semvec/pypi-packages huggingface dataset:
patron-ElioAmado-expoFP 0.1
Print a pattern that can have 3 shapes

Package for Printing a Pattern with 3 Possible Forms
This package has only one module named patron_ElioAmado_expoFP.py.

This Python module defines a function called elio_Patron() which is responsible for printing a pattern in the console. The generated pattern varies according to the specified input parameters. The function accepts four parameters:

alt: Specifies the height of the pattern and must be an integer.
frm: Can take three values: "Up", "Down", or "Mixed". Determines the form of the generated pattern.
rell: Indicates the filling character to be used in the middle of the pattern. Must be a single character.
nom: Is a word or phrase that is printed in the pattern. The way it is printed depends on the value of "frm".
The elio_Patron() function consists of two internal functions:

ptrferiaLine(): This function creates a pattern line according to the provided parameters. It calculates the required spaces, the length of the text to print, and uses a list of special characters to shape the pattern.

ptrferiaimpr() and ptrferiaimprMez(): These functions print the pattern in the console according to the specified form ("Up", "Down", or "Mixed"). They dynamically adjust the height, the amount of filling, and other parameters to create the desired pattern.

The main function elio_Patron() prompts the user to input the necessary parameters and then generates the pattern according to the specifications. If the "Mixed" form is selected, the pattern is generated iteratively, gradually reducing the height and length of the text.

Finally, the function uses Python's standard library to pause execution for a short period of time, resulting in a more animated visualization of the pattern in the console.

This script is interactive.
spotifyrehydrator 0.2.0
A convenience package for creating datasets of track features from self-requested Spotify data.
Requires Python: >=3.6

.. image:: https://github.com/DynamicGenetics/Spotify-Rehydrator/blob/main/docs/image.png?raw=true
  :width: 400
  :alt: Spotify Rehydrator
  
.. image:: https://zenodo.org/badge/333743950.svg
   :target: https://zenodo.org/badge/latestdoi/333743950

.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg
   :target: http://perso.crans.org/besson/LICENSE.html

.. image:: https://img.shields.io/badge/Python-3.9-blue

Recreate a full dataset of audio features of songs downloaded through Spotify's
`download my data <https://support.spotify.com/us/article/data-rights-and-privacy-settings/>`_ facility.  

This requires the files named ``StreamingHistory{n}.json`` where ``{n}`` represents the file number that starts at 0, and goes up to however many files were retrieved.  


Quick start
==============
Extended documentation is `available on ReadTheDocs <https://spotify-rehydrator.readthedocs.io>`_.
First, `install the package using pip. <https://pypi.org/project/spotifyrehydrator/>`_ An example of using the package to rehydrate a folder of json files is then::
  
  # main.py
  from spotifyrehydrator import Rehydrator
  import os
  import pathlib

  if __name__ == "__main__":
      Rehydrator(
          os.path.join(pathlib.Path(__file__).parent.absolute(), "input"),
          os.path.join(pathlib.Path(__file__).parent.absolute(), "output"),
          client_id=os.getenv("SPOTIFY_CLIENT_ID"),
          client_secret=os.getenv("SPOTIFY_CLIENT_SECRET"),
      ).run(return_all=True)


Run takes boolean arguments for ``audio_features`` and ``artist info``, or for ``return_all`` which then returns both. These will determine how much information is retrieved to make up
the full dataset that is saved into the output folder. 

How it works
=============
#. The files for each person are read from the specified input folder.  
#. The name and artist provided are searched with the Spotify API. The first result is taken to be the track, and the track ID is recorded.   
#. Additional information is searched on other endpoints if ``audio_features``, ``artist info`` or ``return_all`` were set to ``True``.
#. The matched track ID and audio features are saved as one **tab delimited** ``.tsv`` file per person into the specified output folder. 

Good to know
===============
* Not all tracks can be retreived from the API. In our experience about 5% of tracks cannot be found on the API. These will have a value of NONE in the output files. 
* There is not a guaranteed match between the first returned item in a search and the track you want. Comparing msPlayed with the track length is a good way to test this since msPlayed should not exceed the track length. 


P.S. Thanks to `Pixel perfect <https://www.flaticon.com/authors/pixel-perfect>`_ for the title `icon <https://www.flaticon.com/>`_. 🙂
cv-xtractor 0.3.0
A Python package for extracting information from CVs (resumes).

# CV-Xtractor

CV-Xtractor is a Python package designed for parsing information from CVs (resumes). It leverages spaCy for natural language processing, providing features to extract key details such as names, phone numbers, email addresses, skills, education information, and more.

## Installation

Install CV-Xtractor using pip:

```bash
pip install cv-xtractor
```

Additionally, download the spaCy English language model:

```bash
python -m spacy download en_core_web_sm
```

## Usage

Here's a simple example of how to use CV-Xtractor in your Python code:

```python
from cv_xtractor.extract_entities import extract_entities_from_file

# Replace 'your_cv.pdf' with the path to your CV file
cv_file_path = 'your_cv.pdf'

# Extract entities from the CV
entities = extract_entities_from_file(cv_file_path)

# Print the extracted information
print(entities)
```

This will output a dictionary containing various details extracted from the CV, such as names, contact information, skills, education, and more.

For more advanced usage and customization, please refer to the [documentation]