pyhepmc

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name: pyhepmc description: Use when the user needs to read or write HepMC3 event records in Python — generator-level event graphs, vertices, particle production chains — with the pyhepmc package. Does NOT cover Les Houches Event files (use pylhe), detector-level ROOT ntuples (use physlite-basics), or 4-vector math on the particles (use vector). Disambiguator phrase: pyhepmc HepMC3 event records. data_scope: both experiment: all

pyhepmc

Overview

pyhepmc provides Python bindings for the HepMC3 event record library. It reads HepMC2, HepMC3, LHEF, and HEPEVT ASCII files produced by Monte Carlo generators (Pythia8, Sherpa, MadGraph, EvtGen, EPOS-LHC, SIBYLL) and exposes particles, vertices, and event metadata as Python objects. It is the standard tool for truth-level generator studies in the Scikit-HEP ecosystem.

Unique features beyond the official HepMC3 Python bindings:

• Simple pyhepmc.open for format auto-detection and compressed-file support
• NumPy array views into particle/vertex data (up to 70× faster than the standard API for large events)
• Event graph rendering in Jupyter notebooks (via graphviz)
• Pythonic properties, context managers, and repr strings

When to Use

• Reading generator-level truth events for particle-level analysis
• Extracting final-state particles for truth jet clustering with fastjet
• Inspecting decay chains or particle status codes
• Writing test events or modified events for generator validation
• Fast vectorized analysis with the NumPy API

Key Concepts

Concept	Notes
GenEvent	One event: particles + vertices + weights + run info
GenParticle	FourVector (px, py, pz, e in GeV) + PDG ID + status code
Status 1	Final-state (stable) particles
Status 2	Decayed particles (intermediate)
Status 3	Parton-level / documentation particles
GenVertex	Connects incoming and outgoing particles
GenRunInfo	Per-run metadata: generator tool, weight names, attributes
GenEventData	Flat in-memory representation; supports NumPy array views
Weights	Named weight list (e.g. scale/PDF variations); access by name

Canonical Patterns

Read a HepMC file

import pyhepmc

# auto-detects HepMC3, HepMC2, LHEF, HEPEVT; handles .gz/.bz2/.xz/.zst
with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        particles = event.particles      # list of GenParticle
        vertices  = event.vertices       # list of GenVertex

Filter final-state particles

with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        final_state = [p for p in event.particles if p.status == 1]
        # FourVector components: .px .py .pz .e  (all in GeV)
        for p in final_state:
            pt = (p.momentum.px**2 + p.momentum.py**2) ** 0.5
            print(p.pid, pt)

NumPy API — fast vectorized processing

Prefer this over Python loops for large events; up to 70× faster on events with thousands of particles.

import numpy as np

with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        p = event.numpy.particles        # read-only array view
        mask = (np.abs(p.pid) == 2212) & (p.status == 1)
        esum = np.sum(p.e[mask])         # sum proton energies
        pt = np.sqrt(p.px[mask]**2 + p.py[mask]**2)

Available columns on event.numpy.particles: pid, status, px, py, pz, e, m. Available columns on event.numpy.vertices: x, y, z, t, status.

Traverse decay tree (parents / children)

with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        for p in event.particles:
            if abs(p.pid) == 23:                   # Z boson
                leptons = [c for c in p.children
                           if abs(c.pid) in (11, 13)]
                for lep in leptons:
                    print("Z->lepton:", lep.pid, lep.parents)

Identify particles by PDG ID

with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        b_hadrons = [p for p in event.particles
                     if abs(p.pid) in (511, 521, 531, 5122)]

Extract four-vectors for fastjet clustering

import pyhepmc, fastjet as fj

with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        pjs = [
            fj.PseudoJet(p.momentum.px, p.momentum.py,
                         p.momentum.pz, p.momentum.e)
            for p in event.particles
            if p.status == 1 and abs(p.pid) not in (12, 14, 16)
        ]
        cs  = fj.ClusterSequence(pjs, fj.JetDefinition(fj.antikt_algorithm, 0.4))
        jets = fj.sorted_by_pt(cs.inclusive_jets(ptmin=20.0))

Write HepMC3 events

# default: HepMC3 ASCII; use format="hepmc2" for legacy output
with pyhepmc.open("output.hepmc3", "w", precision=6) as out:
    with pyhepmc.open("input.hepmc3") as f:
        for event in f:
            out.write(event)

precision controls the number of significant digits (default is full double). Reducing it (e.g. precision=4) can significantly reduce file size.

Access named weights (scale / PDF variations)

with pyhepmc.open("events.hepmc3") as f:
    for event in f:
        # shortcut: event.weight("name") raises RuntimeError if missing
        nominal = event.weight("Default")
        mur_up  = event.weight("MUR2_MUF1")
        # or build the dict yourself:
        w = dict(zip(event.weight_names, event.weights))

Set generator metadata and write

evt.run_info = pyhepmc.GenRunInfo()
evt.run_info.tools = [
    pyhepmc.GenRunInfo.ToolInfo("Pythia", "8.311", "pp collisions at 13 TeV")
]
evt.run_info.weight_names = ["Default", "MUR2_MUF1"]

Visualize an event (Jupyter / graphviz)

from pyhepmc.view import to_dot, savefig

# renders inline in Jupyter (GenEvent also has _repr_html_ automatically)
g = to_dot(event, size=(6, 6))

# save to file
savefig(event, "event.svg")   # also .png, .pdf

Debug: print event listing

import pyhepmc

with pyhepmc.open("events.hepmc3") as f:
    event = f.read()

print(pyhepmc.listing(event))   # tabular particle/vertex listing
print(pyhepmc.content(event))   # raw HepMC3 ASCII dump

Gotchas

• HepMC momentum units are GeV (not MeV): p.momentum.e is in GeV. This is opposite to ATLAS ntuple branches, which are in MeV.
• Status code conventions vary by generator: status=1 for final-state is standard; beyond that, Pythia8 and Sherpa use generator-specific codes.
• Neutrinos pass isolation cuts: exclude neutrino PDG IDs (12, 14, 16) from visible-particle sums and jet inputs.
• HepMC2 vs HepMC3: pyhepmc.open auto-detects the format from the file header. HepMC2 files contain HepMC::IO_GenEvent; HepMC3 files start with HepMC::Asciiv3.
• Attributes deserialise as UnparsedAttribute: when reading back an attribute that was written, HepMC3 does not store the type, so you get an UnparsedAttribute object. Call .astype(bool) / .astype(int) etc. to convert (this also replaces the entry in place).
• NumPy API is read-only: event.numpy.particles is a view; modifying those arrays does not change the GenEvent. Use GenEventData if you need to mutate event content via arrays.
• event.weight_names requires run_info: if event.run_info is None this raises RuntimeError. Check event.run_info is not None first.

Interop

• fastjet: extract final-state particles → build PseudoJet list → cluster.
• particle: identify PDG IDs with Particle.from_pdgid(p.pid) or use from particle import literals as lp; lp.proton.pdgid.
• pylhe: for parton-level LHE files; pyhepmc handles showered / hadronised events. pyhepmc.open can also read LHEF directly (format="lhef").
• awkward: convert final-state particle lists to awkward arrays for vectorised columnar analysis.
• vector: import vector; v = vector.obj(px=p.momentum.px, ...) for Lorentz-boost utilities.
• numpy: use event.numpy.particles or GenEventData for batch array operations across many particles.

Docs

https://scikit-hep.org/pyhepmc/