Jarvis-Operas 1.3.0

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

Architecture and migration references:

• docs/architecture.md
• docs/migration.md

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 '{"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 interp list
jopera interp validate
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

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 a JO-managed source snapshot 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 interp list lists root interpolation namespaces
• jopera interp validate validates root+namespace manifests
• User write policy: the supported user write path is jopera load
• Developer-only write commands (update, delete-func, delete-namespace, interp add/remove) require JARVIS_OPERAS_DEV_WRITE=1

Core API

from jarvis_operas import get_global_operas_registry, get_global_operas

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

operas = get_global_operas()
operas.call("math.add", a=1, b=2)

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>
• User operators cannot register into protected built-in namespaces (for example math, stat, helper, interp, built-in interpolation namespaces).

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]],
)

Behavior:

• Async operator: awaited directly
• Sync operator: offloaded via asyncio.to_thread by default (or custom executor)
• Default numpy async concurrency is CPU-scaled (os.cpu_count() * 3); override with get_global_operas(numpy_concurrency=...)
• 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",
    [
        {"x": 0.1, "y": 0.2},
        {"x": 0.3, "y": 0.4},
        {"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 OperasRegistry, load_user_ops

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

Persistent registration for future Python processes:

from jarvis_operas import get_global_operas_registry, persist_user_ops

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

Persistence store location:

• Default sources registry: ~/.jarvis-operas/sources.json
• Default override rules: ~/.jarvis-operas/overrides.json
• Legacy base env (still supported): JARVIS_OPERAS_PERSIST_FILE=/custom/path/persist_store.json
  • maps to sibling files sources.json and overrides.json
• Optional explicit envs:
  • JARVIS_OPERAS_SOURCES_FILE=/custom/path/sources.json
  • JARVIS_OPERAS_OVERRIDES_FILE=/custom/path/overrides.json
• Managed user source snapshots: ~/.jarvis-operas/user_sources/
• Override snapshot dir: JARVIS_OPERAS_USER_SOURCE_DIR=/custom/path/user_sources
• Legacy single-file user_ops.json is still readable for backward compatibility

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,
}

Integration Contract (Jarvis-HEP / Jarvis-PLOT)

Downstream modules should depend on JO public API only:

• registry provider: get_global_operas_registry()
• runtime calls: call/acall
• lookup/introspection: resolve_name/get/list/info

Avoid importing JO private modules or private symbols.

Function Docs Generation

Function docs are generated from OperaFunction.metadata:

python scripts/generate_function_docs.py --out docs/functions

Generated artifacts:

• docs/functions/functions.json
• docs/functions/<namespace>/<function>.md

Curve publish/runtime cache (namespace manifests + JSON sources)

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

1. Source of truth:
   • root index manifest (interpolations.manifest.json) with namespace list
   • one manifest per namespace (function metadata list)
   • per-function JSON source (x/y arrays)
2. Precompile once: jopera init (bundled library) or jopera init --manifest ./manifest.json (custom)
   • optional: jopera init --namespaces dmdd,interp1 for partial namespace processing
   • optional maintenance: jopera interp list|validate|add|remove
3. Runtime auto-registration: get_global_operas_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

Root index example:

{
  "version": 2,
  "kind": "jarvis_operas_interpolation_namespace_index",
  "namespaces": [
    {
      "namespace": "dmdd",
      "manifest": "dmdd/dmdd.manifest.json"
    }
  ]
}

Namespace manifest example:

{
  "namespace": "dmdd",
  "functions": [
    {
      "name": "demo_curve",
      "source": "dmdd/demo_curve.json",
      "kind": "linear",
      "hot": true,
      "metadata": {
        "group": "dmdd"
      }
    }
  ]
}

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)

Interpolation functions support:

• numpy backend: native callable
• polars backend: map_batches fallback via OperaFunction.return_dtype
• sync: registry.call(...)
• async: await registry.acall(...)

Built-in operators

• math.add(a, b)
• stat.chi2_cov(residual, cov)
• helper.eggbox(x, y)
• math.identity(x)

All built-ins can be called via sync/async registry APIs and accept scalar, NumPy, and Pandas inputs where applicable. Example:

registry.call("math.add", a=1.0, b=2.0)
registry.call("stat.chi2_cov", residual=[1.0, 0.0], cov=[[2.0, 0.0], [0.0, 1.0]])
registry.call("helper.eggbox", 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, backend support fields, and async flag.

Function docs generation (OperaFunction-driven)

Jarvis-Operas can auto-generate per-function docs from OperaFunction declarations.

python scripts/generate_function_docs.py

Output is generated under docs/functions/:

• docs/functions/index.md
• docs/functions/<namespace>/index.md
• docs/functions/<namespace>/<function>.md
• docs/functions/functions.json

Optional filters:

python scripts/generate_function_docs.py --namespaces math,stat
python scripts/generate_function_docs.py --out docs/custom_functions

Workspace clean

Clean local cache/build artifacts:

./scripts/clean_workspace.sh