Distributional source, based on a MCPL-format particle list, on which the Kernel Density Estimation (KDE) method is applied.
Author: O.I.Abbate, N.S.Schmidt, updates for McStas by T.Kittelmann and P.Willendrup
Origin: Instituto Balseiro
Date: Dec 2022 / May 2026
Distributional source, based on a MCPL-format particle list, on which the Kernel Density Estimation (KDE) method is applied.
It allows sampling more particles than the number present in a virtual, previously generated virtual source, without repeating samples, controlled via the nloop input parameter.
To function, this component requires a KDSource installation v.2.0.2 or later, as distributed on conda-forge and pypi and included with (conda-based) McStas 3.7.0 or later.
As inputs the component needs access to all of: <ol> <li>The original MPCL file <li>Outputs of a KDSource-analyzed / optimized KDE source: <ul> <li>An XML parameter-file containing the needed configuration <li>A "bandwidth" file (source_bws) </ul> </ol> For information on performing KDSource analysis, please refer to the KDSource example notebooks and the KDSource online documentation (links below).
Parameters in boldface are required; the others are optional.
|
Name |
Unit |
Description |
Default |
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filename |
str |
Name of the XML parameters file containing KDSource definition. |
0 |
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Emin |
meV |
Lower energy bound. Particles found in the MCPL-file below the limit are skipped. |
0 |
|
Emax |
meV |
Upper energy bound. Particles found in the MCPL-file above the limit are skipped. |
FLT_MAX |
|
nloop |
int |
Number of times to loop through the file. |
1 |
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Component source code found in file KDSource.comp.
N.S. Schmidt et. al Annals of Nuclear Energy, Volume 177, 2022, 109309
KDSource online documentation (not fully updated)