diff --git a/rowers/interactiveplots.py b/rowers/interactiveplots.py
index 0e2e4d23..3e782204 100644
--- a/rowers/interactiveplots.py
+++ b/rowers/interactiveplots.py
@@ -645,10 +645,14 @@ def interactive_otwcpchart(powerdf,promember=0):
 	# message = "CP model fit didn't give correct results"
 
 
+    deltas = fitt.apply(lambda x: timedeltaconv(x))
+    ftime = niceformat(deltas)
+        
     sourcecomplex = ColumnDataSource(
         data = dict(
-            power = fitpower,
-            duration = fitt
+            CP = fitpower,
+            duration = fitt,
+            ftime = ftime
             )
         )
 
@@ -689,7 +693,7 @@ def interactive_otwcpchart(powerdf,promember=0):
  
     hover.mode = 'mouse'
 
-    plot.line('duration','power',source=sourcecomplex,legend="CP Model",
+    plot.line('duration','CP',source=sourcecomplex,legend="CP Model",
 	      color='green')
     
     script, div = components(plot)
diff --git a/rowers/views.py b/rowers/views.py
index 7adcad14..21d06331 100644
--- a/rowers/views.py
+++ b/rowers/views.py
@@ -2897,26 +2897,32 @@ def otwrankings_view(request,theuser=0,
 	
 	thesecs.append(timesecs)
 
+
     if len(thesecs) != 0:
-        maxt = pd.Series(thesecs).max()
+        maxt = 2*pd.Series(thesecs).max()
     else:
         maxt = 1000.
 
+
     maxlog10 = np.log10(maxt)
     logarr = np.arange(100)*maxlog10/100.
     logarr = [int(10.**(la)) for la in logarr]
     logarr = pd.Series(logarr)
     logarr.drop_duplicates(keep='first',inplace=True)
+
     logarr = logarr.values
 
+    
+    
     delta = []
     cpvalue = []
     avgpower = {}
     
     dfgrouped = df.groupby(['workoutid'])
     for id,group in dfgrouped:
-        tt = group['time']
-        ww = group['power']
+        tt = group['time'].copy()
+        ww = group['power'].copy()
+
         try:
             avgpower[id] = int(ww.mean())
         except ValueError:
@@ -2926,40 +2932,44 @@ def otwrankings_view(request,theuser=0,
             dt = []
             cpw = []
             for i in range(length-2):
-                w_roll = ww.rolling(i+2,min_periods=2).mean()
-                # now goes with # data points - should be fixed seconds
-                indexmax = w_roll.idxmax(axis=1)
-                try:
-                    t_0 = tt.ix[indexmax]
-                    t_1 = tt.ix[indexmax-i-2]
-                    deltat = 1.0e-3*(t_0-t_1)
-                    wmax = w_roll.ix[indexmax]
-                    dt.append(deltat)
-                    cpw.append(wmax)
-                except KeyError:
-                    pass
+                w_roll = ww.rolling(i+2).mean().dropna()
+                if len(w_roll):
+                    # now goes with # data points - should be fixed seconds
+                    indexmax = w_roll.idxmax(axis=1)
+                    try:
+                        t_0 = tt.ix[indexmax]
+                        t_1 = tt.ix[indexmax-i-2]
+                        deltat = 1.0e-3*(t_0-t_1)
+                        wmax = w_roll.ix[indexmax]
+                        if not np.isnan(deltat) and not np.isnan(wmax):
+                            dt.append(deltat)
+                            cpw.append(wmax)
+                    except KeyError:
+                        pass
 
+            
             dt = pd.Series(dt)
             cpw = pd.Series(cpw)
+
             cpvalues = griddata(dt.values,
                                 cpw.values,
-                                logarr,method='linear',fill_value=0)
+                                logarr,method='linear',
+                                rescale=True)
 
             for cpv in cpvalues:
                 cpvalue.append(cpv)
             for d in logarr:
                 delta.append(d)
             
-    print avgpower
-    dt = pd.Series(delta,name='Delta')
+    delta = pd.Series(delta,name='Delta')
     cpvalue = pd.Series(cpvalue,name='CP')
 
-            
     powerdf = pd.DataFrame({
         'Delta':delta,
         'CP':cpvalue,
         })
 
+    
     powerdf = powerdf[powerdf['CP']>0]
     powerdf.dropna(axis=0,inplace=True)
     powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)