sign = ['ss']
jets = [0, 1, 2, 3]
processtype=['gg']
threshold=['ept30']
outputdir = 'plots/%s/ControlFakeTau/%s/' % (jobid, channel)
if not os.path.exists(outputdir):
os.makedirs(outputdir)
def remove_name_entry(dictionary):
return dict( [ i for i in dictionary.iteritems() if i[0] != 'name'] )
plotter = BasePlotter(channel,files, lumifiles, outputdir)
EWKDiboson = views.StyleView(
views.SumView(
*[ plotter.get_view(regex) for regex in \
filter(lambda x : x.startswith('WW') or x.startswith('WZ') or x.startswith('ZZ') or x.startswith('WG'), mc_samples )]
), **remove_name_entry(data_styles['WW*'#,'WZ*', 'WG*', 'ZZ*'
])
)
Wplus = views.StyleView(views.SumView( *[ plotter.get_view(regex) for regex in filter(lambda x : x.startswith('Wplus'), mc_samples )]), **remove_name_entry(data_styles['Wplus*Jets*']))
DYLL = views.StyleView(views.SumView( *[ plotter.get_view(regex) for regex in filter(lambda x : x.endswith('skimmedLL'), mc_samples )]), **remove_name_entry(data_styles['Z*jets*LL']))
DYTT = views.StyleView(views.SumView( *[ plotter.get_view(regex) for regex in filter(lambda x : x.endswith('jets_M50_skimmedTT'), mc_samples )]), **remove_name_entry(data_styles['Z*jets*TT']))
TT = views.StyleView(views.SumView( *[ plotter.get_view(regex) for regex in filter(lambda x : x.startswith('TT') , mc_samples)]), **remove_name_entry(data_styles['TTJets*']))
singleT = views.StyleView(views.SumView( *[ plotter.get_view(regex) for regex in filter(lambda x : x.startswith('T_') or x.startswith('Tbar_'), mc_samples)]), **remove_name_entry(data_styles['T*_t*']))
SMH = views.StyleView(views.SumView( *[ plotter.get_view(regex) for regex in filter(lambda x : 'HToTauTau' in x , mc_samples)]), **remove_name_entry(data_styles['GluGluToHToTauTau*']))
from BasePlotter import BasePlotter
from rootpy.io import root_open
import rootpy.plotting.views as views
from ROOT import TProfile
import binning
from pdb import set_trace
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('input', help='input root file')
parser.add_argument('out', help='output dir')
args = parser.parse_args()
tfile = root_open(args.input)
plotter = BasePlotter(
args.out,
defaults={'save':{'pdf' : False}},
)
b_plots = views.StyleView(
views.TitleView(views.SubdirectoryView(tfile, 'B'), 'B'),
linewidth=2,
linecolor='#2aa198',
legendstyle='l',
drawstyle='hist'
)
c_plots = views.StyleView(
views.TitleView(views.SubdirectoryView(tfile, 'C'), 'C'),
linewidth=2,
linecolor='#0055ff',
legendstyle='l',
drawstyle='hist',
def __init__(self, outputDir):
BasePlotter.__init__(self, outputDir)
def initPlotting(self, filePatterns, measurementResult):
BasePlotter.initPlotting(self, filePatterns, measurementResult)
self._createWriters()
self._writeHeader()
def terminate(self):
BasePlotter.terminate(self)
period = '8TeV'
sqrts = 7 if '7TeV' in jobid else 8
def remove_name_entry(dictionary):
return dict([i for i in dictionary.iteritems() if i[0] != 'name'])
sign = ['os']
jets = [0, 1, 2, 3]
outputdir = 'plots/%s/EEAnalyzerMVA/%s/' % (jobid, channel)
if not os.path.exists(outputdir):
os.makedirs(outputdir)
plotter = BasePlotter(files, lumifiles, outputdir)
EWKDiboson = views.StyleView(
views.SumView(
*[ plotter.get_view(regex) for regex in \
filter(lambda x : x.startswith('WW') or x.startswith('WZ') or x.startswith('ZZ') or x.startswith('WG'), mc_samples )]
), **remove_name_entry(data_styles['WW*'#,'WZ*', 'WG*', 'ZZ*'
])
)
Wplus = views.StyleView(
views.SumView(*[
plotter.get_view(regex)
for regex in filter(lambda x: x.startswith('Wplus'), mc_samples)
def initPlotting(self, filePatterns, measurementResult):
BasePlotter.initPlotting(self, filePatterns, measurementResult)
if len(filePatterns) != 2:
raise Exception(
'DiffSampleDiagramPlotter needs exactly two file pattern')
channel = 'et'
import rootpy.plotting.views as views
ROOT.gROOT.SetStyle("Plain")
ROOT.gROOT.SetBatch(True)
ROOT.gStyle.SetOptStat(0)
print "\nPlotting %s for %s\n" % (channel, jobid)
#check if blind
blind = 'blind' not in os.environ or os.environ['blind'] == 'YES'
blind_region=[100, 150] if blind else None
embedded = True
plotter = BasePlotter(blind_region,use_embedded=embedded)
if not args.no_plots:
signs = ['os','ss']
jets = ['0','1','2']
processtype = ['gg']
threshold = ['ept30']
histo_info = [
('tPt', 'p_{T}(#tau) (GeV)', 1),
('tEta', '#eta(#tau)', 1),
('tPhi', '#phi(#tau)', 1),
('ePt', 'p_{T}(e) (GeV)', 1),
('eEta', '#eta(e)', 1),
('ePhi', '#phi(e)', 1),
('e_t_DPhi', 'e#tau #Delta#phi', 1),
def initPlotting(self, filePatterns, measurementResult):
BasePlotter.initPlotting(self, filePatterns, measurementResult)
from rootpy.io import root_open
import rootpy.plotting.views as views
from ROOT import TProfile
import binning
from pdb import set_trace
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('input', help='input root file')
parser.add_argument('out', help='output dir')
args = parser.parse_args()
tfile = root_open(args.input)
plotter = BasePlotter(
args.out,
defaults={'save': {
'pdf': False
}},
)
b_plots = views.StyleView(views.TitleView(views.SubdirectoryView(tfile, 'B'),
'B'),
linewidth=2,
linecolor='#2aa198',
legendstyle='l',
drawstyle='hist')
c_plots = views.StyleView(
views.TitleView(views.SubdirectoryView(tfile, 'C'), 'C'),
linewidth=2,
linecolor='#0055ff',
legendstyle='l',
drawstyle='hist',
channel = 'et'
import rootpy.plotting.views as views
ROOT.gROOT.SetStyle("Plain")
ROOT.gROOT.SetBatch(True)
ROOT.gStyle.SetOptStat(0)
print "\nPlotting %s for %s\n" % (channel, jobid)
#check if blind
blind = 'blind' not in os.environ or os.environ['blind'] == 'YES'
blind_region=[100, 150] if blind else None
embedded = False
plotter = BasePlotter(blind_region,use_embedded=embedded)
cuts= [
['selected', 'tPt30', 'tPt35', 'tPt40', 'tPt45','tPt50','tPt60','tPt70','tPt80','tPt90',
'ePt30', 'ePt35', 'ePt40', 'ePt45','ePt50','ePt60','ePt70','ePt80','ePt90',
'dphi0.50','dphi1.00','dphi1.50','dphi2.20','dphi2.40','dphi2.70', 'dphi3.00',
'tMtToPfMet5','tMtToPfMet10','tMtToPfMet20','tMtToPfMet30','tMtToPfMet35','tMtToPfMet40', 'tMtToPfMet50', 'tMtToPfMet60', 'tMtToPfMet70' , 'tMtToPfMet80', 'tMtToPfMet90' ],
['selected', 'tPt30', 'tPt35', 'tPt40', 'tPt45','tPt50','tPt60','tPt70','tPt80','tPt90',
'ePt30', 'ePt35', 'ePt40', 'ePt45', 'ePt50','ePt60','ePt70','ePt80','ePt90',
'tMtToPfMet5','tMtToPfMet10','tMtToPfMet20','tMtToPfMet30','tMtToPfMet35','tMtToPfMet40' , 'tMtToPfMet50', 'tMtToPfMet60', 'tMtToPfMet70' , 'tMtToPfMet80', 'tMtToPfMet90'],
['selected', 'tPt30', 'tPt35', 'tPt40', 'tPt45','tPt50','tPt60','tPt70','tPt80','tPt90',
'ePt30', 'ePt35', 'ePt40', 'ePt45', 'ePt50','ePt60','ePt70','ePt80','ePt90',
'tMtToPfMet5','tMtToPfMet10','tMtToPfMet20','tMtToPfMet30','tMtToPfMet35','tMtToPfMet40', 'tMtToPfMet50', 'tMtToPfMet60', 'tMtToPfMet70', 'tMtToPfMet80', 'tMtToPfMet90',
'vbf_mass200', 'vbf_mass300', 'vbf_mass400', 'vbf_mass450', 'vbf_mass500', 'vbf_mass550', 'vbf_mass600', 'vbf_mass700', 'vbf_mass800', 'vbf_mass900',
'vbf_deta2.0','vbf_deta2.5','vbf_deta3.0','vbf_deta3.5', 'vbf_deta4.0' ]