A Python toolbox for large-scale Calcium Imaging Analysis.
CaImAn implements a set of essential methods required to analyze calcium and voltage imaging data. It provides fast and scalable algorithms for motion correction, source extraction, spike deconvolution, and registering neurons across multiple sessions. It is suitable for both two-photon and one-photon fluorescence microscopy data, and can be run in both offline and online modes. Documentation is here.
- Caiman Central is the hub for sharing information about CaImAn. Information on quarterly community meetings, workshops, other events, and any other communications between the developers and the user community can be found there.
Follow these three steps to get started quickly, from installation to working through a demo notebook. If you do not already have conda installed, you can find it here. There is a video walkthrough of the following steps here.
The following is all done in your anaconda prompt, starting in your base environment:
conda install -n base -c conda-forge mamba # install mamba in base environment
mamba create -n caiman -c conda-forge caiman # install caiman
conda activate caiman # activate virtual environment
Create a working directory called caiman_data that includes code samples and related data. Run the following command from the same virtual environment that you created in Step 1:
caimanmanager install
Go into the working directory you created in Step 2, and open a Jupyter notebook:
cd <your home>/caiman_data/
jupyter notebook
Jupyter will open. Navigate to demos/notebooks/ and click on demo_pipeline.ipynb to get started with a demo.
Note that what counts as
<your home>in the first line depends on your OS/computer. Be sure to fill in your actual home directory. On Linux/Mac it is~while on Windows it will be something likeC:\Users\your_user_name\
Caiman should install easily on Linux, Mac, and Windows. If you run into problems, we have a dedicated installation page: the details there should help you troubleshoot. If you don't find what you need there, please create an issue at GitHub, and we will help you get it sorted out.
Caiman provides demo notebooks to showcase each of our main features, from motion correction to online CNMF. We recommend starting with the CNMF notebook (demo_pipeline.ipynb), which contains more explanation and details than the other notebooks: it covers many concepts that will be used without explanation in the other notebooks. The CNMFE notebook (demo_pipeline_cnmfE.ipynb), is also more detailed. Once you've gotten things set up and worked through those "anchor" notebooks, the best way to get started is to work through the demo notebook that most closely matches your use case; you should be able to adapt it for your particular needs.
The main use cases and notebooks are listed in the following table:
| Use case | Demo notebook | Paper |
|---|---|---|
| CNMF for 2p or low-noise 1p data | demo_pipeline.ipynb | Pnevmatikakis et al., 2016 |
| CNMFE for 1p data | demo_pipeline_cnmfE.ipynb | Zhou et al., 2018 |
| Volpy for voltage data | demo_pipeline_voltage_imaging.ipynb | Cai et al., 2021 |
| Volumetric (3D) CNMF | demo_caiman_cnmf_3D.ipynb | Mentioned in Giovannucci et al., 2019 |
| CNMF for dendrites | demo_dendritic.ipynb | Pnevmatikakis et al., 2016 |
| Online CNMF (OnACID) | demo_OnACID_mesoscope.ipynb | Giovannucci et al., 2017 |
| Online volumetric CNMF | demo_online_3D.ipynb | Developed by Johannes Friedrich |
| Online CNMFE (OnACID-E) | demo_realtime_cnmfE.ipynb | Friedrich et al. 2020 |
| Motion correction | demo_motion_correction.ipynb | Pnevmatikakis et al., 2017 |
| Seed CNMF with external masks | demo_seeded_CNMF.ipynb | Mentioned in Giovannucci et al., 2019 |
| Register cells across sessions | demo_multisession_registration.ipynb | Pnevmatikakis et al., 2016 |
A comprehensive list of references, where you can find detailed discussion of the methods and their development, can be found here.
