def drawAllTrainStacks(signal, bkg, data, labelCodes, weightsPerSampleA, weightsPerSampleB, corrWeightsA = DEFAULT, corrWeightsB = DEFAULT, subset = 'TrainA'):
"""Draw all train stacks for signal and bkg at once.
Keyword arguments:
signal -- the signal sample
bkg -- the background sample
labelCodes -- the label codes for the type of sample (W, Z for eg.)
weightsPerSample -- the XS weight
subset -- extra identifier for the filename (default TrainA)
"""
if corrWeightsA is DEFAULT:
corrWeightsA = hstack(signal.returnTestCorrectionWeights('A'), bkg.returnTestCorrectionWeights('A'))
if corrWeightsB is DEFAULT:
corrWeightsB = hstack(signal.returnTestCorrectionWeights('B'), bkg.returnTestCorrectionWeights('B'))
# store all histograms in output.root
for x in xrange (0, len(signal.train)):
if x == 0:
subset = 'A'
weightsPerSample = weightsPerSampleA
corrWeights = corrWeightsA
else:
subset = 'B'
weightsPerSample = weightsPerSampleB
corrWeights = corrWeightsB
f = ropen('outputTrain'+str(subset)+'.root','recreate')
c1 = Canvas()
c1.cd()
allStack = []
legendAllStack = []
# get sigA histograms
hist, histDictSigA, testAStack, legendSigStack = createHists(signal.returnTrainSample(subset), labelCodes, 'signal', signal.returnTrainSampleLabels(subset), weightsPerSample[0], signal.returnFoundVariables(), allStack, legendAllStack, corrWeights, str('Train'+subset), True)
# get bkgA histograms
# how to fix legends????
hist2, histDictBkgA, testAStackBkg, legendBkgStack = createHists(bkg.returnTrainSample(subset), labelCodes, 'bkg', bkg.returnTrainSampleLabels(subset), weightsPerSample[1], bkg.returnFoundVariables(), allStack, legendAllStack, corrWeights, str('Train'+subset), True)
for hist2idx in xrange(0,len(hist)):
legend = Legend(3)
legend.AddEntry(hist[hist2idx],'F')
legend.AddEntry(hist2[hist2idx],'F')
hist[hist2idx].draw('hist')
hist[hist2idx].Write()
hist2[hist2idx].draw('histsame')
hist2[hist2idx].Write()
legend.Draw('same')
c1.Write()
#c1.SaveAs(signal.returnFoundVariables()[hist2idx]+".png")
hist2idx+=1
drawStack(testAStack, legendSigStack, signal.returnFoundVariables(), 'Sig', str('Train'+subset)) # draw histograms
drawStack(testAStackBkg, legendBkgStack, bkg.returnFoundVariables(), 'Bkg', str('Train'+subset))
drawStack(allStack, legendAllStack, signal.returnFoundVariables(), 'All', str('Train' + subset))
f.close()
def make_cutflow_table(samples, args):
filters = None
db = datasets.Database(args.db)
# build table
cutflow_table = {}
data_index = []
used_samples = []
i = 0
for latex_name, text_name, sample in samples:
matched_samples = db.search(sample)
if not matched_samples:
if args.warn_missing:
log.warning("sample %s not found" % sample)
continue
raise datasets.NoMatchingDatasetsFound(sample)
total_cutflow = None
for ds in matched_samples:
log.info(ds.name)
for filename in ds.files:
log.debug(filename)
with ropen(filename) as rfile:
cutflow = rfile.cutflow_event
cutflow.SetDirectory(0)
if filters is None:
filters = [cutflow.GetXaxis().GetBinLabel(j + 1) for j in xrange(len(cutflow))]
# scale MC by lumi and xsec
if ds.datatype != datasets.DATA and ds.datatype != datasets.EMBED and not args.no_weight:
lumi = LUMI[args.year]
events = cutflow[1].value
xsec, kfact, effic = ds.xsec_kfact_effic
weight = 1E3 * lumi * xsec * kfact / (effic * events)
if args.verbose:
print '-' * 30
print ds.name
print "xsec: %f [nb]" % xsec
print "effic: %f" % effic
print "events: %d" % events
print "lumi: %f [1/pb]" % lumi
print "weight (1E3 * lumi * xsec / (effic * events)): %f" % weight
cutflow *= weight
if ds.datatype == datasets.DATA:
data_index.append(i)
if total_cutflow is None:
total_cutflow = cutflow
else:
total_cutflow += cutflow
if args.errors:
cutflow_table[i] = list(zip(total_cutflow.y(), total_cutflow.yerrh()))
else:
cutflow_table[i] = list(total_cutflow.y())
used_samples.append((latex_name, text_name, sample))
i += 1
cutflows = [cutflow_table[idx] for idx in xrange(i)]
return used_samples, filters, cutflows, data_index
def run(self):
# ignore sigterm signal and let parent process take care of this
signal.signal(signal.SIGINT, signal.SIG_IGN)
ROOT.gROOT.SetBatch()
os.nice(self.nice)
h = multilogging.QueueHandler(self.logging_queue)
# get the top-level logger
log_root = logging.getLogger()
# clear any existing handlers in the top-level logger
log_root.handlers = []
# add the queuehandler
log_root.addHandler(h)
if not self.gridmode:
# direct stdout and stderr to the local logger
sys.stdout = multilogging.stdout(log)
sys.stderr = multilogging.stderr(log)
try:
filename = 'student-%s-%s.root' % (self.name, self.uuid)
with ropen(os.path.join(
os.getcwd(), filename), 'recreate') as self.output:
ROOT.gROOT.SetBatch(True)
if self.queuemode:
log.info("Receiving files from Supervisor's queue")
else:
log.info(
"Received %i files from Supervisor for processing" %
len(self.files))
self.output.cd()
if self.profile:
profile_filename = 'student-%s-%s.profile' % (
self.name, self.uuid)
profile.runctx('self.work()',
globals=globals(),
locals=locals(),
filename=profile_filename)
self.output_queue.put(
(self.uuid,
[self.filters,
self.output.GetName(),
profile_filename]))
else:
self.work()
self.output_queue.put(
(self.uuid,
[self.filters, self.output.GetName()]))
except:
print sys.exc_info()
traceback.print_tb(sys.exc_info()[2])
self.output_queue.put((self.uuid, None))
self.output_queue.close()
self.logging_queue.close()
def run(self):
# ignore sigterm signal and let parent process take care of this
signal.signal(signal.SIGINT, signal.SIG_IGN)
ROOT.gROOT.SetBatch()
os.nice(self.nice)
h = multilogging.QueueHandler(self.logging_queue)
self.logger = logging.getLogger('Student')
self.logger.addHandler(h)
self.logger.setLevel(logging.DEBUG)
if not self.gridmode:
sys.stdout = multilogging.stdout(self.logger)
sys.stderr = multilogging.stderr(self.logger)
try:
filename = 'student-%s-%s.root' % (self.name, self.uuid)
with ropen(os.path.join(os.getcwd(), filename),
'recreate') as self.output:
ROOT.gROOT.SetBatch(True)
if self.queuemode:
self.logger.info("Receiving files from Supervisor's queue")
else:
self.logger.info(
"Received %i files from Supervisor for processing" %
len(self.files))
self.output.cd()
if self.profile:
profile_filename = 'student-%s-%s.profile' % (self.name,
self.uuid)
profile.runctx('self.work()',
globals=globals(),
locals=locals(),
filename=profile_filename)
self.output_queue.put((self.uuid, [
self.event_filters, self.object_filters,
self.output.GetName(), profile_filename
]))
else:
self.work()
self.output_queue.put((self.uuid, [
self.event_filters, self.object_filters,
self.output.GetName()
]))
except:
print sys.exc_info()
traceback.print_tb(sys.exc_info()[2])
self.output_queue.put((self.uuid, None))
self.output_queue.close()
self.logging_queue.close()
def test_attrs(self):
with ropen(self.temp_file_path) as f:
tree = f.tree
tree.use_cache()
tree.define_object('a', 'a_')
tree.define_collection('b', 'b_', 'b_n')
for event in tree:
# test a setattr before a getattr with caching
new_a_y = random()
event.a_y = new_a_y
assert_almost_equal(event.a_y, new_a_y)
assert event.a_x == event.a.x
assert len(event.b) > 0
def test_attrs(self):
with ropen(self.file_paths[0]) as f:
tree = f.tree
tree.read_branches_on_demand(True)
tree.define_object("a", "a_")
tree.define_collection("b", "b_", "b_n")
for event in tree:
# test a setattr before a getattr with caching
new_a_y = random()
event.a_y = new_a_y
assert_almost_equal(event.a_y, new_a_y)
assert_equals(event.a_x, event.a.x)
assert_equals(len(event.b) > 0, True)
def test_cuts(self):
with ropen(self.file_paths[0]) as f:
tree = f.tree
h1 = Hist(10, -1, 2)
h2 = Hist2D(10, -1, 2, 10, -1, 2)
h3 = Hist3D(10, -1, 2, 10, -1, 2, 10, -1, 2)
tree.draw("a_x", hist=h1)
assert_equals(h1.Integral() > 0, True)
tree.draw("a_x:a_y", hist=h2)
assert_equals(h2.Integral() > 0, True)
tree.draw("a_x:a_y:a_z", hist=h3)
assert_equals(h3.Integral() > 0, True)
h3.Reset()
tree.draw("a_x>0:a_y/2:a_z*2", hist=h3)
assert_equals(h3.Integral() > 0, True)
def run(self):
# ignore sigterm signal and let parent process take care of this
signal.signal(signal.SIGINT, signal.SIG_IGN)
ROOT.gROOT.SetBatch()
os.nice(self.nice)
h = multilogging.QueueHandler(self.logging_queue)
self.logger = logging.getLogger('Student')
self.logger.addHandler(h)
self.logger.setLevel(logging.DEBUG)
if not self.gridmode:
sys.stdout = multilogging.stdout(self.logger)
sys.stderr = multilogging.stderr(self.logger)
try:
filename = 'student-%s-%s.root' % (self.name, self.uuid)
with ropen(os.path.join(os.getcwd(), filename), 'recreate') as self.output:
ROOT.gROOT.SetBatch(True)
if self.queuemode:
self.logger.info("Receiving files from Supervisor's queue")
else:
self.logger.info("Received %i files from Supervisor for processing" % len(self.files))
self.output.cd()
if self.profile:
profile_filename = 'student-%s-%s.profile' % (self.name, self.uuid)
profile.runctx('self.work()', globals=globals(), locals=locals(), filename=profile_filename)
self.output_queue.put((self.uuid, [self.event_filters, self.object_filters, self.output.GetName(), profile_filename]))
else:
self.work()
self.output_queue.put((self.uuid, [self.event_filters, self.object_filters, self.output.GetName()]))
except:
print sys.exc_info()
traceback.print_tb(sys.exc_info()[2])
self.output_queue.put((self.uuid, None))
self.output_queue.close()
self.logging_queue.close()
def createTransformedBDT(bdt_in, classes, bdt_in_B, classes_B, name_A, name_B, bkg_name):
import HistoTransform_ext as ht
from numpy import argmax,argmin,ones,save
from numpy import transpose,bincount,array
from rootpy.interactive import wait
from rootpy.plotting import Canvas, Hist, Hist2D, Hist3D, Legend
from rootpy.io import root_open as ropen, DoesNotExist
from rootpy.plotting import HistStack
print 'saving files as npy'
save('bdt_output_scores_'+name_A+'.npy', bdt_in)
save('bdt_output_classes_'+name_A+'.npy', classes)
save('bdt_output_scores_'+name_B+'.npy', bdt_in_B)
save('bdt_output_classes_'+name_B+'.npy', classes_B)
#need to assign names that follow correct naming convention
return "Going to stop here for now until HistoTransform is fixed"
names = [ bkg_name+'_2tag2jet_mva', 'WlvH125_2tag2jet_mva']
name_x = [name_A, name_B]
for j in xrange(2):
bdtx_inFile_name = "bdt_inFile_"+name_x[j]+'_'+str(j)+"of2.root"
bdtx_inFile = ropen('bdt_inFile_'+name_x[j]+'_'+str(j)+'of2.root','recreate')
bdtxarr = []
for i in xrange(2):
bdtxarr.append(Hist (1000,-1,1, name=names[i], title=names[i]))
bdtxarr[i].fill_array(bdt_in[classes == i])
bdtxarr[i].Write()
bdtx_inFile.close()
for count in xrange(2):
#try:
bdtx_tx = ht.HistoTransform('bdt_inFile_'+name_x[count]+'_'+str(count)+'of2.root', "bdt_outFile_"+name_x[count]+'_'+str(count)+"of2.root")
#except:
# return "Could not create object"
bdtx_tx.transformBkgBDTs = False
bdtx_tx.doMergeKFolds = False
bdtx_tx.doTransformBeforeMerging = False
try:
subDir = bdtx_tx.addSubDirectory("")
except:
return "failed addsubdir"
if count == 1:
bdtx_tx.transformAlgorithm = 5
try:
bdtx_tx.setSignal(subDir, names[0])
except:
return "failed setsignal"
else:
bdtx_tx.transformAlgorithm = 1
try:
bdtx_tx.addBackground(subDir, bkg_name)#"bkg");
except:
return "Failed addBackground"
nFold = 2
maxUncFactor = 1
print 'starting run'
try:
bdtx_tx.addRegion(subDir, "2tag2jet_mva", 0.05 * maxUncFactor, nFold);
except:
return "addRegion failed"
try:
bdtx_tx.run()
return "it ran"
print 'done run ' + str(count) + ' for ' + name_x[count]
except:
print 'Error whilst running HistoTransform run() for ' + name_x[count]
return "Something Failed in .run()"
return "Success!"
