使用facecolors的matplotlib plot_surface的颜色栏 [英] Colorbar for matplotlib plot_surface using facecolors
问题描述
我正在尝试使用facecolors用预定义的颜色对表面进行3D着色绘制.这里的问题是cm.ScalarMappable可以使颜色的表面V归一化，而plt.cm.jet不能归一化，因此颜色和颜色栏不匹配.我已经尝试过尝试标准化V(即V_normalized)，但是结果仍然不太正确.实际上，V的最大值应该在表面的一角，但这实际上并没有反映在图像中.如何绘制以确保表面具有正确的颜色?
I'm trying to plot in 3D colouring the surface with predefined colours using facecolors. The problem here is that cm.ScalarMappable normalizes surface V of colours while plt.cm.jet don't normalizes, so there is a mismatch of colours and colorbar. I've manually tried to normalize V (i.e. V_normalized) but the result is still not quite correct. In fact, the highest value of V should be in a corner of the surface, but this is not reflected in the image in practice. How to plot ensuring to have the corrects colours on the surface?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
# Create data.
X = np.array([[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500],
[ 50, 100, 150, 200, 250, 300, 350, 400, 450, 500]])
Y = np.array([[ 75, 75, 75, 75, 75, 75, 75, 75, 75, 75],
[125, 125, 125, 125, 125, 125, 125, 125, 125, 125],
[175, 175, 175, 175, 175, 175, 175, 175, 175, 175],
[225, 225, 225, 225, 225, 225, 225, 225, 225, 225],
[275, 275, 275, 275, 275, 275, 275, 275, 275, 275],
[325, 325, 325, 325, 325, 325, 325, 325, 325, 325],
[375, 375, 375, 375, 375, 375, 375, 375, 375, 375],
[425, 425, 425, 425, 425, 425, 425, 425, 425, 425],
[475, 475, 475, 475, 475, 475, 475, 475, 475, 475]])
Z = pd.DataFrame([[2.11, 2.14, 2.12, 2.10, 2.09, 2.08, 2.07, 2.07, 2.08, 2.05],
[2.01, 2.03, 1.99, 1.96, 1.95, 1.93, 1.90, 1.90, 1.92, 1.92],
[1.89, 1.90, 1.90, 1.94, 1.92, 1.89, 1.88, 1.87, 1.86, 1.86],
[1.79, 1.79, 1.75, 1.79, 1.77, 1.78, 1.78, 1.78, 1.79, 1.76],
[1.75, 1.77, 1.8, 1.79, 1.8, 1.77, 1.73, 1.73, 1.77, 1.77],
[1.72, 1.76, 1.77, 1.77, 1.79, 1.8, 1.78, 1.78, 1.74, 1.7],
[1.67, 1.66, 1.69, 1.7, 1.65, 1.62, 1.63, 1.65, 1.7, 1.69],
[1.64, 1.64, 1.61, 1.59, 1.61, 1.67, 1.71, 1.7, 1.72, 1.69],
[1.63, 1.63, 1.62, 1.67, 1.7, 1.67, 1.67, 1.69, 1.69, 1.68]],
index=np.arange(75, 525, 50), columns=np.arange(50, 525, 50))
V = pd.DataFrame([[ 7.53, 7.53, 7.53, 7.53, 7.53, 7.53, 7.53, 7.53, 7.53, 7.53],
[ 7.53, 7.53, 7.53, 7.53, 7.66, 8.09, 8.08, 8.05, 8.05, 8.05],
[ 7.53, 7.77, 8.08, 8.05, 8.19, 8.95, 8.93, 8.79,8.79, 8.62],
[ 8.95, 7.92, 8.95, 8.93, 8.62, 7.93, 8.96, 8.95, 9.09, 8.75],
[ 8.61, 8.95, 8.62, 8.61, 8.95, 8.93, 8.82, 9.42, 9.67, 8.48],
[ 9.23, 8.61, 8.95, 9.24, 9.42, 8.48, 8.47, 8.65, 8.92, 9.17],
[ 8.6 , 9.01, 9.66, 8.05, 9.42, 8.92, 8.81, 7.53, 7.53, 7.53],
[ 9.42, 9.25, 8.65, 8.92, 8.25, 7.97, 8.09, 8.49, 8.49, 7.58],
[ 10.15, 9.79, 9.1 , 9.35, 9.35, 9.35, 9.25, 9.3 , 9.3 , 8.19]],
index=np.arange(75, 525, 50), columns=np.arange(50, 525, 50))
# Create the figure, add a 3d axis, set the viewing angle
# % matplotlib inline # If you are using IPython
fig = plt.figure(figsize=[15,10])
ax = fig.add_subplot(111, projection='3d')
ax.view_init(45,60)
# Normalize in [0, 1] the DataFrame V that defines the color of the surface.
V_normalized = (V - V.min().min())
V_normalized = V_normalized / V_normalized.max().max()
# Plot
ax.plot_surface(X, Y, Z, facecolors=plt.cm.jet(V_normalized))
ax.set_xlabel('x', fontsize=18)
ax.set_ylabel('y', fontsize=18)
ax.set_zlabel('z', fontsize=18)
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(V)
plt.colorbar(m)
推荐答案
您的图是正确的，尽管您可以使用matplotlib.colors.Normalize实例简化归一化.
Your plot is correct, although you might simplify the normalization using a matplotlib.colors.Normalize instance.
norm = matplotlib.colors.Normalize(vmin=V.min().min(), vmax=V.max().max())
ax.plot_surface(X, Y, Z, facecolors=plt.cm.jet(norm(V)))
m = cm.ScalarMappable(cmap=plt.cm.jet, norm=norm)
m.set_array([])
plt.colorbar(m)
为什么在网格上看不到10.15的最大值，这一点是不同的:
The point why you don't see the maximum value of 10.15 on the grid, is a different one:
在一个维度上具有N个点时，该图具有(N-1)个面.这意味着根本不会绘制输入颜色数组的最后一行和最后一列.
When having N points along one dimension, the plot has (N-1) faces. That means that the last row and column of the input color array are simply not plotted.
这可以在下图中看到，其中绘制了3x3矩阵，从而形成了2x2面.它们会根据颜色数组中的各个值进行着色，以使第一个面具有该数组中第一个元素所赋予的颜色，等等.对于最后一个元素，则不剩下任何要着色的面.
This can be seen in the following picture, where a 3x3 matrix is plotted, resulting in 2x2 faces. They are colorized according to the respective values in a color array, such that the first face has the color given by the first element in the array etc. For the last elements there is no face to color left.
用于重现此情节的代码:
Code to reproduce this plot:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.colors
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.arange(3)
X,Y = np.meshgrid(x,x)
Z = np.ones_like(X)
V = np.array([[3,2,2],[1,0,3],[2,1,0]])
norm = matplotlib.colors.Normalize(vmin=0, vmax=3)
ax.plot_surface(X, Y, Z, facecolors=plt.cm.jet(norm(V)), shade=False)
m = cm.ScalarMappable(cmap=plt.cm.jet, norm=norm)
m.set_array([])
plt.colorbar(m)
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()
这篇关于使用facecolors的matplotlib plot_surface的颜色栏的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
