Decorators are powerful tools available in python (since version 2.4). They enable developers to add new functionalities to existing functions/classes in a very flexible way that is highly reusable. This is their strongest advantage. Decorator basics are quite easy, but when overused or overcomplicated, they can make the code realy difficult to understand, so you gotta find the proverbial golden mean here.

We'll start with the simplest possible example - just to work out the basics. Let's define a fib function that implements the Fibonacci sequence:

# our example function
def fib(n):
    if n==0 or n==1:
        return n
    else:
        return fib(n-1) + fib(n-2)

For a given non-negative argument n this function will return n-th number in the sequence, e.g.

>>> fib(0)
0
>>> fib(1)
1
>>> fib(2)
1
>>> fib(3)
2
>>> fib(10)
55

Above implementation is an example of a greedy algorithm which is, in short, something that is not going to work fast for big arguments or big data sets. This is because, at each step, 2 previous steps need to be executed no matter if they have already been executed. And that means that a great deal of same operations are going to be performed. To illustrate this behavior, try to run our function on bigger sets:

>>> fib(30)
832040
>>> fib(300)
222232244629420445529739893461909967206666939096499764990979600L

You've waited for the result for quite long, haven't you? This link explains that the complexity of above code is exponential (one of the worst algorithm complexities possible).

What can we do to speed up the calculation? For example, we can store a fib(n) result after it has been calculated for the first time. This technique is called memoization, and its main purpose is to avoid repetitive calculations. The code goes like this: given argument n, we either return function(n), if already calculated, or calculate function(n) now and store it for later:

def memoize_wrapper(m):
    if not values.has_key(m):
        values[m] = fun(m)
    return values[m]

So we've got a small function that has access to values auxiliary structure. And we've got the structure itself, which is not defined in above code (therefore above code is not yet correct).

Now comes the main point in python decorators: a decorator is a callable (something you can call, a function in this example) that should return another callable (again, a function in this example). This may sound tricky, but we'll analyse it. Look at the code below:

# single-argument function decorator
def memoize(fun):
    values = {}
    def memoize_wrapper(m):
        if not values.has_key(m):
            values[m] = fun(m)
        return values[m]
    return memoize_wrapper

The memoize function is the decorator. It takes another function as an argument (could be fib function defined earlier). It also returns a wrapper that has access to the original function (via a closure). Additionally, all already-calculated values are stored in the values dictionary, which keys are fib arguments, and values are their corresponding values.

In fact, when using this decorator, we'll be directly using memoize_wrapper instead of fib function (which will be called inside the wrapper only if the function hasn't been run for a specific argument before). We'll somehow enclose our original function inside the wrapper, using the memoize function. So let's finish our job and decorate our fib function with memoize decorator:

@memoize
def fib(n):
    if n==0 or n==1:
        return n
    else:
        return fib(n-1) + fib(n-2)

I've just added the @memoize line just before the function declaration. That's how you decorate in python. It means that fib is decorated with memoize (no matter what they are, decorators can be any callable, e.g. classes).

After this code is executed, we don't have direct access to original function anymore. The fib function object points to the memoize_wrapper function object returned from memoize. This is because @ notation:

@memoize
def fib(n):
    [...]

is just a shortcut for following assignment:

def fib(n):
    [...]

fib = memoize(fib)

First, fib function is created. Then, it is passed to memoize and finally, the wrapper from within memoize is returned. Finally, we've got no access to the original function.

We may now reload the code and try calling fib for big values, even multiple times:

>>> fib(100)
354224848179261915075L
>>> fib(200)
280571172992510140037611932413038677189525L
>>> fib(300)
222232244629420445529739893461909967206666939096499764990979600L
>>> fib(400)
176023680645013966468226945392411250770384383304492191886725992896575345044216019675L
>>> fib(500)
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L

How long have you been waiting to get the results? Quick? Try out the following then:

>>> for i in range(1000):
...   fib(500)
... 
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L
[...]
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L

I'm pretty sure the work was done in less than a second. So that's the power of memoization.

To see the power of decorators, let's forget about python power operator ** and let's implement a variation of power function:

def power2(n):
    if n==0:
        return 1
    elif n==1:
        return 2
    else:
        return 2 * power2(n-1) + power2(n-2)

it's not original power of 2 just to make this function greedy. If we want to speed the function up, we can reuse our memoize decorator:

@memoize
def power2(n):
    if n==0:
        return 1
    elif n==1:
        return 2
    else:
        return 2 * power2(n-1) + power2(n-2)

to make it as fast as fib. As you can see, we implement the decorator only once and we can use it as much as we want to. And this is the power of decorators.

09 Mar 2014 8:00pm GMT

Decorators are powerful tools available in python (since version 2.4). They enable developers to add new functionalities to existing functions/classes in a very flexible way that is highly reusable. This is their strongest advantage. Decorator basics are quite easy, but when overused or overcomplicated, they can make the code realy difficult to understand, so you gotta find the proverbial golden mean here.

We'll start with the simplest possible example - just to work out the basics. Let's define a fib function that implements the Fibonacci sequence:

# our example function
def fib(n):
    if n==0 or n==1:
        return n
    else:
        return fib(n-1) + fib(n-2)

For a given non-negative argument n this function will return n-th number in the sequence, e.g.

>>> fib(0)
0
>>> fib(1)
1
>>> fib(2)
1
>>> fib(3)
2
>>> fib(10)
55

Above implementation is an example of a greedy algorithm which is, in short, something that is not going to work fast for big arguments or big data sets. This is because, at each step, 2 previous steps need to be executed no matter if they have already been executed. And that means that a great deal of same operations are going to be performed. To illustrate this behavior, try to run our function on bigger sets:

>>> fib(30)
832040
>>> fib(300)
222232244629420445529739893461909967206666939096499764990979600L

You've waited for the result for quite long, haven't you? This link explains that the complexity of above code is exponential (one of the worst algorithm complexities possible).

What can we do to speed up the calculation? For example, we can store a fib(n) result after it has been calculated for the first time. This technique is called memoization, and its main purpose is to avoid repetitive calculations. The code goes like this: given argument n, we either return function(n), if already calculated, or calculate function(n) now and store it for later:

def memoize_wrapper(m):
    if not values.has_key(m):
        values[m] = fun(m)
    return values[m]

So we've got a small function that has access to values auxiliary structure. And we've got the structure itself, which is not defined in above code (therefore above code is not yet correct).

Now comes the main point in python decorators: a decorator is a callable (something you can call, a function in this example) that should return another callable (again, a function in this example). This may sound tricky, but we'll analyse it. Look at the code below:

# single-argument function decorator
def memoize(fun):
    values = {}
    def memoize_wrapper(m):
        if not values.has_key(m):
            values[m] = fun(m)
        return values[m]
    return memoize_wrapper

The memoize function is the decorator. It takes another function as an argument (could be fib function defined earlier). It also returns a wrapper that has access to the original function (via a closure). Additionally, all already-calculated values are stored in the values dictionary, which keys are fib arguments, and values are their corresponding values.

In fact, when using this decorator, we'll be directly using memoize_wrapper instead of fib function (which will be called inside the wrapper only if the function hasn't been run for a specific argument before). We'll somehow enclose our original function inside the wrapper, using the memoize function. So let's finish our job and decorate our fib function with memoize decorator:

@memoize
def fib(n):
    if n==0 or n==1:
        return n
    else:
        return fib(n-1) + fib(n-2)

I've just added the @memoize line just before the function declaration. That's how you decorate in python. It means that fib is decorated with memoize (no matter what they are, decorators can be any callable, e.g. classes).

After this code is executed, we don't have direct access to original function anymore. The fib function object points to the memoize_wrapper function object returned from memoize. This is because @ notation:

@memoize
def fib(n):
    [...]

is just a shortcut for following assignment:

def fib(n):
    [...]

fib = memoize(fib)

First, fib function is created. Then, it is passed to memoize and finally, the wrapper from within memoize is returned. Finally, we've got no access to the original function.

We may now reload the code and try calling fib for big values, even multiple times:

>>> fib(100)
354224848179261915075L
>>> fib(200)
280571172992510140037611932413038677189525L
>>> fib(300)
222232244629420445529739893461909967206666939096499764990979600L
>>> fib(400)
176023680645013966468226945392411250770384383304492191886725992896575345044216019675L
>>> fib(500)
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L

How long have you been waiting to get the results? Quick? Try out the following then:

>>> for i in range(1000):
...   fib(500)
... 
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L
[...]
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L

I'm pretty sure the work was done in less than a second. So that's the power of memoization.

To see the power of decorators, let's forget about python power operator ** and let's implement a variation of power function:

def power2(n):
    if n==0:
        return 1
    elif n==1:
        return 2
    else:
        return 2 * power2(n-1) + power2(n-2)

it's not original power of 2 just to make this function greedy. If we want to speed the function up, we can reuse our memoize decorator:

@memoize
def power2(n):
    if n==0:
        return 1
    elif n==1:
        return 2
    else:
        return 2 * power2(n-1) + power2(n-2)

to make it as fast as fib. As you can see, we implement the decorator only once and we can use it as much as we want to. And this is the power of decorators.

09 Mar 2014 8:00pm GMT

Here's my chart of the month (made using Python). This is a variant of an older post that I cleaned up. I really like the way this turned out. Simple and elegant.

09 Mar 2014 3:00pm GMT

Here's my chart of the month (made using Python). This is a variant of an older post that I cleaned up. I really like the way this turned out. Simple and elegant.

09 Mar 2014 3:00pm GMT

I meant to note this a week or so back - some of the core PyPy dev team (Fijal and Armin) have put together a new consultancy focused on PyPy commercial support. The move was warmly received on reddit. They aim to provide training, tuning and custom work and have a team in various bits of the world. I look forward to watching this develop.

Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

09 Mar 2014 11:52am GMT

I meant to note this a week or so back - some of the core PyPy dev team (Fijal and Armin) have put together a new consultancy focused on PyPy commercial support. The move was warmly received on reddit. They aim to provide training, tuning and custom work and have a team in various bits of the world. I look forward to watching this develop.

Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

09 Mar 2014 11:52am GMT

We've just drawn PyDataLondon 2014 to a close, it has been a wonderfully successful weekend. The growth of Python's use for data science in the last few years here in the UK is pretty phenomenal. Many thanks to Continuum Analytics and NumFocus for backing and organising the PyData conferences.

"Start of the week after busy weekend attending 1st #PyData London. Thanks to organisers & speakers: a smashing set of talks & gr8 community." @lindauruchurtu

"If it weren't for the succession of great talks at #pydata Ldn, I'd be getting quite upset about the AUS v SA test. Thank you @PyDataConf" @davisjmcc

We've had a fab weekend and a packed schedule with training and varied talks including:

• Two great keynotes (Deutsche Börse finance by Felix Fernandez and 'big data' brain research on a budget by Gael Varoquaux)
• Machine learning (mostly scikit-learn) and text processing
• Lots of visualisation (mostly javascript)
• Practical discussion of how and why things work (including some hard-won lessons on processes and statistics) and strong lessons on mistakes to avoid
• Art and economics
• Lots of IPython Notebook
• Some R and Matlab (we need more of this!)
• Great lightning talks including live rocket-science-in-the-Notebook to close the weekend
• Speakers from both industry (including BAE, the Met Office and Hedge Funds through to fresh startups) and academia stretching through Europe

One outcome from Gael's keynote was the importance of citing the open source projects that get used to help highlight their need for funding and resources:

"next time you write an article that uses scikit-learn and friends, cite the software you use, that will help authors, eg get funding #pydata" - @dimazest

I ran a panel asking "Shouldn't more companies be using data science?" - the deliberately loaded question was addressed by a a range of industrial representatives including James from New York, Jonathan, Johnny, Dirk, Ian and Philip. The short answer seemed to be that more companies were taking risks (and winning the rewards) of analysing their data and that some more training (both for scientists and for managers) could help things along.

"#PyData panel first question: have you done anything data analysis related within the last six month? (half the room raises hands)" James Powell

Through my Mor Consulting I talked on The Landscape of High Performance Python by taking a look at profiling techniques and compiler options for single-machine multi-core speed-ups, obviously this is somewhat connected to the High Performance Python book I'm working on (hopefully an early release of the first chapters will be out shortly).

"Like @ianozsvald 's 'team velocity' to describe how clean slow code can be better than complex fast code in terms of team development" - Mark Basham of Diamond Synchrotron

Renowned Brightonian artist Eric Drass spoke on the confluence of art, mass data, surveillance, the redaction of political positions (and how nothing is ever really removed from the internet - AlgoCameron) and Hugh Hefner:

Martin Goodson's "Most Winning A/B Test Results are Illusory" talk has hit HackerNews with good discussion via his published paper.

(reformed string-theorist) Linda spoke on trying #sklearn as an avid R user for music recommendation, highlighting some of the highs and lows of both toolsets (and noting the sillyness of the 'language wars'):

My colleague Bart Baddeley discussed problems and solutions in clustering approaches, IPython Notebook with all examples available online:

"Similarity matrices are a neat way of eye-balling whether you've chosen the right number of clusters #pydata" - Hugo Carr

Kyran Dale (my co-founder from ShowMeDo and StrongSteam) spoke on powering javascript from live Python servers using techniques such as web sockets to visualise robot brain controllers and UK weather patterns:

Neri covered NLP and ML using NLTK and scikitlearn for real-time customer support at Conversocial (a successful London customer support startup):

Philippe Bracke spoke on house price rents and yields, modelled during his PhD:

"Interesting conclusion from @PhilippeBracke #pydata you earn less money from renting more expensive properties" - Ian Taylor

SkimLinks sponsored a fun Saturday party (they're hiring!). The conference series is generously sponsored by Continuum Analytics (it all started in the USA - hello Bryan!) and supported through the non-profit NumFocus organisation (and Leah does a rather ace job of pulling all the loose strands into a cohesive whole!).

Level39 in Canary Wharf provided the venue. Additional sponsors include Lyst who are hiring (hi Seb!), Python Academy (hello Mike!), Python Software Foundation, Knowsis, DataRobot (hello Jeremy and Peter!), Python Weekly and O'Reilly.

The view from Level39 was rather nice (their space is ace - visit it if you get a chance - thanking Jacqui for the photo):

Clearly we have a strong base here to build from for future conferences. EuroSciPy 2014 (Cambridge, August) was discussed and PyDataBerlin was announced, it'll happen in conjunction with EuroPython (July, Berlin). I'll be at all three.

More write-ups are available:

• DataRobot
• Continuum's by Francesc (one of our speakers)
• Ian Huston
• Ian's tweet collection (with many images)
• Florian Rathberger
• SandTable (London based consultancy)

For future events we'll have to work on female attendance (I counted 10% - this surely can be improved), we also want more interdisciplinary talks (we had some R and Matlab - we need more languages and other approaches). Overall I'm super happy with the outcome, we organised this in under two months, we got a fab turn-out and a stellar set of speakers (from nearby, throughout Europe and out to the USA). The next event can only be stronger still.

We collected slides and everything was recorded, videos will hopefully be up in a week.

I thank the organising team - Leah (NumFocus) kept us all on track, Emlyn, Cecilia, Florian, Yves and James here and our past-PyData American supporters all kept things moving in what appeared to be a rather effortless way. It wouldn't have worked without everyone's support including all the custodians of local usergroups who kindly spread the word - many thanks to you all.

Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

09 Mar 2014 11:46am GMT

We've just drawn PyDataLondon 2014 to a close, it has been a wonderfully successful weekend. The growth of Python's use for data science in the last few years here in the UK is pretty phenomenal. Many thanks to Continuum Analytics and NumFocus for backing and organising the PyData conferences.

"Start of the week after busy weekend attending 1st #PyData London. Thanks to organisers & speakers: a smashing set of talks & gr8 community." @lindauruchurtu

"If it weren't for the succession of great talks at #pydata Ldn, I'd be getting quite upset about the AUS v SA test. Thank you @PyDataConf" @davisjmcc

We've had a fab weekend and a packed schedule with training and varied talks including:

• Two great keynotes (Deutsche Börse finance by Felix Fernandez and 'big data' brain research on a budget by Gael Varoquaux)
• Machine learning (mostly scikit-learn) and text processing
• Lots of visualisation (mostly javascript)
• Practical discussion of how and why things work (including some hard-won lessons on processes and statistics) and strong lessons on mistakes to avoid
• Art and economics
• Lots of IPython Notebook
• Some R and Matlab (we need more of this!)
• Great lightning talks including live rocket-science-in-the-Notebook to close the weekend
• Speakers from both industry (including BAE, the Met Office and Hedge Funds through to fresh startups) and academia stretching through Europe

One outcome from Gael's keynote was the importance of citing the open source projects that get used to help highlight their need for funding and resources:

"next time you write an article that uses scikit-learn and friends, cite the software you use, that will help authors, eg get funding #pydata" - @dimazest

I ran a panel asking "Shouldn't more companies be using data science?" - the deliberately loaded question was addressed by a a range of industrial representatives including James from New York, Jonathan, Johnny, Dirk, Ian and Philip. The short answer seemed to be that more companies were taking risks (and winning the rewards) of analysing their data and that some more training (both for scientists and for managers) could help things along.

"#PyData panel first question: have you done anything data analysis related within the last six month? (half the room raises hands)" James Powell

Through my Mor Consulting I talked on The Landscape of High Performance Python by taking a look at profiling techniques and compiler options for single-machine multi-core speed-ups, obviously this is somewhat connected to the High Performance Python book I'm working on (hopefully an early release of the first chapters will be out shortly).

"Like @ianozsvald 's 'team velocity' to describe how clean slow code can be better than complex fast code in terms of team development" - Mark Basham of Diamond Synchrotron

Renowned Brightonian artist Eric Drass spoke on the confluence of art, mass data, surveillance, the redaction of political positions (and how nothing is ever really removed from the internet - AlgoCameron) and Hugh Hefner:

Martin Goodson's "Most Winning A/B Test Results are Illusory" talk has hit HackerNews with good discussion via his published paper.

(reformed string-theorist) Linda spoke on trying #sklearn as an avid R user for music recommendation, highlighting some of the highs and lows of both toolsets (and noting the sillyness of the 'language wars'):

My colleague Bart Baddeley discussed problems and solutions in clustering approaches, IPython Notebook with all examples available online:

"Similarity matrices are a neat way of eye-balling whether you've chosen the right number of clusters #pydata" - Hugo Carr

Kyran Dale (my co-founder from ShowMeDo and StrongSteam) spoke on powering javascript from live Python servers using techniques such as web sockets to visualise robot brain controllers and UK weather patterns:

Neri covered NLP and ML using NLTK and scikitlearn for real-time customer support at Conversocial (a successful London customer support startup):

Philippe Bracke spoke on house price rents and yields, modelled during his PhD:

"Interesting conclusion from @PhilippeBracke #pydata you earn less money from renting more expensive properties" - Ian Taylor

SkimLinks sponsored a fun Saturday party (they're hiring!). The conference series is generously sponsored by Continuum Analytics (it all started in the USA - hello Bryan!) and supported through the non-profit NumFocus organisation (and Leah does a rather ace job of pulling all the loose strands into a cohesive whole!).

Level39 in Canary Wharf provided the venue. Additional sponsors include Lyst who are hiring (hi Seb!), Python Academy (hello Mike!), Python Software Foundation, Knowsis, DataRobot (hello Jeremy and Peter!), Python Weekly and O'Reilly.

The view from Level39 was rather nice (their space is ace - visit it if you get a chance - thanking Jacqui for the photo):

Clearly we have a strong base here to build from for future conferences. EuroSciPy 2014 (Cambridge, August) was discussed and PyDataBerlin was announced, it'll happen in conjunction with EuroPython (July, Berlin). I'll be at all three.

More write-ups are available:

• DataRobot
• Continuum's by Francesc (one of our speakers)
• Ian Huston
• Ian's tweet collection (with many images)
• Florian Rathberger
• SandTable (London based consultancy)

For future events we'll have to work on female attendance (I counted 10% - this surely can be improved), we also want more interdisciplinary talks (we had some R and Matlab - we need more languages and other approaches). Overall I'm super happy with the outcome, we organised this in under two months, we got a fab turn-out and a stellar set of speakers (from nearby, throughout Europe and out to the USA). The next event can only be stronger still.

We collected slides and everything was recorded, videos will hopefully be up in a week.

I thank the organising team - Leah (NumFocus) kept us all on track, Emlyn, Cecilia, Florian, Yves and James here and our past-PyData American supporters all kept things moving in what appeared to be a rather effortless way. It wouldn't have worked without everyone's support including all the custodians of local usergroups who kindly spread the word - many thanks to you all.

Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

09 Mar 2014 11:46am GMT

The subset of Twisted that has been ported to Python 3 can now be pip installed. By either pointing at a version control URL or requiring Twisted 14.0 (once it's released), you can now have Twisted as a dependency for your Python 3 packages.

Here's a slightly edited version of my Travis-CI config for Crochet, demonstrating how I run unit tests on both Python 2 and Python 3 versions of Twisted (slightly tricky because the trial test runner hasn't been ported yet):

language: python

env:
  - TWISTED=Twisted==13.0 RUNTESTS=trial
  - TWISTED=Twisted==13.1 RUNTESTS=trial
  - TWISTED=Twisted==13.2 RUNTESTS=trial

python:
  - 2.6
  - 2.7
  - pypy

matrix:
  include:
    - python: 3.3
      env: TWISTED=git+https://github.com/twisted/twisted.git RUNTESTS="python -m unittest discover"

install:
  - pip install -q --no-use-wheel $TWISTED --use-mirrors
  - python setup.py -q install

script: $RUNTESTS crochet.tests

09 Mar 2014 11:27am GMT

The subset of Twisted that has been ported to Python 3 can now be pip installed. By either pointing at a version control URL or requiring Twisted 14.0 (once it's released), you can now have Twisted as a dependency for your Python 3 packages.

Here's a slightly edited version of my Travis-CI config for Crochet, demonstrating how I run unit tests on both Python 2 and Python 3 versions of Twisted (slightly tricky because the trial test runner hasn't been ported yet):

language: python

env:
  - TWISTED=Twisted==13.0 RUNTESTS=trial
  - TWISTED=Twisted==13.1 RUNTESTS=trial
  - TWISTED=Twisted==13.2 RUNTESTS=trial

python:
  - 2.6
  - 2.7
  - pypy

matrix:
  include:
    - python: 3.3
      env: TWISTED=git+https://github.com/twisted/twisted.git RUNTESTS="python -m unittest discover"

install:
  - pip install -q --no-use-wheel $TWISTED --use-mirrors
  - python setup.py -q install

script: $RUNTESTS crochet.tests

09 Mar 2014 11:27am GMT

PythonAnywhere simple and unique online working environment

My first series in this blog was about Python Cloud Computing: developing Flask application and using Openshift to properly manage and deploy it. This is a series that bring another possibility: why don't I complete this blog with practical hands-on using another Python Cloud Computing solution? The internet is filled with this Platform as a Service solution. Some specifically state that they are Python only solution, other are much more general and open in nature. Having practical hands-on on the usage of some of them will add another advantage to this blog. Although, I am not sure that I can dive to all of the existing Cloud Computing/PaaS solution. For the most part, I will be unable to explore at the fullest on cloud solution that require actual billing to use important features such as database access, e.g Google Cloud or Amazon Web Services. In this regard, I really love Openshift way of attracting developers to use their platform: a 1GB storage including user uploaded files and database. I don't think there are other PaaS solution out there that can surpass this free offering.

