Jarvis-HEP-Sampler

Jarvis-HEP-Sampler is the plug-in SDK for user-defined samplers in the Jarvis ecosystem.

A user writes a sampler as one Python file, loads it once, and from then on uses it in a task card exactly like a built-in method. No Jarvis-HEP source code is changed. The plug-in's points go through the same Redis queue, logging and monitor as built-in samplers. The sampler-enabled HEP branch binds each scan to an exact source snapshot and checks scientific settings before resume. Project packages include the source and its full hash; fetch verifies it and prints a load command.

jsampler init MyLatinHypercube       # fixed template -> ./MyLatinHypercube.py
#   edit draw() (and optionally OPTIONS_SCHEMA)
jsampler check MyLatinHypercube.py   # optional offline check, no Redis
jsampler load MyLatinHypercube.py    # snapshot into ~/.jarvis/samplers; reloading replaces it
Jarvis run task.yaml                 # Sampling.Method: MyLatinHypercube

jsampler is this package's command for creating and checking sampler files and managing registrations in ~/.jarvis/samplers. Its list and info commands show registered external methods. Jarvis sampler is Jarvis-HEP's command and also knows the built-in methods. jsampler -v prints this package's version. jsampler --json prints one plain JSON object. The SDK also supports offline Python use (see Python API).

Scope

Jarvis-HEP-Sampler owns the plug-in format:

• the fixed template
• the PLUGIN metadata rules and method-name rule
• loading a plug-in file
• the user registry in ~/.jarvis/samplers/
• the deterministic random streams
• the output contract (shape, real, finite, inside [0, 1])
• the log facade handed to plug-ins
• the offline driver and conformance checks
• the jsampler command

Jarvis-HEP owns the runtime:

• Jarvis sampler, for built-in methods and loaded plug-ins
• task-card validation
• the run preflight
• mapping unit-cube points to physical parameters, and selection
• Sample UUIDs, Redis, Workers and the Archiver
• checkpoints and resume
• the sampler log, telemetry and monitor
• Jarvis man and project packing

This package does not import Jarvis-HEP, Redis or Portal. jsampler check --card reads Sampling.Variables and Sampling.Bounds from the task YAML.

Complete package installation

Published package: Version 1.0.3. Install from PyPI:

python -m pip install "Jarvis-HEP-Sampler==1.0.3"

From this repository:

python -m pip install .

Dependencies: NumPy, jsonschema, PyYAML and rich. The current Jarvis-HEP source requires host contract 2, which is in this source but not yet in a published release (see CHANGELOG.md); install this source for Jarvis-HEP development.

The end-to-end user tutorial lives in Jarvis-Books, in the TeX manual chapter Jarvis-HEP-Sampler: Implementing Your Own Sampler (TeX/Jarvis-HEP-V2 manual).

A plug-in

"""Latin hypercube design: one point per stratum in every variable."""

import numpy as np

PLUGIN = {
    "api_version": 1,
    "method": "MyLatinHypercube",
    "version": "0.1.0",
    "profile": "independent",
}

OPTIONS_SCHEMA = {  # optional
    "type": "object",
    "properties": {"centered": {"type": "boolean"}},
    "additionalProperties": False,
}


def prepare(setup):
    rng = setup.rng()
    perms = np.stack([rng.permutation(setup.point_number) for _ in range(setup.dimension)])
    return {"perms": perms, "centered": bool(setup.options.get("centered", False))}


def draw(context, rng, plan):
    strata = plan["perms"][:, context.candidate_index]
    offset = 0.5 if plan["centered"] else rng.random(context.dimension)
    return (strata + offset) / context.point_number

Sampling:
  Method: MyLatinHypercube
  Variables:
    - name: mass
      distribution: {type: Flat, parameters: {min: 100.0, max: 1000.0}}
  Bounds:
    seed: 7
    point_number: 10000
    centered: false

The full contract is in Plug-in authoring.

A complete runnable card is provided in examples/MyLatinHypercube.task.yaml. It uses Jarvis-HEP's bundled Eggbox demonstration operator:

jsampler check examples/MyLatinHypercube.py --card examples/MyLatinHypercube.task.yaml
jsampler load examples/MyLatinHypercube.py
Jarvis validate examples/MyLatinHypercube.task.yaml
Jarvis check examples/MyLatinHypercube.task.yaml
Jarvis run examples/MyLatinHypercube.task.yaml

jsampler check accepts a sampler file only and does not need load first. It checks the method name before options schema, prepare or drawing points. A reserved-name failure returns exit 2; algorithm output or repeatability failures return exit 1. For a loaded method, use jsampler info <Method> to find its source file, then check that file. Jarvis check is the calculator smoke: its identity, bound source and checkpoint stay under DATABASE/test and checkpoints/<scan>/<Method>/check/, and the formal scan's science database and resume checkpoint stay in place.

The command-line layout, errors, exit codes and JSON format follow the Jarvis CLI design.

Reloading the same method updates future scans. Removing the registration preserves an existing scan's bound source and its normal Jarvis run ... --resume workflow.

Host integration

HOST_CONTRACT_VERSION = 2 identifies the host-facing interface in this source; plug-in API_VERSION and the stream protocol remain 1. check_method_name and parse_metadata validate syntax and structure. External init, registry load and check_plugin apply check_external_method_name; generic loaders accept packaged names without a trusted-name override. Entry policies run through the optional metadata_validator loader hook before options schemas and hooks.

jarvis_hep_sampler.cli.main(..., catalog=CatalogExtension(...)) accepts host data callbacks augment_list(payload) and resolve_info(method, payload), plus list_sections=((payload_key, panel_title), ...). They add fields to the SDK results; parsing, errors and both text/JSON rendering stay in the SDK. Host info can include conflicts with exact method names, problems and removal commands. Existing JSON fields remain intact.

Python API

from jarvis_hep_sampler import SamplerRegistry, write_template
from jarvis_hep_sampler.testing import check_plugin, run_offline

path = write_template("MyLatinHypercube")                  # jsampler init
report = check_plugin(path, dimension=2, options={})     # jsampler check
for name, result in report.rows():
    print(f"{name:14} {result}")

run = run_offline(path, variable_names=["mass", "coupling"], point_number=1000, seed=7)
print(run.points.shape, run.stop_reason)

SamplerRegistry().load(path)                             # jsampler load

Documentation

• Plug-in authoring
• Developer documentation
• Changelog

Status

Alpha. API version 1 supports the independent execution contract; generational metadata reserves a future profile, whose running state/feedback contract remains under design. This release runs independent plug-ins; generational (feedback samplers) arrives in a later release.

License

MIT. See LICENSE. Copyright (c) 2026 Pengxuan Zhu.