PySpc

Statistical Process Control Charts Library for Humans

PySpc is a Python library aimed to make Statistical Process Control Charts as easy as possible.

Take a look at my other project cchart-online.

Features

Control Charts by Variables

• Mean and Amplitude
• Mean and Standard Deviation
• Individual Values and Moving Range
• Individual values with subgroups
• Exponentially Weighted Moving Average (EWMA)
• Cumulative Sum (CUSUM)

Control Charts by Attributes

• P Chart
• NP Chart
• C Chart
• U Chart

Multivariate Control Charts

• T Square Hotelling
• T Square Hotelling with SubGroup
• Multivariate Exponentially Weighted Moving Average (MEWMA)

##Installation

$ pip install pyspc

Usage

from pyspc import *

a = spc(pistonrings) + ewma()
print(a)

<img src="https://github.com/carlosqsilva/pyspc/blob/master/screenshots/1-screen.png" align="center" height="400" width="450">

adding rules highlighting...

a + rules()

<img src="https://github.com/carlosqsilva/pyspc/blob/master/screenshots/2-screen.png" align="center" height="400" width="450">

adding more control charts to the mix...

a + cusum() + xbar_sbar() + sbar()

<img src="https://github.com/carlosqsilva/pyspc/blob/master/screenshots/3-screen.png" align="center" height="500" width="450">

it comes with 18 sample datasets to play with, available in ./pyspc/sampledata, you can use your own data (of course). Your data can be nested lists, numpy array or pandas DataFrame.

import numpy
from pyspc import *
fake_data = numpy.random.randn(30, 5) + 100
a = spc(fake_data) + xbar_rbar() + rbar() + rules()
print(a)

<img src="https://github.com/carlosqsilva/pyspc/blob/master/screenshots/5-screen.png" align="center" height="400" width="450">

Gtk Gui

Its also available a python gui application for those who do not like to mess with code.

$ python3 pyspc_gui.py