Mostrando entradas con la etiqueta Julia. Mostrar todas las entradas
Mostrando entradas con la etiqueta Julia. Mostrar todas las entradas

lunes, 19 de octubre de 2015

Julia, data analysis’s little sister...meets SAP HANA

This post was originally posted on Julia, data analysis’s little sister...meets SAP HANA.


Julia is not that young right now…as it first appeared on 2012 -:) It is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general purpose programming.


Woaw! That was a big description…so why should we care? Well…maybe because Julia was designed to be the language to rule them all…a language that can be used in any given situation…and without stopping to say if that’s true or not…I must say…Julia is really cool -:)

So…no example or demonstration would be complete if we didn’t hook it up with SAP HANA, right? So…let’s go and do it -;)

First, we need to create a Calculation View and call it “FLIGHTS_BY_CARRIER”. It will be composed of two tables, SCARR and SFLIGHT.

First, we need to create a Join object and link the table by MANDT and CARRID. From here select the following fields as output MANDT, CARRID, CARRNAME, PRICE and CURRENCY.

Then create an Aggregation object selecting the fields CARRNAME, PRICE (As Aggregated Column) and CURRENCY. Filter the CURRENCY field by ‘USD’.

Then create a Projection object and select only PRICE and CARRNAME.

On the Semantics object make sure to select “CROSS CLIENT” as the Default Client.


Now, switch to the SAP HANA Development View and create a new repository. Call it “Flights”.

Create a new “XS Engine” project and call it “Flights” as well. Link it to the “Flights” repository.

Create an empty “.xsapp” file.

Create a file called “.xsaccess” with the following code.

.xsaccess
{
          "exposed" : true,
          "authentication" : [ { "method" : "Basic" } ]
}

Finally create a file called “flights.xsodata” with the following code

flights.xodata
service {
          "Blag/FLIGHTS_BY_CARRIER.calculationview" as "FLIGHTS" keys 
                                                        generate local "Id";
}

Activate your project and launch it on your browser, you should see something like this…


The SAP HANA part is done…so we can move into the Julia part…

Go into your Julia environment and install the following packages

  • HTTPClient
  • Codecs
  • LightXML

You only need to do Pkg.add(“PackageName”) for each of them.

Then create a file called Julia_HANA_XML.jl on your favorite editor and copy the following code

Julia_HANA_XML.jl
using HTTPClient.HTTPC
using Codecs
using LightXML

credentials=encode(Base64,"SYSTEM:YourPassword")
Auth = bytestring(credentials)
Auth = "Basic " * Auth

flights=HTTPC.get("http://YourServer:8000/Flights/flights.xsodata/FLIGHTS",RequestOptions(headers=[("Authorization",Auth)]))

raw_text = takebuf_string(flights.body)
xdoc = parse_string(raw_text)
xroot = root(xdoc)

entry = get_elements_by_tagname(xroot,"entry")

for flights in entry
 print(content(find_element(find_element(find_element(flights,"content"),"properties"),"CARRNAME")),": ",
    content(find_element(find_element(find_element(flights,"content"),"properties"),"PRICE")),"\n")
end

To run this application, simply go to your Julia environment and type

Include(“Julia_HANA_XML.jl”)


If you are wondering…why didn’t I use JSON instead of XML? Well…there’s an easy explanation for that -:) Somehow…the HTTPClient package have a problem using ?$format=json so I was forced to use XML instead…

Greetings,

Blag.
Development Culture.

miércoles, 16 de julio de 2014

Web scrapping with Julia and PhatomJS

As I have been reading some PhantomJS books and I'm always looking to develop something nice using Julia...I thought that integrate them would be an awesome idea -;)

I thought about Twitter and the hashtags...wouldn't it be nice to write a PhantomJS script to webscrape Twitter and get all the hashtags that I have used?

For this particular script...I'm taking the hashtags from the first 5 Twitter pages linked to my profile...

