pcolormesh #

axes.Axes.pcolormesh允许您生成 2D 图像样式的图。请注意，它比类似的要快pcolor。

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

基本 pcolormesh #

我们通常通过定义四边形的边和四边形的值来指定 pcolormesh。请注意，这里x和y在各自的维度中都比 Z 多一个元素。

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)

<matplotlib.collections.QuadMesh object at 0x7f2d00aaeef0>

非直线 pcolormesh #

请注意，我们还可以为X和Y指定矩阵并具有非直线四边形。

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)

<matplotlib.collections.QuadMesh object at 0x7f2d00c610f0>

居中坐标#

通常，用户希望将大小与Z相同的X和Y传递给。如果通过（默认设置为（默认值：）），这也是允许的。在 Matplotlib 3.3 之前，会删除Z的最后一列和最后一行；虽然出于向后兼容的目的仍然允许这样做，但会引发 DeprecationWarning。如果这确实是您想要的，那么只需手动删除 Z 的最后一行和最后一列：axes.Axes.pcolormeshshading='auto'rcParams["pcolor.shading"]'auto'shading='flat'

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[:-1, :-1], vmin=np.min(Z), vmax=np.max(Z),
                  shading='flat')
axs[1].set_title("shading='flat'")

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

使用规范制作关卡#

展示如何结合 Normalization 和 Colormap 实例以在中绘制“级别” axes.Axes.pcolor，axes.Axes.pcolormesh 并axes.Axes.imshow以与轮廓/轮廓的级别关键字参数类似的方式键入绘图。

# 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.colormaps['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()

参考

此示例中显示了以下函数、方法、类和模块的使用：

脚本总运行时间：（0分1.467秒）

由 Sphinx-Gallery 生成的画廊