fix: allow copy to work on overloaded init's

docs/changelog.md

@@ -2,6 +2,14 @@
 
 ## Version 1.3
 
+### WIP
+
+#### Bug Fixes
+
+* Avoid the constructor when creating new histograms from existing ones. [#759][]
+
+[#759]: https://github.com/scikit-hep/boost-histogram/pull/759
+
 ### Version 1.3.2
 
 * Include PyPy 3.9 binary wheels [#730][]

src/boost_histogram/_core/axis/__init__.pyi

@@ -29,6 +29,8 @@ class _BaseAxis:
     def traits_ordered(self) -> bool: ...
     @property
     def metadata(self) -> Any: ...
+    @metadata.setter
+    def metadata(self, item: Any) -> None: ...
     @property
     def size(self) -> int: ...
     @property

src/boost_histogram/_core/hist.pyi

@@ -12,7 +12,10 @@ _axes_limit: int
 
 class _BaseHistogram:
     _storage_type: ClassVar[Type[storage._BaseStorage]]
-    def __init__(self, axis: axis._BaseAxis, storage: storage._BaseStorage) -> None: ...
+    # Note that storage has a default simply because subclasses always handle it.
+    def __init__(
+        self, axis: list[axis._BaseAxis], storage: storage._BaseStorage = ...
+    ) -> None: ...
     def rank(self) -> int: ...
     def size(self) -> int: ...
     def reset(self) -> None: ...
@@ -25,10 +28,17 @@ class _BaseHistogram:
     def to_numpy(self, flow: bool = ...) -> Tuple["np.typing.NDArray[Any]", ...]: ...
     def view(self, flow: bool = ...) -> "np.typing.NDArray[Any]": ...
     def axis(self, i: int = ...) -> axis._BaseAxis: ...
-    def fill(self, *args: ArrayLike, weight: ArrayLike | None = ...) -> None: ...
+    def fill(
+        self,
+        *args: ArrayLike,
+        weight: ArrayLike | None = ...,
+        sample: ArrayLike | None = ...,
+    ) -> None: ...
     def empty(self, flow: bool = ...) -> bool: ...
     def reduce(self: T, *args: Any) -> T: ...
     def project(self: T, *args: int) -> T: ...
+    def sum(self, flow: bool = ...) -> Any: ...
+    def at(self, *args: int) -> Any: ...
 
 class any_int64(_BaseHistogram):
     def __idiv__(self: T, other: any_int64) -> T: ...
@@ -73,7 +83,7 @@ class any_mean(_BaseHistogram):
         self,
         *args: ArrayLike,
         weight: ArrayLike | None = ...,
-        sample: ArrayLike | None = ...
+        sample: ArrayLike | None = ...,
     ) -> None: ...
 
 class any_weighted_mean(_BaseHistogram):
@@ -84,5 +94,5 @@ class any_weighted_mean(_BaseHistogram):
         self,
         *args: ArrayLike,
         weight: ArrayLike | None = ...,
-        sample: ArrayLike | None = ...
+        sample: ArrayLike | None = ...,
     ) -> None: ...

src/boost_histogram/_internal/hist.py

@@ -9,6 +9,7 @@
     TYPE_CHECKING,
     Any,
     Callable,
+    ClassVar,
     Dict,
     Iterable,
     List,
@@ -129,7 +130,10 @@ class Histogram:
     )
     # .metadata and ._variance_known are part of the dict
 
-    _family: object = boost_histogram
+    _family: ClassVar[object] = boost_histogram
+
+    axes: AxesTuple
+    _hist: CppHistogram
 
     def __init_subclass__(cls, *, family: Optional[object] = None) -> None:
         """
@@ -185,22 +189,24 @@ def __init__(
 
         # Allow construction from a raw histogram object (internal)
         if len(axes) == 1 and isinstance(axes[0], tuple(_histograms)):
-            self._hist: Any = axes[0]
-            self.metadata = metadata
-            self.axes = self._generate_axes_()
+            cpp_hist: CppHistogram = axes[0]  # type: ignore[assignment]
+            self._from_histogram_cpp(cpp_hist)
+            if metadata:
+                self.metadata = metadata
             return
 
         # If we construct with another Histogram as the only positional argument,
         # support that too
         if len(axes) == 1 and isinstance(axes[0], Histogram):
-            # Special case - we can recursively call __init__ here
-            self.__init__(axes[0]._hist)  # type: ignore[misc] # pylint: disable=non-parent-init-called
-            self._from_histogram_object(axes[0])
+            normal_hist: Histogram = axes[0]
+            self._from_histogram_object(normal_hist)
+            if metadata:
+                self.metadata = metadata
             return
 
         # Support objects that provide a to_boost method, like Uproot
         if len(axes) == 1 and hasattr(axes[0], "_to_boost_histogram_"):
-            self.__init__(axes[0]._to_boost_histogram_())  # type: ignore[misc, union-attr] # pylint: disable=non-parent-init-called
+            self._from_histogram_object(axes[0]._to_boost_histogram_())  # type: ignore[union-attr]
             return
 
         if storage is None:
@@ -232,6 +238,60 @@ def __init__(
 
         raise TypeError("Unsupported storage")
 
+    @classmethod
+    def _clone(
+        cls: Type[H],
+        _hist: "Histogram | CppHistogram",
+        *,
+        other: "Histogram | None" = None,
+        memo: Any = NOTHING,
+    ) -> H:
+        """
+        Clone a histogram (possibly of a different base). Does not trigger __init__.
+        This will copy data from `other=` if non-None, otherwise metadata gets copied from the input.
+        """
+
+        self = cls.__new__(cls)
+        if isinstance(_hist, tuple(_histograms)):
+            self._from_histogram_cpp(_hist)  # type: ignore[arg-type]
+            if other is not None:
+                return cls._clone(self, other=other, memo=memo)
+            return self
+
+        assert isinstance(_hist, Histogram)
+
+        if other is None:
+            other = _hist
+
+        self._from_histogram_object(_hist)
+
+        if memo is NOTHING:
+            self.__dict__ = copy.copy(other.__dict__)
+        else:
+            self.__dict__ = copy.deepcopy(other.__dict__, memo)
+
+        for ax in self.axes:
+            if memo is NOTHING:
+                ax.__dict__ = copy.copy(ax._ax.metadata)
+            else:
+                ax.__dict__ = copy.deepcopy(ax._ax.metadata, memo)
+        return self
+
+    def _new_hist(self: H, _hist: CppHistogram, memo: Any = NOTHING) -> H:
+        """
+        Return a new histogram given a new _hist, copying current metadata.
+        """
+        return self.__class__._clone(_hist, other=self, memo=memo)
+
+    def _from_histogram_cpp(self, other: CppHistogram) -> None:
+        """
+        Import a Cpp histogram.
+        """
+        self._variance_known = True
+        self._hist = other
+        self.metadata = None
+        self.axes = self._generate_axes_()
+
     def _from_histogram_object(self, other: "Histogram") -> None:
         """
         Convert self into a new histogram object based on another, possibly
@@ -270,32 +330,12 @@ def _generate_axes_(self) -> AxesTuple:
 
         return AxesTuple(self._axis(i) for i in range(self.ndim))
 
-    def _new_hist(self: H, _hist: CppHistogram, memo: Any = NOTHING) -> H:
-        """
-        Return a new histogram given a new _hist, copying metadata.
-        """
-
-        other = self.__class__(_hist)
-        if memo is NOTHING:
-            other.__dict__ = copy.copy(self.__dict__)
-        else:
-            other.__dict__ = copy.deepcopy(self.__dict__, memo)
-        other.axes = other._generate_axes_()
-
-        for ax in other.axes:
-            if memo is NOTHING:
-                ax.__dict__ = copy.copy(ax._ax.metadata)
-            else:
-                ax.__dict__ = copy.deepcopy(ax._ax.metadata, memo)
-
-        return other
-
     @property
     def ndim(self) -> int:
         """
         Number of axes (dimensions) of the histogram.
         """
-        return self._hist.rank()  # type: ignore[no-any-return]
+        return self._hist.rank()
 
     def view(
         self, flow: bool = False
@@ -469,7 +509,7 @@ def fill(
             threads = cpu_count()
 
         if threads is None or threads == 1:
-            self._hist.fill(*args_ars, weight=weight_ars, sample=sample_ars)
+            self._hist.fill(*args_ars, weight=weight_ars, sample=sample_ars)  # type: ignore[arg-type]
             return self
 
         if self._hist._storage_type in {
@@ -640,7 +680,7 @@ def _compute_uhi_index(self, index: InnerIndexing, axis: int) -> SimpleIndexing:
         if isinstance(index, SupportsIndex):
             if abs(int(index)) >= self._hist.axis(axis).size:
                 raise IndexError("histogram index is out of range")
-            return index % self._hist.axis(axis).size  # type: ignore[no-any-return]
+            return int(index) % self._hist.axis(axis).size
 
         return index
 
@@ -719,7 +759,7 @@ def to_numpy(
         hist, *edges = self._hist.to_numpy(flow)
         hist = self.view(flow=flow) if view else self.values(flow=flow)
 
-        return (hist, edges) if dd else (hist, *edges)
+        return (hist, edges) if dd else (hist, *edges)  # type: ignore[return-value]
 
     def copy(self: H, *, deep: bool = True) -> H:
         """
@@ -742,7 +782,7 @@ def empty(self, flow: bool = False) -> bool:
         Check to see if the histogram has any non-default values.
         You can use flow=True to check flow bins too.
         """
-        return self._hist.empty(flow)  # type: ignore[no-any-return]
+        return self._hist.empty(flow)
 
     def sum(self, flow: bool = False) -> Union[float, Accumulator]:
         """
@@ -758,7 +798,7 @@ def size(self) -> int:
         """
         Total number of bins in the histogram (including underflow/overflow).
         """
-        return self._hist.size()  # type: ignore[no-any-return]
+        return self._hist.size()
 
     @property
     def shape(self) -> Tuple[int, ...]:
@@ -779,7 +819,7 @@ def __getitem__(  # noqa: C901
         if not hasattr(indexes, "items") and all(
             isinstance(a, SupportsIndex) for a in indexes
         ):
-            return self._hist.at(*indexes)  # type: ignore[no-any-return]
+            return self._hist.at(*indexes)  # type: ignore[no-any-return, arg-type]
 
         integrations: Set[int] = set()
         slices: List[_core.algorithm.reduce_command] = []
@@ -885,7 +925,7 @@ def __getitem__(  # noqa: C901
                 if ax.traits_overflow and ax.size not in pick_set[i]:
                     selection.append(ax.size)
 
-                new_axis = axes[i].__class__([axes[i].value(j) for j in pick_set[i]])
+                new_axis = axes[i].__class__([axes[i].value(j) for j in pick_set[i]])  # type: ignore[call-arg]
                 new_axis.metadata = axes[i].metadata
                 axes[i] = new_axis
                 reduced_view = np.take(reduced_view, selection, axis=i)

tests/test_subclassing.py

@@ -32,3 +32,20 @@ class MyHist(bh.Histogram):
 
     assert type(h) == MyHist
     assert type(h.axes[0]) == bh.axis.Regular
+
+
+def test_copy():
+    class MyHist(bh.Histogram):
+        def __init__(self, var, bins, weight, **kwargs):
+            super().__init__(
+                bh.axis.Regular(*bins), storage=bh.storage.Weight(), **kwargs
+            )
+
+            self.fill(var, weight=weight)
+
+    b = (2, 0, 1)
+    v = [0.1, 0.5, 0.9]
+    w = [1, 0.5, 1]
+    hist = MyHist(v, b, w)
+
+    hist.copy()