csv-metadata-quality/csv_metadata_quality/app.py

import argparse
import re
import signal
import sys

import pandas as pd
from colorama import Fore

import csv_metadata_quality.check as check
import csv_metadata_quality.experimental as experimental
import csv_metadata_quality.fix as fix
from csv_metadata_quality.version import VERSION


def parse_args(argv):
    parser = argparse.ArgumentParser(description="Metadata quality checker and fixer.")
    parser.add_argument(
        "--agrovoc-fields",
        "-a",
        help="Comma-separated list of fields to validate against AGROVOC, for example: dcterms.subject,cg.coverage.country",
    )
    parser.add_argument(
        "--experimental-checks",
        "-e",
        help="Enable experimental checks like language detection",
        action="store_true",
    )
    parser.add_argument(
        "--input-file",
        "-i",
        help="Path to input file. Can be UTF-8 CSV or Excel XLSX.",
        required=True,
        type=argparse.FileType("r", encoding="UTF-8"),
    )
    parser.add_argument(
        "--output-file",
        "-o",
        help="Path to output file (always CSV).",
        required=True,
        type=argparse.FileType("w", encoding="UTF-8"),
    )
    parser.add_argument(
        "--unsafe-fixes", "-u", help="Perform unsafe fixes.", action="store_true"
    )
    parser.add_argument(
        "--version", "-V", action="version", version=f"CSV Metadata Quality v{VERSION}"
    )
    parser.add_argument(
        "--exclude-fields",
        "-x",
        help="Comma-separated list of fields to skip, for example: dc.contributor.author,dcterms.bibliographicCitation",
    )
    args = parser.parse_args()

    return args


def signal_handler(signal, frame):
    sys.exit(1)


def run(argv):
    args = parse_args(argv)

    # set the signal handler for SIGINT (^C)
    signal.signal(signal.SIGINT, signal_handler)

    # Read all fields as strings so dates don't get converted from 1998 to 1998.0
    df = pd.read_csv(args.input_file, dtype=str)

    for column in df.columns:
        # Check if the user requested to skip any fields
        if args.exclude_fields:
            skip = False
            # Split the list of excludes on ',' so we can test exact matches
            # rather than fuzzy matches with regexes or "if word in string"
            for exclude in args.exclude_fields.split(","):
                if column == exclude and skip is False:
                    skip = True
            if skip:
                print(f"{Fore.YELLOW}Skipping {Fore.RESET}{column}")

                continue

        # Fix: whitespace
        df[column] = df[column].apply(fix.whitespace, field_name=column)

        # Fix: newlines
        if args.unsafe_fixes:
            df[column] = df[column].apply(fix.newlines)

        # Fix: missing space after comma. Only run on author and citation
        # fields for now, as this problem is mostly an issue in names.
        if args.unsafe_fixes:
            match = re.match(r"^.*?(author|citation).*$", column)
            if match is not None:
                df[column] = df[column].apply(fix.comma_space, field_name=column)

        # Fix: perform Unicode normalization (NFC) to convert decomposed
        # characters into their canonical forms.
        if args.unsafe_fixes:
            df[column] = df[column].apply(fix.normalize_unicode, field_name=column)

        # Fix: unnecessary Unicode
        df[column] = df[column].apply(fix.unnecessary_unicode)

        # Check: suspicious characters
        df[column].apply(check.suspicious_characters, field_name=column)

        # Check: mojibake
        df[column].apply(check.mojibake, field_name=column)

        # Fix: mojibake
        if args.unsafe_fixes:
            df[column] = df[column].apply(fix.mojibake, field_name=column)

        # Fix: invalid and unnecessary multi-value separators
        df[column] = df[column].apply(fix.separators, field_name=column)
        # Run whitespace fix again after fixing invalid separators
        df[column] = df[column].apply(fix.whitespace, field_name=column)

        # Fix: duplicate metadata values
        df[column] = df[column].apply(fix.duplicates, field_name=column)

        # Check: invalid AGROVOC subject
        if args.agrovoc_fields:
            # Identify fields the user wants to validate against AGROVOC
            for field in args.agrovoc_fields.split(","):
                if column == field:
                    df[column].apply(check.agrovoc, field_name=column)

        # Check: invalid language
        match = re.match(r"^.*?language.*$", column)
        if match is not None:
            df[column].apply(check.language)

        # Check: invalid ISSN
        match = re.match(r"^.*?issn.*$", column)
        if match is not None:
            df[column].apply(check.issn)

        # Check: invalid ISBN
        match = re.match(r"^.*?isbn.*$", column)
        if match is not None:
            df[column].apply(check.isbn)

        # Check: invalid date
        match = re.match(r"^.*?(date|dcterms\.issued).*$", column)
        if match is not None:
            df[column].apply(check.date, field_name=column)

        # Check: filename extension
        if column == "filename":
            df[column].apply(check.filename_extension)

        # Check: SPDX license identifier
        match = re.match(r"dcterms\.license.*$", column)
        if match is not None:
            df[column].apply(check.spdx_license_identifier)

    ### End individual column checks ###

    # Check: duplicate items
    # We extract just the title, type, and date issued columns to analyze
    duplicates_df = df.filter(
        regex=r"dcterms\.title|dc\.title|dcterms\.type|dc\.type|dcterms\.issued|dc\.date\.issued"
    )
    check.duplicate_items(duplicates_df)

    # Delete the temporary duplicates DataFrame
    del duplicates_df

    ##
    # Perform some checks on rows so we can consider items as a whole rather
    # than simple on a field-by-field basis. This allows us to check whether
    # the language used in the title and abstract matches the language indi-
    # cated in the language field, for example.
    #
    # This is slower and apparently frowned upon in the Pandas community be-
    # cause it requires iterating over rows rather than using apply over a
    # column. For now it will have to do.
    ##

    if args.experimental_checks:
        # Transpose the DataFrame so we can consider each row as a column
        df_transposed = df.T

        for column in df_transposed.columns:
            experimental.correct_language(df_transposed[column])

    # Write
    df.to_csv(args.output_file, index=False)

    # Close the input and output files before exiting
    args.input_file.close()
    args.output_file.close()

    sys.exit(0)