diff options
Diffstat (limited to 'gr-fec/python/fec/LDPC/Generate_LDPC_matrix_functions.py')
| -rw-r--r-- | gr-fec/python/fec/LDPC/Generate_LDPC_matrix_functions.py | 150 |
1 files changed, 77 insertions, 73 deletions
diff --git a/gr-fec/python/fec/LDPC/Generate_LDPC_matrix_functions.py b/gr-fec/python/fec/LDPC/Generate_LDPC_matrix_functions.py index c42fee631f..5774329ecd 100644 --- a/gr-fec/python/fec/LDPC/Generate_LDPC_matrix_functions.py +++ b/gr-fec/python/fec/LDPC/Generate_LDPC_matrix_functions.py @@ -20,6 +20,10 @@ # Boston, MA 02110-1301, USA. # +from __future__ import print_function +from __future__ import division +from __future__ import unicode_literals + import string, sys from numpy import * from numpy.random import shuffle, randint @@ -46,9 +50,9 @@ def read_alist_file(filename): indices = string.split(data[lineNumber]) for index in indices: H[int(index)-1,lineNumber-4] = 1 - # The subsequent lines in the file list the indices for where - # the 1s are in the rows, but this is redundant - # information. + # The subsequent lines in the file list the indices for where + # the 1s are in the rows, but this is redundant + # information. return H @@ -68,7 +72,7 @@ def write_alist_file(filename, H, verbose=0): numRows = H.shape[0] numCols = H.shape[1] - tempstring = `numCols` + ' ' + `numRows` + '\n' + tempstring = repr(numCols) + ' ' + repr(numRows) + '\n' myfile.write(tempstring) tempstring1 = '' @@ -79,12 +83,12 @@ def write_alist_file(filename, H, verbose=0): rowWeight = nonzeros.shape[1] if rowWeight > maxRowWeight: maxRowWeight = rowWeight - tempstring1 = tempstring1 + `rowWeight` + ' ' + tempstring1 = tempstring1 + repr(rowWeight) + ' ' for tempArray in nonzeros: for index in tempArray: - tempstring2 = tempstring2 + `index+1` + ' ' - tempstring2 = tempstring2 + '\n' - tempstring1 = tempstring1 + '\n' + tempstring2 = tempstring2 + repr(index+1) + ' ' + tempstring2 = tempstring2 + '\n' + tempstring1 = tempstring1 + '\n' tempstring3 = '' tempstring4 = '' @@ -94,14 +98,14 @@ def write_alist_file(filename, H, verbose=0): colWeight = nonzeros.shape[1] if colWeight > maxColWeight: maxColWeight = colWeight - tempstring3 = tempstring3 + `colWeight` + ' ' + tempstring3 = tempstring3 + repr(colWeight) + ' ' for tempArray in nonzeros: for index in tempArray: - tempstring4 = tempstring4 + `index+1` + ' ' - tempstring4 = tempstring4 + '\n' - tempstring3 = tempstring3 + '\n' + tempstring4 = tempstring4 + repr(index+1) + ' ' + tempstring4 = tempstring4 + '\n' + tempstring3 = tempstring3 + '\n' - tempstring = `maxColWeight` + ' ' + `maxRowWeight` + '\n' + tempstring = repr(maxColWeight) + ' ' + repr(maxRowWeight) + '\n' # write out max column and row weights myfile.write(tempstring) # write out all of the column weights @@ -116,11 +120,11 @@ def write_alist_file(filename, H, verbose=0): myfile.close() -class LDPC_matrix: +class LDPC_matrix(object): """ Class for a LDPC parity check matrix """ def __init__(self, alist_filename = None, - n_p_q = None, - H_matrix = None): + n_p_q = None, + H_matrix = None): if (alist_filename != None): self.H = self.read_alist_file(alist_filename) elif (n_p_q != None): @@ -128,9 +132,9 @@ class LDPC_matrix: elif (H_matrix != None): self.H = H_matrix else: - print 'Error: provide either an alist filename,', - print 'parameters for constructing regular LDPC parity', - print 'check matrix, or a numpy array.' + print('Error: provide either an alist filename, ', end='') + print('parameters for constructing regular LDPC parity, ', end='') + print('check matrix, or a numpy array.') self.rank = linalg.matrix_rank(self.H) self.numRows = self.H.shape[0] @@ -159,31 +163,31 @@ class LDPC_matrix: # For this algorithm, n/p must be an integer, because the # number of rows in each submatrix must be a whole number. - ratioTest = (n*1.0)/q + ratioTest = (n*1.0) / q if ratioTest%1 != 0: - print '\nError in regular_LDPC_code_contructor: The' - print 'ratio of inputs n/q must be a whole number.\n' + print('\nError in regular_LDPC_code_contructor: The ', end='') + print('ratio of inputs n/q must be a whole number.\n') return # First submatrix first: - m = (n*p)/q # number of rows in H matrix - submatrix1 = zeros((m/p,n)) - for row in arange(m/p): + m = (n*p) / q # number of rows in H matrix + submatrix1 = zeros((m / p,n)) + for row in arange(m / p): range1 = row*q range2 = (row+1)*q submatrix1[row,range1:range2] = 1 - H = submatrix1 + H = submatrix1 # Create the other submatrices and vertically stack them on. submatrixNum = 2 newColumnOrder = arange(n) while submatrixNum <= p: - submatrix = zeros((m/p,n)) + submatrix = zeros((m / p,n)) shuffle(newColumnOrder) for columnNum in arange(n): submatrix[:,columnNum] = \ - submatrix1[:,newColumnOrder[columnNum]] + submatrix1[:,newColumnOrder[columnNum]] H = vstack((H,submatrix)) submatrixNum = submatrixNum + 1 @@ -197,14 +201,14 @@ class LDPC_matrix: for rowNum in arange(rows): nonzeros = array(H[rowNum,:].nonzero()) if nonzeros.shape[1] != q: - print 'Row', rowNum, 'has incorrect weight!' + print('Row', rowNum, 'has incorrect weight!') return # Check the column weights for columnNum in arange(cols): nonzeros = array(H[:,columnNum].nonzero()) if nonzeros.shape[1] != p: - print 'Row', columnNum, 'has incorrect weight!' + print('Row', columnNum, 'has incorrect weight!') return return H @@ -221,10 +225,10 @@ def greedy_upper_triangulation(H, verbose=0): # Per email from Dr. Urbanke, author of this textbook, this # algorithm requires H to be full rank if linalg.matrix_rank(H_t) != H_t.shape[0]: - print 'Rank of H:', linalg.matrix_rank(tempArray) - print 'H has', H_t.shape[0], 'rows' - print 'Error: H must be full rank.' - return + print('Rank of H:', linalg.matrix_rank(tempArray)) + print('H has', H_t.shape[0], 'rows') + print('Error: H must be full rank.') + return size = H_t.shape n = size[1] @@ -253,7 +257,7 @@ def greedy_upper_triangulation(H, verbose=0): # equal to the min positive residual degree, then pick a # random column c. indices = (minResidualDegrees == minimumResidualDegree)\ - .nonzero()[1] + .nonzero()[1] indices = indices + t if indices.shape[0] == 1: columnC = indices[0] @@ -282,7 +286,7 @@ def greedy_upper_triangulation(H, verbose=0): else: # This is the 'choose' case. rowsThatContainNonZeros = H_residual[:,columnC-t]\ - .nonzero()[0] + .nonzero()[0] # Swap column c with column t. (Book says t+1 but we # index from 0, not 1.) @@ -315,8 +319,8 @@ def greedy_upper_triangulation(H, verbose=0): while sub_index < (m - rowInH_t): Htemp[m-sub_index-1,:] = H_t[m-sub_index,:] sub_index = sub_index+1 - H_t = Htemp.copy() - Htemp = H_t.copy() + H_t = Htemp.copy() + Htemp = H_t.copy() # Save temp H as new H_t. H_t = Htemp.copy() @@ -327,7 +331,7 @@ def greedy_upper_triangulation(H, verbose=0): if g == 0: if verbose: - print 'Error: gap is 0.' + print('Error: gap is 0.') return # We need to ensure phi is nonsingular. @@ -348,22 +352,22 @@ def greedy_upper_triangulation(H, verbose=0): except linalg.linalg.LinAlgError: # Phi is singular if verbose > 1: - print 'Initial phi is singular' + print('Initial phi is singular') else: # Phi is nonsingular, so we need to use this version of H. if verbose > 1: - print 'Initial phi is nonsingular' + print('Initial phi is nonsingular') return [H_t, g, t] else: if verbose: - print 'Initial phi is all zeros:\n', phi + print('Initial phi is all zeros:\n', phi) # If the C and D submatrices are all zeros, there is no point in # shuffling them around in an attempt to find a good phi. if not (C.any() or D.any()): if verbose: - print 'C and D are all zeros. There is no hope in', - print 'finding a nonsingular phi matrix. ' + print('C and D are all zeros. There is no hope in',) + print('finding a nonsingular phi matrix. ') return # We can't look at every row/column permutation possibility @@ -378,8 +382,8 @@ def greedy_upper_triangulation(H, verbose=0): while iterationCount < maxIterations: if verbose > 1: - print 'iterationCount:', iterationCount - tempH = H_t.copy() + print('iterationCount:', iterationCount) + tempH = H_t.copy() shuffle(columnsToShuffle) shuffle(rowsToShuffle) @@ -387,7 +391,7 @@ def greedy_upper_triangulation(H, verbose=0): for newDestinationColumnNumber in arange(t,n): oldColumnNumber = columnsToShuffle[index] tempH[:,newDestinationColumnNumber] = \ - H_t[:,oldColumnNumber] + H_t[:,oldColumnNumber] index +=1 tempH2 = tempH.copy() @@ -414,23 +418,23 @@ def greedy_upper_triangulation(H, verbose=0): except linalg.linalg.LinAlgError: # Phi is singular if verbose > 1: - print 'Phi is still singular' + print('Phi is still singular') else: # Phi is nonsingular, so we're done. if verbose: - print 'Found a nonsingular phi on', - print 'iterationCount = ', iterationCount + print('Found a nonsingular phi on',) + print('iterationCount = ', iterationCount) return [H_t, g, t] else: if verbose > 1: - print 'phi is all zeros' + print('phi is all zeros') iterationCount +=1 # If we've reached this point, then we haven't found a # version of H that has a nonsingular phi. if verbose: - print '--- Error: nonsingular phi matrix not found.' + print('--- Error: nonsingular phi matrix not found.') def inv_mod2(squareMatrix, verbose=0): """ @@ -468,16 +472,16 @@ def inv_mod2(squareMatrix, verbose=0): tempTest[rowNum,colNum] = 0 else: if verbose > 1: - print 'In inv_mod2. Rounding error on this', - print 'value? Mod 2 has already been done.', - print 'value:', value + print('In inv_mod2. Rounding error on this',) + print('value? Mod 2 has already been done.',) + print('value:', value) test = tempTest.copy() if (test - eye(t,t) % 2).any(): if verbose: - print 'Error in inv_mod2: did not find inverse.' - # TODO is this the most appropriate error to raise? + print('Error in inv_mod2: did not find inverse.') + # TODO is this the most appropriate error to raise? raise linalg.linalg.LinAlgError else: return C @@ -520,7 +524,7 @@ def get_full_rank_H_matrix(H, verbose=False): tempArray = H.copy() if linalg.matrix_rank(tempArray) == tempArray.shape[0]: if verbose: - print 'Returning H; it is already full rank.' + print('Returning H; it is already full rank.') return tempArray numRows = tempArray.shape[0] @@ -538,8 +542,8 @@ def get_full_rank_H_matrix(H, verbose=False): while i < limit: if verbose: - print 'In get_full_rank_H_matrix; i:', i - # Flag indicating that the row contains a non-zero entry + print('In get_full_rank_H_matrix; i:', i) + # Flag indicating that the row contains a non-zero entry found = False for j in arange(i, numColumns): if tempArray[i, j] == 1: @@ -588,8 +592,8 @@ def get_full_rank_H_matrix(H, verbose=False): newH[:,index] = tempHarray[:,columnOrder[0,index]] if verbose: - print 'original H.shape:', H.shape - print 'newH.shape:', newH.shape + print('original H.shape:', H.shape) + print('newH.shape:', newH.shape) return newH @@ -604,13 +608,13 @@ def get_best_matrix(H, numIterations=100, verbose=False): index = 1 while index <= numIterations: if verbose: - print '--- In get_best_matrix, iteration:', index - index += 1 + print('--- In get_best_matrix, iteration:', index) + index += 1 try: ret = greedy_upper_triangulation(H, verbose) - except ValueError, e: + except ValueError as e: if verbose > 1: - print 'greedy_upper_triangulation error: ', e + print('greedy_upper_triangulation error: ', e) else: if ret: [betterH, gap, t] = ret @@ -632,8 +636,8 @@ def get_best_matrix(H, numIterations=100, verbose=False): return [bestH, bestGap] else: if verbose: - print 'Error: Could not find appropriate H form', - print 'for encoding.' + print('Error: Could not find appropriate H form',) + print('for encoding.') return def getSystematicGmatrix(GenMatrix): @@ -643,7 +647,7 @@ def getSystematicGmatrix(GenMatrix): matrix and P is the parity submatrix. If the GenMatrix matrix provided is not full rank, then dependent rows will be deleted. - This function does not convert parity check (H) matrices to the + This function does not convert parity check (H) matrices to the generator matrix format. Use the function getSystematicGmatrixFromH for that purpose. """ @@ -682,7 +686,7 @@ def getSystematicGmatrix(GenMatrix): tempArray = move_row_to_bottom(i,tempArray) # decrease limit since we just found a row of 0s limit -= 1 - # the rows below i are the dependent rows, which we discard + # the rows below i are the dependent rows, which we discard G = tempArray[0:i,:] return G @@ -696,7 +700,7 @@ def getSystematicGmatrixFromH(H, verbose=False): will be deleted first. """ if verbose: - print 'received H with size: ', H.shape + print('received H with size: ', H.shape) # First, put the H matrix into the form H = [I|m] where: # I is (n-k) x (n-k) identity matrix @@ -716,5 +720,5 @@ def getSystematicGmatrixFromH(H, verbose=False): k = m.shape[1] G = concatenate((identity(k),m.T),axis=1) if verbose: - print 'returning G with size: ', G.shape - return G
\ No newline at end of file + print('returning G with size: ', G.shape) + return G |