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[PR]: Add Z axis support for spatial averaging #606

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Description

This PR adds functionality to compute vertical averages.

Checklist

  • My code follows the style guidelines of this project
  • I have performed a self-review of my own code
  • My changes generate no new warnings
  • Any dependent changes have been merged and published in downstream modules

If applicable:

  • I have added tests that prove my fix is effective or that my feature works
  • New and existing unit tests pass with my changes (locally and CI/CD build)
  • I have commented my code, particularly in hard-to-understand areas
  • I have made corresponding changes to the documentation
  • I have noted that this is a breaking change for a major release (fix or feature that would cause existing functionality to not work as expected)

@github-actions github-actions bot added the type: enhancement New enhancement request label Feb 14, 2024
@pochedls
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I'd appreciate testing with real-world data (and comparisons with CDAT).

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codecov bot commented Feb 14, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 100.00%. Comparing base (5de4d7f) to head (98ea3d7).

Additional details and impacted files
@@            Coverage Diff            @@
##              main      #606   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           15        15           
  Lines         1588      1595    +7     
=========================================
+ Hits          1588      1595    +7     

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Comment on lines +65 to +75
xr.testing.assert_allclose(result["so"], expected)

# check that vertical averaging returns the correct weights
expected = xr.DataArray(
data=np.array([2000, 2000, 1000, 0.0]),
coords={"time": ds.time, "lev": ds.lev, "lat": ds.lat, "lon": ds.lon},
dims=["lev"],
attrs={"xcdat_bounds": True},
)

xr.testing.assert_allclose(result["lev_wts"], expected)
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Are two-in-one tests permitted?

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Yeah there's no problem with that here. As long as the tests are relatively easy to maintain.

@@ -73,10 +73,12 @@ def average(
keep_weights: bool = False,
lat_bounds: Optional[RegionAxisBounds] = None,
lon_bounds: Optional[RegionAxisBounds] = None,
lev_bounds: Optional[RegionAxisBounds] = None,
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I think lev_bounds is a good generic name, but I'm open to other possibilities.

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Sounds good to me

xcdat/spatial.py Show resolved Hide resolved
Comment on lines +512 to +537
def _get_vertical_weights(
self, domain_bounds: xr.DataArray, region_bounds: Optional[np.ndarray]
) -> xr.DataArray:
"""Gets weights for the vertical axis.

This method scales the domain to a region (if selected) and returns weights
proportional to the difference between each pair of level bounds.

Parameters
----------
domain_bounds : xr.DataArray
The array of bounds for the vertical domain.
region_bounds : Optional[np.ndarray]
The array of bounds for vertical selection.

Returns
-------
xr.DataArray
The vertical axis weights.
"""
if region_bounds is not None:
domain_bounds = self._scale_domain_to_region(domain_bounds, region_bounds)

weights = self._calculate_weights(domain_bounds)
return weights

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I think this is sufficiently different from _get_longitude_weights (which deals with the prime meridian) and _get_latitude_weights (sine of bounds) to justify its own function.

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I agree

@tomvothecoder tomvothecoder changed the title Feature/596 vertical averaging [PR]: Add Z axis support for spatial averaging Feb 14, 2024
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Tom:

  • Compare with cdms2 average with Z axis and get floating point diffs
  • Try out in e3sm_diags

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I'm going to pick this back up for review. We might be able to get this in for v0.7.0.

Comment on lines +54 to +56
so = ds["so"]
so[:] = np.array([1, 2, 3, 4])
ds["so"] = so
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@tomvothecoder tomvothecoder Mar 21, 2024

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Suggested change
so = ds["so"]
so[:] = np.array([1, 2, 3, 4])
ds["so"] = so
ds["so"].values = np.array([1, 2, 3, 4])

Assigning the numpy array directly to the DataArray will work too

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I'd appreciate testing with real-world data (and comparisons with CDAT).

Can you provide me some datasets to test with? I wrote a script and tested a single E3SM dataset, which produced nearly identical results (1e-16 max rel diff).

@pochedls
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I'd appreciate testing with real-world data (and comparisons with CDAT).

Can you provide me some datasets to test with? I wrote a script and tested a single E3SM dataset, which produced nearly identical results (1e-16 max rel diff).

Do you have xsearch running? I think you could look for monthly, historical data, for the thetao or ta (Amon table) variables.

@tomvothecoder
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I'd appreciate testing with real-world data (and comparisons with CDAT).

Can you provide me some datasets to test with? I wrote a script and tested a single E3SM dataset, which produced nearly identical results (1e-16 max rel diff).

Do you have xsearch running? I think you could look for monthly, historical data, for the thetao or ta (Amon table) variables.

I'll check on xsearch. Thanks for the pointers.

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type: enhancement New enhancement request
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Add Z axis support for spatial averaging
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