def main():
#read in data and parse
files = ['data/iris.csv'] ###### EDIT ######
train_df = pd.read_csv(files[0], header=None) ###### EDIT ######
file_output = "output/output_iris_new.txt" ###### EDIT ######
#map classifier as binary
train_df[4] = train_df[4].map({
'Iris-setosa': 0,
'Iris-versicolor': 1,
'Iris-virginica': 1
}).astype(int) ###### EDIT ######
#separate models
X_train = train_df.drop(4, axis=1) ###### EDIT ######
Y_train = train_df[4] ###### EDIT ######
X_train, X_test, Y_train, Y_test = train_test_split(X_train,
Y_train,
train_size=0.75,
test_size=0.25)
output = [0] * iters
#print("------Feature Selection Complete------")
for i in range(iters):
models = []
for j in range(0, 15): ###### EDIT ######
#try out a new classifier
pipeline1 = Pipeline([
('dtc',
DecisionTreeClassifier(max_features=random.randint(1, 4),
max_depth=random.randint(1, 200),
random_state=random.randint(1, 5000))
) ###### EDIT ######
])
models.append(pipeline1)
output[i] = {}
# Function calls to create and test ensembles
###### EDIT NAMES AND PROGRESS ######
output[i]['super_dtc'] = add_superlearner('super_dtc', models, X_train,
Y_train, X_test, Y_test)
print("--------------- 10% ---------------")
output[i]['sub_dtc'] = add_subsemble('sub_dtc', models, X_train,
Y_train, X_test, Y_test)
print("--------------- 20% ---------------")
output[i]['blend_dtc'] = add_blend('blend_dtc', models, X_train,
Y_train, X_test, Y_test)
print("--------------- 30% ---------------")
models = []
for j in range(0, 15): ###### EDIT ######
#try out a new classifier
pipeline1 = Pipeline([
('knc', KNeighborsClassifier(n_neighbors=random.randint(1, 20))
) ###### EDIT ######
])
models.append(pipeline1)
###### EDIT NAMES AND PROGRESS ######
output[i]['super_knc'] = add_superlearner('super_knc', models, X_train,
Y_train, X_test, Y_test)
print("--------------- 40% ---------------")
output[i]['sub_knc'] = add_subsemble('sub_knc', models, X_train,
Y_train, X_test, Y_test)
print("--------------- 50% ---------------")
output[i]['blend_knc'] = add_blend('blend_knc', models, X_train,
Y_train, X_test, Y_test)
print("--------------- 60% ---------------")
t = Texttable()
average_acc = {}
average_time = {}
t.add_row([
'Dataset', 'Ensemble', 'Meta Classifier', 'Accuracy Score', 'Runtime'
])
for i in range(iters):
for key, value in output[i].iteritems():
t.add_row([
key, output[i][key]["Ensemble"],
output[i][key]["Meta_Classifier"],
output[i][key]["Accuracy_Score"], output[i][key]["Runtime"]
])
if (i == 0):
average_acc[key] = output[i][key]["Accuracy_Score"]
average_time[key] = output[i][key]["Runtime"]
else:
average_acc[
key] = average_acc[key] + output[i][key]["Accuracy_Score"]
average_time[
key] = average_time[key] + output[i][key]["Runtime"]
for key, value in average_acc.iteritems():
t.add_row(
["Average", key, "SVC", value / iters, average_time[key] / iters])
print(t.draw())
if (file_output != None):
print("Saving output to: {}".format(file_output))
orig_stdout = sys.stdout
f = open(file_output, 'w')
sys.stdout = f
print(t.draw())
sys.stdout = orig_stdout
f.close()
def print_data_training(decoded, actions):
t = Texttable(max_width=130)
t.add_rows([["Decoded", "Action"]])
for decode, action in zip(decoded, actions):
t.add_row([str(decode), str(action)])
print(t.draw())
if neighbors_id1 in killneighbors or neighbors_id2 in killneighbors:
continue
killneighbors.append(neighbors_id1)
killneighbors.append(neighbors_id2)
for item in user_rate_dic[neighbors_id2]:
if item[2] not in killneighbors:
killneighbors.append(item[2])
#且加到宿舍，第三个
break
for item2 in user_rate_dic[item[2]]:
#找第三个的邻居
if item2[2] not in killneighbors:
killneighbors.append(item2[2])
#todo 加到宿舍，第四个
break
table = Texttable()
table.set_deco(Texttable.HEADER)
table.set_cols_dtype(['t', 't', 't', 't']) # automatic
table.set_cols_align(["l", "l", "l", "l"])
#print ("id1:",neighbors_id1)
#print ("id2:",neighbors_id2)
rows.append([neighbors_id1, neighbors_id2, item[2], item2[2]])
table.add_rows(rows)
#取下一个贪心最大值
print(table.draw())
#all_neighbors_dis.sort(reverse=True)
#user_2=[ i[1] for i in all_neighbors_dis]
#user_3=[ i[2] for i in all_neighbors_dis]
#print (all_neighbors_dis)
def do_compare(input, output):
count = 0
sum_percent = 0
bigger = []
not_converted = []
table = [['File', 'Original', 'Transcoded', 'Percent']]
total_i = 0
total_o = 0
for i, o in get_input_output(input, output):
name = os.path.basename(i)
if os.path.exists(o):
i_size = os.path.getsize(i)
o_size = os.path.getsize(o)
percent = o_size / float(i_size) * 100
if percent < 15:
not_converted.append('{} (Too small, {:2f}%)'.format(
name, percent))
elif o_size > i_size:
bigger.append((name, o, i_size, o_size, percent))
else:
count += 1
total_i += i_size
total_o += o_size
sum_percent += percent
table.append([
name,
sizeof_fmt(i_size),
sizeof_fmt(o_size),
_f_percent(percent)
])
else:
not_converted.append(name)
if count > 0:
table.append(['', '', '', ''])
per = total_o / float(total_i) * 100
table.append([
'Total',
sizeof_fmt(total_i),
sizeof_fmt(total_o),
_f_percent(per)
])
avg = sum_percent / count
table.append(['Average', '', '', _f_percent(avg)])
t = Texttable(max_width=0)
t.set_deco(Texttable.VLINES | Texttable.HEADER)
t.set_cols_align(['l', 'r', 'r', 'r'])
t.add_rows(table)
print(t.draw())
print('{} Larger than original'.format(len(bigger)))
for i, o, i_size, o_size, percent in bigger:
print('{}: {} -> {} ({:.2f}%)'.format(i, sizeof_fmt(i_size),
sizeof_fmt(o_size), percent))
if len(not_converted) > 0:
print('Not Converted:')
for i in not_converted:
print(' {}'.format(i))
# Lambda function for resetting terminal window "reset for Raspberry Pi, cls for PC"
clear = lambda: os.system('reset')
#clear = lambda: os.system('cls')
clear()
# Lists hold names of those who want leftovers. Set to be wiped after the reset time: reset_time.
lunch_list = []
dinner_list = []
initialize = 0
last_message_id = ''
# Table creation and initialization
lunch_output_table = Texttable()
dinner_output_table = Texttable()
# Extra spaces to allow for large names to not be on multiple lines
dinner_output_table.add_row([[' Dinner ']])
lunch_output_table.add_row([[' Lunch ']])
lunch_output_table.set_cols_align("c")
dinner_output_table.set_cols_align("c")
# Print Banner
print(text.Text("LATE PLATE BOT", color='0000ff', shadow=True, skew=5))
print(lunch_output_table.draw())
print(dinner_output_table.draw())
# Used to make sure fake names are not put onto the lists.
unadjusted_member_list = open(member_path, "r").read().splitlines()
def __init__(self, rowsArray):
self.__table = Texttable()
self.__rows_array = rowsArray
self.__table.add_rows(rowsArray, header=False)
for collab in r:
sys.stdout.write("%s (%d)\n" %
(collab['login'], collab['contributions']))
if not collab['login'] in t_collab:
t_collab[collab['login']] = 0
t_collab[collab['login']] = t_collab[
collab['login']] + collab['contributions']
else:
sys.stdout.write("-\n")
sys.stdout.write("\n")
print(
colored('[+] %d contributors found, reading profiles...\n' % len(t_collab),
'green'))
tab = Texttable(300)
tab.header(
['Contributions', 'Profile', 'Name', 'Email', 'Company', 'Public repos'])
# tab.set_max_width( 100 )
def grabUserHtmlLight(ghaccount, login):
url = 'https://github.com/' + login
try:
r = requests.get(url, timeout=5)
except Exception as e:
print(colored("[-] error occurred: %s" % e, 'red'))
return False
if not 'Not Found' in r.text:
def select_movies():
clear()
result1= pyfiglet.figlet_format("Select Movie: ".center(40))
print(result1)
movies = {}
# Storing the contents of the movies file in movies dictionary
with open('movies.csv', 'r') as read_file:
csv_reader = list(csv.reader(read_file))
for line in csv_reader:
if line == []:
continue
movies[line[0]] = {}
movies[line[0]]['name'] = line[0]
movies[line[0]]['Rating'] = line[1]
movies[line[0]]['genre'] = line[2]
movies[line[0]]['language'] = line[3]
movies[line[0]]['release'] = line[4]
movies[line[0]]['length'] = line[5]
movies[line[0]]['advisory'] = line[6]
final = [['Movies']]
add = []
print('Movies Now Showing'.title())
print()
for dictmovie in movies.keys():
add.append(dictmovie.title())
final.append(add)
add = []
Cinema = Texttable()
Cinema.add_rows(final)
print(Cinema.draw())
# Enter the movie and check if the movie is present in the movies dictionary
movie = input('Enter your movie: ').lower()
while movie not in movies.keys():
movie = input('Enter valid movie name!!!: ')
movie = movie.upper()
print(pyfiglet.figlet_format(movie.center(40)))
self.movie = movie.lower()
final = []
add = []
# Displaying the movies information table
for dictmovie in movies.values():
if dictmovie['name'] == movie.lower():
for attribute, info in dictmovie.items():
if attribute != 'location' and attribute != 'name':
add.append(attribute)
add.append(info)
final.append(add)
add = []
Cinema = Texttable()
Cinema.add_rows(final)
print(Cinema.draw())
# Asks the user if they want to book tickets for the movie
# if user enters n/no the loop continues
choice = input(('Book tickets? (y/n): ')).lower()
while choice not in ['y','n','yes','no']:
choice = input(('Enter a valid choice: ')).lower()
return choice
def payment(self):
def otp():
self.email = input('Enter your email Address: ')
number = input('Enter your mobile number: ')
while len(number) != 10 and number not in ['7702167946']:
number = input('Input valid mobile number: ')
number = '+91' + number
otp = random.randrange(1000,9999,2)
otp = str(otp)
message = f'Book My Cinema never calls you asking for OTP. OTP is confidential. For security reasons, do not share this OTP with anyone. Your OTP is {otp} '
client = SinchSMS('660367af-18ff-42ca-89d2-8856dcba78dd','fijjTkW5jkuaCARwJlGnKA==')
print("Sending '%s' to %s" % ('OTP', number))
response = client.send_message(number, message)
message_id = response['messageId']
response = client.check_status(message_id)
while response['status'] != 'Successful':
time.sleep(1)
response = client.check_status(message_id)
u_otp = input('Enter the 4 digit otp: ')
while u_otp != otp:
u_otp = input('Enter valid 4 digit otp!!: ')
print('OTP matched!!')
print("\033[1;31;40m")
result1= pyfiglet.figlet_format("Booking summary".center(0))
print(result1)
print("\033[1;37;40m")
format_list = list(_table_formats.keys())
table = [["Movie", self.movie],["Theatre", self.theatre], ["Date",self.date],["Time",self.time],["seats",self.selected_seats], ["Total", self.total]]
print(tabulate(table, tablefmt='grid'))
print('\033[1;31;40mAmount Payable - ' + str(self.total) + '\033[1;37;40m')
result1= pyfiglet.figlet_format("Payment Options".center(0))
print(result1)
format_list = list(_table_formats.keys())
table = [["1", "Credit/Debit card"],["2", "Mobile wallet"],["3","UPI"]]
headers = ['Press', 'Payment method']
print(tabulate(table, headers,tablefmt='pipe'))
choice = input()
while choice not in ['1', '2', '3']:
choice = input('Enter valid payment method: ')
clear()
# Credit card block:
if choice == '1':
today = date.today()
result1= pyfiglet.figlet_format("Credit Card".center(0))
print(result1)
card_num = input('Enter card number: ')
while len(card_num) != 16:
card_num = input('Enter valid card number!!: ')
card_name = input('Name on the card: ')
print('Enter expiry date: ')
check = list(range(1, 13))
check = [str(i) for i in check]
month = input('Enter month: ')
while month not in check:
month = input('Enter valid month: ')
month = int(month)
check = ['2019', '2020', '2021', '2022', '2023', '2024']
year = input('Enter year: ')
while year not in check:
year = input('Enter valid year: ')
year = int(year)
while month <= today.month and year == today.year:
print('Enter valid expiry date: ')
check = list(range(1, 13))
check = [str(i) for i in check]
month = input('Enter month: ')
while month not in check:
month = input('Enter valid month: ')
month = int(month)
check = ['2019', '2020', '2021', '2022', '2023', '2024']
year = input('Enter year: ')
while year not in check:
year = input('Enter valid year: ')
year = int(year)
cvv = input('Enter cvv: ')
while len(cvv) != 3:
cvv = input('Enter cvv: ')
clear()
otp()
clear()
print("Your transaction is underway, Don't refresh: ")
time.sleep(5)
print('Transaction Sucessfull!!')
# Mobile wallet block
elif choice == '2':
result1= pyfiglet.figlet_format("Mobile Wallet".center(0))
print(result1)
format_list = list(_table_formats.keys())
table = [["1", "PayTm"],["2", "PayPal"], ["3","FreeCharge"]]
headers = ['Press', 'Mobile wallet']
print(tabulate(table, headers,tablefmt='pipe'))
choice = input()
while choice not in ['1', '2', '3']:
choice = input('Enter valid mobile wallet: ')
print('Redirecting...')
time.sleep(5)
clear()
if choice == '1':
result1 = pyfiglet.figlet_format("PayTm".center(40))
print(result1)
elif choice == '2':
result1 = pyfiglet.figlet_format("PayPal".center(40))
print(result1)
elif choice == '3':
result1 = pyfiglet.figlet_format("FreeCharge".center(40))
print(result1)
print(f'Book my Cinema order Rs {self.total}')
choice = input('Proceed? (y/n) ').lower()
while choice not in ['y','n','yes','no']:
choice = input('Enter valid choice: ').lower()
if choice in ['n', 'no']:
return choice
otp()
print('Your transaction is underway, Don\'t refresh')
time.sleep(5)
print('Your Transaction is Sucessfull!! ')
# UPI Block
elif choice == '3':
result1= pyfiglet.figlet_format("UPI".center(40))
print(result1)
options = ['PayTm', 'Amazon Pay','PhonePe','BHIM']
print('Enter upi payment option ')
upi = Texttable()
upi.add_rows([['ID', 'Options'],['1','PayTm'],['2','Amazon Pay'],['3','PhonePe'],['4','BHIM']])
print(upi.draw())
upi = input()
while upi not in ['1','2','3','4']:
upi = input('Enter valid choice: ')
mode = options[int(upi) - 1]
result1 = pyfiglet.figlet_format(mode.center(40))
print(result1)
UPI = input('Enter UPI pin: ')
while len(UPI) != 6:
UPI = input('Enter valid UPI pin: ')
print('Enter Bank ID: ')
Bank = Texttable()
Bank.add_rows([['ID', 'Bank'],['1','HDFC Bank'],['2','Axis Bank'],['3','SBI Bank'],['4','Yes Bank'],['5','IDFC Bank']])
print(Bank.draw())
bank = input()
while bank not in ['1','2','3','4','5']:
bank = input('Enter valid choice: ')
otp()
print('Your transaction is underway, Don\'t refresh')
time.sleep(5)
print('Your Transaction is Sucessfull!! ')
def select_time(self):
result1= pyfiglet.figlet_format("Select Time".center(40))
print(result1)
d = date.today()
from datetime import time
from datetime import datetime
# Times is a dictionary to store movie times
times = {}
count = 1
now = datetime.now().time()
# Reads the time.csv file to read time corresponding to the movie and the theater
with open('time.csv', 'r') as read_file:
csv_reader = csv.reader(read_file, delimiter = ',')
for line in csv_reader:
if line[0] == self.theatre and line[1] == self.movie:
for i in range(len(line)):
if i not in [0,1]:
ti = datetime.strptime(line[i],'%H:%M:%S').time()
times[str(count)] = ti
count += 1
read_file.close()
# If the movie is in the current day
# Then enter the available time in the dictionary times_edit
if self.date == d:
times_edit = {}
count = 1
final = [['ID', 'Time']]
add = []
for index, time in times.items():
if time > now:
times_edit[str(count)] = time
add.append(count)
add.append(time)
final.append(add)
add = []
count += 1
times = times_edit
# Else show all the time
else:
count = 1
final = [['ID', 'Time']]
add = []
for index, time in times.items():
add.append(count)
add.append(time)
final.append(add)
count += 1
add = []
Time = Texttable()
Time.add_rows(final)
print(Time.draw())
user_time = input(f'Enter your time (1-{len(times)}): ')
check = list(range(1, len(times) + 1))
check = [str(i) for i in check]
# To check if the user entered the correct option
while user_time not in check:
user_time = input(f'Enter time from (1-{len(times)}): ')
# To convert the time and date entered by the user into string
self.time = str(times[user_time])
self.date = str(self.date)
print(f'Selected movie time - {user_time}')
choice = input('continue? (y/n): ')
while choice not in ['y','n','yes','no']:
choice = input(('Enter valid choice: ')).lower()
return choice
def food_menu(self):
clear()
meal = input('Do you wanna a grab a bite (y/n)?: ')
while meal not in ['y','n','yes','no']:
meal = input(('Enter valid choice!!: ')).lower()
if meal in ['n', 'no']:
return
if meal in ['y', 'yes']:
from texttable import Texttable
def read_food(data, sub):
prices = []
with open('food.csv', 'r') as read_file:
csv_reader = csv.reader(read_file)
for line in csv_reader:
if line[0] == sub:
for i in range(len(line)):
if i != 0:
prices.append(line[i])
data.append(prices)
prices = []
temp = data[0]
del data[0]
data.sort()
k = list(data for data,_ in itertools.groupby(data))
data = k
data.insert(0, temp)
return data
return data
result1 = pyfiglet.figlet_format('Food Menu'.center(40))
print(result1)
data = [['Item', 'Small', 'Medium', 'Large']]
final = read_food(data, 'Iced-Tea')
price = 0
print("Iced-Tea:-")
IcedTea = Texttable()
IcedTea.add_rows(final)
print(IcedTea.draw())
data = [['Item', 'Small', 'Medium', 'Large']]
final = read_food(data, 'Cold Coffee')
i=1
while (i<=4):
i=i+1
print()
print('Cold Coffee:-')
from texttable import Texttable
ColdCoffee = Texttable()
ColdCoffee.add_rows(final)
print(ColdCoffee.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Price']]
final = read_food(data, 'Aerated Drinks (200 ml)')
print ("Aerated Drinks (200 ml):-")
from texttable import Texttable
AeratedDrinks = Texttable()
AeratedDrinks.add_rows(final)
print(AeratedDrinks.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Price']]
final = read_food(data, 'Mineral Water')
print ("Mineral Water:-")
from texttable import Texttable
MineralWater = Texttable()
MineralWater.add_rows(final)
print(MineralWater.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Price']]
final = read_food(data, 'Hot Beverages (80 ml)')
print ("Hot Beverages (80 ml):-")
from texttable import Texttable
HotBeverages = Texttable()
HotBeverages.add_rows(final)
print(HotBeverages.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Small', 'Medium', 'Large']]
final = read_food(data, 'Popcorn')
print ("Popcorn:-")
from texttable import Texttable
Popcorn = Texttable()
Popcorn.add_rows(final)
print(Popcorn.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Price']]
final = read_food(data, 'Appetizers')
print ("Appetizers:-")
from texttable import Texttable
Appetizers = Texttable()
Appetizers.add_rows(final)
print(Appetizers.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Small', 'Medium', 'Large']]
final = read_food(data, 'Rolls')
print ("Rolls:-")
from texttable import Texttable
Rolls = Texttable()
Rolls.add_rows(final)
print(Rolls.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Price']]
final = read_food(data, 'Special Combos')
print ("Special Combos:-")
from texttable import Texttable
SpecialCombos = Texttable()
SpecialCombos.add_rows(final)
print(SpecialCombos.draw())
i=1
while (i<=4):
i=i+1
print()
data = [['Item', 'Single Scoop', 'Double Scoop', 'Cone']]
final = read_food(data, 'Ice Creams')
print ("Ice Creams:-")
from texttable import Texttable
IceCream = Texttable()
IceCream.add_rows(final)
print(IceCream.draw())
choice = 'y'
while choice in ['y', 'yes']:
pre = False
while pre == False:
sub = input('Enter submenu: ')
with open('food.csv', 'r') as read_file:
csv_reader = csv.reader(read_file)
for line in csv_reader:
if line == []:
break
if line[0] == sub:
pre = True
break
if pre == True:
break
pre = False
while pre == False:
item = input('\nEnter item from ' + sub + ': ')
with open('food.csv', 'r') as read_file:
csv_reader = csv.reader(read_file)
for line in csv_reader:
if line == []:
break
if line[0] == sub:
if line[1] == item:
quantity = input('Enter quantity ')
while quantity not in ['0','1', '2', '3', '4', '5', '6', '7']:
quantity = input('Control yourself and enter quantity : ')
quantity = int(quantity)
pre = True
if sub in ['Iced-Tea','Cold Coffee','Popcorn','Rolls']:
Tab = Texttable()
Tab.add_rows([['Press', 'Option'], ['1','To order Small'],['2', 'To order Medium'],['3', 'To order Large']])
print(Tab.draw())
choice = input()
while choice not in ['1','2','3']:
choice = input('Enter 1 or 2 or 3: ')
if choice == '1':
add = int(line[2]) * quantity
price += add
break
elif choice == '2':
add = int(line[3]) * quantity
price += add
break
elif choice == '3':
add = int(line[4]) * quantity
price += add
break
elif sub in ['Ice Creams']:
Tab = Texttable()
Tab.add_rows([['Press', 'Option'], ['1','To order Single Scoop'],['2', 'To order Double Scoop'],['3', 'To order Cone']])
print(Tab.draw())
choice = input()
while choice not in ['1','2','3']:
choice = input('Enter 1 or 2 or 3: ')
if choice == '1':
add = int(line[2]) * quantity
price += add
break
elif choice == '2':
add = int(line[3]) * quantity
price += add
break
elif choice == '3':
add = int(line[4]) * quantity
price += add
break
else:
add = int(line[2]) * quantity
price += add
break
pre = True
if pre == True:
break
self.food_item.append(item)
self.food_price += price
self.total += self.food_price
choice = input('Order more? (y/n): ')
while choice not in ['y','n','yes','no']:
choice = input(('Enter valid choice: ')).lower()
if choice in ['n', 'no']:
print(self.food_item)
break
pre = False
def rrt(lang, module, Input_):
table = kakunin(module)
data = module.Open("./config/setting.conf")
datas = data.read().splitlines()[0]
data.close()
datas = datas.split(",")
datal = []
for i in range(2, len(datas), 2):
datal.append((datas[i + 1], datas[i]))
if len(datal) == 0:
k = ""
while k != "\n":
cuitools.box(lang.lang("エラー"), [
lang.lang("簡単サッカー実行リストにチームが設定されていないのでこの機能は使用できません。"),
lang.lang("settingコマンドでチームの追加ができます。詳しくはsettingコマンドを確認してください。"),
lang.lang("Enterキーを押して続行...")
])
k = cuitools.Key()
elif yes_no_dialog(
title=lang.lang("cszp 簡単サッカー実行プログラム") + "/" + lang.lang("設定確認"),
text=table.draw() + lang.lang("\nEnterキーを押して続行...")).run():
k = ""
csv_save = False
ok = False
ver = open("./version")
v = ver.read()
ver.close()
kantan_list = list(map(lambda n: n[1], datal))
kantan_cmd = list(map(lambda n: n[0], datal))
select = 0
selector = []
synch = False
while k != "\n" or not ok:
cuitools.reset()
print("\033[1;1H\033[0m\033[38;5;172m")
figlet("cszp " + v)
if select == -1:
print("\033[1m\033[38;5;9m> \033[38;5;10m" +
lang.lang("前ページへ戻る(ホーム)") + "\033[0m\n")
else:
print(" \033[38;5;10m" + lang.lang("前ページへ戻る(ホーム)") +
"\033[0m\n")
print("\033[38;5;12m" + lang.lang("総当たり戦をさせるチームを選択してください。"))
for i, j in enumerate(kantan_list):
if i in selector:
if select == i:
print(
"\033[1m\033[38;5;9m>\033[38;5;11m* \033[38;5;10m"
+ j + "\033[0m")
else:
print("\033[38;5;11m * \033[38;5;10m" + j + "\033[0m")
elif select == i:
print("\033[1m\033[38;5;9m> \033[38;5;10m" + j +
"\033[0m")
else:
print(" \033[38;5;10m" + j + "\033[0m")
print("")
if select == len(kantan_list):
print("\033[1m\033[38;5;9m> \033[38;5;14m" +
lang.lang("synchモードでの実行") +
":\033[0m\033[4m\033[38;5;14m" + "No" *
((synch - 1) * -1) + "Yes" * synch + "\033[0m")
else:
print(" \033[38;5;14m" + lang.lang("synchモードでの実行") +
":\033[0m\033[4m\033[38;5;14m" + "No" *
((synch - 1) * -1) + "Yes" * synch + "\033[0m")
if select == len(kantan_list) + 1:
if csv_save is False:
print("\033[1m\033[38;5;9m> \033[38;5;11m" +
lang.lang("csvログの保存") + ":\033[0m\033[4m\033["
"38;5;14mNo\033[0m")
else:
print("\033[1m\033[38;5;9m> \033[38;5;11m" +
lang.lang("csvログの保存") +
":\033[0m\033[4m\033[38;5;14m" + csv_save +
"\033[0m")
else:
if csv_save is False:
print(" \033[38;5;11m" + lang.lang("csvログの保存") +
":\033[0m\033[4m\033[38;5;14mNo\033[0m")
else:
print(" \033[38;5;11m" + lang.lang("csvログの保存") +
":\033[0m\033[4m\033[38;5;14m" + csv_save +
"\033[0m")
if select == len(kantan_list) + 2 and len(selector) > 1:
print("\033[1m\033[38;5;9m> \033[38;5;10m" +
lang.lang("サッカーを実行") + "\033[0m")
elif len(selector) < 2:
print(" \033[38;5;243m" + lang.lang("サッカーを実行") + "\033[0m")
else:
print(" \033[38;5;10m" + lang.lang("サッカーを実行") + "\033[0m")
table = []
tmp = [""]
for i in selector:
tmp.append(kantan_list[i])
table.append(tmp)
for i in selector:
table.append([kantan_list[i]] + [""] * len(selector))
print(Texttable().add_rows(table).draw())
k = cuitools.Key()
if k == "\x1b":
k = cuitools.Key()
if k == "[":
k = cuitools.Key()
if k == "B":
if len(kantan_list) + 2 > select:
select += 1
if len(kantan_list) + 2 == select and len(
selector) < 2:
select -= 1
elif k == "A":
if -1 < select:
select -= 1
if (k == " " or k == "\n") and select == -1:
break
elif (k == " " or k == "\n") and select == len(kantan_list) + 2:
ok = True
break
elif (k == " " or k == "\n") and select == len(kantan_list) + 1:
tmp = yesno(lang.lang("選択"), [lang.lang("csvログを保存しますか？")])
if tmp == 0:
tmp = Input()
tmp = tmp.Input("filename")
if len(tmp.split(".")) == 1:
tmp += ".csv"
csv_save = tmp
else:
csv_save = False
elif (k == " " or k == "\n") and select == len(kantan_list):
tmp = yesno(lang.lang("選択"), [lang.lang("synchモードで実行しますか？")])
if tmp == 0:
synch = True
else:
synch = False
elif (k == " " or k == "\n") and select > -1:
if select in selector:
for i, j in enumerate(selector):
if j == select:
del selector[i]
break
else:
selector.append(select)
if ok:
table = []
tmp = [""]
for i in selector:
tmp.append(kantan_list[i])
table.append(tmp)
for i in selector:
table.append([kantan_list[i]] + [""] * len(selector))
for i in range(len(table)):
if i != 0:
table[i][i] = "N/A"
tmp = []
for i in selector:
for j in selector:
if kantan_cmd[i] != kantan_cmd[j] and not [
kantan_cmd[j], kantan_cmd[i]
] in tmp:
tmp.append([kantan_cmd[i], kantan_cmd[j]])
tmp2 = []
for i in selector:
for j in selector:
if kantan_cmd[i] != kantan_cmd[j] and not [
kantan_list[j], kantan_list[i]
] in tmp2:
tmp2.append([kantan_list[i], kantan_list[j]])
# print(tmp)
data = module.Open("./config/config.conf")
datas = data.read().splitlines()
data.close()
datas = datas[0].split(",")
arg = "server::auto_mode=true server::kick_off_wait=20 server::game_over_wait=20 " \
"server::connect_wait=2000 server::game_log_dir=" + datas[7] + " server::text_log_dir=" + datas[
7] + " server::game_logging=" + (
"true" * (datas[3] == "on") + "false" * (datas[3] == "off")) + " server::text_logging=" + (
"true" * (datas[3] == "on") + "false" * (datas[3] == "off"))
# arg += " server::nr_normal_halfs=1 server::nr_extra_halfs=0 server::penalty_shoot_outs=0 " \
# "server::half_time=10"
s = [0, 1]
results = []
for i, j in zip(tmp, tmp2):
cuitools.reset()
print("\033[1;1H\033[0m\033[38;5;172m")
figlet("cszp " + v)
print("\033[0m")
print(Texttable().add_rows(table).draw() + "\n" + "━" * 50)
try:
_ = soccer([i[0], i[1], arg], lang, 1, module,
[False, False, False, synch], Input_, False,
False, False)
except KeyboardInterrupt:
break
result = _.get_result()
if len(result) == 0:
table[s[0] + 1][s[1] + 1] = "Error"
else:
tmp3 = result[0]
tmp3[1] = table[s[0] + 1][0]
tmp3[2] = table[0][s[1] + 1]
results.append(tmp3)
table[s[0] +
1][s[1] +
1] = str(result[0][3]) + "-" + str(result[0][4])
s[1] += 1
if s[1] > len(selector) - 1:
s[0] += 1
s[1] = 0
tmp3 = list(map(lambda n: n + 1, s))
while [j[1], j[0]] != [table[tmp3[0]][0], table[0][tmp3[1]]]:
print([table[tmp3[0]][0], table[0][tmp3[1]]], [j[1], j[0]])
tmp3[1] += 1
if tmp3[1] > len(selector):
tmp3[0] += 1
tmp3[1] = 0
if len(result) == 0:
table[tmp3[0]][tmp3[1]] = "Error"
else:
table[tmp3[0]][tmp3[1]] = str(result[0][4]) + "-" + str(
result[0][3])
while s[1] <= s[0]:
s[1] += 1
if s[1] > len(selector):
s[0] += 1
s[1] = 0
if csv_save is not False:
with open(datas[7] + "/" + csv_save, "w") as f:
f.write("\n".join(
list(map(lambda n: ",".join(map(str, n)), results))))
cuitools.reset()
print("\033[1;1H\033[0m\033[38;5;172m")
figlet("cszp " + v)
print("\033[0m")
print("\033[1m\033[38;5;11m" + lang.lang("集計結果") + "\033[0m\n" +
"━" * 50 + "\n" + Texttable().add_rows(table).draw())
tmp = []
for i in results:
if not i[1] in list(map(lambda n: n[0], tmp)):
tmp.append([i[1], [0, 0, 0]])
for j, k in enumerate(tmp):
if k[0] == i[1]:
break
if i[3] > i[4]:
tmp[j][1][0] += 1
elif i[3] < i[4]:
tmp[j][1][1] += 1
else:
tmp[j][1][2] += 1
if not i[2] in list(map(lambda n: n[0], tmp)):
tmp.append([i[2], [0, 0, 0]])
for j, k in enumerate(tmp):
if k[0] == i[2]:
break
if i[4] > i[3]:
tmp[j][1][0] += 1
elif i[4] < i[3]:
tmp[j][1][1] += 1
else:
tmp[j][1][2] += 1
table = [["", "W", "D", "L", "Total"]]
for i in tmp:
table.append(
[i[0], i[1][0], i[1][2], i[1][1], i[1][0] - i[1][1]])
print("\n" + "━" * 50 + "\n" + Texttable().add_rows(table).draw())
Input_.Input(lang.lang("Enterキーを押して続行..."), dot=False)
def display_late_info(self):
late_cats = ["1 - 15", "16 - 30", "31 - 60", "61 - 90", "91 - 120"]
# create dict
late_dict = {}
current_count = 0
total_late_count = 0
for cat in late_cats:
late_dict[cat] = {
"count": 0,
"outstanding principal": 0,
"ratings": []
}
