wxPython is a wrapper for the cross-platform GUI API (often referred to as a "toolkit") wxWidgets (which is written in C++) for the Python programming language. It is one of the alternatives to Tkinter. It is implemented as a Python extension module (native code).
In 1995, Robin Dunn needed a GUI application to be deployed on HP-UX systems but also run Windows 3.1 within short time frame. He needed a cross-platform solution. While evaluating free and commercial solutions, he ran across Python bindings on the wxWidgets toolkit webpage (known as wxWindows at the time). This was Dunn's introduction to Python. Together with Harri Pasanen and Edward Zimmerman he developed those initial bindings into wxPython 0.2.
In August 1998, version 0.3 of wxPython was released. It was built for wxWidgets 2.0 and ran on Win32, with a wxGTK version in the works.
The first versions of the wrapper were created by hand. However, the code became difficult to maintain and keep synchronized with wxWidgets releases. By 1997, versions were created with SWIG, greatly decreasing the amount of work to update the wrapper.
In 2010, the Project Phoenix began; an effort to clean up the wxPython implementation and in the process make it compatible with Python 3. The project is a new implementation of wxPython, focused on improving speed, maintainability and extensibility. Like the previous version of wxPython, it wraps the wxWidgets C++ toolkit and provides access to the user interface portions of the wxWidgets API.
With the release of 4.0.0a1 wxPython in 2017, the Project Phoenix version became the official version. wxPython 4.x is the current version being developed as of June 2022.
wxPython enables Python to be used for cross-platform GUI applications requiring very little, if any, platform-specific code.
This is a simple "Hello world" module, depicting the creation of the two main objects in wxPython (the main window object and the application object), followed by passing the control to the event-driven system (by calling MainLoop()) which manages the user-interactive part of the program.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up
prototypes to Spark clusters. It exposes the students to the entire data science pipe
Ce cours est divisé en deux partie. La première partie présente le langage Python et les différences notables entre Python et C++ (utilisé dans le cours précédent ICC). La seconde partie est une intro
This seminar teaches the participants to use advanced Python concepts for writing easier to read, more flexible and faster code.
It teaches concepts in a hands-on and tangible fashion, providing examp
Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to ...
Amer Chemical Soc2024
This dataset supports the publication 'Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid' by Lebo Molefe and John M. Kolinski, Physical Review E, (2023). The data are surface profiles of textured surf ...
Zenodo2023
,
Raw data associated to the manuscript ‘’Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory‘’, Nature Communications (2023); doi: https://doi.org/10.1038/s41467-023-37078-8 Information abou ...