"""
Control = numpy.load(os.path.join(sys.argv[2],"GUI","Control_List.npy"))
dataDir=os.path.join(sys.argv[2],"simulation",sys.argv[1]+"_MeV")
topDir=os.path.join(sys.argv[2])
alphaList=numpy.loadtxt(os.path.join(dataDir,"flat","alphaevents.txt"))
maxNumberOfEvents=float(len(alphaList))
testMass= int(sys.argv[1])+(int(Control[4])/2.)
productionAmplitudes=[]
waves=getwaves(os.path.join(dataDir,"flat"))
normint=numpy.load(os.path.join(dataDir,"flat","normint.npy"))
if os.path.isfile(os.path.join(dataDir,"Vvalues.npy")):
contents=numpy.load(os.path.join(dataDir,"Vvalues.npy"))
orderedContents=sorted(contents.tolist().iteritems(),key=operator.itemgetter(0))
for i in range(0,len(orderedContents),2):
realPart=orderedContents[i][1]
imaginaryPart=orderedContents[i+1][1]
productionAmplitudes.append(numpy.complex(realPart,imaginaryPart))
if sys.argv[3] == "s":
nTrueList = [ntrue(productionAmplitudes,waves,normint)]
for wave in waves:
nTrueList.append(wave.filename.rstrip(".bamp"))
nTrueList.append(ntrueforwave(productionAmplitudes[waves.index(wave)],waves,wave,normint).real)
"""
.. module:: batchFarmServices
:platform: Unix, Windows, OSX
:synopsis: Utilities for doing PWA with the Jlab batch system.
.. moduleauthor:: Joshua Pond <*****@*****.**>
"""
import os
import sys
sys.path.append(os.path.join(sys.argv[4], "pythonPWA"))
import numpy
from pythonPWA.fileHandlers.getWavesGen import getwaves
from pythonPWA.model.normInt import normInt
"""
This is the program that does the work of calculating the normilization integral and returning it as a .npy file.
"""
dataDir = sys.argv[1]
alphaList = numpy.loadtxt(os.path.join(dataDir, sys.argv[2]))
waves = getwaves(dataDir)
rInt = normInt(waves=waves, alphaList=alphaList, beamPolarization=sys.argv[3])
rInt.execute()
rInt.save(dataDir)
from pythonPWA.fileHandlers.getWavesGen import getwaves
from batchFarmServices.fast_like import FASTLikelihood
from batchFarmServices.rhoAA import rhoAA
import fnGenerator
from iminuit import Minuit
indir = sys.argv[2]
Control = numpy.load(os.path.join(indir, "GUI", "Control_List.npy"))
dataDir = os.path.join(indir, "fitting", sys.argv[1] + "_MeV")
alphaList = numpy.loadtxt(os.path.join(dataDir, "data", "alphaevents.txt"))
if os.path.isfile(os.path.join(dataDir, "data", "QFactor.txt")):
QFactor = numpy.loadtxt(os.path.join(dataDir, "data", "QFactor.txt"))
else:
QFactor = [1]
maxNumberOfEvents = float(len(alphaList))
waves = getwaves(os.path.join(dataDir, "data"))
normint = numpy.load(os.path.join(dataDir, "mc", "raw", "normint.npy"))
accNormInt = numpy.load(os.path.join(dataDir, "mc", "acc", "normint.npy"))
acceptedPath = os.path.join(dataDir, "mc", "acc", "alphaevents.txt")
generatedPath = os.path.join(dataDir, "mc", "raw", "alphaevents.txt")
rAA = rhoAA(waves=waves,
alphaList=alphaList,
beamPolarization=float(Control[1]))
rhoAA = rAA.calc()
numpy.save(os.path.join(dataDir, "data", "rhoAA.npy"), rhoAA)
minuitLn = FASTLikelihood(waves=waves,
alphaList=alphaList,
acceptedPath=acceptedPath,
generatedPath=generatedPath,
accNormInt=accNormInt,
Q=QFactor,
from pythonPWA.fileHandlers.getWavesGen import getwaves
from batchFarmServices.fast_like import FASTLikelihood
from batchFarmServices.rhoAA import rhoAA
import fnGenerator
from iminuit import Minuit
indir = sys.argv[2]
Control = numpy.load(os.path.join(indir,"GUI","Control_List.npy"))
dataDir=os.path.join(indir,"fitting",sys.argv[1]+"_MeV")
alphaList=numpy.loadtxt(os.path.join(dataDir,"data","alphaevents.txt"))
if os.path.isfile(os.path.join(dataDir,"data","QFactor.txt")):
QFactor = numpy.loadtxt(os.path.join(dataDir,"data","QFactor.txt"))
else:
QFactor = [1]
maxNumberOfEvents=float(len(alphaList))
waves=getwaves(os.path.join(dataDir,"data"))
normint=numpy.load(os.path.join(dataDir,"mc","raw","normint.npy"))
accNormInt=numpy.load(os.path.join(dataDir,"mc","acc","normint.npy"))
acceptedPath=os.path.join(dataDir,"mc","acc","alphaevents.txt")
generatedPath=os.path.join(dataDir,"mc","raw","alphaevents.txt")
rAA = rhoAA(waves=waves,alphaList=alphaList,beamPolarization=float(Control[1]))
rhoAA = rAA.calc()
numpy.save(os.path.join(dataDir,"data","rhoAA.npy"),rhoAA)
minuitLn=FASTLikelihood(waves=waves,alphaList=alphaList,acceptedPath=acceptedPath,generatedPath=generatedPath,accNormInt=accNormInt,Q=QFactor,rhoAA=rhoAA)
if not os.path.isfile(os.path.join(sys.argv[2],"scripts","minInit.txt")):
generator=fnGenerator.generator()
generator.fileName=os.path.join(os.getcwd(),"generatedFn.py")
generator.createFile(len(waves)*2)
execfile(os.path.join(os.getcwd(),"generatedFn.py"))
m.set_up(0.5)
alphaList=numpy.loadtxt(os.path.join(dataDir,"alphaevents.txt"))
maxNumberOfEvents=float(len(alphaList))
testMass=1025.
#resonances=[resonance(cR=maxNumberOfEvents,wR=[1.],w0=1320.,r0=100000.)]
resonances=[resonance(cR=2.*maxNumberOfEvents/3.,wR=[0.,1.],w0=1320.,r0=107.),resonance(cR=maxNumberOfEvents/3.,wR=[1.,0.],w0=1895.,r0=235.)]
waves=getwaves(dataDir)
rInt=normInt(waves=waves,alphaList=alphaList)
rInt.execute()
rInt.save(dataDir)
normint=numpy.load(os.path.join(dataDir,"normint.npy"))
dSimulator=dataSimulator(mass=testMass,waves=waves,resonances=resonances,normint=normint,alphaList=alphaList)
inputGampFile=open(os.path.join(dataDir,"events.gamp"),'r')
inputPfFile=open(os.path.join(dataDir,"events.pf"),'r')
outputRawGampFile=open(os.path.join(dataDir,"seq_raw_events.gamp"),'w')
outputAccGampFile=open(os.path.join(dataDir,"selected_events.acc.gamp"),'w')
def calcNTrueForDir(dataDir):
"""
This function calculates the value of the total nTrue and the nTrue values for each wave for a single mass bin directory.
Args:
dataDir (string): The complete file path to the "mass_MeV" directory.
"""
#list to hold nTrue errors
errorList=[]
errorListEx=[]
wvNameList=[]
#getting our waves
waves=getwaves(os.path.join(dataDir,"data"))
#loading our accepted normalization integral (NOTE: its from the mc directory)
accNormInt=numpy.load(os.path.join(dataDir,"mc","acc","normint.npy"))
rawNormInt=numpy.load(os.path.join(dataDir,"mc","raw","normint.npy"))
apath = os.path.join(dataDir,"mc","acc","alphaevents.txt")
rpath = os.path.join(dataDir,"mc","raw","alphaevents.txt")
#apath = os.path.join(dataDir,"mc","acc","events.num")
#rpath = os.path.join(dataDir,"mc","raw","events.num")
#instantiating our list of complex production amplitudes, should be
##a list of 2 numpy complexes
contents=numpy.load(os.path.join(dataDir,"Vvalues.npy"))
orderedContents=sorted(contents.tolist().iteritems(),key=operator.itemgetter(0))
vList=[]
for i in range(0,len(orderedContents),2):
realPart=orderedContents[i][1]
imaginaryPart=orderedContents[i+1][1]
vList.append(numpy.complex(realPart,imaginaryPart))
#calculating nTrue for both waves combined
ntrueVal=ntrue(vList,waves,rawNormInt)
nExpVal=nExp(vList,waves,accNormInt,apath,rpath)
nTrueList=[]
nExpList=[]
#then storing it
nTrueList.append(ntrueVal)
nExpList.append(nExpVal)
#next calculating nTrue for each wave
for wave in waves:
nTrueList.append(ntrueforwave(vList[waves.index(wave)],waves,wave,rawNormInt).real)
nExpList.append(nExpforwave(vList[waves.index(wave)],waves,wave,accNormInt,apath,rpath).real)
wvNameList.append(wave.filename)
#next up lets load our raw normalization integral
#loading our minuit covariance matrix obtained from fitting
if os.path.isfile(os.path.join(dataDir,"minuitCovar3.npy")):
covarianceMatrix=numpy.load(os.path.join(dataDir,"minuitCovar3.npy"))
statSquared=calcStatSquaredError(covarianceMatrix,rawNormInt,vList,waves)
statSquaredEx=calcStatSquaredError(covarianceMatrix,accNormInt,vList,waves)
print statSquared
print statSquaredEx
errorList.append(numpy.sqrt(statSquared[0,0].real))
errorListEx.append(numpy.sqrt(statSquaredEx[0,0].real))
if not os.path.isfile(os.path.join(dataDir,"minuitCovar3.npy")):
statSquared=0
errorList.append(0)
statSquaredEx=0
errorListEx.append(0)
for wave in waves:
if statSquared != 0:
buffer=numpy.zeros(shape=rawNormInt.shape)
i=waves.index(wave)
buffer[wave.epsilon,wave.epsilon,i,:]=rawNormInt[wave.epsilon,wave.epsilon,i,:]
statSquared=calcStatSquaredError(covarianceMatrix,buffer,vList,waves)
errorList.append(numpy.sqrt(statSquared[0,0].real))
buffer=numpy.zeros(shape=accNormInt.shape)
i=waves.index(wave)
buffer[wave.epsilon,wave.epsilon,i,:]=accNormInt[wave.epsilon,wave.epsilon,i,:]
statSquared=calcStatSquaredError(covarianceMatrix,buffer,vList,waves)
errorListEx.append(numpy.sqrt(statSquared[0,0].real))
else:
errorList.append(0)
errorListEx.append(0)
#saving our results to be used in plotting later. format is numpy array [[nTrueTotal,nTrueWave1,nTrueWave2],[errorNTrueTotal,errorNTrueWave1,errorNTrueWave2]]
#numpy.save(os.path.join(dataDir,"nTrueError.npy"),numpy.array([nTrueList,errorList]))
retList=[]
for i in range(len(nTrueList)):
if i == 0:
retList.append([dataDir.strip("_MeV"),nTrueList[i],errorList[i],nExpList[i],errorListEx[i]])
elif i > 0:
retList.append([dataDir.strip("_MeV"),nTrueList[i],errorList[i],wvNameList[i-1],nExpList[i],errorListEx[i]])
return retList
"""
Control = numpy.load(os.path.join(sys.argv[2],"GUI","Control_List.npy"))
dataDir=os.path.join(sys.argv[2],"simulation",sys.argv[1]+"_MeV")
topDir=os.path.join(sys.argv[2])
alphaList=numpy.loadtxt(os.path.join(dataDir,"flat","alphaevents.txt"))
maxNumberOfEvents=float(len(alphaList))
testMass= int(sys.argv[1])+(int(Control[4])/2.)
productionAmplitudes=[]
waves=getwaves(os.path.join(dataDir,"flat"))
normint=numpy.load(os.path.join(dataDir,"flat","normint.npy"))
if os.path.isfile(os.path.join(dataDir,"Vvalues.npy")):
contents=numpy.load(os.path.join(dataDir,"Vvalues.npy"))
orderedContents=sorted(contents.tolist().iteritems(),key=operator.itemgetter(0))
for i in range(0,len(orderedContents),2):
realPart=orderedContents[i][1]
imaginaryPart=orderedContents[i+1][1]
productionAmplitudes.append(numpy.complex(realPart,imaginaryPart))
if sys.argv[3] == "s":
nTrueList = [ntrue(productionAmplitudes,waves,normint)]
for wave in waves:
nTrueList.append(wave.filename.rstrip(".bamp"))
nTrueList.append(ntrueforwave(productionAmplitudes[waves.index(wave)],waves,wave,normint).real)
