John B Nelson jbn

## D3_IPython_Freeze_Framing.ipynb

      
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                jbn
                / D3_IPython_Freeze_Framing.ipynb
            
            
              Created
              September 1, 2015 21:26
            
              
                A Little Demo of a Fun Technique for D3.js Development
              
          
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## always_on_jupyter.sh
# 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 &&

## rewrite_oauth_serialize_url_parameters.jl
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)

## nan_to_na.py
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")

## ulp_speed.scala
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

## epsilon_and_scheduling.scala
/*
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

## spark_json_reader.scala
// 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")

## traceroutes.sh
$ 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

## Makefile
# 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)

## execute_makefile.sh
Make
	# 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 &&
	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)
	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")
	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
	/*
	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
	// 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")
	$ 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
	# 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)