signal.py 文件源码

def nonnegative_bandpass_filter(data,fa=None,fb=None,
    Fs=1000.,order=4,zerophase=True,bandstop=False,
    offset=1.0):
    '''
    For filtering data that must remain non-negative. Due to ringing
    conventional fitering can create values less than zero for non-
    negative real inputs. This may be unrealistic for some data.

    To compensate, this performs the filtering on the natural
    logarithm of the input data. For small numbers, this can lead to
    numeric underflow, so an offset parameter (default 1) is added
    to the data for stability.

    Parameters
    ----------
    data (ndarray): 
        data, filtering performed over last dimension
    fa (number): 
        low-freq cutoff Hz. If none, lowpass at fb
    fb (number): 
        high-freq cutoff Hz. If none, highpass at fa
    Fs (int): 
        Sample rate in Hz
    order (1..6): 
        butterworth filter order. Default 4
    zerophase (boolean): 
        Use forward-backward filtering? (true)
    bandstop (boolean): 
        Do band-stop rather than band-pass
    offset (positive number): 
        Offset data to avoid underflow (1)

    Returns
    -------
    filtered : 
        Filtered signal
    '''
    offset -= 1.0
    data = np.log1p(data+offset)
    filtered = bandpass_filter(data,
        fa=fa, fb=fb, Fs=Fs,
        order=order,
        zerophase=zerophase,
        bandstop=bandstop)
    return np.expm1(filtered)