## 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
minmax=0.08, ## stopping criteria
power=2 if 1 != len(plots) else 1, ## tune: effective power
tag="Reweight/%d" % iter) ## tag for printout
with timing('Project weighted MC-dataset:', logger=logger):
# =========================================================================
## 3) make MC-histograms
mcds.project(hmcx, 'x', 'weight')
mcds.project(hmcy, 'y', 'weight')
mcds.project(hmc, 'y:x', 'weight')
with timing('Compare DATA and MC distributions:', logger=logger):
# ==============================================================================
## 4) compare "Data" and "MC" after the reweighting on the given iteration
logger.info('Compare DATA and MC for iteration #%d' % iter)
logger.info ('MCDATA: %s' % mcds )
#
## update weights
#
plots = [ WeightingPlot ( 'y:x' , 'weight' , '2D-reweight' , hdata , hmc ) ]
if 5 <= iter:
plots = [
WeightingPlot ( 'x' , 'weight' , 'x-reweight' , hxdata , hmcx ) ,
WeightingPlot ( 'y' , 'weight' , 'y-reweight' , hydata , hmcy ) ,
WeightingPlot ( 'y:x' , 'weight' , '2D-reweight' , hdata , hmc , 0.5 ) ,
]
## more iteration? number of ``active'' reweightings
more = makeWeights ( mcds , plots , dbname , delta = 0.01 , power = 2 if 1 != len(plots) else 1 )
## make MC-histogram
mcds .project ( hmcx , 'x' , 'weight' )
mcds .project ( hmcy , 'y' , 'weight' )
mcds .project ( hmc , 'y:x' , 'weight' )
logger.info ( 'Compare DATA and MC for iteration #%d' % iter )
#
## compare the basic properties: mean, rms, skewness and kurtosis
#
hxdata.cmp_prnt ( hmcx , 'DATA' , 'MC' , 'DATA(x) vs MC(x)' )
hydata.cmp_prnt ( hmcy , 'DATA' , 'MC' , 'DATA(y) vs MC(y)' )
#
## calculate the distances
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
#
dist = hdata.cmp_dist ( hmc , density = True )
logger.info ('DATA-MC "distance" %s' % dist )
#
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):
# =========================================================================
## 2a) the most important line: perform single iteration step
active = makeWeights(
mcds, ## what to be reweighted
plots, ## reweighting plots/setup
dbname, ## DBASE with reweigting constant
delta=0.01, ## stopping criteria
minmax=0.05, ## stopping criteria
power=power, ## tune: effective power
make_plots=False,
tag=tag) ## tag for printout
with timing(tag + ': project weighted MC-dataset:', logger=logger):
# =========================================================================
## 3) make MC-histograms
mcds.project(hmcx, 'x', 'weight')
mcds.project(hmcy, 'y', 'weight')
mcds.project(hmc, 'y:x', 'weight')
rows = []
with timing(tag + ': compare DATA and MC distributions:', logger=logger):
mcds = selector.data ## dataset
with timing('Add weight to MC-dataset', logger=logger):
## 1b) add "weight" variable to the dataset
mcds.add_reweighting(weighter, name='weight')
logger.info('MCDATA:\n%s' % mcds)
with timing('Make one reweighting iteration:', logger=logger):
# ==============================================================================
## 2) update weights
plots = [WeightingPlot('x', 'weight', 'x-reweight', hdata, hmc)]
more = makeWeights(mcds,
plots,
dbname,
delta=0.001,
tag='Reweight/%s' % iter)
with timing('Project weighted MC-dataset:', logger=logger):
# ==============================================================================
## 3) make MC-histogram
mcds.project(hmc, 'x', 'weight')
with timing('Compare DATA and MC distributions:', logger=logger):
# ==============================================================================
## 4) compare "Data" and "MC" after the reweighting on the given iteration
logger.info('Compare DATA and MC for iteration #%d' % iter)
hh = 'Iteration#%d: ' % iter
with timing(tag + ': add weight to MC-dataset', logger=logger):
## 1b) add "weight" variable to the dataset
mcds.add_reweighting(weighter, name='weight')
if 1 == iter % 10: logger.info((tag + ' MCDATA:\n%s') % mcds)
with timing(tag + ': make actual reweighting:', logger=logger):
# ==============================================================================
## 2) update weights
plots = [WeightingPlot('x', 'weight', 'x-reweight', hdata, hmc)]
more = makeWeights(
mcds,
plots,
dbname,
delta=0.005,
minmax=0.01,
power=1.05, ## tiny ``overreweighting''
make_plots=False,
tag=tag)
with timing(tag + ': project weighted MC-dataset:', logger=logger):
# ==============================================================================
## 3) make MC-histogram
mcds.project(hmc, 'x', 'weight')
with timing(tag + ': compare DATA and MC distributions:', logger=logger):
# ==============================================================================
## 4) compare "Data" and "MC" after the reweighting on the given iteration
logger.info(tag + ': compare DATA and MC for iteration #%d' % iter)