Turbo

18-hlt-intro.md

@@ -43,16 +43,16 @@ available in HLT2 is the same as the offline reconstruction, physics analysis
 can be done with the candidates created in HLT2. If a line is configured to be a
 Turbo line, all information on the candidates that it selects is stored in the
 raw event. These candidates can be resurrected later by the Tesla application and
-written to a microDST. This is similar to stripping streams that go to 
-microDST, where only candidates that are used in passing selections are 
-available to analysts. The Turbo stream is different because information that 
+written to a microDST. This is similar to stripping streams that go to
+microDST, where only candidates that are used in passing selections are
+available to analysts. The Turbo stream is different because information that
 is not saved is lost forever.
 
 We will now have a look at some of the candidates stored by the HLT. We will use the script we
 [used last time](http://lhcb.github.io/first-analysis-steps/05-interactive-dst.html)
 as a starting point, and the file
 `root://eoslhcb.cern.ch//eos/lhcb/user/r/raaij/Impactkit/00051318_00000509_1.turbo.mdst`.
-This file contains some 2016 Turbo events from [run 
+This file contains some 2016 Turbo events from [run
 174252](http://lbrundb.cern.ch/rundb/run/174252/).  Fire up your
 favourite editor, open the script and save a copy to work on as
 `hlt_info.py`. There are a few things in the script that we don't need and can
@@ -83,7 +83,7 @@ The HLT1 selections that are most efficient for hadronic charm and beauty decays
 in Run 2 are called Hlt1TrackMVA and Hlt1TwoTrackMVA. Use the advance function
 to find an event that was accepted by either of these trigger selections.
 
-The DecReports only contains the decisions for each line, 1 or 0. The 
+The DecReports only contains the decisions for each line, 1 or 0. The
 candidates themselves are stored in the
 SelReports ("Hlt{1,2}/SelReports"). Get the HLT1 SelReports from the event store
 and retrieve the one for one of the TrackMVA selections using the selReport function,

19-turbo.md

@@ -0,0 +1,62 @@
+---
+layout: page
+title: Second steps in LHCb
+subtitle: Turbo Stream
+minutes: 30
+---
+
+> ## Learning Objectives {.objectives}
+>
+> * Learn about persited reconstruction
+> * Learn how to make new candidates from turbo candidate and other persisted objects
+> * Learn how to make an NTuple from these new candidates.
+
+ * Online-offline reco the same
+ * Online reconstructed candidates stored in raw event
+ * Tesla pulls them out puts them back in the memory
+
+| The file used in the [lesson about the HLT ](18-hlt-intro.html) can also be used for this lesson:
+| `root://eoslhcb.cern.ch//eos/lhcb/user/r/raaij/Impactkit/00051318_00000509_1.turbo.mdst`.
+
+Python file that defines the data:
+
+~~~ {.python}
+# data.py
+from GaudiConf import IOHelper
+prefix = 'root://eoslhcb.cern.ch/'
+fname = '/eos/lhcb/user/r/raaij/Impactkit/00051318_00000509_1.turbo.mdst'
+IOHelper('ROOT').inputFiles([prefix + fname], clear=True)
+~~~
+
+Basic script for making a turbo NTuple `turbo_intro.py`
+
+~~~ {.python}
+# DaVinci configuration
+from Configurables import DaVinci
+DaVinci().DataType = '2016'
+DaVinci().EvtMax = 1000
+DaVinci().TupleFile = turbo.root'
+
+# Turbo locations:
+turbo_loc = '/Event/Turbo/{0}/Particles'
+dz_line = 'Hlt2CharmHadD02KmPipTurbo'
+
+# Make a DecayTreeTuple
+from Configurables import DecayTreeTuple
+from DecayTreeTuple import Configuration
+
+dtt = DecayTreeTuple('TupleD0ToKpi')
+dtt.Inputs = [turbo_loc.format(dz_line)]
+dtt.Decay = '[D0 -> K- pi+]CC'
+dtt.addBranches({
+    'D0': '[D0 -> K- pi+]CC'
+})
+
+DaVinci().UserAlgorithms = [dtt]
+~~~
+
+Then we run it!
+
+```shell
+lb-run DaVinci gaudirun.py turbo_intro.py data.py
+```

20-persist-reco.md

@@ -0,0 +1,93 @@
+---
+layout: page
+title: Second steps in LHCb
+subtitle: Persisted Reconstruction
+minutes: 45
+---
+
+> ## Learning Objectives {.objectives}
+>
+> * Learn about persited reconstruction
+> * Learn how to make new candidates from turbo candidate and other persisted objects
+> * Learn how to make an NTuple from these new candidates.
+
+Now we want to use the PersistReco to make something more from the candidates,
+in this case a D* -> (D0 -> K pi) pi.
+
+Create a new script, `turbo_persistreco.py`, based on `turbo_intro.py` from the
+[lesson about turbo ](19-turbo.html) to contain your configuration.
+
+There are some general options needed to configure DaVinci to recreate the
+particles created in the HLT.
+
+~~~ {.python}
+# Turbo with PersistReco
+from Configurables import DstConf, TurboConf
+DstConf().Turbo = True
+TurboConf().PersistReco = True
+~~~
+
+Then we need to get the particles that we want to create the D* from and combine
+them.
+
+> ## Persisted Particles {.challenge}
+>
+> Use a GaudiPython script inspired by
+> [another lesson](http://lhcb.github.io/first-analysis-steps/05-interactive-dst.html)
+> to find out which particles are persisted from the
+> online reconstruction by exploring the transient event store.
+
+~~~ {.python}
+# Get the D0 and the pions
+from PhysSelPython.Wrappers import DataOnDemand, Selection, SelectionSequence
+dz = DataOnDemand(turbo_loc.format(dz_line))
+pions = DataOnDemand('Phys/StdAllNoPIDsPions/Particles')
+
+# Combine them
+from GaudiConfUtils.ConfigurableGenerators import CombineParticles
+dst = CombineParticles(
+    DecayDescriptors = ['[D*(2010)+ -> D0 pi+]cc'],
+    CombinationCut = "(ADAMASS('D*(2010)+') < 80 * MeV)",
+    MotherCut = "VFASPF(VCHI2/VDOF) < 6 & ADMASS('D*(2010)+') < 60 * MeV"
+    )
+~~~
+
+To run our combination, we create a selection and a selection sequence.
+
+~~~ {.python}
+dst_sel = Selection(
+    'Sel_DstToD0pi',
+    Algorithm = dst,
+    RequiredSelections = [dz, pions]
+    )
+
+dst_selseq = SelectionSequence(
+    'SelSeq_DstToD0pi',
+    TopSelection = dst_sel
+    )
+~~~
+
+Finally, we create the `DecayTreeTuple` for the D* and use the output of our selection
+sequence as its input.
+
+~~~ {.python}
+# D* in the tuple
+dtt_dst = DecayTreeTuple('TupleDstToD0pi_D0ToKpi_PersistReco')
+dtt_dst.Inputs = dst_selseq.outputLocations()
+dtt_dst.Decay = '[D*(2010)+ -> ^(D0 -> ^K- ^pi+) ^pi+]CC'
+dtt_dst.addBranches({
+    'Dst': '[D*(2010)+ -> (D0 -> K- pi+) pi+]CC',
+    'Dst_pi': '[D*(2010)+ -> (D0 -> K- pi+) ^pi+]CC',
+    'D0': '[D*(2010)+ -> ^(D0 -> K- pi+) pi+]CC',
+    'D0_K': '[D*(2010)+ -> (D0 -> ^K- pi+) pi+]CC',
+    'D0_pi': '[D*(2010)+ -> (D0 -> K- ^pi+) pi+]CC',
+})
+
+DaVinci().UserAlgorithms = [dst_selseq.sequence(), dtt_dst]
+~~~
+
+Then we run it!
+
+```shell
+lb-run DaVinci gaudirun.py turbo_persistreco.py data.py
+```

index.md

@@ -3,26 +3,26 @@ layout: lesson
 title: Second analysis steps in LHCb
 ---
 
-These are the lessons for the second-stage workshop of the [Starterkit 
+These are the lessons for the second-stage workshop of the [Starterkit
 series][starterkit].
-They build on those from [the first workshop][first-ana], teaching LHCb 
+They build on those from [the first workshop][first-ana], teaching LHCb
 software that's more advanced and more focused on specific tasks.
 
-Unlike the first workshop, there may be some lessons here that aren't 
-applicable to everyone's analysis, but all the lessons should still provide a 
+Unlike the first workshop, there may be some lessons here that aren't
+applicable to everyone's analysis, but all the lessons should still provide a
 useful insight in to how things work under the hood.
-It may also be that some lessons don't depend on any others; the prerequisites 
+It may also be that some lessons don't depend on any others; the prerequisites
 will be clearly stated at the beginning of each lesson.
 
-If you have any problems or questions, you can [open an 
-issue][second-ana-issues] on the [GitHub repository where these lessons are 
-developed][second-ana-repo], or you can [send an email to 
+If you have any problems or questions, you can [open an
+issue][second-ana-issues] on the [GitHub repository where these lessons are
+developed][second-ana-repo], or you can [send an email to
 `[email protected]`](mailto:[email protected]).
 
 > ## Prerequisites {.prereq}
 >
-> Before starting, you should be familiar with the [first analysis 
-> steps](https://lhcb.github.io/first-analysis-steps/) and satisfy all of its 
+> Before starting, you should be familiar with the [first analysis
+> steps](https://lhcb.github.io/first-analysis-steps/) and satisfy all of its
 > prerequisites.
 >
 
@@ -36,6 +36,8 @@ developed][second-ana-repo], or you can [send an email to
 1. [Reuse particles from a decay tree](18-filter-in-trees.html)
 1. [HLT intro](18-hlt-intro.html)
 1. [TisTos DIY](18-tistos-diy.html)
+1. [Turbo Stream](19-turbo.html)
+1. [Persisted Reconstruction](20-persist-reco.html)
 1. [Managing files in Ganga](01-managing-files-with-ganga.html)
 1. [Using Ganga with local projects](01-ganga-with-cmake.html)