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()
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')