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        "author": "Eric Boxer",
        "author_email": "ecb2198@columbia.edu",
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            "License :: OSI Approved :: MIT License",
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        "description": "Inkrement\n=========\n\nInkrement provides tools for incremental data analysis. You might find it most useful when the questions you have can be answered from a subset of your data. The tools may also be useful in determining if a partial view of your data can be used to draw conclusions, ie are differences in your data by orders of magnitude or is your data fairly homogeneous.  \n\nTypical usage often looks like this:\n\n    #!usr/bin/env python\n\n\timport inkrement\n\n\timport numpy as np\n\n\tx = np.random.normal(0, 1, 1000)\n\ty = np.random.normal(0, 1, 1000)\n\n\tinkrement.plot_lag(x, y, 10, 53, 8)\n\nOutput looks like this:  \n\n![Example Plot](https://github.com/Ecboxer/inkrement/blob/master/docs/testplot.png)\n\nSummary\n=======\n\nMatplotlib wrapper for incremental data visualization in python\n\nRequirements\n============\n\nMatplotlib\n\n\n",
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