suite2p <img src="suite2p/logo/logo_unshaded.png" width="250" title="sweet two pea" alt="sweet two pea" align="right" vspace = "50">

suite2p includes the following modules:

Registration
ROI detection
Signal extraction
ROI classification
Spike detection
Visualization GUI

For software support, please open an issue. The reference paper is here. The deconvolution algorithm is based on this paper, with settings based on this paper.

See this twitter thread for GUI demonstrations. The matlab version is available here. Note that the algorithm is older and will not work as well on non-circular ROIs. Lectures on how suite2p works are available here. Example notebook on how to use suite2p can be found here:

For more general usage questions, please use 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.

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.

CITATION

If you use this package in your research, please cite the paper:

Carsen Stringer, Chris Ki, Nicholas Del Grosso, Paul LaFosse, Qingqing Zhang, Marius Pachitariu (2026). Extracting large-scale neural activity with Suite2p. bioRxiv.

Read the Documentation at https://suite2p.readthedocs.io/

Local installation (< 2 minutes)

You can install cellpose using conda or with native python if you have python3.8+ on your machine.

System requirements

Linux, Windows and Mac OS are supported for running the code. For running the graphical interface you will need a Mac OS later than Yosemite. At least 8GB of RAM is required to run the software. 16GB-32GB is encouraged for larger recordings. The software has been heavily tested on Windows 10 and Ubuntu 24.04 and less well-tested on Mac OS. Please open an issue if you have problems with installation.

Dependencies

Suite2p relies on the following excellent packages (which are automatically installed with conda/pip if missing):

Suite2p also optionally uses our anatomical segmentation tool Cellpose. In the GUI our tool Rastermap is used for visualization.

Option 1: Installation Instructions with conda

If you have an older suite2p environment you can remove it with conda env remove -n suite2p before creating a new one (we recommend removing pre-2026 envs and re-creating).

If you are using a GPU, make sure its drivers and the cuda libraries are correctly installed.

Install a miniforge distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
Open an anaconda prompt / command prompt which has conda for python 3 in the path
Create a new environment with conda create --name suite2p python=3.11. We recommend python 3.11, but python 3.9-3.12 will also work.
To activate this new environment, run conda activate suite2p
(option 1) To install cellpose with the GUI, run python -m pip install suite2p[gui]. If you're on a zsh server, you may need to use ' ': python -m pip install 'suite2p[gui]'.
(option 2) To install cellpose without the GUI, run python -m pip install suite2p.

To upgrade suite2p (package here), run the following in the environment:

python -m pip install suite2p --upgrade

Note you will always have to run conda activate suite2p before you run cellpose. If you want to run jupyter notebooks in this environment, then also python -m pip install notebook and python -m pip install matplotlib.

You can also try to install Suite2p and the GUI dependencies from your base environment using the command

python -m pip install suite2p[gui]

If you have issues with installation, see here for more details. If these suggestions fail, open an issue.

Option 2: Installation Instructions with python's venv

Venv (tutorial, for those interested) is a built-in tool in python for creating virtual environments. It is a good alternative if you don't want to install conda and already have python3 on your machine. The main difference is that you will need to choose where to install the environment and the packages. Suite2p will then live in this environment and not be accessible from other environments. You will need to navigate to the environment directory and activate it each time before running Suite2p. The steps are similar to the conda installation:

If you are using a GPU, make sure its drivers and the cuda libraries are correctly installed.

Install python3.8 or later from python.org. This will be the version of python that will be used in the environment. You can check your python version with python --version.
Navigate to the directory where you want to create the environment and run python3 -m venv suite2p to create a new environment called suite2p.
Activate the environment with source suite2p/bin/activate on Mac/Linux or suite2p\Scripts\activate on Windows. A prefix (suite2p) should appear in the terminal.
Install suite2p into the suite2p venv using pip with python -m pip install suite2p.
Install the suite2p GUI, with python -m pip install suite2p[gui]. Depending on your terminal software, you may need to use quotes like this: python -m pip install 'suite2p[gui]'.
You can now run suite2p from this environment with python -m suite2p or suite2p if you are in the suite2p directory.
To deactivate the environment, run deactivate.

GPU version (CUDA) on Windows or Linux

If you plan on running Suite2p on longer recordings, we strongly recommend installing a GPU version of torch. To use your NVIDIA GPU with python, you will need to make sure the NVIDIA driver for

Suite2p

Install / Use

README