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some bug fixing for invalid TCX files

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Sander Roosendaal
2017-09-14 17:25:26 +02:00
parent 91547b1af0
commit f1a05db1e4
1 changed files with 19 additions and 10 deletions
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29
rowers/dataprep.py
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@@ -581,8 +581,12 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
try: try:
latavg = row.df[' latitude'].mean() latavg = row.df[' latitude'].mean()
lonavg = row.df[' longitude'].mean() lonavg = row.df[' longitude'].mean()
tf = TimezoneFinder() 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: if timezone_str == None:
timezone_str = tf.closest_timezone_at(lng=lonavg, timezone_str = tf.closest_timezone_at(lng=lonavg,
lat=latavg) lat=latavg)
@@ -613,6 +617,12 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
else: else:
privacy = 'visible' 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 # check for duplicate start times and duration
ws = Workout.objects.filter(startdatetime=workoutstartdatetime, ws = Workout.objects.filter(startdatetime=workoutstartdatetime,
distance=totaldist, 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" message = "Warning: This workout probably already exists in the database"
privacy = 'hidden' 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, w = Workout(user=r,name=title,date=workoutdate,
workouttype=workouttype, workouttype=workouttype,
@@ -650,11 +656,14 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
# delta,cpvalues,avgpower = datautils.getsinglecp(row.df) # delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
thesecs = totaltime thesecs = totaltime
maxt = 1.05*thesecs maxt = 1.05*thesecs
logarr = datautils.getlogarr(maxt) if maxt > 0:
dfgrouped = df.groupby(['workoutid']) logarr = datautils.getlogarr(maxt)
delta,cpvalues,avgpower = datautils.getcp(dfgrouped,logarr) 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: if res:
isbreakthrough = True isbreakthrough = True
res = datautils.updatecp(delta,cpvalues,r) res = datautils.updatecp(delta,cpvalues,r)