def plot_correlation(self):
# Make correlations between estimators
log.info("Correlating N_ch of each estimator")
corr_dir = self.results_post.GetPath().split(":")[1] + '/correlations'
try:
self.f.mkdir(corr_dir, recurse=True)
except:
pass
# Take ntuple from the first estimator and then add friends to this one
nt0 = self.sums[0].FindObject("fEventTuple")
nt0.SetAlias(self.sums[0].GetName(), "fEventTuple")
# build ntuple
for est_dir in self.sums[1:]:
nt0.AddFriend(est_dir.FindObject("fEventTuple"), est_dir.GetName())
for ref_est in self.considered_ests:
for est_dir in self.sums:
log.info("Correlating {0} with {1}".format(ref_est, est_dir.GetName()))
corr_hist = Hist2D(400, 0, 400,
400, 0, 400,
name="corr_hist_{0}_vs_{1}".format(ref_est, est_dir.GetName()))
# Lables are deliberatly swaped, see Projection below!
corr_hist.title = ("Correlation N_{{ch}} in {0} and {1};N_{{ch}} {1};N_{{ch}} {0}"
.format(ref_est, est_dir.GetName()))
# this projects onto y:x, to make coding more adventurous
nt0.Project(corr_hist.name, "{0}.nch:{1}.nch".format(ref_est, est_dir.GetName()),
"ev_weight")
corr_hist.drawstyle = 'colz'
self.f.cd(corr_dir)
corr_hist.write()
def plot_correlation(self):
# Make correlations between estimators
log.info("Correlating N_ch of each estimator")
corr_dir = self.results_post.GetPath().split(":")[1] + '/correlations'
try:
self.f_out.mkdir(corr_dir, recurse=True)
except:
pass
# Take ntuple from the first estimator and then add friends to this one
nt0 = self.sums[0].FindObject("fEventTuple")
nt0.SetAlias(self.sums[0].GetName(), "fEventTuple")
# build ntuple
for est_dir in self.sums[1:]:
nt0.AddFriend(est_dir.FindObject("fEventTuple"), est_dir.GetName())
for ref_est in self.considered_ests:
for est_dir in self.sums:
log.info("Correlating {0} with {1}".format(ref_est, est_dir.GetName()))
corr_hist = Hist2D(400, 0, 400,
400, 0, 400,
name="corr_hist_{0}_vs_{1}".format(ref_est, est_dir.GetName()))
# Lables are deliberatly swaped, see Projection below!
corr_hist.title = ("Correlation N_{{ch}} in {0} and {1};N_{{ch}} {1};N_{{ch}} {0}"
.format(ref_est, est_dir.GetName()))
# this projects onto y:x, to make coding more adventurous
nt0.Project(corr_hist.name, "{0}.nch:{1}.nch".format(ref_est, est_dir.GetName()),
"ev_weight")
corr_hist.drawstyle = 'colz'
self.f_out.cd(corr_dir)
corr_hist.write()
def __iter__(self):
passed_events = 0
entries = 0
total_entries = float(self._tree.GetEntries())
t2 = self._init_time
for i in xrange(self._tree.GetEntries()):
entries += 1
self._tree.GetEntry(i)
for name, (coll_name, mix,
decorate_func) in self._collections.items():
coll = xAODTreeCollection(self._tree,
name,
coll_name,
mix=mix,
decorate_func=decorate_func)
object.__setattr__(self._tree, name, coll)
if self._filters(self._tree):
yield self._tree
passed_events += 1
if self._events == passed_events:
break
if time.time() - t2 > 2:
entry_rate = int(entries / (time.time() - self._init_time))
log.info("{0:d} entries per second. "
"{1:.0f}% done current tree".format(
entry_rate, 100 * entries / total_entries))
t2 = time.time()
self._filters.finalize()
self._store.clear()
def __iter__(self):
passed_events = 0
entries = 0
total_entries = float(self._tree.GetEntries())
t2 = self._init_time
for i in xrange(self._tree.GetEntries()):
entries += 1
self._tree.GetEntry(i)
for name, (coll_name, mix, decorate_func) in self._collections.items():
coll = xAODTreeCollection(
self._tree, name, coll_name,
mix=mix, decorate_func=decorate_func)
object.__setattr__(self._tree, name, coll)
if self._filters(self._tree):
yield self._tree
passed_events +=1
if self._events == passed_events:
break
if time.time() - t2 > 2:
entry_rate = int(entries / (time.time() - self._init_time))
log.info(
"{0:d} entries per second. "
"{1:.0f}% done current tree".format(
entry_rate,
100 * entries / total_entries))
t2 = time.time()
self._filters.finalize()
self._store.clear()
def plot_event_counters(self):
log.info("Creating event counters")
for est_dir in get_est_dirs(self.sums, self.considered_ests):
results_est_dir = self.results_post.__getattr__(est_dir.GetName())
# Nasty, but just use a reference estimator here...
corr = get_correlation_histogram(self.sums, est_dir.GetName(), "EtaLt05")
counter = asrootpy(corr.ProjectionX())
counter.name = "event_counter"
path = results_est_dir.GetPath().split(":")[1] # file.root:/internal/root/path
self.f_out.cd(path)
results_est_dir.WriteTObject(counter)
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_event_counters(self):
log.info("Creating event counters")
for est_dir in get_est_dirs(self.sums, self.considered_ests):
results_est_dir = self.results_post.__getattr__(est_dir.GetName())
# Nasty, but just use a reference estimator here...
corr = get_correlation_histogram(self.sums, est_dir.GetName(), "EtaLt05")
counter = asrootpy(corr.ProjectionX())
counter.name = "event_counter"
path = results_est_dir.GetPath().split(":")[1] # file.root:/internal/root/path
self.f.cd(path)
results_est_dir.WriteTObject(counter)
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 __init__(self, chain, filters=None, events=-1):
self._chain = chain
self._tree = ROOT.xAOD.MakeTransientTree(self._chain)
# Create the TStore that hold the shallow copies
self._store = ROOT.xAOD.TStore()
log.info(self._tree)
self._collections = {}
self._events = events
self._init_time = time.time()
if filters is None:
self._filters = EventFilterList([])
else:
self._filters = filters
def __init__(self, chain, filters=None, events=-1):
self._chain = chain
self._tree = ROOT.xAOD.MakeTransientTree(self._chain)
# Create the TStore that hold the shallow copies
self._store = ROOT.xAOD.TStore()
log.info(self._tree)
self._collections = {}
self._events = events
self._init_time = time.time()
if filters is None:
self._filters = EventFilterList([])
else:
self._filters = filters
def plot_meanpt_vs_ref_mult_for_pids(self):
log.info("Creating mean pT 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]
corr_hist = get_correlation_histogram(self.sums, sums_est_dir.GetName(), "EtaLt05")
# Get the <pT> per classifier bin; then, re-map the classifier value to the reference classifier (eg EtaLt05)
# This might not make a lot of sense, actually. Maybe it would be much more telling if I were to
# put the percentile bins on the x-axis? As in the highest 1% of that classifier has a <pT> of ...
graphs = []
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kPI0, kPIMINUS, kPIPLUS]), corr_hist))
graphs[-1].title = "#pi"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kKMINUS, kKPLUS]), corr_hist))
graphs[-1].title = "K^{#pm}"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kPROTON, kANTIPROTON]), corr_hist))
graphs[-1].title = "p"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kK0S]), corr_hist))
graphs[-1].title = "K^{0}_{S}"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kLAMBDA, kANTILAMBDA]), corr_hist))
graphs[-1].title = "#Lambda"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kXI, kANTIXI]), corr_hist))
graphs[-1].title = "#Xi"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kOMEGAMINUS, kOMEGAPLUS]), corr_hist))
graphs[-1].title = "#Omega"
# sanitize graphs:
for g in graphs:
remove_zero_value_points(g)
remove_points_with_x_err_gt_1NchRef(g)
remove_points_with_equal_x(g)
fig = Figure()
fig.plot.palette = 'root'
fig.plot.ncolors = 7
fig.plot.xmin = 0
fig.plot.xmax = 40
fig.plot.ymin = 0.3
fig.plot.ymax = 2.1
fig.ytitle = "<p_{T}>"
fig.xtitle = "N_{ch}|_{|#eta|<0.5}"
fig.legend.title = make_estimator_title(sums_est_dir.GetName())
[fig.add_plottable(g, g.title) for g in graphs]
fig.save_to_root_file(self.f, "mean_pt", res_dir_str)
figs.append(fig)
return figs
def plot_meanpt_vs_ref_mult_for_pids(self):
log.info("Creating mean pT 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]
corr_hist = get_correlation_histogram(self.sums, sums_est_dir.GetName(), "EtaLt05")
# Get the <pT> per classifier bin; then, re-map the classifier value to the reference classifier (eg EtaLt05)
# This might not make a lot of sense, actually. Maybe it would be much more telling if I were to
# put the percentile bins on the x-axis? As in the highest 1% of that classifier has a <pT> of ...
graphs = []
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kPI0, kPIMINUS, kPIPLUS]), corr_hist))
graphs[-1].title = "#pi"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kKMINUS, kKPLUS]), corr_hist))
graphs[-1].title = "K^{#pm}"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kPROTON, kANTIPROTON]), corr_hist))
graphs[-1].title = "p"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kK0S]), corr_hist))
graphs[-1].title = "K^{0}_{S}"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kLAMBDA, kANTILAMBDA]), corr_hist))
graphs[-1].title = "#Lambda"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kXI, kANTIXI]), corr_hist))
graphs[-1].title = "#Xi"
graphs.append(remap_x_values(get_meanpt_vs_estmult(res_est_dir, [kOMEGAMINUS, kOMEGAPLUS]), corr_hist))
graphs[-1].title = "#Omega"
# sanitize graphs:
for g in graphs:
remove_zero_value_points(g)
remove_points_with_x_err_gt_1NchRef(g)
remove_points_with_equal_x(g)
fig = Figure()
fig.plot.palette = 'root'
fig.plot.ncolors = 7
fig.plot.xmin = 0
fig.plot.xmax = 40
fig.plot.ymin = 0.3
fig.plot.ymax = 2.1
fig.ytitle = "<p_{T}>"
fig.xtitle = "N_{ch}|_{|#eta|<0.5}"
fig.legend.title = make_estimator_title(sums_est_dir.GetName())
[fig.add_plottable(g, g.title) for g in graphs]
fig.save_to_root_file(self.f_out, "mean_pt", res_dir_str)
figs.append(fig)
return figs
def plot_nMPI_vs_Nch(self):
log.info("Creating nMPI(Nch) summary plot")
summary_fig = Figure()
summary_fig.xtitle = "N_{ch}^{est}"
summary_fig.ytitle = "<N_{MPI}>"
summary_fig.plot.palette = 'root'
summary_fig.legend.position = 'br'
summary_fig.plot.logy = True
summary_fig.plot.ymin = 1
for est_dir in get_est_dirs(self.sums, self.considered_ests):
h_tmp = asrootpy(get_correlation_histogram(self.sums, est_dir.GetName(), "nMPI").ProfileX())
summary_fig.add_plottable(h_tmp, make_estimator_title(est_dir.GetName()))
path = self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
summary_fig.save_to_root_file(self.f_out, "nMPI_summary", path=path)
return [summary_fig]
def plot_nMPI_vs_Nch(self):
log.info("Creating nMPI(Nch) summary plot")
summary_fig = Figure()
summary_fig.xtitle = "N_{ch}^{est}"
summary_fig.ytitle = "<N_{MPI}>"
summary_fig.plot.palette = 'root'
summary_fig.legend.position = 'br'
summary_fig.plot.logy = True
summary_fig.plot.ymin = 1
for est_dir in get_est_dirs(self.sums, self.considered_ests):
h_tmp = asrootpy(get_correlation_histogram(self.sums, est_dir.GetName(), "nMPI").ProfileX())
summary_fig.add_plottable(h_tmp, make_estimator_title(est_dir.GetName()))
path = self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
summary_fig.save_to_root_file(self.f, "nMPI_summary", path=path)
return [summary_fig]
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_PNch_summary(self):
log.info("Creating P(Nch) summary plot")
summary_fig = Figure()
summary_fig.xtitle = "N_{ch}^{est}"
summary_fig.ytitle = "P(N_{ch}^{est})"
summary_fig.legend.position = 'tr'
summary_fig.plot.logy = True
for est_dir in get_est_dirs(self.sums, self.considered_ests):
est_name = est_dir.GetName()
h_tmp = get_PNch_vs_estmult(self.sums, est_name)
if h_tmp.Integral() > 0:
h_tmp.Scale(1.0 / h_tmp.Integral())
summary_fig.add_plottable(h_tmp, make_estimator_title(est_name))
path = self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
summary_fig.save_to_root_file(self.f_out, "PNch_summary", path=path)
# list as return type is expected for making the pdf
return [summary_fig]
def plot_PNch_summary(self):
log.info("Creating P(Nch) summary plot")
summary_fig = Figure()
summary_fig.xtitle = "N_{ch}^{est}"
summary_fig.ytitle = "P(N_{ch}^{est})"
summary_fig.legend.position = 'tr'
summary_fig.plot.logy = True
for est_dir in get_est_dirs(self.sums, self.considered_ests):
est_name = est_dir.GetName()
h_tmp = get_PNch_vs_estmult(self.sums, est_name)
if h_tmp.Integral() > 0:
h_tmp.Scale(1.0 / h_tmp.Integral())
summary_fig.add_plottable(h_tmp, make_estimator_title(est_name))
path = self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
summary_fig.save_to_root_file(self.f, "PNch_summary", path=path)
# list as return type is expected for making the pdf
return [summary_fig]
def plot_mult_vs_pt(self):
log.info("Makeing 2D pt plots for each particle kind")
for est_dir in get_est_dirs(self.sums, self.considered_ests):
path = (self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
+ "/" + est_dir.GetName()
+ "/mult_pt")
try:
self.f_out.mkdir(path, recurse=True)
except ValueError:
pass
self.f_out.cd(path)
h3d = asrootpy(est_dir.FindObject('classifier_pT_PID_{0}'.format(est_dir.GetName())))
# loop through all particle kinds:
nPIDs = h3d.zaxis.GetNbins()
for ibin in range(1, nPIDs + 1):
h3d.zaxis.SetRange(ibin, ibin)
mult_pt = asrootpy(h3d.Project3D("yx"))
mult_pt.name = h3d.zaxis.GetBinLabel(ibin)
mult_pt.Write()
def plot_mult_vs_pt(self):
log.info("Makeing 2D pt plots for each particle kind")
for est_dir in get_est_dirs(self.sums, self.considered_ests):
path = (self.results_post.GetPath().split(":")[1] # file.root:/internal/root/path
+ "/" + est_dir.GetName()
+ "/mult_pt")
try:
self.f.mkdir(path, recurse=True)
except ValueError:
pass
self.f.cd(path)
h3d = asrootpy(est_dir.FindObject('classifier_pT_PID_{0}'.format(est_dir.GetName())))
# loop through all particle kinds:
nPIDs = h3d.zaxis.GetNbins()
for ibin in range(1, nPIDs + 1):
h3d.zaxis.SetRange(ibin, ibin)
mult_pt = asrootpy(h3d.Project3D("yx"))
mult_pt.name = h3d.zaxis.GetBinLabel(ibin)
mult_pt.Write()
def plot_dNdetas(self, ratio_to_mb):
# Loop over all estimators in the Sums list:
log.info("Creating dN/deta bin in multiplicity")
figs = []
for est_dir in get_est_dirs(self.sums, self.considered_ests):
# does this estimator have several multiplicity bins?
# Q2, for example only works with pythia and makes no sense to plot
# on Dipsy as it would only be the MB line
if len(self.nch_edges[est_dir.GetName()]) == 1:
continue
results_est_dir = self.results_post.Get(est_dir.GetName())
event_counter = asrootpy(results_est_dir.Get("event_counter"))
fig = Figure()
fig.plot.palette = 'colorblind'
fig.xtitle = '#eta'
fig.ytitle = 'Ratio of dN_{ch}/d#eta over MB result' if ratio_to_mb else '1/N #times dN_{ch}/d#eta'
fig.legend.title = make_estimator_title(est_dir.GetName())
fig.plot.ymin = 0
dNdeta_mb = get_dNdeta_in_classifier_bin_interval(est_dir, event_counter,
[1, event_counter.GetXaxis().GetNbins()])
for cls_bin, perc_bin in zip(self.nch_edges[est_dir.GetName()], self.perc_bins[est_dir.GetName()]):
title = "{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
dNdeta_in_interval = get_dNdeta_in_classifier_bin_interval(est_dir, event_counter, cls_bin)
if ratio_to_mb:
fig.add_plottable(dNdeta_in_interval / dNdeta_mb, legend_title=title)
else:
fig.add_plottable(dNdeta_in_interval, legend_title=title)
# add MB as well, if it is not the ratio plots we are making
if not ratio_to_mb:
title = "MB"
fig.add_plottable(dNdeta_mb, legend_title=title)
path = results_est_dir.GetPath().split(":")[1] # file.root:/internal/root/path
if ratio_to_mb:
fig.save_to_root_file(self.f, "dNdeta_MB_ratio_summary", path=path)
else:
fig.save_to_root_file(self.f, "dNdeta_summary", path=path)
figs.append(fig)
return figs
def plot_dNdetas(self, ratio_to_mb):
# Loop over all estimators in the Sums list:
log.info("Creating dN/deta bin in multiplicity")
figs = []
for est_dir in get_est_dirs(self.sums, self.considered_ests):
# does this estimator have several multiplicity bins?
# Q2, for example only works with pythia and makes no sense to plot
# on Dipsy as it would only be the MB line
if len(self.nch_edges[est_dir.GetName()]) == 1:
continue
results_est_dir = self.results_post.Get(est_dir.GetName())
event_counter = asrootpy(results_est_dir.Get("event_counter"))
fig = Figure()
fig.plot.palette = 'colorblind'
fig.xtitle = '#eta'
fig.ytitle = 'Ratio of dN_{ch}/d#eta over MB result' if ratio_to_mb else '1/N #times dN_{ch}/d#eta'
fig.legend.title = make_estimator_title(est_dir.GetName())
fig.plot.ymin = 0
dNdeta_mb = get_dNdeta_in_classifier_bin_interval(est_dir, event_counter,
[1, event_counter.GetXaxis().GetNbins()])
for cls_bin, perc_bin in zip(self.nch_edges[est_dir.GetName()], self.perc_bins[est_dir.GetName()]):
title = "{0}%-{1}%".format(perc_bin[1] * 100, perc_bin[0] * 100)
dNdeta_in_interval = get_dNdeta_in_classifier_bin_interval(est_dir, event_counter, cls_bin)
if ratio_to_mb:
fig.add_plottable(dNdeta_in_interval / dNdeta_mb, legend_title=title)
else:
fig.add_plottable(dNdeta_in_interval, legend_title=title)
# add MB as well, if it is not the ratio plots we are making
if not ratio_to_mb:
title = "MB"
fig.add_plottable(dNdeta_mb, legend_title=title)
path = results_est_dir.GetPath().split(":")[1] # file.root:/internal/root/path
if ratio_to_mb:
fig.save_to_root_file(self.f_out, "dNdeta_MB_ratio_summary", path=path)
else:
fig.save_to_root_file(self.f_out, "dNdeta_summary", path=path)
figs.append(fig)
return figs
def run(argv=sys.argv):
# Must be done before :py:mod:`rootpy` logs any messages.
import logging
log = logging.getLogger('pyroplot') # set up logging
try:
import ROOT
except ImportError:
# module failed to load - maybe PYTHONPATH is not set correctly?
# guess the right path, but that is only possible if ROOTSYS is set:
if os.environ.get('ROOTSYS') is None:
print "ERROR: Could not load the Python ROOT module. Please make sure that your ROOT installation is compiled with Python support and that your PYTHONPATH is set correctly and includes libPyROOT.so"
exit(1)
sys.path.append(os.path.join(os.environ.get('ROOTSYS'), "lib"))
sys.path.append(os.path.join(os.environ.get('ROOTSYS'), "lib", "root"))
# try again:
try:
import ROOT
except ImportError:
print "ERROR: Could not load the Python ROOT module. Please make sure that your ROOT installation is compiled with Python support and that your PYTHONPATH is set correctly and includes libPyROOT.so"
exit(1)
try:
import rootpy
except ImportError:
# rootpy is not installed; use (old) version provided with EUTelescope
# determine (real) path to subdirectory pymodules (relative to current path)
libdir = os.path.join(
os.path.dirname(os.path.abspath(os.path.realpath(__file__))),
"pymodules", "rootpy")
# search for any rootpy folders
import glob
rootpydirs = glob.glob(libdir + "*")
if not rootpydirs:
print "Error: Could not find the rootpy module provided with EUTelescope in %s!" % (
libdir)
else:
# add last entry to python search path (subfolder rootpy where the modules are located)
sys.path.append(rootpydirs[-1])
# try again loading the module
try:
import rootpy
except ImportError:
print "Error: Could not load the rootpy modules. Please install them from http://www.rootpy.org/install.html"
exit(1)
except SyntaxError:
req_version = (2, 5)
cur_version = sys.version_info
if cur_version < req_version:
print "Error: Python version too old: due to its dependency on rootpy, this script requires a Python interpreter version 2.6 or later (installed: %s.%s.%s)!" % (
cur_version[:3])
exit(1)
print "Error: Failed to load rootpy module! Possibly incompatible with installed Python version (%s.%s.%s)?" % (
cur_version[:3])
exit(1)
from rootpy import log
log = log["/pyroplot"]
rootpy.log.basic_config_colorized()
ROOT.gROOT.SetBatch(True)
ROOT.gErrorIgnoreLevel = 1001
import argparse
# command line argument parsing
parser = argparse.ArgumentParser(
description=
"Python ROOT plotter - A tool for selecting and assembling histogram plots and comparision plots from multiple ROOT files at once"
)
parser.add_argument('--version',
action='version',
version='Revision: $Revision$, $LastChangedDate$')
parser.add_argument(
"-l",
"--log-level",
default="info",
help=
"Sets the verbosity of log messages where LEVEL is either debug, info, warning or error",
metavar="LEVEL")
parser.add_argument(
"--compare",
action="store_true",
default=False,
help=
"Compare the selected histograms between files (ratio plots, chi2) where the first file provides the reference."
