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

rowers/tasks.py

@@ -39,7 +39,7 @@ from django_rq import job
 from django.utils import timezone
 from django.utils.html import strip_tags
 
-from utils import deserialize_list,ewmovingaverage
+from utils import deserialize_list,ewmovingaverage,wavg
 
 from rowers.dataprepnodjango import (
     update_strokedata, new_workout_from_file,
@@ -545,6 +545,101 @@ We have updated Power and Work per Stroke data according to the instructions by
         res = email.send()
     return 1
 
+@app.task
+def handle_calctrimp(id,
+                     csvfilename,
+                     ftp,
+                     sex,
+                     hrftp,
+                     hrmax,
+                     hrmin,
+                     debug=False, **kwargs):
+    if debug:
+        engine = create_engine(database_url_debug, echo=False)
+    else:
+        engine = create_engine(database_url, echo=False)
+    
+    try:
+        rowdata = rdata(csvfilename)
+    except IOError:
+        try:
+            rowdata = rdata(csvfilename + '.csv')
+        except IOError:
+            try:
+                rowdata = rdata(csvfilename + '.gz')
+            except IOError:
+                return 0
+
+    df = rowdata.df
+
+    df['deltat'] = df[' ElapsedTime (sec)'].diff().abs()
+    duration = df['TimeStamp (sec)'].max()-df['TimeStamp (sec)'].min()
+
+    df[' Power (watts)'] = df[' Power (watts)'].abs()
+    
+    df['pwr4'] = df[' Power (watts)']**(4.0)
+    pwr4mean = wavg(df,'pwr4','deltat')
+    pwrmean = wavg(df,' Power (watts)','deltat')
+    if pwr4mean > 0:
+        normp = (pwr4mean)**(0.25)
+    else:
+        normp = pwrmean
+        
+    intensityfactor = pwrmean/float(ftp)
+    intensityfactor = normp/float(ftp)
+    tss = 100.*((duration*normp*intensityfactor)/(3600.*ftp))
+
+    if sex == 'male':
+        f = 1.92
+    else:
+        f = 1.67
+
+    dt = df['TimeStamp (sec)'].diff()/6.e4
+
+    hrr = (df[' HRCur (bpm)']-hrmin)/(hrmax-hrmin)
+    hrrftp = (hrftp-hrmin)/float(hrmax-hrmin)
+    trimp1hr = 60*hrrftp*0.64*np.exp(f*hrrftp)
+
+    trimpdata = dt*hrr*0.64*np.exp(f*hrr)
+    trimp = trimpdata.sum()
+
+    hrtss = 100*trimp/trimp1hr
+
+    pp = 8.0
+    
+    df['v4'] = df[' AverageBoatSpeed (m/s)']**(pp)
+    v4mean = wavg(df,'v4','deltat')
+    normv = v4mean**(1./pp)
+
+    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
+    
+    query = 'UPDATE rowers_workout SET rscore = {tss}, normp = {normp}, trimp={trimp}, hrtss={hrtss}, normv={normv}, normw={normw} WHERE id={id}'.format(
+        tss = int(tss),
+        normp = int(normp),
+        trimp = int(trimp),
+        hrtss = int(hrtss),
+        normv=normv,
+        normw=normw,
+        id = id,
+        )
+
+    with engine.connect() as conn, conn.begin():
+        result = conn.execute(query)
+    conn.close()
+    engine.dispose()
+
+    return 1
+                
+
 @app.task
 def handle_updatedps(useremail, workoutids, debug=False,**kwargs):
     for wid, f1 in workoutids: