def _get_wordcloud(img,
patch,
words,
word_to_frequency=None,
**wordcloud_kwargs):
# get the boolean mask corresponding to each patch
if isinstance(patch, Circle):
# We need to solve two problems here:
# 1) The path of Circle patches is always the unit circle.
# We need to apply the circle transform to the `path` attribute to get
# the correct path.
# 2) The Circle object returned by matplotlib-venn is messed up.
# The transform is not doing what it should do.
# path = transform.transform_path(old_path)
# Create a new patch with the same parameters as the given Circle patch.
dummy = Circle(patch.center, patch.radius)
# Apply transform to get the correct path.
transform = dummy.get_transform()
unit_circle_path = dummy.get_path()
path = transform.transform_path(unit_circle_path)
else:
path = patch.get_path()
mask = path.contains_points(img.pixel_coordinates).reshape(
(img.y_resolution, img.x_resolution))
# make mask matplotlib-venn compatible
mask = (~mask * 255).astype(np.uint8) # black indicates mask position
mask = np.flipud(mask) # origin is in upper left
# create wordcloud
wc = WordCloud(mask=mask,
background_color=None,
mode="RGBA",
**wordcloud_kwargs)
if not word_to_frequency:
# create mapping word : int
word_to_frequency = dict()
for word in words:
try:
word_to_frequency[word] += 1
except KeyError:
word_to_frequency[word] = 1
wc.generate_from_frequencies(
{word: word_to_frequency[word]
for word in words})
return wc
