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flAnalysis.py
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#add info here:
#import libraries here
# _ _________________ ___ ______ _____ _____ _____
#| | |_ _| ___ \ ___ \/ _ \ | ___ \_ _| ___/ ___|
#| | | | | |_/ / |_/ / /_\ \| |_/ / | | | |__ \ `--.
#| | | | | ___ \ /| _ || / | | | __| `--. \
#| |_____| |_| |_/ / |\ \| | | || |\ \ _| |_| |___/\__/ /
#\_____/\___/\____/\_| \_\_| |_/\_| \_|\___/\____/\____/
from optparse import OptionParser
parser = OptionParser()
import sys
import glob
import re
import os
import numpy as np
from array import *
from ROOT import *
rocs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
modName = sys.argv[1]
testDate= sys.argv[2]
myCuFileName = "fluoro_"+modName+"_"+testDate+".root" #"pa207_071615.root"
myAgFileName = myCuFileName; #"fluoro_122915.root"
mySnFileName = myCuFileName; #"fluoro2_122915.root" #"pa207_071615.root"
myInFileName = myCuFileName; #"fluoro2_122915.root"
myfileoutname = "XRFResult_"+modName
parser.add_option('--setup', type='string', action='store',
default='KU',
dest='setup',
help='Setup corresponding to KU or UIC?: Options KU or UIC only')
parser.add_option('--outputfile', type='string', action='store',
default=myfileoutname, #''M_',
dest='outputfile',
help='Set first part of the name of outputfile: Usually M_XX_YYY')
parser.add_option('--histoname', type='string', action='store',
default='Xray/q',
dest='histoname',
help='Histogram that is analyzed')
parser.add_option('--sigma', type='int', action='store',
default=3,
dest='sigma',
help='Value of sigma to be used to find peaks in TSpectrum')
parser.add_option('--threshold', type='float', action='store',
default=0.2,
dest='threshold',
help='Value of the threshold to be used to find peaks in TSpectrum')
parser.add_option('--XRSource', type='string', action='store',
default='Mo',
dest='XRSource',
help='Name of the XRay source, valid options: Cu or Mo ')
parser.add_option('--CuFile', type='string', action='store',
default=myCuFileName, #''Fluorescence.root',
dest='CuFile',
help='Name of the Cu root file (when Mo is the XRaySource) ')
parser.add_option('--MoFile', type='string', action='store',
default='Fluorescence.root',
dest='MoFile',
help='Name of the Mo root file (when Cu is the XRaySource ')
parser.add_option('--AgFile', type='string', action='store',
default=myAgFileName, #''Fluorescence.root',
dest='AgFile',
help='Name of the Ag root file ')
parser.add_option('--SnFile', type='string', action='store',
default=mySnFileName, #''Fluorescence.root',
dest='SnFile',
help='Name of the Sn root file ')
parser.add_option('--InFile', type='string', action='store',
default=myInFileName, #'Fluorescence.root',
dest='InFile',
help='Name of the In root file ')
parser.add_option('--nrocs', type='int', action='store',
default=15,
dest='nrocs',
help='Number or rocs')
parser.add_option('--badrocs', type='string', action='store',
default=' ',
dest='badrocs',
help='List of bad rocs, for example [2,4,5]')
(options, args) = parser.parse_args()
argv = []
options.CuFile = myCuFileName
options.AgFile = myAgFileName
options.SnFile = mySnFileName
options.InFile = myInFileName
options.outputfile = myfileoutname;
os.chdir("./"+modName+"data");
#define functions here:
# _ _ _ _ _ _ _ _ _
# / \ / \ / \ / \ / \ / \ / \ / \ / \
#( f | u | n | c | t | i | o | n | s )
# \_/ \_/ \_/ \_/ \_/ \_/ \_/ \_/ \_/
#Comments on get_gpeaks: To get rid of the polymarkers of the Spectrum method, use the option ='goff' in the search function
def get_gpeaks(h,lrange=[0,180],sigma=6,opt="goff",thres=0.05,niter=1000,exp=0,i=0):
s = TSpectrum(niter,1)
h.GetXaxis().SetRangeUser(lrange[0],lrange[1])
for sigma_temp in range(sigma,0,-1):
s.Search(h,sigma_temp,"",thres)
print "sigma", sigma_temp, s.GetNPeaks()
h.Draw()
c1.Update()
name = h.GetName()
c1.SaveAs(output+'Sigma_'+str(sigma_temp)+name+'.png')
if s.GetNPeaks() >= exp:
break
s.SetAverageWindow(2)
bufX, bufY = s.GetPositionX(), s.GetPositionY()
pos = []
for i in range(s.GetNPeaks()):
pos.append([bufX[i], bufY[i]])
pos.sort()
return pos
#Comments on rm_peak: Gets rid of the additional peak that KU observes
def rm_peak(histo, gaus):
bins = histo.GetNbinsX()
print 'P0',gaus.GetParameter(0),gaus.GetParameter(1),gaus.GetParameter(2)
newhisto = histo.Clone()
newhisto.Reset()
for i in range( 1 , bins) :
#val = histo.GetBinContent(i) - gaus.Eval(i)
val = gaus.Eval(i*2)
newhisto.Fill(i*2,val)
histo.Add(newhisto,-1)
return histo
#Comments on FitPeaks: It has 2 variants depending on the Xray source used. It will get the peaks and fit them and save the stats info in .txt files per Material and ROC.
