def test_parse_line(self):
i = util.parse_line('acc +36')
self.assertEqual(i[0], 'acc')
self.assertEqual(i[1], 36)
i = util.parse_line('acc +36\n')
self.assertEqual(i[0], 'acc')
self.assertEqual(i[1], 36)
i = util.parse_line('acc -36')
self.assertEqual(i[0], 'acc')
self.assertEqual(i[1], -36)
# self.assertEqual(expected, util.func(args))
pass
def test_parse_line(self):
self.assertEqual(
('posh chartreuse', (2, 'faded blue'), (2, 'pale plum'),
(2, 'posh coral')),
util.parse_line(
'posh chartreuse bags contain 2 faded blue bags, 2 pale plum bags, 2 posh coral bags.'
))
self.assertEqual(
('clear purple', (4, 'bright red')),
util.parse_line('clear purple bags contain 4 bright red bags.'))
self.assertEqual(
('shiny crimson', ),
util.parse_line('shiny crimson bags contain no other bags.'))
def _downtime(bot, message, channel):
drinks = ['a beer', 'a scotch', 'a bloody mary', 'a nice glass of wine', 'F****N FOUR LOKO', 'a crisp cider']
action_string = "\001ACTION "
user = parse_line(message).user
if user.lower() == "george" or "thorogood" in user.lower():
bot.say(channel, action_string + ' gets ' + user + ' one bourbon, one scotch, one beer'+ "\001\n")
else:
bot.say(channel, action_string + ' gets ' + user + ' ' + random.choice(drinks)+ "\001\n")
def parse_reverse_replacements(data):
reverse_replacements = dict(parse_line(line)[::-1] for line in data)
ordered_reverse_replacements = OrderedDict()
for key in sorted(reverse_replacements.keys(), key=lambda x: len(x), reverse=True):
ordered_reverse_replacements[key] = reverse_replacements[key]
return ordered_reverse_replacements
def reflect_reload(bot, message, channel):
logger = bot.logger
parse_line(message)
# for k,v in parsed.__dict__.iteritems():
# print k, ": ", v
body = message.split(":")[2:]
recomposed = "".join(body)
if recomposed.startswith(".reflect reload"):
for m in recomposed.split()[2:]:
if m in sys.modules:
try:
imp.reload(sys.modules[m])
bot.brain.notice(channel, 'reloaded ' + m)
logger.write(
Logger.INFO,
bot.getName() +
": reloaded " +
m,
bot.NICK)
except ImportError as e:
bot.brain.notice(channel, 'failed to reload ' + m)
logger.write(
Logger.CRITICAL,
bot.getName() +
": failed to reload " +
m,
bot.NICK)
logger.write(
Logger.CRITICAL,
bot.getName() +
": error was:",
bot.NICK)
logger.write(
Logger.CRITICAL,
bot.getName() + ": " + e,
bot.NICK)
return
def handle(self, event):
self.bot.bare_send("WHOIS " + self.rets_current_nick)
idle_time = 0
for line in list(self.bot.recent_lines):
try:
if int(util.parse_line(line).message_number) == 317:
idle_time = int(line.split()[4])
except BaseException:
pass
# print(idle_time)
since = datetime.datetime.now() - datetime.timedelta(seconds=idle_time)
self.say(
event.channel, self.rets_current_nick + " has been idle since " +
since.strftime("%Y-%m-%d %H:%M:%S"))
def _downtime(bot, message, channel):
drinks = [
'a beer', 'a scotch', 'a bloody mary', 'a nice glass of wine',
'F****N FOUR LOKO', 'a crisp cider',
'a margarita from BoneKin and Jennos\'s house'
]
action_string = "\001ACTION "
user = parse_line(message).user
if user.lower() == "george" or "thorogood" in user.lower():
bot.say(
channel, action_string + ' gets ' + user +
' one bourbon, one scotch, one beer' + "\001\n")
else:
bot.say(
channel, action_string + ' gets ' + user + ' ' +
random.choice(drinks) + "\001\n")
def make(path): f = open(path, "r") width = 0 height = 0 map = [] #generate map count = 0 rows = [] columns = [] for line in f: l = util.parse_line(line) if count == 0: width = int(l[0]) #columns height = int(l[1]) #rows print(l) elif(count <= height): row = [] for segment in l: row.append(int(segment)) rows.append(row) print(row) else: col = [] for segment in l: col.append(int(segment)) columns.append(col) print(col) count += 1 bitmap = util.get_bitmap_vector(max(width, height)) print() gac = GAC() #gen variables vars = gen_variables(rows, columns, width, height, bitmap) for var in vars: gac.add_variable(var) #gen constraints constraints = gen_constraints(width, height) gac.constraints = constraints return gac, width, height
def make(path):
f = open(path, "r")
width = 0
height = 0
map = []
#generate map
count = 0
rows = []
columns = []
for line in f:
l = util.parse_line(line)
if count == 0:
width = int(l[0]) #columns
height = int(l[1]) #rows
print(l)
elif (count <= height):
row = []
for segment in l:
row.append(int(segment))
rows.append(row)
print(row)
else:
col = []
for segment in l:
col.append(int(segment))
columns.append(col)
print(col)
count += 1
bitmap = util.get_bitmap_vector(max(width, height))
print()
gac = GAC()
#gen variables
vars = gen_variables(rows, columns, width, height, bitmap)
for var in vars:
gac.add_variable(var)
#gen constraints
constraints = gen_constraints(width, height)
gac.constraints = constraints
return gac, width, height
def reflect_reload(bot, message, channel):
logger = bot.logger
parsed = parse_line(message)
#for k,v in parsed.__dict__.iteritems():
# print k, ": ", v
body = message.split(":")[2:]
recomposed = "".join(body)
if recomposed.startswith(".reflect reload"):
for m in recomposed.split()[2:]:
if m in sys.modules:
try:
reload(sys.modules[m])
bot.brain.notice(channel, 'reloaded ' + m)
logger.write(Logger.INFO, bot.getName() + ": reloaded " + m, bot.NICK)
except ImportError as e:
bot.brain.notice(channel, 'failed to reload ' + m)
logger.write(Logger.CRITICAL, bot.getName() + ": failed to reload " + m, bot.NICK)
logger.write(Logger.CRITICAL, bot.getName() + ": error was:", bot.NICK)
logger.write(Logger.CRITICAL, bot.getName() + ": " + e, bot.NICK)
return
def reload(bot, message, channel):
logger = bot.logger
parsed = parse_line(message)
if parsed.startswith(".snippets reload"):
try:
bot.load_snippets()
bot.set_snippets()
bot.brain.notice(channel, "snippets reloaded")
except BaseException:
e = sys.exc_info()[0]
logger.write(
Logger.WARNING,
bot.getName() +
": failed to reload snippets",
bot.NICK)
logger.write(
Logger.WARNING,
bot.getName() +
": error was:",
bot.NICK)
logger.write(Logger.WARNING, bot.getName() + ": " + e, bot.NICK)
def main():
# 遍历所有文件
count = 0
size = 0
save_file = open(save_file_path, 'w')
for root, dirs, file_names in os.walk(dataset_folder_path):
if len(dirs) != 0:
continue
print root, dirs, file_names
for file_name in file_names:
if file_name == '.DS_Store': # avoid error in macos
continue
file_path = join(root, file_name)
# open file
size += getsize(file_path)
if size < 5:
continue
save_file.write('###################### %s' % file_name + os.linesep)
with open(file_path, 'r') as f:
count += 1
for line in f:
line = line.rstrip()
if line == '':
continue
# print line
new_line = parse_line(line)
# save line
save_file.write(new_line + os.linesep)
# new article separate
save_file.close()
print 'count: {}'.format(count)
print 'size: {}'.format(size)
print 'size (m): {}'.format(size / (1024 * 1024))
'''
def get(self):
tx = self.request.get('tx')
response = confirm(tx)
if not response.startswith('SUCCESS'):
