semvec/pypi-packages huggingface dataset:
poscidyn 0.1.1
Simulation and Visualization of Nonlinear Oscillator Dynamics in Python
License: MIT
Requires Python: >=3.10
Keywords: nonlinear dynamics, duffing, oscillators, simulation, jax

<h1 align='center'>Poscidyn</h1>
<h2 align='center'>Simulation and Visualization of Nonlinear Oscillator Dynamics in Python</h2>

Poscidyn is a Python toolkit based on [JAX](https://github.com/google/jax), designed to **streamline and accelerate common workflows in nonlinear oscillator dynamics**. It enables the simulation and visualization of (nonlinear) oscillators using experimentally realistic setups, supporting both time- and frequency-domain analyses.

Features include:
- Built-in models of (nonlinear) oscillators
- Frequency sweeping (forward and backward)
- Everything vmappable
---

## Installation
```bash
pip install poscidyn[gpu]
```
Requires Python 3.10 or newer.

## Documentation
Have a look at our extensive documentation on how to install, use and extend this package: [https://rknetemann.github.io/poscidyn/](https://rknetemann.github.io/poscidyn/).

## Quick example

```python
import poscidyn
import numpy as np

Q, omega_0, alpha, gamma = np.array([100.0]), np.array([1.00]), np.zeros((1,1,1)), np.zeros((1,1,1,1))
gamma[0,0,0,0] = 2.55
modal_forces = np.array([1.0])

driving_frequency = np.linspace(0.9, 1.3, 501)
driving_amplitude = np.linspace(0.1, 1.0, 10)

MODEL = poscidyn.NonlinearOscillator(Q=Q, alpha=alpha, gamma=gamma, omega_0=omega_0)
EXCITOR = poscidyn.OneToneExcitation(driving_frequency, driving_amplitude, modal_forces)

frequency_sweep = poscidyn.frequency_sweep(
    model = MODEL, excitor=EXCITOR,
) 

```

## Credits where they are due

[JAX](https://github.com/google/jax): a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

[Diffrax](https://github.com/patrick-kidger/diffrax): JAX-based library providing numerical differential equation solvers.

[Equinox](https://github.com/patrick-kidger/equinox): your one-stop JAX library, for everything you need that isn't already in core JAX.
iesopt 2.13.0
IESopt -- an Integrated Energy System Optimization framework.
License: MIT
Requires Python: >=3.14
Keywords: energy model, integrated energy systems, modelling, optimization

# IESopt: Integrated Energy System Optimization

[![PyPI - Version](https://img.shields.io/pypi/v/iesopt)](https://pypi.org/project/iesopt/)
[![Docs](https://img.shields.io/badge/docs-stable-blue.svg)](https://ait-energy.github.io/iesopt/)
[![License](https://img.shields.io/github/license/ait-energy/iesopt)](LICENSE)

[![Build Status](https://github.com/ait-energy/iesopt/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/ait-energy/iesopt/actions/workflows/pytest.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/ait-energy/iesopt/graph/badge.svg?token=MC2IN9URZY)](https://codecov.io/gh/ait-energy/iesopt)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

**IESopt** (_Integrated Energy System Optimization_) is a modeling and optimization framework for integrated energy
systems.

It is developed and maintained at the **Center for Energy** at
[AIT Austrian Institute of Technology GmbH](https://www.ait.ac.at/). The framework is designed to support the
optimization of energy systems that are characterized by a high degree of integration between different energy carriers
and sectors. It focuses on offering a modular and adaptable tool for modelers, that does not compromise on performance,
while still being user-friendly. This is enabled by reducing energy system assets to abstract building blocks, that are
supported by specialized implementation, and can be combined into complex systems without the need of a detailed
understanding of mathematical modeling or proficiency in any coding-language.

> If you are looking for the framework's core model, written in Julia, head over to the [IESopt.jl repository](https://github.com/ait-energy/IESopt.jl).

## Getting started

Head over to the [documentation](https://ait-energy.github.io/iesopt/) to get started with **IESopt**.

## Contributing

PRs accepted. Checkout the _developer documentation section_ in the [documentation](https://ait-energy.github.io/iesopt/).

## License

[MIT © AIT Austrian Institute of Technology GmbH.](LICENSE)