Metadata-Version: 2.1
Name: grams
Version: 0.0.2
Summary: A package for dealing with histograms.
Home-page: https://github.com/escofresco/makeschool_fsp2_realtweets
Author: Jonasz Rice
Author-email: jonaszakr@gmail.com
License: UNKNOWN
Description: # grams
        
        A Python library for managing the histograms of text corpuses.
        
        [![Build Status](https://travis-ci.com/escofresco/makeschool_fsp2_realtweets.svg?branch=master)](https://travis-ci.com/escofresco/makeschool_fsp2_realtweets)
        
        [![codecov](https://codecov.io/gh/escofresco/makeschool_fsp2_realtweets/branch/master/graph/badge.svg)](https://codecov.io/gh/escofresco/makeschool_fsp2_realtweets)
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install grams.
        
        ```bash
        pip install grams
        ```
        
        ## Usage
        
        ```python
        import grams
        
        # Generate a histogram from a list of sentences
        hist = grams.Histogram(['A sentence here.',
                                'A sentence there.',
                                'A sentence anywhere.'])
        
        # Generate a histogram from a text file
        with open('corpus.txt', 'r') as file:
            file_hist = grams.Histogram(file)
        
        # Find the distance between histograms ∈ [0, 1]
        similarity = hist.similarity(file_hist)
        
        # Sample a random word weighted by its number of occurrences
        word = hist.sample()
        
        # Display word frequencies in the terminal
        hist.visualize()
        #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        # Lorem      : ▇▇▇▇ 4.00
        # Ipsum      : ▇▇▇▇ 4.00
        # is         : ▇ 1.00
        # simply     : ▇ 1.00
        # dummy      : ▇▇ 2.00
        # text       : ▇▇ 2.00
        # of         : ▇▇▇▇ 4.00
        # the        : ▇▇▇▇▇▇ 6.00
        # printing   : ▇ 1.00
        # and        : ▇▇▇ 3.00
        # typesetting: ▇▇ 2.00
        # industry   : ▇ 1.00
        # has        : ▇▇ 2.00
        #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        # Create a distribution object from code coverage data
        cov = grams.Covergram("Documents/.coverage")
        
        # We can visualize our code coverage data as well.
        cov.visualize()
        #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        # __init__.py       : ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100.00
        # grams.py          : ▇▇▇▇▇▇▇▇▇▇▇▇ 25.00
        # hashtable.py      : ▇▇▇▇▇▇▇▇▇▇▇ 22.00
        # linkedlist.py     : ▇▇▇▇▇▇▇ 15.00
        # online.py         : ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40.00
        # stats.py          : ▇▇▇▇▇▇▇▇▇▇ 21.00
        # termgraph.py      : ▇▇▇▇▇▇ 12.00
        # utils.py          : ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 31.00
        #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        # Our distribution also maintains some useful statistics
        avg_code_cov = int(cov.mean)
        frequency_standard_deviation = int(hist.std)
        frequency_variance = int(hist.var)
        
        ```
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        ## License
        [MIT](LICENSE)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
