Metadata-Version: 2.1
Name: noisify
Version: 1.0
Summary: Framework for creating synthetic data with realistic errors for refining data science pipelines.
Home-page: https://github.com/dstl/Noisify
Author: Declan Crew
Author-email: dcrew@dstl.gov.uk
License: MIT
Description: # Noisify
        
        Noisify is a simple light weight library for augmenting and modifying data by adding realistic noise.
         
        ## Introduction
        
        Add some human noise (typos, things in the wrong boxes etc.)
        
            >>> from noisify.recipes import human_error
            >>> test_data = {'this': 1.0, 'is': 2, 'a': 'test!'}
            >>> human_noise = human_error(5)
            >>> print(list(human_noise(test_data)))
            [{'a': 'tset!', 'this': 2, 'is': 1.0}]
            >>> print(list(human_noise(test_data)))
            [{'a': 0.0, 'this': 'test!', 'is': 2}]
        
        Add some machine noise (gaussian noise, data collection interruptions etc.)
        
            >>> from noisify.recipes import machine_error
            >>> machine_noise = machine_error(5)
            >>> print(list(machine_noise(test_data)))
            [{'this': 1.12786393038729, 'is': 2.1387080616716307, 'a': 'test!'}]
        
        If you want both, just add them together
        
            >>> combined_noise = machine_error(5) + human_error(5)
            >>> print(list(combined_noise(test_data)))
            [{'this': 1.23854334573554, 'is': 20.77848220943227, 'a': 'tst!'}]
        
        Add noise to numpy arrays
        
            >>> import numpy as np
            >>> test_array = np.arange(10)
            >>> print(test_array)
            [0 1 2 3 4 5 6 7 8 9]
            >>> print(list(combined_noise(test_array)))
            [[0.09172393 2.52539794 1.38823741 2.85571154 2.85571154 6.37596668
                              4.7135771  7.28358719 6.83600156 9.40973018]]
        
        Read an image
        
            >>> from PIL import Image
            >>> test_image = Image.open(noisify.jpg)
            >>> test_image.show()
        
        
        
        And now with noise
        
            >>> from noisify.recipes import human_error, machine_error
            >>> combined_noise = machine_error(5) + human_error(5)
            >>> for out_image in combined_noise(test_image):
            ...     out_image.show()
        
        
        *Noisify* allows you to build flexible data augmentation pipelines for arbitrary objects.
        All pipelines are built from simple high level objects, plugged together like lego.
        Use noisify to stress test application interfaces, verify data cleaning pipelines, and to make your ML algorithms more
        robust to real world conditions.
        
        ## Installation
        
        #### Prerequisites
        Noisify relies on Python 3.5+
         
        #### Installation from pipy
            $ pip install noisify
        
        ## Additional Information
        
        Full documentation is available at TODO ReadTheDocs Link.
        ## Licence
        
        Dstl (c) Crown Copyright 2019
        
        Noisify is released under the MIT licence
        
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