def run_sequential(dir_model, metric, preprocess_args): for dt_start, dt_end in utils.iter_dt_range(): print_cps(dt_start, dt_end, dir_model, metric, preprocess_args) unsupervised_utils.print_cps_per_name(dt_start, dt_end, metric, "cps_per_mac.csv") unsupervised_utils.print_cps_per_path(dt_start, dt_end, metric, "cps_per_mac.csv")
def run_parallel(metric, eps_hours, min_fraction_of_clients): dt_ranges = list(utils.iter_dt_range()) f_localize_events = functools.partial( localize_events, metric=metric, eps_hours=eps_hours, min_fraction_of_clients=min_fraction_of_clients) utils.parallel_exec(f_localize_events, dt_ranges)
def run_sequential(metric, min_seg_len, filtered, hours_tol): for dt_start, dt_end in utils.iter_dt_range(): print_empty_segs(dt_start, dt_end, metric, min_seg_len, filtered, plot=False) unsupervised_utils.print_per_name(dt_start, dt_end, metric, "empty_segs_per_mac.csv") unsupervised_utils.print_per_path(dt_start, dt_end, metric, "empty_segs_per_mac.csv")
def run_parallel(metric, eps_hours): dt_ranges = list(utils.iter_dt_range()) fp_voting = functools.partial(voting, metric=metric, in_dir="paths", eps_hours=eps_hours) utils.parallel_exec(fp_voting, dt_ranges) fp_voting = functools.partial(voting, metric=metric, in_dir="names", eps_hours=eps_hours) utils.parallel_exec(fp_voting, dt_ranges)
def run_parallel(dir_model, metric, preprocess_args): dt_ranges = list(utils.iter_dt_range()) fp_print_cps = functools.partial(print_cps, dir_model=dir_model, metric=metric, preprocess_args=preprocess_args) utils.parallel_exec(fp_print_cps, dt_ranges) fp_print_per_name = functools.partial(unsupervised_utils.print_per_name, metric=metric, file_name="cps_per_mac.csv") utils.parallel_exec(fp_print_per_name, dt_ranges) fp_print_per_path = functools.partial(unsupervised_utils.print_per_path, metric=metric, file_name="cps_per_mac.csv") utils.parallel_exec(fp_print_per_path, dt_ranges)
def run_parallel(metric, min_seg_len, filtered): dt_ranges = list(utils.iter_dt_range()) fp_print_empty_segs = functools.partial(print_empty_segs, metric=metric, min_seg_len=min_seg_len, filtered=filtered, plot=False) utils.parallel_exec(fp_print_empty_segs, dt_ranges) fp_print_per_name = functools.partial(unsupervised_utils.print_per_name, metric=metric, file_name="empty_segs_per_mac.csv") utils.parallel_exec(fp_print_per_name, dt_ranges) fp_print_per_path = functools.partial(unsupervised_utils.print_per_path, metric=metric, file_name="empty_segs_per_mac.csv") utils.parallel_exec(fp_print_per_path, dt_ranges)
def run_sequential(metric, only_unique_traceroute): for dt_start, dt_end in utils.iter_dt_range(): plot_per_node(dt_start, dt_end, metric, only_unique_traceroute)
def run_sequential(metric, eps_hours, min_fraction_of_clients): for dt_start, dt_end in utils.iter_dt_range(): localize_events(dt_start, dt_end, metric, eps_hours, min_fraction_of_clients)
def run_sequential(metric, preprocess_args): for dt_start, dt_end in utils.iter_dt_range(): plot_per_name(dt_start, dt_end, metric, preprocess_args)
def run_parallel(metric): dt_ranges = list(utils.iter_dt_range()) f_plot_per_name = functools.partial(plot_per_name, metric=metric, preprocess_args=preprocess_args) utils.parallel_exec(f_plot_per_name, dt_ranges)
def run_sequential(): for dt_start, dt_end in utils.iter_dt_range(): process_graphs(dt_start, dt_end)
def run_parallel(): dt_ranges = list(utils.iter_dt_range()) utils.parallel_exec(process_graphs, dt_ranges)
def run_parallel(): mac_node = read_input.get_mac_node() dt_ranges = list(utils.iter_dt_range()) f_print_all = functools.partial(print_all, mac_node=mac_node) utils.parallel_exec(f_print_all, dt_ranges)
def run_sequential(): mac_node = read_input.get_mac_node() for dt_start, dt_end in utils.iter_dt_range(): print_all(dt_start, dt_end, mac_node)
def run_sequential(): for dt_start, dt_end in utils.iter_dt_range(): create_dataset_unsupervised(dt_start, dt_end)
def run_parallel(): dt_ranges = list(utils.iter_dt_range()) utils.parallel_exec(create_dataset_unsupervised, dt_ranges)
def run_parallel(metric, only_unique_traceroute): dt_ranges = list(utils.iter_dt_range()) f_plot_per_node = \ functools.partial(plot_per_node, metric=metric, only_unique_traceroute=only_unique_traceroute) utils.parallel_exec(f_plot_per_node, dt_ranges)
def run_sequential(metric, eps_hours): for dt_start, dt_end in utils.iter_dt_range(): match_cps_different_metrics(dt_start, dt_end, eps_hours)
def run_parallel(preprocess_args): dt_ranges = list(utils.iter_dt_range()) fp = functools.partial(plot_latencies_traceroute, preprocess_args=preprocess_args) utils.parallel_exec(fp, dt_ranges)
def run_sequential(preprocess_args): for dt_start, dt_end in utils.iter_dt_range(): plot_latencies_traceroute(dt_start, dt_end, preprocess_args)
def run_sequential(metric, eps_hours): for dt_start, dt_end in utils.iter_dt_range(): voting(dt_start, dt_end, metric, "paths", eps_hours) voting(dt_start, dt_end, metric, "names", eps_hours)