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GaugeRnR-0.6.0
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A simple library for calculating Gauge RnR
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | GaugeRnR-0.6.0 |
| نام | GaugeRnR |
| نسخه کتابخانه | 0.6.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alexander Poole |
| ایمیل نویسنده | alex.o.poole@gmail.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/GaugeRnR/ |
| مجوز | MIT |
# Gauge R&R
[](https://github.com/owodunni/GaugeRnR)
[](https://pypi.org/project/GaugeRnR/)
[](https://github.com/owodunni/GaugeRnR/blob/master/LICENSE)
## Table of Contents
1. [Install](#Install)
2. [CLI](#CLI)
3. [Example](#Example)
4. [Statistics](#Statistics)
## Install
From PyPi:
``` vim
pip install GaugeRnR
```
From source:
``` console
pip install -e .
```
Development dependencies:
``` vim
pip install -r pip/requirements-dev.txt
```
## CLI
The package can be used to generate reports from CLI:
```vim
GaugeRnR -f data/data_mXop.csv -s 3,5,11 -o outDir
```
This generates a html report that is stored in the outDir folder.
Setting the axes parameter is usefull if the data is not structured correct:
```vim
GaugeRnR -f data/data_opXm.csv -s 5,7,11 -a 2,1,0 -o outDir
```
To calculate linearity and bias ground truth is required:
```vim
GaugeRnR -f data/data_demoGRnR.csv -s 3,10,3 -a 0,2,1 -g 40,42,30,43,29,45,27.5,42,26,35 -o outDir
```
For more help run:
```vim
GaugeRnR -h
```
```
GaugeRnR.
The input data should be structeted
in a 3d array n[i,j,k] where
i = operator, j = part, k = measurement
Stored to file this data would look:
m1 m2 m3
3.29; 3.41; 3.64 # p1 | o1
2.44; 2.32; 2.42 # p2
3.08; 3.25; 3.07 # p1 | o2
2.53; 1.78; 2.32 # p2
3.04; 2.89; 2.85 # p1 | o3
1.62; 1.87; 2.04 # p2
More info: https://github.com/owodunni/GaugeRnR
Usage:
GaugeRnR -f FILE -s STRUCTURE [-a <AXES>] [-d <DELIMITER>] [-o <FOLDER>] [-g <PARTS>]
GaugeRnR -h | --help
GaugeRnR -v | --version
Examples:
GaugeRnR -f data.csv -s5,7,11 -o report
GaugeRnR -f data/data_mXop.csv -s 3,5,11 -o outDir
GaugeRnR -f data/data_opXm.csv -s 5,7,11 -a 2,1,0 -o outDir
GaugeRnR -f data/data_demoGRnR.csv -s 3,10,3 -a 0,2,1 -g 40,42,30,43,29,45,27.5,42,26,35 -o outDir
Options:
-f --file=FILE Load input data.
-s --structure=STRUCTURE Data structure.
Order should be operators, parts, measurements.
-a --axes=<AXES> Order of data axes [default: 0,1,2].
-d --delimiter=<DELIMITER> Order of data axes [default: ;].
-o --output=<FOLDER> Report output directory
-g --groundTruth=<PARTS> Ground Truth data for parts
-h --help Show this screen.
-v --version Show version.
```
## 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 n[i,j,k] where
# i = operator, j = part, k = measurement
# 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.summary())
```
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/tree/master/tests)!
## Statistics
The pacakge can generate the following statistics:
* GaugeRnR
Gauge R&R, which stands for gage repeatability and reproducibility, is a statistical tool that measures the amount of variation in the measurement system arising from the measurement device and the people taking the measurement.
Unfortunately, all measurement data contains a certain percentage of variation. The variation is the difference between the true values and the observed values. The variation represents the amount of measurement error. In addition to measurement error, is the actual product or process variation. When we combine measurement error with product or process variation the resulting value represents the total variation. To assure that our measurement data is accurate we must determine if the amount of variation is acceptable
If the p value is less than 0.05, it means that the source of variation has a significant impact on the results.
For more information take a look at:
* [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)
* Mean, Standard Deviation and bar chart plots.
To get a better feel for our measurement data we can plot it togheter with a bar chart and show some caracteristic statistics of the data.
* Normality test
For Gauge R&R to work it is important that our data is normal distibuted. If we don't have enough data the it might not be normal distributed. We can test if the data is normal distributed using a Shapiro-Wilk Test. Small values of W are evidence of departure from normality. It is important that our parts are normally distributed. A P-value smaller then 0.05 indicates that the data is not Gaussian.
For more information take a look at:
* [Engineering statistics handbook](https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm)
* [Normality tests in python](https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/)
* Linearity and Bias - requires ground truth data
Bias and linearity assess the accuracy of a gage.
* Bias examines the difference between the observed average measurement and a reference value.
Bias indicates how accurate the gage is when compared to a reference value.
* Linearity examines how accurate your measurements are through the expected range of the
measurements. Linearity indicates whether the gage has the same accuracy across all reference values.
A P-value smaller then 0.05 indicates that a linear equation fits well to the data.
For more information take a look at:
* [Measurement System Analysis](http://reliawiki.org/index.php/Measurement_System_Analysis?fbclid=IwAR2uptrlw9MyMaOVLXCOE89GDvN8hNb0qfxgxfxZs7msewQ7ijzqfnGp8oc)
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | scipy |
| - | tabulate |
| - | docopt |
| - | statsmodels |
| - | plotly |
نحوه نصب
نصب پکیج whl GaugeRnR-0.6.0:
pip install GaugeRnR-0.6.0.whl
نصب پکیج tar.gz GaugeRnR-0.6.0:
pip install GaugeRnR-0.6.0.tar.gz