py2lispIDyOM
py2lispIDyOM: A Python package for the information dynamics of music (IDyOM) model - Published in JOSS (2022)
Science Score: 95.0%
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○CITATION.cff file
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✓DOI references
Found 6 DOI reference(s) in README and JOSS metadata -
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1 of 5 committers (20.0%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Scientific Fields
Repository
A Python package for IDyOM
Basic Info
- Host: GitHub
- Owner: xinyiguan
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://xinyiguan.github.io/py2lispIDyOM/
- Size: 60.9 MB
Statistics
- Stars: 12
- Watchers: 1
- Forks: 3
- Open Issues: 1
- Releases: 4
Metadata Files
README.md
py2lispIDyOM: A Python package for IDyOM
py2lispIDyOM is an open-source Python package that serves as a unifying Python interface that simplifies and
streamlines the research workflow for running the information dynamics of music IDyOM model and analyzing output data.
It is broadly aimed at researchers conducting IDyOM-based analysis in Python.
Table of Content
Getting Started
1. Prerequisites: Installing IDyOM
py2lispIDyOM requires IDyOM to be installed on the local machine. To start with, please read
the IDyOM installation page to appropriately install IDyOM.
We also provided a script to automate the IDyOM installation process (for macOS). Some steps to follow:
- Download this folder: install_idyom.
- In the terminal,
- cd to the path the folder "installidyom/" has been downloaded. For example, `cd Downloads/installidyom/
- Typebash install_idyom.sh. You will be prompted to provide
- yourPassword, and
- to follow the subsequent requestPress Enter to continue.`
2. Installing py2lispIDyOM
The code is compatible with >= Python 3.9.
It can be installed using pip or directly from the source code. Basic installation options include:
- From PyPI using pip:
pip install py2lispIDyOM - Download or gitclone this repository
Functionality and Usage
In summary, py2lispIDyOM has three main functionalities for research workflow:
- Running the IDyOM
- Data preprocessing
- Visualizing IDyOM outputs
Please have a look at the tutorials, which guides you through all three basic functionalities of through examples.
Notebook examples
- Running the IDyOM model: 1runningIDyOM_tutorial.ipynb
- Data preprocessing:
- Extracting data: 2adatapreprocessing_extracting.ipynb
- Exporting data: 2bdatapreprocessing_exporting.ipynb
- Visualization: 3visualizingoutputs.ipynb
Citation, Contributions and Acknowledgments
Citation
Guan et al., (2022). py2lispIDyOM: A Python package for the information dynamics of music (IDyOM) model. Journal of Open Source Software, 7(79), 4738, https://doi.org/10.21105/joss.04738
@article{Guan2022, doi = {10.21105/joss.04738}, url = {https://doi.org/10.21105/joss.04738}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {79}, pages = {4738}, author = {Xinyi Guan and Zeng Ren and Claire Pelofi}, title = {py2lispIDyOM: A Python package for the information dynamics of music (IDyOM) model}, journal = {Journal of Open Source Software} }
Contribution guidelines
We tried to make the code accessible and provide some examples in the tutorials for getting started smoothly. But there is still lots of room for better documentation, tutorials and testing. Please contact us if you have any questions or encounter bugs.
You are also welcome to contribute to this project. There are just a few small guidelines you need to follow.
Authors contributions
All authors provided critical feedback on the design of this project, and participated in the writing and editing of the manuscript. X.G. and C.P. conceptualized the project. X.G. and Z.R. planned the code architecture. X.G. carried out the overall computational implementation. C.P. supervised the overall project.
Acknowledgments
We thank Guilhem Marion for his initial contribution in the idea and code that constituted the basis for the development of this software.
We also thank the reviewer Alexander Hayes for providing useful comments on automating the installation steps of IDyOM.
Owner
- Name: Xinyi Guan
- Login: xinyiguan
- Kind: user
- Location: Lausanne
- Company: EPFL
- Twitter: _xinyiguan_
- Repositories: 2
- Profile: https://github.com/xinyiguan
JOSS Publication
py2lispIDyOM: A Python package for the information dynamics of music (IDyOM) model
Authors
Max Planck NYU Center for Language, Music and Emotion, New York, NY 10003 USA
Digital and Cognitive Musicology Lab, École Polytechnique Fédérale de Lausanne, Lausanne, VD 1015 Switzerland
Max Planck NYU Center for Language, Music and Emotion, New York, NY 10003 USA
Editor
Fabian-Robert Stöter
Tags
music cognition IDyOMGitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| xinyiguan | x****2@g****m | 235 |
| Xinyi Guan | x****2@n****u | 56 |
| HackMD | n****y@h****o | 8 |
| hayesall | a****r@b****t | 4 |
| Fabian-Robert Stöter | f****t | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 7
- Total pull requests: 10
- Average time to close issues: 12 days
- Average time to close pull requests: 1 day
- Total issue authors: 3
- Total pull request authors: 3
- Average comments per issue: 3.0
- Average comments per pull request: 0.1
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- hayesall (5)
- siqiyou (1)
- AoifeHughes (1)
Pull Request Authors
- xinyiguan (6)
- hayesall (3)
- faroit (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 107 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 5
- Total maintainers: 1
pypi.org: py2lispidyom
A Python package for the information dynamics of music (IDyOM) model
- Homepage: https://github.com/xinyiguan/py2lispIDyOM
- Documentation: https://py2lispidyom.readthedocs.io/
- License: MIT License
-
Latest release: 1.0.2
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- autoclasstoc *
- myst-parser *
- nbsphinx *
- pydata_sphinx_theme *
- pypandoc *
- sphinx *
- sphinx-autodoc-typehints *
- sphinx-rtd-theme *
- sphinx_gallery *
- matplotlib >=3.5.2
- natsort >=8.1.0
- numpy >=1.22.3
- pandas >=1.4.2
- scipy >=1.8.0
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- actions/setup-python v2 composite
- pypa/gh-action-pypi-publish release/v1 composite
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