def rippe_parameters(matrix, positions, lengths=None, init=None, circ=False):
    """Estimate parameters from the model described in Rippe et al., 2001.
    """

    n, _ = matrix.shape

    if lengths is None:
        lengths = np.abs(np.diff(positions))

    measurements, bins = [], []
    for i in range(n):
        for j in range(1, i):
            mean_length = (lengths[i] + lengths[j]) / 2.
            if positions[i] < positions[j]:
                d = (((positions[j] - positions[i] -
                     lengths[i]) + mean_length) /
                     1000.)
            else:
                d = (((positions[i] - positions[j] -
                     lengths[j]) + mean_length) /
                     1000.)

            bins.append(np.abs(d))
            measurements.append(matrix[i, j])
    parameters = estimate_param_rippe(measurements, bins, init=init, circ=circ)
    print(parameters)
    return parameters[0]
def rippe_parameters ( matrix , positions , lengths = None , init = None , circ = False ) : n , _ = matrix . shape if lengths is None : lengths = np . abs ( np . diff ( positions ) ) measurements , bins = [ ] , [ ] for i in range ( n ) : for j in range ( 1 , i ) : mean_length = ( lengths [ i ] + lengths [ j ] ) / 2. if positions [ i ] < positions [ j ] : d = ( ( ( positions [ j ] - positions [ i ] - lengths [ i ] ) + mean_length ) / 1000. ) else : d = ( ( ( positions [ i ] - positions [ j ] - lengths [ j ] ) + mean_length ) / 1000. ) bins . append ( np . abs ( d ) ) measurements . append ( matrix [ i , j ] ) parameters = estimate_param_rippe ( measurements , bins , init = init , circ = circ ) print ( parameters ) return parameters [ 0 ]
Estimate parameters from the model described in Rippe et al., 2001.