def get_requested_values(cls):
adict = WIMPModel.get_requested_values()
del adict['constant_energy']
del adict['constant_time']
del adict['background_rate']
adict['fix_l_line_ratio'] = ('Fix ratio of the Ge and Zn L-lines', False)
adict['data_file'] = ('Name of data root file', 'temp.root')
adict['object_name'] = ("""Name of object inside data file.
This can be a:
TH1: the bins will be interprected as energy.
A RooDataHist will be used to generate a binned fit.
TTree: with branches, 'ee_energy', 'time', 'weight', plus others.
'time' and ee_energy are both optional, it is assumed that if they
don't exist that time and energy are constant.
'weight' is optional, but when present will adjust the weight of each
entry to 'weight' in the RooDataSet.
A RooDataSet will be used to generate an unbinned fit.
""", 'output_data')
adict['data_set_cuts'] = ("""String of cuts to apply.
This only applies if the object passed
in is a TTree (see object_name). These cuts will be used to generate
a subset of the TTree and pass into RooDataSet.
""", '')
return adict
def get_requested_values(cls):
adict = WIMPModel.get_requested_values()
del adict['constant_energy']
del adict['constant_time']
del adict['wimp_mass']
del adict['variable_quenching']
adict['model_amplitude'] = ('Initial model amplitude', 0.1)
return adict
"""
#####################################
ROOT.RooRandom.randomGenerator().SetSeed(0)
ROOT.RooMsgService.instance().setSilentMode(True)
ROOT.RooMsgService.instance().setGlobalKillBelow(5)
total_mc_entries = 1000
#total_mc_entries = 10
total_entries = 400
exponential_total = 190
basevars = BaseVariables(0, 0.1444,0.5, 3.5)
basevars.get_time().setConstant(True)
# Set up the WIMP class
wimp_class = WIMPModel(basevars,
mass_of_wimp = wimp_mass,
kilograms=0.4,
constant_quenching=False)
model = wimp_class.get_model()
# Set up the background class
background = TestModel(basevars)
list_of_models, list_of_coefficients = \
background.get_list_components()
exp_coef = list_of_coefficients.at(
list_of_coefficients.index("exp_coef_"))
flat_coef = list_of_coefficients.at(
list_of_coefficients.index("flat_coef_"))
exp_coef.setVal(exponential_total)
flat_coef.setVal(180)
#flat_coef.setMin(-5)
def initialize(self):
if self.is_initialized: return
from pyWIMP.DMModels.wimp_model import WIMPModel
from pyWIMP.DMModels.base_model import BaseVariables
from pyWIMP.DMModels.flat_model import FlatModel
self.total_counts = int(self.mass_of_detector*
self.background_rate*
(self.energy_max-self.threshold)*
self.total_time*365)
self.basevars = BaseVariables(time_beginning=0,
time_in_years=self.total_time,
energy_threshold=self.threshold,
energy_max=self.energy_max)
if self.num_energy_bins != 0: self.basevars.get_energy().setBins(int(self.num_energy_bins))
if self.num_time_bins != 0: self.basevars.get_time().setBins(int(self.num_time_bins))
self.variables = ROOT.RooArgSet()
if self.constant_time:
self.basevars.get_time().setVal(0)
self.basevars.get_time().setConstant(True)
else:
self.variables.add(self.basevars.get_time())
if not self.constant_energy:
self.variables.add(self.basevars.get_energy())
self.calculation_class = \
ec.ExclusionCalculation(self.exit_manager)
# This is where we define our models
self.backgroundClass = TritiumDecayModel(self.basevars,
self.tritium_exposure_time,
self.tritium_activation_rate,
self.mass_of_detector,
self.background_rate)
# This model is already an extended model
self.background_model = self.backgroundClass.get_model()
self.background_extend = self.background_model
if not self.do_axioelectric:
# The following has not been normalized to per-nucleon yet.
self.model_normal = ROOT.RooRealVar("model_normal",
"WIMP-nucleus #sigma",
0, -1e-15, 0.1, 'pb')
self.wimpClass = WIMPModel(self.basevars,
mass_of_wimp=self.wimp_mass,
kilograms = self.mass_of_detector,
constant_quenching=(not self.variable_quenching))
self.model = self.wimpClass.get_model()
self.norm = self.wimpClass.get_normalization().getVal()
self.model_extend = ROOT.RooExtendPdf("model_extend",
"model_extend",
self.model,
self.model_normal)
else:
# wimpClass is of course a misnomer here, but we use it for now
self.wimpClass = GaussianSignalModel(self.basevars,
mean_of_signal=self.axion_mass)
self.model_normal = ROOT.RooRealVar("model_normal",
"Counts",
0, -10, 1000)
# This is where we define our model
self.model = self.wimpClass.get_model()
self.norm = self.wimpClass.get_normalization()*self.model.getNorm(
ROOT.RooArgSet(self.basevars.get_energy()))
# We actually want the inverse of the normaliation, since this is later multiplied
# and we want the total counts in a particular gaussian.
self.norm = 1./self.norm
self.model_extend = ROOT.RooExtendPdf("model_extend",
"model_extend",
self.model,
self.model_normal)
self.added_pdf = ROOT.RooAddPdf("b+s",
"Background + Signal",
ROOT.RooArgList(
self.background_extend,
self.model_extend))
self.test_variable = self.model_normal
self.data_set_model = self.background_extend
self.fitting_model = self.added_pdf
self.is_initialized = True
def initialize(self):
# The background model is the same, but now we switch it to the data model
from pyWIMP.DMModels.low_energy_background import LowEnergyBackgroundModel
from pyWIMP.DMModels.base_model import BaseVariables
from pyWIMP.DMModels.wimp_model import WIMPModel
self.total_counts = None
open_file = ROOT.TFile(self.data_file)
self.workspace = open_file.Get(self.object_name)
# Do some introspection, we can handle TH1s, and RooAbsData
self.basevars = BaseVariables(time_beginning=0,
time_in_years=self.total_time,
energy_threshold=self.threshold,
energy_max=self.energy_max,
use_tag=False)
self.variables = ROOT.RooArgSet()
if self.workspace.InheritsFrom(ROOT.TH1.Class()):
self.variables.add(self.basevars.get_energy())
self.basevars.get_time().setVal(0)
self.basevars.get_time().setConstant(True)
self.data_set_model = ROOT.RooDataHist("data", "data",
ROOT.RooArgList(self.basevars.get_energy()),
self.workspace)
elif self.workspace.InheritsFrom(ROOT.TTree.Class()):
# Default to setting them to constant.
self.basevars.get_time().setVal(0)
self.basevars.get_time().setConstant(True)
self.basevars.get_energy().setVal(0)
self.basevars.get_energy().setConstant(True)
if self.num_energy_bins != 0:
self.basevars.get_energy().setBins(int(self.num_energy_bins))
if self.num_time_bins != 0:
self.basevars.get_time().setBins(int(self.num_time_bins))
branches = self.workspace.GetListOfBranches()
efficiency = ""
branch_arg_list = []
for i in range(branches.GetEntries()):
branch_name = branches[i].GetName()
if branch_name == "ee_energy":
self.basevars.get_energy().setConstant(False)
self.variables.add(self.basevars.get_energy())
branch_arg_list.append((self.basevars.get_energy(),
branch_name))
if branch_name == "time":
self.basevars.get_time().setConstant(False)
self.variables.add(self.basevars.get_time())
branch_arg_list.append((self.basevars.get_time(),
branch_name))
elif branch_name == "weight":
efficiency = branch_name
self.variables.add(self.basevars.get_weighting())
if not self.data_set_cuts:
# Load the DataSet the easy way
self.data_set_model = ROOT.RooDataSet("data", "data",
self.workspace,
self.variables,
efficiency)
else:
# Otherwise, we have to get the correct events,
# which requires stepping through all events
self.data_set_model = ROOT.RooDataSet("data", "data",
self.variables,
efficiency)
# Get an event list with the correct cut events
ROOT.gROOT.cd()
el = ROOT.TEventList("el", "el")
cuts = self.data_set_cuts
iter = self.variables.createIterator()
while 1:
obj = iter.Next()
if not obj: break
if obj.GetTitle() == 'weight': continue
cuts += " && ((%s <= %f) && (%s >= %f))" % (obj.GetName(),
obj.getMax(),
obj.GetName(),
obj.getMin())
self.workspace.Draw(">>%s" % el.GetName(), cuts, "goff")
event_list = [el.GetEntry(i) for i in range(el.GetN())]
#event_list = [i for i in range(self.workspace.GetEntries())]
for j in event_list:
self.workspace.GetEntry(j)
eff_val = 1.
if efficiency:
eff_val = getattr(self.workspace, efficiency)
for val in branch_arg_list:
val[0].setVal(getattr(self.workspace, val[1]))
if eff_val != 0: self.data_set_model.add(self.variables, eff_val)
else:
print "Requested: %s, isn't a TTree or TH1!" % self.data_set_name
raise TypeError
if self.num_energy_bins != 0 or self.num_time_bins != 0:
self.original_data_set = self.data_set_model
self.data_set_model = self.original_data_set.binnedClone()
if self.data_set_model.isWeighted(): print "Data set is weighted"
print "Data set has %i entries." % self.data_set_model.sumEntries()
if not self.do_axioelectric:
self.wimpClass = WIMPModel(self.basevars,
mass_of_wimp=self.wimp_mass,
kilograms = self.mass_of_detector,
constant_quenching=(not self.variable_quenching))
# This is where we define our model
self.model = self.wimpClass.get_model()
#self.model = self.wimpClass.get_simple_model()
self.norm = self.wimpClass.get_normalization().getVal()
# The following has not been normalized to per-nucleon yet.
self.model_normal = ROOT.RooRealVar("model_normal",
"WIMP-nucleus #sigma",
1, -10, 100,
"pb")
self.model_extend = ROOT.RooExtendPdf("model_extend",
"model_extend",
self.model,
self.model_normal)
# Getting the number of events for a model_normal of 1
# This gives us number of events per model_normal value
scaler = self.model_extend.expectedEvents(self.variables)
self.model_normal.setMax(2*self.data_set_model.sumEntries()/scaler)
else:
# wimpClass is of course a misnomer here, but we use it for now
self.wimpClass = GaussianSignalModel(self.basevars,
mean_of_signal=self.axion_mass)
self.model_normal = ROOT.RooRealVar("model_normal",
"Counts",
0, -10, 1000)
# This is where we define our model
self.model = self.wimpClass.get_model()
self.norm = self.wimpClass.get_normalization()*self.model.getNorm(
ROOT.RooArgSet(self.basevars.get_energy()))
# We actually want the inverse of the normaliation, since this is later multiplied
# and we want the total counts in a particular gaussian.
self.norm = 1./self.norm
self.model_extend = ROOT.RooExtendPdf("model_extend",
"model_extend",
self.model,
self.model_normal)
self.is_initialized = True
self.calculation_class = \
dat.DataCalculation(self.exit_manager)
self.low_energy = LowEnergyBackgroundModel(self.basevars,
use_ratio=self.fix_l_line_ratio)
list_of_models, list_of_coefficients = self.low_energy.get_list_components()
self.extended_models = []
i = 0
while 1:
amod = list_of_models.at(i)
avar = list_of_coefficients.at(i)
if not amod: break
i += 1
extend = ROOT.RooExtendPdf("extend%s" % amod.GetName(),
"extend%s" % amod.GetName(),
amod, avar)
self.extended_models.append(extend)
temp_list = ROOT.RooArgList()
temp_list.add(self.model_extend)
for amod in self.extended_models:
temp_list.add(amod)
self.added_pdf = ROOT.RooAddPdf("b+s",
"Background + Signal",
temp_list)
self.test_variable = self.model_normal
self.fitting_model = self.added_pdf
Initialization stuff
"""
#####################################
ROOT.RooRandom.randomGenerator().SetSeed(0)
ROOT.RooMsgService.instance().setSilentMode(True)
ROOT.RooMsgService.instance().setGlobalKillBelow(5)
total_mc_entries = 500
total_mc_entries = 400
basevars = BaseVariables(0, 0.14887063655, 0.5, 3.5)
basevars.get_time().setConstant(True)
# Set up the WIMP class
wimp_class = WIMPModel(basevars,
mass_of_wimp=wimp_mass,
kilograms=0.4,
constant_quenching=False)
model = wimp_class.get_model()
normalization = wimp_class.get_normalization().getVal()
# Set up the background class
low_energy = LowEnergyBackgroundModel(basevars)
low_energy_model = low_energy.get_model()
list_of_models, list_of_coefficients = low_energy.get_list_components()
background_normal = ROOT.RooRealVar("flat_normal", "Background event number",
0, 1000)
background_extend = ROOT.RooExtendPdf("background_extend", "background_extend",
low_energy_model, background_normal)
#flat_coef.setMin(-5)
#exp_coef.setMin(-5)
Initialization stuff
"""
#####################################
ROOT.RooRandom.randomGenerator().SetSeed(0)
ROOT.RooMsgService.instance().setSilentMode(True)
ROOT.RooMsgService.instance().setGlobalKillBelow(5)
total_mc_entries = 500
total_mc_entries = 400
basevars = BaseVariables(0, 0.14887063655,0.5, 3.5)
basevars.get_time().setConstant(True)
# Set up the WIMP class
wimp_class = WIMPModel(basevars,
mass_of_wimp = wimp_mass,
kilograms=0.4,
constant_quenching=False)
model = wimp_class.get_model()
normalization = wimp_class.get_normalization().getVal()
# Set up the background class
low_energy = LowEnergyBackgroundModel(basevars)
low_energy_model = low_energy.get_model()
list_of_models, list_of_coefficients = low_energy.get_list_components()
background_normal = ROOT.RooRealVar("flat_normal",
"Background event number",
0,
1000)
background_extend = ROOT.RooExtendPdf("background_extend",
"background_extend",
low_energy_model,
