semvec/pypi-packages huggingface dataset:
grainsy 1.0.2
Library for calculating grains on a chessboard
Requires Python: >=3.11

# Grains Library

This library provides functions to calculate the number of grains on a chessboard.

---

## Install Locally
Run the following command in the terminal to install your library locally for testing:

```bash
pip install .
```

## Usage

Here is an example of how to use this library:

```python
from grains import square, total

print(square(5))  # Grains on square 5
print(total())    # Total grains on chessboard
```
cat-data-tools 0.10.0
A template Python module
Requires Python: >=3.9

# Cat data tools <a href="https://www.islas.org.mx/"><img src="https://www.islas.org.mx/img/logo.svg" align="right" width="256" /></a>

[![codecov](https://codecov.io/gh/IslasGECI/cat_data_tools/branch/develop/graph/badge.svg?token=MvLBzyGSH3)](https://codecov.io/gh/IslasGECI/cat_data_tools)
![licencia](https://img.shields.io/github/license/IslasGECI/cat_data_tools)
![languages](https://img.shields.io/github/languages/top/IslasGECI/cat_data_tools)
![commits](https://img.shields.io/github/commit-activity/y/IslasGECI/cat_data_tools)
![GitHub contributors](https://img.shields.io/github/contributors/IslasGECI/cat_data_tools)

## Overview

Python module with tools to process cat data.


# Adaptadores
Esta nota está inspirada en el tema de arquitectura hexagonal.
La necesidad nació de la petición de que nos hizo el equipo de trampeo por tener las coordenadas de las trampas con capturas.
En el estado actual no tenemos las coordenadas, pero nos hicieron llegar las coordenadas en un archivo GPX.
Ese archivo lo cambiamos a formato GeoJSON y de ahí a csv. 
La promesa es que ese archivo ya no nos lo van a mandar.
El próximo formato serán archivos de mapsource.

## ¿Qué hicimos?
Sabemos que las próximas coordenadas vendrán de un archivo distinto. 
Así que decidimos que un adaptador decida cómo generar el DataFrame a partir de diferentes fuentes de datos.
El adaptador nos regresará objetos que transforan de la fuente de datos a DataFrame.
El nombre del archivo fuente es el criterio para saber el tipo de objeto que nos entregará el DataFrame que necesitamos.

## ¿Dónde lo hicimos?
Trabajamos en el repositorio [`cat_data_tools`](https://github.com/IslasGECI/cat_data_tools). 
¿Cuál es el objetivo de este repositorio?
¿Dónde utilizamos este repositorio?
[Aquí](https://github.com/IslasGECI/cat_data_tools/blob/develop/cat_data_tools/adapters.py) podemos ver la implementación.

## ¿Qué hace el adaptador?
La función `Adapter_for_path_to_dataframe()` nos entrega un objeto del tipo `Adapter_from_path` (creo que podemos mejorar el nombre. Tal vez transformer en lugar de adapter).
Estos objetos tienen un método `get_dataframe()` encargado de entregar el DataFrame con las columnas correctas.
La única razón para cambiar a la función `Adapter_for_path_to_dataframe()` es que agreguemos a una clase "`transformer`" nueva.
Las clases `transformer` cambian de manera independiente, cada una dependen del archivo con el que las inicializamos.
La razón por la que podrían cambiar todas las clases `transformer` es que las columnas de salida cambien de nombre.  

## ¿Qué logramos?
El cambio que nos prometieron que vendría lo tenemos encapsulado.
Cuando la fuente de datos sea distinta tenemos que escribir una nueva clase que cumpla con la misma interfaz. ¿Podemos decir lo mismo sin usar la palabra interfaz?
Además tendremos que agregar un opción en el diccionario [`choice`](https://github.com/IslasGECI/cat_data_tools/blob/develop/cat_data_tools/adapters.py#L6).
spc-export 1.0.1
SPC-Export is a tool designed to facilitate the conversion of SPC from Brama measurements into a format that is both human- and Python-readable. This streamlined conversion ensures easy access, readability, and visualization of measurement data for further analysis and interpretation.
Requires Python: >=3.1
Keywords: spc conversion

# SPC-Export: SPC Data Conversion for Human and Python Readability

**SPC-Export** is a Python-based tool designed to facilitate the conversion of **SPC** files from Raman measurements into a format that is both human- and Python-readable. This streamlined conversion ensures easy access, readability, and visualization of measurement data for further analysis and interpretation.

SPC-Export was developed to streamline the analysis and sharing of SPC measurement data, making it accessible for scientists and engineers.

Feel free to contribute improvements or report issues to enhance this tool for the Raman community!

---

### ZIP File Contents:
1. **Metadata File**:  
   - **`metadata.xml`**: A detailed XML file containing all header data.  
   - The data is condensed and presented in an easily readable, descriptive format.  

2. **Measurement Data**:  
   - Each measurement is saved as a **tab-separated CSV file** with a header line.  
   - CSV filenames are derived from the measurement descriptions.  

---

## Benefits  

- **Human-Readable**:  
  The condensed XML metadata and descriptive filenames ensure clarity for manual review.  

- **Machine-Friendly**:  
  The CSV format enables seamless integration with Python and other data processing workflows.  

- **Organized Output**:  
  The ZIP file structure ensures all related data is packaged together for easy sharing and storage.  

---

## Applications  

- Post-experiment analysis of SPC measurements.  
- Integration into automated workflows for advanced data analysis.  

---

## Installation and Usage  

1. **Instalation**:  
   - Python >= 3.1  
   - pip >= 22.0.2
   - pip install spc-export  

2. **Run the Script**:  
   ```bash
   spc-export --help

    usage: xps-export.py [-h] [--create-plots] [--create-csv]
                         input_file output_file
    
    Utility for parsing files to XML.
    
    positional arguments:
      input_file      Path to the XPS file.
    
    options:
      -h, --help      show this help message and exit
      
   spc-export test/SGL_reduced.spc
   
   ```
2. **Output**:  
   The ZIP file containing metadata, CSVs will be saved as {output_file}.zip .

## Developers and Designers

   Richard W. van Nieuwenhoven <nieuwenhoven@iap.tuwien.ac.at>
   Markus Valtiner <markus.valtiner@tuwien.ac.at>
zenodo-deposit 0.1.5
Zenodo deposit tool
Requires Python: >=3.13

# zenodo-deposit: A command line interface to making file deposits to Zenodo

[![Python tests](https://github.com/willf/zenodo-deposit/actions/workflows/test.yml/badge.svg)](https://github.com/willf/zenodo-deposit/actions/workflows/test.yml)
[![Commit activity](https://img.shields.io/github/commit-activity/m/willf/zenodo-deposit)](https://img.shields.io/github/commit-activity/m/willf/zenodo-deposit)
[![License](https://img.shields.io/github/license/willf/zenodo-deposit)](https://img.shields.io/github/license/willf/zenodo-deposit)

A command line interface to making file deposits to Zenoodo

- **Github repository**: <https://github.com/Public-Environmental-Data-Partners/zenodo-deposit/>
- **Documentation** <https://pypi.org/project/zenodo-deposit/>

## Understanding the use case

Sometimes is it more convenient to make a deposit to [Zenodo](https://zenodo.org) using a
command line interface (CLI) rather than through the web interface. This
is especially true when you have a large number of files to deposit, or
when you want to automate the deposit process as part of a larger workflow.
This script provides tools to make file deposits to Zenodo using
the command line.

## Installation

This can installed using `pipx` or `uv`:

```bash
$ pipx install zenodo-deposit
```

or

```bash
$ uv tool install zenodo-deposit
```

## Usage

```bash
$ zd --help
Usage: zd [OPTIONS] COMMAND [ARGS]...

Options:
  --version                       Show the version and exit.
  --sandbox, --dev / --production, --prod
                                  Set Zenodo environment to sandbox or
                                  production  [default: sandbox]
  --config-file PATH              Path to the configuration file
  --log-level [DEBUG|INFO|WARNING|ERROR|CRITICAL]
                                  Set the log level
  --help                          Show this message and exit.

Commands:
  create    Create a new deposition, without uploading a file
  deposit   Deposit a file
  retrieve  Retrieve deposition details
  search    Search for depositions
  upload    Upload one or more files, with metadata, creating a new deposit

```

Example:

```bash
$ zd --dev --log-level DEBUG upload --title 'Testing URL with larger dataset' --type 'dataset' --keywords 'rmp, epa' --name 'Fitzgerald, Will' --affiliation 'EDGI' --description 'Location database' --metadata metadata.toml https://edg.epa.gov/EPADataCommons/public/OA/EPA_SmartLocationDatabase_V3_Jan_2021_Final.csv
```
regia-ai-sdk 0.7.2
Add your description here
Requires Python: >=3.13

# Regia AI SDK

A lightweight Python SDK to interact with the Regia Vision Extractor API.  
Send PDF documents and automatically extract structured data using Pydantic schemas.

---

## 📦 Installation

Using **pip**:

```bash
pip install regia-ai-sdk
```

or **uv** use the command line:

```bash
uv add regia-ai-sdk
```

## 🚀 Quickstart

```python
from regia_ai_sdk.vision import VisionClient
from pydantic import BaseModel, Field
import os

class InvoiceModel(BaseModel):
  due_date: str = Field(..., description="The due date of the invoice")
  total_amount: float = Field(..., description="The total amount of the invoice")

token = os.getenv("API_TOKEN")

client = VisionClient(token=token)

result = client.extract("./data/sample.pdf", InvoiceModel)
print(result)
```

## 📂 Other Input Methods

### From bytes

```python
with open("./data/sample.pdf", "rb") as f:
  content_bytes = f.read()

result = client.extract(content_bytes, InvoiceModel, filename="sample_bytes.pdf")
print(result)
```

### From base64

```python
import base64

with open("./data/sample.pdf", "rb") as f:
  base64_string = base64.b64encode(f.read()).decode("utf-8")

result = client.extract(base64_string, InvoiceModel, filename="sample_base64.pdf")
print(result)
```

## 🧠 Features

- ✅ Simple interface for sending documents to the Regia Vision API
- ✅ Supports file path, bytes, and base64 inputs
- ✅ Use Pydantic models to define the expected data
- ✅ Automatically converts Pydantic to schema JSON

## 🔐 Authentication

You must provide an API token and base URL:

```bash
export API_TOKEN=your_api_token_here
```

---

Made with 💡 by [Regia.ai](https://regia.ai)
image-processing-JOCILE 0.0.1
Image Processing Package using skimage
Requires Python: >=3.5

# image_processing_JOCILE

- Test the knowledge acquired with a practical exercise on creating Python packages.
- Combine RGB histograms from two images.

Description:

The pakage image_processing is used to:
  Processing:
    - Histogram matching
    - Structural similarity
    - Resize image
  Utils:
    - Read image
    - Save image
    - Plot image
    - Plot result
    - Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_JOCILE
```

## Usage

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Author

[tiemi (Karina Kato) · GitHub](https://github.com/tiemi) | [linkedin.com/in/karina-kato-4b2a56182/](https://www.linkedin.com/in/karina-kato-4b2a56182/)

## License

[MIT](https://choosealicense.com/licenses/mit/)