def getPathData(data, param):
path_data = []
p = param
tree = Grid_adaptiveM(data, p.Eps, p)
tree.buildIndex()
tree.adjustConsistency()
leaf_boxes = []
for (_, _), l1_child in np.ndenumerate(tree.root.children):
if not l1_child.n_isLeaf and l1_child.children is not None:
for (_, _), l2_child in np.ndenumerate(l1_child.children): # child1 is a first-level cell
leaf_boxes.append((l2_child.n_box, l2_child.a_count))
leaf_boxes.append((l1_child.n_box, l1_child.a_count))
for data in leaf_boxes:
# [[x_min,y_min],[x_max,y_max]]
path = []
box = data[0]
# (x_min, y_min) --> (x_min, y_max) --> (x_max, y_max) --> (x_max, y_min) --> (x_min, y_min)
path.append((mpath.Path.MOVETO, (box[0][0], box[0][1])))
path.append((mpath.Path.LINETO, (box[0][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[0][1])))
path.append((mpath.Path.CLOSEPOLY, (box[0][0], box[0][1])))
path_data.append((path, data[1]))
return path_data
def getPathData(data, param):
path_data = []
p = param
tree = Grid_adaptiveM(data, p.Eps, p)
tree.buildIndex()
tree.adjustConsistency()
leaf_boxes = []
for (_, _), l1_child in np.ndenumerate(tree.root.children):
if not l1_child.n_isLeaf and l1_child.children is not None:
for (_, _), l2_child in np.ndenumerate(
l1_child.children): # child1 is a first-level cell
leaf_boxes.append((l2_child.n_box, l2_child.a_count))
leaf_boxes.append((l1_child.n_box, l1_child.a_count))
for data in leaf_boxes:
# [[x_min,y_min],[x_max,y_max]]
path = []
box = data[0]
# (x_min, y_min) --> (x_min, y_max) --> (x_max, y_max) --> (x_max, y_min) --> (x_min, y_min)
path.append((mpath.Path.MOVETO, (box[0][0], box[0][1])))
path.append((mpath.Path.LINETO, (box[0][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[0][1])))
path.append((mpath.Path.CLOSEPOLY, (box[0][0], box[0][1])))
path_data.append((path, data[1]))
return path_data
def run_Grid_adaptiveM(self, param):
logging.debug('building Grid_adaptiveM...')
tree = Grid_adaptiveM(self.data, param)
start = time.clock()
tree.buildIndex()
if Params.CONSTRAINT_INFERENCE:
tree.adjustConsistency()
end = time.clock()
logging.info('[T] Grid_adaptiveM building time: %.2d ' % (end - start))
return self.query(tree, "Grid_adaptiveM")
