semvec/pypi-packages huggingface dataset:
rudi-node-write 1.3.8
Use the internal API of a RUDI Producer node
Requires Python: >=3.11
Keywords: rudi-node-write, rudi-node-put, RUDI, producer node, RUDI node, open-data, Univ. Rennes

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Checked with pyright](https://microsoft.github.io/pyright/img/pyright_badge.svg)](https://microsoft.github.io/pyright/)

# RUDI Node tools: _rudi-node-write_ library

This library offers tools to take advantage of
the [internal API](https://app.swaggerhub.com/apis/OlivierMartineau/RudiProducer-InternalAPI) of a RUDI Producer node (
also
referred as RUDI node), through the API of the backend of the user interface, the "Producer node manager" or "RUDI Manager" module.

## Installation

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install rudi_node_write
```

## Usage: RudiNodeWriter

The Jupyter notebook [`README.ipynb`](https://github.com/OlivierMartineau/rudi-node-write/blob/main/README.ipynb) details how to use the library through the [`RudiNodeWriter`](https://github.com/OlivierMartineau/rudi-node-write/blob/main/src/rudi_node_write/rudi_node_writer.py) object.

## Testing

The [tests](https://github.com/OlivierMartineau/rudi-node-write/tree/main/tests) can be analyzed for further
information about how to call the API

```bash
$ pytest
```
scpert 0.1.1
Single-cell perturbation modeling toolkit
Requires Python: >=3.8

# A Multi-modal LLM-Knowledge Fusion Framework for Predicting Single-cell Genetic Perturbation Effects
        **scPert**, a multi-modal transformer framework that integrates large language model embeddings with structured biological knowledge to predict single-cell transcriptomic responses to genetic perturbations. Through hierarchical fusion of knowledge graph representations, contextual embeddings from foundation models, and gene-specific encodings, scPert achieves significant performance improvements in both single-gene and combinatorial perturbations over existing methods. In cancer-relevant applications, scPert demonstrates the capability to reveal p53 pathway dynamics and immune checkpoint regulatory mechanisms. Systematic evaluation on 42 cancer dependency genes demonstrates scPert's ability to identify critical potential therapeutic targets. Our framework establishes a powerful computational foundation for virtual cell construction and accelerates drug target discovery.
        
        ![scPert Model](./img/ModelArchitecture.png)
        
        ## Installation
        Install [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html), and then do `pip install scpert`
        ## Requirement
        - anndata==0.9.2
        - scanpy==1.9.8
        - torch==2.3.0
        - torch-geometric==2.6.1
        - scvi-tools==0.20.3
        - pandas==2.0.3
        - numpy==1.24.4
        - scipy==1.10.1
        - cell-gears==0.0.2
        - nvidia-cublas-cu12==12.1.3.1
        - nvidia-cudnn-cu12==8.9.2.26
        - flash_attn==0.2.8
        ## Usage
        `embedding_dir`: Directory containing gene embedding files (.npy)
        
        `data_path`: Base directory for perturbation datasets
        
        `model_path`: Pretrained model directory containing model.pt
        
        `pert_file`:CSV file specifying perturbation pairs with columns: gene1,gene2
        
        You can train scPert on your perturbation dataset simply running the Python script:
        ```
        python ./scripts/train.py
        ```
        You can use scPert to predict single gene or gene pairs perturbations by running the scripts:
        
        ``` 
        python ./scripts/infer.py
        ```
        Using API Interface:
        ```
        from scpert import ProcePertdata,scPert
        
        pertData = ProcePertdata(data_path)
        pertData.load(DataName = 'norman')
        
        # training
        SCPert = scPert(pertData, device = 'cuda:0')
        SCPert.model_initialize(hidden_size = 64)
        SCPert.train(epochs = 20)
        
        # saving or loading model
        SCPert.save_model(model_path)
        SCPert.load_pretrained(model_path)
        
        # predict
        SCPert.predict([['PRDM1+CBFA2T3'], ['FEV']])
        SCPert.GI_predict(['CBL', 'CNN1'])
        ```
        ## Cite Us
        This work is currently under peer review.
crackify 0.1.2
Terminal-based Spotify artist autostreamer (headless, requires librespot)
Requires Python: >=3.7
Keywords: spotify, streaming, librespot, music, headless

# crackify

**Terminal-based Spotify artist autostreamer (headless, requires librespot).**

Stream any Spotify artist’s entire catalog in an infinite loop, directly from your terminal, with no need for the Spotify client. Built for automation, bots, or hands-free music streaming.

---

## 🚨 Legal Notice

> **This project is UNOFFICIAL and not affiliated with Spotify.**
>
> By using `crackify`, you accept that it utilizes reverse-engineered streaming with [`librespot`](https://github.com/librespot-org/librespot), which may violate Spotify's Terms of Service. Use at your own risk, for educational or demonstration purposes only.

---

## Features

- Headless/terminal-based audio streaming—no Spotify app needed
- Autostreams all public tracks for any artist (loops forever)
- Minimal Python API: `import crackify; crackify.autostream(...)`
- Requires only Python and the [librespot](https://github.com/librespot-org/librespot) binary

---

## Installation

**1. Install crackify from PyPI:**
```sh
pip install crackify
hkopenai.diagram-as-code-mcp-server 0.2.1
This is a MCP server to provide mermaid js validation tools and prompt for MCP client
Requires Python: >=3.10

# Diagram as Code MCP Server

[![GitHub Repository](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/hkopenai/hk-prompt-mcp-server)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This is a MCP server to provide mermaid js validation tools and prompt for MCP client

## Why Use This MCP Server?

This MCP server is essential for MCP clients working with Mermaid.js diagrams. Large Language Models (LLMs) might be trained with older data and outdated Mermaid.js syntax, and they may not have enough content trained to effectively fix syntax errors. This server addresses these critical limitations by providing specialized tools to assist in authoring, validating, and automatically fixing Mermaid.js syntax, ensuring diagrams are correctly formatted and functional.

## Features

- No Brackets Description: A prompt to instruct bots to avoid using brackets in descriptions.
- Mermaid.js Support: A tool to assist in authoring, validating, and fixing syntax for Mermaid.js diagrams.

## Setup

1. Clone this repository
2. Install Python dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Run the server:
   ```bash
python -m hkopenai.diagram_as_code_mcp_server
   ```

### Running Options

- Default stdio mode: `python -m hkopenai.diagram_as_code_mcp_server`
- SSE mode (port 8000): `python -m hkopenai.diagram_as_code_mcp_server --sse`
- Serve prompt as tool: `python -m hkopenai.diagram_as_code_mcp_server --tool`

## Cline Integration

Cline does not support prompt from mcp server at this moment. The prompt is provided as tool:

To connect this MCP server to Cline using stdio:

1. Add this configuration to your Cline MCP settings (cline_mcp_settings.json):
```json
{
  "hk-prompt-server": {
    "disabled": false,
    "timeout": 3,
    "type": "stdio",
    "command": "python",
    "args": [
      "-m",
      "hkopenai.diagram_as_code_mcp_server",
      "--tool"
    ]
  }
}
```

## Testing

Tests are available in `tests`. Run with:
```bash
pytest
```