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Updated DBs and analizer #1064

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16 changes: 8 additions & 8 deletions machine_learning_hep/analysis/analyzerdhadrons.py
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Original file line number Diff line number Diff line change
Expand Up @@ -304,7 +304,7 @@ def fit(self):
self.lpt_finbinmax[ipt],
lpt_probcutfin[ipt],
)
h_invmass = rfile.Get("hmass" + suffix)
h_invmass = rfile.Get("hmass_" + suffix)
# Rebin
h_invmass.Rebin(self.p_rebin[ipt])
if h_invmass.GetEntries() < 100: # TODO: reconsider criterion
Expand Down Expand Up @@ -341,6 +341,7 @@ def fit(self):
roows.var(fixpar).setConstant(True)
if h_invmass.GetEntries() == 0:
continue

roo_res, roo_ws = self._roofit_mass(
level,
h_invmass,
Expand Down Expand Up @@ -418,7 +419,7 @@ def efficiency(self):
print(self.n_fileff)
lfileeff = TFile.Open(self.n_fileff)
lfileeff.ls()
fileouteff = TFile.Open(f"{self.d_resultsallpmc}/{self.efficiency_filename}{self.case}{self.typean}.root", "recreate")
fileouteff = TFile.Open(f"{self.d_resultsallpmc}/efficiencies{self.case}{self.typean}.root", "recreate")
cEff = TCanvas("cEff", "The Fit Canvas")
cEff.SetCanvasSize(1900, 1500)
cEff.SetWindowSize(500, 500)
Expand Down Expand Up @@ -494,7 +495,7 @@ def makenormyields(self): # pylint: disable=import-outside-toplevel, too-many-b
if not os.path.exists(yield_filename):
self.logger.fatal("Yield file %s could not be found", yield_filename)

fileouteff = f"{self.d_resultsallpmc}/{self.efficiency_filename}{self.case}{self.typean}.root"
fileouteff = f"{self.d_resultsallpmc}/efficiencies{self.case}{self.typean}.root"
if not os.path.exists(fileouteff):
self.logger.fatal("Efficiency file %s could not be found", fileouteff)

Expand All @@ -506,12 +507,13 @@ def makenormyields(self): # pylint: disable=import-outside-toplevel, too-many-b

histonorm = TH1F("histonorm", "histonorm", 1, 0, 1)

filemass = TFile.Open(self.n_filemass)
hevents = filemass.Get("all_events")
hselevents = filemass.Get("sel_events")

if self.p_nevents is not None:
selnorm = self.p_nevents
else:
filemass = TFile.Open(self.n_filemass)
hevents = filemass.Get("all_events")
hselevents = filemass.Get("sel_events")
norm, selnorm = self.calculate_norm(self.logger, hevents, hselevents)
histonorm.SetBinContent(1, selnorm)
self.logger.warning("Number of events %d", norm)
Expand Down Expand Up @@ -554,9 +556,7 @@ def makenormyields(self): # pylint: disable=import-outside-toplevel, too-many-b
f_fileoutcross = TFile.Open(fileoutcross)
if f_fileoutcross:
hcross = f_fileoutcross.Get("hptspectrum")
hcrossbr = f_fileoutcross.Get("hptspectrum_wo_br")
fileoutcrosstot.cd()
hcross.Write()
hcrossbr.Write()
histonorm.Write()
fileoutcrosstot.Close()
16 changes: 9 additions & 7 deletions machine_learning_hep/analysis/analyzerdhadrons_mult.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@
)
from machine_learning_hep.hf_pt_spectrum import hf_pt_spectrum
from machine_learning_hep.logger import get_logger
from machine_learning_hep.utils.hist import get_dim, project_hist
from machine_learning_hep.utils.hist import get_dim


# pylint: disable=too-few-public-methods, too-many-instance-attributes, too-many-statements, fixme
Expand Down Expand Up @@ -320,7 +320,7 @@ def fit(self):
for ipt in range(len(self.lpt_finbinmin)):
lpt_probcutfin[ipt] = self.lpt_probcutfin_tmp[self.bin_matching[ipt]]
self.logger.debug("fitting %s - %i - %i", level, ipt, ibin2)
roows = self.roows.get(ipt)
roows = self.roows.get((ibin2, ipt))
if self.mltype == "MultiClassification":
suffix = "%s%d_%d_%.2f%.2f%s_%.2f_%.2f" % (
self.v_var_binning,
Expand All @@ -342,7 +342,7 @@ def fit(self):
self.lvar2_binmin[ibin2],
self.lvar2_binmax[ibin2],
)
h_invmass = rfile.Get("hmass" + suffix)
h_invmass = rfile.Get("hmass_" + suffix)
# Rebin
h_invmass.Rebin(self.p_rebin[ipt])
if h_invmass.GetEntries() < 100: # TODO: reconsider criterion
Expand Down Expand Up @@ -378,12 +378,14 @@ def fit(self):
break
self.logger.debug("Using fit config for %i: %s", ipt, fitcfg)
if datasel := fitcfg.get("datasel"):
h = rfile.Get(f"h_mass-pthf_{datasel}")
h_invmass = project_hist(h, [0], {1: (ipt + 1, ipt + 1)})
h_invmass = rfile.Get(f"hmass_{datasel}_{suffix}")

for fixpar in fitcfg.get("fix_params", []):
if roows.var(fixpar):
roows.var(fixpar).setConstant(True)
for par in fitcfg.get("free_params", []):
if roows.var(par):
roows.var(par).setConstant(False)
if h_invmass.GetEntries() == 0:
continue

Expand All @@ -405,8 +407,8 @@ def fit(self):
)
# if level == 'mc':
# roo_ws.Print()
self.roo_ws[level][ipt] = roo_ws
self.roows[ipt] = roo_ws
self.roows[(ibin2, ipt)] = roo_ws.Clone()
self.roo_ws[(level, ibin2, ipt)] = roo_ws.Clone()
if roo_res.status() == 0:
if level in ("data", "mc_sig"):
self.fit_mean[level][ipt] = roo_ws.var(self.p_param_names["gauss_mean"]).getValV()
Expand Down
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