correlate.py 文件源码

def time_norm(data, method, clip_factor=1, clip_set_zero=False,
              mute_parts=48, mute_factor=2):
    """
    Calculate normalized data, see e.g. Bensen et al. (2007)

    :param data: numpy array with data to manipulate
    :param str method:
        1bit: reduce data to +1 if >0 and -1 if <0\n
        clip: clip data to the root mean square (rms)\n
        mute_envelope: calculate envelope and set data to zero where envelope
        is larger than specified
    :param float clip_factor: multiply std with this value before cliping
    :param bool clip_mask: instead of clipping, set the values to zero and mask
        them
    :param int mute_parts: mean of the envelope is calculated by dividing the
        envelope into several parts, the mean calculated in each part and
        the median of this averages defines the mean envelope
    :param float mute_factor: mean of envelope multiplied by this
        factor defines the level for muting

    :return: normalized data
    """
    mask = np.ma.getmask(data)
    if method == '1bit':
        data = np.sign(data)
    elif method == 'clip':
        std = np.std(data)
        args = (data < -clip_factor * std, data > clip_factor * std)
        if clip_set_zero:
            ind = np.logical_or(*args)
            data[ind] = 0
        else:
            np.clip(data, *args, out=data)
    elif method == 'mute_envelope':
        N = next_fast_len(len(data))
        envelope = np.abs(hilbert(data, N))[:len(data)]
        levels = [np.mean(d) for d in np.array_split(envelope, mute_parts)]
        level = mute_factor * np.median(levels)
        data[envelope > level] = 0
    else:
        msg = 'The method passed to time_norm is not known: %s.' % method
        raise ValueError(msg)
    return _fill_array(data, mask=mask, fill_value=0.)


# http://azitech.wordpress.com/
# 2011/03/15/designing-a-butterworth-low-pass-filter-with-scipy/