mccode-antlr
ANTLR4-based compiler and Python API for the McStas and McXtrace Monte Carlo particle ray-tracing languages.
Documentation · PyPI · conda-forge · Issues · Contributing
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
pip install "mccode_antlr[ipython]" # with IPython/Jupyter matcher 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/
IPython / Jupyter completions
mccode_antlr can register an IPython matcher for Python authoring with
Assembler and Simulation objects:
from mccode_antlr.integrations.ipython import register_ipython_matcher
register_ipython_matcher()
Or in IPython / Jupyter:
%load_ext mccode_antlr.integrations.ipython
This matcher is intended for Python-side completions such as component names,
component parameter names, and simulation parameter names. It does not provide
raw McCode DSL completion inside notebook cells. The %load_ext path is the
recommended automatic registration mechanism; importing the module alone does
not register the matcher as a side effect.
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.
1 maintainer
Metadata
1 maintainer
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