Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé

Module « scipy.signal »

Fonction iircomb - module scipy.signal

Signature de la fonction iircomb

def iircomb(w0, Q, ftype='notch', fs=2.0, *, pass_zero=False) 

Description

help(scipy.signal.iircomb)

Design IIR notching or peaking digital comb filter.

A notching comb filter consists of regularly-spaced band-stop filters with
a narrow bandwidth (high quality factor). Each rejects a narrow frequency
band and leaves the rest of the spectrum little changed.

A peaking comb filter consists of regularly-spaced band-pass filters with
a narrow bandwidth (high quality factor). Each rejects components outside
a narrow frequency band.

Parameters
----------
w0 : float
    The fundamental frequency of the comb filter (the spacing between its
    peaks). This must evenly divide the sampling frequency. If `fs` is
    specified, this is in the same units as `fs`. By default, it is
    a normalized scalar that must satisfy  ``0 < w0 < 1``, with
    ``w0 = 1`` corresponding to half of the sampling frequency.
Q : float
    Quality factor. Dimensionless parameter that characterizes
    notch filter -3 dB bandwidth ``bw`` relative to its center
    frequency, ``Q = w0/bw``.
ftype : {'notch', 'peak'}
    The type of comb filter generated by the function. If 'notch', then
    the Q factor applies to the notches. If 'peak', then the Q factor
    applies to the peaks.  Default is 'notch'.
fs : float, optional
    The sampling frequency of the signal. Default is 2.0.
pass_zero : bool, optional
    If False (default), the notches (nulls) of the filter are centered on
    frequencies [0, w0, 2*w0, ...], and the peaks are centered on the
    midpoints [w0/2, 3*w0/2, 5*w0/2, ...].  If True, the peaks are centered
    on [0, w0, 2*w0, ...] (passing zero frequency) and vice versa.

    .. versionadded:: 1.9.0

Returns
-------
b, a : ndarray, ndarray
    Numerator (``b``) and denominator (``a``) polynomials
    of the IIR filter.

Raises
------
ValueError
    If `w0` is less than or equal to 0 or greater than or equal to
    ``fs/2``, if `fs` is not divisible by `w0`, if `ftype`
    is not 'notch' or 'peak'

See Also
--------
iirnotch
iirpeak

Notes
-----
For implementation details, see [1]_. The TF implementation of the
comb filter is numerically stable even at higher orders due to the
use of a single repeated pole, which won't suffer from precision loss.

References
----------
.. [1] Sophocles J. Orfanidis, "Introduction To Signal Processing",
       Prentice-Hall, 1996, ch. 11, "Digital Filter Design"

Examples
--------
Design and plot notching comb filter at 20 Hz for a
signal sampled at 200 Hz, using quality factor Q = 30

>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> import numpy as np

>>> fs = 200.0  # Sample frequency (Hz)
>>> f0 = 20.0  # Frequency to be removed from signal (Hz)
>>> Q = 30.0  # Quality factor
>>> # Design notching comb filter
>>> b, a = signal.iircomb(f0, Q, ftype='notch', fs=fs)

>>> # Frequency response
>>> freq, h = signal.freqz(b, a, fs=fs)
>>> response = abs(h)
>>> # To avoid divide by zero when graphing
>>> response[response == 0] = 1e-20
>>> # Plot
>>> fig, ax = plt.subplots(2, 1, figsize=(8, 6), sharex=True)
>>> ax[0].plot(freq, 20*np.log10(abs(response)), color='blue')
>>> ax[0].set_title("Frequency Response")
>>> ax[0].set_ylabel("Amplitude [dB]", color='blue')
>>> ax[0].set_xlim([0, 100])
>>> ax[0].set_ylim([-30, 10])
>>> ax[0].grid(True)
>>> ax[1].plot(freq, (np.angle(h)*180/np.pi+180)%360 - 180, color='green')
>>> ax[1].set_ylabel("Phase [deg]", color='green')
>>> ax[1].set_xlabel("Frequency [Hz]")
>>> ax[1].set_xlim([0, 100])
>>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
>>> ax[1].set_ylim([-90, 90])
>>> ax[1].grid(True)
>>> plt.show()

Design and plot peaking comb filter at 250 Hz for a
signal sampled at 1000 Hz, using quality factor Q = 30

>>> fs = 1000.0  # Sample frequency (Hz)
>>> f0 = 250.0  # Frequency to be retained (Hz)
>>> Q = 30.0  # Quality factor
>>> # Design peaking filter
>>> b, a = signal.iircomb(f0, Q, ftype='peak', fs=fs, pass_zero=True)

>>> # Frequency response
>>> freq, h = signal.freqz(b, a, fs=fs)
>>> response = abs(h)
>>> # To avoid divide by zero when graphing
>>> response[response == 0] = 1e-20
>>> # Plot
>>> fig, ax = plt.subplots(2, 1, figsize=(8, 6), sharex=True)
>>> ax[0].plot(freq, 20*np.log10(np.maximum(abs(h), 1e-5)), color='blue')
>>> ax[0].set_title("Frequency Response")
>>> ax[0].set_ylabel("Amplitude [dB]", color='blue')
>>> ax[0].set_xlim([0, 500])
>>> ax[0].set_ylim([-80, 10])
>>> ax[0].grid(True)
>>> ax[1].plot(freq, (np.angle(h)*180/np.pi+180)%360 - 180, color='green')
>>> ax[1].set_ylabel("Phase [deg]", color='green')
>>> ax[1].set_xlabel("Frequency [Hz]")
>>> ax[1].set_xlim([0, 500])
>>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
>>> ax[1].set_ylim([-90, 90])
>>> ax[1].grid(True)
>>> plt.show()

Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé