def _get_tree(paths, closed):
""" Add all paths to the tree and create mapping from ids of elements added to the
tree to the elements data:
Closed paths:
[id of path, 1st point of segment, 2nd point of segment]
Open paths
[id of path, point of segment]
"""
# TODO: use models aabb?
aabb = get_aabb(np.concatenate(paths))
tree = Quadtree([aabb.min[0], aabb.min[1], aabb.max[0], aabb.max[1]])
mapping = {}
for path in paths:
if closed:
for i, j in zip(range(-1, len(path) - 1), range(len(path))):
# add whole edge into the tree
_add_edge(tree, mapping, path, i, j)
else:
_add_point(tree, mapping, path, 0)
_add_point(tree, mapping, path, -1)
tree.prune()
return tree, mapping
def _get_tree(paths, closed):
""" Add all paths to the tree and create mapping from ids of elements added to the
tree to the elements data:
Closed paths:
[id of path, 1st point of segment, 2nd point of segment]
Open paths
[id of path, point of segment]
"""
# TODO: use models aabb?
aabb = get_aabb(np.concatenate(paths))
tree = Quadtree([aabb.min[0], aabb.min[1], aabb.max[0], aabb.max[1]])
mapping = {}
for path in paths:
if closed:
for i, j in zip(range(-1, len(path) - 1), range(len(path))):
# add whole edge into the tree
_add_edge(tree, mapping, path, i, j)
else:
_add_point(tree, mapping, path, 0)
_add_point(tree, mapping, path, -1)
tree.prune()
return tree, mapping
def __init__(self, definition_file, points):
# Open definition file and background image
definition = ElementTree.parse(open(definition_file)).getroot()
map_file = os.path.join(
os.path.split(definition_file)[0], definition.attrib['bg'])
self.background = Image.open(map_file)
self.north = math.radians(float(definition.attrib['maxlat']))
self.south = math.radians(float(definition.attrib['minlat']))
self.west = math.radians(float(definition.attrib['minlon']))
self.east = math.radians(float(definition.attrib['maxlon']))
# Store ranges color and distance
self.ranges = [(float(r.attrib['distance']), (int(r.attrib['red']),
int(r.attrib['green']),
int(r.attrib['blue'])))
for r in definition.getiterator("range")]
self.width, self.height = self.background.size
self.points = points
# Creating top quadtree element
self.grid = Quadtree(self.north, self.south, self.west, self.east, 6)
for point in points:
self.grid.add(point)
# Precompute latitude and longitude at each row and column
self.pixel_lons = [
self.west + (self.east - self.west) *
(float(i) / float(self.width)) for i in range(self.width)
]
self.pixel_lats = [
self.north - (self.north - self.south) *
(float(j) / float(self.height)) for j in range(self.height)
]
class TwoPhaseModel:
def __init__(self, MI, NI, granulometry, matrixLabel):
self.MI = MI
self.NI = NI
self.granulometry = granulometry
self.matrixLabel = matrixLabel
xv = np.linspace(0, self.MI - 1, self.MI,
endpoint=True).astype(np.int32)
yv = np.linspace(0, self.NI - 1, self.NI,
endpoint=True).astype(np.int32)
X, Y = np.int32(np.meshgrid(xv, yv))
self.coords = {(x, y) for x, y in zip(X.ravel(), Y.ravel())}
depth = 4
self.qtree = Quadtree(int(depth), Rect(0, 0, int(self.MI),
int(self.NI)))
(self.XX, self.YY) = np.meshgrid(range(0, self.NI), range(0, self.MI))
def compute(self):
Objs = []
Image = np.ones((self.MI, self.NI), np.int32) * self.matrixLabel
Image = Image.astype(np.int32)
start = time.time()
for ix, value in enumerate(
zip(self.granulometry.a_reversed,
self.granulometry.c_reversed)):
print(ix, value[0], value[1])
area_set = 0
while area_set < value[1]:
cy, cx = self.coords.pop()
#if Image[cy,cx] == self.matrixLabel:
if True:
b = self.granulometry.getB(value[0])
theta = random.uniform(0, np.pi)
c = Ellipse(cy, cx, int(value[0]), int(b), theta)
objs = self.qtree.query(c)
if len(objs) == 0:
self.qtree.insert(c)
Objs.append(c)
area_set += c.area()
#ellipseMatrix(c.y(), c.x(), c.a(), c.b(), c.theta(), Image, int(self.granulometry.Label), self.XX, self.YY)
#ellipseDiscard(c.y(), c.x(), c.a(), c.b(), c.theta(), self.XX, self.YY, self.coords, Image, self.granulometry.Label)
else:
self.coords.add((cy, cx))
print(time.time() - start)
return Objs, int(self.granulometry.Label), self.XX, self.YY, Image
def test_quadtree_buildup(points: List[Point]) -> float:
tracemalloc.start()
starting_mem, _ = tracemalloc.get_traced_memory()
tree = Quadtree(points)
_, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()
return peak - starting_mem
def __init__(self, definition_file, points):
# Open definition file and background image
definition = ElementTree.parse(open(definition_file)).getroot()
map_file = os.path.join(os.path.split(definition_file)[0], definition.attrib['bg'])
self.background = Image.open(map_file)
self.north = math.radians(float(definition.attrib['maxlat']))
self.south = math.radians(float(definition.attrib['minlat']))
self.west = math.radians(float(definition.attrib['minlon']))
self.east = math.radians(float(definition.attrib['maxlon']))
# Store ranges color and distance
self.ranges = [(float(r.attrib['distance']),
(int(r.attrib['red']), int(r.attrib['green']), int(r.attrib['blue'])))
for r in definition.getiterator("range")]
self.width, self.height = self.background.size
self.points = points
# Creating top quadtree element
self.grid = Quadtree(self.north, self.south, self.west, self.east, 6)
for point in points:
self.grid.add(point)
# Precompute latitude and longitude at each row and column
self.pixel_lons = [self.west + (self.east-self.west)*(float(i)/float(self.width)) for i in range(self.width)]
self.pixel_lats = [self.north - (self.north-self.south)*(float(j)/float(self.height)) for j in range(self.height)]
def main():
pygame.init()
screen = pygame.display.set_mode((width, height))
map = pygame.Surface((map_width, map_height))
pygame.display.set_caption("quadtree")
font = pygame.font.SysFont('arial', 20)
clock = pygame.time.Clock()
particles = Group([
Particle(
random.gauss(map_width / 2, 150) % map_width,
random.gauss(map_height / 2, 150) % map_height, 4, map)
for _ in range(1000)
])
qtree = Quadtree(map.get_rect(), 4, map)
for p in particles:
qtree.insert(p)
while True:
clock.tick(60)
fps_text = font.render(f'fps:{int(clock.get_fps())}', True,
THECOLORS['white'])
fps_rect = fps_text.get_rect()
for event in pygame.event.get():
if event.type == pygame.QUIT:
exit()
qtree = Quadtree(map.get_rect(), 4, map)
for p in particles:
qtree.insert(p)
particles.update()
for p in particles:
p.highlight = False
query_rect = Rect(p.rect.x, p.rect.y, p.radius * 2, p.radius * 2)
if len(qtree.query(query_rect)) != 1:
p.highlight = True
map.fill(THECOLORS['black'])
particles.draw(map)
pygame.transform.scale(map, (width, height), screen)
screen.blit(fps_text, fps_rect)
pygame.display.update()
def capgrids_tree():
tree = Quadtree((-180, -90, 180, 90))
keys = {}
i = 0
for mapid in range(1, 100):
mapid = str(mapid)
for letter in 'abcdefghijklmnop':
for num in range(1, 10):
try:
b = box(mapid, letter + str(num))
except IndexError:
continue
v = "%s/%s" % (mapid, (letter + str(num)).capitalize())
if v not in keys:
tree.add(i, b)
keys[i] = v
i += 1
return keys, tree
def __init__(self, canvas=None):
self._matrix = Matrix()
self._painter = DefaultPainter(self)
self._bounding_box_painter = BoundingBoxPainter(self)
# Handling selections.
### TODO: Move this to a context?
self._selected_items = set()
self._focused_item = None
self._hovered_item = None
self._dropzone_item = None
###/
self._qtree = Quadtree()
self._bounds = Rectangle(0, 0, 0, 0)
self._canvas = None
if canvas:
self._set_canvas(canvas)
def __init__(self, MI, NI, granulometry, matrixLabel):
self.MI = MI
self.NI = NI
self.granulometry = granulometry
self.matrixLabel = matrixLabel
xv = np.linspace(0, self.MI - 1, self.MI,
endpoint=True).astype(np.int32)
yv = np.linspace(0, self.NI - 1, self.NI,
endpoint=True).astype(np.int32)
X, Y = np.int32(np.meshgrid(xv, yv))
self.coords = {(x, y) for x, y in zip(X.ravel(), Y.ravel())}
depth = 4
self.qtree = Quadtree(int(depth), Rect(0, 0, int(self.MI),
int(self.NI)))
(self.XX, self.YY) = np.meshgrid(range(0, self.NI), range(0, self.MI))
def test_quadtree_search(points: List[Point],
rectangles: List[Rectangle]) -> List[float]:
tree = Quadtree(points)
def time_individual(rectangle: Rectangle) -> float:
start_time = default_timer()
tree.find(rectangle)
end_time = default_timer()
return end_time - start_time
return list(map(time_individual, rectangles))
def __init__(self, canvas=None):
self._matrix = Matrix()
self._painter = DefaultPainter(self)
self._bounding_box_painter = BoundingBoxPainter(self)
# Handling selections.
### TODO: Move this to a context?
self._selected_items = set()
self._focused_item = None
self._hovered_item = None
self._dropzone_item = None
###/
self._qtree = Quadtree()
self._bounds = Rectangle(0, 0, 0, 0)
self._canvas = None
if canvas:
self._set_canvas(canvas)
def update_boids():
tree = Quadtree(b[0], 0, 0, WIDTH, HEIGHT)
for i in range(1, len(b)):
tree.insert(b[i])
for boid in b:
accel = [0, 0]
possible_close_boids = tree.findInCircle(boid.pos[0], boid.pos[1], NEIGHBORHOOD_THRESHOLD)
if len(possible_close_boids) > 1:
accel = boid.compute_acceleration(possible_close_boids, SEPARATION_THRESHOLD, C, A, S)
if Quadtree.pointInCircle(tree, boid.pos[0], boid.pos[1], mouse[0], mouse[1], NEIGHBORHOOD_THRESHOLD):
accel = list(map(sum, zip(accel, boid.avoid(mouse))))
boid.update(WIDTH, HEIGHT, accel)
def test():
pygame.init()
screen = pygame.display.set_mode((W, H), 0)
quadtree = Quadtree((0, 0, W, H))
triangles = []
p = (0, 0)
while True:
new_triangles = False
new_point = False
update = False
events = pygame.event.get()
for e in events:
if e.type == pygame.QUIT:
return
elif e.type == pygame.KEYUP and e.key == pygame.K_ESCAPE:
return
elif e.type == pygame.KEYUP and e.key == pygame.K_SPACE:
new_triangles = True
elif e.type == pygame.KEYUP and e.key == pygame.K_RETURN:
new_point = True
elif e.type == pygame.MOUSEBUTTONUP:
p = e.pos
update = True
print p
if new_triangles:
for i in range(N_triangles):
quadtree.remove(i)
print "foo"
triangles = []
for i in range(N_triangles):
triangles.append(get_random_triangle())
quadtree.add(i, triangles[-1])
print triangles[-1]
if new_point:
p = get_random_point()
if new_point or new_triangles or update:
screen.fill(BLACK)
ids = quadtree.query(p)
print ids
for i in range(N_triangles):
color = GREEN if i in ids else BLUE
pygame.draw.lines(screen, color, True, triangles[i])
pygame.draw.line(screen, RED, (p[0]-2, p[1]-2), (p[0]+2, p[1]+2))
pygame.draw.line(screen, RED, (p[0]-2, p[1]+2), (p[0]+2, p[1]-2))
pygame.display.update()
class Map:
def __init__(self, definition_file, points):
# Open definition file and background image
definition = ElementTree.parse(open(definition_file)).getroot()
map_file = os.path.join(os.path.split(definition_file)[0], definition.attrib['bg'])
self.background = Image.open(map_file)
self.north = math.radians(float(definition.attrib['maxlat']))
self.south = math.radians(float(definition.attrib['minlat']))
self.west = math.radians(float(definition.attrib['minlon']))
self.east = math.radians(float(definition.attrib['maxlon']))
# Store ranges color and distance
self.ranges = [(float(r.attrib['distance']),
(int(r.attrib['red']), int(r.attrib['green']), int(r.attrib['blue'])))
for r in definition.getiterator("range")]
self.width, self.height = self.background.size
self.points = points
# Creating top quadtree element
self.grid = Quadtree(self.north, self.south, self.west, self.east, 6)
for point in points:
self.grid.add(point)
# Precompute latitude and longitude at each row and column
self.pixel_lons = [self.west + (self.east-self.west)*(float(i)/float(self.width)) for i in range(self.width)]
self.pixel_lats = [self.north - (self.north-self.south)*(float(j)/float(self.height)) for j in range(self.height)]
def generate(self):
"Generates the output image"
ranges_image = Image.new('RGB', self.background.size)
# Create a lists list with the distance from each pixel to its nearest point
colors = [[self.color(self.distance(i,j)) for i in range(self.width)] for j in range(self.height)]
# Flatten list
colors = [item for sublist in colors for item in sublist]
ranges_image.putdata(colors)
return Image.blend(ranges_image, self.background.convert('RGB'), 0.5)
def distance(self, i, j):
lon = self.pixel_lons[i]
lat = self.pixel_lats[j]
point = Point(lat, lon)
elements = PriorityQueue()
elements.put_nowait((self.grid.distance(point), self.grid))
# We iterate over the priority queue until the nearest element is a point. While it isn't we add its children to the queue.
while True:
(distance, elem) = elements.get_nowait()
#print "Iterating (%d, %d) distance: %f" % (i, j, distance)
if isinstance(elem, Point):
return distance
else:
for child in elem.children:
elements.put_nowait((child.distance(point), child))
def color(self, distance):
"Returns which color represents distance"
return [c for (d,c) in self.ranges if d>distance][0]
pt.normal = -pt.position / np.sqrt(np.dot(pt.position, pt.position))
return pc
def get_dist_func(pc):
def dist(x, y):
pt = Point(x, y, 0)
neighbors = pc.nearest_neighbors(pt, 50)
d = 0
for pt2 in neighbors:
diff = pt.position - pt2.position
d += np.dot(diff, pt2.normal)
return d
return dist
if __name__ == '__main__':
pc = get_circle()
dist = get_dist_func(pc)
quadtree = Quadtree(pc.points, 4)
contour = quadtree.compute_contour(dist)
print 'Found %d edges' % len(contour)
# quadtree.display()
for (pt1, pt2) in contour:
x1, y1, _ = pt1.position
x2, y2, _ = pt2.position
plt.plot([x1, x2], [y1, y2])
# plt.show()
quadtree.display()
class Map:
def __init__(self, definition_file, points):
# Open definition file and background image
definition = ElementTree.parse(open(definition_file)).getroot()
map_file = os.path.join(
os.path.split(definition_file)[0], definition.attrib['bg'])
self.background = Image.open(map_file)
self.north = math.radians(float(definition.attrib['maxlat']))
self.south = math.radians(float(definition.attrib['minlat']))
self.west = math.radians(float(definition.attrib['minlon']))
self.east = math.radians(float(definition.attrib['maxlon']))
# Store ranges color and distance
self.ranges = [(float(r.attrib['distance']), (int(r.attrib['red']),
int(r.attrib['green']),
int(r.attrib['blue'])))
for r in definition.getiterator("range")]
self.width, self.height = self.background.size
self.points = points
# Creating top quadtree element
self.grid = Quadtree(self.north, self.south, self.west, self.east, 6)
for point in points:
self.grid.add(point)
# Precompute latitude and longitude at each row and column
self.pixel_lons = [
self.west + (self.east - self.west) *
(float(i) / float(self.width)) for i in range(self.width)
]
self.pixel_lats = [
self.north - (self.north - self.south) *
(float(j) / float(self.height)) for j in range(self.height)
]
def generate(self):
"Generates the output image"
ranges_image = Image.new('RGB', self.background.size)
# Create a lists list with the distance from each pixel to its nearest point
colors = [[self.color(self.distance(i, j)) for i in range(self.width)]
for j in range(self.height)]
# Flatten list
colors = [item for sublist in colors for item in sublist]
ranges_image.putdata(colors)
return Image.blend(ranges_image, self.background.convert('RGB'), 0.5)
def distance(self, i, j):
lon = self.pixel_lons[i]
lat = self.pixel_lats[j]
point = Point(lat, lon)
elements = PriorityQueue()
elements.put_nowait((self.grid.distance(point), self.grid))
# We iterate over the priority queue until the nearest element is a point. While it isn't we add its children to the queue.
while True:
(distance, elem) = elements.get_nowait()
#print "Iterating (%d, %d) distance: %f" % (i, j, distance)
if isinstance(elem, Point):
return distance
else:
for child in elem.children:
elements.put_nowait((child.distance(point), child))
def color(self, distance):
"Returns which color represents distance"
return [c for (d, c) in self.ranges if d > distance][0]
# y = 2*(np.random.random()-0.5)
# pc.add_point(x, y, 0)
for pt in pc.points:
pt.normal = -pt.position / np.sqrt(np.dot(pt.position, pt.position))
return pc
def get_dist_func(pc):
def dist(x, y):
pt = Point(x, y, 0)
neighbors = pc.nearest_neighbors(pt, 50)
d = 0
for pt2 in neighbors:
diff = pt.position - pt2.position
d += np.dot(diff, pt2.normal)
return d
return dist
if __name__ == '__main__':
pc = get_circle()
dist = get_dist_func(pc)
quadtree = Quadtree(pc.points, 4)
contour = quadtree.compute_contour(dist)
print 'Found %d edges' % len(contour)
# quadtree.display()
for (pt1, pt2) in contour:
x1, y1, _ = pt1.position
x2, y2, _ = pt2.position
plt.plot([x1, x2], [y1, y2])
# plt.show()
quadtree.display()
class View(object):
"""
View class for gaphas.Canvas objects.
"""
def __init__(self, canvas=None):
self._matrix = Matrix()
self._painter = DefaultPainter(self)
self._bounding_box_painter = BoundingBoxPainter(self)
# Handling selections.
### TODO: Move this to a context?
self._selected_items = set()
self._focused_item = None
self._hovered_item = None
self._dropzone_item = None
###/
self._qtree = Quadtree()
self._bounds = Rectangle(0, 0, 0, 0)
self._canvas = None
if canvas:
self._set_canvas(canvas)
matrix = property(lambda s: s._matrix,
doc="Canvas to view transformation matrix")
def _set_canvas(self, canvas):
"""
Use view.canvas = my_canvas to set the canvas to be rendered
in the view.
"""
if self._canvas:
self._qtree.clear()
self._selected_items.clear()
self._focused_item = None
self._hovered_item = None
self._dropzone_item = None
self._canvas = canvas
canvas = property(lambda s: s._canvas, _set_canvas)
def emit(self, *args, **kwargs):
"""
Placeholder method for signal emission functionality.
"""
pass
def queue_draw_item(self, *items):
"""
Placeholder for item redraw queueing.
"""
pass
def select_item(self, item):
"""
Select an item. This adds @item to the set of selected items.
"""
self.queue_draw_item(item)
if item not in self._selected_items:
self._selected_items.add(item)
self.emit('selection-changed', self._selected_items)
def unselect_item(self, item):
"""
Unselect an item.
"""
self.queue_draw_item(item)
if item in self._selected_items:
self._selected_items.discard(item)
self.emit('selection-changed', self._selected_items)
def select_all(self):
for item in self.canvas.get_all_items():
self.select_item(item)
def unselect_all(self):
"""
Clearing the selected_item also clears the focused_item.
"""
self.queue_draw_item(*self._selected_items)
self._selected_items.clear()
self.focused_item = None
self.emit('selection-changed', self._selected_items)
selected_items = property(lambda s: s._selected_items,
select_item, unselect_all,
"Items selected by the view")
def _set_focused_item(self, item):
"""
Set the focused item, this item is also added to the selected_items
set.
"""
if not item is self._focused_item:
self.queue_draw_item(self._focused_item, item)
if item:
self.select_item(item)
if item is not self._focused_item:
self._focused_item = item
self.emit('focus-changed', item)
def _del_focused_item(self):
"""
Items that loose focus remain selected.
"""
self._set_focused_item(None)
focused_item = property(lambda s: s._focused_item,
_set_focused_item, _del_focused_item,
"The item with focus (receives key events a.o.)")
def _set_hovered_item(self, item):
"""
Set the hovered item.
"""
if item is not self._hovered_item:
self.queue_draw_item(self._hovered_item, item)
self._hovered_item = item
self.emit('hover-changed', item)
def _del_hovered_item(self):
"""
Unset the hovered item.
"""
self._set_hovered_item(None)
hovered_item = property(lambda s: s._hovered_item,
_set_hovered_item, _del_hovered_item,
"The item directly under the mouse pointer")
def _set_dropzone_item(self, item):
"""
Set dropzone item.
"""
if item is not self._dropzone_item:
self.queue_draw_item(self._dropzone_item, item)
self._dropzone_item = item
self.emit('dropzone-changed', item)
def _del_dropzone_item(self):
"""
Unset dropzone item.
"""
self._set_dropzone_item(None)
dropzone_item = property(lambda s: s._dropzone_item,
_set_dropzone_item, _del_dropzone_item,
'The item which can group other items')
def _set_painter(self, painter):
"""
Set the painter to use. Painters should implement painter.Painter.
"""
self._painter = painter
painter.set_view(self)
self.emit('painter-changed')
painter = property(lambda s: s._painter, _set_painter)
def _set_bounding_box_painter(self, painter):
"""
Set the painter to use for bounding box calculations.
"""
self._bounding_box_painter = painter
painter.set_view(self)
self.emit('painter-changed')
bounding_box_painter = property(lambda s: s._bounding_box_painter, _set_bounding_box_painter)
def get_item_at_point(self, pos, selected=True):
"""
Return the topmost item located at ``pos`` (x, y).
Parameters:
- selected: if False returns first non-selected item
"""
items = self._qtree.find_intersect((pos[0], pos[1], 1, 1))
for item in self._canvas.sort(items, reverse=True):
if not selected and item in self.selected_items:
continue # skip selected items
v2i = self.get_matrix_v2i(item)
ix, iy = v2i.transform_point(*pos)
if item.point((ix, iy)) < 0.5:
return item
return None
def get_handle_at_point(self, pos, distance=6):
"""
Look for a handle at ``pos`` and return the
tuple (item, handle).
"""
def find(item):
""" Find item's handle at pos """
v2i = self.get_matrix_v2i(item)
d = v2i.transform_distance(distance, 0)[0]
x, y = v2i.transform_point(*pos)
for h in item.handles():
if not h.movable:
continue
hx, hy = h.pos
if -d < (hx - x) < d and -d < (hy - y) < d:
return h
# The focused item is the prefered item for handle grabbing
if self.focused_item:
h = find(self.focused_item)
if h:
return self.focused_item, h
# then try hovered item
if self.hovered_item:
h = find(self.hovered_item)
if h:
return self.hovered_item, h
# Last try all items, checking the bounding box first
x, y = pos
items = self.get_items_in_rectangle((x - distance, y - distance, distance * 2, distance * 2), reverse=True)
found_item, found_h = None, None
for item in items:
h = find(item)
if h:
return item, h
return None, None
def get_port_at_point(self, vpos, distance=10, exclude=None):
"""
Find item with port closest to specified position.
List of items to be ignored can be specified with `exclude`
parameter.
Tuple is returned
- found item
- closest, connectable port
- closest point on found port (in view coordinates)
:Parameters:
vpos
Position specified in view coordinates.
distance
Max distance from point to a port (default 10)
exclude
Set of items to ignore.
"""
v2i = self.get_matrix_v2i
vx, vy = vpos
max_dist = distance
port = None
glue_pos = None
item = None
rect = (vx - distance, vy - distance, distance * 2, distance * 2)
items = self.get_items_in_rectangle(rect, reverse=True)
for i in items:
if i in exclude:
continue
for p in i.ports():
if not p.connectable:
continue
ix, iy = v2i(i).transform_point(vx, vy)
pg, d = p.glue((ix, iy))
if d >= max_dist:
continue
item = i
port = p
# transform coordinates from connectable item space to view
# space
i2v = self.get_matrix_i2v(i).transform_point
glue_pos = i2v(*pg)
return item, port, glue_pos
def get_items_in_rectangle(self, rect, intersect=True, reverse=False):
"""
Return the items in the rectangle 'rect'.
Items are automatically sorted in canvas' processing order.
"""
if intersect:
items = self._qtree.find_intersect(rect)
else:
items = self._qtree.find_inside(rect)
return self._canvas.sort(items, reverse=reverse)
def select_in_rectangle(self, rect):
"""
Select all items who have their bounding box within the
rectangle @rect.
"""
items = self._qtree.find_inside(rect)
map(self.select_item, items)
def zoom(self, factor):
"""
Zoom in/out by factor @factor.
"""
# TODO: should the scale factor be clipped?
self._matrix.scale(factor, factor)
# Make sure everything's updated
#map(self.update_matrix, self._canvas.get_all_items())
self.request_update((), self._canvas.get_all_items())
def set_item_bounding_box(self, item, bounds):
"""
Update the bounding box of the item.
``bounds`` is in view coordinates.
Coordinates are calculated back to item coordinates, so matrix-only
updates can occur.
"""
v2i = self.get_matrix_v2i(item).transform_point
ix0, iy0 = v2i(bounds.x, bounds.y)
ix1, iy1 = v2i(bounds.x1, bounds.y1)
self._qtree.add(item=item, bounds=bounds, data=Rectangle(ix0, iy0, x1=ix1, y1=iy1))
def get_item_bounding_box(self, item):
"""
Get the bounding box for the item, in view coordinates.
"""
return self._qtree.get_bounds(item)
bounding_box = property(lambda s: s._bounds)
def update_bounding_box(self, cr, items=None):
"""
Update the bounding boxes of the canvas items for this view, in
canvas coordinates.
"""
painter = self._bounding_box_painter
if items is None:
items = self.canvas.get_all_items()
# The painter calls set_item_bounding_box() for each rendered item.
painter.paint(Context(cairo=cr,
items=items,
area=None))
# Update the view's bounding box with the rest of the items
self._bounds = Rectangle(*self._qtree.soft_bounds)
def paint(self, cr):
self._painter.paint(Context(cairo=cr,
items=self.canvas.get_all_items(),
area=None))
def get_matrix_i2v(self, item):
"""
Get Item to View matrix for ``item``.
"""
if self not in item._matrix_i2v:
self.update_matrix(item)
return item._matrix_i2v[self]
def get_matrix_v2i(self, item):
"""
Get View to Item matrix for ``item``.
"""
if self not in item._matrix_v2i:
self.update_matrix(item)
return item._matrix_v2i[self]
def update_matrix(self, item):
"""
Update item matrices related to view.
"""
try:
i2v = item._matrix_i2c.multiply(self._matrix)
except AttributeError:
# Fall back to old behaviour
i2v = item._matrix_i2c * self._matrix
item._matrix_i2v[self] = i2v
v2i = Matrix(*i2v)
v2i.invert()
item._matrix_v2i[self] = v2i
def _clear_matrices(self):
"""
Clear registered data in Item's _matrix{i2c|v2i} attributes.
