How to plot multiple regression 3D plot in python
for matplotlib, you can base off the surface example (you're missing 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()
user2738815
Updated on June 04, 2022Comments
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user2738815 almost 2 years
I am not a scientist, so please assume that I do not know the jargon of experienced programmers, or the intricacies of scientific plotting techniques. Python is the only language I know (beginner+, maybe intermediate).
Task : Plot the results of a multiple regression (z = f(x, y) ) as a two dimensional plane on a 3D graph (as I can using OSX’s graphing utility, for example, or as implemented here Plot Regression Surface with R).
After a week searching Stackoverflow and reading various documentations of matplotlib, seaborn and mayavi I finally found Simplest way to plot 3d surface given 3d points which sounded promising. So here is my data and code:
First try with 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()
All I get is an empty 3D coordinate frame with the following error message:
RuntimeError: Error in qhull Delaunay triangulation calculation: singular input data (exitcode=2); use python verbose option (-v) to see original qhull error.
I tried to see if I could play around with the plotting parameters and checked this site http://www.qhull.org/html/qh-impre.htm#delaunay, but I really cannot make sense of what I am supposed to do.
Second try with mayavi:
Same data, divided into 3 numpy arrays:
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 ….]
Code:
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()
All I get is this:
If this matters, I am using the 64 bit version of Enthought's Canopy on OSX 10.9.3
Will be grateful for any input on what I am doing wrong.
EDIT: Posting the final code that worked, in case it helps someone.
'''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()