You can reach me at:
- discord:
parenthetically
- email:
[email protected]
Python toolkit for defining+simulating+visualizing+analyzing attractors, dynamical systems, iterated function systems, roulette curves, and more
Home Page: https://generic-github-user.github.io/attractors/
License: GNU General Public License v2.0
You can reach me at:
parenthetically
[email protected]
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)
For example, taking the position of a point generated by an attractor at a certain instant in time (i.e., a timestep; alternatively, over the course of the entire simulation) and using its x and y values (or polar coordinates) as rotation speeds in another RouletteClass instance (then possibly repeating this process)
This is obviously only applicable to certain types of systems, e.g. roulette curves with constant speed/length values (otherwise, it is difficult or impossible to reduce a long series of changes in the system's state to a simple calculation)
Possibly include some benchmark results in the README/documentation as well for comparison with other similar tools
A similar process could be used for rendering; I may open another issue for this
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
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