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csv-metadata-quality/csv_metadata_quality/app.py
Alan Orth 9ce7dc6716
Add check for uncommon filenames
Generally we want people to upload documents in accessible formats
like PDF, Word, Excel, and PowerPoint. This check warns if a file
is using an uncommon extension.
2019-08-10 23:41:16 +03:00

101 lines
3.5 KiB
Python

from csv_metadata_quality.version import VERSION
import argparse
import csv_metadata_quality.check as check
import csv_metadata_quality.fix as fix
import pandas as pd
import re
import signal
import sys
def parse_args(argv):
parser = argparse.ArgumentParser(description='Metadata quality checker and fixer.')
parser.add_argument('--agrovoc-fields', '-a', help='Comma-separated list of fields to validate against AGROVOC, for example: dc.subject,cg.coverage.country')
parser.add_argument('--input-file', '-i', help='Path to input file. Can be UTF-8 CSV or Excel XLSX.', required=True, type=argparse.FileType('r', encoding='UTF-8'))
parser.add_argument('--output-file', '-o', help='Path to output file (always CSV).', required=True, type=argparse.FileType('w', encoding='UTF-8'))
parser.add_argument('--unsafe-fixes', '-u', help='Perform unsafe fixes.', action='store_true')
parser.add_argument('--version', '-V', action='version', version=f'CSV Metadata Quality v{VERSION}')
args = parser.parse_args()
return args
def signal_handler(signal, frame):
sys.exit(1)
def run(argv):
args = parse_args(argv)
# set the signal handler for SIGINT (^C)
signal.signal(signal.SIGINT, signal_handler)
# Read all fields as strings so dates don't get converted from 1998 to 1998.0
df = pd.read_csv(args.input_file, dtype=str)
for column in df.columns.values.tolist():
# Fix: whitespace
df[column] = df[column].apply(fix.whitespace)
# Fix: newlines
if args.unsafe_fixes:
df[column] = df[column].apply(fix.newlines)
# Fix: unnecessary Unicode
df[column] = df[column].apply(fix.unnecessary_unicode)
# Check: invalid multi-value separator
df[column] = df[column].apply(check.separators)
# Check: suspicious characters
df[column] = df[column].apply(check.suspicious_characters, field_name=column)
# Fix: invalid multi-value separator
if args.unsafe_fixes:
df[column] = df[column].apply(fix.separators)
# Run whitespace fix again after fixing invalid separators
df[column] = df[column].apply(fix.whitespace)
# Fix: duplicate metadata values
df[column] = df[column].apply(fix.duplicates)
# Check: invalid AGROVOC subject
if args.agrovoc_fields:
# Identify fields the user wants to validate against AGROVOC
for field in args.agrovoc_fields.split(','):
if column == field:
df[column] = df[column].apply(check.agrovoc, field_name=column)
# Check: invalid language
match = re.match(r'^.*?language.*$', column)
if match is not None:
df[column] = df[column].apply(check.language)
# Check: invalid ISSN
match = re.match(r'^.*?issn.*$', column)
if match is not None:
df[column] = df[column].apply(check.issn)
# Check: invalid ISBN
match = re.match(r'^.*?isbn.*$', column)
if match is not None:
df[column] = df[column].apply(check.isbn)
# Check: invalid date
match = re.match(r'^.*?date.*$', column)
if match is not None:
df[column] = df[column].apply(check.date)
# Check: filename extension
if column == 'filename':
df[column] = df[column].apply(check.filename_extension)
# Write
df.to_csv(args.output_file, index=False)
# Close the input and output files before exiting
args.input_file.close()
args.output_file.close()
sys.exit(0)