Statistical process control charts (also known as "Shewhart charts" after Walter A. Shewhart) are widely used in manufacturing and industry as a quality-control tool.
The aim of this project is to provide an easy-to-use Python module for generating the following types of control charts:
- X̅ and R
- X̅ and S
- Cumulative Sum (CUSUM)
- P-attribute
In addition to checking the data against the standard 3σ control limits, it can also be evaluated against the well-known "Western Electric rule set" to detect additional statistical anomalies:
An X̅ and R (mean and range) chart can be generated as simply as:
import random
import pyshewhart
measurements = [random.random() for _ in range(200)]
pyshewhart.XbarR(measurements)
If you have time information, you can also provide that. Supported time types include:
datetimetimedelta- various string formats (as supported by
dateutil.parser.parse) - Unix timestamps
- elapsed seconds (
intorfloat)
seconds = range(200)
pyshewhart.XbarR(seconds, measurements)
Notice that now the plot shows both sample numbers and elapsed seconds.
Given a CSV file field_strengths_nT.csv containing timeseries and measurement data:
2022-10-12, 59194.401
2022-10-13, 59176.972
2022-10-14, 59179.485
2022-10-15, 59189.235
...
...
2023-10-08, 59196.725
2023-10-09, 59194.435
2023-10-10, 59187.456
2023-10-11, 59196.135
we can generate an X̅ and S (mean and standard deviation) plot with the following shell command:
$ pyshewhart field_strengths_nT.csv xbar-s 7 --units "nT" --title "Field Strength"
A few other examples can be found in the examples/ directory.
Several of these are recreated from a well-known textbook titled Managing Quality: Integrating the Supply Chain by S. Thomas Foster, including this X̅ and R chart:
Also a P-attribute chart:
The following shows the capabilities of a CUSUM chart to detect small amounts of drift.
First we see normal data:
And here we see the chart detecting a small amount of simulated drift:
