def test_TimeSeriesAlphaModel():
n = TimeIndependent(prior.Gaussian(5, .5))
assert_pickle_roundtrip(n)
st = Sphere(n=n,
r=TimeIndependent(prior.BoundedGaussian(1.6, .1, 0, np.inf)),
center=(prior.Gaussian(10, 1), prior.Gaussian(10, 1),
prior.BoundedGaussian(1.6, .1, 0, np.inf)))
assert_pickle_roundtrip(st)
noise_sd = .1
mt = TimeSeriesAlphaModel(st,
Mie,
noise_sd,
alpha=prior.Uniform(0, 1),
n_frames=2)
assert_pickle_roundtrip(mt)
def test_prior_math():
u = Uniform(1, 2)
g = Gaussian(1, 2)
b = prior.BoundedGaussian(1, 2, 0, 3)
assert_equal(u + 1, Uniform(2, 3))
assert_equal(1 + u, Uniform(2, 3))
assert_equal(-u, Uniform(-2, -1))
assert_equal(1 - u, Uniform(-1, 0))
assert_equal(u - 1, Uniform(0, 1))
assert_equal(2 * u, Uniform(2, 4))
assert_equal(u * 2, Uniform(2, 4))
assert_equal(u / 2, Uniform(0.5, 1))
print(g + 1.)
assert_equal(g + 1., Gaussian(2, 2.))
assert_equal(-g, Gaussian(-1, 2))
assert_equal(b + 1., prior.BoundedGaussian(2., 2, 1., 4.))
assert_equal(-b, prior.BoundedGaussian(-1, 2, -3, 0))
assert_equal(2 * g, Gaussian(2, 4))
assert_equal(g * 2, Gaussian(2, 4))
assert_equal(g / 2, Gaussian(0.5, 1))
assert_equal(g + g, Gaussian(2, np.sqrt(8)))
assert_equal(g + np.array([0, 1]),
np.array([Gaussian(1, 2), Gaussian(2, 2)]))
assert_equal(g * np.array([1, 2]),
np.array([Gaussian(1, 2), Gaussian(2, 4)]))
with assert_raises(TypeError):
u + u
with assert_raises(TypeError):
g + b
with assert_raises(TypeError):
g + [0, 1]
with assert_raises(TypeError):
g * g
def test_updated():
p = prior.BoundedGaussian(1, 2, -1, 2)
d = UncertainValue(1, 0.5, 1)
u = prior.updated(p, d)
assert_equal(u.guess, 1)
assert_obj_close(u.lnprob(0), gold_sigma)
def test_bounded_gaussian():
g = prior.BoundedGaussian(1, 1, 0, 2)
assert_equal(g.lnprob(-1), -np.inf)
assert_equal(g.lnprob(3), -np.inf)
assert_equal(g.guess, 1)