sort of working

rowers/tasks.py

@@ -37,7 +37,7 @@ from django_rq import job
 from django.utils import timezone
 from django.utils.html import strip_tags
 
-from utils import deserialize_list
+from utils import deserialize_list,ewmovingaverage
 
 from rowers.dataprepnodjango import (
     update_strokedata, new_workout_from_file,
@@ -45,7 +45,7 @@ from rowers.dataprepnodjango import (
     update_agegroup_db,fitnessmetric_to_sql,
     add_c2_stroke_data_db,totaltime_sec_to_string,
     create_c2_stroke_data_db,update_empower,
-    database_url_debug,database_url,
+    database_url_debug,database_url,dataprep
 )
 
 
@@ -85,7 +85,7 @@ def handle_strava_import_stroke_data(stravatoken,
     # ready to fetch. Hurray
     fetchresolution = 'high'
     series_type = 'time'
-    authorizationstring = str('Bearer ' + r.stravatoken)
+    authorizationstring = str('Bearer ' + stravatoken)
     headers = {'Authorization': authorizationstring,
 	       'user-agent': 'sanderroosendaal',
 	       'Content-Type': 'application/json',
@@ -109,6 +109,7 @@ def handle_strava_import_stroke_data(stravatoken,
     url = "https://www.strava.com/api/v3/activities/"+str(stravaid)+"/streams/latlng?resolution="+fetchresolution+"&series_type="+series_type
     latlongjson = requests.get(url,headers=headers)
 
+
     try:
 	t = np.array(timejson.json()[0]['data'])
 	nr_rows = len(t)
@@ -161,19 +162,37 @@ def handle_strava_import_stroke_data(stravatoken,
     pace = 500./(1.0*velo2)
     pace[np.isinf(pace)] = 0.0
 
-    df = pd.DataFrame({'t':10*t,
-		       'd':10*d,
-		       'p':10*pace,
-		       'spm':spm,
-		       'hr':hr,
-		       'lat':lat,
-		       'lon':lon,
-		       'strokelength':strokelength,
+    unixtime = starttimeunix+10*t
+
+    nr_strokes = len(t)
+
+    df = pd.DataFrame({'TimeStamp (sec)':unixtime,
+                       ' ElapsedTime (sec)':10*t,
+		       ' Horizontal (meters)':10*d,
+		       ' Stroke500mPace (sec/500m)':10*pace,
+		       ' Cadence (stokes/min)':spm,
+		       ' HRCur (bpm)':hr,
+		       ' latitude':lat,
+		       ' longitude':lon,
+		       ' StrokeDistance (meters)':strokelength,
+                       'cum_dist':10*d,
+	               ' DragFactor':np.zeros(nr_strokes),
+	               ' DriveLength (meters)':np.zeros(nr_strokes),
+	               ' StrokeDistance (meters)':np.zeros(nr_strokes),
+	               ' DriveTime (ms)':np.zeros(nr_strokes),
+	               ' StrokeRecoveryTime (ms)':np.zeros(nr_strokes),
+	               ' AverageDriveForce (lbs)':np.zeros(nr_strokes),
+	               ' PeakDriveForce (lbs)':np.zeros(nr_strokes),
+	               ' lapIdx':np.zeros(nr_strokes),
+                       ' Power (watts)':0*d,
     })
 
+    res = df.to_csv(csvfilename+'.gz',index_label='index',compression='gzip')
+
+    data = dataprep(df,id=workoutid,bands=False,debug=debug)
 	# startdatetime = datetime.datetime.strptime(startdatetime,"%Y-%m-%d-%H:%M:%S")
 
-	return [workoutsummary,df]
+    return 1
     
 
 @app.task