def test_pvalues_type():
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (type(cor) == np.ndarray)
def test_pvalues_similarity():
# test p values for close distributions
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (cor[0, 0] > 0.95 and cor[1, 1] > 0.95)
def test_pvalues_difference():
# test p values for very different distributions
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (cor[0, 1] < 0.05)
def test_pvalues_size():
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (cor.size == 4)
def test_pvalues_type():
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (type(cor) == np.ndarray)
def test_pvalues_difference():
# test p values for very different distributions
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (cor[0, 1] < 0.05)
def test_pvalues_similarity():
# test p values for close distributions
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (cor[0, 0] > 0.95 and cor[1, 1] > 0.95)
def test_pvalues_size():
poisson = np.random.poisson(2, size=100)
normal = np.random.normal(loc=10, scale=2, size=50)
cor = get_pvalues([poisson, normal])
assert (cor.size == 4)
