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CUQIpy software training, 16–17 Sept 2024
UQIPI24 workshop, ICMS, Bayes Centre, Edinburgh, UK
Tentative programme
Monday 16 September
Afternoon
Evening
Tuesday 16 December
Morning
Resources
pip install cuqipy
Plugins
Running the training notebooks
We present three approaches to run the training notebooks
Approach #1: Recommended
Installing CUQIpy and running the notebooks on your local machine
Approach #2: Quick start
Launching the notebooks in the cloud (Google colab and binder)
Approach #3: Technical University of Denmark (DTU) servers
Explained in the next seciton
DTU Jupyter notebook servers
If you are not installing CUQIpy on own computer, you can use one of the two learnmore servers (ask CUQIpy team at event for access):
When logged in to the learnmore servers you should see a "launcher" tab with some icons to start new notebooks with different python packages installed. You can use the following ones:
In the launcher you also see an icon "terminal", click that to start a linux terminal. In the you type/copy-paste:
This will clone/copy the CUQI book to your directory, then you can navigate in the left menu into the training folder and start the training notebooks. You need to change the "kernel" in the top right corner to e.g. the "CUQIpy-env" one, in order for the notebook to have access to the python packages installed in there such as CUQIpy.
Ideas for hackathon projects
List project topics here and please add your name to any project you are interested in working on:
1. Computed tomography with CUQIpy-CIL (proposed by Margaret Duff)

2. PDEs with CUQIpy-FEniCS
3. PSF deconvolution on astronomical images (Proposed by Charlie MacMahon-Geller)

4. Gravity anomaly (proposed by Lara Baalbaki)

5. Surface reconstruction (proposed by Yiyao Zhang)

6. Multistatic radar (Proposed by Joshua Hellier)

7. Semi-blind image deconvolution (proposed by Monica Pragliola)

8. Phase retrieval (proposed by Dawit Hailu)

9. [insert topic title here]
Tuesday morning: Advanced features overview
CUQIpy Benchmarks
Naoki Sakai and Tania Goia, DTU CUQI summer interns
Implicit priors with regularized Gaussian distributions
Jasper Everink
Moreau-Yoshida Unadjusted Langevin Algorithm (MYULA) and Plug & Play priors Unadjusted Langevin Algorithm (PnPULA)
Rémi Laumont
pnpula
pnpula
branch of CUQIpy, which is expected to be merged to the main brach of CUQIpy soon. Instructions for local installation of CUQIpy:demos/cuqi_myula.ipynb
anddemos/cuqi_pnpula.ipynb
.CT with CUQIpy-CIL
Jakob S. Jørgensen
CUQIpy-PyTorch
Jakob S. Jørgensen
UQ for PDEs
Amal M. Alghamdi
Gibbs sampling
Chao Zhang
User showcases
Chao Zhang
Feedback
Acknowledgments
Software issues, problems, comments:
Please let us know about any and all problems, suggestions, etc. about CUQIpy that you may have:
Did you discover any bugs or strange behaviour?
Feature wishlist
Documentation issues
Other