Metadata-Version: 2.4
Name: neuromotifs
Version: 0.1.0a1
Summary: Geometry-aware network motif analysis for neocortical microcircuits
Author: Eyal Gal, Rodrigo Perin, Henry Markram, Michael London, Idan Segev
License: MIT
Project-URL: Homepage, https://github.com/gialdetti/neuromotifs
Project-URL: Issues, https://github.com/gialdetti/neuromotifs/issues
Keywords: neuroscience,motifs,networks,geometry,microcircuit
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.23
Requires-Dist: pandas>=1.5
Provides-Extra: dev
Requires-Dist: jupyter; extra == "dev"
Requires-Dist: ipywidgets; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Dynamic: license-file

# neuromotifs
Python tools to load neuronal microcircuit geometry, generate geometry-aware null models, and quantify over/under-expression of 3-node motifs.

> Paper: *Neuron Morphological Asymmetry Explains Fundamental Network Stereotypy Across Neocortex* (Gal et al.)

## Install
```bash
pip install neuromotifs
# or, for dev
pip install -e .[dev]
```

## Highlights
- Motif counting for directed triplets (#1-#13)
- Geometry-driven random graph generators (1st-5th order) mirroring the paper’s models
- Reproducibility notebooks for Figures 1-4
- Simple CLI: `neuromotifs motifs`, `neuromotifs generate`, `neuromotifs fit`

## Quickstart
```python
# TBD
```

## Data
- `data/nmc/` contains tiny demonstrators only.
- For full datasets, see `data/README.md` for scripted download instructions.

## Citing
Please cite the paper and this package (see `CITATION.cff`).
