import time
import text_utils as txu
import twitter_utils as twu
'''
Implementing the second feature which would produce the median for tweets in a file ft2.txt
'''
# Calculate the time when the processing starts
start = time.clock()
# The mode of input can be a text file or twitter api json
inp, outp = txu.extract_arguments()
tweets = twu.get_input(inp)
#initialize the median dictionary
median_dict = {'median': 0.0,
'length': 0}
outfile = open(outp, 'w')
for tweet in tweets:
median_dict = txu.get_median_iterative(tweet, median_dict)
outfile.write("{0:.2f}".format(round(median_dict['median'],2))+"\n")
outfile.close()
# Calculate the time processing ends
end = time.clock()
# Print total time taken
print "Total time taken in processing median: ", end - start
import time
import text_utils as txu
import twitter_utils as twu
'''
Implementing the first feature which would produce the total count for each word in a file ft1.txt
'''
# Calculate the time when the processing starts
start = time.clock()
inp, outp = txu.extract_arguments()
# The mode of input can be a text file or twitter api json
tweets = '\n'.join(twu.get_input(inp))
words = txu.extract_words(tweets)
counter = txu.get_counter(words)
txu.write_file(
"\n".join("{} \t\t\t\t\t {}".format(k, v)
for k, v in sorted(dict(counter).items())), outp)
# Calculate the time processing ends
end = time.clock()
# Print total time taken
print "Total time taken in processing word count: ", end - start
import time
import text_utils as txu
import twitter_utils as twu
'''
Implementing the second feature which would produce the median for tweets in a file ft2.txt
'''
# Calculate the time when the processing starts
start = time.clock()
# The mode of input can be a text file or twitter api json
inp, outp = txu.extract_arguments()
tweets = twu.get_input(inp)
#initialize the median dictionary
median_dict = {'median': 0.0, 'length': 0}
outfile = open(outp, 'w')
for tweet in tweets:
median_dict = txu.get_median_iterative(tweet, median_dict)
outfile.write("{0:.2f}".format(round(median_dict['median'], 2)) + "\n")
outfile.close()
# Calculate the time processing ends
end = time.clock()
# Print total time taken
print "Total time taken in processing median: ", end - start
import time
import text_utils as txu
import twitter_utils as twu
'''
Implementing the first feature which would produce the total count for each word in a file ft1.txt
'''
# Calculate the time when the processing starts
start = time.clock()
inp, outp = txu.extract_arguments()
# The mode of input can be a text file or twitter api json
tweets = '\n'.join(twu.get_input(inp))
words = txu.extract_words(tweets)
counter = txu.get_counter(words)
txu.write_file("\n".join("{} \t\t\t\t\t {}".format(k, v) for k, v in sorted(dict(counter).items())),outp)
# Calculate the time processing ends
end = time.clock()
# Print total time taken
print "Total time taken in processing word count: ", end - start
