This does not seem to fix the problem 😞

This is trying to fix as issue brought up in the comments of#8024 which was in turn fixing#8012

@QuLogic We are going to unblock this for 2.0.1 to prevent the perfect from becoming the enemy of the good.

So I've had a chance to confirm that this PR does not correct the issue with Cartopy. Is there hope for pursuing an amendment to this PR or do you think a completely different approach is needed?

I am not sure, still suspect that this is the right general direction, but it will depend on exactly what cartopy is feeding in (which I could not dig through the layers to sort out).

I extracted the data and outline path used by Cartopy and put it intoa gist here that uses only Matplotlib.

Here's the blue image with 2.0.0:

and with 2.0.1:

I changed the background patch to red which you can see bleeding through a bit more obviously in the second image.

On a bit deeper inspection I am not sure if this is something that can / should be fixed on the matplotlib side. Tweaking the example a bit I think the issue is that the data / mask are a few pixel too small.

Marking the bad pixels as pink and zooming in gives (ignoring the non-clipping of the patch)

and eroding the mask (because high is the pixels to exclude) by 2 gives

importnumpyasnpimportmatplotlibasmplimportmatplotlib.pyplotaspltimportmatplotlib.patchesasmpatchesimportmatplotlib.pathasmpathimportosimportscipy.ndimage.morphologyimportmatplotlib.cmasmcmplt.style.use('classic')# os.chdir('/tmp/b8ebfd6e1bfc304d39487750bc2a48be')data=np.load('cp-data.npz')path=mpath.Path(vertices=data['verts'],codes=data['codes'],_interpolation_steps=data['steps'],readonly=data['ro'])imshow_kwargs= {'vmin':None,'vmax':None,'resample':None,'filterrad':4.0,'alpha':None,'filternorm':1,'aspect':None,'interpolation':'none','cmap':None,'data':None,'url':None,'shape':None,'extent': [-20037508.342789244,20037508.342789244,-8683259.716434667,8683259.716434667],'norm':None,'imlim':None,'origin':'lower'}forcolorin ['fc','red','green','blue']:mask=data[color+'_mask']# mask = scipy.ndimage.morphology.binary_erosion(mark, iterations=2)img=np.ma.MaskedArray(data[color+'_data'],mask)fig,ax=plt.subplots(figsize=(10,5))background=mpatches.PathPatch(path,edgecolor='none',facecolor='red',zorder=-1,clip_on=False,transform=ax.transData)ax.add_patch(background)outline=mpatches.PathPatch(path,edgecolor='black',facecolor='none',zorder=2.5,clip_on=False,transform=ax.transData)ax.add_patch(outline)# Not necessary?ax.patch=backgroundifcolor!='fc':cmap=mcm.get_cmap(color.title()+'s')cmap.set_bad('pink')imshow_kwargs['cmap']=cmapelse:imshow_kwargs['cmap']=Noneax.imshow(img,**imshow_kwargs)ax.set_title('%s %s'% (color,mpl.__version__))fig.savefig('result-'+color+'.png')plt.show()

From what I can tell, Cartopy determines the mask from the points that don't transform back to a valid point in the source domain. But since this isimshow, Ithink that comes out of the central point and not the corners, so it doesn't know about edge pixels. I'm not sure how easy it would be to change this clipping.

Super-sceded by#8966