如何在Python中将3D数据绘制为2D网格颜色图? [英] How to plot 3D data as 2D grid colormap in Python?

问题描述

我有3D数据作为numpy数组的列(也就是说，array [0] = [0,0,0]等)，例如

I have 3D data as the columns of a numpy array (that is to say that array[0] = [0,0,0], etc.), e.g.

X     Y     Z

0     0     0
0     1     10
1     0     20
1     1     30

我想对此进行绘制，以使每个(X，Y)坐标在该坐标上均具有一个正方形，并带有一个从(例如)0到30的色条显示Z值.

I would like to plot this so that each (X,Y) co-ordinate has a square centered on the co-ordinate, with a colorbar from (e.g.) 0 to 30 showing the Z value.

然后我想覆盖一些轮廓线，但是问题的第一部分是最重要的.

I would then like to overlay some contour lines, but the first part of the question is most important.

对于已经掌握数据的人有帮助，但是我不确定最好的matplotlib例程来调用我的列数据.另外，这是用于科学出版物，因此需要具有良好的质量和外观！希望有人能帮忙！

There is help for people who have already-gridded data, but I am not sure of the best matplotlib routine to call for my column data. Also, this is for scientific publication, so needs to be of a good quality and look! Hope someone can help!

推荐答案

您可以使用matplotlib.mlab中的griddata正确地对数据进行网格化.

You can use griddata from matplotlib.mlab to grid your data properly.

import numpy as np
from matplotlib.mlab import griddata

x = np.array([0,0,1,1])
y = np.array([0,1,0,1])
z = np.array([0,10,20,30])
xi = np.arange(x.min(),x.max()+1)
yi = np.arange(y.min(),y.max()+1)
ar = griddata(x,y,z,xi,yi)

# ar is now
# array([[  0.,  20.],
#        [ 10.,  30.]])

映射的xi和yi点的选择取决于您，它们不必是整数，因为griddata可以为您插值.

The choice of the mapped xi and yi points is up to you, and they do not have to be integers as griddata can interpolate for you.

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