build(agent): melter#14fd4b iteration

mercurymesh/schema.py

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+"""IR schema exporter and sample payloads for conformance tests.
+
+Provides small helpers to extract Pydantic JSON schemas for the
+EnergiBridge-style IR models and to produce small example payloads.
+"""
+from __future__ import annotations
+
+from typing import Dict, Any
+import json
+
+from . import ir
+
+
+def get_ir_schemas() -> Dict[str, Any]:
+    """Return a mapping of IR model names to their JSON schema (as dicts)."""
+    schemas: Dict[str, Any] = {}
+    # Intentionally export a small, curated set of models used in the
+    # interoperability layer.
+    models = [
+        ir.LocalMarketContext,
+        ir.AggregatedSignalIR,
+        ir.PlanDeltaIR,
+        ir.DualVariables,
+        ir.PrivacyBudgetEntry,
+        ir.AuditLogEntry,
+        ir.TimeRound,
+        ir.GoCRegistryEntry,
+    ]
+    for m in models:
+        # pydantic BaseModel has .schema()
+        schemas[m.__name__] = m.schema()
+    return schemas
+
+
+def sample_payloads() -> Dict[str, Any]:
+    """Return small example payloads for a few IR types.
+
+    These are useful for documentation and quick conformance testing.
+    """
+    now = "2020-01-01T00:00:00Z"
+    return {
+        "LocalMarketContext": {
+            "venue_id": "venue-a",
+            "symbol": "ABC",
+            "timeframe": "1m",
+            "objective": "liquidity",
+            "timestamp": now,
+        },
+        "AggregatedSignalIR": {
+            "version": "v1",
+            "venues": ["venue-a", "venue-b"],
+            "feature_vector": {"liquidity_proxy": 0.75},
+            "privacy_budget": 0.1,
+        },
+        "PlanDeltaIR": {
+            "contract_id": "c1",
+            "author": "agent-1",
+            "timestamp": now,
+            "actions": {"adjust": "scale", "scale": 0.5},
+        },
+    }
+
+
+def schemas_json() -> str:
+    """Return the IR schemas as a pretty-printed JSON string."""
+    return json.dumps(get_ir_schemas(), indent=2, sort_keys=True)

tests/test_schema_and_bridge.py

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+from mercurymesh.schema import get_ir_schemas, sample_payloads
+from mercurymesh.bridge_ir import to_ir_market_signal, to_ir_aggregated_signal
+from mercurymesh.adapters.venue_a import VenueAAdapter
+from mercurymesh.adapters.venue_b import VenueBAdapter
+
+
+def test_ir_schemas_present():
+    schemas = get_ir_schemas()
+    # Basic smoke checks for a few required IR models
+    assert "LocalMarketContext" in schemas
+    assert "AggregatedSignalIR" in schemas
+    assert "PlanDeltaIR" in schemas
+
+
+def test_sample_payloads_shape():
+    samples = sample_payloads()
+    assert "LocalMarketContext" in samples
+    assert "AggregatedSignalIR" in samples
+
+
+def test_adapter_to_ir_translation():
+    a = VenueAAdapter()
+    b = VenueBAdapter()
+    msa = a.extract_signal()
+    msb = b.extract_signal()
+
+    ira = to_ir_market_signal(msa)
+    irb = to_ir_market_signal(msb)
+    # Ensure canonical wrapper keys exist
+    assert "local_context" in ira
+    assert "local_context" in irb
+
+    # Build a simple AggregatedSignal-like dict for translation function
+    agg = {
+        "venues": [msa.venue_id, msb.venue_id],
+        "feature_vector": {**msa.features, **msb.features},
+        "privacy_budget_used": 0.05,
+        "nonce": "n-1",
+        "merkle_proof": None,
+    }
+
+    # Convert using the helper; ensure keys are present
+    out = to_ir_aggregated_signal(type("Agg", (), agg)())
+    assert "aggregated" in out
+    assert "feature_vector" in out["aggregated"]