semvec/pypi-packages huggingface dataset:
indycarpy 0.2.1
A Python package to scrape Indycar session data
Requires Python: >=3.8

# IndyCarPy

IndyCarPy is a Python package designed to scrape IndyCar session data from
the official IndyCar website. It provides functionalities to retrieve
information about races, practices, qualifications, and warm-up sessions,
as well as the records associated with these sessions.

## Features

- **Session Data Retrieval**: Fetch data about IndyCar sessions including
  races, practices, qualifications, and warm-ups.
- **Data Cleaning and Preparation**: Clean and prepare the retrieved data
  for further analysis or storage.
- **Export to CSV**: Option to export the data to CSV format for easy
  integration with other tools or databases.

## Installation

You can install IndyCarPy using pip:

```bash
pip install indycarpy
```

Make sure you have the required dependencies installed: `pandas`,
`requests`, `tqdm`, and `numpy`.

## Usage

```bash
import indycarpy

# Retrieve race session data between specific years
indycarpy.get_sessions_records(from_year=1996, to_year=2024, session_type="R", data_format="df")

# Clean session records data
cleaned_data = indycarpy.clean_sessions_records(sessions_df)
```

## Data Files

This package includes a `data/race_track.csv` file containing information
about the track names for each event, which is utilized during data
retrieval.

## Contributing

Contributions to IndyCarPy are welcome! If you encounter any issues or have
suggestions for improvements, feel free to open an issue or submit a pull
request on GitHub.

## License

IndyCarPy is licensed under the GNU General Public License v3.0. See the
LICENSE file for more details.
paz-cli 1.0.2
A CLI tool to analyze password strength and breach status
Requires Python: >=3.10

# Paz – Password Analyzer CLI   
# ![License](https://img.shields.io/badge/license-MIT-green) [![PyPI](https://img.shields.io/pypi/v/paz-cli)](https://pypi.org/project/paz-cli/) ![Package Size](https://img.shields.io/badge/size-5.0KB-red)

یک ابزار خط فرمانی(CLI) ساده برای:

- محاسبهٔ **قدرت** رمز عبور  
- بررسی **لو رفتن** آن در دیتابیس‌های معروف (Have I Been Pwned)   
---
## 🚀 Features

- **Strength Scoring**  
  بر اساس طول، ترکیب حروف بزرگ/کوچک، اعداد، کاراکترهای خاص و جلوگیری از واژه‌های خیلی رایج، به رمز شما از 6 نمره می‌دهد.

- **Breach Check**  
  با استفاده از API سرویس معروف [`Have I Been Pwned`](https://haveibeenpwned.com)، تعداد دفعات افشای رمز در گذشته را نمایش می‌دهد.

---
## 📦 Installation
**From PyPI**  
```bash
pip install paz-cli
```

**From Source**
```bash
git clone https://github.com/MahdiMirshafiee/password-analyzer-cli
cd password-analyzer-cli
python3 -m venv venv
source venv/bin/activate       # Linux/macOS
# venv\Scripts\activate        # Windows PowerShell
pip install -e .
```
---
## ⚙️ Usage
**Direct flag**
```bash
password-analyzer -p 'MyP@ssw0rd!'
password-analyzer --password 'MyP@ssw0rd!'
# Password: MyP@ssw0rd!
# Strength: Strong (score: 5)
# Password found in 183 data breaches!
```

**Hidden prompt**
```bash
paz
# Enter password (input hidden): 
# Password: hidden input received
# Strength: Strong (score: 4)
# Password not found in known breaches
```
---
## 🆘 Help
```bash
paz --help
```
---
## 🛠 Contributing
1.	یک Issue باز کنید برای پیشنهاد فیچر یا گزارش باگ
2.	از برنچ dev :شاخه‌ای بسازید
```bash
git checkout dev
git checkout -b feature/your-feature-name
```
3.	تغییرات را commit کنید و push:
```bash
git add .
git commit -m "[شرح کوتاه تغییرات]"
git push origin feature/your-feature-name
```
4.	یک Pull Request از شاخهٔ خود به dev باز کنید
---
## 📄 License
This project is licensed under the MIT License – see the [LICENSE](./LICENSE) file for details.
cabcode 0.1.0
CabCode - Python 開發工具套件 | CabLate 的開源專案 | 軟體工程師的技術分享
Requires Python: >=3.8
Keywords: cabcode, cablate, python, development, 軟體工程, 開發工具, 技術分享, 台灣開發者, 開源專案, 程式設計

# CabCode - Python 開發工具套件 🐍

[![PyPI version](https://badge.fury.io/py/cabcode.svg)](https://badge.fury.io/py/cabcode)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)


```bash
pip install cabcode
```

## 基本使用

```python
import cabcode

# 驗證安裝
print(cabcode.hello())
# 輸出: Hello from CabCode v0.1.0! This package is ready for development.

# 取得版本
print(cabcode.get_version())
# 輸出: 0.1.0

# 小彩蛋
print(cabcode.ask("這個超強AI套件的創造過程你最感謝誰？"))
```

## 貢獻

目前此專案主要用於名稱佔位，暫不接受外部貢獻。未來開放開發時會更新此說明。

## 授權

此專案採用 MIT 授權條款 - 詳見 [LICENSE](LICENSE) 檔案。

## 關於作者

**CabLate** - 熱愛程式開發與技術分享的軟體工程師

🔗 **社群連結**：

- 🧵 [Threads](https://www.threads.com/@cab_late)
- 💻 [GitHub](https://github.com/CabLate)
- 📦 [PyPI](https://pypi.org/user/CabLate/)

**注意**：此套件目前處於佔位狀態，主要功能尚在開發中。請關注後續版本更新。
CoCoMET 1.0.0
Community Convective cloud Model Evaluation Toolkit (CoCoMET)
Requires Python: >=3.10.0

# CoCoMET
[comment]: # <img src="./docs/images/cocomet_logo.png" alt="Logo" width="200" height="200"/>

Community Cloud Model Evaluation Toolkit.

**Current Features**:

1. **WRF**:  
   1. tobac tracking of variables  
      1. Reflectivity  
      1. Brightness temperature  
      1. Updraft velocity  
      1. Precipitation rate  
      1. Any WRF variables in the dataset (case sensitive)
   1. MOAAP tracking of MCSs and cloud shields
   1. TAMS tracking of MCSs and cloud shields  
1. **RAMS**:  
   1. tobac tracking of variables  
      1. Reflectivity  
      1. Brightness temperature  
      1. Updraft velocity  
      1. Precipitation rate  
      1. Any WRF variables in the dataset (case sensitive)
   1. MOAAP tracking of MCSs and cloud shields  
   1. TAMS tracking of MCSs and cloud shields  
1. **MesoNH**:  
   1. tobac tracking of variables:  
      1. Reflectivity  
      1. Brightness temperature  
      1. Updraft velcoity  
      1. Any MesoNH variables in the dataset (case sensitive)
   1. MOAAP tracking of MCSs and cloud shields
1. **NEXRAD**:  
   1. Automatically grid radars  
   1. tobac tracking of variables:  
      1. Reflectivity  
1. **Multi-NEXRAD**:  
   1. Automatically grid multiple radars  
   1. tobac tracking of variables:  
      1. Reflectivity  
1. **Standardized Radar Grids (CoMET-UDAF Section S1.1.):**  
   1. tobac tracking of variables:  
      1. Reflectivitiy  
1. **GOES**  
   1. tobac tracking of variables:  
      1. Brightness temperature  
1. **Analysis**:  
   1. Calculates areas at given height  
   1. Calculates volume  
   1. Calculates echo top heights  
   1. Identifies mergers and splitters  
   1. Extracts ARM Products:  
      1. Links ARM VAP output to tracks  
      1. Links INTERPSONDE to tracks  
         1. Calculates convective initiation properties from INTERPSONDE data (CAPE, CIN, etc.)


**Planned Features**:
1. Post-processing functions
1. Add ARM Radars
1. Add RWP
1. Add IMERG Satellite Data
1. Calculate ECAPE


[comment]: # ## CoCoMET Workflow

[comment]: # <img src="./docs/images/cocomet_workflow.png" alt="User workflow"/>

## Acknowledgments
This project was supported by the U.S. Department of Energy (DOE) Early Career Research Program, Atmospheric System Research (ASR) program, 
and the Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI).

If you are using this software for a publication, please cite: Hahn et al. (2025) https://doi.org/10.5194/egusphere-2025-1328
pygments-ashen 0.1.3
Pygments style for Ashen
Requires Python: >=3.12

# Pygments Ashen

This repository contains the Pygments style plugin for the
[Ashen](https://sr.ht/~ficd/ashen) theme. Available as the `pygments-ashen`
package on [PyPI](https://pypi.org/project/pygments-ashen/) for use in your
Python projects, or with the Pygments CLI tool. The actual name of the _style_
within `pygments` is `ashen`.

## How To Use

The following shows how to use the style with `uv`, which is currently the
cleanest way to manage Python projects and libraries. You may, of course,
install the package into a virtual environment with `pip`, if you wish:

```sh
pip install pygments-ashen
```

### Pygments CLI

Third-party Pygments styles are Python packages that must be available in the
environment `pygments` is running in. For example, you can use `uv` to create a
one-off environment for a single command:

```sh
uv run --with 'pygments-ashen,pygments' \
pygmentize -O full,style=ashen \
-l python -f html foobar.py > foobar.html
```

The above command invokes `pygmentize` with the `pygments-ashen` and `pygments`
dependencies available, and renders the file `foobar.py` in HTML format with the
Ashen theme applied.

### In Your Projects

If your project uses Pygments for code highlighting, you can easily add support
for Ashen. The `pygments-ashen` package must be installed in the project
environment:

```sh
uv add pygments-ashen
```

Then, you can use `ashen` as the `style` in Pygments formatters. Here's an
example from Zona. This code renders a Markdown code block in HTML with Ashen
highlighting, using the `marko` library:

```python
# method part of subclass of marko.html_renderer.HTMLRenderer
@override
def render_fenced_code(self, element: FencedCode):
    # pygments expects a single string, not a list of lines
    code = "".join(child.children for child in element.children)
    # fallback to "text" if no language specified
    lang = element.lang or "text"

    # load the correct pygments lexer
    try:
        lexer = get_lexer_by_name(lang, stripall=False)
    except Exception:
        lexer = TextLexer(stripall=False)

    # use the "ashen" style, requires pygments-ashen package
    formatter = HtmlFormatter(style="ashen", nowrap=True, noclasses=True)
    # highlight the code
    highlighted = highlight(code, lexer, formatter)

    # return rendered code block
    return (
        f'<pre class="code-block language-{lang}">'
        f"<code>{highlighted}</code></pre>"
    )
```
llm-api-bridge 1.0.0
A unified interface for querying multiple LLM providers via REST APIs
Requires Python: >=3.7
Keywords: llm, ai, openai, claude, gemini, api, chatbot, nlp

# LLMConnect

A unified Python library for querying multiple Large Language Models (LLMs) using direct REST API calls.

## Features

- **Multi-Provider Support**: Connect to OpenAI, Claude, Gemini, Perplexity, Mistral, DeepSeek, and LLaMA
- **Auto-Detection**: Automatically detects the provider based on model name prefix
- **Simple Interface**: Just provide model name and API key
- **Customizable**: Optional temperature and max_tokens parameters
- **No SDKs Required**: Uses direct REST API calls via requests library

## Installation

```bash
pip install llmconnect
```

## Quick Start

```python
from llmconnect import LLMConnect

# Initialize with your preferred model
client = LLMConnect("gpt-4", "your-openai-api-key")

# Send a message
response = client.chat("Hello, how are you?")
print(response)
```

## Supported Models

- **OpenAI**: gpt-4, gpt-3.5-turbo, etc.
- **Claude**: claude-3-sonnet, claude-3-opus, etc.
- **Gemini**: gemini-pro, gemini-ultra, etc.
- **Perplexity**: sonar-pro, sonar-medium, etc.
- **Mistral**: mistral-large-latest, mistral-medium, etc.
- **DeepSeek**: deepseek-chat, deepseek-coder, etc.
- **LLaMA**: llama3-70b, llama2-13b, etc.

