supplement.tex

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\begin{document}

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\vskip-36pt%
{\raggedright\sffamily\bfseries\fontsize{20}{25}\selectfont SymPy: symbolic computing in Python\par}%
\vskip10pt
{\raggedright\sffamily\fontsize{12}{16}\selectfont  Supplementary material\par}
\vskip25pt%

The supplementary material takes a deeper look at certain topics in SymPy
which there was not enough room to discuss in the paper.
Section~\ref{suppsec:Gruntz} discusses the Gruntz algorithm, used to
calculate limits in SymPy.  Sections~\ref{suppsec:Series}--\ref{suppsec:Ten}
discuss in depth some selected submodules.  Section~\ref{suppsec:numsimpl}
discusses numerical simplification.  Section~\ref{suppsec:examples} provides
additional examples for topics discussed in the main paper.  In section~\ref{suppsec:sympy-gamma} the SymPy Gamma
project is discussed.  Finally, section~\ref{suppsec:comp-mma}
has a
brief comparison of SymPy with Wolfram Mathematica.

As in the paper, all examples in the supplement assume that the following
has been run:
\begin{verbatim}
>>> from sympy import *
>>> x, y, z = symbols('x y z')
\end{verbatim}


\section{Limits: The Gruntz Algorithm}
\label{suppsec:Gruntz}
\input{gruntz.tex}

% Series module (Formal Power Series, Fourier Series)
\section{Series}
\label{suppsec:Series}
\input{series.tex}

% Logic module
\section{Logic}
\label{suppsec:Logic}
\input{logic}

\section{Diophantine Equations}
\label{suppsec:Dioph}
\input{diophantine}

% Sets
\section{Sets}
\label{suppsec:Sets}
\input{sets}

\section{Statistics}
\label{suppsec:Stats}
\input{stats}

\section{Category Theory}
\label{suppsec:Cat}
\input{categories}

\section{Tensors}
\label{suppsec:Ten}
\input{tensors}

\section{Numerical Simplification}
\label{suppsec:numsimpl}
\input{nsimplify}

\section{Examples}
\label{suppsec:examples}
\input{examples}

\section{SymPy Gamma}
\label{suppsec:sympy-gamma}
\input{gamma}

\section{Comparison with Mathematica}
\label{suppsec:comp-mma}
\input{comparison_with_mma}

\phantomsection
\bibliography{paper}

\end{document}