Metadata-Version: 2.1
Name: awkwardql
Version: 0.0.1.dev1
Summary: SQL-like language for awkward arrays
Home-page: https://github.com/lgray/AwkwardQL
Author: Lindsey Gray (Fermilab), Jim Pivarski (Princeton)
Author-email: lagray@fnal.gov
Maintainer: Lindsey Gray (Fermilab)
Maintainer-email: lagray@fnal.gov
License: BSD 3-clause
Download-URL: https://github.com/lgray/AwkwardQL/releases
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Dist: lark-parser (>=0.7.8)
Requires-Dist: awkward1 (>=0.1.36)
Requires-Dist: numba (>=0.43.1)
Requires-Dist: numpy (>=1.16.0)
Requires-Dist: matplotlib

.. inclusion-marker-1-5-do-not-remove

This is derived from and inspired by the `demo <https://github.com/jpivarski/PartiQL>`_ by Jim Pivarski (PartiQL) and targets awkward 1.0.

We will start out focusing on particle physics uses cases and see how far it goes from there. :-)

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Installation
============

Install coffea like any other Python package:

.. code-block:: bash

    pip install awkwardql

or similar (use ``sudo``, ``--user``, ``virtualenv``, or pip-in-conda if you wish).

Strict dependencies
===================

- `Python <http://docs.python-guide.org/en/latest/starting/installation/>`__ (3.6+)

The following are installed automatically when you install coffea with pip:

- `awkward1 <https://github.com/scikit-hep/awkward-array>`__ to manipulate complex-structured columnar data, such as jagged arrays;
- `numba <https://numba.pydata.org/>`__ just-in-time compilation of python functions;
- `lark <https://lark-parser.readthedocs.io/en/latest/>`__ a modern and well featured parser/lexer/interpreter engine;
- `numpy <https://scipy.org/install.html>`__ for flat array manipulation;
- `matplotlib <https://matplotlib.org/>`__ as a plotting backend;

