DataJoint Workflow for functional calcium imaging combines multiple DataJoint Elements to process data acquired with ScanImage, Scanbox, Nikon NIS
, or PrairieView
acquisition software, using Suite2p or CaImAn analysis software. DataJoint Elements collectively standardize and automate data collection and analysis for neuroscience experiments. Each Element is a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebook to learn the pipeline.
-
Install Element Calcium Imaging from PyPI
pip install element-calcium-imaging
- If you need help getting started or run into any errors, please contact our team by email at [email protected].
- The easiest way to learn about DataJoint Elements is to use the tutorial notebooks within the included interactive environment configured using DevContainer.
Here are some options that provide a great experience:
-
(recommended) Cloud-based Environment
- Launch using GitHub Codespaces using the
+
option which willCreate codespace on main
in the codebase repository on your fork with default options. For more control, see the...
where you may createNew with options...
. - Build time for a codespace is a few minutes. This is done infrequently and cached for convenience.
- Start time for a codespace is less than 1 minute. This will pull the built codespace from cache when you need it.
- Tip: Each month, GitHub renews a free-tier quota of compute and storage. Typically we run into the storage limits before anything else since Codespaces consume storage while stopped. It is best to delete Codespaces when not actively in use and recreate when needed. We'll soon be creating prebuilds to avoid larger build times. Once any portion of your quota is reached, you will need to wait for it to be reset at the end of your cycle or add billing info to your GitHub account to handle overages.
- Tip: GitHub auto names the codespace but you can rename the codespace so that it is easier to identify later.
- Launch using GitHub Codespaces using the
-
Local Environment
Note: Access to example data is currently limited to MacOS and Linux due to the s3fs utility. Windows users are recommended to use the above environment.
- Install Git
- Install Docker
- Install VSCode
- Install the VSCode Dev Containers extension
git clone
the codebase repository and open it in VSCode- Use the
Dev Containers extension
toReopen in Container
(More info is in theGetting started
included with the extension.)
You will know your environment has finished loading once you either see a terminal open related to Running postStartCommand
with a final message of Done
or the README.md
is opened in Preview
.
-
We recommend you start by navigating to the
notebooks
directory on the left panel and go through thedemo_prepare.ipynb
anddemo_run.ipynb
Jupyter notebooks. Execute the cells in the notebooks to begin your walk through of the tutorial. -
Once you are done, see the options available to you in the menu in the bottom-left corner. For example, in Codespace you will have an option to
Stop Current Codespace
but when running Dev Container on your own machine the equivalent option isReopen folder locally
. By default, GitHub will also automatically stop the Codespace after 30 minutes of inactivity. Once the Codespace is no longer being used, we recommend deleting the Codespace.