Commit 8f29fc53 authored by Jefferson Stafusa E. Portela's avatar Jefferson Stafusa E. Portela
Browse files

Add more information on installation; clean out html and pdf versions of notebooks.

parent 55c30a63
......@@ -190,19 +190,26 @@ git clone git@git.physik.uni-wuerzburg.de:ALF/pyALF.git
\end{lstlisting}
To run the notebooks you need the following installed in your machine:
\begin{itemize}
\item Python
\item Python and packages SciPy, NumPy and matplotlib
\item Jupyter
\item the libraries Lapack and Blas
\item a Fortran compiler, such as \texttt{gfortran} or \texttt{ifort},
\end{itemize}
where the last two are required by the main package \href{https://git.physik.uni-wuerzburg.de:ALF}{ALF}. %, which is automatically handled by pyALF.
Also, add pyALF's path to your environment variable \texttt{PYTHONPATH}. In Linux, this can be achieved, e.g., by adding the following line to \texttt{.bashrc}:
\begin{lstlisting}[style=bash]
export PYTHONPATH="/local/path/to/pyALF:$PYTHONPATH"
\end{lstlisting}
Python and its packages can be easily installed on a variety of platforms using the Anaconda distribution -- check its \href{https://docs.anaconda.com/anaconda/install/}{installation instructions} for your system. Then, from Anaconda, you can issue the command
\begin{lstlisting}[style=bash]
conda install -c anaconda ipython jupyterlab scipy numpy matplotlib
\end{lstlisting}
Anaconda is recommended due to its convenience, but the system's package management (e.g., apt-get) or Python's own package management (pip3) can be used instead if preferred -- see, for instance, SciPy \href{https://www.scipy.org/install.html}{installation instructions}.
A Fortran compiler and the libraries needed for ALF... [CHECK the tutorial's README.md]
maybe mention... example... https://www.scipy.org/install.html
\section*{Starting}
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......@@ -15,9 +15,9 @@ square lattice, at \(U/t=4\) half-band filling, and inverse temperature
We carry out a systematic \(\Delta \tau t\) extrapolation keeping
\(\Delta \tau t L_\text{Trotter} = 2\) constant. Recall that the
formulation of the auxiliary field QMC approach is based on the Trotter
decomposition \[
e^{-\Delta \tau \left( A + B \right ) } = e^{-\Delta \tau A } e^{-\Delta \tau B } + {\cal O} \left( \Delta \tau^2 \right)
formulation of the auxiliary field QMC approach is based on the
symmetric Trotter decomposition \[
e^{ -\Delta \tau \left( \hat{A} + \hat{B} \right) } = e^{ -\Delta \tau \hat{A}/2} e^{ -\Delta \tau \hat{B} } e^{ -\Delta \tau \hat{A}/2} + \mathcal{O} \left (\Delta \tau^3\right)
\] The overall error produced by this approximation is of the order
\(\Delta \tau^2\).
......@@ -27,7 +27,7 @@ reference plot for this analyses is found in
\href{https://git.physik.uni-wuerzburg.de/ALF/ALF_code/-/blob/master/Documentation/ALF_v1.0.pdf}{ALF
documentation}, Sec. 2.3.2 (Symmetric Trotter decomposition).
\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\textbf{1.} Import \texttt{Simulation} class from the \texttt{py\_alf}
python module, which provides the interface with ALF, as well as
......@@ -36,10 +36,10 @@ mathematics and plotting packages:
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{1}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k+kn}{from} \PY{n+nn}{py\PYZus{}alf} \PY{k+kn}{import} \PY{n}{Simulation} \PY{c+c1}{\PYZsh{} Interface with ALF}
\PY{k+kn}{from} \PY{n+nn}{py\PYZus{}alf} \PY{k}{import} \PY{n}{Simulation} \PY{c+c1}{\PYZsh{} Interface with ALF}
\PY{c+c1}{\PYZsh{} }
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np} \PY{c+c1}{\PYZsh{} Numerical library}
\PY{k+kn}{from} \PY{n+nn}{scipy}\PY{n+nn}{.}\PY{n+nn}{optimize} \PY{k+kn}{import} \PY{n}{curve\PYZus{}fit} \PY{c+c1}{\PYZsh{} Numerical library}
\PY{k+kn}{from} \PY{n+nn}{scipy}\PY{n+nn}{.}\PY{n+nn}{optimize} \PY{k}{import} \PY{n}{curve\PYZus{}fit} \PY{c+c1}{\PYZsh{} Numerical library}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt} \PY{c+c1}{\PYZsh{} Plotting library}
\end{Verbatim}
\end{tcolorbox}
......@@ -224,11 +224,11 @@ For Dtau values [0.05 0.1 0.15] the measured energies are:
\end{tcolorbox}
\begin{center}
\adjustimage{max size={0.6\linewidth}{0.9\paperheight}}{trotter_error-output_12_2.png}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{trotter_error-output_12_2.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{exercises}{%
\subsection{Exercises}\label{exercises}}
......
This diff is collapsed.
......@@ -383,9 +383,9 @@ square lattice, at \(U/t=4\) half-band filling, and inverse temperature
We carry out a systematic \(\Delta \tau t\) extrapolation keeping
\(\Delta \tau t L_\text{Trotter} = 2\) constant. Recall that the
formulation of the auxiliary field QMC approach is based on the Trotter
decomposition \[
e^{-\Delta \tau \left( A + B \right ) } = e^{-\Delta \tau A } e^{-\Delta \tau B } + {\cal O} \left( \Delta \tau^2 \right)
formulation of the auxiliary field QMC approach is based on the
symmetric Trotter decomposition \[
e^{ -\Delta \tau \left( \hat{A} + \hat{B} \right) } = e^{ -\Delta \tau \hat{A}/2} e^{ -\Delta \tau \hat{B} } e^{ -\Delta \tau \hat{A}/2} + \mathcal{O} \left (\Delta \tau^3\right)
\] The overall error produced by this approximation is of the order
\(\Delta \tau^2\).
......@@ -395,7 +395,7 @@ reference plot for this analyses is found in
\href{https://git.physik.uni-wuerzburg.de/ALF/ALF_code/-/blob/master/Documentation/ALF_v1.0.pdf}{ALF
documentation}, Sec. 2.3.2 (Symmetric Trotter decomposition).
\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\textbf{1.} Import \texttt{Simulation} class from the \texttt{py\_alf}
python module, which provides the interface with ALF, as well as
......@@ -404,10 +404,10 @@ mathematics and plotting packages:
\begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{1}{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k+kn}{from} \PY{n+nn}{py\PYZus{}alf} \PY{k+kn}{import} \PY{n}{Simulation} \PY{c+c1}{\PYZsh{} Interface with ALF}
\PY{k+kn}{from} \PY{n+nn}{py\PYZus{}alf} \PY{k}{import} \PY{n}{Simulation} \PY{c+c1}{\PYZsh{} Interface with ALF}
\PY{c+c1}{\PYZsh{} }
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np} \PY{c+c1}{\PYZsh{} Numerical library}
\PY{k+kn}{from} \PY{n+nn}{scipy}\PY{n+nn}{.}\PY{n+nn}{optimize} \PY{k+kn}{import} \PY{n}{curve\PYZus{}fit} \PY{c+c1}{\PYZsh{} Numerical library}
\PY{k+kn}{from} \PY{n+nn}{scipy}\PY{n+nn}{.}\PY{n+nn}{optimize} \PY{k}{import} \PY{n}{curve\PYZus{}fit} \PY{c+c1}{\PYZsh{} Numerical library}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt} \PY{c+c1}{\PYZsh{} Plotting library}
\end{Verbatim}
\end{tcolorbox}
......@@ -596,7 +596,7 @@ For Dtau values [0.05 0.1 0.15] the measured energies are:
\end{center}
{ \hspace*{\fill} \\}
\begin{center}\rule{0.5\linewidth}{0.5pt}\end{center}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{exercises}{%
\subsection{Exercises}\label{exercises}}
......
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