jbn

jbn

# Installation: 
# - Put this in your .bash_profile
# - Replace my directory with your exobrain directory on line 10
#
# What you get:
#    A Jupyter notebook open on a well-defined port that is always
#    on for exobrain-ing your ideas...

exec 6<>/dev/tcp/localhost/10000 || (
    source activate py27 && 

jbn

function method_a(options)
    parameterstring = ""
    keyssorted = sort!(collect(keys(options)))
    for key in keyssorted
        parameterstring *= "$key=$(options["$key"])&"
    end
    chop(parameterstring)
end

function method_b(options)

jbn

import os
import numpy as np
import pandas as pd
import rpy2

from rpy2 import robjects
from rpy2.robjects.packages import importr


AMELIA = importr("Amelia")

jbn

import scala.math.ulp

/**
 * ThirdWay calls `ulp` occasionally. This script tries to assess costs.
 */

def timeIt[R](f: => R): Long = {
    val start = System.currentTimeMillis
    f
    System.currentTimeMillis - start

jbn

/*
Consider two activities, A and B, both scheduled at the same time. When 
activated, they schedule themselves for reactivation in a time delta of 0.1 
and 0.2. If this is less than the ulp at a given time, both activities end 
up being scheduled at the same time.
*/
import scala.math.ulp

val MaxTime = 9.007199254740991E15

jbn

// Load a DataFrame of users. Each line in the file is a JSON 
// document, representing one row.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val people = sqlContext.read.json("users.json.bz2")

jbn

$ nslookup donaldjdrumpf.com
54.192.37.{240, 220, 36, 204, 108, 84, 38, 123}

$ nslookup optrump2016.com
104.18.47.68
104.18.46.68

$ nslookup donaldtrump.com
104.16.72.120
104.16.75.120

jbn

# Makefile
FETCH = data/raw/FETCHED
PROCESS = data/clean/CLEANED

all: $(FETCH) $(PROCESS)

$(FETCH): scripts/fetch.py
    @mkdir -p data/raw
    python scripts/fetch.py
    @touch $(FETCH)

jbn

Make