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import numpy as np
from numpy.testing import assert_allclose
from scipy.stats import norm
from astroML.density_estimation import\
EmpiricalDistribution, FunctionDistribution
def test_empirical_distribution(N=1000, rseed=0):
np.random.seed(rseed)
X = norm.rvs(0, 1, size=N)
dist = EmpiricalDistribution(X)
X2 = dist.rvs(N)
meanX = X.mean()
meanX2 = X2.mean()
stdX = X.std()
stdX2 = X2.std()
assert_allclose([meanX, stdX], [meanX2, stdX2], atol=3 / np.sqrt(N))
def test_function_distribution(N=1000, rseed=0):
f = norm(0, 1).pdf
# go from -10 to 10 to check interpolation in presence of zeros
dist = FunctionDistribution(f, -10, 10)
np.random.seed(rseed)
X = dist.rvs(N)
meanX = X.mean()
stdX = X.std()
assert_allclose([meanX, stdX], [0, 1], atol=3 / np.sqrt(N))
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