Recently, I was working in a (Python) project that was completely set up using Poetry. Up to now, I hadn’t had the opportunity (or the need) to try it out, so this seemed like a good learning opportunity.

Except for a few caveats1, Poetry has been very simple and straightforward to use. However, it still has some disadvantages compared to conda (my go-to solution for environment and package management), namely:

1. Poetry only supports Python projects. Sure, I work mostly with Python, but what will happen when I need to use or combine a different framework?
2. Poetry relies on venv for virtual environments, which has given me a lot of headaches in the past (and one of the main reasons why I switched to conda).

But what if you could combine the best of both worlds? For instance: creating an environment with conda and letting Poetry do all the Python package management? That sounds great!

I was looking for a step-by-step guide of how to configure them for Windows. Although I found one or two, I still needed to do some tinkering myself. In this post, I would like to share how I configured conda and Poetry to work together. Hopefully this will save you some time and makes things smoother, should you decide to have this setup yourself.

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This week, I was setting up my new work computer (with Windows). As part of it, I wanted to install my favorite reference manager: JabRef. Unfortunately, the normal installation (i.e., downloading the .msi file) didn’t work, since Windows Protection was blocking it:

I found this weird since I did have admin rights to install stuff. After digging around, I learned that this is because the installer needs to have an electronic certificate. Sadly, this is very expensive, which is a big problem for (Open Source) projects like JabRef which depend mostly on donations. However, there are alternatives. In this post I would like to share a few of them and which one worked for me. Hopefully this will save you a headache.

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Recently, I was developing a package for my job. One of the requirements was to make sure that it was properly documented. Being a big fan of Markdown, MkDocs was a natural choice. Not only is it easy to pick up and customize, but Material for MkDocs offers some fantastic functionality on top of it. With very little effort, you can add admonitions (the nice colorful boxes with handy information), tables , site search , and many others.

After spending time and effort in writing the documentation per se, it was finally time to go live and deploy it. You can do this manually using GitHub Pages. However, in my case it was much handier to automate the documentation deployment after every push. GitHub Actions allow you to do this very easily. Unfortunately, this solution didn’t really work out of the box for me. In this post, I will share the problems I ran into and how I fixed them. Maybe this will be useful for someone else.

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I am happy to share that our latest paper is out. In it, we present a method for measuring the adherence to guidelines in everyday clinical practice. This is a special work since it is the last (large) project to which I contributed during my time at IKNL

The abstract is as follows:

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The reception to Black Panther’s Bust (76215) has been… controversial, to say the least. Although I’m not a big fan of the bust itself (specially because of the scale), I really like the gauntlets. Moreover, I think one of them would fit perfectly side-by-side to the Infinity Gauntlet, the Nano Gauntlet, and Wolverine’s Adamantium Claws.

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Among GitHub’s many cool features, you can create default “community health files” (e.g., CODE_OF_CONDUCT.md, FUNDING.yml, etc.) for all your repositories.

If you are working on repositories from your personal account, you can create a .github repository where you can place the corresponding files. Then, new repositories that you create will use these same files (unless you create community health files for each of them, in which case these will override the defaults). However, when working on an enterprise organization, there are a couple of considerations that you need to take into account to get this up and running.

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In celebration of 2023’s May the 4th, I have created an X-Wing Starfighter using the MINDSTORMS 51515 set, or X-Wing MS5 in short. Not only is this one of the most iconic Star Wars ships, but it also aligns nicely with this year’s release of the X-Wing UCS set. The robot was built using the MINDSTORMS Robot Inventor set 51515 and programmed using Python.

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After more than two years of work, I am happy to share that our latest paper is out! Here, we used federated learning (more specifically, the logistic regression flavor of our previously published GLM model) to identify women at low risk of pN+ cervical cancer. This work has the potential to be used as a guide in shared decision-making process when considering the extent of lymph node dissection.

The abstract is as follows:

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I am happy to share that our latest paper is out! Here, we present a federated implementation of a generalized linear model (GLM) for analyzing horizontally-partitioned data. We show the maths behind it, as well as its validation (done in R). This is part of the work that we are doing with vantage6.

The abstract is as follows:

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As a Biomedical Engineer, I’ve always been fascinated by working with physiological signals. It amazes much how much stuff we can measure on the human body and what we can learn from it. Lately, I’ve been missing working with such data. To scratch that itch, I have been busy with a side project focused on estimating the respiratory rate from the heart rate variability signal (measured from single-lead ECG), as shown in the work by Schäfer and Kratky (2008).

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