Introduction

The purpose of these tutorials is to provide a comprehensive collection of examples targeting a wide range of scientists and engineers interested in ptychography, from experimental users to algorithm and code developers.

Overview

The table below gives an overview of all examples with associated data and a link for downloading the data.

Tutorial	Download Data	File Size	Reference
The Basics
The Parameter Tree	No Data Required	-	-
JSON/YAML Config Files	No Data Required	-	-
Input/Output Parameters	No Data Required	-	-
Scan Models	No Data Required	-	-
Reconstruction Engines	No Data Required	-	-
Projectional Engines	No Data Required	-	-
Stochastic Engines	No Data Required	-	-
Gradient-based Engines	No Data Required	-	-
Experimental X-ray Data
Loading HDF5 data	small_data.zip	126 MB	DOI
Working With Large Data	dls_i08_nanogold_spiral.zip	1.11 GB	DOI
Fixing Issues Related to Data (Loading)	small_data.zip	126 MB	DOI
Partial Coherence	dls_i08_nanogold_spiral.zip	1.11 GB	DOI
Position Refinement	dls_i14_test_structure.zip	3.68 GB	DOI
Missing Detector Frames	dls_i08_nanogold_raster.zip	993 MB	DOI
Testing different algorithms	dls_i13_butterfly.zip	286 MB	DOI
Using multiple GPUs	dls_i08_nanogold_spiral.zip	1.11 GB	DOI
Electron Ptychography Data
Working With Electron Data	dls_epsic_80kV_graphene.zip	270 MB	DOI
Chaining Multiple Engines	dls_epsic_80kV_graphene.zip	270 MB	DOI
Starting From a Previous Reconstruction	dls_epsic_80kV_graphene.zip	270 MB	DOI
Advanced Examples
Nearfield Ptychography	esrf_id16_AlNi_nearfield.zip	532 MB	TBA
Modifying an Engine	esrf_id16_AlNi_nearfield.zip	532 MB	TBA
Live visualisation	dls_i08_nanogold_spiral.zip	1.11 GB	DOI
Simulating Data	No Data Required	-	-
Modified Initial Probe	small_data.zip	126 MB	DOI
User Examples
Template	-	-	-

Jupyter Lab/Notebooks

Note

Most of the content of this tutorial has been created in the form of Jupyter Notebooks and the JupyterLab interface is the recommended way of working through these examples. JupyterLab is very easy to use, should you wish to learn more about the Jupyter project and the tools it provides, visit: https://jupyter.org.

As a minimal introduction, a few words about Jupyter Notebooks, which offer a convenient way to provide executable code (e.g. Python) alongside formatted text (using markdown). All notebooks used here are structured in the same way, with some informative content at the top and a full code example at the bottom, separated by a horizontal line:

In order to execute the code cell (or any other cell), simply select the cell and type SHIFT+ENTER or ALT+ENTER