diff --git a/.gitignore b/.gitignore
index 2c353b4..94ae568 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,4 +26,4 @@
 *.vrb
 __*
 pointprocesses/lecture/pointprocessscetch*.tex
-
+*.DS_Store
diff --git a/Makefile b/Makefile
index a743d0a..140f584 100644
--- a/Makefile
+++ b/Makefile
@@ -3,7 +3,7 @@ BASENAME=scientificcomputing-script
 SUBDIRS=programming debugging plotting codestyle statistics bootstrap regression likelihood pointprocesses designpattern 
 SUBTEXS=$(foreach subd, $(SUBDIRS), $(subd)/lecture/$(subd).tex)
 
-all : script chapters
+all : plots chapters index script exercises
 
 chapters :
 	for sd in $(SUBDIRS); do $(MAKE) -C $$sd/lecture chapter; done
@@ -37,6 +37,11 @@ watchpdf :
 watchscript :
 	while true; do ! make -s -q script && make script; sleep 0.5; done
 
+
+exercises:
+	for sd in $(SUBDIRS); do if test -d $$sd/exercises; then $(MAKE) -C $$sd/exercises pdf; fi; done
+
+
 cleanplots:
 	for sd in $(SUBDIRS); do $(MAKE) -C $$sd/lecture cleanplots; done
 
@@ -46,6 +51,7 @@ cleantex:
 
 clean : cleantex
 	for sd in $(SUBDIRS); do $(MAKE) -C $$sd/lecture clean; done
+	for sd in $(SUBDIRS); do if test -d $$sd/exercises; then $(MAKE) -C $$sd/exercises clean; fi; done
 
 cleanall : clean
 	rm -f $(BASENAME).pdf
diff --git a/README.fonts b/README.fonts
index ab7a941..638013d 100644
--- a/README.fonts
+++ b/README.fonts
@@ -1,6 +1,20 @@
 Fonts for matplotlib
 --------------------
 
+Install Humor Sans font
+```
+sudo apt install fonts-humor-sans
+```
+
+Clear matplotlib font cache:
+```
+cd ~/.cache/matplotlib/
+rm -r *
+```
+
+Older problems
+--------------
+
 Make sure the right fonts are installed:
 ```
 sudo apt-get install ttf-lyx2.0 
diff --git a/bootstrap/code/bootstrapsem.m b/bootstrap/code/bootstrapsem.m
index 18ebae3..f496071 100644
--- a/bootstrap/code/bootstrapsem.m
+++ b/bootstrap/code/bootstrapsem.m
@@ -1,24 +1,25 @@
 nsamples = 100;
 nresamples = 1000;
 
-% draw a SRS (simple random sample, "Stichprobe") from the population:
-x = randn( 1, nsamples );
-fprintf('%-30s  %-5s  %-5s  %-5s\n', '', 'mean', 'stdev', 'sem' )
-fprintf('%30s  %5.2f  %5.2f  %5.2f\n', 'single SRS', mean( x ), std( x ), std( x )/sqrt(nsamples) )
+% draw a simple random sample ("Stichprobe") from the population:
+x = randn(1, nsamples);
+fprintf('%-30s  %-5s  %-5s  %-5s\n', '', 'mean', 'stdev', 'sem')
+fprintf('%30s  %5.2f  %5.2f  %5.2f\n', 'single SRS', mean(x), std(x), std(x)/sqrt(nsamples))
 
 % bootstrap the mean:
-mus = zeros(nresamples,1);  % vector for storing the means
-for i = 1:nresamples        % loop for generating the bootstraps
+mus = zeros(nresamples,1);    % vector for storing the means
+for i = 1:nresamples          % loop for generating the bootstraps
     inx = randi(nsamples, 1, nsamples);  % range, 1D-vector, number
-    xr = x(inx);            % resample the original SRS
-    mus(i) = mean(xr);      % compute statistic of the resampled SRS
+    xr = x(inx);              % resample the original SRS
+    mus(i) = mean(xr);        % compute statistic of the resampled SRS
 end
-fprintf('%30s  %5.2f  %5.2f  -\n', 'bootstrapped distribution', mean( mus ), std( mus ) )
+fprintf('%30s  %5.2f  %5.2f  -\n', 'bootstrapped distribution', mean(mus), std(mus))
 
-% many SRS (we can do that with the random number generator, but not in real life!):
-musrs = zeros(nresamples,1);      % vector for the means of each SRS
+% many SRS (we can do that with the random number generator,
+% but not in real life!):
+musrs = zeros(nresamples,1);  % vector for the means of each SRS
 for i = 1:nresamples
-    x = randn( 1, nsamples );     % draw a new SRS
-    musrs(i) = mean( x );         % compute its mean
+    x = randn(1, nsamples);   % draw a new SRS
+    musrs(i) = mean(x);       % compute its mean
 end
-fprintf('%30s  %5.2f  %5.2f  -\n', 'sampling distribution', mean( musrs ), std( musrs ) )
+fprintf('%30s  %5.2f  %5.2f  -\n', 'sampling distribution', mean(musrs), std(musrs))
diff --git a/bootstrap/code/correlationsignificance.m b/bootstrap/code/correlationsignificance.m
index f5ccb1c..7426cc5 100644
--- a/bootstrap/code/correlationsignificance.m
+++ b/bootstrap/code/correlationsignificance.m
@@ -1,27 +1,27 @@
 % generate correlated data:
-n=200;
-a=0.2;
+n = 200;
+a = 0.2;
 x = randn(n, 1);
 y = randn(n, 1) + a*x;
 
 % correlation coefficient:
 rd = corr(x, y);
-fprintf('correlation coefficient of data r = %.2f\n', rd );
+fprintf('correlation coefficient of data r = %.2f\n', rd);
 
 % distribution of null hypothesis by permutation:
 nperm = 1000;
 rs = zeros(nperm,1);
-for i=1:nperm
-    xr=x(randperm(length(x)));  % shuffle x
-    yr=y(randperm(length(y)));  % shuffle y
+for i = 1:nperm
+    xr = x(randperm(length(x)));  % shuffle x
+    yr = y(randperm(length(y)));  % shuffle y
     rs(i) = corr(xr, yr);
 end
-[h,b] = hist(rs, 20 );
-h = h/sum(h)/(b(2)-b(1));  % normalization
+[h, b] = hist(rs, 20);
+h = h/sum(h)/(b(2)-b(1));         % normalization
 
 % significance:
 rq = quantile(rs, 0.95);
-fprintf('correlation coefficient of null hypothesis at 5%% significance = %.2f\n', rq );
+fprintf('correlation coefficient of null hypothesis at 5%% significance = %.2f\n', rq);
 if rd >= rq
     fprintf('--> correlation r=%.2f is significant\n', rd);
 else
@@ -32,7 +32,7 @@ end
 bar(b, h, 'facecolor', 'b');
 hold on;
 bar(b(b>=rq), h(b>=rq), 'facecolor', 'r');
-plot( [rd rd], [0 4], 'r', 'linewidth', 2 );
+plot([rd rd], [0 4], 'r', 'linewidth', 2);
 xlabel('Correlation coefficient');
 ylabel('Probability density of H0');
 hold off;
diff --git a/bootstrap/code/meandiffsignificance.m b/bootstrap/code/meandiffsignificance.m
new file mode 100644
index 0000000..96a328e
--- /dev/null
+++ b/bootstrap/code/meandiffsignificance.m
@@ -0,0 +1,40 @@
+% generate two data sets:
+n = 200;
+d = 0.2;
+x = randn(n, 1);
+y = randn(n, 1) + d;
+
+% difference of sample means:
+md = mean(y) - mean(x);
+fprintf('difference of means of data d = %.2f\n', md);
+
+% distribution of null hypothesis by permutation:
+nperm = 1000;
+xy = [x; y];                         % x and y data in one column vector
+ds = zeros(nperm,1);
+for i = 1:nperm
+    xyr = xy(randperm(length(xy)));  % shuffle data
+    xr = xyr(1:length(x));           % random x-data
+    yr = xyr(length(x)+1:end);       % random y-data
+    ds(i) = mean(yr) - mean(xr);     % difference of means
+end
+[h, b] = hist(ds, 20);
+h = h/sum(h)/(b(2)-b(1));       % normalization
+
+% significance:
+dq = quantile(ds, 0.95);
+fprintf('difference of means of null hypothesis at 5%% significance = %.2f\n', dq);
+if md >= dq
+    fprintf('--> difference of means d=%.2f is significant\n', md);
+else
+    fprintf('--> d=%.2f is not a significant difference of means\n', md);
+end
+
+% plot:
+bar(b, h, 'facecolor', 'b');
+hold on;
+bar(b(b>=dq), h(b>=dq), 'facecolor', 'r');
+plot([md md], [0 4], 'r', 'linewidth', 2);
+xlabel('Difference of means');
+ylabel('Probability density of H0');
+hold off;
diff --git a/bootstrap/code/meandiffsignificance.out b/bootstrap/code/meandiffsignificance.out
new file mode 100644
index 0000000..e17ad8d
--- /dev/null
+++ b/bootstrap/code/meandiffsignificance.out
@@ -0,0 +1,4 @@
+>> meandiffsignificance
+difference of means of data d = 0.18
+difference of means of null hypothesis at 5% significance = 0.17
+--> difference of means d=0.18 is significant
diff --git a/bootstrap/exercises/Makefile b/bootstrap/exercises/Makefile
index 27691d9..c1e988e 100644
--- a/bootstrap/exercises/Makefile
+++ b/bootstrap/exercises/Makefile
@@ -1,34 +1,3 @@
-TEXFILES=$(wildcard exercises??.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
+TEXFILES=$(wildcard resampling-?.tex)
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
-
-pdf : $(SOLUTIONS) $(EXERCISES)
-
-exercises : $(EXERCISES)
-
-solutions : $(SOLUTIONS)
-
-$(SOLUTIONS) : solutions%.pdf : exercises%.tex instructions.tex
-	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
-	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
-	rm $(patsubst %.pdf,%,$@).[!p]*
-
-$(EXERCISES) : %.pdf : %.tex instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-
-watch :
-	while true; do ! make -q pdf && make pdf; sleep 0.5; done
-
-watchexercises :
-	while true; do ! make -q exercises && make exercises; sleep 0.5; done
-
-watchsolutions :
-	while true; do ! make -q solutions && make solutions; sleep 0.5; done
-
-clean :
-	rm -f *~ *.aux *.log *.out
-
-cleanup : clean
-	rm -f $(SOLUTIONS) $(EXERCISES)
+include ../../exercises.mk
diff --git a/bootstrap/exercises/bootstrapmean.m b/bootstrap/exercises/bootstrapmean.m
index 6f3b494..686df9e 100644
--- a/bootstrap/exercises/bootstrapmean.m
+++ b/bootstrap/exercises/bootstrapmean.m
@@ -10,7 +10,7 @@ function [bootsem, mu] = bootstrapmean(x, resample)
     for i = 1:resample
         % resample:
         xr = x(randi(nsamples, nsamples, 1));
-        % compute statistics on sample:
+        % compute statistics of resampled sample:
         mu(i) = mean(xr);
     end
     bootsem = std(mu);
diff --git a/bootstrap/exercises/correlationbootstrap.m b/bootstrap/exercises/correlationbootstrap.m
index 707285f..8f6f16f 100644
--- a/bootstrap/exercises/correlationbootstrap.m
+++ b/bootstrap/exercises/correlationbootstrap.m
@@ -1,18 +1,18 @@
 %% (a) bootstrap:
 nperm = 1000;
-rb = zeros(nperm,1);
+rb = zeros(nperm, 1);
 for i=1:nperm
     % indices for resampling the data:
     inx = randi(length(x), length(x), 1);
     % resampled data pairs:
-    xb=x(inx);
-    yb=y(inx);
+    xb = x(inx);
+    yb = y(inx);
     rb(i) = corr(xb, yb);
 end
 
 %% (b) pdf of the correlation coefficients:
-[hb,bb] = hist(rb, 20);
-hb = hb/sum(hb)/(bb(2)-bb(1));  % normalization
+[hb, bb] = hist(rb, 20);
+hb = hb/sum(hb)/(bb(2)-bb(1));      % normalization
 
 %% (c) significance:
 rbq = quantile(rb, 0.05);
@@ -25,8 +25,8 @@ end
 
 %% plot:
 hold on;
-bar(b, h, 'facecolor', [0.5 0.5 0.5]);
-bar(bb, hb, 'facecolor', 'b');
+bar(b, h, 'facecolor', [0.5 0.5 0.5]);           % permuation test
+bar(bb, hb, 'facecolor', 'b');                   % bootstrap
 bar(bb(bb<=rbq), hb(bb<=rbq), 'facecolor', 'r');
 plot([rd rd], [0 4], 'r', 'linewidth', 2);
 xlim([-0.25 0.75])
diff --git a/bootstrap/exercises/correlationsignificance.m b/bootstrap/exercises/correlationsignificance.m
index d44af84..c7b1939 100644
--- a/bootstrap/exercises/correlationsignificance.m
+++ b/bootstrap/exercises/correlationsignificance.m
@@ -1,4 +1,4 @@
-%% (a) generate correlated data
+%% (a) generate correlated data:
 n = 1000;
 a = 0.2;
 x = randn(n, 1);
@@ -8,7 +8,7 @@ y = randn(n, 1) + a*x;
 subplot(1, 2, 1);
 plot(x, a*x, 'r', 'linewidth', 3);
 hold on
-%scatter(x, y );   % either scatter ...
+%scatter(x, y );                    % either scatter ...
 plot(x, y, 'o', 'markersize', 2 );  % ... or plot - same plot.
 xlim([-4 4])
 ylim([-4 4])
@@ -20,20 +20,20 @@ hold off
 %c = corrcoef(x, y);  % returns correlation matrix
 %rd = c(1, 2);
 rd = corr(x, y);
-fprintf('correlation coefficient = %.2f\n', rd );
+fprintf('correlation coefficient = %.2f\n', rd);
 
 %% (e) permutation:
 nperm = 1000;
-rs = zeros(nperm,1);
-for i=1:nperm
-    xr=x(randperm(length(x)));  % shuffle x
-    yr=y(randperm(length(y)));  % shuffle y
+rs = zeros(nperm, 1);
+for i = 1:nperm
+    xr = x(randperm(length(x)));      % shuffle x
+    yr = y(randperm(length(y)));      % shuffle y
     rs(i) = corr(xr, yr);
 end
 
 %% (g) pdf of the correlation coefficients:
-[h,b] = hist(rs, 20);
-h = h/sum(h)/(b(2)-b(1));  % normalization
+[h, b] = hist(rs, 20);
+h = h/sum(h)/(b(2)-b(1));           % normalization
 
 %% (h) significance:
 rq = quantile(rs, 0.95);
@@ -49,7 +49,7 @@ subplot(1, 2, 2)
 hold on;
 bar(b, h, 'facecolor', 'b');
 bar(b(b>=rq), h(b>=rq), 'facecolor', 'r');
-plot( [rd rd], [0 4], 'r', 'linewidth', 2);
+plot([rd rd], [0 4], 'r', 'linewidth', 2);
 xlim([-0.25 0.25])
 xlabel('Correlation coefficient');
 ylabel('Probability density of H0');
diff --git a/bootstrap/exercises/exercises01.tex b/bootstrap/exercises/exercises01.tex
deleted file mode 100644
index 0917579..0000000
--- a/bootstrap/exercises/exercises01.tex
+++ /dev/null
@@ -1,205 +0,0 @@
-\documentclass[12pt,a4paper,pdftex]{exam}
-
-\usepackage[english]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 9\stitle}}{{\bfseries\large Bootstrap}}{{\bfseries\large December 9th, 2019}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document}
-
-\input{instructions}
-
-\begin{questions}
-
-\question \qt{Bootstrap the standard error of the mean}
-We want to compute the standard error of the mean of a data set by
-means of the bootstrap method and compare the result with the formula
-``standard deviation divided by the square-root of $n$''.
-\begin{parts}
-  \part Download the file \code{thymusglandweights.dat} from Ilias.
-  This is a data set of the weights of the thymus glands of 14-day old chicken embryos
-  measured in milligram.
-  \part Load the data into Matlab (\code{load} function).
-  \part Compute histogram, mean, and standard error of the mean of the first 80 data points.
-  \part Compute the standard error of the mean of the first 80 data
-  points by bootstrapping the data 500 times. Write a function that
-  bootstraps the standard error of the mean of a given data set. The
-  function should also return a vector with the bootstrapped means.
-  \part Compute the 95\,\% confidence interval for the mean from the
-  bootstrap distribution (\code{quantile()} function) --- the
-  interval that contains the true mean with 95\,\% probability.
-  \part Use the whole data set and the bootstrap method for computing
-  the dependence of the standard error of the mean from the sample
-  size $n$.
-  \part Compare your result with the formula for the standard error
-  $\sigma/\sqrt{n}$.
-\end{parts}
-\begin{solution}
-  \lstinputlisting{bootstrapmean.m}
-  \lstinputlisting{bootstraptymus.m}
-  \includegraphics[width=0.5\textwidth]{bootstraptymus-datahist}
-  \includegraphics[width=0.5\textwidth]{bootstraptymus-meanhist}
-  \includegraphics[width=0.5\textwidth]{bootstraptymus-samples}
-\end{solution}
-
-
-\question \qt{Student t-distribution}
-The distribution of Student's t, $t=\bar x/(\sigma_x/\sqrt{n})$, the
-estimated mean $\bar x$ of a data set of size $n$ divided by the
-estimated standard error of the mean $\sigma_x/\sqrt{n}$, where
-$\sigma_x$ is the estimated standard deviation, is not a normal
-distribution but a Student-t distribution.  We want to compute the
-Student-t distribution and compare it with the normal distribution.
-\begin{parts}
-\part Generate 100000 normally distributed random numbers.
-\part Draw from these data 1000 samples of size $n=3$, 5, 10, and
-50. For each sample size $n$ ...
-\part ... compute the mean $\bar x$ of the samples and plot the
-probability density of these means.
-\part ... compare the resulting probability densities with corresponding
-normal distributions.
-\part ... compute Student's $t=\bar x/(\sigma_x/\sqrt{n})$ and compare its
-distribution with the normal distribution with standard deviation of
-one. Is $t$ normally distributed? Under which conditions is $t$
-normally distributed?
-\end{parts}
-\newsolutionpage
-\begin{solution}
-  \lstinputlisting{tdistribution.m}
-  \includegraphics[width=1\textwidth]{tdistribution-n03}\\
-  \includegraphics[width=1\textwidth]{tdistribution-n05}\\
-  \includegraphics[width=1\textwidth]{tdistribution-n10}\\
-  \includegraphics[width=1\textwidth]{tdistribution-n50}
-\end{solution}
-
-
-\continue
-\question \qt{Permutation test} \label{permutationtest}
-We want to compute the significance of a correlation by means of a permutation test.
-\begin{parts}
-  \part \label{permutationtestdata} Generate 1000 correlated pairs
-  $x$, $y$ of random numbers according to:
-\begin{verbatim}
-n = 1000
-a = 0.2;
-x = randn(n, 1);
-y = randn(n, 1) + a*x;
-\end{verbatim}
-  \part Generate a scatter plot of the two variables.
-  \part Why is $y$ correlated with $x$?
-  \part Compute the correlation coefficient between $x$ and $y$.
-  \part What do you need to do in order to destroy the correlations between the $x$-$y$ pairs?
-  \part Do exactly this 1000 times and compute each time the correlation coefficient.
-  \part Compute and plot the probability density of these correlation
-  coefficients.
-  \part Is the correlation of the original data set significant?
-  \part What does ``significance of the correlation'' mean?
-%  \part Vary the sample size \code{n} and compute in the same way the
-%  significance of the correlation.
-\end{parts}
-\begin{solution}
-  \lstinputlisting{correlationsignificance.m}
-  \includegraphics[width=1\textwidth]{correlationsignificance}
-\end{solution}
-
-\question \qt{Bootstrap the correlation coefficient} 
-The permutation test generates the distribution of the null hypothesis
-of uncorrelated data and we check whether the correlation coefficient
-of the data differs significantly from this
-distribution. Alternatively we can bootstrap the data while keeping
-the pairs and determine the confidence interval of the correlation
-coefficient of the data. If this differs significantly from a
-correlation coefficient of zero we can conclude that the correlation
-coefficient of the data indeed quantifies correlated data.
-
-We take the same data set that we have generated in exercise
-\ref{permutationtest} (\ref{permutationtestdata}).
-\begin{parts}
-  \part Bootstrap 1000 times the correlation coefficient from the data.
-  \part Compute and plot the probability density of these correlation
-  coefficients.
-  \part Is the correlation of the original data set significant?
-\end{parts}
-\begin{solution}
-  \lstinputlisting{correlationbootstrap.m}
-  \includegraphics[width=1\textwidth]{correlationbootstrap}
-\end{solution}
-
-\end{questions}
-
-\end{document}
\ No newline at end of file
diff --git a/bootstrap/exercises/instructions.tex b/bootstrap/exercises/instructions.tex
deleted file mode 100644
index 3041d3e..0000000
--- a/bootstrap/exercises/instructions.tex
+++ /dev/null
@@ -1,6 +0,0 @@
-\vspace*{-7.8ex}
-\begin{center}
-\textbf{\Large Introduction to Scientific Computing}\\[2.3ex]
-{\large Jan Grewe, Jan Benda}\\[-3ex]
-Neuroethology Lab \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
diff --git a/bootstrap/exercises/meandiffpermutation.m b/bootstrap/exercises/meandiffpermutation.m
new file mode 100644
index 0000000..5b02a53
--- /dev/null
+++ b/bootstrap/exercises/meandiffpermutation.m
@@ -0,0 +1,29 @@
+function [md, ds, dq] = meandiffpermutation(x, y, nperm, alpha)
+% Permutation test for difference of means of two independent samples.
+%
+% [md, ds, dq] = meandiffpermutation(x, y, nperm, alpha);
+%
+% Arguments:
+%   x: vector with the samples of the x data set.
+%   y: vector with the samples of the y data set.
+%   nperm: number of permutations run.
+%   alpha: significance level.
+%
+% Returns:
+%   md: difference of the means 
+%   ds: vector containing the differences of the means of the resampled data sets
+%   dq: difference of the means at a significance of alpha.
+
+  md = mean(x) - mean(y);             % measured difference
+  xy = [x; y];                        % merge data sets
+  % permutations:
+  ds = zeros(nperm, 1);
+  for i = 1:nperm
+      xyr = xy(randperm(length(xy))); % shuffle xy
+      xr = xyr(1:length(x));          % random x sample
+      yr = xyr(length(x)+1:end);      % random y sample
+      ds(i) = mean(xr) - mean(yr);
+  end
+  % significance:
+  dq = quantile(ds, 1.0 - alpha);
+end
diff --git a/bootstrap/exercises/meandiffplot.m b/bootstrap/exercises/meandiffplot.m
new file mode 100644
index 0000000..47e8d86
--- /dev/null
+++ b/bootstrap/exercises/meandiffplot.m
@@ -0,0 +1,42 @@
+function meandiffplot(x, y, md, ds, dq, k, nrows)
+% Plot histogram of data sets and of null hypothesis for differences in mean.
+%
+% meandiffplot(x, y, md, ds, dq, k, rows);
+%
+% Arguments:
+%   x: vector with the samples of the x data set.
+%   y: vector with the samples of the y data set.
+%   md: difference of means of the two data sets.
+%   ds: vector containing the differences of the means of the resampled data sets
+%   dq: minimum difference of the means considered significant.
+%   k: current row for the plot panels.
+%   nrows: number of rows of panels in the figure.
+
+  %% (b) plot histograms:
+  subplot(nrows, 2, k*2-1);
+  bmin = min([x; y]);
+  bmax = max([x; y]);
+  bins = bmin:(bmax-bmin)/20.0:bmax;
+  [xh, b] = hist(x, bins);
+  [yh, b] = hist(y, bins);
+  bar(bins, xh, 'facecolor', 'b')
+  hold on
+  bar(bins, yh, 'facecolor', 'r');
+  xlabel('x and y [mV]')
+  ylabel('counts')
+  hold off
+
+  %% (f) pdf of the differences:
+  [h, b] = hist(ds, 20);
+  h = h/sum(h)/(b(2)-b(1));           % normalization
+
+  %% plot:
+  subplot(nrows, 2, k*2)
+  bar(b, h, 'facecolor', 'b');
+  hold on;
+  bar(b(b>=dq), h(b>=dq), 'facecolor', 'r');
+  plot([md md], [0 4], 'r', 'linewidth', 2);
+  xlabel('Difference of means [mV]');
+  ylabel('pdf of H0');
+  hold off;
+end
diff --git a/bootstrap/exercises/meandiffsignificance.m b/bootstrap/exercises/meandiffsignificance.m
new file mode 100644
index 0000000..b5de2fd
--- /dev/null
+++ b/bootstrap/exercises/meandiffsignificance.m
@@ -0,0 +1,37 @@
+n = 200;
+mx = -40.0;
+nperm = 1000;
+alpha = 0.05;
+
+%% (h) repeat for various means of the y-data set:
+mys = [-40.1, -40.2, -40.5];
+for k=1:length(mys)
+
+  %% (a) generate data:
+  my = mys(k);
+  x = randn(n, 1) + mx;
+  y = randn(n, 1) + my;
+
+  %% (d), (e) permutation test:
+  [md, ds, dq] = meandiffpermutation(x, y, nperm, alpha);
+
+  %% (c) difference of means:
+  fprintf('\nmean x = %.1fmV, mean y = %.1fmV\n', mx, my);
+  fprintf('  difference of means = %.2fmV\n', md);
+
+  %% (g) significance:
+  fprintf('  difference of means at 5%% significance = %.2fmV\n', dq);
+  if md >= dq
+      fprintf('  --> measured difference of means is significant\n');
+  else
+      fprintf('  --> measured difference of means is not significant\n');
+  end
+
+  %% (b), (f) plot histograms of data and pdf of differences:
+  meandiffplot(x, y, md, ds, dq, k, length(mys));
+
+  subplot(length(mys), 2, k*2-1);
+  title(sprintf('mx=%.1fmV, my=%.1fmV', mx, my))
+end
+
+savefigpdf(gcf, 'meandiffsignificance.pdf', 12, 10);
diff --git a/bootstrap/exercises/meandiffsignificance.pdf b/bootstrap/exercises/meandiffsignificance.pdf
new file mode 100644
index 0000000..7094421
Binary files /dev/null and b/bootstrap/exercises/meandiffsignificance.pdf differ
diff --git a/bootstrap/exercises/resampling-1.tex b/bootstrap/exercises/resampling-1.tex
new file mode 100644
index 0000000..75b8b81
--- /dev/null
+++ b/bootstrap/exercises/resampling-1.tex
@@ -0,0 +1,116 @@
+\documentclass[12pt,a4paper,pdftex]{exam}
+
+\newcommand{\exercisetopic}{Resampling}
+\newcommand{\exercisenum}{8}
+\newcommand{\exercisedate}{December 14th, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document}
+
+\input{../../exercisestitle}
+
+\begin{questions}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\question \qt{Read chapter 7 of the script on ``resampling methods''!}\vspace{-3ex}
+
+\question \qt{Permutation test of correlations} \label{correlationtest}
+We want to compute the significance of a correlation by means of a permutation test.
+\begin{parts}
+  \part \label{correlationtestdata} Generate 1000 correlated pairs
+  $x$, $y$ of random numbers according to:
+\begin{verbatim}
+n = 1000
+a = 0.2;
+x = randn(n, 1);
+y = randn(n, 1) + a*x;
+\end{verbatim}
+  \part Generate a scatter plot of the two variables.
+  \part Why is $y$ correlated with $x$?
+  \part Compute the correlation coefficient between $x$ and $y$.
+  \part What do you need to do in order to destroy the correlations between the $x$-$y$ pairs?
+  \part Do exactly this 1000 times and compute each time the correlation coefficient.
+  \part Compute and plot the probability density of these correlation
+  coefficients.
+  \part Is the correlation of the original data set significant?
+  \part What does ``significance of the correlation'' mean?
+%  \part Vary the sample size \code{n} and compute in the same way the
+%  significance of the correlation.
+\end{parts}
+\begin{solution}
+  \lstinputlisting{correlationsignificance.m}
+  \includegraphics[width=1\textwidth]{correlationsignificance}
+\end{solution}
+
+\newsolutionpage
+\question \qt{Bootstrap the correlation coefficient} 
+The permutation test generates the distribution of the null hypothesis
+of uncorrelated data and we check whether the correlation coefficient
+of the data differs significantly from this
+distribution. Alternatively we can bootstrap the data while keeping
+the pairs and determine the confidence interval of the correlation
+coefficient of the data. If this differs significantly from a
+correlation coefficient of zero we can conclude that the correlation
+coefficient of the data indeed quantifies correlated data.
+
+We take the same data set that we have generated in exercise
+\ref{correlationtest} (\ref{correlationtestdata}).
+\begin{parts}
+  \part Bootstrap 1000 times the correlation coefficient from the
+  data, i.e.  generate bootstrap data by randomly resampling the
+  original data pairs with replacement. Use the \code{randi()}
+  function for generating random indices that you can use to select a
+  random sample from the original data.
+  \part Compute and plot the probability density of these correlation
+  coefficients.
+  \part Is the correlation of the original data set significant?
+\end{parts}
+\begin{solution}
+  \lstinputlisting{correlationbootstrap.m}
+  \includegraphics[width=1\textwidth]{correlationbootstrap}
+\end{solution}
+
+
+\continue 
+\question \qt{Permutation test of difference of means}
+We want to test whether two data sets come from distributions that
+differ in their mean by means of a permutation test.
+\begin{parts}
+  \part Generate two normally distributed data sets $x$ and $y$
+  containing each $n=200$ samples. Let's assume the $x$ samples are
+  measurements of the membrane potential of a mammalian photoreceptor
+  in darkness with a mean of $-40$\,mV and a standard deviation of
+  1\,mV. The $y$ values are the membrane potentials measured under dim
+  illumination and come from a distribution with the same standard
+  deviation and a mean of $-40.5$\,mV. See section 5.2 ``Scaling and
+  shifting random numbers'' in the script.
+  \part Plot histograms of the $x$ and $y$ data in a single
+  plot. Choose appropriate bins.
+  \part Compute the means of $x$ and $y$ and their difference.
+  \part The null hypothesis is that the $x$ and $y$ data come from the
+  same distribution. How can you generate new samples $x_r$ and $y_r$
+  from the original data that come from the same distribution?
+  \part Do exactly this 1000 times and compute each time the
+  difference of the means of the two resampled samples.
+  \part Compute and plot the probability density of the resulting
+  distribution of the null hypothesis.
+  \part Is the difference of the means of the original data sets significant?
+  \part Repeat this procedure for $y$ samples that are closer or
+  further apart from the mean of the $x$ data set. For this put the
+  computations of the permuation test in a function and all the plotting
+  in another function.
+\end{parts}
+\begin{solution}
+  \lstinputlisting{meandiffpermutation.m}
+  \lstinputlisting{meandiffplot.m}
+  \lstinputlisting{meandiffsignificance.m}
+  \includegraphics[width=1\textwidth]{meandiffsignificance}
+\end{solution}
+
+\end{questions}
+
+\end{document}
\ No newline at end of file
diff --git a/bootstrap/exercises/resampling-2.tex b/bootstrap/exercises/resampling-2.tex
new file mode 100644
index 0000000..e27d1c0
--- /dev/null
+++ b/bootstrap/exercises/resampling-2.tex
@@ -0,0 +1,84 @@
+\documentclass[12pt,a4paper,pdftex]{exam}
+
+\newcommand{\exercisetopic}{Resampling}
+\newcommand{\exercisenum}{X2}
+\newcommand{\exercisedate}{December 14th, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document}
+
+\input{../../exercisestitle}
+
+\begin{questions}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\question \qt{Read chapter 7 of the script on ``resampling methods''!}\vspace{-3ex}
+
+\question \qt{Bootstrap the standard error of the mean}
+We want to compute the standard error of the mean of a data set by
+means of the bootstrap method and compare the result with the formula
+``standard deviation divided by the square-root of $n$''.
+\begin{parts}
+  \part Download the file \code{thymusglandweights.dat} from Ilias.
+  This is a data set of the weights of the thymus glands of 14-day old chicken embryos
+  measured in milligram.
+  \part Load the data into Matlab (\code{load} function).
+  \part Compute histogram, mean, and standard error of the mean of the first 80 data points.
+  \part Compute the standard error of the mean of the first 80 data
+  points by bootstrapping the data 500 times. Write a function that
+  bootstraps the standard error of the mean of a given data set. The
+  function should also return a vector with the bootstrapped means.
+  \part Compute the 95\,\% confidence interval for the mean from the
+  bootstrap distribution (\code{quantile()} function) --- the
+  interval that contains the true mean with 95\,\% probability.
+  \part Use the whole data set and the bootstrap method for computing
+  the dependence of the standard error of the mean from the sample
+  size $n$.
+  \part Compare your result with the formula for the standard error
+  $\sigma/\sqrt{n}$.
+\end{parts}
+\begin{solution}
+  \lstinputlisting{bootstrapmean.m}
+  \lstinputlisting{bootstraptymus.m}
+  \includegraphics[width=0.5\textwidth]{bootstraptymus-datahist}
+  \includegraphics[width=0.5\textwidth]{bootstraptymus-meanhist}
+  \includegraphics[width=0.5\textwidth]{bootstraptymus-samples}
+\end{solution}
+
+
+\question \qt{Student t-distribution}
+The distribution of Student's t, $t=\bar x/(\sigma_x/\sqrt{n})$, the
+estimated mean $\bar x$ of a data set of size $n$ divided by the
+estimated standard error of the mean $\sigma_x/\sqrt{n}$, where
+$\sigma_x$ is the estimated standard deviation, is not a normal
+distribution but a Student-t distribution.  We want to compute the
+Student-t distribution and compare it with the normal distribution.
+\begin{parts}
+\part Generate 100000 normally distributed random numbers.
+\part Draw from these data 1000 samples of size $n=3$, 5, 10, and
+50. For each sample size $n$ ...
+\part ... compute the mean $\bar x$ of the samples and plot the
+probability density of these means.
+\part ... compare the resulting probability densities with corresponding
+normal distributions.
+\part ... compute Student's $t=\bar x/(\sigma_x/\sqrt{n})$ and compare its
+distribution with the normal distribution with standard deviation of
+one. Is $t$ normally distributed? Under which conditions is $t$
+normally distributed?
+\end{parts}
+\newsolutionpage
+\begin{solution}
+  \lstinputlisting{tdistribution.m}
+  \includegraphics[width=1\textwidth]{tdistribution-n03}\\
+  \includegraphics[width=1\textwidth]{tdistribution-n05}\\
+  \includegraphics[width=1\textwidth]{tdistribution-n10}\\
+  \includegraphics[width=1\textwidth]{tdistribution-n50}
+\end{solution}
+
+\end{questions}
+
+\end{document}
\ No newline at end of file
diff --git a/bootstrap/exercises/savefigpdf.m b/bootstrap/exercises/savefigpdf.m
index fbe2dc2..97a28a3 100644
--- a/bootstrap/exercises/savefigpdf.m
+++ b/bootstrap/exercises/savefigpdf.m
@@ -1,28 +1,28 @@
-function savefigpdf( fig, name, width, height )
+function savefigpdf(fig, name, width, height)
 % Saves figure fig in pdf file name.pdf with appropriately set page size
 % and fonts
 
-% default width:
-if nargin < 3
-    width = 11.7;
-end
-% default height:
-if nargin < 4
-    height = 9.0;
-end
+  % default width:
+  if nargin < 3
+      width = 11.7;
+  end
+  % default height:
+  if nargin < 4
+      height = 9.0;
+  end
 
-% paper:
-set( fig, 'PaperUnits', 'centimeters' );
-set( fig, 'PaperSize', [width height] );
-set( fig, 'PaperPosition', [0.0 0.0 width height] );
-set( fig, 'Color', 'white')
+  % paper:
+  set(fig, 'PaperUnits', 'centimeters');
+  set(fig, 'PaperSize', [width height]);
+  set(fig, 'PaperPosition', [0.0 0.0 width height]);
+  set(fig, 'Color', 'white')
 
-% font:
-set( findall( fig, 'type', 'axes' ), 'FontSize', 12 )
-set( findall( fig, 'type', 'text' ), 'FontSize', 12 )
+  % font:
+  set(findall(fig, 'type', 'axes'), 'FontSize', 12)
+  set(findall(fig, 'type', 'text'), 'FontSize', 12)
 
-% save:
-saveas( fig, name, 'pdf' )
+  % save:
+  saveas(fig, name, 'pdf')
 
 end
 
diff --git a/bootstrap/exercises/tdistribution.m b/bootstrap/exercises/tdistribution.m
index 5fe8341..e20aebc 100644
--- a/bootstrap/exercises/tdistribution.m
+++ b/bootstrap/exercises/tdistribution.m
@@ -1,10 +1,10 @@
 %% (a) generate random numbers:
 n = 100000;
-x=randn(n, 1);
+x = randn(n, 1);
 
-for nsamples=[3 5 10 50]
+for nsamples = [3 5 10 50]
     nsamples
-    %% compute mean, standard deviation and t:
+    % compute mean, standard deviation and t:
     nmeans = 10000;
     means = zeros(nmeans, 1);
     sdevs = zeros(nmeans, 1);
@@ -19,14 +19,14 @@ for nsamples=[3 5 10 50]
     % Gaussian pdfs:
     msdev = std(means);
     tsdev = 1.0;
-    dxg=0.01;
+    dxg = 0.01;
     xmax = 10.0;
     xmin = -xmax;
     xg = [xmin:dxg:xmax];
     pm = exp(-0.5*(xg/msdev).^2)/sqrt(2.0*pi)/msdev;
     pt = exp(-0.5*(xg/tsdev).^2)/sqrt(2.0*pi)/tsdev;
     
-    %% plots
+    % plots:
     subplot(1, 2, 1)
     bins = xmin:0.2:xmax;
     [h,b] = hist(means, bins);
@@ -35,7 +35,7 @@ for nsamples=[3 5 10 50]
     hold on
     plot(xg, pm, 'r', 'linewidth', 2)
     title(sprintf('sample size = %d', nsamples));
-    xlim( [-3, 3] );
+    xlim([-3, 3]);
     xlabel('Mean');
     ylabel('pdf');
     hold off;
@@ -48,11 +48,11 @@ for nsamples=[3 5 10 50]
     hold on
     plot(xg, pt, 'r', 'linewidth', 2)
     title(sprintf('sample size = %d', nsamples));
-    xlim( [-8, 8] );
+    xlim([-8, 8]);
     xlabel('Student-t');
     ylabel('pdf');
     hold off;
     
     savefigpdf(gcf, sprintf('tdistribution-n%02d.pdf', nsamples), 14, 5);
-    pause( 3.0 )
+    pause(3.0)
 end
diff --git a/bootstrap/lecture/bootstrap-chapter.tex b/bootstrap/lecture/bootstrap-chapter.tex
index 845b6b1..60c2b5e 100644
--- a/bootstrap/lecture/bootstrap-chapter.tex
+++ b/bootstrap/lecture/bootstrap-chapter.tex
@@ -10,6 +10,8 @@
 \setcounter{page}{\pagenumber}
 \setcounter{chapter}{\chapternumber}
 
+\renewcommand{\exercisesolutions}{here}  % 0: here, 1: chapter, 2: end
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document} 
@@ -17,10 +19,20 @@
 \include{bootstrap}
 
 \section{TODO}
+
 This chapter easily covers two lectures:
 \begin{itemize}
 \item 1. Bootstrapping with a proper introduction of of confidence intervals
-\item 2. Permutation test with a proper introduction of statistical tests (dsitribution of nullhypothesis, significance, power, etc.)
+\item 2. Permutation test with a proper introduction of statistical tests (distribution of nullhypothesis, significance, power, etc.)
+\end{itemize}
+ToDo:
+\begin{itemize}
+\item Add jacknife methods to bootstrapping
+\item Add discussion of confidence intervals to descriptive statistics chapter
+\item Have a separate chapter on statistical tests before. What is the
+  essence of a statistical test (null hypothesis distribution), power
+  analysis, and a few examples of existing functions for statistical
+  tests.
 \end{itemize}
 
 \end{document}
diff --git a/bootstrap/lecture/bootstrap-slides.tex b/bootstrap/lecture/bootstrap-slides.tex
index 31e5655..3801d64 100644
--- a/bootstrap/lecture/bootstrap-slides.tex
+++ b/bootstrap/lecture/bootstrap-slides.tex
@@ -49,7 +49,6 @@
 
 %%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{graphicx}
-\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
 
 %%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{listings}
diff --git a/bootstrap/lecture/bootstrap.tex b/bootstrap/lecture/bootstrap.tex
index 667c833..66a526f 100644
--- a/bootstrap/lecture/bootstrap.tex
+++ b/bootstrap/lecture/bootstrap.tex
@@ -1,20 +1,28 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\chapter{Bootstrap methods}
+\chapter{Resampling methods}
 \label{bootstrapchapter}
-\exercisechapter{Bootstrap methods}
+\exercisechapter{Resampling methods}
 
-Bootstrapping methods are applied to create distributions of
-statistical measures via resampling of a sample. Bootstrapping offers several
-advantages:
+
+\entermde{Resampling-Methoden}{Resampling methods} are applied to
+generate distributions of statistical measures via resampling of
+existing samples. Resampling offers several advantages:
 \begin{itemize}
 \item Fewer assumptions (e.g. a measured sample does not need to be
   normally distributed).
 \item Increased precision as compared to classical methods. %such as?
-\item General applicability: the bootstrapping methods are very
+\item General applicability: the resampling methods are very
   similar for different statistics and there is no need to specialize
   the method to specific statistic measures.
 \end{itemize}
+Resampling methods can be used for both estimating the precision of
+estimated statistics (e.g. standard error of the mean, confidence
+intervals) and testing for significane.
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Bootstrapping}
 
 \begin{figure}[tp]
   \includegraphics[width=0.8\textwidth]{2012-10-29_16-26-05_771}\\[2ex]
@@ -72,10 +80,10 @@ distribution of average values around the true mean of the population
 
 Alternatively, we can use \enterm{bootstrapping}
 (\determ[Bootstrap!Verfahren]{Bootstrapverfahren}) to generate new
-samples from one set of measurements
-(\entermde{Resampling}{resampling}). From these bootstrapped samples
-we compute the desired statistical measure and estimate their
-distribution (\entermde{Bootstrap!Verteilung}{bootstrap distribution},
+samples from one set of measurements by means of resampling. From
+these bootstrapped samples we compute the desired statistical measure
+and estimate their distribution
+(\entermde{Bootstrap!Verteilung}{bootstrap distribution},
 \subfigref{bootstrapsamplingdistributionfig}{c}).  Interestingly, this
 distribution is very similar to the sampling distribution regarding
 its width. The only difference is that the bootstrapped values are
@@ -84,19 +92,20 @@ of the statistical population. We can use the bootstrap distribution
 to draw conclusion regarding the precision of our estimation (e.g.
 standard errors and confidence intervals).
 
-Bootstrapping methods create bootstrapped samples from a SRS by
+Bootstrapping methods generate bootstrapped samples from a SRS by
 resampling. The bootstrapped samples are used to estimate the sampling
 distribution of a statistical measure. The bootstrapped samples have
-the same size as the original sample and are created by randomly
+the same size as the original sample and are generated by randomly
 drawing with replacement. That is, each value of the original sample
-can occur once, multiple time, or not at all in a bootstrapped
+can occur once, multiple times, or not at all in a bootstrapped
 sample. This can be implemented by generating random indices into the
 data set using the \code{randi()} function.
 
 
-\section{Bootstrap of the standard error}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\subsection{Bootstrap the standard error}
 
-Bootstrapping can be nicely illustrated at the example of the
+Bootstrapping can be nicely illustrated on the example of the
 \enterm{standard error} of the mean (\determ{Standardfehler}). The
 arithmetic mean is calculated for a simple random sample. The standard
 error of the mean is the standard deviation of the expected
@@ -116,11 +125,10 @@ population.
     the standard error of the mean.}
 \end{figure}
 
-Via bootstrapping we create a distribution of the mean values
+Via bootstrapping we generate a distribution of mean values
 (\figref{bootstrapsemfig}) and the standard deviation of this
-distribution is the standard error of the mean.
+distribution is the standard error of the sample mean.
 
-\pagebreak[4]
 \begin{exercise}{bootstrapsem.m}{bootstrapsem.out}
   Create the distribution of mean values from bootstrapped samples
   resampled from a single SRS. Use this distribution to estimate the
@@ -138,68 +146,166 @@ distribution is the standard error of the mean.
 \end{exercise}
 
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Permutation tests}
+
 Statistical tests ask for the probability of a measured value to
 originate from a null hypothesis. Is this probability smaller than the
 desired \entermde{Signifikanz}{significance level}, the
-\entermde{Nullhypothese}{null hypothesis} may be rejected.
+\entermde{Nullhypothese}{null hypothesis} can be rejected.
 
 Traditionally, such probabilities are taken from theoretical
-distributions which are based on assumptions about the data.  Thus the
-applied statistical test has to be appropriate for the type of
-data. An alternative approach is to calculate the probability density
-of the null hypothesis directly from the data itself. To do this, we
-need to resample the data according to the null hypothesis from the
-SRS. By such permutation operations we destroy the feature of interest
-while we conserve all other statistical properties of the data.
+distributions which have been derived based on some assumptions about
+the data. For example, the data should be normally distributed.  Given
+some data one has to find an appropriate test that matches the
+properties of the data. An alternative approach is to calculate the
+probability density of the null hypothesis directly from the data
+themselves. To do so, we need to resample the data according to the
+null hypothesis from the SRS. By such permutation operations we
+destroy the feature of interest while conserving all other statistical
+properties of the data.
+
+
+\subsection{Significance of a difference in the mean}
+
+Often we would like to know whether two data sets differ in their
+mean. Whether the ears of foxes are larger in southern Europe compared
+to the ones from Scandinavia, whether a drug decreases blood pressure
+in humans, whether a sensory stimulus increases the firing rate of a
+neuron, etc. The \entermde{Nullhypothese}{null hypothesis} is that
+they do not differ in their means, i.e. that both data sets come from
+the same distribution. But even if the two data sets come from the
+same distribution, their sample means may nevertheless differ by
+chance. We need to figure out how these differences of the means are
+distributed. Only if the measured difference between the means is
+significantly larger than the ones obtained by chance we can reject
+the null hypothesis and consider the two data sets to differ
+significantly in their means.
+
+We can easily estimate the distribution of the null hypothesis by
+putting the data of both data sets in one big bag. By merging the two
+data sets we assume that all the data values come from the same
+distribution. We then randomly separate the data values into two new
+data sets. These random data sets contain data from both original data
+sets and thus come from the same distribution. From these random data
+sets we compute the difference of their sample means. This procedure
+is repeated many, say one thousand, times and each time we get a value
+for a difference of means. The distribution of these values is the
+distribution of the null hypothesis. It is the distribution of
+differences of mean values that we get by chance although the two data
+sets come from the same distribution. For a one-sided test that checks
+whether the measured difference of means is significantly larger than
+zero at a significance level of 5\,\% we compute the value of the
+95\,\% percentile of the null distribution. If the measured value is
+larger, we can reject the null hypothesis and consider the two data
+sets to differ significantly in their means.
+
+By using the original data to estimate the null hypothesis, we make no
+assumption about the properties of the data. We do not need to worry
+about the data being normally distributed. We do not need to memorize
+which test to use in which situation. And we better understand what we
+are testing, because we design the test ourselves. Nowadays, computer
+are powerful enough to iterate even ten thousand times over the data
+to compute the distribution of the null hypothesis --- with only a few
+lines of code. This is why \entermde{Permutationstest}{permutaion
+  test} are getting quite popular.
 
 \begin{figure}[tp]
-  \includegraphics[width=1\textwidth]{permutecorrelation}
-  \titlecaption{\label{permutecorrelationfig}Permutation test for
-    correlations.}{Let the correlation coefficient of a dataset with
-    200 samples be $\rho=0.21$. The distribution of the null
-    hypothesis (yellow), optained from the correlation coefficients of
-    permuted and therefore uncorrelated datasets is centered around
-    zero. The measured correlation coefficient is larger than the
-    95\,\% percentile of the null hypothesis. The null hypothesis may
-    thus be rejected and the measured correlation is considered
-    statistically significant.}
+  \includegraphics[width=1\textwidth]{permuteaverage}
+  \titlecaption{\label{permuteaverage}Permutation test for differences
+    in means.}{We want to test whether two datasets
+    $\left\{x_i\right\}$ (red) and $\left\{y_i\right\}$ (blue) come
+    from different distributions by assessing the significance of the
+    difference in their sample means. The data sets were generated
+    with a difference in their population means of $d=0.7$. For
+    generating the distribution of the null hypothesis, i.e. the
+    distribution of differences in the means if the two data sets come
+    from the same distribution, we randomly select the same number of
+    samples from both data sets (top right). This is repeated many
+    times and results in the desired distribution of differences of
+    means (bottom). The measured difference is clearly beyond the
+    95\,\% percentile of this distribution and thus indicates a
+    significant difference between the distributions of the two
+    original data sets.}
 \end{figure}
 
-A good example for the application of a
-\entermde{Permutationstest}{permutaion test} is the statistical
-assessment of \entermde[correlation]{Korrelation}{correlations}. Given
-are measured pairs of data points $(x_i, y_i)$. By calculating the
+\begin{exercise}{meandiffsignificance.m}{meandiffsignificance.out}
+Estimate the statistical significance of a difference in the mean of two data sets.
+\vspace{-1ex}
+\begin{enumerate}
+\item Generate two independent data sets, $\left\{x_i\right\}$ and
+  $\left\{y_i\right\}$, of $n=200$ samples each, by drawing random
+  numbers from a normal distribution. Add 0.2 to all the $y_i$ samples
+  to ensure the population means to differ by 0.2.
+\item Calculate the difference between the sample means of the two data sets.
+\item Estimate the distribution of the null hypothesis of no
+  difference of the means by generating new data sets with the same
+  number of samples randomly selected from both data sets.  For this
+  lump the two data sets together into a single vector. Then permute
+  the order of the elements in this vector using the function
+  \varcode{randperm()}, split it into two data sets and calculate
+  the difference of their means. Repeat this 1000 times.
+\item Read out the 95\,\% percentile from the resulting distribution
+  of the differences in the mean, the null hypothesis using the
+  \varcode{quantile()} function, and compare it with the difference of
+  means measured from the original data sets.
+\end{enumerate}
+\end{exercise}
+
+
+\subsection{Significance of correlations}
+
+Another nice example for the application of a
+\entermde{Permutationstest}{permutaion test} is testing for
+significant \entermde[correlation]{Korrelation}{correlations}
+(figure\,\ref{permutecorrelationfig}). Given are measured pairs of
+data points $(x_i, y_i)$. By calculating the
 \entermde[correlation!correlation
-coefficient]{Korrelationskoeffizient}{correlation
-  coefficient} we can quantify how strongly $y$ depends on $x$. The
-correlation coefficient alone, however, does not tell whether the
-correlation is significantly different from a random correlation. The
-\entermde{Nullhypothese}{null hypothesis} for such a situation is that
-$y$ does not depend on $x$. In order to perform a permutation test, we
-need to destroy the correlation by permuting the $(x_i, y_i)$ pairs,
-i.e. we rearrange the $x_i$ and $y_i$ values in a random
+coefficient]{Korrelationskoeffizient}{correlation coefficient} we can
+quantify how strongly $y$ depends on $x$. The correlation coefficient
+alone, however, does not tell whether the correlation is significantly
+different from a non-zero correlation that we might get although there
+is no true correlation in the data. The \entermde{Nullhypothese}{null
+  hypothesis} for such a situation is that $y$ does not depend on
+$x$. In order to perform a permutation test, we need to destroy the
+correlation between the data pairs by permuting the $(x_i, y_i)$
+pairs, i.e. we rearrange the $x_i$ and $y_i$ values in a random
 fashion. Generating many sets of random pairs and computing the
-resulting correlation coefficients yields a distribution of
-correlation coefficients that result randomly from uncorrelated
+corresponding correlation coefficients yields a distribution of
+correlation coefficients that result randomly from truly uncorrelated
 data. By comparing the actually measured correlation coefficient with
 this distribution we can directly assess the significance of the
-correlation (figure\,\ref{permutecorrelationfig}).
+correlation.
+
+\begin{figure}[tp]
+  \includegraphics[width=1\textwidth]{permutecorrelation}
+  \titlecaption{\label{permutecorrelationfig}Permutation test for
+    correlations.}{Let the correlation coefficient of a dataset with
+    200 samples be $\rho=0.21$ (top left). By shuffling the data pairs
+    we destroy any true correlation (top right). The resulting
+    distribution of the null hypothesis (bottm, yellow), optained from
+    the correlation coefficients of permuted and therefore
+    uncorrelated datasets is centered around zero. The measured
+    correlation coefficient is larger than the 95\,\% percentile of
+    the null hypothesis. The null hypothesis may thus be rejected and
+    the measured correlation is considered statistically significant.}
+\end{figure}
 
 \begin{exercise}{correlationsignificance.m}{correlationsignificance.out}
 Estimate the statistical significance of a correlation coefficient.
 \begin{enumerate}
-\item Create pairs of $(x_i, y_i)$ values. Randomly choose $x$-values
+\item Generate pairs of $(x_i, y_i)$ values. Randomly choose $x$-values
   and calculate the respective $y$-values according to $y_i =0.2 \cdot x_i + u_i$
   where $u_i$ is a random number drawn from a normal distribution.
 \item Calculate the correlation coefficient.
-\item Generate the distribution of the null hypothesis by generating
+\item Estimate the distribution of the null hypothesis by generating
   uncorrelated pairs. For this permute $x$- and $y$-values
   \matlabfun{randperm()} 1000 times and calculate for each permutation
   the correlation coefficient.
 \item Read out the 95\,\% percentile from the resulting distribution
-  of the null hypothesis and compare it with the correlation
-  coefficient computed from the original data.
+  of the null hypothesis using the \varcode{quantile()} function and
+  compare it with the correlation coefficient computed from the
+  original data.
 \end{enumerate}
 \end{exercise}
 
diff --git a/bootstrap/lecture/bootstrapsem.py b/bootstrap/lecture/bootstrapsem.py
index 3a276bd..d431111 100644
--- a/bootstrap/lecture/bootstrapsem.py
+++ b/bootstrap/lecture/bootstrapsem.py
@@ -24,7 +24,7 @@ for i in range(nresamples) :
     musrs.append(np.mean(rng.randn(nsamples)))
 hmusrs, _ = np.histogram(musrs, bins, density=True)
 
-fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
+fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.1*figure_height))
 fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=1.5))
 ax.set_xlabel('Mean')
 ax.set_xlim(-0.4, 0.4)
diff --git a/bootstrap/lecture/permuteaverage.py b/bootstrap/lecture/permuteaverage.py
new file mode 100644
index 0000000..53cfc0b
--- /dev/null
+++ b/bootstrap/lecture/permuteaverage.py
@@ -0,0 +1,103 @@
+import numpy as np
+import scipy.stats as st
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import matplotlib.ticker as ticker
+from plotstyle import *
+
+rng = np.random.RandomState(637281)
+
+# generate data that differ in their mein by d:
+n = 200
+d = 0.7
+x = rng.randn(n) + d;
+y = rng.randn(n);
+#x = rng.exponential(1.0, n);
+#y = rng.exponential(1.0, n) + d;
+
+# histogram of data:
+db = 0.5
+bins = np.arange(-2.5, 2.6, db)
+hx, _ = np.histogram(x, bins)
+hy, _ = np.histogram(y, bins)
+
+# Diference of means, pooled standard deviation and Cohen's d:
+ad = np.mean(x)-np.mean(y)
+s = np.sqrt(((len(x)-1)*np.var(x)+(len(y)-1)*np.var(y))/(len(x)+len(y)-2))
+cd = ad/s
+
+# permutation:
+nperm = 1000
+ads = []
+xy = np.hstack((x, y))
+for i in range(nperm) :
+    xyp = rng.permutation(xy)
+    ads.append(np.mean(xyp[:len(x)])-np.mean(xyp[len(x):]))
+
+# histogram of shuffled data:
+hxp, _ = np.histogram(xyp[:len(x)], bins)
+hyp, _ = np.histogram(xyp[len(x):], bins)
+    
+# pdf of the differences of means:
+h, b = np.histogram(ads, 20, density=True)
+
+# significance:
+dq = np.percentile(ads, 95.0)
+print('Measured difference of means = %.2f, difference at 95%% percentile of permutation = %.2f' % (ad, dq))
+da = 1.0-0.01*st.percentileofscore(ads, ad)
+print('Measured difference of means %.2f is at %.2f%% percentile of permutation' % (ad, 100.0*da))
+
+ap, at = st.ttest_ind(x, y)
+print('Measured difference of means %.2f is at %.2f%% percentile of test' % (ad, ap))
+
+
+fig = plt.figure(figsize=cm_size(figure_width, 1.8*figure_height))
+gs = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.8,
+                       **adjust_fs(fig, left=5.0, right=1.5, top=1.0, bottom=2.7))
+
+ax = fig.add_subplot(gs[0,0])
+ax.bar(bins[:-1]-0.25*db, hy, 0.5*db, **fsA)
+ax.bar(bins[:-1]+0.25*db, hx, 0.5*db, **fsB)
+ax.annotate('', xy=(0.0, 45.0), xytext=(d, 45.0), arrowprops=dict(arrowstyle='<->'))
+ax.text(0.5*d, 50.0, 'd=%.1f' % d, ha='center')
+ax.set_xlim(-2.5, 2.5)
+ax.set_ylim(0.0, 50)
+ax.yaxis.set_major_locator(ticker.MultipleLocator(20.0))
+ax.set_xlabel('Original x and y values')
+ax.set_ylabel('Counts')
+
+ax = fig.add_subplot(gs[0,1])
+ax.bar(bins[:-1]-0.25*db, hyp, 0.5*db, **fsA)
+ax.bar(bins[:-1]+0.25*db, hxp, 0.5*db, **fsB)
+ax.set_xlim(-2.5, 2.5)
+ax.set_ylim(0.0, 50)
+ax.yaxis.set_major_locator(ticker.MultipleLocator(20.0))
+ax.set_xlabel('Shuffled x and y values')
+ax.set_ylabel('Counts')
+
+ax = fig.add_subplot(gs[1,:])
+ax.annotate('Measured\ndifference\nis significant!',
+            xy=(ad, 1.2), xycoords='data',
+            xytext=(ad-0.1, 2.2), textcoords='data', ha='right',
+            arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
+                connectionstyle="angle3,angleA=-20,angleB=100") )
+ax.annotate('95% percentile',
+            xy=(0.19, 0.9), xycoords='data',
+            xytext=(0.3, 5.0), textcoords='data', ha='left',
+            arrowprops=dict(arrowstyle="->", relpos=(0.1,0.0),
+                connectionstyle="angle3,angleA=40,angleB=80") )
+ax.annotate('Distribution of\nnullhypothesis',
+            xy=(-0.08, 3.0), xycoords='data',
+            xytext=(-0.22, 4.5), textcoords='data', ha='left',
+            arrowprops=dict(arrowstyle="->", relpos=(0.2,0.0),
+                connectionstyle="angle3,angleA=60,angleB=150") )
+ax.bar(b[:-1], h, width=b[1]-b[0], **fsC)
+ax.bar(b[:-1][b[:-1]>=dq], h[b[:-1]>=dq], width=b[1]-b[0], **fsB)
+ax.plot( [ad, ad], [0, 1], **lsA)
+ax.set_xlim(-0.25, 0.85)
+ax.set_ylim(0.0, 5.0)
+ax.yaxis.set_major_locator(ticker.MultipleLocator(2.0))
+ax.set_xlabel('Difference of means')
+ax.set_ylabel('PDF of H0')
+
+plt.savefig('permuteaverage.pdf')
diff --git a/bootstrap/lecture/permutecorrelation.py b/bootstrap/lecture/permutecorrelation.py
index c5f0db7..3357140 100644
--- a/bootstrap/lecture/permutecorrelation.py
+++ b/bootstrap/lecture/permutecorrelation.py
@@ -1,9 +1,11 @@
 import numpy as np
 import scipy.stats as st
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import matplotlib.ticker as ticker
 from plotstyle import *
 
-rng = np.random.RandomState(637281)
+rng = np.random.RandomState(37281)
 
 # generate correlated data:
 n = 200
@@ -19,32 +21,56 @@ rd = np.corrcoef(x, y)[0, 1]
 nperm = 1000
 rs = []
 for i in range(nperm) :
-    xr=rng.permutation(x)
-    yr=rng.permutation(y)
-    rs.append( np.corrcoef(xr, yr)[0, 1] )
+    xr = rng.permutation(x)
+    yr = rng.permutation(y)
+    rs.append(np.corrcoef(xr, yr)[0, 1])
+rr = np.corrcoef(xr, yr)[0, 1]
 
 # pdf of the correlation coefficients:
 h, b = np.histogram(rs, 20, density=True)
 
 # significance:
 rq = np.percentile(rs, 95.0)
-print('Measured correlation coefficient = %.2f, correlation coefficient at 95%% percentile of bootstrap = %.2f' % (rd, rq))
+print('Measured correlation coefficient = %.2f, correlation coefficient at 95%% percentile of permutation = %.2f' % (rd, rq))
 ra = 1.0-0.01*st.percentileofscore(rs, rd)
-print('Measured correlation coefficient %.2f is at %.4f percentile of bootstrap' % (rd, ra))
+print('Measured correlation coefficient %.2f is at %.4f percentile of permutation' % (rd, ra))
 
 rp, ra = st.pearsonr(x, y)
 print('Measured correlation coefficient %.2f is at %.4f percentile of test' % (rp, ra))
 
-fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
-fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=0.5, top=1.0))
+fig = plt.figure(figsize=cm_size(figure_width, 1.8*figure_height))
+gs = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.8,
+                       **adjust_fs(fig, left=5.0, right=1.5, top=1.0, bottom=2.7))
+
+ax = fig.add_subplot(gs[0,0])
+ax.text(0.0, 4.0, 'r=%.2f' % rd, ha='center')
+ax.plot(x, y, **psAm)
+ax.set_xlim(-4.0, 4.0)
+ax.set_ylim(-4.0, 4.0)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(2.0))
+ax.yaxis.set_major_locator(ticker.MultipleLocator(2.0))
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+
+ax = fig.add_subplot(gs[0,1])
+ax.text(0.0, 4.0, 'r=%.2f' % rr, ha='center')
+ax.plot(xr, yr, **psAm)
+ax.set_xlim(-4.0, 4.0)
+ax.set_ylim(-4.0, 4.0)
+ax.xaxis.set_major_locator(ticker.MultipleLocator(2.0))
+ax.yaxis.set_major_locator(ticker.MultipleLocator(2.0))
+ax.set_xlabel('Shuffled x')
+ax.set_ylabel('Shuffled y')
+
+ax = fig.add_subplot(gs[1,:])
 ax.annotate('Measured\ncorrelation\nis significant!',
             xy=(rd, 1.1), xycoords='data',
-            xytext=(rd, 2.2), textcoords='data', ha='left',
-            arrowprops=dict(arrowstyle="->", relpos=(0.2,0.0),
+            xytext=(rd-0.01, 3.0), textcoords='data', ha='left',
+            arrowprops=dict(arrowstyle="->", relpos=(0.3,0.0),
                 connectionstyle="angle3,angleA=10,angleB=80") )
 ax.annotate('95% percentile',
             xy=(0.14, 0.9), xycoords='data',
-            xytext=(0.18, 4.0), textcoords='data', ha='left',
+            xytext=(0.16, 6.2), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.1,0.0),
                 connectionstyle="angle3,angleA=30,angleB=80") )
 ax.annotate('Distribution of\nuncorrelated\nsamples',
@@ -56,7 +82,9 @@ ax.bar(b[:-1], h, width=b[1]-b[0], **fsC)
 ax.bar(b[:-1][b[:-1]>=rq], h[b[:-1]>=rq], width=b[1]-b[0], **fsB)
 ax.plot( [rd, rd], [0, 1], **lsA)
 ax.set_xlim(-0.25, 0.35)
+ax.set_ylim(0.0, 6.0)
+ax.yaxis.set_major_locator(ticker.MultipleLocator(2.0))
 ax.set_xlabel('Correlation coefficient')
-ax.set_ylabel('Prob. density of H0')
+ax.set_ylabel('PDF of H0')
 
 plt.savefig('permutecorrelation.pdf')
diff --git a/chapter.mk b/chapter.mk
index 50d1708..44553f5 100644
--- a/chapter.mk
+++ b/chapter.mk
@@ -1,5 +1,5 @@
 # plots:
-plots : pythonplots gnuplots
+plots : pythonplots
 
 # python plots:
 PYFILES=$(wildcard *.py)
@@ -10,25 +10,12 @@ pythonplots : $(PYPDFFILES)
 
 $(PYPDFFILES) : %.pdf: %.py ../../plotstyle.py
 	PYTHONPATH="../../" python3 $<
+#ps2pdf $@ $(@:.pdf=-temp.pdf)  # strip fonts, saves only about 1.5MB
+#mv $(@:.pdf=-temp.pdf) $@
 
 cleanpythonplots :
 	rm -f $(PYPDFFILES)
 
-
-# gnuplot plots:
-GPTFILES=$(wildcard *.gpt)
-GPTTEXFILES=$(GPTFILES:.gpt=.tex)
-
-gnuplots : $(GPTTEXFILES)
-
-$(GPTTEXFILES) : %.tex: %.gpt whitestyles.gp
-	gnuplot whitestyles.gp $<
-	epstopdf $*.eps
-
-cleangnuplots :
-	rm -f $(GPTTEXFILES)
-
-
 # script:
 chapter : $(BASENAME)-chapter.pdf
 
@@ -42,12 +29,15 @@ $(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex $(wildcard $(B
 watchchapter :
 	while true; do ! make -q chapter && make chapter; sleep 0.5; done
 
+watchplots :
+	while true; do ! make -q plots && make plots; sleep 0.5; done
+
 cleantex:
 	rm -f *~ 
 	rm -f $(BASENAME).aux $(BASENAME).log *.idx
 	rm -f $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out $(BASENAME)-chapter.idx $(BASENAME)-chapter-solutions.tex $(BASENAME)-solutions.tex
 
-cleanplots: cleanpythonplots cleangnuplots
+cleanplots: cleanpythonplots
 
 cleanchapter : cleanplots cleantex
 
diff --git a/codestyle/lecture/codestyle.tex b/codestyle/lecture/codestyle.tex
index 42f7429..8e2ee7e 100644
--- a/codestyle/lecture/codestyle.tex
+++ b/codestyle/lecture/codestyle.tex
@@ -4,6 +4,7 @@
   more months might as well have been written by someone
   else.}{Eagleson's law}
 
+\noindent
 Cultivating a good code style is not just a matter of good taste but
 rather is a key ingredient for readability and maintainability of code
 and, in the end, facilitates reproducibility of scientific
@@ -204,7 +205,7 @@ random walk\footnote{A random walk is a simple simulation of Brownian
 (listing \ref{chaoticcode}) then in cleaner way (listing
 \ref{cleancode})
 
-\begin{lstlisting}[label=chaoticcode, caption={Chaotic implementation of the random-walk.}]
+\begin{pagelisting}[label=chaoticcode, caption={Chaotic implementation of the random-walk.}]
 num_runs = 10; max_steps = 1000;
 
 positions = zeros(max_steps, num_runs);
@@ -217,18 +218,15 @@ for step = 2:max_steps
 x = randn(1);
 if x<0
 positions(step, run)= positions(step-1, run)+1;
-    
        
 elseif x>0
  positions(step,run)=positions(step-1,run)-1;
  end
 end
 end
-\end{lstlisting}
-
-\pagebreak[4]
+\end{pagelisting}
 
-\begin{lstlisting}[label=cleancode, caption={Clean implementation of the random-walk.}]
+\begin{pagelisting}[label=cleancode, caption={Clean implementation of the random-walk.}]
 num_runs = 10;
 max_steps = 1000;
 positions = zeros(max_steps, num_runs);
@@ -243,7 +241,7 @@ for run = 1:num_runs
         end
     end
 end
-\end{lstlisting}
+\end{pagelisting}
 
 \section{Using comments}
 
@@ -275,7 +273,6 @@ avoided:\\ \varcode{ x = x + 2; \% add two to x}\\
     make it even clearer.}{Steve McConnell}
 \end{important}
 
-\pagebreak[4]
 \section{Documenting functions}
 All pre-defined \matlab{} functions begin with a comment block that
 describes the purpose of the function, the required and optional
@@ -335,8 +332,7 @@ used within the context of another function \matlab{} allows to define
 within the same file. Listing \ref{localfunctions} shows an example of
 a local function definition.
 
-\pagebreak[3] 
-\lstinputlisting[label=localfunctions, caption={Example for local
+\pageinputlisting[label=localfunctions, caption={Example for local
   functions.}]{calculateSines.m}
 
 \emph{Local function} live in the same \entermde{m-File}{m-file} as
diff --git a/debugging/lecture/debugging.tex b/debugging/lecture/debugging.tex
index 6be3277..b83c24e 100644
--- a/debugging/lecture/debugging.tex
+++ b/debugging/lecture/debugging.tex
@@ -59,14 +59,14 @@ every opening parenthesis must be matched by a closing one or every
 respective error messages are clear and the editor will point out and
 highlight most syntax errors.
 
-\begin{lstlisting}[label=syntaxerror, caption={Unbalanced parenthesis error.}]
+\begin{pagelisting}[label=syntaxerror, caption={Unbalanced parenthesis error.}]
 >> mean(random_numbers
                       |
 Error: Expression or statement is incorrect--possibly unbalanced (, {, or [.
 
 Did you mean:
 >>   mean(random_numbers)
-\end{lstlisting}
+\end{pagelisting}
 
 \subsection{Indexing error}\label{index_error}
 Second on the list of common errors are the
@@ -125,7 +125,7 @@ dimensions. The command in line 7 works due to the fact, that matlab
 automatically extends the matrix, if you assign values to a range
 outside its bounds.
 
-\begin{lstlisting}[label=assignmenterror, caption={Assignment errors.}]
+\begin{pagelisting}[label=assignmenterror, caption={Assignment errors.}]
 >> a = zeros(1, 100);
 >> b = 0:10;
 
@@ -136,7 +136,7 @@ outside its bounds.
 >> size(a)
 ans =
      110    1
-\end{lstlisting}
+\end{pagelisting}
 
 \subsection{Dimension mismatch error}
 Similarly, some arithmetic operations are only valid if the variables
@@ -154,7 +154,7 @@ in listing\,\ref{dimensionmismatch} does not throw an error but the
 result is something else than the expected elementwise multiplication.
 
 % XXX Some arithmetic operations make size constraints, violating them leads to dimension mismatch errors.
-\begin{lstlisting}[label=dimensionmismatch, caption={Dimension mismatch errors.}]
+\begin{pagelisting}[label=dimensionmismatch, caption={Dimension mismatch errors.}]
   >> a = randn(100, 1);
   >> b = randn(10, 1);
   >> a + b
@@ -171,7 +171,7 @@ result is something else than the expected elementwise multiplication.
   >> size(c)
   ans =
        100    10
-\end{lstlisting}
+\end{pagelisting}
 
 
 
@@ -239,7 +239,7 @@ but it requires a deep understanding of the applied functions and also
 the task at hand.
 
 % XXX Converting a series of spike times into the firing rate as a function of time. Many tasks can be solved with a single line of code. But is this readable?
-\begin{lstlisting}[label=easyvscomplicated, caption={One-liner versus readable code.}]
+\begin{pagelisting}[label=easyvscomplicated, caption={One-liner versus readable code.}]
 % the one-liner
 rate = conv(full(sparse(1, round(spike_times/dt), 1, 1, length(time))), kernel, 'same');
 
@@ -248,7 +248,7 @@ rate = zeros(size(time));
 spike_indices = round(spike_times/dt);
 rate(spike_indices) = 1;
 rate = conv(rate, kernel, 'same');
-\end{lstlisting}
+\end{pagelisting}
 
 The preferred way depends on several considerations. (i) How deep is
 your personal understanding of the programming language?  (ii) What
@@ -291,7 +291,7 @@ example given in the \matlab{} help and assume that there is a
 function \varcode{rightTriangle()} (listing\,\ref{trianglelisting}).
 
 % XXX Slightly more readable version of the example given in the \matlab{} help system. Note: The variable name for the angles have been capitalized in order to not override the matlab defined functions \code{alpha, beta,} and \code{gamma}.
-\begin{lstlisting}[label=trianglelisting, caption={Example function for unit testing.}]
+\begin{pagelisting}[label=trianglelisting, caption={Example function for unit testing.}]
 function angles = rightTriangle(length_a, length_b)
     ALPHA = atand(length_a / length_b);
     BETA = atand(length_a / length_b);
@@ -300,7 +300,7 @@ function angles = rightTriangle(length_a, length_b)
 
     angles = [ALPHA BETA GAMMA];
 end
-\end{lstlisting}
+\end{pagelisting}
 
 This function expects two input arguments that are the length of the
 sides $a$ and $b$ and assumes a right angle between them. From this
@@ -337,7 +337,7 @@ The test script for the \varcode{rightTriangle()} function
 (listing\,\ref{trianglelisting}) may look like in
 listing\,\ref{testscript}.
 
-\begin{lstlisting}[label=testscript, caption={Unit test for the \varcode{rightTriangle()} function stored in an m-file testRightTriangle.m}]
+\begin{pagelisting}[label=testscript, caption={Unit test for the \varcode{rightTriangle()} function stored in an m-file testRightTriangle.m}]
 tolerance = 1e-10;
 
 % preconditions
@@ -373,7 +373,7 @@ angles = rightTriangle(1, 1500);
 smallAngle = (pi / 180) * angles(1); % radians
 approx = sin(smallAngle);
 assert(abs(approx - smallAngle) <= tolerance, 'Problem with small angle approximation')
-\end{lstlisting}
+\end{pagelisting}
 
 In a test script we can execute any code. The actual test whether or
 not the results match our predictions is done using the
@@ -390,16 +390,16 @@ executed. We further define a \varcode{tolerance} variable that is
 used when comparing double values (Why might the test on equality of
 double values be tricky?).
 
-\begin{lstlisting}[label=runtestlisting, caption={Run the test!}]
+\begin{pagelisting}[label=runtestlisting, caption={Run the test!}]
 result = runtests('testRightTriangle')
-\end{lstlisting}
+\end{pagelisting}
 
 During the run, \matlab{} will put out error messages onto the command
 line and a summary of the test results is then stored within the
 \varcode{result} variable. These can be displayed using the function
 \code[table()]{table(result)}.
 
-\begin{lstlisting}[label=testresults, caption={The test results.}, basicstyle=\ttfamily\scriptsize]
+\begin{pagelisting}[label=testresults, caption={The test results.}, basicstyle=\ttfamily\scriptsize]
 table(result)
 ans =
   4x6 table
@@ -411,7 +411,7 @@ _________________________________   ______  ______  ___________  ________  _____
 'testR.../Test_IsoscelesTriangles'   true   false   false        0.004893   [1x1 struct]
 'testR.../Test_30_60_90Triangle'     true   false   false        0.005057   [1x1 struct]
 'testR.../Test_SmallAngleApprox'     true   false   false        0.0049     [1x1 struct]
-\end{lstlisting}
+\end{pagelisting}
 
 So far so good, all tests pass and our function appears to do what it
 is supposed to do. But tests are only as good as the programmer who
@@ -479,11 +479,11 @@ stopped in debug mode (listing\,\ref{debuggerlisting}).
 \end{figure}
 
 
-\begin{lstlisting}[label=debuggerlisting, caption={Command line when the program execution was stopped in the debugger.}]
+\begin{pagelisting}[label=debuggerlisting, caption={Command line when the program execution was stopped in the debugger.}]
 >> simplerandomwalk
 6   for run = 1:num_runs
 K>>
-\end{lstlisting}
+\end{pagelisting}
 
 When stopped in the debugger we can view and change the state of the
 program at this point, we can also issue commands to try the next
diff --git a/designpattern/lecture/designpattern.tex b/designpattern/lecture/designpattern.tex
index 1fb191d..05ee5ab 100644
--- a/designpattern/lecture/designpattern.tex
+++ b/designpattern/lecture/designpattern.tex
@@ -10,7 +10,8 @@ pattern]{design pattern}.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Looping over  vector elements}
 Iterating over vector elements by means of a \mcode{for}-loop is a very commen pattern:
-\begin{lstlisting}[caption={\varcode{for}-loop for accessing vector elements by indices}]
+
+\begin{pagelisting}[caption={\varcode{for}-loop for accessing vector elements by indices}]
 x = [2:3:20];      % Some vector.
 for i=1:length(x)  % For loop over the indices of the vector.
   i                % This is the index (an integer number)
@@ -22,12 +23,14 @@ for i=1:length(x)  % For loop over the indices of the vector.
   % as an argument to a function:
   do_something(x(i));
 end
-\end{lstlisting}
+\end{pagelisting}
 
+\noindent
 If the result of the computation within the loop are single numbers
 that are to be stored in a vector, you should create this vector with
 the right size before the loop:
-\begin{lstlisting}[caption={\varcode{for}-loop for writing a vector}]
+
+\begin{pagelisting}[caption={\varcode{for}-loop for writing a vector}]
 x = [1.2 2.3 2.6 3.1];   % Some vector.
 % Create a vector for the results, as long as the number of loops:
 y = zeros(length(x),1);
@@ -38,13 +41,15 @@ for i=1:length(x)
 end
 % Now the result vector can be further processed:
 mean(y);
-\end{lstlisting}
+\end{pagelisting}
 
+\noindent
 The computation within the loop could also result in a vector of some
 length and not just a single number. If the length of this vector
 (here 10) is known beforehand, then you should create a matrix of
 appropriate size for storing the results:
-\begin{lstlisting}[caption={\varcode{for}-loop for writing rows of a matrix}]
+
+\begin{pagelisting}[caption={\varcode{for}-loop for writing rows of a matrix}]
 x = [2:3:20];             % Some vector.
 % Create space for results -
 % as many rows as loops, as many columns as needed:
@@ -56,30 +61,33 @@ for i=1:length(x)
 end
 % Process the results stored in matrix y:
 mean(y, 1)
-\end{lstlisting}
+\end{pagelisting}
 
+\noindent
 Another possibility is that the result vectors (here of unknown size)
 need to be combined into a single large vector:
-\begin{lstlisting}[caption={\varcode{for}-loop for appending vectors}]
+
+\begin{pagelisting}[caption={\varcode{for}-loop for appending vectors}]
 x = [2:3:20];        % Some vector.
 y = [];              % Empty vector for storing the results.
 for i=1:length(x)
   % The function get_something() returns a vector of unspecified size:
-  z = get_somehow_more(x(i));
+  z = get_something(x(i));
   % The content of z is appended to the result vector y:
-  y = [y; z(:)];
+  y = [y, z(:)];
   % The z(:) syntax ensures that we append column-vectors.
 end
-% Process the results stored in the vector z:
-mean(y)
-\end{lstlisting}
+% Process the results stored in the vector y:
+mean(y, 1)
+\end{pagelisting}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Scaling and shifting random numbers, zeros, and ones}
 Random number generators usually return random numbers of a given mean
 and standard deviation. Multiply those numbers by a factor to change their standard deviation and add a number to shift the mean.
-\begin{lstlisting}[caption={Scaling and shifting of random numbers}]
+
+\begin{pagelisting}[caption={Scaling and shifting of random numbers}]
 % 100 random numbers drawn from a normal distribution 
 % with mean 0 and standard deviation 1:
 x = randn(100, 1);
@@ -89,18 +97,20 @@ x = randn(100, 1);
 mu = 4.8;
 sigma = 2.3;
 y = randn(100, 1)*sigma + mu;
-\end{lstlisting}
+\end{pagelisting}
 
+\noindent
 The same principle can be useful for in the context of the functions
 \mcode{zeros()} or \mcode{ones()}:
-\begin{lstlisting}[caption={Scaling and shifting of \varcode{zeros()} and \varcode{ones()}}]
+
+\begin{pagelisting}[caption={Scaling and shifting of \varcode{zeros()} and \varcode{ones()}}]
 x = -1:0.01:2;      % Vector of x-values for plotting
 plot(x, exp(-x.*x));
 % Plot for the same x-values a horizontal line with y=0.8:
 plot(x, zeros(size(x))+0.8);
 % ... or a line with y=0.5:
 plot(x, ones(size(x))*0.5);
-\end{lstlisting}
+\end{pagelisting}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -119,25 +129,26 @@ can plot the values of the $y$ vector against the ones of the $x$
 vector.
 
 The following scripts compute and plot the function $f(x)=e^{-x^2}$:
-\begin{lstlisting}[caption={Plotting a mathematical function --- very detailed}]
+
+\begin{pagelisting}[caption={Plotting a mathematical function --- very detailed}]
 xmin = -1.0;
 xmax = 2.0;
 dx = 0.01;        % Step size
 x = xmin:dx:xmax; % Vector with x-values.
 y = exp(-x.*x);   % No for loop! '.*' for multiplying the vector elements.
 plot(x, y);
-\end{lstlisting}
+\end{pagelisting}
 
-\begin{lstlisting}[caption={Plotting a mathematical function --- shorter}]
+\begin{pagelisting}[caption={Plotting a mathematical function --- shorter}]
 x = -1:0.01:2;
 y = exp(-x.*x);
 plot(x, y);
-\end{lstlisting}
+\end{pagelisting}
 
-\begin{lstlisting}[caption={Plotting a mathematical function --- compact}]
+\begin{pagelisting}[caption={Plotting a mathematical function --- compact}]
 x = -1:0.01:2;
 plot(x, exp(-x.*x));
-\end{lstlisting}
+\end{pagelisting}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -146,28 +157,34 @@ For estimating probabilities or probability densities from histograms
 we need to normalize them appropriately.
 
 The \mcode{histogram()} function does this automatically with the appropriate arguments:
-\begin{lstlisting}[caption={Probability density with the \varcode{histogram()}-function}]
+
+\begin{pagelisting}[caption={Probability density with the \varcode{histogram()}-function}]
 x = randn(100, 1);         % Some real-valued data.
 histogram(x, 'Normalization', 'pdf');
-\end{lstlisting}
-\begin{lstlisting}[caption={Probability with the \varcode{histogram()}-function}]
+\end{pagelisting}
+
+\begin{pagelisting}[caption={Probability with the \varcode{histogram()}-function}]
 x = randi(6, 100, 1);      % Some integer-valued data.
 histogram(x, 'Normalization', 'probability');
-\end{lstlisting}
+\end{pagelisting}
+
+\noindent
 Alternatively one can normalize the histogram data as returned by the
 \code{hist()}-function manually:
-\begin{lstlisting}[caption={Probability density with the \varcode{hist()}- and \varcode{bar()}-function}]
+
+\begin{pagelisting}[caption={Probability density with the \varcode{hist()}- and \varcode{bar()}-function}]
 x = randn(100, 1);         % Some real-valued data.
 [h, b] = hist(x);          % Compute histogram.
 h = h/sum(h)/(b(2)-b(1));  % Normalization to a probability density.
 bar(b, h);                 % Plot the probability density.
-\end{lstlisting}
-\begin{lstlisting}[caption={Probability with the \varcode{hist()}- and \varcode{bar()}-function}]
+\end{pagelisting}
+
+\begin{pagelisting}[caption={Probability with the \varcode{hist()}- and \varcode{bar()}-function}]
 x = randi(6, 100, 1);      % Some integer-valued data.
 [h, b] = hist(x);          % Compute histogram.
 h = h/sum(h);              % Normalize to probability.
 bar(b, h);                 % Plot the probabilities.
-\end{lstlisting}
+\end{pagelisting}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
diff --git a/exercises.mk b/exercises.mk
new file mode 100644
index 0000000..ae68f24
--- /dev/null
+++ b/exercises.mk
@@ -0,0 +1,33 @@
+EXERCISES=$(TEXFILES:.tex=.pdf)
+SOLUTIONS=$(EXERCISES:%.pdf=%-solutions.pdf)
+
+.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
+
+pdf : $(SOLUTIONS) $(EXERCISES)
+
+exercises : $(EXERCISES)
+
+solutions : $(SOLUTIONS)
+
+$(SOLUTIONS) : %-solutions.pdf : %.tex ../../exercisesheader.tex ../../exercisestitle.tex
+	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
+	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
+	rm $(patsubst %.pdf,%,$@).[!p]*
+
+$(EXERCISES) : %.pdf : %.tex ../../exercisesheader.tex ../../exercisestitle.tex
+	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
+
+watch :
+	while true; do ! make -q pdf && make pdf; sleep 0.5; done
+
+watchexercises :
+	while true; do ! make -q exercises && make exercises; sleep 0.5; done
+
+watchsolutions :
+	while true; do ! make -q solutions && make solutions; sleep 0.5; done
+
+clean :
+	rm -f *~ *.aux *.log $(EXERCISES:.pdf=.out) $(SOLUTIONS:.pdf=.out)
+
+cleanup : clean
+	rm -f $(SOLUTIONS) $(EXERCISES)
diff --git a/exercisesheader.tex b/exercisesheader.tex
new file mode 100644
index 0000000..51a6258
--- /dev/null
+++ b/exercisesheader.tex
@@ -0,0 +1,110 @@
+\usepackage[english]{babel}
+\usepackage{pslatex}
+\usepackage{graphicx}
+\usepackage{tikz}
+\usepackage{xcolor}
+\usepackage{amsmath}
+\usepackage{amssymb}
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+  language=Matlab,
+  basicstyle=\ttfamily\footnotesize,
+  numbers=left,
+  numberstyle=\tiny,
+  title=\lstname,
+  showstringspaces=false,
+  commentstyle=\itshape\color{darkgray},
+  breaklines=true,
+  breakautoindent=true,
+  % columns=flexible,
+  frame=single,
+  xleftmargin=1.5em,
+  xrightmargin=1em,
+  aboveskip=10pt
+}
+
+%%%%% page style %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[left=20mm,right=20mm,top=25mm,bottom=19mm,headsep=2ex,headheight=3ex,footskip=3.5ex]{geometry}
+\pagestyle{headandfoot}
+\ifprintanswers
+\newcommand{\stitle}{Solutions}
+\else
+\newcommand{\stitle}{}
+\fi
+\header{{\bfseries\large \exercisenum. \exercisetopic}}{{\bfseries\large\stitle}}{{\bfseries\large\exercisedate}}
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+\runningfooter{}{\thepage}{}
+
+\shadedsolutions
+\definecolor{SolutionColor}{gray}{0.9}
+
+\setlength{\baselineskip}{15pt}
+\setlength{\parindent}{0.0cm}
+\setlength{\parskip}{0.3cm}
+
+\usepackage{multicol}
+
+%%%%% units %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[mediumspace,mediumqspace,Gray,squaren]{SIunits}      % \ohm, \micro
+
+%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\newcommand{\cin}[1]{[{\rm #1}]_{\text{in}}}
+\newcommand{\cout}[1]{[{\rm #1}]_{\text{out}}}
+\newcommand{\units}[1]{\text{#1}}
+\newcommand{\K}{\text{K$^+$}}
+\newcommand{\Na}{\text{Na$^+$}}
+\newcommand{\Cl}{\text{Cl$^-$}}
+\newcommand{\Ca}{\text{Ca$^{2+}$}}
+\newcommand{\water}{$\text{H}_2\text{O}$}
+
+\usepackage{dsfont}
+\newcommand{\naZ}{\mathds{N}}
+\newcommand{\gaZ}{\mathds{Z}}
+\newcommand{\raZ}{\mathds{Q}}
+\newcommand{\reZ}{\mathds{R}}
+\newcommand{\reZp}{\mathds{R^+}}
+\newcommand{\reZpN}{\mathds{R^+_0}}
+\newcommand{\koZ}{\mathds{C}}
+\newcommand{\RT}{{\rm Re\!\ }}
+\newcommand{\IT}{{\rm Im\!\ }}
+\newcommand{\arccot}{{\rm arccot}}
+\newcommand{\sgn}{{\rm sgn}}
+\newcommand{\im}{{\rm i}}
+\newcommand{\drv}{\mathrm{d}}
+\newcommand{\divis}[2]{$^{#1}\!\!/\!_{#2}$}
+\newcommand{\vect}[2]{\left( \!\! \begin{array}{c} #1 \\ #2 \end{array} \!\! \right)}
+\newcommand{\matt}[2]{\left( \!\! \begin{array}{cc} #1 \\ #2 \end{array} \!\! \right)}
+\renewcommand{\Re}{\mathrm{Re}}
+\renewcommand{\Im}{\mathrm{Im}}
+\newcommand{\av}[1]{\left\langle #1 \right\rangle}
+
+\newcommand{\pref}[1]{(\ref{#1})}
+\newcommand{\eqnref}[1]{(\ref{#1})}
+
+\newcommand{\hr}{\par\vspace{-5ex}\noindent\rule{\textwidth}{1pt}\par\vspace{-1ex}}
+
+\newcommand{\qt}[1]{\textbf{#1}\\}
+
+\newcommand{\extra}{--- Optional ---\ \mbox{}}
+
+\newcommand{\code}[1]{\texttt{#1}}
+
+%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\newcommand{\continue}{\ifprintanswers%
+\else
+\vfill\hspace*{\fill}$\rightarrow$\newpage%
+\fi}
+\newcommand{\continuepage}{\ifprintanswers%
+\newpage
+\else
+\vfill\hspace*{\fill}$\rightarrow$\newpage%
+\fi}
+\newcommand{\newsolutionpage}{\ifprintanswers%
+\newpage%
+\else
+\fi}
+
+%%%%% hyperref %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue!50!black,linkcolor=blue!50!black,urlcolor=blue!50!black]{hyperref}
diff --git a/pointprocesses/exercises/instructions.tex b/exercisestitle.tex
similarity index 98%
rename from pointprocesses/exercises/instructions.tex
rename to exercisestitle.tex
index 3041d3e..b97b56b 100644
--- a/pointprocesses/exercises/instructions.tex
+++ b/exercisestitle.tex
@@ -4,3 +4,4 @@
 {\large Jan Grewe, Jan Benda}\\[-3ex]
 Neuroethology Lab \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
 \end{center}
+\hr
diff --git a/header.tex b/header.tex
index 29f6ba8..73288b8 100644
--- a/header.tex
+++ b/header.tex
@@ -1,8 +1,10 @@
 %%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \title{\textbf{\huge\sffamily\tr{Introduction to\\[1ex] Scientific Computing}%
        {Einf\"uhrung in die\\[1ex] wissenschaftliche Datenverarbeitung}}}
+%\title{\textbf{\huge\sffamily\tr{Scientific Computing for Neurobiologists}%
+%       {Wissenschaftliche Datenverarbeitung f\"ur Neurobiologen}}}
 
-\author{{\LARGE Jan Grewe \& Jan Benda}\\[5ex]Abteilung Neuroethologie\\[2ex]%
+\author{{\LARGE Jan Grewe \& Jan Benda}\\[5ex]Neuroethology Lab\\[2ex]%
         \includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}\vspace{3ex}}
 
 \date{WS 2020/2021\\\vfill%
@@ -66,20 +68,16 @@
 \pagecolor{white}
 
 %%%%% figures %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% gnuplot figures:
-\newcommand{\texinputpath}{}
-\newcommand{\texpicture}[1]{{\sffamily\footnotesize\input{\texinputpath#1.tex}}}
-\newcommand{\figlabel}[1]{\textsf{\textbf{\large \uppercase{#1}}}}
 
 % maximum number of floats:
-\setcounter{topnumber}{2}
-\setcounter{bottomnumber}{0}
+\setcounter{topnumber}{1}
+\setcounter{bottomnumber}{1}
 \setcounter{totalnumber}{2}
 
 % float placement fractions:
-\renewcommand{\textfraction}{0.2}
+\renewcommand{\textfraction}{0.1}
 \renewcommand{\topfraction}{0.9}
-\renewcommand{\bottomfraction}{0.0}
+\renewcommand{\bottomfraction}{0.9}
 \renewcommand{\floatpagefraction}{0.7}
 
 % spacing for floats:
@@ -209,11 +207,41 @@
 \let\l@lstlisting\l@figure
 \makeatother
 
-%%%%% english, german, code and file terms: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% \lstinputlisting wrapped in a minipage to avoid page breaks:
+\newcommand{\pageinputlisting}[2][]{\vspace{-2ex}\noindent\begin{minipage}[t]{1\linewidth}\lstinputlisting[#1]{#2}\end{minipage}}
+
+%%%%% listing environment: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% usage:
+%
+% \begin{pagelisting}[label=listing1, caption={A script.}]
+%   >> x = 6
+% \end{pagelisting}
+%
+% This is the lstlisting environment but wrapped in a minipage to avoid page breaks.
+\lstnewenvironment{pagelisting}[1][]%
+  {\vspace{-2ex}\lstset{#1}\noindent\minipage[t]{1\linewidth}}%
+  {\endminipage}
+
+%%%%% english and german terms: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{ifthen}
 
+% \enterm[en-index]{term}: Typeset term and add it to the index of english terms
+%
+% \determ[de-index]{term}: Typeset term and add it to the index of german terms
+%
+% \entermde[en-index]{de-index}{term}: Typeset term and add it to the index of english terms
+%                                      and de-index to the index of german terms
+%
+% how to specificy an index:
+% \enterm{term}                                 - just put term into the index
+% \enterm[en-index]{term}                       - typeset term and put en-index into the index
+% \enterm[statistics!mean]{term}                - mean is a subentry of statistics
+% \enterm[statistics!average|see{statistics!mean}]{term} - cross reference to statistics mean
+% \enterm[statistics@\textbf{statistics}]{term} - put index at statistics but use 
+%                                               \textbf{statistics} for typesetting in the index
+
 % \enterm[english index entry]{<english term>}
-% typeset the term in italics and add it (or the optional argument) to
+% typeset the term in italics and add it (or rather the optional argument) to
 % the english index.
 \newcommand{\enterm}[2][]{\textit{#2}\ifthenelse{\equal{#1}{}}{\protect\sindex[enterm]{#2}}{\protect\sindex[enterm]{#1}}}
 
@@ -349,11 +377,12 @@
       }{%
         \immediate\write\solutions{\unexpanded{\subsection}{Exercise \thechapter.\arabic{exercisef}}}%
         \immediate\write\solutions{\unexpanded{\label}{solution\arabic{chapter}-\arabic{exercisef}}}%
-        \immediate\write\solutions{\unexpanded{\lstinputlisting[belowskip=0ex,aboveskip=0ex,%
-          nolol=true, title={\textbf{Source code:} \protect\StrSubstitute{#1}{_}{\_}}]}{\codepath#1}}%
+        \immediate\write\solutions{\unexpanded{\begin{minipage}{1\linewidth}\lstinputlisting[belowskip=0ex,aboveskip=0ex,%
+          nolol=true, title={\textbf{Source code:} \protect\StrSubstitute{#1}{_}{\_}}]}{\codepath#1}\unexpanded{\end{minipage}}}%
         \ifthenelse{\equal{#2}{}}{}%
-          {\immediate\write\solutions{\unexpanded{\lstinputlisting[language={},%
-             nolol=true, title={\textbf{Output:}}, belowskip=0ex, aboveskip=1ex]}{\codepath#2}}}%
+          {\immediate\write\solutions{\unexpanded{\begin{minipage}{1\linewidth}\lstinputlisting[language={},%
+             nolol=true, title={\textbf{Output:}}, belowskip=0ex, aboveskip=1ex]}{\codepath#2}\unexpanded{\end{minipage}}}}%
+        \immediate\write\solutions{\unexpanded{\vspace*{\fill}}}%
         \immediate\write\solutions{}%
       }%
   }%
@@ -362,14 +391,18 @@
         { \hypersetup{hypertexnames=false}%
           \ifthenelse{\equal{\exercisesource}{}}{}%
           { \addtocounter{lstlisting}{-1}%
-            \lstinputlisting[belowskip=0pt,aboveskip=1ex,nolol=true,%
+            \par\noindent\begin{minipage}[t]{1\linewidth}%
+            \lstinputlisting[belowskip=1ex,aboveskip=-1ex,nolol=true,%
                              title={\textbf{Solution:} \exercisefile}]%
                             {\exercisesource}%
+            \end{minipage}%
             \ifthenelse{\equal{\exerciseoutput}{}}{}%
               { \addtocounter{lstlisting}{-1}%
+                \par\noindent\begin{minipage}[t]{1\linewidth}%
                 \lstinputlisting[language={},title={\textbf{Output:}},%
-                                 nolol=true,belowskip=0pt]%
+                                 nolol=true,belowskip=0pt,aboveskip=-0.5ex]%
                                 {\exerciseoutput}%
+                \end{minipage}%
               }%
           }%
           \hypersetup{hypertexnames=true}%
@@ -452,12 +485,12 @@
   { \captionsetup{singlelinecheck=off,hypcap=false,labelformat={empty},%
                   labelfont={large,sf,it,bf},font={large,sf,it,bf}}
     \ifthenelse{\equal{#1}{}}%
-    { \begin{mdframed}[linecolor=importantline,linewidth=1ex,%
+    { \begin{mdframed}[nobreak=true,linecolor=importantline,linewidth=1ex,%
                        backgroundcolor=importantback,font={\sffamily}]%
         \setlength{\parindent}{0pt}%
         \setlength{\parskip}{1ex}%
     }%
-    { \begin{mdframed}[linecolor=importantline,linewidth=1ex,%
+    { \begin{mdframed}[nobreak=true,linecolor=importantline,linewidth=1ex,%
                        backgroundcolor=importantback,font={\sffamily},%
                        frametitle={\captionof{iboxf}{#1}},frametitleaboveskip=-1ex,%
                        frametitlebackgroundcolor=importantline]%
diff --git a/likelihood/exercises/Makefile b/likelihood/exercises/Makefile
index 27691d9..cbfb17a 100644
--- a/likelihood/exercises/Makefile
+++ b/likelihood/exercises/Makefile
@@ -1,34 +1,3 @@
-TEXFILES=$(wildcard exercises??.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
+TEXFILES=$(wildcard likelihood-?.tex)
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
-
-pdf : $(SOLUTIONS) $(EXERCISES)
-
-exercises : $(EXERCISES)
-
-solutions : $(SOLUTIONS)
-
-$(SOLUTIONS) : solutions%.pdf : exercises%.tex instructions.tex
-	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
-	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
-	rm $(patsubst %.pdf,%,$@).[!p]*
-
-$(EXERCISES) : %.pdf : %.tex instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-
-watch :
-	while true; do ! make -q pdf && make pdf; sleep 0.5; done
-
-watchexercises :
-	while true; do ! make -q exercises && make exercises; sleep 0.5; done
-
-watchsolutions :
-	while true; do ! make -q solutions && make solutions; sleep 0.5; done
-
-clean :
-	rm -f *~ *.aux *.log *.out
-
-cleanup : clean
-	rm -f $(SOLUTIONS) $(EXERCISES)
+include ../../exercises.mk
diff --git a/likelihood/exercises/instructions.tex b/likelihood/exercises/instructions.tex
deleted file mode 100644
index 7fe599d..0000000
--- a/likelihood/exercises/instructions.tex
+++ /dev/null
@@ -1,41 +0,0 @@
-\vspace*{-8ex}
-\begin{center}
-\textbf{\Large Introduction to scientific computing}\\[1ex]
-{\large Jan Grewe, Jan Benda}\\[-3ex]
-Neuroethology lab \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
-
-% \ifprintanswers%
-% \else
-
-% % Die folgenden Aufgaben dienen der Wiederholung, \"Ubung und
-% % Selbstkontrolle und sollten eigenst\"andig bearbeitet und gel\"ost
-% % werden. Die L\"osung soll in Form eines einzelnen Skriptes (m-files)
-% % im ILIAS hochgeladen werden. Jede Aufgabe sollte in einer eigenen
-% % ``Zelle'' gel\"ost sein. Die Zellen \textbf{m\"ussen} unabh\"angig
-% % voneinander ausf\"uhrbar sein. Das Skript sollte nach dem Muster:
-% % ``variablen\_datentypen\_\{nachname\}.m'' benannt werden
-% % (z.B. variablen\_datentypen\_mueller.m).
-
-% \begin{itemize}
-% \item \"Uberzeuge dich von jeder einzelnen Zeile deines Codes, dass
-%   sie auch wirklich das macht, was sie machen soll! Teste dies mit
-%   kleinen Beispielen direkt in der Kommandozeile.
-% \item Versuche die L\"osungen der Aufgaben m\"oglichst in
-%   sinnvolle kleine Funktionen herunterzubrechen.
-%   Sobald etwas \"ahnliches mehr als einmal berechnet werden soll,
-%   lohnt es sich eine Funktion daraus zu schreiben!
-% \item Teste rechenintensive \code{for} Schleifen, Vektoren, Matrizen
-%   zuerst mit einer kleinen Anzahl von Wiederholungen oder kleiner
-%   Gr\"o{\ss}e, und benutze erst am Ende, wenn alles \"uberpr\"uft
-%   ist, eine gro{\ss}e Anzahl von Wiederholungen oder Elementen, um eine gute
-%   Statistik zu bekommen.
-% \item Benutze die Hilfsfunktion von \code{matlab} (\code{help
-%     commando} oder \code{doc commando}) und das Internet, um
-%   herauszufinden, wie bestimmte \code{matlab} Funktionen zu verwenden
-%   sind und was f\"ur M\"oglichkeiten sie bieten.
-%   Auch zu inhaltlichen Konzepten bietet das Internet oft viele
-%   Antworten!
-% \end{itemize}
-
-% \fi
diff --git a/likelihood/exercises/exercises01.tex b/likelihood/exercises/likelihood-1.tex
similarity index 66%
rename from likelihood/exercises/exercises01.tex
rename to likelihood/exercises/likelihood-1.tex
index 4512e91..8c86079 100644
--- a/likelihood/exercises/exercises01.tex
+++ b/likelihood/exercises/likelihood-1.tex
@@ -1,93 +1,23 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 11\stitle}}{{\bfseries\large Maximum likelihood}}{{\bfseries\large January 7th, 2020}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
+\newcommand{\exercisetopic}{Maximum Likelihood}
+\newcommand{\exercisenum}{10}
+\newcommand{\exercisedate}{January 12th, 2021}
 
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
-
+\input{../../exercisestitle}
 
 \begin{questions}
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\question \qt{Read chapter 9 on ``Maximum likelihood estimation''!}\vspace{-3ex}
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \question \qt{Maximum likelihood of the standard deviation}
 Let's compute the likelihood and the log-likelihood for the estimation
@@ -107,10 +37,11 @@ of the standard deviation.
 \end{parts}
 \begin{solution}
   \lstinputlisting{mlestd.m}
+  \lstinputlisting[language={}]{mlestd.out}
   \includegraphics[width=1\textwidth]{mlestd}\\
 
   The more data the smaller the product of the probabilities ($\approx
-  p^n$ with $0 \le p < 1$) and the smaller the sum of the logarithms
+  p^n$ if $0 \le p < 1$) and the smaller the sum of the logarithms
   of the probabilities ($\approx n\log p$, note that $\log p < 0$).
 
   The product eventually gets smaller than the precision of the
@@ -121,10 +52,10 @@ of the standard deviation.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \question \qt{Maximum-likelihood estimator of a line through the origin} 
 In the lecture we derived the following equation for an
-maximum-likelihood estimate of the slope $\theta$ of a straight line
+maximum-likelihood estimate of the slope $m$ of a straight line
 through the origin fitted to $n$ pairs of data values $(x_i|y_i)$ with
 standard deviation $\sigma_i$:
-\[\theta = \frac{\sum_{i=1}^n \frac{x_i y_i}{\sigma_i^2}}{ \sum_{i=1}^n
+\[ m = \frac{\sum_{i=1}^n \frac{x_i y_i}{\sigma_i^2}}{ \sum_{i=1}^n
   \frac{x_i^2}{\sigma_i^2}} \]
 \begin{parts}
   \part \label{mleslopefunc} Write a function that takes two vectors
@@ -175,7 +106,7 @@ normally-distributed data. Such parameter need to be estimated
 numerically by means of maximum-likelihood from the data.
 
 Let us demonstrate this approach by means of data that are drawn from a
-gamma distribution,
+gamma distribution.
 \begin{parts}
   \part Find out which \code{matlab} function computes the
   probability-density function of the gamma distribution.
@@ -193,7 +124,7 @@ gamma distribution,
   \part Compute and plot a properly normalized histogram of these
   random numbers.
 
-  \part Find out which \code{matlab} function fit a distribution to a
+  \part Find out which \code{matlab} function fits a distribution to a
   vector of random numbers according to the maximum-likelihood method.
   How do you need to use this function in order to fit a gamma
   distribution to the data?
diff --git a/likelihood/exercises/mlestd.m b/likelihood/exercises/mlestd.m
index 8c61e4b..c08f4c1 100644
--- a/likelihood/exercises/mlestd.m
+++ b/likelihood/exercises/mlestd.m
@@ -6,16 +6,19 @@ for k = 1:length(ns)
     fprintf('\nn=%d\n', n);
     % draw random numbers:
     x = randn(n,1)*sigma+mu;
-    fprintf('                                mean of the data is %.2f\n', mean(x))
-    fprintf('                  standard deviation of the data is %.2f\n', std(x))
+    datamean = mean(x);
+    fprintf('                                mean %.3f\n', datamean)
+    % we assume the mean to be given, therefore dof=n:
+    fprintf('                  standard deviation %.3f\n', std(x, 1))
 
     % standard deviation as parameter:
-    psigs = 1.0:0.01:3.0;
+    psigs = 1.0:0.001:3.0;
     % matrix with the probabilities for each x and psigs:
     lms = zeros(length(x), length(psigs));
     for i=1:length(psigs)
         psig = psigs(i);
-        p = exp(-0.5*((x-mu)/psig).^2.0)/sqrt(2.0*pi)/psig;
+        % same mean as for the standard deviation of the data:
+        p = exp(-0.5*((x-datamean)/psig).^2.0)/sqrt(2.0*pi)/psig;
         lms(:,i) = p;
     end
     lm = prod(lms, 1);              % likelihood
@@ -24,8 +27,9 @@ for k = 1:length(ns)
     if length(maxlm) > 1
         maxlm = maxlm(1);
     end
-    fprintf('      likelihood: standard deviation of the data is %.2f\n', maxlm)
-    fprintf('  log-likelihood: standard deviation of the data is %.2f\n', psigs(loglm==max(loglm)))
+    maxloglm = psigs(loglm==max(loglm));
+    fprintf('      likelihood: standard deviation %.3f\n', maxlm)
+    fprintf('  log-likelihood: standard deviation %.3f\n', maxloglm)
 
     % plot likelihood of standard deviation:
     subplot(2, 2, 2*k-1);
diff --git a/likelihood/exercises/mlestd.out b/likelihood/exercises/mlestd.out
new file mode 100644
index 0000000..25fec42
--- /dev/null
+++ b/likelihood/exercises/mlestd.out
@@ -0,0 +1,11 @@
+n=50
+                                mean 3.468
+                  standard deviation 1.833
+      likelihood: standard deviation 1.833
+  log-likelihood: standard deviation 1.833
+
+n=1000
+                                mean 2.993
+                  standard deviation 2.077
+      likelihood: standard deviation 1.000
+  log-likelihood: standard deviation 2.077
diff --git a/likelihood/lecture/likelihood-chapter.tex b/likelihood/lecture/likelihood-chapter.tex
index 736d9eb..40021f4 100644
--- a/likelihood/lecture/likelihood-chapter.tex
+++ b/likelihood/lecture/likelihood-chapter.tex
@@ -20,12 +20,11 @@
 
 \section{TODO}
 \begin{itemize}
-\item Fitting psychometric functions:
+\item Fitting psychometric functions, logistic regression:
   Variable $x_i$, responses $r_i$ either 0 or 1.
-  $p(x_i, \theta)$ is Weibull or Boltzmann function.
+  $p(x_i, \theta)$ is Weibull or Boltzmann or logistic function.
   Likelihood is $L = \prod p(x_i, \theta)^{r_i} (1-p(x_i, \theta))^{1-r_i}$.
-  Use fminsearch for fitting.
-\item GLM model fitting?
+  Use fminsearch for fitting?
 \end{itemize}
 
 \end{document}
diff --git a/likelihood/lecture/likelihood.tex b/likelihood/lecture/likelihood.tex
index 28cc395..7ea2b6c 100644
--- a/likelihood/lecture/likelihood.tex
+++ b/likelihood/lecture/likelihood.tex
@@ -4,111 +4,132 @@
 \label{maximumlikelihoodchapter}
 \exercisechapter{Maximum likelihood estimation}
 
-A common problem in statistics is to estimate from a probability
-distribution one or more parameters $\theta$ that best describe the
-data $x_1, x_2, \ldots x_n$.  \enterm[maximum likelihood
-estimator]{Maximum likelihood estimators} (\enterm[mle|see{maximum
+The core task of statistics is to infer from measured data some
+parameters describing the data. These parameters can be simply a mean,
+a standard deviation, or any other parameter needed to describe the
+distribution the data are originating from, a correlation coefficient,
+or some parameters of a function describing a particular dependence
+between the data. The brain faces exactly the same problem. Given the
+activity pattern of some neurons (the data) it needs to infer some
+aspects (parameters) of the environment and the internal state of the
+body in order to generate some useful behavior. One possible approach
+to estimate parameters from data are \enterm[maximum likelihood
+estimator]{maximum likelihood estimators} (\enterm[mle|see{maximum
   likelihood estimator}]{mle},
-\determ{Maximum-Likelihood-Sch\"atzer}) choose the parameters such
-that they maximize the likelihood of the data $x_1, x_2, \ldots x_n$
-to originate from the distribution.
+\determ{Maximum-Likelihood-Sch\"atzer}).  They choose the parameters
+such that they maximize the likelihood of the specific data values to
+originate from a specific distribution.
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Maximum Likelihood}
+\section{Maximum likelihood}
 
 Let $p(x|\theta)$ (to be read as ``probability(density) of $x$ given
-$\theta$.'') the probability (density) distribution of $x$ given the
-parameters $\theta$. This could be the normal distribution
+$\theta$.'') the probability (density) distribution of data value $x$
+given parameter values $\theta$. This could be the normal distribution
 \begin{equation}
   \label{normpdfmean}
   p(x|\mu, \sigma) = \frac{1}{\sqrt{2\pi \sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}
 \end{equation}
 defined by the mean $\mu$ and the standard deviation $\sigma$ as
-parameters $\theta$.  If the $n$ independent observations of $x_1,
-x_2, \ldots x_n$ originate from the same probability density
-distribution (they are \enterm[i.i.d.|see{independent and identically
-  distributed}]{i.i.d.}, \enterm{independent and identically
-  distributed}) then the conditional probability $p(x_1,x_2, \ldots
-x_n|\theta)$ of observing $x_1, x_2, \ldots x_n$ given some specific
-parameter values $\theta$ is given by
+parameters $\theta$.  If the $n$ observations $x_1, x_2, \ldots, x_n$
+are independent of each other and originate from the same probability
+density distribution (they are \enterm[i.i.d.|see{independent and
+  identically distributed}]{i.i.d.}, \enterm{independent and
+  identically distributed}), then the conditional probability
+$p(x_1,x_2, \ldots, x_n|\theta)$ of observing the particular data
+values $x_1, x_2, \ldots, x_n$ given some specific parameter values
+$\theta$ of the probability density is given by the product of the
+probability densities of each data value:
 \begin{equation}
-  p(x_1,x_2, \ldots x_n|\theta) = p(x_1|\theta) \cdot p(x_2|\theta)
-  \ldots p(x_n|\theta) = \prod_{i=1}^n p(x_i|\theta) \; .
+  \label{probdata}
+  p(x_1,x_2, \ldots, x_n|\theta) = p(x_1|\theta) \cdot p(x_2|\theta)
+  \ldots, p(x_n|\theta) = \prod_{i=1}^n p(x_i|\theta) \; .
 \end{equation}
 Vice versa, the \entermde{Likelihood}{likelihood} of the parameters $\theta$
-given the observed data $x_1, x_2, \ldots x_n$ is
+given the observed data $x_1, x_2, \ldots, x_n$ is
 \begin{equation}
-  {\cal L}(\theta|x_1,x_2, \ldots x_n) = p(x_1,x_2, \ldots x_n|\theta) \; .
+  \label{likelihood}
+  {\cal L}(\theta|x_1,x_2, \ldots, x_n) = p(x_1,x_2, \ldots, x_n|\theta) \; .
 \end{equation}
-Note: the likelihood ${\cal L}$ is not a probability in the
+Note, that the likelihood ${\cal L}$ is not a probability in the
 classic sense since it does not integrate to unity ($\int {\cal
-  L}(\theta|x_1,x_2, \ldots x_n) \, d\theta \ne 1$).
-
-When applying maximum likelihood estimations we are interested in the
-parameter values
+  L}(\theta|x_1,x_2, \ldots, x_n) \, d\theta \ne 1$). For given
+observations $x_1, x_2, \ldots, x_n$, the likelihood
+\eqref{likelihood} is a function of the parameters $\theta$. This
+function has a global maximum for some specific parameter values. At
+this maximum the probability \eqref{probdata} to observe the measured
+data values is the largest.
+
+Maximum likelihood estimators just find the parameter values
 \begin{equation}
-  \theta_{mle} = \text{argmax}_{\theta} {\cal L}(\theta|x_1,x_2, \ldots x_n)
+  \theta_{mle} = \text{argmax}_{\theta} {\cal L}(\theta|x_1,x_2, \ldots, x_n)
 \end{equation}
-that maximize the likelihood.  $\text{argmax}_xf(x)$ is the value of
-the argument $x$ for which the function $f(x)$ assumes its global
-maximum. Thus, we search for the parameter values $\theta$ at which
-the likelihood ${\cal L}(\theta)$ reaches its maximum. For these
-paraemter values the measured data most likely originated from the
-corresponding distribution.
+that maximize the likelihood \eqref{likelihood}.
+$\text{argmax}_xf(x)$ is the value of the argument $x$ for which the
+function $f(x)$ assumes its global maximum. Thus, we search for the
+parameter values $\theta$ at which the likelihood ${\cal L}(\theta)$
+reaches its maximum. For these parameter values the measured data most
+likely originated from the corresponding distribution.
 
 The position of a function's maximum does not change when the values
 of the function are transformed by a strictly monotonously rising
 function such as the logarithm. For numerical reasons and reasons that
-we will discuss below, we search for the maximum of the logarithm of
-the likelihood
-(\entermde[likelihood!log-]{Likelihood!Log-}{log-likelihood}):
-
+we discuss below, we instead search for the maximum of the logarithm
+of the likelihood
+(\entermde[likelihood!log-]{Likelihood!Log-}{log-likelihood})
 \begin{eqnarray}
-  \theta_{mle} & = & \text{argmax}_{\theta}\; {\cal L}(\theta|x_1,x_2, \ldots x_n) \nonumber \\
-              & = & \text{argmax}_{\theta}\; \log {\cal L}(\theta|x_1,x_2, \ldots x_n) \nonumber \\
+  \theta_{mle} & = & \text{argmax}_{\theta}\; {\cal L}(\theta|x_1,x_2, \ldots, x_n) \nonumber \\
+              & = & \text{argmax}_{\theta}\; \log {\cal L}(\theta|x_1,x_2, \ldots, x_n) \nonumber \\
               & = & \text{argmax}_{\theta}\; \log \prod_{i=1}^n p(x_i|\theta) \nonumber \\
               & = & \text{argmax}_{\theta}\; \sum_{i=1}^n \log p(x_i|\theta) \label{loglikelihood}
 \end{eqnarray}
+which is the sum of the logarithms of the probabilites of each
+observation. Let's illustrate the concept of maximum likelihood
+estimation on the arithmetic mean.
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\subsection{Example: the arithmetic mean}
-Suppose that the measurements $x_1, x_2, \ldots x_n$ originate from a
-normal distribution \eqnref{normpdfmean} and we consider the mean
-$\mu$ as the only parameter $\theta$.  Which value of $\theta$
-maximizes the likelihood of the data?
+\subsection{Arithmetic mean}
+Suppose that the measurements $x_1, x_2, \ldots, x_n$ originate from a
+normal distribution \eqref{normpdfmean} and we do not know the
+population mean $\mu$ of the normal distribution
+(\figrefb{mlemeanfig}). In this setting $\mu$ is the only parameter
+$\theta$.  Which value of $\mu$ maximizes the likelihood of the data?
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{mlemean}
   \titlecaption{\label{mlemeanfig} Maximum likelihood estimation of
     the mean.}{Top: The measured data (blue dots) together with three
-    different possible normal distributions with different means
-    (arrows) the data could have originated from.  Bottom left: the
-    likelihood as a function of $\theta$ i.e. the mean. It is maximal
-    at a value of $\theta = 2$. Bottom right: the
-    log-likelihood. Taking the logarithm does not change the position
-    of the maximum.}
+    normal distributions differing in their means (arrows) from which
+    the data could have originated from.  Bottom left: the likelihood
+    as a function of the parameter $\mu$. For the data it is maximal
+    at a value of $\mu = 2$. Bottom right: the log-likelihood. Taking
+    the logarithm does not change the position of the maximum.}
 \end{figure}
 
-The log-likelihood \eqnref{loglikelihood}
+With the normal distribution \eqref{normpdfmean} and applying
+logarithmic identities, the log-likelihood \eqref{loglikelihood} reads
 \begin{eqnarray}
-  \log {\cal L}(\theta|x_1,x_2, \ldots x_n)
-  & = & \sum_{i=1}^n \log \frac{1}{\sqrt{2\pi \sigma^2}}e^{-\frac{(x_i-\theta)^2}{2\sigma^2}} \nonumber \\
-  & = & \sum_{i=1}^n - \log \sqrt{2\pi \sigma^2} -\frac{(x_i-\theta)^2}{2\sigma^2} \; .
+  \log {\cal L}(\mu|x_1,x_2, \ldots, x_n)
+  & = & \sum_{i=1}^n \log \frac{1}{\sqrt{2\pi \sigma^2}}e^{-\frac{(x_i-\mu)^2}{2\sigma^2}} \nonumber \\
+  & = & \sum_{i=1}^n - \log \sqrt{2\pi \sigma^2} -\frac{(x_i-\mu)^2}{2\sigma^2} \; .
 \end{eqnarray}
 Since the logarithm is the inverse function of the exponential
 ($\log(e^x)=x$), taking the logarithm removes the exponential from the
-normal distribution.  To calculate the maximum of the log-likelihood,
-we need to take the derivative with respect to $\theta$ and set it to
-zero:
+normal distribution. This is the second reason why it is useful to
+maximize the log-likelihood. To calculate the maximum of the
+log-likelihood, we need to take the derivative with respect to $\mu$
+and set it to zero:
 \begin{eqnarray}
-  \frac{\text{d}}{\text{d}\theta} \log {\cal L}(\theta|x_1,x_2, \ldots x_n) & = & \sum_{i=1}^n - \frac{2(x_i-\theta)}{2\sigma^2} \;\; = \;\; 0  \nonumber \\
-  \Leftrightarrow \quad \sum_{i=1}^n x_i - \sum_{i=1}^n \theta & = & 0  \nonumber \\
-  \Leftrightarrow \quad n \theta & = & \sum_{i=1}^n x_i  \nonumber \\
-  \Leftrightarrow \quad \theta & = & \frac{1}{n} \sum_{i=1}^n x_i \;\; = \;\; \bar x
+  \frac{\text{d}}{\text{d}\mu} \log {\cal L}(\mu|x_1,x_2, \ldots, x_n) & = & \sum_{i=1}^n - \frac{\text{d}}{\text{d}\mu} \log \sqrt{2\pi \sigma^2} - \frac{\text{d}}{\text{d}\mu} \frac{(x_i-\mu)^2}{2\sigma^2} \;\; = \;\;  0  \nonumber \\
+  \Leftrightarrow \quad \sum_{i=1}^n - \frac{2(x_i-\mu)}{2\sigma^2} & = & 0  \nonumber \\
+  \Leftrightarrow \quad \sum_{i=1}^n x_i - \sum_{i=1}^n \mu & = & 0  \nonumber \\
+  \Leftrightarrow \quad n \mu & = & \sum_{i=1}^n x_i  \nonumber \\
+  \Leftrightarrow \quad \mu & = & \frac{1}{n} \sum_{i=1}^n x_i \;\; = \;\; \bar x \label{arithmeticmean}
 \end{eqnarray}
-Thus, the maximum likelihood estimator is the arithmetic mean. That
-is, the arithmetic mean maximizes the likelihood that the data
-originate from a normal distribution centered at the arithmetic mean
+Thus, the maximum likelihood estimator of the population mean of
+normally distributed data is the arithmetic mean. That is, the
+arithmetic mean maximizes the likelihood that the data originate from
+a normal distribution centered at the arithmetic mean
 (\figref{mlemeanfig}). Equivalently, the standard deviation computed
 from the data, maximizes the likelihood that the data were generated
 from a normal distribution with this standard deviation.
@@ -123,6 +144,17 @@ from a normal distribution with this standard deviation.
   the maxima with the mean calculated from the data.
 \end{exercise}
 
+Comparing the values of the likelihood with the ones of the
+log-likelihood shown in \figref{mlemeanfig}, shows the numerical
+reason for taking the logarithm of the likelihood. The likelihood
+values can get very small, because we multiply many, potentially small
+probability densities with each other. The likelihood quickly gets
+smaller than the samlles number a floating point number of a computer
+can represent. Try it by increasing the number of data values in the
+exercise. Taking the logarithm avoids this problem. The log-likelihood
+assumes well behaving numbers that can be handled well by the
+computer.
+
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Fitting probability distributions}
@@ -130,32 +162,39 @@ Consider normally distributed data with unknown mean and standard
 deviation.  From the considerations above we just have seen that a
 Gaussian distribution with mean at the arithmetic mean and standard
 deviation equal to the standard deviation computed from the data is
-the best Gaussian distribution that fits the data best in a maximum
-likelihood sense, i.e. the likelihood that the data were generated
-from this distribution is the largest. Fitting a Gaussian distribution
-to data is very simple: just compute the two parameter of the Gaussian
-distribution as the arithmetic mean and a standard deviation directly
-from the data.
-
-For non-Gaussian distributions (e.g. a Gamma-distribution), however,
-such simple analytical expressions for the parameters of the
-distribution do not exist, e.g. the shape parameter of a
-\entermde[distribution!Gamma-]{Verteilung!Gamma-}{Gamma-distribution}. How
-do we fit such a distribution to some data? That is, how should we
-compute the values of the parameters of the distribution, given the
-data?
-
-A first guess could be to fit the probability density function by
-minimization of the squared difference to a histogram of the measured
-data. For several reasons this is, however, not the method of choice:
-(i) Probability densities can only be positive which leads, for small
-values in particular, to asymmetric distributions. (ii) The values of
-a histogram estimating the density are not independent because the
-integral over a density is unity. The two basic assumptions of
-normally distributed and independent samples, which are a prerequisite
-make the minimization of the squared difference \eqnref{chisqmin} to a
-maximum likelihood estimation, are violated. (iii) The histogram
-strongly depends on the chosen bin size \figref{mlepdffig}).
+the Gaussian that fits the data best in a maximum likelihood sense,
+i.e. the likelihood that the data were generated from this
+distribution is the largest. Fitting a Gaussian distribution to data
+is very simple: just compute the two parameter of the Gaussian
+distribution $\mu$ and $\sigma$ as the arithmetic mean and a standard
+deviation, respectively, directly from the data.
+
+For non-Gaussian distributions, for example a
+\entermde[distribution!Gamma-]{Verteilung!Gamma-}{Gamma-distribution}
+\begin{equation}
+  \label{gammapdf}
+  p(x|\alpha,\beta) \sim x^{\alpha-1}e^{-\beta x}
+\end{equation}
+with a shape parameter $\alpha$ and a rate parameter $\beta$
+(\figrefb{mlepdffig}), in general no such simple analytical
+expressions for estimating the parameters directly from the data do
+not exist. How do we fit such a distribution to the data?  That is,
+how should we compute the values of the parameters of the
+distribution, given the data?
+
+A first guess could be to fit the probability density function by a
+\enterm{least squares} fit to a normalized histogram of the measured data in
+the same way as we fit a function to some data. For several reasons
+this is, however, not the method of choice: (i) Probability densities
+can only be positive which leads, in particular for small values, to
+asymmetric distributions of the estimated histogram around the true
+density. (ii) The values of a histogram estimating the density are not
+independent because the integral over a density is unity. The two
+basic assumptions of normally distributed and independent samples,
+which are a prerequisite for making the minimization of the squared
+difference to a maximum likelihood estimation (see next section), are
+violated. (iii) The estimation of the probability density by means of
+a histogram strongly depends on the chosen bin size.
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{mlepdf}
@@ -164,20 +203,19 @@ strongly depends on the chosen bin size \figref{mlepdffig}).
     order Gamma-distribution. The maximum likelihood estimation of the
     probability density function is shown in orange, the true pdf is
     shown in red. Right: normalized histogram of the data together
-    with the real (red) and the fitted probability density
-    functions. The fit was done by minimizing the squared difference
-    to the histogram.}
+    with the true probability density (red) and the probability
+    density function obtained by a least squares fit to the
+    histogram.}
 \end{figure}
 
 Instead we should stay with maximum-likelihood estimation.  Exactly in
 the same way as we estimated the mean value of a Gaussian distribution
 above, we can numerically fit the parameter of any type of
 distribution directly from the data by means of maximizing the
-likelihood.  We simply search for the parameter $\theta$ of the
-desired probability density function that maximizes the
-log-likelihood. In general this is a non-linear optimization problem
-that is solved with numerical methods such as the gradient descent
-\matlabfun{mle()}.
+likelihood.  We simply search for the parameter values of the desired
+probability density function that maximize the log-likelihood. In
+general this is a non-linear optimization problem that is solved with
+numerical methods such as the gradient descent \matlabfun{mle()}.
 
 \begin{exercise}{mlegammafit.m}{mlegammafit.out}
   Generate a sample of gamma-distributed random numbers and apply the
@@ -190,46 +228,63 @@ that is solved with numerical methods such as the gradient descent
 \section{Curve fitting}
 
 When fitting a function of the form $f(x;\theta)$ to data pairs
-$(x_i|y_i)$ one tries to adapt the parameter $\theta$ such that the
-function best describes the data. With maximum likelihood we search
-for the parameter value $\theta$ for which the likelihood that the data
-were drawn from the corresponding function is maximal.  If we assume
-that the $y_i$ values are normally distributed around the function
-values $f(x_i;\theta)$ with a standard deviation $\sigma_i$, the
-log-likelihood is
+$(x_i|y_i)$ one tries to adapt the parameters $\theta$ such that the
+function best describes the data. In
+chapter~\ref{gradientdescentchapter} we simply assumed that ``best''
+means minimizing the squared distance between the data and the
+function.  With maximum likelihood we search for those parameter
+values $\theta$ that maximize the likelihood of the data to be drawn
+from the corresponding function.
+
+If we assume that the $y_i$ values are normally distributed around the
+function values $f(x_i;\theta)$ with a standard deviation $\sigma_i$,
+the log-likelihood is
 \begin{eqnarray}
   \log {\cal L}(\theta|(x_1,y_1,\sigma_1), \ldots, (x_n,y_n,\sigma_n))
   & = & \sum_{i=1}^n \log \frac{1}{\sqrt{2\pi \sigma_i^2}}e^{-\frac{(y_i-f(x_i;\theta))^2}{2\sigma_i^2}}  \nonumber \\
-  & = & \sum_{i=1}^n - \log \sqrt{2\pi \sigma_i^2} -\frac{(y_i-f(x_i;\theta))^2}{2\sigma_i^2} \\
+  & = & \sum_{i=1}^n - \log \sqrt{2\pi \sigma_i^2} -\frac{(y_i-f(x_i;\theta))^2}{2\sigma_i^2}
 \end{eqnarray}
-The only difference to the previous example is that the averages in
-the equations above are now given as the function values
-$f(x_i;\theta)$. The parameter $\theta$ should be the one that
-maximizes the log-likelihood. The first part of the sum is independent
-of $\theta$ and can thus be ignored when computing the the maximum:
+The only difference to the previous example of the arithmetic mean is
+that the means $\mu$ in the equations above are given by the function
+values $f(x_i;\theta)$. The parameters $\theta$ should maximize the
+log-likelihood. The first term in the sum is independent of $\theta$
+and can be ignored when computing the the maximum. From the second
+term we pull out the constant factor $-\frac{1}{2}$:
 \begin{eqnarray}
   & = & - \frac{1}{2} \sum_{i=1}^n \left( \frac{y_i-f(x_i;\theta)}{\sigma_i} \right)^2
 \end{eqnarray}
 We can further simplify by inverting the sign and then search for the
-minimum. Also the factor $1/2$ can be ignored since it does not affect
-the position of the minimum:
+minimum. Also the factor $\frac{1}{2}$ can be ignored since it does
+not affect the position of the minimum:
 \begin{equation}
   \label{chisqmin}
   \theta_{mle} = \text{argmin}_{\theta} \; \sum_{i=1}^n \left( \frac{y_i-f(x_i;\theta)}{\sigma_i} \right)^2 \;\; = \;\; \text{argmin}_{\theta} \; \chi^2
 \end{equation}
-The sum of the squared differences normalized by the standard
-deviation is also called $\chi^2$. The parameter $\theta$ which
-minimizes the squared differences is thus the one that maximizes the
-likelihood that the data actually originate from the given
-function. Minimizing $\chi^2$ therefore is a maximum likelihood
-estimation.
+The sum of the squared differences between the $y$-data values and the
+function values, normalized by the standard deviation of the data
+around the function, is called $\chi^2$ (chi squared). The parameter
+$\theta$ which minimizes the squared differences is thus the one that
+maximizes the likelihood of the data to actually originate from the
+given function. 
+
+Whether minimizing $\chi^2$ or the \enterm{mean squared error}
+\eqref{meansquarederror} introduced in
+chapter~\ref{gradientdescentchapter} does not matter. The latter is
+the mean and $\chi^2$ is the sum of the squared differences. They
+simply differ by the constant factor $n$, the number of data pairs,
+which does not affect the position of the minimum. $\chi^2$ is more
+general in that it allows for different standard deviations for each
+data pair. If they are all the same ($\sigma_i = \sigma$), the common
+standard deviation can be pulled out of the sum and also does not
+influence the position of the minimum. Both \enterm{least squares} and
+minimizing $\chi^2$ are maximum likelihood estimators of the
+parameters $\theta$ of a function.
 
 From the mathematical considerations above we can see that the
 minimization of the squared difference is a maximum-likelihood
 estimation only if the data are normally distributed around the
 function. In case of other distributions, the log-likelihood
-\eqnref{loglikelihood} needs to be adapted accordingly and be
-maximized respectively.
+\eqref{loglikelihood} needs to be adapted accordingly.
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{mlepropline}
@@ -242,26 +297,32 @@ maximized respectively.
     the normal distribution of the data around the line (right histogram).}
 \end{figure}
 
+Let's go on and calculate the minimum \eqref{chisqmin} of $\chi^2$
+analytically for a simple function.
 
-\subsection{Example: simple proportionality}
-The function of a line with slope $\theta$ through the origin is
-\[ f(x) = \theta x \; . \]
-The $\chi^2$-sum is thus
-\[ \chi^2 = \sum_{i=1}^n \left( \frac{y_i-\theta x_i}{\sigma_i} \right)^2 \; . \]
-To estimate the minimum we again take the first derivative with
-respect to $\theta$ and equate it to zero:
-\begin{eqnarray}
-  \frac{\text{d}}{\text{d}\theta}\chi^2 & = & \frac{\text{d}}{\text{d}\theta} \sum_{i=1}^n \left( \frac{y_i-\theta x_i}{\sigma_i} \right)^2 \nonumber \\
-  & = & \sum_{i=1}^n \frac{\text{d}}{\text{d}\theta} \left( \frac{y_i-\theta x_i}{\sigma_i} \right)^2 \nonumber \\
-  & = & -2 \sum_{i=1}^n  \frac{x_i}{\sigma_i} \left( \frac{y_i-\theta x_i}{\sigma_i} \right) \nonumber \\
-  & = & -2 \sum_{i=1}^n \left( \frac{x_i y_i}{\sigma_i^2} - \theta \frac{x_i^2}{\sigma_i^2} \right) \;\; = \;\; 0 \nonumber \\
-\Leftrightarrow \quad  \theta \sum_{i=1}^n \frac{x_i^2}{\sigma_i^2} & = & \sum_{i=1}^n \frac{x_i y_i}{\sigma_i^2} \nonumber
-\end{eqnarray}
+
+\subsection{Straight line trough the origin}
+The function of a straight line with slope $m$ through the origin
+is
+\[ f(x) = m x \; . \]
+With this specific function, $\chi^2$ reads
+\[ \chi^2 = \sum_{i=1}^n \left( \frac{y_i-m x_i}{\sigma_i} \right)^2
+\; . \] To calculate the minimum we take the first derivative with
+respect to $m$ and equate it to zero:
 \begin{eqnarray}
-\Leftrightarrow \quad  \theta & = & \frac{\sum_{i=1}^n \frac{x_i y_i}{\sigma_i^2}}{ \sum_{i=1}^n \frac{x_i^2}{\sigma_i^2}} \label{mleslope}
+  \frac{\text{d}}{\text{d}m}\chi^2 & = & \sum_{i=1}^n \frac{\text{d}}{\text{d}m} \left( \frac{y_i-m x_i}{\sigma_i} \right)^2 \nonumber \\
+  & = & -2 \sum_{i=1}^n  \frac{x_i}{\sigma_i} \left( \frac{y_i-m x_i}{\sigma_i} \right) \nonumber \\
+  & = & -2 \sum_{i=1}^n \left( \frac{x_i y_i}{\sigma_i^2} - m \frac{x_i^2}{\sigma_i^2} \right) \;\; = \;\; 0 \nonumber \\
+\Leftrightarrow \quad  m \sum_{i=1}^n \frac{x_i^2}{\sigma_i^2} & = & \sum_{i=1}^n \frac{x_i y_i}{\sigma_i^2} \nonumber \\
+\Leftrightarrow \quad  m & = & \frac{\sum_{i=1}^n \frac{x_i y_i}{\sigma_i^2}}{ \sum_{i=1}^n \frac{x_i^2}{\sigma_i^2}} \label{mleslope}
 \end{eqnarray}
-This is an analytical expression for the estimation of the slope
-$\theta$ of the regression line (\figref{mleproplinefig}).
+This is an analytical expression for the estimation of the slope $m$
+of the regression line (\figref{mleproplinefig}). We do not need to
+apply numerical methods like the gradient descent to find the slope
+that minimizes the squared differences. Instead, we can compute the
+slope directly from the data by means of \eqnref{mleslope}, very much
+like we calculate the mean of some data by means of the arithmetic
+mean \eqref{arithmeticmean}.
 
 \subsection{Linear and non-linear fits}
 A gradient descent, as we have done in the previous chapter, is not
@@ -276,47 +337,68 @@ or the coefficients $a_k$ of a polynomial
 \matlabfun{polyfit()}.
 
 Parameters that are non-linearly combined can not be calculated
-analytically. Consider for example the rate $\lambda$ of the
-exponential decay
+analytically. Consider, for example, the factor $c$ and the rate
+$\lambda$ of the exponential decay
 \[ y = c \cdot e^{\lambda x} \quad , \quad c, \lambda \in \reZ \; . \]
 Such cases require numerical solutions for the optimization of the
 cost function, e.g. the gradient descent \matlabfun{lsqcurvefit()}.
 
 \subsection{Relation between slope and correlation coefficient}
-Let us have a closer look on \eqnref{mleslope}. If the standard
-deviation of the data $\sigma_i$ is the same for each data point,
-i.e. $\sigma_i = \sigma_j \; \forall \; i, j$, the standard deviation drops
-out of \eqnref{mleslope} and we get
+Let us have a closer look on \eqnref{mleslope} for the slope of a line
+through the origin. If the standard deviation of the data $\sigma_i$
+is the same for each data point, i.e. $\sigma_i = \sigma_j \; \forall
+\; i, j$, the standard deviation drops out and \eqnref{mleslope}
+simplifies to
 \begin{equation}
   \label{whitemleslope}
-  \theta = \frac{\sum_{i=1}^n x_i y_i}{\sum_{i=1}^n x_i^2}
+  m = \frac{\sum_{i=1}^n x_i y_i}{\sum_{i=1}^n x_i^2}
 \end{equation}
-To see what this expression is, we need to standardize the data. We
-make the data mean free and normalize them to their standard
-deviation, i.e. $x \mapsto (x - \bar x)/\sigma_x$. The resulting
-numbers are also called \entermde[z-values]{z-Wert}{$z$-values} or
-$z$-scores and they have the property $\bar x = 0$ and $\sigma_x =
-1$. $z$-scores are often used in Biology to make quantities that
-differ in their units comparable. For standardized data the variance
+To see what the nominator and the denominator of this expression
+describe, we need to subtract from the data their mean value. We make
+the data mean free, i.e. $x \mapsto x - \bar x$ and $y \mapsto y -
+\bar y$ . For mean-free data the variance
 \begin{equation}
-  \sigma_x^2 = \frac{1}{n} \sum_{i=1}^n (x_i - \bar x)^2 = \frac{1}{n} \sum_{i=1}^n x_i^2 = 1
+  \sigma_x^2 = \frac{1}{n} \sum_{i=1}^n (x_i - \bar x)^2 = \frac{1}{n} \sum_{i=1}^n x_i^2
 \end{equation}
-is given by the mean squared data and equals one.
-The covariance between $x$ and $y$ also simplifies to
+is given by the mean squared data.  In the same way, the covariance
+between $x$ and $y$ simplifies to
 \begin{equation}
   \text{cov}(x, y) = \frac{1}{n} \sum_{i=1}^n (x_i - \bar x)(y_i -
-  \bar y) =\frac{1}{n} \sum_{i=1}^n x_i y_i
+  \bar y) =\frac{1}{n} \sum_{i=1}^n x_i y_i \; ,
+\end{equation}
+the averaged product between pairs of $x$ and $y$ values. Expanding
+the fraction in \eqnref{whitemleslope} by $\frac{1}{n}$ we get
+\begin{equation}
+  \label{meanfreeslope}
+  m = \frac{\frac{1}{n}\sum_{i=1}^n x_i y_i}{\frac{1}{n}\sum_{i=1}^n x_i^2}
+    = \frac{\text{cov}(x, y)}{\sigma_x^2}
 \end{equation}
-the averaged product between pairs of $x$ and $y$ values.  Recall that
-the correlation coefficient $r_{x,y}$,
-\eqnref{correlationcoefficient}, is the covariance normalized by the
-product of the standard deviations of $x$ and $y$,
-respectively. Therefore, in case the standard deviations equal one,
-the correlation coefficient is identical to the covariance.
-Consequently, for standardized data the slope of the regression line
-\eqnref{whitemleslope} simplifies to
+
+Recall that the correlation coefficient $r_{x,y}$ is the covariance
+normalized by the product of the standard deviations of $x$ and $y$:
 \begin{equation}
-  \theta = \frac{1}{n} \sum_{i=1}^n x_i y_i = \text{cov}(x,y) = r_{x,y}
+  \label{meanfreecorrcoef}
+  r_{x,y} = \frac{\text{cov}(x, y)}{\sigma_x\sigma_y}
+\end{equation}
+If furthermore the standard deviations of $x$ and $y$ are the same,
+i.e. $\sigma_x = \sigma_y$, the slope of a line trough the origin is
+identical to the correlation coefficient.
+
+This relation between slope and correlation coefficient in particular
+holds for standardized data that have been made mean free and have
+been normalized by their standard deviation, i.e. $x \mapsto (x - \bar
+x)/\sigma_x$ and $y \mapsto (y - \bar x)/\sigma_y$. The resulting
+numbers are called \entermde[z-value]{z-Wert}{$z$-values} or
+\enterm[z-score]{$z$-scores}. Their mean equals zero and their
+standard deviation one. $z$-scores are often used to make quantities
+that differ in their units comparable. For standardized data the
+denominators for both the slope \eqref{meanfreeslope} and the
+correlation coefficient \eqref{meanfreecorrcoef} equal one.  For
+standardized data, both the slope of the regression line and the
+corelation coefficient reduce to the covariance between the $x$ and
+$y$ data:
+\begin{equation}
+  m = \frac{1}{n} \sum_{i=1}^n x_i y_i = \text{cov}(x,y) = r_{x,y}
 \end{equation}
 For standardized data the slope of the regression line is the same as the
 correlation coefficient!
@@ -324,63 +406,100 @@ correlation coefficient!
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Neural coding}
-In sensory systems certain aspects of the environment are encoded in
-the neuronal activity of populations of neurons. One example of such a
-population code is the tuning of neurons in the primary visual cortex
-(V1) to the orientation of an edge or bar in the visual
-stimulus. Different neurons respond best to different edge
-orientations. Traditionally, such a tuning is measured by analyzing
-the neuronal response strength (e.g. the firing rate) as a function of
-the orientation of a black bar and is illustrated and summarized
-with the so called \enterm{tuning-curve} (\determ{Abstimmkurve},
-figure~\ref{mlecodingfig}, top).
+Maximum likelihood estimators are not only a central concept for data
+analysis. Neural systems face the very same problem. They also need to
+estimate parameters of the internal and external environment based on
+the activity of neurons.
 
 \begin{figure}[tp]
   \includegraphics[width=1\textwidth]{mlecoding}
   \titlecaption{\label{mlecodingfig} Maximum likelihood estimation of
-    a stimulus parameter from neuronal activity.}{Top: Tuning curve of
-    an individual neuron as a function of the stimulus orientation (a
-    dark bar in front of a white background). The stimulus that evokes
-    the strongest activity in that neuron is the bar with the vertical
-    orientation (arrow, $\phi_i=90$\,\degree). The red area indicates
-    the variability of the neuronal activity $p(r|\phi)$ around the
-    tuning curve. Center: In a population of neurons, each neuron may
-    have a different tuning curve (colors). A specific stimulus (the
-    vertical bar) activates the individual neurons of the population
-    in a specific way (dots). Bottom: The log-likelihood of the
-    activity pattern will be maximized close to the real stimulus
+    a stimulus parameter from neuronal activity.}{Top: Tuning curve
+    $r_i(\phi;c,\phi_i)$ of a specific neuron $i$ as a function of the
+    orientation $\phi$ of a stimulus, a dark bar in front of a white
+    background. The preferred stimulus $\phi_i$ of that neuron, the
+    one that evokes the strongest firing rate response, is a bar with
+    vertical orientation (arrow, $\phi_i=90$\,\degree). The width of
+    the red area indicates the variability of the neuronal activity
+    $\sigma_i$ around the tuning curve. Center: In a population of
+    neurons, each neuron may have a different tuning curve (colors). A
+    specific stimulus activates the individual neurons of the
+    population in a specific way (dots). Bottom: The log-likelihood of
+    the activity pattern has a maximum close to the real stimulus
     orientation.}
 \end{figure}
 
+In sensory systems certain aspects of the environment are encoded in
+the neuronal activity of populations of neurons. One example of such a
+population code is the tuning of neurons in the primary visual cortex
+(V1) to the orientation of a bar in the visual stimulus. Different
+neurons respond best to different bar orientations. Traditionally,
+such a tuning is measured by analyzing the neuronal response strength
+(e.g. the firing rate) as a function of the orientation of a black bar
+and is illustrated and summarized with the so called
+\enterm{tuning-curve} (\determ{Abstimmkurve},
+figure~\ref{mlecodingfig}, top).
+
 The brain, however, is confronted with the inverse problem: given a
 certain activity pattern in the neuronal population, what is the
-stimulus (here the orientation of an edge)? In the sense of maximum
-likelihood, a possible answer to this question would be: the stimulus
-for which the particular activity pattern is most likely given the
-tuning of the neurons.
+stimulus? In our example, what is the orientation of the bar? In the
+sense of maximum likelihood, a possible answer to this question would
+be: the stimulus for which the particular activity pattern is most
+likely given the tuning of the neurons and the noise (standard
+deviation) of the responses.
 
 Let's stay with the example of the orientation tuning in V1. The
-tuning $\Omega_i(\phi)$ of the neurons $i$ to the preferred edge
-orientation $\phi_i$ can be well described using a van-Mises function
-(the Gaussian function on a cyclic x-axis) (\figref{mlecodingfig}):
+tuning of the firing rate $r_i(\phi)$ of neuron $i$ to the preferred
+bar orientation $\phi_i$ can be well described using a van-Mises
+function (the Gaussian function on a cyclic x-axis)
+(\figref{mlecodingfig}):
 \begin{equation}
-  \Omega_i(\phi) = c \cdot e^{\cos(2(\phi-\phi_i))} \quad , \quad c \in \reZ
+  \label{bartuningcurve}
+  r_i(\phi; c, \phi_i) = c \cdot e^{\cos(2(\phi-\phi_i))} \quad , \quad c > 0
 \end{equation} 
+Note the factor two in the cosine, because the response of the neuron
+is the same for a bar turned by 180\,\degree.
+
 If we approximate the neuronal activity by a normal distribution
-around the tuning curve with a standard deviation $\sigma=\Omega/4$,
-which is proportional to $\Omega$, then the probability $p_i(r|\phi)$
-of the $i$-th neuron showing the activity $r$ given a certain
-orientation $\phi$ of an edge is given by
+around the tuning curve with a standard deviation $\sigma_i$, then the
+probability $p_i(r|\phi)$ of the $i$-th neuron having the observed
+activity $r$, given a certain orientation $\phi$ of a bar is
 \begin{equation}
-  p_i(r|\phi) = \frac{1}{\sqrt{2\pi}\Omega_i(\phi)/4} e^{-\frac{1}{2}\left(\frac{r-\Omega_i(\phi)}{\Omega_i(\phi)/4}\right)^2} \; .
+  p_i(r|\phi) = \frac{1}{\sqrt{2\pi\sigma_i^2}} e^{-\frac{1}{2}\left(\frac{r-r_i(\phi; c, \phi_i)}{\sigma_i}\right)^2} \; .
 \end{equation}
-The log-likelihood of the edge orientation $\phi$ given the
+The log-likelihood of the bar orientation $\phi$ given the
 activity pattern in the population $r_1$, $r_2$, ... $r_n$ is thus
 \begin{equation}
-  {\cal L}(\phi|r_1, r_2, \ldots r_n) = \sum_{i=1}^n \log p_i(r_i|\phi)
+  {\cal L}(\phi|r_1, r_2, \ldots, r_n) = \sum_{i=1}^n \log p_i(r_i|\phi)
 \end{equation}
-The angle $\phi$ that maximizes this likelihood is then an estimate of
-the orientation of the edge.
+The angle $\phi_{mle}$ that maximizes this likelihood is an estimate
+of the true orientation of the bar (\figref{mlecodingfig}).
+
+The noisiness of the neuron's responses as quantified by $\sigma_i$
+usually is a function of the neuron's mean firing rate $r_i$:
+$\sigma_i = \sigma_i(r_i)$. This dependence has a major impact on the
+maximum likelihood estimation. Usually, the stronger the response of a
+neuron, the higher its firing rate, the lower the noise. In this case,
+strong responses have a stronger influence on the position of the
+maximum of the log-likelihood.
+
+Whether neural systems really implement maximum likelihood estimators
+is another question. There are many ways how a stimulus property can
+be read out from the activity of a population of neurons. The simplest
+one being a ``winner takes all'' rule. The preferred stimulus
+parameter of the neuron with the strongest response is the
+estimate. Another possibility is to compute a population vector. The
+estimated stimulus parameter is the sum of the preferred stimulus
+parameters of all neurons in the population weighted by the activity
+of the neurons. In case of angular stimulus parameters, like the
+orientation of the bar in our example, a vector pointing in the
+direction of the angle is used instead of the angle to incorporate the
+cyclic nature of the parameter. 
+
+Using maximum likelihood estimators for analyzing neural population
+activity gives us an upper bound of how well a stimulus parameter is
+encoded in the activity of the neurons. The brain would not be able to
+do better.
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
diff --git a/likelihood/lecture/mlecoding.py b/likelihood/lecture/mlecoding.py
index 3edd5a5..8be385f 100644
--- a/likelihood/lecture/mlecoding.py
+++ b/likelihood/lecture/mlecoding.py
@@ -5,11 +5,9 @@ import matplotlib.gridspec as gridspec
 from plotstyle import *
 
 rng = np.random.RandomState(4637281)
-lmarg=0.1
-rmarg=0.1
 
 fig = plt.figure(figsize=cm_size(figure_width, 2.8*figure_height))
-spec = gridspec.GridSpec(nrows=4, ncols=1, height_ratios=[4, 4, 1, 3], hspace=0.2,
+spec = gridspec.GridSpec(nrows=4, ncols=1, height_ratios=[4, 5, 1, 3], hspace=0.2,
                          **adjust_fs(fig, left=4.0))
 ax = fig.add_subplot(spec[0, 0])
 ax.set_xlim(0.0, np.pi)
@@ -17,7 +15,7 @@ ax.set_xticks(np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi))
 ax.set_xticklabels([])
 ax.set_ylim(0.0, 3.5)
 ax.yaxis.set_major_locator(plt.NullLocator())
-ax.text(-0.2, 0.5*3.5, 'Activity', rotation='vertical', va='center')
+ax.text(-0.2, 0.5*3.5, 'Firing rate', rotation='vertical', va='center')
 ax.annotate('Tuning curve',
             xy=(0.42*np.pi, 2.5), xycoords='data',
             xytext=(0.3*np.pi, 3.2), textcoords='data', ha='right',
@@ -32,27 +30,34 @@ ax.text(0.52*np.pi, 0.7, 'preferred\norientation')
 xx = np.arange(0.0, 2.0*np.pi, 0.01)
 pp = 0.5*np.pi
 yy = np.exp(np.cos(2.0*(xx+pp)))
-ax.fill_between(xx, yy+0.25*yy, yy-0.25*yy, **fsBa)
+ss = 1.0/(0.75+2.0*yy)
+ax.fill_between(xx, yy+ss, yy-ss, **fsBa)
 ax.plot(xx, yy, **lsB)
 
 ax = fig.add_subplot(spec[1, 0])
 ax.set_xlim(0.0, np.pi)
 ax.set_xticks(np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi))
 ax.set_xticklabels([])
-ax.set_ylim(0.0, 3.0)
+ax.set_ylim(-0.1, 3.5)
 ax.yaxis.set_major_locator(plt.NullLocator())
-ax.text(-0.2, 0.5*3.5, 'Activity', rotation='vertical', va='center')
+ax.text(-0.2, 0.5*3.5, 'Firing rate', rotation='vertical', va='center')
 xx = np.arange(0.0, 1.0*np.pi, 0.01)
 prefphases = np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi)
 responses = []
-xresponse = 0.475*np.pi
+sigmas = []
+xresponse = 0.41*np.pi
+ax.annotate('Orientation of bar',
+            xy=(xresponse, -0.1), xycoords='data',
+            xytext=(xresponse, 3.1), textcoords='data', ha='left', zorder=-10,
+            arrowprops=dict(arrowstyle="->", relpos=(0.0,0.0)) )
 for pp, ls, ps in zip(prefphases, [lsE, lsC, lsD, lsB, lsD, lsC, lsE],
                       [psE, psC, psD, psB, psD, psC, psE]) :
     yy = np.exp(np.cos(2.0*(xx+pp)))
-    #ax.plot(xx, yy, color=cm.autumn(2.0*np.abs(pp/np.pi-0.5), 1))
     ax.plot(xx, yy, **ls)
     y = np.exp(np.cos(2.0*(xresponse+pp)))
-    responses.append(y + rng.randn()*0.25*y)
+    s = 1.0/(0.75+2.0*y)
+    responses.append(y + rng.randn()*s)
+    sigmas.append(s)
     ax.plot(xresponse, y, **ps)
 responses = np.array(responses)
 
@@ -68,25 +73,23 @@ ax = fig.add_subplot(spec[3, 0])
 ax.set_xlim(0.0, np.pi)
 ax.set_xticks(np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi))
 ax.set_xticklabels([])
-ax.set_ylim(-1600, 0)
+ax.set_ylim(-210, 0)
 ax.yaxis.set_major_locator(plt.NullLocator())
 ax.set_xlabel('Orientation')
-ax.text(-0.2, -800, 'Log-Likelihood', rotation='vertical', va='center')
+ax.text(-0.2, -100, 'Log-Likelihood', rotation='vertical', va='center')
 phases = np.linspace(0.0, 1.1*np.pi, 100)
 probs = np.zeros((len(responses), len(phases)))
-for k, (pp, r) in enumerate(zip(prefphases, responses)) :
+for k, (pp, r, sigma) in enumerate(zip(prefphases, responses, sigmas)) :
     y = np.exp(np.cos(2.0*(phases+pp)))
-    sigma = 0.1*y
-    probs[k,:] = np.exp(-0.5*((r-y)/sigma)**2.0)/np.sqrt(2.0*np.pi)/sigma
+    probs[k,:] = np.exp(-0.5*((r-y)/sigma)**2.0)/np.sqrt(2.0*np.pi*sigma**2)
 loglikelihood = np.sum(np.log(probs), 0)
-maxl = np.max(loglikelihood)
 maxp = phases[np.argmax(loglikelihood)]
 ax.annotate('',
-            xy=(maxp, -1600), xycoords='data',
-            xytext=(maxp, -30), textcoords='data',
+            xy=(maxp, -210), xycoords='data',
+            xytext=(maxp, -10), textcoords='data',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.5),
             connectionstyle="angle3,angleA=80,angleB=90") )
-ax.text(maxp+0.05, -1100, 'most likely\norientation\ngiven the responses')
-ax.plot(phases, loglikelihood, **lsA)
+ax.text(maxp+0.05, -150, 'most likely\norientation\ngiven the responses')
+ax.plot(phases, loglikelihood, clip_on=False, **lsA)
 
 plt.savefig('mlecoding.pdf')
diff --git a/likelihood/lecture/mlemean.py b/likelihood/lecture/mlemean.py
index a1fb974..e18618d 100644
--- a/likelihood/lecture/mlemean.py
+++ b/likelihood/lecture/mlemean.py
@@ -52,7 +52,7 @@ for i, theta in enumerate(thetas) :
 p=np.prod(ps,axis=0)
 # plot it:
 ax = fig.add_subplot(spec[1, 0])
-ax.set_xlabel(r'Parameter $\theta$')
+ax.set_xlabel(r'Parameter $\mu$')
 ax.set_ylabel('Likelihood')
 ax.set_xticks(np.arange(1.6, 2.5, 0.4))
 ax.annotate('Maximum',
@@ -68,7 +68,7 @@ ax.annotate('',
 ax.plot(thetas, p, **lsAm)
 
 ax = fig.add_subplot(spec[1, 1])
-ax.set_xlabel(r'Parameter $\theta$')
+ax.set_xlabel(r'Parameter $\mu$')
 ax.set_ylabel('Log-Likelihood')
 ax.set_ylim(-50,-20)
 ax.set_xticks(np.arange(1.6, 2.5, 0.4))
diff --git a/logistic/code/logistic.py b/logistic/code/logistic.py
new file mode 100644
index 0000000..18d500c
--- /dev/null
+++ b/logistic/code/logistic.py
@@ -0,0 +1,156 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import ttest_ind, mannwhitneyu
+
+
+def auc(n, dx, uniform=False, plot=False):
+    # loser:
+    if uniform:
+        x0 = np.random.rand(n)
+    else:
+        x0 = np.random.randn(n)*0.3
+    y0 = np.zeros(len(x0))
+    # winner:
+    if uniform:
+        x1 = np.random.rand(n) + dx
+    else:
+        x1 = np.random.randn(n)*0.3 + dx
+    y1 = np.ones(len(x1))
+
+    # combine into a single table:
+    data = np.zeros((len(x0) + len(y0), 2))
+    data[:len(x0),0] = x0
+    data[:len(x0),1] = y0
+    data[len(x0):,0] = x1
+    data[len(x0):,1] = y1
+
+    # fraction of overlapping data values:
+    si = np.argsort(data[:,0])
+    i0 = np.argmax(data[si,1] != data[si[0],1])
+    i1 = len(data) - 1 - np.argmax(data[si[::-1],1] != data[si[-1],1])
+    overlap = (i1-i0+1)/len(data)
+
+    # Cohen's d:
+    m0 = np.mean(data[data[:,1] < 0.5,0])
+    v0 = np.var(data[data[:,1] < 0.5,0])
+    m1 = np.mean(data[data[:,1] > 0.5,0])
+    v1 = np.var(data[data[:,1] > 0.5,0])
+    cohensd = (m1 - m0)/np.sqrt(0.5*(v0+v1))
+
+    # t-test:
+    ttest, p = ttest_ind(data[data[:,1] > 0.5,0], data[data[:,1] < 0.5,0])
+
+    # Mann-Whitney U:
+    mannu, p = mannwhitneyu(data[data[:,1] < 0.5,0], data[data[:,1] > 0.5,0])
+
+    # ROC:
+    thresh = np.arange(np.min(data[:,0])-0.1, np.max(data[:,0])+0.2, 0.01)
+    true_pos = np.zeros(len(thresh))
+    false_pos = np.zeros(len(thresh))
+    for k in range(len(thresh)):
+        true_pos[k] = np.sum(data[data[:,0] > thresh[k],1] > 0.5)/np.sum(data[:,1] > 0.5)
+        false_pos[k] = np.sum(data[data[:,0] > thresh[k],1] < 0.5)/np.sum(data[:,1] < 0.5)
+
+    # AUC:
+    droc = 0.001
+    xroc = np.arange(0.0, 1.0+droc, droc)
+    yroc = np.interp(xroc, false_pos[::-1], true_pos[::-1])
+    auc = np.sum(yroc)*droc
+
+    if plot:
+        fig = plt.figure()
+        ax = fig.add_subplot(211)
+        ax.axvline(data[si[i0],0], color='k')
+        ax.axvline(data[si[i1],0], color='k', lw=2)
+        ax.plot(data[:,0], data[:,1], 'o')
+        ax.plot(data[data[:,1] < 0.5,0], np.zeros(len(data[data[:,1] < 0.5,0]))-0.5, 'or')
+        ax.plot(data[data[:,1] > 0.5,0], np.zeros(len(data[data[:,1] > 0.5,0]))-0.5, 'og')
+        ax.text(0.5*(data[si[i0],0]+data[si[i1],0]), 0.65, 'overlap=%.0f%%' % (100.0*overlap), ha='center')
+        ax.text(0.5*(data[si[i0],0]+data[si[i1],0]), 0.35, "Cohen's d=%.2f" % cohensd, ha='center')
+        ax.set_xlabel('x')
+        ax.set_yticks([0, 1])
+        ax.set_yticklabels(['Lose', 'Win'])
+        if uniform:
+            ax.set_title('Uniformly distributed data')
+        else:
+            ax.set_title('Normally distributed data')
+        
+        ax = fig.add_subplot(223)
+        ax.plot(thresh, true_pos, '-og', label='TP')
+        ax.plot(thresh, false_pos, '-or', label='FP')
+        ax.legend()
+        ax.set_xlabel('threshold')
+        
+        ax = fig.add_subplot(224)
+        ax.plot(false_pos, true_pos, '-o')
+        ax.fill_between(xroc, yroc)
+        ax.text(0.5, 0.5, 'AUC=%.0f%%' % (100.0*auc))
+        ax.set_xlabel('FP')
+        ax.set_ylabel('TP')
+        fig.tight_layout()
+        plt.show()
+
+    return auc, overlap, cohensd, ttest, mannu
+
+
+# demo:
+auc(20, 0.5, True, True)
+auc(20, 0.5, False, True)
+
+# AUC versus overlap:
+n = 100
+aucs_uni = []
+overlaps_uni = []
+cohensd_uni = []
+ttest_uni = []
+mannu_uni = []
+aucs_norm = []
+overlaps_norm = []
+cohensd_norm = []
+ttest_norm = []
+mannu_norm = []
+for frac in np.arange(-1.5, 1.5, 0.02):
+    a, o, d, t, u = auc(n, frac, True, False)
+    aucs_uni.append(a)
+    overlaps_uni.append(o)
+    cohensd_uni.append(d)
+    ttest_uni.append(t)
+    mannu_uni.append(u)
+    a, o, d, t, u = auc(n, frac, False, False)
+    aucs_norm.append(a)
+    overlaps_norm.append(o)
+    cohensd_norm.append(d)
+    ttest_norm.append(t)
+    mannu_norm.append(u)
+    
+fig, axs = plt.subplots(2, 2)
+ax = axs[0, 0]
+ax.plot([0.0, 1.0, 0.0], [0.0, 0.5, 1.0], 'k')
+ax.plot(overlaps_uni, aucs_uni, 'o', label='uniform pdfs')
+ax.plot(overlaps_norm, aucs_norm, 'o', label='normal pdfs')
+ax.set_ylim(0, 1)
+ax.set_xlabel('fraction of overlapping data')
+ax.set_ylabel('AUC')
+ax.legend(loc='center left')
+ax = axs[0, 1]
+ax.plot(cohensd_uni, aucs_uni, 'o', label='uniform pdfs')
+ax.plot(cohensd_norm, aucs_norm, 'o', label='normal pdfs')
+ax.set_ylim(0, 1)
+ax.set_xlabel("Cohen's d")
+ax.set_ylabel('AUC')
+#ax.legend(loc='center left')
+ax = axs[1, 1]
+ax.plot(ttest_uni, aucs_uni, 'o', label='uniform pdfs')
+ax.plot(ttest_norm, aucs_norm, 'o', label='normal pdfs')
+ax.set_ylim(0, 1)
+ax.set_xlabel("Student t")
+ax.set_ylabel('AUC')
+ax = axs[1, 0]
+ax.plot(mannu_uni, aucs_uni, 'o', label='uniform pdfs')
+ax.plot(mannu_norm, aucs_norm, 'o', label='normal pdfs')
+ax.set_ylim(0, 1)
+ax.set_xlabel("Mann-Whitney U")
+ax.set_ylabel('AUC')
+fig.savefig('aucoverlap.pdf')
+plt.show()
+
diff --git a/plotstyle.py b/plotstyle.py
index 7e3ba8c..add3e40 100644
--- a/plotstyle.py
+++ b/plotstyle.py
@@ -22,7 +22,7 @@ colors['orange'] = '#FF9900'
 colors['lightorange'] = '#FFCC00'
 colors['yellow'] = '#FFF720'
 colors['green'] = '#99FF00'
-colors['blue'] = '#0010CC'
+colors['blue'] = '#0020BB'
 colors['gray'] = '#A7A7A7'
 colors['black'] = '#000000'
 colors['white'] = '#FFFFFF'
@@ -33,17 +33,19 @@ colors['white'] = '#FFFFFF'
 # general settings for plot styles:
 lwthick = 3.0
 lwthin = 1.8
+edgewidth = 0.0 if xkcd_style else 1.0
 mainline = {'linestyle': '-', 'linewidth': lwthick}
 minorline = {'linestyle': '-', 'linewidth': lwthin}
-largemarker = {'marker': 'o', 'markersize': 9, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
-smallmarker = {'marker': 'o', 'markersize': 6, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
+largemarker = {'marker': 'o', 'markersize': 9, 'markeredgecolor': colors['white'], 'markeredgewidth': edgewidth}
+largeopenmarker = {'marker': 'o', 'markersize': 7, 'markerfacecolor': colors['white'], 'markeredgewidth': 2}
+smallmarker = {'marker': 'o', 'markersize': 6, 'markeredgecolor': colors['white'], 'markeredgewidth': edgewidth}
 largelinepoints = {'linestyle': '-', 'linewidth': lwthick, 'marker': 'o', 'markersize': 10, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
 smalllinepoints = {'linestyle': '-', 'linewidth': 1.4, 'marker': 'o', 'markersize': 7, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
-filllw = 1.0
+filllw = edgewidth
 fillec = colors['white']
 fillalpha = 0.4
 filledge = {'linewidth': filllw, 'joinstyle': 'round'}
-if int(mpl.__version__.split('.')[0]) < 2:
+if mpl_major < 2:
     del filledge['joinstyle']
 
 # helper lines:
@@ -51,6 +53,8 @@ lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1, 'clip_on': Fa
 lsGrid = {'c': colors['gray'], 'linestyle': '--', 'linewidth': 1}
 lsMarker = {'c': colors['black'], 'linestyle': '-', 'linewidth': 2}
 
+psMarker = dict({'color': colors['black'], 'linestyle': 'none'}, **largemarker)
+
 # line (ls), point (ps), and fill styles (fs).
 
 # Each style is derived from a main color as indicated by the capital letter.
@@ -85,6 +89,7 @@ fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
 lsB = dict({'color': colors['red']}, **mainline)
 lsBm = dict({'color': colors['red']}, **minorline)
 psB = dict({'color': colors['red'], 'linestyle': 'none'}, **largemarker)
+psBo = dict({'markeredgecolor': colors['red'], 'linestyle': 'none'}, **largeopenmarker)
 psBm = dict({'color': colors['red'], 'linestyle': 'none'}, **smallmarker)
 lpsB = dict({'color': colors['red']}, **largelinepoints)
 lpsBm = dict({'color': colors['red']}, **smalllinepoints)
diff --git a/plotting/exercises/Makefile b/plotting/exercises/Makefile
new file mode 100644
index 0000000..8e092f8
--- /dev/null
+++ b/plotting/exercises/Makefile
@@ -0,0 +1,3 @@
+TEXFILES=$(wildcard plotting-?.tex)
+
+include ../../exercises.mk
diff --git a/plotting/exercises/instructions.tex b/plotting/exercises/instructions.tex
index 37f737d..8f5d332 100644
--- a/plotting/exercises/instructions.tex
+++ b/plotting/exercises/instructions.tex
@@ -1,17 +1,11 @@
-\vspace*{-8ex}
-\begin{center}
-\textbf{\Large Introduction to scientific computing}\\[1ex]
-{\large Jan Grewe, Jan Benda}\\[-3ex]
-Neuroethology lab \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\ifprintanswers%
+\else
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. In contrast
 to previous exercises, the solutions can not be saved in a single file. Combine the files into a
 single zip archive and submit it via ILIAS. Name the archive according
 to the pattern: ``plotting\_\{surname\}.zip''.
-% \ifprintanswers%
-% \else
 
 % % Die folgenden Aufgaben dienen der Wiederholung, \"Ubung und
 % % Selbstkontrolle und sollten eigenst\"andig bearbeitet und gel\"ost
@@ -43,4 +37,4 @@ to the pattern: ``plotting\_\{surname\}.zip''.
 %   Antworten!
 % \end{itemize}
 
-% \fi
+\fi
diff --git a/plotting/exercises/exercises.tex b/plotting/exercises/plotting-1.tex
similarity index 54%
rename from plotting/exercises/exercises.tex
rename to plotting/exercises/plotting-1.tex
index f66c61c..381e43c 100644
--- a/plotting/exercises/exercises.tex
+++ b/plotting/exercises/plotting-1.tex
@@ -1,88 +1,18 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Plotting}
+\newcommand{\exercisenum}{X}
+\newcommand{\exercisedate}{December 14th, 2020}
 
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 7\stitle}}{{\bfseries\large Plotting}}{{\bfseries\large November 20, 2019}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
+\input{../../exercisestitle}
+
 \input{instructions}
 
 \begin{questions}
diff --git a/plotting/lecture/plotting-chapter.tex b/plotting/lecture/plotting-chapter.tex
index 6a96c4a..85d9cc5 100644
--- a/plotting/lecture/plotting-chapter.tex
+++ b/plotting/lecture/plotting-chapter.tex
@@ -3,7 +3,7 @@
 \input{../../header}
 
 \lstset{inputpath=../code}
-\graphicspath{{images/}}
+\graphicspath{{figures/}}
 
 \typein[\pagenumber]{Number of first page}
 \typein[\chapternumber]{Chapter number}
diff --git a/plotting/lecture/plotting.tex b/plotting/lecture/plotting.tex
index d895f8b..1d30084 100644
--- a/plotting/lecture/plotting.tex
+++ b/plotting/lecture/plotting.tex
@@ -74,20 +74,20 @@ the data was changed or the same kind of plot has to be created for a
 number of datasets.
 
 \begin{important}[Why manual editing should be avoided.]
-  On first glance the manual editing of a figure using tools such as
-  Corel draw, Illustrator, etc.\,appears much more convenient and less
-  complex than coding everything into the analysis scripts. This,
-  however, is not entirely true. What if the figure has to be re-drawn
-  or updated? Then the editing work starts all over again. Rather,
-  there is a great risk associated with the manual editing
-  approach. Axes may be shifted, fonts have not been embedded into the
-  final document, annotations have been copy pasted between figures
-  and are not valid. All of these mistakes can be found in
-  publications and then require an erratum, which is not
-  desirable. Even if it appears more cumbersome in the beginning one
-  should always try to create publication-ready figures directly from
-  the data analysis tool using scripts or functions to properly layout
-  the plot.
+  On first glance manual editing of a figure using tools such as
+  inkscape, Corel draw, Illustrator, etc.\,appears much more
+  convenient and less complex than coding everything into the analysis
+  scripts. This, however, is not entirely true. What if the figure has
+  to be re-drawn or updated, because, for example, you got more data?
+  Then the editing work starts all over again. In addition, there is a
+  great risk associated with the manual editing approach. Axes may be
+  shifted, fonts have not been embedded into the final document,
+  annotations have been copy-pasted between figures and are not
+  valid. All of these mistakes can be found in publications and then
+  require an erratum, which is not desirable. Even if it appears more
+  cumbersome in the beginning, one should always try to generate
+  publication-ready figures directly from the data analysis tool using
+  scripts or functions to properly layout and annotate the plot.
 \end{important}
 
 \subsection{Simple plotting}
@@ -148,7 +148,7 @@ or the color. For additional options consult the help.
 
 The following listing shows a simple line plot with axis labeling and a title
 
-\lstinputlisting[caption={A simple plot showing a sinewave.},
+\pageinputlisting[caption={A simple plot showing a sinewave.},
   label=simpleplotlisting]{simple_plot.m}
 
 
@@ -162,10 +162,10 @@ chosen, and star marker symbols is used. Finally, the name of the
 curve is set to \emph{plot 1} which will be displayed in a legend, if
 chosen.
 
-\begin{lstlisting}[label=settinglineprops, caption={Setting line properties when calling \varcode{plot}.}]
+\begin{pagelisting}[label=settinglineprops, caption={Setting line properties when calling \varcode{plot}.}]
   x = 0:0.1:2*pi; y = sin(x); plot( x, y, 'color', 'r', 'linestyle',
   ':', 'marker', '*', 'linewidth', 1.5, 'displayname', 'plot 1')
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{important}[Choosing the right color.]
   Choosing the perfect color goes a little bit beyond personal
@@ -277,7 +277,7 @@ the last one defines the output format (box\,\ref{graphicsformatbox}).
     listing\,\ref{niceplotlisting}.}\label{spikedetectionfig}
 \end{figure}
 
-\begin{ibox}[t]{\label{graphicsformatbox}File formats for digital artwork.}
+\begin{ibox}[tp]{\label{graphicsformatbox}File formats for digital artwork.}
   There are two fundamentally different types of formats for digital artwork:
   \begin{enumerate}
   \item \enterm[bitmap]{Bitmaps} (\determ{Rastergrafik})
@@ -322,7 +322,7 @@ the last one defines the output format (box\,\ref{graphicsformatbox}).
   efficient.
 \end{ibox}
 
-\lstinputlisting[caption={Script for creating the plot shown in
+\pageinputlisting[caption={Script for creating the plot shown in
     \figref{spikedetectionfig}.},
   label=niceplotlisting]{automatic_plot.m}
 
@@ -380,7 +380,7 @@ draw the data. In the example we also provide further arguments to set
 the size, color of the dots and specify that they are filled
 (listing\,\ref{scatterlisting1}).
 
-\lstinputlisting[caption={Creating a scatter plot with red filled dots.},
+\pageinputlisting[caption={Creating a scatter plot with red filled dots.},
   label=scatterlisting1, firstline=9, lastline=9]{scatterplot.m}
 
 We could have used plot for this purpose and set the marker to
@@ -395,8 +395,7 @@ manipulate the color we need to specify a length(x)-by-3 matrix. For
 each dot we provide an individual color (i.e. the RGB triplet in each
 row of the color matrix, lines 2-4 in listing\,\ref{scatterlisting2})
 
-
-\lstinputlisting[caption={Creating a scatter plot with size and color
+\pageinputlisting[caption={Creating a scatter plot with size and color
     variations. The RGB triplets define the respective color intensity
     in a range 0:1. Here, we modify only the red color channel.},
   label=scatterlisting2, linerange={15-15, 21-23}]{scatterplot.m}
@@ -431,7 +430,7 @@ figures\,\ref{regularsubplotsfig}, \ref{irregularsubplotsfig}).
     also below).}\label{regularsubplotsfig}
 \end{figure}
 
-\lstinputlisting[caption={Script for creating subplots in a regular
+\pageinputlisting[caption={Script for creating subplots in a regular
     grid \figref{regularsubplotsfig}.}, label=regularsubplotlisting,
   basicstyle=\ttfamily\scriptsize]{regular_subplot.m}
 
@@ -458,7 +457,7 @@ create a grid with larger numbers of columns and rows, and specify the
 used cells of the grid by passing a vector as the third argument to
 \code{subplot()}.
 
-\lstinputlisting[caption={Script for creating subplots of different
+\pageinputlisting[caption={Script for creating subplots of different
     sizes \figref{irregularsubplotsfig}.},
   label=irregularsubplotslisting,
   basicstyle=\ttfamily\scriptsize]{irregular_subplot.m}
@@ -516,7 +515,7 @@ its properties. See the \matlab{} help for more information.
     listing\,\ref{errorbarlisting} for A and C and listing\,\ref{errorbarlisting2} }\label{errorbarplot}
 \end{figure}
 
-\lstinputlisting[caption={Illustrating estimation errors using error bars. Script that
+\pageinputlisting[caption={Illustrating estimation errors using error bars. Script that
     creates \figref{errorbarplot}. A, B},
   label=errorbarlisting, firstline=13, lastline=31,
   basicstyle=\ttfamily\scriptsize]{errorbarplot.m}
@@ -550,7 +549,7 @@ leading to invisibility and a value of one to complete
 opaqueness. Finally, we use the normal plot command to draw a line
 connecting the average values (line 12).
 
-\lstinputlisting[caption={Illustrating estimation errors using a shaded area. Script that
+\pageinputlisting[caption={Illustrating estimation errors using a shaded area. Script that
     creates \figref{errorbarplot} C.}, label=errorbarlisting2,
   firstline=33,
   basicstyle=\ttfamily\scriptsize]{errorbarplot.m}
@@ -575,7 +574,7 @@ listing\,\ref{annotationsplotlisting}. For more options consult the
     listing\,\ref{annotationsplotlisting}}\label{annotationsplot}
 \end{figure}
 
-\lstinputlisting[caption={Adding annotations to figures. Script that
+\pageinputlisting[caption={Adding annotations to figures. Script that
     creates \figref{annotationsplot}.},
   label=annotationsplotlisting,
   basicstyle=\ttfamily\scriptsize]{annotations.m}
@@ -632,7 +631,7 @@ Lissajous figure. The basic steps are:
 \item Finally, close the file (line 31).
 \end{enumerate}
 
-\lstinputlisting[caption={Making animations and saving them as a
+\pageinputlisting[caption={Making animations and saving them as a
     movie.}, label=animationlisting, firstline=16, lastline=36,
   basicstyle=\ttfamily\scriptsize]{movie_example.m}
 
diff --git a/pointprocesses/code/binnedRate.m b/pointprocesses/code/binnedRate.m
index e34366c..cf9ad65 100644
--- a/pointprocesses/code/binnedRate.m
+++ b/pointprocesses/code/binnedRate.m
@@ -18,7 +18,7 @@ function [time, rate] = binned_rate(spikes, bin_width, dt, t_max)
     rate = zeros(size(time));
     h = hist(spikes, bins) ./ bin_width;
     for i = 2:length(bins)
-        rate(round(bins(i - 1) / dt) + 1:round(bins(i) / dt)) = h(i);
+        rate(round(bins(i-1)/dt) + 1:round(bins(i)/dt)) = h(i);
     end
 end
 
diff --git a/pointprocesses/code/colorednoisepdf.m b/pointprocesses/code/colorednoisepdf.m
deleted file mode 100644
index 28f421b..0000000
--- a/pointprocesses/code/colorednoisepdf.m
+++ /dev/null
@@ -1,10 +0,0 @@
-function pcn = colorednoisepdf( x, misi, epsilon, tau )
-% returns the ISI pdf for PIF with colored noise drive
-% x: the input ISI
-% misis: the mean isi
-% epsilon: a parameter
-% tau: the correlation time of the noise
-    gamma1 = x/tau+exp(-x/tau)-1.0;
-    gamma2 = 1.0-exp(-x/tau);
-    pcn=exp(-(x-misi).^2./(4.0*epsilon*tau.^2.*gamma1)).*(((misi-x).*gamma2+2*gamma1*tau).^2./(2*gamma1*tau^2)-epsilon*(gamma2.^2-2*gamma1.*exp(-x/tau))) ./ (2*tau*sqrt(4*pi*epsilon*gamma1.^3));
-end
diff --git a/pointprocesses/code/colorednoisepdfisifit.m b/pointprocesses/code/colorednoisepdfisifit.m
deleted file mode 100644
index 1c653c0..0000000
--- a/pointprocesses/code/colorednoisepdfisifit.m
+++ /dev/null
@@ -1,24 +0,0 @@
-% misi = 0.02;
-% epsilon = 1.0;
-% tau = 0.1;
-x=0:0.002:0.1;
-% pcn = colorednoisepdf( x, misi, epsilon, tau )+10.0*randn( size( x ) );
-% plot( x, pcn );
-
-spikes = lifouspikes( 10, 15, 50.0, 1.0, 1.0 );
-isivec = isis( spikes );
-misi = mean( isivec );
-1.0/misi
-isibins = 0:0.0005:0.1;
-[ n, c ] = hist( isivec, isibins );
-n = n / sum(n)/(isibins(2)-isibins(1));
-bar( c, n );
-
-beta0 = [ 1.0, 0.01 ];
-b = nlinfit(c(1:end-2), n(1:end-2), @(b,x)(colorednoisepdf(x, misi, b(1), b(2))), beta0)
-
-pcn = colorednoisepdf( x, misi, b(1), b(2) );
-hold on
-plot( x, pcn, 'r', 'LineWidth', 3 );
-hold off
-
diff --git a/pointprocesses/code/convolutionRate.m b/pointprocesses/code/convolutionRate.m
index a2b1344..4fe5bb8 100644
--- a/pointprocesses/code/convolutionRate.m
+++ b/pointprocesses/code/convolutionRate.m
@@ -10,19 +10,18 @@ function [time, rate] = convolution_rate(spikes, sigma, dt, t_max)
 %   t_max : the trial duration in seconds.
 %
 % Returns:
-    two vectors containing the time and the rate.
+%   two vectors containing the time and the rate.
 
-time = 0:dt:t_max - dt;
-rate = zeros(size(time));
-spike_indices = round(spikes / dt);
-rate(spike_indices) = 1;
-kernel = gaussKernel(sigma, dt);
-
-rate = conv(rate, kernel, 'same');
+    time = 0:dt:t_max - dt;
+    rate = zeros(size(time));
+    spike_indices = round(spikes / dt);
+    rate(spike_indices) = 1;
+    kernel = gaussKernel(sigma, dt);
+    rate = conv(rate, kernel, 'same');
 end
 
 
 function y = gaussKernel(s, step)
     x = -4 * s:step:4 * s;
-    y = exp(-0.5 .* (x ./ s) .^ 2) ./ sqrt(2 * pi) / s;
+    y = exp(-0.5 .* (x ./ s).^ 2) ./ sqrt(2 * pi) / s;
 end
diff --git a/pointprocesses/code/counthist.m b/pointprocesses/code/counthist.m
deleted file mode 100644
index 6885b1a..0000000
--- a/pointprocesses/code/counthist.m
+++ /dev/null
@@ -1,44 +0,0 @@
-function [counts, bins] = counthist(spikes, w)
-% Compute and plot histogram of spike counts.
-%
-% [counts, bins] = counthist(spikes, w)
-%
-% Arguments:
-%   spikes: a cell array of vectors of spike times in seconds
-%   w: observation window duration in seconds for computing the counts
-%
-% Returns:
-%   counts: the histogram of counts normalized to probabilities
-%   bins: the bin centers for the histogram
-
-    % collect spike counts:
-    tmax = spikes{1}(end);
-    n = [];
-    r = [];
-    for k = 1:length(spikes)
-        times = spikes{k};
-%       alternative 1: count the number of spikes in each window:
-%         for tk = 0:w:tmax-w
-%             nn = sum((times >= tk) & (times < tk+w));
-%             %nn = length(find((times >= tk) & (times < tk+w)));
-%             n = [n nn];
-%         end
-%     alternative 2: use the hist() function to do that!
-        tbins = 0.5*w:w:tmax-0.5*w;
-        nn = hist(times, tbins);
-        n = [n nn];
-    end
-
-    % histogram of spike counts:
-    maxn = max(n);
-    [counts, bins] = hist(n, 0:1:maxn+10);
-    % normalize to probabilities:
-    counts = counts / sum(counts);
-
-    % plot:
-    if nargout == 0
-        bar( bins, counts );
-        xlabel( 'counts k' );
-        ylabel( 'P(k)' );
-    end
-end
diff --git a/pointprocesses/code/counthistpoisson.m b/pointprocesses/code/counthistpoisson.m
deleted file mode 100644
index 9d9889a..0000000
--- a/pointprocesses/code/counthistpoisson.m
+++ /dev/null
@@ -1,55 +0,0 @@
-function [counts, bins] = counthist(spikes, w)
-% computes count histogram and compare with Poisson distribution
-%
-% [counts, bins] = counthist(spikes, w)
-%
-% Arguments:
-%   spikes: a cell array of vectors of spike times in seconds
-%   w: observation window duration in seconds for computing the counts
-%
-% Returns:
-%   counts: the histogram of counts normalized to probabilities
-%   bins: the bin centers for the histogram
-
-    % collect spike counts:
-    tmax = spikes{1}(end);
-    n = [];
-    r = [];
-    for k = 1:length(spikes)
-        times = spikes{k};
-%       alternative 1: count the number of spikes in each window:
-%         for tk = 0:w:tmax-w
-%             nn = sum( ( times >= tk ) & ( times < tk+w ) );
-%             %nn = length( find( ( times >= tk ) & ( times < tk+w ) ) );
-%             n = [ n nn ];
-%         end
-%     alternative 2: use the hist function to do that!
-        tbins = 0.5*w:w:tmax-0.5*w;
-        nn = hist(times, tbins);
-        n = [ n nn ];
-        % the rate of the spikes:
-        rate = (length(times)-1)/(times(end) - times(1));
-        r = [ r rate ];
-    end
-
-    % histogram of spike counts:
-    maxn = max( n );
-    [counts, bins ] = hist( n, 0:1:maxn+10 );
-    % normalize to probabilities:
-    counts = counts / sum( counts );
-
-    % plot:
-    if nargout == 0
-        bar( bins, counts );
-        hold on;
-        % Poisson distribution:
-        rate = mean( r );
-        x = 0:1:maxn+10;
-        a = rate*w;
-        y = a.^x.*exp(-a)./factorial(x);
-        plot( x, y, 'r', 'LineWidth', 3 );
-        hold off;
-        xlabel( 'counts k' );
-        ylabel( 'P(k)' );
-    end
-end
diff --git a/pointprocesses/code/counts.m b/pointprocesses/code/counts.m
new file mode 100644
index 0000000..90e53ef
--- /dev/null
+++ b/pointprocesses/code/counts.m
@@ -0,0 +1,25 @@
+function n = counts(spikes, w)
+% Count spikes in time windows.
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%   w: duration of window in seconds for computing the counts
+%
+% Returns:
+%   n: vector with spike counts
+    tmax = spikes{1}(end);
+    n = [];
+    for k = 1:length(spikes)
+        times = spikes{k};
+%       alternative 1: count the number of spikes in each window:
+%         for tk = 0:w:tmax-w
+%             nn = sum((times >= tk) & (times < tk+w));
+%             % nn = length(find((times >= tk) & (times < tk+w)));
+%             n = [n, nn];
+%         end
+%     alternative 2: use the hist() function to do that!
+        tbins = 0.5*w:w:tmax-0.5*w;
+        nn = hist(times, tbins);
+        n = [n, nn];
+    end
+end
diff --git a/pointprocesses/code/fano.m b/pointprocesses/code/fano.m
deleted file mode 100644
index a73cada..0000000
--- a/pointprocesses/code/fano.m
+++ /dev/null
@@ -1,39 +0,0 @@
-function fano( spikes )
-% computes fano factor as a function of window size
-% spikes: a cell array of vectors of spike times
-
-    tmax = spikes{1}(end);
-    windows = 0.01:0.05:0.01*tmax;
-    mc = windows;
-    vc = windows;
-    ff = windows;
-    fs = windows;
-    for j = 1:length(windows)
-        w = windows( j );
-        % collect counts:
-        n = [];
-        for k = 1:length(spikes)
-            for tk = 0:w:tmax-w
-                nn = sum( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) );
-                %nn = length( find( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) ) );
-                n = [ n nn ];
-            end
-        end
-        % statistics for current window:
-        mc(j) = mean( n );
-        vc(j) = var( n );
-        ff(j) = vc( j )/mc( j );
-        fs(j) = sqrt(vc( j )/mc( j ));
-    end
-    
-    subplot( 1, 2, 1 );
-    scatter( mc, vc, 'filled' );
-    xlabel( 'Mean count' );
-    ylabel( 'Count variance' );
-
-    subplot( 1, 2, 2 );
-    scatter( 1000.0*windows, fs, 'filled' );
-    xlabel( 'Window W [ms]' );
-    ylabel( 'Fano factor' );
-end
-
diff --git a/pointprocesses/code/hompoissonspikes.m b/pointprocesses/code/hompoissonspikes.m
index e5caf62..9fe8446 100644
--- a/pointprocesses/code/hompoissonspikes.m
+++ b/pointprocesses/code/hompoissonspikes.m
@@ -2,8 +2,6 @@ function spikes = hompoissonspikes(rate, trials, tmax)
 % Generate spike times of a homogeneous poisson process
 % using the exponential interspike interval distribution.
 %
-% spikes = hompoissonspikes(rate, trials, tmax)
-%
 % Arguments:
 %   rate: the rate of the Poisson process in Hertz
 %   trials: number of trials that should be generated
@@ -11,7 +9,6 @@ function spikes = hompoissonspikes(rate, trials, tmax)
 %
 % Returns:
 %   spikes: a cell array of vectors of spike times in seconds
-
     spikes = cell(trials, 1);
     mu = 1.0/rate;
     nintervals = 2*round(tmax/mu);
diff --git a/pointprocesses/code/instantaneousRate.m b/pointprocesses/code/instantaneousRate.m
index 8928c31..da8ee29 100644
--- a/pointprocesses/code/instantaneousRate.m
+++ b/pointprocesses/code/instantaneousRate.m
@@ -19,6 +19,6 @@ function [time, rate] = instantaneous_rate(spikes, dt, t_max)
     spike_indices = [1 round(spikes ./ dt)];
 
     for i = 2:length(spike_indices)
-        rate(spike_indices(i - 1):spike_indices(i)) = inst_rate(i - 1);
+        rate(spike_indices(i-1):spike_indices(i)) = inst_rate(i-1);
     end
 end
diff --git a/pointprocesses/code/isihist.m b/pointprocesses/code/isihist.m
index 33ad4f5..6c89b38 100644
--- a/pointprocesses/code/isihist.m
+++ b/pointprocesses/code/isihist.m
@@ -1,25 +1,13 @@
 function [pdf, centers] = isihist(isis, binwidth)
 % Compute normalized histogram of interspike intervals.
 %
-% [pdf, centers] = isihist(isis, binwidth)
-%
 % Arguments:
 %   isis: vector of interspike intervals in seconds
-%   binwidth: optional width in seconds to be used for the isi bins
+%   binwidth: width in seconds to be used for the ISI bins
 %
 % Returns:
-%   pdf: vector with probability density of interspike intervals in Hz
+%   pdf: vector with pdf of interspike intervals in Hertz
 %   centers: vector with centers of interspikeintervalls in seconds
-
-    if nargin < 2
-        % compute good binwidth:
-        nperbin = 200;        % average number of data points per bin
-        bins = length(isis)/nperbin;   % number of bins
-        binwidth = max(isis)/bins;
-        if binwidth < 5e-4             % half a millisecond
-            binwidth = 5e-4;
-        end
-    end
     bins = 0.5*binwidth:binwidth:max(isis);
     % histogram data:
     [nelements, centers] = hist(isis, bins);
diff --git a/pointprocesses/code/isireturnmap.m b/pointprocesses/code/isireturnmap.m
deleted file mode 100644
index d790e13..0000000
--- a/pointprocesses/code/isireturnmap.m
+++ /dev/null
@@ -1,24 +0,0 @@
-function isireturnmap( isis, lag2 )
-% plot return maps for lag 1 and lag lag2
-
-    clf;
-    subplot( 1, 2, 1 );
-    lag = 1;
-    scatter( 1000.0*isis(1:end-lag)', 1000.0*isis(1+lag:end)', 'b', 'filled', 'MarkerEdgeColor', 'white' );
-    xlabel( 'ISI T_i [ms]' );
-    ylabel( 'ISI T_{i+1} [ms]' );
-    maxisi = max( isis );
-    maxy = ceil(maxisi/10)*10.0;
-    xlim( [0 1.5*maxy ])
-    ylim( [0 maxy ])
-
-    subplot( 1, 2, 2 );
-    lag = lag2;
-    scatter( 1000.0*isis(1:end-lag)', 1000.0*isis(1+lag:end)', 'b', 'filled', 'MarkerEdgeColor', 'white' );
-    xlabel( 'ISI T_i [ms]' );
-    ylabel( 'ISI T_{i+2} [ms]' );
-    xlim( [0 1.5*maxy ])
-    ylim( [0 maxy ])
-
-end
-
diff --git a/pointprocesses/code/isis.m b/pointprocesses/code/isis.m
index 65a346b..d5eb7d6 100644
--- a/pointprocesses/code/isis.m
+++ b/pointprocesses/code/isis.m
@@ -1,15 +1,11 @@
 function isivec = isis(spikes)
 % returns a single list of isis computed from all trials in spikes
 %
-% isivec = isis(spikes)
-%
 % Arguments:
 %   spikes: a cell array of vectors of spike times in seconds
-%   isivec: a column vector with all the interspike intervalls
 %
 % Returns:
-%   isivec: a column vector with all the interspike intervalls
-
+%   isivec: a column vector with all the interspike intervals
     isivec = [];
     for k = 1:length(spikes)
         difftimes = diff(spikes{k});
diff --git a/pointprocesses/code/isiserialcorr.m b/pointprocesses/code/isiserialcorr.m
index e5b07fb..fc5ec17 100644
--- a/pointprocesses/code/isiserialcorr.m
+++ b/pointprocesses/code/isiserialcorr.m
@@ -1,35 +1,21 @@
-function isicorr = isiserialcorr(isivec, maxlag)
+function [isicorr, lags] = isiserialcorr(isivec, maxlag)
 % serial correlation of interspike intervals
 %
-% isicorr = isiserialcorr(isivec, maxlag)
-%
 % Arguments:
 %   isivec: vector of interspike intervals in seconds
-%   maxlag: the maximum lag in seconds
+%   maxlag: the maximum lag
 %
 % Returns:
 %   isicorr: vector with the serial correlations for lag 0 to maxlag
-
+%   lags: vector with the lags corresponding to isicorr
     lags = 0:maxlag;
     isicorr = zeros(size(lags));
     for k = 1:length(lags)
         lag = lags(k);
         if length(isivec) > lag+10        % ensure "enough" data
 	    % NOTE: the arguments to corr must be column vectors!
-            % We insure this in the isis() function that 
-            % generates the isivec.
+            % We insure this already in the isis() function.
             isicorr(k) = corr(isivec(1:end-lag), isivec(lag+1:end));
         end
     end
-    
-    if nargout == 0
-        % plot:
-        plot(lags, isicorr, '-b');
-        hold on;
-        scatter(lags, isicorr, 50.0, 'b', 'filled');
-        hold off;
-        xlabel('Lag k')
-        ylabel('\rho_k')
-    end
 end
-
diff --git a/pointprocesses/code/plotcounthist.m b/pointprocesses/code/plotcounthist.m
index 7a0a51d..ddfa7fc 100644
--- a/pointprocesses/code/plotcounthist.m
+++ b/pointprocesses/code/plotcounthist.m
@@ -1,24 +1,14 @@
-w = 0.1;
-cmax = 8;
-pmax = 0.5;
-subplot(1, 3, 1);
-counthist(poissonspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('Poisson');
-
-subplot(1, 3, 2);
-counthist(pifouspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('PIF OU');
-
-subplot(1, 3, 3);
-counthist(lifadaptspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('LIF adapt');
-savefigpdf(gcf, 'counthist.pdf', 20, 7);
\ No newline at end of file
+function plotcounthist(spikes, w)
+% Plot histogram of spike counts.
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%   w: duration of window in seconds for computing the counts
+    n = counts(spikes, w);
+    maxn = max(n);
+    [counts, bins] = hist(n, 0:1:maxn+10);
+    counts = counts / sum(counts);
+    bar(bins, counts);
+    xlabel('counts k');
+    ylabel('P(k)');
+end
diff --git a/pointprocesses/code/plotfanofactor.m b/pointprocesses/code/plotfanofactor.m
new file mode 100644
index 0000000..7335bdf
--- /dev/null
+++ b/pointprocesses/code/plotfanofactor.m
@@ -0,0 +1,31 @@
+function plotfanofactor(spikes, wmin, wmax)
+% Compute and plot Fano factor as a function of window size.
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%   wmin: minimum window size in seconds
+%   wmax: maximum window size in seconds
+    windows = logspace(log10(wmin), log10(wmax), 100);
+    mc = zeros(1, length(windows));
+    vc = zeros(1, length(windows));
+    for k = 1:length(windows)
+        w = windows(k);
+        n = counts(spikes, w);
+        mc(k) = mean(n);
+        vc(k) = var(n);
+    end
+    
+    subplot(1, 2, 1);
+    scatter(mc, vc, 'filled');
+    xlabel('Mean count');
+    ylabel('Count variance');
+
+    subplot(1, 2, 2);
+    scatter(1000.0*windows, vc ./ mc, 'filled');
+    xlabel('Window [ms]');
+    ylabel('Fano factor');
+    xlim(1000.0*[windows(1) windows(end)])
+    ylim([0.0 1.1]);
+    set(gca, 'XScale', 'log');
+end
+
diff --git a/pointprocesses/code/plotisihist.m b/pointprocesses/code/plotisihist.m
index 2f3cf93..bfdf179 100644
--- a/pointprocesses/code/plotisihist.m
+++ b/pointprocesses/code/plotisihist.m
@@ -1,24 +1,13 @@
 function plotisihist(isis, binwidth)
 % Plot and annotate histogram of interspike intervals.
 %
-% plotisihist(isis, binwidth)
-%
 % Arguments:
 %   isis: vector of interspike intervals in seconds
-%   binwidth: optional width in seconds to be used for the isi bins
-
-    % compute normalized histogram:
-    if nargin < 2
-        [pdf, centers] = isihist(isis);
-    else
-        [pdf, centers] = isihist(isis, binwidth);
-    end
-
-    % plot:
+%   binwidth: width in seconds to be used for the ISI bins
+    [pdf, centers] = isihist(isis, binwidth);
     bar(1000.0*centers, pdf);     % milliseconds on x-axis
     xlabel('ISI [ms]')
     ylabel('p(ISI) [1/s]')
-    % annotation:
     misi = mean(isis);
     sdisi = std(isis);
     text(0.95, 0.8, sprintf('mean=%.1f ms', 1000.0*misi), ...
diff --git a/pointprocesses/code/plotisiserialcorr.m b/pointprocesses/code/plotisiserialcorr.m
new file mode 100644
index 0000000..ce806e3
--- /dev/null
+++ b/pointprocesses/code/plotisiserialcorr.m
@@ -0,0 +1,14 @@
+function isicorr = plotisiserialcorr(isivec, maxlag)
+% plot serial correlation of interspike intervals
+%
+% Arguments:
+%   isivec: vector of interspike intervals in seconds
+%   maxlag: the maximum lag
+    [isicorr, lags] = isiserialcorr(isivec, maxlag);
+    plot(lags, isicorr, '-b');
+    hold on;
+    scatter(lags, isicorr, 20.0, 'b', 'filled');
+    hold off;
+    xlabel('Lag k')
+    ylabel('\rho_k')
+end
diff --git a/pointprocesses/code/plotspikestats.m b/pointprocesses/code/plotspikestats.m
deleted file mode 100644
index a664eca..0000000
--- a/pointprocesses/code/plotspikestats.m
+++ /dev/null
@@ -1,100 +0,0 @@
-%% load data:
-clear all
-% alternative 1:
-% pro: no structs. contra: global unknown variables
-load poisson.mat
-whos
-poissonspikes = spikes;
-load pifou.mat;
-pifouspikes = spikes;
-load lifadapt.mat;
-lifadaptspikes = spikes;
-clear spikes;
-% alternative 2:
-% pro: clean code. contra: structs that we do not really know yet
-clear all
-x = load( 'poisson.mat' );
-poissonspikes = x.spikes;
-x = load( 'pifou.mat' );
-pifouspikes = x.spikes;
-x = load( 'lifadapt.mat' );
-lifadaptspikes = x.spikes;
-
-%% spike raster plots:
-tmax = 1.0;
-subplot(1, 3, 1);
-spikeraster(poissonspikes, tmax);
-title('Poisson');
-
-subplot(1, 3, 2);
-spikeraster(pifouspikes, tmax);
-title('PIF OU');
-
-subplot(1, 3, 3);
-spikeraster(lifadaptspikes, tmax);
-title('LIF adapt');
-
-%% isi histograms:
-maxisi = 300.0;
-binwidth = 0.002;
-subplot(1, 3, 1);
-poissonisis = isis(poissonspikes);
-isihist(poissonisis, binwidth);
-xlim([0, maxisi])
-title('Poisson');
-
-subplot(1, 3, 2);
-pifouisis = isis(pifouspikes);
-isihist(pifouisis, binwidth);
-xlim([0, maxisi])
-title('PIF OU');
-
-subplot(1, 3, 3);
-lifadaptisis = isis(lifadaptspikes);
-isihist(lifadaptisis, binwidth);
-xlim([0, maxisi])
-title('LIF adapt');
-
-%% serial correlations:
-maxlag = 10;
-rrange = [-0.5, 1.05];
-subplot(1, 3, 1);
-isiserialcorr(poissonisis, maxlag);
-ylim(rrange)
-title('Poisson');
-
-subplot(1, 3, 2);
-isiserialcorr(pifouisis, maxlag);
-ylim(rrange)
-title('PIF OU');
-
-subplot(1, 3, 3);
-isiserialcorr(lifadaptisis, maxlag);
-ylim(rrange)
-title('LIF adapt');
-
-%% spike counts:
-w = 0.1;
-cmax = 8;
-pmax = 0.5;
-subplot(1, 3, 1);
-counthist(poissonspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('Poisson');
-
-subplot(1, 3, 2);
-counthist(pifouspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('PIF OU');
-
-subplot(1, 3, 3);
-counthist(lifadaptspikes, w);
-xlim([0 cmax])
-set(gca, 'XTick', 0:2:cmax)
-ylim([0 pmax])
-title('LIF adapt');
-savefigpdf(gcf, 'counthist.pdf', 20, 7);
diff --git a/pointprocesses/code/poissonisih.m b/pointprocesses/code/poissonisih.m
deleted file mode 100644
index f006e2b..0000000
--- a/pointprocesses/code/poissonisih.m
+++ /dev/null
@@ -1,27 +0,0 @@
-rate = 100.0;
-trials = 50;
-tmax = 100.0;
-
-% generate spikes:
-spikes = poissonspikes( trials, rate, tmax );
-% interspike intervals:
-isivec = isis( spikes );
-% histogram
-f = figure( 1 );
-isihist( isivec );
-hold on
-% theoretical density:
-xmax = 5.0/rate;
-x = 0:0.0001:xmax;
-y = rate*exp(-rate*x);
-plot( 1000.0*x, y, 'r', 'LineWidth', 3 );
-% plot details:
-title( sprintf( 'Poisson spike trains, rate=%g Hz, nisi=%d', rate, length( isivec ) ) )
-xlim( [ 0.0 1000.0*xmax ] )
-ylim( [ 0.0 1.1*rate ] )
-legend( 'data', 'poisson' )
-hold off
-
-% serial correlations:
-f = figure( 2 );
-isiserialcorr( isivec, 10 );
diff --git a/pointprocesses/code/poissonisistats.m b/pointprocesses/code/poissonisistats.m
deleted file mode 100644
index 0c97e3d..0000000
--- a/pointprocesses/code/poissonisistats.m
+++ /dev/null
@@ -1,46 +0,0 @@
-rates = 1:1:100;
-avisi = [];
-sdisi = [];
-cvisi = [];
-
-for rate = rates
-    spikes = poissonspikes( 10, rate, 100.0 );
-    isivec = isis( spikes );
-    av = mean( isivec );
-    sd = std( isivec );
-    cv = sd/av;
-    avisi = [ avisi av ];
-    sdisi = [ sdisi sd ];
-    cvisi = [ cvisi cv ];
-end
-
-f = figure;
-subplot( 1, 3, 1 );
-scatter( rates, 1000.0*avisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] );
-title( 'Mean ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 2 );
-scatter( rates, 1000.0*sdisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] )
-title( 'Standard deviation ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 3 );
-scatter( rates, cvisi, 'b', 'filled' );
-hold on;
-plot( rates, ones( size( rates ) ), 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 2 ] )
-title( 'CV' );
-legend( 'simulation', 'theory' );
diff --git a/pointprocesses/code/psth.m b/pointprocesses/code/psth.m
deleted file mode 100644
index 87da768..0000000
--- a/pointprocesses/code/psth.m
+++ /dev/null
@@ -1,14 +0,0 @@
-function p = psth(spikes, dt, tmax)
-% plots a PSTH of the spikes with binwidth dt
-    t = 0.0:dt:tmax+dt;
-    p = zeros(1, length(t));
-    for k=1:length(spikes)
-        times = spikes{k};
-        [h, b] = hist(times, t);
-        p = p + h;
-    end
-    p = p/length(spikes)/dt;
-    t(end) = [];
-    p(end) = [];
-    plot(t, p);
-end
diff --git a/pointprocesses/code/rasterplot.m b/pointprocesses/code/rasterplot.m
index 0bcdb8d..7551552 100644
--- a/pointprocesses/code/rasterplot.m
+++ b/pointprocesses/code/rasterplot.m
@@ -1,29 +1,32 @@
 function rasterplot(spikes, tmax)
 % Display a spike raster of the spike times given in spikes.
 %
-% rasterplot(spikes, tmax)
+% Arguments:
 %   spikes: a cell array of vectors of spike times in seconds
-%   tmax: plot spike raster upto tmax seconds
-
-ntrials = length(spikes);
-for k = 1:ntrials
-    times = spikes{k};
-    times = times(times<tmax);
-    if tmax < 1.5
-        times = 1000.0*times; % conversion to ms
-    end
-    for i = 1:length( times )
-      line([times(i) times(i)],[k-0.4 k+0.4], 'Color', 'k'); 
+%   tmax: plot spike raster up to tmax seconds
+    spiketimes = [];
+    trials = [];
+    ntrials = length(spikes);
+    for k = 1:ntrials
+        times = spikes{k};
+        times = times(times<tmax);
+        % (x,y) pairs for start and stop of stroke
+        % plus nan separating strokes:
+        spiketimes = [spiketimes, ...
+                       [times(:)'; times(:)'; times(:)'*nan]];
+        trials = [trials, ...
+                   [ones(1, length(times)) * (k-0.4); ...
+                    ones(1, length(times)) * (k+0.4); ...
+                    ones(1, length(times)) * nan]];
     end
-end
-if tmax < 1.5
-    xlabel('Time [ms]');
-    xlim([0.0 1000.0*tmax]);
-else
+    % convert matrices into column vectors of (x,y) pairs:
+    spiketimes = spiketimes(:);
+    trials = trials(:);
+    % plotting this is lightning fast:
+    plot(spiketimes, trials, 'k')
     xlabel('Time [s]');
     xlim([0.0 tmax]);
-end
-ylabel('Trials');
-ylim([0.3 ntrials+0.7 ]);
+    ylabel('Trials');
+    ylim([0.3 ntrials+0.7]);
 end
 
diff --git a/pointprocesses/code/spikecounts.m b/pointprocesses/code/spikecounts.m
deleted file mode 100644
index c3eef4f..0000000
--- a/pointprocesses/code/spikecounts.m
+++ /dev/null
@@ -1,30 +0,0 @@
-function counts = spikecounts(spikes, w)
-% Compute vector of spike counts.
-%
-% counts = spikecounts(spikes, w)
-%
-% Arguments:
-%   spikes: a cell array of vectors of spike times in seconds
-%   w: observation window duration in seconds for computing the counts
-%
-% Returns:
-%   counts: vector of spike counts
-
-    % collect spike counts:
-    tmax = spikes{1}(end);
-    counts = [];
-    for k = 1:length(spikes)
-        times = spikes{k};
-%       method 1: count the number of spikes in each window:
-%         for tk = 0:w:tmax-w
-%             nn = sum((times >= tk) & (times < tk+w));
-%             %nn = length(times((times >= tk) & (times < tk+w)));
-%             %nn = length(find((times >= tk) & (times < tk+w)));
-%             counts = [counts nn];
-%         end
-%     method 2: use the hist() function to do that!
-        tbins = 0.5*w:w:tmax-0.5*w;
-        nn = hist(times, tbins);
-        counts = [counts nn];
-    end
-end
diff --git a/pointprocesses/code/spikecountshists.m b/pointprocesses/code/spikecountshists.m
deleted file mode 100644
index 5876386..0000000
--- a/pointprocesses/code/spikecountshists.m
+++ /dev/null
@@ -1,17 +0,0 @@
-w = 0.1;
-bins = 0.0:1.0:10.0;
-
-counts{1} = spikecounts(poissonspikes, w);
-counts{2} = spikecounts(pifouspikes, w);
-counts{3} = spikecounts(lifadaptspikes, w);
-titles = {'Poisson', 'PIF OU', 'LIF adapt'};
-for k = 1:3
-    subplot(1, 3, k);
-    [h, b] = hist(counts{k}, bins);
-    bar(b, h/sum(h));
-    title(titles{k})
-    xlabel('Spike count');
-    xlim([-0.5, 8.5]);
-    ylim([0.0 0.8]);
-end
-savefigpdf(gcf, 'spikecountshists.pdf', 20, 7);
diff --git a/pointprocesses/code/spikerate.m b/pointprocesses/code/spikerate.m
deleted file mode 100644
index 4efdedb..0000000
--- a/pointprocesses/code/spikerate.m
+++ /dev/null
@@ -1,12 +0,0 @@
-function r = spikerate(spikes, duration)
-% returns the average spike rate of the spikes
-% for the first duration seconds
-% spikes: a cell array of vectors of spike times
-
-    rates = zeros(length(spikes),1);
-    for k = 1:length(spikes)
-        times = spikes{k};
-        rates(k) = sum(times<duration)/duration;
-    end
-    r = mean(rates);
-end
diff --git a/pointprocesses/exercises/Makefile b/pointprocesses/exercises/Makefile
index 0b63fb9..2bbd561 100644
--- a/pointprocesses/exercises/Makefile
+++ b/pointprocesses/exercises/Makefile
@@ -1,35 +1,3 @@
-BASENAME=pointprocesses
-TEXFILES=$(wildcard $(BASENAME)??.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:pointprocesses%=pointprocesses-solutions%)
+TEXFILES=$(wildcard pointprocesses-?.tex) psth.tex
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
-
-pdf : $(SOLUTIONS) $(EXERCISES)
-
-exercises : $(EXERCISES)
-
-solutions : $(SOLUTIONS)
-
-$(SOLUTIONS) : pointprocesses-solutions%.pdf : pointprocesses%.tex instructions.tex
-	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
-	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
-	rm $(patsubst %.pdf,%,$@).[!p]*
-
-$(EXERCISES) : %.pdf : %.tex instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-
-watch :
-	while true; do ! make -q pdf && make pdf; sleep 0.5; done
-
-watchexercises :
-	while true; do ! make -q exercises && make exercises; sleep 0.5; done
-
-watchsolutions :
-	while true; do ! make -q solutions && make solutions; sleep 0.5; done
-
-clean :
-	rm -f *~ *.aux *.log *.out
-
-cleanup : clean
-	rm -f $(SOLUTIONS) $(EXERCISES)
+include ../../exercises.mk
diff --git a/pointprocesses/exercises/counthist.pdf b/pointprocesses/exercises/counthist.pdf
deleted file mode 100644
index 0cbcacf..0000000
Binary files a/pointprocesses/exercises/counthist.pdf and /dev/null differ
diff --git a/pointprocesses/exercises/counts.m b/pointprocesses/exercises/counts.m
new file mode 100644
index 0000000..90e53ef
--- /dev/null
+++ b/pointprocesses/exercises/counts.m
@@ -0,0 +1,25 @@
+function n = counts(spikes, w)
+% Count spikes in time windows.
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%   w: duration of window in seconds for computing the counts
+%
+% Returns:
+%   n: vector with spike counts
+    tmax = spikes{1}(end);
+    n = [];
+    for k = 1:length(spikes)
+        times = spikes{k};
+%       alternative 1: count the number of spikes in each window:
+%         for tk = 0:w:tmax-w
+%             nn = sum((times >= tk) & (times < tk+w));
+%             % nn = length(find((times >= tk) & (times < tk+w)));
+%             n = [n, nn];
+%         end
+%     alternative 2: use the hist() function to do that!
+        tbins = 0.5*w:w:tmax-0.5*w;
+        nn = hist(times, tbins);
+        n = [n, nn];
+    end
+end
diff --git a/pointprocesses/code/fanoplot.m b/pointprocesses/exercises/fanoplot.m
similarity index 51%
rename from pointprocesses/code/fanoplot.m
rename to pointprocesses/exercises/fanoplot.m
index b53c42f..671ced9 100644
--- a/pointprocesses/code/fanoplot.m
+++ b/pointprocesses/exercises/fanoplot.m
@@ -1,18 +1,17 @@
 function fanoplot(spikes, titles)
-% computes and plots fano factor as a function of window size
-% spikes: a cell array of vectors of spike times
-% titles: string that is used as a title for the plots
+% Plots fano factor as a function of window size.
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times
+%   titles: string that is used as a title for the plots
     windows = logspace(-3.0, -0.5, 100);
-    mc = windows;
-    vc = windows;
-    ff = windows;
+    mc = zeros(1, length(windows));
+    vc = zeros(1, length(windows));
     for j = 1:length(windows)
         w = windows(j);
-        counts = spikecounts(spikes, w);
-        % statistics for current window:
-        mc(j) = mean(counts);
-        vc(j) = var(counts);
-        ff(j) = vc(j)/mc(j);
+        spikecounts = counts(spikes, w);
+        mc(j) = mean(spikecounts);
+        vc(j) = var(spikecounts);
     end
     
     subplot(1, 2, 1);
@@ -22,7 +21,7 @@ function fanoplot(spikes, titles)
     ylabel('Count variance');
 
     subplot(1, 2, 2);
-    scatter(1000.0*windows, ff, 'filled');
+    scatter(1000.0*windows, vc ./ mc, 'filled');
     title(titles);
     xlabel('Window [ms]');
     ylabel('Fano factor');
diff --git a/pointprocesses/code/fanoplots.m b/pointprocesses/exercises/fanoplots.m
similarity index 58%
rename from pointprocesses/code/fanoplots.m
rename to pointprocesses/exercises/fanoplots.m
index 949eb91..49d7343 100644
--- a/pointprocesses/code/fanoplots.m
+++ b/pointprocesses/exercises/fanoplots.m
@@ -1,9 +1,9 @@
 spikes{1} = poissonspikes;
 spikes{2} = pifouspikes;
 spikes{3} = lifadaptspikes;
-idents = {'poisson', 'pifou', 'lifadapt'};
+titles = {'poisson', 'pifou', 'lifadapt'};
 for k = 1:3
     figure(k)
     fanoplot(spikes{k}, titles{k});
-    savefigpdf(gcf, sprintf('fanoplots%s.pdf', idents{k}), 20, 7);
+    savefigpdf(gcf, sprintf('fanoplots%s.pdf', titles{k}), 20, 7);
 end
diff --git a/pointprocesses/exercises/fanoplotslifadapt.pdf b/pointprocesses/exercises/fanoplotslifadapt.pdf
index a04d10c..cbe7ccd 100644
Binary files a/pointprocesses/exercises/fanoplotslifadapt.pdf and b/pointprocesses/exercises/fanoplotslifadapt.pdf differ
diff --git a/pointprocesses/exercises/fanoplotspifou.pdf b/pointprocesses/exercises/fanoplotspifou.pdf
index 47cf9cf..a9f9d72 100644
Binary files a/pointprocesses/exercises/fanoplotspifou.pdf and b/pointprocesses/exercises/fanoplotspifou.pdf differ
diff --git a/pointprocesses/exercises/fanoplotspoisson.pdf b/pointprocesses/exercises/fanoplotspoisson.pdf
index 8b1beac..457cc94 100644
Binary files a/pointprocesses/exercises/fanoplotspoisson.pdf and b/pointprocesses/exercises/fanoplotspoisson.pdf differ
diff --git a/pointprocesses/exercises/hompoissonisih.m b/pointprocesses/exercises/hompoissonisih.m
deleted file mode 100644
index cd2bc06..0000000
--- a/pointprocesses/exercises/hompoissonisih.m
+++ /dev/null
@@ -1,18 +0,0 @@
-% generate spike times:
-rate = 20.0;
-spikes = hompoissonspikes( 10, rate, 50.0 );
-% isi histogram:
-isivec = isis( spikes );
-isihist( isivec );
-hold on
-% theoretical density:
-xmax = 5.0/rate;
-x = 0:0.0001:xmax;
-y = rate*exp(-rate*x);
-plot( 1000.0*x, y, 'r', 'LineWidth', 3 );
-% plot details:
-title( sprintf( 'Poisson spike trains, rate=%g Hz, nisi=%d', rate, length( isivec ) ) )
-xlim( [ 0.0 1000.0*xmax ] )
-ylim( [ 0.0 1.1*rate ] )
-legend( 'data', 'poisson' )
-hold off
diff --git a/pointprocesses/exercises/hompoissonisistats.m b/pointprocesses/exercises/hompoissonisistats.m
deleted file mode 100644
index 7ca3cf3..0000000
--- a/pointprocesses/exercises/hompoissonisistats.m
+++ /dev/null
@@ -1,46 +0,0 @@
-rates = 1:1:100;
-avisi = [];
-sdisi = [];
-cvisi = [];
-
-for rate = rates
-    spikes = hompoissonspikes( 10, rate, 100.0 );
-    isivec = isis( spikes );
-    av = mean( isivec );
-    sd = std( isivec );
-    cv = sd/av;
-    avisi = [ avisi av ];
-    sdisi = [ sdisi sd ];
-    cvisi = [ cvisi cv ];
-end
-
-f = figure;
-subplot( 1, 3, 1 );
-scatter( rates, 1000.0*avisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] );
-title( 'Mean ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 2 );
-scatter( rates, 1000.0*sdisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] )
-title( 'Standard deviation ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 3 );
-scatter( rates, cvisi, 'b', 'filled' );
-hold on;
-plot( rates, ones( size( rates ) ), 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 2 ] )
-title( 'CV' );
-legend( 'simulation', 'theory' );
diff --git a/pointprocesses/exercises/hompoissonspikes.m b/pointprocesses/exercises/hompoissonspikes.m
deleted file mode 100644
index 43b4e5c..0000000
--- a/pointprocesses/exercises/hompoissonspikes.m
+++ /dev/null
@@ -1,19 +0,0 @@
-function spikes = hompoissonspikes( trials, rate, tmax )
-% Generate spike times of a homogeneous poisson process
-% trials: number of trials that should be generated
-% rate: the rate of the Poisson process in Hertz
-% tmax: the duration of each trial in seconds
-% returns a cell array of vectors of spike times
-
-    dt = 3.33e-5;
-    p = rate*dt;
-    if p > 0.2
-        p = 0.2
-        dt = p/rate;
-    end
-    x = rand( trials, ceil(tmax/dt) );
-    spikes = cell( trials, 1 );
-    for k=1:trials
-        spikes{k} = find( x(k,:) < p ) * dt;
-    end
-end
diff --git a/pointprocesses/exercises/iafisistats-solutions.pdf b/pointprocesses/exercises/iafisistats-solutions.pdf
deleted file mode 100644
index d1b6c63..0000000
Binary files a/pointprocesses/exercises/iafisistats-solutions.pdf and /dev/null differ
diff --git a/pointprocesses/exercises/iafisistats.pdf b/pointprocesses/exercises/iafisistats.pdf
deleted file mode 100644
index c19617b..0000000
Binary files a/pointprocesses/exercises/iafisistats.pdf and /dev/null differ
diff --git a/pointprocesses/code/inversegauss.m b/pointprocesses/exercises/inversegauss.m
similarity index 100%
rename from pointprocesses/code/inversegauss.m
rename to pointprocesses/exercises/inversegauss.m
diff --git a/pointprocesses/code/inversegaussplot.m b/pointprocesses/exercises/inversegaussplot.m
similarity index 100%
rename from pointprocesses/code/inversegaussplot.m
rename to pointprocesses/exercises/inversegaussplot.m
diff --git a/pointprocesses/exercises/isihist.m b/pointprocesses/exercises/isihist.m
new file mode 100644
index 0000000..a24989c
--- /dev/null
+++ b/pointprocesses/exercises/isihist.m
@@ -0,0 +1,29 @@
+function [pdf, centers] = isihist(isis, binwidth)
+% Compute normalized histogram of interspike intervals.
+%
+% [pdf, centers] = isihist(isis, binwidth)
+%
+% Arguments:
+%   isis: vector of interspike intervals in seconds
+%   binwidth: optional width in seconds to be used for the isi bins
+%
+% Returns:
+%   pdf: vector with pdf of interspike intervals in Hertz
+%   centers: vector with centers of interspikeintervalls in seconds
+
+    if nargin < 2
+        % compute good binwidth:
+        nperbin = 200;     % average number of data points per bin
+        bins = length(isis)/nperbin;   % number of bins
+        binwidth = max(isis)/bins;
+        if binwidth < 5e-4             % half a millisecond
+            binwidth = 5e-4;
+        end
+    end
+    bins = 0.5*binwidth:binwidth:max(isis);
+    % histogram data:
+    [nelements, centers] = hist(isis, bins);
+    % normalization (integral = 1):
+    pdf = nelements / sum(nelements) / binwidth;
+end
+
diff --git a/pointprocesses/exercises/isihist.pdf b/pointprocesses/exercises/isihist.pdf
index 1fdab43..d3eee1d 100644
Binary files a/pointprocesses/exercises/isihist.pdf and b/pointprocesses/exercises/isihist.pdf differ
diff --git a/pointprocesses/exercises/isis.m b/pointprocesses/exercises/isis.m
new file mode 100644
index 0000000..d5eb7d6
--- /dev/null
+++ b/pointprocesses/exercises/isis.m
@@ -0,0 +1,16 @@
+function isivec = isis(spikes)
+% returns a single list of isis computed from all trials in spikes
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%
+% Returns:
+%   isivec: a column vector with all the interspike intervals
+    isivec = [];
+    for k = 1:length(spikes)
+        difftimes = diff(spikes{k});
+        % difftimes(:) ensures a column vector
+        % regardless of the type of vector in spikes{k}
+        isivec = [isivec; difftimes(:)];
+    end
+end
diff --git a/pointprocesses/exercises/isiserialcorr.m b/pointprocesses/exercises/isiserialcorr.m
new file mode 100644
index 0000000..fc5ec17
--- /dev/null
+++ b/pointprocesses/exercises/isiserialcorr.m
@@ -0,0 +1,21 @@
+function [isicorr, lags] = isiserialcorr(isivec, maxlag)
+% serial correlation of interspike intervals
+%
+% Arguments:
+%   isivec: vector of interspike intervals in seconds
+%   maxlag: the maximum lag
+%
+% Returns:
+%   isicorr: vector with the serial correlations for lag 0 to maxlag
+%   lags: vector with the lags corresponding to isicorr
+    lags = 0:maxlag;
+    isicorr = zeros(size(lags));
+    for k = 1:length(lags)
+        lag = lags(k);
+        if length(isivec) > lag+10        % ensure "enough" data
+	    % NOTE: the arguments to corr must be column vectors!
+            % We insure this already in the isis() function.
+            isicorr(k) = corr(isivec(1:end-lag), isivec(lag+1:end));
+        end
+    end
+end
diff --git a/pointprocesses/code/lifadapt.mat b/pointprocesses/exercises/lifadapt.mat
similarity index 100%
rename from pointprocesses/code/lifadapt.mat
rename to pointprocesses/exercises/lifadapt.mat
diff --git a/pointprocesses/code/lifficurves.m b/pointprocesses/exercises/lifficurves.m
similarity index 100%
rename from pointprocesses/code/lifficurves.m
rename to pointprocesses/exercises/lifficurves.m
diff --git a/pointprocesses/code/lifisih.m b/pointprocesses/exercises/lifisih.m
similarity index 100%
rename from pointprocesses/code/lifisih.m
rename to pointprocesses/exercises/lifisih.m
diff --git a/pointprocesses/code/lifisistats.m b/pointprocesses/exercises/lifisistats.m
similarity index 100%
rename from pointprocesses/code/lifisistats.m
rename to pointprocesses/exercises/lifisistats.m
diff --git a/pointprocesses/code/pifou.mat b/pointprocesses/exercises/pifou.mat
similarity index 100%
rename from pointprocesses/code/pifou.mat
rename to pointprocesses/exercises/pifou.mat
diff --git a/pointprocesses/exercises/plotisihist.m b/pointprocesses/exercises/plotisihist.m
new file mode 100644
index 0000000..2f3cf93
--- /dev/null
+++ b/pointprocesses/exercises/plotisihist.m
@@ -0,0 +1,31 @@
+function plotisihist(isis, binwidth)
+% Plot and annotate histogram of interspike intervals.
+%
+% plotisihist(isis, binwidth)
+%
+% Arguments:
+%   isis: vector of interspike intervals in seconds
+%   binwidth: optional width in seconds to be used for the isi bins
+
+    % compute normalized histogram:
+    if nargin < 2
+        [pdf, centers] = isihist(isis);
+    else
+        [pdf, centers] = isihist(isis, binwidth);
+    end
+
+    % plot:
+    bar(1000.0*centers, pdf);     % milliseconds on x-axis
+    xlabel('ISI [ms]')
+    ylabel('p(ISI) [1/s]')
+    % annotation:
+    misi = mean(isis);
+    sdisi = std(isis);
+    text(0.95, 0.8, sprintf('mean=%.1f ms', 1000.0*misi), ...
+         'Units', 'normalized', 'HorizontalAlignment', 'right')
+    text(0.95, 0.7, sprintf('std=%.1f ms', 1000.0*sdisi), ...
+         'Units', 'normalized', 'HorizontalAlignment', 'right')
+    text(0.95, 0.6, sprintf('CV=%.2f', sdisi/misi), ...
+         'Units', 'normalized', 'HorizontalAlignment', 'right')
+end
+
diff --git a/pointprocesses/code/plotisihs.m b/pointprocesses/exercises/plotisihs.m
similarity index 70%
rename from pointprocesses/code/plotisihs.m
rename to pointprocesses/exercises/plotisihs.m
index c2eef9e..a79bd85 100644
--- a/pointprocesses/code/plotisihs.m
+++ b/pointprocesses/exercises/plotisihs.m
@@ -1,19 +1,20 @@
 maxisi = 300.0;
 subplot(1, 3, 1);
+dt = 0.005;    % bin width for ISIH in seconds
 poissonisis = isis(poissonspikes);
-plotisihist(poissonisis, 0.001);
+plotisihist(poissonisis, dt);
 xlim([0, maxisi])
 title('Poisson');
 
 subplot(1, 3, 2);
 pifouisis = isis(pifouspikes);
-plotisihist(pifouisis, 0.001);
+plotisihist(pifouisis, dt);
 xlim([0, maxisi])
 title('PIF OU');
 
 subplot(1, 3, 3);
 lifadaptisis = isis(lifadaptspikes);
-plotisihist(lifadaptisis, 0.001);
+plotisihist(lifadaptisis, dt);
 xlim([0, maxisi])
 title('LIF adapt');
 savefigpdf(gcf, 'isihist.pdf', 20, 7);
diff --git a/pointprocesses/exercises/plotisiserialcorr.m b/pointprocesses/exercises/plotisiserialcorr.m
new file mode 100644
index 0000000..ce806e3
--- /dev/null
+++ b/pointprocesses/exercises/plotisiserialcorr.m
@@ -0,0 +1,14 @@
+function isicorr = plotisiserialcorr(isivec, maxlag)
+% plot serial correlation of interspike intervals
+%
+% Arguments:
+%   isivec: vector of interspike intervals in seconds
+%   maxlag: the maximum lag
+    [isicorr, lags] = isiserialcorr(isivec, maxlag);
+    plot(lags, isicorr, '-b');
+    hold on;
+    scatter(lags, isicorr, 20.0, 'b', 'filled');
+    hold off;
+    xlabel('Lag k')
+    ylabel('\rho_k')
+end
diff --git a/pointprocesses/exercises/plotpoissonisih.m b/pointprocesses/exercises/plotpoissonisih.m
new file mode 100644
index 0000000..01a01b7
--- /dev/null
+++ b/pointprocesses/exercises/plotpoissonisih.m
@@ -0,0 +1,13 @@
+maxisi = 300.0;
+dt = 0.005;
+poissonisis = isis(poissonspikes);
+rate = 1.0/mean(poissonisis);
+plotisihist(poissonisis, dt);
+tt = linspace(0.0, 0.001*maxisi, 200);
+pexp = rate*exp(-tt*rate);
+hold on;
+plot(1000.0*tt, pexp, 'r', 'linewidth', 4)
+hold off;
+xlim([0, maxisi])
+title('Poisson');
+savefigpdf(gcf, 'poissonisihist.pdf', 10, 6);
diff --git a/pointprocesses/code/plotserialcorr.m b/pointprocesses/exercises/plotserialcorr.m
similarity index 53%
rename from pointprocesses/code/plotserialcorr.m
rename to pointprocesses/exercises/plotserialcorr.m
index 05f3ea8..ab33e50 100644
--- a/pointprocesses/code/plotserialcorr.m
+++ b/pointprocesses/exercises/plotserialcorr.m
@@ -1,17 +1,17 @@
 maxlag = 10;
 rrange = [-0.5, 1.05];
 subplot(1, 3, 1);
-isiserialcorr(poissonisis, maxlag);
+plotisiserialcorr(poissonisis, maxlag);
 ylim(rrange)
 title('Poisson');
 
 subplot(1, 3, 2);
-isiserialcorr(pifouisis, maxlag);
+plotisiserialcorr(pifouisis, maxlag);
 ylim(rrange)
 title('PIF OU');
 
 subplot(1, 3, 3);
-isiserialcorr(lifadaptisis, maxlag);
+plotisiserialcorr(lifadaptisis, maxlag);
 ylim(rrange)
 title('LIF adapt');
-savefigpdf(gcf, 'serialcorr.pdf', 20, 7);
\ No newline at end of file
+savefigpdf(gcf, 'serialcorr.pdf', 20, 7);
diff --git a/pointprocesses/code/plotspikeraster.m b/pointprocesses/exercises/plotspikeraster.m
similarity index 60%
rename from pointprocesses/code/plotspikeraster.m
rename to pointprocesses/exercises/plotspikeraster.m
index 93eff59..4bf4a0e 100644
--- a/pointprocesses/code/plotspikeraster.m
+++ b/pointprocesses/exercises/plotspikeraster.m
@@ -1,3 +1,11 @@
+x = load('poisson.mat');
+poissonspikes = x.spikes;
+x = load('pifou.mat');
+pifouspikes = x.spikes;
+x = load('lifadapt.mat');
+lifadaptspikes = x.spikes;
+clear x;
+
 subplot(1, 3, 1);
 rasterplot(poissonspikes, 1.0);
 title('Poisson');
diff --git a/pointprocesses/exercises/pointprocesses01.tex b/pointprocesses/exercises/pointprocesses-1.tex
similarity index 54%
rename from pointprocesses/exercises/pointprocesses01.tex
rename to pointprocesses/exercises/pointprocesses-1.tex
index eb78d42..a27704c 100644
--- a/pointprocesses/exercises/pointprocesses01.tex
+++ b/pointprocesses/exercises/pointprocesses-1.tex
@@ -1,93 +1,23 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 12\stitle}}{{\bfseries\large Point processes}}{{\bfseries\large January 14th, 2020}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
+\newcommand{\exercisetopic}{Point processes}
+\newcommand{\exercisenum}{11}
+\newcommand{\exercisedate}{January 19th, 2021}
 
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
-
+\input{../../exercisestitle}
 
 \begin{questions}
 
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+  \question \qt{Read in chapter 10 on ``Spike-train analysis'' sections 10.1 -- 10.5!}\vspace{-3ex}
+
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
   \question \qt{Statistics of interspike intervals}
   Download the files \code{poisson.mat},
@@ -105,8 +35,8 @@ jan.benda@uni-tuebingen.de}
     How do you access single spike times?
     \begin{solution}
       \begin{lstlisting}
-        clear all
         % not so good:
+        clear all
         load poisson.mat
         whos
         poissonspikes = spikes;
@@ -125,20 +55,30 @@ jan.benda@uni-tuebingen.de}
         lifadaptspikes = x.spikes;
         clear x;
       \end{lstlisting}
+      The files contain cell arrays with 20 trials of spike
+      trains. Each spike train is a vector containing the times of
+      spikes.
+      \begin{lstlisting}
+        % access first trial:
+        trial = poissonspikes{1};
+        % access second spike of first trial:
+        trial(2);
+      \end{lstlisting}
     \end{solution}
 
     \newsolutionpage    
-    \part Write a function that illustrated the spike times of the
+    \part Write a function that displays the spike times of the
     first $t_{max}$ seconds in a raster plot. Each spike train is one
     row in the raster plot. Each spike is a vertical line at the time
     of the spike. When appropriate, the function should use milliseconds 
     for the time axis instead of seconds.
 
-    Use this function to plot the first second of the
-    spike rasters of the three neurons.
+    Use this function to plot the first second of the spike rasters of
+    the three neurons. Carefully look at the raster plots. How do the
+    spike rasters differ between the three neurons?
     \begin{solution}
-      \lstinputlisting{../code/rasterplot.m}
-      \lstinputlisting{../code/plotspikeraster.m}
+      \lstinputlisting{rasterplot.m}
+      \lstinputlisting{plotspikeraster.m}
       \mbox{}\\[-3ex]
       \colorbox{white}{\includegraphics[width=1\textwidth]{spikeraster}}
     \end{solution}
@@ -146,7 +86,7 @@ jan.benda@uni-tuebingen.de}
     \part Write a function that returns a single vector containing the
     interspike intervals of all trials of spike times.
     \begin{solution}
-      \lstinputlisting{../code/isis.m}
+      \lstinputlisting{isis.m}
     \end{solution}
     
     \part Write a function that computes an estimate of the
@@ -163,53 +103,77 @@ jan.benda@uni-tuebingen.de}
     the standard deviation and the coefficient of variation and
     display them in the plot as well.
 
-    Use this and the previous functions to compare the 
-    interspike interval statistics of the three neurons.
+    Use this and the previous functions to compare the interspike
+    interval statistics of the three neurons. How do the ISI
+    histograms differ between the three neurons?
     \begin{solution}
-      \lstinputlisting{../code/isihist.m}
-      \lstinputlisting{../code/plotisihist.m}
-      \lstinputlisting{../code/plotisihs.m}
+      \lstinputlisting{isihist.m}
+      \lstinputlisting{plotisihist.m}
+      \lstinputlisting{plotisihs.m}
       \mbox{}\\[-3ex]
       \colorbox{white}{\includegraphics[width=1\textwidth]{isihist}}
     \end{solution}
-  
+
+    \part Compare the interspike interval histogram of the Poisson
+    spike train to the expected exponential distribution
+    \[ p(T) = \lambda e^{-\lambda T} \; .\] of a Poisson process with
+    firing rate $\lambda$. Estimate the firing rate from the inverse
+    of the mean interspike interval.
+    \begin{solution}
+      \lstinputlisting{plotpoissonisih.m}
+      \mbox{}\\[-3ex]
+      \colorbox{white}{\includegraphics{poissonisihist}}
+    \end{solution}
+    
+    \continue
     % XXX Add return map!!! XXX
-    \part Write a function that computes and plots the serial
-    correlations of interspike intervals for lags upto
-    \code{maxlag}. The serial correlations $\rho_k$ for lag $k$ of the
-    interspike intervals $T_i$ are the correlation coefficients
-    between interspike intervals $T_i$ and the intervals $T_{i+k}$
-    that are shifted by lag $k$:
+    \part Write a function that computes the serial correlations of
+    interspike intervals for lags up to \code{maxlag}. The serial
+    correlations $\rho_k$ for lag $k$ of the interspike intervals
+    $T_i$ are the correlation coefficients between interspike
+    intervals $T_i$ and the intervals $T_{i+k}$ that are shifted by
+    lag $k$:
     \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i -
       \langle T \rangle) \rangle}{\langle (T_i - \langle T
       \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm
-        var}(T_i)} = {\rm corr}(T_{i+k}, T_i) \] 
+        var}(T_i)} = {\rm corr}(T_{i+k}, T_i) \]
 
-    Use this function to compare the serial correlations of the
-    interspike intervals of the three neurons.
+    Write another function that plots the serial correlations.
+
+    Use the functions to compare the serial correlations of the
+    interspike intervals of the three neurons. What do the serial
+    correlations mean? How do they relate to what you see in the
+    raster plots? Do the serial correlations of the Poisson spike
+    train match the expectations?
     \begin{solution}
-      \lstinputlisting{../code/isiserialcorr.m}
-      \lstinputlisting{../code/plotserialcorr.m}
+      \lstinputlisting{isiserialcorr.m}
+      \lstinputlisting{plotisiserialcorr.m}
+      \lstinputlisting{plotserialcorr.m}
       \colorbox{white}{\includegraphics[width=1\textwidth]{serialcorr}}
     \end{solution}
     
   \end{parts}
 
-  \continuepage
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
   \question \qt{Statistics of spike counts}
   Now let's have a look at the statistics of the spike counts.
   \begin{parts}
-    \part Write a function that counts and returns the number of
-    spikes in windows of a given width $W$.
+    \part Write a function that counts and returns a vector with the
+    number of spikes in windows of a given width $W$.
 
     Use this function to generate a properly normalized histogram of
     spike counts for the data of the three types of neurons. Use
     100\,ms for the window width.
+
+    Compare the distributions with the Poisson distribution expected
+    for a Poisson spike train:
+    \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \; , \] where
+    $\lambda$ is the rate of the spike train that you should estimate
+    from the data.
     \begin{solution}
-      \lstinputlisting{../code/spikecounts.m}
+      \lstinputlisting{counts.m}
       \newsolutionpage
-      \lstinputlisting{../code/spikecountshists.m}
+      \lstinputlisting{spikecountshists.m}
       \colorbox{white}{\includegraphics[width=1\textwidth]{spikecountshists}}
     \end{solution}
 
@@ -221,9 +185,9 @@ jan.benda@uni-tuebingen.de}
     (the mean spike count increases for larger window widths). The
     other plot showing the Fano factor as a function of window width.
     \begin{solution}
-      \lstinputlisting{../code/fanoplot.m}
+      \lstinputlisting{fanoplot.m}
       \newsolutionpage
-      \lstinputlisting{../code/fanoplots.m}
+      \lstinputlisting{fanoplots.m}
       \colorbox{white}{\includegraphics[width=1\textwidth]{fanoplotspoisson}}
       \colorbox{white}{\includegraphics[width=1\textwidth]{fanoplotspifou}}
       \colorbox{white}{\includegraphics[width=1\textwidth]{fanoplotslifadapt}}
diff --git a/pointprocesses/exercises/iafisistats.tex b/pointprocesses/exercises/pointprocesses-2.tex
similarity index 76%
rename from pointprocesses/exercises/iafisistats.tex
rename to pointprocesses/exercises/pointprocesses-2.tex
index 53ca479..87ffff3 100644
--- a/pointprocesses/exercises/iafisistats.tex
+++ b/pointprocesses/exercises/pointprocesses-2.tex
@@ -1,49 +1,17 @@
-\documentclass[addpoints,10pt]{exam}
-\usepackage{url}
-\usepackage{color}
-\usepackage{hyperref}
-\usepackage{graphicx}
-
-\pagestyle{headandfoot}
-\runningheadrule
-\firstpageheadrule
-
-\firstpageheader{Scientific Computing}{Integrate-and-fire models}{Oct 28, 2014}
-%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
-\firstpagefooter{}{}{}
-\runningfooter{}{}{}
-\pointsinmargin
-\bracketedpoints
-
-%\printanswers
-\shadedsolutions
-
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
- basicstyle=\ttfamily,
- numbers=left,
- showstringspaces=false,
- language=Matlab,
- breaklines=true,
- breakautoindent=true,
- columns=flexible,
- frame=single,
- captionpos=t,
- xleftmargin=2em,
- xrightmargin=1em,
- aboveskip=10pt,
- %title=\lstname,
- title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
- }
+\documentclass[12pt,a4paper,pdftex]{exam}
 
+\newcommand{\exercisetopic}{Integrate-and-fire models}
+\newcommand{\exercisenum}{13}
+\newcommand{\exercisedate}{January 19th, 2021}
 
+\input{../../exercisesheader}
+
+\firstpagefooter{Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\sffamily
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+\input{../../exercisestitle}
 
 \begin{questions}
   \question \textbf{Statistics of integrate-and-fire neurons}
@@ -78,8 +46,8 @@ spikeraster( spikes )
     $T$ divided by two times the mean cubed). Show in two plots how
     this distribution depends on $\langle T \rangle$ and $D$.
     \begin{solution}
-      \lstinputlisting{simulations/inversegauss.m}
-      \lstinputlisting{simulations/inversegaussplot.m}
+      \lstinputlisting{inversegauss.m}
+      \lstinputlisting{inversegaussplot.m}
       \colorbox{white}{\includegraphics[width=0.98\textwidth]{inversegauss}}
     \end{solution}
 
@@ -102,7 +70,7 @@ text( 0.6, 0.4, sprintf( 'D=%.1f Hz', disi ), 'Units', 'normalized' )
 
     \part Compare intersike interval histograms obtained from the LIF and PIF models with the inverse Gaussian.
     \begin{solution}
-      \lstinputlisting{simulations/lifisih.m}
+      \lstinputlisting{lifisih.m}
       \colorbox{white}{\includegraphics[width=0.48\textwidth]{pifisih01}}
       \colorbox{white}{\includegraphics[width=0.48\textwidth]{pifisih10}}\\
       \colorbox{white}{\includegraphics[width=0.48\textwidth]{lifisih08}}
@@ -114,7 +82,7 @@ text( 0.6, 0.4, sprintf( 'D=%.1f Hz', disi ), 'Units', 'normalized' )
     CV, and diffusion coefficient as a function of the input to the LIF
     and the PIF with noise strength set to 0.01.
     \begin{solution}
-      \lstinputlisting{simulations/lifisistats.m}
+      \lstinputlisting{lifisistats.m}
       Leaky integrate-and-fire:\\
       \colorbox{white}{\includegraphics[width=0.8\textwidth]{lifisistats}}\\
       Perfect integrate-and-fire:\\
@@ -124,7 +92,7 @@ text( 0.6, 0.4, sprintf( 'D=%.1f Hz', disi ), 'Units', 'normalized' )
     \part Plot the firing rate as a function of input of the LIF and the PIF for various values
     of the noise strength.
     \begin{solution}
-      \lstinputlisting{simulations/lifficurves.m}
+      \lstinputlisting{lifficurves.m}
       Leaky integrate-and-fire:\\
       \colorbox{white}{\includegraphics[width=0.7\textwidth]{lifficurves}}\\
       Perfect integrate-and-fire:\\
diff --git a/pointprocesses/exercises/pointprocesses01-de.tex b/pointprocesses/exercises/pointprocesses01-de.tex
deleted file mode 100644
index ea8674d..0000000
--- a/pointprocesses/exercises/pointprocesses01-de.tex
+++ /dev/null
@@ -1,198 +0,0 @@
-\documentclass[12pt,a4paper,pdftex]{exam}
-
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{L\"osungen}
-\else
-\newcommand{\stitle}{\"Ubung}
-\fi
-\header{{\bfseries\large \stitle}}{{\bfseries\large Punktprozesse}}{{\bfseries\large 27. Oktober, 2015}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document}
-
-\input{instructions}
-
-
-\begin{questions}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \question \qt{Statistik von Spiketrains}
-  In Ilias findet ihr die Dateien \code{poisson.mat},
-  \code{pifou.mat}, und \code{lifadapt.mat}.  Jede dieser Dateien
-  enth\"alt mehrere Trials von Spiketrains von einer bestimmten Art
-  von Neuron. Die Spikezeiten sind in Sekunden gemessen.
-
-  Mit den folgenden Aufgaben wollen wir die Statistik der Spiketrains
-  der drei Neurone miteinander vergleichen.
-  \begin{parts}
-    \part Lade die Spiketrains aus den drei Dateien. Achte darauf,
-    dass sie verschiedene Variablen\-namen bekommen.
-    \begin{solution}
-      \begin{lstlisting}
-        clear all
-        % not so good:
-        load poisson.mat
-        whos
-        poissonspikes = spikes;
-        load pifou.mat;
-        pifouspikes = spikes;
-        load lifadapt.mat;
-        lifadaptspikes = spikes;
-        clear spikes;
-        % better:
-        clear all
-        x = load( 'poisson.mat' );
-        poissonspikes = x.spikes;
-        x = load( pifou.mat' );
-        pifouspikes = x.spikes;
-        x = load( 'lifadapt.mat' );
-        lifadaptspikes = x.spikes;
-      \end{lstlisting}
-    \end{solution}
-    
-    \part Schreibe eine Funktion, die die Spikezeiten der ersten
-    $t_{max}$ Sekunden in einem Rasterplot visualisiert.  In jeder
-    Zeile des Rasterplots wird ein Spiketrain dargestellt. Jeder
-    einzelne Spike wird als senkrechte Linie zu der Zeit des
-    Auftretens des Spikes geplottet. Benutze die Funktion, um die
-    Spikeraster der ersten 1\,s der drei Neurone nebeneinander zu plotten.
-    \begin{solution}
-      \lstinputlisting{../code/spikeraster.m}
-      \lstinputlisting{../code/plotspikeraster.m}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=1\textwidth]{spikeraster}}
-    \end{solution}
-  
-    \part Schreibe eine Funktion, die einen einzigen Vektor mit den
-    Interspikeintervallen aller Trials von Spikezeiten zur\"uckgibt.
-    \begin{solution}
-      \lstinputlisting{../code/isis.m}
-    \end{solution}
-    
-    \part Schreibe eine Funktion, die ein normiertes Histogramm aus
-    einem Vektor von Interspikeintervallen, gegeben in Sekunden,
-    berechnet und dieses mit richtiger Achsenbeschriftung plottet.
-    Die Interspikeintervalle sollen dabei in Millisekunden angegeben
-    werden. Die Funktion soll zus\"atzlich den Mittelwert, die
-    Standardabweichung, und den Variationskoeffizienten der
-    Interspikeintervalle berechnen und diese im Plot mit angeben.
-    
-    Benutze die vorherige und diese Funktion, um die
-    Interspikeintervall Verteilung der drei Neurone zu vergleichen.
-    \begin{solution}
-      \lstinputlisting{../code/isihist.m}
-      \lstinputlisting{../code/plotisih.m}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=1\textwidth]{isihist}}
-    \end{solution}
-  
-    \part Schreibe eine Funktion, die die seriellen Korrelationen der
-    Interspikeintervalle f\"ur Lags bis zu \code{maxlag} berechnet und
-    plottet.  Die Seriellen Korrelationen $\rho_k$ f\"ur Lag $k$ der
-    Interspikeintervalle $T_i$ sind die Korrelationskoeffizienten
-    zwischen den Interspikeintervallen $T_i$ und den um das Lag $k$
-    verschobenen Intervallen $T_{i+k}$:
-    \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i -
-      \langle T \rangle) \rangle}{\langle (T_i - \langle T
-      \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm
-        var}(T_i)} = {\rm corr}(T_{i+k}, T_i) \] 
-
-    Benutze diese Funktion, um die Interspikeintervall-Korrelationen
-    der drei Neurone zu vergleichen.
