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# Author: Andreas Christian Mueller <>
# (c) 2012
# Modified by: Paul Nechifor <>
# License: MIT
from __future__ import division
import warnings
from random import Random
import os
import re
import sys
import colorsys
import numpy as np
from operator import itemgetter
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFilter
from PIL import ImageFont
from .query_integral_image import query_integral_image
from .tokenization import unigrams_and_bigrams, process_tokens
FILE = os.path.dirname(__file__)
FONT_PATH = os.environ.get('FONT_PATH', os.path.join(FILE, 'DroidSansMono.ttf'))
STOPWORDS = set(map(str.strip, open(os.path.join(FILE, 'stopwords')).readlines()))
class IntegralOccupancyMap(object):
def __init__(self, height, width, mask):
self.height = height
self.width = width
if mask is not None:
# the order of the cumsum's is important for speed ?!
self.integral = np.cumsum(np.cumsum(255 * mask, axis=1),
self.integral = np.zeros((height, width), dtype=np.uint32)
def sample_position(self, size_x, size_y, random_state):
return query_integral_image(self.integral, size_x, size_y,
def update(self, img_array, pos_x, pos_y):
partial_integral = np.cumsum(np.cumsum(img_array[pos_x:, pos_y:],
axis=1), axis=0)
# paste recomputed part into old image
# if x or y is zero it is a bit annoying
if pos_x > 0:
if pos_y > 0:
partial_integral += (self.integral[pos_x - 1, pos_y:]
- self.integral[pos_x - 1, pos_y - 1])
partial_integral += self.integral[pos_x - 1, pos_y:]
if pos_y > 0:
partial_integral += self.integral[pos_x:, pos_y - 1][:, np.newaxis]
self.integral[pos_x:, pos_y:] = partial_integral
def random_color_func(word=None, font_size=None, position=None,
orientation=None, font_path=None, random_state=None):
"""Random hue color generation.
Default coloring method. This just picks a random hue with value 80% and
lumination 50%.
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
if random_state is None:
random_state = Random()
return "hsl(%d, 80%%, 50%%)" % random_state.randint(0, 255)
class colormap_color_func(object):
"""Color func created from matplotlib colormap.
colormap : string or matplotlib colormap
Colormap to sample from
>>> WordCloud(color_func=colormap_color_func("magma"))
def __init__(self, colormap):
import matplotlib.pyplot as plt
self.colormap =
def __call__(self, word, font_size, position, orientation,
random_state=None, **kwargs):
if random_state is None:
random_state = Random()
r, g, b, _ = np.maximum(0, 255 * np.array(self.colormap(
random_state.uniform(0, 1))))
return "rgb({:.0f}, {:.0f}, {:.0f})".format(r, g, b)
def get_single_color_func(color):
"""Create a color function which returns a single hue and saturation with.
different values (HSV). Accepted values are color strings as usable by
>>> color_func1 = get_single_color_func('deepskyblue')
>>> color_func2 = get_single_color_func('#00b4d2')
old_r, old_g, old_b = ImageColor.getrgb(color)
rgb_max = 255.
h, s, v = colorsys.rgb_to_hsv(old_r / rgb_max, old_g / rgb_max,
old_b / rgb_max)
def single_color_func(word=None, font_size=None, position=None,
orientation=None, font_path=None, random_state=None):
"""Random color generation.
Additional coloring method. It picks a random value with hue and
saturation based on the color given to the generating function.
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
if random_state is None:
random_state = Random()
r, g, b = colorsys.hsv_to_rgb(h, s, random_state.uniform(0.2, 1))
return 'rgb({:.0f}, {:.0f}, {:.0f})'.format(r * rgb_max, g * rgb_max,
b * rgb_max)
return single_color_func
class WordCloud(object):
"""Word cloud object for generating and drawing.
font_path : string
Font path to the font that will be used (OTF or TTF).
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.
width : int (default=400)
Width of the canvas.
height : int (default=200)
Height of the canvas.
prefer_horizontal : float (default=0.90)
The ratio of times to try horizontal fitting as opposed to vertical.
If prefer_horizontal < 1, the algorithm will try rotating the word
if it doesn't fit. (There is currently no built-in way to get only
vertical words.)
mask : nd-array or None (default=None)
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!]
contour_width: float (default=0)
If mask is not None and contour_width > 0, draw the mask contour.
contour_color: color value (default="black")
Mask contour color.
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.
min_font_size : int (default=4)
Smallest font size to use. Will stop when there is no more room in this
font_step : int (default=1)
Step size for the font. font_step > 1 might speed up computation but
give a worse fit.
max_words : number (default=200)
The maximum number of words.
stopwords : set of strings or None
The words that will be eliminated. If None, the build-in STOPWORDS
list will be used. Ignored if using generate_from_frequencies.
background_color : color value (default="black")
Background color for the word cloud image.
max_font_size : int or None (default=None)
Maximum font size for the largest word. If None, height of the image is
mode : string (default="RGB")
Transparent background will be generated when mode is "RGBA" and
background_color is None.
relative_scaling : float (default='auto')
Importance of relative word frequencies for font-size. With
relative_scaling=0, only word-ranks are considered. With
relative_scaling=1, a word that is twice as frequent will have twice
the size. If you want to consider the word frequencies and not only
their rank, relative_scaling around .5 often looks good.
If 'auto' it will be set to 0.5 unless repeat is true, in which
case it will be set to 0.
.. versionchanged: 2.0
Default is now 'auto'.
color_func : callable, default=None
Callable with parameters word, font_size, position, orientation,
font_path, random_state that returns a PIL color for each word.
Overwrites "colormap".
See colormap for specifying a matplotlib colormap instead.
To create a word cloud with a single color, use
``color_func=lambda *args, **kwargs: "white"``.
The single color can also be specified using RGB code. For example
``color_func=lambda *args, **kwargs: (255,0,0)`` sets color to red.
regexp : string or None (optional)
Regular expression to split the input text into tokens in process_text.
If None is specified, ``r"\w[\w']+"`` is used. Ignored if using
collocations : bool, default=True
Whether to include collocations (bigrams) of two words. Ignored if using
.. versionadded: 2.0
colormap : string or matplotlib colormap, default="viridis"
Matplotlib colormap to randomly draw colors from for each word.
Ignored if "color_func" is specified.
.. versionadded: 2.0
normalize_plurals : bool, default=True
Whether to remove trailing 's' from words. If True and a word
appears with and without a trailing 's', the one with trailing 's'
is removed and its counts are added to the version without
trailing 's' -- unless the word ends with 'ss'. Ignored if using
repeat : bool, default=False
Whether to repeat words and phrases until max_words or min_font_size
is reached.
``words_`` : dict of string to float
Word tokens with associated frequency.
.. versionchanged: 2.0
``words_`` is now a dictionary
``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.
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.
def __init__(self, font_path=None, width=400, height=200, margin=2,
ranks_only=None, prefer_horizontal=.9, mask=None, scale=1,
color_func=None, max_words=200, min_font_size=4,
stopwords=None, random_state=None, background_color='black',
max_font_size=None, font_step=1, mode="RGB",
relative_scaling='auto', regexp=None, collocations=True,
colormap=None, normalize_plurals=True, contour_width=0,
contour_color='black', repeat=False):
if font_path is None:
font_path = FONT_PATH
if color_func is None and colormap is None:
# we need a color map
import matplotlib
version = matplotlib.__version__
if version[0] < "2" and version[2] < "5":
colormap = "hsv"
colormap = "viridis"
self.colormap = colormap
self.collocations = collocations
self.font_path = font_path
self.width = width
self.height = height
self.margin = margin
self.prefer_horizontal = prefer_horizontal
self.mask = mask
self.contour_color = contour_color
self.contour_width = contour_width
self.scale = scale
self.color_func = color_func or colormap_color_func(colormap)
self.max_words = max_words
self.stopwords = stopwords if stopwords is not None else STOPWORDS
self.min_font_size = min_font_size
self.font_step = font_step
self.regexp = regexp
if isinstance(random_state, int):
random_state = Random(random_state)
self.random_state = random_state
self.background_color = background_color
self.max_font_size = max_font_size
self.mode = mode
if relative_scaling == "auto":
if repeat:
relative_scaling = 0
relative_scaling = .5
if relative_scaling < 0 or relative_scaling > 1:
raise ValueError("relative_scaling needs to be "
"between 0 and 1, got %f." % relative_scaling)
self.relative_scaling = relative_scaling
if ranks_only is not None:
warnings.warn("ranks_only is deprecated and will be removed as"
" it had no effect. Look into relative_scaling.",
self.normalize_plurals = normalize_plurals
self.repeat = repeat
def fit_words(self, frequencies):
"""Create a word_cloud from words and frequencies.
Alias to generate_from_frequencies.
frequencies : dict from string to float
A contains words and associated frequency.
return self.generate_from_frequencies(frequencies)
def generate_from_frequencies(self, frequencies, max_font_size=None):
"""Create a word_cloud from words and frequencies.
frequencies : dict from string to float
A contains words and associated frequency.
max_font_size : int
Use this font-size instead of self.max_font_size
# make sure frequencies are sorted and normalized
frequencies = sorted(frequencies.items(), key=itemgetter(1), reverse=True)
if len(frequencies) <= 0:
raise ValueError("We need at least 1 word to plot a word cloud, "
"got %d." % len(frequencies))
frequencies = frequencies[:self.max_words]
# largest entry will be 1
max_frequency = float(frequencies[0][1])
frequencies = [(word, freq / max_frequency)
for word, freq in frequencies]
if self.random_state is not None:
random_state = self.random_state
random_state = Random()
if self.mask is not None:
boolean_mask = self._get_bolean_mask(self.mask)
width = self.mask.shape[1]
height = self.mask.shape[0]
boolean_mask = None
height, width = self.height, self.width
occupancy = IntegralOccupancyMap(height, width, boolean_mask)
# create image
img_grey ="L", (width, height))
draw = ImageDraw.Draw(img_grey)
img_array = np.asarray(img_grey)
font_sizes, positions, orientations, colors = [], [], [], []
last_freq = 1.
if max_font_size is None:
# if not provided use default font_size
max_font_size = self.max_font_size
if max_font_size is None:
# figure out a good font size by trying to draw with
# just the first two words
if len(frequencies) == 1:
# we only have one word. We make it big!
font_size = self.height
# find font sizes
sizes = [x[1] for x in self.layout_]
font_size = int(2 * sizes[0] * sizes[1]
/ (sizes[0] + sizes[1]))
# quick fix for if self.layout_ contains less than 2 values
# on very small images it can be empty
except IndexError:
font_size = sizes[0]
except IndexError:
raise ValueError(
"Couldn't find space to draw. Either the Canvas size"
" is too small or too much of the image is masked "
font_size = max_font_size
# we set self.words_ here because we called generate_from_frequencies
# above... hurray for good design?
self.words_ = dict(frequencies)
if self.repeat and len(frequencies) < self.max_words:
# pad frequencies with repeating words.
times_extend = int(np.ceil(self.max_words / len(frequencies))) - 1
# get smallest frequency
frequencies_org = list(frequencies)
downweight = frequencies[-1][1]
for i in range(times_extend):
frequencies.extend([(word, freq * downweight ** (i + 1))
for word, freq in frequencies_org])
# start drawing grey image
for word, freq in frequencies:
# select the font size
rs = self.relative_scaling
if rs != 0:
font_size = int(round((rs * (freq / float(last_freq))
+ (1 - rs)) * font_size))
if random_state.random() < self.prefer_horizontal:
orientation = None
orientation = Image.ROTATE_90
tried_other_orientation = False
while True:
# try to find a position
font = ImageFont.truetype(self.font_path, font_size)
# transpose font optionally
transposed_font = ImageFont.TransposedFont(
font, orientation=orientation)
# get size of resulting text
box_size = draw.textsize(word, font=transposed_font)
# find possible places using integral image:
result = occupancy.sample_position(box_size[1] + self.margin,
box_size[0] + self.margin,
if result is not None or font_size < self.min_font_size:
# either we found a place or font-size went too small
# if we didn't find a place, make font smaller
# but first try to rotate!
if not tried_other_orientation and self.prefer_horizontal < 1:
orientation = (Image.ROTATE_90 if orientation is None else
tried_other_orientation = True
font_size -= self.font_step
orientation = None
if font_size < self.min_font_size:
# we were unable to draw any more
x, y = np.array(result) + self.margin // 2
# actually draw the text
draw.text((y, x), word, fill="white", font=transposed_font)
positions.append((x, y))
colors.append(self.color_func(word, font_size=font_size,
position=(x, y),
# recompute integral image
if self.mask is None:
img_array = np.asarray(img_grey)
img_array = np.asarray(img_grey) + boolean_mask
# recompute bottom right
# the order of the cumsum's is important for speed ?!
occupancy.update(img_array, x, y)
last_freq = freq
self.layout_ = list(zip(frequencies, font_sizes, positions,
orientations, colors))
return self
def process_text(self, text):
"""Splits a long text into words, eliminates the stopwords.
text : string
The text to be processed.
words : dict (string, int)
Word tokens with associated frequency.
..versionchanged:: 1.2.2
Changed return type from list of tuples to dict.
There are better ways to do word tokenization, but I don't want to
include all those things.
stopwords = set([i.lower() for i in self.stopwords])
flags = (re.UNICODE if sys.version < '3' and type(text) is unicode
else 0)
regexp = self.regexp if self.regexp is not None else r"\w[\w']+"
words = re.findall(regexp, text, flags)
# remove stopwords
words = [word for word in words if word.lower() not in stopwords]
# remove 's
words = [word[:-2] if word.lower().endswith("'s") else word
for word in words]
# remove numbers
words = [word for word in words if not word.isdigit()]
if self.collocations:
word_counts = unigrams_and_bigrams(words, self.normalize_plurals)
word_counts, _ = process_tokens(words, self.normalize_plurals)
return word_counts
def generate_from_text(self, text):
"""Generate wordcloud from text.
The input "text" is expected to be a natural text. If you pass a sorted
list of words, words will appear in your output twice. To remove this
duplication, set ``collocations=False``.
Calls process_text and generate_from_frequencies.
..versionchanged:: 1.2.2
Argument of generate_from_frequencies() is not return of
process_text() any more.
words = self.process_text(text)
return self
def generate(self, text):
"""Generate wordcloud from text.
The input "text" is expected to be a natural text. If you pass a sorted
list of words, words will appear in your output twice. To remove this
duplication, set ``collocations=False``.
Alias to generate_from_text.
Calls process_text and generate_from_frequencies.
return self.generate_from_text(text)
def _check_generated(self):
"""Check if ``layout_`` was computed, otherwise raise error."""
if not hasattr(self, "layout_"):
raise ValueError("WordCloud has not been calculated, call generate"
" first.")
def to_image(self):
if self.mask is not None:
width = self.mask.shape[1]
height = self.mask.shape[0]
height, width = self.height, self.width
img =, (int(width * self.scale),
int(height * self.scale)),
draw = ImageDraw.Draw(img)
for (word, count), font_size, position, orientation, color in self.layout_:
font = ImageFont.truetype(self.font_path,
int(font_size * self.scale))
transposed_font = ImageFont.TransposedFont(
font, orientation=orientation)
pos = (int(position[1] * self.scale),
int(position[0] * self.scale))
draw.text(pos, word, fill=color, font=transposed_font)
return self._draw_contour(img=img)
def recolor(self, random_state=None, color_func=None, colormap=None):
"""Recolor existing layout.
Applying a new coloring is much faster than generating the whole
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.
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.
colormap : string or matplotlib colormap, default=None
Use this colormap to generate new colors. Ignored if color_func
is specified. If None, self.color_func (or self.color_map) is used.
if isinstance(random_state, int):
random_state = Random(random_state)
if color_func is None:
if colormap is None:
color_func = self.color_func
color_func = colormap_color_func(colormap)
self.layout_ = [(word_freq, font_size, position, orientation,
color_func(word=word_freq[0], font_size=font_size,
position=position, orientation=orientation,
for word_freq, font_size, position, orientation, _
in self.layout_]
return self
def to_file(self, filename):
"""Export to image file.
filename : string
Location to write to.
img = self.to_image(), optimize=True)
return self
def to_array(self):
"""Convert to numpy array.
image : nd-array size (width, height, 3)
Word cloud image as numpy matrix.
return np.array(self.to_image())
def __array__(self):
"""Convert to numpy array.
image : nd-array size (width, height, 3)
Word cloud image as numpy matrix.
return self.to_array()
def to_html(self):
raise NotImplementedError("FIXME!!!")
def _get_bolean_mask(self, mask):
"""Cast to two dimensional boolean mask."""
if mask.dtype.kind == 'f':
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:
# if all channels are white, mask out
boolean_mask = np.all(mask[:, :, :3] == 255, axis=-1)
raise ValueError("Got mask of invalid shape: %s"
% str(mask.shape))
return boolean_mask
def _draw_contour(self, img):
"""Draw mask contour on a pillow image."""
if self.mask is None or self.contour_width == 0:
return img
mask = self._get_bolean_mask(self.mask) * 255
contour = Image.fromarray(mask.astype(np.uint8))
contour = contour.resize(img.size)
contour = contour.filter(ImageFilter.FIND_EDGES)
contour = np.array(contour)
# make sure borders are not drawn before changing width
contour[[0, -1], :] = 0
contour[:, [0, -1]] = 0
# use gaussian to change width, divide by 10 to give more resolution
radius = self.contour_width / 10
contour = Image.fromarray(contour)
contour = contour.filter(ImageFilter.GaussianBlur(radius=radius))
contour = np.array(contour) > 0
contour = np.dstack((contour, contour, contour))
# color the contour
ret = np.array(img) * np.invert(contour)
if self.contour_color != 'black':
color =, img.size, self.contour_color)
ret += np.array(color) * contour
return Image.fromarray(ret)