def test_2d_mwm_small(self):
values = [[9, 2], [5, 5], [2, 9]]
weights = [[3, 0], [5, 0], [8, 0]]
np.testing.assert_array_equal(
py_mwm_nd(values, weights, 3),
moving_weighted_median(values, weights, (3, 3)),
)
values = [
[8.0, 6.0, 1.0, 5.0],
[6.0, 6.0, 8.0, 10.0],
[8.0, 4.0, 4.0, 7.0],
[10.0, 1.0, 7.0, 8.0],
]
weights = [
[8.0, 6.0, 1.0, 5.0],
[6.0, 6.0, 8.0, 10.0],
[8.0, 4.0, 4.0, 7.0],
[10.0, 1.0, 7.0, 8.0],
]
np.testing.assert_array_equal(
py_mwm_nd(values, weights, 3),
moving_weighted_median(values, weights, (3, 3)),
)
values = [[9, 4], [2, 5]]
weights = [[4, 8], [4, 5]]
np.testing.assert_array_equal(
py_mwm_nd(values, weights, 3),
moving_weighted_median(values, weights, (3, 3)),
)
def test_2d_mwm_big(self):
N = 100
M = 100
N_w = 5
values = np.random.random_sample((N, M))
weights = np.random.random_sample((N, M))
t0 = time.time()
moving_weighted_median(values, weights, (N_w, N_w))
t_cython = time.time() - t0
print("2D moving {}x{} weighted median with {}x{} elements took {}s".
format(N_w, N_w, N, N, t_cython))
def test_2d_mwm(self):
values = np.random.rand(14, 8)
weights = np.random.rand(14, 8)
np.testing.assert_array_equal(
py_mwm_nd(values, weights, 3),
moving_weighted_median(values, weights, (3, 3)),
)
def test_2d_mwm_int(self):
values = np.asarray(np.random.randint(0, 10, (14, 8)), np.float64)
weights = np.asarray(np.random.randint(0, 10, (14, 8)), np.float64)
np.testing.assert_array_equal(
py_mwm_nd(values, weights, (3, 3)),
moving_weighted_median(values, weights, (3, 3)),
)
def medfilt(x, mask, size, *args):
"""Apply a moving median filter to masked data.
The application is done by iterative filling to
overcome the fact we don't have an actual implementation
of a nanmedian.
Parameters
----------
x : np.ndarray
Data to filter.
mask : np.ndarray
Mask of data to filter out.
size : tuple
Size of the window in each dimension.
Returns
-------
y : np.ndarray
The masked data. Data within the mask is undefined.
"""
if np.iscomplexobj(x):
return medfilt(x.real, mask, size) + 1.0j * medfilt(x.imag, mask, size)
# Copy and do initial masking
x = np.ascontiguousarray(x.astype(np.float64))
w = np.ascontiguousarray((~mask).astype(np.float64))
return weighted_median.moving_weighted_median(x, w, size, *args)
def test_small_zero_weight(self):
window = (3, 3)
zero_shape = (2, 2)
shape = (10, 10)
data = np.ones(shape, dtype=np.float64)
weight = np.ones_like(data)
weight[1:zero_shape[0] + 1, 1:zero_shape[1] + 1] = 0.0
np.testing.assert_array_equal(
data, moving_weighted_median(data, weight, window))
def test_1d_mwm_big(self):
N = 100
values = np.random.random_sample(N)
weights = np.random.random_sample(N)
t0 = time.time()
res_py = py_mwm_1d(values, weights, 5)
t_py = time.time() - t0
t0 = time.time()
res_cython = moving_weighted_median(values, weights, 5)
t_cython = time.time() - t0
np.testing.assert_array_equal(res_py, res_cython)
print(
"1D moving weighted median with {} elements took {}s / {}s".format(
N, t_py, t_cython))
def test_med_zero_weight(self):
window = (3, )
zero_shape = (3, )
shape = (10, )
data = np.ones(shape, dtype=np.float64)
weight = np.ones_like(data)
weight[1:zero_shape[0] + 1] = 0.0
result = data.copy()
result[2] = np.nan
np.testing.assert_array_equal(
result, moving_weighted_median(data, weight, window))
def test_2d_mwm_small_large_window(self):
# Try a window that is much larger than the input array.
# This has caused crashes in older versions
values = [
[8.0, 6.0, 1.0, 5.0],
[6.0, 6.0, 8.0, 10.0],
[8.0, 4.0, 4.0, 7.0],
[10.0, 1.0, 7.0, 8.0],
]
weights = [
[8.0, 6.0, 1.0, 5.0],
[6.0, 6.0, 8.0, 10.0],
[8.0, 4.0, 4.0, 7.0],
[10.0, 1.0, 7.0, 8.0],
]
py_res = py_mwm_nd(values, weights, 11)
cy_res = moving_weighted_median(values, weights, (11, 11))
np.testing.assert_array_equal(py_res, cy_res)
# The window is so large all values should be equal, double check that
np.testing.assert_array_equal(cy_res, cy_res[0, 0])
def test_zero_weights(self):
values = [1, 1, 1]
weights = [0, 0, 0]
np.testing.assert_array_equal([0, 0, 0],
moving_weighted_median(
values, weights, 1))
def test_1d_mwm(self):
values = [0.1, 0, 7.7, 0, 9.999, 42, 0, 1, 9, 1]
weights = [0, 7.5, 0.33, 23.23, 0, 4, 7, 8, 9, 0]
np.testing.assert_array_equal(
py_mwm_1d(values, weights, 5),
moving_weighted_median(values, weights, 5))
def test_1d_mwm_int(self):
values = [0, 0, 7, 0, 9, 42, 0, 1, 9, 1]
weights = [0, 7, 1, 23, 0, 4, 7, 8, 9, 0]
np.testing.assert_array_almost_equal(
py_mwm_1d(values, weights, 5),
moving_weighted_median(values, weights, 5))
