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