def test_monotonic5():
logger = getLogger('test_monotonic5')
logger.info("Test monotonic Poly(5)-Distribution")
model = Models.Monotonic_pdf('M4', x, power=4, increasing=False)
result, f = model.fitTo(dataset, silent=True, refit=5)
with wait(1), use_canvas('test_monotonic5'):
model.draw(dataset)
title = 'Positive decreasing polynomial'
logger.info('%s \n%s' % (title, result.table(title=title, prefix='# ')))
models.add(model)
def test_monotonic4():
logger.info("Test monotonic Poly(4)-Distribution")
model = Models.Monotonic_pdf('M4', x, power=4, increasing=False)
with rooSilent():
result, f = model.fitTo(dataset)
result, f = 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:
for phi in model.phis:
logger.info("\tMonotonic4: phi= %-17s " % phi.ve())
models.add(model)
def test_pdf():
logger = getLogger('test_pdf')
effPdf = Models.Monotonic_pdf('P6', xvar=x, power=4, increasing=True)
maxe = margin * effPdf(xmax)
s0 = min(1.0 / emax, 1.0 / maxe)
scale = ROOT.RooRealVar('scaleX', 'scaleX', s0, 0.2 * s0, 5.0 * s0)
eff2 = Efficiency1D('E2', effPdf, cut=acc, scale=scale)
r2 = eff2.fitTo(ds)
logger.info("Fit result using-Monotonic_pdf \n%s" % r2.table(prefix="# "))
logger.info("Compare with true efficiency (using Monotonic_pdf)\n%s" %
make_table(eff2, title='using Monotonic_pdf'))
with wait(2), use_canvas('test_pdf'):
f2 = eff2.draw(ds, nbins=25)
def test_pdf () :
effPdf = Models.Monotonic_pdf ( 'P6' , xvar = x , power = 3 , increasing = True )
maxe = margin * effPdf ( xmax )
s0 = min ( 1.0 / emax , 1.0 / maxe )
scale = ROOT.RooRealVar ( 'scaleX' , 'scaleX' , s0 , 0.2 * s0 , 5.0 * s0 )
eff2 = Efficiency1D ( 'E2' , effPdf , cut = acc , scale = scale )
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 ) )
if '__main__' == __name__ or '__builtin__' == __name__:
logger = getLogger('test_fitting_efficiency')
else:
logger = getLogger(__name__)
# =============================================================================
## make
x = ROOT.RooRealVar('x', 'test', 0, 10)
xmin, xmax = x.minmax()
acc = ROOT.RooCategory('cut', 'cut')
acc.defineType('accept', 1)
acc.defineType('reject', 0)
varset = ROOT.RooArgSet(x, acc)
ds = ROOT.RooDataSet(dsID(), 'test data', varset)
eff0 = Models.Monotonic_pdf('E0', xvar=x, power=2, increasing=True)
eff0.phis = 3.1415 / 2, 3.1415 / 2
margin = 1.25
emax = margin * eff0(x.getMax())
for i in range(10000):
xv = random.uniform(xmin, xmax)
x.setVal(xv)
ev = random.uniform(0, emax)
if eff0(xv) > ev: acc.setIndex(1)
else: acc.setIndex(0)