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
23272260,MDExOlB1bGxSZXF1ZXN0MTAyNzUzMTg=,2,Data objects now have a swappable backend store.,514053,closed,0,,,3,2013-11-25T20:48:40Z,2016-01-04T23:11:54Z,2014-01-29T19:20:58Z,CONTRIBUTOR,,0,pydata/xarray/pulls/2,"- Allows conversion to and from: NetCDF4, scipy.io.netcdf and in
  memory storage.
- Added general test cases, and cases for specific backend stores.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
59730888,MDExOlB1bGxSZXF1ZXN0MzA0MjcxMjU=,359,Raise informative exception when _FillValue and missing_value disagree,514053,closed,0,,1004936,2,2015-03-04T00:22:41Z,2015-03-12T16:33:47Z,2015-03-12T16:32:07Z,CONTRIBUTOR,,0,pydata/xarray/pulls/359,"Previously conflicting _FillValue and missing_value only raised an AssertionError, now it's more informative.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/359/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
58682523,MDExOlB1bGxSZXF1ZXN0Mjk4NjQ5NzA=,334,Fix bug associated with reading / writing of mixed endian data.,514053,closed,0,,799013,1,2015-02-24T01:57:43Z,2015-02-26T04:45:18Z,2015-02-26T04:45:18Z,CONTRIBUTOR,,0,pydata/xarray/pulls/334,"The right solution to this is to figure out how to successfully round trip endian-ness, but that seems to be a deeper issue inside netCDF4 (https://github.com/Unidata/netcdf4-python/issues/346)

