title: Matplotlib: Making 2D Gaussian contours with transparent outermost layer
body: <p>So I have used <a href="http://wiki.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data" rel="noreferrer">matplotlib cookbook</a> to generate the following grayscale gaussian contours:</p>
  
  <pre><code>import numpy as np
  from scipy.interpolate import griddata
  import matplotlib.pyplot as plt
  import numpy.ma as ma
  from numpy.random import uniform, seed
  from matplotlib import cm
  def gauss(x,y,Sigma,mu):
      X=np.vstack((x,y)).T
      mat_multi=np.dot((X-mu[None,...]).dot(np.linalg.inv(Sigma)),(X-mu[None,...]).T)
      return  np.diag(np.exp(-1*(mat_multi)))
  
  def plot_countour(x,y,z):
      # define grid.
      xi = np.linspace(-2.1,2.1,100)
      yi = np.linspace(-2.1,2.1,100)
      ## grid the data.
      zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic')
      # contour the gridded data, plotting dots at the randomly spaced data points.
      CS = plt.contour(xi,yi,zi,6,linewidths=0.5,colors='k')
      #CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)
      CS = plt.contourf(xi,yi,zi,6,cmap=cm.Greys_r)
      #plt.colorbar() # draw colorbar
      # plot data points.
      #plt.scatter(x,y,marker='o',c='b',s=5)
      plt.xlim(-2,2)
      plt.ylim(-2,2)
      plt.title('griddata test (%d points)' % npts)
      plt.show()
  
  
  # make up some randomly distributed data
  seed(1234)
  npts = 1000
  x = uniform(-2,2,npts)
  y = uniform(-2,2,npts)
  z = gauss(x,y,Sigma=np.asarray([[1.,.5],[0.5,1.]]),mu=np.asarray([0.,0.]))
  plot_countour(x,y,z)
  </code></pre>
  
  <p>However I want the outermost layer to be colourless so I could export the image consisting only of the few circular contours of the Gaussian. Is there any way of manipulating this code to do that?
  <img src="https://i.stack.imgur.com/K8cAj.png" alt="enter image description here"></p>