Tutorial on coherent X-ray imaging

Introduction

Jupyter notebooks in Python to assist the tutorial on basics of coherent X-ray imaging. The present notebooks are intended for educational reasons only. They are not be perfect, but keep been gradually improved. Apologies for possible mistakes and missing references.

There three groups of notebooks divided in three folders:

1. FourierTransforms: it contains notebooks to play with the Fourier transforms.
2. Oversampling_CXDI: it contains notebooks to understand oversampling of the speckles and play with CDI phase retrieval.
3. PIE: it contains a notebook to understand ptychography and play with educational simulations.

It is important to mention that the Python codes concerning oversampling and CDI phase retrieval were inspired by the "Tutorial in Diffraction Imaging" by Gösta Huldt and Filipe Maia (originally in MATLAB), which was available until recenlty at: http://xray.bmc.uu.se/~ekeberg/molbiofys/tutorial.pdf

Modifications have been made on the original code for educational reasons and Python compatibility.

The Python implementation in the notebook concerning simulations of ptychography is inspired by the MATLAB code available in the Appendix A of the Diploma thesis by Dr. Martin Dierolf, which is available here: https://www.psi.ch/sls/csaxs/PublicationsEN/thesis_dierolf.pdf

Getting started

Throughout this tutorial, we will run some python scripts using Jupyter Notebook. You can either download the files from this Gitlab repository and copy them in a folder of your choice or, if you prefer, you can clone it by typing:

git clone https://github.com/jcesardasilva/tutorialcoherence.git

Once the files are in your computer, you can open Jupyter Notebook by:

• Windows: click on the Jupyter Notebook icon installed in the start menu.
• Linux/Mac OS: open the terminal and type jupyter notebook at the prompt.

Concerning the use of Jupyter Notebooks, the website:

https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/index.html

shows how to install it and to start it in the different Operational Systems (Linux/Mac OS, Windows). Additionally, some Python packages are required: Numpy, IPython, Matplotlib, and scikit-image.

Dependencies

• python >= 3.6
• numpy
• matplotlib
• scikit-image
• scipy

If you do not have such packages installed, I recommend the installation via pip install:

pip3 install --user numpy, ipython, matplotlib, scikit-image, scipy