sausheong

#!/usr/bin/env ruby
 
require ‘rubygems’
require ‘rmagick’
include Magick
 
left = ImageList.new(ARGV[0]).gamma_correct(1,0,0)
right = ImageList.new(ARGV[1]).gamma_correct(0,1,1)
anaglyph = left.composite right, CenterGravity, ScreenCompositeOp
 

sausheong

reader = Audio::MPEG::Decoder.new
File.open('mrt_closing.mp3', 'rb') do |input|
  File.open('out.wav', 'wb')  do |output|
    reader.decode(input, output)
  end
end

sausheong

FasterCSV.open('wavdata.csv', 'w') do |csv|
 csv << %w(ch1 ch2 combined)
  File.open('out.wav') do |file|
    while !file.eof?
      if file.read(4) == 'data'
        length = file.read(4).unpack('l').first
        wavedata = StringIO.new file.read(length)
        while !wavedata.eof?
          ch1, ch2 = wavedata.read(4).unpack('ss')
          csv << [ch1, ch2,ch1+ch2]

sausheong

script=<<-EOF
  png(file='/Users/sausheong/projects/wavform/mrtplot.png', height=800, width=600, res=72)
  par(mfrow=c(3,1),cex=1.1)
  wav_data <- read.csv(file='/Users/sausheong/projects/wavform/wavdata.csv', header=TRUE)
  plot(wav_data$combined, type='n', main='Channel 1', xlab='Time', ylab='Frequency')
  lines(wav_data$ch1)
  plot(wav_data$combined, type='n', main='Channel 2', xlab='Time', ylab='Frequency')
  lines(wav_data$ch2)
  plot(wav_data$combined, type='n', main='Channel 1 + Channel 2', xlab='Time', ylab='Frequency')
  lines(wav_data$combined)

sausheong

from recordtype import recordtype

# weight is the priority of the task 
# duration is how long it will take to complete the task 
# due is the due date of the task 
# done is the date when the task is completed (starts with 0, 
# set to whenever the task is done)
Task = recordtype('Task', 'weight duration due done')

sausheong

import numpy as np

# create todo tasks
def create_tasks(num): 
    tasks = []
    distribution = np.round(np.random.exponential(5,10000))
    for i in range(num):
        weight = np.random.randint(1,100)
        due = np.random.choice(distribution)+1
        duration = np.random.randint(0,due)

sausheong

# Tallies up the results from running the simulation. Calculates:
# 1. percentage of completed tasks
# 2. percentage of important tasks completed
# 3. percentage of tasks completed in time
def tally(completed_tasks, all_tasks):
    # percentage of tasks completed    
    completed = len(completed_tasks)/len(all_tasks)    

    # set the 75th percentile of all tasks based on weight
    # as the important tasks

sausheong

def duration(task): return task.duration

# run a single simulation
def simulate(ratio, *tasks):
    deadline = ratio * sum(list(map(duration, tasks)))
    completed_tasks = []
    elapsed_time = 0
    for task in tasks:
        elapsed_time += task.duration
        if elapsed_time <= deadline:

sausheong

def add(a, b): return tuple(map(lambda x, y: x + y, a, b))
def divide(a, b): return tuple([x/b for x in a])

# Run multiple iterations of the simulation 
def run(ratio, iterations, num, algorithm):
    results, final_results = (0,0,0), (0,0,0)

    for i in range(iterations):
        tasks = create_tasks(num)
        results = simulate(ratio, *tuple(algorithm(*tasks)))

sausheong

%matplotlib inline
import matplotlib.pyplot as plt

def chart(data, title):
    labels=np.array(['Completed Tasks', 'Completed Weight', 'In Time Tasks'])
    angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
    angles=np.concatenate((angles,[angles[0]]))    
    
    colors = ['blue', 'red', 'green', 'violet', 'blueviolet', 'orange', 'deepskyblue']
    for d, t, c in zip(data, title, colors):