Plugins

Plugin name	short description
binning [2] [6]	particle binning plugin to make histograms with user-defined axes and quantity
charge conservation [5]	maximum difference between electron charge density and div E
checkpoint [2]	stores the primary data of the simulation for restarts.
count particles [5]	count total number of macro particles
count per supercell [2]	count macro particles per supercell
energy histogram [6]	energy histograms for electrons and ions
energy fields	electromagnetic field energy per time step
energy particles [6]	kinetic and total energies summed over all electrons and/or ions
ISAAC	interactive 3D live visualization [Matthes2016]
openPMD [2] [6]	outputs simulation data via the openPMD API
particle calorimeter [2] [3] [6]	spatially resolved, particle energy detector in infinite distance
phase space [2] [5] [6]	calculate 2D phase space [Huebl2014]
PNG [6]	pictures of 2D slices
radiation [2]	compute emitted electromagnetic spectra [Pausch2012] [Pausch2014] [Pausch2018]
shadowgraphy [6]	compute synthetic shadowgrams [Carstens2022]
slice emittance	compute emittance and slice emittance of particles
sum currents [5]	compute the total current summed over all cells
transitionRadiation	compute emitted electromagnetic spectra

Footnotes

[1]

On restart, plugins with that footnote overwrite their output of previous runs. Manually save the created files of these plugins before restarting in the same directory.

[2] (1,2,3,4,5,6,7)

Requires PIConGPU to be compiled with openPMD API.

[3]

Can remember particles that left the box at a certain time step.

[4]

Deprecated

[5] (1,2,3,4)

Only runs on the CUDA backend (GPU).

[6] (1,2,3,4,5,6,7,8)

Multi-Plugin: Can be configured to run multiple times with varying parameters.

Period Syntax

Most plugins allow to define a period on how often a plugin shall be executed (notified). Its simple syntax is: <period> with a simple number.

Additionally, the following syntax allows to define intervals for periods:

<start>:<end>[:<period>]

• <start>: begin of the interval; default: 0

• <end>: end of the interval, including the upper bound; default: end of the simulation

• <period>: notify period within the interval; default: 1

Multiple intervals can be combined via a comma separated list.

Examples

• 42 every 42th time step

• :: equal to just writing 1, every time step from start (0) to the end of the simulation

• 11:11 only once at time step 11

• 10:100:2 every second time step between steps 10 and 100 (included)

• 42,30:50:10: at steps 30 40 42 50 84 126 168 …

• 5,10: at steps 0 5 10 15 20 25 … (only executed once per step in overlapping intervals)

Python Postprocessing

In order to further work with the data produced by a plugin during a simulation run, PIConGPU provides python tools that can be used for reading data and visualization. They can be found under lib/python/picongpu/extra/plugins.

Note

The python plugin tools have been moved to the picongpu.extra submodule.

It is our goal to provide at least three modules for each plugin to make postprocessing as convenient as possible: 1. a data reader (inside the data subdirectory) 2. a matplotlib visualizer (inside the plot_mpl subdirectory) 3. a jupyter widget visualizer (inside the jupyter_widgets subdirectory) for usage in jupyter-notebooks

Further information on how to use these tools can be found at each plugin page.

If you would like to help in developing those classes for a plugin of your choice, please read python postprocessing.

References

[Huebl2014]

A. Huebl. Injection Control for Electrons in Laser-Driven Plasma Wakes on the Femtosecond Time Scale, Diploma Thesis at TU Dresden & Helmholtz-Zentrum Dresden - Rossendorf for the German Degree “Diplom-Physiker” (2014), DOI:10.5281/zenodo.15924

[Matthes2016]

A. Matthes, A. Huebl, R. Widera, S. Grottel, S. Gumhold, and M. Bussmann In situ, steerable, hardware-independent and data-structure agnostic visualization with ISAAC, Supercomputing Frontiers and Innovations 3.4, pp. 30-48, (2016), arXiv:1611.09048, DOI:10.14529/jsfi160403

[Huebl2017]

A. Huebl, R. Widera, F. Schmitt, A. Matthes, N. Podhorszki, J.Y. Choi, S. Klasky, and M. Bussmann. On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective. ISC High Performance Workshops 2017, LNCS 10524, pp. 15-29 (2017), arXiv:1706.00522, DOI:10.1007/978-3-319-67630-2_2

[Pausch2012]

R. Pausch. Electromagnetic Radiation from Relativistic Electrons as Characteristic Signature of their Dynamics, Diploma Thesis at TU Dresden & Helmholtz-Zentrum Dresden - Rossendorf for the German Degree “Diplom-Physiker” (2012), DOI:10.5281/zenodo.843510

[Pausch2014]

R. Pausch, A. Debus, R. Widera, K. Steiniger, A.Huebl, H. Burau, M. Bussmann, and U. Schramm. How to test and verify radiation diagnostics simulations within particle-in-cell frameworks, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 740, pp. 250-256 (2014) DOI:10.1016/j.nima.2013.10.073

[Pausch2018]

R. Pausch, A. Debus, A. Huebl, U. Schramm, K. Steiniger, R. Widera, and M. Bussmann. Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 909, pp. 419-422 (2018) arXiv:1802.03972, DOI:10.1016/j.nima.2018.02.020

[Carstens2022]

F.-O. Carstens, Synthetic few-cycle shadowgraphy diagnostics in particle-in-cell codes for characterizing laser-plasma accelerators Master Thesis at TU Dresden & Helmholtz-Zentrum Dresden - Rossendorf (2022) `DOI:10.5281/zenodo.7755263<https://doi.org/10.5281/zenodo.7755263>`_