semvec/pypi-packages huggingface dataset:
pipscope 0.1.2
Interactive TUI for exploring installed Python packages
Requires Python: >=3.9
Keywords: packages, pip, terminal, textual, tui

# pipscope

A fast, interactive terminal UI for exploring installed Python packages. Think `htop` for `pip`.

![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)

## Features

- Browse all installed Python packages in a split-pane interface
- Live search filtering as you type
- View package details: version, summary, location, dependencies
- See reverse dependencies (which packages depend on each one)
- Sort by name or version
- Export all package data to JSON
- Keyboard-driven, works over SSH
- No subprocess calls — uses native Python APIs

## Installation

```bash
# Clone and install
git clone https://github.com/yourusername/pipscope.git
cd pipscope
pip install .

# Or install in development mode
pip install -e .
```

## Usage

```bash
# If installed via pip
pipscope

# Or run directly
python pipscope.py
```

## Keyboard Controls

| Key | Action |
|-----|--------|
| `↑` / `↓` | Navigate package list |
| `j` / `k` | Navigate (vim-style) |
| `/` | Focus search bar |
| `Esc` | Clear search, return to list |
| `s` | Toggle sort: name / version |
| `e` | Export packages to JSON |
| `h` | Show Environment Health |
| `r` | Export requirements.txt |
| `q` | Quit |

## Interface

```
┌──────────────────────────────────────────────────────────────┐
│ Type to search packages...                                   │
├──────────────────────────────┬───────────────────────────────┤
│ numpy (1.26.4)               │ numpy                         │
│ pandas (2.1.0)               │ Version: 1.26.4               │
│ requests (2.31.0)            │                               │
│ torch (2.0.1)                │ Summary:                      │
│ ...                          │   Fundamental package for     │
│                              │   scientific computing        │
│                              │                               │
│                              │ Location:                     │
│                              │   /usr/lib/python3/site-pkg   │
│                              │                               │
│                              │ Dependencies: (3)             │
│                              │   setuptools                  │
│                              │   ...                         │
│                              │                               │
│                              │ Used by: (12)                 │
│                              │   pandas                      │
│                              │   scipy                       │
│                              │   ...                         │
└──────────────────────────────┴───────────────────────────────┘
```

## Requirements

- Python 3.9+
- [Textual](https://github.com/Textualize/textual) (installed automatically)

## License

MIT
iflow-mcp_yezere-codeql_n1ght_mcp_server 0.1.0
MCP server for CodeQL N1ght tool integration
Requires Python: >=3.11

# CodeQL N1ght MCP Server

A Model Context Protocol (MCP) server for integrating CodeQL N1ght tool with AI assistants.

## Overview

This MCP server provides a standardized interface for AI assistants to interact with the CodeQL N1ght tool, enabling automated code analysis workflows including environment setup, database creation, and security scanning.

## Features

- **Environment Installation**: One-click setup of JDK, Ant, and CodeQL dependencies
- **Database Creation**: Create CodeQL databases from JAR/WAR/ZIP files with configurable decompilers
- **Security Scanning**: Execute security scans with customizable query packs
- **Parallel Processing**: Support for goroutines and multi-threading
- **Flexible Configuration**: Customizable paths, timeouts, and caching options

## Available Tools

### `version`
Get version or help information from the CodeQL N1ght executable.

### `install_environment`
Install required dependencies (JDK, Ant, CodeQL) with optional custom URLs.

### `create_database`
Create a CodeQL database from target files (JAR/WAR/ZIP) with options for:
- Decompiler selection (procyon/fernflower)
- Dependency handling (none/all)
- Parallel processing
- Cache management

### `scan_database`
Execute security scans on CodeQL databases with configurable:
- Database and query pack paths
- Parallel processing options
- Cache control

