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Noisify: All purpose data augmentation
======================================

Release v\ |version|. (:ref:`Installation <install>`)


**Noisify** is a simple light weight library for augmenting and modifying data by adding 'noise'.


-------------------

**Let's make some noise**::

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()

.. image:: _static/noisify.jpg
   :width: 339px
   :height: 158px
   :scale: 70 %
   :alt: unchanged image
   :align: center

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()

.. image:: _static/noisy_noisify.jpg
   :width: 339px
   :height: 158px
   :scale: 70 %
   :alt: image with random noise
   :align: center

**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.

Features
----------------

Noisify provides data augmentation through a simple high level abstraction

- Build reporters to apply augmentation to any object, images, dataframes, database interfaces etc.
- Compose augmentations from configurable flaw objects
- Build recipes to deploy pipelines simply
- Everything is composable, everything is polymorphic

Noisify is built for Python 3+.

The Basics
----------

A brief high level guide of how to use noisify, mostly prose with illustrative examples.

.. toctree::
   :maxdepth: 2

   basics/introduction
   basics/install
   basics/quickstart
   basics/advanced


The Community Guide
-------------------

Our release process and community support process.

.. toctree::
   :maxdepth: 2

   community/support
   community/updates
   community/release-process

The API Documentation / Guide
-----------------------------

Full documentation of the noisify API

.. toctree::
   :maxdepth: 2

   api