Пример #1
0
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)
                masterList = masterList.append(dfi, ignore_index=True)
                
            except:
                continue    
    
    
    # saving updated runs summary to athlete profile
    ath.runsSummary = existingSummary.to_json(orient='records')
    ath.save(update_fields=['runsSummary'])
    
    existingSummary.to_pickle("runsSummary.txt")
    
    # saving updated runs summary to athlete profile
    ath.fitLines = existingFitlines.to_json(orient='records')
    ath.save(update_fields=['fitLines'])
    
    ath.masterList = masterList.to_json(orient='records')
    ath.save(update_fields=['masterList'])
    
    # testing...
    existingSummary = pd.read_json(ath.runsSummary)
    #print(existingSummary)
    
    existingFitlines = pd.read_json(ath.fitLines)
    #print(existingFitlines)

    
    global path
    path = os.path.dirname(__file__)
    # updating dataframe, pickling for use in other views
    #global df
    #df = thresher.masterAssemble(client) 
    
    masterDf = pd.read_json(ath.masterList)
    #print(masterDf)
    masterDf.to_pickle(str(path)+"/"+str(athlete.id)+"masterDf.txt")

    return render(request, 'stravaChimp/authorization.html', {'code':code, 'access_token':access_token, 'athleteId':athleteId})
Пример #2
0
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)
                masterList = masterList.append(dfi, ignore_index=True)

            except:
                continue

    # saving updated runs summary to athlete profile
    ath.runsSummary = existingSummary.to_json(orient='records')
    ath.save(update_fields=['runsSummary'])

    existingSummary.to_pickle("runsSummary.txt")

    # saving updated runs summary to athlete profile
    ath.fitLines = existingFitlines.to_json(orient='records')
    ath.save(update_fields=['fitLines'])

    ath.masterList = masterList.to_json(orient='records')
    ath.save(update_fields=['masterList'])

    # testing...
    existingSummary = pd.read_json(ath.runsSummary)
    #print(existingSummary)

    existingFitlines = pd.read_json(ath.fitLines)
    #print(existingFitlines)

    global path
    path = os.path.dirname(__file__)
    # updating dataframe, pickling for use in other views
    #global df
    #df = thresher.masterAssemble(client)

    masterDf = pd.read_json(ath.masterList)
    #print(masterDf)
    masterDf.to_pickle(str(path) + "/" + str(athlete.id) + "masterDf.txt")

    return render(request, 'stravaChimp/authorization.html', {
        'code': code,
        'access_token': access_token,
        'athleteId': athleteId
    })
Пример #3
0
#import thresher
import pandas as pd
import datetime, os
from pandas import DataFrame, pivot_table
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
import thresher
from math import radians, cos, sin, asin, sqrt
#urllib3.contrib.pyopenssl.inject_into_urllib3()




df_summary = pd.read_pickle("runsSummary.txt")
df_master = thresher.basicClean(pd.read_pickle("master_dfs/10319226masterDf.txt"))

maf = df_summary[['date', 'mafScore']]
print maf

maf_smoothed = thresher.makeLws(df=maf, frac=.20)
print maf_smoothed

fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(maf_smoothed.date, maf_smoothed.mafScore, c='blue', alpha=0.25)
#ax.scatter(df[x], df[y2], c='red', alpha=0.25)
#ax.plot(df['speedLwsX'], df['speedLwsY'], c='blue')
#ax.plot(lwsHatX, lwsHatY, c='red')
#ax.set_title(str(x)+' '+str(y))  
#ax.set_ylabel(str(y))
Пример #4
0
#import urllib3.contrib.pyopenssl
from urllib2 import urlopen
from json import load, dumps
#import thresher
import pandas as pd
import datetime, os
from pandas import DataFrame, pivot_table
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
import thresher
from math import radians, cos, sin, asin, sqrt
#urllib3.contrib.pyopenssl.inject_into_urllib3()

df_summary = pd.read_pickle("runsSummary.txt")
df_master = thresher.basicClean(
    pd.read_pickle("master_dfs/10319226masterDf.txt"))

maf = df_summary[['date', 'mafScore']]
print maf

maf_smoothed = thresher.makeLws(df=maf, frac=.20)
print maf_smoothed

fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(maf_smoothed.date, maf_smoothed.mafScore, c='blue', alpha=0.25)
#ax.scatter(df[x], df[y2], c='red', alpha=0.25)
#ax.plot(df['speedLwsX'], df['speedLwsY'], c='blue')
#ax.plot(lwsHatX, lwsHatY, c='red')
#ax.set_title(str(x)+' '+str(y))
#ax.set_ylabel(str(y))