def test_vars3():
a = ROOT.RooRealVar('A', 'a', 0.05, 0, 1)
b = ROOT.RooRealVar('B', 'b', 0.02, -0.05, 0.1)
c = ROOT.RooRealVar('C', 'c', 0.005, 0, 0.1)
import ostap.fitting.roofuncs as R
from ostap.fitting.funbasic import Fun1D
X = Fun1D(x, xvar=x, name='X')
##F = (X**2) * c + X * b + a
F = a + b * X + c * X**2
eff2 = Efficiency1D('E5', F, cut=acc, xvar=x)
r2 = eff2.fitTo(ds)
f2 = eff2.draw(ds)
print(r2)
for p in points:
e = eff2(p, error=True)
ev = e.value()
e0 = eff0(p) / emax
print(' Point/Eff %4.1f %s%% (%.2f%%)' %
(p, (100 * e).toString('(%5.2f+-%4.2f)'), e0 * 100))
def __init__ ( self ,
name ,
efficiency , ## the function, FUNC or PDF
cut ,
xvar = None ,
accept = 'accept' ,
scale = None ) :
self.__eff = efficiency
if isinstance ( efficiency , PDF ) :
eff_pdf = efficiency
xvar = efficiency.xvar
eff_fun = None
elif isinstance ( efficiency , FUNC ) :
eff_fun = efficiency.fun
xvar = efficiency.xvar
eff_pdf = None
elif isinstance ( efficiency , ROOT.RooAbsPdf ) and xvar and isinstance ( xvar , ROOT.RooAbsReal ) :
eff_pdf = Generic1D_pdf ( efficiency , xvar )
eff_fun = None
elif isinstance ( efficiency , ROOT.RooAbsReal ) and xvar and isinstance ( xvar , ROOT.RooAbsReal ) :
eff_pdf = Fun1D ( efficiency , xvar = xvar )
eff_fun = efficiency
else :
raise AttributeError('Invalid efficiency/xvar combination %s/%s;%s/%s' %
( efficiency , type(efficiency) , xvar , type(xvar) ) )
self.__xvar = xvar
Efficiency.__init__ ( self , name , eff_pdf , eff_fun , ( xvar,) , cut , accept , scale )
def test_vars3():
logger = getLogger('test_vars3')
a = ROOT.RooRealVar('A', 'a', 0.05, 0, 1)
b = ROOT.RooRealVar('B', 'b', 0.02, -0.05, 0.1)
c = ROOT.RooRealVar('C', 'c', 0.005, 0, 0.1)
import ostap.fitting.roofuncs as R
from ostap.fitting.funbasic import Fun1D
X = Fun1D(x, xvar=x, name='X')
##F = (X**2) * c + X * b + a
F = a + b * X + c * X**2
eff2 = Efficiency1D('E5', F, cut=acc, xvar=x)
r2 = eff2.fitTo(ds)
logger.info("Fit result using-Fun1D \n%s" % r2.table(prefix="# "))
logger.info("Compare with true efficiency (using Fun1D)\n%s" %
make_table(eff2, title='using Fnu1D'))
with wait(2), use_canvas('test_vars3'):
f2 = eff2.draw(ds, nbins=25)
def test_transform():
logger = getLogger('test_transform')
if not 62000 <= ROOT.gROOT.GetVersionInt():
logger.info("Not for this version of ROOT")
return
x = ROOT.RooRealVar('x', '', 1, 100000)
mean = 1000
sigma = 1000
## book very simple data set
varset = ROOT.RooArgSet(x)
dataset = ROOT.RooDataSet(dsID(), 'Test Data set', varset)
while len(dataset) < 5000:
v = random.gauss(mean, sigma)
if v in x:
x.value = v
dataset.add(varset)
dataset.add_var('lx', 'log10(x)')
dataset.lx.setMin(0)
dataset.lx.setMax(5)
## original PDF
gauss = Models.Gauss_pdf('G',
xvar=x,
mean=(mean, mean / 2, mean * 2),
sigma=(sigma, sigma / 2, sigma * 2))
with use_canvas('test_transform'), wait(1):
r1, f1 = gauss.fitTo(dataset, draw=True, silent=True)
logger.info('Fit x:\n%s' % r1.table())
lx = dataset.lx
LX = Fun1D(lx, lx)
NX = 10**LX
## transformed PDF
tgauss = TrPDF(pdf=gauss, new_var=NX)
with use_canvas('test_transform'), wait(1):
r2, f2 = tgauss.fitTo(dataset, draw=True, silent=True)
logger.info('Fit log10(x):\n%s' % r2.table())