概述

我不是科学家,所以请假设我不知道有经验的程序员的行话,或者科学绘图技术的复杂性. Python是我所知道的唯一语言(初学者,也许是中级).

任务：将多元回归的结果(z = f(x,y))绘制为3D图形上的二维平面(例如,我可以使用OSX的图形工具,或者在此使用R实现Plot Regression Surface).

经过一周搜索Stackoverflow并阅读matplotlib,seaborn和mayavi的各种文档后,我终于找到了Simplest way to plot 3d surface given 3d points,听起来很有希望.所以这是我的数据和代码：

首先尝试使用matplotlib：

shape: (80,3) 
type: 
  
   ndarray'> 
zmul: 

[[  0.00000000e+00   0.00000000e+00   5.52720000e+00]
 [  5.00000000e+02   5.00000000e-01   5.59220000e+00]
 [  1.00000000e+03   1.00000000e+00   5.65720000e+00]
 [  1.50000000e+03   1.50000000e+00   5.72220000e+00]
 [  2.00000000e+03   2.00000000e+00   5.78720000e+00]
 [  2.50000000e+03   2.50000000e+00   5.85220000e+00]
 ……]

import matplotlib
from matplotlib.ticker import MaxNLocator
from matplotlib import cm

from numpy.random import randn
from scipy import array,newaxis
Xs = zmul[:,0]
Ys = zmul[:,1]
Zs = zmul[:,2]


surf = ax.plot_trisurf(Xs,Ys,Zs,cmap=cm.jet,linewidth=0)

   fig.colorbar(surf)

ax.xaxis.set_major_locator(MaxNLocator(5))
ax.yaxis.set_major_locator(MaxNLocator(6))
ax.zaxis.set_major_locator(MaxNLocator(5))


   fig.tight_layout()


   plt.show()

  

我得到的是一个空的3D坐标框架,其中包含以下错误消息：

RuntimeError：qhull delaunay三角测量计算中的错误：奇异输入数据(exitcode = 2);使用python verbose选项(-v)来查看原始的qhull错误.

我试着看看我是否可以使用绘图参数并检查这个网站http://www.qhull.org/html/qh-impre.htm#delaunay,但我真的无法理解我应该做什么.

第二次尝试使用mayavi：

相同的数据,分为3个numpy数组：

type: 
  
   ndarray'> 
X: [    0   500  1000  1500  2000  2500  3000 ….]

type: 
   
    ndarray'> 
Y: [  0.    0.5   1.    1.5   2.    2.5   3.  ….]

type: 
    
     ndarray'> 
Z: [  5.5272   5.5922   5.6572   5.7222   5.7872   5.8522   5.9172  ….] 

    
   
  

码：

from mayavi import mlab
def multiple3_triple(tpl_lst):

X = xs
Y = ys
Z = zs


# Define the points in 3D space
# including color code based on Z coordinate.
pts = mlab.points3d(X,Y,Z,Z)

# Triangulate based on X,Y with delaunay 2D algorithm.
# Save resulting triangulation.
mesh = mlab.pipeline.delaunay2d(pts)

# Remove the point representation from the plot
pts.remove()

# Draw a surface based on the triangulation
surf = mlab.pipeline.surface(mesh)

# Simple plot.
mlab.xlabel("x")
mlab.ylabel("y")
mlab.zlabel("z")
mlab.show()

我得到的就是：

如果这很重要,我在OSX 10.9.3上使用64位版本的Enthought’s Canopy

对于我做错了什么的输入,将不胜感激.

编辑：发布有效的最终代码,以防有人帮助.

'''After the usual imports'''
def multiple3(tpl_lst):
    mul = []
    for tpl in tpl_lst:
        calc = (.0001*tpl[0]) + (.017*tpl[1])+ 6.166
        mul.append(calc)
    return mul

fig = plt.figure()
ax = fig.gca(projection='3d')
'''some skipped code for the scatterplot'''
X = np.arange(0,40000,500)
Y = np.arange(0,40,.5)
X,Y = np.meshgrid(X,Y)
Z = multiple3(zip(X,Y))

surf = ax.plot_surface(X,rstride=1,cstride=1,cmap=cm.autumn,linewidth=0,antialiased=False,alpha =.1)
ax.set_zlim(1.01,11.01)
ax.set_xlabel(' x = IPP')
ax.set_ylabel('y = UNRP20')
ax.set_zlabel('z = DI')

ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
fig.colorbar(surf,shrink=0.5,aspect=5)
plt.show()

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator,FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5,5,0.25)
Y = np.arange(-5,0.25)
X,Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X,cmap=cm.coolwarm,antialiased=False)
ax.set_zlim(-1.01,1.01)

ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

fig.colorbar(surf,aspect=5)

plt.show()

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