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
374460958,MDU6SXNzdWUzNzQ0NjA5NTg=,2517,Treat accessor dataarrays as members of parent dataset,6153603,closed,0,,,5,2018-10-26T16:37:45Z,2018-11-05T22:40:46Z,2018-11-05T22:40:46Z,CONTRIBUTOR,,,,"#### Code Sample
```python
import xarray as xr
import pandas as pd
# What I'm doing with comparison, I'd like to do with actual
comparison = xr.Dataset({'data': (['time'], [100, 30, 10, 3, 1]),
'altitude': (['time'], [5, 10, 15, 20, 25])},
coords={'time': pd.date_range('2014-09-06', periods=5, freq='1s')})
# With altitude as a data var, I can do the following:
comparison.swap_dims({'time': 'altitude'}).interp(altitude=12.0).data
# And
for (time, g) in comparison.groupby('time'):
print(time)
print(g.altitude.values)
@xr.register_dataset_accessor('acc')
class Accessor(object):
def __init__(self, xarray_ds):
self._ds = xarray_ds
self._altitude = None
@property
def altitude(self):
"""""" An expensive calculation that results in data that not everyone needs. """"""
if self._altitude is None:
self._altitude = xr.DataArray([5, 10, 15, 20, 25],
coords=[('time', self._ds.time)])
return self._altitude
actual = xr.Dataset({'data': (['time'], [100, 30, 10, 3, 1])},
coords={'time': pd.date_range('2014-09-06', periods=5, freq='1s')})
# This doesn't work:
actual.swap_dims({'time': 'altitude'}).interp(altitude=12.0).data
# Neither does this:
for (time, g) in actual.groupby('time'):
print(time)
print(g.acc.altitude.values)
```
#### Problem description
I've been using accessors to extend xarray with some custom computation. The altitude in the above dataset is not used every time the data is loaded, but when it is, it is an expensive computation to make (which is why I put it in as an accessor; if it isn't needed, it isn't computed).
Problem is, once it has been computed, I'd like to be able to use it as if it is a regular data_var of the dataset. For example, to interp on the newly computed column, or use it in a groupby.
Please advise if I'm going about this in the wrong way and how I should think about this problem instead.
#### Output of ``xr.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.16-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
xarray: 0.10.8
pandas: 0.23.1
numpy: 1.14.5
scipy: 1.1.0
netCDF4: 1.4.0
h5netcdf: 0.6.1
h5py: 2.8.0
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.17.5
distributed: 1.21.8
matplotlib: 2.2.2
cartopy: None
seaborn: None
setuptools: 39.2.0
pip: 9.0.3
conda: None
pytest: 3.6.1
IPython: None
sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2517/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
374473176,MDU6SXNzdWUzNzQ0NzMxNzY=,2518,Allow reduce to return an additional dimension,6153603,closed,0,,,2,2018-10-26T17:15:29Z,2018-10-27T14:14:39Z,2018-10-27T14:14:39Z,CONTRIBUTOR,,,,"#### Code Sample, a copy-pastable example if possible
```python
import xarray as xr
from scipy.interpolate import interp1d
airtemps = xr.tutorial.load_dataset('air_temperature')
def interptotarget(y, axis, **kwargs):
x = kwargs['x']
target = kwargs['target']
return interp1d(x, y)(target)
# This works
airtemps.groupby('lat').reduce(interptotarget, dim='lon', x=airtemps.lon, target=213.5)
# This doesn't, but I'd like it to:
airtemps.groupby('lat').reduce(interptotarget, dim='lon', x=airtemps.lon, target=[213.5, 213.6])
```
#### Problem description
In the code above, I give an example of how I'd like to use a reduce to return an additional dimension that I will need to be defined.
The scipy call to interp1d has no trouble calculating the data, but xarray issues `ValueError: dimensions ('time', 'lat') must have the same length as the number of data dimensions, ndim=3`
as it doesn't know what to do with the additional dimension.
I used the above example as a generic case. I know I could use .interp to do an interpolation, but I have different reduction functions in mind that produce an additional dimension. The scipy interp1 function just serves as a working example.
#### Expected Output
```
a =airtemps.groupby('lat').reduce(interptotarget, dim='lon', x=airtemps.lon, target=213.5)
b =airtemps.groupby('lat').reduce(interptotarget, dim='lon', x=airtemps.lon, target=213.6)
import pandas as pd
expected = xr.concat([a, b], pd.Index([213.5, 213.6], name='ilon'))
```
#### Output of ``xr.show_versions()``
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.16-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
xarray: 0.10.8
pandas: 0.23.1
numpy: 1.14.5
scipy: 1.1.0
netCDF4: 1.4.0
h5netcdf: 0.6.1
h5py: 2.8.0
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.17.5
distributed: 1.21.8
matplotlib: 2.2.2
cartopy: None
seaborn: None
setuptools: 39.2.0
pip: 9.0.3
conda: None
pytest: 3.6.1
IPython: None
sphinx: None
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2518/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
340757861,MDU6SXNzdWUzNDA3NTc4NjE=,2284,interp over time coordinate,6153603,closed,0,,,2,2018-07-12T18:54:45Z,2018-07-29T06:09:41Z,2018-07-29T06:09:41Z,CONTRIBUTOR,,,,"Before I start, I'm very excited about the interp addition in 0.10.7. Great addition and thanks to @fujiisoup and @shoyer.
