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| id ▼ | node_id | number | state | locked | title | user | body | created_at | updated_at | closed_at | merged_at | merge_commit_sha | assignee | milestone | draft | head | base | author_association | auto_merge | repo | url | merged_by |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15556956 | MDExOlB1bGxSZXF1ZXN0MTU1NTY5NTY= | 113 | closed | 0 | Most of Python 3 support | takluyver 327925 | 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))`. | 2014-05-06T18:31:56Z | 2014-07-15T20:36:05Z | 2014-05-09T01:39:01Z | 2014-05-09T01:39:01Z | 184fd39c0fa1574a03439998138297bdb193674c | 0.1.1 664063 | 0 | 6dbd8910080e9210700501c0ea671cf0dc44d90f | 8d6fbd7f4469ce73ed94cf09602efa0498f9dab6 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/113 | |||
| 15798892 | MDExOlB1bGxSZXF1ZXN0MTU3OTg4OTI= | 126 | closed | 0 | Return numpy.datetime64 arrays for non-standard calendars | jhamman 2443309 | Fixes issues in #118 and #121 | 2014-05-13T00:22:51Z | 2015-07-27T05:38:06Z | 2014-05-16T00:21:08Z | 2014-05-16T00:21:08Z | e80836b9736fcfba1af500c08aab22bcda4e8912 | 0.1.1 664063 | 0 | e07bc93589bbd23fe3bfa1ae1e1daf15eebf83f2 | ed3143e3082ba339d35dc4678ddabc7e175dd6b8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/126 | |||
| 15820652 | MDExOlB1bGxSZXF1ZXN0MTU4MjA2NTI= | 127 | closed | 0 | initial implementation of support for NetCDF groups | alimanfoo 703554 | 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. | 2014-05-13T13:12:53Z | 2014-06-27T17:23:33Z | 2014-05-16T01:46:09Z | 2014-05-16T01:46:09Z | efece21b5fce99465a52c866b890e34f19d5bd37 | 0.1.1 664063 | 0 | 28b0ba59b33f63dcd6f6cb05666b3cd98211f4b4 | ed3143e3082ba339d35dc4678ddabc7e175dd6b8 | CONTRIBUTOR | xarray 13221727 | https://github.com/pydata/xarray/pull/127 | |||
| 15862812 | MDExOlB1bGxSZXF1ZXN0MTU4NjI4MTI= | 129 | closed | 0 | Require only numpy 1.7 for the benefit of readthedocs | shoyer 1217238 | 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.) | 2014-05-14T06:41:30Z | 2014-06-25T23:40:31Z | 2014-05-15T07:21:22Z | 2014-05-15T07:21:22Z | b020100a03b394cc08b5cb504a08a64af1253ba7 | 0.1.1 664063 | 0 | 0b33e2ab862f27b688d8ababa954265942720164 | ed3143e3082ba339d35dc4678ddabc7e175dd6b8 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/129 | |||
| 16037950 | MDExOlB1bGxSZXF1ZXN0MTYwMzc5NTA= | 134 | closed | 0 | Fix concatenating Variables with dtype=datetime64 | shoyer 1217238 | 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. | 2014-05-19T05:39:46Z | 2014-06-28T01:08:03Z | 2014-05-20T19:09:28Z | 2014-05-20T19:09:28Z | 6e9268f01681c37a9603ef67a46aa96d29955fb8 | 0.1.1 664063 | 0 | e9e1866dfdf13b9656c923c1d8f077e9bad225d8 | c425967c5f23f46ec1100ccdf472a3fbc0a51ade | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/134 |
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