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Make sure integer data arrays can be coregistered #775

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Sep 28, 2018
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Make sure integer data arrays can be coregistered #775

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2 changes: 2 additions & 0 deletions CHANGES.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
## Version 2.0.0.dev21 (in development)

* Make sure integer data variables can be coregistered [#770](https://github.com/CCI-Tools/cate/issues/770)

## Version 2.0.0.dev20

* Changed order and names of new `data_frame_subset` operation inputs.
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2 changes: 1 addition & 1 deletion cate/ops/resampling.py
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Expand Up @@ -529,7 +529,7 @@ def _downsample_2d(src, mask, use_mask, method, fill_value, mode_rank, out):
else:
out[out_y, out_x] = (wvv_sum * w_sum - wv_sum * wv_sum) / w_sum / w_sum
if method == DS_STD:
out = np.sqrt(out)
out = np.sqrt(out).astype(out.dtype)
else:
raise ValueError('invalid downsampling method')

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68 changes: 68 additions & 0 deletions test/ops/test_coregistration.py
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Expand Up @@ -674,3 +674,71 @@ def test_2D(self):
ds_coarse_resampled = coregister(ds_fine, ds_coarse)

assert_almost_equal(ds_coarse_resampled['first'].values, slice_exp)

def test_int_array(self):
"""
Test coregistration on integer arrays
"""
ds_fine = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([np.eye(4, 8), np.eye(4, 8)], dtype='int32')),
'second': (['time', 'lat', 'lon'], np.array([np.eye(4, 8), np.eye(4, 8,)])),
'lat': np.linspace(-67.5, 67.5, 4),
'lon': np.linspace(-157.5, 157.5, 8),
'time': np.array([1, 2])}).chunk(chunks={'lat': 2, 'lon': 4})

ds_coarse = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([np.eye(3, 6), np.eye(3, 6)], dtype='int32')),
'second': (['time', 'lat', 'lon'], np.array([np.eye(3, 6), np.eye(3, 6)])),
'lat': np.linspace(-60, 60, 3),
'lon': np.linspace(-150, 150, 6),
'time': np.array([1, 2])}).chunk(chunks={'lat': 3, 'lon': 3})

# Test that the coarse dataset has been resampled onto the grid
# of the finer dataset.
ds_coarse_resampled = coregister(ds_fine, ds_coarse, method_us='nearest')

expected = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([[[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0]],
[[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0]]])),
'second': (['time', 'lat', 'lon'], np.array([[[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0]],
[[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0]]])),
'lat': np.linspace(-67.5, 67.5, 4),
'lon': np.linspace(-157.5, 157.5, 8),
'time': np.array([1, 2])})
assert_almost_equal(ds_coarse_resampled['first'].values, expected['first'].values)

# Test that the fine dataset has been resampled (aggregated)
# onto the grid of the coarse dataset.
ds_fine_resampled = coregister(ds_coarse, ds_fine, method_ds='mode')
expected = xr.Dataset({
'first': (['time', 'lat', 'lon'], np.array([[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0]],

[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0]]])),
'second': (['time', 'lat', 'lon'], np.array([[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0]],

[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0]]])),
'lat': np.linspace(-60, 60, 3),
'lon': np.linspace(-150, 150, 6),
'time': np.array([1, 2])})

assert_almost_equal(ds_fine_resampled['first'].values, expected['first'].values)