def get_dataset(data_name, varname, max_entries = -1, option = 'merged'):
tree = trees.get(data_name, option)
cfg = config_map[varname]
datasets = []
## Get a dataset for each expression-selection pair
for expr, cuts in zip(cfg.expressions, cfg.selections):
if hasattr(cfg, 'binning') and len(cfg.binning.split(',')) == 3:
nbins, varmin, varmax = map(float, cfg.binning.split(','))
variable = ROOT.RooRealVar(cfg.name, expr, varmin, varmax)
variable.setBins(int(nbins))
else:
variable = ROOT.RooRealVar(cfg.name, expr)
cuts = [cuts]
if max_entries > 0:
cuts.append('Entry$ < %d' % max_entries)
dataset = datasetly.get(tree=tree, variable=variable, cuts=cuts)
variable = dataset.get().first()
variable.SetTitle(cfg.title)
variable.setUnit(cfg.unit)
datasets.append(dataset)
## End of loop over expressions and selections
dataset = datasets[0]
for further_dataset in datasets[1:]:
dataset.append(further_dataset)
return dataset
def get_dataset(data_name, varname, max_entries=-1, option='merged'):
tree = trees.get(data_name, option)
cfg = config_map[varname]
datasets = []
## Get a dataset for each expression-selection pair
for expr, cuts in zip(cfg.expressions, cfg.selections):
if hasattr(cfg, 'binning') and len(cfg.binning.split(',')) == 3:
nbins, varmin, varmax = map(float, cfg.binning.split(','))
variable = ROOT.RooRealVar(cfg.name, expr, varmin, varmax)
variable.setBins(int(nbins))
else:
variable = ROOT.RooRealVar(cfg.name, expr)
cuts = [cuts]
if max_entries > 0:
cuts.append('Entry$ < %d' % max_entries)
dataset = datasetly.get(tree=tree, variable=variable, cuts=cuts)
variable = dataset.get().first()
variable.SetTitle(cfg.title)
variable.setUnit(cfg.unit)
datasets.append(dataset)
## End of loop over expressions and selections
dataset = datasets[0]
for further_dataset in datasets[1:]:
dataset.append(further_dataset)
return dataset
def __init__(self,
name,
varname,
option,
max_entries,
prescale,
prescale_phase=0):
print 'DEBUG', self.__class__.__name__, '__init__'
if prescale > 1:
msg = ', '.join(
['max_entries=%d' % max_entries,
'prescale=%d' % prescale])
raise RuntimeError, 'Illegal arguments ' + msg
self.name = name
self.varname = varname
#self.max_entries = max_entries
#self.option = self.option
tree = trees.get(name, option)
cfg = config_map[varname]
datasets = []
## Get a dataset for each expression-selection pair
for expr, cuts in zip(cfg.expressions, cfg.selections):
if hasattr(cfg, 'qqbinning') and len(
cfg.qqbinning.split(',')) == 3:
nbins, varmin, varmax = map(float, cfg.qqbinning.split(','))
variable = ROOT.RooRealVar(cfg.name, expr, varmin, varmax)
variable.setBins(int(nbins))
else:
variable = ROOT.RooRealVar(cfg.name, expr, 0.)
cuts = [cuts]
## Adds an appropriate prescale
if max_entries > 0:
all_entries = float(tree.GetEntries())
prescale = ROOT.TMath.CeilNint(all_entries / max_entries)
if prescale > 1:
cut = 'Entry$ %% %d == %d' % (prescale, prescale_phase)
print 'Prescaling %s: %s' % (name, cut)
cuts.append(cut)
dataset = datasetly.get(tree=tree, variable=variable, cuts=cuts)
variable = dataset.get().first()
variable.SetTitle(cfg.title)
variable.setUnit(cfg.unit)
datasets.append(dataset)
## End of loop over expressions and selections
dataset = datasets[0]
for further_dataset in datasets[1:]:
dataset.append(further_dataset)
dataset.SetTitle('Raw ' + name.split('-')[0].capitalize())
dataset.SetName('raw_' + name.split('-')[0])
print 'max_entries, numEntries', max_entries, dataset.numEntries()
#if max_entries > 0 and dataset.numEntries() > max_entries:
### Downsample to reduce the size of data
#dataset.Print()
#print 'QQ DEBUG: Downsampling to', max_entries
#dataset = Resampler(dataset).downsample(max_entries)
#dataset.Print()
self.data = dataset
self.xvar = dataset.get().first()
def __init__(self, name, varname, option, max_entries, prescale,
prescale_phase=0):
print 'DEBUG', self.__class__.__name__, '__init__'
if prescale > 1:
msg = ', '.join(['max_entries=%d' % max_entries,
'prescale=%d' % prescale])
raise RuntimeError, 'Illegal arguments ' + msg
self.name = name
self.varname = varname
#self.max_entries = max_entries
#self.option = self.option
tree = trees.get(name, option)
cfg = config_map[varname]
datasets = []
## Get a dataset for each expression-selection pair
for expr, cuts in zip(cfg.expressions, cfg.selections):
if hasattr(cfg, 'qqbinning') and len(cfg.qqbinning.split(',')) == 3:
nbins, varmin, varmax = map(float, cfg.qqbinning.split(','))
variable = ROOT.RooRealVar(cfg.name, expr, varmin, varmax)
variable.setBins(int(nbins))
else:
variable = ROOT.RooRealVar(cfg.name, expr, 0.)
cuts = [cuts]
## Adds an appropriate prescale
if max_entries > 0:
all_entries = float(tree.GetEntries())
prescale = ROOT.TMath.CeilNint(all_entries / max_entries)
if prescale > 1:
cut = 'Entry$ %% %d == %d' % (prescale, prescale_phase)
print 'Prescaling %s: %s' % (name, cut)
cuts.append(cut)
dataset = datasetly.get(tree=tree, variable=variable, cuts=cuts)
variable = dataset.get().first()
variable.SetTitle(cfg.title)
variable.setUnit(cfg.unit)
datasets.append(dataset)
## End of loop over expressions and selections
dataset = datasets[0]
for further_dataset in datasets[1:]:
dataset.append(further_dataset)
dataset.SetTitle('Raw ' + name.split('-')[0].capitalize())
dataset.SetName('raw_' + name.split('-')[0])
print 'max_entries, numEntries', max_entries, dataset.numEntries()
#if max_entries > 0 and dataset.numEntries() > max_entries:
### Downsample to reduce the size of data
#dataset.Print()
#print 'QQ DEBUG: Downsampling to', max_entries
#dataset = Resampler(dataset).downsample(max_entries)
#dataset.Print()
self.data = dataset
self.xvar = dataset.get().first()
'setae',
'sphib',
'sphie',
'sieieb',
'sieiee',
'cieipb',
'cieipe',
's4ratiob',
's4ratioe',
]
output_filename = trees.analysis + '_' + dataset + '_id-histos.root'
destination = os.path.join(trees.base_dir, trees.analysis, 'histos',
output_filename)
tree = trees.get(dataset, option)
outfile = ROOT.TFile(destination, 'recreate')
hist = {}
for variable in variables_to_plot:
cfg = config_map[variable]
canvases.next(cfg.name).SetGrid()
option = ''
for expression, selection in zip(cfg.expressions, cfg.selections):
if not ROOT.gDirectory.Get(cfg.name):
varexp = expression + '>>' + cfg.name
if hasattr(cfg, 'binning'):
varexp += '(' + cfg.binning + ')'
else:
varexp = expression + '>>+' + cfg.name
'pt', 'pt1', 'pt2',
'eta', 'eta1', 'eta2',
'r9b', 'r9e',
'setab', 'setae',
'sphib', 'sphie',
'sieieb', 'sieiee',
'cieipb', 'cieipe',
's4ratiob', 's4ratioe',
]
output_filename = trees.analysis + '_' + dataset + '_id-histos.root'
destination = os.path.join(trees.base_dir, trees.analysis, 'histos',
output_filename)
tree = trees.get(dataset, option)
outfile = ROOT.TFile(destination, 'recreate')
hist = {}
for variable in variables_to_plot:
cfg = config_map[variable]
canvases.next(cfg.name).SetGrid()
option = ''
for expression, selection in zip(cfg.expressions, cfg.selections):
if not ROOT.gDirectory.Get(cfg.name):
varexp = expression + '>>' + cfg.name
if hasattr(cfg, 'binning'):
varexp += '(' + cfg.binning + ')'
else:
varexp = expression + '>>+' + cfg.name
