small speed gain

rowers/dataprep.py

@@ -1111,6 +1111,8 @@ def calculate_goldmedalstandard(rower,workout,recurrance=True):
 
     if df.empty and recurrance:
         df, delta, cpvalues = setcp(workout,recurrance=False)
+        if df.empty:
+            return 0,0
 
     age = calculate_age(rower.birthdate,today=workout.date)
 
@@ -1209,9 +1211,11 @@ def setcp(workout,background=False,recurrance=True):
     filename = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
 
     strokesdf = getsmallrowdata_db(['power','workoutid','time'],ids = [workout.id])
+    if strokesdf['power'].std()==0:
+        return pd.DataFrame(),pd.Series(),pd.Series()
     if background:
         job = myqueue(queuelow,handle_setcp,strokesdf,filename,workout.id)
-        return job.id
+        return pd.DataFrame({'delta':[],'cp':[]}),pd.Series(),pd.Series()
 
 
     if not strokesdf.empty:
@@ -1492,7 +1496,6 @@ def checkduplicates(r,workoutdate,workoutstartdatetime,workoutenddatetime):
         t = ww.duration
         delta = datetime.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second)
         enddatetime = ww.startdatetime+delta
-        print(enddatetime,workoutstartdatetime)
         if enddatetime > workoutstartdatetime:
             ws2.append(ww)
 

rowers/interactiveplots.py

@@ -198,12 +198,10 @@ def build_goldmedalstandards(workouts,kfitness):
     #    fitnesses.append(np.nan)
 
     for w in workouts:
-        #ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=90),
-        #                                     date__lte=w.date)]
-
-        ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=10),
+        ids = [w.id for w in workouts.filter(date__gte=w.date-datetime.timedelta(days=90),
                                              date__lte=w.date)]
 
+
         powerdf = df[df['workout'].isin(ids)]
         indexmax = powerdf['goldmedalstandard'].idxmax()
         theid = powerdf.loc[indexmax,'workout']
@@ -1880,6 +1878,7 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
         markerduration.append(np.nan)
         duration.append(np.nan)
 
+
     df = pd.DataFrame({
         'markerscore':markerscore,
         'markerduration':markerduration,