Metadata-Version: 2.1
Name: measure-noise
Version: 2.58.19240
Summary: Measure deviant noise
Home-page: https://github.com/mozilla/measure-noise
Author: Kyle Lahnakoski
Author-email: kyle@lahnakoski.com
License: MPL 2.0
Description: # measure-noise
        Measure how our data deviates from normal distribution
        
        
        |Branch      |Status   | Coverage |
        |------------|---------|----------|
        |master      | [![Build Status](https://travis-ci.org/mozilla/measure-noise.svg?branch=master)](https://travis-ci.org/mozilla/measure-noise) | |
        |dev         | [![Build Status](https://travis-ci.org/mozilla/measure-noise.svg?branch=dev)](https://travis-ci.org/mozilla/measure-noise)    | [![Coverage Status](https://coveralls.io/repos/github/mozilla/measure-noise/badge.svg)](https://coveralls.io/github/mozilla/measure-noise) |
        
        
        ## Install
        
            pip install measure-noise
        
        ## Usage
        
        The `deviance()` method will return a `(description, score)` pair describing how the samples deviate from a normal distribution, and by how much.  This is intended to screen samples for use in the t-test, and other statistics, that assume a normal distribution.
        
        * `SKEWED` - samples are heavily to one side of the mean
        * `OUTLIERS` - there are more outliers than would be expected from normal distribution
        * `MODAL` - few samples are near the mean (probably bimodal)
        * `OK` - no egregious deviation from normal
        * `N/A` - not enough data to make a conclusion (aka `OK`)
        
        #### Example
        
            from measure_noise import deviance
        
        	>>> desc, score = deviance([1,2,3,4,5,6,7,8])
            >>> desc
            'OK'
        
        ## Development
        
            git clone https://github.com/mozilla/measure-noise.git
            cd measure-noise
            pip install -r requirements.txt
            pip install or tests/requirements.txt
            python -m unittest discover tests 
        
        ## Windows
        
        You must download the `scipy` and `numpy` binary packages. 
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Description-Content-Type: text/markdown
