https://github.com/rsvp/randomsys

Algorithmic study of random systems. / Keywords: probability stochastic process ANU quantum random number generator Gaussian statistics
https://github.com/rsvp/randomsys

algorithm anu generator probability pseudorandom quantum random statistics stochastic

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Algorithmic study of random systems. / Keywords: probability stochastic process ANU quantum random number generator Gaussian statistics

• Host: GitHub
• URL: https://github.com/rsvp/randomsys
• Owner: rsvp
• License: other
• Created: 2015-09-29T22:58:07.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2017-10-03T18:10:53.000Z (over 7 years ago)
• Last Synced: 2023-10-25T21:51:20.399Z (over 1 year ago)
• Topics: algorithm, anu, generator, probability, pseudorandom, quantum, random, statistics, stochastic
• Language: Jupyter Notebook
• Homepage: https://git.io/randquantum
• Size: 173 KB
• Stars: 11
• Watchers: 3
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md

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README

        
# randomsys

[![Join the chat at https://gitter.im/rsvp/randomsys](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/rsvp/randomsys?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

We study the behavior of random systems algorithmically.

## Generating truly random numbers: randquantum

[This notebook gives a DEMONSTRATION](https://github.com/rsvp/randomsys/blob/master/quantum/randquantum-demo.ipynb) 

of the useful functions which yield true random numbers produced live from an

experiment in quantum mechanics. They are contained in the `randquantum` Python

module under the `quantum` directory.

Reliable and unbiased random numbers are needed for a range of applications

from numerical modeling to cryptographic communications. While there are

algorithms that can generate pseudo random numbers, they can never be

perfectly random nor indeterministic. ANU researchers are generating true

random numbers from a physical quantum source by splitting a beam of light

into two beams and then measuring the power in each beam. Because light is

quantised, the light intensity in each beam fluctuates about the mean. Those

fluctuations, due ultimately to the quantum vacuum, can be converted into a

source of random numbers.

The rate at which the live bits are streamed is limited by the bandwidth of

your internet connection. Every number is randomly generated in real time and

cannot be predicted beforehand. Most pseudo-random numbers have a finite

period after which the sequence repeats. The output herein will not have such

periodicity.

We develop a faster stochastic hybrid method which integrates authentic and 

pseudo generators to induce independence and eliminate predictable periodicity. 

This has *PASSED Marsaglia Diehard, NIST STS, and RGB Dieharder tests.* 

## Visualization of digits

For each digit,

[plot/plot_digitangle.py](https://github.com/rsvp/randomsys/blob/master/plot/plot_digitangle.py)

pushes the turtle (an arrow) directionally at a specific angle for a fixed distance.

That push creates a plot where the colors are determined by the input digits.

Thus the script visualizes any sequence of digits.

If circular angles are mapped on a random sequence of digits we will see a

drunkard walk across the screen. Furthermore, if the sequence comes from a

"normal" number, we can expect recurrent behavior, i.e. the drunk turtle

will return to where it started its journey, though it may take a long time

to do so theoretically.

Here is a plot of the first 10,000 digits of pi using

[pi-digits.txt](https://github.com/rsvp/randomsys/blob/master/plot/pi-digits.txt):

![pi-digits.jpg](https://git.io/pi-digits.jpg)

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Revision date : 2017-10-02