graphaelli

#!/usr/bin/env python

import json
import subprocess
import sys


def against():
    try:
        subprocess.check_call("git rev-parse --verify HEAD".split())

graphaelli

"""
The MIT License

Copyright (c) 2008 Gilad Raphaelli <gilad@raphaelli.com>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

graphaelli

#!/usr/bin/env python

from awacs.aws import Action, Condition, Policy, Principal, Statement
from awacs.aws import Deny, Everybody, StringNotEquals
from awacs.s3 import ARN as S3_ARN
import troposphere
import troposphere.s3


def resource_namify(resource_name):

graphaelli

import troposphere
import troposphere.ec2
import troposphere.iam

from awacs.aws import Allow, Statement, Policy
from awacs.s3 import ARN as S3_ARN
import awacs.s3 as s3


def read_only_bucket_access(bucket_name):

graphaelli

#!/usr/bin/env python

""" .vimrc: autocmd BufEnter *.md exe 'noremap <F5> :!mdpreview.py %<CR>' """

import subprocess, sys, tempfile

with tempfile.NamedTemporaryFile(suffix=".html", delete=False) as mdhtml:
    subprocess.check_call(["pandoc", "--to=html", "--from=markdown", "-o", mdhtml.name, sys.argv[1]])
    # webbrowser.open_new_tab ... chrome howto?
    subprocess.check_call(["open", "-a", "Google Chrome", mdhtml.name])

graphaelli

package main

import (
    "flag"
    "fmt"
    "log"
    "net/http"
)

func main() {

graphaelli

#!/usr/bin/env python

import fileinput
import urllib.parse


urllib.parse.uses_netloc.append("postgres")
dsn = next(fileinput.input()).strip()
url = urllib.parse.urlparse(dsn)

graphaelli

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

graphaelli

#!/usr/bin/env python

from io import StringIO
import argparse
import sys
import unittest


def quote_list(r, sep=None):
    # hope r is smallish

graphaelli

#!/usr/bin/env python

import argparse
import os
import subprocess
import sys


def rebase(base, branch, abort=True):
    try: