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            "Operating System :: MacOS",
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            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
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        "description": "vectormath\n==========\n\n\n.. image:: https://img.shields.io/pypi/v/vectormath.svg\n    :target: https://pypi.org/project/vectormath\n    :alt: Latest PyPI version\n\n.. image:: https://img.shields.io/badge/license-MIT-blue.svg\n    :target: https://github.com/seequent/vectormath/blob/master/LICENSE\n    :alt: MIT license\n\n.. image:: https://api.travis-ci.org/seequent/vectormath.svg?branch=master\n    :target: https://travis-ci.org/seequent/vectormath\n    :alt: Travis CI build status\n\n.. image:: https://codecov.io/gh/seequent/vectormath/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/seequent/vectormath\n    :alt: Code test coverage\n\n\nVector math utilities for Python built on `NumPy <http://www.numpy.org/>`_\n\n\nWhy\n---\n\nThe :code:`vectormath` package provides a fast, simple library of vector math\nutilities by leveraging NumPy. This allows explicit\ngeometric constructs to be created (for example, :code:`Vector3` and :code:`Plane`)\nwithout redefining the underlying array math.\n\nScope\n-----\n\nThe :code:`vectormath` package includes :code:`Vector3`/:code:`Vector2` and\n:code:`Vector3Array`/:code:`Vector2Array`.\n\n\nGoals\n-----\n\n* Speed: All low-level operations rely on NumPy arrays. These are densely packed,\n  typed, and partially implemented in C. The :code:`VectorArray` classes in particular\n  take advantage of this speed by performing vector operations on all Vectors at\n  once, rather than in a loop.\n* Simplicty: High-level operations are explicit and straight-forward.\n  This library should be usable by Programmers, Mathematicians, and Geologists.\n\n\nAlternatives\n------------\n\n* `NumPy <http://www.numpy.org/>`_ can be used for any array operations\n* Many small libraries on PyPI (e.g. `vectors <https://github.com/allelos/vectors>`_)\n  implement vector math operations but are are only built with single vectors\n  in mind.\n\nConnections\n-----------\n\n* `properties <https://github.com/seequent/properties>`_ uses :code:`vectormath`\n  as the underlying framework for Vector properties.\n\nInstallation\n------------\n\nTo install the repository, ensure that you have\n`pip installed <https://pip.pypa.io/en/stable/installing/>`_ and run:\n\n.. code::\n\n    pip install vectormath\n\nFor the development version:\n\n.. code::\n\n    git clone https://github.com/seequent/vectormath.git\n    cd vectormath\n    pip install -e .\n\n\nExamples\n========\n\nThis example gives a brief demonstration of some of the notable features of\n:code:`Vector3` and :code:`Vector3Array`\n\n.. code:: python\n\n    import numpy as np\n    import vectormath as vmath\n\n    # Single Vectors\n    v = vmath.Vector3(5, 0, 0)\n    v.normalize()\n    print(v)                          # >> [1, 0, 0]\n    print(v.x)                        # >> 1.0\n\n    # VectorArrays are much faster than a for loop over Vectors\n    v_array = vmath.Vector3Array([[4, 0, 0], [0, 2, 0], [0, 0, 3]])\n    print(v_array.x)                  # >> [4, 0, 0]\n    print(v_array.length)             # >> [4, 2, 3]\n    print(v_array.normalize())        # >> [[1, 0, 0], [0, 1, 0], [0, 0, 1]]\n\n    # Vectors can be accessed individually or in slices\n    print(type(v_array[1:]))          # >> vectormath.Vector3Array\n    print(type(v_array[2]))           # >> vectormath.Vector3\n\n    # All these classes are just numpy arrays\n    print(isinstance(v, np.ndarray))  # >> True\n    print(type(v_array[1:, 1:]))      # >> numpy.ndarray\n\n\nCurrent version: v0.2.2\n",
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