How to use the getdist.gaussian_mixtures.Gaussian2D function in getdist

self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5], label='hammer')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')

        self.tensorlike = Mixture2D([[0, 0.03], [0, 0.03]], [(0.03, 0.03, 0.1), (0.03, 0.06, 0.1)], [0.85, 0.15], ymin=0, label='tensor like')

        self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')

        self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')

        self.cut_correlated = Gaussian2D([0, 0], (0.7, 1, 0.95), ymin=0.3, xmax=1.2, label='cut correlated')

        self.shape_set = [self.gauss, self.bending, self.hammer, self.skew, self.broadtail, self.rotating, self.tight,
                          self.cut_correlated, self.tensorlike]

        self.cut_gaussians = self.cutGaussians((0.7, 1, 0.3))

        # these examples are from Wand and Jones 93
        self.bimodal = []
        self.bimodal.append(Mixture2D([[-1, 0], [1, 0]], [(2. / 3, 2. / 3, 0), (2. / 3, 2. / 3, 0)], label='bimodal WJ1'))
        self.bimodal.append(Mixture2D([[-3. / 2, 0], [3. / 2, 0]], [(1. / 4, 1, 0), (1. / 4, 1, 0)], label='bimodal WJ2'))
        self.bimodal.append(Mixture2D([[-1, 1], [1, -1]], [(2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, 3. / 5)], label='bimodal WJ3'))
        self.bimodal.append(Mixture2D([[1, -1], [-1, 1]], [(2. / 3, 2. / 3, 7. / 10), (2. / 3, 2. / 3, 0)], label='bimodal WJ4'))

        self.trimodal = []
        self.trimodal.append(Mixture2D([[-6. / 5, 6. / 5], [6. / 5, -6. / 5], [0, 0]],
                                   [(3. / 5, 3. / 5, 3. / 10), (3. / 5, 3. / 5, -3. / 5), (0.25, 0.25, 0.2)], weights=[9, 9, 2], label='trimodal WJ1'))

self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5],
                                label='hammer')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')

        self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')

        self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')

        self.cut_correlated = Gaussian2D([0, 0], (0.7, 1, 0.95), ymin=0.3, xmax=1.2, label='cut correlated')

        self.flat = Gaussian2D([0, 0], (1, 2, 0), ymin=-1, ymax=2.1, xmin=-1, xmax=0.2, label='flat')

        self.shape_set = [self.gauss, self.bending, self.hammer, self.skew, self.broadtail, self.rotating, self.tight,
                          self.cut_correlated, self.flat]

        self.cut_gaussians = self.cutGaussians((0.7, 1, 0.3))

        # these examples are from Wand and Jones 93
        self.bimodal = []
        self.bimodal.append(
            Mixture2D([[-1, 0], [1, 0]], [(2. / 3, 2. / 3, 0), (2. / 3, 2. / 3, 0)], label='bimodal WJ1'))
        self.bimodal.append(
            Mixture2D([[-3. / 2, 0], [3. / 2, 0]], [(1. / 4, 1, 0), (1. / 4, 1, 0)], label='bimodal WJ2'))
        self.bimodal.append(
            Mixture2D([[-1, 1], [1, -1]], [(2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, 3. / 5)], label='bimodal WJ3'))
        self.bimodal.append(
            Mixture2D([[1, -1], [-1, 1]], [(2. / 3, 2. / 3, 7. / 10), (2. / 3, 2. / 3, 0)], label='bimodal WJ4'))

def cutGaussians(self, cov, cut_x=[-2, -1, -0.5, 0, 1, 1.5, 2]):
        return [Gaussian2D([0, 0], cov, xmin=cut, label=r'Gaussian [$x>%s$]' % cut) for cut in cut_x]

def __init__(self):

        self.gauss = Gaussian2D([0, 0], (0.7, 1, 0.3), label='Gaussian')

        self.bending = Mixture2D([[0, 0], [2, 1.8]], [(np.sqrt(0.5), 1, 0.9), (1, 1, 0.8)], [0.6, 0.4], xmin=-1, label='bending')

        self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5], label='hammer')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')

        self.tensorlike = Mixture2D([[0, 0.03], [0, 0.03]], [(0.03, 0.03, 0.1), (0.03, 0.06, 0.1)], [0.85, 0.15], ymin=0, label='tensor like')

        self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')

        self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')

def cutGaussians(self, cov, cut_x=[-2, -1, -0.5, 0, 1, 1.5, 2]):
        return [Gaussian2D([0, 0], cov, xmin=cut, label=r'Gaussian [$x>%s$]' % cut) for cut in cut_x]

label='bending')

        self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5],
                                label='hammer')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')

        self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')

        self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')

        self.cut_correlated = Gaussian2D([0, 0], (0.7, 1, 0.95), ymin=0.3, xmax=1.2, label='cut correlated')

        self.flat = Gaussian2D([0, 0], (1, 2, 0), ymin=-1, ymax=2.1, xmin=-1, xmax=0.2, label='flat')

        self.shape_set = [self.gauss, self.bending, self.hammer, self.skew, self.broadtail, self.rotating, self.tight,
                          self.cut_correlated, self.flat]

        self.cut_gaussians = self.cutGaussians((0.7, 1, 0.3))

        # these examples are from Wand and Jones 93
        self.bimodal = []
        self.bimodal.append(
            Mixture2D([[-1, 0], [1, 0]], [(2. / 3, 2. / 3, 0), (2. / 3, 2. / 3, 0)], label='bimodal WJ1'))
        self.bimodal.append(
            Mixture2D([[-3. / 2, 0], [3. / 2, 0]], [(1. / 4, 1, 0), (1. / 4, 1, 0)], label='bimodal WJ2'))
        self.bimodal.append(
            Mixture2D([[-1, 1], [1, -1]], [(2. / 3, 2. / 3, 3. / 5), (2. / 3, 2. / 3, 3. / 5)], label='bimodal WJ3'))

def __init__(self):
        self.gauss = Gaussian2D([0, 0], (0.7, 1, 0.3), label='Gaussian')

        self.bending = Mixture2D([[0, 0], [2, 1.8]], [(np.sqrt(0.5), 1, 0.9), (1, 1, 0.8)], [0.6, 0.4], xmin=-1,
                                 label='bending')

        self.hammer = Mixture2D([[0, 0], [1, 1.8]], [(np.sqrt(0.5), 1, 0.9), (0.3, 1, -0.7)], [0.5, 0.5],
                                label='hammer')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.skew = Mixture2D([[0, 0], [0, 1.2]], [cov, cov / 4], [0.5, 0.5], label='skew')

        cov = make_2D_Cov(np.sqrt(0.5), 1, 0.1)
        self.broadtail = Mixture2D([[0, 0], [0, 0.2]], [cov, cov * 8], [0.9, 0.1], label='broad tail')

        self.rotating = Mixture2D([[0, 0], [0, 0.2]], [(1, 1, 0.5), (2, 2, -0.5)], [0.6, 0.4], label='rotating')

        self.tight = Mixture2D([[0, 0], [2.5, 3.5]], [(1, 1, 0.99), (1, 1.5, 0.98)], [0.6, 0.4], label='tight')

def __init__(self, mean, cov, **kwargs):
        """
        :param mean: 2 element array with mean
        :param cov: 2x2 array of covariance, or list of [sigma_x, sigma_y, correlation] values
        :param kwargs: arguments passed to :class:`Mixture2D`
        """
        super(Gaussian2D, self).__init__([mean], [cov], **kwargs)