def print_chi2_breakdown(chain,mcf):
import models
import variables as v
model = models.get_model_from_file(mcf)
lhoods = models.get_lhood_names(mcf)
MCVdict=v.mc_variables()
if len( model ) > 0 :
print "\nchi2 penalties from gaussian constraints :"
print "==================================================================="
print " Penalty Prediction Name Type Constraint"
print "==================================================================="
for constraint in model:
sn=constraint.short_name
MCV=MCVdict[sn]
v_index = MCV.get_index(mcf)
chi2=chain.treeVars["contributions"][v_index]
pred=chain.treeVars["predictions"][v_index]
#print "{:11g} {:<{width}{precision}{base}}{c!r}".format(chi2, pred, base='g', width=1, precision=4, c=constraint)
print "{:11.2f} {:11.4g} {!r}".format(chi2, pred, constraint)
#print "{chi2:>f} {".format(chi2=chi2)
print "==================================================================="
if len(lhoods.keys()) > 0 : print "\nThe likelihoods give penalties:\n"
for i, lhood in enumerate(lhoods.items()):
chi2=chain.treeVars["lhoods"][i]
print "{:11.2f} {:16} {:16}". format( chi2, lhood[0], lhood[1] )
def get_lhood_names( mcf ) :
lhd = ld.get_lhood_dict()
out = OrderedDict()
mcvars=v.mc_variables()
filename=getattr( mcf, "LHoodFile", None )
if filename is not None :
with open(filename, 'rb') as f:
for line in f :
name = line[:-1] #cut EOL
out[name] = lhd[name]["name"]
return out
def get_lhood_from_file( mcf ) :
lhs = ld.get_lhood_dict()
mcvars=v.mc_variables()
out = OrderedDict()
filename=getattr( mcf, "LHoodFile", None )
if filename is not None :
with open(filename, 'rb') as f:
for line in f :
name = line[:-1] #cut EOL
if name in lhs :
var_ints = [ mcvars[varname].get_index(mcf) for varname in lhs[name]["vars"] ]
out[name] = lhm.LHood( var_ints, lhs[name] )
else :
print "Unknown Likelihood: %s, ignoring!" % name
return out
def get_p_value_n_dof(chain,mcf):
import models
import variables as v
model = models.get_model_from_file(mcf)
lhoods = models.get_lhood_names(mcf)
MCVdict=v.mc_variables()
chi2_s=[]
for constraint in model:
sn=constraint.short_name
chi2_s.append(get_contribution(chain,mcf,sn))
for i, lhood in enumerate(lhoods.items()):
chi2_s.append(chain.treeVars["lhoods"][i])
count=0
for chi2 in chi2_s:
if chi2 > 0 : count += 1
n_dof = count - mcf.Inputs
chi2_tot = chain.treeVars["predictions"][0]
p_value= r.TMath.Prob(chi2_tot, n_dof)
return p_value, n_dof
def recalc_to_file( collection, output_file = "" ) :
model = models.get_model_from_file(collection)
lhoods = models.get_lhood_from_file(collection)
outfile = collection.FileName if output_file == "" else output_file
print "Output file is %s" % outfile
# initialise the MC-variables
MCVdict=v.mc_variables()
chain = MCRecalcChain( collection )
nentries = chain.GetEntries()
begin = getattr( collection, "StartEntry", 0)
end = getattr( collection, "EndEntry", nentries+1)
total_delta = 0
# create trees in scope of outfile
out = r.TFile(outfile,"recreate")
chi2tree = chain.chains["predictions"].CloneTree(0)
# might need to do address of on contirbvars
nTotVars = chain.nTotVars["predictions"]
contribvars = array('d',[0.0]*nTotVars)
contribtree = r.TTree( 'contribtree', 'chi2 contributions')
varsOutName = "vars[%d]/D" % ( nTotVars )
contribtree.SetMaxTreeSize(10*chi2tree.GetMaxTreeSize())
contribtree.Branch("vars",contribvars,varsOutName)
# same with lhood
nLHoods = len(lhoods.keys())
lhoodvars = array('d',[0.0]*nLHoods)
lhoodtree = r.TTree( 'lhoodtree', 'lhood contributions')
varsOutName = "vars[%d]/D" % ( nLHoods )
lhoodtree.SetMaxTreeSize(10*chi2tree.GetMaxTreeSize())
lhoodtree.Branch("vars",lhoodvars,varsOutName)
# want to save best fit point entry number: create new tree and branch
bfname = getattr( collection, "BestFitEntryName", "BestFitEntry" )
bft=r.TTree(bfname, "Entry")
bfn=array('i',[0])
bft.Branch('EntryNo',bfn,'EntryNo/I')
# and the minChi minEntry
minChi=1e9
minEntry=-1
count=-1 # becuase the first entry has number 0
prog = ProgressBar(begin, end, 77, mode='fixed', char='#')
for entry in range(begin,end) :
prog.increment_amount()
print prog,'\r',
stdout.flush()
chain.GetEntry(entry)
if good_point( chain.treeVars["predictions"], collection ) :
delta = 0.
chi2 = 0
for constraint in model :
MCV=MCVdict[constraint.short_name]
v_index = MCV.get_index(collection)
chi2_t = constraint.get_chi2( chain.treeVars["predictions"][v_index] )
contribvars[v_index] = chi2_t
chi2 += chi2_t
for i,lh in enumerate(lhoods.values()) :
chi2_t = lh.get_chi2( chain.treeVars["predictions"] )
lhoodvars[i] = chi2_t
chi2 += chi2_t
chi2 += spectrum_constraints( chain.treeVars["predictions"], collection )
if chi2 > getattr(collection, "MinChi2", 0 ) and \
chi2 < getattr(collection, "MaxChi2", 1e9 ) :
# This was inserted to check on if there was a significant
# calculation error ( average deltachi2 per entry: 1e-15 )
if __DEBUG :
delta_chi2_val = chi2_t - chain.treeVars["contributions"][key]
delta = delta + delta_chi2_val
total_delta = total_delta + abs(delta)
chain.treeVars["predictions"][0] = chi2
contribvars[0] = chi2
chi2tree.Fill()
contribtree.Fill()
lhoodtree.Fill()
count+=1
#dealing with minChi
if chi2 < minChi:
minChi=chi2
minEntry=count
#Saving best fit Entry number
bft.GetEntry(0)
bfn[0]=minEntry
bft.Fill()
bft.AutoSave()
chi2tree.AutoSave()
contribtree.AutoSave()
lhoodtree.AutoSave()
out.Close()
if __DEBUG :
print "\n--------------------------\n"
print " TOTAL ( MEAN )"
print "%10e(%10e)" % ( total_delta, (total_delta/(end-begin)) )
print "\n--------------------------\n"
def get_contribution(chain,mcf,shortname):
import variables as v
MCVdict=v.mc_variables()
index = MCVdict[shortname].get_index(mcf)
contribution=chain.treeVars["contributions"][index]
return contribution
def get_prediction(chain,mcf,shortname):
import variables as v
MCVdict=v.mc_variables()
index = MCVdict[shortname].get_index(mcf)
prediction=chain.treeVars["predictions"][index]
return prediction