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test/test-valencia.jl

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@@ -7,12 +7,7 @@ Validates clustering results and cluster sequence against FastJet C++ reference
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using jet matching to handle numerical precision differences.
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"""
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# Include common test utilities
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include("common.jl")
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import JetReconstruction: pt, rapidity, PseudoJet
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using Test
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using JetReconstruction
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# Reference outputs for R=0.8, beta=1.2, gamma=1.2
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valencia_inclusive = ComparisonTestValencia(events_file_ee,
@@ -69,3 +64,34 @@ valencia_exclusive_d500_b1g1 = ComparisonTestValencia(events_file_ee,
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cs -> exclusive_jets(cs; dcut = 500.0),
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"exclusive dcut=500 beta=1.0 gamma=1.0 R=1.0")
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run_reco_test(valencia_exclusive_d500_b1g1)
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# Bring required types/functions into scope for targeted unit tests
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using JetReconstruction: EERecoJet, PseudoJet, EEJet, ee_genkt_algorithm, dij_dist,
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valencia_distance
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# Test dij_dist for Valencia algorithm
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@testset "dij_dist Valencia" begin
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# Minimal eereco with two reco jets; only nx,ny,nz,E2p are used by valencia_distance
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eereco = EERecoJet[EERecoJet(1, 0, Inf, Inf, 1.0, 0.0, 0.0, 1.0),
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EERecoJet(2, 0, Inf, Inf, 0.0, 1.0, 0.0, 2.0)]
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dij = dij_dist(eereco, 1, 2, 1.0, JetAlgorithm.Valencia, 0.8)
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@test dij valencia_distance(eereco, 1, 2, 0.8)
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end
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# Test ee_genkt_algorithm for Valencia algorithm (covers β override and dij_factor selection)
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@testset "ee_genkt_algorithm Valencia" begin
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particles = [PseudoJet(1.0, 0.0, 0.0, 1.0)]
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cs = ee_genkt_algorithm(particles; algorithm = JetAlgorithm.Valencia, β = 1.2, γ = 1.2,
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R = 0.8)
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@test cs isa JetReconstruction.ClusterSequence
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end
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# Test internal _ee_genkt_algorithm entry (touches StructArray init path too)
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@testset "_ee_genkt_algorithm Valencia dij_factor" begin
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# Ensure cluster_hist_index(i) == i for initial jets
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jets = [EEJet(1.0, 0.0, 0.0, 1.0; cluster_hist_index = 1)]
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cs = JetReconstruction._ee_genkt_algorithm(particles = jets,
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algorithm = JetAlgorithm.Valencia, p = 1.2,
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R = 0.8, γ = 1.2)
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@test cs isa JetReconstruction.ClusterSequence
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end

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