def test_slice_data(self):
"""Test slicing the data."""
import dask.array as da
from xarray import DataArray
from pyresample.bilinear.xarr import XArrayResamplerBilinear
resampler = XArrayResamplerBilinear(self.source_def, self.target_def,
self.radius)
# Too many dimensions
data = DataArray(da.ones((1, 3, 10, 10)))
with self.assertRaises(ValueError):
_ = resampler._slice_data(data, np.nan)
# 2D data
data = DataArray(da.ones((10, 10)))
resampler.slices_x = np.random.randint(0, 10, (100, 4))
resampler.slices_y = np.random.randint(0, 10, (100, 4))
resampler.mask_slices = np.zeros((100, 4), dtype=np.bool)
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertEqual(p_1.shape, (100, ))
self.assertTrue(p_1.shape == p_2.shape == p_3.shape == p_4.shape)
self.assertTrue(
np.all(p_1 == 1.0) and np.all(p_2 == 1.0) and np.all(p_3 == 1.0)
and np.all(p_4 == 1.0))
# 2D data with masking
resampler.mask_slices = np.ones((100, 4), dtype=np.bool)
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertTrue(
np.all(np.isnan(p_1)) and np.all(np.isnan(p_2))
and np.all(np.isnan(p_3)) and np.all(np.isnan(p_4)))
# 3D data
data = DataArray(da.ones((3, 10, 10)))
resampler.slices_x = np.random.randint(0, 10, (100, 4))
resampler.slices_y = np.random.randint(0, 10, (100, 4))
resampler.mask_slices = np.zeros((100, 4), dtype=np.bool)
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertEqual(p_1.shape, (3, 100))
self.assertTrue(p_1.shape == p_2.shape == p_3.shape == p_4.shape)
# 3D data with masking
resampler.mask_slices = np.ones((100, 4), dtype=np.bool)
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertTrue(
np.all(np.isnan(p_1)) and np.all(np.isnan(p_2))
and np.all(np.isnan(p_3)) and np.all(np.isnan(p_4)))
def test_slice_data(self):
"""Test slicing the data."""
import dask.array as da
from xarray import DataArray
from pyresample.bilinear.xarr import XArrayResamplerBilinear
resampler = XArrayResamplerBilinear(self.source_def, self.target_def,
self.radius)
resampler.get_bil_info()
# Too many dimensions
data = DataArray(da.ones((1, 3) + self.source_def.shape))
with self.assertRaises(ValueError):
_ = resampler._slice_data(data, np.nan)
# 2D data
data = DataArray(da.ones(self.source_def.shape))
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertEqual(p_1.shape, resampler.bilinear_s.shape)
self.assertTrue(p_1.shape == p_2.shape == p_3.shape == p_4.shape)
self.assertTrue(
np.all(p_1 == 1.0) and np.all(p_2 == 1.0) and np.all(p_3 == 1.0)
and np.all(p_4 == 1.0))
# 2D data with masking
resampler.mask_slices[:, :] = True
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertTrue(
np.all(np.isnan(p_1)) and np.all(np.isnan(p_2))
and np.all(np.isnan(p_3)) and np.all(np.isnan(p_4)))
# 3D data
data = DataArray(da.ones((3, ) + self.source_def.shape))
resampler.mask_slices[:, :] = False
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertEqual(p_1.shape, (3, ) + resampler.bilinear_s.shape)
self.assertTrue(p_1.shape == p_2.shape == p_3.shape == p_4.shape)
# 3D data with masking
resampler.mask_slices[:, :] = True
p_1, p_2, p_3, p_4 = resampler._slice_data(data, np.nan)
self.assertTrue(
np.all(np.isnan(p_1)) and np.all(np.isnan(p_2))
and np.all(np.isnan(p_3)) and np.all(np.isnan(p_4)))