Well then, lets start at our first stop : PythonAnywhere.

Introducing PythonAnywhere

One striking different between PythonAnywhere and the usual Python Cloud Computing solution that we know of, is that you can totally work on it online using internet browser in developing your Python application. With this, you can bypass the usual delicacies on preparing a local workstation that meet cloud hosting service environment requirement and directly work inside your browser that connected to many consoles provided by Pythonanywhere, such as : Bash, Python/iPython 2.6/2.7/3.3 and MySQL. Want to edit your Python files using ViM through Bash console runs from within browser? No problem. Or simply edit your applications using Pythonanywhere provided editor runs in a browser? They got that for you!

I can predict that some of you may look at this PythonAnywhere online feature with skepticism : "It's always more enjoyable working in a full desktop environment! Why do I want to do all my work inside a browser?" Well, for example, with a great internet connection and a 10" iPad/Galaxy Tab, things can become very interesting for a traveling freelancer. And if you do need to work locally, it's also supported. It has interesting method of synching your application using DropBox (but I can't test it currently. PythonAnywere gives an error message about its current unavailability of using DropBox). Or, you can always git push your local application code into a remote repository, and then git pull it from within PythonAnywere bash console. As this method can be use in a free/basic package, I may not exaggerating too much when I said that working with Pythonanywere is a pleasure.

Running Openshift's Flask Bio Application in Pythonanywhere

To demonstrate its feature, lets run our previously crafted Openshift's Flask Bio Application into Pythonanywhere basic/free package. The availability of a working free plan, --or in other words, not a useless limited features plan--, is a good sign of a service that welcomed the Open Source community (e.g try to compare the free plan in Openshift and Heroku, and you get the idea). We will have a look whether it is reasonably easy to do this migration. But for a start, you can go ahead and signup for a PythonAnywhere free plan. The rest of this article will discuss how to use PythonAnywhere using this free plan.

Prepare a New Web Application

With a free plan, you will get one domain using this name pattern : yourusername.pythonanywhere.com. Currently only web developer plan ($12/month) and above will have the ability to use custom domain. Once login, go to your dashboard and click the Web tab. Click Add a new web app, which will open a wizard dialog that will guide you on the creation of your new Python web application.

A new web application using your username as the subdomain

I find it to be slick and simple, but yet an effective interface. It'll prompt you with the framework selection dialog, that beside Django and Flask, also offers several popular Python web frameworks. Pick Flask as the web framework.

I think Pylon/Pyramid should be on those list

And Python 2.7/Python 3.3 as your Python environment. I am still choosing Python 2.7 by the way..

Your choice of Python version

And finally, the only configuration page for Flask..

This is perfect : flask application in a single *.py file

Done.

If you open the default Flask application created, you will see this source code in flask_app.py:

1
2
3
4
5
6
7

from flask import Flask
 
app = Flask(__name__)
 
@app.route('/')
def hello_world():
    return 'Hello from Flask!'

Just returning a nice welcome message.

And if you inspects its WSGI startup file in (my configuration) /var/www/swdev_pythonanywhere_com_wsgi.py, you will see this source code:

1
2
3
4
5
6
7
8
9

import sys
 
# add your project directory to the sys.path
project_home = u'/home/swdev/mysite'
if project_home not in sys.path:
    sys.path = [project_home] + sys.path
 
# import flask app but need to call it "application" for WSGI to work
from flask_app import app as application

This is important. As this will be the place where we will configured our startup application

Cloned Existing Repository

We already have our Flask Biography application hosted in Openshift at this address, and its code also pushed in this public github repository: github.com/pythonthusiast/bio. So, lets just clone this github repository using Pythonanywhere bash console. Click Consoles tab and choose Bash console from the available consoles:

All the available consoles. In free plan, you will have access to only two consoles (I believe that's enough in most cases)

As we are going to use Pythonanywhere online editing features to edit this application (and hereby git add/commit/push it to origin repository), we will have to register this machine as the legitimate author for the aforementioned Github repository. Inside the console issue the command : ssh-keygen, afterward, add the content of the file ~/.ssh/id_rsa.pub into your own Github Account's SSH keys. You will have to forked first this repository into your own Github repository.

Cloned existing repository after registering this machine public SSH key

The above cloned repository is having an Openshift custom directory structure, saved in ~/bio directory. Whereas due to Pythonanywhere default WSGI startup file, our Pythonanywere site is stored in ~/mysite. Lets keep our Openshift's ~/bio directory structure intact, and lets create symbolic links for required resources from within ~/bio directory into ~/mysite. After several trial and error, this is all symbolic links that needed:

For the unaware, from within ~/mysite directory use the command ln -s <target file> to create the required symbolic links

Database Configuration

It feels like the infamous LAMP stack, where you already have a MySQL database ready to use. Judging by their FAQ, it seems like currently MySQL and SQLite are the only database supported in PythonAnywhere. The default database having the name pattern of <username>$default. It seems like there is no limit of how many database you can create in a free plan. The only restriction is your 500mb space quota. An important task is to your database protected with password using the Database tab.

In our Flask Bio application, we only use single wsgi/main.py file, even for its database configuration. This is okay in the previous Openshift application, as the database URL come from Openshift's environment variable. But how do we do this in PythonAnywhere? Although I have added this new environment variable in ~/.profile:

1

export LOCAL_DB_URL='mysql://<user>:<password>@mysql.server/<databasename>'

The above environment variable won't get detected inside our Python Flask application. The only known work around is to do this from within our WSGI startup file. Here is the modified WSGI startup file:

1
2
3
4
5
6
7
8
9

import sys, os
os.environ['LOCAL_DB_URL']='mysql://<user>:<password>@mysql.server/<databasename>'
# add your project directory to the sys.path
project_home = u'/home/swdev/mysite'
if project_home not in sys.path:
    sys.path = [project_home] + sys.path
 
# import flask app but need to call it "application" for WSGI to work
from flask_app import application

Observe the changes there, where we simply import our Flask application using the name application, because we are using the name application, not app. Our last modification would be to modify wsgi/main.py, this way:

1

application.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('OPENSHIFT_POSTGRESQL_DB_URL') if os.environ.get('OPENSHIFT_POSTGRESQL_DB_URL') else os.environ.get('LOCAL_DB_URL')

As everything already configured and ready, now we have to construct our database by simply running our mysite/flask_app.py file, by issuing this command: ~/mysite$ python flask_app.py. Don't forget that, it will only run successfully if you have already add LOCAL_DB_URL in ~/.profile., and run source ~/.profile to let the environment variable active. The __main__ code of this flask_app.py file will simply drop and recreate the database, making the database readily available for use.

Finally, don't forget to reload your web application and try accessing your application using the URL pattern http://<username>.pythonanywhere.com. In may case, you can open it here.

You have to reload the database for each source code changes

Conclusion

Notice anything unique in our hands-on using Pythonanywhere? No local working environment setup. Pretty interesting, right? Although we are using the already working Flask application, this doesn't stop us to completely work this application in online mode using only internet browser. Actually, this is not a unique thing anymore. Using cloud9.io IDE, you can work fully online using internet browser and have your application hosted in Heroku or Openshift. But as it is a third party IDE, you may or may not like it. The interesting point is, PythonAnywhere was designed to be use fully online using internet browser.

If your solution only use Python, PythonAnywhere brings great advantage to you : as it is a PaaS solution that locked down its technology using only Python. No setup required in your part. Just create an account, login and start working. PythonAnywhere really bring the term Anywhere in Python.

Stay tuned for my next Python Cloud Computing article!

09 Mar 2014 6:55am GMT

PythonAnywhere simple and unique online working environment

My first series in this blog was about Python Cloud Computing: developing Flask application and using Openshift to properly manage and deploy it. This is a series that bring another possibility: why don't I complete this blog with practical hands-on using another Python Cloud Computing solution? The internet is filled with this Platform as a Service solution. Some specifically state that they are Python only solution, other are much more general and open in nature. Having practical hands-on on the usage of some of them will add another advantage to this blog. Although, I am not sure that I can dive to all of the existing Cloud Computing/PaaS solution. For the most part, I will be unable to explore at the fullest on cloud solution that require actual billing to use important features such as database access, e.g Google Cloud or Amazon Web Services. In this regard, I really love Openshift way of attracting developers to use their platform: a 1GB storage including user uploaded files and database. I don't think there are other PaaS solution out there that can surpass this free offering.

Well then, lets start at our first stop : PythonAnywhere.

Introducing PythonAnywhere

One striking different between PythonAnywhere and the usual Python Cloud Computing solution that we know of, is that you can totally work on it online using internet browser in developing your Python application. With this, you can bypass the usual delicacies on preparing a local workstation that meet cloud hosting service environment requirement and directly work inside your browser that connected to many consoles provided by Pythonanywhere, such as : Bash, Python/iPython 2.6/2.7/3.3 and MySQL. Want to edit your Python files using ViM through Bash console runs from within browser? No problem. Or simply edit your applications using Pythonanywhere provided editor runs in a browser? They got that for you!

I can predict that some of you may look at this PythonAnywhere online feature with skepticism : "It's always more enjoyable working in a full desktop environment! Why do I want to do all my work inside a browser?" Well, for example, with a great internet connection and a 10" iPad/Galaxy Tab, things can become very interesting for a traveling freelancer. And if you do need to work locally, it's also supported. It has interesting method of synching your application using DropBox (but I can't test it currently. PythonAnywere gives an error message about its current unavailability of using DropBox). Or, you can always git push your local application code into a remote repository, and then git pull it from within PythonAnywere bash console. As this method can be use in a free/basic package, I may not exaggerating too much when I said that working with Pythonanywere is a pleasure.

Running Openshift's Flask Bio Application in Pythonanywhere

To demonstrate its feature, lets run our previously crafted Openshift's Flask Bio Application into Pythonanywhere basic/free package. The availability of a working free plan, --or in other words, not a useless limited features plan--, is a good sign of a service that welcomed the Open Source community (e.g try to compare the free plan in Openshift and Heroku, and you get the idea). We will have a look whether it is reasonably easy to do this migration. But for a start, you can go ahead and signup for a PythonAnywhere free plan. The rest of this article will discuss how to use PythonAnywhere using this free plan.

Prepare a New Web Application

With a free plan, you will get one domain using this name pattern : yourusername.pythonanywhere.com. Currently only web developer plan ($12/month) and above will have the ability to use custom domain. Once login, go to your dashboard and click the Web tab. Click Add a new web app, which will open a wizard dialog that will guide you on the creation of your new Python web application.

A new web application using your username as the subdomain

I find it to be slick and simple, but yet an effective interface. It'll prompt you with the framework selection dialog, that beside Django and Flask, also offers several popular Python web frameworks. Pick Flask as the web framework.

I think Pylon/Pyramid should be on those list

And Python 2.7/Python 3.3 as your Python environment. I am still choosing Python 2.7 by the way..

Your choice of Python version

And finally, the only configuration page for Flask..

This is perfect : flask application in a single *.py file

Done.

If you open the default Flask application created, you will see this source code in flask_app.py:

1
2
3
4
5
6
7

from flask import Flask
 
app = Flask(__name__)
 
@app.route('/')
def hello_world():
    return 'Hello from Flask!'

Just returning a nice welcome message.

And if you inspects its WSGI startup file in (my configuration) /var/www/swdev_pythonanywhere_com_wsgi.py, you will see this source code:

1
2
3
4
5
6
7
8
9

import sys
 
# add your project directory to the sys.path
project_home = u'/home/swdev/mysite'
if project_home not in sys.path:
    sys.path = [project_home] + sys.path
 
# import flask app but need to call it "application" for WSGI to work
from flask_app import app as application

This is important. As this will be the place where we will configured our startup application

Cloned Existing Repository

We already have our Flask Biography application hosted in Openshift at this address, and its code also pushed in this public github repository: github.com/pythonthusiast/bio. So, lets just clone this github repository using Pythonanywhere bash console. Click Consoles tab and choose Bash console from the available consoles:

All the available consoles. In free plan, you will have access to only two consoles (I believe that's enough in most cases)

As we are going to use Pythonanywhere online editing features to edit this application (and hereby git add/commit/push it to origin repository), we will have to register this machine as the legitimate author for the aforementioned Github repository. Inside the console issue the command : ssh-keygen, afterward, add the content of the file ~/.ssh/id_rsa.pub into your own Github Account's SSH keys. You will have to forked first this repository into your own Github repository.

Cloned existing repository after registering this machine public SSH key

The above cloned repository is having an Openshift custom directory structure, saved in ~/bio directory. Whereas due to Pythonanywhere default WSGI startup file, our Pythonanywere site is stored in ~/mysite. Lets keep our Openshift's ~/bio directory structure intact, and lets create symbolic links for required resources from within ~/bio directory into ~/mysite. After several trial and error, this is all symbolic links that needed:

For the unaware, from within ~/mysite directory use the command ln -s <target file> to create the required symbolic links

Database Configuration

It feels like the infamous LAMP stack, where you already have a MySQL database ready to use. Judging by their FAQ, it seems like currently MySQL and SQLite are the only database supported in PythonAnywhere. The default database having the name pattern of <username>$default. It seems like there is no limit of how many database you can create in a free plan. The only restriction is your 500mb space quota. An important task is to your database protected with password using the Database tab.

In our Flask Bio application, we only use single wsgi/main.py file, even for its database configuration. This is okay in the previous Openshift application, as the database URL come from Openshift's environment variable. But how do we do this in PythonAnywhere? Although I have added this new environment variable in ~/.profile:

1

export LOCAL_DB_URL='mysql://<user>:<password>@mysql.server/<databasename>'

The above environment variable won't get detected inside our Python Flask application. The only known work around is to do this from within our WSGI startup file. Here is the modified WSGI startup file:

1
2
3
4
5
6
7
8
9

import sys, os
os.environ['LOCAL_DB_URL']='mysql://<user>:<password>@mysql.server/<databasename>'
# add your project directory to the sys.path
project_home = u'/home/swdev/mysite'
if project_home not in sys.path:
    sys.path = [project_home] + sys.path
 
# import flask app but need to call it "application" for WSGI to work
from flask_app import application

Observe the changes there, where we simply import our Flask application using the name application, because we are using the name application, not app. Our last modification would be to modify wsgi/main.py, this way:

1

application.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('OPENSHIFT_POSTGRESQL_DB_URL') if os.environ.get('OPENSHIFT_POSTGRESQL_DB_URL') else os.environ.get('LOCAL_DB_URL')

As everything already configured and ready, now we have to construct our database by simply running our mysite/flask_app.py file, by issuing this command: ~/mysite$ python flask_app.py. Don't forget that, it will only run successfully if you have already add LOCAL_DB_URL in ~/.profile., and run source ~/.profile to let the environment variable active. The __main__ code of this flask_app.py file will simply drop and recreate the database, making the database readily available for use.

Finally, don't forget to reload your web application and try accessing your application using the URL pattern http://<username>.pythonanywhere.com. In may case, you can open it here.

You have to reload the database for each source code changes

Conclusion

Notice anything unique in our hands-on using Pythonanywhere? No local working environment setup. Pretty interesting, right? Although we are using the already working Flask application, this doesn't stop us to completely work this application in online mode using only internet browser. Actually, this is not a unique thing anymore. Using cloud9.io IDE, you can work fully online using internet browser and have your application hosted in Heroku or Openshift. But as it is a third party IDE, you may or may not like it. The interesting point is, PythonAnywhere was designed to be use fully online using internet browser.

If your solution only use Python, PythonAnywhere brings great advantage to you : as it is a PaaS solution that locked down its technology using only Python. No setup required in your part. Just create an account, login and start working. PythonAnywhere really bring the term Anywhere in Python.

Stay tuned for my next Python Cloud Computing article!

09 Mar 2014 6:55am GMT

Win Free Copies of new book - Tkinter GUI Application Development Hotshot

Hi guys! Welcome back. As I recently told you that I will be holding a giveaway, the day has finally come. You will be pleased to know that I have teamed up with Packt Publishing to organize a Giveaway of the new book that I have reviewed - Tkinter GUI Application Development Hotshot

And three lucky winners stand a chance to win 3 digital copies of this book. Keep reading to find out how you can be one of the Lucky Winners.

Overview:

• Structure your programs in the model-view framework
• Persist your application data with object serialization
• Work with external libraries and Tkinter extensions
• Write multi-threaded GUI programs
• Re-factor code at every stage of application development
• Integrate your GUI applications to backend database
• Use networking with your Tkinter program
• Apply Internationalization to your GUI applications
• Develop a GUI program framework for maximum code reuse and rapid application development

How to Enter?

All you need to do is head on over to the book page and look through the product description of the book and drop a line via the comments below this post to let me know what interests you the most about this book. Secondly you have to share this post on Twitter and Facebook. It's that simple. Winners will get an e-copy of the Book.

Deadline: The contest will close on 22nd March 2014. Winners will be contacted by email, so be sure to use your real email address when you comment!

08 Mar 2014 4:28pm GMT

Win Free Copies of new book - Tkinter GUI Application Development Hotshot

Hi guys! Welcome back. As I recently told you that I will be holding a giveaway, the day has finally come. You will be pleased to know that I have teamed up with Packt Publishing to organize a Giveaway of the new book that I have reviewed - Tkinter GUI Application Development Hotshot

And three lucky winners stand a chance to win 3 digital copies of this book. Keep reading to find out how you can be one of the Lucky Winners.

Overview:

• Structure your programs in the model-view framework
• Persist your application data with object serialization
• Work with external libraries and Tkinter extensions
• Write multi-threaded GUI programs
• Re-factor code at every stage of application development
• Integrate your GUI applications to backend database
• Use networking with your Tkinter program
• Apply Internationalization to your GUI applications
• Develop a GUI program framework for maximum code reuse and rapid application development

How to Enter?

All you need to do is head on over to the book page and look through the product description of the book and drop a line via the comments below this post to let me know what interests you the most about this book. Secondly you have to share this post on Twitter and Facebook. It's that simple. Winners will get an e-copy of the Book.

Deadline: The contest will close on 22nd March 2014. Winners will be contacted by email, so be sure to use your real email address when you comment!

08 Mar 2014 4:28pm GMT

The IPython project began in 2001 as Fernando Pérez's side project to make a better interactive Python interpreter. In the subsequent 11 years it has grown into what's widely considered one of the...

08 Mar 2014 4:10pm GMT

The IPython project began in 2001 as Fernando Pérez's side project to make a better interactive Python interpreter. In the subsequent 11 years it has grown into what's widely considered one of the...

08 Mar 2014 4:10pm GMT

Good tech lead, bad tech lead. Can we please please stop telling people that coding is easy? Confessions of an Intermediate Programmer. The perception and psychology of competence. The Science of Making your Story Memorable Some interesting advice about presentations. The presentation itself used as example is interesting as well. Thirty percent feedback to iterate faster. The […]

08 Mar 2014 10:55am GMT

Good tech lead, bad tech lead. Can we please please stop telling people that coding is easy? Confessions of an Intermediate Programmer. The perception and psychology of competence. The Science of Making your Story Memorable Some interesting advice about presentations. The presentation itself used as example is interesting as well. Thirty percent feedback to iterate faster. The […]

08 Mar 2014 10:55am GMT

Machine learning (ML) teaches machines how to carry out tasks by themselves. It is that simple. The answer is No. This article will give you a broad overview of the types of learning algorithms...

08 Mar 2014 8:45am GMT

Machine learning (ML) teaches machines how to carry out tasks by themselves. It is that simple. The answer is No. This article will give you a broad overview of the types of learning algorithms...

08 Mar 2014 8:45am GMT

08 Mar 2014 3:21am GMT

08 Mar 2014 3:21am GMT

By Vasudev Ram



I came across the html5lib Python library recently. The site describes it thusly:

"html5lib is a pure-python library for parsing HTML. It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers."

So it doesn't say explicitly that it is for parsing HTML5, though the library name includes "5" in its name. But I tried it out on a simple HTML5 document and it seems to be able to parse HTML5 - at least the few HTML5 elements I tried it on.

Here's the code I used to try out html5lib:

# test_html5lib.py
# A program to try out the html5lib Python library.
# Author: Vasudev Ram - www.dancingbison.com
import html5lib

f = open("html5doc.html")
tree = html5lib.parse(f)
print "tree:"
print repr(tree)
print
print "items in tree:"

for item in tree:
    print item
    for item2 in item:
        print "-" * 4, item2
        for item3 in item2:
            print "-" * 8, item3

And here is the output of running python test_html5lib.py:

<Element u'{http://www.w3.org/1999/xhtml}head' at 0x02B663C8>
<Element u'{http://www.w3.org/1999/xhtml}body' at 0x02B66488>
---- <Element u'{http://www.w3.org/1999/xhtml}header' at 0x02B664B8>
-------- <Element u'{http://www.w3.org/1999/xhtml}h1' at 0x02B66530>
-------- <Element u'{http://www.w3.org/1999/xhtml}h2' at 0x02B664E8>
-------- <Element u'{http://www.w3.org/1999/xhtml}h3' at 0x02B665F0>
---- <Element u'{http://www.w3.org/1999/xhtml}p' at 0x02B66650>
---- <Element u'{http://www.w3.org/2000/svg}svg' at 0x02B66BC0>
-------- <Element u'{http://www.w3.org/2000/svg}defs' at 0x02B66B60>
-------- <Element u'{http://www.w3.org/2000/svg}rect' at 0x02B66B30>
-------- <Element u'{http://www.w3.org/2000/svg}text' at 0x02B66BD8>
---- <Element u'{http://www.w3.org/1999/xhtml}footer' at 0x02B66BF0>

Here is the documentation for html5lib.

And speaking of HTML5, coincidentally, I came across Animatron via Hacker News, today:

Animatron is "a simple and powerful online tool that allows you to create stunning HTML5 animations and interactive content." Animatron is not really related to html5lib, except for the fact that both of them are about HTML5, but it looks cool. Check it out.

Hacker News thread about Animatron.

Enjoy.


- Vasudev Ram - Dancing Bison Enterprises

Contact Page

08 Mar 2014 2:14am GMT

By Vasudev Ram



I came across the html5lib Python library recently. The site describes it thusly:

"html5lib is a pure-python library for parsing HTML. It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers."

So it doesn't say explicitly that it is for parsing HTML5, though the library name includes "5" in its name. But I tried it out on a simple HTML5 document and it seems to be able to parse HTML5 - at least the few HTML5 elements I tried it on.

Here's the code I used to try out html5lib:

# test_html5lib.py
# A program to try out the html5lib Python library.
# Author: Vasudev Ram - www.dancingbison.com
import html5lib

f = open("html5doc.html")
tree = html5lib.parse(f)
print "tree:"
print repr(tree)
print
print "items in tree:"

for item in tree:
    print item
    for item2 in item:
        print "-" * 4, item2
        for item3 in item2:
            print "-" * 8, item3

And here is the output of running python test_html5lib.py:

<Element u'{http://www.w3.org/1999/xhtml}head' at 0x02B663C8>
<Element u'{http://www.w3.org/1999/xhtml}body' at 0x02B66488>
---- <Element u'{http://www.w3.org/1999/xhtml}header' at 0x02B664B8>
-------- <Element u'{http://www.w3.org/1999/xhtml}h1' at 0x02B66530>
-------- <Element u'{http://www.w3.org/1999/xhtml}h2' at 0x02B664E8>
-------- <Element u'{http://www.w3.org/1999/xhtml}h3' at 0x02B665F0>
---- <Element u'{http://www.w3.org/1999/xhtml}p' at 0x02B66650>
---- <Element u'{http://www.w3.org/2000/svg}svg' at 0x02B66BC0>
-------- <Element u'{http://www.w3.org/2000/svg}defs' at 0x02B66B60>
-------- <Element u'{http://www.w3.org/2000/svg}rect' at 0x02B66B30>
-------- <Element u'{http://www.w3.org/2000/svg}text' at 0x02B66BD8>
---- <Element u'{http://www.w3.org/1999/xhtml}footer' at 0x02B66BF0>

Here is the documentation for html5lib.

And speaking of HTML5, coincidentally, I came across Animatron via Hacker News, today:

Animatron is "a simple and powerful online tool that allows you to create stunning HTML5 animations and interactive content." Animatron is not really related to html5lib, except for the fact that both of them are about HTML5, but it looks cool. Check it out.

Hacker News thread about Animatron.

Enjoy.


- Vasudev Ram - Dancing Bison Enterprises

Contact Page

08 Mar 2014 2:14am GMT

So guys I have a good news for you. If you keep up with latest news regarding Python then you might have heard about Neckbeard Republic in the past. If you haven't then let me tell you that Neckbeard Republic is a series of bite sized Python screen-casts created by the awesome Mahdi. It used to be paid. Only a few screen-casts were free. The paid ones required you to pay $9 monthly membership fee. However Mahdi recently announced that all those screen-casts are now free! Due to some personal problems he was not able to continue to awesome series of screen-casts so he just made them free for all of us. The screen-casts have a professional quality. Some of the topics covered in his screen-casts are:

• Django Celery Part 2
• Sphinx & Read the Docs
• Python Mocking
• Pandas Data Frames
• Django Custom Template Tags
• And a whole lot more!
  

Make sure that you check them out and tell me that whether you like them or not. I will be waiting to hear your response.

07 Mar 2014 8:02pm GMT

So guys I have a good news for you. If you keep up with latest news regarding Python then you might have heard about Neckbeard Republic in the past. If you haven't then let me tell you that Neckbeard Republic is a series of bite sized Python screen-casts created by the awesome Mahdi. It used to be paid. Only a few screen-casts were free. The paid ones required you to pay $9 monthly membership fee. However Mahdi recently announced that all those screen-casts are now free! Due to some personal problems he was not able to continue to awesome series of screen-casts so he just made them free for all of us. The screen-casts have a professional quality. Some of the topics covered in his screen-casts are:

• Django Celery Part 2
• Sphinx & Read the Docs
• Python Mocking
• Pandas Data Frames
• Django Custom Template Tags
• And a whole lot more!
  

Make sure that you check them out and tell me that whether you like them or not. I will be waiting to hear your response.

07 Mar 2014 8:02pm GMT

  ImportError: No module named resource_rc 

 pyrcc4 -o App/ui/resources_rc.py App/resources.qrc

The above command runs the PyQt resource compiler which was already installed with PyQt4 on my Ubuntu laptop. pyrcc4 then compiles the resource file into Python which can easily be imported and used in Python. That solution solved my problem.

So that was it. I hope that this post was helpful for you. In order to stay updated with my future posts then don't forget to follow me on Twitter, Facebook and this blog. Stay tuned for my next post which is going to be a book giveaway. Goodbye!

07 Mar 2014 7:40pm GMT

  ImportError: No module named resource_rc 

 pyrcc4 -o App/ui/resources_rc.py App/resources.qrc

The above command runs the PyQt resource compiler which was already installed with PyQt4 on my Ubuntu laptop. pyrcc4 then compiles the resource file into Python which can easily be imported and used in Python. That solution solved my problem.

So that was it. I hope that this post was helpful for you. In order to stay updated with my future posts then don't forget to follow me on Twitter, Facebook and this blog. Stay tuned for my next post which is going to be a book giveaway. Goodbye!

07 Mar 2014 7:40pm GMT

Depending on which government body is talking, Canada is aiming for either 100% broadband access by the end of 2014 or 77% broadband subscriptions by 2019. Now Canada has a population density of 3.7 people/km² which ranks it around 230th in the world in population density. In other words, while 90% of the population lives within 160 km of the longest non-militarized border in the world, 10% don't and they can live as far north as the 76° parallel (as a reference point, the US border is either the 45° or 49° parallel). That can make getting broadband to everyone difficult.

But what if the Canadian government repurposed something that already had a great reach within the country? TV reaches many places thanks to its ability to travel long distances and penetrate walls, etc. This is why using the white spaces between TV channels has been appealing for providing broadband access. But where to get all of that spectrum?

After watching the Olympics this summer on CBC entirely online along with Coronation Street, I realized that I could get all of my CBC content online without having a TV antenna. And that's when I had a crazy idea.

Would it be possible to make the CBC an entirely web-only video broadcaster and then repurpose its TV channel frequencies for white spaces broadband (I'm not worrying about radio since it's easier to claim people have a computer at home than have a smartphone for their car)? That would instantly open up a large chunk of prime spectrum for providing wireless broadband to a huge portion of the Canadian population. It would also cut down on the costs at the CBC as they would no longer have to maintain the broadcast facilities but instead just keep their online services running as they do now (although hopefully improved, e.g. no more Flash players). And with the money saved in the CBC's annual budget, the federal government can repurpose those funds to make sure the broadband gets implemented and that all schools and libraries have computers to give people access to the CBC if they don't have a computer (you don't need anything more than a Raspberry Pi, monitor, keyboard, and mouse for that; Kano is selling such a kit for kids at $129 to give a rough price point). Take this far enough and the government could do municipal broadband across the country for free and then provide Raspberry Pis whose default SD card booted up to just watch the CBC streaming over the free internet connection for those who don't want a computer or lack the technological know-how to stream the CBC. How amazing would that be?

Obviously this idea is absolutely nuts and will never happen. I do not see the CBC supporting the idea of cord cutting the entire nation for fear of losing the portion of the population which lacks broadband or enough computer knowledge to get to the CBC online (just like PBS in the States, the CBC is constantly worried of getting de-funded). I also don't see (at least the Conservative) government giving away that much valuable spectrum without making someone pay for it (they might be willing to auction it off for wireless broadband, though). Heck, I don't see many people over the age of 30 supporting the idea as they probably have not even considered the idea of cord cutting, let alone have actually done it.

07 Mar 2014 7:24pm GMT

Depending on which government body is talking, Canada is aiming for either 100% broadband access by the end of 2014 or 77% broadband subscriptions by 2019. Now Canada has a population density of 3.7 people/km² which ranks it around 230th in the world in population density. In other words, while 90% of the population lives within 160 km of the longest non-militarized border in the world, 10% don't and they can live as far north as the 76° parallel (as a reference point, the US border is either the 45° or 49° parallel). That can make getting broadband to everyone difficult.

But what if the Canadian government repurposed something that already had a great reach within the country? TV reaches many places thanks to its ability to travel long distances and penetrate walls, etc. This is why using the white spaces between TV channels has been appealing for providing broadband access. But where to get all of that spectrum?

After watching the Olympics this summer on CBC entirely online along with Coronation Street, I realized that I could get all of my CBC content online without having a TV antenna. And that's when I had a crazy idea.

Would it be possible to make the CBC an entirely web-only video broadcaster and then repurpose its TV channel frequencies for white spaces broadband (I'm not worrying about radio since it's easier to claim people have a computer at home than have a smartphone for their car)? That would instantly open up a large chunk of prime spectrum for providing wireless broadband to a huge portion of the Canadian population. It would also cut down on the costs at the CBC as they would no longer have to maintain the broadcast facilities but instead just keep their online services running as they do now (although hopefully improved, e.g. no more Flash players). And with the money saved in the CBC's annual budget, the federal government can repurpose those funds to make sure the broadband gets implemented and that all schools and libraries have computers to give people access to the CBC if they don't have a computer (you don't need anything more than a Raspberry Pi, monitor, keyboard, and mouse for that; Kano is selling such a kit for kids at $129 to give a rough price point). Take this far enough and the government could do municipal broadband across the country for free and then provide Raspberry Pis whose default SD card booted up to just watch the CBC streaming over the free internet connection for those who don't want a computer or lack the technological know-how to stream the CBC. How amazing would that be?

Obviously this idea is absolutely nuts and will never happen. I do not see the CBC supporting the idea of cord cutting the entire nation for fear of losing the portion of the population which lacks broadband or enough computer knowledge to get to the CBC online (just like PBS in the States, the CBC is constantly worried of getting de-funded). I also don't see (at least the Conservative) government giving away that much valuable spectrum without making someone pay for it (they might be willing to auction it off for wireless broadband, though). Heck, I don't see many people over the age of 30 supporting the idea as they probably have not even considered the idea of cord cutting, let alone have actually done it.

07 Mar 2014 7:24pm GMT

New This Year - PyCon Dinners!

PyCon Dinner Speakers

Brandon Rhodes

Greg Brockman

PyCon Dinner #1 - Hosted by Brandon Rhodes

Location:

• Place d'Armes Hotel & Suites
  55, Rue St-Jacques, Montréal
  Room: bossa/blues/jazz
  6pm, Friday April 11th

Menu:

• Micro green salad with citrus vinaigrette and orange and grapefruit supremes
• Parmentier of beef braised in its juices, potato & beet purée, taro chips
  -or-
  Vegetarian option: marinated tofu, black bean and soy, fennel and chicory salad
• Royal chocolate (chocolate mousse, lemon financier, crispy praline)
• Coffee or tea

PyCon Dinner #2 - Hosted by Greg Brockman

Location:

• Vieux Port Steakhouse
  39 Saint-Paul E., Montréal
  6pm, Friday April 11th

Menu:

• Goat cheese and dried cranberries on a bed of baby green mesclun salad
• Strip loin steak New York cut, roasted garlic mashed potatoes, peppercorn sauce
  -or-
  Grilled salmon filet, wild rice and sauteed spinach
  -or-
  Vegetarian option available on request
• Joconde cake with blueberry mousse
• Coffee or tea

Registration

• $45.00 Per Person
• Advance registration required:
  1. Edit your existing PyCon registration
  2. Click "Continue"
  3. Click "Add Conference Dinners"
  4. Enter Quantity, click "Continue"

Questions?

• More information: https://us.pycon.org/2014/events/dinners/
• PyCon Organizers: pycon-organizers@python.org

07 Mar 2014 4:09pm GMT

New This Year - PyCon Dinners!

PyCon Dinner Speakers

Brandon Rhodes

Greg Brockman

PyCon Dinner #1 - Hosted by Brandon Rhodes

Location:

• Place d'Armes Hotel & Suites
  55, Rue St-Jacques, Montréal
  Room: bossa/blues/jazz
  6pm, Friday April 11th

Menu:

• Micro green salad with citrus vinaigrette and orange and grapefruit supremes
• Parmentier of beef braised in its juices, potato & beet purée, taro chips
  -or-
  Vegetarian option: marinated tofu, black bean and soy, fennel and chicory salad
• Royal chocolate (chocolate mousse, lemon financier, crispy praline)
• Coffee or tea

PyCon Dinner #2 - Hosted by Greg Brockman

Location:

• Vieux Port Steakhouse
  39 Saint-Paul E., Montréal
  6pm, Friday April 11th

Menu:

• Goat cheese and dried cranberries on a bed of baby green mesclun salad
• Strip loin steak New York cut, roasted garlic mashed potatoes, peppercorn sauce
  -or-
  Grilled salmon filet, wild rice and sauteed spinach
  -or-
  Vegetarian option available on request
• Joconde cake with blueberry mousse
• Coffee or tea

Registration

• $45.00 Per Person
• Advance registration required:
  1. Edit your existing PyCon registration
  2. Click "Continue"
  3. Click "Add Conference Dinners"
  4. Enter Quantity, click "Continue"

Questions?

• More information: https://us.pycon.org/2014/events/dinners/
• PyCon Organizers: pycon-organizers@python.org

07 Mar 2014 4:09pm GMT

Zero to hero

What Prompted me??

I started my third year thinking I should do something that would put me different from the rest and one of my professors suggested me as to why don't I apply for GSoC. I don't know why but I took the suggestion rather seriously, thanks to the bet I had with one of my friend(who is about to complete his MBBS) that whoever earns first will buy the other a "RayBan shades". Well, that's it. I was determined. I started my research early, probably during the start of February(I knew I want to buy my friend, his shades and also buy mine too, in the process).

What experiences I had before??

I started looking at previous years' GSoC projects(having had little experience with Open Source) and started learning how to contribute. I was also very fascinated to the amount of knowledge one could gain just by googling and browsing web pages . I discovered very soon, as to what an immensely great tool , email, through which I could chat with anyone in the open source world and ask seemingly stupid questions and always expect to get a gentle reply back with an answer. Well, that held me spell bound and I knew I want to contribute to Open Source.

How did I begin??

About the middle of March, I discovered that my passion for Python as a programming language increased , after understanding how easy it is as a language. Added to that, my popularity among my fellow classmates increased when I started evangelizing Python(thanks to my seniors for introducing it, I guess I did a decent job popularizing the language). And I started contributing to PSF(Python Software Foundation) , started with a simple bug to fix documentation and slowly my interactivity in IRC increased and I started liking one of the project one of the community member proposed.

A twist in the story??

There I was, still a noob and not knowing how to convince my probable mentor that I could complete the project, given direction. About this juncture, a fellow student(from some university in France) mailed this particular mentor that he was interested in the project . Do, remember, I was part of the mailing list and follow the happenings of it. So, I was furious knowing that I had a competition(having put so much effort) and I was not willing to compromise my project (knowing that this is the one project I actually understood and started researching a little bit too). The other projects require me to have some domain knowledge. I went back to my teachers, seniors, friends and Google and started asking the question , "how would i solve the problem the mentor posted?" . I framed a couple of answers, though very noobish , but at least I could reply the email thread posting my understanding of the problem and how I would solve it and also ask various questions I had in my mind. Well, the mentor replied, immediately to my surprise, and responded back with comments as well as answers to the questions I posed. Again, my nemesis/competitor replied back(he having good knowledge about the problem domain). I knew it was not going to be easy. Hence, I went back again, through all my sources, made further understanding of the problem and posted back again. I guess, about 20 mails in the thread , till we(all three of us) decided we should catch up in IRC and discuss more.

The conclusion:

Well, at IRC , most of senior members from the community were present, and they suggested that they should probably extend the scope of the project(since two students were interested in one project and showed immense passion). Unsurprisingly, over multiple meetings, the project scope was expanded, both the students were given equal important but independent tasks and both the students got opportunity to say they are Google Summer of Code students. Thank goodness, we decided to built the project from scratch giving us more than enough work on our plate.

Movie titles:

1) In the open source world, there is no competition , it is only "COLLABORATION".

2) Why give up, when you can win??

3) From Zero to Hero!!

4) A prodigy in making

p.s. I still owe my friend his shades . *sshole, I am still waiting for him to visit me so that I can buy him his shades and buy mine too. Also, I know its been two years since the story happened, but it is never too late to share, don't you agree??

11 Oct 2013 5:39am GMT