Skip to content
Lectures for the course "Scientific Programming in Python" at Osnabrück University
Branch: master
Clone or download
Rüdiger Busche
Rüdiger Busche Clean notebook
Latest commit 16d0cf1 Jun 17, 2019

README.md

Binder

Lectures in Scientific Computing in Python

This repository contains all lectures from the course Scientific programming in Python that is part of the Cognitive Science program at the University Osnabrück. Each lecture is accompanied by a Jupyter notebook that explains each topic with a combination of code and text. You can view the notebooks directly on GitHub or run them locally and play with the code. If you do not want to install anything, click on the Binder logo above to run all the notebooks in a ready to use environment in the cloud.

Recordings

All lecture recordings from 2018 can be viewed on Youtube and on the Opencast platform.

Lecture YouTube Opencast
01 View View
02 View View
03 View View
04 View View
05 View View
06 View View
07 View View
08 View View
09 View View
10 View View
11 View View
12 View View

Installation

Create a virtual Python environment, name e.g. scientific, for example using conda.

$ conda env create -f environment.yml

Activate the environment

$ conda activate scientific

you might see some error like your shell has not been set up to use conda activate. Follow the instructions given in your shell to make it work.

then start JupyterLab

$ jupyter lab

JupyterLab should open in your browser. From there you can navigate to the notebooks and interact with them.

Contributing

Before committing changes, run the whole notebook from top to bottom using (for fish)

$ env RUNALL=1 jupyter nbconvert --execute --allow-errors --inplace lecture.ipynb 

for bash

$ export RUNALL=1 jupyter nbconvert --execute --allow-errors --inplace lecture.ipynb 

To make new interactive exercises install jupyter-solutions and set up as teacher, by setting

c.JupyterLabRmotrSolutions.role = "teacher"

in the repositories jupyter_notebook_config.py.

Only use markdown headers to structure the lectures. Numbering will be automatically handled by the jupyterlab-toc extension. Also use markdown to talk about the content of the lecture and the next cells. Use comments only if you want to highlight something in a specific line of code. If you write comments, write them in full sentences.

Use nbdime to make working with notebooks and git easier

pip install nbdime
nbdime config-git --enable  

Acknowledgments

Thanks to Auss Abbood for making the videos YouTube ready!

You can’t perform that action at this time.