# Week 9 HW

Here is a curve trace of the J train line, superimposed with 100 equidistant points. Each point was moved in the z-direction by a random number to simulate noise. Going forward in the class I am going to be doing field recording, or some other audio analysis to obtain noise data, and utilize it in a design relating to noise in the anthropocene. Here is the Python script:

import rhinoscriptsyntax as rs
import random

pntList = []

arrObjects = rs.GetObjects(“choose objects”, rs.filter.point, True, True)

for object in arrObjects:
if rs.IsPoint(object):
pntLoc = rs.PointCoordinates(object)
print pntLoc
z = random.uniform(0.0,4.0)
scale = (0, 0, z)
rs.MoveObject(object, scale)
newPnt = rs.PointCoordinates(object)
pntList.append(newPnt)

# Circles w/Recrusion: Joanna import rhinoscriptsyntax as rs
import random

#def random_color():
#return (random.randint(0,255), random.randint(0,255), random.randint(0,255))
color = random.randint(255, 255)

pt = rs.GetPoint(“Pick starting point”)

def RecursiveCircle(pt, d):
if d == 0:
return 1
else:
rs.ObjectColor(c, color)

return RecursiveCircle(pt, d-1)

RecursiveCircle(pt, 10)

# Midterm/Final project idea- Joanna

For my primary project in Sculpting Data into Everyday Objects I partnered up with Ross Goodwin and decided to create a lamp that will visualize the traveling salesman problem between a set of cities that Italo Calvino described in Invisible Cities.

The lamp will be a three-dimensional set of vertices, each a 3D printed city-node designed based on descriptions of various cities in the Calvino’s Invisible Cities. The cities will serve a purpose of little light bulbs with LEDs inside them, that will visualize a computer algorithm running on Raspberry Pi solving the traveling salesman problem in real time between the cities.

Here is the first node-city I have modeled in Rhino, Valdrada: # Sieve and Heatmaps

I attempted to model a sieve but encountered some issues combining the individual components and adding fillets. See below. I’m also interested in investigating sports data, particularly in physically visualizing “heatmaps.” # Homework 3 – Maria

I am teaming up with Martin for this presentation. We would like to explore the relationship between plastic production/consumption and marine pollution.

Rhino Model of water pitcher: