注解

Click here 下载完整的示例代码

彩色网格

axes.Axes.pcolormesh 允许您生成二维图像样式打印。请注意，它比类似的 pcolor .

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

基本颜色

我们通常通过定义四边形的边和四边形的值来指定pcolormesh。注意这里 x 和 y 在各自的维度中，每个元素都比Z多出一个元素。

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)
pcolormesh levels

出：

<matplotlib.collections.QuadMesh object at 0x7fa9fc8e17b8>

非直线多边形网格

请注意，我们还可以为 X 和 Y 具有非直线四边形。

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)
pcolormesh levels

出：

<matplotlib.collections.QuadMesh object at 0x7faa0d2e2dd8>

居中坐标

通常用户想要通过 X 和 Y 尺寸与 Z 到 axes.Axes.pcolormesh . 这也是允许的，如果 shading='auto' 已通过（默认设置为 rcParams["pcolor.shading"] (default: 'flat') ). 前Matplotlib 3.3， shading='flat' 将删除 Z ；虽然出于向后兼容性的目的仍然允许这样做，但会引发一个DeprecationWarning。

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'")
shading='auto' = 'nearest', shading='flat'

出：

/pb1/rst_repos/git/matplotlib/examples/images_contours_and_fields/pcolormesh_levels.py:67: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  axs[1].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='flat')

Text(0.5, 1.0, "shading='flat'")

使用规范制定等级

演示如何将规范化实例和颜色映射实例结合起来绘制 axes.Axes.pcolor ， axes.Axes.pcolormesh 和 axes.Axes.imshow 以类似于轮廓/轮廓的levels关键字参数的方式键入绘图。

# 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()
pcolormesh with levels, contourf with levels