`scipy.signal.windows._windows:blackman`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/windows/_windows.py:491

Signature

def blackman(M, sym = True, *, xp = None, device = None)

Summary

Return a Blackman window.

Extended Summary

The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window.

Parameters

M : int: Number of points in the output window. If zero, an empty array is returned. An exception is thrown when it is negative.
sym : bool, optional: When True (default), generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis.
xp : array_namespace, optional: Optional array namespace. Should be compatible with the array API standard, or supported by array-api-compat. Default: numpy
device : any: optional device specification for output. Should match one of the supported device specification in xp.

Returns

w : ndarray: The window, with the maximum value normalized to 1 (though the value 1 does not appear if M is even and sym is True).

Notes

The Blackman window is defined as

w (n) = 0.42 - 0.5 cos (2 π n / M) + 0.08 cos (4 π n / M)

The "exact Blackman" window was designed to null out the third and fourth sidelobes, but has discontinuities at the boundaries, resulting in a 6 dB/oct fall-off. This window is an approximation of the "exact" window, which does not null the sidelobes as well, but is smooth at the edges, improving the fall-off rate to 18 dB/oct. ^[3]

Most references to the Blackman window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. It is also known as an apodization (which means "removing the foot", i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. It is known as a "near optimal" tapering function, almost as good (by some measures) as the Kaiser window.

Examples

Plot the window and its frequency response:

import numpy as np
from scipy import signal
from scipy.fft import fft, fftshift
import matplotlib.pyplot as plt

✓

window = signal.windows.blackman(51)

✓

plt.plot(window)
plt.title("Blackman window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")

✗

Text(0.5, 0, 'Sample')

plt.figure()

✗

<Figure size 640x480 with 0 Axes>

A = fft(window, 2048) / (len(window)/2.0)
freq = np.linspace(-0.5, 0.5, len(A))
response = np.abs(fftshift(A / abs(A).max()))
response = 20 * np.log10(np.maximum(response, 1e-10))

✓

plt.plot(freq, response)
plt.axis([-0.5, 0.5, -120, 0])
plt.title("Frequency response of the Blackman window")
plt.ylabel("Normalized magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")

✗

Text(0.5, 0, 'Normalized frequency [cycles per sample]')

Aliases

scipy.signal.windows.blackman