What happened?

I'm not sure if this is a bug, normal behaviour or a feature request but if it's expected behaviour, it should be better documented.

I want to interpolate a 2D dataset to a finer spatial grid. I've put together a reproducible example based on the tutorial here: https://docs.xarray.dev/en/stable/user-guide/interpolation.html

The result is this:

When I do this, the edge points are lost (as you can see on the west/north boundaries here).

What did you expect to happen?

I'd expect the interpolation to protect the boundary points and not lose them.

Minimal Complete Verifiable Example

```Python ds = xr.tutorial.load_dataset("air_temperature") subplot_kw = dict(projection=ccrs.PlateCarree()) new_lon = np.linspace(ds.lon[0], ds.lon[-1], ds.dims["lon"] * 4) new_lat = np.linspace(ds.lat[0], ds.lat[-1], ds.dims["lat"] * 4) dsi = ds.interp(lat=new_lat, lon=new_lon, method='nearest', kwargs={'fill_value':None})

fig, axes = plt.subplots(1, 2, figsize=(18, 12), subplot_kw=subplot_kw) axes = np.ravel(axes)

ax = axes[1] ds.air.isel(time=0).plot(x='lon', y='lat', ax=ax, add_colorbar=False) ax.set_extent([ds.lon.min()-5., ds.lon.min()+20., ds.lat.max()-20, ds.lat.max()+5]) ax.coastlines()

ax = axes[0] dsi.air.isel(time=0).plot(x='lon', y='lat', ax=ax, add_colorbar=False) ax.set_extent([ds.lon.min()-5., ds.lon.min()+20., ds.lat.max()-20, ds.lat.max()+5]) ax.coastlines()

plt.show() ```

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

No response

Anything else we need to know?

No response

Environment

The behaviour when specifying "norm" for a colormap normalisation does not work as anticipated.

Below I use the example code from matplotlib and apply the same normalisation to the DataArray version of the data but get very different results.

MCVE Code Sample

```python import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as colors import xarray

example from https://matplotlib.org/3.1.1/tutorials/colors/colormapnorms.html

for colormap normalisation

N = 100 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)] Z1 = np.exp(-X2 - Y2) Z2 = np.exp(-(X - 1)2 - (Y - 1)2) Z = (Z1 - Z2) * 2

fig, ax = plt.subplots(2, 1, figsize=(8, 8)) ax = ax.flatten()

bounds = np.linspace(-1, 1, 10) norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)

ax[0].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')

now add data into dataset and plot it using same normalisation

data = xarray.DataArray(Z, dims=('x', 'y'), coords={'x': X[:,0], 'y': Y[0,:]}) data.plot(ax=ax[1], x='x', y='y', norm=norm, add_colorbar=False)

plt.show() ```

Expected Output

Top is expected, bottom is actual

Problem Description

Colormap normalisation appears to be broken in xarray

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