def get_cites_pubs_on_pub(driver, pub, main_index):
"""Получаем все публикации, в которых цитируется указанная"""
driver.get(_CITES.format(pub.id_scholarcitedby))
utils.check_captcha(driver)
utils.unchecked_citations(driver)
for z in range(1, _CONTINUE_INFO['citations_page']):
time.sleep(0.5)
if not utils.next_page(driver):
print('Такой страницы с публикациями нет')
break
i = _CONTINUE_INFO['citations_page'] - 1
while True:
i = i + 1
_CONTINUE_INFO['citations_page'] = i
_CONTINUE_INFO['last_index_in_result'] = saver.get_last_index()
saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)
add_pubs_in_lib(driver)
pubs = get_pubs_from_lib(driver)
close_window(driver)
for citi in pubs:
saver.save(_RESULT_FILE, citi, main_index)
if not utils.next_page(driver) or i > 9:
break
def save_file(vaults, path):
try:
save(vaults, path)
os.startfile(path)
print('=' * 50)
print('[+]Info saved to your desktop.')
print('=' * 50)
except Exception:
print("Error with saving.Try to re-enter username.")
def dump_sign(self):
curv = self.curves.selected
if curv == None: return
path = []
for u in np.linspace(0, len(curv) - 1, len(curv) * 16):
path.append(curv.get_ptn(u))
trans = np.resize(milling_paths.TRANS, 4)
trans[3] = 0
scale = milling_paths.SCALE
proc = lambda p: (p + trans) * scale
saver.save(-1, '05_sign.k4', path, pre=proc)
def dump_sign( self ) : curv = self.curves.selected if curv == None : return path = [] for u in np.linspace(0,len(curv)-1,len(curv)*16) : path.append( curv.get_ptn( u ) ) trans = np.resize( milling_paths.TRANS , 4 ) trans[3] = 0 scale = milling_paths.SCALE proc = lambda p : (p + trans)*scale saver.save(-1,'05_sign.k4',path, pre = proc )
def __init__(self, curves):
Node.__init__(self, Lines(), (1.0, 0.5, 0.0))
self.curves = curves
self.paths = []
self.init_r = HUGE_DRILL / 2.0 / SCALE
self.flat_r = FLAT_DRILL / 2.0 / SCALE
self.exac_r = SMALL_DRILL / 2.0 / SCALE
print 'Init drill: ', self.init_r
print 'Flat drill: ', self.flat_r
print 'Exact drill: ', self.exac_r
self.gen_paths()
proc = lambda p: (p + TRANS) * SCALE + np.array((0, 0, -SMALL_DRILL))
save(0, '01_init.k16', self.paths, pre=proc)
save(1, '02_border.f12', self.paths, pre=proc)
save(2, '03_flat.f12', self.paths, pre=proc)
save(3, '04_exact.k8', self.paths, pre=proc)
save_commpressed(0, '11_init_small.k16', self.paths, pre=proc)
save_commpressed(1, '12_border_small.f12', self.paths, pre=proc)
save_commpressed(2, '13_flat_small.f12', self.paths, pre=proc)
save_commpressed(3, '14_exact_small.k8', self.paths, pre=proc)
self.set_data(self.paths)
np.set_printoptions(suppress=True)
def __init__( self , curves ) : Node.__init__( self , Lines() , (1.0,0.5,0.0) ) self.curves = curves self.paths = [] self.init_r = HUGE_DRILL / 2.0 / SCALE self.flat_r = FLAT_DRILL / 2.0 / SCALE self.exac_r = SMALL_DRILL / 2.0 / SCALE print 'Init drill: ' ,self.init_r print 'Flat drill: ' ,self.flat_r print 'Exact drill: ' ,self.exac_r self.gen_paths() proc = lambda p : (p + TRANS)*SCALE + np.array((0,0, -SMALL_DRILL)) save(0,'01_init.k16' , self.paths , pre = proc ) save(1,'02_border.f12' , self.paths , pre = proc ) save(2,'03_flat.f12' , self.paths , pre = proc ) save(3,'04_exact.k8' , self.paths , pre = proc ) save_commpressed(0,'11_init_small.k16' , self.paths ,pre = proc ) save_commpressed(1,'12_border_small.f12' , self.paths ,pre = proc ) save_commpressed(2,'13_flat_small.f12' , self.paths ,pre = proc ) save_commpressed(3,'14_exact_small.k8' , self.paths ,pre = proc ) self.set_data( self.paths ) np.set_printoptions(suppress=True)
def file_save():
"""
Function for saving field to file
"""
file = tkinter.filedialog.asksaveasfile(mode='w',
defaultextension=".sav")
if file:
file.write(saver.save(self.game.field, self.game.player))
file.close()
def get_pubs_with_cities(driver):
"""Соединяем публикации с их цитирующими публикацями"""
indexes = []
pubs = get_pubs_from_lib(driver)
for pub in pubs:
indexes.append(saver.save(_RESULT_FILE, pub))
_CONTINUE_INFO['last_index_in_result'] = saver.get_last_index()
saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)
for p in range(_CONTINUE_INFO['cities_index'], len(indexes)):
_CONTINUE_INFO['cities_index'] = p
saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)
if pubs[p].citedby != 0:
get_cites_pubs_on_pub(driver, pubs[p], indexes[p])
_CONTINUE_INFO['citations_page'] = 1
_CONTINUE_INFO['cities_index'] = 0
def make_multiplayer_move(self, first_player):
"""
Method for making multiplayer move
:param first_player: bool
"""
if first_player:
while not self.client.received:
pass
self.field, self.player = saver.load(self.client.recv_str)
else:
data = saver.save(self.field, self.player)
self.client.send(data)
while not self.client.received:
pass
self.field, self.player = saver.load(self.client.recv_str)
self.previous_player = not self.player
self.client.recv_str = ''
self.client.received = False
def run(self, max_iter, solutions, print_all=False):
bees = [
Bee(self.world, self.neighbor_function, self.fitness_function)
for x in range(0, self.n_of_bees)
]
best_solution = None
best_solution_overall = None
best_solution_fitness_overall = 0
worst_solution_fitness_overall = 10000
best_solution_iteration = 0
iteration = 0
best_fitnesses = []
while iteration < max_iter:
print("Iteration ", iteration + 1)
solutions = self.iterate(bees, solutions, print_all)
fitnesses = [
self.fitness_function(self.world, x) for x in solutions
]
fit_sol = list(zip(fitnesses, solutions))
fit_sol.sort(key=lambda x: x[0], reverse=True)
best_solution = fit_sol[0][1]
if fit_sol[0][0] > best_solution_fitness_overall:
best_solution_overall = best_solution
best_solution_iteration = iteration + 1
best_solution_fitness_overall = fit_sol[0][0]
if fit_sol[-1][0] < worst_solution_fitness_overall:
worst_solution_fitness_overall = fit_sol[-1][0]
print("Best fitness in iteration ", iteration + 1, " : ",
fit_sol[0][0], " solution: ", best_solution)
iteration += 1
best_fitnesses.append(fit_sol[0][0])
saver.save(self.world, 'world')
saver.save(best_fitnesses, 'fitness_history')
saver.save(best_solution, 'best_solution')
print('Worst fitness overall: ', worst_solution_fitness_overall)
print('Best solution: ', best_solution_overall)
print('Found in ', best_solution_iteration, ' iteration')
print('Fitness: ', best_solution_fitness_overall)
return best_solution, best_fitnesses
def on_save():
print('on_save: saving...')
file = filedialog.asksaveasfilename(
defaultextension='.jad') # add some options here
save(file, trigger_list)
text = []
try:
html_text = urllib.urlopen(urls[0]).read()
except:
print 'Error on:', urls[0]
soup = BeautifulSoup(html_text)
print 'URL:', urls[0]
urls.pop(0)
print 'Url Count:', len(urls)
for tag in soup.findAll('p'):
if tag.string != None:
text.append(tag.string)
for tag in soup.findAll('a', href=True):
tag['href'] = urlparse.urljoin(start_url, tag['href'])
print 'Tag:', tag['href']
if url_check in tag['href'] and tag['href'] not in visited and not stop:
urls.append(tag['href'])
visited.append(tag['href'])
if len(urls) >= 5000:
stop = True
save(text, FILE_NAME)
print '---------------------'
def test_saver(self):
field, player = self.load_identity()
string = saver.save(field, player)
field2, player2 = saver.load(string)
self.assertEqual((field, player), (field2, player2))
def save(self):
saver.save([self._player, self._map], World.SAVE_DIR)
print(
"step number:", t, "\tpercent done:",
np.round((100.0 * (t - starting_timestep + 1)) /
(final_timestep - starting_timestep + 1), 2),
"\ttime per step estimate:",
np.round(
np.mean(time_taken[max(0,
len(time_taken) - 10):len(time_taken)]),
3), "\ttime estimate:",
np.round(
np.mean(time_taken[max(0,
len(time_taken) - 50):len(time_taken)])
* (final_timestep - t), 1))
# make save
saver.save(vals)
print("he", all_neurons[0].voltage_history.shape)
# make predefined data plots or saves
not_first_plot = False
print(Interpreter.predef_output_lines)
ret = 1
any_plots = False
for line in Interpreter.predef_output_lines:
ret = generate_save_or_plot(line)
if ret == 0:
break
if ret == 2:
not_first_plot = True
any_plots = True
if any_plots:
def save(self, fn):
'''Save project'''
saver.save(fn)
reddit = praw.Reddit(user_agent=user_agent)
subreddit = reddit.get_subreddit('askreddit')
text = []
for submission in subreddit.get_new(limit=5000):
print 'Title:', submission.title
print "Text: ", submission.selftext
print "Score: ", submission.score
text.append(submission.title)
text.append(submission.selftext)
try:
submission.replace_more_comments(limit=50, threshold=0)
except:
continue
for comment in submission.comments:
try:
print 'Comment Body:', comment.body
text.append(comment.body)
except:
pass
print "---------------------------------\n"
save(text, 'new_data.txt')
text = []
def save_streaming_data(time, rdd):
if not rdd.isEmpty():
saver.save(sqlc, config, 'news', rdd)
import pygame
import funcs
import glbl_nms
import saver
pygame.init()
clock = pygame.time.Clock()
pygame.display.set_caption("MIPT study")
saver.load() # загрузка сохранения
while glbl_nms.RUN:
clock.tick(30) # FPS
funcs.rndm_events()
funcs.key_checker()
funcs.stat_decrease() # decrease stats
funcs.timer() # anim timer
funcs.a_u_ok() # checking stats for borders
funcs.draw_screen()
saver.save() # Сохраняем
pygame.quit()
else:
return None
# main
import saver
numRun = 4
dataList = []
for i in range(0, numRun):
print('Run #: ', i, '...')
# First episode
sc0 = generateData(i)
if sc0 is not None:
# if the list is not empty
sc = sc0
saver.save(sc) # save data
if not dataList:
for robot in sc.robots:
dataList.append(robot.data)
else:
for j in range(len(sc.robots)):
dataList[j].append(sc.robots[j].data)
for j in range(1, len(sc.robots)):
dataList[0].append(dataList[j])
dataList[0].store()
#for j in range(len(sc.robots)):
# dataList[j].store()
def test_saver(self):
field, player = self.load_identity()
string = saver.save(field, player)
field2, player2 = saver.load(string)
self.assertEqual((field, player), (field2, player2))
best_friends = []
for i, worker in enumerate(workers):
print('\r calculating friends for worker ',
i + 1,
'/',
len(workers),
end='')
best_friends.append(
(worker, best_friend_for_worker(worker, workers)))
best_workers = []
for i, seat in enumerate(seats):
print('\r calculating workers for seat ',
i + 1,
'/',
len(seats),
end='')
best_workers.append(
(seat, best_worker_for_seat(seat, best_friends)))
print('\r Done kmining! Reorganising output...', end='')
best_workers = list(
reversed(sorted(best_workers, key=lambda x: x[1][0][1])))
output_dict = save(workers, best_workers,
'outputs/result_' + file.split('/')[-1])
print('\r Saved! score: ', end='')
score = score(sizes[fi], output_dict,
{worker.id: worker
for worker in workers})
print(score)
def save(player_pos):
logger.debug('Try to save game')
saver.save([player_pos, world], SAVE_DIR)
logger.debug('Game saved successfully')
