make davinci parallel example python3 compatible

examples/parallel/davinci/pwordfreq.py

@@ -1,80 +1,89 b''
 #!/usr/bin/env python
 """Parallel word frequency counter.
 
 This only works for a local cluster, because the filenames are local paths.
 """
 
 
 import os
 import time
 import urllib
 
 from itertools import repeat
 
 from wordfreq import print_wordfreq, wordfreq
 
 from IPython.parallel import Client, Reference
 
+from __future__ import division 
+
 davinci_url = "http://www.gutenberg.org/cache/epub/5000/pg5000.txt"
 
 def pwordfreq(view, fnames):
     """Parallel word frequency counter.
     
     view - An IPython DirectView
     fnames - The filenames containing the split data.
     """
     assert len(fnames) == len(view.targets)
     view.scatter('fname', fnames, flatten=True)
     ar = view.apply(wordfreq, Reference('fname'))
     freqs_list = ar.get()
     word_set = set()
     for f in freqs_list:
         word_set.update(f.keys())
     freqs = dict(zip(word_set, repeat(0)))
     for f in freqs_list:
-        for word, count in f.iteritems():
+        for word, count in f.items():
             freqs[word] += count
     return freqs
 
 if __name__ == '__main__':
     # Create a Client and View
     rc = Client()
     
     view = rc[:]
 
     if not os.path.exists('davinci.txt'):
         # download from project gutenberg
         print("Downloading Da Vinci's notebooks from Project Gutenberg")
-        urllib.urlretrieve(davinci_url, 'davinci.txt')
+        try : #python2
+            urllib.urlretrieve(davinci_url, 'davinci.txt')
+        except : #python3
+            import urllib.request
+            urllib.request.urlretrieve(davinci_url, 'davinci.txt')
         
     # Run the serial version
     print("Serial word frequency count:")
     text = open('davinci.txt').read()
     tic = time.time()
     freqs = wordfreq(text)
     toc = time.time()
     print_wordfreq(freqs, 10)
     print("Took %.3f s to calcluate"%(toc-tic))
     
     
     # The parallel version
     print("\nParallel word frequency count:")
     # split the davinci.txt into one file per engine:
     lines = text.splitlines()
     nlines = len(lines)
     n = len(rc)
-    block = nlines/n
+    block = nlines//n
     for i in range(n):
         chunk = lines[i*block:i*(block+1)]
         with open('davinci%i.txt'%i, 'w') as f:
             f.write('\n'.join(chunk))
     
-    cwd = os.path.abspath(os.getcwdu())
+    try : #python2
+        cwd = os.path.abspath(os.getcwdu())
+    except : #python3
+        cwd = os.path.abspath(os.getcwd())
     fnames = [ os.path.join(cwd, 'davinci%i.txt'%i) for i in range(n)]
     tic = time.time()
     pfreqs = pwordfreq(view,fnames)
     toc = time.time()
     print_wordfreq(freqs)
     print("Took %.3f s to calcluate on %i engines"%(toc-tic, len(view.targets)))
     # cleanup split files
     map(os.remove, fnames)