Description
Submitting Author: @billbrod
Package Name: plenoptic
One-Line Description of Package: a python library for model-based image synthesis.
Repository Link (if existing): https://github.com/LabForComputationalVision/plenoptic/ (this PR docs build may be more useful for an introduction, still haven't merged into main yet)
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Description
- Include a brief paragraph describing what your package does:
plenoptic
provides tools to help researchers understand their model by synthesizing novel informative stimuli, which help build intuition for what features the model ignores and what it is sensitive to.
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- My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook
Scope
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Please indicate which category or categories.
Check out our package scope page to learn more about our
scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):- Data retrieval
- Data extraction
- Data processing/munging
- Data deposition
- Data validation and testing
- Data visualization
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Domain Specific & Community Partnerships
- [ ] Geospatial
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- [X] Unsure/Other (explain below)
- Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:
I think plenoptic
is actually out of scope, but I wanted to check, because pyopensci looks cool. This package is intended for use by the vision science, machine learning, and neuroscience communities, but could be used by any researcher that builds models that take something image-, video-, or audio-like as input. The package generates new stimuli (for use in further experiments) rather than facilitates the visualization of existing data.
- Who is the target audience and what are the scientific applications of this package?
Researchers in vision science, machine learning, and neuroscience, largely. The goal is to generate novel stimuli (images, videos, audio) that researchers can use in new experiments to better understand their computational models.
- Are there other Python packages that accomplish similar things? If so, how does yours differ?
Not that I'm aware of.
- Any other questions or issues we should be aware of:
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