使用matplotlib在一个表面/轮廓图中绘制3元组数据点

I have some surface data that is generated by an external program as XYZ values. I want to create the following graphs, using matplotlib:

我有一些由外部程序以XYZ值生成的表面数据。我想用matplotlib创建如下图:

Surface plot
曲面图
Contour plot
等高线图
Contour plot overlayed with a surface plot
等高线图与面图重叠

I have looked at several examples for plotting surfaces and contours in matplotlib - however, the Z values seems to be a function of X and Y i.e. Y ~ f(X,Y).

我已经看了一些在matplotlib中绘制曲面和等高线的例子——然而，Z值似乎是X和Y的函数，即Y ~ f(X,Y)。

I assume that I will somehow need to transform my Y variables, but I have not seen any example yet, that shows how to do this.

我假设我需要对Y变量进行变换，但是我还没有看到任何例子，这说明了怎么做。

So, my question is this: given a set of (X,Y,Z) points, how may I generate Surface and contour plots from that data?

所以，我的问题是:给定一组(X,Y,Z)点，如何从这些数据中生成曲面和轮廓图?

BTW, just to clarify, I do NOT want to create scatter plots. Also although I mentioned matplotlib in the title, I am not averse to using rpy(2), if that will allow me to create these charts.

顺便说一句，我不想创建散点图。另外，尽管我在标题中提到了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。

3 个解决方案

#1

for do a contour plot you need interpolate your data to a regular grid http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data

要绘制等高线图，需要将数据插入到一个常规网格http://www.scipy.org/cookbook/matplotlib/gridding_arly_spaced_data

a quick example:

一个快速的例子:

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

for the surface http://matplotlib.sourceforge.net/examples/mplot3d/surface3d_demo.html

表面的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]])

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

3 d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()

#2

Contour plot with rpy2 + ggplot2:

rpy2 + ggplot2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()

Surface plot with rpy2 + lattice:

rpy2 +晶格表面图:

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)

#3

With pandas and numpy to import and manipulate data, with matplot.pylot.contourf to plot the image

使用matplot.pylot和numpy来导入和操作数据。绘制图像的轮廓

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()

Example Image

示例图片

#1