def quantile(data, num_breaks):
    """
    Calculate quantile breaks.

    Arguments:
    data -- Array of values to classify.
    num_breaks -- Number of breaks to perform.
    """

    def scipy_mquantiles(a, prob=list([.25,.5,.75]), alphap=.4, betap=.4, axis=None, limit=()):
        """ function copied from scipy 0.13.3::scipy.stats.mstats.mquantiles """

        def _quantiles1D(data,m,p):
            x = numpy.sort(data.compressed())
            n = len(x)
            if n == 0:
                return numpy.ma.array(numpy.empty(len(p), dtype=float), mask=True)
            elif n == 1:
                return numpy.ma.array(numpy.resize(x, p.shape), mask=numpy.ma.nomask)
            aleph = (n*p + m)
            k = numpy.floor(aleph.clip(1, n-1)).astype(int)
            gamma = (aleph-k).clip(0,1)
            return (1.-gamma)*x[(k-1).tolist()] + gamma*x[k.tolist()]

        # Initialization & checks ---------
        data = numpy.ma.array(a, copy=False)
        if data.ndim > 2:
            raise TypeError("Array should be 2D at most !")
        #
        if limit:
            condition = (limit[0] < data) & (data < limit[1])
            data[~condition.filled(True)] = numpy.ma.masked
        #
        p =  numpy.array(prob, copy=False, ndmin=1)
        m = alphap + p*(1.-alphap-betap)
        # Computes quantiles along axis (or globally)
        if (axis is None):
            return _quantiles1D(data, m, p)
        return numpy.ma.apply_along_axis(_quantiles1D, axis, data, m, p)

    return scipy_mquantiles(data, numpy.linspace(1.0 / num_breaks, 1, num_breaks))
Calculate quantile breaks.

    Arguments:
    data -- Array of values to classify.
    num_breaks -- Number of breaks to perform.