diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 543b3872..dcb39469 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -1037,8 +1037,6 @@ def fetchcp_new(rower,workouts):
     if len(data)>1:
         df = pd.concat(data,axis=0)
 
-    print(df)
-
     df = df = df[df['cp'] == df.groupby(['delta'])['cp'].transform('max')]
 
 
@@ -1054,6 +1052,8 @@ def setcp(workout,background=False):
         job = myqueue(queuelow,handle_setcp,strokesdf,filename,workout.id)
         return job.id
 
+    
+
     if not strokesdf.empty:
         totaltime = strokesdf['time'].max()
         try:
diff --git a/rowers/views/analysisviews.py b/rowers/views/analysisviews.py
index 2e49da27..b96d702e 100644
--- a/rowers/views/analysisviews.py
+++ b/rowers/views/analysisviews.py
@@ -522,11 +522,13 @@ def cpdata(workouts, options):
 
     ids = [w.id for w in workouts]
     delta, cpvalue, avgpower,workoutnames = dataprep.fetchcp_new(r,workouts)
+
     powerdf = pd.DataFrame({
         'Delta':delta,
         'CP':cpvalue,
         'workout':workoutnames,
     })
+    
 
 
     if powerdf.empty:
diff --git a/rowers/views/workoutviews.py b/rowers/views/workoutviews.py
index 31d75fdb..20be4b90 100644
--- a/rowers/views/workoutviews.py
+++ b/rowers/views/workoutviews.py
@@ -1278,8 +1278,9 @@ def workouts_join_select(request,
 def remove_power_view(request,id=0):
     r = getrower(request.user)
     workout = get_workout_by_opaqueid(request,id)
+
     try:
-        os.remove('media/cpdata{id}.parquet.gz'.format(id=workout.id))
+        os.remove('media/cpdata_{id}.parquet.gz'.format(id=workout.id))
     except OSError:
         pass
 
@@ -1294,75 +1295,12 @@ def remove_power_view(request,id=0):
                 powerperc=powerperc, powerzones=r.powerzones)
     row = rdata(f,rower=rr)
     row.df[' Power (watts)'] = 0
+    row.write_csv(f)
     res = dataprep.dataprep(row.df, id=workout.id)
     cpdf,delta,cpvalues = dataprep.setcp(workout)
-    if not cpdf.empty:
-        if workouttype in mytypes.otwtypes:
-            res, btvalues, res2 = utils.isbreakthrough(
-                delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio)
-            cprange = r.cprange
-            firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
 
-            workouts = Workout.objects.filter(
-                date__gte=firstdate,
-                workouttype__in=otwtypes,
-                user = w.user,
-            )
+    dataprep.initiate_cp(r)
 
-            dd,cpcp,avgpower,workoutnames = dataprep.fetchcp_new(r,workouts)
-
-            powerdf = pd.DataFrame({
-                'Delta':dd,
-                'CP':cpcp,
-            })
-
-
-            powerdf = powerdf[powerdf['CP']>0]
-            powerdf.dropna(axis=0,inplace=True)
-            powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
-            powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
-
-            res2 = datautils.cpfit(powerdf)
-            if len(powerdf) != 0:
-                p1 = res2[0]
-                r.p0 = p1[0]
-                r.p1 = p1[1]
-                r.p2 = p1[2]
-                r.p3 = p1[3]
-                r.cpratio = res2[3]
-                r.save()
-
-        elif workouttype in mytypes.otetypes:
-            res, btvalues, res2 = utils.isbreakthrough(
-                delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio)
-            cprange = r.cprange
-            firstdate = datetime.date.today()-datetime.timedelta(days=cprange)
-            workouts = Workout.objects.filter(
-                date__gte=firstdate,
-                workouttype__in=otetypes,
-                user = w.user,
-            )
-            dd,cpcp,avgpower,workoutnames = fetchcp_new(r,workouts)
-            powerdf = pd.DataFrame({
-                'Delta':dd,
-                'CP':cpcp,
-            })
-
-
-            powerdf = powerdf[powerdf['CP']>0]
-            powerdf.dropna(axis=0,inplace=True)
-            powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
-            powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
-            res2 = datautils.cpfit(powerdf)
-            if len(powerdf) != 0:
-                res = datautils.cpfit(powerdf)
-                p1 = res2[0]
-                r.ep0 = p1[0]
-                r.ep1 = p1[1]
-                r.ep2 = p1[2]
-                r.ep3 = p1[3]
-                r.ecpratio = res2[3]
-                r.save()
 
     url = reverse(r.defaultlandingpage,
                   kwargs = {