sort of working

rowers/dataprepnodjango.py

@@ -1030,6 +1030,8 @@ def dataprep(rowdatadf,id=0,bands=True,barchart=True,otwpower=True,
     
     p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
 
+    print p
+    
     try:
         drivespeed = drivelength/rowdatadf[' DriveTime (ms)']*1.0e3
     except KeyError:

rowers/stravastuff.py

@@ -26,6 +26,12 @@ from django.contrib.auth import authenticate, login, logout
 from django.contrib.auth.models import User
 from django.contrib.auth.decorators import login_required
 
+import django_rq
+queue = django_rq.get_queue('default')
+queuelow = django_rq.get_queue('low')
+queuehigh = django_rq.get_queue('low')
+
+
 # Project
 # from .models import Profile
 from rowingdata import rowingdata
@@ -34,10 +40,17 @@ from rowers.models import Rower,Workout
 from rowers.models import checkworkoutuser
 import dataprep
 from dataprep import columndict
-from utils import uniqify,isprorower
+from utils import uniqify,isprorower,myqueue
 from uuid import uuid4
 import stravalib
 from stravalib.exc import ActivityUploadFailed,TimeoutExceeded
+import iso8601
+from iso8601 import ParseError
+
+import pytz
+import arrow
+
+from rowers.tasks import handle_strava_import_stroke_data
 
 from rowsandall_app.settings import C2_CLIENT_ID, C2_REDIRECT_URI, C2_CLIENT_SECRET, STRAVA_CLIENT_ID, STRAVA_REDIRECT_URI, STRAVA_CLIENT_SECRET
 
@@ -46,28 +59,8 @@ try:
 except ImportError:
     JSONDecodeError = ValueError
 
-# Exponentially weighted moving average
-# Used for data smoothing of the jagged data obtained by Strava
-# See bitbucket issue 72
-def ewmovingaverage(interval,window_size):
-    # Experimental code using Exponential Weighted moving average
 
-    try:
+from utils import geo_distance,ewmovingaverage
-        intervaldf = pd.DataFrame({'v':interval})
-        idf_ewma1 = intervaldf.ewm(span=window_size)
-        idf_ewma2 = intervaldf[::-1].ewm(span=window_size)
-        
-        i_ewma1 = idf_ewma1.mean().ix[:,'v']
-        i_ewma2 = idf_ewma2.mean().ix[:,'v']
-
-        interval2 = np.vstack((i_ewma1,i_ewma2[::-1]))
-        interval2 = np.mean( interval2, axis=0) # average
-    except ValueError:
-        interval2 = interval
-
-    return interval2
-
-from utils import geo_distance
 
 
 # Custom exception handler, returns a 401 HTTP message
@@ -155,12 +148,16 @@ def get_strava_workout_list(user):
 def add_stroke_data(user,stravaid,workoutid,startdatetime,csvfilename):
     r = Rower.objects.get(user=user)
 
+    print 'Queueing job for workout {workoutid} (strava {stravaid})'.format(
+        workoutid=workoutid,
+        stravaid=stravaid)
+    
     starttimeunix = arrow.get(startdatetime).timestamp
 
     job = myqueue(queue,
                   handle_strava_import_stroke_data,
                   r.stravatoken,
-                  stravid,
+                  stravaid,
                   workoutid,
                   starttimeunix,
                   csvfilename)
@@ -187,16 +184,14 @@ def get_strava_workouts(rower):
             ])
         newids = [stravaid for stravaid in stravaids if not stravaid in knownstravaids]
 
-        print newids,'aap'
+        for stravaid in newids[:1]:
-
-        for stravaid in newids:
             workoutid = create_async_workout(alldata,rower.user,stravaid)
 
     return 1
 
 def create_async_workout(alldata,user,stravaid):
-    data = alldata[stravid]
+    data = alldata[stravaid]
-
+    r = Rower.objects.get(user=user)
     distance = data['distance']
     stravaid = data['id']
     try:
@@ -240,6 +235,14 @@ def create_async_workout(alldata,user,stravaid):
 	except:
 	    title = 'Imported'
 
+    workoutdate = rowdatetime.astimezone(
+        pytz.timezone(thetimezone)
+    ).strftime('%Y-%m-%d')
+            
+    starttime = rowdatetime.astimezone(
+        pytz.timezone(thetimezone)
+    ).strftime('%H:%m:%S')
+            
     totaltime = data['elapsed_time']
     duration = dataprep.totaltime_sec_to_string(totaltime)
 

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,
+    unixtime = starttimeunix+10*t
-		       'd':10*d,
+
-		       'p':10*pace,
+    nr_strokes = len(t)
-		       'spm':spm,
+
-		       'hr':hr,
+    df = pd.DataFrame({'TimeStamp (sec)':unixtime,
-		       'lat':lat,
+                       ' ElapsedTime (sec)':10*t,
-		       'lon':lon,
+		       ' Horizontal (meters)':10*d,
-		       'strokelength':strokelength,
+		       ' 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

rowers/utils.py

@@ -378,3 +378,23 @@ def isprorower(r):
 
     return result
                                                                                      
+# Exponentially weighted moving average
+# Used for data smoothing of the jagged data obtained by Strava
+# See bitbucket issue 72
+def ewmovingaverage(interval,window_size):
+    # Experimental code using Exponential Weighted moving average
+
+    try:
+        intervaldf = pd.DataFrame({'v':interval})
+        idf_ewma1 = intervaldf.ewm(span=window_size)
+        idf_ewma2 = intervaldf[::-1].ewm(span=window_size)
+        
+        i_ewma1 = idf_ewma1.mean().ix[:,'v']
+        i_ewma2 = idf_ewma2.mean().ix[:,'v']
+
+        interval2 = np.vstack((i_ewma1,i_ewma2[::-1]))
+        interval2 = np.mean( interval2, axis=0) # average
+    except ValueError:
+        interval2 = interval
+
+    return interval2