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  • dcherian · 2 ✖

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  • Improvements to lazy behaviour of `xr.cov()` and `xr.corr()` · 2 ✖

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850650732 https://github.com/pydata/xarray/pull/5390#issuecomment-850650732 https://api.github.com/repos/pydata/xarray/issues/5390 MDEyOklzc3VlQ29tbWVudDg1MDY1MDczMg== dcherian 2448579 2021-05-28T20:19:56Z 2021-05-28T20:20:52Z MEMBER

python # 3. Detrend along the given dim # 4. Compute covariance along the given dim # N.B. `skipna=False` is required or there is a bug when computing # auto-covariance. E.g. Try xr.cov(da,da) for # da = xr.DataArray([[1, 2], [1, np.nan]], dims=["x", "time"]) def _mean(da): return da.sum(dim=dim, skipna=True, min_count=1) / (valid_count) cov = _mean(da_a * da_b) - _mean(da_a.mean(dim=dim) * da_b.mean(dim=dim))

This second term looks very weird to me, it should be a no-op python _mean(da_a.mean(dim=dim) * da_b.mean(dim=dim))

is it just cov = _mean(da_a * da_b) - da_a.mean(dim=dim) * da_b.mean(dim=dim)

 
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   Improvements to lazy behaviour of `xr.cov()` and `xr.corr()` 904153867
850535282 https://github.com/pydata/xarray/pull/5390#issuecomment-850535282 https://api.github.com/repos/pydata/xarray/issues/5390 MDEyOklzc3VlQ29tbWVudDg1MDUzNTI4Mg== dcherian 2448579 2021-05-28T16:31:36Z 2021-05-28T16:31:36Z MEMBER

@AndrewWilliams3142 I think that's right. You can confirm these ideas by profiling a test problem: https://docs.dask.org/en/latest/diagnostics-local.html#example

It does seem like with the new version dask will hold on on to da_a*da_b for a while, which is an improvement over holding da_a and da_b separately for a while

 
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   Improvements to lazy behaviour of `xr.cov()` and `xr.corr()` 904153867

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