Public API
EmpiricalModeDecomposition.ceemd
— Methodceemd(measurements, xvec; num_imfs=6)
Compute the Complete Empirical Mode Decomposition of the time series with values measurements and time steps xvec. numimfs is the Number of Intrinsic Mode Functions. Returns a list of numimfs + 1 Vectors of the same size as measurements.
EmpiricalModeDecomposition.eemd
— Functioneemd(measurements, xvec, numtrails=100)
Return the Intrinsic Mode Functions and the residual of the ensemble Empirical Mode Decomposition of the measurements given on time steps xvec.
EmpiricalModeDecomposition.emd
— Functionemd(measurements, xvec)
Return the Intrinsic Mode Functions and the residual of the Empirical Mode Decomposition of the measurements given on time steps given in xvec.
EmpiricalModeDecomposition.maketestdata
— Methodmaketestdata(seed)
Return a simple example time series with the composing parts
EmpiricalModeDecomposition.EMDIterable
— TypeIterator for the Empirical Mode Decomposition. The time series values are an AbstractVector of type T, and the time positions are an AbstractVector of type U.
EmpiricalModeDecomposition.SiftIterable
— TypeSiftIterable{T, U}
Iterator for the sifting algorithm. The time series values are an AbstractVector of type T, and the time positions are an AbstractVector of type U. Fields:
Internal API
EmpiricalModeDecomposition.colominas2014_s
— Methodcolominas2014_s()
First testdata from Colominas 2014 et al. http://dx.doi.org/10.1016/j.bspc.2014.06.009
EmpiricalModeDecomposition.colominas2014_x
— Methodcolominas2014_x()
Second testdata from Colominas 2014 et al. http://dx.doi.org/10.1016/j.bspc.2014.06.009 It is not exactly the same, because ϕ is not feasable because the arccos is only defined for values between -1 and 1.
EmpiricalModeDecomposition.fosso
— Functionfosso2014()
Make testdata from Fosso 2014 et al.
EmpiricalModeDecomposition.get_edgepoint
— Methodget_edgepoint(y, xvec, extremas, pos, comp)
Compute the edgepoint which should be used as the extrema on the edge for the spline computation.
EmpiricalModeDecomposition.ismonotonic
— Methodismonotonic(x::AbstractVector)
Check wether x is monotonic. This means, every value is either larger or smaller than the preceding value.
EmpiricalModeDecomposition.localmaxmin!
— Methodlocalmaxmin!(x, maxes, mins)
Detect the local extrema of x. Push the maxima into maxes and the minima into mins.
EmpiricalModeDecomposition.sift
— Functionsift(y, xvec)
Sift the vector y whose points have x coordinates given by xvec.
EmpiricalModeDecomposition.zerocrossing!
— Methodzerocrossing!(y, crosses)
Compute the indices of zerocrossings of a vector It searches for elements which are either zero or near a signflip and pushes the indices into crosses.
EmpiricalModeDecomposition.CEEMDIterable
— TypeIterator for the Complete Empirical Mode Decomposition. The time series values are an AbstractVector of type T, and the time positions are an AbstractVector of type U.
EmpiricalModeDecomposition.CEEMDState
— TypeIntermediate results of the CEEMD Iteration
EmpiricalModeDecomposition.SiftState
— TypeSiftState
Handle the intermediate results of the sifting.