def main():
ROOT.gROOT.SetBatch()
# Create workspace to contain full analysis
model = ROOT.RooWorkspace('model', True)
# get toy histogram
toy_data_dh = fh.getHistFromFile( file_name = toy_data_file_name
, hist_name = 'toy__flavor_all__mbl_all__BMINUSL_MET'
, roo_real_var = mbl_real_var
, label = 'toy'
, model = model
)['dh']
model.Print()
print toy_data_dh
# --------------------------------------------------------------------------
bkg_pdf_basic = getFunctionalForm('basic', mbl_real_var, model)
bkg_pdf_basic.fitTo(toy_data_dh)
# --------------------------------------------------------------------------
# draw background template to a canvas
plot_basic_lin = fh.drawToCanvas( roo_object_list = [ toy_data_dh, bkg_pdf_basic ]
, roo_real_var = mbl_real_var
, line_colors = [ROOT.kBlack, ROOT.kBlue]
, label = 'basic'
, range_string_list = ['', '']
, log = False
)
plot_basic_log = fh.drawToCanvas( roo_object_list = [ toy_data_dh, bkg_pdf_basic ]
, roo_real_var = mbl_real_var
, line_colors = [ROOT.kBlack, ROOT.kBlue]
, label = 'basic'
, range_string_list = ['', '']
, log = True
)
# --------------------------------------------------------------------------
out_file = ROOT.TFile('fit.root', 'RECREATE')
out_file.cd()
plot_basic_lin['canv'].Write('c_fit_basic__lin')
plot_basic_log['canv'].Write('c_fit_basic__log')
out_file.Close()
# --------------------------------------------------------------------------
model.writeToFile('fit_workspace.root', True)
def main():
ROOT.gROOT.SetBatch()
# Create workspace to contain full analysis
model = ROOT.RooWorkspace('model', True)
# get template histogram
bkg_template_dict = fh.getHistFromFile( file_name = bkg_template_file_name
, hist_name = 'template__flavor_all__mbl_all__BMINUSL_MET'
, roo_real_var = mbl_real_var
, label = 'bkg_template'
, model = model
)
# get toy histogram
toy_data_dh = fh.getHistFromFile( file_name = toy_data_file_name
, hist_name = 'toy__flavor_all__mbl_all__BMINUSL_MET'
, roo_real_var = mbl_real_var
, label = 'toy'
, model = model
)['dh']
# draw background template to a canvas
template_draw_dict = fh.drawToCanvas( roo_object_list = [bkg_template_dict['pdf']]
, roo_real_var = mbl_real_var
, line_colors = [ROOT.kBlack]
, label = 'mbl_templates'
, range_string_list = ['full_range']
, log = True
)
# draw toy data to a canvas
toy_draw_dict = fh.drawToCanvas( roo_object_list = [toy_data_dh]
, roo_real_var = mbl_real_var
, line_colors = [ROOT.kBlack]
, label = 'mbl_toy'
, range_string_list = ['full_range']
, log = True
)
# do basic fit -- no sliding window
basic_fit_dict = fitAndDrawToCanvas( toy_data_dh
, bkg_template_dict
, mbl_real_var
, 'basic_fit'
, model
)
# do sliding fit window scan
sliding_window_scan = performSlidingWindowScan( toy_data_dh
, bkg_template_dict
, mbl_real_var
, 'sliding_window'
, model
)
# Print a bunch of info to screen
print 'basic fit -- n_bkg: %s' % basic_fit_dict['n_bkg'].getVal()
for sws in sorted(sliding_window_scan.iterkeys()):
print sws
print sliding_window_scan[sws]['n_bkg'].GetName()
print 'fit window string: %s' % sliding_window_scan[sws]['fit_range_string']
print 'norm: %s' % sliding_window_scan[sws]['n_bkg'].getVal()
print 'error: %s' % sliding_window_scan[sws]['n_bkg'].getError()
print 'chi2_sideband: %s' % sliding_window_scan[sws]['chi2_sideband']
print 'ndof_sideband: %s' % sliding_window_scan[sws]['ndof_sideband']
print 'chi2_over_ndof (sideband): %s' % sliding_window_scan[sws]['chi2_over_ndof_sideband']
print 'chi2_search: %s' % sliding_window_scan[sws]['chi2_search']
print 'ndof_search: %s' % sliding_window_scan[sws]['ndof_search']
print 'chi2_over_ndof (search): %s' % sliding_window_scan[sws]['chi2_over_ndof_search']
print ''
# write canvas to file
out_file = ROOT.TFile('fit.root', 'RECREATE')
out_file.cd()
template_draw_dict['canv'].Write()
toy_draw_dict['canv'].Write()
basic_fit_dict['canv_lin'].Write()
basic_fit_dict['canv_log'].Write()
for sws in sorted(sliding_window_scan.iterkeys()):
sliding_window_scan[sws]['canv_lin'].Write()
sliding_window_scan[sws]['canv_log'].Write()
# --------------------------------------------------------------------------
model.writeToFile('fit_workspace.root', True)
def fitAndDrawToCanvas( data_dh
, bkg_template
, roo_real_var
, label
, model
, fit_ranges = []
):
# perform fit to data
fit_dict = performFit( data_dh
, bkg_template
, roo_real_var
, label
, model
, fit_ranges
)
print 'fit and draw to canvas -- linear'
print fit_dict['fit_range_string']
draw_dict_lin = fh.drawToCanvas( [data_dh, fit_dict['pdf'], fit_dict['pdf']]
, roo_real_var
, line_colors = [ROOT.kBlack, ROOT.kRed, ROOT.kGreen]
, label = label
, range_string_list = ['full_range', fit_dict['fit_range_string'], fit_dict['search_range_string']]
, log = False
)
print 'fit and draw to canvas -- log'
print fit_dict['fit_range_string']
draw_dict_log = fh.drawToCanvas( [data_dh, fit_dict['pdf'], fit_dict['pdf']]
, roo_real_var
, line_colors = [ROOT.kBlack, ROOT.kRed, ROOT.kGreen]
, label = label
, range_string_list = ['full_range', fit_dict['fit_range_string'], fit_dict['search_range_string']]
, log = True
)
norm = fit_dict['n_bkg'].getVal()
err = fit_dict['n_bkg'].getError()
# get chi2 for sideband and search regions
chi2_sideband = fit_dict['pdf'].createChi2( data_dh
, ROOT.RooFit.DataError(ROOT.RooAbsData.Poisson)
, ROOT.RooFit.Range(fit_dict['fit_range_string'])
)
chi2_search = fit_dict['pdf'].createChi2( data_dh
, ROOT.RooFit.DataError(ROOT.RooAbsData.Poisson)
, ROOT.RooFit.Range(fit_dict['search_range_string'])
)
# get ndfo for sideband and search regions
ndof_sideband = fh.getNumBinsInRegion( fit_dict['fit_range_string']
, fit_dict['local_roo_real_var']
, bkg_template['hist']
)
ndof_search = fh.getNumBinsInRegion( fit_dict['search_range_string']
, fit_dict['local_roo_real_var']
, bkg_template['hist']
)
# calculate chi2/ndof for sideband and search regions
chi2_over_ndof_sideband = chi2_sideband.getVal()/ndof_sideband
chi2_over_ndof_search = chi2_search.getVal()/ndof_search
# make labels for canvas
search_region_string = ''
if len(fit_ranges) == 0: search_region_string = 'N/A'
# TODO make this more robust
elif len(fit_ranges) == 2:
search_region_string = '%d < m_{bl} < %d' % (fit_ranges[0]['max'], fit_ranges[1]['min'])
label_info = ROOT.TLatex(0.50, 0.85, 'Search region: %s' % (search_region_string))
label_norm = ROOT.TLatex(0.50, 0.80, 'Background: %0.1f #pm %0.1f' % (norm, err))
label_chi2_sideband = ROOT.TLatex(0.50, 0.75, '#chi^{2}/ndof (sideband): %0.2f' % chi2_over_ndof_sideband)
label_chi2_search = ROOT.TLatex(0.50, 0.70, '#chi^{2}/ndof (search): %0.2f' % chi2_over_ndof_search)
label_info.SetTextSize(0.04)
label_norm.SetTextSize(0.04)
label_chi2_sideband.SetTextSize(0.04)
label_chi2_search.SetTextSize(0.04)
label_info.SetNDC()
label_norm.SetNDC()
label_chi2_sideband.SetNDC()
label_chi2_search.SetNDC()
draw_dict_lin['canv'].cd()
label_info.Draw()
label_norm.Draw()
label_chi2_sideband.Draw()
label_chi2_search.Draw()
draw_dict_log['canv'].cd()
label_info.Draw()
label_norm.Draw()
label_chi2_sideband.Draw()
label_chi2_search.Draw()
# save things to dictionary and return them so root doesn't lose them
fit_dict['frame_lin'] = draw_dict_lin['frame']
fit_dict['frame_log'] = draw_dict_log['frame']
fit_dict['canv_lin'] = draw_dict_lin['canv']
fit_dict['canv_log'] = draw_dict_log['canv']
fit_dict['label_info'] = label_info
fit_dict['label_norm'] = label_norm
fit_dict['label_chi2_sideband'] = label_chi2_sideband
fit_dict['label_chi2_search'] = label_chi2_search
fit_dict['chi2_sideband'] = chi2_sideband.getVal()
fit_dict['chi2_search'] = chi2_search.getVal()
fit_dict['ndof_sideband'] = ndof_sideband
fit_dict['ndof_search'] = ndof_search
fit_dict['chi2_over_ndof_sideband'] = chi2_over_ndof_sideband
fit_dict['chi2_over_ndof_search'] = chi2_over_ndof_search
return fit_dict
