def test_summary2():
conf = {
'label_position': 5.6,
'markersize': 1.2,
'text_size': 0.07,
} ## 'line_width' : 2 }
data = [
Limit(2.1, label='Belle', **conf),
Record(VE(3.4, 1.4**2), label='BaBar', **conf),
Record(VE(2.46, 0.64**2), 0.29, label='LHCb', color=2, **conf),
Record(2.18, (0.81, -0.77), label="Global fit", **conf),
Limit(0.59, label='BESIII', **conf),
]
if ROOT.gStyle:
ROOT.gStyle.SetEndErrorSize(5)
ROOT.gStyle.SetTickLength(0.008)
result = draw_summary(data, vmin=0, vmax=8)
if ROOT.gPad:
ROOT.gPad.RedrawAxis()
time.sleep(3)
def test_summary3():
conf = {
'label_position': 5.6,
'markersize': 1.2,
'text_size': 0.07,
} ## 'line_width' : 2 }
data = [
Point(0, **conf),
Point(+19, **conf),
Interval(-1, +19, **conf),
Limit(0, -40, arrow_size=0.01, arrow_style='|-|>', **conf),
Point(+35, **conf),
Point(+52, **conf),
Interval(-3, -1, **conf),
Point(-15, **conf),
Point(+91, **conf),
Point(+60, **conf),
Point(-79, **conf),
Point(+88, **conf),
Point(-215, **conf),
Interval(-70, +124, **conf),
Point(+102, **conf),
Record(VE(7, 12**2), **conf),
Limit(0, -40, arrow_size=0.01, arrow_style='|-|>', **conf),
Point(+100, **conf),
Record(VE(25, 90**2), **conf),
Point(-150, **conf),
Point(0, **conf),
Point(-23, **conf),
Point(+98, **conf),
Point(-149, **conf),
Record(-3, (+4, -15), **conf),
Point(+53, **conf),
Point(+166, **conf),
Point(+260, **conf),
Point(-182, **conf),
Interval(-250, +2, **conf),
Point(+13, **conf),
Point(+164, **conf),
]
if ROOT.gStyle:
ROOT.gStyle.SetEndErrorSize(5)
ROOT.gStyle.SetTickLength(0.008)
result = draw_summary(data, vmin=-300, vmax=300)
if ROOT.gPad:
ROOT.gPad.RedrawAxis()
time.sleep(3)
def test_summary1 () :
conf = { 'label_position' : 3861 , 'markersize' : 1.2 , 'text_size' : 0.05 , } ## 'line_width' : 2 }
ave = Average ( VE(3871.64,0.060**2) , label = 'New average' , **conf )
data = [
Record ( 3871.8 , 3.1 , 3.0 , label = 'D0' , **conf ) ,
Record ( 3873.0 , (+1.8,-1.6) , 1.3 , label = 'BaBar' , **conf ) ,
Record ( 3868.7 , 1.5 , 0.4 , label = 'BaBar-K^{0}' , **conf ) ,
Record ( 3871.4 , 0.6 , 0.1 , label = 'BaBar-K^{+}' , **conf ) ,
Record ( 3871.9 , 0.7 , 0.2 , label = 'BESIII' , **conf ) ,
Record ( 3871.95 , 0.48 , 0.12 , label = "LHCb'2010" , **conf ) ,
Record ( 3871.85 , 0.27 , 0.19 , label = 'Belle' , **conf ) ,
Record ( 3871.61 , 0.16 , 0.19 , label = 'CDF' , **conf ) ,
Record ( 3871.69 , 0.17 , label = "PDG\'2018" , **conf ) ,
Record ( 3871.70 , 0.067 , 0.068 , label = "LHCb'2020" , color = ROOT.kRed , **conf ) ,
Record ( 3871.59 , 0.060 , 0.030 , label = "LHCb'2020" , color = ROOT.kRed , **conf ) ,
ave ,
Record ( 3871.70 , 0.11 , label = 'm_{D^{0}}#plusm_{D^{*0}}' , **conf )
]
if ROOT.gStyle :
ROOT.gStyle.SetEndErrorSize (5 )
ROOT.gStyle.SetTickLength (0.008)
result = draw_summary ( data , average = ave , vmin = 3860 , vmax = 3877 , offset = 1.0 )
if ROOT.gPad :
ROOT.gPad.RedrawAxis()
time.sleep (3)
def __call__ ( self , s ) :
""" Calculate the weigth for the given ``event'' (==record in TTree/TChain or RooDataSet):
>>> weight = Weight ( ... )
>>> tree = ...
>>> w = weight ( tree )
"""
## initialize the weight
weight = VE(1,0)
## loop over functions
for i in self.__vars :
funval = i[1] ## accessor
functions = i[2] ## the functions
## get the weight arguments for given event
v = funval ( s )
ww = VE(1.0)
for f in functions :
if isinstance ( v , tuple ) : w = f ( *v )
else : w = f ( v )
ww *= w # update the weight factor
## keep the statistics
cnt = i[3]
cnt += ww.value()
## update the global weight
weight *= ww
vw = weight.value()
self.__counter += vw
if iszero ( vw ) : self.__nzeroes += 1
return vw
def __init__ ( self ,
dbase = "weights.db" , ## the name of data base with the weights
factors = [] ) :
#
## make some statistic
#
self.__counter = SE ()
self.__nzeroes = 0
self.__vars = []
if not factors : return
## open database
with DBASE.open ( dbase , 'r' ) as db : ## READONLY
for k in db :
e = db[k]
if hasattr ( e , '__len__' ) :
logger.debug( "DBASE ``%.15s'' key ``%.15s'' #%d" % ( dbase , k, len( e ) ) )
## loop over the weighting factors and build the function
for wvar in factors :
funval = wvar.accessor ## accessor to the variable
funname = wvar.address ## address in database
merge = wvar.merge ## merge sequence of callables?
skip = wvar.skip ## skip some of them?
if isinstance ( funval , str ) :
## funval = operator.attrgetter( funval )
funval = AttrGetter( funval )
##
functions = db.get ( funname , [] ) ## db[ funname ]
if not functions :
logger.warning("No reweighting is available for ``%s'', skip it" % funname )
continue
if not isinstance ( functions , ( list , tuple ) ) :
functions = [ functions ]
flen = len(functions)
if 0 < skip and skip < flen :
logger.info ("Use only %d first iterations for ``%s''" % ( skip , funname ) )
functions = functions[:skip]
elif 0 > skip and abs(skip) < flen :
logger.info ("Skip last %d iterations for ``%s''" % ( skip , funname ) )
functions = functions[:-1*skip]
elif 0 == skip :
pass
else :
logger.error("Invalid ``skip'' parameter %s/%d for ``%s''" % ( skip , flen , funname ) )
## nullify the uncertainties except for the last histogram
_functions = []
_first = True
for f in reversed ( functions ) :
if isinstance ( f , ROOT.TH1 ) and _first :
ff = f.clone()
for i in ff :
v = float ( ff[i] )
ff[i] = VE(v,0)
_functions.append ( ff )
_first = False
else :
_functions.append ( f )
_functions.reverse()
functions = _functions
## merge list of functions into single function
if merge and 1 < len ( functions) :
## single_func = functions[0] * functions [1]
single_func = MULT ( functions[0] , functions [1] )
for fun in functions [2:] :
## multiply it
## single_func *= fun
single_func = MULT ( single_func , fun )
functions = [ single_func ]
self.__vars += [ ( funname , funval , functions , SE() ) ]
self.__vars = tuple ( self.__vars )
def makeWeights(
dataset,
plots=[],
database="weights.db",
compare=None, ## comparison function
delta=0.01, ## delta for ``mean'' weight variation
minmax=0.03, ## delta for ``minmax'' weight variation
power=None, ## auto-determination
debug=True, ## save intermediate information in DB
make_plots=False, ## make plots
tag="Reweighting"):
"""The main function: perform one re-weighting iteration
and reweight ``MC''-data set to looks as ``data''(reference) dataset
>>> results = makeWeights (
... dataset , ## data source to be reweighted (DataSet, TTree, abstract source)
... plots , ## reweighting plots
... database , ## datadabse to store/update reweigting results
... delta , ## stopping criteria for `mean` weight variation
... minmax , ## stopping criteria for `min/max` weight variation
... power , ## effective power to apply to the weigths
