semvec/pypi-packages huggingface dataset:
schubert 0.0.6
Schubert`s personal toolbox
Requires Python: >=3.5, <4
Keywords: schubert,toolbox,development,cnab,dac,itau

# Schubert Toolbox

This project intends to be my personal toolbox for solutions of day to day issues.

* `from schubert.itau import dac_calculation` -> Calculate DAC for Itau Invoices
* `from schubert.febraban import generate_codebar_from_bank_slip_digitable_line` -> Get numeric representation of Codebar from Bank Slip digitable line
* `from schubert.febraban import generate_codebar_from_utility_slip_digitable_line` -> Get numeric representation of Codebar from Utility Slip digitable line
BibleHubScrapper-joshpetit 0.0.1
A module to scrape biblehub.com, also cli app
Requires Python: >=3.6

# BibleHubScrapper
A python module to retrieve single verses from biblehub.com. Also includes commandline arguments.

## Commandline
To retrieve all of the currently available information on a reference, use the a tag:

`biblehub -a [Reference]`

To only retrieve specific information pass in the corresponding tag:
```
biblehub -c [Reference] # Retrieves the reference and the cross-references
biblehub -t [Reference] # Retrieves the reference and the Treasury of Scripture
biblehub -l [Reference] # Retrieves the reference and the lexicon
```
Arguments can be stacked with one hyphen such as:
```
biblehub -cl [Reference] # Retrieves the reference, cross-references, and lexicon
```
## Query Usage
```
from BibleHubScrapper import query

bible_hub_query = query('Genesis 1:1')
print(bible_hub_query.text) # In the beginning...
```
You can choose not to query certain fields by indicating in the parameters
for example:
` query('Genesis 1:1', get_lexicons=False) `

## Useful fields
```
print(bible_hub_query.passage) # Genesis 1:1
print(bible_hub_query.version) # NIV (default)
print(bible_hub_query.lexicons) # Text, Hebrew words, translit, strong, and English defintions
print(bible_hub_query.crfs) # Cross References
print(bible_hub_query.tos) # Treasury of Scripture
```
there is also a `bible_hub_query.print_lexicons` method that may prove useful

## CLI


## Todo
- Possibly support multiple verses
- Add the text for verses in cross-references
- Possibly make vim plugin
- Consider fetching commentaries, or a specific one
pyreport2to3 1.2.2
Python 2 to 3 porting HTML report

This package helps in generating better visualization report in python 2 to 3.

Installation
------------

You can install directly after cloning:

use the Python package:

.. code-block:: bash

  $ pip install --user pyreport2to3

Command line tool
-----------------

After installation, you should have ``pyreport2to3`` in your ``$PATH``:


Usage
-----

Python porting 2 to 3 report function:

.. code-block:: python

    >>> from pyreport2to3 import get_HTMLreport


Call the get_HTMLreport function passing command (2to3 or python-modernize) and parameter (python folder or files) :

.. code-block:: python

    >>> get_HTMLreport("python-modernize", "./python_project")


Python porting 2 to 3 HTML file pyreporting.html is generated in current location.

License
~~~~~~~
MIT License
~~~~~~~~~~~


.. code:: rst

    |MIT license|

    .. image:: https://img.shields.io/badge/License-MIT-blue.svg

Authors
~~~~~~~
Maurya Allimuthu ( catchmaurya@gmail.com )
Jitendra Kumar ( gtu.delhi@gmail.com )
Abraham Robert ( abrahamrobert123@gmail.com )

Contact
~~~~~~~
Please submit an issue if you encounter a bug and please email any questions or requests to @authors
matchup-ir 0.4.0
A IR simple library.
Requires Python: >=3.7

[![PyPI version](https://badge.fury.io/py/matchup-ir.svg)](https://badge.fury.io/py/matchup-ir)
![build](https://github.com/marcosfpr/match_up_lib/workflows/build/badge.svg)
[![codecov](https://codecov.io/gh/marcosfpr/match_up_lib/branch/master/graph/badge.svg)](https://codecov.io/gh/marcosfpr/match_up_lib)

# MatchUp

PURE-Python IR library.

Nowadays, through search engines, it is common to make queries that have, as a result, a high number of references that do not meet their contexts. In order to provide relevant results, upon consultation, some models from the Information Retrieval area, called classics, were proposed: the Boolean, the Vector and the Probabilistic. In turn, with a view to improving the quality of the results generated by the application of the classic models of Information Retrieval, extended models of Information Retrieval were defined from them; among them, we have the Extended Boolean, the Generalized Vector and the Belief Network.
 
In 2018/1, the first version of the MatchUp tool was developed: a web tool for calculating similarity between a query, which may be a specific document or set of terms of interest to the user, and create a collection of documents, enabling the generation of a list of documents from this created collection that are relevant to the desired query. To calculate similarity, this version included the classic IR models and the extended Extended Boolean model. Through the analysis of the results of the experiments carried out, it was possible to notice that the Vector model, in general, presented the best results when compared to the other models implemented. However, MatchUp did not include the Generalized Vector and Belief Network extended models, which may present better results than the Vector Model. Therefore, this scientific initiation project has, as main objective, the development of version 2.0 of the MatchUp tool, in order to also include the extended models Generalized Vector and Belief Network. To validate version 2.0 of the MatchUp tool, experiments will be carried out, involving different collections of documents.
 
### Documentation
* [Read the docs](https://match-up-lib.readthedocs.io/en/latest/)

### Technology
* [Python 3.7](https://www.python.org/)