## 0) The weighter object
weighter = Weight(dbname, weightings)
# =========================================================================
## 1a) create new "weighted" mcdataset
mcds = mcds_.Clone()
with timing('Add weight to MC-dataset', logger=logger):
## 1b) add "weight" variable to dataset
mcds.add_reweighting(weighter, name='weight')
logger.info('MCDATA:\n%s' % mcds)
# =========================================================================
## 2) update weights
plots = [WeightingPlot('y:x', 'weight', '2D-reweight', hdata, hmc)]
if 2 < iter:
plots = [
WeightingPlot('x', 'weight', 'x-reweight', hxdata, hmcx),
WeightingPlot('y', 'weight', 'y-reweight', hydata, hmcy),
WeightingPlot('y:x', 'weight', '2D-reweight', hdata, hmc, 0.99),
]
with timing('Make one reweighting iteration:', logger=logger):
# =========================================================================
## 2a) the most important line: perform single iteration step
more = makeWeights(
mcds, ## what to be reweighted
plots, ## reweighting plots/setup
dbname, ## DBASE with reweigting constant
delta=0.04, ## stopping criteria
Variable('weight', 'weight', accessor=weighter)
]
#
## create new "weighted" mcdataset
#
selector = SelectorWithVars(variables, '0<x && x<100 ')
mctree.pprocess(selector, chunk_size=len(mctree) // 20)
##mctree.process ( selector )
mcds = selector.data ## new reweighted dataset
#
## update weights
#
plots = [WeightingPlot('x', 'weight', 'x-reweight', hdata, hmc)]
more = makeWeights(mcds, plots, dbname, delta=0.001)
## make MC-histogram
mcds.project(hmc, 'x', 'weight')
logger.info('Compare DATA and MC for iteration #%d' % iter)
#
## compare the basic properties: mean, rms, skewness and kurtosis
#
hdata.cmp_prnt(hmc, 'DATA', 'MC', 'DATA vs MC')
#
## calculate the distances
#
dist = hdata.cmp_dist(hmc, density=True)
#
## create new "weighted" mcdataset
#
selector = SelectorWithVars (
variables ,
'0<x && x<100 '
)
mctree.process ( selector )
mcds = selector.data ## new reweighted dataset
#
## update weights
#
plots = [
WeightingPlot( 'pt_x' , 'weight' , 'x-reweight' , hdata , hmc )
]
more = makeWeights ( mcds , plots , dbname , delta = 0.001 )
## make MC-histogram
mcds .project ( hmc , 'pt_x' , 'weight' )
logger.info ( 'Compare DATA and MC for iteration #%d' % iter )
#
## compare the basic properties: mean, rms, skewness and kurtosis
#
hdata.cmp_prnt ( hmc , 'DATA' , 'MC' , 'DATA vs MC' )
#
## calculate the distances
#
## 0) The weighter object
weighter = Weight(dbname, weightings)
# =========================================================================
## 1a) create new "weighted" mcdataset
mcds = mcds_.Clone()
with timing(tag + ': add weight to MC-dataset', logger=logger):
## 1b) add "weight" variable to dataset
mcds.add_reweighting(weighter, name='weight')
if 1 == iter % 10: logger.info((tag + ' MCDATA:\n%s') % mcds)
# =========================================================================
## 2) update weights
plots = [
WeightingPlot('x', 'weight', 'x-reweight', hxdata, hmcx),
WeightingPlot('y', 'weight', 'y-reweight', hydata, hmcy),
WeightingPlot('y:x', 'weight', '2D-reweight', hdata, hmc),
]
if iter <= 3: power = 0.75
elif iter <= 10:
power = lambda nactive: 1.5 / nactive if 1 < nactive else 0.90
elif iter <= 15:
power = lambda nactive: 1.3 / nactive if 1 < nactive else 0.90
else:
power = lambda nactive: 1.1 / nactive if 1 < nactive else 0.90
with timing(tag + ': make actual reweighting:', logger=logger):
# =========================================================================