def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
    _np.random.seed(1234)

    x = _np.linspace(0, 4 * _np.pi, N)[:, None]
    s1 = _np.vectorize(lambda x: _np.sin(x))
    s2 = _np.vectorize(lambda x: _np.cos(x) ** 2)
    s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
    sS = _np.vectorize(lambda x: _np.cos(x))

    s1 = s1(x)
    s2 = s2(x)
    s3 = s3(x)
    sS = sS(x)

    s1 -= s1.mean(); s1 /= s1.std(0)
    s2 -= s2.mean(); s2 /= s2.std(0)
    s3 -= s3.mean(); s3 /= s3.std(0)
    sS -= sS.mean(); sS /= sS.std(0)

    Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)

    slist = [sS, s1, s2, s3]
    slist_names = ["sS", "s1", "s2", "s3"]
    Ylist = [Y1, Y2, Y3]

    if plot_sim:
        from matplotlib import pyplot as plt
        import matplotlib.cm as cm
        import itertools
        fig = plt.figure("MRD Simulation Data", figsize=(8, 6))
        fig.clf()
        ax = fig.add_subplot(2, 1, 1)
        labls = slist_names
        for S, lab in itertools.izip(slist, labls):
            ax.plot(S, label=lab)
        ax.legend()
        for i, Y in enumerate(Ylist):
            ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
            ax.imshow(Y, aspect='auto', cmap=cm.gray)  # @UndefinedVariable
            ax.set_title("Y{}".format(i + 1))
        plt.draw()
        plt.tight_layout()

    return slist, [S1, S2, S3], Ylist
Adding a new product version reorders the versions.
dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/dimensionality_reduction.py/_simulate_sincos--crossover with--dataset/ETHPy150Open mozilla/moztrap/tests/model/core/models/test_productversion.py/ProductVersionTest.test_adding_new_version_reorders