def testrowprointervals(self):
csvfile='testdata/RP_interval.csv'
assert_equals(rowingdata.get_file_type(csvfile),'rp')
r=rowingdata.RowProParser(csvfile=csvfile)
row=rowingdata.rowingdata(df=r.df)
assert_equals(row.number_of_rows,1674)
totaldist=row.df['cum_dist'].max()
assert_equals(int(totaldist),19026)
assert_equals(row.rowdatetime,datetime.datetime(2016,1,12,19,23,10,878000,utc))
totaltime=row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
assert_equals(int(totaltime),4800)
checks = row.check_consistency()
assert_equals(checks['velo_time_distance'],True)
def testrowpro(self):
csvfile='testdata/RP_testdata.csv'
assert_equals(rowingdata.get_file_type(csvfile),'rp')
r=rowingdata.RowProParser(csvfile=csvfile)
row=rowingdata.rowingdata(df=r.df)
assert_equals(row.number_of_rows,988)
totaldist=row.df['cum_dist'].max()
assert_equals(totaldist,10000)
assert_equals(row.rowdatetime,datetime.datetime(2016,3,15,19,49,48,863000,utc))
totaltime=row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
assert_equals(int(10*totaltime),22653)
checks = row.check_consistency()
assert_equals(checks['velo_time_distance'],True)
def checkfile(f2, verbose=False):
fileformat = rowingdata.get_file_type(f2)
summary = 'a'
notread = 1
if verbose:
print(fileformat)
if len(fileformat) == 3 and fileformat[0] == 'zip':
with zipfile.ZipFile(f2) as z:
f = z.extract(z.namelist()[0], path='C:/Downloads')
fileformat = fileformat[2]
os.remove(f_to_be_deleted)
if fileformat == 'unknown':
return 0
# handle non-Painsled
if (fileformat != 'csv'):
# handle RowPro:
if (fileformat == 'rp'):
row = rowingdata.RowProParser(f2)
# handle TCX
if (fileformat == 'tcx'):
row = rowingdata.TCXParser(f2)
row.write_csv(f2 + 'o.csv')
row = rowingdata.rowingdata(csvfile=f2 + 'o.csv')
os.remove(f2 + 'o.csv')
notread = 0
# handle Mystery
if (fileformat == 'mystery'):
row = rowingdata.MysteryParser(f2)
# handle Quiske
if (fileformat == 'quiske'):
row = rowingdata.QuiskeParser(f2)
# handle RitmoTime
if (fileformat == 'ritmotime'):
row = rowingdata.RitmoTimeParser(f2)
# handle TCX no HR
if (fileformat == 'tcxnohr'):
row = rowingdata.TCXParserNoHR(f2)
row.write_csv(f2 + 'o.csv')
row = rowingdata.rowingdata(csvfile=f2 + 'o.csv')
os.remove(f2 + 'o.csv')
notread = 0
# handle ErgData
if (fileformat == 'ergdata'):
row = rowingdata.ErgDataParser(f2)
# handle CoxMate
if (fileformat == 'coxmate'):
row = rowingdata.CoxMateParser(f2)
# handle BoatCoachOTW
if (fileformat == 'boatcoachotw'):
row = rowingdata.BoatCoachOTWParser(f2)
# handle BoatCoach
if (fileformat == 'boatcoach'):
row = rowingdata.BoatCoachParser(f2)
# handle painsled desktop
if (fileformat == 'painsleddesktop'):
row = rowingdata.painsledDesktopParser(f2)
# handle speed coach GPS
if (fileformat == 'speedcoach'):
row = rowingdata.speedcoachParser(f2)
# handle speed coach GPS 2
if (fileformat == 'speedcoach2'):
row = rowingdata.SpeedCoach2Parser(f2)
summary = row.allstats()
v = rowingdata.get_empower_firmware(f2)
rig = rowingdata.get_empower_rigging(f2)
if (fileformat == 'rowperfect3'):
row = rowingdata.RowPerfectParser(f2)
# handle ErgStick
if (fileformat == 'ergstick'):
row = rowingdata.ErgStickParser(f2)
# handle Kinomap
if (fileformat == 'kinomap'):
row = rowingdata.KinoMapParser(f2)
if (fileformat == 'eth'):
row = rowingdata.ETHParser(f2)
if (fileformat == 'hero'):
row = rowingdata.HeroParser(f2)
# handle FIT
if (fileformat == 'fit'):
row = rowingdata.FITParser(f2)
row.write_csv(f2 + 'o.csv')
row = rowingdata.rowingdata(csvfile=f2 + 'o.csv')
os.remove(f2 + 'o.csv')
notread = 0
# handle Humon
if (fileformat == 'humon'):
row = rowingdata.HumonParser(f2)
row.write_csv(f2 + 'o.csv')
row = rowingdata.rowingdata(csvfile=f2 + 'o.csv')
os.remove(f2 + 'o.csv')
notread = 0
# handle nklinklogbook
if (fileformat == 'nklinklogbook'):
row = rowingdata.NKLiNKLogbookParser(f2)
row.write_csv(f2 + 'o.csv')
row = rowingdata.rowingdata(csvfile=f2 + 'o.csv')
os.remove(f2 + 'o.csv')
notread = 0
# handle smartrow
if (fileformat == 'smartrow'):
row = rowingdata.SmartRowParser(f2)
row.write_csv(f2 + 'o.csv')
row = rowingdata.rowingdata(csvfile=f2 + 'o.csv')
os.remove(f2 + 'o.csv')
notread = 0
# handle workout log (no stroke data)
if (fileformat == 'c2log'):
return 0
if notread:
row = rowingdata.rowingdata(df=row.df)
else:
row = rowingdata.rowingdata(csvfile=f2)
nr_of_rows = row.number_of_rows
distmax = row.df['cum_dist'].max()
timemax = row.df['TimeStamp (sec)'].max() - row.df['TimeStamp (sec)'].min()
nrintervals = len(row.df[' lapIdx'].unique())
mintime = row.df['TimeStamp (sec)'].min()
maxtime = row.df['TimeStamp (sec)'].max()
if verbose:
print(("nr lines", row.number_of_rows))
print(("data ", row.rowdatetime))
print(("dist ", distmax))
print(("Time ", timemax))
print(("Nr intervals ", nrintervals))
print(("Min time ", mintime))
print(("Max time ", maxtime))
res = row.intervalstats_values()
int1time = res[0][0]
int1dist = res[1][0]
if verbose:
print(("Interval 1 time ", int1time))
print(("Interval 1 dist ", int1dist))
y = row.rowdatetime.year
m = row.rowdatetime.month
d = row.rowdatetime.day
h = row.rowdatetime.hour
tz = row.rowdatetime.tzname()
minute = row.rowdatetime.minute
sec = row.rowdatetime.second
results = {
'type': fileformat,
'nr_lines': nr_of_rows,
'year': y,
'month': m,
'day': d,
'hour': h,
'minute': minute,
'second': sec,
'dist': int(distmax),
'seconds': int(timemax),
'nrintervals': nrintervals,
'lap 1 time': int(int1time),
'lap 1 dist': int(int1dist),
'timezone': tz,
'summary': summary,
}
return results