def get_lumi(filename):
sample_name = extract_sample(filename)
lumi = read_lumi(
os.path.join(
'inputs',
jobid,
sample_name+".lumicalc.sum"
)
)
return lumi
def get_lumi(filename):
sample_name = extract_sample(filename)
lumi = read_lumi(
os.path.join(
'inputs',
jobid,
sample_name+".lumicalc.sum"
)
)
return lumi
import re
#from pudb import set_trace; set_trace()
paths = {
#os.environ['current'],
'current' : 'inputs/%s/' % os.environ['jobid'],
'previous' : 'inputs/2013-Jun-30-8TeV/',
}
for dataset in ['DoubleMu', 'DoubleElectron', 'MuEG']:
lumi_dict = {}
for jobid, path in paths.iteritems():
lumi_dict[jobid] = {}
for lumifile in glob.glob(path+'/data_'+dataset+'*.lumicalc.sum'):
lumi_dict[jobid][extract_sample(lumifile)] = read_lumi(lumifile)
print dataset
print '%60s%20s%20s' % ('', 'current', 'previous')
keys = []
for i in lumi_dict.values():
keys += i.keys()
keys = list(set(keys))
total_lumis = dict([(i, {'current' : 0., 'previous' : 0.}) for i in ['2012A', '2012B', '2012C', '2012D', 'TOTAL',]])
for sample in keys:
curr_l = lumi_dict['current'][sample] if sample in lumi_dict['current'] else 0.
total_lumis['TOTAL']['current'] += curr_l
previous_l = lumi_dict['previous'][sample] if sample in lumi_dict['previous'] else 0.
total_lumis['TOTAL']['previous'] += previous_l
data_lumi = 0.
for i in data_files:
data_lumi += get_lumi(i)
data_tree.Add(i)
num_pass = data_tree.GetEntries(cut_pass.GetTitle())
num_fail = data_tree.GetEntries(cut_fail.GetTitle())
log.info("found %i input files for a total lumi: %f total passing events: %i. total failing events: %i" % (len(data_files), data_lumi, num_pass, num_fail) )
if args.forceLumi:
data_lumi = args.forceLumi
log.info("forcing total lumi to %f", data_lumi )
mc_multiplicative = 1
mc_trees = []
for mc_file in mc_files:
sample_name = extract_sample(mc_file)
log.info('getting %s dataset...' % sample_name)
mc_tree = ROOT.TFile.Open(mc_file).Get(input_tree)
mc_lumi = get_lumi(mc_file)
mc_factor = mc_multiplicative*(data_lumi / mc_lumi) \
if data_lumi <> -1 else 1.
log.info(' Events are going to be scaled by %f' % mc_factor)
mc_trees.append( (sample_name, mc_tree, mc_factor) )
out_tfile.cd()
######################################
## Loading training Tree
######################################
import re
#from pudb import set_trace; set_trace()
paths = {
#os.environ['current'],
'current': 'inputs/%s/' % os.environ['jobid'],
'previous': 'inputs/2013-Jun-30-8TeV/',
}
for dataset in ['DoubleMu', 'DoubleElectron', 'MuEG']:
lumi_dict = {}
for jobid, path in paths.iteritems():
lumi_dict[jobid] = {}
for lumifile in glob.glob(path + '/data_' + dataset +
'*.lumicalc.sum'):
lumi_dict[jobid][extract_sample(lumifile)] = read_lumi(lumifile)
print dataset
print '%60s%20s%20s' % ('', 'current', 'previous')
keys = []
for i in lumi_dict.values():
keys += i.keys()
keys = list(set(keys))
total_lumis = dict([(i, {
'current': 0.,
'previous': 0.
}) for i in [
'2012A',
'2012B',
'2012C',
