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Merge pull request #4305 from minrk/even-more-ways-to-get-ifaces...
Merge pull request #4305 from minrk/even-more-ways-to-get-ifaces Add even more ways to populate localinterfaces use netifaces for faster IPython.utils.localinterfaces when availlable, Parse subprocess output from ifconfig / ip addr / ipconfig. Lower priority than netifaces, but still higher priority than socket.gethostbyname. Fallback to gethostname otherwise. Should be much faster in worst case scenario where machine are badly configurred and can wait up to ~30s to start ipython. Slighly slower in other cases.

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wordfreq.py
69 lines | 2.0 KiB | text/x-python | PythonLexer
"""Count the frequencies of words in a string"""
from __future__ import division
from __future__ import print_function
import cmath as math
def wordfreq(text, is_filename=False):
"""Return a dictionary of words and word counts in a string."""
if is_filename:
with open(text) as f:
text = f.read()
freqs = {}
for word in text.split():
lword = word.lower()
freqs[lword] = freqs.get(lword, 0) + 1
return freqs
def print_wordfreq(freqs, n=10):
"""Print the n most common words and counts in the freqs dict."""
words, counts = freqs.keys(), freqs.values()
items = zip(counts, words)
items.sort(reverse=True)
for (count, word) in items[:n]:
print(word, count)
def wordfreq_to_weightsize(worddict, minsize=25, maxsize=50, minalpha=0.5, maxalpha=1.0):
mincount = min(worddict.itervalues())
maxcount = max(worddict.itervalues())
weights = {}
for k, v in worddict.iteritems():
w = (v-mincount)/(maxcount-mincount)
alpha = minalpha + (maxalpha-minalpha)*w
size = minsize + (maxsize-minsize)*w
weights[k] = (alpha, size)
return weights
def tagcloud(worddict, n=10, minsize=25, maxsize=50, minalpha=0.5, maxalpha=1.0):
from matplotlib import pyplot as plt
import random
worddict = wordfreq_to_weightsize(worddict, minsize, maxsize, minalpha, maxalpha)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_position([0.0,0.0,1.0,1.0])
plt.xticks([])
plt.yticks([])
words = worddict.keys()
alphas = [v[0] for v in worddict.values()]
sizes = [v[1] for v in worddict.values()]
items = zip(alphas, sizes, words)
items.sort(reverse=True)
for alpha, size, word in items[:n]:
# xpos = random.normalvariate(0.5, 0.3)
# ypos = random.normalvariate(0.5, 0.3)
xpos = random.uniform(0.0,1.0)
ypos = random.uniform(0.0,1.0)
ax.text(xpos, ypos, word.lower(), alpha=alpha, fontsize=size)
ax.autoscale_view()
return ax