Who is Installing gbrainy?

While I was flicking through the metrics from installion.co.uk last night I was shocked by just how many hits the Debian and Ubuntu installation pages for gbrainy had had. It seems that from virtually out of nowhere a while back, an increasing number of people are trying to install it on a linux box and it made me wonder why.

The way I look at it there can only be two reasons:

1. It's just really good and so is spreading virally.
2. Everyone is being asked to install it by some educational or training body - maybe it's on some curriculum somewhere.

Having Goggled around and wasted more time than I really should have I came up with nothing except that Edubuntu comes with it installed (and surely schools are using that?)

Anyway, if you have any ideas then let me know. Meanwhile I'm just sitting here playingr gbrainy like the rest of them.

There's a neat article here. 

Python _imaging cannot open shared object file

Okay so I'm using a 64 bit Linux and I was having a lot of trouble getting Calibre ebook tool to convert books into Kindle loving mobi format. I was getting a python error:

ImportError: libjpeg.so.62: cannot open shared object file: No such file or directory

Turns out all you need to do is

sudo apt-get install libjpeg62

You can test this by starting python and doing:

import _imaging

Fastest Way to Burn an ISO to a USB Flash Drive

If your using Linux:

Installing Git and Virtualenv on Linux or Mac

For Debian/Ubuntu Linux:

sudo apt-get install git python-setuptools

For Redhat/Fedora Linux:

sudo yum install git python-setuptools

For Mac:

Install MacPorts and then:

sudo port install git python-setuptools

Continuing on for all of the above

sudo easy_install pip
sudo pip install virtualenv virtualenvwrapper

Now, you may want to set some defaults in your ~/.bashrc. My relevant entries look like this:

export WORKON_HOME=$HOME/Projects
export VIRTUALENVWRAPPER_HOOK_DIR=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_LOG_DIR=$HOME/.virtualenvs
source /usr/local/bin/virtualenvwrapper.sh

git config --global user.name "Terse Col"
git config --global user.email myemail@gmail.com
git config --global http.sslVerify false

Now away you go:

mkdir $HOME/.virtualenvs
source $HOME/.bashrc
mkvirtualenv mynewproject 
cdvirtualenv

Install Google App Engine on Ubuntu 12.10

Google App Engine  is currently on version 1.7.4 and Ubuntu has recently released Ubuntu 12.10 (Quantal Quetzal). Quantal Quetzal comes with Python 2.7 installed, and App Engine has been providing that version of Python as an option since February 2012. So if you are starting a new App Engine project, it's probably a good time to move to Python 2.7.

I'll explain briefly how you start a new project and there's a nice clean copy of the code at the bottom that you can cut and paste.

Let's get the show on the road. Choose a name for the project and create and switch to a virtual environment:

PROJ=gae_project
mkvirtualenv ${PROJ}
cdvirtualenv

Note that note that "--no-site-packages" and "--distribute" are now the defaults for mkvirtualenv. You don't even need to use "--python=python2.7" on Ubuntu 12.10.

Now we need to know what the latest version of App Engine is, but as of writing it's 1.7.4:

GAE=1.7.4
wget -O /tmp/gae.zip http://googleappengine.googlecode.com/files/google_appengine_${GAE}.zip
unzip /tmp/gae.zip

Now let's create an App Engine app. The app will need a name that has been created in the App Engine Console:

GAE_APP_NAME=dummy
mkdir -p gae_app/static

Now create the app.yaml file:

echo """application: ${GAE_APP_NAME}
version: development
runtime: python27
api_version: 1
threadsafe: true

default_expiration: 7d

handlers:
- url: /static
  static_dir: static
- url: .*
  script: wsgi_app.app
""" > gae_app/app.yaml

And finally the app itself:

echo """import webapp2

class MainPage(webapp2.RequestHandler):
  def get(self):
      self.response.headers['Content-Type'] = 'text/plain'
      self.response.out.write('Please replace me with a decent WSGI App Framework such as Flask')

app = webapp2.WSGIApplication([('/', MainPage)],
                              debug=True)
""" > gae_app/wsgi_app.py

And finally to run the development server:

python ./google_appengine/dev_appserver.py gae_app/

I hope that this has all been of some help to you. Did I miss anything? Please comment below.

A Review of the Nagare Microframework

The analytics tell me that the keywords for this blog are "Best Python Framework" which is odd because I don't think I can pretend to have that answer. What I can do is point out what the differences are and let you find the one that suits you best. Nagare for example offers some concepts that you won't be used to if you come from using Django or Flask and the like.

If you have read Install Stackless Python on Ubuntu and could get stackless running the examples I showed you, then just follow these commands to get a stackless virtual environment running a nagare project called 'nagaredemo':

In the examples below, paste the code directly into the file

//negaredemo/demos/demos/app.py

and be aware that when you change the code you need to  restart the development server (Ctrl-C stops it.)

Hello World!

Okay, so example1 shows the basics of rendering a Welcome object.

http://localhost:8080/demos/

You'll notice immediately that we're not using a templating language here, but rather the built in Domain Specific Language (DSL). In reality you would use the DSL to build xhtml snippets and then use meld to insert them into templates. I've not used meld before, but it looks very straight forward - neat in fact. Now onto example2.

Callbacks

Here we've registered two callbacks on the DOM without having to explicitly declare the URL mappings. have a look at the rendered HTML to see how this has been implemented. Now, that's pretty handy.

Tiresome RESTful URLs

Example 3 takes that same code and augments it to allow us to have more ReSTful URLs. I'm not sure this is as obvious as I'd like, but it works I suppose.

http://localhost:8080/demos/Counter?value=7

Saving State Automatically

Nagare can hide the request/response object and global session from the developer. Object states are automatically saved in a server side session during the request/response round-trip. This allows for a more direct style of programming like in a desktop application.

And there's more

There's more documentation on the Nagere website itself, so scoot along there and see what else there is to offer. Nagare is built upon ConfigObj, WebOb, Paste, PEAK-Rules and lxml, so it's on good foundations. I'm told by Hervé Coatanhay that the next release will introduce non stackless support using pypy or CPython.

Conclusion

Nagare is able to implement Continuations and that's a great thing. However I'm not sure I like the way it obfuscates the implementation. I'd much rather be able to see what was going on.

For example, saving state between requests is nice, but is it secure? Do I need to plough through the code to find out? In my opinion it would have been better to have offered a structure that allows for others to plug in solutions/implementations.

The documentation is somewhat limited, which is probably because the community is still fairly small. However sinceteh project has been running since 2008, perhaps that's something to be concerned about.

Thanks to Hervé Coatanhay for suggestion the illustrative code snippets.

An Alternate Explanation of Continuations

What's a continuation?

If you read the Wikipedia item on continuations, make sure you understand the "continuation sandwich" paragraph which is supposed to be the definitive example of what a continuation is. I like to think of it the other way around - with my patent "what a continuation is not" example.

What a continuation is not?

Imagine you have a bookies slip with a four horse accumulator bet (parlay) on it. Your first three horses win and you are in a situation (the state of the bet) where you have a small fortune going onto the last horse. The horse falls.

Now, if the bookies slip had been a continuation it would have maintained the state after the third race and you could have retrieved that state and tried another horse on the last race. But it isn't and you can't, so tough.

Continuations are an important concept in the Nagare Microframework which I'll be looking at next. Continuations allow Nagare to maintain state in an exciting way which may seem unusual to you if you have used some of the other python frameworks like Flask or Bottle or Django.

Good luck fellow travellers.

Stackless Code - The Short Version

So my last couple of posts have been on installing Stackless Python and what exactly the Stackless bit of Stackless Python means. Now it's time for the short version of the example code and an alternate explanation of continuations. Prepare to be underawed.

With Stackless ou get tasklets and channels. Channels let you communicate between tasklets.

Running this you get:

Now the third and final thing that Stackless offers is cooperative multitasking. To see how this works, just uncomment the line with stackless.schedule() in it and rune it again.

That's about it really, but as you know it's not what you got it's how you use it. There's a much more detailed explanation of what we just did here and the point is that this allows you to use continuations.

See my next post An Alternate Explanation of Continuations

Good luck fellow travellers.

Python Rocks - So what is Stackless Python?

Python may be one of the most widely learned and used languages today, but it was conceived in the late 1980's when if you hadn't got a mainframe, you almost certainly were running your code on a single CPU computer of some sort.

For this reason the original implementation of Python was written with the understanding that it was perfectly sensible to use the same single execution stack that C used - after all Python was written in C. Despite being on version 2.7/3.3 nowadays, the standard Python is still written in C, still uses a single execution stack design and is often known as CPython.

English: CPU Zilog Z8 (Photo credit: Wikipedia)

The execution stack - or call stack - or just stack is like a big spike that you stick messages on in a last on, first off way and it's where the low level machine code subroutines used to stick the current code  address before going away to do some jiggery pokery. When the subroutine was finished it returned by pulling the last address off the stack and then execution continued from there. It was all fairly simple in the days of the Z80 and as I understand it, that's still essentially how a single CPU - or Core - works.

AMD Athlon™ X2 Dual-Core Processor 6400+ in AM2 package (Photo credit: Wikipedia)

The problem is that sometime in the early 2000s, dual-core, and then multi-core chips started to be become increasingly affordable and therefore available. Most of you will be using a multi-core system to read this post. This means that your systems are capable of running more than one process a time - what is called concurrency.

This is a bit of a pain for CPython because it only knows how to use a single stack, i.e. a single core, and that is just a bit of a waste of those other cores which are just itching to make it all run super fast.

So Stackless Python is essentially a redesign of CPython which avoids using the call stack and instead uses something called microthreads to get around the problem. This means four things to you:

• Concurrent programming is possible.
• Concurrency can improve on execution time if done properly.
• You need to learn some new concepts: tasklets and channels.
• You get to use some new stuff: tasklets and channels.

I'll introduce those next time.

There's a very informative interview with the creator of Stackless Python here.

You may like to be ready by reading my post about installing Stackless Python.

Good luck fellow travellers.

Install Stackless Python on Ubuntu

I'm just about to write a couple of posts on Stackless Python and the Nagare Micro Framework which runs on it. So I've been installing Stackless on my Ubuntu 12.04. Here are some nice copy and paste instructions if you want to play along.

First install the required libraries and get stackless itself:

Now install stackless :

After the "make" you'll see some failures as below. Just ignore them.

Now it's time to link your standard (CPython) packages so that they can be used with stackless:

...and edit the paths in the site.py file. At about line 300, edit the file to look like this. It's the second sitepackages.append bit we're adding here:

That should be it! Let's test it: