semvec/pypi-packages huggingface dataset:
selenium-pagefactory-impl 0.1.0
A Selenium Page Factory implementation for Python
License: MIT
Requires Python: >=3.12,<4.0

# selenium-pagefactory-impl

![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)

A Selenium Page Factory implementation for Python, providing enhanced functionality for web automation.

## Features

- Simplified and intelligent page factory for Selenium projects.
- Convenient methods for interacting with web elements.
- Improved handling of StaleElementReferenceException.

## Installation

Use [Poetry](https://python-poetry.org/) to install the package and its dependencies:

```bash
poetry install
```
## How to implement in the test

```python
# Import the PageFactory class
from selenium_pagefactory.page_factory_impl import PageFactory

# Your page class using PageFactory
class YourPageClass:
    # Define locators
    locators = {
        'element_name': (By.XPATH, "//your/xpath"),
        # Add more locators as needed
    }

    # Initialize elements using PageFactory
    def __init__(self, driver):
        self.driver = driver
        PageFactory.initialize_elements(self, driver)

# Example usage
driver = webdriver.Chrome()
page_instance = YourPageClass(driver)
page_instance.element_name.click()
```

## License
This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments
Special thanks to the Selenium and pytest communities.
hocmo 1.0.1
A Generalized Higher-Order Correlation Model (HOCMO) tool to generate scores modeling the strength of the relationship between triplicate entities using a tensor-based approach
License: MIT

# Higher-Order Correlation Model

## Description:
A Generalized Higher-Order Correlation Model (HOCMO) tool to generate scores modeling the strength of the relationship among triplicate or quadruplet entities using a tensor-based approach

## Prerequisites:
Developmental environment is provided via dockerfile. We also provide a requirements.txt if a local environment is preferred. Package is also available for installation on PyPi.

## Installation:

```pip install hocmo```

## Contribution:

## License:

MIT License
Copyright (c) 2018 Mollah Lab
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the right to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

## Citation:

## References:
- C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
    nonnegative matrix factorization - Pattern Recognition, 2008
- Gauvin, Laetitia, André Panisson, and Ciro Cattuto. 2014. “Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach.” Edited by Yamir Moreno. PLoS ONE 9 (1): e86028.

- Bro, Rasmus, and Henk A. L. Kiers. 2003. “A New Efficient Method for Determining the Number of Components in PARAFAC Models.” Journal of Chemometrics 17 (5): 274–86. https://doi.org/10.1002/cem.801.
## Contact:
- smollah@wustl.edu, lucharles@wustl.edu
thefuzz 0.22.1
Fuzzy string matching in python
License: MIT
Requires Python: >=3.8

.. image:: https://github.com/seatgeek/thefuzz/actions/workflows/ci.yml/badge.svg
    :target: https://github.com/seatgeek/thefuzz

TheFuzz
=======

Fuzzy string matching like a boss. It uses `Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance>`_ to calculate the differences between sequences in a simple-to-use package.

Requirements
============

-  Python 3.8 or higher
-  `rapidfuzz <https://github.com/maxbachmann/RapidFuzz/>`_

For testing
~~~~~~~~~~~
-  pycodestyle
-  hypothesis
-  pytest

Installation
============

Using pip via PyPI

.. code:: bash

    pip install thefuzz


Using pip via GitHub

.. code:: bash

    pip install git+git://github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzz

Adding to your ``requirements.txt`` file (run ``pip install -r requirements.txt`` afterwards)

.. code:: bash

    git+ssh://git@github.com/seatgeek/thefuzz.git@0.19.0#egg=thefuzz

Manually via GIT

.. code:: bash

    git clone git://github.com/seatgeek/thefuzz.git thefuzz
    cd thefuzz
    python setup.py install


Usage
=====

.. code:: python

    >>> from thefuzz import fuzz
    >>> from thefuzz import process

Simple Ratio
~~~~~~~~~~~~

.. code:: python

    >>> fuzz.ratio("this is a test", "this is a test!")
        97

Partial Ratio
~~~~~~~~~~~~~

.. code:: python

    >>> fuzz.partial_ratio("this is a test", "this is a test!")
        100

Token Sort Ratio
~~~~~~~~~~~~~~~~

.. code:: python

    >>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
        91
    >>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
        100

Token Set Ratio
~~~~~~~~~~~~~~~

.. code:: python

    >>> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
        84
    >>> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
        100

Partial Token Sort Ratio
~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    >>> fuzz.token_sort_ratio("fuzzy was a bear", "wuzzy fuzzy was a bear")
        84
    >>> fuzz.partial_token_sort_ratio("fuzzy was a bear", "wuzzy fuzzy was a bear")
        100

Process
~~~~~~~

.. code:: python

    >>> choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
    >>> process.extract("new york jets", choices, limit=2)
        [('New York Jets', 100), ('New York Giants', 78)]
    >>> process.extractOne("cowboys", choices)
        ("Dallas Cowboys", 90)

You can also pass additional parameters to ``extractOne`` method to make it use a specific scorer. A typical use case is to match file paths:

.. code:: python

    >>> process.extractOne("System of a down - Hypnotize - Heroin", songs)
        ('/music/library/good/System of a Down/2005 - Hypnotize/01 - Attack.mp3', 86)
    >>> process.extractOne("System of a down - Hypnotize - Heroin", songs, scorer=fuzz.token_sort_ratio)
        ("/music/library/good/System of a Down/2005 - Hypnotize/10 - She's Like Heroin.mp3", 61)

.. |Build Status| image:: https://github.com/seatgeek/thefuzz/actions/workflows/ci.yml/badge.svg
   :target: https://github.com/seatgeek/thefuzz