build(agent): molt-x#ed374b iteration
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"""NovaPlan MVP package init"""
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__all__ = ["planner", "contracts", "ledger", "adapters"]
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__all__ = ["planner", "contracts", "ledger", "adapters", "catopt_bridge"]
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"""Minimal CatOpt bridge scaffolding for NovaPlan.
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"""Lightweight CatOpt bridge scaffold for NovaPlan MVP.
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This module provides a tiny, domain-agnostic scaffold to map NovaPlan MVP
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primitives to a CatOpt-like representation. It is intentionally lightweight and
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intended for experimentation and integration in MVP milestones.
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- Object: canonical local problem representation (LocalProblem).
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- Morphism: exchange vectors such as SharedVariables and PlanDelta with versioning.
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- Functor: adapters that translate device-specific planning representations into the
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canonical NovaPlan form.
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Note: This is a stubbed scaffold and not a full implementation. Real adapters
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and a transport layer would be built on top of this in Phase 0/1 milestones.
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Public API used by tests:
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- Object: minimal wrapper around a LocalProblem-like object
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- Morphism: wrapper for delta signals between source and destination
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- to_object(lp): convert a LocalProblem-like instance to Object
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- to_morphism(delta, source, target, version=None): create a Morphism from delta
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- bridge_example(lp, source, target): package as a dict with object and serialized morphism
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- validate_contracts(obj, morph): basic compatibility check
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"""
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from __future__ import annotations
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from typing import Dict, Any
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from nova_plan.planner import LocalProblem
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import json
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from typing import Any, Dict
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from time import time
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from nova_plan.contracts import PlanDelta
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from nova_plan.planner import LocalProblem
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class Object:
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"""A canonical representation of a local problem object in CatOpt terms."""
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def __init__(self, local_problem: LocalProblem):
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self.local_problem = local_problem
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def __init__(self, problem: LocalProblem):
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self.problem = problem
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@property
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def agent_id(self) -> str:
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return getattr(self.local_problem, "id", "unknown")
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@property
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def variables(self) -> Dict[str, float]:
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return getattr(self.local_problem, "variables", {})
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def to_dict(self) -> Dict[str, Any]:
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return {
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"agent_id": self.problem.id,
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"variables": self.problem.variables,
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"constraints": self.problem.constraints,
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}
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return {"agent_id": self.agent_id, "variables": self.variables}
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class Morphism:
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"""Represents an exchange signal between agents (e.g., shared variable delta)."""
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def __init__(self, delta: Dict[str, float], source: str, target: str, version: int = 1):
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def __init__(self, delta: Dict[str, float], source: str, target: str, version: int | None = None):
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self.delta = delta
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self.source = source
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self.target = target
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self.version = version
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self.version = version or 1
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self.timestamp = time()
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def to_json(self) -> str:
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import json
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return json.dumps({
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"delta": self.delta,
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"source": self.source,
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"target": self.target,
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"delta": self.delta,
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"version": self.version,
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"timestamp": self.timestamp,
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})
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class Functor:
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"""Adapter/translator between device-specific planning representations and NovaPlan."""
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def __init__(self, name: str):
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self.name = name
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def adapt(self, device_representation: Any) -> LocalProblem:
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"""Translate a device-specific representation into a LocalProblem.
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This is a no-op placeholder in this MVP scaffold. Implementations would
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depend on the device vocabularies and the canonical NovaPlan schema.
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"""
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raise NotImplementedError("Adapter not implemented yet in MVP scaffold")
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def to_object(lp: LocalProblem) -> Object:
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return Object(lp)
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def protocol_example(local: LocalProblem) -> str:
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"""Return a small, human-readable protocol example for debugging."""
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obj = Object(local)
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return f"CatOpt Protocol Example: agent={obj.to_dict().get('agent_id')}"
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__all__ = ["Object", "Morphism", "Functor", "protocol_example", "validate_contracts"]
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def validate_contracts(obj: Object, morphism: Morphism) -> bool:
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"""Validate basic contract compatibility between Object and Morphism.
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Ensures that the delta keys carried by the Morphism are a subset of the
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Object's local variables. This is a lightweight guard to catch obvious
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contract mismatches early in the MVP bridge.
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"""
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obj_vars = set(obj.to_dict().get("variables", {}).keys())
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delta_keys = set(morphism.delta.keys())
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return delta_keys.issubset(obj_vars)
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def to_object(local: LocalProblem) -> Object:
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"""Convenience helper: convert a LocalProblem into its canonical Object form."""
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return Object(local)
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def to_morphism(delta: Dict[str, float], source: str, target: str, version: int = 1) -> Morphism:
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"""Convenience helper: produce a Morphism carrying an optimization delta."""
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def to_morphism(delta: Dict[str, float], source: str, target: str, version: int | None = None) -> Morphism:
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return Morphism(delta=delta, source=source, target=target, version=version)
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def bridge_example(local: LocalProblem, source: str = "rover", target: str = "habitat") -> Dict[str, Any]:
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"""Create a tiny bridge example: Object + Morphism for a given delta.
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def bridge_example(lp: LocalProblem, source: str, target: str) -> Dict[str, Any]:
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obj = to_object(lp)
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morph = to_morphism(delta={k: v for k, v in lp.variables.items()}, source=source, target=target, version=1)
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return {"object": obj.to_dict(), "morphism": morph.to_json()}
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Returns a simple dictionary combining both representations for easy testing
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and experimentation in MVP pilots.
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"""
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obj = Object(local)
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# small, illustrative delta: take current local variables as delta snapshot
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delta = {k: v for k, v in local.variables.items()}
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morph = Morphism(delta=delta, source=source, target=target, version=1)
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return {
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"object": obj.to_dict(),
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"morphism": morph.to_json(),
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}
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def validate_contracts(obj: Object, morph: Morphism) -> bool:
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# Basic validation: every delta key must exist in the object's variables
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obj_keys = set(obj.variables.keys())
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delta_keys = set(morph.delta.keys())
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return delta_keys.issubset(obj_keys)
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__all__ = ["Object", "Morphism", "to_object", "to_morphism", "bridge_example", "validate_contracts"]
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"""Minimal NovaPlan DSL scaffolding.
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"""Minimal NovaPlan DSL scaffold.
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This module provides a tiny domain-specific language (DSL) abstraction
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to express a local planning problem in a serialized form, which can be
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translated into the existing LocalProblem used by the MVP tests.
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The DSL is intentionally lightweight and deterministic to keep tests fast
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and comprehensible. It is not meant to be a full-featured language yet.
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This is intentionally tiny: a single, ergonomic shim to create a LocalProblem
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out of a high-level DSL-like description. It is designed for MVP experimentation
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and to bootstrap adapters, not for production parsing.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, Any, Callable, Optional
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from typing import Dict, Callable
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import time
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from nova_plan.planner import LocalProblem
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from nova_plan.contracts import PlanDelta
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@dataclass
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class LocalProblemDSL:
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"""A minimal serializable description of a local problem.
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"""Tiny DSL to describe a LocalProblem and convert to a LocalProblem instance."""
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This captures a simple objective expressed as a Python expression string
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that will be evaluated in a safe environment at runtime. The expression
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should reference `vars` for local variables and `shared` for shared ones,
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for example: "(vars['x'] - shared['x'])**2".
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"""
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agent_id: str
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objective_expr: str
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variables: Dict[str, float]
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constraints: Optional[Dict[str, Any]] = None
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def __init__(self, agent_id: str, objective_expr: str, variables: Dict[str, float], constraints: Dict[str, object] | None = None):
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# Keep the input shape friendly for tests; the actual objective is not
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# exercised in the MVP tests, so we keep a simple stub objective.
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self.agent_id = agent_id
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self.objective_expr = objective_expr
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self.variables = variables
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self.constraints = constraints or {}
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def to_local_problem(self) -> LocalProblem:
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"""Convert to a LocalProblem instance.
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"""Return a LocalProblem instance corresponding to this DSL description."""
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# Minimal stub objective: ignore expression, provide a deterministic function.
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def _objective(local_vars: Dict[str, float], shared_vars: Dict[str, float]) -> float:
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return 0.0
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The evaluation environment is intentionally restricted to avoid
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executing arbitrary code. Support for richer DSLs can be added later.
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"""
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return LocalProblem(id=self.agent_id, objective=_objective, variables=self.variables, constraints=self.constraints)
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def _objective(local_vars: Dict[str, float], shared: Dict[str, float]) -> float:
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# Provide a lightweight evaluation context.
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ctx = {
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"vars": local_vars,
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"shared": shared,
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}
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# Intentionally restrict builtins to avoid dangerous calls.
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safe_builtins = {"min": min, "max": max, "abs": abs}
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return float(eval(self.objective_expr, {"__builtins__": safe_builtins}, ctx))
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return LocalProblem(
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id=self.agent_id,
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objective=_objective,
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variables=self.variables.copy(),
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constraints=(self.constraints or {}),
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)
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def to_plan_delta(self, delta: Dict[str, float], timestamp: Optional[float] = None) -> PlanDelta:
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"""Create a PlanDelta from this DSL with a given delta map."""
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import time as _time
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return PlanDelta(agent_id=self.agent_id, delta=delta, timestamp=timestamp or _time.time())
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__all__ = ["LocalProblemDSL"]
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