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| author | Pedram Ashofteh Ardakani <pedramardakani@gmail.com> | 2020-04-29 16:41:38 +0430 |
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| committer | Pedram Ashofteh Ardakani <pedramardakani@gmail.com> | 2020-04-29 16:41:38 +0430 |
| commit | 2c9e797a73fc5f6e2cfa5562ce0772497a6650a5 (patch) | |
| tree | e5825a8c1a2cf5a243d525fbec8f7f0d16d6fb0c /tutorial.html | |
| parent | f14f30c34d78d7e740eb166678815ae5f610d89d (diff) | |
tutorial: Initial tutorial conversion
https://gitlab.com/infantesainz/reproduce-raulfork/-/blob/tutorial/README-tutorial.md
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diff --git a/tutorial.html b/tutorial.html new file mode 100644 index 0000000..0060235 --- /dev/null +++ b/tutorial.html @@ -0,0 +1,792 @@ +<h1>Maneage tutorial</h1> + +<p>Copyright (C) 2020 Raul Infante-Sainz <a href="mailto:infantesainz@gmail.com">infantesainz@gmail.com</a>\ +Copyright (C) 2020 Mohammad Akhlaghi <a href="mailto:mohammad@akhlaghi.org">mohammad@akhlaghi.org</a>\ +See the end of the file for license conditions.</p> + +<p>This document is a tutorial in which it is described how <code>Maneage</code> +(management + lineage) works in practice. It is highly recommended to read +the <code>README-hacking.md</code> in order to have a clear idea of what is this +project about. Actually, in this tutorial it is assumed you have the project +already set up and working properly. In order to do it, please, read and +follow all the steps described in the sections <code>Customization checklist</code> up +to the section <code>Title, short description and author</code> (including the last +one).</p> + +<p>With the current tutorial, the reader will be able to have a fully +reproducible paper describing a small research example carried out step by +step. The research example is very simple: it will consist in analyse a +dataset with two columns (time and population). The analysis will be just to +make a linear fitting of the data, and then, write the results in a small +paragraph into the final paper.</p> + +<p>In the following, the tutorial assume you have three different directories. +You had to set up them in the configure step:</p> + +<ul> +<li><p><code>input-directory</code>: Necessary input data for the project is in this +directory.</p></li> +<li><p><code>project-directory</code>: This directory contains the project itself (source +codes), it is under <code>Git</code> control.</p></li> +<li><p><code>build-directory</code>: Output directory of the project, it is where all the +necessary software and the results of the project are saved.</p></li> +</ul> + +<p><strong><em>IMPORTANT NOTE</em></strong>: the tutorial assume you are always in +<code>project-directory</code> when considering command lines.</p> + +<p><strong>In short:</strong> this hands on tutorial will guide you through a simple +research example in order to show the workflow in <code>Maneage</code>. The tutorial +describes by step how to download a small file containg data, analyse the +data (by making a linear fitting), and finally write a small paragraph with +the fitting parameters into the final paper. All of this will be done in the +same Makefile.</p> + +<h2>Installing available software: Matplotlib</h2> + +<p>If all steps above have been done successfully, you are ready to start +including your own analysis scripts. But, before that, let's install +<code>Matplotlib</code> Python package, which will be used later in the analysis of the +data when obtaining the linear fit figure. This Python package will be used +as an example on how to install programs that are already available in +<code>Maneage</code>. Just open the Makefile +<code>reproduce/software/config/installation/TARGETS.mk</code> and add to the +<code>top-level-python</code> line, the word <code>matplotlib</code>.</p> + +<p><code>shell + # Python libraries/modules. + top-level-python = astropy matplotlib +</code></p> + +<p>After that, run the configure step again with the option <code>-e</code> to continue +using the same configuration options given before (input and build +directories). Also, run the prepare and make steps:</p> + +<p>```shell +$ ./project configure -e +$ ./project prepare +$ ./project make</p> + +<h1>Open 'paper.pdf' and see if everything is fine. Note that now, <code>Matplotlib</code></h1> + +<p>is appearing in the software appendix at the end of the document. +```</p> + +<p>Once you have verified that <code>Matplotlib</code> has been properly installed and it +appears into the final <code>paper.pdf</code>, you are ready to make the first commit +of the project. With the next commands, you will see which files have been +modified, what are the modifications, prepare them to be commited, and make +the commit. In the commit process, <code>Git</code> will open the text editor for +writting the commit message. Take into account that all changes commited +will be preserved in the history of your project. So, it is a good practice +to take some time to describe properly what have been done/changed/added. +Finally, as this is the very first commit of the project, tag this as the +zero-th version.</p> + +<p><code>shell +$ git status # See which files have been changed. +$ git diff # See the lines you have modified. +$ git add -u # Put all tracked changes in staging area. +$ git status # Make sure everything is fine. +$ git commit # Your first commit, add a nice description. +$ git tag -a v0.0 # Tag this as the zero-th version of your project. +</code></p> + +<p>Now, have a look at the <code>Git</code> history of the project. Note that the local +master branch is one commit above than the remote origin/master branch. +After that, push your first commit and its tag to your remote repository +with the next commands. Since you had setup your <code>master</code> branch to follow +<code>origin/master</code>, you can just use <code>git push</code>.</p> + +<p><code>shell +$ git log --oneline --decorate --all --graph # Have a look at the Git history. +$ git push # Push the commit to the remote/origin. +$ git push --tags # Push all tags to the remote/origin. +</code></p> + +<p>Now it is time to start including your own scripts to download and make the +analysis of the data. It is important to bear in mind that the goal of this +tutorial is to give a general view of the workflow in <code>Maneage</code>. In this +sense, only a few basic concepts about <code>Make</code> and how it is used into this +project will be given. <code>Maneage</code> is much more powerfull and much more things +than the ones showed in this tutorial can be done. So, read carefully all +the documentation and comments already available into each file, be creative +and experiment making your own research.</p> + +<p>In the following, the tutorial will be focused in download the data, analyse +the data, and finally write the results into the final paper. As a +consequence, there are a lot of things already done that are not necessary. +For example, all the text of the final paper already written into the +<code>paper.tex</code> file, some Makefiles to download images from the Hubble Space +Telescope and analyse them, etc. In your own research, all of this work +would be removed. However, in this tutorial they are not removed because we +will only show how to do a simple analysis and include a small paragraph +with the result of the linear fitting.</p> + +<p><strong>In short:</strong> in this section you have learnt how to install available +software in <code>Maneage</code>. In this particular case, you installed <code>Matplotlib</code></p> + +<h2>Including Python script to make the analysis</h2> + +<p>You are going to use a small Python script to make the analysis of the data. +This Python script will be invoked from a Makefile that will be set up +later. For now, we are going to just create the Python script and put it in +an appropiate location. All analysis scripts are kept into a subfolder with +the name of the same file type in <code>reproduce/analysis</code>. For example, the +Makefiles are saved into the <code>make</code> directory, and bash scripts are saved +into the <code>bash</code> directory. Since there is any <code>python</code> directory, create it +with the following command.</p> + +<p><code>shell +$ mkdir reproduce/analysis/python +</code></p> + +<p>After that, you need the Python script itself. The code is very simple: it +will take an input file containing two columns (year and population), the +name of the output file in which the parameters of the linear fit will be +saved, and the name of the figure showing the original data and the fitted +curve. Paste the next Python script into a new file named <code>linear-fit.py</code> +into the directory generated in the above step +(<code>reproduce/analysis/python</code>).</p> + +<p>```</p> + +<h1>Make a linear fit of an input data set</h1> + +<p>#</p> + +<h1>This Python script makes a linear fitting of a data consisting in time and</h1> + +<h1>population. It generates a figure in which the original data and the</h1> + +<h1>fitted curve is plotted. Finally, it saves the fitting parameters.</h1> + +<h1>Original author:</h1> + +<h1>Copyright (C) 2020, Raul Infante-Sainz <a href="mailto:infantesainz@gmail.com">infantesainz@gmail.com</a></h1> + +<h1>Contributing author(s):</h1> + +<h1>Copyright (C) YEAR, YourName YourSurname.</h1> + +<p>#</p> + +<h1>This Python script is free software: you can redistribute it and/or modify it</h1> + +<h1>under the terms of the GNU General Public License as published by the</h1> + +<h1>Free Software Foundation, either version 3 of the License, or (at your</h1> + +<h1>option) any later version.</h1> + +<p>#</p> + +<h1>This Python script is distributed in the hope that it will be useful, but</h1> + +<h1>WITHOUT ANY WARRANTY; without even the implied warranty of</h1> + +<h1>MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General</h1> + +<h1>Public License for more details. See <a href="http://www.gnu.org/licenses/">http://www.gnu.org/licenses/</a>.</h1> + +<h1>Necessary packages</h1> + +<p>import sys +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit</p> + +<h1>Fitting function (linear fit)</h1> + +<p>def func(x, a, b): + return a * x + b</p> + +<h1>Define input and output arguments</h1> + +<p>ifile = sys.argv[1] # Input file +ofile = sys.argv[2] # Output file +ofig = sys.argv[3] # Output figure</p> + +<h1>Read the data from the input file.</h1> + +<p>data = np.loadtxt(ifile)</p> + +<h1>Time and population:</h1> + +<h1>time ---------- x</h1> + +<h1>population ---- y</h1> + +<p>x = data[:, 0] +y = data[:, 1]</p> + +<h1>Make the linear fit</h1> + +<p>params, pcov = curve_fit(func, x, y)</p> + +<h1>Make and save the figure</h1> + +<p>plt.clf() +plt.figure()</p> + +<p>plt.plot(x, y, 'bo', label="Original data") +plt.plot(x, func(x, *params), 'r-', label="Fitted curve")</p> + +<p>plt.title('Population along time') +plt.xlabel('Time (year)') +plt.ylabel('Population (million people)') +plt.legend() +plt.grid()</p> + +<p>plt.savefig(ofig, format='PDF', bbox_inches='tight')</p> + +<h1>Save the fitting parameters</h1> + +<p>np.savetxt(ofile, params, fmt='%.3f') +```</p> + +<p>Have a look at this Python script. At the very beginning, it has a block of +commented lines with a descriptive title, a small paragraph describing the +the script, and the copyright with the contact information. For each file, +it is very important to have such kind of meta-data. Below these lines, +there is the source code itself.</p> + +<p>As it can be seen, this Python script (<code>linear-fit.py</code>) is designed to be +invoked from the command line in the following way.</p> + +<p><code>shell +$ python /path/to/linear-fit.py /path/to/input.dat /path/to/output.dat /path/to/figure.pdf +</code></p> + +<p><code>/path/to/input.dat</code> is the input data file, <code>/path/to/output.dat</code> is the +output data file (with the fitted parameters), and <code>/path/to/figure.pdf</code> is +the plotted figure.</p> + +<p>You will do this invokation inside of a Make rule (that will be set up +later). Now that you have included this Python script, make a commit in +order to save this work. With the first command you will see the files with +modifications. With the second command, you can check what are the changes. +Correct, add and modify whatever you want in order to include more +information, comments or clarify any step. After that, add the files and +commit the work. Finally, push the commit to the remote/origin.</p> + +<p><code>shell +$ git status # See which files you have changed. +$ git diff # See the lines you have added/changed. +$ git add reproduce/analysis/python/linear-fit.py # Put all tracked changes in staging area. +$ git commit # Commit, add a nice descriptions. +$ git push # Push the commit to the remote/origin. +</code></p> + +<p>Check that everything is fine having a look at the <code>Git</code> history of the +project. Note that the <code>master</code> branch has been increased in one commit, +while the <code>template</code> branch is behind.</p> + +<p><code>shell +$ git log --oneline --decorate --all --graph # See the `Git` history. +</code></p> + +<p><strong>In short</strong>: in this section you have included a <code>Python</code> script that will +be used for making the linear fitting.</p> + +<h2>Downloading data</h2> + +<p>As it was said before, there are multiple things that are already included +into the project. One of them is to use a dedicated Makefile to manage all +necessary download of the input data +(<code>reproduce/analysis/make/download.mk</code>). By appropiate modifications of this +file, you would be able to download the necessary data. However, in order to +keep this tutorial as simple as possible, we will describe how to download +the data you need more explicity.</p> + +<p>The data needed by this tutorial consist in a simple plain text file +containing two rows: time (year) and population (in million of people). This +data correspond to Spain, and it can be downloaded from this URL: +<code>http://akhlaghi.org/data/template-tutorial/ESP.dat</code>. But don't do that +using your browser, you have to do it into <code>Maneage</code>!</p> + +<p>Let's create a Makefile for downloading the data. Later, you will also +include (in the same Makefile) the necessary work in order to make the +analysis. Save this Makefile in the dedicated directory +(<code>reproduce/analysis/make</code>) with the name <code>getdata-analysis.mk</code>. In that +Makefile, paste the following code.</p> + +<p>```</p> + +<h1>Download data for the tutorial</h1> + +<p>#</p> + +<h1>In this Makefile, data for the tutorial is downloaded.</h1> + +<p>#</p> + +<h1>Copyright (C) 2020 Raul Infante-Sainz <a href="mailto:infantesainz@gmail.com">infantesainz@gmail.com</a></h1> + +<h1>Copyright (C) YYYY Your Name <a href="mailto:your-email@example.xxx">your-email@example.xxx</a></h1> + +<p>#</p> + +<h1>This Makefile is free software: you can redistribute it and/or modify it</h1> + +<h1>under the terms of the GNU General Public License as published by the</h1> + +<h1>Free Software Foundation, either version 3 of the License, or (at your</h1> + +<h1>option) any later version.</h1> + +<p>#</p> + +<h1>This Makefile is distributed in the hope that it will be useful, but</h1> + +<h1>WITHOUT ANY WARRANTY; without even the implied warranty of</h1> + +<h1>MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General</h1> + +<h1>Public License for more details. See <a href="http://www.gnu.org/licenses/">http://www.gnu.org/licenses/</a>.</h1> + +<h1>Download data for the tutorial</h1> + +<h1>------------------------------</h1> + +<p># +pop-data = $(indir)/ESP.dat +$(pop-data): | $(indir) + wget http://akhlaghi.org/data/template-tutorial/ESP.dat -O $@</p> + +<h1>Final TeX macro</h1> + +<h1>---------------</h1> + +<p>#</p> + +<h1>It is very important to mention the address where the data were</h1> + +<h1>downloaded in the final report.</h1> + +<p>$(mtexdir)/getdata-analysis.tex: $(pop-data) | $(mtexdir) + echo "\newcommand{\popurl}{http://akhlaghi.org/data/template-tutorial}" > $@ +```</p> + +<p>Have a look at this Makefile and see the different parts. The first line is +a descriptive title. Below, include your name, contact email, and finally, +the copyright. Please, take your time in order to add all relevant +information in each Makefile you modify. As you can see, these lines start +with <code>#</code> because they are comments.</p> + +<p>After that information, there are five white lines in order to separate the +different parts. Then, you have the Make rule to download the data. Remember +the general structure of a Make rule:</p> + +<p><code> +TARGETS: PREREQUISITES + RECIPE +</code></p> + +<p>In a rule, it is said how to construct the <code>TARGETS</code> from the +<code>PREREQUISITES</code>, following the <code>RECIPE</code>. <strong>Note that the white space at the +beginning of the <code>RECIPE</code> are not spaces but a single <code>TAB</code>. Take into +account this if you copy/paste the code.</strong></p> + +<p>Now you can see this structure in our particular case:</p> + +<p><code> +$(pop-data): | $(indir) + wget http://akhlaghi.org/data/template-tutorial/ESP.dat -O $@ +</code></p> + +<p>Here we have:</p> + +<ul> +<li><p><code>$(pop-data)</code> is the TARGET. It is previously defined just one line above: +<code>pop-data = $(indir)/ESP.dat</code>. As it can be seen, the target is just one +file named <code>ESP.dat</code> into the <code>indir</code> directory.</p></li> +<li><p><code>$(indir)</code> is the PREREQUISITE. In this case, nothing is needed for +obtaining the TARGET, just the output directory in which it is going to be +saved. This is the reason of having the pipe <code>|</code> at the beginning of the +prerequisite (it indicates an order-only-prerequisite).</p></li> +<li><p><code>wget http://akhlaghi.org/data/template-tutorial/ESP.dat -O $@</code> is the +RECIPE. It states how to construct the <code>TARGET</code> from the <code>PREREQUISITE</code>. +In this case, it is just the use of <code>wget</code> to download the file specified +in the <code>URL</code> (<code>http://akhlaghi.org/data/template-tutorial/ESP.dat</code>) and +save it as the target: <code>-O $@</code>. Inisde of a Make rule, <code>$@</code> is the target. +So, in this case: <code>$@</code> is <code>$(pop-data)</code>.</p></li> +</ul> + +<p>With this, you have included the rule that will download the data. Now, to +finish, you have to specify what is the final purpose of the Makefile: +download that data! This is done by setting <code>$(pop-data)</code> as a prerequisite +of the final rule. Remember that each Makefile will build a final target +with the same name as the Makefile, but with the extension <code>.tex</code>. As a +consequence, they will be <code>TeX</code> macros in which relevant information to be +included into the final paper are saved . Here, you are saving the <code>URL</code>.</p> + +<p><code> +$(mtexdir)/getdata-analysis.tex: $(pop-data) | $(mtexdir) + echo "\\newcommand{\\popurl}{http://akhlaghi.org/data/template-tutorial}" > $@ +</code></p> + +<p>In this final rule we have:</p> + +<ul> +<li><p><code>$(mtexdir)/getdata-analysis.tex</code> is the TARGET. It is the <code>TeX</code> macro. +Note that it has the same name as the Makefile itself, but it will be +saved into the <code>$(mtexdir)</code> directory. What do I need for constructing +this target? The prerequisites.</p></li> +<li><p><code>$(pop-data) | $(mtexdir)</code> are the PREREQUISITES. In this case you have +two prerequisites. First, <code>$(pop-data)</code>, which indicates that the final +<code>TeX</code> macro has to be generated after this file has been obtained. The +second prerequisite is order-only-prerequisite, and it is the directory in +which the target is saved: <code>$(mtexdir)</code>.</p></li> +<li><p><code>echo "\\newcommand{\\popurl}{http://akhlaghi.org/data/template-tutorial}" > $@</code> +is the RECIPE. Basically, it writes the text +<code>\\newcommand{\\popurl}{http://akhlaghi.org/data/template-tutorial}</code> into +the TARGET (<code>$@</code>). As you can see, this is the definition of a new +command in <code>TeX</code>. The definition of this new command <code>\popurl</code> will be used +for writting the final paper.</p></li> +</ul> + +<p>Only one step is remaining to finally make the download of the data. You +have to add the name (without the extension .mk) of this Makefile into the +<code>reproduce/analysis/make/top-make.mk</code> Makefile. There it is defined which +Makefiles have to be executed. You have to end up having:</p> + +<p><code> +makesrc = initialize \ + download \ + getdata-analyse \ + delete-me \ + paper +</code></p> + +<p>As allways, read carefully all comments and information in order to know +what is going ong. Also, add your own comments and information in order to +be clear and explain each step with enough level of detail. If everything is +fine, now the project is ready to download the data in the make step. Try +it!</p> + +<p><code>shell +$ ./project make +</code></p> + +<p>Hopefully, it will download and save the file into the folder called +<code>inputs</code> under the <code>build-directory</code>. Check that it is there, and also have +a look at the <code>TeX</code> macro in order to see that the new command has been +included, it is into the top-build directory: +<code>build-directory/tex/macros/getdata-analysis.tex</code>.</p> + +<p>Now that all of this changes have been included and it works fine, it is +time to check little by little everything and make a commit order to save +this work. Remember to put a good commit title and a nice commit message +describing what you have done and why. Then, push the commit to the +remote/origin.</p> + +<p>Congratulations! You have included you first Makefile and the data is now +ready to be analysed!</p> + +<p><strong>In short</strong>, to download the data you did the following:</p> + +<ul> +<li>Create a Makefile: <code>reproduce/analysis/make/getdata-analysis.mk</code></li> +<li>Write meta-data at the beginning: title, your name, email, copyright, etc.</li> +<li>Define the file you want to download, and the rule to do it.</li> +<li>Write the rule to generate the <code>TeX</code> macro, putting as prerequisite, the +file you are downloading.</li> +<li>Add the name of the Makefile (without the <code>.tex</code>) into +<code>reproduce/analysis/make/top-make.mk</code></li> +<li><code>$ ./project make</code> in order to execute the project and download +the data.</li> +<li>Check that everything worked fine by loking at the downloaded file and the +<code>TeX</code> macro.</li> +<li>Commit and push all the work included.</li> +</ul> + +<h2>Adding the analysis rule</h2> + +<p>Until this point, you have included the Python script that will do the +linear fitting, and the rule for downloading the data. Now, it is necessary +to construct the Make rule in which this Python script is invoked to do the +analysis. This rule will be put in the same Makefile you have already +generated for downloading the data. But, before this, define the directory +in which the target is going to be saved.</p> + +<p><code> +odir = $(BDIR)/fit-parameters +</code></p> + +<p>This is a folder under the <code>build-directory</code> called <code>fit-parameters</code>. After +that, define the target: a plain text file in which the linear fit +parameters are saved (by the Python script). Put it into the previously +defined directory. As the data is from Spain, name it <code>ESP.txt</code>.</p> + +<p><code> +param-file = $(odir)/ESP.txt +</code></p> + +<p>Now, include a rule to construct the output directory <code>odir</code>. This is +necessary because this directory is needed for saving the file <code>ESP.txt</code>.</p> + +<p><code> +$(odir): + mkdir $@ +</code></p> + +<p>With all the previous definitions, now it is possible to set the rule for +making the analysis:</p> + +<p><code> +$(param-file): $(indir)/ESP.dat | $(odir) + python reproduce/analysis/python/linear-fit.py $< $@ $(odir)/ESP.pdf +</code></p> + +<p>In this rule you have:</p> + +<ul> +<li><p><code>$(param-file)</code> is the TARGET. It is the file previously defined in which +the fitting parameters will be saved.</p></li> +<li><p><code>$(indir)/ESP.dat | $(odir)</code> are the PREREQUISITES. In this case you have +two prerequisites. First, <code>$(indir)/ESP.dat</code>, which is the input file +previously downloaded by the rule above. In this file there is the input +data that the Python script will use for making the linear fit. <code>$(odir)</code> +is the second prerequisite. It is order-only-prerequisite (indicated by +the pipe <code>|</code>), and it is the directory where the target is saved.</p></li> +<li><p><code>python reproduce/analysis/python/linear-fit.py $< $@ $(odir)/ESP.pdf</code> is +the RECIPE. Basically, it call <code>python</code> to run the script +<code>reproduce/analysis/python/linear-fit.py</code> with the necessary arguments: +the input file <code>$<</code>, the target <code>$@</code>, and the name of the figure +<code>$(odir)/ESP.pdf</code> (a PDF figure saved into the same directory than the +target.</p></li> +</ul> + +<p>Finally, in order to indicate you want to obtain the target you have just +included (<code>$(param-file)</code>), it is necessary to add it as a prerequisite of +the final TARGET <code>$(mtexdir)/linear-fit.tex</code>. So, in the last rule (which +creates the <code>TeX</code> macro), remove <code>$(pop-data)</code> and put <code>$(param-file)</code> +instead. By doing this, you are telling to the Makefile that you want to +obtain the file in which it is saved the fitted parameters. Inside of the +rule, define a couple of bash variables (<code>a</code> and <code>b</code>) that are the fitted +parameters extracted from the prerequisite. For <code>a</code>:</p> + +<p><code> +a=$$(cat $< | awk 'NR==1{print $1}') +</code></p> + +<p>Similarly, for obtaining the parameter <code>b</code> (which is in the second row):</p> + +<p><code> +b=$$(cat $< | awk 'NR==2{print $1}') +</code></p> + +<p>Then you have to specify the new <code>TeX</code> commands for these two parameters, +just write them as it was done before for the <code>URL</code>:</p> + +<p>``` +echo "\newcommand{\afitparam}{$$a}" >> $@ +echo "\newcommand{\bfitparam}{$$b}" >> $@</p> + +<p>```</p> + +<p>So, at the end you will have the final rule like this:</p> + +<p>``` +$(mtexdir)/getdata-analysis.tex: $(param-file) | $(mtexdir)</p> + +<pre><code> echo "\\newcommand{\\popurl}{http://akhlaghi.org/data/template-tutorial}" > $@ + + a=$$(cat $< | awk 'NR==1{print $1}') + b=$$(cat $< | awk 'NR==2{print $1}') + + echo "\newcommand{\afitparam}{$$a}" >> $@ + echo "\newcommand{\bfitparam}{$$b}" >> $@ +</code></pre> + +<p>```</p> + +<p><strong>Important notes: you have to use two <code>$</code> in order to use the bash <code>$</code> +character inside of a Make rule. Also, note that you have to put <code>>></code> in +order to not create a new target each time you write someting into the +target. With the double <code>></code> it will only add the line at the end of the file +without generating a new file.</strong></p> + +<p>With all the above modifications, you are ready to obtain the fitting +parameters. If you add the necessary comments and information, the final +Makefile would look similar to:</p> + +<p>```</p> + +<h1>Download data and linear fitting for the tutorial</h1> + +<p>#</p> + +<h1>In this Makefile, data for the tutorial is downloaded. Then, a Python</h1> + +<h1>script is used to make a linear fitting. Finally, fitted parameters as</h1> + +<h1>well as the URL is saved into a TeX macro.</h1> + +<p>#</p> + +<h1>Copyright (C) 2020 Raul Infante-Sainz <a href="mailto:infantesainz@gmail.com">infantesainz@gmail.com</a></h1> + +<h1>Copyright (C) YYYY Your Name <a href="mailto:your-email@example.xxx">your-email@example.xxx</a></h1> + +<p>#</p> + +<h1>This Makefile is free software: you can redistribute it and/or modify it</h1> + +<h1>under the terms of the GNU General Public License as published by the</h1> + +<h1>Free Software Foundation, either version 3 of the License, or (at your</h1> + +<h1>option) any later version.</h1> + +<p>#</p> + +<h1>This Makefile is distributed in the hope that it will be useful, but</h1> + +<h1>WITHOUT ANY WARRANTY; without even the implied warranty of</h1> + +<h1>MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General</h1> + +<h1>Public License for more details. See <a href="http://www.gnu.org/licenses/">http://www.gnu.org/licenses/</a>.</h1> + +<h1>Download data for the tutorial</h1> + +<h1>------------------------------</h1> + +<p>#</p> + +<h1>The input file is defined and downloaded using the following rule</h1> + +<p>pop-data = $(indir)/ESP.dat +$(pop-data): | $(indir) + # Use wget to download the data + wget http://akhlaghi.org/data/template-tutorial/ESP.dat -O $@</p> + +<h1>Output directory</h1> + +<h1>----------------</h1> + +<p>#</p> + +<h1>Small rule for constructing the output directory, previously defined</h1> + +<p>odir = $(BDIR)/fit-parameters +$(odir): + # Build the output directory + mkdir $@</p> + +<h1>Linear fitting of the data</h1> + +<h1>--------------------------</h1> + +<p>#</p> + +<h1>The output file is defined into the output directory. The fitted</h1> + +<h1>parameters will be saved into this directory by the Python script.</h1> + +<p>param-file = $(odir)/ESP.txt +$(param-file): $(indir)/ESP.dat | $(odir) + # Invoke Python to run the script with the input data + python reproduce/analysis/python/linear-fit.py $< $@ $(odir)/ESP.pdf</p> + +<h1>TeX macros final target</h1> + +<h1>-----------------------</h1> + +<p>#</p> + +<h1>This is how we write the necessary parameters in the final PDF. In this</h1> + +<h1>rule, new TeX parameters are defined from the URL, and the fitted</h1> + +<h1>parameters.</h1> + +<p>$(mtexdir)/getdata-analysis.tex: $(param-file) | $(mtexdir)</p> + +<pre><code> # Write the URL into the target + echo "\newcommand{\popurl}{http://akhlaghi.org/data/template-tutorial}" > $@ + + # Read the fitted parameters and save them into the target + a=$$(cat $< | awk 'NR==1{print $1}') + b=$$(cat $< | awk 'NR==2{print $1}') + + echo "\newcommand{\afitparam}{$$a}" >> $@ + echo "\newcommand{\bfitparam}{$$b}" >> $@ +</code></pre> + +<p>```</p> + +<p>Have look at this Makefile and note that it is what it has been described +above. Take your time for making useful comments and modifying whatever you +think it is necessary. If everything is fine, now the project is ready to +download the data <strong>and</strong> make the linear fitting. Try it!</p> + +<p><code>shell +$ ./project make +</code></p> + +<p>Hopefully, now you will have the fitted parameters into the +<code>build-directory/fit-parameters/ESP.txt</code> file, and the figure in the same +directory. Do not pay to much attention at the quality of the fitting. It is +just an example. Also, check that the <code>TeX</code> macro has been created +successfully by having a look at +<code>build-directory/tex/macros/getdata-analyse.tex</code>. Finally, now that you have +ensured that everything is fine, make a commit in order to keep the work +safe. In the next step, you will see how to include this data into the final +paper.</p> + +<p><strong>In short:</strong> with the work included in this section, the project is able to +download and make the linear fitting of the data. The result is the fitted +parameters that are also saved in a <code>TeX</code> macro, and the figure showing the +data with the fitted curve.</p> + +<h2>Editing the final paper</h2> + +<p>With all the previous work, the project is able to download the file +containing the data (two columns, year and population of Spain), and analyse +them by making a linear fitting (y=ax+b). The result is a <code>TeX</code> macro in +which there are the information about the <code>URL</code> of the data and the linear +fitting parameters (<code>a</code> and <code>b</code>). Now, it is time to add a small paragraph +into the paper, just to ilustrate how to write the relevant parameters from +the analysis.</p> + +<p>Before all, make a copy of the current <code>paper.pdf</code> document you have into +the <code>project-directory</code>. This paper is an example that <code>Maneage</code> constructs +by default. Now, you will modify it by adding a small paragraph including +the fitting parameters and the <code>URL</code>. So, open <code>project-directory/paper.tex</code> +and add the following paragraph just at the beginning of the abstract +section.</p> + +<p><code> +By following the steps described in the tutorial, I have been able to obtain this reproducible paper! +The project is very simple and it consists in download a file (from \popurl), and make an easy linear fit using a Python script. +The linear fitting is $y=a*x+b$, with the following parameters: $a=\afitparam$ and $b=\bfitparam$ +</code></p> + +<p>As you can see, the <code>TeX</code> definitions done before in the Makefiles, are now +included into the paper: <code>\popurl</code>, <code>\afitparam</code>, and <code>\bfitparam</code>. If you +do again the make step <code>$ ./project make</code>, you will re-compile the paper +including this paragraph. Check that it is true and compare with the +previous version, of the paper. Contratulations! You have complete this +tutorial and now you are able to use <code>Maneage</code> for making your exciting +research in a reproducible way!</p> + +<h2>Copyright information</h2> + +<p>This file is part of the reproducible paper template + http://savannah.nongnu.org/projects/reproduce</p> + +<p>This template is free software: you can redistribute it and/or modify it +under the terms of the GNU General Public License as published by the Free +Software Foundation, either version 3 of the License, or (at your option) +any later version.</p> + +<p>This template is distributed in the hope that it will be useful, but +WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY +or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for +more details.</p> + +<p>You should have received a copy of the GNU General Public License along +with Template. If not, see <a href="https://www.gnu.org/licenses/">https://www.gnu.org/licenses/</a>.</p> |