semvec/pypi-packages huggingface dataset:
edfh-data 0.14.1
Python library for the E:D Faction Hub application backend data services
Requires Python: >=3.13

# edfh-data

Python library for the E:D Faction Hub application backend data services:

- EDDN listener service, receives messages from the EDDN stream and publishes them to a RabbitMQ exchange.
- EDDN handler service, polls message from a RabbitMQ queue, parses them and stores the relevant info in a relational database.

## Requirements

- Python 3.13 available in the sytem path.
- Running RabbitMQ and MariaDB instances (the included `docker-compose.yaml` file can set them up for development).

## Configuration

The following configuration variables are defined are defined as environment variables, or, for local development, specified in a `.env` configuration file:

```ini
DB_USER=dbuser
DB_PASSWD=<db_password>
DB_HOST=localhost
DB_PORT=3306
DB_NAME=edfh
RMQ_USER=rmquser
RMQ_PASSWD=<rabbitmq_password>
RMQ_HOST=localhost
RMQ_HANDLER_PREFETCH=10
```

## Usage

Install the package with the `eddn` extra:

```
python -m pip install edfh-data[eddn]
```

Launch the EDDN message handler service with:

```
eddn-handle
```

Launch the EDDN listener service with:

```
eddn-listen
```

## Development

### Python environment

Create a virtual environment using Python 3.13:

```
python3.13 -m venv .venv --prompt edfh-data --upgrade-deps
source .venv/bin/activate
```

Install the package in development mode along with the development dependencies:

```
python -m pip install -e .[dev]
```

Install pre-commit hooks:

```
pre-commit install
```

### Database service

Migrate the database with:

```
alembic upgrade head
```

Generate a new revision:

```
alembic revision --autogenerate -m "Revision message"
```
lindenmayer-fractals 1.1.1
Biblioteca para generar fractales con L-systems
Requires Python: >=3.8

# LINDENMAYER

**Biblioteca para generar fractales con L-systems y tortuga gráfica en Python.**  
Licencia: GNU GPLv3  
🌿 **VIVA PALESTINA LIBRE**

LINDERMAYER permite crear y visualizar **fractales clásicos y personalizados** usando sistemas de Lindenmayer (L-systems) y una tortuga gráfica basada en `matplotlib`.

Incluye **fractales predefinidos** listos para usar:

- `arboleo` → árbol fractal simple  
- `snowflake` → curva de Koch (copo de nieve)  
- `dragon` → curva del dragón  
- `koch` → curva de Koch básica

---

## Instalación

Desde Test PyPI:

```bash
pip install --index-url https://test.pypi.org/simple/ LINDERMAYER
Instalación local editable:


## Fractales predefinidos
from lindermayer import arboleo, snowflake, dragon, koch

# Curva de Koch básica
koch.plt(n=6)

# Árbol fractal simple
arboleo.plot(n=3)

# Curva del Dragón
dragon.plot(n=12)

# Copo de nieve
snowflake.plot(n=6)

# Función generar_fractal
generar_fractal

La función generar_fractal permite construir y dibujar fractales definidos mediante L-systems (Lindenmayer systems) usando una tortuga gráfica.

Definición
generar_fractal(V, P, omega, angle, step, n)

Descripción

Esta función:

Construye un sistema de Lindenmayer (L-system)

Aplica las reglas de producción n veces

Interpreta la cadena resultante con una tortuga gráfica

Muestra el fractal usando matplotlib

Parámetros
V — Alfabeto del sistema

Conjunto de símbolos válidos del L-system.

V = {'F', '+', '-'}


No todos los símbolos necesariamente dibujan; algunos solo controlan el comportamiento.

P — Reglas de producción

Diccionario que define cómo se reescribe cada símbolo.

P = {
    'F': 'F+F--F+F'
}


Cada iteración sustituye los símbolos según estas reglas.

omega — Axioma (estado inicial)

Cadena inicial del sistema antes de aplicar las reglas.

omega = 'F'

angle — Ángulo de giro

Ángulo en radianes usado por la tortuga para rotaciones.

Ejemplos comunes:

pi/2   # 90°
pi/3   # 60°
pi/4   # 45°

step — Tamaño del paso

Longitud del movimiento hacia adelante de la tortuga.

step = 1


Controla la escala del fractal.

n — Número de iteraciones

Cantidad de veces que se aplican las reglas de producción.

n = 5


Más iteraciones → mayor detalle → mayor costo computacional.

Comportamiento de los símbolos (Tortuga)

Por convención:

Símbolo	Acción
F	Avanzar dibujando
f	Avanzar sin dibujar
+	Girar a la derecha
-	Girar a la izquierda

(Esto depende de la implementación de la clase Tortuga.)

Ejemplo

Curva de Koch:

from lindenmayer.funcion_lindermayer import generar_fractal
from math import pi

V = {'F', '+', '-'}
omega = 'F'
P = {
    'F': 'F+F--F+F'
}

generar_fractal(V, P, omega, angle=pi/3, step=1, n=4)

Notas

Iteraciones grandes pueden generar cadenas muy largas

El tiempo de dibujo crece rápidamente con n

El ángulo incorrecto produce geometrías inesperadas
anney 0.0.2
A warm cup of hibiscus tea.
Requires Python: >=3.10
Keywords: valentine, terminal, tui, love, textual

# 🌺 Anney (Hibiscus)

A love letter to my beloved.

> [!IMPORTANT]
> I wrote this because you are the inspiration for every beautiful thing I create.

Built with [Textual](https://textual.textualize.io/) and [Rich](https://rich.readthedocs.io/).

## Install

```bash
pipx install anney
```

## Run

After installing, simply type:

```
hibiscus
```

## License

LOVE

## Hibiscus ; Delicate Beauty
sensa-example 0.0.2
A minimal example package for testing PyPI publishing.
Requires Python: <3.13,>=3.10
Keywords: example, sensa

# sensa-example
[![Test & Build](https://github.com/Tensor46/sensa-example/actions/workflows/python-package.yml/badge.svg?branch=main)](https://github.com/Tensor46/sensa-example/actions/workflows/python-package.yml)

A minimal example package for testing PyPI publishing.

### License
MIT License
midas 0.8.1
Python driver for Honeywell Midas gas detectors.

midas
=====

Python driver for [Honeywell Midas gas detectors](http://www.honeywellanalytics.com/en/products/Midas).

<p align="center">
  <img src="https://s7d1.scene7.com/is/image/Honeywell65/Midas" height="400" />
</p>

Installation
============

```
uv pip install midas
```

Usage
=====

### Command Line

To test your connection and stream real-time data, use the command-line
interface. You can read the state with:

```
$ midas 192.168.1.100
```

This will output a JSON object which can be further manipulated. See below for
object structure.


### Python

For more complex behavior, you can write a python script. This solely uses
Python ≥3.5's async/await syntax.

```python
import asyncio
from midas import GasDetector

async def get():
    async with GasDetector('192.168.1.100') as detector:
        print(await detector.get())

asyncio.run(get())
```

If the detector is operating at that address, this should output a
dictionary of the form:

```python
{
  'alarm': 'none',             # Can be 'none', 'low', or 'high'
  'concentration': 0.0,        # Current gas concentration reading
  'connected': True,           # Monitors heartbeat for connection
  'fault': {                   # Fault data, when applicable
    'code': 'F39',
    'condition': 'User has generated a simulated fault.',
    'description': 'Simulated fault',
    'recovery': 'Reset simulated fault.',
    'status': 'Instrument fault'
  },
  'flow': 514,                 # Flow rate, in cc / minute
  'high-alarm threshold': 2.0, # Threshold concentration for high alarm trigger
  'ip': '192.168.1.192',       # IP address of connection, can be used to link to Honeywell's own web interface
  'life': 538.95,              # Days until cartridge replacement required
  'low-alarm threshold': 1.0,  # Threshold concentration for low alarm trigger
  'state': 'Monitoring',       # Can be any option in `gas_detector.monitoring_status_options`
  'temperature': 26,           # Detector temperature, in celsius
  'units': 'ppm'               # Units for concentration values
}
```

Additionally, there are four commands which can be sent to the device
* Clear all alarms and faults - `detector.clear_alarms_and_faults()`
* Inhibit alarms - `detector.inhibit_alarms()`
* Inhibit alarms and faults - `detector.inhibit_alarms_and_faults()`
* Turn off inhibition - `detector.remove_inhibit()`
jimeng-visual-generation 0.1.4
MCP server for Jimeng Visual Generation API
Requires Python: >=3.10

# Jimeng 视觉生成 MCP 服务器

基于 Volcengine（火山引擎）Jimeng 视觉生成 API 的 MCP 服务器。通过模型上下文协议（MCP）提供图像和视频生成功能。

## 功能

- **文本生成图像（T2I）**：根据文本提示生成图像
- **图像到图像（I2I）**：基于提示和参考图像对图像进行变换
- **多图融合**：合成多张参考图像
- **文本生成视频（T2V）**：根据文本提示生成视频
- **图像到视频（I2V）**：从首帧或首帧+末帧图像生成视频
- **自动本地文件支持**：可传入图像公网URL，同时支持本地文件路径，程序会自动将其转换为 Base64

## 安装

### 选项 1：通过 pip 安装

```bash
pip install jimeng_visual_generation
```

### 选项 2：直接使用 `uvx` 运行（推荐）

无需安装。`uvx` 会自动下载并运行该包：

```bash
uvx jimeng_visual_generation
```

## 在 VS Code / Cursor / Claude Desktop 中的配置

将下列内容添加到你的 MCP 配置文件：

- **VS Code**：`~/.vscode/mcp.json` 或 工作区设置
- **Cursor**：Settings -> MCP Servers
- **Claude Desktop**：`%APPDATA%\Claude\claude_desktop_config.json`

### 示例配置（使用环境变量）

```json
{
  "mcpServers": {
    "jimeng_visual_generation": {
      "command": "uvx",
      "args": ["jimeng_visual_generation"],
      "env": {
        "VOLC_API_KEY": "your_volcengine_api_key_here",
        "VOLC_IMAGE_MODEL": "doubao-seedream-4.5",
        "VOLC_VIDEO_MODEL": "doubao-seedance-1.5-pro-251215"
      }
    }
  }
}
```

### 环境变量

| 变量 | 是否必需 | 描述 |
|------|----------|------|
| `VOLC_API_KEY` | ✅ 必需 | 你的 Volcengine API Key |
| `VOLC_IMAGE_MODEL` | 可选 | 图像模型 ID（默认：doubao-seedream-4.5） |
| `VOLC_VIDEO_MODEL` | 可选 | 视频模型 ID（默认：doubao-seedance-1.5-pro） |

## 可用工具

### `generate_image`

使用文本提示和可选参考图像生成图像。

**参数：**

- `prompt`（必需）：描述目标图像的文本
- `image_urls`（可选）：参考图像列表（支持 URL、Base64 或本地文件路径）
- `model`（可选）：使用的模型 ID
- `size`（可选）：图像尺寸（例如："2048x2048"、"2K"、"4K"）

### `generate_video`

创建视频生成任务。根据输入自动判断模式：

- 无图像 → 文本生成视频（T2V）
- 1 张图像 → 首帧 I2V
- 2 张图像 → 首帧 & 末帧 I2V

**参数：**

- `prompt`（可选）：视频描述的文本
- `image_urls`（可选）：输入图像（支持 URL、Base64 或本地文件路径）
- `model`（可选）：使用的模型 ID
- `ratio`（可选）：宽高比（例如："16:9"、"9:16"）
- `duration`（可选）：视频时长（秒）

### `get_video_task_result`

查询视频生成任务的状态和结果。

**参数：**

- `task_id`（必需）：由 `generate_video` 返回的任务 ID

## 📄 开源协议 (License)

本项目基于 [MIT License](LICENSE) 开源。

## 💖 赞助 (Sponsorship)

维护开源项目不易，如果您觉得 jimeng_visual_generation 对您有帮助，欢迎请作者喝杯咖啡！

<div align="center">

| 平台 | 链接 | 支付方式 |
| :--- | :--- | :--- |
| **爱发电 (Afdian)** | [![Afdian](https://img.shields.io/badge/Afdian-支持我-946ce6?logo=afdian)](https://afdian.com/a/justforever17) | 微信, 支付宝 |

</div>
snowlib 0.4.8
Python-Snowflake utilities
Requires Python: >=3.9
Keywords: snowflake

# ❄️ SnowLib

[![PyPI](https://img.shields.io/pypi/v/snowlib.svg)](https://pypi.org/project/snowlib/)
[![Python Versions](https://img.shields.io/pypi/pyversions/snowlib.svg)](https://pypi.org/project/snowlib/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
[![DOI (latest)](https://zenodo.org/badge/DOI/10.5281/zenodo.17354788.svg)](https://doi.org/10.5281/zenodo.17354788)

Python-Snowflake utilities

