def test_kernel():
n, m = 6, 6
raster = xr.DataArray(np.ones((n, m)), dims=['y', 'x'])
raster['x'] = np.linspace(0, n, n)
raster['y'] = np.linspace(0, m, m)
cellsize_x, cellsize_y = calc_cellsize(raster)
# Passing invalid radius units for `circle`
with pytest.raises(Exception) as e_info:
circle_kernel(cellsize_x, cellsize_y, "10 furlongs")
assert e_info
# Passing invalid radius for `annulus`
with pytest.raises(Exception) as e_info:
annulus_kernel(cellsize_x, cellsize_y, 4, "2 leagues")
assert e_info
# Passing custom kernel with even dimensions
with pytest.raises(Exception) as e_info:
_validate_kernel(np.ones((2, 2)))
assert e_info
# Passing custom kernel of wrong type
with pytest.raises(Exception) as e_info:
_validate_kernel([[1, 1, 1]])
assert e_info
def test_hotspot():
n, m = 10, 10
raster = xr.DataArray(np.zeros((n, m), dtype=float), dims=['y', 'x'])
raster['x'] = np.linspace(0, n, n)
raster['y'] = np.linspace(0, m, m)
cellsize_x, cellsize_y = calc_cellsize(raster)
kernel = circle_kernel(cellsize_x, cellsize_y, 2.0)
all_idx = zip(*np.where(raster.values == 0))
nan_cells = [(i, i) for i in range(m)]
for cell in nan_cells:
raster[cell[0], cell[1]] = np.nan
# add some extreme values
hot_region = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1),
(3, 2), (3, 3)]
cold_region = [(7, 7), (7, 8), (7, 9), (8, 7), (8, 8), (8, 9), (9, 7),
(9, 8), (9, 9)]
for p in hot_region:
raster[p[0], p[1]] = 10000
for p in cold_region:
raster[p[0], p[1]] = -10000
no_significant_region = [
id for id in all_idx if id not in hot_region and id not in cold_region
]
hotspots_output = hotspots(raster, kernel)
# check output's properties
# output must be an xarray DataArray
assert isinstance(hotspots_output, xr.DataArray)
assert isinstance(hotspots_output.values, np.ndarray)
assert issubclass(hotspots_output.values.dtype.type, np.int8)
# shape, dims, coords, attr preserved
assert raster.shape == hotspots_output.shape
assert raster.dims == hotspots_output.dims
assert raster.attrs == hotspots_output.attrs
for coord in raster.coords:
assert np.all(raster[coord] == hotspots_output[coord])
# no nan in output
assert not np.isnan(np.min(hotspots_output))
# output of extreme regions are non-zeros
# hot spots
hot_spot = np.asarray([hotspots_output[p] for p in hot_region])
assert np.all(hot_spot >= 0)
assert np.sum(hot_spot) > 0
# cold spots
cold_spot = np.asarray([hotspots_output[p] for p in cold_region])
assert np.all(cold_spot <= 0)
assert np.sum(cold_spot) < 0
# output of no significant regions are 0s
no_sign = np.asarray([hotspots_output[p] for p in no_significant_region])
assert np.all(no_sign == 0)
def test_apply_crs():
n, m = 6, 6
raster = xr.DataArray(np.ones((n, m)), dims=['y', 'x'])
# add crs
raster = _add_EPSG4326_crs_to_da(raster, res=(1, 1))
raster['x'] = np.linspace(0, n, n)
raster['y'] = np.linspace(0, m, m)
cellsize_x, cellsize_y = calc_cellsize(raster)
# add some nan pixels
nan_cells = [(i, i) for i in range(n)]
for cell in nan_cells:
raster[cell[0], cell[1]] = np.nan
# kernel array = [[1]]
kernel = np.ones((1, 1))
raster_apply = apply(raster, kernel)
assert raster_apply.attrs == raster.attrs
for coord in raster.coords:
assert np.all(raster_apply[coord] == raster[coord])
def test_convolution():
n, m = 6, 6
raster = xr.DataArray(np.ones((n, m)), dims=['y', 'x'])
raster['x'] = np.linspace(0, n, n)
raster['y'] = np.linspace(0, m, m)
cellsize_x, cellsize_y = calc_cellsize(raster)
# add some nan pixels
nan_cells = [(i, i) for i in range(n)]
for cell in nan_cells:
raster[cell[0], cell[1]] = np.nan
# kernel array = [[1]]
kernel = np.ones((1, 1))
# np.nansum(np.array([np.nan])) = 0.0
expected_out_sum_1 = np.array([[0., 1., 1., 1., 1., 1.],
[1., 0., 1., 1., 1., 1.],
[1., 1., 0., 1., 1., 1.],
[1., 1., 1., 0., 1., 1.],
[1., 1., 1., 1., 0., 1.],
[1., 1., 1., 1., 1., 0.]])
