done all except mapmyfitness

rowers/sporttracksstuff.py

@@ -17,7 +17,9 @@ from math import sin,cos,atan2,sqrt
 import urllib
 import c2stuff
 import pytz
-
+import iso8601
+from uuid import uuid4
+import arrow
 # Django
 from django.shortcuts import render_to_response
 from django.http import HttpResponseRedirect, HttpResponse,JsonResponse
@@ -31,10 +33,26 @@ from django.contrib.auth.decorators import login_required
 from rowingdata import rowingdata
 import pandas as pd
 from rowers.models import Rower,Workout,checkworkoutuser
-
+from rowers import types
 from rowsandall_app.settings import C2_CLIENT_ID, C2_REDIRECT_URI, C2_CLIENT_SECRET, STRAVA_CLIENT_ID, STRAVA_REDIRECT_URI, STRAVA_CLIENT_SECRET, SPORTTRACKS_CLIENT_SECRET, SPORTTRACKS_CLIENT_ID, SPORTTRACKS_REDIRECT_URI
 
-from utils import NoTokenError, custom_exception_handler
+from utils import NoTokenError, custom_exception_handler, ewmovingaverage
+
+from time import strftime
+import dataprep
+
+# Splits SportTracks data which is one long sequence of
+# [t,[lat,lon],t2,[lat2,lon2] ...]
+# to [t,t2,t3, ...], [[lat,long],[lat2,long2],...
+def splitstdata(lijst):
+    t = []
+    latlong = []
+    while len(lijst)>=2:
+	t.append(lijst[0])
+	latlong.append(lijst[1])
+	lijst = lijst[2:]
+
+    return [np.array(t),np.array(latlong)]
 
 
 # Checks if user has SportTracks token, renews them if they are expired
@@ -125,7 +143,7 @@ def get_token(code):
 def make_authorization_url(request):
     # Generate a random string for the state parameter
     # Save it for use later to prevent xsrf attacks
-    from uuid import uuid4
+
     state = str(uuid4())
 
     params = {"client_id": SPORTTRACKS_CLIENT_ID,
@@ -178,7 +196,7 @@ def get_sporttracks_workout_list(user):
     return s
 
 # Get workout summary data by SportTracks ID
-def get_sporttracks_workout(user,sporttracksid):
+def get_workout(user,sporttracksid):
     r = Rower.objects.get(user=user)
     if (r.sporttrackstoken == '') or (r.sporttrackstoken is None):
 	return custom_exception_handler(401,s)
@@ -195,7 +213,13 @@ def get_sporttracks_workout(user,sporttracksid):
 	url = "https://api.sporttracks.mobi/api/v2/fitnessActivities/"+str(sporttracksid)
 	s = requests.get(url,headers=headers)
 
-    return s
+    data = s.json()
+        
+    strokedata = pd.DataFrame.from_dict({
+        key: pd.Series(value) for key, value in data.items()
+    })
+        
+    return data,strokedata
 
 # Create Workout Data for upload to SportTracks
 def createsporttracksworkoutdata(w):
@@ -366,3 +390,176 @@ def workout_sporttracks_upload(user,w):
         return message,stid
 
