761 lines
22 KiB
Python
761 lines
22 KiB
Python
# This is Data prep used for testing purposes (no Django environment)
|
|
# Uses the debug SQLite database for stroke data
|
|
from rowingdata import rowingdata as rrdata
|
|
|
|
from rowingdata import rower as rrower
|
|
from rowingdata import main as rmain
|
|
|
|
from pandas import DataFrame,Series
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
import itertools
|
|
|
|
from sqlalchemy import create_engine
|
|
import sqlalchemy as sa
|
|
|
|
from rowsandall_app.settings import DATABASES
|
|
#from rowsandall_app.settings_dev import DATABASES
|
|
|
|
from utils import lbstoN
|
|
|
|
|
|
try:
|
|
user = DATABASES['default']['USER']
|
|
except KeyError:
|
|
user = ''
|
|
try:
|
|
password = DATABASES['default']['PASSWORD']
|
|
except KeyError:
|
|
password = ''
|
|
|
|
try:
|
|
database_name = DATABASES['default']['NAME']
|
|
except KeyError:
|
|
database_name = ''
|
|
try:
|
|
host = DATABASES['default']['HOST']
|
|
except KeyError:
|
|
host = ''
|
|
try:
|
|
port = DATABASES['default']['PORT']
|
|
except KeyError:
|
|
port = ''
|
|
|
|
database_url = 'mysql://{user}:{password}@{host}:{port}/{database_name}'.format(
|
|
user=user,
|
|
password=password,
|
|
database_name=database_name,
|
|
host=host,
|
|
port=port,
|
|
)
|
|
|
|
|
|
database_url_debug = 'sqlite:///'+database_name
|
|
|
|
# mapping the DB column names to the CSV file column names
|
|
columndict = {
|
|
'time':'TimeStamp (sec)',
|
|
'hr':' HRCur (bpm)',
|
|
'pace':' Stroke500mPace (sec/500m)',
|
|
'spm':' Cadence (stokes/min)',
|
|
'power':' Power (watts)',
|
|
'averageforce':' AverageDriveForce (lbs)',
|
|
'drivelength':' DriveLength (meters)',
|
|
'peakforce':' PeakDriveForce (lbs)',
|
|
'distance':' Horizontal (meters)',
|
|
'catch':'catch',
|
|
'finish':'finish',
|
|
'peakforceangle':'peakforceangle',
|
|
'wash':'wash',
|
|
'slip':'wash',
|
|
'workoutstate':' WorkoutState',
|
|
}
|
|
|
|
from scipy.signal import savgol_filter
|
|
|
|
import datetime
|
|
|
|
def niceformat(values):
|
|
out = []
|
|
for v in values:
|
|
formattedv = strfdelta(v)
|
|
out.append(formattedv)
|
|
|
|
return out
|
|
|
|
def strfdelta(tdelta):
|
|
try:
|
|
minutes,seconds = divmod(tdelta.seconds,60)
|
|
tenths = int(tdelta.microseconds/1e5)
|
|
except AttributeError:
|
|
minutes,seconds = divmod(tdelta.view(np.int64),60e9)
|
|
seconds,rest = divmod(seconds,1e9)
|
|
tenths = int(rest/1e8)
|
|
res = "{minutes:0>2}:{seconds:0>2}.{tenths:0>1}".format(
|
|
minutes=minutes,
|
|
seconds=seconds,
|
|
tenths=tenths,
|
|
)
|
|
|
|
return res
|
|
|
|
def nicepaceformat(values):
|
|
out = []
|
|
for v in values:
|
|
formattedv = strfdelta(v)
|
|
out.append(formattedv)
|
|
|
|
|
|
return out
|
|
|
|
def timedeltaconv(x):
|
|
if not np.isnan(x):
|
|
dt = datetime.timedelta(seconds=x)
|
|
else:
|
|
dt = datetime.timedelta(seconds=350.)
|
|
|
|
|
|
return dt
|
|
|
|
def rdata(file,rower=rrower()):
|
|
try:
|
|
res = rrdata(file,rower=rower)
|
|
except IOError:
|
|
try:
|
|
res = rrdata(file+'.gz',rower=rower)
|
|
except IOError:
|
|
res = 0
|
|
|
|
return res
|
|
|
|
# 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,
|
|
summary='',
|
|
makeprivate=False,
|
|
oarlength=2.89,inboard=0.88):
|
|
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)
|
|
|
|
checks = row.check_consistency()
|
|
allchecks = 1
|
|
for key,value in checks.iteritems():
|
|
if not value:
|
|
allchecks = 0
|
|
|
|
if not allchecks:
|
|
#row.repair()
|
|
pass
|
|
|
|
|
|
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()
|
|
if f !=0:
|
|
windowsize = 2*(int(10./(f)))+1
|
|
else:
|
|
windowsize = 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.)
|
|
if minutes>59:
|
|
minutes = 59
|
|
if not message:
|
|
message = 'Warning: there is something wrong with the workout duration'
|
|
|
|
seconds = int(totaltime - 3600.*hours - 60.*minutes)
|
|
if seconds > 59:
|
|
seconds = 59
|
|
if not message:
|
|
message = 'Warning: there is something wrong with the workout duration'
|
|
|
|
tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
|
|
if tenths > 9:
|
|
tenths = 9
|
|
if not message:
|
|
message = 'Warning: there is something wrong with the workout duration'
|
|
|
|
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)
|
|
|
|
if makeprivate:
|
|
privacy = 'private'
|
|
else:
|
|
privacy = 'visible'
|
|
|
|
# check for duplicate start times
|
|
ws = Workout.objects.filter(startdatetime=workoutstartdatetime,
|
|
user=r)
|
|
if (len(ws) != 0):
|
|
message = "Warning: This workout probably already exists in the database"
|
|
privacy = 'private'
|
|
|
|
|
|
|
|
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,
|
|
inboard=inboard,oarlength=oarlength,
|
|
privacy=privacy)
|
|
|
|
|
|
w.save()
|
|
|
|
if privacy == 'visible':
|
|
ts = Team.objects.filter(rower=r)
|
|
for t in ts:
|
|
w.team.add(t)
|
|
|
|
# put stroke data in database
|
|
res = dataprep(row.df,id=w.id,bands=True,
|
|
barchart=True,otwpower=True,empower=True,inboard=inboard)
|
|
|
|
return (w.id,message)
|
|
|
|
def handle_nonpainsled(f2,fileformat,summary=''):
|
|
oarlength = 2.89
|
|
inboard = 0.88
|
|
# handle RowPro:
|
|
if (fileformat == 'rp'):
|
|
row = RowProParser(f2)
|
|
# handle TCX
|
|
if (fileformat == 'tcx'):
|
|
row = TCXParser(f2)
|
|
|
|
# handle Mystery
|
|
if (fileformat == 'mystery'):
|
|
row = MysteryParser(f2)
|
|
|
|
# handle TCX no HR
|
|
if (fileformat == 'tcxnohr'):
|
|
row = TCXParserNoHR(f2)
|
|
|
|
# handle RowPerfect
|
|
if (fileformat == 'rowperfect3'):
|
|
row = RowPerfectParser(f2)
|
|
|
|
# handle ErgData
|
|
if (fileformat == 'ergdata'):
|
|
row = ErgDataParser(f2)
|
|
|
|
# handle Mike
|
|
if (fileformat == 'bcmike'):
|
|
row = BoatCoachAdvancedParser(f2)
|
|
|
|
# handle BoatCoach
|
|
if (fileformat == 'boatcoach'):
|
|
row = BoatCoachParser(f2)
|
|
|
|
# handle painsled desktop
|
|
if (fileformat == 'painsleddesktop'):
|
|
row = painsledDesktopParser(f2)
|
|
|
|
# handle speed coach GPS
|
|
if (fileformat == 'speedcoach'):
|
|
row = speedcoachParser(f2)
|
|
|
|
# handle speed coach GPS 2
|
|
if (fileformat == 'speedcoach2'):
|
|
row = SpeedCoach2Parser(f2)
|
|
try:
|
|
oarlength,inboard = get_empower_rigging(f2)
|
|
summary = row.allstats()
|
|
except:
|
|
pass
|
|
|
|
|
|
# handle ErgStick
|
|
if (fileformat == 'ergstick'):
|
|
row = ErgStickParser(f2)
|
|
|
|
# handle FIT
|
|
if (fileformat == 'fit'):
|
|
row = FITParser(f2)
|
|
s = fitsummarydata(f2)
|
|
s.setsummary()
|
|
summary = s.summarytext
|
|
|
|
|
|
f_to_be_deleted = f2
|
|
# should delete file
|
|
f2 = f2[:-4]+'o.csv'
|
|
row.write_csv(f2,gzip=True)
|
|
|
|
#os.remove(f2)
|
|
try:
|
|
os.remove(f_to_be_deleted)
|
|
except:
|
|
os.remove(f_to_be_deleted+'.gz')
|
|
|
|
return (f2,summary,oarlength,inboard)
|
|
|
|
|
|
# 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
|
|
def new_workout_from_file(r,f2,
|
|
workouttype='rower',
|
|
title='Workout',
|
|
makeprivate=False,
|
|
notes=''):
|
|
message = None
|
|
fileformat = get_file_type(f2)
|
|
summary = ''
|
|
oarlength = 2.89
|
|
inboard = 0.88
|
|
if len(fileformat)==3 and fileformat[0]=='zip':
|
|
f_to_be_deleted = f2
|
|
with zipfile.ZipFile(f2) as z:
|
|
for fname in z.namelist():
|
|
f3 = z.extract(fname,path='media/')
|
|
id,message,f2 = new_workout_from_file(r,f3,
|
|
workouttype=workouttype,
|
|
makeprivate=makeprivate,
|
|
title = title,
|
|
notes='')
|
|
os.remove(f_to_be_deleted)
|
|
return id,message,f2
|
|
|
|
# Some people try to upload Concept2 logbook summaries
|
|
if fileformat == 'c2log':
|
|
os.remove(f2)
|
|
message = "This C2 logbook summary does not contain stroke data. Please download the Export Stroke Data file from the workout details on the C2 logbook."
|
|
return (0,message,f2)
|
|
|
|
if fileformat == 'nostrokes':
|
|
os.remove(f2)
|
|
message = "It looks like this file doesn't contain stroke data."
|
|
return (0,message,f2)
|
|
|
|
# Some people try to upload RowPro summary logs
|
|
if fileformat == 'rowprolog':
|
|
os.remove(f2)
|
|
message = "This RowPro logbook summary does not contain stroke data. Please use the Stroke Data CSV file for the individual workout in your log."
|
|
return (0,message,f2)
|
|
|
|
# Sometimes people try an unsupported file type.
|
|
# Send an email to info@rowsandall.com with the file attached
|
|
# for me to check if it is a bug, or a new file type
|
|
# worth supporting
|
|
if fileformat == 'unknown':
|
|
message = "We couldn't recognize the file type"
|
|
if settings.DEBUG:
|
|
res = handle_sendemail_unrecognized.delay(f2,
|
|
r.user.email)
|
|
|
|
else:
|
|
res = queuehigh.enqueue(handle_sendemail_unrecognized,
|
|
f2,r.user.email)
|
|
return (0,message,f2)
|
|
|
|
# handle non-Painsled by converting it to painsled compatible CSV
|
|
if (fileformat != 'csv'):
|
|
try:
|
|
f2,summary,oarlength,inboard = handle_nonpainsled(f2,
|
|
fileformat,
|
|
summary=summary)
|
|
except:
|
|
errorstring = str(sys.exc_info()[0])
|
|
message = 'Something went wrong: '+errorstring
|
|
return (0,message,'')
|
|
|
|
|
|
|
|
dosummary = (fileformat != 'fit')
|
|
id,message = save_workout_database(f2,r,
|
|
workouttype=workouttype,
|
|
makeprivate=makeprivate,
|
|
dosummary=dosummary,
|
|
summary=summary,
|
|
inboard=inboard,oarlength=oarlength,
|
|
title=title)
|
|
|
|
return (id,message,f2)
|
|
|
|
def delete_strokedata(id,debug=True):
|
|
if debug:
|
|
engine = create_engine(database_url_debug, echo=False)
|
|
else:
|
|
engine = create_engine(database_url, echo=False)
|
|
query = sa.text('DELETE FROM strokedata WHERE workoutid={id};'.format(
|
|
id=id,
|
|
))
|
|
with engine.connect() as conn, conn.begin():
|
|
try:
|
|
result = conn.execute(query)
|
|
except:
|
|
print "Database Locked"
|
|
conn.close()
|
|
engine.dispose()
|
|
|
|
def update_strokedata(id,df,debug=True):
|
|
delete_strokedata(id)
|
|
rowdata = dataprep(df,id=id,bands=True,barchart=True,otwpower=True,
|
|
debug=debug)
|
|
|
|
def testdata(time,distance,pace,spm):
|
|
t1 = np.issubdtype(time,np.number)
|
|
t2 = np.issubdtype(distance,np.number)
|
|
t3 = np.issubdtype(pace,np.number)
|
|
t4 = np.issubdtype(spm,np.number)
|
|
|
|
return t1 and t2 and t3 and t4
|
|
|
|
|
|
def getsmallrowdata_db(columns,ids=[]):
|
|
|
|
data = read_cols_df_sql(ids,columns)
|
|
|
|
return data
|
|
|
|
|
|
|
|
|
|
def read_cols_df_sql(ids,columns,debug=True):
|
|
columns = list(columns)+['distance','spm']
|
|
columns = [x for x in columns if x != 'None']
|
|
columns = list(set(columns))
|
|
cls = ''
|
|
if debug:
|
|
engine = create_engine(database_url_debug, echo=False)
|
|
else:
|
|
engine = create_engine(database_url, echo=False)
|
|
|
|
|
|
for column in columns:
|
|
cls += column+', '
|
|
cls = cls[:-2]
|
|
if len(ids) == 0:
|
|
query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid=0'.format(
|
|
columns = cls,
|
|
))
|
|
elif len(ids) == 1:
|
|
query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid={id}'.format(
|
|
id = ids[0],
|
|
columns = cls,
|
|
))
|
|
else:
|
|
query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids}'.format(
|
|
columns = cls,
|
|
ids = tuple(ids),
|
|
))
|
|
|
|
df = pd.read_sql_query(query,engine)
|
|
engine.dispose()
|
|
return df
|
|
|
|
|
|
def read_df_sql(id,debug=True):
|
|
if debug:
|
|
engine = create_engine(database_url_debug, echo=False)
|
|
else:
|
|
engine = create_engine(database_url, echo=False)
|
|
|
|
df = pd.read_sql_query(sa.text('SELECT * FROM strokedata WHERE workoutid={id}'.format(
|
|
id=id)), engine)
|
|
|
|
engine.dispose()
|
|
return df
|
|
|
|
|
|
|
|
|
|
|
|
def smalldataprep(therows,xparam,yparam1,yparam2):
|
|
df = pd.DataFrame()
|
|
if yparam2 == 'None':
|
|
yparam2 = 'power'
|
|
df[xparam] = []
|
|
df[yparam1] = []
|
|
df[yparam2] = []
|
|
df['distance'] = []
|
|
df['spm'] = []
|
|
for workout in therows:
|
|
f1 = workout.csvfilename
|
|
|
|
try:
|
|
rowdata = dataprep(rrdata(f1).df)
|
|
|
|
rowdata = pd.DataFrame({xparam: rowdata[xparam],
|
|
yparam1: rowdata[yparam1],
|
|
yparam2: rowdata[yparam2],
|
|
'distance': rowdata['distance'],
|
|
'spm': rowdata['spm'],
|
|
}
|
|
)
|
|
df = pd.concat([df,rowdata],ignore_index=True)
|
|
except IOError:
|
|
try:
|
|
rowdata = dataprep(rrdata(f1+'.gz').df)
|
|
rowdata = pd.DataFrame({xparam: rowdata[xparam],
|
|
yparam1: rowdata[yparam1],
|
|
yparam2: rowdata[yparam2],
|
|
'distance': rowdata['distance'],
|
|
'spm': rowdata['spm'],
|
|
}
|
|
)
|
|
df = pd.concat([df,rowdata],ignore_index=True)
|
|
except IOError:
|
|
pass
|
|
|
|
return df
|
|
|
|
|
|
def dataprep(rowdatadf,id=0,bands=True,barchart=True,otwpower=True,
|
|
empower=True,debug=True):
|
|
|
|
if rowdatadf.empty:
|
|
return 0
|
|
|
|
rowdatadf.set_index([range(len(rowdatadf))],inplace=True)
|
|
t = rowdatadf.ix[:,'TimeStamp (sec)']
|
|
t = pd.Series(t-rowdatadf.ix[0,'TimeStamp (sec)'])
|
|
|
|
row_index = rowdatadf.ix[:,' Stroke500mPace (sec/500m)'] > 3000
|
|
rowdatadf.loc[row_index,' Stroke500mPace (sec/500m)'] = 3000.
|
|
|
|
p = rowdatadf.ix[:,' Stroke500mPace (sec/500m)']
|
|
hr = rowdatadf.ix[:,' HRCur (bpm)']
|
|
spm = rowdatadf.ix[:,' Cadence (stokes/min)']
|
|
cumdist = rowdatadf.ix[:,'cum_dist']
|
|
|
|
power = rowdatadf.ix[:,' Power (watts)']
|
|
averageforce = rowdatadf.ix[:,' AverageDriveForce (lbs)']
|
|
drivelength = rowdatadf.ix[:,' DriveLength (meters)']
|
|
try:
|
|
workoutstate = rowdatadf.ix[:,' WorkoutState']
|
|
except KeyError:
|
|
workoutstate = 0*hr
|
|
|
|
peakforce = rowdatadf.ix[:,' PeakDriveForce (lbs)']
|
|
|
|
forceratio = averageforce/peakforce
|
|
forceratio = forceratio.fillna(value=0)
|
|
|
|
f = rowdatadf['TimeStamp (sec)'].diff().mean()
|
|
if f != 0:
|
|
windowsize = 2*(int(10./(f)))+1
|
|
else:
|
|
windowsize = 1
|
|
if windowsize <= 3:
|
|
windowsize = 5
|
|
|
|
if windowsize > 3 and windowsize<len(hr):
|
|
spm = savgol_filter(spm,windowsize,3)
|
|
hr = savgol_filter(hr,windowsize,3)
|
|
drivelength = savgol_filter(drivelength,windowsize,3)
|
|
forceratio = savgol_filter(forceratio,windowsize,3)
|
|
|
|
try:
|
|
t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
|
|
except TypeError:
|
|
t2 = 0*t
|
|
|
|
|
|
p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
|
|
|
|
|
|
drivespeed = drivelength/rowdatadf[' DriveTime (ms)']*1.0e3
|
|
drivespeed = drivespeed.fillna(value=0)
|
|
driveenergy = drivelength*averageforce*lbstoN
|
|
distance = rowdatadf.ix[:,'cum_dist']
|
|
|
|
|
|
|
|
|
|
data = DataFrame(
|
|
dict(
|
|
time = t*1e3,
|
|
hr = hr,
|
|
pace = p*1e3,
|
|
spm = spm,
|
|
cumdist = cumdist,
|
|
ftime = niceformat(t2),
|
|
fpace = nicepaceformat(p2),
|
|
driveenergy=driveenergy,
|
|
power=power,
|
|
workoutstate=workoutstate,
|
|
averageforce=averageforce,
|
|
drivelength=drivelength,
|
|
peakforce=peakforce,
|
|
forceratio=forceratio,
|
|
distance=distance,
|
|
drivespeed=drivespeed,
|
|
)
|
|
)
|
|
|
|
if bands:
|
|
# HR bands
|
|
data['hr_ut2'] = rowdatadf.ix[:,'hr_ut2']
|
|
data['hr_ut1'] = rowdatadf.ix[:,'hr_ut1']
|
|
data['hr_at'] = rowdatadf.ix[:,'hr_at']
|
|
data['hr_tr'] = rowdatadf.ix[:,'hr_tr']
|
|
data['hr_an'] = rowdatadf.ix[:,'hr_an']
|
|
data['hr_max'] = rowdatadf.ix[:,'hr_max']
|
|
data['hr_bottom'] = 0.0*data['hr']
|
|
|
|
if barchart:
|
|
# time increments for bar chart
|
|
time_increments = rowdatadf.ix[:,' ElapsedTime (sec)'].diff()
|
|
time_increments[0] = time_increments[1]
|
|
time_increments = 0.5*time_increments+0.5*np.abs(time_increments)
|
|
x_right = (t2+time_increments.apply(lambda x:timedeltaconv(x)))
|
|
|
|
data['x_right'] = x_right
|
|
|
|
if empower:
|
|
try:
|
|
wash = rowdatadf.ix[:,'wash']
|
|
catch = rowdatadf.ix[:,'catch']
|
|
finish = rowdatadf.ix[:,'finish']
|
|
peakforceangle = rowdatadf.ix[:,'peakforceangle']
|
|
driveenergy = rowdatadf.ix[:,'driveenergy']
|
|
drivelength = driveenergy/(averageforce*4.44822)
|
|
slip = rowdatadf.ix[:,'slip']
|
|
if windowsize > 3:
|
|
wash = savgol_filter(wash,windowsize,3)
|
|
slip = savgol_filter(slip,windowsize,3)
|
|
catch = savgol_filter(catch,windowsize,3)
|
|
finish = savgol_filter(finish,windowsize,3)
|
|
peakforceangle = savgol_filter(peakforceangle,windowsize,3)
|
|
driveenergy = savgol_filter(driveenergy,windowsize,3)
|
|
drivelength = savgol_filter(drivelength,windowsize,3)
|
|
|
|
data['wash'] = wash
|
|
data['catch'] = catch
|
|
data['slip'] = slip
|
|
data['finish'] = finish
|
|
data['peakforceangle'] = peakforceangle
|
|
data['driveenergy'] = driveenergy
|
|
data['drivelength'] = drivelength
|
|
data['peakforce'] = peakforce
|
|
data['averageforce'] = averageforce
|
|
except KeyError:
|
|
pass
|
|
|
|
if otwpower:
|
|
try:
|
|
nowindpace = rowdatadf.ix[:,'nowindpace']
|
|
except KeyError:
|
|
nowindpace = p
|
|
try:
|
|
equivergpower = rowdatadf.ix[:,'equivergpower']
|
|
except KeyError:
|
|
equivergpower = 0*p+50.
|
|
|
|
nowindpace2 = nowindpace.apply(lambda x: timedeltaconv(x))
|
|
ergvelo = (equivergpower/2.8)**(1./3.)
|
|
|
|
ergpace = 500./ergvelo
|
|
ergpace[ergpace == np.inf] = 240.
|
|
ergpace2 = ergpace.apply(lambda x: timedeltaconv(x))
|
|
|
|
velo = 500./p
|
|
|
|
ergpw = 2.8*velo**3
|
|
efficiency = 100.*ergpw/power
|
|
|
|
efficiency = efficiency.replace([-np.inf,np.inf],np.nan)
|
|
efficiency.fillna(method='ffill')
|
|
|
|
|
|
data['ergpace'] = ergpace*1e3
|
|
data['nowindpace'] = nowindpace*1e3
|
|
data['equivergpower'] = equivergpower
|
|
data['fergpace'] = nicepaceformat(ergpace2)
|
|
data['fnowindpace'] = nicepaceformat(nowindpace2)
|
|
data['efficiency'] = efficiency
|
|
|
|
data = data.replace([-np.inf,np.inf],np.nan)
|
|
data = data.fillna(method='ffill')
|
|
|
|
# write data if id given
|
|
if id != 0:
|
|
data['workoutid'] = id
|
|
if debug:
|
|
engine = create_engine(database_url_debug, echo=False)
|
|
else:
|
|
engine = create_engine(database_url, echo=False)
|
|
with engine.connect() as conn, conn.begin():
|
|
data.to_sql('strokedata',engine,if_exists='append',index=False)
|
|
conn.close()
|
|
engine.dispose()
|
|
return data
|