Science Score: 57.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: nature.com -
○Academic email domains
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✓Institutional organization owner
Organization sugiharalab has institutional domain (deepeco.ucsd.edu) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.3%) to scientific vocabulary
Repository
Jupyter notebook for pyEDM
Basic Info
- Host: GitHub
- Owner: SugiharaLab
- License: mit
- Language: Python
- Default Branch: master
- Size: 21.9 MB
Statistics
- Stars: 10
- Watchers: 2
- Forks: 2
- Open Issues: 0
- Releases: 2
Metadata Files
README.md
Empirical Dynamic Modeling (EDM) Jupyter Notebook
A Jupyter notebook GUI front-end for the pyEDM package. An introduction to EDM with documentation is avilable online. The pyEDM package documentation is in the API docs. The EDM packages are developed and maintained by the Sugihara Lab.
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)
Installation
pyEDM Python Package
pyEDM is hosted on the Python Package Index respository (PyPI) at pyEDM.
It can be installed from the command line using the Python pip module: python -m pip install pyEDM.
Jupyter notebook
Download the jpyEDM source.
Start Jupyter notebook.
Open "jpyEDM/notebooks/jpyEDM-0.9.ipynb".
Introduction
A brief video presentation.
Screenshot
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.
Owner
- Name: Sugihara Lab
- Login: SugiharaLab
- Kind: organization
- Email: Sugihara.Lab@gmail.com
- Location: UCSD
- Website: http://deepeco.ucsd.edu/
- Repositories: 4
- Profile: https://github.com/SugiharaLab
Quantitative Ecology and Data-Driven Theory
GitHub Events
Total
- Release event: 2
- Watch event: 1
- Push event: 3
- Fork event: 1
- Create event: 2
Last Year
- Release event: 2
- Watch event: 1
- Push event: 3
- Fork event: 1
- Create event: 2