def correctbaseline(self, nameofstation):
timeofsunmax = self.simtofmax / 100 # time in HH.hh (13.66 = 13h40) instead of HHhh (1366 = 13h40)
# print 'timeofsunmax = ' , timeofsunmax
# print 't1 = ' , int(timeofsunmax - 2), ' ' , int(np.modf(timeofsunmax - 2)[0]*60)
# t1 = utils.doytodate(self.day.year,self.day.day,int(timeofsunmax - 2), int(np.modf(timeofsunmax - 2)[0]*60))
# t2 = utils.doytodate(self.day.year,self.day.day,int(timeofsunmax + 2), int(np.modf(timeofsunmax + 2)[0]*60))
t1 = utils.doytodate(self.day.year,
self.day.timetuple().tm_yday,
int(timeofsunmax - 4),
int(np.modf(timeofsunmax - 4)[0] * 60))
t2 = utils.doytodate(self.day.year,
self.day.timetuple().tm_yday,
int(timeofsunmax + 4),
int(np.modf(timeofsunmax + 4)[0] * 60))
nsradio = self.datasim.data[(self.datasim.timedata < t1) |
(self.datasim.timedata > t2)]
nstemp = self.datasim.tempLL[(self.datasim.timedata < t1) |
(self.datasim.timedata > t2)]
## subtract the mean of the time when no sun is present in the data
radio = self.datasim.data - np.mean(nsradio)
temp = self.datasim.tempLL - np.mean(nstemp)
nstemp = nstemp - np.mean(nstemp)
## get the file with temperature fit result
fitfile = constant.tempfitfolder + '/tempfit_' + nameofstation + '.npy'
# fitfile = constant.tempfitfolder + '/tempfit_'+nameoflist[:-4] + '.npy'
tempfit = np.load(fitfile)
pfit = np.poly1d(tempfit)
radio = radio - pfit(temp)
## baseline corrected
nsradioc = nsradio - np.mean(nsradio)
nsradioc = nsradioc - pfit(nstemp)
self.pointuncert = np.std(nsradioc)
self.cbaseline = radio
# print datafilename
if os.path.isfile(datafilename):
datafile = open(datafilename, 'rb')
else:
continue
thedata = pickle.load(datafile)
simfile = constant.simresultfolder + str(
stid) + '/exptemp_' + stname + '_' + str(t) + '_' + str(
p) + '_' + str(y) + '_' + str(d) + '.txt'
[h, m, temp] = utils.readtempfile(simfile)
sec = 3600 * h + 60 * m
hour = h * 100 + m * (100. / 60.)
thedata.sim = temp
thedata.timesim = hour
day = utils.doytodate(y, d)
an = analyse.Analyse(day, 0, 0, thedata)
simparam = an.getsimparam()
an.correctbaseline(stname)
signalwindow = 4
fitresult = an.fitdata2(signalwindow)
[fithourarray, fitradio] = an.getdataforfit(4)
if (fitresult.success == True):
# fig = plt.figure()
# plt.plot(fithourarray,fitradio)
# plt.plot(fithourarray, fitresult.best_fit, 'r-')
# plt.gca().text(np.mean(fithourarray),np.mean(fitradio), r'$\frac{\chi^2}{ndl} = $' +str(fitresult.redchi), fontsize=15)
# saving part:
outname = constant.datafit2folder + str(
stid) + '/fit2_' + stname + '_' + str(t) + '_' + str(
############################
## load the root file ##
############################
filename = 'alldata.root'
file = constant.datafolder + 'GIGADuck/' + filename
data = dataset.Dataset(file)
data.loaddata()
data = data.getnewdatawithid(stid)
data.selectleafs()
outfolder = constant.dataresultfolder
years = [2015, 2016, 2017]
for y in years:
for d in range(1, constant.doy[y] + 1, 1):
sdatelim1 = utils.doytodate(y, d, hour=3)
deltat = datetime.timedelta(1)
sdatelim2 = sdatelim1 + deltat
cond = np.where((data.date > sdatelim1) & (data.date < sdatelim2))
daydataset = data.getnewdatasetcond(cond)
if (len(daydataset.date) == 0):
continue
ddata = daydata.Daydata('simpledata', y, d, daydataset.date,
daydataset.radio, daydataset.tempLL,
daydataset.humLL, [], [])
#######################################
## save in pkl format the daily data ##
#######################################
outf = outfolder + str(stid)
deltaphis = deltaphis.astype(int)
deltaphis = np.unique(deltaphis)
for delp in deltaphis:
newp = centerphi + delp
# befantsetup = ttime.time()
ant.antennageo = (newt,newp)
# ant.antennageo = (delt,delp)
ant.setrotationmatrix()
# afterantsetup = ttime.time()
# print 'ant setup = ', afterantsetup - befantsetup
print ant.antennageo
for y,d in zip(goodyear,goodday):
y = int(y)
d = int(d)
date = utils.doytodate(y,d)
totsunflux = sflux.getflux(y,d)
pos = sflux.getposition(y,d)
sec = np.linspace(0,24*60*60,len(pos[0]))
posplot = utils.transformsunpos(pos[0],pos[1])
if detector=='dmx':
newpos = utils.transformsunposdmx(pos[0],pos[1])
else:
# beftransfo = ttime.time()
newpos = utils.transformsunpos(pos[0],pos[1])
newpos = utils.transformtrajectory(newpos[0],newpos[1],ant.rotationmatrix)
# aftertransfo = ttime.time()
# print 'transfi = ', aftertransfo - beftransfo
max = -10
daytemp = np.array([])
hourarray = np.array([])
datafilename = outfolder + '/data_' + str(int(y)) + '_' + str(
int(d)) + '.pkl'
datafilenameref = outfolderref + '/data_' + str(int(y)) + '_' + str(
int(d)) + '.pkl'
if os.path.isfile(datafilename) == False or os.path.isfile(
datafilenameref) == False:
continue
else:
print 'aqui !'
datafile = open(datafilename, 'rb')
datafileref = open(datafilenameref, 'rb')
thedata = pickle.load(datafile)
thedataref = pickle.load(datafileref)
time = thedata.timedata
timeofsunmax = constant.exptime[stid]
t1 = utils.doytodate(int(y), int(d), int(timeofsunmax - 2),
int(np.modf(timeofsunmax - 2)[0]))
t2 = utils.doytodate(int(y), int(d), int(timeofsunmax + 2),
int(np.modf(timeofsunmax + 2)[0]))
radio = thedata.data[(thedata.timedata < t1) | (thedata.timedata > t2)]
radior = thedataref.data[(thedata.timedata < t1) |
(thedata.timedata > t2)]
temp = thedata.tempLL[(thedata.timedata < t1) |
(thedata.timedata > t2)]
radio = radio - np.mean(radio)
radior = radior - np.mean(radior)
temp = temp - np.mean(temp)
nosuntime = np.append(
nosuntime, time[(thedata.timedata < t1) | (thedata.timedata > t2)])
nosunradio = np.append(nosunradio, radio)
nosunradioref = np.append(nosunradioref, radior)
nosuntemp = np.append(nosuntemp, temp)