ITK VS pytest

The itkImage.h header is ITK's standard n-dimensional image data structure.

In this post, we review how the Insight Toolkit (ITK) leverages the first-interaction GitHub Action to communicate our appreciation of the efforts of first-time contributors, establish norms for behavior, and provide civil pointers on where to find more information.

Jupyter has emerged as a fundamental component in artificial intelligence (AI) solution development and scientific inquiry. Jupyter notebooks are prevelant in modern education, commercial applications, and academic research. The Insight Toolkit (ITK) is an open source, cross-platform toolkit for N-dimensional processing, segmentation, and registration used to obtain quantitative insights from medical, biomicroscopy, material science, and geoscience images. The ITK community highly values scientific reproducibility and software sustainability. As a result, advanced computational methods in the toolkit have a dramatically larger impact because they can be reproducibly applied in derived research or commercial applications.

It also depends heavily on the toolchain. One of the first successful toolkits used to circumvent image-based security measures was ITK, originally a toolkit for medical image processing. That's not even using AI (at least back then). Here you build "piplines" by lego'ing together functions like building blocks, there are rules to it, but the sleek interface design make it very versatile. It was a nightmare to devise ways to counteract, since the crucial processing steps could easily be switched around as long as the linear algebra made sense. And when you have a toolchain excelling in fourier-space based analysis and interaction, the linear algebra makes sense in a lot of different orders of doing steps.

Yeah, Prolog is pretty cool!

Another technology I found interesting too learn is ITK (https://itk.org/). You need a different mindset using ITK than other image processing libraries.

Lisp is cool as well.

And fully homomorphic encryption.

VTK is a powerful visualization package, but it's more for working with 3D FEM/CFD data. There are lots of things in there though, so it may be useful. The sister project Paraview is an application which can be used to work with data interactively. Both have great Python support. There is also ITK which is focused on with image data, like medical scans - never used it, though.

In this blog post I want to demonstrate how my lab equipment such as a lab power supply or a digital multimeter (DMM) have been integrated into some pytest-based tests. Would love to get your feedback and thoughts! 🚀

Pytest: is a third-party testing framework that supports fixtures, parameterized testing, and easy test discovery while having room to add plugins to extend its functionality.

Next, you need to install a testing framework that will be used for performing unit testing in your project. Several testing frameworks are available depending on the programming language used to create an application. For example, JUnit is commonly used for Java apps, pytest for Python apps, NUnit for .NET apps, Jest for JavaScript apps, and so on. We’ll use the Jest framework for this tutorial since we are using JavaScript.

Yea, read through the pytest docs.

I absolutely agree about fixtures-as-arguments thing. Ward does this a lot better, using default values for the fixture factory. There's a long issue on ideas to implement something like that as a pytest plugin (https://github.com/pytest-dev/pytest/issues/3834), but it seems the resulting plugin relies on something of a hack.

I recommend writing a few tests. py.test makes that quite simple:

Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.