semvec/pypi-packages huggingface dataset:
sharpdock 2.2.0
Automated focused molecular docking pipeline for Autodock Vina
Requires Python: >=3.7

# SHARPDOCK v2.0.0

```text
====================================================
An Integrated Pipeline for Focused Molecular Docking
====================================================
```
**SHARPDOCK** is a high-throughput, automated pipeline for focused molecular docking. It handles the ranking of ligands based on binding affinities by automating grid box generation, receptor/ligand preparation, and parallelized docking execution using AutoDock Vina.

For more tools visit: https://github.com/alpha-horizon

---
## Features

- Automated Grid Box Calculation: No more manual coordinate entry; define sites by chain and residue ID.

- Parallel Ligand Preparation: Process large libraries of ligands in .sdf format.

- Vina Integration: Seamlessly communicates with AutoDock Vina for industry-standard accuracy.

- Formatted Reporting: Generates publication-ready CSVs and detailed log reports.

- Top-Hit Extraction: Automatically isolates the most promising leads for downstream analysis.

---
## Input Requirements

1. Receptor File (-r / --receptor)

    Format: Standard .pdb file.
    Note: It is recommended to remove water molecules, ions, and co-crystallized ligands from the PDB file before running the pipeline to prevent interference with the grid box calculation.

2. Ligand Files (-l / --ligands)

    Format: .sdf (Structure Data File).
    Note: You can input ligands in 2D or 3D structure.

3. Active Site Specification (-s / --sites)

    The site string is the most critical input for Focused Docking. Use the following syntax:

    Single Chain: "A:101 102 105"

    Multiple Chains: "A:101 102; B:70 71"

    Format: ChainID:ResidueID ResidueID; ChainID:ResidueID
    
---
## pip Installation

To install SHARPDOCK, you can use the command mentioned below.

        pip install sharpdock
        
---
## Command Line Usage

This tool supports full argument-based execution for automation and pipelines:

        sharpdock -r receptor.pdb -s "A:101 102; B:70" -l ./ligands -n 10

Options:

        -r	--receptor	Path to receptor PDB file	
        -s	--sites	Active site residues (e.g., 'A:10 11; B:50')	
        -l	--ligands	Folder containing ligand .sdf files |ligands (default)|
        -o	--output	Output directory name |sharpdock_results (default)|
        -p	--padding	Grid box padding in Angstroms (Å) |5.0 (default)|
        -e	--exhaustiveness	Vina search exhaustiveness |32 (default)|
        -n	--top_hits	Number of top ligands to export	|10 (default)|
        
---  
## Directory Structure

- After execution, the output folder contains:

- top_hits/: The best-scoring docked poses.

- docking_results.csv: Comprehensive spreadsheet of all scores.

- binding_affinities.log: A human-readable summary of the run parameters and results.

- receptor.box.txt: The specific Vina configuration used for the grid.

---
## Contribution

For more tools or to report issues, visit the official GitHub repository:

GitHub: https://github.com/alpha-horizon

---
destiny_sdk 0.9.1
A software development kit (sdk) to support interaction with the DESTINY repository
Requires Python: <4,>=3.12

# DESTINY SDK

SDK for interaction with the DESTINY repository. For now this just contains data models for validation and structuring, but will be built out to include convenience functions etc.

## Documentation

The documentation for destiny-sdk is hosted [here](https://destiny-evidence.github.io/destiny-repository/sdk/sdk.html)

## Installation from PyPI

```sh
pip install destiny-sdk
```

```sh
uv add destiny-sdk
```

Some labs functionality may require extra dependencies - these can be installed by:

```sh
pip install destiny-sdk[labs]
uv add destiny-sdk --extra labs
```

## Development

### Dependencies

```sh
uv install
```

### Tests

```sh
uv run pytest
```

### Installing as an editable package of another project

Run the following command in the root folder of the other project (assuming uv as a packaging framework). Pip also has an `--editable` option that you can use.

```sh
uv add --editable ./PATH/TO/sdk/
```

or replace the dependency in `pyproject.toml` with

```toml
destiny-sdk = {path = "./PATH/TO/sdk/", develop = true}
```

### Installing a local wheel

If you want to use a local build of the sdk `z.whl`, do

```sh
uv build
uv add ./PATH/TO/WHEEL.whl
```

### Publishing

Once the package change is merged to main with an iterated `libs/sdk/pyproject.toml` version number, you can run the [github action](https://github.com/destiny-evidence/destiny-repository/actions/workflows/release-sdk-to-pypi.yml) to publish to the test pypi and then production pypi registries.

### Versioning

Follow the [semver](https://semver.org/) guidelines for versioning, tldr;

Given a version number `MAJOR.MINOR.PATCH`, increment the:

- `MAJOR` version when you make incompatible API change
- `MINOR` version when you add functionality in a backward compatible manner
- `PATCH` version when you make backward compatible bug fixes
django-slack-tools 0.3.4
Little helpers working with Slack bot 🤖 in Django.
Requires Python: <4.0,>=3.9

# django-slack-tools

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![codecov](https://codecov.io/gh/lasuillard-s/django-slack-tools/graph/badge.svg?token=c8kzjqjplF)](https://codecov.io/gh/lasuillard-s/django-slack-tools)
[![PyPI - Version](https://img.shields.io/pypi/v/django-slack-tools)](https://pypi.org/project/django-slack-tools/)

Little helpers working with Slack bot 🤖 in Django.

This project aims to implementing helpful features making Slack bot and providing reusable Django apps integrated with database.

## ✨ Features

Key features are:

- [x] Reusable Django app for Slack messaging with various messaging backends for different environments

- [x] Database-backed Slack messaging policies with simple dictionary-based template

- [x] Message histories

- [x] Built-in admin for management working with Slack workspace

- [x] Celery support for messaging backends, management and shortcut tasks, etc.

- [x] Django template support

And more in future roadmap...

- [ ] New Django apps and helpers for Slack features such as modals, event subscription, etc.

- [ ] More fine working example with rich documentation

Currently it is focused on messaging features. In future, hoping to bring more helpful features across Slack Bot ecosystem, such as event subscriptions, modals, bot interactions, etc.

## 🗞️ Documentation

Please check the [documentation](https://lasuillard.github.io/django-slack-tools/) for more details.

## 💖 Contributing

All contributions and helps are welcome. Please check the [CONTRIBUTING.md](/CONTRIBUTING.md) file for about details.

## 📜 License

This project is licensed under the terms of the MIT license.
sly-quantum-walks 1.0.2
A unified Quantum Walk simulation package
Requires Python: >=3.8

# Quantum Walks Package

This package provides a unified interface for various Quantum Walk simulations.

## Installation

```bash
pip install quantumwalks
```

## Usage

```python
import quantumwalks as qw

# 1D/2D/3D Lattice Walks
qw.run_simulation('1d', steps=100)
qw.run_simulation('2d', steps=50)
qw.run_simulation('3d', steps=20)

# Graph Walks
qw.run_simulation('ranking', file_path='graph.edgelist')
qw.run_simulation('pagerank')
qw.run_simulation('hadamard')
qw.run_simulation('szegedy')
qw.run_simulation('community')
```
paintress-cli 0.16.1
TUI reference implementation for pai-agent-sdk
Requires Python: <3.14,>=3.11
Keywords: ai-agent, cli, python, tui

# Paintress CLI

TUI reference implementation for [pai-agent-sdk](https://github.com/youware-labs/pai-agent-sdk).

## Usage

With uvx, run:

```bash
uvx paintress-cli
```

Or to install paintress-cli globally with uv, run:

```bash
uv tool install paintress-cli
...
paintress-cli
```

To update to the latest version:

```bash
uv tool upgrade paintress-cli
```

Or with pip, run:

```bash
pip install paintress-cli
...
paintress-cli
```

Or run as a module:

```bash
python -m paintress_cli
```

## Development

This package is part of the pai-agent-sdk monorepo. To develop locally:

```bash
cd pai-agent-sdk
uv sync --all-packages
```

## License

BSD 3-Clause License - see [LICENSE](LICENSE) for details.
iflow-mcp_truaxki-pdf2png 0.1.0
MCP server to convert a PDF to a PNG file.
Requires Python: >=3.10

# PDF to PNG MCP Server

A Model Context Protocol (MCP) server that provides PDF to PNG conversion capabilities. This server allows you to convert PDF documents into PNG images with a simple MCP tool call.