Instead we force all data to little endian before netCDF4 write.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/334/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
54391570,MDExOlB1bGxSZXF1ZXN0MjczOTI5OTU=,310,More robust CF datetime unit parsing,514053,closed,0,1217238,799013,1,2015-01-14T23:19:07Z,2015-01-14T23:36:34Z,2015-01-14T23:35:27Z,CONTRIBUTOR,,0,pydata/xarray/pulls/310,"This makes it possible to read datasets that don't follow CF datetime conventions perfectly, such as the following example which (surprisingly) comes from NCEP/NCAR (you'd think they would follow CF!)

```
ds = xray.open_dataset('http://thredds.ucar.edu/thredds/dodsC/grib/NCEP/GEFS/Global_1p0deg_Ensemble/members/GEFS_Global_1p0deg_Ensemble_20150114_1200.grib2/GC')
print ds['time'].encoding['units']

u'Hour since 2015-01-14T12:00:00Z'
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/310/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
29008494,MDU6SXNzdWUyOTAwODQ5NA==,55,Allow datetime.timedelta coordinates.,514053,closed,0,,,4,2014-03-07T23:56:39Z,2014-12-12T09:41:01Z,2014-12-12T09:41:01Z,CONTRIBUTOR,,,,"This would allow you to have coordinates which are offsets from a time coordinates which comes in handy when dealing with forecast data where the 'time' coordinate might be the forecast run time and you then want a 'lead' coordinate which is an offset from the run time.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/55/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
36467304,MDExOlB1bGxSZXF1ZXN0MTc1ODI2ODQ=,175,Modular encoding,514053,closed,0,,,9,2014-06-25T10:37:41Z,2014-10-08T20:44:15Z,2014-10-08T20:44:15Z,CONTRIBUTOR,,0,pydata/xarray/pulls/175,"Restructured Backends to make CF conventions handling consistent.

Among other things this includes:
- EncodedDataStores which can wrap other stores and allow
  for modular encoding/decoding.
- Trivial indices ds['x'] = ('x', np.arange(10)) are no longer
  stored on disk and are only created when accessed.
- AbstractDataStore API change.  Shouldn't effect external users.
- missing_value attributes now function like _FillValue

All current tests are passing (though it could use more new ones).
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/175/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
45289935,MDExOlB1bGxSZXF1ZXN0MjI0NTA0NzA=,248,Removed the object oriented encoding/decoding scheme,514053,closed,0,,,0,2014-10-08T20:06:33Z,2014-10-08T20:12:56Z,2014-10-08T20:12:56Z,CONTRIBUTOR,,0,pydata/xarray/pulls/248,"Removed the object oriented encoding/decoding scheme in favor of
a model where encoding/decoding happens when a dataset is
stored to/ loaded from a DataStore.

Conventions can now be enforced at the DataStore level by
overwriting the Datastore.store() and Datastore.load() methods, or
as an optional arg to Dataset.load_store, Dataset.dump_to_store.

Includes miscellaneous cleanup.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/248/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
26545877,MDExOlB1bGxSZXF1ZXN0MTIwMDU3ODk=,8,Datasets now use data stores to allow swap-able backends,514053,closed,0,,,0,2014-01-29T19:25:42Z,2014-06-17T00:35:01Z,2014-01-29T19:30:09Z,CONTRIBUTOR,,0,pydata/xarray/pulls/8,"```
Data objects now have a swap-able backend store.

- Allows conversion to and from: NetCDF4, scipy.io.netcdf and in
  memory storage.
- Added general test cases, and cases for specific backend stores.
- Dataset.translate() can now optionally copy the object.
- Fixed most unit tests, test_translate_consistency still fails.
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
35564268,MDExOlB1bGxSZXF1ZXN0MTcwNDU3Mjk=,153,Fix decode_cf_variable.,514053,closed,0,,,5,2014-06-12T09:42:47Z,2014-06-12T23:33:46Z,2014-06-12T23:33:46Z,CONTRIBUTOR,,0,pydata/xarray/pulls/153,"decode_cf_variable was still using da.data instead of da.values.

It now also works with DataArray as input.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/153/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
35627287,MDExOlB1bGxSZXF1ZXN0MTcwODIzNjc=,154,"Fix decode_cf_variable, without tests",514053,closed,0,,,0,2014-06-12T21:56:10Z,2014-06-12T23:30:15Z,2014-06-12T23:30:15Z,CONTRIBUTOR,,0,pydata/xarray/pulls/154,"same as #153, but without tests.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/154/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
31510183,MDExOlB1bGxSZXF1ZXN0MTQ3NDQzOTI=,102,Dataset.concat() can now automatically concat over non-equal variables.,514053,closed,0,,,3,2014-04-14T22:19:02Z,2014-06-12T17:33:49Z,2014-04-23T03:24:45Z,CONTRIBUTOR,,0,pydata/xarray/pulls/102,"concat_over=True indicates that concat should concat over all variables
that are not the same in the set of datasets that are to be concatenated.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/102/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
28315331,MDExOlB1bGxSZXF1ZXN0MTI5NDE2MDI=,21,Cf time units persist,514053,closed,0,,,4,2014-02-26T08:05:41Z,2014-06-12T17:29:24Z,2014-02-28T01:45:21Z,CONTRIBUTOR,,0,pydata/xarray/pulls/21,"Internally Datasets convert time coordinates to pandas.DatetimeIndex.  The backend function convert_to_cf_variable will convert these datetimes back to CF style times, but the original units were not being preserved.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/21/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
30340123,MDExOlB1bGxSZXF1ZXN0MTQwODExMjk=,86,BUG: Zero dimensional variables couldn't be written to file or serialized.,514053,closed,0,,,0,2014-03-27T20:42:06Z,2014-06-12T17:29:11Z,2014-03-28T03:58:43Z,CONTRIBUTOR,,0,pydata/xarray/pulls/86,"Fixed a bug in which writes would fail if Datasets contained 0d variables.

Also added the ability to open Datasets directly from NetCDF3 bytestrings.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/86/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
34649908,MDExOlB1bGxSZXF1ZXN0MTY1MzU0ODE=,143,Fix decoded_cf_variable was not working.,514053,closed,0,,,0,2014-05-30T14:27:13Z,2014-06-12T09:39:20Z,2014-06-12T09:39:20Z,CONTRIBUTOR,,0,pydata/xarray/pulls/143,"Small bug fix, and a test.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/143/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
35304758,MDExOlB1bGxSZXF1ZXN0MTY4OTY2MjM=,150,Fix DecodedCFDatetimeArray was being incorrectly indexed.,514053,closed,0,,650893,0,2014-06-09T17:25:05Z,2014-06-09T17:43:50Z,2014-06-09T17:43:50Z,CONTRIBUTOR,,0,pydata/xarray/pulls/150,"This was causing an error in the following situation:

```
ds = xray.Dataset()
ds['time'] = ('time', [np.datetime64('2001-05-01') for i in range(5)])
ds['variable'] = ('time', np.arange(5.))
ds.to_netcdf('test.nc')
ds = xray.open_dataset('./test.nc')
ss = ds.indexed(time=slice(0, 2))
ss.dumps()
```

Thanks @shoyer for the fix.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/150/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
33307883,MDExOlB1bGxSZXF1ZXN0MTU3NjcwMTU=,125,Only copy datetime64 data if it is using non-nanosecond precision.,514053,closed,0,,,7,2014-05-12T13:36:22Z,2014-05-20T19:09:40Z,2014-05-20T19:09:40Z,CONTRIBUTOR,,0,pydata/xarray/pulls/125,"In an attempt to coerce all datetime arrays to nano second resolutoin utils.as_safe_array() was creating copies of any datetime64 array (via the astype method).  This was causing unexpected behavior (bugs) for things such as concatenation over times. (see below).

```
import xray
import pandas as pd

ds = xray.Dataset()
ds['time'] = ('time', pd.date_range('2011-09-01', '2011-09-11'))
times = [ds.indexed(time=[i]) for i in range(10)]
ret = xray.Dataset.concat(times, 'time')
print ret['time']

<xray.DataArray 'time' (time: 10)>
array(['1970-01-02T07:04:40.718526408-0800',
       '1969-12-31T16:00:00.099966608-0800',
       '1969-12-31T16:00:00.041748384-0800',
       '1969-12-31T16:00:00.041748360-0800',
       '1969-12-31T16:00:00.041748336-0800',
       '1969-12-31T16:00:00.041748312-0800',
       '1969-12-31T16:00:00.041748288-0800',
       '1969-12-31T16:00:00.041748264-0800',
       '1969-12-31T16:00:00.041748240-0800',
       '1969-12-31T16:00:00.041748216-0800'], dtype='datetime64[ns]')
Attributes:
    Empty
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/125/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
28603092,MDExOlB1bGxSZXF1ZXN0MTMxMDMwODQ=,40,Encodings for object data types are not saved.,514053,closed,0,,,0,2014-03-03T07:22:37Z,2014-04-09T04:10:56Z,2014-03-07T02:21:16Z,CONTRIBUTOR,,0,pydata/xarray/pulls/40,"decode_cf_variable will not save encoding for any 'object' dtypes.

When encoding cf variables check if dtype is np.datetime64 as well as DatetimeIndex.

fixes akleeman/xray/issues/39
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/40/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
30328907,MDExOlB1bGxSZXF1ZXN0MTQwNzQzOTg=,84,Fix: dataset_repr was failing on empty datasets.,514053,closed,0,,,1,2014-03-27T18:29:18Z,2014-03-27T20:09:45Z,2014-03-27T20:05:49Z,CONTRIBUTOR,,0,pydata/xarray/pulls/84,"BUG: dataset_repr was failing on empty datasets.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/84/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
28600785,MDU6SXNzdWUyODYwMDc4NQ==,39,OpenDAP loaded Dataset has lon/lats with type 'object'.,514053,closed,0,,,4,2014-03-03T06:07:17Z,2014-03-24T07:21:02Z,2014-03-24T07:21:02Z,CONTRIBUTOR,,,,"```
ds = xray.open_dataset('http://motherlode.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg/files/GFS_Global_0p5deg_20140303_0000.grib2', decode_cf=False)
In [4]: ds['lat'].dtype
Out[4]: dtype('O')
```

This makes serialization fail.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/39/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
28730473,MDExOlB1bGxSZXF1ZXN0MTMxNzU2NzY=,46,Test lazy loading from stores using mock XArray classes.,514053,closed,0,,,1,2014-03-04T18:50:40Z,2014-03-04T23:24:52Z,2014-03-04T23:10:28Z,CONTRIBUTOR,,0,pydata/xarray/pulls/46,,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/46/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
28445412,MDU6SXNzdWUyODQ0NTQxMg==,26,Allow the ability to add/persist details of how a dataset is stored.,514053,closed,0,,,4,2014-02-27T19:10:38Z,2014-03-03T02:54:16Z,2014-03-03T02:54:16Z,CONTRIBUTOR,,,,"Both Issues https://github.com/akleeman/xray/pull/20 and https://github.com/akleeman/xray/pull/21 are dealing with similar conceptual issues. Namely sometimes the user may want fine control over how a dataset is stored (integer packing, time units and calendars ...).  Taking time as an example, the current model interprets the units and calendar in order to create a DatetimeIndex, but then throws out those attributes so that if the dataset were re-serialized the units may not be preserved.

One proposed solution to this issue is to include a distinct set of encoding attributes that would hold things like 'scale_factor', and 'add_offset' allowing something like this

```
ds['time'] = ('time', pd.date_range('1999-01-05', periods=10))
ds['time'].encoding['units'] = 'days since 1989-08-19'
ds.dump('netcdf.nc')

> ncdump -h
...
    int time(time) ;
        time:units = ""days since 1989-08-19"" ;
...
```

The encoding attributes could also handle masking, scaling, compression etc ...
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