```bash
pip install snowlib
pip install snowlib[snowpark]
pip install snowlib[sqlalchemy]
pip install snowlib[excel]
```

## Compatibility

| Package | 3.14 | 3.13-3.10 | 3.9* |
|---------|:----:|:---------:|:----:|
| **snowflake-connector-python** | ≥3.17.0 | ≥3.17.0, <4.0.0 | ≥3.17.0, <4.0.0 |
| **snowflake-snowpark-python** | - | ≥1.9.0 | ≥1.9.0 |
| **sqlalchemy** | ≥2.0 | ≥2.0 | ≥2.0 |
| **snowflake-sqlalchemy** | ≥1.6.0 | ≥1.6.0 | ≥1.6.0 |
| **pandas** | 2.3.3 | 2.3.1 | 2.3.1 |
| **pyarrow** | ≥22.0.0 | ✓ | <22.0.0 |

**Notes:**
- Version ranges shown when specified in `pyproject.toml`
- ✓ = Supported
- \- = Not available (no compatible wheel or version constraint)
- Python 3.14: Base connector only (Snowpark requires <3.14)
- Python 3.9: PyArrow capped at <22.0.0 (22.0.0+ requires ≥3.10)
- SQLAlchemy support: `pip install snowlib[sqlalchemy]`
- \* Python 3.9 reached [end-of-life in October 2025](https://peps.python.org/pep-0596/#lifespan) - please consider upgrading
sensa 0.1.0
ML Training Library.
Requires Python: <3.13,>=3.10
Keywords: Lightning, MAE, ML-Training, VIT

# Sensa
[![Test & Build](https://github.com/Tensor46/sensa/actions/workflows/python-package.yml/badge.svg?branch=main)](https://github.com/Tensor46/sensa/actions/workflows/python-package.yml)

Sensa (from the Latin sēnsus, meaning “perception”): A PyTorch Lightning based library providing Masked Autoencoder (MAE) pretraining and classification training using customizable Vision Transformer (ViT) backbones.

### Features
* Masked Autoencoder Pretraining with random token masking and reconstruction.
* Classification Training with flexible pooling strategies (avg, full, half, token).

### License
MIT License
pmtvs 0.2.0
Rust-accelerated primitives for signal analysis
Requires Python: >=3.10

# pmtvs

281 pure math functions for signal analysis and dynamical systems.
Rust-accelerated. numpy in, scalars out.

## Install

```
pip install pmtvs
```

## Usage

```python
from pmtvs import lyapunov_exponent, hurst_exponent, permutation_entropy, BACKEND
import numpy as np

signal = np.cumsum(np.random.randn(1000))

le = lyapunov_exponent(signal)      # chaos detection
h = hurst_exponent(signal)          # long-range dependence
pe = permutation_entropy(signal)    # complexity
print(BACKEND)                      # "rust" or "python"
```

## What's inside

Lyapunov exponents, Hurst, FTLE, sample/permutation/transfer entropy,
RQA, phase space embedding, DFA, Granger causality, mutual information,
persistent homology, stationarity tests, spectral analysis, wavelet
decomposition, DTW, KL/JS divergence, and 250+ more.

One import. No config objects. No DataFrames.

## Rust backend

10 performance-critical functions are accelerated via Rust/PyO3.
Falls back to pure Python automatically if the Rust extension
isn't available. Set `PMTVS_USE_RUST=0` to force Python.

## Citing

If you use pmtvs in your research, please cite:

```bibtex
@software{pmtvs,
  author = {Rudder, Jason},
  title = {pmtvs: Signal Analysis Primitives},
  year = {2026},
  url = {https://github.com/rudder-framework/primitives}
}
```

## License

MIT

Part of the [Rudder Framework](https://github.com/rudder-framework).