mirror of
https://github.com/ilri/csv-metadata-quality.git
synced 2024-11-04 21:43:00 +01:00
Alan Orth
040e56fc76
When a user explicitly requests that a field be excluded with -x we skip that field in most checks. Up until now that did not include the item-based checks using a transposed dataframe because we don't know the metadata field names (labels) until we iterate over them. Now the excludes are respected for item-based checks.
557 lines
18 KiB
Python
Executable File
557 lines
18 KiB
Python
Executable File
# SPDX-License-Identifier: GPL-3.0-only
|
||
|
||
import logging
|
||
import os
|
||
import re
|
||
from datetime import datetime, timedelta
|
||
|
||
import country_converter as coco
|
||
import pandas as pd
|
||
import requests
|
||
import requests_cache
|
||
import spdx_license_list
|
||
from colorama import Fore
|
||
from pycountry import languages
|
||
from stdnum import isbn as stdnum_isbn
|
||
from stdnum import issn as stdnum_issn
|
||
|
||
from csv_metadata_quality.util import is_mojibake
|
||
|
||
|
||
def issn(field):
|
||
"""Check if an ISSN is valid.
|
||
|
||
Prints the ISSN if invalid.
|
||
|
||
stdnum's is_valid() function never raises an exception.
|
||
|
||
See: https://arthurdejong.org/python-stdnum/doc/1.11/index.html#stdnum.module.is_valid
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
for value in field.split("||"):
|
||
|
||
if not stdnum_issn.is_valid(value):
|
||
print(f"{Fore.RED}Invalid ISSN: {Fore.RESET}{value}")
|
||
|
||
return
|
||
|
||
|
||
def isbn(field):
|
||
"""Check if an ISBN is valid.
|
||
|
||
Prints the ISBN if invalid.
|
||
|
||
stdnum's is_valid() function never raises an exception.
|
||
|
||
See: https://arthurdejong.org/python-stdnum/doc/1.11/index.html#stdnum.module.is_valid
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
for value in field.split("||"):
|
||
|
||
if not stdnum_isbn.is_valid(value):
|
||
print(f"{Fore.RED}Invalid ISBN: {Fore.RESET}{value}")
|
||
|
||
return
|
||
|
||
|
||
def date(field, field_name):
|
||
"""Check if a date is valid.
|
||
|
||
In DSpace the issue date is usually 1990, 1990-01, or 1990-01-01, but it
|
||
could technically even include time as long as it is ISO8601.
|
||
|
||
Also checks for other invalid cases like missing and multiple dates.
|
||
|
||
Prints the date if invalid.
|
||
"""
|
||
|
||
if pd.isna(field):
|
||
print(f"{Fore.RED}Missing date ({field_name}).{Fore.RESET}")
|
||
|
||
return
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
multiple_dates = field.split("||")
|
||
|
||
# We don't allow multi-value date fields
|
||
if len(multiple_dates) > 1:
|
||
print(
|
||
f"{Fore.RED}Multiple dates not allowed ({field_name}): {Fore.RESET}{field}"
|
||
)
|
||
|
||
return
|
||
|
||
try:
|
||
# Check if date is valid YYYY format
|
||
datetime.strptime(field, "%Y")
|
||
|
||
return
|
||
except ValueError:
|
||
pass
|
||
|
||
try:
|
||
# Check if date is valid YYYY-MM format
|
||
datetime.strptime(field, "%Y-%m")
|
||
|
||
return
|
||
except ValueError:
|
||
pass
|
||
|
||
try:
|
||
# Check if date is valid YYYY-MM-DD format
|
||
datetime.strptime(field, "%Y-%m-%d")
|
||
|
||
return
|
||
except ValueError:
|
||
pass
|
||
|
||
try:
|
||
# Check if date is valid YYYY-MM-DDTHH:MM:SSZ format
|
||
datetime.strptime(field, "%Y-%m-%dT%H:%M:%SZ")
|
||
|
||
return
|
||
except ValueError:
|
||
print(f"{Fore.RED}Invalid date ({field_name}): {Fore.RESET}{field}")
|
||
|
||
return
|
||
|
||
|
||
def suspicious_characters(field, field_name):
|
||
"""Warn about suspicious characters.
|
||
|
||
Look for standalone characters that could indicate encoding or copy/paste
|
||
errors for languages with accents. For example: foreˆt should be forêt.
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# List of suspicious characters, for example: ́ˆ~`
|
||
suspicious_characters = ["\u00B4", "\u02C6", "\u007E", "\u0060"]
|
||
|
||
for character in suspicious_characters:
|
||
# Find the position of the suspicious character in the string
|
||
suspicious_character_position = field.find(character)
|
||
|
||
# Python returns -1 if there is no match
|
||
if suspicious_character_position != -1:
|
||
# Create a temporary new string starting from the position of the
|
||
# suspicious character
|
||
field_subset = field[suspicious_character_position:]
|
||
|
||
# Print part of the metadata value starting from the suspicious
|
||
# character and spanning enough of the rest to give a preview,
|
||
# but not too much to cause the line to break in terminals with
|
||
# a default of 80 characters width.
|
||
suspicious_character_msg = f"{Fore.YELLOW}Suspicious character ({field_name}): {Fore.RESET}{field_subset}"
|
||
print(f"{suspicious_character_msg:1.80}")
|
||
|
||
return
|
||
|
||
|
||
def language(field):
|
||
"""Check if a language is valid ISO 639-1 (alpha 2) or ISO 639-3 (alpha 3).
|
||
|
||
Prints the value if it is invalid.
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# need to handle "Other" values here...
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
for value in field.split("||"):
|
||
|
||
# After splitting, check if language value is 2 or 3 characters so we
|
||
# can check it against ISO 639-1 or ISO 639-3 accordingly.
|
||
if len(value) == 2:
|
||
if not languages.get(alpha_2=value):
|
||
print(f"{Fore.RED}Invalid ISO 639-1 language: {Fore.RESET}{value}")
|
||
elif len(value) == 3:
|
||
if not languages.get(alpha_3=value):
|
||
print(f"{Fore.RED}Invalid ISO 639-3 language: {Fore.RESET}{value}")
|
||
else:
|
||
print(f"{Fore.RED}Invalid language: {Fore.RESET}{value}")
|
||
|
||
return
|
||
|
||
|
||
def agrovoc(field, field_name, drop):
|
||
"""Check subject terms against AGROVOC REST API.
|
||
|
||
Function constructor expects the field as well as the field name because
|
||
many fields can now be validated against AGROVOC and we want to be able
|
||
to inform the user in which field the invalid term is.
|
||
|
||
Logic copied from agrovoc-lookup.py.
|
||
|
||
See: https://github.com/ilri/DSpace/blob/5_x-prod/agrovoc-lookup.py
|
||
|
||
Prints a warning if the value is invalid.
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# enable transparent request cache with thirty days expiry
|
||
expire_after = timedelta(days=30)
|
||
# Allow overriding the location of the requests cache, just in case we are
|
||
# running in an environment where we can't write to the current working di-
|
||
# rectory (for example from csv-metadata-quality-web).
|
||
REQUESTS_CACHE_DIR = os.environ.get("REQUESTS_CACHE_DIR", ".")
|
||
requests_cache.install_cache(
|
||
f"{REQUESTS_CACHE_DIR}/agrovoc-response-cache", expire_after=expire_after
|
||
)
|
||
|
||
# prune old cache entries
|
||
# requests_cache.remove_expired_responses()
|
||
|
||
# Initialize an empty list to hold the validated AGROVOC values
|
||
values = list()
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
for value in field.split("||"):
|
||
request_url = "http://agrovoc.uniroma2.it/agrovoc/rest/v1/agrovoc/search"
|
||
request_params = {"query": value}
|
||
|
||
request = requests.get(request_url, params=request_params)
|
||
|
||
if request.status_code == requests.codes.ok:
|
||
data = request.json()
|
||
|
||
# check if there are any results
|
||
if len(data["results"]) == 0:
|
||
if drop:
|
||
print(
|
||
f"{Fore.GREEN}Dropping invalid AGROVOC ({field_name}): {Fore.RESET}{value}"
|
||
)
|
||
else:
|
||
print(
|
||
f"{Fore.RED}Invalid AGROVOC ({field_name}): {Fore.RESET}{value}"
|
||
)
|
||
|
||
# value is invalid AGROVOC, but we are not dropping
|
||
values.append(value)
|
||
else:
|
||
# value is valid AGROVOC so save it
|
||
values.append(value)
|
||
|
||
# Create a new field consisting of all values joined with "||"
|
||
new_field = "||".join(values)
|
||
|
||
return new_field
|
||
|
||
|
||
def filename_extension(field):
|
||
"""Check filename extension.
|
||
|
||
CSVs with a 'filename' column are likely meant as input for the SAFBuilder
|
||
tool, which creates a Simple Archive Format bundle for importing metadata
|
||
with accompanying PDFs or other files into DSpace.
|
||
|
||
This check warns if a filename has an uncommon extension (that is, other
|
||
than .pdf, .xls(x), .doc(x), ppt(x), case insensitive).
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
values = field.split("||")
|
||
|
||
# List of common filename extentions
|
||
common_filename_extensions = [
|
||
".pdf",
|
||
".doc",
|
||
".docx",
|
||
".ppt",
|
||
".pptx",
|
||
".xls",
|
||
".xlsx",
|
||
]
|
||
|
||
# Iterate over all values
|
||
for value in values:
|
||
# Assume filename extension does not match
|
||
filename_extension_match = False
|
||
|
||
for filename_extension in common_filename_extensions:
|
||
# Check for extension at the end of the filename
|
||
pattern = re.escape(filename_extension) + r"$"
|
||
match = re.search(pattern, value, re.IGNORECASE)
|
||
|
||
if match is not None:
|
||
# Register the match and stop checking for this filename
|
||
filename_extension_match = True
|
||
|
||
break
|
||
|
||
if filename_extension_match is False:
|
||
print(f"{Fore.YELLOW}Filename with uncommon extension: {Fore.RESET}{value}")
|
||
|
||
return
|
||
|
||
|
||
def spdx_license_identifier(field):
|
||
"""Check if a license is a valid SPDX identifier.
|
||
|
||
Prints the value if it is invalid.
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
# Try to split multi-value field on "||" separator
|
||
for value in field.split("||"):
|
||
if value not in spdx_license_list.LICENSES:
|
||
print(f"{Fore.YELLOW}Non-SPDX license identifier: {Fore.RESET}{value}")
|
||
|
||
return
|
||
|
||
|
||
def duplicate_items(df):
|
||
"""Attempt to identify duplicate items.
|
||
|
||
First we check the total number of titles and compare it with the number of
|
||
unique titles. If there are less unique titles than total titles we expand
|
||
the search by creating a key (of sorts) for each item that includes their
|
||
title, type, and date issued, and compare it with all the others. If there
|
||
are multiple occurrences of the same title, type, date string then it's a
|
||
very good indicator that the items are duplicates.
|
||
"""
|
||
|
||
# Extract the names of the title, type, and date issued columns so we can
|
||
# reference them later. First we filter columns by likely patterns, then
|
||
# we extract the name from the first item of the resulting object, ie:
|
||
#
|
||
# Index(['dcterms.title[en_US]'], dtype='object')
|
||
#
|
||
# But, we need to consider that dc.title.alternative might come before the
|
||
# main title in the CSV, so use a negative lookahead to eliminate that.
|
||
#
|
||
# See: https://regex101.com/r/elyXkW/1
|
||
title_column_name = df.filter(
|
||
regex=r"^(dc|dcterms)\.title(?!\.alternative).*$"
|
||
).columns[0]
|
||
type_column_name = df.filter(regex=r"^(dcterms\.type|dc\.type).*$").columns[0]
|
||
date_column_name = df.filter(
|
||
regex=r"^(dcterms\.issued|dc\.date\.accessioned).*$"
|
||
).columns[0]
|
||
|
||
items_count_total = df[title_column_name].count()
|
||
items_count_unique = df[title_column_name].nunique()
|
||
|
||
if items_count_unique < items_count_total:
|
||
# Create a list to hold our items while we check for duplicates
|
||
items = list()
|
||
|
||
for index, row in df.iterrows():
|
||
item_title_type_date = f"{row[title_column_name]}{row[type_column_name]}{row[date_column_name]}"
|
||
|
||
if item_title_type_date in items:
|
||
print(
|
||
f"{Fore.YELLOW}Possible duplicate ({title_column_name}): {Fore.RESET}{row[title_column_name]}"
|
||
)
|
||
else:
|
||
items.append(item_title_type_date)
|
||
|
||
|
||
def mojibake(field, field_name):
|
||
"""Check for mojibake (text that was encoded in one encoding and decoded in
|
||
in another, perhaps multiple times). See util.py.
|
||
|
||
Prints the string if it contains suspected mojibake.
|
||
"""
|
||
|
||
# Skip fields with missing values
|
||
if pd.isna(field):
|
||
return
|
||
|
||
if is_mojibake(field):
|
||
print(
|
||
f"{Fore.YELLOW}Possible encoding issue ({field_name}): {Fore.RESET}{field}"
|
||
)
|
||
|
||
return
|
||
|
||
|
||
def citation_doi(row, exclude):
|
||
"""Check for the scenario where an item has a DOI listed in its citation,
|
||
but does not have a cg.identifier.doi field.
|
||
|
||
Function prints a warning if the DOI field is missing, but there is a DOI
|
||
in the citation.
|
||
"""
|
||
# Check if the user requested us to skip any DOI fields so we can
|
||
# just return before going any further.
|
||
for field in exclude:
|
||
match = re.match(r"^.*?doi.*$", field)
|
||
if match is not None:
|
||
return
|
||
|
||
# Initialize some variables at global scope so that we can set them in the
|
||
# loop scope below and still be able to access them afterwards.
|
||
citation = ""
|
||
|
||
# Iterate over the labels of the current row's values to check if a DOI
|
||
# exists. If not, then we extract the citation to see if there is a DOI
|
||
# listed there.
|
||
for label in row.axes[0]:
|
||
# Skip fields with missing values
|
||
if pd.isna(row[label]):
|
||
continue
|
||
|
||
# If a DOI field exists we don't need to check the citation
|
||
match = re.match(r"^.*?doi.*$", label)
|
||
if match is not None:
|
||
return
|
||
|
||
# Check if the current label is a citation field and make sure the user
|
||
# hasn't asked to skip it. If not, then set the citation.
|
||
match = re.match(r"^.*?[cC]itation.*$", label)
|
||
if match is not None and label not in exclude:
|
||
citation = row[label]
|
||
|
||
if citation != "":
|
||
# Check the citation for "doi: 10.1186/1743-422X-9-218"
|
||
doi_match1 = re.match(r"^.*?doi:\s.*$", citation)
|
||
# Check the citation for a DOI URL (doi.org, dx.doi.org, etc)
|
||
doi_match2 = re.match(r"^.*?doi\.org.*$", citation)
|
||
if doi_match1 is not None or doi_match2 is not None:
|
||
print(
|
||
f"{Fore.YELLOW}DOI in citation, but missing a DOI field: {Fore.RESET}{citation}"
|
||
)
|
||
|
||
return
|
||
|
||
|
||
def title_in_citation(row, exclude):
|
||
"""Check for the scenario where an item's title is missing from its cita-
|
||
tion. This could mean that it is missing entirely, or perhaps just exists
|
||
in a different format (whitespace, accents, etc).
|
||
|
||
Function prints a warning if the title does not appear in the citation.
|
||
"""
|
||
# Initialize some variables at global scope so that we can set them in the
|
||
# loop scope below and still be able to access them afterwards.
|
||
title = ""
|
||
citation = ""
|
||
|
||
# Iterate over the labels of the current row's values to get the names of
|
||
# the title and citation columns. Then we check if the title is present in
|
||
# the citation.
|
||
for label in row.axes[0]:
|
||
# Skip fields with missing values
|
||
if pd.isna(row[label]):
|
||
continue
|
||
|
||
# Find the name of the title column
|
||
match = re.match(r"^(dc|dcterms)\.title.*$", label)
|
||
if match is not None and label not in exclude:
|
||
title = row[label]
|
||
|
||
# Find the name of the citation column
|
||
match = re.match(r"^.*?[cC]itation.*$", label)
|
||
if match is not None and label not in exclude:
|
||
citation = row[label]
|
||
|
||
if citation != "":
|
||
if title not in citation:
|
||
print(f"{Fore.YELLOW}Title is not present in citation: {Fore.RESET}{title}")
|
||
|
||
return
|
||
|
||
|
||
def countries_match_regions(row, exclude):
|
||
"""Check for the scenario where an item has country coverage metadata, but
|
||
does not have the corresponding region metadata. For example, an item that
|
||
has country coverage "Kenya" should also have region "Eastern Africa" acc-
|
||
ording to the UN M.49 classification scheme.
|
||
|
||
See: https://unstats.un.org/unsd/methodology/m49/
|
||
|
||
Function prints a warning if the appropriate region is not present.
|
||
"""
|
||
# Initialize some variables at global scope so that we can set them in the
|
||
# loop scope below and still be able to access them afterwards.
|
||
country_column_name = ""
|
||
region_column_name = ""
|
||
title_column_name = ""
|
||
|
||
# Instantiate a CountryConverter() object here. According to the docs it is
|
||
# more performant to do that as opposed to calling coco.convert() directly
|
||
# because we don't need to re-load the country data with each iteration.
|
||
cc = coco.CountryConverter()
|
||
|
||
# Set logging to ERROR so country_converter's convert() doesn't print the
|
||
# "not found in regex" warning message to the screen.
|
||
logging.basicConfig(level=logging.ERROR)
|
||
|
||
# Iterate over the labels of the current row's values to get the names of
|
||
# the title and citation columns. Then we check if the title is present in
|
||
# the citation.
|
||
for label in row.axes[0]:
|
||
# Find the name of the country column
|
||
match = re.match(r"^.*?country.*$", label)
|
||
if match is not None:
|
||
country_column_name = label
|
||
|
||
# Find the name of the region column
|
||
match = re.match(r"^.*?region.*$", label)
|
||
if match is not None:
|
||
region_column_name = label
|
||
|
||
# Find the name of the title column
|
||
match = re.match(r"^(dc|dcterms)\.title.*$", 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 ({un_region}): {Fore.RESET}{row[title_column_name]}"
|
||
)
|
||
|
||
return
|