<!-- Filename: README.md -->
<!-- Author: Jonathan Delgado -->
<!-- Description: GitHub README -->
<!-- Header -->
<h2 align="center">Stochastic Thermodynamics in Python (STP)</h2>
<p align="center">
Python library to construct random quantities and track their information-theoretic properties. These objects include continuous time rate matrices, discrete time transition matrices, and matrices representing 3-state self assembly models.
<br />
<br />
Status: <em>in progress</em>
<!-- Documentation link -->
·<a href="https://stochastic-thermodynamics-in-python.readthedocs.io/en/latest/"><strong>
Documentation
</strong></a>
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·<a target='_blank' href="https://otanan.notion.site/01f791e958c04bfeaf33cd066f3971c1?v=2ca4c56ad7404d59b2bd751e3fcaaee6"><strong>
Notion Roadmap »
</strong></a>
</p>
</div>
<!-- Project Demo -->
<!--  -->
<!-- ## Table of contents
* [Contact](#contact)
* [Acknowledgments](#acknowledgments) -->
## Installation
This package is pip-installable.
1. Install via pip.
```sh
python3 -m pip install stp
```
1. Import the package
```python
import stp
```
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## Usage
Generating a 3-state time-dependent self-assembly model with this package is as simple as writing
```python
import numpy as np
import stp
# Dimensionless, time-dependent parameter for self assembly matrix
alpha = lambda t : np.cos(t) + 2
W = stp.self_assembly_rate_matrix(alpha)
# The initial matrix
print(W(0))
# [[-2. 3. 9.]
# [ 1. -3. 0.]
# [ 1. 0. -9.]]
# A later matrix
print(W(1))
# [[-2. 2.54030231 6.45313581]
# [ 1. -2.54030231 0. ]
# [ 1. 0. -6.45313581]]
```
_For more examples, please refer to the [Documentation]._
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## Roadmap
Refer to the [Notion Roadmap] for the state of the project.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## Contact
Created by [Jonathan Delgado](https://jdelgado.net/).
<p align="right"><a href="#readme-top">Back to top</a></p>
[Notion Roadmap]: https://otanan.notion.site/01f791e958c04bfeaf33cd066f3971c1?v=2ca4c56ad7404d59b2bd751e3fcaaee6
[Documentation]: https://stochastic-thermodynamics-in-python.readthedocs.io/en/latest/
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