hamming

Builtin index · Signal processing

result = hamming(n)

Input: n · Output: result

What it is

n: a number (integer window length).

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.

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}")

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

How it works

hamming(n) — same raised-cosine family as hann, but with coefficients 0.54 - 0.46*cos(...) chosen to minimize the nearest side-lobe (rather than hann's 0.5/0.5, which gives faster side-lobe roll-off but a higher nearest side-lobe). Same symmetric (n-1) convention as hann. Endpoints are 0.54 - 0.46 = 0.08 exactly, not zero (unlike hann).

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