peakdetect.py 文件源码

def peakdetect_parabole(y_axis, x_axis, points = 9):
    """
    Function for detecting local maximas and minmias in a signal.
    Discovers peaks by fitting the model function: y = k (x - tau) ** 2 + m
    to the peaks. The amount of points used in the fitting is set by the
    points argument.

    Omitting the x_axis is forbidden as it would make the resulting x_axis
    value silly if it was returned as index 50.234 or similar.

    will find the same amount of peaks as the 'peakdetect_zero_crossing'
    function, but might result in a more precise value of the peak.

    keyword arguments:
    y_axis -- A list containg the signal over which to find peaks
    x_axis -- A x-axis whose values correspond to the y_axis list and is used
        in the return to specify the postion of the peaks.
    points -- (optional) How many points around the peak should be used during
        curve fitting, must be odd (default: 9)

    return -- two lists [max_peaks, min_peaks] containing the positive and
        negative peaks respectively. Each cell of the lists contains a list
        of: (position, peak_value) 
        to get the average peak value do: np.mean(max_peaks, 0)[1] on the
        results to unpack one of the lists into x, y coordinates do: 
        x, y = zip(*max_peaks)
    """
    # check input data
    x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis)
    # make the points argument odd
    points += 1 - points % 2
    #points += 1 - int(points) & 1 slower when int conversion needed

    # get raw peaks
    max_raw, min_raw = peakdetect_zero_crossing(y_axis)

    # define output variable
    max_peaks = []
    min_peaks = []

    max_ = _peakdetect_parabole_fitter(max_raw, x_axis, y_axis, points)
    min_ = _peakdetect_parabole_fitter(min_raw, x_axis, y_axis, points)

    max_peaks = map(lambda x: [x[0], x[1]], max_)
    max_fitted = map(lambda x: x[-1], max_)
    min_peaks = map(lambda x: [x[0], x[1]], min_)
    min_fitted = map(lambda x: x[-1], min_)


    #pylab.plot(x_axis, y_axis)
    #pylab.hold(True)
    #for max_p, max_f in zip(max_peaks, max_fitted):
    #    pylab.plot(max_p[0], max_p[1], 'x')
    #    pylab.plot(max_f[0], max_f[1], 'o', markersize = 2)
    #for min_p, min_f in zip(min_peaks, min_fitted):
    #    pylab.plot(min_p[0], min_p[1], 'x')
    #    pylab.plot(min_f[0], min_f[1], 'o', markersize = 2)
    #pylab.show()

    return [max_peaks, min_peaks]