Sikorsky - the Helicopter & the Man

helicopter[wikipedia]
Igor Sikorsky, father of the helicopter[wikipedia]

Zipf's Law & the King James Bible

In 'The Misbehaviour of Markets', Benoit Mandelbrot mentions Zipf's Law - which describes the frequency distribution of word usage in a text.  Zipf's Law is a power law, and a Mandelbrot customization of Zipf's original law bears the name of both - the Zipf-Mandelbrot Law.

Now, to empirically determine the distribution, we use a plain-text version of the King-James Bible.

The python code below features methods that

- open the as-formatted king james, and parse it to a list of text lines
- fragment a list of text lines to a [list] sequence of words, by splitting on punctuation and white-space
- sieve a word sequence to a word frequency distribution dictionary

from time import time

SOURCE_FILE_PATH = 'f:/kjv12_no_header.txt'
DEST_FILE_PATH = 'f:/dist.txt'

from time import time

def p(s):
  print(s.strip())
        
def extract_text_from_file(path):
  '''
  parse king james bible text located at file path 'path'
  strip out all info except for raw text lines, without
  verse/verse number annotations
  returns list of text lines
  '''
  book_count = 0
  line_count = 0
  text_lines = []
  try:
    f = open(path, 'r')
    completed = False
    i = 0
    start_time = time()
    line = ''
    while completed == False:
      line = f.readline()      
      i = i + 1
      if (line == None) or (line == ''):
        completed = True
        continue
      if len(line) < 8:
        continue
      # BOOK
      # Book 01 Genesis
      if line[:4] == 'Book':
        book_count += 1
        line = line.strip()
        splut = line.split('\t')
        book_num_str = splut[0].split(' ')[1]
        book_num = int(book_num_str)
        book_name = splut[1]
        continue
      # INIT VERSE LINE
      # 001:002 And the earth was without form, and void; and darkness was
      if line[3] == ':':
        verse_book_num = int(line[0:3])
        verse_num = int(line[4:7])
        text = line[8:len(line)-1]
        text_lines.append(text)
        line_count += 1
        continue
      # SUBSEQUENT VERSE LINE
      # upon the face of the deep. And the Spirit of God moved upon
      if line[:8] == '        ':
        text = line[8:len(line)-1]
        text_lines.append(text)
        line_count += 1
        continue  
    end_time = time()
    duration = end_time - start_time
    print('file load process took %s seconds' % duration)  
  finally:
    f.close()
  return text_lines   
   
def fragment_text_to_word_sequence(text):  
  '''
  expects text = list of lines of punctuated text
  returns string list corresponding to the word_sequence of the text
  '''
  word_sequence = []  
  init_punc_split_chars = [ ',', '.', '\"', ':', ';', '!', '(', ')', ' ', '?']
  start_time = time()
  for line in text:
    split_chars = init_punc_split_chars    
    current = [line]
    for split_char in split_chars:
      next = []
      for piece in current:
        for fragment in piece.split(split_char):
          if fragment != '':
            next.append(fragment)
      current = next          
    word_sequence.extend(current)
  end_time = time()
  duration = end_time - start_time
  print('split to word sequence took %s seconds' % duration)  
  return word_sequence

def build_word_dist_for_sequence(word_seq):
  '''
  construct word frequency distribution for given word sequence
  '''
  freqs = {}
  keys = []
  start_time = time()
  for word in word_seq:
    word = word.lower()
    if word in keys:
      freqs[word] += 1
    else:
      freqs[word] = 1
      keys.append(word)
  end_time = time()
  duration = end_time - start_time
  print('sieving of word sequence to frequency distribution took %s seconds' % duration)  
  return freqs

def write_word_freqs_to_disc(freqs):
  dest = open(DEST_FILE_PATH, 'w')
  
  sorted_list = sorted(freqs.items(), key = lambda x : x[1])
  sorted_list.reverse()
  
  for key,value in sorted_list:
    s = str(key) + ',' + str(value) + '\n'
    dest.write(s)
  dest.close()    
  
def main():
  text = extract_text_from_file(SOURCE_FILE_PATH)
  word_seq = fragment_text_to_word_sequence(text)
  freqs = build_word_dist_for_sequence(word_seq)
  write_word_freqs_to_disc(freqs)

if __name__ == '__main__':
  main()

These results...


The Top 25 Words

The overall distribution...

And finally, with a logarithmic y axis...