def fill_edge_matrix(nsrcs, match_dict):
    """ Create and fill a matrix with the graph 'edges' between sources.

    Parameters
    ----------
    nsrcs  : int 
        number of sources (used to allocate the size of the matrix)

    match_dict :  dict((int,int):float)    
        Each entry gives a pair of source indices, and the
        corresponding measure (either distance or sigma)

    Returns
    -------
    e_matrix : `~numpy.ndarray`    
        numpy.ndarray((nsrcs,nsrcs)) filled with zeros except for the
        matches, which are filled with the edge measures (either
        distances or sigmas)
    """
    e_matrix = np.zeros((nsrcs, nsrcs))
    for k, v in match_dict.items():
        e_matrix[k[0], k[1]] = v
    return e_matrix
def fill_edge_matrix ( nsrcs , match_dict ) : e_matrix = np . zeros ( ( nsrcs , nsrcs ) ) for k , v in match_dict . items ( ) : e_matrix [ k [ 0 ] , k [ 1 ] ] = v return e_matrix
Create and fill a matrix with the graph 'edges' between sources.

    Parameters
    ----------
    nsrcs  : int 
        number of sources (used to allocate the size of the matrix)

    match_dict :  dict((int,int):float)    
        Each entry gives a pair of source indices, and the
        corresponding measure (either distance or sigma)

    Returns
    -------
    e_matrix : `~numpy.ndarray`    
        numpy.ndarray((nsrcs,nsrcs)) filled with zeros except for the
        matches, which are filled with the edge measures (either
        distances or sigmas)