def __update_sysctl_file_suse(): """ Updates /etc/sysctl.conf file with the HAWQ parameters on SUSE. """ # Backup file backup_file_name = hawq_constants.sysctl_backup_file.format(str(int(time.time()))) try: # Generate file with kernel parameters needed by hawq to temp file File(hawq_constants.hawq_sysctl_tmp_file, content=__convert_sysctl_dict_to_text(), owner=hawq_constants.hawq_user, group=hawq_constants.hawq_group) sysctl_file_dict = utils.read_file_to_dict(hawq_constants.sysctl_suse_file) sysctl_file_dict_original = sysctl_file_dict.copy() hawq_sysctl_dict = utils.read_file_to_dict(hawq_constants.hawq_sysctl_tmp_file) # Merge common system file with hawq specific file sysctl_file_dict.update(hawq_sysctl_dict) if sysctl_file_dict_original != sysctl_file_dict: # Backup file Execute("cp {0} {1}".format(hawq_constants.sysctl_suse_file, backup_file_name), timeout=hawq_constants.default_exec_timeout) # Write merged properties to file utils.write_dict_to_file(sysctl_file_dict, hawq_constants.sysctl_suse_file) # Reload kernel sysctl parameters from /etc/sysctl.conf Execute("sysctl -e -p", timeout=hawq_constants.default_exec_timeout) except Exception as e: Logger.error("Error occurred while updating sysctl.conf file, reverting the contents" + str(e)) Execute("cp {0} {1}".format(hawq_constants.sysctl_suse_file, hawq_constants.hawq_sysctl_tmp_file)) Execute("mv {0} {1}".format(backup_file_name, hawq_constants.sysctl_suse_file), timeout=hawq_constants.default_exec_timeout) Logger.error("Please execute `sysctl -e -p` on the command line manually to reload the contents of file {0}".format( hawq_constants.hawq_sysctl_tmp_file)) raise Fail("Failed to update sysctl.conf file ")
def load_state(self, filename=None): assert self.loadfile is not None or filename is not None if filename is None: filename = self.loadfile d = read_file_to_dict(filename) self.on = bool(d['on']) self.mode = yl.PowerMode(int(d['mode'])) self.h = int(d['h']) self.s = int(d['s']) self.brightness = int(d['brightness']) self.degrees = int(d['degrees']) self.r = int(d['r']) self.g = int(d['g']) self.b = int(d['b']) self.group_name = d['group_name'] self.set_mode(self.mode)
def main(): logger.basicConfig(level="INFO", filename="F:/tmp/custom.log", format="%(asctime)s %(message)s") try: print("ok") extract_convert_lda_input("F:/tmp/test.txt") #extract_fields_by_r1("F:/tmp/full_en3.v.csv",1) extract_hashtag_usage("F:/tmp/full_en3.csv") #create_ml_r1_file_read_line() users_stance = utils.read_file_to_dict("F:/tmp/merged_users.csv", "~") #unique_days = extract_daily_involvement_of_prev_calculated_user_stances("F:/tmp/merged_tweets.csv", users_stance) #write_dict_to_file("F:/tmp/unique_days.csv",unique_days) #filename = "merged_RB_MLMA_out.csv" #filename = "F:/tmp/test.txt" #users_stances = pandas_users_stances("F:/tmp/full_en3.csv_out.csv") #write_dict_to_file("F:/tmp/full_en3_rule_based_out.csv", users_stances) #dict = extract_daily_involvement_of_prev_calculated_user_stances(filename, users_stances) #write_text_list_to_file(filename+"_out.csv", dict) #create_ml_r1_file_read_line() # analyze_group_by_influence("F:/tmp/impact.csv") # analyze_duplicate_tweets("F:/tmp/full_features.csv") # extract_fields_by_r1(globals.INPUT_FILE_NAME_RB, 2) # pandas_extract_tweet_text_by_topic_label_random_n_records("F:/tmp/full_features.csv",5000, 1) # pandas_extract_tweet_text_by_topic_label_random_n_records("F:/tmp/full_features.csv",5000, 2) # extract_neutrals("F:/tmp/full_features.csvl_out.csv") # r1_stats() # rule_based_user_stance() # create_ml_r1_file_read_line() # filename = "C:/mongo/bin/mongo_export_latest_best.csv" # filename = "F:tmp/test-1-out.txt" #filename_user_id_names = "C:/mongo/bin/user_id_name.csv" #filename_stance = "F:tmp/ml_stance.txt" ## filename = "F:tmp/bots-378k.csv" ## filename = "F:tmp/full_fields.csv" #filen#ame = "F:/tmp/user_screen_names.csv" #filename_test = "F:tmp/ml_test.txt" #filename_ml = "F:tmp/pred_data.csv" #filename_write_bot = "F:tmp/bot-378k-wuserid" #filename_write = "F:/tmp/user_screen_names_out_2.csv" #filename_write_ml = "F:tmp/test-1-out.txt" #test = "C:/mongo/bin/tt2.csv" # dict = extract_daily_average_retweet_likes(filename) # users = group_users_by_posts(filename) # dict = extract_post_frequency(users) # write_text_list_to_file(filename_write, dict) # dict_user_names_ids = get_user_names_ids(filename_user_id_names) # dict_user_stances = get_user_id_stances(filename_stance) # add_stance_to_last_column_for_bots2(filename, filename_write, dict_user_names_ids, dict_user_stances) # create_ml_p1_file(filename, filename_ml, filename_write_ml) # dict = extract_tweet_text(filename) # count_total_topic_labels(filename) # extract_desired_field_distinct_user(filename, 1) # texts_1,texts_2 = extract_tweet_text_by_topic_label_random_n_records(filename, 1000) # dict = extract_daily_polarized_tweets(test) # extract_number_of_tweet_ml_labels_topics(filename) # update_mongo_with_ml_tweet_labels(filename_ml) #users_total_topic_counts = extract_users_total_topic_counts("F:/tmp/test.txt") print("ok") # users_stances = extract_users_stances(users_total_topic_counts, False) # extract_write_tweet_text_by_topic_label(filename, filename_write, "0") # logger.info_pro_remain(users_stances) # dict = extract_tweet_text_discover_neutrals("C:/mongo/bin/neutral.csv", "C:/mongo/bin/full_features.csv") # dict = extract_daily_involvement_of_prev_calculated_user_stances(filename, users_stances) # write_text_list_to_file(filename_write,dict) # sorted_hashtags = extract_hashtag_usage(filename) # write_list_to_file(filename_write, sorted_hashtags) # write_dict_to_file(filename_write, dict) # filename_write_1 = filename_write + '1' # filename_write_2 = filename_write + '2' # write_dict_to_file(filename_write_0, texts_0) # write_dict_to_file(filename_write_1, texts_1) # write_dict_to_file(filename_write_2, texts_2) except Exception as ex: logger.info(ex) logger.info(traceback.format_exc())