PAN Blend coherent-diffraction packages

The presented ParaView plugin allows easy access to the algorithm described in Ref 1. It enables analysis of faceted objects that exhibit distortions in their digital representation, e.g. due to tomographic reconstruction artifacts. The contributed functionality can also be used outside ParaView in e.g. command-line programs. The code, data, a test and an example program are included.

Ref 1: Roman Grothausmann, Sebastian Fiechter, Richard Beare, Gaëtan Lehmann, Holger Kropf, Goarke Sanjeeviah Vinod Kumar, Ingo Manke, and John Banhart. Automated quantitative 3D analysis of faceting of particles in tomographic datasets. Ultramicroscopy, 122(0):65 – 75, 2012. ISSN 0304- 3991. doi: 10.1016/j.ultramic.2012.07.024.

PyNX stands for Python tools for Nano-structures Crystallography. It is a python library with the following main modules:

1) pynx.scattering: X-ray scattering computing using graphical processing units, allowing up to 2.5x10^11 reflections/atoms/seconds (single nVidia Titan X). The sub-modulepynx.scattering.gid can be used for Grazing Incidence Diffraction calculations, using the Distorted Wave Born Approximation

2) pynx.ptycho : simulation and analysis of experiments using the ptychography technique, using either CPU (deprecated) or GPU using OpenCL. Examples are available in the pynx/Examples directory. Scripts for analysis of raw data from beamlines are also available, as well as using or producing ptychography data sets in CXI (Coherent X-ray Imaging) format.

3) pynx.wavefront: X-ray wavefront propagation in the near, far field, or continuous (examples available at the end of wavefront.py). Also provided are sub-modules for Fresnel propagation and simulation of the illumination from a Fresnel Zone Plate, both using OpenCL for high performance computing.

4) pynx.cdi: Coherent Diffraction Imaging reconstruction algorithms using GPU.

In addition, it includes :doc:scripts <scripts/index> for command-line processing of ptychography data from generic CXI data (pynx-ptycho-cxi) or specific to beamlines (pynx-ptycho-id01, pynx-ptycho-id13,...).

MoviePy is a Python library for video editing: cutting, concatenations, title insertions, video compositing (a.k.a. non-linear editing), video processing, and creation of custom effects.

PyNX stands for Python tools for Nano-structures Crystallography. It is a python library with the following main modules:

1) pynx.scattering: X-ray scattering computing using graphical processing units, allowing up to 2.5x10^11 reflections/atoms/seconds (single nVidia Titan X). The sub-modulepynx.scattering.gid can be used for Grazing Incidence Diffraction calculations, using the Distorted Wave Born Approximation

2) pynx.ptycho : simulation and analysis of experiments using the ptychography technique, using either CPU (deprecated) or GPU using OpenCL. Examples are available in the pynx/Examples directory. Scripts for analysis of raw data from beamlines are also available, as well as using or producing ptychography data sets in CXI (Coherent X-ray Imaging) format.

3) pynx.wavefront: X-ray wavefront propagation in the near, far field, or continuous (examples available at the end of wavefront.py). Also provided are sub-modules for Fresnel propagation and simulation of the illumination from a Fresnel Zone Plate, both using OpenCL for high performance computing.

4) pynx.cdi: Coherent Diffraction Imaging reconstruction algorithms using GPU.

In addition, it includes :doc:scripts <scripts/index> for command-line processing of ptychography data from generic CXI data (pynx-ptycho-cxi) or specific to beamlines (pynx-ptycho-id01, pynx-ptycho-id13,...).

This package installs the library for Python 3.

Bonsu, the interactive phase retrieval suite, is the first phase retrieval software package for real-time visualisation of the reconstruction of phase information, from coherent X-ray diffraction imaging intensity measurements, in both two and three dimensions. It is complete with an inventory of algorithms and routines for data manipulation and reconstruction.

Bonsu is open-source, is designed around the python language (with c++ bindings) and is largely platform independent. It is also able to handle data in formats such as:

• SPE
• HDF5 (indirectly through h5py)
• VTK
• NumPy

Ptypy was designed with flexibility in mind: it should allow rapid development of new ideas. To this end, much of the "ugly" details have been hidden in advanced containers that manage data and access "views" onto them.

Currently implemented:

• Fully parallelized (using MPI)
• Difference map algorithm with power bound constraint
• Maximum Likelihood with preconditioners and regularizers.
• Mixed-state reconstructions of probe and object
• On-the-fly reconstructions (while data is being acquired)