interrogator

#!/usr/bin/python

def nextbus(where = 'guess', nbus = 5, walking_time = 3):
    """
    tells you when the next bus is from home or to uni

    where: 'h'/'u'/'guess': home, uni, or try to guess based on wifi connection name
    walking_time: don't show buses less than n mins from now
    nbus: show next n buses

interrogator

#!/usr/bin/python

def nextbus(where = 'u', walking_time = 3, nbus = 5):
    """tells you when the next bus is from home or to uni

    where: 'h'/'u'
    walking_time: don't show buses less than n mins from now
    nbus: show next n buses
    """
    walking_time = int(walking_time)

interrogator

LET us go then, you and I,	
When the evening is spread out against the sky	
Like a patient etherized upon a table;	
Let us go, through certain half-deserted streets,	
The muttering retreats	        5
Of restless nights in one-night cheap hotels	
And sawdust restaurants with oyster-shells:	
Streets that follow like a tedious argument	
Of insidious intent	
To lead you to an overwhelming question….	        10

interrogator

import pickle
import os
bnc = pickle.load(open('bnc.p', 'rb'))
lst = ['clinic', 'hospital', 'patient']
for w in lst:
    print '%s: %d occurrences' % ( w, bnc[w])

interrogator

% gobble allows you to indent in your tex file
% rerun code only if it changes
\usepackage[gobble=auto,rerun=modified]{pythontex}

% a figure showing code and the image it produces

\begin{figure}[htb!]

% make a text box
\begin{mdframed}[backgroundcolor=gray!4] \footnotesize \singlespacing

interrogator

from setuptools import setup, find_packages
from setuptools.command.install import install

class install_with_nltk_extras(install):
    """Customized setuptools install command - prints a friendly greeting."""
    def run(self):
        install.run(self)
        import nltk
        nltk.download('punkt')
        nltk.download('wordnet')

interrogator

# keyword calculation for a particular word

# our aim is to use log likelihood to calculate the 'keyness' of a word. common practice.
# a normal application would be to loop through wordlists and gives every word a keyness score.
# here, we'll just get 'apple', as an example

# reference_corpus = a dictionary of words and their frequencies in a large dataset
# target_corpus = a dictionary of words and their frequencies in a smaller dataset

# our example word

interrogator

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>fileTypes</key>
	<array>
		<string>txt</string>
	</array>
	<key>name</key>
	<string>Plain Text</string>

interrogator

 Weeding-Out Urged In Foreign Service: U.S. URGED TO SIFT FOREIGN ...
 By E.W. KENWORTHY Special to The New York Times 
 New York Times (1923-Current file); Dec 29, 1963;
 ProQuest Historical Newspapers: The New York Times (1851-2010) 
 pg.1



Weeding-Out Urged

interrogator

2005 
      -5  -4  -3  -2  -1    0   1   2   3   4   5  Total
NN    52  37  43  32  29   88  47  57  40  36  34    495
JJ    23  27  25  11  36   12  30  20  24  30  24    262
NNS   20  23  19  18  15   50  19  26  25  20  26    261
NNP   25  30  35  27   9    2  16  22  17  31  36    250
risk   6   1   1   0   0  177   0   0   1   1   7    194