semvec/pypi-packages huggingface dataset:
cdk-tutor 0.2.0
A CLI application that teaches AWS CDK in Python to software engineers.
Requires Python: >=3.9

# CDK Tutor

A CLI application that teaches AWS CDK to users through interactive challenges.

## Installation

With `pip`:

```bash
pip install cdk-tutor
```

With `pipx`:

```bash
pipx install cdk-tutor
```

With `uvx`:

```bash
uvx install cdk-tutor
```

## Usage

### List available challenges

```bash
cdk-tutor list-challenges
```

### Start a challenge

```bash
cdk-tutor start [CHALLENGE_NAME]
```

If you don't specify a challenge name, you'll be prompted to choose from available challenges.

### Setup a Python virtual environment

```bash
python -m venv .venv
source .venv/bin/activate  # On Windows use .venv\Scripts\activate
pip install -r requirements.txt
```

### Grade a completed challenge

```bash
cdk-tutor grade [CHALLENGE_DIR]
```

## Features

- Interactive CLI with rich text formatting
- Step-by-step challenges to learn AWS CDK
- Automatic grading of solutions
- Detailed feedback to help users improve

## Challenge Structure

Each challenge includes:

- Starter code with TODOs to complete
- Clear instructions in a README
- Expected CloudFormation output for grading
- Solution files for reference

## Development

To add new challenges:

1. Create a new challenge in `src/cdk_tutor/challenges/`
2. Follow the `Challenge` model structure
3. Add your challenge to the list in `get_available_challenges()`

## License

MIT
paulify 0.1.3
PAUL v1.1 license attribution helper
Requires Python: >=3.6

## About

paulify is a lightweight CLI tool for generating standardized attribution text for projects using the PAUL v1.1 license. PAUL v1.1 is aimed at ethical AI development and expressly forbids commercial use without a paid license while allowing personal and academic use, as long as the developer credits you. It outputs attribution in either plain text or Markdown. 

## Sample Output

`paulify "App Name" "Developer Name" --format markdown`

yields

App Name by Developer Name, used under [PAUL v1.1](https://github.com/cactusflatscoder/PAUL) license.

![License: PAUL v1.1](https://img.shields.io/badge/license-PAUL--v1.1-blue)

## How to Credit Your Work Under PAUL

Those who use your work should credit you like this:

*Built on App Name by Developer Name under [PAUL v1.1](https://github.com/cactusflatscoder/PAUL) license.*

or 

*Incorporates code from App Name by Developer Name under [PAUL v1.1](https://github.com/cactusflatscoder/PAUL) license.*

## License

This project is licensed under the [PAUL v1.1 License](https://github.com/cactusflatscoder/PAUL/blob/main/LICENSE.md).
pygame-starter 0.3.7
Gerador automático de projetos base em Pygame com menu, resolução, cor e recorde
Requires Python: >=3.6

🔧 COMO USAR A BIBLIOTECA 'pygame_starter'
==================================================

📁 1. Estrutura esperada do projeto:

pygame_starter/
├── setup.py
├── README.md (opcional)
└── pygame_starter/
    ├── __init__.py
    └── gerador.py


🚀 2. Instalando com o pip:

Abra o terminal na pasta onde está o setup.py e execute:

    pip install pygame-starter

Isso registra o pacote no seu Python e cria o comando 'pygame-starter'.


📦 3. Usando o gerador de projetos:

Depois de instalado, execute:

    pygame-starter

Ele vai perguntar:
- Nome do projeto (sem espaços, números ou símbolos)
- Largura e altura da tela
- Cor de fundo (em RGB, ex: 0,0,0)

E criará uma estrutura assim:

meu_jogo/
├── main.py
├── config.py
├── recorde.json
└── assets/
    ├── sprites/
    ├── sons/
    └── fontes/


🎮 4. Rodando o jogo:

Entre na pasta do projeto e execute:

    python main.py


💡 Dica:
Você pode editar o main.py para criar seu jogo com base no template!

✅ Pronto para criar jogos com Pygame de forma rápida!
==================================================
pyhighlighter-c 1.0.0
Advanced syntax highlighter with built-in compiler
Requires Python: >=3.7
Keywords: syntax highlighting code editor compiler

# Highlighter

Advanced syntax highlighting library with built-in compiler (dockr) for multiple languages.

## Features

- Syntax highlighting for 15+ languages
- Integrated compiler/runner (dockr)
- Customizable color schemes
- HTML and terminal output formats
- Easy to extend with new languages

## Installation

```bash
pip install highlighter
```

## Usage

### Syntax Highlighting

```python
from highlighter import Highlighter

hl = Highlighter()
code = """
def hello(name):
    print(f"Hello, {name}!")
"""

# Get HTML output
html_output = hl.highlight(code, "python")
print(html_output)

# Get terminal output
term_output = hl.highlight(code, "python", output_format="terminal")
print(term_output)

# Get CSS for the highlighting
print(hl.get_css())
```

### Dockr Compiler

```python
from highlighter import DockrCompiler

compiler = DockrCompiler()
code = """
#include <stdio.h>

int main() {
    printf("Hello from C!\\n");
    return 0;
}
"""

output, error, return_code = compiler.compile_and_run(code, "c")
print(f"Output: {output}")
print(f"Error: {error}")
print(f"Return code: {return_code}")
```

### Command Line Usage

```bash
# Run a Python file
dockr script.py

# Run a C file with input
dockr program.c -i input.txt
```

## Supported Languages

- Python, JavaScript, Ruby, PHP, Lua
- C, C++, Java, Rust, Zig
- HTML, CSS, SCSS
- And more!

## License

MIT
lazyedabf 1.0.3
A package for EDA on CSV, Parquet.
Requires Python: >=3.7


# EDA BF - Exploratory Data Analysis Package

Este paquete permite realizar un análisis exploratorio de datos (EDA) sobre archivos (CSV, Parquet), generando informes en formato Excel para facilitar el análisis de calidad y características de los datos.

## Funcionalidades Principales

### 1. `process_single_file`
Procesa un archivo individual (CSV o Parquet) y genera un informe EDA en formato Excel.

- **Función**: `process_single_file(file_path, output=None, limite=None)`
- **Parámetros**:
  - `file_path` (str): Ruta del archivo a procesar.
  - `output` (str): Ruta de salida para el archivo Excel (opcional).
  - `limite` (int): Límite opcional de filas a leer.
- **Descripción**: Lee el archivo especificado, genera un análisis exploratorio de datos y lo guarda en un archivo Excel que contiene métricas y estadísticas de los datos.

### 2. `process_folder`
Procesa múltiples archivos en una carpeta (CSV y Parquet) y genera un informe EDA consolidado en un archivo Excel.

- **Función**: `process_folder(folder_path, output=None, limite=None)`
- **Parámetros**:
  - `folder_path` (str): Ruta de la carpeta que contiene los archivos a procesar.
  - `output` (str): Ruta de salida para el archivo Excel consolidado.
  - `limite` (int): Límite opcional de filas a leer por archivo.
- **Descripción**: Busca archivos CSV y Parquet en la carpeta especificada, procesa cada archivo, y genera un informe combinado en Excel con métricas detalladas de cada archivo.

### 3. `process_dataframe`
Procesa un DataFrame directamente en memoria y genera un informe EDA en formato Excel.

- **Función**: `process_dataframe(df, output=None, table_name="DataFrame", limite=None)`
- **Parámetros**:
  - `df` (DataFrame): El DataFrame que se desea procesar.
  - `output` (str): Ruta de salida para el archivo Excel.
  - `table_name` (str): Nombre que se asignará al DataFrame en el informe Excel (por defecto: `"DataFrame"`).
  - `limite` (int): Límite opcional de filas a procesar.
- **Descripción**: Procesa un DataFrame cargado en memoria, generando un informe en Excel con estadísticas descriptivas y métricas clave. Si se especifica un límite, solo se procesan las primeras `limite` filas.


## Ejemplos de Uso

### Procesar un Archivo Individual
```python
from edabf import process_single_file
process_single_file("ruta/al/archivo.csv", output="salida", limite=1000)
```

### Procesar una Carpeta de Archivos
```python
from edabf import process_folder
process_folder("ruta/a/carpeta", output="salida_masivo")
```

### Procesar Múltiples Tablas de un Esquema de Base de Datos
```python
from edabf import process_dataframe
import pandas as pd

# Crear un DataFrame de ejemplo
data = {"Columna1": [1, 2, 3], "Columna2": ["A", "B", "C"]}
df = pd.DataFrame(data)

# Generar el informe EDA
process_dataframe(df, output="reporte_dataframe", table_name="Ejemplo", limite=10)
```
## Licencia
Este proyecto está bajo la licencia MIT.
pyedt 0.1.5
Euclidian Distance Transform functions for GPU and parallel CPU

[![](https://badge.fury.io/py/pyedt.svg)](https://pypi.python.org/pypi/pyedt)

# pyedt
Python Euclidian distance transform with numba and cuda

![Benchmarks](doc/benchmarks.png)

Legend
 * cpu - PyEDT running on cpu only
 * gpu - PyEDT running with CUDA, sending the whole image to the GPU
 * gpu_split_n - PyEDT running with CUDA, the image is split in n^2 prisms and processed one at a time in the GPU
 
Benchmark executed in a Windows 10 machine, with 64 Gb of RAM, an AMD Ryzen 7 2700 CPU and a GeForce RTX 3090 Ti GPU

The methods used were developed based on modifications over the work published by Lotufo and Zampirolli[1]

[1]Lotufo, Roberto & Zampirolli, Francisco. (2001). Fast multidimensional parallel Euclidean distance transform based on mathematical morphology. 100 - 105. 10.1109/SIBGRAPI.2001.963043.
offlinesec-connector 0.0.12
Offline Security Connector

# Offline Security Connector
The Offline Security Connector is a package for [Offline Security Client](https://github.com/offlinesec/offlinesec-client) which allows direct connection to SAP systems via RFC protocol (PyRFC library). It collects all needed data for the Offline Security reports.

# Advantages

* You don't need to perform manual actions in SAP anymore
* It supports password authentication, SNC and SSO configurations

## Table of contents

* [Installation](#installation)
* [Quick Start](#quick-start)
* [Use Cases](#use-cases)

## Installation

### Python installation
Install the last version of Python 3.x [from here](https://www.python.org/downloads/)<br />
We support only Python 3.x!

### PyRFC installation
Please refer to [the pyrfc doc](https://github.com/SAP-archive/PyRFC/blob/main/doc/install.rst)
Do not forget to download SAP NWRFC SDK from sap.com. 

### Published version installation (recommended)
```sh
pip3 install offlinesec-connector
```
or
```sh
python3 -m pip install offlinesec-connector
```

Check the installation script output. if you see the following message:
WARNING: The scripts offlinesec_connector and offlinesec_conn_settings are installed in '/Users/<username>/Library/Python/3.8/bin' which is not on PATH.

Then add Python folder to the PATH variable:
```sh
export PATH="$PATH:/Users/<username>/Library/Python/3.8/bin"
```

### Upgrade to the latest published version
```sh
pip3 install --upgrade offlinesec-connector
```
Note: It's recommended to use the last available version each time

### Check what version is installed right now on your laptop
```sh
pip3 show offlinesec-connector
```

## Quick Start
1. Set Connection settings with offlinesec_conn_settings
```sh
offlinesec_conn_settings -f settings.yaml
```
where settings.yaml file - a file with connection settings to SAP. Please refer to [the doc](./docs/offlinesec_conn_settings.md).

2. Run the connector
```sh
offlinesec_connector -c DEV
```
where DEV - a connection name from the settings file
Please refer to [the doc](./docs/offlinesec_connector.md)

```sh
offlinesec_connector -g 'g:productions'
```
where 'g:productions' - a groupname from the settings file

## Use Cases
### Missed SAP Security Notes (Unpatched SAP security vulnerabilities)
```sh
offlinesec_connector -c DEV
```
Please refer to [the doc](./docs/offlinesec_connector.md)

What access we need:
* S_TABU_NAM:ACTVT=03
  S_TABU_NAM:TABLE=CWBNTCUST, PATCHHIST
* S_RFC:ACTVT=16
  S_RFC:RFC_TYPE=FUGR
  S_RFC:RFC_NAME=SDTX, OCS_CRM

To run the following FMs:
* OCS_GET_INSTALLED_COMPS
* RFC_READ_TABLE

and read the following tables:
* CWBNTCUST
* PATCHHIST

## Uninstall
```sh
python3 -m pip uninstall offlinesec-connector
```
apisani-test-project-25063002 2.0.0
A short description of the project
Requires Python: <4.0,>=3.10

# apisani-test-project-25063002

A short description of the project

## Overview

This documentation covers the apisani-test-project-25063002 library....................

## Installation

```bash
pip install apisani-test-project-25063002
```

Or, if you use Poetry:

```bash
poetry add apisani-test-project-25063002
```

## Quick Start

```python
from apisani_test_project_25063002 import Example

# Initialize
example = Example()

# Use the library
result = example.run()
print(result)
```
cdtools-py 0.3.0
Tools for coherent diffractive imaging and ptychography
Requires Python: >3.8

# CDTools

CDTools is a python library for ptychography and CDI reconstructions, using an Automatic Differentiation based approach.

```python
from matplotlib import pyplot as plt
from cdtools.datasets import Ptycho2DDataset
from cdtools.models import FancyPtycho

# Load a data file
dataset = Ptycho2DDataset.from_cxi('ptycho_data.cxi')

# Initialize a model from the data
model = FancyPtycho.from_dataset(dataset)

# Run a reconstruction
for loss in model.Adam_optimize(10, dataset):
    print(model.report())

# Save the results
model.save_to_h5('ptycho_results.h5', dataset)

# And look at them!
model.inspect(dataset) # See the reconstructed object, probe, etc.
model.compare(dataset) # See how the simulated and measured patterns compare
plt.show()
```

Full installation instructions and documentation can be found [here](https://cdtools-developers.github.io/cdtools/).


CDTools was developed in the [photon scattering lab](https://scattering.mit.edu/) at MIT, and further development took place within the [computational x-ray imaging group](https://www.psi.ch/en/cxi) at PSI. The code is distributed under an MIT (a.k.a. Expat) license. If you would like to publish any work that uses CDTools, please contact [Abe Levitan](mailto:abraham.levitan@psi.ch).

Have a wonderful day!