371 lines
11 KiB
Python
Executable File
371 lines
11 KiB
Python
Executable File
# SPDX-License-Identifier: GPL-3.0-only
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import os
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import re
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from datetime import datetime, timedelta
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import pandas as pd
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import requests
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import requests_cache
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import spdx_license_list
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from colorama import Fore
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from pycountry import languages
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from stdnum import isbn as stdnum_isbn
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from stdnum import issn as stdnum_issn
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from csv_metadata_quality.util import is_mojibake
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def issn(field):
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"""Check if an ISSN is valid.
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Prints the ISSN if invalid.
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stdnum's is_valid() function never raises an exception.
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See: https://arthurdejong.org/python-stdnum/doc/1.11/index.html#stdnum.module.is_valid
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# Try to split multi-value field on "||" separator
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for value in field.split("||"):
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if not stdnum_issn.is_valid(value):
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print(f"{Fore.RED}Invalid ISSN: {Fore.RESET}{value}")
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return
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def isbn(field):
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"""Check if an ISBN is valid.
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Prints the ISBN if invalid.
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stdnum's is_valid() function never raises an exception.
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See: https://arthurdejong.org/python-stdnum/doc/1.11/index.html#stdnum.module.is_valid
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# Try to split multi-value field on "||" separator
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for value in field.split("||"):
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if not stdnum_isbn.is_valid(value):
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print(f"{Fore.RED}Invalid ISBN: {Fore.RESET}{value}")
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return
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def date(field, field_name):
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"""Check if a date is valid.
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In DSpace the issue date is usually 1990, 1990-01, or 1990-01-01, but it
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could technically even include time as long as it is ISO8601.
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Also checks for other invalid cases like missing and multiple dates.
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Prints the date if invalid.
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"""
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if pd.isna(field):
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print(f"{Fore.RED}Missing date ({field_name}).{Fore.RESET}")
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return
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# Try to split multi-value field on "||" separator
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multiple_dates = field.split("||")
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# We don't allow multi-value date fields
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if len(multiple_dates) > 1:
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print(
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f"{Fore.RED}Multiple dates not allowed ({field_name}): {Fore.RESET}{field}"
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)
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return
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try:
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# Check if date is valid YYYY format
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datetime.strptime(field, "%Y")
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return
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except ValueError:
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pass
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try:
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# Check if date is valid YYYY-MM format
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datetime.strptime(field, "%Y-%m")
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return
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except ValueError:
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pass
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try:
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# Check if date is valid YYYY-MM-DD format
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datetime.strptime(field, "%Y-%m-%d")
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return
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except ValueError:
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pass
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try:
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# Check if date is valid YYYY-MM-DDTHH:MM:SSZ format
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datetime.strptime(field, "%Y-%m-%dT%H:%M:%SZ")
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return
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except ValueError:
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print(f"{Fore.RED}Invalid date ({field_name}): {Fore.RESET}{field}")
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return
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def suspicious_characters(field, field_name):
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"""Warn about suspicious characters.
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Look for standalone characters that could indicate encoding or copy/paste
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errors for languages with accents. For example: foreˆt should be forêt.
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# List of suspicious characters, for example: ́ˆ~`
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suspicious_characters = ["\u00B4", "\u02C6", "\u007E", "\u0060"]
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for character in suspicious_characters:
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# Find the position of the suspicious character in the string
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suspicious_character_position = field.find(character)
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# Python returns -1 if there is no match
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if suspicious_character_position != -1:
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# Create a temporary new string starting from the position of the
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# suspicious character
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field_subset = field[suspicious_character_position:]
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# Print part of the metadata value starting from the suspicious
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# character and spanning enough of the rest to give a preview,
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# but not too much to cause the line to break in terminals with
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# a default of 80 characters width.
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suspicious_character_msg = f"{Fore.YELLOW}Suspicious character ({field_name}): {Fore.RESET}{field_subset}"
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print(f"{suspicious_character_msg:1.80}")
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return
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def language(field):
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"""Check if a language is valid ISO 639-1 (alpha 2) or ISO 639-3 (alpha 3).
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Prints the value if it is invalid.
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# need to handle "Other" values here...
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# Try to split multi-value field on "||" separator
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for value in field.split("||"):
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# After splitting, check if language value is 2 or 3 characters so we
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# can check it against ISO 639-1 or ISO 639-3 accordingly.
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if len(value) == 2:
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if not languages.get(alpha_2=value):
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print(f"{Fore.RED}Invalid ISO 639-1 language: {Fore.RESET}{value}")
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pass
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elif len(value) == 3:
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if not languages.get(alpha_3=value):
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print(f"{Fore.RED}Invalid ISO 639-3 language: {Fore.RESET}{value}")
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pass
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else:
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print(f"{Fore.RED}Invalid language: {Fore.RESET}{value}")
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return
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def agrovoc(field, field_name):
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"""Check subject terms against AGROVOC REST API.
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Function constructor expects the field as well as the field name because
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many fields can now be validated against AGROVOC and we want to be able
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to inform the user in which field the invalid term is.
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Logic copied from agrovoc-lookup.py.
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See: https://github.com/ilri/DSpace/blob/5_x-prod/agrovoc-lookup.py
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Prints a warning if the value is invalid.
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# enable transparent request cache with thirty days expiry
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expire_after = timedelta(days=30)
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# Allow overriding the location of the requests cache, just in case we are
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# running in an environment where we can't write to the current working di-
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# rectory (for example from csv-metadata-quality-web).
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REQUESTS_CACHE_DIR = os.environ.get("REQUESTS_CACHE_DIR", ".")
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requests_cache.install_cache(
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f"{REQUESTS_CACHE_DIR}/agrovoc-response-cache", expire_after=expire_after
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)
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# prune old cache entries
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requests_cache.core.remove_expired_responses()
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# Try to split multi-value field on "||" separator
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for value in field.split("||"):
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request_url = "http://agrovoc.uniroma2.it/agrovoc/rest/v1/agrovoc/search"
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request_params = {"query": value}
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request = requests.get(request_url, params=request_params)
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if request.status_code == requests.codes.ok:
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data = request.json()
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# check if there are any results
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if len(data["results"]) == 0:
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print(f"{Fore.RED}Invalid AGROVOC ({field_name}): {Fore.RESET}{value}")
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return
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def filename_extension(field):
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"""Check filename extension.
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CSVs with a 'filename' column are likely meant as input for the SAFBuilder
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tool, which creates a Simple Archive Format bundle for importing metadata
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with accompanying PDFs or other files into DSpace.
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This check warns if a filename has an uncommon extension (that is, other
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than .pdf, .xls(x), .doc(x), ppt(x), case insensitive).
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# Try to split multi-value field on "||" separator
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values = field.split("||")
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# List of common filename extentions
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common_filename_extensions = [
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".pdf",
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".doc",
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".docx",
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".ppt",
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".pptx",
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".xls",
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".xlsx",
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]
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# Iterate over all values
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for value in values:
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# Assume filename extension does not match
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filename_extension_match = False
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for filename_extension in common_filename_extensions:
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# Check for extension at the end of the filename
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pattern = re.escape(filename_extension) + r"$"
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match = re.search(pattern, value, re.IGNORECASE)
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if match is not None:
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# Register the match and stop checking for this filename
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filename_extension_match = True
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break
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if filename_extension_match is False:
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print(f"{Fore.YELLOW}Filename with uncommon extension: {Fore.RESET}{value}")
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return
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def spdx_license_identifier(field):
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"""Check if a license is a valid SPDX identifier.
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Prints the value if it is invalid.
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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# Try to split multi-value field on "||" separator
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for value in field.split("||"):
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if value not in spdx_license_list.LICENSES:
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print(f"{Fore.YELLOW}Non-SPDX license identifier: {Fore.RESET}{value}")
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pass
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return
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def duplicate_items(df):
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"""Attempt to identify duplicate items.
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First we check the total number of titles and compare it with the number of
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unique titles. If there are less unique titles than total titles we expand
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the search by creating a key (of sorts) for each item that includes their
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title, type, and date issued, and compare it with all the others. If there
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are multiple occurrences of the same title, type, date string then it's a
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very good indicator that the items are duplicates.
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"""
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# Extract the names of the title, type, and date issued columns so we can
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# reference them later. First we filter columns by likely patterns, then
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# we extract the name from the first item of the resulting object, ie:
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#
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# Index(['dcterms.title[en_US]'], dtype='object')
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#
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title_column_name = df.filter(regex=r"dcterms\.title|dc\.title").columns[0]
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type_column_name = df.filter(regex=r"dcterms\.title|dc\.title").columns[0]
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date_column_name = df.filter(
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regex=r"dcterms\.issued|dc\.date\.accessioned"
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).columns[0]
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items_count_total = df[title_column_name].count()
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items_count_unique = df[title_column_name].nunique()
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if items_count_unique < items_count_total:
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# Create a list to hold our items while we check for duplicates
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items = list()
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for index, row in df.iterrows():
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item_title_type_date = f"{row[title_column_name]}{row[type_column_name]}{row[date_column_name]}"
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if item_title_type_date in items:
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print(
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f"{Fore.YELLOW}Possible duplicate ({title_column_name}): {Fore.RESET}{row[title_column_name]}"
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)
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else:
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items.append(item_title_type_date)
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def mojibake(field, field_name):
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"""Check for mojibake (text that was encoded in one encoding and decoded in
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in another, perhaps multiple times). See util.py.
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Prints the string if it contains suspected mojibake.
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"""
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# Skip fields with missing values
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if pd.isna(field):
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return
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if is_mojibake(field):
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print(
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f"{Fore.YELLOW}Possible encoding issue ({field_name}): {Fore.RESET}{field}"
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)
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return
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