adding fitscore

rowers/dataprep.py

@@ -6,7 +6,9 @@ from __future__ import unicode_literals
 # All the data preparation, data cleaning and data mangling should
 # be defined here
 from __future__ import unicode_literals, absolute_import
-from rowers.models import Workout, Team
+from rowers.models import (
+    Workout, Team, CalcAgePerformance,C2WorldClassAgePerformance,
+    )
 
 import pytz
 import collections
@@ -23,7 +25,10 @@ from rowingdata import (
     get_file_type, get_empower_rigging,get_empower_firmware
     )
 
-from rowers.tasks import handle_sendemail_unrecognized,handle_setcp
+from rowers.tasks import (
+    handle_sendemail_unrecognized,handle_setcp,
+    handle_getagegrouprecords
+    )
 from rowers.tasks import handle_zip_file
 
 from pandas import DataFrame, Series
@@ -1016,6 +1021,67 @@ def fetchcperg(rower,theworkouts):
 
     return cpdf
 
+from rowers.datautils import p0
+from rowers.utils import calculate_age
+from scipy import optimize
+
+def fitscore(rower,workout):
+    cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
+    try:
+        df = pd.read_parquet(cpfile)
+    except:
+        df, delta, cpvalues = setcp(workout)
+
+    age = calculate_age(rower.birthdate,today=workout.date)
+    agerecords = CalcAgePerformance.objects.filter(
+        age=age,
+        sex=rower.sex,
+        weightcategory = rower.weightcategory
+    )
+    wcdurations = []
+    wcpower = []
+    for record in agerecords:
+        wcdurations.append(record.duration)
+        wcpower.append(record.power)
+
+    if len(agerecords)==0:
+        durations = [1,4,10,20,30,60]
+        distances = []
+        df2 = pd.DataFrame(
+            list(
+                C2WorldClassAgePerformance.objects.filter(
+                    sex=rower.sex,
+                    weightcategory=rower.weightcategory
+                ).values()
+            )
+        )
+        jsondf = df2.to_json()
+        job = myqueue(queue,handle_getagegrouprecords,
+                      jsondf,distances,durations,age,rower.sex,rower.weightcategory)
+
+    wcpower = pd.Series(wcpower)
+    wcdurations = pd.Series(wcdurations)
+
+    fitfunc = lambda pars,x: pars[0]/(1+(x/pars[2])) + pars[1]/(1+(x/pars[3]))
+    errfunc = lambda pars,x,y: fitfunc(pars,x)-y
+
+    if len(wcdurations)>4:
+        p1wc, success = optimize.leastsq(errfunc, p0[:],args=(wcdurations,wcpower))
+    else:
+        factor = fitfunc(p0,wcdurations.mean()/wcpower.mean())
+        p1wc = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
+        success = 0
+
+
+    times = df['delta']
+    powers = df['cp']
+    wcpowers = fitfunc(p1wc,times)
+    scores = 100.*powers/wcpowers
+
+    indexmax = scores.idxmax()
+
+    return scores.max(),df.loc[indexmax,'delta']
+
 def fetchcp_new(rower,workouts):
 
     data = []
@@ -3009,6 +3075,7 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
     return data
 
 
+
 def workout_trimp(w):
     r = w.user