> For the complete documentation index, see [llms.txt](https://docs.bezier.octue.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bezier.octue.com/developers/bezier-for-python-users.md).

# Bézier for Python users

The \`bezier\` python package allows you to work with power curve data in python, for time-series and other data analysis purposes.

{% hint style="danger" %}
After four years of internal use, the Bézier python package is presently being open-sourced. We're working with a select few early-stage customers to simplify and improve its performance and documentation, as well as bringing it up to date with the latest evolutions in the upcoming IEC 61400-16 standard.

If you'd like early access to the python library please [contact us](https://bezier.octue.com/contact).

Until general release, the instructions below **won't work** - but this page should give you a flavour of what's coming.
{% endhint %}

## Installation

The [`bezier` package is available on pypi](https://pypi.org/octue/bezier/) so is easily installable with `pip`, `poetry` or any other python package manager. We recommend the use of the `uv` package manager for your python environment and dependencies ([here's why](https://github.com/astral-sh/uv)), so installation is as simple as:

```
uv add bezier

// Or if you prefer to use pip
pip install bezier
```

## Quick Start

{% hint style="info" %}
A **Turbine Label** is a short text label, `written-in-lower-kebab-case-0123456789`, that can uniquely define a power curve document. Mostly you'll use your own documents directly, but it's great to be able to refer to the examples quickly by their label.
{% endhint %}

You can use your own turbine power curves, expressed in `JSON` form. But there are turbines built in to the library for example purposes. You can load them up simply by using the label of that turbine:

```python
>> from bezier import ALL_TURBINE_LABELS
>> print(ALL_TURBINE_LABELS)
[ "gm-5-abc-1", "gm-5-abc-2", ... ]
```

You can load any of the builtin power curves directly from its label, and inspect its data.

```python
>> curve = PowerCurve('gm-5-abc-1')
>> print(curve.get_description())
"The GM-5-ABC-1 turbine, part of Generic Machines' 5MW platform, is the first totally fictional example turbine I could think of"
```

Each top level power curve document can represent multiple "modes" of turbine operation. See which ones are available, and which is the default:

```python
>> print(curve.available_modes)
>> print(curve.default_mode)
```

Use the default mode, and determine the power and thrust for an input time-series of air density and wind speed:

<pre><code><strong>>> mode_label = curve.default_mode
</strong><strong>>> operating_point = {
</strong>  "air-density": np.array([1.223, 1.224, 1.225, 1.220]),
  "wind-speed": np.array([8.63, 10.89, 11.81, 6.28]),
}
>> power, thrust, parameter_labels, xi = curve.get_performance(mode_label, operating_point, as_timeseries=True)
>> print(power)
np.array([4653000, 4853000, 5053000, 2653000])
</code></pre>

## Next steps

Check the [bezier-python library on GitHub](https://github.com/octue/bezier-python) for more advanced use cases and package API documentation.


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