Python Clean-energy Resources

EnergyExpenditure Code for the "Sensing leg movement enhances wearable monitoring of energy expenditure" paper. Additional data for replicating this s

Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks

Peaqev ev-charging Peaqev ev-charging is an attempt of charging an ev without breaching a preset monthly max-peak energy level. In order for this inte

TokenCleaner Don't waste your money paying for new tokens, once you have used your tokens, clean them up and resell them! If you have a very large qua

RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes

Free HTTP Proxy List 🌍 It is a lightweight project that hourly scrapes lots of

TorchMD-net TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular

Minimal implementation of Decision Transformer: Reinforcement Learning via Sequence Modeling in PyTorch for mujoco control tasks in OpenAI gym

Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu

⏱ pytorch-benchmark Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption Install pip install pytor

IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling This is my code, data and approach for the IEEE-CIS Technical Challen

Protection-UB Simple Group Protection userbot running on python3 with ARQ ⚠️ Not

Energy-Estimator-for-Eyeriss-like-Architecture- An energy estimator for eyeriss-like DNN hardware accelerator This is an energy estimator for eyeriss-

Wechat-file-cleaner - Clean files in PC WeChat FileStorage directory

Python based utilities for interacting with digital multimeters that are built on the FS9721-LP3 chipset.

KPACUBO KPACUBO is a set Jupyter notebooks focused on the best practices in both software development and data science, namely, code reuse, explicit d

Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization This repository contains the code for the BBI optimizer, introduced in the p

This is an API developed in python with the FastApi framework and putting into practice the recommendations of the book Clean Architecture in Python by Leonardo Giordani,

The Open Free Energy library Alchemical free energy calculations for the masses.

On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model. Designed sample dashboard with insights and recommendation for customers.

🚩 A simple and clean python banner generator - Banners

Official PyTorch implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 Spotlight Paper) Zhisheng

CO2Ampel This RaspberryPi project uses weather data to estimate the share of ren

This repository contains a utility for measuring energy consumption when running various programs in NVIDIA Jetson-based platforms. Currently TX-2, NX, and AGX are supported.

Welcome to MadAnalysis 5 Outline What is MadAnalysis 5? Requirements Downloading

A clean, easy to scale discord bot template. Develope using nextcord library and can be use with any other discord.py forked library.

GPOEO GPOEO is a micro-intrusive GPU online energy optimization framework for iterative applications. We also implement ODPP [1] as a comparison. [1]

Generates a clean .txt file of contents of a 3 lined csv file. File contents is the .gml file of some function which stores the contents of the csv as a map.

Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.

Kata: Clean Machine Learning From Dirty Code First, open the Kata in Google Colab (or else download it) You can clone this project and launch jupyter-

COResets and Data Subset selection Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order

Finding data is one thing. Getting it ready for analysis is another. Acquiring, cleaning, standardizing and importing publicly available data is time

todo-list-fastapi practice project How to run Install dependencies npm, yarn: standard-version, husky make: script for lint, test pipenv: virtualenv +

Tries to clean your dense mocap graphs like an animator would! So it will produce a usable artist friendly result while maintaining the original graph.

coURLan: Clean, filter, normalize, and sample URLs Why coURLan? “Given that the bandwidth for conducting crawls is neither infinite nor free, it is be

This code extracts line width of phonons from specular energy density (SED) calculated with LAMMPS.

Python client and API for monitoring and controling energy diversion devices from MyEnergi A set of library functions and objects for interfacing with

These are two scripts I use to generate clean bib files. clean_bibfile.py: Removes superfluous fields (which are not included in fields_to_keep.json)

Overview: Easy, clean, reliable Python 2/3 compatibility python-future is the missing compatibility layer between Python 2 and Python 3. It allows you

CleanBlog A clean and simple blog system based on Flask and MongoDB You can access CleanBlog This is the source code of Flask Tutorial Pro,you can buy

Pynavt _____ _ | __ \ | | | |__) | _ _ __ __ ___ _| |_ | ___/ | | | '_ \ / _` \ \ / /

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urb

With this class you can receive, decrypt and handle Yeelight YLKG07YL/YLKG08YL dimmer bluetooth notifications in your python code.

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urba

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and lear

A clean customizable documentation theme for Sphinx

Learning to Compose Visual Relations This is the pytorch codebase for the NeurIPS 2021 Spotlight paper Learning to Compose Visual Relations. Demo Imag

Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T

CaloGAN Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. This repository c

Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis This repository contains all the code used in L. de Oliveira (@lukedeo),

IllustrationGAN A simple, clean TensorFlow implementation of Generative Adversarial Networks with a focus on modeling illustrations. Generated Images

nbdev template Use this template to more easily create your nbdev project. If you are using an older version of this template, and want to upgrade to

Making Simplex Project Testing - Clean and Simple What does this repo do? It organizes the python stack for the coding project What do I need to do in

A clean and extensible PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners A PyTorch re-implementation of Mask Autoencoder trai

Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

Multi-objective constrained optimization for energy applications via tree ensembles

Citylearn Challenge This is the PyTorch implementation for PikaPika team, CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energ

This package is a python library with tools for the Molecular Simulation - Software Gromos. It allows you to easily set up, manage and analyze simulations in python.

