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        "author": "Justin Shenk",
        "author_email": "shenkjustin@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Closely :triangular_ruler:\n [![PyPI version](https://badge.fury.io/py/closely.svg)](https://badge.fury.io/py/closely) [![Build Status](https://travis-ci.com/justinshenk/closely.svg?branch=master)](https://travis-ci.com/justinshenk/closely)\n\n\nFind the closest pairs in an array. \n\nClosely uses principal component analysis (PCA) to reduce the dimensions to 2 and a divide and conquer algorithm to find the closest pair of points.\n\n### Getting Started\n\n```bash\npip install closely\n```\n\nor install from source:\n```bash\ngit clone https://github.com/justinshenk/closely\ncd closely\npip install .\n```\n\n### How to use\n\n```python\nimport closely\n\n# X is an n x m numpy array\npairs, distances = closely.solve(X, n=1)\n```\n\nYou can specify how many pairs you want to identify with `n`.\n\n\n### Example\n```python\nimport closely\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Create dataset\nX = np.random.random((100,2))\npairs, distance = closely.solve(X, n=1)\n\n# Plot points\nz, y = np.split(X, 2, axis=1)\nfig, ax = plt.subplots()\nax.scatter(z, y) \n\nfor i, txt in enumerate(X): \n    if i in pairs: \n        ax.annotate(i, (z[i], y[i]), color='red') \n    else: \n        ax.annotate(i, (z[i], y[i]))\n\nplt.show() \n```\n\nCheck pairs:\n```ipython\nIn [10]: pairs                                                                                                                                \nOut[10]: \narray([[ 7, 16],\n       [96, 50]])\n\n```\n\nOutput:\n![example_plot](example_plot.png)\n\n\n### Caveats\nIf your data has more than 3 features, `closely` will reduce the dimensionality by projecting it onto two directions that explain most of the variance. This speeds up processing, but is not 100% precise. In other words, if your data has four columns (eg, x, y, z, a), it will apply divide-and-conquer on the new projection bases P1 and P2.\n\nIt also removes the first point in a pair if `n`>1. In rare cases this leads to false negatives if the data is highly overlapping.\n\n\n### Credit and Explanation\n\nPython code modified from [Andriy Lazorenko](https://medium.com/@andriylazorenko/closest-pair-of-points-in-python-79e2409fc0b2), packaged and made useful for >2 features by Justin Shenk.\n\n\n",
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        "keywords": "mathematics,geometry,closest pairs,similar data,duplicates",
        "license": "MIT",
        "maintainer": "Justin Shenk",
        "maintainer_email": "shenkjustin@gmail.com",
        "name": "closely",
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