peakdetect.py 文件源码

def zero_crossings(y_axis, window = 11):
    """
    Algorithm to find zero crossings. Smoothens the curve and finds the
    zero-crossings by looking for a sign change.


    keyword arguments:
    y_axis -- A list containg the signal over which to find zero-crossings
    window -- the dimension of the smoothing window; should be an odd integer
        (default: 11)

    return -- the index for each zero-crossing
    """
    # smooth the curve
    length = len(y_axis)
    x_axis = np.asarray(range(length), int)

    # discard tail of smoothed signal
    y_axis = _smooth(y_axis, window)[:length]
    zero_crossings = np.where(np.diff(np.sign(y_axis)))[0]
    indices = [x_axis[index] for index in zero_crossings]

    # check if zero-crossings are valid
    diff = np.diff(indices)
    if diff.std() / diff.mean() > 0.2:
        print(diff.std() / diff.mean())
        print(np.diff(indices))
        raise ValueError("False zero-crossings found, indicates problem {0} or {1}".format(
            "with smoothing window", "problem with offset"))
    # check if any zero crossings were found
    if len(zero_crossings) < 1:
        raise ValueError("No zero crossings found")

    return indices 
    # used this to test the fft function's sensitivity to spectral leakage
    #return indices + np.asarray(30 * np.random.randn(len(indices)), int)

############################Frequency calculation#############################
#    diff = np.diff(indices)
#    time_p_period = diff.mean()
#    
#    if diff.std() / time_p_period > 0.1:
#        raise ValueError, 
#            "smoothing window too small, false zero-crossing found"
#    
#    #return frequency
#    return 1.0 / time_p_period
##############################################################################