More general printing

[dlfivefifty]

This generalises block-printing to include, for example, views of BlockArray:

julia> B = BlockMatrix(randn(6,6), 1:3, 1:3)
3×3-blocked 6×6 BlockArray{Float64,2}:
  0.145757  │   0.246326    0.534004  │  -0.76561    1.14499   -1.96236  
 ───────────┼─────────────────────────┼──────────────────────────────────
 -0.494542  │  -1.02236     0.7017    │  -1.17902   -1.16601   -0.904528 
  0.832341  │  -0.0572088  -0.984644  │  -0.730904   0.433201  -1.39913  
 ───────────┼─────────────────────────┼──────────────────────────────────
 -1.08485   │  -0.522733    0.429331  │   2.43006   -2.25876    0.40058  
  0.269436  │   0.879403   -0.224948  │   1.83561    0.466786   0.0546217
  1.16865   │  -0.685722    2.06924   │   0.377139  -2.12384   -0.303557 

julia> view(B,Block.(1:2),Block.(1:2))
3×3 view(::BlockArray{Float64,2,Array{Array{Float64,2},2},Tuple{BlockedUnitRange{Array{Int64,1}},BlockedUnitRange{Array{Int64,1}}}}, BlockSlice(Block{1,Int64}[Block(1), Block(2)],1:1:3), BlockSlice(Block{1,Int64}[Block(1), Block(2)],1:1:3)) with eltype Float64 with indices 1:1:3×1:1:3:
  0.145757  │   0.246326    0.534004
 ───────────┼───────────────────────
 -0.494542  │  -1.02236     0.7017  
  0.832341  │  -0.0572088  -0.984644

julia> B'
6×6 Adjoint{Float64,BlockArray{Float64,2,Array{Array{Float64,2},2},Tuple{BlockedUnitRange{Array{Int64,1}},BlockedUnitRange{Array{Int64,1}}}}} with indices 1:1:6×1:1:6:
  0.145757  │  -0.494542   0.832341   │  -1.08485    0.269436    1.16865 
 ───────────┼─────────────────────────┼──────────────────────────────────
  0.246326  │  -1.02236   -0.0572088  │  -0.522733   0.879403   -0.685722
  0.534004  │   0.7017    -0.984644   │   0.429331  -0.224948    2.06924 
 ───────────┼─────────────────────────┼──────────────────────────────────
 -0.76561   │  -1.17902   -0.730904   │   2.43006    1.83561     0.377139
  1.14499   │  -1.16601    0.433201   │  -2.25876    0.466786   -2.12384 
 -1.96236   │  -0.904528  -1.39913    │   0.40058    0.0546217  -0.303557

Note it automatically works with LazyArrays.jl:

julia> BroadcastMatrix(exp,B)
6×6 BroadcastArray{Float64,2,typeof(exp),Tuple{BlockArray{Float64,2,Array{Array{Float64,2},2},Tuple{BlockedUnitRange{Array{Int64,1}},BlockedUnitRange{Array{Int64,1}}}}}} with indices 1:1:6×1:1:6:
 1.15692   │  1.27932   1.70575   │   0.46505   3.14242   0.140527
 ──────────┼──────────────────────┼───────────────────────────────
 0.60985   │  0.359745  2.01718   │   0.30758   0.311606  0.404733
 2.29869   │  0.944397  0.373572  │   0.481474  1.54219   0.24681 
 ──────────┼──────────────────────┼───────────────────────────────
 0.337951  │  0.592898  1.53623   │  11.3596    0.10448   1.49269 
 1.30923   │  2.40946   0.798557  │   6.26895   1.59486   1.05614 
 3.21765   │  0.503726  7.91876   │   1.45811   0.119572  0.738188

[codecov]

Codecov Report

Merging #119 into master will increase coverage by 0.35%.
The diff coverage is 86.27%.

@@            Coverage Diff             @@
##           master     #119      +/-   ##
==========================================
+ Coverage   85.24%   85.59%   +0.35%     
==========================================
  Files          15       15              
  Lines         935      965      +30     
==========================================
+ Hits          797      826      +29     
- Misses        138      139       +1     

Impacted Files	Coverage Δ
src/BlockArrays.jl	100.00% <ø> (ø)
src/blockarrayinterface.jl	100.00% <ø> (ø)
src/blockindices.jl	62.58% <63.63%> (+2.29%)	⬆️
src/blockbroadcast.jl	79.48% <88.88%> (+0.03%)	⬆️
src/blocklinalg.jl	91.05% <90.00%> (+1.58%)	⬆️
src/blockarray.jl	97.18% <100.00%> (ø)
src/blocks.jl	88.00% <100.00%> (+1.04%)	⬆️
src/pseudo_blockarray.jl	89.38% <100.00%> (+0.09%)	⬆️
src/show.jl	96.72% <100.00%> (+0.16%)	⬆️
... and 1 more

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