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project template added

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Fabian Sinz 2014-10-30 14:04:55 +01:00
parent 4e5361f6d2
commit 0314208124
5 changed files with 83 additions and 236 deletions
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4
projects/Makefile
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@ -1,7 +1,7 @@
all:
for d in `ls -d project_*/`; do \
echo "Processing $$d" ; \
cd $$d; $(MAKE) zip ; \
cd $$d; $(MAKE) zip ; cd .. ; \
done
mv project_*/*zip .
@ -9,7 +9,7 @@ all:
clean:
for d in `ls -d project_*/`; do \
echo "Cleaning up $$d" ; \
cd $$d; $(MAKE) clean ; \
cd $$d; $(MAKE) clean ; cd .. ; \
done
rm -f *.zip

50
projects/disclaimer.tex
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@ -1,15 +1,37 @@
\fbox{\parbox{0.985\linewidth}{ \small Please answer all questions
in an electronic file (.txt, .doc are ok, but we prefer .pdf) and
submit in ILIAS. If the assignments include programming
exercises, hand in a pdf for the questions, the .py files for
the programs, and the data in one zip file.
Use complete and correct sentences unless otherwise
noted. Please be succinct. Use your own words. Write down a
concise reasoning, not just the result. We expect you to do
exercises on your own, but you are encouraged to discuss the
exercises with your fellow students. If you blindly copy your
results from others, you miss out on a chance to learn something
new. Use all resources available to you, but always make sure
that you truly understand why you give the answer you give.
\fbox{\parbox{0.985\linewidth}{ \small
{\bf Evaluation criteria:}
Each project has three elements that are graded: (i) the code,
(ii) the slides/figures, and (iii) the presentation.
\vspace{.5cm}
The {\bf code} and the {\bf presentation} should be uploaded to
ILIAS before the presentations start on Thursday. Everything
should be bundeled into a {\em single} zip-file. The
presentation should be handed in as pdf.
\vspace{.5cm}
The {\bf code} should be exectuable without any further
adjustments from us. This means that you should include all
additional functions you wrote and the data into the
zip-file. The {\em main script} should produce the same {\em
figures} that you use in your slides. The figures should follow
the guidelines for proper plotting as discussed in the first
statistics lecture. The code should be properly commented and
comprehensible by third persons (use proper and consistent
variable names).
\vspace{.5cm}
The {\bf slides} should be handed in along with the code and in
pdf format. We will store them all on one computer to allow fast
transitions between talks. The {\bf presentation} itself should
be {\em at most} 10min long and be held in English. In the
presentation you should (i) briefly describe the problem, (ii)
explain how you solved it algorithmically (don't show your
entire code), and (iii) present figures showing your results.
}}

217
projects/project_PCA_natural_images/pca_natural_images.tex
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@ -1,217 +0,0 @@
\documentclass[addpoints,10pt]{exam}
\usepackage{url}
\usepackage{color}
\usepackage{hyperref}
\pagestyle{headandfoot}
\runningheadrule
\firstpageheadrule
\firstpageheader{Essential Statistics}{Homework 01 due 10/29/2014 23:59 am}{23. October 2014}
\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
\firstpagefooter{}{}{}
\runningfooter{}{}{}
\pointsinmargin
\bracketedpoints
%\printanswers
\shadedsolutions
\begin{document}
%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
\sffamily
\begin{flushright}
\gradetable[h][questions]
\end{flushright}
\begin{center}
\fbox{\parbox{0.985\linewidth}{ \small Please answer all questions
in an electronic file (.txt, .doc are ok, but we prefer .pdf) and
submit in ILIAS.
Use complete and correct sentences unless otherwise
noted. Please be succinct. Use your own words. Write down a
concise reasoning, not just the result. We expect you to do
exercises on your own, but you are encouraged to discuss the
exercises with your fellow students. If you blindly copy your
results from others, you miss out on a chance to learn something
new. Use all resources available to you, but always make sure
that you truly understand why you give the answer you give.
}}
\end{center}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\begin{questions}
\question {\bf Reading assignment: Do not submit answers to this
question! }
Read chapter 1. up to 2.4 (including) of Samuels/Wittmer/Schaffner.
Pay special attention to the following questions.
\begin{enumerate}
\item What types of scientific evidence do the authors list? How
strong are these evidences?
\item What are the different types of data encountered in
statistical analysis?
\item What is a population? What is a random sample? What are
sampling errors? What are nonsampling errors?
\item What is a descriptive statistic?
\item What property do robust statistics have?
\end{enumerate}
\question Install python and a suitable editor on your computer.
\begin{parts}
\part For installing python, I recommend the anaconda
distribution: \url{http://continuum.io/downloads}. It does not
matter whether you install python 2.7 or 3.4. I will use python
3.4 syntax.
\part As editor I recommend either sublime text (for people new to
programming) or pycharm (for people with programming
experience). I do not recommend to use a text editor that comes
with your operating system (like word pad). Text processing
programs like Mircosoft Word or Libre-Office {\bf won't work at
all}. Programming needs a little more than just typing text and
you will make your life unnecessarily hard by using an editor not
suited for it.
\part Find out how to run a python program on your operating
system and how to install new python packages. Install the
packages {\tt pandas} and {\tt seaborn}.
\end{parts}
\question To publish scientific results, you will usually need
to use statistical methods. Some journals provide you with a brief
description of how they expect you to apply statistical methods. One
example can be found in the author guidelines of the journal
Nature
\begin{center}
\url{http://www.nature.com/neuro/pdf/sm_checklist.pdf}
\end{center}
Please read the ‘checklist’ and answer the following questions:
\begin{parts}
\part[2] Why is it important that statistical methods are applied
correctly?
\begin{solution}
When not applied correctly, the results of statistical methods
might not support your hypothesis and can lead to false
conclusions.
\end{solution}
\part[2] Name two common descriptive statistics and what you have
to specify for them in nature.
\begin{solution}
\begin{itemize}
\item A clearly defined number $n$ of data points should be
specified. If the sample is small, plot points instead of
using descriptive statistics. Errorbars should be clearly
defined.
\item measure of center: mean, median
\item measure of variability: standard deviation, range
\end{itemize}
\end{solution}
\part[3] Name one statistical test that you have heard of or
used. If you were to apply any of them, what would you have to
specify to follow the Nature guidelines?
\begin{solution}
{\bf Student's T-Test} for testing whether the mean of two
populations is the same
\begin{itemize}
\item a clearly defined $n$ for the test
\item a justification for the sample size used
\item a clear description of the statistical method: since the
t-test is very common, stating that a two independent sample
t-test was used should be sufficient.
\item Justify that the data meets the definition: the two
populations should be normally distributed with the same
variance; the data was sampled independently from the two
populations being compared.
\item Is the variance in the different groups different.
\item was it one-sided or two-sided
\end{itemize}
\end{solution}
\part[3] Why are you asked to justify each incidence in which
you exclude some of the data that you collected? What could be a
valid reason to exclude a data point?
\begin{solution}
Excluded data points might make a sample from a population not
representative anymore, and can therefore alter the outcome and
conclusions of a study. They might be excluded if there is a
good reason to believe that they are not part of the population
under investigation.
\end{solution}
\end{parts}
\question {\bf Robust statistics} In 1888, P. Topinard published
data on the brain weights of hundreds of French men and women. Here
are ten brain weights (in Gramm) of female brains from the dataset
\begin{center} [1125, 1027, 1112, 983, 1090, 1247, 1045, 983, 972, 1045]
\end{center}
Open a new file ``brain\_weight.py'' with you text editor to write
the following python program (please hand in the plots and the program).
\begin{parts}
\part[2] Create a list that contains the above brain weights.
\part[2] Create a new list that contains the following ten means:
Each mean is computed from the original list after removing one
element (hint use slicing and adding lists for that; we did this
in the lecture already). {\bf Warning:} I {\em do not} expect you
to use {\tt for}-loops. Only use them if you know them already. If
you do use them, be prepared to explain your code to me to get
credits for this task.
\part[2] Create yet another list that does the same, only for the
median.
\part[2] Make a boxplot with the different means and medias (like
in the lecture). To show the plot at the end of the program
you need to put a {\tt plt.show()} at the end of the program. If
you want to save the plot, put the command {\tt
plt.gcf().savefig('YOUR\_NAME\_homework01.pdf')} before that. Label
the y-axis by using the function {\tt plt.ylabel('FILL IN YOUR LABEL')}
\part[2] What can you observe and what does that tell you about
the robustness of the statistic?
\end{parts}
\begin{solution}
\begin{verbatim}
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_context("paper", font_scale=1.5, rc={"lines.linewidth": 2.5})
w = [1125, 1027, 1112, 983, 1090, 1247, 1045, 983, 972, 1045]
brain_means = [ np.mean(w[1:]), np.mean(w[:1] + w[2:]), np.mean(w[:2] + w[3:]), \
np.mean(w[:3] + w[4:]), np.mean(w[:4] + w[5:]), np.mean(w[:5] + w[6:]), \
np.mean(w[:6] + w[7:]), np.mean(w[:7] + w[8:]), np.mean(w[:8] + w[9:]),\
np.mean(w[:9]) ]
brain_medians = [ np.median(w[1:]), np.median(w[:1] + w[2:]), np.median(w[:2] + w[3:]), \
np.median(w[:3] + w[4:]), np.median(w[:4] + w[5:]), np.median(w[:5] + w[6:]), \
np.median(w[:6] + w[7:]), np.median(w[:7] + w[8:]), np.median(w[:8] + w[9:]),\
np.median(w[:9]) ]
sns.boxplot([brain_means, brain_medians], names=['means', 'medians'])
plt.ylabel('brain weight [g]')
plt.gcf().savefig('fabian_sinz_homework01.pdf')
plt.show()
\end{verbatim}
\end{solution}
\end{questions}
\end{document}

6
projects/project_PCA_natural_images/Makefile → projects/project_template/Makefile
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@ -1,9 +1,9 @@
latex:
pdflatex *.tex
pdflatex *.tex
pdflatex *.tex > /dev/null
pdflatex *.tex > /dev/null
clean:
rm -f *.log *.aux *.zip *.out
rm -f *.log *.aux *.zip *.out auto
rm -f `basename *.tex .tex`.pdf
zip: latex

42
projects/project_template/template.tex Executable file
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@ -0,0 +1,42 @@
\documentclass[addpoints,10pt]{exam}
\usepackage{url}
\usepackage{color}
\usepackage{hyperref}
\pagestyle{headandfoot}
\runningheadrule
\firstpageheadrule
\firstpageheader{Scientific Computing}{Project Assignment}{11/05/2014
-- 11/06/2014}
%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
\firstpagefooter{}{}{}
\runningfooter{}{}{}
\pointsinmargin
\bracketedpoints
%\printanswers
%\shadedsolutions
\begin{document}
%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
\sffamily
% \begin{flushright}
% \gradetable[h][questions]
% \end{flushright}
\begin{center}
\input{../disclaimer.tex}
\end{center}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\begin{questions}
\question What was the questions for 42?
\end{questions}
\end{document}