Clean up APL code and sprinkle an example in comments

zothers/apl.py

@@ -1,31 +1,47 @@
 import sys, string
 import numpy as np
-from itertools import count
 
 # Get an array of characters from the file, make sure it starts and ends with a space
+# Example input: "Hello  World! " 
 characters = np.array([' '] + list(open(sys.argv[1]).read()) + [' '])
-# Stop words
-stop_words = open('../stop_words.txt').read().split(',')
-stop_words.extend(list(string.ascii_lowercase))
-stop_words = np.array(list(set(stop_words)))
+# Result: array([' ', 'H', 'e', 'l', 'l', 'o', ' ', ' ', 
+#                'W', 'o', 'r', 'l', 'd', '!', ' ', ' '], dtype='<U1')
 
 # Normalize
 characters[~np.char.isalpha(characters)] = ' '
 characters = np.char.lower(characters)
-### Split the words
-# indices of the spaces
+# Result: array([' ', 'h', 'e', 'l', 'l', 'o', ' ', ' ', 
+#                'w', 'o', 'r', 'l', 'd', ' ', ' '], dtype='<U1')
+
+### Split the words, by finding the indices of spaces
 sp = np.where(characters == ' ')
+# Result: (array([ 0,  6,  7, 13, 14], dtype=int64),)
+
 # A little trick: let's double each index, and then take pairs
 sp2 = np.repeat(sp, 2)
-# Get the pairs as a 2D matrix
+# Result: array([ 0,  0,  6,  6,  7,  7, 13, 13, 14, 14], dtype=int64)
+
+# Get the pairs as a 2D matrix, skip the first and the last
 w_ranges = np.reshape(sp2[1:-1], (-1, 2))
-# Voila! Words are in between spaces
+# Result: array([[ 0,  6],
+#                [ 6,  7],
+#                [ 7, 13],
+#                [13, 14]], dtype=int64)
+
+# Voila! Words are in between spaces, given as pairs of indices
+# But remember to skip contiguous spaces (the conditional at the end)
 words = [characters[w_ranges[i][0] : w_ranges[i][1]] for i in range(len(w_ranges)) if w_ranges[i][1]-w_ranges[i][0] > 1]
+# Result: [array([' ', 'h', 'e', 'l', 'l', 'o'], dtype='<U1'), 
+#          array([' ', 'w', 'o', 'r', 'l', 'd'], dtype='<U1')]
 
 # But this way too much! Let's reduce rows of characters to strings
 swords = np.array([''.join(row).strip() for row in words])
+# Result: array(['hello', 'world'], dtype='<U5')
 
 # Next, let's remove stop words
+stop_words = open('../stop_words.txt').read().split(',')
+stop_words.extend(list(string.ascii_lowercase))
+stop_words = np.array(list(set(stop_words)))
 non_stop_words = swords[~np.isin(swords, stop_words)]
 
 ### Finally, count the word occurrences
@@ -33,5 +49,5 @@ uniq, counts = np.unique(non_stop_words, axis=0, return_counts=True)
 wf_sorted = sorted(zip(uniq, counts), key = lambda t: t[1], reverse=True)
 
 for w, c in wf_sorted[:25]:
-    print(w, ' - ', c)
+    print(w, '-', c)