Caiman also provides commandline demos, similar to the notebooks, demonstrating how to work with the codebase outside of Jupyter. They take their configuration primarily from json files (which you will want to modify to work with your data and its specifics) and should be reasonably easy to modify if they don't already do what you want them to do (in particular, saving things; a standard output format for Caiman is something intended for future releases). To run them, activate your environment, and find the demos in demos/general under your caiman data directory; you can run them like you would any other python application, or edit them with your code editor. Each demo comes with a json configuration file that you can customise. There is a README in the demos directory that covers some of this.
- Online documentation contains a lot of general information about Caiman, the parameters, how to interpret its outputs, and more.
- GitHub Discussions is our preferred venue for users to ask for help.
- The Gitter forum is our old forum: we sometimes will ask people to join us there when something can best be solved in real time (e.g., installation problems).
- If you have found a bug, we recommend searching the issues at github and opening a new issue if you can't find the solution there.
- If there is a feature you would like to see implemented, feel free to come chat at the above forums or open an issue at Github.
Caiman is an open-source project and improves because of contributions from users all over the world. If there is something about Caiman that you would like to work on, then please reach out. We are always looking for more contributors, so please come read the contributors page for more details about how.
There are multiple online videos by Andrea Giovannucci from past Caiman workshops/events that are an excellent start for newcomers.
The following talk provides a good high-level introduction to Caiman:
https://www.youtube.com/watch?v=5APzPRbzUIA
The following talks are more in depth:
- https://www.youtube.com/watch?v=KjHrjhvhRy0
- https://www.youtube.com/watch?v=rUwIqU6gVvw
- https://www.youtube.com/watch?v=NZZ6_zo0YIM
- https://www.youtube.com/watch?v=z6TlH28MLRo
There are many repositories that use Caiman, or help make using Caiman easier.
- use_cases repo: additional code (unmaintained) demonstrating how to reproduce results in some Caiman-related papers, and how to use/extend Caiman.
- jnormcorre: JAX implementation of NoRMCorre for motion correction using JAX acceleration
- funimag: matrix decomposition for denoising and compression
- mesmerize-core: parameter optimization, data organization and visualizations with Caiman
- improv: a platform for creating online analysis workflows that lets you use Caiman in real time (e.g., for all-optical experiments)
If you have questions about these related packages please reach out to their maintainers directly. If you would like your software to be in this list, please contact one of the developers or open an issue.
If you publish a paper that relied on Caiman, we kindly ask that you cite Giovannucci et al., 2019:
@article{giovannucci2019caiman,
title={CaImAn: An open source tool for scalable Calcium Imaging data Analysis},
author={Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and Kalfon, Jeremie and Brown, Brandon L and Koay, Sue Ann and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L and Zhou, Pengcheng and Khakh, Baljit S and Tank, David W and Chklovskii, Dmitri B and Pnevmatikakis, Eftychios A},
journal={eLife},
volume={8},
pages={e38173},
year={2019},
publisher={eLife Sciences Publications Limited}
}
If possible, we'd also ask that you cite the papers where the original algorithms you use (such as CNMF) were developed. A list of such references can be found here.
- (emeritus) Eftychios A. Pnevmatikakis, Flatiron Institute, Simons Foundation
- (emeritus) Andrea Giovannucci, University of North Carolina, Chapel Hill
- Johannes Friedrich, Allen Institute, Seattle Washington
- Changjia Cai, University of North Carolina, Chapel Hill
- Pat Gunn, Flatiron Institute, Simons Foundation
- Eric Thomson, Flatiron Institute, Simons Foundation
A complete list of contributors can be found here. Currently Pat Gunn, Johannes Friedrich, and Eric Thomson are the most active contributors.
Special thanks to the following people for letting us use their datasets in demo files:
- Weijian Yang, Darcy Peterka, Rafael Yuste, Columbia University
- Bernardo Sabatini, Harvard University
- Sue Ann Koay, David Tank, Princeton University
- Manolis Froudarakis, Jake Reimers, Andreas Tolias, Baylor College of Medicine
- Clay Lacefield, Randy Bruno, Columbia University
- Daniel Aharoni, Peyman Golshani, UCLA
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.