CoursTuring EPFL

<div class="bleu"><br /> <img class="topLogo" src="<?= $path."epfl.png" ?>"><br /> <div class="code"><br /> <?= htmlentities( file_get_contents($path."code.txt")) ?><br /> </div><br /> <div class="caroussel caroussel1"><br /> <?php foreach (glob($path."intro/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE) as $img) { ?><br /> <img id="introImg" src="<?= $img ?>"><br /> <?php } ?><br /> </div><br /> </div><br /> <br /> <div id="menu"><br /> <a class="menuItem menuNotreMenu" href="#notreMenu">Menu</a><br /> <a class="menuItem menuVie" href="#vie"><span>Vie du </span>café</a><br /> <a href="#body" class="title"><br /> <h1>CoursTuring</h1><br /> </a><br /> <a class="menuItem menuInfo" href="#info">Infos<span> pratiques</span></a><br /> <a class="menuItem menuProjet" href="#projet"><span>Le </span>projet</a><br /> <br /> <div class="lineMenu"></div><br /> <div class="fondMenu"></div><br /> </div><br /> <br /> <div class="intro" id="intro"><br /> <div class="introText"><br /> <h2>Un Café <br> pas tout à fait <br> comme <br> les autres</h2><br /> <p class="center"><br /> <?php echo = file_get_contents( 'intro-txt.txt'); ?><br /> </p><br /> </div><br /> </div><br /> <br /> <div class="notreMenu" id="notreMenu"><br /> <div class="notreMenuTop"><br /> <div class="notreMenuTitre"><h2>notre<br><span>menu</span></h2></div><br /> <div class="caroussel caroussel2"><br /> <?php foreach (glob($path."menu/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE) as $img) { ?><br /> <img class="menuImg" src="<?= $img ?>"><br /> <?php } ?><br /> </div><br /> </div><br /> <div class="notreMenuBottom"><br /> <h3><br /> <?php $content = fopen($path.'menu-titre.txt','r'); echo stream_get_contents($content); fclose($content); ?><br /> </h3><br /> <p class=""><br /> <?php $content = fopen($path.'menu-txt.txt','r'); echo stream_get_contents($content); fclose($content); ?><br /> </p><br /> <a href="<?php echo $path.'menu.pdf' ?>" target="_blank"><span class="point2">●</span><div class="menuLeg">Découvrir la carte</div></a><br /> </div><br /> </div><br /> <br /> <div class="vieCafe" id="vie"><br /> <?php<br /> $imgs = glob( $path."vie/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE);<br /> $txts = glob( $path."vie-txt*.txt", GLOB_BRACE);<br /> $legs = file( $path."vie-legends.txt" );<br /> $urls = file( $path."vie-url.txt");<br /> <br /> for ($i=0; $i < 4; $i++) {<br /> if( sizeof($imgs) > $i*3 ) echo '<img class="vie'.($i*3 ).'" src="'.$imgs[$i*3].'">';<br /> if( sizeof($imgs) > $i*3+1 ) echo '<img class="vie'.($i*3+1).'" src="'.$imgs[$i*3+1].'">';<br /> if( sizeof($imgs) > $i*3+2 ) echo '<img class="vie'.($i*3+2).'" src="'.$imgs[$i*3+2].'">';<br /> if( sizeof($txts) > $i ) echo '<div class="vieContainer'.($i%2).'">';<br /> if( sizeof($txts) > $i ) echo '<div class="vietxt">'.nl2br(file_get_contents($txts[$i])).'</div>';<br /> if( sizeof($legs) > $i ) echo '<div class="vieLeg">';<br /> <br /> if( sizeof($urls) > $i && trim($urls[$i]) !== "" ) // double negation !== false ...<br /> echo '<a href="'.((strpos($urls[$i], "http") !== false) ? $urls[$i] : 'http://'.$urls[$i] ).'" target="_blank">';<br /> if( sizeof($legs) > $i && trim($urls[$i]) !== "" )echo '<span class="point2">●</span>';<br /> if( sizeof($legs) > $i ) echo '<div class="vieLien">'.$legs[$i].'</div>'.'</div>';<br /> if( sizeof($urls) > $i && trim($urls[$i]) !== "" )echo '</a>';<br /> if( sizeof($txts) > $i ) echo '</div>';<br /> }<br /> ?><br /> </div><br /> <br /> <div class="separateur"></div><br /> <br /> <div class="infos" id="info"><br /> <div class="infosLeft"><br /> <a href="https://www.openstreetmap.org/?mlat=48.85755&mlon=2.36677#map=13/48.8536/2.3673" target="_blank" class="infosCarte"><br /> <img class="filtreBleu" src="<?=$app->config('assets.path')?>/img/Map_NC.png"><br /> <br /> </a><br /> <br /> <div class="infosText"><br /> <p><span class="point">●</span>NOTRE CAFÉ, PARIS LE MARAIS</p><br><br /> <p>11 allée Arnaud Beltrame 75003 Paris</p><br><br /> <p class="transports">Métro ➑ // Bus <span>➋➒</span></p><br><br /> <div class="réseaux"><br /> <a href="https://www.instagram.com/notrecafemarais/" target="_blank"> <div class="picto2">&hearts;</div><p>Suivez nous sur Instagram</p></a><br /> <br /> <br /> </div><br /> <br />

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.bleu{<br /> width: 100%;<br /> background-color: #0083ae;<br /> }<br /> .bleu.header{ height: 60vh; }<br /> .code{<br /> font-family: "flow circular";<br /> font-size: 0.3em;<br /> }<br /> .header pre{<br /> margin-top: 4.4em;<br /> padding-left: 34vw;<br /> margin-left: -42em;<br /> }<br /> <br /> <?php<br /> if(!empty($_POST)) echo '<div class="messageadmin">';<br /> <br /> if (!empty($_POST['article'])) {<br /> $path = './articles/';<br /> $id = $_POST['article-id'];<br /> $pathId = $path . $_POST['article-id'];<br /> <br /> // ========== article .imgs ===================================================<br /> if ( isset($_FILES['image0']) || isset($_POST['image-name-0']) ) {<br /> foreach(glob($pathId. "/img*") as $name) { rename( $name, $pathId."/tmp---".basename($name) ); }<br /> if ( !is_dir($pathId) ) mkdir($pathId); // create folder<br /> for ($i=0; $i < 29 ; $i++) {<br /> if (isset($_FILES['image'.$i]) AND $_FILES['image'.$i]['error'] == 0) { // upload img<br /> if ($_FILES['image'.$i]['size'] <= 1300000) {<br /> $extension_upload = pathinfo($_FILES['image'.$i]['name'])['extension'];<br /> if (in_array($extension_upload, array('jpg','png','gif','JPG','PNG','GIF') )) {<br /> move_uploaded_file( $_FILES['image'.$i]['tmp_name'], $pathId.'/img'.$i.'.'.$extension_upload );<br /> } else { echo "Mauvais format d'image, format autorisé : .jpg, .png, .gif "; break; }<br /> } else { echo "Image trop volumineuse, taille maximale : 1 Mo par image "; break; }<br /> } else if ( isset($_FILES['image'.$i]) AND $_FILES['image'.$i]['error'] != 0 ){ break; }<br /> if( isset($_POST['image-name-'.$i]) ){ // rename img<br /> rename($pathId.'/tmp---'.$_POST['image-name-'.$i], $pathId.'/img'.$i.'.'.pathinfo($_POST['image-name-'.$i], PATHINFO_EXTENSION) );<br /> }<br /> }<br /> array_map( 'unlink', array_filter((array) glob($pathId. "/tmp---*") ) ); // delete existing files if new img<br /> }<br /> <br /> // ========== article .txt =========================================================<br /> <br /> if (isset($_POST['text' ])){ $content = fopen ( $path.'/'.$id.'-txt.txt', 'w'); fwrite ( $content, $_POST['text'] ); fclose ( $content ); }<br /> if (isset($_POST['titre'])){ $content = fopen ( $path.'/'.$id.'-titre.txt', 'w'); fwrite ( $content, $_POST['titre'] ); fclose ( $content ); }<br /> if (isset($_POST['tel' ])){ $content = fopen ( $path.'/'.$id.'-num.txt', 'w'); fwrite ( $content, $_POST['tel'] ); fclose ( $content ); }<br /> if (isset($_POST['url' ])){ $content = fopen ( $path.'/'.$id.'-url.txt', 'w'); fwrite ( $content, $_POST['url'] ); fclose ( $content ); }<br /> if (isset($_POST['urltxt'])){ $content = fopen ( $path.'/'.$id.'-urltxt.txt', 'w'); fwrite ( $content, $_POST['urltxt'] ); fclose ( $content ); }<br /> if (isset($_POST['credits'])){ $content = fopen ( $path.'/credits.txt', 'w'); fwrite ( $content, $_POST['credits'] ); fclose ( $content ); }<br /> <br /> if (isset($_POST['leg0' ])){<br /> $leg = "";<br /> $url = "";<br /> for ($i=0; $i < 4; $i++) {<br /> $file = fopen ( $path.'/vie-txt'.$i.'.txt', 'w'); fwrite ( $file, $_POST['text'.$i] ); fclose ($file);<br /> $leg .= $_POST['leg'.$i] .PHP_EOL;<br /> $url .= $_POST['url'.$i] .PHP_EOL;<br /> }<br /> $file = fopen ( $path.'/vie-url.txt', 'w'); fwrite ( $file, $url ); fclose ($file);<br /> $file = fopen ( $path.'/vie-legends.txt', 'w'); fwrite ( $file, $leg ); fclose ($file);<br /> }<br /> <br /> // ========== article .pdf =========================================================<br /> if (isset($_FILES['pdf']) AND $_FILES['pdf']['error'] == 0) {<br /> if ($_FILES['pdf']['size'] <= 4300000) {<br /> $extension_upload = pathinfo($_FILES['pdf']['name'])['extension'];<br /> if (in_array($extension_upload, array('pdf','pdf') )) {<br /> array_map( 'unlink', array_filter((array) glob($path. "/menu.*") ) ); // delete existing files if new img<br /> move_uploaded_file( $_FILES['pdf']['tmp_name'], $path.'/menu.'.$extension_upload );<br /> } else { echo "<p>Mauvais format d'image, format autorisé : .pdf</p>"; }<br /> } else { echo "<p>Pdf trop volumineuse, taille maximale : 3 Mo </p>"; }<br /> }<br /> <br /> echo '<script> jQuery(document).ready(function($){ $(document).scrollTop( '.$_POST['scrollTop'].' ); });</script>';<br /> echo $id." enregistrée.";<br /> }<br /> <br /> <br /> if (isset($_FILES['pdf']) AND $_FILES['pdf']['error'] == 0) {<br /> if ($_FILES['pdf']['size'] <= 4300000) {<br /> $extension_upload = pathinfo($_FILES['pdf']['name'])['extension'];<br /> if (in_array($extension_upload, array('pdf','pdf') )) {<br /> array_map( 'unlink', array_filter((array) glob($path. "/menu.*") ) ); // delete existing files if new img<br /> move_uploaded_file( $_FILES['pdf']['tmp_name'], $path.'/menu.'.$extension_upload );<br /> } else { echo "<p>Mauvais format d'image, format autorisé : .pdf</p>"; }<br /> } else { echo "<p>Pdf trop volumineuse, taille maximale : 3 Mo </p>"; }<br /> }<br /> <br /> echo '<script> jQuery(document).ready(function($){ $(document).scrollTop( '.$_POST['scrollTop'].' ); });</script>';<br /> echo $id." enregistrée.";<br /> }<br /> <br /> <br /> <br /> <br /> if(!empty($_POST)) echo '</div>';<br /> ?><br />

<?php include "header.php"; ?><br /> <?php include "admin-process.php"; ?><br /> <br /> <LINK rel="stylesheet" type="text/css" href="<?= $app->config('assets.path'); ?>/admin.css"><br /> <script src="<?= $app->config('assets.path'); ?>/lib-js/jquery-markdown.js"></script><br /> <script src="<?= $app->config('assets.path'); ?>/lib-js/sortable.min.js"></script><br /> <script src="<?= $app->config('assets.path'); ?>/admin.js"></script><br /> <br /> <br /> <br /> <h2>Intro</h2><br /> <br> <br> <br><br /> <?php $path = $app->config('articles.path')."/"; ?><br /> <form action="" method="post" enctype="multipart/form-data" style="display:block"><br /> Images d'accueil <i>30 images maximum</i> <br /><br /> <div class="sortable"><br /> <?php<br /> $list = glob($path."intro/*.{[jJ][pP][gG],[pP][nN][gG],[gG][iI][fF]}", GLOB_BRACE); natsort($list);<br /> foreach ($list as $img) { ?><br /> <div class="inline modifImgContainer"><br /> <img class='modifImg' src='<?=$img.'?'.time()?>'><br /> <button class="supprimer-image">X</button><br /> <input type="hidden" class="image-name" name="image-name-?" id="image-name-?" value="<?=basename($img)?>" /><br /> </div><br /> <?php } ?><br /> <div class="inline modifImgContainer nonDraggable"><br /> <p>Ajouter une image :<br><i>Poid maximum par image 1 Mo</i> </p><br /> <input type="file" name="image?" id="image?" class="addImage" /><br /> </div><br /> </div><br /> <br><br /> <div class="inline text"> Texte de présentation <br><br /> <textarea type="text" name="text" id="text"><?php echo file_get_contents($path.'intro-txt.txt' );?></textarea><br /> </div><br /> <br /><br /> <input type="hidden" class="article-id" name="article-id" id="article-id" value="intro" /><br /> <input type="hidden" name="scrollTop" class="scrollTop" /><br /> <input name="article" type="submit" value="enregistrer" /><br /> </form><br /> <hr/><br /> <br /> <br /> <br /> <h2>notre menu</h2><br /> <br> <br> <br><br /> <?php $path = $app->config('articles.path')."/"; ?><br /> <form action="" method="post" enctype="multipart/form-data" style="display:block"><br /> Images <i>30 images maximum</i> <br /><br /> <div class="sortable"><br /> <?php<br /> $list = glob($path."menu/*.{[jJ][pP][gG],[pP][nN][gG],[gG][iI][fF]}", GLOB_BRACE); natsort($list);<br /> foreach ($list as $img) { ?><br /> <div class="inline modifImgContainer"><br /> <img class='modifImg' src='<?=$img.'?'.time()?>'><br /> <br /> <button class="supprimer-image">X</button><br /> <input type="hidden" class="image-name" name="image-name-?" id="image-name-?" value="<?=basename($img)?>" /><br /> </div><br /> <?php } ?><br /> <div class="inline modifImgContainer nonDraggable"><br /> <p>Ajouter une image :<br><i>Poid maximum par image 1 Mo</i> </p><br /> <input type="file" name="image?" id="image?" class="addImage" /><br /> </div><br /> </div><br /> <br /><br /> <div class="inline">Titre : <input type="text" name="titre" id="titre" value="<?= file($path.'menu-titre.txt')[0] ?>" /></div>    <br /> <br><br /> <div class="inline text adminMenu"><br /> <textarea type="text" name="text" id="text"><?php echo file_get_contents($path.'menu-txt.txt' );?></textarea><br /> </div><br /> <br /><br /> Menu PDF : <i>Poid maximum de 3 Mo</i> <input type="file" name="pdf" /><br /> <br><br /> <input type="hidden" class="article-id" name="article-id" id="article-id" value="menu" /><br /> <input type="hidden" name="scrollTop" class="scrollTop" /><br /> <input name="article" type="submit" value="enregistrer" /><br /> </form><br /> <hr/><br /> <br /> <br /> <br /> <h2>vie du cafe</h2><br /> <br> <br> <br><br /> <?php $path = $app->config('articles.path')."/"; ?><br /> <form action="" method="post" enctype="multipart/form-data" style="display:block"><br /> Images <i>12 images maximum</i> <br /><br /> <div class="sortable"><br /> <?php<br /> $list = glob($path."vie/*.{[jJ][pP][gG],[pP][nN][gG],[gG][iI][fF]}", GLOB_BRACE); natsort($list);<br /> foreach ($list as $img) { ?><br /> <div class="inline modifImgContainer"><br /> <img class='modifImg' src='<?=$img.'?'.time()?>'><br /> <br /> <button class="supprimer-image">X</button><br /> <input type="hidden" class="image-name" name="image-name-?" id="image-name-?" value="<?=basename($img)?>" /><br /> </div><br /> <?php } ?><br /> <div class="inline modifImgContainer nonDraggable"><br /> <p>Ajouter une image :<br><i>Poid maximum par image 1 Mo</i> </p><br /> <input type="file" name="image?" id="image?" class="addImage" /><br /> </div><br /> </div><br />

$('.caroussel').each(function(){<br /> $(this).children().last().addClass("off");<br /> });<br /> $('.caroussel img:not(.off)').hide();<br /> setInterval(function(){<br /> $('.caroussel .off').each(function(){<br /> if( $(this).prev().length > 0 ){<br /> $(this).fadeOut( "slow", function() {}).removeClass('off').prev().addClass('off').show();<br /> } else {<br /> $(this).parent().children().last().addClass('off').fadeIn( "slow", function() {<br /> $(this).parent().children().first().hide().removeClass('off');<br /> });<br /> }<br /> });<br /> <br /> <br /> var tag = $(".code").first();<br /> var i = 0;<br /> var code = $("#code"+Math.floor(Math.random() * (5 - 0) + 0) ).html();<br /> var depart = Math.floor(Math.random() * (2500 - 1500) + 1500);<br /> var long = Math.floor(Math.random() * (3000 - 300) + 300);<br /> console.log("dep : "+depart+" - long : "+long+" - time : "+ (30*(long/Math.floor(long/50) ) ) );<br /> write(tag, depart, depart, long, code, 0);<br /> <br /> $('.code').each(function(){<br /> });<br /> },4000);<br /> <br /> function write(tag, i, depart, long, code, time){<br /> var s = Math.floor(long/50)<br /> if(i < depart + long){<br /> i+= s;<br /> tag.text( code.slice(0, i) );<br /> time++;<br /> setTimeout( () => { write(tag,i, depart, long, code, time) }, 2*time);<br /> }else{<br /> console.log("claer"+ i);<br /> }<br /> }<br /> <br /> <br /> <br /> //////////////////////////////////////////////////////////////////////////<br /> <br /> $("#menu a:not(.title)").hover( function(){ menu( this ); });<br /> $('#menu, #lineMenu').mouseleave(function(){ $(window).scroll(); });<br /> <br /> // smooth scroll<br /> if (!isAdmin) {<br /> $(document).on('click', 'a[href^="#"]', function (event) {<br /> event.preventDefault();<br /> $('html, body').animate({<br /> scrollTop: $($.attr(this, 'href')).offset().top - $('#menu').outerHeight()-20<br /> }, 900, "easeOutExpo");<br /> });<br /> }else{<br /> $('a[href^="#"]').each(function(){<br /> var href = $(this).attr("href");<br /> $(this).attr("href", "index.php" + href);<br /> });<br /> }<br /> <br /> // scroll underline menu<br /> var $w = $(window).scroll(function(){<br /> if (!isAdmin && $('#menu:hover').length == 0 ) {<br /> if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#inscription").offset().top ) { menu( $(".menuInscription") ); }<br /> else if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#direction").offset().top ) { menu( $(".menuDirection") ); }<br /> else if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#enseignants").offset().top ) { menu( $(".menuEnseignants") ); }<br /> else if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#programme").offset().top ) { menu( $(".menuProgramme") ); }<br /> else { menu( $(".menuCoursTuring") ); }<br /> }<br /> });<br /> // menu underline<br /> function menu(elem){<br /> $(".lineMenu").css({<br /> left: $(elem).position().left + "px",<br /> width: $(elem).width() + "px"<br /> });<br /> }<br /> <br /> // responsive mobile<br /> if( $(window).width() > $(window).height() ){<br /> <br /> // hamburger<br /> $("#hamburger, label").hide();<br /> }else{<br /> // déplacement des sections<br /> $('.credits').insertBefore('.imgs');<br /> $('.press').insertAfter('.imgs');<br /> $('.introText').prependTo('#intro');<br /> $('.caroussel1').insertBefore('.center');<br /> $('.infosRight').prependTo('.infos');<br /> $('.vie11').appendTo('.vieCafe');<br />

<div class="bleu header"><br /> <img class="topLogo" src="<?= $path."epfl.png" ?>"><br /> <div class="hidden" id="code0"><?=htmlentities(nl2br(file_get_contents($path."code0.txt"))); ?> </div><br /> <div class="hidden" id="code1"><?=htmlentities(nl2br(file_get_contents($path."code1.txt"))); ?> </div><br /> <div class="hidden" id="code2"><?=htmlentities(nl2br(file_get_contents($path."code2.txt"))); ?> </div><br /> <div class="hidden" id="code3"><?=htmlentities(nl2br(file_get_contents($path."code3.txt"))); ?> </div><br /> <div class="hidden" id="code4"><?=htmlentities(nl2br(file_get_contents($path."code4.txt"))); ?> </div><br /> <div class="hidden" id="code5"><?=htmlentities(nl2br(file_get_contents($path."code5.txt"))); ?> </div><br /> <pre class="code"></pre><br /> <div class="caroussel caroussel1"><br /> <?php foreach (glob($path."intro/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE) as $img) { ?><br /> <img id="introImg" src="<?= $img ?>"><br /> <?php } ?><br /> </div><br /> </div><br /> <br /> <div id="menu"><br /> <a class="menuItem menuCoursTuring" href="#coursTuring">CoursTuring</a><br /> <a class="menuItem menuProgramme" href="#programme">Programme</a><br /> <a class="menuItem menuEnseignants" href="#enseignants">Enseignants</a><br /> <a class="menuItem menuDirection" href="#direction">Direction scientifique</a><br /> <a class="menuItem menuInscription" href="#inscription">Inscription</a><br /> <br /> <div class="lineMenu"></div><br /> </div><br /> <br /> <div class="part intro" id="coursTuring"><br /> <h1>CoursTuring</h1><br /> <div class="introText"><br /> <pre><br /> Algorithmes ? Programmation ?<br /> Ils sont absolument partout dans votre quotidien.<br /> Venez découvrir comment...<br /> </pre><br /> <p><?php echo file_get_contents($path.'intro-txt.txt'); ?> </p><br /> </div><br /> </div><br /> <br /> <div class="part programme" id="programme"><br /> <h2>Programme</h2><br /> <p><br /> <?php echo file_get_contents( $path.'programme-txt.txt'); ?><br /> </p><br /> </div><br /> <br /> <div class="part bleu enseignants" id="enseignants"><br /> <h2>Enseignants & assistants</h2><br /> <p><br /> <?php $json = json_decode( file_get_contents( $path.'enseignants/enseignants.json' ) , true );<br /> foreach ($json as $id => $data) { ?><br /> <div class="peopleContainer"><br /> <pre class="code"><?= substr(htmlentities( nl2br( file_get_contents($path."code2.txt"))),rand(0,300)); ?></pre><br><br /> <br /> <div class="face" style="background: linear-gradient( #000, #000), url('<?=$path.'enseignants/'.$data['img']?>') center/cover, linear-gradient( #2e2e2e, #2e2e2e);"> </div><br /> <br /> <b> <?=$id?> </b><br><br /> <?=$data['attribute']?><br /> <br /> <div class="link"><br /> <?php if( isset($data['mail']) && trim($data['mail'])!=="" ) echo '<a href="mailto:'.$data['mail'].'" class="mail"></a>'; ?><br /> </div><br /> </div><?php<br /> } ?><br /> </p><br /> </div><br /> <br /> <div class="part bleu direction" id="direction"><br /> <h2>Direction scientifique</h2><br /> <p><br /> <?php $json = json_decode( file_get_contents( $path.'enseignants/enseignants.json' ) , true );<br /> foreach ($json as $id => $data) { ?><br /> <div class="peopleContainer"><br /> <pre class="code"><?= substr(htmlentities( nl2br( file_get_contents($path."code2.txt"))),rand(0,300)); ?></pre><br><br /> <br /> <div class="face" style="background: linear-gradient( #000, #000), url('<?=$path.'enseignants/'.$data['img']?>') center/cover, linear-gradient( #2e2e2e, #2e2e2e);"> </div><br /> <br /> <b> <?=$id?> </b><br><br /> <?=$data['attribute']?><br /> <br /> <div class="link"><br /> <?php if( isset($data['mail']) && trim($data['mail'])!=="" ) echo '<a href="mailto:'.$data['mail'].'" class="mail"></a>'; ?><br /> </div><br /> </div><?php<br /> } ?><br /> </p><br /> </div><br /> <br /> <div class="part inscription" id="inscription"><br /> <h2>Inscription</h2><br /> <p></p><br /> <pre><img src="<?=$asset.'img/link.svg'?>" class="svg"> <a href="https://docs.google.com/forms/d/e/1FAIpQLSeT9-RwEBCL-P9ePV9OgXYk3fgpVsTmv-NKc9VjYu_KmeTaqg/viewform">Lien de pré-inscription</a></pre><br /> <p><br /> <?php echo file_get_contents( $path.'inscription-txt.txt'); ?><br /> </p><br /> <p></p><br /> <p></p><br /> <p></p><br /> </div><br />

<div class="vieCafe" id="vie"><br /> <?php<br /> $imgs = glob( $path."vie/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE);<br /> $txts = glob( $path."vie-txt*.txt", GLOB_BRACE);<br /> $legs = file( $path."vie-legends.txt" );<br /> $urls = file( $path."vie-url.txt");<br /> <br /> for ($i=0; $i < 4; $i++) {<br /> if( sizeof($imgs) > $i*3 ) echo '<img class="vie'.($i*3 ).'" src="'.$imgs[$i*3].'">';<br /> if( sizeof($imgs) > $i*3+1 ) echo '<img class="vie'.($i*3+1).'" src="'.$imgs[$i*3+1].'">';<br /> if( sizeof($imgs) > $i*3+2 ) echo '<img class="vie'.($i*3+2).'" src="'.$imgs[$i*3+2].'">';<br /> if( sizeof($txts) > $i ) echo '<div class="vieContainer'.($i%2).'">';<br /> if( sizeof($txts) > $i ) echo '<div class="vietxt">'.nl2br(file_get_contents($txts[$i])).'</div>';<br /> if( sizeof($legs) > $i ) echo '<div class="vieLeg">';<br /> <br /> if( sizeof($urls) > $i && trim($urls[$i]) !== "" ) // double negation !== false ...<br /> echo '<a href="'.((strpos($urls[$i], "http") !== false) ? $urls[$i] : 'http://'.$urls[$i] ).'" target="_blank">';<br /> if( sizeof($legs) > $i && trim($urls[$i]) !== "" )echo '<span class="point2">●</span>';<br /> if( sizeof($legs) > $i ) echo '<div class="vieLien">'.$legs[$i].'</div>'.'</div>';<br /> if( sizeof($urls) > $i && trim($urls[$i]) !== "" )echo '</a>';<br /> if( sizeof($txts) > $i ) echo '</div>';<br /> }<br /> ?><br /> </div><br /> <br /> <div class="separateur"></div><br /> <br /> <div class="infos" id="info"><br /> <div class="infosLeft"><br /> <a href="https://www.openstreetmap.org/?mlat=48.85755&mlon=2.36677#map=13/48.8536/2.3673" target="_blank" class="infosCarte"><br /> <img class="filtreBleu" src="<?=$app->config('assets.path')?>/img/Map_NC.png"><br /> <br /> </a><br /> <br /> <div class="infosText"><br /> <p><span class="point">●</span>NOTRE CAFÉ, PARIS LE MARAIS</p><br><br /> <p>11 allée Arnaud Beltrame 75003 Paris</p><br><br /> <p class="transports">Métro ➑ // Bus <span>➋➒</span></p><br><br /> <div class="réseaux"><br /> <a href="https://www.instagram.com/notrecafemarais/" target="_blank"> <div class="picto2">&hearts;</div><p>Suivez nous sur Instagram</p></a><br /> <br /> <br /> </div><br /> <br /> <div class="bleu"><br /> <img class="topLogo" src="<?= $path."epfl.png" ?>"><br /> <div class="code"><br /> <?= htmlentities( file_get_contents($path."code.txt")) ?><br /> </div><br /> <div class="caroussel caroussel1"><br /> <?php foreach (glob($path."intro/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE) as $img) { ?><br /> <img id="introImg" src="<?= $img ?>"><br /> <?php } ?><br /> </div><br /> </div><br /> <br /> <div id="menu"><br /> <a class="menuItem menuNotreMenu" href="#notreMenu">Menu</a><br /> <a class="menuItem menuVie" href="#vie"><span>Vie du </span>café</a><br /> <a href="#body" class="title"><br /> <h1>CoursTuring</h1><br /> </a><br /> <a class="menuItem menuInfo" href="#info">Infos<span> pratiques</span></a><br /> <a class="menuItem menuProjet" href="#projet"><span>Le </span>projet</a><br /> <br /> <div class="lineMenu"></div><br /> <div class="fondMenu"></div><br /> </div><br /> <br /> <div class="intro" id="intro"><br /> <div class="introText"><br /> <h2>Un Café <br> pas tout à fait <br> comme <br> les autres</h2><br /> <p class="center"><br /> <?php echo = file_get_contents( 'intro-txt.txt'); ?><br /> </p><br /> </div><br /> </div><br /> <br /> <div class="notreMenu" id="notreMenu"><br /> <div class="notreMenuTop"><br /> <div class="notreMenuTitre"><h2>notre<br><span>menu</span></h2></div><br /> <div class="caroussel caroussel2"><br /> <?php foreach (glob($path."menu/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE) as $img) { ?><br /> <img class="menuImg" src="<?= $img ?>"><br /> <?php } ?><br /> </div><br /> </div><br /> <div class="notreMenuBottom"><br /> <h3><br /> <?php $content = fopen($path.'menu-titre.txt','r'); echo stream_get_contents($content); fclose($content); ?><br /> </h3><br /> <p class=""><br /> <?php $content = fopen($path.'menu-txt.txt','r'); echo stream_get_contents($content); fclose($content); ?><br /> </p><br /> <a href="<?php echo $path.'menu.pdf' ?>" target="_blank"><span class="point2">●</span><div class="menuLeg">Découvrir la carte</div></a><br /> </div><br /> </div><br />

"content-hash": "343197bf4bde1202b4e9e5cad2eaa54c",<br /> "packages": [<br /> "dist": {<br /> "type": "zip",<br /> "url": "https://api.github.com/repos/slimphp/Slim/zipball/9224ed81ac1c412881e8d762755e3d76ebf580c0",<br /> "reference": "9224ed81ac1c412881e8d762755e3d76ebf580c0",<br /> "shasum": ""<br /> },<br /> "require": {<br /> "php": ">=5.3.0"<br /> },<br /> "suggest": {<br /> "ext-mcrypt": "Required for HTTP cookie encryption",<br /> "phpseclib/mcrypt_compat": "Polyfil for mcrypt extension"<br /> },<br /> "type": "library",<br /> "autoload": {<br /> "psr-0": {<br /> "Slim": "."<br /> }<br /> },<br /> "notification-url": "https://packagist.org/downloads/",<br /> "license": [<br /> "MIT"<br /> ],<br /> "authors": [<br /> {<br /> "name": "Josh Lockhart",<br /> "email": "info@joshlockhart.com",<br /> "homepage": "http://www.joshlockhart.com/"<br /> }<br /> ],<br /> "description": "Slim Framework, a PHP micro framework",<br /> "homepage": "http://github.com/codeguy/Slim",<br /> "keywords": [<br /> "microframework",<br /> "rest",<br /> "router"<br /> ],<br /> "time": "2017-01-07T12:21:41+00:00"<br /> {<br /> "name": "slim/extras",<br /> "version": "2.0.3",<br /> "target-dir": "Slim/Extras",<br /> "source": {<br /> "type": "git",<br /> "url": "https://github.com/codeguy/Slim-Extras.git",<br /> "reference": "a022ed23dae94e164000acd891e3394d903f9623"<br /> },<br /> "dist": {<br /> "type": "zip",<br /> "url": "https://api.github.com/repos/codeguy/Slim-Extras/zipball/a022ed23dae94e164000acd891e3394d903f9623",<br /> "reference": "a022ed23dae94e164000acd891e3394d903f9623",<br /> "shasum": ""<br /> },<br /> "require": {<br /> "php": ">=5.3.0",<br /> "slim/slim": ">=2.0.0"<br /> },<br /> "type": "library",<br /> "autoload": {<br /> "psr-0": {<br /> "Twig_Extensions_": "Views/Extension/",<br /> "Slim\\Extras": "."<br /> }<br /> },<br /> "notification-url": "https://packagist.org/downloads/",<br /> "license": [<br /> "MIT"<br /> ],<br /> "authors": [<br /> {<br /> "name": "Andrew Smith",<br /> "email": "a.smith@silentworks.co.uk",<br /> "homepage": "http://www.silentworks.co.uk/"<br /> },<br /> {<br /> "name": "Josh Lockhart",<br /> "email": "info@joshlockhart.com",<br /> "homepage": "http://www.joshlockhart.com/"<br /> }<br /> ],<br /> "description": "Extras package for the Slim Framework",<br /> "homepage": "http://github.com/codeguy/Slim-Extras",<br /> "keywords": [<br /> "extensions",<br /> "middleware",<br /> "templating"<br /> ],<br /> "time": "2013-01-07T17:56:10+00:00"<br /> },<br /> {<br /> "name": "slim/slim",<br /> "version": "2.6.3",<br /> "source": {<br /> "type": "git",<br /> "url": "https://github.com/slimphp/Slim.git",<br /> "reference": "9224ed81ac1c412881e8d762755e3d76ebf580c0"<br /> },<br /> }<br /> ],<br /> "packages-dev": [],<br /> "aliases": [],<br /> "minimum-stability": "stable",<br /> "stability-flags": [],<br /> "prefer-stable": false,<br /> "prefer-lowest": false,<br /> "platform": [],<br /> "platform-dev": []<br /> }<br />

