def test_median_axis_none_mask_none(): for i in range(25): size = np.random.randint(1, 10000) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size) expected = np.median(a.astype(np.float32)) actual = stats.median(a) assert np.float32(expected) == actual
def test_median_2d_axis_1_mask_none(set_random_seed): for i in range(5): size1 = np.random.randint(1, 300) size2 = np.random.randint(5, 300) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size=(size1, size2)) expected = np.median(a.astype(np.float32), axis=1) actual = stats.median(a, axis=1) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_2d_axis_1_mask_none(): for i in range(5): size1 = np.random.randint(1, 300) size2 = np.random.randint(1, 300) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size=(size1, size2)) expected = np.median(a.astype(np.float32), axis=1) actual = stats.median(a, axis=1) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_2d_axis_none_mask_none(set_random_seed): for i in range(5): size1 = np.random.randint(1, 300) size2 = np.random.randint(1, 300) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size=(size1, size2)) expected = np.median(a.astype(np.float32)) actual = stats.median(a) assert np.float32(expected) == actual
def test_median_axis_none_mask(): for i in range(25): size = np.random.randint(1, 10000) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size) value_to_mask = np.random.uniform(0, 1.0) mask = np.random.uniform(0, 1, size) < value_to_mask expected = ma.median(ma.array(a, mask=mask, dtype=np.float32)) actual = stats.median(a, mask=mask) assert np.float32(expected) == actual
def test_median_3d_axis_2_mask_none(set_random_seed): for i in range(5): size1 = np.random.randint(1, 50) size2 = np.random.randint(1, 50) size3 = np.random.randint(5, 50) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size=(size1, size2, size3)) expected = np.median(a.astype(np.float32), axis=2) actual = stats.median(a, axis=2) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_2d_axis_1_mask(): for i in range(5): size1 = np.random.randint(1, 300) size2 = np.random.randint(1, 300) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) value_to_mask = np.random.uniform(0, 1) mask = np.random.uniform(0., 1.0, size=(size1, size2)) < value_to_mask a = np.random.normal(mean, sigma, size=(size1, size2)) expected = ma.median(ma.array(a, mask=mask, dtype=np.float32), axis=1) actual = stats.median(a, mask=mask, axis=1) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_2d_axis_1_mask(set_random_seed): for i in range(5): size1 = np.random.randint(1, 300) size2 = np.random.randint(5, 300) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) value_to_mask = np.random.uniform(0, 1) mask = np.random.uniform(0., 1.0, size=(size1, size2)) < value_to_mask a = np.random.normal(mean, sigma, size=(size1, size2)) expected = ma.median(ma.array(a, mask=mask, dtype=np.float32), axis=1) actual = stats.median(a, mask=mask, axis=1) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_2d_axis_none_mask(set_random_seed): for i in range(5): size1 = np.random.randint(1, 300) size2 = np.random.randint(1, 300) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) value_to_mask = np.random.uniform(0, 1) mask = np.random.uniform(0, 1, size=(size1, size2)) < value_to_mask a = np.random.normal(mean, sigma, size=(size1, size2)) expected = ma.median(ma.array(a, mask=mask, dtype=np.float32)) actual = stats.median(a, mask=mask) assert np.float32(expected) == actual
def test_median_3d_axis_2_mask_none(): for i in range(5): size1 = np.random.randint(1, 50) size2 = np.random.randint(1, 50) size3 = np.random.randint(1, 50) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) a = np.random.normal(mean, sigma, size=(size1, size2, size3)) expected = np.median(a.astype(np.float32), axis=2) actual = stats.median(a, axis=2) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_3d_axis_2_mask(set_random_seed): for i in range(5): size1 = np.random.randint(1, 50) size2 = np.random.randint(1, 50) size3 = np.random.randint(5, 50) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) value_to_mask = np.random.uniform(0, 1) mask = np.random.uniform(0, 1, size=(size1, size2, size3)) < value_to_mask a = np.random.normal(mean, sigma, size=(size1, size2, size3)) expected = ma.median(ma.array(a, mask=mask, dtype=np.float32), axis=2) actual = stats.median(a, mask=mask, axis=2) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)
def test_median_3d_axis_2_mask(): for i in range(5): size1 = np.random.randint(1, 50) size2 = np.random.randint(1, 50) size3 = np.random.randint(1, 50) mean = np.random.uniform(-1000, 1000) sigma = np.random.uniform(0, 1000) value_to_mask = np.random.uniform(0, 1) mask = np.random.uniform(0, 1, size=(size1, size2, size3)) < value_to_mask a = np.random.normal(mean, sigma, size=(size1, size2, size3)) expected = ma.median(ma.array(a, mask=mask, dtype=np.float32), axis=2) actual = stats.median(a, mask=mask, axis=2) np.testing.assert_allclose(actual, expected.astype(np.float32), atol=1e-6)