Metadata-Version: 2.4
Name: res-wind-up
Version: 0.5.0
Summary: A tool to assess yield uplift of wind turbines
Author-email: Alex Clerc <alex.clerc@res-group.com>
License: BSD 3-Clause License
        
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# wind-up

A Python package for assessing wind turbine yield uplift from operational data.

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`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 package is published on PyPI as `res-wind-up` and imported in Python as
`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 wind direction
- 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.

## Methodology

The validation methodology that `wind-up` implements is described in detail in
[**wind-up uplift validation methodology v3**](docs/wind-up%20uplift%20validation%20methodology%20v3.pdf)
in the [`docs`](docs) folder.

## Installation

Install the released package with your Python environment manager of choice.
Python 3.10 to 3.13 is supported.

Using `uv`:

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

Using `pip`:

```bash
python -m venv .venv
source .venv/bin/activate
pip install res-wind-up
```

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__)
```

## 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:

```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)
```

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.

> [!NOTE]
> On import, `wind-up` selects the non-interactive matplotlib `Agg` backend
> unless the `MPLBACKEND` environment variable is already set. This lets analyses
> run headless (CI, SSH, batch servers) without an X server. Set `MPLBACKEND`
> yourself before importing `wind_up` if you want an interactive backend.

## 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
- power curve estimation by turbine type
- wind speed estimation from turbine power (preferred for reference turbines) and measured wind speed
- waking state calculation per timestep using turbine coordinates and SCADA
- 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
```

> [!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
```

> [!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 3.10 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

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

## Contact

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