452 lines
13 KiB
Python
452 lines
13 KiB
Python
# All the functionality to connect to SportTracks
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from rowers.imports import *
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import re
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from rowsandall_app.settings import (
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C2_CLIENT_ID, C2_REDIRECT_URI, C2_CLIENT_SECRET,
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STRAVA_CLIENT_ID, STRAVA_REDIRECT_URI, STRAVA_CLIENT_SECRET,
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SPORTTRACKS_CLIENT_SECRET, SPORTTRACKS_CLIENT_ID,
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SPORTTRACKS_REDIRECT_URI
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)
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oauth_data = {
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'client_id': SPORTTRACKS_CLIENT_ID,
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'client_secret': SPORTTRACKS_CLIENT_SECRET,
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'redirect_uri': SPORTTRACKS_REDIRECT_URI,
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'autorization_uri': "https://api.sporttracks.mobi/oauth2/authorize",
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'content_type': 'application/json',
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'tokenname': 'sporttrackstoken',
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'refreshtokenname': 'sporttracksrefreshtoken',
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'expirydatename': 'sporttrackstokenexpirydate',
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'bearer_auth': False,
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'base_url': "https://api.sporttracks.mobi/oauth2/token",
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'scope':'write',
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}
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# Checks if user has SportTracks token, renews them if they are expired
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def sporttracks_open(user):
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return imports_open(user, oauth_data)
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# Refresh ST token using refresh token
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def do_refresh_token(refreshtoken):
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return imports_do_refresh_token(refreshtoken, oauth_data)
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# Exchange ST access code for long-lived ST access token
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def get_token(code):
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return imports_get_token(code,oauth_data)
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# Make authorization URL including random string
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def make_authorization_url(request):
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return imports_make_authorization_url(oauth_data)
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# This is token refresh. Looks for tokens in our database, then refreshes
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def rower_sporttracks_token_refresh(user):
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r = Rower.objects.get(user=user)
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res = do_refresh_token(r.sporttracksrefreshtoken)
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access_token = res[0]
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expires_in = res[1]
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refresh_token = res[2]
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expirydatetime = timezone.now()+timedelta(seconds=expires_in)
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r = Rower.objects.get(user=user)
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r.sporttrackstoken = access_token
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r.tokenexpirydate = expirydatetime
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r.sporttracksrefreshtoken = refresh_token
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r.save()
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return r.sporttrackstoken
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# Get list of workouts available on SportTracks
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def get_sporttracks_workout_list(user):
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r = Rower.objects.get(user=user)
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if (r.sporttrackstoken == '') or (r.sporttrackstoken is None):
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s = "Token doesn't exist. Need to authorize"
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return custom_exception_handler(401,s)
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elif (timezone.now()>r.sporttrackstokenexpirydate):
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s = "Token expired. Needs to refresh."
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return custom_exception_handler(401,s)
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else:
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# ready to fetch. Hurray
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authorizationstring = str('Bearer ' + r.sporttrackstoken)
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headers = {'Authorization': authorizationstring,
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'user-agent': 'sanderroosendaal',
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'Content-Type': 'application/json'}
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url = "https://api.sporttracks.mobi/api/v2/fitnessActivities"
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s = requests.get(url,headers=headers)
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return s
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# Get workout summary data by SportTracks ID
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def get_workout(user,sporttracksid):
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r = Rower.objects.get(user=user)
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if (r.sporttrackstoken == '') or (r.sporttrackstoken is None):
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return custom_exception_handler(401,s)
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s = "Token doesn't exist. Need to authorize"
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elif (timezone.now()>r.sporttrackstokenexpirydate):
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s = "Token expired. Needs to refresh."
