Eric Bidelman has documented some of the common workflows possible with headless Chrome over in https://developers.google.com/web/updates/2017/04/headless-chrome.
If you're looking at this in 2016 and beyond, I strongly recommend investigating real headless Chrome: https://chromium.googlesource.com/chromium/src/+/lkgr/headless/README.md
Windows and Mac users might find using Justin Ribeiro's Docker setup useful here while full support for these platforms is being worked out.
| import datetime | |
| import os | |
| import boto | |
| from boto.emr.instance_group import InstanceGroup | |
| from boto.emr.step import InstallPigStep, PigStep | |
| conn = boto.connect_emr() |
| ## Angular modules | |
| http://ngmodules.org/ | |
| http://angular-ui.github.com/ | |
| http://angular-ui.github.com/bootstrap/ | |
| ## Angular resources | |
| John Lindquist Videos: | |
| http://egghead.io |
| /root/installs/sqoop-1.4.2/build/sqoop-1.4.2.bin__hadoop-1.0.0/bin/sqoop export --connect jdbc:postgresql:where_its_at/muppets --table fulla_stuff --export-dir /more/stuff/part-r-00000 |
| 301 Redirects | |
| Redirect 301 /oldpage.html http://www.yoursite.com/newpage.html | |
| Redirect 301 /oldpage2.html http://www.yoursite.com/folder/ | |
| ------------------------------------------------------------- | |
| Custom Error Pages | |
| ErrorDocument 400 /400.html |
For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.
A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s
| import boto, datetime, time | |
| # Enter your AWS credentials | |
| aws_key = "YOUR_AWS_KEY" | |
| aws_secret = "YOUR_AWS_SECRET" | |
| # Details of instance & time range you want to find spot prices for | |
| instanceType = 'm1.xlarge' | |
| startTime = '2012-07-01T21:14:45.000Z' | |
| endTime = '2012-07-30T23:14:45.000Z' |
| # Protodash | |
| class Instance(Resource): | |
| resource_type = "instance" | |
| metric_resource_type = resource_type | |
| api_key = "instances" | |
| id_key = "instance_id" | |
| metric_id_key = "instance" #our resource ids are chaos | |
| metric_column = "InstanceMetric" |
| angular.module('stateMock',[]); | |
| angular.module('stateMock').service("$state", function($q){ | |
| this.expectedTransitions = []; | |
| this.transitionTo = function(stateName){ | |
| if(this.expectedTransitions.length > 0){ | |
| var expectedState = this.expectedTransitions.shift(); | |
| if(expectedState !== stateName){ | |
| throw Error("Expected transition to state: " + expectedState + " but transitioned to " + stateName ); | |
| } | |
| }else{ |