more Python 3 doctest fixes

docs/example-linear-diophantine-equations.rst

@@ -54,7 +54,7 @@ Note that before we apply LLL to Mb we need to convert it to fpyll IntegerMatrix
 
 ::
 
-  >>> print LLL.reduction(IntegerMatrix.from_matrix([map(int, row) for row in Mb.tolist()]))
+  >>> print(LLL.reduction(IntegerMatrix.from_matrix([map(int, row) for row in Mb.tolist()])))
   [  0 -1  0  0  1  0  0  0  0 0   0      0 ]
   [  0  1  0  0  0  0  1  0 -2 0   0      0 ]
   [ -1  0 -1 -1  0  0  1  0  1 1   0      0 ]

docs/tutorial.rst

@@ -27,7 +27,7 @@ Matrix :math:`A` is a (random) knapsack type matrix. That is of the form  `[ {\b
 
 ::
  
-    >>> print A
+    >>> print(A)
     [  50 1 0 0 0 0 0 0 0 0 ]
     [ 556 0 1 0 0 0 0 0 0 0 ]
     [   5 0 0 1 0 0 0 0 0 0 ]
@@ -60,7 +60,7 @@ Also, the following type of matrices are supported,
 
 For instance::
 
-    >>> print D
+    >>> print(D)
     [ 1 0 0 858 790 620 ]
     [ 0 1 0  72 832 133 ]
     [ 0 0 1 263 121 724 ]
@@ -94,9 +94,9 @@ We can then compute the inner product `r_{i,j} = \langle {\bf b}_i, {\bf b}^{*}_
 ::
 
     >>> i = 3; j = 2;
-    >>> print M.get_r(i,j)
+    >>> print(M.get_r(i,j))
     0.810079798404
-    >>> print M.get_mu(i,j)
+    >>> print(M.get_mu(i,j))
     0.0584569876831
 
 To compute the LLL reduced matrix of `{\bf A}`
@@ -106,7 +106,7 @@ To compute the LLL reduced matrix of `{\bf A}`
     >>> from fpylll import LLL
     >>> A.randomize("qary", bits=10, k=3)
     >>> A_lll = LLL.reduction(A)
-    >>> print A_lll
+    >>> print(A_lll)
     [  -1 -6 -1 10 ]
     [  -7 -5 -7 -4 ]
     [  -4 13 -4  3 ]
@@ -121,7 +121,7 @@ For the BKZ reduction of `{\bf A}` with blocksize say 3 (without pruning),
     >>> block_size = 3
     >>> A.randomize("qary", bits=10, k=3)
     >>> A_bkz = BKZ.reduction(A, BKZ.Param(block_size))
-    >>> print A_bkz
+    >>> print(A_bkz)
     [  75  44   -5 -16 ]
     [  29   7  -52 134 ]
     [ -89 108   56  29 ]
@@ -144,7 +144,7 @@ To compute the norm of a shortest vector of the lattice generated by the rows of
     >>> import numpy as np
     >>> SVP.shortest_vector(A)
     (75, 44, -5, -16)
-    >>> print A[0]
+    >>> A[0]
     (75, 44, -5, -16)
     >>> A[0].norm()
     88.55506761332182
@@ -159,7 +159,7 @@ Also, the ``GaussSieve`` algorithm [MV]_ is implemented,
     >>> v = GaussSieve(A, algorithm=2)()
     >>> tuple(map(lambda x: -1*x, v)) if v[0] < 0 else v 
     (6, -5, 3, -4, 2, -4, 1, -1, -3, -1, -5, 1, 1, 2, -1, -1, -3, 2, 1, 1, 0, 5, -2, 4, -3, 0, 3, -5, 0, -2)
-    >>> print A[0]
+    >>> A[0]
     (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 66, 52, 5, 45, 56, 26, 89, 51, 112, 64, 37, 85, 5, 87, 3)
     >>> A[0].norm()
     237.23827684418887
@@ -170,7 +170,7 @@ The previous code returns a Shortest vector of the lattice generated by `{\bf A}
     >>> A = IntegerMatrix.from_matrix([[1,2,3,4],[30,4,4,5],[1,-2,3,4]])
     >>> t = (1, 2, 5, 5)
     >>> v0 = CVP.closest_vector(A, t)
-    >>> print v0
+    >>> v0
     (1, 2, 3, 4)
 
 In fact the following code was executed::
@@ -183,7 +183,7 @@ In fact the following code was executed::
     >>> _, v2 = E.enumerate(0, A.nrows, 5, 40, M.from_canonical(t))[0]
     >>> v3 = IntegerMatrix.from_iterable(1, A.nrows, map(lambda x: int(x), v2))
     >>> v1 = v3*A
-    >>> print v1
+    >>> v1
     [ 1 2 3 4 ]
 
 Further examples