Paul Butler paulgb

## gist:277567
# different functions that can be passed in
# (num_divisors is used by default)
uniq_factors = lambda x: len([p for p,n in factor(x)])
count_factors = lambda x: sum(n for p,n in factor(x))
num_divisors = lambda x: len(divisors(x))

def plot_divisors(height=100, fun=num_divisors):
    width = 2*height
    m = matrix(height, width)
    l = width / 2

## beaconproxy.coffee
httpProxy = require 'http-proxy' # npm install http-proxy

server = httpProxy.createServer (req, res, proxy) ->
  if req.headers['host'] == PUBLIC_IP
    # react to a request sent from the malicious beacon giving
    # the user's current location
    {query} = url.parse(req.url)
    {location} = querystring.parse(query)
    if location
      console.log "Tracked location: #{location}"

## index.html
<!DOCTYPE html>
<!-- By Jan Wrobel (http://mixedbit.org) -->

<html>

  <head>
    <meta http-equiv="Content-Type" content="text/html;charset=utf-8">
    <title>Random walk</title>
    <script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script>
    <script src="http://code.jquery.com/jquery-1.9.1.min.js"></script>

## gist:5232709

from sklearn import preprocessing, pipeline

label_enc = preprocessing.LabelEncoder()
pipe = pipeline.Pipeline([('enc', label_enc)])

X = ['foo', 'bar', 'baz']

label_enc.fit(X) # works
pipe.fit(X) # fails w/ output below

## convert.py
'''
Convert Yelp Academic Dataset from JSON to CSV

Requires Pandas (https://pypi.python.org/pypi/pandas)

By Paul Butler, No Rights Reserved
'''

import json
import pandas as pd

## Great Circle Flight Maps.ipynb

      
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                paulgb
                / Great Circle Flight Maps.ipynb
            
            
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              February 7, 2021 18:47
            
              
                Visualization of OpenFlight data with gcmap python package
              
          
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## racecondition.py
from IPython.kernel import KernelManager

from time import sleep

def racecondition():
    km = KernelManager()
    km.start_kernel()

    kc = km.client()
    kc.start_channels()

## binom_interval.py
from scipy.stats import beta

def binom_interval(success, total, confint=0.95):
    quantile = (1 - confint) / 2.
    lower = beta.ppf(quantile, success, total - success + 1)
    upper = beta.ppf(1 - quantile, success + 1, total - success)
    return (lower, upper)

## Affairs.ipynb

      
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                paulgb
                / Affairs.ipynb
            
            
              Last active
              December 23, 2015 16:29
            
          
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## file_progress.py
from os.path import getsize
from progressbar import ProgressBar

class ProgOpen(object):
    def __init__(self, filename):
        self.filename = filename

    def __iter__(self):
        return self
	# different functions that can be passed in
	# (num_divisors is used by default)
	uniq_factors = lambda x: len([p for p,n in factor(x)])
	count_factors = lambda x: sum(n for p,n in factor(x))
	num_divisors = lambda x: len(divisors(x))

	def plot_divisors(height=100, fun=num_divisors):
	width = 2*height
	m = matrix(height, width)
	l = width / 2
	httpProxy = require 'http-proxy' # npm install http-proxy

	server = httpProxy.createServer (req, res, proxy) ->
	if req.headers['host'] == PUBLIC_IP
	# react to a request sent from the malicious beacon giving
	# the user's current location
	{query} = url.parse(req.url)
	{location} = querystring.parse(query)
	if location
	console.log "Tracked location: #{location}"
	<!DOCTYPE html>
	<!-- By Jan Wrobel (http://mixedbit.org) -->

	<html>

	<head>
	<meta http-equiv="Content-Type" content="text/html;charset=utf-8">
	<title>Random walk</title>
	<script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script>
	<script src="http://code.jquery.com/jquery-1.9.1.min.js"></script>

	from sklearn import preprocessing, pipeline

	label_enc = preprocessing.LabelEncoder()
	pipe = pipeline.Pipeline([('enc', label_enc)])

	X = ['foo', 'bar', 'baz']

	label_enc.fit(X) # works
	pipe.fit(X) # fails w/ output below
	'''
	Convert Yelp Academic Dataset from JSON to CSV

	Requires Pandas (https://pypi.python.org/pypi/pandas)

	By Paul Butler, No Rights Reserved
	'''

	import json
	import pandas as pd
	from IPython.kernel import KernelManager

	from time import sleep

	def racecondition():
	km = KernelManager()
	km.start_kernel()

	kc = km.client()
	kc.start_channels()
	from scipy.stats import beta

	def binom_interval(success, total, confint=0.95):
	quantile = (1 - confint) / 2.
	lower = beta.ppf(quantile, success, total - success + 1)
	upper = beta.ppf(1 - quantile, success + 1, total - success)
	return (lower, upper)
	from os.path import getsize
	from progressbar import ProgressBar

	class ProgOpen(object):
	def __init__(self, filename):
	self.filename = filename

	def __iter__(self):
	return self