diff -r a749db24e73b -r 555237dbce44 day2/session2.tex --- a/day2/session2.tex Sat Oct 10 08:03:50 2009 +0530 +++ b/day2/session2.tex Mon Oct 12 11:56:07 2009 +0530 @@ -75,7 +75,7 @@ % Title page \title[]{Numerical Computing with Numpy \& Scipy} -\author[FOSSEE Team] {Asokan Pichai\\Prabhu Ramachandran} +\author[FOSSEE Team] {FOSSEE} \institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay} \date[] {11, October 2009} @@ -121,11 +121,11 @@ \begin{frame}[fragile] \frametitle{Broadcasting} \begin{lstlisting} - >>> a = np.arange(4) - >>> b = np.arange(5) + >>> a = arange(4) + >>> b = arange(5) >>> a+b #Does this work? >>> a+3 - >>> c=np.array([3]) + >>> c = array([3]) >>> a+c #Works! >>> b+c #But how? >>> a.shape, b.shape, c.shape @@ -141,29 +141,21 @@ \column{0.65\textwidth} \hspace*{-1.5in} \begin{lstlisting} - >>> a = np.arange(4) + >>> a = arange(4) >>> a+3 array([3, 4, 5, 6]) \end{lstlisting} \column{0.35\textwidth} \includegraphics[height=0.7in, interpolate=true]{data/broadcast_scalar} \end{columns} - \begin{itemize} - \item Allows functions to take inputs that are not of the same shape - \item 2 rules - - \begin{enumerate} - \item 1 is (repeatedly) prepended to shapes of smaller arrays - \item Size 1 in a dimension changed to Largest size in that dimension - \end{enumerate} - \end{itemize} \end{frame} \begin{frame}[fragile] \frametitle{Broadcasting in 3D} \begin{lstlisting} - >>> x = np.ones((3, 5)) - >>> y = np.ones(8) - >>> (x[..., None] + y).shape + >>> x = ones((3, 5, 1)) + >>> y = ones(8) + >>> (x + y).shape (3, 5, 8) \end{lstlisting} \begin{figure} @@ -177,7 +169,7 @@ \frametitle{Copies \& Views} \vspace{-0.1in} \begin{lstlisting} - >>> a = np.arange(1,9); a.shape=3,3 + >>> a = arange(1,9); a.shape=3,3 >>> b = a >>> b is a >>> b[0,0]=0; print a @@ -211,12 +203,10 @@ \begin{frame}[fragile] \frametitle{Copies contd \ldots} \begin{lstlisting} - >>> b = a[np.array([0,1,2])] - array([[1, 2, 3], - [4, 5, 6], - [7, 8, 9]]) + >>> a = arange(1, 10, 2) + >>> b = a[array([0,2,3])] >>> b.flags.owndata - >>> abool=np.greater(a,2) + >>> abool=a>5 >>> c = a[abool] >>> c.flags.owndata \end{lstlisting} @@ -326,7 +316,7 @@ Calculate the area under $(sin(x) + x^2)$ in the range $(0,1)$ \begin{lstlisting} >>> def f(x): - return np.sin(x)+x**2 + return sin(x)+x**2 >>> integrate.quad(f, 0, 1) \end{lstlisting} \end{frame} @@ -340,8 +330,8 @@ \end{align*} \begin{lstlisting} >>> def dx_dt(x,t): - return -np.exp(-t)*x**2 ->>> t = np.linspace(0,2,100) + return -exp(-t)*x**2 +>>> t = linspace(0,2,100) >>> x = integrate.odeint(dx_dt, 2, t) >>> plt.plot(x,t) \end{lstlisting} @@ -354,14 +344,14 @@ \begin{lstlisting} >>> from scipy import interpolate >>> interpolate.interp1d? ->>> x = np.arange(0,2*np.pi,np.pi/4) ->>> y = np.sin(x) +>>> x = arange(0,2*pi,pi/4) +>>> y = sin(x) >>> fl = interpolate.interp1d( x,y,kind='linear') >>> fc = interpolate.interp1d( x,y,kind='cubic') ->>> fl(np.pi/3) ->>> fc(np.pi/3) +>>> fl(pi/3) +>>> fc(pi/3) \end{lstlisting} \end{frame} @@ -370,7 +360,7 @@ Plot the Cubic Spline of $sin(x)$ \begin{lstlisting} >>> tck = interpolate.splrep(x,y) ->>> xs = np.arange(0,2*np.pi,np.pi/50) +>>> xs = arange(0,2*pi,pi/50) >>> ys = interpolate.splev(X,tck,der=0) >>> plt.plot(x,y,'o',x,y,xs,ys) >>> plt.show() @@ -383,15 +373,12 @@ \frametitle{Signal \& Image Processing} \begin{itemize} \item Convolution - \item B-splines \item Filtering \item Filter design \item IIR filter design \item Linear Systems - \item LTI Reresentations - \item Waveforms + \item LTI Representations \item Window functions - \item Wavelets \end{itemize} \end{frame} @@ -409,6 +396,7 @@ \begin{lstlisting} >>> b = ndimage.zoom(A,0.5) >>> imshow(b) +>>> b = ndimage.zoom(A,2) \end{lstlisting} \inctime{5} \end{frame}