Hi Lorenzo,
I'm not a dev, but I've used Mayavi on occasion.
I don't totally understand your question. Does pts = mlab.points3d(x, y, z,
s)
<http://docs.enthought.com/mayavi/mayavi/mlab.html#changing-the-looks-of-the-visual-objects-created>
not
do what you want? You can control the sphere size with s. Once you create
the pts object, if you dig around enough you should be able to find the
vertices that are used to create a polygon to represent that sphere.
Best,
Chris Snyder
On Fri, Apr 15, 2016 at 2:51 AM, Lorenzo Isella <lorenzo.ise...@gmail.com>
wrote:
> Dear All,
> This is what I would like to visualize (scripting as much as
> possible): I have a set of spheres in 3D, with diameter d=2, which I
> can visualize with mayavi.
> On top of that, using scipy.spatial, I can calculate a convex hull
> based on the centers of the spheres.
> The final touch would be to visualize this hull: I should obtain a
> mesh composed by poligons. I'd like it to be partially transparent, so
> that one can see also the spheres inside.
> I paste below a script which visualizes the spheres and calculates the
> convex hull.
> Any suggestions about how to overlay the convex hull is welcome.
> Cheers
>
> Lorenzo
>
>
> ###########################################################################
>
>
> import numpy as np
>
> from scipy.spatial import ConvexHull
>
> from mayavi import mlab
>
> cluster_agglomerate=np.array([[ 0.14513811, -0.18930948, -1.44428171],
> [ 0.88042945, 1.67057596, -1.45742688],
> [-1.66682741, -0.99554261, -1.70267889],
> [-0.95535694, 2.3159907 , -1.93637881],
> [ 1.09396798, 1.7987614 , -3.44524095],
> [-2.63620654, 0.16588691,
> -3.02436539],
> [ 0.19027056, 2.70740725, 0.11479029],
> [ 2.77638842, 1.70535678, -2.10208878],
> [-0.09149779, -0.81908733, 2.07425244],
> [-0.48408772, 0.96793567,
> 1.26652603],
> [ 0.67499278, -2.5386008 , 1.39914827],
> [ 1.02571108, -1.60932884, -0.34500693],
> [ 2.78789155, -1.42050584, 0.59682802],
> [-0.14688239, -2.36883246,
> 3.35177362],
> [-1.71507089, 0.19184887, 2.68408562],
> [-1.87886026, -1.58255618, 3.97006406],
> [ 6.61540229, 1.98324725, 0.82767368],
> [ 7.46818823, 3.00950487,
> -0.66214223],
> [ 4.80777628, 1.97159273, 1.68344958],
> [ 6.3548727 , 2.26459561, 2.92819854],
> [ 4.70026626, 0.44618044, 0.3866837 ],
> [ 3.44366671, 1.87939977,
> -0.2203051 ],
> [ 2.92460648, 1.98510457, 2.37510769],
> [ 5.07053866, -0.10349542, -1.51041234],
> [ 7.21643437, -1.32050186, -0.70707322],
> [ 6.93292243, 0.58458074,
> -1.2458508 ],
> [ 7.84238244, -2.97562362, -1.63914669],
> [ 7.43212373, 0.10620418, 0.68315389],
> [ 9.59692827, -2.0843759 , -1.26623658],
> [ 8.34540867, -1.14927294,
> 1.95073173],
> [ 6.57425162, -2.13797392, -2.94804639],
> [ 6.93340304, -4.4591665 , -0.63578546]])
>
>
> hull = ConvexHull(cluster_agglomerate)
>
>
>
> x=cluster_agglomerate[:,0]
> y=cluster_agglomerate[:,1]
> z=cluster_agglomerate[:,2]
>
>
> mlab.clf()
> pts = mlab.points3d(x, y, z, scale_mode='none', resolution=20,\
> color=(0,0,1),scale_factor=2.)
> #mlab.axes(pts)
>
> mlab.show()
>
>
>
>
>
>
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