Metadata-Version: 2.1
Name: toleranceinterval
Version: 0.0.2
Summary: A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.
Home-page: https://github.com/cjekel/tolerance_interval_py
Author: Charles Jekel
Author-email: cjekel@gmail.com
License: MIT License
Description: # About
        
        ### toleranceinterval
        
        A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.
        
        [![Build Status](https://travis-ci.com/cjekel/tolerance_interval_py.svg?branch=master)](https://travis-ci.com/cjekel/tolerance_interval_py) [![Coverage Status](https://coveralls.io/repos/github/cjekel/tolerance_interval_py/badge.svg?branch=master)](https://coveralls.io/github/cjekel/tolerance_interval_py?branch=master)
        
        # Methods
        
        Checkout the [documentation](https://jekel.me/tolerance_interval_py/index.html). This is what has been implemented so far:
        
        ## twoside
        
        - normal
        - lognormal
        
        ## oneside
        
        - normal
        - lognormal
        - non_parametric
        - hanson_koopmans
        - hanson_koopmans_cmh
        
        # Requirements
        
        ```Python
        "numpy >= 1.14.0"
        "scipy >= 0.19.0"
        "sympy >= 1.4"
        ```
        # Installation
        
        ```
        python -m pip install toleranceinterval
        ```
        
        or clone  and install from source
        
        ```
        git clone https://github.com/cjekel/tolerance_interval_py
        python -m pip install ./tolerance_interval_py
        ```
        
        # Examples
        
        The syntax follows ```(x, p, g)```, where ```x``` is the random sample, ```p``` is the percentile, and ```g``` is the confidence level. Here ```x``` can be a single set of random samples, or sets of random samples of the same size.
        
        Estimate the 10th percentile to 95% confidence, of a random sample ```x``` using the Hanson and Koopmans 1964 method.
        
        ```python
        import numpy as np
        import toleranceinterval as ti
        x = np.random.random(100)
        bound = ti.oneside.hanson_koopmans(x, 0.1, 0.95)
        print(bound)
        ```
        
        Estimate the central 90th percentile to 95% confidence, of a random sample ```x``` assuming ```x``` follows a Normal distribution.
        
        ```python
        import numpy as np
        import toleranceinterval as ti
        x = np.random.random(100)
        bound = ti.twoside.normal(x, 0.1, 0.95)
        print('Lower bound:', bound[:, 0])
        print('Upper bound:', bound[:, 1])
        ```
        
        All methods will allow you to specify sets of samples as 2-D numpy arrays. The caveat here is that each set must be the same size. This example estimates the 95th percentile to 90% confidence using the non-parametric method. Here ```x``` will be 7 random sample sets, where each set is of 500 random samples.
        
        ```python
        import numpy as np
        import toleranceinterval as ti
        x = np.random.random((7, 500))
        bound = ti.oneside.non_parametric(x, 0.95, 0.9)
        # here bound will print for each set of n=500 samples 
        print('Bounds:', bound)
        ```
        
        # Changelog
        
        Changes will be stored in [CHANGELOG.md](https://github.com/cjekel/tolerance_interval_py/blob/master/CHANGELOG.md).
        
        # Contributing
        
        All contributions are welcome! Please let me know if you have any questions, or run into any issues.
        
        # License
        
        MIT License
        
        
Platform: any
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