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FIX: add shading='nearest' and warn on mis-sized shading='flat'
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‎doc/users/next_whats_new/2020-01-18-pcolorshadingoptions.rst

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Pcolor and Pcolormesh now accept shading='nearest' and 'auto'
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-------------------------------------------------------------
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Previously `.axes.Axes.pcolor` and `.axes.Axes.pcolormesh` handled
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the situation where *x* and *y* have the same (respective) size as *C* by
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dropping the last row and column of *C*, and *x* and *y* are regarded as the
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edges of the remaining rows and columns in *C*. However, many users probably
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really want *x* and *y* centered on the rows and columns of *C*.
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To accommodate this, ``shading='nearest'`` and ``shading='auto'`` are
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new allowed strings for the ``shading`` kwarg. ``'nearest'`` will center the
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color on *x* and *y* if *x* and *y* have the same dimensions as *C*
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(otherwise an error will be thrown). ``shading='auto'`` will choose 'flat'
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or 'nearest' based on the size of *X*, *Y*, *C*.
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If ``shading='flat'`` then *X*, and *Y* should have dimensions one larger
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than *C*. If *X* and *Y* have the same dimensions as *C*, then the previous
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behavior is used and the last row and column of *C* are dropped, and a
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Deprecation Warning is raised.
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Users can also specify this by the new :rc:`pcolor.shading` in their
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``.matplotlibrc`` or via `.rcParams`.
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See :doc:`pcolormesh </gallery/images_contours_and_fields/pcolormesh_levels>`
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for examples.

‎examples/images_contours_and_fields/pcolormesh_levels.py

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"""
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==========
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pcolormesh
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Pcolormesh
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==========
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Shows how to combine Normalization and Colormap instances to draw "levels" in
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`~.axes.Axes.pcolor`, `~.axes.Axes.pcolormesh` and `~.axes.Axes.imshow` type
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plots in a similar way to the levels keyword argument to contour/contourf.
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`.axes.Axes.pcolormesh` allows you to generate 2-D image-style plots. Note it
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is somewhat faster than the similar `~.axes.Axes.pcolor`.
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"""
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import matplotlib
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from matplotlib.ticker import MaxNLocator
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import numpy as np
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###############################################################################
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# Basic Pcolormesh
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# ----------------
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#
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# We usually specify a pcolormesh by defining the edge of quadrilaterals and
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# the value of the quadrilateral. Note that here *x* and *y* each have one
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# extra element than Z in the respective dimension.
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np.random.seed(19680801)
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Z = np.random.rand(6, 10)
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x = np.arange(-0.5, 10, 1) # len = 11
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y = np.arange(4.5, 11, 1) # len = 7
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fig, ax = plt.subplots()
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ax.pcolormesh(x, y, Z)
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###############################################################################
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# Non-rectilinear Pcolormesh
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# --------------------------
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#
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# Note that we can also specify matrices for *X* and *Y* and have
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# non-rectilinear quadrilaterals.
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x = np.arange(-0.5, 10, 1) # len = 11
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y = np.arange(4.5, 11, 1) # len = 7
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X, Y = np.meshgrid(x, y)
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X = X + 0.2 * Y # tilt the co-ordinates.
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Y = Y + 0.3 * X
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fig, ax = plt.subplots()
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ax.pcolormesh(X, Y, Z)
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###############################################################################
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# Centered Co-ordinates
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# ---------------------
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#
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# Often a user wants to pass *X* and *Y* with the same sizes as *Z* to
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# `.axes.Axes.pcolormesh`. This is also allowed if ``shading='auto'`` is
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# passed (default set by :rc:`pcolor.shading`). Pre Matplotlib 3.3,
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# ``shading='flat'`` would drop the last column and row of *Z*; while that
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# is still allowed for back compatibility purposes, a Deprecation Warning is
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# raised.
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x = np.arange(10) # len = 10
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y = np.arange(6) # len = 6
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X, Y = np.meshgrid(x, y)
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fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)
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axs[0].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='auto')
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axs[0].set_title("shading='auto' = 'nearest'")
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axs[1].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='flat')
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axs[1].set_title("shading='flat'")
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###############################################################################
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# Making levels using Norms
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# -------------------------
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#
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# Shows how to combine Normalization and Colormap instances to draw
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# "levels" in `.axes.Axes.pcolor`, `.axes.Axes.pcolormesh`
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# and `.axes.Axes.imshow` type plots in a similar
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# way to the levels keyword argument to contour/contourf.
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# make these smaller to increase the resolution
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dx, dy = 0.05, 0.05
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# don't overlap
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fig.tight_layout()
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plt.show()
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#############################################################################
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#
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# ------------

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