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issue 7

  • Structured numpy arrays, xarray and netCDF(4) 2
  • Dataset groups 1
  • Getting DataArrays from netCDF4 files correctly and without hassle 1
  • stack renames single dimensions instead of creating a MultiIndex 1
  • Optional extras to manage dependencies 1
  • Interpolation using non-dimension coordinates 1
  • combine_first of Datasets changes dtype of variable present only in one Dataset 1

user 1

  • equaeghe · 8 ✖

author_association 1

  • NONE · 8 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1112231763 https://github.com/pydata/xarray/issues/1626#issuecomment-1112231763 https://api.github.com/repos/pydata/xarray/issues/1626 IC_kwDOAMm_X85CS09T equaeghe 601177 2022-04-28T13:51:17Z 2022-04-28T13:51:17Z NONE

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

Still relevant.

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  Structured numpy arrays, xarray and netCDF(4) 264582338
1070998603 https://github.com/pydata/xarray/issues/4220#issuecomment-1070998603 https://api.github.com/repos/pydata/xarray/issues/4220 IC_kwDOAMm_X84_1iRL equaeghe 601177 2022-03-17T15:46:28Z 2022-03-17T15:46:28Z NONE

Issue still present in recent xarray.

Output of <tt>xr.show_versions()</tt> INSTALLED VERSIONS ------------------ commit: None python: 3.9.9 (main, Jan 9 2022, 21:37:30) [GCC 11.2.0] python-bits: 64 OS: Linux OS-release: 5.15.26-gentoo-a machine: x86_64 processor: AMD Ryzen 7 PRO 4750U with Radeon Graphics byteorder: little LC_ALL: None LANG: nl_NL.UTF-8 LOCALE: ('nl_NL', 'UTF-8') libhdf5: 1.10.5 libnetcdf: 4.8.1 xarray: 0.21.1 pandas: 1.4.1 numpy: 1.22.2 scipy: 1.7.3 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: 3.3.0 Nio: None zarr: None cftime: 1.5.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2022.02.0 distributed: None matplotlib: 3.5.1 cartopy: None seaborn: 0.11.2 numbagg: None fsspec: 2022.01.0 cupy: None pint: None sparse: None setuptools: 60.9.2 pip: 22.0.3 conda: None pytest: None IPython: 7.31.1 sphinx: 4.4.0 /usr/lib/python3.9/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils. warnings.warn("Setuptools is replacing distutils.")
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  combine_first of Datasets changes dtype of variable present only in one Dataset 656089264
660142287 https://github.com/pydata/xarray/issues/2888#issuecomment-660142287 https://api.github.com/repos/pydata/xarray/issues/2888 MDEyOklzc3VlQ29tbWVudDY2MDE0MjI4Nw== equaeghe 601177 2020-07-17T14:36:57Z 2020-07-17T14:36:57Z NONE

Even if version information is (not yet) available, this would be very useful. Currently, when packaging xarray for downstream distributions, it is a very burdensome task to piece together which optional dependencies there are in the first place. I understand that dealing with the backlog makes this a rather tedious task, but making it a requirement going forward to add such dependency information whenever a dependency is changed or added would be required in any case.

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  Optional extras to manage dependencies 432058005
571806712 https://github.com/pydata/xarray/issues/1888#issuecomment-571806712 https://api.github.com/repos/pydata/xarray/issues/1888 MDEyOklzc3VlQ29tbWVudDU3MTgwNjcxMg== equaeghe 601177 2020-01-07T22:41:44Z 2020-01-07T22:41:44Z NONE

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

I think this is still relevant.

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  Getting DataArrays from netCDF4 files correctly and without hassle 294380515
535169724 https://github.com/pydata/xarray/issues/3343#issuecomment-535169724 https://api.github.com/repos/pydata/xarray/issues/3343 MDEyOklzc3VlQ29tbWVudDUzNTE2OTcyNA== equaeghe 601177 2019-09-25T19:13:46Z 2019-09-25T19:13:46Z NONE

you might be able to achieve what you want by using swap_dims

Yes, that is a nice workaround (I used another, less convenient one). But I would say that the current (lack of) functionality is a bug: the documentation talks about coordinates, not about dimensions. Moreover, it seems like something one would want to support. I do not know how complicated this would be to implement, but if reasonably feasible, I would request to keep this issue open.

