2x~5x speed up for isel() in most cases

[crusaderky]

Yet another major improvement for #2799.

Achieve a 2x to 5x boost in isel performance when slicing small arrays by int, slice, list of int, scalar ndarray, or 1-dimensional ndarray.

import xarray

da = xarray.DataArray([[1, 2], [3, 4]], dims=['x', 'y'])
v = da.variable
a = da.variable.values
ds = da.to_dataset(name="d")

ds_with_idx = xarray.Dataset({
    'x': [10, 20],
    'y': [100, 200],
    'd': (('x', 'y'), [[1, 2], [3, 4]])
})
da_with_idx = ds_with_idx.d

# before -> after
%timeit a[0] # 121 ns
%timeit v[0] # 7 µs
%timeit v.isel(x=0) # 10 µs
%timeit da[0]  # 65 µs -> 15 µs
%timeit da.isel(x=0)  # 63 µs -> 13 µs
%timeit ds.isel(x=0)  # 48 µs -> 24 µs
%timeit da_with_idx[0]  # 209 µs -> 82 µs
%timeit da_with_idx.isel(x=0, drop=False)  # 135 µs -> 34 µs
%timeit da_with_idx.isel(x=0, drop=True)  # 101 µs -> 34 µs
%timeit ds_with_idx.isel(x=0, drop=False)  # 90 µs -> 49 µs
%timeit ds_with_idx.isel(x=0, drop=True)  # 65 µs -> 49 µs

Marked as WIP because this commands running the asv suite to verify there are no regressions for large arrays.
(on a separate note, we really need to add the small size cases to asv - as discussed in #3382).

This profoundly alters one of the most important methods in xarray and I must confess it makes me nervous, particularly as I am unsure if the test coverage of DataArray.isel() is as through as that for Dataset.isel().

[crusaderky]

@shoyer I struggle to understand the meaning of Dataset.indexes. How does it differ from

{
    k: v.to_index()
    for k, v in self._variables.items()
    if isinstance(v, IndexVariable)
}

?

[max-sixty]

Amazing! Great idea!

[shoyer]

Dataset.indexes is currently equivalent to just calling to_index() on each appropriate variable. But eventually we want to separate the notion of an index from variables with names matching dimensions, and this is a prerequisite for that.

[crusaderky]

I would very much appreciate if everybody could play around with this PR in their projects and confirm that nothing breaks!

[crusaderky]

Merging soon if nobody speaks up!

[shoyer]

I haven't tested this but the logic here looks sound.

[crusaderky]

Implemented @shoyer 's suggestions and aligned to master.
Merging into master tomorrow!

[crusaderky]

In it goes *ducks for cover*

[dcherian]

=) thanks @crusaderky