Python packages needed/recommended
Most modern laptops already have Python installed, so always
check, if that is the case, before installing further!
However, we will mainly/also be running Python through a Jupyter
notebook on the science faculty server ERDA,
so in principle a browser is enough. But in order to be able to run
your Python code independently of network, we strongly recommend,
that you also have Python 3.8+ installed on your laptop (possibly
through an Anaconda environment.
Python is often used with packages, which enables new (and often
extremely useful and fast) features possible in your programs.
We will of course make use of packages (the most common ones, and a few
specific for statistics), which in this course are:
os - Allows using operating system dependent functionality.
scipy - A collection of tools for scientific computing.
numpy - Numerical Python, with powerful N-dim arrays and linear algebra commands.
pandas - Data reading, analysis, and manipulation tool.
matplotlib - The Python standard for plotting graphs and histograms.
iminuit - The Python interface to the minimisation package Minuit for fitting.
sympy - Analytic calculation library, very useful for differentiation and integration within Python code.
seaborn - Plotting library built on top of Matplotlib and contains many nice tools for statistical visualization.
If the above doesn't work, please write us.
Last updated 11th of November 2020.