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mirror of https://github.com/ilri/csv-metadata-quality.git synced 2024-11-29 17:18:19 +01:00
csv-metadata-quality/csv_metadata_quality/check.py
Alan Orth 4e2eab68b0
Update requests-cache
Apparently we were stuck on an older version of requests-cache due
to the fact that we were using the caret, which will never update
the left-most (major) version. Upstream requests-cache is currently
version 0.6.4, and there seems to have been some changes to the API.
2021-07-06 15:24:39 +03:00

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# SPDX-License-Identifier: GPL-3.0-only
import os
import re
from datetime import datetime, timedelta
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):
"""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()
# 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:
print(f"{Fore.RED}Invalid AGROVOC ({field_name}): {Fore.RESET}{value}")
return
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')
#
title_column_name = df.filter(regex=r"dcterms\.title|dc\.title").columns[0]
type_column_name = df.filter(regex=r"dcterms\.title|dc\.title").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