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_fit_input( 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_fit_results(dir, variable, "central", channel, "patType1CorrectedPFMet")
# Read initial values
initial_values = read_fit_input(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
path = path_to_JSON + fit_variable + '/' + str( come ) + 'TeV/'
fit_results_electron[fit_variable] = read_fit_results( path,
variable,
category,
'electron',
met_type )
fit_results_muon[fit_variable] = read_fit_results( path,
variable,
category,
'muon',
met_type )
# it doesn't matter which one to use, all of them are identical
# so lets use the 2011 and 2012 default, |eta|
initial_values_electron = read_fit_input( path_to_JSON + 'absolute_eta' + '/' + str( come ) + 'TeV/',
variable,
category,
'electron',
met_type )
initial_values_muon = read_fit_input( path_to_JSON + 'absolute_eta' + '/' + str( come ) + 'TeV/',
variable,
category,
'muon',
met_type )
if not 'closure' in path_to_JSON:
fit_results_electron['before'] = read_fit_results( 'data_single_var_fit/8TeV/',
variable,
category,
'electron',
met_type )
fit_results_muon['before'] = read_fit_results( 'data_single_var_fit/8TeV/',
"absolute_eta_M_bl_angle_bl",
"absolute_eta_M3_angle_bl",
"absolute_eta_M_bl_M3",
"absolute_eta_M_bl_M3_angle_bl",
]
fit_results_electron = {}
fit_results_muon = {}
initial_values_electron = {}
initial_values_muon = {}
for fit_var_input in fit_var_inputs:
path = path_to_JSON + fit_var_input + "/" + str(come) + "TeV/"
fit_results_electron[fit_var_input] = read_fit_results(path, variable, category, "electron", met_type)
fit_results_muon[fit_var_input] = read_fit_results(path, variable, category, "muon", met_type)
# it doesn't matter which one to use, all of them are identical
# so lets use the 2011 and 2012 default, |eta|
initial_values_electron = read_fit_input(
path_to_JSON + "absolute_eta" + "/" + str(come) + "TeV/", variable, category, "electron", met_type
)
initial_values_muon = read_fit_input(
path_to_JSON + "absolute_eta" + "/" + str(come) + "TeV/", variable, category, "muon", met_type
)
if not "closure" in path_to_JSON:
fit_results_electron["before"] = read_fit_results(
"data_single_var_fit/8TeV/", variable, category, "electron", met_type
)
fit_results_muon["before"] = read_fit_results("data_single_var_fit/8TeV/", variable, category, "muon", met_type)
plot_fit_results(fit_results_electron, initial_values_electron, "electron")
plot_fit_results(fit_results_muon, initial_values_muon, "muon")
fit_results_ = read_fit_results( '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_fit_input( '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] )
