Thanks. This looks like it might work for me. I got your example to work, but I
still need to figure out how to apply it to my problem. In particular there is
the note about your Triangulation line which says that we assume there is a
nice projection of the surface into the x/y-plane. Is this just a requirement
of the Triangulation function or of the Poly3Dcollection? My surfaces are
typically enclosed bodies, but since they’re meshed for the Electromagnetic
Simulation I already know the connectivity of the vertices. Also, in your
example it appears that you define one color per triangle but the trisurf files
I have consist of one color per vertex. Will I need to come up with my own
function for defining facecolors based on the three color values associated
with each of my triangles?
Byron Boulton
From: Maximilian Albert [mailto:maximilian.alb...@gmail.com]
Sent: Wednesday, January 21, 2015 11:03 AM
To: Byron K. Boulton
Cc: matplotlib-devel@lists.sourceforge.net
Subject: Re: [matplotlib-devel] trisurf plots with independent color data
Hi Byron,
This is a bit of a workaround, but you can specify facecolors explicitly by
creating a triangulation of your surface explicitly and creating a
Poly3DCollection with these facecolors. I'm attaching an example below which is
a modified version of the plot_trisurf demo [1] in the matplotlib
documentation. It showcases both random colors and a smooth gradient (the
latter in the line that's commented out).
I would have thought that it should be possible to pass an argument like
"facecolors" to plot_trisurf directly, since the documentation [2] states that
"other arguments are passed on to Poly3DCollection". However, I couldn't get
this to work quickly. Maybe someone else knows how?
Best regards,
Max
[1]
http://matplotlib.org/examples/mplot3d/trisurf3d_demo.html<http://cp.mcafee.com/d/avndygscxNJ5xd5VZN5ZwTsSztcQsFzCn4kjqdQPhOCepssKrhKCqekNPbyapJ6WpEVj7cK3HCO1kxlI-to0ExYoOYD8Y_BPp3UNBVehV_bCZd7a5Di33_nVNcQsTu7tuVtdBZXAkhP3aqvbnjIyCGyyeVkffGhBrwqrhdFCXYCONtxdZZBcTsS03lSqNZpDO8s01PMY87_j5lzWPpgOspkryWOwaEuHnwsrlQfAH8-mMXjO7NC2y8DOVJ55CVEVdwLQzh0qmRtCIvmpYy1t3PfDCBQQgjzazsnjh0Ivmt3h0oBcCq89ggZrU8v2pEwJk-Ijh0xIZdKc6NP69lvVcsZE3>
[2]
http://matplotlib.org/mpl_toolkits/mplot3d/api.html<http://cp.mcafee.com/d/FZsS86Qm4QnDT4nS3tPqdQPhOCepshhdETjd7aoVBNOVJ6WpEVj7cK8FCQrFCzBcsOUeKr85i5mPVRw2y7NzbOszP-ndAfz6nAV7DYKrQQsEmt8cfZvD4PhPtUtRXBQSnTKhh7ccFFYJteOaqGa8XBgY-F6lK1FJcSCrLOrb5S4TTSkPtPo0dnpH7RCv8xM075lzNmtqDBpjFyGNZpIEpGQK1NJng-iIzVr3Jf8v6oa8yvbCQkmrCzAS2_id41FrlSqNZpDO85Qfc-uqnjh1ecGdNtd42NZpQd41ykOpEwB13RLwxY9Cy2RjWNd426PQSUMrQ0np3acf>
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.tri import Triangulation
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
n_angles = 36
n_radii = 8
# An array of radii
# Does not include radius r=0, this is to eliminate duplicate points
radii = np.linspace(0.125, 1.0, n_radii)
# An array of angles
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
# Repeat all angles for each radius
angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1)
# Convert polar (radii, angles) coords to cartesian (x, y) coords
# (0, 0) is added here. There are no duplicate points in the (x, y) plane
x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())
# Pringle surface
z = np.sin(-x*y)
tri = Triangulation(x, y) # NOTE: This assumes that there is a nice projection
of the surface into the x/y-plane!
triangle_vertices = np.array([np.array([[x[T[0]], y[T[0]], z[T[0]]],
[x[T[1]], y[T[1]], z[T[1]]],
[x[T[2]], y[T[2]], z[T[2]]]]) for T in
tri.triangles])
midpoints = np.average(triangle_vertices, axis=1)
def find_color_for_point(pt):
x, y, z = pt
col = [(y+1)/2, (1-y)/2, 0]
return col
#facecolors = [find_color_for_point(pt) for pt in midpoints] # smooth gradient
facecolors = [np.random.random(3) for pt in midpoints] # random colors
coll = Poly3DCollection(triangle_vertices, facecolors=facecolors,
edgecolors='black')
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.add_collection(coll)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
ax.elev = 50
plt.show()
------------------------------------------------------------------------------
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