issue_comments: 258695618

After giving this a few more thoughts: 1. if the coordinates are 1D (i.e. the grid is regular in the scipy sense: the coordinates don't have to be evenly spaced), infer_intervals should be set to True by default. This should make everybody happy (including @jhamman and me), and this will cover a huge majority of the geoscientific grids. 2. if the coordinates are 2D, what xarray was doing until now was wrong. _infer_interval_breaks was inferring intervals on the first axis only and ignoring the second, so regardless of whether we were plotting on a projection or not, the plot output was cropped (see example above)

So what do we do with this? @jhamman 's suggestion is to set infer_intervals to False, which is equivalent to saying: "this plot is going to be a bit wrong but this is not xarray's problem". I guess that it's fine to do so, but we'd have to be happy with the following:

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

lon, lat = np.linspace(-20, 20, 5), np.linspace(0, 30, 4) lon, lat = np.meshgrid(lon, lat) lon += lat/10 lat += lon/10 da = xr.DataArray(np.arange(20).reshape(4, 5), dims=['y', 'x'], coords = {'lat': (('y', 'x'), lat), 'lon': (('y', 'x'), lon)})

ax = plt.subplot(1, 2, 1, projection=ccrs.PlateCarree()) da.plot.pcolormesh('lon', 'lat', ax=ax, transform=ccrs.PlateCarree(), infer_intervals=False) ax.scatter(lon, lat, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_title('2d irreg case - pcolor')

ax = plt.subplot(1, 2, 2, projection=ccrs.PlateCarree()) da.plot.contourf('lon', 'lat', ax=ax, transform=ccrs.PlateCarree()) ax.scatter(lon, lat, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_title('2d irreg case - contourf') ```

Note that one line and one column of data disappeared (so that the colorbar range doesn't correspond to what we see) and the coordinates are not understood as being in the cell center. The advantage of this choice are twofold: - it's kind of coherent with how contourf would interpret the coordinates (see example above) - if we agree on "1D = infer per default" and "2D = don't infer per default", we might even get rid of the new keyword altogether.

Let's call this option "Option A".

[edited]

I've been playing around with an "Option B", which includes making _infer_interval_breaks accept an axis kwarg. Regardless of the implementation, we could reach the following result:

[\edited]

This plot is closer to what I think the grid actually represents, but I am biased towards my usage of xarray (gridded geoscientific models). The disadvantages of option B are: - it adds a bit of complexity - if you think about it, the problem of plotting irregular grids is not really an xarray problem. If there was a perfect way to handle an irregular grids without having bounded coordinates, I guess that matplotlib would have a tool for it.

I currently tend to go for option A, since option B is a bit awkward and users who have bounded coordinated available somewhere (as suggested by @ocefpaf ) will have to implement their own plotting routine anyway.