--- a/day1/session2.tex	Mon Nov 16 12:03:49 2009 +0530
+++ b/day1/session2.tex	Fri Nov 20 00:05:50 2009 +0530
@@ -331,7 +331,7 @@
 \frametitle{What about larger data sets?}
 \alert{Data is usually present in a file!} \\
 Lets look at the \typ{pendulum.txt} file.
-\begin{lstlisting}
+\begin{lstlisting} 
 $ cat pendulum.txt 
 1.0000e-01 6.9004e-01
 1.1000e-01 6.9497e-01
@@ -339,7 +339,7 @@
 1.3000e-01 7.5360e-01
 1.4000e-01 8.3568e-01
 1.5000e-01 8.6789e-01
-\end{lstlisting}
+\end{lstlisting}  %$
 \ldots
 \begin{block}{Windows users:}
   C:> type pendulum.txt

--- a/day1/session4.tex	Mon Nov 16 12:03:49 2009 +0530
+++ b/day1/session4.tex	Fri Nov 20 00:05:50 2009 +0530
@@ -156,8 +156,8 @@
        [ 1.,  1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.,  1.]])
 
-In []: ones_like([1, 2, 3, 4, 5]) 
-Out[]: array([1, 1, 1, 1, 1])   
+In []: ones_like([1, 2, 3, 4]) 
+Out[]: array([1, 1, 1, 1])   
 
 In []: identity(2)
 Out[]: 
@@ -515,6 +515,15 @@
   ....
 \end{lstlisting}
 \end{frame}
+
+\begin{frame}[fragile]
+\frametitle{Getting $L$ and $T^2$ \dots}
+\begin{lstlisting}
+In []: l = array(l)
+In []: t = array(t)
+\end{lstlisting}
+\alert{\typ{In []: tsq = t*t}}
+\end{frame}
  
 \begin{frame}[fragile]
 \frametitle{Generating $A$}
@@ -538,7 +547,7 @@
 \item Along with a lot of things, it returns the least squares solution
 \end{itemize}
 \begin{lstlisting}
-In []: result = lstsq(A,TSq)
+In []: result = lstsq(A,tsq)
 In []: coef = result[0]
 \end{lstlisting}
 \end{frame}

--- a/day1/session6.tex	Mon Nov 16 12:03:49 2009 +0530
+++ b/day1/session6.tex	Fri Nov 20 00:05:50 2009 +0530
@@ -328,7 +328,7 @@
 \begin{frame}[fragile]
 \frametitle{Solving ODEs using SciPy \ldots}
 \begin{lstlisting}
-In []: t = arange(0, 12, 0.2)
+In []: t = linspace(0, 12, 61)
 
 In []: y = odeint(epid, 250, t)