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.

Owner
I work primarily on experiments & tools for machine learning, data analysis/visualization, and simulations. Check my README for a list of current projects.
Interactive plotting for Pandas using Vega-Lite

pdvega: Vega-Lite plotting for Pandas Dataframes pdvega is a library that allows you to quickly create interactive Vega-Lite plots from Pandas datafra

Altair 342 Oct 26, 2022
python partial dependence plot toolbox

PDPbox python partial dependence plot toolbox Motivation This repository is inspired by ICEbox. The goal is to visualize the impact of certain feature

Li Jiangchun 723 Jan 07, 2023
The repository is my code for various types of data visualization cases based on the Matplotlib library.

ScienceGallery The repository is my code for various types of data visualization cases based on the Matplotlib library. It summarizes the code and cas

Warrick Xu 2 Apr 20, 2022
Getting started with Python, Dash and Plot.ly for the Data Dashboards team

data_dashboards Getting started with Python, Dash and Plot.ly for the Data Dashboards team Getting started MacOS users: # Install the pyenv version ma

Department for Levelling Up, Housing and Communities 1 Nov 08, 2021
The Spectral Diagram (SD) is a new tool for the comparison of time series in the frequency domain

The Spectral Diagram (SD) is a new tool for the comparison of time series in the frequency domain. The SD provides a novel way to display the coherence function, power, amplitude, phase, and skill sc

Mabel 3 Oct 10, 2022
又一个云探针

ServerStatus-Murasame 感谢ServerStatus-Hotaru,又一个云探针诞生了(大雾 本项目在ServerStatus-Hotaru的基础上使用fastapi重构了服务端,部分修改了客户端与前端 项目还在非常原始的阶段,可能存在严重的问题 演示站:https://stat

6 Oct 19, 2021
a python function to plot a geopandas dataframe

Pretty GeoDataFrame A minimum python function (~60 lines) to draw pretty geodataframe. Based on matplotlib, shapely, descartes. Installation just use

haoming 27 Dec 05, 2022
Plot and save the ground truth and predicted results of human 3.6 M and CMU mocap dataset.

Visualization-of-Human3.6M-Dataset Plot and save the ground truth and predicted results of human 3.6 M and CMU mocap dataset. human-motion-prediction

Gaurav Kumar Yadav 5 Nov 18, 2022
Investment and risk technologies maintained by Fortitudo Technologies.

Fortitudo Technologies Open Source This package allows you to freely explore open-source implementations of some of our fundamental technologies under

Fortitudo Technologies 11 Dec 14, 2022
OpenStats is a library built on top of streamlit that extracts data from the Github API and shows the main KPIs

Open Stats Discover and share the KPIs of your OpenSource project. OpenStats is a library built on top of streamlit that extracts data from the Github

Pere Miquel Brull 4 Apr 03, 2022
Some useful extensions for Matplotlib.

mplx Some useful extensions for Matplotlib. Contour plots for functions with discontinuities plt.contour mplx.contour(max_jump=1.0) Matplotlib has pro

Nico Schlömer 519 Dec 30, 2022
Simple spectra visualization tool for astronomers

SpecViewer A simple visualization tool for astronomers. Dependencies Python = 3.7.4 PyQt5 = 5.15.4 pyqtgraph == 0.10.0 numpy = 1.19.4 How to use py

5 Oct 07, 2021
Homework 2: Matplotlib and Data Visualization

Homework 2: Matplotlib and Data Visualization Overview These data visualizations were created for my introductory computer science course using Python

Sophia Huang 12 Oct 20, 2022
Graphical visualizer for spectralyze by Lauchmelder23

spectralyze visualizer Graphical visualizer for spectralyze by Lauchmelder23 Install Install matplotlib and ffmpeg. Put ffmpeg.exe in same folder as v

Matthew 1 Dec 21, 2021
This is a web application to visualize various famous technical indicators and stocks tickers from user

Visualizing Technical Indicators Using Python and Plotly. Currently facing issues hosting the application on heroku. As soon as I am able to I'll like

4 Aug 04, 2022
Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies

py-self-organizing-map Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies. A SOM is a simple unsuperv

Jonas Grebe 1 Feb 10, 2022
The interactive graphing library for Python (includes Plotly Express) :sparkles:

plotly.py Latest Release User forum PyPI Downloads License Data Science Workspaces Our recommended IDE for Plotly’s Python graphing library is Dash En

Plotly 12.7k Jan 05, 2023
WebApp served by OAK PoE device to visualize various streams, metadata and AI results

DepthAI PoE WebApp | Bootstrap 4 & Vue.js SPA Dashboard Based on dashmin (https:

Luxonis 6 Apr 09, 2022
Statistics and Visualization of acceptance rate, main keyword of CVPR 2021 accepted papers for the main Computer Vision conference (CVPR)

Statistics and Visualization of acceptance rate, main keyword of CVPR 2021 accepted papers for the main Computer Vision conference (CVPR)

Hoseong Lee 78 Aug 23, 2022
Learn Basic to advanced level Data visualisation techniques from this Repository

Data visualisation Hey, You can learn Basic to advanced level Data visualisation techniques from this Repository. Data visualization is the graphic re

Shashank dwivedi 16 Jan 03, 2023