Python 3 compatibility (#86)

docs/source/conf.py

@@ -19,7 +19,7 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('../..'))
-print sys.path
+print(sys.path)
 
 # -- General configuration ------------------------------------------------
 
@@ -48,8 +48,8 @@
 master_doc = 'index'
 
 # General information about the project.
-project = u'GPfit'
-copyright = u'2015, MIT Convex Optimization Group'
+project = 'GPfit'
+copyright = '2015, MIT Convex Optimization Group'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -201,8 +201,8 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-  ('index', 'GPfit.tex', u'GPfit Documentation',
-   u'MIT Convex Optimization Group', 'manual'),
+  ('index', 'GPfit.tex', 'GPfit Documentation',
+   'MIT Convex Optimization Group', 'manual'),
 ]
 
 # The name of an image file (relative to this directory) to place at the top of
@@ -231,8 +231,8 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    ('index', 'gpfit', u'GPfit Documentation',
-     [u'MIT Convex Optimization Group'], 1)
+    ('index', 'gpfit', 'GPfit Documentation',
+     ['MIT Convex Optimization Group'], 1)
 ]
 
 # If true, show URL addresses after external links.
@@ -245,8 +245,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-  ('index', 'GPfit', u'GPfit Documentation',
-   u'MIT Convex Optimization Group', 'GPfit', 'One line description of project.',
+  ('index', 'GPfit', 'GPfit Documentation',
+   'MIT Convex Optimization Group', 'GPfit', 'One line description of project.',
    'Miscellaneous'),
 ]
 

docs/source/examples/hoburgabbeel_ex6_1.py

@@ -1,4 +1,5 @@
 "Fits an example function"
+from __future__ import division
 from numpy import logspace, log, log10, random
 from gpfit.fit import fit
 
@@ -15,6 +16,6 @@
 cSMA, errorSMA = fit(x, y, K, "SMA")
 cISMA, errorISMA = fit(x, y, K, "ISMA")
 
-print "MA RMS Error: %.5g" % errorMA
-print "SMA RMS Error: %.5g" % errorSMA
-print "ISMA RMS Error: %.5g" % errorISMA
+print("MA RMS Error: %.5g" % errorMA)
+print("SMA RMS Error: %.5g" % errorSMA)
+print("ISMA RMS Error: %.5g" % errorISMA)

docs/source/examples/hoburgabbeel_ex6_1_output.txt

@@ -1,12 +1,15 @@
+MA fit from params
 w = 0.807159 * (u_1)**-0.0703921
 w = 0.995106 * (u_1)**-0.431386
 w = 0.92288 * (u_1)**-0.247099
+SMA fit from params
 w**3.4411 = 0.422736 * (u_1)**-2.14843
     + 0.424169 * (u_1)**-2.14784
     + 0.15339 * (u_1)**0.584654
-1 = (0.992648/w**0.35353) * (u_1)**-0.204093
-    + (0.947302/w**0.0920266) * (u_1)**0.017725
-    + (0.961409/w**0.11673) * (u_1)**-0.011164
+ISMA fit from params
+1 = (0.994797/w**0.238961) * (u_1)**-0.138389
+    + (0.949519/w**0.0924504) * (u_1)**0.0165798
+    + (0.967646/w**0.115505) * (u_1)**-0.0131876
 MA RMS Error: 0.0023556
 SMA RMS Error: 2.3856e-05
-ISMA RMS Error: 8.0757e-07
+ISMA RMS Error: 1.0765e-06

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/examples/ex61setup.py

@@ -1,3 +1,4 @@
+from __future__ import division
 from numpy import linspace, logspace, log, exp, log10
 from gpfit.fit import fit
 

gpfit/fit.py

@@ -84,7 +84,7 @@ def fit(xdata, ydata, K, ftype="ISMA"):
     B = params[[i for i in range(K*(d+1)) if i % (d + 1) == 0]]
 
     if ftype == "ISMA":
-        alpha = 1./params[range(-K, 0)]
+        alpha = 1./params[list(range(-K, 0))]
         for k in range(K):
             fitdata["c%d" % k] = exp(alpha[k]*B[k])
             fitdata["a%d" % k] = alpha[k]

gpfit/fit_constraintset.py

@@ -1,7 +1,9 @@
 " fit constraint set "
-from numpy import amax, array, hstack, where
+from __future__ import print_function
+from __future__ import division
 from gpkit import ConstraintSet
 from gpkit import Variable, NomialArray, NamedVariables, VectorVariable
+from numpy import amax, array, hstack, where
 
 # pylint: disable=too-many-instance-attributes, too-many-locals,
 # pylint: disable=too-many-branches, no-member, import-error
@@ -41,7 +43,7 @@ def __init__(self, fitdata, ivar=None, dvars=None, name="",
         monos = [fitdata["c%d" % k]*NomialArray(array(dvars).T**array(
             [fitdata["e%d%d" % (k, i)] for i in
              range(fitdata["d"])])).prod(NomialArray(dvars).ndim - 1)
-            for k in range(fitdata["K"])]
+                 for k in range(fitdata["K"])]
 
         if err_margin == "Max":
             self.mfac = Variable("m_{fac-" + name + "-fit}",
@@ -101,8 +103,8 @@ def get_fitdata(self):
     def get_dataframe(self):
         " return a pandas DataFrame of fit parameters "
         import pandas as pd
-        df = pd.DataFrame(self.fitdata.values()).transpose()
-        df.columns = self.fitdata.keys()
+        df = pd.DataFrame(list(self.fitdata.values())).transpose()
+        df.columns = list(self.fitdata.keys())
         return df
 
     def process_result(self, result):
@@ -112,9 +114,9 @@ def process_result(self, result):
         super(FitCS, self).process_result(result)
 
         if self.mfac not in result["sensitivities"]["constants"]:
-            return None
+            return
         if amax([abs(result["sensitivities"]["constants"][self.mfac])]) < 1e-5:
-            return None
+            return
 
         for dvar in self.dvars:
             if isinstance(dvar, NomialArray):
@@ -135,7 +137,7 @@ 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):
@@ -162,11 +164,11 @@ def process_result(self, result):
         super(XfoilFit, self).process_result(result)
 
         if self.mfac not in result["sensitivities"]["constants"]:
-            return None
+            return
         if amax([abs(result["sensitivities"]["constants"][self.mfac])]) < 1e-5:
-            return None
+            return
         if not self.airfoil:
-            return None
+            return
 
         from .xfoilWrapper import xfoil_comparison
         cl, re = 0.0, 0.0
@@ -185,4 +187,4 @@ 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/implicit_softmax_affine.py

@@ -1,4 +1,5 @@
 "Implements ISMA residual function"
+from __future__ import division
 from numpy import ones, nan, inf, hstack, dot, tile
 from .lse_implicit import lse_implicit
 
@@ -27,7 +28,7 @@ def implicit_softmax_affine(x, params):
     """
 
     npt, dimx = x.shape
-    K = params.size/(dimx+2)
+    K = params.size//(dimx+2)
     ba = params[0:-K]
     alpha = params[-K:]
     if any(alpha <= 0):

gpfit/levenberg_marquardt.py

@@ -1,4 +1,6 @@
 "Implements LM"
+from __future__ import print_function
+from __future__ import division
 from time import time
 from sys import float_info
 import numpy as np
@@ -29,7 +31,7 @@ def levenberg_marquardt(residfun, initparams,
     initparams: np.array (1D)
         Initial fit parameter guesses
     verbose: bool
-        If true, print verbose output
+        If true, print(verbose output)
     lambdainit: float
         Initial value for step size penalty lambda
     maxiter: int
@@ -92,19 +94,19 @@ 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]) <
@@ -155,23 +157,23 @@ 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:
                     print('1st order optimality attained')
                 break
 
             if prev_trial_accepted and itr > 1:
-                lamb = lamb/10
+                lamb = lamb/10.
 
             prev_trial_accepted = True
             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 +1,6 @@
 "Implements lse_implicit"
+from __future__ import print_function
+from __future__ import division
 from numpy import zeros, spacing, exp, log, tile
 
 

gpfit/lse_scaled.py

@@ -1,4 +1,5 @@
 "Implements lse_scaled"
+from __future__ import division
 from numpy import tile, exp, log
 
 

gpfit/max_affine.py

@@ -1,4 +1,5 @@
 "Implements MA residual function"
+from __future__ import division
 import numpy as np
 
 
@@ -26,7 +27,7 @@ def max_affine(x, ba):
             dydba
     """
     npt, dimx = x.shape
-    K = ba.size/(dimx + 1)
+    K = ba.size//(dimx + 1)
     ba = np.reshape(ba, (dimx + 1, K), order='F')  # 'F' gives Fortran indexing
 
     # augment data with column of ones

gpfit/plot_fit.py

@@ -1,6 +1,7 @@
 "Fit plotting"
-import matplotlib.pyplot as plt
+from __future__ import division
 import numpy as np
+import matplotlib.pyplot as plt
 from gpfit.fit import fit
 from gpfit.print_fit import print_MA, print_SMA
 
@@ -24,7 +25,7 @@ def plot_fit_1d(udata, wdata, K=1, fitclass='MA', plotspace='log'):
 
     if fitclass == 'SMA':
         wexps, = cstrt.left.exps
-        alpha, = wexps.values()
+        alpha, = list(wexps.values())
         uvarkey, = cstrt.right.varkeys
         A = [d[uvarkey]/alpha for d in cstrt.right.exps]
         B = np.log(cstrt.right.cs) / alpha

gpfit/print_fit.py

@@ -1,16 +1,18 @@
 "Implements functions for raw fit printing from params"
+from __future__ import print_function
 from numpy import exp
 
 
 # pylint: disable=invalid-name
 def print_ISMA(A, B, alpha, d, K):
-    "print ISMA fit from params"
+    "prints ISMA fit from params"
+    print("ISMA fit from params")
     stringList = [None]*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])
@@ -20,19 +22,20 @@ def print_ISMA(A, B, alpha, d, K):
 
         stringList[k] = printString
 
-    print printString
+    print(printString)
     return stringList
 
 
 # pylint: disable=invalid-name
 def print_SMA(A, B, alpha, d, K):
-    "print SMA fit from params"
+    "prints SMA fit from params"
+    print("SMA fit from params")
     stringList = [None]*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]))
@@ -42,13 +45,14 @@ def print_SMA(A, B, alpha, d, K):
 
         stringList[k] = printString
 
-    print printString
+    print(printString)
     return stringList
 
 
 # pylint: disable=invalid-name
 def print_MA(A, B, d, K):
-    "print MA fit from params"
+    "prints MA fit from params"
+    print("MA fit from params")
     stringList = [None]*K
 
     for k in range(K):
@@ -58,6 +62,6 @@ 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
 
@@ -31,7 +32,7 @@ def softmax_affine(x, params):
     alpha = 1/softness
     if alpha <= 0:
         return inf*ones((npt, 1)), nan
-    K = size(ba)/(dimx+1)
+    K = size(ba)//(dimx+1)
     ba = ba.reshape(dimx+1, K, order='F')
 
     X = hstack((ones((npt, 1)), x))  # augment data with column of ones