brightwind 2.7.0

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            A Python library primarily for wind resource assessments.

Brightwind is an open-source Python library for wind (and solar) resource analysis. It loads meteorological timeseries data, runs common analyses — shear, long-term adjustments, correlations, distributions — and exports results to formats used by wind analysis software such as WAsP.

📚 Full documentation, tutorials and API reference: https://brightwind-dev.github.io/brightwind-docs/

Installation

Install brightwind into its own environment to avoid dependency clashes. Pick whichever option suits you.

Option 1 — venv (quick way to try brightwind out)

Check Python is installed (3.9+ recommended):

python --version

If not, install it from python.org/downloads — on Windows, tick "Add Python to PATH" in the installer.

Then create an environment and install brightwind:

python -m venv brightwind_env
# Windows
brightwind_env\Scripts\activate
# macOS / Linux
source brightwind_env/bin/activate

pip install brightwind

Option 2 — conda

Common if you already use Anaconda or work primarily in Jupyter. Anaconda bundles Python, pip and Jupyter in one installer. From the Anaconda Prompt:

conda create --name brightwind_env python=3.11
conda activate brightwind_env
pip install brightwind

A step-by-step Windows install walkthrough is also available in the tutorials.

Optional — parquet support

Reading .parquet files from BrightHub needs a parquet engine. Install the default (pyarrow):

pip install brightwind[parquet]

Or use fastparquet instead: pip install brightwind[parquet-fastparquet].

Quick start

Most analysts use brightwind from a Jupyter Notebook:

pip install jupyter
jupyter notebook

import brightwind as bw

data = bw.load_csv(bw.demo_datasets.demo_data)
bw.basic_stats(data)

For full examples — loading, plotting, shear, correlations, exporting — see the tutorials and API reference.

Why open-source?

The brightwind library is open-source, making every step of an assessment transparent, auditable and reproducible. The full record of adjustments to a dataset lives in a single file that internal reviewers, third parties and banks can inspect directly — sharpening due diligence and removing the "black box" problem of proprietary tools.

The intent is a shared, validated toolkit that the wind and solar industry builds on together, rather than each consultancy reinventing the same calculations behind closed doors.

Test datasets

Demo datasets are bundled with the library to demonstrate functions and exercise the test suite:

Contributing

Brightwind welcomes contributions from across the wind and solar industry — analysts, engineers, researchers and developers.

• Issues, bugs and feature requests: GitHub issue tracker
• Code contributions and development setup: see contributing.md
• General enquiries: stephen@brightwindanalysis.com

License

MIT — see LICENSE.txt.