Metadata-Version: 2.1
Name: pynamical
Version: 0.3.1
Summary: Model, simulate, and visualize discrete nonlinear dynamical systems
Home-page: https://github.com/gboeing/pynamical
Author: Geoff Boeing
Author-email: boeing@usc.edu
License: MIT
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Requires-Dist: matplotlib (>=3.3)
Requires-Dist: numba (>=0.52)
Requires-Dist: numpy (>=1.19)
Requires-Dist: pandas (>=1.2)


**pynamical** is a Python package for modeling, simulating, visualizing, and animating discrete
nonlinear dynamical systems and chaos. pynamical uses pandas, numpy, and numba for fast simulation,
and matplotlib for beautiful visualizations and animations to explore system behavior. Compatible
with Python 2 and 3. See the examples and demos on `GitHub`_.

You can read/cite the journal article about pynamical: Boeing, G. 2016.
"`Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction`_."
*Systems*, 4 (4), 37. doi:10.3390/systems4040037.

.. _GitHub: https://github.com/gboeing/pynamical
.. _Visual Analysis of Nonlinear Dynamical Systems\: Chaos, Fractals, Self-Similarity and the Limits of Prediction: http://geoffboeing.com/publications/nonlinear-chaos-fractals-prediction/


