pcolormesh_levels.py

# -*- coding: utf-8 -*-
# https://matplotlib.org/3.2.1/gallery/images_contours_and_fields/pcolormesh_levels.html

import sys

import matplotlib.cm as cm
import numpy as np
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.colors import BoundaryNorm
from matplotlib.figure import Figure
from matplotlib.ticker import MaxNLocator
from PyQt5.QtWidgets import QApplication

app = QApplication(sys.argv)

fig = Figure(figsize=(8, 6))
canvas = FigureCanvas(fig)
canvas.resize(640, 480)
canvas.show()

# 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 = cm.get_cmap("PiYG")
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

(ax0, ax1) = fig.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 / 2.0, y[:-1, :-1] + dy / 2.0, 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()

sys.exit(app.exec_())