mirror of
https://github.com/mandiant/capa.git
synced 2026-01-05 01:07:25 -08:00
3cd97ae9f2
Replace the header from source code files using the following script:
```Python
for dir_path, dir_names, file_names in os.walk("capa"):
for file_name in file_names:
# header are only in `.py` and `.toml` files
if file_name[-3:] not in (".py", "oml"):
continue
file_path = f"{dir_path}/{file_name}"
f = open(file_path, "rb+")
content = f.read()
m = re.search(OLD_HEADER, content)
if not m:
continue
print(f"{file_path}: {m.group('year')}")
content = content.replace(m.group(0), NEW_HEADER % m.group("year"))
f.seek(0)
f.write(content)
```
Some files had the copyright headers inside a `"""` comment and needed
manual changes before applying the script. `hook-vivisect.py` and
`pyinstaller.spec` didn't include the license in the header and also
needed manual changes.
The old header had the confusing sentence `All rights reserved`, which
does not make sense for an open source license. Replace the header by
the default Google header that corrects this issue and keep capa
consistent with other Google projects.
Adapt the linter to work with the new header.
Replace also the copyright text in the `web/public/index.html` file for
consistency.
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import networkx
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from networkx.algorithms.components import strongly_connected_components
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def has_loop(edges, threshold=2):
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"""check if a list of edges representing a directed graph contains a loop
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args:
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edges: list of edge sets representing a directed graph i.e. [(1, 2), (2, 1)]
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threshold: min number of nodes contained in loop
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returns:
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bool
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"""
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g = networkx.DiGraph()
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g.add_edges_from(edges)
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return any(len(comp) >= threshold for comp in strongly_connected_components(g))
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