def main():
print_instruction()
board = []
for i in range(9):
board.append(-1)
win = False
move = 0
while not win:
print_board(board)
print "Turn number" + str(move + 1)
if move % 2 == 0:
turn = 'X'
else:
turn = 'O'
user = get_input(turn)
while board[user] != -1:
print "Invalid move! Cell already taken. Please try again.\n"
user = get_input(turn)
board[user] = 1 if turn == 'X' else 0
move += 1
if move > 4:
winner = check_win(board)
if winner != -1:
out = "The winner is"
out += "X" if winner == 1 else "O"
out += "=D"
quit_game(board, out)
elif move >= 9:
quit_game(board, "No winner :(")
def main():
print_instruction()
board = []
for i in range(9):
board.append(-1)
win = False
move = 0
while not win:
print_board(board)
print "Turn number" + str(move+1)
if move % 2 == 0:
turn = 'X'
else:
turn = 'O'
user = get_input(turn)
while board[user] != -1:
print "Invalid move! Cell already taken. Please try again.\n"
user = get_input(turn)
board[user] = 1 if turn == 'X' else 0
move += 1
if move > 4:
winner = check_win(board)
if winner != -1:
out = "The winner is"
out += "X" if winner == 1 else "O"
out += "=D"
quit_game(board,out)
elif move >= 9:
quit_game(board, "No winner :(")
def set_grep_word(stdscr):
word = get_input(stdscr, "Grep for: ")
tab.get_current_tab().grep = word
tab.get_current_tab().name = word
# Clear screen is required since the word size (in the tab) could have changed
globvar.clear_screen = True
globvar.redraw = True
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
image, label = input.get_input(LABEL_PATH, LABEL_FORMAT, IMAGE_PATH, IMAGE_FORMAT)
logits = model.inference(image)
loss = model.loss(logits, label)
train_op = model.train(loss, global_step)
saver = tf.train.Saver(tf.all_variables())
summary_op = tf.merge_all_summaries()
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=input.FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(input.FLAGS.train_dir, graph_def=sess.graph_def)
for step in xrange(input.FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), "Model diverged with loss = NaN"
if step % 1 == 0:
num_examples_per_step = input.FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = "%s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)"
print(format_str % (datetime.now(), step, loss_value, examples_per_sec, sec_per_batch))
if step % 10 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 25 == 0:
checkpoint_path = os.path.join(input.FLAGS.train_dir, "model.ckpt")
saver.save(sess, checkpoint_path, global_step=step)
def get_filters():
"""
Asks user to specify a city, month, and day to analyze.
Returns:
(str) city - name of the city to analyze
(str) month - name of the month to filter by, or "all" to apply no month filter
(str) day - name of the day of week to filter by, or "all" to apply no day filter
"""
print('Hello! Let\'s explore some US bikeshare data!\n')
# TO DO: get user input for city (chicago, new york city, washington)
while True:
city = inp.get_input('Please input the city name:- (chicago), or (new york city), or (washington):__ ',
['chicago', 'new york city', 'washington'])
if city in ['chicago', 'new york city', 'washington']:
break
# TO DO: get user input for month (all, january, february, ... , june)
while True:
month = inp.get_input('\nWhich month? january, february, march, april, may, june, all?__ ',
['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september',
'october', 'november', 'december', 'all'])
try:
month.index(month)
except:
continue
else:
break
# TO DO: get user input for day of week (all, monday, tuesday, ... sunday)
while True:
day = inp.get_input('\nWhich day? monday, tuesday, wednesday, thursday, friday, saturday, sunday, all?__ ',
['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all'])
try:
day.index(day)
except:
continue
else:
break
print('-' * 40)
return city, month, day
def main():
print_instruction()
board = []
