在Python中从嵌套for循环绘制子图

Question

我试图为不同的mu和sigma值绘制直线图（漂移布朗运动），我有一个函数，它迭代可能的mu值和可能的sigma值的列表，然后它应该返回结果图。问题是我不确定如何使subplots返回所需的行数。我已经给了它正确的nrows和ncols，但问题在于索引。有人有办法解决这个问题吗

我已经在下面提供了代码和错误消息

# Drifted BM for varying values mu and sigma respectively

def DriftedBMTest2(nTraj=50,T=5.0,dt=0.01,n=5, sigma = [0.1,1.0,2], mulist=[0,0.5,1,1.5], ValFSize=(18,14)):

    nMu = len(mulist)
    nSigma = len(mulist)
    # Discretize, dt =  time step = $t_{j+1}- t_{j}$
    dt = T/(n-1)

    # Loop on different value sigma
    for z in range(nSigma): 
        # Loop on different value Mu
        for k in range(nMu):

            n=int(T/dt)
            x=np.zeros(n+1,float)

            # Create plot space 
            temp = nSigma*nMu/2
            plt.subplot(temp,2,k+1)
            plt.title("Drifted BM $\sigma$={}, $\mu$={}".format(sigma[z],mulist[k])) 
            plt.xlabel(r'$t$')
            plt.ylabel(r'$W_t$');

            # Container for colours for each trajectory
            colors = plt.cm.jet(np.linspace(0,1,nTraj))
            # Generate many trajectories
            for j in range(nTraj):

                # Time simulation
                # Add the time * constant(mu)
                for i in range(n):
                    x[i+1]=x[i]+np.sqrt(dt)*np.random.randn() + i*mulist[k]

                # Scale Each Tradjectory
                x = x * sigma[z]
                # Plot trajectory just computed
                plt.plot(np.linspace(0,T,n+1),x,'b-',alpha=0.3, color=colors[j], lw=3.0)

DriftedBMTest2( sigma = [1,2], mulist=[-2,1] )

然后我得到了前两个图，但不是全部，还有下面的错误

MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.

很抱歉，如果这是一个糟糕的问题，我是Python新手，但任何帮助都将不胜感激

Answer 1

尝试在两个for循环之间添加fig = plt.figure()

for z in range(nSigma): 
    # Loop on different value Mu
    fig = plt.figure()   # <   Line added here
    for k in range(nMu):

如果这不能提供所需的布局，您可以尝试将其移动到内部for循环，如下所示

for z in range(nSigma): 
    # Loop on different value Mu
    for k in range(nMu):
        fig = plt.figure()  # <   Line added here