rayh

// To add observation
self.label1Binding = [self.model addObserverForKeyPath:@"exampleValue1" block:^(NSDictionary *change) {
  label1.text = [NSString stringWithFormat:@"%d", self.model.exampleValue1];
}];

// To remove observation
self.label1Binding = nil;

rayh

Keybase proof

I hereby claim:

• I am rayh on github.
• I am rayh (https://keybase.io/rayh) on keybase.
• I have a public key whose fingerprint is D532 D25D 30DF 75EF 6CD9 252B A27C 7177 CBBE A264

To claim this, I am signing this object:

rayh

func appController(appController: TVApplicationController, evaluateAppJavaScriptInContext jsContext: JSContext) {
    jsContext.evaluateScript("var console = {log: function() { var message = ''; for(var i = 0; i < arguments.length; i++) { message += arguments[i] + ' ' }; console.print(message) } };")
    let logFunction: @convention(block) (NSString!) -> Void = { (message:NSString!) in
        print("JS: \(message)")
    }
    jsContext.objectForKeyedSubscript("console").setObject(unsafeBitCast(logFunction, AnyObject.self), forKeyedSubscript:"print")
}

rayh

var SimplePage = function(url) {
  var self = this;

  function onSelect(event) {
    var ele = event.target
    var href = ele.getAttribute("href")

    if(href) {
      new SimplePage(href).load();
    }

rayh

App.onLaunch = function(options) {
    new SimplePage('http://mytvservice.com/home.xml').load();
}

rayh

var fs = require('fs');
var Q = require('q');
var spawn = require('child_process').spawn;
var path = require('path');

// Make sure you append the path with the lambda deployment path, so you can 
// execute your own binaries in, say, bin/
process.env["PATH"] = process.env["PATH"] + ":" + process.env["LAMBDA_TASK_ROOT"] + '/bin/';

function spawnCmd(cmd, args, opts) {

rayh

let myTable = MLDataTable(...)

// Fetch the price column (and all it's values)
let priceColumn = myTable["price"]

// Create a new column that contains values that are the result of 
// price * bedroom for each row
let priceTimesBedroomsColumn = myTable["price"] * myTable["bedrooms"]

// Remove rows that contain less that 2 bedrooms

rayh

import kozai.compute as kc

cluster = kc.create_dask_cluster()
dask_client = cluster.client()

print(dask_client)

import dask.bag as db
import numpy as np
from dask.distributed import Client, progress

rayh

# Import the Kozai Python library 
import kozai.compute as kc

# Create a new cluster
cluster = kc.create_dask_cluster()

# Fetch the dask client
dask_client = cluster.client()

# Do some dask work