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  • xarray · 6 ✖
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
33359402 MDExOlB1bGxSZXF1ZXN0MTU3OTg4OTI= 126 Return numpy.datetime64 arrays for non-standard calendars jhamman 2443309 closed 0   0.1.1 664063 5 2014-05-13T00:22:51Z 2015-07-27T05:38:06Z 2014-05-16T00:21:08Z MEMBER   0 pydata/xarray/pulls/126

Fixes issues in #118 and #121

 
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     xarray 13221727 pull
32919692 MDExOlB1bGxSZXF1ZXN0MTU1NTY5NTY= 113 Most of Python 3 support takluyver 327925 closed 0   0.1.1 664063 6 2014-05-06T18:31:56Z 2014-07-15T20:36:05Z 2014-05-09T01:39:01Z MEMBER   0 pydata/xarray/pulls/113

This isn't entirely finished, but I need to stop working on it for a bit, and I think enough of it is ready to be reviewed. The core code is passing its tests; the remaining failures are all in talking to the Scipy and netCDF4 backends. I also have PRs open against Scipy (scipy/scipy#3617) and netCDF4 (Unidata/netcdf4-python#252) to fix bugs I've encountered there.

Particular issues that came up: - There were quite a few circular imports. For now, I've fudged these to work rather than trying to reorganise the code. - isinstance(x, int) doesn't reliably catch numpy integer types - see e.g. numpy/numpy#2951. I changed several such cases to isinstance(x, (int, np.integer)).

 
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     xarray 13221727 pull
33772168 MDExOlB1bGxSZXF1ZXN0MTYwMzc5NTA= 134 Fix concatenating Variables with dtype=datetime64 shoyer 1217238 closed 0   0.1.1 664063 8 2014-05-19T05:39:46Z 2014-06-28T01:08:03Z 2014-05-20T19:09:28Z MEMBER   0 pydata/xarray/pulls/134

This is an alternative to #125 which I think is a little cleaner.

Basically, there was a bug where Variable.values for datetime64 arrays always made a copy of values. This made it impossible to edit variable values in-place.

@akleeman would appreciate your thoughts.

 
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     xarray 13221727 pull
33396232 MDExOlB1bGxSZXF1ZXN0MTU4MjA2NTI= 127 initial implementation of support for NetCDF groups alimanfoo 703554 closed 0   0.1.1 664063 6 2014-05-13T13:12:53Z 2014-06-27T17:23:33Z 2014-05-16T01:46:09Z CONTRIBUTOR   0 pydata/xarray/pulls/127

Just to start getting familiar with xray, I've had a go at implementing support for opening a dataset from a specific group within a NetCDF file. I haven't tested on real data but there are a couple of unit tests covering simple cases. Let me know if you'd like to take this forward, happy to work on it further.

 
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     xarray 13221727 pull
33465275 MDExOlB1bGxSZXF1ZXN0MTU4NjI4MTI= 129 Require only numpy 1.7 for the benefit of readthedocs shoyer 1217238 closed 0   0.1.1 664063 0 2014-05-14T06:41:30Z 2014-06-25T23:40:31Z 2014-05-15T07:21:22Z MEMBER   0 pydata/xarray/pulls/129

ReadTheDocs comes with pre-built packages for the basic scientific python stack, but some of these packages are old (e.g., numpy is 1.7.1). The only way to upgrade packages on readthedocs is to use a virtual environment and a requirements.txt.

Unfortunately, this means we can't upgrade both numpy and pandas simultaneously, because pandas may get built first and link against the wrong version of numpy. We inadvertantly stumbled upon a work around to build the "latest" docs by first installing numpy in the (cached) virtual environment, and then later (in another commit), adding pandas to the requirements.txt file.

However, this is a real hack and makes it impossible to maintain different versions of the docs, such as for tagged releases. Accordingly, this commit relaxes the numpy version requirement so we can use a version that readthedocs already has installed. (We actually don't really need a newer version of numpy for any current functionality in xray, although it's nice to have for support for missing value functions like nanmean.)

 
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     xarray 13221727 pull
28940534 MDU6SXNzdWUyODk0MDUzNA== 53 Python 3 support shoyer 1217238 closed 0   0.1.1 664063 4 2014-03-07T06:14:55Z 2014-05-20T06:22:58Z 2014-05-12T05:56:28Z MEMBER      

This is unlikely to be difficult since all of our dependencies support Python 3, but it will definitely take some work.

 
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   completed xarray 13221727 issue

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