Prerequisites: uproot, pandas, pytables, scipy, matplotlib, numpy

Import the PF candidates and AK4 jets, and run the simple 'seeded cone' reconstruction.

import util, algos
events, ak4jets = util.loadFromFile('debugPF.root')
jets = algos.seedConeJetsAllEvents(events)

Compare kinematic quantities of the newly created jet collection and the base AK4 jets:

import tests
compareCollections(ak4jets,jets)

The script cluster.py contains an example of loading from file, clustering a new jet collectinon and testing its performance.

Coming soon: more algos, performance analysis utilities...

To study the impact of performing jet finding regionally, or the 'de-regionizer': Start by doing: import regions A Region class is provided which simply defines its edges and can check whether particles' coordinates fall within it. One can define a Region and get the particles from the DataFrame which fall within it like:

event = events.loc[0]
region = regions.Region(0, 1, -2, -1) # a region bounded by 0 <= eta < 1 and -2 <= phi < -1
particles_in_region = event[region.in_region(event)]

The 36 FPGA Correlator Layer 1 regionisation of the L1 TDR is defined as a 2D array of these regions regions.PFRegions.

One can get a list (or list-of-lists) of particle DataFrames from a list (or list-of-lists) of regions like:

particles_regionized = regions.regionize(event, regions.PFRegions) # or provide a user-defined list (or list-of-lists) of regions

Then one can merge these regions, applying truncation, like:

particles_merged = regions.merge2D(particles_regionized, truncate=(32, 128), axis=0)

Which will limit the number of particles per group to 32 when merging the first dimension, then 128 for the final list of particles.