done all except mapmyfitness

rowers/stravastuff.py

@@ -11,6 +11,7 @@ from datetime import datetime
 import numpy as np
 from dateutil import parser
 import time
+from time import strftime,strptime
 import math
 from math import sin,cos,atan2,sqrt
 import os,sys
@@ -35,6 +36,7 @@ queuehigh = django_rq.get_queue('low')
 # Project
 # from .models import Profile
 from rowingdata import rowingdata
+from rowingdata import make_cumvalues
 import pandas as pd
 from rowers.models import Rower,Workout
 from rowers.models import checkworkoutuser
@@ -252,7 +254,7 @@ def create_async_workout(alldata,user,stravaid):
 from utils import get_strava_stream    
 
 # Get a Strava workout summary data and stroke data by ID
-def get_strava_workout(user,stravaid):
+def get_workout(user,stravaid):
     r = Rower.objects.get(user=user)
     if (r.stravatoken == '') or (r.stravatoken is None):
 	s = "Token doesn't exist. Need to authorize"
@@ -347,7 +349,7 @@ def get_strava_workout(user,stravaid):
 	# startdatetime = datetime.datetime.strptime(startdatetime,"%Y-%m-%d-%H:%M:%S")
 
 	return [workoutsummary,df]
-
+    
 # Generate Workout data for Strava (a TCX file)
 def createstravaworkoutdata(w,dozip=True):
     filename = w.csvfilename
@@ -482,3 +484,142 @@ def workout_strava_upload(user,w):
                 return message,stravaid
 	return message,stravaid
     return message,stravaid
+
+# Create workout data from Strava or Concept2
+# data and create the associated Workout object and save it
+def add_workout_from_data(user,importid,data,strokedata,
+                          source='strava',splitdata=None,
+                          workoutsource='strava'):
+    try:
+        workouttype = data['type']
+    except KeyError:
+        workouttype = 'rower'
+        
+    if workouttype not in [x[0] for x in Workout.workouttypes]:
+	workouttype = 'other'
+    try:
+	comments = data['comments']
+    except:
+	comments = ' '
+
+    try:
+	thetimezone = tz(data['timezone'])
+    except:
+	thetimezone = 'UTC'
+
+    r = Rower.objects.get(user=user)
+    try:
+	rowdatetime = iso8601.parse_date(data['date_utc'])
+    except KeyError:
+	rowdatetime = iso8601.parse_date(data['start_date'])
+    except ParseError:
+	rowdatetime = iso8601.parse_date(data['date'])
+    
+	
+    try:
+        intervaltype = data['workout_type']
+	
+    except KeyError:
+	intervaltype = ''
+	
+    try:
+	title = data['name']
+    except KeyError:
+	title = ""
+	try:
+	    t = data['comments'].split('\n', 1)[0]
+	    title += t[:20]
+	except:
+	    title = 'Imported'
+
+    starttimeunix = arrow.get(rowdatetime).timestamp
+
+    res = make_cumvalues(0.1*strokedata['t'])
+    cum_time = res[0]
+    lapidx = res[1]
+
+    unixtime = cum_time+starttimeunix
+    seconds = 0.1*strokedata.ix[:,'t']
+
+    nr_rows = len(unixtime)
+
+    try:
+	latcoord = strokedata.ix[:,'lat']
+	loncoord = strokedata.ix[:,'lon']
+    except:
+	latcoord = np.zeros(nr_rows)
+	loncoord = np.zeros(nr_rows)
+
+
+    try:
+	strokelength = strokedata.ix[:,'strokelength']
+    except:
+	strokelength = np.zeros(nr_rows)
+
+    dist2 = 0.1*strokedata.ix[:,'d']
+
+    try:
+        spm = strokedata.ix[:,'spm']
+    except KeyError:
+        spm = 0*dist2
+        
+    try:
+        hr = strokedata.ix[:,'hr']
+    except KeyError:
+        hr = 0*spm
+    pace = strokedata.ix[:,'p']/10.
+    pace = np.clip(pace,0,1e4)
+    pace = pace.replace(0,300)
+
+    velo = 500./pace
+
+    power = 2.8*velo**3
+    
+    # save csv
+    # Create data frame with all necessary data to write to csv
+    df = pd.DataFrame({'TimeStamp (sec)':unixtime,
+		       ' Horizontal (meters)': dist2,
+		       ' Cadence (stokes/min)':spm,
+		       ' HRCur (bpm)':hr,
+		       ' longitude':loncoord,
+		       ' latitude':latcoord,
+		       ' Stroke500mPace (sec/500m)':pace,
+		       ' Power (watts)':power,
+		       ' DragFactor':np.zeros(nr_rows),
+		       ' DriveLength (meters)':np.zeros(nr_rows),
+		       ' StrokeDistance (meters)':strokelength,
+		       ' DriveTime (ms)':np.zeros(nr_rows),
+		       ' StrokeRecoveryTime (ms)':np.zeros(nr_rows),
+		       ' AverageDriveForce (lbs)':np.zeros(nr_rows),
+		       ' PeakDriveForce (lbs)':np.zeros(nr_rows),
+		       ' lapIdx':lapidx,
+		       ' ElapsedTime (sec)':seconds
+		       })
+
+    
+    df.sort_values(by='TimeStamp (sec)',ascending=True)
+	
+    timestr = strftime("%Y%m%d-%H%M%S")
+
+
+    # Create CSV file name and save data to CSV file
+    csvfilename ='media/{code}_{importid}.csv'.format(
+        importid=importid,
+        code = uuid4().hex[:16]
+        )
+
+    res =  df.to_csv(csvfilename+'.gz',index_label='index',
+                     compression='gzip')
+
+
+    id,message = dataprep.save_workout_database(
+        csvfilename,r,
+        workouttype=workouttype,
+        title=title,notes=comments,
+        workoutsource=workoutsource,
+        dosummary=True
+    )
+
+            
+
+    return id,message