def FitPeaks(rootfile,histo,material,rocs,output,XRSource,rebin):
arrayMo = false
arrayCu = false
arrayIn = false
arrayAg = false
arraySn = false
grocs = len(rocs)+1
ArrayG = TObjArray(16)
if material == 'Mo':
arrayMo = true
ArrayMo = TObjArray(16)
if XRSource == 'Mo':
exp = 2
elif material == 'Ag':
arrayAg = true
ArrayAg = TObjArray(16)
if XRSource == 'Mo':
exp = 3
elif material == 'Sn':
arraySn =true
ArraySn = TObjArray(16)
if XRSource == 'Mo':
exp = 3
elif material == 'In':
arrayIn = true
ArrayIn = TObjArray(16)
if XRSource == 'Mo':
exp = 3
elif material == 'Cu':
arrayCu = true
ArrayCu = TObjArray(16)
if XRSource == 'Mo':
exp = 2
#for i in range(0,int(nrocs)-1):
for i in range(0,16):
if i not in rocs:
print 'Skipping roc', i
continue
stats= open(output+material+'C_'+str(i)+'_stats.txt','w')
if material == 'not': #'In':
hl = len( histo )
hist = histo[0 : hl-4] + "Ag_C" + str(i) + "_V0"
else:
hist = histo +str(i)+"_V0"
directory = rootfile.Get('Xray')
keys = directory.GetListOfKeys()
allkeys = []
for key in keys:
allkeys.append(key.GetName())
print hist, hist.strip('Xray/')
if hist.strip('Xray/') in allkeys:
print 'FoilandRoc found:', hist
#if hist not in key.GetName():
#print 'Target foil not found'
#continue
else:
print 'Histogram needed not found',hist.strip('Xray/')
continue
tgt = rootfile.Get(hist)
print "Opening file:"+ hist
tgt.Rebin(rebin)
tgt.Draw()
tgt.GetXaxis().SetRangeUser(0,250)
peaks = get_gpeaks(tgt,[0,250],6,"goff",.05,1000,exp,i)
print len(peaks), "Roc: ", i, material
if len(peaks)==0:
print 'Couldnt find peaks, check here!'
continue
mid1 = peaks[0][1]
mid2 = peaks[1][1]
rebinned = false
if(len(peaks)>3):
print "Too many peaks,rebinning"
tgt.Rebin(2)
peaks = get_gpeaks(tgt,[20,300],6,"goff",.05,1000,exp)
if (len(peaks)==2):
mid1 = peaks[0][1]
mid2 = peaks[1][1]
rebinned = true
#peaks = peaks[1:3]
if (len(peaks)==2 or rebinned):
for t in range(1, int(peaks[0][0])):
if (tgt.GetBinContent(t)/mid1>0.68):
sigma1l = t
break
for j in range(int(peaks[0][0])+1,int(peaks[1][0])):
if (tgt.GetBinContent(j)/mid1<0.68):
sigma1r = j
break
for l in range(int(peaks[1][0])+1,300):
if (tgt.GetBinContent(l)/mid2<0.68):
sigma2r = l
break
for k in range(int(peaks[0][0])+(sigma1r)/2,int(peaks [1][0])):
if (tgt.GetBinContent(k)/mid2>0.68) and abs(k-peaks[1][0])<20:
sigma2l = k
break
if(len(peaks)==3):
if peaks[1][1] > peaks[2][1]:
mid3 = peaks[1][1]
right3 = int(peaks[1][0])
for t in range(1, int(peaks[0][0])):
if (tgt.GetBinContent(t)/mid1>0.68):
sigma1l = t
break
for j in range(int(peaks[0][0])+1,right3):
if (tgt.GetBinContent(j)/mid1<0.68):
sigma1r = j
break
for l in range(right3+1,300):
if (tgt.GetBinContent(l)/mid3<0.68):
sigma2r = l
break
for k in range(int(peaks[0][0])+(sigma1r)/2,right3):
if (tgt.GetBinContent(k)/mid3>0.68):
sigma2l = k
if abs(k-right3)< 20:
break
else:
mid3 = peaks[2][1]
right3 = int(peaks[2][0])
for t in range(1, int(peaks[0][0])):
if (tgt.GetBinContent(t)/mid1>0.68):
sigma1l = t
break
for j in range(int(peaks[0][0])+1,int(peaks[1][0])):
if (tgt.GetBinContent(j)/mid1<0.68):
sigma1r = j
break
for l in range(right3+1,300):
if (tgt.GetBinContent(l)/mid3<0.68):
sigma2r = l
break
for k in range(int(peaks[1][0])+(sigma1r)/2,right3):
if (tgt.GetBinContent(k)/mid3>0.68) and abs(k-right3)<20:
sigma2l = k
break
if (XRSource == 'Mo'):
print "Mo source" ,len(peaks)
if (material == 'Cu'):
gaus1 = TF1("gaus1","gaus",peaks[1][0]-15, peaks[1][0]+15)
gaus2 = TF1("gaus2","gaus",peaks[0][0]-15, peaks[0][0]+15)
elif ((material == 'Sn' or material == 'Ag' or material == 'In') and len(peaks) == 3):
gaus1 = TF1("gaus1","gaus",peaks[1][0]-15, peaks[1][0]+15)
gaus2 = TF1("gaus2","gaus",peaks[2][0]-20, peaks[2][0]+20)
gaus3 = TF1("gaus3","gaus",peaks[2][0]-20, peaks[2][0]+20)
elif ((material == 'Sn' or material == 'Ag' or material == 'In') and len(peaks) == 2):
if peaks[0][0] > 70 and peaks[0][0] < 100:
gaus1 = TF1("gaus1","gaus",peaks[0][0]-15, peaks[0][0]+15)
gaus2 = TF1("gaus2","gaus",peaks[1][0]-15, peaks[1][0]+15)
gaus3 = TF1("gaus3","gaus",peaks[1][0]-15, peaks[1][0]+15)
else:
gaus1 = TF1("gaus1","gaus",peaks[1][0]-15, peaks[1][0]+15)
gaus2 = TF1("gaus2","gaus",peaks[0][0]-15, peaks[0][0]+15)
gaus3 = TF1("gaus3","gaus",peaks[0][0]-15, peaks[0][0]+15)
tgt.Fit("gaus1","R")
tgt.Fit("gaus2","+R")
tgt.Draw()
print tgt.GetFunction("gaus2").GetParameter(1)
mu1 = tgt.GetFunction("gaus1").GetParameter(1)
mu2 = tgt.GetFunction("gaus2").GetParameter(1)
sigma1 = tgt.GetFunction("gaus1").GetParameter(2)
sigma2 = tgt.GetFunction("gaus2").GetParameter(2)
c1.SaveAs(output+'FitC_'+str(i)+material+'.png')
if material == 'Ag' or material == 'In' or material == 'Sn':
newhisto = rm_peak(tgt,tgt.GetFunction("gaus1"))
print "Got new histo"
c1.Update()
newhisto.Fit("gaus3","+R")
newhisto.Draw()
mu2 = newhisto.GetFunction("gaus3").GetParameter(1)
print "new mu2: ", mu2
sigma2 = newhisto.GetFunction("gaus3").GetParameter(2)
c1.Update()
c1.SaveAs(output+'Stripped_C'+str(i)+'_'+material+'.png')
print material, "roc: "+str(i), mu1 , sigma1, mu2, sigma2
stats.writelines(["Mean_Mo_C"+str(i)+":\t"+str(mu1)+"\n", "Sigma_Mo_C"+str(i)+":\t"+str(sigma1)+"\n","Mean_"+material+"_C"+str(i)+":\t"+str(mu2)+"\n", "Sigma_"+material+"_C"+str(i)+":\t"+str(sigma2)+"\n"])
if arrayCu:
ArrayCu.AddAt(tgt,i)
elif arrayAg:
ArrayAg.AddAt(tgt,i)
elif arraySn:
ArraySn.AddAt(tgt,i)
elif arrayIn:
ArrayIn.AddAt(tgt,i)
elif (XRSource == 'Cu'):
gaus1 = TF1("gaus1","gaus",peaks[0][0]-15, peaks[0][0]+15)
if len(peaks)==2: gaus2 = TF1("gaus2","gaus",peaks[1][0]-15,peaks[1][0]+15)
if len(peaks)==3:
if peaks[1][1]>peaks[2][1]:
gaus2 = TF1("gaus2","gaus",peaks[1][0]-20,peaks[1][0]+20)
else:
gaus2 = TF1("gaus2","gaus",peaks[2][0]-20,peaks[2][0]+20)
tgt.Fit("gaus1","R")
tgt.Fit("gaus2","+R")
tgt.Draw()
c1.Update()
mu1 = tgt.GetFunction("gaus1").GetParameter(1)
mu2 = tgt.GetFunction("gaus2").GetParameter(1)
sigma1 = tgt.GetFunction("gaus1").GetParameter(2)
sigma2 = tgt.GetFunction("gaus2").GetParameter(2)
print mu1 , sigma1, mu2, sigma2
c1.SaveAs(output+'FitC_'+str(i)+material+'.png')
stats.writelines(["Mean_Cu_C"+str(i)+":\t"+str(mu1)+"\n", "Sigma_Cu_C"+str(i)+":\t"+str(sigma1)+"\n","Mean_"+material+"_C"+str(i)+":\t"+str(mu2)+"\n", "Sigma_"+material+"_C"+str(i)+":\t"+str(sigma2)+"\n"])
if arrayMo:
ArrayMo.AddAt(tgt,i)
elif arrayAg:
ArrayAg.AddAt(tgt,i)
elif arraySn:
ArraySn.AddAt(tgt,i)
elif arrayIn:
ArrayIn.AddAt(tgt,i)
if arrayCu:
ArrayG = ArrayCu
elif arrayAg:
ArrayG = ArrayAg
elif arraySn:
ArrayG = ArraySn
elif arrayMo:
ArrayG = ArrayMo
elif arrayIn:
ArrayG = ArrayIn
print "fit Peaks Finished"
return ArrayG
#Comments on :
def PlotSameNStats(arrayfithisto1, arrayfithisto2, arrayfithisto3, arrayfithisto4, rocs, output, XRSource):
grocs = len(rocs)
for i in range(0,grocs):
if i not in rocs:
print 'Skipping roc', i
continue
c1 = TCanvas('c1',"Fluorescence test",1)
gStyle.SetOptStat(0)
c1.cd()
gStyle.SetOptTitle(0)
height=[]
if not arrayfithisto1:
height.append(0)
continue
else:
fithisto1 = arrayfithisto1[i]
fithisto1.GetXaxis().SetRange(20,300)
fithisto1.SetLineColor(kBlue)
mu1f = fithisto1.GetFunction("gaus2").GetParameter(1)
sigma1f = fithisto1.GetFunction("gaus2").GetParameter(2)
mu1 = float("{0:.2f}".format(mu1f))
sigma1 = float("{0:.2f}".format(sigma1f))
height.append( fithisto1.GetMaximum())
if not arrayfithisto2:
height.append(0)
continue
else:
fithisto2 = arrayfithisto2[i]
fithisto2.GetXaxis().SetRange(20,300)
fithisto2.SetLineColor(kMagenta)
mu2f = fithisto2.GetFunction("gaus2").GetParameter(1)
sigma2f = fithisto2.GetFunction("gaus2").GetParameter(2)
mu2 = float("{0:.2f}".format(mu2f))
sigma2 = float("{0:.2f}".format(sigma2f))
height.append(fithisto2.GetMaximum())
if not arrayfithisto3:
continue
height.append(0)
else:
fithisto3 = arrayfithisto3[i]
fithisto3.GetXaxis().SetRange(20,300)
fithisto3.SetLineColor(kGreen)
mu3f = fithisto3.GetFunction("gaus2").GetParameter(1)
sigma3f = fithisto3.GetFunction("gaus2").GetParameter(2)
mu3 = float("{0:.2f}".format(mu3f))
sigma3 = float("{0:.2f}".format(sigma3f))
height.append(fithisto3.GetMaximum())
if not arrayfithisto4:
continue
height.append(0)
else:
fithisto4 = arrayfithisto4[i]
fithisto4.GetXaxis().SetRange(20,300)
fithisto4.SetLineColor(kOrange)
mu4f = fithisto4.GetFunction("gaus2").GetParameter(1)
sigma4f = fithisto4.GetFunction("gaus2").GetParameter(2)
mu4 = float("{0:.2f}".format(mu4f))
sigma4 = float("{0:.2f}".format(sigma4f))
height.append( fithisto4.GetMaximum())
print height
maxheight = max(height)
print maxheight
if XRSource == 'Mo':
fithisto1.SetMaximum(maxheight+100)
fithisto1.Draw()
c1.Update()
textmu1 = TLatex()
textmu1.SetNDC()
textmu1.SetTextColor(kBlue)
textmu1.SetTextSize(0.03)
textmu1.DrawText(0.7,0.25,"Cu.Mean():"+ str(mu1))
textsigma1 = TLatex()
textsigma1.SetNDC()
textsigma1.SetTextColor(kBlue)
textsigma1.SetTextSize(0.03)
textsigma1.DrawText(0.7,0.15,"Cu.Sigma():"+ str(sigma1))
c1.Update()
elif XRSource == 'Cu':
fithisto1.SetMaximum(maxheight+100)
fithisto1.Draw()
c1.Update()
textmu1 = TLatex()
textmu1.SetNDC()
textmu1.SetTextColor(kBlue)
textmu1.SetTextSize(0.03)
textmu1.DrawText(0.7,0.25,"Mo.Mean():"+ str(mu1))
textsigma1 = TLatex()
textsigma1.SetNDC()
textsigma1.SetTextColor(kBlue)
textsigma1.SetTextSize(0.03)
textsigma1.DrawText(0.7,0.15,"Mo.Sigma():"+ str(sigma1))
c1.Update()
if not arrayfithisto2:
continue
else:
fithisto2.SetMaximum(maxheight+100)
fithisto2.Draw("sames")
textmu2 = TLatex()
textmu2.SetNDC()
textmu2.SetTextColor(kMagenta)
textmu2.SetTextSize(0.03)
textmu2.DrawText(0.7,0.45,"Ag.Mean():"+ str(mu2))
textsigma2 = TLatex()
textsigma2.SetNDC()
textsigma2.SetTextColor(kMagenta)
textsigma2.SetTextSize(0.03)
textsigma2.DrawText(0.7,0.35,"Ag.Sigma():"+ str(sigma2))
c1.Update()
if not arrayfithisto3:
continue
else:
fithisto3.SetMaximum(maxheight+100)
fithisto3.Draw("sames")
textmu3 = TLatex()
textmu3.SetNDC()
textmu3.SetTextColor(kGreen)
textmu3.SetTextSize(0.03)
textmu3.DrawText(0.7,0.65,"Sn.Mean():"+ str(mu3))
textsigma3 = TLatex()
textsigma3.SetNDC()
textsigma3.SetTextColor(kGreen)
textsigma3.SetTextSize(0.03)
textsigma3.DrawText(0.7,0.55,"Sn.Sigma():"+ str(sigma3))
c1.Update()
if not arrayfithisto4:
continue
else:
fithisto4.SetMaximum(maxheight+100)
fithisto4.Draw("sames")
textmu4 = TLatex()
textmu4.SetNDC()
textmu4.SetTextColor(kOrange)
textmu4.SetTextSize(0.03)
textmu4.DrawText(0.7,0.85,"In.Mean():"+ str(mu4))
textsigma4 = TLatex()
textsigma4.SetNDC()
textsigma4.SetTextColor(kOrange)
textsigma4.SetTextSize(0.03)
textsigma4.DrawText(0.7,0.75,"In.Sigma():"+ str(sigma4))
c1.Update()
text = TLatex()
text.SetNDC()
text.DrawText(0.4,0.95,"Fluorescence tests C_"+str(i))
print 'Saving to:', output
c1.SaveAs(output+"XRayFluoResultsC_"+str(i)+'.png')
c1.Close()
return
def ConversionPlot(rocs,output, XRSource):
grocs = len(rocs)
convfactcu = 8048/3.6
convfactmo = 17479/3.6
convfactag = 22163/3.6
convfactsn= 25271/3.6
convfactin = 24207/3.6
sumoutfile = "SummaryQPlots_" + output + ".txt"
sumout = open(sumoutfile, "w")
sumout.write(myfileoutname + "Summary of QPlots Slopes.\n"+ " e^{-}/Vcal: Intercept: \n" )
qplotfit = open("SummaryQplots.txt",'w')
n_oh = TH1F('n_oh','N_o', 100,0,1000)
slopeh = TH1F('Slope','Slope',100, 0,100)
qmatrix = np.zeros((16,5))
for i in rocs:
mu_cu =[]
sig_cu=[]
mu_mo = []
sig_mo = []
mu_ag =[]
sig_ag =[]
mu_sn =[]
sig_sn = []
mu_in =[]
sig_in = []
for file in glob.glob("*_"+str(i)+"_stats.txt"):
name = os.path.splitext(file)[0]
print "Opening:",name
if XRSource =='Mo':
if('CuC_' in name):
material = 'Cu'
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Mo" in line[k]:
if "Mean_Mo" in line[k]:
words = re.split(':\t',line[k])
meanmo = words[1].strip('\n')
mu_mo.append(meanmo)
elif "Sigma_Mo" in line[k]:
words = re.split(':\t',line[k])
sigmamo = words[1].strip('\n')
sig_mo.append(sigmamo)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_cu.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_cu.append(sigma)
f.close()
elif('AgC_' in name):
material = 'Ag'
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Mo" in line[k]:
if "Mean_Mo" in line[k]:
