                   avg_samples=10, flatten_outliers=False, is_totals=False):
          logger.debug("Initializing %s: (session: %s, display: %s, loss_keys: %s, selections: %s, "
                       "avg_samples: %s, flatten_outliers: %s, is_totals: %s",
                       self.__class__.__name__, session, display, loss_keys, selections, avg_samples,
                       flatten_outliers, is_totals)
  
          warnings.simplefilter("ignore", np.RankWarning)
  
          self.session = session
          self.display = display
 
</s> remove                      "avg_samples: %s, flatten_outliers: %s, is_totals: %s",
 </s> add                      "avg_samples: %s, smooth_amount: %s, flatten_outliers: %s, is_totals: %s", </s> remove                  avg_samples=10, flatten_outliers=False, is_totals=False):
 </s> add                  avg_samples=500, smooth_amount=0.90, flatten_outliers=False, is_totals=False): </s> remove         self.args = {"avg_samples": int(avg_samples),
 </s> add         self.args = {"avg_samples": avg_samples,
                     "smooth_amount": smooth_amount, </s> remove         self.default_view = "avg" if datapoints > self.default_avg * 2 else "raw"
 </s> add         self.default_view = "avg" if datapoints > self.default_avg * 2 else "smoothed"
                     smooth_amount, flatten_outliers, is_totals)
                   avg_samples=10, flatten_outliers=False, is_totals=False):
          logger.debug("Initializing %s: (session: %s, display: %s, loss_keys: %s, selections: %s, "
                       "avg_samples: %s, flatten_outliers: %s, is_totals: %s",
                       self.__class__.__name__, session, display, loss_keys, selections, avg_samples,
                     smooth_amount, flatten_outliers, is_totals)
  
          warnings.simplefilter("ignore", np.RankWarning)
  
          self.session = session
          self.display = display
keep keep keep keep replace keep keep keep keep keep
 Graphing Updates

Add smoothing to Analysis and Training Graph
Replace Trend with Smoothing on Training Graph
Add Smoothing Parameter to Training Graph 
lib/gui/stats.py