def __call__(self, config):
for id, settings in config['trigger_efficiency_fit']:
if not id in config['objects']:
raise ValueError('Requested id {} not found.'.format(id))
eff_fcn = ROOT.TF1("eff_fcn", "(1./2.)*(1 + TMath::Erf((x-[0])/(sqrt(2)*[1])))")
print 'Fitting id {0}'.format(id)
eff_fcn.SetParameters(100., 20.0, 1.)
res = config['objects'][id]['obj'].Fit("eff_fcn", "SEX0", "")
xmin = eff_fcn.GetX(0.5)
eff_fcn.SetRange(xmin, 1000)
res = config['objects'][id]['obj'].Fit("eff_fcn", "SREX0", "")
xmin = eff_fcn.GetParameter(0)
xmin = eff_fcn.GetX(0.7)
eff_fcn.SetRange(xmin, 1000)
res = config['objects'][id]['obj'].Fit("eff_fcn", "SREX0", "")
x99 = math.ceil(eff_fcn.GetX(0.99))
print 'x99 at {0:.2f}'.format(x99)
# Add label with the efficiency factor
text = 's=.99 eff. at {0} GeV'.format(x99)
config['ax_texts'].append(text + '?_bottomright_')
print res.Get()
print res.Status()
if res.Get() == None or res.Status() != 0:
log.critical('Could not fit that function shit.')
vfitter = ROOT.TVirtualFitter.GetFitter()
eff_fcn.SetNpx(1000)
xmin, xmax = config['objects'][id]['obj'].GetXaxis().GetXmin(), config['objects'][id][
'obj'].GetXaxis().GetXmax()
eff_fcn.SetRange(xmin, xmax)
eff_error_graph = get_tgrapherrors(eff_fcn, vfitter)
id_fit = 'fit_{0}'.format(id.strip('_'))
config['objects'].setdefault(id_fit, {})['obj'] = eff_error_graph
def __call__(self, config):
for id, settings in config['fit_obj']:
if id not in config['objects']:
raise ValueError('Requested id {} not found.'.format(id))
log.debug('Fitting object {0}'.format(id))
fcn_name = 'fit_{0}'.format(id)
fcn = ROOT.TF1(fcn_name, settings['fcn'])
if 'fcn_0' in settings:
fcn.SetParameters(*settings['fcn_0'])
options = settings.get('options', '')
xmin = settings.get('xmin', config['objects'][id]['obj'].GetXaxis().GetXmin())
xmax = settings.get('xmax', config['objects'][id]['obj'].GetXaxis().GetXmax())
# Do the fit
if 'N' not in options:
options += 'N'
config['objects'][id]['obj'].Fit(fcn_name, options)
vfitter = ROOT.TVirtualFitter.GetFitter()
fcn.SetNpx(1000)
fcn.SetRange(xmin, xmax)
errorgraph = get_tgrapherrors(fcn, vfitter)
# TODO fix this shit
# config['objects']['fit_{0}'.format(id)] = copy.deepcopy(config['objects'][id])
new_obj_name = 'fit_{0}'.format(id)
config['objects'].setdefault(new_obj_name, {})['obj'] = errorgraph
# TGraph with the same number of points as the fitted object, but y values and errors taken from the fit.
histo_fit_id = '_fit_graph_origbin_{0}'.format(id)
# tmp tgraph of original obj.
config['objects'].setdefault(histo_fit_id, {})['obj'] = ROOT.TGraphAsymmErrors(config['objects'][id]['obj'])
for i in xrange(config['objects'][histo_fit_id]['obj'].GetN()):
tmp_x, tmp_y = ROOT.Double(0), ROOT.Double(0)
config['objects'][histo_fit_id]['obj'].GetPoint(i, tmp_x, tmp_y)
config['objects'][histo_fit_id]['obj'].SetPoint(i, tmp_x, fcn.Eval(tmp_x))
config['objects'][histo_fit_id]['obj'].SetPointEYhigh(i, 0.)
config['objects'][histo_fit_id]['obj'].SetPointEYlow(i, 0.)
