def get_predicts( mcf ) :
v = mc_variables()
p = pc.predictions_to_make()
predicts = []
for predict in p :
opts = v[predict]
predicts.append( MCContribution( mcf, v[predict] ) )
return predicts
def get_contribs( mcf ) :
v = mc_variables()
c = pc.contributions_to_make()
contribs = []
for contrib in c :
opts = v[contrib]
contribs.append( MCContribution( mcf, v[contrib] ) )
return contribs
def init_var_indices(self):
vars = v.mc_variables()
# one will need indices: first of KO's (to find the other 21); m_neutralino; ssi with whith the normal X^2 was calculated
self.KOssi_cen50_index = vars["KOsigma_pp^SI_cen50"].get_index(self.mcf)
self.KOssi_unc50_14_index = vars["KOsigma_pp^SI_unc50_14"].get_index(self.mcf)
self.KOssi_unc50_7_index = vars["KOsigma_pp^SI_unc50_7"].get_index(self.mcf)
self.mneu1_index = vars["neu1"].get_index(self.mcf)
if self.lhood_name is not None:
self.xenon_ssi_index = vars[self.xenon_ssi_sn].get_index(self.mcf)
print self.mneu1_index, self.xenon_ssi_index
def get_plots(mcf) :
v = mc_variables()
plots = pc.plots_to_make()
for plane, opts in plots.iteritems() :
assert len(plane) == len( opts["ranges"] ) == len( opts["nbins"] ), \
"Wrong option numbers for plots (dimensionality doesnt match ranges and/or bins"
d = {}
for plot_vars, options in plots.iteritems() :
d[tuple([ v[var] for var in plot_vars])] = options
spaces = [ MCSpace( mcf, MCV, opts ) for (MCV, opts) in d.iteritems() ]
return spaces
def fill_n_dim_hisogram(chain,n_dim_histo_list,space_dict,mcf):
mc_vars = variables.mc_variables() # this gets all mc-variabls
space_vars = [ [mc_vars[sn] for sn in short_names ] for short_names in space_dict ]
begin = 0
# end = chain.GetEntries()
end = 10000
prog = ProgressBar(begin, end, 77, mode='fixed', char='#')
for entry in xrange(begin,end) :
chi2 = get_chi2(chain,mcf)
for histo, vars in zip(n_dim_histo_list, space_vars ) :
coord=get_and_coordinates(vars,chain,mcf)
fill_histo(entry,chi2,coord)
def calculate_entry_histograms( plots, chain ) :
##assert canvas is not None, "Canvas must be specified in calculate_histograms"
# setup our 2d histos
vars = v.mc_variables()
# KOhack class gets initiated, because it has to be checked "if KOhack is applied"
KOhack=KOhack_class(plots[0].mcf)
histos = []
chi2histos = []
for p in plots :
hname = histo_name( p.short_names, entry_histo_prefix )
cname = histo_name( p.short_names, chi2_histo_prefix )
entryhisto = initialize_histo( p,hname,entry=True )
chi2histo = initialize_histo( p,cname,chi2 =True )
histos.append(entryhisto)
chi2histos.append(chi2histo)
if check_entry_KO_hack(p,KOhack):
KOhack.init_hack(p)
nentries = chain.GetEntries()
prog = ProgressBar(0, nentries+1, 77, mode='fixed', char='#')
for entry in range(0,nentries+1) :
prog.increment_amount()
print prog,'\r',
stdout.flush()
chain.GetEntry(entry)
for h, c, plot in zip( histos, chi2histos, plots ) :
vals_list = get_values_list_from_chain_and_histo(chain,plot,vars,s,KOhack,h)
for vals in vals_list:
nbins = plot.bins
ibin = h.FindBin(*vals)
max_bin = h.FindBin(*plot.max_vals)
if ibin != 0 and ibin < max_bin :
chi2 = get_modified_entry_chi2(vals,chain,KOhack)
if chi2 < c.GetBinContent(ibin) :
c.SetBinContent(ibin, chi2)
h.SetBinContent(ibin, entry)
print
return histos
