def _MakeBackgroundSelections(self):
#First bin the data by the Knuth rule or something else
self.MakeConstantBins (1.)
#self.MakeKnuth()
for i in xrange(len(self.bkgIntervals)):
diffs = abs(self.bins-self.bkgIntervals[i][0])
self.bkgIntervals[i][0] = self.bins[diffs == min(diffs) ][0]
diffs = abs(self.bins-self.bkgIntervals[i][1])
self.bkgIntervals[i][1] = self.bins[diffs == min(diffs) ][0]
evts = self.allEvts
truthTables = []
for sel in self.bkgIntervals:
truthTables.append(logical_and(evts>= sel[0] , evts<= sel[1] ))
tt = truthTables[0]
if len(truthTables)>1:
for y in truthTables[1:]:
tt=logical_or(tt,y)
filteredEvts = evts[tt]
cnts,bins=histtools.histogram(filteredEvts,bins=self.bins)
tt=cnts>0
meanT=[]
for i in xrange(len(bins)-1):
m = mean((bins[i],bins[i+1]))
meanT.append(m)
meanT = array(meanT)
meanT = meanT[tt]
cnts = cnts/self.binWidth
self.optimalPolGrade = self._fitGlobalAndDetermineOptimumGrade(cnts[tt],meanT)
print "Optimal poly grade: %d"% self.optimalPolGrade
def _MakeBackgroundSelections(self):
#First bin the data by the Knuth rule or something else
self.MakeConstantBins (1.)
#self.MakeKnuth()
for i in xrange(len(self.bkgIntervals)):
diffs = abs(self.bins-self.bkgIntervals[i][0])
self.bkgIntervals[i][0] = self.bins[diffs == min(diffs) ][0]
diffs = abs(self.bins-self.bkgIntervals[i][1])
self.bkgIntervals[i][1] = self.bins[diffs == min(diffs) ][0]
evts = self.allEvts
truthTables = []
for sel in self.bkgIntervals:
truthTables.append(logical_and(evts>= sel[0] , evts<= sel[1] ))
tt = truthTables[0]
if len(truthTables)>1:
for y in truthTables[1:]:
tt=logical_or(tt,y)
filteredEvts = evts[tt]
cnts,bins=histtools.histogram(filteredEvts,bins=self.bins)
tt=cnts>0
meanT=[]
for i in xrange(len(bins)-1):
m = mean((bins[i],bins[i+1]))
meanT.append(m)
meanT = array(meanT)
meanT = meanT[tt]
cnts = cnts/self.binWidth
self.optimalPolGrade = self._fitGlobalAndDetermineOptimumGrade(cnts[tt],meanT)
print "Optimal poly grade: %d"% self.optimalPolGrade
def MakeHardnessBlocks(self,binLow,binHi,p0):
self.S2N(3.,minNumberOfEvents=25)
binWidth = diff(self.bins)
#Make two light curves between the energy bins
chanLo1,chanLo2 = map(self._selectEnergy,binLow)
chanHi1,chanHi2 = map(self._selectEnergy,binHi)
tt = logical_and(self.evtExt["PHA"] >= chanLo1, self.evtExt["PHA"]<= chanLo2)
curveLo = self.allEvts[tt]
####Bin the counts using the fine bins
cnts, _ = histtools.histogram(curveLo,self.bins)
cnts = cnts / binWidth
### Get the bkg for this channel selection
bkg = []
for j in xrange(len(self.bins)-1):
tot = 0
for i in xrange(chanLo1,chanLo2+1):
tot+=self.polynomials[i].integral(self.bins[i],self.bins[i+1])
bkg.append(tot)
bkg = array(bkg)
assert len(cnts)==len(bkg), "Background wrong length"
#bkgErr = sqrt(bkg)
#cntErr = sqrt(cnts)
subCntsLo = cnts-bkg
subCntsLoErr = sqrt(cnts+bkg)
#######NEED TO ADD ERROR CALCS HERE
tt = logical_and(self.evtExt["PHA"] >= chanHi1, self.evtExt["PHA"]<= chanHi2)
curveHi = self.allEvts[tt]
cnts, _ = histtools.histogram(curveHi,self.bins)
cnts = cnts / binWidth
### Get the bkg for this channel selection
bkg = []
for j in xrange(len(self.bins)-1):
tot = 0
for i in xrange(chanHi1,chanHi2+1):
tot+=self.polynomials[i].integral(self.bins[i],self.bins[i+1])
bkg.append(tot)
bkg = array(bkg)
assert len(cnts)==len(bkg), "Background wrong length"
subCntsHi = cnts-bkg
subCntsHiErr = sqrt(cnts+bkg)
hardRatio = subCntsHi/subCntsLo
print len(self.bins)
errors = hardRatio**2.*(subCntsLoErr/(subCntsLo**2.) + subCntsHiErr/(subCntsHi**2.))
