mirror of
https://github.com/ilri/csv-metadata-quality.git
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561 lines
18 KiB
Python
Executable File
561 lines
18 KiB
Python
Executable File
# SPDX-License-Identifier: GPL-3.0-only
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import logging
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import re
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from datetime import datetime
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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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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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# Initialize an empty list to hold the validated AGROVOC values
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values = []
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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 = "https://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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# Strip filename descriptions that are meant for SAF Bundler, for
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# example: Annual_Report_2020.pdf__description:Report
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if "__description" in value:
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value = value.split("__")[0]
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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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# List of common non-SPDX licenses to ignore
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# See: https://ilri.github.io/cgspace-submission-guidelines/dcterms-license/dcterms-license.txt
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ignore_licenses = {
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"All rights reserved; no re-use allowed",
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"All rights reserved; self-archive copy only",
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"Copyrighted; Non-commercial educational use only",
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"Copyrighted; Non-commercial use only",
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"Copyrighted; all rights reserved",
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"Other",
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}
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# Skip fields with missing values
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if pd.isna(field) or field in ignore_licenses:
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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 = []
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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!
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match = re.match(r"^.*?region.*$", label)
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if match is not None and "sub" not in label:
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region_column_name = label
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# Find the name of the title column
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match = re.match(r"^(dc|dcterms)\.title.*$", label)
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if match is not None:
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title_column_name = label
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# Make sure the user has not asked to exclude any metadata fields. If so, we
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# should return immediately.
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column_names = [country_column_name, region_column_name, title_column_name]
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if any(field in column_names for field in exclude):
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return
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# Make sure we found the country and region columns
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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 = []
|
||
|
||
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:
|
||
try:
|
||
print(
|
||
f"{Fore.YELLOW}Missing region ({country} → {un_region}): {Fore.RESET}{row[title_column_name]}"
|
||
)
|
||
except KeyError:
|
||
print(
|
||
f"{Fore.YELLOW}Missing region ({country} → {un_region}): {Fore.RESET}<title field not present>"
|
||
)
|
||
|
||
return
|