issue_comments: 366533315

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https://github.com/pydata/xarray/pull/1905#issuecomment-366533315 https://api.github.com/repos/pydata/xarray/issues/1905 366533315 MDEyOklzc3VlQ29tbWVudDM2NjUzMzMxNQ== 1191149 2018-02-18T17:46:48Z 2018-02-18T17:46:48Z CONTRIBUTOR

Indexing support. Do you only support basic-indexing like x[0, :5] or is indexing with integer arrays also supported?

My variable objects present a pure numpy array, so they follow numpy indexing precisely with one exception. If the files are actually netCDF4, they have the same limitations of the netCDF4.Variable object.

Serialization/thread-safety. Can we simultaneously read a file with another process or thread using dask?

I have not tested separate processes. In many cases, I use numpy memmap. So that will be the limitation.

API consistency for scalar arrays. Do these require some sort of special API compared to non-scalar arrays?

Same as numpy, but also has support for the netCDF4 style.

Data types support. Are strings and datetimes converted properly into the format xarray expects?

I use relative dates following netcdf time conventions. Within my software, there are special functions for translation, but I have seen this be treated by xarray separately.

Continuous integration testing in PseudoNetCDF, at a minimum on TravicCI,, but Appveyor would be great too.

I added TravisCI, but haven't looked Appveyor.

A conda-forge package to facilitate easy installs of PseudoNetCDF

I added a ci/requirements-py36-netcdf4-dev.yml as a part of my TravisCI integration. I am also working on a recipe (like https://github.com/conda-forge/xarray-feedstock).

 
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