underarmour (mapmyfitness) done
This commit is contained in:
Sander Roosendaal
@@ -13,10 +13,15 @@ import arrow
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import numpy as np
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from dateutil import parser
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import time
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from time import strftime
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import arrow
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import dataprep
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import math
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from math import sin,cos,atan2,sqrt
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import os,sys
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import urllib
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import iso8601
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from uuid import uuid4
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# Django
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from django.shortcuts import render_to_response
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@@ -43,6 +48,14 @@ from utils import NoTokenError,ewmovingaverage
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from utils import geo_distance, custom_exception_handler
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def splituadata(lijst):
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t = []
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y = []
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for d in lijst:
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t.append(d[0])
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y.append(d[1])
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return np.array(t),np.array(y)
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# Checks if user has UnderArmour token, renews them if they are expired
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@@ -137,7 +150,6 @@ def get_token(code):
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def make_authorization_url(request):
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# Generate a random string for the state parameter
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# Save it for use later to prevent xsrf attacks
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from uuid import uuid4
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state = str(uuid4())
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params = {"client_id": UNDERARMOUR_CLIENT_KEY,
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@@ -170,7 +182,7 @@ def get_underarmour_workout_list(user):
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return s
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# Get workout summary data by Underarmour ID
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def get_underarmour_workout(user,underarmourid):
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def get_workout(user,underarmourid):
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r = Rower.objects.get(user=user)
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if (r.underarmourtoken == '') or (r.underarmourtoken is None):
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return custom_exception_handler(401,s)
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@@ -185,7 +197,14 @@ def get_underarmour_workout(user,underarmourid):
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url = "https://api.ua.com/v7.1/workout/"+str(underarmourid)+"/?field_set=time_series"
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s = requests.get(url,headers=headers)
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return s
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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 Underarmour
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def createunderarmourworkoutdata(w):
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@@ -453,3 +472,171 @@ def workout_ua_upload(user,w):
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return message, uaid
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return message, uaid
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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,
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source='mapmyfitness',
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workoutsource='mapmyfitness'):
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workouttype = 'water'
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try:
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comments = data['notes']
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except:
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comments = ''
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try:
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thetimezone = tz(data['start_locale_timezone'])
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except:
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thetimezone = 'UTC'
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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_datetime'])
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except iso8601.ParseError:
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try:
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rowdatetime = datetime.strptime(data['start_datetime'],"%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_datetime'])
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rowdatetime = thetimezone.localize(rowdatetime).astimezone(utc)
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except:
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rowdatetime = 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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#starttimeunix = mktime(rowdatetime.utctimetuple())
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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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timeseries = data['time_series']
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# position, distance, speed, cadence, power,
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res = splituadata(timeseries['distance'])
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distance = res[1]
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times_distance = res[0]
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try:
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l = timeseries['position']
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res = splituadata(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['lat']
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lon = coord['lng']
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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 = splituadata(timeseries['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 = splituadata(timeseries['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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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/{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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workoutsource='mapmyfitness',
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title=title,
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notes=comments)
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return (id,message)
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@@ -333,7 +333,7 @@ urlpatterns = [
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url(r'^workout/runkeeperimport/$',views.workout_runkeeperimport_view),
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# url(r'^workout/runkeeperimport/(?P<runkeeperid>\d+)/$',views.workout_getrunkeeperworkout_view),
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url(r'^workout/underarmourimport/$',views.workout_underarmourimport_view),
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url(r'^workout/underarmourimport/(?P<underarmourid>\d+)/$',views.workout_getunderarmourworkout_view),
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# url(r'^workout/underarmourimport/(?P<underarmourid>\d+)/$',views.workout_getunderarmourworkout_view),
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url(r'^workout/(?P<id>\d+)/deleteconfirm$',views.workout_delete_confirm_view),
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url(r'^workout/(?P<id>\d+)/c2uploadw/$',views.workout_c2_upload_view),
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url(r'^workout/(?P<id>\d+)/stravauploadw/$',views.workout_strava_upload_view),
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179
rowers/views.py
179
rowers/views.py
@@ -845,14 +845,6 @@ def getidfromuri(uri):
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m = re.search('/(\w.*)\/(\d+)',uri)
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return m.group(2)
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def splituadata(lijst):
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t = []
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y = []
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for d in lijst:
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t.append(d[0])
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y.append(d[1])
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return np.array(t),np.array(y)
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from utils import (
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@@ -1245,177 +1237,6 @@ def add_workout_from_strokedata(user,importid,data,strokedata,
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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_underarmourdata(user,importid,data):
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workouttype = 'water'
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try:
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comments = data['notes']
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except:
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comments = ''
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try:
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thetimezone = tz(data['start_locale_timezone'])
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except:
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thetimezone = 'UTC'
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r = getrower(user)
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try:
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rowdatetime = iso8601.parse_date(data['start_datetime'])
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except iso8601.ParseError:
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try:
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rowdatetime = datetime.datetime.strptime(data['start_datetime'],"%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_datetime'])
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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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#starttimeunix = mktime(rowdatetime.utctimetuple())
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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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timeseries = data['time_series']
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# position, distance, speed, cadence, power,
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res = splituadata(timeseries['distance'])
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distance = res[1]
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times_distance = res[0]
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try:
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l = timeseries['position']
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res = splituadata(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['lat']
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lon = coord['lng']
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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 = splituadata(timeseries['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 = splituadata(timeseries['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 = stravastuff.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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workoutsource='mapmyfitness',
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title=title,
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notes=comments)
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return (id,message)
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Block a user