Encryption, password management, and backups in Linux

An easy introduction to some of the most important tools and concepts

Be it malicious crackers, evil governments, or all-devouring US companies, we have to protect our data from their greedy grasps. But in the beginning it seems to be such a hard task. There are tons of tools and one does neither want to spend several weeks on reading documentation nor to use insecure ones. To alleviate this pain, I will give a short introduction by presenting four simple but essential tools: dm-crypt/LUKS for hard disk encryption, GnuPG for encrypting individual files, emails etc., borg to securely and efficiently backup your data, and pass to manage and store your passwords. »


A package enabling you to download all public data from the ECMWF servers

The European Centre for Medium-Range Weather Forecasts (ECMWF) allows you to access its vast set of model data via a free account and a Python 3 package they provide as well. Unfortunately, a download limit of 30GB prevents the user to retrieve all public data. To circumvent this restriction, I wrote the package ecmwf_retrieve, which chops your request in small parts, sends all of them to the API of the ECMWF, and joins the retrieved data into one single file. »


Reinventing navigation in Emacs

I wrote an Emacs minor mode that provides a much better handling of the navigation in and between buffers in Emacs. In addition, it also features a number of useful and frequently used keybindings, which let you harness both the increased speed and huge flexibility of Emacs. »

Moving from Wordpress to Github Pages+Hugo

I’m moving the blog from Wordpress to Github Pages where I deploy it on my own using the static web package. If you encounter any strange behavior, bugs, bad rendering, or you are not able to follow me or subscribe using RSS, please write me a mail. Sorry for that. I’m still new to this kind of technology 😊 »


Extreme value analysis on climatic time series using R

In this post I present my R package climex for fitting extreme value distributions. It provides improved fitting routines for the generalized extreme value (GEV) and generalized Pareto (GP) distribution to cope with numerical artifacts produced by other packages as well as a better error estimation and an easy handling of parallel calculation. »

on #R,