semvec/pypi-packages huggingface dataset:
egyptian-phone-validator 0.1.1
A Django package to validate Egyptian phone numbers.
Requires Python: >=3.6

# Egyptian Phone Validator

A Django package to validate Egyptian phone numbers.


## Features
- Validates Egyptian mobile phone numbers.

- Ensures the phone number is exactly 11 digits long.

- Checks for valid Egyptian mobile prefixes.

- Raises ValidationError for invalid phone numbers.

- Ensures the phone number is digits.


## Installation

Install the package using pip:

```bash
pip install egyptian-phone-validator
```


## Usage

```bash
from django.db import models
from egyptian_phone_validator.validators import validate_egyptian_phone_number

class UserProfile(models.Model):
    phone = models.CharField(
        max_length=11,
        validators=[validate_egyptian_phone_number],
        help_text="Enter a valid Egyptian phone number (e.g., 01012345678)."
    )
```
evocarshare 0.0.2
Access data from the Evo Car Share api
Requires Python: <4.0,>=3.11

# Evocarshare-py

![https://github.com/jazzz/evocarshare-py/actions/workflows/main-build.yml/badge.svg](https://github.com/jazzz/evocarshare-py/actions/workflows/main-build.yml/badge.svg)
![Status](https://img.shields.io/badge/Project_status-Alpha-orange)

Evocarshare is a module for accessing the data api of the Evo CarShare service which streamlines token management, and queries.

## Installing

```
pip install evocarshare
```

## Supported Versions

- Python 3.11+

## Usage

Fetch all vehicles with range of a location
```python
import aiohttp
import os

from evocarshare import CredentialBundle, EvoApi

API_KEY = os.environ.get("EVOAPI_KEY")
CLIENT_ID = os.environ.get("EVOAPI_CLIENTID")
CLIENT_SECRET = os.environ.get("EVOAPI_CLIENTSECRET")

async with aiohttp.ClientSession() as client_session:
    # Initialize Api
    creds = CredentialBundle(API_KEY, CLIENT_ID, CLIENT_SECRET)
    api = EvoApi(client_session, creds)

    # Query Vehicles
    steam_clock = GpsCoord(latitude=49.284407, longitude=-123.108876)
    vehicles = await api.get_vehicles_within(meters=500, of=steam_clock)

    for v in vehicles:
        print(v)
```


## Quick Start (Dev Containers)

This project supports containerized development via [devcontainers](https://code.visualstudio.com/docs/remote/containers). From Visual Studio Code select `Devcontainers: Reopen in Container` from the command palette


## Quick Start (Poetry)

Install dependencies via poetry
```
poetry install --with dev
```

Install precommit hooks
```
poetry run pre-commit install --install-hooks
```

Run tests
```
 .venv/bin/pytest
```

## Thanks
This project makes heavy use of the reverse engineering work by @jeremy21212121 and @jack-madison.

[evofinder.ca](https://github.com/jeremy21212121/evo-client-nuxt) - Unoffical web interface for Evo

[EvoAppScrape](https://github.com/jack-madison/Evo-Car-Share-App-Scrape/blob/main/evo_app_scrape.py) - Script to extract vehicle data to `csv`

## Contributing
Contributions via PR are welcome.
fastapi-keycloak-auth-lib 0.1.12
FastAPI integration with Keycloak for authentication and role-based access control
Requires Python: >=3.7

# fastapi-keycloak-auth

`fastapi-keycloak-auth` is a Python package that provides an easy integration of Keycloak authentication and role-based authorization into FastAPI applications. The package supports JWT token validation, role checks, and automatic token retrieval, making it simple to implement secure API access.

## Features:
- **Keycloak Integration**: Authenticate and retrieve JWT tokens from Keycloak using the `password` grant type.
- **Token Verification**: Easily verify the JWT token with Keycloak's public key.
- **Role-based Access Control**: Enforce role-based access with custom decorators.
- **Flexible Configuration**: Easily configure Keycloak settings (URL, realm, client ID, username, and password) via environment variables or direct updates.

## Installation:

You can install the package via pip:

```bash
pip install fastapi-keycloak-auth
AdaReader 0.0.2
A versatile library for reading datasets from multiple formats and generating synthetic datasets.
Requires Python: >=3.6

# Flexi DataReader

**Flexi DataReader** is a powerful and versatile Python library for working with datasets in multiple formats. It simplifies the process of reading files, generating synthetic datasets, and loading predefined datasets from scikit-learn.

---

## ðŸŒŸ Features

- Automatically detects and processes files in various formats:
  - CSV
  - JSON
  - Excel (`.xlsx`)
  - MATLAB (`.mat`)
  - Parquet
- Generates synthetic datasets for machine learning using scikit-learn:
  - Classification
  - Regression
  - Clustering
- Loads predefined datasets from scikit-learn:
  - Iris, Digits, California Housing, and more.
- Robust error handling for missing files, invalid data, and unsupported formats.

---

## ðŸš€ Installation

Install the library directly from PyPI:

```bash
pip install flexi-datareader
```
To install the latest development version from GitHub

```bash
pip install git+https://github.com/YousofLHC/flexi-datareader.git
```
CausalBGM 0.1.4
CausalBGM: An AI-powered Bayesian generative modeling approach for causal inference in observational studies
Requires Python: >=3.9

Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome variables. The core innovation of CausalBGM lies in its ability to estimate the individual treatment effect (ITE) by learning individual-specific distributions of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and outcome. This approach not only effectively mitigates confounding effects but also provides comprehensive uncertainty quantification, offering reliable and interpretable causal effect estimates at the individual level. CausalBGM adopts a Bayesian model and uses a novel iterative algorithm to update the model parameters and the posterior distribution of latent features until convergence. This framework leverages the power of AI to capture complex dependencies among variables while adhering to the Bayesian principles. Extensive experiments demonstrate that CausalBGM consistently outperforms state-of-the-art methods, particularly in scenarios with high-dimensional covariates and large-scale datasets. Its Bayesian foundation ensures statistical rigor, providing robust and well-calibrated posterior intervals. By addressing key limitations of existing methods, CausalBGM emerges as a robust and promising framework for advancing causal inference in modern applications in fields such as genomics, healthcare, and social sciences.