olooney

def collapse_intervals(intervals):
    '''intervals is an iterable of 2-tuples representing the start and end of
    closed intervals.  returns a list of 2-tuples after collapsing any
    overlapping intervals. For example:

        collapse_intervals([(1,5), (9,10), (4,7)]) -> [(1,7), (9,10)]

    The algorithm doesn't care if intervals are out of order, or if two points
    of the same interval are out of order. It also doesn't require the points

olooney

 cat ~/.ssh/id_rsa.pub | ssh $USER@$SERVER "umask 077; test -d ~/.ssh || mkdir ~/.ssh ; cat >> ~/.ssh/authorized_keys"

olooney

# http://en.wikipedia.org/wiki/RSA_(algorithm)

p = 61
q = 53
n = p*q
totient = (p-1)*(q-1)

# here, e is 2^4 + 1; in real world applications, e is often 2^16 + 1
e = 17

olooney

"""
Example using Python's itertools and generators to implement the classic FizzBuzz problem using pythonic iterators.


Output looks like this:

	$ python fizz_buzz_with_iterator.py
	1
	2
	Fizz

olooney

#! /usr/bin/env python
"""
Simple wrapper around Github's gist HTTP API.

"""
import argparse
import logging
import requests
import json
from getpass import getuser, getpass

olooney

" Oran Looney, November 2011


""" GENERAL SECTION """
" turn on automatic syntax highlighting.
syntax on
filetype plugin on
set directory=/tmp

""" WHITESPACE """

olooney

Select 
    sum(size) AS total_size_on_disk, 
    sum(size) / (select count(*) from individual) AS per_individual
FROM (
SELECT 
    pg_relation_size(C.oid) AS "size"
  FROM pg_class C
  LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace)
  WHERE 1=1 -- relname in (...)
  ORDER BY pg_relation_size(C.oid) DESC

olooney

#include <iostream>
#include <vector>
#include <tuple>
#include <algorithm>

#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"
#include "xtensor/xmath.hpp"
#include "xtensor/xrandom.hpp"
#include "xtensor/xbuilder.hpp"

olooney

Markdown==2.6.10
numpy==1.6.1
ordereddict==1.2
pandas==0.14.1
patsy==0.4.1
python-dateutil==2.6.1
pytz==2017.3
requests==2.18.4
six==1.11.0

olooney

compare_histograms <- function(sn) { 
  sample_means <- function(r,n) { sqrt(n)*rowMeans(matrix(r(n*10000), ncol=n)) }
  center_scale <- function(v) (v - mean(v))/sd(v)
  
  exp_sample <- center_scale(sample_means(rexp,  sn))
  unif_sample <- center_scale(sample_means(runif, sn)) 

  max_break = ceiling(max(c(unif_sample, exp_sample)))
  min_break = floor(min(c(unif_sample, exp_sample)))
  breaks = seq(min_break,max_break,length.out=(max_break-min_break)*8+1)