)
parser.add_argument(
"-log",
"--log-scale",
action="store_true",
default=False,
help=
"Uses a logarithmic scale for the y axis; only relevant when not using '--compare'."
)
parser.add_argument(
'--select',
'-s',
action='append',
help="Specify regular expression(s) for histogram selection.")
parser.add_argument(
"--selection-from-file",
help=
"Load list of regular expressions for histogram selection from file (plain text file, one reg ex per line).",
metavar="FILE")
parser.add_argument(
"--one-file-per-histogram",
action="store_true",
default=False,
help=
"Writes one file per histogram instead of storing all plots in one single file."
)
parser.add_argument(
"-o",
"--output",
default="./overview.pdf",
help=
"Output path and file name. If the file does not end in '.pdf' it will be assumed to be a path and created if needed. If --one-file-per-histogram is set, this will be the output directory for the plots.",
metavar="FILE/PATH")
parser.add_argument("--with-2D",
"-2D",
action="store_true",
default=False,
help="Also loads TH2-type histograms.")
parser.add_argument(
"--with-3D",
"-3D",
action="store_true",
default=False,
help=
"Also loads TH3-type and Profile2D-type histograms, implies --with-2D."
)
parser.add_argument(
"--list-only",
"--list",
action="store_true",
default=False,
help=
"Do not generate plots but only list objects in ROOT file(s) and indicate which ones would be selected."
)
parser.add_argument(
"--strict",
action="store_true",
default=False,
help=
"Require the selection to match the full histogram path and name (with implied '^' and '$') instead of only a partial match."
)
parser.add_argument(
"files",
help=
"The files to be processed; additional info STRING to be included in the plot legend can be added by specifiying FILE:STRING",
nargs='+')
# parse the arguments
args = parser.parse_args(argv)
# set the logging level
numeric_level = getattr(logging, "INFO",
None) # default: INFO messages and above
if args.log_level:
# Convert log level to upper case to allow the user to specify --log-level=DEBUG or --log-level=debug
numeric_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(numeric_level, int):
log.error('Invalid log level: %s' % args.log_level)
exit(2)
log.setLevel(numeric_level)
log.debug("Command line arguments used: %s ", args)
log.debug("Using rootpy %s from %s" %
(rootpy.__version__, rootpy.__file__))
# laod and combine all specified reg ex
regexs = []
# first from file
if args.selection_from_file:
f = open(args.selection_from_file, 'r')
try:
lines = f.read().splitlines()
for line in lines:
if line: # test if line is not empty (would match anything)
log.debug("Loading reg ex from file " +
args.selection_from_file + ": '" + line + "'")
regexs.append(line)
finally:
f.close()
if args.select:
for arg in args.select:
log.debug("Using reg ex from command line: " + arg)
regexs.append(arg)
# still nothing to select? use default
if not regexs:
import inspect
filepath = os.path.join(
os.path.dirname(os.path.abspath(os.path.realpath(__file__))),
"default.sel")
try:
f = open(filepath, 'r')
try:
lines = f.read().splitlines()
for line in lines:
if line: # test if line is not empty (would match anything)
log.debug("Loading reg ex from file " + filepath +
": '" + line + "'")
regexs.append(line)
finally:
f.close()
except IOError:
log.warn("Could not find the file with the default selection ('" +
filepath + "'), will use default of '.*' (select all)")
regexs.append('.*')
# parse output file name and verify that it ends in '.pdf'
outputFilePath = ""
fileName, fileExtension = os.path.splitext(args.output)
if not fileExtension == '.pdf':
log.debug(
"Output argument does not end in '.pdf': '%s'. Assuming it's meant to be a path"
% args.output)
if not args.one_file_per_histogram:
# append default name for single histogram file
outputFilePath = os.path.join(args.output, "overview.pdf")
else:
outputFilePath = args.output
else:
if args.one_file_per_histogram:
# all we need is the path, strip the file and append a slash
outputFilePath = os.path.dirname(args.output)
else:
outputFilePath = args.output
# parse file names and extract additionally provided info
fileNames = []
fileDescr = {}
for thisFile in args.files:
s = thisFile.strip().split(':', 1) # try to split the string
if (len(s) == 1):
# didn't work, only have one entry
fileNames.append(s[0])
fileDescr[s[0]] = ""
else:
fileNames.append(s[0])
fileDescr[s[0]] = s[1]
histoDicts = [] # our histograms: each element will store a dict()
# of histogram objects with its full path in the
# root file as key
selectedHistos = []
# loop over all files
for idx, thisFile in enumerate(fileNames):
# only search for matching histo names on first iteration if doing a comparison between files
if not idx or not args.compare:
selectedHistos = findHistogramsInFile(thisFile, regexs,
args.strict, args.list_only)
if args.list_only:
continue
h = loadHistogramsFromFile(thisFile, selectedHistos, args.with_2D,
args.with_3D)
histoDicts.append(h) # append to main histo list
if histoDicts:
log.info(
"Input file(s) read. %d histograms matched selection criteria and were loaded"
% (sum(len(histos) for histos in histoDicts)))
makePlotCollection(histoDicts, fileNames, fileDescr, outputFilePath,
args.compare, args.one_file_per_histogram,
args.log_scale)
log.info("done")
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
'ZDC': [(1, 0.7), (.7, .3), (.3, .05), (0.001, 0.0)],
'nMPI': [(1, 0.7), (.7, .4), (.3, .05), (0.001, 0.0)],
'Q2': [(1, 0.7), (.7, .4), (.3, .05), (0.001, 0.0)],
'spherocity': [(1, 0.7), (.7, .4), (.3, .05), (0.001, 0.0)],
'sphericity': [(1, 0.7), (.7, .4), (.3, .05), (0.001, 0.0)],
}
if __name__ == "__main__":
if len(sys.argv) < 4:
print """Usage: python post.py file.root {Inel, InelGt0, V0AND} "<summary name>" """
quit()
# go into batch mode
ROOT.gROOT.SetBatch(True)
log = log["/post"] # set name of this script in logger
log.info("IsBatch: {0}".format(ROOT.gROOT.IsBatch()))
try:
global_trigger = sys.argv[2]
except IndexError:
global_trigger = ""
sums_dir_name = "Sums" + global_trigger
results_dir_name = "results_post" + global_trigger
plotting = Plotting(f_name=sys.argv[1], sums_dir_name=sums_dir_name, results_dir_name=results_dir_name,
percentile_bins=percentile_bins, considered_ests=considered_ests)
latexdoc = Beamerdoc(author="Christian Bourjau", title=sys.argv[3])
# run the actual plots:
sec = latexdoc.add_section(r"$dN/d\eta$")
[sec.add_figure(fig) for fig in plotting.plot_dNdetas(ratio_to_mb=False)]
def run( argv = sys.argv ):
# Must be done before :py:mod:`rootpy` logs any messages.
import logging;
log = logging.getLogger('pyroplot') # set up logging
try:
import ROOT
except ImportError:
# module failed to load - maybe PYTHONPATH is not set correctly?
# guess the right path, but that is only possible if ROOTSYS is set:
if os.environ.get('ROOTSYS') is None:
print "ERROR: Could not load the Python ROOT module. Please make sure that your ROOT installation is compiled with Python support and that your PYTHONPATH is set correctly and includes libPyROOT.so"
exit(1)
sys.path.append(os.path.join(os.environ.get('ROOTSYS'),"lib"))
sys.path.append(os.path.join(os.environ.get('ROOTSYS'),"lib","root"))
# try again:
try:
import ROOT
except ImportError:
print "ERROR: Could not load the Python ROOT module. Please make sure that your ROOT installation is compiled with Python support and that your PYTHONPATH is set correctly and includes libPyROOT.so"
exit(1)
try:
import rootpy
except ImportError:
# rootpy is not installed; use (old) version provided with EUTelescope
# determine (real) path to subdirectory pymodules (relative to current path)
libdir = os.path.join(os.path.dirname(os.path.abspath(os.path.realpath(__file__))),"pymodules","rootpy")
# search for any rootpy folders
import glob
rootpydirs = glob.glob(libdir+"*")
if not rootpydirs:
print "Error: Could not find the rootpy module provided with EUTelescope in %s!"%(libdir)
else:
# add last entry to python search path (subfolder rootpy where the modules are located)
sys.path.append(rootpydirs[-1])
# try again loading the module
try:
import rootpy
except ImportError:
print "Error: Could not load the rootpy modules. Please install them from http://www.rootpy.org/install.html"
exit(1)
except SyntaxError:
req_version = (2,5)
cur_version = sys.version_info
if cur_version < req_version:
print "Error: Python version too old: due to its dependency on rootpy, this script requires a Python interpreter version 2.6 or later (installed: %s.%s.%s)!"%(cur_version[:3])
exit(1)
print "Error: Failed to load rootpy module! Possibly incompatible with installed Python version (%s.%s.%s)?"%(cur_version[:3])
exit(1)
from rootpy import log; log = log["/pyroplot"]
rootpy.log.basic_config_colorized()
ROOT.gROOT.SetBatch(True)
ROOT.gErrorIgnoreLevel = 1001
import argparse
# command line argument parsing
parser = argparse.ArgumentParser(description="Python ROOT plotter - A tool for selecting and assembling histogram plots and comparision plots from multiple ROOT files at once")
parser.add_argument('--version', action='version', version='Revision: $Revision: 2757 $, $LastChangedDate: 2013-06-24 18:14:16 +0200 (Mon, 24 Jun 2013) $')
parser.add_argument("-l", "--log-level", default="info", help="Sets the verbosity of log messages where LEVEL is either debug, info, warning or error", metavar="LEVEL")
parser.add_argument("--compare", action="store_true", default=False, help="Compare the selected histograms between files (ratio plots, chi2) where the first file provides the reference.")
parser.add_argument("-log", "--log-scale", action="store_true", default=False, help="Uses a logarithmic scale for the y axis; only relevant when not using '--compare'.")
parser.add_argument('--select', '-s', action='append', help="Specify regular expression(s) for histogram selection.")
parser.add_argument("--selection-from-file", help="Load list of regular expressions for histogram selection from file (plain text file, one reg ex per line).", metavar="FILE")
parser.add_argument("--one-file-per-histogram", action="store_true", default=False, help="Writes one file per histogram instead of storing all plots in one single file.")
parser.add_argument("-o","--output", default="./overview.pdf", help="Output path and file name. If the file does not end in '.pdf' it will be assumed to be a path and created if needed. If --one-file-per-histogram is set, this will be the output directory for the plots.", metavar="FILE/PATH")
parser.add_argument("--with-2D","-2D", action="store_true", default=False, help="Also loads TH2-type histograms.")
parser.add_argument("--with-3D","-3D", action="store_true", default=False, help="Also loads TH3-type and Profile2D-type histograms, implies --with-2D.")
parser.add_argument("--list-only", "--list", action="store_true", default=False, help="Do not generate plots but only list objects in ROOT file(s) and indicate which ones would be selected.")
parser.add_argument("--strict", action="store_true", default=False, help="Require the selection to match the full histogram path and name (with implied '^' and '$') instead of only a partial match.")
parser.add_argument("files", help="The files to be processed; additional info STRING to be included in the plot legend can be added by specifiying FILE:STRING", nargs='+')
# parse the arguments
args = parser.parse_args(argv)
# set the logging level
numeric_level = getattr(logging, "INFO", None) # default: INFO messages and above
if args.log_level:
# Convert log level to upper case to allow the user to specify --log-level=DEBUG or --log-level=debug
numeric_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(numeric_level, int):
log.error('Invalid log level: %s' % args.log_level)
exit(2)
log.setLevel(numeric_level)
log.debug( "Command line arguments used: %s ", args )
log.debug("Using rootpy %s from %s"%(rootpy.__version__,rootpy.__file__))
# laod and combine all specified reg ex
regexs = []
# first from file
if args.selection_from_file:
f = open(args.selection_from_file, 'r')
try:
lines = f.read().splitlines()
for line in lines:
if line: # test if line is not empty (would match anything)
log.debug("Loading reg ex from file " + args.selection_from_file
+ ": '" + line +"'")
regexs.append(line)
finally:
f.close()
if args.select:
for arg in args.select:
log.debug("Using reg ex from command line: " + arg)
regexs.append(arg)
# still nothing to select? use default
if not regexs:
import inspect
filepath = os.path.join(os.path.dirname(os.path.abspath(os.path.realpath(__file__))),"default.sel")
try:
f = open(filepath, 'r')
try:
lines = f.read().splitlines()
for line in lines:
if line: # test if line is not empty (would match anything)
log.debug("Loading reg ex from file " + filepath + ": '" + line +"'")
regexs.append(line)
finally:
f.close()
except IOError:
log.warn("Could not find the file with the default selection ('"+filepath+"'), will use default of '.*' (select all)")
regexs.append('.*')
# parse output file name and verify that it ends in '.pdf'
outputFilePath = ""
fileName, fileExtension = os.path.splitext(args.output)
if not fileExtension == '.pdf':
log.debug("Output argument does not end in '.pdf': '%s'. Assuming it's meant to be a path"%args.output)
if not args.one_file_per_histogram:
# append default name for single histogram file
outputFilePath = os.path.join(args.output,"overview.pdf")
else:
outputFilePath = args.output
else:
if args.one_file_per_histogram:
# all we need is the path, strip the file and append a slash
outputFilePath = os.path.dirname(args.output)
else:
outputFilePath = args.output
# parse file names and extract additionally provided info
fileNames = []
fileDescr = {}
for thisFile in args.files:
s = thisFile.strip().split(':', 1) # try to split the string
if (len(s)==1):
# didn't work, only have one entry
fileNames.append(s[0])
fileDescr[s[0]] = ""
else:
fileNames.append(s[0])
fileDescr[s[0]] = s[1]
histoDicts = [] # our histograms: each element will store a dict()
# of histogram objects with its full path in the
# root file as key
selectedHistos = []
# loop over all files
for idx, thisFile in enumerate( fileNames ):
# only search for matching histo names on first iteration if doing a comparison between files
if not idx or not args.compare:
selectedHistos = findHistogramsInFile(thisFile, regexs, args.strict, args.list_only)
if args.list_only:
continue
h = loadHistogramsFromFile( thisFile , selectedHistos, args.with_2D, args.with_3D)
histoDicts.append(h) # append to main histo list
if histoDicts:
log.info("Input file(s) read. %d histograms matched selection criteria and were loaded"%(sum(len(histos) for histos in histoDicts)))
makePlotCollection(histoDicts, fileNames, fileDescr, outputFilePath, args.compare, args.one_file_per_histogram,args.log_scale)
log.info("done")
def plot_PNch(self):
log.info("Creating P(Nch_est) and P(Nch_refest) histograms")
# mult_bin_size = 10
figs = []
for ref_est_name in self.ref_ests:
for res_est_dir in get_est_dirs(self.results_post, self.considered_ests):
est_name = res_est_dir.GetName()
# Figure properties:
fig_vs_estmult = Figure()
fig_vs_refmult = Figure()
fig_vs_estmult.plot.logy = True
fig_vs_refmult.plot.logy = True
fig_vs_estmult.plot.palette = 'colorblind'
fig_vs_refmult.plot.palette = 'colorblind'
fig_vs_estmult.legend.position = 'tr'
fig_vs_refmult.legend.position = 'tr'
fig_vs_estmult.xtitle = "N_{{ch}}^{{{0}}}".format(est_name)
fig_vs_refmult.xtitle = "N_{{ch}}^{{{0}}}".format(ref_est_name)
fig_vs_estmult.ytitle = "P(N_{{ch}}^{{{0}}})".format(est_name)
fig_vs_refmult.ytitle = "P(N_{{ch}}^{{{0}}})".format(ref_est_name)
corr_hist = get_correlation_histogram(self.sums, est_name, ref_est_name)
# logic when dealing with fixed bins given in Nch:
# ------------------------------------------------
# mean_nch_est = corr_hist.GetMean(1) # mean of x axis
# nch_max = corr_hist.xaxis.GetNbins()
# nch_cutoff = mean_nch_est * mean_mult_cutoff_factor
# nch_bins = [(low, low + mult_bin_size) for low in range(0, int(nch_cutoff), mult_bin_size)]
# # a large last bin covering the rest:
# nch_bins += [(nch_bins[-1][2], nch_max)]
# legend_tmpl = "{} < N_{ch} < {}"
# logic when dealing with percentile bins:
# ----------------------------------------
# event_counter_est = asrootpy(getattr(res_est_dir, "event_counter"))
legend_tmpl = "{0}% - {1}%"
fig_vs_estmult.legend.title = "Selected in {0}".format(make_estimator_title(ref_est_name))
fig_vs_refmult.legend.title = "Selected in {0}".format(make_estimator_title(est_name))
# WARNING: the following needs tweeking when going back to fixed N_ch bins!
for nch_bin, perc_bin in zip(self.nch_edges[ref_est_name], self.perc_bins[ref_est_name]):
# vs est_mult:
corr_hist.xaxis.SetRange(0, 0) # reset x axis
corr_hist.yaxis.SetRange(nch_bin[0], nch_bin[1])
h_vs_est = asrootpy(corr_hist.ProjectionX(gen_random_name()))
if h_vs_est.Integral() > 0:
h_vs_est.Scale(1.0 / h_vs_est.Integral())
fig_vs_estmult.add_plottable(h_vs_est, legend_tmpl.format(perc_bin[1] * 100, perc_bin[0] * 100))
else:
log.info("No charged particles in {0}*100 percentile bin of estimator {1}. This should not happen".
format(perc_bin, ref_est_name))
for nch_bin, perc_bin in zip(self.nch_edges[est_name], self.perc_bins[est_name]):
# vs ref_mult:
corr_hist.yaxis.SetRange(0, 0) # reset y axis
corr_hist.xaxis.SetRange(*nch_bin)
h_vs_ref = asrootpy(corr_hist.ProjectionY(gen_random_name()))
if h_vs_ref.Integral() > 0:
h_vs_ref.Scale(1.0 / h_vs_ref.Integral())
fig_vs_refmult.add_plottable(h_vs_ref, legend_tmpl.format(perc_bin[1] * 100, perc_bin[0] * 100))
else:
log.info(
"No charged particles in {0}*100 percentile bin of estimator {1}. This should not happen".
format(perc_bin, est_name))
path = res_est_dir.GetPath().split(":")[1]
# vs est_mult
fig_vs_estmult.save_to_root_file(self.f, "PNchEst_binned_in_Nch{0}".format(ref_est_name), path)
# vs est_mult
fig_vs_refmult.save_to_root_file(self.f, "PNch{0}_binned_in_NchEst".format(ref_est_name), path)
figs.append(fig_vs_estmult)
figs.append(fig_vs_refmult)
return figs
def plot_pid_ratio_vs_refmult(self):
log.info("Creating plots vs refmult")
ratios_dir = self.results_post.GetPath().split(":")[1] + '/pid_ratios_vs_refmult'
def get_new_figure():
fig = Figure()
fig.plot.ncolors = len(self.considered_ests)
fig.xtitle = "N_{ch}|_{" + make_estimator_title('EtaLt05') + "}"
fig.plot.xmin = 0
fig.plot.xmax = 60
return fig
figs = []
# Proton / pi_ch
fig = get_new_figure()
pids1, pids2 = ['-2212', '2212'], ['-211', '211']
fig.ytitle = "p/#pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.04, 0.13
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2, )
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# K / pi_ch
fig = get_new_figure()
pids1, pids2 = ['310', '321', '-321'], ['-211', '211']
fig.ytitle = "K^{*}/#pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.09, 0.30
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# Lambda / pi_ch
fig = get_new_figure()
pids1, pids2 = ['3122'], ['-211', '211']
fig.ytitle = "#Lambda / #pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.005, 0.035
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# Xi / pi_ch
fig = get_new_figure()
pids1, pids2 = ['3312'], ['-211', '211']
fig.ytitle = "#Xi / #pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.0004, 0.003
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# Omega / pi_ch
fig = get_new_figure()
pids1, pids2 = ['3334', '-3334'], ['-211', '211']
fig.ytitle = "#Omega / #pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.00001, 0.0005
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# pi_ch/pi0
fig = get_new_figure()
pids1, pids2 = ['-211', '211'], ['111']
fig.ytitle = "#pi^{+-}/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 1.5, 2.2
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# proton / pi0
fig = get_new_figure()
pids1, pids2 = ['-2212', '2212'], ['111']
fig.ytitle = "p/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.09, 0.30
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# K / pi0
fig = get_new_figure()
pids1, pids2 = ['310', '321', '-321'], ['111']
fig.ytitle = "K^{*}/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.15, 0.50
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# Lambda / pi0
fig = get_new_figure()
pids1, pids2 = ['3122'], ['111']
fig.ytitle = "#Lambda/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.014, 0.045
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# Xi / pi0
fig = get_new_figure()
pids1, pids2 = ['3312'], ['111']
fig.ytitle = "#Xi/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.0010, 0.005
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# Omega / pi0
fig = get_new_figure()
pids1, pids2 = ['3334', '-3334'], ['111']
fig.ytitle = "#Omega/#pi^{0}"
fig.legend.position = 'tl'
fig.plot.ymin, fig.plot.ymax = 0.00002, 0.0008
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# K_ch / K0_S
fig = get_new_figure()
pids1, pids2 = ['321', '-321'], ['310']
fig.ytitle = "(K^{+}+K^{-}) / (2#timesK^{0}_{S})"
fig.plot.ymin, fig.plot.ymax = 0.4, 1.5
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2, scale=.5)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# K0_S / Lambda
fig = get_new_figure()
pids1, pids2 = ['310'], ['-3122', '3122']
fig.ytitle = "K^{0}_{S} / #Lambda"
fig.plot.ymin, fig.plot.ymax = 1.3, 3.7
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
# K0_S / Xi
fig = get_new_figure()
pids1, pids2 = ['310'], ['3312']
fig.ytitle = "K^{0}_{S} / #Xi"
fig.plot.ymin, fig.plot.ymax = 15, 80
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f, name, ratios_dir)
figs.append(fig)
return figs
def convert(rfile, hfile, rpath='', entries=-1, userfunc=None, selection=None, indexes=[]):
isatty = check_tty(sys.stdout)
if isatty:
widgets = [Percentage(), ' ', Bar(), ' ', ETA()]
own_h5file = False
if isinstance(hfile, basestring):
hfile = pd.HDFStore(hfile)
own_h5file = True
own_rootfile = False
if isinstance(rfile, basestring):
rfile = root_open(rfile)
own_rootfile = True
for dirpath, dirnames, treenames in rfile.walk(
rpath, class_pattern='TTree'):
# skip root
if not dirpath and not treenames:
continue
# skip directories w/o trees or subdirs
if not dirnames and not treenames:
continue
where_group = '/' + os.path.dirname(dirpath)
current_dir = os.path.basename(dirpath)
#if not current_dir:
#group = hfile.root
#else:
#group = hfile.createGroup(where_group, current_dir, "")
ntrees = len(treenames)
log.info(
"Will convert {0:d} tree{1} in this directory".format(
ntrees, 's' if ntrees != 1 else ''))
for treename in treenames:
input_tree = rfile.Get(os.path.join(dirpath, treename))
path_to_tree = os.path.join(dirpath, treename)
if userfunc is not None:
tmp_file = TemporaryFile()
# call user-defined function on tree and get output trees
log.info("Calling user function on tree '{0}'".format(
input_tree.GetName()))
trees = userfunc(input_tree)
if not isinstance(trees, list):
trees = [trees]
else:
trees = [input_tree]
tmp_file = None
for tree in trees:
log.info("Converting tree '{0}' with {1:d} entries ...".format(
tree.GetName(),
tree.GetEntries()))
#if tree.GetName() in group:
#log.warning(
#"skipping tree '{0}' that already exists "
#"in the output file".format(tree.GetName()))
#continue
total_entries = tree.GetEntries()
pbar = None
if isatty and total_entries > 0:
pbar = ProgressBar(widgets=widgets, maxval=total_entries)
if entries <= 0:
# read the entire tree
if pbar is not None:
pbar.start()
recarray = tree2rec(tree, selection=selection)
recarray = pd.DataFrame(_drop_object_col(recarray))
hfile.append(path_to_tree, recarray, data_columns = indexes)
#table = hfile.createTable(
#group, tree.GetName(),
#recarray, tree.GetTitle())
## flush data in the table
#table.flush()
## flush all pending data
#hfile.flush()
else:
# read the tree in chunks
offset = 0
while offset < total_entries or offset == 0:
if offset > 0:
with warnings.catch_warnings():
warnings.simplefilter(
"ignore",
RootNumpyUnconvertibleWarning)
recarray = tree2rec(
tree,
entries=entries,
offset=offset,
selection=selection)
recarray = pd.DataFrame(_drop_object_col(recarray, warn=False))
#table.append(recarray)
hfile.append(path_to_tree,recarray, data_columns = indexes)
else:
recarray = tree2rec(
tree,
entries=entries,
offset=offset,
selection=selection)
recarray = pd.DataFrame(_drop_object_col(recarray))
if pbar is not None:
# start after any output from root_numpy
pbar.start()
#table = hfile.createTable(
#group, tree.GetName(),
#recarray, tree.GetTitle())
hfile.append(path_to_tree,recarray, data_columns = indexes)
offset += entries
if offset <= total_entries and pbar is not None:
pbar.update(offset)
## flush data in the table
#table.flush()
## flush all pending data
#hfile.flush()
if pbar is not None:
pbar.finish()
input_tree.Delete()
if userfunc is not None:
for tree in trees:
tree.Delete()
tmp_file.Close()
if own_h5file:
hfile.close()
if own_rootfile:
rfile.Close()
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_PNch(self):
log.info("Creating P(Nch_est) and P(Nch_refest) histograms")
# mult_bin_size = 10
figs = []
for ref_est_name in self.ref_ests:
for res_est_dir in get_est_dirs(self.results_post, self.considered_ests):
est_name = res_est_dir.GetName()
# Figure properties:
fig_vs_estmult = Figure()
fig_vs_refmult = Figure()
fig_vs_estmult.plot.logy = True
fig_vs_refmult.plot.logy = True
fig_vs_estmult.plot.palette = 'colorblind'
fig_vs_refmult.plot.palette = 'colorblind'
fig_vs_estmult.legend.position = 'tr'
fig_vs_refmult.legend.position = 'tr'
fig_vs_estmult.xtitle = "N_{{ch}}^{{{0}}}".format(est_name)
fig_vs_refmult.xtitle = "N_{{ch}}^{{{0}}}".format(ref_est_name)
fig_vs_estmult.ytitle = "P(N_{{ch}}^{{{0}}})".format(est_name)
fig_vs_refmult.ytitle = "P(N_{{ch}}^{{{0}}})".format(ref_est_name)
corr_hist = get_correlation_histogram(self.sums, est_name, ref_est_name)
# logic when dealing with fixed bins given in Nch:
# ------------------------------------------------
# mean_nch_est = corr_hist.GetMean(1) # mean of x axis
# nch_max = corr_hist.xaxis.GetNbins()
# nch_cutoff = mean_nch_est * mean_mult_cutoff_factor
# nch_bins = [(low, low + mult_bin_size) for low in range(0, int(nch_cutoff), mult_bin_size)]
# # a large last bin covering the rest:
# nch_bins += [(nch_bins[-1][2], nch_max)]
# legend_tmpl = "{} < N_{ch} < {}"
# logic when dealing with percentile bins:
# ----------------------------------------
# event_counter_est = asrootpy(getattr(res_est_dir, "event_counter"))
legend_tmpl = "{0}% - {1}%"
fig_vs_estmult.legend.title = "Selected in {0}".format(make_estimator_title(ref_est_name))
fig_vs_refmult.legend.title = "Selected in {0}".format(make_estimator_title(est_name))
# WARNING: the following needs tweeking when going back to fixed N_ch bins!
for nch_bin, perc_bin in zip(self.nch_edges[ref_est_name], self.perc_bins[ref_est_name]):
# vs est_mult:
corr_hist.xaxis.SetRange(0, 0) # reset x axis
corr_hist.yaxis.SetRange(nch_bin[0], nch_bin[1])
h_vs_est = asrootpy(corr_hist.ProjectionX(gen_random_name()))
if h_vs_est.Integral() > 0:
h_vs_est.Scale(1.0 / h_vs_est.Integral())
fig_vs_estmult.add_plottable(h_vs_est, legend_tmpl.format(perc_bin[1] * 100, perc_bin[0] * 100))
else:
log.info("No charged particles in {0}*100 percentile bin of estimator {1}. This should not happen".
format(perc_bin, ref_est_name))
for nch_bin, perc_bin in zip(self.nch_edges[est_name], self.perc_bins[est_name]):
# vs ref_mult:
corr_hist.yaxis.SetRange(0, 0) # reset y axis
corr_hist.xaxis.SetRange(*nch_bin)
h_vs_ref = asrootpy(corr_hist.ProjectionY(gen_random_name()))
if h_vs_ref.Integral() > 0:
h_vs_ref.Scale(1.0 / h_vs_ref.Integral())
fig_vs_refmult.add_plottable(h_vs_ref, legend_tmpl.format(perc_bin[1] * 100, perc_bin[0] * 100))
else:
log.info(
"No charged particles in {0}*100 percentile bin of estimator {1}. This should not happen".
format(perc_bin, est_name))
path = res_est_dir.GetPath().split(":")[1]
# vs est_mult
fig_vs_estmult.save_to_root_file(self.f_out, "PNchEst_binned_in_Nch{0}".format(ref_est_name), path)
# vs est_mult
fig_vs_refmult.save_to_root_file(self.f_out, "PNch{0}_binned_in_NchEst".format(ref_est_name), path)
figs.append(fig_vs_estmult)
figs.append(fig_vs_refmult)
return figs
def plot_pid_ratio_vs_refmult(self):
log.info("Creating plots vs refmult")
ratios_dir = self.results_post.GetPath().split(":")[1] + '/pid_ratios_vs_refmult'
def get_new_figure():
fig = Figure()
fig.plot.ncolors = len(self.considered_ests)
fig.xtitle = "N_{ch}|_{" + make_estimator_title('EtaLt05') + "}"
fig.plot.xmin = 0
fig.plot.xmax = 60
return fig
figs = []
# Proton / pi_ch
fig = get_new_figure()
pids1, pids2 = ['-2212', '2212'], ['-211', '211']
fig.ytitle = "p/#pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.04, 0.13
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2, )
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# K / pi_ch
fig = get_new_figure()
pids1, pids2 = ['310', '321', '-321'], ['-211', '211']
fig.ytitle = "K^{*}/#pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.09, 0.30
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# Lambda / pi_ch
fig = get_new_figure()
pids1, pids2 = ['3122'], ['-211', '211']
fig.ytitle = "#Lambda / #pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.005, 0.035
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# Xi / pi_ch
fig = get_new_figure()
pids1, pids2 = ['3312'], ['-211', '211']
fig.ytitle = "#Xi / #pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.0004, 0.003
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# Omega / pi_ch
fig = get_new_figure()
pids1, pids2 = ['3334', '-3334'], ['-211', '211']
fig.ytitle = "#Omega / #pi^{+-}"
fig.plot.ymin, fig.plot.ymax = 0.00001, 0.0005
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# pi_ch/pi0
fig = get_new_figure()
pids1, pids2 = ['-211', '211'], ['111']
fig.ytitle = "#pi^{+-}/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 1.5, 2.2
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# proton / pi0
fig = get_new_figure()
pids1, pids2 = ['-2212', '2212'], ['111']
fig.ytitle = "p/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.09, 0.30
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# K / pi0
fig = get_new_figure()
pids1, pids2 = ['310', '321', '-321'], ['111']
fig.ytitle = "K^{*}/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.15, 0.50
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# Lambda / pi0
fig = get_new_figure()
pids1, pids2 = ['3122'], ['111']
fig.ytitle = "#Lambda/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.014, 0.045
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# Xi / pi0
fig = get_new_figure()
pids1, pids2 = ['3312'], ['111']
fig.ytitle = "#Xi/#pi^{0}"
fig.plot.ymin, fig.plot.ymax = 0.0010, 0.005
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# Omega / pi0
fig = get_new_figure()
pids1, pids2 = ['3334', '-3334'], ['111']
fig.ytitle = "#Omega/#pi^{0}"
fig.legend.position = 'tl'
fig.plot.ymin, fig.plot.ymax = 0.00002, 0.0008
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# K_ch / K0_S
fig = get_new_figure()
pids1, pids2 = ['321', '-321'], ['310']
fig.ytitle = "(K^{+}+K^{-}) / (2#timesK^{0}_{S})"
fig.plot.ymin, fig.plot.ymax = 0.4, 1.5
fig.legend.position = 'tl'
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2, scale=.5)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# K0_S / Lambda
fig = get_new_figure()
pids1, pids2 = ['310'], ['-3122', '3122']
fig.ytitle = "K^{0}_{S} / #Lambda"
fig.plot.ymin, fig.plot.ymax = 1.3, 3.7
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
# K0_S / Xi
fig = get_new_figure()
pids1, pids2 = ['310'], ['3312']
fig.ytitle = "K^{0}_{S} / #Xi"
fig.plot.ymin, fig.plot.ymax = 15, 80
graphs = get_graphs_particle_ratios_vs_refmult(self, pids1, pids2)
[fig.add_plottable(g, legend_title=g.GetTitle()) for g in graphs]
name = "_".join(pids1) + "_div_" + "_".join(pids2)
fig.save_to_root_file(self.f_out, name, ratios_dir)
figs.append(fig)
return figs