toleranceinterval 1.0.3

A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.

About

toleranceinterval

A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.

Methods

Checkout the documentation. This is what has been implemented so far:

twoside

• normal
• normal_factor
• lognormal

oneside

• normal
• lognormal
• non_parametric
• hanson_koopmans
• hanson_koopmans_cmh

Requirements

"numpy >= 1.14.0"
"scipy >= 0.19.0"
"sympy >= 1.4"
"setuptools >= 38.6.0"

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.

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.

import numpy as np
import toleranceinterval as ti
x = np.random.random(100)
bound = ti.twoside.normal(x, 0.9, 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.

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.

Contributing

All contributions are welcome! Please let me know if you have any questions, or run into any issues.

License

MIT License