From 8d9a8b0d2b9e22e968966e3edb7e59e1878df0c5 Mon Sep 17 00:00:00 2001
From: Sander Roosendaal <roosendaalsander@gmail.com>
Date: Sat, 11 Feb 2017 15:51:42 +0100
Subject: [PATCH] Web Import now uses dataprep

---
 rowers/dataprep.py | 213 ++++++++++++++++++++++++---------------------
 rowers/views.py    | 208 +++++++++++++------------------------------
 2 files changed, 176 insertions(+), 245 deletions(-)

diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 906b66fe..9b52d54c 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -217,6 +217,116 @@ def timedeltaconv(x):
         
     return dt
 
+# Processes painsled CSV file to database
+def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
+                          dosummary=True,title='Workout',
+                          notes='',totaldist=0,totaltime=0):
+    message = None
+    powerperc = 100*np.array([r.pw_ut2,
+                              r.pw_ut1,
+                              r.pw_at,
+                              r.pw_tr,r.pw_an])/r.ftp
+
+    # make workout and put in database
+    rr = rrower(hrmax=r.max,hrut2=r.ut2,
+		hrut1=r.ut1,hrat=r.at,
+		hrtr=r.tr,hran=r.an,ftp=r.ftp,
+                powerperc=powerperc,powerzones=r.powerzones)
+    row = rdata(f2,rower=rr)
+    if row == 0:
+	return (0,'Error: CSV data file not found')
+
+    if dosmooth:
+        # auto smoothing
+        pace = row.df[' Stroke500mPace (sec/500m)'].values
+        velo = 500./pace
+                
+        f = row.df['TimeStamp (sec)'].diff().mean()
+        windowsize = 2*(int(10./(f)))+1
+        if not 'originalvelo' in row.df:
+	    row.df['originalvelo'] = velo
+
+        if windowsize > 3 and windowsize<len(velo):
+	    velo2 = savgol_filter(velo,windowsize,3)
+        else:
+	    velo2 = velo
+
+        velo3 = pd.Series(velo2)
+        velo3 = velo3.replace([-np.inf,np.inf],np.nan)
+        velo3 = velo3.fillna(method='ffill')
+                
+        pace2 = 500./abs(velo3)
+        
+        row.df[' Stroke500mPace (sec/500m)'] = pace2
+
+        row.df = row.df.fillna(0)
+
+        row.write_csv(f2,gzip=True)
+        try:
+            os.remove(f2)
+        except:
+            pass
+                
+    # recalculate power data
+    if workouttype == 'rower' or workouttype == 'dynamic' or workouttype == 'slides':
+	try:
+	    row.erg_recalculatepower()
+            row.write_csv(f2,gzip=True)
+	except:
+	    pass
+            
+    averagehr = row.df[' HRCur (bpm)'].mean()
+    maxhr = row.df[' HRCur (bpm)'].max()
+    
+    if totaldist == 0:
+        totaldist = row.df['cum_dist'].max()
+    if totaltime == 0:
+        totaltime = row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
+        totaltime = totaltime+row.df.ix[0,' ElapsedTime (sec)']
+
+
+    
+    hours = int(totaltime/3600.)
+    if hours>23:
+        message = 'Warning: The workout duration was longer than 23 hours'
+        hours = 23
+
+    minutes = int((totaltime - 3600.*hours)/60.)
+    seconds = int(totaltime - 3600.*hours - 60.*minutes)
+    tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
+    duration = "%s:%s:%s.%s" % (hours,minutes,seconds,tenths)
+
+    if dosummary:
+        summary = row.summary()
+        summary += '\n'
+        summary += row.intervalstats()
+
+    workoutdate = row.rowdatetime.strftime('%Y-%m-%d')
+    workoutstarttime = row.rowdatetime.strftime('%H:%M:%S')
+    workoutstartdatetime = thetimezone.localize(row.rowdatetime).astimezone(utc)
+
+    # check for duplicate start times
+    ws = Workout.objects.filter(starttime=workoutstarttime,
+				    user=r)
+    if (len(ws) != 0):
+	message =  "Warning: This workout probably already exists in the database"
+
+    w = Workout(user=r,name=title,date=workoutdate,
+		workouttype=workouttype,
+		duration=duration,distance=totaldist,
+		weightcategory=r.weightcategory,
+		starttime=workoutstarttime,
+		csvfilename=f2,notes=notes,summary=summary,
+		maxhr=maxhr,averagehr=averagehr,
+		startdatetime=workoutstartdatetime)
+
+    w.save()
+    # put stroke data in database
+    res = dataprep(row.df,id=w.id,bands=True,
+                   barchart=True,otwpower=True,empower=True)
+
+    return (w.id,message)
+
 # Create new workout from file and store it in the database
 # This routine should be used everywhere in views.py and mailprocessing.py
 # Currently there is code duplication
@@ -336,105 +446,14 @@ def new_workout_from_file(r,f2,
         except:
             os.remove(f_to_be_deleted+'.gz')
 
-    powerperc = 100*np.array([r.pw_ut2,
-                              r.pw_ut1,
-                              r.pw_at,
-                              r.pw_tr,r.pw_an])/r.ftp
 
-    # make workout and put in database
-    rr = rrower(hrmax=r.max,hrut2=r.ut2,
-		hrut1=r.ut1,hrat=r.at,
-		hrtr=r.tr,hran=r.an,ftp=r.ftp,
-                powerperc=powerperc,powerzones=r.powerzones)
-    row = rdata(f2,rower=rr)
-    if row == 0:
-	return (0,'Error: CSV data file not found')
+    dosummary = (fileformat != 'fit')
+    id,message = save_workout_database(f2,r,
+                                       workouttype=workouttype,
+                                       dosummary=dosummary,
+                                       title=title)
 
-    # auto smoothing
-    pace = row.df[' Stroke500mPace (sec/500m)'].values
-    velo = 500./pace
-                
-    f = row.df['TimeStamp (sec)'].diff().mean()
-    windowsize = 2*(int(10./(f)))+1
-    if not 'originalvelo' in row.df:
-	row.df['originalvelo'] = velo
-
-    if windowsize > 3 and windowsize<len(velo):
-	velo2 = savgol_filter(velo,windowsize,3)
-    else:
-	velo2 = velo
-
-    velo3 = pd.Series(velo2)
-    velo3 = velo3.replace([-np.inf,np.inf],np.nan)
-    velo3 = velo3.fillna(method='ffill')
-                
-    pace2 = 500./abs(velo3)
-
-    row.df[' Stroke500mPace (sec/500m)'] = pace2
-
-    row.df = row.df.fillna(0)
-
-    row.write_csv(f2,gzip=True)
-    try:
-        os.remove(f2)
-    except:
-        pass
-                
-    # recalculate power data
-    if workouttype == 'rower' or workouttype == 'dynamic' or workouttype == 'slides':
-	try:
-	    row.erg_recalculatepower()
-            row.write_csv(f2,gzip=True)
-	except:
-	    pass
-            
-    if fileformat != 'fit' and summary == '':
-	summary = row.summary()
-	summary += '\n'
-	summary += row.intervalstats_painsled()
-
-    averagehr = row.df[' HRCur (bpm)'].mean()
-    maxhr = row.df[' HRCur (bpm)'].max()
-    
-    totaldist = row.df['cum_dist'].max()
-    totaltime = row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
-    totaltime = totaltime+row.df.ix[0,' ElapsedTime (sec)']
-
-    hours = int(totaltime/3600.)
-    if hours>23:
-        message = 'Warning: The workout duration was longer than 23 hours'
-        hours = 23
-
-    minutes = int((totaltime - 3600.*hours)/60.)
-    seconds = int(totaltime - 3600.*hours - 60.*minutes)
-    tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
-    duration = "%s:%s:%s.%s" % (hours,minutes,seconds,tenths)
-
-    workoutdate = row.rowdatetime.strftime('%Y-%m-%d')
-    workoutstarttime = row.rowdatetime.strftime('%H:%M:%S')
-    workoutstartdatetime = thetimezone.localize(row.rowdatetime).astimezone(utc)
-
-    # check for duplicate start times
-    ws = Workout.objects.filter(starttime=workoutstarttime,
-				    user=r)
-    if (len(ws) != 0):
-	message =  "Warning: This workout probably already exists in the database"
-
-    w = Workout(user=r,name=title,date=workoutdate,
-		workouttype=workouttype,
-		duration=duration,distance=totaldist,
-		weightcategory=r.weightcategory,
-		starttime=workoutstarttime,
-		csvfilename=f2,notes=notes,summary=summary,
-		maxhr=maxhr,averagehr=averagehr,
-		startdatetime=workoutstartdatetime)
-
-    w.save()
-    # put stroke data in database
-    res = dataprep(row.df,id=w.id,bands=True,
-                   barchart=True,otwpower=True,empower=True)
-
-    return (w.id,message)
+    return (id,message)
 
 # Compare the data from the CSV file and the database
 # Currently only calculates number of strokes. To be expanded with
diff --git a/rowers/views.py b/rowers/views.py
index 89ad45d6..112cd2ce 100644
--- a/rowers/views.py
+++ b/rowers/views.py
@@ -67,13 +67,8 @@ from shutil import copyfile
 from rowingdata import rower as rrower
 from rowingdata import main as rmain
 from rowingdata import rowingdata as rrdata
-from rowingdata import TCXParser,RowProParser,ErgDataParser,TCXParserNoHR
-from rowingdata import BoatCoachParser,RowPerfectParser,BoatCoachAdvancedParser
-from rowingdata import MysteryParser
-from rowingdata import painsledDesktopParser,speedcoachParser,ErgStickParser
-from rowingdata import SpeedCoach2Parser,FITParser,fitsummarydata
 from rowingdata import make_cumvalues
-from rowingdata import summarydata,get_file_type
+from rowingdata import summarydata
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -451,69 +446,27 @@ def add_workout_from_strokedata(user,importid,data,strokedata,
                      compression='gzip')
 
 
-    # make workout
-    powerperc = 100*np.array([r.pw_ut2,
-                              r.pw_ut1,
-                              r.pw_at,
-                              r.pw_tr,r.pw_an])/r.ftp
-
-    rr = rrower(hrmax=r.max,hrut2=r.ut2,
-		hrut1=r.ut1,hrat=r.at,
-		hrtr=r.tr,hran=r.an,ftp=r.ftp,
-                powerperc=powerperc,
-                powerzones=r.powerzones,
-                )
-    row = rdata(csvfilename,rower=rr)
-
-    averagehr = row.df[' HRCur (bpm)'].mean()
-    maxhr = row.df[' HRCur (bpm)'].max()
-    totaldist = row.df['cum_dist'].max()
-    totaltime = row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
-    totaltime = totaltime+row.df.ix[0,' ElapsedTime (sec)']
-
     # with Concept2
     if source=='c2':
         try:
             totaldist = data['distance']
             totaltime = data['time']/10.
         except KeyError:
-            pass
+            totaldist = 0
+            totaltime = 0
+    else:
+        totaldist = 0
+        totaltime = 0
+
+    id,message = dataprep.save_workout_database(csvfilename,r,
+                                                workouttype=workouttype,
+                                                title=title,notes=comments,
+                                                totaldist=totaldist,
+                                                totaltime=totaltime)
+
             
 
-    hours = int(totaltime/3600.)
-    minutes = int((totaltime - 3600.*hours)/60.)
-    seconds = int(totaltime - 3600.*hours - 60.*minutes)
-    tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
-
-    duration = "%s:%s:%s.%s" % (hours,minutes,seconds,tenths)
-
-
-    summary = row.summary()
-    summary += '\n'
-    summary += row.intervalstats()
-
-    workoutdate = row.rowdatetime.strftime('%Y-%m-%d')
-    workoutstarttime = row.rowdatetime.strftime('%H:%M:%S')
-
-    # check for duplicate start times
-    ws = Workout.objects.filter(starttime=workoutstarttime,
-					user=r)
-    if (len(ws) != 0):
-        warnings.warn("Probably a duplicate workout",UserWarning)
-
-    # Create the Workout object
-    w = Workout(user=r,name=title,
-		date=workoutdate,workouttype=workouttype,
-		duration=duration,distance=totaldist,
-		weightcategory=r.weightcategory,
-		starttime=workoutstarttime,
-		csvfilename=csvfilename,notes=comments,
-		uploadedtoc2=0,summary=summary,
-		averagehr=averagehr,maxhr=maxhr,
-		startdatetime=rowdatetime)
-    w.save()
-    
-    return w.id
+    return id,message
 
 # Create workout from SportTracks Data, which are slightly different
 # than Strava or Concept2 data
@@ -582,7 +535,8 @@ def add_workout_from_stdata(user,importid,data):
 	times_location = times_distance
 	latcoord = np.zeros(len(times_distance))
 	loncoord = np.zeros(len(times_distance))
-
+        if workouttype == 'water':
+            workouttype = 'rower'
 	
     try:
 	res = splitstdata(data['cadence'])
@@ -668,89 +622,17 @@ def add_workout_from_stdata(user,importid,data):
 	
     timestr = strftime("%Y%m%d-%H%M%S")
 
-    # auto smoothing
-    pace = df[' Stroke500mPace (sec/500m)'].values
-    velo = 500./pace
-
-    f = df['TimeStamp (sec)'].diff().mean()
-    windowsize = 2*(int(10./(f)))+1
-
-    df['originalvelo'] = velo
-
-    if windowsize > 3 and windowsize<len(velo):
-	velo2 = savgol_filter(velo,windowsize,3)
-    else:
-	velo2 = velo
-
-    velo3 = pd.Series(velo2)
-    velo3 = velo3.replace([-np.inf,np.inf],np.nan)
-    velo3 = velo3.fillna(method='ffill')
-    
-    pace2 = 500./abs(velo3)
-    df[' Stroke500mPace (sec/500m)'] = pace2
-
-    df = df.fillna(0)
-
-    # end autosmoothing
-
     csvfilename ='media/Import_'+str(importid)+'.csv'
 
     res =  df.to_csv(csvfilename+'.gz',index_label='index',
                      compression='gzip')
 
-    # make workout
-    powerperc = 100*np.array([r.pw_ut2,
-                              r.pw_ut1,
-                              r.pw_at,
-                              r.pw_tr,r.pw_an])/r.ftp
+    id,message = dataprep.save_workout_database(csvfilename,r,
+                                                workouttype=workouttype,
+                                                title=title,
+                                                notes=comments)
 
-    rr = rrower(hrmax=r.max,hrut2=r.ut2,
-		hrut1=r.ut1,hrat=r.at,
-		hrtr=r.tr,hran=r.an,ftp=r.ftp,
-                powerperc=powerperc,powerzones=r.powerzones)
-    row = rdata(csvfilename,rower=rr)
-
-    averagehr = row.df[' HRCur (bpm)'].mean()
-    maxhr = row.df[' HRCur (bpm)'].max()
-
-    totaldist = row.df['cum_dist'].max()
-    totaltime = row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
-    totaltime = totaltime+row.df.ix[0,' ElapsedTime (sec)']
-
-
-    hours = int(totaltime/3600.)
-    minutes = int((totaltime - 3600.*hours)/60.)
-    seconds = int(totaltime - 3600.*hours - 60.*minutes)
-    tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
-
-    duration = "%s:%s:%s.%s" % (hours,minutes,seconds,tenths)
-
-
-    summary = row.summary()
-    summary += '\n'
-    summary += row.intervalstats()
-
-    workoutdate = row.rowdatetime.strftime('%Y-%m-%d')
-    workoutstarttime = row.rowdatetime.strftime('%H:%M:%S')
-
-    # check for duplicate start times
-    ws = Workout.objects.filter(starttime=workoutstarttime,
-					user=r)
-    if (len(ws) != 0):
-	print "Warning: This workout probably already exists in the database"
-
-    w = Workout(user=r,name=title,
-		date=workoutdate,workouttype=workouttype,
-		duration=duration,distance=totaldist,
-		weightcategory=r.weightcategory,
-		starttime=workoutstarttime,
-		csvfilename=csvfilename,notes=comments,
-		uploadedtoc2=0,summary=summary,
-		averagehr=averagehr,maxhr=maxhr,
-		startdatetime=rowdatetime)
-    w.save()
-    
-    return w.id
+    return (id,message)
 
 # Checks if user has Concept2 tokens, resets tokens if they are
 # expired.
@@ -3933,12 +3815,23 @@ def workout_getstravaworkout_view(request,stravaid):
     res = stravastuff.get_strava_workout(request.user,stravaid)
     strokedata = res[1]
     data = res[0]
-    id = add_workout_from_strokedata(request.user,stravaid,data,strokedata,
+    id,message = add_workout_from_strokedata(request.user,stravaid,data,strokedata,
 				     source='strava')
     w = Workout.objects.get(id=id)
     w.uploadedtostrava=stravaid
     w.save()
-    url = "/rowers/workout/"+str(id)+"/edit"
+    if message:
+        url = reverse(workout_edit_view,
+                      kwargs = {
+                          'message':message,
+                          'id':id,
+                          })
+    else:
+        url = reverse(workout_edit_view,
+                      kwargs = {
+                          'id':id,
+                          })
+        
     return HttpResponseRedirect(url)
 
 # Imports a workout from SportTracks
@@ -3947,11 +3840,21 @@ def workout_getsporttracksworkout_view(request,sporttracksid):
     res = sporttracksstuff.get_sporttracks_workout(request.user,sporttracksid)
     data = res.json()
 
-    id = add_workout_from_stdata(request.user,sporttracksid,data)
+    id,message = add_workout_from_stdata(request.user,sporttracksid,data)
     w = Workout.objects.get(id=id)
     w.uploadedtosporttracks=sporttracksid
     w.save()
-    url = "/rowers/workout/"+str(id)+"/edit"
+    if message:
+        url = reverse(workout_edit_view,
+                      kwargs = {
+                          'id':id,
+                          'message':message,
+                          })
+    else:
+        url = reverse(workout_edit_view,
+                      kwargs = {
+                          'id':id,
+                          })
     return HttpResponseRedirect(url)
 
 # Imports a workout from Concept2 
@@ -3987,7 +3890,7 @@ def workout_getc2workout_view(request,c2id):
 	if res2.status_code == 200:
 	    strokedata = pd.DataFrame.from_dict(res2.json()['data'])
             # create the workout
-	    id = add_workout_from_strokedata(request.user,c2id,data,strokedata,
+	    id,message = add_workout_from_strokedata(request.user,c2id,data,strokedata,
 					     source='c2')
 	    w = Workout.objects.get(id=id)
 	    w.uploadedtoc2=c2id
@@ -4021,8 +3924,19 @@ def workout_getc2workout_view(request,c2id):
 
                         rowdata.write_csv(w.csvfilename,gzip=True)
                         dataprep.update_strokedata(w.id,rowdata.df)
-                
-	    url = "/rowers/workout/"+str(id)+"/edit"
+
+            if message:
+                url = reverse(workout_edit_view,
+                              kwargs = {
+                                  'message':message,
+                                  'id':id,
+                              })
+            else:
+                url = reverse(workout_edit_view,
+                              kwargs = {
+                                  'id':id,
+                              })
+
 	    return HttpResponseRedirect(url)
 	else:
 	    # message = json.loads(s.text)['message']
@@ -5175,8 +5089,6 @@ def rower_teams_view(request,message='',successmessage=''):
     otherteams = Team.objects.filter(private='open').exclude(rower=r).exclude(manager=request.user).order_by('name')
     teams.remove_expired_invites()
 
-    print "aap",otherteams
-    
     invites = TeamInvite.objects.filter(user=request.user)
     requests = TeamRequest.objects.filter(user=request.user)
     myrequests = TeamRequest.objects.filter(team__in=myteams)