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"info": {
"author": "Ben Martineau",
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"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Natural Language :: English",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Physics"
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"description": "|Travis|_ |Coveralls|_ |pypi_version|_ |doi|_\n\n.. |Travis| image:: https://travis-ci.org/pyxem/orix.svg?branch=master\n.. _Travis: https://travis-ci.org/pyxem/orix\n\n.. |Coveralls| image:: https://coveralls.io/repos/github/pyxem/orix/badge.svg?branch=master\n.. _Coveralls: https://coveralls.io/github/pyxem/orix?branch=master\n\n.. |pypi_version| image:: http://img.shields.io/pypi/v/orix.svg?style=flat\n.. _pypi_version: https://pypi.python.org/pypi/orix\n\n.. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3459662.svg\n.. _doi: https://doi.org/10.5281/zenodo.3459662\n\norix is an open-source python library for analysing orientations and crystal symmetry.\n\nThe package defines objects and functions for the analysis of orientations represented as quaternions or 3D rotation vectors accounting for crystal symmetry. Functionality buils primarily on top of `numpy `__ and\n`matplotlib `__ and heavily inspired by the `MATLAB `__ package `MTEX `__.\n\nIf analysis using orix forms a part of published work please cite the github repository.\n\norix (this version) is released under the GPL v3 license.\n\n\nGetting started\n---------------\n\nThe use of orix should feel familiar to the use of numpy, but rather\nthan cells of numbers, the cells contain single 3d objects, such as\nvectors or quaternions. They can all be created using tuples, lists,\nnumpy arrays, or other numpy-compatible iterables, and will raise an\nerror if constructed with the incorrect number of dimensions. Basic\nexamples are given below.\n\nVectors\n~~~~~~~\n\nVectors are 3d objects representing positions or directions with\n\"magnitude\". They can be added and subtracted with integers, floats, or\nother vectors (provided the data are of compatible shapes) and have\nseveral further unique operations.\n\n.. code:: python\n\n >>> import numpy as np\n >>> from orix.vector import Vector3d\n >>> v = Vector3d((1, 1, -1))\n >>> w_array = np.array([[[1, 0, 0], [0, 0, -1]], [[1, 1, 0], [-1, 0, -1]]])\n >>> w = Vector3d(w_array)\n >>> v + w\n # Vector3d (2, 2)\n # [[[ 2 1 -1]\n # [ 1 1 -2]]\n #\n # [[ 2 2 -1]\n # [ 0 1 -2]]]\n >>> v.dot(w)\n # array([[1, 1],\n # [2, 0]])\n >>> v.cross(w)\n # Vector3d (2, 2)\n # [[[ 0 -1 -1]\n # [-1 1 0]]\n #\n # [[ 1 -1 0]\n # [-1 2 1]]]\n >>> v.unit\n # Vector3d (1,)\n # [[ 0.5774 0.5774 -0.5774]]\n >>> w[0]\n # Vector3d (2,)\n # [[ 1 0 0]\n # [ 0 0 -1]]\n >>> w[:, 0]\n # Vector3d (2,)\n # [[1 0 0]\n # [1 1 0]]\n\n\nQuaternions\n~~~~~~~~~~~\n\nQuaternions are four-dimensional data structures. Unit quaternions are\noften used for representing rotations in 3d. Quaternion multiplication\nis defined and can be applied to either other quaternions or vectors.\n\n.. code:: python\n\n >>> from orix.quaternion.rotation import Rotation\n >>> p = Rotation([0.5, 0.5, 0.5, 0.5])\n >>> q = Rotation([0, 1, 0, 0])\n >>> p.axis\n # Vector3d (1,)\n # [[0.5774 0.5774 0.5774]]\n >>> p.angle\n # array([2.0943951])\n >>> p * q\n # Rotation (1,)\n # [[-0.5 0.5 0.5 -0.5]]\n >>> p * ~p # (unit rotation)\n # Rotation (1,)\n # [[1. 0. 0. 0.]]\n >>> p.to_euler() # (Euler angles in the Bunge convention)\n # array([[1.57079633, 1.57079633, 0. ]])\n\n\n",
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