Double pendulum simulator using a symplectic Euler's method and Hamiltonian mechanics

Overview

Symplectic Double Pendulum Simulator

Double pendulum simulator using a symplectic Euler's method. The program calculates the momentum and position of each ball using Hamiltonian formulations, and uses those calculations to adjust the pendulum's course. This provides a double pendulum that stays stable and more analagous to real-life for longer periods of time, without the need of more complex math/animation modules. Mathematical methods provided in attatched PDF.

Screen Shot 2022-01-11 at 4 01 07 PM

Functionality

  • Visualization UI that shows scaled changes in pendulum lengths, masses, and starting angles
  • Generates EPS plot of path and phase diagram on command
  • Built to run on a Rasberry Pi/Linux based system

Usage Instructions

Run ./doublependulum.py in Linux terminal. If pendulum animation becomes unstable, the momenta of the masses are most likely too large for the allowed framerate of matplotlib. Try lowering masses or lengthening pendulums.

Required Modules

  • numpy
  • matplotlib
  • threading
  • time

Contributors

Scott Marino / [email protected]

Owner
Scott Marino
Scott Marino
A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images

BaSiC Matlab code accompanying A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images by Tingying Peng, Kurt Thorn, Timm Schr

Marr Lab 34 Dec 18, 2022
Learning Energy-Based Models by Diffusion Recovery Likelihood

Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma Paper: https://arxiv.o

Ruiqi Gao 41 Nov 22, 2022
In this project we predict the forest cover type using the cartographic variables in the training/test datasets.

Kaggle Competition: Forest Cover Type Prediction In this project we predict the forest cover type (the predominant kind of tree cover) using the carto

Marianne Joy Leano 1 Mar 15, 2022
[CVPR'22] Official PyTorch Implementation of Collaborative Transformers for Grounded Situation Recognition

[CVPR'22] Collaborative Transformers for Grounded Situation Recognition Paper | Model Checkpoint This is the official PyTorch implementation of Collab

Junhyeong Cho 29 Dec 10, 2022
3D-aware GANs based on NeRF (arXiv).

CIPS-3D This repository will contain the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis.

Peterou 563 Dec 31, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Jan 05, 2023
EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients. This repository is the official im

Yassir BENDOU 57 Dec 26, 2022
Unofficial Tensorflow Implementation of ConvNeXt from A ConvNet for the 2020s

Tensorflow Implementation of "A ConvNet for the 2020s" This is the unofficial Tensorflow Implementation of ConvNeXt from "A ConvNet for the 2020s" pap

DK 11 Oct 12, 2022
Source code for paper "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling", AAAI 2021

ATLOP Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. If you make use of this co

Wenxuan Zhou 146 Nov 29, 2022
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19) Tianyu Wang*, Xin Yang*, Ke Xu, Shaozhe Chen, Qiang Zhang, Ry

Steve Wong 177 Dec 01, 2022
This repository allows the user to automatically scale a 3D model/mesh/point cloud on Agisoft Metashape

Metashape-Utils This repository allows the user to automatically scale a 3D model/mesh/point cloud on Agisoft Metashape, given a set of 2D coordinates

INSCRIBE 4 Nov 07, 2022
High-Resolution 3D Human Digitization from A Single Image.

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020) News: [2020/06/15] Demo with Google Colab (i

Meta Research 8.4k Dec 29, 2022
Curating a dataset for bioimage transfer learning

CytoImageNet A large-scale pretraining dataset for bioimage transfer learning. Motivation In past few decades, the increase in speed of data collectio

Stanley Z. Hua 9 Jun 20, 2022
Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent

Frank Odom 27 Dec 21, 2022
Code, Data and Demo for Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting

InversePrompting Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting Code: The code is provided in the "chinese_ip"

THUDM 101 Dec 16, 2022
A list of multi-task learning papers and projects.

This page contains a list of papers on multi-task learning for computer vision. Please create a pull request if you wish to add anything. If you are interested, consider reading our recent survey pap

svandenh 297 Dec 17, 2022
Classify music genre from a 10 second sound stream using a Neural Network.

MusicGenreClassification Academic research in the field of Deep Learning (Deep Neural Networks) and Sound Processing, Tel Aviv University. Featured in

Matan Lachmish 453 Dec 27, 2022
Imaginaire - NVIDIA's Deep Imagination Team's PyTorch Library

Imaginaire Docs | License | Installation | Model Zoo Imaginaire is a pytorch library that contains optimized implementation of several image and video

NVIDIA Research Projects 3.6k Dec 29, 2022
masscan + nmap + Finger

说明 个人根据使用习惯修改masnmap而来的一个小工具。调用masscan做全端口扫描,再调用nmap做服务识别,最后调用Finger做Web指纹识别。工具使用场景适合风险探测排查、众测等。 使用方法 安装依赖 pip3 install -r requirements.txt -i https:/

Ryan 3 Mar 25, 2022