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.
Implement the "tighten rule pre-selection" algorithm described here:
https://github.com/mandiant/capa/issues/2063#issuecomment-2100498720
In summary:
> Rather than indexing all features from all rules,
> we should pick and index the minimal set (ideally, one) of
> features from each rule that must be present for the rule to match.
> When we have multiple candidates, pick the feature that is
> probably most uncommon and therefore "selective".
This seems to work pretty well. Total evaluations when running against
mimikatz drop from 19M to 1.1M (wow!) and capa seems to match around
3x more functions per second (wow wow).
When doing large scale runs, capa is about 25% faster when using the
vivisect backend (analysis heavy) or 3x faster when using the
upcoming BinExport2 backend (minimal analysis).
