semvec/pypi-packages huggingface dataset:
metpysdk 0.2.1
METEORA DLMM SDK
License: MIT
Requires Python: <4.0,>=3.9

# metpysdk

**metpysdk** is a Python SDK for interacting with **Meteora DLMM (Dynamic Liquidity Market Maker)** on Solana.

It provides high-level, async-friendly primitives for:
- querying DLMM pairs
- inspecting bins and liquidity
- reading positions and rewards
- parsing on-chain accounts safely and accurately

The SDK is designed as a **library**, not a script:
- clean public API
- explicit versioning
- byte-accurate account parsing
- minimal, modern dependency set

> ⚠️ This project is in active development (`0.x`).  
> APIs may change until `1.0.0`.

---

## Features

- 🚀 Async-first API (`asyncio`)
- 🔍 Accurate parsing of DLMM on-chain accounts
- 🧱 Construct-based binary layouts (no abandoned deps)
- 🧠 Clear separation between public API and internals
- 🛠️ Designed for bots, analytics, and backend services

---

## Installation

### Requirements
- Python **3.9+**
- Poetry (recommended) or pip

### With Poetry (recommended)

```bash
poetry add metpysdk
```
### With pip
```bash
pip install metpysdk
```
### Quick Example
```python
import asyncio

from metpysdk import DLMMClient
from solders.pubkey import Pubkey


async def main():
    client = DLMMClient()

    lb_pair = Pubkey.from_string(
        "INSERT_LB_PAIR_ADDRESS_HERE"
    )

    active_bin = await client.get_active_bin(lb_pair)

    print("Active bin ID:", active_bin.bin_id)
    print("Price:", active_bin.price)


asyncio.run(main())
```
### Project Structure
```text
metpysdk/
├── accounts/     # Parsed on-chain account models
├── dlmm/         # Core DLMM client logic
├── helpers/      # High-level helpers and combinators
├── layouts/      # Binary layouts (construct-based)
├── utils/        # RPC, filters, derivations, constants
└── __init__.py   # Public API surface
```
Only symbols re-exported from package-level modules are considered public API.\
Everything else may change without notice until 1.0.0.

### Public API
The main entry point is:

```python
from metpysdk import DLMMClient
```
Additional data models and enums are exposed via:

```python

from metpysdk.accounts import (
    Position,
    ActiveBin,
    StrategyParameters,
)
```
Deep imports from internal modules are not supported.

### Development
Setup
```bash
git clone https://github.com/yourname/metpysdk.git
cd metpysdk
poetry install
```
Poetry will create a local .venv/ automatically.

### Linting

```
poetry run ruff check src/metpysdk
poetry run pylint src/metpysdk
```
### Tests
```
poetry run pytest
```
### Versioning
This project follows semantic versioning:
```
0.x.y — unstable, breaking changes allowed
1.0.0 — first stable API
>=1.0.0 — no breaking changes without major bump
```
### License
MIT License © 2025 Ivan Bezborodov

See LICENSE for details.

### Disclaimer
This SDK is provided as-is, without warranties.\
Interacting with on-chain programs involves financial risk.\
Always validate results independently before using in production.
---
gyoza 0.3.0
A library for invertible interpretable neural networks.
License: MIT
Requires Python: >=3.12

![Image](https://github.com/TimHenry1995/gyoza/blob/develop/docs/images/logo.png?raw=true)
## Installation
```
pip install gyoza
```

## Documentation
Detailed documentation can be found on the companion website of [read-the-docs](https://gyoza.readthedocs.io/en/latest/)

## Tutorial
A tutorial can be found in the [tutorials](https://github.com/TimHenry1995/gyoza/tree/main/tutorials) folder of the Github repository.

## Experiments
Experiments can be found in the [experiments](https://github.com/TimHenry1995/gyoza/tree/main/experiments) folder of the Github repository.

## Development
Developers can access the package via [GitHub](https://github.com/TimHenry1995/gyoza)
ecodev-core 0.0.81
Low level sqlmodel/fastapi/pydantic building blocks
License: MIT
Requires Python: <4.0,>=3.11

# ecodev-core

<p align="center">
<a href="https://github.com/FR-PAR-ECOACT/ecodev-core/actions" target="_blank">
    <img src="https://github.com/FR-PAR-ECOACT/ecodev-core/blob/main/badges/coverage.svg" alt="Coverage">
</a>
<a href="https://github.com/FR-PAR-ECOACT/ecodev-core/actions" target="_blank">
    <img src="https://github.com/FR-PAR-ECOACT/ecodev-core/blob/main/badges/pylint.svg" alt="Publish">
</a>
<a href="https://github.com/FR-PAR-ECOACT/ecodev-core/actions/workflows/code-quality.yml/badge.svg" target="_blank">
    <img src="https://github.com/FR-PAR-ECOACT/ecodev-core/actions/workflows/code-quality.yml/badge.svg" alt="Package version">
</a>
</p>

Low level ecoact generic code. Aimed at being published in open source with poetry

## Installation of this package

You are strongly encouraged to install this package via Docker.

Starting from a project with a Docker file:
* add the module ecodev-core in the `requirements.txt` file
* make sure the `.env` file includes all required fields (see `BaseSettings` and `AuthenticationConfiguration`)
* build the new version of the Docker container (typically `docker build --tag xxx .`)
* run it with docker compose (`dc up -d`).

## Documentation

Please find it in the [associated mkdoc website!](https://ecodev-doc.lcabox.com/)
qwen3-tts-app 1.0.2
Client library and utilities for Qwen3 TTS text-to-speech API - convert text to natural sounding audio with voice cloning and emotion control
License: MIT
Requires Python: >=3.8
Keywords: tts, text-to-speech, qwen3, audio, speech, synthesis, voice-cloning, emotion, ai

# Qwen3TTS Python Client

Python client library for [Qwen3 TTS](https://qwen3-tts.app/) - Convert text to natural sounding audio with voice cloning and emotion control.

## Installation

```bash
pip install qwen3-tts-app
```

## Features

- Convert text to natural sounding audio in multiple languages
- Voice cloning and custom voice synthesis
- Emotion and tone control (happy, sad, excited, neutral)
- Real-time streaming synthesis
- Voice characteristic adjustment (speed, pitch, volume)

## Quick Start

```python
from qwen3tts import AudioArticulator, EmotionProfile

tts = AudioArticulator(api_key="your-api-key")

# Basic text-to-speech
audio = tts.render_speech("Hello from Qwen3 TTS!", voice_id="default")
audio.save_to_file("output.mp3")

# With emotion
audio = tts.render_speech(
    "This is exciting!",
    emotion=EmotionProfile.EXCITED,
    speed=1.2
)
audio.save_to_file("excited.mp3")
```

## Links

- [Service Website](https://qwen3-tts.app/)
- [Repository](https://github.com/wddaily/qwen3-tts)
- [PyPI Package](https://pypi.org/project/qwen3-tts-app/)

## License

MIT License
mongo-schematic 0.1.4.4
MongoSchematic CLI for MongoDB schema analysis, drift detection, and migrations
License: MIT
Requires Python: >=3.10

# MongoSchematic CLI

**MongoSchematic** is a modern, developer-first tool for MongoDB schema management. It helps you infer schemas from existing data, detect drift, generate code, and handle migrations with confidence.

![License](https://img.shields.io/badge/license-MIT-blue.svg)
![Python](https://img.shields.io/badge/python-3.8+-blue.svg)
![MongoDB](https://img.shields.io/badge/mongodb-4.4+-green.svg)

## Features

- **Schema Inference**: Automatically infer schemas from your MongoDB collections.
- **Full BSON Support**: All MongoDB types including `binData`, `regex`, `long`, `timestamp`, and union types.
- **Drift Detection**: Detect when your data deviates from your schema/models.
- **Code Generation**: Generate Pydantic models (Python) and TypeScript interfaces.
- **Documentation**: Build static HTML documentation for your database.
- **Data Seeding**: Populate your database with realistic dummy data.
- **Migrations**: Generate and execute versioned migration scripts.
- **Validation**: Validate documents against your defined schemas.
- **AI-Powered**: Optional Gemini AI integration for schema recommendations.

