def test_nearest_area_2d_to_area_1n(self):
"""Test 2D area definition to 2D area definition; 1 neighbor."""
import dask.array as da
import xarray as xr
from pyresample.kd_tree import XArrayResamplerNN
from pyresample.test.utils import assert_maximum_dask_computes
data = self.data_2d
resampler = XArrayResamplerNN(self.src_area_2d, self.area_def,
radius_of_influence=50000,
neighbours=1)
with assert_maximum_dask_computes(0):
ninfo = resampler.get_neighbour_info()
for val in ninfo[:3]:
# vii, ia, voi
self.assertIsInstance(val, da.Array)
self.assertRaises(AssertionError,
resampler.get_sample_from_neighbour_info, data)
# rename data dimensions to match the expected area dimensions
data = data.rename({'my_dim_y': 'y', 'my_dim_x': 'x'})
with assert_maximum_dask_computes(0):
res = resampler.get_sample_from_neighbour_info(data)
self.assertIsInstance(res, xr.DataArray)
self.assertIsInstance(res.data, da.Array)
res = res.values
cross_sum = np.nansum(res)
expected = 27706753.0
self.assertEqual(cross_sum, expected)
def test_nearest_area_2d_to_area_1n(self, area_def_stere_source,
data_2d_float32_xarray_dask,
area_def_stere_target):
"""Test 2D area definition to 2D area definition; 1 neighbor."""
resampler = KDTreeNearestXarrayResampler(area_def_stere_source,
area_def_stere_target)
with assert_maximum_dask_computes(0):
resampler.precompute(radius_of_influence=50000)
with assert_maximum_dask_computes(0):
res = resampler.resample(data_2d_float32_xarray_dask,
radius_of_influence=50000)
assert isinstance(res, xr.DataArray)
assert isinstance(res.data, da.Array)
_check_common_metadata(
res, isinstance(area_def_stere_target, AreaDefinition))
res = res.values
cross_sum = float(np.nansum(res))
expected = 303048.0
assert cross_sum == expected
assert res.shape == resampler.target_geo_def.shape
def test_object_type_with_warnings(self, area_def_stere_source,
area_def_stere_target, input_data):
"""Test that providing certain input data causes a warning."""
resampler = KDTreeNearestXarrayResampler(area_def_stere_source,
area_def_stere_target)
with catch_warnings(
PerformanceWarning) as w, assert_maximum_dask_computes(1):
res = resampler.resample(input_data)
assert type(res) is type(input_data)
_check_common_metadata(
res, isinstance(area_def_stere_target, AreaDefinition))
is_data_arr_dask = isinstance(input_data, xr.DataArray) and isinstance(
input_data.data, da.Array)
is_dask_based = isinstance(input_data, da.Array) or is_data_arr_dask
if is_dask_based:
assert not w
else:
assert_warnings_contain(w, "will be converted")
res = np.array(res)
cross_sum = np.nansum(res)
expected = 952386.0 # same as 'test_nearest_area_2d_to_area_1n_no_roi'
assert cross_sum == expected
assert res.shape == resampler.target_geo_def.shape