fix: qc error serializing

pyproject.toml

@@ -17,7 +17,7 @@ readme = "README.md"
 version = "0.3.1"
 
 dependencies = [
-    "aind-behavior-dynamic-foraging[data] @ git+https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.git@ac5ddbf909c9375b9e8875d6a5f90796cfa98653",
+    "aind-behavior-dynamic-foraging[data] @ git+https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.git@v0.0.2rc36",
     "ipykernel",
 ]
 

src/dynamic_foraging_processing/qc/_core/schema.py

@@ -18,6 +18,9 @@
 #: timezone-aware datetimes).
 SEATTLE_TZ = ZoneInfo("America/Los_Angeles")
 
+#: Decimal places metric values are rounded to when converted for serialization.
+VALUE_DECIMALS = 3
+
 #: Map ``contraqctor`` test statuses onto schema statuses. Warnings become
 #: ``PENDING`` (needs review); skips count as passing.
 STATUS_CONVERTER: t.Dict[qc.Status, Status] = {
@@ -81,21 +84,38 @@ def bool_to_status(
 def to_builtin(value: t.Any) -> t.Any:
     """Convert numpy scalars/arrays to JSON-serializable Python builtins.
 
+    Recurses into containers: a numpy scalar nested inside a dict, list, tuple,
+    or set is converted too. Test results carry whole dicts of numpy values, and
+    pydantic refuses to serialize any numpy type that survives.
+
+    Floats are rounded to ``VALUE_DECIMALS`` decimal places — a metric reporting
+    ``float32`` sensor readings otherwise writes out its full binary expansion
+    (``39.563472747802734``), which is noise rather than precision.
+
     Parameters
     ----------
     value : Any
-        A value that may be a numpy scalar or array.
+        A value that may be a numpy scalar or array, or a container holding
+        them at any depth.
 
     Returns
     -------
     Any
-        The equivalent Python builtin (``list`` for arrays, ``item()`` for
-        scalars), or ``value`` unchanged when it is not a numpy type.
+        The equivalent Python builtin (``list`` for arrays and other sequences,
+        rounded ``float`` for floating-point values, dicts rebuilt with
+        converted keys and values), or ``value`` unchanged when it is neither a
+        numpy type, a float, nor a container.
     """
     if isinstance(value, np.ndarray):
-        return value.tolist()
+        return to_builtin(value.tolist())
     if isinstance(value, np.generic):
-        return value.item()
+        return to_builtin(value.item())
+    if isinstance(value, float):
+        return round(value, VALUE_DECIMALS)
+    if isinstance(value, t.Mapping):
+        return {to_builtin(key): to_builtin(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple, set, frozenset)):
+        return [to_builtin(item) for item in value]
     return value
 
 

tests/test_qc/test_schema.py

@@ -56,6 +56,32 @@ def test_to_builtin_converts_numpy_types():
     assert _schema.to_builtin("text") == "text"
 
 
+def test_to_builtin_recurses_into_containers():
+    """Numpy values nested in dicts/sequences are converted too."""
+    nested = {
+        "scalar": np.float32(1.5),
+        "array": np.array([1, 2]),
+        "rows": [(np.int64(3), {"deep": np.float32(0.25)})],
+    }
+    converted = _schema.to_builtin(nested)
+    assert converted == {"scalar": 1.5, "array": [1, 2], "rows": [[3, {"deep": 0.25}]]}
+    assert isinstance(converted["scalar"], float)
+    assert isinstance(converted["rows"][0][0], int)
+    assert isinstance(converted["rows"][0][1]["deep"], float)
+    assert _schema.to_builtin({np.int64(1)}) == [1]
+    assert _schema.to_builtin(frozenset({np.int64(2)})) == [2]
+    assert list(_schema.to_builtin({np.int64(4): "v"})) == [4]
+
+
+def test_to_builtin_rounds_floats():
+    """Floats are rounded to three decimals, at any depth; ints are untouched."""
+    assert _schema.to_builtin(np.float32(39.563472747802734)) == 39.563
+    assert _schema.to_builtin(1.23456) == 1.235
+    assert _schema.to_builtin(np.array([1.23456, 2.0])) == [1.235, 2.0]
+    assert _schema.to_builtin({"mean": np.float32(26.0916690826416)}) == {"mean": 26.092}
+    assert _schema.to_builtin(np.int64(123456)) == 123456
+
+
 def test_make_metric_defaults_and_overrides():
     """``make_metric`` stamps modality/stage and defaults tags to ``{}``."""
     status = _schema.bool_to_status(True)