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  • Polyfit performance on large datasets - Suboptimal dask task graph · 3 ✖

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  • CONTRIBUTOR · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
886905635 https://github.com/pydata/xarray/issues/5629#issuecomment-886905635 https://api.github.com/repos/pydata/xarray/issues/5629 IC_kwDOAMm_X8403Rsj aulemahal 20629530 2021-07-26T17:52:42Z 2021-07-26T17:52:42Z CONTRIBUTOR

@aulemahal what do you think about how easy it might be to change polyfit to use apply_ufunc?

Should be easy! The code was written without apply_ufunc for a few reasons, one being that using apply_ufunc+vectorize was slower than the apply_along_axis trick it currently uses. But, with the issue raised here, that might not be true...

 
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   Polyfit performance on large datasets - Suboptimal dask task graph 950882492
885216203 https://github.com/pydata/xarray/issues/5629#issuecomment-885216203 https://api.github.com/repos/pydata/xarray/issues/5629 IC_kwDOAMm_X840w1PL slevang 39069044 2021-07-22T20:37:04Z 2021-07-22T20:37:04Z CONTRIBUTOR

Potentially a useful comparison: here is the task graph for the same problem posed to da.curvefit, which uses xr.apply_ufunc(scipy.optimize.curvefit, ..., vectorize=True). Looks a lot cleaner, and I think xr.polyfit could be re-implemented this way without much trouble. ```python import xarray as xr import dask.array as dsa

def linear(x, m, b): return m * x + b

da = xr.DataArray(dsa.random.random((4,6, 100), chunks=(1,2,100)), dims=['x','y', 'time']) fit = da.curvefit('time', linear).curvefit_coefficients ``` 

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   Polyfit performance on large datasets - Suboptimal dask task graph 950882492
885086453 https://github.com/pydata/xarray/issues/5629#issuecomment-885086453 https://api.github.com/repos/pydata/xarray/issues/5629 IC_kwDOAMm_X840wVj1 jbusecke 14314623 2021-07-22T17:28:52Z 2021-07-22T17:28:52Z CONTRIBUTOR

Should I close this one then?

 
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   Polyfit performance on large datasets - Suboptimal dask task graph 950882492

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