day1/session6.tex
author Puneeth Chaganti <punchagan@fossee.in>
Wed, 04 Nov 2009 10:35:53 +0530
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%Tutorial slides on Python.
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%
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% Author: FOSSEE
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% Copyright (c) 2009, FOSSEE, IIT Bombay
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\documentclass[14pt,compress]{beamer}
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%\documentclass[draft]{beamer}
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%\documentclass[compress,handout]{beamer}
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%\usepackage{pgfpages} 
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%\pgfpagesuselayout{2 on 1}[a4paper,border shrink=5mm]
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% Modified from: generic-ornate-15min-45min.de.tex
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\mode<presentation>
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{
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  \usetheme{Warsaw}
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  \useoutertheme{infolines}
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  \setbeamercovered{transparent}
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}
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\usepackage[english]{babel}
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\usepackage[latin1]{inputenc}
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%\usepackage{times}
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\usepackage[T1]{fontenc}
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% Taken from Fernando's slides.
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\usepackage{ae,aecompl}
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\usepackage{mathpazo,courier,euler}
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\usepackage[scaled=.95]{helvet}
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\usepackage{amsmath}
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\definecolor{darkgreen}{rgb}{0,0.5,0}
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\usepackage{listings}
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\lstset{language=Python,
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    basicstyle=\ttfamily\bfseries,
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    commentstyle=\color{red}\itshape,
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  stringstyle=\color{darkgreen},
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  showstringspaces=false,
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  keywordstyle=\color{blue}\bfseries}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Macros
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\setbeamercolor{emphbar}{bg=blue!20, fg=black}
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\newcommand{\emphbar}[1]
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{\begin{beamercolorbox}[rounded=true]{emphbar} 
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      {#1}
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 \end{beamercolorbox}
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}
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\newcounter{time}
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\setcounter{time}{0}
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\newcommand{\inctime}[1]{\addtocounter{time}{#1}{\tiny \thetime\ m}}
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\newcommand{\typ}[1]{\lstinline{#1}}
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\newcommand{\kwrd}[1]{ \texttt{\textbf{\color{blue}{#1}}}  }
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%%% This is from Fernando's setup.
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% \usepackage{color}
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% \definecolor{orange}{cmyk}{0,0.4,0.8,0.2}
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% % Use and configure listings package for nicely formatted code
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% \usepackage{listings}
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% \lstset{
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%    language=Python,
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%    basicstyle=\small\ttfamily,
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%    commentstyle=\ttfamily\color{blue},
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%    stringstyle=\ttfamily\color{orange},
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%    showstringspaces=false,
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%    breaklines=true,
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%    postbreak = \space\dots
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% }
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Title page
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\title[ODEs \& Root Finding]{Python for Science and Engg:\\ODEs \& Finding Roots}
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\author[FOSSEE] {FOSSEE}
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\institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
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\date[] {31, October 2009\\Day 1, Session 6}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%\pgfdeclareimage[height=0.75cm]{iitmlogo}{iitmlogo}
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%\logo{\pgfuseimage{iitmlogo}}
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%% Delete this, if you do not want the table of contents to pop up at
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%% the beginning of each subsection:
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\AtBeginSubsection[]
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{
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  \begin{frame}<beamer>
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    \frametitle{Outline}
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    \tableofcontents[currentsection,currentsubsection]
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  \end{frame}
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}
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\AtBeginSection[]
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{
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  \begin{frame}<beamer>
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    \frametitle{Outline}
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    \tableofcontents[currentsection,currentsubsection]
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  \end{frame}
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}
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% If you wish to uncover everything in a step-wise fashion, uncomment
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% the following command: 
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%\beamerdefaultoverlayspecification{<+->}
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%\includeonlyframes{current,current1,current2,current3,current4,current5,current6}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% DOCUMENT STARTS
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\begin{document}
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\begin{frame}
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  \maketitle
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\end{frame}
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%% \begin{frame}
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%%   \frametitle{Outline}
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%%   \tableofcontents
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%%   % You might wish to add the option [pausesections]
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%% \end{frame}
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\section{ODEs}
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\begin{frame}[fragile]
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\frametitle{ODE Integration}
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We shall use the simple ODE of a simple pendulum. 
