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# wind-up
A tool to assess yield uplift of wind turbines

[![image](https://img.shields.io/pypi/v/res-wind-up.svg)](https://pypi.python.org/pypi/res-wind-up)
[![image](https://img.shields.io/pypi/l/res-wind-up.svg)](https://github.com/resgroup/wind-up/blob/main/LICENSE.txt)
[![image](https://img.shields.io/pypi/pyversions/res-wind-up.svg)](https://pypi.python.org/pypi/res-wind-up)
A Python package for assessing wind-turbine yield uplift from operational data.

[![PyPI version](https://img.shields.io/pypi/v/res-wind-up.svg)](https://pypi.python.org/pypi/res-wind-up)
[![License](https://img.shields.io/pypi/l/res-wind-up.svg)](https://github.com/resgroup/wind-up/blob/main/LICENSE.txt)
[![Python versions](https://img.shields.io/pypi/pyversions/res-wind-up.svg)](https://pypi.python.org/pypi/res-wind-up)
[![Lint & Format: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v1.json)](https://github.com/charliermarsh/ruff)
[![Typing: mypy](https://img.shields.io/badge/typing-mypy-yellow.svg)](https://github.com/python/mypy)
[![lint-and-test](https://github.com/resgroup/wind-up/actions/workflows/lint-and-test.yaml/badge.svg)](https://github.com/resgroup/wind-up/actions/workflows/lint-and-test.yaml)
[![Lint and test](https://github.com/resgroup/wind-up/actions/workflows/lint-and-test.yaml/badge.svg)](https://github.com/resgroup/wind-up/actions/workflows/lint-and-test.yaml)

## Getting Started
See [`examples`](examples) folder for example analysis using the wind-up package. [`smarteole_example.ipynb`](examples%2Fsmarteole_example.ipynb) is a good place to start.
`wind-up` compares turbine performance before and after a change to estimate
energy-yield uplift. It is designed for wind-farm SCADA analysis where the
signal of interest is small, operational data is messy, and a credible result
needs more than a simple before/after power-curve plot.

The wind-up package can be installed using any Python environment manager. Examples using [uv](https://docs.astral.sh/uv/) and [pip](https://pypi.org/project/pip/) are shown below:
The package is published on PyPI as `res-wind-up` and imported in Python as
`wind_up`.

### Using uv
```shell
# Install the wind-up package in an existing project
uv add res-wind-up
## What it does

`wind-up` provides a complete analysis workflow for pre/post or toggle-style
uplift assessments:

- prepares and filters wind-farm SCADA data
- builds SCADA-derived power curves by turbine type
- adds reanalysis, mast, or LiDAR reference data
- applies yaw-direction northing corrections
- estimates wind speed and waking state
- detrends test-turbine performance against reference turbines or external references
- performs pre/post power-performance analysis with reversal checks and bootstrapped uncertainty
- combines per-reference results into turbine-level and fleet-level uplift estimates
- writes result tables and diagnostic plots for review

The public examples cover several realistic analysis shapes, including
Smarteole toggle data, Kelmarsh turbine data, and WeDoWind challenge-style
pre/post assessments.

## Installation

Install the released package with your Python environment manager of choice.
Python `>=3.9,<4.0` is supported.

# Or create a new project and install the wind-up package
uv init my-project
cd my-project
Using `uv`:

```bash
uv add res-wind-up
```

### Using pip
```shell
# create and activate a virtual environment, if needed
Using `pip`:

```bash
python -m venv .venv
source .venv/Scripts/activate # or .venv/bin/activate on linux or ".venv/Scripts/activate" in Windows command prompt
# install the wind-up package in the virtual environment
pip install res-wind-up # alternatively clone the repo, navigate to the wind-up folder and run "pip install ."
source .venv/bin/activate
pip install res-wind-up
```
Note that the package is named `wind_up` (with an underscore) in Python code. For example to print the version of the installed package use the following code snippet:

On Windows PowerShell, activate the virtual environment with:

```powershell
.venv\Scripts\Activate.ps1
```

To run the bundled notebooks and example scripts, install the optional example
dependencies as well:

```bash
uv add "res-wind-up[examples]"
# or
pip install "res-wind-up[examples]"
```

Check that the package imports correctly:

```python
import wind_up

print(wind_up.__version__)
```

## Contributing
To start making changes fork the repository or make a new branch from `main`. Note `main` is protected;
if a commit fails to push and you want to undo it try `git reset origin/main --hard`
## First steps

The fastest way to understand the expected data model and analysis flow is to
start from the examples:

- [`examples/smarteole_example.ipynb`](examples/smarteole_example.ipynb) -
a notebook walkthrough and the best place to start
- [`examples/kelmarsh_kaggle.py`](examples/kelmarsh_kaggle.py) -
a script-oriented pre/post example using Kelmarsh data
- [`examples/wedowind_example.py`](examples/wedowind_example.py) -
WeDoWind pitch-angle and vortex-generator analyses

A typical analysis has four stages:

The development environment should be created and managed using [uv](https://docs.astral.sh/uv/). To create the environment:
```shell
uv sync --extra dev
```python
from wind_up.interface import AssessmentInputs
from wind_up.main_analysis import run_wind_up_analysis
from wind_up.models import PlotConfig, WindUpConfig
from wind_up.reanalysis_data import ReanalysisDataset

# 1. Build a WindUpConfig describing the asset, test turbines,
# reference turbines, analysis dates, filters, and output folder.
cfg = WindUpConfig(...)

