smooth

Builtin index · Noise

result = smooth(x, [window=5], [method="savgol"], [order=2])

Input: x, window, method, order · Output: result

What it is

One word for the smoothers. x is a length-N number vector; window is a named/positional odd integer, default 5; method is a named string, one of "savgol" (default), "moving", "median"; order is a named integer, the polynomial order, used only by "savgol", default 2.

Overloads

One call shape — this function is not overloaded.

Examples

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

fs = 1000
n = 500
t = (0 to n - 1) / fs
clean = sin(2 * pi * 7 * t)

spike = 5 * impulse(n, index = 200)
corrupted = clean + spike + 0.01 * (0 to n - 1)   # a spike, plus baseline drift


fixed = medfilt(corrupted, 5)     # the spike

flat = detrend(fixed, 1)          # the ramp

sg = savgol(flat, 11, 2)          # a final polish, shape-preserving

sm = smooth(flat, 11)             # same thing, one word (default method is savgol)

print("after cleanup: {measure_snr(clean, sg):.2f} dB (savgol), {measure_snr(clean, sm):.2f} dB (smooth)")

after cleanup: 17.81 dB (savgol), 17.81 dB (smooth)

from noise

How it works

smooth(x, window, method = ) — one word for the smoothers, which were scattered across filtering, images and statistics. "savgol" a local polynomial fit: keeps a peak's height, which a moving average flattens. The default, because a peak is usually the thing you care about. "moving" a plain box average. "median" a median filter, for outliers rather than noise.

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