This repository contains the PCP Notebooks, which introduce some basic material on Python programming as required for more advanced lab courses offered at FAU study programmes such as Communications and Multimedia Engineering (CME) or Advanced Signal Processing and Communications Engineering (ASC). Furthermore, the PCP notebooks may be used as a gentle introduction to programming as needed in the more advanced FMP Notebooks on Fundamentals of Music Processing. While the first half of the PCP notebooks covers general Python concepts, the second half introduces and requires fundamental concepts in signal processing. The PCP notebooks are not intended to give a comprehensive overview of Python programming, nor are the notebooks self-contained. For a systematic introduction to Python programming, we refer to online sources such as The Python Tutorial or the Scipy Lecture Notes. The PCP notebooks have been inspired and borrow material from the FMP Notebooks on Fundamentals of Music Processing. The PCP Notebooks are freely accessible under the MIT License.

If a static view of the PCP notebooks is enough for you, the exported HTML versions can be used right away without any installation. All material including the explanations, the figures, and the audio examples can be accessed by just following the HTML links. If you want to execute the Python code cells, you have to clone/download the notebooks (along with the data), create an environment, and start a Jupyter server. You then need to follow the IPYNB links within the Jupyter session. The necessary steps are explained in detail in the PCP notebook on how to get started.

If you use the PCP Notebooks in your teaching or research, please consider the following reference.

Meinard Müller and Sebastian Rosenzweig. PCP Notebooks: A Preparation Course for Python with a Focus on Signal Processing. Journal of Open Source Education (JOSE), 5(47), 2022.

This is the preferred and tested variant for using the PCP notebooks.

conda env create -f environment.yml
conda activate PCP
jupyter notebook

The PCP notebooks may be executed using Google colab. However, this needs some preparation. First, you need to be logged in with a Google account. The starting notebook can be accessed via:

https://colab.research.google.com/github/meinardmueller/PCP/blob/master/PCP.ipynb

For the other notebooks, clone the PCP repository to get access to data and the functions in libpcp. To this end, for each colab session, include and execute a code cell at the beginning of the notebook containing the following lines:

%%bash
git clone https://github.com/meinardmueller/PCP.git PCP_temp
mv PCP_temp/* .
rm -rd PCP_temp

One can also use Binder to execute the PCP notebooks. This clones the repository and automatically creates a conda environment. This may take several (maybe even up to ten) minutes when starting binder.

https://mybinder.org/v2/gh/meinardmueller/PCP/master

We are happy for suggestions and contributions. However, to facilitate the synchronization, we would be grateful for either directly contacting us via email ([email protected]) or for creating an issue in our GitHub repository. Please do not submit a pull request without prior consultation with us.

We want to thank the various people who have contributed to the design, implementation, and code examples of the notebooks. We mention the main contributors in alphabetical order: Michael Krause, Heinrich Löllmann, Meinard Müller, Sebastian Rosenzweig, Frank Zalkow. The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.