Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/teaching/scientificComputing

.gitignore

@@ -26,4 +26,4 @@
 *.vrb
 __*
 pointprocesses/lecture/pointprocessscetch*.tex
-
+*.DS_Store

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

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 

bootstrap/code/bootstrapsem.m

@@ -1,24 +1,25 @@
 nsamples = 100;
 nresamples = 1000;
 
-% draw a SRS (simple random sample, "Stichprobe") from the population:
+% draw a simple random sample ("Stichprobe") from the population:
-x = randn( 1, nsamples );
+x = randn(1, nsamples);
-fprintf('%-30s  %-5s  %-5s  %-5s\n', '', 'mean', 'stdev', 'sem' )
+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) )
+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
+mus = zeros(nresamples,1);    % vector for storing the means
-for i = 1:nresamples        % loop for generating the bootstraps
+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
+    xr = x(inx);              % resample the original SRS
-    mus(i) = mean(xr);      % compute statistic of the resampled 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!):
+% many SRS (we can do that with the random number generator,
-musrs = zeros(nresamples,1);      % vector for the means of each SRS
+% 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
+    x = randn(1, nsamples);   % draw a new SRS
-    musrs(i) = mean( x );         % compute its mean
+    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))

bootstrap/code/correlationsignificance.m

@@ -1,27 +1,27 @@
 % generate correlated data:
-n=200;
+n = 200;
-a=0.2;
+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
+for i = 1:nperm
-    xr=x(randperm(length(x)));  % shuffle x
+    xr = x(randperm(length(x)));  % shuffle x
-    yr=y(randperm(length(y)));  % shuffle y
+    yr = y(randperm(length(y)));  % shuffle y
     rs(i) = corr(xr, yr);
 end
-[h,b] = hist(rs, 20 );
+[h, b] = hist(rs, 20);
-h = h/sum(h)/(b(2)-b(1));  % normalization
+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;

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;

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

bootstrap/exercises/Makefile

@@ -1,34 +1,3 @@
-TEXFILES=$(wildcard exercises??.tex)
+TEXFILES=$(wildcard resampling-?.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
 
-.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)

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);

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);
+    xb = x(inx);
-    yb=y(inx);
+    yb = y(inx);
     rb(i) = corr(xb, yb);
 end
 
 %% (b) pdf of the correlation coefficients:
-[hb,bb] = hist(rb, 20);
+[hb, bb] = hist(rb, 20);
-hb = hb/sum(hb)/(bb(2)-bb(1));  % normalization
+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(b, h, 'facecolor', [0.5 0.5 0.5]);           % permuation test
-bar(bb, hb, 'facecolor', 'b');
+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])

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);
+rs = zeros(nperm, 1);
-for i=1:nperm
+for i = 1:nperm
-    xr=x(randperm(length(x)));  % shuffle x
+    xr = x(randperm(length(x)));      % shuffle x
-    yr=y(randperm(length(y)));  % shuffle y
+    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, b] = hist(rs, 20);
-h = h/sum(h)/(b(2)-b(1));  % normalization
+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');

bootstrap/exercises/exercises01.tex

@@ -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}

bootstrap/exercises/instructions.tex

@@ -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}

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

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

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);

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}

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}

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:
+  % default width:
-if nargin < 3
+  if nargin < 3
-    width = 11.7;
+      width = 11.7;
-end
+  end
-% default height:
+  % default height:
-if nargin < 4
+  if nargin < 4
-    height = 9.0;
+      height = 9.0;
-end
+  end
 
-% paper:
+  % paper:
-set( fig, 'PaperUnits', 'centimeters' );
+  set(fig, 'PaperUnits', 'centimeters');
-set( fig, 'PaperSize', [width height] );
+  set(fig, 'PaperSize', [width height]);
-set( fig, 'PaperPosition', [0.0 0.0 width height] );
+  set(fig, 'PaperPosition', [0.0 0.0 width height]);
-set( fig, 'Color', 'white')
+  set(fig, 'Color', 'white')
 
-% font:
+  % font:
-set( findall( fig, 'type', 'axes' ), 'FontSize', 12 )
+  set(findall(fig, 'type', 'axes'), 'FontSize', 12)
-set( findall( fig, 'type', 'text' ), 'FontSize', 12 )
+  set(findall(fig, 'type', 'text'), 'FontSize', 12)
 
-% save:
+  % save:
-saveas( fig, name, 'pdf' )
+  saveas(fig, name, 'pdf')
 
 end
 

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

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}

bootstrap/lecture/bootstrap-slides.tex

@@ -49,7 +49,6 @@
 
 %%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{graphicx}
-\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
 
 %%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{listings}

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
+\entermde{Resampling-Methoden}{Resampling methods} are applied to
-advantages:
+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
+samples from one set of measurements by means of resampling. From
-(\entermde{Resampling}{resampling}). From these bootstrapped samples
+these bootstrapped samples we compute the desired statistical measure
-we compute the desired statistical measure and estimate their
+and estimate their distribution
-distribution (\entermde{Bootstrap!Verteilung}{bootstrap 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
+distributions which have been derived based on some assumptions about
-applied statistical test has to be appropriate for the type of
+the data. For example, the data should be normally distributed.  Given
-data. An alternative approach is to calculate the probability density
+some data one has to find an appropriate test that matches the
-of the null hypothesis directly from the data itself. To do this, we
+properties of the data. An alternative approach is to calculate the
-need to resample the data according to the null hypothesis from the
+probability density of the null hypothesis directly from the data
-SRS. By such permutation operations we destroy the feature of interest
+themselves. To do so, we need to resample the data according to the
-while we conserve all other statistical properties of the data.
+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}
+  \includegraphics[width=1\textwidth]{permuteaverage}
-  \titlecaption{\label{permutecorrelationfig}Permutation test for
+  \titlecaption{\label{permuteaverage}Permutation test for differences
-    correlations.}{Let the correlation coefficient of a dataset with
+    in means.}{We want to test whether two datasets
-    200 samples be $\rho=0.21$. The distribution of the null
+    $\left\{x_i\right\}$ (red) and $\left\{y_i\right\}$ (blue) come
-    hypothesis (yellow), optained from the correlation coefficients of
+    from different distributions by assessing the significance of the
-    permuted and therefore uncorrelated datasets is centered around
+    difference in their sample means. The data sets were generated
-    zero. The measured correlation coefficient is larger than the
+    with a difference in their population means of $d=0.7$. For
-    95\,\% percentile of the null hypothesis. The null hypothesis may
+    generating the distribution of the null hypothesis, i.e. the
-    thus be rejected and the measured correlation is considered
+    distribution of differences in the means if the two data sets come
-    statistically significant.}
+    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
+\begin{exercise}{meandiffsignificance.m}{meandiffsignificance.out}
-\entermde{Permutationstest}{permutaion test} is the statistical
+Estimate the statistical significance of a difference in the mean of two data sets.
-assessment of \entermde[correlation]{Korrelation}{correlations}. Given
+\vspace{-1ex}
-are measured pairs of data points $(x_i, y_i)$. By calculating the
+\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]{Korrelationskoeffizient}{correlation coefficient} we can
-  coefficient} we can quantify how strongly $y$ depends on $x$. The
+quantify how strongly $y$ depends on $x$. The correlation coefficient
-correlation coefficient alone, however, does not tell whether the
+alone, however, does not tell whether the correlation is significantly
-correlation is significantly different from a random correlation. The
+different from a non-zero correlation that we might get although there
-\entermde{Nullhypothese}{null hypothesis} for such a situation is that
+is no true correlation in the data. The \entermde{Nullhypothese}{null
-$y$ does not depend on $x$. In order to perform a permutation test, we
+  hypothesis} for such a situation is that $y$ does not depend on
-need to destroy the correlation by permuting the $(x_i, y_i)$ pairs,
+$x$. In order to perform a permutation test, we need to destroy the
-i.e. we rearrange the $x_i$ and $y_i$ values in a random
+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
+corresponding correlation coefficients yields a distribution of
-correlation coefficients that result randomly from uncorrelated
+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
+  of the null hypothesis using the \varcode{quantile()} function and
-  coefficient computed from the original data.
+  compare it with the correlation coefficient computed from the
+  original data.
 \end{enumerate}
 \end{exercise}
 

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)

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')

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)
+    xr = rng.permutation(x)
-    yr=rng.permutation(y)
+    yr = rng.permutation(y)
-    rs.append( np.corrcoef(xr, yr)[0, 1] )
+    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 = plt.figure(figsize=cm_size(figure_width, 1.8*figure_height))
-fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=0.5, top=1.0))
+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',
+            xytext=(rd-0.01, 3.0), textcoords='data', ha='left',
-            arrowprops=dict(arrowstyle="->", relpos=(0.2,0.0),
+            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')

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
 

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);
@@ -218,17 +219,14 @@ 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}
+\end{pagelisting}
-
-\pagebreak[4]
 
-\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] 
+\pageinputlisting[label=localfunctions, caption={Example for local
-\lstinputlisting[label=localfunctions, caption={Example for local
   functions.}]{calculateSines.m}
 
 \emph{Local function} live in the same \entermde{m-File}{m-file} as

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

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:
+% Process the results stored in the vector y:
-mean(y)
+mean(y, 1)
-\end{lstlisting}
+\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}
+\end{pagelisting}
-\begin{lstlisting}[caption={Probability with the \varcode{histogram()}-function}]
+
+\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}
+\end{pagelisting}
-\begin{lstlisting}[caption={Probability with the \varcode{hist()}- and \varcode{bar()}-function}]
+
+\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}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

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)

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}

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

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{topnumber}{1}
-\setcounter{bottomnumber}{0}
+\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,%
+        \immediate\write\solutions{\unexpanded{\begin{minipage}{1\linewidth}\lstinputlisting[belowskip=0ex,aboveskip=0ex,%
-          nolol=true, title={\textbf{Source code:} \protect\StrSubstitute{#1}{_}{\_}}]}{\codepath#1}}%
+          nolol=true, title={\textbf{Source code:} \protect\StrSubstitute{#1}{_}{\_}}]}{\codepath#1}\unexpanded{\end{minipage}}}%
         \ifthenelse{\equal{#2}{}}{}%
-          {\immediate\write\solutions{\unexpanded{\lstinputlisting[language={},%
+          {\immediate\write\solutions{\unexpanded{\begin{minipage}{1\linewidth}\lstinputlisting[language={},%
-             nolol=true, title={\textbf{Output:}}, belowskip=0ex, aboveskip=1ex]}{\codepath#2}}}%
+             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]%

likelihood/exercises/Makefile

@@ -1,34 +1,3 @@
-TEXFILES=$(wildcard exercises??.tex)
+TEXFILES=$(wildcard likelihood-?.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:exercises%=solutions%)
 
-.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)

likelihood/exercises/instructions.tex

@@ -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

likelihood/exercises/likelihood-1.tex

@@ -1,93 +1,23 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
+\newcommand{\exercisetopic}{Maximum Likelihood}
-\usepackage{pslatex}
+\newcommand{\exercisenum}{10}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+\newcommand{\exercisedate}{January 12th, 2021}
-\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} 
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-\newcommand{\reZpN}{\mathds{R^+_0}}
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-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
 
+\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?

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))
+    datamean = mean(x);
-    fprintf('                  standard deviation of the data is %.2f\n', std(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)
+    maxloglm = psigs(loglm==max(loglm));
-    fprintf('  log-likelihood: standard deviation of the data is %.2f\n', 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);

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

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.
+  Use fminsearch for fitting?
-\item GLM model fitting?
 \end{itemize}
 
 \end{document}

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
+The core task of statistics is to infer from measured data some
-distribution one or more parameters $\theta$ that best describe the
+parameters describing the data. These parameters can be simply a mean,
-data $x_1, x_2, \ldots x_n$.  \enterm[maximum likelihood
+a standard deviation, or any other parameter needed to describe the
-estimator]{Maximum likelihood estimators} (\enterm[mle|see{maximum
+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
+\determ{Maximum-Likelihood-Sch\"atzer}).  They choose the parameters
-that they maximize the likelihood of the data $x_1, x_2, \ldots x_n$
+such that they maximize the likelihood of the specific data values to
-to originate from the distribution.
+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
+$\theta$.'') the probability (density) distribution of data value $x$
-parameters $\theta$. This could be the normal distribution
+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,
+parameters $\theta$.  If the $n$ observations $x_1, x_2, \ldots, x_n$
-x_2, \ldots x_n$ originate from the same probability density
+are independent of each other and originate from the same probability
-distribution (they are \enterm[i.i.d.|see{independent and identically
+density distribution (they are \enterm[i.i.d.|see{independent and
-  distributed}]{i.i.d.}, \enterm{independent and identically
+  identically distributed}]{i.i.d.}, \enterm{independent and
-  distributed}) then the conditional probability $p(x_1,x_2, \ldots
+  identically distributed}), then the conditional probability
-x_n|\theta)$ of observing $x_1, x_2, \ldots x_n$ given some specific
+$p(x_1,x_2, \ldots, x_n|\theta)$ of observing the particular data
-parameter values $\theta$ is given by
+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)
+  \label{probdata}
-  \ldots p(x_n|\theta) = \prod_{i=1}^n p(x_i|\theta) \; .
+  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 ($\int {\cal
-  L}(\theta|x_1,x_2, \ldots x_n) \, d\theta \ne 1$).
+  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
-When applying maximum likelihood estimations we are interested in the
+\eqref{likelihood} is a function of the parameters $\theta$. This
-parameter values
+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
+that maximize the likelihood \eqref{likelihood}.
-the argument $x$ for which the function $f(x)$ assumes its global
+$\text{argmax}_xf(x)$ is the value of the argument $x$ for which the
-maximum. Thus, we search for the parameter values $\theta$ at which
+function $f(x)$ assumes its global maximum. Thus, we search for the
-the likelihood ${\cal L}(\theta)$ reaches its maximum. For these
+parameter values $\theta$ at which the likelihood ${\cal L}(\theta)$
-paraemter values the measured data most likely originated from the
+reaches its maximum. For these parameter values the measured data most
-corresponding distribution.
+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
+we discuss below, we instead search for the maximum of the logarithm
-the likelihood
+of the likelihood
-(\entermde[likelihood!log-]{Likelihood!Log-}{log-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 \\
+  \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 {\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}
+\subsection{Arithmetic mean}
-Suppose that the measurements $x_1, x_2, \ldots x_n$ originate from a
+Suppose that the measurements $x_1, x_2, \ldots, x_n$ originate from a
-normal distribution \eqnref{normpdfmean} and we consider the mean
+normal distribution \eqref{normpdfmean} and we do not know the
-$\mu$ as the only parameter $\theta$.  Which value of $\theta$
+population mean $\mu$ of the normal distribution
-maximizes the likelihood of the data?
+(\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
+    normal distributions differing in their means (arrows) from which
-    (arrows) the data could have originated from.  Bottom left: the
+    the data could have originated from.  Bottom left: the likelihood
-    likelihood as a function of $\theta$ i.e. the mean. It is maximal
+    as a function of the parameter $\mu$. For the data it is maximal
-    at a value of $\theta = 2$. Bottom right: the
+    at a value of $\mu = 2$. Bottom right: the log-likelihood. Taking
-    log-likelihood. Taking the logarithm does not change the position
+    the logarithm does not change the position of the maximum.}
-    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)
+  \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-\theta)^2}{2\sigma^2}} \nonumber \\
+  & = & \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-\theta)^2}{2\sigma^2} \; .
+  & = & \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,
+normal distribution. This is the second reason why it is useful to
-we need to take the derivative with respect to $\theta$ and set it to
+maximize the log-likelihood. To calculate the maximum of the
-zero:
+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 \\
+  \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 x_i - \sum_{i=1}^n \theta & = & 0  \nonumber \\
+  \Leftrightarrow \quad \sum_{i=1}^n - \frac{2(x_i-\mu)}{2\sigma^2} & = & 0  \nonumber \\
-  \Leftrightarrow \quad n \theta & = & \sum_{i=1}^n x_i  \nonumber \\
+  \Leftrightarrow \quad \sum_{i=1}^n x_i - \sum_{i=1}^n \mu & = & 0  \nonumber \\
-  \Leftrightarrow \quad \theta & = & \frac{1}{n} \sum_{i=1}^n x_i \;\; = \;\; \bar x
+  \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
+Thus, the maximum likelihood estimator of the population mean of
-is, the arithmetic mean maximizes the likelihood that the data
+normally distributed data is the arithmetic mean. That is, the
-originate from a normal distribution centered at the arithmetic mean
+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
+the Gaussian that fits the data best in a maximum likelihood sense,
-likelihood sense, i.e. the likelihood that the data were generated
+i.e. the likelihood that the data were generated from this
-from this distribution is the largest. Fitting a Gaussian distribution
+distribution is the largest. Fitting a Gaussian distribution to data
-to data is very simple: just compute the two parameter of the Gaussian
+is very simple: just compute the two parameter of the Gaussian
-distribution as the arithmetic mean and a standard deviation directly
+distribution $\mu$ and $\sigma$ as the arithmetic mean and a standard
-from the data.
+deviation, respectively, directly from the data.
-
+
-For non-Gaussian distributions (e.g. a Gamma-distribution), however,
+For non-Gaussian distributions, for example a
-such simple analytical expressions for the parameters of the
+\entermde[distribution!Gamma-]{Verteilung!Gamma-}{Gamma-distribution}
-distribution do not exist, e.g. the shape parameter of a
+\begin{equation}
-\entermde[distribution!Gamma-]{Verteilung!Gamma-}{Gamma-distribution}. How
+  \label{gammapdf}
-do we fit such a distribution to some data? That is, how should we
+  p(x|\alpha,\beta) \sim x^{\alpha-1}e^{-\beta x}
-compute the values of the parameters of the distribution, given the
+\end{equation}
-data?
+with a shape parameter $\alpha$ and a rate parameter $\beta$
-
+(\figrefb{mlepdffig}), in general no such simple analytical
-A first guess could be to fit the probability density function by
+expressions for estimating the parameters directly from the data do
-minimization of the squared difference to a histogram of the measured
+not exist. How do we fit such a distribution to the data?  That is,
-data. For several reasons this is, however, not the method of choice:
+how should we compute the values of the parameters of the
-(i) Probability densities can only be positive which leads, for small
+distribution, given the data?
-values in particular, to asymmetric distributions. (ii) The values of
+
-a histogram estimating the density are not independent because the
+A first guess could be to fit the probability density function by a
-integral over a density is unity. The two basic assumptions of
+\enterm{least squares} fit to a normalized histogram of the measured data in
-normally distributed and independent samples, which are a prerequisite
+the same way as we fit a function to some data. For several reasons
-make the minimization of the squared difference \eqnref{chisqmin} to a
+this is, however, not the method of choice: (i) Probability densities
-maximum likelihood estimation, are violated. (iii) The histogram
+can only be positive which leads, in particular for small values, to
-strongly depends on the chosen bin size \figref{mlepdffig}).
+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
+    with the true probability density (red) and the probability
-    functions. The fit was done by minimizing the squared difference
+    density function obtained by a least squares fit to the
-    to the histogram.}
+    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
+likelihood.  We simply search for the parameter values of the desired
-desired probability density function that maximizes the
+probability density function that maximize the log-likelihood. In
-log-likelihood. In general this is a non-linear optimization problem
+general this is a non-linear optimization problem that is solved with
-that is solved with numerical methods such as the gradient descent
+numerical methods such as the gradient descent \matlabfun{mle()}.
-\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
+$(x_i|y_i)$ one tries to adapt the parameters $\theta$ such that the
-function best describes the data. With maximum likelihood we search
+function best describes the data. In
-for the parameter value $\theta$ for which the likelihood that the data
+chapter~\ref{gradientdescentchapter} we simply assumed that ``best''
-were drawn from the corresponding function is maximal.  If we assume
+means minimizing the squared distance between the data and the
-that the $y_i$ values are normally distributed around the function
+function.  With maximum likelihood we search for those parameter
-values $f(x_i;\theta)$ with a standard deviation $\sigma_i$, the
+values $\theta$ that maximize the likelihood of the data to be drawn
-log-likelihood is
+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 only difference to the previous example of the arithmetic mean is
-the equations above are now given as the function values
+that the means $\mu$ in the equations above are given by the function
-$f(x_i;\theta)$. The parameter $\theta$ should be the one that
+values $f(x_i;\theta)$. The parameters $\theta$ should maximize the
-maximizes the log-likelihood. The first part of the sum is independent
+log-likelihood. The first term in the sum is independent of $\theta$
-of $\theta$ and can thus be ignored when computing the the maximum:
+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
+minimum. Also the factor $\frac{1}{2}$ can be ignored since it does
-the position of the minimum:
+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
+The sum of the squared differences between the $y$-data values and the
-deviation is also called $\chi^2$. The parameter $\theta$ which
+function values, normalized by the standard deviation of the data
-minimizes the squared differences is thus the one that maximizes the
+around the function, is called $\chi^2$ (chi squared). The parameter
-likelihood that the data actually originate from the given
+$\theta$ which minimizes the squared differences is thus the one that
-function. Minimizing $\chi^2$ therefore is a maximum likelihood
+maximizes the likelihood of the data to actually originate from the
-estimation.
+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
+\eqref{loglikelihood} needs to be adapted accordingly.
-maximized respectively.
 
 \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
+\subsection{Straight line trough the origin}
-\[ f(x) = \theta x \; . \]
+The function of a straight line with slope $m$ through the origin
-The $\chi^2$-sum is thus
+is
-\[ \chi^2 = \sum_{i=1}^n \left( \frac{y_i-\theta x_i}{\sigma_i} \right)^2 \; . \]
+\[ f(x) = m x \; . \]
-To estimate the minimum we again take the first derivative with
+With this specific function, $\chi^2$ reads
-respect to $\theta$ and equate it to zero:
+\[ \chi^2 = \sum_{i=1}^n \left( \frac{y_i-m x_i}{\sigma_i} \right)^2
-\begin{eqnarray}
+\; . \] To calculate the minimum we take the first derivative with
-  \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 \\
+respect to $m$ and equate it to zero:
-  & = & \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}
 \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
+This is an analytical expression for the estimation of the slope $m$
-$\theta$ of the regression line (\figref{mleproplinefig}).
+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
+analytically. Consider, for example, the factor $c$ and the rate
-exponential decay
+$\lambda$ 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
+Let us have a closer look on \eqnref{mleslope} for the slope of a line
-deviation of the data $\sigma_i$ is the same for each data point,
+through the origin. If the standard deviation of the data $\sigma_i$
-i.e. $\sigma_i = \sigma_j \; \forall \; i, j$, the standard deviation drops
+is the same for each data point, i.e. $\sigma_i = \sigma_j \; \forall
-out of \eqnref{mleslope} and we get
+\; i, j$, 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
+To see what the nominator and the denominator of this expression
-make the data mean free and normalize them to their standard
+describe, we need to subtract from the data their mean value. We make
-deviation, i.e. $x \mapsto (x - \bar x)/\sigma_x$. The resulting
+the data mean free, i.e. $x \mapsto x - \bar x$ and $y \mapsto y -
-numbers are also called \entermde[z-values]{z-Wert}{$z$-values} or
+\bar y$ . For mean-free data the variance
-$z$-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
 \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.
+is given by the mean squared data.  In the same way, the covariance
-The covariance between $x$ and $y$ also simplifies to
+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 $r_{x,y}$,
+Recall that the correlation coefficient $r_{x,y}$ is the covariance
-\eqnref{correlationcoefficient}, is the covariance normalized by the
+normalized by the product of the standard deviations of $x$ and $y$:
-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
 \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
+Maximum likelihood estimators are not only a central concept for data
-the neuronal activity of populations of neurons. One example of such a
+analysis. Neural systems face the very same problem. They also need to
-population code is the tuning of neurons in the primary visual cortex
+estimate parameters of the internal and external environment based on
-(V1) to the orientation of an edge or bar in the visual
+the activity of neurons.
-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).
 
 \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
+    a stimulus parameter from neuronal activity.}{Top: Tuning curve
-    an individual neuron as a function of the stimulus orientation (a
+    $r_i(\phi;c,\phi_i)$ of a specific neuron $i$ as a function of the
-    dark bar in front of a white background). The stimulus that evokes
+    orientation $\phi$ of a stimulus, a dark bar in front of a white
-    the strongest activity in that neuron is the bar with the vertical
+    background. The preferred stimulus $\phi_i$ of that neuron, the
-    orientation (arrow, $\phi_i=90$\,\degree). The red area indicates
+    one that evokes the strongest firing rate response, is a bar with
-    the variability of the neuronal activity $p(r|\phi)$ around the
+    vertical orientation (arrow, $\phi_i=90$\,\degree). The width of
-    tuning curve. Center: In a population of neurons, each neuron may
+    the red area indicates the variability of the neuronal activity
-    have a different tuning curve (colors). A specific stimulus (the
+    $\sigma_i$ around the tuning curve. Center: In a population of
-    vertical bar) activates the individual neurons of the population
+    neurons, each neuron may have a different tuning curve (colors). A
-    in a specific way (dots). Bottom: The log-likelihood of the
+    specific stimulus activates the individual neurons of the
-    activity pattern will be maximized close to the real stimulus
+    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
+stimulus? In our example, what is the orientation of the bar? In the
-likelihood, a possible answer to this question would be: the stimulus
+sense of maximum likelihood, a possible answer to this question would
-for which the particular activity pattern is most likely given the
+be: the stimulus for which the particular activity pattern is most
-tuning of the neurons.
+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
+tuning of the firing rate $r_i(\phi)$ of neuron $i$ to the preferred
-orientation $\phi_i$ can be well described using a van-Mises function
+bar orientation $\phi_i$ can be well described using a van-Mises
-(the Gaussian function on a cyclic x-axis) (\figref{mlecodingfig}):
+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 $\sigma=\Omega/4$,
+around the tuning curve with a standard deviation $\sigma_i$, then the
-which is proportional to $\Omega$, then the probability $p_i(r|\phi)$
+probability $p_i(r|\phi)$ of the $i$-th neuron having the observed
-of the $i$-th neuron showing the activity $r$ given a certain
+activity $r$, given a certain orientation $\phi$ of a bar is
-orientation $\phi$ of an edge is given by
 \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 angle $\phi_{mle}$ that maximizes this likelihood is an estimate
-the orientation of the edge.
+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.
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

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**2)
-    probs[k,:] = np.exp(-0.5*((r-y)/sigma)**2.0)/np.sqrt(2.0*np.pi)/sigma
 loglikelihood = np.sum(np.log(probs), 0)
-maxl = np.max(loglikelihood)
 maxp = phases[np.argmax(loglikelihood)]
 ax.annotate('',
-            xy=(maxp, -1600), xycoords='data',
+            xy=(maxp, -210), xycoords='data',
-            xytext=(maxp, -30), textcoords='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.text(maxp+0.05, -150, 'most likely\norientation\ngiven the responses')
-ax.plot(phases, loglikelihood, **lsA)
+ax.plot(phases, loglikelihood, clip_on=False, **lsA)
 
 plt.savefig('mlecoding.pdf')

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))

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()
+

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}
+largemarker = {'marker': 'o', 'markersize': 9, 'markeredgecolor': colors['white'], 'markeredgewidth': edgewidth}
-smallmarker = {'marker': 'o', 'markersize': 6, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
+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)

plotting/exercises/Makefile

@@ -0,0 +1,3 @@
+TEXFILES=$(wildcard plotting-?.tex)
+
+include ../../exercises.mk

plotting/exercises/instructions.tex

@@ -1,17 +1,11 @@
-\vspace*{-8ex}
+\ifprintanswers%
-\begin{center}
+\else
-\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}
 
 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

plotting/exercises/plotting-1.tex

@@ -1,88 +1,18 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
+\newcommand{\exercisetopic}{Plotting}
-\usepackage{pslatex}
+\newcommand{\exercisenum}{X}
-\usepackage[mediumspace,mediumqspace]{SIunits}      % \ohm, \micro
+\newcommand{\exercisedate}{December 14th, 2020}
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
 
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\input{../../exercisesheader}
-\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}{}
 
-\setlength{\baselineskip}{15pt}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
-\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{../../exercisestitle}
+
 \input{instructions}
 
 \begin{questions}

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}

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
+  On first glance manual editing of a figure using tools such as
-  Corel draw, Illustrator, etc.\,appears much more convenient and less
+  inkscape, Corel draw, Illustrator, etc.\,appears much more
-  complex than coding everything into the analysis scripts. This,
+  convenient and less complex than coding everything into the analysis
-  however, is not entirely true. What if the figure has to be re-drawn
+  scripts. This, however, is not entirely true. What if the figure has
-  or updated? Then the editing work starts all over again. Rather,
+  to be re-drawn or updated, because, for example, you got more data?
-  there is a great risk associated with the manual editing
+  Then the editing work starts all over again. In addition, there is a
-  approach. Axes may be shifted, fonts have not been embedded into the
+  great risk associated with the manual editing approach. Axes may be
-  final document, annotations have been copy pasted between figures
+  shifted, fonts have not been embedded into the final document,
-  and are not valid. All of these mistakes can be found in
+  annotations have been copy-pasted between figures and are not
-  publications and then require an erratum, which is not
+  valid. All of these mistakes can be found in publications and then
-  desirable. Even if it appears more cumbersome in the beginning one
+  require an erratum, which is not desirable. Even if it appears more
-  should always try to create publication-ready figures directly from
+  cumbersome in the beginning, one should always try to generate
-  the data analysis tool using scripts or functions to properly layout
+  publication-ready figures directly from the data analysis tool using
-  the plot.
+  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})
 
-
+\pageinputlisting[caption={Creating a scatter plot with size and color
-\lstinputlisting[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}
 

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
 

pointprocesses/code/colorednoisepdf.m

@@ -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

pointprocesses/code/colorednoisepdfisifit.m

@@ -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
-

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;
+    time = 0:dt:t_max - dt;
-rate = zeros(size(time));
+    rate = zeros(size(time));
-spike_indices = round(spikes / dt);
+    spike_indices = round(spikes / dt);
-rate(spike_indices) = 1;
+    rate(spike_indices) = 1;
-kernel = gaussKernel(sigma, dt);
+    kernel = gaussKernel(sigma, dt);
-
+    rate = conv(rate, kernel, 'same');
-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

pointprocesses/code/counthist.m

@@ -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

pointprocesses/code/counthistpoisson.m

@@ -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

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

pointprocesses/code/fano.m

@@ -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
-

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);

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

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);

pointprocesses/code/isireturnmap.m

@@ -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
-

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});

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 
+            % We insure this already in the isis() function.
-            % generates the isivec.
             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
-

pointprocesses/code/plotcounthist.m

@@ -1,24 +1,14 @@
-w = 0.1;
+function plotcounthist(spikes, w)
-cmax = 8;
+% Plot histogram of spike counts.
-pmax = 0.5;
+%
-subplot(1, 3, 1);
+% Arguments:
-counthist(poissonspikes, w);
+%   spikes: a cell array of vectors of spike times in seconds
-xlim([0 cmax])
+%   w: duration of window in seconds for computing the counts
-set(gca, 'XTick', 0:2:cmax)
+    n = counts(spikes, w);
-ylim([0 pmax])
+    maxn = max(n);
-title('Poisson');
+    [counts, bins] = hist(n, 0:1:maxn+10);
-
+    counts = counts / sum(counts);
-subplot(1, 3, 2);
+    bar(bins, counts);
-counthist(pifouspikes, w);
+    xlabel('counts k');
-xlim([0 cmax])
+    ylabel('P(k)');
-set(gca, 'XTick', 0:2:cmax)
+end
-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);

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
+

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
+%   binwidth: width in seconds to be used for the ISI bins
-
+    [pdf, centers] = isihist(isis, binwidth);
-    % 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), ...

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

pointprocesses/code/plotspikestats.m

@@ -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);

pointprocesses/code/poissonisih.m

@@ -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 );

pointprocesses/code/poissonisistats.m

@@ -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' );

pointprocesses/code/psth.m

@@ -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

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
+%   tmax: plot spike raster up to tmax seconds
-
+    spiketimes = [];
-ntrials = length(spikes);
+    trials = [];
-for k = 1:ntrials
+    ntrials = length(spikes);
-    times = spikes{k};
+    for k = 1:ntrials
-    times = times(times<tmax);
+        times = spikes{k};
-    if tmax < 1.5
+        times = times(times<tmax);
-        times = 1000.0*times; % conversion to ms
+        % (x,y) pairs for start and stop of stroke
-    end
+        % plus nan separating strokes:
-    for i = 1:length( times )
+        spiketimes = [spiketimes, ...
-      line([times(i) times(i)],[k-0.4 k+0.4], 'Color', 'k'); 
+                       [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
+    % convert matrices into column vectors of (x,y) pairs:
-if tmax < 1.5
+    spiketimes = spiketimes(:);
-    xlabel('Time [ms]');
+    trials = trials(:);
-    xlim([0.0 1000.0*tmax]);
+    % plotting this is lightning fast:
-else
+    plot(spiketimes, trials, 'k')
     xlabel('Time [s]');
     xlim([0.0 tmax]);
-end
+    ylabel('Trials');
-ylabel('Trials');
+    ylim([0.3 ntrials+0.7]);
-ylim([0.3 ntrials+0.7 ]);
 end
 

pointprocesses/code/spikecounts.m

@@ -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

pointprocesses/code/spikecountshists.m

@@ -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);

pointprocesses/code/spikerate.m

@@ -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

pointprocesses/exercises/Makefile

@@ -1,35 +1,3 @@
-BASENAME=pointprocesses
+TEXFILES=$(wildcard pointprocesses-?.tex) psth.tex
-TEXFILES=$(wildcard $(BASENAME)??.tex)
-EXERCISES=$(TEXFILES:.tex=.pdf)
-SOLUTIONS=$(EXERCISES:pointprocesses%=pointprocesses-solutions%)
 
-.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
+include ../../exercises.mk
-
-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)

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

pointprocesses/exercises/fanoplot.m

@@ -1,18 +1,17 @@
 function fanoplot(spikes, titles)
-% computes and plots fano factor as a function of window size
+% 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
+% 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;
+    mc = zeros(1, length(windows));
-    vc = windows;
+    vc = zeros(1, length(windows));
-    ff = windows;
     for j = 1:length(windows)
         w = windows(j);
-        counts = spikecounts(spikes, w);
+        spikecounts = counts(spikes, w);
-        % statistics for current window:
+        mc(j) = mean(spikecounts);
-        mc(j) = mean(counts);
+        vc(j) = var(spikecounts);
-        vc(j) = var(counts);
-        ff(j) = vc(j)/mc(j);
     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');

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

pointprocesses/exercises/hompoissonisih.m

@@ -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

pointprocesses/exercises/hompoissonisistats.m

@@ -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' );

pointprocesses/exercises/hompoissonspikes.m

@@ -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

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
+

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

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