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Calkit makes it easy to create "single button" reproducible research projects.
Instead of a loosely related collection of files and manual instructions, turn your project into a version-controlled, self-contained "calculation kit," tying together all phases or stages of the project: data collection, analysis, visualization, and writing, each of which can make use of the latest and greatest computational tools and languages. In other words, you, your collaborators, and readers will be able to go from raw data to research article with a single command, improving efficiency via faster iteration cycle time, reducing the likelihood of mistakes, and allowing others to more effectively build upon your work.
Calkit makes this level of automation possible without extensive software engineering expertise by providing a project framework and toolset that unifies and simplifies the use of powerful enabling technologies like Git, DVC, Conda, Docker, and more, while guiding users away from common reproducibility pitfalls.
- A declarative pipeline that guides users to define an environment for every stage, so long lists of instructions in a README and "but it works on my machine" are things of the past.
- A CLI to run the project's pipeline to verify it's reproducible, regenerating outputs as needed and ensuring all computational environments (e.g., Conda, Docker, uv, Julia) match their specification.
- A schema to store structured metadata describing the
project's important outputs (in its
calkit.yamlfile) and how they are created (its computational environments and pipeline). - A command line interface (CLI) to simplify keeping code, text, and larger data files backed up in the same project repo using both Git and DVC.
- A complementary self-hostable and GitHub-integrated cloud system to facilitate backup, collaboration, and sharing throughout the entire research lifecycle.
- Overleaf integration, so code, data, and LaTeX documents can all live in the same repo and be part of a single pipeline (no more manual uploads!)
On Linux, macOS, or Windows Git Bash, install Calkit and uv (if not already installed) with:
curl -LsSf install.calkit.org | shOr with Windows Command Prompt or PowerShell:
powershell -ExecutionPolicy ByPass -c "irm install-ps1.calkit.org | iex"If you already have uv installed, install Calkit with:
uv tool install calkit-pythonYou can also install with your system Python:
pip install calkit-pythonTo effectively use Calkit, you'll want to ensure Git is installed and properly configured. You may also want to install Docker, since that is the default method by which LaTeX environments are created. If you want to use the Calkit Cloud for collaboration and backup as a DVC remote, you can set up cloud integration.
If you want to use Calkit without installing it,
you can use uv's uvx command to run it directly:
uvx calk9 --helpFor Windows users, the Calkit Assistant app is the easiest way to get everything set up and ready to work in VS Code, which can then be used as the primary app for working on all scientific or analytical computing projects.
If you want to use Calkit with an existing project,
navigate into its working directory and use the xr command to start
executing and recording your scripts, notebooks, LaTeX files, etc.,
as reproducible pipeline stages.
For example:
calkit xr scripts/analyze.py
calkit xr notebooks/plot.ipynb
calkit xr paper/main.texCalkit will attempt to detect environments, inputs, and outputs and
save them in calkit.yaml.
If successful,
you'll be able to run the full pipeline with:
calkit runNext, make a change to e.g., a script and look at the output of
calkit status.
You'll see that the pipeline has a stage that is out-of-date:
---------------------------- Pipeline ----------------------------
analyze:
changed deps:
modified: scripts/analyze.pyThis can be fixed with another call to calkit run.
You can save (add and commit) all changes with:
calkit save -am "Add to pipeline"Create a new project from the
calkit/example-basic
template with:
calkit new project my-research \
--title "My research" \
--template calkit/example-basic \
--cloudNote the --cloud flag requires cloud integration
to be set up, but can be omitted if the project doesn't need to be backed up to
the cloud or shared with collaborators.
Cloud integration can also be set up later.
Next, move into the project folder and run the pipeline,
which consists of several stages defined in calkit.yaml:
cd my-research
calkit runNext, make some edits to a script or LaTeX file and run calkit status to
see what stages are out-of-date.
For example:
---------------------------- Pipeline ----------------------------
build-paper:
changed deps:
modified: paper/paper.texExecute calkit run again to bring everything up-to-date.
To back up or save the project, call:
calkit save -am "Run pipeline"We welcome all kinds of contributions! See CONTRIBUTING.md to learn how to get involved.
