# statprocon
**statprocon** is a Python helper library for generating data for use in **Stat**istical **Pro**cess **Con**trol charts.
SPC charts are also known as Process Behaviour Charts, Control charts or Shewhart charts.
SPC Charts help answer questions like:
- How do I know a change has occurred in a process?
- What is the expected variation in a process?
- Is a process stable or unpredictable?
## Installation
```shell
pip install statprocon
```
## Usage
```python
from statprocon import XmR
counts = [10, 50, 40, 30]
xmr = XmR(counts)
moving_ranges = xmr.moving_ranges()
unpl = xmr.upper_natural_process_limit()[0] # 85.7
lnpl = xmr.lower_natural_process_limit()[0] # -20.7
x_cl = xmr.x_central_line()[0] # 32.5
url = xmr.upper_range_limit()[0] # 65.36
mr_cl = xmr.mr_central_line()[0] # 20
```
Currently, this library only supports the data for generating an XmR chart.
An XmR chart is the most universal way of using process behaviour charts.
XmR is short for individual values (X) and a moving range (mR).
More chart data options can be added via pull requests.
For more information, please read [Making Sense of Data by Donald Wheeler](https://www.amazon.com/Making-Sense-Data-Donald-Wheeler/dp/0945320728).
### pandas
Visualize XmR charts using Jupyter Notebooks and [pandas](https://pandas.pydata.org/)
```python
import pandas as pd
from statprocon import XmR
xmr = XmR(counts)
pd.DataFrame(xmr.x_to_dict()).astype(float).plot()
pd.DataFrame(xmr.mr_to_dict()).astype(float).plot()
```
Charts can display X-axis labels by using the following code:
```python
labels = [...]
pd.DataFrame(xmr.x_to_dict(), index=labels).astype(float).plot()
```
Or use built-in methods to generate charts that highlight detection points:
```python
import pandas as pd
labels = [...]
xmr = XmR(counts)
xmr.x_plot(pd, labels)
xmr.mr_plot(pd, labels)
```
Data points that meet detection rules are marked by the following colours:
|Rule|Colour|
|---|---|
|Rule 1|Red|
|Rule 2|Green|
|Rule 3|Orange|
If one data point meets the criteria for multiple detection rules, the lower numbered detection rule will take priority and be marked.
For example, if a data point meets all detection rules, it will be displayed in red.
If a data point meets rule 2 and rule 3, it will be displayed in green.
### CSV
Generate a CSV of all the data needed to create XmR charts.
```python
print(xmr.to_csv())
```
### Google Sheets Charts
Generate XmR Charts in Google Sheets
https://github.com/mattmccormick/statprocon/assets/436801/0de1a9f3-a8ad-4047-8c9d-0f890e0bf453
1. Make a copy of the [statprocon XmR Template sheet](https://docs.google.com/spreadsheets/d/1IdCBpE8FK4qP8B7qHQeXX6amLZ8oyhc8OjlBlGHmWTg/edit?usp=sharing)
1. Paste the CSV output from above into cell A1
1. Click `Data -> Split Text to Columns`
The X and MR charts will appear on the right.
Note that the Lower Natural Process Limit may not make sense if your count data could not possibly go negative.
If LNPL is not needed, remove it with the following steps:
1. Double-click on the X Chart
1. Click the `Setup` tab
1. Under `Series`, find `LNPL`
1. Click the 3 dot menu on the right next to `LNPL`
1. Click `Remove`
The LNPL line will be removed from the X Chart.
## Advanced Usage
### Halfway Lines
Halfway lines between the X central line and the Upper and Lower Natural Process Limits can be returned by using the `include_halfway_lines` argument:
```python
xmr.x_to_dict(include_halfway_lines=True)
```
When the process is predictable, approximately 85% of the X values fall between the Upper and Lower halfway lines.
### Trending Limits
With data points that trend upwards or downwards over time, use Trending Limits to calculate a sloping X central line, Upper Natural Process Limits and Lower Natural Process Limits.
```python
from statprocon import XmRTrending
counts = [...] # data from TrendingTestCase.test_trending_limits
source = XmR(counts)
trending = XmRTrending(source)
pd.DataFrame(trending.x_to_dict()).astype(float).plot()
```
### Use the Median Moving Range
If your data contains extreme outliers, it may be better to compute the limits using the median moving range.
```python
xmr = XmR(counts, moving_range_uses='median')
```
### Use the Median for the X Central Line
```python
xmr = XmR(counts, x_central_line_uses='median')
```
Note: It's assumed that by using the median for the X central line that the median moving range should also be used.
For example, you **cannot** do the following:
```python
xmr = XmR(counts, x_central_line_uses='median', moving_range_uses='average')
```
### Calculate Limits from Subset of Counts
The central lines and limits calculations can be restricted to a subset of the count data.
Use the `subset_start_index` and `subset_end_index` parameters when instantiating the XmR object:
```python
xmr = XmR(counts, subset_start_index=10, subset_end_index=34) # 24 points of data starting at index 10
```
When one or both of these optional arguments are provided, the the X and MR central line calculations will be modified to only use the data from `subset_start_index` up to, but not including, `subset_end_index`.
When these optional arguments are not provided, `subset_start_index` defaults to 0 and `subset_end_index` defaults to the length of `counts`.
## Dependencies
There are a few other Python libraries for generating SPC charts but they all contain large dependencies in order to include the ability to graph the chart.
This package will remain small and light and not require large dependencies.
The user will need to convert the data into charts on their own.
This package also contains extensive tests for verifying the integrity of the calculated data.
---
## Development
Create virtualenv
```shell
python3 -m venv venv
```
Activate virtualenv
```shell
source venv/bin/activate
```
[Build](https://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archiveshttps://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archives)
```shell
python -m build
```
[Upload](https://packaging.python.org/en/latest/tutorials/packaging-projects/#uploading-the-distribution-archives)
```shell
python -m twine upload dist/*
```
### Testing
[Install package from source](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#installing-from-source)
```shell
python3 -m pip install .
```
Run tests
```shell
tox
```
Raw data
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"description": "# statprocon\n\n**statprocon** is a Python helper library for generating data for use in **Stat**istical **Pro**cess **Con**trol charts.\nSPC charts are also known as Process Behaviour Charts, Control charts or Shewhart charts.\n\nSPC Charts help answer questions like:\n- How do I know a change has occurred in a process?\n- What is the expected variation in a process?\n- Is a process stable or unpredictable?\n\n## Installation\n\n```shell\npip install statprocon\n```\n\n## Usage\n\n```python\nfrom statprocon import XmR\n\ncounts = [10, 50, 40, 30]\n\nxmr = XmR(counts)\nmoving_ranges = xmr.moving_ranges()\nunpl = xmr.upper_natural_process_limit()[0] # 85.7\nlnpl = xmr.lower_natural_process_limit()[0] # -20.7\nx_cl = xmr.x_central_line()[0] # 32.5\n\nurl = xmr.upper_range_limit()[0] # 65.36\nmr_cl = xmr.mr_central_line()[0] # 20\n\n```\n\nCurrently, this library only supports the data for generating an XmR chart.\nAn XmR chart is the most universal way of using process behaviour charts.\nXmR is short for individual values (X) and a moving range (mR).\nMore chart data options can be added via pull requests.\n\nFor more information, please read [Making Sense of Data by Donald Wheeler](https://www.amazon.com/Making-Sense-Data-Donald-Wheeler/dp/0945320728).\n\n### pandas\n\nVisualize XmR charts using Jupyter Notebooks and [pandas](https://pandas.pydata.org/)\n\n```python\nimport pandas as pd\nfrom statprocon import XmR\n\nxmr = XmR(counts)\n\npd.DataFrame(xmr.x_to_dict()).astype(float).plot()\npd.DataFrame(xmr.mr_to_dict()).astype(float).plot()\n```\n\n\n\nCharts can display X-axis labels by using the following code:\n\n```python\nlabels = [...]\npd.DataFrame(xmr.x_to_dict(), index=labels).astype(float).plot()\n```\n\nOr use built-in methods to generate charts that highlight detection points:\n\n```python\nimport pandas as pd\n\nlabels = [...]\nxmr = XmR(counts)\nxmr.x_plot(pd, labels)\nxmr.mr_plot(pd, labels)\n```\n\n\n\nData points that meet detection rules are marked by the following colours:\n\n|Rule|Colour|\n|---|---|\n|Rule 1|Red|\n|Rule 2|Green|\n|Rule 3|Orange|\n\nIf one data point meets the criteria for multiple detection rules, the lower numbered detection rule will take priority and be marked.