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import requests | |
r = requests.get('http://gun.io') | |
print r.content |
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# GPLv2+, by fcicq | |
# from http://www.hpl.hp.com/techreports/98/HPL-98-135.pdf | |
IRRED_COEFF = [ | |
4,3,1,5,3,1,4,3,1,7,3,2,5,4,3,5,3,2,7,4,2,4,3,1,10,9,3,9,4,2,7,6,2,10,9, | |
6,4,3,1,5,4,3,4,3,1,7,2,1,5,3,2,7,4,2,6,3,2,5,3,2,15,3,2,11,3,2,9,8,7,7, | |
2,1,5,3,2,9,3,1,7,3,1,9,8,3,9,4,2,8,5,3,15,14,10,10,5,2,9,6,2,9,3,2,9,5, | |
2,11,10,1,7,3,2,11,2,1,9,7,4,4,3,1,8,3,1,7,4,1,7,2,1,13,11,6,5,3,2,7,3,2, | |
8,7,5,12,3,2,13,10,6,5,3,2,5,3,2,9,5,2,9,7,2,13,4,3,4,3,1,11,6,4,18,9,6, | |
19,18,13,11,3,2,15,9,6,4,3,1,16,5,2,15,14,6,8,5,2,15,11,2,11,6,2,7,5,3,8, | |
3,1,19,16,9,11,9,6,15,7,6,13,4,3,14,13,3,13,6,3,9,5,2,19,13,6,19,10,3,11, |
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In [40]: A = randLILMatrix(10,10); A.set_type(FF(2)) | |
In [45]: A | |
Out[45]: | |
⎡1 mod 2 0 mod 2 0 mod 2 0 mod 2 0 mod 2 1 mod 2 0 mod 2 1 mod 2 0 mod 2 1 mod 2⎤ | |
⎢ ⎥ | |
⎢0 mod 2 1 mod 2 1 mod 2 1 mod 2 0 mod 2 0 mod 2 1 mod 2 0 mod 2 1 mod 2 1 mod 2⎥ | |
⎢ ⎥ | |
⎢1 mod 2 0 mod 2 0 mod 2 0 mod 2 0 mod 2 1 mod 2 1 mod 2 1 mod 2 1 mod 2 0 mod 2⎥ | |
⎢ ⎥ |
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import requests | |
import lxml | |
from lxml import html | |
r = requests.get('http://gun.io') | |
tree = lxml.html.fromstring(r.content) | |
elements = tree.get_element_by_id('frontsubtext') | |
for el in elements: | |
print el.text_content() |
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import requests | |
r = requests.get('http://gun.io') | |
print r.content |
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import random | |
class Markov(object): | |
def __init__(self, open_file): | |
self.cache = {} | |
self.open_file = open_file | |
self.words = self.file_to_words() | |
self.word_size = len(self.words) | |
self.database() |
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#!/usr/bin/env python | |
import wave, sys | |
import numpy as np | |
import scipy.signal as signal | |
import math | |
from mpmath import * | |
frq_tab = [261.6, 293.6, 329.6, 349.2, 392, 440, 493.8] |
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from numpy import * | |
from scipy import * | |
from matplotlib.pyplot import * | |
from pylab import * | |
from numpy.fft import * | |
import scipy.signal as signal | |
def mfreqz(b,n,a=1): | |
""" function to plot the response of a filter""" | |
figure(n) |
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# Filtering utilities | |
import numpy as np | |
from numpy import pi | |
from numpy import fft | |
from scipy import signal | |
import scipy.signal as sig | |
import pylab as plt | |
#### Functions ################# |
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