## Installation

```bash
pip install mongo-schematic
```

## Quick Start

### 1. Initialize

Generate a default configuration file:

```bash
mschema init
```

### 2. Analyze a Collection

Infer the schema from an existing collection and save it:

```bash
mschema analyze --collection users --save schemas/users.v1.yml
```

### 3. Generate Code

Create a Pydantic model for your application:

```bash
mschema generate models --schema schemas/users.v1.yml --type pydantic --out models/user.py
```

### 4. Detect Drift

Check if your live data matches your schema:

```bash
mschema drift detect --schema schemas/users.v1.yml --collection users
```

## Documentation

For full usage details including database-wide commands, CI/CD integration, and migration workflows, see the [Usage Guide](docs/USAGE.md).

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
aicommitter 1.0.6
AI powered commit message helper
License: MIT
Requires Python: >=3.7.9
Keywords: git, commit, ai, devops, cli, commit message

![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)
![MIT License](https://img.shields.io/badge/License-MIT-yellow.svg)
![Maintained](https://img.shields.io/badge/Maintained%3F-yes-green.svg)
![GitHub last commit](https://img.shields.io/github/last-commit/ifaakash/ai_commit)
![Python](https://img.shields.io/badge/python-3.8+-blue.svg)
![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)
![Latest Release](https://img.shields.io/badge/Release-1.0.4-orange)
![PyPi](https://img.shields.io/pypi/v/aicommitter)
<!--[![Latest Release](https://img.shields.io/badge/Latest-Release-blue?style=for-the-badge)](https://libraries.io/pypi/aicommitter)-->

### One-Time Setup

1. **Obtain your API Key**  
   Register and get an API key from the DeepSeek AI developer dashboard<br>
   - Get DeepSeek API key from [Deepseek Dashboard](https://platform.deepseek.com/api_keys)
   - Get Gemini API key from [Gemini Dashboard](https://aistudio.google.com/api-keys)

2. **Set the Environment Variable**  
   Set your key as the DEEPSEEK_API_KEY environment variable<br>
   ```bash
   export DEEPSEEK_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxx"
   ```

3. **Install the Git Hook in your repository**  
   Navigate to the root of any Git project and run the install command<br>
   ```bash
   aicommitter install
   ```

### Daily Usage
For every commit after setup:

4. **Stage your changes**  
   Add all or selected changes to the staging area<br>
   ```bash
   git add .
   ```

5. **Commit!**  
   Commit directly with confirmation<br>
   ```bash
   aicommitter generate --commit
   ```

## Changelog

See [CHANGELOG.md](CHANGELOG.md) for a [detailed history](https://libraries.io/pypi/aicommitter) of changes

## Latest Release

**Version 1.0.6** (2026-01-25)
- Updated the version of aicommitter to `1.0.6`
- Fixed the issue of `NotOpenSSLWarning` warning

**Version 1.0.5** (2026-01-25)
- Updated the version of aicommitter to `1.0.5`
- Fixed the issue of `docs.md` file not being found
- Fixed the timeout issue
- Swtiched to `deepseek-chat` model from `deepseek-reasoner` model

**Version 1.0.4** (2025-12-07)
- Updated the version of aicommitter to `1.0.4`
- Added support for `long_description` in pypi library

**Version 1.0.3** (2025-12-05)
- Updated the version of aicommitter to `1.0.3`
- Refactored exception handling
- Increased the session timeout to `180s` for `DEEPSEEK` and `GEMINI`

For full details, see the [CHANGELOG](CHANGELOG.md).
navytraceback 0.2.0
A XP-style traceback handler
License: MIT
Requires Python: >=3.7

# NavyTraceback

<div align="center">
  <img src="https://raw.githubusercontent.com/YOUR_USERNAME/NavyTraceback/main/assets/full_logo.png" alt="NavyTraceback Banner" width="600" />
</div>

Python tracebacks are usually a mess of white text on a black background. **NavyTraceback** replaces them with a Windows XP-style Blue Screen of Death (BSOD) and automatically handles crash dumps so you don't lose your debug data.

## Why?
Standard stack traces are boring. This library makes crashes readable and gives you a `.txt` dump file on your desktop immediately which helps debugging way more than people might expect.

## Safe Mode

NavyTraceback includes a safe mode feature that won't clutter your terminal screen entirely, this
feature helps you look at your print statements you set-up for further debugging.

To enable this feature, type in the snippet below:

```python
import navytraceback

# Initiates safe mode upon starting a script
navytraceback.safe_mode(True)
```

## Installation

To install this library, type in the command below:

```bash
pip install navytraceback