## Advanced Usage

```python
# Custom parameters
client = LLMConnect(
    model_name="claude-3-sonnet",
    api_key="your-claude-api-key",
    temperature=0.9,
    max_tokens=2048
)

response = client.chat("Write a creative story")
```

## API Keys

You'll need API keys from the respective providers:
- OpenAI: https://platform.openai.com/
- Anthropic (Claude): https://console.anthropic.com/
- Google (Gemini): https://makersuite.google.com/
- Perplexity: https://www.perplexity.ai/
- Mistral: https://console.mistral.ai/
- DeepSeek: https://platform.deepseek.com/
- LLaMA: https://www.llama-api.com/

## License

MIT License
gputop 0.3.0
Real-time GPU monitoring tool
Requires Python: >=3.8

# GPUTOP

A simple real-time GPU monitoring tool for NVIDIA, AMD and Intel GPUs.

## Installation

From pypi:

```sh
pip install gputop
```

Form source:

```sh
pip install git+https://github.com/intexcor/gputop.git
```

## Usage


```sh
gputop
```

Run to get more information
```sh
gputop --help
```

![image](https://github.com/user-attachments/assets/a3eca25c-b64d-4efd-94e2-fb7b3ca1e313)


## Donation
You can support the project with money. This will help you develop better new versions faster.
CloudTips: https://pay.cloudtips.ru/p/315e026f

## Contacts
Telegram - @intexcp
pyw-ai-core 0.0.0
Meta-package for PythonWoods AI-powered tools
Requires Python: >=3.10

# 🤖 pyw-ai-core

Meta-pacchetto per orchestrare i pacchetti basati su intelligenza artificiale del framework **PythonWoods**.

## Installazione

```bash
pip install pyw-ai-core[autodocgen]
```

## 📦 Pacchetti inclusi (extras)

- `autodocgen`: Generazione documentazione automatica
- (futuri) `pyw...`, `pyw...`, `pyw...`...

## 📈 Architettura

```mermaid
graph TD
    A[pyw-ai-core] --> B(pyw-autodocgen)
    A --> C(pyw...)
    A --> D(pyw-...)
```

## 📄 Licenza

MIT
countonlyonechar 1.0.0
Package for counting unique characters in a string
Requires Python: >=3.10

# countonlyonechar

Package for counting unique characters in the string occurring only once.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install countonlyonechar.

```bash
pip install countonlyonechar
```

## Usage

```python
import countonlyonechar

# returns 4
countonlyonechar.count_only_one_char('word')

# returns <string in file>
countonlyonechar.read_txt_file(path_to_text_file)
```

```commandline
countonlyonechar --string 'word'
# returns 4

countonlyonechar --file <file_path>
# returns the number of characters in the file occurring only once
```
## License

[MIT](https://choosealicense.com/licenses/mit/)
ultra-crdex 6.1
new compiler to enc files python
Requires Python: >=3.9

# ULTRA - مكتبة قوية للحسابات المتقدمة

[![PyPI - Version](https://img.shields.io/pypi/v/ULTRA.svg)](https://pypi.org/project/ULTRA/)
[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

مرحبًا بك في مكتبة ULTRA! هذه المكتبة مصممة لتوفير طرق حماية قوية وفعالة، مع التركيز على الأداء العالي باستخدام وحدات مجمعة (compiled modules).

## الميزات الرئيسية

*   **سرعة عالية:** تستفيد من وحدات CPython المجمعة لتحقيق أداء فائق.
*   **سهولة الاستخدام:** واجهة برمجة تطبيقات (API) بسيطة وبديهية.
*   **دعم متعدد الإصدارات:** متوافقة مع Python 3.9, 3.11, و 3.12.
*   **دعم معمارية AArch64:** محسنة خصيصا للأجهزة التي تعمل بمعالجات AArch64 (مثل أجهزة Termux).

## التثبيت

يمكنك تثبيت مكتبة ULTRA باستخدام `pip`:

```bash
pip install ULTRA
```