ldpc 0.0.17

Python tools for low density parity check (LDPC) codes

LDPC

This module provides a suite of tools for building and benmarking low density parity check (LDPC) codes. Features include functions for mod2 (binary) arithmatic and a fast implementation of the belief propagation decoder.

Installation from PyPi (recommended method)

Installtion from PyPi requires Python>=3.6. To install via pip, run:

pip install ldpc

Installation (from source)

Installation from source requires Python>=3.6 and a local C compiler (eg. 'gcc' in Linux or 'clang' in Windows). The LDPC package can then be installed by running:

git clone https://github.com/quantumgizmos/ldpc.git
cd ldpc
pip install -e ldpc

Dependencies

This package makes use of the mod2sparse data structure from Radford Neal's Software for Low Density Parity Check Codes C package.

Quick start

Parity check matrices

In this package error correction codes are represented in terms of their parity check matrix stored in numpy.ndarray format. As an example, the parity check matrix for the repetition code can be loaded from the ldpc.codes submodule as follows:

import numpy as np
from ldpc.codes import rep_code
n=5 #specifies the lenght of the repetition code
H=rep_code(n) #returns the repetition code parity check matrix
print(H)

[[1 1 0 0 0]
 [0 1 1 0 0]
 [0 0 1 1 0]
 [0 0 0 1 1]]

To compute the [n,k,d] code parameters we can use functions from the ldpc.mod2 and ldpc.code_util submodules. Below is an example showing how to calculate the code parameters of the Hamming code:

from ldpc.codes import hamming_code #function for generating Hamming codes
from ldpc.mod2 import rank #function for calcuting the mod2 rank
from ldpc.code_util import compute_code_distance #function for calculting the code distance

H=hamming_code(3)
print(H)
n=H.shape[1] #block length of the code
k=n-rank(H) #the dimension of the code computed using the rank-nullity theorem.
d=compute_code_distance(H) #computes the code distance
print(f"Hamming code parameters: [n={n},k={k},d={d}]")

[[0 0 0 1 1 1 1]
 [0 1 1 0 0 1 1]
 [1 0 1 0 1 0 1]]
Hamming code parameters: [n=7,k=4,d=3]

Note that computing the code distance quickly becomes intractable for larger parity check matrices. The ldpc.code_util.compute_code_distance should therefore only be used for small codes.

Belief propagation decoding

To decode using belief propagation, first load an istance of the ldpc.bp_decoder class.

from ldpc import bp_decoder
H=rep_code(3)
n=H.shape[1]

bpd=bp_decoder(
    H, #the parity check matrix
    error_rate=0.1, # the error rate on each bit
    max_iter=n, #the maximum iteration depth for BP
    bp_method="product_sum", #BP method. The other option is `minimum_sum'
    channel_probs=[None] #channel probability probabilities. Will overide error rate.
)

To decode an error, calculate a syndrome and call the bp_decoder.decode function:

error=np.array([0,1,0])
syndrome=H@error%2
decoding=bpd.decode(syndrome)
print(f"Error: {error}")
print(f"Syndrome: {syndrome}")
print(f"Decoding: {decoding}")

Error: [0 1 0]
Syndrome: [1 1]
Decoding: [0 1 0]

If the code bits are subject to different error rates, a channel probability vector can be provided instead of the error rate.

bpd=bp_decoder(
    H, 
    max_iter=n,
    bp_method="product_sum", 
    channel_probs=[0.1,0,0.1] #channel probability probabilities. Will overide error rate.
)

error=np.array([1,0,1])
syndrome=H@error%2
decoding=bpd.decode(syndrome)
print(f"Error: {error}")
print(f"Syndrome: {syndrome}")
print(f"Decoding: {decoding}")

Error: [1 0 1]
Syndrome: [1 1]
Decoding: [1 0 1]

Example: error correction over the binary symmetric channel

import numpy as np
from ldpc.codes import rep_code
from ldpc import bp_decoder

n=13
error_rate=0.3
runs=5
H=rep_code(n)

#BP decoder class. Make sure this is defined outside the loop
bpd=bp_decoder(H,error_rate=error_rate,max_iter=n,bp_method="product_sum")
error=np.zeros(n).astype(int) #error vector

for _ in range(runs):
    for i in range(n):
        if np.random.random()<error_rate:
            error[i]=1
        else: error[i]=0
    syndrome=H@error %2 #calculates the error syndrome
    print(f"Error: {error}")
    print(f"Syndrome: {syndrome}")
    decoding=bpd.decode(syndrome)
    print(f"Decoding: {error}\n")

Error: [1 0 1 0 1 0 1 1 0 0 1 0 0]
Syndrome: [1 1 1 1 1 1 0 1 0 1 1 0]
Decoding: [1 0 1 0 1 0 1 1 0 0 1 0 0]

Error: [1 0 0 0 0 0 1 1 0 0 0 0 0]
Syndrome: [1 0 0 0 0 1 0 1 0 0 0 0]
Decoding: [1 0 0 0 0 0 1 1 0 0 0 0 0]

Error: [0 0 0 0 1 0 0 0 0 0 1 0 0]
Syndrome: [0 0 0 1 1 0 0 0 0 1 1 0]
Decoding: [0 0 0 0 1 0 0 0 0 0 1 0 0]

Error: [0 1 1 1 1 0 0 1 1 1 0 1 1]
Syndrome: [1 0 0 0 1 0 1 0 0 1 1 0]
Decoding: [0 1 1 1 1 0 0 1 1 1 0 1 1]

Error: [1 0 0 0 0 0 1 0 0 0 0 0 0]
Syndrome: [1 0 0 0 0 1 1 0 0 0 0 0]
Decoding: [1 0 0 0 0 0 1 0 0 0 0 0 0]