(spread, min_dist)
return params[0], params[1]
Fit a, b params for the differentiable curve used in lower
    dimensional fuzzy simplicial complex construction. We want the
    smooth curve (from a pre-defined family with simple gradient) that
    best matches an offset exponential decay.
Fit a, b params for the differentiable curve used in lower
    dimensional fuzzy simplicial complex construction. We want the
    smooth curve (from a pre-defined family with simple gradient) that
    best matches an offset exponential decay.
Fit a b params for the differentiable curve used in lower dimensional fuzzy simplicial complex construction . We want the smooth curve ( from a pre - defined family with simple gradient ) that best matches an offset exponential decay .
def find_ab_params(spread, min_dist):
    """Fit a, b params for the differentiable curve used in lower
    dimensional fuzzy simplicial complex construction. We want the
    smooth curve (from a pre-defined family with simple gradient) that
    best matches an offset exponential decay.
    """

    def curve(x, a, b):
        return 1.0 / (1.0 + a * x ** (2 * b))

    xv = np.linspace(0, spread * 3, 300)
    yv = np.zeros(xv.shape)
    yv[xv < min_dist] = 1.0
    yv[xv >= min_dist] = np.exp(-(xv[xv >= min_dist] - min_dist) / spread)
    params, covar = curve_fit(curve, xv, yv)
    return params[0], params[1]
def find_ab_params ( spread , min_dist ) : def curve ( x , a , b ) : return 1.0 / ( 1.0 + a * x ** ( 2 * b ) ) xv = np . linspace ( 0 , spread * 3 , 300 ) yv = np . zeros ( xv . shape ) yv [ xv < min_dist ] = 1.0 yv [ xv >= min_dist ] = np . exp ( - ( xv [ xv >= min_dist ] - min_dist ) / spread ) params , covar = curve_fit ( curve , xv , yv ) return params [ 0 ] , params [ 1 ]