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Rate_Fitter_DEBUG_July3.py
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#!/software/python-2.7-2014q3-el6-x86_64/bin/python
import SNANA_Reader as simread
import REAL_Reader as dataread
import traceback
import scipy
import scipy.stats as stats
import numpy as np
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
import Cosmology
import scipy.stats.mstats as mstats
import scipy.stats as stats
from scipy.interpolate import interp1d
from sys import argv
import glob
import time
import os
import gzip
import shutil
import numpy.ma as ma
import subprocess
import iminuit as iM
from iminuit import Minuit as M
class Rate_Fitter:
def __init__(self, realfilename, realName, simfilename, simName, simgenfilename, MCBeta, zmin=0.1, zmax=1.20 , simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, Rate_Model = 'powerlaw', cheatType = False, cheatZ = False, cheatCCSub = False, cheatCCScale = False, cuts = None, nprint = 5):
self.zmin = zmin
self.zmax = zmax
self.MCBeta = MCBeta
self.Rate_Model = Rate_Model
self.cheatType = cheatType
self.cheatZ = cheatZ
self.cheatCCSub = cheatCCSub
self.cheatCCScale = cheatCCScale
self.cuts = cuts
self.nprint = nprint
self.shiftFlagData = False
self.shiftFlagSim = False
self.globalChi2Storage = []
self.globalNDataStorage = []
self.globalZPhotBinStorage = []
self.globalNDataIaPhotBinStorage = []
self.globalNDataCCPhotBinStorage = []
self.globalZTrueBinStorage = []
self.globalNDataIaTrueBinStorage = []
self.globalNDataCCTrueBinStorage = []
print 'a'
try:
self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
except:
self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 5)
print 'b'
self.simName = simName
self.simgencat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
print 'c'
try:
SIMGEN = np.load(simgenfilename + '.npz')['a']
except:
SIMGEN = np.genfromtxt(simgenfilename, dtype=None, names = True, skip_footer=3, invalid_raise=False)
np.savez_compressed(simgenfilename+'.npz', a = SIMGEN)
print "WHY DO YOU HATE ME WHEN I SHOW YOU NOTHING BUT LOVE"
print simgenfilename
print 'd'
SIMGEN = SIMGEN[SIMGEN['GENZ'] != 'GENZ']
self.simgencat.params = {'flat':True, 'H0': simH0, 'Om0':simOmegaM, 'Ob0': simOb0, 'sigma8': simSigma8, 'ns': simNs}
self.simgencat.cosmo = Cosmology.setCosmology('simCosmo', self.simcat.params)
self.simgencat.OrigCatalog = np.copy(SIMGEN)
self.simgencat.Catalog = np.copy(SIMGEN)
self.simgencat.Catalog = self.simgencat.Catalog[self.simgencat.Catalog['GENZ'] != 'GENZ']
self.simgencat.simname = simName
self.simgencat.NSN = int(len(self.simgencat.Catalog['GENZ']))
print "SIMGEN NUMBER"
print self.simgencat.NSN
print "SIMGENCAT FILE"
print simfilename
self.realName = realName
try:
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 5)
except:
#self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
try:
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
except:
self.realcat = dataread.REAL_Cat(realfilename, realName, skip_header =5 )
if self.cheatType:
print "WARNING, THE FITTER IS CHEATING AND ELIMINATED NON-IAs USING SIM INFO"
self.realcat.Catalog = self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]
self.simcat.Catalog = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]
print "Pre cut Catalog"
print self.realcat.Catalog.shape
for cut in cuts:
self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.realcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]
self.simcat.Catalog = self.simcat.Catalog[(self.simcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.simcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]
print "Post cut Catalog"
print self.realcat.Catalog.shape
def newData(self, realfilename, realName, simIndex = 100):
self.realName = realName
self.shiftFlagData = False
try:
self.simcat = simread.SNANA_Cat(simfilename, simName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95)
except:
self.realcat = simread.SNANA_Cat(realfilename, realName, simOmegaM=0.3, simOmegaL=0.7, simH0=70.0, simw=-1.0, simOb0=0.049, simSigma8=0.81, simNs=0.95, skip_header = 5 )
if self.cheatType:
print "WARNING, THE FITTER IS CHEATING AND ELIMINATED NON-IAs USING SIM INFO"
self.realcat.Catalog = self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]
if simIndex < self.nprint:
print 'N precuts'
print self.realcat.Catalog['FITPROB'].shape
print "Pre cut Catalog"
print self.realcat.Catalog.shape
for cut in cuts:
self.realcat.Catalog = self.realcat.Catalog[(self.realcat.Catalog[cut[0]].astype(type(cut[1])) > cut[1]) & (self.realcat.Catalog[cut[0]].astype(type(cut[2])) < cut[2])]
print "Post cut Catalog"
print self.realcat.Catalog.shape
if simIndex < self.nprint:
print "Minimum Fitprob"
print np.min(self.realcat.Catalog['FITPROB'])
print 'N postcuts'
print self.realcat.Catalog['FITPROB'].shape
def zSystematic(self):
return 0
if self.shiftFlagData:
print "DONT DOUBLE SHIFT"
return 0
if not self.shiftFlagSim:
oldsimz = self.simcat.Catalog['zPHOT']
oldsimtruez = self.simcat.Catalog['SIM_ZCMB']
stat, bins, binnum = stats.binned_statistic(oldsimz, oldsimz - oldsimtruez, bins = self.bins, statistic = 'mean')
self.zBiasShifts = stat
newsimz = oldsimz - stat[binnum]
assert(np.sum(np.abs(newsimz - oldsimz)) > 0)
assert((oldzshape - np.arange(0, oldz.shape[0]).shape[0])< 1)
self.shiftFlagSim = True
oldz = self.realcat.Catalog['zPHOT']
_,_, binnum = stats.binned_statistic(oldz, oldz , bins = self.bins, statistic = 'mean')
newz = oldz - self.zBiasShifts[binnum]
oldzshape = oldz.shape[0]
self.realcat.Catalog['zPHOT'].put(np.arange(0, oldz.shape[0]), newz)
assert(np.sum(np.abs(newz - oldz)) > 0)
assert((oldzshape - np.arange(0, oldz.shape[0]).shape[0])< 1)
self.simFlagData = True
def effCalc(self, fracContamCut = 0.0, nbins = 10, simIndex = 100):
#### Do we want SNIas or all SN for efficiency?
self.nbins = nbins
self.typeString = ''
if self.cheatZ:
ztype = 'SIM_ZCMB'
else:
ztype = 'zPHOT'
zPHOTs = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1][ztype].astype(float)
zTRUEs = self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) == 1]['SIM_ZCMB'].astype(float)
self.typeString = self.typeString + 'A1'
binList = np.linspace(self.zmin, self.zmax, nbins+1)
self.binList = binList
counts, zPhotEdges, zTrueEdges, binnumber = scipy.stats.binned_statistic_2d(zPHOTs, zTRUEs, zTRUEs, statistic = 'count', bins = self.binList)
assert(zPhotEdges.shape[0] == (self.nbins + 1))
self.typeString = self.typeString + 'B1'
zGenHist, zGenBins = np.histogram(self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'].astype(int) == 1]['GENZ'].astype(float), bins = self.binList)
zSim1Hist, zSim1Bins = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) ==1]['SIM_ZCMB'].astype(float), bins = self.binList)
self.effmat = np.zeros((self.nbins,self.nbins))
xMax = zPhotEdges.shape[0] - 2
yMax = zTrueEdges.shape[0] - 2
for zPhotLedge, zPhotRedge, row, i in zip(zPhotEdges[:-1], zPhotEdges[1:], counts, xrange(xMax + 1)):
zPhotCenter = (zPhotLedge + zPhotRedge)/2.0
for zTrueLedge, zTrueRedge, count, j in zip(zTrueEdges[:-1], zTrueEdges[1:], row, xrange(yMax + 1)):
zTrueCenter = (zTrueLedge + zTrueRedge)/2.0
inCell = (zPHOTs > zPhotLedge) & (zPHOTs < zPhotRedge) & (zTRUEs > zTrueLedge)& (zTRUEs < zTrueRedge)
zPhotCell = zPHOTs[inCell];zTrueCell = zTRUEs[inCell]
self.effmat[i][j] = np.sum(inCell)
assert(np.abs(np.sum(inCell) - count < 2))
for row, i in zip(self.effmat, xrange(self.effmat.shape[0])):
for j in xrange(row.shape[0]):
self.effmat[i][j] /= zGenHist[j]
if simIndex < self.nprint:
print 'effmat'
print self.effmat
def fit_rate(self, fixK = False, fixBeta = False, simIndex = 100, trueBeta = 0, CCScale = 1.0, TrueCCScale = 1.0, BetaInit = 0.0, kInit = 1.0, BetaErr = 1, kErr = 1, f_Js = None):
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
if self.cheatZ:
ztype = 'SIM_ZCMB'
else:
ztype = 'zPHOT'
plt.switch_backend('Agg')
if len(self.typeString) <= 4:
self.typeString = self.typeString + 'C1'
nSim, simBins = np.histogram(self.simgencat.Catalog[self.simgencat.Catalog['GENTYPE'].astype(int) == 1]['GENZ'].astype(float), bins=self.binList)
nSim2, simBins2 = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) ==1][ztype].astype(float), bins=self.binList)
nSim3, simBins3 = np.histogram(self.simcat.Catalog[ztype].astype(float), bins=self.binList)
NCC , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1][ztype].astype(float), bins=self.binList)
OrigNCC = np.copy(NCC)
if self.cheatCCSub:
if self.cheatCCScale:
print "WARNING: Only cheating on CC Subtraction not scale"
print "Setting NCC to infinity to make sure that cheating correctly"
print "Diagnostics after this point may be nonsense"
print "NCC BeforeFck"
print NCC
NCC = NCC*1E100
print "NCC AfterFck"
print NCC
elif self.cheatCCScale:
print "NCC Before1"
print NCC
print TrueCCScale
NCC = applyCCScale(NCC, TrueCCScale)
print "NCC After1"
print NCC
else:
print "NCC Before2"
print NCC
print CCScale
NCC = applyCCScale(NCC, CCScale)
print "NCC After2"
print NCC
#assert(0)
NIa , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1][ztype].astype(float), bins=self.binList)
try:
TrueNCC, _ = np.histogram(self.realcat.Catalog[self.realcat.Catalog['SIM_TYPE_INDEX'] !=1][ztype].astype(float), bins=self.binList)
if simIndex < self.nprint:
print "True NCC Data"
print TrueNCC
except:
print "Using real data"
TrueNCC = 0.0
nData, dataBins = np.histogram(self.realcat.Catalog[ztype].astype(float), bins=self.binList)
if not(self.cheatCCSub):
FracBad = NCC*1.0/(1.0*(NCC+NIa))
nCCData = nData*FracBad
else:
nCCData = TrueNCC*1.0
FracBad = TrueNCC*1.0/nData
def chi2func(nData, nSim, effmat, fnorm, zCenters, k = 1.0, Beta = 0.0, zBreak = 1.0, dump = False, complexdump = False, modelError = False, nIA = None, nCC = None, Rate_Model = 'powerlaw', zbins = None, simIndex = 100, BetaPrior = (-3, 3), KPrior = (0.0, 50.0), TrueNCCData = None, f_1 = 1.0, f_2 = 1.0, f_3 = 1.0, f_4 = 1.0, f_5 = 1.0, f_6 = 1.0, f_7 = 1.0, f_8 = 1.0, f_9 = 1.0, f_10 = 1.0):
Chi2Temp = 0.0
if Rate_Model == 'powerlaw':
f_Js = k*(1+zCenters)**Beta
elif Rate_Model == 'discrete':
f_Js = np.array([f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10])
elif Rate_Model == 'brokenpowerlaw':
zCenters = (zbins[1:]+zbins[:-1])/2.0
for zC in zCenters:
if zC < zBreak:
f_Js.append(k*(1+zC)**Beta)
elif not(temp is None):
f_Js.append(temp)
else:
temp = f_Js[-1]
f_Js.append(temp)
else:
assert(0)
if Rate_Model == 'discrete':
print "f_Js init"
print f_Js
else:
print "Beta init"
print Beta
print "k init"
print k
if Rate_Model == 'discrete':
kprior = 0
betaprior = 0
else:
kprior = weakPrior(k, KPrior)
betaprior = weakPrior(Beta, BetaPrior)
if dump and (self.nprint < simInd):
print "kprior"
print kprior
print "betaprior"
print betaprior
if (nIA is None) or (nCC is None):
print "No CC Cut"
fracCCData = np.zeros(nData.shape)
elif self.cheatCCSub:
fracCCData = TrueNCC*1.0/nData
else:
if Rate_Model == 'discrete':
if dump:
print 'f_J adjusted CC Cut'
print Rate_Model
print nCC
print nIA
print np.array(f_Js)
fracCCData = (nCC*1.0)/((1.0*nCC + nIA*np.array(f_Js)))
print fracCCData
else:
if dump:
print "Beta Adjusted CC Cut"
print Rate_Model
#BetaRatio = k*(1+zCenters)**(Beta)#/(1+zCenters)**MCBeta
BetaRatio = (1+zCenters)**(Beta)#/(1+zCenters)**MCBeta
if dump:
print "BadFracCCData"
print (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))
print "bad NCCData"
print (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))*nData
fracCCData = (nCC*1.0)/((1.0*nCC + nIA*BetaRatio))
if dump and (self.nprint < simInd):
print 'abc'
print "fracCCData2"
print fracCCData
print "unscaled fracCCData"
print (1.0*nCC)/(1.0*(nCC+nIA))
if self.cheatCCSub:
print "Cheating CC Sub"
assert(not(TrueNCCData is None))
nCCData = TrueNCCData
else:
print 'def'
print "Normal CC Sub"
nCCData = nData*fracCCData
if dump and (self.nprint < simInd):
print "nCCData2"
print nCCData
if not(TrueNCCData is None):
print "TrueNCCData"
print TrueNCCData
#print f_Js
#Check if I am scaling errors down with increasing MC size. Make MC twice as large as "Data" to test.
if dump: chi2Storage = []
if dump:
print "actually used NCC"
#print nCC
print nCCData
for row, nDataI, nCCDataI, i in zip(effmat, nData, nCCData, xrange(self.nbins)):
if dump and (self.nprint < simInd):
print 'effmat row'
print row
print 'nDataI'
print nDataI
print 'nCCDataI'
print nCCDataI
scaledNSimTemp = 0.0
JSumTempNum = 0.0
JSumTempDen = 0.0
for eff, nSimJ, f_J, j in zip(row, nSim, f_Js, xrange(self.nbins)):
if dump and (i == j) and (self.nprint < simInd):
print 'NGen J'
print nSimJ
print 'JSumTempNum contr'
print nSimJ*f_J*eff*fnorm
print 'JSumTempDen contr'
print nSimJ*f_J*eff*fnorm*f_J*fnorm
if dump and (i != j) and self.cheatZ and (self.nprint < simInd):
if nSimJ*f_J*eff*fnorm > 0:
print " This should be zero but isnt "
print nSimJ*f_J*eff*fnorm
assert(0)
JSumTempNum += nSimJ*f_J*eff*fnorm
JSumTempDen += nSimJ*f_J*eff*fnorm*f_J*fnorm
dataFunc = np.maximum(nDataI ,1)
CCFunc = np.ceil(np.maximum(nCCDataI, 1))
c2t = (nDataI - nCCDataI - JSumTempNum)**2/( dataFunc + CCFunc + JSumTempDen)
if dump and (self.nprint < simInd):
print i
print 'nDataI'
print nDataI
print 'fnCCDataI'
print nCCDataI
print 'fnorm'
print fnorm
print "JSumTempNum tot"
print JSumTempNum
print "JSumTempDen tot"
print JSumTempDen
print "Chi2Bin"
print c2t
if dump:
chi2Storage.append(c2t)
if c2t > 5:
print 'INSANITY CHECK ABOVE'
# Chi2Temp += ((nDataI - nCCDataI - JSumTempNum)**2/(JSumTempNum + JSumTempDen))#*fnorm**2
if nDataI > 1E-11 or JSumTempDen > 1E-11:
Chi2Temp += c2t
if dump:
#print Chi2Temp
print kprior
print betaprior
print chi2Storage
print "nData"
print nData
print "nCCData"
print nCCData
return Chi2Temp+kprior+betaprior, chi2Storage
else:
print Chi2Temp
print kprior
print betaprior
return Chi2Temp+kprior+betaprior
zCenters = (simBins[1:] + simBins[:-1])/2.0
#Is this right? Everything else in the other side of the chi2 function should be Ia only
if self.cheatCCSub:
self.fracCCData = TrueNCC*1.0/nData
else:
self.fracCCData = (NCC*1.0)/(1.0*(NCC + NIa))
fnorm = float(np.sum(nData*(1-self.fracCCData)))/float(np.sum(nSim))
if self.Rate_Model == 'powerlaw':
lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, nIA = NIa, nCC = NCC, simIndex = simIndex, TrueNCCData = TrueNCC)
lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, dump = True, nIA = NIa, nCC = NCC, simIndex = simIndex, TrueNCCData = TrueNCC)
MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), fix_k = fixK, fix_Beta = fixBeta)
c2i, _ = lamChi2Dump(1.0, 0.0)
print "Chi2 init = {0}".format(round(c2i, 4))
elif self.Rate_Model == 'brokenpowerlaw':
lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, 1.0, nCCData = NCCData, simIndex = simIndex, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlaw')
lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, 1.0, dump = True, nCCData = nCCData, simIndex = simIndex, TrueNCCData = TrueNCC, Rate_Model = 'brokenpowerlaw')
MinObj = M(lamChi2, k = kInit, error_k = kErr , Beta = BetaInit, error_Beta = BetaErr, limit_k = (0.0, None), fix_k = fixK, fix_Beta = fixBeta)
c2i, _ = lamChi2Dump(1.0, 0.0)
print "Chi2 init = {0}".format(round(c2i, 4))
elif self.Rate_Model == 'discrete':
lamChi2 = lambda f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10: chi2func(nData, nSim, self.effmat, fnorm, zCenters, 1.0, nIA = NIa, nCC = NCC, simIndex = simIndex, TrueNCCData = TrueNCC, f_1 = f_1, f_2 = f_2,f_3 = f_3, f_4 = f_4,f_5 = f_5, f_6 = f_6,f_7 = f_7, f_8 = f_8,f_9 = f_9, f_10 = f_10, Rate_Model = 'discrete' )
lamChi2Dump = lambda f_1, f_2, f_3, f_4, f_5, f_6, f_7, f_8, f_9, f_10: chi2func(nData, nSim, self.effmat, fnorm, zCenters, 1.0, nIA = NIa, nCC = NCC, simIndex = simIndex, TrueNCCData = TrueNCC, f_1 = f_1, f_2 = f_2,f_3 = f_3, f_4 = f_4,f_5 = f_5, f_6 = f_6,f_7 = f_7, f_8 = f_8,f_9 = f_9, f_10 = f_10, dump = True, Rate_Model = 'discrete')
c2i, _ = lamChi2Dump(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)
print "Chi2 init = {0}".format(round(c2i, 4))
MinObj = M(lamChi2, f_1 = 1.0, error_f_1 = 1.0, limit_f_1 = (0.0, None), f_2 = 1.0, error_f_2 = 1.0, limit_f_2 = (0.0, None), f_3 = 1.0, error_f_3 = 1.0, limit_f_3 = (0.0, None), f_4 = 1.0, error_f_4 = 1.0, limit_f_4 = (0.0, None), f_5 = 1.0, error_f_5 = 1.0, limit_f_5 = (0.0, None), f_6 = 1.0, error_f_6 = 1.0, limit_f_6 = (0.0, None), f_7 = 1.0, error_f_7 = 1.0, limit_f_7 = (0.0, None), f_8 = 1.0, error_f_8 = 1.0, limit_f_8 = (0.0, None), f_9 = 1.0, error_f_9 = 1.0, limit_f_9 = (0.0, None), f_10 = 1.0, error_f_10 = 1.0, limit_f_10 = (0.0, None))
if self.Rate_Model == 'discrete':
c2f, c2stor = lamChi2Dump(MinObj.values['f_1'],MinObj.values['f_2'],MinObj.values['f_3'],MinObj.values['f_4'],MinObj.values['f_5'],MinObj.values['f_6'],MinObj.values['f_7'],MinObj.values['f_8'],MinObj.values['f_9'],MinObj.values['f_10'])
else:
c2f, c2stor = lamChi2Dump(MinObj.values['k'], MinObj.values['Beta'])
#MinObj = M(lamChi2, k = 1.0, fix_k = True, Beta = 0.0, error_Beta = 0.1)
MinObj.set_strategy(2)
fmin, param = MinObj.migrad(nsplit= 10)
plt.scatter(nData, c2stor)
plt.xlabel('nData')
plt.ylabel('chi2 in bin')
plt.savefig(self.realName + 'Chi2VsnData.png')
plt.clf()
for c2, nd in zip(c2stor, nData):
self.globalChi2Storage.append(c2)
self.globalNDataStorage.append(nd)
if self.Rate_Model == 'discrete':
fJList = [MinObj.values['f_1'],MinObj.values['f_2'],MinObj.values['f_3'],MinObj.values['f_4'],MinObj.values['f_5'],MinObj.values['f_6'],MinObj.values['f_7'],MinObj.values['f_8'],MinObj.values['f_9'],MinObj.values['f_10']]
fJErrList = [MinObj.errors['f_1'],MinObj.errors['f_2'],MinObj.errors['f_3'],MinObj.errors['f_4'],MinObj.errors['f_5'],MinObj.errors['f_6'],MinObj.errors['f_7'],MinObj.errors['f_8'],MinObj.errors['f_9'],MinObj.errors['f_10']]
self.fJList = fJList
self.fJErrList = fJErrList
self.Beta = None
self.k = None
self.kErr = None
self.BetaErr = None
print fJList
print fJErrList
else:
k = MinObj.values['k']
kErr = MinObj.errors['k']
Beta = MinObj.values['Beta']
BetaErr = MinObj.errors['Beta']
self.k = k
self.Beta = Beta
self.kErr = kErr
self.BetaErr = BetaErr
#/(self.nbins - 2)
self.BetaRatio = (1+zCenters)**(Beta)
self.fJList = None
self.fracCCData = (NCC*1.0)/(1.0*(1.0*NCC + NIa*self.BetaRatio))
print "Chi2 final = {0}".format(round(lamChi2Dump(self.k, self.Beta)[0], 4))
self.chi2 = fmin.fval
print "Chi2final? = {0}".format(round(fmin.fval, 4))
def chi2V2(self, fJs, fJErrs, zCenters, k, Beta):
fitfJs = k*(1+zCenters)**Beta
Chi2Temp = 0
for fJ, fitfJ, fJErr in zip(fJs, fitfJs, fJErrs):
Chi2Temp += (fJ - fitfJ)**2/(fJ + fJErr)
return Chi2Temp
def weakPrior(value, priorTuple):
if value < priorTuple[1]:
if value > priorTuple[0]:
return 1
else:
return (value - priorTuple[0])**4
else:
return (value - priorTuple[1])**4
def getCCScale(simCat, dataCat, MURESWindow = (-1, 1), zbins = [0.0, 0.3, 0.6, 0.9, 1.2], muresBins = np.linspace(-2, -1, 4), Beta = None, binList = None, fracCCData = None, outfilePrefix = 'Test', Rate_Model = 'powerlaw', f_Js = None):
import iminuit as iM
from iminuit import Minuit as M
print "Check this"
print Rate_Model
print f_Js
print Beta
print fracCCData
print "Done Checking"
CCScales = []
CCScaleErrs = []
if not(f_Js is None):
f_Js = np.array(f_Js)
tempSimCC = simCat[simCat['SIM_TYPE_INDEX'].astype(int) != 1]
tempSimIa = simCat[simCat['SIM_TYPE_INDEX'].astype(int) == 1]
allSimCC = np.copy(tempSimCC)
allSimIa = np.copy(tempSimIa)
allData = np.copy(dataCat)
simHistCC, simBinsCC = np.histogram(tempSimCC['MURES'], bins = muresBins)
simHistIa, simBinsIa = np.histogram(tempSimIa['MURES'], bins = muresBins)
simZHistCC, simZBinsCC = np.histogram(tempSimCC['zPHOT'], bins = binList)
#simZHistIa, simZBinsIa = np.histogram(tempSimIa['zPHOT'], bins = binList)
if Rate_Model == 'discrete':
simZHistIa, simZBinsIa = np.histogram(tempSimIa['zPHOT'], bins = binList)
simHist = simZHistIa*np.array(f_Js) + simZHistCC
else:
simZHistIa, simZBinsIa = np.histogram(tempSimIa['zPHOT'], bins = binList, weights = (1 + tempSimIa['zPHOT'])**Beta)
#simHist = (simZHistIa*(1+binCent)**Beta) + simZHistCC
simHist = simZHistIa + simZHistCC
binCent = (simZBinsIa[1:] + simZBinsIa[:-1])/2.0
print "components of simHist"
print simZHistIa
print binCent
print simHistCC
print 'num and denom of fnorm2'
print float(dataCat.shape[0])
print float(np.sum(simHist))
fnorm2 = float(dataCat.shape[0])/float(np.sum(simHist))
print "precut"
print simCat.shape
print dataCat.shape
simCat = simCat[(simCat['MURES'] < MURESWindow[0]) | (simCat['MURES'] > MURESWindow[1]) ]
dataCat = dataCat[(dataCat['MURES'] < MURESWindow[0]) | (dataCat['MURES'] > MURESWindow[1]) ]
print "postcut"
print simCat.shape
print dataCat.shape
print 'post MURES Cut Ndata and Nsim'
print dataCat.shape
print simCat.shape
for zl, zh in zip(zbins[:-1], zbins[1:]):
tempSim = simCat[(simCat['zPHOT'] < zh) & (simCat['zPHOT'] > zl)]
tempData = dataCat[(dataCat['zPHOT'] < zh) & (dataCat['zPHOT'] > zl)]
allSimCCZbin = allSimCC[(allSimCC['zPHOT'] < zh) & (allSimCC['zPHOT'] > zl)]
allSimIaZbin = allSimIa[(allSimIa['zPHOT'] < zh) & (allSimIa['zPHOT'] > zl)]
print "all Sim CC Zbin/IaZbin"
print allSimCCZbin.shape[0]
print allSimIaZbin.shape[0]
allDataZbin = allData[(allData['zPHOT'] < zh) & (allData['zPHOT'] > zl)]
#binchoice = np.abs(binCent - np.mean(tempData['zPHOT']))
#fracCCCent = fracCCData[np.argmin(binchoice)]
if type(muresBins == int):
histD, muresBins = np.histogram(tempData['MURES'], bins = muresBins)
binsD = muresBins
#histDAll, muresBins = np.histogram(allDataZbin['MURES'], bins = binsD)
else:
histD, binsD = np.histogram(tempData['MURES'], bins = muresBins)
#histDAll = np.histogram(allDataZbin['MURES'], bins = muresBins)
histS, binsS = np.histogram(tempSim['MURES'], bins = muresBins)
tempSimCC = tempSim[tempSim['SIM_TYPE_INDEX'] != 1]
tempSimIa = tempSim[tempSim['SIM_TYPE_INDEX'] == 1]
histSCC, binsSCC = np.histogram(tempSimCC['MURES'], bins = muresBins)
if Rate_Model == 'discrete':
spline = interp1d(binCent, f_Js, kind = 'cubic', bounds_error = False, fill_value = (f_Js[0], f_Js[-1]))
#histSIa, binsSIa = np.histogram(tempSimIa['MURES'], bins = muresBins, weights = f_Js )
histSIa, binsSIa = np.histogram(tempSimIa['MURES'], bins = muresBins, weights = spline(tempSimIa['zPHOT']) )
#histSIa = histSIa*f_Js
else:
histSIa, binsSIa = np.histogram(tempSimIa['MURES'], bins = muresBins, weights = (1+tempSimIa['zPHOT'])**Beta)
#histSAllCC, binsSAllCC = np.histogram(allSimCCZbin['MURES'], bins = muresBins)
#histSAllIa, binsSAllIa = np.histogram(allSimIaZbin['MURES'], bins = muresBins)
#histSIa = histSIa*(1+np.mean(tempSim['zPHOT'])**Beta)
print " MURES tail"
print histS
print histSCC
print histSIa
print "MURES bins"
print binsS
print binsSCC
print binsSIa
print "MURES tail scaled by fnorm2"
print histS*fnorm2
print histSCC*fnorm2
print histSIa*fnorm2
print "Data tail"
print histD
print "fnorm2"
print fnorm2
print "data events outliers only and total"
print histD
print allDataZbin.shape[0]
#R = np.array(histD).astype(float)/np.array(histDAll).astype(float)
R = np.array(histD).astype(float)/float(allDataZbin.shape[0])
print "R"
print R
print "Hist CC, outlier and total"
print histSCC
#print histSAllCC
print allSimCCZbin.shape[0]
print "pre Beta Correction allSimIa"
print allSimIaZbin.shape[0]
#print "Beta Correction Factor"
#print (1+np.mean(allSimIaZbin['zPHOT']))**Beta
#print "BetaCorrected allSimIa"
#print allSimIaZbin.shape[0]*(1+np.mean(allSimIaZbin['zPHOT']))**Beta
#betaCorrAllSimIaZbin = allSimIaZbin.shape[0]*(1+np.mean(allSimIaZbin['zPHOT']))**Beta
if Rate_Model == 'discrete':
hist, bins = np.histogram(allSimIaZbin['zPHOT'], bins = 10)
print 'fJ shape'
print f_Js.shape
print f_Js
print hist
print bins
betaCorrAllSimIaZbin =np.sum(hist*f_Js)
else:
betaCorrAllSimIaZbin =np.sum((1+ allSimIaZbin['zPHOT'])**Beta)
#S = float(np.array(R*histSAllIa) - np.array(histSIa))/float(np.array(histSCC) - np.array(R*histSAllCC))
try:
print "Test S"
print R
print betaCorrAllSimIaZbin
print histSIa
print histSCC
print allSimCCZbin.shape
print 'EEE'
print np.array(R*betaCorrAllSimIaZbin)
print 'DDD'
print np.array(histSIa)
print 'CCC'
print (np.array(histSCC) - np.array(R*allSimCCZbin.shape[0]))
print "AAA"
print (np.array(R*betaCorrAllSimIaZbin) - np.array(histSIa))/(np.array(histSCC) - np.array(R*allSimCCZbin.shape[0]))
print "BBB"
#S = (np.array(R*betaCorrAllSimIaZbin) - np.array(histSIa))/(np.array(histSCC) - np.array(R*allSimCCZbin.shape[0]))
S = float(np.array(R*betaCorrAllSimIaZbin) - np.array(histSIa))/float(np.array(histSCC) - np.array(R*allSimCCZbin.shape[0]))
except:
S = np.nan
print "S WTF"
print S
print "Uncertainty Related Bullshit"
'''
print "Delta R"
dR = np.sqrt(histD + histDAll)
print dR
num1 = np.sqrt(np.sqrt((dR/R)**2 + histSAllIa) + histSIa)
num2 = np.sqrt(np.sqrt((dR/R)**2 + histSAllCC) + histSCC)
den1 = (R*histSAllIa - histSIa)
den2 = (histSCC - R*histSAllCC)
dS = np.sqrt((num1/den1)**2 + (num2/den2)**2)
'''
#ddnCC = np.sqrt(histSCC)*(histSIa - histSAllIa*R)/(histSCC - R*histSAllCC)**2
#ddNCC = np.sqrt(histSAllCC)*R*(histSAllIa*R - histSIa)/(histSCC - R*histSAllCC)**2
#ddnIa = np.sqrt(histSIa)/(histSCC - R*histSAllCC)
#ddNIa = np.sqrt(histSAllIa)*R/(histSCC - R*histSAllCC)
ddnCC = np.sqrt(histSCC)*(histSIa - allSimIaZbin.shape[0]*R)/(histSCC - R*allSimCCZbin.shape[0])**2
ddNCC = np.sqrt(allSimCCZbin.shape[0])*R*(allSimIaZbin.shape[0]*R - histSIa)/(histSCC - R*allSimCCZbin.shape[0])**2
ddnIa = np.sqrt(histSIa)/(histSCC - R*allSimCCZbin.shape[0])
ddNIa = np.sqrt(allSimIaZbin.shape[0])*R/(histSCC - R*allSimCCZbin.shape[0])
#ddR = (histSAllIa*histSCC - histSAllCC * histSIa)/(histSCC - R*histSAllCC)**2
dS = np.sqrt(ddnCC**2 + ddNCC**2 + ddnIa**2 + ddNIa**2)# + ddR**2)
print "ddnCC"
print ddnCC
print "ddNCC"
print ddNCC
print "ddnIa"
print ddnIa
print "ddNIa"
print ddNIa
#print "ddR"
#print ddR
print "Delta S"
print dS
#assert(S > 0)
if S < 0:
S = np.nan
CCScales.append(S)
CCScaleErrs.append(dS[0])
print "CC PlotDebug"
print (simBinsCC[1:] + simBinsCC[:-1])/2.0
print simHistCC
print CCScales[0]
print fnorm2
print histD
print (muresBins[1:] + muresBins[:-1])/2.0
plt.step((simBinsCC[1:] + simBinsCC[:-1])/2.0, simHistCC, c = 'b', where = 'mid', label = 'prescaled Sim CC')
plt.step((simBinsCC[1:] + simBinsCC[:-1])/2.0, CCScales[0]*simHistCC*fnorm2, c = 'g', where = 'post', label = 'postscaledSimCC')
plt.step((muresBins[1:] + muresBins[:-1])/2.0, histD, c = 'r', where = 'mid', label = 'data')
plt.savefig(outfilePrefix + 'ScaledHist.png')
plt.clf()
return CCScales, CCScaleErrs
def applyCCScale(NCC, CCScales, datazbins = None, zbins = None):
if type(CCScales) == list:
if len(CCScales) == 1:
NCCScaled = CCScales[0]*NCC
else:
assert(0)
elif type(CCScales) == np.ndarray:
if CCScales.shape[0] == 1:
NCCScaled = CCScales[0]*NCC
else:
assert(0)
elif (type(CCScales) == int) | (type(CCScales) == float):
NCCScaled = CCScales*NCC
else:
assert(0)
return NCCScaled
if __name__ == '__main__':
from sys import argv
print "argv"
print argv
datadir = argv[1]
simdir = argv[2]
dataname = argv[3]
print "dataname"
simname = argv[4]
print simname
simgenfile = argv[5]
print simgenfile
NNCut = False
cheatType = bool(int(argv[6]))
cheatZ = bool(int(argv[7]))
trueBeta = float(argv[8])
paramFile = argv[9]
cutFiles = argv[10:]
if( ('Combine' in simdir) or ('SALT2' in simdir)) & (('Combine' in datadir) or ('SALT2' in simdir)):
NNCut = True
NNProbCut = 0.95
#if len(argv) > 6:
# NNCut = True
# NNProbCut = 0.9
# NNData = argv[6]
# NNSim = argv[7]
#default params
zmin = 0.1
zmax = 1.2
MJDMin = 0.0
MJDMax = np.inf
bins = "equalSize"
runFit = True
fracContamCuts = [-1]
fixBeta = True
fixK = False
nbins = 10
ScaleMuResCutLow = -1
ScaleMuResCutHigh = 1
muresBins = 1
muresBinsLow = 3
muresBinsHigh = 3
scaleZBins = [0.0, 1.2]
cheatCCSub = False
cheatCCScale = False
ZSysFlag = False
Blind = False
#override file
params = open(paramFile, 'r').readlines()
for p in params:
exec(p)
kmean = []
ksigma = []
kErr = []
BetaMean = []
BetaSigma= []
BetaErr = []
Chi2Mean = []
Chi2Sigma = []
CCScaleStorageGlobal = []
CCScaleErrStorageGlobal = []
#MURES_Cuts = [2.0]
#MURES_Cuts = [1.0, 1.5, 2.0, 3.0, 4.0, 99.0, 2.0]
#for MURES_Cut in MURES_Cuts:
fcc = -1
for cf in cutFiles:
cuts = [] # cuts = [('FITPROB', 0.01, np.inf), ('NN_PROB_IA', NNProbCut, np.inf)]
cutlist = open(cf, 'r').readlines()
for l in cutlist:
spl = l.split()
cuts.append(('{0}'.format(spl[0]), float('{0}'.format(spl[1])), float('{0}'.format(spl[2]))))
ks = []
kErrs = []
Betas = []
BetaErrs = []
Chi2s = []
CCScaleStorage = []
CCScaleErrStorage = []
nFail = 0
simLoaded = False
#print "FUCK MPI"
#if Rate_Model == 'discrete':
# subprocess.call(['python', 'constructChi2Func.py', str(nbins)], shell = False)
#print "MPI Fucked"
if '{' in datadir:
#nfile = 100