using async job to calculate predicted age records and store in db

rowers/metrics.py

@@ -3,6 +3,7 @@ import numpy as np
 from models import C2WorldClassAgePerformance
 import pandas as pd
 from scipy import optimize
+from django.utils import timezone
 
 rowingmetrics = (
     ('time',{
@@ -321,28 +322,36 @@ def calc_trimp(df,sex,hrmax,hrmin):
     return trimp
 
 def getagegrouprecord(age,sex='male',weightcategory='hwt',
-                      distance=2000,duration=None):
-    if not duration:
-        df = pd.DataFrame(
-            list(
-                C2WorldClassAgePerformance.objects.filter(
-                    distance=distance,
-                    sex=sex,
-                    weightcategory=weightcategory
-                ).values()
-            )
-        )
+                      distance=2000,duration=None,indf=pd.DataFrame()):
+
+    if not indf.empty:
+        if not duration:
+            df = indf[indf['distance'] == distance]
+        else:
+            duration = 60*int(duration)
+            df = indf[indf['duration'] == duration]
     else:
-        duration=60*int(duration)
-        df = pd.DataFrame(
-            list(
-                C2WorldClassAgePerformance.objects.filter(
-                    duration=duration,
-                    sex=sex,
-                    weightcategory=weightcategory
-                ).values()
+        if not duration:
+            df = pd.DataFrame(
+                list(
+                    C2WorldClassAgePerformance.objects.filter(
+                        distance=distance,
+                        sex=sex,
+                        weightcategory=weightcategory
+                    ).values()
+                )
+            )
+        else:
+            duration=60*int(duration)
+            df = pd.DataFrame(
+                list(
+                    C2WorldClassAgePerformance.objects.filter(
+                        duration=duration,
+                        sex=sex,
+                        weightcategory=weightcategory
+                    ).values()
+                )
             )
-        )
 
     if not df.empty:
         ages = df['age']