def aggregate(self, glyphset, info, screen):
# TODO: vectorize pretty much this whole method...
(width, height) = screen
# co-iterating on number of points in case glyphset.data() is a non-length-carrying placeholder
# TODO: Should the default placeholder carry length?
infos = [
info(data)
for (data,
_) in zip(glyphset.data(), range(len(glyphset.points())))
]
aggregates = self.allocate(glyphset, screen)
for idx, points in enumerate(glyphset.points()):
self.combine(aggregates, points, glyphset.shaper.code, infos[idx])
return aggregates
def aggregate(self, glyphset, info, screen):
# TODO: vectorize pretty much this whole method...
(width, height) = screen
# co-iterating on number of points in case glyphset.data() is a non-length-carrying placeholder
# TODO: Should the default placeholder carry length?
infos = [info(data) for (data, _)
in zip(glyphset.data(), range(len(glyphset.points())))]
aggregates = self.allocate(glyphset, screen)
for idx, points in enumerate(glyphset.points()):
self.combine(aggregates,
points,
glyphset.shaper.code,
infos[idx])
return aggregates
def aggregate(self, glyphset, info, screen):
points = glyphset.points()
coder = np.vectorize(info)
coded = coder(glyphset.data())
coded = np.array(coded)
cats = coded.max()
(width, height) = screen
dims = (height, width, cats+1)
# Need cats+1 to do proper categorization...
ranges = ((0, screen[1]), (0, screen[0]), (0, cats+1))
data = np.hstack([np.fliplr(points[:, 0:2]), coded[:, np.newaxis]])
dense = np.histogramdd(data, bins=dims, range=ranges)
return dense[0]
def aggregate(self, glyphset, info, screen):
points = glyphset.points()
coder = np.vectorize(info)
coded = coder(glyphset.data())
coded = np.array(coded)
cats = coded.max()
(width, height) = screen
dims = (height, width, cats + 1)
# Need cats+1 to do proper categorization...
ranges = ((0, screen[1]), (0, screen[0]), (0, cats + 1))
data = np.hstack([np.fliplr(points[:, 0:2]), coded[:, np.newaxis]])
dense = np.histogramdd(data, bins=dims, range=ranges)
return dense[0]
