diff --git a/Makefile b/Makefile
index 1702b05..dd62125 100644
--- a/Makefile
+++ b/Makefile
@@ -1,13 +1,15 @@
BASENAME=scientificcomputing-script
-SUBDIRS=programming statistics bootstrap likelihood pointprocesses
-#spike_trains designpattern
-# regression
+SUBDIRS=designpattern statistics bootstrap regression likelihood pointprocesses
+#programming spike_trains
+SUBTEXS=$(foreach subd, $(SUBDIRS), $(subd)/lecture/$(subd).tex)
-pdf : $(BASENAME).pdf
+pdf : chapters $(BASENAME).pdf
+
+chapters :
for sd in $(SUBDIRS); do $(MAKE) -C $$sd/lecture pdf; done
-$(BASENAME).pdf : $(BASENAME).tex header.tex
+$(BASENAME).pdf : $(BASENAME).tex header.tex $(SUBTEXS)
export TEXMFOUTPUT=.; pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
clean :
@@ -17,5 +19,5 @@ clean :
cleanall : clean
rm -f $(PDFFILE)
-watch :
+watchpdf :
while true; do ! make -q pdf && make pdf; sleep 0.5; done
diff --git a/UT_WBMW_Rot_RGB.pdf b/UT_WBMW_Rot_RGB.pdf
new file mode 100644
index 0000000..f54eedc
--- /dev/null
+++ b/UT_WBMW_Rot_RGB.pdf
@@ -0,0 +1,529 @@
+%PDF-1.5
%
+1 0 obj
<>/OCGs[15 0 R 18 0 R 21 0 R 25 0 R 35 0 R 36 0 R 37 0 R 38 0 R]>>/Pages 2 0 R/Type/Catalog>>
endobj
34 0 obj
<>stream
+
+
+
+
+ application/pdf
+
+
+ EKUT_WortBildMarke_W_RGB
+
+
+
+
+ Adobe Illustrator CS3
+ 2010-07-08T15:07+02:00
+ 2010-09-21T13:10:33+02:00
+ 2010-09-21T13:10:33+02:00
+
+
+
+ 256
+ 68
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diff --git a/bootstrap/lecture/bootstrap.tex b/bootstrap/lecture/bootstrap.tex
index 5b62f89..478316e 100644
--- a/bootstrap/lecture/bootstrap.tex
+++ b/bootstrap/lecture/bootstrap.tex
@@ -103,10 +103,10 @@ der gemessene Mittelwert um den Populationsmittelwert streut.
Mittelwertes. Die --- normalerweise unbekannte ---
Stichprobenverteilung des Mittelwerts (rot) ist um den
Populationsmittelwert bei $\mu=0$ zentriert. Die
- Bootstrap-Verteilung (blau) die durch Resampling aus einer
- Stichprobe gewonnen worden ist hat die gleiche Form und Breite wie
- die Stichprobenverteilung, ist aber um den Mittelwert berechnet
- aus der Stichprobe zentriert. Die Standardabweichung der
+ Bootstrap-Verteilung (blau), die durch Resampling aus einer
+ Stichprobe gewonnen worden ist, hat die gleiche Form und Breite
+ wie die Stichprobenverteilung, ist aber um den Mittelwert der
+ Stichprobe zentriert. Die Standardabweichung der
Bootstrapverteilung kann also als Sch\"atzer f\"ur den
Standardfehler des Mittelwertes verwendet werden.}
\end{figure}
diff --git a/designpattern/lecture/designpattern.tex b/designpattern/lecture/designpattern.tex
index f37fa1c..e9d94d7 100644
--- a/designpattern/lecture/designpattern.tex
+++ b/designpattern/lecture/designpattern.tex
@@ -7,72 +7,6 @@ Beim Programmieren sind sich viel Codes in ihrer Grundstruktur sehr
immer wieder in \"ahnlicher Weise vor. In diesem Kapitel stellen wir
einige dieser ``Design pattern'' zusammen.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Plotten einer mathematischen Funktion}
-Eine mathematische Funktion ordnet einem beliebigen $x$-Wert einen
-$y$-Wert zu. Um eine solche Funktion zeichnen zu k\"onnen, m\"ussen
-wir eine Wertetabelle aus vielen $x$-Werten und den
-dazugeh\"origen Funktionswerten $y=f(x)$ erstellen.
-
-Wir erstellen dazu einen Vektor mit geeigneten $x$-Werten, die von
-dem kleinsten bis zu dem gr\"o{\ss}ten $x$-Wert laufen, den wir
-plotten wollen. Die Schrittweite f\"ur die $x$-Werte w\"ahlen wir
-klein genug, um eine sch\"one glatte Kurve zu bekommen. F\"ur jeden
-Wert $x_i$ dieses Vektors berechnen wir den entsprechenden
-Funktionswert und erzeugen damit einen Vektor mit den $y$-Werten. Die
-Werte des $y$-Vektors k\"onnen dann gegen die Werte des $x$-Vektors
-geplottet werden.
-
-Folgende Programme berechnen und plotten die Funktion $f(x)=e^{-x^2}$:
-\begin{lstlisting}
-xmin = -1.0;
-xmax = 2.0;
-dx = 0.01; % Schrittweite
-x = xmin:dx:xmax; % Vektor mit x-Werten
-y = exp(-x.*x); % keine for Schleife! '.*' fuer elementweises multiplizieren
-plot(x, y);
-\end{lstlisting}
-
-\begin{lstlisting}
-x = -1:0.01:2; % Vektor mit x-Werten
-y = exp(-x.*x); % keine for Schleife! '.*' fuer elementweises multiplizieren
-plot(x, y);
-\end{lstlisting}
-
-\begin{lstlisting}
-x = -1:0.01:2; % Vektor mit x-Werten
-plot(x, exp(-x.*x));
-\end{lstlisting}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Skalieren und Verschieben nicht nur von Zufallszahlen}
-Zufallsgeneratoren geben oft nur Zufallszahlen mit festen Mittelwerten
-und Standardabweichungen (auch Skalierungen) zur\"uck. Multiplikation
-mit einem Faktor skaliert die Standardabweichung und Addition einer Zahl
-verschiebt den Mittelwert.
-
-\begin{lstlisting}
-% 100 random numbers draw from a Gaussian distribution with mean 0 and standard deviation 1.
-x = randn(100, 1);
-
-% 100 random numbers drawn from a Gaussian distribution with mean 4.8 and standard deviation 2.3.
-mu = 4.8;
-sigma = 2.3;
-y = randn(100, 1)*sigma + mu;
-\end{lstlisting}
-
-Das gleiche Prinzip ist manchmal auch sinnvoll f\"ur \code{zeros} oder \code{ones}:
-\begin{lstlisting}
-x = -1:0.01:2; % Vektor mit x-Werten
-plot(x, exp(-x.*x));
-% Plotte f\"ur die gleichen x-Werte eine Linie mit y=0.8:
-plot(x, zeros(size(x))+0.8);
-% ... Linie mit y=0.5:
-plot(x, ones(size(x))*0.5);
-\end{lstlisting}
-
-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{for Schleifen \"uber Vektoren}
Manchmal m\"ochte man doch mit einer for-Schleife \"uber einen Vektor iterieren.
@@ -136,6 +70,72 @@ mean(y)
\end{lstlisting}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Skalieren und Verschieben nicht nur von Zufallszahlen}
+Zufallsgeneratoren geben oft nur Zufallszahlen mit festen Mittelwerten
+und Standardabweichungen (auch Skalierungen) zur\"uck. Multiplikation
+mit einem Faktor skaliert die Standardabweichung und Addition einer Zahl
+verschiebt den Mittelwert.
+
+\begin{lstlisting}
+% 100 random numbers draw from a Gaussian distribution with mean 0 and standard deviation 1.
+x = randn(100, 1);
+
+% 100 random numbers drawn from a Gaussian distribution with mean 4.8 and standard deviation 2.3.
+mu = 4.8;
+sigma = 2.3;
+y = randn(100, 1)*sigma + mu;
+\end{lstlisting}
+
+Das gleiche Prinzip ist manchmal auch sinnvoll f\"ur \code{zeros} oder \code{ones}:
+\begin{lstlisting}
+x = -1:0.01:2; % Vektor mit x-Werten
+plot(x, exp(-x.*x));
+% Plotte f\"ur die gleichen x-Werte eine Linie mit y=0.8:
+plot(x, zeros(size(x))+0.8);
+% ... Linie mit y=0.5:
+plot(x, ones(size(x))*0.5);
+\end{lstlisting}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Plotten einer mathematischen Funktion}
+Eine mathematische Funktion ordnet einem beliebigen $x$-Wert einen
+$y$-Wert zu. Um eine solche Funktion zeichnen zu k\"onnen, m\"ussen
+wir eine Wertetabelle aus vielen $x$-Werten und den
+dazugeh\"origen Funktionswerten $y=f(x)$ erstellen.
+
+Wir erstellen dazu einen Vektor mit geeigneten $x$-Werten, die von
+dem kleinsten bis zu dem gr\"o{\ss}ten $x$-Wert laufen, den wir
+plotten wollen. Die Schrittweite f\"ur die $x$-Werte w\"ahlen wir
+klein genug, um eine sch\"one glatte Kurve zu bekommen. F\"ur jeden
+Wert $x_i$ dieses Vektors berechnen wir den entsprechenden
+Funktionswert und erzeugen damit einen Vektor mit den $y$-Werten. Die
+Werte des $y$-Vektors k\"onnen dann gegen die Werte des $x$-Vektors
+geplottet werden.
+
+Folgende Programme berechnen und plotten die Funktion $f(x)=e^{-x^2}$:
+\begin{lstlisting}
+xmin = -1.0;
+xmax = 2.0;
+dx = 0.01; % Schrittweite
+x = xmin:dx:xmax; % Vektor mit x-Werten
+y = exp(-x.*x); % keine for Schleife! '.*' fuer elementweises multiplizieren
+plot(x, y);
+\end{lstlisting}
+
+\begin{lstlisting}
+x = -1:0.01:2; % Vektor mit x-Werten
+y = exp(-x.*x); % keine for Schleife! '.*' fuer elementweises multiplizieren
+plot(x, y);
+\end{lstlisting}
+
+\begin{lstlisting}
+x = -1:0.01:2; % Vektor mit x-Werten
+plot(x, exp(-x.*x));
+\end{lstlisting}
+
+
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Normierung von Histogrammen}
Meistens sollten Histogramme normiert werden, damit sie vergleichbar
diff --git a/header.tex b/header.tex
index 4cf2881..b88c763 100644
--- a/header.tex
+++ b/header.tex
@@ -1,8 +1,8 @@
%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\title{\textbf{\huge\sffamily\tr{Introduction to\\[1ex] Scientific Computing}{Einf\"uhrung in die\\[1ex] wissenschaftliche Datenverarbeitung}}}
-\author{{\LARGE Jan Grewe \& Jan Benda}\\[4ex]Abteilung Neuroethologie\\[2ex]\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
-\date{WS 15/16\\\vfill \includegraphics[width=1\textwidth]{announcements/correlationcartoon}}
+\author{{\LARGE Jan Grewe \& Jan Benda}\\[5ex]Abteilung Neuroethologie\\[2ex]\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}\vspace{3ex}}
+\date{WS 15/16\\\vfill \centerline{\includegraphics[width=0.7\textwidth]{announcements/correlationcartoon}\rotatebox{90}{\footnotesize\url{www.xkcd.com}}}}
%%%% language %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% \newcommand{\tr}[2]{#1} % en
@@ -193,8 +193,9 @@
\usepackage{framed}
\newcounter{maxexercise}
\setcounter{maxexercise}{10000} % show listings up to exercise maxexercise
-\newcounter{theexercise}
-\setcounter{theexercise}{1}
+\newcounter{exercise}[chapter]
+\setcounter{exercise}{0}
+\newcommand{\theexercise}{\arabic{exercise}}
\newcommand{\codepath}{}
\newenvironment{exercise}[2]%
{\newcommand{\exercisesource}{#1}%
@@ -203,11 +204,12 @@
\newcommand{\saveenumi}{\theenumi}\renewcommand{\labelenumi}{(\alph{enumi})}%
\renewcommand{\FrameCommand}{\colorbox{yellow!15}}%
\MakeFramed{\advance\hsize-\width \FrameRestore}%
- \noindent\textbf{\tr{Exercise}{\"Ubung} \arabic{theexercise}:}\newline}%
+ \stepcounter{exercise}%
+ \noindent\textbf{\tr{Exercise}{\"Ubung} \thechapter.\theexercise:}\newline}%
{\ifthenelse{\equal{\exercisesource}{}}{}%
- {\ifthenelse{\value{theexercise}>\value{maxexercise}}{}%
+ {\ifthenelse{\value{exercise}>\value{maxexercise}}{}%
{\lstinputlisting[belowskip=0pt]{\codepath\exercisesource}%
\ifthenelse{\equal{\exerciseoutput}{}}{}%
{\addtocounter{lstlisting}{-1}\lstinputlisting[language={},title={\textbf{\tr{Output}{Ausgabe}:}},belowskip=0pt]{\codepath\exerciseoutput}}}}%
- \endMakeFramed\stepcounter{theexercise}%
+ \endMakeFramed%
\renewcommand{\theenumi}{\saveenumi}}
diff --git a/programming/lectures/programming.tex b/programming/lectures/programming.tex
index d34ad99..9c7f9c6 100644
--- a/programming/lectures/programming.tex
+++ b/programming/lectures/programming.tex
@@ -1,6 +1,6 @@
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\chapter{\tr{Programming basics}{Grundlagen der Programmierung in \matlab}}
+\chapter{\tr{Programming basics}{Programmierung in \matlab}}
\section{Variablen und Datentypen}
@@ -117,7 +117,7 @@ wie eine einzelne Variable gel\"oscht wird.
\subsection{Datentypen}
Der Datentyp bestimmt, wie die im Speicher abgelegten Bitmuster
-interpretiert werden. Die Wichtigsten Datentpyen sind folgende:
+interpretiert werden. Die wichtigsten Datentpyen sind folgende:
\begin{itemize}
\item \textit{integer} - Ganze Zahlen. Hier gibt es mehrere
@@ -134,7 +134,7 @@ unterschiedlichem Speicherbedarf und Wertebreich.
\begin{table}[]
\centering
-\caption{Gel\"aufige Datentypen und ihr Wertebereich.}
+\caption{Grundlegende Datentypen und ihr Wertebereich.}
\label{dtypestab}
\begin{tabular}{llcl}
\hline
@@ -742,10 +742,11 @@ ans =
\section{Graphische Darstellung von Daten}
-
+%%% Wuerde ich als eigenes Kapitel machen! JB
+%%% In einem separaten Verzeichnis...
\begin{figure}
- \includegraphics[width=0.9\columnwidth]{./images/convincing}
+ \includegraphics[width=0.9\columnwidth]{convincing}
\caption{Die Folgen schlecht annotierter
Plots. \url{www.xkcd.com}} \label{xkcdplotting}
\end{figure}
\ No newline at end of file
diff --git a/scientificcomputing-script.tex b/scientificcomputing-script.tex
index 891303c..aa66a08 100644
--- a/scientificcomputing-script.tex
+++ b/scientificcomputing-script.tex
@@ -1,4 +1,4 @@
-\documentclass[12pt]{report}
+\documentclass[12pt]{book}
\input{header}
@@ -12,10 +12,25 @@
\tableofcontents
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\part{Grundlagen des Programmierens}
+
\graphicspath{{programming/lectures/}{programming/lectures/images/}}
\lstset{inputpath=programming/code}
\include{programming/lectures/programming}
+\chapter{Graphische Darstellung}
+
+\graphicspath{{designpattern/lecture}{designpattern/lecture/figures}}
+\lstset{inputpath=designpattern/code/}
+\include{designpattern/lecture/designpattern}
+
+\chapter{Cheat-Sheet}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\part{Grundlagen der Datenanalyse}
+
\graphicspath{{statistics/lecture/}{statistics/lecture/figures/}}
\lstset{inputpath=statistics/code}
\include{statistics/lecture/statistics}
@@ -26,7 +41,7 @@
\graphicspath{{regression/lecture/}{regression/lecture/figures/}}
\lstset{inputpath=regression/code}
-%\include{regression/lecture/linear_regression}
+\include{regression/lecture/regression}
\graphicspath{{likelihood/lecture/}{likelihood/lecture/figures/}}
\lstset{inputpath=likelihood/code}
@@ -41,8 +56,4 @@
\lstset{inputpath=spike_trains/code/}
\include{spike_trains/lecture/psth_sta}
-\graphicspath{{designpattern/lecture}{designpattern/lecture/figures}}
-\lstset{inputpath=designpattern/code/}
-\include{designpattern/lecture/designpattern}
-
\end{document}
diff --git a/statistics/lecture/statistics.tex b/statistics/lecture/statistics.tex
index edb2ac4..d3a68e6 100644
--- a/statistics/lecture/statistics.tex
+++ b/statistics/lecture/statistics.tex
@@ -29,7 +29,7 @@ der Daten eingesetzt:
unimodalen Normalverteilung sind Median, Mittelwert und Modus
identisch. Rechts: bei unsymmetrischen Verteilungen sind die drei
Gr\"o{\ss}en nicht mehr identisch. Der Mittelwert wird am
- st\"arksten von einem starken Schw\"anz der Verteilung
+ st\"arksten von einem starken Schwanz der Verteilung
herausgezogen. Der Median ist dagegen robuster, aber trotzdem
nicht unbedingt identsich mit dem Modus.}
\end{figure}