Hashtags.js
var system = require('system');

var webpage = require('webpage').create();
webpage.viewportSize = { width: 1280, height: 800 };
webpage.scrollPosition = { top: 0, left: 0 };

var userid = system.args[1];
var profileUrl = "http://www.twitter.com/" + userid;

webpage.open(profileUrl, function(status) {
 if (status === 'fail') {
  console.error('webpage did not open successfully');
  phantom.exit(1);
 }
 var i = 0,
 top,
 queryFn = function() {
  return document.body.scrollHeight;
 };
 setInterval(function() {
  top = webpage.evaluate(queryFn);
  i++;
   
  webpage.scrollPosition = { top: top + 1, left: 0 };

  if (i >= 5) {
   var twitter = webpage.evaluate(function () {
    var twitter = [];
    forEach = Array.prototype.forEach;
    var tweets = document.querySelectorAll('[data-query-source="hashtag_click"]');
    forEach.call(tweets, function(el) {
     twitter.push(el.innerText);
    });
    return twitter;
   });

   twitter.forEach(function(t) {
    console.log(t);
   });

   phantom.exit();
  }
}, 3000);
});

If we run this...we're going to have this output...



Now...what I want to do with this information...is to send it to Julia...and get the most used hashtags...so I will summarize them and then get rid of the ones that only appear once...

Let's see the Julia code...


Twitter_Hashtags.jl
tweets = readall(`phantomjs --ssl-protocol=any Hashtags.js Blag`)
tweets = split(tweets,"\n")
hashtags = Dict()
for hash in tweets
 try
  hashtags[hash] += 1
 catch e
  hashtags[hash] = 1
 end
end

filter!((k,v)->v>1,hashtags)

for (k,v) in hashtags
 println("$k has been mentioned $v times")
end

When we run this code...we're going to have this output...


I still don't know how to sort Dicts in Julia...so bear with me -:)

Anyway...by looking at the output...we can have my top 3 hashtags -;)

#LeapMotion ==> 14 times
#Flare3D ==> 11 times
#DevHangout ==> 8 times

Hope you like this and see you next time -:)

Greetings,

Blag.
Development Culture.

jueves, 10 de julio de 2014

LED - My first Julia package

So yesterday I was thinking about Julia and how easy people claim package development is...of course...I need to give it a try...

I wanted to start small and simple...so I build something useless mostly for fun and learning...

The LED Package simply writes an LED representation of any given number...

julia> Using LED

julia > ShowLED(12345)

   _  _       _  
|  _| _| |_| |_  
| |_  _|   |  _| 

As simple as that...and it took me no more than 5 minutes to get it done...

So, I can confirm now that package development in Julia...is a piece of cake -:)

If everything was done nicely...you should be able to do...

julia > Pkg.add("LED")

otherwise...please do...

julia > Pkg.clone("git@github.com:atejada/LED.jl.git")

Of course...this was just an experiment...so of course I'm planning to put my mind into the work and come up with some nice and useful packages -;)

Greetings,

Blag.
Development Culture.

viernes, 16 de mayo de 2014

Julia versus R - Playing around

So...as time goes by, I'm getting more proficient with Julia...which is something fairly easy as the learning curve is pretty fast...

I decided to load a file with 590,209 records that I got from Freebase...the file in question contains Actors and Actresses from movies...you can have a quick look here...


For this test, I'm using my Linux box on VMWare running on 2 GB of RAM...running Ubuntu 12.04.4 (Precise)

For R, I'm not using any special package...just plain R...version 2.14.1 and for Julia version 0.2.1, I'm using the DataFrames package...

Let's take a look at the R source code first along with its runtime processing...

Actors_Info.R
start.time <- Sys.time()
if(!exists("Actors")){
Actors<-read.csv("Actors_Table.csv", header=TRUE, 
                     stringsAsFactors=FALSE, colClasses="character", na.strings = "")
}
Actors<-unique(Actors)
Actors<-Actors[complete.cases(Actors),]
Actor_Info<-data.frame(Actor_Id=Actors$Actor_Id,Name=Actors$Name,Gender=Actors$Gender)
Actor_Info<-Actor_Info[order(Actor_Info$Gender),]
write.csv(Actor_Info,"Actor_Info_R.csv",row.names=TRUE)
end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken

This source will first ask if the file was loaded already, if not...it will load it...then, it will eliminate the repeated records, delete all the null or NA's and the create a new Data Frame, sort it by "Gender" and then write a new CSV file...time will be taken to measure its speed...we will run it twice...first time the file is not loaded...second time it will...and that should improve greatly the execution time...



