minimal working version of alert_get_stats

rowers/alerts.py

@@ -1,12 +1,13 @@
-from rowers.models import Alert, Condition, User, Rower
+from rowers.models import Alert, Condition, User, Rower, Workout
 from rowers.teams import coach_getcoachees
-
+from rowers.dataprep import getsmallrowdata_db,getrowdata_db
+import datetime
 ## BASIC operations
 
 # create alert
 def create_alert(manager, rower, measured,period=7, emailalert=True,
                  reststrokes=False, workouttype='water',
-                 name=name,**kwargs):
+                 name='',**kwargs):
 
     # check if manager is coach of rower. If not return 0
     if manager.rower != rower:
@@ -26,7 +27,7 @@ def create_alert(manager, rower, measured,period=7, emailalert=True,
                   manager=manager,
                   rower=rower,
                   measured=m,
-                  restrokes=reststrokes,
+                  reststrokes=reststrokes,
                   period=period,
                   emailalert=emailalert,
                   workouttype=workouttype
@@ -76,6 +77,75 @@ def alert_add_filters(alert,filter):
 # nperiod = 0: current period, i.e. next_run - n days to today
 # nperiod = 1: 1 period ago , i.e. next_run -2n days to next_run -n days
 def alert_get_stats(alert,nperiod=0):
-    return {}
+    # get strokes
+    workstrokesonly = not alert.reststrokes
+    startdate = (alert.next_run - datetime.timedelta(days=(nperiod+1)*alert.period-1))
+    enddate = alert.next_run - datetime.timedelta(days=(nperiod)*alert.period)
+    columns = [alert.measured.metric]
+
+    for condition in alert.filter.all():
+        columns += condition.metric
+
+    workouts = Workout.objects.filter(date__gte=startdate,date__lte=enddate,user=alert.rower,
+                                      workouttype=alert.workouttype)
+    ids = [w.id for w in workouts]
+    
+    df = getsmallrowdata_db(columns,ids=ids,doclean=True,workstrokesonly=workstrokesonly)
+    if df.empty:
+        return {
+            'workouts':len(workouts),
+            'startdate':startdate,
+            'enddate':enddate,
+            'nr_strokes':0,
+            'nr_strokes_qualifying':0,
+        }
+
+
+    # drop strokes through filter
+    for condition in alert.filter.all():
+        if condition.condition == '>':
+            mask = df[condition.metric] > condition.value1
+            df.loc[mask,alert.measured.metric] = np.nan
+        elif condition.condition == '<':
+            mask = df[condition.metric] < condition.value1
+            df.loc[mask,alert.measured.metric] = np.nan
+        elif condition.condition == 'between':
+            mask = df[condition.metric] > condition.value1
+            mask2 = df[condition.metric] < condition.value2
+            df.loc[mask & mask2,alert.measured.metric] = np.nan
+        elif condition.condition == '=':
+            mask = df[condition.metric] == condition.value1
+            df.loc[mask,alert.measured.metric] = np.nan
+
+        df.dropna(inplace=True,axis=0)
+            
+    # count strokes
+    nr_strokes = len(df)
+    
+
+    # count qualifying
+    if alert.measured.condition == '>':
+        mask = df[alert.measured.metric] > alert.measured.value1
+        df2 = df[mask].copy()
+    elif alert.measured.condition == '<':
+        mask = df[alert.measured.metric] > alert.measured.value1
+        df2 = df[mask].copy()
+    elif alert.measured.condition == 'between':
+        mask = df[alert.measured.metric] > alert.measured.value1 
+        mask2 = df[alert.measured.metric] < alert.measured.value2
+        df2 = df[mask & mask2].copy()
+    else:
+        mask = df[alert.measured.metric] == alert.measured.value1
+        df2 = df[mask].copy()
+
+    nr_strokes_qualifying = len(df2)
+        
+    return {
+        'workouts':len(workouts),
+        'startdate':startdate,
+        'enddate':enddate,
+        'nr_strokes':nr_strokes,
+        'nr_strokes_qualifying':nr_strokes_qualifying
+    }
 
 # run alert report

rowers/models.py

@@ -1023,7 +1023,7 @@ class Condition(models.Model):
     metric = models.CharField(max_length=50,choices=parchoicesy1,verbose_name='Metric')
     value1 = models.FloatField(default=0)
     value2 = models.FloatField(default=0)
-    condition = models.CharField(max_length=2,choices=conditionchoices,null=True)
+    condition = models.CharField(max_length=20,choices=conditionchoices,null=True)
 
 rowchoices = []
 for key,value in mytypes.workouttypes: