The example is a simple parametric surface paneller, that recursively divides a surface according to its Gaussian curvature. The more curved areas get progressively smaller divisions. I use scripting to handle recursion, and Grasshopper component for all the geometry, sliders, etc.

Files here:grasshopper and rhino. Requires current GH Beta.

from RMA.OpenNURBS import *
A = []
B = []
C = []
D = []

def sub(u,v, depth, ud, vd):
  if u>=maxu or v>=maxv:
    return ""
  if depth < 2:
    A.append(On3dPoint(u,v,0))
    B.append(On3dPoint(u+ud,v,0))
    C.append(On3dPoint(u+ud,v+vd,0))
    D.append(On3dPoint(u,v+vd,0))
  else:
    ud/=2
    vd/=2
    for center in ((u,v),(u+ud,v),(u+ud,v+vd),(u,v+vd)):
      u,v = center
      sub(u,v,depth-1, ud, vd)

u = uv.x
v = uv.y
depth = int(G*j)
ud=(maxu-minu)/i
vd=(maxv-minv)/i
sub(u,v,depth,ud,vd)

A first simple experiment, populating a NURBS surface using its UV coordinates.

from __future__ import division

usub = 7
vsub = 20
usize = 8
vsize = 1
depth = 0.5

surfs = selectedNodes()

def pt(surf,u,v):
	return pointOnSurface( surf, u=usize*u/usub, v=vsize*v/vsub)

for surf in surfs:
	for u in range(usub):
		for v in range(vsub):
			pt1 = pt(surf,u,v)
			pt2 = pt(surf,u+1,v)
			pt3 = pt(surf,u+1,v+1)
			pt4 = pt(surf,u,v+1)
			c=curve(p=[pt1,pt2,pt3,pt4,pt1])
			extrude (c, et = 0, upn=True, l= depth)

Much simpler than MEL or the native Python implementation, easier to learn than RhinoScript and more powerful than MaxScript.

genetic housing layout system. Would create and distribute houses on a 3d landscape, mutating and mixing parameters such as:

• plan layout
• height/plan surface
• size
• orientation
• etc.

The fitness function would be a mix of some cost/area calculation and a measure of the negative externalities created by each house, sort of a neighborhood good will indicator. It would be more expensive to be too far away from other houses (urbanization costs), which should create some sort of balance.

As the paper points out, this is not a configurational analysis or a complete one, but the fact that it uses a sample of the possible lines of sight allows it to run in polynomial time O(n²).

The paper can be downloaded here or read online using Issuu:

It’s basically about:

• Definition of “weak heritage” as the elements of a city that make up its character but aren’t part of its “strong” heritage such as churches, avenues, etc. The weak heritage of a city includes individual houses, public spaces, small shops and other anonymous elements that make up the fabric of the city.
• A three dimensional visibility graph analysis model,written in maxscript and python, that allows one to input a 3d mesh of the area one wishes to study and produces a series of graph centrality and clustering measurements.
• An application of points 1 & 2 to Valparaíso, Chile.

A graph I made on many eyes that lets you visualize the correlation between the number of flickr photos per node and visibility graph network centrality measurements for Valparaiso. It’s interactive, you can change the X and Y axes content, etc. Fairly cool.

If you look at the full size version, you can make out the streets in a light grey line. The light-blue area is the bay.

What I think is interesting is the way this constitutes a distributed, spontaneous semantic layer in actual space. It would be cool if you could have people generate these tags in real time, without photographs as intermediaries.

My next step is to correlate this data with the 3D Visibility Graph Analysis.

This method has been used in the discussion and approval of building codes in 5 municipalities in Chile, and presented to the 2005 Sigradi conference in Lima, Perú (PDF, in Spanish) and 2CTV conference in Concepción, Chile.

It is similar to, while more basic than, Benamy Turkienicz’s CityZoom.

The code is a mess of AutoLisp, which encouraged me to learn more OO languages like Java and Python.