Chris Hokamp chrishokamp

d3js Tree Visualizer

This example pulls together various examples of work with trees in D3.js. See the d3js Tree Visualizer live here.

The panning functionality can certainly be improved in my opinion and I would be thrilled to see better solutions contributed.

One can do all manner of housekeeping or server related calls on the drop event to manage a remote tree dataset for example.

Panning can either be done by dragging an empty part of the SVG around or dragging a node towards an edge.

jq — https://jqlang.github.io/jq/ — "like sed for JSON data"

There are several options available for installing jq. I prefer to use Homebrew: brew install jq

	#!/usr/bin/env bash
	# memusg -- Measure memory usage of processes
	# Usage: memusg COMMAND [ARGS]...
	#
	# Author: Jaeho Shin <[email protected]>
	# Created: 2010-08-16
	############################################################################
	# Copyright 2010 Jaeho Shin. #
	# #
	# Licensed under the Apache License, Version 2.0 (the "License"); #

	echo 'export PATH=$HOME/local/bin:$PATH' >> ~/.bashrc
	. ~/.bashrc
	mkdir ~/local
	mkdir ~/node-latest-install
	cd ~/node-latest-install
	curl http://nodejs.org/dist/node-latest.tar.gz \| tar xz --strip-components=1
	./configure --prefix=~/local
	make install # ok, fine, this step probably takes more than 30 seconds...
	curl https://www.npmjs.org/install.sh \| sh

	#!/usr/bin/python2

	import logging
	import sys
	import couchdb
	import json
	from libgreader import GoogleReader, ClientAuthMethod, Feed

	"""
	Original can be obtained here: https://gist.github.com/Nazgolze/5479539

	import logging

	import numpy as np
	import tensorflow as tf
	from tensorflow.contrib import layers


	GO_TOKEN = 0
	END_TOKEN = 1
	UNK_TOKEN = 2

	"""
	Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
	run simple training experiments for NER and text classification.

	Requires the Prodigy annotation tool to be installed: https://prodi.gy
	See here for details on Streamlit: https://streamlit.io.
	"""
	import streamlit as st
	from prodigy.components.db import connect
	from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size