User:Max Dovey/ PT/TRIMESTER 1 ntw6

From XPUB & Lens-Based wiki

Week 1

Alan Turing's Universal Turing Machine (UTM) http://en.wikipedia.org/wiki/Universal_machine 

was a concept for an infinite loop of tape that seperated into frames, each frame would present a different state. This created an infinite programming potential for reading.

deconstructing the seamlessness of the factory line. The pipeline.


in the afternoon we played with Turtle.

http://opentechschool.github.io/python-data-intro/core/recap.html

https://github.com/OpenTechSchool/python/wiki/Facebook-Client

http://bitsofpy.blogspot.nl/2010/04/in-my-cosc-lab-today-few-students-were.html 

facebook page query

[
>>> import json
>>> import urllib2
>>> def load_facebook_page(facebook_id):
...     addy = 'https://graph.facebook.com/548951431'
...     return json.load(urllib2.urlopen(addy))

load_facebook_page(548951431)
{u'username': u'max.dovey', u'first_name': u'Max', u'last_name': u'Dovey', u'name': u'Max Dovey', u'locale': u'en_US', u'gender': u'male', u'link': u'http://www.facebook.com/max.dovey', u'id': u'548951431'}
]

stuff to do & Resources - start fetching data from the twitter api https://code.google.com/p/python-twitter/ http://pzwart3.wdka.hro.nl/wiki/PythonTwitter http://www.lynda.com/Python-tutorials/Up-Running-Python/122467-2.html

mining the social web by o'reilly https://github.com/ptwobrussell/Mining-the-Social-Web updated github for twitter oauth http://www.pythonforbeginners.com/python-on-the-web/how-to-access-various-web-services-in-python/ http://www.greenteapress.com/thinkpython/thinkpython.pdf http://hetland.org/writing/instant-python.html

replacing "Music" with "Crap" on 20 most popular video search from youtube 

import json
import requests 

r = requests.get("http://gdata.youtube.com/feeds/api/standardfeeds/top_rated?v=2&alt=jsonc")
r.text

data = json.loads(r.text)

#print data
for item in data['data']["items"]:   
    print " %s" % (item['category'].replace("Music", "CRAP"))


CRAP
Entertainment
CRAP
CRAP
CRAP
CRAP
CRAP
CRAP
CRAP
Comedy
CRAP
CRAP
Comedy
CRAP
CRAP
CRAP
CRAP
Entertainment
CRAP
CRAP
CRAP

Week2=

In the morning we looked at SVG files, and how you can edit the xml of them in a live editor within inskape. You can also execute python commands by pasting in python drawings and the vectors will be generated within inskape.

In the afternoon we looked at making api grabs , loading them with Json libs

ajax.googleapis.com/ajax

add json extension for firefox

Json turns xml into a javascript object.

json has lists - lists [] append("milk")

and dictionary {} foods = [foods["chocolate"] = "love to eat it"]

runcron - can execute pythonn scripts from a server timed.

i got my twitter search function to write to a text file. Im going to look at automating that text file to network printer for this cloud project.

useful link http://lifehacker.com/5652311/print-files-on-your-printer-from-any-phone-or-remote-computer-via-dropbox http://docs.python.org/2/tutorial/inputoutput.html facebook - https://github.com/OpenTechSchool/python/wiki/Facebook-Client youtube api - http://gdata.youtube.com/ http://nealcaren.web.unc.edu/an-introduction-to-text-analysis-with-python-part-1/

Week3=

This script prints a json grab from twitter api, loops and sends to printer every 55 seconds. 

