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\pgfdeclaremask{tu}{iusealredonwhite-mask}
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\logo{\pgfuseimage{logo}}

\title{Python Tutorial}
\author[B. Zaitlen]{Benjamin L. Zaitlen %{\tt bzaitlen@indiana.edu}
}

\institute{Biocomplexity Institute, Indiana University}
%\date{}

%\author[Author, Another] % (optional, use only with lots of authors)
%\author{Lingyun Yang\inst{1} \and S.~Y.~Lee\inst{2}}
% - Give the names in the same order as the appear in the paper.
% - Use the \inst{?} command only if the authors have different
%   affiliation.

%\institute[Indiana Universities] % (optional, but mostly needed)
%{
%  \inst{1}%
%  Department of Physics\\
%  Indiana University
%  \and
%  \inst{2}%
%  IUCF\\
%  Indiana University}
% - Use the \inst command only if there are several affiliations.
% - Keep it simple, no one is interested in your street address.


\begin{document}

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  \setbeamercolor{title}{fg=red!80!black,bg=black!10!white}
  %\setbeamerfont{title}{shape=\itshape,family=\rmfamily}
 \titlepage
\end{frame}

\section[Outline]{}

\begin{frame}[label=toc]
\tableofcontents
\end{frame}

\section{Introduction}
\begin{frame}
  \frametitle{Introduction}
   \begin{itemize}[<+->]
     \item Computational Science is another approach besides experimental and theoretical.
     \item Some simulations need a lot of computing power.
     \item Distributed computing is practical an economical way.
     \item Some algorithm/physics model widely used are easily parallelized:
       \begin{enumerate}
         \item Monte Carlo (Ising model, particle generating and tracking)
         \item Simulated Anealing (Ising model)
         \item Lattice QCD
       \end{enumerate}
  \end{itemize}
\end{frame}

\section{The Terminal and The Editor}
\begin{frame}
    \frametitle{Open Two Terminals and Xcode}
    \begin{itemize}[<+->]
        \item Start terminal Finder->Applications->Utilities->Terminal (Double Click)
        \item Start Xcode  Finder->Developer->Applications->Xcode
            \begin{itemize}
                \item File->New File->Python Tool->Name file strings.py
            \end{itemize}
    \end{itemize}
    \includegraphics[width=6cm]{setup.png}
\end{frame}


\section{The Terminal and The Interpreter}
\begin{frame}
    \frametitle{Opening the Terminal and Basic Unix}
    \begin{itemize}[<+->]
        \item Start terminal Finder->Applications->Utilities->Terminal (Double Click)
        \item
    \end{itemize}
\end{frame}

\section{Strings and Numbers}
\begin{frame}
    \frametitle{Strings}
    \begin{itemize}
        \item{A simple string "hello world"}
        \item{Concatenation "hello"+"world" $\to$ "hello world"}
        \item{Repetition "hello"*3 $\to$ "hellohellohello"}
        \item{Indexing "world"[3] $\to$ "l"}
        \begin{itemize}
            \item{Note: python lists are zero-offset}
        \end{itemize}
        \item{Searching "o" in "hello" $\to$ True}
    \end{itemize}
\end{frame}

\begin{frame}
    \frametitle{Numbers}
    \begin{itemize}
        \item{Basic math notation: 1.4, 2+2, 2**10}
        \begin{itemize}[<+->]
            \item{Note: Integer division floors values: 2/3 $\to$ 0, 2./3 $\to$ .6666}
        \end{itemize}
        \item{Math functions require import of math}
         \begin{itemize}
            \item{import math}
            \item{math.sqrt(4) $\to$ 2.0}
        \end{itemize}

    \end{itemize}
\end{frame}

%\section{Epithelium}
%\begin{frame}
%	\frametitle{Images}
%
%	  \begin{columns}
%          \begin{column}[T]{4cm}
%\includegraphics[scale=0.30]{images/CPM_lattice.pdf}
%\begin{itemize}
%	\item{Energy minimization formalism - extended by Graner and Glazier, 1992}
%\end{itemize}
%	  \end{column}
%          \begin{column}[T]{6cm}
%		  \begin{itemize}
%			  \item{Example of Potts model with 6 cells and 4 cell types.}
%			  \item{Colors->cell type}
%			  \item{Pixel Number->cell id}
%
%		  \end{itemize}
%	  \end{column}
%  \end{columns}
%
%
%\end{frame}



%\section{Ising Model}

%\section{Monte Carlo Simulation and Metropolis Algorithm}

%\begin{frame}[t]
%  \frametitle{Monte Carlo Simulation and Metropolis Algorithm} %
%  \begin{itemize}
%    \item Randomly move from one state to another.
%    \item Probability at state $S$:
%      \begin{equation}
%        \label{eq:probability}
%        %P(S)=\frac{e^{\displaystyle -\frac{E(S)}{kT}}}{\sum_{A}e^{\displaystyle -\frac{E(A)}{kT}}}
%        P(S)=\frac{e^{-E(S)/kT}}{\sum_{A}e^{-E(A)/kT}}
%      \end{equation}
%    \item Probability of moving to next state:
%      \begin{equation}
%        \label{eq:probability:move}
%        \frac{P(S_{\textrm{after}})}{P(S_{\textrm{before}})}
%         = e^{-\Delta E/kT}
%      \end{equation}
%      where $\Delta E=E_{\textrm{after}}-E_{\textrm{before}}$
%      \item Accept one step with probability:
%        \begin{equation}
%          \label{eq:metropolis}
%          P_{\textrm{accept}}=\min\{1, \frac{P(S_{\textrm{after}})}{P(S_{\textrm{before}})}\}
%        \end{equation}
%  \end{itemize}
%\end{frame}



%\begin{frame}[t]
%\frametitle{Metropolis Algorithm}
  %\vskip-1cm
  %\begin{algorithm}
 %  % \caption{Metropolis Algorithm}
 %   \begin{algorithmic}[1]
 %     \STATE Initialization, Sweep.
 %     \STATE Compute $E_0$.
 %     \STATE Randomly flip one site.
 %     \STATE Compute $E_1$ and $\Delta E=E_1-E_0$.
 %     \IF{$E_1<E_0$ (i.e. $e^{-(E_1-E_0)/kT}>1$)}
 %     \STATE Keep $E_1$
 %     \ELSE
 %     \STATE Keep $E_1$ with probability $\textrm{rand}()<e^{-(E_1-E_0)/kT}$
 %     \ENDIF
 %   \end{algorithmic}
  %\end{algorithm}
%    \hrule
%    5\vskip.5cm
%    \[
%    E=-\sum_{\textrm{neighbor }i,j}S_iS_j
%    \]
    
%\end{frame}

%\section{Physics Result}


% \section{Reference}
%
% \begin{frame}[t]
%   \frametitle{Reference}
%   \begin{thebibliography}{10}
%   \bibitem{Lee04}[S.\ Y.\ Lee, 2004]
%     S.\ Y.\ Lee.
%     \newblock Accelerator Physics, 2nd, 
%     \newblock \emph{World Scientific}, 2004
%   \end{thebibliography}
% \end{frame}

\end{document}