words = re.split(':\t',line[k])
meanmo = words[1].strip('\n')
mu_mo.append(meanmo)
elif "Sigma_Mo" in line[k]:
words = re.split(':\t',line[k])
sigmamo = words[1].strip('\n')
sig_mo.append(sigmamo)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
meanag = words[1].strip('\n')
mu_ag.append(meanag)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_ag.append(sigma)
f.close()
elif('SnC_' in name):
material = 'Sn'
convfact= 25271/3.6
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Mo" in line[k]:
if "Mean_Mo" in line[k]:
words = re.split(':\t',line[k])
meanmo = words[1].strip('\n')
mu_mo.append(meanmo)
elif "Sigma_Mo" in line[k]:
words = re.split(':\t',line[k])
sigmamo = words[1].strip('\n')
sig_mo.append(sigmamo)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_sn.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_sn.append(sigma)
f.close()
elif('InC_' in name):
material = 'In'
convfact= 24207/3.6
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Mo" in line[k]:
if "Mean_Mo" in line[k]:
words = re.split(':\t',line[k])
meanmo = words[1].strip('\n')
mu_mo.append(meanmo)
elif "Sigma_Mo" in line[k]:
words = re.split(':\t',line[k])
sigmamo = words[1].strip('\n')
sig_mo.append(sigmamo)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_in.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_in.append(sigma)
f.close()
elif XRSource == 'Cu':
if('MoC_' in name):
material = 'Mo'
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Cu" in line[k]:
if "Mean_Cu" in line[k]:
words = re.split(':\t',line[k])
meancu = words[1].strip('\n')
mu_cu.append(meancu)
elif "Sigma_Cu" in line[k]:
words = re.split(':\t',line[k])
sigmacu = words[1].strip('\n')
sig_cu.append(sigmacu)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_mo.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_mo.append(sigma)
f.close()
elif('AgC_' in name):
material = 'Ag'
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Cu" in line[k]:
if "Mean_Cu" in line[k]:
words = re.split(':\t',line[k])
meancu = words[1].strip('\n')
mu_cu.append(meancu)
elif "Sigma_Cu" in line[k]:
words = re.split(':\t',line[k])
sigmacu = words[1].strip('\n')
sig_cu.append(sigmacu)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_ag.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_ag.append(sigma)
f.close()
elif('SnC_' in name):
material = 'Sn'
convfact= 25271/3.6
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Cu" in line[k]:
if "Mean_Cu" in line[k]:
words = re.split(':\t',line[k])
meancu = words[1].strip('\n')
mu_cu.append(meancu)
elif "Sigma_Cu" in line[k]:
words = re.split(':\t',line[k])
sigmacu = words[1].strip('\n')
sig_cu.append(sigmacu)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_sn.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_sn.append(sigma)
f.close()
elif('InC_' in name):
material = 'In'
convfact= 24207/3.6
f = open(file,'r')
line = f.readlines()
for k in range(0,len(line)):
if "_Cu" in line[k]:
if "Mean_Cu" in line[k]:
words = re.split(':\t',line[k])
meancu = words[1].strip('\n')
mu_cu.append(meancu)
elif "Sigma_Cu" in line[k]:
words = re.split(':\t',line[k])
sigmacu = words[1].strip('\n')
sig_cu.append(sigmacu)
else:
if "Mean_"+material in line[k]:
words = re.split(':\t',line[k])
mean = words[1].strip('\n')
mu_in.append(mean)
elif "Sigma_"+material in line[k]:
words = re.split(':\t',line[k])
sigma = words[1].strip('\n')
sig_in.append(sigma)
f.close()
n_cu = len(mu_cu)
n_mo = len(mu_mo)
n_ag = len(mu_ag)
n_sn = len(mu_sn)
n_in = len(mu_in)
n_all = n_cu + n_mo + n_sn + n_ag+ n_in
sigma_y = [0]*n_all
mus = mu_cu + mu_mo + mu_ag + mu_sn + mu_in
sigma_x = sig_cu + sig_mo + sig_ag+ sig_sn + sig_in
k_cu = [convfactcu]*n_cu
k_mo = [convfactmo]*n_mo
k_ag = [convfactag]*n_ag
k_sn = [convfactsn]*n_sn
k_in = [convfactin]*n_in
k = k_cu + k_mo + k_ag + k_sn+k_in
matrix = np.zeros(((len(mus)),4))
print "mus", mus, len(matrix)
a=0
for j in [0,1,2, 3, 4 ,5 ,6, 7]: #range(0,len(mus)):
a = j
matrix[a][1] = float(mus[j])
matrix[a][0] = float(k[j])
matrix[a][3] = float(sigma_x[j])
table = open('SummaryTable'+'_'+output+'_'+'C_'+str(i)+'.txt','w')
np.savetxt("SummaryTable"+'_'+output+'_'+"C_"+str(i)+".txt",matrix, delimiter="\t", fmt="%s", newline='\n' )
gStyle.SetOptFit(1)
c1 = TCanvas('c1',"Fluorescence test",1)
c1.cd()
c1.Update()
gStyle.SetOptStat(0)
gr = TGraphErrors("SummaryTable"+'_'+output+'_'+"C_"+str(i)+".txt")
gr.SetMarkerStyle(41)
fit = TF1("fit","pol1",1000,10000)
gr.Fit("fit","w","l",1000,10000)
gr.SetMarkerStyle(20)
n_o = gr.GetFunction("fit").GetParameter(0)
n_o_er = gr.GetFunction("fit").GetParError(0)
slope = gr.GetFunction("fit").GetParameter(1)
pn_o = -(n_o/slope)
pn_o_er =abs(n_o_er/slope)
pslope = 1/slope
slope_err = gr.GetFunction("fit").GetParError(1)
pslope_err = slope_err /( slope*slope)
chisquare = gr.GetFunction("fit").GetChisquare()
ndf = gr.GetFunction("fit").GetNDF()
print "no:",pn_o
print "no error:", pn_o_er
print "slope:",pslope
print "slope error:",pslope_err
sumout.write( "C_" + str(i) + " "+ '{0:.2}'.format(pslope) + " +/- " + '{0:.1}'.format(pslope_err) +" " + '{0:.2}'.format(pn_o) + " +/- " + '{0:.2}'.format(pn_o_er)+ '\n' )
gStyle.SetOptFit(0)
gr.Draw("AP")
#gr.GetYaxis().SetRange(0,300)
#gr.GetXaxis().SetRange(0,8000)
title = "Graph eV vs Vcal for ROC " + str(i) + " "
gr.SetTitle( title )
gr.GetYaxis().SetTitle("Vcal")
gr.GetXaxis().SetTitle("No.Electrons")
gStyle.SetOptStat(0)
c1.Update()
gStyle.SetOptFit(0)
gr.Draw("AP")
gStyle.SetOptFit(0)
#ps = c1.FindObject("Graph").FindObject("stats")
#ps.SetX1NDC(0.15)
#ps.SetX2NDC(0.55)
#gStyle.SetOptStat(0)
c1.SetGrid()
textslope = TLatex()
textslope.SetNDC()
textslope.SetTextColor(kBlack)
textslope.SetTextSize(0.05)
textslope.DrawLatex(0.15,0.9,title)
textslope.DrawLatex(0.15,0.8,"e^{-}/Vcal: "+ '{0:.2}'.format(pslope) + " \pm " + '{0:.1}'.format(pslope_err) +
" Intercept: " + '{0:.2}'.format(pn_o) + " \pm " + '{0:.2}'.format(pn_o_er))
textslope.DrawLatex(0.2,0.2," Cu Mo Ag In Sn")
textslope.DrawLatex(0.15,0.7,"#chi^{2}/ndf = " + '{0:.4}'.format(chisquare/ndf))
