| Name | pyEDM JSON |
| Version |
2.1.0
JSON |
| download |
| home_page | None |
| Summary | Python/Pandas toolset for Empirical Dynamic Modeling. |
| upload_time | 2024-08-03 14:38:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Copyright 2019 The Regents of the University of California. All Rights Reserved. Permission to copy, modify, and distribute this software and its documentation for educational, research and non-profit purposes, without fee, and without a written agreement is hereby granted, provided that the above copyright notice, this paragraph and the following three paragraphs appear in all copies. Those desiring to incorporate this software into commercial products or use for commercial purposes should contact: Office of Innovation & Commercialization University of California, San Diego 9500 Gilman Drive, Mail Code 0910 La Jolla, CA 92093-0910 Ph: (858) 534-5815, FAX: (858) 534-7345 E-MAIL:invent@ucsd.edu. IN NO EVENT SHALL THE UNIVERSITY OF CALIFORNIA BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF THE UNIVERSITY OF CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. THE SOFTWARE PROVIDED HEREIN IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. THE UNIVERSITY OF CALIFORNIA MAKES NO REPRESENTATIONS AND EXTENDS NO WARRANTIES OF ANY KIND, EITHER IMPLIED OR EXPRESS, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY PATENT, TRADEMARK OR OTHER RIGHTS. |
| keywords |
edm
empirical dynamic modeling
nonlinear dynamics
time series
state space
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## Empirical Dynamic Modeling (EDM)
---
This package provides a Python/Pandas DataFrame toolset for [EDM analysis](http://deepeco.ucsd.edu/nonlinear-dynamics-research/edm/ "EDM @ Sugihara Lab"). Introduction and documentation are are avilable [online](https://sugiharalab.github.io/EDM_Documentation/ "EDM Docs"), or in the package [API docs](https://github.com/SugiharaLab/pyEDM/blob/master/doc/pyEDM.pdf "pyEDM API"). A Jupyter notebook interface is available at [jpyEDM](https://github.com/SugiharaLab/jpyEDM#empirical-dynamic-modeling-edm-jupyter-notebook).
Functionality includes:
* Simplex projection ([Sugihara and May 1990](https://www.nature.com/articles/344734a0))
* Sequential Locally Weighted Global Linear Maps (S-Map) ([Sugihara 1994](https://royalsocietypublishing.org/doi/abs/10.1098/rsta.1994.0106))
* Multivariate embeddings ([Dixon et. al. 1999](https://science.sciencemag.org/content/283/5407/1528))
* Convergent cross mapping ([Sugihara et. al. 2012](https://science.sciencemag.org/content/338/6106/496))
* Multiview embedding ([Ye and Sugihara 2016](https://science.sciencemag.org/content/353/6302/922))
---
## Installation
### Python Package Index (PyPI)
Certain MacOS, Linux and Windows platforms are supported with prebuilt binary distributions hosted on PyPI [pyEDM](https://pypi.org/project/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:
```python
>>> 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](https://www.nature.com/articles/344734a0).
Sugihara G. 1994. Nonlinear forecasting for the classification of natural
time series. [Philosophical Transactions: Physical Sciences and
Engineering, 348 (1688) : 477–495](https://royalsocietypublishing.org/doi/abs/10.1098/rsta.1994.0106).
Dixon, P. A., M. Milicich, and G. Sugihara, 1999. Episodic fluctuations in larval supply. [Science 283:1528–1530](https://science.sciencemag.org/content/283/5407/1528).
Sugihara G., May R., Ye H., Hsieh C., Deyle E., Fogarty M., Munch S., 2012.
Detecting Causality in Complex Ecosystems. [Science 338:496-500](https://science.sciencemag.org/content/338/6106/496).
Ye H., and G. Sugihara, 2016. Information leverage in interconnected
ecosystems: Overcoming the curse of dimensionality. [Science 353:922–925](https://science.sciencemag.org/content/353/6302/922).
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"description": "## Empirical Dynamic Modeling (EDM)\n---\nThis package provides a Python/Pandas DataFrame toolset for [EDM analysis](http://deepeco.ucsd.edu/nonlinear-dynamics-research/edm/ \"EDM @ Sugihara Lab\"). Introduction and documentation are are avilable [online](https://sugiharalab.github.io/EDM_Documentation/ \"EDM Docs\"), or in the package [API docs](https://github.com/SugiharaLab/pyEDM/blob/master/doc/pyEDM.pdf \"pyEDM API\"). A Jupyter notebook interface is available at [jpyEDM](https://github.com/SugiharaLab/jpyEDM#empirical-dynamic-modeling-edm-jupyter-notebook).\n\nFunctionality includes:\n* Simplex projection ([Sugihara and May 1990](https://www.nature.com/articles/344734a0))\n* Sequential Locally Weighted Global Linear Maps (S-Map) ([Sugihara 1994](https://royalsocietypublishing.org/doi/abs/10.1098/rsta.1994.0106))\n* Multivariate embeddings ([Dixon et. al. 1999](https://science.sciencemag.org/content/283/5407/1528))\n* Convergent cross mapping ([Sugihara et. al. 2012](https://science.sciencemag.org/content/338/6106/496))\n* Multiview embedding ([Ye and Sugihara 2016](https://science.sciencemag.org/content/353/6302/922))\n\n---\n## Installation\n\n### Python Package Index (PyPI)\nCertain MacOS, Linux and Windows platforms are supported with prebuilt binary distributions hosted on PyPI [pyEDM](https://pypi.org/project/pyEDM/) and can be installed with the Python pip module: `python -m pip install pyEDM`\n\n---\n## Usage\nExamples can be executed in the python command line:\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](https://www.nature.com/articles/344734a0).\n\nSugihara G. 1994. Nonlinear forecasting for the classification of natural \ntime series. [Philosophical Transactions: Physical Sciences and \nEngineering, 348 (1688) : 477\u2013495](https://royalsocietypublishing.org/doi/abs/10.1098/rsta.1994.0106).\n\nDixon, P. A., M. Milicich, and G. Sugihara, 1999. Episodic fluctuations in larval supply. [Science 283:1528\u20131530](https://science.sciencemag.org/content/283/5407/1528).\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](https://science.sciencemag.org/content/338/6106/496).\n\nYe H., and G. Sugihara, 2016. Information leverage in interconnected \necosystems: Overcoming the curse of dimensionality. [Science 353:922\u2013925](https://science.sciencemag.org/content/353/6302/922).\n",
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