manufacturing 1.6.0
Six-Sigma based analysis of manufacturing data
Requires Python: >=3.10

# Purpose

To provide analysis tools and metrics useful in manufacturing environments.

Go to the [documentation](https://slightlynybbled.github.io/manufacturing/index.html).

## Project Maturity

Plots and project are reasonably mature at this point.  Calculations have been refined
and are in-line with commonly accepted standards.

A major v2.0 update is coming to control charts and will be available in 
`manufacturing.alt_vis` module.  For instance, instead of using `from manufacturing import x_mr_chart`,
you would use `from manufacturing.alt_vis import x_mr_chart`.  The new API should
allow for a greater degree of flexibility with recalculation points and the ability
to relabel the axes.  Additionally, alternative axis labels will be able to be supplied.
These changes will eventually become "the way", but are to be considered experimental
until the v2.0 update.

## Installation

To install from `pypi`:

    $>pip install manufacturing

## Building

This package uses [uv](https://docs.astral.sh/uv/) to manage the workflow.

    $>git clone <this repository>
    $>cd manufacturing
    $manufacturing/>uv build

## Testing

Tests will take a while to run - it is generating several hundred plots in the background.

    $>uv run pytest

## Usage

### Cpk Visualization

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)
    
![Screenshot](images/example3.png)

In this example, it appears that the manufacturing processes are not up to the task of 
making consistent product within the specified limits.

### Zone Control Visualization

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

Contributions are welcome!  

### RoadMap

Items marked out were added most recently.

 - ...
 - ~~Add use github actions for deployment~~
 - ~~Transition to `poetry` for releases~~
 - ~~Add `I-MR Chart` (see `examples/imr_chart.py`)~~
 - ~~Add `Xbar-R Chart` (subgroups between 2 and 10)~~
 - ~~Add `Xbar-S Chart` (subgroups of 11 or more)~~
 - ~~Update documentation to reflect recent API changes~~
 - ~~Add `p chart`~~
 - Add `np chart`
 - Add `u chart`
 - Add `c chart`
 - Add automated testing (partially implemented)

## Gallery

![Ppk example](docs/_static/images/ppk_plot.png)

![Cpk example](docs/_static/images/cpk_plot.png)

![X-MR Chart](docs/_static/images/xmr_chart.png)

![Xbar-R Chart](docs/_static/images/xbarr_chart.png)

![Xbar-S Chart](docs/_static/images/xbars_chart.png)