def _generate_serial(self):
timestamp = time.time()
# List of characters that will be excluded from the generator
excluded_chars = ['O']
# List of characters that will be used by the generator
extras = [c for c in string.ascii_uppercase if not c in excluded_chars]
# Generate the key using the current timestamp as the seed
key_custom = generate(3,
SEPARATOR,
LENGTH,
LENGTH,
type_of_value='int',
capital='none',
extras=extras,
seed=timestamp).get_key().upper()
# Generate the signature but only on a key cleared of separator char
key_without_separator = key_custom.replace(SEPARATOR, '')
key_signature = SHA256.SHA256Hash(
key_without_separator.encode()).hexdigest()
# We keep only the first values of the signature as a checksum
key_checksum = key_signature[0:LENGTH].upper()
# The checksum is added at the end of the key
serial = '{}{}{}'.format(key_custom, SEPARATOR, key_checksum)
return serial
def _make_key(self) -> None:
self._KEY = None
if self.ENCRYPT_BYTECODE:
self._KEY = generate(1,
'',
16,
16,
type_of_value='char',
capital='mix',
extras=list(map(str, range(10))),
seed=round(time())).get_key()
def get_account(email):
account = users.find_one({'email': email})
username = account['username']
password = account['password']
key = generate(seed=random.randint(0, 101))
key = key.get_key()
shortkey = key[0:4]
users.update_one({'email': email}, {'$set': {'password': shortkey}})
message = f"Your accound information- Your username is :{username}. Your password is :{password}"
emailuser.get_email(email, username, shortkey)
return {}
def main():
global token
full_path_to_token = os.path.join(os.getcwd(), config.path_to_token)
with open(full_path_to_token, "a+") as f:
if os.stat(full_path_to_token).st_size == 0:
key = generate()
f.write(key.get_key())
_ = requests.post(f"{config.api_post_device}{key.get_key()}")
token = key.get_key()
with open(full_path_to_token, "r") as f:
for line in f:
token = line
break
app = QtWidgets.QApplication(sys.argv) # Новый экземпляр QApplication
window = LoginApp() # Создаём объект класса ExampleApp
window.show() # Показываем окно
app.exec_() # и запускаем приложение
def AddAnnouncement(self,
db_conn,
ID=None,
Title="",
Location="",
Description="",
PictureLink="",
HyperLink="",
PostingTime=None):
ID = generate(num_of_atom=1, min_atom_len=10,
max_atom_len=10).get_key() if (ID == None) else ID
PostingTime = datetime.datetime.now() if (PostingTime
== None) else PostingTime
cur = db_conn.cursor()
query = "INSERT INTO `pesuapp`.`announcement` (`id`, `title`, `location`, `description`, `picture_link`, `hyperlink`, `posting_time`) VALUES (%s, %s, %s, %s, %s, %s, %s);"
cur.execute(query, (ID, Title, Location, Description, PictureLink,
HyperLink, PostingTime))
db_conn.commit()
cur.close()
from key_generator.key_generator import generate
all_sizes_required = [(100, '100'), (500, '500'), (1000, '1K'), (5000, '5K'), (10000, '10K'), (50000, '50K'), (100000, '100K'), (500000, '500K')]
for file_size in all_sizes_required:
OUTPUT_PATH = "./int_test_" + file_size[1] + ".txt"
STRING_COUNT = file_size[0]
output_file = open(OUTPUT_PATH, "w")
for i in range(STRING_COUNT):
integer = generate(
num_of_atom = 1,
min_atom_len = 3,
max_atom_len = 9,
type_of_value = "int",
seed = i
).get_key()
integer = integer.lstrip("0")
if(integer == ""):
output_file.write("0" + "\n")
else:
output_file.write(integer + "\n")
output_file.close()
print("Done with " + OUTPUT_PATH)
test_passed = 0
test_failed = 0
h_total = 100
extras_test = ['^', '%', '&', '#', '!', '@', '$']
pbar = tqdm(total=h_total)
for h in range(h_total):
for i in range(1, 5):
for j in range(1, 5):
for k in range(j, 10):
for l in ['hex', 'char', 'int']:
for m in ['none', 'all', 'mix']:
key = generate(i,
'-',
j,
k,
seed=h,
type_of_value=l,
capital=m).get_key()
flag_check = 0
if (len(key.split('-')) == i):
flag_check += 1
len_atoms = [len(x) for x in key.split('-')]
if (min(len_atoms) >= j and max(len_atoms) <= k):
flag_check += 1
is_in = 0
if (l == 'hex' and m == 'none'):
for _ in ''.join(key.split('-')):
if (_ not in
[str(x) for x in list(range(0, 10))] + [
from key_generator.key_generator import generate
all_sizes_required = [(100, '100'), (500, '500'), (1000, '1K'), (5000, '5K'),
(10000, '10K'), (50000, '50K'), (100000, '100K'),
(500000, '500K')]
for file_size in all_sizes_required:
OUTPUT_PATH = "./double_test_" + file_size[1] + ".txt"
STRING_COUNT = file_size[0]
output_file = open(OUTPUT_PATH, "w")
for i in range(STRING_COUNT):
integer_before_dot = generate(num_of_atom=1,
min_atom_len=1,
max_atom_len=6,
type_of_value="int",
seed=i).get_key()
integer_after_dot = generate(num_of_atom=1,
min_atom_len=1,
max_atom_len=6,
type_of_value="int",
seed=i).get_key()
integer_before_dot = integer_before_dot.lstrip("0")
integer_after_dot = integer_after_dot.rstrip("0")
integer_before_dot = integer_before_dot.lstrip(
"0") if integer_before_dot != "" else "0"
integer_after_dot = integer_after_dot.rstrip(
"0") if integer_after_dot != "" else "0"
def train(args=None):
key = generate().get_key()
model_config = {
'key': key,
'bs': args.bs,
'data': args.data,
'lr': args.lr,
'epochs': args.epochs,
'steps': args.steps,
'loss': args.loss,
'two': args.two_layers
}
config_path = "model_configs/" + key + ".json"
with open(config_path, 'w') as outfile:
json.dump(model_config, outfile)
print("model_config ", model_config)
#Normalisation of features.
x_train, y_train = load_data(args.data)
x_train = x_train.astype(np.float)
y_train = y_train.astype(np.float)
print("x_train.shape[0] ", x_train.shape[0])
callbacks = []
callbacks.append(
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
mode='min',
patience=args.early_stop_patience,
min_delta=0.001,
restore_best_weights=True))
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(os.path.join(
'models', '{name}.h5'.format(name=key)),
verbose=1,
save_best_only=True,
save_weights_only=True,
monitor='val_loss',
mode='min'))
callbacks.append(
tf.keras.callbacks.TensorBoard(log_dir=args.tensorboard_dir + "/" +
key))
#Model building
model_sequence = []
if args.two_layers:
model_sequence.append(layers.Dense(units=int(x_train.shape[0] / 2)))
model_sequence.append(layers.Dense(units=1))
model = tf.keras.Sequential(model_sequence)
input_shape = [None, x_train[0].shape[0]]
model.build(input_shape)
model.summary()
model.compile(optimizer=tf.optimizers.Adam(learning_rate=args.lr),
loss=args.loss)
history = model.fit(
x_train,
y_train,
epochs=args.epochs,
verbose=1,
# steps_per_epoch=args.steps,
batch_size=args.bs,
validation_split=0.1,
callbacks=callbacks)
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist.tail()
all_sizes_required = [(100, '100'), (500, '500'), (1000, '1K'), (5000, '5K'),
(10000, '10K'), (50000, '50K'), (100000, '100K'),
(500000, '500K')]
for file_size in all_sizes_required:
OUTPUT_PATH = "./string_test_" + file_size[1] + ".txt"
STRING_COUNT = file_size[0]
output_file = open(OUTPUT_PATH, "w")
for i in range(STRING_COUNT):
string = ""
recipient = generate(num_of_atom=1,
type_of_value="hex",
capital="mix",
extras=["-", "_"],
seed=i).get_key()
domain = generate(num_of_atom=2,
separator=".",
min_atom_len=3,
max_atom_len=5,
type_of_value="hex",
capital="mix",
extras=["-"],
seed=i).get_key()
string = recipient + "@" + domain
output_file.write(string + "\n")
