Update shuffle() as per review

- Add `rand_bytes`
- Change `for` loop condition for readability and generality.
- Ensure consistency of comment spacing
- Update comments

specs/beacon-chain.md

@@ -325,24 +325,32 @@ def shuffle(values: List[Any],
     values_count = len(values)
 
     # Entropy is consumed from the seed in 3-byte (24 bit) chunks.
-    rand_max = 2 ** 24 - 1
+    rand_bytes = 3
+    # The highest possible result of the RNG.
+    rand_max = 2 ** (rand_bytes * 8) - 1
+
+    # The range of the RNG places an upper-bound on the size of the list that
+    # may be shuffled. It is a logic error to supply an oversized list.
     assert values_count < rand_max
 
     output = [x for x in values]
     source = seed
     index = 0
     while index < values_count - 1:
-        # Re-hash the source
+        # Re-hash the `source` to obtain a new pattern of bytes.
         source = hash(source)
-        for position in range(0, 30, 3):  # Reads indices 3 bytes at a time
-            # Determine the number of indices remaining and exit once the last
-            # index is reached.
+        # Iterate through the `source` bytes in 3-byte chunks.
+        for position in range(0, 32 - (32 % rand_bytes), rand_bytes):
+            # Determine the number of indices remaining in `values` and exit
+            # once the last index is reached.
             remaining = values_count - index
             if remaining == 1:
                 break
 
-            # Read 3-bytes of the seed as a 24-bit big-endian integer.
-            sample_from_source = int.from_bytes(source[position:position + 3], 'big')
+            # Read 3-bytes of `source` as a 24-bit big-endian integer.
+            sample_from_source = int.from_bytes(
+                source[position:position + rand_bytes], 'big'
+            )
 
             # Sample values greater than or equal to `sample_max` will cause
             # modulo bias when mapped into the `remaining` range.
@@ -350,9 +358,9 @@ def shuffle(values: List[Any],
 
             # Perform a swap if the consumed entropy will not cause modulo bias.
             if sample_from_source < sample_max:
-                # Select a replacement index for the present index.
+                # Select a replacement index for the current index.
                 replacement_position = (sample_from_source % remaining) + index
-                # Swap the present index with the replacement index.
+                # Swap the current index with the replacement index.
                 output[index], output[replacement_position] = output[replacement_position], output[index]
                 index += 1
             else: