gistfile1.txt

def group_tweets_by_state(tweets):
    """Return a dictionary that aggregates tweets by their nearest state center.

    The keys of the returned dictionary are state names, and the values are
    lists of tweets that appear closer to that state center than any other.

    tweets -- a sequence of tweet abstract data types

    >>> sf = make_tweet("welcome to san francisco", None, 38, -122)
    >>> ny = make_tweet("welcome to new york", None, 41, -74)
    >>> two_tweets_by_state = group_tweets_by_state([sf, ny])
    >>> len(two_tweets_by_state)
    2
    >>> california_tweets = two_tweets_by_state['CA']
    >>> len(california_tweets)
    1
    >>> tweet_string(california_tweets[0])
    '"welcome to san francisco" @ (38, -122)'
    """

    state_centers_dict = state_centers()
    state_tweets = {}

    for tweet in tweets:
        closest_state = ""
        closest_distance = None
        for state in state_centers_dict:
            distance = geo_distance(state_centers_dict[state], tweet_location(tweet))
            if(closest_distance == None):
                closest_distance = distance
                closest_state = state
            elif(closest_distance > distance):
                closest_distance = distance
                closest_state = state
        if(state_tweets[closest_state]):
            state_tweets[closest_state] = state_tweets[closest_state] + [tweet]
        else:
            state_tweets[closest_state] = [tweet]

    return state_tweets

def state_centers():
    us_state_centers = {}
    for state in us_states:
        us_state_centers[state] = find_state_center(us_states[state])

    return us_state_centers