Daniel Teixeira ddtxra

An interactive version of a Reingold–Tilford tree. Click on the nodes to expand or collapse.

The tree layout implements the Reingold-Tilford algorithm for efficient, tidy arrangement of layered nodes. The depth of nodes is computed by distance from the root, leading to a ragged appearance. Cartesian orientations are also supported. Implementation based on work by Jeff Heer and Jason Davies using Buchheim et al.'s linear-time variant of the Reingold-Tilford algorithm. Data shows the Flare class hierarchy, also courtesy Jeff Heer.

Compare to this Cartesian layout.

	import numpy as np
	from sklearn.neural_network import MLPRegressor
	from sklearn.model_selection import RandomizedSearchCV
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.wrappers.scikit_learn import KerasRegressor
	import sklearn.metrics
	def mean_absolute_percentage_error(y_true, y_pred):
	if any(y_true == 0):
	return 0.0

	import java.time.Duration;
	import java.time.LocalDateTime;

	import io.vertx.core.AbstractVerticle;
	import io.vertx.core.Future;
	import io.vertx.core.Vertx;
	import io.vertx.core.http.HttpServer;
	import io.vertx.ext.web.Router;

	public class HelloVertX {

	{
	"data": [{
	"rowLabel": "Alimentary Sytem",
	"values": [{
	"columnLabel": "Microarray",
	"value": "High"
	}, {
	"columnLabel": "IHC",
	"value": "High"
	}],

	<script src="https://cdn.rawgit.com/calipho-sib/nextprot-js/v0.0.54/dist/nextprot.bundle.js"></script>
	<span id="api-result"></span>
	<script type="text/javascript">

	var applicationName = 'expression-viewer';
	var clientInfo='GSOC';
	var nx = new Nextprot.Client(applicationName, clientInfo);

	var entry = nx.getEntryName();

	body {
	font-family: Helvetica;
	background-color: #fff;
	/#f0f0f0;/

	font-weight: lighter;
	font-size: 10pt;
	margin: 0px;
	width: 100%;
	height: 100%x;

	license: gpl-3.0
	height: 960
	border: no