#!/usr/bin/env python
# this import is required to register the Tree class
import rootpy.tree
from rootpy.io import root_open as ropen
from time import time
from ROOT import TTreeCache
import sys
for cached in (False, True):
try:
f = ropen("test.root")
except IOError:
sys.exit("test.root does not exist. Please run tree_write.py first.")
tree = f.test
if cached:
TTreeCache.SetLearnEntries(1)
tree.SetCacheSize(10000000)
tree.use_cache(cached)
start_time = time()
for event in tree:
event.x
end_time = time()
print "%.2fsec to read one branch" % (end_time - start_time)
start_time = time()
for event in tree:
event.x
float x;
double y;
};
""", ["Thingy"])
# alternatively you can ROOT.gSystem.Load() your library
# define the model
class Event(TreeModel):
event_number = IntCol()
thingy = ObjectCol(C.Thingy)
f = ropen("test.root", "recreate")
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(20):
tree.event_number = i
tree.thingy.i = i
tree.thingy.x = gauss(.3, 2.)
tree.thingy.y = gauss(13., 42.)
tree.fill()
tree.write()
f.close()
# now to read the same tree
with ropen("test.root") as f:
from rootpy.io import open as ropen
with ropen('file.root') as f:
myhist = f.somedirectory.histname
# recursively walk through the file
for path, dirs, objects in f.walk():
# do something
pass
import signals
import optfilter
import rootpy
rootpy.log.basic_config_colorized()
from random import gauss
from rootpy.io import open as ropen
from rootpy.tree import Tree, TreeChain
from rootpy.plotting import Hist
import numpy as np
import random
outfilename = 'ampInAmpOutHeatArbitraryNumpyFilter.root'
rootFileOut = ropen(outfilename, 'recreate')
tree = Tree("t")
branches = {
'amp_in':'F', 'amp_out': 'F', 'amp_out_maxtime': 'F'
}
tree.create_branches(branches)
numEvents = 1000
_pulselength = 512
_pulseStartTime = _pulselength/2
_tukey_alpha = 0.3
amps = [-10, -20, -50, -100, -200, -400, -500, -800, -1000, -1400, -1800, -2000]
#amps = [-100]
#signal
simpulse = signals.HeatSignal(length = _pulselength)
scaleFactor = -1.0*simpulse().min()
==============================
This example demonstrates how basic file operations are made easier in rootpy.
"""
print __doc__
import os
import shutil
import rootpy
rootpy.log.basic_config_colorized()
from rootpy.io import root_open as ropen, DoesNotExist
from rootpy.plotting import Hist, Hist2D
from rootpy import testdata
from rootpy import asrootpy
shutil.copyfile(testdata.get_filepath('test_file_2.root'), 'data.root')
f = ropen('data.root')
print f.a
print f.a.b
try:
print f.a.b.c.d.e.f
except DoesNotExist, e:
print e
for thing in f.walk():
print thing
f.close()
# supports with statements
This example demonstrates how to use the TreeChain class, a more Python-friendly
TChain replacement.
"""
print __doc__
import rootpy
rootpy.log.basic_config_colorized()
from random import gauss
from rootpy.io import root_open as ropen
from rootpy.tree import Tree, TreeChain
from rootpy.plotting import Hist
# Make two files, each with a Tree called "test"
print "Creating test tree in chaintest1.root"
f = ropen("chaintest1.root", "recreate")
tree = Tree("test")
branches = {
'x': 'F',
'y': 'F',
'z': 'F',
'i': 'I'}
tree.create_branches(branches)
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
from rootpy.io import root_open as ropen
from rootpy.plotting import Canvas, Hist, Efficiency, Legend
import rootpy
rootpy.log.basic_config_colorized()
eff_file = ropen("../data/LSB50/uct_tau_efficiency.root")
eff_file_low = ropen("../data/LSB50/uct_mc_efficiency_pu35.root")
eff_file_high = ropen("../data/LSB50/uct_mc_efficiency_pu50.root")
eff_ntuple = eff_file_high["rlxTauEfficiency/Ntuple"]
#binning = (25, 20, 70)
binning = (range(20, 32, 2) + range(32, 45, 1) + range(45, 55, 2) +
range(55, 70, 5), )
l1_pass_vs_pu = Hist(*binning)
uct_pass_vs_pu = Hist(*binning)
uct_iso_pass_vs_pu = Hist(*binning)
total_vs_pu = Hist(*binning)
eff_ntuple.Draw("nPVs", "recoPt > 40", hist=total_vs_pu)
eff_ntuple.Draw("nPVs",
"recoPt > 40 && l1gMatch && max(l1gPt, l1gRegionEt) > 25",
hist=uct_pass_vs_pu)
eff_ntuple.Draw("nPVs",
"recoPt > 40 && l1gMatch && max(l1gPt, l1gRegionEt) > 25"
" && l1gJetPt/max(l1gPt, l1gRegionEt) - 1 < 0.5",
hist=uct_iso_pass_vs_pu)
eff_ntuple.Draw("nPVs",
"recoPt > 40 && l1Match && l1Pt > 44",
tchi2posX = 0.07
tchi2posY = 0.75
h2costphiTitle = '%s, W = [%.3f,%.3f), Q^{2} = [%.3f,%.3f)'
h2costphiXtitle = '#phi^{*} (radians)'
h2costphiYtitle = 'cos(#theta^{*})'
#output data
oroot = 'out/ana_bd.root'
orecords = 'out/ana_bd.dat'
keyorder = ['Wbin', 'Q2bin', 'W', 'Wlo', 'Whi', 'Q2', 'Q2lo', 'Q2hi',
'weight3s', 'weightsb', 'fbgstat', 'fbgchi2', 'fsigstat', 'fsigchi2',
'fbgpar0', 'fbgpar1', 'fbgpar2', 'fbgpar3', 'fbgpar4', 'fbgpar5',
'fsigpar0', 'fsigpar1', 'fsigpar2', 'fsigpar3', 'hsigint3s']
# get histograms for simulation, acceptance, and experiment
fin_t = ropen(ifacc)
fin_e = ropen(ifexp)
h4_t, h4_a = fin_t.Get(ihthr), fin_t.Get(ihacc)
h6_e = fin_e.Get(ihexp)
# open output files
fout = ropen(oroot, 'recreate')
foutrecs = open(orecords, 'w')
# write data header
for i, k in enumerate(keyorder):
if i > 0:
foutrecs.write(',')
foutrecs.write(k)
foutrecs.write(',xsect,err,hcost\n')
# TODO: add cutoff at phase-space edge
Tree models and object collections
==================================
This example demonstrates how to define a tree model and collections of objects
associated to sets of tree branches.
"""
print __doc__
from rootpy.tree import Tree, TreeModel
from rootpy.io import root_open as ropen
from rootpy.types import FloatCol, IntCol
from rootpy.math.physics.vector import LorentzVector
from rootpy import stl
from random import gauss, randint
f = ropen("test.root", "recreate")
# define the model
class Event(TreeModel):
# properties of particle "a"
a_x = FloatCol()
a_y = FloatCol()
a_z = FloatCol()
# properties of particle "b"
b_x = FloatCol()
b_y = FloatCol()
b_z = FloatCol()
sys.exit(1)
channel = sys.argv[-2]
saveplot = str2bool(sys.argv[-1])
print sys.argv
mydirpath = 'results/2013-May-14-8TeV/FakeRates'
mydir = ''.join([mydirpath,channel,'/'])
print 'Analysing Fakerates in dir ', mydir
files = glob(mydir+'alldata.root')
##outputfiles = glob(mydir+'output.root')
##print outputfiles
print files
nfile =0
tfile=ropen(files[0])
dir=tfile.Get('qcd')
subdirlistQCD = GetContent(dir) #[ i for i in GetContent(dir) if i.GetName().startswith('pt10_') or i.GetName().startswith('pt20_')]
total_view = SumView( *[ropen(i) for i in files] )
qcd_view = SubdirectoryView(total_view, 'qcd')
if channel=='EE':
wjet_view = SubdirectoryView(total_view, 'wjetsNoZmass')
else:
wjet_view = SubdirectoryView(total_view, 'wjets')
for m,n in enumerate (subdirlistQCD):
thlistQCD = []
FindTH1( subdirlistQCD[m], '', thlistQCD)
from rootpy.io import root_open as ropen
from ..common import nprong, PRONGS, LEVELS
import os
HERE = os.path.dirname(os.path.abspath(__file__))
P1130_FILE = ropen(os.path.join(HERE, "ParametrizedBDTSelection.root"))
def selection(level, prong):
prong = nprong(prong)
return P1130_FILE.Get("%s_%dp" % (level, prong))
idx1 = self.xmapping[v1]
idx2 = self.ymapping[v2]
return self.matrix[idx1,idx2].value
wps = glob('plots/%s/ctageff/mass_discriminant/%s*' % (jobid, args.algo.lower()))
wp_mapping = { i:j for j, i in enumerate(['Loose', 'Medium', 'Tight'])}
sfs = ['bottomSF', 'charmSF', 'lightSF']
for wp in wps:
wp_name = os.path.basename(wp)[len(args.algo):]
wp_idx = wp_mapping[wp_name]
shifts = ['%s/MaxLikeFit.root' % wp, '%s/MaxLikeFitStatistic.root' % wp] + glob('%s/sys_breakdown/*.root' % wp)
first = True
stat_errs = {}
correlations = CorrelationMatrix(
rpy(ropen('%s/MaxLikeFit.root' % wp).fit_s.correlationHist())
)
for shift in shifts:
tfile = ropen(shift)
pars = rpy(tfile.fit_s.floatParsFinal())
for sf_idx, sf_name in enumerate(sfs):
if sf_name in pars:
var = pars[sf_name]
err = max(abs(i) for i in var.error) if hasattr(var.error, '__len__') else var.error
shift_name = ''
sign = +1
if 'sys_breakdown' in shift:
base = os.path.basename(shift)
shift_name = '_%s' % base.replace('.root', '')
sign = -1 if shift_name in correlations and correlations[sf_name, shift_name] < 0 else +1
elif shift.endswith('Statistic.root'):
"""
This first section of code only creates an example tree chain.
"""
class Event(TreeModel):
"""Event model definition"""
x = FloatCol()
y = FloatCol()
z = FloatCol()
i = IntCol()
# first create several example trees in separate files
fnames = ["test_%d.root" % i for i in xrange(10)]
for fname in fnames:
with ropen(fname, "recreate") as f:
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
"""
This section below takes the example trees and copies it while overwriting a
branch with new values.
ft.SetLineColor(r.kGreen+1)
ft.SetLineStyle(2)
#ft.Draw('same')
h.GetListOfFunctions().Add(ft)
fu = None
if uamp > 0:
fu = r.TF1('fu', '[0]*TMath::Exp(-[1]*x)', -1, 1)
fu.SetParameters(f.GetParameter(len(f.legs)+2), f.GetParameter(len(f.legs)+3))
fu.SetLineColor(r.kYellow+1)
fu.SetLineStyle(2)
#fu.Draw('same')
return (f, fl, ft, fu, h, status)
fin = ropen('top1/ana_bd.root')
df = pd.read_csv('top1/ana_bd.dat')
def intQ2(wmid=1890):
wstr = '_%d_' % wmid
hs = [fin.Get(hn) for hn in df.hcost[df.hcost.apply(lambda x: wstr in x)]]
dQ2 = df[df.hcost.apply(lambda x: wstr in x)][['Q2lo', 'Q2hi']]
dQ2 = dQ2.Q2hi - dQ2.Q2lo
# unscale, add, scale
for i, h in enumerate(hs): h.Scale(dQ2.iloc[i])
htot = hs[0].Clone(wstr)
for i in range(1, len(hs)): htot.Add(hs[i])
for i, h in enumerate(hs): h.Scale(1/dQ2.iloc[i])
htot.Scale(1/dQ2.sum())
import os
HERE = os.path.dirname(os.path.abspath(__file__))
CATEGORIES = {
'_3': Cut('tau_numberOfVertices<=3'),
'3_5': Cut('3<tau_numberOfVertices<=5'),
'5_7': Cut('5<tau_numberOfVertices<=7'),
'7_': Cut('tau_numberOfVertices>7'),
}
if __name__ == '__main__':
with ropen(os.path.join(HERE, 'bdt_selection.root'), 'recreate') as f:
for prong in PRONGS:
for cat_str, category in CATEGORIES.items():
for level_name, level in LEVELS.items():
fname = 'sig-bits-%dp-%s-perfB--%d.txt' % (
prong, category.safe(parentheses=False), level)
with open(fname) as fin:
lines = fin.readlines()[1:]
graph = Graph(len(lines), name='%s_%dp_%s' % (
level_name, prong, cat_str))
for i, line in enumerate(lines):
pt, bdt = map(float, line.strip().split())
graph[i] = (pt, bdt)
graph.Write()
else:
import pandas as pd
from rootpy.io import root_open as ropen
from rootpy.interactive import wait
import ROOT as r
from functools import reduce
r.gSystem.Load('libMathMore.so')
fin = ropen('out/ana_bd.root')
df = pd.read_csv('out/ana_bd.dat')
df1bin = df[(df.Wbin == 15) & (df.Q2bin == 5)]
hs = dict(zip(zip(df.Wbin, df.Q2bin), [fin.Get(hn) for hn in df.hcost]))
h = hs[(15, 5)]
def get_d_legs(legs):
lms = legs
def d_legs(v, par):
retval = reduce(lambda x, y: x + y, [par[i] * r.Math.assoc_legendre(
L, M, v[0]) for i, (L, M) in enumerate(lms)])
return retval
return d_legs
def get_f_legs(legs, par):
retf = r.TF1('flegs', get_d_legs(legs), -1, 1, len(legs))
for i in range(0, len(legs)):
retf.SetParameter(i, par[i])
return retf
from rootpy.io import root_open as ropen
from rootpy.plotting import Canvas, Hist, Efficiency, Legend
import rootpy
rootpy.log.basic_config_colorized()
eff_file = ropen("../data/LSB50/uct_tau_efficiency.root")
eff_file_low = ropen("../data/LSB50/uct_mc_efficiency_pu35.root")
eff_file_high = ropen("../data/LSB50/uct_mc_efficiency_pu50.root")
eff_ntuple = eff_file_high["rlxTauEfficiency/Ntuple"]
#binning = (25, 20, 70)
binning = (range(20, 32, 2) + range(32, 45, 1) + range(45, 55, 2)
+ range(55, 70, 5),)
l1_pass_vs_pu = Hist(*binning)
uct_pass_vs_pu = Hist(*binning)
uct_iso_pass_vs_pu = Hist(*binning)
total_vs_pu = Hist(*binning)
eff_ntuple.Draw("nPVs", "recoPt > 40", hist=total_vs_pu)
eff_ntuple.Draw(
"nPVs",
"recoPt > 40 && l1gMatch && max(l1gPt, l1gRegionEt) > 25",
hist=uct_pass_vs_pu)
eff_ntuple.Draw(
"nPVs",
"recoPt > 40 && l1gMatch && max(l1gPt, l1gRegionEt) > 25"
import ROOT
from rootpy.io import root_open as ropen
from rootpy.tree import Tree
f = ropen("test.root", "recreate")
print ROOT.gDirectory.GetName()
t = Tree()
f2 = ropen("test2.root", "recreate")
f2.Close()
print ROOT.gDirectory.GetName()
#f.cd() <== this should not be needed!
# the tree should "remember" what file it was created in
t.Write()
f.Close()
else:
raise ValueError("invalid working point: %s" % selection)
return passing / total
if __name__ == '__main__':
from samples import MC_TauID
from higgstautau.tauid.p851 import selection, nvtx_to_category, CATEGORIES
from higgstautau.tauid.common import LEVELS, PRONGS
from higgstautau.tauid import EFFIC_UNCERT_2011 as EFFIC_UNCERT
from rootpy.io import open as ropen
with ropen('bdt_uncertainty.root', 'recreate') as f:
ztautau = MC_TauID(systematics=False)
for prong in PRONGS:
for cat_str, category in CATEGORIES.items():
def binary_search(target, selection, shift, min_error=0.0001,
min_change=0.000001,
reverse=False):
print "target:", target
if reverse:
a = 1.
b = 0.
else:
a = 0.
b = 1.
print weightsPerSample
x1A = vstack((sigTestA, bkgTestA))
y1A = hstack((onesInt(len(sigTestA)), zerosInt(len(bkgTestA))))
y1A = transpose(y1A)
from rootpy.interactive import wait
from rootpy.plotting import Canvas, Hist, Hist2D, Hist3D, Legend
from rootpy.io import root_open as ropen, DoesNotExist
from rootpy.plotting import HistStack
import ROOT
ROOT.gROOT.SetBatch(True)
f = ropen('output.root','recreate')
c1 = Canvas()
c1.cd()
histDictSigA = {'W':[],'Z':[],'WW':[],'ZZ':[],'st':[],'ttbar':[],'WZ':[],'WH125':[]}
histDictBkgA = {'W':[],'Z':[],'WW':[],'ZZ':[],'st':[],'ttbar':[],'WZ':[],'WH125':[]}
coloursForStack = ['blue', 'green', 'red', 'yellow', 'black', 'pink', 'magenta', 'cyan']
colourDict = {'W':0,'Z':1,'WW':2,'ZZ':3,'st':4,'ttbar':5,'WZ':6,'WH125':7}
lblcount = 0
sigTestA = transpose(sigTestA)
bkgTestA = transpose(bkgTestA)
h = Hist(10, 0, 1)
def pyroot(tree):
tree.Draw('a_x', '', 'goff', hist=h)
v1 = tree.GetV1()
return set(v1[n] for n in xrange(tree.GetEntries()))
def rootpy(tree):
return np.unique(tree_to_ndarray(tree, branches=["a_x"]))
with ropen('test.root') as f:
tree = f.test
print "Trees has %i entries" % tree.GetEntries()
print
print "Using the ROOT/PyROOT way..."
cProfile.run('pyroot(tree)')
print "time without profiler overhead:"
t1 = time.time()
a = pyroot(tree)
t2 = time.time()
print "%f seconds" % (t2 - t1)
print
print '=' * 40
print
print "Using compiled C extension and numpy..."
if abs(tau.eta) > 1.5:
region = 'forward'
else:
region = 'central'
sf, sf_uncert = EFFIC_SF_2012[wp][np][region]
else:
raise ValueError(
'no efficiency scale factors defined for year %d' % year)
return sf, sf_uncert
HERE = os.path.dirname(os.path.abspath(__file__))
# uncertainty currently only valid for 2011 MC
BDT_UNCERT = {}
with ropen(os.path.join(HERE, 'bdt_uncertainty.root')) as f:
for level in LEVELS.keys():
BDT_UNCERT[level] = {}
for prong in PRONGS:
BDT_UNCERT[level][prong] = {}
for category in CATEGORIES.keys():
BDT_UNCERT[level][prong][category] = {}
for dir in ('high', 'low'):
BDT_UNCERT[level][prong][category][dir] = f.Get(
'%s_%s_%dp_%s' % (
level, dir, prong, category)).Clone()
def uncertainty(score, pt, prong, nvtx):
prong = nprong(prong)
sys.exit(1)
channel = sys.argv[-2]
saveplot = str2bool(sys.argv[-1])
print sys.argv
mydirpath = 'results/2013-May-14-8TeV/FakeRates'
mydir = ''.join([mydirpath, channel, '/'])
print 'Analysing Fakerates in dir ', mydir
files = glob(mydir + 'alldata.root')
##outputfiles = glob(mydir+'output.root')
##print outputfiles
print files
nfile = 0
tfile = ropen(files[0])
dir = tfile.Get('qcd')
subdirlistQCD = GetContent(
dir
) #[ i for i in GetContent(dir) if i.GetName().startswith('pt10_') or i.GetName().startswith('pt20_')]
total_view = SumView(*[ropen(i) for i in files])
qcd_view = SubdirectoryView(total_view, 'qcd')
if channel == 'EE':
wjet_view = SubdirectoryView(total_view, 'wjetsNoZmass')
else:
wjet_view = SubdirectoryView(total_view, 'wjets')
for m, n in enumerate(subdirlistQCD):
thlistQCD = []
from random import gauss
"""
This first section of code only creates an example tree.
"""
# define the model
class Event(TreeModel):
x = FloatCol()
y = FloatCol()
z = FloatCol()
i = IntCol()
# first create a tree "test" in a file "test.root"
f = ropen("test.root", "recreate")
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
"""
This section below takes the example tree and copies it while overwriting a
branch with new values.
from numpy import *
from rootpy.interactive import wait
from rootpy.plotting import Canvas, Hist, Hist2D, Hist3D, Legend
from rootpy.io import root_open as ropen, DoesNotExist
from rootpy.plotting import HistStack
import ROOT
import HistoTransform_ext as ht
print 'loading files from npy'
bdt_in = load('bdt_output_scores_A.npy')
classes = load('bdt_output_classes_A.npy')
bdt_in_B = load('bdt_output_scores_B.npy')
classes_B = load('bdt_output_classes_B.npy')
#need to assign names that follow correct naming convention
names = [ 'bkg_2tag2jet_mva', 'WlvH125_2tag2jet_mva']
bdtx_inFile_name = "bdt_inFile.root"
bdtx_inFile = ropen('bdt_inFile.root','recreate')
#for j in xrange(2):
#bdtx_inFile_name = "bdt_inFile_"+str(j)+"of2.root"
#bdtx_inFile = ropen('bdt_inFile_'+str(j)+'of2.root','recreate')
bdtxarr = []
bdtxarr.append([])
bdtxarr.append([])
for i in xrange(2):
for j in xrange(2):
bdtxarr[i].append(Hist (1000,-1,1, name=str(names[i]+'_' +str(j) + 'of2'), title=str(names[i]+'_' +str(j) + 'of2')))
bdtxarr[i][j].fill_array(bdt_in[classes == i])
bdtxarr[i][j].Write()
bdtx_inFile.close()
