This ntuple-making monitoring algorithm that comes in three versions:

• BranchBySector: a tool for resolution and hit efficiency studies: it produces an ntuple branch for every ST layer, filled event by event by the residual of the hit in that layer.
• BranchByTrack: a tool with a different ntuple layout, where every branch contains a different piece of information: number of hits on each layer, position of the track extrapolation, position of the cluster, cluster charge, noise, residual, inter-strip fraction... This, in turn, comes in two different "flavours":
  • HitsOnTrack: it collects the hits based on the information stored in the track itself
  • TakeEveryHit: it starts from a track, then collected the clusters using the same selection as the preceding hit efficiency track monitors: it uses the STClusterCollector tool, that basically locates the sector in which the hit is expected and collects hits within a default window of 20 mm around the expected position. In this case we are less biased by how the alignment worked when the tracks were created, but we haven't access to informations such as the hit residual error (that is something belonging to the scope of track fitting).

Ntuple layout in BranchByTrack mode:

General information:

• Run Number

Track information:

• p
• pt
• phi
• eta
• charge
• ndof
• chi2
• number of IT hits
• number of TT hits
• for every ST layer, number of hits in layer

Hits information (track by track, arrays of hits information are filled):

• x,y,z position of the track extrapolation
• x,y,z position of the cluster
• ST channel ID of the cluster (can be used to retrieve the station/layer/sector)
• cluster size
• cluster charge
• noise associated to the cluster
• cluster strip
• cluster inter-strip fraction
• residual
• residual error (only in HitsOnTrack mode)
• measurement error (only in HitsOnTrack mode)

Example GaudiPython files to run the algorithm are provided in Analysis/Options.

Runing interactive monitor:

• Install flask
• Put .root file with histograms for each sector to the folder where falsk_test.py is. Pay attention, that histograms in your tuple should be named using schema <Something><SectorName>, i.e. Very_Interesting_Hist_TTaURegionA22
• run falsk_test.py
• open http://127.0.0.1:5000/index in your browser.
• PROFIT!