Merge pull request #32 from aarongarrett/feat/2to3

feat: run 2to3 to update code for py3

docs/conf.py

@@ -55,8 +55,8 @@
 master_doc = 'index'
 
 # General information about the project.
-project = u'inspyred'
-copyright = u"2017, Aaron Garrett"
+project = 'inspyred'
+copyright = "2017, Aaron Garrett"
 
 # The version info for the project you're documenting, acts as replacement
 # for |version| and |release|, also used in various other places throughout
@@ -209,8 +209,8 @@
 # [howto/manual]).
 latex_documents = [
     ('index', 'inspyred.tex',
-     u'inspyred Documentation',
-     u'Aaron Garrett', 'manual'),
+     'inspyred Documentation',
+     'Aaron Garrett', 'manual'),
 ]
 
 # The name of an image file (relative to this directory) to place at
@@ -240,8 +240,8 @@
 # (source start file, name, description, authors, manual section).
 man_pages = [
     ('index', 'inspyred',
-     u'inspyred Documentation',
-     [u'Aaron Garrett'], 1)
+     'inspyred Documentation',
+     ['Aaron Garrett'], 1)
 ]
 
 # If true, show URL addresses after external links.
@@ -255,8 +255,8 @@
 #  dir menu entry, description, category)
 texinfo_documents = [
     ('index', 'inspyred',
-     u'inspyred Documentation',
-     u'Aaron Garrett',
+     'inspyred Documentation',
+     'Aaron Garrett',
      'inspyred',
      'A framework for creating bio-inspired computational intelligence algorithms in Python.',
      'Miscellaneous'),

docs/moonshot.py

@@ -29,7 +29,7 @@ def pairwise(iterable):
     """s -> (s0,s1), (s1,s2), (s2, s3), ..."""
     a, b = itertools.tee(iterable)
     next(b, None)
-    return itertools.izip(a, b)
+    return zip(a, b)
 
 def distance_between(position_a, position_b):
     return math.sqrt((position_a[0] - position_b[0])**2 + (position_a[1] - position_b[1])**2)
@@ -147,7 +147,7 @@ def moonshot(orbital_height, satellite_mass, boost_velocity, initial_y_velocity,
         x = [p[0] for p in position] 
         y = [p[1] for p in position]
         cm = plt.get_cmap('gist_rainbow')
-        lines = plt.scatter(x, y, c=range(len(x)), cmap=cm, marker='o', s=2)
+        lines = plt.scatter(x, y, c=list(range(len(x))), cmap=cm, marker='o', s=2)
         plt.setp(lines, edgecolors='None')  
         plt.axis("equal")
         plt.grid("on")

docs/polyarea.py

@@ -3,7 +3,7 @@
 from time import time
 from time import sleep
 import inspyred
-from Tkinter import *
+from tkinter import *
 import itertools
 #end_imports
 
@@ -12,7 +12,7 @@ def area(p):
     return 0.5 * abs(sum([x0*y1 - x1*y0 for ((x0, y0), (x1, y1)) in segments(p)]))
 
 def segments(p):
-    return zip(p, p[1:] + [p[0]])
+    return list(zip(p, p[1:] + [p[0]]))
 
 def generate_polygon(random, args):
     size = args.get('num_vertices', 6)

examples/custom/custom_variator_example.py

@@ -12,7 +12,7 @@
 def my_variator(random, candidates, args):
     mutants = []
     for c in candidates:
-        points = random.sample(range(len(c)), 2)
+        points = random.sample(list(range(len(c))), 2)
         x, y = min(points), max(points)
         if x == 0:
             mutants.append(c[y::-1] + c[y+1:])

inspyred/benchmarks.py

@@ -133,7 +133,7 @@ def __init__(self, benchmark, dimension_bits):
 
     def _binary_to_real(self, binary):
         real = []
-        for d, lo, hi in zip(range(self.dimensions), self.benchmark.bounder.lower_bound, self.benchmark.bounder.upper_bound):
+        for d, lo, hi in zip(list(range(self.dimensions)), self.benchmark.bounder.lower_bound, self.benchmark.bounder.upper_bound):
             b = binary[d*self.dimension_bits:(d+1)*self.dimension_bits]
             real_val = float(int(''.join([str(i) for i in b]), 2))
             value = real_val / (2**(self.dimension_bits)-1) * (hi - lo) + lo
@@ -513,7 +513,7 @@ def evaluator(self, candidates, args):
         for c in candidates:
             gval = g(c[self.objectives-1:])
             fit = [0.5 * reduce(lambda x,y: x*y, c[:self.objectives-1]) * (1 + gval)]
-            for m in reversed(range(1, self.objectives)):
+            for m in reversed(list(range(1, self.objectives))):
                 fit.append(0.5 * reduce(lambda x,y: x*y, c[:m-1], 1) * (1 - c[m-1]) * (1 + gval))
             fitness.append(emo.Pareto(fit))
         return fitness
@@ -573,7 +573,7 @@ def evaluator(self, candidates, args):
             gval = g(c[self.objectives-1:])
             fit = [(1 + gval) *
                    reduce(lambda x,y: x*y, [math.cos(a * math.pi / 2.0) for a in c[:self.objectives-1]])]
-            for m in reversed(range(1, self.objectives)):
+            for m in reversed(list(range(1, self.objectives))):
                 fit.append((1 + gval) *
                            reduce(lambda x,y: x*y, [math.cos(a * math.pi / 2.0) for a in c[:m-1]], 1) *
                            math.sin(c[m-1] * math.pi / 2.0))
@@ -634,7 +634,7 @@ def evaluator(self, candidates, args):
         for c in candidates:
             gval = g(c[self.objectives-1:])
             fit = [(1 + gval) * reduce(lambda x,y: x*y, [math.cos(a * math.pi / 2.0) for a in c[:self.objectives-1]])]
-            for m in reversed(range(1, self.objectives)):
+            for m in reversed(list(range(1, self.objectives))):
                 fit.append((1 + gval) *
                            reduce(lambda x,y: x*y, [math.cos(a * math.pi / 2.0) for a in c[:m-1]], 1) *
                            math.sin(c[m-1] * math.pi / 2.0))
@@ -697,7 +697,7 @@ def evaluator(self, candidates, args):
             gval = g(c[self.objectives-1:])
             fit = [(1 + gval) *
                    reduce(lambda x,y: x*y, [math.cos(a**self.alpha * math.pi / 2.0) for a in c[:self.objectives-1]])]
-            for m in reversed(range(1, self.objectives)):
+            for m in reversed(list(range(1, self.objectives))):
                 fit.append((1 + gval) *
                            reduce(lambda x,y: x*y, [math.cos(a**self.alpha * math.pi / 2.0) for a in c[:m-1]], 1) *
                            math.sin(c[m-1]**self.alpha * math.pi / 2.0))
@@ -782,7 +782,7 @@ def evaluator(self, candidates, args):
             theta = lambda x: math.pi / (4.0 * (1 + gval)) * (1 + 2 * gval * x)
             fit = [(1 + gval) * math.cos(math.pi / 2.0 * c[0]) *
                    reduce(lambda x,y: x*y, [math.cos(theta(a)) for a in c[1:self.objectives-1]])]
-            for m in reversed(range(1, self.objectives)):
+            for m in reversed(list(range(1, self.objectives))):
                 if m == 1:
                     fit.append((1 + gval) * math.sin(math.pi / 2.0 * c[0]))
                 else:
@@ -849,7 +849,7 @@ def evaluator(self, candidates, args):
             theta = lambda x: math.pi / (4.0 * (1 + gval)) * (1 + 2 * gval * x)
             fit = [(1 + gval) * math.cos(math.pi / 2.0 * c[0]) *
                    reduce(lambda x,y: x*y, [math.cos(theta(a)) for a in c[1:self.objectives-1]])]
-            for m in reversed(range(1, self.objectives)):
+            for m in reversed(list(range(1, self.objectives))):
                 if m == 1:
                     fit.append((1 + gval) * math.sin(math.pi / 2.0 * c[0]))
                 else:
@@ -972,7 +972,7 @@ class TSP(Benchmark):
     def __init__(self, weights):
         Benchmark.__init__(self, len(weights))
         self.weights = weights
-        self.components = [swarm.TrailComponent((i, j), value=(1 / weights[i][j])) for i, j in itertools.permutations(range(len(weights)), 2)]
+        self.components = [swarm.TrailComponent((i, j), value=(1 / weights[i][j])) for i, j in itertools.permutations(list(range(len(weights))), 2)]
         self.bias = 0.5
         self.bounder = ec.DiscreteBounder([i for i in range(len(weights))])
         self.maximize = True

