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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[Matrices \& Curve Fitting]{Python for Science and Engg: Matrices \& Least Square Fit}

\author[FOSSEE] {FOSSEE}

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

\date[] {11 January, 2010\\Day 1, Session 4}

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    \frametitle{Outline}

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\begin{document}

\begin{frame}

  \titlepage

\end{frame}

\begin{frame}

  \frametitle{Outline}

  \tableofcontents

%  \pausesections

\end{frame}

\section{Matrices}

\begin{frame}

\frametitle{Matrices: Introduction}

\alert{All matrix operations are done using \kwrd{arrays}}

\end{frame}

\begin{frame}[fragile]

\frametitle{Matrices: Initializing}

\begin{lstlisting}

In []: c = array([[1,1,2],

                  [2,4,1],

                  [-1,3,7]])

In []: c

Out[]: 

array([[1,1,2],

       [2,4,1],

       [-1,3,7]])

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Initializing some special matrices}

\begin{small}

  \begin{lstlisting}

In []: ones((3,5))

Out[]: 

array([[ 1.,  1.,  1.,  1.,  1.],

       [ 1.,  1.,  1.,  1.,  1.],

       [ 1.,  1.,  1.,  1.,  1.]])

In []: ones_like([1, 2, 3, 4]) 

Out[]: array([1, 1, 1, 1])   

In []: identity(2)

Out[]: 

array([[ 1.,  0.],

       [ 0.,  1.]])

  \end{lstlisting}

Also available \alert{\typ{zeros, zeros_like, empty, empty_like}}

\end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Accessing elements}

  \begin{lstlisting}

In []: c

Out[]: 

array([[1,1,2],

       [2,4,1],

       [-1,3,7]])

In []: c[1][2]

Out[]: 1

In []: c[1,2]

Out[]: 1

In []: c[1]

Out[]: array([2, 4, 1])

  \end{lstlisting}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Changing elements}

  \begin{small}

  \begin{lstlisting}

In []: c[1,1] = -2

In []: c

Out[]: 

array([[ 1,  1,  2],

       [ 2, -2,  1],

       [-1,  3,  7]])

In []: c[1] = [0,0,0]

In []: c

Out[]: 

array([[ 1,  1,  2],

       [ 0,  0,  0],

       [-1,  3,  7]])

  \end{lstlisting}

  \end{small}

How to change one \alert{column}?

\end{frame}

\begin{frame}[fragile]

  \frametitle{Slicing}

\begin{small}

  \begin{lstlisting}

In []: c[:,1]

Out[]: array([1, 0, 3])

In []: c[1,:]

Out[]: array([0, 0, 0])

In []: c[0:2,:]

Out[]: 

array([[1, 1, 2],

       [0, 0, 0]])

In []: c[1:3,:]

Out[]: 

array([[ 0,  0,  0],

       [-1,  3,  7]])

  \end{lstlisting}

\end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Slicing \ldots}

\begin{small}

  \begin{lstlisting}

In []: c[:2,:]

Out[]: 

array([[1, 1, 2],

       [0, 0, 0]])

In []: c[1:,:]

Out[]: 

array([[ 0,  0,  0],

       [-1,  3,  7]])

In []: c[1:,:2]

Out[]: 

array([[ 0,  0],

       [-1,  3]])

  \end{lstlisting}

\end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Striding}

  \begin{small}

  \begin{lstlisting}

In []: c[::2,:]

Out[]: 

array([[ 1,  1,  2],

       [-1,  3,  7]])

In []: c[:,::2]

Out[]: 

xarray([[ 1,  2],

       [ 0,  0],

       [-1,  7]])

In []: c[::2,::2]

Out[]: 

array([[ 1,  2],

       [-1,  7]])

  \end{lstlisting}

  \end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Slicing \& Striding Exercises}

\begin{small}

  \begin{lstlisting}

In []: a = imread('lena.png')

In []: imshow(a)

Out[]: <matplotlib.image.AxesImage object at 0xa0384cc>

  \end{lstlisting}

\end{small}

  \begin{itemize}

  \item Crop the image to get the top-left quarter

  \item Crop the image to get only the face

  \item Resize image to half by dropping alternate pixels

  \end{itemize}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Solutions}

\begin{small}

  \begin{lstlisting}

In []: imshow(a[:256,:256])

Out[]: <matplotlib.image.AxesImage object at 0xb6f658c>

In []: imshow(a[200:400,200:400])

Out[]: <matplotlib.image.AxesImage object at 0xb757c2c>

In []: imshow(a[::2,::2])

Out[]: <matplotlib.image.AxesImage object at 0xb765c8c>

  \end{lstlisting}

\end{small}

\end{frame}

\begin{frame}[fragile]

\frametitle{Transpose of a Matrix}

\begin{lstlisting}

In []: a = array([[ 1,  1,  2, -1],

  ...:            [ 2,  5, -1, -9],

  ...:            [ 2,  1, -1,  3],

  ...:            [ 1, -3,  2,  7]])

In []: a.T

Out[]:

array([[ 1,  2,  2,  1],

       [ 1,  5,  1, -3],

       [ 2, -1, -1,  2],

       [-1, -9,  3,  7]])

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Sum of all elements}

  \begin{lstlisting}

In []: sum(a)

Out[]: 12

  \end{lstlisting}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Matrix Addition}

  \begin{lstlisting}

In []: b = array([[3,2,-1,5],

                  [2,-2,4,9],

                  [-1,0.5,-1,-7],

                  [9,-5,7,3]])

In []: a + b

Out[]: 

array([[  4. ,   3. ,   1. ,   4. ],

       [  4. ,   3. ,   3. ,   0. ],

       [  1. ,   1.5,  -2. ,  -4. ],

       [ 10. ,  -8. ,   9. ,  10. ]])

  \end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Elementwise Multiplication}

\begin{lstlisting}

In []: a*b

Out[]: 

array([[  3. ,   2. ,  -2. ,  -5. ],

       [  4. , -10. ,  -4. , -81. ],

       [ -2. ,   0.5,   1. , -21. ],

       [  9. ,  15. ,  14. ,  21. ]])

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Matrix Multiplication}

\begin{lstlisting}

In []: dot(a, b)

Out[]: 

array([[ -6. ,   6. ,  -6. ,  -3. ],

       [-64. ,  38.5, -44. ,  35. ],

       [ 36. , -13.5,  24. ,  35. ],

       [ 58. , -26. ,  34. , -15. ]])

\end{lstlisting}

\end{frame}

\begin{frame}[fragile]

\frametitle{Inverse of a Matrix}

\begin{lstlisting}

In []: inv(a)

\end{lstlisting}

\begin{small}

\begin{lstlisting}

Out[]: 

array([[-0.5 ,  0.55, -0.15,  0.7 ],

       [ 0.75, -0.5 ,  0.5 , -0.75],

       [ 0.5 , -0.15, -0.05, -0.1 ],

       [ 0.25, -0.25,  0.25, -0.25]])

\end{lstlisting}

\end{small}

\end{frame}

\begin{frame}[fragile]

\frametitle{Determinant}

\begin{lstlisting}

In []: det(a)

Out[]: 80.0

\end{lstlisting}

\end{frame}

%%use S=array(X,Y)

\begin{frame}[fragile]

\frametitle{Eigenvalues and Eigen Vectors}

\begin{small}

\begin{lstlisting}

In []: e = array([[3,2,4],[2,0,2],[4,2,3]])

In []: eig(e)

Out[]: 

(array([-1.,  8., -1.]),

 array([[-0.74535599,  0.66666667, -0.1931126 ],

        [ 0.2981424 ,  0.33333333, -0.78664085],

        [ 0.59628479,  0.66666667,  0.58643303]]))

In []: eigvals(e)

Out[]: array([-1.,  8., -1.])

\end{lstlisting}

\end{small}

\end{frame}

%% \begin{frame}[fragile]

%% \frametitle{Computing Norms}

%% \begin{lstlisting}

%% In []: norm(e)

%% Out[]: 8.1240384046359608

%% \end{lstlisting}

%% \end{frame}

%% \begin{frame}[fragile]

%%   \frametitle{Singular Value Decomposition}

%%   \begin{small}

%%   \begin{lstlisting}

%% In []: svd(e)

%% Out[]: 

%% (array(

%% [[ -6.66666667e-01,  -1.23702565e-16,   7.45355992e-01],

%%  [ -3.33333333e-01,  -8.94427191e-01,  -2.98142397e-01],

%%  [ -6.66666667e-01,   4.47213595e-01,  -5.96284794e-01]]),

%%  array([ 8.,  1.,  1.]),

%%  array([[-0.66666667, -0.33333333, -0.66666667],

%%         [-0.        ,  0.89442719, -0.4472136 ],

%%         [-0.74535599,  0.2981424 ,  0.59628479]]))

%%   \end{lstlisting}

%%   \end{small}

%% \inctime{15}

%% \end{frame}

\section{Least Squares Fit}

\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{itemize}

\item $T^2$ and $L$ have a linear relationship

\item Hence, Least Square Fit Curve is a line

\item we shall use the \typ{lstsq} function

\end{itemize}

\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}