feat: Add combinatoric indices utilitiy

[junggjo9]

Add utility class to loop over all possibility to draw K elements from a set of N numbers
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Tagging: @andiwand, @dimitra97

[github-actions]

📊: Physics performance monitoring for f97d6a6

Full contents

physmon summary

• ✅ Particles fatras
• ✅ Particles geant4
• ✅ Particles ttbar
• ✅ Vertices ttbar
• ✅ Truth tracking (KF)
• ✅ Truth tracking (GSF)
• ✅ Truth tracking (GX2F)
• ✅ Truth tracking (KF refit)
• ✅ Truth tracking (GSF refit)
• ✅ CKF finding performance | trackfinding | single muon | truth smeared seeding
• ✅ CKF fitting performance | trackfinding | single muon | truth smeared seeding
• ✅ CKF track summary | trackfinding | single muon | truth smeared seeding
• ✅ Seeding trackfinding | single muon | truth estimated seeding
• ✅ CKF finding performance | trackfinding | single muon | truth estimated seeding
• ✅ CKF fitting performance | trackfinding | single muon | truth estimated seeding
• ✅ CKF track summary | trackfinding | single muon | truth estimated seeding
• ✅ Seeding trackfinding | single muon | default seeding
• ✅ CKF finding performance | trackfinding | single muon | default seeding
• ✅ CKF fitting performance | trackfinding | single muon | default seeding
• ✅ CKF track summary | trackfinding | single muon | default seeding
• ✅ Seeding trackfinding | single muon | orthogonal seeding
• ✅ CKF finding performance | trackfinding | single muon | orthogonal seeding
• ✅ CKF fitting performance | trackfinding | single muon | orthogonal seeding
• ✅ CKF track summary | trackfinding | single muon | orthogonal seeding
• ✅ Seeding trackfinding | 4 muon x 50 vertices | default seeding
• ✅ CKF finding performance | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ CKF fitting performance | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ CKF track summary | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ Ambisolver finding performance | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ IVF notime | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ AMVF gauss notime | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ AMVF grid time | trackfinding | 4 muon x 50 vertices | default seeding
• ✅ Seeding trackfinding | ttbar with 200 pileup | default seeding
• ✅ CKF finding performance | trackfinding | ttbar with 200 pileup | default seeding
• ✅ CKF fitting performance | trackfinding | ttbar with 200 pileup | default seeding
• ✅ CKF track summary | trackfinding | ttbar with 200 pileup | default seeding
• ✅ Ambisolver finding performance | trackfinding | ttbar with 200 pileup | default seeding
• ✅ ML Ambisolver | trackfinding | ttbar with 200 pileup | default seeding
• ✅ AMVF gauss notime | trackfinding | ttbar with 200 pileup | default seeding
• ✅ AMVF grid time | trackfinding | ttbar with 200 pileup | default seeding
• 🔵️ Comparison - Truth tracking (GX2F vs KF)

[andiwand]

My main concern is that we introduce new non-tracking utility with very targeted use but in the public versioned namespace while clients should not depend on it.

To understand this better:

• What is the use case for this?
• Does boost provide something we can use? Potentially Boost.Hana if we want to push this further into compile time
• STL has std::next_permutation which could be used for something like this

Generally I believe we deal with ordered subsets in a couple of places but I didn't come across generating them yet. If we need this for future code in ACTS I would propose moving this into detail

[junggjo9]

I've not yet seen anything in boost which could provide this functionality, We'll need this to collapse the loop structure in the NSW segment finder from 4-> 1 loops.

[junggjo9]

Permutation isn't neither what we want to do because the number elements themselves may change

219: Iteration: 0-> drawn indices: [0, 1, 2, 3]
219: Iteration: 1-> drawn indices: [0, 1, 2, 4]
219: Iteration: 2-> drawn indices: [0, 1, 2, 5]
219: Iteration: 3-> drawn indices: [0, 1, 2, 6]
219: Iteration: 4-> drawn indices: [0, 1, 2, 7]
219: Iteration: 5-> drawn indices: [0, 1, 2, 8]
219: Iteration: 6-> drawn indices: [0, 1, 2, 9]
219: Iteration: 7-> drawn indices: [0, 1, 2, 10]
219: Iteration: 8-> drawn indices: [0, 1, 2, 11]
219: Iteration: 9-> drawn indices: [0, 1, 2, 12]
219: Iteration: 10-> drawn indices: [0, 1, 2, 13]
219: Iteration: 11-> drawn indices: [0, 1, 2, 14]
219: Iteration: 12-> drawn indices: [0, 1, 3, 4]
219: Iteration: 13-> drawn indices: [0, 1, 3, 5]
219: Iteration: 14-> drawn indices: [0, 1, 3, 6]
219: Iteration: 15-> drawn indices: [0, 1, 3, 7]
219: Iteration: 16-> drawn indices: [0, 1, 3, 8]
219: Iteration: 17-> drawn indices: [0, 1, 3, 9]
219: Iteration: 18-> drawn indices: [0, 1, 3, 10]
219: Iteration: 19-> drawn indices: [0, 1, 3, 11]
219: Iteration: 20-> drawn indices: [0, 1, 3, 12]
219: Iteration: 21-> drawn indices: [0, 1, 3, 13]
219: Iteration: 22-> drawn indices: [0, 1, 3, 14]
219: Iteration: 23-> drawn indices: [0, 1, 4, 5]
219: Iteration: 24-> drawn indices: [0, 1, 4, 6]
219: Iteration: 25-> drawn indices: [0, 1, 4, 7]
219: Iteration: 26-> drawn indices: [0, 1, 4, 8]
219: Iteration: 27-> drawn indices: [0, 1, 4, 9]
219: Iteration: 28-> drawn indices: [0, 1, 4, 10]
219: Iteration: 29-> drawn indices: [0, 1, 4, 11]
219: Iteration: 30-> drawn indices: [0, 1, 4, 12]
219: Iteration: 31-> drawn indices: [0, 1, 4, 13]
219: Iteration: 32-> drawn indices: [0, 1, 4, 14]
219: Iteration: 33-> drawn indices: [0, 1, 5, 6]
219: Iteration: 34-> drawn indices: [0, 1, 5, 7]
219: Iteration: 35-> drawn indices: [0, 1, 5, 8]
219: Iteration: 36-> drawn indices: [0, 1, 5, 9]
219: Iteration: 37-> drawn indices: [0, 1, 5, 10]
219: Iteration: 38-> drawn indices: [0, 1, 5, 11]
219: Iteration: 39-> drawn indices: [0, 1, 5, 12]
219: Iteration: 40-> drawn indices: [0, 1, 5, 13]
219: Iteration: 41-> drawn indices: [0, 1, 5, 14]
219: Iteration: 42-> drawn indices: [0, 1, 6, 7]
219: Iteration: 43-> drawn indices: [0, 1, 6, 8]
219: Iteration: 44-> drawn indices: [0, 1, 6, 9]
219: Iteration: 45-> drawn indices: [0, 1, 6, 10]
219: Iteration: 46-> drawn indices: [0, 1, 6, 11]
219: Iteration: 47-> drawn indices: [0, 1, 6, 12]
219: Iteration: 48-> drawn indices: [0, 1, 6, 13]
219: Iteration: 49-> drawn indices: [0, 1, 6, 14]
219: Iteration: 50-> drawn indices: [0, 1, 7, 8]
219: Iteration: 51-> drawn indices: [0, 1, 7, 9]
219: Iteration: 52-> drawn indices: [0, 1, 7, 10]
219: Iteration: 53-> drawn indices: [0, 1, 7, 11]
219: Iteration: 54-> drawn indices: [0, 1, 7, 12]
219: Iteration: 55-> drawn indices: [0, 1, 7, 13]
219: Iteration: 56-> drawn indices: [0, 1, 7, 14]
219: Iteration: 57-> drawn indices: [0, 1, 8, 9]
219: Iteration: 58-> drawn indices: [0, 1, 8, 10]
219: Iteration: 59-> drawn indices: [0, 1, 8, 11]
219: Iteration: 60-> drawn indices: [0, 1, 8, 12]
219: Iteration: 61-> drawn indices: [0, 1, 8, 13]
219: Iteration: 62-> drawn indices: [0, 1, 8, 14]
219: Iteration: 63-> drawn indices: [0, 1, 9, 10]
219: Iteration: 64-> drawn indices: [0, 1, 9, 11]
219: Iteration: 65-> drawn indices: [0, 1, 9, 12]
219: Iteration: 66-> drawn indices: [0, 1, 9, 13]
219: Iteration: 67-> drawn indices: [0, 1, 9, 14]
219: Iteration: 68-> drawn indices: [0, 1, 10, 11]
219: Iteration: 69-> drawn indices: [0, 1, 10, 12]
219: Iteration: 70-> drawn indices: [0, 1, 10, 13]
219: Iteration: 71-> drawn indices: [0, 1, 10, 14]
219: Iteration: 72-> drawn indices: [0, 1, 11, 12]
219: Iteration: 73-> drawn indices: [0, 1, 11, 13]
219: Iteration: 74-> drawn indices: [0, 1, 11, 14]
219: Iteration: 75-> drawn indices: [0, 1, 12, 13]
219: Iteration: 76-> drawn indices: [0, 1, 12, 14]
219: Iteration: 77-> drawn indices: [0, 1, 13, 14]
219: Iteration: 78-> drawn indices: [0, 2, 3, 4]
219: Iteration: 79-> drawn indices: [0, 2, 3, 5]
219: Iteration: 80-> drawn indices: [0, 2, 3, 6]
219: Iteration: 81-> drawn indices: [0, 2, 3, 7]
219: Iteration: 82-> drawn indices: [0, 2, 3, 8]
219: Iteration: 83-> drawn indices: [0, 2, 3, 9]
219: Iteration: 84-> drawn indices: [0, 2, 3, 10]
219: Iteration: 85-> drawn indices: [0, 2, 3, 11]
219: Iteration: 86-> drawn indices: [0, 2, 3, 12]
219: Iteration: 87-> drawn indices: [0, 2, 3, 13]
219: Iteration: 88-> drawn indices: [0, 2, 3, 14]

[dimitra97]

Actually we are not interested in permutations, we would like to generate unique combinations of elements as subsets of a bigger set.
In my opinion, this cleanly generates combinations of k elements out of N, fine but sill I am a bit skeptical about the usage in athena. I am in the process of evaluating this (dont know how fast I can be in case you want to merge it for this or if you want to merge it either way)
I would agree to put this into detail either way though

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

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