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//

var notificationbox = function (w, which){  // w is a chromeWindow
  //w = w || getMostRecentBrowserWindow();
  switch (which) {
    case "bottom-global":
      //w = w || getMostRecentBrowserWindow();
      let nb = w.gDataNotificationInfoBar._notificationBox;
      //console.log(nb);
      return nb;

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// require() some stuff from npm (like you were using browserify)
// and then hit Run Code to run it on the right

var p = document.createElement("pre");
document.body.appendChild(p);
p.textContent="LOG";

var log = function () {
  p.textContent += "\n" + Array.prototype.slice.call(arguments).join(" ")
}

gregglind

Heartbeat (Self-Repair / Shield) Recipes v1.0pre

Protect. Improve. Extend.

We have the deployment. We have the channels. Time to actually DO THINGS.

Goals:

Today

gregglind

/******/ (function(modules) { // webpackBootstrap
/******/ 	// The module cache
/******/ 	var installedModules = {};
/******/
/******/ 	// The require function
/******/ 	function __webpack_require__(moduleId) {
/******/
/******/ 		// Check if module is in cache
/******/ 		if(installedModules[moduleId])
/******/ 			return installedModules[moduleId].exports;

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#!/usr/bin/env node

require('shelljs/global');
config.fatal = true;

var args = process.argv.slice(2);
var target = {};

var help = function () {
  console.log("== Available targets == \n");

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/******/ (function(modules) { // webpackBootstrap
/******/ 	// The module cache
/******/ 	var installedModules = {};
/******/
/******/ 	// The require function
/******/ 	function __webpack_require__(moduleId) {
/******/
/******/ 		// Check if module is in cache
/******/ 		if(installedModules[moduleId])
/******/ 			return installedModules[moduleId].exports;

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var fs = require("fs"),
    path = require("path");

module.exports = function(blanket){
    var traceur = require("traceur");
    var oldLoaderJS = require.extensions['.js'];

    require.extensions['.js'] = function(localModule, filename) {

        var pattern = blanket.options("filter");

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// license: Public Domain

//it.todo = it.skip;
it.todo = function (title, callback) {
  return it.skip("TODO: " + title, callback)
}

describe("todo vs skip", function () {
  it.skip("skipped test", function () {

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# given a total T, have an algorithm that 
# generates events over (0,t), t <=T with
# proportion t/T
#
require("triangle")

eventSim <- function (sampler_fn, n, events=1, reps=1000) {
  x <- replicate(reps,(function(events){
    r <- sampler_fn(n) # length
    # r <- rexp(n,.01) # mean = var = 1/.01 = 100

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"""
======================================================
Classification of text documents using sparse features
======================================================

This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.sparse
matrix to store the features and demonstrates various classifiers that can
efficiently handle sparse matrices.