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samba-mirror/script/traffic_learner
Noel Power b920d80eca script: PY3 port traffic_learner
Use python3 compatable print

Signed-off-by: Noel Power <noel.power@suse.com>
Reviewed-by: Andrew Bartlett <abartlet@samba.org>
2018-12-10 10:38:23 +01:00

64 lines
2.2 KiB
Python
Executable File

#!/usr/bin/env python
# Generate a traffic model from a traffic summary file
#
# Copyright (C) Catalyst IT Ltd. 2017
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
from __future__ import print_function
import sys
import argparse
sys.path.insert(0, "bin/python")
from samba.emulate import traffic
def main():
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('-o', '--out', type=argparse.FileType('w'),
help="write model here")
parser.add_argument('--dns-mode', choices=['inline', 'count'],
help='how to deal with DNS', default='count')
parser.add_argument('SUMMARY_FILE', nargs='*', type=argparse.FileType('r'),
default=[sys.stdin],
help="read from this file (default STDIN)")
args = parser.parse_args()
if not args.out:
print("No output file was specified to write the model to.", file=sys.stdout)
print("Please specify a filename using the --out option.", file=sys.stdout)
return
if args.SUMMARY_FILE is sys.stdin:
print("reading from STDIN...", file=sys.stderr)
(conversations,
interval,
duration,
dns_counts) = traffic.ingest_summaries(args.SUMMARY_FILE,
dns_mode=args.dns_mode)
model = traffic.TrafficModel()
print("learning model", sys.stderr)
if args.dns_mode == 'count':
model.learn(conversations, dns_counts)
else:
model.learn(conversations)
model.save(args.out)
main()