--- a/day1/session3.tex	Thu Oct 15 22:43:51 2009 +0530
+++ b/day1/session3.tex	Thu Oct 15 23:10:03 2009 +0530
@@ -78,7 +78,7 @@
 \author[FOSSEE] {FOSSEE}
 
 \institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
-\date[] {31, October 2009}
+\date[] {31, October 2009\\Day 1, Session 3}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %\pgfdeclareimage[height=0.75cm]{iitmlogo}{iitmlogo}
@@ -230,7 +230,7 @@
 \end{enumerate}
 \end{itemize}
 \begin{lstlisting}
-coeffs, res, rank, sing = lstsq(A,Tsq)
+In []: coef, res, r, s = lstsq(A,Tsq)
 \end{lstlisting}
 \end{frame}
 
@@ -239,15 +239,15 @@
 \begin{itemize}
 \item Use the poly1d function of pylab, to create a function for the line equation using the coefficients obtained
 \begin{lstlisting}
-p=poly1d(coeffs)
+In []: p=poly1d(coef)
 \end{lstlisting}
 \item Get new $T^2$ values using the function \typ{p} obtained
 \begin{lstlisting}
-Tline = p(L)
+In []: Tline = p(L)
 \end{lstlisting}
 \item Now plot Tline vs. L, to get the Least squares fit line. 
 \begin{lstlisting}
-plot(L, Tline)
+In []: plot(L, Tline)
 \end{lstlisting}
 \end{itemize}
 \end{frame}
@@ -417,27 +417,3 @@
 
 \end{document}
 
-Least squares: Smooth curve fit. 
-Array Operations: Mean, average (etc region wise like district wise and state wise from SSLC.txt) 
-Subject wise average. Introduce idea of dictionary. 
-
-Session 3
-
-import scipy
-from scipy import linalg.
-
-choose some meaningful plot. ??
-Newton's law of cooling. 
-u, v, f - optics
-hooke's law
-Least fit curves. 
-
-
-Choose a named problem. 
-ODE - first order. Whatever. 
-
-
-arrays, etc etc. 
-sum, average, mean. whatever. statistical
-sslc data
-numpy load text??

--- a/day1/session4.tex	Thu Oct 15 22:43:51 2009 +0530
+++ b/day1/session4.tex	Thu Oct 15 23:10:03 2009 +0530
@@ -129,7 +129,9 @@
 \begin{frame}[fragile]
 \frametitle{Matrices: Initializing}
 \begin{lstlisting}
-  In []: a = matrix([[1,2,3],[4,5,6],[7,8,9]])
+  In []: a = matrix([[1,2,3],
+                     [4,5,6],
+                     [7,8,9]])
 
   In []: a
   Out[]: 
@@ -142,6 +144,7 @@
 \subsection{Basic Operations}
 \begin{frame}[fragile]
 \frametitle{Inverse of a Matrix}
+\begin{small}
 \begin{lstlisting}
   In []: linalg.inv(a)
   Out[]: 
@@ -149,6 +152,7 @@
           [ -6.30442381e+15,   1.26088476e+16,  -6.30442381e+15],
           [  3.15221191e+15,  -6.30442381e+15,   3.15221191e+15]])
 \end{lstlisting}
+\end{small}
 \end{frame}
 
 \begin{frame}[fragile]
@@ -164,13 +168,12 @@
 \begin{lstlisting}
   In []: linalg.norm(a)
   Out[]: 16.881943016134134
-  
-  In []: linalg.norm?
 \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])
@@ -182,18 +185,20 @@
           [-0.52532209, -0.08675134, -0.81649658],
           [-0.8186735 ,  0.61232756,  0.40824829]]))
 \end{lstlisting}
+\end{small}
 \end{frame}
 
 \section{Solving linear equations}
 \begin{frame}[fragile]
 \frametitle{Solution of equations}
 Example problem: Consider the set of equations
+\vspace{-0.1in}
   \begin{align*}
     3x + 2y - z  & = 1 \\
     2x - 2y + 4z  & = -2 \\
     -x + \frac{1}{2}y -z & = 0
   \end{align*}
-
+\vspace{-0.08in}
   To Solve this, 
   \begin{lstlisting}
     In []: A = array([[3,2,-1],[2,-2,4],[-1, 0.5, -1]])