def test_compute_lower_upper_errors():
sample = np.random.normal(loc=0, scale=1, size=100000)
median, _ = prob_density.compute_lower_upper_errors(sample, num_sigma=0)
npt.assert_almost_equal(median, 0, decimal=2)
median, [[lower_sigma1, upper_sigma1]
] = prob_density.compute_lower_upper_errors(sample, num_sigma=1)
npt.assert_almost_equal(lower_sigma1, 1, decimal=2)
npt.assert_almost_equal(upper_sigma1, 1, decimal=2)
median, [[lower_sigma1, upper_sigma1], [lower_sigma2, upper_sigma2],
[lower_sigma3, upper_sigma3]
] = prob_density.compute_lower_upper_errors(sample, num_sigma=3)
npt.assert_almost_equal(lower_sigma2, 2, decimal=2)
npt.assert_almost_equal(upper_sigma2, 2, decimal=2)
npt.assert_almost_equal(lower_sigma3, 3, decimal=1)
npt.assert_almost_equal(upper_sigma3, 3, decimal=1)
def test_compute_lower_upper_errors(self):
x_array = np.linspace(-5, 5, 1000)
sigma = 1.
pdf_array = self.gauss(x_array, simga=sigma)
approx = Approx(x_array, pdf_array)
sample = approx.draw(n=20000)
mean_0, _ = prob_density.compute_lower_upper_errors(
sample, num_sigma=0)
mean, [[lower_sigma1, upper_sigma1]] = prob_density.compute_lower_upper_errors(
sample, num_sigma=1)
npt.assert_almost_equal(mean, mean_0, decimal=8)
mean, [[lower_sigma1, upper_sigma1], [lower_sigma2, upper_sigma2]] = prob_density.compute_lower_upper_errors(sample, num_sigma=2)
mean, [[lower_sigma1, upper_sigma1], [lower_sigma2, upper_sigma2], [lower_sigma3, upper_sigma3]] = prob_density.compute_lower_upper_errors(sample, num_sigma=3)
npt.assert_almost_equal(mean, 0, decimal=2)
print(lower_sigma1, lower_sigma2, lower_sigma3)
print(upper_sigma1, upper_sigma2, upper_sigma3)
npt.assert_almost_equal(lower_sigma1, sigma, decimal=1)
npt.assert_almost_equal(lower_sigma2, 2*sigma, decimal=1)
npt.assert_almost_equal(lower_sigma3, 3 * sigma, decimal=1)
draw = approx.draw_one
assert len(draw) == 1
def test_compute_lower_upper_errors(self):
x_array = np.linspace(-5, 5, 1000)
bin_edges = np.linspace(start=-5, stop=5, num=1000 + 1)
sigma = 1.
pdf_array = self.gauss(x_array, simga=sigma)
approx = PDFSampling(bin_edges, pdf_array)
np.random.seed(42)
sample = approx.draw(n=20000)
mean, [[lower_sigma1, upper_sigma1], [lower_sigma2, upper_sigma2],
[lower_sigma3, upper_sigma3]
] = prob_density.compute_lower_upper_errors(sample, num_sigma=3)
npt.assert_almost_equal(mean, 0, decimal=2)
npt.assert_almost_equal(lower_sigma1, sigma, decimal=2)
npt.assert_almost_equal(lower_sigma2, 2 * sigma, decimal=1)
npt.assert_almost_equal(lower_sigma3, 3 * sigma, decimal=1)
draw = approx.draw_one
assert len(draw) == 1
def test_raise(self):
with self.assertRaises(ValueError):
skewGassian = SkewGaussian()
skewGassian.pdf_skew(x=1, mu=1, sigma=1, skw=-1)
with self.assertRaises(ValueError):
prob_density.compute_lower_upper_errors(sample=None, num_sigma=4)
