cutcutcodec.core.signal.window.find_win_law¶

cutcutcodec.core.signal.window.find_win_law(nb_samples: Integral = 129, nb_alphas: Integral = 1000, alpha_min: Real = 0.001, alpha_max: Real = 15.0, win: str = 'kaiser') → tuple[Tensor, Tensor, Tensor][source]¶

For each beta parameter, associate the frequency properties.

Parameters¶

nb_samplesint, default=129: The window size, it has to be >= 3.
nb_alphasint, default=1000: The number of alpha points.
alpha_minfloat, default=ALPHA_MIN: The minimal inclusive alpha value.
alpha_maxfloat, default=15.0: The maximal inclusive alpha value.
winstr, default=”kaiser”: The windows type, “kaiser” or “dpss”

Returns¶

alphastorch.Tensor: The apha values.
attstorch.Tensor: The real positive attenuation of the secondaries lobs in dB.
bandstorch.Tensor: The normalised size of the main lob.

Examples¶

>>> import torch
>>> from cutcutcodec.core.signal.window import find_win_law
>>> alphas, atts, bands = find_win_law()
>>>
>>> # import matplotlib.pyplot as plt
>>> # _ = plt.plot(alphas.numpy(force=True), atts.numpy(force=True), label="attenuation")
>>> # _ = plt.plot(alphas.numpy(force=True), bands.numpy(force=True), label="band")
>>> # _ = plt.legend()
>>> # plt.show()
>>>