Metadata-Version: 2.1
Name: scikit-numerical
Version: 0.0.9
Summary: Tools for numerical math calculations
Home-page: https://github.com/Bellator95/scikit-numerical
Author: Maksym Shpakovych
Author-email: maksym.shpakovych@gmail.com
License: UNKNOWN
Description: ## Tools for numerical math calculations
        
        [![Build Status](https://travis-ci.com/Bellator95/scikit-numerical.svg?branch=master)](https://travis-ci.com/Bellator95/scikit-numerical)
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        This repository contains tools for math numerical computation such as numerical integration and interpolation. The current implementation contains:
        -   numerical integration using Gauss formula
        
            ```python
            import numpy as np
            from numerical.integration import gauss
            
            def f(x):
                return np.power(x[0], 2)
            
            gauss.integrate(f, 0., 1.)  # 0.3333333
          
            ```
        
        
        -   spline functions and theirs derivatives
        
            ```python
            import numpy as np
            from numerical import splines
            import matplotlib.pyplot as plt
            
            x = np.arange(0, 4., 0.05)
            y = splines.schoenberg(x)
            yd1 = splines.schoenberg.deriv(x, order=1)  # first derivative
            yd2 = splines.schoenberg.deriv(x, order=2)  # second derivative
            # visualize results
            plt.plot(x, y)
            plt.plot(x, yd1)
            plt.plot(x, yd2)
            plt.show()
          
            ```
        
        ![spline_derivs](https://github.com/Bellator95/scikit-numerical/blob/master/images/shenberg_spline_derivatives.png)
        
        
        -   function interpolation
        
            ```python
            import numpy as np
            from numerical import interpolate
            import matplotlib.pyplot as plt
            
            def fun(x):
                return 1 - np.power(x[0] - 0.5, 2)
        
            grid = np.array([np.arange(0, 1.0001, 0.25)])
            values = fun(grid)
            itp_fun = interpolate(values, grid)
            
            x = np.arange(0., 1.00001, 0.001).reshape(1, -1)
            y_intp = itp_fun(x)
            y_true = fun(x)
        
            plt.plot(x[0], y_intp)
            plt.plot(x[0], y_true)
            plt.show()
            ```
            
        ![linear_interpolation](https://github.com/Bellator95/scikit-numerical/blob/master/images/linear_interpolation.png)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
