semvec/pypi-packages huggingface dataset:
marhta 0.1.3
Fast string similarity algorithms in Rust
Requires Python: >=3.8
Keywords: string, similarity, levenshtein, jaro-winkler, fuzzy

# marhta
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

Fast string similarity algorithms

*This library is still in development and may not be stable.*

## Installation

```bash
pip install marhta
```

## Usage

```python
from marhta import levenshtein_similarity, jaro_winkler_similarity

# Calculate string similarities
print(levenshtein_similarity("hello", "helo"))  # 0.8
print(jaro_winkler_similarity("martha", "marhta"))  # 0.961

# Find best matches
from marhta import levenshtein_match
strings = ["apple", "banana", "orange", "pear"]
matches = levenshtein_match("aple", strings)
print(matches)  # [("apple", 0.8), ("pear", 0.5)]
```

## Features

- Levenshtein distance and similarity measures
- Jaro-Winkler distance and similarity measures
- Fuzzy string matching with customizable thresholds

## Performance

Written in Rust for improved performance, while maintaining a Pythonic API.
iaparc-inference 0.6.5
Inference service package for IAPARC
Requires Python: >=3.8

# iaparc_inference


[![PyPI version](https://badge.fury.io/py/iaparc-inference.svg)](https://badge.fury.io/py/iaparc-inference)
![PyPI - License](https://img.shields.io/pypi/l/iaparc-inference)


The IA Parc inference plugin allows developers to easily integrate their inference pipeline into IA Parc's production module.   


## Installation

```bash
pip install iaparc-inference
```

## Usage

* If your inference pipeline support batching:
  
    ```python
    from iaparc_inference import IAPListener

    # Define a callback to query your inference pipeline
    # To load your model only once it is recommended to use a class:
    class MyModel:
        def __init__(self, model_path: str):
            ## Load your model in pytorch, tensorflow or any other backend
        
        def batch_query(batch: list, parameters: Optional) -> list:
            ''' execute your pipeline on a batch input
                Note:   "parameters" is an optional argument.
                        It can be used to handle URL's query parameters
                        It's a list of key(string)/value(string) dictionaries
            '''

    if __name__ == '__main__':
        # Initiate your model class
        my_model = MyModel("path/to/my/model")

        # Initiate IAParc listener
        listener = IAPListener(my_model.batch_query)
        # Start the listener
        listener.run()

    ```
* If your inference pipeline **do not** support batching:
  
    ```python
    from iaparc_inference import IAPListener

    # Define a callback to query your inference pipeline
    # To load your model only once it is recommended to use a class:
    class MyModel:
        def __init__(self, model_path: str):
            ## Load your model in pytorch, tensorflow or any other backend
        
        def single_query(one_input, parameters: Optional):
            ''' execute your pipeline on a single input
                Note:   "parameters" is an optional argument.
                        It can be used to handle URL's query parameters
                        It's a key(string)/value(string) dictionary
            '''

    if __name__ == '__main__':
        # Initiate your model class
        my_model = MyModel("path/to/my/model")

        # Initiate IAParc listener
        listener = IAPListener(my_model.single_query, batch=1)  # Note that batch size is forced to 1 here
        # Start the listener
        listener.run()

## Features
* Dynamic batching
* Autoscalling 
* Support both synchronous and asynchronous queries
* Data agnostic


## License
This project is licensed under the Apache License Version 2.0  - see the Apache [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
alpha-miner 0.1.3
The alpha-miner package implements the α-algorithm for process mining, transforming event logs into petri nets.
Requires Python: >=3.6

The alpha-miner package implements the α-algorithm, a foundational method in process mining developed by van der Aalst, Weijters, and Măruşter.This algorithm analyzes event logs—collections of activity sequences—to infer causal (Discovery Limitations: Loops of Length 1 or 2, Faulty petri nets due to non-required implicit places, Missing local dependencies, Bias.) See: https://ieeexplore.ieee.org/document/1316839.relationships between activities. It also outputs a resulting petri net.
printDelay 0.1.1
A Python package for printing text with a delay effect.
Requires Python: >=3.6

# printDelay

A simple Python package created for a project used for printing text with a delay effect.

## Installation

pip install printDelay

## Usage
'''
from printDelay import printf, printl

printf("Hello, world!", 0.05)
printl("This is a typing effect!", 0.05)
'''
script-creator 1.2.0
web script creator for automation

# Django Script Automation Platform

Introducing our Django-powered web applicationâ€”a comprehensive solution for script automation creation and execution. This platform simplifies complex automation tasks, enabling users to effortlessly build, customize, and run scripts through an intuitive web interface.

## Key Features

- **Dynamic Script Creator**  
  Easily craft automation scripts with a user-friendly editor that offers syntax highlighting and real-time error checking.

- **Efficient Script Runner**  
  Execute scripts on-demand or schedule them for automated execution, ensuring seamless workflow management.

- **Centralized Automation Dashboard**  
  Monitor script performance, access detailed logs, and track historical runs to quickly diagnose issues and optimize processes.

- **Modular and Scalable Architecture**  
  Integrate with external APIs and services, allowing the application to adapt and expand as your automation needs evolve.

## Overview

This robust solution empowers both developers and non-technical users to:
- **Streamline Repetitive Tasks:** Reduce manual intervention by automating routine processes.
- **Enhance Productivity:** Save time and minimize errors with automated script execution.
- **Maintain Control:** Gain complete oversight of automation workflows with comprehensive logging and performance monitoring.