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
     

rowers/dataprepnodjango.py

@@ -693,6 +693,7 @@ def testdata(time,distance,pace,spm):
     return t1 and t2 and t3 and t4
 
 
+
 def getsmallrowdata_db(columns,ids=[],debug=False):
 
     data = read_cols_df_sql(ids,columns,debug=debug)

rowers/models.py

@@ -1496,6 +1496,12 @@ class Workout(models.Model):
                                 max_length=100)
     distance = models.IntegerField(default=0,blank=True)
     duration = models.TimeField(default=1,blank=True)
+    trimp = models.IntegerField(default=-1,blank=True)
+    rscore = models.IntegerField(default=-1,blank=True)
+    hrtss = models.IntegerField(default=-1,blank=True)
+    normp = models.IntegerField(default=-1,blank=True)
+    normv = models.FloatField(default=-1,blank=True)
+    normw = models.FloatField(default=-1,blank=True)
     weightcategory = models.CharField(
         default="hwt",
         max_length=10,

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: