#!/usr/bin/env python
from collections import defaultdict
import datetime, glob
from matplotlib import pyplot as plt
from ical_distribution import ECDF
import pybgpstream
"""Code file for CS 6250 BGPM Project
Edit this file according to docstrings.
Do not change the existing function name o...
usrbinenv python from collections import defaultdict import datetime
glob from matplotlib import pyplot as plt from statsmodelsdistributio
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#!/usr/bin/env python
from collections import defaultdict
import datetime, glob
from matplotlib import pyplot as plt
from statsmodels.distributions.empirical_distribution import ECDF
import pybgpstream
"""Code file for CS 6250 BGPM Project
Edit this file according to docstrings.
Do not change the existing function name or arguments in any way.
"""
# Task 1 Part A.
def calculateUniqueIPAddresses(cache_files):
"""Retrieve the number of unique IP prefixes from input BGP data.
Args:
cache_files: A list of absolute file paths.
File paths may not be in order but will end with a timestamp that can be
used for sorting.
For example: ["/rib_files_final/1357027200.120.cache",
"/rib_files_final/1483257600.120.cache"]
Returns:
A list containing the number of unique IP prefixes for each input cache
file.
For example: [2, 5]
"""
files = sorted(cache_files)
unique_prefixes = []
for file in files:
stream = pybgpstream.BGPStream(data_interface="singlefile")
stream.set_data_interface_option("singlefile","rib-file",file)
prefixes = []
for elem in stream:
prefixes.append(elem.fields['prefix'])
prefixes = list(set(prefixes))
unique_prefixes.append(len(prefixes))
return unique_prefixes
# Task 1 Part B.
def calculateUniqueAses(cache_files):
"""Retrieve the number of unique ASes from input BGP data.
Args:
cache_files: A list of absolute file paths.
File paths may not be in order but will end with a timestamp that can be
used for sorting.
For example: ["/rib_files_final/1357027200.120.cache",
"/rib_files_final/1483257600.120.cache"]
Returns:
A list containing the number of the number of unique AS for each input
file.
This study source was downloaded by 100000850872992 from CourseHero.com on 03-29-2023 08:29:41 GMT -05:00
https://www.coursehero.com/file/142684040/bgpmpy/
, For example: [2, 5]
"""
files = sorted(cache_files)
unique_ases = []
for file in files:
stream = pybgpstream.BGPStream(data_interface="singlefile")
stream.set_data_interface_option("singlefile","rib-file",file)
ases = []
for elem in stream:
for _as in elem.fields['as-path'].split(' '):
ases.append(_as)
ases = list(set(ases))
unique_ases.append(len(ases))
return unique_ases
# Task 1 Part C.
def examinePrefixes(cache_files):
"""
Args:
cache_files: A list of absolute file paths.
File paths may not be in order but will end with a timestamp that can be
used for sorting.
For example: ["/rib_files_final/1357027200.120.cache",
"/rib_files_final/1483257600.120.cache"]
Returns:
A list of the top 10 origin ASes according to percentage increase of the
advertised prefixes.
See assignment description for details.
"""
files = sorted(cache_files)
ds = {}
final_list = []
ases = []
for file in files:
stream = pybgpstream.BGPStream(data_interface="singlefile")
stream.set_data_interface_option("singlefile","rib-file",file)
time = datetime.datetime.fromtimestamp(int(file.split('.')[-3]))
for elem in stream:
origin = elem.fields['as-path'].split(' ')[-1]
if origin in ds.keys():
if time in ds[origin].keys():
ds[origin][time].append(elem.fields['prefix'])
else:
ds[origin][time] = [elem.fields['prefix']]
else:
ds[origin] = {}
ds[origin][time] = [elem.fields['prefix']]
for _as in ds.keys():
dates = sorted(list(ds[_as].keys()))
if len(dates) < 2:
final_list.append((_as,0.0))
continue
else:
unique_prefixes_start = list(set(ds[_as][dates[0]]))
unique_prefixes_end = list(set(ds[_as][dates[-1]]))
calc = ((len(unique_prefixes_end) -
len(unique_prefixes_start))/len(unique_prefixes_start)) * 100
This study source was downloaded by 100000850872992 from CourseHero.com on 03-29-2023 08:29:41 GMT -05:00
https://www.coursehero.com/file/142684040/bgpmpy/
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