.. -- mode: rst --

:warning:numpythia is deprecated and unmaintained:warning:

numpythia has not been actively maintained for a couple of years. This is mostly due to the emergence of new alternatives which are both faster and more flexible. For example:

• The official Pythia 8 (SWIG) bindings available from Conda. <https://anaconda.org/conda-forge/pythia8>_
• The impy <https://github.com/impy-project/impy>_ project implementing a generic user interface to popular event generators used in cosmic ray and high-energy particle physics.

numpythia: The interface between PYTHIA and NumPy

.. image:: https://img.shields.io/pypi/v/numpythia.svg :target: https://pypi.python.org/pypi/numpythia

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1471492.svg :target: https://doi.org/10.5281/zenodo.1471492

.. image:: https://github.com/scikit-hep/numpythia/workflows/Main/badge.svg?branch=master :target: https://github.com/scikit-hep/numpythia/actions :alt: Test status

numpythia provides an interface between PYTHIA <http://home.thep.lu.se/Pythia/> and NumPy <http://www.numpy.org/> allowing you to generate events as NumPy arrays of particle four-momenta. By default numpythia only depends on NumPy and builds internal copies of the PYTHIA and HepMC <http://hepmc.web.cern.ch/hepmc/>_ source code.

Standalone Installation

To simply use the built-in PYTHIA and HepMC::

pip install -v numpythia

And you're good to go!

Support for building against an external PYTHIA is on the wishlist.

Note that if you are using a Mac OSX system, then installation may require setting an environment variable as explained here <https://github.com/pytorch/pytorch/issues/1434>_.

Strict dependencies

• Python <http://docs.python-guide.org/en/latest/starting/installation/>__ (2.7+, 3.7+)
• Numpy <https://scipy.org/install.html>__
• six <https://six.readthedocs.io/>__
• Source builds: Cython <https://cython.org/>__ and a compiler.

Getting started

.. code-block:: python

from numpythia import Pythia, hepmc_write, hepmc_read from numpythia import STATUS, HAS_END_VERTEX, ABS_PDG_ID from numpythia.testcmnd import get_cmnd from numpy.testing import assert_array_equal

pythia = Pythia(get_cmnd('w'), random_state=1)

selection = ((STATUS == 1) & ~HAS_END_VERTEX & (ABS_PDG_ID != 12) & (ABS_PDG_ID != 14) & (ABS_PDG_ID != 16))

generate events while writing to ascii hepmc

for event in hepmc_write('events.hepmc', pythia(events=1)): array1 = event.all(selection)

read the same event back from ascii hepmc

for event in hepmc_read('events.hepmc'): array2 = event.all(selection)

assert_array_equal(array1, array2) True

The dtype of any array of particle information is:

.. code-block:: python

np.dtype([('E', 'f8'), ('px', 'f8'), ('py', 'f8'), ('pz', 'f8'), ('pt', 'f8'), ('mass', 'f8'), ('rap', 'f8'), ('eta', 'f8'), ('theta', 'f8'), ('phi', 'f8'), ('prodx', 'f8'), ('prody', 'f8'), ('prodz', 'f8'), ('prodt', 'f8'), ('pdgid', 'i4'), ('status', 'i4')])

Also see pyjet <https://github.com/scikit-hep/pyjet>_ for jet clustering.

Tutorial

Setting PYTHIA

PYTHIA settings can be passed in one of three ways: through the `**kwargs` arguments of the constructor `Pythia(..., **kwargs)`:

.. code-block:: python

   >>> pythia = Pythia(..., Beams_eCM=13000.)

Or as a dictionary:

.. code-block:: python

   >>> pythia = Pythia(..., params={'Beams:eCM':  13000.})

Or via a Python command file:

.. code-block:: python

   >>> pythia = Pythia(config='path/to/config.cmd')

The full list of settings can be found on the  `PYTHIA homepage <http://home.thep.lu.se/Pythia/>`_.

Note that the ":" in settings names is replaced by a "_"  if using `kwargs`.
`kwargs` take precedence over `params` and they both take precedence over `config`.
Example config files can be found under the `numpythia.testcmnd` directory.

Generate events

To generate events do

.. code-block:: python

events = pythia(events=100) events <generator at 0x10cf06f78>

where events is a generator of GenEvent containing all the generated particles.

Generated particles can be accessed through the all, first and last methods which have two optional arguments selection and return_hepmc. Selection is a filter or a combination of filters with bitwise operations (as shown in the getting started example) applied on the particles in the event. The available filters are

.. code-block:: python

STATUS, PDG_ID, ABS_PDG_ID, HAS_END_VERTEX, HAS_PRODUCTION_VERTEX,
HAS_SAME_PDG_ID_DAUGHTER, IS_STABLE, IS_BEAM

return_hepmc is by default set to False when using all:

.. code-block:: python

for e in events: array = e.all(selection)

returns an array of particles, with the dtype described above. return_hepmc is by default set to True for first and last:

.. code-block:: python

>>> for e in events:
>>>     gen_part_f = e.first(selection)
>>>     gen_part_l = e.last(selection)

returns a GenParticle.

Generated particle

``GenParticle`` is the numpythia interface of
`HepMC::GenParticle <http://lcgapp.cern.ch/project/simu/HepMC/205/html/classHepMC_1_1GenParticle.html>`_,
and has the following attributes

.. code-block:: python

    pid, status, e, px, py, pz, pt, eta, phi, mass, theta, rap

``GenParticle`` also has the following methods ``parents``, ``children``, ``ancestors``,
``descendants`` and ``siblings`` both with the two optional arguments ``selection``
and ``return_hepmc`` described before. For instance:

.. code-block:: python

    >>> for e in events:
    >>>     w = e.last((ABS_PDG_ID == 24) & HAS_END_VERTEX))
    >>>     w.children()
    array([(240.60708981, 115.76101664, 126.16766767, -169.03439984, 171.22760682, 0.5, -0.87228439, -0.87228739, 2.34974894, 0.82838703, 0., 0., 0., 0.,  3, 23),
       ( 52.59241372,   9.21296404,  50.77873929,  -10.01763001,  51.60774235, 1.5, -0.19283178, -0.19291222, 1.76252302, 1.39131523, 0., 0., 0., 0., -4, 23)],
      dtype=[('E', '<f8'), ('px', '<f8'), ('py', '<f8'), ('pz', '<f8'), ('pT', '<f8'), ('mass', '<f8'), ('rap', '<f8'), ('eta', '<f8'), ('theta', '<f8'), ('phi', '<f8'), ('prodx', '<f8'), ('prody', '<f8'), ('prodz', '<f8'), ('prodt', '<f8'), ('pdgid', '<i4'), ('status', '<i4')])