def main(N=32, n=32, ndiag=1, main_diag_factor=1.0, off_diag_factor=1.0,
         base_seed=0, seed_range=1, fact_pow2_min=4, fact_pow2_max=18,
         plot=False, npows=0, scan_ndiag=False, savefig='None'):
    """
    Ax = b
    """

    npows = npows or fact_pow2_max - fact_pow2_min
    factors = np.linspace(fact_pow2_min, fact_pow2_max, npows)

    ilu_rmsd, blk_rmsd = [], []
    superiority = []

    if scan_ndiag:
        if seed_range != 1:
            raise ValueError("Cannot plot mulitple seeds and scan ndiag")
        ndiag_range = range(1, ndiag+1)
    else:
        ndiag_range = [ndiag]
    combos = product(ndiag_range, range(seed_range))

    #for seed in range(seed_range):
    #seed = base_seed
    #for ndiag in ndiag_range:

    nseries = 0
    for ndiag, seed in combos:
        nseries += 1
        ilu_rmsd_local, blk_rmsd_local = [], []

        for diag_fact_pow in factors:
            A, b, x_ref, x_ilu, x_blk = get_test_system(
                N, n, ndiag,
                main_diag_factor*2**diag_fact_pow,
                off_diag_factor/2**diag_fact_pow,
                seed+base_seed)
            ilu_err = x_ilu - x_ref
            blk_err = np.array(x_blk).flatten() - x_ref

            ilu_rmsd_local.append(rms(ilu_err))
            blk_rmsd_local.append(rms(blk_err))

        if plot and seed_range == 1 and not scan_ndiag:
            import matplotlib.pyplot as plt

            if npows == 1:
                for idx in (1, 2):
                    plt.subplot(3, 1, idx)
                    plt.plot(ilu_err, label='ILU error')

                for idx in (1, 3):
                    plt.subplot(3, 1, idx)
                    plt.plot(blk_err, label='block error')

                for idx in (1, 2, 3):
                    plt.subplot(3, 1, idx)
                    plt.legend()

                plt.show()
            else:
                plt.semilogy(ilu_rmsd, label="ILU RMSD")
                plt.semilogy(blk_rmsd, label="Block RMSD")
                plt.legend()
                plt.show()

        ilu_rmsd.append(np.array(ilu_rmsd_local))
        blk_rmsd.append(np.array(blk_rmsd_local))
        superiority.append(np.array(blk_rmsd_local) / np.array(ilu_rmsd_local))
        if np.any(superiority[-1] < 1e-3):
            print('1000 x inferior:', seed)

    if plot and (seed_range > 1 or scan_ndiag):
        import matplotlib.pyplot as plt

        plt.figure(figsize=(6, 14))

        if scan_ndiag:
            plot_kwargs = {}
        else:
            decay = exp(-((seed_range-1)/50.0))
            plot_kwargs = dict(alpha=1.0 - 0.9*(1-decay), linewidth=0.2 + 0.8*decay)

        ax = plt.subplot(3, 1, 1)
        ax.set_xscale('log', basex=10)
        ax.set_yscale('log', basey=10)
        clr = lambda idx, rgb: [1.0 - (nseries-idx)/float(nseries) if clridx==rgb else 0.0 for clridx in range(3)]
        for si, series in enumerate(ilu_rmsd):
            if scan_ndiag:
                c = clr(si, 2)  # blue
                lbl = str(ndiag_range[si])
            else:
                c = 'b'
                lbl = None
            plt.plot(2**factors, series, color=c, label=lbl, **plot_kwargs)
        plt.title("ILU")
        plt.xlabel("weight")
        plt.ylabel("RMSD")
        if scan_ndiag:
            plt.legend(loc='best')

        ax = plt.subplot(3, 1, 2)
        ax.set_xscale('log', basex=10)
        ax.set_yscale('log', basey=10)
        for si, series in enumerate(blk_rmsd):
            if scan_ndiag:
                c = clr(si, 1)  # green
                lbl = str(ndiag_range[si])
            else:
                c = 'g'
                lbl = None
            plt.plot(2**factors, series, color=c, label=lbl, **plot_kwargs)
        plt.title("Block RMSD")
        plt.xlabel("weight")
        plt.ylabel("RMSD")
        if scan_ndiag:
            plt.legend(loc='best')

        ax = plt.subplot(3, 1, 3)
        ax.set_xscale('log', basex=10)
        ax.set_yscale('log', basey=10)
        for si, series in enumerate(superiority):
            if scan_ndiag:
                c = clr(si, 0)  # red
                lbl = str(ndiag_range[si])
            else:
                c = 'k'
                lbl = None
            plt.plot(2**factors, series, color=c, label=lbl, **plot_kwargs)
        plt.title("BLOCK RMSD / ILU RMSD")
        plt.xlabel("weight")
        plt.ylabel("RMSD fraction")
        if scan_ndiag:
            plt.legend(loc='best')

        plt.tight_layout()

        if savefig == 'None':
            plt.show()
        else:
            plt.savefig(savefig, dpi=300)