Hall of Justice
Hall of Justice is a Sunlight Foundation Project working with criminal justice data. Hall of Justice was on Heroku while in development
A work in progress. The set up will almost certainly change in the near future.
You can take a properly formatted (fits the data schema) xlsx file and collapse it to a single csv using
scripts/collapse_xls_to_csv.py. This csv can be imported into a Django database (already created using
python manage.py migrate) using the import_datasets management command, i.e.:
python manage.py import_datasets /path/to/data.csv -v 3 2> import_errors.log.
You can look for errors around datasets that failed to import (
grep '^Failed to save dataset' import_errors.log | sort) or categories that don't exist (
grep category$ import_errors.log | uniq | sort). There are also rows from the csv where there wasn't enough data to create a dataset (Not enough data to create a dataset!), but those are likely mostly empty rows from the input csv.
Once you've imported data into the database, you should create (or recreate) the elasticsearch search index using
python manage.py rebuild_index.
There is a Vagrantfile for setting up multiple Virtualbox virtual machines and provisioning them using Ansible. You should be able to run
vagrant up to create the machines after fetching the required git submodules. You'll need to run the setup steps above to populate the data.
Further information is available in NOTES.md.
Front end development
If all you need to do is work on the front end of the website you can follow this process:
- Create a virtual env for the project (optional)
- Create a file in the root of the project called
example-envfor an example of what this should contain.
pip install python-dotenv
python manage.py runserver
You should be all set.
The Elasticsearch features are implemented using haystack and elasticstack, plus a number of custom forms, views, etc. that live in the search app. The core feature supported with this mish-mash is a custom analyzer that supports criminal justice synonyms, as seen in
search/settings.py. The tl;dr on the custom backend is that default haystack search uses the search query language (query_string query) that is incompatible with the synonym filter. Hence
search.backends.PliableSearchBackend which performs an Elasticsearch match query by default, but query_string can be enabled as an alternative (but disables the synonym filter).
There is a crawler app that provides Celery tasks for crawling/inspecting URLs. The
cjdata.tasks.inspect_all_dataset_urls task will spawn subtasks to inspect all Dataset URLs in the database. That function calls
crawler.tasks.inspect_url for each Dataset's URL and records the relation back to the original Dataset.
crawler.tasks should be flexible enough for creating other custom crawler tasks or running one-offs.
Since celerybeat is set up for this project, you could edit
hallofjustice/celeryconfig.py to schedule a regular run that task periodically to monitor for missing or otherwise bad URLs.
This document will instruct on how to check for broken links, links that the crawlers use to capture data. https://github.com/sunlightlabs/hall-of-justice/blob/master/brokenlinks.md
The admin has been customized with
cjdata/admin.py and the grappelli package. There are probably a lot of things that can be done to improve the admin as curation of the data moves into the Django admin.
This project makes use of a number of PostgreSQL-specific features, such as Array and JSON fields. In addition, there a migration that enables the
intarray extension (used getting items from the db by a set of ids in order) and another that creates a GIN index on the tags arrayfield.
The current implementation for exporting CSV streams an http response (see
common.views.CSVExportMixin) so we can support export of custom searches as CSV.
search.views.SearchExportView gets object ids from the haystack SearchQuerySet then performs a custom search to get results from the database in the search result order. This makes use of a PostgreSQL function from the included
There are views for exporting all Datasets or per category as CSV, and they should probably be lightly cached in production, or replaced with a periodic export. The export process does not transform arrayfields from their Django string representation (like
['NC', 'US']), but that may be preferable to transforming values into some custom representation of the lists.