PyXLL using Jupyter and PySimpleGUI Interface (with video) Dec 2022

khoitsma
AISC, Excel, Jupyter, PySimpleGUI, Python, PyXLL, Video
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This post features:
  the PyXLL add-on
  Jupyter
  the PySimpleGUI graphical interface

Python — develop, sell, deploy, protect Nov 2022

In 2022, let’s consider the difficulties of development using Python.

Hint: it isn’t the programming.

Excel — Taming of the Shapes with PyXLL May 2022

Using PyXLL, I have programmed automatic shapes in Excel. That is, I built a function that draws an arbitrary shape in a user spreadsheet: the shape is defined by a range of coordinates; as the coordinates are edited, a new shape can be drawn.

Python, PyXLL, and Pyside2 Nov 2021

I built a full PySide2 menu that allows the selection and presentation of AISC steel properties and the calculation of ASCE wind loads.

Excel Dropdown with NO worksheet data list — redux Aug 2021

I decided to delete this post … There are limits to the number of items that will show in a data validation drop down list: The list can show up to show 32,767 items from a list on the worksheet. If you type the items into the data validation dialog box (a delimited list), the […]

Transform Axes Jun 2021

In structural engineering, we must constantly deal with axes transformations. Mostly we have right handed coordinates (RHC), but sometimes left handed coordinates (LHC).

Protected: Parsing Feet Inches Sixteenths (FIS) Jun 2021

khoitsma
Excel, Mathematics, Python, PyXLL
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ASCE Exact Wind Integration (closed form equations) Jun 2021

khoitsma
ASCE, Calculus, Cocalc, Excel, Jupyter, Mathematics, Python, PyXLL, SageMath
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I developed a Python function to calculate closed form exact values for wind forces and wind CGs.

I use only calculus — no bar charts with 0′ to 15′, 20′ to 25′, et cetera —- a curve rather than a step function. I present derivations and examples.

Multi-Dimension Data Table using PyXLL (with video) Dec 2020

I coded a PyXLL macro that builds a multi-dimension data table.
The native Data Table provided by Excel is useful but limited to two variables.
My version is set up for 5 independent variables and an unlimited (theoretically) number of dependent variables. Five was an arbitrary choice; some limitation is required here.