During the autumn semester of 2017 I took the course “Programming Techniques for Scientific Simulations” as part of my BSc course in Computational Science and engineering at ETH Zürich. This is a summary of the course’s contents I wrote and used during the actual exam. Some things are missing, as it was an open-book exam
I was looking for a way to set up a private git server that I could access from anywhere, to synchronize my work across several computers. I remembered I had a Raspberry Pi lying around that wasn’t getting any real use, so I figured this would be a perfect use case! My first idea was
SSH stands for Secure Shell Host. It effectively allows you to access the terminal (shell) on a remote machine – securely. There are two ways to validate your identity when attempting to access a server via SSH: password, or SSH key pair. Password authentication is the default method: you run a command similar to ssh
Here is a brief example of how to plot data with Python and, specifically, the matplotlib library. The following program calculates an estimate of Pi by generating random points in the range [-1;1) x [-1;1), and using the ratio between the number of points inside the unit circle to the total number of points. There
Building (compiling) large projects can take a long time. Build systems like Make and SCons do a really good job of reusing resources that don’t need recompiling because we didn’t change anything, but compiling a large project for the first time can still take a while. One solution to this issue, especially with multicore and
Here is a more sophisticated example of using gnuplot: set terminal png size 1920, 1080 enhanced font ‘Verdana,15’ # Different log scales set logscale x 2 set logscale y 10 # %L is the exponent of the current log scale set format y “%g” set format x “2e%L” # Move the legend to the top
We already know make, a powerful tool to automate all kinds of building sequences. But if you are using it to compile large C++ projects, you will quickly find that it’s tedious to maintain, and that you’re often repeating the same commands over and over again. This, of course, opens up the door to mistakes
gnuplot is a very easy-to-use tool allowing us to quickly plot data we generated with, for instance, a C++ program. Here is a sample gnuplot script – see a brief explanation of the commands below: set terminal png size 900,675 enhanced font ‘Verdana,9’; set output ‘error_plot.png’; set logscale xy; show logscale; set format xy “%g”;
If you use C++, you’ve almost certainly already used a library of some kind. Even the classic “Hello world” program requires one: #include <iostream> // include the iostream library int main() { std::cout << “Hello world!”; return 0; } But what if we wanted to write our own? This has several advantages. First off, if
make is a very useful tool when working on large projects with many dependencies. A C++ project with many header includes, for instance, can quickly get tedious to compile, when at each compilation you must run several commands: g++ -c main.cpp -o lib/main.o g++ -c myfile.cpp -o lib/myfile.o g++ lib/main.o lib/myfile.o -o main What make