def rankProb(ListOpt):
List = ListOpt.copy()
List.pop(0)
i = 0 #for rank op
rankOp = []
rankOp2 = [None, None, None]
if '--noCal' in List:
noCalFlag = True
List.remove('--noCal')
else:
noCalFlag = False
if '--wof' in List:
wofFlag = True
List.remove('--wof')
else:
wofFlag = False
if '--all' in List:
allFlag = True
List.remove('--all')
else:
allFlag = False
for Arg in List:
try:
if int(Arg) > 0 and int(Arg) < 4:
rankOp.append(int(Arg))
else:
continue
except:
if len(List) <= 1:
rankOp.append(3)
continue
if len(rankOp) == 0:
rankOp.append(3)
rankOp.append(2)
elif len(rankOp) == 1:
if rankOp[0] == 3:
rankOp.append(2)
else:
rankOp.append(3)
rankOp = removeDuplicates(rankOp)
#rankOp2 = [x-4 for x in rankOp]
rankOp2 = [-2, -3]
if len(List) == 0:
print("error: --rank option needs an argument")
return 0
infoFile = List[0]
if not os.path.isfile(infoFile):
print("error: %s does not exist, are you in the right path?" %
(infoFile))
return 10000
if not infoFile.endswith('.info'):
print("error: %s needs a .info extension" % (infoFile))
return 10001
infoDict = getDictFromInfoFile(infoFile)
for arg in List:
if isValidSpecFile(arg):
if arg.endswith('.info'):
pass #infoFile = arg
else:
FileName = arg
try:
if isValidSpecFile(FileName):
FileExt = FileName.split('.')[-1]
except:
print('ERROR: Unexpected error. Not a valid file used.')
return 110
if noCalFlag:
FileDict = functionDictAdv[FileExt](FileName, False)
else:
FileDict = functionDictAdv[FileExt](FileName)
try:
minRange = infoDict['Range']['start']
maxRange = infoDict['Range']['end']
del infoDict['Range']
except:
minRange, maxRange = findRangeInfoDict(infoDict)
idxPairL = []
for DictEle in infoDict.values():
idxPairL.append([DictEle['start'], DictEle['end']])
DBInfoL = []
pathfile = os.path.realpath(__file__)
pathfile = pathfile.rstrip('rank_prob.py')
conexion = OpenDatabase(pathfile)
memoLenDict = {}
isoCountD = {}
DBInfoL = []
DBInfoDL = []
tMinEL = []
tMaxEL = []
ProbLL = []
DiffLL = []
ProbDL = {}
DiffDL = {}
if True:
for infoPair in infoDict.values():
tMinEL.append(infoPair['start'])
tMaxEL.append(infoPair['end'])
tMinE = min(tMinEL)
tMaxE = max(tMaxEL)
else:
tMinE = minRange
tMaxE = maxRange
myDataList = FileDict['theList']
#print("")
#print("Gilmore statistics\n[variables in counts]")
fittingDict = doFittingStuff(infoDict, myDataList)
#gaussData4Print=[]
#fig, ax = plt.subplots()
#for e in fittingDict:
#a,mean,sigma,c,minIdx,maxIdx,myFWHM=fittingDict[e]
# a,mean,sigma,c=fittingDict[e][:-3]
# if a == None:
# print("Skipping failed fit")
# continue
# gaussData4Print.append([e,a,mean,sigma,c])
#plt.annotate(e, xy=[mean,a])
#myGaussRows=['#tags','a','mean','sigma','c']
#pd.set_option('display.max_rows', None)
#dfG = pd.DataFrame(gaussData4Print, columns = myGaussRows)
#gilmoreDict=doGilmoreStuff(infoDict,myDataList)
