fix: some cleanup and a mistake fixed that Copilot noticed

Signed-off-by: Henry Schreiner <[email protected]>

Author: henryiii

src/boost_histogram/serialization/_axis.py

@@ -1,12 +1,11 @@
 from __future__ import annotations
 
 import functools
-from collections.abc import Generator
 from typing import Any
 
 from .. import axis
 
-__all__ = ["_axes_from_dict", "_axis_to_dict"]
+__all__ = ["_axis_from_dict", "_axis_to_dict"]
 
 
 def __dir__() -> list[str]:
@@ -94,43 +93,39 @@ def _(ax: axis.Boolean, /) -> dict[str, Any]:
     return data
 
 
-def _axes_from_dict(
-    data_list: list[dict[str, Any]], /
-) -> Generator[axis.Axis, None, None]:
-    for data in data_list:
-        hist_type = data["type"]
-        opts = {"metadata": data["metadata"]} if "metadata" in data else {}
-        if hist_type == "regular":
-            yield axis.Regular(
-                data["bins"],
-                data["lower"],
-                data["upper"],
-                underflow=data["underflow"],
-                overflow=data["overflow"],
-                circular=data["circular"],
-                **opts,
-            )
-        elif hist_type == "variable":
-            yield axis.Variable(
-                data["edges"],
-                underflow=data["underflow"],
-                overflow=data["overflow"],
-                circular=data["circular"],
-                **opts,
-            )
-        elif hist_type == "category_int":
-            yield axis.IntCategory(
-                data["categories"],
-                overflow=data["flow"],
-                **opts,
-            )
-        elif hist_type == "category_str":
-            yield axis.StrCategory(
-                data["categories"],
-                overflow=data["flow"],
-                **opts,
-            )
-        elif hist_type == "boolean":
-            yield axis.Boolean(**opts)
-        else:
-            raise TypeError(f"Unsupported axis type: {hist_type}")
+def _axis_from_dict(data: dict[str, Any], /) -> axis.Axis:
+    hist_type = data["type"]
+    if hist_type == "regular":
+        return axis.Regular(
+            data["bins"],
+            data["lower"],
+            data["upper"],
+            underflow=data["underflow"],
+            overflow=data["overflow"],
+            circular=data["circular"],
+            metadata=data.get("metadata"),
+        )
+    if hist_type == "variable":
+        return axis.Variable(
+            data["edges"],
+            underflow=data["underflow"],
+            overflow=data["overflow"],
+            circular=data["circular"],
+            metadata=data.get("metadata"),
+        )
+    if hist_type == "category_int":
+        return axis.IntCategory(
+            data["categories"],
+            overflow=data["flow"],
+            metadata=data.get("metadata"),
+        )
+    if hist_type == "category_str":
+        return axis.StrCategory(
+            data["categories"],
+            overflow=data["flow"],
+            metadata=data.get("metadata"),
+        )
+    if hist_type == "boolean":
+        return axis.Boolean(metadata=data.get("metadata"))
+
+    raise TypeError(f"Unsupported axis type: {hist_type}")

src/boost_histogram/serialization/generic.py

@@ -3,7 +3,7 @@
 from typing import Any
 
 from .. import Histogram
-from ._axis import _axes_from_dict, _axis_to_dict
+from ._axis import _axis_from_dict, _axis_to_dict
 from ._storage import _data_from_dict, _storage_from_dict, _storage_to_dict
 
 __all__ = ["from_dict", "to_dict"]
@@ -31,7 +31,7 @@ def from_dict(data: dict[str, Any], /) -> Histogram:
     """Convert a dictionary to an Histogram."""
 
     h = Histogram(
-        *_axes_from_dict(data["axes"]),
+        *(_axis_from_dict(ax) for ax in data["axes"]),
         storage=_storage_from_dict(data["storage"]),
         metadata=data.get("metadata"),
     )

src/boost_histogram/serialization/hdf5.py

@@ -16,6 +16,9 @@ def __dir__() -> list[str]:
 
 
 def write_hdf5_schema(grp: h5py.Group, /, histogram: Histogram) -> None:
+    """
+    Write a histogram to an HDF5 group.
+    """
     hist_dict = to_dict(histogram)
 
     # All referenced objects will be stored inside of /{name}/ref_axes
@@ -43,9 +46,9 @@ def write_hdf5_schema(grp: h5py.Group, /, histogram: Histogram) -> None:
         for key, value in ax_info.items():
             ax_group.attrs[key] = value
         if ax_metadata is not None:
-            ax_metadata = ax_group.create_group("metadata")
-            for k, v in value.items():
-                ax_metadata.attrs[k] = v
+            ax_metadata_grp = ax_group.create_group("metadata")
+            for k, v in ax_metadata.items():
+                ax_metadata_grp.attrs[k] = v
         if ax_edges is not None:
             ax_group.create_dataset("edges", shape=ax_edges.shape, data=ax_edges)
         if ax_cats is not None:
@@ -68,7 +71,9 @@ def write_hdf5_schema(grp: h5py.Group, /, histogram: Histogram) -> None:
 
 
 def read_hdf5_schema(grp: h5py.Group, /) -> Histogram:
-    axes: list[dict[str, Any]] = []
+    """
+    Read a histogram from an HDF5 group.
+    """
     axes_grp = grp["axes"]
     axes_ref = grp["ref_axes"]
     assert isinstance(axes_ref, h5py.Group)

tests/test_serialization_generic.py

@@ -12,9 +12,12 @@
     [
         pytest.param(bh.storage.AtomicInt64(), "int", id="atomic_int"),
         pytest.param(bh.storage.Int64(), "int", id="int"),
-        pytest.param(bh.storage.Unlimited(), "double", id="unlimited"), # This always renders as double
+        pytest.param(
+            bh.storage.Unlimited(), "double", id="unlimited"
+        ),  # This always renders as double
         pytest.param(bh.storage.Double(), "double", id="double"),
-    ])
+    ],
+)
 def test_simple_to_dict(storage_type: bh.storage.Storage, expected_type: str) -> None:
     h = bh.Histogram(
         bh.axis.Regular(10, 0, 1),
@@ -33,6 +36,7 @@ def test_simple_to_dict(storage_type: bh.storage.Storage, expected_type: str) ->
     assert data["storage"]["type"] == expected_type
     assert data["storage"]["data"] == pytest.approx(np.zeros(12))
 
+
 def test_weighed_to_dict() -> None:
     h = bh.Histogram(
         bh.axis.Integer(3, 15),
@@ -51,11 +55,12 @@ def test_weighed_to_dict() -> None:
     assert data["storage"]["data"]["values"] == pytest.approx(np.zeros(14))
     assert data["storage"]["data"]["variances"] == pytest.approx(np.zeros(14))
 
+
 def test_mean_to_dict() -> None:
     h = bh.Histogram(
         bh.axis.StrCategory(["one", "two", "three"]),
         storage=bh.storage.Mean(),
-        metadata = {"name": "hi"},
+        metadata={"name": "hi"},
     )
     data = generic.to_dict(h)
 
@@ -68,6 +73,7 @@ def test_mean_to_dict() -> None:
     assert data["storage"]["data"]["values"] == pytest.approx(np.zeros(4))
     assert data["storage"]["data"]["variances"] == pytest.approx(np.zeros(4))
 
+
 def test_weighted_mean_to_dict() -> None:
     h = bh.Histogram(
         bh.axis.IntCategory([1, 2, 3]),
@@ -81,9 +87,15 @@ def test_weighted_mean_to_dict() -> None:
     assert data["axes"][0]["categories"] == pytest.approx([1, 2, 3])
     assert data["axes"][0]["flow"]
     assert data["storage"]["type"] == "weighted_mean"
-    assert data["storage"]["data"]["sum_of_weights"] == pytest.approx(np.array([20, 40, 60, 10]))
-    assert data["storage"]["data"]["sum_of_weights_squared"] == pytest.approx(np.array([200, 800, 1800, 50]))
-    assert data["storage"]["data"]["values"] == pytest.approx(np.array([100, 200, 300, 1]))
+    assert data["storage"]["data"]["sum_of_weights"] == pytest.approx(
+        np.array([20, 40, 60, 10])
+    )
+    assert data["storage"]["data"]["sum_of_weights_squared"] == pytest.approx(
+        np.array([200, 800, 1800, 50])
+    )
+    assert data["storage"]["data"]["values"] == pytest.approx(
+        np.array([100, 200, 300, 1])
+    )
     assert data["storage"]["data"]["variances"] == pytest.approx(np.zeros(4))
 
 
@@ -94,7 +106,8 @@ def test_weighted_mean_to_dict() -> None:
         pytest.param(bh.storage.Int64(), id="int"),
         pytest.param(bh.storage.Double(), id="double"),
         pytest.param(bh.storage.Unlimited(), id="unlimited"),
-    ])
+    ],
+)
 def test_round_trip_simple(storage_type: bh.storage.Storage) -> None:
     h = bh.Histogram(
         bh.axis.Regular(10, 0, 10),
@@ -126,6 +139,7 @@ def test_round_trip_weighted() -> None:
     assert pytest.approx(np.array(h.axes[0])) == np.array(h2.axes[0])
     assert np.asarray(h) == pytest.approx(h2)
 
+
 def test_round_trip_mean() -> None:
     h = bh.Histogram(
         bh.axis.StrCategory(["1", "2", "3"]),
@@ -139,6 +153,7 @@ def test_round_trip_mean() -> None:
     assert pytest.approx(np.array(h.axes[0])) == np.array(h2.axes[0])
     assert np.asarray(h) == pytest.approx(h2)
 
+
 def test_round_trip_weighted_mean() -> None:
     h = bh.Histogram(
         bh.axis.IntCategory([1, 2, 3]),
@@ -150,4 +165,4 @@ def test_round_trip_weighted_mean() -> None:
     h2 = generic.from_dict(data)
 
     assert pytest.approx(np.array(h.axes[0])) == np.array(h2.axes[0])
-    assert np.asarray(h) == pytest.approx(h2)
+    assert np.asarray(h) == pytest.approx(h2)