graphicle 0.1.7

Encode particle physics data onto graph structures.

graphicle

Utilities for representing high energy physics data as graphs / networks.

Installation

pip install graphicle

Features

Object oriented interface to track-level particle data for collider physics, with routines for constructing and performing calculations over graph-structured data.

Provides data structures for:

• 4-momenta
• PDG codes
• Particle status codes
• Color codes
• Helicity / spin polarisation data
• COO adjacency lists (for graph-structured data)

>>> import graphicle as gcl

# query pdg records
>>> pdgs = gcl.PdgArray([1, 3, 6, -6, 25, 2212])
>>> pdgs.name
['d', 's', 't', 't~', 'H0', 'p'], dtype=object)
>>> pdgs.charge
array([-0.33333333, -0.33333333,  0.66666667, -0.66666667,  0.        ,
        1.        ])

# extract information from momentum data
>>> pmu_data
array([( 1.95057378e-02,  3.12923088e-02,  3.53556064e-01, 3.55473730e-01),
       ( 2.60116947e+01, -3.63466398e+00, -3.33718718e+00, 2.64755711e+01),
       ( 5.91884324e-05, -7.62144267e-06, -6.76385314e-06, 6.00591927e-05),
       ( 2.82881807e+01,  4.32224823e+00,  2.14691072e+02, 2.16589841e+02),
       (-8.73280642e-02, -6.48540201e-02,  3.73744945e-01, 6.28679140e-01),
       ( 1.06204871e-01,  5.78888984e-01, -1.44899819e+02, 1.44901081e+02)],
      dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8'), ('e', '<f8')])
>>> pmu = gcl.MomentumArray(pmu_data)
>>> pmu.pt
array([3.68738715e-02, 2.62644064e+01, 5.96771055e-05, 2.86164812e+01,
       1.08776076e-01, 5.88550704e-01])
>>> pmu.mass
array([-7.45058060e-09,  5.11000489e-04,  9.09494702e-13,  5.10991478e-04,
        4.93680000e-01,  1.39570000e-01])
>>> pmu.eta
array([ 2.95639434, -0.12672178, -0.11309956,  2.71277683,  1.94796328,
       -6.1992861 ])
>>> pmu.phi
array([ 1.01339184, -0.138833  , -0.12806107,  0.15162078, -2.5028134 ,
        1.38935084])

# calculate the inter-particle distances
>>> pmu.delta_R(pmu)
array([[0.        , 3.2913868 , 3.27485993, 0.89554388, 2.94501476,
        9.16339617],
       [3.2913868 , 0.        , 0.01736661, 2.85431528, 3.14526968,
        6.26189934],
       [3.27485993, 0.01736661, 0.        , 2.83968296, 3.14442819,
        6.27249595],
       [0.89554388, 2.85431528, 2.83968296, 0.        , 2.76241933,
        8.99760198],
       [2.94501476, 3.14526968, 3.14442819, 2.76241933, 0.        ,
        8.4908571 ],
       [9.16339617, 6.26189934, 6.27249595, 8.99760198, 8.4908571 ,
        0.        ]])

Graphicle really shines with its composite data structures. These can be used to filter and query heterogeneous particle data records simultaneously, either using user provided boolean masks, or MaskArrays produced with routines in the select module. Additionally, routines in the calculate and transform modules take composite data structures to standardise useful calculations which blends multiple particle data records.

To see an example, let's generate a collision event using Pythia, wrapped with showerpipe.

>>> from showerpipe.generator import PythiaGenerator
...
... lhe_path = "https://zenodo.org/record/6034610/files/unweighted_events.lhe.gz"
... gen = PythiaGenerator("pythia-settings.cmnd", lhe_path)
>>> for event in gen:
...     graph = gcl.Graphicle.from_numpy(
...         pmu=event.pmu,
...         pdg=event.pdg,
...         color=event.color,
...         helicity=event.helicity,
...         status=event.status,
...         final=event.final,
...         edges=event.edges
...         )
...     break

>>> graph.pdg
PdgArray(data=array([2212, 2212,   21, ...,   22,   22,   22], dtype=int32))
>>> graph.edges
array([(   0,   -1), (   0,   -2), (  -6,   -3), ..., (-635, 1211),
       (-636, 1212), (-636, 1213)], dtype=[('in', '<i4'), ('out', '<i4')])
# select all descendants of the W bosons from the hard process
>>> W_mask = gcl.select.hard_descendants(graph, {24})
>>> W_mask
MaskGroup(mask_arrays=["W+", "W-"], agg_op=OR)
# filter data record to get final state W+ boson descendants
>>> Wp_desc = graph[W_mask["W+"] & graph.final]
>>> Wp_desc.pdg
PdgArray(data=array([ 321, -211, -211,  321, -211, -321,  211,  211,  -13,   14,   22,
         22,  211, -211,   22,   22,   22,   22,   22,  211, -211,   22,
         22,   22,   22,  130,   22,   22], dtype=int32))
>>> Wp_desc
Graphicle(particles=ParticleSet(
PdgArray(data=array([ 321, -211, -211,  321, -211, -321,  211,  211,  -13,   14,   22,
         22,  211, -211,   22,   22,   22,   22,   22,  211, -211,   22,
         22,   22,   22,  130,   22,   22], dtype=int32)),
MomentumArray(data=array([(-1.41648688e+00, -2.6653416 , -2.25487483e-01, 3.06676466e+00),
       ( 5.26078595e-01,  0.11325339, -1.85115863e+00, 1.93283550e+00),
       ( 2.92112800e+00,  2.19611382, -9.04351574e+00, 9.75502749e+00),
       ( 1.70197168e+01,  9.65578074, -4.51506419e+01, 4.92110663e+01),
       (-5.70145778e-01, -1.02762625,  1.35915720e-01, 1.19123247e+00),
       (-1.70566595e-01,  0.02598637, -1.34183423e-01, 5.39901276e-01),
       (-1.80439204e-01, -0.51409054,  1.82537117e-01, 5.91309546e-01),
       ( 1.63182285e-01,  0.13788241, -3.17043212e-01, 4.06984277e-01),
       (-2.45719652e+00, -4.10607321,  3.31426006e-01, 4.79777648e+00),
       (-1.08820465e+00, -1.84333164, -1.69547133e-01, 2.14727900e+00),
       (-4.92718715e-01, -0.87998859,  1.11984849e-01, 1.01473753e+00),
       ( 8.90383374e-03, -0.01019132,  4.32869417e-04, 1.35398920e-02),
       (-6.11110402e-01, -0.74064239,  5.47809445e-02, 9.71847628e-01),
       (-2.13853648e-01, -0.34188095, -1.89837677e-01, 4.67048281e-01),
       (-3.57251890e-01, -0.42033772, -1.39634796e-01, 5.69043576e-01),
       (-2.41744268e-01,  0.16830106, -1.53611666e-02, 2.94960174e-01),
       (-8.27775995e-01, -0.4279882 ,  1.03575995e-01, 9.37611318e-01),
       (-3.44298782e-05,  0.14091286, -4.51929191e-02, 1.47982551e-01),
       ( 6.20276481e-02,  0.12552564, -1.96113732e-01, 2.40966203e-01),
       ( 6.32168629e+00,  4.5683574 , -1.69888394e+01, 1.86942171e+01),
       ( 8.77035615e-01,  0.4961944 , -2.38422385e+00, 2.59218122e+00),
       (-1.12781117e+00, -1.41626175, -6.02316244e-02, 1.81145887e+00),
       (-1.52146265e+00, -1.67738354, -3.45502640e-02, 2.26487480e+00),
       ( 1.82715744e+00,  0.28701504, -3.76239153e+00, 4.19243031e+00),
       ( 4.77818092e-01,  0.02881935, -8.63039360e-01, 9.86903046e-01),
       (-3.03560171e+00, -2.76703663,  9.57894838e-02, 4.13861822e+00),
       ( 8.99971241e-01,  0.6677899 , -2.26276823e+00, 2.52507657e+00),
       ( 1.42885287e+00,  0.86196369, -3.46387012e+00, 3.84486646e+00)],
      dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8'), ('e', '<f8')])),
ColorArray(data=array([(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0),
       (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0),
       (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0),
       (0, 0), (0, 0), (0, 0), (0, 0)],
      dtype=[('color', '<i4'), ('anticolor', '<i4')])),
HelicityArray(data=array([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
       9, 9, 9, 9, 9, 9], dtype=int16)),
StatusArray(data=array([83, 84, 84, 84, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91,
       91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91], dtype=int16)),
MaskArray(data=array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True]))
), adj=AdjacencyList(_data=array([(-343,  650), (-343,  651), (-343,  652), (-343,  653),
       (-345,  743), (-349,  744), (-349,  745), (-350,  746),
       (-344,  863), (-344,  864), (-346,  865), (-346,  866),
       (-347,  867), (-347,  868), (-347,  869), (-348,  870),
       (-348,  871), (-351,  872), (-351,  873), (-352,  874),
       (-352,  875), (-518, 1012), (-518, 1013), (-519, 1014),
       (-519, 1015), (-571, 1097), (-572, 1098), (-572, 1099)],
      dtype=[('in', '<i4'), ('out', '<i4')]), weights=array([], dtype=float64)))

# calculate the mass of the W boson from its final state constituents
>>> gcl.calculate.combined_mass(Wp_desc.pmu)
80.419002446

More information on the API is available in the documentation