{
    "info": {
        "author": "Noel Dawe",
        "author_email": "noel@dawe.me",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: MacOS",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: C++",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "pyjet: the interface between FastJet and NumPy\n==============================================\n\n.. image:: https://img.shields.io/pypi/v/pyjet.svg\n   :target: https://pypi.python.org/pypi/pyjet\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1197493.svg\n   :target: https://doi.org/10.5281/zenodo.1197493\n\n.. image:: https://travis-ci.org/scikit-hep/pyjet.svg\n   :target: https://travis-ci.org/scikit-hep/pyjet\n\n.. image:: https://coveralls.io/repos/github/scikit-hep/pyjet/badge.svg?branch=master\n   :target: https://coveralls.io/github/scikit-hep/pyjet?branch=master\n\npyjet allows you to perform jet clustering with `FastJet <http://fastjet.fr/>`_\non `NumPy <http://www.numpy.org/>`_ arrays. By default pyjet only depends on\nNumPy and internally uses FastJet's standalone fjcore release. The interface\ncode is written in `Cython <http://cython.org/>`_ that then becomes compiled\nC++, so it's fast. Remember that if you use pyjet then you are using FastJet\nand should cite the papers listed `here <http://fastjet.fr/about.html>`_.\n\nStrict dependencies\n-------------------\n\n- `Python <http://docs.python-guide.org/en/latest/starting/installation/>`__ (2.7+, 3.4+)\n- `Numpy <https://scipy.org/install.html>`__\n\nGetting started\n---------------\n\npyjet provides the ``cluster()`` function that takes a NumPy array as input\nand returns a ``ClusterSequence`` from which you can access the jets:\n\n.. code-block:: python\n\n   from pyjet import cluster\n   from pyjet.testdata import get_event\n\n   vectors = get_event()\n   sequence = cluster(vectors, R=1.0, p=-1)\n   jets = sequence.inclusive_jets()  # list of PseudoJets\n   exclusivejets = sequence.exclusive_jets(3)  # Find the cluster history when there are 3 jets\n\nThe first four fields of the input array ``vectors`` must be either:\n\n.. code-block:: python\n\n   np.dtype([('pT', 'f8'), ('eta', 'f8'), ('phi', 'f8'), ('mass', 'f8')])\n\nor if ``cluster(..., ep=True)``:\n\n.. code-block:: python\n\n   np.dtype([('E', 'f8'), ('px', 'f8'), ('py', 'f8'), ('pz', 'f8')])\n\nNote that the field names of the input array need not match 'pT', 'eta', 'phi',\n'mass' etc. pyjet only assumes that the first four fields are those quantities.\nThis array may also have additional fields of any type. Additional fields will\nthen become attributes of the ``PseudoJet`` objects.\n\nSee the `examples <https://github.com/scikit-hep/pyjet/tree/master/examples>`_ to\nget started:\n\n.. image:: https://github.com/scikit-hep/pyjet/raw/master/examples/jet_areas.png\n\n\nStandalone Installation\n-----------------------\n\nTo simply use the built-in FastJet source::\n\n   pip install --user pyjet\n\nAnd you're good to go!\n\nGet example.py and run it::\n\n\tcurl -O https://raw.githubusercontent.com/scikit-hep/pyjet/master/examples/example.py\n\tpython example.py\n\tjet#          pT        eta        phi       mass  #constit.\n\t1        983.280     -0.868      2.905     36.457         34\n\t2        901.745      0.221     -0.252     51.850         34\n\t3         67.994     -1.194     -0.200     11.984         32\n\t4         12.465      0.433      0.673      5.461         13\n\t5          6.568     -2.629      1.133      2.099          9\n\t6          6.498     -1.828     -2.248      3.309          6\n\n\tThe 6th jet has the following constituents:\n\tPseudoJet(pt=0.096, eta=-2.166, phi=-2.271, mass=0.000)\n\tPseudoJet(pt=2.200, eta=-1.747, phi=-1.972, mass=0.140)\n\tPseudoJet(pt=1.713, eta=-2.037, phi=-2.469, mass=0.940)\n\tPseudoJet(pt=0.263, eta=-1.682, phi=-2.564, mass=0.140)\n\tPseudoJet(pt=1.478, eta=-1.738, phi=-2.343, mass=0.940)\n\tPseudoJet(pt=0.894, eta=-1.527, phi=-2.250, mass=0.140)\n\n\tGet the constituents as an array (pT, eta, phi, mass):\n\t[( 0.09551261, -2.16560157, -2.27109083,   4.89091390e-06)\n\t ( 2.19975694, -1.74672746, -1.97178728,   1.39570000e-01)\n\t ( 1.71301882, -2.03656511, -2.46861524,   9.39570000e-01)\n\t ( 0.26339374, -1.68243005, -2.56397904,   1.39570000e-01)\n\t ( 1.47781519, -1.7378898 , -2.34304346,   9.39570000e-01)\n\t ( 0.89353864, -1.52729244, -2.24973202,   1.39570000e-01)]\n\n\tor (E, px, py, pz):\n\t[( 0.42190436, -0.06155242, -0.07303395, -0.41095089)\n\t ( 6.50193926, -0.85863306, -2.02526044, -6.11692764)\n\t ( 6.74203628, -1.33952806, -1.06775374, -6.45273802)\n\t ( 0.74600384, -0.22066287, -0.1438199 , -0.68386087)\n\t ( 4.43164941, -1.0311407 , -1.05862485, -4.07096881)\n\t ( 2.15920027, -0.56111108, -0.69538886, -1.96067711)]\n\n    Reclustering the constituents of the hardest jet with the kt algorithm\n    [PseudoJet(pt=983.280, eta=-0.868, phi=2.905, mass=36.457)]\n\n    Go back in the clustering sequence to when there were two jets\n    PseudoJet(pt=946.493, eta=-0.870, phi=2.908, mass=20.117)\n    PseudoJet(pt=36.921, eta=-0.800, phi=2.821, mass=4.119)\n\n    Ask how many jets there are with a given dcut\n    There are 9 jets with a dcut of 0.5\n\n    Get the jets with the given dcut\n    1 PseudoJet(pt=308.478, eta=-0.865, phi=2.908, mass=2.119)\n    2 PseudoJet(pt=256.731, eta=-0.868, phi=2.906, mass=0.140)\n    3 PseudoJet(pt=142.326, eta=-0.886, phi=2.912, mass=0.829)\n    4 PseudoJet(pt=135.971, eta=-0.870, phi=2.910, mass=0.140)\n    5 PseudoJet(pt=91.084, eta=-0.864, phi=2.899, mass=1.530)\n    6 PseudoJet(pt=30.970, eta=-0.831, phi=2.822, mass=2.124)\n    7 PseudoJet(pt=7.123, eta=-0.954, phi=2.939, mass=1.017)\n    8 PseudoJet(pt=5.951, eta=-0.626, phi=2.818, mass=0.748)\n    9 PseudoJet(pt=4.829, eta=-0.812, phi=3.037, mass=0.384)\n\n\nUsing an External FastJet Installation\n---------------------------------------\n\nTo take advantage of the full FastJet library and optimized O(NlnN) kt and\nanti-kt algorithms you can first build and install FastJet and then install\npyjet with the ``--external-fastjet`` flag. Before building FastJet you will\nneed to install `CGAL <http://www.cgal.org/>`_ and `GMP\n<https://gmplib.org/>`_.\n\nOn a Debian-based system (Ubuntu)::\n\n   sudo apt-get install libcgal-dev libcgal11v5 libgmp-dev libgmp10\n\nOn an RPM-based system (Fedora)::\n\n   sudo dnf install gmp.x86_64 gmp-devel.x86_64 CGAL.x86_64 CGAL-devel.x86_64\n\nOn Mac OS::\n\n   brew install cgal gmp wget\n\nThen run pyjet's ``install-fastjet.sh`` script::\n\n   curl -O https://raw.githubusercontent.com/scikit-hep/pyjet/master/install-fastjet.sh\n   chmod +x install-fastjet.sh\n   sudo ./install-fastjet.sh\n\nNow install pyjet like::\n\n   pip install --user pyjet --install-option=\"--external-fastjet\"\n\npyjet will now use the external FastJet installation on your system.\n\n\nNote on units\n-------------\n\nThe package is indifferent to particular units, which are merely \"propagated\"\nthrough the code. 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