gStyle.SetOptFit(0)
c1.Update()
gStyle.SetOptFit(0)
c1.Update()
c1.SaveAs('Qplot'+'_'+output+'_C'+str(i)+'.png')
c1.Close()
c1.Update()
gStyle.SetOptStat(1)
c1.Update()
##get q value of fit and error and add them to a table then use TGraphError to make a distribution of them
n_oh.Fill(-n_o/slope)
slopeh.Fill(1/slope)
qmatrix[i][0] = 1/slope
qmatrix[i][1] = -n_o/slope
qmatrix[i][2] = chisquare
qmatrix[i][3] = ndf
qmatrix[i][4] = slope_err/pow(slope,2)
# Create SummaryQPlots txt file:
np.savetxt("SummaryDistributionTable"+'_'+output+'_'+".txt",qmatrix, delimiter="\t", fmt="%s", newline='\n' )
FormattedFile = open("FluorFormatedOutput" + output + ".txt", "w")
FormattedFile.write("Roc Number Slope (e-/Vcal) Offset (Vcal) Chi2/NDf\n")
for iroc in rocs:
FormattedFile.write("ROC "+str(iroc)+" "+str(round(qmatrix[iroc][0],2))+" +/- "+str(round(qmatrix[iroc][4],4))+" "+str(int(round(qmatrix[iroc][1])))+" "+str(round(qmatrix[iroc][2]/qmatrix[iroc][3],4))+"\n")
FormattedFile.close()
c2 = TCanvas('c2',"Distribution N_o",1)
c2.cd()
gStyle.SetOptStat(1)
n_oh.Draw()
n_oh.SetStats(1)
c2.Update()
ps2 = n_oh.GetListOfFunctions().FindObject("stats")
gStyle.SetOptStat(0)
n_oh.Draw()
c2.Update()
c2.SaveAs(output+'Distribution_NoEle.png')
c3 = TCanvas('c3',"Distribution N_o",1)
c3.cd()
gStyle.SetOptStat(1)
slopeh.Draw()
slopeh.SetStats(1)
c3.Update()
slopeh.GetXaxis().SetTitle("p1[Vcal/No.Ele]")
slopeh.GetYaxis().SetTitle("Entries")
gStyle.SetOptStat(1)
slopeh.Draw()
c3.Update()
c3.SaveAs(output+'Distribution_Slope.png')
sumout.close()
return
# _
# _ __ ___ __ _(_)_ __
#| '_ ` _ \ / _` | | '_ \
#| | | | | | (_| | | | | |
#|_| |_| |_|\__,_|_|_| |_|
if options.setup == 'UIC':
rootfile1 = TFile(options.MoFile)
rootfile1name = options.MoFile
material1 = 'Mo'
XRSource = 'Cu'
elif options.setup =='KU':
rootfile1 = TFile(options.CuFile)
rootfile1name = options.CuFile
material1 = 'Cu'
XRSource = 'Mo'
rootfile2 = TFile(options.AgFile)
rootfile2name = options.AgFile
rootfile3 = TFile(options.SnFile)
rootfile3name = options.SnFile
rootfile4 = TFile(options.InFile)
rootfile4name = options.InFile
outrootfile = TFile('histos.root')
histname = options.histoname
nrocs = options.nrocs
for item in options.badrocs.split():
if item in rocs: rocs.remove( item )
output = options.outputfile
material2 = 'Ag'
material3 = 'Sn'
material4 = 'In'
hist1 = histname+"_"+material1+"_C"
hist2 = histname+"_"+material2+"_C"
hist3 = histname+"_"+material3+"_C"
hist4 = histname+"_"+material4+"_C"
gROOT.SetBatch(kTRUE)
Arraytgt1 = FitPeaks(rootfile1,hist1,material1,rocs,output, XRSource, 2)
Arraytgt2 = FitPeaks(rootfile2,hist2,material2,rocs,output,XRSource, 2)
Arraytgt3 = FitPeaks(rootfile3,hist3,material3,rocs,output,XRSource, 2)
Arraytgt4 = FitPeaks(rootfile4,hist4,material4,rocs,output,XRSource, 2)
PlotSameNStats(Arraytgt1,Arraytgt2,Arraytgt3,Arraytgt4,rocs,output,XRSource)
ConversionPlot(rocs, output,XRSource)
outrootfile.Write()
outrootfile.Close()
print "Thats all Folks!"
os.chdir("../");