inspyred/ec/evaluators.py

@@ -2,15 +2,15 @@
     ===============================================
     :mod:`evaluators` -- Fitness evaluation methods
     ===============================================
-    
-    Evaluator functions are problem-specific. This module provides pre-defined 
+
+    Evaluator functions are problem-specific. This module provides pre-defined
     evaluators for evolutionary computations.
 
     All evaluator functions have the following arguments:
-    
+
     - *candidates* -- the candidate solutions
     - *args* -- a dictionary of keyword arguments
-    
+
     .. Copyright 2012 Aaron Garrett
 
     .. Permission is hereby granted, free of charge, to any person obtaining a copy
@@ -29,43 +29,40 @@
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-       THE SOFTWARE.       
-        
+       THE SOFTWARE.
+
     .. module:: evaluators
     .. moduleauthor:: Aaron Garrett <garrett@inspiredintelligence.io>
     .. moduleauthor:: Jelle Feringa <jelleferinga@gmail.com>
 """
 import functools
-try:
-    import cPickle as pickle
-except ImportError:
-    import pickle
+import pickle
 
 
 def evaluator(evaluate):
     """Return an inspyred evaluator function based on the given function.
-    
+
     This function generator takes a function that evaluates only one
-    candidate. The generator handles the iteration over each candidate 
+    candidate. The generator handles the iteration over each candidate
     to be evaluated.
 
     The given function ``evaluate`` must have the following signature::
-    
+
         fitness = evaluate(candidate, args)
-        
+
     This function is most commonly used as a function decorator with
     the following usage::
-    
+
         @evaluator
         def evaluate(candidate, args):
             # Implementation of evaluation
             pass
-            
+
     The generated function also contains an attribute named
     ``single_evaluation`` which holds the original evaluation function.
     In this way, the original single-candidate function can be
     retrieved if necessary.
-    
+
     """
     @functools.wraps(evaluate)
     def inspyred_evaluator(candidates, args):
@@ -75,59 +72,59 @@ def inspyred_evaluator(candidates, args):
         return fitness
     inspyred_evaluator.single_evaluation = evaluate
     return inspyred_evaluator
-    
+
 
 def parallel_evaluation_pp(candidates, args):
     """Evaluate the candidates in parallel using Parallel Python.
 
     This function allows parallel evaluation of candidate solutions.
     It uses the `Parallel Python <http://www.parallelpython.com>`_  (pp)
-    library to accomplish the parallelization. This library must already 
-    be installed in order to use this function. The function assigns the 
-    evaluation of each candidate to its own job, all of which are then 
+    library to accomplish the parallelization. This library must already
+    be installed in order to use this function. The function assigns the
+    evaluation of each candidate to its own job, all of which are then
     distributed to the available processing units.
-    
+
     .. note::
-    
+
        All arguments to the evaluation function must be pickleable.
        Those that are not will not be sent through the ``args`` variable
        and will be unavailable to your function.
-       
+
     .. Arguments:
        candidates -- the candidate solutions
        args -- a dictionary of keyword arguments
 
     Required keyword arguments in args:
-    
+
     - *pp_evaluator* -- actual evaluation function to be used (This function
       should have the same signature as any other inspyred evaluation function.)
 
