1 Mechanics

There will be

  1. A logic-group activity
  2. A brief review of CSV and urllib
  3. Pairing to work on the lab tasks.
  4. TAs pulling aside students to explain their code.

2 Activities

2.1 Overview

Your code should

  1. Prompt the user to enter a latitude and longitude
  2. Read a list of locations to find the closest location to the user
  3. Open the user’s web browser centered on that location

To test this, you’ll need

  1. A list of locations, such as http://cs1110.cs.virginia.edu/files/wendys.csv (we also have arbys.csv, chickfila.csv, and fiveguys.csv, if you prefer).

    Traditionally, the Wendy’s dataset was the only one we provided so we expect your file to be named wendys.py even if it looks at one of the other datasets.

  2. A test location or two. Rice hall is (38.0317274, -78.5110432); you can find others at http://www.gps-coordinates.net/.

2.2 Finding a location

You’ll need to write code that, given a target latitude and longitude, loops the points of interest in the CSV file and finds the one that is closest to the target location. To do this, you’ll need to create CSV reading code (like we have done in class recently) and also have a way of computing the distance between two GPS coordinates. That distance math is complicated because the earth is a spheroid; the following code should work:

import math

def distance_between(lat_1, lon_1, lat_2, lon_2):
    '''Uses the "great circle distance" formula and the circumference of the earth
    to convert two GPS coordinates to the miles separating them'''
    lat_1, lon_1 = math.radians(lat_1), math.radians(lon_1)
    lat_2, lon_2 = math.radians(lat_2), math.radians(lon_2)
    theta = lon_1 - lon_2
    dist = math.sin(lat_1)*math.sin(lat_2) + math.cos(lat_1)*math.cos(lat_2)*math.cos(theta)
    dist = math.acos(dist)
    dist = math.degrees(dist)
    dist = dist * 69.06         # 69.09 = circumference of earth in miles / 360 degrees

    return dist

You can execute this code by calling:

distance = distance_between(user_lat, user_lon, wendys_lat, wendys_lon)

Here, the user_lat and user_lon are what the user typed in and wendys_lat and wendys_lon are the 0 and 1 columns from the .csv (remember you can get these after you split the line… look at the examples from class!).

The result distance here is the distance in miles.

Loop through the entire file, keeping up with which Wendy’s gave you the shortest distance from the user’s coordinates to that Wendy’s coordinates. Save that information in some variables.

2.3 Opening a map

Now let’s put it on a map!

We can take the address (which is the combination of columns 4, 5, and 6 from the .csv) and create a web address object (URI) that Python can use to open your computer’s default browser.

# You'll need this at the top of your file
import webbrowser

Once you have imported the webbrowser library, you’ll need to create the proper URL for Google Maps.

Pick one of the following mapping websites:


The URL should look like:

http://www.openstreetmap.org/search?query= + the address


The URL should look like:

http://www.mapquest.com/search/results?query= + the address

Google Maps

The URL should look like:

http://maps.google.com/maps?q= + the address

Bing Maps

The URL should look like:

http://www.bing.com/maps?q= + the address

The the address field above is created by concatenating columns 4, 5, and 6 from the .csv, replacing all spaces by plus-signs.

Once you have a URL, open it in a browser using


Give it a shot and see what Wendy’s you can find!

2.4 Submission

At least one partner should submit one .py file named wendys.py to Archimedes (the submission system): https://archimedes.cs.virginia.edu/cs1110/. Please put all partners’ ids in comments at the top of the file.