Welcome to the Pywind, a comprehensive repository about the use of Python in the wind data analysis for wind energy applications. This repository provides tools and examples for reading, plotting, and analyzing wind data. Whether you are a student, developer, engineer, or a Python enthusiast interested in wind data, hope you will find valuable and usable resources. Find below the structure of this project:
Developed and Created by Vortex FdC.
The use of Python in Wind energy is a rapidly growing field, and the effective analysis and manipulation of wind data is crucial for its success. This repository provides wind data analysis solutions, from basic tasks to more technical methodologies.
Pywind uses both public measurements and Vortex simulations, like the SERIES used in the first chapters.
Feel free to browse, comment, share, reuse the code and ideas.
The structure of this repository is based in three main folders.
- Data: Sample wind data from public sources is facilitated for user testing. More in the corresponding data sample documentation which is used in this repository.
- Examples: Python scripts can be executed from terminals (bash). This repository has been tested under Linux.
- Notebooks: Jupyter Notebooks with extended comments and outputs from examples folder.
This repository is created and maintained by:
- Oriol Lacave, a member of the operational technical team at Vortex FDC. With over 15 years of experience in the wind industry, Oriol specializes in data manipulation, analysis, and improvements. He is a scientific programmer dedicated to delivering added value from reanalysis, mesoscale models, and measurements to engineers.
- Arnau Toledano and Vortex technical team are also contributing to the development of the Pywind repository.
- Vortex team in general. Don't hesitate to contact us!
Vortex is a private company that started its technology development in 2005. Comprised of former Wind & Site
engineers, atmospheric physicists, and computer experts, Vortex has developed its own methodology independently.
Their work is based on the Weather Research and Forecasting model (WRF), a state-of-the-art mesoscale model developed collaboratively by atmospheric research centers and a thriving community.
Some active groups we have been inspired are:
- The WRAG and the Wind Resource Assessment Group.
- Pywram stands for Python for Wind Resource Assessment & Metocean Analysis Forum. It originated for Python users within WRAG group.
Clone this repository in your local git environment:
git clone https://github.com/VortexFDC/pywind.git
Each section has its own example and notebook files, located in the examples and notebooks folders, respectively.
For each section a function file is provided, which is imported from the main notebook/example file of the section.
To execute the examples, you can run the following commands from the terminal or your prefered IDE.
python example_1_read_netcdf.py
- Chapter 1 Read netcdf files with the xarray libraries. You will open and make basic operations. A quick overview of the data if done using pandas libraries.
MIT License. Consult LICENSE
Please, use, modify and share if you find it useful.
We encourage collaboration, proposals and new ideas.
Plase follow the collaboration guidelines for work proposals.
You can also use the discussions in this repository for new ideas, Q&A, questions, etc.
