Metadata-Version: 2.4
Name: calkit-python
Version: 0.35.4
Summary: Reproducibility simplified.
Project-URL: Homepage, https://calkit.org
Project-URL: Issues, https://github.com/calkit/calkit/issues
Project-URL: Repository, https://github.com/calkit/calkit
Author-email: Pete Bachant <petebachant@gmail.com>
License-File: LICENSE
Classifier: Framework :: Jupyter
Classifier: Framework :: Jupyter :: JupyterLab
Classifier: Framework :: Jupyter :: JupyterLab :: 4
Classifier: Framework :: Jupyter :: JupyterLab :: Extensions
Classifier: Framework :: Jupyter :: JupyterLab :: Extensions :: Prebuilt
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: >=3.10
Requires-Dist: arithmeval
Requires-Dist: bibtexparser
Requires-Dist: checksumdir
Requires-Dist: docx2pdf
Requires-Dist: dvc==3.66.1
Requires-Dist: fastapi
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Requires-Dist: gitpython
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Requires-Dist: keyring
Requires-Dist: nbconvert
Requires-Dist: papermill>=2.6.0
Requires-Dist: pathspec<1.0.0
Requires-Dist: pillow
Requires-Dist: psutil>=7.0.0
Requires-Dist: pydantic-settings
Requires-Dist: pydantic[email]
Requires-Dist: pyjwt
Requires-Dist: python-dotenv>=1
Requires-Dist: pywin32; platform_system == 'Windows'
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Requires-Dist: tqdm>=4.67.1
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Requires-Dist: uvicorn
Provides-Extra: data
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Requires-Dist: polars>=1.18.0; extra == 'data'
Provides-Extra: xet
Requires-Dist: xet>=0.0.1; extra == 'xet'
Description-Content-Type: text/markdown

    Documentation
   (https://docs.calkit.org)
  |
  
    Tutorials
   (https://docs.calkit.org/tutorials)
  |
  
    Discussions
   (https://github.com/orgs/calkit/discussions)

Calkit makes it easy to create
["single button"](https://doi.org/10.1190/1.1822162)
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.

## Features

- 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](https://docs.conda.io/en/latest/),
  [Docker](https://docker.com), uv, Julia)
  match their specification.
- A schema to store structured metadata describing the
  project's important outputs (in its `calkit.yaml` file)
  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](https://git-scm.com/) and [DVC](https://dvc.org/).
- A complementary self-hostable and GitHub-integrated
  [cloud system](https://github.com/calkit/calkit-cloud)
  to facilitate backup, collaboration,
  and sharing throughout the entire research lifecycle.
- [Overleaf integration](https://docs.calkit.org/overleaf/), so code,
  data, and LaTeX documents can all live in the same repo and be part of a
  single pipeline (no more manual uploads!)

## Installation



On Linux, macOS, or Windows Git Bash,
install Calkit and [uv](https://docs.astral.sh/uv/)
(if not already installed) with:

```sh
curl -LsSf install.calkit.org | sh
```

Or with Windows Command Prompt or PowerShell:

```powershell
powershell -ExecutionPolicy ByPass -c "irm install-ps1.calkit.org | iex"
```

If you already have uv installed, install Calkit with:

```sh
uv tool install calkit-python
```

You can also install with your system Python:

```sh
pip install calkit-python
```

To effectively use Calkit, you'll want to ensure [Git](https://git-scm.com)
is installed and properly configured.
You may also want to install [Docker](https://docker.com),
since that is the default method by which LaTeX environments are created.
If you want to use the [Calkit Cloud](https://calkit.io)
for collaboration and backup as a DVC remote,
you can [set up cloud integration](https://docs.calkit.org/cloud-integration).

### Use without installing

If you want to use Calkit without installing it,
you can use uv's `uvx` command to run it directly:

```sh
uvx calk9 --help
```

### Calkit Assistant

For Windows users, the
[Calkit Assistant](https://github.com/calkit/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.

![Calkit Assistant](https://github.com/calkit/calkit-assistant/blob/main/resources/screenshot.png?raw=true)



## Quickstart



### From an existing project

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:

```sh
calkit xr scripts/analyze.py

calkit xr notebooks/plot.ipynb

calkit xr paper/main.tex
```

Calkit 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:

```sh
calkit run
```

Next, 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:

```sh
---------------------------- Pipeline ----------------------------
analyze:
        changed deps:
                modified:           scripts/analyze.py
```

This can be fixed with another call to `calkit run`.

You can save (add and commit) all changes with:

```sh
calkit save -am "Add to pipeline"
```

### Fresh from a Calkit project template

Create a new project from the
[`calkit/example-basic`](https://github.com/calkit/example-basic)
template with:

```sh
calkit new project my-research \
    --title "My research" \
    --template calkit/example-basic \
    --cloud
```

Note the `--cloud` flag requires [cloud integration](https://docs.calkit.org/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`:



```sh
cd my-research
calkit run
```

Next, make some edits to a script or LaTeX file and run `calkit status` to
see what stages are out-of-date.
For example:

```sh
---------------------------- Pipeline ----------------------------
build-paper:
        changed deps:
                modified:           paper/paper.tex
```

Execute `calkit run` again to bring everything up-to-date.

To back up or save the project, call:

```sh
calkit save -am "Run pipeline"
```



## Get involved

We welcome all kinds of contributions!
See [CONTRIBUTING.md](CONTRIBUTING.md) to learn how to get involved.
