How-To: Compile and run an instrument from Python¶

mccode-antlr can generate C source, compile it into a binary, and execute it entirely from Python — no shell scripts required. This page covers all levels of the API, from the recommended high-level Simulation class down to the individual low-level functions.

Prerequisites¶

The C compiler and McCode runtime must be available on your system (the same prerequisites as for mcstas-antlr / mcxtrace-antlr on the command line).

Recommended: the `Simulation` class¶

The McStas and McXtrace classes (both subclasses of Simulation) are the simplest way to compile an instrument and run simulations from Python.

Compile, run, inspect¶

from mccode_antlr.loader import parse_mcstas_instr
from mccode_antlr.run import McStas

instr = parse_mcstas_instr("MyInstrument.instr")

# compile() creates a temporary directory automatically.
# The directory — and the binary inside it — live as long as `sim` does.
sim = McStas(instr).compile()

# Run a single simulation point.
result, out = sim.run({'E_i': 5.0, 'angle': 30.0}, ncount=1_000_000, seed=42)

# `out` is a SimulationOutput — a dict-like object keyed by detector stem.
print(out['energy'].data.shape)   # e.g. (3, 100) for a 1-D, 100-bin monitor
print(out.directory)              # Path to the output directory on disk

Method chaining is supported for compact one-liners:

result, out = McStas(instr).compile().run({'E_i': 5.0}, ncount=1_000_000)

Use default parameter values¶

Omit parameters (or pass {}) to run with the instrument's compiled-in defaults, equivalent to mcrun -y:

result, out = sim.run(ncount=1_000_000)

Provide a persistent compile directory¶

Pass an explicit directory to compile() if you want the binary to outlive the Simulation object, or to skip recompilation on subsequent runs:

from pathlib import Path

sim = McStas(instr).compile(Path("/tmp/my_build"))
# Reuse the same binary without recompiling:
sim2 = McStas(instr).compile(Path("/tmp/my_build"))  # skips compile if binary exists

Parameter scans¶

scan() iterates over one or more parameter ranges and returns a list of (result, SimulationOutput) tuples — one per scan point.

# 1-D scan: E_i steps from 1 to 5 in increments of 1 (5 points)
results = sim.scan({'E_i': '1:1:5', 'angle': 30.0}, ncount=100_000)

for result, out in results:
    print(out['energy'].data[0].sum())   # total intensity at this scan point

Use an explicit Python list for non-uniform steps:

results = sim.scan({'E_i': [1.0, 2.5, 5.0, 10.0]}, ncount=100_000)

Grid (Cartesian product) scan:

# 3 × 5 = 15 total simulations
results = sim.scan(
    {'E_i': '1:1:3', 'angle': '10:5:30'},
    grid=True, ncount=100_000,
)

Working with `SimulationOutput`¶

SimulationOutput acts as a Mapping from detector stem to loaded data, so existing code that expected a plain dict continues to work. It also exposes additional information about everything written to disk:

result, out = sim.run({'E_i': 5.0}, ncount=1_000_000)

# Dict-like access (backward-compatible)
det = out['energy']      # DatFile1D / DatFile2D / DatFile0D
print(det['I'])          # intensity array
print(det['I_err'])      # uncertainty array
print(len(out))          # number of McCode-format detectors found

# All McCode-format files (any extension — catches Monitor_nD output)
for stem, dat in out.dats.items():
    print(stem, dat.data.shape)

# Files loaded by custom filters (see below)
print(out.other)

# Files that could not be loaded by any filter
for path in out.unrecognized:
    print("Unknown file:", path.name)

# Parsed simulation metadata (.sim file)
if out.sim_file is not None:
    print(out.sim_file)

Registering custom output-file loaders¶

Some components write output in formats other than the McCode .dat format (MCPL particle lists, HDF5 files, etc.). Register a loader for a file extension and it will be called automatically on every matching file:

from mccode_antlr.run import register_output_filter
import h5py

register_output_filter('.h5', h5py.File)

result, out = sim.run({'E_i': 5.0}, ncount=1_000_000)
hdf5_data = out.other.get('nexus_output')   # loaded by h5py.File

Level 1 — Generate C source from an Instr¶

instrument_source translates an Instr object into the C source string that the McCode runtime would compile:

from mccode_antlr import Flavor
from mccode_antlr.compiler.c import instrument_source

config = dict(
    default_main=True,
    enable_trace=False,
    portable=False,
    include_runtime=True,
    embed_instrument_file=False,
    verbose=False,
)

c_source = instrument_source(instr, Flavor.MCSTAS, config)

# Inspect it
print(c_source[:500])

# Write to disk
with open("MyInstrument.c", "w") as f:
    f.write(c_source)

This is useful for debugging the translation step, diffing against known-good output in tests, or feeding the source to a custom build system.

Level 2 — Compile to a binary¶

mccode_compile calls the system C compiler to produce an executable:

from pathlib import Path
from mccode_antlr import Flavor
from mccode_antlr.run import mccode_compile

build_dir = Path("/tmp/my_build")
build_dir.mkdir(exist_ok=True)

binary, target = mccode_compile(instr, build_dir, flavor=Flavor.MCSTAS)
print("Binary at:", binary)

binary is a Path to the compiled executable; target is a CBinaryTarget describing the compilation options used (standard C, MPI, OpenACC, etc.).

Compiler options¶

Pass target and config dicts to control the build:

binary, target = mccode_compile(
    instr, build_dir, flavor=Flavor.MCSTAS,
    target={"mpi": True, "count": 4},   # MPI with 4 processes
    config={"enable_trace": True},       # include TRACE visualisation support
)

Level 3 — Run a compiled binary¶

mccode_run_compiled executes the binary, waits for it to finish, and returns the detector data parsed from the output files:

from pathlib import Path
from mccode_antlr.run import mccode_run_compiled

# The output directory must NOT already exist — McCode creates it.
output_dir = Path("/tmp/my_run_001")

result, out = mccode_run_compiled(
    binary, target,
    output_dir,
    "-n 1000000 E_i=5.0 verbose=0",
)

# `out` is a SimulationOutput (dict-like, keyed by detector stem)
for name, det in out.items():
    print(name, det.data.shape)

The parameters string is passed directly to the binary as command-line arguments (the same syntax as mcrun-antlr). The output directory is passed as --dir to the binary; McCode requires it to not already exist.

Level 4 — Compile and run in one call (temp directory)¶

compile_and_run handles the complete pipeline in a temporary directory that is cleaned up automatically. The .dat files are read and returned before deletion:

from mccode_antlr import Flavor
from mccode_antlr.utils import compile_and_run

result, data = compile_and_run(
    instr,
    parameters="-n 1000000 E_i=5.0",
    flavor=Flavor.MCSTAS,
)

for name, det in data.items():
    print(name, det)

Set run=False to compile only (still in a temp directory):

compile_and_run(instr, parameters="", run=False, flavor=Flavor.MCSTAS)

dump_source=True is the default — compile_and_run writes the generated C file into the current working directory alongside running. Set dump_source=False to suppress this:

result, data = compile_and_run(
    instr, "-n 1e5 E_i=5.0",
    dump_source=False,
    flavor=Flavor.MCSTAS,
)

Putting it all together¶

from mccode_antlr.assembler import Assembler
from mccode_antlr import Flavor
from mccode_antlr.run import McStas

# Build the instrument programmatically
a = Assembler("Quicktest", flavor=Flavor.MCSTAS)
a.parameter("double E_i = 5.0")
a.component("Origin", "Progress_bar", at=(0, 0, 0))
a.component("Source", "Source_simple",
            at=(0, 0, 0),
            parameters={"E0": "E_i", "dE": 0.5, "radius": 0.05,
                        "dist": 1.0, "focus_xw": 0.1, "focus_yh": 0.1})
a.component("Det", "E_monitor",
            at=([0, 0, 1], "Source"),
            parameters={"filename": '"energy.dat"', "Emin": 0.0, "Emax": 10.0,
                        "nchan": 100, "xwidth": 0.1, "yheight": 0.1})
instr = a.instrument

# Compile once, run several times
sim = McStas(instr).compile()

# Single run at the default parameter values
result, out = sim.run(ncount=1_000_000)
print("Default run intensity:", out['energy']['I'].sum())

# Scan over E_i
for result, out in sim.scan({'E_i': '1:1:10'}, ncount=100_000):
    print(out['energy']['I'].sum())

Prerequisites¶

The C compiler and McCode runtime must be available on your system (the same prerequisites as for mcstas-antlr / mcxtrace-antlr on the command line).

Level 1 — Generate C source from an Instr¶

instrument_source translates an Instr object into the C source string that the McCode runtime would compile:

from mccode_antlr import Flavor
from mccode_antlr.compiler.c import instrument_source

config = dict(
    default_main=True,
    enable_trace=False,
    portable=False,
    include_runtime=True,
    embed_instrument_file=False,
    verbose=False,
)

c_source = instrument_source(instr, Flavor.MCSTAS, config)

# Inspect it
print(c_source[:500])

# Write to disk
with open("MyInstrument.c", "w") as f:
    f.write(c_source)

This is useful for debugging the translation step, diffing against known-good output in tests, or feeding the source to a custom build system.

Level 2 — Compile to a binary¶

mccode_compile calls the system C compiler to produce an executable:

from pathlib import Path
from mccode_antlr import Flavor
from mccode_antlr.run import mccode_compile

build_dir = Path("/tmp/my_build")
build_dir.mkdir(exist_ok=True)

binary, target = mccode_compile(instr, build_dir, flavor=Flavor.MCSTAS)
print("Binary at:", binary)

binary is a Path to the compiled executable; target is a CBinaryTarget describing the compilation options used (standard C, MPI, OpenACC, etc.).

Compiler options¶

Pass target and config dicts to control the build:

binary, target = mccode_compile(
    instr, build_dir, flavor=Flavor.MCSTAS,
    target={"mpi": True, "count": 4},   # MPI with 4 processes
    config={"enable_trace": True},       # include TRACE visualisation support
)

Level 3 — Run a compiled binary¶

mccode_run_compiled executes the binary, waits for it to finish, and returns the detector data parsed from the output .dat files:

from pathlib import Path
from mccode_antlr.run import mccode_run_compiled

# The output directory must NOT already exist — McCode creates it.
output_dir = Path("/tmp/my_run_001")

result, data = mccode_run_compiled(
    binary, target,
    output_dir,
    "-n 1000000 E_i=5.0 verbose=0",
)

# `data` is a dict mapping detector stem → McCodeDatData object
for name, det in data.items():
    print(name, det)

The parameters string is passed directly to the binary as command-line arguments (the same syntax as mcrun-antlr). The output directory is passed as --dir to the binary; McCode requires it to not already exist.

Level 4 — Compile and run in one call (temp directory)¶

compile_and_run handles the complete pipeline in a temporary directory that is cleaned up automatically. The .dat files are read and returned before deletion:

from mccode_antlr import Flavor
from mccode_antlr.utils import compile_and_run

result, data = compile_and_run(
    instr,
    parameters="-n 1000000 E_i=5.0",
    flavor=Flavor.MCSTAS,
)

for name, det in data.items():
    print(name, det)

Set run=False to compile only (still in a temp directory):

compile_and_run(instr, parameters="", run=False, flavor=Flavor.MCSTAS)

dump_source=True is the default — compile_and_run writes the generated C file into the current working directory alongside running. Set dump_source=False to suppress this:

result, data = compile_and_run(
    instr, "-n 1e5 E_i=5.0",
    dump_source=False,
    flavor=Flavor.MCSTAS,
)

Putting it all together¶

from textwrap import dedent
from mccode_antlr import Flavor
from mccode_antlr.assembler import Assembler
from mccode_antlr.utils import compile_and_run

# Build the instrument
a = Assembler("Quicktest", flavor=Flavor.MCSTAS)
a.parameter("double E_i = 5.0")
a.component("Origin", "Progress_bar", at=(0, 0, 0))
a.component("Source", "Source_simple",
            at=(0, 0, 0),
            parameters={"E0": "E_i", "dE": 0.5, "radius": 0.05,
                        "dist": 1.0, "focus_xw": 0.1, "focus_yh": 0.1})
a.component("Det", "E_monitor",
            at=([0, 0, 1], "Source"),
            parameters={"filename": '"energy.dat"', "Emin": 0.0, "Emax": 10.0,
                        "nchan": 100, "xwidth": 0.1, "yheight": 0.1})
instr = a.instrument

# Compile and run
result, data = compile_and_run(instr, "-n 1e5 E_i=5.0", flavor=Flavor.MCSTAS)

# Inspect results
print(data.get("energy"))

How-To: Compile and run an instrument from Python¶

Prerequisites¶

Recommended: the Simulation class¶

Compile, run, inspect¶

Use default parameter values¶

Provide a persistent compile directory¶

Parameter scans¶

Working with SimulationOutput¶

Registering custom output-file loaders¶

Level 1 — Generate C source from an Instr¶

Level 2 — Compile to a binary¶

Compiler options¶

Level 3 — Run a compiled binary¶

Level 4 — Compile and run in one call (temp directory)¶

Putting it all together¶

Prerequisites¶

Level 1 — Generate C source from an Instr¶

Level 2 — Compile to a binary¶

Compiler options¶

Level 3 — Run a compiled binary¶

Level 4 — Compile and run in one call (temp directory)¶

Putting it all together¶

Recommended: the `Simulation` class¶

Working with `SimulationOutput`¶