Implement table traits for DiffEq arrays - #222

julia> using OrdinaryDiffEq, DataFrames

julia> f_2dlinear = (du,u,p,t) -> du.=1.01u;

julia> prob = ODEProblem(f_2dlinear,rand(2,2),(0.0,1.0));

julia> sol1 =solve(prob,Euler();dt=1//2^(4));

julia> df = DataFrame(sol1)
17×5 DataFrame
 Row │ timestamp  value1    value2    value3     value4   
     │ Float64    Float64   Float64   Float64    Float64  
─────┼────────────────────────────────────────────────────
   1 │    0.0     0.494427  0.12895   0.0152497  0.330754
   2 │    0.0625  0.525638  0.13709   0.0162124  0.351633
   3 │    0.125   0.558819  0.145744  0.0172358  0.373829
   4 │    0.1875  0.594094  0.154944  0.0183238  0.397427
   5 │    0.25    0.631596  0.164724  0.0194805  0.422515
   6 │    0.3125  0.671466  0.175123  0.0207102  0.449186
   7 │    0.375   0.713852  0.186177  0.0220175  0.477541
   8 │    0.4375  0.758914  0.19793   0.0234074  0.507686
   9 │    0.5     0.806821  0.210424  0.024885   0.539734
  10 │    0.5625  0.857751  0.223707  0.0264558  0.573804
  11 │    0.625   0.911897  0.237828  0.0281259  0.610026
  12 │    0.6875  0.96946   0.252841  0.0299013  0.648534
  13 │    0.75    1.03066   0.268802  0.0317888  0.689472
  14 │    0.8125  1.09572   0.28577   0.0337955  0.732995
  15 │    0.875   1.16488   0.303809  0.0359288  0.779265
  16 │    0.9375  1.23842   0.322987  0.0381968  0.828457
  17 │    1.0     1.31659   0.343376  0.040608   0.880753

julia> time_ = [0.0, 0.1, 0.2]
3-element Vector{Float64}:
 0.0
 0.1
 0.2

julia> DataFrame(sol1(time_))
3×5 DataFrame
 Row │ timestamp  value1    value2    value3     value4   
     │ Float64    Float64   Float64   Float64    Float64  
─────┼────────────────────────────────────────────────────
   1 │       0.0  0.494427  0.12895   0.0152497  0.330754
   2 │       0.1  0.545245  0.142203  0.0168171  0.364749
   3 │       0.2  0.601519  0.15688   0.0185528  0.402394