gknoy / store-locator

Store locator

This repo is a store locator for an un-named retail chain. Stores are located in store-locations.csv.

Installation

First, create an api_keys.json with your credentials:

{
  "GOOGLE_GEOCODING_API_KEY": "<your key>"
}

Unix

This requires python3.6 to be in your path. Barring that, you can likely edit the Makefile to use a different python3.

make prepare-venv
make test

python find_store.py --help

Windows

This also requires a python3 installation. You can either copy win.mk over Makefile, or use make -f win.mk with the same directives:

# puts things in env-win/
make -f win.mk prepare-venv
make -f win.mk win.mk test

python find_store.py --help

Usage

Find Store
  find_store will locate the nearest store (as the vrow flies) from
  store-locations.csv, print the matching store address, as well as
  the distance to that store.

Usage:
  find_store --address="<address>"
  find_store --address="<address>" [--units=(mi|km)] [--output=text|json]
  find_store --zip=<zip>
  find_store --zip=<zip> [--units=(mi|km)] [--output=text|json]

Options:
  --zip=<zip>          Find nearest store to this zip code. If there are multiple best-matches, return the first.
  --address=<address>  Find nearest store to this address. If there are multiple best-matches, return the first.
  --units=(mi|km)      Display units in miles or kilometers [default: mi]
  --output=(text|json) Output in human-readable text, or in JSON (e.g. machine-readable) [default: text]

Example
  find_store --address="1770 Union St, San Francisco, CA 94123"
  find_store --zip=94115 --units=km

How this works

Store locations are stored in store-locations.csv. We parse those to get records for each each store location.

Users provide an address (or a zip code).
We use Google's geocoding API to find the latitude and longitude of the user's location (or the center of the zip code), and then sort the store locations by their proximity to the starting point.