mccode-antlr

PyPI conda-forge License Python

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
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.

Project links

Data verified by PyPI on Aug 13, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.28.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for g5t from gravatar.com g5t

Credits

Author: Gregory Tucker

GitHub Statistics

Data verified by PyPI on Aug 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD License (BSD-3-Clause)

Requires

Python >=3.10

Provides Extra

test hdf5 mcpl ipython display brep scipp docs

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mccode_antlr-0.23.0.tar.gz (1.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mccode_antlr-0.23.0-py3-none-any.whl (486.5 kB view details)

Uploaded Python 3

File details

Details for the file mccode_antlr-0.23.0.tar.gz.

File metadata

  • Download URL: mccode_antlr-0.23.0.tar.gz
  • Upload date:
  • Size: 1.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mccode_antlr-0.23.0.tar.gz
Algorithm Hash digest
SHA256 66b4cc63d6cd1738c1d8df132859218c451b7dc4b3c51416c5ffeff6cf7db955
MD5 bcbd36c37ba492cd7c764ff460afd2f4
BLAKE2b-256 43e7156c50c62a4521b96caf793f5652d00c44b20b1ea89ae48f3bd0a1c224ad

See more details on using hashes here.

Provenance

The following attestation bundles were made for mccode_antlr-0.23.0.tar.gz:

Publisher: wheels.yml on mccode-dev/mccode-antlr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file mccode_antlr-0.23.0-py3-none-any.whl.

File metadata

  • Download URL: mccode_antlr-0.23.0-py3-none-any.whl
  • Upload date:
  • Size: 486.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mccode_antlr-0.23.0-py3-none-any.whl
Algorithm Hash digest
SHA256 cb50b08d843a132d8e2f7b74080ea967f81becd1e436a174ca36253a1a257cb4
MD5 7417c7e638fe1398373f9e47aa116661
BLAKE2b-256 79411164410c2b41983b29aeb3de69ae5e70d171feeed671a9d5145d06f0fef5

See more details on using hashes here.

Provenance

The following attestation bundles were made for mccode_antlr-0.23.0-py3-none-any.whl:

Publisher: wheels.yml on mccode-dev/mccode-antlr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.28.0

2 files

0.27.0

2 files

0.26.0

2 files

0.25.0

2 files

0.24.0

2 files

This release

0.23.0 This release

2 files

0.22.0

2 files

0.21.0

2 files

0.20.3

2 files

0.20.2

2 files

0.20.1

2 files

0.20.0

2 files

0.19.0

2 files

0.18.9

2 files

0.18.8

2 files

0.18.7

2 files

0.18.6

2 files

0.18.5

2 files

0.18.4

2 files

0.18.3

2 files

0.18.2

2 files

0.18.1

2 files

0.18.0

2 files

0.17.2

2 files

0.17.1

2 files

0.17.0

2 files

0.16.2

2 files

0.16.1

2 files

0.16.0

2 files

0.15.2

2 files

0.15.1

2 files

0.15.0

2 files

0.14.0

2 files

0.13.0

2 files

0.12.0

2 files

0.11.0

2 files

0.10.3

2 files

0.10.2

2 files

0.10.1

2 files

0.10.0

2 files

0.9.3

2 files

0.9.2

2 files

0.9.1

2 files

0.9.0

2 files

0.8.4

2 files

0.8.3

2 files

0.8.2

2 files

0.8.1

2 files

0.8.0

2 files

0.7.4

2 files

0.7.3

2 files

0.7.2

2 files

0.7.1

2 files

0.7.0

2 files

0.6.1

2 files

0.6.0

2 files

0.5.3

2 files

0.5.2

2 files

0.5.1

2 files

0.5.0

2 files

0.4.1

2 files

0.4.0

2 files

0.3.3

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.11

2 files

0.2.10

2 files

0.2.9

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.2

2 files

0.1.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page