def LinearFit():
from ConstrainedFit import clsq
xabs= [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
data= [ 1.1, 1.9, 2.9, 4.1, 5.1, 6.1, 6.9, 7.9, 9.1 ]
errors= [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]
covm= clsq.covmFromErrors( errors )
upar= [ 0.1, 1.1 ]
upnames= { 0: "a", 1: "b" }
def linearConstrFun( mpar, upar, xv ):
constraints= []
for mparval, xval in zip( mpar, xv ):
constraints.append( upar[0]+upar[1]*xval - mparval )
return constraints
solver= clsq.clsqSolver( data, covm, upar, linearConstrFun,
uparnames=upnames, args=(xabs,) )
print "Constraints before solution"
print solver.getConstraints()
solver.solve()
ca= clsq.clsqAnalysis( solver )
ca.printResults()
return
def LinearFit():
from ConstrainedFit import clsq
xabs= [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
data= [ 1.1, 1.9, 2.9, 4.1, 5.1, 6.1, 6.9, 7.9, 9.1 ]
errors= [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]
covm= clsq.covmFromErrors( errors )
upar= [ 0.1, 1.1 ]
upnames= { 0: "a", 1: "b" }
def linearConstrFun( mpar, upar, xv ):
constraints= []
for mparval, xval in zip( mpar, xv ):
constraints.append( upar[0]+upar[1]*xval - mparval )
return constraints
solver= clsq.clsqSolver( data, covm, upar, linearConstrFun,
uparnames=upnames, args=(xabs,) )
print( "Constraints before solution" )
print( solver.getConstraints() )
solver.solve()
ca= clsq.clsqAnalysis( solver )
ca.printResults()
return
def printResults( self, ffmt=".4f", cov=False, corr=False ):
if isinstance( self.__solver, minuitSolver ):
self.__solver.printResults( ffmt=ffmt, cov=cov, corr=corr )
elif isinstance( self.__solver, clsq.clsqSolver ):
ca= clsq.clsqAnalysis( self.__solver )
ca.printResults( ffmt=ffmt, cov=cov, corr=corr )
print
return
def printResults(self, ffmt=".4f", cov=False, corr=False):
if isinstance(self.__solver, minuitSolver):
self.__solver.printResults(ffmt=ffmt, cov=cov, corr=corr)
elif isinstance(self.__solver, clsq.clsqSolver):
ca = clsq.clsqAnalysis(self.__solver)
ca.printResults(ffmt=ffmt, cov=cov, corr=corr)
print
return
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 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 PoissonLikelihood( opt="" ):
from ConstrainedFit import clhood
from ConstrainedFit import clsq
from scipy.special import gammaln
from scipy.stats import poisson, norm
from math import log
# Data, two counts, errors not needed(!):
data= [ 9.0, 16.0 ]
# errors= [ 3.0, 4.0 ]
mpnames= { 0: "count 1", 1: "count 2" }
# Fit variable is parameter of poisson distribution:
upar= [ 12.0 ]
upnames= { 0: "mu" }
# Likelihood is sum of log(poisson) for each data point:
def lfun( mpar ):
result= 0.0
for datum, parval in zip( data, mpar ):
parval= parval.item()
result-= log( poisson.pmf( datum, parval ) )
# Calculated log(poisson):
# result-= datum*log( parval ) - gammaln( datum+1.0 ) - parval
return result
# Constraints force poisson distribution with same parameter
# for every data point:
def constrFun( mpar, upar ):
return [ mpar[0] - upar[0],
mpar[1] - upar[0] ]
solver= clhood.clhoodSolver( data, upar, lfun, constrFun,
uparnames=upnames, mparnames=mpnames )
print "Constraints before solution"
print solver.getConstraints()
lBlobel=False
lPrint= False
lCorr= False
if "b" in opt:
lBlobel= True
if "p" in opt:
lPrint= True
if "c" in opt:
lCorr= True
solver.solve( lBlobel=lBlobel, lpr=lPrint )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
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
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
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 _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 PoissonLikelihood( opt="" ):
from ConstrainedFit import clhood
from ConstrainedFit import clsq
from scipy.special import gammaln
from scipy.stats import poisson
from math import log
# Data, two counts, errors not needed(!):
data= [ 9.0, 16.0 ]
# errors= [ 3.0, 4.0 ]
mpnames= { 0: "count 1", 1: "count 2" }
# Fit variable is parameter of poisson distribution:
upar= [ 12.0 ]
upnames= { 0: "mu" }
# Likelihood is sum of log(poisson) for each data point:
def lfun( mpar ):
result= 0.0
for datum, parval in zip( data, mpar ):
parval= parval.item()
result-= poisson.logpmf( datum, parval )
# Calculated log(poisson):
# result-= datum*log( parval ) - gammaln( datum+1.0 ) - parval
return result
# Constraints force poisson distribution with same parameter
# for every data point:
def constrFun( mpar, upar ):
return [ mpar[0] - upar[0],
mpar[1] - upar[0] ]
solver= clhood.clhoodSolver( data, upar, lfun, constrFun,
uparnames=upnames, mparnames=mpnames )
print( "Constraints before solution" )
print( solver.getConstraints() )
lBlobel=False
lPrint= False
lCorr= False
if "b" in opt:
lBlobel= True
if "p" in opt:
lPrint= True
if "c" in opt:
lCorr= True
solver.solve( lBlobel=lBlobel, lpr=lPrint )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
# Minuit fit of the two count likelihood, corresponds
# to max L solution
from AverageTools import minuitSolver
def fcn( n, grad, fval, par, ipar ):
mu= par[0]
lsum= 0.0
for datum in data:
lsum-= poisson.logpmf( datum, mu )
# Calculated log(poisson):
# lsum-= datum*log( mu ) - gammaln( datum+1.0 ) - mu
fval.value= lsum
return
par= [ 12.0 ]
parerr= [ 1.0 ]
parname= [ "mu" ]
ndof= 1
solver= minuitSolver.minuitSolver( fcn, par, parerr, parname, ndof )
solver.minuitCommand( "SET ERRDEF 0.5" )
solver.solve()
solver.printResults()
return
def StraightLine( opt="" ):
from ConstrainedFit import clsq
from numpy import matrix, zeros
# Data, errors and correlations:
xdata= [ 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0 ]
ydata= [ 3.0, 2.5, 3.0, 5.0, 7.0, 5.5, 7.5 ]
xerrs= [ 0.5, 0.3, 0.3, 0.5, 0.5, 0.3, 0.3 ]
yerrs= [ 0.7, 1.0, 0.5, 0.7, 0.7, 1.0, 0.7 ]
xyrho= [ -0.25, 0.5, 0.5, -0.25, 0.25, 0.95, -0.25 ]
#xyrho= [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]
covm= matrix( zeros( (14,14) ) )
data= []
npoints= len(xdata)
for i in range( npoints ):
subcovm= matrix( [ [ xerrs[i]**2, xyrho[i]*xerrs[i]*yerrs[i] ],
[ xyrho[i]*xerrs[i]*yerrs[i], yerrs[i]**2 ] ] )
covm[2*i:2*i+2,2*i:2*i+2]= subcovm
data.append( xdata[i] )
data.append( ydata[i] )
print( covm )
print( data )
# Fit parameters for straight line:
upar= [ 1.0, 0.5 ]
upnames= { 0: "a", 1: "b" }
# or parabola, see possible mpar[.]**2 term in constraints
#upar= [ 0.0, 1.0, 1.0 ]
#upnames= { 0: "a", 1: "b", 2: "c" }
# Constraint function forces y_i = a + b*x_i for every
# pair of measurements x_i, y_i:
def straightlineConstraints( mpar, upar ):
constraints= []
for i in range( npoints ):
constraints.append( upar[0] + upar[1]*mpar[2*i]
# + upar[2]*mpar[2*i]**2
- mpar[2*i+1] )
return constraints
# Setup the solver and solve:
solver= clsq.clsqSolver( data, covm, upar, straightlineConstraints,
uparnames=upnames )
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, "u" )
global tg, lell, tf, tt, canvc, canvp
from ROOT import TGraph, TF1, TText, TCanvas
if "cont" in opt:
canvc= TCanvas( "canv", "Chi^2 Contours", 600, 600 )
_doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2="u" )
# Plot:
from array import array
xarr= array( "f", xdata )
yarr= array( "f", ydata )
tg= TGraph( npoints, xarr, yarr )
tg.SetMarkerStyle( 20 )
tg.SetMinimum( 0.0 )
tg.SetMaximum( 9.0 )
tg.SetTitle( "straight line 2D fit" )
xa= tg.GetXaxis()
ya= tg.GetYaxis()
xa.SetTitle( "X" )
ya.SetTitle( "Y" )
canvp= TCanvas( "canp", "Straight line 2D fit", 600, 400 )
tg.Draw( "ap" )
lell= []
for i in range( npoints ):
te= _makeEllipse( xdata[i], ydata[i], xerrs[i], yerrs[i], xyrho[i] )
lell.append( te )
te.Draw( "s" )
solution= solver.getUpars()
tf= TF1( "tf", "[0]+[1]*x", 0.0, 15.0 )
for i in range( len(upar) ):
tf.SetParameter( i, solution[i] )
tf.SetParName( 0, upnames[i] )
tf.Draw( "same" )
tt= TText( 1, 8, "y= a + b*x" )
tt.Draw( "same" )
return
def Branchingratios( opt="m" ):
from ConstrainedFit import clsq
data= [ 0.265, 0.28, 0.37, 0.166, 0.42, 0.5, 0.20, 0.16,
0.72, 0.6, 0.37, 0.64, 0.45, 0.028, 10.0, 7.5 ]
errors= [ 0.014, 0.05, 0.06, 0.013, 0.15, 0.2, 0.08, 0.08,
0.15, 0.4, 0.16, 0.40, 0.45, 0.009, 5.0, 2.5 ]
# Error scale factor a la Blobel lecture:
if "e" in opt:
print "Apply scaling *2.8 of error[13]"
errors[13]= errors[13]*2.8
covm= clsq.covmFromErrors( errors )
upar= [ 0.33, 0.36, 0.16, 0.09, 0.055 ]
upnames= { 0: "B1", 1: "B2", 2: "B3", 3: "B4", 4: "B5" }
def brConstrFun( mpar, upar ):
constraints= []
x= []
for i in range( 5 ):
x.append( upar[i] )
for i in range( 5, 21 ):
x.append( mpar[i-5] )
constraints.append( x[0]+x[1]+x[2]+x[3]+x[4]-1.0 )
constraints.append( x[3]-x[5]*x[0] )
constraints.append( x[3]-x[6]*x[0] )
constraints.append( x[3]-x[7]*x[0] )
constraints.append( x[3]-(x[1]+x[2])*x[8] )
constraints.append( x[3]-(x[1]+x[2])*x[9] )
constraints.append( x[3]-(x[1]+x[2])*x[10] )
constraints.append( x[3]-(x[1]+x[2])*x[11] )
constraints.append( x[3]-(x[1]+x[2])*x[12] )
constraints.append( x[1]-(x[1]+x[2])*x[13] )
constraints.append( x[1]-(x[1]+x[2])*x[14] )
constraints.append( x[0]-(x[1]+x[2])*x[15] )
constraints.append( x[0]-(x[1]+x[2])*x[16] )
constraints.append( 3.0*x[4]-x[0]*x[17] )
constraints.append( x[3]-x[18] )
constraints.append( (x[1]+x[2])-x[4]*x[19] )
constraints.append( (x[1]+x[2])-x[4]*x[20] )
return constraints
solver= clsq.clsqSolver( data, covm, upar, brConstrFun, epsilon=0.00001,
uparnames=upnames )
print "Constraints before solution"
print solver.getConstraints()
lBlobel= False
if "b" in opt:
lBlobel= True
solver.solve( lBlobel=lBlobel )
lcov= False
lcorr= False
if "corr" in opt:
lcov= True
lcorr= True
ca= clsq.clsqAnalysis( solver )
ca.printResults( cov=lcov, corr=lcorr )
if "m" in opt:
_doMinosAll( solver, "u" )
if "cont" in opt:
_doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2="u" )
return
def StraightLine( opt="" ):
from ConstrainedFit import clsq
from numpy import matrix, zeros
# Data, errors and correlations:
xdata= [ 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0 ]
ydata= [ 3.0, 2.5, 3.0, 5.0, 7.0, 5.5, 7.5 ]
xerrs= [ 0.5, 0.3, 0.3, 0.5, 0.5, 0.3, 0.3 ]
yerrs= [ 0.7, 1.0, 0.5, 0.7, 0.7, 1.0, 0.7 ]
xyrho= [ -0.25, 0.5, 0.5, -0.25, 0.25, 0.95, -0.25 ]
#xyrho= [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]
covm= matrix( zeros( (14,14) ) )
data= []
npoints= len(xdata)
for i in range( npoints ):
subcovm= matrix( [ [ xerrs[i]**2, xyrho[i]*xerrs[i]*yerrs[i] ],
[ xyrho[i]*xerrs[i]*yerrs[i], yerrs[i]**2 ] ] )
covm[2*i:2*i+2,2*i:2*i+2]= subcovm
data.append( xdata[i] )
data.append( ydata[i] )
print covm
print data
# Fit parameters for straight line:
upar= [ 1.0, 0.5 ]
upnames= { 0: "a", 1: "b" }
#upar= [ 0.0, 1.0, 1.0 ]
#upnames= { 0: "a", 1: "b", 2: "c" }
# Constraint function forces y_i = a + b*x_i for every
# pair of measurements x_i, y_i:
def straightlineConstraints( mpar, upar ):
constraints= []
for i in range( npoints ):
constraints.append( upar[0] + upar[1]*mpar[2*i]
# + upar[2]*mpar[2*i]**2
- mpar[2*i+1] )
return constraints
# Setup the solver and solve:
solver= clsq.clsqSolver( data, covm, upar, straightlineConstraints,
uparnames=upnames )
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, "u" )
global tg, lell, tf, tt, canvc, canvp
from ROOT import TGraph, TF1, TText, TCanvas
if "cont" in opt:
canvc= TCanvas( "canv", "Chi^2 Contours", 600, 600 )
_doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2="u" )
# Plot:
from array import array
xarr= array( "f", xdata )
yarr= array( "f", ydata )
tg= TGraph( npoints, xarr, yarr )
tg.SetMarkerStyle( 20 )
tg.SetMinimum( 0.0 )
tg.SetMaximum( 9.0 )
tg.SetTitle( "straight line 2D fit" )
xa= tg.GetXaxis()
ya= tg.GetYaxis()
xa.SetTitle( "X" )
ya.SetTitle( "Y" )
canvp= TCanvas( "canp", "Straight line 2D fit", 600, 400 )
tg.Draw( "ap" )
lell= []
for i in range( npoints ):
te= _makeEllipse( xdata[i], ydata[i], xerrs[i], yerrs[i], xyrho[i] )
lell.append( te )
te.Draw( "s" )
solution= solver.getUpars()
tf= TF1( "tf", "[0]+[1]*x", 0.0, 15.0 )
for i in range( len(upar) ):
tf.SetParameter( i, solution[i] )
tf.SetParName( 0, upnames[i] )
tf.Draw( "same" )
tt= TText( 1, 8, "y= a + b*x" )
tt.Draw( "same" )
return
def GaussLikelihood( opt="" ):
from ConstrainedFit import clhood, clsq
from scipy.stats import norm
from math import log
# Data and errors:
xabs= [ 1.0, 2.0, 3.0, 4.0, 5.0 ]
data= [ 1.1, 1.9, 2.9, 4.1, 5.1 ]
errors= [ 0.1, 0.1, 0.1, 0.1, 0.1 ]
# Linear function (straight line) parameters:
upar= [ 0.0, 1.0 ]
upnames= { 0: "a", 1: "b" }
# Likelihood is sum of log(Gauss) for each data point:
def lfun( mpar ):
result= 0.0
for datum, parval, error in zip( data, mpar, errors ):
parval= parval.item()
result-= log( norm.pdf( datum, parval, error ) )
# result+= 0.5*((datum-parval)/error)**2
return result
# Constraints force linear function for each data point:
def constrFun( mpar, upar ):
constraints= []
for xval, parval in zip( xabs, mpar ):
constraints.append( upar[0] + upar[1]*xval - parval )
return constraints
# Configure options:
lBlobel=False
lPrint= False
lCorr= False
if "b" in opt:
lBlobel= True
if "p" in opt:
lPrint= True
if "c" in opt:
lCorr= True
# Solution using constrained log(likelihood) minimisation:
print "\nMax likelihood constrained fit"
solver= clhood.clhoodSolver( data, upar, lfun, constrFun, uparnames=upnames )
print "Constraints before solution"
print solver.getConstraints()
solver.solve( lBlobel=lBlobel, lpr=lPrint )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
# Solution using constrained least squares:
print "\nLeast squares constrained fit"
covm= clsq.covmFromErrors( errors )
solver= clsq.clsqSolver( data, covm, upar, constrFun, uparnames=upnames )
print "Constraints before solution"
print solver.getConstraints()
solver.solve( lBlobel=lBlobel, lpr=lPrint )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
return
def GaussLikelihood( opt="" ):
from ConstrainedFit import clhood, clsq
from scipy.stats import norm
from math import log
# Data and errors:
xabs= [ 1.0, 2.0, 3.0, 4.0, 5.0 ]
data= [ 1.1, 1.9, 2.9, 4.1, 5.1 ]
errors= [ 0.1, 0.1, 0.1, 0.1, 0.1 ]
# Linear function (straight line) parameters:
upar= [ 0.0, 1.0 ]
upnames= { 0: "a", 1: "b" }
# Likelihood is sum of log(Gauss) for each data point:
def lfun( mpar ):
result= 0.0
for datum, parval, error in zip( data, mpar, errors ):
parval= parval.item()
result-= log( norm.pdf( datum, parval, error ) )
# result+= 0.5*((datum-parval)/error)**2
return result
# Constraints force linear function for each data point:
def constrFun( mpar, upar ):
constraints= []
for xval, parval in zip( xabs, mpar ):
constraints.append( upar[0] + upar[1]*xval - parval )
return constraints
# Configure options:
lBlobel=False
lPrint= False
lCorr= False
if "b" in opt:
lBlobel= True
if "p" in opt:
lPrint= True
if "c" in opt:
lCorr= True
# Solution using constrained log(likelihood) minimisation:
print( "\nMax likelihood constrained fit" )
solver= clhood.clhoodSolver( data, upar, lfun, constrFun, uparnames=upnames )
print( "Constraints before solution" )
print( solver.getConstraints() )
solver.solve( lBlobel=lBlobel, lpr=lPrint )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
# Solution using constrained least squares:
print( "\nLeast squares constrained fit" )
covm= clsq.covmFromErrors( errors )
solver= clsq.clsqSolver( data, covm, upar, constrFun, uparnames=upnames )
print( "Constraints before solution" )
print( solver.getConstraints() )
solver.solve( lBlobel=lBlobel, lpr=lPrint )
ca= clsq.clsqAnalysis( solver )
ca.printResults( corr=lCorr )
return
def Branchingratios( opt="m" ):
from ConstrainedFit import clsq
data= [ 0.265, 0.28, 0.37, 0.166, 0.42, 0.5, 0.20, 0.16,
0.72, 0.6, 0.37, 0.64, 0.45, 0.028, 10.0, 7.5 ]
errors= [ 0.014, 0.05, 0.06, 0.013, 0.15, 0.2, 0.08, 0.08,
0.15, 0.4, 0.16, 0.40, 0.45, 0.009, 5.0, 2.5 ]
# Error scale factor a la Blobel lecture:
if "e" in opt:
print( "Apply scaling *2.8 of error[13]" )
errors[13]= errors[13]*2.8
covm= clsq.covmFromErrors( errors )
# PDG values as start values, last number is 5.5%, not 0.55 as in br.txt
upar= [ 0.33, 0.36, 0.16, 0.09, 0.055 ]
upnames= { 0: "B1", 1: "B2", 2: "B3", 3: "B4", 4: "B5" }
def brConstrFun( mpar, upar ):
constraints= []
x= []
for i in range( 5 ):
x.append( upar[i] )
for i in range( 5, 21 ):
x.append( mpar[i-5] )
constraints.append( x[0]+x[1]+x[2]+x[3]+x[4]-1.0 )
constraints.append( x[3]-x[5]*x[0] )
constraints.append( x[3]-x[6]*x[0] )
constraints.append( x[3]-x[7]*x[0] )
constraints.append( x[3]-(x[1]+x[2])*x[8] )
constraints.append( x[3]-(x[1]+x[2])*x[9] )
constraints.append( x[3]-(x[1]+x[2])*x[10] )
constraints.append( x[3]-(x[1]+x[2])*x[11] )
constraints.append( x[3]-(x[1]+x[2])*x[12] )
constraints.append( x[1]-(x[1]+x[2])*x[13] )
constraints.append( x[1]-(x[1]+x[2])*x[14] )
constraints.append( x[0]-(x[1]+x[2])*x[15] )
constraints.append( x[0]-(x[1]+x[2])*x[16] )
constraints.append( 3.0*x[4]-x[0]*x[17] )
constraints.append( x[3]-x[18] )
constraints.append( (x[1]+x[2])-x[4]*x[19] )
constraints.append( (x[1]+x[2])-x[4]*x[20] )
return constraints
solver= clsq.clsqSolver( data, covm, upar, brConstrFun, epsilon=0.00001,
uparnames=upnames )
print( "Constraints before solution" )
print( solver.getConstraints() )
lBlobel= False
if "b" in opt:
lBlobel= True
lResidual= False
if "r" in opt:
lResidual= True
solver.solve( lBlobel=lBlobel, lResidual=lResidual )
lcov= False
lcorr= False
if "corr" in opt:
lcov= True
lcorr= True
ca= clsq.clsqAnalysis( solver )
ca.printResults( cov=lcov, corr=lcorr )
if "m" in opt:
_doMinosAll( solver, "u" )
if "cont" in opt:
_doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2="u" )
return
