Including Jupyter Notebooks in your Course
Course image by [GustavoAckles](https://pixabay.com/users/gustavoackles-6863234/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=4869815) from...
Keywords: analyse experimental data, virtually explore large scale facility instruments, - teach students data modelling, include common scientific python modules
Resource type: Moodle course, e-learning
Including Jupyter Notebooks in your Course
https://pan-learning.org/moodle/course/view.php?id=70
https://pan-training.eu/materials/including-jupyter-notebooks-in-your-course
Course image by [GustavoAckles](https://pixabay.com/users/gustavoackles-6863234/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=4869815) from [Pixabay](https://pixabay.com/?utm_source=link-attribution&utm_medium=referral&utm_campaign=image&utm_content=4869815).
As a teacher, you can link a JupyterHub instance to your course that runs on our e-learning servers.
The JupyterHub instance runs as a docker container that is launched when a student clicks on the link.
The container will clone your chosen github repository where you can edit and store Jupyter notebooks.
Students will be able to open these Jupyter notebooks and execute them remotely.
Crucially students do not require Jupyter to be installed on their computers or have prior knowledge of python.
Examples where this might be useful:
- Show students how to **reduce** and **analyse** experimental data themselves using **python**.
- Introduce students to **python modules** or **software** used in data reduction and analysis.
- Allow students to **virtually explore** large scale facility instruments. For example by using **McStas** (neutron instrument simulator).
- Teach students **data modelling** and **simulation** techniques that use **python**.
**Requirements:**
What the student will interact with must be in the form of a **Jupyter notebook**.
Your notebooks must be on a public **github repository**.
Note that the **entire repository** you provide will be **cloned** not just selected notebooks. However it does not have to be the master branch.
Our containers already include common scientific python modules such as numpy, matplotlib and ipython.
In addition we have containers with the following software installed:
1. McStas, McXtrace, SCIPP, SasVIew
2. Crispy
3. SimEx
If you require a container with other software to be installed it is possible to have a custom container created.
Please email admin@pan-learning.org for help.
**Useful Links:**
- **[Jupyter notebooks](https://jupyter.org/)**
- [**What is a docker container**?](https://www.docker.com/resources/what-container)(and the **[wiki page](https://en.wikipedia.org/wiki/Docker_(software))**)
- [**Docker and Jupyterhub**](https://hub.docker.com/r/jupyterhub/jupyterhub/#help-and-resources)
If you wish to include a link to a Jupyterhub container for your course.
Please fill the form: Information to set up your container
Once you have filled in the form you will receive an email to let you know the JupyterHub link is being set up and any further instructions that are required.
Please note it may take a few days to be up and running.
analyse experimental data, virtually explore large scale facility instruments, - teach students data modelling, include common scientific python modules