semvec/pypi-packages huggingface dataset:
biomol 1.0.1
ML-ready biomolecular data handling package.
License: MIT
Requires Python: >=3.10
Keywords: bioinformatics, machine-learning, molecular-biology, structural-biology

# BioMol

[![Python Version](https://img.shields.io/pypi/pyversions/biomol.svg)](https://pypi.org/project/biomol/)
[![PyPI](https://img.shields.io/pypi/v/biomol.svg)](https://pypi.org/project/biomol/)

<p align="center">
  <img src="https://raw.githubusercontent.com/CSSB-SNU/BioMol/main/docs/_static/logo-light.png" width="450" alt="BioMol Logo">
</p>

BioMol is a molecular data engine that provides PyMOL-like selections and NumPy-style
operations, designed for machine learning and large-scale molecular analysis.

For more details, see the [documentation](https://biomol.readthedocs.io/en/latest/index.html).

## Installation

BioMol supports Python 3.10 and above.

### Install from PyPI

```bash
pip install biomol
```

### Install from Source

For development, BioMol uses [uv](https://docs.astral.sh/uv/) for fast dependency management:

```bash
git clone https://github.com/CSSB-SNU/BioMol
cd BioMol
uv sync
```

For development with all tools (linting, testing, etc.):

```bash
uv sync --extra dev
```
nanoshot-segmenter 0.6.0
Napari + OpenCV image segmentation UI.
License: MIT
Requires Python: >=3.10

# Nanoshot Segmenter

Napari + OpenCV image segmentation UI.

[![CI](https://github.com/jwestraadt/ImageSegmenter/actions/workflows/ci.yml/badge.svg)](https://github.com/jwestraadt/ImageSegmenter/actions/workflows/ci.yml)
[![Coverage](https://codecov.io/gh/jwestraadt/ImageSegmenter/branch/main/graph/badge.svg?token=1142f2f1-8a0f-4fec-ad97-d950de267b73)](https://codecov.io/gh/jwestraadt/ImageSegmenter)
[![PyPI](https://img.shields.io/pypi/v/nanoshot-segmenter?label=PyPI)](https://pypi.org/project/nanoshot-segmenter/)
[![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)

## Install

```bash
pip install nanoshot-segmenter
```

## Environment

```bash
conda env create -f environment.yml
conda activate nanoshot-segmenter
```

## Run

```bash
nanoshot-segmenter
```

## Development

```bash
pip install -e .
nanoshot-segmenter
```

## Documentation

```bash
pip install -e .[docs]
sphinx-build -b html docs/source docs/build
```