This project consists of a few parts:

• A web scraper to gather data from the CJ IRIS database
• A differencing library which compares changes gathered
• A streamlined pipeline for gathering and differencing data
• A web application for displaying and navigating data

My routine is fairly straightforward: I run pipeline.py to get the most current data, then I run flaskapp_v1.pyto view the updated data via localhost.

If you decide to try to run this, make sure to change the name of sample_repo to just repo (trust me, it's easier this way). You also need to download the most recent chromedriver here.

run_scraper.sh accesses CJ IRIS. Don't be alarmed, but it may open a window and navigate its links. You can do other stuff while this runs, the program doesn't need browser focus to work.

run_flaskapp.sh compiles the data into a simple web application which can be accessed at 127.0.0.1:5000. The home page includes an explanation of what you're looking at.

The scraper uses Selenium to open and navigate the browser and collect data. Flask and Waitress are used to serve the local web app.

bpl_scraper.py is by far the most heavily-documented module. It should not need to be changed at all. If you want to manually access the scraper module instead of using pipeline.py, the method you want is scrape_all_data.

diffy.py contains the ORMs used to manage and compare the data gathered by the scraper. The end product is DiffEntryHistory, which tracks all changes over the lifespan of the archive. You can access an alternate repository with DiffEntryHistory.open(directory). The directory I use is repo/diff.

pipeline.py consists primarily of the scrape_and_diff_today_from_yesterday method. If you specify the directory parameter you can scrape into an alternate repository.

flaskapp_v1.py contains the implementation of a simple web application for navigating the data. This only works for a repo named repo, so if you want to change that you will have to edit this file. But it's just easier to give the repo the proper name in the first place, I promise.

Let's say your archive has grown to a few hundred differences. Maybe it's making the data unwieldy to look at, or maybe it's taking a bit too long to load.

The solution is to rebase your archive. To start, select and open the DiffEntryHistory you want to rebase:

deh = DiffEntryHistory.open("path/to/diff")

Let's say you want to reduce your archive by 200 days. Then call the rebase function like this:

deh.rebase(200)

And voila, your archive has been rebased! This doesn't delete any files, and also produces a _meta_old.txt file which contains the pre-rebase data in case you want to undo.

If you want to automate this process, you could do something like this:

if len(deh) > 365:
    deh.rebase(len(deh) - 365)

This will ensure your archive is always a reasonable size. It is ultimately up to you how much data you want the app to handle.