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    "info": {
        "author": "Philipp Jund",
        "author_email": "jundp@cs.uni-freiburg.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
        ],
        "description": "# Tensorflow Quaternion\nAn implementation of quaternions for tensorflow. Fully differentiable.\n\nThe tfquaternion module provides an implementation of quaternions as a\ntensorflow graph.\n`tfquaternion` offers module functions for the basic quaternion arithmetic\noperations as well as a `Quaternion` class which supports the relevant magic\nmethods. This is similar to the tensorflow API, e.g. `tfq.quaternion_multiply`\nvs. `tf.multiply` and `tfq.Quaternion` vs `tf.Tensor`. Note that all functions\nstarting with `tf.quaternion_...` assume that it's arguments are `tf.Tensor`s\n(or `tfq.Quaternion`s) that can be casted to `tfq.Quaternion`, i.e. the shape\nmust be (..., 4).\n\nThis implementation is mostly compatible with a small subset of\n[moble's quaternion implementation](https://github.com/moble/quaternion/)\n(ensured by using slightly adapted versions of his tests). HOwever, there are\nat least two major differences: First, tfquaternion is type specific as is\ntensorflow, i.e. two quaternions of different dtypes can not be multiplied.\nSecond, tfquaternion supports operations on arrays of quaternions.\n\n### Installation\nYou can either use pypi\n```\npip install tfquaternion\n```\nor install the latest version from git as development package:\n```\ngit clone https://github.com/PhilJd/tf-quaternion.git\ncd tf-quaternion\npip install -e .\n```\nThe -e option only links the working copy to the python site-packages,\nso to upgrade, you only need to run `git pull`.\n\n\n### Usage\n\nBefore getting started, an important note on the division:\nThis library resembles the division behaviour of\n[moble's quaternion](https://github.com/moble/quaternion/). While in\ngeneral the division operator is not defined (from the notation q1/q2 one can\nnot conclude if q1/q2 = q1 * q2^-1 or q1/q2 = q2^-1 * q1), we follow moble's\nimplementation, i.e.  `tfq.quaternion_divide` and `Quaternion.__truediv__`\ncompute `q1/q2 = q1 * 1/q2`.\n\n\n#### Example\nA simple rotation by a quaternion can look like this:\n```\n>>> import tfquaternion as tfq\n>>> import tensorflow as tf\n>>> s = tf.Session()\n>>> points = tf.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=tf.float32)\n>>> quat = tfq.Quaternion([0, 1, 0, 0])  rotate by 180 degrees around x axis\n>>> s.run(tf.matmul(quat.as_rotation_matrix(), points))\narray([[ 1.,  0.,  0.], [ 0., -1.,  0.], [ 0.,  0., -1.]], dtype=float32)\n```\n\n#### API\n\n##### class Quaternion\nThe usage of the `*`-Operator depends on the multiplier. If the multiplier is a\nQuaternion, quaternion multiplication is performed while multiplication with\na tf.Tensor uses tf.multiply. The behaviour of division is similar, except if\nthe dividend is a scalar, then the inverse of the quaternion is computed.\n```\ntfq.Quaternion([1, 0, 0, 0]) * tfq.Quaternion([0, 4, 0, 0])\n>>> tfq.Quaternion([0, 4, 0, 0)\ntfq.Quaternion([1, 0, 0, 0]) * tf.Tensor([0, 4, 0, 0])\n>>> tf.Quaternion([0, 0, 0, 0)\n```\n\n\n",
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        "keywords": "quaternion tensorflow differentiable",
        "license": "Apache 2.0",
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        "name": "tfquaternion",
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