This tutorial is for Python users who would like to create nice 2d plots
with Python.
Audience Level
Students should have a working knowledge of Python. NumPy knowledge is
helpful but not required.
Prerequisites
Please bring your laptop with the operating system of your choice
(Linux, Mac OS X, Windows). In addition to Python 3.6 (2.7 works if
really want to use it), we need:
If you use Anaconda you should have all requirements installed. If you
use a new conda environment install with:
conda install jupyter numpy matplotlib
The same goes for pip:
pip install jupyter numpy matplotlib
Method
This is a hands-on course. Students are strongly encouraged to work
along with the trainer at the interactive prompt. There will be
exercises the students need to do on their own. Experience shows that
this active involvement is essential for an effective learning.
Content
The library matplotlib provides many different types of diagrams from
within Python with only few lines of code. Examples are used to
exercise the use of this library. The tutorial provides an overview
how to create plots . Using matplotlib from Jupyter Notebook provides
an interactive environment for fast testing of ideas. We will be using
this for most of the tutorial.
With a simple plot we learn how to add axis labels, titles and a
legend. The GUI offers zooming, panning, changing of plot sizes and
other interactive ways to modify the plot. We will use Python to
change properties of existing plots such as line colors, marker
symbols, or line styles. There are several ways how to place text on
plots. You will learn about the different coordinate systems relative
to the plot, the canvas or the figure. Another topic are ticks, where
to put them and how to format them to achieve publication-quality
plots. The concepts of figures, subplots, and axes and how they relate
to each other will be explained with examples.
Matplotlib offers many different types of plots. The tutorial
introduces several of them with an example. A more advanced topic will
be creating your own plot types. Finally, we will create a small
animation to explore the possibilities to visualize changes.
