example.py

#;encoding=utf-8
# Example file to redact Social Security Numbers from the
# text layer of a PDF and to demonstrate metadata filtering.

import re
from datetime import datetime

import pdf_redactor

## Set options.

options = pdf_redactor.RedactorOptions()

options.metadata_filters = {
	# Perform some field filtering --- turn the Title into uppercase.
	"Title": [lambda value : value.upper()],

	# Set some values, overriding any value present in the PDF.
	"Producer": [lambda value : "My Name"],
	"CreationDate": [lambda value : datetime.utcnow()],

	# Clear all other fields.
	"DEFAULT": [lambda value : None],
}

# Clear any XMP metadata, if present.
options.xmp_filters = [lambda xml : None]

# Redact things that look like social security numbers, replacing the
# text with X's.
options.content_filters = [
	# First convert all dash-like characters to dashes.
	(
		re.compile(u"[−–—~‐]"),
		lambda m : "-"
	),

	# Then do an actual SSL regex.
	# See https://github.com/opendata/SSN-Redaction for why this regex is complicated.
	(
		re.compile(r"(?<!\d)(?!666|000|9\d{2})([OoIli0-9]{3})([\s-]?)(?!00)([OoIli0-9]{2})\2(?!0{4})([OoIli0-9]{4})(?!\d)"),
		lambda m : "XXX-XX-XXXX"
	),

	# Content filter that runs on the text comment annotation body.
	(
		re.compile(r"comment!"),
		lambda m : "annotation?"
	),
]

# Filter the link target URI.
options.link_filters = [
	lambda href, annotation : "https://www.google.com" 
]

# Perform the redaction using PDF on standard input and writing to standard output.
pdf_redactor.redactor(options)