icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos module

Reconstructions for Cascade Events.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.CascadeLlhVertexFit(tray, name, Pulses, If=<function <lambda>>)

Run CscdLlhVertexFit, seeded with CLast.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.HighLevelFits(tray, _, pulses, InIceCscd=<function <lambda>>)

SPE Reconstructions.

class icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.HoboTimeSplitter(ctx)

Bases: I3ConditionalModule

Split an event into halves on the charge-weighted mean time.

Configure()

Make module configuration: get parameters from IceTray.

Physics(frame)

Split the event by charge weighted mean time.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.OfflineCascadeReco(tray, name, If=<function <lambda>>, BadDOMListName='BadDomsList')

Offline Basic Cascade Recos.

Parameters:

  • BadDOMListName – Name of the Bad DOMs list to use in the FillRatio module Should be the list that matches the input pulses (HLC pulses with HLC bad dom list, etc.)

  • If – Python function or module for conditional execution of all fits

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.Offline_Monopod(tray, _, Pulses='OfflinePulses', amplitude_table='/cvmfs/icecube.opensciencegrid.org/data/photon-tables/splines/cascade_single_spice_ftp-v1_flat_z20_a5.abs.fits', effectivedistance_table='/cvmfs/icecube.opensciencegrid.org/data/photon-tables/splines/cascade_effectivedistance_spice_ftp-v1_z20.eff.fits', effectivedistance_tableprob='/cvmfs/icecube.opensciencegrid.org/data/photon-tables/splines/cascade_effectivedistance_spice_ftp-v1_z20.prob.fits', effectivedistance_tablemod='/cvmfs/icecube.opensciencegrid.org/data/photon-tables/splines/cascade_effectivedistance_spice_ftp-v1_z20.tmod.fits', tilt_table_dir='$I3_BUILD/ice-models/resources/models/ICEMODEL/spice_ftp-v1/', timing_table='/cvmfs/icecube.opensciencegrid.org/data/photon-tables/splines/cascade_single_spice_ftp-v1_flat_z20_a5.prob.v2.fits', minimizer='iMIGRAD', If=<function <lambda>>)

Run Monopod Reconstruction on cleaned and masked pulses.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.SPEFit(tray, name, Pulses='OfflinePulses', Jitter=15.0, NoiseRate=1e-08, Iterations=1, If=<function <lambda>>)

Run SPE Fit.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.SplitFits(tray, name, BasePulses, If=<function <lambda>>)

Run SPEFit and CascadeLlh on split pulse series named {BasePulses}_0 and {BasePulses}_1.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.TimeSplitFits(tray, name, Pulses='OfflinePulses', If=<function <lambda>>)

Split the event in two time halves and run SPEFit+CscdLlh on each half separately.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.TopoSplitFits_Doubles(tray, name, If=<function <lambda>>)

Toological Split Fit on Double events.

icecube.offline_filterscripts.filter_segments.cascade_filter.Cascade_Recos.TopoSplitFits_Singles(tray, name, SRTPulses='SplitRTCleanedInIcePulses', If=<function <lambda>>)

Topological Split Fit.