semvec/pypi-packages huggingface dataset:
yuewen 0.0.38
阅文小说平台的方法封装

# yuewen

#### 介绍
阅文小说平台的方法封装，开放平台的方法封装已经转移到项目：https://pypi.org/project/lazynovel/ 中的open_yuewen

#### 软件架构
软件架构说明


#### 安装教程

1.  pip安装
```shell script
pip3 install yuewen
```
2.  pip安装（使用阿里镜像加速）
```shell script
pip3 install yuewen -i https://mirrors.aliyun.com/pypi/simple
```

#### 使用说明

1.  demo
```python
import yuewen
test_res = yuewen.get_menu(cookie='test')
```

2. 参数含义
```text
coop_id：合作方式代码
    1：微信分销
    9：陌香快应用（共享包）
    11：快应用（独立包）

```
evo-cli 0.1.8
Awesome evo_cli created by maycuatroi

# Evolution CLI (Develop by Dev And for Dev)

[![codecov](https://codecov.io/gh/maycuatroi/evo-cli/branch/main/graph/badge.svg?token=evo-cli_token_here)](https://codecov.io/gh/maycuatroi/evo-cli)
[![CI](https://github.com/maycuatroi/evo-cli/actions/workflows/main.yml/badge.svg)](https://github.com/maycuatroi/evo-cli/actions/workflows/main.yml)

Awesome evo_cli created by maycuatroi

## Install it from PyPI

```bash
pip install evo_cli
```

### Available Commands

#### SSH Setup

Set up SSH with key-based authentication:

```bash
evo setupssh
```

Options:
- `-H, --host` - SSH server hostname or IP address
- `-u, --user` - SSH username
- `-p, --password` - SSH password (not recommended, use interactive mode instead)
- `-i, --identity` - Path to existing identity file to use
- `--help-examples` - Show usage examples

#### Miniconda Installation

Install Miniconda with cross-platform support:

```bash
evo miniconda
```

Options:
- `-p, --prefix` - Installation directory (default: ~/miniconda3 or %USERPROFILE%\miniconda3)
- `-f, --force` - Force reinstallation even if Miniconda is already installed
- `--help-examples` - Show usage examples
grass-gis-helpers 2.3.5
This library provides functions which can be used for developing GRASS GIS addons.
Requires Python: >=3.7

# mundialis GRASS GIS helper library

This library provides functions which can be used for developing GRASS GIS addons and which are used by [mundialis](https://www.mundialis.de/).

The library usage needs to be in a GRASS GIS session, so that the the module `grass` can be imported.

## Installation

```bash
pip install grass-gis-helpers
```

## Small example

Small example how the library can be used inside a GRASS GIS session:

```python3
from grass_gis_helpers import general
general.set_nprocs(2)
```

## DEV setup

pip-tools is required for DEV setup:

```bash
# only once
pip3 install pip-tools
```

then install grass-gis-helpers from the local repository:

```bash
pip3 install -e .

```
blurhash-rs 1.0.0
High-performance BlurHash encoder/decoder
Requires Python: >=3.9
Keywords: blurhash, decoder, encoder, image, loading, placeholder, preview, progressive

# blurhash-rs

High-performance [BlurHash](https://blurha.sh) encoder/decoder for Python.

## Installation

```sh
pip install blurhash-rs
```

## Usage

```py
from PIL import Image
from blurhash_rs import blurhash_encode, blurhash_decode

with Image.open('image.jpg') as img:
  # Reduce resolution for faster encoding
  img.thumbnail((100, 100))

  blurhash = blurhash_encode(img, x_components=4, y_components=3)
  preview = blurhash_decode(blurhash, 32, 32)
  preview.save("preview.png")
```

## Benchmarks

### Run

```sh
benchmark.py --impl blurhash-rs -o blurhash-rs.json --rigorous
benchmark.py --impl blurhash-python -o blurhash-python.json --rigorous
benchmark.py --impl blurhash -o blurhash.json --rigorous
```

### Results

Linux x86_64, CPython 3.14.0:

#### blurhash-python vs blurhash-rs

| Benchmark            | blurhash-python-1.2.2 | blurhash-rs-1.0.0       |
|----------------------|:---------------------:|:-----------------------:|
| encode 32x32 (4x3)   | 486 us                | 5.57 us: 87.29x faster  |
| encode 64x64 (4x3)   | 1.91 ms               | 10.8 us: 177.73x faster |
| encode 128x128 (4x3) | 7.59 ms               | 30.6 us: 247.76x faster |
| decode 32x32 (4x3)   | 160 us                | 12.0 us: 13.31x faster  |
| decode 64x64 (4x3)   | 626 us                | 34.6 us: 18.08x faster  |
| decode 128x128 (4x3) | 2.45 ms               | 122 us: 20.08x faster   |
| Geometric mean       | (ref)                 | 51.46x faster           |

#### blurhash vs blurhash-rs

| Benchmark            | blurhash-1.1.5 | blurhash-rs-1.0.0        |
|----------------------|:--------------:|:------------------------:|
| encode 32x32 (4x3)   | 6.00 ms        | 5.57 us: 1078.28x faster |
| encode 64x64 (4x3)   | 24.2 ms        | 10.8 us: 2249.49x faster |
| encode 128x128 (4x3) | 96.4 ms        | 30.6 us: 3146.95x faster |
| decode 32x32 (4x3)   | 6.91 ms        | 12.0 us: 573.46x faster  |
| decode 64x64 (4x3)   | 27.6 ms        | 34.6 us: 797.99x faster  |
| decode 128x128 (4x3) | 112 ms         | 122 us: 912.88x faster   |
| Geometric mean       | (ref)          | 1213.21x faster          |
osm-shortlink 1.4.1
OpenStreetMap shortlink encoder and decoder
Requires Python: >=3.9
Keywords: decoder, encoder, openstreetmap, osm, shortlink, url

# osm-shortlink

[![PyPI - Python Version](https://shields.monicz.dev/pypi/pyversions/osm-shortlink)](https://pypi.org/project/osm-shortlink)

Fast and correct OpenStreetMap shortlink encoder and decoder implementation in Rust with Python bindings. Shortlinks allow you to represent a location on the map with a short code.

## Installation

```sh
pip install osm-shortlink
```

## Basic usage

```py
from osm_shortlink import shortlink_encode
shortlink_encode(0.054, 51.510, 9)  # -> '0EEQhq--'
shortlink_encode(19.579, 51.876, 19)  # -> '0OP4tR~rx'
shortlink_encode(0, 0, 23)  # ValueError: Invalid zoom: must be between 0 and 22, got 23

from osm_shortlink import shortlink_decode
shortlink_decode('0EEQhq--')  # -> (0.054, 51.510, 9)
shortlink_decode('0OP4tR~rx')  # -> (19.579, 51.876, 19)
shortlink_decode('X')  # ValueError: Invalid shortlink: too short
```

## Format specification

<https://wiki.openstreetmap.org/wiki/Shortlink>
autoops 0.1.1
AutoOps - AI DevOps Agent (Groq backend)
Requires Python: >=3.9


# AutoOps - AI DevOps Agent (Groq)

AutoOps is an AI DevOps Agent that monitors GitHub workflow events, analyzes CI failures, and proposes fixes.
This scaffold uses **Groq (Groq Cloud)** as the AI backend instead of OpenAI.

Quick start:
```bash
pip install -r requirements.txt
export GROQ_API_KEY=your_groq_api_key
export GROQ_MODEL=your_model_id   # e.g. mixtral-8x7b or llama2-70b-4096 depending on your Groq account
autoops setup
autoops run --port 8000
# in another terminal
autoops test --port 8000
```

Notes:
- The Groq Python SDK is used (`pip install groq`). See Groq docs: https://console.groq.com/docs/ and https://github.com/groq/groq-python for more details.
pan123 1.1.0
非官方的123云盘Python包

<div align="center">

# Pan123

![Python Version](https://img.shields.io/badge/Python-3.x-blue)
![GitHub License](https://img.shields.io/github/license/SodaCodeSave/Pan123?)
![GitHub Release](https://img.shields.io/github/v/release/SodaCodeSave/Pan123)
![PyPI - Version](https://img.shields.io/pypi/v/pan123)
![Stars](https://img.shields.io/github/stars/SodaCodeSave/Pan123?style=flat&label=Stars&color=yellow)
![Build](https://img.shields.io/github/actions/workflow/status/SodaCodeSave/Pan123/python-package.yml)

Pan123是123云盘开放平台的非官方Python封装库，用于在Python中与123云盘开放平台进行交互

</div>


## 安装

使用pip进行安装

```
pip install pan123
```

### 导入

```python
# 全量导入
from pan123 import Pan123
from pan123.auth import get_access_token
# 如果已经获取了access_token，则可以直接导入Pan123模块
from pan123 import Pan123
```

### 文档

**[Pan123 Github Wiki](https://github.com/SodaCodeSave/Pan123/wiki)**
dhengine 0.1.0
DhEngine: Lightweight Python game engine built on pygame
Requires Python: >=3.11

# DhEngine

DhEngine is a lightweight Python game engine built on top of pygame.

