semvec/pypi-packages huggingface dataset:
m4d-engine 1.2.1
A premium Native & Web GUI framework for Windows.
Requires Python: >=3.7

# 🚀 M4D Engine (Material 4 Design)

**M4D Engine**은 Windows 사용자를 위한 프리미엄 GUI 및 웹 개발 통합 프레임워크입니다. 
Win32 네이티브 드로잉부터 최신 Chromium 기반 웹 기술까지, 파이썬 코드 한 줄로 전문가급 UI를 구축할 수 있습니다.

---

## 📦 설치 (Installation)

```bash
pip install m4d-engine
```

---

## 🌟 주요 기능 및 사용법 (Key Features)

### 1. Zero-Init 자동 웹 개발 환경 (`m4d.App`)
터미널 명령어 없이 파이썬 실행만으로 React + Vite + Chromium 개발 환경을 즉시 구축합니다.

```python
import m4d

# 앱 이름만 지정하면 자동으로 프로젝트 생성 및 환경 설정이 완료됩니다.
app = m4d.App("my_premium_project")

# 서버 실행 및 브라우저 오픈까지 한 번에!
app.run()
```

### 2. 파이썬 전용 웹사이트 빌더 (`m4d.WebSite`)
프론트엔드 지식 없이 파이썬 객체만으로 고해상도 다크 테마 웹사이트를 제작합니다.

```python
import m4d

site = m4d.WebSite("M4D 공식 가이드")

# 섹션 추가
site.add_hero(title="M4D Web Engine", subtitle="파이썬으로 만드는 프리미엄 웹")
site.add_features([
    ("⚡", "초고속", "최적화된 렌더링"),
    ("🎨", "디자인", "옵시디언 다크 테마")
])

# 실시간 미리보기 실행
site.run(port=8080)
```

### 3. 네이티브 Win32 GUI 엔진 (`m4d.M4DEngine`)
외부 라이브러리 없이 Windows 시스템 API를 직접 호출하여 가볍고 빠른 데스크톱 앱을 만듭니다.

```python
import m4d

app = m4d.M4DEngine("Native App", 800, 600)
app.widgets.append(m4d.widgets.M4DButton(300, 250, 200, 50, "M4D Button"))
app.run()
```

---

## 🛠️ 고급 활용 (CLI)

자동화 기능 외에 수동으로 프로젝트를 생성하고 싶을 때 사용합니다.
```bash
m4d-init [프로젝트명]
```

---

## 💎 디자인 철학
M4D는 **Liquid Glass**와 **Obsidian Dark** 테마를 지향합니다. 사용자에게 "와우"를 선사하는 고해상도 그래픽과 매끄러운 애니메이션을 기본으로 제공합니다.

---

## 📝 라이선스
MIT License. 자유롭게 수정 및 배포가 가능합니다.

---
**M4D Engine** - *Material for Design, Logic for Performance.*
| [PyPI](https://pypi.org/project/m4d-engine/) | [Github](https://github.com/m4d/m4d-engine) |
pyERGM 0.2.9
Exponential Random Graph Models in Python
Requires Python: >=3.11
Keywords: ERGM, network, graph, statistics

# pyERGM - A Python implementation for ERGM's

An exponential random graphs model (**ERGM**) is a statistical model that describes a distribution of random graphs. This package provides a simple and easy way to fit and sample from ERGMs.

An ERGM defines a random variable $\mathbf{Y}$, which is simply a random graph on $n$ nodes. The probability of observing a specific graph $y\in \lbrace 0,1 \rbrace ^{n \times n}$ is given by -

$$\Pr(\mathbf{Y}=y | \theta) = \frac{\exp(\theta^Tg(y))}{\sum_{z\in\mathcal{Y}} \exp(\theta^Tg(z))}$$

where $g(y)$ is a vector of statistics that describe the graph $y$, and $\theta \in \mathbb{R}^q$ is a vector of model parameters. Each graph is represented by a binary adjacency matrix, where $y_{ij}=1$ if there is an edge between nodes $i$ and $j$ (and $y_{ji}=1$ in the undirected case).



Fitting a model for even moderately large graphs can be a computationally challenging task. pyERGM keeps this in mind and is implemented to be efficient and scalable by using `numpy` and `Numba`, as well as providing an interface for fitting models on a distributed computing environment.

[View the full documentation here](https://tomtalp.github.io/pyERGM/)


## Installation
https://pypi.org/project/pyERGM/


## Getting started
Fitting an ERGM model requires a graph and a set of statistics that describe the graph. The model is then fit by maximizing the likelihood of the observed graph under the model. 

The following example demonstrates how to fit a simple ERGM model from [Sampson's monastery data](https://networkdata.ics.uci.edu/netdata/html/sampson.html).

```python
from pyERGM.ergm import ERGM
from pyERGM.metrics import *
from pyERGM.datasets import sampson_matrix


num_nodes = sampson_matrix.shape[0]
is_directed = True
metrics = [NumberOfEdgesDirected(), TotalReciprocity()]

model = ERGM(num_nodes, metrics, is_directed=is_directed)
model.fit(sampson_matrix)
```

The above example fits a model from the Sampson's monastery data using the number of edges and total reciprocity as statistics. The graph is represented as an adjacency matrix.
captcha-router 0.1.3
Reusable captcha router with pluggable solver providers.
Requires Python: >=3.10

# captcha-router

Reusable captcha router with pluggable providers.

## Install

```bash
pip install captcha-router
```

```bash
uv add captcha-router
```

## Supported providers

- `anticaptcha` (alias: `anti-captcha`)
- `2captcha` (alias: `twocaptcha`)

## Router methods

- `CaptchaRouter.list_providers()`: returns all registered provider names and aliases.
- `CaptchaRouter.list_provider_capabilities(include_aliases=False)`: returns provider -> supported captcha kinds.
- `CaptchaRouter.get_supported_task_kinds(provider_name)`: returns supported captcha kinds for one provider.
- `CaptchaRouter.list_supported_task_kinds()`: returns union of captcha kinds across providers.
- `CaptchaRouter.get_solver(provider_name=None, api_key=None, **kwargs)`: creates a solver instance and uses env fallbacks when `api_key` is omitted.

## Usage

### 1) Create a solver
```python
from captcha_router import CaptchaRouter

solver = CaptchaRouter.get_solver(provider_name="anticaptcha", api_key="YOUR_KEY")
```

### 2) Solve a task
```python
import asyncio
from captcha_router import CaptchaRouter, CaptchaTaskSpec


async def main():
    solver = CaptchaRouter.get_solver(provider_name="anticaptcha", api_key="YOUR_KEY")
    try:
        result = await solver.solve_task(
            CaptchaTaskSpec(
                kind="recaptcha_v2",
                params={
                    "website_url": "https://example.com",
                    "site_key": "SITE_KEY_HERE",
                },
            )
        )
        print(result.status, result.token, result.error)
    finally:
        await solver.close()


asyncio.run(main())
```

### 3) Inspect capabilities
```python
from captcha_router import CaptchaRouter

print(CaptchaRouter.list_providers())
print(CaptchaRouter.list_provider_capabilities())
```