ENH: Minor cleanup. Also added slide to introduce IPython's %timeit and

%time.

--HG--
branch : scipy2010

1 files changed, 45 insertions, 16 deletions

diff --git a/day1/session3.tex b/day1/session3.tex
index c46f3b9..02aef4f 100644
--- a/day1/session3.tex
+++ b/day1/session3.tex
@@ -73,13 +73,13 @@
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % Title page
-\title[Statistics]{Python for Science and Engg: Statistics}
+\title[Statistics]{Python for Science and Engg:\\ Basic data processing}
 
 \author[FOSSEE] {FOSSEE}
 
 \institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
 
-\date[] {30 April, 2010\\Day 1, Session 3}
+\date[] {SciPy 2010, Introductory tutorials,\\Day 1, Session 3}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %\pgfdeclareimage[height=0.75cm]{iitmlogo}{iitmlogo}
@@ -127,20 +127,21 @@
 %%   % You might wish to add the option [pausesections]
 %% \end{frame}
 
-\section{Computing mean}
+\section{Computing the mean}
 \begin{frame}
   \frametitle{Value of acceleration due to gravity?}
   \begin{itemize}
-    \item We already have pendulum.txt
+    \item We already have \typ{pendulum.txt}
     \item We know that $ T = 2\pi \sqrt{\frac{L}{g}} $
     \item So $ g = \frac{4 \pi^2 L}{T^2}  $
-    \item Calculate ``g'' - acceleration due to gravity for each pair of L and T
-    \item Hence calculate mean ``g''
+    \item Calculate $g$ - acceleration due to gravity for each pair of
+        $L$ and $T$
+    \item Hence calculate mean $g$
   \end{itemize}
 \end{frame}
 
 \begin{frame}[fragile]
-  \frametitle{Acceleration due to gravity - ``g''\ldots}
+  \frametitle{Acceleration due to gravity - $g$\ldots}
   \begin{lstlisting}
 In []: g_list = []
 In []: for line in open('pendulum.txt'):
@@ -153,7 +154,7 @@ In []: for line in open('pendulum.txt'):
 \end{frame}
 
 \begin{frame}[fragile]
-  \frametitle{Mean ``g'' - Classical method}
+  \frametitle{Mean $g$ - Classical method}
   \begin{lstlisting}
 In []: total = 0
 In []: for g in g_list:
@@ -166,7 +167,7 @@ In []: print 'Mean: ', g_mean
 \end{frame}
 
 \begin{frame}[fragile]
-  \frametitle{Mean ``g'' - Slightly improved method}
+  \frametitle{Mean $g$ - Slightly improved method}
   \begin{lstlisting}
 In []: g_mean = sum(g_list) / len(g_list)
 In []: print 'Mean: ', g_mean
@@ -174,7 +175,7 @@ In []: print 'Mean: ', g_mean
 \end{frame}
 
 \begin{frame}[fragile]
-  \frametitle{Mean ``g'' - One liner}
+  \frametitle{Mean $g$ - One liner}
   \begin{lstlisting}
 In []: g_mean = mean(g_list)
 In []: print 'Mean: ', g_mean
@@ -186,13 +187,15 @@ In []: print 'Mean: ', g_mean
 \begin{frame}
   \frametitle{More on data processing}
   \begin{block}{}
-    We have a huge data file--180,000 records.\\How do we do \emph{efficient} statistical computations, i.e. find mean, median, standard deviation etc; draw pie charts?
+    We have a huge data file--180,000 records.\\How do we do
+    \emph{efficient} statistical computations, i.e. find mean, median,
+    standard deviation etc.;\\How do we draw pie charts?
   \end{block}
 \end{frame}
 
 \begin{frame}
   \frametitle{Structure of the file}
-  Understanding the structure of sslc1.txt
+  Understanding the structure of \typ{sslc1.txt}
   \begin{itemize}
     \item Each line in the file has a student's details(record)
     \item Each record consists of fields separated by ';'
@@ -208,7 +211,8 @@ In []: print 'Mean: ', g_mean
     \item Region Code
     \item Roll Number
     \item Name
-    \item Marks of 5 subjects: SLang, Flang Maths, Science, Social
+    \item Marks of 5 subjects: second lang, first lang., Math, Science,
+        Social Studies
     \item Total marks
     \item Pass/Fail (P/F)
     \item Withheld (W)
@@ -218,7 +222,7 @@ In []: print 'Mean: ', g_mean
 
 \begin{frame}
   \frametitle{Statistical Analysis: Problem statement}
-  1. Read the data supplied in the file \emph{sslc1.txt} and carry out the following:
+  1. Read the data supplied in the file \typ{sslc1.txt} and carry out the following:
   \begin{itemize}
     \item[a] Draw a pie chart representing proportion of students who scored more than 90\% in each region in Science.
     \item[b] Print mean, median and standard deviation of math scores for all regions combined.
@@ -336,7 +340,7 @@ science = {}
     \item Keys will be region codes
     \item Values will be the number students who scored more than 90\% in that region in Science
   \end{itemize}
-  \begin{block}{Sample \emph{science} dictionary}
+  \begin{block}{Sample \typ{science} dictionary}
     \{'A': 729, 'C': 764, 'B': 1120,'E': 414, 'D': 603, 'F': 500\}
   \end{block}
 
@@ -458,13 +462,38 @@ print 'Standard Deviation: ',
 \end{frame}
 
 \begin{frame}[fragile]
+  \frametitle{IPython tip: Timing}
+
+Try the following:
+  \begin{lstlisting}
+In []: %timeit mean(math_scores)
+
+In []: %timeit mean(math_array)
+
+In []: %timeit?
+
+  \end{lstlisting}
+
+  \begin{itemize}
+      \item \typ{\%timeit}: accurate, many measurements
+      \item Can also use \typ{\%time}
+      \item \typ{\%time}: less accurate, one measurement 
+  \end{itemize}
+
+  \inctime{5}
+\end{frame}
+
+\begin{frame}[fragile]
   \frametitle{What tools did we use?}
   \begin{itemize}
+   \item More parsing data
    \item Dictionaries for storing data
    \item Facilities for drawing pie charts
-   \item Efficient array manipulations
    \item Functions for statistical computations - mean, median, standard deviation
+   \item Efficient array manipulations
+   \item Timing in IPython
   \end{itemize}
+
 \end{frame}
 
 \end{document}