From f1a05db1e477165ebfeaa852a90b5d12ec2daf27 Mon Sep 17 00:00:00 2001
From: Sander Roosendaal <roosendaalsander@gmail.com>
Date: Thu, 14 Sep 2017 17:25:26 +0200
Subject: [PATCH] some bug fixing for invalid TCX files

---
 rowers/dataprep.py | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index 33f70b50..223ad583 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -581,8 +581,12 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
     try:
         latavg = row.df[' latitude'].mean()
         lonavg = row.df[' longitude'].mean()
+        
         tf = TimezoneFinder()
-        timezone_str = tf.timezone_at(lng=lonavg,lat=latavg)
+        try:
+            timezone_str = tf.timezone_at(lng=lonavg,lat=latavg)
+        except ValueError:
+            timezone_str = 'UTC'
         if timezone_str == None:
             timezone_str = tf.closest_timezone_at(lng=lonavg,
                                                   lat=latavg)
@@ -613,6 +617,12 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
     else:
         privacy = 'visible'
 
+    # checking for inf values
+    
+    totaldist = np.nan_to_num(totaldist)
+    maxhr = np.nan_to_num(maxhr)
+    averagehr = np.nan_to_num(averagehr)
+
     # check for duplicate start times and duration
     ws = Workout.objects.filter(startdatetime=workoutstartdatetime,
                                 distance=totaldist,
@@ -621,12 +631,8 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
 	message =  "Warning: This workout probably already exists in the database"
         privacy = 'hidden'
 
-    # checking for inf values
-    totaldist = np.nan_to_num(totaldist)
-    maxhr = np.nan_to_num(maxhr)
-    averagehr = np.nan_to_num(averagehr)
     
-        
+
         
     w = Workout(user=r,name=title,date=workoutdate,
 		workouttype=workouttype,
@@ -650,11 +656,14 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
         # delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
         thesecs = totaltime
         maxt = 1.05*thesecs
-        logarr = datautils.getlogarr(maxt)
-        dfgrouped = df.groupby(['workoutid'])
-        delta,cpvalues,avgpower = datautils.getcp(dfgrouped,logarr)
+        if maxt > 0:
+            logarr = datautils.getlogarr(maxt)
+            dfgrouped = df.groupby(['workoutid'])
+            delta,cpvalues,avgpower = datautils.getcp(dfgrouped,logarr)
         
-        res,btvalues = utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3,r.cpratio)
+            res,btvalues = utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3,r.cpratio)
+        else:
+            res = 0
         if res:
             isbreakthrough = True
             res = datautils.updatecp(delta,cpvalues,r)