wordcloud.py 13 KB
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# Author: Andreas Christian Mueller <amueller@ais.uni-bonn.de>
# (c) 2012
# Modified by: Paul Nechifor <paul@nechifor.net>
#
# License: MIT

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from random import Random
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import os
import re
import numpy as np
from operator import itemgetter

from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
from query_integral_image import query_integral_image

item1 = itemgetter(1)

FONT_PATH = "/usr/share/fonts/truetype/droid/DroidSansMono.ttf"
STOPWORDS = set([x.strip() for x in open(os.path.join(os.path.dirname(__file__),
                                                      'stopwords')).read().split('\n')])


def random_color_func(word, font_size, position, orientation, random_state=None):
    if random_state is None:
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        random_state = Random()
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    return "hsl(%d, 80%%, 50%%)" % random_state.randint(0, 255)


class WordCloud(object):
    """Word cloud object for generating and drawing.

    Parameters
    ----------
    font_path : string
        Font path to the font that will be used (OTF or TTF).
        Defaults to DroidSansMono path, but you might not have it.

    width : int (default=400)
        Width of the canvas.

    height : int (default=200)
        Height of the canvas.

    ranks_only : boolean (default=False)
        Only use the rank of the words, not the actual counts.

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    prefer_horizontal : float (default=0.90)
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        The ratio of times to try horizontal fitting as opposed to vertical.

    mask : nd-array or None (default=None)
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        If not None, gives a binary mask on where to draw words. All zero
        entries will be considered "free" to draw on, while all non-zero
        entries will be deemed occupied. If mask is not None, width and height will be
        ignored and the shape of mask will be used instead.
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    max_words : number (default=200)
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        The maximum number of words.

    stopwords : set of strings
        The words that will be eliminated.

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    background_color : color value (default="black")
        Background color for the word cloud image.

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    Attributes
    ----------
    words_ : list of tuples (string, float)
        Word tokens with associated frequency.

    layout_ : list of tuples (string, int, (int, int), int, color))
        Encodes the fitted word cloud. Encodes for each word the string, font
        size, position, orientation and color.
    """

    def __init__(self, font_path=None, width=400, height=200, margin=5,
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                 ranks_only=False, prefer_horizontal=0.9, mask=None, scale=1,
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                 color_func=random_color_func, max_words=200, stopwords=None,
                 random_state=None, background_color='black'):
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        if stopwords is None:
            stopwords = STOPWORDS
        if font_path is None:
            font_path = FONT_PATH
        self.font_path = font_path
        self.width = width
        self.height = height
        self.margin = margin
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        self.ranks_only = ranks_only
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        self.prefer_horizontal = prefer_horizontal
        self.mask = mask
        self.scale = scale
        self.color_func = color_func
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        self.max_words = max_words
        self.stopwords = stopwords
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        if isinstance(random_state, int):
            random_state = Random(random_state)
        self.random_state = random_state
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        self.background_color = background_color
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    def _fit_words(self, words):
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        """Generate the positions for words.

        Parameters
        ----------
        words : array of tuples
            A tuple contains the word and its frequency.

        Returns
        -------
        layout_ : list of tuples (string, int, (int, int), int, color))
            Encodes the fitted word cloud. Encodes for each word the string, font
            size, position, orientation and color.

        Notes
        -----
        Larger canvases with make the code significantly slower. If you need a large
        word cloud, run this function with a lower canvas size, and draw it with a
        larger scale.

        In the current form it actually just uses the rank of the counts, i.e. the
        relative differences don't matter. Play with setting the font_size in the
        main loop for different styles.
        """
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        if self.random_state is not None:
            random_state = self.random_state
        else:
            random_state = Random()
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        if len(words) <= 0:
            print("We need at least 1 word to plot a word cloud, got %d."
                  % len(words))

        if self.mask is not None:
            width = self.mask.shape[1]
            height = self.mask.shape[0]
            # the order of the cumsum's is important for speed ?!
            integral = np.cumsum(np.cumsum(self.mask, axis=1), axis=0).astype(np.uint32)
        else:
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            height, width = self.height, self.width
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            integral = np.zeros((height, width), dtype=np.uint32)

        # create image
        img_grey = Image.new("L", (width, height))
        draw = ImageDraw.Draw(img_grey)
        img_array = np.asarray(img_grey)
        font_sizes, positions, orientations, colors = [], [], [], []

        # intitiallize font size "large enough"
        font_size = height

        # start drawing grey image
        for word, count in words:
            # alternative way to set the font size
            if not self.ranks_only:
                font_size = min(font_size, int(100 * np.log(count + 100)))
            while True:
                # try to find a position
                font = ImageFont.truetype(self.font_path, font_size)
                # transpose font optionally
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                if random_state.random() < self.prefer_horizontal:
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                    orientation = None
                else:
                    orientation = Image.ROTATE_90
                transposed_font = ImageFont.TransposedFont(font,
                                                           orientation=orientation)
                draw.setfont(transposed_font)
                # get size of resulting text
                box_size = draw.textsize(word)
                # find possible places using integral image:
                result = query_integral_image(integral, box_size[1] + self.margin,
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                                              box_size[0] + self.margin, random_state)
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                if result is not None or font_size == 0:
                    break
                # if we didn't find a place, make font smaller
                font_size -= 1

            if font_size == 0:
                # we were unable to draw any more
                break

            x, y = np.array(result) + self.margin // 2
            # actually draw the text
            draw.text((y, x), word, fill="white")
            positions.append((x, y))
            orientations.append(orientation)
            font_sizes.append(font_size)
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            colors.append(self.color_func(word, font_size, (x, y), orientation,
                                          random_state=random_state))
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            # recompute integral image
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            if self.mask is None:
                img_array = np.asarray(img_grey)
            else:
                img_array = np.asarray(img_grey) + self.mask
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            # recompute bottom right
            # the order of the cumsum's is important for speed ?!
            partial_integral = np.cumsum(np.cumsum(img_array[x:, y:], axis=1),
                                         axis=0)
            # paste recomputed part into old image
            # if x or y is zero it is a bit annoying
            if x > 0:
                if y > 0:
                    partial_integral += (integral[x - 1, y:]
                                         - integral[x - 1, y - 1])
                else:
                    partial_integral += integral[x - 1, y:]
            if y > 0:
                partial_integral += integral[x:, y - 1][:, np.newaxis]

            integral[x:, y:] = partial_integral

        self.layout_ = zip(words, font_sizes, positions, orientations, colors)
        return self.layout_

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    def _process_text(self, text):
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        """Splits a long text into words, eliminates the stopwords.

        Parameters
        ----------
        text : string
            The text to be processed.

        Returns
        -------
        words : list of tuples (string, float)
            Word tokens with associated frequency.


        Notes
        -----
        There are better ways to do word tokenization, but I don't want to
        include all those things.
        """

        d = {}
        flags = re.UNICODE if type(text) is unicode else 0
        for word in re.findall(r"\w[\w']*", text, flags=flags):
            if word.isdigit():
                continue

            word_lower = word.lower()
            if word_lower in self.stopwords:
                continue

            # Look in lowercase dict.
            if word_lower in d:
                d2 = d[word_lower]
            else:
                d2 = {}
                d[word_lower] = d2

            # Look in any case dict.
            d2[word] = d2.get(word, 0) + 1

        d3 = {}
        for d2 in d.values():
            # Get the most popular case.
            first = max(d2.iteritems(), key=item1)[0]
            d3[first] = sum(d2.values())

        # merge plurals into the singular count (simple cases only)
        for key in d3.keys():
            if key.endswith('s'):
                key_singular = key[:-1]
                if key_singular in d3:
                    val_plural = d3[key]
                    val_singular = d3[key_singular]
                    d3[key_singular] = val_singular + val_plural
                    del d3[key]

        words = sorted(d3.iteritems(), key=item1, reverse=True)
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        words = words[:self.max_words]
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        maximum = float(max(d3.values()))
        for i, (word, count) in enumerate(words):
            words[i] = word, count / maximum

        self.words_ = words

        return words

    def generate(self, text):
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        """Generate wordcloud from text.

        Calls _process_text and _fit_words.
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        Returns
        -------
        self
        """
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        self._process_text(text)
        self._fit_words(self.words_)
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        return self

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    def _check_generated(self):
        """Check if layout_ was computed, otherwise raise error."""
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        if not hasattr(self, "layout_"):
            raise ValueError("WordCloud has not been calculated, call generate first.")
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    def to_image(self):
        self._check_generated()
        if self.mask is not None:
            width = self.mask.shape[1]
            height = self.mask.shape[0]
        else:
            height, width = self.height, self.width

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        img = Image.new("RGB", (width * self.scale, height * self.scale), self.background_color)
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        draw = ImageDraw.Draw(img)
        for (word, count), font_size, position, orientation, color in self.layout_:
            font = ImageFont.truetype(self.font_path, font_size * self.scale)
            transposed_font = ImageFont.TransposedFont(font,
                                                       orientation=orientation)
            draw.setfont(transposed_font)
            pos = (position[1] * self.scale, position[0] * self.scale)
            draw.text(pos, word, fill=color)
        return img

    def recolor(self, random_state=None, color_func=None):
        """Recolor existing layout.

        Applying a new coloring is much faster than generating the whole wordcloud.

        Parameters
        ----------
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        random_state : RandomState, int, or None, default=None
            If not None, a fixed random state is used. If an int is given, this
            is used as seed for a random.Random state.
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        color_func : function or None, default=None
            Function to generate new color from word count, font size, position
            and orientation.  If None, self.color_func is used.

        Returns
        -------
        self
        """
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        if isinstance(random_state, int):
            random_state = Random(random_state)
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        self._check_generated()
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        if color_func is None:
            color_func = self.color_func
        self.layout_ = [(word, font_size, position, orientation,
                         color_func(word, font_size, position, orientation, random_state))
                        for word, font_size, position, orientation, _ in self.layout_]
        return self

    def to_file(self, filename):
        """Export to image file.

        Parameters
        ----------
        filename : string
            Location to write to.

        Returns
        -------
        self
        """

        img = self.to_image()
        img.save(filename)
        return self

    def to_array(self):
        """Convert to numpy array.

        Returns
        -------
        image : nd-array size (width, height, 3)
            Word cloud image as numpy matrix.
        """
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        return np.array(self.to_image())
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    def __array__(self):
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        """Convert to numpy array.

        Returns
        -------
        image : nd-array size (width, height, 3)
            Word cloud image as numpy matrix.
        """
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        return self.to_array()
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    def to_html(self):
        raise NotImplementedError("FIXME!!!")