def test_pspolsym2D(): logger = getLogger('test_pspolsym2D') logger.info( 'Test PSPol2Dsym_pdf: *SYMMETRIC* product of phase space factors, modulated by positive polynomial in X and Y ' ) ## "fictive phase space" ps = Ostap.Math.PhaseSpaceNL(0, 10, 2, 10) model = Models.PSPol2Dsym_pdf('PS2Ds', m_x, m_y, ps, n=2) with rooSilent(): result, f = model.fitTo(dataset) with use_canvas('test_pspolsym2D'): 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_psxps_BBsym () : logger.info ('Simmetric fit model with non-factorizeable background component: ( Gauss + P1 ) (x) ( Gauss + P1 ) + (PS*P1)**2') PS = Ostap.Math.PhaseSpaceNL( 1.0 , 5.0 , 2 , 5 ) model = Models.Fit2DSym ( suffix = '_12' , signal_x = signal1 , signal_y = signal2s , bkg_1x = -1 , bkg_2D = Models.PSPol2Dsym_pdf ( 'P2D12' , m_x , m_y , ps = PS , 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_psxps_BBs(): logger.info( 'Non-factorizeable symmetric background component: ( Gauss + expo*P1 ) (x) ( Gauss + expo*P1 ) + (PS*P1)**2' ) PS = Ostap.Math.PhaseSpaceNL(1.0, 5.0, 2, 5) model = Models.Fit2D(suffix='_11', 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), power1=1, power2=1, bkg2D=Models.PSPol2Dsym_pdf('P2D11', m_x, m_y, ps=PS, 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) 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)