July 1, 2008


Tag Cloud Image


This Python program extends the tag cloud idea to image generation, doing a little filtering on text to remove stop words and common words first. The program uses the Python Imaging Library (PIL) to generate the image. It doesn't do colors yet, but that will be easy to add. This blog post starts development of a proper software program, called TagCloudImage. You follow may development in the software directory.


Tag clouds are handy visual summaries used to represent the frequency of keywords used on a web site. One of the biggest tag clouds I've seen is the one that shows the frequency of categories of software hosted at launchpad.net . You also see one showing keyword frequency on this web site, which is using using the Plone TagCloud product. As you read in TagCloud's description, the frequencies are classed using a power function, following the suggestion by Anders Pearson . This algorithm is also used in TagCloudImage.


I've played around with applying it to long text items. Tag clouds offer promise for quickly grasping what a body of text concerns. I am thinking of applying it to student reports, for example.


I do a good proportion of my work on the Unix command line, so a simple image-generating program is useful. TagCloudImage could be extended as a Plone software add-on. For example, in the Plone environment, a site administrator could periodically create a new snapshot tag cloud image.


TagCloudImage could also be extended with graphics, using simple outlined or filled rectangles as background to text items, or using specialized cloud, bubble, or other backgrounds. The graphics library currently used is the Python Imaging Library (PIL) .


TagCloudImage does the following:


  • Find all the words and make a list of unique words in the text, removing punctuation.
  • Remove stop words.
  • Remove common words, using a source of 1000 or so common words.
  • Count the words that survive the filtering.
  • Use the power function algorithm mentioned above to put the words into classes.
  • Draw and save a tag cloud image showing the words in alphabetical order and in a font and size according to definition of each class.


Here's the code (Everything is included in this file, except for the list of common words ):


#!/usr/bin/env python


import math

from string import punctuation

import Image

import ImageDraw

import ImageFont


# See also, for the power function to get tag classes:

# http://thraxil.com/users/anders/posts/2005/12/13/scaling-tag-clouds/

# and

# http://behemoth.ccnmtl.columbia.edu/test/clouds/cloud.txt


# For stop words:

# http://www.dcs.gla.ac.uk/idom/ir_resources/linguistic_utils/stop_words


stop_words = """a about above across after afterwards again against all

almost alone along already also although always am among amongst amoungst

amount an and another any anyhow anyone anything anyway anywhere are

around as at back be became because become becomes becoming been before

beforehand behind being below beside besides between beyond bill both

bottom but by call can cannot cant co computer con could couldnt cry

de describe detail do done down due during each eg eight either eleven

else elsewhere empty enough etc even ever every everyone everything

everywhere except few fifteen fify fill find fire first five for

former formerly forty found four from front full further get give go

had has hasnt have he hence her here hereafter hereby herein hereupon

hers herself him himself his how however hundred i ie if in inc indeed

interest into is it its itself keep last latter latterly least less

ltd made many may me meanwhile might mill mine more moreover most

mostly move much must my myself name namely neither never nevertheless

next nine no nobody none noone nor not nothing now nowhere of off

often on once one only onto or other others otherwise our ours

ourselves out over own part per perhaps please put rather re same see

seem seemed seeming seems serious several she should show side since

sincere six sixty so some somehow someone something sometime sometimes

somewhere still such system take ten than that the their them

themselves then thence there thereafter thereby therefore therein

thereupon these they thick thin third this those though three through

throughout thru thus to together too top toward towards twelve twenty

two un under until up upon us very via was we well were what whatever

when whence whenever where whereafter whereas whereby wherein

whereupon wherever whether which while whither who whoever whole whom

whose why will with within without would yet you your yours yourself



stop_words = stop_words.split()


