issue_comments

OK, I've had success with ax.set_aspect('equal', 'box-forced'):

python p = ano.T2M.plot(col='month', col_wrap=4, transform=ccrs.PlateCarree(), subplot_kws={'projection': ccrs.PlateCarree()}); for ax in p.axes.flat: ax.add_feature(cartopy.feature.BORDERS) ax.add_feature(cartopy.feature.COASTLINE) ax.set_extent([70, 100, 25, 40]) ax.set_aspect('equal', 'box-forced')

Note that the result will look better if you set aspect in xarray's facetgrid first, since it will decide on a cleverer figsize based on this information:

python p = ano.T2M.plot(col='month', col_wrap=4, transform=ccrs.PlateCarree(), aspect=ds.dims['lon']/ds.dims['lat'], subplot_kws={'projection': ccrs.PlateCarree()}); for ax in p.axes.flat: ax.add_feature(cartopy.feature.BORDERS) ax.add_feature(cartopy.feature.COASTLINE) ax.set_extent([70, 100, 25, 40]) ax.set_aspect('equal', 'box-forced')

aspect='auto' does "work", but it looks ugly because it doesn't preserve aspect ratio:

The default is aspect='equal', which is good for the maps geometry but it shouldn't be ignored by the axes of the facetgrid, I think we have to do something on the xarray side here, unless there is a way to preserve the aspect ratio already. I'll dig into this.

@darothen did you find out how to control the map extent in facetgrids? See this example:

python p = ano.T2M.plot(col='month', col_wrap=4, transform=ccrs.PlateCarree(), subplot_kws={'projection': ccrs.PlateCarree()}); for ax in p.axes.flat: ax.add_feature(cartopy.feature.BORDERS) ax.add_feature(cartopy.feature.COASTLINE) ax.set_extent([70, 100, 25, 40]) Cartopy basically ignores my ``extent'' since it wants to make the plots more quadratic than they should:

What is it exactly what you are trying to do?

A simple example would go a long way

@CommonClimate, you are now the ideal contributor to create such an example for the docs. 😉

it's simple to add the colorbar, but not easy to make it horizontal

You can try:

ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', cbar_kwargs={'orientation':'horizontal'});

xarray plotting tools, which are useful for quick checks, but not publication-quality figures?

Yes, as a general purpose tool xarray will try to avoid the proliferation of keyword arguments. If you have very specific needs you might need to tweak the plots yourself. Fortunately, this is easy to do on top of xarray since you can control the axes, plots, and so on.

We could add more advanced examples to the docs though (for example in the gallery I had no time to add yet :( )

When I put together the tutorial, I remember that the Robinson projection was the only one that worked reliably. But that was more than a year ago--hopefully updating package versions can fix the issue.

This is what I get:

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

print(xr.version) print(cartopy.version) print(matplotlib.version)

ds = xr.tutorial.load_dataset('rasm') plt.figure(figsize=(14, 4)); ax = plt.axes(projection=ccrs.Robinson()); ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False); ax.coastlines(); plt.tight_layout(); ```

0.9.5-1-gab4ffee 0.14.2 2.0.0

I don't have this problem. did you use the code from the documentation?

http://xarray.pydata.org/en/latest/examples/multidimensional-coords.html

What exactly did you type?

Note that even if you set projection=Robinson() you still need transform=ccrs.PlateCarree() (e.g., see https://github.com/SciTools/cartopy/issues/645).