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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
309100522 MDU6SXNzdWUzMDkxMDA1MjI= 2018 MemoryError when using save_mfdataset() NicWayand 1117224 closed 0     1 2018-03-27T19:22:28Z 2020-03-28T07:51:17Z 2020-03-28T07:51:17Z NONE      

Code Sample, a copy-pastable example if possible

```python import xarray as xr import dask

Dummy data that on disk is about ~200GB

da = xr.DataArray(dask.array.random.normal(0, 1, size=(12,408,1367,304,448), chunks=(1, 1, 1, 304, 448)), dims=('ensemble', 'init_time', 'fore_time', 'x', 'y'))

Perform some calculation on the dask data

da_sum = da.sum(dim='x').sum(dim='y')(2525)/(10**6)

Write to multiple files

c_e, datasets = zip(*da_sum.to_dataset(name='sic').groupby('ensemble')) paths = ['file_%s.nc' % e for e in c_e] xr.save_mfdataset(datasets, paths)

```

Problem description

Results in a MemoryError, when dask should handle writing this OOM DataArray to multiple within-memory-sized netcdf files. Related SO post here

Expected Output

12 netcdf files (grouped by the ensemble dim).

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.4.final.0 python-bits: 64 OS: Linux OS-release: 4.14.12 machine: x86_64 processor: byteorder: little LC_ALL: C LANG: C LOCALE: None.None xarray: 0.10.2 pandas: 0.22.0 numpy: 1.14.1 scipy: 1.0.0 netCDF4: 1.3.1 h5netcdf: 0.5.0 h5py: 2.7.1 Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: None dask: 0.17.1 distributed: 1.21.1 matplotlib: 2.2.2 cartopy: None seaborn: 0.8.1 setuptools: 38.5.1 pip: 9.0.1 conda: None pytest: None IPython: 6.2.1 sphinx: None 
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   completed xarray 13221727 issue
186326698 MDExOlB1bGxSZXF1ZXN0OTE2Mzk0OTY= 1070 Feature/rasterio NicWayand 1117224 closed 0     11 2016-10-31T16:14:55Z 2017-05-22T08:47:40Z 2017-05-22T08:47:40Z NONE   0 pydata/xarray/pulls/1070

@jhamman started a backend for RasterIO that I have been working on. There are two issues I am stuck on that I could use some help:

1) Lat/long coords are not being decoded correctly (missing from output dataset). Lat/lon projection are correctly calculated and added here (https://github.com/NicWayand/xray/blob/feature/rasterio/xarray/backends/rasterio_.py#L117). But, it appears (with my limited knowledge of xarray) that the lat/long coords contained within obj are lost at this line (https://github.com/NicWayand/xray/blob/feature/rasterio/xarray/conventions.py#L930).

2) Lazy-loading needs to be enabled. How can I setup/test this? Are there examples from other backends I could follow?

790

 
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     xarray 13221727 pull
170688064 MDExOlB1bGxSZXF1ZXN0ODA5ODgxNzA= 961 Update time-series.rst NicWayand 1117224 closed 0     3 2016-08-11T16:26:58Z 2017-04-03T05:31:06Z 2017-04-03T05:31:06Z NONE   0 pydata/xarray/pulls/961

Thought it would be helpful to users to know that timezones are not handled here, rather than googling and finding this: https://github.com/pydata/xarray/issues/552

 
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     xarray 13221727 pull
171504099 MDU6SXNzdWUxNzE1MDQwOTk= 970 Multiple preprocessing functions in open_mfdataset? NicWayand 1117224 closed 0     3 2016-08-16T20:01:22Z 2016-08-17T07:01:02Z 2016-08-16T21:46:43Z NONE      

I would like to have multiple functions applied during a open_mfdataset call.

Using one works great:

Python ds = xr.open_mfdataset(files,concat_dim='time',engine='pynio', preprocess=lambda x: x.load())

Does the current behavior include multiple calls? (apologizes if this is defined somewhere, I couldn't find any multiple calls examples)

Something like:

Python ds = xr.open_mfdataset(files,concat_dim='time',engine='pynio', preprocess=[lambda x: x.load(),lambda y: y['time']=100])

 
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   completed xarray 13221727 issue

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