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pyedm

Python package of EDM tools

https://github.com/sugiharalab/pyedm

Science Score: 85.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: nature.com
  • ✓
    Committers with academic emails
    1 of 4 committers (25.0%) from academic institutions
  • ✓
    Institutional organization owner
    Organization sugiharalab has institutional domain (deepeco.ucsd.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Python package of EDM tools

Basic Info
  • Host: GitHub
  • Owner: SugiharaLab
  • License: other
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 54.8 MB
Statistics
  • Stars: 134
  • Watchers: 6
  • Forks: 31
  • Open Issues: 1
  • Releases: 21
Created about 7 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

Empirical Dynamic Modeling (EDM)

This package provides a Python/Pandas DataFrame toolset 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)

Installation

Python Package Index (PyPI)

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

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

Quantitative Ecology and Data-Driven Theory

Citation (CITATION.cff) 

cff-version: 1.2.0
title: pyEDM
message: >-
  If you use this software please cite.
type: software
authors:
  - given-names: Joseph
    family-names: Park
    email: josephpark@ieee.org
    orcid: 'https://orcid.org/0000-0001-5411-1409'
  - {}
identifiers:
  - type: url
    value: 'https://github.com/SugiharaLab/pyEDM'
repository-code: 'https://github.com/SugiharaLab/pyEDM'
url: 'https://github.com/SugiharaLab/pyEDM#readme'
repository: 'https://pypi.org/project/pyEDM/'
abstract: >-
  Python implementation of EDM tools developed at the
  Sugihara Lab, UCSD Scripps Institution of Oceanography.

GitHub Events

Total
  • Create event: 2
  • Release event: 1
  • Issues event: 9
  • Watch event: 15
  • Issue comment event: 18
  • Push event: 7
  • Fork event: 3
Last Year
  • Create event: 2
  • Release event: 1
  • Issues event: 9
  • Watch event: 15
  • Issue comment event: 18
  • Push event: 7
  • Fork event: 3

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 298
  • Total Committers: 4
  • Avg Commits per committer: 74.5
  • Development Distribution Score (DDS): 0.134
Top Committers
Name Email Commits
SoftwareLiteracy J****k@S****g 258
cameronosmith c****8@u****u 37
esabers 3****s@u****m 2
Cameron Smith c****s@g****m 1
Committer Domains (Top 20 + Academic)
ucsd.edu: 1 softwareliteracyfoundation.org: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 61
  • Total pull requests: 0
  • Average time to close issues: 20 days
  • Average time to close pull requests: N/A
  • Total issue authors: 38
  • Total pull request authors: 0
  • Average comments per issue: 3.48
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 0
  • Average time to close issues: about 2 hours
  • Average time to close pull requests: N/A
  • Issue authors: 4
  • Pull request authors: 0
  • Average comments per issue: 0.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • pshankinclarke (12)
  • SteinPanyu (3)
  • UhP88 (3)
  • SoftwareLiteracy (3)
  • forwardState (2)
  • 4rachelgreen (2)
  • wj431364 (2)
  • vishnu020 (2)
  • pmahon3 (2)
  • ecosan327 (2)
  • manihamidi (1)
  • yyw2wyy (1)
  • UCSB-Parker (1)
  • zclandry (1)
  • EllieVahidi (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 1,930 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 73
  • Total maintainers: 1
pypi.org: edm-sugiharalab

Python wrapper for cppEDM using pybind11

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 4.8%
Dependent repos count: 6.3%
Average: 6.7%
Stargazers count: 7.7%
Forks count: 7.8%
Last synced: about 1 year ago
pypi.org: pyedm

Python/Pandas toolset for Empirical Dynamic Modeling.

  • Homepage: https://deepeco.ucsd.edu/
  • Documentation: https://sugiharalab.github.io/EDM_Documentation/
  • 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.
  • Latest release: 2.2.3
    published 7 months ago
  • Versions: 66
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 1,930 Last month
Rankings
Downloads: 5.9%
Dependent packages count: 10.0%
Average: 12.5%
Dependent repos count: 21.7%
Maintainers (1)
sugiharalab
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • matplotlib >=2.2
  • pandas >=1.1
  • pybind11 >=2.3
setup.py pypi
  • matplotlib >=2.2
  • pandas >=1.1
  • pybind11 >=2.3