PyNX: Python tools for Nano-structures Xtallography

NEWS

  • 2020/09: the PyNX article: PyNX: high-performance computing toolkit for coherent X-ray imaging based on operators is out: J. Appl. Cryst. 53 (2020), 1404, also available as arXiv:2008.11511

  • 2020/02/02: version 2020.1 is out ! See the full Changelog

  • 2019/06/19: version 2019.2.6 is out. Note that 2019.2.x (x<6) versions had incorrect Ptycho CUDA scaling, preventing correct ML minimisation

  • 2018/06: all coherent imaging modules have now been converted to use an operator-based API

  • 2016/09: overhaul of the library structure - pynx.gpu has now become pynx.scattering, with pynx.scattering.gid as a sub-module.

Introduction

PyNX stands for Python tools for Nano-structures Xtallography. It can be used for:

  • Coherent X-ray imaging simulation and analysis: coherent diffraction imaging (CDI), Ptychography, Wavefront propagation, near field and far field techniques…

  • Fast scattering calculations from large number of atoms and reciprocal space positions.

PyNX is fully optimised to use Graphical Processing Units, using either CUDA or OpenCL, to provide fast calculations with 1 to 3 orders of magnitude speedup compared to standard processor calculations.

PyNX scripts

PyNX can be used simply with command-line scripts for some applications (2D/3D CDI and 2D Ptychography). These can take generic files as input, such as CXI files (http://cxidb.org), or can analyse data directly from beamlines.

PyNX as a python toolkit

PyNX can be used as 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-module``pynx.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 GPU (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 for Coherent Diffraction Imaging, in 2D or 3D, for small-angle or Bragg diffraction data. This uses either CUDA or OpenCL, but CUDA is strongly recommended for 3D data (significant speedup).

Download

PyNX is available from:

Mailing list, git & issue tracker

There is a mailing list for PyNX: to subscribe, send an email to pynx-subscribe@esrf.fr

To access the git repository and the issue tracker, you can create an account on http://gitlab.esrf and ask the developers for access to the PyNX project at http://gitlab.esrf.fr/favre/PyNX

Changelog

Version 2020.2 (2020-XX-XX)

  • Ptycho-MPI: add ability to distribute computing on several GPU and/or nodes, using MPI. This can be used to distribute independent scans (mpi=multiscan) or to split a large scan (mpi=split) in multiple parts which are automatically stitched (aligned in phase and positions)

  • Ptycho: systematic and reproducible phase ramp removal (far field)

  • Ptycho: keep track of original, absolute coordinates in object

  • Ptycho: use NeXus formatting to automatically display object absolute coordinates

  • Ptycho simulation script: better scale the probe, step size and object according to the frame size. Allow using a siemens star as an object. Use GPU calculations for simulation.

  • Ptycho: add a threshold filter for the position correction, to avoid updating shifts where the object gradient is too small.

  • Ptycho runnner: add obj_max_pix parameter for the maximum object size

  • Ptycho: the absolute position of object & probe is corrected in save plots as well as the CXI file.

  • Ptycho: rename pynx-analyzeprobe.py to pynx-ptycho-analysis.py, and allow using CXI as input files.

  • CDI: add interpolation scheme for masked pixels (e.g. in gaps), using inverse distance wieghted interpolation, and a large confidence interval for amplitude projection during optimisation.

  • CDI-MPI: add ability to distribute multiple scans or runs using MPI

  • CDI & Ptycho runnner: access help text using ‘help’ or ‘–help’ keywords

  • CDI runner: allow processing multiple scans in a more generic way

  • CDI runner: allow importing the initial support from a previous CXI result file, or from an hdf5 file, optionnally with the hdf5 path to the support data array.

  • CDI: add iobs plot to PRTF plots.

  • CDI: save the correct un-masked iobs in CXI files

  • CDI, Ptycho: more accurate reporting of average dt/cycle, including support update, graphical displays.

  • pynx-cdi-analysis: save PRTF in modes.h5 when possible

  • Documentation: now include notebooks in the html docs (using nbsphinx)

Version 2020.1 (2020-02-02)

  • CDI runner: enable combining several masks and interpolating gap maxipix masks

  • CDI runner: enable setting initial support based on command-line equation

  • CDI: Faster cdi array matching and pynx-cdi-analysis using OpenCL

  • CDI: add phase retrieval transfer function (PRTF) plotting code

  • CDI & ptychography: more automatic tests

  • Ptycho: enable position/translation corrections

  • [BUG] Ptycho: correct gradient calculation for maximum likelihood/conjugate gradient algorithm

  • [BUG] Ptycho runner: correctly reshape and rescale probe as needed when loading a previous probe

  • All: use safe import for matplotlib.pyplot in case tk is not available, switching backend to agg

  • pynx-test.py: add option for automatic tests & email reporting

  • More efficient memory usage, especially for tests

  • [INCOMPATIBLE CHANGE] Scattering: change sign in Fhkl_thread, now computing F(hkl)=SUM_i exp(-2j*pi*(h*x_i + k*y_i + l*z_i)) instead of F(hkl)=SUM_i exp(+2j*pi*(h*x_i + k*y_i + l*z_i))

Version 2019.2.6 (2019-06-19)

  • [BUG] Ptycho: Correct CUDA ML operator, which prevented correct minimisation

  • CDI: keep the free pixel mask during successive runs (nb_run=N)

Version 2019.2.5 (2019-06-02)

  • Ptycho: large speedup when using CUDA by increasing stack size (needed for fast, recent GPU) and atomic operations

  • Ptycho: store history of figures of merit and cycle parameters. Export to CXI file.

  • Ptycho: add pynx-simulationpy runner for tests

  • Ptycho: add nanoscopium runner script

  • Ptycho: add dm_loop_obj_probe parameter to control looping over object+probe update

  • Improve pynx-test.py output

  • Improve documentation

  • Improve pynx.test.speed to test for large pinned memory allocations.

  • [BUG] CDI: fix CDI Calc2Obs operator

  • [BUG] CDI & Ptycho: correct nps file import

  • [BUG] Ptycho: correct wavelength calculation for CXI export

  • [BUG] Ptycho CXI runner: correct xyrange parameter interpretation

Version 2019.2 (2019-05-20)

  • CDI & Ptychography: CXI output files follow the NeXus standard, allowing direct display when opened with silx view.

  • CDI: record history of indicators (log-likelihood, support size and levels, …) in CXI output

  • CDI runner: add save=all option to save several steps in the algorithm chain

  • CDI: support update has been improved to avoid diverging, affecting threshold levels to be used.

  • CDI: allow updating support only around the border of the support (support_update_border_n)

  • CDI: add GPS operator

  • CDI: export a more complete set of configuration parameters to CXI files

  • CDI: correct scaling (ML operator, initial scaling)

  • CDI: correct examples

  • CDI runner: add save=all options to export solved object after each step

  • Ptychography: CUDA operators are now preferred to OpenCL (significantly faster for large frame sizes)

  • Ptychography: improve near field algorithm, allowing to specify mask with a zero-phase restraint (vacuum)

  • Ptycho runner: allow to roll (circular shift) data instead of cropping

  • Ptycho: add NanoMAX (MaxIV) runner script

  • Utils: add phase retrieval transfer function estimation (for CDI)

Version 2019.1 (2019-02-07)

  • CDI: add ‘free’ log-likelihood figure-of-merit.

  • CDI: allow to give a range for the support threshold, when performing multiple runs.

  • CDI: allow to keep only the best solutions when performing multiple runs

  • CDI: id01 and id10 scripts will now print the algorithm chain used, when it is not user-supplied

  • CDI: add pynx-cdi-analysis script to analyse proposed solutions.

  • Ptycho: enable CUDA operators, 2x speed improvements, especially for large frame sizes

  • Ptycho: correct probe and object orientation and axis in plots, so that both are seen from the source

  • Ptycho: auto-correct probe centering when necessary (DM)

  • Ptycho: better handling of plots for near field Ptycho

  • Ptycho scripts: add ability to create a movie of the scan

  • CDI & Ptycho: improved speed of calculations from GPU profiling.

  • Add test suite

  • Support for Python 3.7

  • Python 3.4 is deprecated

  • [Incompatible change] Ptycho: now all API functions using x,y(,z) coordinates as input or output will use them in alphabetical order. The inverse order is only used for shapes e.g. (ny, nx). This affects notably declaration of PtychoData, as well as get_view_coord(), calc_obj_shape(), Simulation.scan.values

  • [BUG] CDI: correct handling of smooth parameter in OpenCL SupportUpdate() operator

  • [BUG] CDI: correct handling of masked pixels when using auto-correlation to init the support (OpenCL)

  • [BUG] Ptycho: correct taking into account of mask when using a command-lien script

  • [BUG] Ptycho script: correct taking into account mask in some circumstances

See the full Changelog

Citation & Bibliography

If you use PyNX for scientific work, please consider including a citation:

  • If you use PyNX for coherent X-ray Imaging including CDI and ptychography:

  • If you use PyNX for GPU scattering calculations:

  • Cite the first PyNX article: J. Appl. Cryst. 44(2011), 635-640. A preprint version is also available on ArXiv:1010.2641

  • Give a link to the project: http://ftp.esrf.fr/pub/scisoft/PyNX/

PyNX re-uses or was inspired by features described in the following articles and open-source software packages:
  • PtyPy: 1. B. Enders and P. Thibault, “A computational framework for ptychographic reconstructions”, Proc Math Phys Eng Sci 472(2196), (2016).

  • M. Odstrčil, A. Menzel, and M. Guizar-Sicairos, “Iterative least-squares solver for generalized maximum-likelihood ptychography,” Optics Express 26(3), 3108 (2018).

  • S. Marchesini, A. Schirotzek, C. Yang, H. Wu, and F. Maia, “Augmented projections for ptychographic imaging,” Inverse Problems 29(11), 115009 (2013).

    1. Thibault and A. Menzel, “Reconstructing state mixtures from diffraction measurements,” Nature 494(7435), 68–71 (2013).

    1. Thibault and M. Guizar-Sicairos, “Maximum-likelihood refinement for coherent diffractive imaging,” New J. Phys. 14(6), 063004 (2012).

  • J. N. Clark, X. Huang, R. Harder, and I. K. Robinson, “High-resolution three-dimensional partially coherent diffraction imaging,” Nat Commun 3, 993 (2012).

  • S. Marchesini, “A unified evaluation of iterative projection algorithms for phase retrieval,” Review of Scientific Instruments 78(1), 011301 (2007).

License

The PyNX library is distributed with a CeCILL-B license (an open-source license similar to the FreeBSD one). See http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html

Note that CPU computing of the pynx.scattering module uses the sse_mathfun.h header, which is distributed under the zlib license. See http://gruntthepeon.free.fr/ssemath/

See http://ftp.esrf.fr/pub/scisoft/PyNX/README.txt for more details about the license, copyright, as well as other possible issues regarding ptychography.

Installation

Installation

How to install PyNX (preferably using a python virtual environment)

Automated testing

To automatically test PyNX after installation, you can run the pynx-test.py script, which will run a series of tests and can help diagnose issues specific to GPU languages (OpenCL, CUDA), dependencies or applications (CDI, Ptycho..). Alternatively you can run pytest (if installed) from the root of the PyNX source directory.

Beginner tutorials

To begin using PyNX, you can read the following Tutorials:

Command-line scripts

Scripts Reference

Documentation of scripts included in PyNX

API Documentation

API Reference

Documentation of modules included in PyNX

Indices and tables