Recently, I started getting into LEGO again. More interestingly, I started experimenting with Bricklink Studio (or Studio, in short), a great CAD tool for creating your own (virtual) models brick by brick. One of its best features is the wide variety of parts at your disposal. However, every now and then you can come across a part or two that aren’t registered in Studio’s catalogue, but that exist already as a model. This is especially true if you are using parts from very old sets. Fortunately, there is a way to add them and make them available for your creations.
Today, the version 0.2.0 of DeepSurvK is released!
I started working on my first Python package (which is far from ready, but I will definitely post about it when I have a version worth sharing). When trying to find resources of how to publish it to make it available for the community, I felt a bit overwhelmed. The setup for it to work properly involves a lot of individual files. These need to be in the right structure with the right content. As you can imagine, this is very prone to errors.
Recently, I migrated my personal website from Wordpress to GitHub Pages using Jekyll. After some trial and error, I managed to have everything up and running. However, Jekyll tagging (i.e., generating the pages that contain a collection of posts filtered by a tag) requires additional plugins which are not supported by GitHub Pages. Long Qian wrote a fantastic tutorial on how to implement this functionality with a Python script. Unfortunately, this still requires running the script, adding the files to the staging area, committing them, and pushing them to GitHub. That’s a lot of steps for every time that I want to add a new tag. Not only is it prone to errors, but let’s be honest: ain’t nobody got time for that.
Recently, I finished a personal project in which I analyzed the results of the “Who is your favorite Pokemon” survey. After that, I wanted to generate a more interactive visualization in which the user could choose a specific Pokemon and see its results. After pondering different options, I decided to do so in Bokeh because of a few reasons. First of all, you can generate your visualizations using Python only. Furthermore, it is very easy to incorporate Bokeh in Jupyter notebooks, which is great to generate a first version of the prototype. Lastly, a few colleagues of mine have used it for their projects at work and have been very happy with it.
Previously, I looked into Pokémon’s popularity from all 7 generations. However, I felt the visualizations were a bit static. Furthermore, you couldn’t get details on your preferred Pokémon. Therefore, I decided to extend this project and created an interactive dashboard using Bokeh and Binder to visualize the results of the survey. It allows you to choose whatever Pokémon you want and see its preference ranking compared to all Pokémon, compared to all Pokéon of its generation, and how did people voted for it in time during the period in which the survey was open (notice how the color of that plot corresponds to the Pokémon’s dominant color)
The newest entries of the Poké main series, Pokémon Sword and Pokémon Shield will be released later this year. However, the new generation of pocket monsters came with the news that you won’t be able to complete the national Pokédex. In other words, you won’t be able to capture all the Pokémon from previous 7 generations. In a series where “Gotta catch’em all” is its catch phrase, this caused a huge backslash by some members of the community. Their main argument was that “every Pokémon is someone’s favorite”. Is this actually true?
For some strange reason, I stumbled with a paper that I had downloaded long time ago. It explains a simple algorithm for removing artifacts in ECG signal. Since I have a short time-off after the submission of my PhD thesis (yey!), I thought it would be cool to actually code the algorithm and give it a go.
Recently, I was going through my undergraduate lecture notes. I stumbled upon an exercise which I found interesting. The task consisted in implementing the algorithm proposed by Pei and Tseng, which uses vector projection to minimize the problem of transient values when applying an IIR notch filter to an ECG signal.
Recently, quite a few friends have been finishing their degrees and preparing their final theses. A very common struggle was the actual procedure of writing the thesis document. I don’t mean sitting down and writing the content (which is already quite challenging itself), but to manage big amounts of text, figures, and (specially) references efficiently. Being such a big fan of LaTeX myself, I always recommend it. However, setting up everything for the first time might not be very intuitive for everyone, since it requires a few steps. Therefore, I decided to write this small guide on how to setup LaTeX in your computer for the first time.