ipf, yea! see elsewhere for description of ipf.

ipf.py

# this should be fairly self explanitory if you know ipf
# seed_matrix is your best bet at the totals, col_marginals are
# observed column marginals and row_marginals is the same for rows
def simple_ipf(seed_matrix, col_marginals, row_marginals, tolerance=1, cnt=0):
    assert np.absolute(row_marginals.sum() - col_marginals.sum()) < 5.0

    # first normalize on columns
    ratios = col_marginals / seed_matrix.sum(axis=0)
    seed_matrix *= ratios
    closeness = np.absolute(row_marginals - seed_matrix.sum(axis=1)).sum()
    assert np.absolute(col_marginals - seed_matrix.sum(axis=0)).sum() < .01
    # print "row closeness", closeness
    if closeness < tolerance:
        return seed_matrix

    # first normalize on rows
    ratios = row_marginals / seed_matrix.sum(axis=1)
    ratios[row_marginals == 0] = 0
    seed_matrix = seed_matrix * ratios.reshape((ratios.size, 1))
    assert np.absolute(row_marginals - seed_matrix.sum(axis=1)).sum() < .01
    closeness = np.absolute(col_marginals - seed_matrix.sum(axis=0)).sum()
    # print "col closeness", closeness
    if closeness < tolerance:
        return seed_matrix

    if cnt >= 50:
        return seed_matrix

    return simple_ipf(seed_matrix, col_marginals, row_marginals,
                      tolerance, cnt+1)