# {'1 - 15': {'count': 0, 'outstanding principal': 0, 'ratings': []},
# '16 - 30': {'count': 0, 'outstanding principal': 0, 'ratings': []},
# '31 - 60': {'count': 0, 'outstanding principal': 0, 'ratings': []},
# '61 - 90': {'count': 0, 'outstanding principal': 0, 'ratings': []},
# '91 - 120': {'count': 0, 'outstanding principal': 0, 'ratings': []}}
notes_data = self.notes.pull_notes_table()
for note in notes_data:
if note['note_status_description'] == "CURRENT" and note[
'days_past_due'] == 0:
current_count += 1
if note['note_status_description'] == "CURRENT" and note[
'days_past_due'] > 0 and note['days_past_due'] < 16:
late_dict["1 - 15"]["count"] += 1
late_dict["1 - 15"]["outstanding principal"] += note[
'principal_balance_pro_rata_share']
late_dict["1 - 15"]["ratings"].append(note['prosper_rating'])
if note['note_status_description'] == "CURRENT" and note[
'days_past_due'] > 15 and note['days_past_due'] < 31:
late_dict["16 - 30"]["count"] += 1
late_dict["16 - 30"]["outstanding principal"] += note[
'principal_balance_pro_rata_share']
late_dict["16 - 30"]["ratings"].append(note['prosper_rating'])
if note['note_status_description'] == "CURRENT" and note[
'days_past_due'] > 30 and note['days_past_due'] < 61:
late_dict["31 - 60"]["count"] += 1
late_dict["31 - 60"]["outstanding principal"] += note[
'principal_balance_pro_rata_share']
late_dict["31 - 60"]["ratings"].append(note['prosper_rating'])
if note['note_status_description'] == "CURRENT" and note[
'days_past_due'] > 60 and note['days_past_due'] < 91:
late_dict["61 - 90"]["count"] += 1
late_dict["61 - 90"]["outstanding principal"] += note[
'principal_balance_pro_rata_share']
late_dict["61 - 90"]["ratings"].append(note['prosper_rating'])
if note['note_status_description'] == "CURRENT" and note[
'days_past_due'] > 90:
late_dict["91 - 120"]["count"] += 1
late_dict["91 - 120"]["outstanding principal"] += note[
'principal_balance_pro_rata_share']
late_dict["91 - 120"]["ratings"].append(note['prosper_rating'])
table = Texttable()
table.add_row([
"Late Category", "Count", "Outstanding Principal",
"Comprised Notes"
])
for d in late_dict:
table.add_row([
d, late_dict[d]["count"],
late_dict[d]["outstanding principal"], late_dict[d]["ratings"]
])
total_late_count += late_dict[d]["count"]
self.message += "\nLate Note Data:"
self.message += "\nCurrent notes late is {num}% of all notes\n".format(
num=round((total_late_count /
(total_late_count + current_count) * 100), 2))
self.message += table.draw()
self.message += "\n"
def display_default_rate_tracking(self):
table = Texttable()
table.add_row([
"Rating", "Expected", "Baseline", "Actual", "Diff",
"Diff Baseline", "Late"
])
projected_default_dict, projected_default_dict_prosper, actual_default_dict, actual_default_rates_dict, actual_late_dict = self.notes.default_rate_tracking(
)
total_expected_defaulted_v1 = 0
total_expected_defaulted_prosper = 0
total_actual_num = 0
total_late = 0
self.message += "\n"
late_dict = {}
for k in sorted(projected_default_dict):
try:
actual_num = actual_default_dict[k]
except KeyError:
actual_num = 0
try:
# self.message += "Rating {k}: expected defaulted notes for v1 is {num}, with prosper expected of {prosper_num}, actual is {actual_num} (Including {late_num} late)".format(k=k, num=round(projected_default_dict[k], 4), prosper_num=round(projected_default_dict_prosper[k], 4), actual_num=actual_num, late_num=actual_late_dict[k])
table.add_row([
k,
round(projected_default_dict[k], 4),
round(projected_default_dict_prosper[k], 4),
actual_num,
# "actual is {actual_num} (Including {late_num} late)".format(actual_num=actual_num, late_num=actual_late_dict[k]),
round(projected_default_dict[k], 4) - actual_num,
round(projected_default_dict_prosper[k], 4) - actual_num,
actual_late_dict[k]
])
total_late += actual_late_dict[k]
except KeyError:
# self.message += "Rating {k}: expected defaulted notes for v1 is {num}, with prosper expected of {prosper_num}, actual is {actual_num} (Including {late_num} late)".format(k=k, num=round(projected_default_dict[k], 4), prosper_num=round(projected_default_dict_prosper[k], 4), actual_num=actual_num, late_num=0)
table.add_row([
k,
round(projected_default_dict[k], 4),
round(projected_default_dict_prosper[k], 4),
actual_num,
# "actual is {actual_num} (Including {late_num} late)".format(actual_num=actual_num, late_num=0),
round(projected_default_dict[k], 4) - actual_num,
round(projected_default_dict_prosper[k], 4) - actual_num,
0
])
# self.message += "\n"
total_expected_defaulted_v1 += projected_default_dict[k]
total_expected_defaulted_prosper += projected_default_dict_prosper[
k]
total_actual_num += actual_num
table.add_row([
"Total",
round(total_expected_defaulted_v1, 4),
round(total_expected_defaulted_prosper, 4), total_actual_num,
round(total_expected_defaulted_v1, 4) - total_actual_num,
round(total_expected_defaulted_prosper, 4) - total_actual_num,
total_late
])
self.message += "Default And Late Rate Tracking:\n"
self.message += table.draw()
self.message += "\n"
def similarity_text(self, idx, distance_matrix, data):
"""
Use indici API and Fuzzy Search to select the answer for the Customer
idx: the index of the text we're looking for similar questions to
(data[idx] corresponds to the actual text we care about)
distance_matrix: an m by n matrix that stores the distance between
document m and document n at distance_matrix[m][n]
data: a flat list of text data
"""
t = Texttable()
t.set_cols_width([50, 20])
# these are the indexes of the texts that are most similar to the text at data[idx]
# note that this list of 10 elements contains the index of text that we're comparing things to at idx 0
sorted_distance_idxs = np.argsort(
distance_matrix[idx])[:5] # EX: [252, 102, 239, ...]
#Fuzzy Search Dictionary Index
fuzzy_ratio_array = sorted_distance_idxs.tolist()
fuzzy_ratio_array.pop(0)
print("fuzzy ratio array : %r" % fuzzy_ratio_array)
# this is the index of the text that is most similar to the query (index 0)
most_sim_idx = sorted_distance_idxs[1]
print("sorted distance idx : %r" % most_sim_idx)
# header for texttable
t.add_rows([['Text', 'Similarity']])
print(t.draw())
# set the variable that will hold our matching FAQ
faq_match = None
for similar_idx in sorted_distance_idxs:
# actual text data for display
datum = data[similar_idx]
# distance in cosine space from our text example to the similar text example
distance = distance_matrix[idx][similar_idx]
# how similar that text data is to our input example
similarity = 1 - distance
# add the text + the floating point similarity value to our Texttable() object for display
t.add_rows([[datum, str(round(similarity, 2))]])
print(t.draw())
# set a confidence threshold
if similar_idx == most_sim_idx and similarity >= 0.75:
faq_match = data[most_sim_idx]
elif similar_idx == most_sim_idx and similarity >= 0.4:
fuzzy_search_dict = {}
fuzzy_max_similarity_index = []
for idx in fuzzy_ratio_array:
value = fuzz.ratio(data[0], data[idx])
fuzzy_search_dict[idx] = value
fuzzy_max_similarity_index = max(fuzzy_search_dict.items(),
key=lambda x: x[1])[0]
faq_match = data[fuzzy_max_similarity_index]
else:
sorry = "Sorry, I'm not sure how to respond. Let me find someone who can help you."
# print the appropriate answer to the FAQ, or bring in a human to respond
# TODO
if faq_match is not None:
return faqs[faq_match]
else:
return sorry
def PredictAndShowResult(classifiers, test_data):
y_test = test_data['authors']
X_test = test_data['texts']
for classifier in classifiers:
clf = classifier['clf']
predicted = cross_val_predict(clf, X_test, y_test, cv=10)
#predicted = clf.predict(X_test)
Macro_P = (
metrics.precision_score(y_test, predicted, average='macro') * 100)
A = (metrics.accuracy_score(y_test, predicted) * 100)
Macro_R = (metrics.recall_score(y_test, predicted, average='macro') *
100)
Macro_F = (metrics.f1_score(y_test, predicted, average='macro') * 100)
Micro_P = (
metrics.precision_score(y_test, predicted, average='micro') * 100)
Micro_R = (metrics.recall_score(y_test, predicted, average='micro') *
100)
Micro_F = (metrics.f1_score(y_test, predicted, average='micro') * 100)
fpr, tpr, thresholds = metrics.roc_curve(y_test,
predicted,
pos_label=16)
AUC = (metrics.auc(fpr, tpr) * 100)
######################### Presenting results ###################################################
table = Texttable()
print(classifier['name'] + ' measures: ')
table.add_rows([['Measure', 'Result'], ['ACCURACY ', A],
['Error Rate', 100 - A],
['The MACRO Precission ', Macro_P],
['The MACRO Recall ', Macro_R],
['The MACRO FScore ', Macro_F],
['The MICRO Precission ', Micro_P],
['The MICRO Recall ', Micro_R],
['The MICRO FScore ', Micro_F],
['Area Under Curve', AUC]])
print(table.draw())
print('\n Detailed classification report for ' + classifier['name'] +
':')
print(classification_report(y_test, predicted))
#################################### Confusion Matrix ##########################################
def plot_confusion_matrix(cm,
classes,
title='Confusion matrix',
cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
cmRound = []
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]),
range(cm.shape[1])):
plt.text(j,
i,
cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True authors')
plt.xlabel('Predicted authors')
# Compute confusion matrix
conf_m = confusion_matrix(y_test, predicted)
np.set_printoptions(precision=2)
# Plot normalized confusion matrix for emotions
plt.figure()
plot_confusion_matrix(conf_m,
classes=set(y_test_old),
title=features_name + '. Classifier name: ' +
classifier['name'])
# fname = features_name+'_'+classifier['name']
#fpath = 'D:/Sasha/subversion/trunk/AuthorshipAttributionRussianTexts/results/conf_matrix/'+fname #to save in file
plt.show()
if args.query:
query = args.query
check = args.check
maxresults = args.maxresults
if maxresults < 100:
maxresults = 100
maxwidth = args.maxwidth
inputfile = False
if args.file:
inputfile = args.file
t = Texttable(max_width=maxwidth)
malwaredomains = 'http://mirror1.malwaredomains.com/files/justdomains'
expireddomainsqueryurl = 'https://www.expireddomains.net/domain-name-search'
timestamp = time.strftime("%Y%m%d_%H%M%S")
useragent = 'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0)'
headers = {'User-Agent': useragent}
requests.packages.urllib3.disable_warnings()
# HTTP Session container, used to manage cookies, session tokens and other session information
s = requests.Session()
data = []
items = {
} #1|Toy Story (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)|0|0|0|1|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
f = open(item, "r", encoding="ISO-8859-1")
movie_content = f.readlines()
f.close()
for movie in movie_content:
movieLine = movie.split("|")
items[int(movieLine[0])] = movieLine[1:]
return items
#主程序
if __name__ == '__main__':
itemTemp = getMovieList(r"./ml-100k/u.item") #获取电影列表
fileTemp = readFile(r"./ml-100k/u.data") #读取文件
user_dic, movie_dic = createDict(fileTemp) #创建字典
user_id = 66
movieTemp = recommondation(user_id, user_dic, 80) #对电影排序
rows = []
table = Texttable() #创建表格并显示
table.set_deco(Texttable.HEADER)
table.set_cols_dtype(['t', 'f', 'a'])
table.set_cols_align(["l", "l", "l"])
rows.append(["user name", "recommondation_movie", "from userid"])
for i in movieTemp:
rows.append([user_id, itemTemp[i[0]][0], ""])
table.add_rows(rows)
print(table.draw())
i += 1
if i == 1000:
break
print('Saving training_set to', training_set_filename)
data = np.array(data)
np.save(training_set_filename, data)
with open(images_filename, 'wb') as output:
pickle.dump(images, output, pickle.HIGHEST_PROTOCOL)
print('Predicting the cameras based on the images')
labels = logistic.predict(data)
predictions = dict(zip(images, labels))
print('Prediction list')
text_table = Texttable()
text_table.add_row(['Image', 'Predicted', 'Actual', 'Correct ?'])
right_classifier_counter = 0
for image in images:
images_filename = image.split("/")[-1]
actual_camera = image.split("/")[-2]
predicted_camera = predictions[image]
text_table.add_row([
images_filename, predicted_camera, actual_camera,
predicted_camera == actual_camera
])
if actual_camera == predicted_camera: right_classifier_counter += 1
print(text_table.draw())
print('Accuracy:', right_classifier_counter / len(images))
def __str__(self):
res = Texttable()
for line in self.__cells:
s = " ".join([str(int(value)) for value in line]) + "\n"
res.add_row(s)
return res.draw()
def main(domain, threads, savefile, ports, silent, verbose, enable_bruteforce,
engines):
bruteforce_list = set()
search_list = set()
if is_windows:
subdomains_queue = list()
else:
subdomains_queue = multiprocessing.Manager().list()
# Check Bruteforce Status
if enable_bruteforce or enable_bruteforce is None:
enable_bruteforce = True
# Validate domain
domain_check = re.compile(
"^(http|https)?[a-zA-Z0-9]+([\-\.]{1}[a-zA-Z0-9]+)*\.[a-zA-Z]{2,}$")
if not domain_check.match(domain):
if not silent:
print(R + "Error: Please enter a valid domain" + W)
return []
if not domain.startswith('http://') or not domain.startswith('https://'):
domain = 'http://' + domain
parsed_domain = urlparse.urlparse(domain)
if not silent:
print(B +
"[-] Enumerating subdomains now for %s" % parsed_domain.netloc +
W)
if verbose and not silent:
print(
Y +
"[-] verbosity is enabled, will show the subdomains results in realtime"
+ W)
supported_engines = {
'baidu': BaiduEnum,
'yahoo': YahooEnum,
'google': GoogleEnum,
'bing': BingEnum,
'ask': AskEnum,
'netcraft': NetcraftEnum,
'dnsdumpster': DNSdumpster,
'virustotal': Virustotal,
'threatcrowd': ThreatCrowd,
'ssl': CrtSearch,
'passivedns': PassiveDNS
}
chosenEnums = []
if engines is None:
chosenEnums = [
BaiduEnum, YahooEnum, GoogleEnum, BingEnum, AskEnum, NetcraftEnum,
DNSdumpster, Virustotal, ThreatCrowd, CrtSearch, PassiveDNS
]
else:
engines = engines.split(',')
for engine in engines:
if engine.lower() in supported_engines:
chosenEnums.append(supported_engines[engine.lower()])
# Start the engines enumeration
enums = [
enum(domain, [], q=subdomains_queue, silent=silent, verbose=verbose)
for enum in chosenEnums
]
for enum in enums:
enum.start()
for enum in enums:
enum.join()
subdomains = set(subdomains_queue)
for subdomain in subdomains:
search_list.add(subdomain)
if enable_bruteforce:
if not silent:
print(G + "[-] Starting bruteforce module now using subbrute.." +
W)
record_type = False
path_to_file = os.path.dirname(os.path.realpath(__file__))
subs = os.path.join(path_to_file, 'subbrute', 'names.txt')
resolvers = os.path.join(path_to_file, 'subbrute', 'resolvers.txt')
process_count = threads
output = False
json_output = False
bruteforce_list = subbrute.print_target(parsed_domain.netloc,
record_type, subs, resolvers,
process_count, output,
json_output, search_list,
verbose)
subdomains = search_list.union(bruteforce_list)
if subdomains:
subdomains = sorted(
subdomains,
key=functools.cmp_to_key(subdomain_cmp),
)
if savefile:
write_file(savefile, subdomains)
if not silent:
print(Y +
"[-] Total Unique Subdomains Found: %s" % len(subdomains) +
W)
if ports:
if not silent:
print(G +
"[-] Start port scan now for the following ports: %s%s" %
(Y, ports) + W)
ports = ports.split(',')
pscan = portscan(subdomains, ports)
pscan.run()
elif not silent:
table = Texttable()
table.set_deco(Texttable.HEADER)
for subdomain in subdomains:
try:
geo = geolite2.lookup(socket.gethostbyname(subdomain))
if geo:
table.add_rows(
[["Subdomain", "IP Address", "Location"],
[
subdomain,
socket.gethostbyname(subdomain),
str(geo.country) + " - " + str(geo.location)
]])
else:
table.add_rows(
[["Subdomain", "IP Address", "Location"],
[subdomain,
socket.gethostbyname(subdomain), "-"]])
except socket.gaierror:
table.add_rows([["Subdomain", "IP Address", "Location"],
[subdomain, "-", "-"]])
print(G + table.draw() + W)
return subdomains
def main():
# Enter folder name with video files.
location = input('Directory path: ')
# length of new video in s
length = int(input('Length of sequence (in s): '))
video_files = []
file_names = []
print("\nOutput written to output.txt")
f = open("output.txt", "w")
table = Texttable()
table.add_row(["Clip Position", "Clip Name", "Start Point", "End Point", "Total Duration"])
for root, dirs, files in os.walk(location):
for file in files:
if file.endswith(FILE_EXTENSION):
video_files.append(os.path.join(root, file))
file_names.append(file)
count = 0
while length > 0:
file = random.choice(video_files)
index = video_files.index(file)
if file.endswith(FILE_EXTENSION):
# Video length
clip = VideoFileClip(file)
frame_rate = clip.fps
# random frame
start_frame = random.randint(1, int(frame_rate))
end_frame = random.randint(1, int(frame_rate))
# Start timestamp of clip
a = random.uniform(0, clip.duration)
b = random.uniform(0, clip.duration)
start = 0
end = 0
if a < b:
start = a
end = b
else:
end = a
end = b
duration = end - start
start_string = f'{datetime.timedelta(seconds=round(start))}:{start_frame}'
end_string = f'{datetime.timedelta(seconds=round(end))}:{end_frame}'
table.add_row([count + 1, file_names[index],
start_string,
end_string,
datetime.timedelta(seconds=round(duration))])
count += 1
length -= duration
print(file_names)
f.write(table.draw())
def report(env, filters):
'''Summarise the dependency tree of the current project'''
lCmdHeaders = ['path', 'flags', 'package', 'component', 'lib']
lFilterFormat = re.compile('([^=]*)=(.*)')
lFilterFormatErrors = []
lFieldNotFound = []
lFilters = []
# print ( filters )
for f in filters:
m = lFilterFormat.match(f)
if not m:
lFilterFormatErrors.append(f)
continue
if m.group(1) not in lCmdHeaders:
lFieldNotFound.append(m.group(1))
continue
try:
i = lCmdHeaders.index(m.group(1))
r = re.compile(m.group(2))
lFilters.append((i, r))
except RuntimeError as e:
lFilterFormatErrors.append(f)
if lFilterFormatErrors:
raise click.ClickException(
"Filter syntax errors: " +
' '.join(['\'' + e + '\'' for e in lFilterFormatErrors]))
if lFieldNotFound:
raise click.ClickException(
"Filter syntax errors: fields not found {}. Expected one of {}".
format(
', '.join("'" + s + "'" for s in lFieldNotFound),
', '.join(("'" + s + "'" for s in lCmdHeaders)),
))
# return
lParser = env.depParser
secho('* Variables', fg='blue')
printDictTable(lParser.vars, aHeader=False)
echo()
secho('* Parsed commands', fg='blue')
lPrepend = re.compile('(^|\n)')
for k in lParser.commands:
echo(' + {0} ({1})'.format(k, len(lParser.commands[k])))
if not lParser.commands[k]:
echo()
continue
lCmdTable = Texttable(max_width=0)
lCmdTable.header(lCmdHeaders)
lCmdTable.set_deco(Texttable.HEADER | Texttable.BORDER)
lCmdTable.set_chars(['-', '|', '+', '-'])
for lCmd in lParser.commands[k]:
# print(lCmd)
# lCmdTable.add_row([str(lCmd)])
lRow = [
relpath(lCmd.FilePath, env.srcdir),
','.join(lCmd.flags()),
lCmd.Package,
lCmd.Component,
# lCmd.Map,
lCmd.Lib,
]
if lFilters and not all(
[rxp.match(lRow[i]) for i, rxp in lFilters]):
continue
lCmdTable.add_row(lRow)
echo(lPrepend.sub(r'\g<1> ', lCmdTable.draw()))
echo()
secho('Resolved packages & components', fg='blue')
lString = ''
# lString += '+----------------------------------+\n'
# lString += '| Resolved packages & components |\n'
# lString += '+----------------------------------+\n'
lString += 'packages: ' + ' '.join(iterkeys(lParser.components)) + '\n'
lString += 'components:\n'
for pkg in sorted(lParser.components):
lString += u'* %s (%d)\n' % (pkg, len(lParser.components[pkg]))
lSortCmp = sorted(lParser.components[pkg])
for cmp in lSortCmp[:-1]:
lString += u' ├── ' + str(cmp) + '\n'
lString += u' └── ' + str(lSortCmp[-1]) + '\n'
echo(lString)
if lParser.missing:
lString = ''
if lParser.missingPackages:
secho('Missing packages:', fg='red')
echo(' '.join(list(lParser.missingPackages)))
# ------
lCNF = lParser.missingComponents
if lCNF:
secho('Missing components:', fg='red')
for pkg in sorted(lCNF):
lString += '+ %s (%d)\n' % (pkg, len(lCNF[pkg]))
lSortCNF = sorted(lCNF[pkg])
for cmp in lSortCNF[:-1]:
lString += u' ├──' + str(cmp) + '\n'
lString += u' └──' + str(lSortCNF[-1]) + '\n'
# ------
# ------
echo(lString)
lFNF = lParser.missingFiles
if lFNF:
secho('Missing files:', fg='red')
lFNFTable = Texttable(max_width=0)
lFNFTable.header(
['path expression', 'package', 'component', 'included by'])
lFNFTable.set_deco(Texttable.HEADER | Texttable.BORDER)
for pkg in sorted(lFNF):
lCmps = lFNF[pkg]
for cmp in sorted(lCmps):
lPathExps = lCmps[cmp]
for pathexp in sorted(lPathExps):
lFNFTable.add_row([
relpath(pathexp, env.srcdir),
pkg,
cmp,
'\n'.join([
relpath(src, env.srcdir)
for src in lPathExps[pathexp]
]),
])
echo(lPrepend.sub(r'\g<1> ', lFNFTable.draw()))
def get_table_printer() -> Texttable:
t = Texttable(max_width=130)
t.set_deco(Texttable.BORDER | Texttable.HEADER | Texttable.VLINES)
t.set_precision(2)
return t
def main():
# Метод проверки разностей средних уровней
print(5 * "=", "average difference", 5 * "=")
# USD 2014
# https://net.dn.ua/money/stat.php?valute=12&year=2014&mon=0
usd = [7.99, 8.65, 9.91, 11.63, 11.40, 11.74, 12.90, 13.02, 12.95, 14.64]
# GBP 2014
# https://net.dn.ua/money/stat.php?valute=8&year=2014&mon=0
gbp = [
13.16, 14.31, 16.48, 19.46, 19.17, 20.03, 21.58, 21.27, 20.84, 23.15
]
data = gbp
arr1 = data[:len(data) // 2]
print("arr1", arr1)
arr2 = data[len(data) // 2:]
print("arr2", arr2)
average1 = round(sum(arr1) / len(arr1), 2)
print("average1", average1)
average2 = round(sum(arr2) / len(arr2), 2)
print("average2", average2)
dispersion1 = dispersion(arr1, average1)
print("dispersion1", dispersion1)
dispersion2 = dispersion(arr2, average2)
print("dispersion2", dispersion2)
f_calc = round(
dispersion1 /
dispersion2 if dispersion1 > dispersion2 else dispersion2 /
dispersion1, 2)
assert (len(arr1) - 1 == 4)
assert (len(arr2) - 1 == 4)
f_tabl = 6.39 # because P <= 0.05 and len(arr1) - 1 == 4 and len(arr2) - 1 = 4
print("f_calc", f_calc)
print("f_tabl", f_tabl)
is_accepted = f_calc < f_tabl
print("the hypothesis of homogeneity of dispersion is accepted:",
is_accepted)
if is_accepted:
t_criterion_calc = round(
student_t_criterion(average1, average2, dispersion1, dispersion2,
len(arr1), len(arr2)), 2)
print("t_criterion_calc:", t_criterion_calc)
assert (len(arr1) + len(arr2) - 2 == 8)
t_criterion_tabl = 2.31 # because P <= 0.05 and len(arr1) + len(arr2) - 2 == 8
print("t_criterion_tabl:", t_criterion_tabl)
print("trend exist:", t_criterion_calc > t_criterion_tabl)
# Метод Фостера-Стьюарта
print(5 * "=", "foster–stuart", 5 * "=")
result_list = []
for i, val in enumerate(data):
if i == 0:
result_list.append((None, None, None, None))
else:
# calculate k
j = 0
while j < i:
if val < data[j]:
j = -1
break
j += 1
k_val = 0 if j == -1 else 1
# calculate l
j = 0
while j < i:
if val > data[j]:
j = -1
break
j += 1
l_val = 0 if j == -1 else 1
# calculate s
s_val = k_val + l_val
# calculate d
d_val = k_val - l_val
result_list.append((k_val, l_val, s_val, d_val))
t = Texttable()
t.add_rows([["k", "l ", "s", "d"], *result_list])
print(t.draw())
s_sum = 0
d_sum = 0
for k, l, s, d in result_list[1:]:
s_sum += s
d_sum += d
print("s_sum:", s_sum)
print("d_sum:", d_sum)
assert (len(data) == 10)
m = 3.858 # because len(data) == 10
t_s = abs(d_sum - 0) / 1.964
print("t_s", round(t_s, 2))
t_d = round((s_sum - m) / 1.288, 2)
print("t_d", t_d)
assert (len(data) - 1 == 9)
t_tabl = 2.26 # because P <= 0.05 and len(data) - 1 == 9
print("t_tabl", t_tabl)
print("row trend exist:", t_s > t_tabl)
print("dispersion trend exist:", t_d > t_tabl)
def print_global_status(self):
socat_flag = False
racadm_flag = False
ipmi_console_flag = False
if not self.__node_list:
print "There is no node."
return
for node in self.__node_list:
nd_status = node.get_node_status()
if 'socat' in nd_status:
socat_flag = True
if 'racadm' in nd_status:
racadm_flag = True
if 'ipmi_console' in nd_status:
ipmi_console_flag = True
if racadm_flag and ipmi_console_flag and socat_flag:
break
header_line = ['name', 'bmc pid', 'node pid']
width = [12, 6, 6]
align = ['c', 'l', 'l']
if socat_flag:
header_line.append('socat pid')
width.append(6)
align.append('l')
if racadm_flag:
header_line.append('racadm pid')
width.append(6)
align.append('l')
if ipmi_console_flag:
header_line.append('ipmi-console pid')
width.append(12)
align.append('l')
header_line.append('ports')
port_width = 80 - 14 - 9 - 9 - socat_flag*9 - \
racadm_flag*9 - ipmi_console_flag*15
width.append(port_width - 1)
align.append('l')
rows = []
rows.append(header_line)
for node in self.__node_list:
nd_status = node.get_node_status()
line = [node.get_node_name()]
if 'bmc' in nd_status:
line.append(nd_status['bmc'])
else:
line.append('-')
if 'node' in nd_status:
line.append(nd_status['node'])
else:
line.append('-')
if socat_flag:
if 'socat' in nd_status:
line.append(nd_status['socat'])
else:
line.append('-')
if racadm_flag:
if 'racadm' in nd_status:
line.append(nd_status['racadm'])
else:
line.append('-')
if ipmi_console_flag:
if 'ipmi_console' in nd_status:
line.append(nd_status['ipmi_console'])
else:
line.append('-')
port = ''
for p in node.get_port_status():
port += "{} ".format(p)
if port == '':
line.append('-')
else:
line.append(port)
rows.append(line)
table = Texttable()
table_type = ~Texttable.BORDER
table.set_deco(table_type)
table.set_cols_width(width)
table.set_cols_align(align)
table.add_rows(rows)
print table.draw() + '\n'
def printTextTable(board):
table = Texttable()
table.add_rows(board, [])
print(table.draw())
def spp():
table = Texttable(max_width=900)
no = 0
nama = []
nim = []
kelas = []
bay_uts = []
bay_uas = []
bay_bulan = []
bay_seminar = []
bay_kas = []
admin = []
print("_" * 40)
print(" Program Pembayaran Mahasiswa")
print("_" * 40)
nama = (input("Masukan Nama : "))
nim = (input("Masukan NIM : "))
kelas = (input("Masukan Kelas : "))
print("Pilih jenis pembayaran")
print("1.UTS (Rp.250.000)")
print("2.UAS (Rp.500.000")
pilih1 = (input("Pilih (1/2) ? "))
if pilih1 == '1':
uts_uas = 'UTS'
bay_uts = 250000
bay_uas = 0
jawab = 't'
elif pilih1 == '2':
uts_uas = 'UAS'
bay_uas = 500000
bay_uts = 0
jawab = 't'
else:
print("\n!!! Failed ,Please Try Again !!!")
pilih = (
input("Apakah Anda mau membayar BULANAN (Rp.500.000/bulan) (y/t) ? "))
if pilih == 'y':
bulanan = int(
input("\nPembayaran BULANAN \nDi bayar untuk berapa bulan ? "))
d_bulanan = 'BULANAN'
bay_bulan = 5000000 * bulanan
else:
bulanan_ = ''
bay_bulan = 0
pilih = (input("Apakah Anda mau membayar SEMINAR (Rp.100.000) (y/t) ? "))
if pilih == 'y':
seminar = 'SEMINAR'
bay_seminar = 100000
else:
seminar = ''
bay_seminar = 0
pilih = (input("Apakah Anda mau membayar KAS (Rp.20.000/bulan) (y/t) ? "))
if pilih == 'y':
d_kas = 'KAS'
kas = int(input("\nPembayaran KAS \nDi bayar untuk berapa bulan ? "))
bay_kas = 20000 * kas
else:
kas = ''
bay_kas = 0
print("Anda Akan dikenakan Biaya Admin sebesar (Rp.5.000)")
input('')
admin = 5000
no = +1
for ia in range(no):
total_bayar = bay_uts + bay_uas + bay_bulan + bay_seminar + bay_kas
grand_total = total_bayar + admin
table.set_cols_dtype(
['a', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i', 'i'])
table.add_rows([[
'NO', 'NAMA', 'NIM', 'KELAS', 'UTS', 'UAS', 'BULAN', 'SEMINAR', 'KAS',
'TOTAL', 'ADMIN', 'GRAND TOTAL'
],
[
ia + 1, nama, nim, kelas, bay_uts, bay_uas, bay_bulan,
bay_seminar, bay_kas, total_bayar, admin, grand_total
]])
print("_" * 30)
print(" Rincian Pembayaran")
print("_" * 30)
print(table.draw())
jawab3 = input("\n Tambahkan Data PEMBAYARAN (y/t)? ")
print("")
for token in tokens:
if token.lower() in domains:
domainset.append(token)
# check for bi-grams and tri-grams (example: machine learning)
for token in noun_chunk:
token = token.lower().strip()
if token in domains:
domainset.append(token)
return [i.capitalize() for i in set([i.lower() for i in domainset])]
name = extract_name(text)
phone = extract_mobile_numbers(text)
email = extract_emails(text)
education = extract_education(text)
skills = extract_skills(text)
domains = extract_domains(text)
from texttable import Texttable
t = Texttable()
t.set_cols_dtype(['t','t','t', 't', 't', 't'])
t.set_cols_width([10, 16, 16, 10, 15, 15])
t.add_rows([['Name', 'Mobile Number', 'Email', 'Education', 'Technical Skills', 'Domains'], [name, phone, email, education, skills, domains]])
print(t.draw())
#print(tabulate([[name, phone, email, education]], headers=['Name', 'Mobile Number', 'Email', 'Education'], tablefmt='orgtbl'))
#import spacy
def draw_single_response_table(response):
table = Texttable(max_width=140)
table.add_row(response.keys())
table.add_row(response.values())
return table.draw()
