semvec/pypi-packages huggingface dataset:
wdlc 0.9.1
Web DL Compiler
License: MIT
Requires Python: >=3.8

What will we need to do?

Implement the major layers in pytorch:
- Linear
- Conv2D
- FC
- ReLU
- Pool
- RNN
- LSTM
- Batch Norm
... etc

----------------------------

- Support `nn.Sequential`

- Support hugging face models

- Support saved model.

- Optimization Phase:
nuralshell 0.1.5
A powerful CLI agent for system automation and file management.
License: MIT
Requires Python: >=3.9
Keywords: ai, cli, agent, shell

# NuralShell: Your AI-Powered Terminal Assistant

NuralShell is an intelligent AI assistant designed to streamline your terminal interactions, especially for server deployment and automation tasks. It operates with root permissions, allowing it to execute shell commands (with your explicit confirmation for safety), read and write files, and maintain a persistent chat history across sessions. NuralShell is flexible, supporting both OpenAI/ChatGPT and Google Gemini as its underlying AI model.

## Features

*   **Intelligent Command Execution:** Safely execute shell commands with user confirmation.
*   **File System Interaction:** Read and write files to manage code, configurations, and data.
*   **Persistent Chat History:** Maintains conversation context across sessions for a seamless experience.
*   **Configurable AI Model:** Easily switch between OpenAI/ChatGPT and Google Gemini, with simple API key and model name setup.
*   **Modular Architecture:** Built with a clean, modular design using sub-agents for specific tasks like command execution and file I/O.

## Installation

You can install NuralShell as a Python package using `pip`.

```bash
pip install NuralShell
```

## Configuration

Upon its first run, NuralShell will guide you through a one-time setup process to configure your AI model (ChatGPT or Gemini), API key, and desired model name. This configuration is saved in `~/.NuralShell/config.json`.

You can also set your API keys as environment variables:
*   For OpenAI/ChatGPT: `OPENAI_API_KEY`
*   For Gemini: `GEMINI_API_KEY`

## Usage

Once installed, you can launch NuralShell directly from your terminal:

```bash
nuralshell
```

### Basic Interaction

NuralShell acts like a conversational terminal. You can ask it to perform tasks, run commands, or read/write files.

```
You: List all files in the current directory.
NuralShell: Do you want to Execute `dir`? (Y/N)
You: Y
NuralShell: (output of `dir` command)
```

### Example Commands

*   **Read a file:**
    ```
    You: Read the content of `my_script.py`
    ```
*   **Write to a file:**
    ```
    You: Write "Hello, World!" to `output.txt`
    ```
*   **Execute a shell command:**
    ```
    You: Update the system packages.
    NuralShell: Do you want to Execute `sudo apt update && sudo apt upgrade -y`? (Y/N)
    You: Y
    ```

### Exiting NuralShell

To exit the NuralShell session, simply type `exit` or `quit`:

```
You: exit
```
pytorrentsearch 0.3.0
No distraction way to find torrents
License: MIT
Requires Python: >=3.9

# PyTorrentSearch

CLI + Python library to search for and parse magnet links from the Internet.

It leverages well known search engines such as Yandex, Google and DuckDuckGo. In my tests only Google was able to work reliably.

If you already have the website link you can just paste it as it would be a query then it will look for all magnet links that it can find in the raw HTML.

The idea is to quickly classify good sites and bad sites based on if the magnet link is already in the base HTML without having to load the JavaScript crap.

**DISCLAIMER**: Use at your own risk. This software doesn't store or provide illegal content by itself, it's only a search tool.

## Installation & Usage

Run directly without installation using `uvx`:

```bash
uvx pytorrentsearch "your search query"
```

Or install globally:

```bash
uv tool install pytorrentsearch
pytorrentsearch "your search query"
```

You can also provide a direct URL to parse:

```bash
uvx pytorrentsearch "https://example.com/torrent-page"
```

## Development

This project uses [mise](https://mise.jdx.dev/) for tool management and [uv](https://docs.astral.sh/uv/) for Python dependency management.

### Setup

```bash
# Install mise (if not already installed)
# See: https://mise.jdx.dev/getting-started.html

# Install tools (uv, ruff)
mise install

# Install Python dependencies
uv sync
```

### Available Commands

```bash
# Format code
mise run fmt

# Run linter
mise run lint

# Run tests
mise run test

# Build package
mise run build

# Clean build artifacts
mise run clean
```
qmad 1.1.0
A package for adaptive variational quantum computing algorithms
License: MIT
Requires Python: >=3.6

# QMAD/QMultiAdapt

Welcome to the **QMultiAdapt** repository! This repository is currently undergoing a significant transformation to enhance the usability and execution of adaptive variational techniques. Our goal is to make the code more accessible and seamlessly executable for users. As part of this ongoing morphing process, we will be appending more adaptive variational techniques and offering further optimizations to improve the overall user experience.

---

## Overview

This repository contains the companion code for the paper *Adaptive Variational Simulation for Open Quantum Systems*. The core of the adaptive variational algorithm resides in the `AVQD` directory, which houses the entire package. The root directory is configured as the primary project space, tailored specifically for execution within a cluster computing environment.