An open-source NLP library: fast text cleaning and preprocessing

影像處理-影像降躁化(去躁化) (Image Processing - Make Noise Images Clean) 得力於電腦效能的大幅提升以及GPU的平行運算架構，讓我們能夠更快速且有效地訓練AI，並將AI技術應用於不同領域。本篇將帶給大家的是 「將深度學習應用於影像處理中的影像降躁化 」，

Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur

clean-epub The excellent knock application by Benton Edmondson outputs EPUBs that seem to be DRM-free. However, if you run the application twice on th

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

This is the accompanying repository for the Bloomberg Global Coal Countdown (BGCC) website. Data Sources Dashboard Data Schema and Validation License

Enigma simulator with python and clean code.

Cloudkeeper Housekeeping for Clouds! Table of contents Overview Docker based quick start Cloning this repository Component list Contact License Overvi

EV-charging-impact This repository contains the code that has been used for the Queue modelling for the paper "How will electric vehicles affect traff

ABsolute SOLVantion Free Energy Calculations The absolv framework aims to offer a simple API for computing the change in free energy when transferring

Marching Squares marching Squares algorithm in python with clean code. Tools Python 3 EasyDraw Creators Mohammad Dori Run the Code Installation Requir

toy_gradlogp This repo implements some toy examples of the following score matching algorithms in PyTorch: ssm-vr: sliced score matching with variance

Marching Rectangles marching rectangles algorithm in python with clean code. Tools Python 3 EasyDraw Creators Mohammad Dori Run the Code Installation

Anti-Backdoor Learning PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data. Check the unlearning effect

ebms_proposals Official implementation (PyTorch) of the paper: Learning Proposals for Practical Energy-Based Regression, 2021 [arXiv] [project]. Fredr

An adaptive hierarchical energy management strategy This project contains the source code of an adaptive hierarchical EMS combining heuristic equivale

PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids The electric grid is a key enabling infrastructure for the a

1 Python for Power System Analysis Contents 1 Python for Power System Analysis 1.1 About 1.2 Documentation 1.3 Functionality 1.4 Example scripts as Ju

PROJECT TITLE: CLEAN/DIRTY ROAD DETECTION USING TRANSFER LEARNING Description: This is a project based on ConvNets used to identify whether a road is

Control System Packer is a lightweight, low-level program to transform energy equations into the compact libraries for control systems. Packer supports Python 🐍 , C 💻 and C++ 💻 libraries.

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

model_based_energy_constrained_compression Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Home Assistant Octopus Energy ** WARNING: This component is currently a work in progress ** Custom component built from the ground up to bring your Oc

Amber Electric Usage Summary This is a command line tool that produces a summary CSV report of an Amber Electric customer's energy consumption and cos

PPO-Continuous-Pytorch I found the current implementation of PPO on continuous action space is whether somewhat complicated or not stable. And this is

emhass EMHASS: Energy Management for Home Assistant Context This module was conceived as an energy management optimization tool for residential electr

carbon-footprint-calculator Conda distribution ~/anaconda3/bin/conda install anaconda-client conda-build ~/anaconda3/bin/conda config --set anaconda_u

Hildebrand Glow (DCC) Integration Home Assistant integration for energy consumption data from UK SMETS (Smart) meters using the Hildebrand Glow API. T

BEM(building energy model)-SIM-APP The is a web app presenting my research in BEM(building energy model) calibration. You can play around with some pa

Speech Separation The simple project to separate mixed voice (2 clean voices) to 2 separate voices. Result Example (Clisk to hear the voices): mix ||

Duke Energy Gateway This is a custom integration for Home Assistant. It pulls near-real-time energy usage from Duke Energy via the Duke Energy Gateway

EOW-Softmax This code is for the paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration". Accepted by ICCV21. Usage Commnd exa

This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds. Inspired by the work of Edward Tufte.

document organizer with tags and full-text-search, in a simple and clean sqlite3 schema

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great importance. We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hyperspherical energy which characterizes the diversity of neurons on a hypersphere. See our previous work -- MHE for an in-depth introduction.

Estudo e desenvolvimento de uma API REST 🧑‍💻 Tecnologias Esse projeto utilizará as seguintes tecnologias: Git Python Flask DBeaver Vscode SQLite 🎯

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

Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape