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This workshop will take took place Monday to Friday (April 19th to 23rd) 2021 on Zoom.
Speakers:
Phillip Crout (University of Cambridge)
Magnus Nord (NTNU, Norway)
Francisco de la Peña (University of Lille)
Eric Prestat (University of Manchester- SuperSTEM)
Timothy Poon (Diamond Light Source)
Thomas Slater (ePSIC - Diamond Light Source)
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Agenda
Installation guide- Bundle installation
During the workshop we will have Windows PC workstations prepared with the packages installed and the example notebooks / datasets on disk. We do however encourage the participants to install these packages on their own laptops / workstations and acquaint themselves with the Jupyter Lab environment before the workshop to benefit the most form the presented material.
The easiest way to install hyperspy and other hyperspy extensions (pyxem and atomap) presented in the workshop is to install using the bundles here.
The detailed bundle installation instructions for Windows and MAC can be found through the provided links (courtesy of Eric Prestat).
ParticleSpy is not included in this bundle and should be installed separately following the instructions here.
For this year’s workshop we will be using the bundle above for the workshop workstations.
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Installation guide- Conda installation
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Workshop Software Platform
We will be running the workshop on Google Cloud Platform. Please visit the following link:
https://groups.google.com/g/epsic-hyperspy-workshop-2021
and request to join this Google Group (you will need a Google account to join). This would then give you access to the cluster on GCP. Once you start the server, you will have to clone a github repository that contains all the notebooks for the workshop. The data required for the workshop should appear on your /home directory from the start.
For Q&A both before and during the workshop we will be running a Slack channel.
Installation guide- Conda installation
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. The bundle install is a more rigid package but is guaranteed to work in one step, so it is simpler.
Please follow the steps below for the installation:
Install Python 3.7 from Anaconda
Open an Anaconda Prompt Terminal and create a new environment by running:
Code Block conda create --name hyperspy_env python=3.7
Activate the above environment by:
Code Block WINDOWS: activate hyperspy_env LINUX, macOS: source activate hyperspy_env
Install the packages by running the following commands:
Code Block conda install hyperspy -c conda-forge conda install -c conda-forge pyxem conda install -c conda-forge atomap conda install -c conda-forge particlespy conda install -c conda-forge jupyterlab conda install -c conda-forge notebook conda install -c conda-forge ipympl python -m ipykernel install --user --name=hyperspy_env
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Please note that given the number of participants in this year’s workshop we may not be able to provide technical support for local installations. |
To test your installation, from the same prompt, run:
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jupyter labnotebook |
This would open a Jupyter Lab notebook launcher in your web browser. Under Notebook select Python 3 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.).
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%matplotlib qt5 import hyperspy.api as hs import pyxem as pxm import atomap.api as am import ParticleSpy.api as ps |
Workshop Notebooks and Presentations
We will gradually make the Jupyter Lab notebooks, example datasets and presentations available here for download.to download. In the meantime, we will be updating this github repository:
Notebooks and Presentations
Notebooks can ge accessed here: https://github.com/ePSIC-DLS/Hyperspy_Workshop_2021
HyperSpy Talk
getting started
notebook:
talk:
Introduction to Data Analysis
talk:
Curve Fitting
notebook
Machine Learning
Big Data & Lazy Signals
EELS Analysis
EDX Data Analysis
PyXem
ParticleSpy
HyperSpy Community
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Curve Fitting
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Machine Learning
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EDX Data Analysis
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HyperSpy Community
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