278 lines
7.4 KiB
Python
Executable File
278 lines
7.4 KiB
Python
Executable File
# SPDX-License-Identifier: GPL-3.0-only
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import re
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from unicodedata import normalize
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import pandas as pd
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from colorama import Fore
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from ftfy import fix_text
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from csv_metadata_quality.util import is_mojibake, is_nfc
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def whitespace(field, field_name):
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"""Fix whitespace issues.
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Return string with leading, trailing, and consecutive whitespace trimmed.
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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 cleaned values
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values = list()
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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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# Strip leading and trailing whitespace
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value = value.strip()
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# Replace excessive whitespace (>2) with one space
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pattern = re.compile(r"\s{2,}")
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match = re.findall(pattern, value)
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if match:
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print(
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f"{Fore.GREEN}Removing excessive whitespace ({field_name}): {Fore.RESET}{value}"
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)
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value = re.sub(pattern, " ", value)
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# Save cleaned value
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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 separators(field, field_name):
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"""Fix for invalid and unnecessary multi-value separators, for example:
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value|value
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value|||value
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value||value||
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Prints the field with the invalid multi-value separator.
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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 cleaned values
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values = list()
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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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# Check if the value is blank and skip it
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if value == "":
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print(
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f"{Fore.GREEN}Fixing unnecessary multi-value separator ({field_name}): {Fore.RESET}{field}"
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)
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continue
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# After splitting, see if there are any remaining "|" characters
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pattern = re.compile(r"\|")
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match = re.findall(pattern, value)
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if match:
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print(
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f"{Fore.GREEN}Fixing invalid multi-value separator ({field_name}): {Fore.RESET}{value}"
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)
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value = re.sub(pattern, "||", value)
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# Save cleaned value
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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 unnecessary_unicode(field):
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"""Remove and replace unnecessary Unicode characters.
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Removes unnecessary Unicode characters like:
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- Zero-width space (U+200B)
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- Replacement character (U+FFFD)
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Replaces unnecessary Unicode characters like:
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- Soft hyphen (U+00AD) → hyphen
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- No-break space (U+00A0) → space
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Return string with characters removed or replaced.
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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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# Check for zero-width space characters (U+200B)
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pattern = re.compile(r"\u200B")
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match = re.findall(pattern, field)
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if match:
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print(f"{Fore.GREEN}Removing unnecessary Unicode (U+200B): {Fore.RESET}{field}")
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field = re.sub(pattern, "", field)
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# Check for replacement characters (U+FFFD)
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pattern = re.compile(r"\uFFFD")
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match = re.findall(pattern, field)
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if match:
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print(f"{Fore.GREEN}Removing unnecessary Unicode (U+FFFD): {Fore.RESET}{field}")
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field = re.sub(pattern, "", field)
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# Check for no-break spaces (U+00A0)
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pattern = re.compile(r"\u00A0")
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match = re.findall(pattern, field)
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if match:
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print(
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f"{Fore.GREEN}Replacing unnecessary Unicode (U+00A0): {Fore.RESET}{field}"
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)
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field = re.sub(pattern, " ", field)
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# Check for soft hyphens (U+00AD), sometimes preceeded with a normal hyphen
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pattern = re.compile(r"\u002D*?\u00AD")
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match = re.findall(pattern, field)
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if match:
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print(
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f"{Fore.GREEN}Replacing unnecessary Unicode (U+00AD): {Fore.RESET}{field}"
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)
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field = re.sub(pattern, "-", field)
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return field
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def duplicates(field, field_name):
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"""Remove duplicate metadata values."""
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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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# Initialize an empty list to hold the de-duplicated values
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new_values = list()
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# Iterate over all values
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for value in values:
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# Check if each value exists in our list of values already
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if value not in new_values:
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new_values.append(value)
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else:
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print(
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f"{Fore.GREEN}Removing duplicate value ({field_name}): {Fore.RESET}{value}"
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)
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# Create a new field consisting of all values joined with "||"
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new_field = "||".join(new_values)
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return new_field
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def newlines(field):
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"""Fix newlines.
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Single metadata values should not span multiple lines because this is not
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rendered properly in DSpace's XMLUI and even causes issues during import.
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Implementation note: this currently only detects Unix line feeds (0x0a).
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This is essentially when a user presses "Enter" to move to the next line.
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Other newlines like the Windows carriage return are already handled with
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the string stipping performed in the whitespace fixes.
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Confusingly, in Vim '\n' matches a line feed when searching, but you must
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use '\r' to *insert* a line feed, ie in a search and replace expression.
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Return string with newlines removed.
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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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# Check for Unix line feed (LF)
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match = re.findall(r"\n", field)
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if match:
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print(f"{Fore.GREEN}Removing newline: {Fore.RESET}{field}")
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field = field.replace("\n", "")
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return field
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def comma_space(field, field_name):
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"""Fix occurrences of commas missing a trailing space, for example:
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Orth,Alan S.
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This is a very common mistake in author and citation fields.
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Return string with a space added.
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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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# Check for comma followed by a word character
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match = re.findall(r",\w", field)
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if match:
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print(
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f"{Fore.GREEN}Adding space after comma ({field_name}): {Fore.RESET}{field}"
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)
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field = re.sub(r",(\w)", r", \1", field)
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return field
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def normalize_unicode(field, field_name):
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"""Fix occurrences of decomposed Unicode characters by normalizing them
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with NFC to their canonical forms, for example:
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Ouédraogo, Mathieu → Ouédraogo, Mathieu
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Return normalized string.
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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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# Check if the current string is using normalized Unicode (NFC)
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if not is_nfc(field):
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print(f"{Fore.GREEN}Normalizing Unicode ({field_name}): {Fore.RESET}{field}")
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field = normalize("NFC", field)
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return field
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def mojibake(field, field_name):
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"""Attempts to fix mojibake (text that was encoded in one encoding and deco-
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ded in another, perhaps multiple times). See util.py.
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Return fixed string.
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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 field
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if is_mojibake(field):
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print(f"{Fore.GREEN}Fixing encoding issue ({field_name}): {Fore.RESET}{field}")
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return fix_text(field)
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else:
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return field
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