### `run_codeql_n1ght`
Generic interface for direct command execution with custom arguments.

## Installation

1. Install dependencies:
```bash
pip install -r requirements.txt
```

2. Ensure the CodeQL N1ght executable is available at the configured path (default: `J:\mcp\codeql-n1ght.exe`)

## Usage

Run the MCP server in STDIO mode:

```bash
python codeql_n1ght_mcp_server.py
```

## Configuration

- **Default Executable Path**: `J:\mcp\codeql-n1ght.exe`
- **Path Compatibility**: Supports both Windows (`J:\path`) and Unix-style (`/j:/path`) path formats
- **Timeouts**: Configurable per operation (default: 10 minutes for general operations, 20 hours for database/scan operations)

## Response Format

All tools return a standardized response format:
```json
{
  "returncode": 0,
  "stdout": "command output",
  "stderr": "error output",
  "timeout": false
}
```

## Error Handling

- **Executable Not Found**: Returns error if CodeQL N1ght executable is missing
- **Invalid Parameters**: Validates decompiler and dependency options
- **Timeout Management**: Configurable timeouts with process termination
- **Path Resolution**: Automatic path normalization and validation

## License

This project is open source and available under the MIT License.
spothomelight 1.0.4
Sync Spotify cover color to Home Assistant via Webhook
Requires Python: >=3.8

# SpotHomeLight 🎵🏠💡

**SpotHomeLight** 是一个轻量级的 Python 服务，用于将 Spotify 当前播放封面的主色调同步到 Home Assistant。

它运行在你的家用服务器（Linux 或 Windows）上，通过 K-Means 聚类算法提取封面颜色，并通过 Webhook 实时推送到 Home Assistant，让你的智能灯光跟随音乐氛围律动。

## ✨ 功能特性

* **跨平台支持**：完美支持 Linux (Systemd) 和 Windows (计划任务)。
* **无头模式设计**：专为无显示器的家用服务器设计，支持终端内完成 OAuth 认证。
* **智能取色**：使用 K-Means 聚类算法提取最主要颜色，并进行缩略图预处理以降低 CPU 占用。
* **开机自启**：内置一键配置开机自动运行。
* **低资源占用**：去重机制，仅在切歌时进行下载和计算。

## 🛠️ 前置要求

1.  **Home Assistant**: 一个运行中的 HA 实例。
2.  **Spotify 开发者账号**: 用于获取 API 凭证。

## 📦 安装

使用 pip 安装：

```bash
pip install spothomelight
```

## 🚀 快速开始

### 准备 Spotify API

1. 登录 [Spotify Developer Dashboard](https://developer.spotify.com/dashboard/).
2. 创建一个新的 App (例如命名为 `SpotHomeLight`)。
3. 将 Redirect URI 设置为：

```
http://127.0.0.1:29092/callback
```

4. 记下 **Client ID** 和 **Client Secret**。

### 初始化配置

运行以下命令打开配置文件：

```bash
spothomelight -c
```

conf描述：

```ini
[SPOTIFY]
client_id = Client ID
client_secret = Client Secret
redirect_uri = 重定向URI（一般不需要修改，除非你知道自己在干什么。）

[HOME_ASSISTANT]
ha_url =  Home Assistant 地址（局域网/广域网）
webhook_id = 从 Home Assistant 获取的 Webhook ID

[GENERAL]
interval = 循环周期（秒）
```

### 配置 Home Assistant

登录 Home Assistant 后台，设置，自动化与场景，创建自动化，创建新的自动化，右上角三点菜单，YAML 编辑，将下面的yaml粘贴进去，保存。

右上角三点菜单，可视化编辑，复制 每当 里面的 Webhook ID，注意不要点复制按钮，那会复制API地址，需要选中输入框的内容并复制。

就执行 的实体删除pending，添加目标，选择自己的RGB灯，保存。

```yaml
alias: Spotify Cover Sync
description: ""
mode: restart
trigger:
  - platform: webhook
    webhook_id: ""
    local_only: false
condition: []
action:
  - service: light.turn_on
    target:
      entity_id: light.pending
    data:
      rgb_color: "{{ trigger.json.rgb }}"
      brightness_pct: 100
      transition: 2
```

