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        "description": "## Tools for numerical math calculations\n\n[![Build Status](https://travis-ci.com/Bellator95/scikit-numerical.svg?branch=master)](https://travis-ci.com/Bellator95/scikit-numerical)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e599de8c6de048ef8351811388c63632)](https://www.codacy.com/app/maksym.shpakovych/numerical?utm_source=github.com&utm_medium=referral&utm_content=Bellator95/scikit-numerical&utm_campaign=Badge_Grade)\n[![Codacy Badge](https://api.codacy.com/project/badge/Coverage/e599de8c6de048ef8351811388c63632)](https://www.codacy.com/app/maksym.shpakovych/numerical?utm_source=github.com&utm_medium=referral&utm_content=Bellator95/scikit-numerical&utm_campaign=Badge_Coverage)\n\nThis repository contains tools for math numerical computation such as numerical integration and interpolation. The current implementation contains:\n-   numerical integration using Gauss formula\n\n    ```python\n    import numpy as np\n    from numerical.integration import gauss\n\n    def f(x):\n        return np.power(x[0], 2)\n\n    gauss.integrate(f, 0., 1.)  # 0.3333333\n\n    ```\n\n\n-   spline functions and theirs derivatives\n\n    ```python\n    import numpy as np\n    from numerical import splines\n    import matplotlib.pyplot as plt\n\n    x = np.arange(0, 4., 0.05)\n    y = splines.schoenberg(x)\n    yd1 = splines.schoenberg.deriv(x, order=1)  # first derivative\n    yd2 = splines.schoenberg.deriv(x, order=2)  # second derivative\n    # visualize results\n    plt.plot(x, y)\n    plt.plot(x, yd1)\n    plt.plot(x, yd2)\n    plt.show()\n\n    ```\n\n![spline_derivs](https://github.com/Bellator95/scikit-numerical/blob/master/images/shenberg_spline_derivatives.png)\n\n\n-   function interpolation\n\n    ```python\n    import numpy as np\n    from numerical import interpolate\n    import matplotlib.pyplot as plt\n\n    def fun(x):\n        return 1 - np.power(x[0] - 0.5, 2)\n\n    grid = np.array([np.arange(0, 1.0001, 0.25)])\n    values = fun(grid)\n    itp_fun = interpolate(values, grid)\n\n    x = np.arange(0., 1.00001, 0.001).reshape(1, -1)\n    y_intp = itp_fun(x)\n    y_true = fun(x)\n\n    plt.plot(x[0], y_intp)\n    plt.plot(x[0], y_true)\n    plt.show()\n    ```\n\n![linear_interpolation](https://github.com/Bellator95/scikit-numerical/blob/master/images/linear_interpolation.png)\n\n\n",
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