liamwhite

#!/usr/bin/env ruby

# Task:  Fill in the lines commented 'HERE' to create a working calculator.
# Check: Check your work by executing the file after filling in the lines.

class Lexer
  def initialize(input)
    @input = input.gsub(/\s/, '')
    @tokens = []
  end

liamwhite

class BinaryTree
  attr_accessor :data, :left, :right

  def initialize(data, left, right)
    @data  = data
    @left  = left
    @right = right
  end

  # Return a new tree with all the nodes reversed.

liamwhite

class Sass::Tree::Visitors::Perform
  def visit_variable(node)
    env = @environment
    env = env.global_env if node.global
    if node.guarded
      var = env.var(node.name)
      return [] if var && !var.null?
    end

    val = node.expr.perform(@environment)

liamwhite

/*
 * Fast Cubic Bezier evaluation.
 * Vectorized C.
 */

#include <math.h>

typedef float float4 __attribute__((vector_size(16)));
typedef float float2 __attribute__((vector_size(8)));

liamwhite

#Simple Authentication with Bcrypt

This tutorial is for adding authentication to a vanilla Ruby on Rails app using Bcrypt and has_secure_password.

The steps below are based on Ryan Bates's approach from Railscast #250 Authentication from Scratch (revised).

You can see the final source code here: repo. I began with a stock rails app using rails new gif_vault

##Steps

liamwhite

# writing to a file
f = File.open("/tmp/profile", "w")
f.truncate(0)
for file in profile.keys
  f.puts "\n#{file}"
  File.readlines(file).each_with_index do |line, num|
    if (sample = profile[file][num+1]) and sample[0] > 0
      f.printf "% 8.1fms | %s", sample[0]/1000.0, line
    else
      f.printf "           | %s", line

liamwhite

module Elasticsearch
  module Model
    module ClassMethods
      def msearch!(payload)
        # Extract the search body.
        body = payload.map do |source|
          __index_name    = source.klass.index_name
          __document_type = source.klass.document_type

          # Extract the actual body from each search source.

liamwhite

import cv
from optparse import OptionParser

class Simple2DAnalyser(object):
    def __init__(self, imagename):
        im = cv.LoadImageM(imagename, cv.CV_LOAD_IMAGE_COLOR)
        cv.Smooth(im, im, cv.CV_GAUSSIAN, 3)
        # Now let's get four quadrants and our total
        halfX = int(round(im.cols/2))
        halfY = int(round(im.rows/2))