Python 3 Compatability

Author: alebel14

DataSequence.py

@@ -97,13 +97,13 @@ def from_grid(cls, grid_transcript, trfile):
         """Creates a new DataSequence from a [grid_transript] and a [trfile].
         grid_transcript should be the product of the 'make_simple_transcript' method of TextGrid.
         """
-        data_entries = zip(*grid_transcript)[2]
+        data_entries = list(zip(*grid_transcript))[2]
         if isinstance(data_entries[0], str):
-            data = map(str.lower, zip(*grid_transcript)[2])
+            data = list(map(str.lower, list(zip(*grid_transcript))[2]))
         else:
             data = data_entries
-        word_starts = np.array(map(float, zip(*grid_transcript)[0]))
-        word_ends = np.array(map(float, zip(*grid_transcript)[1]))
+        word_starts = np.array(list(map(float, list(zip(*grid_transcript))[0])))
+        word_ends = np.array(list(map(float, list(zip(*grid_transcript))[1])))
         word_avgtimes = (word_starts + word_ends)/2.0
 
         tr = trfile.avgtr

SemanticModel.py

@@ -1,5 +1,5 @@
 import tables
-import cPickle
+import pickle
 import numpy as np
 
 import logging
@@ -117,11 +117,12 @@ def load(cls, filename):
         """Loads a semantic model from the given filename.
         """
         logger.debug("Loading file: %s"%filename)
-        shf = tables.openFile(filename)
+        shf = tables.open_file(filename)
 
         newsm = cls(None, None)
-        newsm.data = shf.getNode("/data").read()
-        newsm.vocab = shf.getNode("/vocab").read()
+        newsm.data = shf.get_node("/data").read()
+        newsm.vocab = [s.decode('utf-8') for s in shf.get_node("/vocab").read()]
+        
         shf.close()
         logger.debug("Done loading file..")
         return newsm
@@ -277,17 +278,17 @@ def similarity(self, word1, word2):
     def print_best_worst(self, ii, n=10):
         vector = self.data[ii]
         sv = np.argsort(self.data[ii])
-        print "Best:"
-        print "-------------"
+        print ("Best:")
+        print ("-------------")
         for ni in range(1,n+1):
-            print "%s: %0.08f"%(np.array(self.vocab)[sv[-ni]], vector[sv[-ni]])
+            print ("%s: %0.08f"%(np.array(self.vocab)[sv[-ni]], vector[sv[-ni]]))
 
-        print "\nWorst:"
-        print "-------------"
+        print ("\nWorst:")
+        print ("-------------")
         for ni in range(n):
-            print "%s: %0.08f"%(np.array(self.vocab)[sv[ni]], vector[sv[ni]])
+            print ("%s: %0.08f"%(np.array(self.vocab)[sv[ni]], vector[sv[ni]]))
 
-        print "\n"
+        print ("\n")
 
 
 def gaussianize(vec):