## Prerequisites

This server requires the Model Context Protocol (MCP). If you're new to MCP, start by installing the SDK:
```bash
uv pip install mcp
```

Additional requirements:
- Python 3.10 or higher
- [uv](https://github.com/astral-sh/uv) package manager
- poppler (required for pdf2image)

### Installing Poppler

- **Windows**: Download and install from [poppler-windows](https://github.com/oschwartz10612/poppler-windows/releases/)
- **macOS**: `brew install poppler`
- **Linux**: `sudo apt-get install poppler-utils`

## Installation

1. Clone this repository:
   ```bash
   git clone https://github.com/truaxki/mcp-Pdf2png.git
   cd mcp-Pdf2png
   ```

2. Create and activate a virtual environment:
   ```bash
   uv venv
   # Windows
   .venv\Scripts\activate
   # Unix/macOS
   source .venv/bin/activate
   ```

3. Install the package:
   ```bash
   uv pip install -e .
   ```

## Usage

### 1. Configure MCP Client

Add the server configuration to your `claude_desktop_config.json`. The file is typically located in:
- Windows: `%APPDATA%\Claude Desktop\config\claude_desktop_config.json`
- macOS/Linux: `~/.config/Claude Desktop/config/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "pdf2png": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/mcp-Pdf2png",
        "run",
        "pdf2png"
      ]
    }
  }
}
```

Note: Replace `/absolute/path/to/mcp-Pdf2png` with the actual path where you cloned the repository.

### 2. Using the Server

The server provides a single tool `pdf2png` with these parameters:
- `read_file_path`: Absolute path to the input PDF file
- `write_folder_path`: Absolute path to the directory where PNG files should be saved

Output:
- Each PDF page is converted to a PNG image
- Files are named `page_1.png`, `page_2.png`, etc.
- Returns a success message with the conversion count

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
prompt-firewall 0.1.6
Prompt guard library for composing multiple safety checks.
Requires Python: >=3.13

# Prompt Guard

Composable prompt-safety guards as a lightweight Python library. It provides:
- **TF‑IDF guard** for fast lexical similarity against known phrases.
- **RAG guard** for embedding-based retrieval against a sentence corpus.
- **LLM judge** for a final model-based classification.

## Quick start

```python
from prompt_guard import GuardPipeline, PromptGuardConfig

cfg = PromptGuardConfig(
    embed_model_name="mxbai-embed-large",
    judge_model_name="qwen2.5:3b-instruct",
)
guard = GuardPipeline(cfg)
result = await guard.check("Some prompt")
```

## Notes

- TF‑IDF and RAG build in‑memory indexes at first use.
- RAG and LLM judge use local Ollama models by default; ensure Ollama is running and the models are pulled.

## Text file formats

- `phrases.txt`: one phrase per line (blank lines are ignored).
- `sentences.txt`: one sentence per line (blank lines are ignored).
scp-py-client 0.0.1
SCP Client - SCP协议客户端库，支持流式和非流式通信
Requires Python: >=3.10

# scp-client

SCP（Science Context Protocol）相关服务的客户端工具库。

## 安装

```bash
pip install scp-client
```

## 使用

### 非流式（streamable-http / sse）

```python
import asyncio
from scp_client import fetch_scp_Client


async def main():
    client = fetch_scp_Client(
        server_url="https://your-mcp-server.example.com/mcp",
        transport_type="streamable-http",  # or "sse"
        headers_config={},
    )
    await client.connect()
    await client.list_tools()
    await client.disconnect()


if __name__ == "__main__":
    asyncio.run(main())
```

### 流式（streamable-http）

```python
import asyncio
from scp_client import stream_scp_Client


async def main():
    client = stream_scp_Client(
        server_url="https://your-mcp-server.example.com/mcp",
        headers_config={},
    )
    await client.connect()
    await client.list_tools()
    await client.disconnect()


if __name__ == "__main__":
    asyncio.run(main())
```