Open a terminal and change directory to the location of this file.

Run the install_dependencies script:

    $ sh install_dependencies.sh

Best to install the python(x,y) bundle (http://code.google.com/p/pythonxy/).

Afterwards all the packages mentioned in the install_dependencies.sh file can be installed via pip. For example to install xlrd one would issue the following command inside Command Prompt:

    pip install xlrd

If you want to run the web application, you need to have Django installed.

To install from the Ubuntu repos:

    sudo apt-get install python-django

or to run a dev version:

    git clone git://github.com/django/django.git django-trunk
    sudo pip install -e django-trunk/

Set the desired preferences in the configuration file:

    ./foc/forecaster/common/conf.py

Write down the crisis/normal years in the XLS file you defined in your conf file, e.g.:

    ./io/crises-imf-banking.xls

Position yourself inside the irb.foc.forecaster folder (pwd output just to show an example of the correct path):

    $ cd irb.foc.forecaster
    $ pwd
    /media/Data/Drazen/Dropbox/dev/eclipse/w2/irb.foc.forecaster

Run the Python interpreter with the entry script console_run.py as an argument:

    python console_run.py

Position yourself to the code root (the irb.foc.forecaster/ folder) and run the development server:

     python manage.py runserver

Point your web browser to http://127.0.0.1:8000/foc to see the site. If you want to open it from other computers you can also specify that your server listents to outside connections and a specific port (e.g. 8080):

     python manage.py runserver 0.0.0.0:8080

The options are set in .foc/visualiser/data_presenter/vis_conf.py and the script is run by issuing:

     python console_run.py visualise

console_run.py - the entry point for running the console application (useful for getting the data for example)

test - tests which give nice insight into using the module -extractor

cacher - responsible to caching to a mongoDB data store.

extractor - the entry point to grab the data

wb - wrapper for the World Bank API and a parser to a simple model |- api - communicates with the WB server to get data |- model - data structures we want the data to be represented in | |- country - code and list of indicators | - indicator - internal representation: list of dates, list of values | - parser - called by the api module to form data into Country and Indicator instances

common |- conf - configuration file with all the preferences that are used in a console run - exceptions - all the custom exceptions are defined here

model - contains the data structures - DEPRECATED, now part of dracula |- country - code and list of indicators - indicator - internal representation: list of dates, list of values

sources - DEPRECATED, now part of dracula - wb - extracts data from the World Bank

ai - classes regarding pattern recognition, train and test building etc. |- input - parses XLS files to get crisis and normal period years |- output - writes the dataset into a text file in a subgroup-discovery-friendly format |- samples_set - the representation of the train and test datasets that can build samples based on the crisis/normal years input and indicators and countries specified in the conf file; fetches the data live from the World Bank API |- preprocessor - processes the samples to extract useful features (min, max, slope...) - metadata - column labels and data type marks used when writing the dataset

tests - unit tests for individual modules inside the module

data_organiser - prepares data so that it's ready for plotting |- abstract_data_organiser - common functionality for all organisers - complete_multigroup_organiser - gets the complete time series and marks multiple groups of data points inside it

data_presenter - plots the data organised by the data_organiser using matplotlib |- matplotlib | |- visualiser - entry point for local visualisation | |- complete_multigroup_visualisation - one of the implementations of the ivisualisation interface | | ... | |- vis_conf - configuration used by the visualiser - ivisualisation - common interface any visualisation subclass must adher to by overriding the _create_figure(self, item) method

Some scripts used here and there for some manual tasks.

The Django web application resides here.

Input and output files should live here not to make a mess elsewere (except for the conf.py files, which are part of the code for now and they have to be importable).

Some parts of the web app. It would be nice if this could go somewhere else - like in focweb/

Auto-generated files by the pip packaging command.