pyEDM 2.3.2

Python/Pandas toolset for Empirical Dynamic Modeling.

Empirical Dynamic Modeling (EDM)

---

This package provides a Python/Pandas DataFrame toolset for EDM analysis. Introduction and documentation are are avilable online, or in the package API docs. A Jupyter notebook interface is available at jpyEDM.

Functionality includes:

• Simplex projection (Sugihara and May 1990)
• Sequential Locally Weighted Global Linear Maps (S-Map) (Sugihara 1994)
• Multivariate embeddings (Dixon et. al. 1999)
• Convergent cross mapping (Sugihara et. al. 2012)
• Multiview embedding (Ye and Sugihara 2016)

---

Installation

Python Package Index (PyPI)

Certain MacOS, Linux and Windows platforms are supported with prebuilt binary distributions hosted on PyPI pyEDM and can be installed with the Python pip module: python -m pip install pyEDM

---

Usage

Examples can be executed in the python command line:

>>> import pyEDM
>>> pyEDM.Examples()

---

References

Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734–741.

Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688) : 477–495.

Dixon, P. A., M. Milicich, and G. Sugihara, 1999. Episodic fluctuations in larval supply. Science 283:1528–1530.

Sugihara G., May R., Ye H., Hsieh C., Deyle E., Fogarty M., Munch S., 2012. Detecting Causality in Complex Ecosystems. Science 338:496-500.

Ye H., and G. Sugihara, 2016. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science 353:922–925.