def make_2d_contour_plot(input_files, var_pair):
"""Make one plot of all input_files"""
varx, vary = var_pair
graphs = [
f.Get('corr_2d_{}_{}_1sigma'.format(varx, vary)) for f in input_files.values()
]
# These are possibly stored in reversed order so the axis have to be
# flipped
if var_pair == ('r_chic1_jpsi', 'r_chic2_jpsi'):
# try to get them the other way around
graphs_2 = [
f.Get('corr_2d_{}_{}_1sigma'.format(vary, varx)) for f in input_files.values()
]
# Check which graphs are not present and replace them with the flipped axis version
for i, g in enumerate(graphs):
if not g:
graphs[i] = flip_axis(graphs_2[i])
if len(graphs) == 1:
leg = None
leg_entries = None
else:
leg = setup_legend(0.75, 0.8, 0.92, 0.8 + len(graphs) * 0.035)
leg_entries = input_files.keys()
can = mkplot(graphs, drawOpt='L', attr=default_attributes(line=1),
leg=leg, legEntries=leg_entries, legOpt='L',
yLabel=get_label(vary), yRange=get_range(vary),
xLabel=get_label(varx), xRange=get_range(varx))
return can
def make_ratio_plot(uncorr_ratio, corr_ratio, variable):
"""
Make the comparison plot of corrected and uncorrected graphs
"""
leg = setup_legend(*LEGEND_POS[variable])
leg.SetTextSize(0.03)
can = mkplot(corr_ratio,
drawOpt='PE',
attr=[{
'color': 1,
'size': 1,
'marker': 20
}],
yLabel=YLABELS.get('r_chic2_chic1'),
yRange=[0, 0.75],
leg=leg,
legEntries=['corrected'],
**VAR_PLOT[variable])
shift = HORIZONTAL_SHIFT[variable]
mkplot([
shift_graph_horizontal(rescale_graph(uncorr_ratio, corr_ratio), shift),
shift_graph_horizontal(uncorr_ratio, -shift)
],
can=can,
drawOpt='samePE',
attr=default_attributes(open_markers=True),
leg=leg,
legEntries=['fitted (scaled)', 'fitted'])
add_auxiliary_info(can, 2012, prelim=True)
return can
def make_debug_plot(comb_ppd, inputfiles, var):
"""
Make a plot overlaying the combined ppd and the input ppds
"""
can = PLOT_FUNCTIONS[var](rebin(comb_ppd, [(0, 200)]))
set_color(can.pltables[1], 1) # make the combined ppd black
ppds = [get_scaled_ppd(f, var, 200) for f in inputfiles]
mkplot(ppds,
can=can,
drawOpt='samehist',
attr=default_attributes(linewidth=1))
return can
def make_ppd_comp_plot(nom_file, var_files, var):
"""
Make a plot comparing the full ppds directly (shifting all of them by the
median of the nominal ppd).
"""
n_bins = {'dlth': 100, 'dlph': 400, 'dkappa': 400}
nom_ppd = get_scaled_ppd(nom_file, var)
nom_med = get_quantiles(nom_ppd, 0.5)
nom_ppd = shift_by_median(rebin(nom_ppd, [(0, n_bins[var])]), nom_med)
nom_ppd.Scale(1.0 / nom_ppd.Integral())
var_ppds = [
get_scaled_ppd(f, var, n_bins[var]) for f in var_files.values()
]
var_ppds = [shift_by_median(p, nom_med) for p in var_ppds]
[p.Scale(1.0 / p.Integral()) for p in var_ppds]
xran = {'dlth': [-2, 2], 'dlph': [-0.5, 0.5], 'dkappa': [-0.5, 0.5]}
leg = create_legend(0.69, 0.88, 0.94, len(var_ppds) + 1)
label = '{0} - #bar{0}_{{nominal}}'.format(YLABELS.get(var))
can = mkplot(nom_ppd,
attr=[{
'color': 1,
'fillalpha': (1, 0.25)
}],
drawOpt='hist',
yLabel='PPD [a.u.]',
xLabel=label,
xRange=xran[var],
yRange=[0, None],
leg=leg,
legEntries=['nominal'],
legOpt='F')
mkplot(var_ppds,
can=can,
drawOpt='histsame',
attr=default_attributes(linewidth=2),
leg=leg,
legEntries=var_files.keys(),
legOpt='L')
return can
def make_costh_mu_pt_plot(data):
"""Make a comparison of costh distributions for different muon pt cuts"""
pt_cuts = [3.5, 4, 4.5, 5, 5.5]
mu_pt_labels = ['p_{{T}}^{{#mu}} > {:.1f} GeV'.format(p) for p in pt_cuts]
hists = [
hist1d(apply_selections(data, single_muon_sel(flat_pt(p, 1.6))).costh_HX_fold.abs(),
nbins=24, min=0, max=1) for p in pt_cuts
]
[h.Scale(1.0 / h.GetBinContent(1)) for h in hists]
can = mkplot(hists, drawOpt='PE', xRange=[0, 1], xLabel=CTH_LAB,
attr=default_attributes(size=1.0, width=2, open_markers=False),
legPos=(0.65, 0.50, 0.8, 0.92), legEntries=mu_pt_labels,
yLabel='normalized to {} = 0 [a.u.]'.format(CTH_LAB))
can.pltables[0].SetNdivisions(505, 'X')
can.attached_tobjects[0].SetTextSize(0.05)
return can
def make_var_plot(nom_file, var_files, var):
"""
Make a variation plot comparing the nominal PPD to the variations PPDs
"""
nom_ppd = get_scaled_ppd(nom_file, var)
nom_quants = get_quantiles(nom_ppd, QUANTILES)
nom_lo, nom_hi = np.diff(nom_quants)
nom_graph = get_nom_graph(len(var_files), nom_lo, nom_hi)
# var_graphs = get_var_graphs_wrt_nominal(var_files, nom_quants, var)
var_graphs = get_var_graphs_wrt_nominal(
var_files, [nom_ppd.GetMean(), nom_ppd.GetRMS()], var)
xran = np.max([nom_lo, nom_hi]) * 1.75
yran = [-0.25, len(var_files) - 0.75]
can = mkplot(nom_graph,
attr=[{
'fillalpha': (default_colors()[1], 0.5)
}],
drawOpt='E2',
xLabel=get_label(var),
xRange=[-xran, xran],
yRange=yran)
mkplot(r.TLine(0, yran[0], 0, yran[1]),
can=can,
drawOpt='same',
attr=default_attributes(widht=2)[1:])
mkplot(var_graphs, drawOpt='samePE', can=can, attr=VAR_ATTR)
phist = can.pltables[0]
phist.GetYaxis().SetLabelSize(0)
phist.GetYaxis().SetTickSize(0)
ltx = setup_latex()
put_on_latex(ltx, get_labels(var_files.keys()), ndc=True)
ltx.Draw()
can.Draw()
return can
def main(args):
"""Main"""
dfr = get_dataframe(args.mcfile)
costh_bins = get_costh_binning(dfr, args.nbins)
h_int = create_mass_hist(dfr)
h_int.Scale(1.0 / args.nbins)
h_bins = [create_mass_hist(dfr, cbin) for cbin in costh_bins]
attr = [{'color': 1, 'marker': 20, 'size': 1.0}]
attr += default_attributes(size=1.0, open_markers=True) * 3
legEntries = ['costh integrated']
legEntries += [
'{:.2f} < |cos#vartheta^{{HX}}| < {:.2f}'.format(*cbin)
for cbin in costh_bins
]
leg = setup_legend(0.675, 0.6, 0.95, 0.94)
leg.SetTextSize(0.025)
can = r.TCanvas('plot_can', '', 600, 600)
can.SetRightMargin(0.02)
can.SetTopMargin(0.05)
can.SetLeftMargin(0.12)
can = mkplot([h_int] + h_bins,
can=can,
xRange=[3.325, 3.725],
yRange=[0.1, None],
attr=attr,
logy=args.logy,
xLabel='M^{#chi}',
yLabel='# events',
legOpt='PLE',
leg=leg,
legEntries=legEntries)
can.SaveAs(args.output)
def make_lth_plot(ppd_files):
"""Make the plots for the lth1 ppds"""
ppds = [get_scaled_ppd(f, 'lth') for f in ppd_files.values()]
quantiles = [get_quantiles(p, 0.1) for p in ppds]
ppds = [rebin(p, [(0, 100)]) for p in ppds] # for plotting
leg = setup_legend(0.18, 0.7, 0.48, 0.84)
leg.SetTextSize(0.0425)
leg.SetFillColor(0)
leg.SetFillStyle(1001)
# Draw boundaries first to not draw it over the legend in the end
can = mkplot(r.TLine(-1./3., 0, -1./3., get_y_max(ppds) * 1.1),
attr=BOUND_ATTR,
xRange=[-1, 1], xLabel='#lambda_{#vartheta}^{#chi_{c1}}',
yRange=[0, get_y_max(ppds) * 1.1], yLabel='PPD [a.u.]')
can = mkplot(ppds, drawOpt='hist same', can=can,
leg=leg, legOpt='L',
legEntries=['{} < p_{{T}} < {} GeV'.format(*p) for p in ppd_files.keys()])
can.pltables[0].GetYaxis().SetNdivisions(505)
can.pltables[0].GetXaxis().SetNdivisions(505)
mkplot([r.TLine(q, 0, q, 0.01) for q in quantiles], can=can, drawOpt='same')
arrow_attr = default_attributes(open_markers=False)
for att in arrow_attr:
att['fill'] = att['color']
att['width'] = 2
mkplot([
r.TArrow(q, v, q + 0.125, v, 0.025, '<|') for q, v in zip(quantiles, [0.0015, 0.003, 0.0045])
],
can=can, drawOpt='same <|', attr=arrow_attr)
return add_info(can, 'left')
import itertools
import ROOT as r
r.PyConfig.IgnoreCommandLineOptions = True
r.gROOT.SetBatch()
from utils.PlotServer import PlotServer
from utils.plot_helpers import mkplot, default_attributes, get_y_max
from utils.misc_helpers import create_random_str, cond_mkdir
from utils.setup_plot_style import set_TDR_style
# plot gen curves as filled areas
gen_attributes = [
{'color': 1, 'size': 0, 'fillalpha': (1, 0.5), 'width': 0}
]
comp_attributes = default_attributes(size=1, linewidth=1)
# provide consistent styles for the different
def get_plot_histos(pserver, input_sets, plot):
"""
inputsets (list of tuples): which mc, reco, etc. and which selection
selections (list), plot (tuple with var, frame, plot)
Get all the plots for all selections
"""
hists = {}
for iset, sel in input_sets:
input_id = '_'.join([iset, sel])
hists[input_id] = pserver.get_hist(input_id, 2012, '8_Jpsi', 0,
leg = r.TLegend(0.15, 0.15, 0.25, 0.25)
leg.SetBorderSize(0)
can = r.TCanvas('c', 'c', 600, 600)
can.cd()
ratio_pad = r.TPad('ratio_pad', '', 0, 0.3, 1, 1)
r.SetOwnership(ratio_pad, False)
ratio_pad.Draw()
ratio_pad.cd()
ratio_pad = mkplot([gen_ratio, reco_ratio],
yRange=[0, None],
drawOpt='E1',
legOpt='PLE',
attr=default_attributes(size=1.0, linewidth=1),
can=ratio_pad,
leg=leg,
legEntries=['gen', 'reco'],
yLabel='N_{#chi_{c2}} / N_{#chi_{c1}}')
can.cd()
reco_gen_dr = reco_ratio.Clone()
reco_gen_dr.Divide(gen_ratio)
dr_pad = r.TPad('double_ratio_pad', '', 0, 0, 1, 0.3)
r.SetOwnership(dr_pad, False)
dr_pad.Draw()
reco_gen_dr.GetYaxis().SetTitleSize(0.08)
r.gInterpreter.LoadFile(f'{THIS_DIR}/../src/read_sdcs.h')
r.gInterpreter.LoadFile(f'{THIS_DIR}/../interface/nrqcd_helpers.h')
r.gInterpreter.LoadText(AUXILIARY_CPP_FUNCS)
from ROOT import sdc
from ROOT import readPsiSDCs, readChic1SDCs, readChic2SDCs, lth_from_flong, scale_sdc
XRANGE = (1.5, 14.5) # Same as in PLB 773 (2017) 476
# Load plotting helper
from utils.plot_helpers import mkplot, default_attributes, setup_legend
from utils.misc_helpers import cond_mkdir
from utils.setup_plot_style import set_basic_style
# Consistent attributes for plots involving multiple states
ATTRS = {
'3S1_1': [default_attributes(size=0.5)[0]],
'3S1_8': [default_attributes(size=0.5)[1]],
'3PJ_8': [default_attributes(size=0.5)[2]],
'1S0_8': [default_attributes(size=0.5)[3]],
'3P1_1': [default_attributes(size=0.5)[4]],
'3P2_1': [default_attributes(size=0.5)[4]],
}
# Scale factors for comparability with the Shao SDCs from PLB 773 (2017) 476
RAP_SCALE = 1 / 2.4 * 1.5
SCALE_FACTORS = {
'3S1_1': RAP_SCALE * 6,
'3S1_8': RAP_SCALE,
'3PJ_8': RAP_SCALE,
'1S0_8': RAP_SCALE,
'3P1_1': RAP_SCALE * 6,
def make_ratio_pull_plot(ratio, analytic_scens, variable):
"""
Make a comparison plot with an additional pull panel at the bottom
"""
# Scale all analytic scenarios first
chi2_ndfs = get_chi2_ndf(ratio, analytic_scens)
can = mkplot([]) # get a canvas that is already a TCanvasWrapper
can.cd()
rpad = r.TPad('ratio_pad', 'ratio pad', 0, 0.3, 1, 0.98)
r.SetOwnership(rpad, False)
rpad.SetBottomMargin(0)
rpad.Draw()
rpad = mkplot(ratio,
drawOpt='PE',
can=rpad,
attr=[{
'color': 1,
'size': 1,
'marker': 20
}],
yLabel=YLABELS.get('r_chic2_chic1'),
yRange=[0.001, 0.75],
**VAR_PLOT[variable])
leg = setup_legend(0.2, 0.15, 0.65, 0.32)
mkplot(analytic_scens.values(),
drawOpt='sameL',
can=rpad,
attr=ANALYTIC_ATTRS,
leg=leg,
legEntries=analytic_scens.keys(),
legOpt='L')
can.cd()
ppad = r.TPad('pull_pad', 'pull pad', 0, 0, 1, 0.3)
r.SetOwnership(ppad, False)
ppad.SetTopMargin(0)
ppad.SetBottomMargin(0.3)
ppad.Draw()
# Draw grid first so that it is not overlaying the plotted points
xran = VAR_PLOT[variable]['xRange']
ppad = mkplot(
r.TLine(xran[0], 0, xran[1], 0),
can=ppad,
attr=[{
'color': 1,
'width': 2,
'line': 1
}],
yLabel='pulls',
yRange=[-4.99, 4.99], # grid=True,
**VAR_PLOT[variable])
mkplot([r.TLine(xran[0], v, xran[1], v) for v in [-3, 3]],
drawOpt='same',
can=ppad,
attr=[{
'color': 12,
'width': 2,
'line': 7
}])
pulls = [pull_graph(ratio, s) for s in analytic_scens.values()]
ppad = mkplot(pulls,
drawOpt='samePE',
can=ppad,
attr=default_attributes(open_markers=False),
**VAR_PLOT[variable])
phist = ppad.pltables[0]
phist.SetNdivisions(505, "XYZ")
phist.SetLabelFont(42, "XYZ")
phist.SetLabelOffset(0.007, "XYZ")
phist.SetLabelSize(0.12, "XYZ")
phist.GetXaxis().SetTitleOffset(0.95)
phist.GetYaxis().SetTitleOffset(0.52)
phist.SetTitleSize(0.14, "XYZ")
# ppad.SetGridy()
can.add_tobject(rpad)
can.add_tobject(ppad)
add_auxiliary_info(can, 2012, prelim=True)
return can
import ROOT as r
r.PyConfig.IgnoreCommandLineOptions = True
r.gROOT.SetBatch()
from utils.plot_helpers import mkplot, default_attributes, setup_legend
from utils.setup_plot_style import add_auxiliary_info, set_TDR_style
from utils.plot_decoration import YLABELS, VAR_PLOT
from utils.misc_helpers import cond_mkdir_file, create_random_str, fmt_float
from utils.graph_utils import pull_graph
import utils.misc_helpers
utils.misc_helpers.MAX_N_DIGITS = 1
from common_utils import get_graph
ANALYTIC_ATTRS = default_attributes(size=0, line=7)
LINE_STYLES = [7, 9, 2, 3, 5, 1]
for iatt, att in enumerate(ANALYTIC_ATTRS):
att['line'] = LINE_STYLES[iatt % len(LINE_STYLES)]
def r_costh(set_vals, fix_lambdas=True):
"""
R(costh) with possible fixed values
"""
val_to_idx = {'norm': 0, 'lth1': 1, 'lth2': 2}
func = r.TF1(create_random_str(),
'[0] * (1 + [2] * x[0]*x[0]) / (1 + [1] * x[0]*x[0])', 0, 1)
def_vals = {'norm': 1.0, 'lth1': 0, 'lth2': 0}
def_vals.update(set_vals)
def main(args):
c1f = r.TFile.Open(args.corrmaps1)
c2f = r.TFile.Open(args.corrmaps2)
corrmap1 = get_corrmap(c1f, 'HX', args.corr_var, args.mask_acc, args.use_acc_only,
min_acc=args.min_acc, mask_prec=args.min_prec)
corrmap2 = get_corrmap(c2f, 'HX', args.corr_var, args.mask_acc, args.use_acc_only,
min_acc=args.min_acc, mask_prec=args.min_prec)
corrmap1_PX = get_corrmap(c1f, 'PX', args.corr_var, args.mask_acc, args.use_acc_only,
min_acc=args.min_acc, mask_prec=args.min_prec)
corrmap2_PX = get_corrmap(c2f, 'PX', args.corr_var, args.mask_acc, args.use_acc_only,
min_acc=args.min_acc, mask_prec=args.min_prec)
selections = create_selection(args.selection)
scenarios = [
('chic1_R_2o3', 'chic2_R1_2o5_R2_2o5'),
('chic1_R_1', 'chic2_R1_0_R2_1'),
('chic1_R_0', 'chic2_R1_0_R2_0'),
]
attr = default_attributes(size=1.0, open_markers=True)[:3]
attr += [{'color': 13, 'line': 7, 'width': 2, 'marker': 1}]
cond_mkdir(args.outbase)
outfile = r.TFile('/'.join([args.outbase,
'costh_ratios_min_acc_{}_min_prec_{}_gen_{}.root'.format(pfloat(args.min_acc), pfloat(args.min_prec), args.gen_frame)]),
'recreate')
for scen in scenarios:
chi1_d, chi2_d = samples(args.toydatadir, scen, selections)
print(scen)
hists = OrderedDict()
print('uncorr')
hists['uncorr'] = (costh_h(chi2_d), costh_h(chi1_d))
print('corr HX')
hists['corr HX'] = (costh_h(chi2_d, corrmap2, 'HX', args.corr_var),
costh_h(chi1_d, corrmap1, 'HX', args.corr_var))
print('corr PX')
hists['corr PX'] = (costh_h(chi2_d, corrmap2_PX, 'PX', args.corr_var),
costh_h(chi1_d, corrmap1_PX, 'PX', args.corr_var))
if args.gen_frame == 'HX':
ratio_a, delta_lth = analytical_ratio(*scen)
if args.gen_frame == 'CS':
ratio_a, delta_lth = ratio_shape(*scen)
ratios = OrderedDict()
for case in hists:
ratios[case] = divide(*hists[case])
leg = setup_legend(0.15, 0.15, 0.55, 0.25)
leg.SetNColumns(2)
plots = scale_0(ratios.values())
leg_entries = ratios.keys()
if args.gen_frame == 'HX':
plots.append(scale_func(ratio_a))
leg_entries.append('analytical')
can = mkplot(plots,
attr=attr,
xLabel='|cos#vartheta^{HX}|', yLabel='#chi_{c2} / #chi_{c1}', xRange=[0, 1],
leg=leg, legEntries=leg_entries,
drawOpt='E1',
legOpt='PLE')
if args.gen_frame == 'CS':
can = mkplot(scale_0(ratio_a), attr=[attr[-1]], leg=leg, legEntries=['analytical'],
drawOpt='HISTsame', legOpt='PLE',
can=can)
can.SaveAs(pdfname(args.outbase, args.gen_frame, delta_lth, args.min_prec, args.min_acc))
outfile.cd()
for corr, ratio in ratios.iteritems():
ratio.SetName('_'.join([
scen[0], scen[1], 'ratio_costh_HX', corr.replace(' ', '_'), 'gen', args.gen_frame
]))
ratio.Write()
for corr in hists:
hist1, hist2 = hists[corr]
hist1.SetName('_'.join([
scen[0], scen[1], 'chi1_costh_HX', corr.replace(' ', '_'), 'gen', args.gen_frame
]))
hist1.Write()
hist2.SetName('_'.join([
scen[0], scen[1], 'chi2_costh_HX', corr.replace(' ', '_'), 'gen', args.gen_frame
]))
hist2.Write()
outfile.Close()
from collections import OrderedDict
import numpy as np
import ROOT as r
r.PyConfig.IgnoreCommandLineOptions = True
r.gROOT.SetBatch()
from utils.plot_helpers import mkplot, default_attributes, setup_legend
from utils.setup_plot_style import set_TDR_style
from common_helpers import parse_inputs
# define attributes for up to four different confidence levels per input
CONT_ATTRS = []
CONT_ATTRS.append(default_attributes(line=1, width=2))
CONT_ATTRS.append(default_attributes(line=7, width=2))
CONT_ATTRS.append(default_attributes(line=2, width=2))
CONT_ATTRS.append(default_attributes(line=5, width=2))
# attributes for best fit
BF_ATTRS = default_attributes(size=1.5, open_markers=False)
for att in BF_ATTRS:
att['marker'] = 34
# legend attributes for the contours and the best fit
LEG_C_ATTR = [
{'color': 12, 'width': 2, 'line': 1},
{'color': 12, 'width': 2, 'line': 7},
{'color': 12, 'width': 2, 'line': 2},
{'color': 12, 'width': 2, 'line': 5}
r.TLine(1, 1, 1, -0.6),
r.TLine(1, 1, -1. / 3., 1),
r.TLine(-1. / 3., -0.6, 1, -0.6),
r.TLine(-1. / 3., -0.6, -1. / 3., 1)
]
def get_jz_graph():
"""
Get a graph with the markers for the J_z components
"""
return r.TGraph(6, np.array([-1. / 3, -1. / 3, -1. / 3, 1, 1, 1]),
np.array([-0.6, -1. / 3, 1, -0.6, -1. / 3, 1]))
CONT_ATTR = default_attributes(linewidth=4)
CONT_ATTR[0]['line'] = 1
CONT_ATTR[1]['line'] = 9
CONT_ATTR[2]['line'] = 2
CONT_ATTR[2]['color'] = 1
def main(args):
"""Main"""
set_TDR_style()
fitfile = r.TFile.Open(args.fitfile)
cont_1sigma = get_contour(fitfile.Get('chi2_scan_contour_1sigma'))
cont_2sigma = get_contour(fitfile.Get('chi2_scan_contour_2sigma'))
cont_3sigma = get_contour(fitfile.Get('chi2_scan_contour_3sigma'))
('chic1_R_2o3', 'chic2_R1_2o5_R2_2o5'), # unpolarized
),
'costh': (
('chic1_R_2o3', 'chic2_R1_2o5_R2_2o5'), # unpolarized
('chic1_R_14o73', 'chic2_R1_0_R2_25o106'), # nrqcd prediction
('chic1_R_0', 'chic2_R1_0_R2_0'), # max negative
('chic1_R_1', 'chic2_R1_0_R2_1') # max positive
)
}
ANALYSIS_BINNING = {
'phi': parse_binning("0:90,7"),
'costh': parse_binning("0,0.075,0.15,0.225,0.3,0.375,0.45,0.625")
}
SCEN_ATTR = default_attributes(size=0, line=7)
LINE_STYLES = [7, 9, 2, 3, 5, 1]
for iatt, att in enumerate(SCEN_ATTR):
att['color'] = 12
att['line'] = LINE_STYLES[iatt % len(LINE_STYLES)]
RATIO_ATTR = default_attributes(open_markers=False)
LEG_POS = {'phi': (0.2, 0.15, 0.9, 0.33), 'costh': (0.2, 0.15, 0.65, 0.3)}
def get_func(variable):
"""
Get the TF1 belonging to the passed variable, without fixing any parameters
yet
"""
