trimp, tss, hrtss, normv, normw pre-calculated

rowers/dataprep.py

@@ -49,7 +49,8 @@ import itertools
 import math
 from tasks import (
     handle_sendemail_unrecognized, handle_sendemail_breakthrough,
-    handle_sendemail_hard, handle_updatecp,handle_updateergcp
+    handle_sendemail_hard, handle_updatecp,handle_updateergcp,
+    handle_calctrimp,
 )
 
 from django.conf import settings
@@ -1017,6 +1018,8 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
     res = dataprep(row.df, id=w.id, bands=True,
                    barchart=True, otwpower=True, empower=True, inboard=inboard)
 
+    rscore,normp = workout_rscore(w)
+    
     isbreakthrough = False
     ishard = False
     if workouttype == 'water':
@@ -2255,82 +2258,93 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
     return data
 
 
-def workout_trimp(workout):
-    r = workout.user
+def workout_trimp(w):
+    r = w.user
+
+    if w.trimp > 0:
+        return w.trimp,w.hrtss
+    
+    r = w.user
+    ftp = float(r.ftp)
+    if w.workouttype in otwtypes:
+        ftp = ftp*(100.-r.otwslack)/100.
+
     if r.hrftp == 0:
         hrftp = (r.an+r.tr)/2.
         r.hrftp = int(hrftp)
         r.save()
-        
-    df,row = getrowdata_db(id=workout.id)
-    df = clean_df_stats(df,workstrokesonly=False)
-    if df.empty:
-        df,row = getrowdata_db(id=workout.id)
-        df = clean_df_stats(df,workstrokesonly=False)
-    trimp,hrtss = calc_trimp(df,r.sex,r.max,r.rest,r.hrftp)
-    if not np.isnan(trimp):
-        trimp = int(trimp)
-    else:
-        trimp = 0
-    if not np.isnan(hrtss):
-        hrtss = int(hrtss)
-    else:
-        hrtss = 0
 
-    return trimp,hrtss
+        
+
+    job = myqueue(
+        queuehigh,
+        handle_calctrimp,
+        w.id,
+        w.csvfilename,
+        ftp,
+        r.sex,
+        r.hrftp,
+        r.max,
+        r.rest)
+        
+    return 0,0
 
 def workout_rscore(w):
+    if w.rscore > 0:
+        return w.rscore,w.normp
+    
     r = w.user
-    df,row = getrowdata_db(id=w.id)
-    df = clean_df_stats(df,workstrokesonly=False)
-    if df.empty:
-        df,row = getrowdata_db(id=w.id)
-        df = clean_df_stats(df,workstrokesonly=False)
+    ftp = float(r.ftp)
+    if w.workouttype in otwtypes:
+        ftp = ftp*(100.-r.otwslack)/100.
 
-    df['deltat'] = df['time'].diff()
-    duration = df['time'].max()-df['time'].min()
-    duration /= 1.0e3
-    df['pwr4'] = df['power']**(4.0)
-    pwr4mean = wavg(df,'pwr4','deltat')
-    pwrmean = wavg(df,'power','deltat')
-    normp = (pwr4mean)**(0.25)
-    if not np.isnan(normp):
-        ftp = float(r.ftp)
-        if w.workouttype in otwtypes:
-            ftp = ftp*(100.-r.otwslack)/100.
+    if r.hrftp == 0:
+        hrftp = (r.an+r.tr)/2.
+        r.hrftp = int(hrftp)
+        r.save()
 
-        intensityfactor = pwrmean/float(ftp)
-        intensityfactor = normp/float(ftp)
-        tss = 100.*((duration*normp*intensityfactor)/(3600.*ftp))
-    else:
-        tss = 0
+        
 
-    return tss,normp
+    job = myqueue(
+        queuehigh,
+        handle_calctrimp,
+        w.id,
+        w.csvfilename,
+        ftp,
+        r.sex,
+        r.hrftp,
+        r.max,
+        r.rest)
+        
+    return 0,0
 
 def workout_normv(w,pp=4.0):
-    df,row = getrowdata_db(id=w.id)
-    df = clean_df_stats(df,workstrokesonly=False)
-    if df.empty:
-        df,row = getrowdata_db(id=w.id)
-        df = clean_df_stats(df,workstrokesonly=False)
+    if w.normv > 0:
+        return w.normv,w.normw
 
-    df['deltat'] = df['time'].diff()
-    duration = df['time'].max()-df['time'].min()
-    duration /= 1.0e3
-    df['v4'] = df['velo']**(pp)
-    v4mean = wavg(df,'v4','deltat')
-    normv = v4mean**(1./pp)
+    r = w.user
+    ftp = float(r.ftp)
+    if w.workouttype in otwtypes:
+        ftp = ftp*(100.-r.otwslack)/100.
+
+    if r.hrftp == 0:
+        hrftp = (r.an+r.tr)/2.
+        r.hrftp = int(hrftp)
+        r.save()
+
+        
+
+    job = myqueue(
+        queuehigh,
+        handle_calctrimp,
+        w.id,
+        w.csvfilename,
+        ftp,
+        r.sex,
+        r.hrftp,
+        r.max,
+        r.rest)
+        
+    return 0,0
 
-    df['w4'] = df['driveenergy']**(pp)
-    w4mean = wavg(df,'w4','deltat')
-    normw = w4mean**(1./pp)
-    
-
-    if np.isnan(normv):
-        normv =  500./120.
-
-    if np.isnan(normw):
-        normw = 0
-
-    return normv,normw