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  Interpolation using non-dimension coordinates 498297720
469885042 https://github.com/pydata/xarray/issues/2802#issuecomment-469885042 https://api.github.com/repos/pydata/xarray/issues/2802 MDEyOklzc3VlQ29tbWVudDQ2OTg4NTA0Mg== equaeghe 601177 2019-03-05T22:31:44Z 2019-03-05T22:31:44Z NONE

Is this a dupe of #1431 ?

Yes. My example and explanation are a bit clearer, so I do not know which would be best to close.

In any case, I hope this is considered for fixing separately from some big refactoring of the MultiIndex machinery, which seems to have stalled. This seems like a corner case that might not be too big, but I may be mistaken.

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  stack renames single dimensions instead of creating a MultiIndex 417412201
363129063 https://github.com/pydata/xarray/issues/1626#issuecomment-363129063 https://api.github.com/repos/pydata/xarray/issues/1626 MDEyOklzc3VlQ29tbWVudDM2MzEyOTA2Mw== equaeghe 601177 2018-02-05T15:59:50Z 2018-02-05T22:01:35Z NONE

I'd also like to see better support for compound types, writing them for starters. I'll collect some information here:

  • In the code @tfurf linked to (_nc4_values_and_dtype), an elif needs to be added to catch structured dtypes. I think they have kind == 'V'.

  • dtype.builtin can be used to detect whether we are indeed dealing with a structured type. Namely dtype.builtin must be 0.

  • The structured type must fist be added to the netCDF4.Dataset using its method createCompoundType. This must be done recursively, with the deepest levels first.

  • The netCDF variable is created in prepare_variable, which calls _nc4_values_and_dtype. There, via self.ds we also have access to the netCDF4 Dataset to be used for the creation of the as mentioned above. However, is self.ds really the Dataset, or some NetCDF4.Group? In any case _nc4_values_and_dtype and its use in prepare_variable needs to be refactored, because we need access to the underlying netCDF4 Dataset.

Is there anything I've missed? Can someone shed light on whether self.ds in prepare_variable can be assumed to the underlying netCDF4 Dataset?

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  Structured numpy arrays, xarray and netCDF(4) 264582338
363090775 https://github.com/pydata/xarray/issues/1092#issuecomment-363090775 https://api.github.com/repos/pydata/xarray/issues/1092 MDEyOklzc3VlQ29tbWVudDM2MzA5MDc3NQ== equaeghe 601177 2018-02-05T13:53:55Z 2018-02-05T13:53:55Z NONE

I'm late to the discussion and may be repeating some things essentially already said, but I'd still like to add a further voice.

@shoyer said on 8 Nov 2016:

I am reluctant to add the additional complexity of groups directly into the xarray.Dataset data model. For example, how do groups get updated when you slice, aggregate or concatenate datasets? The rules for coordinates are already pretty complex.

If you prepend the paths to all the names (of dimensions, coordinate variables, and variables) and use the resulting strings as names, don't you just get a collection that would fit right in a xarray.Dataset? (Perhaps I'm just repeating what @lamorton said on 28 Mar 2017.) My feeling is that the only thing missing would be coordinate variables defined in a group closer to the root of the hierarchy, but that needs to be dealt with anyway if you want to read from netCDF4 files correctly (see my #1888). My guess would be that having groups inherit all the dimensions/coordinates of their parent that they do not redefine should be the way to go.

My use case is data from a single metmast over time. There are various instruments measuring all kinds of variables of which 10-minute statistics are recorded. I use groups to keep an overview. (I use something like /[wind|air|prec]/<device>/<variable>, /<device>/<variable>, or /<device>/<variable>/statistic as a hierarchy.) Slicing along the time axis for the whole hierarchy would make perfect sense.

@shoyer said on 30 Mar 2017:

We would possibly want to make another Dataset subclass for the sub-datasets to ensure that their variables are linked to the parent, e.g., xarray.LinkedDataset. This would enable ds['flux']['poloidal'] = subset.

But I'm also not convinced this is actually worth the trouble given how easy it is to write ds['flux', 'poloidal'].

I would prefer the former option, as it more clearly shows the hierarchical nature. If also copying the netCDF4-path-separator-convention, then ds['flux/poloidal'] is shorter than ds['flux', 'poloidal']. (Allowing Dataset or DataArray to have names that include '/' would be dangerous anyway in view of netCDF4 serialization.)

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  Dataset groups 187859705

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