So, I’ve been teaching CS101 – Introduction to Computing this semester (Fall 2017). We picked Python as the language. I’ve compiled the videos and all the lecture notebooks. These are being made available in the hopes that they can be useful for someone. Here’s how to get started with these. Continue reading “Beginning Programming with Python”
Here’s a mini howto on backing up files on a remote machine using
rsync. It shows the progress while it does its thing and updates any remote files while keeping files on the remote end that were deleted from your local folder.
rsync -v -r --update --progress -e ssh /media/nam/Documents/ email@example.com:/media/nam/backup/documents/
/media/nam/Documents/ is the local folder and
/media/nam/backup/documents/ is the backup folder on the machine with IP
So, Admob was acquired a while ago by Google and it was recently announced that the publisher reports by Admob would no longer be available through the old APIs. Instead, they now have to be retrieved through the AdSense API — which is based on OAuth 2.0 and thus a real pain for those just getting started.
Turns out, the process is quite straight-forward but extremely poorly documented. You can go through the AdSense reporting docs, the Google API library and the OAuth 2.0 specs but you would soon be lost. After spending a couple of days decoding the requirements, I found out the bare-metal approach to accessing the stats. And here is how.
We’re going to use the the Hadoop tarball we compiled earlier to run a pseudo-cluster. That means we will run a one-node cluster on a single machine. If you haven’t already read the tutorial on building the tarball, please head over and do that first.
Start up your (virtual) machine and login as the user ‘hadoop’. First, we’re going to setup the essentials required to run Hadoop. By the way, if you are running a VM, I suggest you kill the machine used for building Hadoop and re-start from a fresh instance of Ubuntu to avoid any issues with compatibility later. For reference, the OS we are using is 64-bit Ubuntu 12.04.3 LTS.
I wrote a tutorial on getting started with Hadoop back in the day (around mid 2010). Turns out that the distro has moved on quite a bit with the latest versions. The tutorial is unlikely to work. I tried setting up Hadoop on a single-node “cluster” using Michael Knoll’s excellent tutorial but that too was out of date. And of course, the official documentation on Hadoop’s site is lame.
Having struggled for two days, I finally got the steps smoothed out and this is an effort to document it for future use.
So you’ve started working with Django and you love the admin interface that you get for free with your models. You deploy half of your app with the admin interface and are about to release when you figure out that anyone who can modify a model can do anything with it. There is no concept of “ownership” of records!
Let me give you an example. Let’s say we’re creating a little MIS for the computer science department where each faculty member can put in his courses and record the course execution (what was done per lecture). That would be a nice application. (In fact, it’s available open source on github and that is what this tutorial is referring to.) However, the issue is that all instructors can access all the course records and there is no way of ensuring that an instructor can modify only the courses that s/he taught. This isn’t easily possible because admin doesn’t not have “row-level permissions”.
I’ve been teaching “Applied Algorithms and Programming Techniques” and we just reached the topic of AVL Trees. Having taught half of the AVL tree concept, I decided to code it in python — my newest adventure. Bear in mind that I have never actually coded an AVL tree before and I’m not particularly comfortable with python. I thought it would be a good idea to experiment with both of them at the same time. So, I started up my python IDE (that’s Aptana Studio, btw) and started coding.
For the newbie programmer, the code itself may not be very useful since you can find better code online. The benefit is in being able to look at the process. You can take a look at the commits I made along the way over here on github. You can take a look at how I structured the code when I began and how I added bits and pieces. This abstraction should help in solving other problems as well. The final code (along with a rigorous unit test file) can be seen here: https://github.com/recluze/python-avl-tree