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
2235746028,PR_kwDOAMm_X85sQRSg,8924,"Keep attributes for ""bounds"" variables",28786187,open,0,,,0,2024-04-10T14:29:58Z,2024-04-11T14:09:48Z,,CONTRIBUTOR,,0,pydata/xarray/pulls/8924,"Issue #2921 is about mismatching time units between a time variable and its ""bounds"" companion.
However, #2965 does more than fixing #2921, it removes all double attributes from ""bounds"" variables which has the undesired side effect that there is currently no way to save them to netcdf with xarray. Since the mentioned link is a recommendation and not a hard requirement for CF compliance, these attributes should be left to the caller to prepare the dataset variables appropriately if required. Reduces the amount of surprise that attributes are not written to disk and fixes #8368.
<!-- Feel free to remove check-list items aren't relevant to your change -->
- [x] Closes #8368
- [ ] Tests added (simple round trip test would be in issue #8368)
- [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` (not sure about ""user visibility"" here)
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8924/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
1861738444,I_kwDOAMm_X85u99_M,8099,inconsistent dimension propagation in `.interp()` with non-numeric types,28786187,open,0,,,0,2023-08-22T15:46:13Z,2023-08-22T15:55:44Z,,CONTRIBUTOR,,,,"### What happened?
Hi,
tried to find a similar issue, but haven't seen it.
When interpolating a dataset containing non-numeric types using a DataArray for the new coordinates, then the dimensions of the interpolated data set differ from the case with only numeric types.
Not sure this is clear enough, see the example.
Edit: looks like nothing is interpolated at all in the non-numeric case.
### What did you expect to happen?
The output dimensions should be the same in both cases and match the ones of the numeric-only case.
### Minimal Complete Verifiable Example
```Python
import xarray as xr
import numpy as np
ds = xr.Dataset(
{""x"": (""a"", np.arange(4))},
coords={""a"": (np.arange(4) - 1.5)},
)
t = xr.DataArray(
np.random.randn(6).reshape((2, 3)) * 0.5,
dims=[""r"", ""s""],
coords={""r"": np.arange(2) - 0.5, ""s"": np.arange(3) - 1},
)
ds[""m""] = ds.x > 1
# different dimensions for `x`, compare
ds.interp(a=t, method=""linear"")
# to numeric only
ds[[""x""]].interp(a=t, method=""linear"")
```
### MVCE confirmation
- [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [X] Complete example — the example is self-contained, including all data and the text of any traceback.
- [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result.
- [X] New issue — a search of GitHub Issues suggests this is not a duplicate.
### Relevant log output
```Python
# Output in the numeric-only case:
<xarray.Dataset>
Dimensions: (r: 2, s: 3)
Coordinates:
a (r, s) float64 -0.177 -0.2806 -0.1925 -0.1464 0.3165 0.6232
* r (r) float64 -0.5 0.5
* s (s) int64 -1 0 1
Data variables:
x (r, s) float64 1.323 1.219 1.308 1.354 1.816 2.123
# output including non-numeric variables:
<xarray.Dataset>
Dimensions: (a: 4, r: 2, s: 3)
Coordinates:
* a (r, s) float64 -0.177 -0.2806 -0.1925 -0.1464 0.3165 0.6232
* r (r) float64 -0.5 0.5
* s (s) int64 -1 0 1
Data variables:
x (a) int64 0 1 2 3
m (a) bool False False True True
```
### Anything else we need to know?
_No response_
### Environment
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.11.4 | packaged by conda-forge | (main, Jun 10 2023, 18:08:17) [GCC 12.2.0]
python-bits: 64
OS: Linux
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.9.1
xarray: 2023.7.0
pandas: 2.0.3
numpy: 1.25.2
scipy: 1.11.1
netCDF4: 1.6.3
pydap: installed
h5netcdf: 1.2.0
h5py: 3.8.0
Nio: None
zarr: 2.16.0
cftime: 1.6.2
nc_time_axis: None
PseudoNetCDF: None
iris: None
bottleneck: None
dask: 2023.8.0
distributed: 2023.8.0
matplotlib: 3.7.2
cartopy: 0.22.0
seaborn: None
numbagg: None
fsspec: 2023.6.0
cupy: None
pint: None
sparse: None
flox: 0.7.2
numpy_groupies: 0.9.22
setuptools: 68.0.0
pip: 23.2.1
conda: 23.7.2
pytest: 7.4.0
mypy: None
IPython: 8.7.0
sphinx: 5.3.0
</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8099/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
1350803561,I_kwDOAMm_X85Qg6Bp,6953,DataArray.resample().apply() fails to apply custom function,28786187,open,0,,,5,2022-08-25T12:14:04Z,2022-08-29T12:41:47Z,,CONTRIBUTOR,,,,"### What happened?
Hi,
I try to apply a custom function to aggregate a resampled object via `.apply()`.
Maybe it is a documentation issue? But I couldn't find it.
For example calculating the median by passing `np.median` fails with an error as shown in the log below.
### What did you expect to happen?
I would expect the median or any other custom function to be calculated for the resampled data.
It seems to work with pure `pandas`.
### Minimal Complete Verifiable Example
```Python
import numpy as np
import pandas as pd
import xarray as xr
idx = pd.date_range(""2000-01-01"", ""2000-12-31"")
data = xr.DataArray(np.random.randn(len(idx)), coords={""index"": idx})
data.resample(index=""M"").apply(np.median)
```
### MVCE confirmation
- [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [ ] Complete example — the example is self-contained, including all data and the text of any traceback.
- [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result.
- [ ] New issue — a search of GitHub Issues suggests this is not a duplicate.
### Relevant log output
```Python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [425], in <cell line: 3>()
1 idx = pd.date_range(""2000-01-01"", ""2000-12-31"")
2 data = pd.Series(np.random.randn(len(idx)), index=idx).to_xarray()
----> 3 data.resample(index=""M"", label=""left"", loffset=""15d"").apply(np.median)
File ~/Work/miniconda3/envs/20211123_py39/lib/python3.9/site-packages/xarray/core/resample.py:279, in DataArrayResample.apply(self, func, args, shortcut, **kwargs)
267 """"""
268 Backward compatible implementation of ``map``
269
(...)
272 DataArrayResample.map
273 """"""
274 warnings.warn(
275 ""Resample.apply may be deprecated in the future. Using Resample.map is encouraged"",
276 PendingDeprecationWarning,
277 stacklevel=2,
278 )
--> 279 return self.map(func=func, shortcut=shortcut, args=args, **kwargs)
File ~/Work/miniconda3/envs/20211123_py39/lib/python3.9/site-packages/xarray/core/resample.py:253, in DataArrayResample.map(self, func, args, shortcut, **kwargs)
210 """"""Apply a function to each array in the group and concatenate them
211 together into a new array.
212
(...)
249 The result of splitting, applying and combining this array.
250 """"""
251 # TODO: the argument order for Resample doesn't match that for its parent,
252 # GroupBy
--> 253 combined = super().map(func, shortcut=shortcut, args=args, **kwargs)
255 # If the aggregation function didn't drop the original resampling
256 # dimension, then we need to do so before we can rename the proxy
257 # dimension we used.
258 if self._dim in combined.coords:
File ~/Work/miniconda3/envs/20211123_py39/lib/python3.9/site-packages/xarray/core/groupby.py:1095, in DataArrayGroupByBase.map(self, func, args, shortcut, **kwargs)
1093 grouped = self._iter_grouped_shortcut() if shortcut else self._iter_grouped()
1094 applied = (maybe_wrap_array(arr, func(arr, *args, **kwargs)) for arr in grouped)
-> 1095 return self._combine(applied, shortcut=shortcut)
File ~/Work/miniconda3/envs/20211123_py39/lib/python3.9/site-packages/xarray/core/groupby.py:1115, in DataArrayGroupByBase._combine(self, applied, shortcut)
1113 """"""Recombine the applied objects like the original.""""""
1114 applied_example, applied = peek_at(applied)
-> 1115 coord, dim, positions = self._infer_concat_args(applied_example)
1116 if shortcut:
1117 combined = self._concat_shortcut(applied, dim, positions)
File ~/Work/miniconda3/envs/20211123_py39/lib/python3.9/site-packages/xarray/core/groupby.py:559, in GroupBy._infer_concat_args(self, applied_example)
558 def _infer_concat_args(self, applied_example):
--> 559 if self._group_dim in applied_example.dims:
560 coord = self._group
561 positions = self._group_indices
AttributeError: 'numpy.float64' object has no attribute 'dims'
```
### Anything else we need to know?
_No response_
### Environment
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:56:21)
[GCC 10.3.0]
python-bits: 64
OS: Linux
OS-release: 4.4.0-210-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.8.1
xarray: 2022.6.0
pandas: 1.4.3
numpy: 1.23.2
scipy: 1.9.0
netCDF4: 1.6.0
pydap: installed
h5netcdf: None
h5py: 3.7.0
Nio: None
zarr: 2.12.0
cftime: 1.6.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2022.6.1
distributed: 2022.6.1
matplotlib: 3.5.3
cartopy: 0.20.3
seaborn: None
numbagg: None
fsspec: 2022.7.1
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 65.2.0
pip: 22.2.2
conda: 4.14.0
pytest: 7.1.2
IPython: 8.4.0
sphinx: 5.1.1
</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6953/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
937336962,MDExOlB1bGxSZXF1ZXN0NjgzOTA5OTgw,5580,dataset `__repr__` updates,28786187,closed,0,,,8,2021-07-05T20:03:55Z,2021-08-21T22:51:16Z,2021-08-21T22:51:03Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5580,"<!-- Feel free to remove check-list items aren't relevant to your change -->
After discussing the recent change in the dataset `__repr__`, this implements the compromise discussed in #5545. It keeps the dataset `__repr__` short, tunable via `xr.set_options()`, and gives the full output via `ds.coords`, `ds.data_vars`, and `ds.attrs`.
Passes the (updated) testsuite (locally).
- [x] Closes #5545
- [x] Tests added
- [x] Passes `pre-commit run --all-files`
- [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst`
- [ ] New functions/methods are listed in `api.rst`
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5580/reactions"", ""total_count"": 4, ""+1"": 4, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
931591247,MDU6SXNzdWU5MzE1OTEyNDc=,5545,Increase default `display_max_rows`,28786187,closed,0,,,19,2021-06-28T13:46:14Z,2021-08-21T22:51:03Z,2021-08-21T22:51:03Z,CONTRIBUTOR,,,,"This must have been introduced into `xr.set_options()` somewhere around version 0.17.
First of all this limit breaks backwards compatibility in the output format with `print()` or something similar on the console. Second, the default of 12 is much too low imo and makes no sense, in particular since terminals usually have a scrollback buffer and notebook cells can be made scrollable.
I use `print()` frequently to check that **all** variables made it into the data set correctly, which is meaningless when lines are skipped with this default limit. And it broke my doctests that I wrote to do exactly that (thanks for that btw.). So if not removed, could the default be at least increased to a sensible number like 100 or 1000 or 10000? (I'd personally prefer much higher or even no limit, but I guess that is not an option.)
Cheers.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5545/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue