logspace

Builtin index · Signal processing

result = logspace([a=0], [b=1], [n=50])

Input: a, b, n · Output: result

What it is

a, b (optional numbers, decade exponents); n (optional number, integer count).

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = logspace([a=0], [b=1], [n=50])`	`a` number, optional (default `0`)`a`, `b` (optional numbers, decade exponents). `b` number, optional (default `1`)`a`, `b` (optional numbers, decade exponents). `n` number, optional (default `50`)`n` (optional number, integer count).	`result` vector	Returns a length-`n` real vector, `n` logarithmically spaced points, `10^linspace(a, b, n)` — used for frequency axes in Bode-style plots and rLKK's default DRT grid.

Examples

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

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


target = logspace(-1, 5, 10)         # extends past the measured band on both sides

Z_ext = rlkk_extrapolate(Z, freqs, target, lambda=1e-4)
print("extrapolated {length(Z_ext)} points, first = {Z_ext[0]}, last = {Z_ext[9]}")

output

extrapolated 10 points, first = 100.003341 + 0.005947j, last = 0.110709 - 2.404278j

from signal-processing

# A broadband (noise or multisine) excitation, galvanostatic.

Z = impedance(v, i, 1000)
print("coherence at bin 10: {Z.coherence()[10]}")
Z.nyquist()

# A stepped/multisine sweep at known excitation frequencies.

Z = impedance(v, i, 1000, frequencies=logspace(0, 2, 20))

from signal-processing

amp = db2mag(-6)     # amplitude ratio for -6 dB

pow = db2pow(-3)     # power ratio for -3 dB

print("db2mag(-6) = {amp}, db2pow(-3) = {pow}")

w_hamming = hamming(8)
w_blackman = blackman(8)
w_kaiser = kaiser(9, 5)             # beta=5, odd length so there's an exact center sample

print("hamming[0] = {w_hamming[0]}, blackman[0] = {w_blackman[0]}, kaiser center = {w_kaiser[4]}")

f = logspace(0, 3, 4)               # 1, 10, 100, 1000 -- a log-spaced frequency axis

print("logspace(0,3,4) = {f}")

output

db2mag(-6) = 0.501187, db2pow(-3) = 0.501187
hamming[0] = 0.08, blackman[0] = -1.387779e-17, kaiser center = 1
logspace(0,3,4) = [1, 10, 100, 1000]

from signal-processing

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