dh dhfromkorea

## pipeline-cpus.ipynb

      
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                dhfromkorea
                / pipeline-cpus.ipynb
            
            
              Created
              May 25, 2020 21:33
                — forked from mrocklin/pipeline-cpus.ipynb
            
          
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## gist:524bec63f5bf38151afcf05cb5a13550
hKRib2R5hqhkZXRhY2hlZMOpaGFzaF90eXBlCqNrZXnEIwEgn+cKNDjDeEyrkcOlPC8qsMqzn6sdMRm02EctZ3c/TDcKp3BheWxvYWTESpcCB8QgRqSCJYNPNZpSJdLrBtTxP+Ecsg3gRfV2QcAVT4EA+jrEIC51ewx6VipNATlRp7hXSkJBVkObe7jOqFNep6HwZIaAAgHCo3NpZ8RA0JiwMzC/KJmh0kZ6EKAhWwVNV3nVaViklUNZOHj7ER4//G/CwEIJDNc+rzw8CSe5Bfyu5Wa9Y1kiVJz2wQUBB6hzaWdfdHlwZSCkaGFzaIKkdHlwZQildmFsdWXEIO7cxNT/Bgx3n9SCKx1vVAV49PWWc48WLP90rMI/jI5Xo3RhZ80CAqd2ZXJzaW9uAQ==

## fix_drafts_deployment.rb
desc "Default deploy task"
task :deploy do
  # Check if preview posts exist, which should not be published
  if File.exists?(".preview-mode")
    puts "## Found posts in preview mode, regenerating files ..."
    File.delete(".preview-mode")
    Rake::Task[:generate].execute
  end
  Rake::Task[:copydot].invoke(source_dir, public_dir)

## keyword.py
# this model is the most naive one
# find the occurences of the keywords listed and consider them to be matched.
# TODO will be to apply a Bag of Words model or some sort and assign credit score

# read raw caption files
caption_data, metadata = next(load_caption_files(path_to_caption_file))
# combine captions by 10 seconds (i.e. make time blocks)
X = split_caption_to_X(caption_data)

keywords = ['caption', 'type=story', 'type=commercial']

## example.py
fp = FingerPrint(path_to_dejavu_config_file)
timestamp_pb = 20000 # program boundary timestamp in secs from
PAD = 10 # temporal padding to apply to the timestamp

# slice an audio segement off the video
fp.slice_audio_segment(path_to_full_video_or_audio, pb - PAD, pb + PAD, "mp4")

# extract the fingerprint of the segment and save it to db
# db's configured in the .config file
# the path for the full video/audio and the segment may be different

## fizzbuzz.js
var fizzbuzz = function(n){
    var answer = '';
    if (n % 3 === 0) {
        answer = 'Fizz';
    }
    if (n % 5 === 0) {
        answer += 'Buzz';
    }
    if (!answer){
        answer = n;

## memoized_fibonacci.js
var fibonacci = function(){
  var _cache = [];

  var fib = function(i){
      if (i < 0) {
         console.err('invalid input. i must be >= 0');
         return;
      }
      if (i < 2) {
         _cache[i] = i;

## phase_portrait.R
if (!require(phaseR)){
    install.packages('phaseR')
    require(phaseR)
}

# a system of two non-linear autonomous ODE's
ode = function (t, y, parameters) {
    x <- y[1]
    y <- y[2]
    dy <- numeric(2)

## filename_fetcher
// by default, it fetches pdf
// change pdf to an extension you want
var pdflinks = [];
Array.prototype.map.call(document.querySelectorAll("a[href$=\".pdf\"]"), function(e, i) {
  if ((pdflinks || []).indexOf(e.href) == -1) {
    pdflinks.push(e.href);
  }
});
console.log(pdflinks.join(" "));
// copy the logged file names

## gist:6296648
<Response>
    <Dial callerId="16501112222" dialMusic="real">
      <Number>15556667777</Number>
    </Dial>
  </Response>
	desc "Default deploy task"
	task :deploy do
	# Check if preview posts exist, which should not be published
	if File.exists?(".preview-mode")
	puts "## Found posts in preview mode, regenerating files ..."
	File.delete(".preview-mode")
	Rake::Task[:generate].execute
	end
	Rake::Task[:copydot].invoke(source_dir, public_dir)
	# this model is the most naive one
	# find the occurences of the keywords listed and consider them to be matched.
	# TODO will be to apply a Bag of Words model or some sort and assign credit score

	# read raw caption files
	caption_data, metadata = next(load_caption_files(path_to_caption_file))
	# combine captions by 10 seconds (i.e. make time blocks)
	X = split_caption_to_X(caption_data)

	keywords = ['caption', 'type=story', 'type=commercial']
	fp = FingerPrint(path_to_dejavu_config_file)
	timestamp_pb = 20000 # program boundary timestamp in secs from
	PAD = 10 # temporal padding to apply to the timestamp

	# slice an audio segement off the video
	fp.slice_audio_segment(path_to_full_video_or_audio, pb - PAD, pb + PAD, "mp4")

	# extract the fingerprint of the segment and save it to db
	# db's configured in the .config file
	# the path for the full video/audio and the segment may be different
	var fizzbuzz = function(n){
	var answer = '';
	if (n % 3 === 0) {
	answer = 'Fizz';
	}
	if (n % 5 === 0) {
	answer += 'Buzz';
	}
	if (!answer){
	answer = n;
	var fibonacci = function(){
	var _cache = [];

	var fib = function(i){
	if (i < 0) {
	console.err('invalid input. i must be >= 0');
	return;
	}
	if (i < 2) {
	_cache[i] = i;
	if (!require(phaseR)){
	install.packages('phaseR')
	require(phaseR)
	}

	# a system of two non-linear autonomous ODE's
	ode = function (t, y, parameters) {
	x <- y[1]
	y <- y[2]
	dy <- numeric(2)
	// by default, it fetches pdf
	// change pdf to an extension you want
	var pdflinks = [];
	Array.prototype.map.call(document.querySelectorAll("a[href$=\".pdf\"]"), function(e, i) {
	if ((pdflinks \|\| []).indexOf(e.href) == -1) {
	pdflinks.push(e.href);
	}
	});
	console.log(pdflinks.join(" "));
	// copy the logged file names
	<Response>
	<Dial callerId="16501112222" dialMusic="real">
	<Number>15556667777</Number>
	</Dial>
	</Response>