diff --git a/previews/PR71/404.html b/previews/PR71/404.html index 395ee8e..ba0e914 100644 --- a/previews/PR71/404.html +++ b/previews/PR71/404.html @@ -15,7 +15,7 @@
- + \ No newline at end of file diff --git a/previews/PR71/assets/lib_internal.md.BZJZxJcn.js b/previews/PR71/assets/lib_internal.md.BWwoiisY.js similarity index 94% rename from previews/PR71/assets/lib_internal.md.BZJZxJcn.js rename to previews/PR71/assets/lib_internal.md.BWwoiisY.js index 611933e..efca76c 100644 --- a/previews/PR71/assets/lib_internal.md.BZJZxJcn.js +++ b/previews/PR71/assets/lib_internal.md.BWwoiisY.js @@ -1,6 +1,6 @@ -import{_ as e,c as t,o as i,a7 as s}from"./chunks/framework.CICdM3gW.js";const b=JSON.parse('{"title":"Jet Reconstruction Internal Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/internal.md","filePath":"lib/internal.md","lastUpdated":null}'),o={name:"lib/internal.md"},n=s(`

Jet Reconstruction Internal Documentation ​

Documentation for JetReconstruction.jl's internal methods and types.

N.B. no guarantee is made of stability of these interfaces or types.

Index ​

Public Interface ​

# Base.:+ — Method.
julia
+(j1::PseudoJet, j2::PseudoJet)

Addition operator for PseudoJet objects.

Arguments

Returns

A new PseudoJet object with the sum of the momenta and energy of j1 and j2.

source


# Base.copy — Method.
julia
copy(j::TiledJet)

Create a copy of a TiledJet object.

Arguments

Returns

A new TiledJet object with the same attributes as the input object.

source


# Base.iterate — Function.
julia
Base.iterate(t::rightmost_tiles, state=1)

Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.

source


# Base.iterate — Function.
julia
Base.iterate(t::neighbour_tiles, state=1)

Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.

source


# Base.iterate — Method.
julia
Base.iterate(tj::TiledJet)

Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).

Arguments

source


# Base.show — Method.
julia
show(io::IO, jet::PseudoJet)

Print a PseudoJet object to the specified IO stream.

Arguments

source


# Base.tryparse — Method.
julia
Base.tryparse(E::Type{<:Enum}, str::String)

Parser that converts a string to an enum value if it exists, otherwise returns nothing.

source


# JetReconstruction.CosTheta — Method.
julia
CosTheta(p::PseudoJet)

Compute the cosine of the angle between the momentum vector p and the z-axis.

Arguments

Returns

source


# JetReconstruction._ensure_valid_rap_phi — Method.
julia
_ensure_valid_rap_phi(p::PseudoJet)

Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.

Arguments

source


# JetReconstruction._plain_jet_reconstruct — Method.
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)

This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.

Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.

The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.

Arguments

Returns

source


# JetReconstruction._set_rap_phi! — Method.

_set_rap_phi!(p::PseudoJet)

Set the rapidity and azimuthal angle of the PseudoJet p.

Arguments

Description

This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.

Note - the ϕ angle is calculated in the range [0, 2π).

source


# JetReconstruction._tiled_jet_reconstruct — Method.
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}

Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.

Arguments

Returns

Example

julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)

source


# JetReconstruction._tj_diJ — Method.
julia
_tj_diJ(jet)

Compute the dij metric value for a given jet.

Arguments

Returns

Example

source


# JetReconstruction._tj_dist — Method.
julia
_tj_dist(jetA, jetB)

Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.

Arguments

Returns

The squared distance between jetA and jetB.

Examples

source


# JetReconstruction.add_step_to_history! — Method.
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)

Add a new jet's history into the recombination sequence.

Arguments:

This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.

If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.

source


# JetReconstruction.add_untagged_neighbours_to_tile_union — Method.
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)

Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.

Arguments

Returns

The updated number of near tiles.

source


# JetReconstruction.check_algorithm_power_consistency — Method.

Allow a check for algorithm and power consistency

source


# JetReconstruction.detach! — Method.
julia
detach!(jet::TiledJet)

Detach a TiledJet from its linked list by updating the previous and next pointers.

Arguments

source


# JetReconstruction.determine_rapidity_extent — Method.
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat

Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.

This is the heuristic which is used by FastJet (inline comments are from FastJet).

Arguments

Returns

source


# JetReconstruction.dij — Method.
julia
dij(i, kt2_array, nn, nndist)

Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß

Arguments

Returns

source


# JetReconstruction.dist — Method.
julia
dist(i, j, rapidity_array, phi_array)

Compute the distance between points in a 2D space defined by rapidity and phi coordinates.

Arguments

Returns

source


# JetReconstruction.do_iB_recombination_step! — Method.
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)

Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.

Arguments

source


# JetReconstruction.do_ij_recombination_step! — Function.
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)

Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).

Arguments

Returns

Description

This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.

source


# JetReconstruction.energy — Method.
julia
energy(p::PseudoJet)

Return the energy of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.eta — Method.
julia
eta(p::PseudoJet)

Compute the pseudorapidity (η) of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.fast_findmin — Method.
julia
fast_findmin(dij, n)

Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.

Arguments

Returns

source


# JetReconstruction.find_tile_neighbours! — Method.
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)

Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run

Arguments

Returns

The number of neighbouring tiles added to the tile_union.

source


# JetReconstruction.geometric_distance — Method.
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)

Compute the geometric distance between two points in the rap-phi plane.

Arguments

Returns

source


# JetReconstruction.get_algorithm_power_consistency — Method.
julia
set_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})

Get the algorithm and power consistency correct

This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.

Arguments

Returns

A named tuple of the consistent power and algorithm values.

Throws

source


# JetReconstruction.get_all_ancestors — Method.
julia
get_all_ancestors(idx, cs::ClusterSequence)

Recursively finds all ancestors of a given index in a ClusterSequence object.

Arguments

Returns

An array of indices representing the ancestors of the given jet.

source


# JetReconstruction.get_dij_dist — Method.
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)

Compute the dij metric distance between two jets.

Arguments

Returns

The distance between the two jets.

If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.

source


# JetReconstruction.get_tile — Method.
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)

Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.

Arguments

Returns

source


# JetReconstruction.get_tile_cartesian_indices — Method.
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)

Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)

Arguments

Returns

source


# JetReconstruction.initial_history — Method.
julia
initial_history(particles)

Create an initial history for the given particles.

Arguments

Returns

source


# JetReconstruction.insert! — Method.
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)

Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list

Arguments

Example

source


# JetReconstruction.isvalid — Method.
julia
isvalid(t::TiledJet)

Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.

Arguments

Returns

source


# JetReconstruction.jet_ranks — Method.
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)

Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.

Arguments

Returns

A dictionary mapping each jet index to its rank.

Note

This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.

source


# JetReconstruction.m — Method.
julia
m(p::PseudoJet)

Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.

Arguments

Returns

The invariant mass of the PseudoJet object.

source


# JetReconstruction.m2 — Method.
julia
m2(p::PseudoJet)

Calculate the invariant mass squared (m^2) of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.mag — Method.
julia
mag(p::PseudoJet)

Return the magnitude of the momentum of a PseudoJet, |p|.

Arguments

Returns

The magnitude of the PseudoJet object.

source


# JetReconstruction.mass — Method.
julia
mass(p::PseudoJet)

Compute the invariant mass (alias for m(p)).

Arguments

Returns

source


# JetReconstruction.mass2 — Function.

Alias for m2 function

source


# JetReconstruction.merge_steps — Method.
julia
merge_steps(clusterseq::ClusterSequence)

Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).

Arguments

Returns

source


# JetReconstruction.phi — Method.
julia
phi(p::PseudoJet)

Compute the ϕ angle of a PseudoJet object p.

Note this function is a wrapper for phi_02pi(p).

Arguments

Returns

source


# JetReconstruction.phi_02pi — Method.
julia
phi_02pi(p::PseudoJet)

Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).

Arguments

Returns

source


# JetReconstruction.pt — Method.
julia
pt(p::PseudoJet)

Compute the scalar transverse momentum (pt) of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.pt2 — Method.
julia
pt2(p::PseudoJet)

Get the squared transverse momentum of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.px — Method.
julia
px(p::PseudoJet)

Return the x-component of the momentum of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.py — Method.
julia
py(p::PseudoJet)

Return the y-component of the momentum of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.pz — Method.
julia
pz(p::PseudoJet)

Return the z-component of the momentum of a PseudoJet.

Arguments

Returns

source


# JetReconstruction.rapidity — Method.
julia
rapidity(p::PseudoJet)

Compute the rapidity of a PseudoJet object.

Arguments

Returns

The rapidity of the PseudoJet object.

source


# JetReconstruction.reco_state — Method.
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)

This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.

Arguments

during the reconstruction process.

Returns

A dictionary representing a snapshot of the reconstruction state.

Details

The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.

source


# JetReconstruction.rightneighbours — Method.
julia
rightneighbours(center::Int, tiling::Tiling)

Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.

Arguments

Returns

source


# JetReconstruction.set_momentum! — Method.
julia
set_momentum!(j::PseudoJet, px, py, pz, E)

Set the momentum components and energy of a PseudoJet object.

Arguments

source


# JetReconstruction.set_nearest_neighbours! — Method.
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})

This function sets the nearest neighbor information for all jets in the tiledjets vector.

Arguments

Returns

The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.

Note: The diJ values are calculated as the kt distance multiplied by R^2.

source


# JetReconstruction.setup_tiling — Method.
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat

This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.

Arguments

Returns

Description

The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.

source


# JetReconstruction.surrounding — Method.
julia
surrounding(center::Int, tiling::Tiling)

Compute the surrounding indices of a given center index in a tiling.

Arguments

Returns

source


# JetReconstruction.tile_index — Method.
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)

Compute the tile index for a given (eta, phi) coordinate.

Arguments

Returns

The tile index corresponding to the (eta, phi) coordinate.

source


# JetReconstruction.tiledjet_remove_from_tiles! — Method.
julia
tiledjet_remove_from_tiles!(tiling, jet)

Remove a jet from the given tiling structure.

Arguments

Description

This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.

source


# JetReconstruction.tiledjet_set_jetinfo! — Method.
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)

Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)

Arguments:

This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.

Returns:

source


# JetReconstruction.upd_nn_crosscheck! — Method.
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)

Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).

Arguments

source


# JetReconstruction.upd_nn_nocross! — Method.
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)

Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).

Arguments

source


# JetReconstruction.upd_nn_step! — Method.
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)

Update the nearest neighbor information after a jet merge step.

Arguments:

This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.

source


# JetReconstruction.η — Function.
julia
const η = eta

Alias for the pseudorapidity function, eta.

source


# JetReconstruction.FourMomentum — Type.

Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.

source


# JetReconstruction.HistoryElement — Type.
julia
struct HistoryElement

A struct holding a record of jet mergers and finalisations

Fields:

source


# JetReconstruction.HistoryElement — Method.
julia
HistoryElement(jetp_index)

Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.

Arguments

Returns

A HistoryElement object.

source


# JetReconstruction.JetWithAncestors — Type.
julia
struct JetWithAncestors

A struct representing a jet with its origin ancestors.

Fields

Note

This structure needs its associated cluster sequence origin to be useful.

source


# JetReconstruction.Surrounding — Type.
julia
struct Surrounding{N}

Structure used for iterating over neighbour tiles.

Fields

source


# JetReconstruction.TiledJet — Type.
julia
struct TiledJet

TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.

Fields

source


# JetReconstruction.TiledJet — Method.
julia
TiledJet(id)

Constructs a TiledJet object with the given id and initializes its properties to zero.

Arguments

Returns

A TiledJet object with the specified id and values set to zero or noTiledJet.

source


# JetReconstruction.Tiling — Type.
julia
struct Tiling

The Tiling struct represents a tiling configuration for jet reconstruction.

Fields

source


# JetReconstruction.Tiling — Method.
julia
Tiling(setup::TilingDef)

Constructs a intial Tiling object based on the provided setup parameters.

Arguments

Returns

A Tiling object.

source


# JetReconstruction.TilingDef — Type.
julia
struct TilingDef

A struct representing the definition of a spcific tiling scheme.

Fields

Constructor

TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
-	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)

Constructs a TilingDef object with the given parameters.

source


# JetReconstruction.neighbour_tiles — Type.
julia
struct neighbour_tiles

A struct representing the neighbouring tiles.

A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):

XXX
+import{_ as e,c as t,o as i,a7 as s}from"./chunks/framework.CICdM3gW.js";const b=JSON.parse('{"title":"Jet Reconstruction Internal Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/internal.md","filePath":"lib/internal.md","lastUpdated":null}'),o={name:"lib/internal.md"},n=s(`
Jet Reconstruction Internal Documentation ​
Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
Index ​
Public Interface ​
# Base.:+ — Method. 
julia
+(j1::PseudoJet, j2::PseudoJet)
Addition operator for PseudoJet objects.
Arguments
  • j1::PseudoJet: The first PseudoJet object.
  • j2::PseudoJet: The second PseudoJet object.
Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source

# Base.copy — Method. 
julia
copy(j::TiledJet)
Create a copy of a TiledJet object.
Arguments
  • j::TiledJet: The TiledJet object to be copied.
Returns
A new TiledJet object with the same attributes as the input object.
source

# Base.iterate — Function. 
julia
Base.iterate(t::rightmost_tiles, state=1)
Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source

# Base.iterate — Function. 
julia
Base.iterate(t::neighbour_tiles, state=1)
Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source

# Base.iterate — Method. 
julia
Base.iterate(tj::TiledJet)
Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
  • tj::TiledJet: The TiledJet object to start to iterate over.
source

# Base.show — Method. 
julia
show(io::IO, jet::PseudoJet)
Print a PseudoJet object to the specified IO stream.
Arguments
  • io::IO: The IO stream to which the information will be printed.
  • jet::PseudoJet: The PseudoJet object whose information will be printed.
source

# Base.tryparse — Method. 
julia
Base.tryparse(E::Type{<:Enum}, str::String)
Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source

# JetReconstruction.CosTheta — Method. 
julia
CosTheta(p::PseudoJet)
Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
  • p::PseudoJet: The PseudoJet object representing the momentum vector.
Returns
  • The cosine of the angle between p and the z-axis.
source

# JetReconstruction._ensure_valid_rap_phi — Method. 
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source

# JetReconstruction._plain_jet_reconstruct — Method. 
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source

# JetReconstruction._set_rap_phi! — Method. 
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source

# JetReconstruction._tiled_jet_reconstruct — Method. 
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source

# JetReconstruction._tj_diJ — Method. 
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source

# JetReconstruction._tj_dist — Method. 
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source

# JetReconstruction.add_step_to_history! — Method. 
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source

# JetReconstruction.add_untagged_neighbours_to_tile_union — Method. 
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source

# JetReconstruction.check_algorithm_power_consistency — Method. 
Allow a check for algorithm and power consistency
source

# JetReconstruction.detach! — Method. 
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source

# JetReconstruction.determine_rapidity_extent — Method. 
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source

# JetReconstruction.dij — Method. 
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source

# JetReconstruction.dist — Method. 
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source

# JetReconstruction.do_iB_recombination_step! — Method. 
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source

# JetReconstruction.do_ij_recombination_step! — Function. 
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source

# JetReconstruction.energy — Method. 
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source

# JetReconstruction.eta — Method. 
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source

# JetReconstruction.fast_findmin — Method. 
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source

# JetReconstruction.find_tile_neighbours! — Method. 
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source

# JetReconstruction.geometric_distance — Method. 
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source

# JetReconstruction.get_algorithm_power_consistency — Method. 
julia
set_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source

# JetReconstruction.get_all_ancestors — Method. 
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source

# JetReconstruction.get_dij_dist — Method. 
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source

# JetReconstruction.get_tile — Method. 
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source

# JetReconstruction.get_tile_cartesian_indices — Method. 
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source

# JetReconstruction.initial_history — Method. 
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source

# JetReconstruction.insert! — Method. 
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source

# JetReconstruction.isvalid — Method. 
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source

# JetReconstruction.jet_ranks — Method. 
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source

# JetReconstruction.m — Method. 
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source

# JetReconstruction.m2 — Method. 
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source

# JetReconstruction.mag — Method. 
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source

# JetReconstruction.mass — Method. 
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source

# JetReconstruction.mass2 — Function. 
Alias for m2 function
source

# JetReconstruction.merge_steps — Method. 
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source

# JetReconstruction.phi — Method. 
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source

# JetReconstruction.phi_02pi — Method. 
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source

# JetReconstruction.pt — Method. 
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source

# JetReconstruction.pt2 — Method. 
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source

# JetReconstruction.px — Method. 
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source

# JetReconstruction.py — Method. 
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source

# JetReconstruction.pz — Method. 
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source

# JetReconstruction.rapidity — Method. 
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source

# JetReconstruction.reco_state — Method. 
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source

# JetReconstruction.rightneighbours — Method. 
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source

# JetReconstruction.set_momentum! — Method. 
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source

# JetReconstruction.set_nearest_neighbours! — Method. 
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source

# JetReconstruction.setup_tiling — Method. 
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source

# JetReconstruction.surrounding — Method. 
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source

# JetReconstruction.tile_index — Method. 
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source

# JetReconstruction.tiledjet_remove_from_tiles! — Method. 
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source

# JetReconstruction.tiledjet_set_jetinfo! — Method. 
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source

# JetReconstruction.upd_nn_crosscheck! — Method. 
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source

# JetReconstruction.upd_nn_nocross! — Method. 
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source

# JetReconstruction.upd_nn_step! — Method. 
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source

# JetReconstruction.η — Function. 
julia
const η = eta
Alias for the pseudorapidity function, eta.
source

# JetReconstruction.FourMomentum — Type. 
Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.
source

# JetReconstruction.HistoryElement — Type. 
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source

# JetReconstruction.HistoryElement — Method. 
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source

# JetReconstruction.JetWithAncestors — Type. 
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source

# JetReconstruction.Surrounding — Type. 
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source

# JetReconstruction.TiledJet — Type. 
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source

# JetReconstruction.TiledJet — Method. 
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source

# JetReconstruction.Tiling — Type. 
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source

# JetReconstruction.Tiling — Method. 
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source

# JetReconstruction.TilingDef — Type. 
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
+	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source

# JetReconstruction.neighbour_tiles — Type. 
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 X.X
-XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source

# JetReconstruction.rightmost_tiles — Type. 
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source

# JetReconstruction.rightmost_tiles — Type. 
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 O.X
-OOO
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source

`,158),a=[n];function r(l,d,c,p,h,u){return i(),t("div",null,a)}const k=e(o,[["render",r]]);export{b as __pageData,k as default};
+OOO

Note, rapidity coordinate must be in range, ϕ coordinate wraps

Fields

source


`,158),a=[n];function r(l,d,c,p,h,u){return i(),t("div",null,a)}const k=e(o,[["render",r]]);export{b as __pageData,k as default}; diff --git a/previews/PR71/assets/lib_internal.md.BZJZxJcn.lean.js b/previews/PR71/assets/lib_internal.md.BWwoiisY.lean.js similarity index 100% rename from previews/PR71/assets/lib_internal.md.BZJZxJcn.lean.js rename to previews/PR71/assets/lib_internal.md.BWwoiisY.lean.js diff --git a/previews/PR71/assets/lib_public.md.CQ3WjiTs.js b/previews/PR71/assets/lib_public.md.BBcfppu1.js similarity index 92% rename from previews/PR71/assets/lib_public.md.CQ3WjiTs.js rename to previews/PR71/assets/lib_public.md.BBcfppu1.js index bd1816d..41d9940 100644 --- a/previews/PR71/assets/lib_public.md.CQ3WjiTs.js +++ b/previews/PR71/assets/lib_public.md.BBcfppu1.js @@ -1,8 +1,8 @@ -import{_ as e,c as t,o as s,a7 as i}from"./chunks/framework.CICdM3gW.js";const g=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),a={name:"lib/public.md"},n=i(`

Jet Reconstruction Public Documentation ​

Documentation for JetReconstruction.jl's public interfaces.

Index ​

Public Methods and Types ​

# JetReconstruction.constituents — Method.
julia
constituents(j::PseudoJet, cs::ClusterSequence)

Get the constituents of a given jet in a cluster sequence.

Arguments

Returns

An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)

source


# JetReconstruction.exclusive_jets — Method.
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)

Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.

Arguments

Note: Either dcut or njets must be provided (but not both).

Returns

Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)

Exceptions

Examples

julia
exclusive_jets(clusterseq, dcut = 20.0)
-exclusive_jets(clusterseq, njets = 3, T = PseudoJet)

source


# JetReconstruction.final_jets — Function.

Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)

source


# JetReconstruction.final_jets — Function.

Specialisation for final jets from LorentzVectors (TODO: merge into more general function)

source


# JetReconstruction.final_jets — Function.
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)

This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.

Arguments

Returns

A vector of FinalJet objects that satisfy the transverse momentum condition.

source


# JetReconstruction.inclusive_jets — Method.
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)

Return all inclusive jets of a ClusterSequence with pt > ptmin.

Arguments

Returns

An array of T objects representing the inclusive jets.

Description

This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.

Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).

Example

julia
inclusive_jets(clusterseq; ptmin = 10.0)

source


# JetReconstruction.jet_reconstruct — Method.
julia
jet_reconstruct(particles; p = -1, R = 1.0, recombine = +, strategy = RecoStrategy.Best)

Reconstructs jets from a collection of particles using a specified algorithm and strategy

Arguments

Returns

A cluster sequence object containing the reconstructed jets and the merging history.

Details

particles argument

Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.

recombine argument

The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.

Example

julia
jet_reconstruct(particles; p = -1, R = 0.4)

source


# JetReconstruction.loadjets! — Method.
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)

Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.

Everything that was already in jets is not affected as we only use push! on it.

Example

julia
# Load jets from two files into one array
+import{_ as e,c as t,o as s,a7 as i}from"./chunks/framework.CICdM3gW.js";const g=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),a={name:"lib/public.md"},n=i(`
Jet Reconstruction Public Documentation ​
Documentation for JetReconstruction.jl's public interfaces.
Index ​
Public Methods and Types ​
# JetReconstruction.constituents — Method. 
julia
constituents(j::PseudoJet, cs::ClusterSequence)
Get the constituents of a given jet in a cluster sequence.
Arguments
  • cs::ClusterSequence: The cluster sequence object.
  • j::PseudoJet: The jet for which to retrieve the constituents.
Returns
An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)
source

# JetReconstruction.exclusive_jets — Method. 
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
+exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source

# JetReconstruction.final_jets — Function. 
Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)
source

# JetReconstruction.final_jets — Function. 
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source

# JetReconstruction.final_jets — Function. 
Specialisation for final jets from LorentzVectors (TODO: merge into more general function)
source

# JetReconstruction.inclusive_jets — Method. 
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source

# JetReconstruction.jet_reconstruct — Method. 
julia
jet_reconstruct(particles; p = -1, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p=-1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
source

# JetReconstruction.loadjets! — Method. 
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
 jets = []
 loadjets!("myjets1.dat", jets)
-loadjets!("myjets2.dat", jets)
source

# JetReconstruction.loadjets — Method. 
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source

# JetReconstruction.n_exclusive_jets — Method. 
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source

# JetReconstruction.plain_jet_reconstruct — Method. 
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
-jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source

# JetReconstruction.read_final_state_particles — Method. 
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source

# JetReconstruction.savejets — Method. 
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source

# JetReconstruction.tiled_jet_reconstruct — Method. 
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source

# JetReconstruction.ClusterSequence — Type. 
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source

# JetReconstruction.ClusterSequence — Method. 
julia
ClusterSequence(p::Int, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Constructs a ClusterSequence object with a specific power and algorithm.
Arguments
  • p::Real: The power value for the algorithm.
  • algorithm::JetAlgorithm.Algorithm: The algorithm.
  • strategy::RecoStrategy.Strategy: The reconstruction strategy.
  • jets: The jets to be clustered.
  • history: The length of the jets.
  • Qtot: The total energy of the jets.
Returns
A ClusterSequence object.
source

# JetReconstruction.FinalJet — Type. 
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source

# JetReconstruction.FinalJets — Type. 
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source

# JetReconstruction.PseudoJet — Type. 
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
+loadjets!("myjets2.dat", jets)
source

# JetReconstruction.loadjets — Method. 
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source

# JetReconstruction.n_exclusive_jets — Method. 
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source

# JetReconstruction.plain_jet_reconstruct — Method. 
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
+jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source

# JetReconstruction.read_final_state_particles — Method. 
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source

# JetReconstruction.savejets — Method. 
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source

# JetReconstruction.tiled_jet_reconstruct — Method. 
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source

# JetReconstruction.ClusterSequence — Type. 
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source

# JetReconstruction.ClusterSequence — Method. 
julia
ClusterSequence(p::Int, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Constructs a ClusterSequence object with a specific power and algorithm.
Arguments
  • p::Real: The power value for the algorithm.
  • algorithm::JetAlgorithm.Algorithm: The algorithm.
  • strategy::RecoStrategy.Strategy: The reconstruction strategy.
  • jets: The jets to be clustered.
  • history: The length of the jets.
  • Qtot: The total energy of the jets.
Returns
A ClusterSequence object.
source

# JetReconstruction.FinalJet — Type. 
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source

# JetReconstruction.FinalJets — Type. 
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source

# JetReconstruction.PseudoJet — Type. 
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
     _cluster_hist_index::Int,
-    pt2::Float64)
Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
  • _cluster_hist_index::Int: The cluster history index.
  • pt2::Float64: The transverse momentum squared.
Returns
A PseudoJet object.
source

`,47),o=[n];function l(r,p,c,h,d,u){return s(),t("div",null,o)}const b=e(a,[["render",l]]);export{g as __pageData,b as default};
+    pt2::Float64)

Constructs a PseudoJet object with the given momentum components and energy and history index.

Arguments

Returns

A PseudoJet object.

source


`,47),o=[n];function l(r,p,c,d,h,u){return s(),t("div",null,o)}const b=e(a,[["render",l]]);export{g as __pageData,b as default}; diff --git a/previews/PR71/assets/lib_public.md.CQ3WjiTs.lean.js b/previews/PR71/assets/lib_public.md.BBcfppu1.lean.js similarity index 85% rename from previews/PR71/assets/lib_public.md.CQ3WjiTs.lean.js rename to previews/PR71/assets/lib_public.md.BBcfppu1.lean.js index 5b8a2da..f3a5569 100644 --- a/previews/PR71/assets/lib_public.md.CQ3WjiTs.lean.js +++ b/previews/PR71/assets/lib_public.md.BBcfppu1.lean.js @@ -1 +1 @@ -import{_ as e,c as t,o as s,a7 as i}from"./chunks/framework.CICdM3gW.js";const g=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),a={name:"lib/public.md"},n=i("",47),o=[n];function l(r,p,c,h,d,u){return s(),t("div",null,o)}const b=e(a,[["render",l]]);export{g as __pageData,b as default}; +import{_ as e,c as t,o as s,a7 as i}from"./chunks/framework.CICdM3gW.js";const g=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),a={name:"lib/public.md"},n=i("",47),o=[n];function l(r,p,c,d,h,u){return s(),t("div",null,o)}const b=e(a,[["render",l]]);export{g as __pageData,b as default}; diff --git a/previews/PR71/assets/lib_visualisation.md.v23FfpTh.js b/previews/PR71/assets/lib_visualisation.md.CiRAusqZ.js similarity index 97% rename from previews/PR71/assets/lib_visualisation.md.v23FfpTh.js rename to previews/PR71/assets/lib_visualisation.md.CiRAusqZ.js index 9b356b8..0dbccf3 100644 --- a/previews/PR71/assets/lib_visualisation.md.v23FfpTh.js +++ b/previews/PR71/assets/lib_visualisation.md.CiRAusqZ.js @@ -7,7 +7,7 @@ import{_ as e,c as s,o as a,a7 as i}from"./chunks/framework.CICdM3gW.js";const b elevation = 0.5, framerate = 5, ancestors = false, - Module = Makie)

Animate the jet reconstruction process and save it as a video file.

Arguments

Optional Arguments

For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.

Returns

source


# JetReconstruction.jetsplot — Method.

jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
+            Module = Makie)

Animate the jet reconstruction process and save it as a video file.

Arguments

Optional Arguments

For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.

Returns

source


# JetReconstruction.jetsplot — Method.

jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
 using CairoMakie # use any other Makie that you have here
 
 jetsplot([object1, object2, object3], [[1], [2, 3]])

The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.

This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:

import CairoMakie
@@ -17,7 +17,7 @@ import{_ as e,c as s,o as a,a7 as i}from"./chunks/framework.CICdM3gW.js";const b
 jetsplot(my_objects, my_colour_arrays, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, my_colour_arrays, Module=Main) #default

source


# JetReconstruction.jetsplot — Method.
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
+jetsplot(my_objects, my_colour_arrays, Module=Main) #default

source


# JetReconstruction.jetsplot — Method.
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
 using CairoMakie # use any other Makie that you have here
 jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)

This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:

import CairoMakie
 jetsplot(my_objects, cs, Module=CairoMakie)
@@ -26,4 +26,4 @@ import{_ as e,c as s,o as a,a7 as i}from"./chunks/framework.CICdM3gW.js";const b
 jetsplot(my_objects, cs, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, cs, Module=Main) #default

source


`,11),o=[n];function l(p,c,r,h,d,u){return a(),s("div",null,o)}const g=e(t,[["render",l]]);export{b as __pageData,g as default}; +jetsplot(my_objects, cs, Module=Main) #default

source


`,11),o=[n];function l(p,c,r,h,d,u){return a(),s("div",null,o)}const g=e(t,[["render",l]]);export{b as __pageData,g as default}; diff --git a/previews/PR71/assets/lib_visualisation.md.v23FfpTh.lean.js b/previews/PR71/assets/lib_visualisation.md.CiRAusqZ.lean.js similarity index 100% rename from previews/PR71/assets/lib_visualisation.md.v23FfpTh.lean.js rename to previews/PR71/assets/lib_visualisation.md.CiRAusqZ.lean.js diff --git a/previews/PR71/examples.html b/previews/PR71/examples.html index 2f86c29..b0bc1e9 100644 --- a/previews/PR71/examples.html +++ b/previews/PR71/examples.html @@ -21,7 +21,7 @@ ... julia --project=. examples/jetreco.jl --maxevents=100 --strategy=N2Tiled test/data/events.hepmc3 ...

There are options to explicitly set the algorithm (use --help to see these).

instrumented-jetreco.jl ​

This is a more sophisticated example that allows performance measurements to be made of the reconstruction, as well as profiling (flamegraphs and memory profiling). Use the --help option to see usage. e.g., to extract timing performance for the AntiKt algorithm using the tiled strategy:

sh
julia --project instrumented-jetreco.jl -S N2Tiled -A AntiKt --nsamples 100 ../test/data/events.hepmc3

visualise-jets.jl ​

This script will produce a PNG/PDF showing the results of a jet reconstruction. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.

visualise-jets.ipynb ​

Similar to visualise-jets.jl this notebook will produce a visualisation of jet reconstruction in the browser. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.

animate-reconstruction.jl ​

Performs jet reconstruction and then produces and animation of the process, showing how the jets merge from their different constituents.

- + \ No newline at end of file diff --git a/previews/PR71/hashmap.json b/previews/PR71/hashmap.json index aa42374..f472611 100644 --- a/previews/PR71/hashmap.json +++ b/previews/PR71/hashmap.json @@ -1 +1 @@ -{"examples.md":"BUHmLLLn","index.md":"BniuZdwB","lib_internal.md":"BZJZxJcn","lib_public.md":"CQ3WjiTs","lib_visualisation.md":"v23FfpTh"} +{"examples.md":"BUHmLLLn","index.md":"BniuZdwB","lib_internal.md":"BWwoiisY","lib_public.md":"BBcfppu1","lib_visualisation.md":"CiRAusqZ"} diff --git a/previews/PR71/index.html b/previews/PR71/index.html index c2e0910..a50c215 100644 --- a/previews/PR71/index.html +++ b/previews/PR71/index.html @@ -39,7 +39,7 @@ archivePrefix={arXiv}, primaryClass={hep-ex} }

Code in this package is authored by:

and is Copyright 2022-2024 The Authors, CERN.

The code is under the MIT License.

- + \ No newline at end of file diff --git a/previews/PR71/lib/internal.html b/previews/PR71/lib/internal.html index 55525ef..403e97a 100644 --- a/previews/PR71/lib/internal.html +++ b/previews/PR71/lib/internal.html @@ -12,18 +12,18 @@ - + -
JetReconstruction.jl

Appearance

Jet Reconstruction Internal Documentation ​

Documentation for JetReconstruction.jl's internal methods and types.

N.B. no guarantee is made of stability of these interfaces or types.

Index ​

Public Interface ​

# Base.:+ — Method.
julia
+(j1::PseudoJet, j2::PseudoJet)

Addition operator for PseudoJet objects.

Arguments

  • j1::PseudoJet: The first PseudoJet object.

  • j2::PseudoJet: The second PseudoJet object.

Returns

A new PseudoJet object with the sum of the momenta and energy of j1 and j2.

source


# Base.copy — Method.
julia
copy(j::TiledJet)

Create a copy of a TiledJet object.

Arguments

  • j::TiledJet: The TiledJet object to be copied.

Returns

A new TiledJet object with the same attributes as the input object.

source


# Base.iterate — Function.
julia
Base.iterate(t::rightmost_tiles, state=1)

Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.

source


# Base.iterate — Function.
julia
Base.iterate(t::neighbour_tiles, state=1)

Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.

source


# Base.iterate — Method.
julia
Base.iterate(tj::TiledJet)

Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).

Arguments

  • tj::TiledJet: The TiledJet object to start to iterate over.

source


# Base.show — Method.
julia
show(io::IO, jet::PseudoJet)

Print a PseudoJet object to the specified IO stream.

Arguments

  • io::IO: The IO stream to which the information will be printed.

  • jet::PseudoJet: The PseudoJet object whose information will be printed.

source


# Base.tryparse — Method.
julia
Base.tryparse(E::Type{<:Enum}, str::String)

Parser that converts a string to an enum value if it exists, otherwise returns nothing.

source


# JetReconstruction.CosTheta — Method.
julia
CosTheta(p::PseudoJet)

Compute the cosine of the angle between the momentum vector p and the z-axis.

Arguments

  • p::PseudoJet: The PseudoJet object representing the momentum vector.

Returns

  • The cosine of the angle between p and the z-axis.

source


# JetReconstruction._ensure_valid_rap_phi — Method.
julia
_ensure_valid_rap_phi(p::PseudoJet)

Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.

Arguments

  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.

source


# JetReconstruction._plain_jet_reconstruct — Method.
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)

This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.

Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.

The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.

Arguments

  • particles: A vector of PseudoJet objects representing the input particles.

  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.

  • R=1.0: The jet radius parameter.

  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).

Returns

  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.

source


# JetReconstruction._set_rap_phi! — Method.

_set_rap_phi!(p::PseudoJet)

Set the rapidity and azimuthal angle of the PseudoJet p.

Arguments

  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.

Description

This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.

Note - the ϕ angle is calculated in the range [0, 2π).

source


# JetReconstruction._tiled_jet_reconstruct — Method.
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}

Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.

Arguments

  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.

  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.

  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.

  • recombine::Function = +: The recombination function used for combining pseudojets.

Returns

  • Vector{PseudoJet}: A vector of reconstructed jets.

Example

julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)

source


# JetReconstruction._tj_diJ — Method.
julia
_tj_diJ(jet)

Compute the dij metric value for a given jet.

Arguments

  • jet: The input jet.

Returns

  • The dij value for the jet.

Example

source


# JetReconstruction._tj_dist — Method.
julia
_tj_dist(jetA, jetB)

Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.

Arguments

  • jetA: The first jet.

  • jetB: The second jet.

Returns

The squared distance between jetA and jetB.

Examples

source


# JetReconstruction.add_step_to_history! — Method.
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)

Add a new jet's history into the recombination sequence.

Arguments:

  • clusterseq::ClusterSequence: The cluster sequence object.

  • parent1: The index of the first parent.

  • parent2: The index of the second parent.

  • jetp_index: The index of the jet.

  • dij: The dij value.

This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.

If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.

source


# JetReconstruction.add_untagged_neighbours_to_tile_union — Method.
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)

Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.

Arguments

  • center_index: The index of the center tile.

  • tile_union: An array to store the indices of neighbouring tiles.

  • n_near_tiles: The number of neighbouring tiles.

  • tiling: The tiling object containing the tile tags.

Returns

The updated number of near tiles.

source


# JetReconstruction.check_algorithm_power_consistency — Method.

Allow a check for algorithm and power consistency

source


# JetReconstruction.detach! — Method.
julia
detach!(jet::TiledJet)

Detach a TiledJet from its linked list by updating the previous and next pointers.

Arguments

  • jet::TiledJet: The TiledJet object to detach.

source


# JetReconstruction.determine_rapidity_extent — Method.
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat

Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.

This is the heuristic which is used by FastJet (inline comments are from FastJet).

Arguments

  • eta::Vector{T}: A vector of rapidity values.

Returns

  • minrap::T: The minimum rapidity value.

  • maxrap::T: The maximum rapidity value.

source


# JetReconstruction.dij — Method.
julia
dij(i, kt2_array, nn, nndist)

Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß

Arguments

  • i: The index of the element.

  • kt2_array: An array of kt2 values.

  • nn: An array of nearest neighbors.

  • nndist: An array of nearest neighbor distances.

Returns

  • The computed dij value.

source


# JetReconstruction.dist — Method.
julia
dist(i, j, rapidity_array, phi_array)

Compute the distance between points in a 2D space defined by rapidity and phi coordinates.

Arguments

  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).

  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).

  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.

  • phi_array::Vector{Float64}: Array of phi coordinates.

Returns

  • distance::Float64: The distance between the two points.

source


# JetReconstruction.do_iB_recombination_step! — Method.
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)

Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.

Arguments

  • clusterseq::ClusterSequence: The cluster sequence object.

  • jet_i: The index of the jet.

  • diB: The diB value.

source


# JetReconstruction.do_ij_recombination_step! — Function.
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)

Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).

Arguments

  • clusterseq::ClusterSequence: The cluster sequence object.

  • jet_i: The index of the first jet to be recombined.

  • jet_j: The index of the second jet to be recombined.

  • dij: The distance between the two jets.

  • recombine=+: The recombination function to be used. Default is addition.

Returns

  • newjet_k: The index of the newly created jet.

Description

This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.

source


# JetReconstruction.energy — Method.
julia
energy(p::PseudoJet)

Return the energy of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object.

Returns

  • The energy of the PseudoJet.

source


# JetReconstruction.eta — Method.
julia
eta(p::PseudoJet)

Compute the pseudorapidity (η) of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.

Returns

  • The pseudorapidity (η) of the PseudoJet.

source


# JetReconstruction.fast_findmin — Method.
julia
fast_findmin(dij, n)

Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.

Arguments

  • dij: An array of values.

  • n: The number of elements to consider in the dij array.

Returns

  • dij_min: The minimum value in the first n elements of the dij array.

  • best: The index of the minimum value in the dij array.

source


# JetReconstruction.find_tile_neighbours! — Method.
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)

Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run

Arguments

  • tile_union: The tile union to which the neighbouring tiles will be added.

  • jetA: The first jet.

  • jetB: The second jet.

  • oldB: The old second jet.

  • tiling: The tiling information.

Returns

The number of neighbouring tiles added to the tile_union.

source


# JetReconstruction.geometric_distance — Method.
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)

Compute the geometric distance between two points in the rap-phi plane.

Arguments

  • eta1::AbstractFloat: The eta coordinate of the first point.

  • phi1::AbstractFloat: The phi coordinate of the first point.

  • eta2::AbstractFloat: The eta coordinate of the second point.

  • phi2::AbstractFloat: The phi coordinate of the second point.

Returns

  • distance::Float64: The geometric distance between the two points.

source


# JetReconstruction.get_algorithm_power_consistency — Method.
julia
set_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})

Get the algorithm and power consistency correct

This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.

Arguments

  • p::Union{Real, Nothing}: The power value.

  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.

Returns

A named tuple of the consistent power and algorithm values.

Throws

  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.

source


# JetReconstruction.get_all_ancestors — Method.
julia
get_all_ancestors(idx, cs::ClusterSequence)

Recursively finds all ancestors of a given index in a ClusterSequence object.

Arguments

  • idx: The index of the jet for which to find ancestors.

  • cs: The ClusterSequence object containing the jet history.

Returns

An array of indices representing the ancestors of the given jet.

source


# JetReconstruction.get_dij_dist — Method.
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)

Compute the dij metric distance between two jets.

Arguments

  • nn_dist: The nearest-neighbor distance between two jets.

  • kt2_1: The squared momentum metric value of the first jet.

  • kt2_2: The squared momentum metric value of the second jet.

  • R2: The jet radius parameter squared.

Returns

The distance between the two jets.

If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.

source


# JetReconstruction.get_tile — Method.
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)

Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.

Arguments

  • tiling_setup: A TilingDef object that contains the tiling setup parameters.

  • eta: The eta value for which to calculate the tile index.

  • phi: The phi value for which to calculate the tile index.

Returns

  • ieta: The tile index along the eta direction.

  • iphi: The tile index along the phi direction.

source


# JetReconstruction.get_tile_cartesian_indices — Method.
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)

Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)

Arguments

  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.

  • i_η::Int: The index of the tile in the η dimension.

  • i_ϕ::Int: The index of the tile in the ϕ dimension.

Returns

  • The linear index of the tile.

source


# JetReconstruction.initial_history — Method.
julia
initial_history(particles)

Create an initial history for the given particles.

Arguments

  • particles: The initial vector of stable particles.

Returns

  • history: An array of HistoryElement objects.

  • Qtot: The total energy in the event.

source


# JetReconstruction.insert! — Method.
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)

Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list

Arguments

  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.

  • jettomove::TiledJet: The TiledJet object to be inserted.

Example

source


# JetReconstruction.isvalid — Method.
julia
isvalid(t::TiledJet)

Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.

Arguments

  • t::TiledJet: The TiledJet object to check.

Returns

  • Bool: true if the TiledJet object is valid, false otherwise.

source


# JetReconstruction.jet_ranks — Method.
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)

Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.

Arguments

  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.

  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.

Returns

A dictionary mapping each jet index to its rank.

Note

This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.

source


# JetReconstruction.m — Method.
julia
m(p::PseudoJet)

Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.

Returns

The invariant mass of the PseudoJet object.

source


# JetReconstruction.m2 — Method.
julia
m2(p::PseudoJet)

Calculate the invariant mass squared (m^2) of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.

Returns

  • The invariant mass squared (m^2) of the PseudoJet.

source


# JetReconstruction.mag — Method.
julia
mag(p::PseudoJet)

Return the magnitude of the momentum of a PseudoJet, |p|.

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.

Returns

The magnitude of the PseudoJet object.

source


# JetReconstruction.mass — Method.
julia
mass(p::PseudoJet)

Compute the invariant mass (alias for m(p)).

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the mass.

Returns

  • The mass of the PseudoJet.

source


# JetReconstruction.mass2 — Function.

Alias for m2 function

source


# JetReconstruction.merge_steps — Method.
julia
merge_steps(clusterseq::ClusterSequence)

Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).

Arguments

  • clusterseq::ClusterSequence: The cluster sequence object.

Returns

  • merge_steps::Int: The number of merge steps.

source


# JetReconstruction.phi — Method.
julia
phi(p::PseudoJet)

Compute the ϕ angle of a PseudoJet object p.

Note this function is a wrapper for phi_02pi(p).

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.

Returns

  • The azimuthal angle of p in the range [0, 2π).

source


# JetReconstruction.phi_02pi — Method.
julia
phi_02pi(p::PseudoJet)

Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.

Returns

  • The azimuthal angle of p in the range [0, 2π).

source


# JetReconstruction.pt — Method.
julia
pt(p::PseudoJet)

Compute the scalar transverse momentum (pt) of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.

Returns

  • The transverse momentum (pt) of the PseudoJet.

source


# JetReconstruction.pt2 — Method.
julia
pt2(p::PseudoJet)

Get the squared transverse momentum of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object.

Returns

  • The squared transverse momentum of the PseudoJet.

source


# JetReconstruction.px — Method.
julia
px(p::PseudoJet)

Return the x-component of the momentum of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object.

Returns

  • The x-component of the momentum of the PseudoJet.

source


# JetReconstruction.py — Method.
julia
py(p::PseudoJet)

Return the y-component of the momentum of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object.

Returns

  • The y-component of the momentum of the PseudoJet.

source


# JetReconstruction.pz — Method.
julia
pz(p::PseudoJet)

Return the z-component of the momentum of a PseudoJet.

Arguments

  • p::PseudoJet: The PseudoJet object.

Returns

  • The z-component of the momentum of the PseudoJet.

source


# JetReconstruction.rapidity — Method.
julia
rapidity(p::PseudoJet)

Compute the rapidity of a PseudoJet object.

Arguments

  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.

Returns

The rapidity of the PseudoJet object.

source


# JetReconstruction.reco_state — Method.
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)

This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.

Arguments

  • cs::ClusterSequence: The ClusterSequence object to update.

  • ranks: The ranks of the original clusters, that are inherited by peudojets

during the reconstruction process.

  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).

  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).

Returns

A dictionary representing a snapshot of the reconstruction state.

Details

The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.

source


# JetReconstruction.rightneighbours — Method.
julia
rightneighbours(center::Int, tiling::Tiling)

Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.

Arguments

  • center::Int: The center index.

  • tiling::Tiling: The tiling object.

Returns

  • Surrounding: An object containing the indices of the right neighbors.

source


# JetReconstruction.set_momentum! — Method.
julia
set_momentum!(j::PseudoJet, px, py, pz, E)

Set the momentum components and energy of a PseudoJet object.

Arguments

  • j::PseudoJet: The PseudoJet object to set the momentum for.

  • px: The x-component of the momentum.

  • py: The y-component of the momentum.

  • pz: The z-component of the momentum.

  • E: The energy of the particle.

source


# JetReconstruction.set_nearest_neighbours! — Method.
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})

This function sets the nearest neighbor information for all jets in the tiledjets vector.

Arguments

  • clusterseq::ClusterSequence: The cluster sequence object.

  • tiling::Tiling: The tiling object.

  • tiledjets::Vector{TiledJet}: The vector of tiled jets.

Returns

  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.

  • diJ::Vector{Float64}: The vector of diJ values.

The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.

Note: The diJ values are calculated as the kt distance multiplied by R^2.

source


# JetReconstruction.setup_tiling — Method.
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat

This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.

Arguments

  • eta::Vector{T}: A vector of rapidities.

  • Rparam::AbstractFloat: The jet radius parameter.

Returns

  • tiling_setup: A TilingDef object containing the tiling setup parameters.

Description

The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.

source


# JetReconstruction.surrounding — Method.
julia
surrounding(center::Int, tiling::Tiling)

Compute the surrounding indices of a given center index in a tiling.

Arguments

  • center::Int: The center index.

  • tiling::Tiling: The tiling object.

Returns

  • Surrounding: An object containing the surrounding indices.

source


# JetReconstruction.tile_index — Method.
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)

Compute the tile index for a given (eta, phi) coordinate.

Arguments

  • tiling_setup: The tiling setup object containing the tile size and number of tiles.

  • eta::Float64: The eta coordinate.

  • phi::Float64: The phi coordinate.

Returns

The tile index corresponding to the (eta, phi) coordinate.

source


# JetReconstruction.tiledjet_remove_from_tiles! — Method.
julia
tiledjet_remove_from_tiles!(tiling, jet)

Remove a jet from the given tiling structure.

Arguments

  • tiling: The tiling structure from which the jet will be removed.

  • jet: The jet to be removed from the tiling structure.

Description

This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.

source


# JetReconstruction.tiledjet_set_jetinfo! — Method.
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)

Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)

Arguments:

  • jet::TiledJet: The TiledJet object to set the information for.

  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.

  • tiling::Tiling: The Tiling object containing the tile information.

  • jets_index: The index of the jet in the ClusterSequence.

  • R2: The jet radius parameter squared.

  • p: The power to raise the pt2 value to.

This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.

Returns:

  • nothing

source


# JetReconstruction.upd_nn_crosscheck! — Method.
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)

Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).

Arguments

  • i::Int: The index of the particle to update and check against.

  • from::Int: The starting index of the range of particles to check against.

  • to::Int: The ending index of the range of particles to check against.

  • rapidity_array: An array containing the rapidity values of all particles.

  • phi_array: An array containing the phi values of the all particles.

  • R2: The squared jet distance threshold for considering a particle as a neighbour.

  • nndist: The array that stores the nearest neighbor distances.

  • nn: The array that stores the nearest neighbor indices.

source


# JetReconstruction.upd_nn_nocross! — Method.
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)

Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).

Arguments

  • i::Int: The index of the particle to update and check against.

  • from::Int: The starting index of the range of particles to check against.

  • to::Int: The ending index of the range of particles to check against.

  • rapidity_array: An array containing the rapidity values of all particles.

  • phi_array: An array containing the phi values of the all particles.

  • R2: The squared jet distance threshold for considering a particle as a neighbour.

  • nndist: The array that stores the nearest neighbor distances.

  • nn: The array that stores the nearest neighbor indices.

source


# JetReconstruction.upd_nn_step! — Method.
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)

Update the nearest neighbor information after a jet merge step.

Arguments:

  • i: Index of the first particle in the last merge step.

  • j: Index of the second particle in the last merge step.

  • k: Index of the current particle for which the nearest neighbour will be updated.

  • N: Total number of particles (currently vaild array indexes are [1:N]).

  • Nn: Number of nearest neighbors to consider.

  • kt2_array: Array of transverse momentum squared values.

  • rapidity_array: Array of rapidity values.

  • phi_array: Array of azimuthal angle values.

  • R2: Distance threshold squared for nearest neighbors.

  • nndist: Array of nearest neighbor geometric distances.

  • nn: Array of nearest neighbor indices.

  • nndij: Array of metric distances between particles.

This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.

source


# JetReconstruction.η — Function.
julia
const η = eta

Alias for the pseudorapidity function, eta.

source


# JetReconstruction.FourMomentum — Type.

Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.

source


# JetReconstruction.HistoryElement — Type.
julia
struct HistoryElement

A struct holding a record of jet mergers and finalisations

Fields:

  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)

  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)

  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.

  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.

  • dij: The distance corresponding to the recombination at this stage of the clustering.

  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.

source


# JetReconstruction.HistoryElement — Method.
julia
HistoryElement(jetp_index)

Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.

Arguments

  • jetp_index: The index of the jetp.

Returns

A HistoryElement object.

source


# JetReconstruction.JetWithAncestors — Type.
julia
struct JetWithAncestors

A struct representing a jet with its origin ancestors.

Fields

  • self::PseudoJet: The PseudoJet object for this jet.

  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.

  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).

  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors

Note

This structure needs its associated cluster sequence origin to be useful.

source


# JetReconstruction.Surrounding — Type.
julia
struct Surrounding{N}

Structure used for iterating over neighbour tiles.

Fields

  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.

source


# JetReconstruction.TiledJet — Type.
julia
struct TiledJet

TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.

Fields

  • id::Int: The ID of the jet.

  • eta::Float64: The rapidity of the jet.

  • phi::Float64: The azimuthal angle of the jet.

  • kt2::Float64: The transverse momentum squared of the jet.

  • NN_dist::Float64: The distance to the nearest neighbor.

  • jets_index::Int: The index of the jet in the jet array.

  • tile_index::Int: The index of the tile in the tile array.

  • dij_posn::Int: The position of this jet in the dij compact array.

  • NN::TiledJet: The nearest neighbor.

  • previous::TiledJet: The previous jet.

  • next::TiledJet: The next jet.

source


# JetReconstruction.TiledJet — Method.
julia
TiledJet(id)

Constructs a TiledJet object with the given id and initializes its properties to zero.

Arguments

  • id: The ID of the TiledJet object.

Returns

A TiledJet object with the specified id and values set to zero or noTiledJet.

source


# JetReconstruction.Tiling — Type.
julia
struct Tiling

The Tiling struct represents a tiling configuration for jet reconstruction.

Fields

  • setup::TilingDef: The tiling definition used for the configuration.

  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).

  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.

  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).

source


# JetReconstruction.Tiling — Method.
julia
Tiling(setup::TilingDef)

Constructs a intial Tiling object based on the provided setup parameters.

Arguments

  • setup::TilingDef: The setup parameters for the tiling.

Returns

A Tiling object.

source


# JetReconstruction.TilingDef — Type.
julia
struct TilingDef

A struct representing the definition of a spcific tiling scheme.

Fields

  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.

  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.

  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).

  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).

  • _n_tiles_eta::Int: The number of tiles across rapidity.

  • _n_tiles_phi::Int: The number of tiles across phi.

  • _n_tiles::Int: The total number of tiles.

  • _tiles_ieta_min::Int: The minimum rapidity tile index.

  • _tiles_ieta_max::Int: The maximum rapidity tile index.

Constructor

TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
-	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)

Constructs a TilingDef object with the given parameters.

source


# JetReconstruction.neighbour_tiles — Type.
julia
struct neighbour_tiles

A struct representing the neighbouring tiles.

A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):

XXX
+    
JetReconstruction.jl
Appearance
Jet Reconstruction Internal Documentation ​
Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
Index ​
Public Interface ​
# Base.:+ — Method. 
julia
+(j1::PseudoJet, j2::PseudoJet)
Addition operator for PseudoJet objects.
Arguments
  • j1::PseudoJet: The first PseudoJet object.
  • j2::PseudoJet: The second PseudoJet object.
Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source

# Base.copy — Method. 
julia
copy(j::TiledJet)
Create a copy of a TiledJet object.
Arguments
  • j::TiledJet: The TiledJet object to be copied.
Returns
A new TiledJet object with the same attributes as the input object.
source

# Base.iterate — Function. 
julia
Base.iterate(t::rightmost_tiles, state=1)
Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source

# Base.iterate — Function. 
julia
Base.iterate(t::neighbour_tiles, state=1)
Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source

# Base.iterate — Method. 
julia
Base.iterate(tj::TiledJet)
Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
  • tj::TiledJet: The TiledJet object to start to iterate over.
source

# Base.show — Method. 
julia
show(io::IO, jet::PseudoJet)
Print a PseudoJet object to the specified IO stream.
Arguments
  • io::IO: The IO stream to which the information will be printed.
  • jet::PseudoJet: The PseudoJet object whose information will be printed.
source

# Base.tryparse — Method. 
julia
Base.tryparse(E::Type{<:Enum}, str::String)
Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source

# JetReconstruction.CosTheta — Method. 
julia
CosTheta(p::PseudoJet)
Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
  • p::PseudoJet: The PseudoJet object representing the momentum vector.
Returns
  • The cosine of the angle between p and the z-axis.
source

# JetReconstruction._ensure_valid_rap_phi — Method. 
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source

# JetReconstruction._plain_jet_reconstruct — Method. 
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source

# JetReconstruction._set_rap_phi! — Method. 
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source

# JetReconstruction._tiled_jet_reconstruct — Method. 
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source

# JetReconstruction._tj_diJ — Method. 
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source

# JetReconstruction._tj_dist — Method. 
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source

# JetReconstruction.add_step_to_history! — Method. 
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source

# JetReconstruction.add_untagged_neighbours_to_tile_union — Method. 
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source

# JetReconstruction.check_algorithm_power_consistency — Method. 
Allow a check for algorithm and power consistency
source

# JetReconstruction.detach! — Method. 
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source

# JetReconstruction.determine_rapidity_extent — Method. 
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source

# JetReconstruction.dij — Method. 
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source

# JetReconstruction.dist — Method. 
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source

# JetReconstruction.do_iB_recombination_step! — Method. 
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source

# JetReconstruction.do_ij_recombination_step! — Function. 
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source

# JetReconstruction.energy — Method. 
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source

# JetReconstruction.eta — Method. 
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source

# JetReconstruction.fast_findmin — Method. 
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source

# JetReconstruction.find_tile_neighbours! — Method. 
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source

# JetReconstruction.geometric_distance — Method. 
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source

# JetReconstruction.get_algorithm_power_consistency — Method. 
julia
set_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source

# JetReconstruction.get_all_ancestors — Method. 
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source

# JetReconstruction.get_dij_dist — Method. 
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source

# JetReconstruction.get_tile — Method. 
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source

# JetReconstruction.get_tile_cartesian_indices — Method. 
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source

# JetReconstruction.initial_history — Method. 
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source

# JetReconstruction.insert! — Method. 
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source

# JetReconstruction.isvalid — Method. 
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source

# JetReconstruction.jet_ranks — Method. 
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source

# JetReconstruction.m — Method. 
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source

# JetReconstruction.m2 — Method. 
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source

# JetReconstruction.mag — Method. 
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source

# JetReconstruction.mass — Method. 
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source

# JetReconstruction.mass2 — Function. 
Alias for m2 function
source

# JetReconstruction.merge_steps — Method. 
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source

# JetReconstruction.phi — Method. 
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source

# JetReconstruction.phi_02pi — Method. 
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source

# JetReconstruction.pt — Method. 
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source

# JetReconstruction.pt2 — Method. 
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source

# JetReconstruction.px — Method. 
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source

# JetReconstruction.py — Method. 
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source

# JetReconstruction.pz — Method. 
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source

# JetReconstruction.rapidity — Method. 
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source

# JetReconstruction.reco_state — Method. 
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source

# JetReconstruction.rightneighbours — Method. 
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source

# JetReconstruction.set_momentum! — Method. 
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source

# JetReconstruction.set_nearest_neighbours! — Method. 
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source

# JetReconstruction.setup_tiling — Method. 
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source

# JetReconstruction.surrounding — Method. 
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source

# JetReconstruction.tile_index — Method. 
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source

# JetReconstruction.tiledjet_remove_from_tiles! — Method. 
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source

# JetReconstruction.tiledjet_set_jetinfo! — Method. 
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source

# JetReconstruction.upd_nn_crosscheck! — Method. 
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source

# JetReconstruction.upd_nn_nocross! — Method. 
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source

# JetReconstruction.upd_nn_step! — Method. 
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source

# JetReconstruction.η — Function. 
julia
const η = eta
Alias for the pseudorapidity function, eta.
source

# JetReconstruction.FourMomentum — Type. 
Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.
source

# JetReconstruction.HistoryElement — Type. 
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source

# JetReconstruction.HistoryElement — Method. 
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source

# JetReconstruction.JetWithAncestors — Type. 
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source

# JetReconstruction.Surrounding — Type. 
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source

# JetReconstruction.TiledJet — Type. 
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source

# JetReconstruction.TiledJet — Method. 
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source

# JetReconstruction.Tiling — Type. 
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source

# JetReconstruction.Tiling — Method. 
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source

# JetReconstruction.TilingDef — Type. 
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
+	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source

# JetReconstruction.neighbour_tiles — Type. 
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 X.X
-XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source

# JetReconstruction.rightmost_tiles — Type. 
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source

# JetReconstruction.rightmost_tiles — Type. 
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 O.X
-OOO
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source


-    
+OOO

Note, rapidity coordinate must be in range, ϕ coordinate wraps

Fields

  • n_η::Int: Number of η tiles

  • n_ϕ::Int: Number of ϕ tiles

  • start_η::Int: Centre η tile coordinate

  • start_ϕ::Int: Centre ϕ tile coordinate

source


+ \ No newline at end of file diff --git a/previews/PR71/lib/public.html b/previews/PR71/lib/public.html index ad23860..9c741bd 100644 --- a/previews/PR71/lib/public.html +++ b/previews/PR71/lib/public.html @@ -12,20 +12,20 @@ - + -
JetReconstruction.jl

Appearance

Jet Reconstruction Public Documentation ​

Documentation for JetReconstruction.jl's public interfaces.