#data4print=[]
#for e in gilmoreDict:
# gL=gilmoreDict[e]
# data4print.append(gL[0:6])
#realXVals=myDataList[0]
#myHStr4=['Tags','NetArea','Area+ExtBkgd','GrossInt','Background','Sigma_A']
#pd.set_option('display.max_rows', len(data4print))#imprime todas las filas
#df = pd.DataFrame([data for data in data4print], columns = myHStr4)
#print(df)
#print('\nGauss Parameters')
#print(dfG)
fittingDictKeys = list(fittingDict.keys())
PeakNum = -1
IdxRemove = []
#idxPairL_copy=idxPairL.copy()
for idxR in idxPairL:
PeakNum += 1
iEner = idxR[0]
fEner = idxR[1]
# DBInfoL.append(GetIntensities(conexion,iEner,fEner))
# DBInfo = DBInfoL[-1]
DBInfo = GetIntensities(conexion, iEner, fEner)
DiffL, ProbL = MeanDistance(DBInfo,
fittingDict[fittingDictKeys[PeakNum]])
if DiffL == None or ProbL == None:
continue
else:
IdxRemove.append(PeakNum)
DBInfoL.append(DBInfo)
DiffLL.append(DiffL)
ProbLL.append(ProbL)
DBInfoD = {}
for e, fs, gs in zip(DBInfo, DiffL, ProbL):
if e[-1] not in DBInfoD:
DBInfoD[e[-1]] = [e]
ProbDL[gs[0]] = [gs]
DiffDL[fs[0]] = [fs]
else:
DBInfoD[e[-1]].append(e)
ProbDL[gs[0]].append(gs)
DiffDL[fs[0]].append(fs)
DBInfoDL.append(DBInfoD)
for Ele in DBInfo:
iso = Ele[-1]
if iso not in memoLenDict:
memoLenDict[iso] = [
len(GetIntensities(conexion, tMinE, tMaxE, iso)), 1,
Ele[10], [PeakNum]
]
isoCountD[iso] = [Ele]
else:
memoLenDict[iso][1] += 1
memoLenDict[iso][2] += Ele[10]
memoLenDict[iso][3].append(PeakNum)
isoCountD[iso].append(Ele)
idxPairL = list(map(idxPairL.__getitem__, IdxRemove))
memoLenDictKeys = memoLenDict.copy().keys()
for Ele in memoLenDictKeys:
if memoLenDict[Ele][0] == 0 or memoLenDict[Ele][2] == 0:
del memoLenDict[Ele]
del isoCountD[Ele]
for DBInfoD in DBInfoDL:
try:
del DBInfoD[Ele]
except KeyError:
continue
memoLenDictKeys = memoLenDict.keys()
Ranges = []
rankList = []
try:
if rankOp[0] == 1:
myfilename = infoFile.strip('.info') + '_rank_B.txt'
elif rankOp[0] == 2:
myfilename = infoFile.strip('.info') + '_rank_C.txt'
elif rankOp[0] == 3:
myfilename = infoFile.strip('.info') + '_rank_Prob.txt'
else:
myfilename = infoFile.strip('.info') + '_rank_Prob.txt'
except:
myfilename = 'FileNameCouldNotBeRecovered.txt'
for idxR, DBInfoD in zip(idxPairL, DBInfoDL):
iEner = idxR[0]
fEner = idxR[1]
Ranges.append([iEner, fEner])
Eg , Ig , Decay, Half , Parent, rank, rank2, rank3, ProbRank, DiffRank = [],[],[],[],[],[],[],[],[],[]
for Key in DBInfoD:
#Ele = DBInfoD[Key]
for Ele, DiffEle, ProbEle in zip(DBInfoD[Key], DiffDL[Key],
ProbDL[Key]):
Eg.append(Ele[1])
Ig.append(round(Ele[3], 2))
Decay.append(Ele[5])
x = halfLifeUnit(Ele)
if x == 0:
y = str(x)
else:
y = str('{0:.2e}'.format(x))
Half.append(y + ' [s] ')
Parent.append(Ele[-1])
rank.append(memoLenDict[Ele[-1]][1])
rank2.append(
round(memoLenDict[Ele[-1]][1] / memoLenDict[Ele[-1]][0],
3))
rank3.append(round(memoLenDict[Ele[-1]][2], 3))
ProbRank.append(ProbEle[1])
DiffRank.append(DiffEle[1])
Eg, Ig, Decay, Half, Parent, rank3, ProbRank, DiffRank = (
list(t) for t in zip(*sorted(zip(Eg, Ig, Decay, Half, Parent,
rank3, ProbRank, DiffRank),
key=itemgetter(*rankOp2),
reverse=True)))
print(
'\nThe energy range consulted is between %.2f keV and %.2f keV.\n'
% (iEner, fEner))
if allFlag:
pd.set_option('display.max_rows', None) #imprime todas las filas
pd.options.display.float_format = '{:,.5f}'.format
df = pd.DataFrame(list(
zip(Eg, Ig, Decay, Half, Parent, rank3, ProbRank, DiffRank)),
columns=[
'Eg [keV]', 'Ig (%)', 'Decay m', 'Half Life',
'Parent', 'Rank3', 'Probability', 'Distance'
]) #crea la tabla
print(df)
else:
pd.set_option('display.max_rows', len(Ele))
df = pd.DataFrame(list(
zip(Eg, Ig, Decay, Half, Parent, rank3, ProbRank, DiffRank)),
columns=[
'Eg [keV]', 'Ig (%)', 'Decay mode',
'Half Life', 'Parent', 'Rank3',
'Probability', 'Distance'
]) #crea la tabla
print(df)
if wofFlag:
try:
if allFlag:
WriteOutputFileRR(myfilename, df, iEner, fEner)
else:
WriteOutputFileRR(myfilename, df.head(10), iEner, fEner)
except IOError:
print(
'ERROR: An unexpected error ocurrs. Data could not be saved.'
)
break
if wofFlag:
print('-----------------------------------------')
print('The file was saved as:')
print(myfilename)
print('-----------------------------------------')
return 0
def rankDist(ListOpt):
List = ListOpt.copy()
List.pop(0)
i = 0 #for rank op
rankOp = []
rankOp2 = [None,None,None]
if '--noCal' in List:
noCalFlag = True
List.remove('--noCal')
else:
noCalFlag = False
if '--wof' in List:
wofFlag = True
List.remove('--wof')
else:
wofFlag = False
if '--all' in List:
allFlag = True
List.remove('--all')
else:
allFlag = False
for Arg in List:
try:
if int(Arg) > 0 and int(Arg) < 4:
rankOp.append(int(Arg))
else:
continue
except:
if len(List) <= 1:
rankOp.append(3)
continue
if len(rankOp) == 0:
rankOp.append(3)
rankOp.append(2)
elif len(rankOp) == 1:
if rankOp[0] == 3:
rankOp.append(2)
else:
rankOp.append(3)
rankOp = removeDuplicates(rankOp)
rankOp2 = [-1,-3,1]
if len(List) == 0:
sys.stderr.write("error: --rank option needs an argument\n")
return 0
for arg in List:
if isValidSpecFile(arg):
if arg.endswith('.info'):
infoFile = arg
else:
FileName = arg
try:
if isValidSpecFile(FileName):
FileExt = FileName.split('.')[-1]
except:
sys.stderr.write('ERROR: Unexpected error. Not a valid file used.\n')
return 110
if not os.path.isfile(infoFile):
sys.stderr.write("error: %s does not exist, are you in the right path?\n" %(infoFile))
return 10000
if not infoFile.endswith('.info'):
sys.stderr.write("error: %s needs a .info extension\n" % (infoFile))
return 10001
infoDict=getDictFromInfoFile(infoFile)
if noCalFlag:
FileDict = functionDictAdv[FileExt](FileName,False)
else:
FileDict = functionDictAdv[FileExt](FileName)
try:
minRange = infoDict['Range']['start']
maxRange = infoDict['Range']['end']
del infoDict['Range']
except:
minRange, maxRange = findRangeInfoDict(infoDict)
idxPairL = []
for DictEle in infoDict.values():
idxPairL.append([DictEle['start'],DictEle['end']])
DBInfoL = []
pathfile = os.path.realpath(__file__)
if operatingSystem == 'Linux' or operatingSystem == 'Darwin':
pathfile = pathfile.replace('/modules/rank_dist.py','')
elif operatingSystem == 'Windows':
pathfile = pathfile.replace('\\modules\\rank_dist.py','')
conexion = OpenDatabase(pathfile)
memoLenDict={}
isoCountD = {}
DBInfoL = []
DBInfoDL = []
ProbDL = []
DiffDL = []
tMinEL = []
tMaxEL = []
ProbLL = []
DiffLL = []
if True:
for infoPair in infoDict.values():
tMinEL.append(infoPair['start'])
tMaxEL.append(infoPair['end'])
tMinE = min(tMinEL)
tMaxE = max(tMaxEL)
else:
tMinE = minRange
tMaxE = maxRange
myDataList = FileDict['theList']
fittingDict=doFittingStuff(infoDict,myDataList)
fittingDictKeys = list(fittingDict.keys())
PeakNum = -1
IdxRemove=[]
for idxR in idxPairL:
PeakNum += 1
iEner = idxR[0]
fEner = idxR[1]
DBInfo = GetIntensities(conexion,iEner,fEner)
DiffL, ProbL = MeanDistance(DBInfo,fittingDict[fittingDictKeys[PeakNum]])
if DiffL == None or ProbL == None:
continue
else:
IdxRemove.append(PeakNum)
DBInfoL.append(DBInfo)
DiffLL.append(DiffL)
ProbLL.append(ProbL)
DBInfoD = {}
ProbD= {}
DiffD = {}
for e,fs,gs in zip(DBInfo,DiffL,ProbL):
if e[-1] not in DBInfoD:
DBInfoD[e[-1]] = [e]
ProbD[gs[0]] = [gs]
DiffD[fs[0]] = [fs]
else:
DBInfoD[e[-1]].append(e)
ProbD[gs[0]].append(gs)
DiffD[fs[0]].append(fs)
DBInfoDL.append(DBInfoD)
ProbDL.append(ProbD)
DiffDL.append(DiffD)
for Ele in DBInfo:
iso = Ele[-1]
if iso not in memoLenDict:
memoLenDict[iso]=[len(GetIntensities(conexion,tMinE,tMaxE,iso)),1,Ele[10],[PeakNum]]
isoCountD[iso] = [Ele]
else:
memoLenDict[iso][1] += 1
memoLenDict[iso][2] += Ele[10]
memoLenDict[iso][3].append(PeakNum)
isoCountD[iso].append(Ele)
idxPairL=list(map(idxPairL.__getitem__,IdxRemove))
memoLenDictKeys = memoLenDict.copy().keys()
for Ele in memoLenDictKeys:
if memoLenDict[Ele][0] == 0 or memoLenDict[Ele][2] == 0:
del memoLenDict[Ele]
del isoCountD[Ele]
for DBInfoD in DBInfoDL:
try:
del DBInfoD[Ele]
except KeyError:
continue
memoLenDictKeys = memoLenDict.keys()
Ranges = []
rankList = []
try:
if rankOp[0] == 1:
myfilename = infoFile.strip('.info') + '_rank_B.txt'
elif rankOp[0] == 2:
myfilename = infoFile.strip('.info') + '_rank_C.txt'
elif rankOp[0] == 3:
myfilename = infoFile.strip('.info') + '_rank_A.txt'
else:
myfilename = infoFile.strip('.info') + '_rank_A.txt'
except:
myfilename = 'FileNameCouldNotBeRecovered.txt'
for idxR,DBInfoD,DiffD,ProbD in zip(idxPairL,DBInfoDL,DiffDL,ProbDL):
iEner = idxR[0]
fEner = idxR[1]
