Merge branch 'follow_Moore2418' into 'master'

Remake DaVinci input files

See merge request !928

DaVinciTutorials/python/DaVinciTutorials/tutorial1_functors_specialfield.py

@@ -91,7 +91,7 @@ def main(options: Options):
     print(F.PT.code_repr())
 
     # Define the TES location (see previous example for explanation)
-    turbo_line = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
+    turbo_line = "Hlt2B2CC_BsToJpsiPhi_Detached"
     input_data = get_particles(f"/Event/HLT2/{turbo_line}/Particles")
 
     # Define a filter (see previous example for explanation)

DaVinciTutorials/python/DaVinciTutorials/tutorial2_LoKi.py

@@ -96,7 +96,7 @@ def main(options: Options):
     }
 
     # Load data from dst onto a TES
-    turbo_line = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
+    turbo_line = "Hlt2B2CC_BsToJpsiPhi_Detached"
     input_data = get_particles(f"/Event/HLT2/{turbo_line}/Particles")
 
     # Add a filter

DaVinciTutorials/python/DaVinciTutorials/tutorial3_ThOrfunctors.py

@@ -94,7 +94,7 @@ def main(options: Options):
     }
 
     # Load data from dst onto a TES
-    turbo_line = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
+    turbo_line = "Hlt2B2CC_BsToJpsiPhi_Detached"
     input_data = get_particles(f"/Event/HLT2/{turbo_line}/Particles")
 
     # Add a filter

DaVinciTutorials/python/DaVinciTutorials/tutorial4_trigger_eventinfo.py

@@ -116,8 +116,8 @@ def main(options: Options):
     selection_type = (
         "Hlt2"  # User defined and will be used as prefix for TBranch in the root file
     )
-    turbo_line = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
-    turbo_line2 = "Hlt2BsToJpsiPhi_JPsi2ee_PhiToKK_Line"
+    turbo_line = "Hlt2B2CC_BsToJpsiPhi_Detached"
+    turbo_line2 = "Hlt2B2CC_BuToJpsiKplus_JpsiToMuMu_Prompt"
     line_names = [f"{turbo_line}Decision", f"{turbo_line2}"]
     selinfo = FC.SelectionInfo(selection_type=selection_type, trigger_lines=line_names)
     print(selinfo)

DaVinciTutorials/python/DaVinciTutorials/tutorial5_MCTruth.py

@@ -52,7 +52,7 @@ def main(options: Options):
     from DaVinciMCTools import MCTruthAndBkgCat
 
     # Load data from dst onto a TES
-    turbo_line = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
+    turbo_line = "Hlt2B2CC_BsToJpsiPhi_Detached"
     input_data = get_particles(f"/Event/HLT2/{turbo_line}/Particles")
     # Define an algorithm that builds a map i.e. one-to-one relation b/w Reco Particle -> Truth MC Particle.
     MCTRUTH = MCTruthAndBkgCat(input_data, name="MCTruthAndBkgCat_tuto")

DaVinciTutorials/python/DaVinciTutorials/tutorial6_DecayTreeFit.py

@@ -65,7 +65,7 @@ def main(options: Options):
     }
 
     # Load data from dst onto a TES (See Example7)
-    turbo_line = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
+    turbo_line = "Hlt2B2CC_BsToJpsiPhi_Detached"
     input_data = get_particles(f"/Event/HLT2/{turbo_line}/Particles")
 
     # get kinematic functors

DaVinciTutorials/python/DaVinciTutorials/tutorial7_multiple_sel_lines.py

@@ -29,16 +29,16 @@ def main(options: Options):
         "mum": "B_s0 -> (J/psi(1S) -> mu+ ^mu-) (phi(1020) ->K+ K-)",
     }
     fields2 = {
-        "Bs": "B_s0 -> (J/psi(1S) ->  e+  e-) (phi(1020) ->K+ K-)",
-        "ep": "B_s0 -> (J/psi(1S) -> ^e+  e-) (phi(1020) ->K+ K-)",
-        "em": "B_s0 -> (J/psi(1S) ->  e+ ^e-) (phi(1020) ->K+ K-)",
+        "B": "[B+ -> (J/psi(1S) -> mu+ mu- ) K+]CC",
+        "Jpsi": "[B+ -> ^(J/psi(1S) -> mu+ mu- ) K+]CC",
+        "Kp": "[B+ -> (J/psi(1S) -> mu+ mu- ) ^K+]CC",
     }
 
