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1535 lines (1401 loc) · 56 KB
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<!DOCTYPE html>
<html>
<meta charset="utf-8">
<!-- Example based on http://bl.ocks.org/mbostock/3887118 -->
<!-- Tooltip example from http://www.d3noob.org/2013/01/adding-tooltips-to-d3js-graph.html -->
<!-- The css script below determines the appearance of the webpage -->
<style>
body {
font: 11px sans-serif;
}
.lasso path {
stroke: rgb(80,80,80);
stroke-width:2px;
}
.lasso .drawn {
fill-opacity:.05 ;
}
.lasso .loop_close {
fill:none;
stroke-dasharray: 4,4;
}
.lasso .origin {
fill:#3399FF;
fill-opacity:.5;
}
.not_possible {
fill:rgb(200,200,200);
}
.possible {
fill:#EC888C;
}
.dot{
stroke: #000;
}
.axis path,
.axis line {
fill: none;
stroke: #000;
shape-rendering: crispEdges;
}
.tooltip {
position: absolute;
width: 250px;
height: 28px;
background: rgba(255,255,255,0.80);
pointer-events: none;
}
.tooltip1 {
position: absolute;
width: 100px;
height:20px;
background: rgba(255,255,255,0.9);
pointer-events: none;
}
/*The dropdown selectors*/
/*Coloring*/
.select1{
margin-left: 122px;
margin-top: 810px;
position: absolute;
}
/*Searching*/
.select2{
margin-left: 140px;
margin-top: 960px;
position: absolute;
}
/*?? Used in table in tabulate*/
.select3{
position: absolute;
margin-left: 50px;
margin-top: 600px;
}
/*Transparent*/
.select4{
margin-left: 237px;
margin-top: 1060px;
position: absolute;
}
/*Click on feature*/
.select5{
margin-left: 190px;
margin-top: 870px;
position: absolute;
}
/*Shaping*/
.select6{
margin-left: 135px;
margin-top: 910px;
position: absolute;
}
/*?? Default input textbox*/
input{
width: 145px;
}
/*The checkbox*/
.input1{
width : 40px;
}
/*Input textbox for circle and zoom*/
.input2{
width : 225px;
height : 25px;
}
/*Transparent*/
.form1 {
text-align: left;
position: absolute;
margin-left: 20px;
margin-top: 1100px;
}
/*Search*/
.form2 {
text-align: left;
position: absolute;
margin-left: 20px;
margin-top: 1010px;
}
/*Unused*/
.form3 {
text-align: left;
position: absolute;
margin-left: 680px;
margin-top: 850px;
}
/*Wrapper for each option*/
/*Coloring column*/
.p1 {
margin-left: 20px;
margin-top: 810px;
position: absolute;
font-size: 150%;
}
/*Searching column*/
.p2 {
margin-left: 20px;
margin-top: 960px;
position: absolute;
font-size: 150%;
}
/*Side table*/
.p3 {
margin-left: 1000px;
margin-top: 20px;
position: absolute;
white-space: nowrap;
font-size: 150%;
}
/*Transparent column*/
.p4 {
margin-left: 20px;
margin-top: 1060px;
position: absolute;
font-size: 150%;
}
/*Spectrum and log*/
.p5 {
margin-left: 20px;
margin-top: 835px;
position: absolute;
font-size: 150%;
}
/*Click on feature*/
.p6 {
margin-left: 20px;
margin-top: 870px;
position: absolute;
font-size: 150%;
}
/*Shaping column*/
.p7 {
margin-left: 20px;
margin-top: 910px;
position: absolute;
font-size: 150%;
}
/*Zoom*/
.p8 {
margin-left: 20px;
margin-top: 770px;
position: absolute;
font-size: 150%;
}
/*Circle*/
.p9 {
margin-left: 20px;
margin-top: 730px;
position: absolute;
font-size: 150%;
}
/*Exact match - search*/
.p10 {
margin-left: 20px;
margin-top: 985px;
position: absolute;
font-size: 150%;
}
/*Exact match - transparent*/
.p11 {
margin-left: 20px;
margin-top: 1085px;
position: absolute;
font-size: 150%;
}
/*Display top predicted words*/
.p12 {
margin-left: 20px;
margin-top: 10px;
position: absolute;
font-size: 200%;
}
/*Display top frequent words*/
.p13 {
margin-left: 20px;
margin-top: 60px;
position: absolute;
font-size: 200%;
}
.bar rect {
fill: steelblue;
}
.bar text {
fill: #fff;
font: 10px sans-serif;
}
/*Never referenced - unused*/
.div1 {
position: absolute;
height: 400px;
width: 600px;
margin-left:1000px;
}
/*Never referenced - unused*/
.div2 {
position: absolute;
margin-top: 400px;
height: 400px;
width: 600px;
margin-left: 1000px;
}
/*Never referenced - unused*/
#legend-svg {
vertical-align: top;
}
.point {
fill: steelblue;
stroke: #000;
}
</style>
<body>
<p class="p12" id="predicted_words"></p>
<p class="p13" id="frequent_words"></p>
<!-- <p class="p9"><b>Circle</b><input class="input1" type="checkbox" id="cbox4" value="fourth_checkbox"></input><input class = "input2" type="text" id="drawxy" placeholder="Select 1 pt after check"> </input> <button class = "input2" onclick="handleClick4()" value="Draw" >Draw</button></p> -->
<p class="p8"><b>Zoom</b><input class="input1" type="checkbox" id="cbox3" value="third_checkbox"></input><input class = "input2" type="text" id="zoomxy" placeholder="Select 2 pts after check"> </input> <button class = "input2" onclick="handleClick4()" value="Zoom" >Zoom</button></p>
<p class="p1" id="demo" ><b>Color by: </b></p>
<p class="p5">Options: spectrum<input class="input1" type="checkbox" id="cbox1" value="first_checkbox" onclick="handleClick3()">log<input class="input1" type="checkbox" id="cbox2" value="second_checkbox" onclick="handleClick3()"></p>
<p class="p6"><b>Click on feature: </b></p>
<p class="p7"><b>Shape by: </b></p>
<p class="p2" id="demo2" ><b>Search by: </b></p>
<p class="p10">Exact match: <input class="input1" type="checkbox" id="cbox5" value="fifth_checkbox" onclick="handleCheck()"></p>
<p class="p4" id="demo4" ><b>Transparent attribute: </b></p>
<p class="p11">Exact match: <input class="input1" type="checkbox" id="cbox6" value="sixth_checkbox" onclick="handleCheck1()"></p>
<!-- Side Table -->
<p class="p3" id="demo3"></p>
<!-- The handleClick() function will be executed when the search button is pressed -->
<form name="myform" class="form2" onSubmit="return handleClick()">
<input type="text" id="searchText" placeholder="Search text">
<input name="Submit" type="submit" value="Apply" >
</form>
<!-- The handleClick1() function will be executed when the transparent button is pressed -->
<form name="myform1" class="form1" onSubmit="return handleClick1()">
<p>Enter attribute value, and opacity values b/w 0 and 1:</p>
<input type="text" id="transpText" placeholder="Match attri. val">
<input type="text" id="opacityMatch" placeholder="Opacity (match)">
<input type="text" id="opacityNoMatch" placeholder="Opacity (no match)">
<input name="Submit" type="submit" value="Apply">
</form>
<!-- These javascript modules are used to draw the interactive plot -->
<script src="http://d3js.org/d3.v3.min.js" charset="utf-8"></script>
<script src="//d3js.org/d3-path.v0.1.min.js"></script>
<script src="//d3js.org/d3-shape.v0.6.min.js"></script>
<script src="http://axc.net/code_libraries/lasso/lasso.min.js"></script>
<script src="vendor/crossfilter.min.js"></script>
<script src="math.min.js"></script>
<script>
// The location of svg plot is determined by the following margins
var margin = {top: 90, right: 40, bottom: 40, left: 40},
width = 700 - margin.left - margin.right,
height = 750 - margin.top - margin.bottom;
// This step is performed to parse the url to identify the dataset and the default coloring column
var query = window.location.search.substring(1);
var temp_query = query.split("&");
var dicts = {};
var tvars;
for(var i=0;i<temp_query.length;i++) {
tvars = temp_query[i].split("=");
dicts[tvars[0]]=tvars[1].replace(/%20/g, " ");
}
if ("dataset" in dicts) {
dataset = dicts["dataset"];
} else {
dataset = "joined_data.csv";
}
var weights_2darray = [], biases_1darray = [], vocab_1darray = [], vectorspace_2darray = [], bow_2darray = [];
// Semantic model option set up
if ("semantic_model" in dicts && dicts["semantic_model"] == "true") {
console.log('Using semantic model.\nGetting matrices...');
var weightsfile = dataset.split(/\.t[a-z]{2}$/)[0]+'_weights.txt';
var biasesfile = dataset.split(/\.t[a-z]{2}$/)[0]+'_biases.txt';
var vocabfile = dataset.split(/\.t[a-z]{2}$/)[0]+'_vocab.txt';
var vectorfile = 'VS-' + dataset.split("_semantic")[0]+'.txt';
var bowfile = dataset.split(/\.t[a-z]{2}$/)[0]+'_bow.txt';
console.log("Reading " + bowfile);
d3.tsv(bowfile, function(text){
bow_2darray = text.map( Object.values );
bow_2darray = bow_2darray.map(function(entry) {
return entry.map(function(elem) {
return Math.round(parseFloat(elem));
});
});
});
console.log("Reading " + vectorfile);
d3.tsv(vectorfile, function(text){
vectorspace_2darray = text.map( Object.values );
vectorspace_2darray = vectorspace_2darray.map(function(arr) {
// username column ends up last in the dictionary, due to alphanumeric sort
return arr.slice(0,-1).map(function(elem) {
return parseFloat(elem);
});
});
});
console.log("Reading " + weightsfile);
d3.tsv(weightsfile, function(text){
weights_2darray = text.map( Object.values );
weights_2darray = weights_2darray.map(function(entry) {
return entry.map(function(elem) {
return parseFloat(elem);
});
});
});
console.log("Reading " + biasesfile);
d3.tsv(biasesfile, function(text){
biases_1darray = text.map( Object.values );
biases_1darray = Object.values(biases_1darray.map(Number));
});
console.log("Reading " + vocabfile);
d3.tsv(vocabfile, function(text){
vocab_1darray = text.map( Object.values );
vocab_1darray = Object.values(vocab_1darray.map(String));
});
}
/*
* value accessor - returns the value to encode for a given data object.
* scale - maps value to a visual display encoding, such as a pixel position.
* map function - maps from data value to display value
* axis - sets up axis
*/
// setup x
var xValue = function(d) { return d.x;}, // data -> value
xScale = d3.scale.linear().range([0, width]), // value -> display
xMap = function(d) {return xScale(xValue(d));}, // data -> display
xAxis = d3.svg.axis().scale(xScale).orient("bottom");
// setup y
var yValue = function(d) { return d["y"];}, // data -> value
yScale = d3.scale.linear().range([height, 0]), // value -> display
yMap = function(d) { return yScale(yValue(d));}, // data -> display
yAxis = d3.svg.axis().scale(yScale).orient("left");
// add the tooltip area to the webpage
var tooltip = d3.select("body").append("div")
.attr("class", "tooltip")
.style("opacity", 0);
var tooltip1 = d3.select("body").append("div")
.attr("class", "tooltip1")
.style("opacity", 0);
// to print all the key values pairs of a point (used to display the summary on the webpage)
var print_array = function(arr, d) {
var x = "";
for (var i=0; i<arr.length; i++) {
x = x + "<b>" + arr[i] + "</b>: " + d[arr[i]] + "<br>"
}
x = x + d.x + "<br>" + d["y"];
return x;
};
// setup fill color
var color_column;
// Can generate more with http://jnnnnn.github.io/category-colors-2L-inplace.html if want more
var d3_category20_shuffled = [
"#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf",
"#aec7e8", "#ffbb78", "#98df8a", "#ff9896", "#c5b0d5", "#c49c94", "#f7b6d2", "#c7c7c7", "#dbdb8d", "#9edae5"
];
// coloring will be done according to the values determined by cValue
var cValue = function(d) {return d[color_column];},
cValue2 = function(d) {return Math.log(parseFloat(d[color_column]));},
color = d3.scale.ordinal().range(d3_category20_shuffled);
// used to search a particular substring in the list of requested feature column
// used to determine whether we should add find to arri, hence the t/f -> f/t
var searchdic = function(arri, find) {
for(var i=0;i<arri.length;i++) {
if(JSON.stringify(find) === JSON.stringify(arri[i])){
return false;
}
}
return true;
}
// displays the summary in tabular form
var tabulate = function(data_tab, columns) {
var table = d3.select("body").append("table")
.attr("class", "select3"),
thead = table.append("thead"),
tbody = table.append("tbody");
// append the header row
thead.append("tr")
.selectAll("th")
.data(columns)
.enter()
.append("th")
.text(function(column) { return column; });
// create a row for each object in the data
var rows = tbody.selectAll("tr")
.data(data_tab)
.enter()
.append("tr");
// create a cell in each row for each column
var cells = rows.selectAll("td")
.data(function(row) {
return columns.map(function(column) {
return {column: column, value: row[column]};
});
})
.enter()
.append("td")
.attr("style", "font-family: Courier") // sets the font style
.html(function(d) { return d.value; });
/*
crossfilter dimensions and group by
http://animateddata.co.uk/articles/crossfilter/
*/
var output = "";
var cf = crossfilter(data_tab);
/* crossfilter currently only supports up to 32 columns) */
for (var i=0;i<columns.length && i<32;i++) {
var byParty = cf.dimension(function(p) {
return p[columns[i]]; });
output = output + "<b>" +columns[i] + "</b>" + "<br>";
var groupByParty = byParty.group();
groupByParty.top(5).forEach(function(p, i) {
output = output + p.key + ": " + p.value + "<br>";
console.log(p.key + ": " + p.value);
});
output = output + "<br>";
}
// side table
document.getElementById("demo3").innerHTML = output;
return table;
}
// create the dropdown menu
// Coloring
var dropDown = d3.select("body").append("select")
.attr("class", "select1")
.attr("name", "color_column");
// Searching
var dropDown1 = d3.select("body").append("select")
.attr("class", "select2")
.attr("name", "color_column");
// Transparent
var dropDown2 = d3.select("body").append("select")
.attr("class", "select4")
.attr("name", "color_column");
// Click on feature
var dropDown3 = d3.select("body").append("select")
.attr("class", "select5")
.attr("name", "color_column");
// Shaping
var dropDown4 = d3.select("body").append("select")
.attr("class", "select6")
.attr("name", "color_column");
var categories = [];
// category_search stores the name of column according to which searching is to be done
var category_search_data = [];
// categories stores the name of all the columns
var category_search;
categories.push("Select");
// check whether the searching column is provided in the url or not
if ("search" in dicts) {
category_search = dicts["search"];
category_search_data.push(category_search);
}
// color_column stores the name of column according to which coloring is to be done
// check whether the coloring column is provided in the url or not
if ("color" in dicts) {
color_column = dicts["color"];
categories.push(color_column);
} else {
color_column = "Select";
}
// categories_copy_color is just the copy of categories
var categories_copy_color = [];
categories_copy_color.push(color_column);
var columns = [], temp = [];
var x_max, x_min, y_max, y_min;
// column for the transparent value
var transparent_column = "Select", feature_column = "", shaping_column = "Select";
console.log('Loading main data')
// getting header from csv file to make drowdown menus
d3.tsv(dataset, function(data) {
console.log(data[0]);
temp = Object.keys(data[0]);
// remove x and y
temp.splice(temp.indexOf('x'), 1);
temp.splice(temp.indexOf('y'), 1);
for(var i=0;i<temp.length;i++)
if (temp[i] != category_search) {
category_search_data.push(temp[i]);
}
for(var i=0;i<temp.length;i++) {
// color_column already pushed
if (temp[i] != color_column) {
categories.push(temp[i]);
categories_copy_color.push(temp[i]);
}
columns.push(temp[i]);
}
// check whether the coloring column is provided in the url or not
// ?? is this necessary? color_column is already defined with the same procedure outside the function
if ("color" in dicts) {
color_column = categories[1]; // since color would be first, start with next
} else {
// color_column = "Select"
color_column = categories[0];
}
category_search = category_search_data[0];
// Searching
dropDown1.selectAll("option")
.data(category_search_data)
.enter()
.append("option")
.text(function(d) { return d;})
.text(function(d) {return d;});
// Coloring
dropDown.selectAll("option")
.data(categories_copy_color)
.enter()
.append("option")
.text(function(d) { return d;})
.text(function(d) {return d;});
// Transparent
dropDown2.selectAll("option")
.data(category_search_data)
.enter()
.append("option")
.text(function(d) { return d;})
.text(function(d) {return d;});
// Click on feature
dropDown3.selectAll("option")
.data(category_search_data)
.enter()
.append("option")
.text(function(d) { return d;})
.text(function(d) {return d;});
// Shaping
dropDown4.selectAll("option")
.data(categories)
.enter()
.append("option")
.text(function(d) { return d;})
.text(function(d) {return d;});
shaping_column = categories[0];
feature_column = category_search_data[0];
transparent_column = category_search_data[0];
});
// whenever any one of the drowdown menu's selected column is changes the plot is generated according to the value of dropdown menu selected
// Coloring
dropDown.on("change", plotting);
// Searching
dropDown1.on("change", plotting2);
// Transparent
dropDown2.on("change", plotting3);
// Click on feature
dropDown3.on("change", plotting4);
// Shaping
dropDown4.on("change", plotting5);
if ("q" in dicts) {
highlighting(dicts["q"], "", "");
} else {
highlighting("", "", "");
}
// the functions to call when the value of dropdown menu is changes
// Click on feature
function plotting4(){
feature_column = d3.event.target.value;
}
// Transparent
function plotting3(){
transparent_column = d3.event.target.value;
}
// Searching
function plotting2(){
category_search = d3.event.target.value;
}
// function to call for change event
// Coloring
function plotting(){
color_column = d3.event.target.value;
cValue = function(d) { return d[color_column];};
val_search = document.getElementById("searchText").value;
val_transp = document.getElementById("transpText").value;
val_opacityMatch = document.getElementById("opacityMatch").value;
val_opacityNoMatch = document.getElementById("opacityNoMatch").value;
highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch);
}
// function to call for change event
// Shaping
function plotting5(){
shaping_column = d3.event.target.value;
cValue = function(d) { return d[color_column];};
val_search = document.getElementById("searchText").value;
val_transp = document.getElementById("transpText").value;
val_opacityMatch = document.getElementById("opacityMatch").value;
val_opacityNoMatch = document.getElementById("opacityNoMatch").value;
highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch);
}
var zoomed = 0;
var needZoom = false;
var needDrawCircle = false;
// search event
// it will be executed when search button is pressed and points that matches the searched string will be highlighted
function handleClick(event) {
console.log(document.getElementById("searchText").value);
val_search = document.getElementById("searchText").value;
val_transp = document.getElementById("transpText").value;
val_opacityMatch = document.getElementById("opacityMatch").value;
val_opacityNoMatch = document.getElementById("opacityNoMatch").value;
highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch);
return false;
}
function handleCheck(event) {
if (document.getElementById("searchText").value) {
handleClick();
}
}
// transparent event
// it will be executed when Transparent button is pressed and points that satisfies the condition will be highlighted
function handleClick1(event) {
console.log(document.getElementById("transpText").value);
val_search = document.getElementById("searchText").value;
val_transp = document.getElementById("transpText").value;
val_opacityMatch = document.getElementById("opacityMatch").value;
val_opacityNoMatch = document.getElementById("opacityNoMatch").value;
highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch);
return false;
}
function handleCheck1(event) {
if (document.getElementById("transpText").value) {
handleClick1();
}
}
// ?? I believe this function is unused, and draw also maps to handleClick4
// it will be executed when Draw button is pressed and the plot will highlight those points that covers fixed percentage of point from the point obtained by mouse click
function handleClick2(event){
shaping_column = "Select";
color_column = "Select";
myForm.searchText.value = 0;
myForm1.transpText.value = 0;
myForm1.opacityMatch.value = 0;
myForm1.opacityNoMatch.value = 0;
dropDown4.property( "value", "Select" );
dropDown.property( "value", "Select" );
highlighting("", "", "");
return false;
}
// spectrum / log event
// it will be executed when spectrum/log is checked
// ?? Can we collapse handleClick1,3,4?
function handleClick3(event) {
val_search = document.getElementById("searchText").value;
val_transp = document.getElementById("transpText").value;
val_opacityMatch = document.getElementById("opacityMatch").value;
val_opacityNoMatch = document.getElementById("opacityNoMatch").value;
highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch);
}
// it will be executed when (?? draw and) zoom button is pressed and the plot will zoomed out according to the points obtained by mouse click event
function handleClick4(){
if (!document.getElementById('cbox3').checked) {
document.getElementById("zoomxy").value = ""; // clear the textbox
}
val_search = document.getElementById("searchText").value;
val_transp = document.getElementById("transpText").value;
val_opacityMatch = document.getElementById("opacityMatch").value;
val_opacityNoMatch = document.getElementById("opacityNoMatch").value;
needZoom = true;
highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch);
}
// Checks the url query for name=value and extracts the value
function getParameterByName(name, url) {
if (!url) {
url = window.location.href;
}
name = name.replace(/[\[\]]/g, "\\$&");
var regex = new RegExp("[?&]" + name + "(=([^&#]*)|&|#|$)"),
results = regex.exec(url);
if (!results) return null;
if (!results[2]) return '';
return decodeURIComponent(results[2].replace(/\+/g, " "));
}
function linspace(start, end, n) {
var out = [];
var delta = (end - start) / (n - 1);
var i = 0;
while(i < (n - 1)) {
out.push(start + (i * delta));
i++;
}
out.push(end);
return out;
}
coordinatesx = [];
coordinatesy = [];
// provides different colored spectrum
var scale_d = {
'puOr11': ['#7f3b08', '#b35806', '#e08214', '#fdb863', '#fee0b6', '#f7f7f7', '#d8daeb', '#b2abd2', '#8073ac', '#542788', '#2d004b'],
'spectral8': ['#d53e4f', '#f46d43', '#fdae61', '#fee08b', '#e6f598', '#abdda4', '#66c2a5', '#3288bd'],
'redBlackGreen': ['#ff0000', '#AA0000', '#550000', '#005500', '#00AA00', '#00ff00'],
};
scale = scale_d['spectral8'];
// function for plotting
function highlighting(val_search, val_transp, val_opacityMatch, val_opacityNoMatch) {
var svg;
var temp1 = [], temp2 = [], temp3 = [];
var dict1 = {};
// to remove the existing svg plot if any and clear side table
document.getElementById("demo3").innerHTML = "";
document.getElementById("predicted_words").innerHTML = "";
document.getElementById("frequent_words").innerHTML = "";
d3.select("svg").remove();
d3.select("table").remove();
// function zoom() {
// svg.attr("transform", "translate(" + d3.event.translate + ")scale(" + d3.event.scale + ")");
// }
// the location of svg image will be determined
svg = d3.select("body").append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform","translate(" + margin.left + "," + margin.top + ")");
/* https://github.com/skokenes/D3-Lasso-Plugin
plugin also handles selected and possible settings */
// Lasso starts
var lasso_start = function() {
d3.select("table").remove();
document.getElementById("demo3").innerHTML = "";
lasso.items()
.attr("r",3.5) // reset size
.style("fill",null) // clear all of the fills (greys out)
.classed({"not_possible":true,"selected":false}); // style as not possible
};
var lasso_draw = function() {
// Style the possible dots
lasso.items().filter(function(d) {return d.possible===true})
.classed({"not_possible":false,"possible":true});
// Style the not possible dot
lasso.items().filter(function(d) {return d.possible===false})
.classed({"not_possible":true,"possible":false})
.style("stroke", "#000");
};
var lasso_end = function() {
// Reset the color of all dots
lasso.items()
.style("fill", function(d) { return color(d[color_column]); });
// Style the selected dots
lasso.items().filter(function(d) {return d.selected===true})
.classed({"not_possible":false,"possible":false})
.attr("r",6.5);
// get values for table -> array inside a list
var zsx = lasso.items().filter(function(d) {return d.selected===true});
x_values = [];
y_values = [];
// adjust the x and y values
for (var i=0; i<zsx[0].length; i++) {
x_values.push(((((zsx[0][i].getBBox().x+6.5) * (x_max - x_min))/width + x_min )));
y_values.push(((((zsx[0][i].getBBox().y+6.5) * (y_min - y_max))/height + y_max)));
}
var selected_data=[], selected_data_indices=[];
// Compare every selected point to all points (tempX)
// in order to match coordinates with actual data
for (var ii=0;ii<x_values.length;ii++) {
console.log("lasso_end gathering selected data");
console.log(temp1.length);
for (var jj=0;jj<temp1.length;jj++) {
x_values[ii] = +(x_values[ii].toFixed(3));
y_values[ii] = +(y_values[ii].toFixed(5));
if ( (x_values[ii] === +(temp1[jj].toFixed(3))) && (y_values[ii] === +(temp2[jj].toFixed(5))) ) {
all_values = {};
for (var k=1;k<categories.length;k++) {
all_values[categories[k]] = (dict1[categories[k]][jj]);
}
if(searchdic(selected_data,all_values)==true){
selected_data.push(all_values);
selected_data_indices.push(jj);
break;
}
}
}
}
// render the table for the points selected by lasso
if (selected_data.length > 0) {
console.log("Rendering table...");
console.log(selected_data);
console.log(columns);
console.log(x_values);
var peopleTable = tabulate(selected_data, columns, x_values);
if ("semantic_model" in dicts && dicts["semantic_model"] == "true") {
console.log("Predicting words...");
classify(selected_data_indices, vectorspace_2darray, weights_2darray, biases_1darray, vocab_1darray);
benchmark(selected_data_indices, bow_2darray, vocab_1darray);
}
}
// Reset the style of the not selected dots (we made them 0.5 smaller)
lasso.items().filter(function(d) {return d.selected===false})
.classed({"not_possible":false,"possible":false})
.attr("r",3)
.style("stroke", "#000");
};
// Create the area where the lasso event can be triggered
var lasso_area = svg.append("rect")
.attr("width",width)
.attr("height",height)
.style("opacity",0);
// Define the lasso
var lasso = d3.lasso()
.closePathDistance(75) // max distance for the lasso loop to be closed
.closePathSelect(true) // can items be selected by closing the path?
