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yukawa_plots.py
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yukawa_plots.py
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#!/usr/bin/env python
"""
Thomas Klijnsma
"""
########################################
# Imports
########################################
from OptionHandler import flag_as_option
import LatestPaths
import LatestBinning
import differentials
import differentialutils
import logging
from collections import namedtuple
from time import strftime
datestr = strftime( '%b%d' )
import ROOT
from array import array
import sys
import copy
########################################
# Main
########################################
x_coupling = 'kappac'
y_coupling = 'kappab'
titles = { 'kappac': '#kappa_{c}', 'kappab' : '#kappa_{b}' }
yukawa_x_min = -35
yukawa_x_max = 35
yukawa_y_min = -11
yukawa_y_max = 12
couplingdependentBRs_x_min = -8.
couplingdependentBRs_x_max = 8.
couplingdependentBRs_y_min = -2.
couplingdependentBRs_y_max = 2.
floatingBRs_x_min = -75.
floatingBRs_x_max = 75.
floatingBRs_y_min = -30.
floatingBRs_y_max = 40.
@flag_as_option
def all_plots_Yukawa(args):
multicont_Yukawa(args)
points_on_contour_Yukawa(args)
args = differentialutils.force_asimov(args)
multicont_Yukawa(args)
multicont_Yukawa_highLumi(args)
multicont_Yukawa_profiledTotalXS(args)
scandict_G = differentials.core.AttrDict()
# scandict_G.G0A = LatestPaths.scan.yukawa.reweighted.asimov.combination
scandict_G.G0A = 'out/Scan_Yukawa_May11_combination_G0A_asimov'
# scandict_G.G0B = 'out/Scan_Yukawa_May11_combination_G0B_asimov_0'
scandict_G.G1A = 'out/Scan_Yukawa_May11_combination_G1A_asimov'
# scandict_G.G1B = 'out/Scan_Yukawa_May11_combination_G1B_asimov_0'
# scandict_G.G2A = 'out/Scan_Yukawa_May11_combination_G2A_asimov'
scandict_G.G1BKV = 'out/Scan_Yukawa_May11_combination_G1BKV_asimov_0'
scandict_G.G0B = 'out/Scan_Yukawa_May14_combination_G0B_asimov'
scandict_G.G1B = 'out/Scan_Yukawa_May14_combination_G1B_asimov'
scandict_G.G2A = 'out/Scan_Yukawa_May14_combination_G2A_asimov'
scandict_G.G1A_unreweighted = 'out/Scan_Yukawa_May14_combination_G1A_unreweighted_asimov/'
scandict_G.G1B_reweighted = 'out/Scan_Yukawa_May14_combination_G1B_reweighted_asimov'
# scandict_G.G0B_reweighted = 'out/Scan_Yukawa_May16_combination_G0B_reweighted_asimov'
scandict_G.G0B_reweighted = 'out/Scan_Yukawa_May17_combination_G0B_reweighted_asimov'
# approval = differentials.core.AttrDict()
# approval.scalingbbH_couplingdependentBRs = differentials.core.AttrDict()
# approval.scalingbbH_couplingdependentBRs.data = 'out/Scan_Yukawa_May24_combination_scalingbbH_couplingdependentBRs'
# approval.scalingbbH_couplingdependentBRs.asimov = 'out/Scan_Yukawa_May22_combination_scalingbbH_couplingdependentBRs_asimov'
# approval.scalingbbH_fixedSMBRs = 'out/Scan_Yukawa_May18_combination_scalingbbH_asimov'
approval = differentials.core.AttrDict.create_tree(['fixedBRs', 'couplingdependentBRs', 'floatingBRs'], ['asimov', 'observed'])
approval.couplingdependentBRs.asimov.combination = 'out/Scan_Yukawa_Jun05_combination_NONscalingbbH_couplingdependentBRs_asimov'
approval.couplingdependentBRs.observed.combination = 'out/Scan_Yukawa_May30_combination_NONscalingbbH_couplingdependentBRs'
approval.couplingdependentBRs.observed.hgg = 'out/Scan_Yukawa_Jun07_hgg_NONscalingbbH_couplingdependentBRs'
approval.couplingdependentBRs.observed.hzz = 'out/Scan_Yukawa_Jun11_hzz_NONscalingbbH_couplingdependentBRs'
# approval.floatingBRs.asimov.combination = 'out/Scan_Yukawa_May24_combination_NONscalingbbH_floatingBRs_asimov'
approval.floatingBRs.asimov.combination = 'out/Scan_Yukawa_Jun09_combination_NONscalingbbH_floatingBRs_asimov'
approval.floatingBRs.observed.combination = 'out/Scan_Yukawa_May30_combination_NONscalingbbH_floatingBRs'
approval.floatingBRs.observed.hgg = 'out/Scan_Yukawa_Jun07_hgg_NONscalingbbH_floatingBRs'
approval.floatingBRs.observed.hzz = 'out/Scan_Yukawa_Jun11_hzz_NONscalingbbH_floatingBRs_0'
#____________________________________________________________________
def latest_couplingdependentBRs(args, decay_channel=None, asimov=None, splined=False):
if not(asimov is None): args = differentialutils.force_asimov(args, asimov)
if decay_channel is None: decay_channel = differentialutils.get_decay_channel_tag(args)
scandir = approval.couplingdependentBRs['asimov' if args.asimov else 'observed'][decay_channel]
scan = differentials.scans.Scan2D(
'kbkc_{0}'.format(decay_channel), x_coupling, y_coupling,
scandir = scandir
)
scan.title = differentials.core.standard_titles.get(decay_channel, decay_channel)
scan.color = 1
scan.read()
if splined: return spline_couplingdependentBRs(scan)
return scan
def spline_couplingdependentBRs(scan):
# Careful with ranges; too far outside causes noise
spline = scan.to_spline(
x_min = -8.,
x_max = 8.,
y_min = -1.8,
y_max = 2.1,
)
spline.disallow_negativity = False
hist = spline.to_hist(nx=300, ny=300)
# hist = spline.to_hist(nx=600, ny=600)
hist.color = scan.color
hist.name = scan.name + '_splined'
hist.title = scan.title
return hist
def latest_floatingBRs(args, decay_channel=None, asimov=None, splined=False):
if not(asimov is None): args = differentialutils.force_asimov(args, asimov)
if not(decay_channel is None): args = differentialutils.set_one_decay_channel(args, decay_channel)
decay_channel = differentialutils.get_decay_channel_tag(args)
scandir = approval.floatingBRs['asimov' if args.asimov else 'observed'][decay_channel]
scan = differentials.scans.Scan2D(
'kbkc_{0}'.format(decay_channel), x_coupling, y_coupling,
scandir = scandir
)
scan.title = differentials.core.standard_titles.get(decay_channel, decay_channel)
scan.color = 1
scan.read()
if splined: return spline_floatingBRs(args, scan)
return scan
def spline_floatingBRs(args, scan):
# Careful with ranges; too far outside causes noise
x_min = -75.
x_max = 75.
y_min = -30.
y_max = 40.
eps = 2.2
deltaNLL_cutoff = 30.
if args.hzz:
x_min = -35.
x_max = 40.
y_min = -9.
y_max = 19.
eps = 2.5
deltaNLL_cutoff = 10.
spline = scan.to_spline(
x_min = x_min,
x_max = x_max,
y_min = y_min,
y_max = y_max,
eps = eps,
deltaNLL_cutoff = deltaNLL_cutoff
)
spline.disallow_negativity = False
hist = spline.to_hist(nx=300, ny=300)
hist.color = scan.color
hist.name = scan.name + '_splined'
hist.title = scan.title
return hist
def latest_scenario3(args, splined=False):
if args.asimov:
scandir = 'out/Scan_Yukawa_May11_combination_G0A_asimov'
else:
scandir = LatestPaths.scan.yukawa.reweighted.observed.combination
scan = differentials.scans.Scan2D(
'scenario3', x_coupling, y_coupling,
scandir = scandir
)
scan.title = differentials.core.standard_titles['hgg'] + ' + ' + differentials.core.standard_titles['hzz']
scan.color = 1
scan.read()
if not splined: return scan
x_min = -18.
x_max = 18.
y_min = -5.
y_max = 6.5
eps = 2.2
deltaNLL_cutoff = 30.
spline = scan.to_spline(
x_min = x_min,
x_max = x_max,
y_min = y_min,
y_max = y_max,
# eps = eps,
# deltaNLL_cutoff = deltaNLL_cutoff
)
spline.disallow_negativity = False
hist = spline.to_hist(nx=180, ny=180, x_min=1.1*x_min, x_max=1.1*x_max, y_min=1.2*y_min, y_max=1.3*y_max)
hist.color = scan.color
hist.name = scan.name + '_splined'
hist.title = scan.title
return hist
#____________________________________________________________________
@flag_as_option
def multicont_Yukawa_NONscalingbbH_floatingBRs(args):
scans = []
scans.append(latest_floatingBRs(args, decay_channel='combination', splined=True))
if not args.asimov:
# hzz = latest_floatingBRs(args, decay_channel='hzz', splined=True)
hzz = latest_floatingBRs(args, decay_channel='hzz', splined=False)
hzz.color = differentials.core.safe_colors.blue
scans.append(hzz)
hgg = latest_floatingBRs(args, decay_channel='hgg', splined=True)
hgg.color = differentials.core.safe_colors.red
scans.append(hgg)
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_floatingBRs' + ('_asimov' if args.asimov else ''),
scans,
x_title = differentials.core.standard_titles['kappac'], y_title = differentials.core.standard_titles['kappab'],
x_min=floatingBRs_x_min, x_max=floatingBRs_x_max, y_min=floatingBRs_y_min, y_max=floatingBRs_y_max
)
plot.draw(wait=True)
plot.add_BR_floating_text()
plot.wrapup()
@flag_as_option
def multicont_Yukawa_NONscalingbbH_couplingdependentBRs(args):
do_spline = True
scans = []
scans.append(latest_couplingdependentBRs(args, decay_channel='combination', splined=do_spline))
if not args.asimov:
hzz = latest_couplingdependentBRs(args, decay_channel='hzz', splined=do_spline)
hzz.color = differentials.core.safe_colors.blue
scans.append(hzz)
hgg = latest_couplingdependentBRs(args, decay_channel='hgg', splined=do_spline)
hgg.color = differentials.core.safe_colors.red
scans.append(hgg)
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_couplingdependentBRs' + ('_asimov' if args.asimov else ''),
scans,
x_title = differentials.core.standard_titles['kappac'], y_title = differentials.core.standard_titles['kappab'],
x_min=couplingdependentBRs_x_min, x_max=couplingdependentBRs_x_max, y_min=couplingdependentBRs_y_min, y_max=couplingdependentBRs_y_max
)
plot.draw(wait=True)
plot.add_BR_parametrized_text()
plot.wrapup()
@flag_as_option
def multicont_Yukawa_NONscalingbbH(args):
args = differentialutils.force_asimov(args)
hist = latest_scenario3(args, splined=True)
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_scenario3' + ('_asimov' if args.asimov else ''),
[ hist ],
x_title = differentials.core.standard_titles['kappac'], y_title = differentials.core.standard_titles['kappab'],
# x_min=1.1*x_min, x_max=1.1*x_max, y_min=1.1*y_min, y_max=1.1*y_max
)
plot.legend.set(
x1 = lambda c: c.GetLeftMargin() + 0.02,
x2 = lambda c: c.GetLeftMargin() + 0.30,
y1 = lambda c: 1. - c.GetTopMargin() - 0.11,
y2 = lambda c: 1. - c.GetTopMargin() - 0.01,
)
plot.draw()
#____________________________________________________________________
@flag_as_option
def onedimscans_Yukawa_scalingbbH_couplingdependentBRs(args):
args = differentialutils.set_one_decay_channel(args, 'combination')
obs2D = latest_couplingdependentBRs(args, splined=True)
exp2D = latest_couplingdependentBRs(args, splined=True, asimov=True)
onedimscans_Yukawa('kappab', exp2D, obs2D, x_min=-4.0, x_max=4.0, apply_smoothing=True, tag='couplingdependentBRs')
onedimscans_Yukawa('kappac', exp2D, obs2D, x_min=-15., x_max=15., apply_smoothing=True, tag='couplingdependentBRs')
@flag_as_option
def onedimscans_Yukawa_scalingbbH_floatingBRs(args):
args = differentialutils.set_one_decay_channel(args, 'combination')
obs2D = latest_floatingBRs(args, splined=True)
exp2D = latest_floatingBRs(args, splined=True, asimov=True)
onedimscans_Yukawa('kappab', exp2D, obs2D, x_min=-30.0, x_max=30.0, tag='floatingBRs')
onedimscans_Yukawa('kappac', exp2D, obs2D, x_min=-75., x_max=75., tag='floatingBRs')
@flag_as_option
def onedimscans_Yukawa_scalingbbH_scenario3(args):
args = differentialutils.set_one_decay_channel(args, 'combination', asimov=True)
# obs2D = latest_floatingBRs(args, splined=True)
exp2D = latest_scenario3(args, splined=True)
onedimscans_Yukawa('kappab', exp2D, x_min=-10.0, x_max=10.0, tag='scenario3')
onedimscans_Yukawa('kappac', exp2D, x_min=-20., x_max=20., tag='scenario3')
def f2digits(number):
if abs(number) >= 10.:
n_decimals = 0
elif abs(number) >= 1.:
n_decimals = 1
else:
n_decimals = 2
return '{0:.{n_decimals}f}'.format(number, n_decimals=n_decimals)
def onedimscans_Yukawa(kappa, exp2D, obs2D=None, x_min=-10., x_max=10., apply_smoothing=False, tag=None):
do_95percent_CL = True
if not obs2D is None:
onedimscanner_obs = differentials.onedimscanner.OneDimScanner(
obs2D,
'kappac', 'kappab',
do_95percent_CL = do_95percent_CL
)
obs1D = onedimscanner_obs.get_1d(kappa)
obs1D.draw_style = 'repr_smooth_line'
obs1D.title = '{0} observed; ({1} - {2}) ({3}% CL)'.format(
differentials.core.standard_titles[kappa],
f2digits(obs1D.unc.left_bound), f2digits(obs1D.unc.right_bound),
'95' if do_95percent_CL else '68'
)
obs1D.color = 1
if apply_smoothing: obs1D.smooth_y(10)
onedimscanner_exp = differentials.onedimscanner.OneDimScanner(
exp2D,
'kappac', 'kappab',
do_95percent_CL = do_95percent_CL
)
exp1D = onedimscanner_exp.get_1d(kappa)
exp1D.draw_style = 'repr_dashed_line'
exp1D.title = '{0} expected; ({1} - {2}) ({3}% CL)'.format(
differentials.core.standard_titles[kappa],
f2digits(exp1D.unc.left_bound), f2digits(exp1D.unc.right_bound),
'95' if do_95percent_CL else '68'
)
exp1D.color = 1
if apply_smoothing: exp1D.smooth_y(10)
plotname = 'onekappascan_kbkc'
if not(tag is None): plotname += '_' + tag
plotname += '_' + kappa
differentials.plotting.canvas.c.resize_temporarily(850, 800)
plot = differentials.plotting.plots.MultiScanPlot(plotname)
plot.do_95percent_CL = do_95percent_CL
plot.y_max = 7.0
plot.y_cutoff = 5.0
if not obs2D is None: plot.manual_graphs.append(obs1D)
plot.manual_graphs.append(exp1D)
plot.x_title = differentials.core.standard_titles[kappa]
plot.x_min = x_min
plot.x_max = x_max
plot.leg.SetNColumns(1)
plot.leg._y1 = lambda c: 1. - c.GetTopMargin() - 0.20
plot.draw()
plot.wrapup()
texstr_var = lambda cmdname, value: '\\newcommand{{\\{0}}}{{{1}}}'.format(cmdname, value)
print texstr_var('{0}LeftAsimov'.format(kappa), f2digits(exp1D.unc.left_bound))
print texstr_var('{0}RightAsimov'.format(kappa), f2digits(exp1D.unc.right_bound))
if not obs2D is None:
print texstr_var('{0}LeftObserved'.format(kappa), f2digits(obs1D.unc.left_bound))
print texstr_var('{0}RightObserved'.format(kappa), f2digits(obs1D.unc.right_bound))
#____________________________________________________________________
@flag_as_option
def multicont_Yukawa_scalingbbH_couplingdependentBRs(args):
if args.asimov:
scandir = approval.scalingbbH_couplingdependentBRs.asimov
else:
scandir = approval.scalingbbH_couplingdependentBRs.data
scalingbbH = differentials.scans.Scan2D(
'scalingbbH', x_coupling, y_coupling,
scandir = scandir
)
scalingbbH.title = 'Combination (incl. bbH / BR(#vec{#kappa}))'
scalingbbH.color = 1
scalingbbH.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_scalingbbH_couplingdependentBRs' + ('_asimov' if args.asimov else ''),
[ scalingbbH ],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_scalingbbH_floatingBRs(args):
if args.asimov:
scandir = 'out/Scan_Yukawa_May23_combination_scalingbbH_floatingBRs_asimov_1'
else:
scandir = 'out/Scan_Yukawa_May24_combination_scalingbbH_floatingBRs'
scalingbbH = differentials.scans.Scan2D(
'scalingbbH', x_coupling, y_coupling,
scandir = scandir
)
scalingbbH.title = 'Combination (incl. bbH / BRs free)'
scalingbbH.color = 1
scalingbbH.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_scalingbbH_floatingBRs' + ('_asimov' if args.asimov else ''),
[ scalingbbH ],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_scalingbbH(args):
scans = []
compare_bbH = False
if compare_bbH:
G0A = differentials.scans.Scan2D('nominal', x_coupling, y_coupling, scandir = scandict_G.G0A)
G0A.title = 'Nominal'
G0A.color = 2
G0A.read()
scans.append(G0A)
scalingbbH = differentials.scans.Scan2D('scalingbbH', x_coupling, y_coupling, scandir = 'out/Scan_Yukawa_May18_combination_scalingbbH_asimov')
scalingbbH.title = 'incl. scaling bbH'
scalingbbH.color = 1
scalingbbH.read()
scans.append(scalingbbH)
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_scalingbbH' + ( '_comparebbH' if compare_bbH else '' ),
scans,
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
#____________________________________________________________________
@flag_as_option
def multicont_Yukawa_G2A(args):
G2A = differentials.scans.Scan2D('G2A', x_coupling, y_coupling, scandir = scandict_G.G2A)
G2A.color = 2
G2A.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_G2A',
[
G2A,
],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_G0(args):
G0A = differentials.scans.Scan2D('G0A', x_coupling, y_coupling, scandir = scandict_G.G0A)
G0A.color = 4
G0A.read()
G0B_reweighted = differentials.scans.Scan2D('G0B_reweighted', x_coupling, y_coupling, scandir = scandict_G.G0B_reweighted)
G0B_reweighted.title = 'G0B'
G0B_reweighted.color = 2
G0B_reweighted.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_G0',
[
G0A,
G0B_reweighted,
],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_G1(args):
G1A = differentials.scans.Scan2D('G1A', x_coupling, y_coupling, scandir = scandict_G.G1A)
G1A.color = 38
G1A.read()
G1B_reweighted = differentials.scans.Scan2D('G1B_reweighted', x_coupling, y_coupling, scandir = scandict_G.G1B_reweighted)
G1B_reweighted.color = 46
G1B_reweighted.read()
G1A_unreweighted = differentials.scans.Scan2D('G1A_unreweighted', x_coupling, y_coupling, scandir = scandict_G.G1A_unreweighted)
G1A_unreweighted.color = 4
G1A_unreweighted.read()
G1B = differentials.scans.Scan2D('G1B', x_coupling, y_coupling, scandir = scandict_G.G1B)
G1B.color = 2
G1B.read()
# G1BKV = differentials.scans.Scan2D('G1BKV', x_coupling, y_coupling, scandir = scandict_G.G1BKV)
# G1BKV.color = 9
# G1BKV.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_G1',
[
G1A_unreweighted,
G1B,
G1A,
G1B_reweighted,
],
x_min=0.35*yukawa_x_min, x_max=0.35*yukawa_x_max, y_min=0.35*yukawa_y_min, y_max=0.35*yukawa_y_max
)
plot.only_1sigma_contours = True
plot.draw()
scandict_G.G1A_noTheoryUnc = 'out/Scan_Yukawa_May15_combination_G1A_reweighted_noTheoryUnc_asimov'
# scandict_G.G1B_noTheoryUnc = 'out/Scan_Yukawa_May15_combination_G1B_reweighted_noTheoryUnc_asimov'
# scandict_G.G1A_noTheoryUnc_scaledByMuTotalXS = 'out/Scan_Yukawa_May16_combination_G1A_reweighted_noTheoryUnc_scaledByMuTotalXS_asimov'
scandict_G.G1B_noTheoryUnc = 'out/Scan_Yukawa_May16_combination_G1B_reweighted_noTheoryUnc_asimov'
scandict_G.G1A_noTheoryUnc_scaledByMuTotalXS = 'out/Scan_Yukawa_May16_combination_G1A_reweighted_noTheoryUnc_scaledByMuTotalXS_asimov_0'
@flag_as_option
def multicont_Yukawa_G1_noTheoryUnc(args):
G1A = differentials.scans.Scan2D('G1A', x_coupling, y_coupling, scandir = scandict_G.G1A_noTheoryUnc)
G1A.color = 2
G1A.read()
G1B = differentials.scans.Scan2D('G1B', x_coupling, y_coupling, scandir = scandict_G.G1B_noTheoryUnc)
G1B.color = 4
G1B.read()
# G1A_scaledByMuTotalXS = differentials.scans.Scan2D('G1A_scaledByMuTotalXS', x_coupling, y_coupling, scandir = scandict_G.G1A_noTheoryUnc_scaledByMuTotalXS)
# G1A_scaledByMuTotalXS.color = 3
# G1A_scaledByMuTotalXS.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_G1_noTheoryUnc',
[
G1A,
G1B,
# G1A_scaledByMuTotalXS,
],
x_min=0.35*yukawa_x_min, x_max=0.35*yukawa_x_max, y_min=0.35*yukawa_y_min, y_max=0.35*yukawa_y_max
)
plot.only_1sigma_contours = True
plot.draw()
@flag_as_option
def multicont_Yukawa_compareBRuncertainties(args):
args = differentialutils.set_one_decay_channel(args, 'combination', asimov=True)
scandict = LatestPaths.scan.yukawa.reweighted.asimov
# combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir = scandict.combination)
# combination.color = 1
# combination.read()
# hgg = differentials.scans.Scan2D('hgg', x_coupling, y_coupling, scandir = scandict.hgg)
# hgg.color = 2
# hgg.read()
hzz = differentials.scans.Scan2D('hzz', x_coupling, y_coupling, scandir = scandict.hzz)
hzz.color = 4
hzz.read()
hzz_BRU = differentials.scans.Scan2D('hzz_BRU', x_coupling, y_coupling, scandir = 'out/Scan_Yukawa_May08_hzz_withBRuncertainties_asimov')
hzz_BRU.color = 2
hzz_BRU.title = differentials.core.standard_titles['hzz'] + ' with BR unc.'
hzz_BRU.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_compareBRuncertainties' + ('_asimov' if args.asimov else ''),
[hzz, hzz_BRU],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa(args):
multicont_Yukawa_reweighted(args)
multicont_Yukawa_unreweighted(args)
@flag_as_option
def multicont_Yukawa_reweighted(args):
scandict = LatestPaths.scan.yukawa.reweighted.asimov if args.asimov else LatestPaths.scan.yukawa.reweighted.observed
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir = scandict.combination)
combination.color = 1
combination.read()
hgg = differentials.scans.Scan2D('hgg', x_coupling, y_coupling, scandir = scandict.hgg)
hgg.color = 2
hgg.read()
hzz = differentials.scans.Scan2D('hzz', x_coupling, y_coupling, scandir = scandict.hzz)
hzz.color = 4
hzz.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_reweighted' + ('_asimov' if args.asimov else ''),
[combination, hgg, hzz],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_reweighted_paper(args):
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir = scandict.combination)
combination.color = 1
combination.read()
hgg = differentials.scans.Scan2D('hgg', x_coupling, y_coupling, scandir = scandict.hgg)
hgg.color = 2
hgg.read()
hzz = differentials.scans.Scan2D('hzz', x_coupling, y_coupling, scandir = scandict.hzz)
hzz.color = 4
hzz.read()
plot = differentials.plotting.plots.Single2DHistPlot(
'multicont_Yukawa_paper' + ('_asimov' if args.asimov else ''),
combination.to_hist()
)
# plot = differentials.plotting.plots.MultiContourPlot(
# 'multicont_Yukawa_reweighted' + ('_asimov' if args.asimov else ''),
# [combination, hgg, hzz],
# x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
# )
plot.draw()
plot.wrapup()
@flag_as_option
def multicont_Yukawa_unreweighted(args):
scandict = LatestPaths.scan.yukawa.unreweighted.asimov if args.asimov else LatestPaths.scan.yukawa.unreweighted.observed
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir = scandict.combination)
combination.color = 1
combination.read()
hgg = differentials.scans.Scan2D('hgg', x_coupling, y_coupling, scandir = scandict.hgg)
hgg.color = 2
hgg.read()
hzz = differentials.scans.Scan2D('hzz', x_coupling, y_coupling, scandir = scandict.hzz)
hzz.color = 4
hzz.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_unreweighted' + ('_asimov' if args.asimov else ''),
[combination, hgg, hzz],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
#____________________________________________________________________
# Extra studies
@flag_as_option
def multicont_Yukawa_highLumi(args):
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir=LatestPaths.scan.yukawa.reweighted.asimov.combination)
combination.color = 1
combination.title = '35.9 fb^{-1}'
combination.read()
lumi300fb = differentials.scans.Scan2D('lumi300fb', x_coupling, y_coupling, scandir=LatestPaths.scan.yukawa.lumi300fb)
lumi300fb.color = 4
lumi300fb.title = '300 fb^{-1}'
lumi300fb.read()
lumi300fb.contour_filter_method = 'max_distance_to_com'
# raise NotImplementedError('Need to multiply values from 300 fb-1')
# lumi_3000 = differentials.scans.Scan2D('lumi_3000', x_coupling, y_coupling,
# scandir=LatestPaths.scan_combined_Yukawa_lumiStudy_asimov
# )
# lumi_3000.color = 4
# lumi_3000.title = '3000 fb^{-1}'
# lumi_3000.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_highLumi',
[combination, lumi300fb],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_profiledTotalXS(args):
# Range too small... range should be higher to observe starlike pattern.
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir=LatestPaths.scan.yukawa.reweighted.asimov.combination)
combination.color = 1
combination.title = 'Nominal'
combination.read()
profiled_total_XS = differentials.scans.Scan2D('profiled_total_XS', x_coupling, y_coupling, scandir=LatestPaths.scan.yukawa.profiledTotalXS)
profiled_total_XS.color = 4
profiled_total_XS.title = '#sigma_{tot} profiled'
profiled_total_XS.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_profiledTotalXS',
[combination, profiled_total_XS],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
#____________________________________________________________________
# To redo
@flag_as_option
def multicont_Yukawa_ratio_of_BRs(args):
containers = []
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_asimov
)
combination.color = 1
combination.title = 'SM BR'
combination.read()
ratio_of_BRs = differentials.scans.Scan2D('ratio_of_BRs', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_ratioOfBRs_asimov
)
ratio_of_BRs.color = 4
ratio_of_BRs.title = 'floating BR_{H#gamma#gamma}/BR_{HZZ}'
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_ratio_of_BRs',
[combination, ratio_of_BRs],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_onlyNormalization(args):
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_asimov
)
combination.color = 1
combination.title = 'Nominal'
combination.read()
only_normalization = differentials.scans.Scan2D('only_normalization', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_onlyNormalization_asimov
)
only_normalization.color = 4
only_normalization.title = 'Only normalization'
only_normalization.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_onlyNormalization',
[combination, only_normalization],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_theoryCrossCheck(args):
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_asimov
)
combination.color = 1
combination.title = 'Nominal'
combination.read()
no_theory_unc = differentials.scans.Scan2D('no_theory_unc', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_noTheoryUncertainties_asimov
)
no_theory_unc.color = 4
no_theory_unc.title = 'No th. uncertainty'
no_theory_unc.read()
uncorr_theory_unc = differentials.scans.Scan2D('uncorr_theory_unc', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_uncorrelatedTheoryUncertainties_asimov
)
uncorr_theory_unc.color = 2
uncorr_theory_unc.title = 'Uncorr. uncertainty'
uncorr_theory_unc.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_theoryCrossCheck',
[combination, no_theory_unc, uncorr_theory_unc],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_BRdependencyComparison(args):
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_asimov
)
combination.color = 1
combination.title = 'Nominal'
combination.read()
brscal_kappaV_fixed = differentials.scans.Scan2D('brscal_kappaV_fixed', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_couplingDependentBR_fixedKappaV_asimov
)
brscal_kappaV_fixed.color = 4
brscal_kappaV_fixed.title = 'BR(#kappa_{b}, #kappa_{c})' # + ' (#kappa_{V} fixed)'
brscal_kappaV_fixed.read()
brscal_profiledTotalXS = differentials.scans.Scan2D('brscal_profiledTotalXS', x_coupling, y_coupling,
scandir='out/Scan_Yukawa_Feb07_combination_couplingDependentBR_profiledTotalXS'
)
brscal_profiledTotalXS.color = 2
brscal_profiledTotalXS.title = 'BR(#kappa_{b}, #kappa_{c}, #sigma_{tot})' # + ' (#kappa_{V} fixed)'
brscal_profiledTotalXS.read()
brscal_kappaV_max1 = differentials.scans.Scan2D('brscal_kappaV_max1', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_couplingDependentBR_kappaVMaxOne_asimov
)
brscal_kappaV_max1.color = 8
brscal_kappaV_max1.title = 'BR(#kappa_{t}, #kappa_{V}#leq1)'
brscal_kappaV_max1.read()
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_BRdependencyComparison',
[combination, brscal_kappaV_fixed, brscal_profiledTotalXS, brscal_kappaV_max1],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
@flag_as_option
def multicont_Yukawa_atfchi2(args):
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling,
scandir=LatestPaths.scan_combined_Yukawa_noTheoryUncertainties_asimov
)
combination.color = 1
combination.title = 'Full likelihood (no theor. unc.)'
combination.read()
raise NotImplementedError('Have to fix the \'as theorist\' scan')
# Get relevant info from a canvas that has the histogram and the bestfit point
atfchi2_rootFp = ROOT.TFile.Open( 'plots_Nov14_Yukawa/AfterTheFactChi2Fit.root' )
canvas = atfchi2_rootFp.Get('ctc')
# Get TH2F
atfchi2_H = canvas.GetPrimitive('H2')
ROOT.SetOwnership( atfchi2_H, False )
# Get values of x and y for bestfit
bestfitpoint_Tg = canvas.GetPrimitive('bestfitpoint')
x_Double = ROOT.Double(0)
y_Double = ROOT.Double(0)
bestfitpoint_Tg.GetPoint( 0, x_Double, y_Double )
xBestfit = float(x_Double)
yBestfit = float(y_Double)
atfchi2_rootFp.Close()
# Construct container
atfchi2 = Container()
atfchi2.H2 = atfchi2_H
atfchi2.xBestfit = xBestfit
atfchi2.yBestfit = yBestfit
atfchi2.color = 2
atfchi2.name = 'atfchi2'
atfchi2.title = 'Simple #chi^{2} fit'
containers.append( atfchi2 )
plot = differentials.plotting.plots.MultiContourPlot(
'multicont_Yukawa_atfchi2',
[combination, atfchi2],
x_min=yukawa_x_min, x_max=yukawa_x_max, y_min=yukawa_y_min, y_max=yukawa_y_max
)
plot.draw()
# ======================================
# Other plots
@flag_as_option
def points_on_contour_Yukawa(args):
if args.asimov:
scandir = LatestPaths.scan.yukawa.reweighted.asimov.combination
else:
# scandir = LatestPaths.scan.yukawa.reweighted.observed.combination
scandir = approval.scalingbbH_couplingdependentBRs.data
combination = differentials.scans.Scan2D('combination', x_coupling, y_coupling, scandir=scandir)
combination.color = 1
combination.read()
obs = LatestBinning.obs_pth_ggH
obs.drop_bins_up_to_value(125.)
obs_name = 'pth_ggH'
scandict = LatestPaths.scan.pth_ggH.asimov if args.asimov else LatestPaths.scan.pth_ggH.observed
pth_ggH_combination = differentials.scans.DifferentialSpectrum('combination', scandict.combination)
pth_ggH_combination.color = 1
pth_ggH_combination.no_overflow_label = True
pth_ggH_combination.draw_method = 'repr_point_with_vertical_bar'
pth_ggH_combination.set_sm(obs.crosssection_over_binwidth(normalize_by_second_to_last_bin_width=False))
pth_ggH_combination.drop_bins_up_to_value(125.)
pth_ggH_combination.read()
# ws = LatestPaths.ws_combined_TopHighPt
# ws = LatestPaths.ws_combined_Yukawa
# ws = 'out/workspaces_Feb01/combinedCard_Nov03_CouplingModel_Yukawa_withTheoryUncertainties.root'
# ws = LatestPaths.ws.yukawa.nominal.combination
ws = 'out/workspaces_May30/combination_Yukawa_reweighted_couplingdependentBRs.root'
# ======================================
# Load into plot
plot = differentials.plotting.plots.BottomPanelPlotWithParametrizations('points_on_contour_Yukawa')
plot.scan2D = combination
plot.ws_file = ws
plot.ptspectrum = pth_ggH_combination
plot.obs = obs
# plot.top_y_min = top_y_min
plot.top_y_max = 100.
obs_title = 'p_{T}'
obs_unit = 'GeV'
plot.x_title = obs_title + (' ({0})'.format(obs_unit) if not(obs_unit is None) else '')
plot.y_title_top = '#Delta#sigma/#Delta{0} (pb{1})'.format(
obs_title,
'/' + obs_unit if not(obs_unit is None) else ''
)
plot.y_title_bottom = '#mu'
plot.draw()
@flag_as_option
def one_kappa_scan(args):
one_kappa_scan_plot_manualProfile(args, 'kappab')
one_kappa_scan_plot(args, 'kappac')
def one_kappa_scan_plot(args, kappa):
differentials.plotting.canvas.c.resize_temporarily(850, 800)
scandict = LatestPaths.scan.yukawa.onekappa
scans = []
observed = differentials.scans.Scan(kappa, scandir=scandict.observed[kappa])
observed.color = 1
observed.title = '{0} observed'.format(differentials.core.standard_titles[kappa])
observed.read()
observed.create_uncertainties()
observed.title += '; ({0:.2f} - {1:.2f}) @ 68% CL'.format(observed.unc.left_bound, observed.unc.right_bound)
scans.append(observed)
expected = differentials.scans.Scan(kappa, scandir=scandict.asimov[kappa])
expected.color = 1
expected.title = '{0} expected'.format(differentials.core.standard_titles[kappa])
expected.read()
expected.create_uncertainties()
expected.title += '; ({0:.2f} - {1:.2f}) @ 68% CL'.format(expected.unc.left_bound, expected.unc.right_bound)
expected.draw_style = 'repr_dashed_line'
scans.append(expected)
plot = differentials.plotting.plots.MultiScanPlot('onekappascan_{0}'.format(kappa))
plot.scans.extend(scans)
plot.x_title = differentials.core.standard_titles[kappa]
if kappa == 'kappab':
plot.x_min = -7.