def kernel_gaussian(size=100, sigma=None, forwardOnly=False):
    """
    return a 1d gassuan array of a given size and sigma.
    If sigma isn't given, it will be 1/10 of the size, which is usually good.
    """
    if sigma is None:sigma=size/10
    points=np.exp(-np.power(np.arange(size)-size/2,2)/(2*np.power(sigma,2)))
    if forwardOnly:
        points[:int(len(points)/2)]=0
    return points/sum(points)
def kernel_gaussian(size=100, sigma=None, forwardOnly=False):
    """
    return a 1d gassuan array of a given size and sigma.
    If sigma isn't given, it will be 1/10 of the size, which is usually good.
    """
    if sigma is None:sigma=size/10
    points=np.exp(-np.power(np.arange(size)-size/2,2)/(2*np.power(sigma,2)))
    if forwardOnly:
        points[:int(len(points)/2)]=0
    return points/sum(points)
def kernel_gaussian ( size = 100 , sigma = None , forwardOnly = False ) : if sigma is None : sigma = size / 10 points = np . exp ( - np . power ( np . arange ( size ) - size / 2 , 2 ) / ( 2 * np . power ( sigma , 2 ) ) ) if forwardOnly : points [ : int ( len ( points ) / 2 ) ] = 0 return points / sum ( points )
return a 1d gassuan array of a given size and sigma.
    If sigma isn't given, it will be 1/10 of the size, which is usually good.
return a 1d gassuan array of a given size and sigma . If sigma isn t given it will be 1 / 10 of the size which is usually good .