BPTK-Py 0.6.3

A python simulation engine for System Dynamics & Agent based models

Business Prototyping Toolkit for Python

System Dynamics and Agent-based Modeling in Python

The Business Prototyping Toolkit for Python (BPTK_Py) provides you with a computational modeling framework that allows you to build and run simulation models using System Dynamics and/or agent-based modeling and manage simulation scenarios with ease.

It gives you the power to quickly build simulation models in Python. If you use the framework with Jupyter Notebooks, you to create beautiful plots of the simulation results - or just run the simulation in Python and use the results however you wish.

The framework also includes our sdcc parser for transpiling System Dynamics models conforming to the XMILE standard into Python code. This means you can build models using your favorite XMILE environment (such as iseesystems Stella and then experiment with them in Juypter.

Main Features

• The BPTK_Py framework supports System Dynamics models in XMILE Format, native SD models, Agent-based models and hybrid SD-ABM-Models
• The objective of the framework is to provide the infrastructure for managing model settings and scenarios and for running and plotting simulation results, so that the modeller can concentrate on modelling.
• The framework automatically collect statistics on agents, their states and their properties, which makes plotting simulation results very easy.
• All plotting is done using Matplotlib.
• Simulation results can also be returned as Pandas dataframes.
• The framework uses some advanced Python metaprogramming techniques to ensure the amount of boilerplate code the modeler has to write is kept to a minimum.
• Model settings and scenarios are kept in JSON files. These settings are automatically loaded by the framework upon initialization, as are the model classes themselves. This makes interactive modeling, coding and testing very painless, especially if using the Jupyter notebook environment.

Getting Help

The first place to go to for help and installation instructions is the online documentation.

You should also download the BPTK_Py tutorial, which contains the sample models and Jupyter notebooks referenced in the online documentation. You can download the tutorial from our website.

BPTK_Py is developed and maintained by transentis Labs GmbH.

For questions regarding installation, usage and other help please contact us at: [e-mail].