rlkk_validate

Builtin index · Signal processing

result = rlkk_validate(Z, freqs, [lambda=1e-3], [threshold=2.0])

Input: Z, freqs, lambda, threshold · Output: result

What it is

Z: a length-N complex vector (CVec); freqs: a length-N real vector (Hz); lambda (optional number); threshold (optional number, percent).

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = rlkk_validate(Z, freqs, [lambda=1e-3], [threshold=2.0])`	`Z` vector or CVec`Z`: a length-N complex vector (`CVec`). `freqs` vector`freqs`: a length-N real vector (Hz). `lambda` number, optional (default `1e-3`)`lambda` (optional number). `threshold` number, optional (default `2.0`)`threshold` (optional number, percent).	`Model` —kind `"rlkk_validation"` `valid` booleana boolean, true iff every point's percent residual is under `threshold` `residuals` vectora length-N real vector, percent `max_residual` numbera number	Returns a `Model` (kind `"rlkk_validation"`) with fields `valid` (a boolean, true iff every point's percent residual is under `threshold`), `residuals` (a length-N real vector, percent), `max_residual` (a number). Checks `Z` against its own rLKK reconstruction.

Examples

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

Z = [100 - 20i, 80 - 45i, 50 - 55i, 30 - 35i, 22 - 12i]   # measured impedance

freqs = [0.1, 1, 10, 100, 1000]                          # Hz


fit = rlkk_reconstruct(Z, freqs, lambda = 1e-4) # fit.z, fit.gamma

v = rlkk_validate(Z, freqs, lambda = 1e-3, threshold = 2.0)
if v.valid
    print("consistent within {v.max_residual:.2f}%")
else
    print("bad points: {where(abs(v.residuals) > 2.0)}")
end if

output

consistent within 0.00%

from specialized

freqs = logspace(0, 4, 20)
R = 100
C = 1e-6
w = 2 * pi * freqs
Z = R / (1 + 1j * w * R * C)         # a simple RC impedance, as measured data


fit = rlkk_reconstruct(Z, freqs, lambda=1e-4)     # fit.z, fit.gamma

v = rlkk_validate(Z, freqs, lambda=1e-3, threshold=2.0)
if v.valid
    print("measurement is causal/linear/stable within {v.max_residual}%")
else
    print("bad points: {where(abs(v.residuals) > 2.0)}")
end

nyquist(Z)   # Re(Z) vs -Im(Z), the standard EIS view

output

measurement is causal/linear/stable within 0.013416%

from signal-processing

How it works

rlkk_validate(Z, freqs, [lambda=1e-3], [threshold=2.0]) — checks Z against its own rLKK reconstruction; valid is true when every point's residual is under threshold percent. Model kind "rlkk_validation", fields valid/ residuals/max_residual.

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