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        "author": "Joseph Park & Cameron Smith",
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        "description": "## Empirical Dynamic Modeling (EDM)\n---\nThis package provides a Python/Pandas DataFrame interface to the [cppEDM](https://github.com/SugiharaLab/cppEDM) library for [EDM analysis](http://deepeco.ucsd.edu/nonlinear-dynamics-research/edm/).  [Documentation](https://github.com/SugiharaLab/pyEDM/blob/master/doc/pyEDM.pdf) is available at [pyEDM](https://github.com/SugiharaLab/pyEDM).\n\nFunctionality includes:\n* Simplex projection (Sugihara and May 1990)\n* Sequential Locally Weighted Global Linear Maps (S-map) (Sugihara 1994)\n* Multivariate embeddings (Dixon et. al. 1999)\n* Convergent cross mapping (Sugihara et. al. 2012)\n* Multiview embedding (Ye and Sugihara 2016)\n\n---\n## Installation\n\n### Python Package Index (PyPI)\nCertain Mac OSX and Windows platforms are supported with prebuilt binary distributions and can  be installed using the Python pip module.  The module is located at [pypi.org/project/pyEDM](https://pypi.org/project/pyEDM/).\n\nInstallation can be executed as: `python -m pip install pyEDM`\n\n\n### Manual Install\nUnfortunately, 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.\n\nNote that the [Eigen C++ Template Library](http://eigen.tuxfamily.org/) is required to build cppEDM.  It is assumed that the `Eigen` directory is available in the compiler `INCLUDE` path.  If not, you can add the directory to the `CFLAGS -I` option in the makefile, appropriately define the `INCLUDE` environment variable, or, override the make command line with `CFLAGS=` to specify the location.\n\n#### OSX and Linux\n1. Download pyEDM: `git clone https://github.com/SugiharaLab/pyEDM`\n2. Build cppEDM library: `cd pyEDM/cppEDM/src; make`\n3. Build and install package: `cd ../..; python -m pip install . --user --trusted-host pypi.org`\n\n#### Windows\n1. Download pyEDM: `git clone https://github.com/SugiharaLab/pyEDM`\n2. Build cppEDM library: `cd pyEDM\\cppEDM\\src; nmake /f makefile.windows`\n3. Build and install package: `cd ..\\..; python -m pip install . --user --trusted-host pypi.org`\n\n---\n## Usage\nExample usage at the python prompt:\n```python\n>>> import pyEDM\n>>> pyEDM.Examples()\n```\n\n---\n### References\nSugihara G. and May R. 1990.  Nonlinear forecasting as a way of distinguishing \nchaos from measurement error in time series. Nature, 344:734\u2013741.\n\nSugihara G. 1994. Nonlinear forecasting for the classification of natural \ntime series. Philosophical Transactions: Physical Sciences and \nEngineering, 348 (1688) : 477\u2013495.\n\nDixon, P. A., M. Milicich, and G. Sugihara, 1999. Episodic fluctuations in larval supply. Science 283:1528\u20131530.\n\nSugihara G., May R., Ye H., Hsieh C., Deyle E., Fogarty M., Munch S., 2012.\nDetecting Causality in Complex Ecosystems. Science 338:496-500.\n\nYe H., and G. Sugihara, 2016. Information leverage in interconnected \necosystems: Overcoming the curse of dimensionality. Science 353:922\u2013925.\n\n\n",
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