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