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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

\author[FOSSEE] {FOSSEE}

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

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  \begin{frame}<beamer>

    \frametitle{Outline}

    \tableofcontents[currentsection,currentsubsection]

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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{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([[11,12,13],

                  [21,22,23],

                  [31,32,33]])

In []: c

Out[]: 

array([[11, 12, 13],

       [21, 22, 23],

       [31, 32, 33]])

\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{small}

  \begin{lstlisting}

In []: c

Out[]: 

array([[11, 12, 13],

       [21, 22, 23],

       [31, 32, 33]])

In []: c[1][2]

Out[]: 23

In []: c[1,2]

Out[]: 23

In []: c[1]

Out[]: array([21, 22, 23])

  \end{lstlisting}

  \end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Changing elements}

  \begin{small}

  \begin{lstlisting}

In []: c[1,1] = -22

In []: c

Out[]: 

array([[ 11,  12,  13],

       [ 21, -22,  23],

       [ 31,  32,  33]])

In []: c[1] = 0

In []: c

Out[]: 

array([[11, 12, 13],

       [ 0,  0,  0],

       [31, 32, 33]])

  \end{lstlisting}

  \end{small}

How do you access one \alert{column}?

\end{frame}

\begin{frame}[fragile]

  \frametitle{Slicing}

\begin{small}

  \begin{lstlisting}

In []: c[:,1]

Out[]: array([12,  0, 32])

In []: c[1,:]

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

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

Out[]: 

array([[11, 12, 13],

       [ 0,  0,  0]])

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

Out[]: 

array([[ 0,  0,  0],

       [31, 32, 33]])

  \end{lstlisting}

\end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Slicing \ldots}

\begin{small}

  \begin{lstlisting}

In []: c[:2,:]

Out[]: 

array([[11, 12, 13],

       [ 0,  0,  0]])

In []: c[1:,:]

Out[]: 

array([[ 0,  0,  0],

       [31, 32, 33]])

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

Out[]: 

array([[ 0,  0],

       [31, 32]])

  \end{lstlisting}

\end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Striding}

  \begin{small}

  \begin{lstlisting}

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

Out[]: 

array([[11, 12, 13],

       [31, 32, 33]])

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

Out[]: 

array([[11, 13],

       [ 0,  0],

       [31, 33]])

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

Out[]: 

array([[11, 13],

       [31, 33]])

  \end{lstlisting}

  \end{small}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Shape of a matrix}

  \begin{lstlisting}

In []: c

Out[]: 

array([[11, 12, 13],

       [ 0,  0,  0],

       [31, 32, 33]])

In []: c.shape

Out[]: (3, 3)

  \end{lstlisting}

\emphbar{Shape specifies shape or dimensions of a matrix}

\end{frame}

\begin{frame}[fragile]

  \frametitle{Elementary image processing}

\begin{small}

  \begin{lstlisting}

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

In []: imshow(a)

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

  \end{lstlisting}

  \end{small}

\typ{imread} returns an array of shape (512, 512, 4) which represents an image of 512x512 pixels and 4 shades.\\

\typ{imshow} renders the array as an image.

\end{frame}

\begin{frame}[fragile]

\frametitle{Slicing \& Striding Exercises}

  \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{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}

\end{lstlisting}

\begin{small}

\begin{lstlisting}

In []: inv(a)

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}

\emphbar{Try this: \typ{I = dot(a, inv(a))}}

\end{frame}

\begin{frame}[fragile]

\frametitle{Determinant and sum of all elements}

\begin{lstlisting}

In []: det(a)

Out[]: 80.0

\end{lstlisting}

  \begin{lstlisting}

In []: sum(a)

Out[]: 12

  \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}

\end{frame}

\section{Summary}

\begin{frame}

  \frametitle{What did we learn?}

  \begin{itemize}

  \item Matrices

    \begin{itemize}

      \item Initializing

      \item Accessing elements

      \item Slicing and Striding

      \item Transpose

      \item Addition

      \item Multiplication

      \item Inverse of a matrix

      \item Determinant

      \item Eigenvalues and Eigen vector

      \item Singular Value Decomposition

    \end{itemize}

  \item Least Square Curve fitting