uth = twitter.oauth.OAuth(OAUTH_TOKEN, OAUTH_TOKEN_SECRET,
                           CONSUMER_KEY, CONSUMER_SECRET)

twitter_api = twitter.Twitter(domain='api.twitter.com', 
                              api_version='1.1',
                              auth=auth
                                             )
filepath = "Desktop/autoprinting/todo/" #define file path 

filename = "cloud101" #def file name 
#if file path does not exist make this
if not os.path.exists('Desktop/autoprinting/todo'):
    os.makedirs('Desktop/autoprinting/todo')
#add filename to path 
completepath = os.path.join(filepath, filename+".txt")
    

while True:
    q = "the_cloud"
    count = 30
    f = open (completepath, "w",)
    search_results= twitter_api.search.tweets(q=q, count=count)
    # search_results['meta_data']
    for status in search_results['statuses']:
        text = status['text']
        date = status['created_at']
        simplejson.dump(text + date, f)
        f.writelines("\n")

    f.close()
    
    time.sleep(55)
#if path exists 
    if os.path.exists('Desktop/autoprinting/todo/cloud101.txt'):
#lbr print 
        os.system("lpr -p -r Desktop/autoprinting/todo/cloud101.txt")

week 4

basic arithmetic (moving the digit to another column when u reach *power 10*) keeping the count. binary systems would use 10 figures ( 0,1,2,3,4,5,6,7,8,9) and then when it gets to 10 will give to another cog to start counting giving you an infinite counting system. The difference engine

George Boole began implementing numerical binary into text in logic. He is the inventor of what is known as Boolean logic, true and false statements and conditionals.

Lewis carroll wrote logic conditionals in alice and wonderland "It is a very inconvenient habit of kittens (Alice had once made the remark) that, whatever you say to them, they Always purr.  'If them would only purr for "yes" and mew for "no," or any rule of that sort,' she had said, 'so that one could keep up a conversation!  But how can you talk with a person if they always say the same thing?' Lewis Carroll (1832 - 1898)

Alice predicament allows us to talk with computers. Alice's whole adventure is based on encountering scenarios and applying logic conditionals.

8-bit = 8 on and offs. giving you 255 characters to allocate. all commmands are distributed via the ascII table. http://en.wikipedia.org/wiki/ASCII unicode (utf-8) (UTF-16) Think about the task of the translator by Walter Benjamin/ Everything is interpreted. every webpage is being encoded all bytes are counters for their 8 bit reference gives them a Glyph. see last years page for more on human computation http://pzwart3.wdka.hro.nl/wiki/Human_Computation_(Slides) PYTHON AND AUDIO wav files have a header like how utf is encoded at the top of html. (16 bit little endian , rate 44100 hz, stereo) number of samples / sample rate = time of piece. using this example http://zacharydenton.com/generate-audio-with-python/

This code generates a sound byte the same frequency as the temperature of Rotterdam that day. 

#!/usr/bin/env python
#-*- coding:utf-8 -*-
import json
import requests
import urllib2

url = ('http://api.openweathermap.org/data/2.5/weather?q=rotterdam,nl')
r = requests.get(url)
data = r.text

r.text

      
#urllib2.urlopen(url)

#json.dump(url)
data = json.loads(r.text)

t = data["main"]["temp"]


import wave, struct
 
filename = "rotterdam.wav"
nframes=None
nchannels=2
sampwidth=1 # in bytes so 2=16bit, 1=8bit
framerate=44100
bufsize=2048
 
w = wave.open(filename, 'w')
w.setparams((nchannels, sampwidth, framerate, nframes, 'NONE', 'not compressed'))
 
max_amplitude = float(int((2 ** (sampwidth * 8)) / 2) - 1)
 
# split the samples into chunks (to reduce memory consumption and improve performance)
#for chunk in grouper(bufsize, samples):
#    frames = ''.join(''.join(struct.pack('h', int(max_amplitude * sample)) for sample in channels) for channels in chunk if channels is not None)
#    w.writeframesraw(frames)
 
freq = int(t)
# this means that FREQ times a second, we need to complete a cycle
# there are FRAMERATE samples per second
# so FRAMERATE / FREQ = CYCLE LENGTH
cycle = framerate / freq
 
 
for x in range(freq):
    data = ''
    for i in range(1):
        for x in range(cycle/2):
            data += struct.pack('h', int(0.5 * max_amplitude))
            data += struct.pack('h', 0)
        for x in range(cycle/2):
            data += struct.pack('h', int(-0.5 * max_amplitude))
            data += struct.pack('h', 0)
 

    w.writeframesraw(data)
 
w.close()

