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  • JSKenyon · 2 ✖

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  • Behaviour change in xarray.Dataset.sortby/sel between dask==2.25.0 and dask==2.26.0 · 2 ✖

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  • NONE · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
693552440 https://github.com/pydata/xarray/issues/4428#issuecomment-693552440 https://api.github.com/repos/pydata/xarray/issues/4428 MDEyOklzc3VlQ29tbWVudDY5MzU1MjQ0MA== JSKenyon 6582745 2020-09-16T17:31:54Z 2020-09-16T17:31:54Z NONE

Thanks! I will definitely give that a go when I am back at my work PC. My personal take is that this level of automated rechunking is dangerous. I have constructed the chunking in my code with great care and for a reason. Having it changed "invisibly" by operations which didn't have this behaviour previously seems problematic to me.

 
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   Behaviour change in xarray.Dataset.sortby/sel between dask==2.25.0 and dask==2.26.0 702646191
693385409 https://github.com/pydata/xarray/issues/4428#issuecomment-693385409 https://api.github.com/repos/pydata/xarray/issues/4428 MDEyOklzc3VlQ29tbWVudDY5MzM4NTQwOQ== JSKenyon 6582745 2020-09-16T12:54:39Z 2020-09-16T12:54:39Z NONE

Finally managed to reproduce. Here it is: ```python import xarray import dask.array as da import numpy as np

if name == "main":

data = da.random.random([10000, 16, 4], chunks=(10000, 16, 4))

dtype = np.float32

xds = xarray.Dataset(
    data_vars={"DATA1": (("x", "y", "z"), data.astype(dtype))})

upsample_factor = 1024//xds.dims["y"]

# Create a selection which will upsample the y axis.
selection = np.repeat(np.arange(xds.dims["y"]), upsample_factor)

print("xarray.Dataset prior to resampling:\n", xds)

xds = xds.sel({"y": selection})

print("xarray.Dataset post resampling:\n", xds)

```

With dask==2.25.0 this gives: xarray.Dataset prior to resampling: <xarray.Dataset> Dimensions: (x: 10000, y: 16, z: 4) Dimensions without coordinates: x, y, z Data variables: DATA1 (x, y, z) float32 dask.array<chunksize=(10000, 16, 4), meta=np.ndarray> xarray.Dataset post resampling: <xarray.Dataset> Dimensions: (x: 10000, y: 1024, z: 4) Dimensions without coordinates: x, y, z Data variables: DATA1 (x, y, z) float32 dask.array<chunksize=(10000, 1024, 4), meta=np.ndarray>

With dask==2.26.0 this gives: xarray.Dataset prior to resampling: <xarray.Dataset> Dimensions: (x: 10000, y: 16, z: 4) Dimensions without coordinates: x, y, z Data variables: DATA1 (x, y, z) float32 dask.array<chunksize=(10000, 16, 4), meta=np.ndarray> xarray.Dataset post resampling: <xarray.Dataset> Dimensions: (x: 10000, y: 1024, z: 4) Dimensions without coordinates: x, y, z Data variables: DATA1 (x, y, z) float32 dask.array<chunksize=(10000, 512, 4), meta=np.ndarray>

And finally, the most distressing part - changing the dtype changes the chunking! With dtype = np.complex64, dask==2.26.0 gives: xarray.Dataset prior to resampling: <xarray.Dataset> Dimensions: (x: 10000, y: 16, z: 4) Dimensions without coordinates: x, y, z Data variables: DATA1 (x, y, z) complex64 dask.array<chunksize=(10000, 16, 4), meta=np.ndarray> xarray.Dataset post resampling: <xarray.Dataset> Dimensions: (x: 10000, y: 1024, z: 4) Dimensions without coordinates: x, y, z Data variables: DATA1 (x, y, z) complex64 dask.array<chunksize=(10000, 342, 4), meta=np.ndarray>

 
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   Behaviour change in xarray.Dataset.sortby/sel between dask==2.25.0 and dask==2.26.0 702646191

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