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})
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
})
#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))
#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))
