# =============================================================================
logger.info ( 'Test for histogtram fits')
# =============================================================================
## make simple test
mass2 = ROOT.RooRealVar ( 'test_mass2' , 'Some test mass' , 0 , 10 )
x = mass2
## book very simple data set
varset2 = ROOT.RooArgSet ( mass2 )
import Ostap.FitModels as Models
events = 50000
logger.debug('Make a test data using Gamma-Distribution')
m_gamma0 = Models.GammaDist_pdf( 'GD0' , x )
m_gamma0.k .setVal( 2 )
m_gamma0.theta.setVal( 2 )
dataset2 = m_gamma0.pdf.generate ( varset2 , events )
#
##
h1 = ROOT.TH1D ( hID() , '' , 10 , 0 , 10 ) ; h1.Sumw2()
h2 = ROOT.TH1D ( hID() , '' , 20 , 0 , 10 ) ; h2.Sumw2()
h3 = ROOT.TH1D ( hID() , '' , 100 , 10 , 10 ) ; h3.Sumw2()
random.seed(10)
bins = [ 0 ]
for i in range(0, 9 ) : bins.append ( random.uniform ( 0 , 10 ) )
else:
logger = getLogger(__name__)
logger.info('Create 1D dataset for Ostap Tutorial ')
# ============================================================================
RAD = ROOT.RooAbsData
if RAD.Tree != RAD.getDefaultStorageType():
logger.info('DEFINE default storage type to be TTree! ')
RAD.setDefaultStorageType(RAD.Tree)
# ============================================================================
x = ROOT.RooRealVar('x', 'x', 0, 10)
varset = ROOT.RooArgSet(x)
import Ostap.FitModels as Models
gamma = Models.GammaDist_pdf('GPDF', x)
gamma.k.setVal(2)
gamma.theta.setVal(1)
data = gamma.pdf.generate(varset, 100000)
h1 = x.histo(100)
h2 = x.histo(500)
h3 = h1_axis([0, 1, 2, 3, 6, 8, 10])
data.project(h1, 'x')
data.project(h2, 'x')
data.project(h3, 'x')
h4 = h1_axis(h2.equal_edges(5))
h5 = h1_axis(h2.equal_edges(10))