semvec/pypi-packages huggingface dataset:
mysql-connection-helper 0.2.0
A lightweight MySQL connection helper using mysql-connector-python
Requires Python: >=3.7

# mysql-helper

A lightweight Python utility to create and manage MySQL database connections using
`mysql-connector-python`.

---

## ✨ Features

- Simple MySQL connection helper
- Built-in error handling
- PyPI & CI friendly
- Easy to test with `pytest`
- Minimal and clean API

---

## 📦 Installation

```bash
pip install mysql-connection-helper
eden-simple-knn 0.1.0a0
Simple KNN CUDA implementation for 3D Gaussian Splatting
Requires Python: >=3.7

simple-knn
---

> ⚠️ LICENSE WARNING: This project is a derivative of simple-knn and is licensed under the GRAPHDECO research group.


Description: It compute the **average distance to the nearest neighbors** for a set of 3D points.

Install:
```bash
pip install git+https://github.com/camenduru/simple-knn
```

Usage:
```python
from simple_knn import distCUDA2

# shape: [N, 3]
demopc

# shape: [N]
mean_distances = distCUDA2(demopc)
```
moncef-color-utils-demo 0.0.8
A simple utility package for handling colors by Moncef
Requires Python: >=3.7

# Moncef's Color Utils

This is a demo package created for Moncef to handle color conversions and generation.

## Installation

```bash
pip install moncef-color-utils-demo
```

## Usage

```python
from moncef_colors import get_random_color, rgb_to_hex, moncef_signature

print(moncef_signature())
print(f"Random Color: {get_random_color()}")
print(f"RGB(255, 0, 0) to Hex: {rgb_to_hex(255, 0, 0)}")
```
chekml 0.1.18
A versatile library for data manipulation
Requires Python: >=3.7

 ## Chek's Math Logic (chekml)
 ---
 ## Features
 - Preprocessing
   - Data crawler using LLM through ollama
   - Featurization (maximizing importance of information)
 - Model Construction
   - Machine learning
   - Custom layer
 - Optimization
   - Tuning
 
 ---
 ## Future Direction
 - exploring binary neural network
 - viewing model structure in detail

 ---
 feel free to point out any potential bug, great appreciation for any contribution
easylcsc4kicad 0.1.2
Search LCSC and EasyEDA for schematics and footprints
Requires Python: >=3.10

# EasyLCSC4KiCad

A CLI tool to search for components on LCSC/EasyEDA and download footprints, symbols, and 3D models directly for KiCad.

## Installation

```bash
pip install easylcsc4kicad
```

Or using uv:

```bash
uv pip install easylcsc4kicad
```

## Usage

### Search

Search for components by keyword.

```bash
# Basic search
easylcsc4kicad search "ESP32-S3"

# Search with pagination
easylcsc4kicad search "STM32F103" --page 1 --page-size 10
```

### Download

Download component footprint, symbol, and 3D models using the LCSC ID.

```bash
# Download a specific component
easylcsc4kicad download C1337258

# Download to a specific directory (default: easylcsc_lib)
easylcsc4kicad download C1337258 -o ./my_libs

# Download multiple components
easylcsc4kicad download C1337258 C2980300

# Download without generating footprint or symbol
easylcsc4kicad download C1337258 --no-footprint --no-symbol
```

## Generated Files

The tool generates KiCad files in the specified output directory:

```
output_dir/
├── footprint/
│   ├── component_name.kicad_mod
│   └── packages3d/
│       └── component_name.step
└── symbol/
    └── component_name.kicad_sym
```
canvas-instructor-tools 0.1.0a0
A collection of tools for automating Canvas LMS instructor operations.
Requires Python: >=3.11

# Canvas Tools

A Python package for automating common instructor operations on the Canvas LMS.

## Features

*   **Download Submissions**: Bulk download all file submissions for a specific assignment, automatically renaming them with the student's name.

## Installation

```bash
pip install canvas-tools
```

## Configuration

Create a `.env` file in your working directory with your Canvas credentials:

```ini
CANVAS_API_URL=https://your.institution.instructure.com
CANVAS_API_KEY=your_api_key_here
```

## Usage

### Command Line Interface

To download submissions for a specific assignment:

```bash
# Syntax: canvas-tools download <course_id> <assignment_id>
canvas-tools download 12345 67890
```

Optional arguments:
*   `--output` or `-o`: Specify the output directory (default: current directory)

### Python API

```python
from canvas_tools import download_assignment_submissions

download_assignment_submissions(
    course_id=12345, 
    assignment_id=67890, 
    output_dir="my_downloads"
)
```

## Development

1.  Clone the repository.
2.  Install dependencies: `pip install -e .`
3.  Run tests: `python -m unittest discover tests`

## CI/CD & Publishing

This project uses GitHub Actions for automated testing and publishing.

### Workflow Overview

*   **CI (`.github/workflows/ci.yml`)**: Runs on every Pull Request and push to `main`.
    *   Tests across Python 3.11, 3.12, and 3.13.
    *   Verifies the package builds successfully.
*   **Publish (`.github/workflows/publish.yml`)**: Runs when a GitHub Release is published.
    *   Builds the package.
    *   Publishes to **TestPyPI** and **PyPI** using Trusted Publishing (OIDC).

### How to Publish a New Version

1.  **Update Version**:
    *   Edit `pyproject.toml` and increment the `version` (e.g., `0.0.1` -> `0.0.2`).
    *   Commit and push to `main`.

2.  **Create Release**:
    *   Go to the GitHub repository page.
    *   Click **Releases** > **Draft a new release**.
    *   **Tag version**: `v0.0.2` (matching your `pyproject.toml`).
    *   **Title**: `v0.0.2`.
    *   Click **Publish release**.

3.  **Verify**:
    *   Check the **Actions** tab to see the `Publish to PyPI` workflow running.
    *   Once green, verify the new version is available on [PyPI](https://pypi.org/project/canvas-tools/).