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?)