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return custom_exception_handler(401,s)
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else:
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# ready to fetch. Hurray
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authorizationstring = str('Bearer ' + r.sporttrackstoken)
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headers = {'Authorization': authorizationstring,
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'user-agent': 'sanderroosendaal',
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'Content-Type': 'application/json'}
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url = "https://api.sporttracks.mobi/api/v2/fitnessActivities/"+str(sporttracksid)
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s = requests.get(url,headers=headers)
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data = s.json()
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strokedata = pd.DataFrame.from_dict({
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key: pd.Series(value) for key, value in data.items()
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})
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return data,strokedata
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# Create Workout Data for upload to SportTracks
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def createsporttracksworkoutdata(w):
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timezone = pytz.timezone(w.timezone)
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filename = w.csvfilename
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try:
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row = rowingdata(filename)
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except:
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return 0
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averagehr = int(row.df[' HRCur (bpm)'].mean())
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maxhr = int(row.df[' HRCur (bpm)'].max())
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duration = w.duration.hour*3600
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duration += w.duration.minute*60
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duration += w.duration.second
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duration += +1.0e-6*w.duration.microsecond
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# adding diff, trying to see if this is valid
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#t = row.df.ix[:,'TimeStamp (sec)'].values-10*row.df.ix[0,'TimeStamp (sec)']
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t = row.df.ix[:,'TimeStamp (sec)'].values-row.df.ix[0,'TimeStamp (sec)']
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t[0] = t[1]
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d = row.df.ix[:,'cum_dist'].values
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d[0] = d[1]
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t = t.astype(int)
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d = d.astype(int)
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spm = row.df[' Cadence (stokes/min)'].astype(int).values
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spm[0] = spm[1]
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hr = row.df[' HRCur (bpm)'].astype(int).values
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haslatlon=1
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try:
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lat = row.df[' latitude'].values
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lon = row.df[' longitude'].values
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if not lat.std() and not lon.std():
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haslatlon = 0
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except KeyError:
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haslatlon = 0
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haspower = 1
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try:
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power = row.df[' Power (watts)'].astype(int).values
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except KeyError:
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haspower = 0
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locdata = []
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hrdata = []
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spmdata = []
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distancedata = []
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powerdata = []
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for i in range(len(t)):
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hrdata.append(t[i])
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hrdata.append(hr[i])
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distancedata.append(t[i])
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distancedata.append(d[i])
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spmdata.append(t[i])
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spmdata.append(spm[i])
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if haslatlon:
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locdata.append(t[i])
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locdata.append([lat[i],lon[i]])
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if haspower:
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powerdata.append(t[i])
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powerdata.append(power[i])
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try:
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w.notes = w.notes+'\n from '+w.workoutsource+' via rowsandall.com'
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except TypeError:
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w.notes = 'from '+w.workoutsource+' via rowsandall.com'
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st = w.startdatetime.astimezone(timezone)
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st = st.replace(microsecond=0)
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if haslatlon:
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data = {
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"type": "Rowing",
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"name": w.name,
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"start_time": st.isoformat(),
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"total_distance": int(w.distance),
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"duration": duration,
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"notes": w.notes,
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"avg_heartrate": averagehr,
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"max_heartrate": maxhr,
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"location": locdata,
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"distance": distancedata,
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"cadence": spmdata,
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"heartrate": hrdata,
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}
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else:
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data = {
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"type": "Rowing",
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"name": w.name,
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"start_time": st.isoformat(),
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"total_distance": int(w.distance),
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"duration": duration,
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"notes": w.notes,
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"avg_heartrate": averagehr,
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"max_heartrate": maxhr,
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"distance": distancedata,
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"cadence": spmdata,
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"heartrate": hrdata,
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}
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if haspower:
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data['power'] = powerdata
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return data
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# Obtain SportTracks Workout ID from the response returned on successful
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# upload
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def getidfromresponse(response):
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t = response.json()
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uri = t['uris'][0]
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regex = '.*?sporttracks\.mobi\/api\/v2\/fitnessActivities/(\d+)\.json$'
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m = re.compile(regex).match(uri).group(1)
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id = int(m)
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return int(id)
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def workout_sporttracks_upload(user,w):
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message = "Uploading to SportTracks"
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stid = 0
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# ready to upload. Hurray
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r = w.user
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thetoken = sporttracks_open(user)
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if (checkworkoutuser(user,w)):
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data = createsporttracksworkoutdata(w)
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if not data:
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message = "Data error"
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stid = 0
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return message,stid
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authorizationstring = str('Bearer ' + thetoken)
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headers = {'Authorization': authorizationstring,
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'user-agent': 'sanderroosendaal',
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'Content-Type': 'application/json'}
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url = "https://api.sporttracks.mobi/api/v2/fitnessActivities.json"
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response = requests.post(url,headers=headers,data=json.dumps(data))
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# check for duplicate error first
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if (response.status_code == 409 ):
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message = "Duplicate error"
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w.uploadedtosporttracks = -1
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stid = -1
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w.save()
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return message, stid
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elif (response.status_code == 201 or response.status_code==200):
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s= response.json()
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stid = getidfromresponse(response)
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w.uploadedtosporttracks = stid
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w.save()
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return 'Successfully synced to SportTracks',stid