As we can see...the times are really good...and the different between the first and second run are pretty obvious...for the record...the generated file contains 105874 records...

Now...let's see the Julia version of the code...

Actors_Info.jl
using DataFrames
start = time()
isdefined(:Actors) || (Actors = readtable("Actors_Table.csv", header=true, nastrings=["","NA"]))
drop_duplicates!(Actors)
complete_cases!(Actors)
Actor_Info = DataFrame(Actor_Id=Actors["Actor_Id"],Name=Actors["Name"],Gender=Actors["Gender"])
sortby!(Actor_Info, [:Gender])
writetable("Actor_Info_Julia.csv", Actor_Info)
finish = time()
println("Time: ", finish-start)


Here...we're doing the same...we load the DataFrames package (But exclude that from the execution time), check if the file is loaded so we don't load it again on the second run...eliminate duplicates, delete all null or NA, create a new DataFrame, sort it by "Gender" and finally write a new CVS file...


Well...the difference between the second and first run is very significative...but of course...way slower than R...

But...let me tell you one simple thing...Julia is still a brand new language...the DataFrames package is not part of the core Julia language, which means...that its even newer...and optimizations are being performed as we speak...I would say that for a young language...18 seconds to process 590,209 records is pretty awesome...and of course...my R experience surpasses greatly my Julia experience...

So...I don't really want to leave you with the impression that Julia is not good or not fast enough...because believe me...it is...and you going to love my next experiment -;)

Let's take a look at the R source code first...

Random_Names.R
start.time <- Sys.time()
names<-c("Anne","Gigi","Blag","Juergen","Marek","Ingo","Lars","Julia",
         "Danielle","Rocky","Julien","Uwe","Myles","Mike", "Steven")

last_names<-c("Hardy","Read","Tejada","Schmerder","Kowalkiewicz","Sauerzapf",
              "Karg","Satsuta","Keene","Ongkowidjojo","Vayssiere","Kylau",
              "Fenlon","Flynn","Taylor")
full_names<-c()
for(i in 1:100000){
  name<-sample(1:15, 1)
  last_name<-sample(1:15, 1)
  full_name<-paste(names[name],last_names[last_name],sep=" ")
  full_names<-append(full_names,full_name)
}
end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken

So this code is fairly simple...we have a couple of vectors with names and last names...then we loop 100000 times and then generate a couple of random numbers simply to read the vectors, create a full name and populate a new vector... with some random funny name combinations...



Well....the different between both runs is not really good...second time was a little bit higher...and 1 minute is kind of a lot...let's see how Julia behaves...

Here's the Julia source code...

Random_Numbers.jl
start = time()
names=["Anne","Gigi","Blag","Juergen","Marek","Ingo","Lars","Julia",
       "Danielle","Rocky","Julien","Uwe","Myles","Mike", "Steven"]
last_names=["Hardy","Read","Tejada","Schmerder","Kowalkiewicz","Sauerzapf",
            "Karg","Satsuta","Keene","Ongkowidjojo","Vayssiere","Kylau","Fenlon","Flynn","Taylor"]
full_names=String[]
full_name = ""
for i = 1:100000
        name=rand(1:15)
        last_name=rand(1:15)
        full_name = names[name] * " " * last_names[last_name]
        push!(full_names,full_name)
end
finish = time()
println("Time: ", finish-start)

So this code as well, creates two arrays with names and last names, do a loop 100000 times, generate a couple of random numbers, mix a name with a last name and then populate a new array with some mixed full names...


Just like in the R code...the second time took Julia a little bit more...but...less than a second?! That's something like...amazingly fast and really took R by storm...

