Python3 compatibility.

gpfit/ba_init.py

@@ -1,4 +1,5 @@
 "Implements ba_init"
+from __future__ import print_function
 from numpy import ones, hstack, zeros, tile, argmin
 from numpy.linalg import lstsq, matrix_rank
 from numpy.random import permutation as randperm

gpfit/fit_constraintset.py

@@ -1,4 +1,5 @@
 " fit constraint set "
+from __future__ import print_function
 from numpy import amax, array, hstack, where
 from gpkit import ConstraintSet
 from gpkit import Variable, NomialArray, NamedVariables, VectorVariable
@@ -135,7 +136,7 @@ def process_result(self, result):
                        + " %s bound. Solution is %.4f but"
                        " bound is %.4f" %
                        (direct, amax([num]), bnd))
-                print "Warning: " + msg
+                print("Warning: " + msg)
 
 
 class XfoilFit(FitCS):
@@ -185,4 +186,4 @@ def process_result(self, result):
                    " Xfoil cd=%.6f, GP sol cd=%.6f" %
                    (", ".join(self.ivar.descr["models"]), err[i], re[i],
                     cl[i], cd[i], cdx[i]))
-            print "Warning: %s" % msg
+            print("Warning: %s" % msg)

gpfit/levenberg_marquardt.py

@@ -1,4 +1,5 @@
 "Implements LM"
+from __future__ import print_function
 from time import time
 from sys import float_info
 import numpy as np
@@ -92,19 +93,19 @@ def levenberg_marquardt(residfun, initparams,
               'Norm of \n')
         print('Iter        Residual        optimality            Lambda'
               '            step        Jwarp \n')
-        print formatstr1 % (itr, rms, maxgrad)
+        print(formatstr1 % (itr, rms, maxgrad))
 
     # Main Loop
     while True:
 
         if itr == maxiter:
             if verbose:
-                print 'Reached maximum number of iterations'
+                print('Reached maximum number of iterations')
             break
 
         elif time() - t > maxtime:
             if verbose:
-                print 'Reached maxtime (%s seconds)' % maxtime
+                print('Reached maxtime (%s seconds)' % maxtime)
             break
         elif (itr >= 2 and
               abs(RMStraj[itr] - RMStraj[itr-2]) <
@@ -155,8 +156,8 @@ def levenberg_marquardt(residfun, initparams,
             # dsp here so that all grad info is for updated point,
             # but lambda not yet updated
             if verbose:
-                print formatstr % (itr, trialrms, maxgrad, lamb, norm(step),
-                                   max(diagJJ)/min(diagJJ))
+                print(formatstr % (itr, trialrms, maxgrad, lamb, norm(step),
+                                   max(diagJJ)/min(diagJJ)))
 
             if maxgrad < tolgrad:
                 if verbose:
@@ -170,8 +171,8 @@ def levenberg_marquardt(residfun, initparams,
             params_updated = True
         else:
             if verbose:
-                print formatstr % (itr, trialrms, maxgrad, lamb, norm(step),
-                                   max(diagJJ)/min(diagJJ))
+                print(formatstr % (itr, trialrms, maxgrad, lamb, norm(step),
+                                   max(diagJJ)/min(diagJJ)))
             lamb = lamb*10
             prev_trial_accepted = False
             params_updated = False

gpfit/lse_implicit.py

@@ -1,4 +1,5 @@
 "Implements lse_implicit"
+from __future__ import print_function
 from numpy import zeros, spacing, exp, log, tile
 
 

gpfit/print_fit.py

@@ -1,4 +1,5 @@
 "Implements functions for raw fit printing from params"
+from __future__ import print_function
 from numpy import exp
 
 
@@ -10,7 +11,7 @@ def print_ISMA(A, B, alpha, d, K):
     printString = '1 = '
     for k in range(K):
         if k > 0:
-            print printString
+            print(printString)
             printString = '    + '
 
         printString += '({0:.6g}/w**{1:.6g})'.format(exp(alpha[k] * B[k]), alpha[k])
@@ -20,7 +21,7 @@ def print_ISMA(A, B, alpha, d, K):
 
         stringList[k] = printString
 
-    print printString
+    print(printString)
     return stringList
 
 
@@ -32,7 +33,7 @@ def print_SMA(A, B, alpha, d, K):
     printString = 'w**{0:.6g} = '.format(alpha)
     for k in range(K):
         if k > 0:
-            print printString
+            print(printString)
             printString = '    + '
 
         printString += '{0:.6g}'.format(exp(alpha * B[k]))
@@ -42,7 +43,7 @@ def print_SMA(A, B, alpha, d, K):
 
         stringList[k] = printString
 
-    print printString
+    print(printString)
     return stringList
 
 
@@ -58,6 +59,6 @@ def print_MA(A, B, d, K):
             printString += ' * (u_{0:d})**{1:.6g}'.format(i+1, A[d*k + i])
 
         stringList[k] = printString
-        print printString
+        print(printString)
 
     return stringList

gpfit/softmax_affine.py

@@ -1,4 +1,5 @@
 "Implements SMA residual function"
+from __future__ import division
 from numpy import size, inf, nan, ones, hstack, dot, tile
 from .lse_scaled import lse_scaled
 

gpfit/xfoil_wrapper.py

@@ -1,4 +1,5 @@
 " python wrapper to call xfoil "
+from __future__ import print_function
 import subprocess
 import numpy as np
 
@@ -43,12 +44,12 @@ def xfoil_comparison(airfoil, Cl, Re, Cd):
         try:
             x = single_call(topline, cl, re, 0.0)
             if "VISCAL:  Convergence failed" in x:
-                print "Convergence Warning: %s" % failmsg
+                print("Convergence Warning: %s" % failmsg)
                 cdx = cd
             else:
                 cdx = x[0]
         except subprocess.CalledProcessError:
-            print "Unable to start Xfoil: %s" % failmsg
+            print("Unable to start Xfoil: %s" % failmsg)
             cdx = cd
 
         err.append(1 - cd/cdx)