Inconsistency between sel and isel when working with slice
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Description
What happened:
Slice do not have the same effect when working with sel and isel.
The stop bound is not selected with isel while it is with sel.
What you expected to happen:
Either to select or not select the "stop" bound of the slice in both case.
Minimal Complete Verifiable Example:
import xarray as xr
da = xr.Dataset()
da.coords["lat"] = [0, 1, 2, 3]
da["value"] = (('lat'),da["lat"].values+10)
print("Isel result is \n %s \n\n"%da.isel(lat=slice(1,2)))
print("Sel result is \n %s"%da.sel(lat=slice(1,2)))
Gives the following results
Isel result is
<xarray.Dataset>
Dimensions: (lat: 1)
Coordinates:
* lat (lat) int64 1
Data variables:
value (lat) int64 11
Sel result is
<xarray.Dataset>
Dimensions: (lat: 2)
Coordinates:
* lat (lat) int64 1 2
Data variables:
value (lat) int64 11 12
Anything else we need to know?:
Environment:
Output of xr.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.8.2 | packaged by conda-forge | (default, Mar 5 2020, 17:11:00)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 4.15.0-118-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
libhdf5: 1.10.5
libnetcdf: 4.7.3
xarray: 0.15.0
pandas: 1.0.1
numpy: 1.18.1
scipy: 1.4.1
netCDF4: 1.5.3
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.4.0
cftime: 1.0.4.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: 0.9.8.3
iris: None
bottleneck: None
dask: 2.12.0
distributed: 2.12.0
matplotlib: 3.2.0
cartopy: 0.17.0
seaborn: None
numbagg: None
setuptools: 46.0.0.post20200308
pip: 20.0.2
conda: None
pytest: 5.4.1
IPython: 7.13.0
sphinx: None
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by running the supplied Python example and tracing the sel and isel entry points. Determine the intended stop-bound behavior for each indexing mode, then make the behavior consistent and add regression coverage for the reproducer.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100