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Luigi Dello Stritto committed Nov 5, 2024
1 parent fe563e2 commit cb7f08c
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Showing 2 changed files with 24 additions and 177 deletions.
144 changes: 6 additions & 138 deletions machine_learning_hep/analysis/analyzerdhadrons_mult.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,6 @@ def __init__(self, datap, case, typean, period):
self.lvar2_binmax = datap["analysis"][self.typean]["sel_binmax2"]
self.v_var2_binning = datap["analysis"][self.typean]["var_binning2"]
self.v_var2_binning_gen = datap["analysis"][self.typean]["var_binning2_gen"]
self.triggerbit = datap["analysis"][self.typean]["triggerbit"]
self.p_nbin2 = len(self.lvar2_binmin)

dp = datap["analysis"][typean]
Expand Down Expand Up @@ -126,12 +125,6 @@ def __init__(self, datap, case, typean, period):
self.p_cctype = datap["analysis"]["cctype"]
self.p_sigmamb = datap["analysis"]["sigmamb"]
self.p_inputfonllpred = datap["analysis"]["inputfonllpred"]
self.p_triggereff = datap["analysis"][self.typean].get("triggereff", [1] * 10)
self.p_triggereffunc = datap["analysis"][self.typean].get("triggereffunc", [0] * 10)

self.apply_weights = \
datap["analysis"][self.typean]["triggersel"].get("usetriggcorrfunc", None) \
is not None
self.root_objects = []

self.get_crossmb_from_path = datap["analysis"][self.typean].get("get_crossmb_from_path", \
Expand Down Expand Up @@ -319,9 +312,8 @@ def fit(self):
if self.cfg('mass_fit'):
fit_res, _, func_bkg = self._fit_mass(
h_invmass,
f'fit/'
f'h_mass_fitted_pthf-{ptrange[0]}-{ptrange[1]}_\
{self.v_var2_binning}-{multrange[0]}-{multrange[1]}_{level}.png')
f'fit/h_mass_fitted_pthf-{ptrange[0]}-{ptrange[1]}'
f'_{self.v_var2_binning}-{multrange[0]}-{multrange[1]}_{level}.png')
if fit_res and fit_res.Get() and fit_res.IsValid():
self.fit_mean[level][ipt] = fit_res.Parameter(1)
self.fit_sigma[level][ipt] = fit_res.Parameter(2)
Expand Down Expand Up @@ -357,8 +349,8 @@ def fit(self):
roo_res, roo_ws = self._roofit_mass(
level, h_invmass, ipt, self.p_pdfnames, self.p_param_names, fitcfg, roows,
f'roofit/mult_{multrange[0]}-{multrange[1]}/'
f'h_mass_fitted_pthf-{ptrange[0]}-{ptrange[1]}_\
{self.v_var2_binning}-{multrange[0]}-{multrange[1]}_{level}.png')
f'h_mass_fitted_pthf-{ptrange[0]}-{ptrange[1]}'\
f'_{self.v_var2_binning}-{multrange[0]}-{multrange[1]}_{level}.png')
# if level == 'mc':
# roo_ws.Print()
self.roo_ws[level][ipt] = roo_ws
Expand Down Expand Up @@ -634,9 +626,6 @@ def makenormyields(self): # pylint: disable=import-outside-toplevel, too-many-br
nameyield = "hyields%d" % imult
fileoutcrossmult = "%s/finalcross%s%smult%d.root" % \
(self.d_resultsallpdata, self.case, self.typean, imult)
labeltrigger = "hbit%svs%s" % (self.triggerbit, self.v_var2_binning_gen)
if self.apply_weights is True:
labeltrigger = labeltrigger + "_weight"

#Bin1 is all events. Bin2 is all sel events. Mult bins start from Bin3.
norm = histonorm.GetBinContent(imult + 3)
Expand Down Expand Up @@ -747,126 +736,5 @@ def plotternormyields(self):
cCrossvsvar1.SaveAs("%s/CorrectedYieldsNorm%s%sVs%s.eps" % (self.d_resultsallpdata,
self.case, self.typean,
self.v_var_binning))
def plottervalidation(self):
if self.p_performval is False:
self.logger.fatal(
"The validation step was set to false. You dont \
have produced the histograms you need for the \
validation stage. Please rerun the histomass \
step"
)
self.logger.info("I AM RUNNING THE PLOTTER VALIDATION STEP")
# You can find all the input files in the self.n_filemass. At the
# moment we dont do tests for the MC file that would be in any case
# self.n_filemass_mc. This function will be run on only the single
# merged LHC16,LHC17, LHC18 file or also on the separate years
# depending on how you set the option doperperiod in the
# default_complete.yml database.

def do_validation_plots(input_file_name,
output_path,
ismc=False,
pileup_fraction=True,
tpc_tof_me=True):
gROOT.SetBatch(True)

input_file = TFile(input_file_name, "READ")
if not input_file or not input_file.IsOpen():
self.logger.fatal("Did not find file %s", input_file.GetName())

def get_histo(namex, namey=None, tag="", strictly_require=True):
"""
Gets a histogram from a file
"""
h_name = f"hVal_{namex}"
if namey:
h_name += f"_vs_{namey}"
h_name += tag
h = input_file.Get(h_name)
if not h:
if strictly_require:
input_file.ls()
self.logger.fatal(
"Did not find %s in file %s", h_name, input_file.GetName()
)
else:
self.logger.warning(
"Did not find %s in file %s", h_name, input_file.GetName()
)
return None
return h

def do_plot(histo):
"""
Plots the histogram in a new canvas, if it is a TH2, it also plots the profile.
The canvas has the same name as the histogram and it is saved to the output_path
"""
canvas = TCanvas(histo.GetName(), histo.GetName())
profile = None
histo.Draw("COLZ")
if "TH2" in histo.ClassName():
if "nsig" in histo.GetYaxis().GetTitle():
histo.GetYaxis().SetRangeUser(-100, 100)
profile = histo.ProfileX(histo.GetName() + "_profile")
profile.SetLineWidth(2)
profile.SetLineColor(2)
profile.Draw("same")
gPad.SetLogz()
gPad.Update()
save_root_object(canvas, path=output_path)

# Fraction of pileup events
if pileup_fraction:
hnum = get_histo("n_tracklets_corr", tag="pileup", strictly_require=False)
if hnum is not None:
hnum.SetName(hnum.GetName() + "_eventfraction")
hden = get_histo("n_tracklets_corr", tag="_EvtSel")
hnum.Divide(hnum, hden)
hnum.GetYaxis().SetTitle("Fraction of events")
do_plot(hnum)

def plot_tpc_tof_me(tag):
# Compute TPC-TOF matching efficiency
if tpc_tof_me:
to_plot = [["Pi", "K", "Pr"],
["0", "1"],
["p_prong0", "pt_prong0", "pt_cand"]
]
for spec, prong, observable in itertools.product(*to_plot):
hname = [f"{observable}",
f"nsigTOF_{spec}_{prong}", tag]
hnum = get_histo(*hname,
strictly_require=False)
if hnum is None:
continue
hnum = hnum.ProjectionX(
hnum.GetName() + "_num", 2, -1)
hden = get_histo(*hname)
hden = hden.ProjectionX(
hden.GetName() + "_den")
hnum.Divide(hnum, hden, 1, 1, "B")
hnum.SetName(
hnum.GetName().replace(
"_num", "_TPC-TOF_MatchingEfficiency"
)
)
hnum.GetYaxis().SetTitle("TPC-TOF_MatchingEfficiency")
do_plot(hnum)

plot_tpc_tof_me(tag="")
# Part dedicated to MC Checks
if ismc:
plot_tpc_tof_me(tag="MC")

# Plot all other validation histogram
for i in range(0, input_file.GetListOfKeys().GetEntries()):
key_name = input_file.GetListOfKeys().At(i).GetName()
if not key_name.startswith("hVal_"):
continue
do_plot(input_file.Get(key_name))

input_file.Close()

do_validation_plots(self.n_filemass, self.d_resultsallpdata)
do_validation_plots(self.n_filemass_mc,
self.d_resultsallpmc, ismc=True)

fileoutcrosstot.Close()
57 changes: 18 additions & 39 deletions machine_learning_hep/processerdhadrons_mult.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
import os
import numpy as np
import pandas as pd
from ROOT import TFile, TH1F
from ROOT import TFile, TH1F, TH2F
from machine_learning_hep.utilities_files import create_folder_struc
from machine_learning_hep.utilities import seldf_singlevar, seldf_singlevar_inclusive
from machine_learning_hep.utilities import mergerootfiles, read_df
Expand Down Expand Up @@ -66,25 +66,12 @@ def __init__(self, case, datap, run_param, mcordata, p_maxfiles,
self.mc_cut_on_binning2 = datap["analysis"][self.typean].get("mc_cut_on_binning2", True)

self.bin_matching = datap["analysis"][self.typean]["binning_matching"]
#self.sel_final_fineptbins = datap["analysis"][self.typean]["sel_final_fineptbins"]
self.s_evtsel = datap["analysis"][self.typean]["evtsel"]
self.event_cand_validation = datap["analysis"][self.typean].get("event_cand_validation", "")
if "event_cand_validation" not in datap["analysis"][self.typean]:
self.event_cand_validation = False
self.usetriggcorrfunc = \
datap["analysis"][self.typean]["triggersel"].get("usetriggcorrfunc", None)
self.weightfunc = None
self.weighthist = None
if self.usetriggcorrfunc is not None and self.mcordata == "data":
filename = os.path.join(self.d_mcreweights, "trigger%s.root" % self.typean)
if os.path.exists(filename):
weight_file = TFile.Open(filename, "read")
self.weightfunc = weight_file.Get("func%s_norm" % self.typean)
self.weighthist = weight_file.Get("hist%s_norm" % self.typean)
self.weighthist.SetDirectory(0)
weight_file.Close()
else:
print("trigger correction file", filename, "doesnt exist")
self.nbinshisto = datap["analysis"][self.typean]["nbinshisto"]
self.minvaluehisto = datap["analysis"][self.typean]["minvaluehisto"]
self.maxvaluehisto = datap["analysis"][self.typean]["maxvaluehisto"]
Expand All @@ -93,6 +80,7 @@ def __init__(self, case, datap, run_param, mcordata, p_maxfiles,
# Event re-weighting MC
self.event_weighting_mc = datap["analysis"][self.typean].get("event_weighting_mc", {})
self.event_weighting_mc = self.event_weighting_mc.get(self.period, {})
self.v_var2_binning_weigths = datap["analysis"][self.typean].get("var_binning2_weights")

@staticmethod
def make_weights(col, func, hist, use_func):
Expand Down Expand Up @@ -189,20 +177,11 @@ def process_histomass_single(self, index):
self.lvar2_binmin[ibin2], self.lvar2_binmax[ibin2])
h_invmass = TH1F("hmass" + suffix, "", self.p_num_bins,
self.p_mass_fit_lim[0], self.p_mass_fit_lim[1])
#h_invmass_weight = TH1F("h_invmass_weight" + suffix, "", self.p_num_bins,
# self.p_mass_fit_lim[0], self.p_mass_fit_lim[1])
df_bin = seldf_singlevar_inclusive(df, self.v_var2_binning, \
self.lvar2_binmin[ibin2], self.lvar2_binmax[ibin2])
fill_hist(h_invmass, df_bin[self.v_invmass])
#if self.usetriggcorrfunc is not None and self.mcordata == "data":
# weights = self.make_weights(df_bin[self.v_var2_binning_gen], self.weightfunc,
# self.weighthist, self.usetriggcorrfunc)

# weightsinv = [1./weight for weight in weights]
# fill_hist(h_invmass_weight, df_bin[self.v_invmass], weights=weightsinv)
myfile.cd()
h_invmass.Write()
#h_invmass_weight.Write()

if self.mcordata == "mc":
df_bin_sig = df_bin[df_bin[self.v_ismcsignal] == 1]
Expand All @@ -219,6 +198,22 @@ def process_histomass_single(self, index):
fill_hist(histomult, dfevtevtsel[self.v_var2_binning_gen])
histomult.Write()

if self.v_var2_binning_weigths is not None:
label = "h%s" % self.v_var2_binning_weigths
histomult_weigths = TH1F(label, label, self.nbinshisto,
self.minvaluehisto, self.maxvaluehisto)
fill_hist(histomult_weigths, dfevtevtsel[self.v_var2_binning_weigths])

label = "h%s_%s" % (self.v_var2_binning_weigths, self.v_var2_binning_gen)
histomult_weigths_2d = TH2F(label, label,
self.nbinshisto, self.minvaluehisto, self.maxvaluehisto,
self.nbinshisto, self.minvaluehisto, self.maxvaluehisto)
fill_hist(histomult_weigths_2d, dfevtevtsel[[self.v_var2_binning_weigths, self.v_var2_binning_gen]])

histomult_weigths.Write()
histomult_weigths_2d.Write()


def get_reweighted_count(self, dfsel, ibin=None):
"""Apply event weights
Expand Down Expand Up @@ -298,16 +293,8 @@ def make_histo(name, title,
"Prompt Generated in acceptance |y|<0.5")
h_presel_pr = make_histo("h_presel_pr",
"Prompt Reco in acc |#eta|<0.8 and sel")
h_presel_pr_wotof = make_histo("h_presel_pr_wotof",
"Prompt Reco in acc woTOF |#eta|<0.8 and pre-sel")
h_presel_pr_wtof = make_histo("h_presel_pr_wtof",
"Prompt Reco in acc wTOF |#eta|<0.8 and pre-sel")
h_sel_pr = make_histo("h_sel_pr",
"Prompt Reco and sel in acc |#eta|<0.8 and sel")
h_sel_pr_wotof = make_histo("h_sel_pr_wotof",
"Prompt Reco and sel woTOF in acc |#eta|<0.8")
h_sel_pr_wtof = make_histo("h_sel_pr_wtof",
"Prompt Reco and sel wTOF in acc |#eta|<0.8")
h_gen_fd = make_histo("h_gen_fd",
"FD Generated in acceptance |y|<0.5")
h_presel_fd = make_histo("h_presel_fd",
Expand Down Expand Up @@ -365,16 +352,8 @@ def set_content(df_to_use, histogram,
histogram.SetBinError(b_c + 1, err)

set_content(df_gen_sel_pr, h_gen_pr)
if "nsigTOF_Pr_0" in df_reco_presel_pr:
set_content(df_reco_presel_pr[df_reco_presel_pr.nsigTOF_Pr_0 < -998],
h_presel_pr_wotof)
set_content(df_reco_presel_pr[df_reco_presel_pr.nsigTOF_Pr_0 > -998],
h_presel_pr_wtof)
set_content(df_reco_presel_pr, h_presel_pr)
set_content(df_reco_sel_pr, h_sel_pr)
if "nsigTOF_Pr_0" in df_reco_sel_pr:
set_content(df_reco_sel_pr[df_reco_sel_pr.nsigTOF_Pr_0 < -998], h_sel_pr_wotof)
set_content(df_reco_sel_pr[df_reco_sel_pr.nsigTOF_Pr_0 > -998], h_sel_pr_wtof)
set_content(df_gen_sel_fd, h_gen_fd)
set_content(df_reco_presel_fd, h_presel_fd)
set_content(df_reco_sel_fd, h_sel_fd)
Expand Down

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