color version - PET: Python Entre Todos - Python Argentina

(presidential advisors for example).
In this case the value of the information would be 10,000, and since we define
value as “much” and what value we define as “little”, this value is “large” (much).
This, we can consider as correct as any medium that can influence important
people should have a high value.
Keeping the values, but changing our function by AUDIENCE + IMPACT the
value of the information would be 1010 which, keeping the above reasoning
remains high.
Now, if we replace the value of the audience by a large number 1000, and keep the
impact, the value of the original function would be 1 million and the second case
in 2000.
If we consider we now likely impact on 1000 presidential advisors 2000 value
remains small, since we are presence of a medium likely to generate a global
impact. Which shows that multiplication represents much better variation of
parameters.
• The value vanishes in the absence of audience or impact: When the audience
or impact are 0 (zero) (No one sees or pays attention to the medium) the value of
information is also 0 (zero).
This is not trivial because it suggests that the information is worthless if nobody
cares to see it or no one pays attention.
Infopython
Since there is a wide variety of public services that extract statistics and data on new
media (web, twitter, etc), including:
• Klout (http://klout.com/)
• Compete (http://www.compete.com/)
• Alexa (http://www.alexa.com/)
To quote a few. Infopython focuses on providing a simple API to value through
agenda-setting (in the future to implement other theories) to the media regardless of
type, using aforementioned services
Architecture:
Analyzing every layer:
• Internet Service: Corresponds to the different services that exist
on the Web for statistics and data mining of new media.
• Other Sources: They are other data which feeds Infopython, such as databases,
excel templates, etc.
• Scipy: It is an open source library of algorithms and
mathematical tools.
This handles the necessary number crunching.
• Third Parties Apis: These are third-party libraries that connect to services that
exist in the network. For example:
• Tweepy used to manipulate data from twitter.
• Koutpy that connects to Klout
• Session: This sub-layer is a module that is responsible for centralizing all
necessary settings to access internet services.
• Interpolation Normalization: This is a layer of abstraction for different
interpolators which has scipy and defines some new all with the same API.
• API Normalization: will convert all the answers of all Internet services and third
party APIs to common structures (dictionaries) using if needed information
contained in the session.
• Information Sources: These are the classes that represent our sources. These are
connected in a “auto-magical” way to the various standardized API’s.