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        "author": "Nico Schl\u00f6mer",
        "author_email": "nico.schloemer@gmail.com",
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            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# tspsolve\n\n[![CircleCI](https://img.shields.io/circleci/project/github/nschloe/tspsolve/master.svg)](https://circleci.com/gh/nschloe/tspsolve/tree/master)\n[![codecov](https://img.shields.io/codecov/c/github/nschloe/tspsolve.svg)](https://codecov.io/gh/nschloe/tspsolve)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n[![PyPi Version](https://img.shields.io/pypi/v/tspsolve.svg)](https://pypi.org/project/tspsolve)\n[![GitHub stars](https://img.shields.io/github/stars/nschloe/tspsolve.svg?logo=github&label=Stars)](https://github.com/nschloe/tspsolve)\n\nAlgorithms for the [traveling salesman problem\n(TSP)](https://en.wikipedia.org/wiki/Travelling_salesman_problem) in Python.\n\nImplemented so far:\n\n  * Nearest neighbor algorithm\n    ```python\n    import tspsolve\n\n    # Create matrix of distances d\n    path = tspsolve.nearest_neighbor(d)\n    ```\n\n  * [2-opt](https://en.wikipedia.org/wiki/2-opt) improvement\n    ```python\n    import tspsolve\n\n    # Create matrix of distances d and an initial path\n    new_path = tspsolve.two_opt(d, path, verbose=True)\n    ```\n\nFor [Euclidiean\nTSP](https://en.wikipedia.org/wiki/Travelling_salesman_problem#Euclidean_TSP), the\ndistance matrix can be computed efficiently with\n```python\ndx = numpy.subtract.outer(x, x)\ndy = numpy.subtract.outer(y, y)\nd = numpy.sqrt(dx ** 2 + dy ** 2)\n```\n\n### Installation\n\ntspsolve is [available from the Python Package\nIndex](https://pypi.org/project/tspsolve/), so simply type\n```\npip install -U tspsolve\n```\nto install or upgrade.\n\n### Testing\n\nTo run the tspsolve unit tests, check out this repository and type\n```\npytest\n```\n\n### Distribution\n\nTo create a new release\n\n1. bump the `__version__` number,\n\n2. publish to PyPi and GitHub:\n    ```\n    make publish\n    ```\n\n### License\n\ntspsolve is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).\n\n\n",
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