Module « scipy.signal »

Fonction firwin - module scipy.signal

Signature de la fonction firwin

def firwin(numtaps, cutoff, width=None, window='hamming', pass_zero=True, scale=True, nyq=None, fs=None) 

Description

firwin.__doc__

    FIR filter design using the window method.

    This function computes the coefficients of a finite impulse response
    filter. The filter will have linear phase; it will be Type I if
    `numtaps` is odd and Type II if `numtaps` is even.

    Type II filters always have zero response at the Nyquist frequency, so a
    ValueError exception is raised if firwin is called with `numtaps` even and
    having a passband whose right end is at the Nyquist frequency.

    Parameters
    ----------
    numtaps : int
        Length of the filter (number of coefficients, i.e. the filter
        order + 1).  `numtaps` must be odd if a passband includes the
        Nyquist frequency.
    cutoff : float or 1-D array_like
        Cutoff frequency of filter (expressed in the same units as `fs`)
        OR an array of cutoff frequencies (that is, band edges). In the
        latter case, the frequencies in `cutoff` should be positive and
        monotonically increasing between 0 and `fs/2`. The values 0 and
        `fs/2` must not be included in `cutoff`.
    width : float or None, optional
        If `width` is not None, then assume it is the approximate width
        of the transition region (expressed in the same units as `fs`)
        for use in Kaiser FIR filter design. In this case, the `window`
        argument is ignored.
    window : string or tuple of string and parameter values, optional
        Desired window to use. See `scipy.signal.get_window` for a list
        of windows and required parameters.
    pass_zero : {True, False, 'bandpass', 'lowpass', 'highpass', 'bandstop'}, optional
        If True, the gain at the frequency 0 (i.e., the "DC gain") is 1.
        If False, the DC gain is 0. Can also be a string argument for the
        desired filter type (equivalent to ``btype`` in IIR design functions).

        .. versionadded:: 1.3.0
           Support for string arguments.
    scale : bool, optional
        Set to True to scale the coefficients so that the frequency
        response is exactly unity at a certain frequency.
        That frequency is either:

        - 0 (DC) if the first passband starts at 0 (i.e. pass_zero
          is True)
        - `fs/2` (the Nyquist frequency) if the first passband ends at
          `fs/2` (i.e the filter is a single band highpass filter);
          center of first passband otherwise

    nyq : float, optional
        *Deprecated. Use `fs` instead.* This is the Nyquist frequency.
        Each frequency in `cutoff` must be between 0 and `nyq`. Default
        is 1.
    fs : float, optional
        The sampling frequency of the signal. Each frequency in `cutoff`
        must be between 0 and ``fs/2``.  Default is 2.

    Returns
    -------
    h : (numtaps,) ndarray
        Coefficients of length `numtaps` FIR filter.

    Raises
    ------
    ValueError
        If any value in `cutoff` is less than or equal to 0 or greater
        than or equal to ``fs/2``, if the values in `cutoff` are not strictly
        monotonically increasing, or if `numtaps` is even but a passband
        includes the Nyquist frequency.

    See Also
    --------
    firwin2
    firls
    minimum_phase
    remez

    Examples
    --------
    Low-pass from 0 to f:

    >>> from scipy import signal
    >>> numtaps = 3
    >>> f = 0.1
    >>> signal.firwin(numtaps, f)
    array([ 0.06799017,  0.86401967,  0.06799017])

    Use a specific window function:

    >>> signal.firwin(numtaps, f, window='nuttall')
    array([  3.56607041e-04,   9.99286786e-01,   3.56607041e-04])

    High-pass ('stop' from 0 to f):

    >>> signal.firwin(numtaps, f, pass_zero=False)
    array([-0.00859313,  0.98281375, -0.00859313])

    Band-pass:

    >>> f1, f2 = 0.1, 0.2
    >>> signal.firwin(numtaps, [f1, f2], pass_zero=False)
    array([ 0.06301614,  0.88770441,  0.06301614])

    Band-stop:

    >>> signal.firwin(numtaps, [f1, f2])
    array([-0.00801395,  1.0160279 , -0.00801395])

    Multi-band (passbands are [0, f1], [f2, f3] and [f4, 1]):

    >>> f3, f4 = 0.3, 0.4
    >>> signal.firwin(numtaps, [f1, f2, f3, f4])
    array([-0.01376344,  1.02752689, -0.01376344])

    Multi-band (passbands are [f1, f2] and [f3,f4]):

    >>> signal.firwin(numtaps, [f1, f2, f3, f4], pass_zero=False)
    array([ 0.04890915,  0.91284326,  0.04890915])