Not that I am aware of. We kind of brute-force it in the plot_surface()
function:
polys = []
# Only need these vectors to shade if there is no cmap
if cmap is None and shade :
totpts = int(np.ceil(float(rows - 1) / rstride) *
np.ceil(float(cols - 1) / cstride))
v1 = np.empty((totpts, 3))
v2 = np.empty((totpts, 3))
# This indexes the vertex points
which_pt = 0
#colset contains the data for coloring: either average z or the
facecolor
colset = []
for rs in xrange(0, rows-1, rstride):
for cs in xrange(0, cols-1, cstride):
ps = []
for a in (X, Y, Z) :
ztop = a[rs,cs:min(cols, cs+cstride+1)]
zleft = a[rs+1:min(rows, rs+rstride+1),
min(cols-1, cs+cstride)]
zbase = a[min(rows-1, rs+rstride), cs:min(cols,
cs+cstride+1):][::-1]
zright = a[rs:min(rows-1, rs+rstride):, cs][::-1]
z = np.concatenate((ztop, zleft, zbase, zright))
ps.append(z)
# The construction leaves the array with duplicate points,
which
# are removed here.
ps = zip(*ps)
lastp = np.array([])
ps2 = [ps[0]] + [ps[i] for i in xrange(1, len(ps)) if ps[i]
!= ps[i-1]]
avgzsum = sum(p[2] for p in ps2)
polys.append(ps2)
if fcolors is not None:
colset.append(fcolors[rs][cs])
else:
colset.append(avgzsum / len(ps2))
# Only need vectors to shade if no cmap
if cmap is None and shade:
i1, i2, i3 = 0, int(len(ps2)/3), int(2*len(ps2)/3)
v1[which_pt] = np.array(ps2[i1]) - np.array(ps2[i2])
v2[which_pt] = np.array(ps2[i2]) - np.array(ps2[i3])
which_pt += 1
if cmap is None and shade:
normals = np.cross(v1, v2)
else :
normals = []
If you find a better way to do this, I will owe you some beers.
Cheers!
Ben Root
On Wed, Mar 26, 2014 at 7:17 AM, <clau...@br.ibm.com> wrote:
> Dear colleagues,
>
> Exploring the 3D support for plotting a simple trapezoid isosceles based
> on eight locations with x,y,z (imagine a water tank). When doing a manual
> selection of the collections that defines each surface plane, the drawing
> works well (see a sample below). Watching for a more automated process that
> could work with a complex surface based on any Polygons.
>
> My question: Is there an algorithm, or function in Numpy or Matplotlib
> that identifies the quartet of each plane in the sample below? I've tried
> to apply Numpy function "combinations", but it generates too many
> collections.
>
> Thanks in advance for your hint to optimize this drawing with the
> Matplotlib with Poly3DCollection
>
> Sample Code
> -----------
> from mpl_toolkits.mplot3d import Axes3D
> from mpl_toolkits.mplot3d.art3d import Poly3DCollection
> from mpl_toolkits.mplot3d.art3d import Line3DCollection
> import matplotlib.pyplot as plt
> from matplotlib import cm
> import matplotlib.colors as colors
> from numpy import random
> fig = plt.figure()
> ax = Axes3D(fig)
> # for random color settings
> color = colors.rgb2hex(random.rand(3))
> # blue color
> color = 'b'
> #mypoly = [[2, 0, -1], [2, 0, 1], [4, 0, 1], [4, 0, -1], [0, 4, -2], [0,
> 4, 2], [6, 4, 2], [6, 4, -2]]
> # A B C D E
> F G H
> # Colections for drawing 3D plot with polygon (each plane defined
> separately)
> #plane a: A,E,H,D
> #plane b: A,E,F,B
> #plane c: B,F,G,C
> #plane d: C,G,H,D
> #plane e: E,F,G,H
> #plane f: A,B,C,D
> #plane collection
> xa = [2,0,6,4]
> ya = [0,4,4,0]
> za = [-1,-2,-2,-1]
> #second collection
> xb = [2,0,0,2]
> yb = [0,4,4,0]
> zb = [-1,-2,2,1]
> #third collection
> xc = [2,0,6,4]
> yc = [0,4,4,0]
> zc = [1,2,2,1]
> #fourth collection
> xd = [4,6,6,4]
> yd = [0,4,4,0]
> zd = [1,2,-2,-1]
> #fifth collection (kept open, to watch the plot result)
> xe = [0,0,6,6]
> ye = [4,4,4,4]
> ze = [-2,2,2,-2]
> #sixth collection
> xf = [2,2,4,4]
> yf = [0,0,0,0]
> zf = [-1,1,1,-1]
> # to do
> verts = [zip(xa, ya,za),zip(xb, yb,zb),zip(xc, yc,zc),zip(xd,
> yd,zd),zip(xf, yf,zf)]
> ax.add_collection3d(Poly3DCollection(verts, facecolors=color,
> linewidths=1, alpha=0.5))
> ax.add_collection3d(Line3DCollection(verts, colors='k', linewidths=0.2,
> linestyles=':'))
> # set axis view
> # add grid
> ax.grid(True)
> # view
> ax.set_xlim(-1,6)
> ax.set_ylim(-1,6)
> ax.set_zlim(-5,5)
> ax.view_init(elev=10., azim=110.)
> ax.get_xaxis().set_visible(True)
> ax.get_yaxis().set_visible(True)
> ax.set_autoscale_on(True)
> plt.show()
>
> Thanks for support.
>
> Regards,
> Claude
>
>
>
>
>
>
>
>
>
> * Claude Falbriard Certified IT Specialist L2 - Middleware AMS Hortolândia
> / SP - Brazil phone: +55 13 9 9760 0453 <%2B55%2013%209%209760%200453>
> cell: +55 13 9 8117 3316 <%2B55%2013%209%208117%203316> e-mail:
> clau...@br.ibm.com <clau...@br.ibm.com> *
>
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"Graph Databases" is the definitive new guide to graph databases and their
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this first edition is now available. Download your free book today!
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