mccode-antlr

ANTLR4-based compiler and Python API for the McStas and McXtrace Monte Carlo particle ray-tracing languages.

Documentation · PyPI · conda-forge · Issues

Quick start

Command line

pip install mccode_antlr

mcstas-antlr my_instrument.instr   # translate to C
mcrun-antlr  my_instrument.instr -n 1e6 --E_i=5.0   # compile & run

Python API

from mccode_antlr import Flavor
from mccode_antlr.assembler import Assembler

a = Assembler("BrillouinSpec", flavor=Flavor.MCSTAS)
a.parameter("double E_i = 5.0")   # meV

src = a.component("Source", "Source_simple",
                  at=(0, 0, 0),
                  parameters={"E0": "E_i", "radius": 0.05})

instr = a.instrument()
instr.to_file("BrillouinSpec.instr")

# In Jupyter: just put `instr` on the last line of a cell for an interactive view

Installation

# pip
pip install mccode_antlr                 # latest release
pip install "mccode_antlr[hdf5]"         # with HDF5 output
pip install "mccode_antlr[mcpl]"         # with MCPL file support

# conda / mamba (conda-forge)
conda install conda-forge::mccode-antlr
mamba install -c conda-forge mccode-antlr

# development version
pip install git+https://github.com/mccode-dev/mccode-antlr.git

Documentation

Full documentation — including a getting-started guide, core concepts, how-to guides, and API reference — is at:

https://mccode-dev.github.io/mccode-antlr/

Why ANTLR4?

included in-rule code to implement some language features and called the code-generator to construct the intermediate instrument source file. The mixture of language parsing and multiple layers of generated functionality made understanding the program operation, and debugging introduced errors, difficult. Worst of all, there is no easy-to-use tooling available to help the programmer identify syntax errors on-the-fly.

This project reimplements the McCode languages using ANTLR4 which both tokenizes and parses the language into a recursive descent parse tree. ANTLR can include extra in-rule parsing code, but since it can produce output suited for multiple languages (and the extra code must be in the targeted language) this feature is not implemented in this project.

Other benefits of ANTLR4 include integration with Integrated Development Environments, including the freely available Community edition of PyCharm from JetBrains. IDE integration can identify syntax mistakes in the language grammar files, plus help to understand and debug language parsing.

McCode languages

Traditionally, McCode identifies as a single language able to read, parse, and construct programs to perform single particle statistical ray tracing. While McCode-3 uses a single language.l and language.y file pair for lexing and parsing, it actually implemented at least two related languages: one for component definitions in .comp files, one for instrument definitions in .instr files, plus arguably more for other specialised tasks. Notably the mcdisplay utilities of McCode make use of a special runtime output mode to identify the positions and shapes of components, and the paths of particles, which is then read by an independent ply parser to generated visualizations.

This project makes use of ANTLR's language dependency feature to separate the languages into McComp for components and McInstr for instruments, with common language features defined in a McCommon grammar.

Language translation

For use with the McCode runtimes (McStas and McXtrace), the input languages must be translated to C following the C99 standard. This translation was previously performed in C since the lex|flex, yacc|bison workflow produced programs written in C. The C programming language is a very good choice where execution speed is important, as in the McCode runtimes, but less so if speed is not the main goal and memory safety or cross-platform development is important. The McCode-3 translators do not always deallocate memory used in their runtime, and newly developed features are likely to introduce unallocated, out-of-bounds, or double-free memory errors which are then difficult to track down.

ANTLR4 is a Java program, but produces parse-trees in multiple languages. This project uses the Python target so that language-translation can proceed in a language which is well suited to new-feature development, while removing memory handling issues and making cross-platform development significantly easier.