semvec/pypi-packages huggingface dataset:
pythed 0.0.5
A vinted api wrapper in python.
Keywords: python, vinted, pypi, api, wrapper


# Vinted App in Python



## Description



This is a Vinted Api Wrapper which you can use in python for various projects in which you need to include some vinted features (e.g : Searching items based on query).



## Installation



1. Install package : `pip install pythed`



## Basic example



```

from pythed.app.Vinted import Vinted



app = Vinted()



brands = app.get_brands(brand_names=["Jordan", "Nike"])

brand1 = brands[0]["id"]

brand2 = brands[1]["id"]



items = app.search_items(search_text="Jordan", perPage=1, brand_ids=[brand1, brand2], price_to=15)



print(items)

```



## Retrieve similar items 



```

similar_items = app.similar_items(item_id="4695939285")



print(similar_items)

```



## Emoji Code for commits



- 🔄 Updated README file.

- 🛠️ Created new feature.

- 🐛 Fixed a bug.

- ✅ Added tests.

- 🔒️ Fixed security issues.

- 🔖 New release.

- 🚧 Work in progress.

- ⬆️ Updgraded dependencies.

- ⬇️ Downgraded dependencies.

- ♻️ Refactored code.

- ➕ Added a dependency.

- ➖ Removedg a dependency.

- 🔧 Changed configuration files.

- 🙈 Updating .gitignore file.



## Contact



Discord: archibald1789
gcardenas-db 0.6
Optimización del proceso de carga de datos con procesamiento paralelo


    La función carga_datos_efficient está diseñada para optimizar el proceso de carga de datos mediante técnicas avanzadas de procesamiento paralelo. 
    Esta función mejora significativamente el rendimiento y la eficiencia en la gestión de grandes volúmenes de datos, asegurando una carga rápida y segura. 
    A continuación se detallan sus características principales:

    - Procesamiento Paralelo: Distribuye la carga de trabajo entre múltiples unidades de procesamiento para acelerar el tiempo de carga.
    - Robustez y Fiabilidad: Implementa mecanismos avanzados de control y recuperación de errores para mantener la integridad de los datos durante todo el proceso.
    - Escalabilidad: Permite ajustar los parámetros de carga para adaptarse a diferentes volúmenes de datos y capacidades del sistema.
    - Registro y Monitoreo: Proporciona registros detallados y en tiempo real del proceso de carga, facilitando el monitoreo, análisis y resolución de problemas.

    Esta función es ideal para escenarios donde se requiere una gestión eficiente de grandes conjuntos de datos, aprovechando al máximo los recursos del sistema.

    Parámetros:

    - ruta_archivo: La ruta del archivo de datos a cargar.
    - configuracion: Diccionario opcional con configuraciones adicionales para personalizar el comportamiento de la función, como el nivel de detalle en los registros y las opciones de recuperación de errores.

    Retorno:

    - resultado: Un diccionario con el estado de la carga, incluyendo detalles sobre el éxito, posibles errores y estadísticas de rendimiento.
pysegul 0.4.1
Python bindings for the SEGUL high-performance and memory-efficient phylogenomic tools.
Requires Python: >=3.8
Keywords: phylogenomics, bioinformatics, phylogenetics, alignment, SEGUL

# PySEGUL

![Test](https://github.com/hhandika/pysegul/workflows/ci/badge.svg)
[![PyPI version](https://badge.fury.io/py/pysegul.svg)](https://badge.fury.io/py/pysegul)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pysegul)

PySEGUL is a python bindings of the [SEGUL](https://segul.app) high-performance and memory-efficient phylogenomic tools. It is well-suited for large-scale phylogenomic projects involving thousands of loci, but it is just as capable of handling small Sanger sequences effectively.

Learn more on using PySEGUL in the [documentation](https://www.segul.app/docs/api-usage/python/intro).

## Quick Start

```bash
pip install pysegul
```

To concatenate alignments:

```python
import pysegul

def concat_alignments():
    input_dir = 'tests/concat'
    input_format = 'nexus'
    datatype = 'dna'
    output_format = 'fasta'
    partition_format = 'raxml'
    prefix = 'concatenated'
    output_dir = 'results/concat'
    concat = pysegul.AlignmentConcatenation(
        input_format,  
        datatype, 
        output_dir, 
        output_format, 
        partition_format, 
        prefix
        )
    concat.from_dir(input_dir)

if __name__ == '__main__':
    concat_alignments()
```
video-audio-recorder 0.2.0
A package to record video and audio simultaneously
Requires Python: >=3.6

### Video Audio Recorder: Recording Audio and Video on the device simultaneously using device microphone and camera module/ device camera

Features
* Record audio and video simultaneously
* Use device's built-in microphone and camera
* Save recordings in common formats
* Easy-to-use interface

Note: This package is in beta testing.

### Installation and usage
# Steps to Install video-audio-recorder

1. **(Optional but Highly Recommended) Create a virtual environment with Python (version > 3.10). If you are using conda, run the following on terminal:**
    ```bash
    conda create -n <env name> python=3.9
    conda activate <env name>
    ```

2. **On terminal, install video-audio-recorder by:**
    ```bash
    pip install video-audio-recorder
    ```

3. **Once done, the application can be launched in a Jupyter notebook session via:**
    ```python
    from video_audio_recorder.recorder import record_video_and_audio
    record_video_and_audio(video_path='my_video.mp4', audio_path='output_audio.wav', output_path='merged_video.mp4', record_time=10)
    ```
    where output_path is the folder where you want to store the recording in .mp4 format. This will record video for 10 sec.
simpleLLMP 0.1.9
A simple library for interacting with OpenAI models.
Requires Python: >=3.6

# simpleLLMP

`simpleLLMP` is a simple Python library for interacting with OpenAI models. This library provides easy-to-use methods for setting up the OpenAI API and generating responses from a given prompt. It also includes OCR and PDF preprocessing capabilities.

## Features

- Simplified interaction with OpenAI's ChatGPT models.
- Easy setup with API key and model version.
- Generate responses from text prompts.
- Additional capabilities for OCR and PDF preprocessing (if needed).

## Installation

You can install the library using pip:

```bash
pip install simpleLLMP