Allow slicing of DenseAxisArray

[odow]

DenseAxisArrays with OneTo axes used a dictionary that mapped i => i. This PR replaces that with a Base.OneTo for improved efficiency. But in doing so, I realized we can now define get for vectors, and support proper slicing of DenseAxisArray.

Closes #287

[codecov]

Codecov Report

Merging #2524 (b07b264) into master (f6be919) will decrease coverage by 0.18%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master    #2524      +/-   ##
==========================================
- Coverage   93.69%   93.51%   -0.19%     
==========================================
  Files          43       45       +2     
  Lines        5360     5473     +113     
==========================================
+ Hits         5022     5118      +96     
- Misses        338      355      +17     

Impacted Files	Coverage Δ
src/Containers/DenseAxisArray.jl	87.00% <100.00%> (+3.22%)	⬆️
src/_Derivatives/topological_sort.jl	95.12% <0.00%> (-4.88%)	⬇️
src/_Derivatives/coloring.jl	95.31% <0.00%> (-2.50%)	⬇️
src/_Derivatives/sparsity.jl	95.18% <0.00%> (-2.41%)	⬇️
src/_Derivatives/conversion.jl	96.49% <0.00%> (-1.76%)	⬇️
src/constraints.jl	95.02% <0.00%> (-0.50%)	⬇️
src/nlp.jl	92.21% <0.00%> (-0.45%)	⬇️
src/JuMP.jl	82.90% <0.00%> (ø)
src/precompile.jl	95.00% <0.00%> (ø)
src/feasibility_checker.jl	100.00% <0.00%> (ø)

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[odow]

Here's the proof of @mlubin's example:

julia> using JuMP
[ Info: Precompiling JuMP [4076af6c-e467-56ae-b986-b466b2749572]

julia> model = Model()
A JuMP Model
Feasibility problem with:
Variables: 0
@variableModel mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.

julia> @variable(model, x[0:5])
1-dimensional DenseAxisArray{VariableRef,1,...} with index sets:
    Dimension 1, 0:5
And data, a 6-element Array{VariableRef,1}:
 x[0]
 x[1]
 x[2]
 x[3]
 x[4]
 x[5]

julia> x[0:2]
1-dimensional DenseAxisArray{VariableRef,1,...} with index sets:
    Dimension 1, [0, 1, 2]
And data, a 3-element Array{VariableRef,1}:
 x[0]
 x[1]
 x[2]