Sourcery refactored develop branch (#544)

* 'Refactored by Sourcery'

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix: cleanup tests and sets

Co-authored-by: Sourcery AI <>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Henry Schreiner <henryschreineriii@gmail.com>

src/boost_histogram/_internal/axis.py

@@ -345,10 +345,13 @@ def __init__(
             ax = ca.regular_uflow(bins, start, stop)
         elif options == {"overflow"}:
             ax = ca.regular_oflow(bins, start, stop)
-        elif options == {"circular", "underflow", "overflow"} or options == {
-            "circular",
-            "overflow",
-        }:
+        elif options in [
+            {"circular", "underflow", "overflow"},
+            {
+                "circular",
+                "overflow",
+            },
+        ]:
             # growth=True, underflow=False is also correct
             ax = ca.regular_circular(bins, start, stop)
 
@@ -449,10 +452,17 @@ def __init__(
             ax = ca.variable_uflow(edges)
         elif options == {"overflow"}:
             ax = ca.variable_oflow(edges)
-        elif options == {"circular", "underflow", "overflow",} or options == {
-            "circular",
-            "overflow",
-        }:
+        elif options in [
+            {
+                "circular",
+                "underflow",
+                "overflow",
+            },
+            {
+                "circular",
+                "overflow",
+            },
+        ]:
             # growth=True, underflow=False is also correct
             ax = ca.variable_circular(edges)
         elif options == set():

src/boost_histogram/_internal/hist.py

@@ -75,8 +75,8 @@ def _fill_cast(value: T, *, inner: bool = False) -> Union[T, np.ndarray, Tuple[T
 
 
 def _arg_shortcut(item: Union[Tuple[int, float, float], Axis, CppAxis]) -> CppAxis:
-    msg = "Developer shortcut: will be removed in a future version"
     if isinstance(item, tuple) and len(item) == 3:
+        msg = "Developer shortcut: will be removed in a future version"
         warnings.warn(msg, FutureWarning)
         return _core.axis.regular_uoflow(item[0], item[1], item[2])  # type: ignore
     elif isinstance(item, Axis):
@@ -364,10 +364,10 @@ def _compute_inplace_op(
                         len(other.shape), self.ndim
                     )
                 )
-            elif all((a == b or a == 1) for a, b in zip(other.shape, self.shape)):
+            elif all(a in {b, 1} for a, b in zip(other.shape, self.shape)):
                 view = self.view(flow=False)
                 getattr(view, name)(other)
-            elif all((a == b or a == 1) for a, b in zip(other.shape, self.axes.extent)):
+            elif all(a in {b, 1} for a, b in zip(other.shape, self.axes.extent)):
                 view = self.view(flow=True)
                 getattr(view, name)(other)
             else:
@@ -494,13 +494,11 @@ def __str__(self) -> str:
         """
         # TODO check the terminal width and adjust the presentation
         # only use for 1D, fall back to repr for ND
-        if self._hist.rank() == 1:
-            s = str(self._hist)
-            # get rid of first line and last character
-            s = s[s.index("\n") + 1 : -1]
-        else:
-            s = repr(self)
-        return s
+        if self._hist.rank() != 1:
+            return repr(self)
+        s = str(self._hist)
+        # get rid of first line and last character
+        return s[s.index("\n") + 1 : -1]
 
     def _axis(self, i: int = 0) -> Axis:
         """
@@ -547,15 +545,14 @@ def __setstate__(self, state: Any) -> None:
                 msg = "Cannot open boost-histogram pickle v{}".format(state[0])
                 raise RuntimeError(msg)
 
-            self.axes = self._generate_axes_()
-
         else:  # Classic (0.10 and before) state
             self._hist = state["_hist"]
             self._variance_known = True
             self.metadata = state.get("metadata", None)
             for i in range(self._hist.rank()):
                 self._hist.axis(i).metadata = {"metadata": self._hist.axis(i).metadata}
-            self.axes = self._generate_axes_()
+
+        self.axes = self._generate_axes_()
 
     def __repr__(self) -> str:
         newline = "\n  "
@@ -779,14 +776,13 @@ def __getitem__(  # noqa: C901
 
         if not integrations:
             return self._new_hist(reduced)
-        else:
-            projections = [i for i in range(self.ndim) if i not in integrations]
+        projections = [i for i in range(self.ndim) if i not in integrations]
 
-            return (
-                self._new_hist(reduced.project(*projections))
-                if projections
-                else reduced.sum(flow=True)
-            )
+        return (
+            self._new_hist(reduced.project(*projections))
+            if projections
+            else reduced.sum(flow=True)
+        )
 
     def __setitem__(
         self, index: IndexingExpr, value: Union[ArrayLike, Accumulator]

src/boost_histogram/_internal/view.py

@@ -111,13 +111,16 @@ def __array_ufunc__(
             return ufunc(np.asarray(inputs[0]), np.asarray(inputs[1]), **kwargs)  # type: ignore
 
         # Support unary + and -
-        if method == "__call__" and len(inputs) == 1:
-            if ufunc in {np.negative, np.positive}:
-                (result,) = kwargs.pop("out", [np.empty(self.shape, self.dtype)])
+        if (
+            method == "__call__"
+            and len(inputs) == 1
+            and ufunc in {np.negative, np.positive}
+        ):
+            (result,) = kwargs.pop("out", [np.empty(self.shape, self.dtype)])
 
-                ufunc(inputs[0]["value"], out=result["value"], **kwargs)
-                result["variance"] = inputs[0]["variance"]
-                return result.view(self.__class__)  # type: ignore
+            ufunc(inputs[0]["value"], out=result["value"], **kwargs)
+            result["variance"] = inputs[0]["variance"]
+            return result.view(self.__class__)  # type: ignore
 
         if method == "__call__" and len(inputs) == 2:
             input_0 = np.asarray(inputs[0])

src/boost_histogram/numpy.py

@@ -114,12 +114,12 @@ def histogram2d(
         threads=threads,
     )
 
-    if isinstance(result, tuple):
-        data, (edgesx, edgesy) = result
-        return data, edgesx, edgesy
-    else:
+    if not isinstance(result, tuple):
         return result
 
+    data, (edgesx, edgesy) = result
+    return data, edgesx, edgesy
+
 
 def histogram(
     a: ArrayLike,
@@ -162,12 +162,12 @@ def histogram(
         storage=storage,
         threads=threads,
     )
-    if isinstance(result, tuple):
-        data, (edges,) = result
-        return data, edges
-    else:
+    if not isinstance(result, tuple):
         return result
 
+    data, (edges,) = result
+    return data, edges
+
 
 # Process docstrings
 for f, n in zip(

tests/test_accumulators.py

@@ -115,12 +115,12 @@ def test_sum_mean(list1, list2):
 
     ab = a + b
     assert ab.value == approx(c.value)
-    assert ab.variance == approx(c.variance, nan_ok=True, abs=1e-9, rel=1e-9)
+    assert ab.variance == approx(c.variance, nan_ok=True, abs=1e-7, rel=1e-3)
     assert ab.count == approx(c.count)
 
     a += b
     assert a.value == approx(c.value)
-    assert a.variance == approx(c.variance, nan_ok=True, abs=1e-9, rel=1e-9)
+    assert a.variance == approx(c.variance, nan_ok=True, abs=1e-7, rel=1e-3)
     assert a.count == approx(c.count)
 
 
@@ -129,7 +129,7 @@ def test_sum_mean(list1, list2):
         st.lists(float_st, min_size=n, max_size=n),
         st.lists(
             st.floats(
-                allow_nan=False, allow_infinity=False, min_value=1e-4, max_value=1e5
+                allow_nan=False, allow_infinity=False, min_value=1e-2, max_value=1e3
             ),
             min_size=n,
             max_size=n,
@@ -151,12 +151,12 @@ def test_sum_weighed_mean(pair1, pair2):
 
     ab = a + b
     assert ab.value == approx(c.value)
-    assert ab.variance == approx(c.variance, nan_ok=True, abs=1e-9, rel=1e-9)
+    assert ab.variance == approx(c.variance, nan_ok=True, abs=1e-7, rel=1e-3)
     assert ab.sum_of_weights == approx(c.sum_of_weights)
     assert ab.sum_of_weights_squared == approx(c.sum_of_weights_squared)
 
     a += b
     assert a.value == approx(c.value)
-    assert a.variance == approx(c.variance, nan_ok=True, abs=1e-9, rel=1e-9)
+    assert a.variance == approx(c.variance, nan_ok=True, abs=1e-7, rel=1e-3)
     assert a.sum_of_weights == approx(c.sum_of_weights)
     assert a.sum_of_weights_squared == approx(c.sum_of_weights_squared)

tests/test_minihist_title.py

@@ -14,14 +14,14 @@ class NamedAxesTuple(bh.axis.AxesTuple):
     __slots__ = ()
 
     def _get_index_by_name(self, name):
-        if isinstance(name, str):
-            for i, ax in enumerate(self):
-                if ax.name == name:
-                    return i
-            raise KeyError(f"{name} not found in axes")
-        else:
+        if not isinstance(name, str):
             return name
 
+        for i, ax in enumerate(self):
+            if ax.name == name:
+                return i
+        raise KeyError(f"{name} not found in axes")
+
     def __getitem__(self, item):
         if isinstance(item, slice):
             item = slice(