def test_stl():
generate('map<int,vector<float> >', '<vector>;<map>')
generate('map<int,vector<int> >', '<vector>;<map>')
generate('vector<TLorentzVector>', '<vector>;TLorentzVector.h')
ROOT.std.map('int,vector<float>')
ROOT.std.map('int,vector<int>')
ROOT.std.vector('TLorentzVector')
temp = CPPType.from_string('vector<vector<vector<int> > >')
temp.ensure_built()
stl.vector('vector<map<int, string> >')
stl.vector(stl.string)()
stl.vector('string')()
stl.vector(int)
stl.map("string", "string")
stl.map(stl.string, stl.string)
stl.map(int, stl.string)
stl.map(stl.string, int)
stl.map("string", ROOT.TLorentzVector)
histmap = stl.map("string", ROOT.TH1D)()
a = ROOT.TH1D("a", "a", 10, -1, 1)
histmap["a"] = a
StrHist = stl.pair(stl.string, "TH1*")
generate('pair<map<string,TH1*>::iterator,bool>', '<map>;<TH1.h>')
histptrmap = stl.map(stl.string, "TH1*")()
histptrmap.insert(StrHist("test", a))
assert histptrmap["test"] is a
def __init__( self, fit_data ):
MapStrRootPtr = stl.map( stl.string, "TH1*" )
StrHist = stl.pair( stl.string, "TH1*" )
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory( 'sample', 'sample' )
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable( 'n_' + sample,
'number of ' + sample + " events",
fit_data_1.normalisation[sample],
N_min,
N_max,
"events" )
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model( variable, self.observables )
self.sample.defineType( variable )
self.sample.setLabel ( variable )
data = deepcopy( fit_input.real_data_histogram() )
input_hists.insert( StrHist( variable, data ) )
self.roofit_variables.append( fit_input.fit_variable )
self.comb_data = RooDataHist( "combData",
"combined data",
RooArgList( self.roofit_variables[0] ),
self.sample,
input_hists,
)
import rootpy.stl as stl
import ROOT
# Create a vector type
StrVector = stl.vector(stl.string)
# Instantiate
strvector = StrVector()
strvector.push_back("Hello")
# etc.
MapStrRoot = stl.map(stl.string, ROOT.TH1D)
MapStrRootPtr = stl.map(stl.string, "TH1D*")
StrHist = stl.pair(stl.string, "TH1*")
m = MapStrRootPtr()
a = ROOT.TH1D('t1', 't1', 10, 0, 1)
m.insert(StrHist("test", a))
print m
# include "RooAddPdf.h"
# include "RooSimultaneous.h"
# include "RooCategory.h"
# include "TCanvas.h"
# include "TAxis.h"
# include "RooPlot.h"
from ROOT import RooFit, RooRealVar, RooGaussian, RooChebychev, RooAddPdf, \
RooArgList, RooArgSet, RooDataSet, RooCategory, RooPlot, TCanvas, gPad, \
RooSimultaneous, kDashed, RooDataHist
import numpy as np
from rootpy.plotting import Hist
import rootpy.stl as stl
MapStrRootPtr = stl.map(stl.string, "TH1*")
StrHist = stl.pair(stl.string, "TH1*")
def get_data():
N_bkg1_ctl = 10000
N_signal_ctl = 2000
N_bkg1_obs = 1000
N_signal_obs = 200
mu1, mu2, sigma1, sigma2 = 100, 140, 15, 5
x1_ctl = mu1 + sigma1 * np.random.randn(N_bkg1_ctl)
x2_ctl = mu2 + sigma2 * np.random.randn(N_signal_ctl)
x1_obs = mu1 + sigma1 * np.random.randn(N_bkg1_obs)
x2_obs = mu2 + sigma2 * np.random.randn(N_signal_obs)
h1 = Hist(100, 40, 200, title='data')
h2 = Hist(100, 40, 200, title='data_ctl')