Is your feature request related to a problem?

I encountered an issue with the open_dataset function in my code when the server I fetch data from experienced a network issue. The whole script froze because open_dataset was unable to fetch the data from the server.

Describe the solution you'd like

An argument that allows you to specify how long open_dataset tries to fetch the data before timing out.

Describe alternatives you've considered

Right now I'm considering trying to send a HEAD request to the server and checking the response with a try-except block to catch a bad status code. I'm not sure how robust this alternative is, and I would prefer if there would be a way to natively specify a timeout in open_dataset.

Additional context

No response

What happened?

When I try to select a subset of the data in a dataset/array with a list containing dates it fails when the time index is in cftime, and I get the following error message:

KeyError: "not all values found in index 'time'"

What did you expect to happen?

I expect selecting a set of dates with a list to work the same way as when the time index is in datetime64.

Minimal Complete Verifiable Example

```Python import xarray as xr import numpy as np

ds = xr.open_dataset("https://thredds.met.no/thredds/dodsC/osisaf/met.no/ice/index/v2p1/nh/osisaf_nh_sie_daily.nc")

Time coordinates are in datetime64, and selecting dates with a list works.

print(ds.time) print(ds.sel(time=["2023-01-01", "2023-01-02"]))

Converting the calendar to all_leap changes the time coordinates to use cftime instead of datetime64.

ds = ds.convert_calendar("all_leap", missing=np.nan).interpolate_na()

Time coordinates are in cftime, and selecting dates with a list fails.

print(ds.time) print(ds.sel(time=["2023-01-01", "2023-01-02"])) ```

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, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

Python (geoscience) [michael@localhost ~]$ python minimal.py <xarray.DataArray 'time' (time: 16107)> array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000', '1979-01-03T00:00:00.000000000', ..., '2023-02-03T00:00:00.000000000', '2023-02-04T00:00:00.000000000', '2023-02-05T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2023-02-05 sic_threshold float32 ... lat float32 ... lon float32 ... Attributes: standard_name: time long_name: time of the observation (centered) coverage_content_type: auxiliaryInformation axis: T <xarray.Dataset> Dimensions: (time: 2, nv: 2) Coordinates: * time (time) datetime64[ns] 2023-01-01 2023-01-02 sic_threshold float32 ... lat float32 ... lon float32 ... Dimensions without coordinates: nv Data variables: lat_bounds (nv) float32 ... lon_bounds (nv) float32 ... area |S64 ... sie (time) float64 ... source (time) float64 ... Attributes: (12/35) title: Daily Northern Hemisphere Sea Ice Extent from EU... product_id: OSI-420 product_name: OSI SAF Sea Ice Index product_status: demonstration version: v2p1 summary: Time series of Daily Sea Ice Extent (SIE) for No... ... ... distribution_statement: Free copyright_statement: Copyright 2023 EUMETSAT references: Product User Manual for OSI-420, Lavergne et al.... featureType: timeSeries DODS.strlen: 2 DODS.dimName: nchar <xarray.DataArray 'time' (time: 16140)> array([cftime.DatetimeAllLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeAllLeap(1979, 1, 2, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeAllLeap(1979, 1, 3, 0, 0, 0, 0, has_year_zero=True), ..., cftime.DatetimeAllLeap(2023, 2, 3, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeAllLeap(2023, 2, 4, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeAllLeap(2023, 2, 5, 0, 0, 0, 0, has_year_zero=True)], dtype=object) Coordinates: * time (time) object 1979-01-01 00:00:00 ... 2023-02-05 00:00:00 lat float32 90.0 lon float32 0.0 sic_threshold float32 0.15 Attributes: standard_name: time long_name: time of the observation (centered) coverage_content_type: auxiliaryInformation axis: T Traceback (most recent call last): File "/var/home/michael/minimal.py", line 15, in <module> print(ds.sel(time=["2023-01-01", "2023-01-02"])) File "/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/xarray/core/dataset.py", line 2554, in sel query_results = map_index_queries( File "/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/xarray/core/indexing.py", line 183, in map_index_queries results.append(index.sel(labels, **options)) # type: ignore[call-arg] File "/var/home/michael/mambaforge/envs/geoscience/lib/python3.10/site-packages/xarray/core/indexes.py", line 480, in sel raise KeyError(f"not all values found in index {coord_name!r}") KeyError: "not all values found in index 'time'"

Anything else we need to know?

No response

Environment

What happened:

I'm trying to do some analysis of CMIP6 model data, and I want to plot multi-model ensembles. In order to do that I need to regrid all of the models to a common grid. Whenever I try to plot data from a regridded model there's a white line along the central longitude and the poles. I use the PlateCarree projection and it doesn't matter what I choose as the central longitude; there's always a white line there.

The code I've included below produces 4 plots. The first one is of data that hasn't been interpolated and there's no white line:

The next three are with interpolated data and with different central longitudes. They all have a white line at the central longitude.

central_longitude=0

central_longitude=33

central_longitude=164

What you expected to happen:

No plot artefacts.

Minimal Complete Verifiable Example:

```python import xarray as xr import matplotlib.pyplot as plt import cartopy.crs as ccrs

cesm2_waccm = xr.open_dataset('pr_day_CESM2-WACCM_ssp245_r2i1p1f1_gn_20750101-20841231.nc') mpi = xr.open_dataset('pr_day_MPI-ESM1-2-LR_ssp245_r1i1p1f1_gn_20750101-20941231.nc')

cesm2_waccm_subset = cesm2_waccm.sel(time=slice('2075-01-01', '2075-12-31')).mean(dim='time') mpi_subset = mpi.sel(time=slice('2075-01-01', '2075-12-31')).mean(dim='time')

map_proj = ccrs.PlateCarree()

This works.

plot = mpi_subset.pr.plot(subplot_kws={'projection': map_proj}) plot.axes.coastlines() plt.show()

mpi_interp = mpi_subset.interp(lat=cesm2_waccm_subset['lat'], lon=cesm2_waccm_subset['lon'])

A randomly chosen set of central longitudes for plots.

longitudes = [0, 33, 164]

for lon in longitudes: map_proj = ccrs.PlateCarree(central_longitude=lon)

# Has a white line at the central longitude. plot = mpi_interp.pr.plot(subplot_kws={'projection': map_proj}) plot.axes.coastlines() plt.show()

```

Anything else we need to know?:

Here's the data I used for plotting:

https://climate.uiogeo-apps.sigma2.no/ESGF/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r2i1p1f1/day/pr/gn/v20200224/pr_day_CESM2-WACCM_ssp245_r2i1p1f1_gn_20750101-20841231.nc

https://climate.uiogeo-apps.sigma2.no/ESGF/CMIP6/ScenarioMIP/MPI-M/MPI-ESM1-2-LR/ssp245/r1i1p1f1/day/pr/gn/v20190710/pr_day_MPI-ESM1-2-LR_ssp245_r1i1p1f1_gn_20750101-20941231.nc

Environment: