build(agent): molt-c#9d26e0 iteration

.gitignore

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+node_modules/
+.npmrc
+.env
+.env.*
+__tests__/
+coverage/
+.nyc_output/
+dist/
+build/
+.cache/
+*.log
+.DS_Store
+tmp/
+.tmp/
+__pycache__/
+*.pyc
+.venv/
+venv/
+*.egg-info/
+.pytest_cache/
+READY_TO_PUBLISH

AGENTS.md

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+# NovaPlan SWARM Architecture
+
+This repository contains a minimal, testable MVP for the NovaPlan concept:
+- Decentralized, offline-friendly multi-agent planning with privacy-preserving considerations.
+- Local problem solving with federated aggregation (ADMM-like) and secure data contracts.
+- A lightweight mission ledger anchored when ground links are available.
+- Adapters for common hardware (rovers, habitat modules) and simulation-ready interfaces.
+
+Guiding rules
+- Keep changes small and well-scoped. Favor minimal viable features that demonstrate core ideas.
+- Tests and packaging must pass before publishing. See test.sh and README for workflow.
+- All components are Python-based for this MVP unless the user explicitly requests another language.
+
+Architecture overview
+- planner.py: Local problem and a tiny ADMM-style solver interface.
+- contracts.py: Data contracts (PlanDelta, SharedSchedule, ResourceUsage, PrivacyBudget, AuditLog).
+- ledger.py: Simple, auditable decision ledger with optional anchoring to external ground links.
+- adapters/: Lightweight stubs for rover and habitat module adapters.
+- tests/: Unit tests validating the core workflow and contract encoding/decoding.
+
+Development workflow
+- Implement features as independent modules with small, focused tests.
+- Run test.sh to verify end-to-end viability, including packaging build check.
+- Update README.md with usage notes and API surface.
+
+Testing commands
+- Run tests: ./test.sh
+- Build package: python -m build
+
+Contribution
+- Open a PR with a focused feature, include tests, and ensure all tests pass.

README.md

@@ -1,3 +1,26 @@
-# novaplan-decentralized-privacy-preservin
+# NovaPlan MVP
 
-A novel open-source software framework enabling offline-first, privacy-preserving coordination and planning across heterogeneous space robotics fleets (rovers, drones, habitat bots) operating in deep-space habitats or spacecraft with intermittent com
+A minimal, open-source MVP for decentralized, privacy-preserving multi-agent mission planning in deep-space robotic constellations.
+
+- Offline-first, privacy-aware coordination across heterogeneous fleets (rovers, drones, habitat bots).
+- Local problem solving with a tiny ADMM-like core and federation of agents.
+- A lightweight mission ledger that can anchor decisions when ground links are available.
+- Lightweight adapters for common hardware and simulation-ready interfaces.
+- A clear test and packaging path to verify end-to-end viability.
+
+This repository focuses on a small, well-scoped subset of the NovaPlan ecosystem to demonstrate core ideas and enable further expansion.
+
+How to run tests
+- Run: `./test.sh`
+- This will execute unit tests and verify packaging with `python -m build`.
+
+Directory layout
+- nova_plan/          Core MVP implementation (planner, contracts, ledger, adapters)
+- tests/              Unit tests for core workflow and contracts
+- adapters/           Stubs for rover and habitat modules
+- README.md, AGENTS.md  Documentation and governance for the project
+- pyproject.toml        Build metadata for packaging
+- AGENTS.md            Architecture and contribution guidelines
+- READY_TO_PUBLISH     (created after the repo is ready for publishing)
+
+Note: This is a minimal MVP intended for demonstration and testing; it is not a production-ready system.

nova_plan/__init__.py

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+"""NovaPlan MVP package init"""
+
+__all__ = ["planner", "contracts", "ledger", "adapters"]

nova_plan/adapters/.gitkeep

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+# keep directory for adapters

nova_plan/adapters/habitat_adapter.py

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+"""Lightweight habitat module adapter stub for NovaPlan MVP."""
+from __future__ import annotations
+
+class HabitatAdapter:
+    def __init__(self, module_id: str):
+        self.module_id = module_id
+
+    def get_status(self) -> dict:
+        return {"module_id": self.module_id, "status": "ready"}
+
+    def plan_task(self, task: dict) -> dict:
+        return {"module_id": self.module_id, "accepted": True, "task": task}

nova_plan/adapters/rover_adapter.py

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+"""Lightweight rover adapter stub for NovaPlan MVP."""
+from __future__ import annotations
+
+class RoverAdapter:
+    def __init__(self, rover_id: str):
+        self.rover_id = rover_id
+
+    def get_status(self) -> dict:
+        # In a real adapter this would fetch telemetry; here we return a stub.
+        return {"rover_id": self.rover_id, "status": "idle"}
+
+    def plan_task(self, task: dict) -> dict:
+        # Accept a plan and return an acknowledgment.
+        return {"rover_id": self.rover_id, "accepted": True, "task": task}

nova_plan/contracts.py

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+"""Simple data contracts used by NovaPlan MVP.
+
+- PlanDelta: delta between local and global plans.
+- SharedSchedule: aggregated schedule signals from agents.
+- ResourceUsage: energy, time, or other resource consumptions.
+- PrivacyBudget: basic DP-like budget for an agent (simulated).
+- AuditLog: lightweight log entries for governance.
+"""
+from __future__ import annotations
+from dataclasses import dataclass, asdict
+from typing import Dict, Any, List
+import json
+
+@dataclass
+class PlanDelta:
+    agent_id: str
+    delta: Dict[str, float]
+    timestamp: float
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self))
+
+@dataclass
+class SharedSchedule:
+    schedule: Dict[str, Any]
+    timestamp: float
+
+@dataclass
+class ResourceUsage:
+    agent_id: str
+    resources: Dict[str, float]
+    timestamp: float
+
+@dataclass
+class PrivacyBudget:
+    agent_id: str
+    budget: float
+    timestamp: float
+
+@dataclass
+class AuditLog:
+    entry_id: str
+    message: str
+    timestamp: float
+
+def serialize(obj: object) -> str:
+    if hasattr(obj, "__dict__"):
+        return json.dumps(obj.__dict__)
+    return json.dumps(obj)

nova_plan/ledger.py

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+"""A lightweight mission ledger with optional anchoring capability.
+
+- append-only log of decisions
+- optional anchoring to an external ground link (simulated for MVP)
+"""
+from __future__ import annotations
+from datetime import datetime
+from typing import List
+
+class LedgerEntry:
+    def __init__(self, key: str, value: str, anchor: str | None = None):
+        self.key = key
+        self.value = value
+        self.timestamp = datetime.utcnow().isoformat()
+        self.anchor = anchor
+
+    def __repr__(self) -> str:
+        return f"LedgerEntry(key={self.key}, timestamp={self.timestamp}, anchor={self.anchor})"
+
+class Ledger:
+    def __init__(self):
+        self.entries: List[LedgerEntry] = []
+
+    def log(self, key: str, value: str, anchor: str | None = None) -> LedgerEntry:
+        e = LedgerEntry(key, value, anchor)
+        self.entries.append(e)
+        return e
+
+    def last_anchor(self) -> str | None:
+        for e in reversed(self.entries):
+            if e.anchor:
+                return e.anchor
+        return None

nova_plan/planner.py

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+"""Minimal local planning core for NovaPlan MVP.
+
+This module provides a tiny LocalProblem definition and a naive ADMM-like
+heartbeat to combine local objectives with a shared variable. The implementation
+is intentionally small and deterministic to enable unit tests and demonstrations.
+"""
+from __future__ import annotations
+from typing import Dict, Any
+
+class LocalProblem:
+    """A tiny local optimization problem.
+
+    Attributes:
+        id: Unique identifier for the agent.
+        objective: A callable that computes a scalar objective given a dict of variables.
+        variables: Local decision variables for this agent.
+        constraints: Optional dict describing simple bound constraints.
+    """
+
+    def __init__(self, id: str, objective, variables: Dict[str, float], constraints: Dict[str, Any] | None = None):
+        self.id = id
+        self.objective = objective
+        self.variables = variables
+        self.constraints = constraints or {}
+
+    def evaluate(self, shared_vars: Dict[str, float]) -> float:
+        """Evaluate local objective using local variables and shared_vars."""
+        return float(self.objective(self.variables, shared_vars))
+
+def simple_admm_step(local: LocalProblem, shared_vars: Dict[str, float], rho: float = 1.0) -> Dict[str, float]:
+    """Perform a toy ADMM-like update step and return updated local variables.
+
+    This is intentionally simple: we adjust each local variable toward the corresponding
+    shared variable with a step proportional to the difference times rho.
+    """
+    new_vars = {}
+    for k, v in local.variables.items():
+        s = shared_vars.get(k, 0.0)
+        new_vars[k] = v + rho * (s - v)
+    local.variables = new_vars
+    return new_vars

pyproject.toml

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+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "novaplan-mvp"
+version = "0.1.0"
+description = "Minimal MVP for decentralized, privacy-preserving multi-agent mission planning in space robotics"
+readme = "README.md"
+requires-python = ">=3.8"
+
+[tool.setuptools.packages.find]
+where = ["."]

test.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+echo "Installing package in editable mode..."
+pip install -e . >/dev/null 2>&1 || true
+
+echo "Running unit tests..."
+pytest -q
+
+echo "Building package..."
+python -m build
+
+echo "All tests passed and package built."

tests/test_core.py

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+import time
+from nova_plan.planner import LocalProblem, simple_admm_step
+from nova_plan.contracts import PlanDelta
+from nova_plan.ledger import Ledger
+
+
+def test_local_problem_evaluation_and_admm_step():
+    # Simple local problem: minimize (x - s)^2 with x in [0, 10], shared_var s
+    def objective(local_vars, shared):
+        x = local_vars.get("x", 0.0)
+        s = shared.get("x", 0.0)
+        return (x - s) ** 2
+
+    lp = LocalProblem(id="agent-1", objective=objective, variables={"x": 5.0}, constraints={"min": 0, "max": 10})
+    shared = {"x": 7.0}
+
+    # Evaluate
+    val = lp.evaluate(shared)
+    assert isinstance(val, float)
+
+    # Perform a few ADMM-like steps
+    for _ in range(3):
+        simple_admm_step(lp, shared, rho=1.0)
+        assert isinstance(lp.variables["x"], float)
+
+
+def test_plan_delta_serialization():
+    pd = PlanDelta(agent_id="agent-1", delta={"x": 1.0}, timestamp=time.time())
+    s = pd.to_json() if hasattr(pd, "to_json") else None
+    assert s is not None
+
+
+def test_ledger_logging_and_anchor():
+    ledger = Ledger()
+    e = ledger.log("decision", "alloc-1", anchor="ground-link-001")
+    assert e.anchor == "ground-link-001"
+    assert ledger.last_anchor() == "ground-link-001"