# 2. Configure diagnostic plot output.
plot_cfg = PlotConfig(
show_plots=False,
save_plots=True,
plots_dir=cfg.out_dir / "plots",
)

# 3. Prepare inputs from SCADA, metadata, and reference datasets.
assessment_inputs = AssessmentInputs.from_cfg(
cfg=cfg,
plot_cfg=plot_cfg,
scada_df=scada_df,
metadata_df=metadata_df,
reanalysis_datasets=[ReanalysisDataset(id="ERA5", data=reanalysis_df)],
cache_dir=cache_dir,
)

# 4. Run the assessment and save the per-test/per-reference results.
results_per_test_ref_df = run_wind_up_analysis(assessment_inputs)
results_per_test_ref_df.to_csv(cfg.out_dir / "results_per_test_ref.csv", index=False)
```
To run the formatting, linting and testing:
```shell
uv run poe all # or all-fast to skip slow tests

For complete, executable versions of this pattern, use the example files above.
The configuration is intentionally explicit: wind-resource assessments are
sensitive to turbine metadata, analysis periods, filters, and reference choices,
so those assumptions should be visible in code.

## Inputs and outputs

At a high level, an assessment needs:

| Input | Purpose |
| --- | --- |
| SCADA time series | turbine power, wind speed, yaw, pitch, RPM, downtime, and related operational signals |
| turbine metadata | turbine names, turbine type, rated power, rotor diameter, and location where available |
| analysis configuration | test/reference turbines, pre/post or toggle periods, filters, long-term settings, and output paths |
| reference data | reanalysis, mast, LiDAR, or reference-turbine signals used for detrending and validation |

The main analysis returns a `pandas.DataFrame` with per-test/per-reference uplift
results, uncertainty columns, warning counts, and supporting diagnostic metrics.
When plot saving is enabled, diagnostic figures are written under
`PlotConfig.plots_dir`; CSV results are written under the configured assessment
output directory.

## Analysis features

`wind-up` includes utilities for the parts of an uplift study that usually need
careful handling:

- SCADA cleaning and filtering for unavailable or implausible operating data
- turbine-type power-curve estimation
- wind-speed estimation from turbine power and measured wind speed
- wake-state calculation using turbine coordinates and wind direction
- yaw-direction northing checks and optional optimized northing corrections
- long-term distribution calculations
- wind-speed drift checks
- pre/post and toggle-based splitting
- reference selection and combined uplift calculations
- diagnostic plots for input data, detrending, power curves, yaw direction,
reanalysis comparison, waking state, long-term distributions, and final results

## Development

This project uses `uv` for dependency management, `poethepoet` for task running,
Ruff for formatting/linting, mypy for type checking, and pytest with coverage for
tests.

Create the development environment:

```bash
uv sync --all-extras --dev
```

> [!TIP]
> For normal local development and pre-push checks, start with the fast suite.
> It runs formatting, linting, type checking, and tests that are not marked as
> slow.

```bash
uv run poe all-fast
```

Run the full local verification suite before releases or when you need the slow
regression tests as well:

```bash
uv run poe all
```
Or simply
```shell
poe all # or all-fast to skip slow tests

> [!NOTE]
> `uv run poe all` includes tests marked as slow. On a typical local machine it
> can take 15 minutes or more; `uv run poe all-fast` is much quicker for everyday
> iteration.

Individual tasks are also available:

```bash
uv run poe lint-check # formatter, linter, and mypy checks
uv run poe test-fast # tests excluding the slow marker
uv run poe test # full test suite with coverage report
uv run poe jupy # start JupyterLab for example exploration
```
if you have activated the virtual environment.

> [!WARNING]
> `uv run poe jupy` starts a local JupyterLab server and keeps running until you
> stop it. Use it when you want an interactive notebook session, not as a
> one-shot verification command.

The GitHub Actions workflow runs linting and tests on Python 3.9 and 3.13.

## Project status

The package is marked as beta in the Python package metadata. Interfaces and
configuration options may still evolve, but the repository includes a substantial
test suite and example analyses for regression coverage.

## License
See [`LICENSE.txt`](LICENSE.txt)

`wind-up` is released under the BSD 3-Clause License. See
[`LICENSE.txt`](LICENSE.txt) for the full license text.

## Contact
Alex.Clerc@res-group.com

For questions about the package, contact Alex Clerc at
[Alex.Clerc@res-group.com](mailto:Alex.Clerc@res-group.com).