for i in range(9):
board.append(-1)
win = False
move = 0
while not win:
print_board(board)
print "Turn number" + str(move+1)
if move % 2 == 0:
#this is the user
turn = 'X'
user = get_input(turn)
while board[user] != -1:
print "Invalid move! Cell already taken. Please try again.\n"
user = get_input(turn)
board[user] = 1
else:
#this will be the computer
turn = 'O'
comp=generate_o(board)#computer behaviour define
board[comp]=0
move += 1
if move > 4:
winner = check_win(board)
if winner != -1:
out = "The winner is"
out += "X" if winner == 1 else "O"
out += "=D"
quit_game(board,out)
elif move >= 9:
quit_game(board, "No winner :(")
def do_fizzbuzz():
try:
user_input = get_input()
end = user_input
curr = 1
while curr < end:
if curr % 3 == 0:
print("fizz")
elif curr % 5 == 0:
print("buzz")
elif curr % 15 == 0:
print("fizzbuzz")
else:
print(curr)
curr += 1
except ValueError:
return
def test(): with tf.Graph().as_default(): image, label = input.get_input(LABEL_PATH, LABEL_FORMAT, IMAGE_PATH, IMAGE_FORMAT) logits = model.inference(image) top_k_op = tf.nn.in_top_k(logits, label, 1) variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) # Get summaries for TENSOR BOARD summary_op = tf.merge_all_summaries() graph_def = tf.get_default_graph().as_graph_def() summary_writer = tf.train.SummaryWriter(input.FLAGS.eval_dir, graph_def=graph_def) while True: evaluate_model(saver, summary_writer, top_k_op, summary_op) if input.FLAGS.run_once: break time.sleep(input.FLAGS.eval_interval_secs)
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
image, label = input.get_input(LABEL_PATH, LABEL_FORMAT, IMAGE_PATH,
IMAGE_FORMAT)
logits = model.inference(image)
loss = model.loss(logits, label)
train_op = model.train(loss, global_step)
saver = tf.train.Saver(tf.all_variables())
summary_op = tf.merge_all_summaries()
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=input.FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(input.FLAGS.train_dir,
graph_def=sess.graph_def)
for step in xrange(input.FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 1 == 0:
num_examples_per_step = input.FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)'
)
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 10 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 25 == 0:
checkpoint_path = os.path.join(input.FLAGS.train_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +']
SHIP_LIST = [('Destroyer', 2, 'd2'),
('Cruiser', 3, 'c3'),
('Submarine', 3, 's3'),
('Battleship', 4, 'b4'),
('Aircraft Carrier', 5, 'a5')]
if __name__ == '__main__':
board = BLANK_BOARD
taken = set()
for ship_tuple in SHIP_LIST:
board, taken = get_input(board, ship_tuple, taken)
def highlight(stdscr):
globvar.redraw = True
word = get_input(stdscr, "Highlight word: ")
if not word:
return
tab.get_current_tab().highlight.append(word)
def goto_backward(stdscr):
globvar.redraw = True
word = get_input(stdscr, "Go to: ")
if not word:
return
tab.get_current_tab().goto = (word, BACK)
def main(): input_string = input.get_input() vowels = count_vowels(input_string, True) vowel_summary(vowels)
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +',
'| | | | | | | | | | |',
'+ -- + -- + -- + -- + -- + -- + -- + -- + -- + -- +'
]
SHIP_LIST = [('Destroyer', 2, 'd2'), ('Cruiser', 3, 'c3'),
('Submarine', 3, 's3'), ('Battleship', 4, 'b4'),
('Aircraft Carrier', 5, 'a5')]
if __name__ == '__main__':
board = BLANK_BOARD
taken = set()
for ship_tuple in SHIP_LIST:
board, taken = get_input(board, ship_tuple, taken)
def load_profile(stdscr):
""" Save profile to file"""
word = get_input(stdscr, "Loading: Profile name:")
# Get resulting contents
load_profile_from_file(word)
def save_profile(stdscr):
""" Save profile to file"""
word = get_input(stdscr, "Saving: Profile name:")
# Get resulting contents
save_profile_to_file(word.strip())
def build(regime: str, epochs: int, batch_size: int, wd: float,
learning_rate: float, buffer_size: int) -> list:
summaries = []
with tf.name_scope('inputs'):
batch, dataset_len = get_input(regime, epochs, batch_size, buffer_size)
cues, responses, lens, target = batch
global_step = _locate_variable('global_step', [],
tf.constant_initializer(0), False, None)
with tf.variable_scope('embeddings_matrices', reuse=False):
cues_embeddings = _locate_variable(
'cues_embeddings',
shape=[CUES_MAP_SIZE, 100],
initializer=tf.random_normal_initializer(),
trainable=True,
wd=wd)
responses_embeddings = _locate_variable(
'responses_embeddings',
shape=[RESPONSES_MAP_SIZE, 100],
initializer=tf.random_normal_initializer(),
trainable=True,
wd=wd)
with tf.name_scope('lookup'):
cues_embedded = tf.nn.embedding_lookup(cues_embeddings, cues)
summaries.append(tf.summary.histogram('cues_embeddings',
cues_embedded))
responses_embedded_raw = tf.nn.embedding_lookup(
responses_embeddings, responses)
mask = _return_mask(lens)
responses_embedded_masked = tf.multiply(mask, responses_embedded_raw)
responses_embedded = tf.reduce_sum(
responses_embedded_masked, axis=1) / tf.cast(
tf.expand_dims(lens, axis=1), dtype=tf.float32)
summaries.append(
tf.summary.histogram('response_embeddings', responses_embedded))
with tf.name_scope('dense_body'):
dense_input = tf.concat((cues_embedded, responses_embedded), axis=1)
summaries.append(tf.summary.histogram('dense_input', dense_input))
with tf.variable_scope('first_dense', reuse=False):
w1 = _locate_variable(
'w', [200, 1024],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
b1 = _locate_variable('b', [
1024,
], tf.random_normal_initializer(), True, wd)
z1 = tf.nn.elu(tf.matmul(dense_input, w1) + b1)
with tf.variable_scope('second_dense', reuse=False):
w2 = _locate_variable(
'w', [1024, 512],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
b2 = _locate_variable('b', [
512,
], tf.random_normal_initializer(), True, wd)
z2 = tf.nn.elu(tf.matmul(z1, w2) + b2)
with tf.variable_scope('third_dense', reuse=False):
w3 = _locate_variable(
'w', [512, 256],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
b3 = _locate_variable('b', [
256,
], tf.random_normal_initializer(), True, wd)
z3 = tf.nn.elu(tf.matmul(z2, w3) + b3)
with tf.variable_scope('forth_dense', reuse=False):
w4 = _locate_variable(
'w', [256, 128],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
b4 = _locate_variable('b', [
128,
], tf.random_normal_initializer(), True, wd)
z4 = tf.nn.elu(tf.matmul(z3, w4) + b4)
with tf.variable_scope('fivth_dense', reuse=False):
w5 = _locate_variable(
'w', [128, 64],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
b5 = _locate_variable('b', [
64,
], tf.random_normal_initializer(), True, wd)
z5 = tf.nn.elu(tf.matmul(z4, w5) + b5)
with tf.variable_scope('sixth_dense', reuse=False):
w6 = _locate_variable(
'w', [64, 32],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
b6 = _locate_variable('b', [
32,
], tf.random_normal_initializer(), True, wd)
z6 = tf.nn.elu(tf.matmul(z5, w6) + b6)
with tf.variable_scope('final_dense', reuse=False):
w_final = _locate_variable(
'w', [32, 1], tf.contrib.layers.variance_scaling_initializer(),
True, wd)
b_final = _locate_variable('b', [
1,
], tf.random_normal_initializer(), True, wd)
with tf.variable_scope('skip_connection', reuse=False):
w_sc = _locate_variable(
'w', [200, 1],
tf.contrib.layers.variance_scaling_initializer(), True, wd)
z_sc = tf.matmul(dense_input, w_sc)
logits = tf.nn.sigmoid(
tf.squeeze(tf.matmul(z6, w_final) + b_final + z_sc),
name='sig_logits')
summaries.append(tf.summary.histogram('logits', logits))
with tf.name_scope('mae'):
mae, metric_update_op = tf.metrics.mean_absolute_error(
labels=target, predictions=logits)
running_variables = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES,
scope='mae/mean_absolute_error')
running_variables_init = tf.variables_initializer(
var_list=running_variables)
mae_summary = tf.summary.scalar('mae', mae)
with tf.name_scope('loss'):
loss = _return_loss(target, logits)
summaries.append(tf.summary.scalar('loss', loss))
with tf.name_scope('optimization'):
with tf.variable_scope('optimizer', reuse=False):
optimizer = tf.train.AdamOptimizer(learning_rate)
grads_vars = optimizer.compute_gradients(loss)
train_step = optimizer.apply_gradients(grads_vars,
global_step=global_step)
with tf.name_scope('gradients'):
for grad, var in grads_vars:
if grad is not None:
summaries.append(
tf.summary.histogram('gradient_at_{}'.format(var.op.name),
grad))
with tf.name_scope('variables'):
for var in tf.trainable_variables():
summaries.append(
tf.summary.histogram('var_at_{}'.format(var.op.name), var))
merge_op = tf.summary.merge(summaries)
dense_activations = tf.get_collection(tf.GraphKeys.SUMMARIES,
scope='dense_body')
input_summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope='lookup')
test_summaries_op = tf.summary.merge(dense_activations + input_summaries)
mae_merge_op = tf.summary.merge([mae_summary])
with tf.name_scope('saving'):
saver = tf.train.Saver()
return global_step, train_step, merge_op, test_summaries_op, \
saver, dataset_len, mae_merge_op, running_variables_init, \
metric_update_op
mario.move(input , board)
if input == 'q' :
os.system('clear')
sys.exit()
time.sleep(0.2)
'''
a='r'
quit_val = 1
timer = time.time()
timer1 = time.time()
while a=='r':
if config.level == 2 :
break
p_input = (input.get_input())
if p_input == 'q':
quit_val = 0
break
# cur_round = datetime.datetime.now()
""" if (cur_round - prev_round) >= datetime.timedelta(seconds=1):
# bd.update_frame()
# prev_round = cur_round"""
mario.move(p_input , board)
board.render((mario._y - 25))
#board.printer(0,20,0,190)
print(mario.get_coods())
def main(): input_string = input.get_input() reversed_string = reverse_string(input_string) print reversed_string print "This string a Palindrome?: %s"%(isPalindrome(reversed_string))
def main():
input.get_input()
NumnodeS = cirdraw.draw()
prepare.cube_ready(NumnodeS)
caculater.caclulater(NumnodeS)
def main(): #file = read_file(file_path) computer_v2 = get_input()
if eat_coins(screen, player):
play(coin)
count += 1
coin_count -= 1
if killPlayer(screen, player):
player = restart(screen, player)
health -= 1
if health == 0:
break
player.draw(screen, dir)
mission_comp = checkMissions(mission_comp)
make_scene(screen)
try:
keypress = input.get_input()
if keypress == 'd':
iter_count += 1
dir = 0
move = checkMove(screen, player)
if move != 2 and move != 1:
screen.move_right()
elif keypress == 'a':
dir = 1
move = checkMove(screen, player)
if move != 3 and move != 1:
screen.move_left()
elif keypress == 'w':
play(jump)
player.jump(screen)
make_scene(screen)
h2_150 = np.array(Ne150)[1, :]
G_150 = np.add(h1_150, h2_150)
'''
def cal_diff(x, y):
assert isinstance(x, int)
a = table_1.iloc[:, x]
b = h1
diff = 0
for i in range(0, len(b)-1):
if a.loc[i] == b[i]:
diff = diff
else:
diff += 1
return diff
'''
s, h, r, s_ID = input.get_input()
s_ID = np.array(s_ID)
answer1 = h[:, 2]
answer2 = h[:, 3]
answer_G = np.add(answer1, answer2)
def locus_diff(my_id, his_id):
different = []
for i in range(len(my_id)):
if int(my_id[i]) != his_id[i]:
different.append(i)
np.array(different)
return different
GLOBAL.enemy_list[j].next()
if level == 2:
to_del = []
for j in GLOBAL.bullet_list:
oldx = GLOBAL.bullet_list[j].x
GLOBAL.bullet_list[j].next()
# GLOBAL.bullet_list[j].collision()
if oldx == GLOBAL.bullet_list[j].x:
to_del.append(j)
for j in to_del:
del GLOBAL.bullet_list[j]
a = input.get_input()
GLOBAL.board.header["points"] = GLOBAL.points
GLOBAL.board.header["health"] = player.health
b = int(player.x / config.boardWidth * 100)
if level == 1:
GLOBAL.board.header[
"progress"] = '\n[' + "\033[31m#\033[39m" * b + "-" * (100 -
b) + ']'
if level == 2:
GLOBAL.board.header["boss health"] = GLOBAL.boss.health
if player.x >= config.boardWidth - (
config.dispWidth) / 2 and level != 2:
fin_score = GLOBAL.points
os.system("tput reset")
print("final score: " + str(fin_score))
return lst
def randomize(self, lst):
off = numpy.random.random() - 0.5
for i in range(0, len(lst)):
lst[i] += (numpy.random.random()-0.5)*0.009 + off*0.02
def deconvolute(self, convoluted):
matrix = self._a().I
return numpy.asarray(matrix * numpy.array(convoluted, ndmin=2).transpose()).transpose()[0]
if __name__ == "__main__":
original = get_input()
sigma = 0.02
disc = Discretizer(sigma, original)
convoluted = disc.convolute_with_integral()
convoluted2 = disc.convolute_with_matrix()
plt.figure()
plt.plot(original, "r")
plt.plot(convoluted, "b")
plt.figure()
plt.plot(convoluted, "b")
plt.plot(disc.deconvolute(convoluted2), "r")
plt.figure()
plt.plot(original, "r")
plt.plot(convoluted2, "b")
def main(): input_string = input.get_input() pl_string = pig_latin_phrase(input_string) print pl_string
import prepare
from input import get_input
from AI.SemanticAnalysis import SemanticAnalysis
from config import Config
config = Config()
ai = SemanticAnalysis(config)
sentence = get_input(config.INPUT)
while sentence:
print("客户： " + sentence)
ai.get_response(sentence)
if ai.is_end():
break
sentence = get_input(config.INPUT)
'''
This is the entry point for the program, everything will happen through here
'''
import fetch
import input
import plot
import simplify
data = 0
while data == 0:
#Get the input from the user
symbol = input.get_input()
#Fetch the data from Google Finance
if symbol:
data = fetch.get_data(symbol)
#Simplify the data from Google
simplified_Data = simplify.simplify_data(data)
#Plot the graph of the stock price over time
plot.plot_graph(simplified_Data)
def init():
star_spawner = logic.star_spawner()
#Game loop
while 1:
var.window.setscreencolors('white','black',clear=True)
input.get_input()
for d in var.debris:
if not var.pause or d.owner==var.player:
d.tick()
d.draw()
for b in var.bullets:
if not var.pause and var.state=='game': b.tick()
b.draw()
for s in var.ships:
if not var.pause and var.state=='game': s.tick()
s.draw()
if var.state=='game' and not var.player in var.ships and not var.lives:
var.pause = True
_count = 0
for d in var.debris:
if d.owner==var.player: _count+=1
if not _count:
sound.pause_song()
var.window.putchars('YOU ARE DEAD',fgcolor=(255,255,255),x=9,y=15)
elif var.state=='game' and not var.player in var.ships and var.lives and not var.cleaning:
var.cleaning = True
sound.pause_song()
for d in var.debris:
if d.owner==var.player: continue
d.move_speed_max = d.move_speed_max/2
d.move_speed = 0
for s in var.ships:
s.move_speed_max = 1#s.move_speed_max/2
s.move_speed = 0
s.direction = 'south'
s.y_limit = var.win_size[1]+1
elif var.state=='menu':
_r = random.randint(-100,0)
var.window.putchars('ASCII SHOOTER',fgcolor=(255+_r,255+_r,255+_r),x=9,y=13-(len(var.main_menu)/2))
for entry in var.main_menu:
_i = var.main_menu.index(entry)
if _i == var.menu_select:
var.window.putchars('> '+entry['text'],fgcolor=(255,255,255),x=12,y=15-(len(var.main_menu)/2)+_i)
else:
var.window.putchars(entry['text'],fgcolor=(255,255,255),x=12,y=15-(len(var.main_menu)/2)+_i)
if var.cleaning:
var.window.putchars('LIFE -1',fgcolor=(255,255,255),x=12,y=15)
if not len(var.ships):
var.player = ship.fighter(x=15,y=25,player=True)
var.player.move_speed_max = 3
var.lives-=1
sound.unpause_song()
var.cleaning = False
elif var.pause and var.player in var.ships:
var.window.putchars('PAUSED',fgcolor=(255,255,255),x=12,y=15)
var.window.putchars('Score %s' % (var.score),fgcolor=(255,255,255),x=0,y=0)
var.window.putchars('Lives %s' % (var.lives),fgcolor=(255,255,255),x=23,y=0)
if not var.pause and not var.cleaning and var.state=='game': var.ship_spawner.tick()
if not var.pause: star_spawner.tick()
var.window.update()
var.clock.tick(var.fps)
