### Markdown Note - The best Note app around

That title is obviously click bait. However, it is also true, at least for me, as I wrote Markdown Note (MDN) because I was not satisfied with any existing solution. It allows you to write notes in Markdown, with any editor you prefer, and display them in the Browser. It also has some bibliographic features.

### An introduction to Jupyter - and why I don’t like it

Jupyter Notebooks are hugely popular. In this post I’ll give an introduction into what they are, why I would actually not recommend using them, and what I do instead.

### A Python Course

Recenty I’ve been asked whether I could teach Python to a fellow PhD student and said yes. My plan was not to invest too much time into this, and just assemble a list of resources to read, and a few practice tasks. However, by now two more people have asked whether they could join, so I decided to gather everything in one place and this place is here.

### The best way to create a Pathfinder character sheet? Python!

I love playing Pathfinder. And I’ve tried many different ways to manage my character sheets. You can do it by hand, use an Excel-sheet or use one of the many available programs to do it for you. The problem with the programs is that even if they actually contain everything, you get into trouble if you want to use custom items or house rules. This is obviously no problem if you do it by hand but that is honestly kinda complex and it is very hard to make no mistakes. Lately, I’ve been writing one in markdown, and thought to myself: “If you could combine the possibilities from markdown and Excel, that would be very nice.”

# Intro

Currently, I am learning Clojure reading the great (and free) book Clojure for the brave and true. In chapter 5, the game Peg-Thing is implemented. I thought this would be a good opportunity to compare Python and Clojure. I reimplemented the game in Python two times. First in the ‘normal’ Python way and a second time in a way that seemed to me like the ideal functional implementation in Python.

### How to code basic psychological experiments with Python quickly

A few days ago, I read an article: How to code basic psychological experiments with Python by Mathias Gatti. It advertises PsychoPy, which is a python library to create psychological paradigms, which is really just a fancy name for what is basically a very simple mini game that will usually measure reaction times or the like. In his post, he walks the reader through the code necessary for a very simple paradigm that consist of only 2 screens, the first one says “press any key to continue”, and once you do, you are taken to the second screen which says “press [ n ] to continue” and “press [ q ] to exit”. It will then measure your reaction time and take you back to the beginning if you press n or exit and save the results to a csv-file if you press q.

In this post I will implement exactly the same paradigm but using PyParadigm, which is a library for paradigm creation that I wrote. The advantage that I want to demonstrate is that it requires much less code to write paradigms with PyParadigm than with PsychoPy, therefore you can work more quickly. Also, less code means less bugs.

### How well can an SVM deal with noise and small samples?

What I do at my job is called “Multi Voxel Pattern Analysis” (MVPA). It involves applying classification algorithms to functional MRI (fMRI) data, i.e. recorded brain-activity, to predict some parameter or a behavior. The classification algorithm of choice in most studies is the Support Vector Machine (SVM). The reason for this is that commonly we only have small samples (usually $$10 \leq n \leq 300$$) while the number of features tends to be large (all voxels within multiple volumes, which sums up to ~120000 and more potential features) and SVMs are supposedly good at dealing with small samples and large numbers of features. The accuracies we reach are pretty low most of the time. High enough to yield significant p-values, but not in a useful range to do any predictions. Also fMRI images are quite noisy and a lot of times a positive result becomes insignificant when evaluating it with previously unseen data. This generally made me a little suspicious as to how well an SVM can deal with these difficulties.

### Accessing pandas DataFrame using SQL-like select statements

Recently I was writing the following code:

processing_frame = \
sl_results[sl_results.c == coi][["subscript", "kappa"]].rename(
columns={"kappa": "value"})


Another piece of code that might look familiar to the habituated pandas user is:

my_frame[(my_frame.col1 == a) & (my_frame.col2 == b) & (my_frame.col3 < c)]


Typing this kind of stuff is annoying, and it triggers me every time I write something like this. Ideally, this would look more like:

### Functionalish Programming

There already is a myriad of blog posts on functional programming, with this post, I don’t try to give an introduction to it, but I want to highlight, how we can get inspirations from it, to improve our code. This is why the post is called “functionalish” programming, instead of “functional” programming.