def smoothSpikePosCount(self, x1, npos, sigma=3.0, shuffle=None):
    '\n        Returns a spike train the same length as num pos samples that has been\n        smoothed in time with a gaussian kernel M in width and standard\n        deviation equal to sigma\n\n        Parameters\n        --------------\n        x1 : np.array\n            The pos indices the spikes occured at\n        npos : int\n            The number of position samples captured\n        sigma : float\n            the standard deviation of the gaussian used to smooth the spike\n            train\n        shuffle: int\n            The number of seconds to shift the spike train by. Default None\n\n        Returns\n        -----------\n        smoothed_spikes : np.array\n            The smoothed spike train\n        '
    spk_hist = np.bincount(x1, minlength=npos)
    if (shuffle is not None):
        spk_hist = np.roll(spk_hist, int((shuffle * 50)))
    h = signal.gaussian(13, sigma)
    h = (h / float(np.sum(h)))
    return signal.filtfilt(h.ravel(), 1, spk_hist)
Returns a spike train the same length as num pos samples that has been
smoothed in time with a gaussian kernel M in width and standard
deviation equal to sigma

Parameters
--------------
x1 : np.array
    The pos indices the spikes occured at
npos : int
    The number of position samples captured
sigma : float
    the standard deviation of the gaussian used to smooth the spike
    train
shuffle: int
    The number of seconds to shift the spike train by. Default None

Returns
-----------
smoothed_spikes : np.array
    The smoothed spike train
def smoothSpikePosCount(self, x1, npos, sigma=3.0, shuffle=None):
    '\n        Returns a spike train the same length as num pos samples that has been\n        smoothed in time with a gaussian kernel M in width and standard\n        deviation equal to sigma\n\n        Parameters\n        --------------\n        x1 : np.array\n            The pos indices the spikes occured at\n        npos : int\n            The number of position samples captured\n        sigma : float\n            the standard deviation of the gaussian used to smooth the spike\n            train\n        shuffle: int\n            The number of seconds to shift the spike train by. Default None\n\n        Returns\n        -----------\n        smoothed_spikes : np.array\n            The smoothed spike train\n        '
    spk_hist = np.bincount(x1, minlength=npos)
    if (shuffle is not None):
        spk_hist = np.roll(spk_hist, int((shuffle * 50)))
    h = signal.gaussian(13, sigma)
    h = (h / float(np.sum(h)))
    return signal.filtfilt(h.ravel(), 1, spk_hist)