#bb = BayesianBlocks(binWidth,hardRatio,errors,self.bins[0])
bb = BayesianBlocks(hardRatio,binWidth,self.bins[0])
bins,_ = bb.globalOpt(ncp_prior=p0)
lastBin = bins[-1]
newBins=array(bins)[::2]
newBins =newBins.tolist()
newBins.append(lastBin)
self.bins = array(newBins)
print len(self.bins)
self.bType = "hr"
def MakeHardnessBlocks(self, binLow, binHi, p0):
self.S2N(3., minNumberOfEvents=25)
binWidth = diff(self.bins)
#Make two light curves between the energy bins
chanLo1, chanLo2 = map(self._selectEnergy, binLow)
chanHi1, chanHi2 = map(self._selectEnergy, binHi)
tt = logical_and(self.evtExt["PHA"] >= chanLo1,
self.evtExt["PHA"] <= chanLo2)
curveLo = self.allEvts[tt]
####Bin the counts using the fine bins
cnts, _ = histtools.histogram(curveLo, self.bins)
cnts = cnts / binWidth
### Get the bkg for this channel selection
bkg = []
for j in xrange(len(self.bins) - 1):
tot = 0
for i in xrange(chanLo1, chanLo2 + 1):
tot += self.polynomials[i].integral(self.bins[i],
self.bins[i + 1])
bkg.append(tot)
bkg = array(bkg)
assert len(cnts) == len(bkg), "Background wrong length"
#bkgErr = sqrt(bkg)
#cntErr = sqrt(cnts)
subCntsLo = cnts - bkg
subCntsLoErr = sqrt(cnts + bkg)
#######NEED TO ADD ERROR CALCS HERE
tt = logical_and(self.evtExt["PHA"] >= chanHi1,
self.evtExt["PHA"] <= chanHi2)
curveHi = self.allEvts[tt]
cnts, _ = histtools.histogram(curveHi, self.bins)
cnts = cnts / binWidth
### Get the bkg for this channel selection
bkg = []
for j in xrange(len(self.bins) - 1):
tot = 0
for i in xrange(chanHi1, chanHi2 + 1):
tot += self.polynomials[i].integral(self.bins[i],
self.bins[i + 1])
bkg.append(tot)
bkg = array(bkg)
assert len(cnts) == len(bkg), "Background wrong length"
subCntsHi = cnts - bkg
subCntsHiErr = sqrt(cnts + bkg)
hardRatio = subCntsHi / subCntsLo
print len(self.bins)
errors = hardRatio**2. * (subCntsLoErr /
(subCntsLo**2.) + subCntsHiErr /
(subCntsHi**2.))
#bb = BayesianBlocks(binWidth,hardRatio,errors,self.bins[0])
bb = BayesianBlocks(hardRatio, binWidth, self.bins[0])
bins, _ = bb.globalOpt(ncp_prior=p0)
lastBin = bins[-1]
newBins = array(bins)[::2]
newBins = newBins.tolist()
newBins.append(lastBin)
self.bins = array(newBins)
print len(self.bins)
self.bType = "hr"
