def test_kisa2 () :
from ostap.fitting.selectors import SelectorWithVars, Variable
variables = [
Variable ( 'mass' , 'mass(mu+mu-)' , 3.09 , 3.11 ) ,
Variable ( 'c2dtf' , 'chi2(dtf)' , -1 , 10 ) ,
Variable ( 'mass2' , 'mass(mu+mu-)' , 1 , 5 , MASS() ) ,
Variable ( 'mass3' , 'mass(mu+mu-)' , 1 , 5 , 'mass' )
]
from sys import version_info as python_version
if python_version.major < 3 :
variables.append ( Variable ( 'mass1' , 'mass(mu+mu-)' , 2 , 4 , lambda s : s.mass ) )
ppservers = () ## 'lxplus051' , )
## ppservers = 'auto'
nf = len ( data.files )
nf //= 40
nf += 1
nf = min ( nf , 25 )
with timing('%d files in sequence %s' % ( nf , len( data.chain ) ) ) :
selector = SelectorWithVars (
variables = variables ,
selection = '2<=mass && mass<4 && 0<=c2dtf && c2dtf<5' ,
silence = False
)
chain = data.chain[:nf]
st = chain.process ( selector , silent = False , shortcut = True )
ds = selector.data
del selector
logger.info ( 'Dataset:\n%s' % ds )
with timing('%s files in parallel %s' % ( len ( data.files ) , len( data.chain ) ) ) :
selector = SelectorWithVars (
variables = variables ,
selection = '2<=mass && mass<4 && 0<=c2dtf && c2dtf<5' ,
silence = True
)
st = data.chain.pprocess ( selector ,
silent = False ,
chunk_size = -1 ,
max_files = 1 ,
ppservers = ppservers )
ds = selector.data
del selector
logger.info ( 'Dataset:\n%s' % ds )
def process(self, jobid, item):
import ROOT
from ostap.logger.logger import logWarning
with logWarning():
import ostap.core.pyrouts
import ostap.trees.trees
## reconstruct chain from the item
chain = item.chain
ll = len(chain)
first = item.first
nevents = item.nevents
all = 0 == first and (nevents < 0 or ll <= nevents)
if self.trivial and all:
import ostap.fitting.selectors
self.__output = chain.make_dataset(self.variables,
self.selection,
silent=True)
return self.__output
from ostap.fitting.selectors import SelectorWithVars
## use selector
selector = SelectorWithVars(self.variables,
self.selection,
silence=True)
args = ()
if not all: args = nevents, first
num = chain.process(selector,
*args,
shortcut=all and self.trivial,
use_frame=self.use_frame)
self.__output = selector.data, selector.stat
if num < 0:
logger.warning("Processing status %s (jobid #%s)" % (num % jobid))
##del selector.data
##del selector
logger.debug('Processed %s and filled %d entries (jobid #%s)' %
(item, len(self.__output), jobid))
return self.__output
## configuration of reweighting
weightings = (
## variable address in DB
Weight.Var('x', 'x-reweight'),
Weight.Var('y', 'y-reweight'),
Weight.Var(('x', 'y'), '2D-reweight'),
)
# =============================================================================
## variables to be used in MC-dataset
variables = [
Variable('x', 'x-var', 0, 20),
Variable('y', 'y-var', 0, 15),
]
selector = SelectorWithVars(variables,
'0<x && x<20 && 0<y && y<20',
silence=True)
mctree.process(selector, silent=True)
mcds_ = selector.data ## dataset
# =============================================================================
## start reweighting iterations:
for iter in range(1, maxIter + 1):
logger.info(allright('Reweighting iteration %d ' % iter))
with timing('Prepare MC-dataset:', logger=logger):
# =========================================================================
## 0) The weighter object
weighter = Weight(dbname, weightings)
# =========================================================================
Weight.Var ( lambda s : (s.x,s.y) , '2D-reweight' ) ,
)
weighter = Weight( dbname , weightings )
## variables to be used in MC-dataset
variables = [
Variable( 'x' , 'x-var' , 0 , 20 , lambda s : s.x ) ,
Variable( 'y' , 'y-var' , 0 , 15 , lambda s : s.y ) ,
Variable( 'weight' , 'weight' , accessor = weighter )
]
#
## create new "weighted" mcdataset
#
selector = SelectorWithVars (
variables ,
'0<x && x<20 && 0<y && y<20'
)
mctree.process ( selector )
mcds = selector.data ## new reweighted dataset
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 ) ,
## variable address in DB
Weight.Var( accessor = lambda s : s.x , address = 'x-reweight' ) ,
)
weighter = Weight( dbname , weighting )
## variables to be used in MC-dataset
variables = [
Variable ( 'pt_x' , 'pt_x' , 0 , 100 , lambda s : s.x ) ,
Variable ( 'weight' , 'weight' , accessor = weighter )
]
#
## 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
## method = reader[m]
## logger.info ( 'Method %12s , response %s' % ( m , method ( 1.1 , 0.8 , 0.3 ) ) )
## del method
## # =============================================================================
## ## 2.2) declare/add TMVA variables with TMVAreader
## for m in methods :
## variables += [ Variable( 'tmva_%s' % m , 'TMVA(%s)' % m , accessor = reader[m] ) ]
## # =============================================================================
## ## The END of TMVA.Reader fragment
## # =============================================================================
## 3) Run Ostap to fill RooDataSet
dsS = SelectorWithVars (
variables = variables ,
selection = "var1 < 100" ,
)
dsB = SelectorWithVars (
variables = variables ,
selection = "var1 < 100" ,
)
## read input data file
with ROOT.TFile.Open( data_file ,'READ') as datafile :
datafile.ls()
tSignal = datafile['S']
tBkg = datafile['B']
from ostap.utils.timing import timing
with timing("Process signal" ) : tSignal.process ( dsS )
#Variable( 'var1' , 'variable#1' , accessor = lambda s : s.var1 ) ,
#Variable( 'var2' , 'variable#2' , accessor = lambda s : s.var2 ) ,
#Variable( 'var3' , 'variable#3' , accessor = lambda s : s.var3 ) ,
Variable('var1', 'variable#1'),
Variable('var2', 'variable#2'),
Variable('var3', 'variable#3'),
]
## ## 3) declare/add TMVA variables
## for m in methods :
## variables += [ Variable( 'tmva_%s' % m , 'TMVA(%s)' % m , accessor = reader[m] ) ]
## 4) Run Ostap to fill RooDataSet
dsS = SelectorWithVars(
variables=
variables, ## + [ Variable ( 'signal' , 'signal' , -1 , 3 , lambda s : 1 ) ] ,
selection="var1 < 100",
)
dsB = SelectorWithVars(
variables=
variables, ## + [ Variable ( 'signal' , 'signal' , -1 , 3 , lambda s : 0 ) ] ,
selection="var1 < 100",
)
## read input data file
with ROOT.TFile.Open(data_file, 'READ') as datafile:
datafile.ls()
tSignal = datafile['S']
tBkg = datafile['B']
# =============================================================================
## start iterations:
for iter in range(1, maxIter + 1):
logger.info(allright('Reweighting iteration %d ' % iter))
with timing('Prepare MC-dataset:', logger=logger):
# =============================================================================
## 0) The weighter object
weighter = Weight(dbname, weighting)
# ===============================================================================
## 1a) create mcdataset
selector = SelectorWithVars(variables, '0<x && x<100 ', silence=True)
mctree.process(selector, silent=True)
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,