     Optional keyword arguments in args:
-    
-    - *pp_dependencies* -- tuple of functional dependencies of the serial 
+
+    - *pp_dependencies* -- tuple of functional dependencies of the serial
       evaluator (default ())
-    - *pp_modules* -- tuple of modules that must be imported for the 
+    - *pp_modules* -- tuple of modules that must be imported for the
       functional dependencies (default ())
-    - *pp_servers* -- tuple of servers (on a cluster) that will be used 
+    - *pp_servers* -- tuple of servers (on a cluster) that will be used
       for parallel processing (default ("*",))
     - *pp_secret* -- string representing the secret key needed to authenticate
       on a worker node (default "inspyred")
     - *pp_nprocs* -- integer representing the number of worker processes to
       start on the local machine (default "autodetect", which sets it to the
       number of processors in the system)
-      
+
     For more information about these arguments, please consult the
     documentation for `Parallel Python <http://www.parallelpython.com>`_.
-    
+
     """
     import pp
     logger = args['_ec'].logger
-    
+
     try:
         evaluator = args['pp_evaluator']
     except KeyError:
         logger.error('parallel_evaluation_pp requires \'pp_evaluator\' be defined in the keyword arguments list')
-        raise 
+        raise
     secret_key = args.setdefault('pp_secret', 'inspyred')
     try:
         job_server = args['_pp_job_server']
@@ -138,7 +135,7 @@ def parallel_evaluation_pp(candidates, args):
         args['_pp_job_server'] = job_server
     pp_depends = args.setdefault('pp_dependencies', ())
     pp_modules = args.setdefault('pp_modules', ())
-        
+
     pickled_args = {}
     for key in args:
         try:
@@ -147,10 +144,10 @@ def parallel_evaluation_pp(candidates, args):
         except (TypeError, pickle.PickleError, pickle.PicklingError):
             logger.debug('unable to pickle args parameter {0} in parallel_evaluation_pp'.format(key))
             pass
-            
+
     func_template = pp.Template(job_server, evaluator, pp_depends, pp_modules)
     jobs = [func_template.submit([c], pickled_args) for c in candidates]
-    
+
     fitness = []
     for i, job in enumerate(jobs):
         r = job()
@@ -166,46 +163,46 @@ def parallel_evaluation_mp(candidates, args):
     """Evaluate the candidates in parallel using ``multiprocessing``.
 
     This function allows parallel evaluation of candidate solutions.
-    It uses the standard multiprocessing library to accomplish the 
+    It uses the standard multiprocessing library to accomplish the
     parallelization. The function assigns the evaluation of each
     candidate to its own job, all of which are then distributed to the
     available processing units.
-    
+
     .. note::
-    
+
        All arguments to the evaluation function must be pickleable.
        Those that are not will not be sent through the ``args`` variable
        and will be unavailable to your function.
-    
+
     .. Arguments:
        candidates -- the candidate solutions
        args -- a dictionary of keyword arguments
 
     Required keyword arguments in args:
-    
+
     - *mp_evaluator* -- actual evaluation function to be used (This function
       should have the same signature as any other inspyred evaluation function.)
 
     Optional keyword arguments in args:
-    
-    - *mp_nprocs* -- number of processors that will be used (default machine 
+
+    - *mp_nprocs* -- number of processors that will be used (default machine
       cpu count)
-    
+
     """
     import time
     import multiprocessing
     logger = args['_ec'].logger
-    
+
     try:
         evaluator = args['mp_evaluator']
     except KeyError:
         logger.error('parallel_evaluation_mp requires \'mp_evaluator\' be defined in the keyword arguments list')
-        raise 
+        raise
     try:
         nprocs = args['mp_nprocs']
     except KeyError:
         nprocs = multiprocessing.cpu_count()
-        
+
     pickled_args = {}
     for key in args:
         try:
@@ -228,4 +225,4 @@ def parallel_evaluation_mp(candidates, args):
     else:
         end = time.time()
         logger.debug('completed parallel_evaluation_mp in {0} seconds'.format(end - start))
-        
+