File size: 5,126 Bytes
99a41ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
"""
String processing pipeline functions for testing function analysis.
"""
import re
from typing import List
def normalize_whitespace(text):
"""Normalize whitespace by removing extra spaces and newlines."""
# Replace multiple whitespace with single space
text = re.sub(r'\s+', ' ', text)
# Strip leading and trailing whitespace
return text.strip()
def remove_special_characters(text, keep_chars=""):
"""Remove special characters, optionally keeping specified characters."""
# Keep alphanumeric, spaces, and specified characters
pattern = fr"[^a-zA-Z0-9\s{re.escape(keep_chars)}]"
return re.sub(pattern, '', text)
def convert_to_lowercase(text):
"""Convert text to lowercase."""
return text.lower()
def remove_stopwords(text, stopwords=None):
"""Remove common stopwords from text."""
if stopwords is None:
stopwords = {
'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to',
'for', 'of', 'with', 'by', 'is', 'are', 'was', 'were', 'be',
'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did',
'will', 'would', 'could', 'should', 'may', 'might', 'must'
}
words = text.split()
filtered_words = [word for word in words if word.lower() not in stopwords]
return ' '.join(filtered_words)
def extract_keywords(text, min_length=3):
"""Extract keywords (words longer than min_length)."""
words = text.split()
keywords = [word for word in words if len(word) >= min_length]
return keywords
def count_word_frequency(text):
"""Count frequency of each word in text."""
words = text.split()
frequency = {}
for word in words:
frequency[word] = frequency.get(word, 0) + 1
return frequency
def capitalize_words(text, exceptions=None):
"""Capitalize first letter of each word, with exceptions."""
if exceptions is None:
exceptions = {'a', 'an', 'the', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'}
words = text.split()
capitalized = []
for i, word in enumerate(words):
if i == 0 or word.lower() not in exceptions:
capitalized.append(word.capitalize())
else:
capitalized.append(word.lower())
return ' '.join(capitalized)
def truncate_text(text, max_length=100, suffix="..."):
"""Truncate text to specified length with suffix."""
if len(text) <= max_length:
return text
truncated = text[:max_length - len(suffix)]
# Try to break at last complete word
last_space = truncated.rfind(' ')
if last_space > max_length * 0.8: # If we can break at a word boundary
truncated = truncated[:last_space]
return truncated + suffix
def text_processing_pipeline(text, operations=None):
"""Process text through a pipeline of operations."""
if operations is None:
operations = [
'normalize_whitespace',
'remove_special_characters',
'convert_to_lowercase',
'remove_stopwords'
]
# Map operation names to functions
operation_map = {
'normalize_whitespace': normalize_whitespace,
'remove_special_characters': remove_special_characters,
'convert_to_lowercase': convert_to_lowercase,
'remove_stopwords': remove_stopwords,
'capitalize_words': capitalize_words,
'truncate_text': truncate_text
}
result = text
processing_steps = []
for operation in operations:
if operation in operation_map:
before = result
result = operation_map[operation](result)
processing_steps.append({
'operation': operation,
'before': before[:50] + "..." if len(before) > 50 else before,
'after': result[:50] + "..." if len(result) > 50 else result
})
return result, processing_steps
def analyze_text_statistics(text):
"""Analyze various statistics about the text."""
words = text.split()
stats = {
'character_count': len(text),
'word_count': len(words),
'sentence_count': len(re.findall(r'[.!?]+', text)),
'average_word_length': sum(len(word) for word in words) / len(words) if words else 0,
'longest_word': max(words, key=len) if words else "",
'shortest_word': min(words, key=len) if words else ""
}
return stats
if __name__ == "__main__":
sample_text = """
This is a SAMPLE text with various formatting issues!!!
It has multiple spaces, special @#$% characters, and
needs some serious cleaning & processing...
"""
print("Original text:")
print(repr(sample_text))
processed_text, steps = text_processing_pipeline(sample_text)
print("\nProcessing steps:")
for step in steps:
print(f"After {step['operation']}:")
print(f" {step['after']}")
print(f"\nFinal result: {processed_text}")
stats = analyze_text_statistics(processed_text)
print(f"\nText statistics: {stats}")
|