Awesome
<div align="center"> <h1 align="center"> <img src="https://github.com/flechtenberg/flechtenberg_images/blob/main/Pulpo-Logo_INKSCAPE.png?raw=true" width="300" /> <h3>◦ Python-based User-defined Lifecycle Production Optimization!</h3> <h3>◦ Developed with the software and tools below.</h3> <p align="center"> <img src="https://img.shields.io/badge/Jupyter-F37626.svg?style&logo=Jupyter&logoColor=white" alt="Jupyter" /> <img src="https://img.shields.io/badge/Python-3776AB.svg?style&logo=Python&logoColor=white" alt="Python" /> <img src="https://img.shields.io/badge/Markdown-000000.svg?style&logo=Markdown&logoColor=white" alt="Markdown" /> </p> <img src="https://img.shields.io/github/license/flechtenberg/pulpo?style=flat&color=5D6D7E" alt="GitHub license" /> <img src="https://img.shields.io/github/last-commit/flechtenberg/pulpo?style=flat&color=5D6D7E" alt="git-last-commit" /> <img src="https://img.shields.io/github/commit-activity/m/flechtenberg/pulpo?style=flat&color=5D6D7E" alt="GitHub commit activity" /> <img src="https://img.shields.io/github/languages/top/flechtenberg/pulpo?style=flat&color=5D6D7E" alt="GitHub top language" /> </div>📖 Table of Contents
📍 Overview
Pulpo is a comprehensive Life Cycle Optimization (LCO) tool designed to streamline the optimization of environmental impacts across the entire lifecycle of products. It facilitates the import of data from the LCI databases accessed via brightway, converts inputs into optimization-ready formats, defines and solves optimization models using the Pyomo package, and saves and summarizes results. Pulpo empowers users to efficiently optimize and analyze environmental impacts, supporting sustainable decision-making through lifecycle-based strategies.
⚙️ Modules
<details closed><summary>Root</summary>| File | Summary |
|---|---|
| .gitconfig | This code fragment configures a git filter to clean Jupyter Notebook files in the.gitconfig file. It uses the Jupyter nbconvert command to remove the output cells and smudge to display the file's contents. |
| pulpo.py | The code implements a PulpoOptimizer class that provides functionalities for data import, optimization, solving, retrieval, saving and summarizing results related to life cycle assessments. It uses modules like optimizer, bw_parser, converter, and saver for different operations. |
| bw_parser.py | The code in bw_parser.py provides functions for importing, saving, and retrieving life cycle inventory (LCI) data from the Ecoinvent database using the Brightway2 library. It includes functions for importing data, checking if data needs to be reloaded, performing LCA calculations, saving LCI data to files, and reading LCI data from files. Additionally, it provides functions for retrieving activities and environmental flows from the database based on specified criteria. |
| converter.py | The code in pulpo\utils\converter.py combines various inputs into a dictionary for an optimization model. It converts sparse matrices to dictionaries, modifies the technosphere matrix, creates sets, specifies demand, limits, and supply, assigns weights, and assembles the final data dictionary for the model. This function serves as a crucial step in preparing the inputs for the optimization process. |
| optimizer.py | The code defines an optimization model using the pyomo package. It includes sets, parameters, variables, constraints, and an objective function. The model is created and solved using different solvers. |
| saver.py | The code provides two main functionalities:1. save_results: Saves the results of a Pyomo model to an Excel file, including raw results, metadata, and constraints.2. summarize_results: Prints a summary of the model results, including demand, impacts, choices, and constraints. |
🚀 Getting Started
🔧 Installation
PULPO has been deployed to the pypi index and can now be installed via:
pip install pulpo-dev
🤖 Running pulpo
See pypi for a description of how to use PULPO via pip.
Find example notebooks for a hydrogen case, an electricity case, and a plastic case here.
There is also a "workshop" repository (here), which has been created for the Brightcon 2024 conference. It contains several notebooks that guide you through the PULPO package and its functionalities, as well as an exercise.
🧪 Tests
Calling from the package folder:
python -m unittest discover -s tests
Updates
ℹ️ The package is currently under development.
🛣 Roadmap
ℹ️ Task 1: Implement integer cuts to allow a fast calculation of a ranked list of best optionsℹ️ Task 2: Implement functionality to treat uncertainty in the optimization problem (robust)ℹ️ ... Requests are welcome.
🤝 Contributing
Contributions are always welcome! Please follow these steps:
- Fork the project repository. This creates a copy of the project on your account that you can modify without affecting the original project.
- Clone the forked repository to your local machine using a Git client like Git or GitHub Desktop.
- Create a new branch with a descriptive name (e.g.,
new-feature-branchorbugfix-issue-123).
git checkout -b new-feature-branch
- Make changes to the project's codebase.
- Commit your changes to your local branch with a clear commit message that explains the changes you've made.
git commit -m 'Implemented new feature.'
- Push your changes to your forked repository on GitHub using the following command
git push origin new-feature-branch
- Create a new pull request to the original project repository. In the pull request, describe the changes you've made and why they're necessary. The project maintainers will review your changes and provide feedback or merge them into the main branch.
📄 License
This project is licensed under the ℹ️ BSD 3-Clause License. See the LICENSE file for additional info.
Copyright (c) 2024, Fabian Lechtenberg. All rights reserved.
👏 Acknowledgments
We would like to acknowledge the authors and contributors of these main packages that pulpo is based on: