mirror of
https://github.com/ilri/csv-metadata-quality.git
synced 2024-11-10 16:25:45 +01:00
Alan Orth
7cc49b500d
spdx-license-list has been deprecated[1] and already has outdated information compared to recent SPDX data releases. Now I use the JSON license data directly from SPDX[2] (currently version 3.19). The JSON file is loaded from the package's data directory using Python 3's stdlib functions from importlib[3], though we now need Python 3.9 as a minimum for importlib.resources.files[4]. Also note that the data directory is not properly packaged via setuptools, so this only works for local installs, and not via versions published to pypi, for example (I'm currently not doing this anyways). If I want to publish this in the future I will need to modify setup.py/pyproject.toml to include the data files. [1] https://gitlab.com/uniqx/spdx-license-list [2] https://github.com/spdx/license-list-data/blob/main/json/licenses.json [3] https://copdips.com/2022/09/adding-data-files-to-python-package-with-setup-py.html [4] https://docs.python.org/3/library/importlib.resources.html#importlib.resources.files
558 lines
18 KiB
Python
Executable File
558 lines
18 KiB
Python
Executable File
# SPDX-License-Identifier: GPL-3.0-only
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import logging
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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 country_converter as coco
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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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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, load_spdx_licenses
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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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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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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, drop):
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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.remove_expired_responses()
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# Initialize an empty list to hold the validated AGROVOC values
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values = list()
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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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if drop:
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print(
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f"{Fore.GREEN}Dropping invalid AGROVOC ({field_name}): {Fore.RESET}{value}"
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)
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else:
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print(
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f"{Fore.RED}Invalid AGROVOC ({field_name}): {Fore.RESET}{value}"
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)
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# value is invalid AGROVOC, but we are not dropping
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values.append(value)
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else:
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# value is valid AGROVOC so save it
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values.append(value)
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# Create a new field consisting of all values joined with "||"
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new_field = "||".join(values)
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return new_field
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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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spdx_licenses = load_spdx_licenses()
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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_licenses:
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print(f"{Fore.YELLOW}Non-SPDX license identifier: {Fore.RESET}{value}")
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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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# But, we need to consider that dc.title.alternative might come before the
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# main title in the CSV, so use a negative lookahead to eliminate that.
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#
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# See: https://regex101.com/r/elyXkW/1
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title_column_name = df.filter(
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regex=r"^(dc|dcterms)\.title(?!\.alternative).*$"
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).columns[0]
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type_column_name = df.filter(regex=r"^(dcterms\.type|dc\.type).*$").columns[0]
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date_column_name = df.filter(
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regex=r"^(dcterms\.issued|dc\.date\.accessioned).*$"
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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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def citation_doi(row, exclude):
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"""Check for the scenario where an item has a DOI listed in its citation,
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but does not have a cg.identifier.doi field.
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Function prints a warning if the DOI field is missing, but there is a DOI
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in the citation.
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"""
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# Check if the user requested us to skip any DOI fields so we can
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# just return before going any further.
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for field in exclude:
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match = re.match(r"^.*?doi.*$", field)
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if match is not None:
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return
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# Initialize some variables at global scope so that we can set them in the
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# loop scope below and still be able to access them afterwards.
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citation = ""
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# Iterate over the labels of the current row's values to check if a DOI
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# exists. If not, then we extract the citation to see if there is a DOI
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# listed there.
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for label in row.axes[0]:
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# Skip fields with missing values
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if pd.isna(row[label]):
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continue
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# If a DOI field exists we don't need to check the citation
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match = re.match(r"^.*?doi.*$", label)
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if match is not None:
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return
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# Check if the current label is a citation field and make sure the user
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# hasn't asked to skip it. If not, then set the citation.
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match = re.match(r"^.*?[cC]itation.*$", label)
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if match is not None and label not in exclude:
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citation = row[label]
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if citation != "":
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# Check the citation for "doi: 10.1186/1743-422X-9-218"
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doi_match1 = re.match(r"^.*?doi:\s.*$", citation)
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# Check the citation for a DOI URL (doi.org, dx.doi.org, etc)
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doi_match2 = re.match(r"^.*?doi\.org.*$", citation)
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if doi_match1 is not None or doi_match2 is not None:
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print(
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f"{Fore.YELLOW}DOI in citation, but missing a DOI field: {Fore.RESET}{citation}"
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)
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return
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def title_in_citation(row, exclude):
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"""Check for the scenario where an item's title is missing from its cita-
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tion. This could mean that it is missing entirely, or perhaps just exists
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in a different format (whitespace, accents, etc).
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Function prints a warning if the title does not appear in the citation.
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"""
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# Initialize some variables at global scope so that we can set them in the
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# loop scope below and still be able to access them afterwards.
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title = ""
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citation = ""
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# Iterate over the labels of the current row's values to get the names of
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# the title and citation columns. Then we check if the title is present in
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# the citation.
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for label in row.axes[0]:
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# Skip fields with missing values
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if pd.isna(row[label]):
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continue
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# Find the name of the title column
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match = re.match(r"^(dc|dcterms)\.title.*$", label)
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if match is not None and label not in exclude:
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title = row[label]
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# Find the name of the citation column
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match = re.match(r"^.*?[cC]itation.*$", label)
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if match is not None and label not in exclude:
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citation = row[label]
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if citation != "":
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if title not in citation:
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print(f"{Fore.YELLOW}Title is not present in citation: {Fore.RESET}{title}")
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return
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def countries_match_regions(row, exclude):
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"""Check for the scenario where an item has country coverage metadata, but
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does not have the corresponding region metadata. For example, an item that
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has country coverage "Kenya" should also have region "Eastern Africa" acc-
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ording to the UN M.49 classification scheme.
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See: https://unstats.un.org/unsd/methodology/m49/
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Function prints a warning if the appropriate region is not present.
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"""
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# Initialize some variables at global scope so that we can set them in the
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# loop scope below and still be able to access them afterwards.
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country_column_name = ""
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region_column_name = ""
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title_column_name = ""
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# Instantiate a CountryConverter() object here. According to the docs it is
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# more performant to do that as opposed to calling coco.convert() directly
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# because we don't need to re-load the country data with each iteration.
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cc = coco.CountryConverter()
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# Set logging to ERROR so country_converter's convert() doesn't print the
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# "not found in regex" warning message to the screen.
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logging.basicConfig(level=logging.ERROR)
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# Iterate over the labels of the current row's values to get the names of
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# the title and citation columns. Then we check if the title is present in
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# the citation.
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for label in row.axes[0]:
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# Find the name of the country column
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match = re.match(r"^.*?country.*$", label)
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if match is not None:
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country_column_name = label
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# Find the name of the region column, but make sure it's not subregion!
|
||
match = re.match(r"^.*?region.*$", label)
|
||
if match is not None and "sub" not in label:
|
||
region_column_name = label
|
||
|
||
# Find the name of the title column
|
||
match = re.match(r"^(dc|dcterms)\.title.*$", label)
|
||
if match is not None:
|
||
title_column_name = label
|
||
|
||
# Make sure the user has not asked to exclude any metadata fields. If so, we
|
||
# should return immediately.
|
||
column_names = [country_column_name, region_column_name, title_column_name]
|
||
if any(field in column_names for field in exclude):
|
||
return
|
||
|
||
# Make sure we found the country and region columns
|
||
if country_column_name != "" and region_column_name != "":
|
||
# If we don't have any countries then we should return early before
|
||
# suggesting regions.
|
||
if row[country_column_name] is not None:
|
||
countries = row[country_column_name].split("||")
|
||
else:
|
||
return
|
||
|
||
if row[region_column_name] is not None:
|
||
regions = row[region_column_name].split("||")
|
||
else:
|
||
regions = list()
|
||
|
||
for country in countries:
|
||
# Look up the UN M.49 regions for this country code. CoCo seems to
|
||
# only list the direct region, ie Western Africa, rather than all
|
||
# the parent regions ("Sub-Saharan Africa", "Africa", "World")
|
||
un_region = cc.convert(names=country, to="UNRegion")
|
||
|
||
if un_region != "not found" and un_region not in regions:
|
||
print(
|
||
f"{Fore.YELLOW}Missing region ({country} → {un_region}): {Fore.RESET}{row[title_column_name]}"
|
||
)
|
||
|
||
return
|