Metadata-Version: 2.1
Name: cuTradeNet
Version: 0.0.5
Summary: GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks
Home-page: https://github.com/Qsanti/cuTradeNet
Author: Santiago Cuevas
Author-email: san.cuevas@protonmail.com
Classifier: Programming Language :: Python :: 3
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 3 - Alpha
Description-Content-Type: text/markdown
License-File: LICENSE

# cuTradeNet

**cuTradeNet** is a library that provides classes to easily create & run [*kinetic wealth exchange models*](https://rf.mokslasplius.lt/elementary-kinetic-exchange-models/ "online mini simulations") on [*complex networks*](https://en.wikipedia.org/wiki/Complex_network "complex networks wiki"). 

Leads the user to set one (or ensemble) of *complex networks* as a contact structure agents use to trade about. The following wealth exchange models were implemented:
* [Yard-sale model](https://www.sciencedirect.com/science/article/pii/S0378437120309237 "model details here")
* [Merger-Spinoff model](https://www.sciencedirect.com/science/article/pii/S0378437120309237 "model details here")

It is written in Python and uses Cuda module from [Numba](https://numba.pydata.org/ "Numba page") package to accelerate the simulation runnin in GPU, *paralelizing some transaccions* in the same graph and *paralelizing runs* in multiple graphs, leading to  easier & faster averaging of system properties.
It's completely abstracted from the CUDA knowledge for the user, so you can use it as a regular Python library.

### How to use
There is a [Demo notebook](https://github.com/Qsanti/cuTradeNet/blob/master/Models%26Demo/Demo.ipynb) in the repository that can be tryed in it's [Google Colab](https://colab.research.google.com/github/Qsanti/cuTradeNet/blob/master/Models%26Demo/Demo.ipynb) version too.
There is also a [General explanation](https://github.com/Qsanti/cuTradeNet/blob/master/Models%26Demo/ModelsList.ipynb) of Kinetic Wealth Exchange Models used.

### How to install
You can install it from [PyPi](https://pypi.org/project/cuTradeNet/ "cuTradeNet page in PyPi") with the following command:
```bash
pip install cuTradeNet
```

### Repository&Questions
The repository is in [GitHub](https://github.com/Qsanti/cuTradeNet/), and you can ask questions or contact us in the [Discussions](https://github.com/Qsanti/cuTradeNet/discussions/ "cuTradeNet discussions") section. 
