@dcherian Interesting! There should ideally be a way to set that because 32-64 seconds is way to long to wait before timing out in my opinion.

Ah, got it! In that case it's a real server since I don't mount anything.

I'm using netCDF4. I'm not sure what the difference between a real server and a remote file system is, but I use OPeNDAP to fetch the data I need.

One example is the following data file (the first link under the access subheader): https://thredds.met.no/thredds/catalog/osisaf/met.no/ice/index/v2p1/nh/catalog.html?dataset=osisaf/met.no/ice/index/v2p1/nh/osisaf_nh_sie_monthly.nc

Ok, great! Thanks for the tip.

On Mon, Feb 6, 2023 at 13:51, Spencer Clark @.***> wrote:

Indeed currently we do not support indexing a CFTimeIndex-backed array using a list of strings, but that's something I think we would be happy to change (e.g. we do accept a list of strings to interp for CFTimeIndex-backed arrays).

For the time being you should be able to use cftime.DatetimeAllLeap values themselves:

ds

.

sel

(

time

=

[

cftime

.

DatetimeAllLeap

(

2023

,

1

,

1

),

cftime

.

DatetimeAllLeap

(

2023

,

1

,

2

)])

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.***>

I also have an issue where xarray doesn't produce the correct plot when normalizing with BoundaryNorm:

```python import xarray as xr import matplotlib.pyplot as plt import matplotlib.colors as colors from cmcrameri import cm

airtemps = xr.tutorial.open_dataset("air_temperature")

Convert to Celsius.

air = airtemps.air - 273.15 air.attrs = airtemps.air.attrs air.attrs["units"] = "deg C"

Select a timestep.

air2d = air.isel(time=500)

Plotting discrete bounds with matplotlib works fine.

bounds = [x for x in range(-30, 31, 10)] norm = colors.BoundaryNorm(boundaries=bounds, extend='both', ncolors=cm.vik.N)

fig, ax = plt.subplots() cs = ax.pcolormesh(air2d.lon, air2d.lat, air2d, cmap=cm.vik, norm=norm) fig.colorbar(cs) plt.show()

Plotting with xarray doesn't work.

fig, ax = plt.subplots() air2d.plot.pcolormesh(ax=ax, norm=norm) plt.show() ``` First one is from matplotlib:

Second one is from xarray:

I also get the following traceback after running the script:

bash Traceback (most recent call last): File "/home/michael/miniconda3/envs/testing_xarray/lib/python3.10/site-packages/matplotlib/cbook/__init__.py", line 287, in process func(*args, **kwargs) File "/home/michael/miniconda3/envs/testing_xarray/lib/python3.10/site-packages/matplotlib/backend_bases.py", line 3056, in mouse_move s = self._mouse_event_to_message(event) File "/home/michael/miniconda3/envs/testing_xarray/lib/python3.10/site-packages/matplotlib/backend_bases.py", line 3048, in _mouse_event_to_message data_str = a.format_cursor_data(data).rstrip() File "/home/michael/miniconda3/envs/testing_xarray/lib/python3.10/site-packages/matplotlib/artist.py", line 1282, in format_cursor_data neighbors = self.norm.inverse( File "/home/michael/miniconda3/envs/testing_xarray/lib/python3.10/site-packages/matplotlib/colors.py", line 1832, in inverse raise ValueError("BoundaryNorm is not invertible") ValueError: BoundaryNorm is not invertible

@mathause Sorry for the late reply! I've been very busy lately, but yes, please move it to a discussion.

@spencerkclark Thanks for the suggestion! I haven't made any serious tests yet, but my initial tests worked fine =)

I decided to also look at what happens when you plot with contourf. In this case both the plot of the original data and the interpolated data have a white line at the central longitude, but the interpolated data also has white lines at the poles:

Original MPI-ESM

Interpolated MPI-ESM

Here's the code that produced the plots:

```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()

Now this also produces a white line.

plot = mpi_subset.pr.plot.contourf(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'])

Has a white line at the central longitude.

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