def search(query, page = 1, size=10):
lasttime = time.clock()
suggestor.add_query(query)
tokens = stop_and_stem_seq(parse(query, ''))
result = get_doc_list(query, tokens)
result = collect(result[(page - 1)*size:page*size], tokens, len(result), lasttime)
return result
def search(query, page=1, size=10):
lasttime = time.clock()
suggestor.add_query(query)
tokens = stop_and_stem_seq(parse(query, ''))
result = get_doc_list(query, tokens)
result = collect(result[(page - 1) * size:page * size], tokens,
len(result), lasttime)
return result
def run(self, command):
parsed = analysis.parse(command)
print parsed
print self._call_api(parsed)
while error != "":
error = input("Error " + email + " is subscribed to: ").upper()
if error == "":
continue
if error in emails:
emails[error].add(email)
else:
print("Invalid error")
num_emails = {}
for error in emails:
for email in emails[error]:
num_emails[email] = 0
timestamps = {}
#populate datasets
analysis.parse(logs, errors, emails, num_emails, timestamps)
num_errors = analysis.num_errors(errors)
worst_to_best = analysis.most_to_least(errors)
frequent_to_unfrequent = analysis.most_to_least(timestamps)
#output results of analyses
if num_emails != {}:
print("Email Summary:")
for email in num_emails:
print(email + ": " + str(num_emails[email]) + " emails sent")
print("There are " + str(num_errors) + " unique errors in the log.")
print("From most to least problematic, the different error categories are:")
for error in worst_to_best:
print(error[0] + ": " + str(error[1]) + " times")
print(
"From most to least common, the hours of the day when errors occured are:")
for hour in frequent_to_unfrequent:
)
sys.exit()
for i in range(1, len(sys.argv)):
try:
if sys.argv[i] == '-t':
thorough = True
if sys.argv[i] == '-r':
ratio = float(sys.argv[i+1])
if sys.argv[i].endswith('.csv'):
file = '../files/' + sys.argv[i]
except:
print("'python main.py help' to see a list of possible arguments")
sys.exit()
'''
print('[' + str(datetime.datetime.now().time()) + '] Initializing parsing')
object_list = analysis.parse(file)
print('\n[' + str(datetime.datetime.now().time()) + '] Updating vendor list')
analysis.pull_vendors()
print('[' + str(datetime.datetime.now().time()) + '] Identifying vendors')
vendorlist = analysis.find_vendors(object_list)
print('[' + str(datetime.datetime.now().time()) +
'] Pulling vendor product lists')
for vendor in vendorlist:
analysis.pull_products(vendor)
print('[' + str(datetime.datetime.now().time()) + '] Identifying products')
object_list = identify.determine_products(object_list, ratio, thorough)
