semvec/pypi-packages huggingface dataset:
nwtools 3.0.0
A Python package for negatively optimizing your Windows/Linux system performance
Requires Python: >=3.6

# nwtools 🐌

一个用于"负优化"你的Windows/Linux系统性能的Python包。这个包主要用于演示、测试和教育目的，展示如何降低系统性能的各种方法。

**警告：请谨慎使用！仅在测试环境或虚拟机中使用！**

## 功能特性

- **CPU负优化**：创建大量计算密集型任务消耗CPU资源
- **内存负优化**：分配大量内存但不释放
- **磁盘负优化**：频繁读写磁盘，创建大量临时文件
- **网络负优化**：发送大量网络请求，消耗带宽
- **进程负优化**：创建大量子进程
- **UI干扰**：干扰用户界面（如闪烁窗口、移动鼠标等）
- **安全模式**：所有操作都是可逆的，有安全限制

## 安装

```bash
pip install nwtools
```

或者从源代码安装：

```bash
git clone https://github.com/yourusername/nwtools.git
cd nwtools
pip install -e .
```

## 使用方法

### 命令行界面

```bash
# 显示帮助信息
nwtools --help

# 运行所有负优化功能（默认强度）
nwtools run

# 运行特定强度的负优化
nwtools run --intensity high

# 只运行CPU负优化
nwtools cpu --duration 30

# 运行内存负优化
nwtools memory --size 1024

# 运行磁盘负优化
nwtools disk --files 100

# 运行网络负优化
nwtools network --requests 50

# 运行进程负优化
nwtools process --count 20

# 停止所有负优化任务
nwtools stop

# 显示系统状态
nwtools status
```

### Python API

```python
from nwtools import NegativeOptimizer

# 创建优化器实例
optimizer = NegativeOptimizer()

# 运行CPU负优化
optimizer.optimize_cpu(duration=30, intensity="medium")

# 运行内存负优化
optimizer.optimize_memory(size_mb=512, duration=60)

# 运行磁盘负优化
optimizer.optimize_disk(num_files=50, file_size_kb=1024)

# 运行网络负优化
optimizer.optimize_network(num_requests=100, target_url="http://example.com")

# 运行进程负优化
optimizer.optimize_process(num_processes=10)

# 停止所有优化
optimizer.stop_all()

# 获取系统状态
status = optimizer.get_system_status()
print(f"CPU使用率: {status['cpu_percent']}%")
print(f"内存使用率: {status['memory_percent']}%")
```

## TUI界面（稳定跨平台）

nwtools提供了三种TUI界面，**推荐使用稳定TUI**：

### 🎯 1. 稳定TUI界面（推荐 - 跨平台稳定）
```bash
# 运行稳定TUI界面
nwtools-stable-tui
```
**特点**：
- ✅ 纯Python实现，无任何外部依赖
- ✅ 跨平台支持（Windows/Linux/Termux/macOS）
- ✅ 不会崩溃，稳定可靠
- ✅ 数字选择菜单，操作简单
- ✅ 支持所有nwtools功能

### 2. 基于dialog的TUI界面（仅Linux）
```bash
# 安装dialog工具
pkg install dialog  # Termux
sudo apt install dialog  # Ubuntu/Debian

# 运行TUI界面
nwtools-tui
```
**特点**：
- 使用dialog工具创建图形对话框
- 支持真正的鼠标点击
- ❌ Windows不支持

### 3. 简单TUI界面（实验性）
```bash
# 运行简单TUI界面
nwtools-simple-tui
```
**特点**：
- 使用curses库
- ❌ 在Termux上可能不稳定
- ❌ 在Windows上需要额外配置

**推荐使用稳定TUI**，因为它：
1. 在所有平台上都能工作
2. 不会崩溃
3. 无需安装额外工具
4. 操作简单直观

## 安全特性

1. **资源限制**：默认有资源使用上限
2. **超时保护**：所有操作都有超时限制
3. **可逆操作**：所有创建的文件和进程都可以清理
4. **确认提示**：危险操作前需要确认
5. **强度控制**：可以控制负优化的强度

## 配置

创建配置文件 `~/.nwtools/config.yaml`：

```yaml
defaults:
  intensity: medium
  duration: 60
  auto_cleanup: true
  
limits:
  max_cpu_percent: 80
  max_memory_mb: 2048
  max_disk_files: 100
  max_processes: 20
  
safety:
  require_confirmation: true
  max_total_duration: 300
```

## 许可证

MIT License

## 免责声明

本软件仅供测试、演示和教育目的使用。作者不对使用本软件造成的任何损害负责。请勿在生产环境或重要系统上使用。

**使用风险自负！**
torchfinufft-ryan 1.2.1
NUFFT module for PyTorch.
Keywords: NUFFT, Toeplitz, non-Carteisan, mri, PyTorch

# NUFFT module for PyTorch

## Introduction
There is no built-in NUFFT function in PyTorch, neither is Toeplitz MSE loss module, which is important for non-Cartesian MRI reconstruction works. To fill this gap, this package provides:
1. A high performance NUFFT `torch.nn` module wrapping `cufinufft` [3] and `finufft` [1,2] - they are the fastest NUFFT backends to the best of my knowledge.
2. Another elegant MSE loss (mean square of l2 loss) module for non-Cartesian reconstruction with **DCF preconditioning** boosted by **Toeplitz** operator. Basically, this is done by replacing the two-pass NUFFTs with a Cartesian fast Fourier convolution. This method is also fast but slightly slower than cufinufft in practice. Use as you need.

Both CPU and GPU are supported. Benchmark indicates a 2ms (NUFFT module) or a 3ms (Toeplitz MSE loss module) time cost per iteration in a 256×256 inverse NUFFT problem using a RTX3090 GPU.

## Install
For offline installing:
```bash
$ bash install.bash
```
To install with pip:
```bash
$ pip install torchfinufft-ryan
```
Optionally, to enable CUDA computation, `cufinufft` has to be installed. To solve for the density compensation function, I recommend my `mrarbdcf` package.

## Usage
Please refer to the `exmaple` folder - there are minimal example(s) for tutorial. Please run:
```bash
$ pip install -r example/requirements.txt
```
to install essential packages for the examples.

## References
[1] Barnett AH. Aliasing error of the kerne exp(β√(1-z²)) in the nonuniform fast Fourier transform. Applied and Computational Harmonic Analysis. 2021 Mar 1;51:1–16.

[2] Barnett AH, Magland J, af Klinteberg L. A Parallel Nonuniform Fast Fourier Transform Library Based on an “Exponential of Semicircle" Kernel. SIAM J Sci Comput. 2019 Jan;41(5):C479–504.

[3] Shih Y hsuan, Wright G, Anden J, Blaschke J, Barnett AH. cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 2021 June;688–97.
swiftshadow 2.4.0
Free IP Proxy rotator for python
Requires Python: >=3.10

# Swiftshadow

![PyPI - Downloads](https://img.shields.io/pypi/dm/swiftshadow)  
![GitHub release (latest by date including pre-releases)](https://img.shields.io/github/v/release/sachin-sankar/swiftshadow?include_prereleases&style=flat)

> [!WARNING]  
> **Heads up!** If you're using **versions 1.2.1 or below**, please note that **version 2.0.0 and above** includes breaking changes. Before upgrading, read the [documentation](https://sachin-sankar.github.io/swiftshadow/) to understand the changes and ensure compatibility with your code. If you encounter issues, please review the docs before opening a GitHub issue.

## About

**Swiftshadow** is a lightweight and efficient Python library designed to simplify IP proxy rotation for web scraping, data mining, and other automated tasks. It helps you bypass common challenges like IP bans, rate limits, and detection mechanisms, ensuring smooth and uninterrupted data collection.

### Key Features
- **Speed**: Optimized for fast proxy retrieval and rotation.
- **Reliability**: Automatically switches to working proxies if one fails.
- **Customization**: Configure proxy filters, rotation frequency, and caching behavior.
- **Low Dependencies**: Only one third-party dependency (`requests`), making it easy to use and maintain.
- **Caching**: Built-in caching mechanism to reduce load times and improve performance.

Whether you're a seasoned developer or a beginner, **Swiftshadow** makes proxy management effortless.

---

## Installation

Install the library using pip:

```bash
pip install swiftshadow
```

---

## Quick Start

### Get a Proxy in 2 Lines
Fetch a random proxy with just two lines of code:

```python
from swiftshadow import QuickProxy

print(QuickProxy())  # Output: http://<ip>:<port>
```

### Advanced Usage
For more control, use the `ProxyInterface` class:

```python
from swiftshadow.classes import ProxyInterface

# Fetch HTTPS proxies from the US
swift = ProxyInterface(countries=["US"], protocol="https")
print(swift.get().as_string())  # Output: https://<ip>:<port>
```

---

## Documentation

Explore the full documentation to learn more about **Swiftshadow**'s features and advanced usage:

📚 [Documentation](https://sachin-sankar.github.io/swiftshadow/)

---

## Why Swiftshadow?

- **Lightweight**: Minimal dependencies and easy to integrate.
- **Flexible**: Supports filtering by country and protocol.
- **Scalable**: Ideal for both small scripts and large-scale scraping projects.
- **Open Source**: Free to use, modify, and contribute to under the MIT License.

---

## Contributing

Contributions are welcome! If you'd like to improve **Swiftshadow**, feel free to open an issue or submit a pull request on [GitHub](https://github.com/sachin-sankar/swiftshadow).

---

## License

**Swiftshadow** is licensed under the MIT License. See the [LICENSE](https://github.com/sachin-sankar/swiftshadow/blob/main/LICENSE) file for details.
traccia-sdk 0.1.0
Tracing SDK for agents
Requires Python: >=3.9

# Agent Trace SDK

This repository scaffolds the tracing SDK described in `SDK_Architecture_Detail.md` and is organized to match the phased delivery plan in `phases.txt`.

## Layout
- `traccia_sdk/` — Python client implementation.
  - `tracer/` — span, span context, tracer, and provider logic.
  - `instrumentation/` — decorator and monkey-patching helpers.
  - `exporter/` — HTTP exporter and related delivery utilities.
  - `context/` — context variable management and propagators.
  - `processors/` — span processors, sampling, and batching utilities.
  - `examples/` — usage examples and quickstarts.
- `backend/` — ingest API and storage components.
- `proto/` — protobuf definitions for wire formats.
- `docs/` — product and developer documentation.

## Status
Phases completed: 0–6 (foundation, core tracing engine, processors/queue, exporter stubs, instrumentation, propagation, cost intelligence). Phase 7 adds DX helpers (env/CLI/console). Remaining: Phases 8–10 per `phases.txt`.
parq-blockmodel 0.1.8
A Python package for efficient storage, manipulation, and analysis of mining block models using Parquet files.
Requires Python: <3.13,>=3.11

# parq-blockmodel

[![Run Tests](https://github.com/Elphick/parq-blockmodel/actions/workflows/build_and_test.yml/badge.svg?branch=main)](https://github.com/Elphick/parq-blockmodel/actions/workflows/build_and_test.yml)
[![PyPI](https://img.shields.io/pypi/v/parq-blockmodel.svg?logo=python&logoColor=white)](https://pypi.org/project/parq-blockmodel/)
![Coverage](https://raw.githubusercontent.com/elphick/parq-blockmodel/main/docs/source/_static/badges/coverage.svg)
[![Python Versions](https://img.shields.io/pypi/pyversions/parq-blockmodel.svg)](https://pypi.org/project/parq-blockmodel/)
[![License](https://img.shields.io/github/license/Elphick/parq-blockmodel.svg?logo=apache&logoColor=white)](https://pypi.org/project/parq-blockmodel/)
[![Publish Docs](https://github.com/Elphick/parq-blockmodel/actions/workflows/docs_to_gh_pages.yml/badge.svg?branch=main)](https://github.com/Elphick/parq-blockmodel/actions/workflows/docs_to_gh_pages.yml)
[![Open Issues](https://img.shields.io/github/issues/Elphick/parq-blockmodel.svg)](https://github.com/Elphick/parq-blockmodel/issues)
[![Open PRs](https://img.shields.io/github/issues-pr/Elphick/parq-blockmodel.svg)](https://github.com/Elphick/parq-blockmodel/pulls)


## Overview
A Python package for efficient storage, manipulation, and analysis of mining block models using Parquet files. 
parq-blockmodel provides tools for reading, writing, indexing, and transforming large-scale block model datasets, 
leveraging the performance of Apache Arrow and Parquet for scalable geoscience data workflows.
pympostor 0.1.0
CLI impostor game with AI
Requires Python: >=3.13

# pympostor

CLI impostor game with AI - A word deduction game where players must identify the impostor among them.

## Description

"Adivina El Impostor" (Guess the Impostor) is a word-based deduction game where participants must figure out who the impostor is among them. It's a game of strategy and deduction that requires skill to give clues without being too obvious.

## Installation

Install using pip:

```bash
pip install pympostor
```

Or using uv:

```bash
uv pip install pympostor
```

## Requirements

- Python 3.13 or higher
- OpenAI API key (set in your environment or `.env` file)

## Usage

After installation, run the game with:

```bash
pympostor play
```

You can customize the number of players and impostors:

```bash
pympostor play --jugadores 5 --impostores 1
```

Options:
- `--jugadores, -j`: Total number of players (minimum 3)
- `--impostores, -i`: Number of impostors (must be less than players/2)

## How It Works

- Most players receive the same secret keyword
- One or more impostors only know the category of the keyword, not the word itself
- Players take turns saying a word related to the keyword
- Players vote to eliminate who they think is the impostor
- The game continues until the impostor is found or wins

## License

This project is open source and available under the MIT License.

## Author

Facundo Goiriz - facundogoiriz@gmail.com
pattern-scope 0.1.0
Tiny CSV pattern analysis tool.
Requires Python: >=3.8

# pattern-scope

A tiny (<200 lines) CSV pattern-analysis tool.

It computes:
- Monotonicity score
- Outlier count
- Inflection-point estimates
- Optional matplotlib plots

## Install

pip install pattern-scope

## Usage

from patternscope.core import analyze
summary = analyze("data.csv", plot=True)
print(summary)
inspectr 0.1.2
A collection of python tools to inspect code quality. 

# inspectr
A collection of python tools to inspect code quality.

## Installation
```bash
python -m venv .venv/
source .venv/bin/activate
pip install inspectr
```

## Usage
Generally, the syntax goes:
```bash
inspectr <subtool> [options] [files...]
```
where `<subtool>` is one of the following:

- `authenticity`: looks for TODO comments, empty try/except blocks, and stub functions
- `bare_ratio`: checks for the ratio of bare excepts to meaningful exception usage
- `compare_funcs`: compares function/method names across two directory versions
- `complexity`: analyzes algorithmic complexity of Python code
- `count_exceptions`: counts how many of each type of exception there are (including bare except)
- `duplicates`: looks for occurrences of duplicate or similar code (>80% similarity, default: 10+ lines, 3+ occurrences)
- `size_counts`: various linecount-related code complexity checks
- `with_open`: checks for `open` in the absense of `with` and manual calls to `close()`

### Command-Line Options
All tools accept command-line options in the format `--option-name value`. 
Options are passed as keyword arguments to the tool's main function. For example:
```bash
inspectr duplicates --block-size 15 --min-occur 2 file1.py file2.py
```

Recognized options include:
- `duplicates`:
  - `--block-size N`: number of consecutive lines in a block (default: 10)
  - `--min-occur N`: minimum number of occurrences to report (default: 3

**Note**: any unrecognized options will be silently ignored.

### Usage for compare_funcs
The `compare_funcs` tool compares functions across two directory versions:
```bash
inspectr compare_funcs files_list.txt dir1 dir2
```
where `files_list.txt` contains relative paths to compare, one per line.

## Local Testing
First install in development mode with test dependencies, then run the tests:
```bash
git clone https://github.com/ajcm474/inspectr.git
cd inspectr
pip install -e ".[test]"
pytest tests/
```

**Please note:** this project is in the early alpha stage, so don't expect the above subtool names 
to be stable between versions. I might even merge/split them at some point.