It provides:
- Window, Clock, and Event management
- Sprites, Groups, and Scenes
- Physics, Camera, UI, Audio, Assets
- ECS, Tilemaps, Animations, and Effects

Created by Ayaan Dhalait.
particleEX 0.0.1
PDG-powered particle data tool with multi-name variant support
Requires Python: >=3.12

# particleEX

A lightweight, PDG-powered particle data tool with multi-variant name support and real-time data synchronization.

## Overview
particleEX simplifies particle property retrieval by unifying local fast lookup and PDG's authoritative API. It natively supports multiple particle name variants (LaTeX, EvtGen, Unicode, etc.) and automatically fetches updated data from PDG, making particle physics data access intuitive and efficient.

## Installation
```bash
pip install particleEX
```

## Quick Start
```python
from particleEX import Particle

# 1. Query by name (supports multiple variants)
p = Particle(name="π⁺")  # Or "pi+", "\\pi^+", "pi_plus", "π<sup>+</sup>"
print(f"Mass: {p.mass} ± {p.mass_err} MeV/c²")
print(f"Lifetime: {p.lifetime} s")
print(f"Quantum J/P: {p.quantum_J}/{p.quantum_P}")

# 2. Query by PDG MCID
p_proton = Particle(mcid=2212)
print(f"Name: {p_proton.name}, Charge: {p_proton.charge}e")

# 3. Access decay properties
print("Top 2 branching fractions:", p.branching_fractions[:2])
```

## Core Features
- **Multi-Variant Name Compatibility**: Seamlessly match particles by standard name, LaTeX, EvtGen, HTML, Unicode, or custom aliases.
- **Comprehensive Particle Properties**: Retrieve mass, lifetime, charge, branching fractions, quantum numbers (J/P/C/I/G), and particle type flags (baryon/meson/lepton/etc.).
- **PDG Data Synchronization**: Automatically supplements local data with up-to-date PDG API results, with built-in caching to avoid redundant requests.
- **Dual Query Modes**: Look up particles by name (flexible) or PDG MCID (precise).
- **Robust Fallback**: Retains local data access even if PDG API is unavailable.

## Key Properties
| Category | Properties |
|----------|------------|
| Basic Info | `name`, `mcid`, `programmatic_name`, `latex_name`, `evtgen_name` |
| Dynamic Data | `mass`/`mass_err`, `lifetime`/`lifetime_err`, `width`/`width_err` |
| Decay Data | `branching_fractions`, `inclusive_branching_fractions`, `exclusive_branching_fractions` |
| Classification | `charge`, `is_baryon`, `is_meson`, `is_lepton`, `is_boson`, `is_quark` |
| Quantum Numbers | `quantum_J`, `quantum_P`, `quantum_C`, `quantum_I`, `quantum_G` |

## Dependencies
- `pdg` (PDG official API client)
- `particle` (particle name variant support)
- Python 3.8+

## Contributors
- [zhangbolun](zhangbolun@ihep.ac.cn)
- [liaoyipu](liaoyp@ihep.ac.cn)

## License
MIT License
avanak-api 0.1.0
Python client library for Avanak voice message REST API
Requires Python: >=3.8

# Avanak API

Python client library for Avanak voice message REST API.

## Installation

```bash
pip install avanak-api
```

## Usage

```python
from avanak_api import AvanakClient

client = AvanakClient(token="your_token")

# Get account status
status = client.account_status()
print(status.account_name)

# Send OTP
response = client.send_otp(length=4, number="09120000000")
print(response.generated_code)
```

## Development

This project uses uv for dependency management, ruff for linting and formatting.

```bash
uv sync
uv run ruff check .
uv run ruff format .
```