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. 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)
Certain MacOS, Linux and Windows platforms are supported with prebuilt binary distributions installed from PyPI pyEDM using the Python pip module.
Command line using the Python pip module: python -m pip install pyEDM
Unfortunately, we do not have the resources to provide pre-built binary distributions for all computer platforms. In this case the user can build the cppEDM library, then install the Python package using pip. On OSX and Linux this requires g++. On Windows, mingw and 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.
- Download pyEDM:
git clone https://github.com/SugiharaLab/pyEDM - Build cppEDM library:
cd pyEDM/cppEDM/src; make - Build and install package:
cd ../..; python -m pip install . --user --trusted-host pypi.org
- We do not have resources to maintain windows build support. These suggestions may be useful.
- Requires mingw installation.
- Requires gfortran libraries.
- Download pyEDM:
git clone https://github.com/SugiharaLab/pyEDM - Build cppEDM library:
cd pyEDM\cppEDM\src; make - Adjust paths to find gfortran and openblas libraries (pyEDM/pyEDM/etc/windows/libopenblas.a). You may need to rename libEDM.a to EDM.lib, and openblas.a to openblas.lib.
- Build and install package in
pyEDM\:python -m pip install . --user --trusted-host pypi.org
Example usage at the python prompt:
>>> import pyEDM
>>> pyEDM.Examples()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.