Update _get_weights() method for SpatialAccessor and TemporalAccessor
#252
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| elif isinstance(weights, xr.DataArray): | ||
| dv_weights = weights | ||
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| self._validate_weights(dv, axis, dv_weights) | ||
| dataset[dv.name] = self._averager(dv, axis, dv_weights) | ||
| return dataset | ||
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| def get_weights( |
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I assume this is the only line that changed in this file (aside from calls to _get_weights)? Github is highlighting this whole function...probably because it got moved?
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Yes, this method was moved and renamed. Nothing else was changed in this file.
tests/test_spatial.py
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| # FIXME: ValueError when domain bounds contains lower bound larger than | ||
| # upper bound |
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I think this should throw an error? I'm kind of confused why it wasn't before based on this check. Should this test have region bounds such that lon_bounds=np.array([350, 20]) or something like that?
I think region bounds should be able to accept a larger right hand bound, but I don't think this should be true for domain bounds.
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I ended up deleting this test because it is faulty and we already have a test for raising a ValueError if the domain bounds has a larger lower bound vs. upper bound.
| ), | ||
| "lon": xr.DataArray( | ||
| name="lon_wts", | ||
| data=np.array([1, 2, 3, 4]), | ||
| coords={"lon": self.ds.lon}, | ||
| dims=["lon"], | ||
| ), | ||
| } | ||
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| def test_weights_for_single_axis_are_identical(self): | ||
| axis_weights = self.axis_weights | ||
| del axis_weights["lon"] | ||
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| result = self.ds.spatial._combine_weights(axis_weights=self.axis_weights) | ||
| expected = self.axis_weights["lat"] | ||
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| assert result.identical(expected) | ||
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| def test_weights_for_multiple_axis_is_the_product_of_matrix_multiplication(self): | ||
| result = self.ds.spatial._combine_weights(axis_weights=self.axis_weights) | ||
| expected = xr.DataArray( | ||
| data=np.array([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12], [4, 8, 12, 16]]), | ||
| coords={"lat": self.ds.lat, "lon": self.ds.lon}, | ||
| dims=["lat", "lon"], | ||
| ) | ||
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| assert result.identical(expected) | ||
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| class TestAverager: | ||
| @pytest.fixture(autouse=True) | ||
| def setup(self): | ||
| self.ds = generate_dataset(cf_compliant=True, has_bounds=True) | ||
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| @requires_dask | ||
| def test_chunked_weighted_avg_over_lat_and_lon_axes(self): | ||
| ds = self.ds.copy().chunk(2) | ||
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| weights = xr.DataArray( | ||
| data=np.array([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12], [4, 8, 12, 16]]), | ||
| coords={"lat": ds.lat, "lon": ds.lon}, | ||
| dims=["lat", "lon"], | ||
| ) | ||
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| result = ds.spatial._averager(ds.ts, axis=["X", "Y"], weights=weights) | ||
| expected = xr.DataArray( | ||
| name="ts", data=np.ones(15), coords={"time": ds.time}, dims=["time"] | ||
| ) | ||
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| assert result.identical(expected) | ||
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| def test_weighted_avg_over_lat_axis(self): | ||
| weights = xr.DataArray( | ||
| name="lat_wts", | ||
| data=np.array([1, 2, 3, 4]), | ||
| coords={"lat": self.ds.lat}, | ||
| dims=["lat"], | ||
| ) | ||
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| result = self.ds.spatial._averager(self.ds.ts, axis=["Y"], weights=weights) | ||
| expected = xr.DataArray( | ||
| name="ts", | ||
| data=np.ones((15, 4)), | ||
| coords={"time": self.ds.time, "lon": self.ds.lon}, | ||
| dims=["time", "lon"], | ||
| ) | ||
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| assert result.identical(expected) | ||
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| def test_weighted_avg_over_lon_axis(self): | ||
| weights = xr.DataArray( | ||
| name="lon_wts", | ||
| data=np.array([1, 2, 3, 4]), | ||
| coords={"lon": self.ds.lon}, | ||
| dims=["lon"], | ||
| ) | ||
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| result = self.ds.spatial._averager(self.ds.ts, axis=["X"], weights=weights) | ||
| expected = xr.DataArray( | ||
| name="ts", | ||
| data=np.ones((15, 4)), | ||
| coords={"time": self.ds.time, "lat": self.ds.lat}, | ||
| dims=["time", "lat"], | ||
| ) | ||
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| assert result.identical(expected) | ||
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| def test_weighted_avg_over_lat_and_lon_axis(self): | ||
| weights = xr.DataArray( | ||
| data=np.array([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12], [4, 8, 12, 16]]), | ||
| coords={"lat": self.ds.lat, "lon": self.ds.lon}, | ||
| dims=["lat", "lon"], | ||
| ) | ||
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| result = self.ds.spatial._averager(self.ds.ts, axis=["X", "Y"], weights=weights) | ||
| expected = xr.DataArray( | ||
| name="ts", data=np.ones(15), coords={"time": self.ds.time}, dims=["time"] | ||
| ) | ||
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| assert result.identical(expected) |
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I kind of remember you commenting on this...is this all removed because they are private methods (and we don't need to test them)?
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Correct, we should test the public method which will cover the private methods (implementation details).
No need to review any of these changes here so I am just porting some tests over from the private methods to the public methods.
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I have no objection to making get_weights() public, though I didn't fully understand the the changes to the unit tests. Should I view the changes in tests/ as a reorganization or something that I should review more carefully?
I think the error for test_weights_for_region_in_lon_domain_with_both_spanning_p_meridian is correct and we should represent the problematic line as ds.lon_bnds.data[:] = np.array([[-1, 1], [1, 90], [90, 180], [180, 359]]).
I addressed this comment in one of the PR review comments.
I ended up removing tests where the domain bounds has a larger left hand value than right hand value because they are supposed to throw an error, rather than pass. There's already a test to check that an error is thrown for this case. I kept |
Codecov Report
@@ Coverage Diff @@
## main #252 +/- ##
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Coverage 100.00% 100.00%
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Files 9 8 -1
Lines 742 736 -6
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- Hits 742 736 -6
Continue to review full report at Codecov.
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tomvothecoder
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Hi @pochedls, @lee1043, and @chengzhuzhang, instead of making
Example for Lines 332 to 339 in 44620c0 Lines 742 to 745 in 44620c0 Example for Lines 465 to 472 in 44620c0 Lines 624 to 629 in 44620c0
Lines 1369 to 1402 in 44620c0 If this looks good, we can implement |
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Hi @tomvothecoder Thank you for the discussion. I don't recall that I have a use case that requires to get/keep the weights to pass down to another operation. With the exception that, they are useful to be examed during xcdat development/validation. So I don't have strong opinion on the implementation. I will defer to Steve and Jiwoo. |
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I like I think it would be good to keep |
There is only one Keeping weights with Climatology
Keeping weights with Departures
Sounds good, I added
Hmmm, we can address this if users open up an issue about it, unless you think it is worth addressing now. |
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Got it – thanks Tom. |
- Add kwarg `keep_weights` to both methods - Make `_get_weights()` a public method for `SpatialAccessor` - Delete redundant tests for private methods - Delete faulty tests with domain bounds:
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SpatialAccessor._get_weights() and TemporalAccessor._get_weights() public_get_weights() method for SpatialAccessor and TemporalAccessor
Description
Summary of Changes
_get_weights()method forSpatialAccessorandTemporalAccessor#251Nonevalue forlat_boundsandlon_boundskeyword args toSpatialAccessor.get_weights()keep_weightsto both methods_get_weights()a public method forSpatialAccessorChecklist
If applicable: