def plot_dNdpT(self, pid_selection):
"""
Plot dNdpT particles in pid_selection
Parameters
----------
pid_selection : str
Either all charged particles ('ch') or 'pi', 'K' or 'p'
"""
log.info("1/N_evts dN_ch/dpT plots")
figs = []
for sums_est_dir, res_est_dir in zip(get_est_dirs(self.sums, self.considered_ests),
get_est_dirs(self.results_post, self.considered_ests)):
if sums_est_dir.GetName() != res_est_dir.GetName():
raise IndexError("Order of estimator dirs is different in sums and results_post")
res_dir_str = res_est_dir.GetPath().split(":")[1]
fig = Figure()
fig.plot.palette = 'colorblind'
# fig.plot.ncolors = 5
fig.legend.position = 'tr'
fig.ytitle = "1/N_{evts} dN/dp_{T} (" + make_estimator_title(sums_est_dir.GetName()) + ")"
fig.xtitle = "p_{T} (GeV)"
fig.plot.logy = True
hists = []
if pid_selection == 'ch':
fig.legend.title = "#pi^{#pm}, K^{#pm}, p, #Lambda, #Xi, #Omega"
pid_numbers = [kPIMINUS, kPIPLUS, kKMINUS, kKPLUS, kPROTON, kANTIPROTON,
kLAMBDA, kANTILAMBDA, kXI, kANTIXI, kOMEGAMINUS, kOMEGAPLUS]
if pid_selection == 'pi':
fig.legend.title = "#pi^{#pm}"
pid_numbers = [kPIMINUS, kPIPLUS]
if pid_selection == 'K':
fig.legend.title = "K^{#pm}"
pid_numbers = [kKMINUS, kKPLUS]
if pid_selection == 'p':
fig.legend.title = "p, #bar{p}"
pid_numbers = [kPROTON, kANTIPROTON]
for perc_bin, classifier_bin in zip(self.perc_bins[sums_est_dir.GetName()], self.nch_edges[sums_est_dir.GetName()]):
hists.append(get_pT_distribution(res_est_dir, pid_numbers, classifier_bin, normalized=False))
hists[-1].title = "{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
# add MB last to be consistent with colors in other plots; the very first and very last bin we look at
classifier_bin_mb = (self.nch_edges[sums_est_dir.GetName()][0][0], self.nch_edges[sums_est_dir.GetName()][-1][-1])
hists.append(get_pT_distribution(res_est_dir, pid_numbers, classifier_bin_mb, normalized=False))
hists[-1].title = "MB"
# scale by bin width
[h.Scale(1, "width") for h in hists]
[fig.add_plottable(p, p.title) for p in hists]
fig.save_to_root_file(self.f_out, "dN{0}dpT".format(pid_selection), res_dir_str)
figs.append(fig)
return figs
def plot_dNdpT(self, pid_selection):
"""
Plot dNdpT particles in pid_selection
Parameters
----------
pid_selection : str
Either all charged particles ('ch') or 'pi', 'K' or 'p'
"""
log.info("1/N_evts dN_ch/dpT plots")
figs = []
for sums_est_dir, res_est_dir in zip(get_est_dirs(self.sums, self.considered_ests),
get_est_dirs(self.results_post, self.considered_ests)):
if sums_est_dir.GetName() != res_est_dir.GetName():
raise IndexError("Order of estimator dirs is different in sums and results_post")
res_dir_str = res_est_dir.GetPath().split(":")[1]
fig = Figure()
fig.plot.palette = 'colorblind'
# fig.plot.ncolors = 5
fig.legend.position = 'tr'
fig.ytitle = "1/N_{evts} dN/dp_{T} (" + make_estimator_title(sums_est_dir.GetName()) + ")"
fig.xtitle = "p_{T} (GeV)"
fig.plot.logy = True
hists = []
if pid_selection == 'ch':
fig.legend.title = "#pi^{#pm}, K^{#pm}, p, #Lambda, #Xi, #Omega"
pid_numbers = [kPIMINUS, kPIPLUS, kKMINUS, kKPLUS, kPROTON, kANTIPROTON,
kLAMBDA, kANTILAMBDA, kXI, kANTIXI, kOMEGAMINUS, kOMEGAPLUS]
if pid_selection == 'pi':
fig.legend.title = "#pi^{#pm}"
pid_numbers = [kPIMINUS, kPIPLUS]
if pid_selection == 'K':
fig.legend.title = "K^{#pm}"
pid_numbers = [kKMINUS, kKPLUS]
if pid_selection == 'p':
fig.legend.title = "p, #bar{p}"
pid_numbers = [kPROTON, kANTIPROTON]
for perc_bin, classifier_bin in zip(self.perc_bins[sums_est_dir.GetName()], self.nch_edges[sums_est_dir.GetName()]):
hists.append(get_pT_distribution(res_est_dir, pid_numbers, classifier_bin, normalized=False))
hists[-1].title = "{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
# add MB last to be consistent with colors in other plots; the very first and very last bin we look at
classifier_bin_mb = (self.nch_edges[sums_est_dir.GetName()][0][0], self.nch_edges[sums_est_dir.GetName()][-1][-1])
hists.append(get_pT_distribution(res_est_dir, pid_numbers, classifier_bin_mb, normalized=False))
hists[-1].title = "MB"
# scale by bin width
[h.Scale(1, "width") for h in hists]
[fig.add_plottable(p, p.title) for p in hists]
fig.save_to_root_file(self.f, "dN{0}dpT".format(pid_selection), res_dir_str)
figs.append(fig)
return figs
def plot_pT_HM_div_pt_MB(self, scale_nMPI):
log.info("Plot dN_{HM}/dpT / dN_{MB}/dpT ratios scaled with nMPI")
figs = []
for sums_est_dir, res_est_dir in zip(get_est_dirs(self.sums, self.considered_ests),
get_est_dirs(self.results_post, self.considered_ests)):
if sums_est_dir.GetName() != res_est_dir.GetName():
raise IndexError("Order of estimator dirs is different in sums and results_post")
res_dir_str = res_est_dir.GetPath().split(":")[1]
fig = Figure()
fig.plot.palette = 'root'
fig.plot.ncolors = 7
fig.xtitle = "p_{T} (GeV)"
fig.legend.title = make_estimator_title(sums_est_dir.GetName())
if scale_nMPI:
fig.ytitle = ("#left[ #frac{dN^{HM}}{dp_{T}} / #frac{dN^{MB}}{dp_{T}} #right] "
"#times #left[ #frac{<N_{MPI}^{MB}>}{<N_{MPI}^{HM}>} #right]")
else:
fig.ytitle = "#frac{dN^{HM}}{dp_{T}} / #frac{dN^{MB}}{dp_{T}}"
charged_particles = [kPIMINUS, kPIPLUS, kKMINUS, kKPLUS, kPROTON, kANTIPROTON,
kLAMBDA, kANTILAMBDA, kXI, kANTIXI, kOMEGAMINUS, kOMEGAPLUS]
# get the MB distribution which will be used to devide the nch-binned distributions
classifier_bin_mb = (self.nch_edges[sums_est_dir.GetName()][0][0],
self.nch_edges[sums_est_dir.GetName()][-1][-1])
pt_dist_mb = get_pT_distribution(res_est_dir, charged_particles, classifier_bin_mb, normalized=False)
mean_nmpi_mb = get_mean_nMPI(sums_est_dir, classifier_bin_mb)
for perc_bin, classifier_bin in zip(self.perc_bins[sums_est_dir.GetName()],
self.nch_edges[sums_est_dir.GetName()]):
# get the pt distribution in this Nch interval
pt_dist_in_interval = get_pT_distribution(res_est_dir, charged_particles,
classifier_bin, normalized=False)
title = "{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
if scale_nMPI:
mean_nmpi_hm = get_mean_nMPI(sums_est_dir, classifier_bin)
fig.add_plottable((pt_dist_in_interval / pt_dist_mb) * (mean_nmpi_mb / mean_nmpi_hm), title)
name = "pt_hm_div_pt_mb_scaled_nMPI"
else:
fig.add_plottable((pt_dist_in_interval / pt_dist_mb), title)
name = "pt_hm_div_pt_mb"
fig.save_to_root_file(self.f_out, name, res_dir_str)
figs.append(fig)
return figs
def plot_pT_HM_div_pt_MB(self, scale_nMPI):
log.info("Plot dN_{HM}/dpT / dN_{MB}/dpT ratios scaled with nMPI")
figs = []
for sums_est_dir, res_est_dir in zip(get_est_dirs(self.sums, self.considered_ests),
get_est_dirs(self.results_post, self.considered_ests)):
if sums_est_dir.GetName() != res_est_dir.GetName():
raise IndexError("Order of estimator dirs is different in sums and results_post")
res_dir_str = res_est_dir.GetPath().split(":")[1]
fig = Figure()
fig.plot.palette = 'root'
fig.plot.ncolors = 7
fig.xtitle = "p_{T} (GeV)"
fig.legend.title = make_estimator_title(sums_est_dir.GetName())
if scale_nMPI:
fig.ytitle = ("#left[ #frac{dN^{HM}}{dp_{T}} / #frac{dN^{MB}}{dp_{T}} #right] "
"#times #left[ #frac{<N_{MPI}^{MB}>}{<N_{MPI}^{HM}>} #right]")
else:
fig.ytitle = "#frac{dN^{HM}}{dp_{T}} / #frac{dN^{MB}}{dp_{T}}"
charged_particles = [kPIMINUS, kPIPLUS, kKMINUS, kKPLUS, kPROTON, kANTIPROTON,
kLAMBDA, kANTILAMBDA, kXI, kANTIXI, kOMEGAMINUS, kOMEGAPLUS]
# get the MB distribution which will be used to devide the nch-binned distributions
classifier_bin_mb = (self.nch_edges[sums_est_dir.GetName()][0][0],
self.nch_edges[sums_est_dir.GetName()][-1][-1])
pt_dist_mb = get_pT_distribution(res_est_dir, charged_particles, classifier_bin_mb, normalized=False)
mean_nmpi_mb = get_mean_nMPI(sums_est_dir, classifier_bin_mb)
for perc_bin, classifier_bin in zip(self.perc_bins[sums_est_dir.GetName()],
self.nch_edges[sums_est_dir.GetName()]):
# get the pt distribution in this Nch interval
pt_dist_in_interval = get_pT_distribution(res_est_dir, charged_particles,
classifier_bin, normalized=False)
title = "{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
if scale_nMPI:
mean_nmpi_hm = get_mean_nMPI(sums_est_dir, classifier_bin)
fig.add_plottable((pt_dist_in_interval / pt_dist_mb) * (mean_nmpi_mb / mean_nmpi_hm), title)
name = "pt_hm_div_pt_mb_scaled_nMPI"
else:
fig.add_plottable((pt_dist_in_interval / pt_dist_mb), title)
name = "pt_hm_div_pt_mb"
fig.save_to_root_file(self.f, name, res_dir_str)
figs.append(fig)
return figs
def plot_pt_distribution_ratios(self):
# create particle ratio vs pT plots
log.info("Computing histograms vs pt")
results_path = self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
# Loop over all estimators in the Sums list:
figs = []
def get_new_figure():
fig = Figure()
fig.xtitle = 'p_{T} (GeV)'
fig.plot.ymin = 0
fig.plot.xmax = 10
fig.plot.palette = 'colorblind'
# fig.plot.palette_ncolors = len(nch_edges) - 1
fig.legend.position = 'br'
return fig
for est_dir in get_est_dirs(self.results_post, self.considered_ests):
dirname = '{0}/{1}/pid_ratios/'.format(results_path, est_dir.GetName())
mult_binned_pt_dists = {}
mult_binned_pt_dists['proton'] = [
get_pT_distribution(est_dir, [kANTIPROTON, kPROTON], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['pi_ch'] = [
get_pT_distribution(est_dir, [kPIMINUS, kPIPLUS], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['xi'] = [
get_pT_distribution(est_dir, [kANTIXI, kXI], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['omega'] = [
get_pT_distribution(est_dir, [kOMEGAMINUS, kOMEGAPLUS], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['lambda'] = [
get_pT_distribution(est_dir, [kANTILAMBDA, kLAMBDA], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['k0s'] = [
get_pT_distribution(est_dir, [kK0S], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['k_ch'] = [
get_pT_distribution(est_dir, [kKPLUS, kKMINUS], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['pi0'] = [
get_pT_distribution(est_dir, [kPI0], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
perc_titles = ["{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
for perc_bin in self.perc_bins[est_dir.GetName()]]
fig = get_new_figure()
name = "proton_over_pich__vs__pt"
fig.ytitle = "(p+#bar{p})/#pi^{+-}"
fig.plot.ymax = .3
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['proton'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Xi_over_pich__vs__pt"
fig.plot.ymax = .06
fig.legend.position = 'tl'
fig.ytitle = "#Xi/#pi^{+-}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['xi'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "OmegaCh_over_pich__vs__pt"
fig.plot.ymax = .005
fig.legend.position = 'tl'
fig.ytitle = "#Omega_{ch}/#pi^{+-} "
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['omega'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
# Ratios to pi0
fig = get_new_figure()
name = "pich_over_pi0__vs__pt"
fig.plot.ymax = 2.5
fig.legend.position = 'bl'
fig.ytitle = "#pi^{+-}/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['pi_ch'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "proton_over_pi0__vs__pt"
