title: Python - integrate 2D Kernel Density Estimation within contour lines
body: <p>I would like to draw a contour plot of a Kernel Density Estimation, where the KDE is integrated within each of the contour plot filled areas. </p>
  
  <p>As an example, imagine I calculate the KDE of 2D data:</p>
  
  <pre><code>data = np.random.multivariate_normal((0, 0), [[1, 1], [2, 0.7]], 100)
  x = data[:, 0]
  y = data[:, 1]
  xmin, xmax = min(x), max(x)
  ymin, ymax = min(y), max(y)
  xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
  positions = np.vstack([xx.ravel(), yy.ravel()])
  values = np.vstack([x, y])
  kernel = st.gaussian_kde(values)
  f = np.reshape(kernel(positions).T, xx.shape)
  </code></pre>
  
  <p>I know how to draw the contourplot of the KDE.</p>
  
  <pre><code>fig = plt.figure()
  ax = fig.gca()
  ax.set_xlim(xmin, xmax)
  ax.set_ylim(ymin, ymax)
  cfset = ax.contourf(xx, yy, f, cmap='Blues')
  cset = ax.contour(xx, yy, f, colors='k')
  plt.show()
  </code></pre>
  
  <p>However, this contourplot shows what is the probability density within each of the filled areas. Instead, I would like the plot to indicate the total probability to fall within each of the filled areas. </p>