def create_fit_data( path, variables, fit_variables, mc_samples, COMEnergies, channels ):
'''
Creates the fit data in path + fit_check_data.txt in the JSON format.
The resulting dictionary is of the form
{centre-of-mass-energy: {
channel : {
variable : {
variable_bin : {
fit_variable : {
templates : { sample : []},
initial_values : { sample : []}
}
}
}
}
}
}
'''
# first step is to read the data
raw_data = {}
# the folder structure is
# path/fit_variable/centre-of-mass-energy/variable/fit_results/central/*
for fit_variable in fit_variables:
raw_data[fit_variable] = {}
for COMEnergy in COMEnergies:
raw_data[fit_variable][COMEnergy] = {}
for variable in variables:
raw_data[fit_variable][COMEnergy][variable] = {}
for channel in channels:
raw_data[fit_variable][COMEnergy][variable][channel] = {}
data_path = path + '/' + fit_variable + '/' + str( COMEnergy ) + 'TeV'
templates = read_fit_templates( data_path, variable,
channel = channel )
initial_values = read_initial_normalisation( data_path,
variable,
channel = channel )
raw_data[fit_variable][COMEnergy][variable][channel]['templates'] = templates
raw_data[fit_variable][COMEnergy][variable][channel]['initial_values'] = initial_values
# put it into the new structure
fit_data = {}
for COMEnergy in COMEnergies:
fit_data[COMEnergy] = {}
for channel in channels:
fit_data[COMEnergy][channel] = {}
for variable in variables:
fit_data[COMEnergy][channel][variable] = {}
for v_bin in variable_bins_ROOT[variable]:
fit_data[COMEnergy][channel][variable][v_bin] = {}
for fit_variable in fit_variables:
fit_data[COMEnergy][channel][variable][v_bin][fit_variable] = {}
fit_data[COMEnergy][channel][variable][v_bin][fit_variable]['templates'] = raw_data[fit_variable][COMEnergy][variable][channel]['templates']
fit_data[COMEnergy][channel][variable][v_bin][fit_variable]['initial_values'] = raw_data[fit_variable][COMEnergy][variable][channel]['initial_values']
write_data_to_JSON( fit_data, path + '/fit_check_data.txt', indent = False )
def makeClosureTestTable():
fitVariable='absolute_eta_M3_angle_bl'
for channel in channels:
print 'CHANNEL :',channel
for variable in variables:
print '--->',variable
# Read fit results
dir = 'data/'+fitVariable+'/'+com+'/'
if closureTest : dir = 'data/closure_test/simple/'+fitVariable+'/'+com+'/'
fit_results = read_normalisation( dir,
variable,
'central',
channel,
'patType1CorrectedPFMet' )
# Read initial values
initial_values = read_initial_normalisation( dir,
variable,
'central',
channel,
'patType1CorrectedPFMet' )
# for whichBin in range (0,len(bin_edges[variable])-1):
for whichBin in range (0,1):
for process in processes:
scale = closure_tests['simple'][process]
line = '%s ' % (samples_latex[process])
line += '& %.0f ' % initial_values[process][whichBin][0]
line += '& %.0f ' % (initial_values[process][whichBin][0]*scale)
line += '& %.0f \pm %.0f ' % (fit_results[process][whichBin][0],fit_results[process][whichBin][1])
line += '\\\\'
print line
# print process
# print scale
# print initial_values[process][whichBin][0]
# print initial_values[process][whichBin][0]*scale
# print fit_results[process][whichBin][0],'+/-',fit_results[process][whichBin][1]
pass
pass
print '\n'
pass
pass
pass
def makeClosureTestTable():
fitVariable = 'absolute_eta_M3_angle_bl'
for channel in channels:
print 'CHANNEL :', channel
for variable in variables:
print '--->', variable
# Read fit results
dir = 'data/' + fitVariable + '/' + com + '/'
if closureTest:
dir = 'data/closure_test/simple/' + fitVariable + '/' + com + '/'
fit_results = read_normalisation(dir, variable, 'central', channel,
'patType1CorrectedPFMet')
# Read initial values
initial_values = read_initial_normalisation(
dir, variable, 'central', channel, 'patType1CorrectedPFMet')
# for whichBin in range (0,len(bin_edges[variable])-1):
for whichBin in range(0, 1):
for process in processes:
scale = closure_tests['simple'][process]
line = '%s ' % (samples_latex[process])
line += '& %.0f ' % initial_values[process][whichBin][0]
line += '& %.0f ' % (initial_values[process][whichBin][0] *
scale)
line += '& %.0f \pm %.0f ' % (
fit_results[process][whichBin][0],
fit_results[process][whichBin][1])
line += '\\\\'
print line
# print process
# print scale
# print initial_values[process][whichBin][0]
# print initial_values[process][whichBin][0]*scale
# print fit_results[process][whichBin][0],'+/-',fit_results[process][whichBin][1]
pass
pass
print '\n'
pass
pass
pass
def create_fit_data(path, variables, fit_variables, mc_samples, COMEnergies,
channels):
'''
Creates the fit data in path + fit_check_data.txt in the JSON format.
The resulting dictionary is of the form
{centre-of-mass-energy: {
channel : {
variable : {
variable_bin : {
fit_variable : {
templates : { sample : []},
initial_values : { sample : []}
}
}
}
}
}
}
'''
# first step is to read the data
raw_data = {}
# the folder structure is
# path/fit_variable/centre-of-mass-energy/variable/fit_results/central/*
for fit_variable in fit_variables:
raw_data[fit_variable] = {}
for COMEnergy in COMEnergies:
raw_data[fit_variable][COMEnergy] = {}
for variable in variables:
raw_data[fit_variable][COMEnergy][variable] = {}
for channel in channels:
raw_data[fit_variable][COMEnergy][variable][channel] = {}
data_path = path + '/' + fit_variable + '/' + str(
COMEnergy) + 'TeV'
templates = read_fit_templates(data_path,
variable,
channel=channel)
initial_values = read_initial_normalisation(
data_path, variable, channel=channel)
raw_data[fit_variable][COMEnergy][variable][channel][
'templates'] = templates
raw_data[fit_variable][COMEnergy][variable][channel][
'initial_values'] = initial_values
# put it into the new structure
fit_data = {}
for COMEnergy in COMEnergies:
fit_data[COMEnergy] = {}
for channel in channels:
fit_data[COMEnergy][channel] = {}
for variable in variables:
fit_data[COMEnergy][channel][variable] = {}
for v_bin in variable_bins_ROOT[variable]:
fit_data[COMEnergy][channel][variable][v_bin] = {}
for fit_variable in fit_variables:
fit_data[COMEnergy][channel][variable][v_bin][
fit_variable] = {}
fit_data[COMEnergy][channel][variable][v_bin][
fit_variable]['templates'] = raw_data[
fit_variable][COMEnergy][variable][channel][
'templates']
fit_data[COMEnergy][channel][variable][v_bin][
fit_variable]['initial_values'] = raw_data[
fit_variable][COMEnergy][variable][channel][
'initial_values']
write_data_to_JSON(fit_data, path + '/fit_check_data.txt', indent=False)
def main(argv=None):
'''Command line options.'''
program_name = os.path.basename(sys.argv[0])
program_version = "v0.1"
program_build_date = "%s" % __updated__
program_version_string = '%%prog %s (%s)' % (
program_version, program_build_date)
# program_usage = '''usage: spam two eggs''' # optional - will be
# autogenerated by optparse
program_longdesc = '''''' # optional - give further explanation about what the program does
program_license = "Copyright 2015 user_name (organization_name) \
Licensed under the Apache License 2.0\nhttp://www.apache.org/licenses/LICENSE-2.0"
if argv is None:
argv = sys.argv[1:]
# setup option parser
parser = OptionParser(
version=program_version_string, epilog=program_longdesc, description=program_license)
parser.add_option(
"-i", "--in", dest="input_path", help="set input path [default: %default]")
parser.add_option(
"-o", "--out", dest="output_path", help="set output path [default: %default]")
parser.add_option("-v", "--variable", dest="variable",
help="set the variable to analyse (MET, HT, ST, MT, WPT) [default: %default]")
parser.add_option("-c", "--centre-of-mass-energy", dest="com", type=int,
help="set the centre of mass energy in TeV for analysis. [default: %default]")
parser.add_option('--visiblePS', dest="visiblePS", action="store_true",
help="Unfold to visible phase space")
# set defaults
parser.set_defaults(
output_path="tables/background_subtraction/",
input_path="data/normalisation/background_subtraction/",
com=13,
variable='MET',
)
# process options
(opts, _) = parser.parse_args(argv)
# MAIN BODY #
config = XSectionConfig(opts.com)
met_type = config.translate_options['type1']
variable = opts.variable
output_path = opts.output_path
input_path = opts.input_path
visiblePS = opts.visiblePS
if not output_path.endswith('/'):
output_path += '/'
phase_space = 'FullPS'
if visiblePS:
phase_space = 'VisiblePS'
path_to_JSON = '{path}/{com}TeV/{variable}/{phase_space}/'.format(
path=input_path,
com=opts.com,
variable=variable,
phase_space=phase_space,
)
# categories_and_prefixes = config.categories_and_prefixes
# categories = deepcopy(categories_and_prefixes.keys())
for channel in ['electron', 'muon']: # , 'combined']:
# read results
unfolded_normalisation = read_unfolded_normalisation(
path_to_JSON=path_to_JSON,
category='central',
channel=channel,
met_type=met_type,
)
initial_normalisation = read_initial_normalisation(
path_to_JSON=path_to_JSON,
category='central',
channel=channel,
met_type=met_type,
)
mylog.debug('initial normalisation entries: ')
mylog.debug(initial_normalisation.keys())
mylog.debug('unfolded normalisation entries: ')
mylog.debug(unfolded_normalisation.keys())
print_result_table(
config,
channel, opts, initial_normalisation, unfolded_normalisation,
)
print_before_unfolding=False,
)
print_error_table(
normalised_xsection_measured_unfolded,
normalised_xsection_measured_errors,
channel,
toFile=True,
print_before_unfolding=True,
)
if channel == "combined":
print_typical_systematics_table(
normalised_xsection_measured_unfolded,
normalised_xsection_unfolded_errors,
channel,
toFile=True,
print_before_unfolding=False,
)
print_typical_systematics_table(
normalised_xsection_measured_unfolded,
normalised_xsection_measured_errors,
channel,
toFile=True,
print_before_unfolding=True,
)
if not channel == "combined" and not channel == "combinedBeforeUnfolding":
fit_input = read_initial_normalisation(path_to_JSON, variable, "central", channel, met_type)
fit_results = read_normalisation(path_to_JSON, variable, "central", channel, met_type)
print_fit_results_table(fit_input, fit_results, channel, toFile=True)
fit_results_ = read_normalisation( 'data/closure_test/'+test+'/absolute_eta_M3_angle_bl/8TeV/',
variable,
'central',
'electron',
'patType1CorrectedPFMet' )
fit_templates_ = read_fit_templates( 'data/closure_test/'+test+'/absolute_eta_M3_angle_bl/8TeV/',
variable,
'central',
'electron',
'patType1CorrectedPFMet' )
initial_values_ = read_initial_normalisation( 'data/closure_test/'+test+'/absolute_eta_M3_angle_bl/8TeV/',
variable,
'central',
'electron',
'patType1CorrectedPFMet' )
for whichBin in range (0,len(fit_results_['TTJet'])):
for fitVariable in fit_variable_properties:
fitTemplates = fit_templates_[fitVariable]
histograms = {}
for template in fitTemplates:
nBins = len(fitTemplates[template][whichBin])
histograms[template] = Hist(nBins,fit_variable_properties[fitVariable]['min'],fit_variable_properties[fitVariable]['max'],name=template)
for bin in range(0,nBins):
histograms[template].SetBinContent( bin, fitTemplates[template][whichBin][bin] )