-    \begin{solution}
-      \lstinputlisting{../code/isiserialcorr.m}
-      \lstinputlisting{../code/plotserialcorr.m}
-      \colorbox{white}{\includegraphics[width=1\textwidth]{serialcorr}}
-    \end{solution}
-  
-    \part Schreibe eine Funktion, die aus Spikezeiten
-    Histogramme aus der Anzahl von Spikes, die in Fenstern gegebener L\"ange $W$
-    gez\"ahlt werden, erzeugt und plottet.
-
-    Wende diese Funktion auf die drei
-    Datens\"atze an. Probiere verschiedene Fenstergr\"o{\ss}en $W$ aus.
-    \begin{solution}
-      \lstinputlisting{../code/counthist.m}
-      \lstinputlisting{../code/plotcounthist.m}
-      \colorbox{white}{\includegraphics[width=1\textwidth]{counthist}}
-    \end{solution}
-
-    
-  \end{parts}
-  
-\end{questions}
-
-\end{document}
\ No newline at end of file
diff --git a/pointprocesses/exercises/pointprocesses02.tex b/pointprocesses/exercises/pointprocesses02.tex
deleted file mode 100644
index c943457..0000000
--- a/pointprocesses/exercises/pointprocesses02.tex
+++ /dev/null
@@ -1,199 +0,0 @@
-\documentclass[12pt,a4paper,pdftex]{exam}
-
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{L\"osungen}
-\else
-\newcommand{\stitle}{\"Ubung}
-\fi
-\header{{\bfseries\large \stitle}}{{\bfseries\large Punktprozesse 2}}{{\bfseries\large 27. Oktober, 2015}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document}
-
-\input{instructions}
-
-
-\begin{questions}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\question \qt{Homogener Poisson Prozess}
-Wir wollen den homogenen Poisson Prozess benutzen um Spikes zu generieren,
-mit denen wir die Analysfunktionen des vorherigen \"Ubungszettel \"uberpr\"ufen k\"onnen.
-
-Ein homogener Poisson Prozess mit der Rate $\lambda$ (gemessen in Hertz) ist ein Punktprozess,
-bei dem die Wahrschienlichkeit eines Ereignisses unabh\"angig von der Zeit $t$ und
-unabh\"angig von vorherigen Ereignissen ist.
-Die Wahrscheinlichkeit $P$ eines Ereignisses innerhalb eines Bins der Breite $\Delta t$ ist
-\[ P = \lambda \cdot \Delta t \]
-f\"ur gen\"ugend kleine $\Delta t$.
-\begin{parts}
-  
-  \part Schreibe eine Funktion die $n$ homogene Poisson Spiketrains
-  einer gegebenen Dauer $T_{max}$ mit Rate $\lambda$ erzeugt.
-  \begin{solution}
-    \lstinputlisting{hompoissonspikes.m}
-  \end{solution}
-  
-  \part Benutze diese Funktion um einige Trials von Spikes zu erzeugen
-  und plotte diese als Spikeraster.
-  \begin{solution}
-    \begin{lstlisting}
-      spikes = hompoissonspikes( 10, 100.0, 0.5 );
-      spikeraster( spikes )
-    \end{lstlisting}
-    \mbox{}\\[-3ex]
-    \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
-  \end{solution}
-  
-  \part Berechne Histogramme aus den Interspikeintervallen von $n$
-  Poisson Spiketrains mit der Rate $\lambda=100$\,Hz. Ver\"andere
-  \"uber die Dauer $T_{max}$ der Spiketrains und die Anzahl $n$ der
-  Trials die Anzahl der Intervalle und ver\"andere auch die Binbreite
-  des Histograms (fang mit 1\,ms an). Wieviele Interspikeintervalle
-  werden ben\"otigt, um ein ``sch\"ones'' Histogramm zu erhalten? Wie
-  lange m\"usste man also von dem Neuron ableiten?
-  \begin{solution}
-    About 5000 intervals for 25 bins. This corresponds to a $5000 /
-    100\,\hertz = 50\,\second$ recording of a neuron firing with
-    100\,\hertz.
-  \end{solution}
-  
-  \part Vergleiche Interspike-Intervall Histogramme von Poisson-Spikes
-  verschiedener Raten $\lambda$ mit der theoretisch zu erwartenden Verteilung
-  der Intervalle $T$ des Poisson Prozesses
-  \[ p(T) = \lambda e^{-\lambda T} \; .\]
-  \begin{solution}
-    \lstinputlisting{hompoissonisih.m}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
-  \end{solution}
-  
-  \part \extra Was pasiert mit den Histogrammen, wenn die Binbreite
-  der Histogramme kleiner als das bei der Erzeugung der Poisson
-  Spiketrains verwendete $\Delta t$ ist?
-  \begin{solution}
-    Die Bins zwischen der durch $\Delta t$ vorgegebenen
-    Diskretisierung haben den Wert 0. Dadurch werden aber die anderen
-    durch die Normierung h\"oher als sie sein sollten.
-  \end{solution}
-  
-  \part Plotte den Mittelwert der Interspikeintervalle, die
-  dazugeh\"orige Standardabweichung und den Variationskoeffizienten
-  als Funktion der Rate $\lambda$ des Poisson Prozesses. Vergleiche
-  die Ergebnisse mit den theoretischen Erwartungen (siehe Vorlesungsskript).
-  \begin{solution}
-    \lstinputlisting{hompoissonisistats.m}
-    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
-  \end{solution}
-  
-  \part Plotte die seriellen Korrelationen von Poisson-Spiketrains und
-  erkl\"are kurz das Ergebniss.
-  \begin{solution}
-    \mbox{}\\[-2ex]\hspace*{2cm}
-    \colorbox{white}{\includegraphics[width=0.8\textwidth]{poissonserial100hz}}\\
-    Alle Korrelationen zwischen Interspikeintervallen sind Null, da
-    beim Poisson Prozess das Auftreten jedes Spikes unabh\"angig von
-    den vorherigen Spikes ist.
-  \end{solution}
-  
-  \part Vergleiche Histogramme von Spikecounts gez\"ahlt in Fenstern
-  der Breite $W$ mit der Poisson Verteilung
-  \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \; , \] 
-  wobei die Rate $\lambda$ aus den Daten bestimmt werden
-  soll. Hinweis: es gibt eine \code{matlab} Funktion, die die
-  Fakult\"at $n!$ berechnet.
-  \begin{solution}
-    \lstinputlisting{../code/counthist.m}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}}
-  \end{solution}
-  
-  \part Schreibe eine Funktion, die die mittlere Anzahl, die Varianz
-  und den Fano-Faktor der Anzahl der Spikes in einem Fenster der
-  Breite $W$ bestimmt. Benutze die Funktion, um diese Parameter f\"ur
-  verschiedene Fensterbreiten $W$ zu bestimmen. Zwei Plots sollen aus
-  den Ergebnissen angefertigt werden: (i) Varianz gegen Mittelwert der counts.
-  (ii) Fano Faktor als Funktion der Fensterbreite.
-  \begin{solution}
-    \lstinputlisting{../code/fano.m}
-    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonfano100hz}}
-  \end{solution}
-  
-\end{parts}
-
-\end{questions}
-
-\end{document}
diff --git a/pointprocesses/exercises/pointprocesses03.tex b/pointprocesses/exercises/pointprocesses03.tex
deleted file mode 100644
index 31bb760..0000000
--- a/pointprocesses/exercises/pointprocesses03.tex
+++ /dev/null
@@ -1,236 +0,0 @@
-\documentclass[12pt,a4paper,pdftex]{exam}
-
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: L\"osungen}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large \"Ubung 8\stitle}}{{\bfseries\large Spiketrain Analyse}}{{\bfseries\large 6. Dezember, 2016}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document}
-
-\input{instructions}
-
-
-\begin{questions}
-  
-  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
-  \question \qt{Statistik von Spiketrains 2}
-  In Ilias findet ihr die Dateien \code{poisson.mat},
-  \code{pifou.mat}, und \code{lifadapt.mat}.  Jede dieser Dateien
-  enth\"alt mehrere Trials von Spiketrains von einer bestimmten Art
-  von Neuron. Die Spikezeiten sind in Sekunden gemessen.
-
-  Mit den folgenden Aufgaben wollen wir die Statistik der Spiketrains
-  der drei Neurone miteinander vergleichen.
-
-  Bereits im letzten \"Ubungszettel erstellte Funktionen d\"urfen (sollen!)
-  wiederverwendet werden.
-  \begin{parts}
-    \part Lade die Spiketrains aus den drei Dateien. Stelle sie in Rasterplots dar.
-    
-    \part Plotte die Interspike-Intervall Verteilungen.
-
-    Annotiere die Plots mit dem Mittelwert, der
-    Standardabweichung, und dem Variationskoeffizienten der
-    Interspikeintervalle, sowie der mittleren Feuerrate.
-
-    \part Vergleiche die ISI-Histogramme mit der ISI Verteilung eines Poisson Prozesses
-    der Rate $\lambda$:
-    \[ p(T) = \lambda e^{-\lambda T} \; .\]
-
-    \part Erstelle Return-Maps f\"ur die drei Spiketrains, also jedes
-    Interspike-Intervall $T_{i+1}$ gegen das vorherige Intervall $T_i$
-    geplottet.
-  
-    \part Schreibe eine Funktion, die die seriellen Korrelationen der
-    Interspikeintervalle f\"ur Lags bis zu \code{maxlag} berechnet und
-    plottet.  Die Seriellen Korrelationen $\rho_k$ f\"ur Lag $k$ der
-    Interspikeintervalle $T_i$ sind die Korrelationskoeffizienten
-    zwischen den Interspikeintervallen $T_i$ und den um das Lag $k$
-    verschobenen Intervallen $T_{i+k}$:
-    \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i -
-      \langle T \rangle) \rangle}{\langle (T_i - \langle T
-      \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm
-        var}(T_i)} = {\rm corr}(T_{i+k}, T_i) \] 
-
-    Benutze diese Funktion, um die Interspikeintervall-Korrelationen
-    der drei Neurone zu vergleichen.
-    \begin{solution}
-      \lstinputlisting{../code/isiserialcorr.m}
-      \lstinputlisting{../code/plotserialcorr.m}
-      \colorbox{white}{\includegraphics[width=1\textwidth]{serialcorr}}
-    \end{solution}
-    
-  \end{parts}
-
-  \continue
-  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
-  \question \qt{Homogener Poisson Prozess}
-  Wir wollen den homogenen Poisson Prozess benutzen um Spikes zu
-  generieren, mit denen wir die Analysfunktionen des vorherigen
-  Aufgaben \"uberpr\"ufen k\"onnen.
-
-  Ein homogener Poisson Prozess mit der Rate $\lambda$ (gemessen in
-  Hertz) ist ein Punktprozess, bei dem die Wahrscheinlichkeit eines
-  Ereignisses unabh\"angig von der Zeit $t$ und unabh\"angig von
-  vorherigen Ereignissen ist.  Wenn wir die Zeitachse in kleine Bins
-  der Breite $\Delta t$ einteilen, dann ist
-  \[ P = \lambda \cdot \Delta t \]
-  die Wahrscheinlichkeit innerhalb eines Bins ein Ereignis (``spike'')
-  zu erhalten. $\Delta t$ muss daf\"ur klein genug sein, so dass $P<0.1$.
-  \begin{parts}
-    
-    \part Schreibe eine Funktion die $n$ homogene Poisson Spiketrains
-    einer gegebenen Dauer $T_{max}$ mit Rate $\lambda$ erzeugt.
-
-    Falls das nicht gelingt, benutze f\"ur die folgenden Aufgaben
-    soweit m\"oglich spikes aus der Datei \code{poisson.mat}.
-    \begin{solution}
-      \lstinputlisting{hompoissonspikes.m}
-    \end{solution}
-    
-    \part Benutze diese Funktion um einige Trials von Spikes zu erzeugen
-    und plotte diese als Spikeraster.
-    \begin{solution}
-      \begin{lstlisting}
-        spikes = hompoissonspikes( 10, 100.0, 0.5 );
-        spikeraster( spikes )
-      \end{lstlisting}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
-    \end{solution}
-    
-    \part Berechne Histogramme aus den Interspikeintervallen von $n$
-    Poisson Spiketrains mit der Rate $\lambda=100$\,Hz. Wie viele bins
-    werden f\"ur ein ``sch\"ones'' ISI-Histogramm ungef\"ahr ben\"otigt?
-    Ver\"andere \"uber die Dauer $T_{max}$ der Spiketrains und die
-    Anzahl $n$ der Trials die Anzahl der Intervalle. Wieviele
-    Interspikeintervalle werden ben\"otigt, um ein ``sch\"ones''
-    Histogramm zu erhalten? Wie lange m\"usste man also von dem Neuron
-    ableiten?
-    \begin{solution}
-      About 5000 intervals for 25 bins. This corresponds to a $5000 /
-      100\,\hertz = 50\,\second$ recording of a neuron firing with
-      100\,\hertz.
-    \end{solution}
-    
-    \part Vergleiche Interspike-Intervall Histogramme von Poisson-Spikes
-    verschiedener Raten $\lambda$ mit der theoretisch zu erwartenden Verteilung
-    der Intervalle $T$ des Poisson Prozesses
-    \[ p(T) = \lambda e^{-\lambda T} \; .\]
-    Achte darauf, dass die Bins des Histograms nicht kleiner als $\Delta t$ sind!
-    \begin{solution}
-      \lstinputlisting{hompoissonisih.m}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
-    \end{solution}
-    
-    \part \extra Was passiert mit den Histogrammen, wenn die Binbreite
-    der Histogramme kleiner als das bei der Erzeugung der Poisson
-    Spiketrains verwendete $\Delta t$ ist?
-    \begin{solution}
-      Die Bins zwischen der durch $\Delta t$ vorgegebenen
-      Diskretisierung haben den Wert 0. Dadurch werden aber die anderen
-      durch die Normierung h\"oher als sie sein sollten.
-    \end{solution}
-    
-    \part Plotte den Mittelwert der Interspikeintervalle, die
-    dazugeh\"orige Standardabweichung und den Variationskoeffizienten
-    als Funktion der Rate $\lambda$ des Poisson Prozesses. Vergleiche
-    die Ergebnisse mit den theoretischen Erwartungen (siehe Vorlesungsskript).
-    \begin{solution}
-      \lstinputlisting{hompoissonisistats.m}
-      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
-    \end{solution}
-    
-    \part Plotte die seriellen Korrelationen von Poisson-Spiketrains und
-    erkl\"are kurz das Ergebniss.
-    \begin{solution}
-      \mbox{}\\[-2ex]\hspace*{2cm}
-      \colorbox{white}{\includegraphics[width=0.8\textwidth]{poissonserial100hz}}\\
-      Alle Korrelationen zwischen Interspikeintervallen sind Null, da
-      beim Poisson Prozess das Auftreten jedes Spikes unabh\"angig von
-      den vorherigen Spikes ist.
-    \end{solution}
-    
-  \end{parts}
-
-
-\end{questions}
-
-\end{document}
diff --git a/pointprocesses/exercises/poisson-solutions.pdf b/pointprocesses/exercises/poisson-solutions.pdf
deleted file mode 100644
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diff --git a/pointprocesses/code/poisson.mat b/pointprocesses/exercises/poisson.mat
similarity index 100%
rename from pointprocesses/code/poisson.mat
rename to pointprocesses/exercises/poisson.mat
diff --git a/pointprocesses/exercises/poissoncounthistdist100hz100ms.pdf b/pointprocesses/exercises/poissoncounthistdist100hz100ms.pdf
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diff --git a/pointprocesses/exercises/poissonfano100hz.pdf b/pointprocesses/exercises/poissonfano100hz.pdf
deleted file mode 100644
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diff --git a/pointprocesses/exercises/poissonisihist.pdf b/pointprocesses/exercises/poissonisihist.pdf
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diff --git a/pointprocesses/exercises/poissonisistats.pdf b/pointprocesses/exercises/poissonisistats.pdf
deleted file mode 100644
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diff --git a/pointprocesses/exercises/poissonraster100hz.pdf b/pointprocesses/exercises/poissonraster100hz.pdf
deleted file mode 100644
index a20fa0e..0000000
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diff --git a/pointprocesses/exercises/poissonserial100hz.pdf b/pointprocesses/exercises/poissonserial100hz.pdf
deleted file mode 100644
index ff667d9..0000000
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diff --git a/pointprocesses/exercises/psth.tex b/pointprocesses/exercises/psth.tex
index a662af7..a9ff599 100644
--- a/pointprocesses/exercises/psth.tex
+++ b/pointprocesses/exercises/psth.tex
@@ -1,42 +1,20 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Time-dependent firing rates}
+\newcommand{\exercisenum}{12}
+\newcommand{\exercisedate}{January 19th, 2021}
 
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot} \header{{\bfseries\large Exercise
-    }}{{\bfseries\large Time-dependent firing rate}}{{\bfseries\large January, 14, 2020}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de} \runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solution}\par\noindent}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to scientific computing.}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Abteilung Neuroethologie \hfill --- \hfill Institut f\"ur Neurobiologie \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 \begin{questions}
+  
   \question Plot the time-dependent firing rate of a neuron. Calculate
   the firing rate from the \emph{instantaneous firing rate} (based on the
   interspike interval). Use the \code{lifoustim.mat}. The dataset
@@ -45,14 +23,14 @@
   duration of each trial is 30 seconds.
 
   \begin{parts}
-    \part{} Write a function that takes three arguments: the spike
+    \part Write a function that takes three arguments: the spike
     times of a single trial, the trial duration and the temporal
     resolution. The function should return two variables: the time axis and the
     time-dependent firing rate.
-    \part{} Write a script that applies the above function to estimate
+    \part Write a script that applies the above function to estimate
     the time-dependent firing rate of each trial. Plot the firing rates of the individual responses
     and the average response as a function of time into the same graph.
-    \part{} Extend your program that it saves the figure with the width and height of 8.5\,cm using a fontsize of 10\,pt for labels.
+    \part Extend your program that it saves the figure with the width and height of 8.5\,cm using a fontsize of 10\,pt for labels.
     See Chapter 3 in the script, or browse the Matlab help for more
     information. Store the figure in pdf format.
   \end{parts}
@@ -65,10 +43,10 @@
 
   \question{} Some trials are different than the others.
   \begin{parts}
-    \part{} Use a rasterplot to identify them. In which sense
+    \part Use a rasterplot to identify them. In which sense
     are they different? Save the rasterplot in pdf
     format. Use the same figure specifications as above and make sure it is properly labeled.
-    \part{} Identify those trials in which the spike count
+    \part Identify those trials in which the spike count
     deviates more than $2\sigma$ (twice the standard deviation) from the average.
   \end{parts}
 \end{questions}
diff --git a/pointprocesses/exercises/rasterplot.m b/pointprocesses/exercises/rasterplot.m
new file mode 100644
index 0000000..db92539
--- /dev/null
+++ b/pointprocesses/exercises/rasterplot.m
@@ -0,0 +1,43 @@
+function rasterplot(spikes, tmax)
+% Display a spike raster of the spike times given in spikes.
+%
+% rasterplot(spikes, tmax)
+%
+% Arguments:
+%   spikes: a cell array of vectors of spike times in seconds
+%   tmax: plot spike raster up to tmax seconds
+    in_msecs = tmax < 1.5;
+    spiketimes = [];
+    trials = [];
+    ntrials = length(spikes);
+    for k = 1:ntrials
+        times = spikes{k};
+        times = times(times<tmax);
+        if in_msecs
+            times = 1000.0*times;               % conversion to ms
+        end
+        % (x,y) pairs for start and stop of stroke
+        % plus nan separating strokes:
+        spiketimes = [spiketimes, ...
+                       [times(:)'; times(:)'; times(:)'*nan]];
+        trials = [trials, ...
+                   [ones(1, length(times)) * (k-0.4); ...
+                    ones(1, length(times)) * (k+0.4); ...
+                    ones(1, length(times)) * nan]];
+    end
+    % convert matrices into simple vectors of (x,y) pairs:
+    spiketimes = spiketimes(:);
+    trials = trials(:);
+    % plotting this is lightning fast:
+    plot(spiketimes, trials, 'k')
+    if in_msecs
+        xlabel('Time [ms]');
+        xlim([0.0 1000.0*tmax]);
+    else
+        xlabel('Time [s]');
+        xlim([0.0 tmax]);
+    end
+    ylabel('Trials');
+    ylim([0.3 ntrials+0.7]);
+end
+
diff --git a/pointprocesses/code/savefigpdf.m b/pointprocesses/exercises/savefigpdf.m
similarity index 100%
rename from pointprocesses/code/savefigpdf.m
rename to pointprocesses/exercises/savefigpdf.m
diff --git a/pointprocesses/exercises/serialcorr.pdf b/pointprocesses/exercises/serialcorr.pdf
index 5ce399b..51b3d2b 100644
Binary files a/pointprocesses/exercises/serialcorr.pdf and b/pointprocesses/exercises/serialcorr.pdf differ
diff --git a/pointprocesses/exercises/spikecountshists.m b/pointprocesses/exercises/spikecountshists.m
new file mode 100644
index 0000000..e18516c
--- /dev/null
+++ b/pointprocesses/exercises/spikecountshists.m
@@ -0,0 +1,22 @@
+w = 0.1;
+bins = 0:1:10;
+
+spikecounts{1} = counts(poissonspikes, w);
+spikecounts{2} = counts(pifouspikes, w);
+spikecounts{3} = counts(lifadaptspikes, w);
+titles = {'Poisson', 'PIF OU', 'LIF adapt'};
+for k = 1:3
+    subplot(1, 3, k);
+    [h, b] = hist(spikecounts{k}, bins);
+    bar(b, h/sum(h));
+    mu = mean(spikecounts{k});
+    ppois = poisspdf(bins, mu);
+    hold on;
+    scatter(bins, ppois, 'filled');
+    hold off;
+    title(titles{k})
+    xlabel('Spike count');
+    xlim([-0.5, 8.5]);
+    ylim([0.0 0.8]);
+end
+savefigpdf(gcf, 'spikecountshists.pdf', 20, 7);
diff --git a/pointprocesses/exercises/spikecountshists.pdf b/pointprocesses/exercises/spikecountshists.pdf
index a940a04..3a217f0 100644
Binary files a/pointprocesses/exercises/spikecountshists.pdf and b/pointprocesses/exercises/spikecountshists.pdf differ
diff --git a/pointprocesses/exercises/spikeraster.pdf b/pointprocesses/exercises/spikeraster.pdf
index 68c6e28..f75555a 100644
Binary files a/pointprocesses/exercises/spikeraster.pdf and b/pointprocesses/exercises/spikeraster.pdf differ
diff --git a/pointprocesses/lecture/countexamples.py b/pointprocesses/lecture/countexamples.py
new file mode 100644
index 0000000..8c8a8f3
--- /dev/null
+++ b/pointprocesses/lecture/countexamples.py
@@ -0,0 +1,51 @@
+import numpy as np
+from scipy.stats import poisson
+import matplotlib.pyplot as plt
+from plotstyle import *
+
+
+rate = 20.0
+trials = 20
+duration = 100.0
+
+
+def hompoisson(rate, trials, duration) :
+    spikes = []
+    for k in range(trials) :
+        times = []
+        t = 0.0
+        while t < duration :
+            t += np.random.exponential(1/rate)
+            times.append( t )
+        spikes.append( times )
+    return spikes
+
+    
+def plot_count_hist(ax, spikes, win, pmax):
+    counts = []
+    for times in spikes:
+        c, _ = np.histogram(times, np.arange(0.0, duration, win))
+        counts.extend(c)
+    cb = np.arange(0.0, 15.5, 1.0)
+    h, b = np.histogram(counts, cb, density=True)
+    ax.bar(b[:-1], h, bar_fac, align='center', **fsA)
+    ax.plot(cb, poisson.pmf(cb, rate*win), **lsBm)
+    ax.plot(cb, poisson.pmf(cb, rate*win), **psBm)
+    ax.text(0.9, 0.9, 'T=%.0fms' % (1000.0*win), transform=ax.transAxes, ha='right')
+    ax.set_xlim(-0.5, 10.5)
+    ax.set_ylim(0.0, pmax)
+    ax.set_xticks(np.arange(0.0, 11.0, 5.0))
+    ax.set_xlabel('Counts k')
+    ax.set_ylabel('P(k)')
+
+    
+if __name__ == "__main__":
+    spikes = hompoisson(rate, trials, duration)
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
+    plot_count_hist(ax1, spikes, 0.02, 0.7)
+    ax1.set_yticks(np.arange(0.0, 0.7, 0.2))
+    plot_count_hist(ax2, spikes, 0.2, 0.22)
+    ax2.set_yticks(np.arange(0.0, 0.25, 0.1))
+    plt.savefig('countexamples.pdf')
+    plt.close()
diff --git a/pointprocesses/lecture/fanoexamples.json b/pointprocesses/lecture/fanoexamples.json
new file mode 100644
index 0000000..a71ed0f
--- /dev/null
+++ b/pointprocesses/lecture/fanoexamples.json
@@ -0,0 +1,512 @@
+{
+    "inh_mean_counts": [
+        0.019790539581079163,
+        0.021220735104578375,
+        0.02275429204781155,
+        0.024398678386941673,
+        0.026161922211558546,
+        0.028052531609684187,
+        0.030079856276940473,
+        0.03225362212552356,
+        0.03458450709147974,
+        0.03708354257703291,
+        0.03976375421436924,
+        0.04263742087823543,
+        0.04571863535977971,
+        0.04902204607381719,
+        0.05256472913103067,
+        0.05636367261148674,
+        0.0604368857915921,
+        0.06480431194823566,
+        0.06948757382321753,
+        0.07450942742688474,
+        0.07989350278152325,
+        0.08566741121673332,
+        0.09185813351157156,
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+}
\ No newline at end of file
diff --git a/pointprocesses/lecture/fanoexamples.py b/pointprocesses/lecture/fanoexamples.py
new file mode 100644
index 0000000..3a68d58
--- /dev/null
+++ b/pointprocesses/lecture/fanoexamples.py
@@ -0,0 +1,143 @@
+import os
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mpt
+from plotstyle import *
+
+
+rate = 20.0
+trials = 20
+duration = 500.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
+    
+def hompoisson(rate, trials, duration) :
+    spikes = []
+    for k in range(trials) :
+        times = []
+        t = 0.0
+        while t < duration :
+            t += np.random.exponential(1/rate)
+            times.append(t)
+        spikes.append(times)
+    return spikes
+
+
+def inhompoisson(rate, trials, dt) :
+    spikes = []
+    p = rate*dt
+    for k in range(trials) :
+        x = np.random.rand(len(rate))
+        times = dt*np.nonzero(x<p)[0]
+        spikes.append(times)
+    return spikes
+
+
+def pifspikes(input, trials, dt, D=0.1) :
+    vreset = 0.0
+    vthresh = 1.0
+    tau = 1.0
+    spikes = []
+    for k in range(trials) :
+        times = []
+        v = vreset
+        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
+        for k in range(len(noise)) :
+            v += (input[k]+noise[k])*dt/tau
+            if v >= vthresh :
+                v = vreset
+                times.append(k*dt)
+        spikes.append(times)
+    return spikes
+
+
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
+    # OU noise:
+    rng = np.random.RandomState(54637281)
+    time = np.arange(0.0, duration, dt)
+    x = np.zeros(time.shape)+rate
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
+        x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    x[x<0.0] = 0.0
+    spikes = pifspikes(x, trials, dt, D)
+    return spikes
+
+
+def count_stats(spikes, wins):
+    mean_counts = np.zeros(len(wins))
+    var_counts = np.zeros(len(wins))
+    for k, win in enumerate(wins):
+        counts = []
+        for times in spikes:
+            c, _ = np.histogram(times, np.arange(0.0, duration, win))
+            counts.extend(c)
+        mean_counts[k] = np.mean(counts)
+        var_counts[k] = np.var(counts)
+    return mean_counts, var_counts
+
+
+def plot_count_fano(ax1, ax2, wins, mean_counts, var_counts):
+    ax1.plot(mean_counts, var_counts, zorder=100, **lsA)
+    ax1.set_xlabel('Mean count')
+    ax1.set_xlim(0.0, 20.0)
+    ax1.set_ylim(0.0, 20.0)
+    ax1.set_xticks(np.arange(0.0, 21.0, 10.0))
+    ax1.set_yticks(np.arange(0.0, 21.0, 10.0))
+    ax2.plot(1000.0*wins, var_counts/mean_counts, **lsB)
+    ax2.set_xlabel('Window', 'ms')
+    ax2.set_ylim(0.0, 1.2)
+    ax2.set_xscale('log')
+    ax2.set_xticks([10, 100, 1000])
+    ax2.set_xticklabels(['10', '100', '1000'])
+    ax2.xaxis.set_minor_locator(mpt.NullLocator())
+    ax2.set_yticks(np.arange(0.0, 1.2, 0.5))
+
+
+def plot_fano(ax, wins, mean_counts, var_counts):
+    ax.plot(1000.0*wins, var_counts/mean_counts, **lsB)
+    ax.set_xlabel('Window', 'ms')
+    ax.set_ylim(0.0, 1.2)
+    ax.set_xscale('log')
+    ax.set_xticks([1, 10, 100, 1000])
+    ax.set_xticklabels(['1', '10', '100', '1000'])
+    ax.xaxis.set_minor_locator(mpt.NullLocator())
+    ax.set_yticks(np.arange(0.0, 1.2, 0.5))
+
+    
+if __name__ == "__main__":
+    if not os.path.exists('fanoexamples.json'):
+        homspikes = hompoisson(rate, trials, duration)
+        inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
+        wins = np.logspace(-3, 0.0, 100)
+        hom_mean_counts, hom_var_counts = count_stats(homspikes, wins)
+        inh_mean_counts, inh_var_counts = count_stats(inhspikes, wins)
+        with open('fanoexamples.json', 'w') as df:
+            json.dump({'wins': wins.tolist(),
+                       'hom_mean_counts': hom_mean_counts.tolist(),
+                       'hom_var_counts': hom_var_counts.tolist(),
+                       'inh_mean_counts': inh_mean_counts.tolist(),
+                       'inh_var_counts': inh_var_counts.tolist()}, df, indent=4)
+    else:
+        with open('fanoexamples.json', 'r') as sf:
+            data = json.load(sf)
+            wins = np.array(data['wins'])
+            hom_mean_counts = np.array(data['hom_mean_counts'])
+            hom_var_counts = np.array(data['hom_var_counts'])
+            inh_mean_counts = np.array(data['inh_mean_counts'])
+            inh_var_counts = np.array(data['inh_var_counts'])
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=2.0))
+    plot_fano(ax1, wins, hom_mean_counts, hom_var_counts)
+    ax1.set_ylabel('Fano factor')
+    ax1.text(0.1, 0.95, 'Poisson', transform=ax1.transAxes)
+    ax2.axhline(1.0, **lsGrid)
+    plot_fano(ax2, wins, inh_mean_counts, inh_var_counts)
+    ax2.annotate('', xy=(45.0, 0.0), xytext=(45.0, 0.4), arrowprops=dict(arrowstyle="->"))
+    ax2.text(60.0, 0.25, 'most\nreliable', va='center')
+    ax2.text(0.1, 0.95, 'OU noise', transform=ax2.transAxes)
+    plt.savefig('fanoexamples.pdf')
+    plt.close()
diff --git a/pointprocesses/lecture/firingrates.py b/pointprocesses/lecture/firingrates.py
index 087e7ee..1195e3a 100644
--- a/pointprocesses/lecture/firingrates.py
+++ b/pointprocesses/lecture/firingrates.py
@@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
 from plotstyle import *
 
 
-def get_instantaneous_rate(times, max_t=30., dt=1e-4):
+def instantaneous_rate(times, max_t=30., dt=1e-4):
     time = np.arange(0., max_t, dt)
     indices = np.asarray(times / dt, dtype=int)
     intervals = np.diff(np.hstack(([0], times)))
@@ -19,12 +19,12 @@ def get_instantaneous_rate(times, max_t=30., dt=1e-4):
 
 def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
     times = np.squeeze(spike_times[0][0])[:50000]
-    time, rate = get_instantaneous_rate(times, max_t=50000*dt)
+    time, rate = instantaneous_rate(times, max_t=50000*dt)
 
     rates = np.zeros((len(rate), len(spike_times)))
     for i in range(len(spike_times)):
-        _, rates[:, i] = get_instantaneous_rate(np.squeeze(spike_times[i][0])[:50000],
-                                                max_t=50000*dt)
+        _, rates[:, i] = instantaneous_rate(np.squeeze(spike_times[i][0])[:50000],
+                                            max_t=50000*dt)
     avg_rate = np.mean(rates, axis=1)
     rate_std = np.std(rates, axis=1)
 
@@ -48,7 +48,7 @@ def plot_isi_rate(spike_times, max_t=30, dt=1e-4):
     plt.close()
 
 
-def get_binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
+def binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
     time = np.arange(0., max_t, dt)
     bins = np.arange(0., max_t, bin_width)
     bin_indices = np.asarray(bins / dt, np.int)
@@ -62,10 +62,10 @@ def get_binned_rate(times, bin_width=0.05, max_t=30., dt=1e-4):
 
 def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
     times = np.squeeze(spike_times[0][0])
-    time, rate = get_binned_rate(times)
+    time, rate = binned_rate(times)
     rates = np.zeros((len(rate), len(spike_times)))
     for i in range(len(spike_times)):
-        _, rates[:, i] = get_binned_rate(np.squeeze(spike_times[i][0]))
+        _, rates[:, i] = binned_rate(np.squeeze(spike_times[i][0]))
     avg_rate = np.mean(rates, axis=1)
     rate_std = np.std(rates, axis=1)
 
@@ -93,7 +93,7 @@ def plot_bin_rate(spike_times, bin_width, max_t=30, dt=1e-4):
     plt.close()
   
 
-def get_convolved_rate(times, sigma, max_t=30., dt=1.e-4):
+def convolved_rate(times, sigma, max_t=30., dt=1.e-4):
     time = np.arange(0., max_t, dt)
     kernel = spst.norm.pdf(np.arange(-8*sigma, 8*sigma, dt),loc=0,scale=sigma)
     indices = np.asarray(times/dt, dtype=int)
@@ -105,11 +105,11 @@ def get_convolved_rate(times, sigma, max_t=30., dt=1.e-4):
 
 def plot_conv_rate(spike_times, sigma=0.05, max_t=30, dt=1e-4):
     times = np.squeeze(spike_times[0][0])
-    time, rate = get_convolved_rate(times, sigma)
+    time, rate = convolved_rate(times, sigma)
    
     rates = np.zeros((len(rate), len(spike_times)))
     for i in range(len(spike_times)):
-        _, rates[:, i] = get_convolved_rate(np.squeeze(spike_times[i][0]), sigma)
+        _, rates[:, i] = convolved_rate(np.squeeze(spike_times[i][0]), sigma)
     avg_rate = np.mean(rates, axis=1)
     rate_std = np.std(rates, axis=1)
     
@@ -139,9 +139,9 @@ def plot_conv_rate(spike_times, sigma=0.05, max_t=30, dt=1e-4):
 
 def plot_comparison(spike_times, bin_width, sigma, max_t=30., dt=1e-4):
     times = np.squeeze(spike_times[0][0])
-    time, conv_rate = get_convolved_rate(times, bin_width/np.sqrt(12.))
-    time, inst_rate = get_instantaneous_rate(times)
-    time, binn_rate = get_binned_rate(times, bin_width)
+    time, conv_rate = convolved_rate(times, bin_width/np.sqrt(12.))
+    time, inst_rate = instantaneous_rate(times)
+    time, binn_rate = binned_rate(times, bin_width)
    
     fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=cm_size(figure_width, 1.8*figure_height))
     fig.subplots_adjust(**adjust_fs(fig, left=6.0, right=1.5, bottom=3.0, top=1.0))
diff --git a/pointprocesses/lecture/isihexamples.py b/pointprocesses/lecture/isihexamples.py
index b1c7daa..c3e2d87 100644
--- a/pointprocesses/lecture/isihexamples.py
+++ b/pointprocesses/lecture/isihexamples.py
@@ -2,6 +2,15 @@ import numpy as np
 import matplotlib.pyplot as plt
 from plotstyle import *
 
+
+rate = 20.0
+trials = 10
+duration = 100.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
+
 def hompoisson(rate, trials, duration) :
     spikes = []
     for k in range(trials) :
@@ -13,6 +22,7 @@ def hompoisson(rate, trials, duration) :
         spikes.append( times )
     return spikes
 
+
 def inhompoisson(rate, trials, dt) :
     spikes = []
     p = rate*dt
@@ -40,12 +50,27 @@ def pifspikes(input, trials, dt, D=0.1) :
         spikes.append( times )
     return spikes
 
+
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
+    # OU noise:
+    rng = np.random.RandomState(54637281)
+    time = np.arange(0.0, duration, dt)
+    x = np.zeros(time.shape)+rate
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
+        x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    x[x<0.0] = 0.0
+    spikes = pifspikes(x, trials, dt, D)
+    return spikes
+
+
 def isis( spikes ) :
     isi = []
     for k in range(len(spikes)) :
         isi.extend(np.diff(spikes[k]))
     return isi
 
+
 def plotisih( ax, isis, binwidth=None ) :
     if binwidth == None :
         nperbin = 200.0    # average number of isis per bin
@@ -55,46 +80,37 @@ def plotisih( ax, isis, binwidth=None ) :
             binwidth = 5e-4
     h, b = np.histogram(isis, np.arange(0.0, np.max(isis)+binwidth, binwidth), density=True)
     ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
-    ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
-    ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.7, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.55, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
     ax.set_xlabel('ISI', 'ms')
     ax.set_ylabel('p(ISI)', '1/s')
-    ax.bar( 1000.0*b[:-1], h, bar_fac*1000.0*np.diff(b), facecolor=colors['blue'])
+    ax.bar( 1000.0*b[:-1], h, bar_fac*1000.0*np.diff(b), **fsA)
 
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 100.0
-dt = 0.001
-tau = 0.1;
+    
+def plot_hom_isih(ax):
+    homspikes = hompoisson(rate, trials, duration)
+    ax.set_xlim(0.0, 150.0)
+    ax.set_ylim(0.0, 31.0)
+    ax.set_xticks(np.arange(0.0, 151.0, 50.0))
+    ax.set_yticks(np.arange(0.0, 31.0, 10.0))
+    tt = np.linspace(0.0, 0.15, 100)
+    ax.plot(1000.0*tt, rate*np.exp(-rate*tt), **lsB)
+    plotisih(ax, isis(homspikes), 0.005)
+
+    
+def plot_inhom_isih(ax):
+    inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
+    ax.set_xlim(0.0, 150.0)
+    ax.set_ylim(0.0, 31.0)
+    ax.set_xticks(np.arange(0.0, 151.0, 50.0))
+    ax.set_yticks(np.arange(0.0, 31.0, 10.0))
+    plotisih(ax, isis(inhspikes), 0.005)
 
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
-
-# OU noise:
-rng = np.random.RandomState(54637281)
-time = np.arange(0.0, duration, dt)
-x = np.zeros(time.shape)+rate
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
-for k in range(1,len(x)) :
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
-
-# pif spike trains:
-inhspikes = pifspikes(x, trials, dt, D=0.3)
-
-fig, (ax1, ax2) = plt.subplots(1, 2)
-fig.subplots_adjust(**adjust_fs(fig, top=1.5))
-ax1.set_title('stationary')
-ax1.set_xlim(0.0, 200.0)
-ax1.set_ylim(0.0, 40.0)
-plotisih(ax1, isis(homspikes))
-
-ax2.set_title('non-stationary')
-ax2.set_xlim(0.0, 200.0)
-ax2.set_ylim(0.0, 40.0)
-plotisih(ax2, isis(inhspikes))
-
-plt.savefig('isihexamples.pdf')
-plt.close()
+    
+if __name__ == "__main__":
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=1.5))
+    plot_hom_isih(ax1)
+    plot_inhom_isih(ax2)
+    plt.savefig('isihexamples.pdf')
+    plt.close()
diff --git a/pointprocesses/lecture/pointprocesses-chapter.tex b/pointprocesses/lecture/pointprocesses-chapter.tex
index 89081cc..9fcf180 100644
--- a/pointprocesses/lecture/pointprocesses-chapter.tex
+++ b/pointprocesses/lecture/pointprocesses-chapter.tex
@@ -20,14 +20,13 @@
 
 \section{TODO}
 \begin{itemize}
-\item Explain difference stationary versus non-stationary point process
 \item Show different types of ISI histograms (regular, noisy, poisson, bursty, locking)
 \item Multitrial firing rates
 \item Better explain difference between ISI method and PSTHes. The
   latter is dependent on precision of spike times the former not.
-\item Choice of bin width for PSTH, kernel width, also in relation sto
+\item Choice of bin width for PSTH, kernel width, also in relation to
   stimulus time scale
-\item Kernle firing rate: discuss different kernel shapes, in
+\item Kernel firing rate: discuss different kernel shapes, in
   particular causal kernels (gamma, exponential), relation to synaptic
   potentials
 \end{itemize}
diff --git a/pointprocesses/lecture/pointprocesses-slides.tex b/pointprocesses/lecture/pointprocesses-slides.tex
index ff49cb2..75a084c 100644
--- a/pointprocesses/lecture/pointprocesses-slides.tex
+++ b/pointprocesses/lecture/pointprocesses-slides.tex
@@ -49,7 +49,6 @@
 
 %%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{graphicx}
-\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
 
 %%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{listings}
@@ -95,7 +94,7 @@
 \begin{frame}
   \frametitle{Point process}
   \vspace{-3ex}
-  \texpicture{pointprocessscetchA}
+  \includegraphics{pointprocesssketch}
 
   A point process is a stochastic (or random) process that generates a sequence of events
   at times $\{t_i\}$, $t_i \in \reZ$.
@@ -113,7 +112,7 @@
 
 \begin{frame}
   \frametitle{Point process}
-  \texpicture{pointprocessscetchB}
+  \includegraphics{pointprocesssketch}
 \end{frame}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
diff --git a/pointprocesses/lecture/pointprocesses.tex b/pointprocesses/lecture/pointprocesses.tex
index 1cf796e..f6796d2 100644
--- a/pointprocesses/lecture/pointprocesses.tex
+++ b/pointprocesses/lecture/pointprocesses.tex
@@ -1,34 +1,41 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\chapter{Spiketrain analysis}
-\exercisechapter{Spiketrain analysis}
+\chapter{Spike-train analysis}
+\label{pointprocesseschapter}
+\exercisechapter{Spike-train analysis}
 
 \entermde[action potential]{Aktionspotential}{Action potentials}
-(\enterm[spike|seealso{action potential}]{spikes}) are the carriers of
-information in the nervous system. Thereby it is the time at which the
-spikes are generated that is of importance for information
-transmission. The waveform of the action potential is largely
-stereotyped and therefore does not carry information.
-
-The result of the pre-processing of electrophysiological recordings are
-series of spike times, which are termed \enterm{spiketrains}. If
-measurements are repeated we get several \enterm{trials} of
-spiketrains (\figref{rasterexamplesfig}).
-
-Spiketrains are times of events, the action potentials. Analyzing
-spike trains leads into the realm of the so called \entermde[point
-  process]{Punktprozess}{point processes}.
-
-\begin{figure}[ht]
+(\enterm[spike|seealso{action potential}]{spikes}) carry information
+within neural systems. More precisely, the times at which action
+potentials are generated contain the information.  The waveform of the
+action potential is largely stereotyped and therefore conveys no
+information. Analyzing the statistics of spike times and their
+relation to sensory stimuli or motor actions is central to
+neuroscientific research. With multi-electrode arrays it is nowadays
+possible to record from hundreds or even thousands of neurons
+simultaneously. The open challenge is how to analyze such huge data
+sets in smart ways in order to gain insights into the way neural
+systems work. Let's start with the basics in this chapter.
+
+The result of the pre-processing of electrophysiological recordings
+are series of spike times, which are termed \enterm[spike train]{spike
+  trains}. If measurements are repeated we get several \enterm{trials}
+of spike trains (\figref{rasterexamplesfig}).  Spike trains are lists
+of times of events, the action potentials. Analyzing spike trains
+leads into the realm of the statistics of so called \entermde[point
+process]{Punktprozess}{point processes}.
+
+\begin{figure}[bt]
   \includegraphics[width=1\textwidth]{rasterexamples}
-  \titlecaption{\label{rasterexamplesfig}Raster-plot.}{Raster-plots of
-    ten trials of data illustrating the times of the action
-    potentials. Each vertical dash illustrates the time at which an
-    action potential was observed. Each line displays the events of
-    one trial. Shown is a stationary point process (homogeneous point
-    process with a rate $\lambda=20$\;Hz, left) and an non-stationary
-    point process (perfect integrate-and-fire neuron driven by
-    Ohrnstein-Uhlenbeck noise with a time-constant $\tau=100$\,ms,
+  \titlecaption{\label{rasterexamplesfig}Raster plots of spike
+    trains.}{Raster plots of ten trials of data illustrating the times
+    of action potentials. Each vertical stroke illustrates the time at
+    which an action potential was observed. Each row displays the
+    events of one trial. Shown is a stationary point process
+    (homogeneous point process with a rate $\lambda=20$\;Hz, left) and
+    an non-stationary point process with a rate that varies in time
+    (noisy perfect integrate-and-fire neuron driven by
+    Ornstein-Uhlenbeck noise with a time-constant $\tau=100$\,ms,
     right).}
 \end{figure}
 
@@ -42,7 +49,7 @@ spike trains leads into the realm of the so called \entermde[point
   crosses some threshold. For example:\vspace{-1ex}
   \begin{itemize}
   \item Action potentials/heart beat: created by the dynamics of the
-    neuron/sinoatrial node
+    neuron/sinoatrial node.
   \item Earthquake: defined by the dynamics of the pressure between
     tectonical plates.
   \item Communication calls in crickets/frogs/birds: shaped by
@@ -50,51 +57,130 @@ spike trains leads into the realm of the so called \entermde[point
   \end{itemize}
 \end{ibox}
 
-\begin{figure}[t]
-  \texpicture{pointprocessscetch}
-  \titlecaption{\label{pointprocessscetchfig} Statistics of point
-    processes.}{A point process is a sequence of instances in time
-    $t_i$ that can be characterized through the inter-event-intervals
-    $T_i=t_{i+1}-t_i$ and the number of events $n_i$. }
+\begin{figure}[tb]
+  \includegraphics{pointprocesssketch}
+  \titlecaption{\label{pointprocesssketchfig} Statistics of point
+    processes.}{A temporal point process is a sequence of events in
+    time, $t_i$, that can be also characterized by the corresponding
+    inter-event intervals $T_i=t_{i+1}-t_i$ and event counts $n_i$,
+    i.e. the number of events that occurred so far.}
 \end{figure}
 
+\noindent
 A temporal \entermde{Punktprozess}{point process} is a stochastic
-process that generates a sequence of events at times $\{t_i\}$, $t_i
-\in \reZ$.  In the neurosciences, the statistics of point processes is
-of importance since the timing of neuronal events (action potentials,
-post-synaptic potentials, events in EEG or local-field recordings,
-etc.) is crucial for information transmission and can be treated as
-such a process.
+process that generates a sequence of events at times $\{t_i\}$.  In
+the neurosciences, the statistics of point processes is of importance
+since the timing of neuronal events (action potentials, post-synaptic
+potentials, events in EEG or local-field recordings, etc.) is crucial
+for information transmission and can be treated as such a process.
 
 The events of a point process can be illustrated by means of a raster
 plot in which each vertical line indicates the time of an event. The
 event from two different point processes are shown in
-\figref{rasterexamplesfig}.  Point processes can be described using
-the intervals between successive events $T_i=t_{i+1}-t_i$ and the
-number of observed events within a certain time window $n_i$
-(\figref{pointprocessscetchfig}).
-
-\begin{exercise}{rasterplot.m}{}
-  Implement a function \varcode{rasterplot()} that displays the times of
-  action potentials within the first \varcode{tmax} seconds in a raster
-  plot. The spike times (in seconds) recorded in the individual trials
-  are stored as vectors of times within a cell array.
-\end{exercise}
+\figref{rasterexamplesfig}.  In addition to the event times, point
+processes can be described using the intervals $T_i=t_{i+1}-t_i$
+between successive events or the number of observed events $n_i$
+within a certain time window (\figref{pointprocesssketchfig}).
+
+In \enterm[point process!stationary]{stationary} point processes the
+statistics does not change over time. In particular, the rate of the
+process, the number of events per time, is constant. The homogeneous
+point process shown in \figref{rasterexamplesfig} on the left is an
+example of a stationary point process. Although locally within each
+trial there are regions with many events per time and others with long
+intervals between events, the average number of events within a small
+time window over trials does not change in time. In the first sections
+of this chapter we introduce various statistics for characterizing
+stationary point processes.
+
+On the other hand, the example shown in \figref{rasterexamplesfig} on
+the right is a non-stationary point process. The rate of the events in
+all trials first decreases and then increases again. This common
+change in rate may have been caused by some sensory input to the
+neuron. How to estimate temporal changes in event rates (firing rates)
+is covered in section~\ref{nonstationarysec}.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Homogeneous Poisson process}
+
+Before we are able to start analyzing point processes, we need some
+data. As before, because we are working with computers, we can easily
+simulate them. To get us started we here briefly introduce the
+homogeneous Poisson process. The Poisson process is to point processes
+what the Gaussian distribution is to the statistics of real-valued
+data. It is a standard against which everything is compared.
+
+In a \entermde[Poisson
+process!homogeneous]{Poissonprozess!homogener}{homogeneous Poisson
+  process} events at every given time, that is within every small time
+window of width $\Delta t$ occur with the same constant
+probability. This probability is independent of absolute time and
+independent of any events occurring before. To observe an event right
+after an event is as likely as to observe an event at some specific
+time later on.
+
+The simplest way to simulate events of a homogeneous Poisson process
+is based on the exponential distribution \eqref{hompoissonexponential}
+of event intervals. We randomly draw intervals from this distribution
+and then sum them up to convert them to event times. The only
+parameter of a homogeneous Poisson process is its rate. It defines how
+many events per time are expected.
+
+Here is a function that generates several trials of a homogeneous
+Poisson process:
+
+\lstinputlisting[caption={hompoissonspikes.m}]{hompoissonspikes.m}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\section{Raster plot}
+
+Let's generate some events with this function and display them in a
+raster plot as in \figref{rasterexamplesfig}. For the raster plot we
+need to draw for each event a line at the corresponding time of the
+event and at the height of the corresponding trial. You can go with a
+one for-loop through the trials and then with another for-loop through
+the event times and every time plot a two-point line. This, however,
+is very slow. The fastest way is to concatenate the coordinates of all
+strokes into large vectors, separate the events by \varcode{nan}
+entries, and pass this to a single call of \varcode{plot()}:
+
+\pageinputlisting[caption={rasterplot.m}]{rasterplot.m}
+
+Adapt this function to your needs and use it where ever possible to
+illustrate your spike train data to your readers. They appreciate
+seeing your raw data and being able to judge the data for themselves
+before you go on analyzing firing rates, interspike-interval
+correlations, etc.
+
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
 \section{Interval statistics}
 
-The intervals $T_i=t_{i+1}-t_i$ between successive events are real
-positive numbers. In the context of action potentials they are
-referred to as \entermde{Interspikeintervalle}{interspike
-  intervals}. The statistics of interspike intervals are described
-using common measures for describing the statistics of stochastic
-real-valued variables:
+Let's start with the interval statistics of stationary point
+processes.  The intervals $T_i=t_{i+1}-t_i$ between successive events
+are real positive numbers. In the context of action potentials they
+are referred to as \entermde[interspike
+interval]{Interspikeintervall}{interspike intervals}, in short
+\entermde[ISI|see{interspike
+  interval}]{ISI|see{Interspikeintervall}}{ISI}s.  For analyzing event
+intervals we can use all the usual statistics that we know from
+describing univariate data sets of real numbers:
 
 \begin{figure}[t]
-  \includegraphics[width=0.96\textwidth]{isihexamples}\vspace{-2ex}
-  \titlecaption{\label{isihexamplesfig}Interspike interval
-    histograms}{of the spike trains shown in \figref{rasterexamplesfig}.}
+  \includegraphics[width=0.96\textwidth]{isihexamples}
+  \titlecaption{\label{isihexamplesfig}Interspike-interval
+    histograms}{of the spike trains shown in
+    \figref{rasterexamplesfig}.  The intervals of the homogeneous
+    Poisson process (left) are exponentially distributed according to
+    \eqnref{hompoissonexponential} (red). Typically for many sensory
+    neurons that fire more regularly is an ISI histogram like the one
+    shown on the right. There is a preferred interval where the
+    distribution peaks. The distribution falls off quickly towards
+    smaller intervals, and very small intervals are absent, probably
+    because of refractoriness of the spike generator. The tail of the
+    distribution again approaches an exponential distribution as for
+    the Poisson process.}
 \end{figure}
 
 \begin{exercise}{isis.m}{}
@@ -104,49 +190,102 @@ real-valued variables:
   trial are stored as vectors within a cell-array.
 \end{exercise}
 
-%\subsection{First order interval statistics}
 \begin{itemize}
 \item Probability density $p(T)$ of the intervals $T$
-  (\figref{isihexamplesfig}). Normalized to $\int_0^{\infty} p(T) \; dT
-  = 1$.
-\item Average interval: $\mu_{ISI} = \langle T \rangle =
-  \frac{1}{n}\sum\limits_{i=1}^n T_i$.
-\item Standard deviation of the interspike intervals: $\sigma_{ISI} = \sqrt{\langle (T - \langle T
-    \rangle)^2 \rangle}$\vspace{1ex}
-\item \entermde[coefficient of variation]{Variationskoeffizient}{Coefficient of variation}:
-  $CV_{ISI} = \frac{\sigma_{ISI}}{\mu_{ISI}}$.
-\item \entermde[diffusion coefficient]{Diffusionskoeffizient}{Diffusion coefficient}: $D_{ISI} =
-  \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$.
+  (\figref{isihexamplesfig}). Normalized to $\int_0^{\infty} p(T) \;
+  dT = 1$. Commonly referred to as the \enterm[interspike
+  interval!histogram]{interspike interval histogram}. Its shape
+  reveals many interesting aspects like locking or bursting that
+  cannot be inferred from the mean or standard deviation. A particular
+  reference is the exponential distribution of intervals
+  \begin{equation}
+    \label{hompoissonexponential}
+    p_{exp}(T) = \lambda e^{-\lambda T}
+  \end{equation}
+  of a homogeneous Poisson spike train with rate $\lambda$.
+\item Mean interval
+  \begin{equation}
+    \label{meanisi}
+    \mu_{ISI} = \langle T \rangle = \frac{1}{n}\sum_{i=1}^n T_i
+  \end{equation}
+  The average time it takes from one event to the next. For stationary
+  point processes the inverse of the mean interval is identical with
+  the mean rate $\lambda$ (number of events per time, see below) of
+  the process.
+\item Standard deviation of intervals
+  \begin{equation}
+    \label{stdisi}
+    \sigma_{ISI} = \sqrt{\langle (T - \langle T \rangle)^2 \rangle}
+  \end{equation}
+  Periodically spiking neurons have little variability in their
+  intervals, whereas many cortical neurons cover a wide range with
+  their intervals. The standard deviation of homogeneous Poisson spike
+  trains also equals the inverse rate.  Whether the standard deviation
+  of intervals is low or high, however, is better quantified by the
+\item \entermde[coefficient of
+  variation]{Variationskoeffizient}{Coefficient of variation}, the
+  standard deviation of the ISIs relative to their mean:
+  \begin{equation}
+    \label{cvisi}
+    CV_{ISI} = \frac{\sigma_{ISI}}{\mu_{ISI}}
+  \end{equation}
+  Homogeneous Poisson spike trains have an $CV$ of exactly one. The
+  lower the $CV$ the more regularly a neuron is firing. $CV$s larger than
+  one are also possible in spike trains with small intervals separated
+  by really long ones.
+%\item \entermde[diffusion coefficient]{Diffusionskoeffizient}{Diffusion coefficient}: $D_{ISI} =
+%  \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$.
 \end{itemize}
 
 \begin{exercise}{isihist.m}{}
-  Implement a function \varcode{isiHist()} that calculates the normalized
-  interspike interval histogram. The function should take two input
-  arguments; (i) a vector of interspike intervals and (ii) the width
-  of the bins used for the histogram. It further returns the
+  Implement a function \varcode{isihist()} that calculates the
+  normalized interspike interval histogram. The function should take a
+  vector of interspike intervals and the width of the bins to be used
+  for the histogram as input arguments. The function returns the
   probability density as well as the centers of the bins.
 \end{exercise}
 
 \begin{exercise}{plotisihist.m}{}
-  Implement a function that takes the return values of
-  \varcode{isiHist()} as input arguments and then plots the data. The
-  plot should show the histogram with the x-axis scaled to
-  milliseconds and should be annotated with the average ISI, the
-  standard deviation and the coefficient of variation.
+  Implement a function that uses the \varcode{isihist()} function from
+  the previous exercise to plot an ISI histogram. The plot shows the
+  histogram with the x-axis scaled to milliseconds, annotated with the
+  average ISI, the standard deviation, and the coefficient of
+  variation of the ISIs (\figref{isihexamplesfig}).
 \end{exercise}
 
 \subsection{Interval correlations}
-So called \entermde[return map]{return map}{return maps} are used to
-illustrate interdependencies between successive interspike
-intervals. The return map plots the delayed interval $T_{i+k}$ against
-the interval $T_i$. The parameter $k$ is called the \enterm{lag}
-(\determ{Verz\"ogerung}) $k$. Stationary and non-stationary return
-maps are distinctly different \figref{returnmapfig}.
+Intervals are not just numbers without an order, like weights of
+tigers.  Intervals are temporally ordered and there could be temporal
+structure in the sequence of intervals. For example, short intervals
+could be followed by more longer ones, and vice versa. Such
+dependencies in the sequence of intervals do not show up in the
+interval histogram.  We need additional measures to also quantify the
+temporal structure of the sequence of intervals.
+
+We can use the same techniques we know for visualizing and quantifying
+correlations in bivariate data sets, i.e. scatter plots and
+correlation coefficients. We form $(x,y)$ data pairs by taking the
+series of intervals $T_i$ as $x$-data values and pairing them with the
+$k$-th next intervals $T_{i+k}$ as $y$-data values. The parameter $k$
+is called \enterm{lag} (\determ{Verz\"ogerung}). For lag one we pair
+each interval with the next one. A \entermde{return map}{return map}
+illustrates dependencies between successive intervals by simply
+plotting the intervals $T_{i+k}$ against the intervals $T_i$ in a
+scatter plot (\figref{returnmapfig}). For Poisson spike trains there
+is no structure beyond the one expected from the exponential
+interspike interval distribution, hinting at neighboring interspike
+intervals being independent of each other. For the spike train based
+on an Ornstein-Uhlenbeck process the return map is more clustered
+along the diagonal, hinting at a positive correlation between
+succeeding intervals. That is, short intervals are more likely to be
+followed by short ones and long intervals more likely by long
+ones. This temporal structure was already clearly visible in each
+trial of the spike raster shown in \figref{rasterexamplesfig} on the
+right.
 
-\begin{figure}[t]
-  \includegraphics[width=1\textwidth]{returnmapexamples}
+\begin{figure}[tp]
   \includegraphics[width=1\textwidth]{serialcorrexamples}
-  \titlecaption{\label{returnmapfig}Interspike interval
+  \titlecaption{\label{returnmapfig}Interspike-interval
     correlations}{of the spike trains shown in
     \figref{rasterexamplesfig}. Upper panels show the return maps and
     lower panels the serial correlations of successive intervals
@@ -160,170 +299,178 @@ maps are distinctly different \figref{returnmapfig}.
     the mean firing rate of the spike trains.}
 \end{figure}
 
-Such dependencies can be further quantified using the
+Such dependencies can be further quantified by
 \entermde[correlation!serial]{Korrelation!serielle}{serial
-  correlations} \figref{returnmapfig}. The serial correlation is the
-correlation coefficient of the intervals $T_i$ and the intervals
-delayed by the lag $T_{i+k}$:
-\[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} 
-= {\rm corr}(T_{i+k}, T_i) \] The resulting correlation coefficient
-$\rho_k$ is usually plotted against the lag $k$
-\figref{returnmapfig}. $\rho_0=1$ is the correlation of each interval
-with itself and is always 1.
+  correlations}. These quantify the correlations between successive
+intervals by Pearson's correlation coefficients between the intervals
+$T_{i+k}$ and $T_i$ in dependence on lag $k$:
+\begin{equation}
+  \label{serialcorrelation}
+  \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} 
+= {\rm corr}(T_{i+k}, T_i)
+\end{equation}
+The serial correlations $\rho_k$ are usually plotted against the lag
+$k$ for a small range of lags (\figref{returnmapfig}). $\rho_0=1$ is
+the correlation of each interval with itself and always equals one.
+
+If the serial correlations all equal zero, $\rho_k =0$ for $k>0$, then
+the length of an interval is independent of all the previous
+ones. Such a process is a \enterm{renewal process}
+(\determ{Erneuerungsprozess}). Each event, each action potential,
+erases the history. The occurrence of the next event is independent of
+what happened before. To a first approximation an action potential
+erases all information about the past from the membrane voltage and
+thus spike trains may approximate renewal processes.
+
+However, other variables like the intracellular calcium concentration
+or the states of slowly switching ion channels may carry information
+from one interspike interval to the next and thus introduce
+correlations between intervals. Such non-renewal dynamics is then
+characterized by the non-zero serial correlations
+(\figref{returnmapfig}).
 
 \begin{exercise}{isiserialcorr.m}{}
-  Implement a function \varcode{isiserialcorr()} that takes a vector of
-  interspike intervals as input argument and calculates the serial
-  correlation. The function should further plot the serial
-  correlation.
+  Implement a function \varcode{isiserialcorr()} that takes a vector
+  of interspike intervals as input and computes serial correlations up
+  to some maximum lag.
 \end{exercise}
 
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Count statistics}
-
-% \begin{figure}[t]
-%   \includegraphics[width=0.48\textwidth]{poissoncounthist100hz10ms}\hfill
-%   \includegraphics[width=0.48\textwidth]{poissoncounthist100hz100ms}
-%   \titlecaption{\label{countstatsfig}Count Statistik.}{}
-% \end{figure}
-Counting the number of events $n_i$ (counts) in time windows $i$ of duration $W$
-yields positive integer random numbers that are commonly quantified
-using the following measures:
-\begin{itemize}
-\item Histogram of the counts $n_i$.
-\item Average number of counts: $\mu_n = \langle n \rangle$.
-\item Variance of counts: $\sigma_n^2 = \langle (n - \langle n \rangle)^2 \rangle$.
-\item \entermde{Fano Faktor}{Fano factor} (variance of counts divided by average count): $F = \frac{\sigma_n^2}{\mu_n}$.
-\end{itemize}
-Of particular interest is the \enterm[firing rate!average]{average
-  firing rate} $r$ (spike count per time interval, \determ{Feuerrate})
-that is given in Hertz
-\begin{equation}
-  \label{firingrate}
-  r = \frac{\langle n \rangle}{W} \; .
-\end{equation}
-
-% \begin{figure}[t]
-%   \begin{minipage}[t]{0.49\textwidth}
-%     Poisson process $\lambda=100$\,Hz:\\
-%     \includegraphics[width=1\textwidth]{poissonfano100hz}
-%   \end{minipage}
-%   \hfill
-%   \begin{minipage}[t]{0.49\textwidth}
-%     LIF $I=10$, $\tau_{adapt}=100$\,ms:\\
-%     \includegraphics[width=1\textwidth]{lifadaptfano10-100ms}
-%   \end{minipage}
-%   \titlecaption{\label{fanofig}Fano factor.}{}
-% \end{figure}
-
-\begin{exercise}{counthist.m}{}
-  Implement a function \varcode{counthist()} that calculates and plots
-  the distribution of spike counts observed in a certain time
-  window. The function should take two input arguments: (i) a
-  cell-array of vectors containing the spike times in seconds observed
-  in a number of trials, and (ii) the duration of the time window that
-  is used to evaluate the counts.
+\begin{exercise}{plotisiserialcorr.m}{}
+  Implement a function \varcode{plotisiserialcorr()} that takes a
+  vector of interspike intervals as input argument and generates a
+  plot of the serial correlations.
 \end{exercise}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Homogeneous Poisson process}
-
-The Gaussian distribution is, because of the central limit theorem,
-the standard distribution for continuous measures. The equivalent in
-the realm of point processes is the
-\entermde[distribution!Poisson]{Verteilung!Poisson-}{Poisson distribution}.
-
-In a \entermde[Poisson process!homogeneous]{Poissonprozess!homogener}{homogeneous Poisson
-  process} the events occur at a fixed rate $\lambda=\text{const}$ and
-are independent of both the time $t$ and occurrence of previous events
-(\figref{hompoissonfig}). The probability of observing an event within
-a small time window of width $\Delta t$ is given by
-\begin{equation}
-  \label{hompoissonprob}
-  P = \lambda \cdot \Delta t \; .
-\end{equation}
-
-In an \entermde[Poisson process!inhomogeneous]{Poissonprozess!inhomogener}{inhomogeneous Poisson
-  process}, however, the rate $\lambda$ depends on time: $\lambda =
-\lambda(t)$.
-
-\begin{exercise}{poissonspikes.m}{}
-  Implement a function \varcode{poissonspikes()} that uses a homogeneous
-  Poisson process to generate events at a given rate for a certain
-  duration and a number of trials. The rate should be given in Hertz
-  and the duration of the trials is given in seconds. The function
-  should return the event times in a cell-array. Each entry in this
-  array represents the events observed in one trial. Apply
-  \eqnref{hompoissonprob} to generate the event times.
-\end{exercise}
-
-\begin{figure}[t]
-  \includegraphics[width=1\textwidth]{poissonraster100hz}
-  \titlecaption{\label{hompoissonfig}Rasterplot of spikes of a
-    homogeneous Poisson process with a rate $\lambda=100$\,Hz.}{}
-\end{figure}
+\section{Count statistics}
 
 \begin{figure}[t]
-  \includegraphics[width=0.45\textwidth]{poissonisihexp20hz}\hfill
-  \includegraphics[width=0.45\textwidth]{poissonisihexp100hz}
-  \titlecaption{\label{hompoissonisihfig}Distribution of interspike
-    intervals of two Poisson processes.}{The processes differ in their
-    rate (left: $\lambda=20$\,Hz, right: $\lambda=100$\,Hz). The red
-    lines indicate the corresponding exponential interval distribution
-    \eqnref{poissonintervals}.}
+  \includegraphics{countexamples}
+  \titlecaption{\label{countstatsfig}Count statistics.}{Probability
+    distributions of counting $k$ events $k$ (blue) within windows of
+    20\,ms (left) or 200\,ms duration (right) of a homogeneous Poisson
+    spike train with a rate of 20\,Hz
+    (\figref{rasterexamplesfig}). For Poisson spike trains these
+    distributions are given by Poisson distributions (red).}
 \end{figure}
 
-The homogeneous Poisson process has the following properties:
+The most commonly used measure for characterizing spike trains is the
+\enterm[firing rate!average]{average firing rate}. The firing rate $r$
+is the average number of spikes counted within some time interval $W$
+\begin{equation}
+  \label{firingrate}
+  r = \frac{\langle n \rangle}{W}
+\end{equation}
+and is measured in Hertz. The average of the spike counts is taken
+over trials. For stationary spike trains (no change in statistics, in
+particular the firing rate, over time), the firing rate based on the
+spike count equals the inverse average interspike interval
+$1/\mu_{ISI}$.
+
+The firing rate based on an averaged spike counts is one example of
+many statistics based on event counts. Stationary spike trains can be
+split into many segments $i$, each of duration $W$, and the number of
+events $n_i$ in each of the segments can be counted. The integer event
+counts can be quantified in the usual ways:
 \begin{itemize}
-\item Intervals $T$ are exponentially distributed (\figref{hompoissonisihfig}):
+\item Histogram of the counts $n_i$ appropriately normalized to
+  probability distributions. For homogeneous Poisson spike trains with
+  rate $\lambda$ the resulting probability distributions follow a
+  Poisson distribution (\figref{countstatsfig}), where the probability
+  of counting $k$ events within a time window $W$ is given by
   \begin{equation}
-    \label{poissonintervals}
-    p(T) = \lambda e^{-\lambda T} \; .
+    \label{poissondist}
+    P(k) = \frac{(\lambda W)^k e^{\lambda W}}{k!}
   \end{equation}
-\item The average interval is $\mu_{ISI} = \frac{1}{\lambda}$ .
-\item The variance of the intervals is $\sigma_{ISI}^2 = \frac{1}{\lambda^2}$ .
-\item Thus, the coefficient of variation is always $CV_{ISI} = 1$ .
-\item The serial correlation is $\rho_k =0$ for $k>0$, since the
-  occurrence of an event is independent of all previous events. Such a
-  process is also called a \enterm{renewal process} (\determ{Erneuerungsprozess}).
-\item The number of events $k$ within a temporal window of duration
-  $W$ is Poisson distributed:
-\begin{equation}
-  \label{poissoncounts}
-  P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!}
-\end{equation}
-(\figref{hompoissoncountfig})
-\item The Fano Faktor is always $F=1$ .
+\item Average number of counts: $\mu_n = \langle n \rangle$.
+\item Variance of counts:
+  $\sigma_n^2 = \langle (n - \langle n \rangle)^2 \rangle$.
+\end{itemize}
+Because spike counts are unitless and positive numbers the
+\begin{itemize}
+\item \entermde{Fano Faktor}{Fano factor} (variance of counts divided
+  by average count)
+  \begin{equation}
+    \label{fano}
+    F = \frac{\sigma_n^2}{\mu_n}
+  \end{equation}
+  is a commonly used measure for quantifying the variability of event
+  counts relative to the mean number of events. In particular for
+  homogeneous Poisson processes the Fano factor equals exactly one and
+  is independent of the time window $W$.
 \end{itemize}
-
-\begin{exercise}{hompoissonspikes.m}{}
-  Implement a function \varcode{hompoissonspikes()} that uses a
-  homogeneous Poisson process to generate spike events at a given rate
-  for a certain duration and a number of trials. The rate should be
-  given in Hertz and the duration of the trials is given in
-  seconds. The function should return the event times in a
-  cell-array. Each entry in this array represents the events observed
-  in one trial. Apply \eqnref{poissonintervals} to generate the event
-  times.
-\end{exercise}
 
 \begin{figure}[t]
-  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}\hfill
-  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}
-  \titlecaption{\label{hompoissoncountfig}Distribution of counts of a
-    Poisson spiketrain.}{The count statistics was generated for two
-    different windows of width $W=10$\,ms (left) and width $W=100$\,ms
-    (right).  The red line illustrates the corresponding Poisson
-    distribution \eqnref{poissoncounts}.}
+  \includegraphics{fanoexamples}
+  \titlecaption{\label{fanofig} Fano factor.}{Counting events in time
+    windows of given duration and then dividing the variance of the
+    counts by their mean results in the Fano factor. Here, the Fano
+    factor is plotted as a function of the duration of the window used
+    to count events.  For Poisson spike trains the variance always
+    equals the mean counts and consequently the Fano factor equals one
+    irrespective of the count window (left). A spike train with
+    positive correlations between interspike intervals (caused by an
+    Ornstein-Uhlenbeck process) has a minimum in the Fano factor, that
+    is an analysis window for which the relative count variance is
+    minimal somewhere close to the correlation time scale of the
+    interspike intervals (right).}
 \end{figure}
 
+Note that all of these statistics depend in general on the chosen
+window length $W$ used for counting the events. The average spike
+count, for example, grows linearly with $W$ for sufficiently large
+time windows: $\langle n \rangle = r W$, \eqnref{firingrate}. Doubling
+the counting window doubles the spike count. As does the spike-count
+variance (\figref{fanofig}). At smaller time windows the statistics of
+the event counts might depend on the particular duration of the
+counting window. There could be an optimal time window for which the
+variance of the spike count is minimal. The Fano factor plotted as a
+function of the time window illustrates such properties of point
+processes in a single graph (\figref{fanofig}).
+
+This also has consequences for information transmission in neural
+systems. The membrane time constant of a post-synaptic neuron defines
+a counting window. If input spikes arrive within about one time
+constant they are added up. The lower the variance in spike count
+relative to the averaged count, the higher the signal-to-noise ratio
+at which information encoded in the mean spike count is
+transmitted. If the membrane time constant of the target neuron
+matches the counting window where the Fano factor is minimal, then
+information is potentially transmitted at the highest possible
+signal-to-noise ratio. For this reason, the Fano factor is used in the
+Neurosciences to quantify and analyze reliability of neuronal
+responses.
+
+\begin{exercise}{counts.m}{}
+  Write a function \varcode{counts()} that counts the number of spikes
+  in windows of given duration and returns the counts in a single
+  vector. Spike times are passed as a cell-array of vectors,
+  containing the spike times in seconds observed in a number of
+  trials, to the function.
+\end{exercise}
+
+\begin{exercise}{plotcounthist.m}{}
+  Write a function that takes a cell-array with spike times as input
+  and plots a normalized histogram of the spike counts counted in
+  windows of a given duration.
+\end{exercise}
+
+\begin{exercise}{plotfanofactor.m}{}
+  Write a function that takes a cell-array with spike times as input
+  and plots in one plot count variances a function of the
+  corresponding mean counts and in a second plot the Fano factor as a
+  function of the duration of the count window in logarithmic
+  scale. Two arguments of the function take the minimum and maximum
+  duration of the count window.
+\end{exercise}
+
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Time-dependent firing rate}
+\label{nonstationarysec}
 
-So far we have discussed stationary spiketrains. The statistical properties
+So far we have discussed stationary spike trains. The statistical properties
 of these did not change within the observation time (stationary point
 processes). Most commonly, however, this is not the case. A sensory
 neuron, for example, might respond to a stimulus by modulating its
@@ -350,7 +497,7 @@ justifications, their pros- and cons.
 
 \begin{figure}[tp]
   \includegraphics[width=\columnwidth]{isimethod}
-  \titlecaption{Instantaneous firing rate.}{The recorded spiketrain
+  \titlecaption{Instantaneous firing rate.}{The recorded spike train
     (top).  Arrows illustrate the interspike intervals and numbers
     give the intervals in milliseconds. The inverse of the interspike
     intervals is the \emph{instantaneous firing rate}
@@ -411,7 +558,7 @@ methods make an assumption about the relevant observation time-scale
 \begin{figure}[tp]
   \includegraphics[width=\columnwidth]{binmethod}
   \titlecaption{Estimating the PSTH using the binning method.}{The
-    same spiketrain as shown in \figref{instratefig} (top). Vertical
+    same spike train as shown in \figref{instratefig} (top). Vertical
     gray lines indicate the borders between adjacent bins in which the
     number of action potentials is counted (red numbers). The firing
     rate is then the histogram normalized to the binwidth
@@ -447,8 +594,8 @@ time-scale.
 \begin{figure}[tp]
   \includegraphics[width=\columnwidth]{convmethod}
   \titlecaption{Estimating the firing rate using the convolution
-    method.}{The same spiketrain as in \figref{instratefig} (top). The
-    convolution of the spiketrain with a kernel replaces each spike
+    method.}{The same spike train as in \figref{instratefig} (top). The
+    convolution of the spike train with a kernel replaces each spike
     event with the kernel (red). A Gaussian kernel is used here, but
     other kernels are also possible. If the kernel is properly
     normalized the firing rate results directly form the superposition
@@ -489,6 +636,7 @@ relevate time-scale.
 \end{exercise}
 
 \section{Spike-triggered Average}
+\label{stasec}
 
 The graphical representation of the neuronal activity alone is not
 sufficient tot investigate the relation between the neuronal response
diff --git a/pointprocesses/lecture/pointprocesses_de.tex b/pointprocesses/lecture/pointprocesses_de.tex
index 4b0850f..60c0f80 100644
--- a/pointprocesses/lecture/pointprocesses_de.tex
+++ b/pointprocesses/lecture/pointprocesses_de.tex
@@ -56,7 +56,7 @@ erzeugt. Zum Beispiel:
 \end{ibox}
 
 \begin{figure}[t]
-  \texpicture{pointprocessscetch}
+  \includegraphics{pointprocesssketch}
   \titlecaption{\label{pointprocessscetchfig} Statistik von
     Punktprozessen.}{Ein Punktprozess ist eine Abfolge von
     Zeitpunkten $t_i$ die auch durch die Intervalle $T_i=t_{i+1}-t_i$
diff --git a/pointprocesses/lecture/pointprocessscetch.gpt b/pointprocesses/lecture/pointprocessscetch.gpt
deleted file mode 100644
index d045938..0000000
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+++ /dev/null
@@ -1,205 +0,0 @@
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diff --git a/pointprocesses/lecture/pointprocessscetchA.eps b/pointprocesses/lecture/pointprocessscetchA.eps
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diff --git a/pointprocesses/lecture/pointprocessscetchB.gpt b/pointprocesses/lecture/pointprocessscetchB.gpt
deleted file mode 100644
index e1cda52..0000000
--- a/pointprocesses/lecture/pointprocessscetchB.gpt
+++ /dev/null
@@ -1,204 +0,0 @@
-set out 'pointprocessscetchB.tex'
-
-set border 0
-set lmarg 4
-set rmarg 1
-set tmarg 1
-set bmarg 1
-set xrange [0:11]
-unset xtics
-set yrange [-0.7:1.8]
-unset ytics
-set label 1 at graph -0.07, graph 1.1
-
-set multiplot
-
-set origin 0, 0.7
-set size 1, 0.3
-set label 1 '\normalsize Event times $\{t_i\}$'
-set arrow 1 from 0, 0.5 to 11, 0.5 head filled
-set label 2 'Time' at 11, -0.3 center
-set label 3 "$t_{1}$" at 0.724649, -0.6 center
-set label 4 "$t_{2}$" at 1.67586, -0.6 center
-set label 5 "$t_{3}$" at 3.02389, -0.6 center
-set label 6 "$t_{4}$" at 3.57466, -0.6 center
-set label 7 "$t_{5}$" at 4.15121, -0.6 center
-set label 8 "$t_{6}$" at 5.00412, -0.6 center
-set label 9 "$t_{7}$" at 6.64549, -0.6 center
-set label 10 "$t_{8}$" at 7.81657, -0.6 center
-set label 11 "$t_{9}$" at 9.77964, -0.6 center
-plot '-' w l lt 1 lc rgb 'black' lw 10
-0.724649 0
-0.724649 1
-
-1.67586 0
-1.67586 1
-
-3.02389 0
-3.02389 1
-
-3.57466 0
-3.57466 1
-
-4.15121 0
-4.15121 1
-
-5.00412 0
-5.00412 1
-
-6.64549 0
-6.64549 1
-
-7.81657 0
-7.81657 1
-
-9.77964 0
-9.77964 1
-e
-unset label 3
-unset label 4
-unset label 5
-unset label 6
-unset label 7
-unset label 8
-unset label 9
-unset label 10
-unset label 11
-
-set origin 0, 0.4
-set label 1 '\normalsize Intervals $\{T_i\}, \; T_i = t_{i+1} - t_i$'
-set label 3 "$T_{1}$" at 1.20025, -0.5 center
-set arrow 3 from 0.724649, 0.2 to 1.67586, 0.2 heads
-set label 4 "$T_{2}$" at 2.34987, -0.5 center
-set arrow 4 from 1.67586, 0.2 to 3.02389, 0.2 heads
-set label 5 "$T_{3}$" at 3.29927, -0.5 center
-set arrow 5 from 3.02389, 0.2 to 3.57466, 0.2 heads
-set label 6 "$T_{4}$" at 3.86293, -0.5 center
-set arrow 6 from 3.57466, 0.2 to 4.15121, 0.2 heads
-set label 7 "$T_{5}$" at 4.57767, -0.5 center
-set arrow 7 from 4.15121, 0.2 to 5.00412, 0.2 heads
-set label 8 "$T_{6}$" at 5.82481, -0.5 center
-set arrow 8 from 5.00412, 0.2 to 6.64549, 0.2 heads
-set label 9 "$T_{7}$" at 7.23103, -0.5 center
-set arrow 9 from 6.64549, 0.2 to 7.81657, 0.2 heads
-set label 10 "$T_{8}$" at 8.79811, -0.5 center
-set arrow 10 from 7.81657, 0.2 to 9.77964, 0.2 heads
-plot '-' w l lt 1 lc rgb 'black' lw 10
-0.724649 0
-0.724649 1
-
-1.67586 0
-1.67586 1
-
-3.02389 0
-3.02389 1
-
-3.57466 0
-3.57466 1
-
-4.15121 0
-4.15121 1
-
-5.00412 0
-5.00412 1
-
-6.64549 0
-6.64549 1
-
-7.81657 0
-7.81657 1
-
-9.77964 0
-9.77964 1
-e
-unset label 3
-unset label 4
-unset label 5
-unset label 6
-unset label 7
-unset label 8
-unset label 9
-unset label 10
-unset arrow 3
-unset arrow 4
-unset arrow 5
-unset arrow 6
-unset arrow 7
-unset arrow 8
-unset arrow 9
-unset arrow 10
-
-set origin 0, 0
-set size 1, 0.4
-set border 2
-set yrange [-0.5:10.5]
-set ytics 2 nomirror out
-set arrow 1 from 0, 0.0 to 11, 0.0 head filled
-set label 2 'Time' at 11, -2.2 center
-set label 1 '\normalsize Event counts $\{ n_i \}$' at graph -0.07, graph 1.2
-plot '-' w l lt 1 lc rgb 'black' lw 3, \
-     '-' w p lt 1 lc rgb 'black' pt 7 ps 1.5 lw 2, \
-     '-' w p lt 1 lc rgb 'white' pt 7 ps 1.0 lw 2, \
-     '-' w p lt 1 lc rgb 'black' pt 7 ps 1.5 lw 2
-0 0
-0.724649 0
-
-0.724649 1
-1.67586 1
-
-1.67586 2
-3.02389 2
-
-3.02389 3
-3.57466 3
-
-3.57466 4
-4.15121 4
-
-4.15121 5
-5.00412 5
-
-5.00412 6
-6.64549 6
-
-6.64549 7
-7.81657 7
-
-7.81657 8
-9.77964 8
-
-9.77964 9
-10.7899 9
-e
-0.724649 0
-1.67586 1
-3.02389 2
-3.57466 3
-4.15121 4
-5.00412 5
-6.64549 6
-7.81657 7
-9.77964 8
-e
-0.724649 0
-1.67586 1
-3.02389 2
-3.57466 3
-4.15121 4
-5.00412 5
-6.64549 6
-7.81657 7
-9.77964 8
-e
-0.724649 1
-1.67586 2
-3.02389 3
-3.57466 4
-4.15121 5
-5.00412 6
-6.64549 7
-7.81657 8
-9.77964 9
-e
-
-unset multiplot
diff --git a/pointprocesses/lecture/pointprocessscetchB.pdf b/pointprocesses/lecture/pointprocessscetchB.pdf
deleted file mode 100644
index 2055658..0000000
Binary files a/pointprocesses/lecture/pointprocessscetchB.pdf and /dev/null differ
diff --git a/pointprocesses/lecture/pointprocesssketch.py b/pointprocesses/lecture/pointprocesssketch.py
new file mode 100644
index 0000000..fad7f5c
--- /dev/null
+++ b/pointprocesses/lecture/pointprocesssketch.py
@@ -0,0 +1,69 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
+
+
+spikes = [0.724649, 1.67586, 3.02389, 3.57466, 4.15121, 5.00412, 6.64549, 7.81657, 9.77964]
+
+
+def plot_spikes(ax, spikes):
+    ax.show_spines('')
+    ax.text(-0.06, 1.1, r'Event times $\{t_i\}$', transform=ax.transAxes)
+    ax.annotate('', xy=(0.0, 0.5), xytext=(11.0, 0.5), zorder=-10,
+                arrowprops=dict(arrowstyle='<-'))
+    ax.text(10.5, -0.2, 'Time')
+    for i, t in enumerate(spikes):
+        ax.plot([t, t], [0.0, 1.0], **lsA)
+        ax.text(t, -0.6, '$t_{%d}$' % i, ha='center')
+    ax.set_xlim(0.0, 11.0)
+    ax.set_ylim(-0.7, 1.2)
+    
+
+def plot_isis(ax, spikes):
+    ax.show_spines('')
+    ax.text(-0.06, 1.1, r'Intervals $\{T_i\}, \; T_i = t_{i+1} - t_i$', transform=ax.transAxes)
+    ax.annotate('', xy=(0.0, 0.5), xytext=(11.0, 0.5), zorder=-10,
+                arrowprops=dict(arrowstyle='<-'))
+    ax.text(10.5, -0.2, 'Time')
+    for i in range(len(spikes)):
+        t1 = spikes[i]
+        ax.plot([t1, t1], [0., 1.], **lsA)
+        if i > 0:
+            t0 = spikes[i-1]
+            ax.annotate('', xy=(t0, 0.2), xytext=(t1, 0.2), arrowprops=dict(arrowstyle='<->'))
+            ax.text(0.5*(t0+t1), -0.6, '$T_{%d}$' % i, ha='center')
+    ax.set_xlim(0.0, 11.0)
+    ax.set_ylim(-0.7, 1.2)
+
+        
+def plot_counts(ax, spikes):
+    ax.show_spines('l')
+    ax.text(-0.06, 1.1, r'Event counts $\{ n_i \}$', transform=ax.transAxes)
+    ax.annotate('', xy=(0.0, 0.0), xytext=(11.0, 0.0), zorder=-10,
+                arrowprops=dict(arrowstyle='<-'))
+    ax.text(10.5, -2.0, 'Time')
+    c = 0
+    t0 = 0.0
+    for i, t in enumerate(spikes):
+        ax.plot([t0, t], [c, c], zorder=10, clip_on=False, **lsBm)
+        ax.plot([t], [c], zorder=20, clip_on=False, **psBo)
+        c += 1
+        ax.plot([t], [c], zorder=30, clip_on=False, **psB)
+        t0 = t
+    ax.plot([t0, 10.5], [c, c], zorder=10, **lsBm)
+    ax.set_xlim(0.0, 11.0)
+    ax.set_ylim(0, 11)
+    ax.set_yticks(np.arange(0.0, 11.0, 2.0))
+
+
+if __name__ == "__main__":
+    fig = plt.figure(figsize=cm_size(figure_width, 1.7*figure_height))
+    gs = gridspec.GridSpec(3, 1, height_ratios=[2, 2, 4])
+    gs.update(hspace=0.6, **adjust_fs(fig, bottom=1.5, top=1.5, left=3.0, right=1.5))
+    ax1, ax2, ax3 = [fig.add_subplot(gs[i,0]) for i in range(3)]
+    plot_spikes(ax1, spikes)
+    plot_isis(ax2, spikes)
+    plot_counts(ax3, spikes)
+    plt.savefig('pointprocesssketch.pdf')
+    plt.close()
diff --git a/pointprocesses/lecture/raster.pdf b/pointprocesses/lecture/raster.pdf
deleted file mode 100644
index a20fa0e..0000000
Binary files a/pointprocesses/lecture/raster.pdf and /dev/null differ
diff --git a/pointprocesses/lecture/rasterexamples.py b/pointprocesses/lecture/rasterexamples.py
index 042bda2..7a9f4f0 100644
--- a/pointprocesses/lecture/rasterexamples.py
+++ b/pointprocesses/lecture/rasterexamples.py
@@ -1,5 +1,15 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
+
+
+rate = 20.0
+trials = 10
+duration = 2.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
 
 def hompoisson(rate, trials, duration) :
     spikes = []
@@ -8,17 +18,18 @@ def hompoisson(rate, trials, duration) :
         t = 0.0
         while t < duration :
             t += np.random.exponential(1/rate)
-            times.append( t )
-        spikes.append( times )
+            times.append(t)
+        spikes.append(times)
     return spikes
 
+
 def inhompoisson(rate, trials, dt) :
     spikes = []
     p = rate*dt
     for k in range(trials) :
         x = np.random.rand(len(rate))
         times = dt*np.nonzero(x<p)[0]
-        spikes.append( times )
+        spikes.append(times)
     return spikes
 
 
@@ -36,51 +47,47 @@ def pifspikes(input, trials, dt, D=0.1) :
             if v >= vthresh :
                 v = vreset
                 times.append(k*dt)
-        spikes.append( times )
+        spikes.append(times)
     return spikes
 
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 2.0
-dt = 0.001
-tau = 0.1;
 
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
-
-# OU noise:
-rng = np.random.RandomState(54637281)
-time = np.arange(0.0, duration, dt)
-x = np.zeros(time.shape)+rate
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
-for k in range(1,len(x)) :
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
+    # OU noise:
+    rng = np.random.RandomState(54637281)
+    time = np.arange(0.0, duration, dt)
+    x = np.zeros(time.shape)+rate
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
+        x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    x[x<0.0] = 0.0
+    spikes = pifspikes(x, trials, dt, D)
+    return spikes
 
-# inhomogeneous spike trains:
-#inhspikes = inhompoisson(x, trials, dt)
-# pif spike trains:
-inhspikes = pifspikes(x, trials, dt, D=0.3)
+    
+def plot_homogeneous_spikes(ax):
+    homspikes = hompoisson(rate, trials, duration)
+    ax.set_title('stationary')
+    ax.set_xlim(0.0, duration)
+    ax.set_ylim(-0.5, trials-0.5)
+    ax.set_xlabel('Time [s]')
+    ax.set_ylabel('Trial')
+    ax.eventplot(homspikes, colors=[lsA['color']], linelength=0.8, lw=1)
 
-fig = plt.figure( figsize=(9,4) )
-ax = fig.add_subplot(1, 2, 1)
-ax.set_title('stationary')
-ax.set_xlim(0.0, duration)
-ax.set_ylim(-0.5, trials-0.5)
-ax.set_xlabel('Time [s]')
-ax.set_ylabel('Trials')
-ax.eventplot(homspikes, colors=[[0, 0, 0]], linelength=0.8)
+    
+def plot_inhomogeneous_spikes(ax):
+    inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
+    ax.set_title('non-stationary')
+    ax.set_xlim(0.0, duration)
+    ax.set_ylim(-0.5, trials-0.5)
+    ax.set_xlabel('Time [s]')
+    ax.set_ylabel('Trial')
+    ax.eventplot(inhspikes, colors=[lsA['color']], linelength=0.8, lw=1)
 
-ax = fig.add_subplot(1, 2, 2)
-ax.set_title('non-stationary')
-ax.set_xlim(0.0, duration)
-ax.set_ylim(-0.5, trials-0.5)
-ax.set_xlabel('Time [s]')
-ax.set_ylabel('Trials')
-ax.eventplot(inhspikes, colors=[[0, 0, 0]], linelength=0.8)
 
-plt.tight_layout()
-plt.savefig('rasterexamples.pdf')
-plt.close()
+if __name__ == "__main__":
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(**adjust_fs(fig, left=4.0, right=1.0, top=1.2))
+    plot_homogeneous_spikes(ax1)
+    plot_inhomogeneous_spikes(ax2)
+    plt.savefig('rasterexamples.pdf')
+    plt.close()
diff --git a/pointprocesses/lecture/returnmapexamples.py b/pointprocesses/lecture/returnmapexamples.py
deleted file mode 100644
index db9326d..0000000
--- a/pointprocesses/lecture/returnmapexamples.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from plotstyle import *
-
-def hompoisson(rate, trials, duration) :
-    spikes = []
-    for k in range(trials) :
-        times = []
-        t = 0.0
-        while t < duration :
-            t += np.random.exponential(1/rate)
-            times.append( t )
-        spikes.append( times )
-    return spikes
-
-def inhompoisson(rate, trials, dt) :
-    spikes = []
-    p = rate*dt
-    for k in range(trials) :
-        x = np.random.rand(len(rate))
-        times = dt*np.nonzero(x<p)[0]
-        spikes.append( times )
-    return spikes
-
-
-def pifspikes(input, trials, dt, D=0.1) :
-    vreset = 0.0
-    vthresh = 1.0
-    tau = 1.0
-    spikes = []
-    for k in range(trials) :
-        times = []
-        v = vreset
-        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
-        for k in range(len(noise)) :
-            v += (input[k]+noise[k])*dt/tau
-            if v >= vthresh :
-                v = vreset
-                times.append(k*dt)
-        spikes.append( times )
-    return spikes
-
-def isis( spikes ) :
-    isi = []
-    for k in range(len(spikes)) :
-        isi.extend(np.diff(spikes[k]))
-    return np.array( isi )
-
-def plotreturnmap(ax, isis, lag=1, max=None) :
-    ax.set_xlabel(r'ISI$_i$', 'ms')
-    ax.set_ylabel(r'ISI$_{i+1}$', 'ms')
-    if max != None :
-        ax.set_xlim(0.0, 1000.0*max)
-        ax.set_ylim(0.0, 1000.0*max)
-    ax.scatter(1000.0*isis[:-lag], 1000.0*isis[lag:], c=colors['blue'])
-
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 10.0
-dt = 0.001
-tau = 0.1;
-
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
-
-# OU noise:
-rng = np.random.RandomState(54637281)
-time = np.arange(0.0, duration, dt)
-x = np.zeros(time.shape)+rate
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
-for k in range(1,len(x)) :
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
-
-# pif spike trains:
-inhspikes = pifspikes(x, trials, dt, D=0.3)
-
-fig, (ax1, ax2) = plt.subplots(1, 2)
-fig.subplots_adjust(**adjust_fs(fig, left=6.5, top=1.5))
-ax1.set_title('stationary')
-plotreturnmap(ax1, isis(homspikes), 1, 0.3)
-
-ax2.set_title('non-stationary')
-plotreturnmap(ax2, isis(inhspikes), 1, 0.3)
-
-plt.savefig('returnmapexamples.pdf')
-plt.close()
diff --git a/pointprocesses/lecture/serialcorrexamples.json b/pointprocesses/lecture/serialcorrexamples.json
new file mode 100644
index 0000000..ee0b48e
--- /dev/null
+++ b/pointprocesses/lecture/serialcorrexamples.json
@@ -0,0 +1,398126 @@
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+        0.036000000000001364,
+        0.045000000000015916,
+        0.028999999999996362,
+        0.02600000000001046
+    ]
+}
\ No newline at end of file
diff --git a/pointprocesses/lecture/serialcorrexamples.py b/pointprocesses/lecture/serialcorrexamples.py
index d45a6be..22d2e0f 100644
--- a/pointprocesses/lecture/serialcorrexamples.py
+++ b/pointprocesses/lecture/serialcorrexamples.py
@@ -1,7 +1,19 @@
+import os
+import json
 import numpy as np
 import matplotlib.pyplot as plt
 from plotstyle import *
 
+    
+# parameter:
+rate = 20.0
+trials = 20
+duration = 500.0
+dt = 0.001
+drate = 50.0
+tau = 0.1;
+
+
 def hompoisson(rate, trials, duration) :
     spikes = []
     for k in range(trials) :
@@ -13,6 +25,7 @@ def hompoisson(rate, trials, duration) :
         spikes.append( times )
     return spikes
 
+
 def inhompoisson(rate, trials, dt) :
     spikes = []
     p = rate*dt
@@ -40,11 +53,35 @@ def pifspikes(input, trials, dt, D=0.1) :
         spikes.append( times )
     return spikes
 
+
+def oupifspikes(rate, trials, duration, dt, D, drate, tau):
+    # OU noise:
+    rng = np.random.RandomState(54637281)
+    time = np.arange(0.0, duration, dt)
+    x = np.zeros(time.shape)+rate
+    n = rng.randn(len(time))*drate*tau/np.sqrt(dt) + rate
+    for k in range(1,len(x)) :
+        x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+    x[x<0.0] = 0.0
+    spikes = pifspikes(x, trials, dt, D)
+    return spikes
+
+
 def isis( spikes ) :
     isi = []
     for k in range(len(spikes)) :
         isi.extend(np.diff(spikes[k]))
-    return np.array( isi )
+    return np.array(isi)
+
+
+def plotreturnmap(ax, isis, lag=1, max=1.0) :
+    ax.set_xlabel(r'ISI$_i$', 'ms')
+    ax.set_ylabel(r'ISI$_{i+1}$', 'ms')
+    ax.set_xlim(0.0, 1000.0*max)
+    ax.set_ylim(0.0, 1000.0*max)
+    isiss = isis[isis<max]
+    ax.plot(1000.0*isiss[:-lag], 1000.0*isiss[lag:], clip_on=False, **psAm)
+
 
 def plotserialcorr(ax, isis, maxlag=10) :
     lags = np.arange(maxlag+1)
@@ -55,39 +92,60 @@ def plotserialcorr(ax, isis, maxlag=10) :
     ax.set_ylabel(r'ISI correlation $\rho_k$')
     ax.set_xlim(0.0, maxlag)
     ax.set_ylim(-1.0, 1.0)
-    ax.plot(lags, corr, '.-', markersize=15, c=colors['blue'])
+    ax.plot([0, 10], [0.0, 0.0], **lsGrid)
+    ax.plot(lags, corr, clip_on=False, zorder=100, **lpsBm)
+
+
+def plot_hom_returnmap(ax, isis):
+    plotreturnmap(ax, isis[:200], 1, 0.3)
+    ax.set_xticks(np.arange(0.0, 301.0, 100.0))
+    ax.set_yticks(np.arange(0.0, 301.0, 100.0))
 
-# parameter:
-rate = 20.0
-drate = 50.0
-trials = 10
-duration = 500.0
-dt = 0.001
-tau = 0.1;
 
-# homogeneous spike trains:
-homspikes = hompoisson(rate, trials, duration)
+def plot_inhom_returnmap(ax, isis):
+    plotreturnmap(ax, isis[:200], 1, 0.3)
+    ax.set_ylabel('')
+    ax.set_xticks(np.arange(0.0, 301.0, 100.0))
+    ax.set_yticks(np.arange(0.0, 301.0, 100.0))
 
-# OU noise:
-rng = np.random.RandomState(54637281)
-time = np.arange(0.0, duration, dt)
-x = np.zeros(time.shape)+rate
-n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
-for k in range(1,len(x)) :
-    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
-x[x<0.0] = 0.0
 
-# pif spike trains:
-inhspikes = pifspikes(x, trials, dt, D=0.3)
+def plot_hom_serialcorr(ax, isis):
+    plotserialcorr(ax, isis)
+    ax.annotate('independent\nintervals', xy=(2.5, 0.05), xytext=(4.0, 0.4),
+                arrowprops=dict(arrowstyle="->", relpos=(0.0,0.0),
+                connectionstyle="angle3,angleA=20,angleB=70"))
+    ax.set_ylim(-0.2, 1.0)
 
-fig, (ax1, ax2) = plt.subplots(1, 2)
-fig.subplots_adjust(**adjust_fs(fig, left=7.0, right=1.0))
 
-plotserialcorr(ax1, isis(homspikes))
-ax1.set_ylim(-0.2, 1.0)
+def plot_inhom_serialcorr(ax, isis):
+    plotserialcorr(ax, isis)
+    ax.annotate('positive\ncorrelations', xy=(1.5, 0.28), xytext=(3.5, 0.5),
+                arrowprops=dict(arrowstyle="->", relpos=(0.0,0.4),
+                connectionstyle="angle3,angleA=20,angleB=60"))
+    ax.set_ylabel('')
+    ax.set_ylim(-0.2, 1.0)
 
-plotserialcorr(ax2, isis(inhspikes))
-ax2.set_ylim(-0.2, 1.0)
 
-plt.savefig('serialcorrexamples.pdf')
-plt.close()
+if __name__ == "__main__":
+    data_file = 'serialcorrexamples.json'
+    if not os.path.exists(data_file):
+        homspikes = hompoisson(rate, trials, duration)
+        inhomspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
+        homisis = isis(homspikes)
+        inhomisis = isis(inhomspikes)
+        with open(data_file, 'w') as df:
+            json.dump({'homisis': homisis.tolist(),
+                       'inhomisis': inhomisis.tolist()}, df, indent=4)
+    else:
+        with open(data_file, 'r') as sf:
+            data = json.load(sf)
+            homisis = np.array(data['homisis'])
+            inhomisis = np.array(data['inhomisis'])
+    fig, axs = plt.subplots(2, 2, figsize=cm_size(figure_width, 1.8*figure_height))
+    fig.subplots_adjust(**adjust_fs(fig, left=6.5, right=1.5))
+    plot_hom_returnmap(axs[0,0], homisis)
+    plot_inhom_returnmap(axs[0,1], inhomisis)
+    plot_hom_serialcorr(axs[1,0], homisis)
+    plot_inhom_serialcorr(axs[1,1], inhomisis)
+    plt.savefig('serialcorrexamples.pdf')
+    plt.close()
diff --git a/pointprocesses/lecture/thumbs.pdf b/pointprocesses/lecture/thumbs.pdf
deleted file mode 100644
index 61cb4e6..0000000
Binary files a/pointprocesses/lecture/thumbs.pdf and /dev/null differ
diff --git a/pointprocesses/lecture/whitestyles.gp b/pointprocesses/lecture/whitestyles.gp
deleted file mode 100644
index 1eeb255..0000000
--- a/pointprocesses/lecture/whitestyles.gp
+++ /dev/null
@@ -1,67 +0,0 @@
-set tics scale 0.8
-set tics out
-set format '$%g$'
-unset grid
-unset key
-
-# on white background:
-set style line  1 lt -1 lc rgb "black" lw 2              # border
-set border back ls 1
-set style line  2 lt 1 lc rgb "black" lw 4 pt 7 ps 2     # thick line
-set style line  3 lt 1 lc rgb "black" lw 2               # thin line
-set style line  4 lt 3 lc rgb "black" lw 1               # grid line
-
-set style line 10 lt 1 lc rgb "#00DD00" lw 6          # stimulus thick, AM
-set style line 11 lt 1 lc rgb "#00DD00" lw 3          # stimulus thin, AM
-set style line 15 lt 1 lc rgb "black" lw 15              # stimulus bar
-
-set style line 20 lt 1 lc rgb "yellow" lw 4              # thin spikes cell 1
-set style line 21 lt 1 lc rgb "orange" lw 4              # thin spikes cell 2
-set style line 22 lt 1 lc rgb "yellow" lw 6              # thick spikes cell 1
-set style line 23 lt 1 lc rgb "orange" lw 6              # thick spikes cell 2
-set style line 24 lt 1 lc rgb "blue" lw 22               # big sync spikes
-set style line 25 lt 1 lc rgb "blue" lw 6                # thick sync spikes
-set style line 26 lt 1 lc rgb "orange" lw 4              # all spikes
-
-set style line 30 lt 1 lc rgb "dark-red" lw 6 pt 7 ps 2   # firing rate 1 thick
-set style line 31 lt 1 lc rgb "red" lw 6 pt 7 ps 2        # firing rate 2 thick
-set style line 32 lt 1 lc rgb "orange" lw 6 pt 7 ps 2     # firing rate 3 thick
-set style line 33 lt 1 lc rgb "blue" lw 6 pt 7 ps 2       # firing rate 4 thick
-set style line 34 lt 1 lc rgb "gray" lw 6                 # firing rate gray thick
-set style line 35 lt 1 lc rgb "dark-red" lw 3 pt 7 ps 0.7 # firing rate 1 thin
-set style line 36 lt 1 lc rgb "red" lw 3                  # firing rate 2 thin
-set style line 37 lt 1 lc rgb "orange" lw 3               # firing rate 3 thin
-set style line 38 lt 1 lc rgb "blue" lw 3                 # firing rate 4 thin
-set style line 39 lt 1 lc rgb "gray" lw 3                 # firing rate gray thin
-
-set style line 40 lt 1 lc rgb "green" lw 6 pt 5 ps 2     # onset f-I curve thick
-set style line 41 lt 1 lc rgb "green" lw 3 pt 5 ps 2     # onset f-I curve thin
-set style line 42 lt 1 lc rgb "red" lw 6 pt 7 ps 2            # steady-state f-I curve thick
-set style line 43 lt 1 lc rgb "red" lw 3 pt 7 ps 2            # steady-state f-I curve thin
-set style line 44 lt 1 lc rgb "blue" lw 6 pt 9 ps 2           # adapted f-I curve thick
-set style line 45 lt 1 lc rgb "blue" lw 3 pt 9 ps 2           # adapted f-I curve thin
-set style line 47 lt 1 lc rgb "cyan" lw 6                     # adaptation level thick
-set style line 48 lt 1 lc rgb "cyan" lw 3                     # adaptation level thin
-set style line 49 lt 1 lc rgb "yellow" lw 3 pt 7 ps 4         # current adaptation point
-
-set style line 50 lt 1 lc rgb "dark-red" lw 4         # membrane voltage 1
-set style line 51 lt 1 lc rgb "red" lw 4              # membrane voltage 2
-set style line 52 lt 1 lc rgb "orange" lw 4           # membrane voltage 3
-set style line 55 lt 1 lc rgb "orange" lw 4           # nerve potential
-
-set style line 60 lt 1 lc rgb "blue" lw 4             # gating variable 
-set style line 61 lt 1 lc rgb "cyan" lw 4             # gating variable average
-
-set style line 70 lt 1 lc rgb "red" lw 4              # ionic current
-set style line 71 lt 1 lc rgb "orange" lw 4           # ionic current average
-
-set style line 80 lt 1 lc rgb "#77FFFF" lw 4 pt 13 ps 2.4# baseline
-set style line 81 lt 1 lc rgb "#FF5533" lw 4 pt 9 ps 2.8 # beat
-set style line 82 lt 1 lc rgb "yellow" lw 4 pt 7 ps 2.4  # chirp
-set style line 83 lt 1 lc rgb "#FF7700" lw 4 pt 11 ps 2.8# beat shuffled
-set style line 84 lt 1 lc rgb "#FFAA00" lw 4 pt 5 ps 2.2 # chirp shuffled
-
-set style line 90 lt 1 lc rgb "#77FFFF" lw 2             # EOD thin
-set style line 91 lt 1 lc rgb "#77FFFF" lw 4             # EOD thick
-
-set term epslatex input color dashed rounded size 11.4cm, 6.6cm
diff --git a/programming/code/vectorsize.m b/programming/code/vectorsize.m
index 26ebfd5..54e3d52 100644
--- a/programming/code/vectorsize.m
+++ b/programming/code/vectorsize.m
@@ -1,4 +1,5 @@
-a = [2 4 6 8  10];   % row vector with five elements
-s = size(a) % store the return value of size() in a new variable
-s(2)        % get the second element of s, i.e. the length along the 2nd dimension
-size(a,2)   % the shortcut
+a = [2 4 6 8 10];   % row vector with five elements
+s = size(a)  % store the return value of size() in a new variable
+s(2)         % get the second element of s, 
+             % i.e. the length along the 2nd dimension
+size(a, 2)   % the shortcut
diff --git a/programming/code/vectorsize.out b/programming/code/vectorsize.out
index 429d61e..580f4d5 100644
--- a/programming/code/vectorsize.out
+++ b/programming/code/vectorsize.out
@@ -1,3 +1,3 @@
-s =  1    10
-ans = 10
-ans = 10
+s =  1    5
+ans = 5
+ans = 5
diff --git a/programming/exercises/Makefile b/programming/exercises/Makefile
new file mode 100644
index 0000000..7f3247f
--- /dev/null
+++ b/programming/exercises/Makefile
@@ -0,0 +1,11 @@
+TEXFILES= \
+  boolean_logical_indexing.tex \
+  control_flow.tex \
+  matrices.tex \
+  scripts_functions.tex \
+  structs_cells.tex \
+  variables_types.tex \
+  vectors_matrices.tex \
+  vectors.tex
+
+include ../../exercises.mk
diff --git a/programming/exercises/boolean_logical_indexing.tex b/programming/exercises/boolean_logical_indexing.tex
index f166f3d..fa75aef 100644
--- a/programming/exercises/boolean_logical_indexing.tex
+++ b/programming/exercises/boolean_logical_indexing.tex
@@ -1,47 +1,25 @@
-\documentclass[12pt,a4paper,pdftex, answers]{exam}
-
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot} \header{{\bfseries\large Exercise 4
-    }}{{\bfseries\large Boolean expressions and logical indexing}}{{\bfseries\large 29. Oktober, 2019}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de} \runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-\newcommand{\code}[1]{\texttt{#1}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\documentclass[12pt,a4paper,pdftex]{exam}
+
+\newcommand{\exercisetopic}{Logical indexing}
+\newcommand{\exercisenum}{4}
+\newcommand{\exercisedate}{17. November, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to scientific computing}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Abteilung Neuroethologie \hfill --- \hfill Institut f\"ur Neurobiologie \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. Your solution
 should be submitted as a single script (m-file) in the Ilias
 system. Each task should be solved in its own ``cell''. Each cell must
 be executable on its own. The file should be named according to the
-following pattern: ``variables\_datatypes\_\{lastname\}.m''
-(e.g. variables\_datentypes\_mueller.m).
+following pattern: ``boolean\_logical\_indexing\_\{lastname\}.m''
+(e.g. boolean\_logical\_indexing\_mueller.m).
 
 \section{Boolean expressions}
 
diff --git a/programming/exercises/control_flow.tex b/programming/exercises/control_flow.tex
index 2dd88fd..29ba300 100644
--- a/programming/exercises/control_flow.tex
+++ b/programming/exercises/control_flow.tex
@@ -1,53 +1,30 @@
-\documentclass[12pt,a4paper,pdftex, answers]{exam}
+\documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Control flow}
+\newcommand{\exercisenum}{5}
+\newcommand{\exercisedate}{24. November, 2020}
 
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large Exercise 5}}{{\bfseries\large Control Flow}}{{\bfseries\large 05. November, 2019}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-\newcommand{\code}[1]{\texttt{#1}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
-\vspace*{-6.5ex}
 
-\begin{center}
-  \textbf{\Large Introduction to scientific computing}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Neuroethologie \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. Your solution
 should be submitted as a single script (m-file) in the Ilias
 system. Each task should be solved in its own ``cell''. Each cell must
 be executable on its own. The file should be named according to the
-following pattern: ``variables\_datatypes\_\{lastname\}.m''
-(e.g. variables\_datentypes\_mueller.m).
+following pattern: ``control\_flow\_\{lastname\}.m''
+(e.g. control\_flow\_mueller.m).
 
 \begin{questions}
-  \question Implement \code{for} loops in which the  \emph{running variable}:
+  \question Implement \code{for} loops in which the \emph{running variable}:
   \begin{parts}
-    \part ... assumes values from 0 to 10. Print (\code{disp}) the value of the running variable for each iteration step.
+    \part ... assumes values from 0 to 10. Display (\code{disp}) the value of the running variable for each iteration of the loop.
     \begin{solution}
       for i = 1:10 disp(i); end;
     \end{solution}
@@ -252,7 +229,7 @@ following pattern: ``variables\_datatypes\_\{lastname\}.m''
     \part Create a variable
     \verb+filename = '2015-10-12_100Hz_1.25V.dat'+. Obviously, the
     underscore was used as a delimiter.
-    \part Use a \verb+for+ loop to find the positions of the underscores and store these in a vector.
+    \part Use a \verb+for+ loop to find the positions of the underscores and store these positions in a vector.
     \begin{solution}
       \begin{verbatim}
       positions = [];
diff --git a/programming/exercises/matrices.tex b/programming/exercises/matrices.tex
index 84127a4..79b5b4c 100644
--- a/programming/exercises/matrices.tex
+++ b/programming/exercises/matrices.tex
@@ -1,40 +1,17 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large Exercise 3}}{{\bfseries\large Matrices}}{{\bfseries\large 22. Oktober, 2019}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solutions:}\par\noindent}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\newcommand{\exercisetopic}{Matrices}
+\newcommand{\exercisenum}{3}
+\newcommand{\exercisedate}{22. October, 2019}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to Scientific Computing}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Neuroethology \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. Your solution
@@ -54,11 +31,11 @@ following pattern:
     \begin{solution}
       \code{x = [7 3 5; 1 8 3; 8 6 4];\\disp(size(x))}
     \end{solution}
-    \part Use the help to figure out how to get only the size along a certain dimension. Display the sizes of each dimension.
+    \part Use the help to figure out how to get only the size along a certain dimension. Display the sizes of each dimension separately.
     \begin{solution}
       \code{disp(size(x, 1))}\\\code{disp(size(x, 2))}
     \end{solution}
-    \part Copy the content at the position 3rd line, 2nd column to a new variable.
+    \part Copy the content at the position 3rd line, 2nd column into a new variable.
     \begin{solution}
       \code{c = x(3, 2)}
     \end{solution}
@@ -109,7 +86,7 @@ following pattern:
       \code{y = M(:, [2:2:size(M,2)])}
     \end{solution}
 
-    \part Calculate the averages of lines 1, 3, and 5 (use the function \verb+mean+}, see help).
+    \part Calculate the averages of lines 1, 3, and 5 (use the function \verb+mean+, see help).
     \begin{solution}
       \code{mean(M([1 3 5],:), 2)}
     \end{solution}
diff --git a/programming/exercises/plotsinewave.m b/programming/exercises/plotsinewave.m
index f2960da..25667a3 100644
--- a/programming/exercises/plotsinewave.m
+++ b/programming/exercises/plotsinewave.m
@@ -2,12 +2,12 @@ function plotsinewave(time, sine)
 % plot precomputed sinewave
 % time: vector with timepoints
 % sine: corresponding vector with sinewave 
-if time(end)-time(1) <= 1.0
-	plot(1000.0*time, sine);
+if time(end) - time(1) <= 1.0
+	plot(1000.0 * time, sine);
 	xlabel('Time [ms]');
 else
 	plot(time, sine);
 	xlabel('Time [s]');
 end
-ylim([1.2*min(sine) 1.2*max(sine)]);
+ylim([1.2 * min(sine) 1.2 * max(sine)]);
 ylabel('Sinewave');
diff --git a/programming/exercises/scripts_functions.tex b/programming/exercises/scripts_functions.tex
index 2b3282a..f5fbf50 100644
--- a/programming/exercises/scripts_functions.tex
+++ b/programming/exercises/scripts_functions.tex
@@ -1,62 +1,17 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
-%\documentclass[answers,12pt,a4paper,pdftex]{exam}
-
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large Exercise 6}}{{\bfseries\large Scripts and functions}}{{\bfseries\large 06. November, 2018}}
-\firstpagefooter{Prof. Jan Benda}{Phone: 29 74 573}{Email:
-  jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-\newcommand{\code}[1]{\texttt{#1}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+
+\newcommand{\exercisetopic}{Scripts and Functions}
+\newcommand{\exercisenum}{6}
+\newcommand{\exercisedate}{1. December, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to Scientific Computing}\\[1ex]
-         {\large Jan Grewe, Jan Benda}\\[-3ex]
-         Neuroethology \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. In contrast
@@ -71,7 +26,7 @@ to the pattern: ``scripts\_functions\_\{surname\}.zip''.
   \begin{parts}
     \part{}
     Version 1: Write a script that calculates the factorial of 5 and
-    prints out the result.
+    displays the result on the command line.
     \begin{solution}
       \lstinputlisting{factorialscripta.m}
     \end{solution}
@@ -86,8 +41,8 @@ to the pattern: ``scripts\_functions\_\{surname\}.zip''.
 
     \part{}
     Version 3: like version 2, but the calculated result should not be
-    printed on the command line but returned by the function. Write a
-    script that calls the function and prints out the result.
+    displayed on the command line but returned by the function. Write a
+    script that calls the function, accepts the returned value and then displays the result.
     \begin{solution}
       \lstinputlisting{myfactorial.m}
       \lstinputlisting{factorialscriptc.m}
@@ -116,7 +71,7 @@ to the pattern: ``scripts\_functions\_\{surname\}.zip''.
 
     \part{}
     Write a script that calls the function and controls the
-    plotting. Change the function in a way that it returns a proper
+    plotting. Change the function in a way that it returns 
     time-axis and the calculated sinwave.
     \begin{solution}
       \lstinputlisting{sinewave.m}
@@ -148,10 +103,11 @@ to the pattern: ``scripts\_functions\_\{surname\}.zip''.
 
   \begin{parts}
     \part{}
-    Read the exercise completely before starting the implementation
-    and then come up with a proper program layout of scripts and
-    functions. What would be a suitable function to solve the task? Which
-    arguments should it take? Which results should it return?
+    Read the exercise instructions completely before you start
+    implementing a solution. Analyze the requirements and then come up with a
+    program layout of scripts and functions. What would be a
+    suitable function to solve the core task? Which arguments should it
+    take? Which results should it return?
     \begin{solution}
       One function that computes one realisation of a random walk.
       Scripts for plotting and analysis.
diff --git a/programming/exercises/structs_cells.tex b/programming/exercises/structs_cells.tex
index a4431bc..b60a81d 100644
--- a/programming/exercises/structs_cells.tex
+++ b/programming/exercises/structs_cells.tex
@@ -1,40 +1,17 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Structs and Cell Arrays}
+\newcommand{\exercisenum}{6}
+\newcommand{\exercisedate}{16. October, 2015}
 
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large \"Ubung 6}}{{\bfseries\large Strukturen und Cell Arrays}}{{\bfseries\large 16. Oktober, 2015}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-\newcommand{\code}[1]{\texttt{#1}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Einf\"uhrung in die wissenschaftliche Datenverarbeitung}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Abteilung Neuroethologie \hfill --- \hfill Institut f\"ur Neurobiologie \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 Die folgenden Aufgaben dienen der Wiederholung, \"Ubung und
 Selbstkontrolle und sollten eigenst\"andig bearbeitet und gel\"ost
diff --git a/programming/exercises/variables_types.tex b/programming/exercises/variables_types.tex
index 3c4f017..89261c4 100644
--- a/programming/exercises/variables_types.tex
+++ b/programming/exercises/variables_types.tex
@@ -1,41 +1,17 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[english]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{lipsum}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large Exercise 1}}{{\bfseries\large Variables und Datatypes}}{{\bfseries\large 03. November, 2020}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solutions:}\par\noindent}
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\newcommand{\exercisetopic}{Variables und Datatypes}
+\newcommand{\exercisenum}{1}
+\newcommand{\exercisedate}{3. November, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to Scientific Computing}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Neuroethology \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. Your solution
diff --git a/programming/exercises/vectors.tex b/programming/exercises/vectors.tex
index 1ea0e8e..a870b39 100644
--- a/programming/exercises/vectors.tex
+++ b/programming/exercises/vectors.tex
@@ -1,40 +1,17 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Vectors}
+\newcommand{\exercisenum}{2}
+\newcommand{\exercisedate}{3. November, 2020}
 
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large Exercise 2}}{{\bfseries\large Vectors}}{{\bfseries\large 03. November, 2020}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solutions:}\par\noindent}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to Scientific Computing}\\[1ex]
-         {\large Jan Grewe, Jan Benda}\\[-3ex]
-         Neuroethology \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the
 lecture. You should try to solve them on your own. Your solution
diff --git a/programming/exercises/vectors_matrices.tex b/programming/exercises/vectors_matrices.tex
index 5726b64..d64c5fa 100644
--- a/programming/exercises/vectors_matrices.tex
+++ b/programming/exercises/vectors_matrices.tex
@@ -1,40 +1,17 @@
-\documentclass[12pt,a4paper,pdftex, answers]{exam}
+\documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Vectors}
+\newcommand{\exercisenum}{2}
+\newcommand{\exercisedate}{3. November, 2020}
 
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\header{{\bfseries\large Exercise 2}}{{\bfseries\large Vectors}}{{\bfseries\large 03. November, 2020}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solutions:}\par\noindent}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to Scientific Computing}\\[1ex]
-         {\large Jan Grewe, Jan Benda}\\[-3ex]
-         Neuroethology \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+
+\input{../../exercisestitle}
 
 The exercises are meant for self-monitoring and revision of the lecture
 topic. You should try to solve them on your own. Your solution should
diff --git a/programming/lecture/programming-chapter.tex b/programming/lecture/programming-chapter.tex
index 2a76f64..941c688 100644
--- a/programming/lecture/programming-chapter.tex
+++ b/programming/lecture/programming-chapter.tex
@@ -19,7 +19,7 @@
 \section{TODO}
 \begin{itemize}
 \item Ausgabeformat: \varcode{format} ?
-\item Expliziter die \varcode{(a:b:c)} Notation einf\"uhren!
+\item Expliziter die \varcode{(a:b:c)} Notation einf\"uhren! Done!
 \item Mathematische Funktionen sin(), cos(), exp()
 \item Rundungsfunktionen round(), floor(), ceil()
 \item Zeitvektoren, deltat, Umrechnung Zeit und Index.
diff --git a/programming/lecture/programming.tex b/programming/lecture/programming.tex
index e05e381..b75cc0d 100644
--- a/programming/lecture/programming.tex
+++ b/programming/lecture/programming.tex
@@ -60,7 +60,8 @@ variable.
 In \matlab{} variables can be created at any time on the command line
 or any place in a script or function. Listing~\ref{varListing1} shows
 three different ways of creating a variable:
-\begin{lstlisting}[label=varListing1, caption={Creating variables.}]
+
+\begin{pagelisting}[label=varListing1, caption={Creating variables.}]
 >> x = 38
 x =
     38
@@ -72,7 +73,7 @@ y =
 >> z = 'A'
 z =
     A
-\end{lstlisting}
+\end{pagelisting}
 
 Line 1 can be read like: ``Create a variable with the name \varcode{x}
 and assign the value 38''. The equality sign is the so called
@@ -93,8 +94,7 @@ information but it is not suited to be used in programs (see
 also the \code{who} function that returns a list of all defined
 variables, listing~\ref{varListing2}).
 
-\newpage
-\begin{lstlisting}[label=varListing2, caption={Requesting information about defined variables and their types.}]
+\begin{pagelisting}[label=varListing2, caption={Requesting information about defined variables and their types.}]
 >>class(x)
 ans =
       double
@@ -110,7 +110,7 @@ x  y  z
   x         1x1                 8  double              
   y         0x0                 0  double              
   z         1x1                 2  char                
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{important}[Naming conventions]
   There are a few rules regarding variable names. \matlab{} is
@@ -120,7 +120,6 @@ x  y  z
   variable names.
 \end{important}
 
-\pagebreak[4]
 \subsection{Working with variables}
 We can certainly work, i.e. do calculations, with variables. \matlab{}
 knows all basic \entermde[Operator!arithmetic]{Operator!arithmetischer}{arithmetic operators}
@@ -131,7 +130,7 @@ such as \code[Operator!arithmetic!1add@+]{+},
 \code[Operator!arithmetic!5pow@\^{}]{\^{}}. Listing~\ref{varListing3}
 shows their use.
 
-\begin{lstlisting}[label=varListing3, caption={Working with variables.}]
+\begin{pagelisting}[label=varListing3, caption={Working with variables.}]
 >> x = 1;
 >> x + 10
 ans = 
@@ -149,13 +148,12 @@ ans =
 z =
     3
 
->> z = z * 5;
->> z 
+>> z = z * 5
 z = 
     15
 
 >> clear z      % deleting a variable
-\end{lstlisting}
+\end{pagelisting}
 
 Note: in lines 2 and 10 the variables have been used without changing
 their values. Whenever the value of a variable should change, the
@@ -176,11 +174,12 @@ As mentioned above, the data type associated with a variable defines how the sto
   part.
 \item \enterm{logical}: Boolean values that can be evaluated to
   \code{true} or \code{false}.
-\item \enterm{character}: ASCII characters.
+\item \enterm{character}: Unicode characters.
+\item \enterm{string}: Strings of unicode characters, i.e. text. 
 \end{itemize}
 There is a variety of numeric data types that require different
 amounts of memory and have different ranges of values that can be
-represented (table~\ref{dtypestab}).
+represented (table~\ref{dtypestab}). There are also more advanced data types which will not be used in this chapter but see box~\ref{box:advanced_dtypes} and box about strings \ref{box:strings}.
 
 \begin{table}[t]
   \centering
@@ -271,8 +270,8 @@ step-sizes unequal to 1. Line 5 can be read like: ``Create a variable
 \varcode{b} and assign the values from 0 to 9 in increasing steps of
 1.''. Line 9 reads: ``Create a variable \varcode{c} and assign the
 values from 0 to 10 in steps of 2''.
-\pagebreak
-\begin{lstlisting}[label=generatevectorslisting, caption={Creating simple row-vectors.}]
+
+\begin{pagelisting}[label=generatevectorslisting, caption={Creating simple row-vectors.}]
 >> a = [0 1 2 3 4 5 6 7 8 9]  % Creating a row-vector
 a =
     0  1   2   3   4   5   6   7   8   9
@@ -284,7 +283,7 @@ b =
 >> c = (0:2:10)                   
 c = 
     0  2  4  6  8  10
-\end{lstlisting}
+\end{pagelisting}
 
 The length of a vector, that is the number of elements, can be
 requested using the \code{length()} or \code{numel()}
@@ -292,14 +291,14 @@ functions. \code{size()} provides the same information in a slightly,
 yet more powerful way (listing~\ref{vectorsizeslisting}). The above
 used vector \varcode{a} has the following size:
 
-\begin{lstlisting}[label=vectorsizeslisting, caption={Size of a vector.}]
+\begin{pagelisting}[label=vectorsizeslisting, caption={Size of a vector.}]
 >> length(a)
 ans =
       10
 >> size(a)
 ans =
       1  10
-\end{lstlisting}
+\end{pagelisting}
 
 The answer provided by the \code{size()} function demonstrates that
 vectors are nothing else but 2-dimensional matrices in which one
@@ -310,7 +309,7 @@ create a column-vector and how the \code[Operator!Matrix!']{'} ---
 operator is used to transpose the column-vector into a row-vector
 (lines 14 and following).
 
-\begin{lstlisting}[label=columnvectorlisting, caption={Column-vectors.}]
+\begin{pagelisting}[label=columnvectorlisting, caption={Column-vectors.}]
 >> b = [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]  % Creating a column-vector
 b =
      1
@@ -333,7 +332,7 @@ b =
 >> size(b)
 ans =
       1  10
-\end{lstlisting}
+\end{pagelisting}
 
 
 \subsubsection{Accessing elements of a vector}
@@ -354,7 +353,7 @@ number of elements irrespective of the type of vector.
   Elements of a vector are accessed via their index. This process is
   called \entermde{Indizierung}{indexing}.
 
-  In \matlab{} the first element has the index one.
+  In \matlab{} the first element in a vector has the index one.
   
   The last element's index equals the length of the vector.
 \end{important}
@@ -362,28 +361,28 @@ number of elements irrespective of the type of vector.
 Listings~\ref{vectorelementslisting} and~\ref{vectorrangelisting} show
 how the index is used to access elements of a vector. One can access
 individual values by providing a single index or use the
-\code[Operator!Matrix!:]{:} notation to access multiple values with a
-single command.
+\code[Operator!Matrix!:]{:} operator to access multiple values with a
+single command (see also the info box below \ref{important:colon_operator}).
 
-\begin{lstlisting}[label=vectorelementslisting, caption={Access to individual elements of a vector.}]
->> a = (11:20)
+\begin{pagelisting}[label=vectorelementslisting, caption={Access to individual elements of a vector.}]
+>> a = (11:20)   % generate a vector
 a = 
     11    12    13    14    15    16    17    18    19    20
 
->> a(1)     % the 1. element
+>> a(1)          % the 1. element
 ans = 11 
->> a(5)     % the 5. element
+>> a(5)          % the 5. element
 ans = 15
->> a(end)   % the last element
+>> a(end)        % the last element
 ans = 20
-\end{lstlisting}
+\end{pagelisting}
 
-\begin{lstlisting}[caption={Access to multiple elements.}, label=vectorrangelisting]
+\begin{pagelisting}[caption={Access to multiple elements.}, label=vectorrangelisting]
 >> a([1 3 5])   % 1., 3. and 5. element
 ans =
       11  13  15
 
->> a(2:4)       % all elements with the indices 2 to 4
+>> a(2:4)       % elements at indices 2 to 4
 ans =
       12  13  14 
 
@@ -394,7 +393,7 @@ ans =
 >> a(:)         % all elements as row-vector
 ans = 
       11  12  13  14  15  16  17  18  19  20
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{exercise}{vectorsize.m}{vectorsize.out}
   Create a row-vector \varcode{a} with 5 elements. The return value of
@@ -403,13 +402,14 @@ ans =
 \end{exercise}
 
 \begin{important}[The : (colon) operator]
+  \label{important:colon_operator}
   The colon \code[Operator!colon@:]{:} operator is often used when working with vectors. It has multiple purposes.
   \begin{enumerate}
     \item In the simplest form,  \code{x = a:b} with \code{a} and \code{b} being two numbers, it creates 
   a vector \code{x} containing the numbers \code{a} to \code{b} in integer steps. In \matlab{} the borders $a$ and $b$ are included $[a, b]$ or $a\leq x \leq b$.
-    \item In the form \code{x = a:c:b} the vector \code{x} uses a \emph{stepsize} of \code{c} to create the range of numbers.
+    \item In the form \code{x = a:c:b} the vector \code{x} uses a \emph{stepsize} of \code{c} to create the range of numbers ranging from $a$ to $b$ in steps of $c$.
     \item When used in the context of indexing such as \code{x(:)} all elements of the vector x are accessed.
-    \item As vectors are often used for indexing in other vectors one use the colon operator to create such vectors implicitely, e.g. \varcode{x(1:2:end)} to access every seond element of \code{x}. 
+    \item Vectors are often used for indexing in other vectors. To do so the colon operator is used to create such vectors implicitely, e.g. \varcode{x(1:2:end)} to access every second element of \code{x}. 
   \end{enumerate}  
 \end{important}
 
@@ -423,7 +423,7 @@ how vectors and scalars can be combined with the operators \code[Operator!arithm
 \code[Operator!arithmetic!4div@/]{/}
 \code[Operator!arithmetic!5powe@.\^{}]{.\^}.
 
-\begin{lstlisting}[caption={Calculating with vectors and scalars.},label=vectorscalarlisting]
+\begin{pagelisting}[caption={Calculations with vectors and scalars.},label=vectorscalarlisting]
 >> a = (0:2:8)
 a =
       0   2   4   6   8
@@ -447,7 +447,7 @@ ans =
 >> a .^ 2           % exponentiation
 ans =
       0   4  16  36  64
-\end{lstlisting}
+\end{pagelisting}
 
 When doing calculations with scalars and vectors the same mathematical
 operation is done to each element of the vector. In case of, e.g. an
@@ -460,7 +460,7 @@ element-wise operations of two vectors, e.g. each element of vector
 layout (row- or column vectors). Addition and subtraction are always
 element-wise (listing~\ref{vectoradditionlisting}).
 
-\begin{lstlisting}[caption={Element-wise addition and subtraction of two vectors.},label=vectoradditionlisting]
+\begin{pagelisting}[caption={Element-wise addition and subtraction of two vectors.},label=vectoradditionlisting]
 >> a = [4   9  12];
 >> b = [4   3   2];
 >> a + b           % addition 
@@ -479,7 +479,7 @@ Matrix dimensions must agree.
 >> a + d           % both vectors must have the same layout!
 Error using +
 Matrix dimensions must agree.
-\end{lstlisting}
+\end{pagelisting}
 
 Element-wise multiplication, division, or raising a vector to a given power requires a
 different operator with a preceding '.'.  \matlab{} defines the
@@ -489,7 +489,7 @@ following operators for element-wise operations on vectors
 \code[Operator!arithmetic!5powe@.\^{}]{.\^{}}
 (listing~\ref{vectorelemmultiplicationlisting}).
 
-\begin{lstlisting}[caption={Element-wise multiplication, division and
+\begin{pagelisting}[caption={Element-wise multiplication, division and
     exponentiation of two vectors.},label=vectorelemmultiplicationlisting]
 >> a .* b           % element-wise multiplication
 ans = 
@@ -509,7 +509,7 @@ Matrix dimensions must agree.
 >> a .* d           % Both vectors must have the same layout!
 Error using .*
 Matrix dimensions must agree.
-\end{lstlisting}
+\end{pagelisting}
 
 The simple operators \code[Operator!arithmetic!3mul@*]{*},
 \code[Operator!arithmetic!4div@/]{/} and
@@ -519,7 +519,7 @@ matrix-operations known from linear algebra (Box~
 of a row-vectors $\vec a$ with a column-vector $\vec b$ the
 scalar-poduct (or dot-product) $\sum_i = a_i b_i$.
 
-\begin{lstlisting}[caption={Multiplication of vectors.},label=vectormultiplicationlisting]
+\begin{pagelisting}[caption={Multiplication of vectors.},label=vectormultiplicationlisting]
 >> a * b            % multiplication of two vectors
 Error using  * 
 Inner matrix dimensions must agree.
@@ -536,14 +536,13 @@ ans =
     16    12     8
     36    27    18
     48    36    24
-\end{lstlisting}
-
-\pagebreak[4] 
+\end{pagelisting}
 
 To remove elements from a vector an empty value
 (\code[Operator!Matrix!{[]}]{[]}) is assigned to the respective
 elements:
-\begin{lstlisting}[label=vectoreraselisting, caption={Deleting elements of a vector.}]
+
+\begin{pagelisting}[label=vectoreraselisting, caption={Deleting elements of a vector.}]
 >> a = (0:2:8);
 >> length(a) 
 ans = 5
@@ -556,7 +555,7 @@ a = 4  8
 
 >> length(a)
 ans = 2
-\end{lstlisting}
+\end{pagelisting}
 
 In addition to deleting of vector elements one also add new elements
 or concatenate two vectors. When performing a concatenation the two
@@ -718,7 +717,7 @@ remapping, but can be really helpful
     rows in each column and so on.}\label{matrixlinearindexingfig}
 \end{figure}
 
-\begin{lstlisting}[label=matrixLinearIndexing, caption={Lineares indexing in matrices.}]
+\begin{pagelisting}[label=matrixLinearIndexing, caption={Lineares indexing in matrices.}]
 >> x = randi(100, [3, 4, 5]); % 3-D matrix filled with random numbers 
 >> size(x)
 ans =
@@ -735,17 +734,17 @@ ans =
 >> min(x(:))  % or even simpler
 ans =
 4
-\end{lstlisting}
+\end{pagelisting}
 
 \matlab{} defines functions that convert subscript indices to linear indices and back (\code{sub2ind()} and \code{ind2sub()}).
 
-\begin{ibox}[tp]{\label{matrixmultiplication} The matrix--multiplication.}
+\begin{ibox}[t]{\label{matrixmultiplication} The matrix--multiplication.}
   The matrix--multiplication from linear algebra is \textbf{not} an
   element--wise multiplication of each element in a matrix \varcode{A}
   and the respective element of matrix \varcode{B}. It is something
   completely different. Confusing element--wise and
   matrix--multiplication is one of the most common mistakes in
-  \matlab{}. \linebreak 
+  \matlab{}.
 
   The matrix--multiplication of two 2-D matrices is only possible if
   the number of columns in the first matrix agrees with the number of
@@ -795,8 +794,7 @@ box~\ref{matrixmultiplication}). To do a matrix-multiplication the
 inner dimensions of the matrices must agree
 (box~\ref{matrixmultiplication}).
 
-\pagebreak[4]
-\begin{lstlisting}[label=matrixOperations, caption={Two kinds of multiplications of matrices.}]
+\begin{pagelisting}[label=matrixOperations, caption={Two kinds of multiplications of matrices.}]
 >> A = randi(5, [2, 3])   % 2-D matrix 
 A =
      1     5     3
@@ -822,7 +820,7 @@ ans =
     10    15    20
     24    23    35
     16    17    25
-\end{lstlisting}
+\end{pagelisting}
 
 \section{Boolean expressions}
 
@@ -854,7 +852,7 @@ synonymous for the logical values 1 and
 0. Listing~\ref{logicaldatatype} exemplifies the use of the logical
 data type.
 
-\begin{lstlisting}[caption={The logical data type. Please note that the actual \matlab{} output looks a little different.}, label=logicaldatatype]
+\begin{pagelisting}[caption={The logical data type. Please note that the actual \matlab{} output looks a little different.}, label=logicaldatatype]
 >> true
 ans = 1
 >> false
@@ -871,7 +869,7 @@ ans = 0
 ans = 1   1   1   1
 >> logical([1 2 3 4 0 0 10])
 ans = 1   1   1   1   0   0   1
-\end{lstlisting}
+\end{pagelisting}
 
 \varcode{true} and \varcode{false} are reserved keywords that evaluate
 to the logical values 1 and 0, respectively. If you want to create a
@@ -884,7 +882,7 @@ code of each character in ``test'' is non-zero value, thus, the result
 of casting it to logical is a vector of logicals. A similar thing
 happens upon casting a vector (or matrix) of numbers to logical. Each
 value is converted to logical and the result is true for all non-zero
-values (line 21).
+values (line 15).
 
 Knowing how to represent true and false values in \matlab{} using the
 logical data type allows us to take a step towards more complex
@@ -922,8 +920,8 @@ stored in variable \varcode{b}?''.
 
 The result of such questions is then given as a logical
 value. Listing~\ref{relationaloperationslisting} shows examples using relational operators.
-\pagebreak
-\begin{lstlisting}[caption={Relational Boolean expressions.}, label=relationaloperationslisting]
+
+\begin{pagelisting}[caption={Relational Boolean expressions.}, label=relationaloperationslisting]
 >> true == logical(1)
 ans = 1
 >> false ~= logical(1)
@@ -941,7 +939,7 @@ ans = 0  0  1  0  0
 ans = 0  1  0  0  1 
 >> [2  0  0  5  0] >= [1  0  3  2  0]
 ans = 1  1  0  1  1 
-\end{lstlisting}
+\end{pagelisting}
 
 Testing the relations between numbers and scalar variables is straight
 forward. When comparing vectors, the relational operator will be
@@ -1038,7 +1036,7 @@ for implementing such
 expressions. Listing~\ref{logicaloperatorlisting} shows a few
 examples and respective illustrations are shown in figure~\ref{logicaloperationsfig}.
 
-\begin{lstlisting}[caption={Boolean expressions.}, label=logicaloperatorlisting]
+\begin{pagelisting}[caption={Boolean expressions.}, label=logicaloperatorlisting]
 >> x = rand(1)   % create a single random number in the range [0, 1]
 x = 0.3452
 >> x > 0.25 & x < 0.75
@@ -1049,7 +1047,7 @@ x = 0.4920, 0.9106, 0.7218, 0.8749, 0.1574, 0.0201, 0.9107, 0.8357, 0.0357, 0.47
 ans = 1  0  1  0  0  0  0  0  0  1
 >> x < 0.25 | x > 0.75  
 ans = 0  1  0  1  1  1  1  1  1  0
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{figure}[ht]
   \includegraphics[]{logical_operations}
@@ -1064,7 +1062,6 @@ ans = 0  1  0  1  1  1  1  1  1  0
     data.}\label{logicaloperationsfig}
 \end{figure}
 
-\pagebreak
 \begin{important}[Assignment and equality operators]
   The assignment operator \code[Operator!Assignment!=]{=} and the
   relational equality operator \code[Operator!relational!==]{==} are
@@ -1093,7 +1090,7 @@ elements of \varcode{x} where the Boolean expression \varcode{x < 0}
 evaluates to true and store the result in the variable
 \varcode{x\_smaller\_zero}''.
 
-\begin{lstlisting}[caption={Logical indexing.}, label=logicalindexing1]
+\begin{pagelisting}[caption={Logical indexing.}, label=logicalindexing1]
 >> x = randn(1, 6)   % a vector with 6 random numbers
 x =
    -1.4023   -1.4224    0.4882   -0.1774   -0.1961    1.4193
@@ -1110,7 +1107,7 @@ elements_smaller_zero =
 >> elements_smaller_zero = x(x < 0)
 elements_smaller_zero =
    -1.4023   -1.4224   -0.1774   -0.1961
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{exercise}{logicalVector.m}{logicalVector.out}
   Create a vector \varcode{x} containing the values 0--10.
@@ -1119,8 +1116,7 @@ elements_smaller_zero =
   \item Display the content of \varcode{y} in the command window.
   \item  What is the data type of \varcode{y}?
   \item  Return only those elements \varcode{x} that are less than 5.
-  \end{enumerate}
-  \pagebreak[4]
+  \end{enumerate}\vspace{-1ex}
 \end{exercise}
 
 \begin{figure}[t]
@@ -1142,7 +1138,6 @@ segment of data of a certain time span (the stimulus was on,
 \begin{exercise}{logicalIndexingTime.m}{}
   Assume that measurements have been made for a certain time. Usually
   measured values and the time are stored in two vectors.
-  
   \begin{itemize}
   \item Create a vector that represents the recording time \varcode{t
     = 0:0.001:10;}.
@@ -1150,12 +1145,12 @@ segment of data of a certain time span (the stimulus was on,
     that has the same length as \varcode{t}. The values stored in
     \varcode{x} represent the measured data at the times in
     \varcode{t}.
-  \item Use logical indexing to select those values that have been
-    recorded in the time span from 5--6\,s.
-  \end{itemize}
+  \item Use logical indexing to select values that have been
+    recorded in the time span 5--6\,s.
+  \end{itemize}\vspace{-1ex}
 \end{exercise}
 
-\begin{ibox}[ht]{\label{advancedtypesbox}Advanced data types}
+\begin{ibox}[!ht]{\label{advancedtypesbox}Advanced data types}
   Thoughout this script and the exercises we will limit ourselves to
   the basic data types introduced above (int, double, char, scalars,
   vectors, matrices and strings). There are, however, \matlab{}-
@@ -1211,6 +1206,50 @@ ans =
 
 \end{ibox}
 
+\begin{ibox}[!ht]{\label{box:string_dtype}The \enterm[string]{string} data type}
+    With release 2017a \matlab{} introduced the string data type which offers more convenience for handling text. A string scalar is created using double quotes: \varcode{s = "Hello world!";}.
+    The string object \varcode{s} now offers several methods to work on the stored information. Some of the most commonly used methods are listed below. Refer to the documentation for complete descriptions.
+    \begin{enumerate}
+        \item \varcode{append} -- appends another string to the string and returns the new string. As with the other functions the original is not affected.\vspace{-0.5em}
+        \item \varcode{contains} -- searches the string for the occurence of a another string, which is passed as an argument. Returns a logical that is true if the pattern was found.\vspace{-0.5em} 
+        \item \varcode{split} -- splits the string into substrings. By default a blank is used as delimiter. You can pass any other string as an argument to spilt the string at each occurence of the passed pattern. Returns a vector of substrings.\vspace{-0.5em}
+        \item \varcode{join} --  The opposite of spilt. \varcode{join} takes a vector of strings and concatenates them to a single string. By default a blank is used as delimiter.\vspace{-0.5em}
+        \item \varcode{lower} -- returns a version of the string in which all characters are lower case. This is particularly useful when one compares user input or string arguments in a function in an case insensitive way.\vspace{-0.5em}
+        \item \varcode{upper} -- as above but all characters are converted to upper case.        
+    \end{enumerate} 
+    \paragraph{Example:}
+    A dataset is recorded on a specific recording date using an experimental subject and applying certain stimulus conditions. The recorded data should be stored in a file that contains this information separated by undersores.
+
+    \begin{lstlisting}[basicstyle=\ttfamily\scriptsize, caption={Using \varcode{join} to combine strings.}, label=usingjoin]
+date = "2020-12-24";
+subject = "subjectA";
+condition = "control";
+file_extension = ".dat";
+
+filename = join([date, subject, condition], "_");
+filename = filename.append(file_extension)
+        
+disp(filename)
+
+ans = 
+     "2020-12-24_subjectA_control.dat"
+    \end{lstlisting}
+
+    When reading the dataset during the analysis, we may want to extract the stimulus condtion or the subject name.
+    
+    \begin{lstlisting}[basicstyle=\ttfamily\scriptsize, caption={Using \varcode{split} to cut a string at a delimiter.}, label=usingsplit]
+disp(filename)
+ans = 
+    "2020-12-24_subjectA_control.dat"
+    
+filename = filename.erase(".dat");  % remove file extension
+string_parts = filename.split("_");  % split at the underscore
+% string_parts is a vector of the substrings
+date = string_parts(1);
+subject = string_parts(2);
+condition = string_parts(3);
+    \end{lstlisting}
+\end{ibox}
 \section{Control flow}\label{controlstructsec}
 
 Generally, a program is executed line by line from top to
@@ -1230,7 +1269,7 @@ As the name already suggests loops are used to execute the same parts
 of the code repeatedly. In one of the earlier exercises the factorial of
 five has been calculated as depicted in listing~\ref{facultylisting}.
 
-\begin{lstlisting}[caption={Calculation of the factorial of 5 in five steps}, label=facultylisting]
+\begin{pagelisting}[caption={Calculation of the factorial of 5 in five steps}, label=facultylisting]
 >> x = 1;
 >> x = x * 2;
 >> x = x * 3;  
@@ -1239,7 +1278,7 @@ five has been calculated as depicted in listing~\ref{facultylisting}.
 >> x
 x =
     120
-\end{lstlisting}
+\end{pagelisting}
 
 This kind of program solves the taks but it is rather repetitive. The only
 thing that changes is the increasing factor. The repetition of such
@@ -1279,7 +1318,7 @@ a certain purpose. The \varcode{for}-loop is closed with the keyword
 \code{end}. Listing~\ref{looplisting} shows a simple version of such a
 for-loop.
 
-\begin{lstlisting}[caption={Example of a \varcode{for}-loop.}, label=looplisting]
+\begin{pagelisting}[caption={Example of a \varcode{for}-loop.}, label=looplisting]
 >> for x = 1:3 % head
      disp(x)   % body
    end
@@ -1288,7 +1327,7 @@ for-loop.
      1
      2
      3
-\end{lstlisting}
+\end{pagelisting}
 
 
 \begin{exercise}{factorialLoop.m}{factorialLoop.out}
@@ -1308,11 +1347,11 @@ keyword \code{while} that is followed by a Boolean expression. If this
 can be evaluated to true, the code in the body is executed. The loop
 is closed with an \code{end}.
 
-\begin{lstlisting}[caption={Basic structure of a \varcode{while} loop.}, label=whileloop]
-while x == true % head with a Boolean expression
+\begin{pagelisting}[caption={Basic structure of a \varcode{while} loop.}, label=whileloop]
+while x == true   % head with a Boolean expression
    % execute this code if the expression yields true
 end
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{exercise}{factorialWhileLoop.m}{}
   Implement the factorial of a number \varcode{n} using a \varcode{while}-loop.
@@ -1367,7 +1406,7 @@ expression to provide a default case. The last body of the
 \varcode{if} - \varcode{elseif} - \varcode{else} statement has to be
 finished with the \code{end} (listing~\ref{ifelselisting}).
   
-\begin{lstlisting}[label=ifelselisting, caption={Structure of an \varcode{if} statement.}]
+\begin{pagelisting}[label=ifelselisting, caption={Structure of an \varcode{if} statement.}]
 if x < y % head
    % body I, executed only if x < y
 elseif x > y
@@ -1375,7 +1414,7 @@ elseif x > y
 else
    % body III, executed only if the previous conditions did not match
 end  
-  \end{lstlisting}
+  \end{pagelisting}
 
 \begin{exercise}{ifelse.m}{}
   Draw a random number and check with an appropriate \varcode{if}
@@ -1403,7 +1442,7 @@ that were not explicitly stated above (listing~\ref{switchlisting}).
 As usual the \code{switch} statement needs to be closed with an
 \code{end}.
 
-\begin{lstlisting}[label=switchlisting, caption={Structure of a \varcode{switch} statement.}]
+\begin{pagelisting}[label=switchlisting, caption={Structure of a \varcode{switch} statement.}]
 mynumber = input('Enter a number:');
 switch mynumber
   case -1
@@ -1413,7 +1452,7 @@ switch mynumber
   otherwise
       disp('something else');
 end
-\end{lstlisting}
+\end{pagelisting}
 
 
 \subsubsection{Comparison \varcode{if}  and \varcode{switch} -- statements}
@@ -1437,7 +1476,7 @@ skip the execution of the body under certain circumstances, one can
 use the keywords \code{break} and \code{continue}
 (listings~\ref{continuelisting} and \ref{continuelisting}).
 
-\begin{lstlisting}[caption={Stop the execution of a loop using \varcode{break}.}, label=breaklisting]
+\begin{pagelisting}[caption={Stop the execution of a loop using \varcode{break}.}, label=breaklisting]
 >> x = 1;
    while true
      if (x > 3)
@@ -1450,9 +1489,9 @@ use the keywords \code{break} and \code{continue}
      1
      2
      3
-\end{lstlisting}
+\end{pagelisting}
 
-\begin{lstlisting}[caption={Skipping iterations using \varcode{continue}.}, label=continuelisting]
+\begin{pagelisting}[caption={Skipping iterations using \varcode{continue}.}, label=continuelisting]
 for x = 1:5 
   if(x > 2 & x < 5)
     continue;
@@ -1463,7 +1502,7 @@ end
      1
      2
      5
-\end{lstlisting}
+\end{pagelisting}
 
 \begin{exercise}{logicalIndexingBenchmark.m}{logicalIndexingBenchmark.out}
   Above we claimed that logical indexing is faster and much more
@@ -1535,11 +1574,11 @@ has one \entermde{Argument}{argument} $x$ that is transformed into the
 function's output value $y$. In \matlab{} the syntax of a function
 declaration is very similar (listing~\ref{functiondefinitionlisting}).
 
-\begin{lstlisting}[caption={Declaration of a function in \matlab{}}, label=functiondefinitionlisting]
+\begin{pagelisting}[caption={Declaration of a function in \matlab{}}, label=functiondefinitionlisting]
 function [y] = functionName(arg_1, arg_2)
 %         ^                  ^       ^
 % return value        argument_1, argument_2
-\end{lstlisting}
+\end{pagelisting}
 
 The keyword \code{function} is followed by the return value(s) (it can
 be a list \varcode{[]} of values), the function name and the
@@ -1572,7 +1611,7 @@ The following listing (\ref{badsinewavelisting}) shows a function that
 calculates and displays a bunch of sine waves with different amplitudes.
 
 
-\begin{lstlisting}[caption={Bad example of a function that displays a series of sine waves.},label=badsinewavelisting]
+\begin{pagelisting}[caption={Bad example of a function that displays a series of sine waves.},label=badsinewavelisting]
 function myFirstFunction() % function head
    t = (0:0.01:2); 
    frequency = 1.0;
@@ -1583,7 +1622,7 @@ function myFirstFunction() % function head
        hold on;
    end 
 end
-\end{lstlisting}
+\end{pagelisting}
 
 \varcode{myFirstFunction} (listing~\ref{badsinewavelisting}) is a
 prime-example of a bad function. There are several issues with it's
@@ -1644,7 +1683,7 @@ define (i) how to name the function, (ii) which information it needs
 Having defined this we can start coding
 (listing~\ref{sinefunctionlisting}).
 
-\begin{lstlisting}[caption={Function that calculates a sine wave.}, label=sinefunctionlisting]
+\begin{pagelisting}[caption={Function that calculates a sine wave.}, label=sinefunctionlisting]
 function [time, sine] = sinewave(frequency, amplitude, t_max, t_step)
 % Calculate a sinewave of a given frequency, amplitude, 
 % duration and temporal resolution. 
@@ -1662,7 +1701,7 @@ function [time, sine] = sinewave(frequency, amplitude, t_max, t_step)
    time = (0:t_step:t_max);
    sine = sin(frequency .* time .* 2 .* pi) .* amplitude;
 end
-\end{lstlisting}
+\end{pagelisting}
   
 
 \paragraph{II. Plotting a single sine wave}
@@ -1689,7 +1728,7 @@ specification of the function:
 With this specification we can start to implement the function
 (listing~\ref{sineplotfunctionlisting}).
 
-\begin{lstlisting}[caption={Function for the graphical display of data.}, label=sineplotfunctionlisting]
+\begin{pagelisting}[caption={Function for the graphical display of data.}, label=sineplotfunctionlisting]
 function plotFunction(x_data, y_data, name)
 % Plots x-data against y-data and sets the display name.
 %
@@ -1701,8 +1740,38 @@ function plotFunction(x_data, y_data, name)
 %   name  : the displayname
   plot(x_data, y_data, 'displayname', name) 
 end
-\end{lstlisting}
+\end{pagelisting}
+
+\begin{ibox}[!ht]{\label{box:sprintf}\enterm[Format strings]{Formatted strings} with \mcode{sprintf}.}
+    We can use \varcode{disp} to dispaly the content of a variable on the command line. But how can we embed variable contents nicely into a text and store it in a variable or show it on the command line? \varcode{sprintf} is the standard function to achieve this. We introduce only a fraction of its real might here. For a more complete description visit the \matlab{} help or e.g. \url{https://en.wikipedia.org/wiki/Printf_format_string}. 
+    
+    Consider the following code:
+    \begin{lstlisting}[commentstyle=\color{black}, basicstyle=\ttfamily\scriptsize, caption={Using sprintf.}]
+n = 100;
+numbers = randn(n, 1);
+    
+sprintf("Drawing %i random numbers yields an average of %.3f and a standard deviation of %.3f.", n, mean(numbers), std(numbers))
+
+ans = 
+    "Drawing 100 random numbers yields an average of -0.121 and a standard deviation of 0.921."
 
+    \end{lstlisting}
+    \varcode{sprintf} takes a variable number of arguments. The first one is the string that contains placehoders for the variable content. Here we used three placehoders (two different types, \varcode{\%i, \%.2f}). Accordingly, \varcode{sprintf} expects three additional arguments (\varcode{n, mean(numbers), std(numbers)}). These are the content we want to show in the text. The function will try to convert the variable content and format is according to the placehoder type:
+
+   \footnotesize
+   \begin{tabular}{l||p{4cm}|p{7.5cm}}
+     type & purpose & example \\ \hline \hline
+    \%i & integer values & \varcode{sprintf("I drew \%i numbers", 100)}\\ \hline
+    \%s & string values & \varcode{name = "John Doe";} \varcode{sprintf("My name is \%s", name)}\\ \hline
+    \%f & double values, accepts width and precision arguments & \varcode{sprintf("Number pi is \%.2f", pi)}\\ \hline
+    \%g & double values, uses exponential style when appropriate& \varcode{sprintf("Large int \%g", 1000000)}
+    \end{tabular}
+   \normalsize
+
+    Simplified, the placehoder has the form \varcode{\%[width][.precision]type}. Square brackets indicates optional parameters. \varcode{width} controls the total number of characters used for the content. \varcode{.precision} controls how many of the total characters may de devoted to the positions after the decimal point. The example above (\varcode{\%.2f}) limits the output of the $\pi$ to 2 positions after the decimal point.
+
+    Many programming languages offer versions of \varcode{sprintf} that work essentially in the same way.
+\end{ibox}
 
 \paragraph{III. One script to rule them all}
 
@@ -1728,7 +1797,7 @@ Again, we need to specify what needs to be done:
 
 The implementation is shown in listing~\ref{sinesskriptlisting}.
 
-\begin{lstlisting}[caption={Control script for the plotting of sine waves.},label=sinesskriptlisting]
+\begin{pagelisting}[caption={Control script for the plotting of sine waves. Note the use of \varcode{sprintf} in the function call in line 11. This function creates a so called formatted string based on the passed string which contains placeholders for variables. For some more details see~\ref{box:sprintf}.},label=sinesskriptlisting]
 amplitudes = 0.25:0.25:1.25;
 frequency = 2.0;
 duration = 10.0;  % seconds
@@ -1739,16 +1808,16 @@ hold on
 for i = 1:length(amplitudes)
   [x_data, y_data] = sinewave(frequency, amplitudes(i), ... 
                               duration, stepsize);
-  plotFunction(x_data, y_data, sprintf('freq: %5.2f, ampl: %5.2f',...
+  plotFunction(x_data, y_data, sprintf("freq: %5.2f, ampl: %5.2f",...
                                        frequency, amplitudes(i)))
 end
 hold off
 legend('show')
-\end{lstlisting}
+\end{pagelisting}
 
+\pagebreak[4]
 \begin{exercise}{plotMultipleSinewaves.m}{}
   Extend the program to plot also a range of frequencies.
-  \pagebreak[4]
 \end{exercise}
 
 
diff --git a/regression/code/funcPlotter.m b/regression/code/funcPlotter.m
new file mode 100644
index 0000000..ed4936e
--- /dev/null
+++ b/regression/code/funcPlotter.m
@@ -0,0 +1,4 @@
+function funcPlotter(func)
+  x = 0:0.1:10.0;
+  plot(x,func(x));
+end
diff --git a/regression/code/funcplotterexamples.m b/regression/code/funcplotterexamples.m
new file mode 100644
index 0000000..7b12d06
--- /dev/null
+++ b/regression/code/funcplotterexamples.m
@@ -0,0 +1,2 @@
+funcPlotter(@sin);
+
diff --git a/regression/code/gradientDescent.m b/regression/code/gradientDescent.m
index 1919035..f0f7ef1 100644
--- a/regression/code/gradientDescent.m
+++ b/regression/code/gradientDescent.m
@@ -1,32 +1,41 @@
-% x, y from exercise 8.3
+function [p, ps, mses] = gradientDescent(x, y, func, p0, epsilon, threshold)
+% Gradient descent for fitting a function to data pairs.
+%
+% Arguments: x, vector of the x-data values.
+%            y, vector of the corresponding y-data values.
+%            func, function handle func(x, p)
+%            p0, vector with initial parameter values
+%            epsilon: factor multiplying the gradient.
+%            threshold: minimum value for gradient
+%
+% Returns:   p, vector with the final parameter values.
+%            ps: 2D-vector with all the parameter vectors traversed.
+%            mses: vector with the corresponding mean squared errors
 
-% some arbitrary values for the slope and the intercept to start with:
-position = [-2.0, 10.0];  
+  p = p0;
+  gradient = ones(size(p0)) * 1000.0;
+  ps = [];
+  mses = [];
+  while norm(gradient) > threshold
+      ps = [ps, p(:)];
+      mses = [mses, meanSquaredError(x, y, func, p)];
+      gradient = meanSquaredGradient(x, y, func, p);
+      p = p - epsilon * gradient;
+  end
+end
+
+function mse = meanSquaredError(x, y, func, p)
+    mse = mean((y - func(x, p)).^2);
+end
 
-% gradient descent:
-gradient = [];
-errors = [];
-count = 1;
-eps = 0.0001;
-while isempty(gradient) || norm(gradient) > 0.1
-    gradient = meanSquaredGradient(x, y, position);
-    errors(count) = meanSquaredError(x, y, position);
-    position = position - eps .* gradient; 
-    count = count + 1;
+function gradmse = meanSquaredGradient(x, y, func, p)
+  gradmse = zeros(size(p));
+  h = 1e-7;               % stepsize for derivatives
+  ph = eye(length(p))*h;  % ... for each parameter
+  mse = meanSquaredError(x, y, func, p);
+  for i = 1:length(p)     % for each coordinate ...
+    msepi = meanSquaredError(x, y, func, p(:) + ph(:,i));
+    gradmse(i) = (msepi - mse)/h;
+  end
 end
 
-figure()
-subplot(2,1,1)
-hold on
-scatter(x, y, 'displayname', 'data')
-xx = min(x):0.01:max(x);
-yy = position(1).*xx + position(2);
-plot(xx, yy, 'displayname', 'fit')
-xlabel('Input')
-ylabel('Output')
-grid on
-legend show
-subplot(2,1,2)
-plot(errors)
-xlabel('optimization steps')
-ylabel('error')
diff --git a/regression/code/gradientDescentCubic.m b/regression/code/gradientDescentCubic.m
new file mode 100644
index 0000000..a495221
--- /dev/null
+++ b/regression/code/gradientDescentCubic.m
@@ -0,0 +1,25 @@
+function [c, cs, mses] = gradientDescentCubic(x, y, c0, epsilon, threshold)
+% Gradient descent for fitting a cubic relation.
+%
+% Arguments: x, vector of the x-data values.
+%            y, vector of the corresponding y-data values.
+%            c0, initial value for the parameter c. 
+%            epsilon: factor multiplying the gradient.
+%            threshold: minimum value for gradient
+%
+% Returns:   c, the final value of the c-parameter.
+%            cs: vector with all the c-values traversed.
+%            mses: vector with the corresponding mean squared errors
+  c = c0;
+  gradient = 1000.0;
+  cs = [];
+  mses = [];
+  count = 1;
+  while abs(gradient) > threshold
+      cs(count) = c;
+      mses(count) = meanSquaredErrorCubic(x, y, c);
+      gradient = meanSquaredGradientCubic(x, y, c);
+      c = c - epsilon * gradient; 
+      count = count + 1;
+  end
+end
diff --git a/regression/code/meanSquaredError.m b/regression/code/meanSquaredError.m
deleted file mode 100644
index 6eeea7b..0000000
--- a/regression/code/meanSquaredError.m
+++ /dev/null
@@ -1,12 +0,0 @@
-function mse = meanSquaredError(x, y, parameter)
-% Mean squared error between a straight line and data pairs.
-%
-% Arguments: x, vector of the input values
-%            y, vector of the corresponding measured output values
-%            parameter, vector containing slope and intercept 
-%                       as the 1st and 2nd element, respectively. 
-%
-% Returns:   mse, the mean-squared-error.
-
-  mse = mean((y - x * parameter(1) - parameter(2)).^2);
-end
diff --git a/regression/code/meanSquaredErrorCubic.m b/regression/code/meanSquaredErrorCubic.m
new file mode 100644
index 0000000..8953d50
--- /dev/null
+++ b/regression/code/meanSquaredErrorCubic.m
@@ -0,0 +1,11 @@
+function mse = meanSquaredErrorCubic(x, y, c)
+% Mean squared error between data pairs and a cubic relation.
+%
+% Arguments: x, vector of the x-data values
+%            y, vector of the corresponding y-data values
+%            c, the factor for the cubic relation. 
+%
+% Returns:   mse, the mean-squared-error.
+
+  mse = mean((y - c*x.^3).^2);
+end
diff --git a/regression/code/meanSquaredErrorLine.m b/regression/code/meanSquaredErrorLine.m
deleted file mode 100644
index c395934..0000000
--- a/regression/code/meanSquaredErrorLine.m
+++ /dev/null
@@ -1 +0,0 @@
-mse = mean((y - y_est).^2);
diff --git a/regression/code/meanSquaredGradientCubic.m b/regression/code/meanSquaredGradientCubic.m
new file mode 100644
index 0000000..99d5b3b
--- /dev/null
+++ b/regression/code/meanSquaredGradientCubic.m
@@ -0,0 +1,14 @@
+function dmsedc = meanSquaredGradientCubic(x, y, c)
+% The gradient of the mean squared error for a cubic relation.
+%
+% Arguments: x, vector of the x-data values
+%            y, vector of the corresponding y-data values
+%            c, the factor for the cubic relation. 
+%
+% Returns: the derivative of the mean squared error at c.
+
+  h = 1e-7;   % stepsize for derivatives
+  mse = meanSquaredErrorCubic(x, y, c);
+  mseh = meanSquaredErrorCubic(x, y, c+h);
+  dmsedc = (mseh - mse)/h;
+end
diff --git a/regression/code/meansquarederrorline.m b/regression/code/meansquarederrorline.m
new file mode 100644
index 0000000..4eca4a3
--- /dev/null
+++ b/regression/code/meansquarederrorline.m
@@ -0,0 +1,15 @@
+n = 40;
+xmin = 2.2;
+xmax = 3.9;
+c = 6.0;
+noise = 50.0;
+
+% generate data:
+x = rand(n, 1) * (xmax-xmin) + xmin;
+yest = c * x.^3;
+y = yest + noise*randn(n, 1);
+
+% compute mean squared error:
+mse = mean((y - yest).^2);
+
+fprintf('the mean squared error is %.0f kg^2\n', mse)
diff --git a/regression/code/plotcubiccosts.m b/regression/code/plotcubiccosts.m
new file mode 100644
index 0000000..6c90f03
--- /dev/null
+++ b/regression/code/plotcubiccosts.m
@@ -0,0 +1,9 @@
+cs = 2.0:0.1:8.0;
+mses = zeros(length(cs));
+for i = 1:length(cs)
+    mses(i) = meanSquaredErrorCubic(x, y, cs(i));
+end
+
+plot(cs, mses)
+xlabel('c')
+ylabel('mean squared error')
diff --git a/regression/code/plotcubicgradient.m b/regression/code/plotcubicgradient.m
new file mode 100644
index 0000000..d00bf6e
--- /dev/null
+++ b/regression/code/plotcubicgradient.m
@@ -0,0 +1,11 @@
+meansquarederrorline;               % generate data
+
+cs = 2.0:0.1:8.0;
+mseg = zeros(length(cs));
+for i = 1:length(cs)
+    mseg(i) = meanSquaredGradientCubic(x, y, cs(i));
+end
+
+plot(cs, mseg)
+xlabel('c')
+ylabel('gradient')
diff --git a/regression/code/plotgradientdescentcubic.m b/regression/code/plotgradientdescentcubic.m
new file mode 100644
index 0000000..6aadc12
--- /dev/null
+++ b/regression/code/plotgradientdescentcubic.m
@@ -0,0 +1,28 @@
+meansquarederrorline;               % generate data
+
+c0 = 2.0;
+eps = 0.00001;
+thresh = 1.0;
+[cest, cs, mses] = gradientDescentCubic(x, y, c0, eps, thresh);
+
+subplot(2, 2, 1);                   % top left panel
+hold on;
+plot(cs, '-o');
+plot([1, length(cs)], [c, c], 'k'); % line indicating true c value
+hold off;
+xlabel('Iteration');
+ylabel('C');
+subplot(2, 2, 3);                   % bottom left panel
+plot(mses, '-o');
+xlabel('Iteration steps');
+ylabel('MSE');
+subplot(1, 2, 2);                   % right panel
+hold on;
+% generate x-values for plottig the fit:
+xx = min(x):0.01:max(x);
+yy = cest * xx.^3;
+plot(xx, yy);
+plot(x, y, 'o');                    % plot original data
+xlabel('Size [m]');
+ylabel('Weight [kg]');
+legend('fit', 'data', 'location', 'northwest');
diff --git a/regression/code/plotgradientdescentpower.m b/regression/code/plotgradientdescentpower.m
new file mode 100644
index 0000000..2d2a08f
--- /dev/null
+++ b/regression/code/plotgradientdescentpower.m
@@ -0,0 +1,30 @@
+meansquarederrorline;               % generate data
+
+p0 = [2.0, 1.0];
+eps = 0.00001;
+thresh = 1.0;
+[pest, ps, mses] = gradientDescent(x, y, @powerLaw, p0, eps, thresh);
+pest
+
+subplot(2, 2, 1);                   % top left panel
+hold on;
+plot(ps(1,:), ps(2,:), '.');
+plot(ps(1,end), ps(2,end), 'og');
+plot(c, 3.0, 'or'); % dot indicating true parameter values
+hold off;
+xlabel('Iteration');
+ylabel('C');
+subplot(2, 2, 3);                   % bottom left panel
+plot(mses, '-o');
+xlabel('Iteration steps');
+ylabel('MSE');
+subplot(1, 2, 2);                   % right panel
+hold on;
+% generate x-values for plottig the fit:
+xx = min(x):0.01:max(x);
+yy = powerLaw(xx, pest);
+plot(xx, yy);
+plot(x, y, 'o');                    % plot original data
+xlabel('Size [m]');
+ylabel('Weight [kg]');
+legend('fit', 'data', 'location', 'northwest');
diff --git a/regression/code/plotlsqcurvefitpower.m b/regression/code/plotlsqcurvefitpower.m
new file mode 100644
index 0000000..3d39605
--- /dev/null
+++ b/regression/code/plotlsqcurvefitpower.m
@@ -0,0 +1,13 @@
+meansquarederrorline;               % generate data
+p0 = [2.0, 1.0];
+pest = lsqcurvefit(@powerLaw, p0, x, y)
+
+hold on;
+% generate x-values for plottig the fit:
+xx = min(x):0.01:max(x);
+yy = powerLaw(xx, pest);
+plot(xx, yy);                       % plot fit
+plot(x, y, 'o');                    % plot original data
+xlabel('Size [m]');
+ylabel('Weight [kg]');
+legend('fit', 'data', 'location', 'northwest');
diff --git a/regression/code/powerLaw.m b/regression/code/powerLaw.m
new file mode 100644
index 0000000..f51898e
--- /dev/null
+++ b/regression/code/powerLaw.m
@@ -0,0 +1,9 @@
+function y = powerLaw(x, p)
+% Power law function y = c*x^alpha.
+%
+% Arguments: x, vector of the x-data values.
+%            p, vector with parameter values c and alpha
+%
+% Returns:   y, vector of the computed y-data values.
+  y = p(1)*x.^p(2);
+end
diff --git a/regression/exercises/Makefile b/regression/exercises/Makefile
index 27691d9..ba0b38c 100644
--- a/regression/exercises/Makefile
+++ b/regression/exercises/Makefile
@@ -1,34 +1,4 @@
-TEXFILES=$(wildcard exercises??.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
+TEXFILES=$(wildcard gradientdescent-?.tex)
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
+include ../../exercises.mk
 
-pdf : $(SOLUTIONS) $(EXERCISES)
-
-exercises : $(EXERCISES)
-
-solutions : $(SOLUTIONS)
-
-$(SOLUTIONS) : solutions%.pdf : exercises%.tex instructions.tex
-	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
-	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
-	rm $(patsubst %.pdf,%,$@).[!p]*
-
-$(EXERCISES) : %.pdf : %.tex instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-
-watch :
-	while true; do ! make -q pdf && make pdf; sleep 0.5; done
-
-watchexercises :
-	while true; do ! make -q exercises && make exercises; sleep 0.5; done
-
-watchsolutions :
-	while true; do ! make -q solutions && make solutions; sleep 0.5; done
-
-clean :
-	rm -f *~ *.aux *.log *.out
-
-cleanup : clean
-	rm -f $(SOLUTIONS) $(EXERCISES)
diff --git a/regression/code/checkdescent.m b/regression/exercises/checkdescent.m
similarity index 100%
rename from regression/code/checkdescent.m
rename to regression/exercises/checkdescent.m
diff --git a/regression/code/descent.m b/regression/exercises/descent.m
similarity index 100%
rename from regression/code/descent.m
rename to regression/exercises/descent.m
diff --git a/regression/code/descentfit.m b/regression/exercises/descentfit.m
similarity index 100%
rename from regression/code/descentfit.m
rename to regression/exercises/descentfit.m
diff --git a/statistics/exercises/fitting.tex b/regression/exercises/exercises00-de.tex
similarity index 100%
rename from statistics/exercises/fitting.tex
rename to regression/exercises/exercises00-de.tex
diff --git a/regression/exercises/exp2errorsurface.m b/regression/exercises/exp2errorsurface.m
new file mode 100644
index 0000000..7dc4e4f
--- /dev/null
+++ b/regression/exercises/exp2errorsurface.m
@@ -0,0 +1,15 @@
+expdecaydata;                               % generate data
+close all;
+
+cs = 0.5:0.02:1.5;                          % range of c values
+taus = 4.0:0.2:16.0;                        % range of tau values
+msesurf = zeros(length(taus), length(cs));  % mean squared error
+for i = 1:length(taus)
+    for k = 1:length(cs)
+        msesurf(i, k) = mean((voltage - ...
+                              exp2func(time, [cs(k), taus(i)])).^2);
+    end
+end
+%surface(cs, taus, mses)
+%contour(cs, taus, mses)
+contourf(cs, taus, log10(msesurf))
diff --git a/regression/exercises/exp2fit.m b/regression/exercises/exp2fit.m
new file mode 100644
index 0000000..47e833a
--- /dev/null
+++ b/regression/exercises/exp2fit.m
@@ -0,0 +1,15 @@
+expdecaydata;
+close all;
+
+p0 = [0.5, 6.0];
+pest = lsqcurvefit(@(p, x) exp2func(x, p), p0, time, voltage);
+fprintf('c=%4.1fmV, tau=%4.1fms\n', pest(1), pest(2));
+
+hold on;
+plot(time, voltage)
+plot(time, exp2func(time, pest), 'LineWidth', 2)
+hold off;
+xlabel('time [ms]')
+ylabel('voltage [mv]')
+savefigpdf(gcf, 'exp2fit.pdf', 15, 8.5);
+
diff --git a/regression/exercises/exp2fit.pdf b/regression/exercises/exp2fit.pdf
new file mode 100644
index 0000000..de17294
Binary files /dev/null and b/regression/exercises/exp2fit.pdf differ
diff --git a/regression/exercises/exp2func.m b/regression/exercises/exp2func.m
new file mode 100644
index 0000000..6bdb6c0
--- /dev/null
+++ b/regression/exercises/exp2func.m
@@ -0,0 +1,4 @@
+function x = exp2func(t, p)
+% return the exponential function x = c*e^{-t/tau}
+  x = p(1)*exp(-t./p(2));
+end
diff --git a/regression/exercises/exp2plot.m b/regression/exercises/exp2plot.m
new file mode 100644
index 0000000..cc2dd91
--- /dev/null
+++ b/regression/exercises/exp2plot.m
@@ -0,0 +1,27 @@
+...
+    
+p0 = [0.5, 6.0];
+[pest, ps, mses] = gradientdescent(time, voltage, @exp2func, p0, 1.0, 0.00001);
+fprintf('c=%4.1fmV, tau=%4.1fms\n', pest(1), pest(2));
+
+subplot(1, 2, 1);
+hold on;
+%surface(cs, taus, msesurf);
+%plot3(ps(1,:), ps(2,:), mses, '.r');
+%view(80, 35);
+contourf(cs, taus, log10(msesurf));
+plot(ps(1,:), ps(2,:), '.r');
+hold off;
+xlabel('c [mV]')
+ylabel('tau [ms]')
+zlabel('mse [mV^2]')
+
+subplot(1, 2, 2);
+hold on;
+plot(time, voltage)
+plot(time, exp2func(time, pest), 'LineWidth', 2)
+hold off;
+xlabel('time [ms]')
+ylabel('voltage [mv]')
+savefigpdf(gcf, 'exp2plot.pdf', 15, 8.5);
+
diff --git a/regression/exercises/exp2plot.pdf b/regression/exercises/exp2plot.pdf
new file mode 100644
index 0000000..f42670b
Binary files /dev/null and b/regression/exercises/exp2plot.pdf differ
diff --git a/regression/exercises/expdecay.m b/regression/exercises/expdecay.m
new file mode 100644
index 0000000..febd36a
--- /dev/null
+++ b/regression/exercises/expdecay.m
@@ -0,0 +1,4 @@
+function x = expdecay(t, tau)
+% return the exponential function x = e^{-t/tau}
+  x = exp(-t./tau);
+end
diff --git a/regression/exercises/expdecaydata.m b/regression/exercises/expdecaydata.m
new file mode 100644
index 0000000..be5b0c7
--- /dev/null
+++ b/regression/exercises/expdecaydata.m
@@ -0,0 +1,10 @@
+tau = 10.0;                      % membrane time constant in ms
+dt = 0.05;                       % sampling interval in ms
+noisesd = 0.05;                  % measurement noise in mV
+
+time = 0.0:dt:6*tau;             % time vector
+voltage = expdecay(time, tau);   % exponential decay
+voltage = voltage + noisesd*randn(1, length(voltage));  % plus noise
+
+plot(time, voltage);
+
diff --git a/regression/exercises/expdecaydescent.m b/regression/exercises/expdecaydescent.m
new file mode 100644
index 0000000..b75b63b
--- /dev/null
+++ b/regression/exercises/expdecaydescent.m
@@ -0,0 +1,25 @@
+function [tau, taus, mses] = expdecaydescent(t, x, tau0, epsilon, threshold)
+% Gradient descent for fitting a decaying exponential.
+%
+% Arguments: t, vector of time points.
+%            x, vector of the corresponding measured data values.
+%            tau0, initial value for the time constant. 
+%            epsilon: factor multiplying the gradient.
+%            threshold: minimum value for gradient
+%
+% Returns:   tau, the final value of the time constant.
+%            taus: vector with all the tau-values traversed.
+%            mses: vector with the corresponding mean squared errors
+  tau = tau0;
+  gradient = 1000.0;
+  taus = [];
+  mses = [];
+  count = 1;
+  while abs(gradient) > threshold
+      taus(count) = tau;
+      mses(count) = expdecaymse(t, x, tau);
+      gradient = expdecaygradient(t, x, tau);
+      tau = tau - epsilon * gradient; 
+      count = count + 1;
+  end
+end
diff --git a/regression/exercises/expdecaygradient.m b/regression/exercises/expdecaygradient.m
new file mode 100644
index 0000000..1b10400
--- /dev/null
+++ b/regression/exercises/expdecaygradient.m
@@ -0,0 +1,11 @@
+function gradient = expdecaygradient(t, x, tau)
+% Gradient of MSE for a decaying exponential.
+%
+% Arguments: t, vector of time points.
+%            x, vector of the corresponding measured data values.
+%            tau, value for the time constant. 
+%
+% Returns: gradient: the derivative of the MSE with respect to tau.
+  h = 1e-7;   % stepsize for derivative
+  gradient = (expdecaymse(t, x, tau+h) - expdecaymse(t, x, tau))/h;
+end
diff --git a/regression/exercises/expdecaygradientplot.m b/regression/exercises/expdecaygradientplot.m
new file mode 100644
index 0000000..62baf42
--- /dev/null
+++ b/regression/exercises/expdecaygradientplot.m
@@ -0,0 +1,15 @@
+...
+
+gradients = zeros(length(taus), 1);
+for i = 1:length(taus)
+  gradients(i) = expdecaygradient(time, voltage, taus(i));
+end
+
+subplot(2, 1, 2)
+plot(taus, gradients);
+xlabel('tau [ms]')
+ylabel('gradient [mV^2/ms]')
+savefigpdf(gcf, 'expdecaygradientplot.pdf', 10, 10);
+
+
+
diff --git a/regression/exercises/expdecaygradientplot.pdf b/regression/exercises/expdecaygradientplot.pdf
new file mode 100644
index 0000000..ac46b86
Binary files /dev/null and b/regression/exercises/expdecaygradientplot.pdf differ
diff --git a/regression/exercises/expdecaymse.m b/regression/exercises/expdecaymse.m
new file mode 100644
index 0000000..4e16dd6
--- /dev/null
+++ b/regression/exercises/expdecaymse.m
@@ -0,0 +1,10 @@
+function mse = expdecaymse(t, x, tau)
+% Mean squared error for a decaying exponential.
+%
+% Arguments: t, vector of time points.
+%            x, vector of the corresponding measured data values.
+%            tau, value for the time constant. 
+%
+% Returns:   mse: mean squared error
+  mse = mean((x - expdecay(t, tau)).^2);
+end
diff --git a/regression/exercises/expdecaymseplot.m b/regression/exercises/expdecaymseplot.m
new file mode 100644
index 0000000..1e1ebd9
--- /dev/null
+++ b/regression/exercises/expdecaymseplot.m
@@ -0,0 +1,13 @@
+expdecaydata;                             % generate data
+close all;
+
+taus = 0.0:0.1:20.0;
+mses = zeros(length(taus), 1);
+for i = 1:length(taus)
+  mses(i) = expdecaymse(time, voltage, taus(i));
+end
+
+subplot(2, 1, 1)
+plot(taus, mses);
+xlabel('tau [ms]')
+ylabel('mean squared error [mV^2]')
diff --git a/regression/exercises/expdecayplot.m b/regression/exercises/expdecayplot.m
new file mode 100644
index 0000000..baa6d16
--- /dev/null
+++ b/regression/exercises/expdecayplot.m
@@ -0,0 +1,27 @@
+expdecaydata;                             % generate data
+
+tau0 = 2.0;
+eps = 1.0;
+thresh = 0.00001;
+[tauest, taus, mses] = expdecaydescent(time, voltage, tau0, eps, thresh);
+
+subplot(2, 2, 1);                         % top left panel
+hold on;
+plot(taus, '-o');
+plot([1, length(taus)], [tau, tau], 'k'); % line indicating true tau
+hold off;
+xlabel('Iteration');
+ylabel('tau [ms]');
+subplot(2, 2, 3);                         % bottom left panel
+plot(mses, '-o');
+xlabel('Iteration steps');
+ylabel('MSE');
+subplot(1, 2, 2);                         % right panel
+hold on;
+% generate x-values for plottig the fit:
+plot(time, voltage);                      % plot original data
+plot(time, expdecay(time, tauest), 'LineWidth', 2); % plot fit
+xlabel('Time [ms]');
+ylabel('Voltage [mV]');
+legend('data', 'fit', 'location', 'northeast');
+savefigpdf(gcf, 'expdecayplot.pdf', 15, 10);
diff --git a/regression/exercises/expdecayplot.pdf b/regression/exercises/expdecayplot.pdf
new file mode 100644
index 0000000..8b16af9
Binary files /dev/null and b/regression/exercises/expdecayplot.pdf differ
diff --git a/regression/exercises/gradientdescent-1.tex b/regression/exercises/gradientdescent-1.tex
new file mode 100644
index 0000000..b940202
--- /dev/null
+++ b/regression/exercises/gradientdescent-1.tex
@@ -0,0 +1,200 @@
+\documentclass[12pt,a4paper,pdftex]{exam}
+
+\newcommand{\exercisetopic}{Gradient descent}
+\newcommand{\exercisenum}{9}
+\newcommand{\exercisedate}{December 22th, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document}
+
+\input{../../exercisestitle}
+
+\begin{questions}
+
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+  \question \qt{Read sections 8.1 -- 8.5 of chapter 8 on ``optimization
+    and gradient descent!}\vspace{-3ex}
+
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+  \question \qt{Fitting the time constant of an exponential function}
+  Let's assume we record the membrane potential from a photoreceptor
+  neuron.  We define the resting potential of the neuron to be at
+  0\,mV. By means of a brief current injection we increase the
+  membrane potential by exactly 1\,mV. We then record how the membrane
+  potential decays exponentially down to the resting potential. We are
+  interested in the membrane time constant and therefore want to fit
+  an exponential function to the recorded time course of the membrane
+  potential.
+
+  \begin{parts}
+    \part Implement (and document!) the exponential function 
+    \begin{equation}
+      \label{expfunc}
+      x(t) = e^{-t/\tau} \quad , \qquad \tau \in \reZ
+    \end{equation}
+    with the membrane time constant $\tau$ as a matlab function
+    \code{expdecay(t, tau)} that takes as arguments a vector of time
+    points and the value of the membrane time constant. The function
+    returns \eqnref{expfunc} computed for each time point as a vector.
+    \begin{solution}
+      \lstinputlisting{expdecay.m}
+    \end{solution}
+
+    \part Let's first generate the data. Set the membrane time
+    constant to 10\,ms. Generate a time vector with sample times
+    between zero and six times the membrane time constant and a
+    sampling interval of 0.05\,ms. Then compute a vector containing
+    the corresponding measurements of the membrane potential using the
+    \code{expdecay()} function and adding some measurement noise with
+    a standard deviation of 0.05\.mV (\code{randn()} function).
+    Plot the data.
+    \begin{solution}
+      \lstinputlisting{expdecaydata.m}
+    \end{solution}
+
+    \part Write a function that returns the mean squared error.  The
+    function takes as arguments the measured data (time points and
+    corresponding voltage measurements) and a value for the membrane
+    time constant at which the mean squred error should be
+    evaluated. Plot the mean squared error as a function of $\tau$.
+    \begin{solution}
+      \lstinputlisting{expdecaymse.m}
+      \lstinputlisting{expdecaymseplot.m}
+    \end{solution}
+
+    \part Write a function that returns the gradient of the mean
+    squared error, i.e. its derivative with respect to the $\tau$
+    parameter.  The function takes as arguments the measured data
+    (time points and corresponding voltage measurements) and a value
+    for the membrane time constant at which the gradient should be
+    evaluated. Plot the gradient as a function of $\tau$.
+    \begin{solution}
+      \lstinputlisting{expdecaygradient.m}
+      \newsolutionpage
+      \lstinputlisting{expdecaygradientplot.m}
+      \includegraphics{expdecaygradientplot}
+    \end{solution}
+
+    \part Implement the gradient descent algorithm for finding the
+    least squares for the exponential function \eqref{expfunc}. The
+    function takes as arguments the measured data, an initial value
+    for the estimation of the membrane time constant, the $\epsilon$
+    factor, and the threshold for the length of the gradient where to
+    terminate the algorithm. The function should return the estimated
+    membrane time constant at the minimum of the mean squared error,
+    and a vector with the time constants as well as a vector with the
+    mean squared errors for each step of the algorithm.
+    \begin{solution}
+      \lstinputlisting{expdecaydescent.m}
+    \end{solution}
+
+    \part Call the gradient descent function with the generated data.
+    Watch the value of the gradient and of tau and adapt $\epsilon$
+    and the threshold accordingly (they differ quite dramatically from
+    the ones in the script for the cubic fit).
+
+    \part Generate three plots: (i) the values of the time constant
+    for each iteration step, (ii) the mean squared error for each
+    iteration step, and (iii) the measured data and the fitted
+    exponential function.
+    \begin{solution}
+      \lstinputlisting{expdecayplot.m}
+      \includegraphics{expdecayplot}
+    \end{solution}
+
+  \end{parts}
+
+  \continuepage
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+  \question \qt{Read sections 8.6 -- 8.8 of chapter 8 on ``optimization
+    and gradient descent!}\vspace{-3ex}
+
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+  \question \qt{Fitting an exponential function with two parameters}
+  Usually we do not know the exact value of the voltage step. So let's
+  make this a paramter, too:
+  \begin{equation}
+    \label{exp2func}
+    x(t) = c e^{-t/\tau} \quad , \qquad c, \tau \in \reZ
+  \end{equation}
+  
+  \begin{parts}
+    \part Implement the exponential function \eqref{exp2func} as a
+    matlab function \code{exp2func(t, p)} that takes as arguments a
+    vector of time points and a vector with the two parameter values
+    $c$ and $\tau$. The function returns \eqnref{exp2func} computed for
+    each time point as a vector.
+    \begin{solution}
+      \lstinputlisting{exp2func.m}
+    \end{solution}
+
+    \part Plot the error surface, the mean squared error, for a range
+    of values for the two parameters $c$ and $\tau$. Try the
+    \code{surface()}, the \code{contour()} as well as the
+    \code{contourf()} functions to visualize the error surface for the
+    data from the previous exercise. You may also want to try to plot
+    the logarithm of the mean squared error.
+    \begin{solution}
+      \lstinputlisting{exp2errorsurface.m}
+    \end{solution}
+
+    \newsolutionpage
+    \part Implement the gradient descent algorithm for finding the
+    least squares of an arbitrary function. The function takes as
+    arguments the measured data, a vector holding the initial values
+    of the function parameters, the $\epsilon$ factor, and the
+    threshold for the length of the gradient where to terminate the
+    algorithm. The function should return the estimated parameter
+    values in a vector, and a 2D vector with the parameter values as
+    well as a vector with the mean squared errors for each step of the
+    algorithm.
+    \begin{solution}
+      \lstinputlisting{gradientdescent.m}
+    \end{solution}
+
+    \part Call the gradient descent function with the generated data.
+    Set $\epsilon=1.0$ and the threshold value initially to 0.001.
+
+    \part Plot the path of the gradient descent algorithm into the
+    error surface.
+
+    Why does (potentially) the path of the gradient descent algorithm
+    appear to be not perpendicular to the contour lines of the error
+    surface?
+    \begin{solution}
+      Because on the plot the $c$ and $\tau$ axes are (in general) not
+      equally scaled. That is a unit step in $c$ direction covers a
+      physical distance on paper or the screen than a unit stpe in
+      $\tau$ direction. Use \code{axis equal} to force the plot to
+      have equally scaled $c$ and $\tau$ axes.
+    \end{solution}
+
+    \part Plot the data together with the fitted exponential function.
+
+    \part Then adapt $\epsilon$ and the threshold value to improve the
+    convergence of the algortihm to the minimum of the cost function.
+    \begin{solution}
+      \lstinputlisting{exp2plot.m}
+      \includegraphics{exp2plot}
+    \end{solution}
+
+    \part Use the function \code{lsqcurvefit()} provided by matlab to
+    find $c$ and $\tau$ for the exponential function describing the
+    data.  Compare the resulting fit parameters of this function with
+    the ones of your gradient descent algorithm.  Plot the data
+    together with the fitted exponential function.
+    \begin{solution}
+      \lstinputlisting{exp2fit.m}
+      \includegraphics{exp2fit}\\
+      Resulting values for $c$ and $\tau$ are similar but not identical.
+    \end{solution}
+
+  \end{parts}
+
+\end{questions}
+
+\end{document}
diff --git a/regression/exercises/exercises01.tex b/regression/exercises/gradientdescent-2.tex
similarity index 63%
rename from regression/exercises/exercises01.tex
rename to regression/exercises/gradientdescent-2.tex
index 9e7c5b3..e10be99 100644
--- a/regression/exercises/exercises01.tex
+++ b/regression/exercises/gradientdescent-2.tex
@@ -1,63 +1,21 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 10\stitle}}{{\bfseries\large Gradient descent}}{{\bfseries\large December 16th, 2019}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solution:}\par\noindent}
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\newcommand{\exercisetopic}{Gradient descent}
+\newcommand{\exercisenum}{9}
+\newcommand{\exercisedate}{December 22th, 2020}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
+\input{../../exercisestitle}
 
 \begin{questions}
 
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
   \question We want to fit the straigth line \[ y = mx+b \] to the
   data in the file \emph{lin\_regression.mat}.
 
@@ -65,7 +23,7 @@
   (\code{meanSquaredError()}), and the gradient
   (\code{meanSquaredGradient()}) (read chapter 8 ``Optimization and
   gradient descent'' in the script, in particular section 8.4 and
-  exercise 8.4!). With these functions in place we here want to
+  exercise 8.5!). With these functions in place we here want to
   implement a gradient descend algorithm that finds the minimum of the
   cost function and thus the slope and intercept of the straigth line
   that minimizes the squared distance to the data values.
@@ -96,7 +54,7 @@
     the parameter values at the minimum of the cost function and a vector
     with the value of the cost function at each step of the algorithm.
     \begin{solution}
-      \lstinputlisting{../code/descent.m}
+      \lstinputlisting{descent.m}
     \end{solution}
 
     \part Plot the data and the straight line with the parameter
@@ -105,7 +63,7 @@
     \part Plot the development of the costs as a function of the
     iteration step.
     \begin{solution}
-      \lstinputlisting{../code/descentfit.m}
+      \lstinputlisting{descentfit.m}
     \end{solution}
 
     \part For checking the gradient descend method from (a) compare
@@ -116,7 +74,7 @@
     minimum gradient. What are good values such that the gradient
     descent gets closest to the true minimum of the cost function?
     \begin{solution}
-      \lstinputlisting{../code/checkdescent.m}
+      \lstinputlisting{checkdescent.m}
     \end{solution}
 
     \part Use the functions \code{polyfit()} and \code{lsqcurvefit()}
@@ -124,7 +82,7 @@
     line that fits the data. Compare the resulting fit parameters of
     those functions with the ones of your gradient descent algorithm.
     \begin{solution}
-      \lstinputlisting{../code/linefit.m}
+      \lstinputlisting{linefit.m}
     \end{solution}
 
   \end{parts}
diff --git a/regression/exercises/gradientdescent.m b/regression/exercises/gradientdescent.m
new file mode 100644
index 0000000..b7d61a5
--- /dev/null
+++ b/regression/exercises/gradientdescent.m
@@ -0,0 +1,39 @@
+function [p, ps, mses] = gradientdescent(t, x, func, p0, epsilon, threshold)
+% Gradient descent for leas squares fit.
+%
+% Arguments: t, vector of time points.
+%            x, vector of the corresponding measured data values.
+%            p0, vector with initial parameter values.
+%            func, function to be fit to the data. 
+%            epsilon: factor multiplying the gradient.
+%            threshold: minimum value for gradient
+%
+% Returns:   p, vector with the final estimates of the parameter values.
+%            ps: 2D vector with all the parameter values traversed.
+%            mses: vector with the corresponding mean squared errors
+  p = p0;
+  gradient = ones(size(p0)) * 1000.0;
+  ps = [];
+  mses = [];
+  while norm(gradient) > threshold
+      ps = [ps, p(:)];
+      mses = [mses, meansquarederror(t, x, func, p)];
+      gradient = msegradient(t, x, func, p);
+      p = p - epsilon * gradient; 
+  end
+end
+
+function mse = meansquarederror(t, x, func, p)
+  mse = mean((x - func(t, p)).^2);
+end
+
+function gradmse = msegradient(t, x, func, p)
+  gradmse = zeros(size(p));
+  h = 1e-7;   % stepsize for derivative
+  ph = eye(length(p))*h;  % ... for each parameter
+  mse = meansquarederror(t, x, func, p);
+  for i = 1:length(p)     % for each coordinate ...
+    msepi = meansquarederror(t, x, func, p(:) + ph(:,i));
+    gradmse(i) = (msepi - mse)/h;
+  end
+end
diff --git a/regression/exercises/instructions.tex b/regression/exercises/instructions.tex
deleted file mode 100644
index 3041d3e..0000000
--- a/regression/exercises/instructions.tex
+++ /dev/null
@@ -1,6 +0,0 @@
-\vspace*{-7.8ex}
-\begin{center}
-\textbf{\Large Introduction to Scientific Computing}\\[2.3ex]
-{\large Jan Grewe, Jan Benda}\\[-3ex]
-Neuroethology Lab \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
diff --git a/regression/code/linefit.m b/regression/exercises/linefit.m
similarity index 100%
rename from regression/code/linefit.m
rename to regression/exercises/linefit.m
diff --git a/regression/exercises/savefigpdf.m b/regression/exercises/savefigpdf.m
new file mode 100644
index 0000000..97a28a3
--- /dev/null
+++ b/regression/exercises/savefigpdf.m
@@ -0,0 +1,28 @@
+function savefigpdf(fig, name, width, height)
+% Saves figure fig in pdf file name.pdf with appropriately set page size
+% and fonts
+
+  % default width:
+  if nargin < 3
+      width = 11.7;
+  end
+  % default height:
+  if nargin < 4
+      height = 9.0;
+  end
+
+  % paper:
+  set(fig, 'PaperUnits', 'centimeters');
+  set(fig, 'PaperSize', [width height]);
+  set(fig, 'PaperPosition', [0.0 0.0 width height]);
+  set(fig, 'Color', 'white')
+
+  % font:
+  set(findall(fig, 'type', 'axes'), 'FontSize', 12)
+  set(findall(fig, 'type', 'text'), 'FontSize', 12)
+
+  % save:
+  saveas(fig, name, 'pdf')
+
+end
+
diff --git a/regression/lecture/cubiccost.py b/regression/lecture/cubiccost.py
new file mode 100644
index 0000000..ac145fa
--- /dev/null
+++ b/regression/lecture/cubiccost.py
@@ -0,0 +1,82 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mt
+from plotstyle import *
+
+def create_data():
+    # wikipedia:
+    # Generally, males vary in total length from 250 to 390 cm and
+    # weigh between 90 and 306 kg
+    c = 6
+    x = np.arange(2.2, 3.9, 0.05)
+    y = c * x**3.0
+    rng = np.random.RandomState(32281)
+    noise = rng.randn(len(x))*50
+    y += noise
+    return x, y, c
+
+    
+def plot_mse(ax, x, y, c):
+    ccs = np.linspace(0.5, 10.0, 200)
+    mses = np.zeros(len(ccs))
+    for i, cc in enumerate(ccs):
+        mses[i] = np.mean((y-(cc*x**3.0))**2.0)
+    imin = np.argmin(mses)
+        
+    ax.plot(ccs, mses, **lsAm)
+    ax.plot(c, 500.0, **psB)
+    ax.plot(ccs[imin], mses[imin], **psC)
+    ax.annotate('Minimum of\ncost\nfunction',
+                xy=(ccs[imin], mses[imin]*1.2), xycoords='data',
+                xytext=(4, 7000), textcoords='data', ha='left',
+                arrowprops=dict(arrowstyle="->", relpos=(0.2,0.0),
+                connectionstyle="angle3,angleA=10,angleB=90") )
+    ax.text(2.2, 500, 'True\nparameter\nvalue')
+    ax.annotate('', xy=(c-0.2, 500), xycoords='data',
+                xytext=(4.1, 700), textcoords='data', ha='left',
+                arrowprops=dict(arrowstyle="->", relpos=(1.0,0.0),
+                connectionstyle="angle3,angleA=-10,angleB=0") )
+    ax.set_xlabel('c')
+    ax.set_ylabel('Mean squared error')
+    ax.set_xlim(2, 8.2)
+    ax.set_ylim(0, 10000)
+    ax.set_xticks(np.arange(2.0, 8.1, 2.0))
+    ax.set_yticks(np.arange(0, 10001, 5000))
+
+    
+def plot_mse_min(ax, x, y, c):
+    ccs = np.arange(0.5, 10.0, 0.05)
+    mses = np.zeros(len(ccs))
+    for i, cc in enumerate(ccs):
+        mses[i] = np.mean((y-(cc*x**3.0))**2.0)
+    imin = np.argmin(mses)
+    di = 25
+    i0 = 16
+    dimin = np.argmin(mses[i0::di])*di + i0
+        
+    ax.plot(c, 500.0, **psB)
+    ax.plot(ccs, mses, **lsAm)
+    ax.plot(ccs[i0::di], mses[i0::di], **psAm)
+    ax.plot(ccs[dimin], mses[dimin], **psD)
+    #ax.plot(ccs[imin], mses[imin], **psCm)
+    ax.annotate('Estimated\nminimum of\ncost\nfunction',
+                xy=(ccs[dimin], mses[dimin]*1.2), xycoords='data',
+                xytext=(4, 6700), textcoords='data', ha='left',
+                arrowprops=dict(arrowstyle="->", relpos=(0.8,0.0),
+                connectionstyle="angle3,angleA=0,angleB=85") )
+    ax.set_xlabel('c')
+    ax.set_xlim(2, 8.2)
+    ax.set_ylim(0, 10000)
+    ax.set_xticks(np.arange(2.0, 8.1, 2.0))
+    ax.set_yticks(np.arange(0, 10001, 5000))
+    ax.yaxis.set_major_formatter(mt.NullFormatter())
+
+
+if __name__ == "__main__":
+    x, y, c = create_data()
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 1.1*figure_height))
+    fig.subplots_adjust(**adjust_fs(left=8.0, right=1.2))
+    plot_mse(ax1, x, y, c)
+    plot_mse_min(ax2, x, y, c)
+    fig.savefig("cubiccost.pdf")
+    plt.close()
diff --git a/regression/lecture/cubicerrors.py b/regression/lecture/cubicerrors.py
index e072d9e..d74c3bf 100644
--- a/regression/lecture/cubicerrors.py
+++ b/regression/lecture/cubicerrors.py
@@ -15,28 +15,13 @@ def create_data():
     return x, y, c
 
     
-def plot_data(ax, x, y, c):
-    ax.plot(x, y, zorder=10, **psAm)
-    xx = np.linspace(2.1, 3.9, 100)
-    ax.plot(xx, c*xx**3.0, zorder=5, **lsBm)
-    for cc in [0.25*c, 0.5*c, 2.0*c, 4.0*c]:
-        ax.plot(xx, cc*xx**3.0, zorder=5, **lsDm)
-    ax.set_xlabel('Size x', 'm')
-    ax.set_ylabel('Weight y', 'kg')
-    ax.set_xlim(2, 4)
-    ax.set_ylim(0, 400)
-    ax.set_xticks(np.arange(2.0, 4.1, 0.5))
-    ax.set_yticks(np.arange(0, 401, 100))
-    
-    
 def plot_data_errors(ax, x, y, c):
     ax.set_xlabel('Size x', 'm')
-    #ax.set_ylabel('Weight y', 'kg')
+    ax.set_ylabel('Weight y', 'kg')
     ax.set_xlim(2, 4)
     ax.set_ylim(0, 400)
     ax.set_xticks(np.arange(2.0, 4.1, 0.5))
     ax.set_yticks(np.arange(0, 401, 100))
-    ax.set_yticklabels([])
     ax.annotate('Error',
                 xy=(x[28]+0.05, y[28]+60), xycoords='data',
                 xytext=(3.4, 70), textcoords='data', ha='left',
@@ -52,31 +37,30 @@ def plot_data_errors(ax, x, y, c):
         yy = [c*x[i]**3.0, y[i]]
         ax.plot(xx, yy, zorder=5, **lsDm)
 
+        
 def plot_error_hist(ax, x, y, c):
     ax.set_xlabel('Squared error')
     ax.set_ylabel('Frequency')
-    bins = np.arange(0.0, 1250.0, 100)
+    bins = np.arange(0.0, 11000.0, 750)
     ax.set_xlim(bins[0], bins[-1])
-    #ax.set_ylim(0, 35)
-    ax.set_xticks(np.arange(bins[0], bins[-1], 200))
-    #ax.set_yticks(np.arange(0, 36, 10))
+    ax.set_ylim(0, 15)
+    ax.set_xticks(np.arange(bins[0], bins[-1], 5000))
+    ax.set_yticks(np.arange(0, 16, 5))
     errors = (y-(c*x**3.0))**2.0
     mls = np.mean(errors)
     ax.annotate('Mean\nsquared\nerror',
                 xy=(mls, 0.5), xycoords='data',
-                xytext=(800, 3), textcoords='data', ha='left',
+                xytext=(4500, 6), textcoords='data', ha='left',
                 arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
                 connectionstyle="angle3,angleA=10,angleB=90") )
     ax.hist(errors, bins, **fsC)
 
 
-
 if __name__ == "__main__":
     x, y, c = create_data()
-    fig, (ax1, ax2) = plt.subplots(1, 2)
-    fig.subplots_adjust(wspace=0.2, **adjust_fs(left=6.0, right=1.2))
-    plot_data(ax1, x, y, c)
-    plot_data_errors(ax2, x, y, c)
-    #plot_error_hist(ax2, x, y, c)
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 0.9*figure_height))
+    fig.subplots_adjust(wspace=0.5, **adjust_fs(left=6.0, right=1.2))
+    plot_data_errors(ax1, x, y, c)
+    plot_error_hist(ax2, x, y, c)
     fig.savefig("cubicerrors.pdf")
     plt.close()
diff --git a/regression/lecture/cubicfunc.py b/regression/lecture/cubicfunc.py
index e8c0565..3b34c03 100644
--- a/regression/lecture/cubicfunc.py
+++ b/regression/lecture/cubicfunc.py
@@ -2,7 +2,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 from plotstyle import *
 
-if __name__ == "__main__":
+def create_data():
     # wikipedia:
     # Generally, males vary in total length from 250 to 390 cm and
     # weigh between 90 and 306 kg
@@ -12,10 +12,20 @@ if __name__ == "__main__":
     rng = np.random.RandomState(32281)
     noise = rng.randn(len(x))*50
     y += noise
+    return x, y, c
+
     
-    fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.4*figure_height))
-    fig.subplots_adjust(**adjust_fs(left=6.0, right=1.2))
-    
+def plot_data(ax, x, y):
+    ax.plot(x, y, **psA)
+    ax.set_xlabel('Size x', 'm')
+    ax.set_ylabel('Weight y', 'kg')
+    ax.set_xlim(2, 4)
+    ax.set_ylim(0, 400)
+    ax.set_xticks(np.arange(2.0, 4.1, 0.5))
+    ax.set_yticks(np.arange(0, 401, 100))
+
+        
+def plot_data_fac(ax, x, y, c):
     ax.plot(x, y, zorder=10, **psA)
     xx = np.linspace(2.1, 3.9, 100)
     ax.plot(xx, c*xx**3.0, zorder=5, **lsB)
@@ -27,6 +37,14 @@ if __name__ == "__main__":
     ax.set_ylim(0, 400)
     ax.set_xticks(np.arange(2.0, 4.1, 0.5))
     ax.set_yticks(np.arange(0, 401, 100))
+
     
+if __name__ == "__main__":
+    x, y, c = create_data()
+    print('n=%d' % len(x))
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 0.9*figure_height))
+    fig.subplots_adjust(wspace=0.5, **adjust_fs(fig, left=6.0, right=1.2))
+    plot_data(ax1, x, y)
+    plot_data_fac(ax2, x, y, c)
     fig.savefig("cubicfunc.pdf")
     plt.close()
diff --git a/regression/lecture/cubicgradient.py b/regression/lecture/cubicgradient.py
new file mode 100644
index 0000000..e8557d1
--- /dev/null
+++ b/regression/lecture/cubicgradient.py
@@ -0,0 +1,89 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mt
+from plotstyle import *
+
+def create_data():
+    # wikipedia:
+    # Generally, males vary in total length from 250 to 390 cm and
+    # weigh between 90 and 306 kg
+    c = 6
+    x = np.arange(2.2, 3.9, 0.05)
+    y = c * x**3.0
+    rng = np.random.RandomState(32281)
+    noise = rng.randn(len(x))*50
+    y += noise
+    return x, y, c
+
+
+def gradient_descent(x, y):
+    n = 20
+    dc = 0.01
+    eps = 0.0001
+    cc = 1.1
+    cs = []
+    mses = []
+    for k in range(n):
+        m0 = np.mean((y-(cc*x**3.0))**2.0)
+        m1 = np.mean((y-((cc+dc)*x**3.0))**2.0)
+        dmdc = (m1 - m0)/dc
+        cs.append(cc)
+        mses.append(m0)
+        cc -= eps*dmdc
+    return cs, mses
+
+    
+def plot_gradient(ax, x, y, c):
+    ccs = np.linspace(0.5, 10.0, 200)
+    mses = np.zeros(len(ccs))
+    for i, cc in enumerate(ccs):
+        mses[i] = np.mean((y-(cc*x**3.0))**2.0)
+    cmin = ccs[np.argmin(mses)]
+    gradient = np.diff(mses)/(ccs[1]-ccs[0])
+        
+    ax.plot([cmin, cmin], [-10000, 10000], **lsSpine)
+    ax.plot([ccs[0], ccs[-1]], [0, 0], **lsSpine)
+    ax.plot(ccs[:-1], gradient, **lsBm)
+    ax.set_xlabel('c')
+    ax.set_ylabel('Derivative')
+    ax.set_xlim(0, 10)
+    ax.set_ylim(-10000, 10000)
+    ax.set_xticks(np.arange(0.0, 10.1, 2.0))
+    ax.set_yticks(np.arange(-10000, 10001, 10000))
+    ax.set_yticklabels(['', '0', ''])
+
+    
+def plot_mse(ax, x, y, c):
+    ccs = np.linspace(0.5, 10.0, 200)
+    mses = np.zeros(len(ccs))
+    for i, cc in enumerate(ccs):
+        mses[i] = np.mean((y-(cc*x**3.0))**2.0)
+    cmin = ccs[np.argmin(mses)]
+    gradient = np.diff(mses)/(ccs[1]-ccs[0])
+
+    ay = 1500.0
+    asB = dict(arrowprops=dict(arrowstyle="->", shrinkA=0, shrinkB=0,
+                               color=lsB['color'], lw=2))
+    ax.annotate('', xy=(3.0, ay), xytext=(1.0, ay), **asB)
+    ax.annotate('', xy=(5.0, ay), xytext=(3.8, ay), **asB)
+    ax.annotate('', xy=(6.2, ay), xytext=(7.4, ay), **asB)
+    ax.annotate('', xy=(8.0, ay), xytext=(10.0, ay), **asB)
+    ax.plot([cmin, cmin], [0, 30000], **lsSpine)
+    ax.plot(ccs, mses, zorder=10, **lsAm)
+    ax.set_xlabel('c')
+    ax.set_ylabel('Mean squared error')
+    ax.set_xlim(0, 10)
+    ax.set_ylim(0, 25000)
+    ax.set_xticks(np.arange(0.0, 10.1, 2.0))
+    ax.set_yticks(np.arange(0, 30001, 10000))
+    ax.set_yticklabels(['0', '', '', ''])
+
+
+if __name__ == "__main__":
+    x, y, c = create_data()
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 1.1*figure_height))
+    fig.subplots_adjust(wspace=0.5, **adjust_fs(left=5.0, right=1.2))
+    plot_gradient(ax1, x, y, c)
+    plot_mse(ax2, x, y, c)
+    fig.savefig("cubicgradient.pdf")
+    plt.close()
diff --git a/regression/lecture/cubicmse.py b/regression/lecture/cubicmse.py
index f687885..0d956c5 100644
--- a/regression/lecture/cubicmse.py
+++ b/regression/lecture/cubicmse.py
@@ -14,6 +14,7 @@ def create_data():
     y += noise
     return x, y, c
 
+
 def gradient_descent(x, y):
     n = 20
     dc = 0.01
@@ -29,6 +30,7 @@ def gradient_descent(x, y):
         mses.append(m0)
         cc -= eps*dmdc
     return cs, mses
+
     
 def plot_mse(ax, x, y, c, cs):
     ms = np.zeros(len(cs))
@@ -54,7 +56,8 @@ def plot_mse(ax, x, y, c, cs):
     ax.set_ylim(0, 25000)
     ax.set_xticks(np.arange(0.0, 10.1, 2.0))
     ax.set_yticks(np.arange(0, 30001, 10000))
-    
+
+        
 def plot_descent(ax, cs, mses):
     ax.plot(np.arange(len(mses))+1, mses, **lpsBm)
     ax.set_xlabel('Iteration')
@@ -69,8 +72,8 @@ def plot_descent(ax, cs, mses):
 if __name__ == "__main__":
     x, y, c = create_data()
     cs, mses = gradient_descent(x, y)
-    fig, (ax1, ax2) = plt.subplots(1, 2)
-    fig.subplots_adjust(wspace=0.2, **adjust_fs(left=8.0, right=0.5))
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 1.1*figure_height))
+    fig.subplots_adjust(**adjust_fs(left=8.0, right=1.2))
     plot_mse(ax1, x, y, c, cs)
     plot_descent(ax2, cs, mses)
     fig.savefig("cubicmse.pdf")
diff --git a/regression/lecture/error_surface.py b/regression/lecture/error_surface.py
deleted file mode 100644
index da0d867..0000000
--- a/regression/lecture/error_surface.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import numpy as np
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-from plotstyle import *
-
-def create_data():
-    m = 0.75
-    n=  -40
-    x = np.arange(10.,110., 2.5)
-    y = m * x + n;
-    rng = np.random.RandomState(37281)
-    noise = rng.randn(len(x))*15
-    y += noise
-    return x, y, m, n
-    
-
-def plot_error_plane(ax, x, y, m, n):
-    ax.set_xlabel('Slope m')
-    ax.set_ylabel('Intercept b')
-    ax.set_zlabel('Mean squared error')
-    ax.set_xlim(-4.5, 5.0)
-    ax.set_ylim(-60.0, -20.0)
-    ax.set_zlim(0.0, 700.0)
-    ax.set_xticks(np.arange(-4, 5, 2))
-    ax.set_yticks(np.arange(-60, -19, 10))
-    ax.set_zticks(np.arange(0, 700, 200))
-    ax.grid(True)
-    ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
-    ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
-    ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
-    ax.invert_xaxis()
-    ax.view_init(25, 40)
-    slopes = np.linspace(-4.5, 5, 40)
-    intercepts = np.linspace(-60, -20, 40)
-    x, y = np.meshgrid(slopes, intercepts)
-    error_surf = np.zeros(x.shape)
-    for i, s in enumerate(slopes) :
-        for j, b in enumerate(intercepts) :
-                error_surf[j,i] = np.mean((y-s*x-b)**2.0)
-    ax.plot_surface(x, y, error_surf, rstride=1, cstride=1, cmap=cm.coolwarm,
-                    linewidth=0, shade=True)
-    # Minimum:
-    mini = np.unravel_index(np.argmin(error_surf), error_surf.shape)
-    ax.scatter(slopes[mini[1]], intercepts[mini[0]], [0.0], color='#cc0000', s=60)
-
-
-if __name__ == "__main__":
-    x, y, m, n = create_data()
-    fig = plt.figure()
-    ax = fig.add_subplot(1, 1, 1, projection='3d')
-    plot_error_plane(ax, x, y, m, n)
-    fig.set_size_inches(7., 5.)
-    fig.subplots_adjust(**adjust_fs(fig, 1.0, 0.0, 0.0, 0.0))
-    fig.savefig("error_surface.pdf")
-    plt.close()
diff --git a/regression/lecture/lin_regress.py b/regression/lecture/lin_regress.py
deleted file mode 100644
index ae854d5..0000000
--- a/regression/lecture/lin_regress.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from plotstyle import *
-
-def create_data():
-    m = 0.75
-    n=  -40
-    x = np.arange(10.,110., 2.5)
-    y = m * x + n;
-    rng = np.random.RandomState(37281)
-    noise = rng.randn(len(x))*15
-    y += noise
-    return x, y, m, n
-
-    
-def plot_data(ax, x, y):
-    ax.plot(x, y, **psA)
-    ax.set_xlabel('Input x')
-    ax.set_ylabel('Output y')
-    ax.set_xlim(0, 120)
-    ax.set_ylim(-80, 80)
-    ax.set_xticks(np.arange(0,121, 40))
-    ax.set_yticks(np.arange(-80,81, 40))
-    
-
-def plot_data_slopes(ax, x, y, m, n):
-    ax.plot(x, y, **psA)
-    xx = np.asarray([2, 118])
-    for i in np.linspace(0.3*m, 2.0*m, 5):
-        ax.plot(xx, i*xx+n, **lsBm)
-    ax.set_xlabel('Input x')
-    #ax.set_ylabel('Output y')
-    ax.set_xlim(0, 120)
-    ax.set_ylim(-80, 80)
-    ax.set_xticks(np.arange(0,121, 40))
-    ax.set_yticks(np.arange(-80,81, 40))
-
-
-def plot_data_intercepts(ax, x, y, m, n):
-    ax.plot(x, y, **psA)
-    xx = np.asarray([2, 118])
-    for i in np.linspace(n-1*n, n+1*n, 5):
-        ax.plot(xx, m*xx + i, **lsBm)
-    ax.set_xlabel('Input x')
-    #ax.set_ylabel('Output y')
-    ax.set_xlim(0, 120)
-    ax.set_ylim(-80, 80)
-    ax.set_xticks(np.arange(0,121, 40))
-    ax.set_yticks(np.arange(-80,81, 40))
-
-
-if __name__ == "__main__":
-    x, y, m, n = create_data()
-    fig, axs = plt.subplots(1, 3)
-    fig.subplots_adjust(wspace=0.5, **adjust_fs(fig, left=6.0, right=1.5))
-    plot_data(axs[0], x, y)
-    plot_data_slopes(axs[1], x, y, m, n)
-    plot_data_intercepts(axs[2], x, y, m, n)
-    fig.savefig("lin_regress.pdf")
-    plt.close()
diff --git a/regression/lecture/linear_least_squares.py b/regression/lecture/linear_least_squares.py
deleted file mode 100644
index d36fd32..0000000
--- a/regression/lecture/linear_least_squares.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from plotstyle import *
-
-def create_data():
-    m = 0.75
-    n=  -40
-    x = np.concatenate( (np.arange(10.,110., 2.5), np.arange(0.,120., 2.0)) )
-    y = m * x + n;
-    rng = np.random.RandomState(37281)
-    noise = rng.randn(len(x))*15
-    y += noise
-    return x, y, m, n
-
-    
-def plot_data(ax, x, y, m, n):
-    ax.set_xlabel('Input x')
-    ax.set_ylabel('Output y')
-    ax.set_xlim(0, 120)
-    ax.set_ylim(-80, 80)
-    ax.set_xticks(np.arange(0,121, 40))
-    ax.set_yticks(np.arange(-80,81, 40))
-    ax.annotate('Error',
-                xy=(x[34]+1, y[34]+15), xycoords='data',
-                xytext=(80, -50), textcoords='data', ha='left',
-                arrowprops=dict(arrowstyle="->", relpos=(0.9,1.0),
-                connectionstyle="angle3,angleA=50,angleB=-30") )
-    ax.plot(x[:40], y[:40], zorder=0, **psAm)
-    inxs = [3, 13, 16, 19, 25, 34, 36]
-    ax.plot(x[inxs], y[inxs], zorder=10, **psA)
-    xx = np.asarray([2, 118])
-    ax.plot(xx, m*xx+n, **lsBm)
-    for i in inxs :
-        xx = [x[i], x[i]]
-        yy = [m*x[i]+n, y[i]]
-        ax.plot(xx, yy, zorder=5, **lsDm)
-    
-
-def plot_error_hist(ax, x, y, m, n):
-    ax.set_xlabel('Squared error')
-    ax.set_ylabel('Frequency')
-    bins = np.arange(0.0, 602.0, 50.0)
-    ax.set_xlim(bins[0], bins[-1])
-    ax.set_ylim(0, 35)
-    ax.set_xticks(np.arange(bins[0], bins[-1], 100))
-    ax.set_yticks(np.arange(0, 36, 10))
-    errors = (y-(m*x+n))**2.0
-    mls = np.mean(errors)
-    ax.annotate('Mean\nsquared\nerror',
-                xy=(mls, 0.5), xycoords='data',
-                xytext=(350, 20), textcoords='data', ha='left',
-                arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
-                connectionstyle="angle3,angleA=10,angleB=90") )
-    ax.hist(errors, bins, **fsD)
-
-
-
-if __name__ == "__main__":
-    x, y, m, n = create_data()
-    fig, axs = plt.subplots(1, 2)
-    fig.subplots_adjust(**adjust_fs(fig, left=6.0))
-    plot_data(axs[0], x, y, m, n)
-    plot_error_hist(axs[1], x, y, m, n)
-    fig.savefig("linear_least_squares.pdf")
-    plt.close()
diff --git a/regression/lecture/powergradientdescent.py b/regression/lecture/powergradientdescent.py
new file mode 100644
index 0000000..56566e3
--- /dev/null
+++ b/regression/lecture/powergradientdescent.py
@@ -0,0 +1,67 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mt
+from plotstyle import *
+
+
+def power_law(x, c, a):
+    return c*x**a
+
+
+def create_data():
+    # wikipedia:
+    # Generally, males vary in total length from 250 to 390 cm and
+    # weigh between 90 and 306 kg
+    c = 6.0
+    x = np.arange(2.2, 3.9, 0.05)
+    y = power_law(x, c, 3.0)
+    rng = np.random.RandomState(32281)
+    noise = rng.randn(len(x))*50
+    y += noise
+    return x, y, c
+
+
+def gradient_descent(x, y, func, p0):
+    n = 20000
+    h = 1e-7
+    ph = np.identity(len(p0))*h
+    eps = 0.00001
+    p = p0
+    ps = np.zeros((n, len(p0)))
+    mses = np.zeros(n)
+    for k in range(n):
+        m0 = np.mean((y-func(x, *p))**2.0)
+        gradient = np.array([(np.mean((y-func(x, *(p+ph[:,i])))**2.0) - m0)/h
+                             for i in range(len(p))])
+        ps[k,:] = p
+        mses[k] = m0
+        p -= eps*gradient
+    return ps, mses
+
+    
+def plot_gradient_descent(ax, x, y, c, ps, mses):
+    cs = np.linspace(0.0, 10.0, 300)
+    bs = np.linspace(1.0, 5.5, 180)
+    mse = np.zeros((len(bs), len(cs)))
+    for i in range(len(bs)):
+        for k in range(len(cs)):
+            mse[i, k] = np.mean((y-power_law(x, cs[k], bs[i]))**2.0)
+    z = np.log10(mse)
+    ax.contourf(cs, bs, z, levels=(3.3, 3.36, 3.5, 4.0, 4.5, 5.5, 6.5, 7.5, 8.5),
+                cmap='Blues_r')
+    ax.plot(ps[::5,0], ps[::5,1], **lsBm)
+    ax.plot(ps[-1,0], ps[-1,1], **psC)
+    ax.set_xlabel('c')
+    ax.set_ylabel('a')
+    ax.yaxis.set_major_locator(mt.MultipleLocator(1.0))
+    ax.set_aspect('equal')
+
+
+if __name__ == "__main__":
+    x, y, c = create_data()
+    ps, mses = gradient_descent(x, y, power_law, [1.0, 1.0])
+    fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.3*figure_height))
+    fig.subplots_adjust(**adjust_fs(left=4.5, right=1.0))
+    plot_gradient_descent(ax, x, y, c, ps, mses)
+    fig.savefig("powergradientdescent.pdf")
+    plt.close()
diff --git a/regression/lecture/regression-chapter.tex b/regression/lecture/regression-chapter.tex
index 792dfcd..05c049a 100644
--- a/regression/lecture/regression-chapter.tex
+++ b/regression/lecture/regression-chapter.tex
@@ -21,43 +21,30 @@
 \subsection{Notes}
 \begin{itemize}
 \item Fig 8.2 right: this should be a chi-squared distribution with one degree of freedom!
+\item Gradient ascent is used by bacteria and worms (and others) to
+  approach food sources along chemical and temperature gradients (using biased random walks).
 \end{itemize}
 
-\subsection{Start with one-dimensional problem!}
+\subsection{New chapter: non-linear fits}
 \begin{itemize}
-\item Let's fit a cubic function $y=cx^3$ (weight versus length of a tiger)\\
-\includegraphics[width=0.8\textwidth]{cubicfunc}
-\item Introduce the problem, $c$ is density and form factor
-\item How to generate an artificial data set (refer to simulation chapter)
-\item How to plot a function (do not use the data x values!)
-\item Just the mean square error as a function of the factor c\\
-\includegraphics[width=0.8\textwidth]{cubicerrors}
-\item Also mention the cost function for a straight line
-\item 1-d gradient, NO quiver plot (it is a nightmare to get this right)\\
-\includegraphics[width=0.8\textwidth]{cubicmse}
-\item 1-d gradient descend
-\item Describe in words the n-d problem.
-\item Homework is to do the 2d problem with the straight line!
-\end{itemize}
-
-\subsection{Linear fits}
-\begin{itemize}
-\item Polyfit is easy: unique solution! $c x^2$ is also a linear fit.
-\item Example for overfitting with polyfit of a high order (=number of data points)
+\item Move 8.7 to this new chapter.
+\item Example that illustrates the Nebenminima Problem (with error
+  surface). Maybe data generate from $1/x$ and fitted with
+  $c\exp(\lambda x)$ induces local minima.
+\item You need initial values for the parameter!
+\item Example that fitting gets harder the more parameter you have.
+\item Try to fix as many parameters before doing the fit.
+\item Ensure that all parameters in the function are unique, e.g. $a e^{x-c} = be^x$.
+\item How to test the quality of a fit? Residuals. $\chi^2$ test. Run-test.
+\item Important box: summary of fit howtos.
 \end{itemize}
 
-\section{Fitting in practice}
-
-Fit with matlab functions lsqcurvefit, polyfit
-
-
-\subsection{Non-linear fits}
+\subsection{New chapter: linear fits --- generalized linear models}
 \begin{itemize}
-\item Example that illustrates the Nebenminima Problem (with error surface)
-\item You need got initial values for the parameter!
-\item Example that fitting gets harder the more parameter yuo have.
-\item Try to fix as many parameter before doing the fit.
-\item How to test the quality of a fit? Residuals. $\chi^2$ test. Run-test.
+\item Polyfit is easy: unique solution! $c x^3$ is also a linear fit.
+\item Example for \emph{overfitting} with polyfit of a high order (=number of data points)
+\item Higher order and cross terms
+\item Link function, logistic regression
 \end{itemize}
 
 
diff --git a/regression/lecture/regression.tex b/regression/lecture/regression.tex
index e63e3f9..6199650 100644
--- a/regression/lecture/regression.tex
+++ b/regression/lecture/regression.tex
@@ -1,202 +1,218 @@
 \chapter{Optimization and gradient descent}
+\label{gradientdescentchapter}
 \exercisechapter{Optimization and gradient descent}
 
 Optimization problems arise in many different contexts. For example,
-to understand the behavior of a given system, the system is probed
-with a range of input signals and then the resulting responses are
-measured. This input-output relation can be described by a model. Such
-a model can be a simple function that maps the input signals to
-corresponding responses, it can be a filter, or a system of
-differential equations. In any case, the model has some parameters that
-specify how input and output signals are related.  Which combination
-of parameter values are best suited to describe the input-output
-relation? The process of finding the best parameter values is an
-optimization problem. For a simple parameterized function that maps
-input to output values, this is the special case of a \enterm{curve
-  fitting} problem, where the average distance between the curve and
-the response values is minimized. One basic numerical method used for
-such optimization problems is the so called gradient descent, which is
-introduced in this chapter.
-
-%%% Weiteres einfaches verbales Beispiel? Eventuell aus der Populationsoekologie?
+to understand the behavior of a given neuronal system, the system is
+probed with a range of input signals and then the resulting responses
+are measured. This input-output relation can be described by a
+model. Such a model can be a simple function that maps the input
+signals to corresponding responses, it can be a filter, or a system of
+differential equations. In any case, the model has some parameters
+that specify how input and output signals are related.  Which
+combination of parameter values are best suited to describe the
+input-output relation? The process of finding the best parameter
+values is an optimization problem. For a simple parameterized function
+that maps input to output values, this is the special case of a
+\enterm{curve fitting} problem, where the average distance between the
+curve and the response values is minimized. One basic numerical method
+used for such optimization problems is the so called gradient descent,
+which is introduced in this chapter.
 
 \begin{figure}[t]
-  \includegraphics[width=1\textwidth]{lin_regress}\hfill
-  \titlecaption{Example data suggesting a linear relation.}{A set of
-    input signals $x$, e.g. stimulus intensities, were used to probe a
-    system. The system's output $y$ to the inputs are noted
-    (left). Assuming a linear relation between $x$ and $y$ leaves us
-    with 2 parameters, the slope (center) and the intercept with the
-    y-axis (right panel).}\label{linregressiondatafig}
+  \includegraphics{cubicfunc}
+  \titlecaption{Example data suggesting a cubic relation.}{The length
+    $x$ and weight $y$ of $n=34$ male tigers (blue, left). Assuming a
+    cubic relation between size and weight leaves us with a single
+    free parameters, a scaling factor. The cubic relation is shown for
+    a few values of this scaling factor (orange and red,
+    right).}\label{cubicdatafig}
 \end{figure}
 
-The data plotted in \figref{linregressiondatafig} suggest a linear
-relation between input and output of the system. We thus assume that a
-straight line
+For demonstrating the curve-fitting problem let's take the simple
+example of weights and sizes measured for a number of male tigers
+(\figref{cubicdatafig}). Weight $y$ is proportional to volume
+$V$ via the density $\rho$. The volume $V$ of any object is
+proportional to its length $x$ cubed. The factor $\alpha$ relating
+volume and size cubed depends on the shape of the object and we do not
+know this factor for tigers. For the data set we thus expect a cubic
+relation between weight and length
 \begin{equation}
-  \label{straightline}
-  y = f(x; m, b) = m\cdot x + b
+  \label{cubicfunc}
+  y = f(x; c) = c\cdot x^3
 \end{equation}
-is an appropriate model to describe the system.  The line has two free
-parameter, the slope $m$ and the $y$-intercept $b$. We need to find
-values for the slope and the intercept that best describe the measured
-data.  In this chapter we use this example to illustrate the gradient
-descent and how this methods can be used to find a combination of
-slope and intercept that best describes the system.
+where $c=\rho\alpha$, the product of a tiger's density and form
+factor, is the only free parameter in the relation. We would like to
+find out which value of $c$ best describes the measured data.  In the
+following we use this example to illustrate the gradient descent as a
+basic method for finding such an optimal parameter.
 
 
 \section{The error function --- mean squared error}
 
-Before the optimization can be done we need to specify what exactly is
-considered an optimal fit. In our example we search the parameter
-combination that describe the relation of $x$ and $y$ best. What is
-meant by this? Each input $x_i$ leads to an measured output $y_i$ and
-for each $x_i$ there is a \emph{prediction} or \emph{estimation}
-$y^{est}(x_i)$ of the output value by the model. At each $x_i$
-estimation and measurement have a distance or error $y_i -
-y^{est}(x_i)$. In our example the estimation is given by the equation
-$y^{est}(x_i) = f(x_i;m,b)$. The best fitting model with parameters
-$m$ and $b$ is the one that minimizes the distances between
-observation $y_i$ and estimation $y^{est}(x_i)$
-(\figref{leastsquareerrorfig}).
-
-As a first guess we could simply minimize the sum $\sum_{i=1}^N y_i -
-y^{est}(x_i)$. This approach, however, will not work since a minimal sum
-can also be achieved if half of the measurements is above and the
-other half below the predicted line. Positive and negative errors
-would cancel out and then sum up to values close to zero. A better
-approach is to sum over the absolute values of the distances:
-$\sum_{i=1}^N |y_i - y^{est}(x_i)|$. This sum can only be small if all
-deviations are indeed small no matter if they are above or below the
-predicted line. Instead of the sum we could also take the average
-\begin{equation}
-  \label{meanabserror}
-  f_{dist}(\{(x_i, y_i)\}|\{y^{est}(x_i)\}) = \frac{1}{N} \sum_{i=1}^N |y_i - y^{est}(x_i)|
-\end{equation}
-Instead of the averaged absolute errors, the \enterm[mean squared
-error]{mean squared error} (\determ[quadratischer
-Fehler!mittlerer]{mittlerer quadratischer Fehler})
+Before we go ahead finding the optimal parameter value we need to
+specify what exactly we consider as an optimal fit. In our example we
+search the parameter that describes the relation of $x$ and $y$
+best. What is meant by this? The length $x_i$ of each tiger is
+associated with a weight $y_i$ and for each $x_i$ we have a
+\emph{prediction} or \emph{estimation} $y^{est}(x_i)$ of the weight by
+the model \eqnref{cubicfunc} for a specific value of the parameter
+$c$. Prediction and actual data value ideally match (in a perfect
+noise-free world), but in general the estimate and measurement are
+separated by some distance or error $y_i - y^{est}(x_i)$. In our
+example the estimate of the weight for the length $x_i$ is given by
+equation \eqref{cubicfunc} $y^{est}(x_i) = f(x_i;c)$. The best fitting
+model with parameter $c$ is the one that somehow minimizes the
+distances between observations $y_i$ and corresponding estimations
+$y^{est}(x_i)$ (\figref{cubicerrorsfig}).
+
+As a first guess we could simply minimize the sum of the distances,
+$\sum_{i=1}^N y_i - y^{est}(x_i)$. This, however, does not work
+because positive and negative errors would cancel out, no matter how
+large they are, and sum up to values close to zero. Better is to sum
+over absolute distances: $\sum_{i=1}^N |y_i - y^{est}(x_i)|$. This sum
+can only be small if all deviations are indeed small no matter if they
+are above or below the prediction.  The sum of the squared distances,
+$\sum_{i=1}^N (y_i - y^{est}(x_i))^2$, turns out to be an even better
+choice. Instead of the sum we could also minimize the average distance
 \begin{equation}
   \label{meansquarederror}
   f_{mse}(\{(x_i, y_i)\}|\{y^{est}(x_i)\}) = \frac{1}{N} \sum_{i=1}^N (y_i - y^{est}(x_i))^2
 \end{equation}
-is commonly used (\figref{leastsquareerrorfig}). Similar to the
-absolute distance, the square of the errors, $(y_i - y^{est}(x_i))^2$, is
-always positive and thus positive and negative error values do not
+This is known as the \enterm[mean squared error]{mean squared error}
+(\determ[quadratischer Fehler!mittlerer]{mittlerer quadratischer
+  Fehler}). Similar to the absolute distance, the square of the errors
+is always positive and thus positive and negative error values do not
 cancel each other out. In addition, the square punishes large
 deviations over small deviations. In
 chapter~\ref{maximumlikelihoodchapter} we show that minimizing the
-mean square error is equivalent to maximizing the likelihood that the
+mean squared error is equivalent to maximizing the likelihood that the
 observations originate from the model, if the data are normally
 distributed around the model prediction.
 
-\begin{exercise}{meanSquaredErrorLine.m}{}\label{mseexercise}%
-  Given a vector of observations \varcode{y} and a vector with the
-  corresponding predictions \varcode{y\_est}, compute the \emph{mean
-    square error} between \varcode{y} and \varcode{y\_est} in a single
-  line of code.
+\begin{exercise}{meansquarederrorline.m}{}\label{mseexercise}
+  Simulate $n=40$ tigers ranging from 2.2 to 3.9\,m in size and store
+  these sizes in a vector \varcode{x}. Compute the corresponding
+  predicted weights \varcode{yest} for each tiger according to
+  \eqnref{cubicfunc} with $c=6$\,\kilo\gram\per\meter\cubed. From the
+  predictions generate simulated measurements of the tiger's weights
+  \varcode{y}, by adding normally distributed random numbers to the
+  predictions scaled to a standard deviation of 50\,\kilo\gram.
+
+  Compute the \emph{mean squared error} between \varcode{y} and
+  \varcode{yest} in a single line of code.
 \end{exercise}
 
 
-\section{Objective function}
+\section{Cost function}
 
 The mean squared error is a so called \enterm{objective function} or
 \enterm{cost function} (\determ{Kostenfunktion}). A cost function
 assigns to a model prediction $\{y^{est}(x_i)\}$ for a given data set
 $\{(x_i, y_i)\}$ a single scalar value that we want to minimize.  Here
-we aim to adapt the model parameters to minimize the mean squared
-error \eqref{meansquarederror}. In general, the \enterm{cost function}
-can be any function that describes the quality of the fit by mapping
-the data and the predictions to a single scalar value.
+we aim to adapt the model parameter to minimize the mean squared error
+\eqref{meansquarederror}. In general, the \enterm{cost function} can
+be any function that describes the quality of a fit by mapping the
+data and the predictions to a single scalar value.
 
 \begin{figure}[t]
-  \includegraphics[width=1\textwidth]{linear_least_squares}
-  \titlecaption{Estimating the \emph{mean square error}.}  {The
-    deviation error, orange) between the prediction (red
-    line) and the observations (blue dots) is calculated for each data
-    point (left). Then the deviations are squared and the aveage is
+  \includegraphics{cubicerrors}
+  \titlecaption{Estimating the \emph{mean squared error}.}  {The
+    deviation error (orange) between the prediction (red line) and the
+    observations (blue dots) is calculated for each data point
+    (left). Then the deviations are squared and the average is
     calculated (right).}
-  \label{leastsquareerrorfig}
+  \label{cubicerrorsfig}
 \end{figure}
 
 Replacing $y^{est}$ in the mean squared error \eqref{meansquarederror}
-with our model, the straight line \eqref{straightline}, the cost
-function reads
+with our cubic model \eqref{cubicfunc}, the cost function reads
 \begin{eqnarray}
-  f_{cost}(m,b|\{(x_i, y_i)\}) & = & \frac{1}{N} \sum_{i=1}^N (y_i - f(x_i;m,b))^2 \label{msefunc} \\
-  & = & \frac{1}{N} \sum_{i=1}^N (y_i - m x_i - b)^2 \label{mseline}
+  f_{cost}(c|\{(x_i, y_i)\}) & = & \frac{1}{N} \sum_{i=1}^N (y_i - f(x_i;c))^2 \label{msefunc} \\
+  & = & \frac{1}{N} \sum_{i=1}^N (y_i - c x_i^3)^2 \label{msecube}
 \end{eqnarray}
-The optimization process tries to find the slope $m$ and the intercept
-$b$ such that the cost function is minimized. With the mean squared
-error as the cost function this optimization process is also called
-method of the \enterm{least square error} (\determ[quadratischer
+The optimization process tries to find a value for the factor $c$ such
+that the cost function is minimized. With the mean squared error as
+the cost function this optimization process is also called method of
+ \enterm{least squares} (\determ[quadratischer
 Fehler!kleinster]{Methode der kleinsten Quadrate}).
 
-\begin{exercise}{meanSquaredError.m}{}
-  Implement the objective function \eqref{mseline} as a function
-  \varcode{meanSquaredError()}.  The function takes three
+\begin{exercise}{meanSquaredErrorCubic.m}{}
+  Implement the objective function \eqref{msecube} as a function
+  \varcode{meanSquaredErrorCubic()}.  The function takes three
   arguments. The first is a vector of $x$-values and the second
   contains the measurements $y$ for each value of $x$. The third
-  argument is a 2-element vector that contains the values of
-  parameters \varcode{m} and \varcode{b}. The function returns the
-  mean square error.
+  argument is the value of the factor $c$. The function returns the
+  mean squared error.
 \end{exercise}
 
 
-\section{Error surface}
-For each combination of the two parameters $m$ and $b$ of the model we
-can use \eqnref{mseline} to calculate the corresponding value of the
-cost function. The cost function $f_{cost}(m,b|\{(x_i, y_i)\}|)$ is a
-function $f_{cost}(m,b)$, that maps the parameter values $m$ and $b$
-to a scalar error value.  The error values describe a landscape over the
-$m$-$b$ plane, the error surface, that can be illustrated graphically
-using a 3-d surface-plot. $m$ and $b$ are plotted on the $x$- and $y$-
-axis while the third dimension indicates the error value
-(\figref{errorsurfacefig}).
+\section{Graph of the cost function}
+For each value of the parameter $c$ of the model we can use
+\eqnref{msecube} to calculate the corresponding value of the cost
+function. The cost function $f_{cost}(c|\{(x_i, y_i)\}|)$ is a
+function $f_{cost}(c)$ that maps the parameter value $c$ to a scalar
+error value. For a given data set we thus can simply plot the cost
+function as a function of the parameter $c$ (\figref{cubiccostfig}).
+
+\begin{exercise}{plotcubiccosts.m}{}
+  Calculate the mean squared error between the data and the cubic
+  function for a range of values of the factor $c$ using the
+  \varcode{meanSquaredErrorCubic()} function from the previous
+  exercise. Plot the graph of the cost function.
+\end{exercise}
 
 \begin{figure}[t]
-  \includegraphics[width=0.75\textwidth]{error_surface}
-  \titlecaption{Error surface.}{The two model parameters $m$ and $b$
-    define the base area of the surface plot. For each parameter
-    combination of slope and intercept the error is calculated. The
-    resulting surface has a minimum which indicates the parameter
-    combination that best fits the data.}\label{errorsurfacefig}
+  \includegraphics{cubiccost}
+  \titlecaption{Minimum of the cost function.}{For a given data set
+    the cost function, the mean squared error \eqnref{msecube}, as a
+    function of the unknown parameter $c$ has a minimum close to the
+    true value of $c$ that was used to generate the data
+    (left). Simply taking the absolute minimum of the cost function
+    computed for a pre-set range of values for the parameter $c$, has
+    the disadvantage to be limited in precision (right) and the
+    possibility to entirely miss the global minimum if it is outside
+    the computed range.}\label{cubiccostfig}
 \end{figure}
 
-\begin{exercise}{errorSurface.m}{}\label{errorsurfaceexercise}
-  Generate 20 data pairs $(x_i|y_i)$ that are linearly related with
-  slope $m=0.75$ and intercept $b=-40$, using \varcode{rand()} for
-  drawing $x$ values between 0 and 120 and \varcode{randn()} for
-  jittering the $y$ values with a standard deviation of 15.  Then
-  calculate the mean squared error between the data and straight lines
-  for a range of slopes and intercepts using the
-  \varcode{meanSquaredError()} function from the previous exercise.
-  Illustrates the error surface using the \code{surface()} function.
-  Consult the documentation to find out how to use \code{surface()}.
-\end{exercise}
-
-By looking at the error surface we can directly see the position of
-the minimum and thus estimate the optimal parameter combination. How
-can we use the error surface to guide an automatic optimization
-process?
-
-The obvious approach would be to calculate the error surface for any
-combination of slope and intercept values and then find the position
-of the minimum using the \code{min} function. This approach, however
-has several disadvantages: (i) it is computationally very expensive to
-calculate the error for each parameter combination. The number of
-combinations increases exponentially with the number of free
-parameters (also known as the ``curse of dimensionality''). (ii) the
-accuracy with which the best parameters can be estimated is limited by
-the resolution used to sample the parameter space. The coarser the
-parameters are sampled the less precise is the obtained position of
-the minimum.
+By looking at the plot of the cost function we can visually identify
+the position of the minimum and thus estimate the optimal value for
+the parameter $c$. How can we use the cost function to guide an
+automatic optimization process?
+
+The obvious approach would be to calculate the mean squared error for
+a range of parameter values and then find the position of the minimum
+using the \code{min()} function. This approach, however has several
+disadvantages: (i) The accuracy of the estimation of the best
+parameter is limited by the resolution used to sample the parameter
+space. The coarser the parameters are sampled the less precise is the
+obtained position of the minimum (\figref{cubiccostfig}, right).  (ii)
+The range of parameter values might not include the absolute minimum.
+(iii) In particular for functions with more than a single free
+parameter it is computationally expensive to calculate the cost
+function for each parameter combination at a sufficient
+resolution. The number of combinations increases exponentially with
+the number of free parameters. This is known as the \enterm{curse of
+  dimensionality}.
 
 So we need a different approach. We want a procedure that finds the
 minimum of the cost function with a minimal number of computations and
 to arbitrary precision.
 
-\begin{ibox}[t]{\label{differentialquotientbox}Difference quotient and derivative}
+\section{Gradient}
+
+\begin{figure}[t]
+  \includegraphics{cubicgradient}
+  \titlecaption{Derivative of the cost function.}{The gradient, the
+    derivative \eqref{costderivative} of the cost function, is
+    negative to the left of the minimum (vertical line) of the cost
+    function, zero (horizontal line) at, and positive to the right of
+    the minimum (left). For each value of the parameter $c$ the
+    negative gradient (arrows) points towards the minimum of the cost
+    function (right).} \label{gradientcubicfig}
+\end{figure}
+
+\begin{ibox}[b]{\label{differentialquotientbox}Difference quotient and derivative}
   \includegraphics[width=0.33\textwidth]{derivative}
   \hfill
   \begin{minipage}[b]{0.63\textwidth}
@@ -206,34 +222,231 @@ to arbitrary precision.
       m = \frac{f(x + \Delta x) - f(x)}{\Delta x}
     \end{equation}
     of a function $y = f(x)$ is the slope of the secant (red) defined
-    by the points $(x,f(x))$ and $(x+\Delta x,f(x+\Delta x))$ with the
+    by the points $(x,f(x))$ and $(x+\Delta x,f(x+\Delta x))$ at
     distance $\Delta x$.
 
-    The slope of the function $y=f(x)$ at the position $x$ (yellow) is
+    The slope of the function $y=f(x)$ at the position $x$ (orange) is
     given by the derivative $f'(x)$ of the function at that position.
     It is defined by the difference quotient in the limit of
-    infinitesimally (orange) small distances $\Delta x$:
+    infinitesimally (red and yellow) small distances $\Delta x$:
     \begin{equation}
       \label{derivative}
       f'(x) = \frac{{\rm d} f(x)}{{\rm d}x} = \lim\limits_{\Delta x \to 0} \frac{f(x + \Delta x) - f(x)}{\Delta x} \end{equation}
   \end{minipage}\vspace{2ex} 
 
-  It is not possible to calculate the derivative, \eqnref{derivative},
-  numerically. The derivative can only be estimated using the
-  difference quotient, \eqnref{difffrac}, by using sufficiently small
-  $\Delta x$.
+  It is not possible to calculate the exact value of the derivative
+  \eqref{derivative} numerically. The derivative can only be estimated
+  by computing the difference quotient \eqref{difffrac} using
+  sufficiently small $\Delta x$.
 \end{ibox}
 
-\begin{ibox}[tp]{\label{partialderivativebox}Partial derivative and gradient}
-  Some functions that depend on more than a single variable:
+Imagine to place a ball at some point on the cost function
+\figref{cubiccostfig}. Naturally, it would roll down the slope and
+eventually stop at the minimum of the error surface (if it had no
+inertia). We will use this analogy to develop an algorithm to find our
+way to the minimum of the cost function. The ball always follows the
+steepest slope. Thus we need to figure out the direction of the slope
+at the position of the ball.
+
+In our one-dimensional example of a single free parameter the slope is
+simply the derivative of the cost function with respect to the
+parameter $c$ (\figref{gradientcubicfig}, left). This derivative is called
+the \entermde{Gradient}{gradient} of the cost function:
+\begin{equation}
+  \label{costderivative}
+  \nabla f_{cost}(c) = \frac{{\rm d} f_{cost}(c)}{{\rm d} c}
+\end{equation}
+There is no need to calculate this derivative analytically, because it
+can be approximated numerically by the difference quotient
+(Box~\ref{differentialquotientbox}) for small steps $\Delta c$:
+\begin{equation}
+  \label{costderivativediff}
+  \frac{{\rm d} f_{cost}(c)}{{\rm d} c} =
+    \lim\limits_{\Delta c \to 0} \frac{f_{cost}(c + \Delta c) - f_{cost}(c)}{\Delta c}
+    \approx \frac{f_{cost}(c + \Delta c) - f_{cost}(c)}{\Delta c}
+\end{equation}
+Choose, for example, $\Delta c = 10^{-7}$.  The derivative is positive
+for positive slopes. Since want to go down the hill, we choose the
+opposite direction (\figref{gradientcubicfig}, right).
+
+\begin{exercise}{meanSquaredGradientCubic.m}{}\label{gradientcubic}
+  Implement a function \varcode{meanSquaredGradientCubic()}, that
+  takes the $x$- and $y$-data and the parameter $c$ as input
+  arguments. The function should return the derivative of the mean
+  squared error $f_{cost}(c)$ with respect to $c$ at the position
+  $c$.
+\end{exercise}
+
+\begin{exercise}{plotcubicgradient.m}{}
+  Using the \varcode{meanSquaredGradientCubic()} function from the
+  previous exercise, plot the derivative of the cost function as a
+  function of $c$.
+\end{exercise}
+
+\section{Gradient descent}
+
+\begin{figure}[t]
+  \includegraphics{cubicmse}
+  \titlecaption{Gradient descent.}{The algorithm starts at an
+    arbitrary position. At each point the gradient is estimated and
+    the position is updated as long as the length of the gradient is
+    sufficiently large. The dots show the positions after each
+    iteration of the algorithm.} \label{gradientdescentcubicfig}
+\end{figure}
+
+Finally, we are able to implement the optimization itself. By now it
+should be obvious why it is called the gradient descent method. From a
+starting position on we iteratively walk down the slope of the cost
+function against its gradient. All ingredients necessary for this
+algorithm are already there. We need: (i) the cost function
+(\varcode{meanSquaredErrorCubic()}), and (ii) the gradient
+(\varcode{meanSquaredGradientCubic()}). The algorithm of the gradient
+descent works as follows:
+\begin{enumerate}
+\item Start with some arbitrary value $p_0$ for the parameter $c$.
+\item \label{computegradient} Compute the gradient
+  \eqnref{costderivative} at the current position $p_i$.
+\item If the length of the gradient, the absolute value of the
+  derivative \eqnref{costderivative}, is smaller than some threshold
+  value, we assume to have reached the minimum and stop the search.
+  We return the current parameter value $p_i$ as our best estimate of
+  the parameter $c$ that minimizes the cost function.
+
+  We are actually looking for the point at which the derivative of the
+  cost function equals zero, but this is impossible because of
+  numerical imprecision. We thus apply a threshold below which we are
+  sufficiently close to zero.
+\item \label{gradientstep} If the length of the gradient exceeds the
+  threshold we take a small step into the opposite direction:
+  \begin{equation}
+    \label{gradientdescent}
+    p_{i+1} = p_i - \epsilon \cdot \nabla f_{cost}(p_i)
+  \end{equation}
+  where $\epsilon$ is a factor linking the gradient to
+  appropriate steps in the parameter space.
+\item Repeat steps \ref{computegradient} -- \ref{gradientstep}.
+\end{enumerate}
+
+\Figref{gradientdescentcubicfig} illustrates the gradient descent ---
+the path the imaginary ball has chosen to reach the minimum. We walk
+along the parameter axis against the gradient as long as the gradient
+differs sufficiently from zero.  At steep slopes we take large steps
+(the distance between the red dots in \figref{gradientdescentcubicfig}
+is large).
+
+\begin{exercise}{gradientDescentCubic.m}{}\label{gradientdescentexercise}
+  Implement the gradient descent algorithm for the problem of fitting
+  a cubic function \eqref{cubicfunc} to some measured data pairs $x$
+  and $y$ as a function \varcode{gradientDescentCubic()} that returns
+  the estimated best fitting parameter value $c$ as well as two
+  vectors with all the parameter values and the corresponding values
+  of the cost function that the algorithm iterated trough. As
+  additional arguments that function takes the initial value for the
+  parameter $c$, the factor $\epsilon$ connecting the gradient with
+  iteration steps in \eqnref{gradientdescent}, and the threshold value
+  for the absolute value of the gradient terminating the algorithm.
+\end{exercise}
+
+\begin{exercise}{plotgradientdescentcubic.m}{}\label{plotgradientdescentexercise}
+  Use the function \varcode{gradientDescentCubic()} to fit the
+  simulated data from exercise~\ref{mseexercise}. Plot the returned
+  values of the parameter $c$ as as well as the corresponding mean
+  squared errors as a function of iteration step (two plots). Compare
+  the result of the gradient descent method with the true value of $c$
+  used to simulate the data. Inspect the plots and adapt $\epsilon$
+  and the threshold to make the algorithm behave as intended. Finally
+  plot the data together with the best fitting cubic relation
+  \eqref{cubicfunc}.
+\end{exercise}
+
+The $\epsilon$ parameter in \eqnref{gradientdescent} is critical. If
+too large, the algorithm does not converge to the minimum of the cost
+function (try it!). At medium values it oscillates around the minimum
+but might nevertheless converge. Only for sufficiently small values
+(here $\epsilon = 0.00001$) does the algorithm follow the slope
+downwards towards the minimum. Change $\epsilon$ by factors of ten to
+adapt it to a specific problem.
+
+The terminating condition on the absolute value of the gradient
+influences how often the cost function is evaluated. The smaller the
+threshold value the more often the cost is computed and the more
+precisely the fit parameter is estimated. If it is too small, however,
+the increase in precision is negligible, in particular in comparison
+to the increased computational effort. Have a look at the derivatives
+that we plotted in exercise~\ref{gradientcubic} and decide on a
+sensible value for the threshold. Run the gradient descent algorithm
+and check how the resulting $c$ parameter values converge and how many
+iterations were needed. Then reduce the threshold (by factors of ten)
+and check how this changes the results.
+
+Many modern algorithms for finding the minimum of a function are based
+on the basic idea of the gradient descent. Luckily these algorithms
+choose $\epsilon$ in a smart adaptive way and they also come up with
+sensible default values for the termination condition. On the other
+hand, these algorithm often take optional arguments that let you
+control how they behave. Now you know what this is all about.
+
+\section{N-dimensional minimization problems}
+
+So far we were concerned about finding the right value for a single
+parameter that minimizes a cost function.  Often we deal with
+functions that have more than a single parameter, in general $n$
+parameters. We then need to find the minimum in an $n$ dimensional
+parameter space.
+
+For our tiger problem, we could have also fitted the exponent $a$ of
+the power-law relation between size and weight, instead of assuming a
+cubic relation with $a=3$:
+\begin{equation}
+  \label{powerfunc}
+  y = f(x; c, a) = f(x; \vec p) = c\cdot x^a
+\end{equation}
+We then could check whether the resulting estimate of the exponent
+$a$ indeed is close to the expected power of three.  The
+power-law \eqref{powerfunc} has two free parameters $c$ and $a$.
+Instead of a single parameter we are now dealing with a vector $\vec
+p$ containing $n$ parameter values. Here, $\vec p = (c, a)$. All
+the concepts we introduced on the example of the one dimensional
+problem of tiger weights generalize to $n$-dimensional problems. We
+only need to adapt a few things. The cost function for the mean
+squared error reads
+\begin{equation}
+  \label{ndimcostfunc}
+  f_{cost}(\vec p|\{(x_i, y_i)\}) = \frac{1}{N} \sum_{i=1}^N (y_i - f(x_i;\vec p))^2
+\end{equation}
+
+\begin{figure}[t]
+  \includegraphics{powergradientdescent}
+  \titlecaption{Gradient descent on an error surface.}{Contour plot of
+    the cost function \eqref{ndimcostfunc} for the fit of a power law
+    \eqref{powerfunc} to some data. Here the cost function is a long
+    and narrow valley on the plane spanned by the two parameters $c$
+    and $a$ of the power law. The red line marks the path of the
+    gradient descent algorithm. The gradient is always perpendicular
+    to the contour lines. The algorithm quickly descends into the
+    valley and then slowly creeps on the shallow bottom of the valley
+    towards the global minimum where it terminates (yellow circle).
+  } \label{powergradientdescentfig}
+\end{figure}
+
+For two-dimensional problems the graph of the cost function is an
+\enterm{error surface} (\determ{{Fehlerfl\"ache}}). The two parameters
+span a two-dimensional plane. The cost function assigns to each
+parameter combination on this plane a single value. This results in a
+landscape over the parameter plane with mountains and valleys and we
+are searching for the position of the bottom of the deepest valley
+(\figref{powergradientdescentfig}).
+
+\begin{ibox}[t]{\label{partialderivativebox}Partial derivatives and gradient}
+  Some functions depend on more than a single variable. For example, the function
   \[ z = f(x,y) \]
-  for example depends on $x$ and $y$. Using the partial derivative
+  depends on both $x$ and $y$. Using the partial derivatives
   \[ \frac{\partial f(x,y)}{\partial x} = \lim\limits_{\Delta x \to 0} \frac{f(x + \Delta x,y) - f(x,y)}{\Delta x} \]
   and
   \[ \frac{\partial f(x,y)}{\partial y} = \lim\limits_{\Delta y \to 0} \frac{f(x, y + \Delta y) - f(x,y)}{\Delta y} \]
-  one can estimate the slope in the direction of the variables
-  individually by using the respective difference quotient
-  (Box~\ref{differentialquotientbox}).  \vspace{1ex}
+  one can calculate the slopes in the directions of each of the
+  variables by means of the respective difference quotients
+  (see box~\ref{differentialquotientbox}).  \vspace{1ex}
 
   \begin{minipage}[t]{0.5\textwidth}
     \mbox{}\\[-2ex]
@@ -242,184 +455,254 @@ to arbitrary precision.
   \hfill
   \begin{minipage}[t]{0.46\textwidth}
     For example, the partial derivatives of
-    \[ f(x,y) = x^2+y^2 \] are 
+    \[ f(x,y) = x^2+y^2 \vspace{-1ex} \] are 
     \[ \frac{\partial f(x,y)}{\partial x} = 2x \; , \quad \frac{\partial f(x,y)}{\partial y} = 2y \; .\]
 
-    The gradient is a vector that is constructed from the partial derivatives:
+    The gradient is a vector with the partial derivatives as coordinates:
     \[ \nabla f(x,y) = \left( \begin{array}{c} \frac{\partial f(x,y)}{\partial x} \\[1ex] \frac{\partial f(x,y)}{\partial y} \end{array} \right) \]
     This vector points into the direction of the strongest ascend of
     $f(x,y)$.
   \end{minipage}
 
-  \vspace{0.5ex} The figure shows the contour lines of a bi-variate
+  \vspace{0.5ex} The figure shows the contour lines of a bivariate
   Gaussian $f(x,y) = \exp(-(x^2+y^2)/2)$ and the gradient (thick
-  arrows) and the corresponding two partial derivatives (thin arrows)
-  for three different locations.
+  arrows) together with the corresponding partial derivatives (thin
+  arrows) for three different locations.
 \end{ibox}
 
-
-\section{Gradient}
-Imagine to place a small ball at some point on the error surface
-\figref{errorsurfacefig}. Naturally, it would roll down the steepest
-slope and eventually stop at the minimum of the error surface (if it had no
-inertia). We will use this picture to develop an algorithm to find our
-way to the minimum of the objective function. The ball will always
-follow the steepest slope. Thus we need to figure out the direction of
-the steepest slope at the position of the ball.
-
-The \entermde{Gradient}{gradient} (Box~\ref{partialderivativebox}) of the
-objective function is the vector
+When we place a ball somewhere on the slopes of a hill, it rolls
+downwards and eventually stops at the bottom. The ball always rolls in
+the direction of the steepest slope. For a two-dimensional parameter
+space of our example, the \entermde{Gradient}{gradient}
+(box~\ref{partialderivativebox}) of the cost function is a vector
+\begin{equation}
+  \label{gradientpowerlaw}
+  \nabla f_{cost}(c, a) = \left( \frac{\partial f_{cost}(c, a)}{\partial c},
+    \frac{\partial f_{cost}(c, a)}{\partial a} \right)
+\end{equation}
+that points into the direction of the strongest ascend of the cost
+function.  The gradient is given by the partial derivatives
+(box~\ref{partialderivativebox}) of the mean squared error with
+respect to the parameters $c$ and $a$ of the power law
+relation. In general, for $n$-dimensional problems, the gradient is an
+$n-$ dimensional vector containing for each of the $n$ parameters
+$p_j$ the respective partial derivatives as coordinates:
 \begin{equation}
   \label{gradient}
-  \nabla f_{cost}(m,b) = \left( \frac{\partial f(m,b)}{\partial m},
-    \frac{\partial f(m,b)}{\partial b} \right)
+  \nabla f_{cost}(\vec p) = \left( \frac{\partial f_{cost}(\vec p)}{\partial p_j} \right)
+\end{equation}
+Despite the fancy words this simply means that we need to calculate the
+derivatives in the very same way as we have done it for the case of a
+single parameter, \eqnref{costderivativediff}, for each parameter
+separately.
+
+The iterative equation \eqref{gradientdescent} of the gradient descent
+stays exactly the same, with the only difference that the current
+parameter value $p_i$ becomes a vector $\vec p_i$ of parameter values:
+\begin{equation}
+  \label{ndimgradientdescent}
+  \vec p_{i+1} = \vec p_i - \epsilon \cdot \nabla f_{cost}(\vec p_i)
 \end{equation}
-that points to the strongest ascend of the objective function.  The
-gradient is given by partial derivatives
-(Box~\ref{partialderivativebox}) of the mean squared error with
-respect to the parameters $m$ and $b$ of the straight line. There is
-no need to calculate it analytically because it can be estimated from
-the partial derivatives using the difference quotient
-(Box~\ref{differentialquotientbox}) for small steps $\Delta m$ and
-$\Delta b$. For example, the partial derivative with respect to $m$
-can be computed as
+For each parameter we subtract the corresponding derivative multiplied
+with $\epsilon$.  The algorithm proceeds along the negative gradient
+(\figref{powergradientdescentfig}).
+
+For the termination condition we need the length of the gradient. In
+one dimension it was just the absolute value of the derivative. For
+$n$ dimensions this is according to the \enterm{Pythagorean theorem}
+(\determ[Pythagoras!Satz des]{Satz des Pythagoras}) the square root of
+the sum of the squared partial derivatives:
 \begin{equation}
-  \frac{\partial f_{cost}(m,b)}{\partial m} = \lim\limits_{\Delta m \to
-  0} \frac{f_{cost}(m + \Delta m, b) - f_{cost}(m,b)}{\Delta m}
-\approx \frac{f_{cost}(m + \Delta m, b) - f_{cost}(m,b)}{\Delta m} \; . 
+  \label{ndimabsgradient}
+  |\nabla f_{cost}(\vec p_i)| = \sqrt{\sum_{i=1}^n \left(\frac{\partial f_{cost}(\vec p)}{\partial p_i}\right)^2}
 \end{equation}
-The length of the gradient indicates the steepness of the slope
-(\figref{gradientquiverfig}). Since want to go down the hill, we
-choose the opposite direction.
+The \code{norm()} function implements this given a vector with the
+partial derivatives.
 
+\subsection{Passing a function as an argument to another function}
 
-\begin{figure}[t]
-  \includegraphics[width=0.75\textwidth]{error_gradient}
-  \titlecaption{Gradient of the error surface.}  {Each arrow points
-    into the direction of the greatest ascend at different positions
-    of the error surface shown in \figref{errorsurfacefig}. The
-    contour lines in the background illustrate the error surface. Warm
-    colors indicate high errors, colder colors low error values. Each
-    contour line connects points of equal
-    error.}\label{gradientquiverfig}
-\end{figure}
+So far, all our code for the gradient descent algorithm was tailored
+to a specific function, the cubic relation \eqref{cubicfunc}. It would
+be much better if we could pass an arbitrary function to our gradient
+algorithm. Then we would not need to rewrite it every time anew.
 
-\begin{exercise}{meanSquaredGradient.m}{}\label{gradientexercise}%
-  Implement a function \varcode{meanSquaredGradient()}, that takes the
-  $x$- and $y$-data and the set of parameters $(m, b)$ of a straight
-  line as a two-element vector as input arguments. The function should
-  return the gradient at the position $(m, b)$ as a vector with two
-  elements.
-\end{exercise}
+This is possible. We can indeed pass a function as an argument to
+another function. For this use the \code{@}-operator. As an example
+let's define a function that produces a standard plot for a function:
 
-\begin{exercise}{errorGradient.m}{}
-  Extend the script of exercises~\ref{errorsurfaceexercise} to plot
-  both the error surface and gradients using the
-  \varcode{meanSquaredGradient()} function from
-  exercise~\ref{gradientexercise}. Vectors in space can be easily
-  plotted using the function \code{quiver()}. Use \code{contour()}
-  instead of \code{surface()} to plot the error surface.
-\end{exercise}
+\pageinputlisting[caption={Example function taking a function as argument.}]{funcPlotter.m}
 
+This function can then be used as follows for plotting a sine wave. We
+pass the built in \varcode{sin()} function as \varcode{@sin} as an
+argument to our function:
 
-\section{Gradient descent}
-Finally, we are able to implement the optimization itself. By now it
-should be obvious why it is called the gradient descent method. All
-ingredients are already there. We need: (i) the cost function
-(\varcode{meanSquaredError()}), and (ii) the gradient
-(\varcode{meanSquaredGradient()}). The algorithm of the gradient
-descent works as follows:
-\begin{enumerate}
-\item Start with some given combination of the parameters $m$ and $b$
-  ($p_0 = (m_0, b_0)$).
-\item \label{computegradient} Calculate the gradient at the current
-  position $p_i$.
-\item If the length of the gradient falls below a certain value, we
-  assume to have reached the minimum and stop the search. We are
-  actually looking for the point at which the length of the gradient
-  is zero, but finding zero is impossible because of numerical
-  imprecision. We thus apply a threshold below which we are
-  sufficiently close to zero (e.g. \varcode{norm(gradient) < 0.1}).
-\item \label{gradientstep} If the length of the gradient exceeds the
-  threshold we take a small step into the opposite direction:
-  \begin{equation}
-    p_{i+1} = p_i - \epsilon \cdot \nabla f_{cost}(m_i, b_i)
-  \end{equation}
-  where $\epsilon = 0.01$ is a factor linking the gradient to
-  appropriate steps in the parameter space.
-\item Repeat steps \ref{computegradient} -- \ref{gradientstep}.
-\end{enumerate}
+\pageinputlisting[caption={Passing a function handle as an argument to a function.}]{funcplotterexamples.m}
 
-\Figref{gradientdescentfig} illustrates the gradient descent --- the
-path the imaginary ball has chosen to reach the minimum. Starting at
-an arbitrary position on the error surface we change the position as
-long as the gradient at that position is larger than a certain
-threshold. If the slope is very steep, the change in the position (the
-distance between the red dots in \figref{gradientdescentfig}) is
-large.
 
-\begin{figure}[t]
-  \includegraphics[width=0.45\textwidth]{gradient_descent}
-  \titlecaption{Gradient descent.}{The algorithm starts at an
-    arbitrary position. At each point the gradient is estimated and
-    the position is updated as long as the length of the gradient is
-    sufficiently large.The dots show the positions after each
-    iteration of the algorithm.} \label{gradientdescentfig}
-\end{figure}
+\subsection{Gradient descent algorithm for arbitrary functions}
+
+Now we are ready to adapt the gradient descent algorithm from
+exercise~\ref{gradientdescentexercise} to arbitrary functions with $n$
+parameters that we want to fit to some data.
 
 \begin{exercise}{gradientDescent.m}{}
-  Implement the gradient descent for the problem of fitting a straight
-  line to some measured data. Reuse the data generated in
-  exercise~\ref{errorsurfaceexercise}.
-  \begin{enumerate}
-  \item Store for each iteration the error value.
-  \item Plot the error values as a function of the iterations, the
-    number of optimization steps.
-  \item Plot the measured data together with the best fitting straight line.
-  \end{enumerate}\vspace{-4.5ex}
+  Adapt the function \varcode{gradientDescentCubic()} from
+  exercise~\ref{gradientdescentexercise} to implement the gradient
+  descent algorithm for any function \varcode{func(x, p)} that takes
+  as first argument the $x$-data values and as second argument a
+  vector with parameter values.  The new function takes a vector $\vec
+  p_0$ for the initial parameter values and also returns the best
+  parameter combination as a vector. Use a \varcode{for} loop over the
+  two dimensions for computing the gradient.
 \end{exercise}
 
+For testing our new function we need to implement the power law
+\eqref{powerfunc}:
 
-\section{Summary}
+\begin{exercise}{powerLaw.m}{}
+  Write a function that implements \eqref{powerfunc}. The function
+  gets as arguments a vector $x$ containing the $x$-data values and
+  another vector containing as elements the parameters for the power
+  law, i.e. the factor $c$ and the power $a$. It returns a vector
+  with the computed $y$ values for each $x$ value. 
+\end{exercise}
+
+Now let's use the new gradient descent function to fit a power law to
+our tiger data-set:
+
+\begin{exercise}{plotgradientdescentpower.m}{}
+  Use the function \varcode{gradientDescent()} to fit the
+  \varcode{powerLaw()} function to the simulated data from
+  exercise~\ref{mseexercise}. Plot the returned values of the two
+  parameters against each other. Compare the result of the gradient
+  descent method with the true values of $c$ and $a$ used to simulate
+  the data. Observe the norm of the gradient and inspect the plots to
+  adapt $\epsilon$ and the threshold if necessary. Finally plot the
+  data together with the best fitting power-law \eqref{powerfunc}.
+\end{exercise}
+
+Note that in our specific example on tiger sizes and weights the
+simulated data look on a first glance like being linearly related
+(\figref{cubicdatafig}). The true cubic relation between weights and
+sizes is not that obvious, because of the limited range of tiger
+sizes. Nevertheless, the cost function has a minimum at the bottom of
+a valley that is very narrow in the direction of the expontent
+(\figref{powergradientdescentfig}). The exponent of about three is
+thus clearly defined by the data.
+
+
+\section{Fitting non-linear functions to data}
 
-The gradient descent is an important numerical method for solving
+The gradient descent is a basic numerical method for solving
 optimization problems. It is used to find the global minimum of an
-objective function.
-
-Curve fitting is a common application for the gradient descent method.
-For the case of fitting straight lines to data pairs, the error
-surface (using the mean squared error) has exactly one clearly defined
-global minimum. In fact, the position of the minimum can be analytically
-calculated as shown in the next chapter.
-
-Problems that involve nonlinear computations on parameters, e.g. the
-rate $\lambda$ in an exponential function $f(x;\lambda) = e^{\lambda
-  x}$, do not have an analytical solution for the least squares. To
-find the least squares for such functions numerical methods such as
-the gradient descent have to be applied.
-
-The suggested gradient descent algorithm can be improved in multiple
-ways to converge faster.  For example one could adapt the step size to
-the length of the gradient. These numerical tricks have already been
-implemented in pre-defined functions. Generic optimization functions
-such as \matlabfun{fminsearch()} have been implemented for arbitrary
-objective functions, while the more specialized function
-\matlabfun{lsqcurvefit()} i specifically designed for optimizations in
-the least square error sense.
-
-%\newpage
+objective function.  
+
+Curve fitting is a specific optimization problem and a common
+application for the gradient descent method.  For the case of fitting
+straight lines to data pairs, the error surface (using the mean
+squared error) has exactly one clearly defined global minimum. In
+fact, the position of the minimum can be analytically calculated as
+shown in the next chapter. For linear fitting problems numerical
+methods like the gradient descent are not needed.
+
+Fitting problems that involve nonlinear functions of the parameters,
+e.g. the power law \eqref{powerfunc} or the exponential function
+$f(t;\tau) = e^{-t/\tau}$, do in general not have an analytical
+solution for the least squares. To find the least squares for such
+functions numerical methods such as the gradient descent have to be
+applied.
+
+The suggested gradient descent algorithm requires manually tuned
+values for $\epsilon$ and the threshold for terminating the iteration.
+The algorithm can be improved in multiple ways to converge more
+robustly and faster.  Most importantly, $\epsilon$ is made dependent
+on the changes of the gradient from one iteration to the next. These
+and other numerical tricks have already been implemented in
+pre-defined functions. Generic optimization functions such as
+\mcode{fminsearch()} have been implemented for arbitrary objective
+functions, while the more specialized function \mcode{lsqcurvefit()}
+is specifically designed for optimizations in the least square error
+sense.
+
+\begin{exercise}{plotlsqcurvefitpower.m}{}
+  Use the \matlab-function \varcode{lsqcurvefit()} instead of
+  \varcode{gradientDescent()} to fit the \varcode{powerLaw()} function
+  to the simulated data from exercise~\ref{mseexercise}. Plot the data
+  and the resulting best fitting power law function.
+\end{exercise}
+
+
 \begin{important}[Beware of secondary minima!]
   Finding the absolute minimum is not always as easy as in the case of
-  fitting a straight line. Often, the error surface has secondary or
+  fitting a straight line. Often, the cost function has secondary or
   local minima in which the gradient descent stops even though there
   is a more optimal solution, a global minimum that is lower than the
   local minimum. Starting from good initial positions is a good
   approach to avoid getting stuck in local minima. Also keep in mind
-  that error surfaces tend to be simpler (less local minima) the fewer
+  that cost functions tend to be simpler (less local minima) the fewer
   parameters are fitted from the data. Each additional parameter
   increases complexity and is computationally more expensive.
 \end{important}
 
 
+\section{Evolution as an optimization problem}
+
+Evolution is a biological implementation of an optimization
+algorithm. The objective function is an organism's fitness. This needs
+to be maximized (this is the same as minimizing the negative fitness).
+The parameters of this optimization problem are all the many genes on
+the DNA. This is a very high-dimensional optimization problem. By
+cross-over and mutations a population of a species moves along the
+high-dimensional parameter space. Selection processes make sure that
+only organisms with higher fitness pass on their genes to the next
+generations. In this way the algorithm is not directed towards higher
+fitness, as the gradient descent method would be. Rather, some
+neighborhood of the parameter space is randomly probed. That way it is
+even possible to escape a local maximum and find a potentially better
+maximum. For this reason, \enterm[genetic algorithm]{genetic
+  algorithms} try to mimic evolution in the context of
+high-dimensional optimization problems, in particular with discrete
+parameter values. In biological evolution, the objective function,
+however, is not a fixed function. It may change in time by changing
+abiotic and biotic environmental conditions, making this a very
+complex but also interesting optimization problem.
+
+\subsection{Optimal design of neural systems}
+
+How should a neuron or neural network be designed? As a particular
+aspect of the general evolution of a species, this is a fundamental
+question in the neurosciences. Maintaining a neural system is
+costly. By their simple presence neurons incur costs. They need to be
+built and maintained, they occupy space and consume
+resources. Equipping a neuron with more ion channels also costs. And
+neural activity makes it more costly to maintain concentration
+gradients of ions. This all boils down to the consumption of more ATP,
+the currency of metabolism. On the other hand each neuron provides
+some useful function. In the end, neurons make the organism to behave
+in some sensible way to increase the overall fitness of the
+organism. On the level of neurons that means that they should
+faithfully represent and process behaviorally relevant sensory
+stimuli, make a sensible decision, store some important memory, or
+initiate and control movements in a directed way. Unfortunately there
+is a tradeoff. Better neural function usually involves higher
+costs. More ion channels reduce intrinsic noise which usually favors
+the precision of neural responses. Higher neuronal activity improves
+the quality of the encoding of sensory stimuli. More neurons are
+required for more complex computations. And so on.
+
+Understanding why a neuronal system is designed in some specific way
+requires to also understand these tradeoffs. The number of neurons,
+the number and types of ion channels, the length of axons, the number
+of synapses, the way neurons are connected, etc. are all parameters
+that could be optimized. For the objective function the function of
+the neurons needs to be quantified, for example by measures from
+information or detection theory, and their dependence on the
+parameters. From these benefits the costs need to be subtracted. And
+then one is interested in finding the maximum of the resulting
+objective function. Maximization (or minimization) problems are not
+only a tool for data analysis, rather they are at the core of many ---
+not only biological or neuroscientific --- problems.
+
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \printsolutions
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similarity index 96%
rename from further_topics/lectures/topics-chapter.tex
rename to replicability/lectures/topics-chapter.tex
index 32acf92..e3bfbde 100644
--- a/further_topics/lectures/topics-chapter.tex
+++ b/replicability/lectures/topics-chapter.tex
@@ -59,10 +59,6 @@
 
 % figures:
 \setlength{\fboxsep}{0pt}
-\newcommand{\texpicture}[1]{{\sffamily\footnotesize\input{#1.tex}}}
-%\newcommand{\texpicture}[1]{\fbox{\sffamily\footnotesize\input{#1.tex}}}
-%\newcommand{\texpicture}[1]{\setlength{\fboxsep}{2mm}\fbox{#1}}
-%\newcommand{\texpicture}[1]{}
 \newcommand{\figlabel}[1]{\textsf{\textbf{\large \uppercase{#1}}}}
 
 % maximum number of floats:
diff --git a/further_topics/lectures/topics-slides.tex b/replicability/lectures/topics-slides.tex
similarity index 99%
rename from further_topics/lectures/topics-slides.tex
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index 295e684..157d1ab 100644
--- a/further_topics/lectures/topics-slides.tex
+++ b/replicability/lectures/topics-slides.tex
@@ -50,7 +50,6 @@
 
 %%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{graphicx}
-\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
 
 %%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{listings}
diff --git a/scientificcomputing-script.tex b/scientificcomputing-script.tex
index 4847455..016a6a4 100644
--- a/scientificcomputing-script.tex
+++ b/scientificcomputing-script.tex
@@ -5,7 +5,6 @@
 \newcommand{\includechapter}[1]{%
   \graphicspath{{#1/lecture/}{#1/lecture/figures/}{#1/images/}}%
   \setcodepath{#1/code}%
-  \renewcommand{\texinputpath}{#1/lecture/}%
   \include{#1/lecture/#1}%
 }
 
@@ -39,8 +38,28 @@
 \listofiboxfs
 
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \mainmatter
 
+% add introduction:
+% - data analysis skills way beyond basic statistical test are at the
+%   core of modern neuroscience
+% - *understanding* of basic concepts of data analysis concepts is important
+% - math is the universal language of natural sciences, including neuroscience.
+%   it is the most precise way to express relation.
+%   We therefore expose you to some mathematical equations 
+%   that do not go beyond what you learned at school.
+% - most concepts are also quite relevant for the brain itself!
+%   The brain also needs to analyse the data coming in from the senses.
+% - modern approaches:
+%   * open source science (open data, open code, open algorithmns in contrast 
+%     to closed source license models), FAIR
+%   * knowledge is freedom ...
+%   * do not consume what companies offer you but know what you want and implement it
+%   * no screenshots on how to use a stupid GUI program
+%   * python as a modern and popular programming language in modern science
+%     (i.e. machine learning, others?) with a strong community support.
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \part{Programming basics}
 
@@ -57,10 +76,10 @@
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \part{Data analysis}
+%\subpart{Basic statistics}
+% With an intro saying statistical tests are overrated!
 
-% add chapter on simulations (draw random numbers, draw random functions, euler forward, odeint)
-% !!! this would be a nice and simple starter !!!
-% introduces derivatives which are also needed for fitting
+% add chapter on stochastic simulations (draw random numbers, draw random functions)
 %\includechapter{simulations}
 
 \includechapter{statistics}
@@ -71,38 +90,74 @@
 
 \includechapter{regression}
 
-% add chapter on generalized linear models (versus ANOVA)
-
-% add chapter on nonlinear fitting (methods, initial values, local minima)
+% add chapter on nonlinear fitting (methods, initial values, local minima, power law fits on log-log data)
 
 \includechapter{likelihood}
 
-\includechapter{pointprocesses}
+% add chapter on multivariate analysis/generalized linear models (in addition to ANOVA)
 
-% add chapter on time series (amplitude distribution, autocorrelation, crosscorrelation)
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%\subpart{Time series}
+
+% add chapter on simulations of dynamical systems/time series (euler
+% forward, odeint, iterated maps, dynamical system, bifurcations?)
+% !!! this would be a nice and simple starter !!!
+% introduces derivatives which are also needed for fitting
+
+% add chapter on time series (amplitude distribution, autocorrelation, crosscorrelation)
 %\includechapter{spectral}
 
-% add chapter on digital filtering
+% add chapter on filtering (1. order, butter and kernel convolution) and envelopes
 
-% add chapter on event detection, signal detection, ROC
+% add chapter on event detection (local maxima, threshold crossings,
+% peak detection), signal detection, ROC
 
-% add chapter on mutual information
+\includechapter{pointprocesses}
+
+% add a chapter on simulating point-processes/spike trains
+% (Poisson spike trains, LMP models, integrate-and-fire models, full HH like models)
 
-% add chapters on linear algebra, PCA, clustering
+% add chapter on information theory, mutual information, stimulus reconstruction, coherence
+% move STA here!
+
+% add a chapter on Bayesian inference (the Neuroscience of it and a
+% bit of application for statistical problems).
 
-% add chapter on simple machine learning
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%\subpart{Machine learning}
+
+% add chapters on linear algebra, PCA, clustering, see linearalgebra/
 
+% add chapter on logistic regression, see logistic/
+
+% add chapter on simple machine learning, perceptron
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %\part{Tools}
-% Latex
-% markdown
+
+% add chapter on Markup and LaTeX -> see latex/
+% - if you know LaTeX you also know how to better use word/libreoffice
+% - with an introduction on mark-up in general, an outlook to markdown, html, xml, jason, yaml
+% - make notes, data, and texts understandable by computers!
+% - easily generate markup from python scripts via mako .
+
+% bash basics:
+% - calling a (python) script/program with arguments
+% - files: ls, cp, mkdir, find, etc.
+% - text file manipulations: grep, diff, sed, awk
+% - pipes, for loop, if, test
+
 % Makefile
-% version control (git and github) only put source files into repository!
+
 % distributed computing (ssh and grid engines)
-% data handling (structure, data bases, storage (rsync), backup)
-% data annotation, meta data
+
+% see replicability/ :
+% - version control (git and github) only put source files into repository!
+% - data handling (structure, storage (rsync), backup, data bases)
+% - data annotation, meta data, FAIR
+
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \appendix
@@ -111,7 +166,7 @@
 %\chapter{Cheat-Sheet}
 
 %%%% solutions: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% \printallsolutions
+\printallsolutions
 
 %%%% indices: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
diff --git a/simulations/code/exprandom.m b/simulations/code/exprandom.m
new file mode 100644
index 0000000..be9c5a3
--- /dev/null
+++ b/simulations/code/exprandom.m
@@ -0,0 +1,8 @@
+n = 200;                     % number of time intervals
+rate = 50.0;                 % event rate
+t = exprnd(1.0/rate, n, 1);  % exponentially distributed intervals
+m = mean(t);                 % mean time interval
+s = std(t);                  % corresponding standard deviation
+fprintf('Generated n=%d random time intervals with rate=%.0fHz:\n', n, rate);
+fprintf('  Expected mean interval=%.3fs, std=%.3fs\n', 1.0/rate, 1.0/rate);
+fprintf('  Measured mean interval=%.3fs, std=%.3fs\n', m, s);
diff --git a/simulations/code/exprandom.out b/simulations/code/exprandom.out
new file mode 100644
index 0000000..7053ee8
--- /dev/null
+++ b/simulations/code/exprandom.out
@@ -0,0 +1,3 @@
+Generated n=200 random time intervals with rate=50Hz:
+  Expected mean interval=0.020s, std=0.020s
+  Measured mean interval=0.020s, std=0.019s
diff --git a/simulations/code/probability.m b/simulations/code/probability.m
new file mode 100644
index 0000000..c7da8d4
--- /dev/null
+++ b/simulations/code/probability.m
@@ -0,0 +1,6 @@
+n = 20;               % number of coin flips
+P = 0.6;              % probability of head
+u = rand(n, 1);       % n random numbers between 0 and 1
+heads = u < P;        % true if head showed up
+nheads = sum(heads);  % number of heads
+fprintf('Of %d coin flips, %d heads showed up.\n', n, nheads);
diff --git a/simulations/code/probability.out b/simulations/code/probability.out
new file mode 100644
index 0000000..8ae62ee
--- /dev/null
+++ b/simulations/code/probability.out
@@ -0,0 +1 @@
+Of 20 coin flips, 12 heads showed up.
diff --git a/simulations/code/unirandom.m b/simulations/code/unirandom.m
new file mode 100644
index 0000000..0c6f1fb
--- /dev/null
+++ b/simulations/code/unirandom.m
@@ -0,0 +1,16 @@
+n = 10000;                    % number of action potentials
+T = 100.0;                    % total time interval
+spikes = sort(T*rand(n, 1));  % sorted times of action potentials
+isis = diff(spikes);          % interspike intervals
+rate = n/T;                   % firing rate
+misi = mean(isis);            % mean interspike interval
+sisi = std(isis);             % and standard deviation
+fprintf('firing rate = %.1fHz\n', rate);
+fprintf('   mean ISI = %.1fms\n', 1000.0*misi);  % inverse of rate
+fprintf('    std ISI = %.1fms\n', 1000.0*sisi);  % same as mean
+hist(1000.0*isis, 50);        % exponential distribution
+xlabel('ISI [ms]');
+ylabel('count');
+
+
+
diff --git a/simulations/code/unirandom.out b/simulations/code/unirandom.out
new file mode 100644
index 0000000..4abe24c
--- /dev/null
+++ b/simulations/code/unirandom.out
@@ -0,0 +1,3 @@
+firing rate = 100.0Hz
+   mean ISI = 10.0ms
+    std ISI = 10.0ms
diff --git a/simulations/lecture/randomwalkneuron.py b/simulations/lecture/randomwalkneuron.py
new file mode 100644
index 0000000..da248ce
--- /dev/null
+++ b/simulations/lecture/randomwalkneuron.py
@@ -0,0 +1,58 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
+
+def random_walk(n, p, rng):
+    steps = rng.rand(n)
+    steps[steps>=1.0-p] = 1
+    steps[steps<1.0-p] = -1
+    x = np.hstack(((0.0,), np.cumsum(steps)))
+    return x
+
+def random_walk_neuron(n, p, thresh, rng):
+    x = random_walk(n, p, rng)
+    spikes = []
+    j = -1
+    i = 1
+    while i > 0:
+        if j > 0:
+            spikes.append(j+i)
+        j += i + 1
+        x[j:] -= x[j]
+        i = np.argmax(x[j:] >= thresh - 0.1)
+    return x, spikes
+    
+
+if __name__ == "__main__":
+    fig = plt.figure()
+    spec = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[2, 1], wspace=0.4,
+                             **adjust_fs(fig, left=6.5, right=1.5))
+    ax = fig.add_subplot(spec[0, 0])
+    rng = np.random.RandomState(52281)
+    p = 0.6
+    thresh = 10.0
+    nmax = 500
+    n = np.arange(0.0, nmax+1, 1.0)
+    x, spikes = random_walk_neuron(nmax, p, thresh, rng)
+    ax.axhline(0.0, **lsGrid)
+    ax.axhline(thresh, **lsAm)
+    ax.plot(n, x, **lsBm)
+    for tspike in spikes:
+        ax.plot([tspike, tspike], [12.0, 16.0], **lsC)
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Potential')
+    ax.set_xlim(0, nmax)
+    ax.set_ylim(-10, 17)
+    ax.set_yticks(np.arange(-10, 11, 10))
+    ax = fig.add_subplot(spec[0, 1])
+    nmax = 100000
+    x, spikes = random_walk_neuron(nmax, p, thresh, rng)
+    isis = np.diff(spikes)
+    ax.hist(isis, np.arange(0.0, 151.0, 10.0), **fsAs)
+    ax.set_xlabel('ISI')
+    ax.set_ylabel('Count')
+    ax.set_xticks(np.arange(0, 151, 50))
+    ax.set_yticks(np.arange(0, 401, 100))
+    fig.savefig("randomwalkneuron.pdf")
+    plt.close()
diff --git a/simulations/lecture/randomwalkone.py b/simulations/lecture/randomwalkone.py
new file mode 100644
index 0000000..9b4724a
--- /dev/null
+++ b/simulations/lecture/randomwalkone.py
@@ -0,0 +1,42 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from plotstyle import *
+
+def random_walk(n, p, rng):
+    steps = rng.rand(n)
+    steps[steps>=1.0-p] = 1
+    steps[steps<1.0-p] = -1
+    x = np.hstack(((0.0,), np.cumsum(steps)))
+    return x
+
+def plot_random_walk(ax, nmax, p, rng, ymin=-20.0, ymax=20.0):
+    nn = np.linspace(0.0, nmax, 200)
+    m = 2*p-1
+    v = 4*p*(1-p)
+    ax.fill_between(nn, m*nn+np.sqrt(nn*v), m*nn-np.sqrt(nn*v), **fsAa)
+    ax.plot([0.0, nmax], [0.0, m*nmax], **lsAm)
+    lcs = [colors['red'], colors['orange'],
+           colors['yellow'], colors['green']]
+    n = np.arange(0.0, nmax+1, 1.0)
+    for k in range(12):
+        x = random_walk(nmax, p, rng)
+        ls = dict(**lsAm)
+        ls['color'] = lcs[k%len(lcs)]
+        ax.plot(n, x, **ls)
+    ax.set_xlabel('Iteration $n$')
+    ax.set_ylabel('Position $x_n$')
+    ax.set_xlim(0, nmax)
+    ax.set_ylim(ymin, ymax)
+    ax.set_yticks(np.arange(ymin, ymax+1.0, 10.0))
+
+    
+if __name__ == "__main__":
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=cm_size(figure_width, 2.0*figure_height))
+    fig.subplots_adjust(**adjust_fs(fig, right=1.0))
+    rng = np.random.RandomState(52281)
+    plot_random_walk(ax1, 80, 0.5, rng)
+    ax1.text(5.0, 20.0, 'symmetric', va='center')
+    plot_random_walk(ax2, 80, 0.6, rng, -10.0, 30.0)
+    ax2.text(5.0, 30.0, 'with drift', va='center')
+    fig.savefig("randomwalkone.pdf")
+    plt.close()
diff --git a/simulations/lecture/randomwalktwo.py b/simulations/lecture/randomwalktwo.py
new file mode 100644
index 0000000..79221d1
--- /dev/null
+++ b/simulations/lecture/randomwalktwo.py
@@ -0,0 +1,38 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from plotstyle import *
+
+def random_walk(n, rng):
+    p = 0.25
+    steps = rng.rand(n)
+    xsteps = np.zeros(len(steps))
+    ysteps = np.zeros(len(steps))
+    xsteps[(steps>=0*p)&(steps<1*p)] = +1
+    xsteps[(steps>=1*p)&(steps<2*p)] = -1
+    ysteps[(steps>=2*p)&(steps<3*p)] = +1
+    ysteps[(steps>=3*p)&(steps<4*p)] = -1
+    x = np.hstack(((0.0,), np.cumsum(xsteps)))
+    y = np.hstack(((0.0,), np.cumsum(ysteps)))
+    return x, y
+
+if __name__ == "__main__":
+    fig, ax = plt.subplots(figsize=cm_size(figure_width, 0.65*figure_width))
+    fig.subplots_adjust(**adjust_fs(fig, right=1.0))
+    rng = np.random.RandomState(42281)
+    nmax = 500
+    lcs = [colors['blue'], colors['red'], colors['orange'],
+           colors['yellow'], colors['green']]
+    for k in range(len(lcs)):
+        x, y = random_walk(nmax, rng)
+        ls = dict(**lsAm)
+        ls['color'] = lcs[k%len(lcs)]
+        ax.plot(x, y, **ls)
+    ax.plot([0], [0], **psMarker)
+    ax.set_xlabel('Position $x_n$')
+    ax.set_ylabel('Position $y_n$')
+    ax.set_xlim(-30, 30)
+    ax.set_ylim(-20, 20)
+    ax.set_xticks(np.arange(-30, 31, 10))
+    ax.set_yticks(np.arange(-20, 21, 10))
+    fig.savefig("randomwalktwo.pdf")
+    plt.close()
diff --git a/simulations/lecture/simulations.tex b/simulations/lecture/simulations.tex
index 6d69396..f2deb1d 100644
--- a/simulations/lecture/simulations.tex
+++ b/simulations/lecture/simulations.tex
@@ -1,8 +1,8 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\chapter{Simulations}
-\label{simulationschapter}
-\exercisechapter{Simulations}
+\chapter{Stochastic simulations}
+\label{stochasticsimulationschapter}
+\exercisechapter{Stochastic simulations}
 
 The real power of computers for data analysis is the possibility to
 run simulations. Experimental data of almost unlimited sample sizes
@@ -12,33 +12,48 @@ well controlled data sets that are free of confounding pecularities of
 real data sets. With simulated data we can also test our own analysis
 functions. More importantly, by means of simulations we can explore
 possible outcomes of our experiments before we even started the
-experiment or we can explore possible results for regimes that we
+experiment. We could even explore possible results for regimes that we
 cannot test experimentally. How dynamical systems, like for example
-predator-prey interactions or the activity of neurons, evolve in time
-is a central application for simulations. Computers becoming available
-from the second half of the twentieth century on pushed the exciting
-field of nonlinear dynamical systems forward. Conceptually, many kinds
-of simulations are very simple and are implemented in a few lines of
-code.
+predator-prey interactions or the activity of neurons or whole brains,
+evolve in time is a central application for simulations. The advent of
+computers at the second half of the twentieth century pushed the
+exciting field of nonlinear dynamical systems forward. Conceptually,
+many kinds of simulations are very simple and are implemented in a few
+lines of code.
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Random numbers}
+
+\begin{figure}[t]
+  \includegraphics{randomnumbers}
+  \titlecaption{\label{randomnumbersfig} Random numbers.}  {Numerical
+    random number generators return sequences of numbers that are as
+    random as possible. The returned values approximate a given
+    probability distribution. Here, for example, a uniform
+    distribution between zero and one (top left). The serial
+    correlation (auto-correlation) of the returned sequences is zero
+    at any lag except for zero (bottom left). However, by setting a
+    seed, defining the state, or spawning the random number generator,
+    the exact same sequence can be generated (right).}
+\end{figure}
+
 At the heart of many simulations are random numbers that we get from
 \enterm[random number generator]{random number generators}.  These are
 numerical algorithms that return sequences of numbers that appear to
-be as random as possible. If we draw random numbers using, for
-example, the \code{rand()} function, then these numbers are indeed
-uniformly distributed and have a mean of one half. Subsequent numbers
-are independent of each other, i.e. the autocorrelation function is
-zero everywhere except at lag zero. However, numerical random number
-generators have a period, after which they repeat the exact same
-sequence of numbers. This differentiates them from truely random
-numbers and hence they are called \enterm[random number
-generator!pseudo]{pseudo random number generators}. In rare cases this
-periodicity can induce problems in simulations whenever more random
-numbers than the period of the random number generator are
-used. Luckily, nowadays the periods of random nunmber generators are
-very large, $2^{64}$, $2^{128}$, or even larger.
+be as random as possible (\figref{randomnumbersfig}). If we draw
+random numbers using, for example, the \code{rand()} function, then
+these numbers are indeed uniformly distributed and have a mean of one
+half. Subsequent numbers are independent of each other, i.e. the
+autocorrelation function is zero everywhere except at lag
+zero. However, numerical random number generators have a period, after
+which they repeat the exact same sequence of numbers. This
+differentiates them from truely random numbers and hence they are
+called \enterm[random number generator!pseudo]{pseudo random number
+  generators}. In rare cases this periodicity can induce problems in
+simulations whenever more random numbers than the period of the random
+number generator are used. Luckily, nowadays the periods of random
+nunmber generators are very large. Periods are at least in the range
+of $2^{64}$, that is about $10^{18}$, or even larger.
 
 The pseudo randomness of numerical random number generators also has
 an advantage.  They allow to repeat exactly the same sequence of
@@ -63,45 +78,23 @@ script is run.
   the sequence. This is the serial correlation at lag one.
 \end{exercise}
 
-\begin{figure}[t]
-  \includegraphics[width=1\textwidth]{randomnumbers}
-  \titlecaption{\label{randomnumbersfig} Random numbers.}  {Numerical
-    random number generators return sequences of numbers that are as
-    random as possible. The returned values approximate a given
-    probability distribution. Here, for example, a uniform
-    distribution between zero and one (top left). The serial
-    correlation (auto-correlation) of the returned sequences is zero
-    at any lag except for zero (bottom left). However, by setting a
-    seed, defining the state, or spawning the random number generator,
-    the exact same sequence can be generated (right).}
-\end{figure}
-
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
 \section{Univariate data}
-The most basic type of simulation is to draw random numbers from a
-given distribution like, for example, the normal distribution. This
-simulates repeated measurements of some quantity (e.g., weight of
-tigers or firing rate of neurons). Doing so we must specify from which
+A basic type of stochastic simulations is to draw random numbers from
+a given distribution like, for example, the normal distribution. This
+simulates repeated measurements of some quantity (weight of tigers,
+firing rate of neurons). Doing so we must specify from which
 probability distribution the data should originate from and what are
-the parameters (mean, standard deviation, shape parameters, etc.)
-that distribution. How to illuastrate and quantify univariate data, no
-matter whether they have been actually measured or whether they have
-been simulated as described in the following, is described in
+the parameters of that distribution (mean, standard deviation, shape
+parameters). How to illustrate and quantify univariate data, no matter
+whether they have been actually measured or whether they have been
+simulated as described in the following, is described in
 chapter~\ref{descriptivestatisticschapter}.
 
 \subsection{Normally distributed data}
-For drawing numbers $x_i$ from a normal distribution we use the
-\code{randn()} function. This function returns normally distributed
-numbers $\xi_i$ with zero mean and unit standard deviation. For
-changing the standard deviation we need to multiply the returned data
-values with the required standard deviation $\sigma$. For changing the
-mean we just add the desired mean $\mu$ to the random numbers:
-\begin{equation}
-  x_i = \sigma \xi_i + \mu
-\end{equation}
 
 \begin{figure}[t]
-  \includegraphics[width=1\textwidth]{normaldata}
+  \includegraphics{normaldata}
   \titlecaption{\label{normaldatafig} Generating normally distributed
     data.}{With the help of a computer the weight of 300 tigers can be
     measured in no time using the \code{randn()} function (left).  By
@@ -111,6 +104,21 @@ mean we just add the desired mean $\mu$ to the random numbers:
     histogram).}
 \end{figure}
 
+For drawing numbers from a normal distribution
+\begin{equation}
+  p_{norm}(x) = \frac{1}{\sqrt{2\pi}}e^{-\frac{1}{2}x^2}
+\end{equation}
+with zero mean and unit standard deviation we use the \code{randn()}
+function. This function returns normally distributed numbers $\xi_i$
+with zero mean and unit standard deviation. For changing the standard
+deviation we need to multiply the returned data values with the
+required standard deviation $\sigma$. For changing the mean we just
+add the desired mean $\mu$ to the random numbers
+(\figref{normaldatafig}):
+\begin{equation}
+  x_i = \sigma \xi_i + \mu
+\end{equation}
+
 \begin{exercise}{normaldata.m}{normaldata.out}
   Write a little script that generates $n=100$ normally distributed
   data simulating the weight of Bengal tiger males with mean 220\,kg
@@ -124,18 +132,191 @@ mean we just add the desired mean $\mu$ to the random numbers:
 \end{exercise}
 
 \subsection{Other probability densities}
-\code{rand()}
-gamma
+We can draw random numbers not only from normal
+distributions. Functions are provided that let you draw random numbers
+of almost any probability distribution. They differ in their shape and
+in the number of parameters they have. Most probability distributions
+are parameterized by a location parameter that usually coincides with
+their mean, and a scale parameter that often coincides with their
+standard deviation. Some distributions have additional shape
+parameters.
+
+For example, the time intervals $t$ between randomly generated action
+potentials (a so called Poisson spike train, see
+chapter~\ref{pointprocesseschapter}) are exponentially distributed ---
+as are the intervals between state transitions of ion channels, or the
+intervals between radioactive decays. The exponential distribution is
+only defined for positive time intervals:
+\begin{equation}
+  \label{expdistribution}
+  p_{exp}(t) = \lambda e^{-\lambda t}  \; , \quad t \ge 0, \; \lambda > 0
+\end{equation}
+A single rate parameter $\lambda$ measured in Hertz parameterizes the
+exponential distribution. It defines how often per time unit the event
+happens. Both the mean interval between the events and the
+corresponding standard deviation equal the inverse rate.
+
+\begin{exercise}{exprandom.m}{exprandom.out}
+  Draw $n=200$ random time intervals from an exponential distribution
+  with rate $\lambda=50$\,Hz. Calculate the mean and the standard
+  deviation of the random numbers and compare them with the expected
+  values.
+\end{exercise}
+
+
+\subsection{Uniformly distributed random numbers}
+
+\begin{figure}[t]
+  Exponential distribution of ISIs.
+\end{figure}
+
+The \code{rand()} function returns uniformly distributed random
+numbers between zero and one
+\begin{equation}
+  p_{uni}(x) = \left\{\begin{array}{rcl} 1 & ; & 0 \le x < 1 \\
+                               0 & ; & \text{else} \end{array}\right.
+\end{equation}
+For random numbers $x_i$ uniformly distributed between $x_{\rm min}$
+and $x_{\rm max}$ we multiply by the desired range $x_{\rm max} -
+x_{\rm min}$ and add $x_{\rm min}$ to the numbers $u_i$ returned by
+\code{rand()}:
+\begin{equation}
+  x_i = (x_{\rm max} - x_{\rm min}) u_i + x_{\rm min}
+\end{equation}
+
+\begin{exercise}{unirandom.m}{unirandom.out}
+  Simulate the times of $n=10\,000$ action potentials occuring
+  randomly within a time interval of 100\,s using the \code{rand()}
+  function.  Sort the times using \code{sort()}, compute the time
+  intervals between the action potentials (interspike intervals, ISIs)
+  using the \code{diff()} function, plot a histogram of the interspike
+  intervals in milliseconds, and compute the mean and standard
+  deviation of the interspike intervals, also in milliseconds. Compare
+  the mean ISI with the firing rate (number of action potentials per
+  time).
+\end{exercise}
+
+
+\subsection{Simulating probabilities}
+Simulating events that happen with some probability $P$ is also
+possible. That could be the probability of head showing up when
+flipping a coin, getting an action potential within some time, a ion
+channel to open, ... For this draw a random number $u_i$ from a
+uniform distribution between zero and one (\code{rand()}). If the
+random number is lower than $P$, the event happens, if it is larger
+the event does not occur.
+
+\begin{exercise}{probability.m}{probability.out}
+  Let's flip a coin 20 times. The coin is biased and shows head with a
+  probability of $P=0.6$. Count the number of heads.
+\end{exercise}
+
+
+The gamma distribution (\code{gamrnd()}) phenomenologically describes
+various types of interspike interval dsitributions
+(chapter~\ref{pointprocesseschapter}). scale and shape. exercise.
 
 \subsection{Random integers}
 \code{randi()}
 
 
+\section{Random walks}
+A basic concept for stochastic models is the \enterm{random walk}. A
+walker starts at some initial position $x_0$. At every iteration $n$
+it takes a step $\delta_n$: $x_n = x_{n-1} + \delta_n$. The walker
+either steps to the right ($\delta_n = +1$) with some probability $p$
+or to the left ($\delta_n = -1$) with probability $q = 1-p$.
+
+\begin{figure}[tb]
+  \includegraphics{randomwalkone}
+  \titlecaption{\label{randomwalkonefig} Random walk in one
+    dimension.}{Twelve random walks starting at $x_0=0$. The ensemble
+    average of the position of the random walkers marked by the blue
+    line and the corresponding standard deviation by the shaded
+    area. The standard deviation increases with the square root of the
+    number of steps taken. The top plot shows a symmetric random walk
+    where the probability of a step to the right equals the one for a
+    step to the left ($p=q=\frac{1}{2}$). The bottom plot shows a
+    random walk with drift. Here the probability of taking a step to
+    the right ($p=0.6$) is larger than the probability for a step to
+    the left ($q=0.4$).}
+\end{figure}
+
+For a \enterm[random walk!symmetric]{symmetric random walk}
+(\figref{randomwalkonefig} top), the probabilities for a step to the
+left or to the right are the same, $p = q = \frac{1}{2}$. The average
+position of many walkers is then independent of the iteration step and
+equals the initial position $x_0$. The variance of each step is
+$\sigma_{\delta_n}^2 = p \cdot 1^2 + q \cdot(-1)^2 = 1$. Since each
+step is independent of the steps before, the variances of all the
+steps add up such that the total variance of the walker position at
+step $n$ equals $\sigma_{x_n}^2 = \sum_{i=1}^n \sigma_{\delta_n}^2 =
+n$. The standard deviation of the walker positions grows with the
+square root of the number of iteration steps $n$: $\sigma_{x_n} =
+\sqrt{n}$.
+
+\enterm[random walk!with drift]{Random walks with drift} are biased to
+one direction (\figref{randomwalkonefig} bottom). The probability to
+step to the left does not equal the probability to step to the right,
+$p \ne q$. Consequently, the average step is no longer zero. Instead
+it equals $\mu_{\delta_n} = p \cdot 1 + q \cdot (-1) = 2p - 1$. The
+average position grows proportional to the number of iterations:
+$\mu_{x_n} = n(2p - 1)$. The variance of each step turns out to be
+$\sigma_{\delta_n}^2 = 4pq$ (this indeed equals one for
+$p=q=\frac{1}{2}$). Again, because of the independence of the steps,
+the variance of the position at the $n$-th iteration, $\sigma_{x_n}^2
+= 4pqn$, is proportional to $n$ and the standard deviation,
+$\sigma_{x_n} = 2\sqrt{pqn}$, grows with the square root of $n$.
+
+\begin{exercise}{}{}
+  Random walk exercise.
+\end{exercise}
+
+\begin{figure}[tb]
+  \includegraphics{randomwalkneuron}
+  \titlecaption{\label{randomwalkneuronfig} Random walk spiking neuron.}{.}
+\end{figure}
+
+The sub-threshold membrane potential of a neuron can be modeled as a
+one-dimensional random walk. The thousands of excitatory and
+inhibitory synaptic inputs of a principal neuron in the brain make the
+membrane potential to randomly step upwards or downwards. Once the
+membrane potential hits a threshold an action potential is generated
+and the membrane potential is reset to the resting potential. The
+threshold in this context is often called an ``absorbing
+boundary''. The time it takes from the initial position to cross the
+threshold is the ``first-passage time''. For neurons this is the
+interspike interval. For symmetric random walks the first-passage
+times are again exponentially distributed. ??? Is that so ???
+
+% Gerstein and Mandelbrot 1964
+
+\begin{exercise}{}{}
+  Random walk neuron exercise.
+\end{exercise}
+
+\begin{figure}[tb]
+  \includegraphics{randomwalktwo}
+  \titlecaption{\label{randomwalktwofig} Random walk in two
+    dimensions.}{Fivehundred steps of five random walkers starting at
+    the origin (black dot) and taking steps of distance one either up,
+    down, to the left, or to the right with equal probability.}
+\end{figure}
+
+Higher dimensions (\figref{randomwalktwofig}), Brownian motion. Crystal growth.
+
+\begin{exercise}{}{}
+  Random walk in 2D exercise.
+\end{exercise}
+
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Bivariate data and static nonlinearities}
+\section{Bivariate data}
+
+\subsection{Static nonlinearities}
 
 \begin{figure}[t]
-  \includegraphics[width=1\textwidth]{staticnonlinearity}
+  \includegraphics{staticnonlinearity}
   \titlecaption{\label{staticnonlinearityfig} Generating data
     fluctuating around a function.}{The conductance of the
     mechontransducer channels in hair cells of the inner ear is a
@@ -145,9 +326,10 @@ gamma
     distributed (right, red line).}
 \end{figure}
 
-Example: mechanotransduciton!
+Example: mechanotransduciton! (\figref{staticnonlinearityfig})
+
+draw (and plot) random functions.
 
-draw (and plot) random functions (in statistics chapter?)
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Dynamical systems}
diff --git a/spectral/code/crosscorrelation.ipynb b/spectral/code/crosscorrelation.ipynb
new file mode 100644
index 0000000..0973ec8
--- /dev/null
+++ b/spectral/code/crosscorrelation.ipynb
@@ -0,0 +1,1602 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "Correlation coefficient, autocorrelation, and cross-correlograms"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "skip"
+      }
+     },
+     "source": [
+      "Initialize python"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import scipy.stats as stats\n",
+      "import scipy.signal as sig\n",
+      "import matplotlib.pylab as plt\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "skip"
+      }
+     },
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A function for printing out x-y values:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def printdata( x, y ) :\n",
+      "    print 'x ||', ' | '.join( [ '%5.2f' % v for v in x[:10] ] ), '...'\n",
+      "    print 'y ||', ' | '.join( [ '%5.2f' % v for v in y[:10] ] ), '...'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "Correlations coefficient (Pearson r)"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
+     "source": [
+      "Paired data (independent)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x = np.random.randn( 1000 )\n",
+      "y = np.random.randn( 1000 )\n",
+      "printdata( x, y )"
+     ],
+     "language": "python",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "x || -0.56 |  2.81 |  2.90 |  0.95 | -0.11 |  0.85 |  0.19 |  0.55 |  0.69 | -0.69 ...\n",
+        "y || -1.23 |  0.81 | -0.18 | -0.99 |  0.59 | -1.31 | -0.18 |  0.84 | -0.79 |  0.26 ...\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
+     "source": [
+      "Pearson's correlation coefficient"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plotcorr( x, y ) :\n",
+      "    r = stats.pearsonr( x, y )\n",
+      "    plt.scatter( x, y )\n",
+      "    plt.xlabel( 'x' )\n",
+      "    plt.ylabel( 'y' )\n",
+      "    plt.text( 0.05, 0.9, 'Pearson r = %.2f' % r[0], transform=plt.gca().transAxes )\n",
+      "    plt.show()\n",
+      "\n",
+      "plt.title( 'Independent data' )\n",
+      "plotcorr( x, y )\n"
+     ],
+     "language": "python",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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yUFIjOHLkKJLyquvSpUu8fv16jnW///57TpkyhevXr3+sfDcmk4uDMiZ1uj50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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8ed5b50>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Mean $$\\langle x \\rangle = \\frac{1}{N}\\sum_{i=1}^N x_i$$\n",
+      "\n",
+      "Variance $$\\sigma_{x,x}^2 = \\langle (x - \\langle x \\rangle)^2 \\rangle$$\n",
+      "\n",
+      "Covariance $$\\sigma_{x,y}^2 = \\langle (x - \\langle x \\rangle) (y - \\langle y \\rangle) \\rangle$$\n",
+      "\n",
+      "Pearson's correlation coefficient $$r_{x,y}=\\frac{\\sigma_{x,y}^2}{\\sqrt{\\sigma_{x,x}^2 \\sigma_{y,y}^2}}$$\n",
+      "\n",
+      "* Correlation coefficient is the covariance normalized by the variance: $$-1 < r < 1$$\n",
+      "* positively correlated: $$r>0$$\n",
+      "* negatively correlated, anti-correlated: $$r<0$$\n",
+      "* uncorrelated: $$r \\sim 0$$"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for r, t in zip( [ 1.0, 0.8, 0.5, 0.0, -0.7, -1.0 ],\n",
+      "                 [ 'perfectly correlated', 'positively correlated', 'weakly positively correlated', \n",
+      "                   'uncorrelated', 'negatively correlated', 'perfectly anticorrelated' ] ) :\n",
+      "    z = x*10.0\n",
+      "    plt.title( t )\n",
+      "    plotcorr( z, r*x+np.sqrt(1.0-r*r)*y )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Ap3F1LSM39wLOzs46JBVCXzJrSNRJd911F+HhdwBhQDe0k8O+BH4GXkBrCfQB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\nGViP1hp4DxiDdnLY3wADbdq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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab894d390>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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5kwkJCQVcPv/3f/9Hk8lDccl8TSCQwFxaLFW4adOme3qXFStW0GTyUdxO02ky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oPBAIPBgMcffxzA9KH5ZWVliIqK8qsp6HYNdXV1+b6+9WSz7OxsHD9+HF6vFw6HA11dXb537QOh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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8ebcf50>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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kJMLCwnDy5Mkcsn3++edo3bo1GjVqhMaNGz/Qc65duxY9evQAAERHR2Pbtm13vDa3miTB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esKy71f46jThXPYsCcq/f5LL/2ZWq2BGo2BffsOZX5+/l17/4RwN1IIaklubi59fQPsl1vyqVK9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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8718b10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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XsNlsaNu2LUJCQgAA5cvXxKFD0UhLexPALlgsA3HixO/w9/cvWIEFuWLlylXo3n0wkpKG\nQqO5CmfnFTh0aF+mcykEzz7CNCT415w5cwYxMdWQnNwMpA4Gwyrs3v0TgoKC4OHhh/T0BGQsJp2c\nWuOLLzqgXbt2BSu0IFcULx6Fs2enAJADMzSaN/Duu+4YO3ZMgcolyF+EaUjwrxk1agISEvohNXUe\n0tJmITHxPQwfPk45m8EGIF4paQV5Ac7OzgUorSAvpKQkA/C0f7ZavZCYmFxwAgkKBUIRCLJw/fot\n2Gyh9s9kGP7++zb0ej1Gjx4DSaoFYDQkqTHCw91Rr169ApNVkDe6dGkPSeoH4BcA/wdJmol27VoX\ntFiCAkbEAwqy0K5dY+za9SGSkiIB6CFJY9C27SsAgJEjhyM6Ohx79uxFQEBrdO/eXYSVPkOMHz8K\nGo0Gy5e/BrPZjEmTFqFixYoFLZaggBE+AkEWSOKDDyZh2rRPYbNZ0afPa/jwwzFQq5/OAnL79u1Y\nu3Y9XF2d0bdvb3h7ez+VfgWC/wrCWSx4plmx4mv06DEEyckDoNNdhJvbZhw5sh9eXl4FLZpA8Mwg\nnMWCZ5q33/4fkpNXAHgXaWmf4fbt2liwYEFBiyUQ/KcRxt1Cwt27d3H+/Hls2rQJKSn30apVywI7\nJ5gk1q9fj3PnziEqKgrVq1d/an0nJycC8LN/TkvzR0JC4lPrXyB4HhGmoQKGJIYMGYHZs2chLU0N\nwAVqdXMYjSuxceNq1KxZ86nL06lTL6xevRlpaUkAUlG9elXExn7/VHwEffsOweLFR5GcPB3ABUhS\nd2zfvgHly5d/4n0LBP8VhGnoGWPVqlX44ovvkZZ2AcAdAO1hs/2JpKRZGDJk9FOX58CBA1izZh3S\n0twA7AZwEDt3XsEHH0y0lyGJkydP4sCBA0hJScnX/j/5ZCJ69iwHP7/WCAsbhVWrFgolIBA8YYQi\nKGD27v0ViYkdAHgAUAHoC+A3ACG4ffvOU5fn+vXrsFrNAN4FUApAcQCTsWbNJgCA1WpF27ZdEBlZ\nG7VqdUVoaATOnz+fb/3r9Xp8+ulkXL16AidP/orGjRvnW9sCgSB7hCIoYEJDg2EybQeQrlz5CYAv\nJGkY2rZt+tTliY6OBnAbwCmHqyfg5yeHcC5evBibNp1DcvJpJCQcRnx8T7z6at9ct3/ixAlUqFAX\nnp5BqFu3Ba5evZqv8ueVy5cvY+3atdi1a9d/3gwpEOQICzGFXLx84f79+6xevSEtljI0GmtSpTLT\n2dmbAwa8ybS0tAKRacWKFVSpzAQ6E3iNkuTBQ4cOkSQHD36LwIcEqPycpqdnUK7avXPnDj09i1Kl\n+pTAGWo07zE0tBzT09Of4NPkzNatW2k2e9LZuQnN5jC2adOZNputQGQRCPKTvI6dwllcCLBardi1\naxfu3buHihUrwsPDo6BFwpUrV7BixYos2UcXLlyIgQO/QGLijwAkaDQfoUqVndixY+Mj24yNjUXL\nliNx587PyhXCbA7CoUOxKFas2JN7mBzw8QnBtWtzAdQHkAKLpTKWLx+LZs2aPXVZBIL8JK9jpwgf\nLQRoNBrUqFGjoMXIRJEiRfDmm29mud61a1ds2rQd69YVh1brDhcXK5Yu3ZyrNi0WC9LT/wKQCkAP\n4C7S0u7CYrHYy9hsNsTFxSExMRHR0dGZ7uUnNpsNN25cAlBLuWJEenolXLhwwV4mMTERBoNBpNAQ\n/OcRPgJBnlCr1VixYiEOHdqJHTuW4fTpgwgKCsqx/JYtW1ChQj2UKVMFW7fuQJUqZSFJDQF8BLO5\nDrp27WJPIZGWlob69VuiZs32aNZsGEJDI3D27Nkn9hylSsVArf5UuXIeavV6hIeH4/jx46hUqS5c\nXT1hMjlh/PiJD21LIHjWKVDT0KVLl9ClSxdcu3YNKpUKr7/+Ot54440Hwj0npqFnmdOnT2PZsuXY\nsmUHTpw4h/v30+DqakF0dDS6dm2Ljh1fR3LyTABekKQhePfdV+Dp6YoTJ86gfPlIvPLKK1CpVACA\nmTNnYtiw75CcvAGADmr1FJQu/TVatWoMf38/dO/eHQaDId9kP3v2LOrUaYa//voLNlsKmjZthvXr\n1yE9XQ2bTQKwA4AZklQbX389DU2bPn3nvUDwOOR57MxnH0WeiI+PZ1xcHEkyISGBJUqU4B9//GG/\nX8DiCR7BwYMHabF4UaWqRiCGwEEC2wj4Ua3uQqPR/R+O5T0MCSmXY3t9+w4iMMWh/AgCvgTep8nU\nkOXL12Rqamq+PoPVauWff/7J7du3U5KKEDir9D2ZQAXl9/9x2LB38rXf3HDlyhX26NGP9eq15pQp\nH9NqtT51GQTPJnkdOwvUNOTr64vIyEgAsv34hRdeKPBwwmeNuLg4tGnTBfXrt8XKlatyVefixYv4\n8MOPMG7ceJw8efKx+x45cgISE98FmQRgJoAIyDb3YbDZLLBa3aFSOe6FSIBer8+xvfLlIyBJqwHc\ngxxOOx3AXgBjkZy8AceP38f333//2PJmh1qtho+PDw4fPgybrSmAEOXOIMj7OdJhMu1DYGCRfO33\nUdy+fRvR0dWwZIkFW7a0x6hRKzFgQFafjUCQHxQaL9j58+cRFxcncqPngSNHjqB69fpITHwPgBd2\n7RqOhIR76Nmze451Tp8+jZiYakhKagPSgIkTq2L79k2IiYmxl/njjz/QunUXnDlzFEFBJbF69SK7\nwnbk1q27IIMBmAE4KvArACzQ6z2h1X6OlBQjSG9I0ocYM2ZqjrJ169YN27btxapVQdBoLEhKsgLI\nOEtXDSAYd+5k3WR39uxZ/PXXX3B3d8eGDRuQmpqKVq1aoWTJkjm/vH8QGBgIjeZzACkAjJDNQk6w\nWKqiZEkjevbsmeu28oMNGzYgMTEc6emyfyIpqS4+/7wIPv10CjQazVOVRfAc8IRWJnkiISGBMTEx\n/PbbbzNdB8DRo0fbf7Zt21YwAhZS3njjTQKjHUwpWxkWFvPQOp07v061+n8Odeawbt2W9vtJSUn0\n8gqiSjWXQAKBJXRz8+edO3eytDVr1meUpHACcwl4EBhFoC8BT2q13RgYWIoHDhzg668P5Cuv9OSm\nTZsy1U9NTeWsWbM4ePBbXL58uT2G/8qVKzx58iQrVapLna4/gUsE1tBi8eL58+cztfHmm+/SaPSi\nk1MMVSqJWm1TarWDaTZ7cu/evbl+lzabjW3adKbZXILOzs1oMnnwnXfe4fr16/PdHJUblixZQrO5\ntcP3dIcajb7A9pYICjfbtm3LNFbmdWgvcEWQmprK+vXrc/r06VnuFRI9VWgZMGAogbEOg8UOFi8e\nnalMQkICd+3axSNHjtBms7FJkw4EFjvU2cTo6Nr28gcPHqST0wsO90kXlxe5e/fuLP3bbDaOGzeB\nXl4hdHMrwurV67Bu3QasVasJ+/YdzOvXr2cqv337dk6bNo1r1qxhamoqa9ZsTEl6icBHNJsj2b//\n0Ezlb9y4wUaN2tLZ2ZehoVH8+eefs7RnNhcj8Lci61oCIcrvC1m1asM8vU+bzcadO3dyzZo1vHjx\nYo7lrFYrx42bwGLFolimTGV+9913eeonN1y/fp0eHgFUq8cT+J6SVI+dO7+W7/0I/ps8U4rAZrOx\nc+fOHDx4cLb3hSJ4OHFxcZQkT2VG/g0lqQRnzpxjv//HH3/Q0zOQzs4vUpIC2K5dVy5ZspSSVJJA\nHIFjlKTynDRpmr3OpUuXaDC4OQyud2gy+fLEiRO5kik5OZm9ew9ikSKlGB5elTt27CBJTpw4jZIU\nSL1+IM3maNas2ZBmcykCaUo/N6nXO/Hvv//OVT8rV66in18xqlQdHJSWlYCGwH0CO/jCC5Wyrfvp\np7Pp6upPSXJj1659mJKSkqs+Mxg3bgIlKYbAbgLraDL5cPv27XlqIzecPXuWrVq9yvLl63LkyLEF\nsjIRPJs8U4pg586dVKlULFeuHCMjIxkZGcnvv//efl8ogkezZ88eNmjQhlWrNuaCBYsy3YuIqEqV\nao4ySCbSbK7AZcuWcdq0T+jlFUIPj0C+996YLNEoQ4aMoNlcknr9IJrNZfnaawNzLU+nTr1oNDYl\ncIjASkqSJ3/77Tfq9WYCFxVZkmk0FqMkRTkM4jaaTD4PnYlnsGnTJppM/kpkT1ECfyptrCAQTOAM\nJak6R44cm6Xu//3f/1GSQhT5rtJkasgBA97K9fORZEhIJIE9DrJP4uuv5/4dCQRPmmdKETyK51ER\n3Lhxg7Nnz+b06dN5+vTpf9WWxeJF4KrDgDWSo0aNJimvxq5evcobN25kW/f777/nlClTuG7dujzl\n3zGZXBwGZlKn60t3d18CTpnMTUAVarVmqlSzCJyiVvs2S5d+MVchku3adScwW2nnAwLOVKuDaTJ5\n0NXVly4ufnzjjbezzWHUs2d/AtMd5PiNQUHhuX4+kixdujKB9fY21OrhHDp0WJ7aEAieJEIRKJw5\nc4Yvv9yVVas25oQJU56JGOw///yTPj4hNJna02DoQ7PZk7/88stjt/fii7WpVk9WBqzbNJvLcdWq\nVbx9+zYrVqxDo9GDer0TO3XqlW+J31xd/ZTZdsZA24RANwKlCEwgkEhgHQEv6vWV6eMTRk/PIL70\nUiv++eefueqje/e+VKnGZ3J4ly1bkYmJiY+s+847I6nT9XGou5zR0TXt93ft2sXixcvRYvFknTrN\n+ddff2VpQ15V+BKYTLV6OJ2dfXjmzJlcvyNH1q9fz5o1m7FWrebcsGHDY7UhEPwToQgoD6hubv5U\nq8cRWEtJqpLFEVkYGTJkGLXagQ6D1AJWrlz/sds7c+YMAwJK0MmpBA0Gd77++hu02Wzs1Ok16vU9\nCKQTSKAkVeOMGTPz5Rk6duxCwIWAJ4FyBFwJ3KK8UauKYsMvRmAHgViWLVs1123//fffbNeuG4sW\nLUuNxo3AIAKTKEne/OGHH3LVxrVr1+jrW4wmU3vqdAMoSZ52J/TFixeVVdQaAvHUaocyKqp6tu3E\nxsbytdcGcPDgtx9bCWzYsIGS5EdgGYFllCQ/bty48bHaEggcEYqA5Ny5cylJrzgMqH9SrzcX+hTD\nHTv2cjB5/EmgAbVaLzZt2p5Xr17Nc3spKSmMi4vj1q1beenSJaalpfHOnTssXjyawD6H9zOX7dt3\n/9fyr1+/nnq9v9L2Kco7c10IpCj9/EXASOCOYlKZzIYN2+aq7fT0dIaHV1LCSfdRoxlJi8WH3bv3\n4bRp09iuXXd2796XR48ezbGNU6dOsX377qxWrRFffrk9J0+ezOPHj9vvL1++nE5ObRzei5VarYl3\n79791+8mO+rVa01giUN/S1ivXusn0pfg+SKvY2eh2VCWn8i5a+hwhTkVfSocOHAAly5dQkREhD2d\ncwapqalYtGgRLly4hKJFPSFJHyMpqRqAjgBeQnr6B9i0aQ2qVq2PY8d+zZRr5+zZs+jY8XUcP/4H\nwsJK4auv5iE0NBSAfABMrVqNkZioRWrqdVStWhk//7wdNhthMLhCo/kBVmsFADYYjT+hZMmySElJ\nwaBBw7Fx44/w9PTErFkTUKVKlVw/56pV65CaOgxABeXKpwAaw2iMRmpqcxiNa6DRuIJsB0CCTrcP\nM2bE5qrtX3/9FceOnUV6+i4AalitFaBSfY/ixQMwcuQUJCWNhEp1C6tW1cL+/dvxwgsvZKp/9epV\nlC9fHQkJA2CzNYYkfYgSJV7ItOnM1dUV5HkAVgAayBvjCKPRmOt3kBfUapXSVwbpyjWB4CnzhBRS\nvvC44v311190dy9CjWYM5bDKShw4MG+RIfnFG28MoyQVpbNzE0qSJ1evXmO/l56ezipVXlJi6UdT\nksJYrlx56vXOBPwJ2OwRNU5OZfjbb7/Z6yYnJ9PfP1TxAVykWj2dPj4hdjt5mTIVFafoXcqhoIEE\nviBgo1o9mFqtC52da9DJKZKRkVV57949tmvXjUZjc8o5g5bTbPbkyZMnc/2s/fsPItDfYYa7gmp1\nUfbv359jx47lhg0bePfuXX799df88ssvee3atVy1e+fOHfr6hiiriySl7XSazSVYrFg4gQ32PlWq\n0ezff0iWNj7++GMaDD0cZDtDJyevTGXS0tKU76MuVap3aTYX48SJU7O0derUKW7evDnL5ra88uOP\nP1KSfJTE5R8+AAAgAElEQVTv5QuaTN788ccf/1WbAgEpTEN2zp07xw4derBGjWacPHl6gTiL9+/f\nT0kKVGzkcoSKyeRi3x26efNmWixRiq2elCN8dNTpXiHg7mBSuU9JCuSRI0fsbf/++++0WDJv/JKk\nMty/fz9tNhvVarNihpEINCLQh8BEpWwSVSotmzdvzU6dOvPcuXMkqYR4/m1vz2jszRkzZuT6eS9f\nvky12olAVwJvEXCnweDCo0ePcunSpZw1a1au9yM48uWXX9JsbkzgFQJ1KO+baMBKleoyNDSGcgTP\nbAJTCQxjz579s7QhK4JeDu9rPjUadzZq1I6xsbH2cvfv3+cXX3zBMWP+x82bN2dpZ+rUGTSZvOji\nUocmkwcXL176UNk3bdrEBg3aslGjdvzpp5+yvd+o0cts3Lh9tvcFgsdBKIJCxKpVq+js3DLTYG0w\nuNlnwqtWraKTU/NMNmnAogzGTQhUJTCbJlND1qvXPJOP4+zZs1SpXCingZD3CQCu3LJlCxctWkyV\nqhSBG5Q3bHUl4ENglVJ2i9LPh9RoBtLdvQgvX75MJycvAscUxTSHGk0Jtm37cp42XO3fv58+PoEE\n1HR29uE333zDEiWiaDbXp9HYi5Lk+dBUIUePHuW4ceM5adIku19k4cKFNJvbK8/yMYHOVKl0vHfv\nHkeMeJdyaGpbAr0JSJw7d26Wdi9evEhnZx+qVJMIvEvAmUBtAjVoMLhw586dj3y2c+fO0WTyJHBB\neY9HaTS68NatW9mW37hxI00mXwILCcynyeRtH+zT0tLYtWtvajR6ajR6durUS6SPEOQbQhHkgM1m\n47x5X7Bhw3bs1q3Pv17W54YTJ07QZPIicIQZoYre3kH21cnVq1fp5ORNYDnlzVYDlcGfBFKp1Tqz\nZcuOnDRpapZdpXfv3lXOFS5PYAyBitRqS/DLL79k1659KOf8GUDgPQI/UqNxp9lclk5ObalSyUog\nQwFptf35/vujOX36DEpSMQKVKDt6Z1Cnq8/q1Rtku6JKS0vjuHETWK9ea/btOzjTnoSMcNTp06fT\naGzDB2autQwLi87SFknu3r2bkuRJjeZN6nSv0d29CC9evMjNmzdTq3UnUJHARJpMTfjyy114+/Zt\nms0eBHo6KNMvGRNTK9v2jx07xpYtOymhn24EphGYRcCDVarUyVR248aNLF26EoOCwvnOO6OZnp7O\nbdu20cWlWibF7uRUItNKzZFatZoT+NKh/Ods0qQ9SXL8+ImUpFqUHed3KUn1OHr0+GzbEQjyilAE\nOTB69HhKUgSBL6lWv083N3/Gx8fnW/s5sWTJlzQanWky+dDLK8h+/kIG+/fvZ6lSL9LZ2VcJifyS\nQDLV6mkMDCyVY3x/eno6dTpJMYe8S2AUVSofhoREslSpcGUFME1RBl588cVaXLp0Kbt27UqLxZNA\nrMMANZ5DhrxNklywYAFVKidlhUECadRoinL//v1ZZGjXrislqR6Br6nT9WdISJkssfzDhr1DwDHJ\n3Vm6uxfN9pkqVapPxzxIKtVbDAl5gQaDq/Kc86lSebBJk5Y8cuQIfXyKUzZ/fezQ/q8MCgrn6dOn\nc4z2cXcP+Ued5ZSkAPv9vXv3UpK8CfwfgV8oSVU5bNhIXr16lZLkQeA3pd42WiyeTEhIyLYfWREs\nd+hnPhs1akeSrFGjGYFvHO6tY+XKecuNJBDkhFAEOSCbPU7Z//GMxi55sn//G5KTk+3hmw9j586d\n9PcPo1qtZZkyFR+5s1jO/lmEOt2rBMyUnY4/KbNdx/DQV+ji4kWDwZUGQ09qtW0U08hmAhtpMvnY\nY+nXrVtHOZOozaF+Kc6cmXmfwd27dxVFdI8ZfgeLpVKWTVGyQzSQwB8E7tFgeJUvv9w12+cpVaoi\ngZ0O/X5GOWXEB5kGTA+P4tTp3BVF941S5gjlkNua1OvdaTYH0WBw5uefL8jST8mSMQTmObS5loCr\nXekOHTrsH8rrIP39S5IkV69eQ5PJlWZzMJ2cvLhly5Ycv59169ZRkvwp7xNYQpPJx+4M7tKlN7Xa\n4fY+NJr38yWE15ELFy5wwoQJHD/+gzw5/QXPPkIR5IBsQrho/8czGF7LNuNpQWOz2Th9+gxWqPAS\nGzRoywMHDmS6d+HCBZ48edI+aO3evZuNGjWiWj3IYeDy5YOTtkjgTcpRQ7McZtuDaTb7sESJ8pw7\ndy7nzp3L2bNn09XVl3L+njcI/E7Z7GSmSqW35+65e/cuGzZsTUCn9NVSmZkb6OoakMXsNmvWZzSb\nPajR6NmoUdscZ+rvvz+OklRNUdi/Ud54Vl9ZDWQ8yw8E/ChvVMu49qmi2ExUq50JfKVcP0GTyZvH\njh3L1M+IESMUZbmKwHcEAqlS6exyjRo1hhrNAIf2t7BYsUh7/Xv37vHUqVNMSkp65Pe5bt061q7d\ngnXqtMzkfL5y5Qp9fEJosTSmxdKU3t5BucqzlFtOnjxJZ2cfarV9qdEMotnsmWU1KvjvIhRBDgwa\nNIySVIXAJqpUH9PJyfup+AnyimzCiqIcCTOLZrMnT5w4wbS0NLZo8QqNRk9KUlGGh1eyZ+qcOHEi\ndbreDgNXZ8q7ekdSNht5ECii/BQn8AmB+WzdujNjY2NpNntSkrpRr69FlcpPUSKvEChJOWTzHIE/\naTQW57p16xgTU5kqVTCBxgR6KAP2NcpO5p50di6a7ernURv60tPT2blzD2q1LspAP4Ryhk8vyiaj\n/6McVjuT8gropH01olIVYaNGLWgwuDu8B9LZuRVXrlyZqZ+zZ89Sr3ei7HOoRZWqNUuXftF+//Ll\ny3Rz86dGM5jAJJpMfvz665X/FPdfc+vWLa5YsYJfffUVb968ma9td+rUS9lZn/EuZrFBgzb52oeg\n8CIUQQ6kp6dz/PiJjImpw4YN2+bo4Msvli//iiVLVmDx4tGcOvWTXO9q9vIK4QPnMqnRDOXYseM4\nZcp0SlJdynH0Vur1/dmuXTeS8sDl6upHtfo9AvOp0/lSrXYl0E4Z+MsSKKO0e4BAmDK46ihH3Kxk\nxn4FoBXlrJ4kcOUfM+936etbVLm2lPKuWDdmdtYeJ+DyWLPPI0eO0Gz2pEo1VlFWHgQ+pU73El1c\nghgTU4cqlUGZxb+rrAw60Gh8gR06dGdqaiolyY3AXkWWG5Skovz111+z9LV582Z6ewdTo9ExOroG\nL126lOn+hg0bWKdOPdap04A//PAD79+/z7FjP2STJh04fPhI3rt3L8/P9zRp1KgdMzuqN/LFF+sV\ntFiCp4RQBIWA9evXU5ICKNvgf6YkleGnn87OVV3ZARpn/wfWagdy/PgPlIybjnbtn+nq6s/Kleux\nb99BPHjwIF97bQBbtOhErVaibJMngWTKZqEJDnWXUt6ncI6yff2kw70PKZtjtisz5sbMcBrLieO8\nlfoZ5RcqbViZERmjVntmMmnllh49+v0jmdwa6nTefOWVnrxz5w4PHDhArdaZwIsEggiE0NOzCNeu\nXWtXtN999x0lyZMuLnUpSX4cPnzUQ/vMTkEvW/YVTSYfGgz9aTbXYMWKddigQSuaTI0ILKXR+Aqj\noqoV6nDPRYuWUJJKUd4ceIySFMMpUz4uaLEETwmhCLLBarVy7NiPGBwcwZIlX+Tq1avzpd2caNOm\nK+VNTxkD2mZGRtbMVd3Jk6cr/8DLqFJ9SCcnb547d45jx35Ao7ElH2w+C1LMI6UJOLFo0TCmpKTw\n1q1b1OksmcwjKlVDAt0drv1PWSWcpBx/34ZylNAppV03OjsHUqt1UvqoTiCUsqmoLDPnx5mvrBDK\nEnhJKW9hRERVzpv3hX2gPX36NHfu3PnQg2fkXEszHNrekikpXalS5fkgqiiFGk0kg4JKsWTJChwz\n5gO73+Ty5cvctGnTQ/MOPQw5sCAjMshKSapKnc6JDzb4WWmxlM7TUZhPG5vNxkmTptHLK5ju7kWz\nPXdC8N9FKIJskGO2YwjsJ7CJJpPfQ6M9/i1duvSmSvWhw4C2jFWq5C400GazcdGiJaxfvy07dOhh\nT4qWnJzMypXr0WIpoZwwZiFwRml/OwGJmzZtos1mY7Fi4VSrpyiz9D00GuUdvvI+hT4ETAReoGwe\nGkLZPKRTrkcSaMnevXszPj6eNWvWo7wzeasyCAdQdhAvVJSAMyMiXuSDXcw+lB3Ni6jXl+WwYSP5\nzjtjlN24lWixeGXaySsruXEcNWoMFyxYoGzA+pbAT5SkFzKtpCTJnXLiuoz3OkxRZDspSZU4YsQo\nXrt2jUePHmVycvJjfXdWq5VqtdZh0CeNxl7U6VwclLCNTk7RWY7OFAgKC0IRZIOchuBnhwFkOrt3\n75svbWfH0aNHabF4UaUaSeAjSpJXvqQPSE9P5/79+zly5EgClTPN+gEPDhs2jD/88APd3f2Vmbma\nJpMbPT2LUKXSKgO9hnJUzecEGlK28espR93MoBz37svevfuQzEiT4UnZQRtI2TzkRMCHarU7tVoz\n5fQO1SifO+C4k/oKdTqJJlNRys5kOerH1dWXNpuNJ06coLOzNzWagVSr36bZ7MmpU6cyMrImS5eu\nzE8/nZ3JdFO+fC2q1W8QuK20V4zy2QYkcIRmsy8NBhc6OZWgh0fAY0fJVKlSnzrdIMq7tnfRZPJi\nmTLlaTB0J7CNGk1rms2+7NPnDXt6DoGgMCEUQTaUK1ddmWXKA5Ra/Q4HDnwzX9rOiWPHjvGNN95k\n376D8t2EcPz4carVGdE8VJSckSZTOapUzpTz6acRmKbsPl5I2RH8gzJrH0XZlLOawCRFQQymo0lG\npXLnuHHjePPmTe7YsYNhYWWpUmUM8qcIzKNe78EHfoueyuy8o0M7t6hW62k2O54rTGo0Rnbr1pvB\nweFUqV51uDePdeq0yPK8ycnJrFGjliKnr7L60FP2YWTU3UXZRHWFcqK9WfT3D3us93vt2jVWr96I\nWq2RHh5FuXbtWt65c4fdu/djkSJh1Gh8CMykWj2CLi6++Rr2KRDkB0IRZIOc88WbwEdUq9/+VydK\nFRYmTZpGjcaJQAll9r9MGezbE3jfYcD2+sfKIZqyw9fxFLG+BOo5fN5KoBjV6uL08irK1atXc/Dg\nIVSr33Yoc0npP8Nmf0BZVbhSznoaS6AOJclH2VR1SSk3lyqVRDn1xTTKkT8ZbWyij09J9uzZn+vX\nrycpp7EoXryMogS2KuUOUQ5r9SYwnMAsarV+1GqrKs8uUY44Muf7jD0wsAwdN71pNAM4Zsz/8rUP\ngeDf8kwpgu7du9Pb25tly5bN9n5+Rg3t2rWL/fsP4VtvjeDZs2fzrd2H8ffff3Po0OFs3boL58yZ\nm+8H41y5coWBgaUJfO8wQM93mJUfImDgg410t5UB0kLgsEOdfsoM+zPKm6yKU96DEETAnVpthg/A\nicCPBM7SaGzJWrUaUTYtfa3Uy3AuexMIJ9CH7u5BnDhxmmKyeYEGgxOBDoocToos/oo8pahSVSfw\nIbVaN1avXo8TJ06kWu1F2SzlqNBiCExRVgW+NBqdaDD4UjZdXVOU4lusXbtZpneWlpbGEydOZEkv\n8umnM6nVehJwpVbrwYULF5KUY/1jY2N5+PBhkqSvb+g/lOhIjhjxXqa2bt26xW3btjEuLi7P3/m2\nbdv43nvv8+OPP84xdYVA8CieKUWwY8cOHjhw4Ikrgj179rB69caMiKjOjz7Kv/OL09PTuXnzZq5Y\nsSKLeSAhIYHBwaWp179OYD4lqfwTOROhadM21GhiKId03qRGU5E6nT+Nxq7Ual0YFFSSBoMvJakr\nJSmUpUpFUa32o7yv4BtlVi4RUCsDciPKUUGvKbP7TpTPGPhKGbRDqFJZ+OqrrzEpKYlDhw5VchP5\nKbN2d8qRTMUI1GGnTr1IymdEHDp0iAMGDFIUQDnKvoWOSrtmymafF5W2XiUwmVptgKJsLJR3OlNR\nbE6UN8i1InCVarWWMTGVKJu9aC+nUpntO3ovXLjAoKDSNJuDaTC42o/uXLt2rbLC6EE5WmgCAYmj\nRo2ii4svXVyqUJKKsEuX3nznnVGUpIxUGF9RkjKHyh48eJBubv50dq5KSQpis2btef/+/Vx9l/Pn\nL6QkFSEwikZjW4aFRRb6/QqCwskzpQhIOWrkSSqCI0eOKM7OBQS2UJLKc+TIf7+UT0tLY+3aTWmx\nRNDJqTXNZk/u2LHDfn/lypW0WOpR3tTzCoFu1Gj0j4w9P3DgAEuUiKJWa6bB4Mno6KqZomwc+eKL\nBTSZ/KhSvU6gHFUqJ3bs2IOrV6+ml1cwdboOBCbTYCjK1q1f5rZt23j37l26uwcoA/xLlLOM+lLO\nGVRemfm7UN4b4ErZSTyO8hnEJgKdqdEY+M033/DkyZM0m32oVvtTjhZyo5zvR96P4OoaZE9Cl56e\nznnz5rFu3QYEtHxw7oGNQBTllcunBEYocmUM5sf5ICLJWVkBWJTyr1EOiw2nJPlwxowZNJlqUvaP\nkMAi5Z4nz58/z6pVG1KjGaf0eZs6XWkWK1aWXl6hSpvpDv2Wp7xKqqfIdYdmcwS//fZbjh37EUuW\nrMDy5evYU2pv3bqVY8b8j76+QXzgN0kiUIZarfGR5xaQpKurn4Oys1GSmnD+/Pm5+nsUCBwRiuAf\njBw5imr1cId/8MP09i7+r9tdunQpzebqDoPOdwwMLG2/v2zZMur1kZRj7xcTGE3A9NBEctevX6fZ\n7KnMdJ2UOlOp1brx+++/z1TWarXSaHSifH4ACaTRbI7munXruGzZMlos9fkgcdw5GgwWu5ni6NGj\nDA+vQoPBVUkpcYryrL8UgdZUq90VZZBx8lcqZV9EtDL4ulGn86bR6E3gZQIbKYel+lHOa0QCp+nu\nHkhSDol9+eUuSh6hkZRDVVMdvpNalE09VBSP407l2wT0VKtdlMN2XAnMoewoL005euon6vVdGBNT\ng9Wq1ads0mqgKLg4WiwN2LNnTxqNznTcrCfvp+hAOSWHjrJpzEbgPuWVTSPlWg0C3ajXD+KUKVOy\nfG8zZ86h0RigKLEaikz3lT5GEBhISfJ55G52OYnfLbt8BkO/QpkPS1D4yevYWejPLB4zZoz991q1\naqFWrVp5qq/X66BS3XK4kgit9t8/9uXLl5GSUhFARltVce3aZfv9evXqIS2tD4CfAUQAAFSqK1i9\nejWGDx+epT2r1YqXX34FiYnuAO4DeBvA+wCA9PRAvPfeRFSpUgU///wztFotypQpg/v3kwGUUFrQ\nIjExEHv27EFISAhsNh8AGeffeiM9PRU2mw0ajQalS5fGoUO7cOTIEVSoUB/JyVcAfAfgKAAn2GwX\nAZQD4KTUXwvAGcA+AGoA/ZCWVgtpaWoAX0E+37chgO0AflXqfImSJUsBAC5cuIB1675HSsp5ABKA\n9QBeATACwE6lTsY5xw0BjAJQF0C48g6CQP4JlUoCMAdAG6XshwB6AbAhNTUcBw9+i5UrF2Pv3o5I\nT38XwFIAV3Dv3s9YurQt0tLqAagP4DcAXgB+AOAPYK/SznSljk159nXKs70KIABarRsiIuZm+t5I\nYsiQt5CWFgcgTKlbWalbW3mvg2C17sWMGTMwbdo0mM3mTG3cvHkT165dQ/36TbBlS3/cv/8hgD+g\n0azESy/FQiB4FLGxsYiNjX38Bp6MPso9T3pFcOHCBbq4+FKtHkXgC0pSMc6a9Zm97zlz5nDhwoW8\nc+dOntrdtm0bJSmIwHkCNmq177JKlfqZyri4+BM4YZ/haTSDOH78B1naSktLY+XKdSibXvwoO2tn\nOsxctzA4uBz9/IrT2bk2LZaKNBgyVg7DKZt1tlHeEezNs2fP0mLxouw3OEq9vg1r1mzIhIQE2mw2\nHjt2jHv27GFCQgKHDBlBnc5Vme07OmOdFDnWKTPccMohmlRmuxpldp4xs7cRCKFswgmmTudmT+on\n76so7rBCSaBW60NX10D6+oaxTp0m1GjMyqojgrK/wF9ZofQncJOAlmr1C8yckvpDAlUom4y6EBhM\no9GDgwYNoST502R6jRqNH2WnsuMmNB/K0VTNlb6u8IEpx4eyGaySQx0rARf27Tsoy3eXmppK2b/i\nuMJpo7w/Z8q+Dk8CHWk01mdoaESmv7XJkz+mweBMiyWU7u5FWK9eU7q6+jMkJII//PBDnv4mBYIM\n8jp2/ucVASmnN+jRox9bt+7CVavk9BK//vorLRYvmkzdaTY3Z9GiJR+a/iA7pkz5mDqdiTqdmWXL\nVrQfrZjBiBGjKEkVKOccmmPPJJpBQkICN2/ezFdffZUqVRjlXb2NlcHUi3Ke/C8pp4U2KxvUSDkj\nZzEClwnUpWwvlwjUoUZj5NatW7lv3z6Gh1dWzEcmGo2+NJncWKNGQ5pM/nR2jqabWxF6eQVSpfJR\nlFAHZdD7jLJ5ZSJlu38jhwF0AWW7vK8yiDaivB+hozL46RkdXS1TZtfU1FQWLx5BrfZdAkep0XxA\nX98QfvPNN9y/fz9jY2OVtr6kbGbKcBpnKI7rlP0KXooM3ZUfibKD1/Es4tUsW7YK9+/fz9mzZ7N4\n8Wjl/WfcX6YouOqU91J401EBWix16ebmQ9lnMIrAfup0vVm2bMUcgwxkZ3kfymdOryPgzPr169Nk\ncqdaXYRyVJWsLPX6DvzoowkkyV9++UVxDmdEdX1FP7/szZZLlnzJmjWbs3Hjdty3b1+e/k6z4/bt\n27xw4UKOBx85kpiYyAMHDhTKbL2C7HmmFEGHDh3o5+dHvV7PgIAALliQ+RCRJ7lgqVChrjKoyQOA\nTvca33vv4QnKsiM1NTXHM2utVis/+mgKo6Nrs27dlvztt9/s9y5dukR//1CazZWVAa6TMiitpxy1\n4qUMrB6U7fVFKB86Q8opJco5DGB3+eAM3j40mby4evVqFisWTnmn7wRl8GtCObVExmEyMynPwkn5\nfONAAiql358oOz2bOQzI+yg7bk3K4NtOGcBrUz4vuBsBEz08grhw4WKSsvN76tSprFy5JiXJn0aj\nN8PDK9Bs9qKLSz2azcWUldNEh+fZTMBZ2Wz2GeWVQW3Kp625KsrApLwXF2ae8cexaNEy9vc8ZswH\nlKTqBOIpr97KUnaEZ4TDWihvqrtH4FuaTB68cuUKd+/ezXr1WjAkJJLt23d/aJroGjUaKmdEexEo\nQb3ejb/++itPnz5NF5cifODHIYEJbNBADmmVz2J23FBno0ZjyBIpNG/eF5SkUMpZYmdTkv7d2QLv\nvfc/6vUWSpIfQ0LK8sKFCzmWPXz4MD09i9LZOZxGowcHDHgr38OgBfnPM6UIHsWTVATBwREEfnX4\nJ5zBbt2eXNqJf9K8+SvUaDJCHX+h7JxcTHk2nBEhM0gZhBMoR7FEUjZBXKdK5U7gLcphjFUoO0dt\nlA9W706NxkyNpijlWarsvJUH7REOzxyvKJqMz29TdlBbKIdRTlYG34z71/kgkijj2gjKG9LeoLya\nuURgHyUpgG+/PZyS5EuVqqOigF6iVvsGtVo3PtjpnbEj2JOy4/kG5X0RLlSrS1MOD31VGfwvUHY2\nW5TnvUL5yElPRUmdpyS9xEGDhtnfc3p6Ovv1G8IHaTS8KGdXjVe+/4yEeXoCwbRYPPL0Pf7www8M\nDg6nVutKlcpCV1dfrljxtf3+Sy81p6yMEygn+QumTmei1Wrlzp07KUkhlE1fJPAj3dz8sgy0YWEx\nlE1/Ge98LPv1G/xYf3cbN26k2VyCcs4mGzWa8axQoU6O5eXT3D5X+r1Fs/mFLKfQCQofQhHkkj59\nBtNobE45KuUMJakkV61a9a/bXbt2LTt27MUBA4Y+NPVA6dKVCexw+Od+nfIsV8sH9nnHTV/TCVio\n0zlTpzOzRYv2ynGN5SibUYYrg70vZbv6W0o73sqAmUg5MqY45QPTM2zsYUo/K5Xfy9HLK0TJIeSp\nDLo/KgN8O8qz6R8d5FpMOTOpPx+kviaBD6hWmxyupSjt96PsX0ikrKS8Ke9bOKsolDKUlaLeQU4q\nCqEc5dBSbzoeOwq0pCR508XFj6+//gZTU1OzvG83tyIEWijPdMKh7jjKZi8SSKdKpWZKSkquvuvD\nhw8rocnrCZyh0diOrVu/mqnMF198QY0mlLL5zpXAx9RqJbufYMiQETSZfOniUp1mc/ZHX4aFleeD\nXdVytFP//kNyJeM/GT9+/D+i6K7TZHLNsbxOZ6K84sxYOQ/m5MmTH6tvwdNDKIJckpyczHbtulKr\nNdJkcuEHH0z6123OmTNPmeHNpkbzNt3di2TxG2TQo0d/GgxdKIef3qOctK0VTSZ/ligRTjnLZ0YG\n01QCNdmjRy/euHGDt2/fZkJCAps0aU212oMqVVllkHmZD9JLkHKOoZKKkmlDwMTAwFDq9c6UpCAl\nD5Er5RVINQI+VKncOG/ePBqNbkqdEgRcqVKZqVZ7UE5hUYFy+OYxyr4EHWUTzXf2vjWaLlSpdHxg\nVrJSNjPFUDb7TFGUTzMHea3KgFme8qy9Dx84omMoz+YTKZuKvrLXMxhe5aRJD//+hg9/X3nW8Exy\nys/zlvL7VKpUbqxZs3GuNoFNnTqVer3jiukGjUbnTGVOnDhBScpIa22jSvUJQ0Iy+8T27t3LsmXL\nU63WUaczccKEzCGqn38+n5JUjMAKAjMpSZ78/fffHylfdixbtoxmcyU+yK66nGFhUTmWDwuLUv6O\nSOA2zebS9vQfgsKLUAR5xGaz5ZvN08srmEBXyjPnSdRqe3DixInZlr179y6rVHmJBoM7dTpnFi9e\njq+++ho3bdrE+/fvs1u3XooTsjTV6gBWrFjbPjilpaUxKqoaDYZXCXxNtbop5dm/C+UD7DMGpp8o\nm2Q8KK8ELMqMWK+cNfAtZZPLdmZE86jVQTSbXSlH71QhsIVy3L6Jn3wyg9WrN1UGNkn5MVM+U3i1\n0v5gAq3o6xvCYsXKKum4R1O2yWsor17C6eERqJQvyQeH2sQrSuUFAnupUnkRmEG9vqPyLsKV5whS\n2iLDaU4AACAASURBVKtCo/FlBgaWytFPk4HVaqWvbwnKs39Pyma3ZjSZPCivPlwo+0haUa0O4dCh\nQ3n27FkOHPgmu3fvm+1Mfd68eZSk5g7v+wDd3IpkKbdq1WpKkiu1WiOLFQvnqVOnMt1v1qwD9fre\nlCcFFylJxblx48ZMZZYt+4p16rRks2av8Jdffnnosz7qPTRr1p5mcyhdXOrS2dkn2xPcMjh06BA9\nPALo7BxFk8mbffsOET6CZwChCAqIlJQUJSNod8qRKfUIRHDs2HE51rHZbIyPj+f169czXbdarSxZ\nMoYaTXcCn1Gt7kV//1D7Ll054slxAE2lbBKaSTnCaC/lYyljlAHOTDn65yOl/BFlEE6hbIqyOQxm\nbZXB1okPzjsggT6cOHEid+/erdSdRtnZXkUZSK8QGEiz2ZXDhg3jzZs3ee7cOTo7e1MOK71KOTyz\nGYFgNmjQhI0aNadst69H2URTXJHVj8AEligRw27d+rJFi1Y0GF6iPKPPOLjmGgF/9uvXj9u3b+fq\n1at5/Phx7t69m1FRNRkYWJb9+g21m3nOnDnDVq3aU6t1psFQm3p9IIsUCWF8fLxyPOjHlE1OcwjM\noEbjTElyo1o9gsDHlCT/LOcWJyQksFixcBoM7alSjaEkBXD+/IWZyqSlpdknGzmli3BzC+CDTLKy\n6WfYsHfy+ieYa2w2G/fu3ctNmzZl+dvLjoSEBO7fv/+ZT9T4PCEUQQGxefNmqtVlHAbVRALGHA8v\nuXnzJnv1GsBKlRpwwIC3Mg0SM2fOVAZuq31wMJmiuH37dpLkvn376OTk2Fe6ogBOUM7f46YMsN6U\n/QUWyv6HG0r5NGXA3U3ZUTpHuX5KmS0HKvUdz07uxqlTp3LChAlKnVmU/QXjKMfjyzuPX3ghgu3b\n9+BXX60gSbq6BlE+f3i0ohCCCZhoMBTl+PHjFSViUmT0pryi6UeVysk+823Tpj3lFYCWstM1w179\nBmv+P3vnHWZFlbTxuvl23zA5wTCEIYcZco4iIklAokRhFQEBFQmCmNAFFyOLYVVgwZxQMSFiQEFF\nVHDXXQyI4CqIKAiI5Lm/7486PfeOhAVEd91v6nl8cOZ2nz7dt6fqnLfeeqvNWfj92VhWZwKBdPz+\nqAnEf8blKkcwmENychkDg01GIacIOTn5xfLgHTv2wuWqSckWnHdSkpn1MlWqNGD37t0lxOB27drF\nLbfcwuTJU3nttdeKf799+3ZatTobt9uLZSVx1133HPPdqV69EYkUU8vqypw5c07xTSy1UisNBP8x\n69dvICWLsg7i9Ub59ttvSxy3c+dOevcegtebgss1HJHnCQYH0LRpe4qKiojFYgSDScaZ/4Su2mcg\nkk6PHr04cOAABw4coHr1Bvh8o1CqZX80YfsjCgc5TvwfKO0yiq7wHzJz0z7GbneYcLgRImG83lTc\n7iC6ixiFUk4ro/jwZFJSyrB582aef/554rpCnxQ7r7gG0ChE/oJtV2fGjFmUKVMZhYMaouwZv3Ho\naXg8YTyediaQJJPYASwcrsuKFSt48803saw8tGaiNvHcwB6CwSrojsdpevOpCRgvmDk+ifZgqIhI\nGxS2aotKZN+Nbafx/vvvs3XrViyrLKqg6nx/88ycnZ/fx7Ky8XotvF6Lfv3OP6Zu1HPPPUc4nIvm\nef6IyDpsO/eYmlFvv/024XAG4XA/wuHm1K3b4pQ7rJVaqUFpIPjNbP369bRocTY5OVXp2rUfubnV\nUez6SuOMe5OTU+UIPLV58w54PF1RHDy+orftsqxfv549e/bg8fhRymRbVJCtAyJz8Xo70L59N2Kx\nGE888QQeTzK6wk5BpIVxstnmX0fNM2gcqNOKsr75/Vn4/TXo1q0bzz33HJs3b+add94xejzpaE1A\nZ3SnEcTt9jF48IUcOHCAunVbGGfu1CNgnH3XhJ8/IxRK4+mnnzbzG4wWn+1BBefqobUTqSgskkFc\nnydGOFybt956ixkzZuB2N0brFq4xDr42bnc6ZctWomRzGsw47VCox/ndKyjslEq8LwKIDMPt9jN6\n9GU8+uhjppPaU4g8TiCQZdp7LkLkLbzeQrzemmgQ3YNlncn06TN57733eOihh4oVSFesWIFlZaEJ\n6XdMMJmJ2z2F6dOnH/N9+vLLL7n//vt55plnTlittNRK7VhWGgh+A9u1axcZGeVxu29B5J/4fJcR\nCGSgDesHo8JjjRk3bnyJ83bu3InHYxsHkRgIDmFZOdSu3QS324vbHUGTzpON43PkCw7g9WZSoUId\ngsF0lO3yKpqXqIjPdx6WlW366z5qzvkSpXb+xQSC2831Y4hMx+erhWVlFOPfs2fPNqJzt5nr26h8\nw05suz3Tpk3n0KFDVKxYG2UVrTfO0m/m7DjZb/F4LLp06YvL5bB1Xkn4/EFEzjXntUappWegRWx9\nKVOmKj/88ANZWZXQoHg3mlhOQmGoSuYZhNCcCGjyOxWFmK5PuJYTjFIpWdw1CJE/EQrV46GHHuLR\nRx+jSZOzaNbsbJ599lmWLFlC3bqtqVKlIWXLVkdpos65T1ChQh1sO5dwuA/BYCaTJ09jxIixlCyO\nW4VIIbZ9NvfcE4eHXnvtNfLz65KSUpZevQaftMRJqZXa8aw0EPwG9sorrxCNtkz4Y48RCGQRDKYi\ncjVe72hSU8vy1VdflThv79696Mr8G1TL5nxEnsLt7o5tZ+N2TzMrzjdQmMWNwgux4uvoCv0u4th/\nLRTC6Uy1atUZOXIkbrefkqvkAWhi14Xi9Q481NL8fg2WlUwsFqN69SaUbHQz0TjRAYgsoWHDMwGM\nlHVbdEdSF915hNFg+DYq4RDB6+2M1gBEKakTNA5NEjdE6wjKmjG0fiEQyGD69OmEw+0T7v8cdCfh\n5BWyzP/bCY4+lbJlq+D1RhG5AU2gJ5vjypr5zkXkMvMsv0XkRsaOHX+0r7rY+vcfjtc7pXj+Xu8w\nPJ4oWrvRDk1wJ1G2bFVcrvEJ9/kCLlc29eu3Kk5cK6U0Hd01bCQQGELHjuf+Oi9rqf2/tNJA8BuY\nYrrViUtQ78Lvj/Lyyy8zefJUpk+/ns2bNx/13Gi0LErNnInKKGeRnp6H2+0jMTkcDg/kiiuuIC0t\nD4/nUnQVf7FxhAeNc6yLwj6XImLj852P1zvWOMa7zFjbjfMbS6VKBaSn52FZ9VB4qD+Ky8cQ8bJp\n0yYqVCgkvsIGzRX0RCQFl+saevYcRFFRkQkq61DWkNOV7FEUi08xQeYmcx2vudcQCnO1RHMWFrp6\nvtV85mjxz0ckk4YNG+L1dkmYS54JBrvRArRK5l6fRIPrYUSac9111/HRRx/RtWtv3O5kdOV/pTk2\ngEJfVVCdpgEEAi3/bXJ28+bN5OTkE4m0JxJpQ1ZWeSKRhigNdYi59gH8/q74/UmmaOtm/P50Jk6c\nWALuufPOO7GsRH0khQNPlZYZi8WYNGkaoVAqtp3C+PFXnLbmS6X2+7TSQPAr2aJFi6hQoQ4ZGRUZ\nOfISWrQ4C8vqhMit2HYThg07MXmK6dNn4vdXQGmagxFJxedrbBK1ThXuIcLherzwwgt8++23nHvu\nYCpWrIvCIhuNc+6OQj7plCyKApH7cLtTCQaboFINUcqXr8nnn3/O7t27efDBBw2LZq0JArcgUobx\n4yfTvn1nFLZaiUIqmWiRWjLBYAobNmygqKjI1BKUQemc29FVdi660k+UsXgeXZE7ap93oDuhMsZB\nO6v1xGY0IGLj9Raiu4w/oxISqQnBAhTm6kg8NxHDslqwePFiAM4+uzdxRhSI3EYgkGFovk5h2R/x\neCJHtC997rnnGDr0Ilq1ak+7dt0YN24CmzZtYvHixTz77LN89dVXRuG1AZqQdq7xKC1bduLSSydy\n4YVjihvXJJr2suhAfKfzT4LBZAoKmpKeXoWOHXuwdevWE343Z8++A9tugGop/QvbbsKsWbce8/hd\nu3bRs+dAotEsypevxUsvvXTC1yq134eVBgKUujdu3AS6dOnPrbfO/sWro5UrV2Lb2SjG/QmW1YGR\nIy/l1ltvY8SIscyfP/+Er1FUVMTAgUNNQ5hWqNrmNygskoLIcFyuOrRqdXYJZchYLEbdus3R1X0b\nswq+GC0Wq0HJQrLXqV69CY899hh//etf+eijj45YbebkOLUCAZQmeQ3t23clO7sKcRZPeRS/txFJ\nNeqlnXG7vUZ6oMzPnHc9tE7gpoTfvWXG+RDNORSieYVkVASvDQo/5RGXMliLQmjnonkDp8CtAvFG\n95rs1YCai1I/e+PzRfnkk08AaNGiM5q/cI5/hPbte2JZycQVPyEYPL/EjkArxCuYQDMKkTS83l5U\nrlzAihUrWLZsGTt27GD58uX4fCloBXQMkSICgcGMH3/Fcd+BvXv3Ur16A4LBnohcQyBQ1hTM9URZ\nYOPIyKjAG2+8ccydZaK1a9fdvA/OfT5L8+adjnl8p069TVX7V4i8hG1n8M9//vPfXqfUfj/2/z4Q\n7Nmzh4oVa+P3j0TkAWy75Qmv1o9lEyZMRuTahD+0dWRlHV0u+MMPP2Tq1GlMn379ETmCzz77jObN\nO5KcXA6PJ4847fFytBp5NSKzcbu70bfv+cXnxWIxioqK2L59u3GQPybMpTFpaTlYVhW0qfoX2HZL\nOnfuSTCYSlJSEywrlUWLnuLQoUNccskkMjMrYVlZuFzZKDX1ZUQiBIM10V1HIYqfV0Apn8lohXOy\noazuR/WGQsQF0/aYQOZU6T6DCuJVN4HhJxMUyhHX678VTQA/hu4kKiBythm3NiIPo7uLsLlvJzfQ\nF60HyCuem67MUxBpQDCYysMPP8r8+Quw7apmHm9g2/k89NAjhEKpaBLdCQRDuOOOOwBYsmQJfn8a\nIotNEDgP7ccQwu1OJxgsTzTahuTkHNauXcu2bduoVKkOkUgh4XBNCgubs3v3bgBeeOEFJk2axOOP\nlyxEc97T2bNnM3XqNFq1am+eWWJPg1rYdhWCwRRuu+34sFW/fsNwu6cXn+t230jPnoOOebwG8biO\nUyAwitmzZx/3GqX2+7L/94Fg8eLFRCJtiW+7d+HxBNi7d+8pz2P06ItRHNj5I11KpUqFRxz35ptv\nYtvpuFxT8XrHkJycw8aNGwFlDGku4DYUAhpjHNkTuN0VjNOL0x3r1m0DwI033kwwGMHj8dOxY0+8\n3iBaoesc2wzF4P1GeC2bYcMuMonrT9DCqAsIBMIMGzYSr7e5uf7rKKxUzzje7mhv3o3oCjsXpVI+\napy+34jIfZ9w7XyUo385IgW43Snk59cmEEgzDjrJ/PuxObY+cXltC91tDDXH9UfbYCbhcgUoSfM8\n1wSU2mjCOQ3ND3RB2U2OjMbX5vi/4fWGue+++5gyZRqVKtUlP78e99xzHwCTJk0zUMqTuN3Xk5yc\nw5YtW3jmmWewrBwznwwUEltg7rGZeU77zDUWUqNGI0Cryt966y1WrVrF+++/z6JFi+jSpbu59+6I\nZNG27bFX6J0790GT6c7YTv7ndUQ2YVmZrFu37pjnb9iwgeTkHILBIQSDw0hKyirR9+LnFo1mkdgb\nORTqyIIFC/7dn0Gp/Y7s/30gWLRoEZFIpwQnsh+vN1iiGvRk7ODBgwYPz0Z76V6NSPSoGkKNG59J\nvGgL3O4pxXLBy5YtIxptlTCvGB5PGk2bdqBt245YVmd0pX2IQGAQF110CU8//TSWVQmliH6PSFcs\nK9Mc+yKqm5OJ9rldj0gKffsOoH79tvh85VGWTFUTxFJwuZIoibHfiLKBQmg/gQEojNMehX3qGyec\ngUiAlJQyaJI1FZEuBAItadmyNWlpZbEsrepV/D3HjJmHw+SJC+gdwO9vTVyWoiK6ur/BBB8LXf1/\nmzDP3mY+zYmrj44yn92KsocaJBwPIhlYVgcsK4t7753HmjVraNy4PXl5tRk2bDSzZt1K27bn0L//\ncDZs2MCHH35IlSqFaG+DJDSP4Yz1d/MsPQnO+ltsO7XE93/ttTOw7TKEw472kyPpsQyRIFOmTDmq\nMqoW6SWh+Y7HzfdVH0cYLhrtwjPPPHPc93TLli3MmTOHOXPm8PXXXx/32Hnz/optl8XluhLL6kH1\n6g1+0UKp1P777P99INixYwcZGXl4PDcg8hrBYE86d+59ynMYO3YCSuPcapzZVPz+Ttx7771HHKvU\ny0Rp6bsYMOACAN566y3C4RrEmUY78fujfPfddxw4cIBOnXoRDKZhWVk0a3YmP/74Iy1btjcOtYJx\nRI8ikk79+i1NPwKbknpAw3G7K6FQS1PjiB2VyfXoqvO5hOMvRlfqCxJ+Nw6FYpoTr/R9HLc72SiS\nPm2c9KVYVia7d+8mP7+2CTgfmUCThkI8ERS7L0dJ/v5N5r7qGofn7N6+MgHifLRAbqk5Nhmlezrn\nX4HbnUkg0BDNcbjNs/gbzo5Nx38Hkc/w+yMmsTsPkTUEAr04++xzi/Mmo0Zdhm3n4nY3M/deG5Gx\nCdf7DA2GSWhPhhgezx9p0qR98Xe/fv16LCvDvCeJz/tONDCOxu9vRvPmHY5akayU3zw04IUTvifd\nEXz88cen/A4fzd58802uueZa5syZwxtvvMHChQuLJTdK7fdv/+8DAWj7y3POOY86dVoxduzEX7Ta\nycmpapywU0y0A58v76hskOnTZ2LbTVBph7ew7fI8++yzgDZIKShogsdTF5GxWFZD/vCHi4vPjcVi\nfP3113z55ZfEYjH+8Y9/mH4Dnxc7Y1351iUUKmOcrU28a9kBtKbA4bA/jK7sMcHnCeMwU9HmLsPN\nfeUbhxkPXurQrkz43dd4PBEikV4JvytCxE8olIFCTInSzq3QPMMt5uceiEw1Dn+PCQAh4/AcB+3M\n02vurTfxRHgLSiZDX6BcuToGy3eYT44eUQUzHxtNJrc1QayzOfdJ46ADpKfnMXfuXEKhysQx8zfM\nM0pDGUcvI1IHy0pn6NAL8Psj2HYuFSrU4t1336VVq05Eo1lUqFCdcLh5whwxc4kQ12wqIhxuylNP\nPXXEu1OpUgFxePB5REJYVi2CwRRuv/2OU35//51df/2fTFHcAGw7j6uvvuFXu1ap/XZWGgjQRFz/\n/sNJTi5DXl6tX6Sfnp9fD4ULMtBVcipNmrQ56rFFRUVMmXINWVn55ObWYO7ceOvNWbNmEdf9zyEU\nyjpuA5RHHnmEcLjXzxxLEJFz8XgyTWAIGUfTA12RR9AV6xvoytKNrjKbobmAfmYOQeItJ9PQgrQv\nEfknlpXPwIEDzfkfo7DIKEQihEIFxHcJX5rzG6JQjaMKCtoXIYV4AdnXaMApY55jgQkUH6NBKBct\nRLsQ3UncZ45zKonT0B3ObjTp3BaXy+niNguHsSPiNoHYNk74QhSW+QMuV6a5F2enoMHSttOw7QEJ\nc4+hu5KViHTD5crlzDM7FX9X33//PV988QUHDhygatV6eDzTzP3dbq77doIzt9B6i0PF49v2+Ufd\nTb733nskJWWTlNQG2y5Pjx7n8e677x6zn8XpsM2bN5tdntPF7luCwbTS3sT/A1YaCIDevYcQDPZB\nedUvY1kZp9zjdfHixUY7ZjIeTzeSk7P55ptvjjhu//79x9WIUdmIF80f3F5EqnLZZcfuMvXuu+9i\n23koRx+UcWMTiaQTClUxDusNtElMmJo165OfXwefbyi6ol1ijhmNwi+OA3/TON4kPJ5BiDyAy9UM\ntztKOJzJ9OkzWLx4MV5vrgkWZRCx8XhsGjdua+CTySi2Pwpl7CQZpzwGhZss/P6QcYw3odTWNBTP\n34TqEq1McL6O2uoA8/kA4r2aK6I7jA7obsFn5r8RFZmrjuZlPsCyknnuuee47rrr0N2TU6AXw+Uq\nZ55LYkU4uFxpBIMZxCG2+ehOpSF+fz7t2nXl4MGD7N+/n0svnUytWi3o2LGXEcPLIlHC27IKjWBg\nxFz/FVQL6RITxJZj2+nF9Naf2/fff8/LL7/MtddeR9++5zN+/ORimehYLMbs2Xdwxhk9GDDgD0fU\nPZyKrVmzhmi0TonnEY02KIWI/gfsdxUIlixZQrVq1ahcuTI33njjEZ+faiCw7RTiWC14veOPOv6J\n2ttvv82ECZO57rrpRwSBAwcO0KvXYDwePx6Pn+HDR5fg/+/Zs4dBgy40zqEF8ardC+jevTuHDx/m\nk08+YcOGDUdw/SdMuBLLyiEabUcgkMLkyZPZvHkzVarUxeebgMi7+HwTqFy5kAMHDrBjxw7atOmA\nrqYdrPsStEOZ88f+o3GmR0pmh8PVCQaTyMurboKAM9cPcLlCLFiwAL8/GZWWuBeFYJ5CV9ptEAli\nWSmkp5dD+f8O0yYJxfLfNdesQ0mW1HjiOkCFKGVznjnfoZPWQ+GnxsQb6YDIXDyeGlhWBk888SQA\nH3/8MZaVS3wlfhhN9qcYB/0Dcew/wC233G40oDLQncRKRObidsdbSp577kAsqxsiy3C5LsCyIobd\n9J0Z6yChUDXeeOMNOnfuhd8/0Dj/13G7U/F6g2RmVvi3/X6vvPI6bLsOIvfi840mN7cqO3fuZOLE\nKwmFGiLyBB7PtaSklDmporOj2e7duw2DaDEO5BaJZP7bJj+l9t9vv5tAcPjwYfLz89m4cSMHDx6k\nsLDwCIrcqQaC9PTyJMokWFZP7rrrrtMx7SNs4sRphsXzEyrM1oqbbrqt+PMuXfoSCPRDE5mXGAf9\nMCLp3HLLLdSp0xTbzsPrTSYzswrjxl3O9u3bi8//xz/+wdKlS0sUFn377bf06DGQ/Pz6dOvWn82b\nN7Np0yZeeOEF0+TkeuPsniRO4/w7ylO/3DjiugnO9KA5Ziu6Wi9E2UHxlaLfXweFOuaiyc8Mcz/O\nMf/C7Q7TsWNHNPdwj/m3nnHyucRX9KnGuU9CmVg2ffqcZ/jtBQkBaqcJSO3N9VahlNF7iq/r8Yzn\njDPOLvHubNu2jVAoGy2MewStO3BkuDNRZlRvRJJIScll6NARFBTUQ1k7S9ACvy2I2LRu3ZF9+/YZ\nRdhtJjjVR1f6ETQRrhRcvz+VPn36MXz4RTRp0havN0g0msl99807oXcpFouZnVScOhsKdWPhwoVH\nFMFZ1qDT8k6/8847pKeXw+sNkppa9pj9M0rt92WnPRDMnj2bHTt2nPKEjmVvv/02HTt2LP555syZ\nzJw5s+TkTjEQPPTQw9h2Di7XNILBvlSqVLu4yOdE7eDBg6xYsYLly5cfN9lcv347lKWyA01c3kuH\nDr0ADXbqQH5AoY2mKF/epnbtQtLS8o0jqYNi7X/F47mAChVqsmfPHg4fPsyKFStYunQpO3fuLHHd\nnTt30rZtFzQP4OD9ycY5JXYca2KcftQcm4SIH5crjBZrvY5WtDr9iMugImqJjefXozBPoXGQDvTS\nP+E6K3C7k3C52qAr/Ew0L9AQTZa+iu4gHHjsWTPXawkGM/n000956qmnCIXOTBjzgAkYVVHcvhqa\nP0hB5Hw8nt5kZOTx9ddfc+DAAQ4fPsy2bdvIzq5kgtBw4/BHmp9B4SnbPItMM94EM5eocfSp5pn1\nxeXKpHv3/uZ7nGqCivN8b0ED3HkoWyoL3X3dgMuVzLx5JxYAHCsqKsLrDZBY7GXbg7jvvvuwrCSU\nbqu/DwaHFhfB/VKLxWLs3r27tAXl/5Cd9kAwdepU8vPz6dOnD0uWLDltL8sTTzzBBRdcUPzzAw88\nwJgxY0pO7hQDAagu/FVXXc1tt9120hK/u3fvpk6dpoTDBUQijShfvsZR8wIAPXoMxOXqb5xHVZze\nvJ0792H37t0Eg04FbRviOP3r6M7gQUReQlfK24v/yMPh9jzyyCO0aHEW4XBNotE2pKeXK1Ek1LPn\nIDyepsap9UehmBTjwJ3k33608CoJxdltRLymovhqFF/PQPMIY4xTc3oMXG/GykUDTBOUWloRZfIs\nM2NfiArHhc0xjpP80Fx3TYJj/xMqkAe6Q8lDcekCVq9ezXvvvUdKSlm0puBJE0jSzBwdmesGxfeU\nlJTJpk2b6NKlDx6PH683SPPm7fB4LjQOvZw5/j5zD9eiuYXbjOP/u5nLe8aJOzDPSvP5LES6EApV\npG/fQXg8uSgd1LmfD8w9/oAGx9EJny0lEMg66fe2b9/zsayuiLyNy3UXkUgmX331FePGTcS2myHy\nHG73jSQlZZ+Q/MSpWCwW46GHHqZv32GMGzfhmO9+qf332q8CDRUVFbFkyRL69etHfn4+U6ZM4fPP\nPz+lCTr25JNPnlAguOaaa4r/Oxpl83Ta9u3bee+99xg1ahyBwOBip+b1Tioh+ZBoa9euNQ7zA+MA\n1iKSit/fi379hnHjjTfj86UZR+s4iV0oZg5aoOQnXqgE4fA5DBkyxEBOGjxcrttp3jy+g0pPr2Ac\n6f3E2S79jMPLQVfljU2QaIBIIWlpeUQiGcQ5/UvMGDVQAbwmKBvpC3Q1HjZjPog2lemNSkdHUZx+\nm3GAjdAVdmIu4ieOrFsYhSZrFxonPw23exbZ2ZVo3LgdgUCOqY+IoDuBa1AWTn8U40+U406nfPla\nXHDBWEMM2IfIVrze6mY+NdAE+2uIZJKRUcY8mzLEg+YXaOL+IjQXkcjQchr7fEJSUiteeeUVBgwY\nhMdTaBz/IbTeIdkEkkmITE84/yNEoif9Du7fv5+xYydStWoj2rTpwt///ndA/wZnzryZpk070qPH\nwONWDjsWi8X45JNPWL16dXG/6xOxGTNmYds1ELkXr/dSMjMr8P3335/0vZTab2evv/56CV/5q+UI\n1q5dy7hx46hatSojR46kbt26TJgw4aQn7Ng777xTAhqaMWPGEQnd31J99Omnn8G2U4lG6+J2pxjn\n5/xRv0ZBQaujnvfee+8RiRT+zIloS8X09AqAUke93nQUIjmEFitVSzi+vXFEb+J2/4nU1LIMGXIh\ncR4+iKwjO7tK8XWrVWuIQi3/TDjmJpyOYiJT0AK03Yhk4vc34uKLL8frtdDdxwoUGnnSOEunRKA0\n2wAAIABJREFUYvgcM+656OrfYd7sR1fnZxKnnc5FZDqWlUogkGnOewUtOLsADSypxqFfgO5ILOLV\nxlFcriT69h2E19sbDXq3oQnb1gn35fRYdjDyLYj4eeCBB4wq63sJx95pnHNiE5n5+P0ZaD4igga/\nED5fYzRp3sQEBqdm4ykzxj8RWUBKShl27NhBLBZjxIhx+Hw2Hk/Y1HkE0B3LAHPOS+hOoyWRSM5v\n8u4ezYqKiujff5ghG9QlM7MCn3322QmdGw6no4l0Jx/Rj7vvvvtXnnGpnU477YHg9ttvp379+nTo\n0IHHHnusuES+qKiISpUqndosgUOHDlGpUiU2btzIgQMHTmuy+GRt165d2HZqgkMZbxzRPlTyYTAj\nRowDdJU1a9atVKhQQJUqDbjzzruwrBTiRUPrjNMqpFq1hsXXUAG0ZHTl7ODRXdCipVxE6uPxpNOx\n47msX7+eBQsWYNsN0YRpEW73WNq3P6d4vKlTp5pxzjXz/NKM08IEGcfx1UIkwuDBF/C3v/3NtLfs\nhuLaiSqhK4jLNNxsnHZ6gvM9bH7OQFe+EURSCIfLMXXqNIPLjyRO++yNrpwnGcfrQwPb9yjkkolS\nNcuQmVkVFakD3Xlcb+bi7AB+NA63JiKX4HKV5fzzRwDQosXZuFyO1HQMv/980zTnr8X35nLdaO7n\nYTPvZERSqFq1Bi6Xo0t1Dw7tMxrNpkaNBgSDyeTm1jiC6fPjjz/y/fffM3LkOLRW4m3zTLIRScXl\nysDnSyrRzP5E7emnn6ZBgzOoW7cNEyZMZPr06cyfP7/47+7HH3/ktddeY9WqVSXYaT+3Bx98kFCo\nMboz0x1lgwZtj3rsW2+9xT333FO849Z2pXHWXSBwYako3e/MTnsguPrqq49ZYPJLpWtffPFFqlat\nSn5+PjNmzDhycr9RIPjoo4+IRKonOMWDeL1l8fmS8fvTiERyycioxBlnnMN1111PKFQHLUp6Bdsu\nR5cu3dCVbmN0pbsAkTzuvPPOEtfZv3+/CRJd0QKs+ohEcLv9lC1bhY8++qj42Fgsxjnn9EVX96mI\nlCMSyWTDhg0AzJ8/n2CwA5rA9RhHG6B169b4/ZloRewdiDyC12uzc+dO5s2bh22fh3L9Q5SsHn4e\nLZhbiO4qbkdzAiloVXJ/E2ichvXp5h5exrZrcNVV11CjRiNcLg8a5BqgO50QGRllCQTqm/NeNQ7T\nYwJGHXy+ZHRFXWSu80c0sT4AxfcbE++JfBUiqYwbd2nxdxeNZhEO9yEcbk9+fh1efPFF0wHselyu\nKwmF0klLK2ue40o0r1EFtzsJjyexQczn+P1h9u3bx8SJ07DtskSjnbDtdObMuYOGDdsSDmdQWNiS\njz/+mNGjL8Xlui7h/KWI5JKcnFksNngytmTJEmy7DCrj8SIK8Z2DbbeldetOrF+/nqysikSjzQiF\nqtGyZcdjFiVOnToN3Yk5c/uKaPTInIXqI5XHtocTCuVz2WVXMHz4aCzrTPOs7iUczjgtdQul9tvZ\n74Y+eiL2WwWCnTt3mlW9g/N/gmWlsXLlSrKzK+HxzEDkEzyeawwckAg73IvbnW2c3wqcRG043PqI\nhh9vvvmmcbJO0vhHRELcfPPNxGIxvvvuO5588kmef/559u3bR+vWXdDWkv9CZAsu1/hiWYrt27eT\nmVkBhW/GGifp7DR8+Hw1ELmGUKgmEyZMBVTcLBisgkJXDdGV8dWo6miy+bc6iq079zecOCPpG/O7\nregK21EifY309HzGjbuUwsJm+HydEVmMyzWarKxKrF69Gp8vG8Xk000wiKE6RCmI2IRC6ejOIcmM\nPQhl7qQY5z8J7VvwGCIN8XhCLFqkUg1btmzhgQce4PHHH2fPnj0AfPDBB4wZM57LLpvIxx9/zMyZ\nM1FxuzYoTPUOygIKodDYDkQGkptbgzVr1mDbuQn39y4qQ32T+R7uICOjPKtWrcLrdcgA96O01Pvx\n+U4tEHTvPtAEPufZL0bZZocIh+tSWNgCt/tGHLjMsrpw8823HHWshx9+mFCoAY5kudt9M40bn1Hi\nmG+++YZAIPF73VGsdDpx4jSqVWtMq1adWbNmzUnfy+/ZYrEYTz31FFdffQ0PPvjg77LbW2kgOEVb\ntOgpkyNogGWlMm/eAt59912i0US+fQy3uxxxJU1QSGAEumq9DJHPcLnuJC0t94jCnFdeeQW/P1El\nswiRZJYtW8aSJUtISSlLJNKFSKQ5NWo0pFatZoi8gEJIKYhEyM6uWgwTfP7557hcPkQ6G8e5wTjK\nVHy+ZP7whwt54IEHuOaa6xg5chxXX321aYCSgsJJH6O7g2RycioZSmmYkgJxV6D1ArVRKQqHipnY\nUWwxmoR15KUdobsYkUhzXnrpJWrUaIAGrcYJ54HmA5rhcoWoXbsApa5uRGGk/mju4kYTIFqjbKBh\niERp2vToUMfRbOXKlQSDeSg76HDC9WsRL8DrQTCYzt1330002s0EgCvRnVFOiXlHo/V55513qFGj\nPgqH9UTzLZtwuYLHpSs/8cSTpKfn4feH6NChRzE9u0+f89EciXOdh8x3D5FID9LSKhAX1gOR2Qwf\nPvqo14jFYgwZchHBYAaRSE3KlKl8BMHj73//O5FIDUTeN/d4CeFwnf/3tQRjxlxOKFQbkWmEQk3p\n2XPg745aWxoIfoFt27aNVatWFdPl/vGPf2Db5RIc20/4/ekEAikojj3ZOKh1aIK0Ey5XlMaNzziq\nfvzu3bvJyqqIyzUDkQ9xu0eRnJyL35+Ey5VmnGsBIpVwuSrRtGlLvN5yKN5+EJF9+P0dmD5dYbSi\noiJTgORDWT6OgxiI39+COXPmkJdXHb9/GCI3m2s4apgO/j4TXaXfY4JaFIVl3kUZRCkoE+oF4+gb\nm3mmoTTPO835KThMmcRAIFJAbm4VxowZQ7zJvFPZu8mM2QuRJKpUcfoNxKEaHa+rObd2ghNfi88X\n5vDhw1x//Y3UrNmM5s078tZbbxU/7x9++IEXX3yR5cuXc/DgQQYNchLWPxEPxJVIlLuIRuvwzDPP\n4PdHifckGEZc0K4CItMIBnNYt24dQ4ZchMvVGN1JDUckk8LCpsd8xz744AMsKxPNK+zE7x9Bhw49\nACUeKKR1Ewq7paCBfRHhcAZnndUTn+8S81x/xLab85e/3HPcd3rjxo18+OGH7Nu374jPfvrpJyIR\n57ubYb5/+1dn5/03m+6Skog3XNqHbZc/ZYma/5SVBoLTaLFYjPbtu+LzVUakO8FgQ/r0GcLq1asZ\nM2Y83br1NP0CtiJShM83jk6dji95/cUXX3DGGedQrlwtGjVqi20XoDTMVHRVPcI43hkEAqlkZFTh\n5z1xnYK1Dz/8kMLCFiYQbEpwvq0JBstx6aWXYtvdE86tgVbnZqI7gb+ikM8rCce0Mc6yjAkYlnES\nZ5pj70dXz14UVgmhgaqbOd/5/2dQumg1dGXr1CRkoDuK3miuYIAZrx3VqzcgFKpR/Dz1WXRAA04e\nJXsEHMTt9jJx4pXYdlO0NmMhbneEtm078/jjj5OeXo5otB3hcAGNGrVl3759tGvXGb+/FSJzCQb7\n4PUmG6e7DZdrDpmZFdi7dy/Z2dXQ4AeaZC6PQof/RKQm1aoV0KvXEFq16kIkkkEw2JRAoC5JSZl8\n+eWXx/z+b7nlFvz+cQn3sROfzy7+/P3332fw4BH06DGAgoKmhEJp5OcX8tprr3HppROJRMrg82UQ\nCCQzcOAFvxi20B4aiW1Ob+Pccwf/ojF/z/bJJ58QDldMeB6QlNSc5cuX/6endlJWGghOo61fv57k\n5By83kF4PIOwrNQSgmGxWIypU6/F57Pw+yPUr9+qWCTsRGzSpClmFfYscYiiqPgFDAab0qbNWXi9\nk4qdfCBwAWPHTmDdunUGV78FrQHIQXcpnXG5UujZcyCzZ8/G5/tDwks9EU1Q9zJBoQwK17yNitGV\nQ1fgjuTzd2ZOVY1DTzYB6wETcF7F708iEMhDdwVfoLuBwebYfmiQ+xe6cxpsAlAUTf5WNP9fG5Fs\nKlasS69e/fB6g/h8YbzeqJljFF1xp5m57kVkPKFQtmFBORXQoFXCnXG7U3C5HPptEW53ZypUqMF9\n983j4ovH0bhxO0aNGsvatWspKGiObadQUNCi+PtV+XGn4Ow8SvZsWIrbnYbLdTMij2BZVRg8+Hwe\nfPDBf8u3X7Bgwc8a179Nenrecc85fPgwTZqcQTDYG5EFBALtadnyrNMCVzRv3gmlyzr39gAdO/b5\nxeP+Xu3gwYPk5VXH7Z6JyDe4XPeRmlr2iMr+/3YrDQSn0fr0GYrbHYcq3O6Z9O495Ijj9u3bV8wz\nPxmbN28eoVArdJXd2Tg8p4H7YcLhWjzzzDPk5VUnGm1OJNKQatXq88MPPzBhwhW4XFMS/oBnEQql\nM3jw4OIK8FWrVqGr+0eMU9OEckZGJfz+NFyudHSVXd04+BeNA99oxhyDFlvFUAXPC83nj6CMlEeJ\nRFpz4YUjCYWyUPjE6TJW2Tj/5ijOf3nCXOejbKeqZn5J6Kr7MWy7LDNmzDAUxigiA1Gm0oPm5xx0\nN5KJ212IrtRXJ4w9Ct3BlKMknn47Ip3xeivi82USifTBtjO5//4Hj/rdXHLJJCzrDHPt7mhS3Rnr\nbrQgbr15Nu+Sm1vjhL7zffv2UVjYnFCoA37/OCwrs1gs71i2du1a0zPBgcUOYFllWL9+/Um9b0ez\nBQvux7Yrownzl7Ht8jz55KJfPO7v2TZu3EjjxmcQDqdTu3bTEmy+34uVBoLTaK1bd/vZaulpWrbs\nQiwW48EHH2TixCuYP3/+cfncx7PDhw/TtWtfo5Sp8IgWN81BpCORSFnWrVvHnj17ePnll3n11VeL\n6YKXXz4ZhVXGo7uKJeTl1S4x/sqVKwmFaqLVvNURGYNl5ZGTU8VADrWMY+1lHDMo5XMAurJvh0pC\n6PiaFE5BdxXTULG1JG688UbS03PRnEkUlZ94AN0llEEhoHsTnuMbZpxR6K5hXsJnjxIO56EKp15K\n5j56oRCVU8j1KZrbqGj+vcLcz1eINMXtvhDdYe00c52E7nZqm/H+QSAQOap8+MGDBxkz5nLS0yuQ\nk1MZy0rF670Yl+tiNK+RY+7tHETeoUyZasXn7t+/ny+++OKYGlX79u3j+uuvZ+jQoTzxxBP/9j1Z\nvXo1kUiiWmwRoVCF09a17C9/uZdq1RpTvXoTFiy4v8Rnr732Gn37DmPQoAv54IMPTsv1Su3Xt9JA\ncBrtttvmmKKuTYhswrYbccstsxk6dKSRBL4e225B9+7nnfI2PRaLsXbtWubNm0fduq1wu0Mos+U8\nRJrg9WZw1VXTjwg206dfbxziTDRJGWHq1KtKHPOvf/3LNLF38gcvoyvwFxDZgtvdGE3+1iCexN2J\nUkvdJjg1MA6+ClrQFSGuR/QjIlGzcm6DJlKTzWflzLFOv+dyqP7QBkQaULVqXdzua8x9xtVERR4k\nEMhBC+UCxAvaYmYufgKBDEo2kn8StzsLtzsZTV7Px7LSsO0scw8+E6Ca49RkxOG3tBOSc/7yyy+5\n/vobKChogtc7wgSYA4h0wuvNYdasWwEt9Y9GMwmFymFZycUU10SbMeMmbDuHaPQcLCuLP//5+Cqi\nBw4cMNLjlyKyHL//IgoKmp3yAuRE7cUXXzQ9F+5A5CZsO53333//V71mqZ0eKw0Ep2Dbtm2ja9d+\nZGdXoVmzs4px4lgsxqRJ0wiF0giFUpkwYSqbNm0iGEwjDuHsw7bL8fbbbzN06EiqVm1E5859jpsw\nPJbt2LEDvz+CqkyWRXV8niEQaMro0SWb2EQi2Sizx3GgfSgoiFcy//DDDwwceKE5LoyIbaijZdCV\n+ploYdUNxPMHlVB1Ta1QdrvLE1+Rf2McaoWEa2ICxJqEMVLQnUM6urPIRiUthuC0kBw8+AIWL15M\nNJqB39/GOOm/IDIXlyuJihWr4/U2RCGmPJQ+2huRHOrUaUgo1MLMsyMKq/WioKAZ99//AC1bduHM\nM3syYsRIgsFOqORDkHhtxBYzj78j8leysyudVMK1Vq3mlOyHsJDy5WuzbNkydu3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kAAAg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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab88169d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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J2FHQas/DbDbfdr03C0k0b94cdvspKBviBAFoCGAgkpI23PBeHx8/KBvZAwBh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1AAAc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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab87f5a90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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8ALh06SoGg4vMShaiAkmLQFSo9u3bs3nzfrSlrN2A+4B1aPMN3gbS0bbNzKJ2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\n/eaCz+5tEAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab898fed0>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "Time series data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A time axis:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tmax=500.0\n",
+      "deltat = 0.01  # 10 msec resolution\n",
+      "time = np.arange( 0.0, tmax, deltat )\n",
+      "print 'time ||', ' | '.join( [ '%5.2f' % v for v in time[:10] ] ), '...'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "time ||  0.00 |  0.01 |  0.02 |  0.03 |  0.04 |  0.05 |  0.06 |  0.07 |  0.08 |  0.09 ...\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And some white noise:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "white_noise = np.random.randn( len( time ) )\n",
+      "printdata( time, white_noise )\n",
+      "tseg = (time>10.0) & (time<15.0)\n",
+      "plt.plot( time[tseg], white_noise[tseg] )\n",
+      "plt.xlabel( 'Time [s]' )\n",
+      "plt.ylabel( 'Signal' )\n",
+      "plt.show()\n",
+      "plt.hist( white_noise, 50 )\n",
+      "plt.xlabel( 'Signal' )\n",
+      "plt.ylabel( 'Count' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "x ||  0.00 |  0.01 |  0.02 |  0.03 |  0.04 |  0.05 |  0.06 |  0.07 |  0.08 |  0.09 ...\n",
+        "y ||  1.72 | -0.09 | -0.22 |  0.78 | -1.09 |  1.61 | -0.50 | -1.33 | -0.62 | -0.09 ...\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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CIzxuL9tOv365RMBjyX1kmxs6NNo8Alm/VBi67UFrSMYNtt/eP4/F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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab894db90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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1KtolRdSpEzxdLpduvfXWURkOkrR37149//zz2r17t9xut9xut6qqqqJdVkyI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\nCAgAgCkCAgBgioAAAJgiIAAApv4fYdctzdSrXjMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8765490>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Low-pass filtered white noise:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "colored_noise = np.zeros( len( white_noise ) )\n",
+      "x = 0.0\n",
+      "for k in xrange( len( white_noise ) ) :\n",
+      "    x += ( 8.0*white_noise[k] - x )*0.03\n",
+      "    colored_noise[k] = x\n",
+      "printdata( time, colored_noise )\n",
+      "plt.plot( time[tseg], colored_noise[tseg] )\n",
+      "plt.xlabel( 'Time [s]' )\n",
+      "plt.ylabel( 'Signal' )\n",
+      "plt.show()\n",
+      "plt.hist( colored_noise, 50 )\n",
+      "plt.xlabel( 'Signal' )\n",
+      "plt.ylabel( 'Count' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "x ||  0.00 |  0.01 |  0.02 |  0.03 |  0.04 |  0.05 |  0.06 |  0.07 |  0.08 |  0.09 ...\n",
+        "y ||  0.41 |  0.38 |  0.31 |  0.49 |  0.21 |  0.59 |  0.46 |  0.12 | -0.03 | -0.05 ...\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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6O6nGfV6e9wSSmUkn2+jRSjzju98Ffv1r4PrrQxuz2sKwIhjt7TTZlJWR1dPW\nFnxJCy3+YhiA80pzs2C0tZF7sajIXBCYBcPudre8COC9HYD+Dv6RI+l44/9xUDWYGEZnJx2b//43\nTXpJScBvf0sC+vOfk5AMDOin+CYqakt6xAi6PmUKbdw9+2yqEt3R4W2JtrTQd6jepxUq9fX1qK+v\nD/r5UROMUaNGoYFtWwANDQ0o1dTWzlXlks2bNw8333wzTp48iQJ2BqooLFyBCy8kFfejFQCUTU+h\nwP1xOzrMC4bWwhgxwnfiZf/2sGGKYHCTnFBdUjy5WBUMHg9Ak2VLi72C4e9zOa2shdtN30FzM620\nCwu9/dBG8ERgV/yCUX8/bW20Ge+++3wfxwkUdrikurroc197Lf12l19Or6u1PNxuEQxGXR16+HC6\nPnky8OKLtAjLzFRSark1cUsLWfN2Whg1Nd6L6ZUrV1p6ftRcUjNnzsSePXtw4MAB9Pb24tlnn8Ui\nzdbjpqam0zGMLVu2wOPx6IoFQF+6mclFHXgOBbVg+EO9gtZaGIWFvhMvi8+oUXQQdXcrGQ2hTpz8\n/fDeECuoBUObJx4KgVxSThMMFsusLDoGioqsuaTstjC0O/h/+EPgkkt8H3fffcD3vqfEOUIRjFOn\nlMAtH5udnXReffAB8Le/0X2PPGLvsRLLqIt9jh5Nx/yZZ9Lt9HSlR/uJE4qrimMYdloYoRI1CyMl\nJQWrV6/G3LlzMTAwgGXLlmHSpEl49NFHAQDLly/H888/j0ceeQQpKSnIysrCM888Y/h62dkkBoGy\nauzatcyCEag2jz8L49xzfVecfLukRNn1zRMSv2ewpKQAmzebL22uRmth2EWsWRgtLUotL7OC8e67\nFAROSbHfwlDvr/EXmyoupgmdx8ouqWAtDP4cnJjAgrF+vZLrv2oVcMEFwNy51t8jHnj6aXLNfeMb\nioXxwQd0/Pz0p8pisaVFEYyTJ2mT7wcf0PXW1uDbsoaDqAkGQG6mefPmed23XFWR7ZZbbsEtt9xi\n6rXMWhgsFtq+x1bhGjyBLAz1Cjo/X2mO1NEBjBsHzJzp/XieyEtK6ABqaFAycUKt+7NvH3DdddRI\n3qp7K5yC4S+G4bT6YBzDyMqik3nYMGXnt9HxtGkTXc6eHdwE7Q8rO/izs5UFFVsYVmMY3M9cKzhd\nXSSg7e3ev1eoi5xY5mtfowy1b3xDsTAqK+l/XGzzm98kF/rbb3tbGCUldKydOGFvWm2oxM1Ob/YD\n+jshe3t8TrvmAAAgAElEQVQpcyMnJ/QT16xLSs/C0OuKxnAAvKCAPs+xY0ranV2F4oJJ0Q2HYHg8\nsWdh8D4Mdi1x+rM/K6Ozk/YlnHNO+C2MQIJhxcJoawN+8xvv+3inOgfY+flsYWhTehNZMAA6tnfu\npDIu6uOcBePxx5UEl64uOrcWL6YEl5YW4Phx71pz0SauBCPQRq+uLnOPM0MwgsH+/95eWnn4W1nn\n5Sl7MoYModLHoea2V1XRpVXBqK2l1RJgr2AMDCh7CYxQr6CdAOfNWxGMkyeVvRvhiGGoS774Ewx1\nirKZGMauXb77brgWFgsDn0ednfQ7aSutJrpgZGQAV11F57HaDXz77eSaUj+uqYmOpTlzyPJgwdBW\nJI4mUXVJ2Ul2trfJrQev6tPT7RGM5OTgLAwz6YbjxtEEwBaGmd4VgdiyhT6/1TIjr76qXLdTMMwI\nl9MsDF5h88TPx50/wWhpod++rS08WVLsImpp8b8a5fPD4zHnkmptpaBrVxfFvr70JaU8+/jxlBWl\nFgxAyUDkkuoiGBST2LXL+1i/8Ubfxx0+rOwG5+y7tDT7FxmhEHcWRiDXgF0WRlcXKb+VoDdbGHpN\nbtR0d1OONrukrOzK9kdqKvnau7qCT9EVwfAWDLMWRkEBTbLqLop2wC6p3buB8nL/32dyMn2f3d30\n3aem0ucpLlbKfajhzXd1dUqzJw54b9gA3HabdwwjI0MRjJIS+swiGEpGmb8NrxkZJMBsTWRn07nm\nJOsCiCPBmDTJvIVhh2D09tKPuWmTUoROD3VQly2MQGmtfNJnZNDj7ShtDJAbjLMxghWMIUPs69oW\nKOANOC/ofeoUfYcs+GYEgwPll14K/OQn9o6HLYyPPjLXI4XPke5ucnsePEjHL1ezVcMpuDt2KJ+P\nXVJcH01tYZSXKwJy1VXAHXeIYGRk0Hfz0EPAF79o/LjMTBIU9a6B8nJnxS+AOBKMRYsia2Fw9cm/\n/AVYu1b/MR4Pncx6FoaZfRDsvrBLMAA6gLkpUjAEmhytYMY15nQLg11S/lyTbGGEAxZUs4LB50h3\nNx2PW7bQ/S+9RMKghgXjgw+U35xdUvxa6qB3ebny3Joa4KtfFcFgwRg92n/BP45vqC0KEYwwUloa\neQuDJ4GDB/Uf09dHJzQfKFxW+NAhc24mFgy7XFKAUnMr2GqVgSZHK5h1STkp6K3nksrJMRfDCAcs\nqA0NNCkFggVfXQwPAB54AJg/3/uxWsHweLz3YHBmD+AtGEOG0HcydKh3o7BEJCMjsAsaUL5TtUCM\nHu08wYiboDdg3sJISqLrL70ELFwY3Hv19CjZPZ99pv8YdfyCyc8H/vpX3zaMekyaFNzY/MEuqWAJ\nNDlaIdZiGNxDIiPDfAzD41GypMIBlzjv7DRniapdUjNmAPPmUVzr/vt98/3b2mghtm8ffY7eXmML\no6tLEYzf/IZemydLjyfxWrpy4cX0dOMUejVlZVQm5NxzlfvOPVffVRhN4sbCAAJbGOq02p07yY0V\nLL29yr4ItYVx6pTSrEZPMAoKaDfshRcGfg+eZOw8aDIyQqtJZZdL6p//pMyvQDEMJwkGx35cLvOC\n0dlJnyHUSsNGWC33wouqnh4Kdq9YoVgm2h3Fra3k5uLJj/tRqwWjq4sEa3CQxAUArrmGxpKSogTW\nEw31osxMVqTLBXz5y96ifcUVwJ13hmd8wRJXghHIwuCTivc3hEJvL/WI+PnPvQUjKwv4wQ+Ux+hZ\nGK2twHnnmXufTz8F/vCH0Maqhl1SwZKTo7ikfvSj4Ps7NzUBH38cWxaGur1qVhZNiGlp/gXDav90\nq7DLzozbA1DGqk58KCmhy6NHabJnWDAYFgytS4pX0IWF9JpcLgRQCuklGrxPpafH/G8TCxgKRk5O\nDnJzc3X/hoTzDAgBNpGN6vDzJpisLEUwgq3Z39NDeyW++13KMunvB956i/7HxQ2NLIyzzzZf/I/L\nBNiFnRbGpk1UTj4YuD1ooLE4KUtKKxj8G/oTDHWDnHCQnk7jMltKnK1wtrYBcn0WFlKs4p13KGB9\n+LC+YOi5pFgwRozw/ayJLhi9veZcUrGCYQyjw67IZgRJSlJOIL3NLsePk5/V41FKMff3e6+IzMJi\nkJqqFD08/3z6H/eF0ksbLSgApk61/n52YadguN3e9futwCIQqxZGSQlVYwXoOzHam8I9mcPFsGH0\nG1h1SakfP3YsxfNmzaLP0damCMaZZ9J5NTjo65JiC2PfPnJHjRvnu8E0UQWD3XDBVod2KqaD3seO\nHUO3yjF3xhlnhGVAocIrKD3BaG6mYn8dHUoJA85ksoo6YJue7l0ynTfo6KWN3ntveF0UgQjVJcWr\nysFBpThaMLAImIlhOCVLSi0YycnAkiV0PScH2L6dMlqOH/d+TrgFY8QIyrozu4plwddOYlzltq2N\n/OlcZaCoiGIdXKHg5Eklu4qPhY0bgYsuos/5i194v1+iCkZfH33X3d3G81EsEjCGsX79elRUVGDM\nmDGYM2cOysvLfSrMOgl/cYzjx+kEy8pSmt4Eu3pVu5vS0rw37/H767mkxo6NbqpcqBZGUpJ3obRE\ntTDUjB4NrFun/120t4d3dTliBC1WzK5ihw4ly9BIMACywHftorGPHQv8/vdUOLGzE/jkE6p2DCjn\n2saNVG9Mj0QVjN5e+uytrXSM+6uXFksEFIx7770X//rXvzBhwgTs378fr732GmbNmhWJsQWFvz0W\nzc00WWdlKVZAsKtXtfWQnk6CMWMGtar0JxjRJlTBABQXTFdX8ILB33s8CMakSYoLQhsTi4SFwYJh\nxsIYPZrSwP0JBgC88grtTE5Koh3qeXl0XH/8sVLepLSUXLsffWS8izknh+JciZZW29tLn/3kyfhx\nRwEmBCM1NRXDhw/H4OAgBgYGcOGFF+Ldd9+NxNiCwl8Aki0MXiEBwU9G6vhEWhplmBQUeL+/EwUj\nVJcUQCcAW2jhtjCcGvRWM2aM8jm4bDgTCcFoaFBSWAPBgqG1fDIyvJ9fX0/JGUx2Nr3Pp59STSyA\nPtfs2SQWRg25cnN9d5AnAiwYAwPxE/AGTMQw8vPz0d7ejtmzZ+OrX/0qCgsLkeNgyRw5kpoUaRsT\neTyKhaHefq+2MDo7yVwfNcr/e3DJDz7B2MKIBcGww8LweJQVZSRiGE4XjORkWoTs2KHUmmIiIRj7\n95tfxZaX0/nBExrjcpGV0d1N1kd3t3epj6wsqg2VkuL9+X74Q//7LHJzlaZhiQS7pIDw/v6RJqCF\n8eKLLyIrKwsPPfQQLr30UowfPx4vvfRSJMYWFJdeCrz8svd93LCIN1ypFV89GT3/PPD97wd+j95e\nEgtudsQxDK1gbNgQnt3aoWCHYKgngEjEMJwY9NbyyCP6jbkiIRjd3eZXsUYWBkB7g2bMUG5zth8A\n7N1Ll1p37znn+N+EmpurJJgEm8Iei6gF2WkVZ0MhoIXB1kRycjKuv/76cI8nZObOpc10av70J+DZ\nZ5WdqAC5kC64wHsy6ugwNwFqLQe2MKZNUwRjcJB6G+/bF9LHsR07XFJqJOhNnHceTd56ghHIYg0F\nzr7RusKMyMmh5zQ3+wrGiy8C3/42xRxOnvQ+X/Ly6Pi2mlGYm6uUzlEX4ox3ensVEQ9XWZhoENDC\neOGFF1BRUYEhQ4bYvnGvrq4OEydOREVFBR588EHdx9x+++2oqKhAVVUVtprYVlxa6lshc/du4PXX\nvYuzjRxJJ45aMDjzJxDadFmthfH22+Tbzc6m93ESdlgYb76pBD6DrUsVT0FvRq9FcLgtDICEykpV\nWHY16aV6lpQom/XUQvfMM8B771kfW1mZcoyYFbV4oK9PObYd7MG3TEDBuOuuu7B+/Xq0tbWhvb0d\n7e3taNM27g2CgYEB3Hrrrairq8POnTuxZs0a7Nq1y+sxGzZswN69e7Fnzx489thjuOmmmwK+bmYm\nmb7qE/fIEfoB1T5ZwDeg2tVFK6tAaDfksYWRn6+sKt5+21z10Ehjh2DMnk1B0W3b6Lvm2llWMBvD\ncFLQu7PTfz59tATjK1+x9vjycprEknTO/nvuofpSXBGBSUkJriS+ug1pIgmG2guRUIIxcuRITAqD\nI37Lli0YP348ysvLkZqaiiVLlmDdunVej1m/fj2WLl0KAJg1axbcbjeaAhSBcrl8u8KxD1UrGFr/\nuFnBWLXKe6JLSyPXzJAh3r5kJwpGXp53WetgKSqiHuHBxhhi0SUVqPyGutw309YWfsF46CFrrs/R\no40/R2oqUFEBXHyxPWNTZySGUiU51ohXwQi4Zpg5cyauueYaLF68GGmffwMulwtXXHFFSG98+PBh\nlKmiaqWlpXjnnXcCPubQoUMo0pbV1JCfTym0LS1kXh8+TKsj7QSuZ2G43RR/0Ft9AVRK5De/Udou\nAjTpdXf7BtSduBn+O9/x3yrSKjyhG6VVGhGrguEvHqFnYezeraShhovUVNpgZxa2MIwoKyMXlB2o\nXXhiYcQ+AQWjtbUVmZmZePXVV73uD1UwXCZ38ng0qRVGz1uxYsXp60lJNbjiihrs20eT4+HDwM03\nU60cNdrVMRcubG01DlTxhOB2e78OoHRfAyh24UQLI9hOe0YE6zKKxSypQOU3tILR2Unu0HALhlUC\nCYbd/PWvFExPNAuDEwScJBj19fWor68P+vkBp48//elPQb+4P0aNGoWGhobTtxsaGlCqTsvQecyh\nQ4cwymCJpxaMf/+bUloBSgHt7wd+9Svf3aZ6Fgag9GDWgw969STGk15WliIef/oTcNZZ+q8RTwRr\nAfT1kXgFEgw9N0+0CLSbWt1QCKAd0BMn2i/SoXLuucCtt0bu/RYsoGq4TrQwZsygJA67J3W1heGk\njXs1NTWoqak5fXvlypWWnh/wUL7tttvgcrlOr/RdLheGDh2KmTNn4stf/rK10aqYOXMm9uzZgwMH\nDqCkpATPPvss1qxZ4/WYRYsWYfXq1ViyZAk2b96MvLy8gO4oQPnxKyuB114jc13PMNGLYQAUxzAy\n8fU2KWkPjNdfpxLRiVAOIZQYxv33U9MYf+TleVtz0cSqhbFrl3fQ1ykMHw4sWxbZ90xPd6ZgbN1K\nO9g5TDswQFUMQvUOqAXDaXuxQiGgYHR3d+OTTz7B1VdfDY/HgxdeeAFjxozB9u3bsWnTJvzqV78K\n7o1TUrB69WrMnTsXAwMDWLZsGSZNmoRHH30UALB8+XLMnz8fGzZswPjx45GdnY0nnnjC1GtzEteE\nCcDf/kZBPD30LAyXy3/gW08w1BYGYK6bXrwQrIXR20vxpUA56toEhmjw1FPk1jQT9FYfH+3t9iQY\nxAOhtgYOJ+oFz5tvUgO0f/0r9NfktH2nWZihEPCjfPDBB3jrrbeQ8vmnvvnmm3H++efjn//8Jyor\nK0N683nz5vlUvl2+fLnX7dWrV1t+XZ7wx4wBHn0UMMrG5X7ITFcX7aXw1wpE76B3oukZKUKJYZjZ\nxJWXF33BaGig8htWLYxA+zYSCadaGID3HHDsGG3qDZXeXjp240ksABNptW6326uZUkdHB06ePImU\nlBRkWE2NiRCTJ1Omx7RptMozCjpqV8enTlH+ub/aOP4sjEQUjFBiGGYEIyeHXj+amVLd3XQciWAE\nT0aG8wSD82nUgnH8OHXjTE4OvooB4Mw6cnYQUP/uuusuVFdXY86cOQCAN954Az/4wQ/Q2dmJi+1K\n1raZ3/+efJHJycBPf+pddVONnoVhVjDUr8kHRiJODqG4pMycUC6XEscoLLT+PnbQ3U1uzkBB78xM\n794PXV2+ZcMTFU49dxKcXq4WshMnlHG63cHXgUpYwVi2bBnmzZuHLVu2wOVyYdWqVSj5vMn0z7VF\nmxxCcrLSsOSTT4wfpxf0HjfOv2B0d1O9qro65b70dJosjPZuxDOhBL3NnlAcx4i2YJixMNQlOk6d\ncl5pmGjhRJcUH7fq811tVYQyXnVabTxhOMVxmY733nsPjY2NKCsrQ2lpKRobG/H+++9HbIDhRC/o\nXVAQ2MLQWhJpafHTgtEqoVgYZk+oaMcxTp0y55LS9mIx6i2fiDgx6N3fT5fq813dYjeU8SachfHL\nX/4Sjz/+OO68807dzXKbNm0K68AigZ6FMWyY/wNFTzDS0xMzfgFYD3oPDioxCasWRrTo7qbNnF1d\n/n9n7TglhqHgZAtDvc/nxAklFuVv4RiIeBUMQwvjW9/6Fo4ePYr6+nps2rQJ119/PXJzczF16lQ8\n//zzkRxj2FBPdlyw0EwMQywMBasWxt/+Bixdaq3UdX5+dPdidHeTqyk11X/WS0GBd0p2V5cIBmNF\nMMaOpR7p4cbIwpg2zfd+qyScYCxfvhzpn6f/vPnmm7j77ruxdOlSDB061Cf1NVZRWxjd3TQhZGcH\njmFok8MS2cKwKhhHjtCkauWEGjHCnlTHYOnuNtekqKDA2wcuFoaCFZfU/v2h74Mwg1EM49FHqfhi\nKIIRr70/DNdLg4ODKCgoAAA8++yzWL58Oa688kpceeWVqKqqitgAw0lqqnc5kIICOrCtWhjp6Ylt\nYZgNend3K1koVgRj5kyl1Es04IkukGAMG+ZtYYhgKFh1SUWi/pLWwhgcpDYFFRUUNxMLwxdDC2Ng\nYAB9n88Ef//733GhavtyP3/TMU5aGvDuu7SCvfVWOuEzM63HMNLSxMIww3e+QynPp05ZC3qfdx71\nF4kWPHFoy+Nr0bqkJOitYHUfRiQEQ2thHD5M7s+sLP3Kw1aIV8EwtDCuvfZazJkzB8OHD0dWVhZm\nz54NANizZw/y4qTeQWoq1Zq67DJawZ5zTuADRc81MWdO9FI+o42VoPexY8CePVQSxMoJNW4c7YE4\nfpzqIEUaXkAEqi+UnU2TELstJYahYHUfRiQtDPYy7NtHxxoQeOEYiHhNqzUUjHvuuQdf+tKX0NjY\niEsuuQRJn28y8Hg8ePjhhyM2wHDC/uZ776USzGxhBHJJaSetYcOA888P3zidjBULgze1dXXRatNs\n5z/evNfWFl3BCGRhuFxkZbS0AMXF4pJSk5qqTND+4FV/JIpIaC0MrWCIheGL34175557rs99E9Qt\ntGKc7Gya7LnR0fDhwcUwEplgBKOlhZ5npc5OTo73Hge7KCsD1q6lOIkR3d1UHv/GGwO/3rBhtBAR\nwfAmJcWcYHDhUDubfBmhjWGoBYPnAY+HLGMTRbK9iFfBSMC9yQp33QU0N5M7KSkpcAzj3Xcpg8Kh\nJbSigpWgN08Gbrf1mE9Ojv+ikMFy6BAQqAhydzdw1VXmWq2qM6VEMBRSUswdJ5w+HYkwqdbCOHZM\n2ZmfmUkB8F/+Mrjd+vEqGHFWS9E6LheVESksJAvDnynKgVcRDAWzFsbKlYpgANYFIzs7PIIBAG+9\n5f//p06Z/83HjqU4zZw55HqToDdh1iXV2kqXAwPhHQ/gKxhtbUrtr8xM4Ne/Vh7r8VjrbxOvabUJ\nbWGoKS4OHMPo7ATuuANYsiSyY3MyZoLeg4PAihXeexSsBjXD5ZICgL17leuNjVQrTI3e3hsjqqqA\n7dtJ3Kw8L94x65JiCyMSgqENere1KVak1jJUlwwxQ7xaGCIYnzNjBpVB14thbNpEq4s//5l8mYnQ\nSc8saWn0fflr+ckrueRkctkkJQXvklqxwv4SE2rBe/VV+mM8nuAEgyeeRCxIqYdZwWALI1IuqdRU\nRaTa2oAhQ+g6C8abb1Kr5X37rL22CEac8/vfU76/noWxfz9dfvhh8OWO45W0NFqh/+Y3Sn8BLSwY\nubnUjCg3N3iX1MqVyu9hF2rf+sGDvv9LSVGqHwdi+nQSDAC47z57xhcPmI1hRNIl1d9Pm/QOHKDb\n7e2KYPACoayMAuHBCEZCpdUmKnpBb7UrJBppnU4mLU0pId/bq58qyyv4IUOUTVHBuKR4JWhXF7P+\nfrIWU1KUNF+tYFh1Kw0bBlx5JTXhWbHCnnHGA2ZjGGw9RsrCGD0a+PRTOsfVFgb/5iNHUhZlQ4O1\n145XC0MEQ0NWFh086iBXZyetNBoaxMLQkpYGfF4JH6dO+RcMdtNkZARnYRw+TNftckn19JB4ZWXR\nZDFihK9gzJ/v3RTJDA8/7B3gF8y7pPhYiZSFkZZG+2v27/eOYbCXISND2VtjhXgVjKi4pE6ePIna\n2lpMmDABl1xyCdwGpUjLy8sxbdo0VFdX42yjtnk2k55Oqwx1kKuzE5g4ka6LYHijNruN0pHVLimA\nJulgYhiHDtF1uwSDrYchQ5QJXlvkMFAGlR7Z2ZREIShYFYxIWRgpKYrLqb1dOUbVAmG1vL7HQ+OP\nR5dUVATjgQceQG1tLXbv3o2LLroIDzzwgO7jXC4X6uvrsXXrVmzZsiVi4ystVSYngATjzDPpugiG\nN2edpVw3EgythRGsS4p/E7sa8egJBm8Y48tx44Ddu+15v0TGbAwj0hZGaiolu2zfThYBT/LXXAP8\n93/TdauCwcH0eEyOiYpgrF+/HkuXLgUALF26FC+++KLhYz1GkdQwoicYFRUkFp8X8BU+p6qKVmYV\nFYEtjPPOo8tgXVLsRw6nYPBYeeKSvRT2YNbCYOsxUvswUlKAqVOpnDrHLwDKhrz9drpuVTDi1R0F\nREkwmpqaUPT5XvuioiI0NTXpPs7lcuHiiy/GzJkz8fjjj0dsfHqCkZ9PPnS7Aq7xRE4OTar+LIyp\nU5WsoWAtDD5M7HZJ5eYaC4ZUnLUHs0FvTpyIhEuKLYypU8n1aLSTPz+f9hCp9+v4I54FI2zTX21t\nLRobG33uv//++71uu1wu3RawAPDWW2+huLgYzc3NqK2txcSJE09XzdWyQpWSUlNTg5qamqDHricY\n2dnmi+UlIv5qcGlPoGBjGExDg3dGS7B0d9NYxMIIP1ZiGFlZkbUwpkwhK9moXlR+PrBtG1nRZhwe\nTk6pra+vR319fdDPD5tgbNy40fB/RUVFaGxsxMiRI3H06FEUGtQGL/48cjhixAhcfvnl2LJliynB\nCJXRo4Fnn1Vus2AIxvirwaUtkxCMS0pdWvyee+g3+d73rI9TjT+XVE8PTXCcSSOEhhXByMyMrIWR\nm0up0EYxh/x8a6/rZAtDu5heuXKlpedHxSW1aNEiPPnkkwCAJ598EosXL/Z5TFdXF9o/z2fs7OzE\nq6++isrKyoiM76qrgJ07gc2b8fn7i2AEQq8FZ20t8IMf+K64rr4a0CmE7BdOOgDIPdDcHPxYGa4R\npRWM7GwaMxcPjMfgZaSxEvSOtIUBAM8/Dzz3nP7jrLb/cbJghEpUBOPuu+/Gxo0bMWHCBLz++uu4\n++67AQBHjhzBggULAACNjY2YPXs2pk+fjlmzZuGyyy7DJZdcEpHxZWUBZ5+t7AAVwQiMnmB88gnw\ns5/Ral19An3lK+QGsILLBXzrW+QW9Hi8O9sFizqGwXsttIIh7ih7sBLDyMqKrIURCLO7/Jl4Foyo\nhHALCgrw97//3ef+kpISvPzyywCAsWPHYtu2bZEe2mnUJUJEMAKjV1JlzBgl3mCHT/exx2iCf/hh\n70KGwcKCkZbmLRjDhtFJL/EL+7Dqkoq0hRGIM89UKhqYed14FQypJWVAVpZSxVIEIzB6FgZ/f263\nfScQl2yw08JQl2hnC6OnRwTDTpwY9Layue6dd8wnWYiFkYCIYFhDTzC4Bldrq30nEGeq2WVhZGbS\npNHXp+zQZZcUIA2Q7MJKDCNSQW/eYGcGKz3J41kwxMIwIDOTBMPjEcEwg5FgpKeThWFXmmG4LIy+\nPpqkkpPpvvZ24JVXxMKwC6daGGZdUunpNLZXXgk8Nien1YaKWBgGZGXRyriry7tkgKCPXgyjs5MK\n+tnpkmILIxTBeOYZ7z4X3ASKXRRpacB//ieVi4hQnkXcYyXoPXx4aBbGwIBSgdjfcWfFwnC56Ni7\n7jpyT02YYPxYsTASEHZJcc9vwT9GFgYLht0WRmdn8Du+r7uO/jitll1SPIGkpSk9Lexu1pSoRDLo\nfeQIXbJL2QgrFgagWMsdHcALLxi72EQwEhB2SR07RpOe4B+tYAwO0mQ7bFh4LIzCwuCtDH6Njg7a\nQc5Bb7VgMDrFCoQgiKRLipsdGVUeYKxYGICyWOnoAG67jfpo6CGCkYBkZdEBd+yYWBhm0ApGV5dS\nAsTOoDfvEs/LC77nBAsGlxfRWhjqEjAGZc4Ei0Qq6F1fD1x4IV0PJBgnT1o7LtWC0dZm/PqSVpuA\niEvKGurSIH19wPnnK82J7HRJpaeTVaAuGBjMawBK/wOtYDDJyUqXPyE0UlLIaghUiykUC+P554HP\nC0gA8C8Ye/cCGzcCX/6y+dfn48btJpeokcsrni0MCXobIC4pa2RmKmm0H39MMYD0dLIG7N6HkZND\nloHVTngMj4UtDLVLKiVF+Rx//rPSY1oIDZeLBJgD0kZwF8RgLIynnvJueOVPMLZvB2bPNi44qAdb\nGBwjMVtsM54QwTBA7ZKS7mmBGTGCvivAO2AcDgsjO9u7/pNV+GRWd1hTWxi8cvzKV0Ifr6DAcQx/\nghFK0LulhdxMv/0tib0/wWhupmwsK7BgcKtgfxZGvGZVikvKALVLSiyMwJSUKO1NWTAA+h7b2+1b\ncQ0dSoH0UARDHcPIzVX2YbBgsIUh2IuZOEYotaTYfVhcbFxun6sNHT9u/bzm4yaRLQwRDAPYJdXQ\nAIwaFe3ROJ/iYuVEUtfc4Y1vdp1As2YBa9faF8PgoLc6S0oEIzyY2YsRqoUB0LGoty8IIDdUaysJ\nRqgWhgiGcBp2Se3ZQ41TBP8UFND3xXEfnpR5h7xdJrrLRVaGnRaGNugtghEezKTWhhL0ZgujpMRY\nMHp6KDkjGM+BFZeUCEaCkZVFq5ATJ6gDn+AflwsYOZLcUk1N1NzoC1+w38JgQgl6ay0M7T4MEYzw\nYEUwrLqk+vpIDC64gI5DPcHweJTHBWNh8HHjz8L47DPggQe8G37FEyIYBmRmUr71mDHW6+EnKhzH\nOAJ8rrQAABc8SURBVHaMuuJt2aIIht1BwFBcUjyW7m4an9bCePJJ746Lgj0EimF4PMG7pFpaaG/O\nG2/Qb6gnGOpuisEGvfPzldfRszAOHQIqK4Fvf9vaa8cKkiVlALtSrLZnTGRGjqT8do9H+f7CFf8J\nxSU1OEiXSUlkGWmD3nPn2jdOQSGQhcEZVGbrTqlxu7074+kJBlcg7u0NLuidkUHHOMdK9CyM1lZy\nmcYrIhgGpKTQQRhot6igMGwYBbwLC5W2pl/6EjB1qv0mejCC8fHHilsCIMEAfIsPCuEhkBBwOirv\n17BCS4v34s6fYGzfTtblyJHW3iM9nZ6zaxfF7PQsjNZW6y1dYwkRDD8MHRrfqwW7KSigk0m9M97l\nAnbssP+9hgwB1q8HXn+dRMkMy5YBb79NmVY8XsB4p7dgL4EsjOZmWnSYrTulxu32FQxtMUxeKPzq\nV8DNN3uXgDFDXh6VHamtpdfirECAjvGpU+PfwpAYhmAbBQW0ireyezZYzj6bkhHeecf8c6qq6HL3\nbjr5n36abmt3egvhIZAQNDQAZ5wRnIWh3oQJ+LcwjhwBxo+39voA8KMfAXffDfzv/00Ws/r1p02j\n+InbLYJhO8899xymTJmC5ORkvP/++4aPq6urw8SJE1FRUYEHH3wwgiMUgmHYMIphWDX1gyEnB/jm\nN813QQOUFWZLC53cbJmIhREZAgW9Dx4kweC6U1bg3iaMP8Fwu4PrpJierhwf6o6cXB8rLU0sjLBQ\nWVmJtWvX4oILLjB8zMDAAG699VbU1dVh586dWLNmDXbt2hXBUQpWKSigEz1SKYXqk9YM6glEneYr\nghEZjh4FZs40/n9DA1BWRhaGVZeUVjD0dnqzYHR2ej82GNSCxGnYP/4xpdTGcwwjKoIxceJETPDX\nsgrAli1bMH78eJSXlyM1NRVLlizBunXrIjRCIRg4JnDGGZF5P95caYZ336U0aUYtDByMjecaQE4g\nUG+RUCyMnp7AFobaugm1VztXggAo9gIAf/sbXYqFEQUOHz6MsrKy07dLS0txmHfMCI6EBUP1s4UV\n9UkbiKuvJtFgQVALg8tFk1RXlwhGOAnU7jZUC0MdxPbnkuL/h4LaumXBYOJZMMIW4qutrUWjzpJi\n1apVWLhwYcDnuzgv0yQrVqw4fb2mpgY1NTWWni+EzrBhdBlJC8OsYHBrzREjyDWiFYa0NBGMcFNX\nR2IwOKikNKtpaaFFRzBBbysxDP5/KIwbB+zfD2zeTNUgADq2mpspg8+p1NfXo76+Pujnh00wNm7c\nGNLzR40ahYaGhtO3GxoaUOqnRodaMIToUFBAE26kSqmYFYzBQQpGejwkakeP+pYq4bLmIhjhw+Ui\nK4B32GvhUi3BpNX29ETWwigsBO68E1izBqiupvs4wy5Qk6hool1Mr1y50tLzo+6S8hh8uzNnzsSe\nPXtw4MAB9Pb24tlnn8WiRYsiPDrBCpmZlLJqNb89lPczIxgdHcpJzFaQVhi4hpQIRnjRtvJVww2t\nwmVh2BnDAIDp04EPP1RcUh0dVEPNTy5PzBMVwVi7di3KysqwefNmLFiwAPPmzQMAHDlyBAsWLAAA\npKSkYPXq1Zg7dy4mT56Ma665BpMmTYrGcAULlJdH7r3MBr3VXfOMBCMtjU74eK0y6hT0NtQxXD04\n2LTaSFoYAG3U27ED2LSJbnd0UMFNPXdbvBCVbUqXX345Lr/8cp/7S0pK8PLLL5++PW/evNNiIgha\nzLqk1H25ueCcnoVx4gQwdqx94xN8MbIwPB5l893gID3G41FKzATCTJaU3YJRXEzWxYcfUlLFc8/F\n/4IjjrVQiHfMuqT0LAxtBeK0NDr54znDxQkYdcLr6VFiHJmZFA+zkhQZ6aA3QOPdvBnYuVMpthkp\nd2y0EMEQYhazFoZWMNLSfFeuqakiGJHAyMLggDczYQLFw8xiJejNBQ7tYNYsqjrAloVYGILgUMzG\nMNgllZKitGTVe60jR0Qwwo2RYHDAm7EqGGZ2enPQ2w7rQosIhiA4HCsuqSFD6PHcMElLYWH8F45z\nAkZBb23xwDPP9O4NHwht0DsjgywK7n0CKBaGCEbwiGAIMQtPPupJQctHH5EQlJWRWBgJBlfYFcEI\nL0YxDK2FMWoU7Zcxizbo7XL5WjPc/jUcgsHHVLzHMKSYsxCzJCX53wgGUOrj2LHAlClkjWRm6q8C\nuYeHCEZ48eeSUlsYVhtkaS0MQIlj8LHR20vxhlALD+ohFoYgxABm4hiffgqcfz6JgVgY0cVs0Ntq\nz3athQH4Br77+kgwxCUVPCIYQkyTm+u9z8KIK64AXnuNJgsjwUhKoglFCB/qGMbatdRbG/CNYQwZ\nQveZRRv05vdSC0ZvL71HOF1SIhiC4GDKy4EDB8w9rqAAKClROu+pKSqiScpizUvBIuoYxi9+Abz3\nHl3v6FD2MgD2uqSY3l5Kqw6HFclCEe8xDBEMIaYZM4aqhvpj+nSlMNyoUbQjV0txsVKeXQgfapdU\nZ6d3U6NQBMOMS6q3l/px//nPwY3dH+KSEoQYYOxYilEYkZQE/PvfgV9n0iQgxALLggm0gtHTo1xX\nuwM5hmG28qsZC6Ovj94/HB3xRDAEIQYYO9bXwli3juIVXMAuxUQuoMsldaQigTqG4c/CSE8nsWdB\nCYTZGEa4JvRESasVwRBimjFjfC2MV18F/vGP8E4QQnCoYxhqC0MbwwDMu6U8Ht/SINr3AsLbgpeP\ns3gvjy+CIcQ0ei6p1lbKsOnpEcFwGupVv9YlpScYR4/S7+tyefezUNPaqp8unZXlfWx0dfm+h12k\npZElG8+lzQERDCHGKSqiiUCdgsmCIRaG88jJIWuit5e66hm5pAASjCuvpHaoALBnj/5rHjum7KNR\n893vAj/5iZK629ERvrTp1NTEONZEMISYxuWilFl1HMPtVgQj3n3KsQa7mTo76bbaJaWdzIcMAfbt\nU27v2OH9/23bqA94U5OyU1/NrFnAjBnGqbt2kpaWGMeaCIYQ82gD362tyio2EVZ9sURuLok5C4Y/\nC0NrNWgFo7oa+PGPSTD0LAwAOOssb8EIl4WRlpYYx5oIhhDzaDfvSQzDuWgFw18MY+FC79tqa4Mz\n4IYNI5eUnoUBkIWxdavyHiIYoREVwXjuuecwZcoUJCcn4/333zd8XHl5OaZNm4bq6mqcffbZERyh\nEEvk5Xln06hdUolwEscSRi4pPcFYtIiKRgIULO/oUP738cd06XL5tzBGjCC3FSAxDDuISrXayspK\nrF27FsuXL/f7OJfLhfr6ehTIFlzBDzk5wMmTdH1wkMSCXVKJ4FeOJYxcUnqT+dChFKdITQVKS70F\n48MP6ZKtycpK/fdT7/sIt0sqEY61qFgYEydOxIQJE0w91mN2q6eQsHDmDUCTh8cjFoZTseKSApQu\niaWlynMA4MQJSmF1u4GGBqoRpoc6jTecgjFhAvCf/xme13YSjo5huFwuXHzxxZg5cyYef/zxaA9H\ncChqweCueRLDcCY5Od5p0L29tL+iv994hZ6fTzXA1BbGyZMUu2ptpSZZ7LrSwqVIuPteuI6HnBzg\nG98Iz2s7ibC5pGpra9HY2Ohz/6pVq7BQG80y4K233kJxcTGam5tRW1uLiRMnYvbs2XYPVYhx1ILR\n2kqrzU8+oYlCBMNZJCXRhrpjxyj+0NOjWBdGlYILCsjCeOMN5b6WFtrlf+gQvZZRWRe2MDjgLdWI\nQyNsgrHRhkpuxcXFAIARI0bg8ssvx5YtWwwFY8WKFaev19TUoKamJuT3F2IDrUsqL49Wlm53YviV\nY43cXKCxkSzBnh4KgvsrOZ6f7+uSamkhC+OPf6RqxMnJ+s9lCyOcGVKxRH19Perr64N+ftRbtBrF\nKLq6ujAwMIDc3Fx0dnbi1VdfxX333Wf4OmrBEBILtWDwajU3l/zcYmE4jyFDSDDy88lV1NrqXzC+\n8x3qYXLHHcp9LS3AzJkUr9Lrb8KwhRHOTXuxhHYxvXLlSkvPj0oMY+3atSgrK8PmzZuxYMECzJs3\nDwBw5MgRLFiwAADQ2NiI2bNnY/r06Zg1axYuu+wyXHLJJdEYruBwcnIUnzgLxrBhwOHDIhhOhC2M\n/HyyMAIJxtVXAxUVtPeC60mxSwoAzjvP+LlsYYQz4J1IRMXCuPzyy3H55Zf73F9SUoKXX34ZADB2\n7Fhs27Yt0kMTYhA9C6OiglIvhw+P7tgEX1gwCgoUC0Pdz1sPl4t+185OcjmqBeP8842fl5FBotTe\nLoJhB47OkhIEM+gJxplnUikJiWE4D65CW1BgzsJg1L/zyZOU3HDfffRbG+FykZV54oS4pOxABEOI\nefQEY8IE4OBBcUk5kdxc2p3NLqlAQW9G/Tu3tNDzV6wInPmUmUkCk5kZ8tATHhEMIebh3bwDA94W\nBiCC4URyc2nfhdolZUYw2CXV1UXPz801934ZGSQwIhihI4IhxDxJScpkwoJRXU3/GxyM7tgEXzhe\nEaxLau9e2ndhdk8FWxhZWcGPWSBEMIS4gDOlWDDYX60tiS1EH7YMzKbVMrwo2LOHkhrMkpkpFoZd\niGAIccHQoUoVVBaLqirg872fgoNgwbBqYeTnU/B6zx6KUZklI0NiGHYR9Y17gmAHeXm0sauuDuDt\nOu+9F/89lmMRdkmZ3YfBlJRQdtXu3cA555h/Pwl624ecTkJckJcHcGsVtjCSk6V2kBNhC2P4cEpW\naG83F8AuKQGOHLHukhILwz5EMIS4IC+PitABkm/vdFgcRoygjCej0uZa1IJhxSUlMQz7EMEQ4oK8\nPOW6CIazYZdUbi6lPZvNYCoupirE7e3G/S/0EAvDPkQwhLhALRgZGdEbhxCY3FxyFWZmkrgfO2be\nwvjgA2D8eGuuRm7vKoIROiIYQlwgFkbskJtLFoXLRZcdHeYsDLYqeI+NWfLz6VL2YYSOZEkJcQEL\nRm8v9YAWnEtREXD77XSdxd2MyGdnA7t2UR8MK4wYQZdiYYSOCIYQF+TnU2qmiIXzSU8HVq2i67zq\nNzuZT5xo/f1EMOxDXFJCXJCXRz0whNgiO5tiTkYd8+yAS9yLYISOCIYQF5xxBjB5crRHIVglKyv8\nsQWxMOxDBEOICyZNAl56KdqjEKySlRX+JAURDPsQwRAEIWqoC0WGCxEM+xDBEAQhakTCJcVptdIb\nJXSiIhjf//73MWnSJFRVVeGKK65Aa2ur7uPq6uowceJEVFRU4MEHH4zwKAVBCDeRsDCSk4G1ayUp\nwg6iIhiXXHIJPvroI2zfvh0TJkzAz372M5/HDAwM4NZbb0VdXR127tyJNWvWYNeuXVEYbWxRX18f\n7SE4BvkuFJz6XUTCwgCAxYuV3eFO/S5igagIRm1tLZI+rzs9a9YsHDp0yOcxW7Zswfjx41FeXo7U\n1FQsWbIE69ati/RQYw45GRTku1Bw6ncRCQtDi1O/i1gg6jGMP/7xj5g/f77P/YcPH0ZZWdnp26Wl\npTh8+HAkhyYIQpiJlIUh2EPYdnrX1taisbHR5/5Vq1Zh4cKFAID7778faWlpuO6663we55JGBoIQ\n91RWyu78mMITJZ544gnPF7/4Rc+pU6d0//+vf/3LM3fu3NO3V61a5XnggQd0Hztu3DgPAPmTP/mT\nP/mz8Ddu3DhL87bL4/F4EGHq6upw55134o033sBw3revob+/H2eeeSZee+01lJSU4Oyzz8aaNWsw\nadKkCI9WEARBAKIUw7jtttvQ0dGB2tpaVFdX4+abbwYAHDlyBAsWLAAApKSkYPXq1Zg7dy4mT56M\na665RsRCEAQhikTFwhAEQRBij6hnSVnhxhtvRFFRESorK0/fd/LkSdTW1mLChAm45JJL4Ha7ozjC\nyKH3XTz33HOYMmUKkpOT8f7770dxdJFF77swuzk03tD7Ln74wx+iqqoK06dPx0UXXYSGhoYojjBy\n6H0XzC9+8QskJSXh5MmTURhZ5NH7LlasWIHS0lJUV1ejuroadXV1AV8npgTjhhtu8PlQDzzwAGpr\na7F7925cdNFFeOCBB6I0usii911UVlZi7dq1uOCCC6I0quig912Y2Rwaj+h9F3fddRe2b9+Obdu2\nYfHixVi5cmWURhdZ9L4LAGhoaMDGjRsxevToKIwqOuh9Fy6XC3fccQe2bt2KrVu34tJLLw34OjEl\nGLNnz0Y+F4b5nPXr12Pp0qUAgKVLl+LFF1+MxtAijt53MXHiREyYMCFKI4oeet+Fmc2h8Yjed5Gb\nm3v6ekdHh2GiSbyh910AwB133IH/+q//isKIoofRd2E1IhFTgqFHU1MTioqKAABFRUVoamqK8ogE\np2G0OTSRuOeee3DGGWfgySefxN133x3t4USNdevWobS0FNOmTYv2UBzBww8/jKqqKixbtsyUOz/m\nBUONy+WSDX+CF/42hyYS999/Pw4ePIjrr78e3/ve96I9nKjQ1dWFVatWebnkEjnn56abbsL+/fux\nbds2FBcX48477wz4nJgXjKKiotM7yo8ePYrCwsIoj0hwCn/605+wYcMGPP3009EeimO47rrr8O9/\n/zvaw4gK+/btw4EDB1BVVYUxY8bg0KFDOOuss3Ds2LFoDy0qFBYWnl5kf/Ob38SWLVsCPifmBWPR\nokV48sknAQBPPvkkFi9eHOUROYNEXjkBtDn05z//OdatW4eMjIxoDyeq7Nmz5/T1devWobq6Ooqj\niR6VlZVoamrC/v37sX//fpSWluL9999P2EXm0aNHT19fu3atbjaZD5b2hUeZJUuWeIqLiz2pqame\n0tJSzx//+EfPiRMnPBdddJGnoqLCU1tb62lpaYn2MCOC9rv4wx/+4Fm7dq2ntLTUk5GR4SkqKvJc\neuml0R5mRND7LsaPH+8544wzPNOnT/dMnz7dc9NNN0V7mBFB77u48sorPVOnTvVUVVV5rrjiCk9T\nU1O0hxkR+LtIS0s7PV+oGTNmjOfEiRNRGl1k0Tsuvv71r3sqKys906ZN83z5y1/2NDY2Bnwd2bgn\nCIIgmCLmXVKCIAhCZBDBEARBEEwhgiEIgiCYQgRDEARBMIUIhiAIgmAKEQxBEATBFCIYgiAIgilE\nMARBxYkTJ073ByguLj7dLyA3Nxe33nqr7e93/fXXY+zYsXjssccMH/PPf/4TkydPNrcTVxDCiGzc\nEwQDVq5cidzcXNxxxx1he48bbrgBCxcuxBVXXOH3cZ999hkuu+wy7NixI2xjEYRAiIUhCH7g9VR9\nfT0WLlwIgDqVLV26FBdccAHKy8vxl7/8Bf/xH/+BadOmYd68eejv7wcAvPfee6ipqcHMmTNx6aWX\nni6SafQeAHVNrKysxPTp0zFnzhzdxwhCtBDBEIQg2L9/PzZt2oT169fja1/7Gmpra/HBBx8gMzMT\nL7/8Mvr6+nDbbbfhhRdewLvvvosbbrgB99xzT8DX/clPfoJXX30V27Ztw0svvRSBTyII5kmJ9gAE\nIdZwuVyYN28ekpOTMXXqVAwODmLu3LkAqCLqgQMHsHv3bnz00Ue4+OKLAQADAwMoKSkJ+NrnnXce\nli5diq985SsB3VSCEGlEMAQhCNLS0gAASUlJSE1NPX1/UlIS+vv74fF4MGXKFLz99tuWXveRRx7B\nli1b8PLLL+Oss87Ce++9h4KCAlvHLgjBIi4pQbCImXjCmWeeiebmZmzevBkA0NfXh507dwZ83r59\n+3D22Wdj5cqVGDFiRML0IhdiA7EwBMEP3PJX3f5X2wpY2xbY5XIhNTUVzz//PG6//Xa0traiv78f\n3/ve9zB58mTD9wCAu+66C3v27IHH48HFF18svacFRyFptYIQRW644QZcdtlluPLKK/0+7sCBA1i4\ncKGk1QpRRVxSghBFhg4dih/+8Id+N+794x//wKJFizBixIgIjkwQfBELQxAEQTCFWBiCIAiCKUQw\nBEEQBFOIYAiCIAimEMEQBEEQTCGCIQiCIJji/wP1C+oiAZ9ZTgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab883dc10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8765cd0>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "Autocorrelation"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Autocorrelation shows how a timeseries correlates with itself, if you shift the timeseries against itself by various time lags. With no time shift (lag = 0) the timeseries is obviously perfectly correlated with itself:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plotdatacorr( time, data, lagi, tseg, title ) :\n",
+      "    plt.figure( figsize=(16, 5))\n",
+      "    plt.subplot( 1, 2, 1 )\n",
+      "    plt.title( title )\n",
+      "    plt.plot( time[tseg], data[tseg], 'b-', lw=2 )\n",
+      "    if lagi > 0 :\n",
+      "        plt.plot( time[tseg], data[lagi:][tseg[:-lagi]], 'r-' )\n",
+      "    else :\n",
+      "        plt.plot( time[tseg], data[tseg], 'r-' )\n",
+      "    plt.subplot( 1, 2, 2 )\n",
+      "    plt.title( title )\n",
+      "    if lagi > 0 :\n",
+      "        plotcorr( data[:-lagi], data[lagi:] )\n",
+      "    else :\n",
+      "        plotcorr( data, data )\n",
+      "    \n",
+      "tseg = (time>10.0) & (time<11.0)\n",
+      "plotdatacorr( time, white_noise, 0, tseg, 'White noise' )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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d3ScxxhYXyrKZsRaZySKCu9k/1EMcMh4P2HATNlnjx3SlJRI8hyF9SGu1tVig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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab877b6d0>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plotdatacorr( time, colored_noise, 0, tseg, 'Colored noise' )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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fIDBwBFZrgxLSPmb3SWlODsxKWYPft+8eF9PpROxNVhaMIYMKnzCcnc2ktBRv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XTop88ZBny+YCOnCSL9d2PR9TpERERERAKLmlIJCA2RNmw4Kf6X00o3b4bJjqW80m0u02eoIxUR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\nqYiIiIiIiJhGTamIiIiIiIiY5v8DvxV2+VtttZ8AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab86abc90>"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Time lags"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we shift the time series data by a range of lags. For the colored noise we see that the correlation decreases with increasing lags."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lags = [ 0.1, 0.5, 1.0 ]  # lags in seconds\n",
+      "for lag in lags :\n",
+      "    lagi = int( lag/deltat )       # lag in indices\n",
+      "    tseg = (time>10.0) & (time<15.0)\n",
+      "    plotdatacorr( time, colored_noise, lagi, tseg, 'Lag %gs' % lag )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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//qvEx/9FnTp13P0DAwPZvHkdLVqsRq+vQq1aE/nuu9VU0S548rBnzx5ycsp7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\n/qKL7E2VKoHysyHh5FhGYNw/NnJywo9gEIDSqOQIUPrTT1JKjROCxf1ly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o71I4LEkehR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9eyZctYtmxZqIehKoiWLVuya9fvpKWlMW7cRG6++U4cjiimTXvqnAmkIK+fYmNjOXx4KLJn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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab883d3d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9PQiLJZY5c2bi7a0HtOEPffD2rgmcALYAfezTR4GxgI65c99h8uTJLm3vqaee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/1c8zsIbO5DEN0ChYiOMP/fhQAl3rR/AwpRLURgMsaMkb0qdi9SPR82/QsYf0AQmYwg+i0zNhXM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ZSUraxfX5Fnnnkx3/2CcX5EsYPSJk1g+XLYsCFvJeGcitKlZV08fDinUtq2Lfz1V4Eq1Z5S\nBLDvZk/99L/Bbt8exo2DmRc+RZ3JQwWdJUqIHAPllBYyHA4dwuv1VeItTEydqhzYvn2zvp5XoaMg\nlBYCMFNT9Q/xVJRSr9dIkX7SewAodZhCR1cufgXuuy/goa0HtJZjNIdSarNBtWr6EDRrlr9SWiUr\nlAZSSq3o2TP/bXKLvP6p5JX7bJkd/JfxrImoKLlBgtbdYJwj8eWX0+natTcTJpRh7NhDXHRRMw5m\nL0GPrIpTp07l2WefY8iQ4aSm3gXE4naPICzsRtq330m/fvuZNesrrriiAbKBXo4UqQeQDfU5VCCn\nHAK3WkjtuhTloW4FFhAa+g92+zcINtIQhJZDMPMMaiPyOAK6ighk7kSK2O0oV/VmBGLHEQiFIyjb\nhkAqFcFSd3PMoUgF7Ysg2IZyHysjiy8IvuqbfZ9BBY/eMuM/jiAvAymJach+usGcW0UEuM0QdK9A\nAHo7qjL7HQK7iahwkhPd+nrN2JYFyoFyPL/F1+bmBWRprousuekIfNujfNxDZi0cZp+V5niWmm1D\nxYgWmHO/z7x2i9/77RF0/m3G62fm+DJqMROCrzCUF+WGgizNyxF0fmvmm4SqGTcxX7tR3vECpFrP\nNOu3EBWFWgR4sNsbcsklVxAZ2QhZsa8z52RVdS6Hw3ERL730LK1btyY8PAKp8bcD1+J0/kFGxlIy\nMoZw+eWXM2nSjfz00yTefPMprrnmB+Li1nHwYDwDBjxBeqAnscDatZvIyOiB1Ss3Pf0W1qzZFHCf\nYJw/Ueyg9LyLUqWklB46lLukUr9+zteKOiIiBAYtW+b6ln8eb3ZuHjoUrouej+3VV3SHbsk7bdr4\nNspu3z2JsNSfW28t/L6zZ+f+etOmvumBDzCCUFpAKE1K0gOIEiVODUpTUvS58c+dDmDfdWQ6qcE/\nxB3bCTfdlOdhvV5fp5dq1fQ9yxRjYqB7d4FtQgJ065aTMr/5Bjwejh31UoIk4qplte/m8/cZux1O\ntv6Mv+X3ySfVtxeyTnHwYDlG0tN9SulZCaXR0XrwFoTSYJwj8cQTL5Ca+gnwFG73OyQkdOb99z/I\nso3H46Fr1x488sgURo1ykZwcgWy3C4DLcLlC6NChHW+//Tpt27blxRdHEBHhQPbV/+JrfTIAAUoi\nApw3ECAtR6ACsBubLYSmTa8kJMRSRH/1m80KBDYlkZXThtTDWHxFlTqinM8yCHTjESg9gVRAB7Ke\nuhA07kVq6gsI4A6a3zeiIkDWH9clyI4ah2DnJ6T+XYyU02/NMZ9CFtG2CHz/g8D1fqSmWn98OyPI\na47g7TCCuhhkZd2PlMRPUcEkG4K4S5CldazZdhKCty/M3F9CeZMPmPmUQXbdcShH02HGftSc3wKk\ncP4fgvgJZo1fNfsfQ7CcgHJ1B6Ic21FmrE8QRIaZ+fRDsN8QgXIIAsMt5hp9ZdbqIaQQx5jr1RWp\n3p0QMFvrVBMIweU6yqZNx0hNXYaU6wfNuJYF+E88ntWkpqby+++/IxAeiD5bHbGqN2dmtufgwSPc\nfvvtXHPNNXTs2IGlS5dz8OAo9u79nGnT/ua++wYQKC65pCHh4VYF6EwiIr6madOGAfcJxvkTQSg9\n1yMyUnfPu3advM/vVMNmg1mz1Fgxlyhd2vdzrlNsaP7DWbgQrr4a7r1XN/hWFAGUjhql7ydT7OiC\nC3K+VrWqDxay55SehJh7fkVBoXTPHtGezXZq9t3k5Jywko99tyQJZISXEPXlEYcPy75bqpTvoUaW\nKYaFwddfC6gjI3OO6fVC795kHjqCw5mKkzAiS4SemB7kr5RGRWXt51vY2LpV+dxjx/pynP3HfPNN\nuUYWLz7LoTQqKgilwTinIjU1BSl0CperEsePZ82NW7p0KcuWbSYl5Re83tEol3EHygu8B5jJ9ddf\nf2L79u3bM2bMU6hSrH+eZDkEiNVQEZxVqEdkKILE54GuOJ3lWb06Hrf7dwSwX5l9KyPgsyEgegBZ\nOEPN948ROP6EYOt+BDSrkHI7Eqm3kxC0XYbU1W+RXfVxVAipM1I1HzBj3Yhsq52R4noMFRmy2uDs\nMT9/Yc5zm/megSD2RvN7CzPHBATJk/HZUG9FwHkfUjpvMft/i6B+J1KMfzXr3xtfa5iWZm3aIFi/\n1Zz3SwjW01Au6Axkx30JKakLkfrbDcHVNcim2wcprosQnFcy51cPqdRWuNHDhfcQbCabNXgZwfZl\nCICjEEgfNnNpjooVjUBQegBB9Vwz9qMoz/MnBJQjgTKEhOwhJKQxPlh9xYzbF1mJr8Tr7UKfPvez\nevVq3O6D5v3LgakIuN2Eh0+iRQurAjL8+OOPuN23oQcMF5GW9gFffz2dQDF+/Cjq199IdHQ9oqPr\nctFFB3jhhREB9wnG+RMnDaXTp0+ncePGOBwO8+Qk95g1axYNGjSgbt26vPzyyyc7XDDyCptNd80b\nN/qqCp2JaNw4zztsfyitUCGXDaz94uN1HP/eFODLKXW7TxpKH3zQ93Nh80qzn1aXLrBli28qwZzS\nbFFQwNy92ydBnopSmpGR04sfAErtHhdxJOJ0ROb6vv/0QFO00hgDTjH7mMnJ4HTiTNF4icSdUC8L\nCqWRgaeYb9Spo39SuY3p9fq2CwuTWmrx9VkXUVH6/yEIpcE4R6J37x5ERfVDFtMfiYqaxM0335hl\nm8TEROz26vhgYBeCtWHAe0RHR9HY+gds4tFHB/Daa88RFfUYsBSYjc02ghtvbI+AZDTKO6yCQNMC\n3SuRmphkxtiLKqfOR21LYhHYuhDYxiFFzHpKVQvZfN0I4Dz4QNL6o2q1qtmMoPcIyl+0ohqCOqt6\nbQmk9jlQzunfZo6HkPq3FVXWHYaAsBkC9hZmnsbKQhcEZhURrL+LADQV5Z/uRsBcBqmGi8y496M8\n17Z+c2xvrsd4s3+kmdOl5msHgt9j5lyfQpA4AAH9FUhV9Zo1/Rkpwv+Y47nNMceZ43yI1NLZqHLu\n2+aYlq34IgS+VpGAMuacOiAg3ISU8xgzh2tQQaRrkEr7PIL+TATaz6M2PWWBydjtCUyZ8i4ez3yk\n7KajBwPRZo3Gos/FdGAYL7/8FldddSFRUV2AYzgcO7DbyxMeXo5LL93LxIm++/yoqCgcjkN+a3uQ\n8PDAf2Di4uL4/ffFLFv2LcuXf8eyZXOJDv6/X2zipDWdJk2a8O233/Kg/91+tsjMzOSRRx5h7ty5\nVKlShSuuuIIbbriBhg2DUnyRRunSsH697wb/TERkZJ7Jav55pblOMTVVd8WJibkTnb9SepIypN0u\nh2dGhm6+C1PAM/tp1a+f9cY9mFOaLUJCYPXqnMWHssfu3ZKc4dSgND09J5TmY9+NI5E9RyJJ2wG1\nauV+WEv4P2koNeVznXsP044/OW6PO6GbFNS+W5SAmB1Kk/3a1bVtq+9xcSdX6fe0R1ApDcY5Fq+8\nMhq7fSTTp99GdHQMr702hSuvvDLLNmXLlsXlWo3snR1QS5UPUf5fAunplzJnzhw6duwIwPHjx0lK\nSqJChbK0bFmZuXO7AXa83ih++mkxdrsbj+cVpKg9gKBnNrJkes3XMQSFoWasBghOH0aK2iIE0lcj\nuFlofp5mZh2NCv40RRbSPUix7IYqw0YgqHrXHPtuBGBbkF22J4KvD5H6twBBYydz/MkIhJsjoG6H\n1NbDCH6/QKrftagYz0sIfjNRbu0cBHBh6BZ3PyrKcxWyyJZA8FbNjNMNFSVqjYBxMoLHA/iA2FLq\nngV6mXVtZbZPQ2pkCALu/aha8FakVjc3+05EKmyEOZf95mcQsIYgZTQN2XM/QUrk1+brPnRdDyJ7\n9gKkkk5Flt4GSPmehuDze2Tt7YWvUnIrpJam0bLllXTp0oXevXtRp04dypcvT58+dxIfvxep3HOR\nyl7TnMdvQBqHDx8lISERp3MZsbEbefzxhxk4cCAul4ty5cph8/sD0qtXL1588TUOHrwfl6sBUVFv\nM2rUSPILh8NBkyZN8t0uGOdfnLRS2qBBA+rVqxdwm5UrV1KnTh1q1qxJaGgovXr1YsaMGSc7ZDDy\nitKl5R0901Canp5VfjHhr5TmOsW0NJXpTUrKH0pPgfgsEC1IcRkrvN6c22efgvW7VTy42EOpwwFf\nfAHvvRd4O3+l9FTsuxkZOUvNBip0ZKA0nQjefDPw9EBTtJ6FBJxiVFTWMQ8fBiDslTF8Qh9S7L6y\ntgVVSouyGJv/mF6vr+WMf5yV1l0IFjoKxjkXoaGhvP76S+zZs4nNm3+jm39KCvDJJ5/Rrt112O2N\nsNkGEBJSH4FQd7NFSRyOTvz111+sX7+eunUvoVSp8lStWou77hrP3LkrUdXZ5cAInM50PJ50fPDp\nQQVr6iAYvBVZcGNR5dYyCIys2I0UxxAEnLcita4bUhMHoMqtSQjutpkxViB76dMIZp9BqmxJ1F6k\nEQKjBxB4T0PAWAtBVX0zthPB51Dz/iqk6M5Cqu5DZv5PIVX1TtSb04aURxsC1s3IThuHwNONYO0x\ns1ZTkOI51Gx/BOW/ljDnucGsR2sE7hf5rdEl5vi7EbDuNfvsMa81Nes3Atled2ZbXyeCPQ8C0AdR\nPm4/BMLrkVr+JioCFYaUy4Hma4A5dmd8RYiOmPV3mvnuM2txvVkXf6W9Ibr+sHz5H/Tr9zB16qhd\nTefOnTlwYLt5KLkZKb7t0WdsHbI4NwXc/Pbbpbjdazl+vDHPPz+B22+/H6/XmwVIQarn2rVLGT68\nGg8+uIsvvphwohdpMIKRW5zWnNK9e/dSzY9Cqlatyt69e0/nkMUzSpUSCJxJ+67DoTt3q4eGX+QJ\npWlp8OWX/xqUFqbiKWi4iy6Czz7LOZ3C/F7sYtkyfc8vx7ko7bsFhVKPB7snkzgSSSMyR+Gt7NMD\nibkFVkr9ZXVDfe5okV5yiI/4CtKn1DpkUYXlFgAtmWHmLHHWViSPjlb/Yv+q3MEIxjka6enp3Hff\nQ6SlzSclZRFe79+43aFIhfzEbBWPwzGXrVu3c8klV7NtW2cyM1OBv3C54hGsTEBq5YMo//Fr1Bfy\nKZRjuR3BTytUXKcSUj/TESDegVSwB1BBoSsQ3IxDRW7CkEL3LoLLaghq+iO1bi6yrO5FsBeJ1Mq2\nSJENRxVv1yDA/QG1R5mHLK83oGq++xBgDkOq8Vdmjt+YY8Sigk6NEfRa/UJLmHHGIKBrg5RGB1IZ\nfzbbLkUqpeXsG4dUxO/xqcUbkcqaiaD+TWQHftascwukJjdB0LcD5eP+x6yTA6nCVRE0voZyMweh\n3NVHUS7nX8hqm45azryNIDUUgXAdc91ArWO6mWu5DSmsM5Ga/YS5pj3NmixAQD3K7B+PLNQvmXM7\nhHJIbwFi8Hi60r799Xj88pnsdjuRkbHm3DDnkGKuR1ezJnXxeq9GDxouwev9nNmzY2nZsiNOv/u/\nlJQUnnnmBR566HHi4kry9tuvcd111xGMYASKgFDasWNHmjRpkuNr5syZBTp49qcmwThNUbq0Ot6f\n6f59eVh4/R23Zf1rM2zYoF6qqamC0oyM3ImuCHJKwaeU5tUSI3ts3KgiMdkjPwgt9oWOBg7U94yM\nwNvt3QtVqujnf8u+a+RsSynNRdjPMj0oJJRaMiScoD5XuCoVpfhBaUH6lPpvV1RhHS8lJXelNLfX\nzoqIilLj1kBPEYIRjLM4XC4X27Zt4+jRo8THx2OzRSHbLEhVa4ZgciAREfUID69H796def/9T/F6\n3aj4jg3led6Cr3orCKQOIXVyFFIzX0BAYeUmesz+ZRAEfoYssD8hEOyO1NFGyIYbgpSyNxDQfY+U\nPztSId9GIPY9Ugp/N+N9gpS7ELPf50htdJmvI+b3d1A+ZAukEEciG2xtBEAg5baGOR/rP8sM83tD\nBIAXojzTGgii0lCu6CXmfOcgxbgNsqPGmddAMOk151gHwXR/BI0bkULoRHmVzwHvm9czzbyTEDh7\nzHG+xaesdjZjfWjm9QN6cFADAZ3T7PsRgsnbzGuH0YOAR5FNeg+CzslmjGpI8U02r4cgeMW8VxUf\nPHeiZEkvethQ17x/j7lO77Ft2y7+sRpio3v2V155maioTthsI4iI6INPdcfv5z+RslsLuAWPZzbb\nt2/lk0/0QMXlctGqVWfGj9/IV1+1ZsSIb7jjjsDt14IRDMgnp3TOnDmB3s43qlSpwm5LbgB2795N\nVSuHLJd47rnnTvzctm1b2lqJTsEIHKVLn1nrrhWWfTFbc1J/CMzynCIxUf1NQ0OhZk295t/Ww4oi\nyCmFwiuleQFIdgjNPuVir5TeeCMMGJA/cR075pPR/y37roHSGFJIIzLgFI+Z+70yZXyqacAphoRA\n7dpS/3v2PEF43mRV3EwLPbP2XYAaNZTqO26crwCSf+Smnp4VERWlJNh8oHTBggUsWLDg35lTMIJR\nwNiyZQvt2nUjMdGNy3WEJ54YQnR0KGlpXyIYWYty9t4B4mjdejZTp77LypUr+fLLXaSlbUZW2C7I\njroSAWNzlFc5BxWuaY2gKQJZaP9CcHQXssZWQbA6BVk0Z5rjTECFfbxIXeuEIDHR7yzCEJB0QoBl\nQ+reYKTYjUbqpwWUqxAEfY8q2G428+qF1Lt+CFJbmzWYhqB8J4LmSqjtzTYEWm0Q5E5HgPoHyhP9\nE6mGcxBMOhEcfoNgPwSpnQ+ZeZVAymUcUkyjUU6n1T5vKwLOlxH0RaFCUJ3N+6/gUz6jkW25jlmL\nRHOcL5GKnIpgcii+BwhudK3rmXktQTbqGdhsLrzeDLO2b5ntL0DwdzUwhsjIy3E4rsLjWcyQIU/x\n0kvjcTp/Mes0Bdl/GwHJ2O21ads2ljJlSvHxxz/gdB5BCvkrgIPMzDTCs/3tfPjhB2jYsB4LFiyk\nYsXu/PhjSWbP7k5Gxv2Ehs4nOvoYqalv4XSmI/W6urmuZbnnnkHccMMNbNy4kW3bjpOe/ilgJzW1\nF998U5nDh8dRLvhgMRgBokg0HW8ecsPll1/O1q1b2blzJ5UrV+aLL77gs+xeSL/wh9JgFCJKlTo7\noDQPpdRqlZIjEr7pg+4AACAASURBVBNl2Y2M1J0//Cs5pQVVSv0LwWSfTmF+L5YRoNDQiTh+3GfH\nLGr7bkRE7sTnZy/KD0qPH9f32NgCKqUAU6dC376CUkN43uP6IKWFnln7LsDbb0OrVvDOO/Dss0V7\n7NMa1j/efCzh2R9mPv/886dxUsEIRsHi+ut7s2+flRN4gNdea8lrrz3NyJFDOXLkfjyeTFSwpiZh\nYeu4+OLGVK5cmbp16+Jy/YZsk30RmKwH9mO3O4mOLsXx46uRuhaG7KlfoUI3I3E43gUmkZn5B1LH\n2iLoqgB8B1TEZvsZr3ceuh2MRlbZUub3nxCcXYpgthyCre/NXGabeQ1BgLfDjBONT91sidS5GmY1\nRiBL6xcIpCciCGuF4Ote1J80zszhUWQl/gNfpdkGQHPs9il4PG6U1/oeAt/1SElcjRRTB1YfTYXd\nrNet5lglkY25j9l/GVIW05Bq+iu+Xq+g9idXmGvyhTneXrP+l5pzH4qUzKvM+WQioG6NgLsaAvty\nOBwVcDhSiY4uybFjt6OHB/9BaukP6DNzFNlzPTgc6cTELOepp55g4MCBtGnTki5dbsLlKoMqDB9F\nduwYypb9lokTF1OlShV69ryBhx4axJ499XE6ISqqG127dqNy5cpkD///R++55x5Gjx7HokUfU7du\ndcaMWcOwYcOYMuUTc/3a4APo+xg8+EnuuqsXTmcoPjNmBC6Xl507dwahNBgB46RzSr/99luqVavG\n8uXL6datG9deey0A+/btO5HQHxISwltvvUXnzp1p1KgRPXv2DFbePR3RqpXUqTMd2Qu9mLj3Xn2/\n9dZsbyQmClySkv4VKC2sUppXT9MglBYgCgqlVgPQorbvWoW3socflKYTUfRQ2qwZ7N+f1R+bmsIR\nSrOtpK9/W1723ezHL2ooveoqtWVKTlaB5OzRp0/RjldkYRU4Ct7QBOMcisTERNq27caWLetQvuTL\nQEUyM7uRlpbG/v3bWbDge+LiYomJ+YyYmGuoUmUpw4cPBaBx48Y899wwIiL6ERtbhfDw2bz44l0c\nP36A9PQUevXqSXh4NFIlhxEauoprr72aiIhZwMVkZr5LZmYIAq8OKL/yFaS4jgFSuO++W4mNdSNQ\nGo3stanIdtsMKZi9ESTGINXyanOGncyx1yMYcyGV7m5UFOcmZK+1igeB4PIAav8CPmuoG8HfBKR2\nHkFgWRZBn1VR92ukTk7H40k0+32OQLEPguSZSJFNN/uMQCrilwiQL0S3v16UdzoH5Y/aEMSuQQD/\nClKCByMQboPgfCiC2mvNXN9FCmwiUkGTzJotN+N4zHwOIEX6f2be4PUm4fG4OH7ciSA43ZxfBeAe\nbLZ6xuo9HXiX5ORkDhxIY9CgYbz77ru0b9+e6tXrIhtwZzPuSOLibGzduo6qVatis9no2LEjGzb8\nzjPPdKZ3798YM+ZmPv/8Q/KLsLAwnn9+BPPn/4/33ptA2bJlSUlJM+ebgD5LNqy+s9u27aZZs2Y4\nnX+h9jPLgQex2UqzzKo3EYxg5BEnrZR2796d7t2753i9cuXK/PDDDyd+v/baa08AazBOU7Rrd6Zn\noMhDKe3WDTZvzqX1RqKxBvnbOANBqdv9r9p3ExNzfz2YU1qAiIjwUV1ekZTkU0r/ZfsuFE4pLVD1\nXRC91qsHmzadgFJbSjJTuIdF1e44sZk1XadTIGpBr9/0gNNTeKhxYzh4EBYuzPp61aowaVLRj1ck\nYSmlQSgNxjkU99wzgGXLKiLV7SACw1qEhCykdu1OOBwOWrduzebNa5k7dy5hYWF07do1S1/GJ554\nlF69bmHPnj3Ur1+fMtYDXOCVV15kzZob2LixNl6vmxYtLmfnzoM4nQOQelkbFfT5D1JQJyJw7I+g\n4kH++99pREbWx+H4i9DQabjdt+LxfE1YWAjp6bMQbDyA8iutyq57kRV4p/l5N7KwVgaWYLN9Qps2\n7ViwoAmyDrdEEBqCAHAYguJYBGvXowrBoUgxXYFyIFeh3NXNZrxxKGd2Keqv+i6CvqNwouHWQZRv\nOhcBaQ1k8X0UWXGfRbD0Pcq93AN0RDmofyE7aktznCSUyzvAXLs3EJx3QfBaFuXyvmWOeZ+51pch\nCL4RFXVqh0DUjhTqA8jC3BKPZyEez28Igu0I9I6g3N90vN4dyMI7Flmz55r5LWbQoOvo3bs3drsd\nPUiwIoWePW+hRIkS+EdERARPP/0kpxq7dh1FDw/eQepsF8CLzfYerVpdTkxMDJGRoaSlrUbrfClh\nYS3NPIMRjLwjePscjKKLAG04cu0e5E99gaDUKnSUnp4TPgoRhbXvFhRKs+eUnsIUz5+IiAicoOj1\nSk2MURGgf63QUSHsu5ZSXiilFKBRI1iyRFWI69bFnpKMk7AsnwubzTfFjAzfZzN78eqiVkpBUDpv\nngRd/7j22rO440oB7bvBCMbZFIsX/4rT+TOCrarAXYSEPMjNN9+WpRJphQoVuOOOO/I6DNWrV6d6\n9eo5Xi9RogQrVsxj+/btOBwOypUrR4kSJfF6lyPACkHqrA3ZP2uhvqORCLSSgcmkpb1PSEh/Lr54\nGT161KdduzmMGPEys2bdiOy4jyOYyzDncTEq6LMBKX81kHV4PTAMr3cSW7fuJCxsBU5nClIvX0Wg\n2R5Vj62N7LhVEVz+iQo41UH5l4cQRN+AgBQE01bP0B1IPa2G4G+QOecfUfXZzWZeF+NrW9MKQWEL\n82UpnoMRWE/H1+rmMvN6VQScmH3ikNp7AWrT8rMZ9wazTSRSLJcjxfZPBK+zkZL7GWpZsw8pq14E\n1WFmfk2Q6nsbdvt0PB4HKrL0N8pPbWnGaY3dXol169Zx6NAu1CJnFKra/BqDB6+kIJGQkMCUKVM4\ncuQY117bmVatWuW7T/nyZdD1+RkVkSoPZNKmzdWMGqXrM2jQQCZM+J7U1Cex2zcRGTmJW255tUBz\nCkbxjeBji2AUXeRh380zLOoLDfXZOAMppUlJp9RI8d9SSs/aXo//ZuRn301JEb1bEqR1jU8mAiml\n2fPd/cYojH3XmmYuHY9yRuPGMGIE3HQTNG6MPS0FJ2G5Oowh6+fx34LS3CK3GmNnTQSV0mCcg1Gx\nYiVUlAjAS1jYcgYNupePPppUZN0J7HY71atXZ/jwUZQsWQav14YgMAKpdunIKvomym+MRrd+9yCb\nag3gOG63k9Wr1/H88+N46qlniY/fi2BuI4K3IUgJbW+OGYvUyodRUaXDSAFsBkSwd+87xlkyG0FX\nJFIDOwDrCAn5m7vu6o1yNrcge2wlpDomIsVwltnf+o/6F2Qh7ovgugcq9vSKmWe02XYzAshE4BGk\nasahYkerzFcqyg9tiiy4MciKXM6c5zwEqUcROIKgHLONHYF9PIL/j8x761BhpDRUaOlOpMK+ig+u\nj1C2bFVUFCoMtcXpgHJQf6d69Uy6d99C7dolEfTfgIpBHcDX+/RvnM7dbN++HY/nYlTldwkCclfA\noqJWJCYmcsklLRk+fDVjx9rp3Pk2Pvvs83z3GznyMaKjR6EHHlcTEWFj5sxPmTdvJhHmD92YMc/x\n8sv30b79p/TqtYvff19CpUqVchwrISGBJUuW8Pfff+d4LxjFL4JKaTCKLvKw7+YZFvVFRvruvvOC\n0owMbX8KxHe6lNLsv2dzzBTPyA9K/fNJQa1hfv315MbKDUr9++b6v+d0Sqb0egMqpW63pm+363Nj\nda7ZtasA8+neXYQ3YADceSeO9JxKKegjf+xYVij9N+y7bdroM9uwoTpJzZql189qKI2O1jXNVtk7\nGME4m+ODD96gfftuSDHbS61abl544bNTBlKn08nPP//MsWPHyMzM5OuvZ/LLLylkZh5B9tz2hIT0\nwuu1k5l5NQK+dAR4DyBI+wnlUQ5EdtEPcbt/JDn5bebMWYSUwo8QWFmVa+9GQFgZAW04KuLzPILU\nzciWmggsx+MZiJTLhqjf5gQEeZcTEdGVe++9iy++WERamhdB3oco33MBAr6ZSKlsgpTV5Uj1rWLG\nq47Uw67InjucChVqcOWVF7Jq1R/s35+KQLYp4CE09BeioyNISChtzi8KAeMYpI6uRvDXF9mJQQpr\nX5Q/O8mcu9UveR4C1lDULmayWcuhqCeo28ztArP2HqAH1atvoGnTS5gx4wDqM9oQnwL8AsnJU6lX\nrwkzZiww42PO+Sak/DYD1hIaGktiYqK55l3NVzI226cFssp+/PHHHDp0IRkZauWSmtqZRx/9D717\n98qy3bZt2/jww2lkZmZyxx29uOyyy1i2bB7vv/8RXq+Xe++dz8UXX5xlH5vNxiOPPMwjjzyc5/jL\nli2jS5fuQA2czh3063cfr746Jt95B+P8jSCUBqPo4mSV0qio/KE0KSlrtdaTiKJSSvNrARNUSikY\nlPpfy7p14cP8iy7kGrnZd/3nkB1KY2Lg+PGAUOqvktpsPvv51q0FmE+jRvoCCA3FkZ5CBuG5Fgi2\npu8/Pf84HUppgwZKd42JgSFDzhEojYpSk+NgTlIwzqG44oor2LhxNQsWLCAmJoauXbvmaMFR2EhP\nT+eqqzqxZYuL1NQyeDwLsNlK4/W+j9Q+gGE0aDCBsWP/y3vvfcDMmd8jsNqAbLeR+Oy9XlQVtoXZ\ndyk+1XELAsJRCGDLICg8gqDrYgREEQh625uvzUj1i0dK4HOoSFBrZGd1kprqoF+/oaSl7cCX69kO\nWXaXo6JLHZDS2AfZXTNQ25hOCFhfQ1A3HOWA2liyZA02m43atZuYeT2DiiMlERoaj6+36t+oKNFY\nM+eG5twWoZ6rzVDRoGTUYmYZsqx6zLaVzP6pCFK7odzU+/FZeUNQvux887sduJ7du+eya9chlGc6\n3qyxFReRmprBhAmT8Hga4bNNh5lzrocAvB4REb248MILKV36PdLTB+JytSIqajI33tg7S15yXpGU\nlERGRnWkSG8CypGSkrXC48aNG2nWrC2pqXfi8YQzcWJ7fvllJs2bN+fNN1/Jd4xAcdNNt5OU9B5a\nr6NMnnwFN9zQmTZt2pzScYNx7kbwL3wwii5ORim1gNSSMfPKKT12TNudQhUha4iitu9mv5kPQin5\nQ6l/kSMQ9W3ZcnJj5aaUQu45zi7XiTzWQPZd/3xSgAsuEJzu2FFIl3FYGCFGKS2IfTf7sU8HlIJE\nars962f5rM6Frl0b3njjTM8iGMEodFSrVo2+ffvSvXv3UwZSgA8++IBNm0qQnLwQj6c1cDFebwqy\nsypCQn6jZcvLue666/jmm+m0a9cc5TJORq1T6uNr+zIIAeQ21C+zMQJSEACFIdj6ClmARyAQm4qU\n07II1BwI3OYgQHQj0ElCeYe/INj9D6qc6+WPP/5EKuOFCDyrIEi0IWvtdagH6afAckJDbbRqtZw6\ndQYQGXnAzC0R5ap+TIkSl7F9+3YWL16Mbm9/QHbWCOAQTqeXyMgoBJ0dgGGEhFyIzfYIAuK1CHS3\nAx/jK7jkRippaQSw0cimOwubLQS7PRP1Re2FIPY9BPvJZp3+QIpyKvA+Xm9LlBu6GrgGqcv7zNcL\n2O1OQkMvQLmajyKA/hN4AJtNeb0OxxfExCTQokULVq1axN1327jmmi94+unOTJs2OZdPTs7o0qUL\ndvsHSJXeDoykcuWstt/Ro18jJeUxPJ7xwIukpo7l6adfKtDxA4XT6eTw4d0I2gFK4/W2YcvJ3gcE\n47yIIJQGo+giQKGjXCMxUf1V/e27uck10dGwb98p+2KtIYravuv/QDI09Cy/uf+3orBKacWK2j4h\nofBjFQZKLaWUwIWO/JVS0OlUr65CRzt2FGJuoaGEutJyte/m1qs0u1J6Ouy7/uH/z+2sVkrDw6FX\nr/y3C0YwzvPYvXsfaWlXIkVuLoLK7igXsz0xMd0oV24GL7zwNKDWfPPm/UTVqmUR/HVGythFQD/g\nvyjf8lcEXgsRXHpRdVUbUi7fAmYTEnKAmJgyWICl3Ek7gtL1Zpa/IzWzFrK4zjNjrkZw9R/UauY4\nygfdi9UiRWpkhtluNQJpB5CE3R7Lyy+PoFSpGFwuJ1Jn30RW5MtIS1vLsmXL2LZtG1IumyPY2gCM\nxu1+laNHUwkPv4vo6P8QE9OOhg09JCXtplevW7DZMlHBpuoI3PcRGTmNdu3aExl5B9HRl5h1Oga8\njsMxjBo16uPxPI1yPRORpXkpsh1XM7+nI2W1Msq9vQ61lhmJrMpHzFrVB67B4wnB7d6CCh69a86j\nBRERU2jWrAxVqvSkTZsVLF06l+joaMqWLcvkyW8yZ87XDB/+BCHm4f2UKVO58MKraNy4JV988WWO\nz1KZMmWMzXclUqjXsGvXHnb4/ZFLTEzB6/XvZVqZpKS8ms8XPMLCwqhc+QLU6xXgADbbPBrnVfQg\nGMUigvbdYBRdREUVXimtVk3fA9l3/7+9e49rum7/OP7axoCNk5oiHvB8wCOSpmUeSkVN09RMPJR2\nsOyuLNOs7uyglefKtLP+6k4r005qlpKameUhNQ+V5hnP5xMIjNO23x8fvjDmgAGDgVzPx4MHA77b\nLr4M2HvX5xAaqsZNFrEFWVz7lDouCBoSojpq5V5B55TqdNCggfo533RTwe4rJSV7n9v8aihkKAU1\nwvjoUVWiy9WkXclMemn4UimXTmlew3eLOyg6PpZLdSgVQgDQsWN75swZjcVyDhXgTKgFen4mNHQ/\ns2e/wR133EGI0//LefNmMWDACCyWW1GhT9u67y7Ufpt3o1aFvQsVmhIBI08++RAHDsSxenUEoKdZ\ns1Y89NBzjBkzC5vtGKpT+ix16vzExYsTSEtbQ2rq36iwmoyaU/oE2avGzkGtMjsg8+NHUXM6OwDR\n+Pl9SWBgKBcvDkUNRz6HWjioFampL9Ghwx3Y7TVQHV0d0BSdriZ2+wns9rZMn74Xu30FRmNd0tOt\nqOD8EmouLaSmBhAZOZu+feuwZ4+FRo0a8uyz/+X77w9ht58D/NHrh9G48UHGjn2BU6dOUb9+fV59\ndQJHjhzhxRf3ceyYH3b7fKxWE8eO7c48l/ehQmiVzPvqiQqkD6GGM7+ETncIu30BaoGmhajhzH0y\nz4UVFcBfxGaby8KF/+PBBx8jIeESBsOztGkTxWuvLaRr165uPU4WLPicRx99hfT0uUAaQ4Y8wNWr\nCYwcOTLrmAsXLuDvH05amjZXvwK+vjW4ePEidTP38Lvvvv788st/SU5uAPhhNj/P8OGPXnN/hbF8\n+WK6detDevprpKWd4plnxtO+ffv8ryiuWxJKhecUtFOakKA2R0xLyz+UxsVBmzZFKs/dhY7sdjhy\nxP1OqWMolUWOMvn7qw5mblzND65TR6W+gobSYhi+q4VSx5+nts+uW4sdaRxCqauFjiDv4bvaVjTF\npcx0SoUQAPTu3ZuxY7cyefI0VIdyOGrRoZZcufI7RqPvNYEU1FDNlSu/4umnn2PXrtbYbNpXaqPT\nXQUWYre3Q3Uw7UACBkM1evXqRXR0NDabjeTkZIKDg5kw4SVstuGoAAbwJJcvf8HMma/y+OOjUft1\nrkFt72JEDVHVxGXe/gHU0NjLQBINGlxl8GAdbdvOYvToF7h40Rc4hup4xmRe91Ps9hCgDmoe5vtA\nMnZ7AjCEjIy5mSv+zsHH51XgRlQH9kaH+9eTnJzMG298iMVyIyokm1Eryar/DTbbaDIyxvD6629x\n8WI4sAujcTNffTWf8+eTsNv3oA00tNlaZtbTBngTvf50Zud0Jmp48SmaN69P7dp/ULVqRTZurM+h\nQz3JyLBgt49AhVZQw6f/i9kcTUzMUPr370+/fv1ITEwkMDCwwItjTZ/+Punpc1CdcbDbZ/D00xNz\nhNKIiAiMxouoBa0GAV/j43OBiIiIrGNiYgZx5Uo8kyc/gs1m5fHHH8xz8aKCiIqK4vjx/Rw8eJDQ\n0FDCwsI8crui7JJQKjzHbIazZ/M/7s03oXFjlfpq1IAzZ/KeUxoaqsZNemj4bn65edYsGDcu9687\nT2t17pQKCj58F9TJ09JgQeQWSv39Cz1813lOqVaeVrrbMh/PhR2+W9zr+kgoFaLsef31iWzb9hdr\n13YlPf0yarisP2lpfzJ8eDQDBvS/JsT8+OOPPPbYc1y+fA6dbi8q7EXg7/88vXvfQ0ZGOsuW/YgK\nU7cBM7BafenXbxQdOrRi5cpvCM78H1yvXh3M5i9ITk5Hhc6fSU+HceM+xmq9B/gC1Smti5oPuQa4\nH7VY0ofodCaMxlswGNqTkbGFHj26snTpYgyZr8I9+eSLqLC4BrVqrR3VFT2NetraBhVITaiubzpq\nhWFNWzIyDKj5qFtR82ADADNG4xguXTJisbQhe1GneQ416jAYfiEjI4VTpzqTnv4BAHr9W7z22ixs\ntlTUFjL+qO5mGjAGnS4ZvX40VmtL1BzNlcAS9Pp2nD9/iLVr53Dy5EkWLfqK9PQeqMD9Lmoxper4\n+EykVq0wXn75MYYPHw6oFWyDCrm4Y2pqCmo+ryaBxMRL2O32rMdGQEAAa9f+yIABwzly5GFq127C\nkiU/EqjtH55p1KiHGTXq4ULVkR+z2UzLli2L5bZF2SOhVHiOu53SZ55Rx/r5QbVq+W8Jo6W+IiY+\ndxc6+u9/8/661ZrzY8ey5Il9poIudATq49zGTOclt9V3TaZra0hPz3oguNMpdSxRe02kQKHUoVOa\n20JHjjU4d0qLO5TK8F0hyqalSxdy1113s2ZNGDab9sflRlJSEklNTc3aLxJg586d3HPPA1gsXwCN\nMBqH4+8/Ej8/H3r27M7cubMxm8188MEHPPXUy2RkfIBaCOh9UlKeZsOGM7z22mu0b9+eG2+8kREj\nRvDFF0vZujUSvT6cjIw/sdvDSE7eiHpauQ01JPhF1LzVGNSiPQcAG2ZzBitWfMeZM2eoX/8VWrdu\nnVWr3W7nlltacuzYQGy2KNTQ17tRq+G+i+pQHkYF1VigKTAOtUDSYCAYg2ESVmsLYFjmW0NgLBBI\nenoTLl78A7UI022o+aPPZn6/TQkMrEpg4HEaNYoiLq5tVl02202cP/810dFdWLOmDxbLYNSw3Rqo\nTmwcVmtqZk2mzJp3YbM9w6VL3/DRR3P5559DJCe/kFkLwDB0uibo9TZuv70333yzPiv4F9WIEf15\n+eXHUSsgpwHTqFy50jUvVrRs2ZKDB3d65D6FKCpZ6Eh4TkGG71osqltapYraezCvUBocrAJsCS10\npA3TzI1zcHD8G59XDitXCtMpDQ72bKc0t4WO/Pyw+/kVeE6pdrlAuTmPTqmrzr10SoUQ7vD39+et\nt2bg7/87aoEhO3r9mzRs2CJHIAVYtWoV6en3olbRrU16+tfY7SlcvHiML774PwICAtDpdPTp0wdf\nX19U6JsHDAVaYrHEM3Xqx8TEzKB+/eZs3bqVNWuWsXTpOwwdWo82bVpgtd5Edp8jEjUstycqNM4H\nnkOn+4yGDeuyceNaOnXqxKBBg3IEUoDXX5/OsmU7sNmmAs0wGNJo0+YktWrNp2fPKNq0uQm9/mfU\ncNMbUR3LacAVoA56fSVatEjAZEpHdVEB3kPtS3oMtehSdVRY3oJa8TYIeA2T6TRffvkM+/btoF+/\n7pjN76NCXTIm05t069aR7777nEmT7qBevXfw8dkFjAFmYbcfyfz+tTkX2hMDO+nptbly5SpJSSmo\nxZ06oYb8niIysh0pKcmsWrUk30B69OhRevUaRMOGbbjvvlGZe5S69uKLL9KlSwd8fKZiNM7FZIIP\nP5zFrbf2ICSkGq1adWTPnj153l9ezp49y/r16zly5Eihb0MIZxJKhedk7v+Yr9q11fvGjWHgQHj3\nXTV5ztfXdSjV6VS3tIidUm2V3PxKrFMn+3KXLvDcc7BxY/bnMjKuuUoWCaWZnEPpmTOQ5LBin6tQ\n6ulOaW4LHRmN4OubI5T+9RcMGJC9D2leobSwndLC7FMqnVIhRG6aNWvGvHmzMZk6YTCYqF//C1au\n/Oaa44KDgzEajzh8Jg6z+doAFBISQkbGVdQwWVCrxu4ErKSn7yc+/meuXv2IQYMewGaz8cILk5k/\n/yjr1zcjLe1bVIc0HbW3Zl2gHnAQgyGVtWvXYLUmsX//rjyHa86c+RbJyUtRQ2lnYjT25f77h3P0\n6B5Wrvye336LpVevdqghy9rE2L/Q6018+OF7pKVZ2Lr1V7p0qYrZ3JDg4EgMhmOoAKsFxZtQ4fBp\n1NzUSAyGe1i+/FvuvPNOgoODGTXqYUaOvB0fn5oYDBXp3t3MjBmv4uPjw/jxY9m//0+ef34EOt39\nqKG/nTO/38GoobvjgKMAmM3vc9ddvRk06A5gRub3tgZozOXLF7NWy83L1atXufnmLqxa1ZKDB9/j\nq68y6N69P3a73eXxOp2ONWt+ZMOGH1i69B327/+bZ555hT/+6EBCwlb++uteOnXqkWewzc3y5T9Q\nr14z+vb9L02b3sSMGbMKfBtCuCKhVHhOeLh7q8A4hlKDIXtcbUBA7s+MQ0OL3CkND1fv8yvRca59\n3bowbRrcckv25/L6/5HX2j7linMgfOEFWLw4++OkpJx76UDJdErT09VjzM+PNJ0/Vqt6keHzz2HJ\nEvj22+zyIGeJ2sOvsJ3SwuxTKgsdCSHyMnToYBITLxEff5H9+3dkrZrqaNiwYVSrdgB//xh0upcw\nmfoze/ZUl7c3cuRDGAxtUCvmtkUtFHQbajEggB6cPn2YNWvWsGdPAhbLD6hhtW+hVpP1R6f7ENWl\nPIzqND7Lc8+95tZiPVZrBmr4q5KS4sO///6b9bG/vz/ffvsVrVv74+fXAaNxJP7+/fjuu88YNeph\nDAYDPj4+zJw5iQceuIthwzrQuXMn4H+ovUWTUPNd56A6zF2A/Xzyybs5VrbV6XTMnj0DiyWR5OSr\nLF26MEcH2mAwMHLkCEwmE2rI7luolYL3odM9hl7/McHBBsLDx/G//82mY8eOBAYGYjJ1AB5ELQL1\nPqdPH+PyOdzgnAAAIABJREFU5cv5npdNmzaRnFwDq/VFoB1paXP566+/OHPmTK7X0el0tG3bll69\nemGxWDh/PjHz+jWx20eRnl6LHTt25HvfjlJSUhgyZATJyT8SH78Bi2UHEydOZ+/evQW6HSFckVAq\nPKdOHbVsbX60Z+ONG+f8/Nq1ajivKx7olNaqpd4fO5Z3t9Nxzqhjp2r2bNVNi47O/boSSjM5h9KE\nhJzpy1UoDQoqmeG7RiO6Z57hsn81QJWp3a1WsqtQ6ulOqTvDd7t1o1hJp1SIsk+v1xPg/PfUQVBQ\nEDt2bGDatPa8+KKe1au/ZujQITmOiY2NpXr1enz22Sqs1vPAGVTH8yvge7TuqU43j8aNo0hMTESn\nq0H208gHMRp9uHjxPEOH9kKnG4JanVeH1foI27Zt5ty5c/l+Lz179kTtu/oLajGj5SxYsDhHR9DX\n15eNG1fz6adPMmtWFFu2rOWuu+7K+vrWrVu56aZOfPihH/Pmwdatf1KxohG4FbV9zlrU/pi3AI3x\n9/ejV69eLuvx8fHJHNJ8rWrVqmE0WlHb3tQHFmE0HmPq1Ec5e/Yg8fFnOHZsN4MG3QNAYGAgBsMF\nsju8F7DZ0jODbd78/Pyw2686XNeCzZaea23OgoODSU+/gtpLFSAVq/VUgeewnjt3DrvdHzXHF6Am\nvr5RmXvDClE0stCR8JywMBU+XAUOR2lp0LUrOO9H1apV7tdp3Dg7VRaSvz9Urw6nTsHJk9kNW1fl\naR57LPvyk0+qN1c6dYL166FHjyKVeP3w81MJz25Xw6+Tk3Oe2ORk16G0uIfvap3S555DPwOwqEO0\necZaiXmFUk/NKc1r+O7gwfDRR8W/xZB0SoUoH4KDg3nqqadcfu2nn36ib98BpKc/CUxCbU/SGXgb\nFTpCsNka4utbkYoVTSxd+mPmqrCPA58BHTAaZxEZ2ZpKlSrRu3dvvv32LVJSUlF7dcZit1dm6NBH\nWLNmaZ51du58M8uWbcJqfQo1BHgtFkt7rl69miNA+fr6MnjwYJe38eyzr5GUNBW1NQ0kJVVh2LBT\nDBt2NzabjcOHjzJ+/DB8fSNIS9vLRx+9R+XKld0/mQ41/PjjN/TuPZCMDH+s1svMnfsh9903zOXx\nt99+O02bhvD3332xWG4lIOALnnhi/DVzgF1p37499esHsHfvUFJSumE2f8Zddw3kBld7dLtQtWpV\nHnroAT7+uD1paffg47OS9u0jiYqKwm638+2337Jnzx4iIiK45557cu1qh4WF4eOTAaxGzVHeT1ra\nnzm2kRGisCSUCs/R61XSO3oUmjbN/bi0NDUmtlo19297lmfmLNSpo0JpXFz+ofTDD/POyY6++QYW\nLYLMldyFwaDGOWshMCkpZxvZW8N309Ky0pdjKNRCqFZiXsN3Pb36rqvhu/7+JbPnrXRKhSi/LBYL\nMTH3smLFb1itj6Pmj0aj5juqQKjTJREYmMT27bux2WyEh4dnzYH85ZcV3H//aE6dmkC7du1YsEDN\nZ42JiWHixBns398ANUx1L/A/tm69P896tm3bxgsvvI7VGoFaKCkD2EfFipULtDXKlSsJqIWEFJut\nDgkJB+nh8Kpx3769OXz4MA0aNKBGjRpu37azW2+9lTNn4jhx4gRhYWHXbKfiyMfHh/XrVzJ37lzi\n4o5z662TGDBggFv3YzQa+f33n5g+/U327t1Ahw738PjjBdsv1GTyRw2r3onNVpG//vqL+Ph4xoz5\nL4sWrSQ1NQKTaRE//PAzCxZ85PI2fH19WbZsMX37xgCVSE8/zezZb9GgQYMC1SKEKxJKhWfVqaMS\nn3Mo/f576NlTPfN1CAYlrW5dtWhRXBzcdlv25199FRYsgC1bskNpzZru326VKjB6tEdLLfu0TqUW\nSh07pbkN3y1Mp7QQw3e18rSrayFUK1HrnBbn8N289iktqV8P6ZQKUT5lZGTQqdMdbNu2BbVoUARq\nm5XOwFz0+v1UqDCBevUaMn/+Kmq7eBW3devW/P33xms+r9frGTfuPzz55P+RmvocamGhtVStmnf4\nu+++x0hKmoXaxiUD6IjZ/Ag//LDKrfmompiYPuzf/wLJyTUAC2bzFAYPfi3HMeHh4YRrC00Ukb+/\nv9uhzM/Pj9GFfLIQEBDAq6++XKjr2mw23nlnNunph4EwbDZITOzHRx99xIIFC7Hbo4BaWCzbWbz4\na1566RkaNmzo8rZuu+02Tp06xJEjR6hevTqVKlUqVE1COJM5pcKz6tZVic/Z44/D/v3qspdDKVxb\n4iuvwKFD8OmnJR8MrluOw2edh+96slNamOG75OyUaiHUnU6ppxc6crVPaUk99qRTKkT59Mcff/Dv\nv+dRq+XWy/ysDqiB0fg8c+bM4OLF42zdupamTZtit9v55ZdfWLRoEXGu/sc7uf/++2ndugKBgRMI\nCnqYwMDHWbDgvTyvc+rUMdSiSqB6Jt0YPfo/tG3bNvcrufDss0/z1FO9qFz5DqpWHcSUKU8yeHBM\ngW7jemS323BcRMpuN7Fx40bs9paoObxzgUWkp9u5cuVK1nEJCQlYrVbsdjuzZ79LjRoRNGrUmh9/\n/ImKFSuW9LchrmMSSoVn1a3rerGjhARITFSXvRhKte1ecluPyW6XUOoxjqHQneG7zgsd7dsHly7l\nfz+Jia7nMLvqlGqdW1wP33VnTunVq+pxsn49tGsHu3blUZtDp9S5RC2UOu6Uo50u6ZQKIYpTSkoK\nBkMF1HDdMaiVY2Px8/uJ1at/5PHHR2Uda7PZ6NdvKH37PsEjj3xD8+ZtiY2NzfP2fX19+fXXFXz9\n9WTmzYth794d3HzzzXlep3Xrtvj4zEF1bM8QEPAVHTrcWuDvTa/XM2XKRM6fP8KZMwd56qknCnwb\neVm48EsaNmxNnTotmTp1Zq7bspQki8XC1q1b2b9/v8t69Ho9gwcPx2QahBqW/RZG4zoaN26M2sNV\n60S3xG5PoWnTphw9epTGjW/khhuqERBQgZEjR/HCC+9y6tRnnD79Ja+99ikffjivBL9Lcb2TUCo8\nSxu+68hmU8/ktcBRCjulGr1eQqnHmEzZicvdTqn24sVvv8HEiap1nZeMDLh8GVwt9hASosZjO+7D\nlpSk9tMlZyh0Z6Ejo1GNErZaITISOndWN9+3bx71ZbYiU/EjNPTabxfUTjn79uW83zymJXmUdEqF\nKJ/atWuH2XwGvT4K2A/Ux2i8l++/X0Tnzp1zHPvDDz/w8897SUyM5erVL0lO/oZ773043/vw8fGh\nZ8+exMTEuDVvc+HCuURE/IqfXyWMxno8/fS93HnnnYX8DovHypUrefjhZzl4cAZHj/4fr7++kLfe\nmuPVmg4ePEi9es3p1u1hWrW6jaFDH8Jms11z3Mcfv8vo0e1o2XIiPXps4o8/1tG/f3/8/RejtsdJ\nAZ7Hzy+EIUNG0rPn3Rw8OJCMjERSU7fy6affkZw8ETUc+0aSkl7n88/zXrhKiIKQUCo8y9XwXa21\nVAo6pa5CqeOLigZDdkPP1TRFUQBBQdk/c3c6pQEBqlW4YgWMHw9XrsDu3Xnfx8WLULGi681jhwxR\nWwm95zBkzOF+HTufuS10pG2hq9GC5N9/Z38ur31vrQb1OE/H95rc3KMHtGihLmslaqcrr8WrPcnx\n11Ae70KUH4GBgWzevJZu3fZSt+5FYmLu4ezZA3Tv3v2aY7ds2UJS0gmgFXADcJTLl0+7DD6ubN68\nmTlz5rBkyZI8rxMWFsZff23i+PH9XLlygddee6lw31wxmj//G5KTXwC6Am1JTn6LTz/92qs1DRny\nCOfOjSYhYScWy0GWL9/DwoULrznO19eX6dNfZdeu9axc+TWNGzfmlltu4YMPphMY2BUIRKf7ndTU\nz1m5sg579/6LzfYkEAcMxGZLBxz/4Z0gJKSEXkEV5YKEUuFZrobvap2qUhBKa9ZU3dBTp7IDiPOc\nPumUeog2HNdmU8NotRNrt7veEkanU5/bulUF0vh4+Ocf17etbTR7/jzXtCA1/v4QEwPbt2d/LjEx\nqw3pGErd6ZQ6Xic/n30Gy5ZBfLJqRQbd4IvBkPMYX1+YOVNd1vYvl06pEKKgUlJSeOqp52jZsiN9\n+gx2a84nQO3atfnpp285fHgHixZ94nJ+oN1uZ+7cL4CpwEVgIzAGu93ExImTcwwVXbt2LQ0aRFGp\nUk0GDhxOQkIC77//EV27DuTZZ/dx332TueuuIVnXsVqtbN++nY0bN2LJnGqh0+kIDAzk3Xff45FH\nRvPZZ5+ViuGxmqAgMzqd436rZwkIMOd6fEk4cGAvNtvdmR+ZSUq6gz179rp9/fvvH84ff/yK2VwL\nu/1foBsZGVOAMOBLYCgwAvgdmAk8jU73HAEBE3n99ec9+82Ick1W3xWedcMN6pl9fLwaPgnZoVQb\nvpua6rVnwEYjhIerXWuOHYOGDXMuXJOYKKHUY7RQqs3r1E5sWlr2ljHOgoNh82Y1JFevh+PHVajV\nO7x+duAADBig2pXnzuUeSgGiouAlh1fbC9gpdTXC2JWrV7Nv7+TJ7K2BDsz3pRJQKdT1n9qoKPV+\n504yV0N0fb/FReaUClF2WSwWYmNjef31GezZE0JKyqvs3r2JzZs7s2/fTo+sinr58mXi4y+h7fkJ\nzYF22O038dZb31K9ehiPPvow+/bto0+fGJKTPwFa8sMPLzNo0P388ssq0tJ2AfVJTU1j3bobWbdu\nHbfccgvR0XexbdsewEBwsI3t2zcRGhpKhw492LOnMikpnVi48G22bNnFO++8UeTvxRPGjx/N4sUd\nSUpKxmYLxmyezZQpi7xaU0REU7ZuXYzN9gyQSEDADzRv/nSBbsPPzw+73QKkofaWzcDfPwO7fTyp\nqVeB9YAvsBGDYQhdu1Zh9uzfZH9S4VHSKRWepdNd2y3VUl8p6JTCtUN4HaccSij1IC2Uukp8zuNi\nNe3awe+/q1B65Yr6YTiPj/3jDzhxQl3OL5Q2agRnz6rbghydUq0bGR+fMzdrjVy4NhxqIdLZv/9m\nX965M/vy2cu+pOJLaFXX2xmEhkKNGqqsgwdLvlMqoVSIsik+Pp7IyFsYPnwO27cHkpKyE7Bisz1L\namoL1q5d65H7CQkJwcdHj9o2BuAqcADoQVLS83z33U8ArFmzBputP9AHqE1q6vusWfMjarSutrqv\nL1CPKVPeokGDNmzY8BcpKTZSUjpy7pydDh168uuvv7J/fxIpKd8AY0hKWs2HH75PkuOKcF7UqFEj\ntm/fwNixekaPvsQvv/xAly5dvFrTwoVzCQv7iKCgZphM9enfP4ohQ4YU6Dbq1avH7bd3wGzuA3yE\nyTSAdu2a8/ffWwkKCgV+zTwyHJPJymOPPSqBVHichFLheTVrqnaRphQN34Xs/UdPnVLvJZQWEy2U\nuhobm1sr8O7MIUhWqwqc9eqp9462b1cvdNjt+YdSgwGaNMlOjS46pefPZx+elqays82mfv7OzVyt\nPGeOt+EYSo+dNpKGb54lavNK9+4t+U6pDN8VonSKi4tjwID7uOmmbkyYMIl0bb+oTG+88TbHjkWS\nmLgWWA1MAGKA+mRknMXH1UiUQjAYDCxY8DEmUw98fLoDTYHewK3o9XupWlV1Y4ODgzEYjqJWzgU4\nir9/EHXrNkSvnwqkAqtISvqV9etv4OTJV7DbE4BNwGfAXxw9eoL9+/ej11ch++lpCHq9LynO23t5\nUYMGDZg5cypz5rxZ4O1qikO9evU4dOhv1q//gn/+2cRnn80t0L6uoIZNL126kFdf7UVMzBZefrkT\nq1YtoWHDhixd+gUBAUMJDu5LQEAk3bs3p0+fPsX03YjyTIbvCs8LDMx+dg05h+/abGo+oOOz4RKm\nhRHthVfHUHr1qoRSj8mrU5pb6rrzThg2DH75RXU4q1bNuWcKqFCqjXXNL5SCGnOr3YaLOaVnz2Yf\nmpqa+9BdgG7dXN+FY4l//pl9+fAJ33xDqTbKPSnJu3NKvfgrKYRwcOHCBW66qRNXrozCar2X3bvf\n5MiR//DFF/+XdcyRI6dITW1H9lYetwC1gNFYLE+ydetO+vXrd81t//HHHxw6dIgWLVrQQntFLB93\n3z2AVq0iWb58ORMmbMNqTQYewGxexWuvbQBg4MCBTJkymyNHBpKS0gKz+RPeeGMqd9zRnbvuGsbf\nf79CSEgoFksIKSmfoLqt1TJrBqiIwVCfatWq4ePzDzrdu9jtnfH1fY9WrW70yFDk65m/vz+tWrUq\n0m0YjUbGjRtzzee7dOnCv/9uZ9u2bVSpUoVbb721wKFXCHcUulP69ddf06xZMwwGA9sdFxJxUqdO\nHVq2bElUVFSpeEVJlIDAwJzP0uPjVccqMVGtJOTrq4b5eokWNrTcLJ3SYuIcSt3plAYHw+efQ4UK\nKq05P5YA/vpL7ecSH+9eKHXeL9UplJ45k31oWlreodTPT40c1oaAaxxfg3Fcm+nQsfw7pdrWNCkp\nJd8pdZyq67wQkxDCO3766SdSUtpgtb4I9MBi+ZbFiz/L0S3t2rU9ZvM84DyqC/kGcCvwAJDB229/\nzooVK3Lc7vjxL9KlSwyjRi3j5pu78+67H7pdU/369RkzZgz//ruT6dObM3PmjezevY06mZt/m0wm\ntm37lenTO/PCC1aWL/+URx99mNq1a7Nz5+9kZKSxcePPmX9zbEDtzLonAP2ADuj1++nQoQO//76a\nm29eTo0ag+jb10Js7LcSgrwsPDyc/v3706FDB/lZiGJT6E5pixYtWLJkCaNGjcrzOJ1Ox7p16+RV\nrvLEVae0WrXsxOfltKd1oVx1SiWUelBQkFpRKjlZJR53QqmmYkWV0gICcj6WMjLU0N0GDdQP7syZ\ngoXSxMRrhu86dkrzC6Wg5oA6b8frmJsvXMi+/OfRymzg1jxL9PdX71NSSr5TWlLhVwjhPr1ej06X\n4fCZDHQ6XY4wMGLEcP75Zx9vv10Tq9UG3AFMBzYAwSQnD2bz5j/o1asXAHv27OG99z7GYtkNVALi\neOaZVtx772AqVKjgdm21atXiqaeecvm1EydOUL9+fXr16kWDBg1yfE2n09GoUSNatYpg+/ZhpKTc\ng8FQE6v1HeA9wIRO9zQ//7yWwYNj2LjxJ7drEkJcHwrdKY2IiKBRo0ZuHVualvMWJcA5SCQkqGfy\n2tjYUhJKXXVKHYfvynDGIgoMzO6UVqjg3vBdTcWKqlMaEHBt1z0kRN1efDzs368WM8qLyaRWMrLb\nXc4pdeyU5jd8V+P8QrF2HZst5+Ppr2MVuIdvcHp+loMWSi2Wku+U+vvDtm05910VQnhXr169CAzc\ng4/PeOArzOY+PPjgqBzzRHU6HW+8MQWLJZGRIx9Fp9uMWmSoP/A/zOZN1K5dK+v4kydP4usbgQqk\nAHUxGm/gvOOE+CJ48805REV1Ytiwd2jZsj1z5358zTF6vZ7Vq5fyxBMNuP32z6lXLw211cx9wEBS\nU9/jzTfneaQeIUTZU+wLHel0Orp160abNm2YN0/+2JQLrobvVq9e6jqlWgBw3BLmyhWVXQwGGc5Y\nZI7DdytWzE77rvYodVahgnpzfixdvpw9tPf8edWJbdgw79vSOqWpqeqHmvlqgxZKHddRcqdTCrmH\nUm3atLOmTfMuD7zTKQVo3RqaNy+5+xNC5C0kJITt239n+PBkunVbzKRJA/ngg1kujzUajcyb9w4L\nFryFybSdwMBIAgNfoE0bP4Zre1OhRrdlZPwD/Jb5ma/x9U2jVq1aLm+3II4dO8aLL76KxbKN+PhY\nLJaNPPXUOC44DhvJZDabmTlzMmvXLuXGGyMBq8NXM2RoqBDlWJ7Dd6Ojoznj2EbINGXKFLdX3tqw\nYQPVqlXj/PnzREdHExERQceOHQtXrSgbAgNzjmGMj1dL3h47VipCqRY2XA3fvXRJvZehux7guPpu\nxYrZ+9S62ynVjnMMpVeuqK9VqABbt6pxtPn9sLRQ6jCfVCsPcobIwnZKtRc4Ll92/a2EheV+W9qc\nUovFvfsWQlz/qlWrxscfv+f28ffeey/dunVj06ZNhISE0LlzZwwOr6yGhYXx3XdfMHDgAFJSUqhY\n8QZWrFiGn59fkWs9duwYfn4NSUkJz/xMA4zGGpw8eZLKlSvner0xYx5h+fI+JCcbARNm8wReeMH9\n71kIcX3JM5SuXr26yHdQrVo1AKpUqUL//v3ZsmVLrqF04sSJWZdvu+02brvttiLfv/AC5yCRmKha\nRaV0+K6WlUBCqUcFBamTnJQElSplv1ARH68WNMqLFmKdh4I7dko3bsy7BakxmbJXEXJIe1oodZSW\nlt05z6vE3Dql2naojpo2zXtdL61Tevmy6tL7+0uXvrDWrVvHunXrvF2GEMVm5cqVjBr1DPHxF+na\nNZr5898nKPOPWVhYGP3798/1ut27d+fKlbMkJCQQEhLisa5ko0aNSE8/AGwGbgZ+wW4/R7169fK8\n3s0338zq1cuYPv090tMzeOKJeVlzYIUQ5Y9HtoTJbc5ocnIyVquVoKAgkpKSWLVqFa+88kqut+MY\nSkUZ5rzQUVKS2tqjlA7f1bbRhOyumYRSD3DulGrDdxMSsvdByU3TpioV6vU5u+6XL2fPN92yBcaO\nzb8Of//sCZsuOqWO3A2lvXuD42t2Wih11Slt1iz/8iD725QuaeE5v5g5adIk7xUjhIf9/fff3H33\ncCyWL4CmrFjxX4YOfZjlyxe5fRt6vb5ACxu5IzQ0lK++mk9MTG/sdhMGQxpLlizKCst5ad++PcuW\ntfdoPUKIsqnQc0qXLFlCeHg4mzdvpnfv3txxxx0AnDp1it69ewNw5swZOnbsSKtWrWjXrh133nkn\n3bt390zlovTKLZSWkk6p8/Bd5x1HwOslXh8c55Q6LnSUkJB/pzQmBsaNcz2nVAulSUngzjZTjsN3\n3eiUat3OvHLz44/D4sXwYeaOCnmF0ptuyr88yA6lJTmfVIg333wTvV7PJW2YiCi11qxZg9UaA3QH\napKa+g6rVv3g7bIA6N27NxcunGT37t85f/44Xbt29XZJQogyptCd0v79+7scJlK9enV+/PFHAOrV\nq8fOnTsLX50om5yH7yYnqy1hbDa1OE0p65S6CqUemGYjclvoKD5ezQV1h6s5pdrwXYCbb87/Nkwm\nuHjxmk5pbuHv4kX1Pq/c7OMDgwbBqlXqY+2xpAVax11o2ufTBNDmlEqnVJS048ePs3r1amrXru3t\nUoQbKlSogI/PYdLS7IAOOExAQD6jTkqQv79/1r6lQghRUMW++q4oh1x1SgMD1d6Su3eXmlCqZR1t\n+K7WsQKvl3h9KMrwXY2rOaVap7RRI8hjEY0s2vBdp06pwQBm87WHazskuFOi82NJ65Q6bicUEZF/\neZAdhqVTKkrK2LFjmTFjhrfLEG6KiYmhZs0zmEwD0OkmYDb3Ydasad4uSwghPEJCqfA8V6HUbFYh\n4p9/vJ74tFzi3Clt1Sr7GAmlHmAyQXq66oxWrKiG79rt7i10pHHulGoLHbVtC+PHu3cbuQzfBddD\neLVQ6k6JzkPBtVA6cKB63727mhabX3kgnVJRspYtW0bNmjVp2bKlt0sRbjKbzWzf/hszZnTl5Zd9\n+emnxYwYcZ+3yxJCCI/wyEJHQuTgHCS0MNCoEaxZowKKF+U2fDcqCjZvVpcllHqATqdO9tmz2YsW\nWa0F65Q6zynVtoSJiMi/BanRQqnFkj1WNlNQkCrPkRYO3SnR+QUObfhuVBRMm+beQ10rSRvu61Si\nEIWW27ZukydPZurUqazSxp+T+4KFonQJCAjgiSee8HYZQgjhcRJKhec5d0qTk9Wz94YN4e23wcsL\nIJhMKi+lpKiMpA3flU5pMQgKgjNnVKfc11d1S4vaKS3oixomkwqkuYRSZ57olFasCKGh7pXnOGxc\nK1cIT8htW7d//vmHuLg4IiMjAThx4gStW7dmy5YthLp44Mp2bUIIIQrL3e3aJJQKz3MMpenpaoEj\no1F1tpKSvJ74dLrsqYpJSTk7pRrHOYGiCLRWZECA+rlre64UJJQ6vsBx8iSEhRWshjw6pa7mbxZl\nTumJE+p91aoFK8+RhFJR3Jo3b85ZhyECdevW5c8//6RSpUouj5ft2oQQQhSWu9u1yZxS4Xm+vqoF\nmZ6ePXRXp4PMV+VJT/dufeQcwqsFCsepVS5GvInCCApS42HNZrWksRZKC7LQkfYDstng8GG1YFZB\n5DN815k7+5RqtIWSkpNVeQcPqo8bNixYeXl9LERx0+l03i5BCCFEOSedUuF52lzCpCT1bF175q49\n2961y3u1ZdJC6ZUrkJGhtvhw3AZm3z7v1HXdCQpSixtpndKUFJX63NhUHcg5p/TkSTUutqArAWnD\nd1NS3AqlGndCqcGQnXkvXoRTp1SXPTy8YOXl9bEQxe3w4cPeLkEIIUQ5J51SUTyCglT4cF7xNDAQ\njhzxWlkaLYycPq3ea7n5xhu9U891SzvRAQEq9V+5osKpu+OjTSbVXU1PV23IgnZJIWen1KkN6RhK\nnVfJdbeZq4XXHTvU+3r1VFgtSHmOJJQKIYQQoryRUCqKR4UKqm20eXPOULptG2zY4L26MmmBo1s3\n9V4r8fPPVTNuzhzv1HXd0VKfttDR+fPupz1QSbFKFTh3zjOhNI9OaYUK2ZcNBvfDoTZ/9Pff1fuC\nDN3VynMkoVQIIYQQ5Y0M3xXFIyQEvv4a3nkHWrfO/nzjxt6ryYFjAIHsUNqkiRqGKVOsPEQbJ60N\n3z13zv2hu5pq1VRL+8CBgic+UCnPjVBaqRJcuqQuBwe7/xioVg3+/ht++019XNDcLMN3hRBCCFHe\nSadUFI8KFdSiNOnpBZ8DWAKcm3WO3SoJpB6kpT6TSb1duFD4UFqUTqkbW8LccIPrz7tTHsD69ep9\nQUt0nMsMEkqFEEIIUf5IKBXFIyREhVLInrBZijh3SrUVV4WHBQWpUGgwqBcntJV4C8ITodSNTmmV\nKtkzM9HyAAAM5ElEQVSXC1KiFkptNvW+oCUaDDmn2EooFUIIIUR5I6FUFA+tUwqlslPqHErPnfNO\nHde9oKDsn7/ZrOaUFvTxEBam5icfOgT16xe8Bm34bj6r71aunH25IKHUedvUwuZmjYRSIYQQQpQ3\nEkpF8QgJUZuAQqkMpc7Dd1NSvFPHdS8oKDvhmc2F75Ru367mp7qzT4szo1Ht+5OYmGsoNZmyp79C\nwR6yWqcU1NZCtWsXvETHsiSUCiGEEKK8kVAqiofWiqxQoUwM3xXFxLlTWthQ+ttvhWtBgpokbDKp\nFaxyCaVmc86yCjN8F6BOHRVMC8qxLOfVeIUQQgghrncSSkXx0FqRXbtCrVrercUF506p4wLBwoOc\nO6Xnzxc8lIaHq/1N27QpfB033AAnT+YaSgMCCh9Ka9TIvtykSeHL00inVAghhBDljWwJI4qH1op8\n6SWIjPRuLS44dkoHDFA714hi0LgxdO+uLhd2Tmnr1rB3b+E7paAmjB4/fk0bMjxcdTbr1Cl8KK1b\nF15+Wa3FNHZs4cvTSCgVQgghRHkjoVQUD60VWamSd+vIhWMonTABqlf3Xi3Xtbp1YepUdTkgQA2h\nLWinVKcr+v622tK6TokvNBR27lTvv/oq+/MFKVGng0mTilaehFIhhBBClGcSSkXx0FJfKQ2ljsN3\nS2mJ1x+zGaxW78wx1lKfi8TXrJl679jALekSJZQKIYQQojyTOaWieISEgJ9fqVzkCHJ2Sh3n84li\n5Di3tKTlEUo1jmWV9ILREkqFEEIIUZ5JKBXFo1o16NxZjW0shRw7pY5bgYhipKU+b2wRpKW+PJa2\nLeycUk/QRheDhFIhhBBClD8yfFcUjwoV4KefvF1Frnx8YNMm9b6U5ubrj7c7pf7+ef6wvRlKpVMq\nhBBCiPJMQqkot26+2dsVlDOO+5WWtCpV8k173gyljkPIZZ9SIYQQQpQ3MnxXCFEyvN0pzSeUenOh\nI8fh5D7yUqEQQgghyhkJpUKIkuHNOaWhofnerzcXOqpYsWTvTwghhBCiNJHX5IUQJcObndImTSA2\nNs9DvDl8t25dGDdOrQ8mhBBCCFHeFLpTOn78eJo0aUJkZCQDBgwgPj7e5XGxsbFERETQsGFDpk+f\nXuhChRBlnDdDqU4H9erleYg3QynAG2+oYCqEEEIIUd4UOpR2796d3bt3s2vXLho1asTUqVOvOcZq\ntfLEE08QGxvLnj17+PLLL/n333+LVLDI3bp167xdQpkn57Docj2H3gylbnAsy9sr4MrjUAj3yO9K\n0ck59Aw5j0Un57DoyvI5LHQojY6ORq9XV2/Xrh0nTpy45pgtW7bQoEED6tSpg9FoZPDgwSxbtqzw\n1Yo8leUHYmkh57Docj2H3lx91w1Go+vL3iCPQyHcI78rRSfn0DPkPBadnMOiK8vn0CMLHX3yySf0\n6tXrms+fPHmS8PDwrI9r1qzJyZMnPXGXQoiyxpsLHQkhhBBCiFIrz4WOoqOjOXPmzDWfnzJlCn36\n9AFg8uTJ+Pr6MnTo0GuO0+WxUb0QopwxGmHMmFK9EafZDMnJ+U4/FUIIIYQQnmQvgv/973/29u3b\n2y0Wi8uvb9q0yd6jR4+sj6dMmWKfNm2ay2Pr169vB+RN3uRN3uRN3jzyVr9+/aL8ixN2u71z585e\n/znKm7zJm7zJ2/Xz1rlzZ5f/b3R2u91OIcTGxjJu3Dh+/fVXKleu7PKYjIwMGjduzM8//0z16tVp\n27YtX375JU2aNCnMXQohhBBCCCGEuM4Uek7p6NGjSUxMJDo6mqioKB577DEATp06Re/evQHw8fHh\n3XffpUePHjRt2pSYmBgJpEIIIYQQQgghshS6UyqEEEIIIYQQQhSVR1bfLYgHH3yQqlWr0qJFi6zP\nXbp0iejoaBo1akT37t25cuVKSZdV5rg6j19//TXNmjXDYDCwfft2L1ZXNrg6h+PHj6dJkyZERkYy\nYMAA4uPjvVhh6efqHL700ktERkbSqlUrunbtyvHjx71YYenn6hxq3nzzTfR6PZcuXfJCZWWHq3M4\nceJEatasSVRUFFFRUcTGxnqxQlES5Pel8OR/X+HFxsYSERFBw4YNmT59urfLKXOOHz/O7bffTrNm\nzWjevDlz5szxdkllltVqJSoqKmsx2rKmxEPpAw88cM2Tg2nTphEdHc3+/fvp2rUr06ZNK+myyhxX\n57FFixYsWbKETp06eamqssXVOezevTu7d+9m165dNGrUiKlTp3qpurLB1Tl89tln2bVrFzt37qRf\nv35MmjTJS9WVDa7OIah/1KtXr6Z27dpeqKpscXUOdTodY8eOZceOHezYsYOePXt6qTpREuT3pWjk\nf1/hWK1WnnjiCWJjY9mzZw9ffvkl//77r7fLKlOMRiOzZs1i9+7dbN68mffee0/OYSHNnj2bpk2b\nltndT0o8lHbs2JGKFSvm+Nz333/PiBEjABgxYgRLly4t6bLKHFfnMSIigkaNGnmporLH1TmMjo5G\nr1e/Fu3atePEiRPeKK3McHUOg4KCsi4nJibmuhCaUFydQ4CxY8cyY8YML1RU9uR2DmV2Svkhvy9F\nI//7CmfLli00aNCAOnXqYDQaGTx4MMuWLfN2WWVKWFgYrVq1AiAwMJAmTZpw6tQpL1dV9pw4cYIV\nK1YwcuTIMvu/r8RDqStnz56latWqAFStWpWzZ896uSIh4JNPPqFXr17eLqNMmjBhArVq1WL+/Pk8\n//zz3i6nzFm2bBk1a9akZcuW3i6lTHvnnXeIjIzkoYcekmkh1zH5ffEs+d/nvpMnTxIeHp71cc2a\nNTl58qQXKyrbjhw5wo4dO2jXrp23Sylznn76aWbOnJn14lJZVOoq1+l0ZbbtLK4fkydPxtfXl6FD\nh3q7lDJp8uTJHDt2jPvvv5+nn37a2+WUKcnJyUyZMiXHsOey+qqnN/3nP/8hLi6OnTt3Uq1aNcaN\nG+ftkkQRREdH06JFi2vevv/+e6ZOnSq/L27I7RwuX7486xj531cw8nzVcxITExk4cCCzZ88mMDDQ\n2+WUKT/88AOhoaFERUWV6b9/Pt4uAFR39MyZM4SFhXH69GlCQ0O9XZIoxz799FNWrFjBzz//7O1S\nyryhQ4fKK+4FdOjQIY4cOUJkZCSghuS0bt2aLVu2yN/GAnA8VyNHjiyzCz8IZfXq1S4//88//xAX\nFye/L27I7Rxq5H9fwdWoUSPHYn7Hjx+nZs2aXqyobEpPT+fuu+/m3nvvpV+/ft4up8zZuHEj33//\nPStWrCAlJYWEhASGDx/OggULvF1agZSKTmnfvn2ZP38+APPnz5cHpAeU5VdKvCk2NpaZM2eybNky\n/P39vV1OmXTgwIGsy8uWLSMqKsqL1ZQ9LVq04OzZs8TFxREXF0fNmjXZvn27PMEuoNOnT2ddXrJk\nicvVjUXZ17x5c/l98QD531c4bdq04cCBAxw5coS0tDQWL15M3759vV1WmWK323nooYdo2rQpY8aM\n8XY5ZdKUKVM4fvw4cXFxLFq0iC5dupS5QApe6JQOGTKEX3/9lQsXLhAeHs6rr77K888/z6BBg/j4\n44+pU6cOX331VUmXVeY4n8dJkyZRqVIlRo8ezYULF+jduzdRUVGsXLnS26WWWq7O4dSpU0lLSyM6\nOhqAW265hffff9/LlZZers7hihUr2LdvHwaDgfr16/PBBx94u8xSTTuHFy9ezPqb+MADD2R9XYaH\n5c/V43DdunXs3LkTnU5H3bp1+eijj7xdpigB8vtSOKNHj5b/fYXg4+PDu+++S48ePbBarTz00EM0\nadLE22WVKRs2bODzzz+nZcuWWS9iT506VVZML4Ky+ndQZ5eWmhBCCCGEEEIILykVw3eFEEIIIYQQ\nQpRPEkqFEEIIIYQQQniNhFIhhBBCCCGEEF4joVQIIYQQQgghhNdIKBVCCCGEEEII4TUSSoUQQggh\nhBBCeI2EUiGEEEIIIYQQXiOhVAghhBBCCCGE10goFUIIIYQQooC2bt1KZGQkqampJCUl0bx5c/bs\n2ePtsoQok3R2u93u7SKEEEIIIYQoa1566SVSUlKwWCyEh4fz3HPPebskIcokCaVCCCGEEEIUQnp6\nOm3atMFkMrFp0yZ0Op23SxKiTJLhu0IIIYQQQhTChQsXSEpKIjExEYvF4u1yhCizpFMqhBBCCCFE\nIfTt25ehQ4dy+PBhTp8+zTvvvOPtkoQok3y8XYAQQgghhBBlzYIFC/Dz82Pw4MHYbDbat2/PunXr\nuO2227xdmhBljnRKhRBCCCGEEEJ4jcwpFUIIIYQQQgjhNRJKhRBCCCGEEEJ4jYRSIYQQQgghhBBe\nI6FUCCGEEEIIIYTXSCgVQgghhBBCCOE1EkqFEEIIIYQQQniNhFIhhBBCCCGEEF4joVQIIYQQQggh\nhNf8P5Xw9Q3rYuBcAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab86b2c50>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9c+eUaoPE+yB8N7UofeghJTS8vNRjZ4jSQuGUliihRKmbmxKjuQ3fNRrB27vY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889dZtGgx6emTgDsJBEaxfftxTJ48mSlT3sPvz2DXrmT69y/+lIb/sh0WUFq3rvdzw4aFX5c/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+vMPjYzTg4E8bciNfkHcx3aIKV5lGmnHlJ9rXVVe5vx+hBNDJRGqnESLtckLr+8LS7XUyiv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\n2osAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab877e850>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The autocorrelation function is the correlation coefficient plotted against the lags:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plotautocorr( data, deltat, lags, rmin=0.0 ) :\n",
+      "    auto_corr = []\n",
+      "    for lag in lags :\n",
+      "        lagi = int( lag/deltat )\n",
+      "        if lagi > 0 :\n",
+      "            r = stats.pearsonr( data[:-lagi], data[lagi:] )\n",
+      "        elif lagi < 0 :\n",
+      "            r = stats.pearsonr( data[-lagi:], data[:lagi] )\n",
+      "        else :\n",
+      "            r = stats.pearsonr( data[:], data[:] )\n",
+      "        auto_corr.append( r[0] )\n",
+      "    plt.plot( lags, auto_corr )\n",
+      "    plt.xlabel( 'Lag [s]' )\n",
+      "    plt.ylabel( 'Autocorrelation' )\n",
+      "    plt.ylim( rmin, 1.0 )\n",
+      "    plt.show()\n",
+      "    \n",
+      "lags = np.arange( 0.0, 2.0, 0.05 )\n",
+      "plt.title( 'Colored noise' )\n",
+      "plotautocorr( colored_noise, deltat, lags )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89f9890>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The time it needs for the autocorrelation to drop to zero is the correlation time of the time series. The correlation time is often measured as the time consant of the exponential decay of the autocorrelation. The colored noise example shown has a correlation time of about 0.5 s.\n",
+      "\n",
+      "You can also plot the autocorrelation for negative time lags. However, the autocorrelation is symmetric and therefore the negative lags do not provide any new information about the time series."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lags = np.arange( -2.0, 2.0, 0.05 )\n",
+      "plt.title( 'Colored noise' )\n",
+      "plotautocorr( colored_noise, deltat, lags )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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000/LGorIHMXFwFdf2dfchcpERwMffABY8aZAqoGMFoaXX35ZP8bg4OCA4uJi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\nDEREVAYLAxERlfH/AUSJx4Vl0aAWAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab898bc50>"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The white noise data has no correlation except for the time lag at zero, where every time series has a correlation coefficient of one. White noise has an infinitesimal small correlation time. You cannot predict any future value of white noise from the knowledge of the previous ones."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.title( 'White noise' )\n",
+      "plotautocorr( white_noise, deltat, lags )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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554CYGODpp4HDh/WXa20F8vLEGsPDgeBgse6vvzb8OltagKoqcd35+cD+/eJz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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab884be10>"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In python (similar to other programming languages) the numpy.correlate() function just computes $$c_k = \\sum_{n} x_{n+k} x_n$$"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "acc = np.correlate( colored_noise, colored_noise, mode='full' )\n",
+      "lags = deltat*(np.arange( len( acc ) ) - len( acc )/2)\n",
+      "plt.plot(lags, acc)\n",
+      "plt.xlim( -2.0, 2.0 )\n",
+      "plt.ylim( 0.0, 60000.0 )\n",
+      "plt.xlabel( 'Lag [s]' )\n",
+      "plt.ylabel( 'Autocorrelation' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8ebce50>"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "but we want\n",
+      "$$\\rho_k = \\frac{\\frac{1}{N}\\sum_n (x_{n+k}-\\langle x \\rangle)(x_n-\\langle x \\rangle)}{\\frac{1}{N}\\sum_n (x_n-\\langle x \\rangle)^2}$$\n",
+      "In order to get the correlation coefficients from that we need to first subtract the mean of the data before we then compute the covariance with the correlate function. The result we then divide by the number of available data to turn the sum into an average. Finally we divide by the variance of the data to get the correlation coefficient."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# subtract mean:\n",
+      "x = colored_noise - np.mean( colored_noise )\n",
+      "# \"covariance\":\n",
+      "acc = np.correlate( x, x, mode='full' )\n",
+      "# divide by n to make it a mean:\n",
+      "acc /= np.arange( len( acc ) )+1\n",
+      "# divide by variance to make it a correlation coefficient:\n",
+      "acc /= np.var( x )\n",
+      "lags = deltat*(np.arange( len( acc ) ) - len( acc )/2)\n",
+      "plt.plot(lags, acc)\n",
+      "plt.xlim( -2.0, 2.0 )\n",
+      "plt.ylim( 0.0, 1.0 )\n",
+      "plt.xlabel( 'Lag [s]' )\n",
+      "plt.ylabel( 'Autocorrelation' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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PPYZz584BAN555x0899xzWLlyJXr16oV27dpV2bBFl9zcXIwdOxZl\nZWUAgHnz5sn3jyEyAxYGsnulpaU6v65rs6LmzZvj559/hoODA1JSUnDw4EE4OTnVef6QkBD8+uuv\nOr/HVe9JjXgricgI58+fR/fu3REWFoZp06Zh6dKlOo9r1qyZQRPcvvvuO85lINXhRj1ERFQFWwxE\nRFQFCwMREVXBwkBERFWwMBARURUsDEREVAULAxERVfH/lEN51X311kAAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab87fa090>"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another example: white noise filtered by a resonance filter - a band-pass filter with peak-gain at about 2Hz. This produces a almost periodic but not perfectly periodic signal."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "band_noise = np.zeros( len( white_noise ) )\n",
+      "x = 0.0\n",
+      "y = 0.0\n",
+      "w0 = 2.0*np.pi*2.0\n",
+      "xi = 0.1\n",
+      "for k in xrange( len( white_noise ) ) :\n",
+      "    y += (-2.0*xi*w0*y-w0*w0*x+white_noise[k])*deltat\n",
+      "    x += y*deltat\n",
+      "    band_noise[k] = x\n",
+      "tseg = (time>20.0) & (time<25.0)\n",
+      "plt.plot( time[tseg], band_noise[tseg], 'b-' )\n",
+      "plt.xlabel( 'Time [s]' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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X5HK66SZ95/3BD6i1x6ZN9PrXv6YHQFaW/2ONBDONWhTyTFpEY0A7pMc6MUYB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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab877d050>"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For such quasi-periodic signal we have positive and negative autocorrelations in dependence on the lag."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lags = np.arange( 0.125, 0.6, 0.125 )\n",
+      "for lag in lags :\n",
+      "    lagi = int( lag/deltat )\n",
+      "    tseg = (time>20.0) & (time<25.0)\n",
+      "    plotdatacorr( time, band_noise, lagi, tseg, 'Lag %gs' % lag )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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LF4wxwI7pJTftmkzaDVoppRT8738/kZb2JFANcCE9/Rk2bSrc6+lKNWtWB0yI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\nB10MJpY7eNYJUu63vvdzgprjHbxU6LpPnAC4ud23iYPznKA96KC/g9Psm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US3EVTgqBXg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YnVmk9iYipJQEAhg2CuNYjeQoF4sdsflPvvfyTDGMuWLZPg4EoCboEZAneJqrLzDMK1UiBE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\nREREREREduH/A43nQfURaFb1AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab898b950>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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7//3PyciYDvwT8LE8a61kKpgZzcLd8g84ISGBLl1uIzl5KobRhqNHX+PkycdZ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waF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\nABMREREREZEmcANMREREREREmvA/vQ77RUqp96gAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89bd750>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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6ukNCK6XUTcNisdC0aTu++krYtu0FZs9OolWrjrkWbBIR2rfvxjvvbGHx4na8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kUK9Fyk1t+DNgdSbHzq2ZgXRJsn19o4nkAbC0/a/FyMYoBfR6ryChv/FDSPT6D1Vhep2OUR3Cej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\ngfXbxdZPp6NNhoVow+NC64O3LP9X0DnwjUgx7YXG2fUG6GNl9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QfmkJvbm0aN6lKvXj1A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brB1Td/p5TKsJ37+4IFC3L8+HGCgoLYunUr7du3Z/fu3fjd9WK0dxklS8rT3804eBAaNswT\nk15ekuzp8GHh2WXLyvcnT0opktDQHPGF7MPbW/4uXoSQkPTbeBTgfy/cRXlvW5D3JuQwC7SbALuv\ngUwRGChMtFChrJdBussK8Dmt+sMtvPsuJsooUSL1fY8ed83s/z1y+96cXXiSqnjwoKBWrVrs2bOV\nNWvW4OvrS7169UhISKBIkSKYzebr7QoUKIDZHE5ycjSwHfgGpf6LkLE2SIZmqTH8669/U7t2U+Lj\nS+NyhfDGG++xdu0yqlWrlu4YDAY9LpdXmm/MCIkDOIpOZyQy8ixHjpRAqSCELNtRKoABA94hKelT\nIAlv71d5//13GTjQrVY/CrwMdKBq1Z1s3erCbh+OENtmCKktgtQJ/gwpE6VHslinAL4Yjc1Rqhel\nSj3E/v2HcDhsSBkpPdAOSRDmi5BqqXXrck1ClORFQE0MhvdxOnVIZutLQHMgmcKFC3DsWFql0olO\np2P8+Am8//6HOBwp9OnTi7/+WsvQoe+zbds1YmKG4nBsAPYgCwTPIgq3P1JiaiWiTj+NJBVrQvny\n1Wnfvj0Gg4Ho6I1Mnvwl585dpFWrb2nWrBkAo0a9i8Xixfffv4iXl5nx47+jUjplCQIDA1m8eHYG\n59HAunXLGDBgENu2vUTZsiX58stVeHl5pds+qyhVqiS//76Oq1f1LFy4FJMpAm/veFasWECtWrXY\nsuV3XnttKLGxf9KkSS1GjHgnR/YedGRKgFeuXJnhtvDwcM6cOUP+/Pk5ffo0YemoF4UKFeL48ePX\nP584cYJChQpl2t9sNl//salSpQrFixfnwIEDVKlS5Zb9Dxs27Pr7hg0b0jCPiOJdwcMPw8SJt36/\nZw88/3yema1QQQjw5s2pBHjPHnl1f74nKFxYmPhdJMDapUlmooaHAN8ncBPgOyk1cLdcoEEIsHax\nZKkMUi4owHc0PoRzQ4559/81fvvtN3777bd7PYw7Rm7fm48fP36DupseMruf34wH6t7swb8GkZGR\nPP3004wd+zE9e5bCZArBx0exZs3PlCtXDgCLxYLLdYXUkj83LwLpgCYkJw+mXLlHsNsvI67FM4B6\nDBw4mE2bbv3/1el09Or1FLNmdSIxcTA63Q6UWoq4++4CvkYpL2JjY5GMyZUQsmchOfkCkhn6cQAS\nE6+watUGrFYLCQnuhxUdUIOwsK3Urh3Mpk2rSUoKRkjo28AujMYxOJ0nUKoLUAN4hXr16tC5cyeU\nUjz66AhKlChBvnwRxMe70szd7Ya8BKiHEGodsJ+QkHz4+w/l7NmTVKxYmc2bXTidAUiW6ZEEBiZR\nsWIVjh17F8kQbQVexWotz7BhX2Kz/Qr48vnnnVi6dAVz506nWrVqzJo1ix49nkGpWMT9Oglxp34H\ncWE+jLhZa7X3uEzlyhdZuXIlc+cuITjYn5deGnDL757BYMDPz5eTJw9jNhemS5enWLZsHnXr1s3o\nskkXISEhzJkz9Y763A5PPvkMcXHdkUWNw6SkhJGSsoR27bpw9uwRIiIi+PHH6blq817hrtybsxs8\nPGjQoOuZIz/44IN0E22kpKSoqKgoFRsbq5KTk29JgpVe//PnzyuHw6GUkkDyQoUKpZvFMgdDvz/h\ncCgVGChZn91wOpWyWpW6dCnPzE6cKAHzPXumfjdhQmpW1nuG5s2V+vnnjLe/9ZZS77+fqya3bpV5\nV6iQ/naHQymdTtpol6gH9wq1ayv1xx931uftt5UaOTLbJtetk3Nfu3YWGrdpo9TChcrlUkqvl34p\nKZm0L1pUqcOHsz02pZTatUvslC6dtfazZkn7zp1zZPaBwoNwX8npvdmNhg0bqi1btlz/7E6ClZyc\nrA4fPqyioqKUS0v0lhYPwjH04N+LjRs3Kqs1QsFxLZnQt6pYsdSEm06nUzVt2lZ5ezdX8InS6cKV\nTtddwRIFPbRkU5uUZFX+Wknm5JVKsi6vUMWKVVQbNmxQ/v4FFAQqL69Q1bHjk2rChAmqSpX6ymj0\nVV5eBVSZMtWVweCnJZDqrCWi+k1JVudnFURq+1YKhmlJqF5RUFNBddWyZQc1bNho5eVVScEGBfMV\nBCkvrzD1+eeTVfv2nRQEa9uUgqsKmikvr/zKYsmvwsNvzA6fFn///bcyGAIVPKUks3QPJdmhKymd\nLlBBMwUvKm/vMLVgwYLr/SZMmKDM5j5pEjXFKH///KpMmZpKsi4/qeAJBS+qAgXKaMfP3fY3BWWU\nj0+oiomJUUeOHFFW680Zq6sraKhggdLpCqnUZF5KwWjVsGEL7dxOUAbDKyo4uJA6derUDXPbsWOH\nlsDqqNbvZxUYmD9HGZxzC97egQomacfbPS+XMhi8rmcYf1CRF/eVbMcAv/XWW6xcuZKSJUuyZs0a\n3nrrLUDihFq1agVIoPhnn31G8+bNKVu2LE8++SRlypTJtP/vv/9OxYoVqVy5Mp06dWLKlCkEBgam\nP4gHCQYDNGgAaVf2jx0TWcft25gHaNxYXr//HmJi5P19owCfOJHx9osXUyWvXDQJGZu9fFl+bgID\n5XR5cA+RnURYdzMGODQULlzAZgOXSzy1jZn525w7l2MpNlTLtXLhQtbaexTgBxM5vTcvWLCAiIgI\nNm3aRKtWrWjZsiUAZcuWpXPnzpQtW5aWLVvyxRdfeFygPXjgEB0djbgMu5XBPhw5soeUFFF89Xo9\ny5b9xKhRzenVaw8ffPAqTzwh7aR+rF7r74245eqQmNoKmEzv0ahRHRo0aM2VK5WBH0lO/g8//bSG\nV155j23bWuBwHCE5uT0xMYe1uNh2SE3gzkgSrCBgEpD2QaUpkmjqMJIAqi3Ll69g06Z/0OuPAu2R\nWN2vSU7+haFDR1OyZAnE7XkNotg+CQSQnPwTKSkDUcpBvzS5IHbt2kWPHs/Rrl13Tpw4wZkzB5DE\nWB8h7s+L8fauzMCBPRk8uBoffFCAP/5YRvv27QEJgUxMTESptLVwvXA6HRQqFI7BcFnb31zM5muE\nhfljMBxI0/YAUIzExN788MOPFC5cmNBQP/T6D5EayT9hsRyiU6dC1K37DY0bP4zV2heYD3yN1TqB\n3bv3YrPNAV7G6fyYK1daM23a9BvOf0xMDEZjbWAvUk+5PZcv29ixY0dGlwwAKSkpOJ3OTNvkFMWK\nlUTO8TqkNjXAEoKDwz2xvtlBrlPqu4T/46FnjAULRAV21x76+WelmjXLU5Mul1JFishKUoECUgK0\nTh35vGpVnprOHO+9p9TQoRlv79jxNoVV7xwul1Jms8w9vcW0AwdkW1RUrpr1IDvo0SMLBXlvQu/e\nSk2dmm2Tx4/L+c9SBaZBg5QaM0adPi19wsMzaXvtmlLe3tfLJmUXdru7PmDWPBSGDJH2w4fnyOwD\nhQfyvnKX4TmGHvw/Y+XKlcrHp5SS8jxKwTKVL1/R2/YbN26CkhI9EUpKB/kqOKLcJWkgWDVr1kYt\nWrRISf3cxDQqXjMFIWk+l1dSGzjt9lppFN+tSsolXVVgV9BJs91QQRtNga6ndLpQTYnuo6CVgpIK\n9iqLJVB99dVXymKpqSnJdZSUDrJft+nv30QtWbJEKaXU3r17lY9PqNLpxiiYqqzWomr69BmqUaPW\nymx+TsEpBcuU1Rqq9u3bd8uxWb9+vQoJKaz0epPS6aya2rtI6fX5lcHgp0wmP+XlFaJ8fZspP7/a\n6qGHKqldu3apkJDCSspRDVBS9miLMhoHqpEjRymlxEu0UqW6ymSyqqJFy6lNmzbdYHf69BmqTp3H\nVLNmj6v169er0NBIBXuvz1Gne1sNGXLjc+a2bdu0mtAhClZr52moKl26Wrrn3WazqbZtuyiDwayM\nRosaNGhIup4x2UFycrLavn272rdvnzp//rwKCSmkHYcwBVZlMJRQAQH51caNG3PF3v2MvLivZFsB\n9iAP0L49zJwJI0fK/+fmzZBO8H1uQqeT3FuFC0vyp0mTYOtW2Va+fJ6azhxZUYBzORBXp0tVgdOL\nA/ZkgL6PEBSUxWxUaZBLSbDuRAHOUgkkt/qbQzXNZBLvBJcra4mwPAqwBx544MGNaNKkCd27N8dq\nLUtAQCN8fXszb97317dfvHiRw4cP43A4bug3aNDLrFq1BB+fBKzWhpjNEeh0VbBa++DjU41nn+3G\nr78upmDBgojimpKmdwoSJ6z9KJOIJHByoxqwG6gDvAA0QsoRhSBlf/4EIoC3gLZIGaTdKJUCfAtM\nA5YCtYDWVKxYiZSUFCpWtGC1GvD2jkcyNCdp9hRKXbuej+err6Zis/0Hpd4EnsJmm8aoUROZN28G\nzZpdxs+vIkWLDmLx4jm3lFC7dOkSLVs+TlzcFFyuZJQaidH4LkFBL6LXN8LpvEhKymkMhvJ06lSU\n2bPfJjr6T8qVK8fu3Vvo0CEFk+kHYBB6/TKs1rnXy/9ERUWxbdsf2O0JHDmyixo1atxgu3fvnqxf\n/zO//jqPChUq8NBDkRgMvYHNiGL8FR07Pn5Dn0qVKtGmTRPgEe04WoDhHDy453rG5bR4/vlXWbr0\nb5zOUByOUkyaNIfvvptxS7s7xenTpylTphp16z5JpUqNaNSoFQkJdZCyS6uAaXh5xXH8+H5q1qyZ\nY3v/RngI8P2Gli3Fv7ZfP1i8GDSXtbxEkSIwdqy8f/NNqS5TpQpolTTuDW5HgOPi8oSJZuYG7S4b\nk8ue1x5kB/fABdpqFZKZnJyFCkw3EeC8ToCV1ixkzQ06wyzQHnjggQf/Uuh0OqZM+YRNm5bz009D\nOHRoF/Xq1QNgyJDhFCgQSYUKDYmKKs+hQ4du6NukSRP279/J5Mnt+f77Yfz22wI++aQuixdP5quv\nJgFQrVo1oqLKIm7Lc4ABwEmgGjpdNQyGNzAaUzAY+gCbgJ8wmSbj5aWAhojb8iKgCGZzALVq1QSS\ngR+RhFbPIiTZD8lz645lcwGl0enOsnPnZQYN+ofo6F307v0os2e/z5NPdsVqfQyYitncnaCgS+zd\nu5e3336HrVu33eS6bMHpdGK1WsmfPwydzkBSUhInTqTW1k1KSmLmzJmMGDECpfIhtZV1SIKrggQE\nBOJwDNDG6IPN1ptLl5Jp1aoV3lp1h/DwcObP/4lp0ybRqtU2unc/wZYtf1DkDmvl2Ww2qlVrwD//\nFMTpDEGna0V4+BCWLv0h3QzPffs+jdV6nNRFikPo9Tp8fHxuaTtnzgJcrkbARuBdkpPjmD9/2S3t\nLly4wPTp05k2bRrn3avPmeDZZ1/i2LHWXLsWQ2LiIWJiTCQlHUUIeQWgPkqpB79CTh4i0yzQHtwD\n6PWwdq0Q39hYqF37rpjt2hX27hXxGe4K784ctyPAuVA2JiOzAGmSnd5gEjyK2X2BoKDUYtVZRQ6z\nQLurL507l4UKTKGhEBd310ogpTV78KAQ4FKlMm/ruZ498MADD9JHhQoVqFChwvXPv/76KxMnzsRu\nP4jdHkZS0sd07NiHbdtuLF9ZsGBBevXqdf1z/fr1b9n3vn3/8Oyz/2Hdug+Ij4/DYNBRqVIwffr0\n5dixY0RFjWHPnoPMnj0APz9fRo/+L61btwVG4868bDBU49lng/jggw8IDY0iJSUpjYVkpLzRPCT2\ntzXwOmBDKV9sth+QuOJ5TJ68nJkz57FgwSwSEyezZs0wbLYrnDoVyEsvjQIKYzZb0OvH4XIVAg5g\nMEylfPlavPDCIGbNOkhi4iauXDnN8893pHDhgtSuXZuqVetz7FgADkcJkpOPArOBrsBpkpNPUKJE\nA44fX4XTWQdQeHmtoWTJyHTPRffuXenevWu622w2G/v37ydfvnwZZqVftmwZp08HYbfPBHQodYa4\nuGI0aNAg3faNGzemfv1S/PFHHRyO6hgMi/joowkYbkr+YrfbSUq6AHwBmJDFidlaiahUHDt2jKpV\n65KYWAPQ8frrQ9my5Q+KFSt2Q7utW7dy+PBhypcvz86de3A4hiDn24LT2QUYAkwFymA2D6Fr127p\njt+DrMFDgO9H+PnB/PmwZYtITncBOh2MGAFOJ8yYAb173xWzGaNQoYzrESklT/h58OTu/v08derW\nbR7CcB8hOwpwDl2g3WbdBDjTCkyaAuwugXRbF2iPAuyBBx54cN9i+/bt2O1tkYRP4HI9w54972Zr\nX0ajkenTv71tu5Ejh15/HxVVjsOHv0CpAcBBvLx+49lnf8bf35+PPx7Jyy93xOkcBRxDyuS8islk\nICVlF7AWWI8oh2OBJ4BzQDRQlKtXf+GxxzoCxbDbvwT24XINAV4E1mO3P4O393h8fEZw8aIBp/M1\nVq7cgMPxXxyOVUgd3qLYbAOZOfMHunbtw/nzxZFavDokgVcHfHyWodQ6Bg8eTK9eXahRoxE22xqU\nSqBIEQNDhnzBoUOHiI+Pp0yZMukqrmmxfft2mjZtQ0pKIMnJJ3n55RcYM2b4Le0SExOBUFLLNgWh\nlAuHw3ELqQVJdLZ06Q8sXLiQkydPUqPGvFvcq0HOo8nkRUrKCaQEk0KnO8yTT754Q7vBg0cQH98H\np3MEAElJI3nzzeE3lCwaPHgYkyZ9i9FYjZSUP4mKKoZO9yNKPYwo0fOBvojSf4QCBUx8+eUvmR4f\nDzKHhwDfrwgNhRYt7rrZ0aPl754jMBCSksTX9Gap7coVMJvBYkm/bw7gdvs+e/bWbW7C4CHA9wHu\nEQF2e93f1nRISNZdoM+fz3UC7HbXzwye69kDDzzwIGuIiorCy+sHHI5EJMPzcgoXLn5Dm2+/ncb7\n70/C6XTywgvP8OqrL6abKT05OZmZM2dy5swZ6tWrl65KfDOWLZtL06ZtOX/+PVyuJD76aAKVK1cG\nYODA/ly5Es+QIa8g6q8Ts3k0K1Ys4rPPPmfxYgMOx8Pant5AVOGWCHEFaI7dbgfmAsURd+X9wKcI\n6TqAwVCGixd/xuU6BoSRlPQaOl1FYCEg4zCZDrFixTouXCgLPPy/9u48Psrq/vv/a7ITghIEEiAg\nEAhhDaFhcQM0hIhgUNwgyo2Kyxew/CpVoUVb/VYQtLRsKlpF8G5FihpBxdygKGJFo7KIBDGUAGFJ\nkCVANrJdvz8uJ0aTmy0AACAASURBVCSZmeyTmUnez8cjj0muOTNnuSZz5jPnXOdwKeDsj7+/heXL\nY+nT57cMGTIEgJ9/3sVXX32Fr68v11xzDY888hj/+te/8fXtgJ/fWbZu/Zg+VWxHcuut93D69HPA\nPcApli8fxo03Xm+z73hsbCxeXo8D/wAGExDwPCNGjMPf39/hc3t7e3Obuby3Q15eXixY8BxPPnkD\n+fn/Bz+/FHr18mPSpIqj1UePnqSk5Kayv0tKBpKe/hk33zyRHTt207FjB/bs2cPFi/swA/Wf+fnn\nQRjGXszp7icxV6SejznSvJRBg77Ft5EGyJoqBcDiniwWMxrNyoKuXSve56TpzwChoeZtZqbtfRoB\ndiMuuAbYmi3UIOvaXgNsfeHV0xVXmLfVjQAXFprbenl765p2EZHqTJgwgXXrPuSjj/rg43MlsJ+1\naz8ou/+dd95l5sy/kJe3CvDjT3+aSmBgC6ZNe6jC8xQVFXHNNaPZu7eAixf7EhCwgkWLnrJJV1pa\nytKlL5GUtImQkDbMmzeXQ4dSOXnyJMHBwTbB2z//mYSX1xxKSx8H9uLlFUvbtm158MEH2LTpCXJy\nLgL+wE7AG3MrneNAR+DzX5+l/MJexUA7zK2dbsIwtuLl5U1p6a+dDF4EBHShqOgFSkt34+PjTZs2\nu/jll2MYxgrMoDQRiMDbezY33BDHvffeW6HMrVq1Ij4+HoD333+fNWu+ID8/jfz8y7BYXuHOO+/n\nxx+/tns+SktLOXx4H+b2TQBtKSkZxd69e20C4I4dO7Jt2yYefvgxjh9fyvXXX8vy5S/Yfd7amjVr\nJn379mLr1m106jSO+++/v2zxMKuxY0fy3XeLyMsbDlho0eJ5Dh8+xu7d0RQVPUlm5kYMYyfm+QGI\nwNc3mNLSs5SUvA6kA49gjt6X0LLlcp58UqO/9aVFsMR9hYbaH4pVACy1DYBLSyE3F4KC6p0t1CDr\n4GC4cIHcbHMRjcYeAa5ujY3yr2Uv9QIiIlXy8vLi7bff4Isv3uWdd57kv//9kZiYmLL733hjHXl5\nTwPDgWHk5S1k1ap3bJ7nn//8Jzt27KWg4AKG8Tn5+d149NHfY+70cskf//g0c+eu4osv7uXddyMY\nPHg4J06cIDQ01Cb4LSwsZP/+XZSWPoY56toPb+8xpKSkMHr0aGJj+xEUFENQ0N0EBo5h3bp/8eyz\ncwkIGMDllw8hKOhO7r77DsyR37eBv2COPGbh43OSnj238cknHxITcxV+fjMw98h9jaKiL/HxCcPX\nNx/YwsqVywkJ6YI5Cv085ihza/r02cdbb/2jyvbdt28fBQXxgNlZGsadHDiQWuX56NSpJ5D065Gz\neHtvITIy0m76AQMGsH37Jg4f3sOqVS8TVMvPAidPnuSGGxJo1aod4eFRfPnll2X3xcfHM3/+s8yY\nMaNsAa/yHnvsd9x771B8fbvg4xPG+PHdyc0tpKjor0A/SkufwDDCAOtq40n4+5cycmQsAQF/Bdri\n5XUrgYFLeOihX9i+fQuDBg2qVfnFlkaAxX2FhjqORJ0cADuKu0HXTLqF2gbAOTnmMs71jPZqHAB7\neUH79hgnMoHONdsGqQFYr0u2dw17efoyR0SkdiwWi8PAo1WrQCyWTC7FsVkEBQXapHvppVUYRiKw\nBCgB7uDixUKKiooqjBy++OLL5OWlAN0oLYWCggO8++67zJw50+Y5fX19admyNRcufA8MBi5iseyk\nY8e78PLyIinpX3zyySccP36coUOfKgsSJ0+eyNGjR+nVqxdt2rQhNPQJFi9+BDDw8SnmlVdWMKXc\ngjAbN65j6tSZfPXVOFq0CCQzszsFBSmY03LfY/r0J3jrrX8wduwdeHtfRWlpG0aMGM6GDW/jVU3f\n27t3bwIC/kRu7pPAZVgs/6ZHD8fTnwGSkv5JXFwCpaXPU1h4hAceuJ/Y2NgqH1NXY8bcwQ8/xFBc\n/Co5OV8zZswE9u79zmZF6p07dzJv3mLy8gp48MFJ3HrrLXh5efHii39j2bK/AmYwnZTUG3PrqyCg\nmICAfAxjDvAUQUGBbNyYRP/+/Zk9+89s2/YsPXpcyeLFuxwu9CW1pwBY3FdISKMHwNZrgDUC7OZq\nGwA3wPRna7ZQw6zDwvDJPAp0brQR4KpWMS9PC2CJiDScJ5+cxYcfXk9eXjaG4U9g4HL+8pf1NunO\nns3BXBTKgvkRfDzBwbttps2aI8Llrx+22IwSl91jsbBq1atMnjwWL69YDGMPo0ZFceOv68hYLBbi\n4uJsHtelS5cKAdxf//oC8+fPIzMzk5CQEJuR5uDgYN57zxylnD9/Pn/60znM4BdgBJmZRxgxYgQ/\n/bSDlJQUrrjiCoYPH47FYpb9tddW8tln2+nePYwnnpjFZeU6xvHjx3P33Z/x5ps98PMLxd//POvW\nfWy3vlYxMTEcPvwTqamptG/fnu7du1eZvq5ycnL44YdvKS7+DHPi7C1YLP/iP//5T4X227NnD9dd\nN5rc3LlAG7Zu/f9YsSKXyZPvBij7EiA0NJQ777yTd98dTV7enbRosYnBgyPYtGkv2dnZtGvXrizt\n0qXPO6VOogBY3FlVI8BO+uTepg34+EB2trkGV/l1thQAuxHrNCN7i6TZU88tkKysAfCZMzVI3Lkz\nfr+YW3lVew1wA72oqtrHujy9lkVEGk6/fv34/vsvee21NyguzmXKlM1295gdPHgghw+vobj4GqAY\nL6+3ePhh2+1sZsyYxvLld5CXNxcvr58ICNjIbbfZrnBsNWHCrfTr15dvvvmGDh2mEhsba3cBrur4\n+fnVaJ/dIUOG4O//EHl5M4GOeHsvITraXCk5LCyMMGtn9Kvf/vYx3njjC/LyHsDf/z+888717Nr1\nHwJ+/ZBl3X95zpzfcebMGa688kq2bt3Kzp07GTVqFO0dfOa77LLLGDZsWK3rWRu5ubmUlhYBj2Ne\n1zwQw0indevWFdKtWLGS3NyZwO8AyMsL5bnn/lwWAJe3atXLjBjxBt98s4vevUczY8Z0/Pz8CLGO\nwojTKQAW9xUaCnv32h4/ebKaPWjqzsvLHAU+dszMxtoPXLxoxlA+PuYC1eJi1k15q92Q91cNsAI0\n1H4EOGi/ORTrMPY2jAad0WD9zHHsmHnZs6NZZxoBFhFpWL169eKFFxZUmWb58uf54YcxHDnSk9LS\nAoYNi+bpp5+ySffcc88QEtKOpKTXCAlpw/z5W22CysoiIiKIiIioVx1qatSoUcydO42nn+6Jl5c/\n3bqFs26d7Yg3QEFBAStWvEhJyXGgDRcvPsTx49fxySefMG7cuAppu3XrRnBwMDExw8nKagu0xsfn\nMbZvd3x973//+18SEx/ip59SiYjozZo1r9KjRw+HZT916hSLFy/j5MkzJCTE25ShctqBA6/Gy+sO\nSku9gVH4+19JVFSIzah6cXEJl0bEAfwoKSmx+7xeXl5MnTqVqVMdZi1OpuVPxH2FhsKJE7bHG3Df\nVEfZQsWsy28ZU4cvVcUZajMN2kUBcKtz1YwAX7hgfqsSaHutWF0EBpqzGAoLq14JWiPAIiKN74or\nrmD37q/Yvv19du7cwiefbLC7HY+XlxezZs1k27YPeeedNxstsK2NP/7xcc6fP8PRo2mkpn7r8PrU\n4uJiLBYvwPpNsAVozcWLF+2mX7BgERkZMeTkfEpOznucOzeb6dOfsJu2oKCA666L57vvbuL8+W/Z\nsSOB666LJy8vz276s2fPMmDAMJ5/Pot//KM7d901kxdfXOGwjq+88iqnT4+kuPgtzIW93uLyy3P5\n7LMP8fGpOIb44IP/h8DARcAbwHoCAx/i0UcfcPjc4loKgMV9tWtnfznb48edNgIMl2Lr8llbFxVy\nYrZSW7UJgC9caNAp0DUNgC/PqSYAdsKXOTWZBq0RYBER1/Dx8aFfv35ERERUO005KSmJAQOuIzJy\nKIsXL3N4HbCrBAQE0LZt2yrrERQUxPDho/D3vxdIwWJZjK/vLkaMGGE3/aFDxyksHIb1GmjDGEZG\nhv2VHfft20dOTgClpb8Hwigt/R15eUGkptpfQXrNmjVkZ8dQVLQCeJS8vPd58sm/OCz72bPnKSrq\nVu5INywWw+4evDExMSQnv8cNN6xn2LCXWb58Lv/zPw/ZpBP3oABY3Fe7dvaHsU6ccGokat1KpnzW\n1tFgBcBupLYjwC4IgK/IN6NQh1k7YUG3miyEVX5Gg4iIuJ9PPvmEe+6ZwZ49s9m//wXmzn2FZcte\ncnWx6mT9+rdITGxNePg0Ro7cwvbtW2hr/bBVyahR1xAY+ApwCiggIOCv3HDDNXbTXnbZZRQV/QLk\n/Hokl6KiXyossFVeXl4excXlO772FBTkMXnyQ4wefTsvv/xqhS8ZEhJuIjDwZeBLIJ0WLR7l1ltv\ndljP6667jk8/fZ/t25O5774pDtOJ6ykAFvflohFga1CgEWA3V5sAODv7UvRazyyh5gFwSKEZhTq8\nbvzo0UsRawOpyQiw9b4GzlpERBrIG2+sJS/vj8A4YDh5eUv5xz/WuLpYdRIUFMTKlS9y4MD3bNmy\nocop3VOn3seDD47C27sT3t6Xc/31Bn//+3N204aHh3P77eNp2fJ64BlatryBW24ZQ8+ePe2mHzdu\nHL6+bwNrgV0EBNxHaamFt97qyObNt/PYYy/x5z8/W5Z++PDhvPbaIjp1eoDg4OtITOzB4sVVX+ct\nnkGLYIn7Cg6Gc+eguNi8ThLM/VyLiuDyy52WrfVLSQXAbq62AXADrF5WqwC4Y0falWbhTTGtWzt4\nqz1y5NJKaw2kc2fztqoA+MiRimlFRMS9BAYGYLGcLbe38Bm71ws3NRaLhcWLF/LCC89SUlJStlK0\nI2+++Qpr1qzhxx9T6dt3JpMmTXI4JTsyMpL/9/+SeOSRP/662nQHUlLGUVr6NAB5eYNZvPgq/vd/\nLy1MNmnSRCZNmthg9RP3oABY3Je3txlxnDlz6WLFEyegQwenrkRlHQEuPwVaAbAbsr42aiI7G668\nst5ZBgaCr6+5Knh1OzAVlPpxlivo7JtFQICDzeuPHGmQcpVX3QhwXh6cPg1+froGWETEXc2aNZ23\n3x5Bbm4JhtGKFi2e59lnV7u6WI3G19fX7rW2lVksFhITbbeScuTaa69l164vAFiyZAnffvtj+Vwp\nLS2tbVHFA2kKtLi3ytOgnTz92Zol2B8B7tDBqVlLbbhgBNi6+xJUn3V2NhwljMigo46/r8nIaPAR\n4OoCYOu1wWFhjrdJEhER1+rduzfffvsF06ad4/77D7Jp07vceOONri5WkzJhwgT8/T/AYvkbsJHA\nwLt48EGt3Nwc6OOPuLe2bW2HYp0cANtbBEsjwG7IBQEwmNsMQc0D4B4B1cxFbuB5yNUtgmWd/tzA\ncbeIiDSwyMhIXnzx77z++otce+21Vabdv38/ffsOxc8vkPDwKHbs2NFIpfRcnTt3Zvv2LYwdm8KQ\nIYt58snbWLRovquLJY1AU6DFvVUeAT50yOkXLlYeATYMM1vQNZNuxUUBcG1HgLv7VrEcs5NHgA3D\n9moBa2CsAFhEpGm4ePEiI0aM4eTJxzCMyRw8+AGxseNIT0+ldQP1fZ5o27ZtPPronzl37jx33nkz\nzzwz12b/3j59+vDBB2+7qITiKhoBFvdWeQR4717o18/pWcKlADgjw9xGtn37S/eJG3DzAPjsWcig\nM50tDkaA8/PNcoWENEi5rIKCzKpevGhe61uZvswREWlaDh48SF6eD4YxHWgFJGIYXdmzZ4+ri+Yy\nP/74IzfeOIHvv3+AAweWsXjxpzz22FxXF0vchAJgcW+VR4D37HF6ANy6tbn+1vnzZhBh7T+cnK3U\nlpsHwNnZcIQuhJUcsp9g3z6IiHDKhbjW4DY93fa+vXvN28jIBs9WRERcIDg4mMLCXzD3zgXIobDw\nMFdccYUri+VS772XREHBfUAicBV5ea/zf/+vZ24jJQ1PAbC4ty5dLg1ZFRXBzz9Dnz5OzdLL69I0\n0kOH4MdfFwhUAOxmPCAA/pF+dL3g4Bv4H36AAQMapEyV9e1r3tr78t96zElZi4hIIwsNDWXmzEdo\n2fIafH0fpWXLa7jrrvH0cfB56dy5c8yc+Tixsbfyhz/8mYKCgkYusfO1aBGAt3f5jvosfn5Nfxsp\nqRkFwOLeevWC/fvN3w8cMCPTwMBGyRbMeFsBsJtq06ZmAXBpqTmH/bLLGiRbawBc3Q5M2dmwn15c\nkXvE3Huosj17oH//BilTZVFR5u3u3RWP5+aa/0Y+PhoBFhFpSp5//i+8++5S5s/vxL/+9b+sXPmi\n3XRFRUVce208r756mi1b7mbJkt2MG3cnxqUNh93e+vXrGTYsnqFDR/Pvf6+zm2by5Mlcdlky3t6/\nB5YTGHgnzzwzu3ELKm5Li2CJe+vVC376yfx9x45Ln+wbIdtNm8zY+/vvzWONlLXUlHUT3uo25D1/\nHlq1Mue1N4DajAAX48uZdr0I2bsXBg+umOCHH+DRRxukTJVZR3d/+KHi8dRUc2GsyEhzH2AREWk6\n4uPjiY+PrzLNt99+y+HDeVy8+DpgIT8/gf/8pzNHjhzhygbel94ZPvroIyZNmk5+/lLAm/vum4mX\nlxe3335bhXShoaHs2rWdRYuWcvr0Hu64Yxk333yzawotbkcBsLi3kBAoLDRX8/n2W9sgwkkiIszb\n774z429fXwXAbsk6DbqqALgBpz9bs4SaBcAA2V2iCNm92/a1u2eP0+Yhlx8BLr8StDUgdtLAs4iI\nuDlzpLf8F8JeWCxedkeAt23bxjPP/I38/ItMm3YP99yT6NSyXbx4kd27d+Pt7c3AgQPxtvPF9fLl\nq8nPnw+YAW9eXiFLl66yCYABwsLC+Pvfn3dqmcUzaQq0uDeL5dI06O++gyFDGiVb6xTotWvNAGLA\nAPDXpSPupybXAbs4AL7QZwhs317xzpMnzRXWOnVqsHKV17EjhIaaZbReQQC6/rc5OHPmDHFxcURE\nRDB69GiyrS/ESpKTk4mMjKRnz54sXLiw7Pi6devo27cv3t7eFfYRPXToEC1atCA6Opro6GimT5/u\n9LqISMOLiYkhNBR8fWcCyQQETCE6ur/N6O8333xDfPwEPv30Zr766gEefvgpVq5c5bRynTp1iv79\nhzFq1AOMHJnIsGGx5Obm2qTz8fEGyl+znI+vb8PM8JLmQwGwuL+rroI33oBdu+A3v2mULAcMqLg4\nbyMNPEtt1SQAPnvWpQFwweDh8MUXFe+0jv5W3qS3gVgsMHy4+Xv5rDUC3PQtWLCAuLg4fv75Z2Jj\nY1mwYIFNmpKSEh555BGSk5NJTU1lzZo17Nu3D4D+/fuTlJTEcOsLqJwePXqwc+dOdu7cyUsvveT0\nuohIw/P39+errzaTmFjM4MGLeOCBDmzalISlUn/06qtvkp//BHA/MIG8vBX87W//qJAmNzeXN954\ngyVLlpCammo3v9zcXF555RUWLlxY4Uu1ymbOnMOhQyO4cGE3OTmp/PhjKM8+u9Am3ezZMwgMfApY\nBrxEYOAT/PGPv61lK0hzpwBY3N/cufDvf8OMGQ22kFF1QkLgf/7n0t93390o2UptecAIsE9UX3MK\n/4kTl+784QenR6HW+GXrVvPWMC4FwBoBbro2bNjAlClTAJgyZQrvv/++TZqUlBR69OhB165d8fX1\nZeLEiaxfvx6AyMhIIqzXgIhIk9S2bVtWrXqZlJTNLFv2V1q2bGmTxgyIS8odKa4QJOfk5BAdfS2P\nPLKO2bP3M3jwCDZv3lzhOaxpZs3ayJNPnuS668aUvddUtnfvzxQVjQcsgDcFBTeze/d+m3TXXnst\nmze/zx137OD221P4+ON1xMbG1qEVpDnTNcDi/kJCzP2IGjCIqYn586FlSxgzBq69tlGzlprygAC4\ndRsviIuDd96B3/76LfUPP8CwYQ1WJntuuMG8/eADOHfOXAH69GmzKazbfEnTk5WVRUhICAAhISFk\nZWXZpDl27BidrZtFY14n980331T73Onp6URHR3P55Zfz7LPPcq3eGEWarBkzprJmzWjy8loCrQkM\nnMsf/nBpRPb1118nIyOcgoJ1mEHrOB5++DEOHry0/cCbb77J0aNdyc9/D7BQXJzA9OkPMX78eJv8\nBg3qx/79b3Hx4gigmBYt/s2QIfan31199dVcffXVDVpfaV4UAItnsEYdjejyy+F5rZ3g3jwhAG6N\nudrzpEkwbZq5B1FKyqVg2El69zaD4C1b4JVXLi3sNmiQ02ZeSyOJi4sjMzPT5vi8efMq/G2xWGym\nNVqP11bHjh3JyMggODiYHTt2cMstt7B3715atWpV6+cSEfcXHR3N559vZP78JRQUFPLww0u55ZZb\nyu4/efIUBQV9MYNfgL5kZ5+u8Bxnz56lsDCiXJoIzp+3v4fg3/8+n127xvHzz90wjCKuuuo3/OEP\njzd4vURAAbCIeDIXBMCBgeaq4BcvOt6ByTAqBcDDhkH79rB5s3lN+5EjjTIPedYsMwB+7TWwfm65\n6iqnZytOVnmaYXkhISFkZmYSGhrKiRMnaN++vU2aTp06kZGRUfZ3RkYGYdVMC/Dz88Pv172zBg0a\nRHh4OGlpaQwaNMgm7dNPP132+8iRIxk5cmQ1NRIRdzR48GCSkv5p9764uFgWL55MXt4EoCv+/nMZ\nNWpUhTSjRo1i/vxbyMu7BQjH3/8xRo++0e7ztW7dmu+//4IDBw7g7e1N9+7d6/RlnXi+zz//nM8/\n/9ypeSgAFhHPFRwMBw9WnSY7G7p1a7AsLRYz25MnHe/AVFBg7t7l7w8BAb8evO8+eP11c3W1mBhz\nJNjJ4uOhQwdIS4MXXjCPadZY05aQkMDq1auZPXs2q1evrjBiYxUTE0NaWhqHDh2iY8eOrF27ljVr\n1tikK78tyqlTpwgODsbb25uDBw+SlpZG9+7d7ZahfAAsIk3TyJEjWbr0f5k1K578/PPExSXw+usv\nV0gzdOhQVq5cwsyZd5OTk018/E2sXv2yg2cELy8vrUEgNl+cPvPMMw2ehxbBEhHPVdMR4AaeQt+m\njXnrKGvr8QoDzxMnwr59cMcd5oXljcDHp+Jibt7eTr/0WFxszpw5bN68mYiICLZs2cKcOXMAOH78\nOGPHjgXAx8eH5cuXEx8fT58+fbjrrrvo3bs3AElJSXTu3Jmvv/6asWPHMubX1+rWrVuJiooiOjqa\nO+64g1deeYXWjbwug4i4l6lT7+PcuUwKC/P44IO37V4Scdddd5KVdZDc3DO8994/CQoKckFJRSqy\nGPZ2vvYAFovF7qbdItKMbNgAr74KH37oOM348XD//eZtA7n6anNr323b7C+QlpoKffua+0n/9FO5\nO4qKICurUVehKikxp0B/953ZBOPGNVrWHkf9Sv2pDUVEpCE5o1/RFGgR8VwuuAbYmi04zrrC9b/l\n+fo2+hLM3t7w8MPmj4iIiEhzpynQIuK5PC0AFhERERGXUgAsIp6rbVs4darqNE4IgK+4wrz95RfH\nWYICYBERERF3owBYRDxXu3bmMGxxseM0TgiAQ0PN26wsx1mCS7avFhEREZEqKAAWEc/l7W0uyexo\nKLa4GHJzwc7KlPVRXQBsnRp9+eUNmq2IiIiI1JMCYBHxbKGhjiPR06fNANmrYd/qQkLM28xM+/db\n4/F27Ro0WxERERGpJwXAIuLZQkOrjkSdEIVaR4AVAIuIiIh4FgXAIuLZQkIcR6InT7o0AG7fvsGz\nFhEREZF6UAAsIp7NBSPA7dqBxWIuQF1SYnv/yZOX0omIiIiI+1AALCKerboA2AnDsL6+5lZIpaX2\n19/SFGgRERER96QAWEQ8W1UBsJOmQFuzBdusDUMBsIiIiIi7UgAsIp6tUyc4etT+fU6aAm3NFmyz\nPn8eioogKAhatHBK1iIiIiJSRwqARcSzXXklHD5s/z4nBsBXXmneVs5a1/+KiIiIuC8FwCLi2Tp1\nMvcBLiqyve/kSactxewoANb0ZxERERH3pQBYRDybr6+5FdKxY7b3nThx6WLdBtali3lbOQA+fty8\ndVK2IiIiIlIPCoBFxPPZmwZdVARHjkC3bk7LEmyzPXDAvA0Pd0q2IiIiIlIPCoBFxPPZC4APH4YO\nHcDf32lZghljl5eWZt727OmUbEVERESkHhQAi4jnu/JKSE+veCwtzalRaMeO4ONjzrLOza2YLSgA\nFhEREXFHCoBFxPMNGgTff1/x2IEDTo1CfXygXz/z9x07Lh1XACwiIiLivhQAi4jnGzYMvv4aDOPS\nMSePAFuzBTNrgAsXIDMT/PwgLMypWYuIiIhIHSgAFhHPFxZmRp3lp0Hv2AH9+zs128oBsHUkuE8f\n8PZ2atYiIiIiUgcKgEWkaRg+HD76yPy9sBB27YKhQ52a5TXXmLebN8O5c7B9u/n31Vc7NVsRERER\nqSMFwCLSNMyYAYsXQ0mJGfyGh0OrVk7NskcPGDnSnPr86quXAuCrrnJqtiIiIiJSRxbDKH/RnOew\nWCx4aNFFxBkMwxwFnjoVjh0zf156yenZbtwIY8eai2IVF5vHDhzQPsCeSP1K/akNRUSkITmjX1EA\nLCJNx/btcOutUFoKn30Gffs6PUvDgBEjYNs28+/hw2HrVqdnK06gfqX+1IYiItKQFACXo05WROz6\n+GNITYXf/77RskxLg/Hj4eBBM+7WFGjPpH6l/tSGIiLSkJzRr9T5GuAzZ84QFxdHREQEo0ePJjs7\n22665ORkIiMj6dmzJwsXLiw7vm7dOvr27Yu3tzc7ym+iCTz33HP07NmTyMhINm3aVNciikhzNGZM\nowa/YO62tGcPnDih4Fdcq7598+OPP07v3r2JiopiwoQJnDt3ruw+9c0iItIU1DkAXrBgAXFxcfz8\n88/ExsayYMECmzQlJSU88sgjJCcnk5qaypo1a9i3bx8A/fv3JykpieHDh1d4TGpqKmvXriU1NZXk\n5GSmT59Ov9/LLAAADIdJREFUaWlpXYsp1fj8889dXQSPpzasv6bQht7eEBzsuvybQhtK/dW3bx49\nejR79+5l9+7dRERE8NxzzwHqmytrLv9vqmfTono2Lc2lns5Q5wB4w4YNTJkyBYApU6bw/vvv26RJ\nSUmhR48edO3aFV9fXyZOnMj69esBiIyMJCIiwuYx69evZ9KkSfj6+tK1a1d69OhBSkpKXYsp1dA/\nT/2pDetPbVh/akOB+vfNcXFxeHmZHw2GDh3K0aNHAfXNlTWX/zfVs2lRPZuW5lJPZ6hzAJyVlUVI\nSAgAISEhZGVl2aQ5duwYnTt3Lvs7LCyMY8eOVfm8x48fJywsrFaPERERkYbtm1euXMlNN90EqG8W\nEZGmw6eqO+Pi4sjMzLQ5Pm/evAp/WywWLBaLTTp7x+qioZ5HRETE0zVG3zxv3jz8/PxITEx0mEZ9\ns4iIeKIqA+DNmzc7vC8kJITMzExCQ0M5ceIE7du3t0nTqVMnMjIyyv7OyMio8A2yPZUfc/ToUTp1\n6mSTLjw8XJ1vA3nmmWdcXQSPpzasP7Vh/akN6yfcQzZvdnbfvGrVKjZu3Minn37q8DHqm5vP/5vq\n2bSonk1Lc6inM/rmKgPgqiQkJLB69Wpmz57N6tWrueWWW2zSxMTEkJaWxqFDh+jYsSNr165lzZo1\nNunKL22dkJBAYmIis2bN4tixY6SlpTFkyBCbxxw4cKCuRRcREWmS6ts3Jycn88ILL7B161YCAgIq\nPK/6ZhERaQrqfA3wnDlz2Lx5MxEREWzZsoU5c+YA5nVCY8eOBcDHx4fly5cTHx9Pnz59uOuuu+jd\nuzcASUlJdO7cma+//pqxY8cyZswYAPr06cOdd95Jnz59GDNmDC+99FKz+TZZRESkPurbN//2t78l\nJyeHuLg4oqOjmT59OqC+WUREmg6LoR3rRUREREREpBmo8whwY8rIyOD666+nb9++9OvXj6VLlwJw\n5swZ4uLiiIiIYPTo0WRnZ7u4pO7LURuuW7eOvn374u3tzY4dO1xcSvfmqA0ff/xxevfuTVRUFBMm\nTODcuXMuLqn7ctSGTz31FFFRUQwcOJDY2NgK1xpKRY7a0GrRokV4eXlx5swZF5XQMzhqx6effpqw\nsDCio6OJjo4mOTnZxSV1DzXtb5OTk4mMjKRnz54sXLiw7HhV75PPPfccPXv2JDIykk2bNjm9LlWp\nbz0d9amHDh2iRYsWZa8r68i6qzirntC0zqejx7vL+XRU7vJmzpxJz549iYqKYufOndU+1h0/Wzuj\nnu74Xl+fet5///2EhITQv3//Cumb2vl0VM9an0/DA5w4ccLYuXOnYRiGceHCBSMiIsJITU01Hn/8\ncWPhwoWGYRjGggULjNmzZ7uymG7NURvu27fP2L9/vzFy5Ejj+++/d3Ep3ZujNty0aZNRUlJiGIZh\nzJ49W6/DKjhqw/Pnz5elWbp0qTF16lRXFdHtOWpDwzCMI0eOGPHx8UbXrl2N06dPu7KYbs9ROz79\n9NPGokWLXFw691OT/ra4uNgIDw830tPTjcLCQiMqKqrstenofXLv3r1GVFSUUVhYaKSnpxvh4eFl\n6VyhvvV01Kemp6cb/fr1a5xK1ICz6tnUzqejx7vD+ayq3FYfffSRMWbMGMMwDOPrr782hg4dWu1j\n3e2ztbPq6W7v9fWpp2EYxhdffGHs2LHD5nXZlM6nYTiuZ23Pp0eMAIeGhjJw4EAAgoKC6N27N8eO\nHWPDhg1MmTIFgClTpvD++++7sphuzV4bHj9+nMjISCIiIlxcOs/gqA3j4uLw8jL/lYYOHcrRo0dd\nWUy35qgNW7VqVZYmJyeHtm3buqqIbs9RGwLMmjWL559/3pXF8xiO+hWouDCjmGrS36akpNCjRw+6\ndu2Kr68vEydOZP369QAO3yfXr1/PpEmT8PX1pWvXrvTo0YOUlJRGqpWt+tbTU/pUZ9WzqZ1Pd/6c\nWVW5rcqXf+jQoWRnZ5OZmelRdXZWPcG93uvrU0+A6667juDgYJvnbUrnExzXE2p3Pj0iAC7v0KFD\n7Ny5k6FDh5KVlUVISAhgbv2QlZXl4tJ5hvJtKHXjqA1XrlzJTTfd5KJSeZbKbTh37ly6dOnC6tWr\nyxbukaqVb8P169cTFhbGgAEDXF0sj2Ntx2HDhgGwbNkyoqKimDp1qltMF3MHNelvjx07RufOncv+\nDgsLK/tSobzy75PHjx+vsAWTo8c0loasZ2Xp6elER0czcuRIvvzyy4YrdB04q55N7XxW9XhXn8+a\nnB9HaY4fP16nOruCs+oJ7vVeX596VqUpnc/q1OZ8elQAnJOTw2233caSJUsqjBgBWCwWrUhZAzk5\nOdx+++0sWbKEoKAgVxfHIzlqw3nz5uHn50diYqILS+cZ7LXhvHnzOHLkCPfeey+PPvqoi0vo/sq3\noZeXF/Pnz6+wH6A7fbPtziq/FqdNm0Z6ejq7du2iQ4cO/P73v3d1ERtNXFwc/fv3t/nZsGFDhXSO\n+tua9ME1eZ90dl/eGPWsrGPHjmRkZLBz507+9re/kZiYyIULF+pch5pwRT3t8bTzaRiGw3TW4644\nn/bKUxM16QtqUmdXach6ludu7/V1rWdtzo8nn8/qHlfb81nnfYAbW1FREbfddhuTJ08u29cwJCSE\nzMxMQkNDOXHiBO3bt3dxKd2btQ3vueceu3tDSvUcteGqVavYuHEjn376qQtL5xmqex0mJiZqFL0a\nldtwz549HDp0iKioKACOHj3Kb37zG1JSUvS+WAV7r8Xy7fXAAw9w8803u6p4jW7z5s0O76tJf9up\nU6cKC9hlZGRUGA209z5Z+TFHjx6lU6dO9a1KlZxdT3v8/Pzw8/MDYNCgQYSHh5OWlsagQYPqWIvq\nuaKeTeF8li+zo8e74nxWV25758de3cLCwigqKqp1nV2lIetZ/rHu9l5f13pW9//VVM5ndfWs7fn0\niBFgwzCYOnUqffr04Xe/+13Z8YSEBFavXg3A6tWrFdRVwVEbVk4jjjlqw+TkZF544QXWr19PQECA\nC0vo/hy1YVpaWtnv69evJzo62hXF8wj22rB///5kZWWRnp5Oeno6YWFh7Nixw+UdnTtz9Fo8ceJE\n2e9JSUk2K002VzXpb2NiYkhLS+PQoUMUFhaydu1aEhISAMfvkwkJCbz99tsUFhaSnp5OWloaQ4YM\naZxK2VHfepZXvk89deoUJSUlABw8eJC0tDS6d+/upFpUz1n1bGrn09Hj3eF81uT8JCQk8OabbwLw\n9ddf07p1a0JCQupUZ1dxVj3d7b2+PvWsSlM6n1Wp9fms8XJZLrRt2zbDYrEYUVFRxsCBA42BAwca\nH3/8sXH69GkjNjbW6NmzpxEXF2ecPXvW1UV1W/bacOPGjUZSUpIRFhZmBAQEGCEhIcaNN97o6qK6\nLUdt2KNHD6NLly5lx6ZNm+bqorotR2142223Gf369TOioqKMCRMmGFlZWa4uqtty1IbldevWTatA\nV8NRO06ePNno37+/MWDAAGP8+PFGZmamq4vqFhz1t8eOHTNuuummsnQbN240IiIijPDwcGP+/Pll\nx6t6n5w3b54RHh5u9OrVy0hOTm68StlR33q+9957dvvUd955x+jbt68xcOBAY9CgQcaHH37YuBWr\nxFn1NIymdT4dPf7dd991i/Npr9wrVqwwVqxYUZZmxowZRnh4uDFgwIAKK3bXts6u5Ix6uuN7fX3q\nOXHiRKNDhw6Gn5+fERYWZqxcudIwjKZ3Ph3Vs7bn02IYGvYTERERERGRps8jpkCLiIiIiIiI1JcC\nYBEREREREWkWFACLiIiIiIhIs6AAWERERERERJoFBcAiIiIiIiLSLCgAFhERERERkWZBAbCIiIiI\niIg0CwqARUREREREpFlQACwiIiIi0gi+/fZboqKiuHjxIrm5ufTr14/U1FRXF0ukWbEYhmG4uhAi\nIiIiIs3BU089RUFBAfn5+XTu3JnZs2e7ukgizYoCYBERERGRRlJUVERMTAwtWrRg+/btWCwWVxdJ\npFnRFGgRERERkUZy6tQpcnNzycnJIT8/39XFEWl2NAIsIiIiItJIEhISSExM5ODBg5w4cYJly5a5\nukgizYqPqwsgIiIiItIcvPnmm/j7+zNx4kRKS0u5+uqr+fzzzxk5cqSriybSbGgEWERERERERJoF\nXQMsIiIiIiIizYICYBEREREREWkWFACLiIiIiIhIs6AAWERERERERJoFBcAiIiIiIiLSLCgAFhER\nERERkWZBAbCIiIiIiIg0CwqARUREREREpFn4/wEH9Kz89McowwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4adce1f390>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9QD/gGbKzzwGv8+ijN/HAA/fhWujCMnLkOM6ebQLMBP4G+gOjgSzgF/75p7kd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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89b61d0>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The resulting autocorrelation function is periodic with a frequency of about 2Hz. In addition this periodic correlation decays towards zero with larger lags."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lags = np.arange( -2.0, 2.0, 0.02 )\n",
+      "plotautocorr( band_noise, deltat, lags, rmin=-1.0 )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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t6s9VXdeu1DqQ1f1z7JjckVNmsruo9Bg9JbN/W3ZNA5Df0pBZz6hO5iiqf/2L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\ntucdAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab86bf350>"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As a final example a pure sine wave with 2Hz. It has as a 2Hz sine wave as an autocorrelation with infinite correlation time."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sine = np.sin( 2.0*np.pi*2.0*time)\n",
+      "#sine += 10.0*white_noise\n",
+      "tseg = (time>20.0) & (time<26.0)\n",
+      "plt.title( 'Sine wave with 2Hz' )\n",
+      "plt.xlabel( 'Time [s]' )\n",
+      "plt.plot( time[tseg], sine[tseg] )\n",
+      "plt.show()\n",
+      "lags = np.arange( -3.0, 3.0, 0.02 )\n",
+      "plt.title( 'Autocorrelation of sine wave' )\n",
+      "plotautocorr( sine, deltat, lags, rmin=-1.0 )\n",
+      "#plt.psd( sine, 1024, Fs=1.0/deltat )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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h0z5jotMKn5MtBclXxPLJts8OlBV9lXpjivTF8ukcvbW2WtuQBuDliKmpvDzR\nKshXxPJRpO8iVOqNKdIXy6dz9GbHNid8TnzFTgks+YpYPjtQVvRV6o2JT12+jg4efaemyuGzY5ts\nvuRkPrqw86wmnX3FCz47UFb0VeqNiU9dPoPLykSnCD6r0JlPFV/xgs8OlBV9lXpj4hPHZ3VDGqd8\nKkTeuvOlpfGyUjvPatL5XrALZUXfaZ5Pds5bJp/sPKbM9mxu5hvMmN2QximfXVGUmWN3wid7DsFO\newYCXbvJWYUX955MPjtQWvQbGuRNJqlUTaPz4iy7ImWXz276QyX7ZEb6KrSnStpiB45Ff/fu3Zgw\nYQLGjh2L9evXhz3m4YcfxtixYzFt2jTs27fPKSWArrK2lhbr31Uh0ne6FFxWpN/WxuvDrWwaAdi3\nz0kkLDP9ITvSt9ueKkT6gFx/UUlb7MCR6Hd0dGD16tXYvXs3Dh48iG3btuHQoUPdjnnrrbdw9OhR\nHDlyBJs2bcKqVascnXAonDiC3yN92Q9ccxq52ZnolB0Jy5zolB3p221PVSJ9nf1FqUi/vLwceXl5\nyM3NRXJyMpYtW4YdO3Z0O2bnzp0oLS0FAMyaNQuXL19GdXW1E9ogZEZvKSlAezv/swqZkb6dTSMA\nvSM3gCL9SKBIPzxU0RY7cCT6VVVVGDFiRPB1Tk4OqqqqYh5z+vRpJ7RByOyNnWz8ITPSb262vmkE\nQJFbJFCkHx7kL+EhW1vswMJi76sRMDmmZz1mRCJ9b+3atcH/FxYWorCwMOrvelWm1rev+e/Y2TQC\n6L7q0UrqRIUSPOLzF9+oUXL5dG9Pt7WlrKwMZWVl1km+hSPRHz58OCorK4OvKysrkZOTE/WY06dP\nY/jw4WF/L1T0zUCFBSl2No0AeElir148creSqlFhsQ3xReezm46oqbHHp3t76sbXMyBet26dJS5H\n6Z2ZM2fiyJEjOHHiBFpbW/H666+jpKSk2zElJSX4zW9+AwDYu3cv+vfvj+zsbCe0QajQ+9vlks2n\nQlsSH/ERn3M4ivSTkpKwYcMGLFy4EB0dHVixYgXy8/OxceNGAMDKlStxyy234K233kJeXh569+6N\nLVu2CDlxwF5vbGwaYfVZKnb57Pb8svmSk3kqqa3N2qhEx0iK+IhPBW2xC0eiDwDFxcUoLi7u9t7K\nlSu7vd6wYYNTmrCw0zs2NvJ0idWJTrt8qkT6oRt/9O/vPp8qkVRDA2BnYKqSfXb5LlyQy+f39pSt\nLXah7IpnrS2uAAAcNUlEQVRcwN6MviqRN2CvgkAV++xu/CG7+kOVyFR2dZIK9tl9ThOghrbYhdKi\nb8cRVIm8AXuOp4p9djf+kF3nrUpkKnsdggr2tbRY35DGgAraYhdKi75sUbQbCTtxBDuOJzPacOro\nstrT7vNUaHFWeKiwOMvJvaeCttiF0qIvO/1hNxJ2IsJ2hpgyow2nQ1pZ7Wls/NHWZu17tDgrPOzY\nZ6w6TUmxzifTVwA1tMUulBZ9FXpj2ZEw2RcZMv1FhUjYCZ+T+R+rz2kC9PYVg48ifRNQoTeWHQmT\nfZEh01/sbvwhc+KRMXvPaQLIV9zgo0jfBFTojVWLhHW3T2YJrJ2NP2RG+s3NfNW31Q1pAPIVN/go\n0jcBFXpj1aIb3e3T1V/sTFSrYhtAviISjhdneQmdF0sZfLW1cvl0b08/89ndkAbovvGH2RWhOrdl\nPPB99ZW9SXGlRV+FBRSNjcCAAfb4evcGzp61zqfC4izAm8VnfvYXJxOdQFd7mhV98pXI8LuvAMDm\nzcDIkda54i69o1Lvr/PiLMAb+6z4i7EhjawhuxPbAOvt6YTPzsYfOvuKCD5ZJalKi74Kky0qOV48\n2GfFX5qa7G1IE8pnxT6nom+1PZ3w2dn4Q2dfEcEna/GZ0qKvSm+siuP53T5jQxpZKQIRIqxrpA/I\ntS8tjV87KxPVKnUyMu89pUVf9io9FfKYOtvX2srLC61uSGPAjijatc3gs5PTtws7nZoq9iUm8pRS\nc7McPhW0JS5F3+7kjgq9MaDGYxhk2ic7/eGkLQ0+P0f6utsnO9JX5d5TWvSTk/lkm5XnqVDOOzKs\n2tfRwdvezqYRgPx0i+xORuecPqC3fapoS9xN5Bobf8icTPJzJOyUz07k5qTE0O+Rvko5b0DvnD4g\n1z4VtCUuJ3IBuY5gZ+MPmZG+sWmEnWepAGpEbk5z0BTpd+eTmdNX0T4dtUV50bcbndqBnY0/ZC7N\nbmrik112nqVih0/ExKPfc9Cy7ZPJJzvSJ3+JDDvaEteiL3tyTlZ0Y6c6QhXbAP9Hirq3p+72xYO/\nxF1OH/BmyG6Wr7WV/2vnWSoAn0wKBLp+JxZUypkCeueEveDTvT11t88KnzHBbEdblBd9P0/OOeUC\nrDmC7HSEFzeV7Dp23UWK/CUy/OwvTriUF30rjtDRYe0phJH4zDqC0xwmYM3xnDpdaiofVZjd+EP2\nYiKnw2danNUdXqSTdPUX2doS16JvxRGMXYLsPkvF4PNrpO/Uya0+T0W14bMXOVo/R8K62yfTX7zQ\nFtuTxva+5h9YTX+IEGFZogjIjfQB+aJv5XkqqomG3ycedbbPKF+WVbIp+95z0mErL/qyRdHPkb7T\ndAQg177QjT/M8qmU/lAhEtY1p9/c3OVfdqGrtigv+rr2xgb8HOnLts+LnD6VUEaGn+0T4Zt+1pa4\nFn2roig7EnbKJ9vxdLbP6sYfXkTCqqwgBazZZ7S73fJlwJt7wc/aEreiL3Oi0+Dza05fd/uc8lmd\nqBYxsvBrzhuQa59hm93nNAHejHr9rC1xO5ErO8fudz5Vog27fCrZJ3ui2optxlaQdp/TZJWPfEUs\nX1xP5MqOTGXnvMk+7/ictmdiIk9nmN34Q2bk7XQrSKt85Cv+4VNe9P3cGxMf8Znlc7oVpBUuQO+2\nJL7o0EL0ZU+2yOTze46d7BPD19rKo267W0EC1jb+ULEtyVfE8Ckv+l5Mtvi19xfx2Ac/2ye7Wkim\nv4hoSysbf5CvxIZffcUpn/Ki7+cFFPHQyaji6AbM+ovTrSBD+czYJ8I2QG4nY2XjD519BaD0jlTo\nuoDCgBdlarrbZ1YU09OdlRgafGbsE2EbYL49RfBZ2fhDZ18RyUcTuSbg9wUUNISODrP2iXiWCmBN\nFJ1yGXxWOhmnkBnpA3LtM2wzUwKrak6fIn0T8PsCCpmOp7N9TreCNGDWX0SmW/wY6atoX3IyH13I\nmqj2u7bE7USurmVVdvlUiTas8nkRecuOhFWLvK3yqWafX+8FIM4ncq1s/CE776Zi5G2Wz8l2bXb4\nVIxMrfCRff7j87O2xHV6x8rzVOIh0pcZeeseCRNffPPpqi3Kiz5g7cKIWiBidjJJ1oIUY7s2J89S\nscIncuLRj5GpF/ZRe4rlU80+WdqihejLrIW2svGHzN7feHiW0xJDP0duMnPQotIRfm1PEZVQVvjI\nX2LDirbEdU4f8Gfvb5QYysrzUY5WLJ/u7am7fTq3p1Nt0UL0/RhttLTwEjOnJYYpKXzSNNZkkoo5\nU+IjPuKzztfSwkcEdrVFG9H324IbUVzGZJIZPhWHz14sJvKbr4jk03lxFkD+YnA5sU0L0TfjCEa0\nnJIihi+WI4hyOsCc44lyurQ0Pj8QazJJ1ZtKtmj4dXGWqhOrfvQXL7TFiW22Rf/SpUsoKirCuHHj\ncPPNN+Py5cthj8vNzcXUqVMxY8YMXH/99bZPNBrM9I5GDszpRKdVPhGQyZeYyJ031vNUZE90Eh/x\n+ZVPNW2xLfrPPfccioqKUFFRgfnz5+O5554Le1wgEEBZWRn27duH8vJy2ycaDTLTH8Qnls+LyJv4\n9OYztoKUFemrdu/ZFv2dO3eitLQUAFBaWoo//elPEY9lZjcJtYk+fWI3VH09P474/MXXqxePkFpb\n5fCZsY341OZrbORpSidbQVrhU+3es90s1dXVyM7OBgBkZ2ejuro67HGBQAALFizAzJkz8etf/9ou\nXVT06cMbIhpEX5i6uujH1NXJ5RNtX6z2lG2fKD4ztgFiRSqWbYBY+8zwibQvVnsaW0GKiIa9uPd0\n05akaB8WFRXh3LlzV73/y1/+stvrQCCAQISE1kcffYShQ4fi/PnzKCoqwoQJEzB37tywx65duzb4\n/8LCQhQWFsY4fY6MDHMNlZFh6udM8ZlxBJl8ou0z08kMHiyOr74eGDAgOp8I+1JS+PC/tTX6c4NE\ntaeZaweIs88sn0j7YvlKYyN/jo3T8mWDT/a95zdtKS8vw8mTZQiRS0uIKvp//vOfI36WnZ2Nc+fO\nYciQITh79iwGR1CAoUOHAgAGDRqE22+/HeXl5aZE3woyMoAwfVM3yBZFLyJh2faNHi2GT6Z9gUCX\nfdE6GZmiaPDJivSNyFsEXzzcC37TlhEjCjF7dmFQ9NetW2eJw3Z6p6SkBFu3bgUAbN26FUuXLr3q\nmMbGRtR9a0FDQwPeeecdTJkyxS5lRHgxBPNbpE/2mYdMf0lJ4eV8ZuYsZEX6jY1i9iYAyFe84nPS\nnrZF/8knn8Sf//xnjBs3Du+99x6efPJJAMCZM2dw6623AgDOnTuHuXPnYvr06Zg1axYWL16Mm2++\n2f7ZRoAXQzDiIz4zMEYW0W5kxsQJh85tSXxi+KKmd6IhKysL77777lXvDxs2DLt27QIAjB49Gl98\n8YX9szMJv062DBokj48mcq3xeeEvWVnhPxcZeaekAO3tfMFQcnL4Y8hXrPHppi1arMj142RLPEzk\nqtye0ewTGXkbfNHsE2mbmZEF+Yo1Pt20hUTfRT6VJ6/i2T4j8k6yPQ62xieyLYHY7SmyLVNTu0YW\n0fh09RWv+Dyp0/cT/DoEkx1NyR6yqxp5x7JPJJfBJ0uEgdjtKdK+QCB2e6omiqHQUVu0EH0dh2BW\n+XQesjc18Zp6mZG3aBGWld4xw+dFJyNzZEHpnejQRvT9mOeTvSJXluO5EXnrLsJ+Su940cmoPLLQ\nTVu0EP3UVN7zt7dHPiYe8oqy7Gtq4pUhOue8dU7vkH3moaO2aCH6Ru8vy/H8tkDEiLxldTJu5Lz9\nFnnLHlnIbE+V5xAAuf6io7ZoIfqA3CF7WhpfYRmr95clws3N4iNvP4lGPKR3VM2xG3zkL2K4ZGiL\nNqIvc8geK68ou87bi5ywSD4vImHZ6Q+Z9pG/qOsvMrRFG9H3U/TW3Myj7kgrIq0iLY1vhhyp91c9\ncqOJXLntSf5iDX7yFxHaoo3oR3MExuRGN6KdLlbvTxNz1kATuWSfFeimLdqIvszI2+CLJMKih5dA\nbNEXeVOlp8ceWag6MQf4MydME7nm4Sd/UVFbtBL9aA0l0gkMPlmRfiw+0fYZI4tI27bRxJx1Pj9F\nwqqnd6LZ50bkrZu2aCP6sYZEbkTexCcGsUYW8ZDeIT4xfM3N/Gml0XZFE8mn4r2njeh70RsTnxgE\nAnz/1EgjC9mRKfGJ4xO9hiQWn+r3ggw+rUT/ypXwn7mVbiE+NfmMkUVHhxy+aLbpztfSAiQkiI28\ndfZNGXzaiH7//kBtbfjPLl/mn4vmi3Rhamvd4ZNtn67tGQgAffvKsy+abYB4+6Jdu85Ofi59+8rh\nU91XDD6d7NNK9C9fDv+ZWxeG+IjPDFJT+b/NzXL4otlWX89HOqJWb8fiU/3a6chHok98xOchX1sb\nT4H07i2Oq29fHil2dl79mc5tSXzmQKJPfMQXAsb4+/36yeGrreVcgYA4rqQk3omEqwDR+doRnzmQ\n6BMf8YWgsZFPOoqceIzG54ZtsvnS0/lDwlpb5fD5xVdU5SPRJz5f83V08FJO0RUSOouwbL5AIPJk\npxt8/frx32VMDp9f7gVRfCT6xOdrvitXuOAnCPZUnUVYdz5jJNbYKIfPL/eCKD5tRL9PH+4E4VZ1\nqnhh/MrX3MwjLKMixW0+HUSK+NTm001btBH9hISuqoWeUPHCmOVzo+46Gp9hm8iJRzN8okF8xGcW\nummLNqIPyHWEaLXXMh3BjbrraHw63MTER3x+5nNbW0j0BfO5UXcNRK691sHJiY/4iC82nyht0V70\n3aq7jsTnRt01ELn2WmcnJz7i8zOfqtqivei7VXcdic8tp5PNF6n2WuebmPiIzwqfqtqivejrIsKy\n+SLVXrvFF6n2WmfRID6xfG6t6YjEp+q9TqJPfL7gi1R7rbNIEZ9YPrfWdETiU/XeI9Envrjki1R7\nrbMoyuZza01HJD5dfNNtPhJ94otLvki11zqLsFtrOiLxGbaJLmqIxecGdOLTSvQzM4FLl7q/V1Pj\n3oUhPuIzi9RULn4901du8YWzra7OnTUdkfh0uXa68Wkl+gMHAhcudH/v/Hlg0CDiIz7/8TU08Ohb\n9JoOgE+MNzZ2r77SuS2Jzzy0Ev1Bg3jDhMLNCzNoEPDNN93f++Ybd/l62uc2X0/7dG7Plhagqcmd\numuDL9Q+oy3dSH8kJAADBnQXDjfbcsAAHomG7juss68A6tpHok98vuVrb+clo1lZcvguXOARlhsi\nHI7PzbaUzZeYyFMPFy/K4TMi4dASX53vBZF82on+N990dwTde3+ZjtfZ2SWMMvguXuS5zcRE9/hC\n29NNXzH4ZIt+uJGFm3yh9rnZnikpfH4idLJTxr2ng7ZoJfrGJFV9fdd7588Dgwe7wzd4cPgLI4uP\nMXcdvSdfTQ2vg3ZjBWI4PjfbkviIzwp00hatRB+QG01lZPCJsqYmOXw9baur4wKcliaHT6d0BPER\nn5/53NQW7UQ/Oxs4d67rdXU1f88NBAL8t6ur+ev6ep4C6dPHHb7MTF7xYTxy9dw592wD5LZlOD6y\nT20+3dtTVW3RTvSHDweqqvj/jUcRu7EYJRxfVRWQk+PeRGBCAjBsGHDmTBff8OHucAE8d9/Q0BVt\nnD7N7XMLOTldbQm4b1/otQO4fTL5DH9xC35oT1n2MSbXPpW1RTvRD3UEt0XY4Dt9mv/fbSc3+Az7\n3OYLBHgn07M93UJWFu9gGhr4ay86Gbf5DF8B5Nsng8+wT4YIh/LV1natsnaTTwdt0VL0ZYtwKJ+b\nTg7w39fVvkCge3Tjtn39+/ONKYw9CmSKosHndmTqlQjX1PD5JrdSnYD394Kq9552ot/TEWSIsKxI\nGOjuCG7fxIBcEQaujqZkdzJu8vXty8XXSA2cOSNHhBnj5a/p6fzPLejsK4A+2qKd6I8cCZw4wf9/\n4gR/TXzEF4uvrY1Pmg0b5h5XINDFd+4cH2m48QRKA0Z57YULctpyxAguhh0devuK6ny2Rf+NN97A\npEmTkJiYiM8//zzicbt378aECRMwduxYrF+/3i6daUyYAHz9NY9uvv6av5bBB+jN19kJVFQA48fL\n4aut5WkXt6NFg+/oUS5aKSly+GRcO9l86em8jvzECTl8o0bxMsaGBrn3guraYlv0p0yZgu3bt2Pe\nvHkRj+no6MDq1auxe/duHDx4ENu2bcOhQ4fsUprCwIFAcjKPpA4dAvLzXaXDuHHA8eM8UozGV1ZW\nJoQvP5/zNDfzId+YMUJ+NiZfZSV/Jk2459KIsi2U79Ah7uRuTpT15HP72pnlEwmd7UtMBMaOBQ4f\nlmOfX7XFKmyL/oQJEzBu3Liox5SXlyMvLw+5ublITk7GsmXLsGPHDruUppGfD3zxBXDkiPuRaWoq\nj0Y/+og/KyZS3a4oxxsxgkfAe/cC11zDndBN5OfzKOPAAXmi8be/8T9ZohiLT6R9EyfGh30HD+pp\nnx+1xSpczelXVVVhxIgRwdc5OTmoCq0hcwmFhcD/+3/A1KnuPLa2JwoKgEcf5f+6HZkGAsDcuZyv\nsNBdLoBH9mPHAj/7GbfPbYwbxx8JvGGDHPuuvx7Yvx947TU5fPPmAX/5C7Brl5z2LCgA3nwT+OAD\n7jcy+H77Wx4oXHedHL4XX+T/Hz3afT4dtCWq6BcVFWHKlClX/b355pumfjzgtgJGwJ138t74nnvk\n8N19N7BvH7B8uRy+e+7hfMuWyeX73vfc50pI4O35xRdASYn7fH36AIsW8cj0H//Rfb4RI3guuqEB\nmDbNfb6ZM3neOz8fGDrUfb6bb+ajwiVL3Hs8SCiWLuW+ec897gdcgCbawhyisLCQffbZZ2E/+7//\n+z+2cOHC4OtnnnmGPffcc2GPHTNmDANAf/RHf/RHfxb+xowZY0mzhWycxkKfNxqCmTNn4siRIzhx\n4gSGDRuG119/Hdu2bQt77NGjR0WcCoFAIBCiwHZOf/v27RgxYgT27t2LW2+9FcXFxQCAM2fO4NZb\nbwUAJCUlYcOGDVi4cCEmTpyI73//+8iXMbtDIBAIhLAIsEhhOoFAIBC0g/QVuZWVlfiHf/gHTJo0\nCZMnT8ZLL70EALh06RKKioowbtw43HzzzbgcuiWOQohkn9nFbH5HJPsef/xx5OfnY9q0abjjjjtQ\nW1vr8ZnaQyT7fvrTn2LatGmYPn065s+fj8rKSo/P1Doi2Wbg+eefR0JCAi5duuTRGTpDJPvWrl2L\nnJwczJgxAzNmzMDu3bs9PlN7iHb9fvWrXyE/Px+TJ0/GE088Ef2HLM0ACMDZs2fZvn37GGOM1dXV\nsXHjxrGDBw+yxx9/nK1fv54xxthzzz3HnnjiCdmnJgSR7Dt06BA7fPhw1IlvFRDJvnfeeYd1dHQw\nxhh74okntLt+V65cCR7z0ksvsRUrVnh1irYRyTbGGDt16hRbuHAhy83NZRcvXvTyNG0jkn1r165l\nzz//vMdn5xyR7HvvvffYggULWGtrK2OMsW+++Sbq70iP9IcMGYLp06cDAPr06YP8/HxUVVVh586d\nKC0tBQCUlpbiT3/6k+xTE4Jw9p05c8bUYjYVEMm+oqIiJCRwd5o1axZOhz5OUiFEsi8jIyN4TH19\nPQa6tVGwi4hkGwA8+uij+Pd//3cvT88xImkLELnYRCVEsu+///u/8dRTTyH525Wag2Jsr+XpA9dO\nnDiBffv2YdasWaiurkb2t0vOsrOzUW1sGaMwQu3TEZHse+WVV3DLLbd4dFbi0NO+NWvWYOTIkdi6\ndSuefPJJj8/OGUJt27FjB3JycjB16lSvT0sYDPtmz54NgKc/pk2bhhUrViibOg5F6PWrqKjAhx9+\niNmzZ6OwsBCffvpp9C+7PSSJhLq6Onbttdey7du3M8YY69+/f7fPMzMzvTgtYairq2PXXXdd0D4D\nqqd3DESy7xe/+AW74447PDorcYhkH2OMPfvss+z+++/34KzEINS2hoYGdv3117Pa2lrGGGO5ubns\nwoULHp+hM/S8dtXV1ayzs5N1dnayNWvWsAcffNDjM3SGnvZNnjyZPfzww4wxxsrLy9k111wT9fue\niH5rayu7+eab2Ysvvhh8b/z48ezs2bOMMcbOnDnDxo8f78WpCUE4+wzoIPqR7NuyZQubM2cOa2pq\n8ujMxCDa9WOMsZMnT7JJkyZJPisx6Gnb/v372eDBg1lubi7Lzc1lSUlJbNSoUay6utrjM7WHWNfu\n+PHjbPLkyZLPShzC2bdo0SJWVlYWfD1mzJioHbf09A5jDCtWrMDEiRPxyCOPBN8vKSnB1q1bAQBb\nt27F0qVLZZ+aEESyr+cxqiKSfbt378Z//Md/YMeOHUh18yHxLiOSfUeOHAn+f8eOHZgxY4YXp+cI\n4WybMmUKqqurcfz4cRw/fhw5OTn4/PPPMXjwYI/P1joiXbuzZ88G/799+3ZMmTLFi9NzjEj2LV26\nFO+99x4AoKKiAq2trRgwYEDUH5KKPXv2sEAgwKZNm8amT5/Opk+fzt5++2128eJFNn/+fDZ27FhW\nVFTEampqZJ+aEISz76233mLbt29nOTk5LDU1lWVnZ7NFixZ5faq2EMm+vLw8NnLkyOB7q1at8vpU\nbSGSfd/97nfZ5MmT2bRp09gdd9yhZCQcybZQXHPNNcpW70Sy77777mNTpkxhU6dOZbfddhs7d+6c\n16dqC5G0s7W1ld17771s8uTJ7Nprr2Xvv/9+1N+hxVkEAoEQR9Buu0QCgUAgRAaJPoFAIMQRSPQJ\nBAIhjkCiTyAQCHEEEn0CgUCII5DoEwgEQhyBRJ9AIBDiCCT6BO1w8eLF4LPThw4dGnyWekZGBlav\nXi2c7/7778fo0aOxadOmiMf89a9/xcSJE5VdDUrQB7Q4i6A11q1bh4yMDDz66KOucTzwwANYsmQJ\n7rjjjqjHnTx5EosXL8aBAwdcOxcCIRYo0idoDyOuKSsrw5IlSwDw3ZRKS0sxb9485Obm4o9//CMe\ne+wxTJ06FcXFxWhvbwcAfPbZZygsLMTMmTOxaNEinDt3LioHwHdJmzJlCqZPn46CgoKwxxAIXoFE\nnxC3OH78ON5//33s3LkT9957L4qKirB//36kpaVh165daGtrwz//8z/jD3/4Az799FM88MADWLNm\nTczf/bd/+ze88847+OKLL/Dmm29KsIRAMI8kr0+AQPACgUAAxcXFSExMxOTJk9HZ2YmFCxcC4E+e\nPHHiBCoqKvC3v/0NCxYsAAB0dHRg2LBhMX/7xhtvRGlpKe66666YKR8CQTZI9Alxi169egEAEhIS\nglvNGa/b29vBGMOkSZPw8ccfW/rdl19+GeXl5di1axeuu+46fPbZZ8jKyhJ67gSCXVB6hxCXMJNf\nHz9+PM6fP4+9e/cCANra2nDw4MGY3zt27Biuv/56rFu3DoMGDVJ2v2CCnqBIn6A9AoFA8N9w/w89\nJvR1cnIyfv/73+Phhx9GbW0t2tvb8a//+q+YOHFiRA4A+MlPfoIjR46AMYYFCxZotfcsQX1QySaB\n4BAPPPAAFi9ejO9+97tRjztx4gSWLFlCJZsET0HpHQLBIfr164ef/vSnURdn7dmzByUlJRg0aJDE\nMyMQrgZF+gQCgRBHoEifQCAQ4ggk+gQCgRBHINEnEAiEOAKJPoFAIMQRSPQJBAIhjvD/AcfbT1MH\n2aQnAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab868cc10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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2gTEW1EAEQtSDjUal7Xa+wMiFgs8uIyFC0ehNH4iMJKKNLzaW12VEnC4YjYVk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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab6b1e410>"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's look at the sine wave autocorrelation in more detail."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lags = np.arange( 0.0, 0.57, 0.5/8 )\n",
+      "for lag in lags :\n",
+      "    lagi = int( lag/deltat )\n",
+      "    tseg = (time>20.0) & (time<22.0)\n",
+      "    plotdatacorr( time, sine, lagi, tseg, 'Lag %gs' % lag )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SHVRIuvtnBDvrfqYVUkAlhw+7//i+qmnPUcYIxLRkvkeOn7soHAdGTuyq9cjx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\nAKBICisAAABFUlgBAAAo0k93IxY/1p27MQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89f9310>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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AFyUZP/3pT2lsbKSwsJBJkyZx9913A6oM9s9//jNTp05l8+bNpKend7md9evX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\nipmO4zFpaABSUrTPYPVcYhTHbYVnsFK0SAIyDsBHAfwWwP8GMJaJCfU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hAI8DqIVptoR9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Brrq0eNvPYesaj8u6677jpUV1djzJgxePHFF7Fq1SocPnw4CrVzJ7+8N1TovjccT1g7Oztx5513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\nAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab878de90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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++lW48UboqqMQMz/4AXzlK/D5z8PPfuaOTM9xyinw29/2bA51g7Iy+OUvOT5n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\nhJWIiIiIiIhs6f8Dqh0iA+rQP7kAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab878e510>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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btm3Mnj2b6dOn87Of/Yz777+fxMREnn76ab7xjW8wZcoU4uLiuP3223vsaxhG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\n3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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab874de90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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xQrFpk31zaC0zZ6q5oHHclH3bNujshNNOg+xse46ZlDRwPV6Iy56c09sLH300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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4adcdda8d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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XGtS//OUvXZ8dJO24uLiY0rDS7o2NjZxzzjnMnz+fRYsWUVxczGWXXeaKnIqi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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab878d310>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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L46abburxXsuyep1TX393g0ceeYQvfOELTJw4kQkTJnD++ecD8Kc//anzf582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zj0840bN2Lnzp0AgJ07d4ZtBC5fvhwnT57E2bNn0dPTg6effhobN24083B9I564dHR0oLW1FQCG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\niIiIiIjIkZiwEhERERERkSP9f6YuTufJOLcKAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4adcddaed0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Aww8/zPz58xk7dix//etf+fWvf+3K8QVBEHyN6RHsDOVXvzJNMM1bbrFxwAsv\nNM3XX2/39m9/q/Z588029vnVr5rmq6/aEAwOdXWmmZSkzmFNjabw0qWmecEF7d6urzfNXr3UPg8d\n0tznG2+Y5rRpmkLB45571Pl74AEbwqNGmebq1e3evvdetc//+A8b+xw71jRXrrQhGBwqK9X5693b\nNBsbNYWfeso0r7uu3dsHDqh9pqaaZkOD5j7/8hfTvOYaTSFv46FbnG+RcygIgiC4iZ37iq8jrLbT\n6Uyzw9RTkMZLTtmwQa1KM2wY9OmjKdxB6ilAQoKKooONjMUJE5RQU5OmYLCwHck7flwtQ3Pmme0+\nksZLzrDO2/jxKiNXi04yRLKzYfBgpbYOVunoGomwCoIgCILgQQLhsGrXSpaWqoLVDor5xo1T6Y7r\n1qlum1pI+qn9hwjQYeqphe1lVLKzIStLFXHGKNbzGbDZcOmMMzpc5DhUJ9J4SR+3a70tbNvKsGFQ\nWamaAwiCIAiCIHgE3zqsR46oMrjERBg1SlO4i8leerqat9XXq2ihFhI1cl4rGVLLE4qjaF6MR452\n7FA+SE6OarqkRRe2MmSIasS8b5+N0m2xFfu20tioHiS4bSvx8apVujReEgRBEATBQ/jWYd24Uf0+\n4wzVJViLzz9XE7NOsNJP16/X3O+QIVBTA4cOaQoGB+ucdXF6O+bwYeX5DB3a4cfW/rR1Akqh69bZ\nEAwGoTrR7h/Sha0YhgNbGT1aNQ6K4TVybdvKjh0qc6CTtSHFVgRBEARBCBK+dVityZh2dBVU6LQL\nQatcT3vCZxgwcqSN0GwwMM3Wc9ZByWPXbNwII0Z0WsxnLVv0xRcq+q3FqFExqxNwoBOInK307q1C\nvl9+aWNQ/ufECZWlHh8Pw4drCnejE+sjWw5rjNuKIAiCIAjew7cOqzWnsj0J70LQmvDZmredeWbM\nTvj27lXB5cxMGDBAU7gbnfTurQLY9fU2ylFjWCfQ+q9rP9wxTbGVCPHFF6oP2NChNjJEunFYBw+G\ntDRljwcPau47hnUiCIIgCII38b3Dqj0Jr6tTTZeGDet0E4lQ2CM06q2detrNJNzab+hxwqa4GCoq\nVOvUGMS2rezZo4rE+/fvdBNHDqvYiv0MkS4eIsTFqUQPa1MtLJ1od9ESBEEQBEGIDLHnsG7erEJ1\nHXQ9tSguVh/v2qWaO2kxapRNT9f/OIp6r18ftsOqPQlPSFChrE2bbAzM3zQ0qGgetDoxYaOhk40b\nbawcJLYvEeWdAAAgAElEQVRiz2GNpK3066cuftpdtARBEARBECKDLx3W6moVMOvVS/meWnQTnQDl\n34wYoV5bzZ3CJoZT6hxNwsPQi6Sf6rN9u0oqGDTIxrq4YeikXz+1OtTRo+oBjxYxqhNwYCsNDbB1\na2tRdyeIrQiCIAiCEBR86bBac6mRI1XTEm3hMGaJttNPBw5UHsL+/ZqC/sd2muOhQ6pL8KBBXW5m\nu8GPNagYnIQ7foigYSvap3fECOV8aXfR8j+2G2Ft364KxFNTu9xMbEUQBEEQhKDga4c1Uul00Drh\n0563GUZMTvhMszUarT0J37BBPX3opvDVaiK8dat6JqBFjKafRjpNO3Tf2l/51FTIz7fRRcvfHDum\nVqZJTOx0FafOWb8+LGU6ri2OQVsRBEEQBMGb+NJhjWTDEgtJqdOjvFwFSfv1U6uVaBGmTlJS4LTT\n1NKdW7ZoHiMGdQIObMV6AhHJbASISb1YpdTDhimnVYswo94FBSoF/MABtbyxFjGoE0EQBEEQvIsv\nHVbbEdbaWlX8Wlzc7abSKViPSK6LG4ptvQwZotK0tbto+RvbtlJWptYSysrqdlPpFKxHT9iKtSR0\n6PHCZtQo9bBCOgULgiAIguABfO2waqc5btqknNWEhG43HTJERfR271ZNnrSIwZS6nkg9Dd2/to8T\nHw/Dh9voouVf6utVJNowuu3R0x4NnVibbdoknYLDwfO2kpkJ6ek2umgJXuLGG28kNzeX0aNHd7rN\nXXfdxdChQxk7diwlJSU9ODpBEARBCB/fOawHD0JlJaSlddujpz0akbz4+NZJvvaEz0qpi6EIRU80\n9wndv6Rqd4/Vz2jIEGUvWmjoJDNTlaLW1qraTC1iTCfgwFbq6+HLL1tbmHeD2Epsc8MNN7Bs2bJO\nP3/99dfZtm0bW7du5YknnuDWW2/twdEJgiCAYRhtfiIlI/gf3zms1pqSVgMebWGNUJO1qXXMsMnJ\nUaGmAwc0Bf2LdY60I3mHDsHx48rjCQPbOgH1pdm82YagP7GtE1DnqSdsZehQ5YQ1NGgK+hfbetm+\nXdlJr15hbe7YVmwJCl5h6tSp9O3bt9PPlyxZwvXXXw/A5MmTqa6uprKysqeGJwhCjKOczT5Af2AI\n0LtbB1R9ng70BYYCqeK0xgi+c1itZjvDhtkU1hC0NtVu8GMYKv1UW9C/2NaLpZMwLzhWV9Xt2234\nOMOGxZTD6shWNm/uGVtJSYG8PCgt1RT0J3V16l+Ni1MNxLTYskVdV8LEtk4s4Ri6fsUiFRUVFBYW\ntvxdUFBAeXl5FEckCIIfUZHOVAwjG8PI0nAgDWA8UA58CdwFZIQhV9gsswV4uEsZNbZkG2MTvEb3\nxZwew5pDaczbWtm8WUvQ2tT2hG/zZpgyxYawvzh4EKqqVI+eAQM0hTV1kpamOqCWl8POnXD66RrH\nitGHCLZsRdM5cmQrw4er70EYzdD8zvbtqlLgtNMgOVlTWPMhwqBB6hh79qheY+npGscaPhwWL9Yc\noOA3zFPKVjqbzM2fP7/l9YwZM5gxY0YERyUIgr9IA4YBj6CcyHswDKPd9aU9vYGrgaTmv78B/D6M\n410JWGuRXwX8uIttk4Ei4DGgArgjzLEJbrJ8+XKWL1/uaB++c1itAJl21KipSa33qLHwoXUMW0G5\nGHKOQnWi/fBKM+ptHae8XB1Xy2EtLm5NPw2j8ZbfsW0r1dUqTVvj6YMjW7GieZdeakPYX9jWCahz\nNHFi2JvHx6uv/IYN2qISYY0B8vPzKSsra/m7vLyc/E5KM0IdVkEQhLY0AS+g0novAj4HFoYhdxR4\nBrgZ5Vj+NczjPY9yUvs0yyR1sW1y8zHGN/+9BfhlmMcR3OLUB50LFizQ3odvU4K1o0ZlZWqJjt69\nwxaxfNtt29Tan1rEUPppT0a9Q49jO/10505NQX9iWy+aadqhx3AUYY0BHNuKpqdrWy8FBerBRYwt\nAxVLzJkzh0WLFgHw8ccfk5mZSW5ubpRHJQhCNFCps30xjD4YRqZm6mwccCLk7+NhypnAeiAPKAD+\nCNSEIbcHyAcGAfO7kTFOGVstysE+ZSvDwDDSMYyM5vMgqcNew1dhpsZG5TyCVqBUYSOS17u36nFS\nUaHqziT9tGMcR41sRFhDj6strB2a9R9VVarnV1qazTRtTZ0MGgRJScpWjh7Vei6kjvXii3pj9CmO\nbUXT07VtK3FxKjy7dStMmKApLHiB6667jnfffZcDBw5QWFjIggULqK+vB2DevHnMnj2b119/neLi\nYtLS0njyySejPGJBEKKBcs5SgZ+hIqT/BfyvRupsAzALWABsBsIrJzFNM8QxrGl5rzuZ1jEfDUPm\nKPB14OdAGfAEylE+ld7AWOAPzdtdK6nDHsNXDuuuXappycCBmvVYYGuyB0qkokKJ20o/bWxUuXkB\nxnbUqKlJTYh7KmpkCW/ZArNn2xD2D6ENl2ylaWsq00o/3bhRqXT8+O5lWpAIa/fU1KgnAQMHaom5\nEvkWh9WXPPPMM91u8/jjj/fASARB8D5jgDuaX/8X8LSGbB2wC/g+UA/Uhu3o2XUIw99/Q7Nz+8Mw\nxvZnVC3uaOBO4Be2xiZEBl+lBDuKTtiIGoUeS3s+nZqqlreJgfRT23opL1eLeGo+fXClXjLgWOfG\ndsMlG7ZiHUtbL4WFanmjo0e1j+k3bNuKjTTt0OOIrQiCIAidsw+wat9qUM5deJimiWk2YZpVmOYR\nz0Ul1fi6G1scKtXYooKOUoeF6OErh7Unu55aOO4UHPAJX2iatq1JuA2dFBVBYmJr+qkWMRLNc7yk\njQ292F5GJTT9NMBYadqpqWEvO9yKw4cIW7ao7sTawgG/fgmCIAQBVYMZh2Gk2Ky/3A9cgmpIdB6Q\n5DnHM7IcBq4A/j/ge6jGTm1R5zip+UdqXHsaXzmsvoqwQkw4R1aa9oABNtK0berESj8FGz5ODDxE\nAAcRVitNW7tI3EGEFWKiSZmjNG2bDxGys1WvuaNH1fI2WsSATgRBEPyOcp7SgG+imhDlYBiJYcsr\nx/QI8Bbwn8AXWPWhsYI6B9WoOtw/AcfbOOzqHKcAP0ClF9t9MCDYxVcOq+0I64kTarZWVKR9TImw\ndk00ot6hx9M+vYMGqYVjjx2zdVy/YDvCWlEBGRnQp4/2MW1HWCEmonmObcXWkzoHtmJdv2LqKbsg\nCIIfmQH8L/AT4E26XuqlPSpt1sQ0a1texxrq/z6BaTZ28P9noKKvv2j+/TCQ2dNDjGl85bDajrBu\n2wZDhthae3PwYJV+Wl5uw8eJgQhrNKLeocez1f309NMDnX5qBUnBQa2kDUIjrNr3uhiI5jm2FZsP\nd2zbSt++0KsX7N1r67iCIAhCT1EY8noArfWogjskAKFNDwcCwW6o6jV847AeP66WUk1IUL6nFg4i\neQkJDtJPJWrUvXBPR1gt4QDrpaxMJRXk5dkIlDrQSXa28nGOHIHKSk3hgOsEHNiKadrqpm0htiII\nghB0FgEvAhuB76AbYRW6owqVCvwx8AkqNbg6qiOKNXzjsFqNfU47zUagdMsWWzV5FrZTHQsLYf9+\n5W0HFOucaJ/eujqVfmojTRscpp8OHRroSbhtnVjCNm3FMBzoxdJJgNOQbOtl9261sK2NNG0QWxEE\nQQgyKn31OHATcC7wAaomVXAL02xCdRG+BJjZ/Fqi2D2JbxzW7dvVbyvaqS1sS1Bhrb9qjSFs4uOV\nQ7Zjh+1jex3beiktVQ59YviNAUKxrRNQg7Ul6A98aStZWSpd++BB28f2MqbpQC/R0gkE3lYEQRCi\njeo+m4lh9MIwMmw181H1l4cwzRpM83BM1qBGGtOsxzSrm39Otpxjpb8+zR2aM6UZU4TwncNqTb60\n2LbNpmDbY9qat51+emt4OGDU16tlZg3DRpr29u2OdJKXp5YHqaqCat2sjADrBHxqK4ahhAPqHO3b\np2rgMzOVb66FSzr58ktV36wtHGBbEQRBiCbKuemNapZUBjwGpIrT4xNa9fcKUApchmrQJLhNbDis\nDp0jxw5rQCfhu3apdVjz81VvFi0cTsINQ6WHg43TG2CdgINIXlOTygawTqwN5OFOx0Tz+pWeDv37\nqyz83bs1hQNuK4IgCNGnF/BvQH/gesBeHwkhWvwLcD6QC/welZ4tuE3wHda6OhXeKCzsfttOEIe1\nY6I5CQ89rvbpzc+HQ4cCW1tsWy+7d6sQYFqa7WOLrXSMb23F0omklwmCIESIo4BVDnMCqIjiWAR9\nNgHWPXIrYK/UTega3zms2lGjHTuUs2pjSRuLwYNVOWp5ufJ/tYiBSbjtWkmHk3DruNqnNy5O1RZ/\n+aWj43uR0FpJ7dMbTZ2A2EpXwtHSS9++6tp54ICj4wuCIAjtUXWQ8cAEVFrwWUBtVMck6LIG+Cpw\nD3ARor/I4AuHNZq1kqD6Ag0erJwB7f5JMTAJtx01ctBIJvS4trJIA6qXykpVK9m3r/rRwgVbyc1V\nAdqqKhXE1iLADX6s72i0I6xiK4IgCF7jGLALeARYDxyRpkk+QenpCPAW8GugprmjsOA2vnBYd+5U\ntZIFBZCcrCnswmQPHKTUDRmiFsZsaHA8Bq9h22FtalJdgh3USoYeV6J5rUQ79VRqizvGtl4OHVLX\njn79HB1fbEUQBCEyqC6xhu1GSarDb9sfwT90pDun3wmhPY4d1mXLljFixAiGDh3Kww8/3O7z5cuX\nk5GRwfjx4xk/fjwPPvig9jGiPQkPPbb2vC05GXJylNMaMGzrpaJChf9SUx0dXybh7fG1rQwcqFo+\nHzvmeAxew3GatsObntiKIAiC+yiHJA1IBpLEQRGavwO9UN+JFPlOuIQjh7WxsZE77riDZcuWsXHj\nRp555hk2bdrUbrvp06dTUlJCSUkJ9913n/ZxorlMh4V0P22LabaWgEajVhJg0CBVW1xRASdOaAoH\nUCfgsFYy2rYSF6cyEgJWW3zkCOzfr55dDRyoKRxtnVjCAbQVQRAEJyhHJBV4HNUs6QNkSZrYRuk+\nBXgV9Z14HnFa3cGRw7py5UqKi4spKioiMTGRa6+9lldeeaXddk7TG3wdNbKEAxahsGols7JUY1kt\nXNKJ1Ba3R2zFe1j/zmmnKZ9cW9gFneTkqNri6mpVX6xFAHUiCILgDgnAd5tfTwImR28ogkcYjGq+\nBHAparkbwSmOHNaKigoKQ5aLKSgooKKibTtuwzD48MMPGTt2LLNnz2bjxo3ax7E9CW9sdKVWMvTY\ntgINAWwmE+0mMhZSW9wW27ZSVaVqix3WSoYeWxxWRbSbk4HKKLatlwBevwRBENzhBGpZE4DDqKZJ\nQmxTBuxtfl0OVEZxLMHBkcMaToh7woQJlJWV8dlnn3HnnXdyxRVXaB/HUa1kdrbjWklo9Xl37FB+\nsBYyCW8v7MIkPPT4tmqL8/Jg1y5XxuEVol0rGXpsW1/5ADpHXoh6hx5f+/QOGACHD8PRo66MQxAE\nIQio7MFGVFT1MmA4cEKaJsUwrbofBXwNGANIIy03sL84KZCfn09ZSDOhsrIyCgoK2myTnp7e8nrW\nrFncdtttVFVVkZWV1W5/8+fPb3k9Y8YMZsyYEfV1JS1691ZLdlRWKj940CANYXFY2wtHexJuCW/f\n7koE3gscPqxqJXv1Uj6GFi7qxKotLi+H2lpISdEQPv10ePVVV8bhFXxvK6G1xWPGuDKWSLF8+XKW\nL18e7WEIghAjmGZDc/DmH81/i2MS65jm0ebvxGvNf8t3wg0cOayTJk1i69atlJaWMnDgQJ577jme\neeaZNttUVlaSk5ODYRisXLkS0zQ7dFahrcNqsWePmvRmZ0evVtKiuFg5rNu323RYTdOVCJYXcNTc\nx0W9WMd3tL7kxRe7MpZoE+oYRatWElRtcVGR2uWOHTBypIZwAB/uWN9NbVuprYUDB9R6Xi7giq14\n3GG1HnRaLFiwIHqDEQQhJhCHRDgV+U64j6OU4ISEBB5//HFmzpzJyJEjmTt3LmeccQYLFy5k4cKF\nACxevJjRo0czbtw47r77bp599lmtY1jNdGwFwb780tXoWWhasBYZGSoFdf9+18YSbWzr5dAhVSvZ\nyUMLXWzrxBIOUEfaQNhKUZGqLdbOu/cutvVSWtoarnYBsRVBEIS2yHqZQiSR75d7OIqwgkrznTVr\nVpv35s2b1/L69ttv5/bbb7e9f2tyNWSITeFLL7V97FMpKlK/S0ttCu/cqdp1BgDbetmxQwm5ZLyD\nB6vfpaU2AthFRbB2rSvj8AKObeXaa10bi21bSUqC/v1h924IaejmVxoaWsukre9q2Fi24hKOr1/i\nsAqCECBa11BtAEwMIw7TbIryqISg0Pr9agSaMAxDIq8OcBRh7QkcT8K9NOGzJeg9amth716V+qm9\nrqTLOsnIgL591Tqs+/ZpCgdIJyC24kXKy1WweOBAVVushcs6scoYysttNMcOkE4EQRCUM9EbeBjV\n6XcV0FsiYYIrtDqrjwO1wIdAmny/HOAbh9WaAGtRWiqT8Ahg/Ru2shVdnoRDq1527rQhGBCdgANb\naWiw0Umsa2zrxBIOiF68dP1KTlaOc2OjUrcWAdKJIAiC4iRwW/Pr0cD5URyLEDwMWtfonYis0esM\nzzus1hxJe952/DjU1KilS1xCHFaFo0heaanN2Xvn2NZLbq5qrXv8uKvjiRa29VJers5FUpJrYxFb\nUdi+foFSqFdsxdKJpDMJghAoSpp/HwPWRHMgQuA4CWxofn0YWBfFsfgfzzustifhVsMS7XapnVNQ\noHZXUQH19ZrCMglXRDDCqn164+LUd8RWGNBbmKYDvXhJJ5ZwQGzFS2na4EAvmZnKXg4dcnU8giAI\n0UDVEp4EpgGzUWuo1kR1TEJwaF2j9xxav1+yRq8TPO2wNjSohqEQ/YYloAJQ+fmqyW15uaawTMJb\nhb0yCbeEA6CXfftUbXFWFvTpoykcAZ3k5Sl72b8fjh3TFA6ITkBsRRAEwaso5+EYsBSoAI6IQyG4\nhmk2AEdR36+9mObhKI/I33jaYQ1tWJKcrCnscv2Xhe0J3+DBgUmps12XZ4UBvZLmaAkHYBLuqFYy\nAo5RXFzrQ6ZYri22/g1tvdTUwMmT0K+fq+MRWxEEQWjFNM02P4LgJvLdcg9PO6yOoxMuO0bgYMLX\np49qE3rggMsj6nls66WyEtLSoHdvV8cjk3DvRfLAgV4KC1ufVvkcRyUNRUWuLf9kIbYiCIIgCILf\nCLbD6qVJuCUcgAmfo0l4BHRy6lqsWsS6TsB72QjJya1rsfqYujr1L8TH21hSVq5fgiAI3WIYhiwV\nIngW6/sp31HniMOqSaxP+GpqVN+VlBTIydEUjpBOrLVYa2tVzaQWAdAJeK8bLYit7NypHqAUFkJC\ngqZwhGzFWrmorEzWYhUEwb8oJ6APkAgkYBjp4hQInqJ1LdZeQLJ8Px3iaYfVdv2XJSyTcNcJ1Ym2\n7UXIMQIXluvwObZrWE+cUGnq+fluD0lspVT99tL1S9ZiFQQhGKQB56GWCzmEWkdVt9mJIEQG5Zym\nAo8DtcCHQJo4rQ7wtMNqO8IaoYYl0Db9VJsATPi8GPUGWYvVtl527lQhwPh418fkyGG1mpT5GK/a\niu1rmKzFKgiCZ+gF/LD5d2/gLpQTKwheIQ74bvPrCcDk6A0lAATTYbUmexF4klFYqHZbXh6ba7F6\ndRIey2uxNjbCrl3qtXZQzos6sYTFVlwdj4WsxSoIgv9pAN4N+ftdoC5KYxGEjjgJbGh+fRhYF8Wx\n+B/POqwnTrQ2LCko0BSOUBMZkLVYHdVKRlAvsewc7d6tHp7k5anaYi0iqJNYX4vVtsMaoeWfLGLZ\nVgRBCAo1wG+BqcDZwF9Qa6oKQvRRy9g0AucAs4FhwAlZ3sYBnnVYrYiR7YYlEZrsQWyvxWq7VrKx\nUXV6sbq+uEwsT8Idr8EaIVuJ9bVYbdewHjyoLnqZmS6PSBHLtiIIQjBQE//jwPvAp8AxcQYET2Ga\nDcBRYClQiWkejvKI/I1nHVZHDUt27mydKUeAWF6L1XI8tPWyZw9kZan/PwLE8iTctk4sYS/ayqBB\nvl+L1bZevKoTS9jHthJLLFu2jBEjRjB06FAefvjhdp8vX76cjIwMxo8fz/jx43nwwQejMEpBsI9p\nmm1+BMFryPfTPXRjlz2Go0n4rl3wla+4OZw2uDLh69/ftfH0JFbkW3s+vWtXRCfhp67FqlW+XFQE\nL78cgVH1DLZ1Ygl70TlKTlZN03bvtrGIafQ5cQIqK1WgdMAATWGv6sQS/vJL9wYjRITGxkbuuOMO\n3nrrLfLz8znrrLOYM2cOZ5xxRpvtpk+fzpIlS6I0SkEQBEEID89GWC2HNVCTcEvYpxGKw4ehulrV\nSWZnawpHWCexvBar5bDayrYWW4kIVn17QYGNBswR1omsxRp8Vq5cSXFxMUVFRSQmJnLttdfyyiuv\ntNtOnvoLgiAIfsCzDqs1J7I1b9u5M2K1khC7k/BQx0i7AXOEdQKxuxar9XBH+/SePAn79tkIAYZP\nrNqKbZ1YwhG0lV69lMplLdbgUlFRQWFIZkJBQQEVpyjbMAw+/PBDxo4dy+zZs9m4cWNPD1OIcQzD\naPMjCEFBvtfu41mH1XaE9fhxOHIkoim3sToJdxzJ86rD6vO1WG3rpaJCeS7aXc3CR2zFprBXbUXW\nYvUF4UySJkyYQFlZGZ999hl33nknV1xxRQ+MTBAU6juaBpwGZADpMrkXAoFhJAApwFAgBcNwf537\nWCR4NaxlZarmLS5yvvipa7EmJmoIFxXBsmWRGlpEcTwJ/+pXXR3PqVjfFe2OtKFrsZ5S4+V1TNOB\nXnrQMbK1zG1REXz8sYuj6Tn84LB+9JENvYSuxZqVFYmhCS6Qn59PWVlZy99lZWUUnLI+XHp6esvr\nWbNmcdttt1FVVUVWB3qdP39+y+sZM2YwY8YM18csxBoZwP3AD1HL0ZwDrI/qiATBKeqhSwqwChgJ\nrAXOwzCMmC7BWL58OcuXL3e0D086rPX1KvhjGDbWYO2ByZ61Fmt5ufrRWmdx8GCbs/fo44dJONgM\nyll68ZnDeuiQWuM0PV3V8WrRAzqx1mLdt08FsFNTNYSLiuC55yI1tIgSE7YiDqtnmTRpElu3bqW0\ntJSBAwfy3HPP8cwzz7TZprKykpycHAzDYOXKlZim2aGzCm0dVkFwh3rgmubXacDVgKSlC0GgCOWs\nAowDcoHSaA3GE5z6oHPBggXa+/BkSnBFBTQ1wcCBarKrRYQblljE4lqsXu5GC7GZfhqqE+1sqh7Q\nSayuxWrbVk6cUJ3N8vJcH1MosWgrsURCQgKPP/44M2fOZOTIkcydO5czzjiDhQsXsnDhQgAWL17M\n6NGjGTduHHfffTfPPvtslEctxBaJgPWdOwY8DzRFbziC4BqlwIbm1yXA3ugNJUB4MsLq5YZLFkVF\n8P77NuZtGRnKCz94UC3b4SNsR40OH1Zh8759XR9TKKFL22jj00m4o0jezp0wYYKr4+mIwYNh61Z1\nerUC2KFrsWq32o0utpsulZWp9I0IljSAOKyxwKxZs5g1a1ab9+bNm9fy+vbbb+f222/v6WEJQjM1\nwALg98AhwH8P8QXhVEzTbK5hnQTkA7uBkzGdDuwWnoywOl7SpoccVoitCZ/tSbilkwg3VDh1LVYt\nYk0n4H1bSU5W6yft2ePyiCJLaF2x9hKyXteJJexDWxEEwTuoCfwxYCdwGNM8LJN6IRCYZgNwAtgO\n1GKajVEeUTDwtMOq3XAJZMIXIRoaWpfA8GJdMah+MJmZqlbywAFNYR/qBLxfKwmxZyv790NdnUoo\nCOlrEx49pBNZi1UQhGhjmmbLjyAECfluu4+nHVaJsHqHPXtUZmZengp8adFDOoHYW4vVtsNqhQG9\nbitWzbeP8MNDBGst1oYG2L1bU9intiIIgiAIgj8JlsPa1KRq3rTz8PSJtXpJxw2XvO6w5uWpZje1\ntS6PKLLY1suhQ2r91T59XB/TqcTawx2/2Irta5isxSoIgiAIQg/iSYfVdtOlykrV1Cglxe0htePU\ntVi18PEk3HbUqAc6N4MD5yh0LVYf4WgNVq/rxBL2ma04qiveudP7esnMVL+rq10cjSAIgiAIQsd4\nzmFtalJ1VWBj3taD0YnkZLXsjhXU1SIWJ+Fej7Bawj7Sy8mTKlU7Lk59F7XoQZ0MGACJia1rsWrh\nM52AP1KCwYGtGIYv9SIIgrsYhoFh9MIwsjCMJIwIN1YUBC+i7CCt2Q7ixA4ihOcc1r171US8f39I\nTdUU7sHJHsTWWqyBn4Rbwj6ahJeXq69Qfr7K7tWiB3USa2uxOqorLivrkZIGiC1bEQTBXdSkPB24\nDPgdcG7z34IQOyg7SAXmAb8GTgN0G70I4eA5h9UPDZcsbE/4rLVYtVvZRg/bk/CGBhUGzM93fUwd\n4XgSvmOHe4OJMH55iAAO9DJokHLiGv3TFt62Xvbvh7Q09dMDxJKtCIIQCfoBfwe+A/wTaJLokhCD\nzAR+A9yAsgOxgUjgOYfVLw1LoHWM1pi1ha3cZx9gexK+Z48KlycluT6mjgjViXYAO1Z0Ygn7wVZ6\n9YKsLFWf7hMc1RX7QSeWsI9sRRCESNCL1mlkIqCb6iMIQaB3yOs0wD/Zk37Csw6r1xuWQOsYbU34\nfNbgx/aDhB5s7gMqeJ2eDkeP2ugJEys6sYTFVlyntlbV6iYmqtpdLXr4+mVlHpeVqVp8LXykE0EQ\nIsUu4MfAe8B3Ad0LiSAEgcXA74HlwNdRD28Et/Gcw+qouU8PRygcT8JtCfY8NTXqJzVVBbu06MHm\nPqD6wdjWi490Ai483ImCrdjycXykFyvoWFCgane16OHrV1oaZGerngH79mkK+0gngiC4j2mawDHg\nCeBKYAlwpPl9QYgN1Pe9FrgPuAr4HDga1TEFFc85rH5Kc4yVSXioTrTLU3pYJ+BALwMHqtRT7XWK\nokKRfgYAACAASURBVIPthzt1dap+WjsEaJ9Yebjjp+sXOLAVH+lEEITIYJomplmNaR7ENGvEWRVi\nEmUHh5rt4KjYQYQIjsN67Jj66d/f9TF1RugkXPv76aMJn18n4dqnNzERcnNh927XxxQJbOulokI5\n5/Hxro+pM8RhDVO4h23Fdh1rTg4cPqxyoAVBEARBECKIZx1W7VIuazmIHuxQl54OffuqgNX+/ZrC\nMgmPGI6ayfhEL6bpn+Y+0FovWV5uo+GvT3QC/rMV2w8S4uJU3rM0XhIEQRAEIcJ4ymE9cgQOHVKN\nQfv10xTu4Zo8C9sTvsGDfTcJ90PnZnAYzfOJXqqq4Phx1WQqI0NTOAo6SU6GvDzlrO7Zoyk8eLBv\nGvw4spUoXMNiwVYEQRAEQfA3nnJYHddK9mCHTQvbE768POV1nDjh+pjcxnHUqIf1Egsdaf2mE4iN\neknbdcW1tSrFNjfX9TF1RSzYiiAIgiAI/sazDqst4ShGWLXnbXFxkJ+vciQ9ju1JeE2NCqllZro+\npq6IhWZYjrpp+y0bITtbPdg5csT1MbmN7WtYWZnN1sLOiAVbEQShawzDwDDiMIwEjB4sqxIEP9Fq\nJ3FiJ1FAHFaHxEIzGce1kj1s2AMHqnn/nj1qyQ4tgq4TS9hPtmKtVeTxesmmptYhWjW7YeM3nVjC\nPrAVQRA6R028U4BbUGuqpslkXBBOodVOvg/8AEgRO+lhxGF1SNAb/DQ0qKayhqECwlpESSeJiWqs\npqnGroUPdAJiK15k/37VgC0rC3r31hSOkk5ycyEpCQ4eVE3WtfCBTgRB6I4U4A7gv4GfA/8D9I3q\niATBe2QCDwK/Bn6FspWezR6MdTzlsFppaX5pWALBb1qye7eKHOXlqcY5WkRpEg4Om2Ht3GljnaKe\nxbbDarUXFltxHUcPEaJ0/YqLa40GawewfaATQRC6IxEInXTppocIQiwQD4Teowc1vyf0FJ5yWG1P\n+BobVShNOw/POUFvWuK4Q3AUmvuAA71kZKhZfHW162NyE9t6qapSTx7S010fU3cE3VYcPXDzo60U\nFiovt6nJ9TEJgtBTHAbmA+8D64HbAe83gxSEnqUa+BGwGlgD/BCoieqIYo1gOKyVlWpB1F69XB9T\nd+TlQUIC7NunGn1q4YOUOj9GjSD4zWRs60V0EjH8mKYNDvSSkgJ9+qiLnyAIvsQ0TeAgcClwHrAF\n0J3MCELQaQR2AxcA5ze/bojqiGINTzmsVsPcggJNwShO9uLjW8ernVLng0m4o260HpiEB7Fesq5O\nNZSKj4cBAzSFo6iT7Gzl49TUqB8tPK4T8L/DGkRbEQShe0yzCdOswTQPY5rHm51YQRAsTNPENOua\n7aSm+bXYSU/iKYe1sVE1AdEOlEZxsgcOmskUFiohD3/pZRLuPawHO/n5KrqvRRR1YjX7hWA+3LFt\nK1Z74SiUNEDwm2EJgiAIguBvPOWwgv8cI3DgHPXuDampcOCA62NyC9uT8IYG2LvXRmthdwjyJNyv\nDxHAgV4KClQHsMZG18fkFrb1sn+/qilOTXV9TOEQ5Ic7giAIgiD4H085rJMmwbhxNgSjWJcHwe5+\naru5z+7dkJOj1piJAqE60Q5gB1UnlrAfbSU5WeUU79nj+pjcwnbTJbl+CYIgCIIgdIqnHNZPP4Un\nnrAhGMUOmxDs7qe2o0ZR1klGhuoHc+wYHDqkKRxUnVjCHrCVoDVeOn5cJUokJqqyBi2irJPQZW20\nG/563FYEQRAEQfA/nnJYbeORqFHQJuHV1XD4MKSlqSbMWkQ5agQO9OJhnYDDRlh+juZ5WC9WTW5h\noVoVSYsoX79SU6FfP6ivV1n8WnhYJ4IQqxiGgWEY0R6GIPgOsR3vIg6rCwS1XjI0kqdtv1HWCThw\njgYOVEt11Ne7PiY3sB1hratT67Dm5bk+pnAJqsPq57picHAN87BOBCHWUJPtdCABSMAw0mXyLQhh\noGwnA0hE2U5KtIcknIL/HdajR1U+Xr9+URuC45Q6j074YnYSnpCgnLqKCtfH5Aa29VJerpzx+HjX\nxxQujh7ueLhe0u+2YvtBQv/+rddgQRCiTC9gPFAN1AATgeg0cxMEf5EBXIdag3gX0F8e9ngM/zus\n1mQvil+s3r0hK0sFsPbv1xSWSXjECGIzGdN0WFccZZ3k5ytTrahQjaS18HC9pJ/TtMGBrcTFqSd2\n2usUCYLgPqnAD4C05tc/BCRSJAjd0wDcC8QDA4B/RWUqCF4hGA5rFBuWWNie8Hl4Eu64G22U9RLE\nZlgHD0JtLWRmqqZSWnhAJ8nJKnjd2KgaSWvhg2wEsRVBEKLHSWBFyN/vNb8nCELXJAAfNL9uAt5F\nObGCVwiGwxrl6AQ4aPCTm6u6G9XWuj4mp9iO5Jmmp6JGQWqG5Sjq7QGdgMOHOx7UCTjQy/HjcOSI\nSq2NIkG0FUGIPY4CC4HzgK8AfwCORHVEguAPalBR1UuA0cDK6A5HaIc4rC5huzYvLg4KClR9ocew\nneZYU6N+Z2S4Oh5dgtjgx1HqqUdsxbZesrLg5EnVutpj2HZYy8psthZ2l6A2jhOEWMI0TeAY8BEq\nWnSs+T1BELpC2Ukt8E9gI3BEbMdjiMPqEkF0jhzXSka5YN3qL7Rnj6ov1iJoOrGE/WwrhqE8K4/V\nSzY1tV3WRgu/68QS9qCtCEIsYppmmx9BEMJDbMfb+N9h9XuaoyXssQlffb2qMTQM1ShHC49MwhMS\nWseu3fDXgzqBYDisjqN5HquX3LdPBX6zs9WaxVp45PrVv7+qL66qUk1/tfCorQiCIAiCEAz877B6\nZBIetI60u3eryNHAgZCUpCnsEZ2AA71YOvHYUzbbzX2s9sIe0EvQ6iWt/8N2wyUP6MRq9gs2Atge\nvH4JgiAIghAc/O2wWq1GtfPw3CdoXTYdR/I80LkZHOilTx+VT3zokOtjcoJtvRw8CCkpag2mKBO0\nbISYtxWrBl97EWpBEARBEITuceywLlu2jBEjRjB06FAefvjhDre56667GDp0KGPHjqWkpMTpIVvZ\nu1c1YklOdm+fNsnLg8REtQ7r8eOawh6chPt9XUmLoEbztE+vB3UiDiueibCCA1tJSVEN1iorXR+T\nYJ+o3psFQRAEwUUcOayNjY3ccccdLFu2jI0bN/LMM8+wadOmNtu8/vrrbNu2ja1bt/LEE09w6623\nOhpwGzw02XOUUieT8IgRJOfoxAnlE8THw4ABmsIe0klWFqSmqma/VkPpsPFg+mlQbEU6BQeHqN+b\nBdcwDKPNjyAIeogNBQNHDuvKlSspLi6mqKiIxMRErr32Wl555ZU22yxZsoTrr78egMmTJ1NdXU2l\nW0/iPRQ1AgfOUWGh8nI9VC8pk3A8Nwm3Vj4qKFBOqxYe0onV7BdsrsUalPR5q72wB0oaIFgPd/zC\nY489xqEIlB1E/d4suIKaXKcDxcC3gUwMIzG6gxIEH6FsKAWYi1qfuLc4rT7FkcNaUVFBYchkq6Cg\ngIpT2rF2tE25W2uOemgSDg4mfGlpqrZw/37Xx2QX28196utVGHDgQNfHZIcgNcOyrRNL2IO2ou17\n5uerdYoaGlwfk11sN12qrFSptCkpro/JDkGyFb9QWVnJWWedxTXXXMOyZctcW0oh6vdmwUUygHXA\nIuBTQKJEghA+GcBTwLPA+8CFUR2NYJ8EJ8LhXjRPvQl3Jjd//vyW1zNmzGDGjBld73jXLhg2LKwx\n9ASuNF7KyXF1THaxHTXavbu1oNcDhOrENDWXhh00CNasici47OA46j15sqvjcYJtW0lKUmuw7Nnj\nmciko/WKPdJwCVy4fn35pavjscPy5ctZvnx5tIcRNg899BA/+9nPeOONN3jqqae44447uOaaa7jp\npps4/fTTbe836vdmwUVGAL2aXxejpm310RuOIPiKJmB882sDmAy8Hr3hxChu3JsdOaz5+fmUhRRs\nlpWVUVBQ0OU25eXl5HeysGfoTTEsdu2Ciy7Sk4kgrjT4OessV8dkB9MMRnMfUM1+MzJUreTBg9Cv\nn4awx9IcHTmsHtOLK+mnHnBYjx1T36ukJBvPmjwW9Q6twW9s1Ew7HzQIPOAonupMLViwIHqDCZO4\nuDjy8vLIzc0lPj6eQ4cOcfXVV3PRRRfxy1/+0tY+o35vFlzkQ+BdYArwKKBbDyIIsYwB3A88CewB\n/gt54NPzuHFvdpQSPGnSJLZu3UppaSknT57kueeeY86cOW22mTNnDosWLQLg448/JjMzk9zcXCeH\nbcVjE76g1EtWV8PRoypLOTNTU9hjOgEHzpGHdAIOOzd7TC9BqZe05vuFharxmhYe00lKinK6GxpU\nA3YtPKQTP/Hoo48yceJEfvzjHzNlyhTWr1/PH/7wB1avXs2LL75oe79RvzcLrqAi4MeB2UASsAA4\n6lrquCAEn8PAUqA3KlvhoNiPT3EUYU1ISODxxx9n5syZNDY2ctNNN3HGGWewcOFCAObNm8fs2bN5\n/fXXKS4uJi0tjSeffNKVgQPBjBp5gNBInnapjMcm4aCGs26dGtqECRqCAwaouuKTJ1UILcrYjrCe\nOKHWk83Lc31MdnFcL+mRxkuOo95Dhrg6HqcMGgT79qn/q5NgW+eCHrl++YmqqipefPFFBp+SGh4X\nF8err75qe79RvzcLrmFNrg3DwDQPR3k0guAvQu0HmsRZ9TGOHFaAWbNmMWvWrDbvzZs3r83fjz/+\nuNPDtOfwYairg+xs9/dtk9CUuqYmzYjLoEHw4YcRGZcujmslx4xxdTxOsR35TkhQTmtFhSccC9t6\nKS9X3od2CDByWDqxnT6/caOr47GL46j39OmujscpgwbBqlVqaOeeqyHYv7/Kjz52TDWRE8Kiq7So\nkSNHOtp31O7NQkSQibYg2Efsx/94ZwarS1mZzRBg5EhLU/7zyZMqSqGFh7psBqkbLQSj+6lpOmzu\n4zGd5Ocr0929WzWW1sJD0TyxlWYMQwlrL0ItCIIgCILQNf51WD3WYdPCUb1kENIcPagXV7o3R5kD\nB1Rmb9++kJ6uKexBnSQlqeB1U5NyWrXwoMMqtoJnbEUQBEEQhGDhb4fVY9EJcJDqmJOj0pxra10f\nky62J+FWe2GP6cWV7s1RJkgdgi2C0AzLtl6s9Nn+/V0fkxMcp2p7RC+CIAiCIAQHcVhdxvYkPC4O\nCgo8kVJnuy6vulr9HxkZro/JCUFohhWkDsEWtvXSt69ad6WmxvUx6WLbYS0rU0XvHippgGDYiiAI\ngiAIwcK/DmvQokaWsAcmfEGqlQSVehofr5bqqKvTFPa7TixhD+rFcb1klPXS1NR2WRst5PolCDGN\nYRgYRiaGkY1h9GnuYioIQnco28nCMPphGCliOzGCfx3WoE3CLeEoT/jq61VNYVyc5rIW4FmdJCSo\n4DWohrlaeEAnELzmPuD/9NO9e5W99O8Pqamawh7VSf/+kJysVkE6ckRT2AM6EQQ/oCbYqcBtwPPA\nBYBucwJBiD2U7aQADwF/A4qb/xaCjr8dVo81LAH/ry9ZUaFKUQcOhMRETWGP6gRcaIYV5ZbojuqK\nPeoc+f3hTtAaLkFr8BpsVCd44PolCP5hFPBzlLP6d6BeIkWCEBY3AbcCXwVewM+ujBA+/tRyY6MK\nA2qHACOP36NGQWzuAw4aL/Xpo1raVlW5PiYdbOtl/3613pIH18aMeYfVo7Zi+xpWUKCeeDU2uj4m\nQQgedYD1ILQeaIriWATBT5w45bU86IkF/Omw7tkD/fqp3DWPkZOj/JuDB1UTUC08MAkPYnMf8L9z\nZFsvPtCJrQC2B9bHDarDattWevVSDbEqK10fkyAEjy+BG1FpjTOBZMwoZ/IIgj/4K/AA8Bfga8DJ\n6A5H6BH86bB6OJIXF9fagEU7pc4DjpFMwjsRjqJeamth3z5Vi5uXpynsYZ307asCv0eP2mj464E1\nPx093PHwNczPtiIIfkA5pkdRqcB3AqsA3aJxQYg9lO3UAr8B7gbKME3dbpqCH/Gnw+rhSTg4mPAV\nFiovtyl6qUFBdVitNEc/TsKtRlEFBarbsRYe1olhOEg/9YBjZNtWGhtV6qzVCcxjiMMqCJHHNE1M\n8ximeRDTPCHRVUEIE2U7R5ptR1LpYwVxWCOA7Qlfaqqqmdy/3/UxhYvtbrT19SoMOHCg62NyA8fN\nsKI4CQ9ih2AL23rJz1dtehsaXB9TuNjWS2UlZGZCijc7G/rZVnbuhA8/jOolVBAEQRAEl/Gvw+rB\nDpsWjqN5UUx1tB01qqhQC54mJLg+Jjew0rR37bJRL+lXnVjCHrYV285RYqIqGN+92/UxhYuj9Yo9\nrBM/X7/+9jeYMgUeeSRqQxAEQRAEwWX867D6IGrkt07B1goo1jC08HBNHqjAdWamqgc9cEBTOMpp\njkHt3Az+jeYdPaoaRycnq7VLtfD49St0zWLthr9+thVBEARBEDyJPx3WIE/CozjhO3RITcTT0yEj\nQ1PY45NwcLgWa5TTHK1haONxvTi2lShF86yGaoWFqtGaFh6/fvXqBbm5Ktt6zx5NYY84rB4OYAuC\nIAiCoIk/HdagT8KjNOELjU5or1/ucZ2AA70MGKDCsnXR6URnO2pUWwvV1cr78Ch+zUYI8kME8O/D\nHYmwCl7BMIw2P4IgtEfsRAgX/zmsNTWqwU9WVrRH0imhy9poN/z1gMMaxOY+4KA2Lz5eNZOqqHB9\nTOFgWy/l5Sq/UzsE2HP4tXtz0G3FtsPar9//3969h1dV3XkD/+6EQAgJ4RZCbhAIxECQgEWptigq\nQbmI0HaUOm2Zx047b9sZx847FntV22qxtxlbp2PHqW9xOmOtUxFalYJKBBUay8UbyD0aEhJCQiBX\nEpL9/vHLTk7IOclZa5/Lvnw/z5MnITkrZ7HXWSfrt3/rIjdKmpsjXqdwMGAlJ5CB92gAWQDmA0jh\nYJzoEtIn0gBMAVACYBT7CYXk3JFsKJWVMkp08Is6JUXGbZ2dsiGokilT4jbN0cub+wARWC8Zh3YJ\nXFds3QgJmwvaJCdHunJ1tfQXJQ4IWNlXLmEYcWuXc+eA8+flbN+xY2P+9ESXmA6gAsCbAB4FoLrO\nhsjrEgBcA+AYgP0AvglgTFxrRM7lvoDVBdkJwJ3nS3p5cx/AndNP6+pkJvK4cUBqqmJhF7RJUpIk\nr01TI4Edx02XvHpesUX7/QuIW18JnKbt4PuZ5BtLAQzv+fpmAPFZUkLkXKMArABgHTC/FADPI6bg\n3BewumAQDtjIUGRkAE1NQGtrxOs0FO11eVYa0OHt4sa1xV5fKwnYXC/5wQca5xTZpx2wNjdL354w\nIeJ1iiQ39hVOByZn+W8A9ZAB+H8ASI5vdYgcpwnAEwCaAXQD+GV8q0OO5r6A1euD8IQEmftpbUMa\nQ9oDvrNn5fzV0aMjXqdI8t0g3GV9RTmbZ21lfe5cROsTDu0bCVabODwF6Oa+4vDZ1uQbdQByAIwH\n8GMAjfGtDpEjHQEwEcA4yE2e2P89J3dgwBolbh7waQ/CHS4rS+Lq2lqgvV2xsNvaxCrsgnbR3ngp\nTuslu7pkPytAc12xC9rEV+9fRBFmmiZMsxkyDfgsgGaYcZgJQuRk0k+aALRBAtUm9hMKyZ0Bqwtu\nodve4CfGA76ODjlzMSFBNsJR4pI2SUyUTXOBvoAjbHFaL2l7N1oXtIvbgqOaGjmjdOJEYORIxcIu\naZMJE+T/1tgoGxkpiXNfYcBKTiEDcpODcKJBsJ9QONwZsLpgRGL7uI4Y70hbVSVLAXNyJAupxCVt\nAtgIjvLypFCM31C1B+GmKdPKlVOAsee2gNUPWW8reQ1orE6Iw/sXwICViIjIq9wVsF68KGlA5RRg\n7LltR1qv7xBs0W6XtDRgxAigvj7idRqMdrucPi3bCqekRLxOkea244bYV4aQmyvnFHV1RbxOg7G1\nQRkRERE5lrsC1upq2UV3+PChHxtnGRkS3zQ0yMagSuIQsPphN1rAfdk825v7uEBgYKScwHZTmwCu\nbBflyztihJzDVFMT8TqF0tkpfx4MwxX3M4mIiEiBuwJWFw32rM1+Ac0pdW7KsLqoXdwUsLa1yTms\nSUnApEmKhV3UJmPGSDK4pUU2nFbCvhI1buor1dVAd7dsrOaC+5nkIYZh9Psg8iP2AYo2BqxRZGu9\n5MmTMgKLEdub+7ikXWyvLY7hINy60ZGXJzdAlLioTQzDRrvEMWBV7itdXRJZWTt/OZybAlYeaUPx\nIIPzNMjRNekA0jhgJ9+R1/woANkAUmAY7AcUee4LWF00ItEehI8cKWdMnj4d8TqFop016ugAzpwB\nsrMjXqdocNPuzbYzeS7qK9rtkp0t5xR1dka8TqFot0tNjUyVTU6OeJ2iwdbNHTf1FSJtaQBWATjd\n83ENAHf0b6LISQfwAIAqACcBZMS3OuRJ7gpYXbRhCRCBjZdiuJmM9oDv5EmZh5eYGPE6RYM1TVtr\nw1+3tAng2r6iHONY86WrqyNep1C028WlbeLp9y8iW5IA/A1kKDUcwOcAOH+jO6LIagewtufrsQA+\nFce6kFcxYI0it0ypM02goqLvaZW4rE3S0oCxY4H2dlkfqiTG0xy12wRwXbu4JTg6d07W2SYny8Zq\nSlzWJtbM5aoq2aBdiZv6CpG2iwCeBWAC6O75ui2uNSKKvREANvV83Qrgj3GsC3mVuwLWigpg6tR4\n1yJsbglY6+pkg5+xY2UmshKXtQlgo13iNAjXurwuaxe39BUrLs7Pl7W3SlzWJiNGSPK6q0tOE1Pi\npr5CpO08gA0ApgOYAmArGLCS/5wHcBeAYgB5kGnBRJHlnoDVNGW06Id1eVbhGA34rMFefr5mYa2C\n8aO9Nm/SJDmn6MKFiNcpGO12OX9eUsgTJkS4RtHjls2wAgNWrcIu6ytuu7njsstLLmeaJoBmAMch\ng/Smnu8R+Ye85lsBHADQAPYDigb3BKxnzsg8vNGj412TsFnrJU+elCyFkhhuWuK3gFV7EJ6YKIc8\nnozN3UPtdrECIxft0sebO86kfSNh/Hi5sdPUFPE6Xaq7u+9GgovuZ5JHmKbZ74PIj9gPKNrcE7C6\ncLA3ciQwcaJsYFpbq1g4huvymDVSLByDdunslLWDCQkap6C4sE2ys+X/euqUbDythDd3oka7rxhG\nzG4knD4tsfGECXKeLxEREXkLA9Yo095MhlmjqLG9wU8M2sU6hjcnBxg+XLGwC9skKUmCVtPUSGDH\n8OaOdl9x4ZIGwB19hdOBiYiIvI0Ba5RpZygyMoCWFvmIMu0B38WLkhJTTgHGlxumn/rtJgIQgfWS\nMZiGpN0utbWyRfWoURGuUXR5vq8QERGR4zFgjTJbU+ry8oDKyojX6VLWgE85+XPyJJCZKekxF3HT\nIFwrIVdR4bpMHmCjXdLTZT5xY2PE63Qp7eDIb21iFXZ6X/GghoYGlJaWorCwEEuWLEFjiH6Rn5+P\nOXPmYN68ebjqqqtiXEv3MgwDhpEOwxgBw0X7BBANRl7XST2v7QS+tslxGLBGmdMHfIFnsCoP+Fza\nJllZEmOfPi3H+ShxQ9bIpe3i9J2Cz5+XTaKTk2VtuhKXtonT378AZlgvtX79epSWluLw4cO48cYb\nsX79+qCPMwwDZWVl2LdvH8rLy2NcS3cyjGQAkwH8AMBaACkc2JPryWs4DcByAA8DmAuAGwKQszBg\njTJbg/AYbCZz5gzQ2gqMGSMfSlzaJoEbGSmvl4zRBj9+DFhtBUcxaBfbZ7C6sE3GjwdSUoBz5+RD\niRv6igdt3rwZa9euBQCsXbsWzz33XMjHcjdPHa8A+DKA/wBwXZzrQhQpWQCeBfB/AJQB6OTNGHIU\ndwSsVhrQhXO+bG9aEuXNZPwYGAE22iUvLybrJbXbpalJ7kBkZES4RtHn9N2b/dhXrM1+Ac21xTHY\nDIsBa3+1tbXIzMwEAGRmZqI2xBb1hmFg8eLFmD9/Ph5//PFYVtHFugGMDfi3e866JhpcOvpCglEA\nhsWxLkQDueMV6cIzWC22B+FlZZGszgC2B+HXXhu5ysSQdrukpsp5RWfORDUo9NMZrBan70hru6/c\nckvkKhNDkycD778vl/fyyxUK5uQA1dVyCHViYlTqZm2+DLjyfqa20tJS1NTUDPj+gw8+2O/fsi4t\n+HvB66+/jqysLNTV1aG0tBRFRUVYuHBhVOrrHckAboNMCX4bwP/GtzpEEfMegPUAFgP4OdySzyL/\ncEfA6tLsBCAxzYgRwNmzkvxKS1Mo7IZB+Oc+F7nKxFBE1uZFKWDt7JSpyta+W0pc3FcC28Q0FWPu\nyZOB/fujUi+LHzOsgI2+MmKEHI4axZ3Ea2uB9naZuqz03upy27ZtC/mzzMxM1NTUYNKkSTh16hQm\nhlhwnZWVBQDIyMjA6tWrUV5eHjJgvf/++3u/XrRoERYtWqRdd3drAlAOYEnPv9s4rZpczzTNnhtb\nD/d8dANo4mubIqasrAxlNhNwDFijzJpSd+SIbPg7a5ZCYTcErC5tl4hs8PORj0S0ThbrDNbcXH+c\nwWoZM0YmUVibG40fr1DYyX3FpWewWiJycydKASunAw+0cuVKbNiwAevWrcOGDRuwatWqAY9pbW1F\nV1cX0tLS0NLSgq1bt+K+++4L+TsDA1Y/4wCevIqvbYqmS290PvDAA8q/wx05fxcPwgEbwVFeXl/0\nEiV+O4PV4uQNfvx6EwGw0S4x2ODH1hmsqany4UJO3jiOAetA9957L7Zt24bCwkK88soruPfeewEA\n1dXVWL58OQCgpqYGCxcuxNy5c7FgwQKsWLECS5YsGezXEhERxY17MqxFRfGuhTbtQXhyMjB2LFBT\nA2RnR7xegI0Bn3UGq3IK0BmcvMGP7YDVxWcqTp4MvPuutMu8eQoFs7LknKKOjqi9Jm2dweriiMqz\nfcWjxo0bh5deemnA97Ozs/H8888DAKZNm4b9UZ5CT0REFCnMsMaAU3cK9uMZrJbAQbhyAtvJwQ6l\nPQAAIABJREFUg3CPtIvy5R02TIJW5XOKwuPHM1gtTn3/AhiwEhER+YE7AlZr51OXsqp+4oRmYWtU\nFmF+PIPVMmqU7Adz4YIksJVEsU0AfwesTu0rts5gdfn7V16enF188qQksJU4ua8QhUl2W06DYYyH\nYaTzfEpyHHmNjoVhTIBhpPA1Sp7j/IDVxWewWqZOlc9a47apU6M24PNzYATYaJcotgngzzNYLU7t\nK4EBqzKX95WkJFmqbpqycZwSp/YVojDJwH80gBsA/BbA3wEYxYCAHENeiyMBfAfAbwAUQc5SJfIO\n5wes9fWyJs2FZ7BabGeNtAoOze8Bq3a7WKnZ8+cjXSUA/jyD1eL0vqJ138zPfcXKsEZhB0q/nsFK\n8dAJ4BkApQB+CKAwvtUhGuA2AF8FcDOATQC64lsdoghzfsDqgcFeTo5kKWpqgLY2xcJTpzJgjRIr\nm6d8eQ0jasGRX89gtQS2iXKMw74SNdp9JTVV5t/X1ka8Tn49g5XioRt9AYAJCWCJnORCwNeqazeI\nnI8BawwkJvZtXKI1/ZSD8KjQHoRbhaPQLtYpRjk5/jqD1TJhgsQ3588DZ88qFnZiX3H5GawWJ/YV\nTgemWJDzKYdDMle/BfB5ABXxrBJREJsBfAvA/wBYGue6EEUeA9YYsf4LygHrlCmycKwr8tM7/HoG\nq8X2ekmnDcI90FcMw5lri7Xb5fRpicBdegarRfv9C4hauzBgpdhpAvBnAF8B8DSA5p5Alij+5LXY\nCuARAP8A4DiA9rjWiSjSGLDGiHaGIjlZ5rxVV0e8Tn49g9XiuayR3/tKdrZsfd0e+T/Ufj2D1eK5\nvkKkwDRNmGY7TLMeptnCYJUcR16jTT2v0Yt8jZLnMGCNEacN+Px8Bqsl8CzWixcVC0fpuA4GrDY2\n+ElMlIW/ET73s6lJ9n5LTpb7NEo80ia23r+itN6bASsREZE/MGCNEacFrH4+g9WSnCxJua4uSRor\ncWLWyCPt4rS+ErgTrfIGzB5pk+xs2TiutlbeN5Q4sa8QERGRazg7YPXAGawWW2vAopDNY2AkbK2X\n1NrKdnB+PoPVYmttMftKVARuHKecwOYaVnI5wzBgGGkwjPEwjHSewUoRJa+v0Xx9EQ3C2QGrdQZr\nenq8a2Kb07JGts+V9MBNBMBGu6Sny2vzzJmI1ke7XaydaD3wh86pfUU7YPV7X5kyRaYwRHDjOJ7B\nSrEiwcNoADdAdgn+OwCjGFRQRMjrKA2yC/XTANYCSOXri+gSzg5YPZKdAGTtW3Iy0NAgR3YoceIg\n3CPtor1eEoh4u/j9DFZL4GwEJ5zFyr4itAPWESPkvKKqqojVxTqDddw4nsFKsdAJ4BkApQB+CKAw\nvtUhj0mAHEezGLLTb058q0PkQAxYY8QwbARHHIRHjZM2k7HOYM3OljG+Eg+1SXo6MHYs0NYmgYkS\nJ/UVj5zBarF1cyfCfYXTgSm2ugFYO/OZkACWKFK6ej4sfH0RXUo7YG1oaEBpaSkKCwuxZMkSNDY2\nBn1cfn4+5syZg3nz5uGqq65SexIPDcIBG2vzcnOBmhqgoyNidbF9BqtyCtCZbJ/FGsG1ebYG4R98\nwL4COGsN6+nTQEqKZ1KAnukrRArkeJDhkCmbvwVwJ4CKeFaJPMcAsALy+voMgLr4VofIgbQD1vXr\n16O0tBSHDx/GjTfeiPXr1wd9nGEYKCsrw759+1BeXq72JB4dhCsnGpKSJO1WWRmxumgP+KqqgIkT\nXX8Gq8VJ6yWZ9e6j3S6ZmUBLC9DcHLG6aLcL37/6F3ZKXyFS1gSgHMBXAPwOQDPPuaSIkNdRE4DX\nIK+v5wA08fVFdAntgHXz5s1Yu3YtAGDt2rV47rnnQj5Wu+OdOOGpEYk14Dt+XLOwVsGBAs9gVb68\nHmuT3FzZAbW6WtbEKYlgmwA2B+EeaxftvmLNvY9Qu9g6g5Vt0r+wU/oKkSLTNGGa7TDNephmC4MJ\niih5fbXx9UU0CO2Atba2Fpk9I7jMzEzUhlhsZhgGFi9ejPnz5+Pxxx9Xe5Jjx4CCAt0qOs60afJZ\na9xWUBCxAZ91luK4cRpnsHqsTYYNkyWGgUF82CLYJoBcWuvXahX2ULs4pa9Yv2baNI0NmD3WJhMn\nAqNGAY2NsnmcEif1FSIiInKVYYP9sLS0FDU1NQO+/+CDD/b7t5whFXw09/rrryMrKwt1dXUoLS1F\nUVERFi5cOHTNurslgrBGrh4wfbp8PnpUo3BBQd8ozSbr+RkYienTZSx99ChQVKRQMD9fpmlfvCiR\nr03a7dLQIP1l/HjbdXAKz/SVBQsiUg8nMAy5Dm+/Lf+1ceMUCkewTQCb7UKkScY5I3s+ugCcYzbM\n5+Q1kQpZ58zXBFG0DDrK3rZtW8ifZWZmoqamBpMmTcKpU6cwceLEoI/LysoCAGRkZGD16tUoLy8P\nGbDef//9vV8vmjULi8aNA0aOHOr/4BpW7H3ihBxJmJioULigAHj66YjUw3Ym79ZbI1IPp7Cug/J4\nesQIYNIk4MMPI3JjRbtdrJsIHjq3TbtNrMIHD0akHrb7yh13RKQeThEYsF55pULBrCzg3DlZW5ya\naqsOHR3S5QJ3XreUlZWhrKzM1u8nCi0VQDGA7wF4B8C3YBgGAxSf6jtD9QYA/wDgJQA/52uCKAq0\n00IrV67Ehg0bsG7dOmzYsAGrVq0a8JjW1lZ0dXUhLS0NLS0t2Lp1K+67776QvzMwYMX27Z67fZ6a\nKuvgamtl76LJkxUKFxRoppsG4tTT/mwHR0eP2g5Yz58HzpyRtZI993jC58E2mTxZktZVVXK8jdJ9\nq4IC4A9/iEg9bPWVo0c91y7afSUhoW8d65w5turwwQcyoWDy5IHHPy1atAiLFi3q/fcDDzxg67mI\n+rsAYDOAiZAzWf8C4Km41ojirROyEddwADdCgta/xLVGRF6kvYb13nvvxbZt21BYWIhXXnkF9957\nLwCguroay5cvBwDU1NRg4cKFmDt3LhYsWIAVK1ZgyZIl4T2BBwfhgI0BnzWlLgJ37bQH4abJQXiw\nwhGY6mj9imnTZGyvxINtYq0tBjQ2l43g9FPtvtLWJncgPHL8k8VJfcVjL3lyBQMStFra4lURcgwT\n/c/ovTDIY4lIl3aGddy4cXjppZcGfD87OxvPP/88AGDatGnYv3+/3hN4OGB94w35711/vULBMWMk\nnXD6tMZ2pf1pD/gaGiRo9dBaScADg/Bjx4BrrrFdB6exLu2xY8CsWQoF8/OBkyeBzk45EsoG7XY5\ncUJSgErz/p3P9X2FyJZEAEsAfAvAWwC2xrc6FFemacIw0gDcBOBLALYBiNzxXUTURzvDGnUeDlgB\nzXHb9OnxHfAdOyZ18NBaSWDg2mIlThiEs6/0N2KEzKv+8ENbz2+tlUxI0Dg+xeorHsOAlfytDcBR\nAHcB+HcArVyr6HvNAN6ErGHlGb1E0cKANcbiPeDjWsmBRo2SvZM6OmTNpBInDMI92i7x7ivWWsm8\nPGD4cMXCHm2TS9cWK3FCXyGyQc7L7Ow5L7OJgQnxjF6iGHFmwOrRtZJA/AfhgYM95bWSHh2EAzbX\nFh8/bnttsfW8ykm5tjagvh7IzbX1/E7klL6ilSj16PuX7bXFEdg4zla7EEWQHOk3EoYxGoaRDsOw\ntwSBYkvabwwMI63ns7dmjxF5iTMD1vp6mXaqdNCfOwSeL6kc40RgEM4zWIPTPvczPV3S1bW1tp5f\nu12OH5cIwmNrJYH4n8XKvhKcdrvk50tqtrNT+7m7u5lhJScZDiAfwOuQ3WEzGfS4ymgAtwF4G8Av\nAIxk+xE5lDMDVg+eK2mZMAFIS5OpufX1ioUjnGHVKuzRUWI8s3kXLgCVlZLxtrJXYfNwm1hriysq\n4rO2mH0lOO2+Mny4rEP44APt5z51Cmhvl/fR0aO1fw1RhKQC+FcAlwO4EsCDAMbGtUYUHglMWyFr\nkacCuAPAonhWiYgG4eyA1YMMIwJH29jAQXhw8QxYKyok2z55MtdKBkpJkfims1MCeiXx7CtdXbJb\n09Sptp7fqeLZV5hdJWcxAQS+OX2AviNOyB2s2VHd6N+WROQkzg1YPbxASXvAl5UFNDXJhyZb50o2\nNAA5OdrP7WTx3L3Z9k0E9pXgBW2uLdZul8pKSQGOHKn93E7m2r5CFHFnAfwjgHsA/D2A9QD0/z5T\n7MjmSEkArgLwPcjRNPqzP4goupwbsHp4RKI94LPSs8ePaz+39oDv+HFZg+bBtZJA/zaJ9dpiZr1D\n0+4ro0dLilZzbXF3d1830z7+yaOYYSUSEvS0AvgJZA1kW+8usbKhT98HRZ7da2yarQCqAHwXsgaZ\nOz8TOZUzA1aP7rBpscayR45oFtYqKGu/Kisl5lReK+nxNhk/XvZPOn8eqKtTLGyjTQCbm/t4vF3i\n1Veqq6W/ZGTImnMlHm+TwHOLL6rOfoxnXyGKAjnWpLvnoy9YBdIATAQwB0AKg9YIk+uZCmA6ZOOr\nNM2g1TqqyGSwSuRgzgxYDx0CLrss3rWImsJC+Xz4sGZhrYJ9OxNPnaqxVtLjbWIYNtrFahPNP3aH\nDsln5cvb0SFrJT08eo9XX9FuE6uwh/tKSoqcTXvxosbRNjbaBLDZLkQxNQ0yxfQtyMZM6fGtjuek\nAVgL4DCAYwBWAEiJa42IKHqcF7A2NMi2qZMmxbsmUWMNtg4d0ohxbAz4rGJag73Dh/uiB4+yrovy\n5Z0wQT6fOaP1vNrtcvy4RA7Kdx/cQ7tNAPaVKNJul4IC2WVMOTUr75XW83n88pInrACQHPB1Rxzr\n4kXDAdwKwIAMZW9F3/UmIq9xXsBqDfY8PH0mM1OmGTY2asQ4l13Wl2ZQZBXTGux5PGsE9F0X5ctr\nGNrt0t4u4/fExL6plmHzQZtYU4KPHdOIcdhXoka7r4wcKZvHKadm5QjX1la5P+TBI7rJc56CbMoE\nAE8AGBHHunhRB4D/BNDV8/X/g6wnJiIvcm7A6mFWfANoTj/VSs0yazSUeGTzrE2etKZp+6BNRo2S\nJHJnpwT2SuKRYbWmaSvffXCXePQVW+9fRDF3GkA2ZB3regCN/X4qGwWNhmEkwTDSfbfGVf7/6TCM\n4TCMUTAM1eFoE4AXAUzo+dgFoD3S1SQih3BewOqD7ARgI0ORkSERTn298nNqZ40aGyW1kZWl/Jxu\not0mgHY2j5m8oWm3y/TpMm1aY/qpdrtY07RHeDub4rq+QhRDsoFPEySAqgPQ3G9DHwlORwL4DYAG\nAP8MINVnQetoAIsB1AB4DcBYpf+/XM8myI2AJgDnuWkSkYc5L2D1QdYIsJGhsHYH0hjwaWcofDBN\nGwBmzJDPR48CXV2KheORNWJfGdzIkbIW/gO1s/UuXJBsbkKCxn5WbJOhMcNKPmHtPBs8kFoAYCVk\n86BvAQgdbDn5eBz9unUB+CmAcQDmAfgSZD1q+AKvL4NVIm9zZsDqgxGJ7QyF4oCvvl4+Ro3SSJT6\nJJM3ahSQm6s5/ZQZ1qiJdTbv2DE5h5W7aYc2ebJcm+pqoKlJsTAzrFH1zDPPoLi4GImJidi7d2/I\nx23ZsgVFRUWYMWMGHn744RjWkMQxABd6vq5CqE2ZJBAcAdlkaJSjglapSzKkbqpH9yQCONDztQng\nHQwWtBORvzkrYO3uljP6rFSXh9k+rkNxwBe4u6by3zufZI0AG+1iTT9VTM1q73ra2Ai0tADZ2YoF\n3SfWR9vY2onWJ30lMdHGGbk2M6w+uLy2XH755di4cSOuvfbakI/p6urC3//932PLli04cOAAnnrq\nKRw8eDCGtSSZyjofwD8AuAJA4oAsoQSAoyBTZtsB/ARDBa2S7Uzp+Qjvj72USYZhhD8tuW9a8xZI\n4P0E1ILW8wD+CpJZXQLglTDLEZEfOStgPXkSGDtWttD1OGvQpTX9VCPDynMlwxN45JCSlBTZ/lkx\nNavdLj6Zpg3YaBOrsGJB9pXwaLdLXh5w9qxSavbCBdlY2DD6AmUKrqioCIVDRPXl5eWYPn068vPz\nkZSUhDVr1mDTpk0xqiH1rb98F8CjAOpgmm0hHn0NJLA1APwdgO6Qv1eCxRQA9wL4OsIJIPsCz68C\n+A6A0QpB5ywA1/V8fTskuA6PXIMWAI8BeAmXrvMlIgrkrID10CHf3D5PTZXkWEeH8hI72xlWZT7J\nGgGxzeYFTtNWTpT6qE2mTOmbftrcrFiYGdao0e4rCQkSdSqkZo8flwk4+fme388qJqqqqpCXl9f7\n79zcXFRVVcWxRv4T/vrLdwBYb3wHAAy2idwYAD+EBJ7fBvDjnu8NJgXA/wXwAwBfgxwPM3bo/wAA\nmdZsHd1zApI1DR/XoBJRuIbFuwL9+GT9quWyy2QQfuiQ4ikYM2b0TT9NTAyriHbWyJqm7ZNBeESy\neUuXhvVwW9O0fZTJs6afHjgg1+yKKxQKxzLDeu6cRNQ5OYoF3SkifSXMxrSV9fag0tJS1NTUDPj+\nQw89hFtuuWXI8qrrIO+///7erxctWoRFixYplSc7WgAUAfgIgO0AugcJ7hIAjA/49wTIWtHBJALI\nuKTM0EzThGGM7qnb1QDKer9PRBSorKwMZWVltn6HswJWH2VYAfmvbt8ug/AwYxyRkiLH23zwQdiR\nrnbWqKoKSE/3xTRtIAIZ1vffD/vhtjN5K1dqFHSnwkLNgDUvD2hokEAyNTWsItrtcviw3EzywTRt\nILazEbh+tb9t27bZKp+Tk4PKysref1dWViI3Nzfk4wMDVoodCQoNyPThqt7vhdYA4J8gZ78mALi7\n53uDaQJwH4DpkGzsFwG0hlm/8z312xRG3YjIry690fnAAw8o/w5nTQn2YYYViP7avK6uvtl3ygM+\nn91EyM+X6acnT2pMP1XM5tneIdhH7aLdVxSnn549C9TVyT0h5USpj9tEeZway77iY6ECiPnz5+PI\nkSOoqKhAR0cHnn76aaz00Q0wN1GZNis/Pw3gUwA+AaAWphl6zWtfmUYAnwGwAkAF+nYvVqsfEVG0\nOCtg/c53gKuvjnctYmbmTPl84MDgjwtZOMyCJ07IpiU5OcDo0YrPc+AAMGuWev1cKjGxb1CskCwV\nCm0C9D1U+fJ2dcnNHesF5AOx6ivWw2bO1EiU+qyvTJggH83NcoNHSaz6ig9t3LgReXl52L17N5Yv\nX46lPdN3qqursXz5cgDAsGHD8Oijj+Kmm27CrFmzcPvtt2Omj95PvMw0L8I0G3o+BlvvGljGDCjT\nzuCTiBzHWVOCr7km3jWIqeJi+fzuu5KhUBogz54N7N4d1kPffbf/8yl57z2gpESjoHsVF8s1e/dd\nYP58hYJ5eXLUTEMDMG7ckA/Xbpfjx2VH4jCnuHpBYF/RKvzee2E91HZf+dznNAq6V3Ex8Oqrct0C\n9vAZ2qxZctMljHX4ptnXfFrt4jOrV6/G6tWrB3w/Ozsbzz//fO+/ly5d2hvMEhEROZmzMqw+k5sr\nGc/6euD0acXCCoNw62GzZys+h1XYZ6NE678b5uXtYxhht0trq8SdgRndsPmwTazkz+HDsrO2ktmz\nlfuKdsDqs3ax3lOU+8qoUcCkScCxY0M+tLJSTsDJyAAmTlSvIxEREbkbA9Y4suIbQGPAV1ws8+S6\nB1+fEvi7lcfSpimpEw7Cw2elZ4fw/vtyeQsLNY7p8GGbjBol+4tdvKh0GooIs00AGzd3WltlgzKf\nHRKq/f5lFQ6jXZhdJSIi8jcGrHGmPeAbM0Z27/3wwyEfqj3gq64GkpNloZqP2Jp+GmY2z3YmTytd\n7m7afaWgADh1SoLKIWhPCT54UO4+DHPWKotosxWwxqKvEBERkesxYI0zK+6I1tq8ixf7Ng9S3rDE\nh1McAYlvRoyQqYjn1c5BD3tKsO21kj5sF+0bCcOGyXEzBw8O+rAzZ2RqfmoqMHmy4nP4vE3eey+s\nyR4DCyv0FR/eoyEiIiIwYI27aE+pO3pU1vxNmaJxlKoPp54Csq5Ue1daxWmOyoPwzk6ZE1tUpFjQ\n/WxN1Z49e8h2CczkKe8Q7NO+Mn68LEVtbZVjoZVwSjARERGFgQFrnAUOwpV3kg9jSp3tDZd8mtbQ\nzuZlZcnOp0PsoqU9CD96VHbrSklRLOh+tm/uDFHQdtbbp31F+0ZCUZFsutTZGfIh3d19N40YsBIR\nEfkTA9Y4y8yUE1AaG2WZnZIwBuHc9VRPNHcKbm4GKiqApCSNPXp83CZFRUBCgsTs7e2KhdlXoka7\nr4wcKWfhDLKL1gcfSPY2Kyusk6KIiIjIgxiwxlngTsHK2bxZs2RdXldXyIdoZ42sww99PgjXXls8\nSEErY3TZZRK0KvHp1FNA9v8qKJCX+6FDioXDmH6q3VeamiSjPnWqYkFviGZfsZX1JiIiIk9gwOoA\n2lPq0tLkYMITJ0I+RHtK8Icfyi7EY8YoFvQG2+slBynIadr6tNtl6lTZVampKeiPrfszgc8RtgMH\nZNFzYqJiQW9wbF8hIiIiT2DA6gDRylB0dACHD0sWV3mPHh9n8gAgP1+WiZ46BTQ0KBaOZtbI5+2i\n3VcSE6UThAiOamqkndPTgexsxd/t8zaxdh8/eFB2JVfCDCsRERENgQGrA5SUyOd9+zQKz50L7N8f\n9EfvvisDyMJCjT169u+X3+1TCQnA5ZfL18rtUlICvP12yHM+rN9ntXvYWlpkUZ8Pdwi2RKuvBLaJ\n8g7BPu8r6elyg6e9XWOq9iBtAtjoK0REROQZDFgdYO5cGSS/+y5w4YJi4SuuAPbsCfqjvXvl80c+\nolGpPXvkd/uYdd2s6xi2cePkvI8gm8mYpo12eestSWcNH65Y0Dusa7Znj8au2uwrUaPdV2bMkPW/\nZ88O+FFLi5whPWxY380jIiIi8h8GrA6Qmiob8HR2akx1/MhHQo4SrbG51lh6717N0bt3WNdNeRAO\nhGyX48eBc+dkd+isLMXfyTZBfj4wdixQVwdUVSkWHqSvWN9W7itdXZJNnzdPsaC3WNctxP2A0BIT\n5Y5dkJT5W2/JJIXiYtlwi4iIiPyJAatDaGcopkwB2tpkEd4ltLNG9fWS8Zg2TbGgtwRm85SFyOYF\ntony1FNm8mAYNm4kzJkjCy2DTGOwmkq5rxw6JHce0tMVC3qL9vsXIA0apKCtrDcRERF5BgNWh9DO\nUFgj+EsGfJ2dkqEANJbX7d0rGaMEf788rNm3R44A588rFg4xCLed9fZ5wArY6CspKXIuziUbL505\nI5tip6TIem8lvIkAoC/BvG9fyKXboYW4uWOrrxAREZFn+DsicZBITz+1EkkFBRon0+zZw7QGJFjV\n3njJClgvWWipnTVqa5PImYv57GfzLgmOrN8zd67GyTScpg1ATtfKzQWam4Mu3R5ciKna2tO0iYiI\nyFMYsDqElaF4+23JjioJMgjXnuIIMJMXQDs4mjhRzsk9frz3W6ZpI2v0zjuS/uNiPv0MKxA0OOKG\nS5GhPYW+qAg4ebLfNIa2NkmEJyRwh2AiIiK/Y8DqEOnpwPTpkhU9cECx8CCDcK2xNDOsvWxnvgNG\n7x9+KGd9TpgA5OUp/i62Sa+CAmD0aDkj99QpxcKDZFiV+0p3txzJwoAVgI2+MmyYrC8OON7mnXdk\nP6uZMzWO5CIiIiJPYcDqINoZimnTgMZGWYyH/r9DOcY5e1aOmZgxQ7GgN9neeClg9B6YXVXecIlZ\n714JCX0zEpSDo7lzZSvugGkM2n3l6FE5vmjcOMWC3hSNvsJ7NERERMSA1UG0pzpaI/ieAV9XV9+G\nS8qnbezbJ3PwlBfzedPs2ZIAev99WZ+n5JJsHqeeRo52cJSaKjtr90xjaGyUWdvJyZLNU8I26Scw\nw2p34yVbM0Qefxzo6NAoSERERE7EgNVBrrxSPu/erVH4qqt6C779NtDaKonX8eMVf8/u3fK7CIAE\nMnPmyPrTN99ULHzVVVKoqwsAsGuXfNtq57C1tMjxKcrbPXtXpPqKVX7ePLkxoYR9pZ+sLCAnR5ai\nvv++YuGANgFs9JXKSuAb3wCSkhQLEhERkVMxYHWQBQtknLV/v8YxKtdeC+zYAaD3ExYu1KjEjh3y\nu6iXdR2t6xq2iROBSZOAd95BR0ffIPzjH1f8Pbt3S7A6cqRiQe+y2uT113vvB4SPfSVqtPtKcbGc\n/3zqFOrrZcOl5GRg/nzF37Nzp1RCec49ERERORUDVgdJSZEBWnc38MYbioU/9jHgz38GOjuxc6d8\nS3ksffGiRFXKEZW3WddReRAOyOB5xw7s3Ss7n86cCWRkKP4OBkYD5OTIDILz52VGgZKeNoFp9rap\n8uU9d07Ob+Eiy36s62i9B4UtIUHed3buxGuvybc++lE5WkoJ+woREZHnMGB1GO3gaOxYYNo0mHv2\n6g/C33pLDlOcMEGxoLdZWaNduzSWxl17LbBzp71MnpU1on60s3kzZgCdnWg/9AHefFOScR/7mOLv\neP11mcaqHFF5m/We8+qrA44gDq/wjh3671+A9BUGrERERJ7CgNVhtAfhPYXrnt2JujqZiVpQoFie\ngVFQGRlyVGRbm8autD3ZvB2vyuhdeSzd0QGUl2tEVN6nfXPHMICFC3Hiv3aio0PWKI8Zo/g72FeC\nmjlTNk2uqgIqKhQLL1xo7+ZOXZ2c58qDW4mIiDyFAavDfOxjMp5+800JkJRcey3a/rTD+lJ9GRen\n04WkHRxNmQJzxAic2nEEgMYgfM8eoLBQDuqlfqxruXOnXjYvsK8oY18JKiGhf7soueIKmMeP48Te\nsxg2DLj6asXyr70GXHMNdzgnIiLyGAasDjNmjGR8rMSakoULMf7912CgWz0wMk1mjQahPQg3DDRe\nfi3mNe/AlCnA5MmK5XfsYJuEMH26zCSoq5NNlJVcey0yDkpjKl/etjaZPv/RjyoW9AftWSJJSWiY\n8VF8tPt1XHEFMGqUYnm+fxEREXkSA1YH0t64JCsLdd3jUYz31JM/778vZ1Tm5SkW9Icrxkp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Az7iigtLUVNTc2A7z/00EO45ZZbwvodXnlt270WXntNhboeDz74YL9/+3GMEG5bX5o18tprxBLO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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4adce03310>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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UPkdkhGI6HKPeg2l5Tp166jCeMzGaJzMdp2X8kH3U25lrvR0cfWVkv/SV6XD0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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4adce1f390>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ZHiHaeOaMdBr7jBmjgsiIGDtWmsl0x+bNRovcOsck7HVxHQYMUJNg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rVzJ7+8N1TovjccT1g7Oztx55134tOf/jRWrVoFQO7M1dfXY/Lkyairq8OkSZMGPC43NxfV1dV9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\nE1YiIiIiIiJypf8PMmYnhO3nhkgAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89f97d0>"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Crosscorrelation"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A crosscorrelation is computed between two different time series x(t) and y(t): $$\\rho_{x,y}(\\tau) = \\frac{\\langle (x(t-\\tau)-\\langle x \\rangle) (y(t) -\\langle y \\rangle)\\rangle}{\\sqrt{\\sigma_{x,x}^2 \\sigma_{y,y}^2}}$$\n",
+      "\n",
+      "For an example we generate a low-pass filtered version of the colored noise. The low-pass filtering introduces a time shift such that the filtered signal lags behind the original colored noise signal."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "nyq = 0.5/deltat\n",
+      "lowf = 0.5\n",
+      "low = lowf/nyq\n",
+      "b, a = sig.butter( 4, low, btype='lowpass' )\n",
+      "filtered_noise = sig.lfilter( b, a, colored_noise )\n",
+      "filtered_noise += 0.1*np.random.randn( len(filtered_noise) )\n",
+      "\n",
+      "tseg = (time>10.0) & (time<15.0)\n",
+      "plt.plot( time[tseg], colored_noise[tseg], '-b' )\n",
+      "plt.plot( time[tseg], filtered_noise[tseg], '-r' )\n",
+      "plt.xlabel( 'Time [s]' )\n",
+      "plt.ylabel( 'Signal' )\n",
+      "plt.show()   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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qMREY/sy9NGMk4/wAYQC085dPFCBMUnqQm0snrquLVtvYWBGYfvQolQKVf4MX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\nggWDYRiG0QQLBsMwDKOJ/wPJJ0jEheAXpgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89b66d0>"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And now we cross correlate the two signals."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plotcrosscorr( datax, datay, deltat, lags, rmin=0.0 ) :\n",
+      "    cross_corr = []\n",
+      "    for lag in lags :\n",
+      "        lagi = int( lag/deltat )\n",
+      "        if lagi > 0 :\n",
+      "            r = stats.pearsonr( datax[:-lagi], datay[lagi:] )\n",
+      "        elif lagi < 0 :\n",
+      "            r = stats.pearsonr( datax[-lagi:], datay[:lagi] )\n",
+      "        else :\n",
+      "            r = stats.pearsonr( datax[:], datay[:] )\n",
+      "        cross_corr.append( r[0] )\n",
+      "    plt.plot( lags, cross_corr )\n",
+      "    plt.xlabel( 'Lag [s]' )\n",
+      "    plt.ylabel( 'Cross-correlation' )\n",
+      "    plt.ylim( rmin, 1.0 )\n",
+      "    plt.show()\n",
+      "\n",
+      "lags = np.arange( -2.0, 2.0, 0.05 )    \n",
+      "plotcrosscorr( colored_noise, filtered_noise, deltat, lags, rmin=-0.1 )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Q0nbsABYuFK0HOcearHaCG1Fdd+cO8MYbwKpVLAokDBkibkTYt0/pJPqxMBDJ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\ndPw/uV+Mmt4sV8EAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab84e3b50>"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "There are several things to note in contrast to the auto correlation:\n",
+      "* the maximum cross correlation can be smaller than one (here this occurs because of the filtering and because of the added independent noise)\n",
+      "* the maximum cross correlation is not at zero lag\n",
+      "* the cross-correlation function is not symmetric with respect to lag zero\n",
+      "* the shifted peak tells us that the filtered noise lags behind the colored noise\n",
+      "* and again there is a finite correlation time between the two signals."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "Point process data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Point process data are sets of time points $t_i$. As a countinuous time series, point processes can be expressed as delta function combs:\n",
+      "$$ x(t) = \\sum_{i=1}^n \\delta(t-t_i) $$\n",
+      "This is a sum of Dirac delta functions shifted to the time points $t_i$ of the point process."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "Generate periodic time points that occur every 12 seconds with some jitter:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def printpoints( x ) :\n",
+      "    print 'ti ||', ' | '.join( [ '%5.2f' % v for v in x[:10] ] ), '...'\n",
+      "\n",
+      "tmax = 10000.0\n",
+      "period = 12.0\n",
+      "periodic_chirps = np.arange( 0.0, tmax, period )\n",
+      "periodic_chirps += 0.1*np.random.randn( len( periodic_chirps ) )\n",
+      "printpoints( periodic_chirps )"
+     ],
+     "language": "python",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "-"
+      }
+     },
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "ti || -0.07 | 12.07 | 24.02 | 35.91 | 48.05 | 60.06 | 72.05 | 83.97 | 95.94 | 108.08 ...\n"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.eventplot( periodic_chirps[periodic_chirps<100.0], colors=[['#ff0000']] )\n",
+      "plt.xlabel( 'Time [s]' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEPCAYAAABIut/fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab88428d0>"
+       ]
+      }
+     ],
+     "prompt_number": 43
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The mean of the point process is\n",
+      "$$\\langle x \\rangle = \\frac{1}{T}\\int x(t) dt = \\frac{1}{T}\\int \\sum_{i=1}^n \\delta(t-t_i) dt = \\frac{1}{T} \\sum_{i=1}^n \\int \\delta(t-t_i) dt = \\frac{1}{T} \\sum_{i=1}^n 1 = \\frac{n}{T} = r$$\n",
+      "its average rate $r$ (number of events per time).\n",
+      "\n",
+      "However, the variance of a point process is not defined/infinity:\n",
+      "$$\\begin{array}{rcl}\n",
+      "\\sigma^2_{x,x} & = & \\langle x^2 \\rangle - \\langle x \\rangle^2\\\\\n",
+      "& = & \\frac{1}{T}\\int x^2(t) dt - r^2\\\\\n",
+      "& = & \\frac{1}{T}\\int \\sum_{i=1}^n \\delta(t-t_i) \\sum_{k=1}^n \\delta(t-t_k) dt - r^2\\\\\n",
+      "& = & \\frac{1}{T} \\sum_{i=1}^n \\sum_{k=1}^n \\int \\delta(t-t_i)\\delta(t-t_k) dt - r^2\\\\\n",
+      "& = & \\frac{1}{T} \\sum_{i=1}^n \\sum_{k=1}^n \\delta(t_k-t_i) - r^2\\\\\n",
+      "& = & \\frac{1}{T} \\sum_{i=1}^n \\delta(0) - r^2\n",
+      "\\end{array}$$\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "rate = len(periodic_chirps)/tmax\n",
+      "print('rate: %g Hz' % rate)\n",
+      "print('inverse period: %g Hz' % (1.0/period))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "rate: 0.0834 Hz\n",
+        "inverse period: 0.0833333 Hz\n"
+       ]
+      }
+     ],
+     "prompt_number": 75
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "For computing the auto or cross correlation we need to convert point process data to a time series.\n",
+      "\n",
+      "One way to do this is to use the histogram function and create a time series that is one whenever there is an event and zero otherwise. After multiplying the histogram with the inverse binwidth, this is an approximation of the delta function comb. The mean over the time series is then the expeted firing rate."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "deltat = 0.02  # 20 msec resolution\n",
+      "time = np.arange( 0.0, tmax, deltat )\n",
+      "data, time = np.histogram( periodic_chirps, time )\n",
+      "data /= deltat\n",
+      "print('rate: %g Hz' % (np.mean(data)))\n",
+      "plt.plot( time[time<100.0], data[time[:-1]<100.0], 'r-' )\n",
+      "plt.ylim( -5, 60 )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "rate: 0.0833002 Hz\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab883b3d0>"
+       ]
+      }
+     ],
+     "prompt_number": 54
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The variance of this time series depends on the bin width:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "deltats = np.arange(0.01, 0.2, 0.01)\n",
+      "variances = []\n",
+      "for dt in deltats :\n",
+      "    vtime = np.arange( 0.0, tmax, dt )\n",
+      "    vdata, vtime = np.histogram( periodic_chirps, vtime )\n",
+      "    vdata /= dt\n",
+      "    variances.append(np.var(vdata))\n",
+      "plt.xlabel('Time bin [s]')\n",
+      "plt.ylabel('Variance [Hz^2]')\n",
+      "plt.plot(deltats, variances)\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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f1q1bl/3eqlUr9u/fb1VxIiJSC5Yl/6BAmWctIlIHVbHgq2e0bNmSvXv3lv2+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\nAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89a0090>"
+       ]
+      }
+     ],
+     "prompt_number": 64
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In particular, the variance diverges for small time bins."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Autocorrelation of this point process:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lags = np.arange( -30.0, 30.0, 0.1 )\n",
+      "plotautocorr( data, deltat, lags, rmin=-0.1 )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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zZsxQbW2tJGnZsmXmdQa4BAQCLnt+v7/R/Y098KZHjx5at26doqKi9Nlnn+nL\nL79UTExMs8cfOnSotm3b1uhr3H0ekYQpI6AVDhw4oFGjRsntduvBBx/U3//+90bbde/evUUXpr3x\nxhtci4CIwQNyAACSGCEAAOoQCAAASQQCAKAOgQAAkEQgAADqEAgAAEnS/wN298/6DMuk8QAAAABJ\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab881d410>"
+       ]
+      }
+     ],
+     "prompt_number": 65
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This depends on the bin width of the time series:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "deltat = 1.0  # 1 sec resolution\n",
+      "time = np.arange( 0.0, tmax, deltat )\n",
+      "data, time = np.histogram( periodic_chirps, time )\n",
+      "plotautocorr( data, deltat, lags, rmin=-0.1 )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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GviLT82uhGBBmzpyJFStWoK6uDnV1dVi+fDlmzpypuOGqqiqkpaUhJSUFMTExKC4uRmVl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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8765b90>"
+       ]
+      }
+     ],
+     "prompt_number": 66
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Alternatively to the histogram binning we can convolve the point-process with a Gaussian kernel with some standard deviation. The standard deviation of the Gaussian determines its width and thus is the equivalent to the bins of the histogram method.\n",
+      "\n",
+      "Convolution of the delta function comb simplifies to placing the kernel $k(t)$ at each time point and summing up:\n",
+      "$$ \\begin{array}{rcl}\n",
+      "    x * k & = & \\int_{\\infty}^{\\infty} x(\\tau) k(t-\\tau) d\\tau \\\\\n",
+      "    & = & \\int_{\\infty}^{\\infty} \\sum_i \\delta(\\tau-t_i) k(t-\\tau) d\\tau \\\\\n",
+      "    & = & \\sum_i \\int_{\\infty}^{\\infty} \\delta(\\tau-t_i) k(t-\\tau) d\\tau \\\\ \n",
+      "    & = & \\sum_i k(t-t_i)\n",
+      "\\end{array}$$\n",
+      "\n",
+      "So let's convolve the point process with Gaussian kernels:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def gauss_convolve( time, points, sd ) :\n",
+      "    gtime = np.arange( -4.0*sd, 4.0*sd, time[1]-time[0] )\n",
+      "    gauss = stats.norm.pdf( gtime, scale=sd )\n",
+      "    ngauss = len( gauss )/2\n",
+      "    data = np.zeros( len( time ) )\n",
+      "    for x in points :\n",
+      "        inx = int(x/deltat)\n",
+      "        if inx-ngauss < 0 :\n",
+      "            n = ngauss-inx\n",
+      "            data[:inx+ngauss] += gauss[n:]\n",
+      "        elif inx + ngauss >= len( data ) :\n",
+      "            n = inx+ngauss-len(data)\n",
+      "            data[inx-ngauss:] += gauss[:-n]\n",
+      "        else :\n",
+      "            data[inx-ngauss:inx+ngauss] += gauss\n",
+      "    return data\n",
+      "\n",
+      "deltat = 0.01  # 10 msec resolution for plotting\n",
+      "time = np.arange( 0.0, tmax, deltat )\n",
+      "data = gauss_convolve( time, periodic_chirps, 0.3 )\n",
+      "plt.plot( time[time<50.0], data[time<50.0], 'r-' )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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lZaisrMTBgwfNrbmiAjh82NLG5oSPP1b/R5vBDCeykt/8+Wp+7AtfZDW/Q4dS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\nIqIswqJORJRFWNSJiLIIizoRURb5f6AN0TDZ5j3wAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab894d290>"
+       ]
+      }
+     ],
+     "prompt_number": 67
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is now a time series and we can compute the autocorrelation as usual:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plotautocorr( data, deltat, lags, rmin=-0.2 )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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kX/jNMN58801UVVVhzpw5WLBggTvD0Aol99T46KOPsG3bNnz66ac+3y8vL3c/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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab881da50>"
+       ]
+      }
+     ],
+     "prompt_number": 68
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Note the peak values and the negative values. The latter get larger, the wider we make the kernel.\n",
+      "\n",
+      "The autocorrelation thus heavily depends on the bin width we choose!"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Alternatively, we can compute the autocorrelation integral for point processes before we turn the point process into a time series:\n",
+      "$$ \\begin{array}{rcl}\n",
+      "  \\int x(t) x(t-\\tau) dt & = & \\int \\sum_i \\delta(t-t_i) \\sum_k \\delta(t-\\tau-t_k) dt \\\\\n",
+      "  & = & \\int \\sum_i \\sum_k \\delta(t-t_i) \\delta(t-\\tau-t_k) dt \\\\\n",
+      "  & = & \\sum_i \\sum_k \\int \\delta(t-t_i) \\delta(t-\\tau-t_k) dt \\\\\n",
+      "  & = & \\sum_i \\sum_k \\delta(\\tau + t_k-t_i) \\\\\n",
+      "  & = & \\sum_k \\sum_i \\delta(\\tau -(t_i-t_k)) \n",
+      "  \\end{array} $$\n",
+      "\n",
+      "That is, shift one point-process to the times of the other point process and compute a histogram or kernel density."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      " def points_autocorr( data, lags ) :\n",
+      "    rel = []\n",
+      "    for x in data :\n",
+      "        rel.extend( data[(data>=x+lags[0]) & (data<=x+lags[-1])] - x )\n",
+      "    h,_ = np.histogram( rel, lags )\n",
+      "    return h\n",
+      "\n",
+      "h = points_autocorr( periodic_chirps, lags )\n",
+      "plt.xlabel( 'Lag [s]' )\n",
+      "plt.plot( lags[:-1], h )\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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lcgh25EV8yy0wMJD451nISXKvlfgNrAB5edDbO7tl02F+cxXfojHj/CJmmltv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\nYQAAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab89715d0>"
+       ]
+      }
+     ],
+     "prompt_number": 69
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Better is it to normalize the result to probabilities, i.e. rates."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for bin_width in [0.2, 1.0, 6.0, 12.0] :\n",
+      "    lags = np.arange( -50.0, 50.0+0.5*bin_width, bin_width )\n",
+      "    h = points_autocorr( periodic_chirps, lags )\n",
+      "    plt.xlabel( 'Lag [s]' )\n",
+      "    plt.ylim(0.0, 1.1/bin_width)\n",
+      "    plt.plot( [lags[0], lags[-1]], [rate, rate], 'r')\n",
+      "    plt.plot( lags[:-1], h/(lags[1]-lags[0])/len(periodic_chirps), 'b' )\n",
+      "    plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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gwe3++ERHAoEA7r33XowZMwb5+fl4+umnO2MTiGLGwCZXmjhxIt5//31UVlbi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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab6ad5810>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ZhCGN8Ubg2Dpo6pqRZGQc0tihQ3fnDUXqOHUpQxqdOHuK9xg5pzQAMrZFMjHS\n1CXV1JnIEutgUu9Awv5wSkdLi7XNoz2ow9bAmnr3MdLUJTRYWwdrp4l1SKmpO/mQDJq6nIQsYVsk\nE5p6Ckl0mu2m8ou0WRp1Pc6utdW6SzHys23cVJaLN0OjrUFKUqepa6Yv1NSdmglPQinKNrJ4UyFL\neUhGqpO6hP0ByCg59JWkzpq6ABJ1HCcuRsV7jBxgmZwTOiSkEAkaIok19W6qk7qUbSFBh4QDi61D\nwhlDsjDS1CU0WLtmGyudAjR1Gyfn77bRldQlnUVKaBfxzFRKUjeu/FJXV4eCggLk5+djxYoVUZeZ\nO3cu8vPzUVRUhD179iRdZHeRsJMSabB1pLp+KmVbxOu8OmbCk5rUMzKsUSGpPsBJaBdSxodL2BbJ\nJK6pB4NBzJkzB3V1dThw4ABqampw8ODBsGVqa2tx+PBh1NfX49lnn8Xs2bNTKrgr6EhDPp8v7Pd4\nUwSkUkckydoWkevXXQ1dOcA5eYobmtTtdZNQU7fLcsk82EfbdxKMrCu17K60id60za7qMMbUd+zY\ngby8POTk5CAjIwMVFRVYu3Zt2DJvvvkmHn/cmt1s3LhxaGpqwqlTp1KnuAvouBgV2bC6amS6O44k\nU9eV1O11k5DUgeRvi56YuhNGlkhDVx9QkWpTN6qm3tjYiOzsjunTvF4vGhsbEy5z/PjxJMvsHl3Z\nSakueyQ6sADODGuUkMgSnWYDzqchqTV1wD1luURDGp3UoXtbJJO4U+96PJ4uvYmKmFu4q/+XKhI1\n2IEDgS1bgClTkveZhw4Bu3Z1/P73v1ufE4+BA4HZs4HLLkuejkh27gQqKuJrePvtxNsicv26wxdf\nJDaygQOBGTOAiy/u2Wd0l3PnrPp1KBkZwMaNyW0XkXz+ede2xUMPJV6uq0Tbd3v2xH//gQOBN94A\nPv44ORqi8emnwJVXxl9m4EBg2rTO+yqU3rRNADhwIP62GDDA2m99BhWHrVu3qrKysvbfly5dqpYv\nXx62TGVlpaqpqWn/ffjw4erkyZOd3is3N1cB4Be/+MUvfnXjKzc3N55NdyJuUi8uLkZ9fT0aGhow\nePBgrFmzBjU1NWHLlJeXo6qqChUVFdi2bRuuuOIKXHfddZ3e6/Dhw/E+ihBCSBKIa+rp6emoqqpC\nWVkZgsEgZs6cicLCQlRXVwMAKisrMXnyZNTW1iIvLw8XX3wxVq9e7YhwQgghnfGoyII4IYSQPkvK\n7yh9+ukiMCz0AAAHm0lEQVSnUVhYiBEjRmDBggXtf1+2bBny8/NRUFCADRs2pFpGSlm1ahXS0tLw\necjVFBPWb/78+SgsLERRURGmTp2KL774ov01E9YP6NrNdX0Jv9+P22+/Hd/85jcxYsQI/O53vwMA\nfP755ygtLcWwYcNw1113oampSbPSnhMMBjF69GhM+f9XtE1at6amJkyfPh2FhYW48cYbsX379u6v\nX7cq8N3krbfeUnfeeadqbm5WSin16aefKqWU+stf/qKKiopUc3OzOnr0qMrNzVXBYDCVUlLGsWPH\nVFlZmcrJyVFnzpxRSpmzfhs2bGjXvWDBArVgwQKllDnr19raqnJzc9XRo0dVc3OzKioqUgcOHNAt\nq1ecOHFC7dmzRyml1JdffqmGDRumDhw4oObPn69WrFihlFJq+fLl7fuyL7Jq1Sr10EMPqSlTpiil\nlFHr9thjj6nnn39eKaVUS0uLampq6vb6pdTU77//frV58+ZOf48cRVNWVqa2bt2aSikpY/r06Wrf\nvn1hpm7S+tm8/vrr6uGHH1ZKmbN+W7ZsCRvdtWzZMrVs2TKNipLPPffcozZu3Bg2Ku3EiRNq+PDh\nmpX1DL/fryZMmKDeeust9e1vf1sppYxZt6amJjVkyJBOf+/u+qW0/FJfX493330Xt9xyC0pKSrBz\n504AwCeffAKv19u+XLSbmvoCa9euhdfrxciRI8P+bsr6hfLCCy9g8uTJAMxZv67cXNeXaWhowJ49\nezBu3DicOnWqfVTaddddp/2u757y4x//GCtXrkRayOOSTFm3o0eP4pprrsGMGTMwZswYzJo1C199\n9VW31y/u6JeuUFpaipMnT3b6+5IlS9Da2oqzZ89i27Zt+OCDD/DAAw/gyJEjUd9H9w1LsYi3fsuW\nLQurJ6s415z72votXbq0vWa5ZMkS9O/fHw899FDM95G6fvHoi5q7yt///ndMmzYNv/3tb3HppZeG\nvebxePrkuv/pT3/Ctddei9GjR8ecGqCvrhsAtLa2Yvfu3aiqqsLYsWMxb948LF++PGyZrqxfr019\n48aNMV/7/e9/j6lTpwIAxo4di7S0NJw+fRpZWVnw+/3tyx0/fhxZWVm9lZISYq3fRx99hKNHj6Ko\nqAiAtQ4333wztm/fbsT62bz44ouora3F5s2b2//Wl9YvHpHr4ff7w85A+iotLS2YNm0aHn30Udx7\n770ArIR38uRJXH/99Thx4gSuvfZazSq7z5YtW/Dmm2+itrYWFy5cwLlz5/Doo48asW6Adabo9Xox\nduxYAMD06dOxbNkyXH/99d1bv1TUhmyeeeYZ9ctf/lIppdShQ4dUdna2UqrjQlsgEFBHjhxRQ4cO\nVW1tbamUknKiXSjt6+u3bt06deONN6rPPvss7O+mrF9LS4saOnSoOnr0qAoEAkZcKG1ra1OPPvqo\nmjdvXtjf58+f334dZNmyZX36YqJSSvl8vvaauknrNn78eHXo0CGllFILFy5U8+fP7/b6pdTUm5ub\n1SOPPKJGjBihxowZo95+++3215YsWaJyc3PV8OHDVV1dXSplOMKQIUPaTV0pM9YvLy9PfeMb31Cj\nRo1So0aNUrNnz25/zYT1U0qp2tpaNWzYMJWbm6uWLl2qW06vee+995TH41FFRUXt+23dunXqzJkz\nasKECSo/P1+Vlpaqs2fP6pbaK3w+X/voF5PWbe/evaq4uFiNHDlS3Xfffaqpqanb68ebjwghxCCM\nfJwdIYS4FZo6IYQYBE2dEEIMgqZOCCEGQVMnhBCDoKkTQohB0NSJEVxyySVJf89+/fphzJgxOHHi\nRMxl5s+fjxtuuAGrVq1K+ucT0hN6PU0AIRJIxXwfF110EXbv3h13mZUrV6bkgEJIT2FSJ8byxz/+\nEbfccgvGjBmD0tJSfPrppwCAzz77DKWlpRgxYgRmzZqFnJycsAecRCMYDOK73/0ubrrpJowcORK/\n+c1vnFgFQroNTZ0Yy/jx47Ft2zbs3r0b3/nOd/DUU08BABYvXow777wTH330EaZPn45jx44lfK+9\ne/fik08+wYcffoj9+/djxowZqZZPSI9g+YUYi9/vxwMPPICTJ0+iubkZQ4cOBQC8//77eOONNwAA\nZWVlGDRoUML3ys3NxZEjRzB37lzcfffduOuuu1KqnZCewqROjOWHP/wh5s6di/3796O6uhpff/11\n+2vdnfLoiiuuwP79+1FSUoJnnnkGTzzxRLLlEpIUaOrEWM6dO4fBgwcDsOaFt7ntttvw2muvAQA2\nbNiAs2fPJnyvM2fOoLW1FVOnTsWvf/3rhBdQCdEFyy/ECM6fPx/2aLqf/OQnWLRoEe6//34MGjQI\nd9xxB/72t78BABYuXIgHH3wQL730Em699VZcf/31nZ4OFEljYyNmzJiBtrY2AOj0RBpCpEBTJ0YQ\nDAaj/r28vLzT3y6//HKsX78e/fr1w9atW7Fz505kZGTEff+RI0di165dUV/j7NVEEiy/ENdx7Ngx\njB07FqNGjcKPfvQj/OEPf4i63GWXXdalm49eeeUVjlUnYuBDMgghxCCY1AkhxCBo6oQQYhA0dUII\nMQiaOiGEGARNnRBCDIKmTgghBvH/AOHPv3q1NfFjAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab86d8a90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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kkZ2C2V+7JtcssaO0zYgOCrHQltCwesI8LU1+rt7SYAqxsMtks7NlfLVtFxPB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\nA4NeplCe0+CmAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab87f4090>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8842c10>"
+       ]
+      }
+     ],
+     "prompt_number": 91
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "However, what we want to compute is\n",
+      "$$\\begin{array}{rcl}\n",
+      "\\rho_{x,y}(\\tau) & = & \\frac{\\langle (x(t-\\tau)-\\langle x \\rangle) (y(t) -\\langle y \\rangle)\\rangle}{\\sqrt{\\sigma_{x,x}^2 \\sigma_{y,y}^2}} \\\\\n",
+      " & = & \\frac{\\langle x(t-\\tau) y(t) \\rangle-\\langle x \\rangle\\langle y \\rangle}{\\sqrt{\\sigma_{x,x}^2 \\sigma_{y,y}^2}} \\\\\n",
+      " & = & \\frac{\\frac{1}{T}\\int x(t-\\tau) y(t) dt - \\frac{1}{T}\\int x(t) dt \\cdot \\frac{1}{T} \\int y(t) dt}{\\sqrt{\\frac{1}{T}\\int x(t)^2 dt \\cdot \\frac{1}{T}\\int y(t)^2 dt}} \\\\\n",
+      "\\end{array}$$\n",
+      "\n",
+      "We still need to subtract the average firing rates (shuffle corrector) and divide by the variance..."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "hn=(h/tmax-(len(periodic_chirps)/tmax)**2)\n",
+      "plt.plot(lags[:-1], hn)\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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kK4YBkQYMA5IVw4DS+tvfgPHxXNci/3A6ipmOHgUOHsx1LUiU0DgDktd//idw+jSwbx+w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\n+H/akTHSoqx76gAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ab8768ed0>"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/statistics/code/checkmymedian.m b/statistics/code/checkmymedian.m
index d2d101d..31c7e1a 100644
--- a/statistics/code/checkmymedian.m
+++ b/statistics/code/checkmymedian.m
@@ -1,11 +1,11 @@
 % check whether the median returned by mymedian 
 % really separates a vector into two halfs
-for i = 1:140                         % loop over different length
-  for k = 1:10                        % try several times
-    a = randn( i, 1 );                % generate some data
-    m = mymedian( a );                % compute median
-    if length( a(a>m) ) ~= length( a(a<m) ) % check
-      disp( 'error!' )
+for i = 1:140                           % loop over different length
+  for k = 1:10                          % try several times
+    a = randn(i, 1);                    % generate some data
+    m = mymedian(a);                    % compute median
+    if length(a(a>m)) ~= length(a(a<m)) % check
+      disp('error!')
     end
   end
 end
diff --git a/statistics/code/correlations.m b/statistics/code/correlations.m
index d328592..e6eb0b7 100644
--- a/statistics/code/correlations.m
+++ b/statistics/code/correlations.m
@@ -1,16 +1,16 @@
 n = 200;
-corrs = [ 1.0, 0.6, 0.0, -0.9 ];
+corrs = [1.0, 0.6, 0.0, -0.9];
 for k = [1:length(corrs)]
     r = corrs(k);
     x = randn(n, 1);
-    y = r*x; % linear dependence of y on x
+    y = r*x;      % linear dependence of y on x
     % add noise to destroy perfect correlations:
     y = y + sqrt(1.0-r*r)*randn(n, 1);
     % compute correlation coefficient of data:
     rho = corr(x, y);
     subplot(2, 2, k)
-    scatter( x, y )
-    text( -2, 2.5, sprintf('r=%.1f', rho) )
+    scatter(x, y)
+    text(-2, 2.5, sprintf('r=%.1f', rho))
     xlabel('x')
     ylabel('y')
     xlim([-3.0, 3.0])
diff --git a/statistics/code/cumulative.m b/statistics/code/cumulative.m
index 44ebbc5..71c3323 100644
--- a/statistics/code/cumulative.m
+++ b/statistics/code/cumulative.m
@@ -5,7 +5,7 @@ plot(xs, cdf);
 hold on;
 
 dx = 0.01;
-xx = [-4:dx:4];                     % x-values for Gaussian pdf
+xx = [-4:dx:4];                        % x-values for Gaussian pdf
 gauss = exp(-0.5*xx.^2)/sqrt(2.0*pi);  % Gaussian pdf
 gausscdf = cumsum(gauss)*dx;
 plot(xx, gausscdf);
diff --git a/statistics/code/diehistograms.m b/statistics/code/diehistograms.m
index 5b47597..313fd2d 100644
--- a/statistics/code/diehistograms.m
+++ b/statistics/code/diehistograms.m
@@ -20,5 +20,5 @@ for i = [1:length(nrolls)]
   bar(b, h, 'facecolor', 'b')
   hold off
   title(sprintf('N=%d', length(d)))
-  pause( 2.0 )
+  pause(2.0)
 end
diff --git a/statistics/code/gaussiankerneldensity.m b/statistics/code/gaussiankerneldensity.m
index 8f97818..b01a1c2 100644
--- a/statistics/code/gaussiankerneldensity.m
+++ b/statistics/code/gaussiankerneldensity.m
@@ -1,15 +1,15 @@
-data = randn(100, 1);             % generate some data
-sigma = 0.2;                      % std. dev. of Gaussian kernel
-xmin = -4.0;                      % minimum x value for kernel density
-xmax = 4.0;                       % maximum x value for kernel density
-dx = 0.05*sigma;                  % step size for kernel density
-xg = [-4.0*sigma:dx:4.0*sigma];   % x-axis for single Gaussian kernel  
+data = randn(100, 1);           % generate some data
+sigma = 0.2;                    % std. dev. of Gaussian kernel
+xmin = -4.0;                    % minimum x value for kernel density
+xmax = 4.0;                     % maximum x value for kernel density
+dx = 0.05*sigma;                % step size for kernel density
+xg = [-4.0*sigma:dx:4.0*sigma]; % x-axis for single Gaussian kernel  
 % single Gaussian kernel:
 kernel = exp(-0.5*(xg/sigma).^2)/sqrt(2.0*pi)/sigma;
 ng = floor((length(kernel)-1)/2); % half the length of the Gaussian
-x = [xmin:dx:xmax+0.5*dx];        % x-axis for kernel density
-kd = zeros(1, length(x));         % vector for kernel density
-for i = 1:length(data)            % for every data value ...                
+x = [xmin:dx:xmax+0.5*dx];      % x-axis for kernel density
+kd = zeros(1, length(x));       % vector for kernel density
+for i = 1:length(data)          % for every data value ...                
   xd = data(i);
   % index of data value in kernel density vector:
   inx = round((xd-xmin)/dx)+1;
@@ -17,8 +17,8 @@ for i = 1:length(data)            % for every data value ...
   k0 = inx-ng;
   % end index for Gaussian in kernel density vector:
   k1 = inx+ng;
-  g0 = 1;                         % start index in Gaussian
-  g1 = length(kernel);            % end index in Gaussian
+  g0 = 1;                       % start index in Gaussian
+  g1 = length(kernel);          % end index in Gaussian
   % check whether left side of Gaussian extends below xmin:
   if inx < ng+1
     % adjust start indices accordingly:
@@ -34,7 +34,7 @@ for i = 1:length(data)            % for every data value ...
   % add Gaussian on kernel density:
   kd(k0:k1) = kd(k0:k1) + kernel(g0:g1);
 end
-kd = kd/length(data);              % normalize by number of data points
+kd = kd/length(data);           % normalize by number of data points
 
 % plot the computed kernel density:
 plot(x, kd, 'b', 'linewidth', 4, 'displayname', 'manual')
@@ -45,4 +45,4 @@ plot(x, kd, '--r', 'linewidth', 4, 'displayname', 'ksdensity()')
 hold off
 xlabel('x')
 ylabel('Probability density')
-legend('show')
\ No newline at end of file
+legend('show')
diff --git a/statistics/code/gaussianpdf.m b/statistics/code/gaussianpdf.m
index 7f9b58b..ef3de26 100644
--- a/statistics/code/gaussianpdf.m
+++ b/statistics/code/gaussianpdf.m
@@ -12,15 +12,15 @@ hold off
 
 % compute integral between x1 and x2:
 P = sum(p((x>=x1)&(x<x2)))*dx;
-fprintf( 'Integral between %.2g and %.2g: %.3g\n', x1, x2, P );
+fprintf('Integral between %.2g and %.2g: %.3g\n', x1, x2, P);
 
 % draw random numbers:
-r = randn( 10000, 1 );
+r = randn(10000, 1);
 
 % check P:
 Pr = sum((r>=x1)&(r<x2))/length(r);
-fprintf( 'Probability of a number between %.2g and %.2g: %.3g\n', x1, x2, Pr );
+fprintf('Probability of a number between %.2g and %.2g: %.3g\n', x1, x2, Pr);
 
 % infinite integral:
 P = sum(p)*dx;
-fprintf( 'Integral between -infinity and +infinity: %.3g\n', P );
+fprintf('Integral between -infinity and +infinity: %.3g\n', P);
diff --git a/statistics/code/histogramquartiles.m b/statistics/code/histogramquartiles.m
index e700eb4..a7a2995 100644
--- a/statistics/code/histogramquartiles.m
+++ b/statistics/code/histogramquartiles.m
@@ -1,24 +1,24 @@
 % generate data:
-x = randn( 1, 100000 );
+x = randn(1, 100000);
 
 % histogram:
-[h,b] = hist( x, 100 );
+[h,b] = hist(x, 100);
 % normalize:
 bs = b(2)-b(1);
 h = h/sum(h)/bs;
 
 % plot:
-bar( b, h );
-xlabel( 'x' );
+bar(b, h);
+xlabel('x');
 
 % median, quartile:
-q = quartiles( x );
-%q = quantile( x, [0.25, 0.5, 0.75 ] );
+q = quartiles(x);
+%q = quantile(x, [0.25, 0.5, 0.75 ]);
 
 % plot:
 hold on;
-bar( b(b<q(1)), h(b<q(1)), 'FaceColor', [0.5 0 0.5] );
-bar( b((b>=q(1)) & (b<q(2))), h((b>=q(1)) & (b<q(2))), 'FaceColor', [0.9 0 0] );
-bar( b((b>=q(2)) & (b<q(3))), h((b>=q(2)) & (b<q(3))), 'FaceColor', [0 0 0.9] );
-bar( b(b>=q(3)), h(b>=q(3)), 'FaceColor', [0.5 0 0.5] );
+bar(b(b<q(1)), h(b<q(1)), 'FaceColor', [0.5 0 0.5]);
+bar(b((b>=q(1)) & (b<q(2))), h((b>=q(1)) & (b<q(2))), 'FaceColor', [0.9 0 0]);
+bar(b((b>=q(2)) & (b<q(3))), h((b>=q(2)) & (b<q(3))), 'FaceColor', [0 0 0.9]);
+bar(b(b>=q(3)), h(b>=q(3)), 'FaceColor', [0.5 0 0.5]);
 hold off;
diff --git a/statistics/code/quartiles.m b/statistics/code/quartiles.m
index 8123733..d78a8db 100644
--- a/statistics/code/quartiles.m
+++ b/statistics/code/quartiles.m
@@ -1,16 +1,16 @@
-function q = quartiles( x )
+function q = quartiles(x)
   % returns a vector with the first, second, and third quartile
   % of the vector x
-  xs = sort( x );
-  if ( length( xs ) == 0 )                            % no data
+  xs = sort(x);
+  if (length(xs) == 0)                          % no data
     q = [];
-  elseif ( rem( length( xs ), 2 ) == 0 )              % even number of data
-    index = length( xs )/2;
-    m = (xs( index ) + xs( index+1 ))/2;
-    q = [ round( xs(length(xs)/4) ), m, xs(round(3*length(xs)/4)) ];
-  else                                                % odd number of data
-    index = (length( xs ) + 1)/2;
-    m = xs( index );
-    q = [ round( xs(length(xs)/4) ), m, xs(round(3*length(xs)/4)) ];
+  elseif (rem(length(xs), 2) == 0)              % even number of data
+    index = length(xs)/2;
+    m = (xs(index) + xs(index+1))/2;
+    q = [round(xs(length(xs)/4)), m, xs(round(3*length(xs)/4))];
+  else                                          % odd number of data
+    index = (length(xs) + 1)/2;
+    m = xs(index);
+    q = [round(xs(length(xs)/4)), m, xs(round(3*length(xs)/4))];
   end
 end
diff --git a/statistics/code/rollthedie.m b/statistics/code/rollthedie.m
index 1842da8..1eeefc6 100644
--- a/statistics/code/rollthedie.m
+++ b/statistics/code/rollthedie.m
@@ -1,4 +1,4 @@
-function x = rollthedie( n )
+function x = rollthedie(n)
 % return a vector with the result of rolling a die n times
-  x = randi( [1, 6], n, 1 );
+  x = randi([1, 6], n, 1);
 end
diff --git a/statistics/code/sprintfexamples.m b/statistics/code/sprintfexamples.m
index b8f6b75..b08a5c2 100644
--- a/statistics/code/sprintfexamples.m
+++ b/statistics/code/sprintfexamples.m
@@ -1,48 +1,48 @@
 % sprintf returns a string.
 % This string can be used to annotate plots using the text() function.
-s = sprintf( 'x=%f', pi )
+s = sprintf('x=%f', pi)
 
 % fprintf writes directly to console (or into files).
 % for fprintf you usually want to add the line break '\n':
 
 % '%f' formats floating point numbers:
-fprintf( 'x=%f\n', pi )
+fprintf('x=%f\n', pi)
 % The '%f' formatting string can be anywhere in the string:
-fprintf( 'x=%fms\n', pi )
+fprintf('x=%fms\n', pi)
 % There can be arbitrary many '%' formatting strings:
-fprintf( 'x=%fms, y=%fkHz\n', pi, 2*pi )
+fprintf('x=%fms, y=%fkHz\n', pi, 2*pi)
 % The '%' itself is generated by '%%':
-fprintf( 'x=%f%%\n', pi )
+fprintf('x=%f%%\n', pi)
 % A point followed by a number sets the number of digits after the point:
-fprintf( 'x=%.2fms\n', pi )
+fprintf('x=%.2fms\n', pi)
 % The numbers are appropriately rounded:
-fprintf( 'x=%.3fms\n', pi )
+fprintf('x=%.3fms\n', pi)
 % A number right before the point sets the width of the generated output:
-fprintf( 'x=%10.3fms\n', pi )
+fprintf('x=%10.3fms\n', pi)
 % '%e' also formats floating point numbers but forces to write in
 % exponential style:
-fprintf( 'x=%e\n', pi )
+fprintf('x=%e\n', pi)
 % again, a point and number set the number of digits after the point.
-fprintf( 'x=%.1e\n', pi )
+fprintf('x=%.1e\n', pi)
 % '%g% formats the floating point number to a given number of valid digits
 % (default is 5):
-fprintf( 'x=%g\n', pi )
+fprintf('x=%g\n', pi)
 % The number of valid digits is not the number of digits after the point:
-fprintf( 'x=%.2g\n', pi )
-fprintf( 'x=%.2g\n', 10.123 )
-fprintf( 'x=%.2g\n', 18765.123 )
-fprintf( 'x=%.5g\n', 18765.123 )
+fprintf('x=%.2g\n', pi)
+fprintf('x=%.2g\n', 10.123)
+fprintf('x=%.2g\n', 18765.123)
+fprintf('x=%.5g\n', 18765.123)
 
 % '%d' formats integers:
-fprintf( 'x=%d\n', 5 )
+fprintf('x=%d\n', 5)
 % the number defines the width of the output:
-fprintf( 'x=%3d\n', 5 )
+fprintf('x=%3d\n', 5)
 % precedig the width with a '0' fills up the space with leading zeros:
-fprintf( 'x=%03d\n', 5 )
+fprintf('x=%03d\n', 5)
 
 % '%s' formats a string:
-fprintf( 'x=%s\n', 'hallo' )
+fprintf('x=%s\n', 'hallo')
 % ... aligned to the right:
-fprintf( 'x=%10s\n', 'hallo' )
+fprintf('x=%10s\n', 'hallo')
 % ... unless the width is negative:
-fprintf( 'x=%-10s!\n', 'hallo' )
+fprintf('x=%-10s!\n', 'hallo')
diff --git a/statistics/exercises/Makefile b/statistics/exercises/Makefile
index 27691d9..fd49c98 100644
--- a/statistics/exercises/Makefile
+++ b/statistics/exercises/Makefile
@@ -1,34 +1,3 @@
-TEXFILES=$(wildcard exercises??.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
+TEXFILES=$(wildcard statistics-?.tex)
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
-
-pdf : $(SOLUTIONS) $(EXERCISES)
-
-exercises : $(EXERCISES)
-
-solutions : $(SOLUTIONS)
-
-$(SOLUTIONS) : solutions%.pdf : exercises%.tex instructions.tex
-	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
-	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
-	rm $(patsubst %.pdf,%,$@).[!p]*
-
-$(EXERCISES) : %.pdf : %.tex instructions.tex
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-
-watch :
-	while true; do ! make -q pdf && make pdf; sleep 0.5; done
-
-watchexercises :
-	while true; do ! make -q exercises && make exercises; sleep 0.5; done
-
-watchsolutions :
-	while true; do ! make -q solutions && make solutions; sleep 0.5; done
-
-clean :
-	rm -f *~ *.aux *.log *.out
-
-cleanup : clean
-	rm -f $(SOLUTIONS) $(EXERCISES)
+include ../../exercises.mk
diff --git a/statistics/exercises/instructions.tex b/statistics/exercises/instructions.tex
deleted file mode 100644
index 3041d3e..0000000
--- a/statistics/exercises/instructions.tex
+++ /dev/null
@@ -1,6 +0,0 @@
-\vspace*{-7.8ex}
-\begin{center}
-\textbf{\Large Introduction to Scientific Computing}\\[2.3ex]
-{\large Jan Grewe, Jan Benda}\\[-3ex]
-Neuroethology Lab \hfill --- \hfill Institute for Neurobiology \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
diff --git a/statistics/exercises/exercises01.tex b/statistics/exercises/statistics-1.tex
similarity index 73%
rename from statistics/exercises/exercises01.tex
rename to statistics/exercises/statistics-1.tex
index 68455b3..b7acb51 100644
--- a/statistics/exercises/exercises01.tex
+++ b/statistics/exercises/statistics-1.tex
@@ -1,88 +1,18 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[english]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 8\stitle}}{{\bfseries\large Statistics}}{{\bfseries\large December 2nd, 2019}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
+\newcommand{\exercisetopic}{Statistics}
+\newcommand{\exercisenum}{7}
+\newcommand{\exercisedate}{December 8th, 2020}
+
+\input{../../exercisesheader}
 
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- bonus question ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
+\input{../../exercisestitle}
 
 \ifprintanswers%
 \else
@@ -97,12 +27,13 @@ jan.benda@uni-tuebingen.de}
 \item Initially test computationally expensive \code{for} loops, vectors,
   matrices, etc. with small numbers of repetitions and/or
   sizes. Once it is working use large repetitions and/or sizes for
-  getting a good statistics.
+  getting a good statistics, i.e. smooth curves.
 \item Use the help functions of \code{matlab} (\code{help command} or
   \code{doc command}) and the internet to figure out how specific
   \code{matlab} functions are used and what features they offer.  In
   addition, the internet offers a lot of material and suggestions for
   any question you have regarding your code !
+\item Work in groups! Nevertheless everybody should write down his/her own solution.
 \item Please upload your solution to the exercises to ILIAS as a zip-archive with the name
   ``statistics\_\{last name\}\_\{first name\}.zip''.
 \end{itemize}
diff --git a/statistics/exercises/exercises02.tex b/statistics/exercises/statistics-2.tex
similarity index 69%
rename from statistics/exercises/exercises02.tex
rename to statistics/exercises/statistics-2.tex
index 4b1cc3c..e58f16b 100644
--- a/statistics/exercises/exercises02.tex
+++ b/statistics/exercises/statistics-2.tex
@@ -1,89 +1,18 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: Solutions}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large Exercise 7\stitle}}{{\bfseries\large Statistics}}{{\bfseries\large November 21st, 2017}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- bonus question ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
+\newcommand{\exercisetopic}{Statistics}
+\newcommand{\exercisenum}{X2}
+\newcommand{\exercisedate}{XXX}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
-
+\input{../../exercisestitle}
 
 \begin{questions}
 
diff --git a/statistics/exercises/exercises03.tex b/statistics/exercises/statistics-3.tex
similarity index 56%
rename from statistics/exercises/exercises03.tex
rename to statistics/exercises/statistics-3.tex
index 11e4320..8456122 100644
--- a/statistics/exercises/exercises03.tex
+++ b/statistics/exercises/statistics-3.tex
@@ -1,89 +1,20 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
+\newcommand{\exercisetopic}{Statistics -- Random Walk}
+\newcommand{\exercisenum}{X3}
+\newcommand{\exercisedate}{XXX}
+
+\input{../../exercisesheader}
+
+\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+
 \usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{: L\"osungen}
-\else
-\newcommand{\stitle}{}
-\fi
-\header{{\bfseries\large \"Ubung 8\stitle}}{{\bfseries\large Statistik}}{{\bfseries\large 29. November, 2016}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\input{instructions}
-
+\input{../../exercisestitle}
 
 \begin{questions}
 
diff --git a/statistics/lecture/diehistograms.py b/statistics/lecture/diehistograms.py
index 2458412..241bde7 100644
--- a/statistics/lecture/diehistograms.py
+++ b/statistics/lecture/diehistograms.py
@@ -15,7 +15,7 @@ ax1.set_xticks(range(1, 7))
 ax1.set_xlabel('x')
 ax1.set_ylim(0, 98)
 ax1.set_ylabel('Frequency')
-fs = fsC
+fs = dict(**fsC)
 fs['color'] = [fsC['facecolor'], fsE['facecolor']]
 del fs['facecolor']
 ax1.hist([x2, x1], bins, **fs)
@@ -26,9 +26,11 @@ ax2.set_xlabel('x')
 ax2.set_ylim(0, 0.23)
 ax2.set_ylabel('Probability')
 ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=-10, **lsAm)
-if mpl_major > 1:
-    ax2.hist([x2, x1], bins, density=True, zorder=-5, **fs)
-else:
-    ax2.hist([x2, x1], bins, normed=True, zorder=-5, **fs)
+h1, b1 = np.histogram(x1, bins)
+h2, b2 = np.histogram(x2, bins)
+h1 = h1/np.sum(h1)
+h2 = h2/np.sum(h2)
+ax2.bar(b1[:-1]+0.3, h1, zorder=-5, width=0.4, **fsC)
+ax2.bar(b2[:-1]+0.7, h2, zorder=-5, width=0.4, **fsE)
 fig.subplots_adjust(left=0.125)
 fig.savefig('diehistograms.pdf')
diff --git a/statistics/lecture/statistics-chapter.tex b/statistics/lecture/statistics-chapter.tex
index 4331ed1..c8edb4b 100644
--- a/statistics/lecture/statistics-chapter.tex
+++ b/statistics/lecture/statistics-chapter.tex
@@ -10,6 +10,8 @@
 \setcounter{page}{\pagenumber}
 \setcounter{chapter}{\chapternumber}
 
+\renewcommand{\exercisesolutions}{here}  % 0: here, 1: chapter, 2: end
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document} 
@@ -18,12 +20,21 @@
 
 \section{TODO}
 \begin{itemize}
-\item The content of this lecture easily covers two lectures!
-\item 1. mymedian and debugging, rolling a die, normalized histogram
-\item 2. densities, quantiles, cumulative distribution, kernel histogram
-\item Adapt the exercises to that!
+\item Statistics is to estimate some parameter from data (see below).
+\item Introduce this concept on a range of examples: mean, other
+  parameters of a distribution, parameters of a function.
+\item This is also what the brain needs to do: estimate some
+  information about the environment from noisy/incomplete data!
 \end{itemize}
 
+\section{Exercises}
+The content of this lecture easily covers two lectures!
+\begin{enumerate}
+\item mymedian and debugging, rolling a die, normalized histogram
+\item densities, quantiles, cumulative distribution, kernel histogram
+\end{enumerate}
+ Adapt the exercises to that!
+
 \end{document}
 
 
diff --git a/statistics/lecture/statistics-slides.tex b/statistics/lecture/statistics-slides.tex
index dc1af03..e143170 100644
--- a/statistics/lecture/statistics-slides.tex
+++ b/statistics/lecture/statistics-slides.tex
@@ -49,7 +49,6 @@
 
 %%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{graphicx}
-\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
 
 %%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{listings}