Working cross section estimators. Only multiclass optimization remains

Author: saganatt

machine_learning_hep/analysis/analyzerdhadrons.py

@@ -293,7 +293,7 @@ def makenormyields(self):  # pylint: disable=import-outside-toplevel, too-many-b
 
         yield_filename = self.make_file_path(self.d_resultsallpdata, self.yields_filename, "root",
                                              None, [self.case, self.typean])
-        fileouteff = "/data8/majak/crosssec/merged_yields_fdd_approvals_fd_0.00_0.00_0.00_0.00_0.00_0.00_0.00_0.00_0.00.root"
+        yield_filename = "/data8/majak/crosssec/merged_yields_fdd_approvals_fd_0.00_0.00_0.00_0.00_0.00_0.00_0.00_0.00_0.00.root"
         if not os.path.exists(yield_filename):
             self.logger.fatal(
                 "Yield file %s could not be found", yield_filename)

machine_learning_hep/data/data_run3/database_ml_parameters_LcToPKPi_multiclass.yml

@@ -380,11 +380,12 @@ LcpKpi:
     probcutoptimal: [[0.02, 0.0, %fd12%], [0.02, 0.0, %fd23%], [0.02, 0.0, %fd34%], [0.06, 0.0, %fd45%], [0.06, 0.0, %fd56%], [0.10, 0.0, %fd68%], [0.20, 0.0, %fd812%], [%bkg1216%, 0.0, %fd1216%], [%bkg1624%, 0.0, %fd1624%]] #list of nbins
   analysis:
     anahptspectrum: "LctopKpi" #D0Kpi, DplusKpipi, DstarD0pi, DsKKpi, LctopKpi, LcK0Sp
-    fd_method: "dd" # fc, Nb
+    fd_method: "dd" # fc, Nb, dd, dd_N
     crosssec_prompt: True # True for prompt, False for non-prompt
     cctype: "pp"
     sigmamb: 59.4e+9 # 50.87e+9 pp5TeV, 57.8e+9 pp13TeV, 59.4e+9 pp Run3, pb
-    inputfonllpred: /data8/majak/fdd-results/07112024-check-approvals/fdd-results-merged-sept-yields-eff/CutVarLc_pp13TeV_LHC24d3.root #inputfonllpred: data/fonll/DmesonLcPredictions_13TeV_y05_FFptDepLHCb_BRpythia8_PDG2020.root
+    inputfonllpred: /data8/majak/fdd-results/07112024-check-approvals/fdd-results-merged-sept-yields-eff/CutVarLc_pp13TeV_LHC24d3.root
+    #inputfonllpred: data/fonll/DmesonLcPredictions_13TeV_y05_FFptDepLHCb_BRpythia8_PDG2020.root
     dir_general_plots: analysis_plots
 
     Run3analysis:

machine_learning_hep/hf_analysis_utils.py

@@ -377,7 +377,7 @@ def compute_fraction_dd(
     der_same_same = (acc_eff_same * (yield_times_acceff_same + yield_times_acceff_other) - \
                    acc_eff_same**2 * corryields_same) / denom
     der_same_other = -acc_eff_same * acc_eff_other * corryields_same / denom
-    unc = np.sqrt(der_same_same**2 * cov_same + der_same_other * cov_other + \
+    unc = np.sqrt(der_same_same**2 * cov_same + der_same_other**2 * cov_other + \
                   2 * der_same_same * der_same_other * cov_comb)
     print(f"denom {denom} der_same_same {der_same_same} der_same_other {der_same_other} " \
           f"cov same {cov_same} cov other {cov_other} cov comb {cov_comb} final unc {unc}")

machine_learning_hep/hf_pt_spectrum.py

@@ -206,7 +206,7 @@ def hf_pt_spectrum(channel, # pylint: disable=too-many-locals, too-many-argument
         rawy_unc = histos["rawyields"].GetBinError(i_pt + 1)
         eff_times_acc_prompt = histos["acceffp"].GetBinContent(i_pt + 1)
         eff_times_acc_nonprompt = histos["acceffnp"].GetBinContent(i_pt + 1)
-        if frac_method != "dd":
+        if frac_method not in ("dd", "dd_N"):
             ptmin_fonll = (
                 histos["FONLL"]["nonprompt"]["central"].GetXaxis().FindBin(ptmin * 1.0001)
             )
@@ -267,6 +267,7 @@ def hf_pt_spectrum(channel, # pylint: disable=too-many-locals, too-many-argument
                     histos["covariances"][1 - pnp_ind].GetBinContent(i_pt + 1),
                     histos["covariances"][2].GetBinContent(i_pt + 1))
         elif frac_method == "dd_N":
+            pnp_ind = 0 if crosssec_prompt else 1
             frac = [histos["corryields_fdd"][pnp_ind].GetBinContent(i_pt + 1)] * 3