Now...I believe you will start to take Julia more seriously -:D

Hope you liked this blog...

Greetings,

Blag.
Development Culture.

martes, 13 de mayo de 2014

My first post on Julia

So...what Julia? Just another nice programming language -;)

According to it's creators...

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.



I just started learning it a couple of days ago...and I must say that I really like it...it has a Python like syntax so I felt comfortable from the very start...

Of course...it's kind of a brand new language, so things are being added and fixed while we speak...but the community is growing and I'm glad to be amongst it's "early" supporters -:)

What I did right after I read the documentation and watch a couple of videos was to simply port one my old Python applications to Julia...the app was "LCD Numbers" which ask for a number and return it printed like in LCD format...

This is the Python code...

LCD_Numbers.py
global line1, line2, line3

line1 = ""
line2 = ""
line3 = ""

zero = {1: ' _  ', 2: '| | ', 3: '|_| '}
one = {1: '  ', 2: '| ', 3: '| '}
two = {1: ' _  ', 2: ' _| ', 3: '|_  '}
three = {1: '_  ', 2: '_| ', 3: '_| '}
four = {1: '    ', 2: '|_| ', 3: '  | '}
five = {1: ' _  ', 2: '|_  ', 3: ' _| '}
six = {1: ' _  ', 2: '|_  ', 3: '|_| '}
seven = {1: '_   ', 2: ' |  ', 3: ' |  '}
eight = {1: ' _  ', 2: '|_| ', 3: '|_| '}
nine = {1: ' _  ', 2: '|_| ', 3: ' _| '}

num_lines = {0: zero, 1: one, 2: two, 3: three, 4: four,
             5: five, 6: six, 7: seven, 8: eight, 9: nine}

def Lines(number):
    global line1, line2, line3
    line1 += number.get(1, 0)
    line2 += number.get(2, 0)
    line3 += number.get(3, 0)

number = str(input("\nEnter a number: "))
length = len(number)
for i in range(0, length):
    Lines(num_lines.get(int(number[i:i+1]), 0))

print ("\n")
print line1
print line2
print line3
print ("\n") 
And this is in turn...the Julia version of it...

LCD_Numbers.jl
zero = [1=> " _  ", 2=> "| | ", 3=> "|_| "]
one = [1=> "  ", 2=> "| ", 3=> "| "]
two = [1=> " _  ", 2=> " _| ", 3=> "|_  "]
three = [1=> "_  ", 2=> "_| ", 3=> "_| "]
four = [1=> "    ", 2=> "|_| ", 3=> "  | "]
five = [1=> " _  ", 2=> "|_  ", 3=> " _| "]
six = [1=> " _  ", 2=> "|_  ", 3=> "|_| "]
seven = [1=> "_   ", 2=> " |  ", 3=> " |  "]
eight = [1=> " _  ", 2=> "|_| ", 3=> "|_| "]
nine = [1=> " _  ", 2=> "|_| ", 3=> " _| "]

num_lines = [0=> zero, 1=> one, 2=> two, 3=> three, 4=> four,
             5=> five, 6=> six, 7=> seven, 8=> eight, 9=> nine]

line = ""; line1 = ""; line2 = ""; line3 = ""

function Lines(number, line1, line2, line3)
    line1 *= number[1]
    line2 *= number[2]
    line3 *= number[3]
    line1, line2, line3
end

println("Enter a number: "); number = chomp(readline(STDIN))
len = length(number)
for i in [1:len]
    line = Lines(num_lines[parseint(string(number[i]))],line1,line2,line3)
    line1 = line[1]; line2 = line[2]; line3 = line[3]
end

println(line1)
println(line2)
println(line3 * "\n")

As you can see...the code looks somehow similar...but of course...I got rid of those ugly global variables...and used some of the neat Julia features, like multiple value return and variable definition on one line... If you want to see the output...here it is...


Of course...this is just a test...things are going to become interesting when I port some R code into Julia and run some speed comparisons -;)

Greetings,

Blag.
Development Culture.