Update DBs and small fit update (#705)

* prelim. finalisation of D0 and Lc DBs for MB and V0HM analysis

* fitter doesn't anymore fail when pre-fits are non successful but
  sigma should be taken from user anyway

Co-authored-by: Benedikt Volkel <[email protected]>

Author: benedikt-voelkel

machine_learning_hep/data/data_prod_20200304/database_ml_parameters_LcpK0spp_0304.yml

@@ -538,9 +538,9 @@ LcpK0spp:
       usesinglebineff: null
       fprompt_from_mb: true
       corresp_mb_typean: null
-      corrEffMult: [false, true, true]
-      sel_binmin2:  [0,30,0.1] #list of var2 splittng nbins
-      sel_binmax2: [100,100,30] #list of var2 splitting nbins
+      corrEffMult: [false, false, false, false]
+      sel_binmin2:  [0,30,0.1,0] #list of var2 splittng nbins
+      sel_binmax2: [100,100,30,0.1] #list of var2 splitting nbins
       var_binning2: perc_v0m
       var_binning2_gen: perc_v0m
       nbinshisto: 100000
@@ -579,11 +579,23 @@ LcpK0spp:
       masspeak: 2.2864
       massmin: [2.14,2.14,2.14,2.14,2.14,2.14]
       massmax: [2.436,2.436,2.436,2.436,2.436,2.436]
-      rebin: [[6,6,8,9,10,6], [6,6,8,9,10,6], [6,6,8,9,10,6]]
-
-      includesecpeak: [[false, false, false, false, false, false],[false, false, false, false, false, false],[false, false, false, false, false, false]]
+      rebin:
+        - [6,6,8,9,10,6]
+        - [6,6,8,9,10,6]
+        - [6,6,8,9,10,6]
+        - [6,6,8,9,10,6]
+
+      includesecpeak:
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
       masssecpeak: 2.2864
-      fix_masssecpeak: [[false, false, false, false, false, false],[false, false, false, false, false, false],[false, false, false, false, false, false]]
+      fix_masssecpeak:
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
       widthsecpeak: 0.01
       fix_widthsecpeak: true
 

machine_learning_hep/data/data_prod_20200304/database_ml_parameters_LcpK0spp_0304_HM_V0.yml

@@ -348,9 +348,9 @@ LcpK0spp:
       usesinglebineff: null
       fprompt_from_mb: true
       corresp_mb_typean: MBvspt_perc_v0m
-      corrEffMult: [false, false, true, true]
-      sel_binmin2: [0] #list of var2 splittng nbins
-      sel_binmax2: [20] #list of var2 splitting nbins
+      corrEffMult: [false]
+      sel_binmin2: [0,] #list of var2 splittng nbins
+      sel_binmax2: [0.1] #list of var2 splitting nbins
       var_binning2: perc_v0m
       var_binning2_gen: perc_v0m
       nbinshisto: 100000
@@ -370,6 +370,8 @@ LcpK0spp:
         data: "trigger_hasbit_HighMultV0==1"
         mc: null
         usetriggcorrfunc: Null
+      get_crossmb_from_path: True
+      path_for_crossmb: /data/DerivedResults/LckAnywithJets_sub/vAN-20200304_ROOT6-1/pp_data/resultsMBvspt_perc_v0m/finalcrossLcpK0sppMBvspt_perc_v0mmult3.root
       data:
         runselection: [null, null, null]
         results: [/data/DerivedResults/LckAnywithJets_sub/vAN-20200304_ROOT6-1/pp_2016_data/374_20200304-2028/resultsV0vspt_perc_v0m,

machine_learning_hep/data/data_prod_20200417/database_ml_parameters_D0pp_0417.yml

@@ -658,9 +658,9 @@ D0pp:
       usesinglebineff: null
       fprompt_from_mb: true
       corresp_mb_typean: null
-      corrEffMult: [false,true,true]
-      sel_binmin2:  [0,30,0.1] #list of var2 splittng nbins
-      sel_binmax2: [100,100,30] #list of var2 splitting nbins
+      corrEffMult: [false,false,false,false]
+      sel_binmin2:  [0,30,0.1,0] #list of var2 splittng nbins
+      sel_binmax2: [100,100,30,0.1] #list of var2 splitting nbins
       var_binning2: perc_v0m
       var_binning2_gen: perc_v0m
       nbinshisto: 100000
@@ -701,10 +701,22 @@ D0pp:
       masspeak: 1.864
       massmin: [1.72,1.72,1.72,1.72,1.72,1.72]
       massmax: [2.03,2.03,2.03,2.03,2.03,2.03]
-      rebin: [[6,6,6,6,6,6], [6,6,6,6,6,6], [6,6,6,6,6,6]]
-      includesecpeak: [[false, false, false, false, false, false],[false, false, false, false, false, false],[false, false, false, false, false, false]]
+      rebin:
+        - [6,6,6,6,6,6]
+        - [6,6,6,6,6,6]
+        - [6,6,6,6,6,6]
+        - [6,6,6,6,6,6]
+      includesecpeak:
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
       masssecpeak: -1.0
-      fix_masssecpeak: [[false, false, false, false, false, false],[false, false, false, false, false, false],[false, false, false, false, false, false]]
+      fix_masssecpeak:
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
+        - [false, false, false, false, false, false]
       #Fraction Dplus/Ds (taken from pp5TeV as we don't store it in TTrees)
       widthsecpeak: -1
       fix_widthsecpeak: false

machine_learning_hep/data/data_prod_20200417/database_ml_parameters_D0pp_0417_HM_V0.yml

@@ -288,17 +288,17 @@ D0pp:
       usesinglebineff: null
       fprompt_from_mb: true
       corresp_mb_typean: MBvspt_perc_v0m
-      corrEffMult: [false, false]
-      sel_binmin2: [0.1, 0.] #list of var2 splittng nbins
-      sel_binmax2: [30, 0.1] #list of var2 splitting nbins
+      corrEffMult: [false]
+      sel_binmin2: [0.] #list of var2 splittng nbins
+      sel_binmax2: [0.1] #list of var2 splitting nbins
       var_binning2: perc_v0m
       var_binning2_gen: perc_v0m
       nbinshisto: 100000
       minvaluehisto: -0.0005
       maxvaluehisto: 99.9995
       # here the trigger efficiency is set to 1. Corrections are implemented in the analysis step
-      triggereff: [1.,1.]
-      triggereffunc: [0.,0.]
+      triggereff: [1.]
+      triggereffunc: [0.]
       triggerbit: HighMultV0
       isNbx2: False    #Estimate the feeddown systematic with Nb and Nbx2 method convolution
       event_cand_validation: False
@@ -337,10 +337,13 @@ D0pp:
       masspeak: 1.864
       massmin: [1.72,1.72,1.72,1.72,1.72,1.72]
       massmax: [2.03,2.03,2.03,2.03,2.03,2.03]
-      rebin: [[6,6,6,6,6,6], [6,6,6,6,6,6]]
-      includesecpeak: [[false, false, false, false, false, false],[false, false, false, false, false, false]]
+      rebin:
+        - [6,6,6,6,6,6]
+      includesecpeak:
+        - [false, false, false, false, false, false]
       masssecpeak: -1.0
-      fix_masssecpeak: [[false, false, false, false, false, false],[false, false, false, false, false, false]]
+      fix_masssecpeak:
+        - [false, false, false, false, false, false]
       #Fraction Dplus/Ds (taken from pp5TeV as we don't store it in TTrees)
       widthsecpeak: -1
       fix_widthsecpeak: false

machine_learning_hep/fitting/helpers.py

@@ -485,18 +485,19 @@ def perform_central_fits(self):
             self.perform_pre_fits()
 
         for (ibin1, ibin2), fit in self.central_fits.items():
-            override_init_pars = None
             pre_fit = None
             if self.init_central_fits_from[(ibin1, ibin2)] == "mc":
                 pre_fit = self.pre_fits_mc[ibin1]
             else:
                 pre_fit = self.pre_fits_data[ibin1]
-            if not pre_fit.success:
+            if not pre_fit.success and self.lock_override_init[(ibin1, ibin2)] \
+                    and "sigma" not in self.lock_override_init[(ibin1, ibin2)]:
                 self.logger.warning("Requested pre-fit on %s not successful but requested for " \
                                     "central fit in bins (%i, %i). Skip...",
                                     self.init_central_fits_from[(ibin1, ibin2)], ibin1, ibin2)
                 continue
-            override_init_pars = pre_fit.get_fit_pars()
+
+            override_init_pars = pre_fit.get_fit_pars() if pre_fit and pre_fit.success else {}
             if self.lock_override_init[(ibin1, ibin2)]:
                 for name in self.lock_override_init[(ibin1, ibin2)]:
                     _ = override_init_pars.pop(name, None)