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else:
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s = response
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message = "Something went wrong in workout_sporttracks_upload_view: %s" % s.reason
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stid = 0
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return message,stid
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else:
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message = "You are not authorized to upload this workout"
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stid = 0
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return message,stid
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return message,stid
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# Create workout from SportTracks Data, which are slightly different
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# than Strava or Concept2 data
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def add_workout_from_data(user,importid,data,strokedata,source='sporttracks',
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workoutsource='sporttracks'):
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try:
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workouttype = data['type']
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except KeyError:
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workouttype = 'other'
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if workouttype not in [x[0] for x in Workout.workouttypes]:
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workouttype = 'other'
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try:
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comments = data['comments']
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except:
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comments = ''
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r = Rower.objects.get(user=user)
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try:
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rowdatetime = iso8601.parse_date(data['start_time'])
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except iso8601.ParseError:
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try:
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rowdatetime = datetime.datetime.strptime(data['start_time'],"%Y-%m-%d %H:%M:%S")
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rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
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except:
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try:
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rowdatetime = dateutil.parser.parse(data['start_time'])
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rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
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except:
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rowdatetime = datetime.datetime.strptime(data['date'],"%Y-%m-%d %H:%M:%S")
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rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
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starttimeunix = arrow.get(rowdatetime).timestamp
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try:
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title = data['name']
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except:
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title = "Imported data"
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try:
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res = splitstdata(data['distance'])
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distance = res[1]
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times_distance = res[0]
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except KeyError:
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try:
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res = splitstdata(data['heartrate'])
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times_distance = res[0]
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distance = 0*times_distance
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except KeyError:
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return (0,"No distance or heart rate data in the workout")
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try:
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l = data['location']
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res = splitstdata(l)
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times_location = res[0]
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latlong = res[1]
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latcoord = []
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loncoord = []
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for coord in latlong:
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lat = coord[0]
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lon = coord[1]
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latcoord.append(lat)
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loncoord.append(lon)
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except:
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times_location = times_distance
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latcoord = np.zeros(len(times_distance))
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loncoord = np.zeros(len(times_distance))
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if workouttype in types.otwtypes:
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workouttype = 'rower'
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try:
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res = splitstdata(data['cadence'])
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times_spm = res[0]
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spm = res[1]
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except KeyError:
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times_spm = times_distance
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spm = 0*times_distance
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try:
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res = splitstdata(data['heartrate'])
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hr = res[1]
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times_hr = res[0]
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except KeyError:
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times_hr = times_distance
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hr = 0*times_distance
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# create data series and remove duplicates
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distseries = pd.Series(distance,index=times_distance)
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distseries = distseries.groupby(distseries.index).first()
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latseries = pd.Series(latcoord,index=times_location)
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latseries = latseries.groupby(latseries.index).first()
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lonseries = pd.Series(loncoord,index=times_location)
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lonseries = lonseries.groupby(lonseries.index).first()
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spmseries = pd.Series(spm,index=times_spm)
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spmseries = spmseries.groupby(spmseries.index).first()
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hrseries = pd.Series(hr,index=times_hr)
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hrseries = hrseries.groupby(hrseries.index).first()
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# Create dicts and big dataframe
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d = {
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' Horizontal (meters)': distseries,
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' latitude': latseries,
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' longitude': lonseries,
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' Cadence (stokes/min)': spmseries,
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' HRCur (bpm)' : hrseries,
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}
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df = pd.DataFrame(d)
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df = df.groupby(level=0).last()
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cum_time = df.index.values
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df[' ElapsedTime (sec)'] = cum_time
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velo = df[' Horizontal (meters)'].diff()/df[' ElapsedTime (sec)'].diff()
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df[' Power (watts)'] = 0.0*velo
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nr_rows = len(velo.values)
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df[' DriveLength (meters)'] = np.zeros(nr_rows)
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df[' StrokeDistance (meters)'] = np.zeros(nr_rows)
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df[' DriveTime (ms)'] = np.zeros(nr_rows)
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df[' StrokeRecoveryTime (ms)'] = np.zeros(nr_rows)
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df[' AverageDriveForce (lbs)'] = np.zeros(nr_rows)
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df[' PeakDriveForce (lbs)'] = np.zeros(nr_rows)
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df[' lapIdx'] = np.zeros(nr_rows)
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unixtime = cum_time+starttimeunix
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unixtime[0] = starttimeunix
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df['TimeStamp (sec)'] = unixtime
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dt = np.diff(cum_time).mean()
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wsize = round(5./dt)
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velo2 = ewmovingaverage(velo,wsize)
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df[' Stroke500mPace (sec/500m)'] = 500./velo2
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df = df.fillna(0)
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df.sort_values(by='TimeStamp (sec)',ascending=True)
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timestr = strftime("%Y%m%d-%H%M%S")
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# csvfilename ='media/Import_'+str(importid)+'.csv'
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csvfilename ='media/{code}_{importid}.csv'.format(
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importid=importid,
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code = uuid4().hex[:16]
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)
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res = df.to_csv(csvfilename+'.gz',index_label='index',
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compression='gzip')
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id,message = dataprep.save_workout_database(csvfilename,r,
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workouttype=workouttype,
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title=title,
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notes=comments,
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workoutsource='sporttracks')
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return (id,message)
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