Merge pull request oseledets#33 from rballester/oseledets-develop

Fixed print() for Python 2 and 3
stareforever · Mar 18, 2017 · e3c24e0 · e3c24e0
2 parents efccde8 + fc18a82
commit e3c24e0
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Showing 7 changed files with 33 additions and 26 deletions.
diff --git a/tt/__init__.py b/tt/__init__.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 try:
     from core.tt import *
 except:
@@ -8,7 +9,7 @@
         try:
             ctypes.CDLL("liblapack.so", ctypes.RTLD_GLOBAL)
         except:
-            print "Did not find MKL or LAPACK library"
+            print("Did not find MKL or LAPACK library")
     from core.tt import *
 
 from multifuncrs import multifuncrs

diff --git a/tt/amen/amen_mv.py b/tt/amen/amen_mv.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import tt as _tt
 import numpy as _np
 from scipy.sparse import spdiags as _spdiags
@@ -112,7 +113,7 @@ def amen_mv(A, x, tol, y=None, z=None, nswp=20, kickrank=4,
     '''
 
     if renorm is 'gram':
-        print "Not implemented yet. Renorm is switched to 'direct'"
+        print("Not implemented yet. Renorm is switched to 'direct'")
         renorm = 'direct'
 
     if isinstance(x, _tt.vector):
@@ -436,12 +437,12 @@ def amen_mv(A, x, tol, y=None, z=None, nswp=20, kickrank=4,
                     phizy[i + 1], z[i], y[i], 'rl', return_norm=False)
 
         if (verb > 1):
-            print 'amen-mv: swp=[%d,%d], dx=%.3e, r=%d, |y|=%.3e, |z|=%.3e' % (swp, i, dx, r, _np.linalg.norm(cry), nrmz)
+            print('amen-mv: swp=[%d,%d], dx=%.3e, r=%d, |y|=%.3e, |z|=%.3e' % (swp, i, dx, r, _np.linalg.norm(cry), nrmz))
 
         # Stopping or reversing
         if ((direct > 0) and (i == d - 1)) or ((direct < 0) and (i == 0)):
             if (verb > 0):
-                print 'amen-mv: swp=%d{%d}, max_dx=%.3e, max_r=%d' % (swp, (1 - direct) / 2, max_dx, max(ry))
+                print('amen-mv: swp=%d{%d}, max_dx=%.3e, max_r=%d' % (swp, (1 - direct) / 2, max_dx, max(ry)))
             if ((max_dx < tol) or (swp == nswp)) and (direct > 0):
                 break
             else:
@@ -679,4 +680,4 @@ def _bfun3(Phi1, A, Phi2, x):
                 kickrank2=0, verb=True, init_qr=True, renorm='gram', fkick=False)
     d = _tt.matvec(A, b).round(eps)
 
-    print (c[0] - d).norm() / d.norm()
+    print((c[0] - d).norm() / d.norm())
diff --git a/tt/core/tools.py b/tt/core/tools.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import numpy as _np
 import math as _math
 import copy as _cp
@@ -59,7 +60,7 @@ def get_core(i):
                 newr = min(rl * n, rr)
                 ccr = u[:, :newr].reshape((rl, n, newr), order='F')
                 v = _np.dot(_np.diag(s[:newr]), v[:newr, :])
-            # print ccr.shape
+            # print(ccr.shape)
             # r x r . r x n x R -> r x n x R
             nrm = _np.tensordot(nrm, ccr, (0, 0))
             # r x n x R . r x n x R -> n x R x n x R
@@ -68,9 +69,9 @@ def get_core(i):
             nrm = nrm.sum(axis=2)  # R x R x n -> R x R
         if nrm.size > 1:
             raise Exception('too many numbers in norm')
-        # print "Norm calculated:", nrm
+        # print("Norm calculated:", nrm)
         nrm = _np.sqrt(_np.linalg.norm(nrm))
-        # print "Norm predicted:", nrm
+        # print("Norm predicted:", nrm)
         compression = compression * nrm / _np.sqrt(d - 1)
         v = _np.array([[1.0]])
 
@@ -87,15 +88,15 @@ def get_core(i):
                 ss = _np.cumsum(s[::-1])[::-1]
                 newr = max(min([r for r in range(ss.size) if ss[
                            r] <= compression] + [min(rl * n, rr)]), 1)
-                # print "Rank % 4d replaced by % 4d" % (rr, newr)
+                # print("Rank % 4d replaced by % 4d" % (rr, newr))
                 ccr = u[:, :newr].reshape((rl, n, newr), order='F')
                 v = _np.dot(_np.diag(s[:newr]), v[:newr, :])
         ccrs.append(ccr)
     result = _vector.vector.from_list(ccrs)
     if compression:
-        # print result
-        print "Norm actual:", result.norm(), " mean rank:", result.rmean()
-        # print "Norm very actual:", matvec(a,b).norm()
+        # print(result)
+        print("Norm actual:", result.norm(), " mean rank:", result.rmean())
+        # print("Norm very actual:", matvec(a,b).norm())
     return result
 
 

diff --git a/tt/multifuncrs.py b/tt/multifuncrs.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import numpy as np
 from numpy import prod, nonzero, size
 import math
@@ -229,7 +230,7 @@ def multifuncrs(X, funs, eps=1E-6,
                 s = np.ones((r, ))
 
         if verb > 1:
-            print '=multifuncrs=   block %d{%d}, dy: %3.3e, r: %d' % (i, dirn, dy[i], r)
+            print('=multifuncrs=   block %d{%d}, dy: %3.3e, r: %d' % (i, dirn, dy[i], r))
 
         # kicks and interfaces
         if dirn > 0 and i < d - 1:
@@ -398,8 +399,8 @@ def multifuncrs(X, funs, eps=1E-6,
         if cur_order[order_index] == 0:
             order_index = order_index + 1
             if verb > 0:
-                print '=multifuncrs= sweep %d{%d}, max_dy: %3.3e, erank: %g' % (swp, order_index, max_dy,
-                                                                                math.sqrt(np.dot(ry[:d], n * ry[1:]) / np.sum(n)))
+                print('=multifuncrs= sweep %d{%d}, max_dy: %3.3e, erank: %g' % (swp, order_index, max_dy,
+                                                                                math.sqrt(np.dot(ry[:d], n * ry[1:]) / np.sum(n))))
 
             if last_sweep:
                 break

diff --git a/tt/multifuncrs2.py b/tt/multifuncrs2.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import numpy as np
 from numpy import prod, nonzero, size
 import math
@@ -176,7 +177,7 @@ def multifuncrs2(X, funs, eps=1e-6,
         try:
             dy = np.linalg.norm(newy - oldy) / np.linalg.norm(newy)
         except ZeroDivisionError:
-            print 'Bad initial indices, the solution is exactly zero. Restarting'
+            print('Bad initial indices, the solution is exactly zero. Restarting')
             return
         max_dy = max(max_dy, dy)
         # truncation
@@ -214,7 +215,7 @@ def multifuncrs2(X, funs, eps=1e-6,
                 r = u.shape[1]
                 s = np.ones((r, ))
         if verb > 1:
-            print '=multifuncrs2=   block %d{%d}, dy: %3.3e, r: %d' % (i, dirn, dy, r)
+            print('=multifuncrs2=   block %d{%d}, dy: %3.3e, r: %d' % (i, dirn, dy, r))
         #Kicks and interfaces
         if dirn > 0 and i < d - 1:
             u = u[:, :r]
@@ -470,8 +471,8 @@ def multifuncrs2(X, funs, eps=1e-6,
         if cur_order[order_index] == 0:
             order_index = order_index + 1
             if verb > 0:
-                print '=multifuncrs= sweep %d{%d}, max_dy: %3.3e, erank: %g' % (swp, order_index, max_dy,
-                                                                                math.sqrt(np.dot(ry[:d], n * ry[1:]) / np.sum(n)))
+                print('=multifuncrs= sweep %d{%d}, max_dy: %3.3e, erank: %g' % (swp, order_index, max_dy,
+                                                                                math.sqrt(np.dot(ry[:d], n * ry[1:]) / np.sum(n))))
             if max_dy < eps_exit and dirn > 0:
                 break
             if order_index >= len(cur_order):  # New global sweep

diff --git a/tt/optimize/tt_min.py b/tt/optimize/tt_min.py
@@ -1,6 +1,7 @@
 """This module contains a prototype implementation of the
 TT-cross-based minimization procedure
 """
+from __future__ import print_function
 import numpy as np
 import math
 import tt
@@ -102,7 +103,7 @@ def min_func(fun, bounds_min, bounds_max, d=None, rmax=10,
             x_full = J[ind_cur, :]
             val = fun(x_full)
             if verb:
-                print 'New record:', val, 'Point:', x_full, 'fevals:', fun_evals
+                print('New record:', val, 'Point:', x_full, 'fevals:', fun_evals)
         cry = smooth_fun(cry, lm)
         if (dirn < 0 and i > 0):
             cry = reshape(cry, (ry[i], n[i] * ry[i + 1]))
@@ -213,7 +214,7 @@ def min_tens(tens, rmax=10, nswp=10, verb=True, smooth_fun=None):
             x_full = J[ind_cur, :]
             val = tens[x_full]
             if verb:
-                print 'New record:', val, 'Point:', x_full, 'elements seen:', elements_seen
+                print('New record:', val, 'Point:', x_full, 'elements seen:', elements_seen)
         cry = smooth_fun(cry, lm)
         if dirn < 0 and i > 0:
             cry = reshape(cry, (ry[i], n[i] * ry[i + 1]))

diff --git a/tt/solvers.py b/tt/solvers.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 import numpy as np
 import tt
 
@@ -17,23 +18,23 @@ def GMRES(A, u_0, b, eps=1E-6, restart=20, verb=0):
         bnorm = b.norm()
         curr_beta = beta
         if verb:
-            print "/ Initial  residual  norm: %lf; mean rank:" % beta, r0.rmean()
+            print("/ Initial  residual  norm: %lf; mean rank:" % beta, r0.rmean())
         m = restart
         V = np.zeros(m + 1, dtype=object)  # Krylov basis
         V[0] = r0 * (1.0 / beta)
         H = np.mat(np.zeros((m + 1, m), dtype=np.complex128, order='F'))
         j = 0
         while j < m and curr_beta / bnorm > eps:
             delta = eps / (curr_beta / beta)
-            # print "Calculating new Krylov vector"
+            # print("Calculating new Krylov vector")
             w = A(V[j], delta)
             #w = w.round(delta)
             for i in range(j + 1):
                 H[i, j] = tt.dot(w, V[i])
                 w = w + (-H[i, j]) * V[i]
             w = w.round(delta)
             if verb > 1:
-                print "|% 3d. New Krylov vector mean rank:" % (j + 1), w.rmean()
+                print("|% 3d. New Krylov vector mean rank:" % (j + 1), w.rmean())
             H[j + 1, j] = w.norm()
             V[j + 1] = w * (1 / H[j + 1, j])
 
@@ -44,14 +45,14 @@ def GMRES(A, u_0, b, eps=1E-6, restart=20, verb=0):
             y, curr_beta, rank, s = np.linalg.lstsq(Hj, betae)
             curr_beta = curr_beta[0]
             if verb:
-                print "|% 3d. LSTSQ residual norm:" % (j + 1), curr_beta
+                print("|% 3d. LSTSQ residual norm:" % (j + 1), curr_beta)
             j += 1
         x = u_0
         for i in range(j):
             x = x + V[i] * y[i]
         x = x.round(eps)
         if verb:
-            print "\\ Solution mean rank:", x.rmean()
+            print("\\ Solution mean rank:", x.rmean())
         u_0 = x
         do_restart = (curr_beta / bnorm > eps)
     return x