Python 制作散点轮廓
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Make contour of scatter
提问by JuanPablo
In python, If I have a set of data
在python中，如果我有一组数据
x, y, z
I can make a scatter with
我可以散布
import matplotlib.pyplot as plt
plt.scatter(x,y,c=z)
How I can get a plt.contourf(x,y,z)of the scatter ?
我怎样才能得到一个plt.contourf(x,y,z)分散的？
采纳答案by elyase
You can use tricontourfas suggested in case b.of this other answer:
您可以按照情况b 的建议使用tricontourf 。的这个其他答案：
import matplotlib.tri as tri
import matplotlib.pyplot as plt
plt.tricontour(x, y, z, 15, linewidths=0.5, colors='k')
plt.tricontourf(x, y, z, 15)
Old reply:
旧回复：
Use the following function to convert to the format required by contourf:
使用以下函数转换成contourf需要的格式：
from numpy import linspace, meshgrid
from matplotlib.mlab import griddata
def grid(x, y, z, resX=100, resY=100):
"Convert 3 column data to matplotlib grid"
xi = linspace(min(x), max(x), resX)
yi = linspace(min(y), max(y), resY)
Z = griddata(x, y, z, xi, yi)
X, Y = meshgrid(xi, yi)
return X, Y, Z
Now you can do:
现在你可以这样做：
X, Y, Z = grid(x, y, z)
plt.contourf(X, Y, Z)
回答by David Zwicker
contourexpects regularly gridded data. You thus need to interpolate your data first:
contour期望定期网格数据。因此，您需要先插入数据：
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import numpy.ma as ma
from numpy.random import uniform, seed
# make up some randomly distributed data
seed(1234)
npts = 200
x = uniform(-2,2,npts)
y = uniform(-2,2,npts)
z = x*np.exp(-x**2-y**2)
# define grid.
xi = np.linspace(-2.1,2.1,100)
yi = np.linspace(-2.1,2.1,100)
# grid the data.
zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic')
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x,y,marker='o',c='b',s=5)
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.title('griddata test (%d points)' % npts)
plt.show()
Note that I shamelessly stole this code from the excellent matplotlib cookbook
请注意，我无耻地从优秀的matplotlib 食谱中窃取了这段代码
回答by ImportanceOfBeingErnest
The solution will depend on how the data is organized.
解决方案将取决于数据的组织方式。
Data on regular grid
常规网格上的数据
If the xand ydata already define a grid, they can be easily reshaped to a quadrilateral grid. E.g.
如果x和y数据已经定义了网格，则可以轻松地将它们重新调整为四边形网格。例如
#x y z
4 1 3
6 1 8
8 1 -9
4 2 10
6 2 -1
8 2 -8
4 3 8
6 3 -9
8 3 0
4 4 -1
6 4 -8
8 4 8
can plotted as a contourusing
可以绘制为contour使用
import matplotlib.pyplot as plt
import numpy as np
x,y,z = np.loadtxt("data.txt", unpack=True)
plt.contour(x.reshape(4,3), y.reshape(4,3), z.reshape(4,3))
Arbitrary data
任意数据
a. Interpolation
一种。插值
In case the data is not living on a quadrilateral grid, one can interpolate the data on a grid. One way to do so is scipy.interpolate.griddata
如果数据不在四边形网格上，可以在网格上插入数据。一种方法是 scipy.interpolate.griddata
import numpy as np
from scipy.interpolate import griddata
xi = np.linspace(4, 8, 10)
yi = np.linspace(1, 4, 10)
zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='linear')
plt.contour(xi, yi, zi)
b. Non-gridded contour
湾 非网格轮廓
Finally, one can plot a contour completely without the use of a quadrilateral grid. This can be done using tricontour.
最后，可以在不使用四边形网格的情况下完全绘制轮廓。这可以使用tricontour.
plt.tricontour(x,y,z)
An example comparing the latter two methods is found on the matplotlib page.
在matplotlib 页面上可以找到比较后两种方法的示例。
