def draw_centered_map(center_state='TX', n=10): """Draw the n states closest to center_state.""" center = us_centers[center_state.upper()] dist_from_center = lambda name: geo_distance(center, us_centers[name]) for name in sorted(us_states.keys(), key=dist_from_center)[:int(n)]: draw_state(us_states[name]) draw_name(name, us_centers[name]) draw_dot(center, 1, 10) # Mark the center state with a red dot wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state."""
us_centers = {n: find_state_center(s) for n, s in us_states.items()}
center = us_centers[center_state.upper()]
dist_from_center = lambda name: geo_distance(center, us_centers[name])
for name in sorted(us_states.keys(), key=dist_from_center)[:int(n)]:
draw_state(us_states[name])
draw_name(name, us_centers[name])
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state."""
centers = {name: find_state_center(us_states[name]) for name in us_states}
center = centers[center_state.upper()]
distance = lambda name: geo_distance(center, centers[name])
for name in sorted(centers, key=distance)[:int(n)]:
draw_state(us_states[name])
draw_name(name, centers[name])
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state."""
centers = {name: find_state_center(us_states[name]) for name in us_states}
center = centers[center_state.upper()]
distance = lambda name: geo_distance(center, centers[name])
for name in sorted(centers, key=distance)[:int(n)]:
draw_state(us_states[name])
draw_name(name, centers[name])
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_centered_map(center_state='TX', n=10, canvas=None):
"""Draw the n states closest to center_state."""
us_centers = {n: find_center(s) for n, s in us_states.items()}
center = us_centers[center_state.upper()]
dist_from_center = lambda name: geo_distance(center, us_centers[name])
for name in sorted(us_states.keys(), key=dist_from_center)[:int(n)]:
draw_state(us_states[name], canvas=canvas)
draw_name(name, us_centers[name], canvas=canvas)
draw_dot(center, 1, 10, canvas=canvas) # Mark the center state with a red dot
wait(canvas=canvas)
def draw_map_for_query(test, new_file_name=None):
if new_file_name == None:
random.seed()
new_file_name = str(random.randint(0, 1000000000))
"""Draw the frequency map corresponding to the tweets that pass the test.
"""
tweets = load_tweets(make_tweet, test, new_file_name)
tweets_by_state = count_tweets_by_state(tweets)
draw_state_frequencies(tweets_by_state)
wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state."""
us_centers = make_database()
for state, s in get_items(us_states):
us_centers = add_value(us_centers, state, find_state_center(s))
center = get_value_from_key(us_centers, center_state.upper())
dist_from_center = lambda name: geo_distance(center, get_value_from_key(us_centers, name))
for name in sorted(get_keys(us_centers), key=dist_from_center)[:int(n)]:
draw_state(get_value_from_key(us_states, name))
draw_name(name, get_value_from_key(us_centers, name))
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_map_by_hour(term='my job', pause=0.5):
"""Draw the sentiment map for tweets that match term, for each hour."""
tweets = load_tweets(make_tweet, term)
tweets_by_hour = group_tweets_by_hour(tweets)
for hour in range(24):
current_tweets = tweets_by_hour.get(hour, [])
tweets_by_state = group_tweets_by_state(current_tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
message("{0:02}:00-{0:02}:59".format(hour))
wait(pause)
def draw_map_by_hour(term='my job', pause=0.5):
"""Draw the sentiment map for tweets that match term, for each hour."""
tweets = load_tweets(make_tweet, term)
tweets_by_hour = group_tweets_by_hour(tweets)
for hour in range(24):
current_tweets = tweets_by_hour.get(hour, [])
tweets_by_state = group_tweets_by_state(current_tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
message("{0:02}:00-{0:02}:59".format(hour))
wait(pause)
def draw_map_for_term(term='my job'):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
draw_dot(tweet_location(tweet), analyze_tweet_sentiment(tweet))
wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state."""
us_centers = make_database()
for state, s in get_items(us_states):
us_centers = add_value(us_centers, state, find_state_center(s))
center = get_value_from_key(us_centers, center_state.upper())
dist_from_center = lambda name: geo_distance(
center, get_value_from_key(us_centers, name))
for name in sorted(get_keys(us_centers), key=dist_from_center)[:int(n)]:
draw_state(get_value_from_key(us_states, name))
draw_name(name, get_value_from_key(us_centers, name))
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_map_for_query(tweets, term='my job'):
"""
Draw the sentiment map corresponding to the tweets that contain term.
"""
relevant_tweets = filter_tweets(tweets, term)
tweets_by_state = group_tweets_by_state(relevant_tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in relevant_tweets:
s = analyze_tweet_sentiment(tweet)
if has_sentiment(s):
display_tweet(tweet)
draw_dot(tweet_location(tweet), sentiment_value(s))
wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state.
For example, to draw the 20 states closest to California (including California):
# python3 trends.py CA 20
"""
us_centers = {n: find_center(s) for n, s in us_states.items()}
center = us_centers[center_state.upper()]
dist_from_center = lambda name: geo_distance(center, us_centers[name])
for name in sorted(us_states.keys(), key=dist_from_center)[:int(n)]:
draw_state(us_states[name])
draw_name(name, us_centers[name])
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state.
For example, to draw the 20 states closest to California (including California):
# python3 trends.py CA 20
"""
us_centers = {n: find_center(s) for n, s in us_states.items()}
center = us_centers[center_state.upper()]
dist_from_center = lambda name: geo_distance(center, us_centers[name])
for name in sorted(us_states.keys(), key=dist_from_center)[:int(n)]:
draw_state(us_states[name])
draw_name(name, us_centers[name])
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_map_for_query(term='my job', file_name='tweets2014.txt'):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term, file_name)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
s = analyze_tweet_sentiment(tweet)
if has_sentiment(s):
draw_dot(tweet_location(tweet), sentiment_value(s))
wait()
def draw_state_sentiments(state_sentiments={}):
"""Draw all U.S. states in colors corresponding to their sentiment value.
Unknown state names are ignored; states without values are colored grey.
state_sentiments -- A dictionary from state strings to sentiment values
"""
for name, shapes in us_states.items():
sentiment = state_sentiments.get(name, None)
draw_state(shapes, sentiment)
for name, shapes in us_states.items():
center = find_center(shapes)
if center is not None:
draw_name(name, center)
wait()
def draw_map_for_query(term='my job', file_name='tweets2014.txt'):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term, file_name)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
s = analyze_tweet_sentiment(tweet)
if has_sentiment(s):
draw_dot(tweet_location(tweet), sentiment_value(s))
wait()
def draw_map_by_hour(find_state, term='my job', pause=0.5, canvas=None, imglist=None):
"""Draw the sentiment map for tweets that match term, for each hour."""
word_sentiments = load_sentiments()
tweets = load_tweets(term)
tweets_by_hour = group_tweets_by_hour(tweets)
for hour in range(24):
current_tweets = tweets_by_hour[hour]
tweets_by_state = group_tweets_by_state(current_tweets, find_state)
state_sentiments = average_sentiments(tweets_by_state,word_sentiments)
draw_state_sentiments(state_sentiments, canvas=canvas)
message("{0:02}:00-{0:02}:59".format(hour), canvas=canvas)
wait(pause, canvas=canvas)
if imglist is not None:
imglist.append(get_img_copy(canvas))
def draw_map_for_term(find_state, term='my job', canvas=None):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
word_sentiments = load_sentiments()
tweets = load_tweets(term)
tweets_by_state = group_tweets_by_state(tweets, find_state)
state_sentiments = average_sentiments(tweets_by_state,word_sentiments)
draw_state_sentiments(state_sentiments, canvas=canvas)
for tweet in tweets:
s = tweet.get_sentiment(word_sentiments)
if s != None:
draw_dot(tweet.get_location(), s, canvas=canvas)
wait(canvas=canvas)
def run(*args):
"""Read command-line arguments and calls corresponding functions."""
import argparse
parser = argparse.ArgumentParser(description="Run Trends")
parser.add_argument('--print_sentiment', '-p', action='store_true')
parser.add_argument('--run_doctests', '-t', action='store_true')
parser.add_argument('--draw_centered_map', '-d', action='store_true')
parser.add_argument('--draw_state_sentiments', '-s', action='store_true')
parser.add_argument('--draw_map_for_term', '-m', action='store_true')
parser.add_argument('--draw_map_by_hour', '-b', action='store_true')
parser.add_argument('--containing_state', '-c', action='store_true')
parser.add_argument('--file', '-f', type=str, default=None)
parser.add_argument('text', metavar='T', type=str, nargs='*',
help='Text to process')
args = parser.parse_args()
if (args.__dict__['containing_state']):
find_state = find_containing_state(us_states)
else:
us_centers = {n: find_center(s) for n, s in us_states.items()}
find_state = find_closest_state(us_centers)
if args.__dict__['file']:
canvas = MapImage(960,500)
else:
canvas = None
for name, execute in args.__dict__.items():
if name != 'text' and name != 'containing_state' and name != 'file' and execute:
if name == 'draw_map_for_term':
draw_map_for_term(find_state, ' '.join(args.text), canvas=canvas)
if canvas: canvas._img.save(args.file+'.png', "PNG")
elif name == 'draw_map_by_hour':
imglist = [] if canvas else None
draw_map_by_hour(find_state, ' '.join(args.text), canvas=canvas, imglist=imglist)
if canvas:
for i in range(24):
imglist[i].save(args.file+'_'+str(i).zfill(2)+'.png', "PNG")
elif name == 'draw_centered_map':
draw_centered_map(' '.join(args.text), canvas=canvas)
if canvas: canvas._img.save(args.file+'.png', "PNG")
elif name == 'draw_state_sentiments':
draw_state_sentiments(average_sentiments(group_tweets_by_state(load_tweets(' '.join(args.text)), find_state),load_sentiments()), canvas=canvas)
wait(canvas=canvas)
if canvas: canvas._img.save(args.file+'.png', "PNG")
else:
globals()[name](' '.join(args.text))
def draw_centered_map(center_state='TX', n=10):
"""Draw the n states closest to center_state.
For example, to draw the 20 states closest to California (including California),
enter in the terminal:
# python3 trends.py CA 20
"""
us_centers = make_idict()
for i, s in idict_items(us_states):
us_centers = idict_insert(us_centers, i, find_center(s))
center = idict_select(us_centers, center_state.upper())
dist_from_center = lambda name: geo_distance(center, idict_select(us_centers, name))
for name in sorted(idict_keys(us_states), key=dist_from_center)[:int(n)]:
draw_state(idict_select(us_states, name))
draw_name(name, idict_select(us_centers, name))
draw_dot(center, 1, 10) # Mark the center state with a red dot
wait()
def draw_map_for_term(term='my job'):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
s = analyze_tweet_sentiment(tweet)
if has_sentiment(s):
draw_dot(tweet_location(tweet), sentiment_value(s))
if len(tweets) != 0:
draw_top_states(most_talkative_states(term))
else:
draw_top_states(None)
wait()
def draw_map_for_term(term='my job'):
"""Draw the sentiment map corresponding to the tweets that contain term.
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
s = analyze_tweet_sentiment(tweet)
if has_sentiment(s):
draw_dot(tweet_location(tweet), sentiment_value(s))
if len(tweets) != 0:
draw_top_states(most_talkative_states(term))
else:
draw_top_states(None)
wait()
def draw_map_for_term(term='Berkeley'):
"""
Draw the sentiment map corresponding to the tweets that match term.
term -- a word or phrase to filter the tweets by.
To visualize tweets containing the word "obama":
# python3 trends.py obama
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = calculate_average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
draw_dot(tweet_location(tweet), analyze_tweet_sentiment(tweet))
wait()
def draw_map_for_term(term='Berkeley'):
"""
Draw the sentiment map corresponding to the tweets that match term.
term -- a word or phrase to filter the tweets by.
To visualize tweets containing the word "obama":
# python3 trends.py obama
Some term suggestions:
New York, Texas, sandwich, my life, justinbieber
"""
tweets = load_tweets(make_tweet, term)
tweets_by_state = group_tweets_by_state(tweets)
state_sentiments = calculate_average_sentiments(tweets_by_state)
draw_state_sentiments(state_sentiments)
for tweet in tweets:
draw_dot(tweet_location(tweet), analyze_tweet_sentiment(tweet))
wait()