if (isset($_FILES['pdf']) AND $_FILES['pdf']['error'] == 0) {<br /> if ($_FILES['pdf']['size'] <= 4300000) {<br /> $extension_upload = pathinfo($_FILES['pdf']['name'])['extension'];<br /> if (in_array($extension_upload, array('pdf','pdf') )) {<br /> array_map( 'unlink', array_filter((array) glob($path. "/menu.*") ) ); // delete existing files if new img<br /> move_uploaded_file( $_FILES['pdf']['tmp_name'], $path.'/menu.'.$extension_upload );<br /> } else { echo "<p>Mauvais format d'image, format autorisé : .pdf</p>"; }<br /> } else { echo "<p>Pdf trop volumineuse, taille maximale : 3 Mo </p>"; }<br /> }<br /> <br /> echo '<script> jQuery(document).ready(function($){ $(document).scrollTop( '.$_POST['scrollTop'].' ); });</script>';<br /> echo $id." enregistrée.";<br /> }<br /> <br /> <br /> <br /> <br /> if(!empty($_POST)) echo '</div>';<br /> ?><br /> <br /> .bleu{<br /> width: 100%;<br /> background-color: #0083ae;<br /> }<br /> .bleu.header{ height: 60vh; }<br /> .code{<br /> font-family: "flow circular";<br /> font-size: 0.3em;<br /> }<br /> .header pre{<br /> margin-top: 4.4em;<br /> padding-left: 34vw;<br /> margin-left: -42em;<br /> }<br /> <br /> <?php<br /> if(!empty($_POST)) echo '<div class="messageadmin">';<br /> <br /> if (!empty($_POST['article'])) {<br /> $path = './articles/';<br /> $id = $_POST['article-id'];<br /> $pathId = $path . $_POST['article-id'];<br /> <br /> // ========== article .imgs ===================================================<br /> if ( isset($_FILES['image0']) || isset($_POST['image-name-0']) ) {<br /> foreach(glob($pathId. "/img*") as $name) { rename( $name, $pathId."/tmp---".basename($name) ); }<br /> if ( !is_dir($pathId) ) mkdir($pathId); // create folder<br /> for ($i=0; $i < 29 ; $i++) {<br /> if (isset($_FILES['image'.$i]) AND $_FILES['image'.$i]['error'] == 0) { // upload img<br /> if ($_FILES['image'.$i]['size'] <= 1300000) {<br /> $extension_upload = pathinfo($_FILES['image'.$i]['name'])['extension'];<br /> if (in_array($extension_upload, array('jpg','png','gif','JPG','PNG','GIF') )) {<br /> move_uploaded_file( $_FILES['image'.$i]['tmp_name'], $pathId.'/img'.$i.'.'.$extension_upload );<br /> } else { echo "Mauvais format d'image, format autorisé : .jpg, .png, .gif "; break; }<br /> } else { echo "Image trop volumineuse, taille maximale : 1 Mo par image "; break; }<br /> } else if ( isset($_FILES['image'.$i]) AND $_FILES['image'.$i]['error'] != 0 ){ break; }<br /> if( isset($_POST['image-name-'.$i]) ){ // rename img<br /> rename($pathId.'/tmp---'.$_POST['image-name-'.$i], $pathId.'/img'.$i.'.'.pathinfo($_POST['image-name-'.$i], PATHINFO_EXTENSION) );<br /> }<br /> }<br /> array_map( 'unlink', array_filter((array) glob($pathId. "/tmp---*") ) ); // delete existing files if new img<br /> }<br /> <br /> // ========== article .txt =========================================================<br /> <br /> if (isset($_POST['text' ])){ $content = fopen ( $path.'/'.$id.'-txt.txt', 'w'); fwrite ( $content, $_POST['text'] ); fclose ( $content ); }<br /> if (isset($_POST['titre'])){ $content = fopen ( $path.'/'.$id.'-titre.txt', 'w'); fwrite ( $content, $_POST['titre'] ); fclose ( $content ); }<br /> if (isset($_POST['tel' ])){ $content = fopen ( $path.'/'.$id.'-num.txt', 'w'); fwrite ( $content, $_POST['tel'] ); fclose ( $content ); }<br /> if (isset($_POST['url' ])){ $content = fopen ( $path.'/'.$id.'-url.txt', 'w'); fwrite ( $content, $_POST['url'] ); fclose ( $content ); }<br /> if (isset($_POST['urltxt'])){ $content = fopen ( $path.'/'.$id.'-urltxt.txt', 'w'); fwrite ( $content, $_POST['urltxt'] ); fclose ( $content ); }<br /> if (isset($_POST['credits'])){ $content = fopen ( $path.'/credits.txt', 'w'); fwrite ( $content, $_POST['credits'] ); fclose ( $content ); }<br /> <br /> if (isset($_POST['leg0' ])){<br /> $leg = "";<br /> $url = "";<br /> for ($i=0; $i < 4; $i++) {<br /> $file = fopen ( $path.'/vie-txt'.$i.'.txt', 'w'); fwrite ( $file, $_POST['text'.$i] ); fclose ($file);<br /> $leg .= $_POST['leg'.$i] .PHP_EOL;<br /> $url .= $_POST['url'.$i] .PHP_EOL;<br /> }<br /> $file = fopen ( $path.'/vie-url.txt', 'w'); fwrite ( $file, $url ); fclose ($file);<br /> $file = fopen ( $path.'/vie-legends.txt', 'w'); fwrite ( $file, $leg ); fclose ($file);<br /> }<br /> <br /> // ========== article .pdf =========================================================<br /> if (isset($_FILES['pdf']) AND $_FILES['pdf']['error'] == 0) {<br /> if ($_FILES['pdf']['size'] <= 4300000) {<br /> $extension_upload = pathinfo($_FILES['pdf']['name'])['extension'];<br /> if (in_array($extension_upload, array('pdf','pdf') )) {<br /> array_map( 'unlink', array_filter((array) glob($path. "/menu.*") ) ); // delete existing files if new img<br /> move_uploaded_file( $_FILES['pdf']['tmp_name'], $path.'/menu.'.$extension_upload );<br /> } else { echo "<p>Mauvais format d'image, format autorisé : .pdf</p>"; }<br /> } else { echo "<p>Pdf trop volumineuse, taille maximale : 3 Mo </p>"; }<br /> }<br /> <br /> echo '<script> jQuery(document).ready(function($){ $(document).scrollTop( '.$_POST['scrollTop'].' ); });</script>';<br /> echo $id." enregistrée.";<br /> }<br />

<div class="part bleu enseignants" id="enseignants"><br /> <h2>Enseignants & assistants</h2><br /> <p><br /> <?php $json = json_decode( file_get_contents( $path.'enseignants/enseignants.json' ) , true );<br /> foreach ($json as $id => $data) { ?><br /> <div class="peopleContainer"><br /> <pre class="code"><?= substr(htmlentities( nl2br( file_get_contents($path."code2.txt"))),rand(0,300)); ?></pre><br><br /> <br /> <div class="face" style="background: linear-gradient( #000, #000), url('<?=$path.'enseignants/'.$data['img']?>') center/cover, linear-gradient( #2e2e2e, #2e2e2e);"> </div><br /> <br /> <b> <?=$id?> </b><br><br /> <?=$data['attribute']?><br /> <br /> <div class="link"><br /> <?php if( isset($data['mail']) && trim($data['mail'])!=="" ) echo '<a href="mailto:'.$data['mail'].'" class="mail"></a>'; ?><br /> </div><br /> </div><?php<br /> } ?><br /> </p><br /> </div><br /> <br /> <div class="part bleu direction" id="direction"><br /> <h2>Direction scientifique</h2><br /> <p><br /> <?php $json = json_decode( file_get_contents( $path.'enseignants/enseignants.json' ) , true );<br /> foreach ($json as $id => $data) { ?><br /> <div class="peopleContainer"><br /> <pre class="code"><?= substr(htmlentities( nl2br( file_get_contents($path."code2.txt"))),rand(0,300)); ?></pre><br><br /> <br /> <div class="face" style="background: linear-gradient( #000, #000), url('<?=$path.'enseignants/'.$data['img']?>') center/cover, linear-gradient( #2e2e2e, #2e2e2e);"> </div><br /> <br /> <b> <?=$id?> </b><br><br /> <?=$data['attribute']?><br /> <br /> <div class="link"><br /> <?php if( isset($data['mail']) && trim($data['mail'])!=="" ) echo '<a href="mailto:'.$data['mail'].'" class="mail"></a>'; ?><br /> </div><br /> </div><?php<br /> } ?><br /> </p><br /> </div><br /> <br /> <div class="part inscription" id="inscription"><br /> <h2>Inscription</h2><br /> <p></p><br /> <pre><img src="<?=$asset.'img/link.svg'?>" class="svg"> <a href="https://docs.google.com/forms/d/e/1FAIpQLSeT9-RwEBCL-P9ePV9OgXYk3fgpVsTmv-NKc9VjYu_KmeTaqg/viewform">Lien de pré-inscription</a></pre><br /> <p><br /> <?php echo file_get_contents( $path.'inscription-txt.txt'); ?><br /> </p><br /> <p></p><br /> <p></p><br /> <p></p><br /> </div><br /> <br /> <div class="bleu header"><br /> <img class="topLogo" src="<?= $path."epfl.png" ?>"><br /> <div class="hidden" id="code0"><?=htmlentities(nl2br(file_get_contents($path."code0.txt"))); ?> </div><br /> <div class="hidden" id="code1"><?=htmlentities(nl2br(file_get_contents($path."code1.txt"))); ?> </div><br /> <div class="hidden" id="code2"><?=htmlentities(nl2br(file_get_contents($path."code2.txt"))); ?> </div><br /> <div class="hidden" id="code3"><?=htmlentities(nl2br(file_get_contents($path."code3.txt"))); ?> </div><br /> <div class="hidden" id="code4"><?=htmlentities(nl2br(file_get_contents($path."code4.txt"))); ?> </div><br /> <div class="hidden" id="code5"><?=htmlentities(nl2br(file_get_contents($path."code5.txt"))); ?> </div><br /> <pre class="code"></pre><br /> <div class="caroussel caroussel1"><br /> <?php foreach (glob($path."intro/*.{jpg,png,gif,JPG,PNG,GIF}", GLOB_BRACE) as $img) { ?><br /> <img id="introImg" src="<?= $img ?>"><br /> <?php } ?><br /> </div><br /> </div><br /> <br /> <div id="menu"><br /> <a class="menuItem menuCoursTuring" href="#coursTuring">CoursTuring</a><br /> <a class="menuItem menuProgramme" href="#programme">Programme</a><br /> <a class="menuItem menuEnseignants" href="#enseignants">Enseignants</a><br /> <a class="menuItem menuDirection" href="#direction">Direction scientifique</a><br /> <a class="menuItem menuInscription" href="#inscription">Inscription</a><br /> <br /> <div class="lineMenu"></div><br /> </div><br /> <br /> <div class="part intro" id="coursTuring"><br /> <h1>CoursTuring</h1><br /> <div class="introText"><br /> <pre><br /> Algorithmes ? Programmation ?<br /> Ils sont absolument partout dans votre quotidien.<br /> Venez découvrir comment...<br /> </pre><br /> <p><?php echo file_get_contents($path.'intro-txt.txt'); ?> </p><br /> </div><br /> </div><br /> <br /> <div class="part programme" id="programme"><br /> <h2>Programme</h2><br /> <p><br /> <?php echo file_get_contents( $path.'programme-txt.txt'); ?><br /> </p><br /> </div><br />

<br /> <br /> //////////////////////////////////////////////////////////////////////////<br /> $("#menu a:not(.title)").hover( function(){ menu( this ); });<br /> $('#menu, #lineMenu').mouseleave(function(){ $(window).scroll(); });<br /> <br /> // smooth scroll<br /> if (!isAdmin) {<br /> $(document).on('click', 'a[href^="#"]', function (event) {<br /> event.preventDefault();<br /> $('html, body').animate({<br /> scrollTop: $($.attr(this, 'href')).offset().top - $('#menu').outerHeight()-20<br /> }, 900, "easeOutExpo");<br /> });<br /> }else{<br /> $('a[href^="#"]').each(function(){<br /> var href = $(this).attr("href");<br /> $(this).attr("href", "index.php" + href);<br /> });<br /> }<br /> <br /> // scroll underline menu<br /> var $w = $(window).scroll(function(){<br /> if (!isAdmin && $('#menu:hover').length == 0 ) {<br /> if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#inscription").offset().top ) { menu( $(".menuInscription") ); }<br /> else if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#direction").offset().top ) { menu( $(".menuDirection") ); }<br /> else if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#enseignants").offset().top ) { menu( $(".menuEnseignants") ); }<br /> else if ( $w.scrollTop()+ $('#menu').outerHeight()+200 > $("#programme").offset().top ) { menu( $(".menuProgramme") ); }<br /> else { menu( $(".menuCoursTuring") ); }<br /> }<br /> });<br /> // menu underline<br /> function menu(elem){<br /> $(".lineMenu").css({<br /> left: $(elem).position().left + "px",<br /> width: $(elem).width() + "px"<br /> });<br /> }<br /> <br /> // responsive mobile<br /> if( $(window).width() > $(window).height() ){<br /> <br /> // hamburger<br /> $("#hamburger, label").hide();<br /> }else{<br /> // déplacement des sections<br /> $('.credits').insertBefore('.imgs');<br /> $('.press').insertAfter('.imgs');<br /> $('.introText').prependTo('#intro');<br /> $('.caroussel1').insertBefore('.center');<br /> $('.infosRight').prependTo('.infos');<br /> $('.vie11').appendTo('.vieCafe');<br /> $('.caroussel').each(function(){<br /> $(this).children().last().addClass("off");<br /> });<br /> $('.caroussel img:not(.off)').hide();<br /> setInterval(function(){<br /> $('.caroussel .off').each(function(){<br /> if( $(this).prev().length > 0 ){<br /> $(this).fadeOut( "slow", function() {}).removeClass('off').prev().addClass('off').show();<br /> } else {<br /> $(this).parent().children().last().addClass('off').fadeIn( "slow", function() {<br /> $(this).parent().children().first().hide().removeClass('off');<br /> });<br /> }<br /> });<br /> <br /> <br /> var tag = $(".code").first();<br /> var i = 0;<br /> var code = $("#code"+Math.floor(Math.random() * (5 - 0) + 0) ).html();<br /> var depart = Math.floor(Math.random() * (2500 - 1500) + 1500);<br /> var long = Math.floor(Math.random() * (3000 - 300) + 300);<br /> console.log("dep : "+depart+" - long : "+long+" - time : "+ (30*(long/Math.floor(long/50) ) ) );<br /> write(tag, depart, depart, long, code, 0);<br /> <br /> $('.code').each(function(){<br /> });<br /> },4000);<br /> <br /> function write(tag, i, depart, long, code, time){<br /> var s = Math.floor(long/50)<br /> if(i < depart + long){<br /> i+= s;<br /> tag.text( code.slice(0, i) );<br /> time++;<br /> setTimeout( () => { write(tag,i, depart, long, code, time) }, 2*time);<br /> }else{<br /> console.log("claer"+ i);<br /> }<br /> }<br />

<br /> <br /> <h2>vie du cafe</h2><br /> <br> <br> <br><br /> <?php $path = $app->config('articles.path')."/"; ?><br /> <form action="" method="post" enctype="multipart/form-data" style="display:block"><br /> Images <i>12 images maximum</i> <br /><br /> <div class="sortable"><br /> <?php<br /> $list = glob($path."vie/*.{[jJ][pP][gG],[pP][nN][gG],[gG][iI][fF]}", GLOB_BRACE); natsort($list);<br /> foreach ($list as $img) { ?><br /> <div class="inline modifImgContainer"><br /> <img class='modifImg' src='<?=$img.'?'.time()?>'><br /> <br /> <button class="supprimer-image">X</button><br /> <input type="hidden" class="image-name" name="image-name-?" id="image-name-?" value="<?=basename($img)?>" /><br /> </div><br /> <?php } ?><br /> <div class="inline modifImgContainer nonDraggable"><br /> <p>Ajouter une image :<br><i>Poid maximum par image 1 Mo</i> </p><br /> <input type="file" name="image?" id="image?" class="addImage" /><br /> </div><br /> </div><br /> <?php include "header.php"; ?><br /> <?php include "admin-process.php"; ?><br /> <br /> <LINK rel="stylesheet" type="text/css" href="<?= $app->config('assets.path'); ?>/admin.css"><br /> <script src="<?= $app->config('assets.path'); ?>/lib-js/jquery-markdown.js"></script><br /> <script src="<?= $app->config('assets.path'); ?>/lib-js/sortable.min.js"></script><br /> <script src="<?= $app->config('assets.path'); ?>/admin.js"></script><br /> <br /> <br /> <br /> <h2>Intro</h2><br /> <br> <br> <br><br /> <?php $path = $app->config('articles.path')."/"; ?><br /> <form action="" method="post" enctype="multipart/form-data" style="display:block"><br /> Images d'accueil <i>30 images maximum</i> <br /><br /> <div class="sortable"><br /> <?php<br /> $list = glob($path."intro/*.{[jJ][pP][gG],[pP][nN][gG],[gG][iI][fF]}", GLOB_BRACE); natsort($list);<br /> foreach ($list as $img) { ?><br /> <div class="inline modifImgContainer"><br /> <img class='modifImg' src='<?=$img.'?'.time()?>'><br /> <button class="supprimer-image">X</button><br /> <input type="hidden" class="image-name" name="image-name-?" id="image-name-?" value="<?=basename($img)?>" /><br /> </div><br /> <?php } ?><br /> <div class="inline modifImgContainer nonDraggable"><br /> <p>Ajouter une image :<br><i>Poid maximum par image 1 Mo</i> </p><br /> <input type="file" name="image?" id="image?" class="addImage" /><br /> </div><br /> </div><br /> <br><br /> <div class="inline text"> Texte de présentation <br><br /> <textarea type="text" name="text" id="text"><?php echo file_get_contents($path.'intro-txt.txt' );?></textarea><br /> </div><br /> <br /><br /> <input type="hidden" class="article-id" name="article-id" id="article-id" value="intro" /><br /> <input type="hidden" name="scrollTop" class="scrollTop" /><br /> <input name="article" type="submit" value="enregistrer" /><br /> </form><br /> <hr/><br /> <br /> <br /> <br /> <h2>notre menu</h2><br /> <br> <br> <br><br /> <?php $path = $app->config('articles.path')."/"; ?><br /> <form action="" method="post" enctype="multipart/form-data" style="display:block"><br /> Images <i>30 images maximum</i> <br /><br /> <div class="sortable"><br /> <?php<br /> $list = glob($path."menu/*.{[jJ][pP][gG],[pP][nN][gG],[gG][iI][fF]}", GLOB_BRACE); natsort($list);<br /> foreach ($list as $img) { ?><br /> <div class="inline modifImgContainer"><br /> <img class='modifImg' src='<?=$img.'?'.time()?>'><br /> <br /> <button class="supprimer-image">X</button><br /> <input type="hidden" class="image-name" name="image-name-?" id="image-name-?" value="<?=basename($img)?>" /><br /> </div><br /> <?php } ?><br /> <div class="inline modifImgContainer nonDraggable"><br /> <p>Ajouter une image :<br><i>Poid maximum par image 1 Mo</i> </p><br /> <input type="file" name="image?" id="image?" class="addImage" /><br /> </div><br /> </div><br /> <br /><br /> <div class="inline">Titre : <input type="text" name="titre" id="titre" value="<?= file($path.'menu-titre.txt')[0] ?>" /></div>    <br /> <br><br /> <div class="inline text adminMenu"><br /> <textarea type="text" name="text" id="text"><?php echo file_get_contents($path.'menu-txt.txt' );?></textarea><br /> </div><br /> <br /><br /> Menu PDF : <i>Poid maximum de 3 Mo</i> <input type="file" name="pdf" /><br /> <br><br /> <input type="hidden" class="article-id" name="article-id" id="article-id" value="menu" /><br /> <input type="hidden" name="scrollTop" class="scrollTop" /><br /> <input name="article" type="submit" value="enregistrer" /><br /> </form><br /> <hr/><br />

Équipe pédagogique

import numpy as np<br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Michael Kapralov
Direction Scientifique

Mikhail Kapralov est un spécialiste de l’informatique théorique. Ses recherches sur les algorithmes sublinéaires ont abouti à des percées majeures dans les secteurs du big data et de l’analyse des données. Mikhail Kapralov a reçu plusieurs prix, dont une bourse ERC Starting Grant en 2017. Alors que ses travaux sont de nature théorique, il entretient néanmoins des liens forts avec l’industrie. Ses contacts avec des partenaires industriels comme le Microsoft Research Lab et Google Research ouvrent des perspectives professionnelles inédites à ses étudiantes et étudiants.

<span style="color:red">import numpy as np</span><br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Romain Edelmann
Enseignant

Romain Edelmann enseigne l'informatique au Gymnase de Burier, là-même où il avait obtenu sa maturité et découvert sa passion pour l'informatique. Sa maturité en poche, Romain a décidé d'étudier à l'EPFL et y a obtenu un bachelor, un master et finalement un doctorat en informatique.

Durant ses études, Romain a largement profité des nombreuses occasions de partir à l'étranger. Il a notamment passé une année à l'université nationale de Singapour et 6 mois à Google Zurich. Depuis la fin de ses études, Romain a été très actif dans l'enseignement de l'informatique. Récemment, il a été impliqué dans la création de ressources pour l'enseignement de l'informatique au gymnase et donne des Summer Schools à l'EPFL pour les gymnasiens.

import numpy as np<br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Olivier Lévêque
Enseignant

Olivier Lévêque est maître d’enseignement et de recherche à la Faculté Informatique et Communications de l’EPFL. Ayant d’abord obtenu son diplôme de physique à l’EPFL en 1995, puis soutenu sa thèse en mathématiques en 2001, toujours à l’EPFL, il a ensuite rejoint la Faculté Informatique et Communications. Son domaine de spécialisation est celui des probabilités: pendant sa thèse, il a travaillé plus spécifiquement sur les équations aux dérivées partielles stochastiques, et depuis son arrivée à la Facuilté I&C, il s’est intéressé plus particulièrement à la théorie des matrices aléatoires, ainsi qu’à ses nombreuses applications.

D’autre part, Olivier s’est beaucoup investi ces dernières années dans la formation des enseignants d’informatique au gymnase (avec le programme GymInf, notamment) pour préparer l’arrivée de la nouvelle discipline obligatoire.

import numpy as np<br />
import matplotlib.pyplot as pl<br />
<br />
# ============ define relevant functions =============<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
def apply_laplacian(mat):<br />
    """This function applies a discretized Laplacian<br />
    in periodic boundary conditions to a matrix<br />
    For more information see<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
    """<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
    # the total difference<br />
    neigh_mat = -4*mat.copy()<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
    # the discrete difference formula<br />
    neighbors = [<br />
                    ( 1.0,  (-1, 0) ),<br />
                    ( 1.0,  ( 0,-1) ),<br />
                    ( 1.0,  ( 0, 1) ),<br />
                    ( 1.0,  ( 1, 0) ),<br />
                ]<br />
<br />
    # shift matrix according to demanded neighbors<br />
    # and add to this cell with corresponding weight<br />
    for weight, neigh in neighbors:<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
    return neigh_mat<br />
<br />
# Define the update formula for chemicals A and B<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
    # compute the diffusion part of the update<br />
    diff_A = DA * apply_laplacian(A)<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
    # Apply chemical reaction<br />
    reaction = A*B**2<br />
    diff_A -= reaction<br />
    diff_B += reaction<br />
<br />
    # Apply birth/death<br />
    diff_A += f * (1-A)<br />
    diff_B -= (k+f) * B<br />
<br />
    A += diff_A * delta_t<br />
    B += diff_B * delta_t<br />
<br />
    return A, B<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
    """get the initial chemical concentrations"""<br />
<br />
    # get initial homogeneous concentrations<br />
    A = (1-random_influence) * np.ones((N,N))<br />
    B = np.zeros((N,N))<br />
<br />
    # put some noise on there<br />
    A += random_influence * np.random.random((N,N))<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
    # get center and radius for initial disturbance<br />
    N2, r = N//2, 50<br />
<br />
    # apply initial disturbance<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
    return A, B<br />
<br />
def draw(A, B):<br />
    """return the matplotlib artists for animation"""<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
    ax[0].axis('off')<br />
    ax[1].axis('off')<br />
    ax[0].set_title('A')<br />
    ax[1].set_title('B')<br />
<br />
    return fig, imA, imB<br />
<br />
# =========== define model parameters ==========<br />
<br />
# update in time<br />
delta_t = 1.0<br />
<br />
# Diffusion coefficients<br />
DA = 0.16<br />
DB = 0.08<br />
<br />
# define birth/death rates<br />
f = 0.060<br />
k = 0.062<br />
<br />
# grid size<br />
N = 200<br />
<br />
# intialize the chemical concentrations<br />
A, B = get_initial_A_and_B(N)<br />
<br />
N_simulation_steps = 10000<br />
for step in range(N_simulation_steps):<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
draw(A, B)<br />
<br />
# show the result<br />
pl.show()<br />

Joachim Hugonot
Enseignant

Joachim Hugonot est enseignant d'informatique au gymnase de Renens. Il a obtenu son master et son bachelor en informatique à l'EPFL et a travaillé pendant 5 ans comme ingénieur de recherche dans deux laboratoires de l'EPFL. Joachim est passionné par l'intelligence artificielle, les réseaux de neurones, les modèles génératifs et la façon dont ces algorithmes se sont insinués dans nos vies. Il a pour volonté de développer la pensée critique, la curiosité et la prise de conscience chez ses élèves quant aux enjeux de société liés à l’informatique.

import numpy as np<br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Cédric Donner
Enseignant

Cédric Donner enseigne l’informatique au Collège du Sud (Gymnase), à Bulle, où il a obtenu sa maturité et développé sa passion pour l’informatique. Il a ensuite débuté des études d’informatique / physique à l’Université de Fribourg, pour bifurquer ensuite en mathématiques et y obtenir son master en 2008.

Déjà bien avant l’introduction de l’informatique comme discipline obligatoire au gymnase, Cédric a été très actif dans le développement de ressources pédagogiques d’enseignement de l’informatique, dans le cadre de son enseignement, du TJGroup et l'ETHZ. Il a contribué à la traduction et à la rédaction de plusieurs ouvrages pédagogiques pour l’école obligatoire et le gymnase. Actuellement, il contribue au développement d’outils et environnements d’apprentissage de l’informatique, notamment le nouvel IDE WebTigerPython, successeur de TigerJython.

import numpy as np<br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Solal Pirelli
Enseignant

Solal Pirelli est chercheur en systèmes informatiques et méthodes formelles.
Il a obtenu son bachelor, master, et doctorat en informatique à l'EPFL, se spécialisant dans la vérification automatisée de logiciels réseaux comme les pare-feux.

À l'EPFL, il a co-enseigné les cours de Génie Logiciel et de Projet de Développement Logiciel, permettant aux étudiants de dépasser la simple écriture de code pour comprendre comment planifier, développer, et maintenir un produit logiciel.
Il a également organisé des évènements tels que le Helvetic Coding Contest, plus grand concours suisse de programmation, et LauzHack, un hackathon par et pour des étudiants.

import numpy as np<br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Pierre Quinton
Enseignant

Pierre Quinton est chercheur et développeur passionné par l'informatique et les mathématiques. Il a obtenu son baccalauréat à Toulouse, puis a poursuivi ses études en informatique avec un Bachelor, suivi d'un Master en Data Science à l'EPFL. Son parcours l'a ensuite mené au Danemark, où il a effectué un stage chez B&O. Par la suite, il est retourné à l'EPFL pour y réaliser un doctorat en théorie de l'information.

Ses recherches se concentrent sur un large éventail de domaines, notamment la théorie de l'information, la théorie des probabilités, la théorie de la mesure, la théorie de l'ordre et l'optimisation multi-objective. En parallèle de ses activités de recherche, il est le développeur et le mainteneur de TorchJD (https://github.com/TorchJD/torchjd), une bibliothèque Python qui permet de faire de l'optimisation multi-objective basée sur PyTorch.

import numpy as np<br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Jennyfer Steiner
Assistante

Jennyfer est en 3ème année de Bachelor de Systèmes de Communication à l'EPFL et a notamment pu enseigner dans les cours "Internet et code pour les filles" ainsi que "Toi aussi crée ton appli" proposés par l'EPFL.

<span style="color:red">import numpy as np</span><br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Ernest Guisset
Assistant

Ernest Guisset est étudiant en master de mathématiques de l'EPFL, avec un intérêt particulier pour l'informatique. Il a fait auparavant du volontariat dans un Coding Club, où il enseignait la programmation à des enfants très jeunes. Il a aussi participé à l'enseignement de la "Semaine Théorie des jeux & IA." Il vient de Bruxelles, où il a effectué son bachelor.

<span style="color:red">import numpy as np</span><br />
<br />
import matplotlib.pyplot as pl<br />
<br />
<br />
<br />
# ============ define relevant functions =============<br />
<br />
<br />
<br />
# an efficient function to compute a mean over neighboring cells<br />
<br />
def apply_laplacian(mat):<br />
<br />
    """This function applies a discretized Laplacian<br />
<br />
    in periodic boundary conditions to a matrix<br />
<br />
    For more information see<br />
<br />
    https://en.wikipedia.org/wiki/Discrete_Laplace_operator#Implementation_via_operator_discretization<br />
<br />
    """<br />
<br />
<br />
<br />
    # the cell appears 4 times in the formula to compute<br />
<br />
    # the total difference<br />
<br />
    neigh_mat = -4*mat.copy()<br />
<br />
<br />
<br />
    # Each direct neighbor on the lattice is counted in<br />
<br />
    # the discrete difference formula<br />
<br />
    neighbors = [<br />
<br />
                    ( 1.0,  (-1, 0) ),<br />
<br />
                    ( 1.0,  ( 0,-1) ),<br />
<br />
                    ( 1.0,  ( 0, 1) ),<br />
<br />
                    ( 1.0,  ( 1, 0) ),<br />
<br />
                ]<br />
<br />
<br />
<br />
    # shift matrix according to demanded neighbors<br />
<br />
    # and add to this cell with corresponding weight<br />
<br />
    for weight, neigh in neighbors:<br />
<br />
        neigh_mat += weight * np.roll(mat, neigh, (0,1))<br />
<br />
<br />
<br />
    return neigh_mat<br />
<br />
<br />
<br />
# Define the update formula for chemicals A and B<br />
<br />
def update(A, B, DA, DB, f, k, delta_t):<br />
<br />
    """Apply the Gray-Scott update formula"""<br />
<br />
<br />
<br />
    # compute the diffusion part of the update<br />
<br />
    diff_A = DA * apply_laplacian(A)<br />
<br />
    diff_B = DB * apply_laplacian(B)<br />
<br />
<br />
<br />
    # Apply chemical reaction<br />
<br />
    reaction = A*B**2<br />
<br />
    diff_A -= reaction<br />
<br />
    diff_B += reaction<br />
<br />
<br />
<br />
    # Apply birth/death<br />
<br />
    diff_A += f * (1-A)<br />
<br />
    diff_B -= (k+f) * B<br />
<br />
<br />
<br />
    A += diff_A * delta_t<br />
<br />
    B += diff_B * delta_t<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def get_initial_A_and_B(N, random_influence = 0.2):<br />
<br />
    """get the initial chemical concentrations"""<br />
<br />
<br />
<br />
    # get initial homogeneous concentrations<br />
<br />
    A = (1-random_influence) * np.ones((N,N))<br />
<br />
    B = np.zeros((N,N))<br />
<br />
<br />
<br />
    # put some noise on there<br />
<br />
    A += random_influence * np.random.random((N,N))<br />
<br />
    B += random_influence * np.random.random((N,N))<br />
<br />
<br />
<br />
    # get center and radius for initial disturbance<br />
<br />
    N2, r = N//2, 50<br />
<br />
<br />
<br />
    # apply initial disturbance<br />
<br />
    A[N2-r:N2+r, N2-r:N2+r] = 0.50<br />
<br />
    B[N2-r:N2+r, N2-r:N2+r] = 0.25<br />
<br />
<br />
<br />
    return A, B<br />
<br />
<br />
<br />
def draw(A, B):<br />
<br />
    """return the matplotlib artists for animation"""<br />
<br />
    fig, ax = pl.subplots(1,2,figsize=(5.65,3))<br />
<br />
    imA = ax[0].imshow(A, animated=True,vmin=0,cmap='Greys')<br />
<br />
    imB = ax[1].imshow(B, animated=True,vmax=1,cmap='Greys')<br />
<br />
    ax[0].axis('off')<br />
<br />
    ax[1].axis('off')<br />
<br />
    ax[0].set_title('A')<br />
<br />
    ax[1].set_title('B')<br />
<br />
<br />
<br />
    return fig, imA, imB<br />
<br />
<br />
<br />
# =========== define model parameters ==========<br />
<br />
<br />
<br />
# update in time<br />
<br />
delta_t = 1.0<br />
<br />
<br />
<br />
# Diffusion coefficients<br />
<br />
DA = 0.16<br />
<br />
DB = 0.08<br />
<br />
<br />
<br />
# define birth/death rates<br />
<br />
f = 0.060<br />
<br />
k = 0.062<br />
<br />
<br />
<br />
# grid size<br />
<br />
N = 200<br />
<br />
<br />
<br />
# intialize the chemical concentrations<br />
<br />
A, B = get_initial_A_and_B(N)<br />
<br />
<br />
<br />
N_simulation_steps = 10000<br />
<br />
for step in range(N_simulation_steps):<br />
<br />
    A, B = update(A, B, DA, DB, f, k, delta_t)<br />
<br />
<br />
<br />
draw(A, B)<br />
<br />
<br />
<br />
# show the result<br />
<br />
pl.show()<br />
<br />

Pauline Raffestin
Administration

Pauline Raffestin est assistante administrative au sein de la Faculté Informatique et Communication de l’EPFL depuis 2016. Elle est gestionnaire du Laboratoire de Théorie du Calcul (THL4) du Prof. Michael Kapralov et du Laboratoire d’Informatique Réaliste (RGL) du Prof. Wenzel Jakob.

N’hésitez pas à la contacter pour toute question relative au Cours Turing. Elle se fera un plaisir de vous répondre.

CoursTuring

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Programme

Séance d'introduction

Année 1, Module 1 : Programmation (Romain Edelmann)

Année 1, Module 2 : Cryptographie (Olivier Lévêque)

Année 1, Module 3 : Traitement d'images (Joachim Hugonot)

Intermezzo

Année 2, Module 1 : Génie logiciel (Solal Pirelli)

Année 2, Module 2 : Programmation fonctionnelle (Pierre Quinton)

Année 2, Module 3 : Optimisation combinatoire (Cédric Donner)

Cérémonie de clôture

Équipe pédagogique

Partenaires

Contact