Image Generation

Outputrandom2.png

The sound of weather - Rotterdam, Netherlands and Lagos, Nigeria 

#!/usr/bin/env python
#-*- coding:utf-8 -*-
import json
import requests
import urllib2
import time 
url = ('http://api.openweathermap.org/data/2.5/weather?q=lagos,ni')
r = requests.get(url)
data = r.text

r.text

      
#urllib2.urlopen(url)

#json.dump(url)
data = json.loads(r.text)

t1 = data["main"]["temp"]

url = ('http://api.openweathermap.org/data/2.5/weather?q=rotterdam,nl')
r = requests.get(url)
data = r.text

r.text

      
#urllib2.urlopen(url)

#json.dump(url)
data = json.loads(r.text)

t2 = data["main"]["temp"]
import wave, struct
 
filename = "weatherreport.wav"
nframes=None
nchannels=2
sampwidth=2 # in bytes so 2=16bit, 1=8bit
framerate=22150
bufsize=2048
 
if nframes is None:
    nframes = -1
 
w = wave.open(filename, 'w')
w.setparams((nchannels, sampwidth, framerate, nframes, 'NONE', 'not compressed'))
 
max_amplitude = float(int((2 ** (sampwidth * 8)) / 2) - 1)
 
# split the samples into chunks (to reduce memory consumption and improve performance)
#for chunk in grouper(bufsize, samples):
#    frames = ''.join(''.join(struct.pack('h', int(max_amplitude * sample)) for sample in channels) for channels in chunk if channels is not None)
#    w.writeframesraw(frames)
 
freq = int(t2 * 10)
freq2 = int(t1 * 10)
# this means that FREQ times a second, we need to complete a cycle
# there are FRAMERATE samples per second
# so FRAMERATE / FREQ = CYCLE LENGTH
cycle = framerate / freq
 
 
for x in range(5):
    data2 = ''
    data = ''
    for i in range(1):
        for x in range(cycle*1000):
            data += struct.pack('h', int(0.5 * max_amplitude))
            data += struct.pack('h', 1)
        for x in range(cycle*1000):
            data += struct.pack('h', int(-0.5 * max_amplitude))
            data += struct.pack('h', 1)


    for i in range(1):
        #if freq2 >= 600:
            for x in range(cycle):
                data2 += struct.pack('h', int(0.5 * max_amplitude))
                data2 += struct.pack('h', 0)
            
            for x in range(cycle):
                data2 += struct.pack('h', int(-0.5 * max_amplitude))
                data2 += struct.pack('h', 0)
            




    #   newstring = ''.join([data] + [data2])
    w.writeframesraw(data)
    w.writeframesraw(data2)
 
w.close()

Audio output

File:Weatherreport.ogg

WEATHER visualization graphic 

import struct, array
import random 

import json
import requests
import urllib2

url = ('http://api.openweathermap.org/data/2.5/weather?q=antartica')
r = requests.get(url)
data = r.text

r.text

data = json.loads(r.text)

temp = data["main"]["temp"]

width = 100
height = 400
 
filename="antartica.tga"
datafile = open(filename, "wb")
# TGA format: http://gpwiki.org/index.php/TGA
# Offset, ColorType, ImageType, PaletteStart, PaletteLen, PalBits, XOrigin, YOrigin, Width, Height, BPP, Orientation
header = struct.pack("<BBBHHBHHHHBB", 0, 0, 2, 0, 0, 8, 0, 0, width, height, 24, 1 << 5)
datafile.write(header)

base = (height - int(temp)) #because the co-ordinates begin at 0 , deduct temp from height to make base that will show white, and color will fill from then on. 
 
 
data = ''
for y in xrange(height):
    for x in xrange(width):
        r, g, b = 0, 0, 0
 
 
        if y > 0 and y < 400:
            b = 255
            g = 255
            r = 255

            if y > base and y < 400:
                r = 0
                g = 0
                b = 240

        if x <= 100 and y <= 1: #bottom border
            b = 0
            g = 0
            r = 0
        if x <= 100 and y >= 398: #top border 
            b = 0
            g = 0
            r = 0
        if y <= 400 and x <= 1: #left border 
            b = 0
            g = 0
            r = 0
        if y <= 400 and x >= 98: #right border 
            b = 0
            g = 0
            r = 0 

        data += struct.pack('B', b)
        data += struct.pack('B', g)
        data += struct.pack('B', r)
 
datafile.write(data)
datafile.close()

Rotterdam temp, Lagos Temp and Anartica

Tempvizrotterdam.png
Tempviznigeria.png
Tempvizantartica.png


http://palewi.re/posts/2008/04/20/python-recipe-grab-a-page-scrape-a-table-download-a-file/

week 6

Looking at EPUB, Calbire and free publishing tools. http://dpt.automatist.org/digitalworkflows/

week 7

Working with text - NLTK toolkit for natural language processing in python http://nltk.org/ Bitnik Deivery for Mr Assange http://wwwwwwwwwwwwwwwwwwwwww.bitnik.org/assange/ 

 
import time 
f = open('emma.txt')
print f

out = open('janedump.txt', "w")
sawWord = False 
for line in f:
    line = line.strip().lower()
    group = line.split()
    for word in group:
        if(word.lower() == 'emma'): #if emma = true sawWord becomes true
            sawWord = True 
            #print 
            out.write(word)
            out.write("\n") 
            #time.sleep(0.2)
        elif (sawWord): #if saW
            out.write(word)
            out.write("\n") 
            sawWord = False 
            #time.sleep(0.2)
    

out.close()

Jane austen emma (+next word) emma author: emma

emma by emma woodhouse, emma doing emma first emma was emma could emma smiled emma spared emma playfully. emma woodhouse, emma herself, emma bears emma turned emma should emma was emma could emma found emma knew emma particularly emma was emma well emma help emma allowed emma lost emma had emma was emma encouraged emma watched emma was emma thought emma for emma imagined emma and emma must emma good. emma wants emma is emma has emma such emma could emma as emma imagine emma altogether--face emma always emma errs emma shall emma with emma in emma could emma exclaimed, emma wished emma began emma drew emma knew emma thought emma was emma was emma rather emma continued: emma persevered emma waited emma felt emma assured emma continued emma believed emma could emma judged emma will emma had emma knew emma was emma as emma not emma made emma laughed emma had emma remained emma could emma was emma assisted emma was emma than emma could emma spoke emma could emma only emma saw emma could emma thanked emma and emma smilingly emma could emma had emma could emma laughed, emma was emma time emma experienced emma passed emma felt emma quietly emma could emma felt emma only emma could emma could emma long emma could emma called emma sat emma was emma soon emma did emma thought emma smiled emma was emma might emma spoke emma liked emma wished emma found emma listened, emma were emma was emma saw emma tried emma should emma could emma on emma settled emma hoped emma in emma found, emma felt, emma sat emma was emma got emma to emma was emma was emma was emma had emma felt emma was emma immediately emma and emma knew emma said emma had emma was emma could emma was emma left emma was emma saw emma procure emma could emma could emma saw emma thinks emma had emma and emma said, emma was emma felt emma wish emma learned emma had emma thought emma was emma guessed emma saw emma would emma observed emma collected emma must emma consider emma could emma could emma was emma was emma wondered emma remained emma had emma to emma could emma watched emma was emma recollected emma would emma felt emma found emma heard emma want emma could emma did emma dine emma should emma comes emma thus emma had emma could emma to emma said, emma watched emma suspected emma should emma could emma divined emma restrained emma began emma guessed emma found emma soon emma rather emma best emma would emma could emma found emma had emma to emma did emma was emma went emma caught emma watched emma would emma wondered emma again. emma did emma could emma joined emma wished emma a emma still emma found emma said emma demurred. emma perceived emma up, emma was emma nor emma could emma felt emma disappointed; emma was emma looked emma was emma had emma felt emma continued emma had emma could emma grew emma could emma felt emma attacked emma continued, emma feel emma for emma had emma would emma thought emma made emma made emma doubted emma was emma could emma was emma was emma could emma had emma was emma hoped emma had emma woodhouse-ing emma had emma felt emma returned emma could emma triumphantly emma had emma was emma would emma apprehended emma both emma began emma found emma heard emma read emma could emma doubted emma has emma as emma heard emma saw emma were emma could emma perceived emma found emma longed emma most emma could emma in emma could emma that emma must emma was emma felt emma thought emma could emma had emma was emma considerable emma could emma acquainted emma engaging emma thought, emma had emma would emma and emma with emma was emma read emma saw emma was emma was emma then emma could emma was emma was emma herself emma and emma was emma had emma could emma was emma had emma had emma opposing emma some emma was emma had emma could emma denied emma had emma felt emma was emma and emma would emma received emma found emma walked emma very emma had emma had emma listened, emma returned emma were, emma and emma and emma was emma could emma found emma was emma grew emma recollected, emma felt emma seriously emma was emma made emma time emma was emma was emma has emma could emma communicated emma could emma was emma it emma listened emma felt emma did emma afterwards emma was emma found emma distinctly emma thought emma even emma might emma scarcely emma pondered emma began emma dryly, emma could emma feelingly. emma could emma was emma did emma looked emma could emma turned emma the emma knew emma felt emma herself.--the emma came, emma came emma felt) emma was emma to emma had emma as emma had; emma smiled, emma again. emma with emma resolved emma was emma understood emma could emma could emma was emma had emma a emma take emma woodhouse, emma woodhouse, emma agreed emma knew emma would emma had emma was emma could emma to emma fancied emma proposed emma could, emma was emma could emma saw emma could emma could emma guessed emma would emma amused emma felt emma thought emma warmly, emma laughed, emma grieved emma was emma accepted emma having emma hung emma first emma could emma would emma was emma gave emma could emma was emma began emma spoke emma was emma could emma soon emma had emma had emma could emma had emma became emma admitted emma must emma attended emma and emma

TWITTERBOT

CREATING AN AUTOMATED TWITTERBOT

STILL NEED TO CREATE A 140 CHAR PARAMETER SO THAT TWEETS ARE NOT REJECTED. 

 

import tweepy
import re
import os, sys
import time

CONSUMER_KEY = "cqPrXvSrS5WcSVpldOSNQ"
CONSUMER_SECRET = "uswR9EK1TuUorv3RfzFpcn46tljG98RTWnMKCD6yk"
ACCESS_KEY = "2247492469-1fr0vrCXA0SVS3oXFZ8FRu9X6gBesv9KnelWVZd"
ACCESS_SECRET = "cvM618YmJErThPZiLALJtmr7oUjBlmikfq1I3wUp8L05R"

# setup the twitter connection...
auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(ACCESS_KEY, ACCESS_SECRET)
api = tweepy.API(auth)

text = "".join(open("cruise.txt").readlines())
sentences = re.split(r' *[\.\?!][\'"\)\]]* *', text)


# f = open('journeylog1.txt')

for line in sentences:
    line = line.strip()
    api.update_status(line)

    print line 
    time.sleep(25)

Twitterbots & CRON JOBS

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