emd

Builtin index · Signal processing

result = emd(x, [max_imfs=])

Input: x, max_imfs · Output: result

What it is

x: a length-N real vector. max_imfs (optional number): caps the number of IMFs extracted. Empirical Mode Decomposition: splits x into oscillatory intrinsic mode functions (IMFs, highest frequency first) plus a residual trend, via the standard sifting algorithm.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = emd(x, [max_imfs=])`	`x` vector`x`: a length-N real vector. `max_imfs` number, optional`max_imfs` (optional number): caps the number of IMFs extracted.	`Model` —kind `"emd"` `imfs` matrixa `K`×N matrix, one IMF per row `residual` vectora length-N vector `count` numbera number, `K`	Returns a `Model` (kind `"emd"`) with fields `imfs` (a `K`×N matrix, one IMF per row), `residual` (a length-N vector), `count` (a number, `K`). `sum(e.imfs, axis=0) + e.residual` reconstructs `x` exactly.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

  e = emd(signal)
  recon = sum(e.imfs, axis=0) + e.residual   # == signal, to floating precision

  first_imf = e.imfs[0, :]                    # the highest-frequency mode

from numerics-dsp

How it works

emd(x, [max_imfs=]) — Empirical Mode Decomposition: splits x into a small number of oscillatory intrinsic mode functions (IMFs, highest frequency first) plus a residual trend, via the standard sifting algorithm. Model kind "emd", fields imfs (a matrix, one IMF per row — e.imfs has zero rows if x had no oscillation to extract at all), residual (a Vec), and count (the number of IMFs). sum(e.imfs, axis=0) + e.residual reconstructs x exactly.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.