使用 matplotlib 在曲面/等高线图中绘制 3 元组数据点

Question

我有一些由外部程序生成的表面数据作为 XYZ 值。我想使用 matplotlib 创建以下图形：

• 曲面图
• 轮廓图
• 覆盖有曲面图的轮廓图

我已经查看了在 matplotlib 中绘制曲面和轮廓的几个示例 - 然而，Z 值似乎是 X 和 Y 的函数即Y ~ f(X,Y)。

我假设我需要以某种方式转换我的 Y 变量，但我还没有看到任何示例来说明如何执行此操作。

所以，我的问题是：给定一组 (X,Y,Z) 点，如何从该数据生成曲面图和等高线图？

顺便说一句，只是为了澄清，我不想创建散点图。另外，虽然我在标题中提到了 matplotlib，但我并不反对使用 rpy(2)，如果这允许我创建这些图表的话。

Answer 1

要绘制等高线图，您需要将数据插值到规则网格http: //www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data

一个简单的例子：

>>> xi = linspace(min(X), max(X))
>>> yi = linspace(min(Y), max(Y))
>>> zi = griddata(X, Y, Z, xi, yi)
>>> contour(xi, yi, zi)

对于表面 http://matplotlib.sourceforge.net/examples/mplot3d/surface3d_demo.html

>>> from mpl_toolkits.mplot3d import Axes3D
>>> fig = figure()
>>> ax = Axes3D(fig)
>>> xim, yim = meshgrid(xi, yi)
>>> ax.plot_surface(xim, yim, zi)
>>> show()

>>> help(meshgrid(x, y))
    Return coordinate matrices from two coordinate vectors.
    [...]
    Examples
    --------
    >>> X, Y = np.meshgrid([1,2,3], [4,5,6,7])
    >>> X
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    >>> Y
    array([[4, 4, 4],
           [5, 5, 5],
           [6, 6, 6],
           [7, 7, 7]])

3D 轮廓 http://matplotlib.sourceforge.net/examples/mplot3d/contour3d_demo.html

>>> fig = figure()
>>> ax = Axes3D(fig)
>>> ax.contour(xi, yi, zi) # ax.contourf for filled contours
>>> show()

Answer 2

使用 pandas 和 numpy 导入和操作数据，使用 matplot.pylot.contourf 绘制图像

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata

PATH='/YOUR/CSV/FILE'
df=pd.read_csv(PATH)

#Get the original data
x=df['COLUMNNE']
y=df['COLUMNTWO']
z=df['COLUMNTHREE']

#Through the unstructured data get the structured data by interpolation
xi = np.linspace(x.min()-1, x.max()+1, 100)
yi = np.linspace(y.min()-1, y.max()+1, 100)
zi = griddata(x, y, z, xi, yi, interp='linear')

#Plot the contour mapping and edit the parameter setting according to your data (http://matplotlib.org/api/pyplot_api.html?highlight=contourf#matplotlib.pyplot.contourf)
CS = plt.contourf(xi, yi, zi, 5, levels=[0,50,100,1000],colors=['b','y','r'],vmax=abs(zi).max(), vmin=-abs(zi).max())
plt.colorbar()

#Save the mapping and save the image
plt.savefig('/PATH/OF/IMAGE.png')
plt.show()

示例图像

Answer 3

使用 rpy2 + ggplot2 绘制等高线图：

from rpy2.robjects.lib.ggplot2 import ggplot, aes_string, geom_contour
from rpy2.robjects.vectors import DataFrame

# Assume that data are in a .csv file with three columns X,Y,and Z
# read data from the file
dataf = DataFrame.from_csv('mydata.csv')

p = ggplot(dataf) + \
    geom_contour(aes_string(x = 'X', y = 'Y', z = 'Z'))
p.plot()

使用 rpy2 + 晶格绘制曲面图：

from rpy2.robjects.packages import importr
from rpy2.robjects.vectors import DataFrame
from rpy2.robjects import Formula

lattice = importr('lattice')
rprint = robjects.globalenv.get("print")

# Assume that data are in a .csv file with three columns X,Y,and Z
# read data from the file
dataf = DataFrame.from_csv('mydata.csv')

p = lattice.wireframe(Formula('Z ~ X * Y'), shade = True, data = dataf)
rprint(p)