Magnetic Words: Firefly-inspired Chinese Words & Phrases

While doing some Christmas shopping at ThinkGeek I looked at their Firefly Magnetic prose and was disappointed by their rendering of the Chinese (Mandarin) phrases included. (Of course, the Chinese used in Firefly is 25th century Chinese, so it could be Anglicized, but that isn't quite as fun). So I made my own:



And so can you: Words and phrases file (PDF), English-Chinese List, Chinese (by Pinyin)-English List. You’ll also need a pair of scissors and some adhesive-backed magnetic sheets such as those by MagPro [e.g. amazon] (also available at Michael’s arts & craft stores).
These PDFs are creative commons licensed. Attribution, Non-Commercial, Share-Alike

2112 in Mayan

The other day I was browsing a book on Mayan Civilization and Archeology and was reading about their base-20 number system and how it was juxtaposed to their calendar system in terms of complexity. I started to play a bit with the number system (I love messing about mathematically in different bases — place-value systems are so brilliant!). Thinking of the prog-rock band Rush’s palindromic album title, 2112, I quickly converted in my head and came to a surprising conclusion:
The number twenty-one twelve is a palindrome in both Hindu-Arabic base-ten numerals:
and in Mayan base-twenty numerals:
However, Mayan is normally written along the vertical axis rather than the horizontal:
Granted, it is a palindrome only graphically, as transliterating to Hindu-Arabic numerals would give us 5.5.12 (base 20, using decimals as place separators), not exactly palindromic. Even so, I thought this was a neat observation.

Vectors for Custom Map(s) of Portland (Maine)

The other day I started working on a cartography project that I have been thinking about for some time: A Personalized Map of Portland, Maine. I've only spent a couple hours digitizing off of aerial imagery from MEGIS. Originally, I wanted to use 3-inch resolution imagery, but it just was too much data, so I reduced the 3-in mosaic to a 12-in resolution mosaic. The 12-in image was better, but the image dimensions exceeded Illustrator's size limits on placed images, so I proportionally reduced it in Photoshop, so it's now about 14-in resolution imagery. In any case, I started digitizing I have two draft results:


Next I wanted to experiment with some more detail, so I started working on a map of the University of Southern Maine's Portland Campus, I have roughed out green spaces and buildings in about two hours. The draft results look a little too cartoonish, which might work in the school paper for orientation week, but is just not the look I was going for, so I need to spend some more time tidying up and generalizing; — it also needs labels.

Basic 2D & 3D Multi-dimensional scaling (MDS) in R

A couple of weeks ago I needed to examine several thousand 4-dimensional data points for separability. I turned to R and MDS to help me explore the data.

Multi-dimensional scaling is related to PCA. Basically it translates data points between differently dimensioned spaces, through the use of a similarity metric. Thus the result is a plot in 2- or 3-D similarity space; that is, what you're plotting is the similarity between data points as measured by the metric.

For 2-D plots, you can use the dist and cmdscale functions of R:

#2D
setwd("~/Data/")
getwd()

#Load Data
data <- read.csv("myData.csv", header=TRUE)

#Caclulate Dist.
data.dist <- dist(data[,1:4])

#Calculate MDS
data.mds <- cmdscale(data.dist, k=2)

#Create x,y refs
data.x <- data.mds[,1]
data.y <- data.mds[,2]

#Plot
plot(data.x, data.y, col=as.integer(data$CLASS))

this results in a plot like this:

2-D MDS Plot

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3-D plotting relies on the amazing rgl library. Which, on MacOS, I believe requires X11 to be installed. The code for this is very similar, except in the call to cmdscale, k equals 3, there is an additional axis, and a call to plot3d (rgl library call):

#3D
library(rgl)

setwd("~/Data/")
getwd()

#Load Data
data <- read.csv("myData.csv", header=TRUE)

#Caclulate Dist.
data.dist <- dist(data[,1:4])

#Calculate MDS
data.mds <- cmdscale(data.dist, k=3)

#Create x,y refs
data.x <- data.mds[,1]
data.y <- data.mds[,2]
data.z <- data.mds[,3]

#Plot
plot3d(data.x, data.y, data.z, col=as.integer(data$CLASS))

which result in a plot like this:

3-D MDS Plot

which you can even animate by adding:

#Animate by spinning on Y & Z axes play3d(spin3d(axis=c(0,1,1), rpm=3), duration=30)

Additionally, by changing play3d to movie3d and adding a movie = "namewithnoext" argument you can produce animated gifs of your 3D plot:

Animated 3-D MDS Plot

Data from SatLog dataset, from the UCI Machine Learning Repository