INFO 206B

INFO 206B, Assignment 1. Submit your Jupyter notebook (.ipynb file) that contains your well-commented code. Please name your file that you submit {firstname_lastname}_A1.ipynb.

In this assignment, you will read in a text file in which each line describes a world city, store the information about each city in its own object within a list, sort the list using quicksort, write the results out to a file, and display part of the output file in a visualization on a world map.

Define a City class. Each City object will need instance variables to store the following information:
1. The city's country code, which is a two-letter string.
2. The city's name, which is a string.
3. The city's region, a two-character string.
4. The city's population, which is an int.
5. The city's latitude, which is a float.
6. The city's longitude, which is also a float.
You will need five methods in the City class. The __init__ method does the usual: it takes six parameters (plus self) and stores each in the appropriate instance variable. The __str__ method returns a string consisting of the city's name, population, latitude, and longitude, separated by commas and with no spaces around the commas. For example, here's the string that __str__ should return for a town in California (notice that the country code and region do not appear in this string): Berkeley,100158,37.8716667,-122.2716667. And then you will need three functions get_population, get_latitude, and get_name that simply returns the corresponding data value.
The input file world_cities.txt consists of 47,913 lines. Each line has information about a world city, specifically the six attributes you'll store in the City object. These attributes are separated by commas without surrounding spaces, in the same order as enumerated above. Call the split method on each line of the file to separate it into its individual components. Then, call the City constructor with these components. Some of the instance variables are not strings, and so you will have to convert these strings to the appropriate types. Repeat this process for each line of world_cities.txt and build a list of 47,913 references to City objects.
I provide the code for sorting your list based on name, population, or latitude. To sort based on an attribute, call the function sort with your list of cities and the appropriate comparison function that I provide. Sort your list and then save the sorted file as:
1. cities_alpha.txt contains the list of cities sorted alphabetically.
2. cities_population.txt contains the list of cities sorted by population, from most to least populous.
3. cities_latitude.txt contains the list of cities sorted by latitude, from south to north.
Here are the first and last few lines of each of the files:
1. cities_alpha.txt
```
A,1145,63.966667,10.2
A,1145,63.966667,10.216667
A Coruna,236010,43.366667,-8.383333
```
2. cities_population.txt
```
Tokyo,31480498,35.685,139.7513889
Shanghai,14608512,31.005,121.4086111
Bombay,12692717,18.975,72.825833
```
3. cities_latitude.txt
```
Ushuaia,58045,-54.8,-68.3
Punta Arenas,117432,-53.15,-70.9166667
Rio Gallegos,93234,-51.6333333,-69.2166667
```
Lastly, write some code that visualizes the output of cities_population.txt. Display the image world.jpg over which you should plot a small dot over each of the top 30 most populous cities along with the name of the city. I used matplotlib.pyplot to display the image (using imshow) and plot the city locations (using plot). Here, for example, is a code snippet for loading and displaying an image and plotting a single data point over the image:
```
import matplotlib.pyplot as plt
WIDTH = 720 # image (world.png) width
HEIGHT = 360 # image height
DPI = 60 # display image at this dots-per-inch resolution
img = plt.imread("world.jpg")
plt.figure(figsize=(WIDTH/DPI,HEIGHT/DPI))
plt.imshow(img) # Display the image
plt.axis('off')
plt.plot(120, 100, 'bo') # plot one blue data point
plt.show()
```
Your final animation should look something like this. The function longlat_to_pixel will convert longitude/lattidue to pixel coordinates -- this function assumes that you have defined two variables WIDTH and HEIGHT corresponding to the size of the image world.jpg of size 720 x 360.

To make the animation smooth and render each frame in the same figure axis, I used the commands sleep(0.5); clear_output(wait=True) between each frame of the animation (you'll need to import these from the time and IPython.display libraries).

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