Web Import now uses dataprep
This commit is contained in:
Sander Roosendaal
@@ -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
|
||||
|
||||
208
rowers/views.py
208
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):
|
||||
def workout_getstravaworkout_view(request,stravaid):
|
||||
res = stravastuff.get_strava_workout(request.user,stravaid)
|
||||
strokedata = res[1]
|
||||
data = res[0]
|
||||
data = res[0]
|
||||
id,message = add_workout_from_strokedata(request.user,stravaid,data,strokedata,
|
||||
source='strava')
|
||||
w = Workout.objects.get(id=id)
|
||||
w.uploadedtostrava=stravaid
|
||||
w.save()
|
||||
w.save()
|
||||
if message:
|
||||
url = reverse(workout_edit_view,
|
||||
kwargs = {
|
||||
'message':message,
|
||||
'id':id,
|
||||
})
|
||||
else:
|
||||
url = reverse(workout_edit_view,
|
||||
kwargs = {
|
||||
'id':id,
|
||||
})
|
||||
|
||||
return HttpResponseRedirect(url)
|
||||
|
||||
@@ -3947,11 +3840,21 @@ def workout_getsporttracksworkout_view(request,sporttracksid):
|
||||
def workout_getsporttracksworkout_view(request,sporttracksid):
|
||||
res = sporttracksstuff.get_sporttracks_workout(request.user,sporttracksid)
|
||||
data = res.json()
|
||||
|
||||
|
||||
id,message = add_workout_from_stdata(request.user,sporttracksid,data)
|
||||
w = Workout.objects.get(id=id)
|
||||
w.uploadedtosporttracks=sporttracksid
|
||||
w.save()
|
||||
w.save()
|
||||
if message:
|
||||
url = reverse(workout_edit_view,
|
||||
kwargs = {
|
||||
'id':id,
|
||||
'message':message,
|
||||
})
|
||||
else:
|
||||
url = reverse(workout_edit_view,
|
||||
kwargs = {
|
||||
'id':id,
|
||||
})
|
||||
return HttpResponseRedirect(url)
|
||||
|
||||
@@ -3987,7 +3890,7 @@ def workout_getc2workout_view(request,c2id):
|
||||
# We have stroke data
|
||||
if res2.status_code == 200:
|
||||
strokedata = pd.DataFrame.from_dict(res2.json()['data'])
|
||||
# create the workout
|
||||
# create the workout
|
||||
id,message = add_workout_from_strokedata(request.user,c2id,data,strokedata,
|
||||
source='c2')
|
||||
w = Workout.objects.get(id=id)
|
||||
@@ -4021,8 +3924,19 @@ def workout_getc2workout_view(request,c2id):
|
||||
units,types,results)
|
||||
|
||||
rowdata.write_csv(w.csvfilename,gzip=True)
|
||||
dataprep.update_strokedata(w.id,rowdata.df)
|
||||
|
||||
dataprep.update_strokedata(w.id,rowdata.df)
|
||||
|
||||
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:
|
||||
@@ -5175,8 +5089,6 @@ def rower_teams_view(request,message='',successmessage=''):
|
||||
myteams = Team.objects.filter(manager=request.user)
|
||||
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)
|
||||
|
||||
Reference in New Issue
Block a user