%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Tutorial slides on Python. % % Author: FOSSEE % Copyright (c) 2009, FOSSEE, IIT Bombay %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass[14pt,compress]{beamer} %\documentclass[draft]{beamer} %\documentclass[compress,handout]{beamer} %\usepackage{pgfpages} %\pgfpagesuselayout{2 on 1}[a4paper,border shrink=5mm] % Modified from: generic-ornate-15min-45min.de.tex \mode { \usetheme{Warsaw} \useoutertheme{infolines} \setbeamercovered{transparent} } \usepackage[english]{babel} \usepackage[latin1]{inputenc} %\usepackage{times} \usepackage[T1]{fontenc} % Taken from Fernando's slides. \usepackage{ae,aecompl} \usepackage{mathpazo,courier,euler} \usepackage[scaled=.95]{helvet} \definecolor{darkgreen}{rgb}{0,0.5,0} \usepackage{listings} \lstset{language=Python, basicstyle=\ttfamily\bfseries, commentstyle=\color{red}\itshape, stringstyle=\color{darkgreen}, showstringspaces=false, keywordstyle=\color{blue}\bfseries} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Macros \setbeamercolor{emphbar}{bg=blue!20, fg=black} \newcommand{\emphbar}[1] {\begin{beamercolorbox}[rounded=true]{emphbar} {#1} \end{beamercolorbox} } \newcounter{time} \setcounter{time}{0} \newcommand{\inctime}[1]{\addtocounter{time}{#1}{\tiny \thetime\ m}} \newcommand{\typ}[1]{\lstinline{#1}} \newcommand{\kwrd}[1]{ \texttt{\textbf{\color{blue}{#1}}} } % Title page \title{Python for Scientific Computing : Least Square Fit} \author[FOSSEE] {FOSSEE} \institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay} \date{} % DOCUMENT STARTS \begin{document} \begin{frame} \maketitle \end{frame} \begin{frame} \frametitle{About the Session} \begin{block}{Goal} Finding least square fit of given data-set \end{block} \begin{block}{Checklist} \begin{itemize} \item pendulum.txt \end{itemize} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{$L$ vs. $T^2$ - Scatter} \vspace{-0.15in} \begin{figure} \includegraphics[width=4in]{data/L-Tsq-points} \end{figure} \end{frame} \begin{frame}[fragile] \frametitle{$L$ vs. $T^2$ - Line} \vspace{-0.15in} \begin{figure} \includegraphics[width=4in]{data/L-Tsq-Line} \end{figure} \end{frame} \begin{frame}[fragile] \frametitle{$L$ vs. $T^2$ } \frametitle{$L$ vs. $T^2$ - Least Square Fit} \vspace{-0.15in} \begin{figure} \includegraphics[width=4in]{data/least-sq-fit} \end{figure} \end{frame} \begin{frame} \frametitle{Least Square Fit Curve} \begin{center} \begin{itemize} \item $L \alpha T^2$ \item Best Fit Curve $\rightarrow$ Linear \begin{itemize} \item Least Square Fit \end{itemize} \item \typ{lstsq()} \end{itemize} \end{center} \end{frame} \begin{frame}[fragile] \frametitle{\typ{lstsq}} \begin{itemize} \item We need to fit a line through points for the equation $T^2 = m \cdot L+c$ \item In matrix form, the equation can be represented as $T_{sq} = A \cdot p$, where $T_{sq}$ is $\begin{bmatrix} T^2_1 \\ T^2_2 \\ \vdots\\ T^2_N \\ \end{bmatrix}$ , A is $\begin{bmatrix} L_1 & 1 \\ L_2 & 1 \\ \vdots & \vdots\\ L_N & 1 \\ \end{bmatrix}$ and p is $\begin{bmatrix} m\\ c\\ \end{bmatrix}$ \item We need to find $p$ to plot the line \end{itemize} \end{frame} \begin{frame}[fragile] \frametitle{Summary} \begin{block}{} Obtaining the least fit curve from a data set \end{block} \end{frame} \begin{frame} \frametitle{Thank you!} \begin{block}{} This session is part of \textcolor{blue}{FOSSEE} project funded by: \begin{center} \textcolor{blue}{NME through ICT from MHRD, Govt. of India}. \end{center} \end{block} \end{frame} \end{document}