SciPy covers a broad variety of typical numerical tasks encountered in scientific computing ranging from the statistical analysis of data, curve fitting, and fast Fourier transform to numerical integration and special functions to name just a few topics. To avoid reinventing the wheel, it is always a good idea to check whether a desired functionality is already provided by SciPy.

In the main part of the tutorial, we will demonstrate how some real-world data taken with a smartphone can be analyzed by means of SciPy.

Installation instructions

The tutorial requires the following packages on top of a Python 3 installation:

• numpy
• scipy
• matplotlib
• jupyter

Any recent version of the Anaconda distribution should allow to run the Jupyter notebooks used in this tutorial (see below) just fine. If you do not have the Anaconda distribution installed and are not short of disk space and want to do scientific work with Python, seriously consider installing it. It is free and pretty straightforward to install.

Alternatively, you can install miniconda and build a specific environment euroscipy-scipy-tutorial for the tutorial by running

conda env create -f environment.yml

with the environment.yml file provided in the repository of this tutorial. For more detailed instruction on how to create a conda environment, see the conda documentation. Note that you need to activate the environment by means of

conda activate euroscipy-scipy-tutorial

Finally, it nothing else works, the notebooks can also be run on binder (provided wifi is available during the tutorial session).

Get the tutorial notebooks

Unless you are using binder, you will need the notebooks of the tutorial to actively follow along.

You can either clone the repository gertingold/euroscipy-scipy-tutorial or go to https://github.com/gertingold/euroscipy-scipy-tutorial/archive/master.zip to download a zipped version of the repository. All files needed during the tutorial are located in the directory notebooks.