def main(args):
# Write arparse arguments to YAML config
logger.debug("Write argparse arguments to YAML config.")
output_config = {}
output_config["base_path"] = args.base_path
output_config["output_path"] = args.output_path
output_config["output_filename"] = args.output_filename
output_config["tree_path"] = args.tree_path
output_config["event_branch"] = args.event_branch
output_config["training_weight_branch"] = args.training_weight_branch
# Define era
if "2016" in args.era:
from shape_producer.estimation_methods_2016 import DataEstimation, HTTEstimation, ggHEstimation, qqHEstimation, VHEstimation, ZTTEstimation, ZTTEstimationTT, ZLEstimationMTSM, ZLEstimationETSM, ZLEstimationTT, ZJEstimationMT, ZJEstimationET, ZJEstimationTT, WEstimationRaw, TTTEstimationMT, TTTEstimationET, TTTEstimationTT, TTJEstimationMT, TTJEstimationET, TTJEstimationTT, VVEstimation, QCDEstimationMT, QCDEstimationET, QCDEstimationTT, ZTTEmbeddedEstimation, TTLEstimationMT, TTLEstimationET, TTLEstimationTT, TTTTEstimationMT, TTTTEstimationET, EWKWpEstimation, EWKWmEstimation, EWKZllEstimation, EWKZnnEstimation
from shape_producer.era import Run2016
era = Run2016(args.database)
else:
logger.fatal("Era {} is not implemented.".format(args.era))
raise Exception
############################################################################
# Channel: mt
if args.channel == "mt":
channel = MTSM()
# Set up `processes` part of config
output_config["processes"] = {}
# Additional cuts
additional_cuts = Cuts()
logger.warning("Use additional cuts for mt: %s",
additional_cuts.expand())
# MC-driven processes
# NOTE: Define here the mappig of the process estimations to the training classes
classes_map = {
"ggH": "ggh",
"qqH": "qqh",
"ZTT": "ztt",
"EMB": "ztt",
"ZL": "zll",
"ZJ": "zll",
"TTT": "tt",
"TTL": "tt",
"TTJ": "tt",
"W": "w",
"EWKWp": "w",
"EWKWm": "w",
"VV": "misc",
"EWKZll": "misc",
"EWKZnn": "misc"
}
for estimation in [
ggHEstimation(era, args.base_path, channel),
qqHEstimation(era, args.base_path, channel),
ZTTEstimation(era, args.base_path, channel),
#ZTTEmbeddedEstimation(era, args.base_path, channel),
ZLEstimationMTSM(era, args.base_path, channel),
ZJEstimationMT(era, args.base_path, channel),
TTTEstimationMT(era, args.base_path, channel),
#TTLEstimationMT(era, args.base_path, channel),
TTJEstimationMT(era, args.base_path, channel),
WEstimationRaw(era, args.base_path, channel),
EWKWpEstimation(era, args.base_path, channel),
EWKWmEstimation(era, args.base_path, channel),
VVEstimation(era, args.base_path, channel),
EWKZllEstimation(era, args.base_path, channel),
#EWKZnnEstimation(era, args.base_path, channel)
]:
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path + "/", "")
for f in estimation.get_files()
],
"cut_string": (estimation.get_cuts() + channel.cuts +
additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
classes_map[estimation.name]
}
# Same sign selection for data-driven QCD
estimation = DataEstimation(era, args.base_path, channel)
estimation.name = "QCD"
channel_ss = copy.deepcopy(channel)
channel_ss.cuts.get("os").invert()
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path + "/", "")
for f in estimation.get_files()
],
"cut_string": (estimation.get_cuts() + channel_ss.cuts +
additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
"ss"
}
############################################################################
# Channel: et
if args.channel == "et":
channel = ETSM()
# Set up `processes` part of config
output_config["processes"] = {}
# Additional cuts
additional_cuts = Cuts()
logger.warning("Use additional cuts for et: %s",
additional_cuts.expand())
# MC-driven processes
# NOTE: Define here the mappig of the process estimations to the training classes
classes_map = {
"ggH": "ggh",
"qqH": "qqh",
"ZTT": "ztt",
"EMB": "ztt",
"ZL": "zll",
"ZJ": "zll",
"TTT": "tt",
"TTL": "tt",
"TTJ": "tt",
"W": "w",
"EWKWp": "w",
"EWKWm": "w",
"VV": "misc",
"EWKZll": "misc",
"EWKZnn": "misc"
}
for estimation in [
ggHEstimation(era, args.base_path, channel),
qqHEstimation(era, args.base_path, channel),
ZTTEstimation(era, args.base_path, channel),
#ZTTEmbeddedEstimation(era, args.base_path, channel),
ZLEstimationETSM(era, args.base_path, channel),
ZJEstimationET(era, args.base_path, channel),
TTTEstimationET(era, args.base_path, channel),
#TTLEstimationET(era, args.base_path, channel),
TTJEstimationET(era, args.base_path, channel),
WEstimationRaw(era, args.base_path, channel),
EWKWpEstimation(era, args.base_path, channel),
EWKWmEstimation(era, args.base_path, channel),
VVEstimation(era, args.base_path, channel),
EWKZllEstimation(era, args.base_path, channel),
#EWKZnnEstimation(era, args.base_path, channel)
]:
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path + "/", "")
for f in estimation.get_files()
],
"cut_string": (estimation.get_cuts() + channel.cuts +
additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
classes_map[estimation.name]
}
# Same sign selection for data-driven QCD
estimation = DataEstimation(era, args.base_path, channel)
estimation.name = "QCD"
channel_ss = copy.deepcopy(channel)
channel_ss.cuts.get("os").invert()
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path + "/", "")
for f in estimation.get_files()
],
"cut_string": (estimation.get_cuts() + channel_ss.cuts +
additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
"ss"
}
############################################################################
# Channel: tt
if args.channel == "tt":
channel = TTSM()
# Set up `processes` part of config
output_config["processes"] = {}
# Additional cuts
additional_cuts = Cuts()
logger.warning("Use additional cuts for tt: %s",
additional_cuts.expand())
# MC-driven processes
# NOTE: Define here the mappig of the process estimations to the training classes
classes_map = {
"ggH": "ggh",
"qqH": "qqh",
"ZTT": "ztt",
"EMB": "ztt",
"ZL": "misc",
"ZJ": "misc",
"TTT": "misc",
"TTL": "misc",
"TTJ": "misc",
"W": "misc",
"EWKWp": "misc",
"EWKWm": "misc",
"VV": "misc",
"EWKZll": "misc",
"EWKZnn": "misc"
}
for estimation in [
ggHEstimation(era, args.base_path, channel),
qqHEstimation(era, args.base_path, channel),
ZTTEstimationTT(era, args.base_path, channel),
#ZTTEmbeddedEstimation(era, args.base_path, channel),
ZLEstimationTT(era, args.base_path, channel),
ZJEstimationTT(era, args.base_path, channel),
TTTEstimationTT(era, args.base_path, channel),
#TTLEstimationTT(era, args.base_path, channel),
TTJEstimationTT(era, args.base_path, channel),
WEstimationRaw(era, args.base_path, channel),
EWKWpEstimation(era, args.base_path, channel),
EWKWmEstimation(era, args.base_path, channel),
VVEstimation(era, args.base_path, channel),
EWKZllEstimation(era, args.base_path, channel),
#EWKZnnEstimation(era, args.base_path, channel)
]:
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path + "/", "")
for f in estimation.get_files()
],
"cut_string": (estimation.get_cuts() + channel.cuts +
additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
classes_map[estimation.name]
}
# Same sign selection for data-driven QCD
estimation = DataEstimation(era, args.base_path, channel)
estimation.name = "QCD"
channel_iso = copy.deepcopy(channel)
channel_iso.cuts.remove("tau_2_iso")
channel_iso.cuts.add(
Cut("byTightIsolationMVArun2v1DBoldDMwLT_2<0.5", "tau_2_iso"))
channel_iso.cuts.add(
Cut("byLooseIsolationMVArun2v1DBoldDMwLT_2>0.5",
"tau_2_iso_loose"))
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path + "/", "")
for f in estimation.get_files()
],
"cut_string": (estimation.get_cuts() + channel_iso.cuts +
additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
"noniso"
}
############################################################################
# Write output config
logger.info("Write config to file: {}".format(args.output_config))
yaml.dump(output_config,
open(args.output_config, 'w'),
default_flow_style=False)
def main(args):
# Define era
if "2016" in args.era:
from shape_producer.era import Run2016
era = Run2016(args.datasets)
elif "2017" in args.era:
from shape_producer.era import Run2017
era = Run2017(args.datasets)
else:
logger.fatal("Era {} is not implemented.".format(args.era))
raise Exception
# Load variables
variables = yaml.load(open(args.variables))["selected_variables"]
# Define bins and range of binning for variables in enabled channels
channel_dict = {
"em": {
"2016": EMSM2016(),
"2017": EMSM2017()
},
"et": {
"2016": ETSM2016(),
"2017": ETSM2017()
},
"mt": {
"2016": MTSM2016(),
"2017": MTSM2017()
},
"tt": {
"2016": TTSM2016(),
"2017": TTSM2017()
},
}
friend_directories_dict = {
"em": args.em_friend_directories,
"et": args.et_friend_directories,
"mt": args.mt_friend_directories,
"tt": args.tt_friend_directories,
}
percentiles = [
1.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 99.0
]
config = {"gof": {}}
for ch in channel_dict:
# Get properties
if "2016" in args.era:
eraname = "2016"
elif "2017" in args.era:
eraname = "2017"
channel = channel_dict[ch][eraname]
logger.info("Channel: %s" % ch)
dict_ = {}
additional_cuts = Cuts()
logger.warning("Use additional cuts for %s: %s" %
(ch, additional_cuts.expand()))
dict_ = get_properties(dict_, era, channel, args.directory,
additional_cuts)
# Build chain
dict_["tree_path"] = "%s_nominal/ntuple" % ch
chain = build_chain(dict_, friend_directories_dict[ch])
# Get percentiles and calculate 1d binning
binning = get_1d_binning(ch, chain, variables[int(eraname)][ch],
percentiles)
# Add binning for unrolled 2d distributions
binning = add_2d_unrolled_binning(variables[int(eraname)][ch], binning)
# Append binning to config
config["gof"][ch] = binning
# Write config
logger.info("Write binning config to %s.", args.output)
yaml.dump(config, open(args.output, 'w'))
def main(args):
# Write arparse arguments to YAML config
logger.debug("Write argparse arguments to YAML config.")
output_config = {}
output_config["base_path"] = args.base_path
output_config["friend_paths"] = args.friend_paths
output_config["output_path"] = args.output_path
output_config["output_filename"] = args.output_filename
output_config["tree_path"] = args.tree_path
output_config["event_branch"] = args.event_branch
output_config["training_weight_branch"] = args.training_weight_branch
# Define era
if "2016" in args.era:
from shape_producer.estimation_methods_2016 import DataEstimation, ggHEstimation, qqHEstimation, ZTTEstimation, ZLEstimation, ZJEstimation, WEstimation, TTTEstimation, TTJEstimation, ZTTEmbeddedEstimation, TTLEstimation, EWKZEstimation, VVLEstimation, VVJEstimation, VVEstimation, VVTEstimation
#QCDEstimation_SStoOS_MTETEM, QCDEstimationTT, EWKWpEstimation, EWKWmEstimation, , VHEstimation, HTTEstimation,
from shape_producer.era import Run2016
era = Run2016(args.database)
elif "2017" in args.era:
from shape_producer.estimation_methods_2017 import DataEstimation, ZTTEstimation, ZJEstimation, ZLEstimation, TTLEstimation, TTJEstimation, TTTEstimation, VVTEstimation, VVJEstimation, VVLEstimation, WEstimation, ggHEstimation, qqHEstimation, EWKZEstimation, ZTTEmbeddedEstimation
from shape_producer.era import Run2017
era = Run2017(args.database)
elif "2018" in args.era:
from shape_producer.estimation_methods_2018 import DataEstimation, ZTTEstimation, ZJEstimation, ZLEstimation, TTLEstimation, TTJEstimation, TTTEstimation, VVTEstimation, VVJEstimation, VVLEstimation, WEstimation, ggHEstimation, qqHEstimation, EWKZEstimation, ZTTEmbeddedEstimation
from shape_producer.era import Run2018
era = Run2018(args.database)
else:
logger.fatal("Era {} is not implemented.".format(args.era))
raise Exception
def estimationMethodAndClassMapGenerator():
###### common processes
classes_map = {"ggH": "ggh", "qqH": "qqh", "EWKZ": "misc"}
estimationMethodList = [
ggHEstimation("ggH", era, args.base_path, channel),
qqHEstimation("qqH", era, args.base_path, channel),
EWKZEstimation(era, args.base_path, channel),
VVLEstimation(era, args.base_path, channel),
WEstimation(era, args.base_path, channel)
]
######## Check for emb vs MC
if args.training_z_estimation_method == "emb":
classes_map["EMB"] = "ztt"
estimationMethodList.extend(
[ZTTEmbeddedEstimation(era, args.base_path, channel)])
elif args.training_z_estimation_method == "mc":
classes_map["ZTT"] = "ztt"
estimationMethodList.extend([
ZTTEstimation(era, args.base_path, channel),
TTTEstimation(era, args.base_path, channel),
VVTEstimation(era, args.base_path, channel)
])
else:
logger.fatal(
"No valid training-z-estimation-method! Options are emb, mc. Argument was {}"
.format(args.training_z_estimation_method))
raise Exception
##### TT* zl,zj processes
estimationMethodList.extend([
TTLEstimation(era, args.base_path, channel),
ZLEstimation(era, args.base_path, channel)
])
# less data-> less categories for tt
if args.channel == "tt":
classes_map.update({
"TTT": "misc",
"TTL": "misc",
"TTJ": "misc",
"ZL": "misc",
"ZJ": "misc"
})
estimationMethodList.extend([
ZJEstimation(era, args.base_path, channel),
TTJEstimation(era, args.base_path, channel)
])
## not TTJ,ZJ for em
elif args.channel == "em":
classes_map.update({"TTT": "tt", "TTL": "tt", "ZL": "misc"})
else:
classes_map.update({
"TTT": "tt",
"TTL": "tt",
"TTJ": "tt",
"ZL": "zll",
"ZJ": "zll"
})
estimationMethodList.extend([
ZJEstimation(era, args.base_path, channel),
TTJEstimation(era, args.base_path, channel)
])
###w:
# estimation metho already included, just different mapping fror et and mt
if args.channel in ["et", "mt"]:
classes_map["W"] = "w"
else:
classes_map["W"] = "misc"
##### VV/[VVT,VVL,VVJ] split
# VVL in common, VVT in "EMBvsMC"
if args.channel == "em":
classes_map.update({"VVT": "db", "VVL": "db"})
else:
classes_map.update({"VVT": "misc", "VVL": "misc", "VVJ": "misc"})
estimationMethodList.extend([
VVJEstimation(era, args.base_path, channel),
])
### QCD class
if args.channel == "tt":
classes_map["QCD"] = "noniso"
else:
classes_map["QCD"] = "ss"
return ([classes_map, estimationMethodList])
channelDict = {}
channelDict["2016"] = {
"mt": MTSM2016(),
"et": ETSM2016(),
"tt": TTSM2016(),
"em": EMSM2016()
}
channelDict["2017"] = {
"mt": MTSM2017(),
"et": ETSM2017(),
"tt": TTSM2017(),
"em": EMSM2017()
}
channelDict["2018"] = {
"mt": MTSM2018(),
"et": ETSM2018(),
"tt": TTSM2018(),
"em": EMSM2018()
}
channel = channelDict[args.era][args.channel]
# Set up `processes` part of config
output_config["processes"] = {}
# Additional cuts
additional_cuts = Cuts()
logger.warning("Use additional cuts for mt: %s", additional_cuts.expand())
classes_map, estimationMethodList = estimationMethodAndClassMapGenerator()
##MC+/Embedding Processes
for estimation in estimationMethodList:
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path.rstrip("/") + "/", "")
for f in estimation.get_files()
],
"cut_string":
(estimation.get_cuts() + channel.cuts + additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
classes_map[estimation.name]
}
###
# Same sign selection for data-driven QCD
estimation = DataEstimation(era, args.base_path, channel)
estimation.name = "QCD"
channel_qcd = copy.deepcopy(channel)
if args.channel != "tt":
## os= opposite sign
channel_qcd.cuts.get("os").invert()
# Same sign selection for data-driven QCD
else:
channel_qcd.cuts.remove("tau_2_iso")
channel_qcd.cuts.add(
Cut("byTightIsolationMVArun2017v2DBoldDMwLT2017_2<0.5",
"tau_2_iso"))
channel_qcd.cuts.add(
Cut("byLooseIsolationMVArun2017v2DBoldDMwLT2017_2>0.5",
"tau_2_iso_loose"))
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path.rstrip("/") + "/", "")
for f in estimation.get_files()
],
"cut_string":
(estimation.get_cuts() + channel_qcd.cuts + additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
"class":
classes_map[estimation.name]
}
#####################################
# Write output config
logger.info("Write config to file: {}".format(args.output_config))
yaml.dump(output_config,
open(args.output_config, 'w'),
default_flow_style=False)
def main(args):
# Write arparse arguments to YAML config
logger.debug("Write argparse arguments to YAML config.")
output_config = {}
output_config["base_path"] = args.base_path
output_config["friend_paths"] = args.friend_paths
output_config["output_path"] = args.output_path
output_config["output_filename"] = args.output_filename
output_config["tree_path"] = args.tree_path
output_config["event_branch"] = args.event_branch
output_config["training_weight_branch"] = args.training_weight_branch
# Define era
if "2016" in args.era:
from shape_producer.estimation_methods_2016 import ggHEstimation, qqHEstimation, HWWEstimation
from shape_producer.era import Run2016
era = Run2016(args.database)
elif "2017" in args.era:
from shape_producer.estimation_methods_2017 import ggHEstimation, qqHEstimation, HWWEstimation, DataEstimation
from shape_producer.era import Run2017
era = Run2017(args.database)
else:
logger.fatal("Era {} is not implemented.".format(args.era))
raise Exception
def estimationMethodAndClassMapGenerator():
estimationMethodList = [
DataEstimation(era, args.base_path, channel),
ggHEstimation("ggH125", era, args.base_path, channel),
qqHEstimation("qqH125", era, args.base_path, channel),
HWWEstimation(era, args.base_path, channel),
]
return (estimationMethodList)
channelDict = {}
channelDict["2016"] = {
"mt": MTSM2016(),
"et": ETSM2016(),
"tt": TTSM2016(),
"em": EMSM2016()
}
channelDict["2017"] = {
"mt": MTSM2017(),
"et": ETSM2017(),
"tt": TTSM2017(),
"em": EMSM2017()
}
channel = channelDict[args.era][args.channel]
# Set up `processes` part of config
output_config["processes"] = {}
# Additional cuts
additional_cuts = Cuts()
logger.warning("Use additional cuts for mt: %s", additional_cuts.expand())
estimationMethodList = estimationMethodAndClassMapGenerator()
for estimation in estimationMethodList:
output_config["processes"][estimation.name] = {
"files": [
str(f).replace(args.base_path.rstrip("/") + "/", "")
for f in estimation.get_files()
],
"cut_string":
(estimation.get_cuts() + channel.cuts + additional_cuts).expand(),
"weight_string":
estimation.get_weights().extract(),
}
# Write output config
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
logger.info("Write config to file: {}".format(args.output_config))
yaml.dump(output_config,
open(args.output_config, 'w'),
default_flow_style=False)
def main(args):
# Define era
if "2016" in args.era:
from shape_producer.era import Run2016
era = Run2016(args.datasets)
else:
logger.fatal("Era {} is not implemented.".format(args.era))
raise Exception
# Load variables
variables = yaml.load(open(args.variables))["variables"]
# Define bins and range of binning for variables in enabled channels
channels = ["et", "mt", "tt"]
num_borders = 9
min_percentile = 1.0
max_percentile = 99.0
config = {"gof": {}}
# Channel: ET
if "et" in channels:
# Get properties
channel = ETSM()
logger.info("Channel: et")
dict_ = {}
additional_cuts = Cuts()
logger.warning("Use additional cuts for et: %s",
additional_cuts.expand())
dict_ = get_properties(dict_, era, channel, args.directory,
additional_cuts)
# Build chain
dict_["tree_path"] = "et_nominal/ntuple"
chain = build_chain(dict_)
# Get percentiles and calculate 1d binning
binning = get_1d_binning("et", chain, variables, min_percentile,
max_percentile, num_borders)
# Add binning for unrolled 2d distributions
binning = add_2d_unrolled_binning(variables, binning)
# Append binning to config
config["gof"]["et"] = binning
# Channel: MT
if "mt" in channels:
# Get properties
channel = MTSM()
logger.info("Channel: mt")
dict_ = {}
additional_cuts = Cuts()
logger.warning("Use additional cuts for mt: %s",
additional_cuts.expand())
dict_ = get_properties(dict_, era, channel, args.directory,
additional_cuts)
# Build chain
dict_["tree_path"] = "mt_nominal/ntuple"
chain = build_chain(dict_)
# Get percentiles
binning = get_1d_binning("mt", chain, variables, min_percentile,
max_percentile, num_borders)
# Add binning for unrolled 2d distributions
binning = add_2d_unrolled_binning(variables, binning)
# Append binning to config
config["gof"]["mt"] = binning
# Channel: TT
if "tt" in channels:
# Get properties
channel = TTSM()
logger.info("Channel: tt")
dict_ = {}
additional_cuts = Cuts()
logger.warning("Use additional cuts for tt: %s",
additional_cuts.expand())
dict_ = get_properties(dict_, era, channel, args.directory,
additional_cuts)
# Build chain
dict_["tree_path"] = "tt_nominal/ntuple"
chain = build_chain(dict_)
# Get percentiles
binning = get_1d_binning("tt", chain, variables, min_percentile,
max_percentile, num_borders)
# Add binning for unrolled 2d distributions
binning = add_2d_unrolled_binning(variables, binning)
# Append binning to config
config["gof"]["tt"] = binning
# Write config
logger.info("Write binning config to %s.", args.output)
yaml.dump(config, open(args.output, 'w'))
