PEP8 fixes

pyevolve/Interaction.py

@@ -17,69 +17,70 @@
 import logging
 
 try:
-   import pylab
+    import pylab
 except:
-   logging.debug("cannot import Matplotlib ! Plots will not be available !")
-   print "Warning: cannot import Matplotlib ! Plots will not be available !"
+    logging.debug("cannot import Matplotlib ! Plots will not be available !")
+    print "Warning: cannot import Matplotlib ! Plots will not be available !"
 
 try:
-   import numpy
+    import numpy
 except:
-   logging.debug("cannot import Numpy ! Some functions will not be available !")
-   print "Warning: cannot import Numpy ! Some functions will not be available !"
+    logging.debug("cannot import Numpy ! Some functions will not be available !")
+    print "Warning: cannot import Numpy ! Some functions will not be available !"
 
 def getPopScores(population, fitness=False):
-   """ Returns a list of population scores
+    """ Returns a list of population scores
 
-   Example:
-      >>> lst = Interaction.getPopScores(population)
+    Example:
+       >>> lst = Interaction.getPopScores(population)
 
-   :param population: population object (:class:`GPopulation.GPopulation`)
-   :param fitness: if is True, the fitness score will be used, otherwise, the raw.
-   :rtype: list of population scores
+    :param population: population object (:class:`GPopulation.GPopulation`)
+    :param fitness: if is True, the fitness score will be used, otherwise, the raw.
+    :rtype: list of population scores
+
+    """
+    score_list = []
+    for individual in population:
+        score_list.append(individual.fitness if fitness else individual.score)
+    return score_list
 
-   """
-   score_list = []
-   for individual in population:
-      score_list.append(individual.fitness if fitness else individual.score)
-   return score_list
 
 def plotPopScore(population, fitness=False):
-   """ Plot the population score distribution 
+    """ Plot the population score distribution
 
-   Example:
-      >>> Interaction.plotPopScore(population)
+    Example:
+       >>> Interaction.plotPopScore(population)
 
-   :param population: population object (:class:`GPopulation.GPopulation`)
-   :param fitness: if is True, the fitness score will be used, otherwise, the raw.
-   :rtype: None
+    :param population: population object (:class:`GPopulation.GPopulation`)
+    :param fitness: if is True, the fitness score will be used, otherwise, the raw.
+    :rtype: None
 
-   """
-   score_list = getPopScores(population, fitness)
-   pylab.plot(score_list, 'o')
-   pylab.title("Plot of population score distribution")
-   pylab.xlabel('Individual')
-   pylab.ylabel('Score')
-   pylab.grid(True)
-   pylab.show()
+    """
+    score_list = getPopScores(population, fitness)
+    pylab.plot(score_list, 'o')
+    pylab.title("Plot of population score distribution")
+    pylab.xlabel('Individual')
+    pylab.ylabel('Score')
+    pylab.grid(True)
+    pylab.show()
 
-def plotHistPopScore(population, fitness=False):
-   """ Population score distribution histogram 
-
-   Example:
-      >>> Interaction.plotHistPopScore(population)
-
-   :param population: population object (:class:`GPopulation.GPopulation`)
-   :param fitness: if is True, the fitness score will be used, otherwise, the raw.
-   :rtype: None
-
-   """
-   score_list = getPopScores(population, fitness)
-   n, bins, patches = pylab.hist(score_list, 50, facecolor='green', alpha=0.75, normed=1)
-   pylab.plot(bins, pylab.normpdf(bins, numpy.mean(score_list), numpy.std(score_list)), 'r--')
-   pylab.xlabel('Score')
-   pylab.ylabel('Frequency')
-   pylab.grid(True)
-   pylab.title("Plot of population score distribution")
-   pylab.show()
 
+def plotHistPopScore(population, fitness=False):
+    """ Population score distribution histogram
+
+    Example:
+       >>> Interaction.plotHistPopScore(population)
+
+    :param population: population object (:class:`GPopulation.GPopulation`)
+    :param fitness: if is True, the fitness score will be used, otherwise, the raw.
+    :rtype: None
+
+    """
+    score_list = getPopScores(population, fitness)
+    n, bins, patches = pylab.hist(score_list, 50, facecolor='green', alpha=0.75, normed=1)
+    pylab.plot(bins, pylab.normpdf(bins, numpy.mean(score_list), numpy.std(score_list)), 'r--')
+    pylab.xlabel('Score')
+    pylab.ylabel('Frequency')
+    pylab.grid(True)
+    pylab.title("Plot of population score distribution")
+    pylab.show()