JuliaDynamics/ComplexityMeasures.jl
Improve `PowerSpectrum` estimator: add threhsold
Ouverte
#104 ouverte le 27 sept. 2022
good first issueimprovement
Métriques du dépôt
- Stars
- (79 étoiles)
- Métriques de merge PR
- (Métriques PR en attente)
Description
The power spectrum estimator will yield rather uncoverged results for small timeseries of regular signals, due to the noise induced by the FOurier transform affecting the signal a lot. We should add a threshold that reduces to 0 all spectral power less than this threshold. This would make the results much more reasonable for signals, and better for normalized. Here is the example code I have:
using DynamicalSystems
N1, N2, a = 101, 100001, 10
for N in (N1, N2)
t = LinRange(0, 2*a*π, N)
x = sin.(t) # periodic
y = sin.(t .+ cos.(t/0.5)) # periodic, complex spectrum
z = sin.(rand(1:15, N) ./ rand(1:10, N)) # random
w = trajectory(Systems.lorenz(), N÷10; Δt = 0.1, Ttr = 100)[:, 1] # chaotic
for q in (x, y, z, w)
h = entropy(q, PowerSpectrum())
n = entropy_normalized(q, PowerSpectrum())
println("entropy: $(h), normalized: $(n).")
end
end