diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 2c5b46fa..12744d7f 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -1,6 +1,6 @@
 # All the data preparation, data cleaning and data mangling should
 # be defined here
-from rowers.models import Workout, User, Rower
+from rowers.models import Workout, User, Rower,StrokeData
 from rowingdata import rowingdata as rrdata
 
 from rowers.tasks import handle_sendemail_unrecognized
@@ -67,6 +67,98 @@ from scipy.signal import savgol_filter
 
 import datetime
 
+def clean_df_stats(datadf,workstrokesonly=True):
+    # clean data remove zeros and negative values
+    datadf=datadf.clip(lower=0)
+    datadf.replace(to_replace=0,value=np.nan,inplace=True)
+
+    # clean data for useful ranges per column
+    mask = datadf['hr'] < 30 
+    datadf.loc[mask,'hr'] = np.nan
+
+    mask = datadf['rhythm'] < 5
+    datadf.loc[mask,'rhythm'] = np.nan
+
+    mask = datadf['rhythm'] > 70
+    datadf.loc[mask,'rhythm'] = np.nan
+
+    mask = datadf['power'] < 20
+    datadf.loc[mask,'power'] = np.nan
+
+    mask = datadf['drivelength'] < 0.5
+    datadf.loc[mask,'drivelength'] = np.nan
+
+    mask = datadf['forceratio'] < 0.2
+    datadf.loc[mask,'forceratio'] = np.nan
+    
+    mask = datadf['forceratio'] > 1.0
+    datadf.loc[mask,'forceratio'] = np.nan
+
+    mask = datadf['spm'] < 10
+    datadf.loc[mask,'spm'] = np.nan
+
+    
+    mask = datadf['spm'] > 60
+    datadf.loc[mask,'spm'] = np.nan
+
+    mask = datadf['drivespeed'] < 0.5
+    datadf.loc[mask,'drivespeed'] = np.nan
+    
+    mask = datadf['drivespeed'] > 4
+    datadf.loc[mask,'drivespeed'] = np.nan
+
+    mask = datadf['driveenergy'] > 2000
+    datadf.loc[mask,'driveenergy'] = np.nan
+    
+    mask = datadf['driveenergy'] < 100
+    datadf.loc[mask,'driveenergy'] = np.nan
+
+    workoutstateswork = [1,4,5,8,9,6,7]
+    workoutstatesrest = [3]
+    workoutstatetransition = [0,2,10,11,12,13]
+
+    if workstrokesonly=='True' or workstrokesonly==True:
+        try:
+            datadf = datadf[~datadf['workoutstate'].isin(workoutstatesrest)]
+        except:
+            pass
+
+    return datadf
+
+def getstatsfields():
+    # Get field names and remove those that are not useful in stats
+    fields = StrokeData._meta.get_fields()
+
+    fielddict = {field.name:field.verbose_name for field in fields}
+        
+    fielddict.pop('workoutid')
+    fielddict.pop('ergpace')
+    fielddict.pop('hr_an')
+    fielddict.pop('hr_tr')
+    fielddict.pop('hr_at')
+    fielddict.pop('hr_ut2')
+    fielddict.pop('hr_ut1')
+    fielddict.pop('time')
+    fielddict.pop('distance')
+    fielddict.pop('nowindpace')
+    fielddict.pop('fnowindpace')
+    fielddict.pop('fergpace')
+    fielddict.pop('equivergpower')
+#    fielddict.pop('workoutstate')
+    fielddict.pop('fpace')
+    fielddict.pop('pace')
+    fielddict.pop('id')
+    fielddict.pop('ftime')
+    fielddict.pop('x_right')
+    fielddict.pop('hr_max')
+    fielddict.pop('hr_bottom')
+    fielddict.pop('cumdist')
+
+    fieldlist = [field for field,value in fielddict.iteritems()]
+
+    return fieldlist,fielddict
+
+
 # A string representation for time deltas
 def niceformat(values):
     out = []
diff --git a/rowers/models.py b/rowers/models.py
index c801799f..8c75db10 100644
--- a/rowers/models.py
+++ b/rowers/models.py
@@ -83,9 +83,14 @@ class PowerZonesField(models.TextField):
 
