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  • max-sixty · 4 ✖

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  • Writing a a dataset to .zarr in a loop makes all the data NaNs · 4 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1099643203 https://github.com/pydata/xarray/issues/6456#issuecomment-1099643203 https://api.github.com/repos/pydata/xarray/issues/6456 IC_kwDOAMm_X85BizlD max-sixty 5635139 2022-04-14T21:31:37Z 2022-04-14T21:31:37Z MEMBER

@max-sixty could you explain which bit isn't working for you? The initial example I shared works fine in colab for me, so that might be a you problem. The second one required specifying the chunks when making the datasets (I've editted above).

Right, you changed the example after I responded

But this bug report was more about the fact that overwriting was converting data to NaNs (in two different ways depending on the code apparently).

In my case there is no longer any need to do the overwriting, but this doesn't seem like the expected behaviour of overwriting, and I'm sure there are some valid reasons to overwrite data - hence me opening the bug report.

Something surprising is indeed going on here. To focus on the surprising part;

```python print(ds3.low_dim.values)

ds3.to_zarr('zarr_bug.zarr', mode='w')

print(ds3.low_dim.values) ```

returns:

[[2. 3. 2. ... 8. 0. 9.] [6. 2. 6. ... 2. 4. 3.] [0. 8. 8. ... 6. 5. 4.] ... [1. 0. 5. ... 2. 0. 3.] [5. 5. 7. ... 9. 6. 2.] [5. 7. 8. ... 4. 8. 9.]] [[nan nan nan ... nan nan nan] [nan nan nan ... nan nan nan] [nan nan nan ... nan nan nan] ... [ 1. 0. 5. ... 2. 0. 3.] [ 5. 5. 7. ... 9. 6. 2.] [ 5. 7. 8. ... 4. 8. 9.]]

Similarly:

```python

In [50]: ds3.low_dim.count().compute() Out[50]: <xarray.DataArray 'low_dim' ()> array(1000000)

In [51]: ds3.to_zarr('zarr_bug.zarr', mode='w') Out[51]: <xarray.backends.zarr.ZarrStore at 0x16a27c6d0>

In [55]: ds3.low_dim.count().compute() Out[55]: <xarray.DataArray 'low_dim' ()> array(500000) ```

So it's changing the result in memory just from writing to the Zarr store. I'm not sure what the cause is.

We can still massively reduce the size of this example — it's currently doing pickling, got a bunch of repeated code, etc. Does it work without the pickling? What if ds3 = xr.concat([ds1, ds1.copy(deep=True)]), etc.

 
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   Writing a a dataset to .zarr in a loop makes all the data NaNs 1197117301
1095585081 https://github.com/pydata/xarray/issues/6456#issuecomment-1095585081 https://api.github.com/repos/pydata/xarray/issues/6456 IC_kwDOAMm_X85BTU05 max-sixty 5635139 2022-04-11T21:29:27Z 2022-04-11T21:29:27Z MEMBER

@tbloch1 it doesn't copy in to someone else's python atm — that's the "C" part of MCVE...

 
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   Writing a a dataset to .zarr in a loop makes all the data NaNs 1197117301
1094412198 https://github.com/pydata/xarray/issues/6456#issuecomment-1094412198 https://api.github.com/repos/pydata/xarray/issues/6456 IC_kwDOAMm_X85BO2em max-sixty 5635139 2022-04-10T23:46:53Z 2022-04-10T23:46:53Z MEMBER

Have you tried asking on stackoverflow with the xarray tag?

Or GH Discussions! But it would need a smaller MCVE

 
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   Writing a a dataset to .zarr in a loop makes all the data NaNs 1197117301
1093253883 https://github.com/pydata/xarray/issues/6456#issuecomment-1093253883 https://api.github.com/repos/pydata/xarray/issues/6456 IC_kwDOAMm_X85BKbr7 max-sixty 5635139 2022-04-08T19:05:12Z 2022-04-08T19:05:12Z MEMBER

Hi @tbloch1 — thanks for the issue

So I understand — is this loading the existing dataset, adding one a slice, and then writing the whole result? Have you considered using mode='a' if you want to write from different processes?

For the example — would it be possible to slim that down a bit further? Does it happen with with one read & write after the initial one?

 
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   Writing a a dataset to .zarr in a loop makes all the data NaNs 1197117301

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