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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[Basic Python]{Matrices, Solution of equations and Integration\\}

\author[FOSSEE] {FOSSEE}

\institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
\date[] {31, October 2009\\Day 1, Session 4}
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%% the beginning of each subsection:
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  \begin{frame}<beamer>
    \frametitle{Outline}
    \tableofcontents[currentsection,currentsubsection]
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% DOCUMENT STARTS
\begin{document}

\begin{frame}
  \titlepage
\end{frame}

\begin{frame}
  \frametitle{Outline}
  \tableofcontents
%  \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 = matrix([[3,2,-1],[2,-2,4],[-1, 0.5, -1]])
    In []: b = matrix([[1], [-2], [0]])
    In []: x = linalg.solve(A, b)
    In []: Ax = dot(A, x)
    In []: allclose(Ax, b)
    Out[]: True
  \end{lstlisting}
\end{frame}

\begin{frame}[fragile]
\frametitle{Solution:}
\begin{lstlisting}
In []: x
Out[]: 
array([[ 1.],
       [-2.],
       [-2.]])

In []: Ax
Out[]: 
matrix([[  1.00000000e+00],
        [ -2.00000000e+00],
        [  2.22044605e-16]])
\end{lstlisting}
\end{frame}

\section{Matrices}
\subsection{Initializing}
\begin{frame}[fragile]
\frametitle{Matrices: Initializing}
\begin{lstlisting}
  In []: a = matrix([[1,2,3],
                     [4,5,6],
                     [7,8,9]])

  In []: a
  Out[]: 
  matrix([[1, 2, 3],
         [4, 5, 6],
         [7, 8, 9]])
\end{lstlisting}
\end{frame}

\subsection{Basic Operations}
\begin{frame}[fragile]
\frametitle{Inverse of a Matrix}

\begin{small}
\begin{lstlisting}
In []: linalg.inv(A)
Out[]: 
matrix([[ 0.07734807,  0.01657459,  0.32044199],
        [ 0.09944751, -0.12154696, -0.01657459],
        [-0.02762431, -0.07734807,  0.17127072]])

\end{lstlisting}
\end{small}
\end{frame}

\begin{frame}[fragile]
\frametitle{Determinant}
\begin{lstlisting}
  In []: linalg.det(a)
  Out[]: -9.5171266700777579e-16
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]
\frametitle{Computing Norms}
\begin{lstlisting}
  In []: linalg.norm(a)
  Out[]: 16.881943016134134
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]
\frametitle{Eigen Values and Eigen Matrix}
\begin{small}
\begin{lstlisting}
  In []: linalg.eigvals(a)
  Out[]: array([1.61168440e+01, -1.11684397e+00, -1.22196337e-15])

  In []: linalg.eig(a)
  Out[]: 
  (array([ 1.61168440e+01, -1.11684397e+00, -1.22196337e-15]),
   matrix([[-0.23197069, -0.78583024,  0.40824829],
          [-0.52532209, -0.08675134, -0.81649658],
          [-0.8186735 ,  0.61232756,  0.40824829]]))
\end{lstlisting}
\end{small}
\end{frame}


\section{Integration}

\subsection{Quadrature}

\begin{frame}[fragile]
\frametitle{Quadrature}
\begin{itemize}
\item We wish to find area under a curve
\item Area under $(sin(x) + x^2)$ in $(0,1)$
\item scipy has functions to do that
\end{itemize}
\small{\typ{In []: from scipy.integrate import quad}}
\begin{itemize}
\item Inputs - function to integrate, limits
\end{itemize}
\begin{lstlisting}
In []: x = 0
In []: integrate.quad(sin(x)+x**2, 0, 1)
\end{lstlisting}
\alert{\typ{error:}}
\typ{First argument must be a callable function.}
\end{frame}

\begin{frame}[fragile]
\frametitle{Functions - Definition}
\begin{lstlisting}
In []: def f(x):
           return sin(x)+x**2
In []: integrate.quad(f, 0, 1)
\end{lstlisting}
\begin{itemize}
\item \typ{def}
\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[]: 0.0
In []: f(1)
Out[]: 1.8414709848078965
\end{lstlisting}
\end{frame}


\begin{frame}[fragile]
\frametitle{Functions - Default Arguments}
\begin{lstlisting}
In []: def f(x=1):
           return sin(x)+x**2
In []: f(10)
Out[]: 99.455978889110625
In []: f(1)
Out[]: 1.8414709848078965
In []: f()
Out[]: 1.8414709848078965
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]
\frametitle{Functions - Keyword Arguments}
\begin{lstlisting}
In []: def f(x=1, y=pi):
           return sin(y)+x**2
In []: f()
Out[]: 1.0000000000000002
In []: f(2)
Out[]: 4.0
In []: f(y=2)
Out[]: 1.9092974268256817
In []: f(y=pi/2,x=0)
Out[]: 1.0
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]
  \frametitle{More on functions}
  \begin{itemize}
  \item Scope of variables in the function is local
  \item Mutable items are \alert{passed by reference}
  \item First line after definition may be a documentation string
    (\alert{recommended!})
  \item Function definition and execution defines a name bound to the
    function
  \item You \emph{can} assign a variable to a function!
  \end{itemize}
\end{frame}

\begin{frame}[fragile]
\frametitle{Quadrature \ldots}
\begin{lstlisting}
In []: integrate.quad(f, 0, 1)
\end{lstlisting}
Returns the integral and an estimate of the absolute error in the result.
\end{frame}

\subsection{ODEs}

\begin{frame}[fragile]
\frametitle{ODE Integration}
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\quad & \&\quad  \omega = 0 \nonumber
\end{align}
\end{frame}

\begin{frame}[fragile]
\frametitle{Solving ODEs using SciPy}
\begin{itemize}
\item We use the \typ{odeint} function from scipy to do the integration
\item Define a function as below
\end{itemize}
\begin{lstlisting}
In []: def pend_int(unknown, t, p):
  ....     theta, omega = unknown
  ....     g, L = p
  ....     f=[omega, -(g/L)*sin(theta)]
  ....     return f
  ....
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]
\frametitle{Solving ODEs using SciPy \ldots}
\begin{itemize}
\item \typ{t} is the time variable \\ 
\item \typ{p} has the constants \\
\item \typ{initial} has the initial values
\end{itemize}
\begin{lstlisting}
In []: t = linspace(0, 10, 101)
In []: p=(-9.81, 0.2)
In []: initial = [10*2*pi/360, 0]
\end{lstlisting}
\end{frame}

\begin{frame}[fragile]
\frametitle{Solving ODEs using SciPy \ldots}

\small{\typ{In []: from scipy.integrate import odeint}}
\begin{lstlisting}
In []: pend_sol = odeint(pend_int, 
                         initial,t, 
                         args=(p,))
\end{lstlisting}
\end{frame}

\end{document}