feat: to_numpy matching uproot, with mode

Author: henryiii

src/boost_histogram/_internal/hist.py

@@ -552,18 +552,25 @@ def _compute_commonindex(self, index):
 
         return indexes
 
-    @inject_signature("self, flow=False, *, dd=False")
+    @inject_signature("self, flow=False, *, dd=False, mode='numpy'")
     def to_numpy(self, flow=False, **kwargs):
         """
-        Convert to a Numpy style tuple of return arrays.
+        Convert to a Numpy style tuple of return arrays. Edges are converted
+        to exactly match NumPy standards, with upper edge inclusive, unlike
+        boost-histogram, where upper edge is exclusive.
 
         Parameters
         ----------
-
         flow : bool = False
             Include the flow bins.
         dd : bool = False
-            Use the histogramdd return syntax, where the edges are in a tuple
+            Use the histogramdd return syntax, where the edges are in a tuple.
+            Otherwise, this is the histogram/histogram2d return style.
+        mode : Literal["numpy", "view"] = "numpy"
+            The behavior for the return value. "numpy" will return the NumPy
+            array of the values only regardless of the storage (which is all
+            NumPy's histogram function can do). "view" will leave the
+            boost-histogram view of the storage untouched.
 
         Return
         ------
@@ -575,13 +582,23 @@ def to_numpy(self, flow=False, **kwargs):
 
         with KWArgs(kwargs) as kw:
             dd = kw.optional("dd", False)
+            mode = kw.optional("mode", "numpy")
 
+        # Python 3+ would be simpler
         return_tuple = self._hist.to_numpy(flow)
+        hist = return_tuple[0]
+
+        if mode == "numpy":
+            hist = self.values(flow=flow)
+        elif mode == "view":
+            hist = self.view(flow=flow)
+        else:
+            raise KeyError("Invalid mode")
 
         if dd:
-            return return_tuple[0], return_tuple[1:]
+            return hist, return_tuple[1:]
         else:
-            return return_tuple
+            return (hist,) +  return_tuple[1:]
 
     @inject_signature("self, *, deep=True")
     def copy(self, **kwargs):

src/boost_histogram/numpy.py

@@ -79,7 +79,10 @@ def histogramdd(
         density = hist.view() / hist.sum() / areas
         return (density, hist.to_numpy()[1:])
 
-    return hist if bh_cls is not None else hist.to_numpy(dd=True)
+    # Note: this is mode="view" since users have to ask explicilty for special
+    # storages, so mode="numpy" would throw away part of what they are asking
+    # for. Users can use a histogram return type if they need mode="numpy".
+    return hist if bh_cls is not None else hist.to_numpy(dd=True, mode="view")
 
 
 @_inject_signature(

tests/test_internal_histogram.py

@@ -135,6 +135,31 @@ def test_numpy_dd():
     assert_array_equal(x1, x2)
     assert_array_equal(y1, y2)
 
+def test_numpy_weights():
+    h = bh.Histogram(
+        bh.axis.Regular(10, 0, 1), bh.axis.Regular(5, 0, 1), storage=bh.storage.Weight()
+    )
+
+    for i in range(10):
+        for j in range(5):
+            x, y = h.axes[0].centers[i], h.axes[1].centers[j]
+            v = i + j * 10 + 1
+            h.fill([x] * v, [y] * v)
+
+    h2, x2, y2 = h.to_numpy(mode="numpy")
+    h1, (x1, y1) = h.to_numpy(dd=True, mode="numpy")
+
+    assert_array_equal(h1, h2)
+    assert_array_equal(x1, x2)
+    assert_array_equal(y1, y2)
+
+    h1, (x1, y1) = h.to_numpy(dd=True, mode="numpy")
+    h2, x2, y2 = h.to_numpy(mode="view")
+
+    assert_array_equal(h1, h2.value)
+    assert_array_equal(x1, x2)
+    assert_array_equal(y1, y2)
+
 
 def test_numpy_flow():
     h = bh.Histogram(