def test_mean_transfer_function_cpu():
# numpy case
numpy_agg = xr.DataArray(data_random)
numpy_mean = mean(numpy_agg)
general_output_checks(numpy_agg, numpy_mean)
# dask + numpy case
dask_numpy_agg = xr.DataArray(da.from_array(data_random, chunks=(3, 3)))
dask_numpy_mean = mean(dask_numpy_agg)
general_output_checks(dask_numpy_agg, dask_numpy_mean)
# both output same results
np.testing.assert_allclose(numpy_mean.data,
dask_numpy_mean.data.compute(),
equal_nan=True)
def test_mean_transfer_function_cpu():
# numpy case
numpy_agg = xr.DataArray(data_random)
numpy_mean = mean(numpy_agg)
assert isinstance(numpy_mean.data, np.ndarray)
# dask + numpy case
dask_numpy_agg = xr.DataArray(da.from_array(data_random, chunks=(3, 3)))
dask_numpy_mean = mean(dask_numpy_agg)
assert isinstance(dask_numpy_mean.data, da.Array)
# both output same results
assert np.isclose(
numpy_mean, dask_numpy_mean.compute(), equal_nan=True
).all()
assert numpy_agg.shape == numpy_mean.shape
def test_mean_transfer_function():
da = xr.DataArray(data_random)
# add crs for tests
da = _add_EPSG4326_crs_to_da(da)
da_mean = mean(da)
assert da.shape == da_mean.shape
# crs tests
assert da_mean.attrs == da.attrs
for coord in da.coords:
assert np.all(da_mean[coord] == da[coord])
# Overall mean value should be the same as the original array.
# Considering the default behaviour to 'mean' is to pad the borders
# with zeros, the mean value of the filtered array will be slightly
# smaller (considering 'data_random' is positive).
assert da_mean.mean() <= data_random.mean()
# And if we pad the borders with the original values, we should have a
# 'mean' filtered array with _mean_ value very similar to the original one.
da_mean[0, :] = data_random[0, :]
da_mean[-1, :] = data_random[-1, :]
da_mean[:, 0] = data_random[:, 0]
da_mean[:, -1] = data_random[:, -1]
assert abs(da_mean.mean() - data_random.mean()) < 10**-3
def test_mean_transfer_function_gpu_equals_cpu():
import cupy
# cupy case
cupy_agg = xr.DataArray(cupy.asarray(data_random))
cupy_mean = mean(cupy_agg)
assert isinstance(cupy_mean.data, cupy.ndarray)
# numpy case
numpy_agg = xr.DataArray(data_random)
numpy_mean = mean(numpy_agg)
assert np.isclose(numpy_mean, cupy_mean.data.get(), equal_nan=True).all()
# dask + cupy case not implemented
dask_cupy_agg = xr.DataArray(
da.from_array(cupy.asarray(data_random), chunks=(3, 3))
)
with pytest.raises(NotImplementedError) as e_info:
mean(dask_cupy_agg)
assert e_info
def test_mean_transfer_function_gpu_equals_cpu():
import cupy
# cupy case
cupy_agg = xr.DataArray(cupy.asarray(data_random))
cupy_mean = mean(cupy_agg)
general_output_checks(cupy_agg, cupy_mean)
# numpy case
numpy_agg = xr.DataArray(data_random)
numpy_mean = mean(numpy_agg)
np.testing.assert_allclose(numpy_mean.data,
cupy_mean.data.get(),
equal_nan=True)
# dask + cupy case not implemented
dask_cupy_agg = xr.DataArray(
da.from_array(cupy.asarray(data_random), chunks=(3, 3)))
with pytest.raises(NotImplementedError) as e_info:
mean(dask_cupy_agg)
assert e_info
trees += bump(W,
H,
count=T // 3,
height_func=partial(heights,
src=src,
src_range=(1700, 2000),
height=5))
tree_colorize = trees.copy()
tree_colorize.data[tree_colorize.data == 0] = np.nan
LAND_CONSTANT = 50.0
water = terrain.copy()
water.data = np.where(water.data > 0, LAND_CONSTANT, 0)
water = mean(water, passes=50, excludes=[LAND_CONSTANT])
water.data[water.data == LAND_CONSTANT] = np.nan
def create_map(azimuth):
global cvs
global terrain
global water
global trees
img = stack(
shade(terrain, cmap=Elevation, how='linear'),
shade(water, cmap=['aqua', 'white']),
shade(hillshade(terrain + trees, azimuth=azimuth),
cmap=['black', 'white'],
