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An agent-based modeling framework for Python with a shallow learning curve and powerful visualization capabilities.

Project description

Helipad

Helipad is an agent-based modeling framework for Python with powerful visualization capabilities and a shallow learning curve. Documentation and API reference can be found at https://helipad.dev

Features

  • ⚓️ A simple hook-based API makes it easy to build a model without worrying about the features you don’t need
  • 📈 Interactive and live-updating visualizations, including time series, bar charts, networks, spatial, and an API for writing custom visualizations
  • 👋🏻 Flexible parameter API allows parameter values to be set programmatically, adjusted manually from the control panel while the model is running, or shocked stochastically
  • 🪐 Cross-platform and multimodal. Models can be written and run with a Tkinter GUI, in Jupyter notebooks, or without a GUI at all
  • 🤹🏻‍♂️ Agents can barter, buy and sell with money, reproduce both haploid and polyploid, and more
  • 🕺🏻 A variety of model types: sequential or random-activation models, matching models, multi-level models, network models, spatial models, and more

How to use

You can install Helipad using Pip.

pip install helipad

Once installed, getting started with a model is very simple.

from helipad import *
heli = Helipad()

#Use the heli object to set up here

heli.launchCpanel()

The included bootstrap model contains a more detailed template, and the sample models exemplify various use cases. The documentation also includes a complete hook and function reference.

Requirements

Helipad requires Python 3.7 or higher. The following libraries are also required:

The following libraries are optional but recommended:

Version History

  • 1.3: Allow mixing time series and other plots, display networks on spatial maps, goods API improvements
  • 1.2: Extensible visualization API, events, performance profiling, Jupyterlab support
  • 1.1: Virtual parameters, improved Jupyter flexibility, spatial pre-alpha, misc improvements
  • 1.0: Jupyter integration, hook decorators, and separated control panel from plotting
  • 0.7: Ability to output stackplots, parameter sweeps, and an updated parameter identification pattern
  • 0.6: Support for multi-level models
  • 0.5: Support for matching models, and the checkGrid class
  • 0.4: Initial PyPI release

Project details


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