Jarvis-Operas 1.1.3

Operator registry and async-friendly execution layer for Jarvis

Jarvis-Operas

Jarvis-Operas is a standalone operator layer for registering, loading, and calling Python callables.

• Scope: operators only (likelihood, chi2, prior mapping, data transforms, etc.)
• No dependency on Jarvis-HEP internals
• Native async entrypoint for Jarvis-HEP style execution (await registry.acall(...))
• Stable "<namespace>.<name>" naming for references from Jarvis-HEP/Jarvis-PLOT YAML

Install

pip install .

Or for development (includes pytest and installs terminal command):

pip install -e ".[dev]"

Terminal command (jopera)

After install, you can use:

jopera
jopera help examples
jopera list --namespace math
jopera list --namespace math --json
jopera info stat.chi2_cov --json
jopera call math.add --kwargs '{"a": 1, "b": 2}'
jopera acall stat.chi2_cov --arg residual=[1.0,-0.5] --arg cov=[[2.0,0.1],[0.1,1.0]]
jopera call helper.eggbox --kwargs '{"observables":{"x":0.5,"y":0.0}}'
jopera list --namespace math --log-mode info
jopera call math.add --kwargs '{"a": 1, "b": 2}' --log-mode debug
jopera init
jopera init --manifest ./manifest.json --cache-root ~/.jarvis-operas/curve-cache
jopera --help-advanced

Load user operators directly in CLI:

jopera load /absolute/path/to/my_ops.py
jopera call my_ops.my_op --user-ops /absolute/path/to/my_ops.py --arg x=10
jopera info my_ops.my_op --json
jopera update /absolute/path/to/my_ops.py
jopera update /absolute/path/to/my_ops.py --function my_op
jopera delete-func my_ops.old_op
jopera delete-func h12345
jopera delete-namespace legacy_ops

CLI behavior:

• jopera (no args) prints a quick start card
• jopera list prints grouped human-readable output by default (id + name)
• jopera list --json prints machine-readable JSON objects with id/name/namespace
• jopera info <name_or_id> prints metadata (including a unique id) and a suggested next command
• jopera load <path> persists this source path by default for future processes
• jopera load <path> --session-only loads without persisting
• jopera init precompiles bundled interpolation manifest library
• jopera init --manifest <manifest.json> precompiles a custom curve JSON manifest
• jopera update <path> updates all functions in script namespaces (default behavior)
• jopera update <path> --function <name> updates one function from script
• jopera delete-func <name_or_id> / delete-namespace remove persisted functions/namespaces

Core API

from jarvis_operas import get_global_registry

registry = get_global_registry()
registry.call("math.add", a=1, b=2)
registry.call("math.identity", x={"k": 1})

Supported namespace convention:

• Built-ins use feature namespaces, e.g.:
  • math.<name>
  • stat.<name>
  • helper.<name>
• User operator files default to <script_name>.<func_name>

Async call (Jarvis-HEP Factory/Module friendly)

result = await registry.acall(
    "stat.chi2_cov",
    residual=[1.0, -0.5],
    cov=[[2.0, 0.1], [0.1, 1.0]],
    observables={"obs": 1.0},
    sample_info={"id": 42},
    cfg={"mode": "demo"},
)

Behavior:

• Async operator: awaited directly
• Sync operator: offloaded via asyncio.to_thread by default (or custom executor)
• Batch helper concurrency: await registry.acall_helper_many("eggbox", [...])

Example batch helper execution for Factory-side concurrent scans:

results = await registry.acall_helper_many(
    "eggbox",
    [
        {"observables": {"x": 0.1, "y": 0.2}},
        {"observables": {"x": 0.3, "y": 0.4}},
        {"observables": {"x": 0.5, "y": 0.6}},
    ],
)

Logger injection

registry methods accept optional logger and operators can optionally define logger argument.

• If no logger is provided, Jarvis-Operas uses loguru.logger bound with module="Jarvis-Operas"
• If logger is provided, Jarvis-Operas reuses it (no duplicate handler creation)
• Console format follows Jarvis-HEP style (module -> time - [level] >>> message)
• Default mode is warning (only warning/error/critical are shown)
• Optional modes: info, debug

Utility:

from jarvis_operas import get_logger, set_log_mode

set_log_mode("info")   # or "debug", default is "warning"
logger = get_logger()

Load user operators from file

from jarvis_operas import OperatorRegistry, load_user_ops

registry = OperatorRegistry()
loaded = load_user_ops("./my_ops.py", registry)
print(loaded)

Persistent registration for future Python processes:

from jarvis_operas import get_global_registry, persist_user_ops

persist_user_ops("/absolute/path/to/my_ops.py")
registry = get_global_registry()  # auto-loads persisted sources

Persistence store location:

• Default: ~/.jarvis-operas/user_ops.json
• Override with env: JARVIS_OPERAS_PERSIST_FILE=/custom/path/user_ops.json
• The same store keeps persistent delete/update overrides for functions and namespaces

By default, load_user_ops("./my_ops.py", ...) uses my_ops as namespace, so my_op becomes my_ops.my_op.

my_ops.py can export operators with either style:

1. Decorator (recommended)

from jarvis_operas import oper

@oper("my_chi2")
def my_chi2(residual, cov, logger=None):
    ...

When loaded by load_user_ops("./my_ops.py", ...), this is registered as my_ops.my_chi2.

2. Explicit whitelist

def my_op(x):
    return x

__JARVIS_OPERAS__ = {
    "my_op": my_op,
}

Curve publish/runtime cache (manifest + JSON sources)

Use this flow when you have many 1D interpolation curves and want runtime speed:

1. Source of truth: manifest.json + per-curve JSON (x/y arrays)
2. Precompile once: jopera init (bundled library) or jopera init --manifest ./manifest.json (custom)
3. Runtime auto-registration: get_global_registry() registers hot curves as interp.<curve_id>
4. Runtime load path uses index.json + *.pkl only (no source JSON in hot path)

Namespace rule for registered interpolation operators:

• If namespace is set in curve item, register as <namespace>.<curve_id>
• Else if metadata.group (or group) is set, register as <group>.<curve_id>
• Else fallback to interp.<curve_id>

Bundled interpolation manifest library resource:

• jarvis_operas/manifests/interpolations.manifest.json

Minimal manifest example:

{
  "curves": [
    {
      "curve_id": "demo_curve",
      "source": "curves/demo_curve.json",
      "kind": "linear",
      "hot": true
    }
  ]
}

Curve source JSON example:

{
  "x": [0.0, 1.0, 2.0],
  "y": [0.0, 1.0, 4.0]
}

Python integration API:

from jarvis_operas import (
    init_curve_cache,
    interpolation_manifest_resource,
    load_hot_curve_function_table,
    load_interpolation_manifest_library,
    register_hot_curves,
)

library_manifest = load_interpolation_manifest_library()
library_path = interpolation_manifest_resource()

init_curve_cache("./manifest.json")
table = load_hot_curve_function_table()

funcs = {}
updated = register_hot_curves(funcs)

Built-in operators

• math.add(a, b)
• stat.chi2_cov(residual, cov)
• helper.eggbox(observables) where observables must be {"x": ..., "y": ...} (scalar, NumPy, or Pandas)
• math.identity(x)

All built-ins can be called via sync/async registry APIs and accept scalar, NumPy, and Pandas inputs where applicable. Built-ins also support observables dict input (Jarvis-HEP style), e.g.:

registry.call("math.add", observables={"a": 1.0, "b": 2.0})
registry.call("stat.chi2_cov", observables={"residual": [1.0, 0.0], "cov": [[2.0, 0.0], [0.0, 1.0]]})
registry.call("helper.eggbox", observables={"x": 0.5, "y": 0.0})

Query registry for external UIs (JHEP/JPlot)

registry.list()
registry.list(namespace="math")
registry.info("stat.chi2_cov")

registry.info(...) returns metadata, signature, docstring summary, module/qualname, and async flag.