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_pbxpb_BBsym () : logger.info ('Symmetric fit model with non-factorizeable background component: ( Gauss + P1 ) (x) ( Gauss + P1 ) + Sym(expo*P1)**2') model = Models.Fit2DSym ( suffix = '_9' , signal_x = signal1 , signal_y = signal2s , bkg_1x = -1 , bkg_2D = Models.ExpoPol2Dsym_pdf ( 'P2D9' , m_x , m_y , n = 1 ) ) ## 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 ) 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] /2 ) ) logger.info ('B1xS2 : %20s' % ( result ( model.BS ) [0] /2 ) ) logger.info ('B1xB2 : %20s' % result ( model.BB ) [0] ) models.add ( model )
def test_expopolsym2D(): logger = getLogger('test_expopolsym2D') logger.info( 'Test ExpoPol2Dsym_pdf: symmetric exponential times exponential modulated by positive polynomial in X and Y' ) model = Models.ExpoPol2Dsym_pdf('EPs', m_x, m_y, n=2) with rooSilent(): result, f = model.fitTo(dataset) with use_canvas('test_expopolsym2D'): with wait(1): model.draw1(dataset) with wait(1): 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_pbxpb_BBs(): logger.info( 'Non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Sym(expo*P1)**2' ) model = Models.Fit2D(suffix='_8', signal_1=signal1, signal_2=signal2s, bkg1=1, bkg2=1, bkg2D=Models.ExpoPol2Dsym_pdf('P2D8', m_x, m_y, n=1)) model.bkg1.tau.fix(0) model.bkg2.tau.fix(0) ## fit with fixed mass and sigma with rooSilent(): result, frame = model.fitTo(dataset) model.signal1.sigma.release() model.signal2.sigma.release() model.signal1.mean.release() model.signal2.mean.release() result, frame = model.fitTo(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)
def test_pbxpb_BBsym(): logger.info( 'Symmetric fit model with non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Sym(expo*P1)**2' ) model = Models.Fit2DSym(suffix='_9', signal_1=Models.Gauss_pdf('Gx', m_x.getMin(), m_x.getMax(), mass=m_x), signal_2=Models.Gauss_pdf('Gy', m_y.getMin(), m_y.getMax(), mass=m_y), bkg1=1, bkg2D=Models.ExpoPol2Dsym_pdf('P2D9', m_x, m_y, n=1)) model.signal1.sigma.fix(m.error()) model.signal2.sigma.fix(m.error()) model.signal1.mean.fix(m.value()) model.signal2.mean.fix(m.value()) model.signal1.mean.fix(m.value()) model.signal2.mean.fix(m.value()) model.bkg1.tau.fix(0) ## fit with fixed mass and sigma with rooSilent(): result, frame = model.fitTo(dataset) model.signal1.sigma.release() model.signal2.sigma.release() model.signal1.mean.release() model.signal2.mean.release() result, frame = model.fitTo(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] / 2)) logger.info('B1xS2 : %20s' % (result(model.bs)[0] / 2)) logger.info('B1xB2 : %20s' % result(model.bb)[0]) models.add(model)
def test_pbxpb_BBs(): logger = getLogger('test_pbxpb_BBs') logger.info( 'Non-factorizeable background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + Sym(expo*P1)**2' ) model = Models.Fit2D(suffix='_8', signal_x=signal1, signal_y=signal2s, bkg_1x=1, bkg_1y=1, bkg_2D=Models.ExpoPol2Dsym_pdf('P2D8', m_x, m_y, n=1)) model.bkg_1x.tau.fix(0) model.bkg_1y.tau.fix(0) ## fit with fixed mass and sigma with rooSilent(), use_canvas('test_pbxpb_BBs'): 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) with wait(1): model.draw1(dataset) with wait(1): model.draw2(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)