htt/plot-cuts at master · qbuat/htt · GitHub

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#!/usr/bin/env python
from mva.cmd import get_parser

args = get_parser(actions=False).parse_args()

from mva.categories import (
    Category_Preselection,
    Category_VBF_NO_DETAJJ_CUT,
    Category_Boosted_NO_PTH_CUT,
    Category_Preselection_NO_MET_CENTRALITY)
from mva.analysis import get_analysis
from mva.plotting import draw_channel_array
from mva.variables import VARIABLES
from rootpy.tree import Cut
from rootpy.plotting import Hist
from math import pi


for year in (2011, 2012):
    analysis = get_analysis(
        args, year=year,
        systematics=False)
    # fakes_region='nOS_ISOL' for met centrality cut
    analysis.normalize(Category_Preselection)

    # show justification of the dEta cut location in VBF
    draw_channel_array(
        analysis, {'dEta_jets': VARIABLES['dEta_jets']},
        mass=125, mode=['gg', 'VBF'], signal_scale=100,
        stack_signal=False,
        signal_colors=['blue', 'red'],
        signal_linestyles=['dashed', 'solid'],
        category=Category_VBF_NO_DETAJJ_CUT,
        region=analysis.target_region,
        show_ratio=False,
        legend_leftmargin=0.28,
        output_dir='plots/categories',
        output_suffix='_{0}'.format(year % 1000),
        output_formats=['png', 'eps'],
        arrow_values=[2.])

    templates = {'resonance_pt': Hist(30, 0, 300)}
    # show justification of the ptH cut in boosted
    draw_channel_array(
        analysis, {'resonance_pt': VARIABLES['resonance_pt']},
        templates=templates,
        mass=125, mode=['gg', 'VBF'], signal_scale=100,
        stack_signal=False,
        signal_colors=['blue', 'red'],
        signal_linestyles=['dashed', 'solid'],
        category=Category_Boosted_NO_PTH_CUT,
        region=analysis.target_region,
        show_ratio=False,
        legend_leftmargin=0.28,
        output_dir='plots/categories',
        output_suffix='_{0}'.format(year % 1000),
        output_formats=['png', 'eps'],
        arrow_values=[100],
        log_ypadding=(0.3, 0))

    # show justification of the MET centrality cut at preselection
    draw_channel_array(
        analysis, {'MET_bisecting': VARIABLES['MET_bisecting']},
        mass=125, mode='combined', signal_scale=50,
        stack_signal=True,
        separate_legends=True,
        category=Category_Preselection_NO_MET_CENTRALITY,
        region=analysis.target_region,
        show_ratio=False,
        output_dir='plots/categories',
        output_suffix='_{0}'.format(year % 1000),
        output_formats=['png', 'eps'],
        ypadding=(0.55, 0))

    draw_channel_array(
        analysis, {'dPhi_min_tau_MET': VARIABLES['dPhi_min_tau_MET']},
        mass=125, mode='combined', signal_scale=50,
        stack_signal=True,
        separate_legends=True,
        category=Category_Preselection_NO_MET_CENTRALITY,
        region=analysis.target_region,
        show_ratio=False,
        output_dir='plots/categories',
        output_suffix='_{0}'.format(year % 1000),
        output_formats=['png', 'eps'],
        cuts=Cut('!MET_bisecting'),
        arrow_values=[pi / 4])