import numpy as np
    import math
    from pylab import cm,imshow,colorbar,title,show
    import pylab as pyl

    from mpl_toolkits import mplot3d
    import matplotlib.pyplot as plt

    #Parameters
    N = 10**2                        #Step of discretization
 
    # Cost function
    T = np.linspace(0,1,N, False) #Discretization of [0,1]
    S = np.linspace(1,2,N,False) #Discretization of [1,2]
    X,Y = np.meshgrid(T,S)
    C = (X-Y)**2                    #Matrix of c[i,j]=(xi-yj)²

    def Sinkhorn(M, r, c, lam):
    """
    Computes the optimal transport matrix and Slinkhorn distance using the
    Sinkhorn-Knopp algorithm

    Inputs:
        - M : cost matrix (n x m)
        - r : vector of marginals (n, )
        - c : vector of marginals (m, )
        - lam : strength of the entropic regularization
        - arret : convergence parameter

    Outputs:
        - P : optimal transport matrix (n x m)
        - dist : Sinkhorn distance
    """

    # Uniform measure over [0;1]
    uni1 = np.ones(N)

    # Uniform measure over [1;2]
    uni2 = np.ones(N)

    n = 1000

    fig = plt.figure()
    ax = plt.axes(projection='3d')
    # I'm going to compute a matrix which is a approximation of a probability over R^{2}
    Gamma_star = Sinkhorn(C, uni1, uni2, 1/10**4)
    ax.scatter(X, Y, Gamma_star)
    plt.title("Gamma bar 1/{} entre une uniforme([0;1]) et uniforme([1;2])".format(1/10**4))    
    plt.show()

我的问题是：Gamma bar收敛到一个我想调查的度量，所以我想打印子图，如下所示：（当然它没有工作，它只是告诉你什么在我的想法）

for i in range(4):
        plt.subplot(2,2,i+1) 
        Gamma_star = Sinkhorn(C, uni1, uni2, 1/10**i)
        ax.scatter(X, Y, Gamma_star)
        plt.title("Gamma bar 1/{} between uniform([0;1]) and uniform([1;2])".format(1/10**i))    
    plt.plot()

我还想绘制（子图以非常相同的方式）3D直方图，其中X，Y和Z = Gamma_bar，如下所示：