Open Course Work from MIT

MIT's open course work website can be found here.

Call for Abstracts

A good resource for writing abstract can be found here.

Variance

Different programs implement variance in different ways. This can lead to confusing results when you move from one set of functions to another.

scipy.stats.cov() normalizes by (N-1), by default, bias=False. By setting bias=True, you will normalize by N.

However, scipy.var() normalizes by N.

In Octave, var( x, 0 ) biases by N-1, and var( x, 1 ) biases by N.

Matlab and R, I think, do things slightly differently.

NumPy and Input/Output

Super dooper resource for NUmPy I/O can be found here. Includes information on how to interpret binary data, and use the NumPy alias of the the MATLAB fread() function.

GNU Octave

GNU Octave Manual, version 3, is found here.

Tilings

Girih tiles can be found here.

Penrose tiles can be found here.

Aperiodic tiles in general are found here.

CUDA, PyCUDA, and PyCUBLAS

Andreas Klöckner's PyCUDA page.

Some documentation for PyCUDA and PyOpenCL.

Moire Patterns using PyCUDA and PyGame.

Use PyCUBLAS to multiply matrices smaller than 65536-by-65536.