build(agent): new-agents-3#dd492b iteration
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node_modules/
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.npmrc
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.env
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.env.*
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__tests__/
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coverage/
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.nyc_output/
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dist/
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build/
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.cache/
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*.log
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.DS_Store
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tmp/
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.tmp/
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__pycache__/
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*.pyc
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.venv/
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venv/
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*.egg-info/
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.pytest_cache/
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READY_TO_PUBLISH
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# NebulaForge Architecture and Contribution Guide
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Overview
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- NebulaForge is an offline-resilient, federated foundation-model platform designed for space robotics with intermittent connectivity. This repository contains a production-ready MVP scaffold including a device runtime, secure aggregation, data contracts, governance ledger, and a simulator stub.
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Architecture Overview
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- Runtime (nebulaforge.runtime): lightweight on-device inference and planning primitives tailored for ARM/RISC-V platforms.
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- Federated (nebulaforge.federated): secure aggregation with optional differential privacy budgets.
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- Contracts (nebulaforge.contracts): per-message data contracts such as LocalProblem, SharedVariables, PlanDelta, PrivacyBudget, AuditLog.
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- Governance (nebulaforge.governance): crypto-signed decisions and tamper-evident provenance ledger.
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- Simulator (nebulaforge.simulator): stubbed integration points for Gazebo/ROS-based HIL validation.
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Getting Started
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- Install Python dependencies via test.sh which runs packaging checks and tests.
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- Run unit tests with pytest.
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Contribution Rules
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- Follow PEP 8 where applicable; keep modules small and focused.
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- Add tests for new functionality; ensure all tests pass before proposing changes.
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- Update AGENTS.md if you introduce new major components or interfaces.
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Testing and Validation
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- The test.sh script in the repo automates: building the package (python3 -m build) and running tests (pytest).
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- Use pytest to validate behavior of runtime, federated aggregation, and contracts.
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Release Process (high level)
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- Bump version in nebulaforge/__init__.py or pyproject.toml when features are merged.
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- Run test.sh to ensure green tests and packaging integrity.
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- Create READY_TO_PUBLISH file to signal completion for publishing workflow.
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19
README.md
19
README.md
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# nebulaforge-offline-resilient-federated-
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# NebulaForge: Offline-Resilient Federated Foundation Models for Space Robotics
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A novel platform to deploy, train, and govern lightweight foundation models on space robotics fleets (rovers, drones, habitat modules) that operate with intermittent communications. It enables on-device inference, privacy-preserving federated fine-tu
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NebulaForge is an offline-resilient, federated foundation-model platform designed for space robotics with intermittent connectivity. It enables on-device inference, privacy-preserving federated fine-tuning, and contract-driven safety guarantees across fleets of rovers, drones, and habitat modules.
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Key Components
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- Runtime: compact on-device runtime optimized for ARM/RISC-V for real-time planning and perception tasks.
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- Federated: secure aggregation with optional differential privacy budgets.
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- Contracts: CatOpt-inspired data-contract layer with versioned schemas (LocalProblem, SharedVariables, PlanDelta, PrivacyBudget, AuditLog).
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- Governance: crypto-signed decisions and tamper-evident provenance ledger.
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- Simulator: Gazebo/ROS-ready stub for rapid HIL validation.
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MVP Plan (8–12 weeks)
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- Phase 0: runtime core + two starter adapters (rover planner, habitat supervisor) + delta-sync scaffold.
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- Phase 1: secure aggregation, governance ledger, delta reconciliation proofs.
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- Phase 2: HIL validation with Gazebo/ROS and two physical rigs; cross-domain demo.
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- Phase 3: governance conformance tests and a lightweight adapter marketplace entry.
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This repository contains a production-oriented skeleton to enable collaborative extension by multiple teams. See AGENTS.md for architectural guidelines and testing commands.
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"""NebulaForge package initialization."""
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__version__ = "0.1.0"
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from .runtime import DeviceRuntime
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from .federated import SecureAggregator
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from .contracts import LocalProblem, SharedVariables, PlanDelta, PrivacyBudget, AuditLog
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from .governance import GovernanceLedger
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from .simulator import SimulatorStub
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__all__ = [
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"__version__",
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"DeviceRuntime",
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"SecureAggregator",
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"LocalProblem",
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"SharedVariables",
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"PlanDelta",
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"PrivacyBudget",
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"AuditLog",
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"GovernanceLedger",
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"SimulatorStub",
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]
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from __future__ import annotations
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from dataclasses import dataclass, asdict
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from typing import Any, Dict
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import json
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@dataclass
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class LocalProblem:
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id: str
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domain: str
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description: str
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version: int = 1
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@dataclass
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class SharedVariables:
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variables: Dict[str, Any]
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version: int = 1
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@dataclass
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class PlanDelta:
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delta: Dict[str, Any]
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timestamp: float
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version: int = 1
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@dataclass
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class PrivacyBudget:
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lambda_privacy: float
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@dataclass
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class AuditLog:
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entry: str
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signer: str
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timestamp: float
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contract_id: str
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version: int = 1
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def to_json(obj: Any) -> str:
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if hasattr(obj, "__dict__"):
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return json.dumps(asdict(obj), default=str)
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return json.dumps(obj, default=str)
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from __future__ import annotations
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from typing import Any, List
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class SecureAggregator:
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"""Very small, deterministic secure-aggregation surface.
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This MVP aggregates a list of numeric vectors and optionally applies a
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per-vector differential-privacy budget by clipping/noising if budgets are provided.
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"""
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def __init__(self, dp_budget: float | None = None) -> None:
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self.dp_budget = dp_budget
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def aggregate(self, updates: List[List[float]]) -> List[float]:
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if not updates:
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return []
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# Simple elementwise average as a placeholder aggregation
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n = len(updates)
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m = max(len(u) for u in updates)
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sums = [0.0] * m
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for u in updates:
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for i, v in enumerate(u):
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sums[i] += v
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agg = [s / n for s in sums]
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if self.dp_budget is not None:
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# Very naive DP: clip to +/- budget around 0 for each dimension
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limit = float(self.dp_budget)
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agg = [min(max(x, -limit), limit) for x in agg]
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return agg
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from __future__ import annotations
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from typing import List
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from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey, Ed25519PublicKey
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from cryptography.hazmat.primitives import serialization
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import time
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class GovernanceLedger:
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"""Tamper-evident ledger of governance decisions (signature-based)."""
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def __init__(self) -> None:
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self.entries: List[dict] = []
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self._private_key = Ed25519PrivateKey.generate()
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self.public_key = self._private_key.public_key()
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def sign(self, message: bytes) -> bytes:
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return self._private_key.sign(message)
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def verify(self, message: bytes, signature: bytes) -> bool:
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try:
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self.public_key.verify(signature, message)
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return True
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except Exception:
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return False
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def add_entry(self, decision: str, contract_id: str) -> None:
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timestamp = time.time()
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payload = f"{decision}:{contract_id}:{timestamp}".encode()
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sig = self.sign(payload)
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self.entries.append({"decision": decision, "contract_id": contract_id, "timestamp": timestamp, "signature": sig.hex()})
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from __future__ import annotations
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from typing import Any
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class DeviceRuntime:
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"""Minimal on-device runtime abstraction.
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This is intentionally lightweight and designed to be extended.
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"""
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def __init__(self, device: str = "generic-arm-v8") -> None:
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self.device = device
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self.initialized = False
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def initialize(self, config: dict[str, Any] | None = None) -> None:
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# Initialize runtime with optional device-specific config
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self.initialized = True
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def infer(self, inputs: Any) -> Any:
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if not self.initialized:
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raise RuntimeError("DeviceRuntime not initialized")
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# Placeholder: return inputs as mock inference result
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return {"result": inputs}
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def plan(self, state: Any) -> Any:
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if not self.initialized:
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raise RuntimeError("DeviceRuntime not initialized")
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# Placeholder planning: echo back state as plan delta
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return {"plan_delta": state}
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from __future__ import annotations
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from typing import Any
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class SimulatorStub:
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"""Tiny stub to emulate a Gazebo/ROS-style simulator interface."""
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def __init__(self) -> None:
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self.connected = False
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def connect(self) -> None:
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self.connected = True
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def run(self, scene: str, params: dict[str, Any] | None = None) -> dict[str, Any]:
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if not self.connected:
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raise RuntimeError("Simulator not connected")
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# Return a minimal synthetic result
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return {"scene": scene, "params": params or {}, "status": "ok"}
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[build-system]
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requires = ["setuptools>=42", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "nebulaforge"
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version = "0.1.0"
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description = "Offline-resilient federated foundation models for space robotics"
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requires-python = ">=3.9"
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readme = "README.md"
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license = { file = "LICENSE" }
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authors = [
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{ name = "NebulaForge Contributors" }
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]
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dependencies = [
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"cryptography>=38.0.0",
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"pydantic>=1.10.2",
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"pytest>=7.4.0",
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"numpy>=1.23",
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"protobuf>=3.20",
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"setuptools>=42",
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"wheel"
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]
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#!/usr/bin/env bash
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set -euo pipefail
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# MVP sanity: run packaging and tests
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export PYTHONPATH="${PWD}${PYTHONPATH:+:${PYTHONPATH}}:/usr/local/lib/python3.11/site-packages"
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echo "==> Building package (Python) ..."
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python3 -m build
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echo "==> Running tests (pytest) ..."
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pytest -q
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# If we reach here, tests passed
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echo "All tests passed."
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from nebulaforge.contracts import LocalProblem, SharedVariables, PlanDelta, AuditLog
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import json
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def test_contract_dataclasses():
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lp = LocalProblem(id="lp1", domain="rover", description="test", version=1)
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sv = SharedVariables(variables={"a": 1}, version=1)
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pd = PlanDelta(delta={"x": 1}, timestamp=123.456, version=1)
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al = AuditLog(entry="update", signer="tester", timestamp=123.456, contract_id="lp1", version=1)
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assert isinstance(lp, LocalProblem)
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assert lp.id == "lp1"
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assert isinstance(sv.variables, dict)
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assert isinstance(pd.delta, dict)
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assert isinstance(al.entry, str)
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# JSON serialization sanity check
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from nebulaforge.contracts import to_json
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json_str = to_json(lp)
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assert isinstance(json_str, str)
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data = json.loads(json_str)
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assert data["id"] == "lp1"
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from nebulaforge.federated import SecureAggregator
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def test_aggregate_basic():
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agg = SecureAggregator()
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updates = [[1.0, 2.0], [3.0, 4.0]]
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out = agg.aggregate(updates)
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assert out == [2.0, 3.0]
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def test_dp_budget_clipping():
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agg = SecureAggregator(dp_budget=1.0)
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updates = [[2.0, -2.0]]
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out = agg.aggregate(updates)
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# clipped to +/-1
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assert all(-1.0 <= v <= 1.0 for v in out)
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from nebulaforge.runtime import DeviceRuntime
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def test_runtime_basic():
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rt = DeviceRuntime(device="cpu-test")
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rt.initialize()
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res = rt.infer({"input": 1})
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assert isinstance(res, dict)
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assert res.get("result") == {"input": 1}
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pl = rt.plan({"state": 42})
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assert isinstance(pl, dict)
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assert "plan_delta" in pl
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