diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 46fe140e..951b287c 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -1037,7 +1037,7 @@ def fetchcp_new(rower,workouts):
     if len(data)>1:
         df = pd.concat(data,axis=0)
 
-    df = df.groupby(['delta']).max()
+    df = df = df[df['cp'] == df.groupby(['delta'])['cp'].transform('max')]
 
 
     df = df.sort_values(['delta']).reset_index()
diff --git a/rowers/urls.py b/rowers/urls.py
index fe50d571..236f8e4d 100644
--- a/rowers/urls.py
+++ b/rowers/urls.py
@@ -428,6 +428,8 @@ urlpatterns = [
     re_path(r'^workout/(?P<id>\b[0-9A-Fa-f]+\b)/stats/$',views.workout_stats_view,name='workout_stats_view'),
     re_path(r'^workout/(?P<id>\b[0-9A-Fa-f]+\b)/data/$',views.workout_data_view,
         name='workout_data_view'),
+    re_path(r'^workout/(?P<id>\b[0-9A-Fa-f]+\b)/zeropower/$',views.remove_power_view,
+            name='remove_power_view'),
     re_path(r'^workout/(?P<id>\b[0-9A-Fa-f]+\b)/otwsetpower/$',views.workout_otwsetpower_view,name='workout_otwsetpower_view'),
     re_path(r'^workout/(?P<id>\b[0-9A-Fa-f]+\b)/interactiveotwplot/$',views.workout_otwpowerplot_view,name='workout_otwpowerplot_view'),
     re_path(r'^workout/(?P<id>\b[0-9A-Fa-f]+\b)/wind/$',views.workout_wind_view,name='workout_wind_view'),
diff --git a/rowers/views/workoutviews.py b/rowers/views/workoutviews.py
index a7625a57..31d75fdb 100644
--- a/rowers/views/workoutviews.py
+++ b/rowers/views/workoutviews.py
@@ -1274,6 +1274,103 @@ def workouts_join_select(request,
                    'teams':get_my_teams(request.user),
                    })
 
+@login_required()
+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))
+    except OSError:
+        pass
+
+    f = workout.csvfilename
+    powerperc = 100*np.array([r.pw_ut2,
+                              r.pw_ut1,
+                              r.pw_at,
+                              r.pw_tr,r.pw_an])/r.ftp
+    rr = rrower(hrmax=r.max, hrut2=r.ut2,
+                hrut1=r.ut1, hrat=r.at,
+                hrtr=r.tr, hran=r.an, ftp=r.ftp,
+                powerperc=powerperc, powerzones=r.powerzones)
+    row = rdata(f,rower=rr)
+    row.df[' Power (watts)'] = 0
+    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,
+            )
+
+            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 = {
+                      'id':id,
+                      }
+                  )
+    return HttpResponseRedirect(url)
+
 # Team comparison
 @user_passes_test(ispromember,login_url='/rowers/paidplans/',
                   message="This functionality requires a Pro plan or higher. If you are already a Pro user, please log in to access this functionality. If you are already a Pro user, please log in to access this functionality",