title: Plotting stochastic processes in Python
body: <p>Say I have a stochastic process defined between <code>[0... N]</code>, e.g. <code>N=50</code>. For every location, I have several samples (e.g. <code>m=100</code> samples) (representing my sampling distribution at each location). One way to look at this is as a numpy 2D array of size <code>(m,N)</code>.</p>
  
  <p>How can I plot this intuitively in <code>matplotlib</code>? </p>
  
  <p>One possibility is to plot the process as a 1D plot along with an <strong>envelope of varying thickness</strong> and shade that captures the density of these distributions, something along the lines of what I show below. How can I do this in <code>matplotlib</code>?</p>
  
  <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <img src="https://i.stack.imgur.com/STj8k.png" alt="enter image description here"></p>
  
  <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <img src="https://i.stack.imgur.com/RPBbx.png" alt="enter image description here"></p>
  
  <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <img src="https://i.stack.imgur.com/sx776.png" alt="enter image description here"></p>