def check_device_status(self):
'''Check if the device responds, and also save the size of the tracklogs
which's inside the respond.'''
verbose('checking device status.. ', newline=False)
buf = self._communicate(CHECK_STATUS)
success = True if OK in buf else False
if success:
verbose('ok')
self.logsize = convert_logsize_value(buf[-4:])
else:
fprint('error: is the device turned on?')
return success
def purge_log_on_device(self):
'''Delete all tracklogs from the device.'''
self.check_if_device_is_empty()
fprint('purge log on device.. ', newline=False)
buf = self._communicate(PURGE_LOG)
if OK not in buf:
die('error while trying to purge the log')
else:
while FINISH not in buf:
time.sleep(1)
buf += self.ser.read(self.ser.inWaiting())
fprint('ok')
def main():
twitter = get_twitter_API()
open('collect.txt', 'w')
f = open('collect.txt', 'a')
fprint('Collecting tweets........................', f)
fprint('Started collecting tweets From Twitter Based on hashtags', f)
min_id = 0
for tags in hashtags:
tweets, tweets_from_file, since_id, users = [], [], [], []
count = 0
fprint("Collecting data for : " + tags, f)
tweets_from_file = json_loader(tweets_from_file, tags)
if tweets_from_file:
for i in tweets_from_file:
since_id.append(i['since_id'])
if i['screen_name'] not in users:
users.append(i['screen_name'])
count += 1
min_id = min(since_id)
tweets = get_tweets(twitter, tags, tweets, min_id)
tweets = process_data(tweets)
for z in range(len(tweets)):
if tweets[z]['screen_name'] not in users:
users.append(tweets[z]['screen_name'])
count += 1
fprint(
"Total No. of Users who tweets for this hashtag : " + str(count),
f)
for i in tweets:
tweets_from_file.append(i)
json_writer(tweets_from_file, tags, f)
print('Tweets saved to each hashtags file \n')
f.close()
def download_data(self):
'''Download all tracklogs from the device.'''
self.check_if_device_is_empty()
verbose('switching device to download mode.. ', newline=False)
chunks = int(math.ceil(self.logsize / 128.0))
size_of_chunks = chunks * 128
if OK in self._communicate(INIT_DOWNLOAD_MAINNAV_MG_950D) or \
OK in self._communicate(INIT_DOWNLOAD_QSTART_BT_Q2000):
verbose('ok')
buf = ''
# download first chunk:
chunk = 1
buf += self._communicate(DOWNLOAD_CHUNK_FIRST, bytes=132)[3:-1]
while chunk < chunks: # download remaining chunks
chunk += 1
buf += self._communicate(DOWNLOAD_CHUNK_NEXT, bytes=132)[3:-1]
fprint('\rdownloading: %s%%' % int((len(buf) / float(size_of_chunks)) * 100), newline=False)
fprint('')
# only unnecessary data left, aborting:
self._communicate(ABORT_TRANSMISSION, answer=False)
verbose('switching device back to standard mode.. ', newline=False)
self._communicate(INIT_STANDARD, answer=False)
verbose('ok')
return buf
else:
fprint('error, unknown device')
def main():
print(
"--------------------------------- Started Summary -----------------------------------"
)
collector_details = open("collect.txt", 'r').read()
cluster_details = open("cluster.txt", 'r').read()
classify_details = open("classify.txt", 'r', encoding='utf-8').read()
open("summary.txt", 'w')
f = open("summary.txt", 'a', encoding='utf-8')
fprint("collect.py : \n" + collector_details, f)
fprint("cluster.py : \n" + cluster_details, f)
fprint("classify.py : \n" + classify_details, f)
f.close()
print("Details saved to : summary.txt")
print(
"---------------------------------- Finished summary -----------------------------------"
)
def main():
print(
"--------------------------------- Started Clustering -----------------------------------"
)
tweets = []
names = {}
open('cluster.txt', 'w')
f = open('cluster.txt', 'a')
for tags in hashtags:
tweets = json_loader(tweets, tags)
tweeter_names, _ = return_unique_user(tweets)
names[tags] = tweeter_names
teams = list(names.keys())
for i in range(len(teams)):
for j in range(i + 1, len(teams)):
a = list(set(names[teams[i]]) & set(names[teams[j]]))
fprint(
"Common number of followers for the team " + teams[i] +
" and " + teams[j] + " : " + str(len(a)), f)
graph = plot_graph(names)
g = draw_network_graph(graph, names, "clusters.png")
fprint(
'Graph has %d nodes and %d edges' %
(graph.order(), graph.number_of_edges()), f)
result = girvan_newman(g, 5, 20)
for i in range(1, len(result)):
fprint(
'Cluster %d Number of nodes or followers %d' %
(i, len(result[i])), f)
draw_network_graph(graph.subgraph(result[i]), result[i],
"cluster_" + str(i) + ".png")
print("Cluster %d nodes" % i, result[i], file=f)
f.close()
print(
"---------------------------------- Finished Clustering -----------------------------------"
)
def print_data(tag, test_data,predicted_labels):
pos_instance, neg_instance, neutral_instance, index_pos_instance, index_neg_instance, index_neutral_instance = 0,0,0,0,0,0
for i in range(len(predicted_labels)):
if predicted_labels[i] == "negative":
index_neg_instance = i
neg_instance+=1
elif predicted_labels[i] == "positive":
index_pos_instance = i
pos_instance+=1
elif predicted_labels[i] == "neutral":
index_neutral_instance = i
neutral_instance+=1
with open("classify.txt",'a', encoding="utf8") as fp:
fprint("*********************************"+tag +"**************************************", fp)
fprint("Positive Number of Tweets for "+tag+" : " + str(pos_instance), fp)
fprint("Negative Number of Tweets for "+tag+" : "+ str(neg_instance), fp)
fprint("Neutral Number of Tweets for "+tag+" : "+ str(neutral_instance), fp)
fprint("Positive Tweets Example for "+tag+" : " + str(test_data[index_pos_instance]).replace("\n",""), fp)
fprint("Negative Tweets Example for "+tag+" : " + str(test_data[index_neg_instance]).replace("\n",""), fp)
fprint("Neutral Tweets Example for "+tag+" : " + str(test_data[index_neutral_instance]).replace("\n",""), fp)
fp.close()