     # Define variables dictionary "field name" -> Collections of functor
     variables = {"ALL": FC.Kinematics()}
 
     # Load data from dst onto a TES
-    turbo_line1 = "Hlt2BsToJpsiPhi_JPsi2MuMu_PhiToKK_Line"
+    turbo_line1 = "Hlt2B2CC_BsToJpsiPhi_Detached"
     input_data1 = get_particles(f"/Event/HLT2/{turbo_line1}/Particles")
     my_filter1 = create_lines_filter("HDRFilter_SeeNoEvil1", lines=[f"{turbo_line1}"])
     mytuple1 = Funtuple(
@@ -50,7 +50,7 @@ def main(options: Options):
     )
 
     # If running over several sprucing lines (e.g. for calibration) one can define multiple instances of FunTuple
-    turbo_line2 = "Hlt2BsToJpsiPhi_JPsi2ee_PhiToKK_Line"
+    turbo_line2 = "Hlt2B2CC_BuToJpsiKplus_JpsiToMuMu_Prompt"
     input_data2 = get_particles(f"/Event/HLT2/{turbo_line2}/Particles")
     my_filter2 = create_lines_filter("HDRFilter_SeeNoEvil2", lines=[f"{turbo_line2}"])
     mytuple2 = Funtuple(

DaVinciTutorials/tests/qmtest/test_tutorial1_functors_specialfield.qmt

@@ -44,7 +44,7 @@ df = get_pandas_dataframe(ntuple, 'TDirectoryName/TTreeName')
 # Check ntuple structure
 if df.empty:
     raise Exception(f"File {ntuple}: ntuple does not contain any branches")
-if df.shape != (30, 22):
+if df.shape != (41, 22):
     causes.append("Ntuple not with expected number of entries and/or branches")
 
 # Check there are no NaN values in the ntuple

DaVinciTutorials/tests/qmtest/test_tutorial2_LoKi.qmt

@@ -45,7 +45,7 @@ df = get_pandas_dataframe(ntuple, 'TDirectoryName/TTreeName')
 # Check ntuple structure
 if df.empty:
     causes.append(f"File {ntuple}: ntuple does not contain any branches")
-if df.shape != (30, 50):
+if df.shape != (41, 50):
     causes.append("Ntuple not with expected number of entries and/or branches")
 
 # Check there are no NaN values in the ntuple

DaVinciTutorials/tests/qmtest/test_tutorial3_ThOrfunctors.qmt

@@ -45,12 +45,18 @@ df = get_pandas_dataframe(ntuple, 'TDirectoryName/TTreeName')
 # Check ntuple structure
 if df.empty:
     causes.append(f"File {ntuple}: ntuple does not contain any branches")
-if df.shape != (30, 26):
+if df.shape != (41, 26):
     causes.append("Ntuple not with expected number of entries and/or branches")
 
-# Check there are no NaN values in the ntuple
-if df_has_nan(df):
-    causes.append("Ntuple contains NaN entries")
+# Check there are no NaN values in the ntuple except where expected.
+from DaVinciTests.QMTest.check_helpers import list_fields_with_nan
+
+l_branches_with_nans = ['Bs_phi_mass_kk', 'Bs_phi_mass_kpi']  # MASSWITHHYPOTHESES variables
+
+l_test = list_fields_with_nan(ntuple, "TDirectoryName/TTreeName")
+if sorted(l_test) != sorted(l_branches_with_nans):
+    causes.append("Unexpected list of branches with NaN values")
+
 
 # Checks PIDs are correctly assigned
 if not ( (df["Bs_Kp_ID"].abs() == 321).all() and (df["Bs_jpsi_ID"].abs() == 443).all() and (df["Bs_phi_ID"].abs() == 333).all() ):

DaVinciTutorials/tests/qmtest/test_tutorial4_trigger_eventinfo.qmt

@@ -45,7 +45,7 @@ df = get_pandas_dataframe(ntuple, 'TDirectoryName/TTreeName')
 # Check ntuple structure
 if df.empty:
     causes.append(f"File {ntuple}: ntuple does not contain any branches")
-if df.shape != (30, 12):
+if df.shape != (41, 12):
     causes.append("Ntuple not with expected number of entries and/or branches")
 
 # Check there are no NaN values in the ntuple

DaVinciTutorials/tests/qmtest/test_tutorial5_MCTruth.qmt

@@ -45,28 +45,27 @@ df = get_pandas_dataframe(ntuple, 'TDirectoryName/TTreeName')
 # Check ntuple structure
 if df.empty:
     causes.append(f"File {ntuple}: ntuple does not contain any branches")
-if df.shape != (30, 39):
+if df.shape != (41, 39):
     causes.append("Ntuple not with expected number of entries and/or branches")
 
 # Check there are no NaN values in the ntuple except where expected
 from DaVinciTests.QMTest.check_helpers import list_fields_with_nan
-l_branches_with_nans = ['Bs_TRUEP', 'Jpsi_TRUEP', 'Phi_TRUEP', 'Mup_TRUEP', 'Mum_TRUEP', 'Kp_TRUEEPHI', 'Kp_TRUEP', 'Km_TRUEP']
+l_branches_with_nans = ['Bs_TRUEP', 'Phi_TRUEP']
 l_test = list_fields_with_nan(ntuple, 'TDirectoryName/TTreeName')
 if sorted(l_test) != sorted(l_branches_with_nans):
     causes.append("Unexpected list of branches with NaN values")
 
 # Checks PIDs are correctly assigned
-if not ((df.filter(regex=("[B|J|P].*TRUEID"))==0).all().all()  # no matched MC
-        and ( (df["Km_TRUEID"].abs()==0).sum() + (df["Kp_TRUEID"].abs()==0).sum() == 14 )
-        and ( (df["Mum_TRUEID"].abs()==0).sum() + (df["Mup_TRUEID"].abs()==0).sum() == 3 )
-       ):
-    causes.append("Ntuple contains unexpected TRUEID values")
+if ((df.filter(regex=("[B|J|P].*TRUEID"))==0).all().all() or (df.filter(regex=("[K|Mu].*TRUEID"))==0).all().all()):  # If all TRUEIDS are 0 something is wrong
+  causes.append("Ntuple contains unexpected TRUEID values")  # no matched MC
+
+if not ((df["Km_TRUEID"]).sum() + (df["Kp_TRUEID"]).sum() + (df["Mum_TRUEID"]).sum() + (df["Mup_TRUEID"]).sum() == 0 ):  # ABS(TRUEID) should be consistent for particles and particles in final state
+  causes.append("Ntuple contains unexpected TRUEID values")
 
 # Check background categories
-if not ((df.filter(regex=("[B|J|P].*BKGCAT"))!=0).all().all()
-         and (df.filter(regex=("[K|Mu].*BKGCAT"))==-1).all().all()  # for kaons and muons, all entries are -1
-       ):
-    causes.append("Ntuple contains unexpected BKGCAT values")
+if not (df.filter(regex=("[K|Mu].*BKGCAT"))==-1).all().all() or ((df.filter(regex=("[B|J|P].*BKGCAT"))!=0).all().all()): #Should have some true signal and K and Mu have BKGCAT=-1
+  causes.append("Ntuple contains unexpected BKGCAT values")
+
 
 print('Test successfully completed!')
 os.system(f"rm {ntuple}")

DaVinciTutorials/tests/qmtest/test_tutorial6_DecayTreeFit.qmt

@@ -43,7 +43,7 @@ df = get_pandas_dataframe(ntuple, 'TDirectoryName/TTreeName')
 
 # Check ntuple structure
 if df.empty: raise Exception(f"File {ntuple}: ntuple does not contain any branches. Please check.")
-assert df.shape == (30, 48)
+assert df.shape == (41, 48)
 
 # Check there are no NaN values in the ntuple
 if df_has_nan(df):

DaVinciTutorials/tests/qmtest/test_tutorial7_multiple_sel_lines.qmt

@@ -48,9 +48,9 @@ if df1.empty:
     causes.append(f"File {ntuple}: ntuple 'TDirectoryName1/TTreeName1' does not contain any branches")
 if df2.empty:
     causes.append(f"File {ntuple}: ntuple 'TDirectoryName2/TTreeName2' does not contain any branches")
-if df1.shape != (30, 23):
+if df1.shape != (41, 23):
     causes.append("Ntuple 'TDirectoryName1/TTreeName1' not with expected number of entries and/or branches")
-if df2.shape != (13, 23):
+if df2.shape != (26, 23):
     causes.append("Ntuple 'TDirectoryName2/TTreeName2' not with expected number of entries and/or branches")
 
 # Check there are no NaN values in the ntuples