UNIVERSITY OF CALIFORNIA Los Angeles - Users - UCLA

Tools In fall 2007, I took a course called Statistical Computing from
Mark Hansen in the UCLA Statistics Department. During this class, he
introduced tools that could be used in different ways for different
purposes and explore the artifacts of communication. An outline of these
tools is given below:
[E]macs, Unix tools, Python
Regular expressions, manipulating text
R, data types, basic computations, writing functions, objects and methods
Code distribution, R packages, software licenses
Scripting visualization via Processing
Databases, SQL, R interface to MySQL, XML
Statistical computation in real-time systems
Figure 40. Beaufort wind
scale is an empirical
measure for describing wind
speed based mainly on
observed sea conditions.
[Wiki]
For my data analysis stage, I mainly used Python urllib, 59 which is
scripting language, to acquire and parse the large sample data from the
website. R 60 , an integrated suite of software facilities was used for data
manipulation, calculation and graphical display of samples. Next, like Ben
Fry, I used Processing 61 as a necessary supplementary component.
Figure 41. Examples of wind data analysis for 4weeks: station 40007
In my project, I used Python to get and update data continuously (in
this project, every 10min). The Python module saves each station’s
updated data files in the proceeding data folder. Processing was used to get
data and phase it and send signals to Arduino 62 and simulate data on the
computer screen.
59
http://www.python.org/ (accessed March.12.2008)
60
http://www.r-project.org/ (accessed March.12.2008)
61
http://processing.org/ (accessed March.12.2008) Processing is a simple programming
environment that was created to make it easier to develop visually oriented applications.
62
http://www.arduino.cc/ (accessed March.20.2008)
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