... debug = True , ## store debuig information in database
... make_plots = True , ## produce useful comparison plots
... tag = 'RW' ) ## tag for better printout
If `make_plots = False`, it returns the tuple of active reweitings:
>>> active = makeWeights ( ... , make_plots = False , ... )
Otherwise it also returns list of comparison plots
>>> active, cmp_plots = makeWeights ( ... , make_plots = True , ... )
>>> for item in cmp_plots :
... what = item.what
... hdata = item.data
... hmc = item.mc
... hweight = item.weight
If no more rewighting iteratios required, <code>active</code> is an empty tuple
"""
assert 0 < delta, "makeWeights(%s): Invalid value for ``delta'' %s" % (
tag, delta)
assert 0 < minmax, "makeWeights(%s): Invalid value for ``minmax'' %s" % (
tag, minmax)
from ostap.logger.colorized import allright, attention, infostr
from ostap.utils.basic import isatty
nplots = len(plots)
## if 1 < nplots :
## import math
## fudge_factor = math.sqrt ( 1.0 / max ( 2.0 , nplots - 1.0 ) )
## delta = delta * fudge_factor
## minmax = minmax * fudge_factor
## list of plots to compare
cmp_plots = []
## reweighting summary table
header = ('Reweighting', 'wmin/wmax', 'OK?', 'wrms[%]', 'OK?', 'chi2/ndf',
'ww', 'exp')
rows = {}
save_to_db = []
## number of active plots for reweighting
for wplot in plots:
what = wplot.what ## variable/function to plot/compare
how = wplot.how ## weight and/or additional cuts
address = wplot.address ## address in database
hdata0 = wplot.data ## original "DATA" object
hmc0 = wplot.mc_histo ## original "MC" histogram
ww = wplot.w ## relative weight
projector = wplot.projector ## projector for MC data
ignore = wplot.ignore ## ignore for weigtht building?
#
# normalize the data
#
hdata = hdata0
if isinstance(hdata, ROOT.TH1): hdata = hdata.density()
# =====================================================================
## make a plot on (MC) data with the weight
# =====================================================================
hmc0 = projector(dataset, hmc0, what, how)
st = hmc0.stat()
mnmx = st.minmax()
if iszero(mnmx[0]):
logger.warning("%s: statistic goes to zero %s/``%s''" %
(tag, st, address))
elif mnmx[0] <= 0:
logger.warning("%s: statistic is negative %s/``%s''" %
(tag, st, address))
# =====================================================================
## normalize MC
# =====================================================================
hmc = hmc0.density()
# =====================================================================
## calculate the reweighting factor : a bit conservative (?)
# this is the only important line
# =====================================================================
# try to exploit finer binning if/when possible
hboth = isinstance(hmc, ROOT.TH1) and isinstance(hdata, ROOT.TH1)
if hboth and 1 == hmc.dim () and 1 == hdata.dim () and \
len ( hmc ) >= len( hdata ) :
w = (1.0 / hmc) * hdata ## NB!
elif hboth and 2 == hmc.dim () and 2 == hdata.dim () and \
( hmc.binsx() >= hdata.binsx() ) and \
( hmc.binsy() >= hdata.binsy() ) :
w = (1.0 / hmc) * hdata ## NB!
elif hboth and 3 == hmc.dim () and 3 == hdata.dim () and \
( hmc.binsx() >= hdata.binsx() ) and \
( hmc.binsy() >= hdata.binsy() ) and \
( hmc.binsz() >= hdata.binsz() ) :
w = (1.0 / hmc) * hdata ## NB!
else:
w = hdata / hmc ## NB!
# =====================================================================
## scale & get the statistics of weights
w /= w.stat().mean().value()
cnt = w.stat()
#
mnw, mxw = cnt.minmax()
wvar = cnt.rms() / cnt.mean()
good1 = wvar.value() <= delta
good2 = abs(mxw - mnw) <= minmax
good = good1 and good2 ## small variance?
#
c2ndf = 0
for i in w:
c2ndf += w[i].chi2(1.0)
c2ndf /= (len(w) - 1)
## build the row in the summary table
row = address , \
'%-5.3f/%5.3f' % ( cnt.minmax()[0] , cnt.minmax()[1] ) , \
allright ( '+' ) if good2 else attention ( '-' ) , \
(wvar * 100).toString('%6.2f+-%-6.2f') , \
allright ( '+' ) if good1 else attention ( '-' ) , '%6.2f' % c2ndf
## make plots at the start of each iteration?
if make_plots:
item = ComparisonPlot(what, hdata, hmc, w)
cmp_plots.append(item)
row = tuple(list(row) + ['%4.3f' % ww if 1 != ww else ''])
rows[address] = row
#
## make decision based on the variance of weights
#
mnw, mxw = cnt.minmax()
if (not good) and (not ignore): ## small variance?
save_to_db.append((address, ww, hdata0, hmc0, hdata, hmc, w))
# =====================================================================
## make a comparison (if needed)
# =====================================================================
if compare: compare(hdata0, hmc0, address)
active = tuple([p[0] for p in save_to_db])
nactive = len(active)
if power and callable(power):
eff_exp = power(nactive)
elif isinstance(power, num_types) and 0 < power <= 1.5:
eff_exp = 1.0 * power
elif 1 == nactive and 1 < len(plots):
eff_exp = 0.95
elif 1 == nactive:
eff_exp = 1.00
else:
eff_exp = 1.10 / max(nactive, 1)
while database and save_to_db:
entry = save_to_db.pop()
address, ww, hd0, hm0, hd, hm, weight = entry
cnt = weight.stat()
mnw, mxw = cnt.minmax()
## avoid too large or too small weights
for i in weight:
w = weight[i]
if w.value() < 0.5:
weight[i] = VE(0.5, w.cov2())
elif w.value() > 2.0:
weight[i] = VE(2.0, w.cov2())
if 1 < nactive and 1 != ww:
eff_exp *= ww
logger.info("%s: apply ``effective exponent'' of %.3f for ``%s''" %
(tag, eff_exp, address))
if 1 != eff_exp and 0 < eff_exp:
weight = weight**eff_exp
row = list(rows[address])
row.append('%4.3f' % eff_exp)
rows[address] = tuple(row)
with DBASE.open(database) as db:
db[address] = db.get(address, []) + [weight]
if debug:
addr = address + ':REWEIGHTING'
db[addr] = db.get(addr, []) + list(entry[2:])
del hd0, hm0, hd, hm, weight, entry
table = [header]
for row in rows:
table.append(rows[row])
import ostap.logger.table as Table
logger.info(
'%s, active:#%d \n%s ' %
(tag, nactive,
Table.table(table, title=tag, prefix='# ', alignment='lccccccc')))
cmp_plots = tuple(cmp_plots)
return (active, cmp_plots) if make_plots else active
def __init__(
self,
dbase="weights.db", ## the name of data base with the weights
factors=[]):
#
## make some statistic
#
self.__counter = SE()
self.__nzeroes = 0
self.__vars = []
if not factors: return
self.__dbase = dbase
## open database
self.__table = [('Reweighting', 'accessor', '#', 'merged?', 'skip')]
rows = []
with DBASE.open(dbase, 'r') as db: ## READONLY
logger.debug('Reweigting database: \n%s' % db.table(prefix='# '))
## loop over the weighting factors and build the function
for wvar in factors:
funval = wvar.accessor ## accessor to the variable
funname = wvar.address ## address in database
merge = wvar.merge ## merge sequence of callables?
skip = wvar.skip ## skip some of them?
row = []
row.append(funname)
if isinstance(funval, str):
row.append(funval)
## funval = operator.attrgetter( funval )
funval = AttrGetter(funval)
elif isinstance(funval, AttrGetter):
atts = funval.attributes
if 1 == len(atts): atts = atts[0]
row.append(str(atts))
else:
row.append('')
##
functions = db.get(funname, []) ## db[ funname ]
if not functions:
logger.warning(
"No reweighting is available for ``%s'', skip it" %
funname)
continue
if not isinstance(functions, (list, tuple)):
functions = [functions]
flen = len(functions)
if 0 < skip and skip < flen:
logger.info("Use only %d first iterations for ``%s''" %
(skip, funname))
functions = functions[:skip]
elif 0 > skip and abs(skip) < flen:
logger.info("Skip last %d iterations for ``%s''" %
(skip, funname))
functions = functions[:skip]
elif 0 == skip:
pass
else:
logger.error(
"Invalid ``skip'' parameter %s/%d for ``%s''" %
(skip, flen, funname))
row.append('%d' % flen)
## nullify the uncertainties except for the last histogram
_functions = []
_first = True
for f in reversed(functions):
if isinstance(f, ROOT.TH1) and _first:
ff = f.clone()
for i in ff:
v = float(ff[i])
ff[i] = VE(v, 0)
_functions.append(ff)
_first = False
else:
_functions.append(f)
_functions.reverse()
functions = _functions
row.append('+' if merge else '-')
row.append('%s' % skip)
## merge list of functions into single function
if merge and 1 < len(functions):
## single_func = functions[0] * functions [1]
single_func = MULT(functions[0], functions[1])
for fun in functions[2:]:
## multiply it
## single_func *= fun
single_func = MULT(single_func, fun)
functions = [single_func]
self.__vars += [(funname, funval, functions, SE())]
self.__table.append(row)
self.__vars = tuple(self.__vars)
from ostap.utils.cleanup import CleanUp
files = [
CleanUp.tempfile(prefix='ostap-test-trees-addbranch-%d-' % i,
suffix='.root') for i in range(nfiles)
]
for f in progress_bar(files):
create_tree(f, nentries)
return files
# =============================================================================
## let h2 be a weigth histogram
h2 = ROOT.TH2D('h2', '', 20, 0, 10, 15, 2, 5)
h2 += lambda x, y: VE(10 + x + y, (0.25 * (10 + x + y))**2)
cut = ROOT.TCut('abs(mass-3.1)<1*0.015')
# =============================================================================
## Add pseudoexepriments into TTree/TChain
def test_modify_initial_tree(NEXP=10):
"""Add pseudoexepriments into TTree/TChain
"""
files = prepare_data(1, 100000)
logger.info('Add %s pseudoexepriments into TTree/TChain' % NEXP)
logger.info('#files: %s' % len(files))
import ostap.utils.cleanup as CU
import ostap.io.zipshelve as zipshelve
import ostap.io.bz2shelve as bz2shelve
if 2 < python_version.major :
import ostap.io.lzshelve as lzshelve
else :
lzshelve = None
import ostap.io.sqliteshelve as sqliteshelve
import ostap.io.rootshelve as rootshelve
# =============================================================================
bins = 1000
data = {}
h1 = ROOT.TH1D('h1','1D-histogram',bins,-5,5) ; h1.Sumw2()
m1 = VE(1,2)
for i in range ( 0, 100000) : h1.Fill( m1.gauss() )
bins = 50
h2 = ROOT.TH2D('h2','2D-histogram',bins,-5,5,bins,-5,5) ; h2.Sumw2()
for i in range ( 0, 100000) : h2.Fill( m1.gauss() , m1.gauss() )
data [ 'histo-1D' ] = h1
data [ 'histo-2D' ] = h2
data [ 'both' ] = (123 , h1 , {'a':2}, h2,'comment',())
data [ 'histos' ] = {}
for i in range ( 5000 ) :
ht = 'histo#%d' % i
hh = ROOT.TH1D ( ht , '' , 500 , 0 , 100 )
for j in range ( 200 ) :
hh.Fill ( random.gauss ( 50 , 10) )
import ostap.io.zipshelve as zipshelve
import ostap.io.sqliteshelve as sqliteshelve
import ostap.io.rootshelve as rootshelve
# =============================================================================
db_sql_name = tempfile.mktemp(suffix='.msql')
db_zip_name = tempfile.mktemp(suffix='.zdb')
db_root_name = tempfile.mktemp(suffix='.root')
bins = 1000
data = {}
h1 = ROOT.TH1D('h1', '1D-histogram', bins, -5, 5)
h1.Sumw2()
m1 = VE(1, 2)
for i in range(0, 100000):
h1.Fill(m1.gauss())
bins = 50
h2 = ROOT.TH2D('h2', '2D-histogram', bins, -5, 5, bins, -5, 5)
h2.Sumw2()
for i in range(0, 100000):
h2.Fill(m1.gauss(), m1.gauss())
data['histo-1D'] = h1
data['histo-2D'] = h2
data['both'] = (123, h1, {'a': 2}, h2, 'comment')
def test_shelves():