 # For future Team functionality
 class Team(models.Model):
+    choices = (
+        ('private','private'),
+        ('open','open'),
+        )
     name = models.CharField(max_length=150,unique=True)
     notes = models.CharField(blank=True,max_length=200)
     manager = models.ForeignKey(User)
+    private = models.CharField(max_length=30,choices=choices,default='open')
 
     def __unicode__(self):
         return self.name
diff --git a/rowers/views.py b/rowers/views.py
index 2a49720c..402c6828 100644
--- a/rowers/views.py
+++ b/rowers/views.py
@@ -2738,97 +2738,17 @@ def cumstats(request,theuser=0,
 
     ids = [int(workout.id) for workout in allergworkouts]
 
-    # Get field names and remove those that are not useful in stats
-    fields = StrokeData._meta.get_fields()
-
-    fielddict = {field.name:field.verbose_name for field in fields}
-
-        
-    fielddict.pop('workoutid')
-    fielddict.pop('ergpace')
-    fielddict.pop('hr_an')
-    fielddict.pop('hr_tr')
-    fielddict.pop('hr_at')
-    fielddict.pop('hr_ut2')
-    fielddict.pop('hr_ut1')
-    fielddict.pop('time')
-    fielddict.pop('distance')
-    fielddict.pop('nowindpace')
-    fielddict.pop('fnowindpace')
-    fielddict.pop('fergpace')
-    fielddict.pop('equivergpower')
-#    fielddict.pop('workoutstate')
-    fielddict.pop('fpace')
-    fielddict.pop('pace')
-    fielddict.pop('id')
-    fielddict.pop('ftime')
-    fielddict.pop('x_right')
-    fielddict.pop('hr_max')
-    fielddict.pop('hr_bottom')
-    fielddict.pop('cumdist')
-
-    fieldlist = [field for field,value in fielddict.iteritems()]
+    fieldlist,fielddict = dataprep.getstatsfields()
     
     # prepare data frame
     datadf = dataprep.read_cols_df_sql(ids,fieldlist)
 
-    # clean data remove zeros and negative values
-    datadf=datadf.clip(lower=0)
-    datadf.replace(to_replace=0,value=np.nan,inplace=True)
-
-    # clean data for useful ranges per column
-    mask = datadf['hr'] < 30 
-    datadf.loc[mask,'hr'] = np.nan
-
-    mask = datadf['rhythm'] < 5
-    datadf.loc[mask,'rhythm'] = np.nan
-
-    mask = datadf['rhythm'] > 70
-    datadf.loc[mask,'rhythm'] = np.nan
-
-    mask = datadf['power'] < 20
-    datadf.loc[mask,'power'] = np.nan
-
-    mask = datadf['drivelength'] < 0.5
-    datadf.loc[mask,'drivelength'] = np.nan
-
-    mask = datadf['forceratio'] < 0.2
-    datadf.loc[mask,'forceratio'] = np.nan
+    datadf = dataprep.clean_df_stats(datadf,workstrokesonly=workstrokesonly)
     
-    mask = datadf['forceratio'] > 1.0
-    datadf.loc[mask,'forceratio'] = np.nan
-
-    mask = datadf['spm'] < 10
-    datadf.loc[mask,'spm'] = np.nan
-
-    
-    mask = datadf['spm'] > 60
-    datadf.loc[mask,'spm'] = np.nan
-
-    mask = datadf['drivespeed'] < 0.5
-    datadf.loc[mask,'drivespeed'] = np.nan
-    
-    mask = datadf['drivespeed'] > 4
-    datadf.loc[mask,'drivespeed'] = np.nan
-
-    mask = datadf['driveenergy'] > 2000
-    datadf.loc[mask,'driveenergy'] = np.nan
-    
-    mask = datadf['driveenergy'] < 100
-    datadf.loc[mask,'driveenergy'] = np.nan
     
     if datadf.empty:
         return HttpResponse("No data found")
 
