## Latest Posts:

• Aug, 2019
• Aug 15, 2019Matplotlib for publications
• This article shows how to create plots with matplotlib for publications where fonts and font sizes match the LaTeX document and graphics are not blocky, but allow for infinite zooming.

• Apr, 2019
• Apr 14, 2019Numba - @vectorize and @guvectorize
• In this post, I will explain how to use the @vectorize and @guvectorize decorator from Numba. You can use the former if you want to write a function which extrapolates from scalars to elements of arrays and the latter for a function which extrapolates from arrays to arrays of higher dimensions.

• Mar, 2019
• Mar 25, 2019Numpy - Views vs. Copies
• In one of my recent projects, I needed to accelarate a discrete choice dynamic programming model. After I changed a part of the implementation, the program was indeed faster. But, the most expensive operation according to profiling with snakeviz was now ~:0(<method 'copy' of 'numpy.ndarray' objects>). I was puzzled. I was sure that there was no use of np.copy() at all. After reading some StackOverflow posts and blog entries, it became clear that some operations and more importantly indexing methods return copies instead of views. The difference between the two is that views refer to the same underlying data in memory whereas a copy creates a new object. The disadvantages of a copy are:

• takes more time
• takes more memory

But, what operations return copies?

• Oct, 2018
• Oct 17, 2018A time series course with Julia
• Last semester, I had a time series course where we implemented some models like the Hodrick-Prescott filter or structural vector autoregressive processes in Julia. The whole thing is available online with the notebooks running on Binder which allows you to run the programming examples in your browser. So, if you plan to use Julia yourself and want to play around a little, that is a place to start.

As I had not used Julia before and only heard about how fast it is, that it is statically typed, and so on, I was very interested in the beginning, but that changed quickly.

The main cause of frustration was that the Julia developers released three versions during the time of the course. Version 0.6.4 was released on 9 July 2018, version 0.7.0 and 1.0.0 followed on 8 and 9 August respectively. All versions changed the …

• Aug, 2018
• Aug 27, 2018Facilitate reproducible research with cookiecutter-research-template
• This DAG is produced by a sample project for reproducible research from https://github.com/hmgaudecker/econ-project-templates. I extended this template with the templating engine cookiecutter and various other software engineering tools.

• Aug 21, 2018Identifying Software Patents
• In 2015, I wrote my Bachelor's thesis on identifying software patents. This is useful and necessary in two ways. First, there is no official system to sort patents this way. The main system used by the USPTO focuses on the technological and functional form. A subclass dealing with dispensing solids contains manure spreaders and toothpaste tubes. In contrast, researchers are more interested in topics like automation or software. Second, I learned Python and made my first steps into the world of machine learning.

You can find the whole project on Github as well as the paper. There is also a script to download different kinds of data sets. The raw data uses approximately 90GB of disk space whereas the data for replicating the previous results based on a simple algorithm is currently less than 1GB.

Now, let us see what has been done so far.

• Jun, 2018
• This article shows how to compile and distribute R packages on anaconda.org to be used in your data science projects. This is useful as R has not really a neat dependency pinning tool like Python with requirements.txt or environment.yml with conda and R is shipped with conda anyway. But, if you want to use the MKL accelerated Microsoft R Open instead of plain R, there are some packages which are currently not provided in conda's default channels or conda-forge. Here is how to lift this obstacle.