Depending on the number of samples in your actual problem this might not be
very performant (because ... is slow for large arrays) but here’s an
approach using vcat. I had to generate the data slightly differently from
you, because I had no function flatten.
julia> data = [[t,lowpass(s,sin(t),0.5)...] for t in linspace(0,2pi,20)]
20-element Array{Any,1}: [0.0,0.0,0.0]
# etc...
julia> vcat([d' for d in data]...)
20x3 Array{Float64,2}: 0.0 0.0 0.0 0.330694 0.16235
0.324699 # etc...
On Monday, June 16, 2014 8:54:57 AM UTC+2, Andrew Simper wrote:
When I'm working with time series data I often end up with things like this:
>
> function lowpass(lp::Float64, input::Float64, g::Float64)
> hp::Float64 = input - lp
> lp += g * hp
> [lp, hp]
> end
> s = 0.0;
> data=[flatten([t, lowpass(s, sin(t), 0.5)]) for t in linspace(0,2pi,20)]
>
> 20-element Array{Any,1}:
> [0.0,0.0,0.0]
> [0.330694,0.16235,0.324699]
> [0.661388,0.307106,0.614213]
> [0.992082,0.418583,0.837166]
> [1.32278,0.4847,0.9694]
> [1.65347,0.498292,0.996584]
> [1.98416,0.457887,0.915773]
> [2.31486,0.367862,0.735724]
> [2.64555,0.237974,0.475947]
> [2.97625,0.0822973,0.164595]
> [3.30694,-0.0822973,-0.164595]
> [3.63763,-0.237974,-0.475947]
> [3.96833,-0.367862,-0.735724]
> [4.29902,-0.457887,-0.915773]
> [4.62972,-0.498292,-0.996584]
> [4.96041,-0.4847,-0.9694]
> [5.2911,-0.418583,-0.837166]
> [5.6218,-0.307106,-0.614213]
> [5.95249,-0.16235,-0.324699]
> [6.28319,-1.22465e-16,-2.44929e-16]
>
>
>
> Can anyone please help out with how to turn an array of arrays like this
> into a 2D array? (or even to pass data as a single chunk into DataFrames to
> do the job?)
>
>
>
>
>
>
