semvec/pypi-packages huggingface dataset:
scab 0.2.4
Single cell analysis of B cells
Requires Python: >=3.8

![](https://img.shields.io/pypi/v/scab.svg?colorB=blue)
[![Build Status](https://travis-ci.com/briney/scab.svg?branch=master)](https://travis-ci.com/briney/scab)
[![Documentation Status](https://readthedocs.org/projects/scab/badge/?version=latest)](https://scab.readthedocs.io/en/latest/?badge=latest)
![](https://img.shields.io/pypi/pyversions/scab.svg)
![](https://img.shields.io/badge/license-MIT-blue.svg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# scab

Single cell analysis of B cells.
scab is a core component of the ab\[x\] toolkit for antibody sequence analysis.
  
  - Source code: [github.com/briney/scab](https://github.com/briney/scab)  
  - Documentation: [scab.readthedocs.org](https://scab.readthedocs.io)  
  - Download: [pypi.python.org/pypi/scab](https://pypi.python.org/pypi/scab)  
  
### install  
`pip install scab`  


### api  
The intended use of scab is through the public API, enabling incorporation of scab's methods and utilities into integrated analysis pipelines or for interative analysis of antibody repertoires in notebook-style computing environments like Jupyter. See the scab [documentation](http://scab.readthedocs.io) for more detail about the API.  


### testing  
To run the test suite, clone or download the repository and run `pytest ./` from the top-level directory. The same tests are run after every commit using TravisCI.  
  

### requirements  
Python 3.6+  
abstar
abutils>=0.2.5
anndata
dnachisel
fastcluster
harmonypy
leidenalg
matplotlib
mnemonic
natsort
numpy
pandas
prettytable
pytest
python-Levenshtein
scanpy
scanorama
scipy
scrublet
scvelo
seaborn>=0.11
umap-learn
  
All of the above dependencies can be installed with pip, and will be installed automatically when installing scab with pip.  
If you're new to Python, a great way to get started is to install the [Anaconda Python distribution](https://www.continuum.io/downloads), which includes pip as well as a ton of useful scientific Python packages.
acmsg 0.1.0
Automatically generate git commit messages using AI.
Requires Python: <3.14,>=3.12

# AI Commit Message Generator (acmsg)

A Python tool that automatically generates meaningful git commit messages using AI models through the OpenRouter API.

## Features

- Analyzes staged changes in your git repository
- Generates contextual commit messages using AI
- Supports multiple AI models via [OpenRouter](https://openrouter.ai)
- Automatically commits changes with generated message

## Prerequisites
- OpenRouter API Key

## Installation

### With Nix Flakes:
```bash
# Add this flake as an input
inputs.acmsg.url = "github:quinneden/acmsg";

# Add the overlay & include the package in your configuration
nixpkgs.overlays = [ inputs.acmsg.overlays.default ];
environment.systemPackages = [ pkgs.acmsg ];

# Or just add the package directly
environment.systemPackages = [ inputs.acmsg.packages.${pkgs.system}.acmsg ];
```

## Usage

```bash
# Save api_token configuration value
$ acmsg config set api_token <token_value>

# Optional: Change AI model
acmsg config set model <model>

# Stage your changes first
git add <files>

# Review message & commit
$ acmsg commit
Commit message:

feat(content): add portfolio site content


Do you want to commit? (y/n):
```

## Configuration

You can also configure default settings in `~/.config/git-commit-ai/config.yaml`:

```yaml
api_token: **-**-**-****************************************************************
model: deepseek/deepseek-r1:free # Default model
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a Pull Request

## License

MIT License - See LICENSE file for details
dp-parser 0.1.0
A package for parsing and analyzing code dependency graphs
Requires Python: >=3.8

# DG-Parser

A Python package for parsing and analyzing code dependency graphs.

## Features

- Parse Python code to generate dependency graphs
- Analyze module dependencies and relationships
- Command-line interface for quick analysis
- Export dependency graphs (i.e. Pickle)
<!-- - Built on `networkx` and `astroid` -->

## Getting Started

### Prerequisites

- Python 3.8 or higher
- pip package manager

### Installation

1. From PyPI (not yet published):
```bash
pip install dg-parser
```

2. From source:
```bash
git clone https://github.com/nmd2k/DG-parser.git
cd DG-parser
pip install -e .
```

### Basic Usage

#### 1. Command Line Interface

The package provides a convenient CLI for quick analysis:

```bash
dp-parser analyze tests/test_data/sample_project             

>>> Analyzing project: tests/test_data/sample_project
        Function Signature: validate_input
        Dependencies:
        Calls: from utils import validate_input, transform_data
        Parameters: ['data']
----------------------------------------------------------------------------------------------------
        Function Signature: transform_data
        Dependencies:
        Calls: from utils import validate_input, transform_data
        Parameters: ['data']
----------------------------------------------------------------------------------------------------
        Function Signature: ValueError
        Dependencies:
        Calls: ValueError
        Parameters: []
----------------------------------------------------------------------------------------------------
        Function Signature: process_data
        Dependencies:
                transform_data
                ValueError
                validate_input
        Calls: def process_data(data):
        Parameters: ['data']
----------------------------------------------------------------------------------------------------
Dependency graph saved to: output/sample_project_graph.pkl
```

#### 2. Python API

```python
from dp_graph import PythonParser

# Create a dependency graph instance
parser = PythonParser()

# Parse a Python project
graph = parser.parse("/path/to/project")

# Get all dependencies
for node in graph.nodes.values():
    print("\tFunction Signature:", node.signature)
    print("\tDependencies:")
    for dep in node.dependencies:
        print(f"\t\t{dep.signature}")
    print("\tCalls:", node.code.split("\n")[0])
    print("\tParameters:", node.parameters)
    print("-" * 100)

# Export the graph
# Comming soon
```

## License

MIT License