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https://github.com/cjekel/tolerance_interval_py
A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.
https://github.com/cjekel/tolerance_interval_py
Last synced: 2 days ago
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A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.
- Host: GitHub
- URL: https://github.com/cjekel/tolerance_interval_py
- Owner: cjekel
- License: mit
- Created: 2019-10-02T21:22:48.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-27T19:29:12.000Z (over 1 year ago)
- Last Synced: 2024-09-06T14:44:26.989Z (27 days ago)
- Language: Python
- Homepage:
- Size: 145 KB
- Stars: 17
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# About
### toleranceinterval
A small Python library for one-sided tolerance bounds and two-sided tolerance intervals.
[](https://travis-ci.com/cjekel/tolerance_interval_py) [](https://codecov.io/gh/cjekel/tolerance_interval_py)
# Methods
Checkout the [documentation](https://jekel.me/tolerance_interval_py/index.html). This is what has been implemented so far:
## twoside
- normal
- normal_factor
- lognormal
## oneside
- normal
- lognormal
- non_parametric
- hanson_koopmans
- hanson_koopmans_cmh
# Requirements
```Python
"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.
```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, 0.9, 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.
```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