"""
==========
pcolormesh
==========

`.axes.Axes.pcolormesh` allows you to generate 2-D image-style plots.  Note it
is faster than the similar `~.axes.Axes.pcolor`.

"""

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator
import numpy as np

###############################################################################
# Basic pcolormesh
# ----------------
#
# We usually specify a pcolormesh by defining the edge of quadrilaterals and
# the value of the quadrilateral.  Note that here *x* and *y* each have one
# extra element than Z in the respective dimension.

np.random.seed(19680801)
Z = np.random.rand(6, 10)
x = np.arange(-0.5, 10, 1)  # len = 11
y = np.arange(4.5, 11, 1)  # len = 7

fig, ax = plt.subplots()
ax.pcolormesh(x, y, Z)

###############################################################################
# Non-rectilinear pcolormesh
# --------------------------
#
# Note that we can also specify matrices for *X* and *Y* and have
# non-rectilinear quadrilaterals.

x = np.arange(-0.5, 10, 1)  # len = 11
y = np.arange(4.5, 11, 1)  # len = 7
X, Y = np.meshgrid(x, y)
X = X + 0.2 * Y  # tilt the coordinates.
Y = Y + 0.3 * X

fig, ax = plt.subplots()
ax.pcolormesh(X, Y, Z)

###############################################################################
# Centered Coordinates
# ---------------------
#
# Often a user wants to pass *X* and *Y* with the same sizes as *Z* to
# `.axes.Axes.pcolormesh`.  This is also allowed if ``shading='auto'`` is
# passed (default set by :rc:`pcolor.shading`).  Pre Matplotlib 3.3,
# ``shading='flat'`` would drop the last column and row of *Z*; while that
# is still allowed for back compatibility purposes, a DeprecationWarning is
# raised.

x = np.arange(10)  # len = 10
y = np.arange(6)  # len = 6
X, Y = np.meshgrid(x, y)

fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)
axs[0].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='auto')
axs[0].set_title("shading='auto' = 'nearest'")
axs[1].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='flat')
axs[1].set_title("shading='flat'")

###############################################################################
# Making levels using Norms
# -------------------------
#
# Shows how to combine Normalization and Colormap instances to draw
# "levels" in `.axes.Axes.pcolor`, `.axes.Axes.pcolormesh`
# and `.axes.Axes.imshow` type plots in a similar
# way to the levels keyword argument to contour/contourf.

# make these smaller to increase the resolution
dx, dy = 0.05, 0.05

# generate 2 2d grids for the x & y bounds
y, x = np.mgrid[slice(1, 5 + dy, dy),
                slice(1, 5 + dx, dx)]

z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)

# x and y are bounds, so z should be the value *inside* those bounds.
# Therefore, remove the last value from the z array.
z = z[:-1, :-1]
levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())


# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap = plt.get_cmap('PiYG')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

fig, (ax0, ax1) = plt.subplots(nrows=2)

im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax0)
ax0.set_title('pcolormesh with levels')


# contours are *point* based plots, so convert our bound into point
# centers
cf = ax1.contourf(x[:-1, :-1] + dx/2.,
                  y[:-1, :-1] + dy/2., z, levels=levels,
                  cmap=cmap)
fig.colorbar(cf, ax=ax1)
ax1.set_title('contourf with levels')

# adjust spacing between subplots so `ax1` title and `ax0` tick labels
# don't overlap
fig.tight_layout()

plt.show()

#############################################################################
#
# ------------
#
# References
# """"""""""
#
# The use of the following functions and methods is shown in this example:

matplotlib.axes.Axes.pcolormesh
matplotlib.pyplot.pcolormesh
matplotlib.axes.Axes.contourf
matplotlib.pyplot.contourf
matplotlib.figure.Figure.colorbar
matplotlib.pyplot.colorbar
matplotlib.colors.BoundaryNorm
matplotlib.ticker.MaxNLocator