# For more stop words:

# http://dev.mysql.com/tech-resources/articles/full-text-revealed.html


more_stop_words = """a's, able, about, above, according, accordingly,

across, actually, after, afterwards, again, against, ain't, all,

allow, allows, almost, alone, along, already, also, although, always,

am, among, amongst, an, and, another, any, anybody, anyhow, anyone,

anything, anyway, anyways, anywhere, apart, appear, appreciate,

appropriate, are, aren't, around, as, aside, ask, asking, associated,

at, available, away, awfully, be, became, because, become, becomes,

becoming, been, before, beforehand, behind, being, believe, below,

beside, besides, best, better, between, beyond, both, brief, but, by,

c'mon, c's, came, can, can't, cannot, cant, cause, causes, certain,

certainly, changes, clearly, co, com, come, comes, concerning,

consequently, consider, considering, contain, containing, contains,

corresponding, could, couldn't, course, currently, definitely,

described, despite, did, didn't, different, do, does, doesn't, doing,

don't, done, down, downwards, during, each, edu, eg, eight, either,

else, elsewhere, enough, entirely, especially, et, etc, even, ever,

every, everybody, everyone, everything, everywhere, ex, exactly,

example, except, far, few, fifth, first, five, followed, following,

follows, for, former, formerly, forth, four, from, further,

furthermore, get, gets, getting, given, gives, go, goes, going, gone,

got, gotten, greetings, had, hadn't, happens, hardly, has, hasn't,

have, haven't, having, he, he's, hello, help, hence, her, here,

here's, hereafter, hereby, herein, hereupon, hers, herself, hi, him,

himself, his, hither, hopefully, how, howbeit, however, i'd, i'll,

i'm, i've, ie, if, ignored, immediate, in, inasmuch, inc, indeed,

indicate, indicated, indicates, inner, insofar, instead, into,

inward, is, isn't, it, it'd, it'll, it's, its, itself, just, keep,

keeps, kept, know, knows, known, last, lately, later, latter,

latterly, least, less, lest, let, let's, like, liked, likely, little,

look, looking, looks, ltd, mainly, many, may, maybe, me, mean,

meanwhile, merely, might, more, moreover, most, mostly, much, must,

my, myself, name, namely, nd, near, nearly, necessary, need, needs,

neither, never, nevertheless, new, next, nine, no, nobody, non, none,

noone, nor, normally, not, nothing, novel, now, nowhere, obviously,

of, off, often, oh, ok, okay, old, on, once, one, ones, only, onto,

or, other, others, otherwise, ought, our, ours, ourselves, out,

outside, over, overall, own, particular, particularly, per, perhaps,

placed, please, plus, possible, presumably, probably, provides, que,

quite, qv, rather, rd, re, really, reasonably, regarding, regardless,

regards, relatively, respectively, right, said, same, saw, say,

saying, says, second, secondly, see, seeing, seem, seemed, seeming,

seems, seen, self, selves, sensible, sent, serious, seriously, seven,

several, shall, she, should, shouldn't, since, six, so, some,

somebody, somehow, someone, something, sometime, sometimes, somewhat,

somewhere, soon, sorry, specified, specify, specifying, still, sub,

such, sup, sure, t's, take, taken, tell, tends, th, than, thank,

thanks, thanx, that, that's, thats, the, their, theirs, them,

themselves, then, thence, there, there's, thereafter, thereby,

therefore, therein, theres, thereupon, these, they, they'd, they'll,

they're, they've, think, third, this, thorough, thoroughly, those,

though, three, through, throughout, thru, thus, to, together, too,

took, toward, towards, tried, tries, truly, try, trying, twice, two,

un, under, unfortunately, unless, unlikely, until, unto, up, upon,

us, use, used, useful, uses, using, usually, value, various, very,

via, viz, vs, want, wants, was, wasn't, way, we, we'd, we'll, we're,

we've, welcome, well, went, were, weren't, what, what's, whatever,

when, whence, whenever, where, where's, whereafter, whereas, whereby,

wherein, whereupon, wherever, whether, which, while, whither, who,

who's, whoever, whole, whom, whose, why, will, willing, wish, with,

within, without, won't, wonder, would, would, wouldn't, yes, yet,

you, you'd, you'll, you're, you've, your, yours, yourself,

yourselves, zero"""


more_stop_words = more_stop_words.split(', ')


stop_words = stop_words + filter(lambda x:x not in stop_words, more_stop_words)


# For common words:


# http://www.uri.edu/comm_service/cued_speech/amerpron.html


# which says about the source fo the 1000 or so words:


#     compiled mostly from words in a special dictionary by Robert

#     Shaw, The New Horizon Ladder Dictionary, New York: Popular

#     Library, Inc., 1969, for the United States Information Agency.

#     The bias favors words in print rather than frequency of spoken

#     English words.


common_words = open('common-words.txt').read().split()


excluded_words = stop_words + filter(lambda x:x not in stop_words, common_words)


width = 450

height = width


image_left_margin = 5

image_right_margin = 5


# Find fonts on your system and set this path:



arial18 = ImageFont.truetype(fontPath,18)

arial28 = ImageFont.truetype(fontPath,28)

arial36 = ImageFont.truetype(fontPath,36)

arial48 = ImageFont.truetype(fontPath,48)

arial64 = ImageFont.truetype(fontPath,64)


font_classes = [arial18, arial28, arial36, arial48, arial64]


words_and_counts = {}


for line in open('geojeff_twitter.txt').readlines():

    line = line.strip()

    possible_start_times = ['%02d:' % num for num in range(24)]

    for possible_start_time in possible_start_times:

        if possible_start_time in line:

            line = line[:line.find(possible_start_time)]

    words = line.split()

    for word in words:

        word = word.strip(punctuation)

        if len(word) > 0:

            if word.endswith('\'s'):

                word = word[:-2]

            exclude = False


                number = int(word)

            except (ValueError, IndexError):

                number = None

            if number:

                exclude = True

            elif 'http' in word:

                exclude = True

            elif len(word) == 1:

                exclude = True

            if not exclude:

                word = word.lower()

                if word not in excluded_words:

                    words_and_counts[word] \

                        = words_and_counts.get(word, 0) + 1


tags = words_and_counts.items()



levels = 5


def ex_weights(l): return [int(w) for (t,w) in l]


max_weight = max(ex_weights(tags))

min_weight = min(ex_weights(tags))


thresholds = [math.pow(max_weight - min_weight + 1,float(i) \

                / float(levels)) for i in range(0,levels)]


def class_from_weight(w,thresholds):

    i = 0

    for t in thresholds:

        i += 1

        if w <= t:

            return i

    return i


im = Image.new("RGB",(width,height),"#ddd")

draw = ImageDraw.Draw(im)


textsizes = [draw.textsize('Boy', font_classes[0]),

             draw.textsize('Boy', font_classes[1]),

             draw.textsize('Boy', font_classes[2]),

             draw.textsize('Boy', font_classes[3]),

             draw.textsize('Boy', font_classes[4])]


x = 0

# fix width to at least fit the longest item

for (t,w) in tags:

    c = class_from_weight(w,thresholds) - 1

    font = font_classes[c]

    word_w,word_h = draw.textsize(t, font=font)

    if (x + word_w) > width:

        width = x + word_w


lines = []

current_line = []

x = 0

y = 0

word_left_margin = 0

max_font_index = 0

for (t,w) in tags:

    c = class_from_weight(w,thresholds) - 1

    font = font_classes[c]

    word_w,word_h = draw.textsize(t, font=font)


    # set the x position

    if len(current_line) == 0:

        x = image_left_margin

        word_left_margin = 0


        previous_word_w,previous_word_h = current_line[-1][3]

        word_left_margin = int((float(previous_word_w) * 0.05 \

                                   + float(word_w) * 0.05) / 2.0)

        x = x + previous_word_w + word_left_margin


    if (x + word_w) < width:

        current_line.append((t, c, (x,y), (word_w,word_h),

                             font, word_left_margin))

        if c > max_font_index:

            max_font_index = c



        y = y + textsizes[max_font_index][1]

        max_font_index = 0

        current_line = []

        x = image_left_margin

        word_left_margin = 0

        current_line.append((t, c, (x,y), (word_w,word_h),

                             font, word_left_margin))

        if c > max_font_index:

            max_font_index = c


image_width = 0

image_height = 0

lines_adjusted = []

for line in lines:

    width_for_line = 0

    max_height_for_line = 0

    for word_data in line:

        x,y = word_data[2]

        if x > width_for_line:

            width_for_line = x

        word_left_margin = word_data[5]

        word_w,word_h = word_data[3]

        width_for_line += word_left_margin + word_w

        if word_h > max_height_for_line:

            max_height_for_line = word_h

    line_adjusted = []

    for word_data in line:

        word_w,word_h = word_data[3]

        if word_h != max_height_for_line:

            x,y = word_data[2]

            y_adjusted = y + int((float(max_height_for_line) \

                                    - float(word_h)) * 0.5)

            line_adjusted.append((word_data[0], word_data[1],

                                  (x,y_adjusted), word_data[3],

                                  word_data[4], word_data[5]))




    if image_width < width_for_line:

        image_width = width_for_line

    image_height += max_height_for_line


im = Image.new("RGB",(image_width,image_height),"#ddd")

draw = ImageDraw.Draw(im)

for line in lines_adjusted:

    for word_data in line:

        word = word_data[0]

        x,y = word_data[2]

        font = word_data[4]





for line in lines_adjusted:

    for word_data in line:

        print word_data[0]


And here is the image that is produced when I apply the program to my first 42 Twitter updates, to summarize the subjects I had been tweeting about: