semvec/pypi-packages huggingface dataset:
t-linter 0.1.0
Intelligent syntax highlighting and validation for Python template strings (PEP 750)
License: MIT
Requires Python: >=3.9
Keywords: linter, template-strings, pep750, syntax-highlighting, python

# t-linter 🐍✨

Intelligent syntax highlighting and validation for Python template strings (PEP 750).

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## Features

- 🎨 **Smart Syntax Highlighting** - Detects embedded languages in `t"..."` strings
- 🔍 **Type-based Detection** - Understands `Annotated[Template, "html"]` annotations
- 🚀 **Fast** - Built with Rust and Tree-sitter for optimal performance
- 🔧 **Extensible** - Support for HTML, SQL, JavaScript, CSS, and more

## Installation

Install using pip:

```bash
pip install t-linter
```

## Usage

Run the language server:

```bash
t-linter lsp
```

Check files:

```bash
t-linter check file.py
```

## Development

For development, you can also build from source:

```bash
git clone https://github.com/koxudaxi/t-linter
cd t-linter
cargo install --path crates/t-linter
```
libmogra 0.3.4
"A Python Toolbox for Indian (Classical) Music"
License: MIT
Requires Python: <4.0,>=3.11
Keywords: music, indian classical music, raga, raag, taal, swar, indian music, classical music, hindustani classical music

# libmogra

A Python Toolbox for Indian (Classical) Music

## The Why

For playing around (understanding, modifying, etc.) with music & audio specifically in the Indian context. Music theory standardization as well as tool development has been done extensively for western music, but other music cultures lag behind. This library will start with small features, but the dream is to have a Photoshop for Indian music.

## The What

This is intended to be a higher-level layer on top of the widely used [librosa](https://github.com/librosa/librosa).
If you must have an acronym, here it is: a LIBrary for the Manipulation, Organization, Generation, and Raag-aware Analysis of music. If that's too much, think of [mogra](https://en.wikipedia.org/wiki/Jasminum_sambac) the flower `:)`

# mogra CLI

```
pip install libmogra
```
will also install the `mogra` command-line interface.


To find info about a raag
```
mogra info bairagi
```
To visualize its tonnetz diagram (if available)
```
mogra info bairagi --tonnetz=window
```
To look at just the Tonnetz diagram by itself
```
mogra info all --tonnetz=window
```

To search for a raag given its notes among SrRgGmMPdDnN (note: follow the convention m = shuddha and M = teevra)
```
mogra search SrmPn
```
lm-raindrop-integrations 0.1.6
Python SDK for integrating LiquidMetal's Raindrop API with popular frameworks and tools
License: MIT
Requires Python: >=3.8

# LiquidMetal Raindrop Integrations

Python SDK for integrating LiquidMetal's Raindrop API with popular frameworks and tools.

## Installation

Install the base package:

```shell
pip install lm-raindrop-integrations
```

## Supported Integrations

Raindrop SmartBuckets currently supports the following integrations:

### LangChain Retriever

Example usage with LangChain:

```python
from lm_raindrop_integrations import LangchainSmartBucketRetriever
import os

# Initialize the retriever
retriever = LangchainSmartBucketRetriever(
    api_key="your-api-key",  # or use RAINDROP_API_KEY env var
    bucket_name="my-bucket"
)

# Search for documents
results = retriever.invoke("What is machine learning?")

# Process results
for doc in results:
    print(f"Content: {doc.page_content}")
    print(f"Score: {doc.metadata['score']}")
    print(f"Source: {doc.metadata['source']}")
    print("---")
```