))
results = parallel.run(self.model_parameters)
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["TestingModelAdaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
self.assertTrue(
np.array_equal(results["feature_adaptive"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["feature_adaptive"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["feature_adaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_postprocess_error_value(self):
parallel = Parallel(model=PostprocessErrorValue())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_run_model_no_time(self):
parallel = Parallel(model=TestingModelNoTime())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
class TestParallel(unittest.TestCase):
def setUp(self):
self.output_test_dir = ".tests/"
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
os.makedirs(self.output_test_dir)
self.features = TestingFeatures(features_to_run=[
"feature0d", "feature1d", "feature2d", "feature_invalid",
"feature_adaptive"
])
self.parallel = Parallel(model=TestingModel1d(),
features=self.features)
self.model_parameters = {"a": 0, "b": 1}
self.t = np.arange(0, 10)
self.values = np.arange(0, 10) + 1
def tearDown(self):
if os.path.isdir(self.output_test_dir):
shutil.rmtree(self.output_test_dir)
def test_init(self):
Parallel(TestingModel1d())
def test_feature(self):
self.parallel.features = Features
self.assertIsInstance(self.parallel.features, Features)
def test_feature_function(self):
def feature_function(time, values):
return "time", "values"
self.parallel.features = feature_function
self.assertIsInstance(self.parallel.features, Features)
time, values = self.parallel.features.feature_function(None, None)
self.assertEqual(time, "time")
self.assertEqual(values, "values")
self.assertEqual(self.parallel.features.features_to_run,
["feature_function"])
def test_feature_functions(self):
def feature_function(time, values):
return "time", "values"
def feature_function2(time, values):
return "time2", "values2"
self.parallel.features = [feature_function, feature_function2]
self.assertIsInstance(self.parallel.features, Features)
time, values = self.parallel.features.feature_function(None, None)
self.assertEqual(time, "time")
self.assertEqual(values, "values")
time, values = self.parallel.features.feature_function(None, None)
self.assertEqual(time, "time")
self.assertEqual(values, "values")
time, values = self.parallel.features.feature_function2(None, None)
self.assertEqual(time, "time2")
self.assertEqual(values, "values2")
self.assertEqual(self.parallel.features.features_to_run,
["feature_function", "feature_function2"])
def test_model(self):
self.parallel.model = model_function
self.assertIsInstance(self.parallel.model, Model)
self.assertEqual(self.parallel.model.name, "model_function")
# def test_sort_features(self):
# results = {"TestingModel1d": {"values": np.arange(0, 10) + 1,
# "time": np.arange(0, 10)},
# "feature1d": {"values": np.arange(0, 10),
# "time": np.arange(0, 10)},
# "feature0d": {"values": 1,
# "time": np.nan},
# "feature2d": {"values": np.array([np.arange(0, 10),
# np.arange(0, 10)]),
# "time": np.arange(0, 10)},
# "feature_adaptive": {"values": np.arange(0, 10) + 1,
# "time": np.arange(0, 10),
# "interpolation": "interpolation object"},
# "feature_invalid": {"values": np.nan,
# "time": np.nan}}
# features_0d, features_1d, features_2d = self.parallel.sort_features(results)
# self.assertEqual(features_0d, ["feature0d", "feature_invalid"])
# self.assertEqual(set(features_1d),
# set(["feature1d", "TestingModel1d", "feature_adaptive"]))
# self.assertEqual(features_2d, ["feature2d"])
def test_create_interpolations(self):
results = {
"TestingModel1d": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
},
"feature1d": {
"values": np.arange(0, 10),
"time": np.arange(0, 10)
},
"feature0d": {
"values": 1,
"time": np.nan
},
"feature2d": {
"values": np.array([np.arange(0, 10),
np.arange(0, 10)]),
"time": np.arange(0, 10)
},
"feature_adaptive": {
"values": np.arange(0, 10) + 1,
"time": np.arange(0, 10)
},
"feature_invalid": {
"values": np.nan,
"time": np.nan
}
}
results = self.parallel.create_interpolations(results)
self.assertTrue(
np.array_equal(results["TestingModel1d"]["time"], np.arange(0,
10)))
self.assertTrue(
np.array_equal(results["TestingModel1d"]["values"],
np.arange(0, 10) + 1))
self.assertTrue(
np.array_equal(results["feature1d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature1d"]["values"], np.arange(0, 10)))
self.assertTrue(np.isnan(results["feature0d"]["time"]))
self.assertEqual(results["feature0d"]["values"], 1)
self.assertTrue(
np.array_equal(results["feature2d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature2d"]["values"],
np.array([np.arange(0, 10),
np.arange(0, 10)])))
self.assertTrue(np.isnan(results["feature_invalid"]["time"]))
self.assertTrue(np.isnan(results["feature_invalid"]["values"]))
self.assertTrue(
np.array_equal(results["feature_adaptive"]["time"],
np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature_adaptive"]["values"],
np.arange(0, 10) + 1))
self.assertIsInstance(results["feature_adaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_create_interpolations_feature_1d_no_t(self):
results = {
"feature_adaptive": {
"values": np.arange(0, 10),
"time": np.nan
}
}
with self.assertRaises(AttributeError):
self.parallel.create_interpolations(results)
def test_create_interpolations_feature_0d(self):
results = {"feature_adaptive": {"values": 1, "time": np.arange(0, 10)}}
with self.assertRaises(AttributeError):
self.parallel.create_interpolations(results)
def test_create_interpolations_feature_2d(self):
results = {
"feature_adaptive": {
"values": np.array([np.arange(0, 10),
np.arange(0, 10)]),
"time": np.arange(0, 10)
}
}
with self.assertRaises(NotImplementedError):
self.parallel.create_interpolations(results)
def test_create_interpolations_model_0d(self):
self.parallel.model.adaptive = True
results = {"TestingModel1d": {"values": 1, "time": np.arange(0, 10)}}
with self.assertRaises(AttributeError):
self.parallel.create_interpolations(results)
def test_create_interpolations_model_2d(self):
self.parallel.model.adaptive = True
results = {
"TestingModel1d": {
"values": np.array([np.arange(0, 10),
np.arange(0, 10)]),
"time": np.arange(0, 10)
}
}
with self.assertRaises(NotImplementedError):
self.parallel.create_interpolations(results)
def test_run(self):
results = self.parallel.run(self.model_parameters)
self.assertTrue(self.parallel.features.is_preprocess_run)
self.assertTrue(
np.array_equal(results["TestingModel1d"]["time"], np.arange(0,
10)))
self.assertTrue(
np.array_equal(results["TestingModel1d"]["values"],
np.arange(0, 10) + 1))
self.assertTrue(
np.array_equal(results["feature1d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature1d"]["values"], np.arange(0, 10)))
self.assertTrue(np.isnan(results["feature0d"]["time"]))
self.assertEqual(results["feature0d"]["values"], 1)
self.assertTrue(
np.array_equal(results["feature2d"]["time"], np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature2d"]["values"],
np.array([np.arange(0, 10),
np.arange(0, 10)])))
self.assertTrue(np.isnan(results["feature_invalid"]["time"]))
self.assertTrue(np.isnan(results["feature_invalid"]["values"]))
self.assertTrue(
np.array_equal(results["feature_adaptive"]["time"],
np.arange(0, 10)))
self.assertTrue(
np.array_equal(results["feature_adaptive"]["values"],
np.arange(0, 10) + 1))
self.assertIsInstance(results["feature_adaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_run_adaptive(self):
parallel = Parallel(
model=TestingModelAdaptive(),
features=TestingFeatures(features_to_run="feature_adaptive"))
results = parallel.run(self.model_parameters)
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["TestingModelAdaptive"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["TestingModelAdaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
self.assertTrue(
np.array_equal(results["feature_adaptive"]["time"],
np.arange(0, 11)))
self.assertTrue(
np.array_equal(results["feature_adaptive"]["values"],
np.arange(0, 11) + 1))
self.assertIsInstance(results["feature_adaptive"]["interpolation"],
scipy.interpolate.fitpack2.UnivariateSpline)
def test_run_model_no_time(self):
parallel = Parallel(model=TestingModelNoTime())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_run_feature_no_time(self):
parallel = Parallel(
model=TestingModel1d(),
