dmerrick

#!/bin/bash
# take a directory of images and combine them into a movie

# create an editable list of frames for the movie
ls -1v | grep JPG > files.txt

# 1080p at 24 fps
mencoder -nosound -ovc lavc -lavcopts vcodec=mpeg4:vbitrate=21600000 -o timelapse.1080p.24fps.avi -mf type=jpeg:fps=24 mf://@files.txt -vf scale=1920:1080

# 4K at 60 fps

mbostock

license: gpl-3.0
border: no
height: 1060
redirect: https://beta.observablehq.com/@mbostock/d3-radial-tidy-tree

mbostock

license: gpl-3.0
height: 960
border: no
redirect: https://observablehq.com/@d3/bubble-chart

emeeks

// Word cloud layout by Jason Davies, http://www.jasondavies.com/word-cloud/
// Algorithm due to Jonathan Feinberg, http://static.mrfeinberg.com/bv_ch03.pdf
(function(exports) {
  function cloud() {
    var size = [256, 256],
        text = cloudText,
        font = cloudFont,
        fontSize = cloudFontSize,
        rotate = cloudRotate,
        padding = cloudPadding,

xim

import sys

import numpy
from nltk.cluster import KMeansClusterer, GAAClusterer, euclidean_distance
import nltk.corpus
from nltk import decorators
import nltk.stem

stemmer_func = nltk.stem.EnglishStemmer().stem
stopwords = set(nltk.corpus.stopwords.words('english'))

onyxfish

import nltk

with open('sample.txt', 'r') as f:
    sample = f.read()
     
sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)