zebranoise 0.1.0

Zebra noise stimuli for Python

This library will efficiently generate visual stimulus videos using Perlin noise, including zebra noise. The core algorithm is in optimized C.

Example zebra noise

Installation

From pypi

Run:

pip install zebranoise

From source

Make sure you have a C compiler installed. The easiest way to do this is by installing Cython.

Then, install using the standard

python setup.py install

This will compile the sources and install.

I have only tested this on Linux.

Usage

Basic usage

If you would like to generate a zebra noise of size 640x480 for 2 minutes, you can run

import zebranoise
zebranoise.zebra_noise("output.mp4", xsize=640, ysize=480, tdur=60*2, fps=30, seed=0)

Other parameters control the spatial and temporal frequencies. For instance,

zebranoise.zebra_noise("output2.mp4", xsize=640, ysize=480, tdur=60*2, levels=10, xyscale=.2, tscale=50, fps=30, xscale=1.0, yscale=1.0, seed=0)

The meaning of these parameters is:

• xsize and ysize: the x and y dimensions of the output video. (Sometimes these will be rounded up to multiples of 16.)
• tdur: the duration in seconds
• levels: The number of octaves to use when approximating the 1/f spectrum. The default of 10 should be more than enough.
• xyscale: The spatial scale of the Perlin noise, from 0 to 1. Low values will make the video smoother and high values choppier.
• tscale: The speed of the video
• xscale and yscale: Resize the x and y dimensions of the output.
• fps: Frames per second
• seed: Random seed

Advanced usage

This package can be used to generate new stimuli based on Perlin noise.

To generate a Perlin noise-based video, follow two steps with the package. First, generate the Perlin noise. This is the most time consuming step, and will also use a lot of temporary disk space. This is accomplished with:

noise = zebranoise.PerlinStimulus(xsize=400, ysize=100, tdur=60*2, xyscale=.2, tscale=50, seed=0)

See the function documentation for perlstim.Perl for more information about modifying the properties of the noise.

Second, apply "filters" to the Perlin noise and save the resulting video. Filters include thresholds, sync squares in the corner, etc. If using a filter with no arguments, just pass a string naming the filter. If using a filter with arguments, pass a tuple where the first element is the name of the filter as a string, and the remaining elements are the arguments. For example,

noise.save_video("noise.mp4", filters=["reverse", ("comb", .05)])

The above example applies the "reverse" filter (with no arguments) and the "comb" filter with the argument 0.1.

Filters

The following filters are currently defined:

• "threshold" (1 argument): set all values above the threshold argument to white, and all below to black.
• "softthresh" (1 argument): set values much greater than 0 to white and much less than zero to blac. Use shades of grey for intermediate values, according to a sigmoid with a temperature given as the argument.
• "comb" (1 argument): Alternate black and white thresholds at intervals given by the argument.
• "reverse" (0 arguments): play the video in reverse
• "invert" (0 arguments): switch white and black
• "wood" (1 argument): similar to "comb", but use a sawtooth wave
• "blur" (1 argument): Gaussian blur with the given spatial standard deviation
• "photodiode" (1 argument): Draw a sync square in the corner, with a size given by the argument.
• "photodiode_anywhere" (3 arguments): Draw a sync square at the coordinates given by the first two arguments (x and y), and of size given by argument 3.
• [any function] (0 arguments): If you pass a function, the function will be applied to each chunk of the video. Chunks consist of the entire x and y but a subset of z, guaranteed to be an even number.

Example

To get started, try the following:

import zebranoise
stim = zebranoise.PerlinStimulus(xsize=480, ysize=128, tdur=60*5, xyscale=.2, tscale=50)
stim.save_video("perlin_stimulus.mp4", loop=1, filters=[("comb", .08), ("photodiode", 30)])

Other information

Much of the C code is based on Casey Duncan's "noise" package for Python, under the MIT license.