semvec/pypi-packages huggingface dataset:
wsp-tasha-toolbox 0.1.4
Python tools for working with TASHA-based models
Requires Python: >=3.10

# wsp-tasha-toolbox

![PyPI - Version](https://img.shields.io/pypi/v/wsp-tasha-toolbox)

A toolbox for working with travel demand models based upon the *Travel Activity Scheduler for Household Agents (TASHA)* model structure.

Tools included in this toolbox are designed to work with models built using GTAModel V4.0+.

The modelling platform for all TASHA-based models (such as GTAModel V4.0+ in the GTHA and implementations in other regions) is developed by the [Travel Modelling Group](https://tmg.utoronto.ca/) at the University of Toronto.

## Installation

Binary installer for the latest released version is available on the Python Package Index:

```batch
pip install wsp-tasha-toolbox
```
python-audio 0.1.2
Package for working on audio in python
Requires Python: >=3.10

# python-audio

This package is desgined to replace the deprecated audioop package.
Future plan is to make it a one-stop solution for anything audio related for python.

## How to use?

Install using one of the methods below.

```bash
pip install git+https://github.com/AstralScribe/python-audio.git
```

```bash
pip install python-audio
```

## Available features:

- All functionality of audioop has been implemented except three functions `ratecv, adpcm2lin, lin2adpcm`.
  - To use the package just replace `import audioop` with `import audio.op as audioop`
pycatima 1.981
Python interface to CATIMA library
Requires Python: >=3.8

![Build](https://github.com/hrosiak/pycatima/actions/workflows/build.yml/badge.svg)

# pycatima


Python Module
-------------
Python module can be installed directly from PyPI using pip:
```bash
pip install pycatima
```

Or installed by cloning the Git repo
```bash
git clone https://github.com/hrosiak/pycatima.git
```
and installing with pip
```bash
pip install ./pycatima
```

The C++ library takes additional CMake configuration options which can be provided to the build backend with `scikit-build-core`. See [the documentation for `scikit-build-core`](https://scikit-build-core.readthedocs.io/en/latest/readme.html#configuration) for more information.
dynamic-bitmap 0.3.3
Dynamic Parallel Bitmap con soporte de IA para búsquedas optimizadas, ahora con una vision sobre la conexion p2p.
Requires Python: >=3.8

# Dynamic Parallel Bitmap (DPB)

**Dynamic Parallel Bitmap** es un sistema optimizado para **búsquedas y deduplicación en mapas de bits segmentados**, con soporte de **IA** y procesamiento **paralelo**. Es ideal para manejar grandes volúmenes de datos en sistemas CRM, multiempresa o distribuidos.

---

## Características

- **Vectorizado** → Operaciones bit a bit altamente optimizadas usando **NumPy**  
- **Dinámico** → Soporta inserciones y eliminaciones de registros  
- **Paralelo** → Búsquedas y joins acelerados mediante **multiprocessing**  
- **Segmentado + IA** → Integración con **TensorFlow** para aprendizaje de patrones  
- **Escalable** → Maneja millones de registros sin degradar el rendimiento  
- **Aplicación típica** → Joins, deduplicación y sincronización de datos en CRM  

---

## DPB-NET-Q (Extensión experimental)

DPB-NET-Q introduce un **entrelazamiento lógico entre nodos de red** mediante **qbits lógicos**:  

- Cada nodo mantiene segmentos sincronizables y un vector de qbits asociados  
- Los valores de **score** y **confianza** evolucionan según la interacción de datos  
- Permite sincronizaciones inteligentes y medición de correlaciones entre nodos distribuidos  

Esta extensión es ideal para **redes de datos distribuidas** y entornos de alta complejidad.

---

## Instalación

Clonar el repositorio e instalar el paquete en modo editable:

```bash
git clone https://github.com/JesusDeg8061/Dynamic_bitmap.git
cd Dynamic_bitmap
pip install dynamic-bitmap