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        "author": "Thomas Oberbichler",
        "author_email": "thomas.oberbichler@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
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        "description": "![HyperJet](https://github.com/oberbichler/HyperJet/raw/main/docs/HyperJet.png?raw=true)\n\n<p align=\"center\"><b>HyperJet \u2014 Algorithmic Differentiation with Hyper-Dual numbers for Python and C++</b></p>\n\n---\n\nA header-only library for algorithmic differentiation with hyper-dual numbers. Written in C++17 with an extensive Python interface.\n\n[![PyPI](https://img.shields.io/pypi/v/hyperjet)](https://pypi.org/project/hyperjet) [![DOI](https://zenodo.org/badge/165487832.svg)](https://zenodo.org/badge/latestdoi/165487832) [![Build Status](https://github.com/oberbichler/HyperJet/workflows/Python%20package/badge.svg?branch=master)](https://github.com/oberbichler/HyperJet/actions) ![PyPI - License](https://img.shields.io/pypi/l/hyperjet) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hyperjet) ![PyPI - Format](https://img.shields.io/pypi/format/hyperjet)\n\n## Installation\n\n```\npip install hyperjet\n```\n\n## Quickstart\n\nImport the module:\n\n```python\nimport hyperjet as hj\n```\n\nCreate a set of variables e.g. `x=3` and `y=6`:\n\n```python\nx, y = hj.variables([3, 6])\n```\n\n`x` and `y` are hyper-dual numbers. This is indicated by the postfix `hj`:\n\n```python\nx\n>>> 3hj\n```\n\nGet the value as a simple `float`:\n\n```python\nx.f\n>>> 3\n```\n\nThe hyper-dual number stores the derivatives as a numpy array.\n\nGet the first order derivatives (Gradient) of a hyper-dual number:\n\n```python\nx.g  # = [dx/dx, dx/dy]\n>>> array([1., 0.])\n```\n\nGet the second order derivatives (Hessian matrix):\n\n```python\nx.hm()  # = [[d^2 x/ dx**2 , d^2 x/(dx*dy)],\n        #    [d^2 x/(dx*dy), d^2 x/ dy**2 ]]\n>>> array([[0., 0.],\n           [0., 0.]])\n```\n\nFor a simple variable these derivatives are trivial.\n\nNow do some computations:\n\n```python\nf = (x * y) / (x - y)\nf\n>>> -6hj\n```\n\nThe result is again a hyper-dual number.\n\nGet the first order derivatives of `f` with respect to `x` and `y`:\n\n```python\nf.g  # = [df/dx, df/dy]\n>>> array([-4.,  1.])\n```\n\nGet the second order derivatives of `f`:\n\n```python\nf.hm()  # = [[d^2 f/ dx**2 , d^2 f/(dx*dy)],\n        #    [d^2 f/(dx*dy), d^2 f/ dy**2 ]]\n>>> array([[-2.66666667,  1.33333333],\n           [ 1.33333333, -0.66666667]])\n```\n\nYou can use numpy to perform vector and matrix operations.\n\nCompute the nomalized cross product of two vectors `u = [1, 2, 2]` and `v = [4, 1, -1]` with hyper-dual numbers:\n\n```python\nimport numpy as np\n\nvariables = hj.DDScalar.variables([1, 2,  2,\n                                   4, 1, -1])\n\nu = np.array(variables[:3])  # = [1hj, 2hj,  2hj]\nv = np.array(variables[3:])  # = [4hj, 1hj, -1hj]\n\nnormal = np.cross(u, v)\nnormal /= np.linalg.norm(normal)\nnormal\n>>> array([-0.331042hj, 0.744845hj, -0.579324hj], dtype=object)\n```\n\nThe result is a three-dimensional numpy array containing hyper-dual numbers.\n\nGet the value and derivatives of the x-component:\n\n```python\nnormal[0].f\n>>> -0.3310423554409472\n\nnormal[0].g\n>>> array([ 0.00453483, -0.01020336,  0.00793595,  0.07255723, -0.16325376, 0.12697515])\n\nnormal[0].hm()\n>>> array([[ 0.00434846, -0.01091775,  0.00647611, -0.0029818 , -0.01143025, -0.02335746],\n           [-0.01091775,  0.02711578, -0.01655522,  0.00444165,  0.03081974,  0.04858632],\n           [ 0.00647611, -0.01655522,  0.0093492 , -0.00295074, -0.02510461, -0.03690759],\n           [-0.0029818 ,  0.00444165, -0.00295074, -0.02956956,  0.03025289, -0.01546811],\n           [-0.01143025,  0.03081974, -0.02510461,  0.03025289,  0.01355789, -0.02868433],\n           [-0.02335746,  0.04858632, -0.03690759, -0.01546811, -0.02868433,  0.03641839]])\n```\n\n## Reference\n\nIf you use HyperJet, please refer to the official GitHub repository:\n\n```bibtex\n@misc{HyperJet,\n  author = \"Thomas Oberbichler\",\n  title = \"HyperJet\",\n  howpublished = \"\\url{http://github.com/oberbichler/HyperJet}\",\n}\n```\n\n\n",
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