Andrew Moa Blog Site

Lately, I’ve frequently encountered situations in my work where I need to fit function curves to scattered data. Typical examples include fitting porous media parameters using the pressure drop of a heat exchanger and fitting polynomial density to a fluid medium. While these tasks can be performed using Excel, Origin, and other tools, they are cumbersome and support limited function types. Here, I’ve developed a simple toolkit in Python that implements function fitting using numpy and scipy. The user interface is written in PySide, allowing for the entire process from input data to output fitting function parameters. The tool also supports data prediction and custom functions.

The executable program compiled using the Qt dynamic link library will rely on a large number of dynamic link library files. If you want to publish the program written on your local computer to other people’s computers, you need to package and publish the dependent dynamic link library files together with the executable program. There are many ways to package and publish, and the principle is the same. The executable program and the dependent files are packaged and compressed into a separate executable file through a compression program. When the user executes the executable file, it is automatically decompressed and run.

For frequent Markdown users, image storage has always been a challenge. Markdown itself does not support embedded images, traditionally requiring external links to local or online files. However, publishing articles online raises storage issues. While image hosting services exist, the process can be cumbersome.

ortunately, Markdown supports image rendering via Base64 encoding. This tool Image2Base64 simplifies conversions between image files and Base64 encoding.

1. Features

Below is the graphical interface of the software (this screenshot uses Base64 rendering):