确定再现性的随机状态创建RGB合成图像。

import numpy as np
import matplotlib.pyplot as plt

# Fixing random state for reproducibility
np.random.seed(19680801)

n = 100000
x = np.random.standard_normal(n)
y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
xmin = x.min()
xmax = x.max()
ymin = y.min()
ymax = y.max()

fig, axs = plt.subplots(ncols=2, sharey=True, figsize=(7, 4))
fig.subplots_adjust(hspace=0.5, left=0.07, right=0.93)
ax = axs[0]
hb = ax.hexbin(x, y, gridsize=50, cmap='inferno')
ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax))
ax.set_title("Hexagon binning")
cb = fig.colorbar(hb, ax=ax)
cb.set_label('counts')

ax = axs[1]
hb = ax.hexbin(x, y, gridsize=50, bins='log', cmap='inferno')
ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax))
ax.set_title("With a log color scale")
cb = fig.colorbar(hb, ax=ax)
cb.set_label('log10(N)')

plt.show()
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes


def get_demo_image():
    # prepare image
    delta = 0.5

    extent = (-3, 4, -4, 3)
    x = np.arange(-3.0, 4.001, delta)
    y = np.arange(-4.0, 3.001, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = np.exp(-X**2 - Y**2)
    Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
    Z = (Z1 - Z2) * 2

    return Z, extent


def get_rgb():
    Z, extent = get_demo_image()

    Z[Z < 0] = 0.
    Z = Z / Z.max()

    R = Z[:13, :13]
    G = Z[2:, 2:]
    B = Z[:13, 2:]

    return R, G, B


fig = plt.figure()
ax = RGBAxes(fig, [0.1, 0.1, 0.8, 0.8])

r, g, b = get_rgb()
kwargs = dict(origin="lower", interpolation="nearest")
ax.imshow_rgb(r, g, b, **kwargs)

ax.RGB.set_xlim(0., 9.5)
ax.RGB.set_ylim(0.9, 10.6)

plt.show()