## Examples and Applications

The **QMultiAdapt** repository demonstrates the versatility and applicability of the UAVQD (Unrestricted Adaptive Variational Quantum Dynamics) vectorization method across multiple open quantum system scenarios. The examples provided include:

- **Amplitude Damping Channel**
- **FMO Complex**
- **Dicke Superradiance**

### Key Insights:
- The vectorization method excels in handling continuous updates, particularly in complex scenarios like Dicke Superradiance, where the state evolves through numerous possible pathways due to successive emissions.
- The UAVQD operator pool developed here is well-suited to manage the complexity of multi-qubit systems with polynomial runtime scaling (as opposed to exponential scaling in classical methods).
- An appendix demonstrates how the scaling can be further improved to linear with a scheme requiring only \( O(\log_2 N) \) gates, given all-to-all connectivity in qubits. Trapped ion quantum computers have shown promise for handling such ansatz constructions.

**Note**: The repository is under active development and will be incrementally updated. Contributions and feedback are welcome.

---

### Citation
For academic use, please cite our work as mentioned in the related paper *Designing variational ansatz for quantum-enabled simulation of non-unitary dynamical evolution an excursion into Dicke supperradiance.*


Feel free to reach out with any questions, suggestions, or contributions!
upcat 0.1.0
图片文件夹可视化工具
License: MIT
Requires Python: >=3.6

# upcat - 图片文件夹可视化工具
        
        一个简单易用的图片文件夹可视化工具，可以快速生成HTML预览网页并启动本地服务器。
        
        ## 功能特性
        
        - 自动扫描指定文件夹中的所有图片文件
        - 生成美观的响应式HTML预览页面
        - 提供图片缩略图网格视图和点击放大预览功能
        - 自动启动本地Web服务器并打开浏览器
        - 支持多种图片格式：JPG、PNG、GIF、WebP、BMP、TIFF、SVG等
        
        ## 安装
        
        使用pip安装：
        
        ```bash
        pip install upcat
        ```
        
        ## 使用方法
        
        ### 基本使用
        
        ```bash
        upcat -d /path/to/image/folder
        ```
        
        这将扫描指定文件夹中的所有图片，生成HTML预览文件，并在默认端口(8000)启动Web服务器。
        
        ### 指定端口
        
        ```bash
        upcat -d /path/to/image/folder -p 8080
        ```
        
        这将在端口8080上启动Web服务器。
        
        ### 外网访问
        
        服务器默认支持外网访问，启动后会显示本地访问地址和外网访问地址。在同一局域网内的其他设备可以通过外网访问地址查看图片。
        
        **注意：** 要实现真正的互联网访问，需要确保您的电脑有公网IP或已配置端口映射。
        
        ### 命令行参数
        
        - `-d`, `--directory`: 要可视化的图片文件夹路径（必需）
        - `-p`, `--port`: 服务器端口（可选，默认8000）
        
        ## 预览效果
        
        - 响应式网格布局，适配不同屏幕大小
        - 图片卡片支持悬停效果和点击放大
        - 模态框预览大图，支持ESC键关闭
        - 显示文件夹信息、图片数量和生成时间
        
        ## 系统要求
        
        - Python 3.6 或更高版本
        - 无需其他第三方依赖
        
        ## 示例
        
        ```bash
        # 查看当前目录下的图片
        upcat -d .
        
        # 查看指定文件夹的图片，使用端口9000
        upcat -d /Users/username/Pictures -p 9000
        ```
        
        ## 许可证
        
        MIT License
colorgradtext 1.0.0
Apply amazing horizontal and vertical color gradients to text and ASCII art in the terminal.
License: MIT
Requires Python: >=3.8
Keywords: text, gradient, ansi, colors, terminal

# ColorGradText

**ColorGradText** is a Python module that allows you to apply amazing color gradients to text and ASCII art in your terminal.  
It supports **horizontal and vertical gradients**, works with multi-line text, and comes with **10 creative gradient presets**.

![](https://github.com/OgTen/GradientText/blob/main/images/aqua_wave.png)
![](https://github.com/OgTen/GradientText/blob/main/images/rose_blush.png)
![](https://github.com/OgTen/GradientText/blob/main/images/violet_dream.png)
![](https://github.com/OgTen/GradientText/blob/main/images/sunflare.png)
![](https://github.com/OgTen/GradientText/blob/main/images/frostbite.png)
![](https://github.com/OgTen/GradientText/blob/main/images/ember_glow.png)
![](https://github.com/OgTen/GradientText/blob/main/images/tidepool.png)
![](https://github.com/OgTen/GradientText/blob/main/images/evenfall.png)
![](https://github.com/OgTen/GradientText/blob/main/images/lime_light.png)
![](https://github.com/OgTen/GradientText/blob/main/images/dreamcloud.png)
![](https://github.com/OgTen/GradientText/blob/main/images/gradtexts.png)

```
from colorgradtext import aqua_wave

text = "Og_Ten"
print(aqua_wave(text))
```
```
from colorgradtext import lime_light

ascii = r"""
   ____   _____            _______ ______ _   _ 
  / __ \ / ____|          |__   __|  ____| \ | |
 | |  | | |  __              | |  | |__  |  \| |
 | |  | | | |_ |             | |  |  __| | . ` |
 | |__| | |__| |             | |  | |____| |\  |
  \____/ \_____|   _______   |_|  |______|_| \_|                        
                  |_______|   
"""

print(lime_light(ascii, direction="vertical"))
```

**Download: ``pip install colorgradtext``**