Python toolkit for defining+simulating+visualizing+analyzing attractors, dynamical systems, iterated function systems, roulette curves, and more

Overview

Attractors

A small module that provides functions and classes for very efficient simulation and rendering of iterated function systems; dynamical systems, roulette curves, (strange) attractors, and so on.

Installation

Clone this repository and install with pip or another package manager. Alternatively, just clone/download the repo and use a relative import to include the scripts in your project.

Dependencies

  • Numba
  • NumPy
  • Matplotlib
  • SciPy (optional, only needed for image postprocessing)
  • nbdev (if building from source/developing)

Documentation

A brief overview of the project's main features is given below. For a more comprehensive API reference, documentation of specific classes, and functions, etc., see https://generic-github-user.github.io/attractors/.

Usage

attractors tries to conform to the principle of least astonishment wherever possible (and variable names, classes, parameters etc. aim to be readable), so using the tools should be fairly intuitive.

If we want to make a new RouletteCurve, for instance, the following will initialize one with the default parameters (including randomized arm lengths/rotation speeds):

R = RouletteCurve(num_sections=2)

Then, we can run simulate and render; function chaining is usually available since most class methods return the class instance ("self"):

R.simulate_accelerated(steps=10000).render(mode='hist', hist_args=dict(bins=150))

   

   

png

Other rendering modes are available; line will trace between each generated point.

RouletteCurve(num_sections=2).simulate_accelerated(steps=200).render(mode='line')

   

   

png

A softer render can be achieved using dist (and an optional falloff value that corresponds to the norm order when generating the brush).

RouletteCurve(num_sections=3).simulate_accelerated(steps=10000).render(mode='dist', falloff=3)
[[0.31748021 0.37475618 0.39893899 0.39893899 0.37475618]
 [0.37475618 0.52913368 0.65863376 0.65863376 0.52913368]
 [0.39893899 0.65863376 1.58740105 1.58740105 0.65863376]
 [0.39893899 0.65863376 1.58740105 1.58740105 0.65863376]
 [0.37475618 0.52913368 0.65863376 0.65863376 0.52913368]]






   

   

png

License

This project is licensed under GPL v2.0. The license file may be viewed here.

Tools

attractors is built using nbdev and Jupyter Lab, two open-source projects whose developers are owed much credit for making the development process highly efficient and enjoyable.

Issues
  • Add function for evolving visually interesting attractor systems

    Add function for evolving visually interesting attractor systems

    The same optimization process (probably evolutionary in this case) could probably be used for both this and optimizing based on an objective function (i.e., attractor/dynamical system inference)

    opened by generic-github-user 0
  • Allow incremental rendering

    Allow incremental rendering

    This may not be useful (or possible) for every rendering method (especially since rendering is usually much quicker than simulation), but could make rapid previewing/interactive iteration faster

    opened by generic-github-user 0
Owner
I work primarily on experiments & tools for machine learning, data analysis/visualization, and simulations. Check my README for a list of current projects.
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