silkie VS pytest

[metadata] name = silkie version = 1.0.7 ... description = Static site generator with the smoothness of silk long_description = file: README.md long_description_content_type = text/markdown url = https://github.com/oliver-pham/silkie project_urls = Bug Tracker = https://github.com/oliver-pham/silkie/issues classifiers = Programming Language :: Python :: 3 License :: OSI Approved :: MIT License Operating System :: OS Independent [options] packages = silkie python_requires = >=3.9 install_requires = click >= 8.0.0 markdown >= 3.3.0 yattag >= 1.14.0 python-frontmatter >= 1.0.0 [options.entry_points] console_scripts = silkie = silkie.cli:silkie

Last week, I already set up some automation tests for Silkie, my static site generator (SSG). Instead of running tests manually on each Pull Request (PR), I made an attempt to configure GitHub Actions to automate this Continuous Integration (CI) workflow. Moreover, I also helped my friend, Luke, add a test case to his SSG this week.

According to my parter's issue, I create a new test file named. I pull a new PR, the partner's Actions passed it. Before that, I found that many projects have the function of automatic error checking. I wonder how to do it. After lab9, I also created my own GitHub actions. I'm very excited.

After setting up static analysis tools last week, it's time to configure a testing framework for Continuous Integration (CI). There are several options for Silkie, my work-in-progress static site generator, but I decided to give Pytest a try. In this blog, I'll show you how I set up:

If you are tired of maintaining your coding style, I have good news for you. Fortunately, there are developer tools that can automate and streamline mundane development tasks. In this blog, I'll show you how I integrated 2 static code analysis tools and a package manager for pre-commit hooks into Silke, my work-in-progress static site generator.

After wandering the world of static site generators (SSG), I came across an eye-catching, well-documented, and developer-friendly one focusing on documentation sites: Docusaurus. After diving a bit deeper into their documentation, I realized they have many out-of-the-box features, which I can try integrating into Silke, an SSG I wrote from scratch.

This week, I noticed that some functions in my static site generator (SSG) were hardcoded with complex logic and "magic values", so I decided to focus on refactoring them. Without cleaning them up, maintaining them would be a tragedy. For instance, there was a function spanning 36 lines of code with 8 if/elif statements. Some of the statements even have nested if/elif statements themselves. You can find the function referenced in this issue.

This week on my Open Source journey, I attempted to add support for JSON formatted configuration files for an open source Static Site Generator (SSG). The owner of the repo, Tengzhen, also contributed the same feature to my SSG, Silkie. However, I made a step forward by testing his code from a tracking branch before merging it.

After establishing Markdown support for my static site generator (SSG), I decided to enable parsing Markdown horizontal rules along with HTML document language support. However, I developed the two features on separate branches this time, so I could switch between the two if I encountered any obstacle. Little did I know the obstacle was awaiting me at the end.

As for Eugene, he also contributed the same feature to Silkie, my SSG. I noticed his code might need to be fixed and refactored, so we worked together on both Slack and GitHub to resolve those issues. Given our time constraint and Eugene's lack of experience with Python, it was a success that we managed to add a new feature without breaking the existing ones.

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.