### 首次运行与认证

在终端运行：

```bash
spothomelight
```

考虑到服务器通常没有浏览器，程序会打印一个认证 URL。

1. 复制该 URL 到你电脑的浏览器中打开。
2. 登录并点击“同意”。
3. 浏览器会跳转到一个 `127.0.0.1` 的无法连接页面。
4. 复制浏览器地址栏中完整的 URL。
5. 回到服务器终端，粘贴 URL 并回车。

认证成功后，程序应该开始监控并在终端输出日志，观察输出是否正常。

### 第五步：设置开机自启

确认运行正常后，Ctrl+C 停止程序，然后运行：

```bash
spothomelight -a
```

* **Linux**: 会自动创建并启用 Systemd User Service。
* **Windows**: 会自动创建 Windows 计划任务（登录时运行）。

手动启动服务：

```bash
spothomelight --start
```

停止服务：

```bash
spothomelight --stop
```

## 🐛 已知问题

* **取色结果与客户端不一致**: 
  本工具目前使用 **K-Means 聚类算法** 来提取封面主色调。由于 Spotify 官方客户端的动态取色算法（涉及饱和度筛选、亮度平衡、以及如何排除纯黑/纯白背景等逻辑）并未公开且较为复杂，**本工具计算出的颜色可能与你在 Spotify 电脑或手机端看到的背景色存在差异**。

## 📝 License

MIT License. Copyright (c) 2026 ZGQ Inc.
imspy-core 0.4.0
Core data structures and utilities for processing timsTOF ion mobility spectrometry data.
Requires Python: <3.14,>=3.11

# imspy-core

Core data structures and utilities for processing timsTOF ion mobility spectrometry data.

## Installation

```bash
pip install imspy-core
```

## Features

- **Data Structures**: MzSpectrum, TimsSpectrum, PeptideSequence, and more
- **Chemistry Utilities**: Elements, amino acids, UNIMOD modifications, CCS/mobility conversions
- **TimsTOF Readers**: Read DDA and DIA datasets from Bruker timsTOF instruments
- **Low Dependencies**: Only essential packages (numpy, pandas, scipy, numba)

## Quick Start

```python
from imspy_core.timstof import TimsDatasetDDA
from imspy_core.data import PeptideSequence

# Read a DDA dataset
dataset = TimsDatasetDDA("/path/to/data.d")
frame = dataset.get_tims_frame(1)
print(frame)

# Work with peptides
peptide = PeptideSequence("PEPTIDEK")
print(f"Mass: {peptide.mono_isotopic_mass}")
```

## Related Packages

- **imspy-predictors**: ML-based predictions (CCS, RT, intensity) - requires TensorFlow
- **imspy-search**: Database search functionality - requires sagepy, mokapot
- **imspy-simulation**: Simulation tools for timsTOF data
- **imspy-vis**: Visualization tools - requires Plotly, Matplotlib

## License

MIT License - see LICENSE file for details.
pretix-map 0.1.9
An overview map of the catchment area of previous orders. Measured by postcode
Keywords: pretix

# Pretix Sales Map Plugin

A powerful geographic analytics and visualization plugin for [pretix](https://github.com/pretix/pretix). This plugin allows you to visualize where your attendees are coming from, track sales growth over time, and analyze regional market penetration.

## ✨ Features

- **📍 Automatic Geocoding:** Automatically converts attendee addresses to map coordinates upon payment.
- **🗺️ Interactive Map:** Switch between **Pin View** (with clustering), **Heatmap**, and **Density Grid**.
- **📈 Sales Analytics:** Track total revenue, average travel distance, and top regions (cities/items).
- **⏱️ Timeline Animation:** Play back the history of your sales to see geographic growth over time.
- **★ Marketing Milestones:** Define important dates (e.g., newsletters) in the UI to see their impact on the timeline.
- **🔄 Event Comparison:** Overlay data from previous events to compare reach and performance.
- **⚠️ Quality Control:** Dedicated view for orders with failed geocoding to manage data issues.
- **🌙 Nightly Sync:** Automated background task to retry failed geocoding attempts.
- **🔘 Manual Trigger:** Admin button to re-run geocoding for all orders of an event.

## 🚀 Installation & Setup

To use this plugin with your local pretix instance (`C:\Users\SCJA03\Desktop\Programmieren\pretix`):

### 1. Register the Plugin
Open your terminal, navigate to this directory, and ensure your pretix virtual environment is active.
```bash
python setup.py develop
```

### 2. Run Migrations
Apply the database changes for geodata and milestones:
```bash
# From your pretix/src directory
python manage.py migrate
```

### 3. Configuration
Add a User-Agent for the geocoding service in your `pretix.cfg`:
```ini
[pretix_mapplugin]
nominatim_user_agent = YourProjectName/1.0 (contact@yourdomain.com)
```

### 4. Enable the Plugin
1. Log in to your Pretix Control Panel.
2. Go to **Organizer Settings > Plugins** and enable **Map-Plugin**.
3. In your specific **Event > Settings > Plugins**, also enable **Map-Plugin**.

## 🛠️ Usage

- **Map View:** Navigate to **Sales Map > Map View** in the event sidebar.
- **Milestones:** Go to **Sales Map > Milestones** to add marketing dates.
- **Revenue Weighting:** Use the toggle button on the map to see "where the money comes from" instead of just "where the people are".

## 🚀 Starting the Application (Development)

1. **Pretix Server:**
   ```bash
   cd C:\Users\SCJA03\Desktop\Programmieren\pretix\src
   python manage.py runserver
   ```
2. **Celery Worker:**
   ```bash
   cd C:\Users\SCJA03\Desktop\Programmieren\pretix\src
   celery -A pretix.celery_app worker -l info
   ```
   *Note: If you don't have a broker like Redis or RabbitMQ running, the plugin will automatically fall back to synchronous geocoding (eager mode) if configured in `pretix.cfg`.*

## 🛡️ Requirements

- pretix >= 2.7
- geopy
- A running Celery worker (essential for background geocoding)

---
Developed by MarkenJaden. Released under the Apache License 2.0.
carcent 2.1.0
Discord Clipboard System
Requires Python: >=3.7
Keywords: discord, clipboard, utility, productivity

# carcent - Discord Clipboard System

A clipboard utility for Discord that allows you to copy and paste message content.

## Features
- Copy Discord message content to clipboard
- Paste clipboard content
- Persistent storage of clipboard items
- Simple API for integration

## Installation
```bash
pip install carcent
isage-tooluse-benchmark 0.1.0.1
SAGE Tool Use Benchmark - Tool selection and use evaluation framework
Requires Python: >=3.11
Keywords: sage, benchmark, tool-selection, tool-use, planning, timing-detection, evaluation, intellistream

# SAGE Tool Use Benchmark

Configuration-driven experiment framework for evaluating tool selection and use capabilities.

## Features

- **Tool Selection Evaluation**: Tool retrieval and ranking benchmarks
- **Planning Evaluation**: Multi-step planning with tool composition
- **Timing Detection**: Timing judgment for tool invocation decisions

## Quick Start

```bash
# Install
pip install isage-tooluse-benchmark

# Run tool selection experiment
sage-tooluse-bench tool-selection --config config/tool_selection_exp.yaml

# Run planning experiment
sage-tooluse-bench planning --config config/planning_exp.yaml
```

## Documentation

See [benchmark_agent/README.md](src/sage/benchmark/benchmark_agent/README.md) for detailed documentation.

## Development

```bash
# Clone
git clone https://github.com/intellistream/sage-tooluse-benchmark.git
cd sage-tooluse-benchmark

# Setup virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest
```

## License

MIT License - see [LICENSE](LICENSE) for details.