def main(prefix): result = re.load_result(prefix) result.import_dict(ml_glpp_parameters(result)) re.save_result(prefix, result) return result
def run_trial(trial, prefix="experiment", *args): ''' Runs a particular trial. Parameters: - trial: name of a function in trials.py - prefix: file in which the results should be stored. - *args: arguments to the trial function ''' reload(trials) resu = getattr(trials, trial)(*args) result.save_result(config.results_dir + prefix, resu) return resu
def main(prefix): result = re.load_result(prefix) try: an = lnp_result_info(result) except: print " (not a LNP simulation)" an = information_analysis(result.intensity) setattr(result, "info", an) re.save_result(prefix, result) return result
if catch_no_event: twitter.obtain_tweets(date_since, date_until, csv_with_duplicate + no_event_folder + date, csv_without_duplicate + no_event_folder + date) # Get and show total tweets statistics print("\nGet number of total tweets") total_tweets_counts = twitter.count_total_tweets( twitter.get_teams(), csv_with_duplicate + no_event_folder + date) graphic.create_graph(total_tweets_counts, ["Team", "Count"]) # Save results in CSV file print("\nSave results -> Total tweets") result.save_result(total_tweets_counts, ["Position", "Team", "Total tweets"], csv_result + no_event_folder + date, "ResultTotalTweets") # Get and show unique users' tweets statistics print("\nGet number of tweets created by unique users") total_tweets_per_unique_user_counts = twitter.count_tweets_per_unique_user( twitter.get_teams(), csv_without_duplicate + no_event_folder + date) graphic.create_graph(total_tweets_per_unique_user_counts, ["Team", "Count"]) # Save results in CSV file print("\nSave results -> Total tweets per unique user") result.save_result(total_tweets_per_unique_user_counts, ["Position", "Team", "Total tweets per unique user"], csv_result + no_event_folder + date,