I'm trying to plot a 3d surface where each of the three dimensions in a separate array of values and the colouring of the surface at each coordinate is a function of x,y,z. A sort of numpy.pcolormesh but in 4D, rather than 3D.
The 3D plot is given by:
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.logspace(-1.,np.log10(5),50)
y = np.linspace(6,9,50)
z = np.linspace(-1,1,50)
colors = LikeBeta(y,range(50),range(50))
ax.plot_trisurf(x,y,z,cmap=colors,linewidth=0.2)
where
def LikeBeta(rho0,r0,beta):
M0 = 10**rho0*r0_array[r0]**3
I = cst*M0*sigma_los_beta[beta,:,r0]
S = dv**2+I
res = (np.log(S) + (v-u)**2/S).sum()
return res/2.
Probably the cmap=colors
is wrong, but the problem lies elsewhere. I get the following error:
----> 8 colors = LikeBeta(y,range(50),range(50))
----> 4 I = cst*M0*sigma_los_beta[beta,:,r0]
ValueError: operands could not be broadcast together with shapes (50,) (50,353)
Indeed sigma_los_beta
is an array that I evaluate separately and has shape (50,353,50)
and those 353 are data that I must have.
How can I cast this function into a form that is compatible with the other entries of plot_trisurf
?
Sorry, but I can't supply a minimal working code, because dv,v and u are data.
Thank you very much for your help. Cheers
This answer addresses the 4d surface plot problem. It uses matplotlib's plot_surface
function instead of plot_trisurf
.
Basically you want to reshape your x, y and z variables into 2d arrays of the same dimension. To add the fourth dimension as a colormap, you must supply another 2d array of the same dimension as your axes variables.
Below is example code for a 3d plot with the colormap corresponding to the x values. The facecolors
argument is used to alter the colormap to your liking. Note that its value is acquired from the to_rgba()
function in the matplotlib.cm.ScalarMappable
class.
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
# domains
x = np.logspace(-1.,np.log10(5),50) # [0.1, 5]
y = np.linspace(6,9,50) # [6, 9]
z = np.linspace(-1,1,50) # [-1, 1]
# convert to 2d matrices
Z = np.outer(z.T, z) # 50x50
X, Y = np.meshgrid(x, y) # 50x50
# fourth dimention - colormap
# create colormap according to x-value (can use any 50x50 array)
color_dimension = X # change to desired fourth dimension
minn, maxx = color_dimension.min(), color_dimension.max()
norm = matplotlib.colors.Normalize(minn, maxx)
m = plt.cm.ScalarMappable(norm=norm, cmap='jet')
m.set_array([])
fcolors = m.to_rgba(color_dimension)
# plot
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X,Y,Z, rstride=1, cstride=1, facecolors=fcolors, vmin=minn, vmax=maxx, shade=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
fig.canvas.show()
The answer I referenced (and others) mentions that you should normalize your fourth dimension data. It seems that this may be avoided by explicitly setting the limits of the colormap as I did in the code sample.
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