Skip to main content

Python wrapper for cppEDM using pybind11

Project description

Empirical Dynamic Modeling (EDM)


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:


Installation

Python Package Index (PyPI)

Certain MacOS, Linux and Windows platforms are supported with prebuilt binary distributions hosted on PyPI pyEDM.

Command line using the Python pip module: python -m pip install pyEDM

Manual Install

If a pre-built binary distribution is not available the user can build the cppEDM library, then install the Python package using pip. On OSX and Linux this requires g++. On Windows, the mingw-w64 GCC is available as in MSYS2.

Note the LAPACK library is required to build cppEDM and pyEDM. As of version 1.15.1, LAPACK is not required on Windows.

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

Windows

  1. If a Windows binary is not available, these suggestions may be useful.
  2. mingw-w64 GCC is available in MSYS2.
  3. Prior to version 1.15.1, gfortran and OpenBLAS libraries are required.
  4. Download pyEDM: git clone https://github.com/SugiharaLab/pyEDM
  5. Build cppEDM library: cd pyEDM\cppEDM\src; make
  6. 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.
  7. Build and install package in pyEDM\: python -m pip install . --user

Usage

Example usage at the python prompt:

>>> 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

pyEDM-1.15.2.0-cp312-cp312-win_amd64.whl (765.5 kB view hashes)

Uploaded CPython 3.12 Windows x86-64

pyEDM-1.15.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB view hashes)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

pyEDM-1.15.2.0-cp312-cp312-macosx_10_9_universal2.whl (756.2 kB view hashes)

Uploaded CPython 3.12 macOS 10.9+ universal2 (ARM64, x86-64)

pyEDM-1.15.2.0-cp311-cp311-win_amd64.whl (766.0 kB view hashes)

Uploaded CPython 3.11 Windows x86-64

pyEDM-1.15.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.9 MB view hashes)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pyEDM-1.15.2.0-cp311-cp311-macosx_10_9_universal2.whl (752.5 kB view hashes)

Uploaded CPython 3.11 macOS 10.9+ universal2 (ARM64, x86-64)

pyEDM-1.15.2.0-cp310-cp310-win_amd64.whl (765.6 kB view hashes)

Uploaded CPython 3.10 Windows x86-64

pyEDM-1.15.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.9 MB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pyEDM-1.15.2.0-cp310-cp310-macosx_11_0_x86_64.whl (430.0 kB view hashes)

Uploaded CPython 3.10 macOS 11.0+ x86-64

pyEDM-1.15.2.0-cp39-cp39-win_amd64.whl (685.2 kB view hashes)

Uploaded CPython 3.9 Windows x86-64

pyEDM-1.15.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.9 MB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pyEDM-1.15.2.0-cp39-cp39-macosx_11_0_x86_64.whl (430.2 kB view hashes)

Uploaded CPython 3.9 macOS 11.0+ x86-64

pyEDM-1.15.2.0-cp38-cp38-win_amd64.whl (765.1 kB view hashes)

Uploaded CPython 3.8 Windows x86-64

pyEDM-1.15.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.9 MB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pyEDM-1.15.2.0-cp38-cp38-macosx_11_0_x86_64.whl (429.9 kB view hashes)

Uploaded CPython 3.8 macOS 11.0+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page