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diff --git a/gr-fec/python/fec/polar/helper_functions.py b/gr-fec/python/fec/polar/helper_functions.py
new file mode 100644
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+++ b/gr-fec/python/fec/polar/helper_functions.py
@@ -0,0 +1,169 @@
+#!/usr/bin/env python
+#
+# Copyright 2015 Free Software Foundation, Inc.
+#
+# GNU Radio is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 3, or (at your option)
+# any later version.
+#
+# GNU Radio is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with GNU Radio; see the file COPYING.  If not, write to
+# the Free Software Foundation, Inc., 51 Franklin Street,
+# Boston, MA 02110-1301, USA.
+#
+
+import numpy as np
+import time, sys
+import copy
+
+
+def power_of_2_int(num):
+    return int(np.log2(num))
+
+
+def is_power_of_two(num):
+    if type(num) != int:
+        return False  # make sure we only compute integers.
+    return num != 0 and ((num & (num - 1)) == 0)
+
+
+def bit_reverse(value, n):
+    # is this really missing in NumPy???
+    seq = np.int(value)
+    rev = np.int(0)
+    rmask = np.int(1)
+    lmask = np.int(2 ** (n - 1))
+    for i in range(n // 2):
+        shiftval = n - 1 - (i * 2)
+        rshift = np.left_shift(np.bitwise_and(seq, rmask), shiftval)
+        lshift = np.right_shift(np.bitwise_and(seq, lmask), shiftval)
+        rev = np.bitwise_or(rev, rshift)
+        rev = np.bitwise_or(rev, lshift)
+        rmask = np.left_shift(rmask, 1)
+        lmask = np.right_shift(lmask, 1)
+    if not n % 2 == 0:
+        rev = np.bitwise_or(rev, np.bitwise_and(seq, rmask))
+    return rev
+
+
+def bit_reverse_vector(vec, n):
+    return np.array([bit_reverse(e, n) for e in vec], dtype=vec.dtype)
+
+
+def get_Bn(n):
+    # this is a bit reversal matrix.
+    lw = power_of_2_int(n)  # number of used bits
+    indexes = [bit_reverse(i, lw) for i in range(n)]
+    Bn = np.zeros((n, n), type(n))
+    for i, index in enumerate(indexes):
+        Bn[i][index] = 1
+    return Bn
+
+
+def get_Fn(n):
+    # this matrix defines the actual channel combining.
+    if n == 1:
+        return np.array([1, ])
+    nump = power_of_2_int(n) - 1  # number of Kronecker products to calculate
+    F2 = np.array([[1, 0], [1, 1]], np.int)
+    Fn = F2
+    for i in range(nump):
+        Fn = np.kron(Fn, F2)
+    return Fn
+
+
+def get_Gn(n):
+    # this matrix is called generator matrix
+    if not is_power_of_two(n):
+        print "invalid input"
+        return None
+    if n == 1:
+        return np.array([1, ])
+    Bn = get_Bn(n)
+    Fn = get_Fn(n)
+    Gn = np.dot(Bn, Fn)
+    return Gn
+
+
+def unpack_byte(byte, nactive):
+    if np.amin(byte) < 0 or np.amax(byte) > 255:
+        return None
+    if not byte.dtype == np.uint8:
+        byte = byte.astype(np.uint8)
+    if nactive == 0:
+        return np.array([], dtype=np.uint8)
+    return np.unpackbits(byte)[-nactive:]
+
+
+def pack_byte(bits):
+    if len(bits) == 0:
+        return 0
+    if np.amin(bits) < 0 or np.amax(bits) > 1:  # only '1' and '0' in bits array allowed!
+        return None
+    bits = np.concatenate((np.zeros(8 - len(bits), dtype=np.uint8), bits))
+    res = np.packbits(bits)[0]
+    return res
+
+
+def show_progress_bar(ndone, ntotal):
+    nchars = 50
+
+    fract = (1. * ndone / ntotal)
+    percentage = 100. * fract
+    ndone_chars = int(nchars * fract)
+    nundone_chars = nchars - ndone_chars
+    sys.stdout.write('\r[{0}{1}] {2:5.2f}% ({3} / {4})'.format('=' * ndone_chars, ' ' * nundone_chars, percentage, ndone, ntotal))
+
+
+
+def mutual_information(w):
+    '''
+    calculate mutual information I(W)
+    I(W) = sum over y e Y ( sum over x e X ( ... ) )
+    .5 W(y|x) log frac { W(y|x) }{ .5 W(y|0) + .5 W(y|1) }
+    '''
+    ydim, xdim = np.shape(w)
+    i = 0.0
+    for y in range(ydim):
+        for x in range(xdim):
+            v = w[y][x] * np.log2(w[y][x] / (0.5 * w[y][0] + 0.5 * w[y][1]))
+            i += v
+    i /= 2.0
+    return i
+
+
+def bhattacharyya_parameter(w):
+    '''bhattacharyya parameter is a measure of similarity between two prob. distributions'''
+    # sum over all y e Y for sqrt( W(y|0) * W(y|1) )
+    dim = np.shape(w)
+    ydim = dim[0]
+    z = 0.0
+    for y in range(ydim):
+        z += np.sqrt(w[0, y] * w[1, y])
+    # need all
+    return z
+
+
+def main():
+    print 'helper functions'
+
+    for i in range(9):
+        print(i, 'is power of 2: ', is_power_of_two(i))
+    n = 6
+    m = 2 ** n
+
+
+    pos = np.arange(m)
+    rev_pos = bit_reverse_vector(pos, n)
+    print(pos)
+    print(rev_pos)
+
+
+if __name__ == '__main__':
+    main()