I see there was a bit of a discussion in the interp pull request, #2104, about interpolating over times and that it was suggested to wait for use cases. I can think of an immediate use case in my line of work.
I frequently use regular gridded geophysical data (time, lat, lon), not unlike the sample tutorial air_temperature data, and the data must be interpolated to line up with corresponding satellite measurements that are irregularly spaced in lat, lon and time.
Being able to interpolate in one quick step would be fantastic. For example:
```python
ds = xr.tutorial.load_dataset('air_temperature')
ds.interp(lat=60.5, lon=211, time='2013-01-01T03:14:37')
```
#### Problem description
Currently issues `TypeError: cannot perform reduce with flexible type`.
#### Desired Output
```
Dimensions: ()
Coordinates:
lat float64 60.5
lon int64 211
time datetime64[ns] 2013-01-01T03:14:37
Data variables:
air float64 273.5
```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2284/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
292653302,MDExOlB1bGxSZXF1ZXN0MTY1ODg2Mzky,1869,Add '_FillValue' to set of valid_encodings for netCDF4 backend,6153603,closed,0,,,9,2018-01-30T04:57:24Z,2018-02-13T18:34:44Z,2018-02-13T18:34:37Z,CONTRIBUTOR,,0,pydata/xarray/pulls/1869," - [x] Closes #1865 (remove if there is no corresponding issue, which should only be the case for minor changes)
- [x] Tests added (for all bug fixes or enhancements)
- [x] Tests passed (for all non-documentation changes)
- Relevant tests passed on my cloned version of the code. I had some skipped tests as I did not install rasterio, Nio, or Zarr. Four unrelated tests failed (2 in netcdf3 and 2 in h5netcdf)
- [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1869/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
292585285,MDU6SXNzdWUyOTI1ODUyODU=,1865,Dimension Co-ordinates incorectly saving _FillValue attribute,6153603,closed,0,,,6,2018-01-29T22:31:43Z,2018-02-13T18:34:36Z,2018-02-13T18:34:36Z,CONTRIBUTOR,,,,"#### Code Sample, a copy-pastable example if possible
```python
import xarray as xr
import pandas as pd
import numpy as np
temp = 273.15 + 25 * np.random.randn(2, 2)
lon = [0.0, 5.0]
lat = [10.0, 20.0]
ds = xr.Dataset({'temperature': (['lat', 'lon'], temp)},
coords={'lat': lat,
'lon': lon})
ds['lat'].attrs = {
'standard_name': 'latitude',
'long_name': 'latitude',
'units': 'degrees_north',
'axis': 'Y'}
ds['lon'].attrs = {
'standard_name': 'longitude',
'long_name': 'longitude',
'units': 'degrees_east',
'axis': 'X'}
ds['temperature'].attrs = {
'standard_name': 'air_temperature',
'units': 'K'}
ds.attrs = {
('title', 'non-conforming CF 1.6 data produced by xarray 0.10'),
('Conventions', 'CF-1.6')}
ds.to_netcdf('/tmp/test.nc')
```
#### Problem description
According to the last sentence of the first paragraph of 2.5.1. *Missing data, valid and actual range of data* in NetCDF Climate and Forecast (CF) Metadata Conventions 1.7:
> Missing data is not allowed in coordinate variables.
When I use the [conformance checker](http://pumatest.nerc.ac.uk/cgi-bin/cf-checker-dev.pl?cfversion=auto) it issues an INFO message to this point for the co-ordinate variables.
Output of CF-Checker follows...
```
CHECKING` NetCDF FILE: /tmp/29428.nc
=====================
Using CF Checker Version 3.0.0
Checking against CF Version CF-1.6
Using Standard Name Table Version 48 (2017-11-28T15:32:48Z)
Using Area Type Table Version 6 (22 February 2017)
------------------
Checking variable: temperature
------------------
------------------
Checking variable: lat
------------------
INFO: attribute _FillValue is being used in a non-standard way
------------------
Checking variable: lon
------------------
INFO: attribute _FillValue is being used in a non-standard way
ERRORS detected: 0
WARNINGS given: 0
INFORMATION messages: 2
```
#### Expected Output
Co-ordinate variables should not store a _FillValue attribute
#### Output of ``xr.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.15-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
xarray: 0.10.0
pandas: 0.22.0
numpy: 1.14.0
scipy: 1.0.0
netCDF4: 1.3.1
h5netcdf: 0.5.0
Nio: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.16.1
matplotlib: 2.1.2
cartopy: None
seaborn: None
setuptools: 38.4.0
pip: 9.0.1
conda: None
pytest: None
IPython: None
sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1865/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
216833414,MDU6SXNzdWUyMTY4MzM0MTQ=,1327,Add 'count' as option for how in dataset resample,6153603,closed,0,,,2,2017-03-24T16:11:25Z,2018-02-13T18:05:57Z,2018-02-13T18:05:57Z,CONTRIBUTOR,,,,"All of the usual aggregations are included in `resample`, but 'count' is missing.
At present, `how='count'` fails because the function doesn't accept the kwarg `skipna` that is being passed to it.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1327/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
216836795,MDExOlB1bGxSZXF1ZXN0MTEyNDkzMzQ4,1328,Add how=count option to resample,6153603,closed,0,,,3,2017-03-24T16:23:32Z,2017-09-01T15:57:36Z,2017-09-01T15:57:35Z,CONTRIBUTOR,,0,pydata/xarray/pulls/1328," - [x] closes #1327
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1328/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull