Accident

Question

Accident

Can someone explain GROUP , SOURCE , VALUE and TARGET in the Les Misérables match matrix and how they are related .

It would be helpful if an explanation would be made using an example.

I tried the existing example, taking 5 characters, but could not relate the source , target and values .

Also, if someone tried other input (json data) than the standard example “miserables.json”, please share so that I can understand well to put my data and see the visualization.

Thanks at Advance

+7

d3.js

Kish Dec 9 '13 at 5:12

source share

1 answer

duhaime · Answer 1 · 2015-07-28T12:02:01+0000

I was also interested in this task, and this is what I came up with. First I created json, which will load into the visualization using Python ( json sample ). Json should have the following form:

 { "nodes": [ {"name":node_name, "group":node_group}, {"name":another_node_name, "group":another_node_group}, ... ], "links":[ {"source"link_source, "target":link_target, "value":link_value, {"source"another_link_source, "target":another_link_target, "value":another_link_value}, ... ] }

Then it is just a matter of pointing your application to your json files. I used the Python Flask library to create a simple server that could serve my json files:

 #!flask/bin/python from flask import Flask, jsonify, request, Response from flask.ext.cors import CORS import json application = Flask(__name__) # Utilize CORS to allow cross-origin API requests cors = CORS(application) ############################# # Network Visualization App # ############################# @application.route('/api/json/<request>', methods=['GET']) def parse_json_request(request): with open("json/" + request, 'r') as json_in: cooccurrence_json = json.load(json_in) return jsonify(cooccurrence_json) @application.errorhandler(404) def page_not_found(e): return "Sorry, the page you requested could not be found." if __name__ == '__main__': application.run(debug=True)

Then I downloaded the server and my json into Amazon web services using this elastic bean glass tutorial to deploy the application and change the javascript code as follows:

 <!DOCTYPE html> <html class="ocks-org do-not-copy"> <meta charset="utf-8"> <title>Dramatic Co-occurrence</title> <style> @import url(../style.css?aea6f0a); d3_plot { font-size: 80%; } body.svg { margin-left: 0px; } .background { fill: #eee; } line { stroke: #fff; } text.active { fill: red; } </style> <script type="text/javascript" src="http://d3js.org/d3.v2.min.js?2.8.1"></script> <script type="text/javascript" src="http://code.jquery.com/jquery-1.7.2.min.js"></script> <header> </header> <h1>Character Co-occurrence in Shakespearean Drama</h1> <aside style="margin-top:20px;"> <p>Play: <select id="selected_json"> <option value='"http://tdm-api-dev.elasticbeanstalk.com/api/json/king_henry_the_fifth.json"'>Henry VIII</option> <option value='"http://tdm-api-dev.elasticbeanstalk.com/api/json/romeo_and_juliet.json"'>Romeo and Juliet</option> <option value='"http://tdm-api-dev.elasticbeanstalk.com/api/json/hamlet.json"'>Hamlet</option> </select> <i> </i> Order: <select id="order"> <option value="name">by Name</option> <option value="count">by Frequency</option> <option value="group">by Cluster</option> </select> <p>This application visualizes the degree to which characters in Shakespeare plays appear together. <p>Each colored cell represents two characters that appeared in the same scene, and darker cells indicate characters that co-occurred more frequently. <p>Use the drop-down menus to select a different play, reorder the matrix, and explore the data. <p>Built with data from ProQuest Chadwyck Healey <a href="http://www.proquest.com/products-services/literature_online.html">Literature Online Collections</a>. </aside> <d3_plot></d3_plot> <script> function select_json(new_json) { var margin = { top: 120, right: 0, bottom: 10, left: 160 }, width = 800, height = 800; var x = d3.scale.ordinal().rangeBands([0, width]), z = d3.scale.linear().domain([0, 4]).clamp(true), c = d3.scale.category10().domain(d3.range(10)); var svg = d3.select("d3_plot").append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .style("margin-left", "0px") .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); // Based on the user-selected input text above, make the appropriate api call and retrieve the json d3.json(new_json, function(miserables) { console.log(new_json) var matrix = [], nodes = miserables.nodes, n = nodes.length; // Compute index per node. nodes.forEach(function(node, i) { node.index = i; node.count = 0; matrix[i] = d3.range(n).map(function(j) { return { x: j, y: i, z: 0 }; }); }); // Convert links to matrix; count character occurrences. miserables.links.forEach(function(link) { matrix[link.source][link.target].z += link.value; matrix[link.target][link.source].z += link.value; matrix[link.source][link.source].z += link.value; matrix[link.target][link.target].z += link.value; nodes[link.source].count += link.value; nodes[link.target].count += link.value; }); // Precompute the orders. var orders = { name: d3.range(n).sort(function(a, b) { return d3.ascending(nodes[a].name, nodes[b].name); }), count: d3.range(n).sort(function(a, b) { return nodes[b].count - nodes[a].count; }), group: d3.range(n).sort(function(a, b) { return nodes[b].group - nodes[a].group; }) }; // The default sort order. x.domain(orders.name); svg.append("rect") .attr("class", "background") .attr("width", width) .attr("height", height); var row = svg.selectAll(".row") .data(matrix) .enter().append("g") .attr("class", "row") .attr("transform", function(d, i) { return "translate(0," + x(i) + ")"; }) .each(row); row.append("line") .attr("x2", width); row.append("text") .attr("x", -6) .attr("y", x.rangeBand() / 2) .attr("dy", ".32em") .attr("text-anchor", "end") .text(function(d, i) { return nodes[i].name; }); var column = svg.selectAll(".column") .data(matrix) .enter().append("g") .attr("class", "column") .attr("transform", function(d, i) { return "translate(" + x(i) + ")rotate(-90)"; }); column.append("line") .attr("x1", -width); column.append("text") .attr("x", 6) .attr("y", x.rangeBand() / 2) .attr("dy", ".32em") .attr("text-anchor", "start") .text(function(d, i) { return nodes[i].name; }); function row(row) { var cell = d3.select(this).selectAll(".cell") .data(row.filter(function(d) { return dz; })) .enter().append("rect") .attr("class", "cell") .attr("x", function(d) { return x(dx); }) .attr("width", x.rangeBand()) .attr("height", x.rangeBand()) .style("fill-opacity", function(d) { return z(dz); }) .style("fill", function(d) { return nodes[dx].group == nodes[dy].group ? c(nodes[dx].group) : null; }) .on("mouseover", mouseover) .on("mouseout", mouseout); } function mouseover(p) { d3.selectAll(".row text").classed("active", function(d, i) { return i == py; }); d3.selectAll(".column text").classed("active", function(d, i) { return i == px; }); } function mouseout() { d3.selectAll("text").classed("active", false); } d3.select("#order").on("change", function() { clearTimeout(timeout); order(this.value); }); function order(value) { x.domain(orders[value]); var t = svg.transition().duration(2500); t.selectAll(".row") .delay(function(d, i) { return x(i) * 4; }) .attr("transform", function(d, i) { return "translate(0," + x(i) + ")"; }) .selectAll(".cell") .delay(function(d) { return x(dx) * 4; }) .attr("x", function(d) { return x(dx); }); t.selectAll(".column") .delay(function(d, i) { return x(i) * 4; }) .attr("transform", function(d, i) { return "translate(" + x(i) + ")rotate(-90)"; }); } var timeout = setTimeout(function() { order("group"); d3.select("#order").property("selectedIndex", 2).node().focus(); }, 5000); }); } // set initial json selection select_json("http://tdm-api-dev.elasticbeanstalk.com/api/json/king_henry_the_fifth.json"); // handle on click event d3.select('#selected_json').on('change', function() { // erase old image d3.select("svg").remove(); var new_json = eval(d3.select(this).property('value')); select_json(new_json); }); </script> [1]: http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/create-deploy-python-flask.html

This created a web page that looks like the image below, which you can see in action here . Hope this helps others!

PS When creating the page, it was very useful for me to open my html in Chrome, and then use more tools --> developer tools to help me debug javascript errors.

Accident

More articles: