| { | |
| "lines.linewidth": 2.0, | |
| "examples.download": true, | |
| "patch.linewidth": 0.5, | |
| "legend.fancybox": true, | |
| "axes.color_cycle": [ | |
| "#30a2da", | |
| "#fc4f30", | |
| "#e5ae38", | |
| "#6d904f", |
This is part of a series of examples that describe the basic operation of the D3.js force layout. Eventually they may end up in a blog post that wraps everything together. If you missed the beginning of the series, here's a link to first example.
Earlier examples looked at the operation of force layout from a (very) high level. This example begins examining the important properties of the force layout in more detail.
Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.
| #!/bin/bash | |
| # Backs up the OpenShift PostgreSQL database for this application | |
| # by Skye Book <skye.book@gmail.com> | |
| NOW="$(date +"%Y-%m-%d")" | |
| FILENAME="$OPENSHIFT_DATA_DIR/$OPENSHIFT_APP_NAME.$NOW.backup.sql.gz" | |
| pg_dump $OPENSHIFT_APP_NAME | gzip > $FILENAME |
| -- | |
| -- This is SQL query to extract AddressBook info from respective sqlite3 file from iOS backup. | |
| -- It has been tested only on my own backup, so, if anything is wrong, fork, edit, and stuff. | |
| -- | |
| -- # parse-manifest.py from http://stackoverflow.com/questions/3085153/how-to-parse-the-manifest-mbdb-file-in-an-ios-4-0-itunes-backup | |
| -- | |
| -- $ python ../parse-manifest.py | grep AddressBook.sqlitedb | |
| -- -rw-r--r-- 000001f5 000001f5 1654784 1312812502 1312812502 1287387943 (31bb7ba8914766d4ba40d6dfb6113c8b614be442)HomeDomain::Library/AddressBook/AddressBook.sqlitedb | |
| -- | |
| -- $ sqlite3 31bb7ba8914766d4ba40d6dfb6113c8b614be442 |
| data = sqlContext.load("/home/rxin/ints.parquet") | |
| data.groupBy("a").agg(col("a"), avg("num")).collect() |
| <!DOCTYPE HTML> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title></title> | |
| <script src="http://d3js.org/d3.v2.js"></script> | |
| <style> | |
| line.arrow { | |
| stroke: #666; |
| ./elastic-mapreduce --create --alive \ | |
| --name "Hive cluster" \ | |
| --hive-interactive --hive-versions latest \ | |
| --hive-site=s3://${s3bucket}/emr/conf/hive-site.xml \ | |
| --ami-version latest \ | |
| --availability-zone us-east-1d --log-uri s3n://york-emr-logs \ | |
| --instance-group master --instance-type m1.medium --instance-count 1 --bid-price 0.175 \ | |
| --instance-group core --instance-type m1.large --instance-count 3 --bid-price 0.35 \ | |
| --enable-debugging --log-uri s3n://${s3bucket}/emr/logs \ | |
| --bootstrap-action s3://elasticmapreduce/bootstrap-actions/run-if \ |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# The unreasonable effectiveness of Character-level Language Models\n", | |
| "## (and why RNNs are still cool)\n", | |
| "\n", | |
| "###[Yoav Goldberg](http://www.cs.biu.ac.il/~yogo)\n", |