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package thunder.streaming

import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.mllib.clustering.KMeansModel

import scala.util.Random.nextDouble

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/**
 * bisecting <master> <input> <nNodes> <subIterations>
 *
 * divisive hierarchical clustering using bisecting k-means
 * assumes input is a text file, each row is a data point
 * given as numbers separated by spaces
 *
 */

import org.apache.spark.SparkContext

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import java.io.{FileFilter, File}
import org.apache.hadoop.fs.Path
import org.apache.hadoop.io.{LongWritable, BytesWritable}
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
import org.apache.hadoop.mapreduce.{JobContext, InputSplit, RecordReader, TaskAttemptContext}

/**
 * Custom Input Format for reading and splitting flat binary files that contain records, each of which
 * are a fixed size in bytes. The fixed record size is specified through a parameter recordLength
 * in the Hadoop configuration.

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var THREE = require('three.js');
var _ = require('lodash');


var ParticleTest = function(selector, data, images, opts) {

    var width = $(selector).width();
    var height = width * 0.7;

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Notes on compiling chromium using an EC2 cluster

This note lays out the machine specifications and configurations used to get a distributed Chome build working on cloud compute (in this case AWS EC2) using the icecc tool.

These links were a good starting point:

• https://chromium.googlesource.com/chromium/src/+/master/docs/linux_faster_builds.md
• https://github.com/icecc/icecream
• http://mkollaro.github.io/2015/05/08/compiling-chromium-with-clang-and-icecc/

but I couldn't fine a full walk through, so hopefully we can make it easier for others.

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from numpy import array
from thunder import Images, ThunderContext

tsc = ThunderContext.start()

rawpath = ''
savepath = ''

data = tsc.loadImages(rawpath, inputFormat='tif', nplanes=1)

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