Python Client.get_activity_streams方法代码示例

# 需要导入模块: from stravalib import Client [as 别名]
# 或者: from stravalib.Client import get_activity_streams [as 别名]
def compare (request):

    athlete_id = request.GET.get("athlete_id")

    # how do I get what is in the URL?
    # I want to send an arbitrary array and get that in the URL
    # code = request.GET.get("code")  <-- works for one item
    # what if the URL is just /compare/runs=123,456,789
    # runs = request.GET.get("runs")
    # Then split that string into an array...

    # use the token URL pattern as a template

    # By now the athlete should exist
    current_athlete = Athlete.objects.get(id_strava = athlete_id)

    # Use the access_token ### "58298d33c3a183c12673691a1ae53d261b08c3a4"
    client = Client(access_token=current_athlete.access_token)


    # hardcoded sleighride activities. 
    activities = [547540459, 547545741, 547550929, 559626235]

    max_speed = 0.0
    ride_names = []
    
    # combined_array is to collect all the values to do some calculations later.
    combined_array = []
    all_speeds = []


    for ac in activities:
        #activity id 
        strava_ride = client.get_activity(ac)

        # Get and update the highest maxspeed across rides. 
        t_max_speed = float(strava_ride.max_speed * 2.23693629)
        if t_max_speed > max_speed:
            max_speed = t_max_speed
        
        # Add the activity name to this array    
        ride_names.append(strava_ride.name)


        # Streams
        stream_types = "time","distance","latlng","altitude","grade_smooth","velocity_smooth"
        streams = client.get_activity_streams(strava_ride.id, types=stream_types)

        stream_time = streams["time"].data 
        stream_distance = streams["distance"].data 
        stream_lat_lng = streams["latlng"].data 
        stream_altitude = streams["altitude"].data 
        stream_grade = streams["grade_smooth"].data 
        stream_velocity = streams["velocity_smooth"].data

        stream_tuple = zip(stream_time, stream_distance, stream_lat_lng, stream_altitude, stream_grade, stream_velocity)

        # Getting info from the streams and combining it all into a CSV format. 
        for (tTime,tDistance,tLatLng,tAltitude,tGrade,tVelocity) in stream_tuple:
            current_time = strava_ride.start_date_local + timedelta(seconds=tTime)
            # current_speed = format(float(tVelocity * 2.23693629), '.9f')
            current_speed = tVelocity * 2.23693629
            
            if current_speed > 0.5:
                all_speeds.append(current_speed)

            temp_stuff = []
            temp_stuff.append(format(current_time))
            temp_stuff.append(format(tLatLng[0]))
            temp_stuff.append(format(tLatLng[1]))
            temp_stuff.append(format(current_speed))

            combined_array.append(temp_stuff)
        # End inner FOR
    
        # insert splitter between runs
        combined_array.append(["$$$"])
    # END outer FOR


    # make special Shred Analytics average speeds that remove all 0 values.     
    sa_average_speed = mean(all_speeds)

    # combined_string is a string version of the array to send to the template.
    combined_string = ""

    # Make a string version of the arracy to send to Javascript. 
    for i in combined_array:
        combined_string += ','.join(i) + "@"

    context = {'sa_average_speed': sa_average_speed, 'max_speed': max_speed, 'ride_names': ride_names, 'start_lat': combined_array[3][1], 'start_lon': combined_array[3][2], 'file_string': combined_string}
    
    template = loader.get_template('shred/compare.html')
    return HttpResponse(template.render(context))

# 需要导入模块: from stravalib import Client [as 别名]
# 或者: from stravalib.Client import get_activity_streams [as 别名]
def map(request, athlete_id, activity_id):

    # athlete_id = athlete_id #421122 #750228
    # activity_id = activity_id #577320490 #476912675


    path = "../../../../../data/" + format(athlete_id) + "_" + format(activity_id) + ".txt"
    file_to_write_to = open(path, "w")
    # print(file_to_write_to.path)
    writer = csv.writer(file_to_write_to, delimiter=',', quotechar='', quoting=csv.QUOTE_NONE)
    #write header row for text file
    activity_tuple = "AthleteID", "ActivityID", "StartTime", "TotalElapsedTime", "TotalDistanceMeters", "MaxSpeedMPH", "MeasuredTime", "Latitude", "Longitude", "AltitudeMeters", "DistanceMeters", "current_speedMPH", "CurrentGrade"
    writer.writerow(activity_tuple)


    # By now the athlete should exist
    current_athlete = Athlete.objects.get(id_strava = athlete_id)

    # Use the access_token ### "58298d33c3a183c12673691a1ae53d261b08c3a4"
    client = Client(access_token=current_athlete.access_token)

    #activity id 
    strava_ride = client.get_activity(activity_id)
    
    # values we are using in calculations and sending to the template
    max_speed = format(float(strava_ride.max_speed * 2.23693629), '.9f')
    average_speed = format(float(strava_ride.average_speed * 2.23693629), '.9f')
    ride_name = strava_ride.name

    # Streams
    stream_types = "time","distance","latlng","altitude","grade_smooth","velocity_smooth"
    streams = client.get_activity_streams(activity_id, types=stream_types)

    stream_time = streams["time"].data 
    stream_distance = streams["distance"].data 
    stream_lat_lng = streams["latlng"].data 
    stream_altitude = streams["altitude"].data 
    stream_grade = streams["grade_smooth"].data 
    stream_velocity = streams["velocity_smooth"].data

    stream_tuple = zip(stream_time, stream_distance, stream_lat_lng, stream_altitude, stream_grade, stream_velocity)

    # combined_array is to collect all the values to do some calculations later.
    combined_array = []

    # combined_string is a string version of the array to send to the template.
    combined_string = ""

    # Getting info from the streams and combining it all into a CSV format. 
    for (tTime,tDistance,tLatLng,tAltitude,tGrade,tVelocity) in stream_tuple:
        current_time = strava_ride.start_date_local + timedelta(seconds=tTime)
        # current_speed = format(float(tVelocity * 2.23693629), '.9f')
        current_speed = tVelocity * 2.23693629
        activity_tuple = athlete_id, activity_id, strava_ride.start_date_local, strava_ride.elapsed_time, \
            float(strava_ride.distance), max_speed, current_time, tLatLng[0], tLatLng[1], \
            tAltitude, tDistance, current_speed, tGrade
        writer.writerow(activity_tuple)

        temp_stuff = []
        temp_stuff.append(format(current_time))
        temp_stuff.append(format(tLatLng[0]))
        temp_stuff.append(format(tLatLng[1]))
        temp_stuff.append(format(current_speed))

        combined_array.append(temp_stuff)
    
    file_to_write_to.close()


    # make special Shred Analytics average speeds that remove all 0 values.     
    sa_average_speed = 0.0
    sa_avg_index = 0

    for i in combined_array:
        # i[3] is speed
        if float(i[3]) > 0.5:
            sa_average_speed = sa_average_speed + float(i[3])
            sa_avg_index = sa_avg_index + 1 

        # Make a string version of the arracy to send to Javascript. 
        combined_string += ','.join(i) + "@"

    # the important calculation
    sa_average_speed = sa_average_speed / sa_avg_index

    context = {'sa_average_speed': sa_average_speed, 'max_speed': max_speed, 'average_speed': average_speed, 'ride_name': ride_name, 'athlete_id': athlete_id, 'activity_id': activity_id, 'start_lat': combined_array[3][1], 'start_lon': combined_array[3][2], 'file_string': combined_string}
    
    template = loader.get_template('shred/map.html')
    return HttpResponse(template.render(context))

# 需要导入模块: from stravalib import Client [as 别名]
# 或者: from stravalib.Client import get_activity_streams [as 别名]
def authorization(request):
    client = Client()
    code = request.GET['code']
    access_token = client.exchange_code_for_token(client_id=MY_STRAVA_CLIENT_ID, client_secret=MY_STRAVA_CLIENT_SECRET, code=code)   
    
