#!/usr/bin/env python
#
# Copyright 2015 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#


from .Generate_LDPC_matrix_functions import (
    LDPC_matrix,
    get_best_matrix,
    get_full_rank_H_matrix,
    linalg,
    write_alist_file,
)

# This is an example of how to generate a parity check matrix for
# use with the LDPC Richardson Urbanke encoder. A significant amount
# of matrix manipulation is required, so this process should be done
# before using the encoder at run-time. This process can take quite
# a while, with more time required for larger matrices.

# Not all attempts to create a parity check matrix will be
# successful. The script will terminate and output error messages
# when the process fails. To increase verbosity, edit the verbose
# variable at the top of Generate_LDPC_matrix_functions.py.

# Because random number generation and
# shuffling methods are used, it is not possible to predict what
# starting conditions will result in success. It requires a bit of
# trial and error.

# ----------------------------------------------------------------- #

# First, generate a regular LDPC parity check matrix. Specify
# the properties desired. For example:
n = 200  # number of columns, corresponds to codeword length
p = 3  # column weight
q = 5  # row weight

parity_check_matrix = LDPC_matrix(n_p_q=[n, p, q])

# Richardson and Urbanke's preprocessing method requires a full rank
# matrix to start. The matrices generated by the
# regular_LDPC_code_contructor function will never be full rank. So,
# use the get_full_rank_H_matrix function.
newH = get_full_rank_H_matrix(parity_check_matrix.H)

# At this point, the matrix is no longer regular. (The row/column
# weights are not the same for all rows/columns.)

# Next, some preprocessing steps need to be performed as described
# Richardson and Urbanke in Modern Coding Theory, Appendix A. This
# can take a while...
[bestH, g] = get_best_matrix(newH, 100)

# Print(out some of the resulting properties.)
n = bestH.shape[1]
k = n - bestH.shape[0]
print("Parity check matrix properties:")
print("\tSize :", bestH.shape)
print("\tRank :", linalg.matrix_rank(bestH))
print("\tRate : %.3f" % ((k * 1.0) / n))
print("\tn    :", n, " (codeword length)")
print("\tk    :", k, " (info word length)")
print("\tgap  : %i" % g)

# Save the matrix to an alist file for future use:
alist_filename = "n_%04i_k_%04i_gap_%02i.alist" % (n, k, g)
write_alist_file(alist_filename, bestH)
print("\nMatrix saved to alist file:", alist_filename, "\n")
