Metadata-Version: 2.1
Name: GaugeRnR
Version: 0.1.4
Summary: A simple library for calculating Gauge RnR
Home-page: UNKNOWN
Author: Alexander Poole
Author-email: alex.o.poole@gmail.com
License: MIT
Project-URL: Source Code, https://github.com/owodunni/GaugeRnR
Description: # Gauge RnR
        
        [![GitHub](https://github.com/owodunni/gaugernr/workflows/Python%20package/badge.svg)](https://github.com/owodunni/GaugeRnR)
        [![PyPi](https://img.shields.io/pypi/v/GaugeRnR)](https://pypi.org/project/GaugeRnR/)
        [![License](https://img.shields.io/github/license/owodunni/GaugeRnR)](https://github.com/owodunni/GaugeRnR/blob/master/LICENSE)
        
        ## Install
        
        ``` console
        pip install GaugeRnR
        ```
        
        ## Example
        
        The package can be used in the following way:
        
        ``` python
        from gaugeRnR import GaugeRnR
        import numpy as np
        
        # The input should be structeted in a 3d
        # numpy array with shape [operators parts measurements]
        # Example:
        #       m1    m2    m3
        data = np.array(            #
            [[[3.29, 3.41, 3.64],   # p1 | o1
              [2.44, 2.32, 2.42],   # p2
              [4.34, 4.17, 4.27],   # p3
              [3.47, 3.5, 3.64],    # p4
              [2.2, 2.08, 2.16]],   # p5
             [[3.08, 3.25, 3.07],   # p1 | o2
              [2.53, 1.78, 2.32],   # p2
              [4.19, 3.94, 4.34],   # p3
              [3.01, 4.03, 3.2],    # p4
              [2.44, 1.8, 1.72]],   # p5
             [[3.04, 2.89, 2.85],   # p1 | o3
              [1.62, 1.87, 2.04],   # p2
              [3.88, 4.09, 3.67],   # p3
              [3.14, 3.2, 3.11],    # p4
              [1.54, 1.93, 1.55]]]) # p5
        
        g = GaugeRnR(data)
        g.calculate()
        print(g.toTabulare())
        ```
        
        This will result in the following table:
        
        | Sources of Variance   |   DF |     SS |    MS |   Var (σ²) |   Std (σ) | F-value   | P-value   |
        |-----------------------|------|--------|-------|------------|-----------|-----------|-----------|
        | Operator              |    2 |  1.63  | 0.815 |      0.054 |     0.232 | 100.322   | 0.000     |
        | Part                  |    4 | 28.909 | 7.227 |      0.802 |     0.896 | 889.458   | 0.000     |
        | Operator by Part      |    8 |  0.065 | 0.008 |      0     |     0     | 0.142     | 0.996     |
        | Measurment            |   30 |  1.712 | 0.057 |      0.057 |     0.239 |           |           |
        | Total                 |   44 | 32.317 | 0.734 |      0.913 |     0.956 |           |           |
        
        To access the result from the Gauge RnR data directly:
        
        ``` python
        from gaugeRnR import GaugeRnR, Component, Result
        
        .
        .
        .
        
        g = GaugeRnR(data)
        result = g.calculate()
        F = result[Result.F]
        >>> print(F[Component.OPERATOR])
        100.322
        ```
        
        For more examples of how to use this library take a look at the [unit tests](https://github.com/owodunni/GaugeRnR/blob/master/tests/test.py)!
        
        ## Documentations
        
        This GaugeRnR package was built and tested using the resources bellow. If you want to learn more about Gauge RnR and ANOVA they are a great place to start!
        
        * [anova-gage-rr-part-1](https://www.spcforexcel.com/knowledge/measurement-systems-analysis/anova-gage-rr-part-1)
        * [anova-gage-rr-part-2](https://www.spcforexcel.com/knowledge/measurement-systems-analysis/anova-gage-rr-part-2)
        * [Introduction to Statistical Quality Control 6th Edition](https://www.amazon.com/Introduction-Statistical-Quality-Control-Montgomery/dp/0470169923)
        
Keywords: statistics gagueR&R gageRnR anova
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