features=TestingFeatures(features_to_run="feature_no_time"))
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_run_neuron_model(self):
path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"models/interneuron_modelDB/")
model = NeuronModel(path=path, adaptive=True)
parallel = Parallel(model=model)
model_parameters = {"cap": 1.1, "Rm": 22000}
with Xvfb() as xvfb:
parallel.run(model_parameters)
def test_postprocess_error_numpy(self):
parallel = Parallel(model=PostprocessErrorNumpy())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_postprocess_error_one(self):
parallel = Parallel(model=PostprocessErrorOne())
with self.assertRaises(TypeError):
parallel.run(self.model_parameters)
def test_postprocess_error_value(self):
parallel = Parallel(model=PostprocessErrorValue())
with self.assertRaises(ValueError):
parallel.run(self.model_parameters)
def test_use_info_arg(self):
def model_function(**model_parameters):
return 1, 2, True
def feature_function(time, values, info=False):
self.assertTrue(info)
return "time", "values"
self.parallel.model = model_function
self.parallel.features = feature_function
self.parallel.run(self.model_parameters)
def test_use_info_arg_dict(self):
def model_function(**model_parameters):
return 1, 2, {"1": 1, "2": 2}
def feature_function(time, values, info):
self.assertEqual(info["1"], 1)
self.assertEqual(info["2"], 2)
return "time", "values"
self.parallel.model = model_function
self.parallel.features = feature_function
self.parallel.run(self.model_parameters)
def test_use_model_feature_arguments_error(self):
def model_function(**model_parameters):
return 1, 2, 3
def feature_function(time, values):
return "time", "values"
self.parallel.model = model_function
self.parallel.features = feature_function
with self.assertRaises(TypeError):
self.parallel.run(self.model_parameters)
def test_none_to_nan(self):
values_irregular = np.array(
[None, np.array([1, 2, 3]), None,
np.array([1, 2, 3])])
result = self.parallel.none_to_nan(values_irregular)
values_correct = np.array([[np.nan, np.nan, np.nan], [1, 2, 3],
[np.nan, np.nan, np.nan], [1, 2, 3]])
result = np.array(result)
self.assertTrue(((result == values_correct) |
(np.isnan(result) & np.isnan(values_correct))).all())
values_irregular = np.array([
None,
np.array([None,
np.array([1, 2, 3]), None,
np.array([1, 2, 3])]),
np.array([None,
np.array([1, 2, 3]), None,
np.array([1, 2, 3])]),
np.array([None,
np.array([1, 2, 3]), None,
np.array([1, 2, 3])]), None
])
result = self.parallel.none_to_nan(values_irregular)
values_correct = np.array([[[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan]],
[[np.nan, np.nan, np.nan], [1, 2, 3],
[np.nan, np.nan, np.nan], [1, 2, 3]],
[[np.nan, np.nan, np.nan], [1, 2, 3],
[np.nan, np.nan, np.nan], [1, 2, 3]],
[[np.nan, np.nan, np.nan], [1, 2, 3],
[np.nan, np.nan, np.nan], [1, 2, 3]],
[[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan]]])
result = np.array(result)
self.assertTrue(((result == values_correct) |
(np.isnan(result) & np.isnan(values_correct))).all())
values_irregular = np.array([
np.array([1, 2, 3]),
np.array([1, 2, 3]),
np.array([1, 2, 3]),
np.array([1, 2, 3])
])
result = self.parallel.none_to_nan(values_irregular)
result = np.array(result)
self.assertTrue(np.array_equal(result, values_irregular))
values_irregular = np.array([
None,
np.array([np.array(1), np.array(2),
np.array(3)]), None,
np.array([np.array(1), np.array(2),
np.array(3)])
])
result = self.parallel.none_to_nan(values_irregular)
values_correct = np.array([[np.nan, np.nan, np.nan], [1, 2, 3],
[np.nan, np.nan, np.nan], [1, 2, 3]])
result = np.array(result)
self.assertTrue(((result == values_correct) |
(np.isnan(result) & np.isnan(values_correct))).all())
values_irregular = np.array([np.array(1), np.array(2), np.array(3)])
result = self.parallel.none_to_nan(values_irregular)
values_correct = np.array([np.array(1), np.array(2), np.array(3)])
result = np.array(result)
self.assertTrue(np.array_equal(result, values_irregular))
values_irregular = np.array([None, None, None])
result = self.parallel.none_to_nan(values_irregular)
values_correct = np.array([np.nan, np.nan, np.nan])
result = np.array(result)
self.assertTrue(np.all(np.isnan(result)))
self.assertEqual(len(result), 3)