underarmour (mapmyfitness) done

rowers/underarmourstuff.py

@@ -13,10 +13,15 @@ import arrow
 import numpy as np
 from dateutil import parser
 import time
+from time import strftime
+import arrow
+import dataprep
 import math
 from math import sin,cos,atan2,sqrt
 import os,sys
 import urllib
+import iso8601
+from uuid import uuid4
 
 # Django
 from django.shortcuts import render_to_response
@@ -43,6 +48,14 @@ from utils import NoTokenError,ewmovingaverage
 
 from utils import geo_distance, custom_exception_handler
 
+def splituadata(lijst):
+    t = []
+    y = []
+    for d in lijst:
+        t.append(d[0])
+        y.append(d[1])
+
+    return np.array(t),np.array(y)
 
 
 # Checks if user has UnderArmour token, renews them if they are expired
@@ -137,7 +150,6 @@ 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": UNDERARMOUR_CLIENT_KEY,
@@ -170,7 +182,7 @@ def get_underarmour_workout_list(user):
     return s
 
 # Get workout summary data by Underarmour ID
-def get_underarmour_workout(user,underarmourid):
+def get_workout(user,underarmourid):
     r = Rower.objects.get(user=user)
     if (r.underarmourtoken == '') or (r.underarmourtoken is None):
 	return custom_exception_handler(401,s)
@@ -185,7 +197,14 @@ def get_underarmour_workout(user,underarmourid):
 	url = "https://api.ua.com/v7.1/workout/"+str(underarmourid)+"/?field_set=time_series"
 	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 Underarmour
 def createunderarmourworkoutdata(w):
@@ -453,3 +472,171 @@ def workout_ua_upload(user,w):
         return message, uaid
 
     return message, uaid
+
+# Create workout from SportTracks Data, which are slightly different
+# than Strava or Concept2 data
+def add_workout_from_data(user,importid,data,strokedata,
+                          source='mapmyfitness',
+                          workoutsource='mapmyfitness'):
+    workouttype = 'water'
+
+    try:
+	comments = data['notes']
+    except:
+	comments = ''
+    
+    try:
+	thetimezone = tz(data['start_locale_timezone'])
+    except:
+	thetimezone = 'UTC'
+
+    r = Rower.objects.get(user=user)
+    try:
+	rowdatetime = iso8601.parse_date(data['start_datetime'])
+    except iso8601.ParseError:
+	try:
+	    rowdatetime = datetime.strptime(data['start_datetime'],"%Y-%m-%d %H:%M:%S")
+	    rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
+	except:
+	    try:
+		rowdatetime = dateutil.parser.parse(data['start_datetime'])
+		rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
+	    except:
+		rowdatetime = datetime.strptime(data['date'],"%Y-%m-%d %H:%M:%S")
+		rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
+    starttimeunix = arrow.get(rowdatetime).timestamp
+    #starttimeunix = mktime(rowdatetime.utctimetuple())
+        
+	
+    try:
+	title = data['name']
+    except:
+	title = "Imported data"
+
+    timeseries = data['time_series']
+
+    # position, distance, speed, cadence, power, 
+    
+    res = splituadata(timeseries['distance'])
+
+    distance = res[1]
+
+    times_distance = res[0]
+
+
+    try:
+	l = timeseries['position']
+    
+	res = splituadata(l)
+	times_location = res[0]
+	latlong = res[1]
+	latcoord = []
+	loncoord = []
+
+	for coord in latlong:
+	    lat = coord['lat']
+	    lon = coord['lng']
+	    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 = splituadata(timeseries['cadence'])
+	times_spm = res[0]
+	spm = res[1]
+    except KeyError:
+	times_spm = times_distance
+	spm = 0*times_distance
+    
+    try:
+	res = splituadata(timeseries['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)
+
+    df = df.fillna(0)
+
+    df.sort_values(by='TimeStamp (sec)',ascending=True)
+	
+    timestr = strftime("%Y%m%d-%H%M%S")
+
+    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,
+                                                workoutsource='mapmyfitness',
+                                                title=title,
+                                                notes=comments)
+
+    return (id,message)
+

rowers/urls.py

@@ -333,7 +333,7 @@ urlpatterns = [
     url(r'^workout/runkeeperimport/$',views.workout_runkeeperimport_view),
 #    url(r'^workout/runkeeperimport/(?P<runkeeperid>\d+)/$',views.workout_getrunkeeperworkout_view),
     url(r'^workout/underarmourimport/$',views.workout_underarmourimport_view),
-    url(r'^workout/underarmourimport/(?P<underarmourid>\d+)/$',views.workout_getunderarmourworkout_view),
+#    url(r'^workout/underarmourimport/(?P<underarmourid>\d+)/$',views.workout_getunderarmourworkout_view),
     url(r'^workout/(?P<id>\d+)/deleteconfirm$',views.workout_delete_confirm_view),
     url(r'^workout/(?P<id>\d+)/c2uploadw/$',views.workout_c2_upload_view),
     url(r'^workout/(?P<id>\d+)/stravauploadw/$',views.workout_strava_upload_view),

rowers/views.py

@@ -845,14 +845,6 @@ def getidfromuri(uri):
     m = re.search('/(\w.*)\/(\d+)',uri)
     return m.group(2)
 
-def splituadata(lijst):
-    t = []
-    y = []
-    for d in lijst:
-        t.append(d[0])
-        y.append(d[1])
-
-    return np.array(t),np.array(y)
 
 	
 from utils import (
@@ -1245,177 +1237,6 @@ def add_workout_from_strokedata(user,importid,data,strokedata,
     
 
 
-# Create workout from SportTracks Data, which are slightly different
-# than Strava or Concept2 data
-def add_workout_from_underarmourdata(user,importid,data):
-    workouttype = 'water'
-
-    try:
-	comments = data['notes']
-    except:
-	comments = ''
-    
-    try:
-	thetimezone = tz(data['start_locale_timezone'])
-    except:
-	thetimezone = 'UTC'
-
-    r = getrower(user)
-    try:
-	rowdatetime = iso8601.parse_date(data['start_datetime'])
-    except iso8601.ParseError:
-	try:
-	    rowdatetime = datetime.datetime.strptime(data['start_datetime'],"%Y-%m-%d %H:%M:%S")
-	    rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
-	except:
-	    try:
-		rowdatetime = dateutil.parser.parse(data['start_datetime'])
-		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
-    #starttimeunix = mktime(rowdatetime.utctimetuple())
-        
-	
-    try:
-	title = data['name']
-    except:
-	title = "Imported data"
-
-    timeseries = data['time_series']
-
-    # position, distance, speed, cadence, power, 
-    
-    res = splituadata(timeseries['distance'])
-
-    distance = res[1]
-
-    times_distance = res[0]
-
-
-    try:
-	l = timeseries['position']
-    
-	res = splituadata(l)
-	times_location = res[0]
-	latlong = res[1]
-	latcoord = []
-	loncoord = []
-
-	for coord in latlong:
-	    lat = coord['lat']
-	    lon = coord['lng']
-	    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 = splituadata(timeseries['cadence'])
-	times_spm = res[0]
-	spm = res[1]
-    except KeyError:
-	times_spm = times_distance
-	spm = 0*times_distance
-    
-    try:
-	res = splituadata(timeseries['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 = stravastuff.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,
-                                                workoutsource='mapmyfitness',
-                                                title=title,
-                                                notes=comments)
-
-    return (id,message)
-