There are two submodules to this git repo!
So far, there are two scripts of interest:
- towers_by_lai.py: Breaks OpenCellID DB into files for each LAI, in CSV and
KML format. KML goes into the submodule rooted at
data/derived/kml. - report_outliers.py: Takes the files produced by the first script and produces
CSV and MD reports on nearest neighbor CGI, with distances beyond 30km
appearing in bold on MD reports. These files go into the other submodule,
rooted at
reports/
In order to run these scripts, you will need API access to Twilio, because that's where we get the information to correlate MCC+MNC with the owning provider. You will also need to get a copy of the OpenCellID database from http://opencellid.org/#action=database.downloadDatabase
Place the OpenCellID database at lai-analysis/data/source. It should be named
cell_towers.csv.gz when you download it. Just move it into place without
decompressing or renaming it.
This is written for Python 2.7. Required modules are in requirements.txt:
Install requirements: pip install -r requirements.txt
The towers_by_lai file is the first stage. It places the feed in files by LAI, ready to be processed by report_outliers.py.
The report_outliers.py script feeds rabbitmq on localhost, and you'll need to
run celery worker -A celeryutils to process the jobs. If you want a nice web
interface to watch the jobs go by, install flower (from pip) and
celery flower -A celeryutils. Go to the URL that comes up after starting
and you'll be able to adjust your queues, etc...
Navigate to code/tests/profile and run
python -m cProfile -o profile_data.pyprof profile_process_one_file.py, and
then dissect it with Kcachegrind or Qcachegrind.
Pytest is required for testing.
Navigate to code/tests/ and run py.test