Science Score: 36.0%
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
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○Academic publication links
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✓Committers with academic emails
8 of 22 committers (36.4%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary
Keywords from Contributors
Repository
Adaptive Platform for Neuroscience Experiments
Basic Info
- Host: GitHub
- Owner: project-improv
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://project-improv.github.io
- Size: 14.9 MB
Statistics
- Stars: 40
- Watchers: 4
- Forks: 13
- Open Issues: 20
- Releases: 6
Metadata Files
README.md
Adaptive experiments for neuroscience
improv is a streaming software platform designed to enable adaptive experiments. By analyzing data as they arrive, we can obtain information about the current brain state in real time and use it to adaptively modify an experiment as data collection is ongoing.
This video shows raw 2-photon calcium imaging data in zebrafish, with cells detected in real time by CaImAn, and directional tuning curves (shown as colored neurons) and functional connectivity (lines) estimated online, during a live experiment. Here only a few minutes of data have been acquired, and neurons are colored by their strongest response to visual simuli shown so far. We also provide up-to-the-moment estimates of the functional connectivity by fitting linear-nonlinear-Poisson models online, as each new piece of data is acquired. Simple visualizations offer real-time insights, allowing for adaptive experiments that change in response to the current state of the brain.
How improv works
improv allows users to flexibly specify and manage adaptive experiments to integrate data collection, preprocessing, visualization, and user-defined analytics. All kinds of behavioral, neural, or modeling data can be incorporated, and input and output data streams are managed independently and asynchronously. With this design, streaming analyses and real-time interventions can be easily integrated into various experimental setups. improv manages the backend engineering of data flow and task execution for all steps in an experimental pipeline in real time, without requiring user oversight. Users need only define their particular processing pipeline with simple text files and are free to define their own streaming analyses via Python classes, allowing for rapid prototyping of adaptive experiments.
improv's design is based on a streamlined version of the actor model for concurrent computation. Each component of the system (experimental pipeline) is considered an 'actor' and has a unique role. They interact via message passing, without the need for a central broker. Actors are implemented as user-defined classes that inherit from improv's Actor class, which supplies all queues for message passing and orchestrates process execution and error handling. Messages between actors are composed of keys that correspond to items in a shared, in-memory data store. This both minimizes communication overhead and data copying between processes.
Installation
For installation instructions, please consult the documentation.
Contact
To get in touch, feel free to reach out on Twitter @annedraelos or @jmxpearson.
Owner
- Name: project-improv
- Login: project-improv
- Kind: organization
- Repositories: 1
- Profile: https://github.com/project-improv
GitHub Events
Total
- Issues event: 3
- Watch event: 1
- Delete event: 1
- Issue comment event: 10
- Push event: 26
- Pull request review event: 22
- Pull request review comment event: 57
- Pull request event: 4
- Fork event: 2
- Create event: 4
Last Year
- Issues event: 3
- Watch event: 1
- Delete event: 1
- Issue comment event: 10
- Push event: 26
- Pull request review event: 22
- Pull request review comment event: 57
- Pull request event: 4
- Fork event: 2
- Create event: 4
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Anne Draelos | h****s | 158 |
| saraliszeski | 7****i | 146 |
| cyzhou1028 | c****u@d****u | 132 |
| draelos | 96 | |
| Chaichontat Sriworarat | c****s@d****u | 77 |
| eaogorman | e****1@d****u | 56 |
| eaogorman | e****n@g****m | 48 |
| Daniel Sprague | d****9@d****u | 48 |
| John Pearson | j****n@g****m | 24 |
| NICOLE MOISEYEV | n****0@g****m | 24 |
| Annie Do | v****o@d****u | 24 |
| Anne Draelos | 4****s | 18 |
| RaymondChenDuke | r****4@d****u | 10 |
| Tony | 4****a | 10 |
| Sara Liszeski | s****i@d****u | 4 |
| Shiyang Pan | 5****l | 3 |
| Chang Li | 9****i | 2 |
| eaogorman | e****3@g****m | 2 |
| dobaongocvi | v****9@d****u | 2 |
| chaichontat | 3****t | 1 |
| Kushal Kolar | k****r@g****m | 1 |
| Daniel Sprague | 4****e | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 63
- Total pull requests: 128
- Average time to close issues: 8 months
- Average time to close pull requests: 2 months
- Total issue authors: 11
- Total pull request authors: 20
- Average comments per issue: 1.16
- Average comments per pull request: 1.25
- Merged pull requests: 89
- Bot issues: 0
- Bot pull requests: 2
Past Year
- Issues: 5
- Pull requests: 11
- Average time to close issues: 21 days
- Average time to close pull requests: about 7 hours
- Issue authors: 4
- Pull request authors: 6
- Average comments per issue: 0.6
- Average comments per pull request: 0.91
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- jmxpearson (27)
- draelos (16)
- kushalkolar (5)
- rwschonberg (4)
- clewis7 (2)
- eaogorman (2)
- Tonynanra (1)
- chaichontat (1)
- cyzhou1028 (1)
- raymondhechen (1)
- WaAaaAterfall (1)
Pull Request Authors
- jmxpearson (31)
- draelos (29)
- rwschonberg (19)
- chaichontat (12)
- dysprague (9)
- cyzhou1028 (7)
- WaAaaAterfall (6)
- eaogorman (5)
- saraliszeski (4)
- myniax1024 (3)
- clewis7 (3)
- kushalkolar (3)
- dependabot[bot] (3)
- ameliacang7 (3)
- MarleneLi (3)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 31 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 4
(may contain duplicates) - Total versions: 7
- Total maintainers: 2
proxy.golang.org: github.com/project-improv/improv
- Documentation: https://pkg.go.dev/github.com/project-improv/improv#section-documentation
- License: mit
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Latest release: v0.2.2
published over 2 years ago
Rankings
pypi.org: improv
Platform for adaptive neuroscience experiments
- Documentation: https://improv.readthedocs.io/
- License: MIT License Copyright (c) 2019 Pearson Lab at Duke University Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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Latest release: 0.2.2
published over 2 years ago
Rankings
Maintainers (2)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- coverallsapp/github-action v2 composite
- psf/black stable composite
- actions/checkout v2 composite
- actions/setup-python v1 composite
- peaceiris/actions-gh-pages v3.6.1 composite
- jupyter-book *
- matplotlib *
- numpy *
- PyQt5 *
- h5py *
- lmdb *
- matplotlib *
- numpy *
- psutil *
- pyarrow ==9.0.0
- pyyaml *
- pyzmq *
- scipy *
- textual ==0.15.0