-    workoutstateswork = [1,4,5,8,9,6,7]
-    workoutstatesrest = [3]
-    workoutstatetransition = [0,2,10,11,12,13]
-
-    if workstrokesonly=='True' or workstrokesonly==True:
-        try:
-            datadf = datadf[~datadf['workoutstate'].isin(workoutstatesrest)]
-        except:
-            pass
 
     # Create stats
     stats = {}
@@ -2908,51 +2828,8 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
         url = reverse(workouts_view,args=[str(message)])
         return HttpResponseRedirect(url)
 
+    datadf = dataprep.clean_df_stats(datadf,workstrokesonly=workstrokesonly)
 
-    # clean data remove zeros and negative values
-    datadf=datadf.clip(lower=0)
-    datadf.replace(to_replace=0,value=np.nan,inplace=True)
-
-    # clean data for useful ranges per column
-    mask = datadf['hr'] < 30 
-    datadf.loc[mask,'hr'] = np.nan
-
-    mask = datadf['rhythm'] < 5
-    datadf.loc[mask,'rhythm'] = np.nan
-
-    mask = datadf['rhythm'] > 70
-    datadf.loc[mask,'rhythm'] = np.nan
-
-    mask = datadf['power'] < 20
-    datadf.loc[mask,'power'] = np.nan
-
-    mask = datadf['drivelength'] < 0.5
-    datadf.loc[mask,'drivelength'] = np.nan
-
-    mask = datadf['forceratio'] < 0.2
-    datadf.loc[mask,'forceratio'] = np.nan
-    
-    mask = datadf['forceratio'] > 1.0
-    datadf.loc[mask,'forceratio'] = np.nan
-
-    mask = datadf['spm'] < 10
-    datadf.loc[mask,'spm'] = np.nan
-
-    
-    mask = datadf['spm'] > 60
-    datadf.loc[mask,'spm'] = np.nan
-
-    mask = datadf['drivespeed'] < 0.5
-    datadf.loc[mask,'drivespeed'] = np.nan
-    
-    mask = datadf['drivespeed'] > 4
-    datadf.loc[mask,'drivespeed'] = np.nan
-
-    mask = datadf['driveenergy'] > 2000
-    datadf.loc[mask,'driveenergy'] = np.nan
-    
-    mask = datadf['driveenergy'] < 100
-    datadf.loc[mask,'driveenergy'] = np.nan
         
     if datadf.empty:
         return HttpResponse("CSV data file not found")
@@ -2961,44 +2838,12 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
     workoutstatesrest = [3]
     workoutstatetransition = [0,2,10,11,12,13]
 
-    if workstrokesonly=='True' or workstrokesonly==True:
-        try:
-            datadf = datadf[~datadf['workoutstate'].isin(workoutstatesrest)]
-        except:
-            pass
-        workstrokesonly = True
 
     # Create stats
     stats = {}
 
-    # Get field names and remove those that are not useful in stats
-    fields = StrokeData._meta.get_fields()
-
-    fielddict = {field.name:field.verbose_name for field in fields}
-
-        
-    fielddict.pop('workoutid')
-    fielddict.pop('ergpace')
-    fielddict.pop('hr_an')
-    fielddict.pop('hr_tr')
-    fielddict.pop('hr_at')
-    fielddict.pop('hr_ut2')
-    fielddict.pop('hr_ut1')
-    fielddict.pop('time')
-    fielddict.pop('distance')
-    fielddict.pop('nowindpace')
-    fielddict.pop('fnowindpace')
-    fielddict.pop('fergpace')
-    fielddict.pop('equivergpower')
-    fielddict.pop('workoutstate')
-    fielddict.pop('fpace')
-    fielddict.pop('pace')
-    fielddict.pop('id')
-    fielddict.pop('ftime')
-    fielddict.pop('x_right')
-    fielddict.pop('hr_max')
-    fielddict.pop('hr_bottom')
-    fielddict.pop('cumdist')
+    fieldlist,fielddict = dataprep.getstatsfields()
+    
 
     for field,verbosename in fielddict.iteritems():
         thedict = {