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    "info": {
        "author": "Andrew Hynes",
        "author_email": "andrewjhynes@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": "\n.. figure:: images/logo.svg\n         :align: left\n         :width: 70%\n\n.. image:: https://img.shields.io/pypi/v/scikit-spatial.svg\n         :target: https://pypi.python.org/pypi/scikit-spatial\n\n.. image:: https://img.shields.io/pypi/pyversions/scikit-spatial.svg\n         :target: https://pypi.python.org/pypi/scikit-spatial\n\n.. image:: https://img.shields.io/travis/ajhynes7/scikit-spatial.svg\n         :target: https://travis-ci.org/ajhynes7/scikit-spatial\n\n.. image:: https://readthedocs.org/projects/scikit-spatial/badge/?version=latest\n         :target: https://scikit-spatial.readthedocs.io/en/latest/?badge=latest\n         :alt: Documentation Status\n\n.. image:: https://pyup.io/repos/github/ajhynes7/scikit-spatial/shield.svg\n         :target: https://pyup.io/account/repos/github/ajhynes7/scikit-spatial/\n\n.. image:: https://codecov.io/gh/ajhynes7/scikit-spatial/branch/master/graph/badge.svg\n         :target: https://codecov.io/gh/ajhynes7/scikit-spatial\n\n| \n\nIntroduction\n------------\n\nThis package provides spatial objects based on NumPy arrays, as well as computations using these objects. The package includes computations for 2D, 3D, and higher-dimensional space.\n\nThe following spatial objects are provided:\n\n   - Point\n   - Points\n   - Vector\n   - Line\n   - Plane\n   - Circle\n   - Sphere\n\nThe computations can be grouped into the following main categories:\n\n   - Measurement\n   - Comparison\n   - Projection\n   - Intersection\n   - Fitting\n   - Transformation\n\nAll spatial objects are equipped with plotting methods based on ``matplotlib``. Both 2D and 3D plotting are supported. Spatial computations can be easily visualized by plotting multiple objects at once.\n\n\nWhy this instead of ``scipy.spatial`` or ``sympy.geometry``?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThis package has little to no overlap with the functionality of ``scipy.spatial``. It can be viewed as an object-oriented extension.\n\nWhile similar spatial objects and computations exist in the ``sympy.geometry`` module, ``scikit-spatial`` is based on NumPy rather than symbolic math. The primary objects of ``scikit-spatial`` (``Point``, ``Points``, and ``Vector``) are actually subclasses of the NumPy *ndarray*. This gives them all the regular functionality of the *ndarray*, plus additional methods from this package.\n\n>>> from skspatial.objects import Vector\n\n>>> vector = Vector([2, 0, 0])\n\nBehaviour inherited from NumPy:\n\n>>> vector.size\n3\n>>> vector.mean().round(3)\n0.667\n\nAdditional methods from ``scikit-spatial``:\n\n>>> vector.norm()\n2.0\n>>> vector.unit()\nVector([1., 0., 0.])\n\n``Point`` and ``Vector`` are based on a 1D NumPy array, and ``Points`` is based on a 2D NumPy array, where each row represents a point in space.  The ``Line`` and ``Plane`` objects have ``Point`` and ``Vector`` objects as attributes. \n\nBecause the computations of ``scikit-spatial`` are also based on NumPy, keyword arguments can be passed to NumPy functions. For example, a tolerance can be specified while testing for collinearity. The ``tol`` keyword is passed to ``numpy.linalg.matrix_rank``.\n\n>>> from skspatial.objects import Points\n\n>>> points = Points([[1, 2, 3], [4, 5, 6], [7, 8, 8]])\n\n>>> points.are_collinear()\nFalse\n>>> points.are_collinear(tol=1)\nTrue\n\n\n\nInstallation\n------------\n\nThe package can be installed via pip.\n\n.. code-block:: bash\n\n   $ pip install scikit-spatial\n\n\n\nExample Usage\n-------------\n\nMeasurement\n~~~~~~~~~~~\n\nMeasure the cosine similarity between two vectors.\n\n>>> from skspatial.objects import Vector\n\n>>> Vector([1, 0]).cosine_similarity([1, 1]).round(3)\n0.707\n\n\nComparison\n~~~~~~~~~~\n\nCheck if multiple points are collinear.\n\n>>> from skspatial.objects import Points\n\n>>> points = Points([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n\n>>> points.are_collinear()\nTrue\n\n\nProjection\n~~~~~~~~~~\n\nProject a point onto a line.\n\n>>> from skspatial.objects import Line\n\n>>> line = Line(point=[0, 0, 0], direction=[1, 1, 0])\n\n>>> line.project_point([5, 6, 7])\nPoint([5.5, 5.5, 0. ])\n\n\nAn error is raised if the computation is undefined.\n\n>>> line_a = Line([0, 0], [1, 0])\n>>> line_b = Line([1, 0], [1, 0])\n\n>>> line_a.intersect_line(line_b)\nTraceback (most recent call last):\n...\nValueError: The lines must not be parallel.\n\n\nIntersection\n~~~~~~~~~~~~\n\nFind the intersection of two planes.\n\n>>> from skspatial.objects import Plane\n\n>>> plane_a = Plane([0, 0, 0], [0, 0, 1])\n>>> plane_b = Plane([5, 16, -94], [1, 0, 0])\n\n>>> plane_a.intersect_plane(plane_b)\nLine(point=Point([5., 0., 0.]), direction=Vector([0, 1, 0]))\n\n\nFitting\n~~~~~~~\n\nFind the plane of best fit for multiple points.\n\n>>> points = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]]\n\n>>> Plane.best_fit(points)\nPlane(point=Point([0.5, 0.5, 0. ]), normal=Vector([0., 0., 1.]))\n\n\nTransformation\n~~~~~~~~~~~~~~\n\nTransform multiple points to 1D coordinates along a line.\n\n>>> line = Line(point=[0, 0, 0], direction=[1, 2, 0])\n>>> points = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n\n>>> line.transform_points(points).round(3)\narray([ 2.236,  6.261, 10.286])\n\n\nAcknowledgment\n--------------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n",
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