Psifr
Psifr: Analysis and visualization of free recall data - Published in JOSS (2020)
Science Score: 93.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
○Committers with academic emails
-
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Repository
Psifr: Analysis and visualization of free recall data
Basic Info
- Host: GitHub
- Owner: mortonne
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Homepage: https://psifr.readthedocs.io/en/stable/
- Size: 12.2 MB
Statistics
- Stars: 11
- Watchers: 3
- Forks: 4
- Open Issues: 0
- Releases: 17
Topics
Metadata Files
README.md
Psifr
Advanced analysis and visualization of free recall data in Python.
Features: * A large library of advanced analyses, tested against published benchmarks * Flexible analysis customization and plotting * Tools for exploratory analysis of large datasets * Extensive automated testing to ensure analysis correctness * Based around a simple and flexible table-based data format * Comprehensive documentation and user guide
The name Psifr is pronounced "cipher". It's taken from Psi, in reference to the field of psychology, and FR for free recall.
Installation
You can install the latest stable version of Psifr using pip:
bash
pip install psifr
You can also install the development version directly from the code repository on GitHub:
bash
pip install git+https://github.com/mortonne/psifr
Quickstart
To plot a serial position curve for a sample dataset:
python
from psifr import fr
df = fr.sample_data('Morton2013')
data = fr.merge_free_recall(df)
recall = fr.spc(data)
g = fr.plot_spc(recall)
See the user guide for detailed documentation on importing and analyzing free recall datasets.
Also see the Jupyter notebooks for more analysis examples: * Recall performance * Temporal clustering
Importing data
Generally the best way to get your data into shape for analysis in Psifr is to create a CSV (or TSV) file with one row for each event in the experiment, including study events (i.e., item presentations) and all recall attempts (including repeats and intrusions). See importing data for details.
A number of archival free recall datasets are available in the Matlab-based EMBAM format.
Data archives for a number of studies are available from the UPenn and Vanderbilt memory labs.
If you have data in EMBAM format, use matlab/frdata2table.m to convert your data struct to a table with standard format.
Then use the Matlab function writetable to write a CSV file which can then be read into Python for analysis.
Citation
If you use Psifr, please cite the paper:
Morton, N. W., (2020). Psifr: Analysis and visualization of free recall data. Journal of Open Source Software, 5(54), 2669, https://doi.org/10.21105/joss.02669
Publications using Psifr
Hong, B., Barense, M. D., Pace-Tonna, C. A. & Mack, M. L. (2022). Emphasizing associations from encoding affects free recall at retrieval. Proceedings of the Annual Meeting of the Cognitive Science Society 453–460. https://escholarship.org/uc/item/2gw1s36q
Related projects
EMBAM
Analyses supported by Psifr are based on analyses implemented in the Matlab toolbox EMBAM.
pybeh
pybeh is a direct Python port of EMBAM that supports a wide range of analyses.
Quail
Quail runs automatic scoring of free recall data, supports calculation and plotting of some common free recall measures, and has tools for measuring the "memory fingerprint" of individuals.
Contributing to Psifr
Contributions are welcome to suggest new features, add documentation, and identify bugs. See the contributing guidelines for an overview.
Owner
- Name: Neal W Morton
- Login: mortonne
- Kind: user
- Location: Austin, TX
- Company: The University of Texas at Austin
- Website: https://nealwmorton.com
- Repositories: 8
- Profile: https://github.com/mortonne
JOSS Publication
Psifr: Analysis and visualization of free recall data
Authors
Research Fellow, Center for Learning and Memory, The University of Texas at Austin
Editor
Christopher R. Madan
Tags
psychology memory researchGitHub Events
Total
- Create event: 2
- Issues event: 1
- Release event: 2
- Issue comment event: 2
- Push event: 15
Last Year
- Create event: 2
- Issues event: 1
- Release event: 2
- Issue comment event: 2
- Push event: 15
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Neal Morton | m****e@g****m | 703 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 12
- Total pull requests: 6
- Average time to close issues: 4 months
- Average time to close pull requests: 3 minutes
- Total issue authors: 4
- Total pull request authors: 1
- Average comments per issue: 2.08
- Average comments per pull request: 0.17
- Merged pull requests: 6
- 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
- paxtonfitzpatrick (6)
- mortonne (3)
- samhforbes (2)
- githubpsyche (1)
Pull Request Authors
- mortonne (6)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 3,706 last-month
- Total dependent packages: 0
- Total dependent repositories: 2
- Total versions: 18
- Total maintainers: 1
pypi.org: psifr
Psifr: Analysis and visualization of free recall data
- Documentation: https://psifr.readthedocs.io/
- License: GPL-3.0-or-later
-
Latest release: 0.10.0
published 8 months ago