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_rasingcosine(): logger.info("Test RaisingCosine") model = Models.Fit1D(signal=Models.RaisingCosine_pdf( 'RC', xvar=mass, mean=signal_gauss.mean), background=1) 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) 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('Signal & Background are: %-28s & %-28s ' % (result('S')[0], result('B')[0])) logger.info('Mean is: %-28s ' % result(signal.mean)[0]) logger.info('Scale is: %-28s ' % result(signal.scale)[0]) models.add(model)