Practical Numerical Computing Using Python

Description

Preface Chapter One Introduction to Computing Computer Hardware Supercomputers & Computation Complexity Computer Software Brief Overview of Python Anaconda Python, Notebooks, and Prutor Applications of Computers Chapter Two Integers Floating Point and Complex Numbers Python Variables and Logical Variables Chapter Three Character & Strings List Numpy Arrays Dictionary Mutable and Immutable Objects in Python Chapter Four Simple Statements Conditional Flows in Python Looping in Python Chapter Five Functions in Python Python Namespace and Scope Recursive Functions Chapter Six Good Progamming Practices Prime Numbers Searching and Sorting Chapter Seven Matplotlib & Field Plots Miscellaneous Plots Animation using Python Chapter Eight Reading & Writing Text Files in Python Reading & Writing Numerical Data in Python Chapter Nine Error Analysis Nondimensionalization of Equations Numerical Methods Chapter Ten Lagrange Interpolation Splines Chapter Eleven Newton-Cotes Formulas Gaussian Quadrature Python's Quad & Multidimensional Integrals Chapter Twelve Computing Numerical Derivatives Chapter Thirteen General Overview Euler Forward Method, Accuracy & Stability Implicit Schemes Higher-order Methods Multistep Method Solving a System of Equations Stiff Equations Chapter Fourteen Fourier Transform One-dimensional Discrete Fourier Transforms Mutlidimensional Fourier Transform Chapter Fifteen Solving PDEs Using Spectral Method: Diffusion Equation Solving Wave, Burgers, and KdV Equations Spectral Solution of Naiver-Stokes Equation Spectral Solution of Schrödinger Equation Chapter Sixteen General Overview & Diffusion Equation Solver Solving Wave Equation Burgers and Navier-Stokes Equations Schrodinger equation Chapter Seventeen Root Finders Chapter Eighteen Shooting Method Eigenvalue Calculation Chapter Nineteen Solving Laplace Equation Solving Poisson Equation Chapter Twenty Solution of Algebraic Equations Eigenvalues and Eigenvectors Chapter Twenty-One Random numbers Integration Using Random Numbers Regression Analysis Applications in Statmech Machine Learning Epilogue Appendix A: Errors in Lagrange Interopolation Appendix B: Improving Accuracy Using Richardson Method References