    # making a global variable to be used across views. don't know how this will work in practice
    
    client = Client(access_token=access_token)
    athlete = client.get_athlete() # Get current athlete details
    
    global athleteId 
    athleteId = athlete.id
    
    # if athlete doesn't exist, add them
    if len(Athlete.objects.filter(athleteId=athleteId)) == 0:
        ath = Athlete.objects.create(name=str(athlete.firstname+' '+athlete.lastname), athleteId=athleteId, profilePic=athlete.profile, city=athlete.city, country=athlete.country, sex=athlete.sex, premium=athlete.premium, created_at=athlete.created_at, updated_at=athlete.updated_at, followers=athlete.follower_count, friends=athlete.friend_count, email=athlete.email, weight=athlete.weight, meas_pref=athlete.measurement_preference, runsSummary = DataFrame({}).to_json(orient='records'), fitLines = DataFrame({}).to_json(orient='records'), masterList = DataFrame({}).to_json(orient='records'))

        ath.profilePic.name = "rudyzPic"
        ath.save(update_fields=['profilePic'])
 
    # if athlete already exists, draw their file
    elif len(Athlete.objects.filter(athleteId=athleteId)) == 1:
        ath = Athlete.objects.get(athleteId=athleteId)
           
    ############################################ 
    ##### compiling new runs, updating summary
        
    # athlete's existing runs summary   
    existingSummary = DataFrame(pd.read_json(ath.runsSummary))
    existingFitlines = DataFrame(pd.read_json(ath.fitLines)) 
    masterList = DataFrame(pd.read_json(ath.masterList))
     
    activities = list(client.get_activities()) 
    
    # activity IDs of runs already in the system
    try:
        ids = existingSummary.activityId
    except AttributeError:
        ids = []
         
    for i in range(len(activities)):   
    #for i in range(30,37):
        # Ignoring activities already in the system 
        if (len(ids) == 0) or (float(activities[i].id) not in list(ids)):
            
            try:
                # compiling df for raw json-ization
                activityId = activities[i].id
                run = client.get_activity_streams(activityId, types=['time','latlng','distance','heartrate','altitude','cadence'])
                latlng = run['latlng'].data
                time = run['time'].data
                distance = run['distance'].data
                heartrate = run['heartrate'].data
                altitude = run['altitude'].data
                cadence = run['cadence'].data
                date = activities[i].start_date_local 
                activity = activityId   
                dfi = thresher.assemble(date, activityId, heartrate, distance, time, altitude, latlng, cadence) 
                
                
                # basic cleanup, only removing totally unreasonable values
                dfi = thresher.basicClean(dfi)


                # if we ever want to try our hand at improving strava's speed data (ie by predicting speed when GPS blanks), intervene here:
                    
                #dfi = thresher.addDistDeltas(dfi)
                             
                                        
                try: 
                    fitline = thresher.getFitlineLws(dfi) # this adds speed-shifted columns
                except:
                    fitline = pd.DataFrame({})
                    
                try:
                    mafScore = fitline[fitline.hr == 140.0].avgSpeed.iloc[0]
                    print "MAF "
                    print mafScore
                except:
                    mafScore = np.nan
                    
                fitline_json = fitline.to_json(orient='records')
                
                 # getting summary info for run (as one-entry dict)
                runSummary = thresher.getSingleSummaryDf(dfi)
                
                # adding mafScore to summary
                runSummary['mafScore'] = mafScore
                
                print runSummary
                
                # adding predicted hr and speed values
                #dfi = thresher.getPred(dfi)

                # saving entry to database
                Activity.objects.create(act_id = activityId, name=str(activities[i].name), description=activities[i].description, act_type=activities[i].type, date=activities[i].start_date_local, timezone=activities[i].timezone, df=dfi.to_json(orient='records'), avgHr=runSummary['avgHr'], hrVar=runSummary['variation'], realMiles=runSummary['realMiles'], recovery=runSummary['recovery'], easy=runSummary['easy'], stamina=runSummary['stamina'], impulse=runSummary['impulse'], totalTime=runSummary['totalTime'], totalDist=runSummary['totalDist'], climb=runSummary['climb'], fitline=fitline_json, mafScore=mafScore, athlete=ath)
                
                # updating runs summary
                existingSummary = existingSummary.append(runSummary, ignore_index=True)
                existingFitlines = existingFitlines.append(fitline, ignore_index=True)
#.........这里部分代码省略.........