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b920d80eca
Use python3 compatable print Signed-off-by: Noel Power <noel.power@suse.com> Reviewed-by: Andrew Bartlett <abartlet@samba.org>
64 lines
2.2 KiB
Python
Executable File
64 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python
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# Generate a traffic model from a traffic summary file
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#
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# Copyright (C) Catalyst IT Ltd. 2017
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation; either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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#
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from __future__ import print_function
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import sys
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import argparse
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sys.path.insert(0, "bin/python")
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from samba.emulate import traffic
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def main():
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parser = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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parser.add_argument('-o', '--out', type=argparse.FileType('w'),
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help="write model here")
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parser.add_argument('--dns-mode', choices=['inline', 'count'],
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help='how to deal with DNS', default='count')
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parser.add_argument('SUMMARY_FILE', nargs='*', type=argparse.FileType('r'),
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default=[sys.stdin],
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help="read from this file (default STDIN)")
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args = parser.parse_args()
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if not args.out:
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print("No output file was specified to write the model to.", file=sys.stdout)
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print("Please specify a filename using the --out option.", file=sys.stdout)
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return
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if args.SUMMARY_FILE is sys.stdin:
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print("reading from STDIN...", file=sys.stderr)
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(conversations,
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interval,
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duration,
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dns_counts) = traffic.ingest_summaries(args.SUMMARY_FILE,
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dns_mode=args.dns_mode)
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model = traffic.TrafficModel()
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print("learning model", sys.stderr)
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if args.dns_mode == 'count':
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model.learn(conversations, dns_counts)
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else:
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model.learn(conversations)
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model.save(args.out)
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main()
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