Ranges.append([iEner,fEner])
Eg , Ig , Decay, Half , Parent, rank, rank2, rank3, ProbRank, DiffRank = [],[],[],[],[],[],[],[],[],[]
for Key in DBInfoD:
#Ele = DBInfoD[Key]
for Ele,DiffEle,ProbEle in zip(DBInfoD[Key],DiffD[Key],ProbD[Key]):
Eg.append(Ele[1])
Ig.append(round(Ele[3],2))
Decay.append(Ele[5])
x=halfLifeUnit(Ele)
if x == 0:
y = str(x)
else:
y = str('{0:.2e}'.format(x))
Half.append(y+ ' [s] ')
Parent.append(Ele[-1])
rank.append(memoLenDict[Ele[-1]][1])
rank2.append(round(memoLenDict[Ele[-1]][1]/memoLenDict[Ele[-1]][0],3))
rank3.append(round(memoLenDict[Ele[-1]][2],3))
ProbRank.append(ProbEle[1])
DiffRank.append(DiffEle[1])
print('\nThe energy range consulted is between %.2f keV and %.2f keV.\n' % (iEner,fEner))
if allFlag:
pd.set_option('display.max_rows', None) #imprime todas las filas
pd.options.display.float_format = '{:,.5f}'.format
df = pd.DataFrame(list(zip(Eg,Ig,Decay,Half,Parent,rank3,ProbRank,DiffRank)),columns=['Eg [keV]','Ig (%)','Decay m','Half Life','Parent','Rank F','Probability','Distance'])#crea la tabla
df = df.sort_values(by=['Distance','Rank F','Ig (%)'],ascending=[True,False,False],ignore_index=True)
print(df)
else:
pd.set_option('display.max_rows', len(Ele))
df = pd.DataFrame(list(zip(Eg,Ig,Decay,Half,Parent,rank3,ProbRank,DiffRank)),columns=['Eg [keV]','Ig (%)','Decay mode','Half Life','Parent','Rank F','Probability','Distance'])#crea la tabla
df = df.sort_values(by=['Distance','Rank F','Ig (%)'],ascending=[True,False,False],ignore_index=True)
print(df.head(n=10))
if wofFlag:
try:
if allFlag:
WriteOutputFileRR(myfilename,df,iEner,fEner)
else:
WriteOutputFileRR(myfilename,df.head(10),iEner,fEner)
except IOError:
sys.stderr.write('ERROR: An unexpected error ocurrs. Data could not be saved.\n')
break
if wofFlag:
print('-----------------------------------------')
print('The file was saved as:')
print(myfilename)
print('-----------------------------------------')
return 0
def statsFun(ListOpt):
List = ListOpt.copy()
List.pop(0)
if '--noCal' in List:
noCalFlag = True
List.remove('--noCal')
else:
noCalFlag = False
if '--noPlot' in List:
noPlotFlag = True
List.remove('--noPlot')
else:
noPlotFlag = False
if '--wof' in List:
wofFlag = True
List.remove('--wof')
else:
wofFlag = False
if '--log' in List:
logFlag = True
List.remove('--log')
else:
logFlag = False
if '--rebin' in List:
rebinFlag = True
List.remove('--rebin')
rebinNum = None
for Arg in List:
try:
rebinNum = int(Arg)
List.remove(Arg)
break
except:
continue
# if rebinNum == None and :
# return 120
else:
rebinFlag = False
#infoFile=List[0]
for arg in List:
if isValidSpecFile(arg):
if arg.endswith('.info'):
infoFile = arg
else:
FileName = arg
try:
if isValidSpecFile(FileName):
FileExt = FileName.split('.')[-1]
except:
print('ERROR: Unexpected error. Not a valid file used.')
return 110
if not os.path.isfile(infoFile):
print("ERROR: %s does not exist, are you in the right path?" %(infoFile))
return 111
if not infoFile.endswith('.info'):
print("ERROR: %s needs a .info extension" % (infoFile))
return 112
infoDict=getDictFromInfoFile(infoFile)
try:
del infoDict['Range']
except:
pass
if noCalFlag:
FileDict = functionDictAdv[FileExt](FileName,False)
else:
FileDict = functionDictAdv[FileExt](FileName)
#####
if rebinFlag:
if isinstance(rebinNum, int) == False:
rebinNum=5
print("There was no rebin integer detected, the default rebin value used was 5")
if "theRebinedList" not in FileDict:
FileDict["theRebinedList"]=getRebinedList(FileDict["theList"],rebinNum)
myDataList = FileDict["theRebinedList"]
myDataList[0] = list(myDataList[0])
myDataList[1] = list(myDataList[1])
else:
print("There was no rebin option detected, the rebin option is --rebin")
myDataList = FileDict['theList']
#####
print("")
print("Gilmore statistics\n[variables in counts]")
fittingDict=doFittingStuff(infoDict,myDataList)
gaussData4Print=[]
#fig, ax = plt.subplots()
for e in fittingDict:
#a,mean,sigma,c,minIdx,maxIdx,myFWHM=fittingDict[e]
a,mean,sigma,c=fittingDict[e][:-3]
if a == None:
print("Skipping failed fit")
continue
gaussData4Print.append([e,a,mean,sigma,c])
#plt.annotate(e, xy=[mean,a])
myGaussRows=['#tags','a','mean','sigma','c']
pd.set_option('display.max_rows', None)
dfG = pd.DataFrame(gaussData4Print, columns = myGaussRows)
gilmoreDict=doGilmoreStuff(infoDict,myDataList)
data4print=[]
for e in gilmoreDict:
gL=gilmoreDict[e]
data4print.append(gL[0:6])
realXVals=myDataList[0]
myHStr4=['Tags','NetArea','Area+ExtBkgd','GrossInt','Background','Sigma_A']
pd.set_option('display.max_rows', len(data4print))#imprime todas las filas
df = pd.DataFrame([data for data in data4print], columns = myHStr4)
print(df)
print('\nGauss Parameters')
print(dfG)
#keyboard.press_and_release('\n')
if wofFlag:
doOutputFile(FileName,df,dfG)
count = 0
AnnotateArg = []
idxPairL = []
for e in gilmoreDict:
#tag,netArea,G,B,sigma_A,EBA,extSigma_A,myFWHMSigma_A,myFWHMExtSigma_A,max_index,max_value=gilmoreDict[e]
a,mean,sigma,c,_,_=[str(val) for val in fittingDict[e][:-1]]
#a,mean,sigma,c,minIdx,maxIdx=[str(val) for val in fittingDict[e][:-1]]
max_index = gilmoreDict[e][-2]
max_value = gilmoreDict[e][-1]
floatMean=fittingDict[e][1]
idxPairL.append([infoDict[e]['start'],infoDict[e]['end']])
AnnotateArg.append(["%s,%2.1f"%(e,floatMean),[realXVals[max_index],max_value]])
count += 1
#if None != floatMean:
# plt.annotate("%s,%2.1f" %(e,floatMean),xy=[realXVals[max_index],max_value])
#else:
# plt.annotate(e, xy=[realXVals[max_index],max_value])
#erase this part?
# plt.hist(myArr, bins=16384)
# plt.bar(np.arange(len(li)),li)
# plt.yscale('log', nonposy='clip')
#print("exposure time = ", FileDict["expoTime"])
complexPlot(FileDict,idxPairL,fittingDict,AnnotateArg,logFlag=logFlag,noCalFlag=noCalFlag,Show=not(noPlotFlag),FitCurve=True, rebinFlag=rebinFlag)
return 0
def signal2noise(ListOpt):
List = ListOpt.copy()
List.pop(0)
if '--noCal' in List:
noCalFlag = True
List.remove('--noCal')
else:
noCalFlag = False
if '--noPlot' in List:
noPlotFlag = True
List.remove('--noPlot')
else:
noPlotFlag = False
if '--wof' in List:
wofFlag = True
List.remove('--wof')
else:
wofFlag = False
if '--log' in List:
logFlag = True
List.remove('--log')
else:
logFlag = False
if '--rebin' in List:
rebinFlag = True
List.remove('--rebin')
rebinNum = None
for Arg in List:
try:
rebinNum = int(Arg)
List.remove(Arg)
break
except:
continue
else:
rebinFlag = False
FileName = ''
for arg in List:
if isValidSpecFile(arg):
if arg.endswith('.info'):
infoFile = arg
else:
FileName = arg
try:
if isValidSpecFile(FileName):
FileExt = FileName.split('.')[-1]
except:
sys.stderr.write('\nERROR: Unexpected error. Not a valid file used.\n')
return 12010
if not os.path.isfile(infoFile):
sys.stderr.write(
"\nERROR: %s does not exist, are you in the right path?" %
(infoFile) + "\n")
return 12011
if not infoFile.endswith('.info'):
sys.stderr.write("\nERROR: %s needs a .info extension" % (infoFile) +
"\n")
return 12012
infoDict = getDictFromInfoFile(infoFile)
try:
del infoDict['Range']
except:
pass
if noCalFlag:
FileDict = functionDictAdv[FileExt](FileName, False)
else:
FileDict = functionDictAdv[FileExt](FileName)
if rebinFlag:
if isinstance(rebinNum, int) == False:
rebinNum = 5
sys.stderr.write(
"\nThere was no rebin integer detected, the default rebin value used was 5\n"
)
if "theRebinedList" not in FileDict:
FileDict["theRebinedList"] = getRebinedList(
FileDict["theList"], rebinNum)
myDataList = FileDict["theRebinedList"]
myDataList[0] = list(myDataList[0])
myDataList[1] = list(myDataList[1])
else:
sys.stderr.write(
"\nThere was no rebin option detected, the rebin option is --rebin\n"
)
myDataList = FileDict['theList']
print("")
print("Gilmore statistics\n[variables in counts]")
fittingDict = doFittingStuff(infoDict, myDataList, typeSpec='s')
gaussData4Print = []
areaSpectrum = []
areaUnderPeak = []
underPeakDict = {}
for e in fittingDict:
underPeakDict[e] = fittingDict[e].copy()
for e in underPeakDict:
underPeakDict[e][0] = 0
underPeakDict[e][1] = 0
underPeakDict[e][2] = 0
for e in fittingDict:
k, mean, sigma, a, b, c = fittingDict[e][:-3]
areaSpectrum.append(
calculateIntegral(fittingDict[e],
infoDict[e]['start'],
infoDict[e]['end'],
type='s'))
areaUnderPeak.append(
calculateIntegral(underPeakDict[e],
infoDict[e]['start'],
infoDict[e]['end'],
type='s'))
if k == None:
sys.stderr.write("Fit failed for " + str(e) + " in fiting.py" +
"\n")
continue
gaussData4Print.append([e, k, mean, sigma, a, b, c])
myGaussRows = ['#tags', 'k', 'mean', 'sigma', 'a', 'b', 'c']
pd.set_option('display.max_rows', None)
dfG = pd.DataFrame(gaussData4Print, columns=myGaussRows)
gaussData4Print = []
gilmoreDict = doGilmoreStuff(infoDict, myDataList)
data4print = []
for e in gilmoreDict:
gL = gilmoreDict[e]
data4print.append(gL[0:6])
realXVals = myDataList[0]
myHStr4 = [
'Tags', 'NetArea', 'Area+ExtBkgd', 'GrossInt', 'Background', 'Sigma_A'
]
pd.set_option('display.max_rows', len(data4print))
df = pd.DataFrame([data for data in data4print], columns=myHStr4)
print('\nSpectra information')
print(df)
print('\nGaussian Parameters + 2nd Order Polynomial')
print(dfG)
SNratio = [aS / aB for aS, aB in zip(areaSpectrum, areaUnderPeak)]
SNratiodB = [10 * log10(SNR) for SNR in SNratio]
print('\nSignal to Noise ratio per Peak')
SNdf = pd.DataFrame(
[[tag, SNR, SNRdB]
for tag, SNR, SNRdB in zip(fittingDict.keys(), SNratio, SNratiodB)],
columns=['#tags', 'SNR', 'SNR[dB]'])
print(SNdf)
if wofFlag:
doOutputFile(FileName, df, dfG)
count = 0
AnnotateArg = []
idxPairL = []
for e in gilmoreDict:
k, mean, sigma, a, b, c, _, _ = [
str(val) for val in fittingDict[e][:-1]
]
max_index = gilmoreDict[e][-2]
max_value = gilmoreDict[e][-1]
floatMean = fittingDict[e][1]
idxPairL.append([infoDict[e]['start'], infoDict[e]['end']])
try:
AnnotateArg.append([
"%s,%2.1f" % (e, floatMean), [realXVals[max_index], max_value]
])
except:
pass
count += 1
complexPlot(FileDict,
idxPairL,
fittingDict,
AnnotateArg,
logFlag=logFlag,
noCalFlag=noCalFlag,
Show=not (noPlotFlag),
FitCurve=True,
rebinFlag=rebinFlag)
return 0