def _doMinos( solver, ipar=0, ptype="u" ):
from ConstrainedFit import clsq
par= clsq.createClsqPar( ipar, ptype, solver )
result= par.getParVal()
name= par.getParName()
ca= clsq.clsqAnalysis( solver )
errhi, errlo= ca.minos( par )
fmtstr= "{0:>10s}: {1:10.4f} + {2:6.4f} - {3:6.4f}"
print fmtstr.format( name, result, errhi, abs(errlo) )
def _doMinos( solver, ipar=0, ptype="u" ):
from ConstrainedFit import clsq
par= clsq.createClsqPar( ipar, ptype, solver )
result= par.getParVal()
name= par.getParName()
ca= clsq.clsqAnalysis( solver )
errhi, errlo= ca.minos( par )
fmtstr= "{0:>10s}: {1:10.4f} + {2:6.4f} - {3:6.4f}"
print( fmtstr.format( name, result, errhi, abs(errlo) ) )
def _doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2= "m" ):
from ConstrainedFit import clsq
from ROOT import TGraph2D, TMarker
from array import array
global tg2d, hist, te1, te2, te3, tm
par1= clsq.createClsqPar( ipar1, type1, solver )
par2= clsq.createClsqPar( ipar2, type2, solver )
parval1, parerr1, name1= _getUMParErrName( par1 )
parval2, parerr2, name2= _getUMParErrName( par2 )
print "\nChi^2 profile plot " + name1 + " - " + name2 + ":"
corr= solver.getCorrMatrix()
icorr1= ipar1
icorr2= ipar2
if type1 == "u" or type2 == "u":
nmpar= len(solver.getMpars())
if type1 == "u":
icorr1= nmpar + ipar1
if type2 == "u":
icorr2= nmpar + ipar2
rho= corr[icorr1,icorr2]
te1= _makeEllipse( parval1, parval2, parerr1, parerr2, rho )
te2= _makeEllipse( parval1, parval2, 2.0*parerr1, 2.0*parerr2, rho )
te3= _makeEllipse( parval1, parval2, 3.0*parerr1, 3.0*parerr2, rho )
ca= clsq.clsqAnalysis( solver )
points= ca.profile2d( par1, par2 )
npoints= len(points)
tg2d= TGraph2D( npoints )
for i in range( npoints ):
point= points[i]
tg2d.SetPoint( i, point[0], point[1], point[2] )
hist= tg2d.GetHistogram()
contourlevels= array( "d", [ 1.0, 4.0, 9.0 ] )
hist.SetContour( 3, contourlevels )
xa= hist.GetXaxis()
ya= hist.GetYaxis()
xa.SetTitle( name1 )
ya.SetTitle( name2 )
hist.Draw( "cont1" )
tm= TMarker( parval1, parval2, 20 )
tm.Draw( "s" )
te1.Draw( "s" )
te2.Draw( "s" )
te3.Draw( "s" )
return
def _doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2= "m" ):
from ConstrainedFit import clsq
from ROOT import TGraph2D, TMarker, gPad
from array import array
global tg2d, hist, te1, te2, te3, tm
par1= clsq.createClsqPar( ipar1, type1, solver )
par2= clsq.createClsqPar( ipar2, type2, solver )
parval1, parerr1, name1= _getUMParErrName( par1 )
parval2, parerr2, name2= _getUMParErrName( par2 )
print( "\nChi^2 profile plot " + name1 + " - " + name2 + ":" )
corr= solver.getCorrMatrix()
icorr1= ipar1
icorr2= ipar2
if type1 == "u" or type2 == "u":
nmpar= len(solver.getMpars())
if type1 == "u":
icorr1= nmpar + ipar1
if type2 == "u":
icorr2= nmpar + ipar2
rho= corr[icorr1,icorr2]
te1= _makeEllipse( parval1, parval2, parerr1, parerr2, rho )
te2= _makeEllipse( parval1, parval2, 2.0*parerr1, 2.0*parerr2, rho )
te3= _makeEllipse( parval1, parval2, 3.0*parerr1, 3.0*parerr2, rho )
ca= clsq.clsqAnalysis( solver )
points= ca.profile2d( par1, par2 )
npoints= len(points)
tg2d= TGraph2D( npoints )
for i in range( npoints ):
point= points[i]
tg2d.SetPoint( i, point[0], point[1], point[2] )
hist= tg2d.GetHistogram()
contourlevels= array( "d", [ 1.0, 4.0, 9.0 ] )
hist.SetContour( 3, contourlevels )
hist.GetXaxis().SetTitle( name1 )
hist.GetYaxis().SetTitle( name2 )
hist.SetTitle( "Triangle fit "+name2+" vs "+name1 )
hist.Draw( "cont1" )
tm= TMarker( parval1, parval2, 20 )
tm.Draw( "s" )
te1.Draw( "s" )
te2.Draw( "s" )
te3.Draw( "s" )
gPad.Print( "triangle_errorellipse.png" )
return
def Triangle( opt="" ):
from ConstrainedFit import clsq
from math import sqrt, tan
data= [ 10.0, 7.0, 9.0, 1.0 ]
errors= [ 0.05, 0.2, 0.2, 0.02 ]
covm= clsq.covmFromErrors( errors )
mpnames= { 0: "a", 1: "b", 2: "c", 3: "gamma" }
upar= [ 30.0 ]
upnames= { 0: "A" }
def triangleConstrFun( mpar, upar ):
a= mpar[0]
b= mpar[1]
c= mpar[2]
gamma= mpar[3]
aa= upar[0]
p= (a+b+c)/2.0
s= sqrt( p*(p-a)*(p-b)*(p-c) )
return [ tan(gamma/2.0)-s/(p*(p-c)), aa-s ]
solver= clsq.clsqSolver( data, covm, upar, triangleConstrFun,
uparnames=upnames, mparnames=mpnames )
print( "Constraints before solution" )
print( solver.getConstraints() )
lBlobel= False
lCorr= False
lResidual= True
if "b" in opt:
lBlobel= True
if "corr" in opt:
lCorr= True
if "r" in opt:
lResidual= False
solver.solve( lBlobel=lBlobel, lResidual=lResidual )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
if "m" in opt:
_doMinosAll( solver )
if "cont" in opt:
_doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2="m" )
print( "Profile A" )
par= clsq.createClsqPar( 0, "u", solver )
results= ca.profile( par )
print( results )
return solver
def Triangle( opt="" ):
from ConstrainedFit import clsq
from math import sqrt, tan
data= [ 10.0, 7.0, 9.0, 1.0 ]
errors= [ 0.05, 0.2, 0.2, 0.02 ]
covm= clsq.covmFromErrors( errors )
mpnames= { 0: "a", 1: "b", 2: "c", 3: "gamma" }
upar= [ 30.0 ]
upnames= { 0: "A" }
def triangleConstrFun( mpar, upar ):
a= mpar[0]
b= mpar[1]
c= mpar[2]
gamma= mpar[3]
aa= upar[0]
p= (a+b+c)/2.0
s= sqrt( p*(p-a)*(p-b)*(p-c) )
return [ tan(gamma/2.0)-s/(p*(p-c)), aa-s ]
solver= clsq.clsqSolver( data, covm, upar, triangleConstrFun,
uparnames=upnames, mparnames=mpnames )
print "Constraints before solution"
print solver.getConstraints()
lBlobel= False
lCorr= False
if "b" in opt:
lBlobel= True
if "corr" in opt:
lCorr= True
solver.solve( lBlobel=lBlobel )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
if "m" in opt:
_doMinosAll( solver )
if "cont" in opt:
_doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2="m" )
print "Profile A"
par= clsq.createClsqPar( 0, "u", solver )
results= ca.profile( par )
print results
return solver
