Speakers:

Carter Francis (University of Wisconsin-Madison)
Jacob Madsen (University of Vienna)
Kate MacArthur (Oxford Instruments)
Magnus Nord (NTNU, Norway)
Francisco de la Peña (University of Lille)
Eric Prestat (UKAEA)
Stephanie Ribet (LBL)
Thomas Slater (Cardiff University)
Tara Mishra (LBL)
Steven Zeltmann (Cornell University)

Joining also as panellists:
Ben Savitzky (LBL - hbar instruments)

Diamond Light Source (ePSIC) organising team:

Mohsen Danaie - mohsen.danaie@diamond.ac.uk

David Hopkinson and Chris Allen

Diamond’s Events Page: https://www.diamond.ac.uk/Home/Events/2024/HyperSpy-Workshop-2024.html

BYOD Session Organiser

If you would like to have a more focussed discussion with one of the Speakers about your own data please express interest via this link:

BYOD_Session.docx

Add to the table your name, a brief description of the type of data analysis and relevant packages, plus links to any existing code / example datasets, if possible. These will then be assigned to Speakers and we will have break-out rooms on Friday’s BYOD session.

Workshop Software Platform

We will be running the workshop on your local python installations [Instructions below]. It is crucial that you check your installation ahead of the week of workshop and if you have any issues to get in touch with us.

Installation guide- Using the bundle (Preferred route!)

You can use this bundle installer to get all the packages needed for the workshop all at once:

https://hyperspy.org/hyperspy-bundle/

For testing the installation, write click on a location on your machine and select Jupyter lab here and open a Python 3 notebook and try importing the packages as below in testing section.

Google Colab

For some of the demos - and as a back up in case your local installation develops an issue - we may run notebooks on Google Colab during the workshop.
Please make sure you have an active Google account for this.
We will share the link to those notebooks during the workshop.

Installation guide- Using Miniforge / Mamba (Potentially not as stable as above)

These instructions are provided to enable you to have your personalised hyperspy (and other packages covered in this workshop) installed on you local machine. Having the installation done through this route allows you to have these packages updated independent of each other, in case there is a new release in future.

Please follow the steps below for the installation:

• Install Python from Mambaforge

• Open a Miniforge terminal. This should look like a regular terminal but with the prompt set to (base)

• Create a new environment by running:
  
  

  conda create --name hyperspy_env python=3.10

• Activate the above environment by:
  
  

  WINDOWS: activate hyperspy_env LINUX, macOS: source activate hyperspy_env

  Now your prompt should read: (hyperspy_env)

• Install the packages by running the following commands:

  mamba install hyperspy mamba install abtem nglview mamba install pyxem mamba install atomap mamba install particlespy mamba install jupyterlab notebook ipympl mamba install py4dstem pymatgen

Testing your installation

To test your installation, from the same prompt, run:

This would open a Jupyter lab launcher in your web browser (You can choose the default browser here). If you have installed using the Bundle you can right click and select Jupyter Lab here! Under Notebook select Python 3 (ipykernel) button to open a new notebook. Copy this code to the first cell and run (press shift + enter). If the installation is correct you should not get any error messages (Note that you may get a Warning about pyOpenCl that can be ignored.).

Workshop Notebooks and Presentations

We will gradually make the Jupyter notebooks, example datasets and presentations available here to download. In the meantime, we will be updating this github repository:

https://github.com/ePSIC-DLS/Hyperspy_Workshop_2024

Large Data Download link

Link to the ZIP file

Recordings of the workshop tutorials

Online Resources

HyperSpy webpage - HyperSpy Demos

HyperSpy Gitter chat

Atomap

PyXem - PyXem Demos

ParticleSpy

abTEM

py4DSTEM