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  • dcherian · 1 ✖

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  • `xr.save_mfdataset()` doesn't honor `compute=False` argument · 1 ✖

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  • MEMBER · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
656403217 https://github.com/pydata/xarray/issues/4209#issuecomment-656403217 https://api.github.com/repos/pydata/xarray/issues/4209 MDEyOklzc3VlQ29tbWVudDY1NjQwMzIxNw== dcherian 2448579 2020-07-09T23:43:17Z 2020-07-09T23:43:17Z MEMBER

Here's an alternative map_blocks solution:

``` python def write_block(ds, t0): if len(ds.time) > 0: fname = (ds.time[0] - t0).values.astype("timedelta64[h]").astype(int) ds.to_netcdf(f"temp/file-{fname:06d}.nc")

# dummy return
return ds.time

ds = xr.tutorial.open_dataset("air_temperature", chunks={"time": 100}) ds.map_blocks(write_block, kwargs=dict(t0=ds.time[0])).compute(scheduler="processes") ```

There are two workarounds here though. 1. The user function always has to return something. 2. We can't provide template=ds.time because it has no chunk information and ds.time.chunk({"time": 100}) silently does nothing because it is an IndexVariable. So the user function still needs the len(ds.time) > 0 workaround.

I think a cleaner API may be to have dask.compute([write_block(block) for block in ds.to_delayed()]) where ds.to_delayed() yields a bunch of tasks; each of which gives a Dataset wrapping one block of the underlying array.

 
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   `xr.save_mfdataset()` doesn't honor `compute=False` argument 653442225

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