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        "author": "Christoph Gohlke",
        "author_email": "cgohlke@uci.edu",
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            "Development Status :: 7 - Inactive",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: C",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "Homogeneous Transformation Matrices and Quaternions\r\n===================================================\r\n\r\nTransformations is a Python library for calculating 4x4 matrices for\r\ntranslating, rotating, reflecting, scaling, shearing, projecting,\r\northogonalizing, and superimposing arrays of 3D homogeneous coordinates\r\nas well as for converting between rotation matrices, Euler angles,\r\nand quaternions. Also includes an Arcball control object and\r\nfunctions to decompose transformation matrices.\r\n\r\n:Author:\r\n  `Christoph Gohlke <https://www.lfd.uci.edu/~gohlke/>`_\r\n\r\n:Organization:\r\n  Laboratory for Fluorescence Dynamics. University of California, Irvine\r\n\r\n:License: 3-clause BSD\r\n\r\n:Version: 2019.4.22\r\n\r\nRequirements\r\n------------\r\n* `CPython 2.7 or 3.5+ <https://www.python.org>`_\r\n* `Numpy 1.11.3 <https://www.numpy.org>`_\r\n* A Python distutils compatible C compiler  (build)\r\n\r\nRevisions\r\n---------\r\n2019.4.22\r\n    Fix setup requirements.\r\n2019.1.1\r\n    Update copyright year.\r\n\r\nNotes\r\n-----\r\nTransformations.py is no longer actively developed and has a few known issues\r\nand numerical instabilities. The module is mostly superseded by other modules\r\nfor 3D transformations and quaternions:\r\n\r\n* `Scipy.spatial.transform <https://github.com/scipy/scipy/tree/master/\r\n  scipy/spatial/transform>`_\r\n* `Transforms3d <https://github.com/matthew-brett/transforms3d>`_\r\n  (includes most code of this module)\r\n* `Numpy-quaternion <https://github.com/moble/quaternion>`_\r\n* `Blender.mathutils <https://docs.blender.org/api/master/mathutils.html>`_\r\n\r\nThe API is not stable yet and is expected to change between revisions.\r\n\r\nPython 2.7 and 3.4 are deprecated.\r\n\r\nThis Python code is not optimized for speed. Refer to the transformations.c\r\nmodule for a faster implementation of some functions.\r\n\r\nDocumentation in HTML format can be generated with epydoc.\r\n\r\nMatrices (M) can be inverted using numpy.linalg.inv(M), be concatenated using\r\nnumpy.dot(M0, M1), or transform homogeneous coordinate arrays (v) using\r\nnumpy.dot(M, v) for shape (4, \\*) column vectors, respectively\r\nnumpy.dot(v, M.T) for shape (\\*, 4) row vectors (\"array of points\").\r\n\r\nThis module follows the \"column vectors on the right\" and \"row major storage\"\r\n(C contiguous) conventions. The translation components are in the right column\r\nof the transformation matrix, i.e. M[:3, 3].\r\nThe transpose of the transformation matrices may have to be used to interface\r\nwith other graphics systems, e.g. OpenGL's glMultMatrixd(). See also [16].\r\n\r\nCalculations are carried out with numpy.float64 precision.\r\n\r\nVector, point, quaternion, and matrix function arguments are expected to be\r\n\"array like\", i.e. tuple, list, or numpy arrays.\r\n\r\nReturn types are numpy arrays unless specified otherwise.\r\n\r\nAngles are in radians unless specified otherwise.\r\n\r\nQuaternions w+ix+jy+kz are represented as [w, x, y, z].\r\n\r\nA triple of Euler angles can be applied/interpreted in 24 ways, which can\r\nbe specified using a 4 character string or encoded 4-tuple:\r\n\r\n  *Axes 4-string*: e.g. 'sxyz' or 'ryxy'\r\n\r\n  - first character : rotations are applied to 's'tatic or 'r'otating frame\r\n  - remaining characters : successive rotation axis 'x', 'y', or 'z'\r\n\r\n  *Axes 4-tuple*: e.g. (0, 0, 0, 0) or (1, 1, 1, 1)\r\n\r\n  - inner axis: code of axis ('x':0, 'y':1, 'z':2) of rightmost matrix.\r\n  - parity : even (0) if inner axis 'x' is followed by 'y', 'y' is followed\r\n    by 'z', or 'z' is followed by 'x'. Otherwise odd (1).\r\n  - repetition : first and last axis are same (1) or different (0).\r\n  - frame : rotations are applied to static (0) or rotating (1) frame.\r\n\r\nReferences\r\n----------\r\n(1)  Matrices and transformations. Ronald Goldman.\r\n     In \"Graphics Gems I\", pp 472-475. Morgan Kaufmann, 1990.\r\n(2)  More matrices and transformations: shear and pseudo-perspective.\r\n     Ronald Goldman. In \"Graphics Gems II\", pp 320-323. Morgan Kaufmann, 1991.\r\n(3)  Decomposing a matrix into simple transformations. Spencer Thomas.\r\n     In \"Graphics Gems II\", pp 320-323. Morgan Kaufmann, 1991.\r\n(4)  Recovering the data from the transformation matrix. Ronald Goldman.\r\n     In \"Graphics Gems II\", pp 324-331. Morgan Kaufmann, 1991.\r\n(5)  Euler angle conversion. Ken Shoemake.\r\n     In \"Graphics Gems IV\", pp 222-229. Morgan Kaufmann, 1994.\r\n(6)  Arcball rotation control. Ken Shoemake.\r\n     In \"Graphics Gems IV\", pp 175-192. Morgan Kaufmann, 1994.\r\n(7)  Representing attitude: Euler angles, unit quaternions, and rotation\r\n     vectors. James Diebel. 2006.\r\n(8)  A discussion of the solution for the best rotation to relate two sets\r\n     of vectors. W Kabsch. Acta Cryst. 1978. A34, 827-828.\r\n(9)  Closed-form solution of absolute orientation using unit quaternions.\r\n     BKP Horn. J Opt Soc Am A. 1987. 4(4):629-642.\r\n(10) Quaternions. Ken Shoemake.\r\n     http://www.sfu.ca/~jwa3/cmpt461/files/quatut.pdf\r\n(11) From quaternion to matrix and back. JMP van Waveren. 2005.\r\n     http://www.intel.com/cd/ids/developer/asmo-na/eng/293748.htm\r\n(12) Uniform random rotations. Ken Shoemake.\r\n     In \"Graphics Gems III\", pp 124-132. Morgan Kaufmann, 1992.\r\n(13) Quaternion in molecular modeling. CFF Karney.\r\n     J Mol Graph Mod, 25(5):595-604\r\n(14) New method for extracting the quaternion from a rotation matrix.\r\n     Itzhack Y Bar-Itzhack, J Guid Contr Dynam. 2000. 23(6): 1085-1087.\r\n(15) Multiple View Geometry in Computer Vision. Hartley and Zissermann.\r\n     Cambridge University Press; 2nd Ed. 2004. Chapter 4, Algorithm 4.7, p 130.\r\n(16) Column Vectors vs. Row Vectors.\r\n     http://steve.hollasch.net/cgindex/math/matrix/column-vec.html\r\n\r\nExamples\r\n--------\r\n>>> alpha, beta, gamma = 0.123, -1.234, 2.345\r\n>>> origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]\r\n>>> I = identity_matrix()\r\n>>> Rx = rotation_matrix(alpha, xaxis)\r\n>>> Ry = rotation_matrix(beta, yaxis)\r\n>>> Rz = rotation_matrix(gamma, zaxis)\r\n>>> R = concatenate_matrices(Rx, Ry, Rz)\r\n>>> euler = euler_from_matrix(R, 'rxyz')\r\n>>> numpy.allclose([alpha, beta, gamma], euler)\r\nTrue\r\n>>> Re = euler_matrix(alpha, beta, gamma, 'rxyz')\r\n>>> is_same_transform(R, Re)\r\nTrue\r\n>>> al, be, ga = euler_from_matrix(Re, 'rxyz')\r\n>>> is_same_transform(Re, euler_matrix(al, be, ga, 'rxyz'))\r\nTrue\r\n>>> qx = quaternion_about_axis(alpha, xaxis)\r\n>>> qy = quaternion_about_axis(beta, yaxis)\r\n>>> qz = quaternion_about_axis(gamma, zaxis)\r\n>>> q = quaternion_multiply(qx, qy)\r\n>>> q = quaternion_multiply(q, qz)\r\n>>> Rq = quaternion_matrix(q)\r\n>>> is_same_transform(R, Rq)\r\nTrue\r\n>>> S = scale_matrix(1.23, origin)\r\n>>> T = translation_matrix([1, 2, 3])\r\n>>> Z = shear_matrix(beta, xaxis, origin, zaxis)\r\n>>> R = random_rotation_matrix(numpy.random.rand(3))\r\n>>> M = concatenate_matrices(T, R, Z, S)\r\n>>> scale, shear, angles, trans, persp = decompose_matrix(M)\r\n>>> numpy.allclose(scale, 1.23)\r\nTrue\r\n>>> numpy.allclose(trans, [1, 2, 3])\r\nTrue\r\n>>> numpy.allclose(shear, [0, math.tan(beta), 0])\r\nTrue\r\n>>> is_same_transform(R, euler_matrix(axes='sxyz', *angles))\r\nTrue\r\n>>> M1 = compose_matrix(scale, shear, angles, trans, persp)\r\n>>> is_same_transform(M, M1)\r\nTrue\r\n>>> v0, v1 = random_vector(3), random_vector(3)\r\n>>> M = rotation_matrix(angle_between_vectors(v0, v1), vector_product(v0, v1))\r\n>>> v2 = numpy.dot(v0, M[:3,:3].T)\r\n>>> numpy.allclose(unit_vector(v1), unit_vector(v2))\r\nTrue\r\n\r\n\r\n",
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