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# Fortran-like array with arbitrary starting indices
#
# usage:
#
# julia> include("FArray.jl")
# size (generic function with 51 methods)
# 
# julia> y = FArray(Float64, -1:1, -7:7, -128:512, -5:5, -1:1, -3:3, -2:2, -1:1);
# 
# julia> y[-1,-7,-128,-5,-1,-3,-2,-1] = 14

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// takes arguments and produces an array of functions that accept context
function handle_args() {
  var args = Array.prototype.slice.call(arguments);
  return args.map(function(arg) {
    if(arg instanceof Function) return arg; // Presumably already a contex-accepting function.
    if(arg instanceof Array) return and.apply(this, arg); // make arrays behave as and.
    // Presuming a string, build a function to check.
    var my_regex = new RegExp(arg.toString(), 'i');
    return function(context) {
      return context.search(my_regex) > -1;

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function query() {
  var 
    total = 0, shown = 0,
    // HN is done with very unsemantic classes.
    job_list = Array.prototype.slice.call(document.querySelectorAll('.c5a,.cae,.c00,.c9c,.cdd,.c73,.c88')),
    query_list = Array.prototype.slice.call(arguments);
    
  // This traverses up the dom stack trying to find a match of a specific class
  function up_to(node, klass) {
    if (node.className === klass) {

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@doc doc"""
    Takes a 2d array where each column is a pattern which maps to a row in
    the resulting dataframe. Each pattern index is its own column in 
    the dataframe.
""" ->
function DataFrame(data::Array{Float64, 2})
    m = data'
    result = DataFrame()
    
    for i in 1:size(data, 1)

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# http://dlo.me/archives/2014/09/08/pythonrc/
# vim: set ft=python :

from __future__ import print_function

import json
import sys
import datetime

from redis import StrictRedis as Redis

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#!/usr/bin/env python

import os
import logging
from collections import Counter
import gffutils
from gffutils.create import logger
logger.setLevel(logging.INFO)

usage = """

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.ipynb_checkpoints

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