title: Intelligent averaging of time series data with python
body: <p>I have the following (time-series) data: </p>
  
  <pre><code>t = [5.13,   5.27,   5.40,   5.46,  190.99, 191.13, 191.267, 368.70, 368.83,  368.90, 368.93]
  y = [17.17, 17.18, 17.014, 17.104,  16.981,  16.96,   16.85,  17.27, 17.66,   17.76, 18.01]
  </code></pre>
  
  <p>so, groups of data in short (time) intervals then separated cleanly by a long time gap. </p>
  
  <p>I'm looking for a simple method that will intelligently average these together; sort of a 'Bayesian blocks' but for non-histogram data. </p>
  
  <p>One could do a simple moving average, or numpy convolution, but I'm looking for something a bit smarter that will generalize to larger, similar, but not identical  datasets. </p>