csv-metadata-quality/csv_metadata_quality/util.py

# SPDX-License-Identifier: GPL-3.0-only


import json
from importlib.resources import files

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(files("csv_metadata_quality").joinpath("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"]]