Contents
   Preface and acknowledgements
Preface and acknowledgements
1  Introduction to McXtrace
1.1  Development of Monte Carlo x-ray simulation
1.2  Scientiﬁc background
1.3  The design of McXtrace
1.4  Overview
2  New features in McXtrace 3.9
2.1  Kernel
2.2  Run-time
2.3  Components and Library
2.4  Tools, installation
3  Installing McXtrace
4  Monte Carlo Techniques and simulation strategy
4.1  X-ray simulations
4.2  The x-ray weight
4.3  Weight factor transformations during a Monte Carlo choice
4.4  Stratiﬁed sampling
4.5  Accuracy of Monte Carlo simulations
5  Running McXtrace
5.1  Brief introduction to the graphical user interface
5.2  Running the instrument compiler
5.3  Running the simulations
5.4  Using simulation front-ends
5.5  Data formats - Analyzing and visualizing the simulation results
5.6  Using MPI for parallel computing
6  The McXtrace kernel and meta-language
6.1  Notational conventions
6.2  Syntactical conventions
6.3  Writing instrument deﬁnitions
6.4  Writing instrument deﬁnitions - complex arrangements and syntax
6.5  Writing component deﬁnitions
6.6  Extending component deﬁnitions
6.7  MxDoc, the McXtrace library documentation tool
7  The Union components
7.1  Motivation
7.2  Component classes
7.3  The propagation algorithm
7.4  A complete example
7.5  Further examples
8  The component library: Abstract
8.1  Component categories
8.2  Data ﬁles
8.3  Component and instrument examples
A  Random numbers in McXtrace
A.1  Transformation of random numbers
A.2  Random generators
B  Libraries and conversion constants
B.1  Run-time calls and functions (mcxtrace-r)
B.2  Reading a data ﬁle into a vector/matrix (Table input, read_table-lib)
B.3  Constants for unit conversion etc.
C  The McXtrace terminology
Bibliography
   Bibliography
   Index and keywords

User and Programmers’ Guide to the X Ray-Tracing Package McXtrace, version 3.9

¹A working jupyter lab installation is required; it is not currently bundled with the standard conda-forge McXtrace package.