# Convolution will return np.nan, so convert nan to 0
assert np.all(np.nan_to_num(expected_out_sum_1) == expected_out_sum_1)
# np.nanmean(np.array([np.nan])) = nan
mean_output_1 = convolve_2d(raster.values, kernel / kernel.sum())
for cell in nan_cells:
assert np.isnan(mean_output_1[cell[0], cell[1]])
# remaining cells are 1s
for i in range(n):
for j in range(m):
if i != j:
assert mean_output_1[i, j] == 1
# kernel array: [[0, 1, 0],
# [1, 1, 1],
# [0, 1, 0]]
kernel = circle_kernel(cellsize_x, cellsize_y, 2)
sum_output_2 = convolve_2d(np.nan_to_num(raster.values), kernel, pad=False)
expected_out_sum_2 = np.array([[2., 2., 4., 4., 4., 3.],
[2., 4., 3., 5., 5., 4.],
[4., 3., 4., 3., 5., 4.],
[4., 5., 3., 4., 3., 4.],
[4., 5., 5., 3., 4., 2.],
[3., 4., 4., 4., 2., 2.]])
assert np.all(sum_output_2 == expected_out_sum_2)
mean_output_2 = convolve_2d(np.ones((n, m)),
kernel / kernel.sum(),
pad=True)
expected_mean_output_2 = np.ones((n, m))
assert np.all(mean_output_2 == expected_mean_output_2)
# kernel array: [[0, 1, 0],
# [1, 0, 1],
# [0, 1, 0]]
kernel = annulus_kernel(cellsize_x, cellsize_y, 2.0, 0.5)
sum_output_3 = convolve_2d(np.nan_to_num(raster.values), kernel, pad=False)
expected_out_sum_3 = np.array([[2., 1., 3., 3., 3., 2.],
[1., 4., 2., 4., 4., 3.],
[3., 2., 4., 2., 4., 3.],
[3., 4., 2., 4., 2., 3.],
[3., 4., 4., 2., 4., 1.],
[2., 3., 3., 3., 1., 2.]])
assert np.all(sum_output_3 == expected_out_sum_3)
mean_output_3 = convolve_2d(np.ones((n, m)),
kernel / kernel.sum(),
pad=True)
expected_mean_output_3 = np.ones((n, m))
assert np.all(mean_output_3 == expected_mean_output_3)
def test_apply():
n, m = 6, 6
raster = xr.DataArray(np.ones((n, m)), dims=['y', 'x'])
raster['x'] = np.linspace(0, n, n)
raster['y'] = np.linspace(0, m, m)
cellsize_x, cellsize_y = calc_cellsize(raster)
# test apply() with calc_sum and calc_mean function
# add some nan pixels
nan_cells = [(i, i) for i in range(n)]
for cell in nan_cells:
raster[cell[0], cell[1]] = np.nan
# kernel array = [[1]]
kernel = np.ones((1, 1))
sum_output_1 = apply(raster, kernel, func=calc_sum)
# np.nansum(np.array([np.nan])) = 0.0
expected_out_sum_1 = np.array([[0., 1., 1., 1., 1., 1.],
[1., 0., 1., 1., 1., 1.],
[1., 1., 0., 1., 1., 1.],
[1., 1., 1., 0., 1., 1.],
[1., 1., 1., 1., 0., 1.],
[1., 1., 1., 1., 1., 0.]])
assert np.all(sum_output_1.values == expected_out_sum_1)
# np.nanmean(np.array([np.nan])) = nan
mean_output_1 = apply(raster, kernel, func=calc_mean)
for cell in nan_cells:
assert np.isnan(mean_output_1[cell[0], cell[1]])
# remaining cells are 1s
for i in range(n):
for j in range(m):
if i != j:
assert mean_output_1[i, j] == 1
# kernel array: [[0, 1, 0],
# [1, 1, 1],
# [0, 1, 0]]
kernel = circle_kernel(cellsize_x, cellsize_y, 2)
sum_output_2 = apply(raster, kernel, func=calc_sum)
expected_out_sum_2 = np.array([[2., 2., 4., 4., 4., 3.],
[2., 4., 3., 5., 5., 4.],
[4., 3., 4., 3., 5., 4.],
[4., 5., 3., 4., 3., 4.],
[4., 5., 5., 3., 4., 2.],
[3., 4., 4., 4., 2., 2.]])
assert np.all(sum_output_2.values == expected_out_sum_2)
mean_output_2 = apply(raster, kernel, func=calc_mean)
expected_mean_output_2 = np.ones((n, m))
assert np.all(mean_output_2.values == expected_mean_output_2)
# kernel array: [[0, 1, 0],
# [1, 0, 1],
# [0, 1, 0]]
kernel = annulus_kernel(cellsize_x, cellsize_y, 2.0, 0.5)
sum_output_3 = apply(raster, kernel, func=calc_sum)
expected_out_sum_3 = np.array([[2., 1., 3., 3., 3., 2.],
[1., 4., 2., 4., 4., 3.],
[3., 2., 4., 2., 4., 3.],
[3., 4., 2., 4., 2., 3.],
[3., 4., 4., 2., 4., 1.],
[2., 3., 3., 3., 1., 2.]])
assert np.all(sum_output_3.values == expected_out_sum_3)
mean_output_3 = apply(raster, kernel, func=calc_mean)
expected_mean_output_3 = np.ones((n, m))
assert np.all(mean_output_3.values == expected_mean_output_3)