print "Suspicious: invalid autoreturn"
self.response.write("Suspicious: invalid autoreturn")
return
params = util.parse_line(response)
if not util.check_transaction(params):
return
name, email = params['custom'].split('|')
name = name.replace('+', ' ')
#assume email is already vetted
if not my_db.get(email):
user = my_db.new_user(name, email, params['txn_id'])
user.put()
passwordReset = my_db.new_reset(email)
passwordReset.put()
#add password reset key
html2 = html.password_reset(key=str(passwordReset.key()), email=email)
self.response.write(html2)
def extract_items_features(train_file_path,begin_date,end_date):
# 按商品id排序
generate_sortedfile(train_file_path, "sorted_by_item-" + train_file_path, 1)
train_file = open("sorted_by_item-" + train_file_path)
items_feat_file_path = "./feature/" + begin_date + "-" + end_date + "-itemfeat.csv"
items_features_file = open(items_feat_file_path, 'w')
userful_feat_file_path = "./feature/" + begin_date + "-" + end_date + "-itemusefulfeat.csv"
useful_features_file = open(userful_feat_file_path, 'w')
tmp_features = {}
initial_tmp_features(tmp_features)
# 输出栏位名
other_features = get_other_basic_item_features(tmp_features) # 获取用户其他特征
item_features,userful_features = merge_features(tmp_features, other_features,item_feat_name,useful_feat_name)
items_features_file.write("item_id" + "," + get_features_key(item_features) + "\n")
useful_features_file.write("item_id" + "," + get_features_key(userful_features) + "\n")
initial_tmp_features(tmp_features)
global lastdate
lastdate = datetime.strptime(end_date, "%Y-%m-%d").date() - timedelta(days=1)
pre_item_id = train_file.readline().split(delimiter)[1] # 获取第一行的item_id
train_file.seek(0)
for line in train_file:
user_id, item_id, behavior_type, user_geohash, item_category, date = parse_line(line)
# 如果前一个物品pre_item_id和读取到的item_id不一样则输出当前item_features并置空
if not item_id == pre_item_id:
other_features = get_other_basic_item_features(tmp_features) # 获取用户其他特征
item_features,userful_features = merge_features(tmp_features, other_features,item_feat_name,useful_feat_name)
items_features_file.write(pre_item_id + "," + get_features_key(item_features) + "\n")
useful_features_file.write(pre_item_id + "," + get_features_key(userful_features) + "\n")
initial_tmp_features(tmp_features) # 初始化置空item_features
##计算当前item基本特征
#行为基本特征计算
xxx_counts = basic_feature_behaviour[behavior_type][0]
xxx_user = basic_feature_behaviour[behavior_type][1]
user_xxx_first_time = basic_feature_behaviour[behavior_type][2]
eachday_xxx_counts = basic_feature_behaviour[behavior_type][3] #每天销售量、收藏量
tmp_features[xxx_counts] += 1
tmp_features[xxx_user][user_id] = tmp_features[xxx_user].get(user_id, 0) + 1
tmp_features[eachday_xxx_counts][date.date()] = tmp_features[eachday_xxx_counts].get(date.date(), 0) + 1
#new-added
if behavior_type == 4:
week = datetime.datetime(date.date()).strftime("%w")
tmp_features['buy_week_list'][week] += 1#一周购买量分布
tmp_features['multibuy_time_list'][user_id].append(date.date()) #多次购买时间间隔均值
#收藏到购买时间间隔均值
if behavior_type == 2:
tmp_features['save_to_buy_time'][user_id]['save_time'].append(date.date())
if behavior_type == 4:
tmp_features['save_to_buy_time'][user_id]['buy_time'].append(date.date())
#收藏到购买时间间隔均值
if behavior_type == 2:
tmp_features['cart_to_buy_time'][user_id]['cart_time'].append(date.date())
if behavior_type == 4:
tmp_features['cart_to_buy_time'][user_id]['buy_time'].append(date.date())
if user_id not in tmp_features[user_xxx_first_time]:
tmp_features[user_xxx_first_time][user_id] = date.date()
pre_item_id = item_id
#最后一个item特征
other_features = get_other_basic_item_features(tmp_features)
item_features,userful_features = merge_features(tmp_features, other_features,item_feat_name,useful_feat_name)
items_features_file.write(pre_item_id + "," + get_features_key(item_features) + "\n")
useful_features_file.write(pre_item_id + "," + get_features_key(userful_features) + "\n")
train_file.close()
items_features_file.close()
return items_feat_file_path
def extract_items_features(train_file_path, begin_date, end_date):
# 按商品id排序
generate_sortedfile(train_file_path, "sorted_by_item-" + train_file_path,
1)
train_file = open("sorted_by_item-" + train_file_path)
items_feat_file_path = "./feature/" + begin_date + "-" + end_date + "-itemfeat.csv"
items_features_file = open(items_feat_file_path, 'w')
userful_feat_file_path = "./feature/" + begin_date + "-" + end_date + "-itemusefulfeat.csv"
useful_features_file = open(userful_feat_file_path, 'w')
tmp_features = {}
initial_tmp_features(tmp_features)
# 输出栏位名
other_features = get_other_basic_item_features(tmp_features) # 获取用户其他特征
item_features, userful_features = merge_features(tmp_features,
other_features,
item_feat_name,
useful_feat_name)
items_features_file.write("item_id" + "," +
get_features_key(item_features) + "\n")
useful_features_file.write("item_id" + "," +
get_features_key(userful_features) + "\n")
initial_tmp_features(tmp_features)
global lastdate
lastdate = datetime.strptime(end_date,
"%Y-%m-%d").date() - timedelta(days=1)
pre_item_id = train_file.readline().split(delimiter)[1] # 获取第一行的item_id
train_file.seek(0)
for line in train_file:
user_id, item_id, behavior_type, user_geohash, item_category, date = parse_line(
line)
# 如果前一个物品pre_item_id和读取到的item_id不一样则输出当前item_features并置空
if not item_id == pre_item_id:
other_features = get_other_basic_item_features(
tmp_features) # 获取用户其他特征
item_features, userful_features = merge_features(
tmp_features, other_features, item_feat_name, useful_feat_name)
items_features_file.write(pre_item_id + "," +
get_features_key(item_features) + "\n")
useful_features_file.write(pre_item_id + "," +
get_features_key(userful_features) +
"\n")
initial_tmp_features(tmp_features) # 初始化置空item_features
##计算当前item基本特征
#行为基本特征计算
xxx_counts = basic_feature_behaviour[behavior_type][0]
xxx_user = basic_feature_behaviour[behavior_type][1]
user_xxx_first_time = basic_feature_behaviour[behavior_type][2]
eachday_xxx_counts = basic_feature_behaviour[behavior_type][
3] #每天销售量、收藏量
tmp_features[xxx_counts] += 1
tmp_features[xxx_user][user_id] = tmp_features[xxx_user].get(
user_id, 0) + 1
tmp_features[eachday_xxx_counts][date.date(
)] = tmp_features[eachday_xxx_counts].get(date.date(), 0) + 1
#new-added
if behavior_type == 4:
week = datetime.datetime(date.date()).strftime("%w")
tmp_features['buy_week_list'][week] += 1 #一周购买量分布
tmp_features['multibuy_time_list'][user_id].append(
date.date()) #多次购买时间间隔均值
#收藏到购买时间间隔均值
if behavior_type == 2:
tmp_features['save_to_buy_time'][user_id]['save_time'].append(
date.date())
if behavior_type == 4:
tmp_features['save_to_buy_time'][user_id]['buy_time'].append(
date.date())
#收藏到购买时间间隔均值
if behavior_type == 2:
tmp_features['cart_to_buy_time'][user_id]['cart_time'].append(
date.date())
if behavior_type == 4:
tmp_features['cart_to_buy_time'][user_id]['buy_time'].append(
date.date())
if user_id not in tmp_features[user_xxx_first_time]:
tmp_features[user_xxx_first_time][user_id] = date.date()
pre_item_id = item_id
#最后一个item特征
other_features = get_other_basic_item_features(tmp_features)
item_features, userful_features = merge_features(tmp_features,
other_features,
item_feat_name,
useful_feat_name)
items_features_file.write(pre_item_id + "," +
get_features_key(item_features) + "\n")
useful_features_file.write(pre_item_id + "," +
get_features_key(userful_features) + "\n")
train_file.close()
items_features_file.close()
return items_feat_file_path
import util
input_path = 'elb_log_file.txt'
output_path = 'anonymized_data.txt'
o_f = open(output_path, "w+")
with open(input_path) as fp:
line = fp.readline()
record = ""
while line:
stripped_line = line.strip()
if line == "\n":
parsed_line = util.parse_line(record)
if len(parsed_line) > 0:
o_f.write(parsed_line)
o_f.write('\n')
record = ""
else:
record = record + " " + stripped_line
line = fp.readline()
o_f.close()
def main():
# 遍历所有文件
count = 0
size = 0
article_count = 0
# 词在语料库中的总次数
total_word_counter = Counter()
# 词在文章中出现的次数
articles_counter = Counter()
# 文章中每一个词出现的次数
article_word_counter_dict = {}
for root, dirs, file_names in os.walk(dataset_folder_path):
if len(dirs) != 0:
continue
print root, dirs, file_names
for file_name in file_names:
if file_name == '.DS_Store': # avoid error in macos
continue
file_path = join(root, file_name)
# open file
size += getsize(file_path)
if size < 5:
continue
article_word_counter = Counter()
article_token_words = []
with codecs.open(file_path, 'r', encoding='utf-8') as f:
count += 1
for line in f:
# 某个词是否在某篇文章中出现
line = line.rstrip()
if line == '':
continue
# print line
new_line = parse_line(line)
# token_words = new_line.split()
cleaned_words = remove_words(new_line, stop_words)
total_word_counter.update(cleaned_words)
article_token_words += cleaned_words
article_word_counter.update(article_token_words)
article_word_counter_dict[file_name] = article_word_counter
# 变为set
article_token_words_set = set(article_token_words)
articles_counter.update(article_token_words_set)
article_count += 1
words_idf = {}
# 计算每一个词的IDF（逆文档频率）
for word, count in articles_counter.iteritems():
idf = math.log(article_count / (count + 1))
words_idf[word] = idf
pickle.dump(words_idf, open("./../data/people's_daily_words_idf.pkl", "wb"))
pickle.dump(article_word_counter_dict, open("./../data/people's_daily_article_word_counter_dict.pkl", "wb"))
pickle.dump(total_word_counter, open("./../data/people's_daily_word_counter.pkl", "wb"))
print('words_idf: {}'.format(words_idf))
computers = set()
auth_types = set()
logon_types = set()
filenames = os.listdir(dir_path)
for filename in tqdm(filenames):
if not re.findall("txt$", filename):
continue
filepath = os.path.join(dir_path, filename)
if not os.path.isfile(filepath):
continue
f = open(filepath)
for line in f:
try:
values = parse_line(line)
except AssertionError:
os.remove(filepath)
continue
src_user, dest_user = values['src_user'], values['dest_user']
src_comp, dest_comp = values['src_comp'], values['dest_comp']
auth_type, logon_type = values['auth_type'], values['logon_type']
for user in [src_user, dest_user]:
if user and user not in users:
users.add(user)
for computer in [src_comp, dest_comp]:
if computer and computer not in computers:
computers.add(computer)
if auth_type not in auth_types:
def extract_items_features(train_file_path,begin_date,end_date):
# 按商品id排序
print "\n" + begin_date + "---" + end_date + "extracting items features..."
generate_sortedfile(train_file_path, "temp/sorted_by_item-" + train_file_path.split('/')[-1], 1)
train_file = open("temp/sorted_by_item-" + train_file_path.split('/')[-1])
items_feat_file_path = "./feature/" + begin_date + "-" + end_date + "-itemfeat.csv"
items_features_file = open(items_feat_file_path, 'w')
tmp_features = {}
initial_tmp_features(tmp_features)
# 输出栏位名
other_features = get_other_basic_item_features(tmp_features) # 获取用户其他特征
all_features = merge_features(tmp_features, other_features)
# print get_features_key(all_features)
items_features_file.write("item_id" + "," + get_features_key(all_features) + "\n")
initial_tmp_features(tmp_features)
global lastday
lastday = datetime.strptime(end_date, "%Y-%m-%d").date() - timedelta(days=1)
pre_item_id = train_file.readline().split(delimiter)[1] # 获取第一行的item_id
train_file.seek(0)
for line in train_file:
user_id, item_id, behavior_type, user_geohash, item_category, date = parse_line(line)
# 如果前一个物品pre_item_id和读取到的item_id不一样则输出当前item_features并置空
if not item_id == pre_item_id:
other_features = get_other_basic_item_features(tmp_features) # 获取用户其他特征
all_features = merge_features(tmp_features,other_features)
items_features_file.write(pre_item_id + "," + get_item_features_str(all_features) + "\n") # 输出当前item_features
initial_tmp_features(tmp_features) # 初始化置空item_features
##计算当前item基本特征
#行为基本特征计算
xxx_counts = basic_feature_behaviour[behavior_type][0]
xxx_user = basic_feature_behaviour[behavior_type][1]
user_xxx_first_time = basic_feature_behaviour[behavior_type][2]
eachday_xxx_counts = basic_feature_behaviour[behavior_type][3]
tmp_features[xxx_counts] += 1
tmp_features[xxx_user][user_id] = tmp_features[xxx_user].get(user_id, 0) + 1
tmp_features[eachday_xxx_counts][date.date()] = tmp_features[eachday_xxx_counts].get(date.date(), 0) + 1
if user_id not in tmp_features[user_xxx_first_time]:
tmp_features[user_xxx_first_time][user_id] = date.date()
pre_item_id = item_id
#最后一个item特征
other_features = get_other_basic_item_features(tmp_features)
all_features = merge_features(tmp_features,other_features)
# print item_features
# 输出最后一个item_features到文件
items_features_file.write(pre_item_id + "," + get_item_features_str(all_features) + "\n")
train_file.close()
items_features_file.close()
print "extract" + begin_date + "---" + begin_date + "items features completed"
return items_feat_file_path
def extract_categorys_features(train_file_path, begin_date, end_date):
# 按商品id排序
print "\n" + begin_date + "---" + end_date + "extracting categorys features..."
