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Utilities for representing high energy physics data as graphs / networks.
.. code:: bash
pip install graphicle
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:
.. code:: python3
import graphicle as gcl
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. ])
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 MomentumArray([[ 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.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])
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
MaskArray instances 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.
.. code:: python3
from showerpipe.generator import PythiaGenerator ... ... lhe_path = "https://zenodo.org/record/6034610/files/unweighted_events.lhe.gz" ... gen = PythiaGenerator("pythia-settings.cmnd", lhe_path, 1) for event in gen: ... graph = gcl.Graphicle.from_event(event) ... break
print(graph) name px py pz energy color anticolor helicity status final src dst p 0.00E+00 0.00E+00 6.50E+03 6.50E+03 0 0 9 -12 False 0 -1 p 0.00E+00 0.00E+00 -6.50E+03 6.50E+03 0 0 9 -12 False 0 -2 g 0.00E+00 0.00E+00 2.99E+02 2.99E+02 503 502 1 -21 False -6 -3 g -0.00E+00 -0.00E+00 -5.99E+02 5.99E+02 501 503 1 -21 False -7 -3 t 2.34E+02 -2.20E+01 -4.76E+02 5.58E+02 501 0 0 -22 False -3 -4 ... ... ... ... ... ... ... ... ... ... ... ... gamma 1.30E-02 -1.30E+00 -3.24E+00 3.49E+00 0 0 9 91 True -969 979 gamma 1.70E-01 -8.21E-01 -2.32E+00 2.47E+00 0 0 9 91 True -970 980 gamma 3.12E-01 -2.26E+00 -6.82E+00 7.19E+00 0 0 9 91 True -970 981 gamma 9.38E-03 -3.58E-01 -7.98E-01 8.75E-01 0 0 9 91 True -971 982 gamma 3.08E-02 -4.36E-02 -4.56E-02 7.02E-02 0 0 9 91 True -971 983
[1065 particles × 12 attributes]
graph.pdg PdgArray([2212 2212 21 ... 22 22 22], dtype=int32) graph.adj AdjacencyList([[ 0 -1] [ 0 -2] [ -6 -3] ... [-970 981] [-971 982] [-971 983]], dtype=[('src', '<i4'), ('dst', '<i4')])
W_mask = gcl.select.hard_descendants(graph, {24}) W_mask MaskGroup(mask_arrays=["W+", "W-"], agg_op=OR)
Wp_desc = graph[W_mask["W+"] & graph.final] print(Wp_desc) name px py pz energy color anticolor helicity status final src dst gamma 2.46E-05 -5.65E-06 -1.54E-05 2.95E-05 0 0 9 51 True -350 353 nu(tau) 1.72E+02 3.52E+01 -3.18E+02 3.63E+02 0 0 9 52 True -351 354 nu(tau)~ 1.73E+01 -4.48E+00 -1.08E+01 2.09E+01 0 0 9 91 True -352 687 pi+ 1.19E+01 -3.15E+00 -7.51E+00 1.44E+01 0 0 9 91 True -352 690 gamma 4.12E+00 -1.09E+00 -2.19E+00 4.79E+00 0 0 9 91 True -688 879 gamma 1.54E+00 -4.72E-01 -8.87E-01 1.84E+00 0 0 9 91 True -688 880 gamma 2.11E+00 -4.94E-01 -9.96E-01 2.38E+00 0 0 9 91 True -689 881 gamma 3.22E+00 -7.42E-01 -1.71E+00 3.72E+00 0 0 9 91 True -689 882
[8 particles × 12 attributes]
Wp_sum = np.sum(Wp_desc.pmu, axis=0) Wp_sum.mass 80.419002446
More information on the API is available in the
documentation <https://graphicle.readthedocs.io>__
graphicle offers a function wrapper around fastjet to cluster
MomentumArray objects using their optimised generalised-kT algorithm.
However, this library cannot build wheels for all systems, including Windows
and the latest macOS systems using ARM architectures.
Therefore, in order to use graphicle.select.fastjet_clusters(), you must
install graphicle with fastjet as an optional dependency.
This enables users who don't want the fastjet wrapper to ignore it, and
still make the most of the many other features of graphicle.
Use the following to get started:
.. code:: bash
pip install "graphicle[fastjet]"
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FAQs
Encode particle physics data onto graph structures.
We found that graphicle demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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