chemikadze

demo: lib420.so
        LD_PRELOAD=./lib420.so ls -l
.PHONY: demo

lib420.so: lib420.c
        gcc -shared -o lib420.so lib420.c

clean:
        rm lib420.so

chemikadze

from pyspark.mllib.clustering import KMeans, KMeansModel
from pyspark.mllib.linalg.Vectors
from numpy import isnan
from IPython.lib.pretty import pprint

# hypotesis: mtb offroad normal, mtb road normal, mtb flat intervals, mtb hill intervals, road normal
def do_clustering(sc, sqlCtx):
    ss = sqlCtx.sparkSession
    activities = ss.read.json("gs://chemikadze/strava-analysis/activities_full.json")
    train_data = activities.rdd \

chemikadze

scala> case class Scalar(s: String)
scala> case class Sequence(s: Seq[String])
scala> object ScalarMatcher { def unapply(x: Scalar) = Some(x.s) }
scala> object SequenceMatcher { def unapply(x: Sequence) = Some(x.s) }

scala> Scalar("x").s match { case ScalarMatcher(x) => x ; case _ => "because fuck you" }
<console>:11: error: scrutinee is incompatible with pattern type;
 found   : Scalar
 required: String
              Scalar("x").s match { case ScalarMatcher(x) => x ; case _ => "because fuck you" }

chemikadze

        - get-properties:
            action: getEnvironmentProperties
            output:
              props: result

        - compute-properties:
            action: serialize
            precedingPhases: [get-properties]
            parameters:
              format: JSON

chemikadze

# launch and add to env
application:
   interfaces:
     values:
       subnet: bind(const#network.subnet)
       prefixLength: bind(const#network.prefixLength)
     configuration: 
       values: bind(const#configuration.values)
   configuration:
     configuration.values:

chemikadze

application:
  configuration:
    input.image: "us-east-1/ami-aede32c6"
    input.image-user: "Administrator"
    input.instance-size: "m1.medium"
    input.password: "123QweAsd"
    input.recipe-url: "https://dl.dropboxusercontent.com/u/250836/jenkins.tar.gz"
    input.jenkin-server-url: "http://23.20.248.221:8080/"
    input.quantity: "1"
    

chemikadze

launch:
  steps:
    provision-do:
      action: provisionVms
      parameters:
        jcloudsNodeNamePrefix: test
        roleName: cloud
        imageId: us-east-1/ami-f666929e
        hardwareId: m1.medium
        vmIdentity: Administrator

chemikadze

function setup_mcollective() {
  CFGNAME=$1
  HOST=$2
  PORT=$3
  USER=$4
  PASSWORD=$5
  cp /etc/mcollective/$CFGNAME.cfg /etc/mcollective/$CFGNAME.cfg.orig
  sed -re 's/connector = .*/connector = rabbitmq/' < /etc/mcollective/$CFGNAME.cfg.orig > /etc/mcollective/$CFGNAME.cfg 
  cat >> /etc/mcollective/$CFGNAME.cfg <<EOF
direct_addressing = 1 # ???

chemikadze

Compiled from "anon-class.scala"
public final class Parent$$anon$1 implements IChild {
  private java.lang.Object notReferenced;                      # notReferenced field is not prefixed 
  private java.lang.Object Parent$$anon$$referenced;           # referenced    field is     prefixed
  private java.lang.Object notReferenced();                    # notReferenced field is private
  private void notReferenced_$eq(java.lang.Object);
  public java.lang.Object Parent$$anon$$referenced();          # referenced field is    public
  private void Parent$$anon$$referenced_$eq(java.lang.Object);
  private scala.Function0<java.lang.Object> lambda();
  public Parent$$anon$1(Parent);

chemikadze

import org.scalameter.api._
import scala.util.Random
import scala.collection.mutable
import scala.collection.mutable.Builder

object MirrorBenchmark extends PerformanceTest.Quickbenchmark {
  val sizes = Gen.range("size")(100, 500, 100)
  val maps = for (sz <- sizes) yield {
    val r = (0 until sz)
    val random = new Random()