def setUp(self):
self.x = np.array([1, 2, 3])
self.y = np.array([1, 2, 3])
self.first_likelihood = GaussianLikelihood(
x=self.x,
y=self.y,
func=lambda x, param1, param2: (param1 + param2) * x,
sigma=1,
)
self.second_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x, param2, param3: (param2 + param3) * x
)
self.third_likelihood = ExponentialLikelihood(
x=self.x, y=self.y, func=lambda x, param4, param5: (param4 + param5) * x
)
self.joint_likelihood = JointLikelihood(
self.first_likelihood, self.second_likelihood, self.third_likelihood
)
self.first_likelihood.parameters["param1"] = 1
self.first_likelihood.parameters["param2"] = 2
self.second_likelihood.parameters["param2"] = 2
self.second_likelihood.parameters["param3"] = 3
self.third_likelihood.parameters["param4"] = 4
self.third_likelihood.parameters["param5"] = 5
self.joint_likelihood.parameters["param1"] = 1
self.joint_likelihood.parameters["param2"] = 2
self.joint_likelihood.parameters["param3"] = 3
self.joint_likelihood.parameters["param4"] = 4
self.joint_likelihood.parameters["param5"] = 5
def test_log_likelihood_dummy(self):
""" Merely tests if it goes into the right if else bracket """
poisson_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x: np.linspace(1, 100, self.N))
with mock.patch('numpy.sum') as m:
m.return_value = 1
self.assertEqual(1, poisson_likelihood.log_likelihood())
def setUp(self):
self.N = 100
self.mu = 5
self.x = np.linspace(0, 1, self.N)
self.y = np.random.poisson(self.mu, self.N)
self.yfloat = np.copy(self.y) * 1.0
self.yneg = np.copy(self.y)
self.yneg[0] = -1
def test_function(x, c):
return c
def test_function_array(x, c):
return np.ones(len(x)) * c
self.function = test_function
self.function_array = test_function_array
self.poisson_likelihood = PoissonLikelihood(self.x, self.y, self.function)
# get radioactive counts
counts = np.random.poisson(rates)
theoretical = decay_rate(delta_t, **injection_parameters)
# We quickly plot the data to check it looks sensible
fig, ax = plt.subplots()
ax.semilogy(time[:-1], counts, "o", label="data")
ax.semilogy(time[:-1], theoretical, "--r", label="signal")
ax.set_xlabel("time")
ax.set_ylabel("counts")
ax.legend()
fig.savefig("{}/{}_data.png".format(outdir, label))
# Now lets instantiate a version of the Poisson Likelihood, giving it
# the time intervals, counts and rate model
likelihood = PoissonLikelihood(delta_t, counts, decay_rate)
# Make the prior
priors = dict()
priors["halflife"] = LogUniform(1e-5, 1e5, latex_label="$t_{1/2}$", unit="min")
priors["n_init"] = LogUniform(
1e-25 / atto, 1e-10 / atto, latex_label="$N_0$", unit="attomole"
)
# And run sampler
result = bilby.run_sampler(
likelihood=likelihood,
priors=priors,
sampler="dynesty",
sample="unif",
nlive=1000,
def test_repr(self):
likelihood = PoissonLikelihood(self.x, self.y, self.function)
expected = "PoissonLikelihood(x={}, y={}, func={})".format(
self.x, self.y, self.function.__name__
)
self.assertEqual(expected, repr(likelihood))
def test_log_likelihood_zero_func_return_element(self):
poisson_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x: np.array([3, 6, 0])
)
self.assertEqual(-np.inf, poisson_likelihood.log_likelihood())
def test_log_likelihood_negative_func_return_element(self):
poisson_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x: np.array([3, 6, -2])
)
with self.assertRaises(ValueError):
poisson_likelihood.log_likelihood()
def test_log_likelihood_wrong_func_return_type(self):
poisson_likelihood = PoissonLikelihood(
x=self.x, y=self.y, func=lambda x: "test"
)
with self.assertRaises(ValueError):
poisson_likelihood.log_likelihood()
def test_neg_rate_array(self):
likelihood = PoissonLikelihood(self.x, self.y, self.function_array)
likelihood.parameters["c"] = -2
with self.assertRaises(ValueError):
likelihood.log_likelihood()
def test_init__y_negative(self):
with self.assertRaises(ValueError):
PoissonLikelihood(self.x, self.yneg, self.function)
def test_init_y_non_integer(self):
with self.assertRaises(ValueError):
PoissonLikelihood(self.x, self.yfloat, self.function)
