brightwind 2.7.0

Scripts for wind resource data processing.

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            A Python library primarily for wind resource assessments.

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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/

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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].

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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.

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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.

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Test datasets

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

Dataset	Source	Notes
demo_data.csv	BrightWind	A modified 2-year met mast dataset in CSV and Campbell Scientific format.
MERRA-2_XX_2000-01-01_2017-06-30.csv	NASA GES DISC	4 × MERRA-2 18-year datasets to complement the demo data for long-term analyses.
demo_cleaning_file.csv	BrightWind	Periods to clean out from the demo data.
windographer_flagging_log.txt	BrightWind	Same cleaning info as demo_cleaning_file.csv formatted as a Windographer flagging file.
demo_data_iea43_wra_data_model.json	BrightWind	A JSON file formatted to the IEA Wind Task 43 WRA Data Model standard, describing the mast configuration for the demo data.

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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: [e-mail]

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License

MIT — see LICENSE.txt.