csv-metadata-quality/csv_metadata_quality/util.py

66 lines
1.9 KiB
Python

# SPDX-License-Identifier: GPL-3.0-only
import json
import os
from ftfy.badness import is_bad
def is_nfc(field):
"""Utility function to check whether a string is using normalized Unicode.
Python's built-in unicodedata library has the is_normalized() function, but
it was only introduced in Python 3.8. By using a simple utility function we
are able to run on Python >= 3.6 again.
See: https://docs.python.org/3/library/unicodedata.html
Return boolean.
"""
from unicodedata import normalize
return field == normalize("NFC", field)
def is_mojibake(field):
"""Determines whether a string contains mojibake.
We commonly deal with CSV files that were *encoded* in UTF-8, but decoded
as something else like CP-1252 (Windows Latin). This manifests in the form
of "mojibake", for example:
- CIAT Publicaçao
- CIAT Publicación
This uses the excellent "fixes text for you" (ftfy) library to determine
whether a string contains characters that have been encoded in one encoding
and decoded in another.
Inspired by this code snippet from Martijn Pieters on StackOverflow:
https://stackoverflow.com/questions/29071995/identify-garbage-unicode-string-using-python
Return boolean.
"""
if not is_bad(field):
# Nothing weird, should be okay
return False
try:
field.encode("sloppy-windows-1252")
except UnicodeEncodeError:
# Not CP-1252 encodable, probably fine
return False
else:
# Encodable as CP-1252, Mojibake alert level high
return True
def load_spdx_licenses():
"""Returns a Python list of SPDX short license identifiers."""
with open(os.path.join(os.path.dirname(__file__), "data/licenses.json")) as f:
licenses = json.load(f)
# List comprehension to extract the license ID for each license
return [license["licenseId"] for license in licenses["licenses"]]