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Spectral Forecast model for signals.html
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Spectral Forecast model for signals.html
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<!-- ############################################################################################################################## -->
<!-- # John Wiley & Sons, Inc. # -->
<!-- # # -->
<!-- # Book: Algorithms in Bioinformatics: Theory and Implementation # -->
<!-- # Author: Dr. Paul A. Gagniuc # -->
<!-- # # -->
<!-- # Institution: # -->
<!-- # University Politehnica of Bucharest # -->
<!-- # Faculty of Engineering in Foreign Languages # -->
<!-- # Department of Engineering in Foreign Languages # -->
<!-- # # -->
<!-- # Area: European Union # -->
<!-- # Date: 04/01/2021 # -->
<!-- # # -->
<!-- # Mode: Native JavaScript # -->
<!-- # # -->
<!-- # Cite this work as: # -->
<!-- # Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, 2021, ISBN: 9781119697961. # -->
<!-- # # -->
<!-- ############################################################################################################################## -->
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<style>
input[type=text] {
width: 800px;
background-color: white;
padding: 12px 20px 12px 20px;
-webkit-transition: width 0.4s ease-in-out;
transition: width 0.4s ease-in-out;
}
input[type=text]:focus {
width: 100%;
}
</style>
<div class="w3-bar w3-black">
<h4 class="w3-left">Powered by the <i>Spectral Forecast</i> model</h4>
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" class="w3-right w3-image" style='height:50px;'>
</div>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p>Height:</p>
</div>
<div class="w3-container w3-cell">
<p><input type="range" min="120" max="1000" value="300" class="slider" id="canvas_h"></p>
</div>
<div class="w3-container w3-cell">
<p><div class="slidecontainer" id="info_canvas_h">300 px</div></p>
</div>
<div class="w3-container w3-cell">
<p>Width:</p>
</div>
<div class="w3-container w3-cell">
<p><input type="range" min="450" max="2000" value="1100" class="slider" id="canvas_w"></p>
</div>
<div class="w3-container w3-cell">
<p><div class="slidecontainer" id="info_canvas_w">1100 px</div></p>
</div>
</div>
<canvas id="bio" height="300" width="1100" class="w3-animate-zoom"></canvas>
<hr>
<p> Signals:</p>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p><label>A</label></p>
</div>
<div class="w3-container w3-cell" style="width:1100px;">
<p><input class="w3-input w3-border w3-round-xxlarge w3-animate-right" type="text" id="sA"></p>
</div>
</div>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p><label>P</label></p>
</div>
<div class="w3-container w3-cell" style="width:1100px;">
<p><input class="w3-input w3-border w3-round-xxlarge w3-animate-right" type="text" id="sP"></p>
</div>
</div>
<div class="slidecontainer">
<div class="w3-container w3-cell">
<p><label>B</label></p>
</div>
<div class="w3-container w3-cell" style="width:1100px;">
<p><input class="w3-input w3-border w3-round-xxlarge w3-animate-right" type="text" id="sB"></p>
</div>
</div>
<script>
//A text
document.getElementById("sA").oninput = function() {
A = document.getElementById("sA").value;
run();
}
//B text
document.getElementById("sB").oninput = function() {
B = document.getElementById("sB").value;
run();
}
//P text
document.getElementById("sP").oninput = function() {
P = document.getElementById("sP").value;
run();
}
//Canvas Height
document.getElementById("canvas_h").oninput = function() {
document.getElementById('bio').height = this.value;
document.getElementById("info_canvas_h").innerHTML = this.value + " px";
forecast();
}
//Canvas Width
document.getElementById("canvas_w").oninput = function() {
document.getElementById('bio').width = this.value;
document.getElementById("info_canvas_w").innerHTML = this.value + " px";
forecast();
}
//Spectral forecast model for signals
var t = '';
var tA = [];
var tB = [];
var tP = [];
var maxA;
var maxB;
var max;
var A ='10.3,23.4,44.8,63.2,44.1,35.1,46.5,62.6,50.4,28.9,24.7,22.7,43.2,17.2,31.5,8.3,17.9,3.9,4.1,2.3';
var B ='18.8,43.1,52.2,45.5,46.8,46.6,67.9,66.3,70.4,62,39.7,50.3,75.9,52.9,44.9,32,64.8,37.4,19.3,9.4';
var P ='18.8,43.1,52.2,45.5,46.8,46.6,67.9,66.3,70.4,62,39.7,50.3,75.9,52.9,44.9,32,64.8,37.4,19.3,9.4';
run();
document.getElementById("sA").value = A;
document.getElementById("sP").value = P;
document.getElementById("sB").value = B;
function run(){
tA = A.split(',');
tB = B.split(',');
tP = P.split(',');
maxA = Math.max.apply(null, tA);
maxB = Math.max.apply(null, tB);
max = Math.max(maxA, maxB)
forecast();
}
function forecast(){
var t = '';
for(var i=0; i<max; i++) {
var tmp = spectral(i);
t+=tmp;
if(i<max-1){t+=',';}
}
Chart(t, '#000000', 'y');
}
function spectral(d){
var M='';
for(var i=0; i<tA.length; i++) {
var tmp=((d/maxA)*tA[i])+(((max-d)/maxB)*tB[i]);
M+=tmp.toFixed(2);
if(i<tA.length-1){M+=','}
}
return index(M);
}
function index(M){
var u=0;
var k=0;
var h=0;
var tM = M.split(',');
for(var i=0; i<tM.length; i++) {
u+=(Number(tP[i])*Number(tM[i]));//(Pi*Mi)
k+=Number(tP[i])**2;//(Pi^2)
h+=Number(tM[i])**2;//(Mi^2)
}
return u**2/(k*h); //s[d] = (Pi*Mi)^2 / ((Pi^2) * (Mi^2))
}
// just a chart
function Chart(q,c,e) {
var s = q.split(",");
var mx = Math.max.apply(null, s);
var mn = Math.min.apply(null, s);
var canvas = document.getElementById('bio');
var w = canvas.width;
var h = canvas.height;
if (canvas.getContext) {
var ctx = canvas.getContext('2d');
if(e=='y'){ctx.clearRect(0, 0, w, h);}
ctx.moveTo(0, 0);
ctx.beginPath();
for (var i=0; i<=s.length-1; i++)
{
var y = (h-80) - ((h-80) / (mx-mn)) * (s[i]-mn);
var x = ((w-80) / s.length) * i;
ctx.lineTo(x+40, y+40);
ctx.font = "20px Arial";
if(i==0){
ctx.textAlign = 'left';
ctx.fillText(Number(s[i]).toFixed(5), x+45, y+60);
}
if(i==s.length-1){
ctx.textAlign = 'right';
ctx.fillText(Number(s[i]).toFixed(5), x+35, y+60);
}
}
ctx.lineWidth = 2;
ctx.strokeStyle = c;
ctx.stroke();
//left vertical line
ctx.moveTo(x+40, 0);
ctx.lineTo(x+40, h);
ctx.stroke();
//right vertical line
ctx.moveTo(40, 0);
ctx.lineTo(40, h);
ctx.stroke();
//top line
ctx.moveTo(40, 1);
ctx.lineTo(x+40, 1);
ctx.stroke();
//bottom line
ctx.moveTo(40, h-1);
ctx.lineTo(x+40, h-1);
ctx.stroke();
//text
var text = 'Model B';
var dim = ctx.measureText(text).width;
ctx.save();
ctx.translate((40/2)+10,(h/2)+(dim/2)+14);
ctx.rotate(Math.PI + (Math.PI/2));
ctx.font = "30px Arial";
ctx.fillStyle = "#000000";
ctx.textAlign = "left";
ctx.fillText(text, 0, 0);
ctx.restore();
text = 'Model A';
dim = ctx.measureText(text).width
ctx.save();
ctx.translate(x+40+10,(h/2)-(dim/2)-14);
ctx.rotate(Math.PI / 2);
ctx.font = "30px Arial";
ctx.fillStyle = "#000000";
ctx.textAlign = "left";
ctx.fillText(text, 0, 0);
ctx.restore();
text = 'Model P vs M';
dim = ctx.measureText(text).width;
ctx.save();
ctx.translate((w/2)-(dim/2)-30,h-10);
ctx.font = "30px Arial";
ctx.fillStyle = "#000000";
ctx.textAlign = "left";
ctx.fillText(text, 0, 0);
ctx.restore();
}
}
</script>
<hr>
Paul A. Gagniuc. <i>Algorithms in Bioinformatics: Theory and Implementation</i>. John Wiley & Sons, USA, 2021, ISBN: 9781119697961.
<hr>
Paul A. Gagniuc et al. <i><a href="https://aip.scitation.org/doi/10.1063/1.5120818">Spectral forecast: A general purpose prediction model as an alternative to classical neural networks</a></i>. Chaos 30, 033119 (2020).