fig.plot.ymax = 1
fig.legend.position = 'tr'
fig.ytitle = "p/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['proton'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "K0S_over_pi0__vs__pt"
fig.plot.ymax = 1.4
fig.legend.position = 'tl'
fig.ytitle = "K^{0}_{S}/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['k0s'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Lambda_over_pi0__vs__pt"
fig.plot.ymax = .9
fig.legend.position = 'tl'
fig.ytitle = "#Lambda/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['lambda'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Xi_over_pi0__vs__pt"
fig.plot.ymax = .08
fig.legend.position = 'tl'
fig.ytitle = "#Xi/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['xi'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "OmegaCh_over_pi0__vs__pt"
fig.plot.ymax = .005
fig.legend.position = 'tl'
fig.ytitle = "#Omega_{ch}/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['omega'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
# Ratios to K0S
fig = get_new_figure()
name = "proton_over_K0S__vs__pt"
fig.plot.ymax = 2.6
fig.legend.position = 'tr'
fig.ytitle = "p/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['proton'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Lambda_over_K0S__vs__pt"
fig.plot.ymax = 1
fig.legend.position = 'bl'
fig.ytitle = "#Lambda/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['lambda'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Xi_over_K0S__vs__pt"
fig.plot.ymax = .2
fig.legend.position = 'tl'
fig.ytitle = "#Xi/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['xi'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "OmegaCh_over_K0S__vs__pt"
fig.plot.ymax = .012
fig.legend.position = 'tl'
fig.ytitle = "#Omega_{ch}/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['omega'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Kaon_over_pich__vs__pt"
fig.plot.ymax = 1
fig.legend.position = 'tl'
fig.ytitle = "(K^{+} + K^{-}) / (#pi^{+} +#pi^{-})"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['k_ch'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f_out, name, dirname)
figs.append(fig)
return figs
def plot_pt_distribution_ratios(self):
# create particle ratio vs pT plots
log.info("Computing histograms vs pt")
results_path = self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
# Loop over all estimators in the Sums list:
figs = []
def get_new_figure():
fig = Figure()
fig.xtitle = 'p_{T} (GeV)'
fig.plot.ymin = 0
fig.plot.xmax = 10
fig.plot.palette = 'colorblind'
# fig.plot.palette_ncolors = len(nch_edges) - 1
fig.legend.position = 'br'
return fig
for est_dir in get_est_dirs(self.results_post, self.considered_ests):
dirname = '{0}/{1}/pid_ratios/'.format(results_path, est_dir.GetName())
mult_binned_pt_dists = {}
mult_binned_pt_dists['proton'] = [
get_pT_distribution(est_dir, [kANTIPROTON, kPROTON], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['pi_ch'] = [
get_pT_distribution(est_dir, [kPIMINUS, kPIPLUS], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['xi'] = [
get_pT_distribution(est_dir, [kANTIXI, kXI], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['omega'] = [
get_pT_distribution(est_dir, [kOMEGAMINUS, kOMEGAPLUS], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['lambda'] = [
get_pT_distribution(est_dir, [kANTILAMBDA, kLAMBDA], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['k0s'] = [
get_pT_distribution(est_dir, [kK0S], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['k_ch'] = [
get_pT_distribution(est_dir, [kKPLUS, kKMINUS], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
mult_binned_pt_dists['pi0'] = [
get_pT_distribution(est_dir, [kPI0], classifier_bin_interval)
for classifier_bin_interval in self.nch_edges[est_dir.GetName()]
]
perc_titles = ["{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
for perc_bin in self.perc_bins[est_dir.GetName()]]
fig = get_new_figure()
name = "proton_over_pich__vs__pt"
fig.ytitle = "(p+#bar{p})/#pi^{+-}"
fig.plot.ymax = .3
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['proton'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Xi_over_pich__vs__pt"
fig.plot.ymax = .06
fig.legend.position = 'tl'
fig.ytitle = "#Xi/#pi^{+-}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['xi'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "OmegaCh_over_pich__vs__pt"
fig.plot.ymax = .005
fig.legend.position = 'tl'
fig.ytitle = "#Omega_{ch}/#pi^{+-} "
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['omega'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
# Ratios to pi0
fig = get_new_figure()
name = "pich_over_pi0__vs__pt"
fig.plot.ymax = 2.5
fig.legend.position = 'bl'
fig.ytitle = "#pi^{+-}/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['pi_ch'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "proton_over_pi0__vs__pt"
fig.plot.ymax = 1
fig.legend.position = 'tr'
fig.ytitle = "p/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['proton'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "K0S_over_pi0__vs__pt"
fig.plot.ymax = 1.4
fig.legend.position = 'tl'
fig.ytitle = "K^{0}_{S}/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['k0s'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Lambda_over_pi0__vs__pt"
fig.plot.ymax = .9
fig.legend.position = 'tl'
fig.ytitle = "#Lambda/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['lambda'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Xi_over_pi0__vs__pt"
fig.plot.ymax = .08
fig.legend.position = 'tl'
fig.ytitle = "#Xi/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['xi'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "OmegaCh_over_pi0__vs__pt"
fig.plot.ymax = .005
fig.legend.position = 'tl'
fig.ytitle = "#Omega_{ch}/#pi^{0}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['omega'], mult_binned_pt_dists['pi0'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
# Ratios to K0S
fig = get_new_figure()
name = "proton_over_K0S__vs__pt"
fig.plot.ymax = 2.6
fig.legend.position = 'tr'
fig.ytitle = "p/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['proton'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Lambda_over_K0S__vs__pt"
fig.plot.ymax = 1
fig.legend.position = 'bl'
fig.ytitle = "#Lambda/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['lambda'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Xi_over_K0S__vs__pt"
fig.plot.ymax = .2
fig.legend.position = 'tl'
fig.ytitle = "#Xi/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['xi'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "OmegaCh_over_K0S__vs__pt"
fig.plot.ymax = .012
fig.legend.position = 'tl'
fig.ytitle = "#Omega_{ch}/K^{0}_{S}"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['omega'], mult_binned_pt_dists['k0s'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
fig = get_new_figure()
name = "Kaon_over_pich__vs__pt"
fig.plot.ymax = 1
fig.legend.position = 'tl'
fig.ytitle = "(K^{+} + K^{-}) / (#pi^{+} +#pi^{-})"
fig.legend.title = make_estimator_title(est_dir.GetName())
[
fig.add_plottable(h1 / h2, legend_title=title)
for h1, h2, title in zip(mult_binned_pt_dists['k_ch'], mult_binned_pt_dists['pi_ch'], perc_titles)
]
fig.save_to_root_file(self.f, name, dirname)
figs.append(fig)
return figs