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Rate_Fitter_April29.py
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Rate_Fitter_April29.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 astropy.cosmology as cosmo
import traceback
import scipy
import scipy.stats as stats
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
import Cosmology
import emcee as MC
import corner
import scipy.stats.mstats as mstats
from sys import argv
import glob
import time
import os
import gzip
import shutil
import numpy.ma as ma
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, cuts = None, nprint = 10):
self.zmin = zmin
self.zmax = zmax
self.MCBeta = MCBeta
self.Rate_Model = Rate_Model
self.cheatType = cheatType
self.cheatZ = cheatZ
self.cuts = cuts
self.nprint = nprint
self.globalChi2Storage = []
self.globalNDataStorage = []
self.globalZPhotBinStorage = []
self.globalNDataIaPhotBinStorage = []
self.globalNDataCCPhotBinStorage = []
self.globalZTrueBinStorage = []
self.globalNDataIaTrueBinStorage = []
self.globalNDataCCTrueBinStorage = []
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)
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)
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)
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)
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]
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])]
def newData(self, realfilename, realName, simIndex = 100):
self.realName = realName
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
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])]
if simIndex < self.nprint:
print "Minimum Fitprob"
print np.min(self.realcat.Catalog['FITPROB'])
print 'N postcuts'
print self.realcat.Catalog['FITPROB'].shape
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'
'''
if (fracContamCut > 0.000000001) & (fracContamCut < 1.0):
print " Cutting based on Frac Contam"
histTot, binsX, binsY = np.histogram2d(self.simcat.Catalog[ztype], self.simcat.Catalog['MURES'], bins = nbins)
histCC, binsX, binsY = np.histogram2d(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) != 1][ztype], self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'].astype(int) != 1]['MURES'], bins = (binsX, binsY))
fracContam = histCC.astype(np.float)/histTot.astype(np.float)
for fcRow, i in zip(fracContam, xrange(binsX.shape[0])):
for fc, j in zip(fcRow, xrange(binsY.shape[0])):
if fc < fracContamCut:
continue
else:
simInBin = (self.simcat.Catalog[ztype] > binsX[i]) & (self.simcat.Catalog[ztype] < binsX[i+1]) & (self.simcat.Catalog['MURES'] > binsY[j]) & (self.simcat.Catalog['MURES'] < binsY[j+1])
realInBin = (self.realcat.Catalog[ztype] > binsX[i]) & (self.realcat.Catalog[ztype] < binsX[i+1]) & (self.realcat.Catalog['MURES'] > binsY[j]) & (self.realcat.Catalog['MURES'] < binsY[j+1])
self.simcat.Catalog = self.simcat.Catalog[np.invert(simInBin)]
self.realcat.Catalog = self.realcat.Catalog[np.invert(realInBin)]
'''
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)
if simIndex < self.nprint:
print "Type Location A"
print "Choice A1"
print zPHOTs.shape
print zTRUEs.shape
print binList
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))
if simIndex < self.nprint:
print "Type Location B"
print "Choice B1"
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)
if simIndex < self.nprint:
print "counts of zTrue in each zPhot vs zTrue bin"
print counts.astype(int)
print "zGen Bins"
print zGenBins
print 'zGen Histogram'
print zGenHist
print "sum zGen events"
print np.sum(zGenHist)
print "sum zPhot events"
print np.sum(counts)
#print "DEBUG HERE"
#assert(0)
self.effmat = np.zeros((self.nbins,self.nbins))
xMax = zPhotEdges.shape[0] - 2
yMax = zTrueEdges.shape[0] - 2
if simIndex < self.nprint:
print zGenHist
print counts.astype(int)
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
if simIndex == 0:
extent = [zPhotEdges[0], zPhotEdges[-1], zTrueEdges[0], zTrueEdges[-1]]
plt.figure()
plt.imshow(np.flipud(counts), extent = extent, cmap = 'Blues')
plt.colorbar()
plt.savefig(self.realName + 'redshiftDistro.png')
plt.clf()
plt.close()
plt.figure()
plt.imshow(np.flipud(self.effmat), extent = extent, cmap = 'Blues', norm=mpl.colors.LogNorm())
plt.colorbar()
plt.savefig(self.realName + 'efficiencyMatrixLog.png')
plt.clf()
plt.close()
plt.figure()
plt.imshow(np.flipud(self.effmat), extent = extent, cmap = 'Blues')
plt.colorbar()
plt.savefig(self.realName + 'efficiencyMatrix.png')
plt.clf()
plt.close()
def fit_rate(self, fixK = False, fixBeta = False, simIndex = 100, trueBeta = 0):
import iminuit as iM
from iminuit import Minuit as M
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 simIndex < self.nprint:
print "Type Location C"
print "Choice C1"
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)
NIa , _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1][ztype].astype(float), bins=self.binList)
DebugNIaPhot, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]['zPHOT'].astype(float), bins=self.binList)
DebugNCCPhot, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1]['zPHOT'].astype(float), bins=self.binList)
DebugNIaTrue, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] == 1]['SIM_ZCMB'].astype(float), bins=self.binList)
DebugNCCTrue, _ = np.histogram(self.simcat.Catalog[self.simcat.Catalog['SIM_TYPE_INDEX'] != 1]['SIM_ZCMB'].astype(float), bins=self.binList)
for niap, nccp, niat, ncct, zb in zip(DebugNIaPhot, DebugNCCPhot, DebugNIaTrue, DebugNCCTrue,self.binList ):
self.globalZTrueBinStorage.append(zb)
self.globalZPhotBinStorage.append(zb)
self.globalNDataIaPhotBinStorage.append(niap)
self.globalNDataCCPhotBinStorage.append(nccp)
self.globalNDataIaTrueBinStorage.append(niat)
self.globalNDataCCTrueBinStorage.append(ncct)
FracBad = NCC*1.0/(1.0*(NCC+NIa))
nData, dataBins = np.histogram(self.realcat.Catalog[ztype].astype(float), bins=self.binList)
nCCData = nData*FracBad
if True or simIndex < self.nprint:
print "Fraction of CCs in each bin"
print FracBad
print 'NCC'
print NCC
print 'nSim2'
print nSim2
print "nData, dataBins, realcat shape pre contam correction"
print nData
print dataBins
print np.sum(self.realcat.Catalog[ztype].astype(float) > zmax)
print np.sum(self.realcat.Catalog[ztype].astype(float) < zmin)
print self.realcat.Catalog[ztype].shape
print "Ratio nData/nSim"
print 1.0*nData/(1.0*nSim)
print "Ratio nData/nSim2"
print 1.0*nData/(1.0*nSim2)
print "Ratio nSim/nData"
print 1.0*nSim2/(1.0*nData)
print "Ratio nSim2/nData"
print 1.0*nSim2/(1.0*nData)
print "FracBad"
print FracBad
print 'NCCData'
print nCCData
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
print "Ratio NCCData/TrueNCC"
print nCCData*1.0/(TrueNCC*1.0)
print "Sum of TrueNCC"
print np.sum(TrueNCC)
print "Sum of Data"
print np.sum(nData)
print "Sum of nCCData"
print np.sum(nCCData)
print "True NCC/NData"
print TrueNCC*1.0/(1.0*nData)
except:
print "Using real data"
if simIndex < self.nprint:
print "overall Contam"
print np.sum(NCC)*1.0/(np.sum(nSim3)*1.0)
def chi2func(nData, nSim, effmat, fnorm, zCenters, k, Beta, 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)):
Chi2Temp = 0.0
if Rate_Model == 'powerlaw':
f_Js = k*(1+zCenters)**Beta
else:
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)
chi2Mat = np.zeros((self.nbins))
adjNMC = np.zeros((self.nbins))
if (nIA is None) or (nCC is None):
print "No CC Cut"
fracCCData = np.zeros(nData.shape)
else:
print "Beta Adjusted CC Cut"
#BetaRatio = k*(1+zCenters)**(Beta)#/(1+zCenters)**MCBeta
BetaRatio = (1+zCenters)**(Beta)#/(1+zCenters)**MCBeta
fracCCData = (nCC*1.0)/(1.0*(nCC + nIA*BetaRatio))
if dump and (self.nprint < simInd):
print "fracCCData2"
print fracCCData
print "unscaled fracCCData"
print 1.0*(1.0*nCC)/(1.0*(nCC+nIA))
nCCData = nData*fracCCData
if dump and (self.nprint < simInd):
print "nCCData2"
print nCCData
#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: scaledNSimStor = []
for row, nDataI, nCCDataI, i in zip(effmat, nData, nCCData, xrange(self.nbins)):
#print "FUCKME"
#print nDataI
#print nCCDataI
#assert((nDataI - nCCDataI) > 0)
if dump and (self.nprint < simInd):
print 'effmat row'
print row
print 'nDataI'
print nDataI
print 'nCCDataI'
print nCCDataI
scaledNSimTemp = 0.0
#if dump:
# print "nDataI"
# print nDataI
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
if dump and (self.nprint < simInd):
print i
print 'nDataI'
print nDataI
print 'nCCDataI'
print nCCDataI
print 'fnorm'
print fnorm
print "JSumTempNum tot"
print JSumTempNum
print "JSumTempDen tot"
print JSumTempDen
print "Chi2Bin"
dataFunc = np.maximum(nDataI ,1)
#print dataFunc
c2t = (nDataI - nCCDataI - JSumTempNum)**2/( dataFunc + nCCDataI + JSumTempDen)
print c2t
if dump:
dataFunc = np.maximum(nDataI ,1)
c2t = (nDataI - nCCDataI - JSumTempNum)**2/( dataFunc + nCCDataI + JSumTempDen)
chi2Storage.append(c2t)
if c2t > 5:
print 'INSANITY CHECK ABOVE'
dataFunc = np.maximum(nDataI ,1)
#print dataFunc
c2t = (nDataI - nCCDataI - JSumTempNum)**2/( dataFunc + nCCDataI + JSumTempDen)
#if nDataI < 5:
# JSumTempDen += 10*nDataI
#if nDataI > 1E-11 or JSumTempDen > 1E-11 and modelError:
dataFunc = np.maximum(nDataI ,1)
# Chi2Temp += ((nDataI - nCCDataI - JSumTempNum)**2/(JSumTempNum + JSumTempDen))#*fnorm**2
if nDataI > 1E-11 or JSumTempDen > 1E-11:
Chi2Temp += ((nDataI - nCCDataI - JSumTempNum)**2/(dataFunc + nCCDataI + JSumTempDen))#*fnorm**2
if dump:
return Chi2Temp, chi2Storage
else:
return Chi2Temp
zCenters = (simBins[1:] + simBins[:-1])/2.0
#Is this right? Everything else in the other side of the chi2 function should be Ia only
fnorm = float(np.sum(nData))/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)
lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, dump = True, nIA = NIa, nCC = NCC, simIndex = simIndex)
elif self.Rate_Model == 'brokenpowerlaw':
lamChi2 = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, 1.0, nCCData = NCCData, simIndex = simIndex)
lamChi2Dump = lambda k, Beta: chi2func(nData, nSim, self.effmat, fnorm, zCenters, k, Beta, 1.0, dump = True, nCCData = nCCData, simIndex = simIndex)
MinObj = M(lamChi2, k = 1.0, error_k = 0.1, Beta = trueBeta, error_Beta = 0.1, 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))
#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)
c2f, c2stor = lamChi2Dump(MinObj.values['k'], MinObj.values['Beta'])
print c2stor
print nData
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)
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.chi2 = fmin.fval#/(self.nbins - 2)
self.BetaRatio = (1+zCenters)**(Beta)#/(1+zCenters)**MCBeta
self.fracCCData = (NCC*1.0)/(1.0*(NCC + NIa*self.BetaRatio))
print "Chi2 final = {0}".format(round(lamChi2Dump(self.k, self.Beta)[0], 4))
#fJs = np.ones(zCenters.shape)
'''
xgrid,ygrid, sigma, rawdata = MinObj.mncontour_grid('k', 'Beta', numpoints=400, sigma_res = 4, nsigma = 2.0)
plt.figure()
plt.clf()
CS = plt.contour(xgrid, ygrid + self.MCBeta, sigma, levels = [0.5, 1.0, 1.5, 2.0])
plt.clabel(CS, fontsize=7, inline=1)
plt.xlabel('k')
plt.ylabel('Beta')
plt.savefig('{0}_{1}_k_beta_contour.png'.format(self.realName, self.simName))
plt.close()
'''
#plt.axhline(y = self.MCBeta, c = 'k', label = 'True Beta')
#plt.axhline(y = Beta + self.MCBeta, c = 'g', label= 'Best Fit Beta')
#plt.axvline(x = k, label = 'Best Fit k')
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):
return 1
def getCCScaleSplit(simCat, dataCat, MURESWindow = (-1, 1), zbins = [0.0, 0.3, 0.6, 0.9, 1.2], muresBinslow = np.linspace(-2, -1, 4), muresBinshigh = np.linspace(1, 2, 4)):
import iminuit as iM
from iminuit import Minuit as M
CCScaleslow = []
CCScaleshigh = []
fnorm2 = float(dataCat.shape[0])/simCat.shape[0]
simCatlow = simCat[(simCat['MURES'] < MURESWindow[0]) ]#| (simCat['MURES'] > MURESWindow[1]) ]
dataCatlow = dataCat[(dataCat['MURES'] < MURESWindow[0])]# | (dataCat['MURES'] > MURESWindow[1]) ]
simCathigh = simCat[(simCat['MURES'] > MURESWindow[1]) ]#| (simCat['MURES'] > MURESWindow[1]) ]
dataCathigh = dataCat[(dataCat['MURES'] > MURESWindow[1])]# | (dataCat['MURES'] > MURESWindow[1]) ]
for zl, zh in zip(zbins[:-1], zbins[1:]):
tempSimlow = simCatlow[(simCatlow['zPHOT'] < zh) & (simCatlow['zPHOT'] > zl)]
tempDatalow = dataCatlow[(dataCatlow['zPHOT'] < zh) & (dataCatlow['zPHOT'] > zl)]
tempSimhigh = simCathigh[(simCathigh['zPHOT'] < zh) & (simCathigh['zPHOT'] > zl)]
tempDatahigh = dataCathigh[(dataCathigh['zPHOT'] < zh) & (dataCathigh['zPHOT'] > zl)]
if type(muresBinslow == int):
histDlow, muresBinslow = np.histogram(tempDatalow['MURES'], bins = muresBinslow)
binsDlow = muresBinslow
else:
histDlow, binsDlow = np.histogram(tempDatalow['MURES'], bins = muresBinslow)
histSlow, binsSlow = np.histogram(tempSimlow['MURES'], bins = muresBinslow)
histSCClow, binsSCClow = np.histogram(tempSimlow['MURES'][tempSimlow['SIM_TYPE_INDEX'] != 1], bins = muresBinslow)
histSIalow, binsSIalow = np.histogram(tempSimlow['MURES'][tempSimlow['SIM_TYPE_INDEX'] == 1], bins = muresBinslow)
if type(muresBinshigh == int):
histDhigh, muresBinshigh = np.histogram(tempDatahigh['MURES'], bins = muresBinshigh)
binsDhigh = muresBinshigh
else:
histDhigh, binsDhigh = np.histogram(tempDatahigh['MURES'], bins = muresBinshigh)
histShigh, binsShigh = np.histogram(tempSimhigh['MURES'], bins = muresBinshigh)
histSCChigh, binsSCChigh = np.histogram(tempSimhigh['MURES'][tempSimhigh['SIM_TYPE_INDEX'] != 1], bins = muresBinshigh)
histSIahigh, binsSIahigh = np.histogram(tempSimhigh['MURES'][tempSimhigh['SIM_TYPE_INDEX'] == 1], bins = muresBinshigh)
#histDhigh, binsDhigh = np.histogram(tempDatahigh['MURES'], bins = muresBinshigh)
print "low MURES tail"
print histSlow
print histSCClow
print histSIalow
print "high MURES tail"
print histShigh
print histSCChigh
print histSIahigh
print "Data tails"
print histDlow
print histDhigh
def chi2(SimCC, SimIa, Data, scale, fnorm2):
#ms = ma.masked_array((SimCC*scale + SimIa)*fnorm2, mask = (SimCC+SimIa) == 0)
ms = (SimCC*scale + SimIa)*fnorm2
return stats.chisquare(Data, ms)[0]
lamChi2low = lambda s: chi2(histSCClow, histSIalow, histDlow, s, fnorm2)
lamChi2high = lambda s: chi2(histSCChigh, histSIahigh, histDhigh, s, fnorm2)
MinObjlow = M(lamChi2low, s = 1.0, error_s = 1, limit_s = (0.0, None))
MinObjhigh = M(lamChi2high, s = 1.0, error_s = 1, limit_s = (0.0, None))
print "Chi2 init low = {0}".format(round(lamChi2low(1.0), 4))
print "Chi2 init high = {0}".format(round(lamChi2high(1.0), 4))
#MinObj = M(lamChi2, k = 1.0, fix_k = True, Beta = 0.0, error_Beta = 0.1)
MinObjlow.set_strategy(2)
MinObjhigh.set_strategy(2)
fminlow, paramlow = MinObjlow.migrad(nsplit= 10)
fminhigh, paramhigh = MinObjhigh.migrad(nsplit= 10)
CCScaleslow.append(MinObjlow.values['s'])
CCScaleshigh.append(MinObjhigh.values['s'])
return CCScaleslow, CCScaleshigh
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 = 0.0, binList = None, fracCCData = None, outfilePrefix = 'Test'):
import iminuit as iM
from iminuit import Minuit as M
CCScales = []
CCScaleErrs = []
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)
'''
print "total sim CC, Ia, all including outliers"
print tempSimCC.shape
print tempSimIa.shape
print tempSimIa.shape[0] + tempSimCC.shape[0]
NCCTOTSIM = tempSimCC.shape[0]
NIATOTSIM = tempSimIa.shape[0]
NTOTSIM = tempSimIa.shape[0] + tempSimCC.shape[0]
print "total data, all including outliers"
print dataCat.shape
NTOTDATA = dataCat.shape[0]
'''
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)
binCent = (simZBinsIa[1:] + simZBinsIa[:-1])/2.0
#dataIaHist = dataHist*(1-fracCCData)
#dataCCHist = dataHist*fracCCData
print "components of simHist"
print simZHistIa
print binCent
print Beta
print simHistCC
simHist = (simZHistIa*(1+binCent)**Beta) + simZHistCC
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]) ]
#simCat = simCat[ (simCat['MURES'] > MURESWindow[1]) ]
#dataCat = dataCat[(dataCat['MURES'] > MURESWindow[1]) ]
print "postcut"
print simCat.shape
print dataCat.shape
'''
print "outlier sim CC, Ia, total"
print np.sum([simCat['SIM_TYPE_INDEX'].astype(int) != 1])
print np.sum([simCat['SIM_TYPE_INDEX'].astype(int) == 1])
print simCat.shape
noutccsim = np.sum([simCat['SIM_TYPE_INDEX'].astype(int) != 1])
noutiasim = np.sum([simCat['SIM_TYPE_INDEX'].astype(int) == 1])
noutsim = simCat.shape[0]
print "outlier data total"
print dataCat.shape
noutdata = dataCat.shape[0]
'''
print 'post MURES Cut Ndata and Nsim'
print dataCat.shape
print simCat.shape
'''
print "---Rick Test Here---"
R = float(noutdata)/float(NTOTDATA)
print "R"
print R
S = float(R*NIATOTSIM - noutiasim)/float(noutccsim - R*NCCTOTSIM)
print "S"
print S
print "---End Rick Test---"
'''
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)
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
#histSIa = histSIa*((1+np.mean(tempSim['zPHOT'])))**Beta
#print "beta scaling"
#print ((1+np.mean(tempSim['zPHOT'])))**Beta
#print "mean zphot"
#print np.mean(tempSim['zPHOT'])
#print "beta"
#print Beta
#print "PostCorr HistSIa "
#print histSIa
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
betaCorrAllSimIaZbin =np.sum((1+ allSimIaZbin['zPHOT'])**Beta)
#S = float(np.array(R*histSAllIa) - np.array(histSIa))/float(np.array(histSCC) - np.array(R*histSAllCC))
S = float(np.array(R*betaCorrAllSimIaZbin) - np.array(histSIa))/float(np.array(histSCC) - np.array(R*allSimCCZbin.shape[0]))
print "S"
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
CCScales.append(S)
CCScaleErrs.append(dS[0])
'''
def chi2(SimCC, SimIa, Data, scale, fnorm2):
#ms = ma.masked_array((SimCC*scale + SimIa)*fnorm2, mask = (SimCC+SimIa) == 0)
#totSimCC = np.sum(simcat['SIM_TYPE_INDEX'] !=1)
#totSimIa = np.sum(simcat['SIM_TYPE_INDEX'] ==1)
#fnorm2 = float(datacat.shape[0])/(totSimCC*scale+totSimIa)
ms = (SimCC*scale + SimIa)*fnorm2
chi2 = np.sum((Data - ms)**2/(np.maximum(Data*1.0, 1.0)))
return chi2
#return stats.chisquare(Data, ms)[0]
#lamChi2 = lambda s: chi2(histSCC, histSIa, histD, s, dataCat, simCat)
lamChi2 = lambda s: chi2(histSCC, histSIa, histD, s, fnorm2)
MinObj = M(lamChi2, s = 1.0, error_s = 1, limit_s = (0.0, None))
print "Chi2 init low = {0}".format(round(lamChi2(1.0), 4))
#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)
CCScales.append(MinObj.values['s'])
CCScaleErrs.append(MinObj.errors['s'])
'''
#plt.plot((simBinsCC[1:] + simBinsCC[:-1])/2.0, simHistCC, c = 'b', label = 'prescaled Sim CC')
#plt.plot((simBinsCC[1:] + simBinsCC[:-1])/2.0, CCScales[0]*simHistCC*fnorm2, c = 'g', label = 'postscaledSimCC')
#plt.plot(muresBins, histD, c = 'r', label = 'data')
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
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]
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
combinedScaleFit = True
ScaleMuResCutLow = -1
ScaleMuResCutHigh = 1
muresBins = 1
muresBinsLow = 3
muresBinsHigh = 3
scaleZBins = [0.0, 1.2]
#override file
params = open(paramFile, 'r').readlines()
for p in params:
exec(p)
kmean = []
ksigma = []
kErr = []
BetaMean = []
BetaSigma= []
BetaErr = []
Chi2Mean = []
Chi2Sigma = []
if combinedScaleFit:
CCScaleStorageGlobal = []
CCScaleErrStorageGlobal = []
else:
CCScaleStorageLowGlobal = []
CCScaleStorageHighGlobal = []
#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: