wordcloud.py 15.4 KB
Newer Older
Andreas Mueller's avatar
Andreas Mueller committed
1
2
# Author: Andreas Christian Mueller <t3kcit@gmail.com>
#
3
4
5
6
7
# (c) 2012
# Modified by: Paul Nechifor <paul@nechifor.net>
#
# License: MIT

Andreas Mueller's avatar
Andreas Mueller committed
8
import warnings
9
from random import Random
10
11
import os
import re
12
import sys
13
14
15
16
17
18
import numpy as np
from operator import itemgetter

from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
19
from .query_integral_image import query_integral_image
20
21
22

item1 = itemgetter(1)

23
FONT_PATH = os.environ.get("FONT_PATH", "/usr/share/fonts/truetype/droid/DroidSansMono.ttf")
24
25
26
27
STOPWORDS = set([x.strip() for x in open(os.path.join(os.path.dirname(__file__),
                                                      'stopwords')).read().split('\n')])


Andreas Mueller's avatar
Andreas Mueller committed
28
29
def random_color_func(word=None, font_size=None, position=None,
                      orientation=None, font_path=None, random_state=None):
Andreas Mueller's avatar
Andreas Mueller committed
30
31
32
33
34
35
36
37
38
39
40
41
42
    """Random hue color generation.

    Default coloring method. This just picks a random hue with value 80% and
    lumination 50%.

    Parameters
    ----------
    word, font_size, position, orientation  : ignored.

    random_state : random.Random object or None, (default=None)
        If a random object is given, this is used for generating random numbers.

    """
43
    if random_state is None:
44
        random_state = Random()
45
46
47
48
49
50
51
52
53
54
    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).
Andreas Mueller's avatar
Andreas Mueller committed
55
56
        Defaults to DroidSansMono path on a Linux machine. If you are on
        another OS or don't have this font, you need to adjust this path.
57
58
59
60
61
62
63
64
65
66

    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.

Andreas Mueller's avatar
Andreas Mueller committed
67
    prefer_horizontal : float (default=0.90)
68
69
70
        The ratio of times to try horizontal fitting as opposed to vertical.

    mask : nd-array or None (default=None)
Andreas Mueller's avatar
Andreas Mueller committed
71
72
73
74
75
        If not None, gives a binary mask on where to draw words. If mask is not
        None, width and height will be ignored and the shape of mask will be
        used instead. All white (#FF or #FFFFFF) entries will be considerd
        "masked out" while other entries will be free to draw on. [This
        changed in the most recent version!]
76

77
78
79
80
81
    scale : float (default=1)
        Scaling between computation and drawing. For large word-cloud images,
        using scale instead of larger canvas size is significantly faster, but
        might lead to a coarser fit for the words.

Andreas Mueller's avatar
Andreas Mueller committed
82
    max_words : number (default=200)
83
84
85
86
87
        The maximum number of words.

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

88
89
90
    background_color : color value (default="black")
        Background color for the word cloud image.

Andreas Mueller's avatar
Andreas Mueller committed
91
92
93
94
    max_font_size : int or None (default=None)
        Maximum font size for the largest word. If None, height of the image is
        used.

95
96
    Attributes
    ----------
Andreas Mueller's avatar
Andreas Mueller committed
97
    ``words_``: list of tuples (string, float)
98
99
        Word tokens with associated frequency.

Andreas Mueller's avatar
Andreas Mueller committed
100
    ``layout_`` : list of tuples (string, int, (int, int), int, color))
101
102
        Encodes the fitted word cloud. Encodes for each word the string, font
        size, position, orientation and color.
103
104
105
106
107
108
109
110
111

    Notes
    -----
    Larger canvases with make the code significantly slower. If you need a large
    word cloud, try a lower canvas size, and set the scale parameter.

    The algorithm might give more weight to the ranking of the words
    than their actual frequencies, depending on the ``max_font_size`` and the
    scaling heuristic.
112
113
114
    """

    def __init__(self, font_path=None, width=400, height=200, margin=5,
Andreas Mueller's avatar
Andreas Mueller committed
115
                 ranks_only=False, prefer_horizontal=0.9, mask=None, scale=1,
116
                 color_func=random_color_func, max_words=200, stopwords=None,
Andreas Mueller's avatar
Andreas Mueller committed
117
                 random_state=None, background_color='black', max_font_size=None):
118
119
120
121
122
123
124
125
        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
Andreas Mueller's avatar
Andreas Mueller committed
126
        self.ranks_only = ranks_only
127
128
129
130
        self.prefer_horizontal = prefer_horizontal
        self.mask = mask
        self.scale = scale
        self.color_func = color_func
Andreas Mueller's avatar
Andreas Mueller committed
131
132
        self.max_words = max_words
        self.stopwords = stopwords
133
134
135
        if isinstance(random_state, int):
            random_state = Random(random_state)
        self.random_state = random_state
136
        self.background_color = background_color
Andreas Mueller's avatar
Andreas Mueller committed
137
138
139
        if max_font_size is None:
            max_font_size = height
        self.max_font_size = max_font_size
140

141
142
143
144
    def fit_words(self, frequencies):
        """Create a word_cloud from words and frequencies.

        Alias to generate_from_frequencies.
145
146
147

        Parameters
        ----------
148
        frequencies : array of tuples
149
150
151
152
            A tuple contains the word and its frequency.

        Returns
        -------
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
        self
        """
        return self.generate_from_frequencies(frequencies)

    def generate_from_frequencies(self, frequencies):
        """Create a word_cloud from words and frequencies.

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

        Returns
        -------
        self
168
169

        """
170
171
172
173
        if self.random_state is not None:
            random_state = self.random_state
        else:
            random_state = Random()
174

175
        if len(frequencies) <= 0:
176
            print("We need at least 1 word to plot a word cloud, got %d."
177
                  % len(frequencies))
178
179

        if self.mask is not None:
180
            mask = self.mask
Andreas Mueller's avatar
Andreas Mueller committed
181
182
            width = mask.shape[1]
            height = mask.shape[0]
183
            if mask.dtype.kind == 'f':
Andreas Mueller's avatar
Andreas Mueller committed
184
185
186
187
188
189
190
191
                warnings.warn("mask image should be unsigned byte between 0 and"
                              "255. Got a float array")
            if mask.ndim == 2:
                boolean_mask = mask == 255
            elif mask.ndim == 3:
                # "OR" the color channels
                boolean_mask = np.sum(mask[:, :, :3] == 255, axis=-1)
            else:
192
                raise ValueError("Got mask of invalid shape: %s" % str(mask.shape))
193
            # the order of the cumsum's is important for speed ?!
Andreas Mueller's avatar
Andreas Mueller committed
194
            integral = np.cumsum(np.cumsum(boolean_mask * 255, axis=1), axis=0).astype(np.uint32)
195
        else:
Andreas Mueller's avatar
Andreas Mueller committed
196
            height, width = self.height, self.width
197
198
199
200
201
202
203
204
            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 = [], [], [], []

Andreas Mueller's avatar
Andreas Mueller committed
205
        font_size = self.max_font_size
206
207

        # start drawing grey image
208
        for word, count in frequencies:
209
210
211
212
213
214
215
            # 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
216
                if random_state.random() < self.prefer_horizontal:
217
218
219
220
221
222
223
224
225
226
                    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,
227
                                              box_size[0] + self.margin, random_state)
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
                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)
Andreas Mueller's avatar
Andreas Mueller committed
243
244
245
246
247
            colors.append(self.color_func(word, font_size=font_size,
                                          position=(x, y),
                                          orientation=orientation,
                                          random_state=random_state,
                                          font_path=self.font_path))
248
            # recompute integral image
Andreas Mueller's avatar
Andreas Mueller committed
249
250
251
            if self.mask is None:
                img_array = np.asarray(img_grey)
            else:
Andreas Mueller's avatar
Andreas Mueller committed
252
                img_array = np.asarray(img_grey) + boolean_mask
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
            # 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

270
271
        self.layout_ = list(zip(frequencies, font_sizes, positions, orientations, colors))
        return self
272

273
    def process_text(self, text):
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
        """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 = {}
293
294
        flags = (re.UNICODE if sys.version < '3' and type(text) is unicode
                 else 0)
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
        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.
defacto133's avatar
defacto133 committed
316
            first = max(d2.items(), key=item1)[0]
317
318
319
            d3[first] = sum(d2.values())

        # merge plurals into the singular count (simple cases only)
defacto133's avatar
defacto133 committed
320
        for key in list(d3.keys()):
321
322
323
324
325
326
327
328
            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]

defacto133's avatar
defacto133 committed
329
        words = sorted(d3.items(), key=item1, reverse=True)
Andreas Mueller's avatar
Andreas Mueller committed
330
        words = words[:self.max_words]
331
332
333
334
335
336
337
338
        maximum = float(max(d3.values()))
        for i, (word, count) in enumerate(words):
            words[i] = word, count / maximum

        self.words_ = words

        return words

339
    def generate_from_text(self, text):
340
341
        """Generate wordcloud from text.

342
        Calls process_text and fit_words.
343
344
345
346
347

        Returns
        -------
        self
        """
348
349
        self.process_text(text)
        self.fit_words(self.words_)
350
351
        return self

352
353
354
355
356
357
358
359
360
361
362
363
364
    def generate(self, text):
        """Generate wordcloud from text.

        Alias to generate_from_text.

        Calls process_text and fit_words.

        Returns
        -------
        self
        """
        return self.generate_from_text(text)

365
    def _check_generated(self):
Andreas Mueller's avatar
Andreas Mueller committed
366
        """Check if ``layout_`` was computed, otherwise raise error."""
367
368
        if not hasattr(self, "layout_"):
            raise ValueError("WordCloud has not been calculated, call generate first.")
369
370
371
372
373
374
375
376
377

    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

378
        img = Image.new("RGB", (width * self.scale, height * self.scale), self.background_color)
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
        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
        ----------
396
397
398
        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.
399
400
401
402
403
404
405
406
407

        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
        """
408
409
        if isinstance(random_state, int):
            random_state = Random(random_state)
410
        self._check_generated()
411
412
413
414

        if color_func is None:
            color_func = self.color_func
        self.layout_ = [(word, font_size, position, orientation,
Andreas Mueller's avatar
Andreas Mueller committed
415
416
417
                         color_func(word=word, font_size=font_size,
                                    position=position, orientation=orientation,
                                    random_state=random_state, font_path=self.font_path))
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
                        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.
        """
Andreas Mueller's avatar
Andreas Mueller committed
446
        return np.array(self.to_image())
447

Andreas Mueller's avatar
Andreas Mueller committed
448
    def __array__(self):
449
450
451
452
453
454
455
        """Convert to numpy array.

        Returns
        -------
        image : nd-array size (width, height, 3)
            Word cloud image as numpy matrix.
        """
Andreas Mueller's avatar
Andreas Mueller committed
456
        return self.to_array()
457
458
459

    def to_html(self):
        raise NotImplementedError("FIXME!!!")