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This library contains useful functionality for working with Huffman trees
and codes.
There is function for directly creating a Huffman code from a frequency
map: bitarray.util.huffman_code()
from __future__ import print_function
from heapq import heappush, heappop
from bitarray import bitarray
class Node(object):
def __init__(self):
self.child = [None, None]
self.symbol = None
self.freq = None
def __lt__(self, other):
# heapq needs to be able to compare the nodes
return self.freq < other.freq
def huff_tree(freq):
Given a dictionary mapping symbols to thier frequency, construct a Huffman
tree and return its root node.
minheap = []
# create all the leaf nodes and push them onto the queue
for sym in sorted(freq):
nd = Node()
nd.symbol = sym
nd.freq = freq[sym]
heappush(minheap, nd)
# repeat the process until only one node remains
while len(minheap) > 1:
# take the nodes with smallest frequencies from the queue
child_0 = heappop(minheap)
child_1 = heappop(minheap)
# construct the new internal node and push it onto the queue
parent = Node()
parent.child = [child_0, child_1]
parent.freq = child_0.freq + child_1.freq
heappush(minheap, parent)
# return the one remaining node, which is the root of the Huffman tree
return minheap[0]
def huff_code(tree):
Given a Huffman tree, traverse the tree and return the Huffman code, i.e.
a dictionary mapping symbols to bitarrays.
result = {}
def traverse(nd, prefix=bitarray()):
if nd.symbol is None: # parent, so traverse each of the children
traverse(nd.child[0], prefix + bitarray([0]))
traverse(nd.child[1], prefix + bitarray([1]))
else: # leaf
result[nd.symbol] = prefix
return result
def insert_symbol(tree, ba, sym):
Insert symbol into a tree at the position described by the bitarray,
creating nodes as necessary.
if sym is None:
raise ValueError("symbol cannot be None")
nd = tree
for k in ba:
prev = nd
nd = nd.child[k]
if nd and nd.symbol is not None:
raise ValueError("ambiguity")
if not nd:
nd = Node()
prev.child[k] = nd
if nd.symbol is not None or nd.child[0] or nd.child[1]:
raise ValueError("ambiguity")
nd.symbol = sym
def make_tree(codedict):
Create a tree from the given code dictionary, and return its root node.
Unlike trees created by huff_tree, all nodes will have .freq set to None.
tree = Node()
for sym, ba in codedict.items():
insert_symbol(tree, ba, sym)
return tree
def traverse(tree, it):
Traverse tree until a leaf node is reached, and return its symbol.
This function consumes an iterator on which next() is called during each
step of traversing.
nd = tree
while 1:
nd = nd.child[next(it)]
if not nd:
raise ValueError("prefix code does not match data in bitarray")
if nd.symbol is not None:
return nd.symbol
if nd != tree:
raise ValueError("decoding not terminated")
def iterdecode(tree, bitsequence):
Given a tree and a bitsequence, decode the bitsequence and generate
the symbols.
it = iter(bitsequence)
while True:
yield traverse(tree, it)
except StopIteration:
def write_dot(tree, fn, binary=False):
Given a tree (which may or may not contain frequencies), write
a graphviz '.dot' file with a visual representation of the tree.
special_ascii = {' ': 'SPACE', '\n': 'LF', '\r': 'CR', '\t': 'TAB',
'\\': r'\\', '"': r'\"'}
def disp_sym(i):
if binary:
return '0x%02x' % i
c = chr(i)
res = special_ascii.get(c, c)
assert res.strip(), repr(c)
return res
def disp_freq(f):
if f is None:
return ''
return '%d' % f
with open(fn, 'w') as fo: # dot -Tpng -O
def write_nd(fo, nd):
if nd.symbol is not None: # leaf node
a, b = disp_freq(nd.freq), disp_sym(nd.symbol)
fo.write(' %d [label="%s%s%s"];\n' %
(id(nd), a, ': ' if a and b else '', b))
else: # parent node
fo.write(' %d [shape=circle, style=filled, '
'fillcolor=grey, label="%s"];\n' %
(id(nd), disp_freq(nd.freq)))
for k in range(2):
if nd.child[k]:
fo.write(' %d->%d;\n' % (id(nd), id(nd.child[k])))
for k in range(2):
if nd.child[k]:
write_nd(fo, nd.child[k])
fo.write('digraph BT {\n')
fo.write(' node [shape=box, fontsize=20, fontname="Arial"];\n')
write_nd(fo, tree)
def print_code(freq, codedict):
Given a frequency map (dictionary mapping symbols to thier frequency)
and a codedict, print them in a readable form.
special_ascii = {0: 'NUL', 9: 'TAB', 10: 'LF', 13: 'CR', 127: 'DEL'}
def disp_char(i):
if 32 <= i < 127:
return repr(chr(i))
return special_ascii.get(i, '')
print(' symbol char hex frequency Huffman code')
print(70 * '-')
for i in sorted(codedict, key=lambda c: (freq[c], c), reverse=True):
print('%7r %-4s 0x%02x %10i %s' % (
i, disp_char(i), i, freq[i], codedict[i].to01()))
def test():
freq = {'a': 10, 'b': 2, 'c': 1}
tree = huff_tree(freq)
code = huff_code(tree)
assert len(code['a']) == 1
assert len(code['b']) == len(code['c']) == 2
code = {'a': bitarray('0'),
'b': bitarray('10'),
'c': bitarray('11')}
tree = make_tree(code)
txt = 'abca'
a = bitarray()
a.encode(code, txt)
assert a == bitarray('010110')
assert list(iterdecode(tree, a)) == ['a', 'b', 'c', 'a']
if __name__ == '__main__':
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