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%Tutorial slides on Python.

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% Author: FOSSEE

% Copyright (c) 2009, FOSSEE, IIT Bombay

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% Title page

\title[Solving Equations \& ODEs]{Python for Science and Engg:\\Solving Equations \& ODEs}

\author[FOSSEE] {FOSSEE}

\institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}

\date[] {7 November, 2009\\Day 1, Session 6}

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\begin{document}

\begin{frame}

  \maketitle

\end{frame}

%% \begin{frame}

%%   \frametitle{Outline}

%%   \tableofcontents

%%   % You might wish to add the option [pausesections]

%% \end{frame}

\section{Solving linear equations}

\begin{frame}[fragile]

\frametitle{Solution of equations}

Consider,

  \begin{align*}

    3x + 2y - z  & = 1 \\

    2x - 2y + 4z  & = -2 \\

    -x + \frac{1}{2}y -z & = 0

  \end{align*}

Solution:

  \begin{align*}

    x & = 1 \\

    y & = -2 \\

    z & = -2

  \end{align*}

\end{frame}

\begin{frame}[fragile]

\frametitle{Solving using Matrices}

Let us now look at how to solve this using \kwrd{matrices}

  \begin{lstlisting}

    In []: A = array([[3,2,-1],

                      [2,-2,4],                   

                      [-1, 0.5, -1]])

    In []: b = array([[1], [-2], [0]])

    In []: x = solve(A, b)

    In []: Ax = dot(A,x)

  \end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Solution:}

\begin{lstlisting}

In []: x

Out[]: 

array([[ 1.],

       [-2.],

       [-2.]])

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Let's check!}

\begin{lstlisting}

In []: Ax

Out[]: 

array([[  1.00000000e+00],

       [ -2.00000000e+00],

       [  2.22044605e-16]])

\end{lstlisting}

\begin{block}{}

The last term in the matrix is actually \alert{0}!\\

We can use \kwrd{allclose()} to check.

\end{block}

\begin{lstlisting}

In []: allclose(Ax, b)

Out[]: True

\end{lstlisting}

\inctime{15}

\end{frame}

\subsection{Exercises}

\begin{frame}[fragile]

Solve the set of equations:

\begin{align*}

  x + y + 2z -w & = 3\\

  2x + 5y - z - 9w & = -3\\

  2x + y -z + 3w & = -11 \\

  x - 3y + 2z + 7w & = -5\\

\end{align*}

\inctime{10}

\end{frame}

\begin{frame}[fragile]

\frametitle{Solution}

Use \kwrd{solve()}

\begin{align*}

  x & = -5\\

  y & = 2\\

  z & = 3\\

  w & = 0\\

\end{align*}

\end{frame}

\section{Finding Roots}

\begin{frame}[fragile]

\frametitle{Scipy Methods - \typ{roots}}

\begin{itemize}

\item Calculates the roots of polynomials

\end{itemize}

\begin{lstlisting}

  In []: coeffs = [1, 6, 13]

  In []: roots(coeffs)

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Scipy Methods - \typ{fsolve}}

\begin{small}

\begin{lstlisting}

  In []: from scipy.optimize import fsolve

\end{lstlisting}

\end{small}

\begin{itemize}

\item Finds the roots of a system of non-linear equations

\item Input arguments - Function and initial estimate

\item Returns the solution

\end{itemize}

\end{frame}

\begin{frame}[fragile]

\frametitle{\typ{fsolve}}

Find the root of $sin(x)+cos^2(x)$ nearest to $0$

\begin{lstlisting}

In []: fsolve(sin(x)+cos(x)**2, 0)

NameError: name 'x' is not defined

In []: x = linspace(-pi, pi)

In []: fsolve(sin(x)+cos(x)**2, 0)

\end{lstlisting}

\begin{small}

\alert{\typ{TypeError:}}

\typ{'numpy.ndarray' object is not callable}

\end{small}

\end{frame}

\begin{frame}[fragile]

\frametitle{Functions - Definition}

We have been using them all along. Now let's see how to define them.

\begin{lstlisting}

In []: def f(x):

           return sin(x)+cos(x)**2

\end{lstlisting}

\begin{itemize}

\item \typ{def}

\item name

\item arguments

\item \typ{return}

\end{itemize}

\end{frame}

\begin{frame}[fragile]

\frametitle{Functions - Calling them}

\begin{lstlisting}

In [15]: f()

---------------------------------------

\end{lstlisting}

\alert{\typ{TypeError:}}\typ{f() takes exactly 1 argument}

\typ{(0 given)}

\begin{lstlisting}

In []: f(0)

Out[]: 1.0

In []: f(1)

Out[]: 1.1333975665343254

\end{lstlisting}

More on Functions later \ldots

\end{frame}

\begin{frame}[fragile]

\frametitle{\typ{fsolve} \ldots}

Find the root of $sin(x)+cos^2(x)$ nearest to $0$

\begin{lstlisting}

In []: fsolve(f, 0)

Out[]: -0.66623943249251527

\end{lstlisting}

\end{frame}

%% \begin{frame}[fragile]

%% \frametitle{Scipy Methods \dots}

%% \begin{small}

%% \begin{lstlisting}

%% In []: from scipy.optimize import fixed_point

%% In []: from scipy.optimize import bisect

%% In []: from scipy.optimize import newton

%% \end{lstlisting}

%% \end{small}

%% \end{frame}

\section{ODEs}

\begin{frame}[fragile]

We shall use the simple ODE of a simple pendulum. 

\begin{equation*}

\ddot{\theta} = -\frac{g}{L}sin(\theta)

\end{equation*}

\begin{itemize}

\item This equation can be written as a system of two first order ODEs

\end{itemize}

\begin{align}

\dot{\theta} &= \omega \\

\dot{\omega} &= -\frac{g}{L}sin(\theta) \\

 \text{At}\ t &= 0 : \nonumber \\

 \theta = \theta_0(10^o)\quad & \&\quad  \omega = 0\ (Initial\ values)\nonumber 

\end{align}

\end{frame}

\begin{frame}[fragile]

\begin{itemize}

\end{itemize}

\begin{lstlisting}

In []: def pend_int(initial, t):

  ....     theta, omega = initial

  ....     g, L = 9.81, 0.2

  ....     f=[omega, -(g/L)*sin(theta)]

  ....     return f

  ....

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\begin{itemize}

\item \typ{t} is the time variable \\ 

\item \typ{initial} has the initial values

\end{itemize}

\begin{lstlisting}

In []: t = linspace(0, 10, 101)

In []: initial = [10*2*pi/360, 0]

\end{lstlisting} 

\end{frame}

\begin{frame}[fragile]

%%\begin{small}

\typ{In []: from scipy.integrate import odeint}

%%\end{small}

\begin{lstlisting}

In []: pend_sol = odeint(pend_int, 

                         initial,t)

\end{lstlisting}

\end{frame}

\begin{frame}

  \frametitle{Things we have learned}

  \begin{itemize}

  \item Solving Linear Equations

  \item Defining Functions

  \item Finding Roots

  \item Solving ODEs

  \end{itemize}

\end{frame}

\end{document}