def testCosmicTimeMasking(self):
"""
tests the cosmic time masking function
"""
ymax = sys.float_info.max/100.0
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
seq = '121229'
fn = FileName.FileName(run, sundownDate, obsDate+"-"+seq)
beginTime = 123.247400
endTime = 123.248400
cosmic = Cosmic(fn, beginTime=beginTime, endTime=endTime)
stride = 10
threshold = 100
populationMax=2000
nSigma=10
dictionary0 = cosmic.findCosmics(stride=stride,
threshold=threshold,
populationMax=populationMax,
nSigma=nSigma)
interval = dictionary0['interval']
plt.clf()
plt.plot(dictionary0['timeHgValues'])
for oneInt in interval:
x0 = (oneInt[0]-beginTime)*cosmic.file.ticksPerSec
x1 = (oneInt[1]-beginTime)*cosmic.file.ticksPerSec
plt.fill_between((x0,x1),(0,0),(ymax,ymax),alpha=0.2,color='red')
plt.title("timeHgValues stride=%d threshold=%d nSigma=%d"%(stride,threshold,nSigma))
plt.xlabel("ticks after t=%f seconds"%beginTime)
plt.yscale("symlog",linthreshy=0.5)
plt.ylim(0,dictionary0['timeHgValues'].max())
plt.savefig("testCosmicTimeMasking-0.png")
ObsFile.ObsFile.writeCosmicIntervalToFile(interval,
cosmic.file.ticksPerSec,
'junk.h5')
cosmic.file.loadCosmicMask('junk.h5')
dictionary1 = cosmic.findCosmics(populationMax=1000)
plt.clf()
hist = dictionary0['populationHg'][0]
bins = np.arange(len(hist))
plt.plot(bins, hist)
plt.ylabel("no mask")
plt.xscale('log')
plt.yscale('log')
hist = dictionary1['populationHg'][0]
bins = np.arange(len(hist))
plt.plot(bins, hist, color='g')
plt.ylabel("cosmic mask")
plt.xscale('log')
plt.yscale('log')
plt.savefig("testCosmicTimeMasking.png")
def testSecondBoundary2(self):
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
seq = '121229'
t0 = 123
t1 = 133
nSigma = 1
stride = 1
threshold = 15
fn = FileName.FileName(run, sundownDate, obsDate+"-"+seq)
cosmic = Cosmic(fn, beginTime=t0, endTime=t1,
loggingLevel=logging.CRITICAL)
fc = cosmic.findCosmics(stride=stride,
threshold=threshold,
populationMax=2000,
nSigma=nSigma)
print "len if timeHgValues=",len(fc['timeHgValues'])
plt.clf()
t = np.arange(len(fc['timeHgValues']))/1e6
plt.plot(t,fc['timeHgValues'])
plt.ylabel("number of photons per tick")
plt.xlabel("time after %.1f (sec)"%t0)
plt.title("run=%s obsDate=%s seq=%s t0=%d t1=%d"%(run,obsDate,seq,t0,t1))
plt.grid(axis='x')
#plt.yscale('symlog', linthreshy=0.9)
plt.yscale('log')
plt.ylim(ymin=-0.1)
plt.savefig(inspect.stack()[0][3]+".png")
def findv1(self):
populationMax=2000
ySum = np.zeros(populationMax)
frameSum = 'none'
seq5 = self.s['seq5'].split()
for seq in seq5:
print "seq=",seq
outfileName = "cosmicTimeList-"+seq+".pkl"
if not os.path.exists(outfileName):
fn = FileName(self.s['run'],
self.s['sundownDate'],
self.s['obsDate']+"-"+str(seq))
cosmic = Cosmic(fn,
beginTime=self.s['beginTime'],
endTime=self.s['endTime'],
loggingLevel = logging.INFO)
fc = cosmic.findCosmics(stride=int(self.s['stride']),
threshold=int(self.s['threshold']),
populationMax=populationMax,
nSigma=float(self.s['nSigma']))
outfile = open(outfileName, "wb")
pickle.dump(fc['cosmicTimeList'],outfile)
pickle.dump(fc['binContents'],outfile)
outfile.close()
cfn = "cosmicMask-%s.h5"%seq
ObsFile.writeCosmicIntervalToFile(fc['interval'],1.0e6, cfn,
self.s['beginTime'],
self.s['endTime'],
int(self.s['stride']),
int(self.s['threshold']),
float(self.s['nSigma']),
populationMax)
del cosmic
def run(self):
for obs in self.param['obsSequence']:
fn = FileName.FileName(run=self.param['run'],
date=self.param['sunsetDate'],
tstamp = obs)
cosmic = Cosmic(fn,
loggingLevel=logging.INFO,
beginTime=self.s['beginTime'],
endTime=self.s['endTime'])
fc = cosmic.findCosmics(stride=int(self.s['stride']),
threshold=int(self.s['threshold']),
populationMax=int(self.s['populationMax']),
nSigma=float(self.s['nSigma']),
writeCosmicMask=True)
cosmic = Cosmic(fn,
loggingLevel=logging.INFO,
beginTime=self.s['beginTime'],
endTime=self.s['endTime'],
applyCosmicMask=True)
fcm = cosmic.findCosmics(stride=int(self.s['stride']),
threshold=int(self.s['threshold']),
populationMax=int(self.s['populationMax']),
nSigma=float(self.s['nSigma']),
writeCosmicMask=False)
p = {
'populationHg':fc['populationHg'][0],
'populationHgM':fcm['populationHg'][0],
'pps':fc['pps'],
'ppsM':fcm['pps'],
'ppmMasked':fc['ppmMasked'],
'ppsTime':fc['ppsTime'],
'beginTime':cosmic.beginTime,
'endTime':cosmic.endTime
}
outfileName = "cosmic-summary-"+obs+".pkl"
outfile = open(outfileName, "wb")
pickle.dump(p,outfile)
outfile.close()
def test_findCosmics(self):
run = 'PAL2012'
obsDate = '20121211'
seq = '20121212-113212'
fn = FileName.FileName(run, obsDate, seq)
cosmic = Cosmic(fn, beginTime= 222, endTime=232,\
nBinsPerSec = 10, loggingLevel=logging.FATAL)
stride = 10
threshold = 20
populationMax = 2000
fc = cosmic.findCosmics(stride, threshold, populationMax)
cosmicTimeList = fc['cosmicTimeList']
# 4 events found on 2013-09-27
self.assertEquals(4,len(cosmicTimeList))
def testExponAndGaussFit(self):
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
seq = '131254'
fn = FileName(run, sundownDate, obsDate+"-"+seq)
beginTime=79.248482
endTime = beginTime+200*1e-6
cosmic = Cosmic(fn, beginTime=beginTime,endTime=endTime)
dictionary = cosmic.findCosmics(populationMax=1000)
hist = np.array(dictionary['timeHgValues'])
bins = np.arange(len(hist))
plt.plot(bins, hist)
plt.savefig(inspect.stack()[0][3]+".png")
def testSecondBoundary(self):
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
seq = '121229'
t0 = 123
t1 = 133
nSigma = 1
stride = 1
threshold = 15
fn = FileName.FileName(run, sundownDate, obsDate+"-"+seq)
tsum = None
for t in range(t0,t1,1):
beginTime = t-15e-5
endTime = t+15e-5
cosmic = Cosmic(fn, beginTime=beginTime, endTime=endTime,
loggingLevel=logging.CRITICAL)
fc = cosmic.findCosmics(stride=stride,
threshold=threshold,
populationMax=2000,
nSigma=nSigma)
if tsum == None:
tsum = np.array(fc['timeHgValues'])
else:
tsum += np.array(fc['timeHgValues'])
plt.clf()
dt = (endTime-beginTime)
x0 = (t-beginTime)*(len(tsum)-1)/dt
print "dt=",dt
print "x0=",x0
print "len(tsum)=",len(tsum)
x = np.arange(len(tsum))-x0
plt.plot(x,tsum, drawstyle='steps-mid')
ymin = -1
ymax = 1.1*tsum.max()
xAtMax = tsum.argmax()-x0
#plt.vlines(xAtMax, ymin, ymax, linestyles='dotted', label="beginning of second")
plt.text(xAtMax+10,ymax/2, "t at max=%.1f"%xAtMax, ha="left",va="center")
plt.ylim(-1,ymax)
plt.ylabel("number of photons per time tick")
plt.xlabel("ticks since beginning of second")
plt.title("run=%s obsDate=%s seq=%s t0=%d t1=%d"%(run,obsDate,seq,t0,t1))
plt.savefig(inspect.stack()[0][3]+".png")
def testCosmic(self):
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
seq = '121229'
fn = FileName.FileName(run, sundownDate, obsDate+"-"+seq)
cosmic = Cosmic(fn, beginTime=123, endTime=125)
dictionary0 = cosmic.findCosmics(threshold=500, populationMax=2000)
print "dictionary0 keyst=",dictionary0.keys()
print "interval=",dictionary0['interval']
hist = dictionary0['populationHg'][0]
bins = np.arange(len(hist))
plt.plot(bins, hist, label="no mask")
plt.xscale('log')
plt.yscale('log')
plt.legend()
plt.savefig(inspect.stack()[0][3]+".png")
#seq5 = ['133303']
nSigma = 7
stride = 10
threshold = 50
populationMax=2000
ySum = np.zeros(populationMax)
frameSum = 'none'
for seq in seq5:
print "seq=",seq
fn = FileName(run, sundownDate, obsDate+"-"+seq)
#cosmic = Cosmic(fn, endTime='exptime')
cosmic = Cosmic(fn, beginTime=0)
fc = cosmic.findCosmics(stride=stride,
threshold=threshold,
populationMax=populationMax,
nSigma=nSigma)
if frameSum == 'none':
frameSum = fc['frameSum']
else:
frameSum += fc['frameSum']
outfile = open("cosmicTimeList-"+seq+".pkl", "wb")
pickle.dump(fc['cosmicTimeList'],outfile)
pickle.dump(fc['binContents'],outfile)
outfile.close()
cfn = "cosmicMax-%s.h5"%seq
ObsFile.writeCosmicIntervalToFile(fc['interval'],1.0e6, cfn)
populationHg = fc['populationHg']
yPlot = populationHg[0].astype(np.float)
ySum += yPlot
norm = yPlot.sum()
threshold = 25
stride = 10
populationMax = 1000
nlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, 40]
nlist2 = [10, 20, 30]
for n in nlist:
beginTime = n-1
endTime = n
cosmic = Cosmic(fn, beginTime, endTime)
dictionary0 = cosmic.findCosmics(nSigma, threshold, stride, populationMax)
interval = dictionary0['interval']
hist0 = dictionary0['populationHg'][0]
bins0 = np.arange(len(hist0))
muNum = (bins0*hist0).sum()
muDen = float(hist0.sum())
mu1 = muNum/muDen
mu1Err = np.sqrt(mu1/muDen)
print "mu1=",mu1," mu1Err=",mu1Err," muNum=",muNum," muDen=",muDen
mean.append(mu1)
meanErr.append(mu1Err)
for n in nlist2:
import pickle
run = 'PAL2013'
sundownDate = '20131204'
obsDate = '20131205'
seq = '033506'
populationMax=1000
beginTime = 0
expTime = 300
fn = FileName(run, sundownDate, obsDate+"-"+seq)
cosmic = Cosmic(fn, beginTime=0, endTime=10, loggingLevel=logging.INFO)
fcd = cosmic.findCosmics(nSigma=10, stride=10, threshold=15)
print "done: fcd.keys=",fcd.keys()
maskedTime = Cosmic.countMaskedBins(fcd['interval'])
maskedPercent = 100*maskedTime/(cosmic.endTime-cosmic.beginTime)
print "maskedPercent=",maskedPercent
cosmic.file.setCosmicMask(fcd['interval'])
fcd2 = cosmic.findCosmics()
#plt.xkcd()
plt.plot(fcd['populationHg'][0],drawstyle='steps-mid',label="all data")
plt.plot(fcd2['populationHg'][0],drawstyle='steps-mid',label="cosmics masked")
plt.xlim(xmax=1000)
plt.xscale('symlog',linthreshx=0.9)
plt.yscale('symlog',linthreshy=0.5)
plt.title('%s %s %s %s'%(run,sundownDate,obsDate,seq))
Makes a plot for the entire file showing the mean for every ten seconds of
data and the cosmic events. This plot is saved as MeanAndCosmicEvents.png
Makes a scatter plot of the number of cosmics masked out and the mean which is
saved as scatter.png
"""
#loads the file
run = 'PAL2012'
sundownDate = '20121211'
obsDate = '20121212'
seq = '121229'
fn = FileName.FileName(run, sundownDate, obsDate+"-"+seq)
#this particular plot is for only ten seconds of data
cosmic = Cosmic(fn, beginTime=123, endTime=133)
dictionary0 = cosmic.findCosmics(nSigma=6, threshold=15, populationMax=1000)
interval = dictionary0['interval']
ObsFile.ObsFile.writeCosmicIntervalToFile(interval,
cosmic.file.ticksPerSec,
'junk.h5')
cosmic.file.loadCosmicMask('junk.h5')
dictionary1 = cosmic.findCosmics(nSigma=6, threshold=15, populationMax=1000)
nSigma = 6
threshold = 15
plt.clf()
hist0 = dictionary0['populationHg'][0]
bins0 = np.arange(len(hist0))