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_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_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)
