pyEDM 1.3.2.0

Python wrapper for cppEDM using pybind11

Empirical Dynamic Modeling (EDM)

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This package provides a Python/Pandas DataFrame interface to the cppEDM library for EDM analysis. Introduction and documentation are are avilable online, or in the package API docs.

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)

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Installation

Python Package Index (PyPI)

Certain Mac OSX and Windows platforms are supported with prebuilt binary distributions and can be installed using the Python pip pyEDM module on PyPI.

Installation can be executed as: python -m pip install pyEDM

Manual Install

Unfortunately, we do not have the resources to provide pre-built binary distributions for all computer platforms. In this case the user is required to first build the cppEDM library on their machine, and then install the Python package using pip. On OSX and Linux this requires g++, on Windows, Microsoft Visual Studio Compiler (MSVC) which can be obtained from Build Tools for Visual Studio 2019. Only the Windows SDK is needed.

Note that the LAPACK library is required to build cppEDM.

OSX and Linux

1. Download pyEDM: git clone https://github.com/SugiharaLab/pyEDM
2. Build cppEDM library: cd pyEDM/cppEDM/src; make
3. Build and install package: cd ../..; python -m pip install . --user --trusted-host pypi.org

Windows

1. Download pyEDM: git clone https://github.com/SugiharaLab/pyEDM
2. Build cppEDM library: cd pyEDM\cppEDM\src; nmake /f makefile.windows
3. Build and install package: cd ..\..; python -m pip install . --user --trusted-host pypi.org

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Usage

Example usage at the python prompt:

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

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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.