docs: mention stacked setting for histograms

docs/usage/accumulators.rst

@@ -159,4 +159,8 @@ Most of the accumulators (except Sum) support a View. This is what is returned f
 a histogram when ``.view()`` is requested. This is a structured NumPy ndarray, with a few small
 additions to make them easier to work with. Like a NumPy recarray, you can access the fields with
 attributes; you can even access (but not set) computed attributes like ``.variance``. A view will
-also return an accumulator instance if you select a single item.
+also return an accumulator instance if you select a single item. You can set a view's contents
+with a stacked array, and each item in the stack will be used for the (computed) values that a
+normal constructor would take. For example, WeighedMean can take an array with a final
+dimension four long, with ``sum_of_weights``, ``sum_of_weights_squared``, ``value``, and ``variance``
+elements, even though several of these values are computed from the internal representation.

docs/usage/histogram.rst

@@ -24,8 +24,20 @@ The primary values from a histogram are always available as ``.values()``. The v
 Views
 ^^^^^
 
-While Histograms do conform to the Python buffer protocol, the best way to get access to the raw contents of a histogram as a NumPy array is with ``.view()``. This way you can optionally pass ``flow=True`` to get the flow bins, and if you have an accumulator storage, you will get a View, which is a slightly augmented ndarrray subclass (see :ref:`usage-accumulators`).
+While Histograms do conform to the Python buffer protocol, the best way to get access to the raw contents of a histogram as a NumPy array is with ``.view()``. This way you can optionally pass ``flow=True`` to get the flow bins, and if you have an accumulator storage, you will get a View, which is a slightly augmented ndarrray subclass (see :ref:`usage-accumulators`). Views support setting as well for non-computed properties; you can use an expression like this to set the values of an accumulator storage:
 
+.. code:: python3
+
+   h.view().value = values
+
+
+You can also used stacked arrays (N+1 dimensional) to set a histogram's contents. This is especially useful if you need to set a computed value, like variance on a Mean/WeightedMean storage, which cannot be set using the above method:
+
+.. code:: python3
+
+   h[...] = np.stack([values, variances], axis=-1)
+
+If you leave endpoints off (such as with ``...`` above), then you can match the size with or without flow bins.
 
 Operations
 ^^^^^^^^^^