Henry Schreiner

Two ways: ```python h = bh.Histogram(bh.axis.Regular(10,0,1), storage=bh.storage.Weight()) values = np.ones(10) variances = np.ones(10) * .1 ``` Using the shortcut for Weight Histograms: ```python h[...] = np.stack([values, variances], axis=-1) ``` For...

`.view()` returns a view of the storage. It's always an ndarray, though for accumulator storages, it is also RecArray/RecordArray (I forget the naming scheme)-like, which allows attribute access into the...

PS: of course, you can use `h[...] =`, and it works well, though you had to build an extra dimension in for the weighted storage (or you can use the...

If we ever add the ability to use existing memory allocated in Python as the Storage's memory, this would suddenly make histograms initialized this way more efficient (and would have...

Note: `.view()` does what it says it does - it returns a view of the underlying data. If it's non-simple, the view is non-simple, as it should be. I've proposed...

I'm intending to implement this in Hist first (it's already available for pandas data frame storages, actually - but that one can't be upstreamed to boost-histogram because it depends on...

Update: this is supported in Hist (as you can see from the linked merged PR above). I'd be fine to upstream it (as with any non-dependency addition to Hist) if...

This is a bit tricky to implement; I've started it, but pybind11 doesn't provide runtime utilities for array access, and I don't want to generate 32 copies of this, so...

I think we should make sure UHI is powerful enough that users could create a smart locator - see #485. This would greatly complicate UHI by having to track multiple...

Tiny clarification: I want *the ability for users to write* smart locators; I'm proposing the default locators like `bh.loc` stay "dumb". With one exception: I have been long bothered by...