This is the Django source code that runs http://wardiaries.wikileaks.org, a tool which enables rich browsing and searching of the WikiLeaks Afghan War Diaries document archive.
You may be interested in the DocuDig fork, which generalizes this application to any structured data set.
The latest version of afgexplorer uses Solr as its search backend. Previous versions of afgexplorer only used the database. The latest version should work with any Django-compatible database (the previous version depended on postgreql); however, the management command to import data assumes postgresql for efficiency's sake.
It is recommended that you install using pip and virtualenv. To install dependencies:
pip install -r requirements.txt -E /path/to/your/virtualenv
If you use postgresql (recommended), you will need to install
egenix-mx-base, which cannot be installed using pip.
To install it, first activate your virtualenv, and then:
easy_install -i http://downloads.egenix.com/python/index/ucs4/ egenix-mx-base
Install Solr. For the purposes of testing and development, the example server should be adequate, though you will need to add add the schema.xml file as described below.
Style sheets are compiled using Compass. If you
wish to modify the style sheets, you will need to install that as well. After
compass is installed, stylesheets can be compiled as you modify the .sass
files as follows:
cd media/css/sass/ compass watch
Copy the file example.settings.py to settings.py, and add your database settings.
This project contains only the code to run the site, and not the documents themselves. The documents themselves must be separately obtained at: http://wikileaks.org/wiki/Afghan_War_Diary,_2004-2010
To import the documents, download the CSV format file. Then, start the process as follows.:
python manage.py import_wikileaks path/to/file.csv "2010 July 25"
The first argument is the path to the data file, and the second argument is the release label for that file (used as an additional facet to allow viewers to search within particular document releases). If there are multiple document releases to import at once, add additional filename and label pairs as subsequent arguments.
The script will first collate the entries and extract phrases that are in common between the documents. Then, it will construct a new csv file which contains the cleaned database fields for for efficient bulk importing with postgres. Following this colation, you will need to enter the database password to execute the bulk import.
To generate the Solr schema, run the following management command:
python manage.py build_solr_schema > schema.xml
Copy or link this file to the Solr conf directory (if you're using the example
Solr server, this will be apache-solor-1.4.1/example/solr/conf), replacing
any schema.xml file that is already there, and then restart Solr. After
restarting Solr, the following management command will rebuild the index:
python manage.py rebuild_index
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