def test_expopspol2D() :
logger.info ('Test ExpoPSPol2D_pdf: Exponential times phase space factor, modulated by positive polynomial in X and Y ')
## "fictive phase space"
psy = Ostap.Math.PhaseSpaceNL ( 0 , 10 , 2 , 10 )
model = Models.ExpoPSPol2D_pdf ( 'EPS',
m_x , m_y ,
psy ,
nx = 2 , ny = 2 )
with rooSilent() :
result, f = model.fitTo ( dataset )
model.draw1 ( dataset )
model.draw2 ( dataset )
result, f = model.fitTo ( dataset , silent = True )
if 0 != result.status() or 3 != result.covQual() :
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' % ( result.status() , result.covQual () ) )
print(result)
else :
logger.info ( 'Bernstein Coefficients:\n%s' % model.pars() )
models.add ( model )
def test_comp_2dSymfit():
logger.info('Test multi-component 2d Sym fit')
model = Models.Fit2DSym(name='fit_comp',
signal_x=signal_x1,
signal_y=signal_y1,
bkg_1x=bkg_x,
components=[ss_cmp, sb_cmp, bb_cmp])
with rooSilent():
## components
model.SS.fix(5000)
model.SB.fix(1000)
model.BB.fix(1000)
model.C[0].fix(5000)
model.C[1].fix(1000)
model.C[2].fix(1000)
r = model.fitTo(dataset, ncpu=8)
model.SS.release()
model.SB.release()
model.BB.release()
model.C[0].release()
model.C[1].release()
model.C[2].release()
r = model.fitTo(dataset, ncpu=8)
logger.info('Model %s Fit result \n#%s ' % (model.name, r))
def test_2gauss():
logger.info('Test ResoGauss2: double Gaussian resolution model')
from ostap.fitting.resolution import ResoGauss2
reso_2gauss = ResoGauss2('G2', mass, 0.1)
reso_2gauss.sigma.release()
reso_2gauss.scale.release()
reso_2gauss.fraction.release()
from ostap.logger.utils import rooSilent
with rooSilent():
result, frame = reso_2gauss.fitTo(dataset0)
result, frame = reso_2gauss.fitTo(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print(result)
else:
logger.info('ResoGauss2: RMS %s ' % reso_2gauss.rms())
logger.info('ResoGauss2: FWHM %s ' % reso_2gauss.fwhm())
logger.info('ResoGauss2: Resolution %s ' % result.sigma_G2.ve())
logger.info('ResoGauss2: Scale %s ' % result.SigmaScale_G2.ve())
logger.info('ResoGauss2: Fraction %s ' % result.CoreFraction_G2.ve())
models.add(reso_2gauss)
def test_losev():
logger.info("Test Losev: asymmetric hyperbilic secant distribution")
model = Models.Fit1D(signal=Models.Losev_pdf('LOSEV',
xvar=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgLOSEV',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.mean.release()
signal.alpha.release()
signal.beta.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Asymmetric hyperbolic secant/Losev distribution\n%s" %
result.table(prefix="# "))
models.add(model)
def test_voigt():
logger.info('Test Voigt_pdf: Breit-Wigner convoluted with Gauss')
model = Models.Fit1D(signal=Models.Voigt_pdf('V',
xvar=mass,
m0=signal_gauss.mean,
sigma=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgV', xvar=mass, power=0),
S=S,
B=B)
signal = model.signal
signal.sigma.fix(m.error())
signal.gamma.fix(0.002)
signal.m0.fix()
model.B.setVal(500)
model.S.setVal(5000)
with rooSilent():
result, frame = model.fitTo(dataset0)
result, frame = model.fitTo(dataset0)
signal.sigma.release()
result, frame = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Voigt function\n%s" % result.table(prefix="# "))
models.add(model)
def test_johnsonSU():
logger.info("Test JohnsonSU-Distribution")
model = Models.Fit1D(signal=Models.JohnsonSU_pdf('JSU',
xvar=mass,
xi=signal_gauss.mean),
background=Models.Bkg_pdf('BkgJSU',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.lambd.release()
signal.delta.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Johnson-SU function\n%s" % result.table(prefix="# "))
models.add(model)
def test_gauss():
logger.info('Test Gauss_pdf: simple Gaussian signal')
## release the sigma of signal:
signal_gauss.mean.release()
## simple fit with gaussian only
result = signal_gauss.fitTo(dataset0, silent=True)
model_gauss.background.tau.fix(0)
with rooSilent():
result, frame = model_gauss.fitTo(dataset0)
result, frame = model_gauss.fitTo(dataset0)
model_gauss.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Simple Gaussian model\n%s' % result.table(prefix="# "))
models.add(model_gauss)
signal_gauss.mean.fix(m.value())
def test_hyperbolic():
logger.info(
'Test Hyperbolic_pdf: hyperbolic distribution (exponential tails)')
model_hyperbolic = Models.Fit1D(signal=Models.Hyperbolic_pdf(
name='HB',
xvar=mass,
mu=signal_gauss.mean,
sigma=signal_gauss.sigma,
zeta=(1, -1, 1e+6),
kappa=(0, -1, 1)),
background=Models.Bkg_pdf('BkgHB',
xvar=mass,
power=0),
S=S,
B=B)
model_hyperbolic.S.value = 5000
model_hyperbolic.B.value = 500
with rooSilent():
model_hyperbolic.signal.kappa.fix(0)
result, frame = model_hyperbolic.fitTo(dataset0)
model_hyperbolic.signal.kappa.release()
result, frame = model_hyperbolic.fitTo(dataset0)
model_hyperbolic.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Hyperbolic function\n%s' % result.table(prefix="# "))
models.add(model_hyperbolic)
def test_crystalball_RS():
logger.info('Test CrystalBallRS_pdf: right-side Crystal Ball function')
model_cbrs = Models.Fit1D(signal=Models.CrystalBallRS_pdf(
name='CBRS',
xvar=mass,
sigma=signal_gauss.sigma,
alpha=(1.5, 0.5, 3.0),
n=(5, 1, 10),
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgCBRS',
xvar=mass,
power=0),
S=S,
B=B)
model_cbrs.S.value = 5000
model_cbrs.B.value = 500
with rooSilent():
result, frame = model_cbrs.fitTo(dataset0)
model_cbrs.signal.alpha.release()
result, frame = model_cbrs.fitTo(dataset0)
result, frame = model_cbrs.fitTo(dataset0)
model_cbrs.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('right-side Crystal Ball function\n%s' %
result.table(prefix="# "))
models.add(model_cbrs)
def test_crystalball():
logger.info('Test CrystalBall_pdf: Crystal Ball function')
## composite model: signal + background
model_cb = Models.Fit1D(
signal=Models.CrystalBall_pdf(
name='CB', ## the name
xvar=mass, ## the variable
alpha=(2, 1, 5), ## tail parameter
n=(3, 1, 9), ## tail parameter
sigma=signal_gauss.sigma, ## reuse sigma from gauss
mean=signal_gauss.mean), ## reuse mean from gauss
background=Models.Bkg_pdf('BkgCB', xvar=mass, power=0),
S=S,
B=B)
model_cb.signal.n.fix(8)
with rooSilent():
result, frame = model_cb.fitTo(dataset0)
model_cb.signal.alpha.release()
result, frame = model_cb.fitTo(dataset0)
result, frame = model_cb.fitTo(dataset0)
model_cb.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Crystal Ball function\n%s' % result.table(prefix="# "))
models.add(model_cb)
def test_rasingcosine():
logger.info("Test RaisingCosine")
model = Models.Fit1D(signal=Models.RaisingCosine_pdf(
'RC', xvar=mass, mean=signal_gauss.mean),
background=1,
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.mean.release()
signal.scale.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Raising cosine function\n%s" % result.table(prefix="# "))
models.add(model)
def test_laplace():
logger.info('Test Asymmetric Laplace shape')
model = Models.Fit1D(
signal=Models.AsymmetricLaplace_pdf(name='AL',
xvar=mass,
mean=signal_gauss.mean,
slope=signal_gauss.sigma),
background=None,
S=S,
B=B)
signal = model.signal
signal.slope.release()
signal.mean.fix()
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, frame = model.fitTo(dataset0)
result, frame = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("asymmetric Laplace function\n%s" % result.table(prefix="# "))
models.add(model)
def test_polypos2D() :
logger.info ('Test PolyPos2D_pdf: positive polynomial in 2D' )
model = Models.PolyPos2D_pdf ( 'P2D' ,
m_x ,
m_y ,
nx = 2 ,
ny = 2 )
with rooSilent() :
result, f = model.fitTo ( dataset )
model.draw1 ( dataset )
model.draw2 ( dataset )
result, f = model.fitTo ( dataset , silent = True )
if 0 != result.status() or 3 != result.covQual() :
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' % ( result.status() , result.covQual () ) )
print(result)
else :
logger.info ( 'Bernstein Coefficients:\n%s' % model.pars() )
models.add ( model )
def test_spline2D() :
logger.info ('Test Spline2D_pdf : 2D-spline')
s1 = Ostap.Math.BSpline ( m_x.xmin(), m_x.xmax() , 1 , 2 )
s2 = Ostap.Math.BSpline ( m_y.xmin(), m_y.xmax() , 1 , 2 )
s3 = Ostap.Math.PositiveSpline2D ( s1 , s2 )
model = Models.Spline2D_pdf ( 'S2D' , m_x , m_y, s3 )
model = Models.ExpoPol2Dsym_pdf ( 'S2DS',
m_x ,
m_y ,
n = 2 )
with rooSilent() :
result, f = model.fitTo ( dataset )
model.draw1 ( dataset )
model.draw2 ( dataset )
result, f = model.fitTo ( dataset , silent = True )
if 0 != result.status() or 3 != result.covQual() :
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' % ( result.status() , result.covQual () ) )
print(result)
models.add ( model )
def test_qgauss():
logger.info('Test QGaussian_pdf: q-Gaussian')
model_qgauss = Models.Fit1D(signal=Models.QGaussian_pdf(
name='qG',
xvar=mass,
q=(1, 0.7, 1.2),
mean=signal_gauss.mean,
scale=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgQG',
xvar=mass,
power=0),
S=S,
B=B)
s = model_qgauss.signal
s.scale = 0.015
with rooSilent():
result, frame = model_qgauss.fitTo(dataset0)
model_qgauss.signal.scale.release()
result, frame = model_qgauss.fitTo(dataset0)
model_qgauss.signal.q.release()
result, frame = model_qgauss.fitTo(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Q-Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_qgauss)
def test_needham():
logger.info('Test Needham_pdf: Crystal Ball with alpha=f(sigma)')
model_matt = Models.Fit1D(signal=Models.Needham_pdf(
name='Matt',
xvar=mass,
sigma=signal_gauss.sigma,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgMATT',
xvar=mass,
power=0),
S=S,
B=B)
with rooSilent():
result, frame = model_matt.fitTo(dataset0)
model_matt.signal.mean.release()
model_matt.signal.sigma.release()
result, frame = model_matt.fitTo(dataset0)
result, frame = model_matt.fitTo(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Needham function\n%s' % result.table(prefix="# "))
models.add(model_matt)
def test_studentT():
logger.info('Test StudentT_pdf: Student-t distribution')
model_student = Models.Fit1D(signal=Models.StudentT_pdf(
name='ST', xvar=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgST',
xvar=mass,
power=0),
S=S,
B=B)
model_student.signal.n.setVal(20)
model_student.signal.sigma.setVal(0.013)
model_student.S.setVal(5000)
model_student.B.setVal(500)
with rooSilent():
result, frame = model_student.fitTo(dataset0)
result, frame = model_student.fitTo(dataset0)
model_student.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Student's t-function\n%s" % result.table(prefix="# "))
models.add(model_student)
def test_apollonios():
logger.info(
'Test Apollonios_pdf: Modified gaussian with power-law and exponential tails'
)
model_apollonios = Models.Fit1D(signal=Models.Apollonios_pdf(
name='APO',
xvar=mass,
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
b=1,
n=10,
alpha=3),
background=Models.Bkg_pdf('BkgAPO',
xvar=mass,
power=0),
S=S,
B=B)
model_apollonios.S.setVal(5000)
model_apollonios.B.setVal(500)
with rooSilent():
result, frame = model_apollonios.fitTo(dataset0)
result, frame = model_apollonios.fitTo(dataset0)
model_apollonios.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Apollonios function\n%s' % result.table(prefix="# "))
models.add(model_apollonios)
def test_atlas():
logger.info("Test ATLAS: Modified Gaussian, used by ATLAS/Zeus")
model = Models.Fit1D(signal=Models.Atlas_pdf('ATLAS',
xvar=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgATLAS',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.mean.release()
signal.sigma.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("ATLAS function\n%s" % result.table(prefix="# "))
models.add(model)
def test_apollonios2():
logger.info(
'Test Apollonios2_pdf: modified Gaussian with exponential tails')
model_apollonios2 = Models.Fit1D(signal=Models.Apollonios2_pdf(
name='AP2',
xvar=mass,
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
beta=(0.5, 2),
asymmetry=0),
background=Models.Bkg_pdf('BkgAPO2',
xvar=mass,
power=0),
S=S,
B=B)
model_apollonios2.signal.mean.fix(m.value())
model_apollonios2.S.value = 5000
model_apollonios2.B.value = 500
model_apollonios2.signal.sigma.release()
with rooSilent():
result, frame = model_apollonios2.fitTo(dataset0)
model_apollonios2.signal.asym.release()
result, frame = model_apollonios2.fitTo(dataset0)
model_apollonios2.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Apollonios2 function\n%s' % result.table(prefix="# "))
models.add(model_apollonios2)
def test_sech():
logger.info("Test SECH: Sech(1/cosh) distribution")
model = Models.Fit1D(signal=Models.Sech_pdf('SECH',
xvar=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgSECH',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.mean.release()
signal.sigma.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Hyperbolic secant/sech function\n%s" %
result.table(prefix="# "))
models.add(model)
def test_bifurcated():
logger.info('Test BifurcatedGauss_pdf: Bifurcated Gaussian')
signal_bifurcated = Models.BifurcatedGauss_pdf(name='BfGau',
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
xvar=mass)
signal_bifurcated.asym.setVal(0)
model_bifurcated = Models.Fit1D(signal=signal_bifurcated,
background=Models.Bkg_pdf('BkgBFG',
xvar=mass,
power=0),
S=S,
B=B)
model_bifurcated.B.setVal(500)
model_bifurcated.S.setVal(6000)
with rooSilent():
result, frame = model_bifurcated.fitTo(dataset0)
result, frame = model_bifurcated.fitTo(dataset0)
model_bifurcated.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('Bifurcated Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_bifurcated)
def test_logistic():
logger.info("Test LOGISTIC: Logistic distribution")
model = Models.Fit1D(signal=Models.Logistic_pdf('LOGI',
xvar=mass,
mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgLOGI',
xvar=mass,
power=0),
S=S,
B=B)
signal = model.signal
model.S.setVal(5000)
model.B.setVal(500)
with rooSilent():
result, f = model.fitTo(dataset0)
result, f = model.fitTo(dataset0)
signal.mean.release()
signal.sigma.release()
result, f = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("Logistic distribution\n%s" % result.table(prefix="# "))
models.add(model)
def test_2gauss():
logger.info('Test DoubleGauss_pdf: Double Gaussian')
signal_2gauss = Models.DoubleGauss_pdf(name='Gau2',
mean=signal_gauss.mean,
sigma=signal_gauss.sigma,
xvar=mass,
fraction=0.9,
scale=1.2)
model_2gauss = Models.Fit1D(signal=signal_2gauss,
background=Models.Bkg_pdf('Bkg22G',
xvar=mass,
power=0),
S=S,
B=B)
model_2gauss.B.setVal(500)
model_2gauss.S.setVal(6000)
with rooSilent():
result, frame = model_2gauss.fitTo(dataset0)
signal_2gauss.fraction.release()
result, frame = model_2gauss.fitTo(dataset0)
model_2gauss.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('double Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_2gauss)
def test_pvoigt():
logger.info('Test PSeudoVoigt_pdf: fast approximation to Voigt profile')
model = Models.Fit1D(
signal=Models.PseudoVoigt_pdf('PV',
xvar=mass,
m0=signal_gauss.mean,
sigma=signal_gauss.sigma),
background=Models.Bkg_pdf('BkgPV', xvar=mass, power=0),
S=S,
B=B)
signal = model.signal
signal.m0.fix()
signal.sigma.fix(m.error())
signal.gamma.fix(0.002)
model.B.setVal(500)
model.S.setVal(5000)
with rooSilent():
result, frame = model.fitTo(dataset0)
result, frame = model.fitTo(dataset0)
model.signal.sigma.release()
result, frame = model.fitTo(dataset0)
model.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info("pseudo-Voigt function\n%s" % result.table(prefix="# "))
models.add(model)
def test_gengauss_v1():
logger.info('Test GenGaussV1_pdf: Generalized Gaussian V1')
model_gauss_gv1 = Models.Fit1D(signal=Models.GenGaussV1_pdf(
name='Gv1', xvar=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgGGV1',
xvar=mass,
power=0),
S=S,
B=B)
model_gauss_gv1.signal.beta.fix(2)
model_gauss_gv1.signal.mean.fix(m.value())
model_gauss_gv1.S.setVal(5000)
model_gauss_gv1.B.setVal(500)
with rooSilent():
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.signal.alpha.release()
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.signal.mean.release()
result, frame = model_gauss_gv1.fitTo(dataset0)
model_gauss_gv1.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('generalized Gaussian(v1) function\n%s' %
result.table(prefix="# "))
models.add(model_gauss_gv1)
def test_cb2():
logger.info(
'Test ResoCB2: symmetric double-sided Crystal Ball resolution model')
from ostap.fitting.resolution import ResoCB2
reso_cb2 = ResoCB2('CB', mass, 0.1)
reso_cb2.sigma.release()
reso_cb2.alpha.release()
reso_cb2.n.release()
from ostap.logger.utils import rooSilent
with rooSilent():
result, frame = reso_cb2.fitTo(dataset0)
result, frame = reso_cb2.fitTo(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print(result)
else:
logger.info('ResoCB2: RMS %s ' % reso_cb2.rms())
logger.info('ResoCB2: FWHM %s ' % reso_cb2.fwhm())
logger.info('ResoCB2: Resolution %s ' % result.sigma_CB.ve())
logger.info('ResoCB2: Alpha %s ' % result.ResoAlpha_CB.ve())
logger.info('ResoCB2: N %s ' % result.ResoN_CB.ve())
models.add(reso_cb2)
def test_skewgauss():
logger.info('Test SkewGauss_pdf: Skew Gaussian function')
model_gauss_skew = Models.Fit1D(signal=Models.SkewGauss_pdf(
name='GSk', xvar=mass, mean=signal_gauss.mean),
background=Models.Bkg_pdf('BkgSkG',
xvar=mass,
power=0),
S=S,
B=B)
model_gauss_skew.signal.alpha.fix(0)
model_gauss_skew.S.setVal(5000)
model_gauss_skew.B.setVal(500)
with rooSilent():
result, frame = model_gauss_skew.fitTo(dataset0)
result, frame = model_gauss_skew.fitTo(dataset0)
model_gauss_skew.draw(dataset0)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
logger.info('skew Gaussian function\n%s' % result.table(prefix="# "))
models.add(model_gauss_skew)
def test_extended2():
logger.info('Test extended combined multi-component fit')
model = Models.Fit1D(
name='E2',
signal=signal_1,
othersignals=[signal_2, signal_3],
background=Models.Bkg_pdf('P1', xvar=mass, power=0, tau=0),
otherbackgrounds=[wide_1, wide_2],
others=[narrow_1, narrow_2],
combine_signals=True, ## ATTENTION!
combine_others=True, ## ATTENTION!
combine_backgrounds=True, ## ATTENTION!
)
with rooSilent():
model.S.fix(15000)
model.B.fix(7000)
model.C.fix(2000)
r, f = model.fitTo(dataset, draw=False, silent=True)
model.S.release()
model.B.release()
model.C.release()
r, f = model.fitTo(dataset, draw=False, silent=True)
logger.info('Model %s Fit result \n#%s ' % (model.name, r))
def test_const():
logger.info(
'Simplest (factorized) fit model: ( Gauss + const ) x ( Gauss + const ) '
)
model = Models.Fit2D(
signal_x=signal1,
signal_y=signal2s,
)
## fit with fixed mass and sigma
with rooSilent():
result, frame = model.fitTo(dataset)
model.signal_x.sigma.release()
model.signal_y.sigma.release()
model.signal_x.mean.release()
model.signal_y.mean.release()
result, frame = model.fitTo(dataset)
model.draw1(dataset)
model.draw2(dataset)
if 0 != result.status() or 3 != result.covQual():
logger.warning('Fit is not perfect MIGRAD=%d QUAL=%d ' %
(result.status(), result.covQual()))
print(result)
else:
logger.info('S1xS2 : %20s' % result(model.SS)[0])
logger.info('S1xB2 : %20s' % result(model.SB)[0])
logger.info('B1xS2 : %20s' % result(model.BS)[0])
logger.info('B1xB2 : %20s' % result(model.BB)[0])
models.add(model)