\nFor example, if a data point meets all detection rules, it will be displayed in red.\nIf a data point meets rule 2 and rule 3, it will be displayed in green.\n\n### CSV\n\nGenerate a CSV of all the data needed to create XmR charts.\n\n```python\nprint(xmr.to_csv())\n```\n\n### Google Sheets Charts\n\nGenerate XmR Charts in Google Sheets\n\nhttps://github.com/mattmccormick/statprocon/assets/436801/0de1a9f3-a8ad-4047-8c9d-0f890e0bf453\n\n1. Make a copy of the [statprocon XmR Template sheet](https://docs.google.com/spreadsheets/d/1IdCBpE8FK4qP8B7qHQeXX6amLZ8oyhc8OjlBlGHmWTg/edit?usp=sharing)\n1. Paste the CSV output from above into cell A1\n1. Click `Data -> Split Text to Columns`\n\nThe X and MR charts will appear on the right.\n\nNote that the Lower Natural Process Limit may not make sense if your count data could not possibly go negative.\nIf LNPL is not needed, remove it with the following steps:\n\n1. Double-click on the X Chart\n1. Click the `Setup` tab\n1. Under `Series`, find `LNPL`\n1. Click the 3 dot menu on the right next to `LNPL`\n1. Click `Remove`\n\nThe LNPL line will be removed from the X Chart.\n\n## Advanced Usage\n\n### Halfway Lines\n\nHalfway lines between the X central line and the Upper and Lower Natural Process Limits can be returned by using the `include_halfway_lines` argument:\n\n```python\nxmr.x_to_dict(include_halfway_lines=True)\n```\n\nWhen the process is predictable, approximately 85% of the X values fall between the Upper and Lower halfway lines.\n\n### Trending Limits\n\nWith data points that trend upwards or downwards over time, use Trending Limits to calculate a sloping X central line, Upper Natural Process Limits and Lower Natural Process Limits.\n\n```python\nfrom statprocon import XmRTrending\n\ncounts = [...] # data from TrendingTestCase.test_trending_limits\n\nsource = XmR(counts)\ntrending = XmRTrending(source)\npd.DataFrame(trending.x_to_dict()).astype(float).plot()\n```\n\n\n\n\n### Use the Median Moving Range\n\nIf your data contains extreme outliers, it may be better to compute the limits using the median moving range.\n\n```python\nxmr = XmR(counts, moving_range_uses='median')\n```\n\n### Use the Median for the X Central Line\n\n```python\nxmr = XmR(counts, x_central_line_uses='median')\n```\n\nNote: It's assumed that by using the median for the X central line that the median moving range should also be used.\nFor example, you **cannot** do the following:\n\n```python\nxmr = XmR(counts, x_central_line_uses='median', moving_range_uses='average')\n```\n\n### Calculate Limits from Subset of Counts\n\nThe central lines and limits calculations can be restricted to a subset of the count data.\nUse the `subset_start_index` and `subset_end_index` parameters when instantiating the XmR object:\n\n```python\nxmr = XmR(counts, subset_start_index=10, subset_end_index=34) # 24 points of data starting at index 10\n```\n\nWhen one or both of these optional arguments are provided, the the X and MR central line calculations will be modified to only use the data from `subset_start_index` up to, but not including, `subset_end_index`.\nWhen these optional arguments are not provided, `subset_start_index` defaults to 0 and `subset_end_index` defaults to the length of `counts`.\n\n## Dependencies\n\nThere are a few other Python libraries for generating SPC charts but they all contain large dependencies in order to include the ability to graph the chart.\nThis package will remain small and light and not require large dependencies.\nThe user will need to convert the data into charts on their own.\n\nThis package also contains extensive tests for verifying the integrity of the calculated data.\n\n---\n## Development\n\nCreate virtualenv\n\n```shell\npython3 -m venv venv\n```\n\nActivate virtualenv\n\n```shell\nsource venv/bin/activate\n```\n\n[Build](https://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archiveshttps://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archives)\n\n```shell\npython -m build\n```\n\n[Upload](https://packaging.python.org/en/latest/tutorials/packaging-projects/#uploading-the-distribution-archives)\n\n```shell\npython -m twine upload dist/*\n```\n\n### Testing\n\n[Install package from source](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#installing-from-source)\n```shell\npython3 -m pip install .\n```\n\nRun tests\n```shell\ntox\n```\n",
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