semvec/pypi-packages huggingface dataset:
itu-r-468-weighting 2.0.3
A zero dependency Python ITU-R 468 noise weighting filter (1 kHz and 2 kHz)
License: MIT
Requires Python: >=3.6
Keywords: ITU-R BS.468-4 SMPTE RP 2054:2010 noise weighting filter

# itu-r-468-weighting

A zero dependency Python ITU-R 468 noise weighting filter (1 kHz and 2 kHz)

Master branch: [![Build Status](https://travis-ci.com/cinelexi/itu-r-468-weighting.svg?branch=master)](https://travis-ci.com/cinelexi/itu-r-468-weighting?branch=master) [![Coverage Status](https://img.shields.io/coveralls/github/cinelexi/itu-r-468-weighting/master)](https://coveralls.io/github/cinelexi/itu-r-468-weighting?branch=master) \
Dev branch: [![Build Status](https://travis-ci.com/cinelexi/itu-r-468-weighting.svg?branch=dev)](https://travis-ci.com/cinelexi/itu-r-468-weighting?branch=dev) [![Coverage Status](https://img.shields.io/coveralls/github/cinelexi/itu-r-468-weighting/dev)](https://coveralls.io/github/cinelexi/itu-r-468-weighting?branch=dev)

## Introduction

This project consists of a sole function named `r468()`. The function takes a frequency value and returns a weighted gain value. For weightening, the [ITU-R BS.468-4](https://www.itu.int/rec/R-REC-BS.468-4-198607-I/en) standard and the [SMPTE RP 2054:2010](https://ieeexplore.ieee.org/document/7290513) recommended practice are followed.

The math for this project is taken from Wikipedia (as of 2019-08-08):

- https://en.wikipedia.org/wiki/ITU-R_468_noise_weighting ([archived version](https://web.archive.org/web/20190808084536/https:/en.wikipedia.org/wiki/ITU-R_468_noise_weighting))

### Filter with gain in dB ("db" option):

![](https://raw.githubusercontent.com/cinelexi/itu-r-468-weighting/master/images/filter_gain_db.png)

### Filter with gain factor ("factor" option):

![](https://raw.githubusercontent.com/cinelexi/itu-r-468-weighting/master/images/filter_gain.png)

## Installation

```
pip install itu-r-468-weighting
```

More infos on the project page at [PyPI](https://pypi.org/project/itu-r-468-weighting/).

## Example Usage

```
from itu_r_468_weighting.filter import r468

r468(1000, "1khz", "db")      # returns gain in dB
r468(1000, "2khz", "db")      # returns gain in dB
r468(1000, "1khz", "factor")  # returns gain factor
r468(1000, "2khz", "factor")  # returns gain factor
```

## Documentation

See docstring of function `r468()` in `itu_r_468_weighting/filter.py` or create and read the docs from source in `docs/` or read them online at [readthedocs.io](https://itu-r-468-weighting.readthedocs.io/en/latest/index.html).

## Contributing

See CONTRIBUTING.md file.

## Project page

This project is hosted on [GitHub](https://github.com/cinelexi/itu-r-468-weighting).
names_comparator 1.0.0
Simple tool to compare almost similar names which are coming from the same source (for example list of all company owners and officers of that company). Helps to cluster together persons with a slight difference in name spelling/typos. Better suited for Cyrillic names, but should work everywhere
License: MIT
Keywords: names fuzzy comparison levenshtein

comparator
================

Simple tool to compare almost similar names which are coming **from the same source** (for example list of all company owners and officers of that company). Helps to cluster together persons with a slight difference in name spelling/typos. Better suited for Cyrillic names, but should work everywhere.

It's heuristic based and as any algorithm of such a nature it **make errors** sometime (see Accuracy section below for details). 

Installation
==================================
Install from PyPI.

.. code-block:: bash

    $ pip install names_comparator

Accuracy
==================================

You can test your installation on a ground truth data and check confusion matrix by running:

.. code-block:: bash

    $ python comparator/__init__.py comparator/data/ground_truth.csv
	+--------------------+----------+----------+
	|                    | Positive | Negative |
	+--------------------+----------+----------+
	| Predicted positive |   290    |    9     |
	| Predicted negative |    14    |   687    |
	+--------------------+----------+----------+
	Precision:  0.97
	Recall:  0.95
	F1 score:  0.96

You can also run it with debug flag to see all the errors algorithm made:

.. code-block:: bash

    $ python comparator/__init__.py comparator/data/ground_truth.csv yes

Usage
==================================

.. code-block:: python

    >>> from comparator import full_compare
    >>> full_compare("Barack Hussein Obama", "Obama, Barak")
    True
    >>> full_compare("Петро Мазепа", "Мазепа Петро")
    True
    >>> full_compare("Марченко Петро Миколайович", "Панченко Петро Миколайович")
    False
    >>> full_compare("Овдієнко Сергій Костантинович", "Овдієнко Сергій Костянтинович")
    True
    >>> full_compare("Іванов Михайло Юрійович", "Іванов Юрій Михайлович")
    False