Metadata-Version: 2.3
Name: spectranorm
Version: 0.1.0
Summary: A Python package for spectral normative modeling of neuroimaging and other high-dimensional data.
License: Dual license: AGPLv3 for non-commercial use; proprietary commercial license available. See LICENSE file for details.
Author: Sina Mansour L.
Author-email: sina.mansour.lakouraj@gmail.com
Requires-Python: >=3.10,<3.14
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary 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: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Dist: arviz (>=0.21)
Requires-Dist: joblib (>=1.5)
Requires-Dist: nibabel (>=5.3)
Requires-Dist: numpy (>=1.25)
Requires-Dist: pandas (>=2.2)
Requires-Dist: patsy (>=1.0)
Requires-Dist: pymc (>=5.23)
Requires-Dist: pytensor (>=2.31)
Requires-Dist: scipy (>=1.15)
Requires-Dist: tqdm (>=4.67)
Project-URL: Documentation, https://sina-mansour.github.io/spectranorm
Project-URL: Homepage, https://sina-mansour.github.io/spectranorm
Project-URL: Repository, https://github.com/sina-mansour/spectranorm
Description-Content-Type: text/markdown

# spectranorm

[![PyPI](https://img.shields.io/pypi/v/spectranorm?style=flat-square)](https://pypi.python.org/pypi/spectranorm/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/spectranorm?style=flat-square)](https://pypi.python.org/pypi/spectranorm/)
[![PyPI - License](https://img.shields.io/pypi/l/spectranorm?style=flat-square)](https://pypi.python.org/pypi/spectranorm/)
[![Coookiecutter - Wolt](https://img.shields.io/badge/cookiecutter-Wolt-00c2e8?style=flat-square&logo=cookiecutter&logoColor=D4AA00&link=https://github.com/woltapp/wolt-python-package-cookiecutter)](https://github.com/woltapp/wolt-python-package-cookiecutter)


---

**Documentation**: [https://sina-mansour.github.io/spectranorm](https://sina-mansour.github.io/spectranorm)

**Source Code**: [https://github.com/sina-mansour/spectranorm](https://github.com/sina-mansour/spectranorm)

**PyPI**: [https://pypi.org/project/spectranorm/](https://pypi.org/project/spectranorm/)

---

A Python package for spectral normative modeling of neuroimaging and other high-dimensional data.

## Installation

```sh
pip install spectranorm
```

## Development

* Clone this repository
* Requirements:
  * [Poetry](https://python-poetry.org/)
  * Python 3.8+
* Create a virtual environment and install the dependencies

```sh
poetry install
```

* Activate the virtual environment

```sh
poetry shell
```

### Testing

```sh
pytest
```

### Documentation

The documentation is automatically generated from the content of the [docs directory](https://github.com/sina-mansour/spectranorm/tree/master/docs) and from the docstrings
 of the public signatures of the source code. The documentation is updated and published as a [Github Pages page](https://pages.github.com/) automatically as part each release.

### Releasing

Trigger the [Draft release workflow](https://github.com/sina-mansour/spectranorm/actions/workflows/draft_release.yml)
(press _Run workflow_). This will update the changelog & version and create a GitHub release which is in _Draft_ state.

Find the draft release from the
[GitHub releases](https://github.com/sina-mansour/spectranorm/releases) and publish it. When
 a release is published, it'll trigger [release](https://github.com/sina-mansour/spectranorm/blob/master/.github/workflows/release.yml) workflow which creates PyPI
 release and deploys updated documentation.

### Pre-commit

Pre-commit hooks run all the auto-formatting (`ruff format`), linters (e.g. `ruff` and `mypy`), and other quality
 checks to make sure the changeset is in good shape before a commit/push happens.

You can install the hooks with (runs for each commit):

```sh
pre-commit install
```

Or if you want them to run only for each push:

```sh
pre-commit install -t pre-push
```

Or if you want e.g. want to run all checks manually for all files:

```sh
pre-commit run --all-files
```

---

This project was generated using the [wolt-python-package-cookiecutter](https://github.com/woltapp/wolt-python-package-cookiecutter) template.