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\begin{equation*}
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\ddot{\theta} = -\frac{g}{L}sin(\theta)
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\end{equation*}
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\begin{itemize}
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\item This equation can be written as a system of two first order ODEs
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\end{itemize}
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\begin{align}
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\dot{\theta} &= \omega \\
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\dot{\omega} &= -\frac{g}{L}sin(\theta) \\
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 \text{At}\ t &= 0 : \nonumber \\
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 \theta = \theta_0(10^o)\quad & \&\quad  \omega = 0\ (Initial\ values)\nonumber 
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\end{align}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Solving ODEs using SciPy}
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\begin{itemize}
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\item We use the \typ{odeint} function from scipy to do the integration
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\item Define a function as below
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\end{itemize}
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\begin{lstlisting}
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In []: def pend_int(initial, t):
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  ....     theta, omega = initial
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  ....     g, L = -9.81, 0.2
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  ....     f=[omega, -(g/L)*sin(theta)]
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  ....     return f
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  ....
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\end{lstlisting}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Solving ODEs using SciPy \ldots}
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\begin{itemize}
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\item \typ{t} is the time variable \\ 
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\item \typ{initial} has the initial values
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\end{itemize}
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\begin{lstlisting}
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In []: t = linspace(0, 10, 101)
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In []: initial = [10*2*pi/360, 0]
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\end{lstlisting} 
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Solving ODEs using SciPy \ldots}
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%%\begin{small}
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\typ{In []: from scipy.integrate import odeint}
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%%\end{small}
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\begin{lstlisting}
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In []: pend_sol = odeint(pend_int, 
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                         initial,t)
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\end{lstlisting}
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\end{frame}
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\section{Finding Roots}
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\begin{frame}[fragile]
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\frametitle{Roots of $f(x)=0$}
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\begin{itemize}
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\item Roots --- values of $x$ satisfying $f(x)=0$
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\item $f(x)=0$ may have real or complex roots
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\item Presently, let's look at real roots
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\end{itemize}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Roots of $f(x)=0$}
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\begin{itemize}
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\item Given function $cosx-x^2$ 
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\item First we find \alert{a} root in $(-\pi/2, \pi/2)$
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\item Then we find \alert{all} roots in $(-\pi/2, \pi/2)$
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\end{itemize}
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\end{frame}
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%% \begin{frame}[fragile]
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%% \frametitle{Fixed Point Method}
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%% \begin{enumerate}
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%% \item Convert $f(x)=0$ to the form $x=g(x)$
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%% \item Start with an initial value of $x$ and iterate successively
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%% \item $x_{n+1}=g(x_n)$ and $x_0$ is our initial guess
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%% \item Iterate until $x_{n+1}-x_n \le tolerance$
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%% \end{enumerate}
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%% \end{frame}
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%% \begin{frame}[fragile]
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%% \frametitle{Fixed Point \dots}
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%% \begin{lstlisting}
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%% In []: def our_g(x):
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%%  ....:     return sqrt(cos(x))
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%%  ....: 
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%% In []: tolerance = 1e-5
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%% In []: while abs(x1-x0)>tolerance:
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%%  ....:     x0 = x1
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%%  ....:     x1 = our_g(x1)
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%%  ....:   
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%% In []: x0
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%% \end{lstlisting}
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%% \end{frame}
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\begin{frame}[fragile]
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\frametitle{Bisection Method}
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\begin{enumerate}
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\item Start with the given interval $(-\pi/2, \pi/2)$ ($(a, b)$)
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\item $f(a)\cdot f(b) < 0$
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\item Bisect the interval. $c = \frac{a+b}{2}$
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\item Change the interval to $(a, c)$ if $f(a)\cdot f(c) < 0$
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\item Else, change the interval to $(c, b)$
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\item Go back to 3 until $(b-a) \le$ tolerance
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\end{enumerate}
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\alert{\typ{tolerance = 1e-5}}
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\end{frame}
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%% \begin{frame}[fragile]
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%% \frametitle{Bisection \dots}
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%% \begin{lstlisting}
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%% In []: tolerance = 1e-5
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%% In []: a = -pi/2
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%% In []: b = 0
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%% In []: while b-a > tolerance:
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%%  ....:     c = (a+b)/2
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%%  ....:     if our_f(a)*our_f(c) < 0:
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%%  ....:         b = c
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%%  ....:     else:
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%%  ....:         a = c
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%%  ....:         
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%% \end{lstlisting}
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%% \end{frame}
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\begin{frame}[fragile]
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\frametitle{Newton-Raphson Method}
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\begin{enumerate}
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\item Start with an initial guess of x for the root
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\item $\Delta x = -f(x)/f^{'}(x)$
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\item $ x \leftarrow x + \Delta x$
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\item Go back to 2 until $|\Delta x| \le$ tolerance
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\end{enumerate}
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\end{frame}
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%% \begin{frame}[fragile]
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%% \frametitle{Newton-Raphson \dots}
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%% \begin{lstlisting}
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%% In []: def our_df(x):
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%%  ....:     return -sin(x)-2*x
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%%  ....: 
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%% In []: delx = 10*tolerance
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%% In []: while delx > tolerance:
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%%  ....:     delx = -our_f(x)/our_df(x)
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%%  ....:     x = x + delx
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%%  ....:     
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%%  ....:     
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%% \end{lstlisting}
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%% \end{frame}
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%% \begin{frame}[fragile]
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%% \frametitle{Newton-Raphson \ldots}
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%% \begin{itemize}
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%% \item What if $f^{'}(x) = 0$?
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%% \item Can you write a better version of the Newton-Raphson?
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%% \item What if the differential is not easy to calculate?
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%% \item Look at Secant Method
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%% \end{itemize}
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%% \end{frame}
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\begin{frame}[fragile]
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\frametitle{Initial Estimates}
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\begin{itemize}
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\item Given an interval
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\item How to find \alert{all} the roots?
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\end{itemize}
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\begin{enumerate}
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\item Check for change of signs of $f(x)$ in the given interval
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\item A root lies in the interval where the sign change occurs
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\end{enumerate}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Initial Estimates \ldots}
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\begin{lstlisting}
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In []: def our_f(x):
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 ....:     return cos(x) - x*x
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 ....: 
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In []: x = linspace(-pi/2, pi/2, 11)
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In []: y = our_f(x)
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\end{lstlisting}
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Get the intervals of x, where sign changes occur
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Initial Estimates \ldots}
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\begin{lstlisting}
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In []: pos = y[:-1]*y[1:] < 0
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In []: rpos = zeros(x.shape, dtype=bool)
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In []: rpos[:-1] = pos
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In []: rpos[1:] += pos
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In []: rts = x[rpos]
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\end{lstlisting}
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Now use Newton-Raphson?
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Scipy Methods - \typ{roots}}
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\begin{itemize}
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\item Calculates the roots of polynomials
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\end{itemize}
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\begin{lstlisting}
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  In []: coeffs = [1, 6, 13]
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  In []: roots(coeffs)
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\end{lstlisting}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Scipy Methods - \typ{fsolve}}
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\begin{small}
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\begin{lstlisting}
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  In []: from scipy.optimize import fsolve
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\end{lstlisting}
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\end{small}
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\begin{itemize}
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\item Finds the roots of a system of non-linear equations
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\item Input arguments - Function and initial estimate
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\item Returns the solution
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\end{itemize}
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\begin{lstlisting}
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  In []: fsolve(our_f, -pi/2)
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\end{lstlisting}
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\end{frame}
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\begin{frame}[fragile]
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\frametitle{Scipy Methods \dots}
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\begin{small}
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\begin{lstlisting}
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In []: from scipy.optimize import fixed_point
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In []: from scipy.optimize import bisect
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In []: from scipy.optimize import newton
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\end{lstlisting}
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\end{small}
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\end{frame}
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\begin{frame}
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  \frametitle{Things we have learned}
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  \begin{itemize}
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  \item Solving ODEs
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  \item Finding Roots
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    \begin{itemize}
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    \item Estimating Interval
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    \item Newton-Raphson
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    \item Scipy methods
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    \end{itemize}
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  \end{itemize}
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\end{frame}
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\end{document}