"""
for item in self.canvas.get_all_items():
try:
del item._matrix_i2v[self]
del item._matrix_v2i[self]
except KeyError:
pass
from quadtree import Quadtree import numpy as np import time np.random.seed(5) p = Quadtree(0, 0, 100, 100, 5) N = 5000000 data = 100 * np.random.random(size=(N, 2)) data = data.astype(np.float32) start = time.time() p.insert(data) print(time.time() - start) start = time.time() r = .1 d = p.select_from(data, r) print(time.time() - start) print(d[0]) print()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d', type=int, action='store',
dest='data_num', help='choose which data set to use')
if len(sys.argv) != 3:
print 'Command e.g.: python findNearPlace.py -d 0(1,2)'
sys.exit(1)
para = parser.parse_args()
if para.data_num == 0:
location_infile = settings["ROOT_PATH"] + settings["SRC_DATA_FILE1_1"]
nearplace_outfile = settings["ROOT_PATH"] + settings["NEAR_PLACE_FILE1"]
elif para.data_num == 1:
location_infile = settings["ROOT_PATH"] + settings["SRC_DATA_FILE2_1"]
nearplace_outfile = settings["ROOT_PATH"] + settings["NEAR_PLACE_FILE2"]
elif para.data_num == 2:
location_infile = settings["ROOT_PATH"] + settings["SRC_DATA_FILE3_3"]
nearplace_outfile = settings["ROOT_PATH"] + settings["NEAR_PLACE_FILE3"]
else:
print 'Invalid choice of data set'
sys.exit(1)
loc_latlng = {}
try:
for entry in csv.reader(open(location_infile, 'rU')):
pid, lat, lng = int(entry[0]), float(entry[2]), float(entry[3])
loc_latlng[pid] = (lat, lng)
except:
print entry
sys.exit(1)
# directly scanning all POIs to get answer, which is too slow
'''writer = csv.writer(open(nearplace_outfile, "w"), lineterminator="\r\n")
pids = loc_latlng.keys()
for i in xrange(len(pids)):
pid1 = pids[i]
near_place = []
for j in xrange(len(pids)):
pid2 = pids[j]
dis = distance.distance(loc_latlng[pid1], loc_latlng[pid2]).miles
if dis < settings["DISTANCE_THRESHOLD"]:
near_place.append(pid2)
writer.writerow([pid1] + near_place)
print i'''
# quad tree
index_extent = (-90, -180, 90, 180)
index = Quadtree(index_extent)
for pid in loc_latlng:
index.add(pid, loc_latlng[pid])
for pid in loc_latlng:
start_time = time.clock()
pid_set = findNearPlaceByQuadtree(loc_latlng,
loc_latlng[pid],
index.struct(),
settings["DISTANCE_THRESHOLD"])
end_time = time.clock()
print "Time Cost: %f(s)" % (end_time-start_time)
raw_input()
print len(pid_set)
raw_input()
gmaxx = GALAXY_WIDTH
gmaxy = GALAXY_HEIGHT
testimage = Image.new('RGBA', (int(gmaxx), int(gmaxy)))
nebulae = Image.new('RGB', (int(gmaxx), int(gmaxy)))
draw = ImageDraw.Draw(testimage)
draw2 = ImageDraw.Draw(nebulae)
povfile = open("stars.pov", "w")
squares = {}
numstars = 0
totalstars = 0
sectors = {}
planets = []
nplanets = []
tree = Quadtree((gminx, gminy, gmaxx, gmaxy), maxdepth=16)
ntree = Quadtree((gminx, gminy, gmaxx, gmaxy), maxdepth=16)
def is_nan2(num):
return str(num) == "nan"
def setsize(color):
basesize = .05
sizemods = {'blue': 2.0, 'red': .7, 'yellow': 1.0, 'orange': 4, 'green': 4}
size = basesize * sizemods[color]
if color == 'red' and random.random() < .011:
# red giant star
size *= random.randint(4, 5)
class View(object):
"""
View class for gaphas.Canvas objects.
"""
def __init__(self, canvas=None):
self._matrix = Matrix()
self._painter = DefaultPainter(self)
self._bounding_box_painter = BoundingBoxPainter(self)
# Handling selections.
### TODO: Move this to a context?
self._selected_items = set()
self._focused_item = None
self._hovered_item = None
self._dropzone_item = None
###/
self._qtree = Quadtree()
self._bounds = Rectangle(0, 0, 0, 0)
self._canvas = None
if canvas:
self._set_canvas(canvas)
matrix = property(lambda s: s._matrix,
doc="Canvas to view transformation matrix")
def _set_canvas(self, canvas):
"""
Use view.canvas = my_canvas to set the canvas to be rendered
in the view.
"""
if self._canvas:
self._qtree.clear()
self._selected_items.clear()
self._focused_item = None
self._hovered_item = None
self._dropzone_item = None
self._canvas = canvas
canvas = property(lambda s: s._canvas, _set_canvas)
def emit(self, *args, **kwargs):
"""
Placeholder method for signal emission functionality.
"""
pass
def queue_draw_item(self, *items):
"""
Placeholder for item redraw queueing.
"""
pass
def select_item(self, item):
"""
Select an item. This adds @item to the set of selected items.
"""
self.queue_draw_item(item)
if item not in self._selected_items:
self._selected_items.add(item)
self.emit('selection-changed', self._selected_items)
def unselect_item(self, item):
"""
Unselect an item.
"""
self.queue_draw_item(item)
if item in self._selected_items:
self._selected_items.discard(item)
self.emit('selection-changed', self._selected_items)
def select_all(self):
for item in self.canvas.get_all_items():
self.select_item(item)
def unselect_all(self):
"""
Clearing the selected_item also clears the focused_item.
"""
self.queue_draw_item(*self._selected_items)
self._selected_items.clear()
self.focused_item = None
self.emit('selection-changed', self._selected_items)
selected_items = property(lambda s: s._selected_items, select_item,
unselect_all, "Items selected by the view")
def _set_focused_item(self, item):
"""
Set the focused item, this item is also added to the selected_items
set.
"""
if not item is self._focused_item:
self.queue_draw_item(self._focused_item, item)
if item:
self.select_item(item)
if item is not self._focused_item:
self._focused_item = item
self.emit('focus-changed', item)
def _del_focused_item(self):
"""
Items that loose focus remain selected.
"""
self._set_focused_item(None)
focused_item = property(lambda s: s._focused_item, _set_focused_item,
_del_focused_item,
"The item with focus (receives key events a.o.)")
def _set_hovered_item(self, item):
"""
Set the hovered item.
"""
if item is not self._hovered_item:
self.queue_draw_item(self._hovered_item, item)
self._hovered_item = item
self.emit('hover-changed', item)
def _del_hovered_item(self):
"""
Unset the hovered item.
"""
self._set_hovered_item(None)
hovered_item = property(lambda s: s._hovered_item, _set_hovered_item,
_del_hovered_item,
"The item directly under the mouse pointer")
def _set_dropzone_item(self, item):
"""
Set dropzone item.
"""
if item is not self._dropzone_item:
self.queue_draw_item(self._dropzone_item, item)
self._dropzone_item = item
self.emit('dropzone-changed', item)
def _del_dropzone_item(self):
"""
Unset dropzone item.
"""
self._set_dropzone_item(None)
dropzone_item = property(lambda s: s._dropzone_item, _set_dropzone_item,
_del_dropzone_item,
'The item which can group other items')
def _set_painter(self, painter):
"""
Set the painter to use. Painters should implement painter.Painter.
"""
self._painter = painter
painter.set_view(self)
self.emit('painter-changed')
painter = property(lambda s: s._painter, _set_painter)
def _set_bounding_box_painter(self, painter):
"""
Set the painter to use for bounding box calculations.
"""
self._bounding_box_painter = painter
painter.set_view(self)
self.emit('painter-changed')
bounding_box_painter = property(lambda s: s._bounding_box_painter,
_set_bounding_box_painter)
def get_item_at_point(self, pos, selected=True):
"""
Return the topmost item located at ``pos`` (x, y).
Parameters:
- selected: if False returns first non-selected item
"""
items = self._qtree.find_intersect((pos[0], pos[1], 1, 1))
for item in self._canvas.sort(items, reverse=True):
if not selected and item in self.selected_items:
continue # skip selected items
v2i = self.get_matrix_v2i(item)
ix, iy = v2i.transform_point(*pos)
if item.point((ix, iy)) < 0.5:
return item
return None
def get_handle_at_point(self, pos, distance=6):
"""
Look for a handle at ``pos`` and return the
tuple (item, handle).
"""
def find(item):
""" Find item's handle at pos """
v2i = self.get_matrix_v2i(item)
d = v2i.transform_distance(distance, 0)[0]
x, y = v2i.transform_point(*pos)
for h in item.handles():
if not h.movable:
continue
hx, hy = h.pos
if -d < (hx - x) < d and -d < (hy - y) < d:
return h
# The focused item is the prefered item for handle grabbing
if self.focused_item:
h = find(self.focused_item)
if h:
return self.focused_item, h
# then try hovered item
if self.hovered_item:
h = find(self.hovered_item)
if h:
return self.hovered_item, h
# Last try all items, checking the bounding box first
x, y = pos
items = self.get_items_in_rectangle(
(x - distance, y - distance, distance * 2, distance * 2),
reverse=True)
found_item, found_h = None, None
for item in items:
h = find(item)
if h:
return item, h
return None, None
def get_port_at_point(self, vpos, distance=10, exclude=None):
"""
Find item with port closest to specified position.
List of items to be ignored can be specified with `exclude`
parameter.
Tuple is returned
- found item
- closest, connectable port
- closest point on found port (in view coordinates)
:Parameters:
vpos
Position specified in view coordinates.
distance
Max distance from point to a port (default 10)
exclude
Set of items to ignore.
"""
v2i = self.get_matrix_v2i
vx, vy = vpos
max_dist = distance
port = None
glue_pos = None
item = None
rect = (vx - distance, vy - distance, distance * 2, distance * 2)
items = self.get_items_in_rectangle(rect, reverse=True)
for i in items:
if i in exclude:
continue
for p in i.ports():
if not p.connectable:
continue
ix, iy = v2i(i).transform_point(vx, vy)
pg, d = p.glue((ix, iy))
if d >= max_dist:
continue
item = i
port = p
# transform coordinates from connectable item space to view
# space
i2v = self.get_matrix_i2v(i).transform_point
glue_pos = i2v(*pg)
return item, port, glue_pos
def get_items_in_rectangle(self, rect, intersect=True, reverse=False):
"""
Return the items in the rectangle 'rect'.
Items are automatically sorted in canvas' processing order.
"""
if intersect:
items = self._qtree.find_intersect(rect)
else:
items = self._qtree.find_inside(rect)
return self._canvas.sort(items, reverse=reverse)
def select_in_rectangle(self, rect):
"""
Select all items who have their bounding box within the
rectangle @rect.
"""
items = self._qtree.find_inside(rect)
map(self.select_item, items)
def zoom(self, factor):
"""
Zoom in/out by factor @factor.
"""
# TODO: should the scale factor be clipped?
self._matrix.scale(factor, factor)
# Make sure everything's updated
#map(self.update_matrix, self._canvas.get_all_items())
self.request_update((), self._canvas.get_all_items())
def set_item_bounding_box(self, item, bounds):
"""
Update the bounding box of the item.
``bounds`` is in view coordinates.
Coordinates are calculated back to item coordinates, so matrix-only
updates can occur.
"""
v2i = self.get_matrix_v2i(item).transform_point
ix0, iy0 = v2i(bounds.x, bounds.y)
ix1, iy1 = v2i(bounds.x1, bounds.y1)
self._qtree.add(item=item,
bounds=bounds,
data=Rectangle(ix0, iy0, x1=ix1, y1=iy1))
def get_item_bounding_box(self, item):
"""
Get the bounding box for the item, in view coordinates.
"""
return self._qtree.get_bounds(item)
bounding_box = property(lambda s: s._bounds)
def update_bounding_box(self, cr, items=None):
"""
Update the bounding boxes of the canvas items for this view, in
canvas coordinates.
"""
painter = self._bounding_box_painter
if items is None:
items = self.canvas.get_all_items()
# The painter calls set_item_bounding_box() for each rendered item.
painter.paint(Context(cairo=cr, items=items, area=None))
# Update the view's bounding box with the rest of the items
self._bounds = Rectangle(*self._qtree.soft_bounds)
def paint(self, cr):
self._painter.paint(
Context(cairo=cr, items=self.canvas.get_all_items(), area=None))
def get_matrix_i2v(self, item):
"""
Get Item to View matrix for ``item``.
"""
if self not in item._matrix_i2v:
self.update_matrix(item)
return item._matrix_i2v[self]
def get_matrix_v2i(self, item):
"""
Get View to Item matrix for ``item``.
