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.devon/wiki.json

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{
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"repo_notes": [
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{
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"content": "Suite2p is a complete processing pipeline for neural imaging",
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"author": "Carsen Stringer & Marius Pachitariu"
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}
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],
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"pages": [
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{
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"title": "Overview",
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"purpose": "High-level overview of suite2p's imaging pipeline and how db, settings etc interact"
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},
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{
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"title": "Installation and Setup",
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"purpose": "How to install suite2p and configure the environment",
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"parent": "Overview"
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},
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{
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"title": "Core Concepts",
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"purpose": "Foundational concepts including pipeline architecture, configuration, and data formats"
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},
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{
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"title": "Pipeline Architecture",
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"purpose": "How the processing stages connect: registration, detection, extraction, deconvolution, classification",
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"parent": "Core Concepts"
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},
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{
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"title": "Configuration (db and settings)",
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"purpose": "The db dictionary and settings dictionary, and all configurable parameters for controlling pipeline behavior",
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"parent": "Core Concepts"
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},
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{
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"title": "Data Structures and File Formats",
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"purpose": "Input file formats (tiff, h5, sbx, etc.) and output files (stat.npy, F.npy, iscell.npy, etc.)",
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"parent": "Core Concepts"
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},
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{
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"title": "Processing Pipeline",
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"purpose": "Detailed documentation of each processing stage in the suite2p pipeline"
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},
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{
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"title": "Data Input and Conversion",
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"purpose": "How raw imaging data is loaded, converted to binary format, and prepared for processing",
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"parent": "Processing Pipeline"
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},
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{
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"title": "Registration (Motion Correction)",
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"purpose": "Rigid and nonrigid registration methods for correcting motion artifacts in imaging data",
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"parent": "Processing Pipeline"
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},
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{
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"title": "ROI Detection",
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"purpose": "How regions of interest (neurons) are detected using sparse decomposition and clustering",
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"parent": "Processing Pipeline"
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},
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{
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"title": "Signal Extraction and Deconvolution",
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"purpose": "Extracting fluorescence traces from ROIs, neuropil subtraction, and spike deconvolution via OASIS",
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"parent": "Processing Pipeline"
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},
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{
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"title": "Cell Classification",
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"purpose": "Classifier that distinguishes cells from non-cell ROIs using shape and activity features",
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"parent": "Processing Pipeline"
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},
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{
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"title": "User Interfaces",
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"purpose": "The different ways to run and interact with suite2p"
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},
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{
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"title": "Command-Line Interface",
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"purpose": "Running suite2p from the command line with arguments and config files",
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"parent": "User Interfaces"
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},
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{
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"title": "Python API",
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"purpose": "Using suite2p programmatically in Python scripts and notebooks",
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"parent": "User Interfaces"
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},
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{
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"title": "Graphical User Interface",
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"purpose": "The PyQt-based GUI for running suite2p and visualizing results",
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"parent": "User Interfaces"
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},
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{
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"title": "Main Visualization GUI",
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"purpose": "Visualizing ROIs, traces, and registration results in the main GUI window",
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"parent": "Graphical User Interface"
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},
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{
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"title": "Run Configuration Window",
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"purpose": "Setting parameters and file paths through the GUI run dialog",
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"parent": "Graphical User Interface"
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},
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{
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"title": "Advanced Topics",
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"purpose": "Multi-plane recordings, distributed processing, and data format integrations"
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},
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{
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"title": "Multi-Plane and Multi-Channel Processing",
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"purpose": "Handling multi-plane (volumetric) and multi-channel imaging sessions, including multi-day alignment",
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"parent": "Advanced Topics"
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},
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{
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"title": "Distributed Processing",
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"purpose": "Running suite2p across multiple machines or in parallel for large datasets",
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"parent": "Advanced Topics"
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},
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{
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"title": "NWB Integration",
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"purpose": "Reading and writing Neurodata Without Borders (NWB) format files",
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"parent": "Advanced Topics"
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},
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{
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"title": "Development",
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"purpose": "Contributing to suite2p, package structure, and testing"
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},
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{
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"title": "Package Structure",
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"purpose": "Organization of the suite2p codebase: modules, subpackages, and their responsibilities",
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"parent": "Development"
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},
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{
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"title": "Testing Framework",
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"purpose": "How tests are structured, regression tests, and how to run the test suite",
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"parent": "Development"
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},
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{
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"title": "Contributing Guidelines",
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"purpose": "How to contribute code, report issues, and follow project conventions",
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"parent": "Development"
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},
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{
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"title": "API Reference",
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"purpose": "Auto-generated API docs from Python docstrings for all suite2p subpackages",
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}
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]
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}

README.md

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# suite2p <img src="suite2p/logo/logo_unshaded.png" width="250" title="sweet two pea" alt="sweet two pea" align="right" vspace = "50">
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[![Documentation Status](https://readthedocs.org/projects/suite2p/badge/?version=latest)](https://suite2p.readthedocs.io/en/latest/?badge=latest)
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[![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fsuite2p.json&query=%24.topic_list.tags.0.topic_count&colorB=brightgreen&suffix=%20topics&logo=data:image/png;base64,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)](https://forum.image.sc/tag/suite2p)
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[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/MouseLand/suite2p)
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![tests](https://github.com/mouseland/suite2p/actions/workflows/test_and_deploy.yml/badge.svg)
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[![codecov](https://codecov.io/gh/MouseLand/suite2p/branch/main/graph/badge.svg?token=OJEC3mty85)](https://codecov.io/gh/MouseLand/suite2p)
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* Spike detection
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* Visualization GUI
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For support, please open an [issue](https://github.com/MouseLand/suite2p/issues). The reference paper is [here](https://www.biorxiv.org/content/10.64898/2026.02.04.703741v1). The deconvolution algorithm is based on [this paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005423), with settings based on [this paper](http://www.jneurosci.org/content/early/2018/08/06/JNEUROSCI.3339-17.2018).
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For software support, please open an [issue](https://github.com/MouseLand/suite2p/issues). The reference paper is [here](https://www.biorxiv.org/content/10.64898/2026.02.04.703741v1). The deconvolution algorithm is based on [this paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005423), with settings based on [this paper](http://www.jneurosci.org/content/early/2018/08/06/JNEUROSCI.3339-17.2018).
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See this **twitter [thread](https://twitter.com/marius10p/status/1032804776633880583)** for GUI demonstrations. The matlab version is available [here](https://github.com/cortex-lab/Suite2P). Note that the algorithm is older and will not work as well on non-circular ROIs. Lectures on how suite2p works are available [here](https://youtu.be/HpL5XNtC5wU?list=PLutb8FMs2QdNqL4h4NrNhSHgLGk4sXarb).
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For more general usage questions, please use **[forum.image.sc](https://forum.image.sc/)**. Also, apologies, we had github discussions open but completely forgot about it - if there was any info there that was lost let us know and we'll move it to our FAQ.
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**Note on pull requests**: we accept very few pull requests due to the maintenance efforts required to support new code, and we do not accept pull requests from automated code checkers. If you wrote code that interfaces/changes suite2p behavior, a common approach would be to keep that in a fork and pull periodically from the main branch to make sure you have the latest updates.
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### CITATION

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