pylfsr 1.0.5

Linear Feedback Shift Register

LFSR -Linear Feedback Shift Register

Github Page

PyPi - project

Documentation

Python

Requirement : numpy

Updates:

• Fixed the bugs (1) missing initial bit (2) exception
• Added test properties of LFSR
• (1) Balance Property
• (2) Runlength Property
• (3) Autocorrelation Property

Installation

with pip

pip install pylfsr

Build from the source

Download the repository or clone it with git, after cd in directory build it from source with

python setup.py install

Examples

Example 1: 5-bit LFSR with feedback polynomial x^5 + x^2 + 1

# import LFSR
import numpy as np
from pylfsr import LFSR

L = LFSR()

# print the info
L.info()

5 bit LFSR with feedback polynomial  x^5 + x^2 + 1
Expected Period (if polynomial is primitive) =  31
Current :
State        :  [1 1 1 1 1]
Count        :  0
Output bit   : -1
feedback bit : -1

L.next()
L.runKCycle(10)
L.runFullCycle()
L.info()

Example 2**: 5-bit LFSR with custum state and feedback polynomial

state = [0,0,0,1,0]
fpoly = [5,4,3,2]
L = LFSR(fpoly=fpoly,initstate =state, verbose=True)
L.info()
tempseq = L.runKCycle(10)
L.set(fpoly=[5,3])

Example 3 ## To visualize the process with 3-bit LFSR, with default counter_start_zero = True

state = [1,1,1]
fpoly = [3,2]
L = LFSR(initstate=state,fpoly=fpoly)
print('count \t state \t\toutbit \t seq')
print('-'*50)
for _ in range(15):
    print(L.count,L.state,'',L.outbit,L.seq,sep='\t')
    L.next()
print('-'*50)
print('Output: ',L.seq)

Output :

count 	        state 		outbit 	 seq
--------------------------------------------------
0		[1 1 1]		-1	[-1]
1		[0 1 1]		1	[1]
2		[0 0 1]		1	[1 1]
3		[1 0 0]		1	[1 1 1]
4		[0 1 0]		0	[1 1 1 0]
5		[1 0 1]		0	[1 1 1 0 0]
6		[1 1 0]		1	[1 1 1 0 0 1]
7		[1 1 1]		0	[1 1 1 0 0 1 0]
8		[0 1 1]		1	[1 1 1 0 0 1 0 1]
9		[0 0 1]		1	[1 1 1 0 0 1 0 1 1]
10		[1 0 0]		1	[1 1 1 0 0 1 0 1 1 1]
11		[0 1 0]		0	[1 1 1 0 0 1 0 1 1 1 0]
12		[1 0 1]		0	[1 1 1 0 0 1 0 1 1 1 0 0]
13		[1 1 0]		1	[1 1 1 0 0 1 0 1 1 1 0 0 1]
14		[1 1 1]		0	[1 1 1 0 0 1 0 1 1 1 0 0 1 0]
--------------------------------------------------
Output:  [1 1 1 0 0 1 0 1 1 1 0 0 1 0 1]

Example 4 ## To visualize the process with 3-bit LFSR, with default counter_start_zero = False

state = [1,1,1]
fpoly = [3,2]
L = LFSR(initstate=state,fpoly=fpoly,counter_start_zero=False)
print('count \t state \t\toutbit \t seq')
print('-'*50)
for _ in range(14):
    print(L.count,L.state,'',L.outbit,L.seq,sep='\t')
    L.next()
print('-'*50)
print('Output: ',L.seq)

Output

count 	  state 		outbit 	 seq
--------------------------------------------------
1	  [1 1 1]		1	[1]
2	  [0 1 1]		1	[1 1]
3	  [0 0 1]		1	[1 1 1]
4	  [1 0 0]		1	[1 1 1 0]
5	  [0 1 0]		0	[1 1 1 0 0]
6	  [1 0 1]		0	[1 1 1 0 0 1]
7	  [1 1 0]		1	[1 1 1 0 0 1 0]
8	  [1 1 1]		0	[1 1 1 0 0 1 0 1]
9	  [0 1 1]		1	[1 1 1 0 0 1 0 1 1]
10	  [0 0 1]		1	[1 1 1 0 0 1 0 1 1 1]
11	  [1 0 0]		1	[1 1 1 0 0 1 0 1 1 1 0]
12	  [0 1 0]		0	[1 1 1 0 0 1 0 1 1 1 0 0]
13	  [1 0 1]		0	[1 1 1 0 0 1 0 1 1 1 0 0 1]
14	  [1 1 0]		1	[1 1 1 0 0 1 0 1 1 1 0 0 1 0]
--------------------------------------------------
Output:  [1 1 1 0 0 1 0 1 1 1 0 0 1 0 1]

Example 5 ## 23 bit LFSR with custum state and feedback polynomial

fpoly = [23,19]
L1 = LFSR(fpoly=fpoly,initstate ='ones', verbose=False)
L1.info()

Output

23 bit LFSR with feedback polynomial  x^23 + x^19 + 1
Expected Period (if polynomial is primitive) =  8388607
Current :
 State        :  [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
 Count        :  0
 Output bit   :  -1
 feedback bit :  -1

seq = L1.runKCycle(100)

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1])

Example 6 ## testing the properties

state = [1,1,1,1,0]
fpoly = [5,3]
L = LFSR(initstate=state,fpoly=fpoly)
result  = L.test_properties(verbose=2)

Output

1. Periodicity
------------------
 - Expected period = 2^M-1 = 31
 - Pass?:  True

2. Balance Property
-------------------
 - Number of 1s = Number of 0s+1 (in a period): (N1s,N0s) =  (16, 15)
 - Pass?:  True

3. Runlength Property
-------------------
 - Number of Runs in a period should be of specific order, e.g. [4,2,1,1]
 - Runs:  [8 4 2 1 1]
 - Pass?:  True

4. Autocorrelation Property
-------------------
 - Autocorrelation of a period should be noise-like, specifically, 1 at k=0, -1/m everywhere else
 - Pass?:  True

==================
Passed all the tests
==================

Testing individual property

# get a full period sequence
p = L.getFullPeriod()
p
array([0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,
       0, 1, 0, 0, 1, 0, 1, 1, 0])

L.balance_property(p.copy())
# (True, (16, 15))

L.runlength_property(p.copy())
# (True, array([8, 4, 2, 1, 1]))

L.autocorr_property(p.copy())[0]
#True

Example 7 ## testing the properties for non-primitive polynomial

state = [1,1,1,1,0]
fpoly = [5,1]
L = LFSR(initstate=state,fpoly=fpoly)
result = L.test_properties(verbose=1)

Output

1. Periodicity
------------------
 - Expected period = 2^M-1 = 31
 - Pass?:  False

2. Balance Property
-------------------
 - Number of 1s = Number of 0s+1 (in a period): (N1s,N0s) =  (17, 14)
 - Pass?:  False

3. Runlength Property
-------------------
 - Number of Runs in a period should be of specific order, e.g. [4,2,1,1]
 - Runs:  [10  2  1  1  2]
 - Pass?:  False

4. Autocorrelation Property
-------------------
 - Autocorrelation of a period should be noise-like, specifically, 1 at k=0, -1/m everywhere else
 - Pass?:  False

==================
Failed one or more tests, check if feedback polynomial is primitive polynomial
==================

Example 8**: Get the feedback polynomial or list

Reference : http://www.partow.net/programming/polynomials/index.html

L = LFSR()
# list of 5-bit feedback polynomials
fpoly = L.get_fpolyList(m=5)

# list of all feedback polynomials as a dictionary
fpolyDict = L.get_fpolyList()

Changing feedback polynomial in between

L.changeFpoly(newfpoly =[23,14],reset=False)
seq1 = L.runKCycle(20)

# Change after 20 clocks
L.changeFpoly(newfpoly =[23,9],reset=False)
seq2 = L.runKCycle(20)

For A5/1 GSM Stream cipher generator

Reference Article: Enhancement of A5/1: https://doi.org/10.1109/ETNCC.2011.5958486

# Three LFSRs initialzed with 'ones' though they are intialized with encription key
R1 = LFSR(fpoly = [19,18,17,14])
R2 = LFSR(fpoly = [23,22,21,8])
R3 = LFSR(fpoly = [22,21])

# clocking bits
b1 = R1.state[8]
b2 = R1.state[10]
b3 = R1.state[10]

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MATLAB

For matlab files download it from here

Folder : https://github.com/Nikeshbajaj/Linear_Feedback_Shift_Register/tree/master/matlabfiles

Description Genrate randon binary sequence using LFSR for any given feedback taps (polynomial), This will also check Three fundamental Property of LFSR

1. Balance Property
2. Runlength Property
3. Autocorrelation Property

This MATLAB Code work for any length of LFSR with given taps (feedback polynomial) -Universal, There are three files LFSRv1.m an LFSRv2.m, LFSRv3.m

LFSRv1

This function will return all the states of LFSR and will check Three fundamental Property of LFSR (1) Balance Property (2) Runlength Property (3) Autocorrelation Property

EXAMPLE

s=[1 1 0 0 1]
t=[5 2]
[seq c] =LFSRv1(s,t)

LFSRv2

This function will return only generated sequence will all the states of LFSR, no verification of properties are done here. Use this function to avoid verification each time you execute the program.

EXAMPLE

s=[1 1 0 0 1]
t=[5 2]
[seq c] =LFSRv2(s,t)

LFSRv3 (faster)

seq = LFSRv3(s,t,N) this function generates N bit sequence only. This is faster then other two functions, as this does not gives each state of LFSR

EXAMPLE

s=[1 1 0 0 1]  
t=[5 2]
seq =LFSRv3(s,t,50)

Tips

• If you want to use this function in middle of any program, use LFSRv2 or LFSRv1 with verification =0.
• If you want to make it fast for long length of LFSR,use LFSRv3.m

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Contacts:

If any doubt, confusion or feedback please contact me

• Nikesh Bajaj
• http://nikeshbajaj.in
• [e-mail]
• bajaj[dot]nikkey [AT]gmail[dot]com

PhD Student: Queen Mary University of London & University of Genoa

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