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Fonction iirdesign - module scipy.signal

Signature de la fonction iirdesign

def iirdesign(wp, ws, gpass, gstop, analog=False, ftype='ellip', output='ba', fs=None) 

Description

iirdesign.__doc__

Complete IIR digital and analog filter design.

    Given passband and stopband frequencies and gains, construct an analog or
    digital IIR filter of minimum order for a given basic type. Return the
    output in numerator, denominator ('ba'), pole-zero ('zpk') or second order
    sections ('sos') form.

    Parameters
    ----------
    wp, ws : float or array like, shape (2,)
        Passband and stopband edge frequencies. Possible values are scalars
        (for lowpass and highpass filters) or ranges (for bandpass and bandstop
        filters).
        For digital filters, these are in the same units as `fs`. By default,
        `fs` is 2 half-cycles/sample, so these are normalized from 0 to 1,
        where 1 is the Nyquist frequency. For example:

            - Lowpass:   wp = 0.2,          ws = 0.3
            - Highpass:  wp = 0.3,          ws = 0.2
            - Bandpass:  wp = [0.2, 0.5],   ws = [0.1, 0.6]
            - Bandstop:  wp = [0.1, 0.6],   ws = [0.2, 0.5]

        For analog filters, `wp` and `ws` are angular frequencies (e.g., rad/s).
        Note, that for bandpass and bandstop filters passband must lie strictly
        inside stopband or vice versa.
    gpass : float
        The maximum loss in the passband (dB).
    gstop : float
        The minimum attenuation in the stopband (dB).
    analog : bool, optional
        When True, return an analog filter, otherwise a digital filter is
        returned.
    ftype : str, optional
        The type of IIR filter to design:

            - Butterworth   : 'butter'
            - Chebyshev I   : 'cheby1'
            - Chebyshev II  : 'cheby2'
            - Cauer/elliptic: 'ellip'
            - Bessel/Thomson: 'bessel'

    output : {'ba', 'zpk', 'sos'}, optional
        Filter form of the output:

            - second-order sections (recommended): 'sos'
            - numerator/denominator (default)    : 'ba'
            - pole-zero                          : 'zpk'

        In general the second-order sections ('sos') form  is
        recommended because inferring the coefficients for the
        numerator/denominator form ('ba') suffers from numerical
        instabilities. For reasons of backward compatibility the default
        form is the numerator/denominator form ('ba'), where the 'b'
        and the 'a' in 'ba' refer to the commonly used names of the
        coefficients used.

        Note: Using the second-order sections form ('sos') is sometimes
        associated with additional computational costs: for
        data-intense use cases it is therefore recommended to also
        investigate the numerator/denominator form ('ba').

    fs : float, optional
        The sampling frequency of the digital system.

        .. versionadded:: 1.2.0

    Returns
    -------
    b, a : ndarray, ndarray
        Numerator (`b`) and denominator (`a`) polynomials of the IIR filter.
        Only returned if ``output='ba'``.
    z, p, k : ndarray, ndarray, float
        Zeros, poles, and system gain of the IIR filter transfer
        function.  Only returned if ``output='zpk'``.
    sos : ndarray
        Second-order sections representation of the IIR filter.
        Only returned if ``output=='sos'``.

    See Also
    --------
    butter : Filter design using order and critical points
    cheby1, cheby2, ellip, bessel
    buttord : Find order and critical points from passband and stopband spec
    cheb1ord, cheb2ord, ellipord
    iirfilter : General filter design using order and critical frequencies

    Notes
    -----
    The ``'sos'`` output parameter was added in 0.16.0.

    Examples
    --------

    >>> from scipy import signal
    >>> import matplotlib.pyplot as plt
    >>> import matplotlib.ticker

    >>> wp = 0.2
    >>> ws = 0.3
    >>> gpass = 1
    >>> gstop = 40

    >>> system = signal.iirdesign(wp, ws, gpass, gstop)
    >>> w, h = signal.freqz(*system)

    >>> fig, ax1 = plt.subplots()
    >>> ax1.set_title('Digital filter frequency response')
    >>> ax1.plot(w, 20 * np.log10(abs(h)), 'b')
    >>> ax1.set_ylabel('Amplitude [dB]', color='b')
    >>> ax1.set_xlabel('Frequency [rad/sample]')
    >>> ax1.grid()
    >>> ax1.set_ylim([-120, 20])
    >>> ax2 = ax1.twinx()
    >>> angles = np.unwrap(np.angle(h))
    >>> ax2.plot(w, angles, 'g')
    >>> ax2.set_ylabel('Angle (radians)', color='g')
    >>> ax2.grid()
    >>> ax2.axis('tight')
    >>> ax2.set_ylim([-6, 1])
    >>> nticks = 8
    >>> ax1.yaxis.set_major_locator(matplotlib.ticker.LinearLocator(nticks))
    >>> ax2.yaxis.set_major_locator(matplotlib.ticker.LinearLocator(nticks))