PyMois Applications for interactive visualization of numerical methods
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interactive Python library visualization numerical interactive visualization numerical methods

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Applications for interactive visualization of numerical methods The WhyBeing able to calculate something is not very interesting, but being able to visualize it makes the calcuations mean something. The primary goal of these utilities where to make sens of the different numerical methods and be able to play with differnt types of input data. Also I had some spare time on my hands.Application DescriptionschebychevThe function of the users choice (or from a set of presets) will be approximated using Chebchev Approximation. The user can chose the start and end range of the function, as wel as the number of points(in that range) that will be used to plot the function. The range of Chebychev series can also be set.integralShows differenty types of algorithms used to integrate functions. The fucntion and expected result are user configurable, as well as the range of the integration and number of subdivition. The first algorithm is an implementation of the simplest method used to integrate function(calculates the area of a trapeze) while the other method uses GSL's QAP algorithm.The resutls from the calculation can be displayed on the plot. There are 3 types of plots aviablable. The first displays the error rate in corrolation to the number of subdivisions used. The second displays the corrolation between adjecent errors. The final plot displays the actual result per number of subdivions.There is also a table displaying the number of subdivision needed to achive an error less than 1E-3, 1E-4, 1E-5 and 1E-6. All raw data used for plotting is also available.interpolationEnables a user to see the precision of different interpolation methods implemented in GSL. The user can input his own custom function, as well as describe the start and end range and the number of points that the interpolation methods will use. The follwing GSL interpolation methods are available: akima, akima_periodic, cspline, cspline_periodic, polynominaland linear. The user can chose any number of interpolation methods to be displayed on the plot. The input point are displayed as dots. All plot data is avialable to the user.lineareqShows the time relations beetween matrix decomposition(LU) time and the time required to solve the matrix. Each matrix is populated with random numbers. The start size and end size of the matracies can be specified as well as the step size.rootsShows different root finding methods and the error assosiated with each iteration of the convergance process. Both the function as well as the bracket range are configurable. The maximum number of iteration can also be specified. Data generated by the methods can be displayed and analyzed.There are a total of 6 methods avaialbe, false postion, bisection, brent, newton, newton-steffenson, scant.Methods which have exceded the maximum amount of iterations are discarded and not displayed. Requirements: · Python

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