.hoverSelect(true) // can items by selected by hovering over them?
.area(lasso_area) // area where the lasso can be started
.on("start",lasso_start) // lasso start function
.on("draw",lasso_draw) // lasso draw function
.on("end",lasso_end); // lasso end function
// Init the lasso object on the svg:g that contains the dots
svg.call(lasso);
var classify = function(indices, vs_source, weights_source, biases_source, vocab_source) {
var sum_vectors = vs_source[indices[0]];
for (i=1; i < indices.length; i++) {
sum_vectors = math.add(sum_vectors, vs_source[indices[i]]);
}
var avg_vector = sum_vectors.map(function(x) { return x / indices.length; });
var mul = math.multiply(avg_vector, weights_source);
var add = math.add(mul, biases_source);
// get indices of 10 greatest elements
var topIndices = findIndicesOfMax(add, 10);
console.log("Top predicted vocab words:");
var strbuilder = "Predicted words:";
for (i=0; i<10; i++) {
console.log((i+1) + ": " + vocab_source[topIndices[i]]);
strbuilder += " " + vocab_source[topIndices[i]] + ",";
}
document.getElementById("predicted_words").innerHTML = strbuilder.slice(0, -1);
}
var benchmark = function(indices, bow_source, vocab_source) { // may need to adjust
var num_indices = indices.length;
var vocab_freq = bow_source[indices[0]];
for (i=1; i < num_indices; i++) {
vocab_freq = math.add(vocab_freq, bow_source[indices[i]]);
}
// get indices of 10 greatest elements
var topIndices = findIndicesOfMax(vocab_freq, 10);
console.log("Most frequent words:");
var strbuilder = "Most frequent words:";
for (i=0; i<10; i++) {
console.log((i+1) + ": " + vocab_source[topIndices[i]]);
strbuilder += " " + vocab_source[topIndices[i]] + ",";
}
document.getElementById("frequent_words").innerHTML = strbuilder.slice(0, -1);
}
// Utility function used for predicting words in semantic setting
// Source: https://stackoverflow.com/a/11792230/7100714
function findIndicesOfMax(inp, count) {
var outp = new Array();
for (var i = 0; i < inp.length; i++) {
outp.push(i);
if (outp.length > count) {
outp.sort(function(a, b) { return inp[b] - inp[a]; });
outp.pop();
}
}
return outp;
}
console.log('Loading main data, again') // load data
d3.tsv(dataset, function(error, data) {
// change string (from CSV) into number format
var numerics = {}, symbol = {};
//Omitting Select (0)
for(var i=1;i<categories.length;i++) {
// initialize the value for each category key to empty list
dict1[categories[i]] = [];
// initialize all categories as numeric
numerics[categories[i]] = 1;
}
counter = 0;
data.forEach(function(d) {
// coerce the data to numbers
d.x = +d.x;
d["y"] = +d["y"];
for(var i=1;i<categories.length;i++){
// add every attribute of point to the {category:[val1,val2,...]}
dict1[categories[i]].push(d[categories[i]]);
// revoke a category's numerics status if find an entry has a non-Int or non-null value for that category
numerics[categories[i]] = numerics[categories[i]] && (d[categories[i]] == "" || d[categories[i]] == parseFloat(d[categories[i]]));
}
// fill the symbol dictionary with all possible values of the shaping column as keys
// value is the order of points
if (!(d[shaping_column] in symbol)) {
symbol[d[shaping_column]] = counter;
counter = counter + 1;
}
// push all x values, y values, and all category search values into temp1/2/3
temp1.push(d.x);
temp2.push(d["y"]);