     return message,stid
+
+# Create workout from SportTracks Data, which are slightly different
+# than Strava or Concept2 data
+def add_workout_from_data(user,importid,data,strokedata,source='sporttracks',
+                          workoutsource='sporttracks'):
+    try:
+        workouttype = data['type']
+    except KeyError:
+        workouttype = 'other'
+        
+    if workouttype not in [x[0] for x in Workout.workouttypes]:
+	workouttype = 'other'
+    try:
+	comments = data['comments']
+    except:
+	comments = ''
+
+        
+    r = Rower.objects.get(user=user)
+    try:
+	rowdatetime = iso8601.parse_date(data['start_time'])
+    except iso8601.ParseError:
+	try:
+	    rowdatetime = datetime.datetime.strptime(data['start_time'],"%Y-%m-%d %H:%M:%S")
+	    rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
+	except:
+	    try:
+		rowdatetime = dateutil.parser.parse(data['start_time'])
+		rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
+	    except:
+		rowdatetime = datetime.datetime.strptime(data['date'],"%Y-%m-%d %H:%M:%S")
+		rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
+    starttimeunix = arrow.get(rowdatetime).timestamp
+
+    try:
+	title = data['name']
+    except:
+	title = "Imported data"
+
+    try:
+        res = splitstdata(data['distance'])
+        distance = res[1]
+        times_distance = res[0]
+    except KeyError:
+        try:
+            res = splitstdata(data['heartrate'])
+            times_distance = res[0]
+            distance = 0*times_distance
+        except KeyError:
+            return (0,"No distance or heart rate data in the workout")
+
+
+    try:
+	l = data['location']
+    
+	res = splitstdata(l)
+	times_location = res[0]
+	latlong = res[1]
+	latcoord = []
+	loncoord = []
+
+	for coord in latlong:
+	    lat = coord[0]
+	    lon = coord[1]
+	    latcoord.append(lat)
+	    loncoord.append(lon)
+    except:
+	times_location = times_distance
+	latcoord = np.zeros(len(times_distance))
+	loncoord = np.zeros(len(times_distance))
+        if workouttype in types.otwtypes:
+            workouttype = 'rower'
+	
+    try:
+	res = splitstdata(data['cadence'])
+	times_spm = res[0]
+	spm = res[1]
+    except KeyError:
+	times_spm = times_distance
+	spm = 0*times_distance
+    
+    try:
+	res = splitstdata(data['heartrate'])
+	hr = res[1]
+	times_hr = res[0]
+    except KeyError:
+	times_hr = times_distance
+	hr = 0*times_distance
+
+
+    # create data series and remove duplicates
+    distseries = pd.Series(distance,index=times_distance)
+    distseries = distseries.groupby(distseries.index).first()
+    latseries = pd.Series(latcoord,index=times_location)
+    latseries = latseries.groupby(latseries.index).first()
+    lonseries = pd.Series(loncoord,index=times_location)
+    lonseries = lonseries.groupby(lonseries.index).first()
+    spmseries = pd.Series(spm,index=times_spm)
+    spmseries = spmseries.groupby(spmseries.index).first()
+    hrseries = pd.Series(hr,index=times_hr)
+    hrseries = hrseries.groupby(hrseries.index).first()
+
+
+    # Create dicts and big dataframe
+    d = {
+	' Horizontal (meters)': distseries,
+	' latitude': latseries,
+	' longitude': lonseries,
+	' Cadence (stokes/min)': spmseries,
+	' HRCur (bpm)' : hrseries,
+	}
+
+    
+
+    df = pd.DataFrame(d)
+
+    df = df.groupby(level=0).last()
+
+    cum_time = df.index.values
+    df[' ElapsedTime (sec)'] = cum_time
+
+    velo = df[' Horizontal (meters)'].diff()/df[' ElapsedTime (sec)'].diff()
+    
+    df[' Power (watts)'] = 0.0*velo
+
+    nr_rows = len(velo.values)
+
+    df[' DriveLength (meters)'] = np.zeros(nr_rows)
+    df[' StrokeDistance (meters)'] = np.zeros(nr_rows)
+    df[' DriveTime (ms)'] = np.zeros(nr_rows)
+    df[' StrokeRecoveryTime (ms)'] = np.zeros(nr_rows)
+    df[' AverageDriveForce (lbs)'] = np.zeros(nr_rows)
+    df[' PeakDriveForce (lbs)'] = np.zeros(nr_rows)
+    df[' lapIdx'] = np.zeros(nr_rows)
+
+
+
+    unixtime = cum_time+starttimeunix
+    unixtime[0] = starttimeunix
+ 
+    df['TimeStamp (sec)'] = unixtime
+
+ 
+    dt = np.diff(cum_time).mean()
+    wsize = round(5./dt)
+
+    velo2 = ewmovingaverage(velo,wsize)
+
+    df[' Stroke500mPace (sec/500m)'] = 500./velo2
+
+
+    df = df.fillna(0)
+
+    df.sort_values(by='TimeStamp (sec)',ascending=True)
+	
+    timestr = strftime("%Y%m%d-%H%M%S")
+
+#    csvfilename ='media/Import_'+str(importid)+'.csv'
+    csvfilename ='media/{code}_{importid}.csv'.format(
+        importid=importid,
+        code = uuid4().hex[:16]
+        )
+
+    res =  df.to_csv(csvfilename+'.gz',index_label='index',
+                     compression='gzip')
+
+    id,message = dataprep.save_workout_database(csvfilename,r,
+                                                workouttype=workouttype,
+                                                title=title,
+                                                notes=comments,
+                                                workoutsource='sporttracks')
+
+    return (id,message)