gluex-rs

Python tools for GlueX analysis, conditions data, luminosity calculations, and HDDM event generation. The package installs the gluex Python module and the matching gluex command-line program.

Install

Use Python 3.10 or newer. With pip:

python -m pip install gluex-rs

Or add it to a uv project:

uv add gluex-rs

The package installs laddu for channel construction and generation. Confirm the installation:

python -c "import gluex; print(gluex.__version__)"
gluex --help

Python

import gluex

period = gluex.RunPeriod("f18")
print(period.min_run, period.max_run)

GlueX conditions and luminosity utilities are exposed through the gluex.ccdb, gluex.rcdb, and gluex.lumi modules. The command-line interface is useful for common one-off tasks:

gluex info runs f18
gluex info rest f18
gluex lumi --run f18=2 --rcdb rcdb.sqlite --ccdb ccdb.sqlite

Event generation

Generation configurations are authored in Python. GlueX turns a Laddu channel, an optional model, its parameter values, and any additional scalar branches into a versioned JSON execution manifest. The JSON is an interchange artifact for gluex gen check and gluex gen run; users do not need to write or edit it.

This example creates (\gamma p \to \pi^+ n) and writes a model-less configuration:

import laddu as ld
from gluex import generation

channel = ld.Channel(
    "gamma p -> pi+ n",
    edges=[
        ld.Edge(
            "beam",
            particle=ld.particles.PHOTON,
            initial_momentum=ld.InitialMomentum.uniform_energy(
                low=8.0,
                high=9.0,
                direction=[0.0, 0.0, 1.0],
            ),
        ),
        ld.Edge(
            "target",
            particle=ld.particles.PROTON,
            initial_momentum=ld.InitialMomentum.momentum([0.0, 0.0, 0.0]),
        ),
        ld.Edge("pi_plus", particle=ld.particles.PI_PLUS, output=True),
        ld.Edge("neutron", particle=ld.particles.NEUTRON, output=True),
    ],
    vertices=[
        ld.Vertex(
            "production",
            incoming=["beam", "target"],
            outgoing=["pi_plus", "neutron"],
            generation=ld.VertexProposal.t_exchange(
                incoming="beam",
                outgoing="pi_plus",
            ),
        ),
    ],
)

config = generation.GenerationConfig(channel)
config.write("generation.json")

generation.config_json(channel) remains available when a JSON string is more convenient. GenerationConfig.write(...) is preferred for complete scripts because the same object can configure and validate every generation option.

gluex gen check generation.json
gluex gen run generation.json --events 100000 --run-number 90000

The default seed is 0, and this command writes generation.hddm beside the manifest. Use --seed or --output to override either default.

Users can also run a Monte Carlo generation in Python rather than using the CLI:

report = config.run(
    "events.hddm",
    events=10_000,
    run_number=30_000,
    seed=1,
)
print(report["acceptance_rate"])

The optional model and additional scalar branches are configured in the same Python script. Parameters may be supplied by name or in model.parameter_names order:

scale = ld.parameter("scale", initial=1.0)
model = ld.Model(scale * (ld.scalar("polarization") + 1.0))

config = generation.GenerationConfig(
    channel,
    model=model,
    parameters={"scale": 2.0},
    scalars={
        "polarization": generation.Scalar.uniform(-0.5, 0.5),
        "setting": generation.Scalar.fixed(7.0),
    },
    pilot_proposals=20_000,
    safety_scale=2.0,
)
config.add_scalar(
    "calibration",
    generation.Scalar.histogram(
        edges=[0.0, 1.0, 2.0],
        weights=[1.0, 3.0],
    ),
)
config.validate()
config.write("generation.json")

Scalar.uniform(low, high) samples on [low, high). Scalar branches are available to Laddu expressions through ld.scalar(name) while the model is evaluated; fixed and histogram-backed scalar sources are also supported. Set config.max_weight if you have an independently known bound, or leave it unset to use the pilot estimate for a model-backed configuration.

Transfer proposals

The production vertex above pairs the incoming beam with outgoing pi_plus. t_exchange(...) with no slope samples uniformly in the corresponding Mandelstam transfer (t=(p_\gamma-p_{\pi^+})^2). The range is the physical (t) interval for that event’s sampled beam energy and final-state masses, so it changes across the 8–9 GeV beam-energy range. It is not a fixed interval and it is not a separate uniform angular proposal.

To restrict the proposal, provide t_min and/or t_max; they are intersected with the physical interval. For example, use t_min=-4.0, t_max=0.0 in ld.VertexProposal.t_exchange(...). The example’s unconstrained uniform proposal uses the entire physical interval.

Certified unweighting

For unit-model generation, this library uses a branch-and-bound envelope solver to obtain a certified finite upper bound for the proposal weight. This avoids tuning a pilot sample or relying on an observed maximum. The generation report records the certified envelope and proof statistics when available.

When a model is present and no manual bound is supplied, the generator estimates the envelope from config.pilot_proposals proposals and multiplies the observed maximum by config.safety_scale. These values can also be overridden for one run with --pilot-proposals, --max-weight, and --safety-scale. If a pilot or manual envelope is exceeded, GlueX grows it, retrospectively thins buffered events, and prints a warning instead of failing.

License

Dual-licensed under Apache-2.0 or MIT.

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gluex-rs 0.1.7

gluex-rs

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BLAKE2b-256	`d545e1be0b2dd8f0cbd0b266b32cf6a779491596c23f900860b39dbd7284f152`

Algorithm	Hash digest
SHA256	`53e8f9d45c70befb352e837477d406f6fc959e21c3b32b4f5658f8690ac614b1`
MD5	`64ba4df72532859c35aac36478ea2b9f`
BLAKE2b-256	`c16a1225ccb33636bd9033773d0c4be82d95bd6c55dfafa65ea89321b4bdc016`

Algorithm	Hash digest
SHA256	`6a98c2f889b5fcbe1042bad569aae4055688b7ff88556b60830fef054a7b3742`
MD5	`4a110fa2aaa4323ac149193eaf2a33ca`
BLAKE2b-256	`48ce664da925645e4737a4599660d37ed91e752aebf519d52d6af6eb121eff0b`

Algorithm	Hash digest
SHA256	`37c2fe78dd14d9144d5c6b21f72c6fc4a93c716c38cab0ddaa5e6b91e6d4e9fd`
MD5	`e549fcb8547b572b05b582951609c7e9`
BLAKE2b-256	`9278d91d7fb1fbb218fd2816682f61977642f7f8dbced360b006439ea97a10f3`

Algorithm	Hash digest
SHA256	`94d6475c8f9b091c894e0a064a11af258d6f8407d74d162ab2fed91c350f140b`
MD5	`1199fd123951f8be6af3376f908dcc6e`
BLAKE2b-256	`072457f9aaeac1aa95b07f03f14eaf37817a23d2b35f34a6e1546852ec737faa`

Algorithm	Hash digest
SHA256	`535f4c26c6d8e0e59b698a5705570640885bb9a666233495a69b132a5204dd28`
MD5	`7a7a447eb4f07a63462465559e5a4055`
BLAKE2b-256	`b2fa8056d944ee692ef9f088750f49e253ec841f0ac52e605575a3afeee537f4`

Algorithm	Hash digest
SHA256	`2f88fddf01cecf9edbc79e94256bf936e29739f26ffa33abae1ed8205c312e0c`
MD5	`a95d885f676a1acb14f11793d0c43ae2`
BLAKE2b-256	`3ecd2f3808596b9eaf6e20ddd4faf9f7d5b076b921b8db886ce605e86fc5ddb2`

Algorithm	Hash digest
SHA256	`3d38429271c17f375a2e53b1b8c22b95c3e43c2287de47ee189842eb80546c7c`
MD5	`28d0db0aeabd141fd22c743a770a647c`
BLAKE2b-256	`782049609722c12bdca33b1bfeeb31a771bdea0e6655100a6890f0309f2c6672`

Algorithm	Hash digest
SHA256	`a31b7f5a8d808261eb9ebcce0a2eaf958f7a022131817f96673b178ead3bbffd`
MD5	`7e165e7bbfc81d33acfb36d29f94ba35`
BLAKE2b-256	`28df9ca88c945458e382fcedc04f68dd79debe043f57b849c86a97d27c84b7c4`

Algorithm	Hash digest
SHA256	`1931a795038f83c2d79ff02ae7c1b4cbe1298326cac1221fc80ca07c2f2b5ee1`
MD5	`15dfdebf5729ef3804ecc1f47be44710`
BLAKE2b-256	`7f5b6ff846660a256857118ce36cdd1ee320c35107bf91f900fcf93c196eb213`