generate_sortedfile(
train_file_path,
"temp/sorted_by_category-" + train_file_path.split('/')[-1], 4)
train_file = open("temp/sorted_by_category-" +
train_file_path.split('/')[-1])
categorys_feat_file_path = "./feature/" + begin_date + "-" + end_date + "-categoryfeat.csv"
categorys_features_file = open(categorys_feat_file_path, 'w')
tmp_features = {}
initial_tmp_features(tmp_features)
# 输出栏位名
other_features = get_other_basic_category_features(tmp_features) # 获取其他特征
all_features = merge_features(tmp_features, other_features)
# print "category_featurename is :\n",get_features_key(all_features)
categorys_features_file.write("category_id" + "," +
get_features_key(all_features) + "\n")
initial_tmp_features(tmp_features)
global lastday
lastday = datetime.strptime(end_date,
"%Y-%m-%d").date() - timedelta(days=1)
pre_category_id = train_file.readline().split(delimiter)[
4] # 获取第一行的category_id
train_file.seek(0)
for line in train_file:
user_id, item_id, behavior_type, user_geohash, item_category, date = parse_line(
line)
# 如果前一个物品pre_category_id和读取到的item_category不一样则输出当前category_features并置空
if not item_category == pre_category_id:
other_features = get_other_basic_category_features(
tmp_features) # 获取用户其他特征
all_features = merge_features(tmp_features, other_features)
categorys_features_file.write(
pre_category_id + "," +
get_category_features_str(all_features) +
"\n") # 输出当前category_features
initial_tmp_features(tmp_features) # 初始化置空category_features
##计算当前category基本特征
#行为基本特征计算
xxx_counts = basic_feature_behaviour[behavior_type][0]
xxx_user = basic_feature_behaviour[behavior_type][1]
user_xxx_first_time = basic_feature_behaviour[behavior_type][2]
eachday_xxx_counts = basic_feature_behaviour[behavior_type][3]
tmp_features[xxx_counts] += 1
tmp_features[xxx_user][user_id] = tmp_features[xxx_user].get(
user_id, 0) + 1
tmp_features[eachday_xxx_counts][date.date(
)] = tmp_features[eachday_xxx_counts].get(date.date(), 0) + 1
if user_id not in tmp_features[user_xxx_first_time]:
tmp_features[user_xxx_first_time][user_id] = date.date()
pre_category_id = item_category
#最后一个category特征
other_features = get_other_basic_category_features(tmp_features)
all_features = merge_features(tmp_features, other_features)
# print category_features
# 输出最后一个category_features到文件
categorys_features_file.write(pre_category_id + "," +
get_category_features_str(all_features) +
"\n")
train_file.close()
categorys_features_file.close()
print "extract" + begin_date + "---" + begin_date + "categorys features completed"
return categorys_feat_file_path
def processline(self, line):
"""
Grab newline-delineated lines sent to us, and determine what to do with them.
This function handles our initial low-level IRC stuff, as well; if we haven't joined, it waits for the MOTD message (or message indicating there isn't one) and then issues our own JOIN calls.
Also immediately passes off PING messages to PONG.
Args:
line: string.
"""
self.recent_lines.appendleft(line)
if self.DEBUG:
if os.name == "posix": # because windows doesn't like the color codes.
self.debug_print(util.bcolors.OKBLUE + "<< " +
util.bcolors.ENDC + line)
else:
self.debug_print("<< " + ": " + line)
message_number = line.split()[1]
try:
first_word = line.split(":", 2)[2].split()[0]
channel = line.split()[2]
except IndexError:
pass
else:
if first_word in self.command_function_map:
self.command_function_map[first_word](self,
parse_line(line).message,
channel)
try:
for e in self.events_list:
if e.matches(line):
e.notifySubscribers(line)
# don't bother going any further if it's a PING/PONG request
if line.startswith("PING"):
ping_response_line = line.split(":", 1)
self.pong(ping_response_line[1])
# pings we respond to directly. everything else...
else:
# patch contributed by github.com/thekanbo
if self.JOINED is False and (message_number == "376"
or message_number == "422"):
# wait until we receive end of MOTD before joining, or
# until the server tells us the MOTD doesn't exist
if not self.chan_list:
self.chan_list = self.conf.getChannels(self.network)
for c in self.chan_list:
self.send(('JOIN ' + c + ' \n').encode())
self.JOINED = True
line_array = line.split()
user_and_mask = line_array[0][1:]
usr = user_and_mask.split("!")[0]
channel = line_array[2]
try:
message = line.split(":", 2)[2]
self.brain.respond(usr, channel, message)
except IndexError:
try:
message = line.split(":", 2)[1]
self.brain.respond(usr, channel, message)
except IndexError:
print(("index out of range.", line))
except Exception:
print(("Unexpected error:", sys.exc_info()[0]))
traceback.print_exc(file=sys.stdout)
