7 July 2008
Pymel fixes Maya’s python API.
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.
19 December 2007
I recently published a paper on 3d isovist analysis at the 2007 SIGraDi conference in Mexico. The illustrations are in greyscale and they screwed up some of the notation (basically, not rendering any sub- or super-scripts).
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:
12 December 2007
If you wish, you can read my thesis online using this little Issuu widget (yeah, I know, stupid 2.0 name, it’s still pretty slick). You can page through the tiny version using the arrows, or click on a page to pop open a full reader (which can even be put into fullscreen mode).
19 October 2007
Finished writing my thesis (!). PDF here. In spanish.
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.
30 August 2007
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.
26 August 2007
A geolocated tag-cloud for all the flickr photographs that have been placed within Valparaiso, Chile. The size of the text relates to the frequency of use of each tag in each position (not the overall frequency). This is part of my MArch thesis, and makes use of James Clarke’s flicker.py to retrieve the data.
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.
10 August 2007
I just found an online reference to one of my papers (co-written with Sebastián Guevara), in Portuguese: “Sigradi 2006 - Santiago de Chile Um novo paradigma para o uso da informática na arquitetura“.
7 August 2007
Some work done with Claudio Labarca on the simulation of building codes in 3d. It takes as its input an area’s building codes, lots and road centerlines, and generates 3d models of possible buildings that maximize their allowed heights and surface areas.
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.
4 August 2007
A few preliminary graphics from my thesis, an application of three-dimensional visibility graph analysis to “weak heritage” sites.
A simple 3d model is represented as an array of tiles(30×30 in the example) with each of the tiles as a vertex vi of a graph G, with an edge (vi,vj) in G if vi and vj are mutually visible from each other.
I then run standard graph centrality analysis on the graph, as shown below. The color scale is normalized for each graph, with red being the highest value, green the midway and blue the lowest.
The white “T” shaped object in the middle represents an inaccessible visual barrier.
The analysis is done in a Python program (from a simple elevation grid exported from 3ds Max via a maxscript), using the excellent NetworkX package. The results are read back into 3ds Max to generate the graphics shown.
The Clustering Centrality analysis seems particularly promising, as it can be construed as a measure of how Local or Global the visibility relationships are for any given point, which should allow for an understanding of how space is understood by both locals and visitors.
Betweenness Centrality
Closesness Centrality
Clustering Coefficient
Degree Centrality
