Merge branch 'feature/newranking' into develop

rowers/dataprep.py

@@ -1102,6 +1102,62 @@ def workout_goldmedalstandard(workout):
     else:
         return 0,0
 
+def check_marker(workout):
+    r = workout.user
+    gmstandard,gmseconds = workout_goldmedalstandard(workout)
+    if gmseconds<60:
+        return None
+
+    dd = arrow.get(workout.date).datetime-datetime.timedelta(days=r.kfit)
+    ws = Workout.objects.filter(date__gte=dd,
+                                date__lte=workout.date,
+                                user=r,duplicate=False,
+                                workouttype__in=mytypes.rowtypes,
+                                ).order_by("date")
+    ids = []
+    gms = []
+    for w in ws:
+        gmstandard,gmseconds = workout_goldmedalstandard(w)
+        if gmseconds>60:
+            ids.append(w.id)
+            gms.append(gmstandard)
+
+    df = pd.DataFrame({
+        'id':ids,
+        'gms':gms,
+    })
+
+    if df.empty:
+        workout.ranking = True
+        workout.save()
+        return workout
+
+    indexmax = df['gms'].idxmax()
+    theid = df.loc[indexmax,'id']
+
+    wmax = Workout.objects.get(id=theid)
+    gms_max = wmax.goldmedalstandard
+
+    # check if equal, bigger, or smaller than previous
+    if not wmax.rankingpiece:
+        rankingworkouts = ws.filter(rankingpiece=True)
+        if len(rankingworkouts) == 0:
+            wmax.rankingpiece = True
+            wmax.save()
+            return wmax
+
+        lastranking = rankingworkouts[len(rankingworkouts)-1]
+        if lastranking.goldmedalstandard+0.2 < wmax.goldmedalstandard:
+            wmax.rankingpiece = True
+            wmax.save()
+            return wmax
+    else:
+        return wmax
+
+    return None
+
+
+
 def calculate_goldmedalstandard(rower,workout,recurrance=True):
     cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
     try:
@@ -1470,6 +1526,9 @@ def checkbreakthrough(w, r):
 
     # submit email task to send email about breakthrough workout
     if isbreakthrough:
+        if not w.duplicate:
+            w.rankingpiece = True
+            w.save()
         if r.getemailnotifications and not r.emailbounced:
             job = myqueue(queuehigh,handle_sendemail_breakthrough,
                           w.id,
@@ -1480,6 +1539,9 @@ def checkbreakthrough(w, r):
 
     # submit email task to send email about breakthrough workout
     if ishard:
+        if not w.duplicate:
+            w.rankingpiece = True
+            w.save()
         if r.getemailnotifications and not r.emailbounced:
             job = myqueue(queuehigh,handle_sendemail_hard,
                           w.id,
@@ -1724,6 +1786,8 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
 
     # check for duplicate start times and duration
     duplicate = checkduplicates(r,workoutdate,workoutstartdatetime,workoutenddatetime)
+    if duplicate:
+        rankingpiece = False
 
     # test title length
     if title is not None and len(title)>140:
@@ -1771,6 +1835,7 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
     job = myqueue(queuehigh,handle_calctrimp,w.id,f2,r.ftp,r.sex,r.hrftp,r.max,r.rest)
 
     isbreakthrough, ishard = checkbreakthrough(w, r)
+    marker = check_marker(w)
 
     return (w.id, message)
 

rowers/interactiveplots.py

@@ -1801,9 +1801,17 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
                                       duplicate=False)
 
     # marker workouts
-    dates,testpower,testduration,fatigues,fitnesses,impulses, outids = build_goldmedalstandards(
+    workouts = Workout.objects.filter(user=user.rower,date__gte=startdate,
-        workouts,42
+                                      date__lte=enddate,
-    )
+                                      workouttype__in=mytypes.rowtypes,
+                                      duplicate=False).order_by('date')
+
+    markerworkouts = workouts.filter(rankingpiece=True)
+    outids = [w.id for w in markerworkouts]
+    dates = [arrow.get(w.date).datetime for w in markerworkouts]
+    testpower = [w.goldmedalstandard if w.rankingpiece else np.nan for w in markerworkouts]
+
+    testduration = [w.goldmedalseconds if w.rankingpiece else 0 for w in markerworkouts]
 
     df = pd.DataFrame({
         'id':outids,
@@ -1813,8 +1821,8 @@ def goldmedalscorechart(user,startdate=None,enddate=None):
     })
     df.sort_values(['date'],inplace=True)
 
-    df['testdup'] = df['testpower'].shift(1)
+    #df['testdup'] = df['testpower'].shift(1)
-    df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
+    #df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
     #df['date'] = df.apply(lambda x: newtestpowerdate(x), axis=1)
 
 
@@ -1985,41 +1993,16 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
     workouts = Workout.objects.filter(user=user.rower,date__gte=startdate,
                                       date__lte=enddate,
                                       workouttype__in=mytypes.rowtypes,
-                                      duplicate=False)
+                                      duplicate=False).order_by('date')
-    dates,testpower,testduration,fatigues,fitnesses,impulses, outids = build_goldmedalstandards(
-        workouts,kfitness
-        )
 
-
+    markerworkouts = workouts.filter(rankingpiece=True)
-    df = pd.DataFrame({
+    outids = [w.id for w in markerworkouts]
-        'id': outids,
+    dates = [arrow.get(w.date).datetime for w in workouts]
-        'date':dates,
+    testpower = [w.goldmedalstandard if w.rankingpiece else np.nan for w in workouts]
-        'testpower':testpower,
+    impulses = [np.nan for w in workouts]
-        'testduration':testduration,
+    testduration = [w.goldmedalseconds if w.rankingpiece else 0 for w in workouts]
-        'fatigue':fatigues,
+    fitnesses = [np.nan for w in workouts]
-        'fitness':fitnesses,
+    fatigues = [np.nan for w in workouts]
-        'impulse':impulses,
-    })
-    df.sort_values(['date'],inplace=True)
-    if showtests:
-        df['testdup'] = df['testpower'].shift(1)
-        df['testpower'] = df.apply(lambda x: newtestpower(x),axis=1)
-        df['id'] = df.apply(lambda x: newtestpowerid(x),axis=1)
-
-        #try:
-        #    df['testpower'].iloc[-1] = df['testdup'].iloc[-1]
-        #except IndexError:
-        #    pass
-
-
-        dates = [d for d in df['date']]
-        testpower = df['testpower'].values.tolist()
-        fatigues = df['fatigue'].values.tolist()
-        fitnesses = df['fitness'].values.tolist()
-        testduration = df['testduration'].values.tolist()
-        impulses = df['impulse'].tolist()
-
-    outids = df['id'].unique()
 
     fatigues,fitnesses,dates,testpower,testduration,impulses = getfatigues(fatigues,
                                                      fitnesses,
@@ -2031,7 +2014,6 @@ def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
                                                      kfatigue,kfitness)
 
 
-
     df = pd.DataFrame({
         'date':dates,
         'testpower':testpower,