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        "author": "Alexander Butyaev",
        "author_email": "alexander.butyaev@mail.mcgill.ca",
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        "description": "KernSmooth for Python\n=====================\nPorting popular R library KernSmooth to python.\n\nFunctions for Kernel Smoothing and Density Estimation.\n\nTransformed R and Fortran functions into Python(2,3) code.\n\nInstallation:\n-------------\n\n.. code:: shell\n\n\t\tpip install kern-smooth\n\n\nMain function of the module:\n----------------------------\n\n.. code:: python\n        \n        def densCols(x, y=None, nbin=128, bandwidth=None)\n\n\nProduces a vector of numbers which encode the local densities at each point in dataset.\n\nx, y : 1D numpy array with coordinates of the points density will be estimated on\n\nnbin : [optional] int or [int, int] - number of bins along each axis\n    (in case of single value - [nbin, nbin] will be used). Default value 128.\n\nbandwidth : [optional] numeric vector (len of 1 or 2) of smoothing bandwidth.\n\nReturns: numpy array with numerical representation (in range [0,1]) of point densities.\n\nAttention: For return value numpy.nan values are allowed in case of nan / infinite values in original dataset \n\nSource: R::grDevices::densCols\n\n\nUsage\n-----\n\nGenerate data for plotting\n\n.. code:: python\n\n    from matplotlib import pyplot as plt\n    from matplotlib import cm\n    import numpy as np\n    np.random.seed(0)\n    # create two 'bulbs' with normal distributions\n    mean1 = [0, 0]\n    cov1 = [[5, 0], [0, 30]]  # diagonal covariance\n    x1, y1 = np.random.multivariate_normal(mean1, cov1, 50000).T\n    mean2 = [5, 17]\n    cov2 = [[30, 0], [0, 5]]  # diagonal covariance\n    x2, y2 = np.random.multivariate_normal(mean2, cov2, 50000).T\n    x = np.hstack([x1,x2])\n    y = np.hstack([y1,y2])\n\n\nGenerate point densities:\n\n.. code:: python\n\n    from kern_smooth import densCols\n    densities = densCols(x, y, nbin = 128)\n\n\nPlot the result\n\n.. code:: python\n\n    sc = plt.scatter(x, y, c=densities, s=15, edgecolors='none', alpha=0.75, cmap=cm.jet)\n    plt.colorbar(sc)\n    plt.show()\n\n\nResult\n------\n![Result](https://github.com/AlexanderButyaev/kern_smooth/blob/master/example_density.png) \n\nAuthor\n------\nAlexander Butyaev",
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        "keywords": "statistics,probability,KDE,PDF,kernel density estimation",
        "license": "MIT",
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        "name": "kern-smooth",
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        "summary": "A python implementation of KernSmooth package (https://cran.r-project.org/web/packages/KernSmooth):kernel smoothing and density estimation functions based on the book: Wand, M.P. and Jones, M.C. (1995) \"Kernel Smoothing\".",
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