Due by 11:59pm Tuesday, November 7
Download and unzip the folder assign9_files.zip, which contains all of the files you'll need for this assignment. You should put your code for this assignment in the file assign9.py, which is included in the folder. Test cases for the Autotester are available for all exercises except Part 2 (playTTT).
Copy and paste your code for the five functions from Lab 9 listed below into your assign9.py file. You DO NOT need to include any other functions from Lab 9 (such as readGrid, numRows, numColumns, printGrid, directionNames, nextPosition, etc.). Checklist:
In class, we developed the function playTTT(size) to play the game of Tic-Tac-Toe (shown below). However, this function does not currently detect the case of a "cat's game" — that is, when all spaces on the board have been filled in without either player having won the game. Modify this program so that if a cat's game occurs, it will print "Sorry, cat's game" and then terminate the game.
def playTTT(size):
board = makeBoard(size)
printBoard(board)
gameOver = False
while gameOver == False:
makeMove(board, 'x')
if checkForWin(board, 'x') == True:
print("x wins!")
gameOver = True
else:
makeMove(board, 'o')
if checkForWin(board, 'o') == True:
print("o wins!")
gameOver = True
else:
pass
print("Thanks for playing.")
What U.S. city has the zip code 12345? How about 55555? What is the zip code for Truth Or Consequences, NM? How far is it from Fairbanks to Fort Lauderdale? What U.S. city with a population over 100,000 is closest to Minot, ND? What towns with a population over 25,000 are within 100 miles of Lawton, OK? In class, we created a dictionary database from a file of zip code information, which we will use to answer such questions. Each line of the file contains seven fields separated by commas representing, respectively, the zip code, latitude (degrees), longitude (degrees), city, county, state, and population. For example, the entry for Bronxville is shown below:
10708,40.938448,-073.829978,Bronxville,Westchester,NY,6547
The full zipDataAll.txt file has entries for 41,824 unique zip codes, but it is by no means complete. Some zip codes are missing, and some have no population data (the population is 0). For others the latitude/longitude data may be redundant or inaccurate. If your favorite city or hometown is missing and you know all the relevant info, let me know and I'll be happy to update the database, but I am not trying to maintain a comprehensive list. The file zipDataSample.txt is a much smaller version of the database that will be easier for you to work with while developing and debugging your functions.
The function makeZipcodeDatabase, which we wrote in class, builds a database of records indexed by zip code, and returns it as a single Python dictionary. Each record in the database is itself a dictionary, containing all of the information about a particular zip code:
Key Associated value Example
'city' city name "Bronxville"
'state' 2-letter state code "NY"
'zip' zip code string "10708"
'county' county name "Westchester"
'coords' latitude/longitude represented as a list [40.938448, -73.829978]
'pop' population 6547
Write the function milesBetween(database), which takes the database dictionary as input, asks the user for two zip codes, and reports the distance in miles between the locations, as shown in the examples below. You should use the function lookupByZip that we wrote in class as a helper function to ask the user for a zip code and retrieve the appropriate record from the database. You can use the helper function geographicDistance (included in assign9.py) to calculate the distance in miles, based on the latitude/longitude coordinates of the two zip codes.
>>> milesBetween(db) Enter zip code: 10708 Enter zip code: 10001 Bronxville, NY 10708 and New York, NY 10001 are 15.6 miles apart >>> milesBetween(db) Enter zip code: 90210 Enter zip code: 12345 Beverly Hills, CA 90210 and Schenectady, NY 12345 are 2451.3 miles apart >>> milesBetween(db) Enter zip code: 99701 Enter zip code: 33330 Fairbanks, AK 99701 and Fort Lauderdale, FL 33330 are 3935.0 miles apart
Many cities have multiple zip codes. For example, the database contains 162 different zip codes for New York, NY. Write a function called samePlace(zip1, zip2, database) that takes two zip codes and the database dictionary as input, and returns True if the corresponding records have the same city and state, or False otherwise. For example:
db['10003'] => {'city': 'New York', 'state': 'NY', 'zip': '10003', ...}
db['10035'] => {'city': 'New York', 'state': 'NY', 'zip': '10035', ...}
>>> print(samePlace("10003", "10035", db))
True
db['10701'] => {'city': 'Yonkers', 'state': 'NY', 'zip': '10701', ...}
db['10708'] => {'city': 'Bronxville', 'state': 'NY', 'zip': '10708', ...}
>>> print(samePlace("10701", "10708", db))
False
db['10601'] => {'city': 'White Plains', 'state': 'NY', 'zip': '10601', ...}
db['23893'] => {'city': 'White Plains', 'state': 'VA', 'zip': '23893', ...}
>>> print(samePlace("10601", "23893", db))
False
Write a function called allZips(database) that uses lookupByCity to ask the user for a location in "city, state" form, and prints out all zip codes in the database that represent the same city and state, along with the total number of such zip codes. Hint: use your samePlace function as a helper to determine whether two zip codes represent the same city and state. Your program should work as shown below (but remember that the order of items in a dictionary is unspecified, so your program might print out the zip codes in a different order, which is fine):
>>> allZips(db) Enter city, state: Yonkers, NY Yonkers, NY 10701 Yonkers, NY 10702 Yonkers, NY 10703 Yonkers, NY 10704 Yonkers, NY 10705 Yonkers, NY 10710 6 zip codes found for Yonkers, NY >>> allZips(db) Enter city, state: New York, NY New York, NY 10001 New York, NY 10002 New York, NY 10003 New York, NY 10004 New York, NY 10005 ... 162 zip codes found for New York, NY
You should finish the other problems first before attempting these.
Write a function called neighbors(database) that uses lookupByZip to ask the user for a zip code, and prints out all zip codes in neighboring cities or towns up to 3 miles away. Zip codes that are 0 miles away or have the same city and state as the specified zip code should not be printed. If there are no neighboring cities within 3 miles, the message "No neighboring cities or towns" should be printed. Your program should work as shown below (but it might print out the neighboring cities in a different order, which is fine):
>>> neighbors(db) Enter zip code: 10701 Neighbors of Yonkers, NY 10701: Mount Vernon, NY 10550 is 3.0 miles away Hastings On Hudson, NY 10706 is 2.9 miles away Tuckahoe, NY 10707 is 2.8 miles away Bronxville, NY 10708 is 2.0 miles away Eastchester, NY 10709 is 2.9 miles away >>> neighbors(db) Enter zip code: 10003 Neighbors of New York, NY 10003: Hoboken, NJ 07030 is 2.6 miles away Jersey City, NJ 07310 is 2.6 miles away Long Island City, NY 11101 is 3.0 miles away Brooklyn, NY 11201 is 2.5 miles away Brooklyn, NY 11206 is 2.9 miles away Brooklyn, NY 11211 is 2.7 miles away Brooklyn, NY 11222 is 2.2 miles away >>> neighbors(db) Enter zip code: 87901 Neighbors of Truth Or Consequences, NM 87901: No neighboring cities or towns
Write a function called closest(database) that uses lookupByCity to ask the user for a location in "city, state" form, and reports the closest big city with a population over 100,000. Remember that many cities have multiple zip codes. For example, the closest city with a population over 100,000 to New York, NY should not be New York, NY, but some other city. Examples:
>>> closest(db) Enter city, state: Bronxville, NY Closest big city is Yonkers, NY (population 196086) >>> closest(db) Enter city, state: New York, NY Closest big city is Jersey City, NJ (population 240055) >>> closest(db) Enter city, state: Kansas City, KS Closest big city is Kansas City, MO (population 441545) >>> closest(db) Enter city, state: Truth Or Consequences, NM Closest big city is El Paso, TX (population 563662)
Write a function called nearby(database) that prompts the user for a city and state, a distance in miles, and a minimum population size, and prints out all cities within the specified distance having a population greater than or equal to the specified size. List the cities by name, state, and population only (no zip codes or distances). You should not report the same city more than once. If there are no cities that match the search criteria, you should print out the message "None found". Examples:
>>> nearby(db) Enter city, state: Lawton, OK Finding cities near Lawton, OK... Up to how many miles away? 100 With at least how many people? 25000 Cities of size 25000 or more within 100 miles of Lawton, OK: Edmond, OK (population 68315) Norman, OK (population 95694) Oklahoma City, OK (population 506132) Shawnee, OK (population 28692) Wichita Falls, TX (population 104197) >>> nearby(db) Enter city, state: Lawton, OK Finding cities near Lawton, OK... Up to how many miles away? 100 With at least how many people? 750000 Cities of size 750000 or more within 100 miles of Lawton, OK: None found
Save all of your program definitions in the file assign9.py. Make sure to include your name and the assignment number in a comment at the top of your file. Submit your file electronically using the Homework Upload Site. Please DO NOT email your file to me.
If you have questions about anything, don't hesitate to ask!