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  • sjpfenninger · 5 ✖

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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
212940728 https://github.com/pydata/xarray/issues/439#issuecomment-212940728 https://api.github.com/repos/pydata/xarray/issues/439 MDEyOklzc3VlQ29tbWVudDIxMjk0MDcyOA== sjpfenninger 141709 2016-04-21T14:23:54Z 2016-04-21T14:23:54Z CONTRIBUTOR

Would it not make sense to use a pandas.DatetimeIndex instead of pure-numpy datetimes? It seems that DatetimeIndex already solves lots of the weirdness in the underlying numpy datetime objects and adds a bunch of useful functionality for things like groupby operations.

 
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   Display datetime64 arrays without showing local timezones 89866276
206155779 https://github.com/pydata/xarray/issues/191#issuecomment-206155779 https://api.github.com/repos/pydata/xarray/issues/191 MDEyOklzc3VlQ29tbWVudDIwNjE1NTc3OQ== sjpfenninger 141709 2016-04-06T06:52:47Z 2016-04-06T06:52:47Z CONTRIBUTOR

Ok, I'll extend this into a pull request one of these days

 
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   interpolate/sample array at point 38849807
204418102 https://github.com/pydata/xarray/issues/191#issuecomment-204418102 https://api.github.com/repos/pydata/xarray/issues/191 MDEyOklzc3VlQ29tbWVudDIwNDQxODEwMg== sjpfenninger 141709 2016-04-01T14:32:40Z 2016-04-01T14:32:40Z CONTRIBUTOR

I've written a wrapper around scipy's map_coordinates. I cleaned this up a bit and pushed it here: https://github.com/sjpfenninger/xray/commit/0e0d88d6dc6f428d24ae3df2161ff10deded2a5a

It's not exactly fully-featured but scratches the itch I had, which is to pass DataArrays through map_coordinates with support for xarray's coordinates.

@shoyer If this seems of use, I could add some tests and perhaps an example, then submit a pull request?

 
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   interpolate/sample array at point 38849807
73732815 https://github.com/pydata/xarray/issues/316#issuecomment-73732815 https://api.github.com/repos/pydata/xarray/issues/316 MDEyOklzc3VlQ29tbWVudDczNzMyODE1 sjpfenninger 141709 2015-02-10T16:37:16Z 2015-02-10T16:37:16Z CONTRIBUTOR

I noticed that you already have a fallback option and I simply made use of that, which solves this problem in a way that looks reasonable to me.

However, I'm still not quite happy with the way errors were/are handled: before this change, reading a file with the non-standard datetime format would work without errors, but trying to do anything with the dataset after opening it would produce a ValueError, which from an end-user perspective doesn't help much to figure out where the error is coming from.

I suppose with the change in my PR, for cases where python-netCDF4 is not installed, one would instead get an ImportError without explanation (one wouldn't know why we tried to import the optional dependency). Maybe something like a wrapper to import netCDF4 which raises a context-specific ImportError message would be useful?

 
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   Not-quite-ISO timestamps 56817968
73519971 https://github.com/pydata/xarray/issues/316#issuecomment-73519971 https://api.github.com/repos/pydata/xarray/issues/316 MDEyOklzc3VlQ29tbWVudDczNTE5OTcx sjpfenninger 141709 2015-02-09T14:47:39Z 2015-02-09T14:47:39Z CONTRIBUTOR

I think falling back to netcdftime would be preferrable to a datetime_format argument since it would just automagically work (although a datetime_format could be useful for other, trickier netCDF files). I could have a go at doing this later in the week and submit a PR, if it's helpful.

 
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   Not-quite-ISO timestamps 56817968

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