To provide analysis tools and metrics useful in manufacturing environments.
Go to the documentation.
Project is currently undergoing frequent updates for documentation and to add functionality and update documentation!! Screenshots and features that you see on here may be out of date, but are in progress.
Current focus is to add more plot types.
To install from pypi
:
pip install manufacturing
To install from source download and install using poetry:
poetry install
The most useful feature of the manufacturing
package is the visualization of Cpk.
As hinted previously, the ppk_plot()
function is the primary method for display of
Cpk visual information. First, get your data into a list
, numpy.array
, or
pandas.Series
; then supply that data, along with the lower_control_limit
and
upper_control_limit
into the ppk_plot()
function.
manufacturing.ppk_plot(data, lower_specification_limit=-2, upper_specification_limit=2)
In this example, it appears that the manufacturing processes are not up to the task of making consistent product within the specified limits.
Another useful feature is the zone control visualization.
manufacturing.control_chart(data)
There are X-MR charts, Xbar-R charts, and Xbar-S charts available as well. If you call the
control_chart()
function, the appropriate sample size will be selected and data grouped as
the dataset requires. However, if you wish to call a specific type of control chart, use
x_mr_chart
xbar_r_chart
xbar_s_chart
p_chart
Contributions are welcome!
Items marked out were added most recently.
- ...
Add use github actions for deploymentTransition topoetry
for releasesAddI-MR Chart
(seeexamples/imr_chart.py
)AddXbar-R Chart
(subgroups between 2 and 10)AddXbar-S Chart
(subgroups of 11 or more)Update documentation to reflect recent API changesAddp chart
- Add
np chart
- Add
u chart
- Add
c chart
- Add automated testing (partially implemented)