def IPv_analysis(IPv_type, exp_n, res_directory, coll, from_d, to_d, ext):
input_file_path = res_directory + exp_n + '/5.split_data_for_analysis/' + IPv_type + '/' + coll + '_' + from_d + '-' + to_d + ext
output_file_path = res_directory + exp_n + '/6.more_specifics_analysis/' + IPv_type + '/' + coll + '_' + from_d + '-' + to_d + '.csv'
write_flag = f.overwrite_file(output_file_path)
if write_flag:
print "Loading " + input_file_path + "..."
df = f.read_file(file_ext, input_file_path)
df_sort = df.sort_values(by=['MONITOR', 'PREFIX', 'TIME'])
df_sort = df_sort.reset_index(drop=True)
df_sort = df_sort.drop(['Unnamed: 0'], axis=1)
print "Data loaded successfully"
# 1.Prefix visibility analysis
print 'Getting visibility per prefix...'
monitors, prefixes, visibilities_per_prefix, updates_per_prefix, ASes = prefix_visibility_analysis(
df_sort, exp_n)
df_prefixes_per_monitor = pd.DataFrame({
'MONITOR': monitors,
'PREFIX': prefixes
})
# 2.Clustering prefixes into more specifics, least_specifics and uniques (non-specifics)
pref_types, deeps = clustering_prefixes(df_prefixes_per_monitor)
df_visibility_per_prefix = pd.DataFrame({
'MONITOR': monitors,
'PREFIX': prefixes,
'VISIBILITY': visibilities_per_prefix,
'UPDATES': updates_per_prefix,
'TYPE': pref_types,
'DEEP': deeps,
'ORIGIN': ASes
})
# 3.Clustering more specifics prefixes into TOP, single level and more specifics of other more specifics
df_more_specifics = df_visibility_per_prefix[
df_visibility_per_prefix['TYPE'] == 'more_specific']
df_more_specifics = df_more_specifics.reset_index(drop=True)
df_more_specifics = df_more_specifics.drop(['Unnamed: 0'], axis=1)
pref_types, deeps = clustering_prefixes(df_more_specifics)
# Replace types for more detailed types
df_more_specifics['TYPE'] = pref_types
df_others = df_visibility_per_prefix[
df_visibility_per_prefix['TYPE'] != 'more_specific']
df_others = df_others.reset_index(drop=True)
df_others = df_others.drop(['Unnamed: 0'], axis=1)
df_visibility_per_prefix = df_more_specifics.append(df_others,
ignore_index=True)
output_file_path = res_directory + exp_n + '/6.more_specifics_analysis/' + IPv_type + '/' + collector + '_' + from_d + '-' + to_d + '.csv'
f.save_file(df_visibility_per_prefix, ext, output_file_path)
result_directory = experiment['resultDirectory']
file_ext = experiment['resultFormat']
step_dir = '/5.split_data_for_analysis'
exp.per_step_dir(exp_name, step_dir)
step_dir = '/5.split_data_for_analysis/IPv4'
exp.per_step_dir(exp_name, step_dir)
step_dir = '/5.split_data_for_analysis/IPv6'
exp.per_step_dir(exp_name, step_dir)
input_file_path = result_directory + exp_name + '/4.concatenate_RIB_data/' + collector + '_' + from_date + '-' + to_date + file_ext
output_file_path = result_directory + exp_name + step_dir + '/' + collector + '_' + from_date + '-' + to_date + file_ext
write_flag = f.overwrite_file(output_file_path)
if write_flag == 1:
print "Loading " + input_file_path + "..."
df_advises = f.read_file(file_ext, input_file_path)
print "Data loaded successfully"
print "Splitting {} advises...".format(len(df_advises))
df_IPv4_updates, df_IPv6_updates = separate_IPv_types(df_advises)
df_IPv4_updates = df_IPv4_updates.drop(['Unnamed: 0'], axis=1)
df_IPv6_updates = df_IPv6_updates.drop(['Unnamed: 0'], axis=1)
file_ext = experiment['resultFormat']
# this will be used to filter data that would be out of the experiment window
from_time = exp.get_experiment_from_time(exp_name)
to_time = exp.get_experiment_to_time(exp_name)
# VARIABLES (pathlib)
file_path = '/srv/agarcia/passive_mrai/bgp_updates/' + collector + '/'
bgpdump_path = '/srv/agarcia/TFM/bgpdump'
# bgpdump_path = '/usr/local/bin/bgpdump'
step_dir = '/1.load_data'
exp.per_step_dir(exp_name, step_dir)
output_file_path = result_directory + exp_name + step_dir + '/' + collector + '_' + from_date + '-' + to_date + file_ext
write_flag = exp.check_date_ok(from_date) and exp.check_date_ok(to_date)
write_flag = write_flag and f.overwrite_file(output_file_path)
if 'rrc' in collector:
hop_size = 5
elif 'route-views' in collector:
hop_size = 15
else:
print('Collector type could not be recognised. Finishing execution...')
write_flag = False
if write_flag:
# UPDATES LOAD
from_min = int(from_date.split('.')[1][2:4])
to_min = int(to_date.split('.')[1][2:4])