Bug report

Bug summary

#16258 deprecatesshading="flat" when data and coordinates have the same shape and introducesshading="nearest" which interpolates the coordinates. However, the logic to interpolate the coordinates breaks down if they are not monotonically increasing.

Of course non-monotonic coordinates are a bit exotic but they make sense for geographic data.

Code for reproduction

importnumpyasnpdef_interp_grid(X):# helper for belowifnp.shape(X)[1]>1:dX=np.diff(X,axis=1)/2.X=np.hstack((X[:, [0]]-dX[:, [0]],X[:, :-1]+dX,X[:, [-1]]+dX[:, [-1]]))returnXx=np.array([1,2,0])y=np.array([1,2])X,Y=np.meshgrid(x,y)_interp_grid(X)

Actual outcome

Results in

array([[0.5,1.5,1. ,-1. ],       [0.5,1.5,1. ,-1. ]])

While having cooridinates[1, 2, 0] may seem just as wrong as coordinates[0.5, 1.5, 1, -1] the latter can cause some real-world problems in conjunction with cartopy (SciTools/cartopy#1638):

Expected outcome

• A warning for non-monotonic data
• A convenient option to restore the old behaviour, e.g.shading="drop"

cc@greglucas

Matplotlib version

• Operating system: Linux Mint
• Matplotlib version: '3.3.1'
• Matplotlib backend (print(matplotlib.get_backend())): TkAgg
• Python version: 3.8
• Jupyter version (if applicable):
• Other libraries: