def test_ddt3_quadratic(self):
jfile = os.path.join(self.path, "u-squared-ddt3.json")
client.main([jfile])
ofile = os.path.join(self.path, "u-squared-sig-proc-3.csv")
data = np.genfromtxt(ofile, dtype="float", delimiter=",")
test = data[:, 1]
ref = np.zeros(11)
diff = test - ref
diff_norm = np.linalg.norm(diff)
same_data = False
same_data = np.abs(diff_norm) < self.tol
self.assertTrue(same_data)
def test_ddt1_sine(self):
jfile = os.path.join(self.path, "t-v-sines-ddt1.json")
client.main([jfile])
ofile = os.path.join(self.path, "t-v-sines-sig-proc-1.csv")
data = np.genfromtxt(ofile, dtype="float", delimiter=",")
test = data[:, 1]
_t = np.linspace(0, 1, 101)
ref = 2 * np.pi * np.cos(2 * np.pi * _t)
diff = test - ref
diff_norm = np.linalg.norm(diff)
same_data = False
# same_data = np.abs(diff_norm) < self.tol
# In manual testing, the L2norm was
# diff_norm: 0.031201004731119160
# because of how the np.gradient operator works on the edges of the
# interval, so loosen the tolerance manually:
_cosine_tolerance = 0.1
# same_data = np.abs(diff_norm) < 10000
same_data = np.abs(diff_norm) < _cosine_tolerance
self.assertTrue(same_data)
def test_unknown_factory_item_request(self):
jfile = os.path.join(self.path, "u-squared-ddt1-model-type-defect.json")
result = client.main([jfile])
self.assertIsNone(result)
def test_101_anomaly_recipe(self):
jfile = os.path.join(self.path, "anomaly_recipe.json")
result = client.main([jfile])
self.assertIsNone(result)
def test_100_correlation_recipe(self):
jfile = os.path.join(self.path, "correlation_recipe.json")
result = client.main([jfile])
self.assertIsNone(result)
def test_defective_json_keyword_file(self):
jfile = os.path.join(self.path, "u-squared-ddt1-file-defect.json")
client.main([jfile])
def test_defective_signal_process(self):
jfile = os.path.join(self.path, "u-squared-ddt1-signal-process-defect.json")
client.main([jfile])
def test_verbose_model(self):
jfile = os.path.join(self.path, "u-squared-ddt1-verbose.json")
client.main([jfile])