"""
if self not in item._matrix_v2i:
self.update_matrix(item)
return item._matrix_v2i[self]
def update_matrix(self, item):
"""
Update item matrices related to view.
"""
try:
i2v = item._matrix_i2c.multiply(self._matrix)
except AttributeError:
# Fall back to old behaviour
i2v = item._matrix_i2c * self._matrix
item._matrix_i2v[self] = i2v
v2i = Matrix(*i2v)
v2i.invert()
item._matrix_v2i[self] = v2i
def _clear_matrices(self):
"""
Clear registered data in Item's _matrix{i2c|v2i} attributes.
"""
for item in self.canvas.get_all_items():
try:
del item._matrix_i2v[self]
del item._matrix_v2i[self]
except KeyError:
pass
dimensions = {'xmin': 0.0, 'xmax': 8.0, 'ymin': 0.0, 'ymax': 8.0}
resolution = 1.0/32.0
data = sample_data(doubletorus_f, resolution, dimensions)
print "done"
print "dual contouring..."
[dc_verts, dc_edges] = dual_contour(data, resolution, dimensions)
print "done."
#non_manifold_verts = detectManifolds2d(dc_edges)
print "transforming into objects..."
vertex_set, edge_set = transform_into_object_sets(dc_verts, dc_edges)
print "done."
print "building quadtree..."
qt = Quadtree(8.0, np.array([0,0]))
qt.add_dataset(vertex_set)
print "quadtree of depth "+str(qt.get_depth())+" constructed."
print "done."
print "plotting..."
import matplotlib.pyplot as plt
#fig = plt.figure()
#plot_qt(qt)
#plot_vertices(vertex_set)
#plot_edges(edge_set)
#plot_non_manifold_vertices(dc_verts, non_manifold_verts)
plot_qt(qt, 'b--')
from quadtree import Quadtree, RGB
from PIL import Image
def is_valid_read_file(parser, arg):
if not os.path.exists(arg):
parser.error("The file %s does not exist!" % arg)
else:
return arg
parser = argparse.ArgumentParser()
parser.add_argument('image',
help='image file',
type=lambda x: is_valid_read_file(parser, x))
parser.add_argument('operation', help='operation to do on image', type=str)
args = parser.parse_args()
img_raw = Image.open(args.image)
img_rgb = img_raw.convert('RGB')
width, height = img_rgb.size
shape = (height, width)
colors = np.empty(shape, dtype=RGB)
pixels = img_rgb.load()
for h in range(height):
for w in range(width):
colors[h][w] = RGB(pixels[w, h])
qtree = Quadtree(colors)
modified = qtree.outline()
def test_quadtree_buildup(points: List[Point]) -> float:
start_time = default_timer()
_ = Quadtree(points)
end_time = default_timer()
return end_time - start_time
nargs='?',
default=10)
p.add_argument("-p",
"--maxpoints",
help="the maximum number of points in each area (required)",
type=int,
required=True)
p.add_argument("-u",
"--upper",
help="upper left point (required)",
type=float,
nargs=2,
metavar=('X', 'Y'),
required=True)
p.add_argument("-l",
"--lower",
help="loewr right point (required)",
type=float,
nargs=2,
metavar=('X', 'Y'),
required=True)
args = p.parse_args()
X = read_data(args.infile)
qtree = Quadtree(args.upper[0], args.upper[1], args.lower[0], args.lower[1],
args.maxpoints, args.maxdepth)
qtree.fit(X)
pickle.dump(qtree, args.outfile)
import numpy as np
import matplotlib.pyplot as plt
from quadtree import Point, Rectangle, Quadtree
DPI = 72
np.random.seed(60)
width, height = 600, 400
N = 500
coords = np.random.randn(N, 2) * height / 3 + (width / 2, height / 2)
points = [Point(*coord) for coord in coords]
print(len(points))
center = Point(width / 2, height / 2)
domain = Rectangle(center, height, width)
qtree = Quadtree(domain, 3)
for point in points:
qtree.insert(point)
print('Number of points in the domain =',
len(qtree)) # it might not be adding all the points for some reason
fig = plt.figure(figsize=(700 / DPI, 500 / DPI), dpi=DPI)
ax = plt.subplot()
ax.set_xlim(0, width)
ax.set_ylim(0, height)
qtree.draw(ax)
ax.scatter([p.x for p in points], [p.y for p in points], s=4)
ax.set_xticks([])
ax.set_yticks([])
import numpy as np
from quadtree import Quadtree
def read_data(f):
data = []
for line in f:
entries = line.rstrip().split(' ')
lat = float(entries[0])
lng = float(entries[1])
data.append((lat,lng))
return np.array(data)
p = argparse.ArgumentParser()
p.add_argument("-i", "--infile", help="input file (default=STDIN)", type=argparse.FileType('r'), nargs='?', default=sys.stdin)
p.add_argument("-o", "--outfile", help="output file (default=STDOUT)", type=argparse.FileType('w'), nargs='?', default=sys.stdout)
p.add_argument("-d", "--maxdepth", help="the maximum number of quadtree depth (default=10)", type=int, nargs='?', default=10)
p.add_argument("-p", "--maxpoints", help="the maximum number of points in each area (required)", type=int, required=True)
p.add_argument("-u", "--upper", help="upper left point (required)", type=float, nargs=2, metavar=('X','Y'), required=True)
p.add_argument("-l", "--lower", help="loewr right point (required)", type=float, nargs=2, metavar=('X', 'Y'), required=True)
args = p.parse_args()
X = read_data(args.infile)
qtree = Quadtree(args.upper[0],args.upper[1],args.lower[0],args.lower[1],args.maxpoints,args.maxdepth)
X_trans = qtree.fit_transform(X)
print '"area ID","upper left x","upper left y","lower right x","lower right y"'
for i in range(len(X_trans)):
a = qtree.leaves_[X_trans[i]]
print >>args.outfile, "%s,%s,%s,%s,%s" % (a.aid,a.x1,a.y1,a.x2,a.y2)