def test_weight_and_integration_1d(self):
"""
Test that the Gaussian distribution intagrates to the weight.
"""
weight = 1.0
gaussian = Gaussian(cov=2.0,
mean=4.0,
log_weight=np.log(weight),
var_names=["a"])
definite_integral, _ = integrate.quad(gaussian.potential, -100.0,
100.0)
self.assertAlmostEqual(definite_integral, weight)
# test another weight and with canonical form
weight = 2.0
gaussian = Gaussian(cov=2.0,
mean=4.0,
log_weight=np.log(weight),
var_names=["a"])
gaussian._update_canform()
gaussian.covform = False
definite_integral, _ = integrate.quad(gaussian.potential, -100.0,
100.0)
self.assertAlmostEqual(definite_integral, weight)
def test_log_potential_no_form(self):
"""
Test that the log_potential function raises an exception if the Gaussian has no form.
"""
gaussian = Gaussian(cov=1.0,
mean=1.0,
log_weight=np.log(1.0),
var_names=["a"])
gaussian.covform = False
with self.assertRaises(Exception):
gaussian.log_potential(x_val=0)
def test_copy_no_form(self):
"""
Test that the copy function raises an exception when a Gaussian does not have either of its form updated.
"""
gaussian_no_form = Gaussian(cov=1.0,
mean=0.0,
log_weight=0.0,
var_names=["a"])
gaussian_no_form.covform = False
with self.assertRaises(Exception):
gaussian_no_form.copy()