Index ​

Public Methods and Types ​

# JetReconstruction.constituents — Method.
julia
constituents(j::PseudoJet, cs::ClusterSequence)

Get the constituents of a given jet in a cluster sequence.

Arguments

  • cs::ClusterSequence: The cluster sequence object.

  • j::PseudoJet: The jet for which to retrieve the constituents.

Returns

An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)

source


# JetReconstruction.exclusive_jets — Method.
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)

Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.

Arguments

  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.

  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.

  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.

  • T = LorentzVectorCyl: The return type used for the selected jets.

Note: Either dcut or njets must be provided (but not both).

Returns

  • An array of T objects representing the exclusive jets.

Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)

Exceptions

  • ArgumentError: If neither dcut nor njets is provided.

  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.

  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.

Examples

julia
exclusive_jets(clusterseq, dcut = 20.0)
-exclusive_jets(clusterseq, njets = 3, T = PseudoJet)

source


# JetReconstruction.final_jets — Function.

Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)

source


# JetReconstruction.final_jets — Function.

Specialisation for final jets from LorentzVectors (TODO: merge into more general function)

source


# JetReconstruction.final_jets — Function.
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)

This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.

Arguments

  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.

  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.

Returns

A vector of FinalJet objects that satisfy the transverse momentum condition.

source


# JetReconstruction.inclusive_jets — Method.
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)

Return all inclusive jets of a ClusterSequence with pt > ptmin.

Arguments

  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.

  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.

  • T = LorentzVectorCyl: The return type used for the selected jets.

Returns

An array of T objects representing the inclusive jets.

Description

This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.

Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).

Example

julia
inclusive_jets(clusterseq; ptmin = 10.0)

source


# JetReconstruction.jet_reconstruct — Method.
julia
jet_reconstruct(particles; p = -1, R = 1.0, recombine = +, strategy = RecoStrategy.Best)

Reconstructs jets from a collection of particles using a specified algorithm and strategy

Arguments

  • particles: A collection of particles used for jet reconstruction.

  • p=-1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).

  • R=1.0: The jet radius parameter.

  • recombine=+: The recombination scheme used for combining particles.

  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.

Returns

A cluster sequence object containing the reconstructed jets and the merging history.

Details

particles argument

Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.

recombine argument

The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.

Example

julia
jet_reconstruct(particles; p = -1, R = 0.4)

source


# JetReconstruction.loadjets! — Method.
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)

Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.

Everything that was already in jets is not affected as we only use push! on it.

Example

julia
# Load jets from two files into one array
+    
JetReconstruction.jl
Appearance
Jet Reconstruction Public Documentation ​
Documentation for JetReconstruction.jl's public interfaces.
Index ​
Public Methods and Types ​
# JetReconstruction.constituents — Method. 
julia
constituents(j::PseudoJet, cs::ClusterSequence)
Get the constituents of a given jet in a cluster sequence.
Arguments
  • cs::ClusterSequence: The cluster sequence object.
  • j::PseudoJet: The jet for which to retrieve the constituents.
Returns
An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)
source

# JetReconstruction.exclusive_jets — Method. 
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
+exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source

# JetReconstruction.final_jets — Function. 
Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)
source

# JetReconstruction.final_jets — Function. 
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source

# JetReconstruction.final_jets — Function. 
Specialisation for final jets from LorentzVectors (TODO: merge into more general function)
source

# JetReconstruction.inclusive_jets — Method. 
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source

# JetReconstruction.jet_reconstruct — Method. 
julia
jet_reconstruct(particles; p = -1, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p=-1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
source

# JetReconstruction.loadjets! — Method. 
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
 jets = []
 loadjets!("myjets1.dat", jets)
-loadjets!("myjets2.dat", jets)
source

# JetReconstruction.loadjets — Method. 
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source

# JetReconstruction.n_exclusive_jets — Method. 
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source

# JetReconstruction.plain_jet_reconstruct — Method. 
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
-jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source

# JetReconstruction.read_final_state_particles — Method. 
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source

# JetReconstruction.savejets — Method. 
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source

# JetReconstruction.tiled_jet_reconstruct — Method. 
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source

# JetReconstruction.ClusterSequence — Type. 
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source

# JetReconstruction.ClusterSequence — Method. 
julia
ClusterSequence(p::Int, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Constructs a ClusterSequence object with a specific power and algorithm.
Arguments
  • p::Real: The power value for the algorithm.
  • algorithm::JetAlgorithm.Algorithm: The algorithm.
  • strategy::RecoStrategy.Strategy: The reconstruction strategy.
  • jets: The jets to be clustered.
  • history: The length of the jets.
  • Qtot: The total energy of the jets.
Returns
A ClusterSequence object.
source

# JetReconstruction.FinalJet — Type. 
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source

# JetReconstruction.FinalJets — Type. 
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source

# JetReconstruction.PseudoJet — Type. 
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
+loadjets!("myjets2.dat", jets)
source

# JetReconstruction.loadjets — Method. 
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source

# JetReconstruction.n_exclusive_jets — Method. 
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source

# JetReconstruction.plain_jet_reconstruct — Method. 
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
+jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source

# JetReconstruction.read_final_state_particles — Method. 
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source

# JetReconstruction.savejets — Method. 
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source

# JetReconstruction.tiled_jet_reconstruct — Method. 
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source

# JetReconstruction.ClusterSequence — Type. 
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source

# JetReconstruction.ClusterSequence — Method. 
julia
ClusterSequence(p::Int, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Constructs a ClusterSequence object with a specific power and algorithm.
Arguments
  • p::Real: The power value for the algorithm.
  • algorithm::JetAlgorithm.Algorithm: The algorithm.
  • strategy::RecoStrategy.Strategy: The reconstruction strategy.
  • jets: The jets to be clustered.
  • history: The length of the jets.
  • Qtot: The total energy of the jets.
Returns
A ClusterSequence object.
source

# JetReconstruction.FinalJet — Type. 
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source

# JetReconstruction.FinalJets — Type. 
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source

# JetReconstruction.PseudoJet — Type. 
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source

# JetReconstruction.PseudoJet — Method. 
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
     _cluster_hist_index::Int,
-    pt2::Float64)
Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
  • _cluster_hist_index::Int: The cluster history index.
  • pt2::Float64: The transverse momentum squared.
Returns
A PseudoJet object.
source


-    
+    pt2::Float64)

Constructs a PseudoJet object with the given momentum components and energy and history index.

Arguments

  • px::Float64: The x-component of the momentum.

  • py::Float64: The y-component of the momentum.

  • pz::Float64: The z-component of the momentum.

  • E::Float64: The energy.

  • _cluster_hist_index::Int: The cluster history index.

  • pt2::Float64: The transverse momentum squared.

Returns

A PseudoJet object.

source


+ \ No newline at end of file diff --git a/previews/PR71/lib/visualisation.html b/previews/PR71/lib/visualisation.html index 520218e..957db06 100644 --- a/previews/PR71/lib/visualisation.html +++ b/previews/PR71/lib/visualisation.html @@ -12,7 +12,7 @@ - + @@ -26,7 +26,7 @@ elevation = 0.5, framerate = 5, ancestors = false, - Module = Makie)

Animate the jet reconstruction process and save it as a video file.

Arguments

Optional Arguments

For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.

Returns

source


# JetReconstruction.jetsplot — Method.

jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
+            Module = Makie)

Animate the jet reconstruction process and save it as a video file.

Arguments

Optional Arguments

For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.

Returns

source


# JetReconstruction.jetsplot — Method.

jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
 using CairoMakie # use any other Makie that you have here
 
 jetsplot([object1, object2, object3], [[1], [2, 3]])

The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.

This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:

import CairoMakie
@@ -36,7 +36,7 @@
 jetsplot(my_objects, my_colour_arrays, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, my_colour_arrays, Module=Main) #default

source


# JetReconstruction.jetsplot — Method.
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
+jetsplot(my_objects, my_colour_arrays, Module=Main) #default

source


# JetReconstruction.jetsplot — Method.
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
 using CairoMakie # use any other Makie that you have here
 jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)

This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:

import CairoMakie
 jetsplot(my_objects, cs, Module=CairoMakie)
@@ -45,8 +45,8 @@
 jetsplot(my_objects, cs, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, cs, Module=Main) #default

source


- +jetsplot(my_objects, cs, Module=Main) #default

source


+ \ No newline at end of file