semvec/pypi-packages huggingface dataset:
blockhouse 1.0.31
Blockhouse SDK
Keywords: python, blockhouse, sdk, stock, market, trading, algotrading, finance, financial, stocks, market, data, api, wrapper


## Blockhouse SDK API Package

This is a Python SDK package that is used for various functionality from the Blockhouse API. The package is published on PyPI and can be installed using pip.

## Table of Contents

1. Features
2. Usage
3. Available Functions
4. License
5. Contributing
6. Support
7. Changelog

## Features

- Fetch trade data from the Blockhouse API and send them to our kafka topic.

## Usage

Get the API key from the Blockhouse API and install the package using pip:

```bash
pip install blockhouse
```

Using as a Python Library

```python
from blockhouse import Transfer

client = Transfer(api_key="your_api_key_here")

send = client.send_file(local_file_path="test123.txt", bucket_name="blockhouse-sdk")

print(send)

trades = client.trades_data()

print(trades)
```

Sending SOR data to the Blockhouse API

```python
from blockhouse import TradeClient

client = TradeClient("your_api_key_here")
res = client.send_trade(
    order_id="12345",
    symbol="AAPL",
    quantity=100,
    side="buy",
    price=150.50,
    order_type="limit",
)
print(res)
```

## Available Functions:

- TransferData: Fetch trade data from the Blockhouse API and send them to our kafka topic.

## License

This project is licensed under the MIT License. See the LICENSE file for more details.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

## Support

If you encounter any issues or have questions, feel free to open email us.

## Changelog

Version 1.0.9

- Added the ability to send files to the Blockhouse API.
- Added the ability to fetch trade data from the Blockhouse API.
- Added the ability to send SOR data to the Blockhouse API and create FIX messages.
chill-pip 0.1.0
A simple Dash application with secret functionality
Requires Python: >=3.6

# Chill Pip

A simple Dash application with secret functionality.

## Installation

Install directly from PyPI:

```bash
pip install chill_pip
```

## Usage

After installation, you can run the application with:

```bash
chill-pip
```

Or you can use it in your own Python code:

```python
from chill_pip.app import app

app.run(debug=True)
```

## Features

- Simple web interface with Bootstrap styling
- Secret function that processes numeric inputs
- Easy to integrate into your own projects

## License

This is a commercial product with obfuscated code. While the package is freely available on PyPI, using it for commercial purposes requires a license key.

Contact us at your.email@example.com for licensing information.
xrayvisim 0.2.1
An open-source Python library for Fourier or synthesis X-Ray imaging instruments.
Requires Python: >=3.9
Keywords: solar, physics, solar, sun, x-rays

XRAYVISION - X-RAY VIsibility Synthesis ImagiNg
===============================================

|Powered By| |Build Status| |Doc Status| |Python Versions|

.. |Powered By| image:: http://img.shields.io/badge/powered%20by-SunPy-orange.svg?style=flat
    :target: https://www.sunpy.org
    :alt: Powered by SunPy Badge

.. |Build Status| image:: https://github.com/TCDSolar/xrayvision/actions/workflows/ci.yml/badge.svg
    :target: https://github.com/TCDSolar/xrayvision/actions/workflows/ci.yaml
    :alt: Build Status

.. |Doc Status| image:: https://readthedocs.org/projects/xrayvision/badge/?version=stable
    :target: https://xrayvision.readthedocs.io/en/latest/?badge=stable
    :alt: Documentation Status

.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/xrayvisim
    :target: https://pypi.python.org/pypi/xrayvisim/
    :alt: Python Versions

XRAYVISION is an open-source Python library for Fourier or synthesis imaging of X-Rays. The most
common usage of this technique is radio interferometry however there have been a number of solar
X-ray missions which also use this technique but obtain the visibilities via a different method.

Installation
------------

It is strongly advised that you use and isolated environment through python's venv, virturalenv, anaconda or similar.


**The name 'xrayvision' was already taken on PyPi so the package name is 'xrayvisim' e.g.**

.. code-block::

    pip install xrayvisim

Usage
-----

See the `example gallery`_.

Getting Help
------------

See the `issue tracker`_.

Contributing
------------
When you are interacting with the community you are asked to
follow the `Code of Conduct`_.

.. _Code of Conduct: http://docs.sunpy.org/en/stable/coc.html
.. _example gallery: https://xrayvision.readthedocs.io/en/latest/generated/gallery/index.html
.. _issue tracker: https://github.com/TCDSolar/xrayvision/issues
mcp-music-analysis 0.1.41
\A MCP server with music tools\
Requires Python: >=3.11.11

# MCP Music Analysis

This repository contains a **Model Context Provider (MCP)** that uses MCP and [librosa](https://librosa.org/) for audio analysis on audio in local, youtube link, or audio link.

## Usage with Claude Desktop

<div style="display: flex; gap: 1rem;">
  <img src="public/screen.png" alt="alt text" width="40%">
  <img src="public/screen1.png" alt="alt text" width="40%">
</div>

## Installation

```bash
# Clone repository
git clone git@github.com:hugohow/mcp-music-analysis.git
cd mcp-music-analysis

# Create virtual environment and install
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .
```

### Usage with Claude Desktop

#### Locate Configuration File

The configuration file location depends on your operating system:

- **macOS**:
  ```
  ~/Library/Application\ Support/Claude/claude_desktop_config.json
  ```

- **Windows**:
  ```
  %APPDATA%\Claude\claude_desktop_config.json
  ```

- **Linux**:
  ```
  ~/.config/Claude/claude_desktop_config.json
  ```

Add the following to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "music-analysis": {
      "command": "uvx",
      "args": ["-n", "mcp-music-analysis"]
    }
  }
}
```

## Example Prompts

Here are some sample prompts you might use in a conversational or chat-based context once the server is running. The MCP will understand these requests and execute the relevant tools:

```
Can you analyze the beat of /Users/hugohow-choong/Desktop/sample-6s.mp3?
Could you give me the duration of https://download.samplelib.com/mp3/sample-15s.mp3 ?
Please compute the MFCC for this file: /path/to/another_audio.mp3
What are the spectral centroid values for /path/to/music.wav?
I'd like to know the onset times for https://www.youtube.com/watch?v=8HFiFd9vx1c
```

## To-Do List

- [x] Add URL to audio file download
- [x] Add YouTube to audio file transformation
- [ ] Experiment with multiple Python environments (testing)
- [ ] Improve installation guide
- [ ] Integrate Whisper for lyrics
- [ ] Implement a Docker solution

## Author

Hugo How-Choong
llamaplastics-llamasearch 0.1.0
LlamaPlastics - A LlamaBench project
Requires Python: >=3.8

# Project LlamaPlastics: LLM-Powered Materials Discovery Platform

## Overview

(TODO: Add project overview)

## Architecture

(TODO: Add architecture diagram/description)

## Setup

(TODO: Add setup instructions)

## Usage

### Running the Agent

\`\`\`bash
# Run the autonomous discovery agent
python main_agent.py --config config.yaml --output-dir agent_results --num-iterations 20

# Run the active learning loop
python main_active_learning.py --config config.yaml --output-dir results

# Run multi-objective optimization
python main_optimize.py --config config.yaml --output-dir optimization_results
\`\`\`

### Using the MLX Deployment

(TODO: Add MLX usage example)

## Repository Structure

\`\`\`
llamaplastics_project/
│
├── README.md
├── requirements.txt
├── config.yaml
│
├── data/
├── llm_interface/
├── composition_encoding/
├── property_predictor/
├── active_learning/
├── robotics_interface/
├── simulation_interface/
├── optimization/
├── mlx_deployment/
├── training/
├── evaluation/
├── agent/
│
├── main_active_learning.py
├── main_optimize.py
├── main_agent.py
│
└── notebooks/
\`\`\`

## Contributing

(TODO: Add contribution guidelines)

## License

(TODO: Add license information)
hf-ocr-llamasearchai 0.1.0rc625
LlamaSearch AI Package
Requires Python: >=3.8

# Hf Ocr

## Overview

Hf Ocr is a Python package developed by LlamaSearch AI that provides tools and utilities for AI and data processing tasks.

## Installation

You can install the package directly from PyPI:

```bash
pip install hf_ocr
```

## Quick Start

```python
import hf_ocr

# Example usage
result = hf_ocr.process_data(your_data)
```

## Features

- Feature 1: Description of feature 1
- Feature 2: Description of feature 2
- Feature 3: Description of feature 3

## Documentation

For more detailed documentation, see the [docs](./docs) directory.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
hf-ocr 0.1.0rc625
LlamaSearch AI Package
Requires Python: >=3.8

# Hf Ocr

## Overview

Hf Ocr is a Python package developed by LlamaSearch AI that provides tools and utilities for AI and data processing tasks.

## Installation

You can install the package directly from PyPI:

```bash
pip install hf_ocr
```

## Quick Start

```python
import hf_ocr

# Example usage
result = hf_ocr.process_data(your_data)
```

## Features

- Feature 1: Description of feature 1
- Feature 2: Description of feature 2
- Feature 3: Description of feature 3

## Documentation

For more detailed documentation, see the [docs](./docs) directory.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
hf-ocr-llamasearch 0.1.0rc625
LlamaSearch AI Package
Requires Python: >=3.8

# Hf Ocr

## Overview

Hf Ocr is a Python package developed by LlamaSearch AI that provides tools and utilities for AI and data processing tasks.

## Installation

You can install the package directly from PyPI:

```bash
pip install hf_ocr
```

## Quick Start

```python
import hf_ocr

# Example usage
result = hf_ocr.process_data(your_data)
```

## Features

- Feature 1: Description of feature 1
- Feature 2: Description of feature 2
- Feature 3: Description of feature 3

## Documentation

For more detailed documentation, see the [docs](./docs) directory.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.