Update on notebook to make working with PyRolL easier and more effective

[GRPlan]

@sag-kalibreur and I have put together an improved version of a PyRolL-notebook:

With this template notebook we try to provide an easy introduction to using PyRolL.

The basic ideas behind the template notebook are:

• demonstrate PyRolL's capabilities without to much python-Syntax

• it is assumed that in real life applications roll and other process data are available in table form
  for convenience we use an Excel-file (process_data.xlsx)

• a simple mechanism to manage units is provided (UnitConversion.py function convert_SI)

• result investigation is done in a pandas dataframe (in the dataframe units are converted to user specification)

• report generation is based on powerpoint and can be easily adopted to companyspecific presentation templates

Basic structure of the notebook:

• to get a look at the data structures created comment out DEBUG = False in cell 1
• to get a look at the functions that are run in the cells comment out FUNC_SOURCE = False in cell 1
• all the required user inputs are in cell 10
  there is no need to distinguish between 2-Roll and 3-Roll processes
• cells 29, 30 and 31 demonstrate that new user defined resultvariables can be added to the dataframe
• the next cells demonstrate various ways to plot data and how to add them to the powerpoint-report
• cell 60 demonstrates a way to create native powerpoint-charts based on PyRolL-results
• cell 64 shows how to add PyRolL-plots to the powerpoint-report

In the powerpoint-report the comparison-plots are scaled in a way so that in presentation mode a simple animation can be emulated by scrolling with the mouse wheel

During development of the template notebook python 3.12.6 was used.
The template notebook was mostly run using VSCode 1.99.2.
It is recommended to use python environments. To setup the environment requirements.txt is provided
(possible pitfall: an older Version of kaleido==0.1.0.post1 is required to reliably plot to file)

We are certain that for many solutions there are more elegant and/or pythonic ways to implement them.
Feel free to provide or suggest improvements.

With the template we hope to get more people involved and discussions running on what data to look at and how to interpret the results provided by PyRolL.

TemplateMay2025.zip

If you have questions or suggestions add to this thread or start a new one.
Have fun.