def _workflow(sample, jecv):
_phs = []
_add_cuts = [_ac['cut'] for _ac in _ADDITIONAL_CUTS]
_SAMPLES = []
for _ac in _ADDITIONAL_CUTS:
_SAMPLES.append(deepcopy(sample))
_SAMPLES[-1]['color'] = _ac['color']
_SAMPLES[-1]['source_label'] = '{}'.format(_ac['label'])
_ph = PlotHistograms1D(
basename="control_plots_hist1d_17Nov2017",
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_CORR_FOLDER,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
show_ratio_to_first=True,
show_cut_info_text=False,
plot_label="Fall17 JEC {}".format(jecv))
_ph2 = PlotHistograms1D(
basename="control_plots_hist1d_norm_17Nov2017",
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_CORR_FOLDER,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
normalize_to_first=True,
show_ratio_to_first=True,
show_cut_info_text=False)
for _plot in _ph._plots:
pass
#_phs.append(_ph)
_phs.append(_ph2)
for _ph in _phs:
_ph.make_plots()
def _workflow(sample_data, sample_mc, jecv):
_phs = []
#_add_cuts = [_ac['cut'] for _ac in RUN_PERIOD_CUT_DICTS]
#_add_cuts.insert(0, None)
_SAMPLES = []
_SAMPLES.append(deepcopy(sample_mc))
_SAMPLES[-1]['color'] = '#7293cb' #'royalblue'
_SAMPLES[-1]['marker'] = 'bar'
_SAMPLES[-1]['step_flag'] = False
_SAMPLES[-1]['source_label'] = sample_mc['source_label']
_SAMPLES.append(deepcopy(sample_data))
_SAMPLES[-1]['color'] = 'k'
_SAMPLES[-1]['marker'] = '.'
_SAMPLES[-1]['step_flag'] = False
_SAMPLES[-1]['source_label'] = sample_data['source_label']
#for _corr_level in ('L1L2L3', 'L1L2L3Res'):
for _corr_level in ('L1L2L3', ):
_ph = PlotHistograms1D(
basename="data_mc_hist_allData_07Aug2017_JEC{}".format(jecv),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_corr_level,
additional_cuts=None, #[None]*len(_SAMPLES),
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
normalize_to_first_histo=True,
show_ratio_to_first=True,
show_cut_info_text=True,
show_corr_folder_text=False,
plot_label="Run2016, 35.82 fb$^{-1}$ (13 TeV)",
y_subplot_label="Data/MC",
y_subplot_range=[0.78, 1.22],
)
_phs.append(_ph)
# add cut labels as text
for _p in _ph._plots:
if _p._qs[0].name in ('zpt_400', 'jet1pt', 'jet2pt', 'jet3pt'):
_p._basic_dict['y_log'] = True
_p._basic_dict['formats'] = ['pdf', 'png']
_p._basic_dict['texts'].extend(
[r"$\\bf{CMS}$", r"$\\it{Preliminary}$"])
_p._basic_dict['texts_size'].extend([25, 20])
_p._basic_dict['texts_x'].extend([0.04, 0.04])
_p._basic_dict['texts_y'].extend([0.94, 0.84])
for _ph in _phs:
_ph.make_plots()
def _workflow(sample):
_phs = []
for _run_period_cut_name, _run_period_cut in _run_period_cuts.iteritems():
if _run_period_cut is None:
_add_cuts = [_ac['cut'] for _ac in RUN_PERIOD_CUT_DICTS]
else:
_add_cuts = [(_ac['cut'] + _run_period_cut)
if _ac['cut'] is not None else _run_period_cut
for _ac in RUN_PERIOD_CUT_DICTS]
_source_label = "{}".format(_run_period_cut_name)
_SAMPLES = []
for _ac in _ADDITIONAL_CUTS:
_SAMPLES.append(deepcopy(sample))
_SAMPLES[-1]['color'] = _ac['color']
_SAMPLES[-1]['source_label'] = '{}, {}'.format(
_source_label, _ac['label'])
_ph = PlotHistograms1D(
basename="adhoc_etaphiveto_jetpthist_07Aug2017_{}".format(
_source_label),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_CORR_FOLDER,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
show_ratio_to_first=True,
show_cut_info_text=False)
_eta_range = _eta_ranges.get(_run_period_cut_name)
_phi_range = _phi_ranges.get(_run_period_cut_name)
for _plot in _ph._plots:
if _plot._q.name.endswith('eta') and _eta_range is not None:
_plot._basic_dict['vertical_lines'] = list(_eta_range)
if _plot._q.name.endswith('phi') and _phi_range is not None:
_plot._basic_dict['vertical_lines'] = list(_phi_range)
_phs.append(_ph)
for _ph in _phs:
_ph.make_plots()
def _workflow(sample_data, sample_mc, jecv):
_phs = []
_add_cuts = [_ac['cut'] for _ac in RUN_PERIOD_CUT_DICTS]
_add_cuts.insert(0, None)
_SAMPLES = []
_SAMPLES.append(deepcopy(sample_mc))
_SAMPLES[-1]['color'] = 'k'
_SAMPLES[-1]['source_label'] = sample_mc['source_label']
for _ac in RUN_PERIOD_CUT_DICTS:
_SAMPLES.append(deepcopy(sample_data))
_SAMPLES[-1]['color'] = _ac['color']
_SAMPLES[-1]['source_label'] = '{}'.format(_ac['label'])
#for _corr_level in ('L1L2L3', 'L1L2L3Res'):
for _corr_level in ('L1L2L3', ):
_ph = PlotHistograms1D(
basename="pf_fractions_hist_07Aug2017_JEC{}".format(jecv),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_corr_level,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
normalize_to_first_histo=True,
show_ratio_to_first=True,
show_cut_info_text=False,
plot_label="Summer16 JEC {}".format(jecv),
y_log_scale=True)
_phs.append(_ph)
for _ph in _phs:
_ph.make_plots()
def _old_workflow(sample_data, sample_mc, jecv):
_phs = []
_add_cuts = [_ac['cut'] for _ac in RUN_PERIOD_CUT_DICTS]
_add_cuts.insert(0, None)
_SAMPLES = []
_SAMPLES.append(deepcopy(sample_mc))
_SAMPLES[-1]['color'] = 'k'
_SAMPLES[-1]['source_label'] = sample_mc['source_label']
for _ac in RUN_PERIOD_CUT_DICTS:
_SAMPLES.append(deepcopy(sample_data))
_SAMPLES[-1]['color'] = _ac['color']
_SAMPLES[-1]['source_label'] = '{}'.format(_ac['label'])
#for _corr_level in ('L1L2L3', 'L1L2L3Res'):
for _corr_level in ('L1L2L3', 'L1L2Res'):
_ph = PlotHistograms1D(
basename="data_mc_hist_17Nov2017_JEC{}".format(jecv),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_corr_level,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
normalize_to_first=True,
show_ratio_to_first=True,
show_cut_info_text=False,
plot_label="Fall17 JEC {}".format(jecv))
#_phs.append(_ph)
for _bin_cut in _ADDITIONAL_CUTS_ETA:
break
_ph2 = PlotHistograms1D(
basename="data_mc_hist_etabins_17Nov2017_JEC{}".format(jecv),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_corr_level,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=[
_sel_cut + _bin_cut['cut'] for _sel_cut in _SELECTION_CUTS
],
normalize_to_first=True,
show_ratio_to_first=True,
show_cut_info_text=False,
plot_label="Fall17 JEC {}".format(jecv))
_phs.append(_ph2)
for _bin_cut in _ADDITIONAL_CUTS_ZPT:
break
_ph2 = PlotHistograms1D(
basename="data_mc_hist_zptbins_17Nov2017_JEC{}".format(jecv),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_corr_level,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=[
_sel_cut + _bin_cut['cut'] for _sel_cut in _SELECTION_CUTS
],
normalize_to_first=True,
show_ratio_to_first=True,
show_cut_info_text=False,
plot_label="Fall17 JEC {}".format(jecv))
_phs.append(_ph2)
for _bin_cut_1 in _ADDITIONAL_CUTS_ETA[17:]:
for _bin_cut_2 in _ADDITIONAL_CUTS_ZPT:
break
_ph2 = PlotHistograms1D(
basename="data_mc_hist_zptbins_17Nov2017_JEC{}".format(
jecv),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_corr_level,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=[
_sel_cut + _bin_cut_1['cut'] + _bin_cut_2['cut']
for _sel_cut in _SELECTION_CUTS
],
normalize_to_first=True,
show_ratio_to_first=True,
show_cut_info_text=False,
plot_label="Fall17 JEC {}".format(jecv))
_phs.append(_ph2)
for _ph in _phs:
_ph.make_plots()
def _workflow(sample, which='hist1D'):
_phs = []
for _run_period_cut_name, _run_period_cut in _run_period_cuts.iteritems():
if _run_period_cut is None:
_add_cuts = [_ac['cut'] for _ac in RUN_PERIOD_CUT_DICTS]
else:
_add_cuts = [(_ac['cut'] + _run_period_cut)
if _ac['cut'] is not None else _run_period_cut
for _ac in RUN_PERIOD_CUT_DICTS]
_source_label = "{}".format(_run_period_cut_name)
_SAMPLES = []
for _ac in _ADDITIONAL_CUTS:
_SAMPLES.append(deepcopy(sample))
_SAMPLES[-1]['color'] = _ac['color']
_SAMPLES[-1]['source_label'] = '{}, {}'.format(
_source_label, _ac['label'])
if which == 'hist1D':
_ph = PlotHistograms1D(
basename="adhoc_etaphiveto_1D_07Aug2017_{}".format(
_source_label),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_CORR_FOLDER,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantities=_QUANTITIES,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
show_ratio_to_first=True,
show_cut_info_text=False)
_eta_range = _hcal_hot_eta_ranges.get(_run_period_cut_name)
_phi_range = _hcal_hot_phi_ranges.get(_run_period_cut_name)
for _plot in _ph._plots:
if _plot._q.name.endswith('eta') and _eta_range is not None:
_plot._basic_dict['vertical_lines'] = list(_eta_range)
if _plot._q.name.endswith('phi') and _phi_range is not None:
_plot._basic_dict['vertical_lines'] = list(_phi_range)
if _plot._q.name == 'zpt':
del _plot._basic_dict['x_lims']
_plot._basic_dict['x_bins'] = 'zpt'
_plot._basic_dict['x_log'] = True
_phs.append(_ph)
elif which == 'hist2D':
for _ac in _add_cuts:
_cutname = "all" if _ac is None else _ac.name
_ph = PlotHistograms2D(
basename="adhoc_etaphiveto_2D_07Aug2017_{}_{}".format(
_cutname, _source_label),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_CORR_FOLDER,
additional_cuts=[_ac],
# each quantity cut generates a different plot
quantity_pairs=_QUANTITY_PAIRS,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
# show_ratio_to_first=True
)
_eta_range = _hcal_hot_eta_ranges.get(_run_period_cut_name)
_phi_range = _hcal_hot_phi_ranges.get(_run_period_cut_name)
for _plot in _ph._plots:
if (_plot._qy.name.endswith('eta')
or _plot._qy.name.endswith('eta_zoom')
) and _eta_range is not None:
_plot._basic_dict['lines'] = list(_eta_range)
if (_plot._qx.name.endswith('phi')
or _plot._qx.name.endswith('phi_zoom')
) and _phi_range is not None:
_plot._basic_dict['vertical_lines'] = list(_phi_range)
_phs.append(_ph)
elif which == 'profile':
_ph = PlotHistograms2D(
basename="adhoc_etaphiveto_profile_07Aug2017_{}".format(
_source_label),
# there is one subplot per sample and cut in each plot
samples=_SAMPLES,
jec_correction_string=_CORR_FOLDER,
additional_cuts=_add_cuts,
# each quantity cut generates a different plot
quantity_pairs=_QUANTITY_PAIRS_PROFILE,
# each selection cut generates a new plot
selection_cuts=_SELECTION_CUTS,
#show_ratio_to_first=True,
show_as_profile=True,
)
for _plot in _ph._plots:
del _plot._basic_dict['y_bins']
if _plot._qy.name == 'mpf' or _plot._qy.name == 'ptbalance':
_plot._basic_dict['y_lims'] = [0.8, 1.2]
if _plot._qx.name == 'zpt':
del _plot._basic_dict['x_lims']
_plot._basic_dict['x_bins'] = 'zpt'
_plot._basic_dict['x_log'] = True
_phs.append(_ph)
else:
raise ValueError("UNKNOWN 'which' = '{}'".format(which))
for _ph in _phs:
# for _plot in _ph._plots:
# print _plot.get_dict()
# import json
# with open('test.json', 'w') as f:
# json.dump(_plot.get_dict(), f)
# exit(44)
_ph.make_plots()
if which == 'hist1D':
for _plot in _ph._plots:
_plot.count_events(write_to_file=True)