python Python Decorators, Explained Decorators in Python are a very powerful albeit mind-bending and under-documented tool. They are useful for many things, including changing how a function is executed (e.g. for denoting that an API endpoint requires authentication) or to register a function as a handler (e.g. to register different parser functions
javascript Programming R like it's JavaScript / Python R is the king of statistics languages for a good reason: There are hundreds of packages on CRAN for all sorts of analyses and the language is fluent for processing data in a variety of ways, especially when taking advantage of the hadleyverse packages. In my opinion, dplyr is the
python Compiling Python, NumPy and SciPy with Intel Compilers and Intel MKL Compute clusters are shared machines so they often contain very outdated versions of software in the name of backward compatibility. While the environment modules project goes a long way of alleviating this problem, I often encounter problems with the Python installation on clusters that I'm working on. In such a
python Inspecting multidimensional C/C++ matrices by putting NumPy into GDB Developing numerical programs in C, C++ or Fortran or even just fast C extensions like in my previous post, the chance of introducing a bug increases as complexity increases. One of my favorite types of bugs are caused when working with multi-dimensional arrays. While multi-dimensional arrays are relatively painless in
hpc Fast Interstellar Travel with NumPy and C I absolutely love Python (and NumPy!) for the flexibility it gives me in quickly building up complex data structures and programs to arrive at interesting solutions. Still, sometimes handling larger amounts of data is too slow even after profiling my code and subsequent performance tuning. In those cases, going more