def __call__(self, config):
ROOT.TVirtualFitter.SetMaxIterations(99999)
res_pars = {}
for id in config['resolution']:
if id not in config['objects']:
raise ValueError('Requested id {} not found.'.format(id))
gen_bins = config['objects'][id]['obj'].GetNbinsY()
resolution_graph = ROOT.TGraphErrors(gen_bins)
for i in xrange(1, gen_bins + 1):
# Get slice of 2d histogram
print 'ptbin', i, config['objects'][id]['obj'].GetYaxis().GetBinLowEdge(i), config['objects'][id]['obj'].GetYaxis().GetBinLowEdge(i+1)
pt_bin_obj = config['objects'][id]['obj'].ProjectionX("{0}_slice_{1}".format(id, i), i, i)
# Continue if histogram if empty
if pt_bin_obj.GetEntries() < 1:
continue
# If fit fails continue instead of failing
id_slice = '_{0}_slice_{1}'.format(id.strip('_'), i)
res = pt_bin_obj.Fit("gaus", "SQO")
fcn = pt_bin_obj.GetFunction("gaus")
if res.Get() == None or res.Status() != 0:
continue
xmin, xmax = pt_bin_obj.GetXaxis().GetXmin(), pt_bin_obj.GetXaxis().GetXmax()
vfitter = ROOT.TVirtualFitter.GetFitter()
fcn.SetNpx(1000)
fcn.SetRange(xmin, xmax)
error_graph = get_tgrapherrors(fcn, vfitter)
# Omit bin with too large errors > 5%
if (fcn.GetParError(2) / fcn.GetParameter(2)) > 0.05:
continue
# Uncomment to save also individual gauss fits to dict
config['objects'].setdefault(id_slice, {})
config['objects'][id_slice]['obj'] = pt_bin_obj
config['objects'].setdefault('{0}_fit'.format(id_slice), {})
config['objects']['{0}_fit'.format(id_slice)]['obj'] = error_graph
resolution_graph.SetPoint(i, config['objects'][id]['obj'].GetYaxis().GetBinCenter(i),
fcn.GetParameter(2))
resolution_graph.SetPointError(i, config['objects'][id]['obj'].GetYaxis().GetBinWidth(i) / 2.,
fcn.GetParError(2))
# Store TGraph and fit TGraph
id_res = 'resolution_{0}'.format(id.strip('_'))
config['objects'].setdefault(id_res, {})
config['objects'][id_res]['obj'] = resolution_graph
res_fcn = ROOT.TF1("res_fcn", "sqrt(([0]/x)**2 + (([1]**2)/x) + [2]**2)")
# res_fcn = ROOT.TF1("res_fcn", "sqrt(TMath::Sign(1.,[0])*([0]/x)**2 + (([1]**2)/x) + [2]**2)")
# res_fcn = ROOT.TF1("res_fcn", "sqrt(TMath::Sign(1.,[0])*([0]**2) + ((x)**[1])*([2]**2/x) + [3]**2)")
res_fcn.SetParameters(5., 1.0, 0.03, -0.5)
res_fcn.SetRange(30., 9999.)
print 'Fitting id {0}'.format(id_res)
res = resolution_graph.Fit("res_fcn", "RSOEX0", "")
# if res.Get() == None or res.Status() != 0:
# raise Exception('Fit Failed')
res_pars[id] = [res_fcn.GetParameter(0),res_fcn.GetParameter(1),res_fcn.GetParameter(2),res_fcn.GetParameter(3)]
xmin, xmax = resolution_graph.GetXaxis().GetXmin(), resolution_graph.GetXaxis().GetXmax()
vfitter = ROOT.TVirtualFitter.GetFitter()
res_fcn.SetNpx(1000)
res_fcn.SetRange(xmin, xmax)
res_error_graph = get_tgrapherrors(res_fcn, vfitter)
config['objects'].setdefault('{0}_fit'.format(id_res), {})['obj'] = res_error_graph
print res_pars
for k,v in res_pars.iteritems():
print '\'{0}\' : {1},'.format(k, v)
