如何在 python 中绘制多元回归 3D 图
How to plot multiple regression 3D plot in python
我不是科学家,所以请假设我不知道经验丰富的程序员的行话,或者科学绘图技术的复杂性。 Python 是我唯一知道的语言(初学者+,也许是中级)。
Task :将多元回归 (z = f(x, y) ) 的结果绘制为 3D 图上的二维平面(我可以使用 OSX 的图形实用程序,例如,或此处 Plot Regression Surface 使用 R 实现的)。
经过一周的搜索 Whosebug 并阅读了 matplotlib、seaborn 和 mayavi 我终于找到了 Simplest way to plot 3d surface given 3d points 这听起来很有希望。所以这是我的数据和代码:
首先尝试使用matplotlib:
shape: (80, 3)
type: <type 'numpy.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()
我得到的只是一个带有以下错误消息的空 3D 坐标框:
RuntimeError:qhull Delaunay三角剖分计算错误:奇异输入数据(exitcode=2);使用 python 详细选项 (-v) 查看原始 qhull 错误。
我试图看看我是否可以使用绘图参数并检查了这个站点 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:
同样的数据,分成3个numpy数组:
type: <type 'numpy.ndarray'>
X: [ 0 500 1000 1500 2000 2500 3000 ….]
type: <type 'numpy.ndarray'>
Y: [ 0. 0.5 1. 1.5 2. 2.5 3. ….]
type: <type 'numpy.ndarray'>
Z: [ 5.5272 5.5922 5.6572 5.7222 5.7872 5.8522 5.9172 ….]
代码:
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()
我得到的是这个:
如果这很重要,我在 OSX 10.9.3
上使用 64 位版本的 Enthought's Canopy
对于我做错的任何意见,我们将不胜感激。
编辑:发布有效的最终代码,以防对某人有所帮助。
'''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, Y, Z, 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()
对于 matplotlib,您可以基于 surface example(您缺少 plt.meshgrid):
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, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, 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, shrink=0.5, aspect=5)
plt.show()
我不是科学家,所以请假设我不知道经验丰富的程序员的行话,或者科学绘图技术的复杂性。 Python 是我唯一知道的语言(初学者+,也许是中级)。
Task :将多元回归 (z = f(x, y) ) 的结果绘制为 3D 图上的二维平面(我可以使用 OSX 的图形实用程序,例如,或此处 Plot Regression Surface 使用 R 实现的)。
经过一周的搜索 Whosebug 并阅读了 matplotlib、seaborn 和 mayavi 我终于找到了 Simplest way to plot 3d surface given 3d points 这听起来很有希望。所以这是我的数据和代码:
首先尝试使用matplotlib:
shape: (80, 3)
type: <type 'numpy.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()
我得到的只是一个带有以下错误消息的空 3D 坐标框:
RuntimeError:qhull Delaunay三角剖分计算错误:奇异输入数据(exitcode=2);使用 python 详细选项 (-v) 查看原始 qhull 错误。
我试图看看我是否可以使用绘图参数并检查了这个站点 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:
同样的数据,分成3个numpy数组:
type: <type 'numpy.ndarray'>
X: [ 0 500 1000 1500 2000 2500 3000 ….]
type: <type 'numpy.ndarray'>
Y: [ 0. 0.5 1. 1.5 2. 2.5 3. ….]
type: <type 'numpy.ndarray'>
Z: [ 5.5272 5.5922 5.6572 5.7222 5.7872 5.8522 5.9172 ….]
代码:
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()
我得到的是这个:
如果这很重要,我在 OSX 10.9.3
上使用 64 位版本的 Enthought's Canopy对于我做错的任何意见,我们将不胜感激。
编辑:发布有效的最终代码,以防对某人有所帮助。
'''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, Y, Z, 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()
对于 matplotlib,您可以基于 surface example(您缺少 plt.meshgrid):
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, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, 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, shrink=0.5, aspect=5)
plt.show()